2.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<h1>Load the Data</h1>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "HgfKplaYDGZJ"
      },
      "outputs": [],
      "source": [
        "#importing all the libraries\n",
        "import os\n",
        "import numpy as np\n",
        "import csv\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.dates import DateFormatter\n",
        "import math\n",
        "\n",
        "import statsmodels.api as sm\n",
        "from numpy import *\n",
        "from math import sqrt\n",
        "from pandas import *\n",
        "from datetime import datetime, timedelta\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from pickle import dump\n",
        "\n",
        "import tensorflow as tf\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import LeakyReLU\n",
        "from tensorflow.keras.layers import GRU\n",
        "from matplotlib import pyplot\n",
        "from pickle import load\n",
        "\n",
        "import time\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv1D, LeakyReLU\n",
        "from tensorflow.keras import Sequential\n",
        "\n",
        "import pandas_profiling as pp\n",
        "from matplotlib import style"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "WdR6J8UXDGZN",
        "outputId": "8274d301-e7b6-43aa-be08-61e007d6df3f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'DATE', 'HEADLINE', 'TICKER', 'COMPANY NAME', 'SECTOR',\n",
              "       'SERIES', 'DATE OF LISTING', 'PAID UP VALUE', 'MARKET LOT',\n",
              "       'ISIN NUMBER', 'FACE VALUE', 'high', 'low', 'open', 'close', 'volume',\n",
              "       'adj close', 'avg high', 'avg low', 'avg open', 'avg close',\n",
              "       'avg volume', 'avg adj close', 'compound', 'negative_score',\n",
              "       'positive_score', 'neutral_score', 'trend'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv('result.csv.xls')\n",
        "df.columns"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<h1>Data Visualization</h1>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler=MinMaxScaler()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\rowle\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\rowle\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
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AlbXiSa1SyrFJfiPJCU3TPCLJgUnOTPKiJB9pmuahST5S36eU8rA6/eFJTkryylLKgXV2r0pyVpKH1r+TVnBVAAAAAJiTed1+eFCSw0opByVZl2RLklOTnFunn5vktPr61CRvbZrm9qZpLktyaZJHlVIelGRD0zSfbJqmSfKm3mcAAAAA2I+teFKraZrNSf5XkiuSXJ1ke9M0H0zywKZprq4xVyc5un7k2CRX9mZxVR13bH09Ph4AAACA/dw8bj88Mu3VVw9J8m1JDi+l/Nysj0wY18wYP2mZZ5VSLi6lXHzdddfd3SIDAAAAsMrM4/bDn0xyWdM01zVNc2eStyf590muqbcUpg6vrfFXJXlw7/PHpb1d8ar6enz8HpqmeW3TNCc0TXPCxo0b9+nKAAAAALDy5pHUuiLJo0sp6+p/K3xikq8muSjJc2rMc5JcWF9flOTMUsqhpZSHpH0g/GfqLYo7SimPrvN5du8zAAAAAOzHDlrpBTZN8+lSyvlJPpdkZ5LPJ3ltkiOSnFdKeW7axNcZNf4rpZTzklxS41/QNM2uOrvnJ3ljksOSvK/+AQAAALCfW/GkVpI0TfPSJC8dG3172qu2JsW/LMnLJoy/OMkj9nkBAQAAAFjV5nH7IQAAAADcK5JaAAAAAAyOpBYAAAAAgyOpBQAAAMDgSGoBAAAAMDiSWgAAAAAMjqQWAAAAAIMjqQUAAADA4EhqAQAAADA4kloAAAAADI6kFgAAAACDI6kFAAAAwOBIagEAAAAwOJJaAAAAAAyOpBYAAAAAgyOpBQAAAMDgSGoBAAAAMDiSWgAAAAAMjqQWAAAAAIMjqQUAAADA4EhqAQAAADA4kloAAAAADM5eJbVKKf9fKeVJpZTDl7tAAAAAALCUvb1S6/Ikz0pycSnlM6WU/11KOXX5igUAAAAA0+1VUqtpmjc0TfNLSX48yZuTnFGHAAAAALDiDtqboFLKnyd5WJJrkvxdkmcm+dwylgsAAAAAptrb2w8fkOTAJNuS3JDk+qZpdi5XoQAAAABglr26UqtpmqcnSSnl+5KcmORjpZQDm6Y5bjkLBwAAAACT7O3th09N8rgkj09yZJKPpr0NEQAAAABW3F4ltZKcnOTjSf6kaZoty1geAAAAAFjS3t5++IJSygOT/LtSyg8n+UzTNNcub9EAAAAAYLK9elB8KeWMJJ9JckaSn07y6VLKM5ezYAAAAAAwzd7efvi7Sf5dd3VWKWVjkg8nOX+5CgYAAAAA0+zVlVpJDhi73fBbd+OzAAAAALBP7e2VWu8vpXwgyVvq+59J8t7lKRIAAAAAzLa3D4r/L6WU05P8aJKS5LVN07xjWUsGAAAAAFPs7ZVaaZrmgiQXLGNZAAAAAGCvzExqlVJ2JGkmTUrSNE2zYVlKBQAAAAAzzExqNU2zfqUKAgAAAAB7y38wBAAAAGBwJLUAAAAAGBxJLQAAAAAGR1ILAAAAgMGR1AIAAABgcCS1AAAAABgcSS0AAAAABkdSCwAAAIDBkdQCAAAAYHAktQAAAAAYnIPmXQAAANiXNm3alK1bt+aYY47JOeecM+/iAADLRFILAID9ytatW7N58+Z5FwMAWGZuPwQAAABgcCS1AAAAABgcSS0AAAAABkdSCwAAAIDBkdQCAAAAYHAktQAAAAAYHEktAAAAAAZHUgsAAACAwZHUAgAAAGBwJLUAAAAAGBxJLQAAAAAGR1ILAAAAgMGR1AIAAABgcCS1AAAAABgcSS0AAAAABkdSCwAAAIDBmUtSq5Ryv1LK+aWUfy6lfLWU8phSyv1LKR8qpXy9Do/sxZ9dSrm0lPK1UsqJvfGPLKV8qU57RSmlzGN9AAAAAFhZ87pS60+SvL9pmu9N8gNJvprkRUk+0jTNQ5N8pL5PKeVhSc5M8vAkJyV5ZSnlwDqfVyU5K8lD699JK7kSAAAAAMzHiie1Sikbkjw+yeuTpGmaO5qm2Zbk1CTn1rBzk5xWX5+a5K1N09zeNM1lSS5N8qhSyoOSbGia5pNN0zRJ3tT7DAAAAAD7sXlcqfWdSa5L8n9LKZ8vpfx5KeXwJA9smubqJKnDo2v8sUmu7H3+qjru2Pp6fDwAAAAA+7l5JLUOSvLDSV7VNM0PJbk59VbDKSY9J6uZMX7PGZRyVinl4lLKxdddd93dLS8AAAAAq8w8klpXJbmqaZpP1/fnp01yXVNvKUwdXtuLf3Dv88cl2VLHHzdh/B6apnlt0zQnNE1zwsaNG/fZigAAAAAwHyue1GqaZmuSK0sp/6aOemKSS5JclOQ5ddxzklxYX1+U5MxSyqGllIekfSD8Z+otijtKKY+u//Xw2b3PAAAAALAfO2hOy/31JH9ZSjkkyTeS/GLaBNt5pZTnJrkiyRlJ0jTNV0op56VNfO1M8oKmaXbV+Tw/yRuTHJbkffUPAAAAgP3cXJJaTdN8IckJEyY9cUr8y5K8bML4i5M8Yp8WDgAAAIBVbx7P1AIAAACAe0VSCwAAAIDBkdQCAAAAYHAktQAAAAAYHEktAAAAAAZHUgsAAACAwZHUAgAAAGBwDpp3AQAA9iebNm3K1q1bc8wxx+Scc86Zd3EAAPZbkloAAPvQ1q1bs3nz5nkXAwBgv+f2QwAAAAAGR1ILAAAAgMGR1AIAAABgcCS1AAAAABgcSS0AAAAABkdSCwAAAIDBkdQCAAAAYHAktQAAAAAYHEktAAAAAAZHUgsAAACAwZHUAgAAAGBwJLUAAAAAGBxJLQAAAAAGR1ILAAAAgMGR1AIAAABgcCS1AAAAABgcSS0AAAAABkdSCwAAAIDBkdQCAAAAYHAktQAAAAAYHEktAAAAAAZHUgsAAACAwZHUAgAAAGBwJLUAAAAAGBxJLQAAAAAGR1ILAAAAgME5aN4FAABYTk+54DUj729f2J4k2bKwffe095z+KyteLgAA7h1XagEAAAAwOJJaAAAAAAyOpBYAAAAAgyOpBQAAAMDgSGoBAAAAMDiSWgAAAAAMjqQWAAAAAIMjqQUAAADA4EhqAQAAADA4kloAAAAADI6kFgAAAACDI6kFAAAAwOBIagEAAAAwOJJaAAAAAAyOpBYAAAAAgyOpBQAAAMDgSGoBAAAAMDiSWgAAAAAMjqQWAAAAAIMjqQUAAADA4EhqAQAAADA4kloAAAAADI6kFgAAAACDc9C8CwAAALCcNm3alK1bt+aYY47JOeecM+/iALCPSGoBAAD7ta1bt2bz5s3zLgYA+5jbDwEAAAAYHEktAAAAAAZHUgsAAACAwZHUAgAAAGBwJLUAAAAAGBxJLQAAAAAGZ25JrVLKgaWUz5dS3l3f37+U8qFSytfr8Mhe7NmllEtLKV8rpZzYG//IUsqX6rRXlFLKPNYFAAAAgJU1zyu1fjPJV3vvX5TkI03TPDTJR+r7lFIeluTMJA9PclKSV5ZSDqyfeVWSs5I8tP6dtDJFBwAAAGCe5pLUKqUcl+QpSf68N/rUJOfW1+cmOa03/q1N09zeNM1lSS5N8qhSyoOSbGia5pNN0zRJ3tT7DAAAAAD7sXldqfXyJJuS3NUb98Cmaa5Okjo8uo4/NsmVvbir6rhj6+vx8QAAAADs51Y8qVVKeWqSa5um+ezefmTCuGbG+EnLPKuUcnEp5eLrrrtuLxcLAAAAwGo1jyu1fjTJKaWUy5O8NclPlFLenOSaekth6vDaGn9Vkgf3Pn9cki11/HETxu+haZrXNk1zQtM0J2zcuHFfrgsAAAAAc7DiSa2mac5umua4pmmOT/sA+I82TfNzSS5K8pwa9pwkF9bXFyU5s5RyaCnlIWkfCP+ZeovijlLKo+t/PXx27zMAAAAA7McOmncBev4gyXmllOcmuSLJGUnSNM1XSinnJbkkyc4kL2iaZlf9zPOTvDHJYUneV/8AAAAA2M/NNanVNM3fJPmb+vpbSZ44Je5lSV42YfzFSR6xfCUEAAAAYDWa138/BAAAAIB7TFILAAAAgMGR1AIAAABgcCS1AAAAABgcSS0AAAAABkdSCwAAAIDBkdQCAAAAYHAktQAAAAAYHEktAAAAAAZHUgsAAACAwTlo3gVgsk2bNmXr1q055phjcs4558y7OAAAAACriqTWKrV169Zs3rx53sUAAAAAWJXcfggAAADA4EhqAQAAADA4kloAAAAADI5nagEAAACwpNX2T+0ktQAAAABY0mr7p3aSWgAAAMB+Z7VdVcS+J6kFALCKPfX8vxx5f9vCjiTJloUdI9Pe/cyfXdFyAcBqt9quKmLf86B4AAAAAAZHUgsAAACAwZHUAgAAAGBwJLUAAAAAGBxJLQAAAAAGR1ILAAAAgMGR1AIAAABgcA6adwGYj02bNmXr1q055phjcs4558y7OAAAAAB3i6TWGrV169Zs3rx53sUAAAAAuEcktQAAAIB7zR1BrDRJLQAAAOBec0cQK82D4gEAAAAYHEktAAAAAAbH7YcAA+WZBQAAwFomqQUwUJ5ZACtDAhkAYHWS1AIAmEECGQBgdfJMLQAAAAAGR1ILAAAAgMGR1AIAAABgcCS1AAAAABgcSS0AAAAABkdSCwAAAIDBkdQCAAAAYHAktQAAAAAYHEktAAAAAAZHUgsAAACAwZHUAgAAAGBwJLUAAAAAGJyD5l0AAAAAWOs2bdqUrVu35phjjsk555wz7+LAIEhqAQAAwJxt3bo1mzdvnncxYFDcfggAAADA4EhqAQAAADA4bj8E7hH3/AMAADBPklrAPeKefwAAAOZJUgsAVjFXRQIAwGSSWgCwirkqEpZ26vnvH3l/88ItSZItC7eMTLvwmSetaLkAgOXlQfEAAAAADI4rtQDYZ9wqBwAArBRJLQD2GbfKAQAAK0VSCwDgXnjqBW8ceX/bwk1Jki0LN41Me/fpv7ByhQIAWAM8UwsAAACAwZHUAgAAAGBw3H7IquDh0gAAAMDdIanFquDh0gAAAMDd4fZDAAAAAAbHlVrAfsNtrAAAAGuHpBaw33AbKwAAwNrh9kMAAAAABkdSCwAAAIDBWfGkVinlwaWUj5VSvlpK+Uop5Tfr+PuXUj5USvl6HR7Z+8zZpZRLSylfK6Wc2Bv/yFLKl+q0V5RSykqvz1qxadOmPPvZz86mTZvmXRTWEPsdAAAA08zjmVo7k/ynpmk+V0pZn+SzpZQPJfmFJB9pmuYPSikvSvKiJL9TSnlYkjOTPDzJtyX5cCnle5qm2ZXkVUnOSvKpJO9NclKS9634Gq0BnlXEPNjvAFaef7oBAHSufeV5I+93bV/YPexPO/pXf3pFy9VZ8Su1mqa5ummaz9XXO5J8NcmxSU5Ncm4NOzfJafX1qUne2jTN7U3TXJbk0iSPKqU8KMmGpmk+2TRNk+RNvc8AAHAPdD8obN26dd5FAQCYaa7P1CqlHJ/kh5J8OskDm6a5OmkTX0mOrmHHJrmy97Gr6rhj6+vx8QAAAADs5+aW1CqlHJHkgiQvbJrmplmhE8Y1M8ZPWtZZpZSLSykXX3fddXe/sAAAAACsKnNJapVSDk6b0PrLpmneXkdfU28pTB1eW8dfleTBvY8fl2RLHX/chPF7aJrmtU3TnNA0zQkbN27cdysCAAAAwFzM478fliSvT/LVpmn+qDfpoiTPqa+fk+TC3vgzSymHllIekuShST5Tb1HcUUp5dJ3ns3ufAQAAAGA/No//fvijSX4+yZdKKV+o416c5A+SnFdKeW6SK5KckSRN03yllHJekkvS/ufEF9T/fJgkz0/yxiSHpf2vh/7zIQAAAMAasOJJraZpPpHJz8NKkidO+czLkrxswviLkzxi35Vufq599StG3u/avm33sJt29PN+Y6WLBQAAALAqzfW/HwIAAADAPSGpBQAAAMDgzOOZWrBf2rRpU7Zu3Zpjjjkm55xzzryLAwAAADMN/XuspBbsI1u3bs3mzZvnXQwAAJL893dcvfv1DQu7dg/741/89AeteLkAVpOhf491+yEAAAAAgyOpBQAAAMDgSGoBAAAAMDieqbVGbH3l742837X9ht3D/rRjfnU0Dti/DP1BkAAA+4rzIhg+SS2ANWToD4IEANhXnBfB8ElqAQCwprlaAwCGSVILAIA1zdUaADBMkloAAMCguLoOgERSC2BQXv5XJ+5+vW3HzjrcPDL+hf/hAyteLgBYSa6uAya55k8+NfJ+17bbdg/70x74m49e0XKxfA6YdwEAAAAA4O5ypRbAMnFrBAD3hP4DGIqtf3TJyPtd2+7YPexPO+a3H7ai5WLtkNQCWCZr4daI/3reSSPvv7VwZx1uHpn2+z/9/n2yPF/0WAlPueD1I+9vX7gpSbJl4aaRae85/bn7ZHn2a8athf4DAPYFSS1gr130hpN3v775pjvqcPPI+FN+6X0rXi7WDl/02B/ZrwEA7hlJLQAAAFhhV/zR1pH3O7ft2j3sT/v23z5mRcsFQyKpBQDV3b0NzG1j7C+eev5f735928JCkmTLwsLI+Hc/84wVLxerh/YOgNVIUgtYNc77v6PPZ1q46c463Lx72k//4r55NtM94YR+/3d3bwNz2xiwVmjvAFiNJLUA9pITeoD58KMCADCJpFacKAEArGar7UcF544AsDpIamX1nSgBALB6OXcEgNVBUgv2U35FBgAAYH8mqQX7Kb8ig+QuAADszyS19hO+uK28T7/mqSPvb9t+Wx1uGZn2I7/y7hUtF7BoiMndn3rn74y8v+Pm65MkW26+fmTae0/7w32yPP0HwHxofxkC+ymrnaTWfmKIX9wAmL+723+stpPb1VYegL3l/J0hsJ+y2klqAYP1l288ceT9jpt21uHmkWk/+wsfWNFywf5stZ3crrbyAACwcg6YdwEAAAAA4O5ypRYAACwjt8kCsFpc+2fvGHm/a/vC7mF/2tEvePqKluuektS6B5yY3HuXv+K0kfc7ty3U4ZaRacf/xjtXrlAAAMvAbbIAsDwkte4BJyYA3FPL/cOIH14AAFgrJLUAmJu1mIBZ7h9G/PACwFq1Fs8rYK2T1IIpdIqw/CRgAIB9xXnF/s93NMZJasEUa61T1EGwFp180Skj7++4+ZYkyeabt4xMe98pF61ouYC15xkXfHL36x0LtyVJrl64bWT8209/zIqXi+Fyuzv7o7X2HY2lSWox0ZY/+08j73dtv373sD/t217wv1e0XGvZcp846CD29Po3PXnk/U07dtXh5pFpz332B1e0XMCwPfX8N428v21hR5Jky8KOkWnvfuazV7Rc94Yvtyy3s98xeo5y/cLO3cP+tP/x9GNXtFyrmdvd9/TF11078v6Om3btHvan/cB/PHpFywX70lrrkyW1YCCGeOIAwNowtD7qtPM/NPJ+YaG9SnPLwi0j0975zCetaLn2J2vtSxVwz2gr9r2h9cn3lqQWANwNJ1/4S7tf33HzNUmSzTdfMzL+fae+YcXLBbCarLUvVawNEjD7nraCe0tSC2CVcKIEAEzjPGH+JGBg9ZHUAlglnCgBANM4TwDYk6QWAPuNn33nSSPvr7v5ziTJ1ps3j0z7y9Pev6LlgnlydQcAe2vr//76yPtdN965e9hNO+Y/PXTFywXTSGoBwBryU+942cj7OxZuSJJsWbhhZNp7n/6SGn/OWPyNNf7GkWnvffqmZSkv956rOwCA/ZWkFsA+8pq/OHHk/fYdO+tw88i0X/n5D6xouWB/85S3v3L369sXtidJtixsHxn/nmf86oqXCwD6XCkLy09SizVBhwIAe6esXz8yZG366Qu+OvL+xoU7kiRXL9wxMu28079vRcsFQ+JKWVh+kloDdc2rRhMzu7bfuHvYn/bA57sdJNGhwEo5dH1J0tThvferbx99Rta1C3fW4eaRaa98hmdkrVZ+VBieQ5/21HkXIaec/+6R97cs3Jwk2bJw88i0i545/7ICAPMjqQXAPvPwp+hWGOVHBQAAlotvH7Cf+NCf/9TI+1tuuqMOt4xMe9Ivv3dFywUsr596x0tH3t+x8K0kyZaFb41Me+/T/98VLRcALLeL33DtyPvbb9q1e9ifdsIvHb2i5YJpXMG+763JpNZ1r3rzyPtd23fsHvanbXz+z61ouWBINhze3mbWDpfH+iPaZbRDGKaT3/kbI+/vuPm6JMnmm6/bPe19p71ixcsFLJ+nX/B3I+8XFm5Nkly9cOvItHec/rgVLRcA8+UK9n1vTSa1gHvv6U88eNmX8ZQnaqL2N//l/NFnZF1fn5F1/cLmkWn/85mekQUAAMzmG+NeuO5V/3fk/a7tN+0e9qdtfP4vrmi52Lc+9+qnjby/ffutdbhl97Qfft67pn7+yHrF0pHLeOUS3F3nvOXEkfc37thZh5t3T9v0rA+seLlWq7KhpKlDAFbOWrgl5y/eft3I+x0Lu3YP+9N+/hkbV7RcsJpd8/KLR97v2nb77mF/2gNfeMKKlmslrYX28d6Q1Fqjjlp36MiQe++Xfsy2HBodBOMOfvph8y4CsAqddsHHRt53txNuWbh1ZNo7T//xFS3X/sQtOfPnvAhWJ+3jbJJaa9TZj/v+eRcB5u7udhCHHz46BJikrD9iZDi0+a8EX57ZH7zu7dcuGfMfn7FyDyi/8K+vH3l/88Jdu4f9aaeecdTEz/vizEq45o+/OPJ+17Y7dg/70x74Wz+wouUasmv/bPRuol3bb9497E87+gWjdybtLyS1VqmNh68bGQLz9xNPPnDeRQAG4NBTfvJuxZf1h48Ml5z/005aOmiZPe38t4+8v3VhIUmyZWFh97R3PfMZUz/vy/P+52fe/o2R9zfUZyZevXDnyLS3PeM7V7Rc7Dsf/qvR2ydv2bFr97A/7Sf/g9sn76mjDjtqZAgsTVJrlXrx4//9vIvAnH3sz58y8v7Wm26vwy0j0378l99zj+b/vtf/1Mj7W266ow63jEw7+bnvvUfzZ2l/+ubR511tq8+72rZj88i0X/u5yc+8Oqz+d8jD/HdI7o0N90mpw72LP6zGD/dWzbubRFpuhz7tyfMuAgATXPKqa0be37F91+5hf9rDnv/AfbK8//wjZ++T+cBaIqm1AlxiDyyHx5zsyrGlHLi+Tfy1w32vrD+ofbD8+uF2p4ec+kN3M374D2I95JQfm3cRYKYD1t9vZMjKc/7OPXHp/xlNgt25bdfuYX/ad//6vkmCrYSj1j1gZMj+Z+O6DSPDoRnuWfiAuMR+39ubE40vvfKU3a/v2H5LHW4ZGf/9v3rR8hYUmKv7n7a83dzBT1+Bk9L1h7ZXRq33zygmKRvWjQyXZRmr7Mou9n+Hn+I/ai/l0A0bR4b72mo4f//rC0afkbVQn5G1sHDXyLQzTnerGsvn7Ef95ryLcK9sXHfkyJA9vfixp867CPeKpBaDtBpONABWwiGnfc+8i7CqHXLK41ZgGT+x7Mtgvg5Yf9/cVYdDtdauLPreUzfdrfjDNhw1MoQkOXLdxpEh995Rhz1gZDhvZz/GjwT7O0ktmJO1dvIJwMp46vlvHXl/28KOJMmWhR0j0979zDNXrExPO//Ckfe3Ltxcy3TzyLR3PXM+vxave9rKbYvl4ge/2X7o1BfNuwisQs/78RfPuwj7nbN/5AXzLsKqc+3/+fDu17u23bJ72B9/9K8v/pOZa//0fSOfH/lMnXb0r528bOUdGkktmBMnn8D+6Clv/6OR97cvbEuSbFnYNjLtPc/47ZUsFgBrwJGHbxwZAvs/SS1gRazFK9MOr/+d8HD/nZCVdHf/myEA7Cd++Ylr68qrtXh+zfxtXLd+ZDhvklrAilgNV6ad+8Ynj7y/6aZddbh5ZNpzfuGD+2R5P/Zk/51wKYfU/054yDL9d8K16JBTHzHvIgAAK2A1nF+z9rz4cU+ZdxFGSGotk+te/Zrdr3dt37572B+/8Xm/suLlGqqv/dnoMzbu3H5zHW4ZmfZvXjD6zI5748jDy8gQ2Pce+rS11w2V9QenqUMAmJf3vW30vyveUv+74i0Ld41MO/lnPFx/f+HKLvZHa+/bxAQb1x0xMhyijesOGxkOzQPWHZDkrjpcHc56vFt3YLU5eEN7ZVc7HKaDTzt+3kWAwSnrN4wMh6asv18OqMN95fQLLh55f9PC7UmSqxdu3z3tgtNP2GfL29d+5R1XjLy/dmHn7mF/2mue/u0rWi7Yn+0PV3ZtPOzIkSFIaiV5yeNPnHcR7rWzH796T1r2xn/69+vmXQRgAB58im4L9gdl/fqR4VIOe9rpy1mcZXf4Kc+edxHutQPXP2BkyOq3fv3GkSGsRnc3SXX2Y355ny7/mld8Yq/iHvgbj92ny11OG9dtGBnu73w7YE24/7oyMpyHT7z2qSPvb9t+Wx1uGZn22LPevU+Wd9/Dk6TUIeyd//a2vUvy/+7PfGCZSwLsz+7ztKfPuwjcTRtO+bW7FX/ghqNGhkPwJ+/Yuvv1toVdu4f98b/59GNWvFz31NOf+pJ5F2HNe8C6jSPDe+vqP7x65P2uG3ftHvanPeh3HrRPlrcSzn708+ZdhBW1cd19R4Z79ZnDN4wMl/Lixz7j7hdswCS17oH94XbFteb5jxvmbZn3xpk/fsi8i5C3/9+Tdr9euOnOOtw8Mv4Zv/j+FS/XauW/JQJDNfTbA9ei5X62zn1P+a19Ps/9zREbNo4M93f7w/Oc7m6S6oU/evZyFocBevGPPusefOaMZSjJ/kNS6x54yeOfeLfiNx5++MhwiI5ad8jIEPYHRxwxOpy3Hz3Rf0tkbXrK21+xZMx7nvEbK1AS7qn7PO2UeRdhzTtg/ZEjw6XsD8/WGbonn3L3rqQa+u2E92SfW22JsBc8fnhJqq3/85u7X++6cefuYX/8Mf/lO1a8XPuL1baPrkWDT2qVUk5K8idJDkzy503T/MGci7SHlzz+x+ZdhHvtdx77XfMuwoj71wfL338VPVie4XnST0oiwXIrGw4bGcJKOGD9htxVh8sz//vW+e/97SPL7YhTZj9n5pkXfHHk/faFO5IkVy/cMTLt/NN/YN8Xjn3iqXczCXZ3bThi48hwKfetcfedEf/xN1+3+/WtO3btHvbHP/7npn9e8pXVzj46f4NOapVSDkzyZ0melOSqJP9YSrmoaZpL5luyteeqP33uXsUd92uv3yfL+43Hrr0vR/c9vIwM97UN9RlcG4Z7QWGOqLfvHeH2PVg1DjnlR+ZdBNagw57208s6/3Wn/Oyyzh/m4Yyn3L2k2bNO3vdJtk+ee93I+9tu2rV72E17zHOGeaXacjjqsKNGhqyMa17xN7tf79p26+5hf/wDf+MJK1uoNWzQSa0kj0pyadM030iSUspbk5yaRFKL/c7PLvMzss74ieHfWnqSK68A9nB3/9MgAMPwoke+aN5FgLkbelLr2CRX9t5flcRPwgzC/eoVV/dbpiuvVpsN9SqqDa6iAlhRhz7tp+ZdBFahu/sMrrVmXf2vjesG9N8bV7v71dsU77eXtzfe088wnSu79r2N6+43MmTllaZp5l2Ge6yUckaSE5um+eX6/ueTPKppml8fizsryVn17b9J8rUJszsqyfV3Y/HLHb8SyxA//2WIn/8y1lr8SixD/PyXIX7+y1hr8SuxDPHzX4b4+S9jrcWvxDLEz38Z4ue/jH0V/x1N06xsFrppmsH+JXlMkg/03p+d5Ox7OK+LV1P8aizTWotfjWVaa/GrsUxDj1+NZVpr8auxTGstfjWWaejxq7FMay1+NZZprcWvxjINPX41lmmtxa/GMq21+NVYpnuyDsv1N/R/HfePSR5aSnlIKeWQJGcmuWjOZQIAAABgmQ36mVpN0+wspfxakg8kOTDJG5qm+cqciwUAAADAMht0UitJmqZ5b5L37oNZvXaVxa/EMsTPfxni57+MtRa/EssQP/9liJ//MtZa/EosQ/z8lyF+/stYa/ErsQzx81+G+PkvYyXWYVkM+kHxAAAAAKxNQ3+mFgAAAABr0Kq9/bCU8uAkb0pyTJK7kry2aZo/KaXcP8nbkhyf5PIkP900zY2llP+Q5DVJDk7SJHl90zS/Vko5NskXktw3yR1JXtc0zW9Nik+yOclZSR5cx92e5HVJfifJO5I8scbvSrIlyZeSPCzJA5McknZ77kzy1SRXJnlkkqOTlLpat9YyfCvJg5IcVpdzfdqH3o/H35jk0jru29MmIe+q8T9fy/WEGr+zlv+wupwHd5syyQ21PEnyA3XcXUm2JbkzyYYk96llWSp+V5JDkxwxNv8jktyU9l97HlDjttXpByU5vM7/rlq++9TpG2t8k+Tmuh239OqgH39DkiPr8lPX+bYad2BdzkH1/a66PQ6s2+6uuq631LJeWZd9eG+7frMXX+p8vlU/tzPJt9XxJcn2Wp4Da1m7ddhe4++q22LWOidtXd6Zdh8qtdzT4u9Ku0/06+yuvYifVGe7allvHiv/eB2Mx29PcuyEOtiV9tg4qH4+dftcleR+Y9v01vq569Lu20fUabcm+ZdefFdnW7O4X3fzKWmPv7tqfLcO/W3a1dmsdd6V5J/r9r9v2vpsZsT359/V2V0z4ifV2fj8+9u023f727TftuysdXNQ3RbdvG6vf9dntG3p9usuvrMzyUvTtnf9tuVbaY/jg8bW+Ya6vb8jo/vFjUnW1c/0j+UbavkPq9Nnxe9K8uokP5Hku2odLDX/7Wn3hwdk8ZifNf9pddzF35jk/rUOStp2+ra0++auWp6l6uDWGndt2v16Wh30l/3lOm68Dv4iyRlp94uuP7gpyVfS7tPj7eMtdb2OqrHdcTatPR2P7+Z/RxbbiqXiu7alq+NZ8Xek7WP7dTweX9Ieywdnsd5uSXvM7MxiG9/1mbfVuGNr7IF1OQfVbbgui+37zfUzXXy3X3dxO2ts17ZsS3t+8LNjdXBD/Tu0jp+1zrvqNp3W/n4syffV+aSWYUdG+8xZ8ZPqeDz+e9KeQx3cGz+tvf5Y2v12w9g2mtZef7OWdzy+32d2264k+WKSP6x//XXYkdE+c9Y6l978J7W/4/FdHUxrf6fFTzvP2ZG2XrpyzqqzLv6wLO7Xk+qg375vqetz4Iw66LfvC3V7dPHd+cN4n9k/d3xdkmf35j/pPGfWOt+Z9rx0Uns6KX5WnU2Kn1UH36rrtC5te71UHXwri23I/er6dvtj0u7DD8zoueP1dRkHp93fD+xtv8vr5/rnjjenrZeD67wOzGL73u8z+23Lq5L8XJLjenXQP9+f1V43dVsu1Z7240vdpv0+864l5v+vGa3j8fiS9nz8gDq/OzPaZ941Y5seUOfZ7zPvP2Wb9uPH+8x+2/I/kpxTt2l669zvM7vxu5J8NO2xf1wtY1PLP609HY/v5t/vM5eKH+8zZ8VPquPx+O+p69Ztn7uyZ5/Zte8frdvyyDpsav30+8zDM9q+31Tju2MkGe0zu9hZdTDeZ85a57uydPu7Lu3x150rL9X+9uNn1fGk+Fl13MXfJ+2+29VB175330vH2/dddVxXB01Gv5eOt+/X9+IP7tVBv4/tt++vSfKc3jZNpvex/XU4Mu1xt66Wc13aeu++y6+vsQu97fieJJ9P8uI63w1Jrk7y52nPXw/J4nf/Lv6zSV5St817mqbZVEp5VBZvYSxJfq9pmnckSSnlkUneWLfJe5P8ZjPrFsN5//vFGf8i8kFJfri+Xp/2y+7D0h4wL6rjX5TkD+vrJyV5cn39qFqBD0vyR2kTYqkb8sokJ0+I35n2i8+fJHlL2gb9d2v8X6Y9EfjBtImnx6ZthG9P8iNJ3lUr/SFJLkvbMd1Ul3FKklOT/Eyt2M+mfbD9Qtqd7iV1R7m5xp+YNmHXlemmJO+rf5fXMm2t8W9O8uS0O/Tvpd1hb+utw5VpTyCvTPLu+pknpz0wbuit88VpO99p8UcluaYXf0naxugbSf5rjf8PaRvsk+s6XpbFL5OXJfmbJFek3eGvSvILaZNOJ9X4P6jz39Ur/3j8lUl+uC7/V3p18NE6j21J/qlup/fUOjwx7b7z+CT/u9bBpWkPuh1pG+L/J+1BvKUX//m0DUN/v3hPXd5Lx+KfXF+/pxf/minr0F/nX+nFX5Pkw0vEb59QZ9Piv5C24ZtUZ69LW/8XpR4HM+pgPP6H0h4HX03bgXV18Pkkn6rL/Im0jevXe8fmv9RydcfBh2t5bqp1885aHyf34h+fZFNG9+u/rut44Vj8k+v7l2XPY3nWOr+rzr+Lf/sS8ZPqbFb8pDqbtk2/Vrfb9XWbvjvJP2S0bflAXf9uG301bZvQJPm77Nm2bK3z6+KfV7dZ01vn8bali39aLVO3zjfXehpvW55XP9Ot82/01vnSJH+/RPyvZDHBd/1ezP9rWWxbJrV14/GT6ng8/tpaB5ekPe67/fqrtfyz6uDGJL9ct+n/TfK5GXVwadr95sl1G22r5Tkvi31O98X2jBp3ddr9fWedb1cH/WN/e319Uto+souf1p724/8o7b62s27bzdmzvR6f/xsn1PGs+NdNqOPx+M1p2/dz6+tvZc8+tmvfb691cWLaH7n+JskFtQ5+MG3f2LXvL63L3tqL/70s9rG3Jjk/o+371fUzz0x7vNyc9ke2nbVcn1pim76iLn9a+3tj2jbt9LR1fE7afW68z5wVP6mOx+N/vLc93pnZ7fXOtH39k+o2+h+Z3l5fmHYf/Zle/OXZs88cb6+39dbh79MeK+N95qx1fl2mt7/j8f8z7ZeFae3vePy0Ouu3FTfVbTarzsbjr1uiDrZlsW25Pe0+NKsOurbld+uyb+jF/1727DPH2/ddvXW+Iu0PtuN95qx1/mxmt6fj8ZPqbFr8rOOmO9fcXrfZyTPqoIvfXLf/jWnP3d+V9hzh+rpNx9uWK9LuM13b8sEkv5a2j2rS/sjbP3f8H7WsXdvykSS/ncW2pesz+23L1jqta9//KsmvZvR8v9+2bK7l7dqWc2v8tPZ0Uvy/ZM8+c1b8eybU8Xh8116/K8lzM7nPnLRNP5TJfea0bfqxTO4z+23LQl1+t19fl+R/Zc8+szv2t9UydfHnpm0vprWn4/FvTvLH2bPPnDX/386efeas+El1PB7fte/vqvMd7zO79r1LgmxM21Z8MG1/Nt5ndu37e9PW/5m9+Cdmzz5zWh2cXGM2Zc8+c9o6vzWTv7N0bcXmtG1d11acm8nfM2fFT6rjafHT6ng8vmvf35XkBZn8vfQhaZOEt2axfe+26fj30q59/28Zbd8/mPZ73Xgf22/fu7aw26ZfqHU53sdOWodvq9vouWmPty+m7aP+3xr/kCSfrPFX1+92b0p7LP/7Gv+/k7y8xh+XNoF+dS/+rXXeG+v7c+s2WJfkoF7u59re+88keUzaZNf7kpw8K3e0am8/bJrm6qZpPldf70jbUB6bNkF0bg07N8lpNeZDTdN8sI7/x7Q7yfFJnpK2I0mSN6TNhh43IT5pK+yktI3PpWl3iCPSHqAvbJrmC03TfDZtY3R42oNzQ9ps+efT/iL64bSVf2eSjzdNc1HTNBcm+c60O9tVaTvGf0ybJX5DFjOZH2+a5gNN01zeK9O2Gv9LaQ+sL6TNGB+W5NfrOqxL8pNZzPh263BJ2i+6R6Td6W5L8rd1vrvS7iSfTPLdaXfOafEPSXsy2G2jI9N2VF+v631E2l9N7kjbsByT9qBuavyhaXfy29IemIenTax9OW3C8ugkZ/fK1ZV/PP6StL9YrE+b1e3q4LC0B8K3at18KO0JzKW1fg5L22mensVfjx6X9sD5h7QN7GFpD84ufnvaX3i6dT4p7UH7zbQNVRd/U13no5P8dC/+CVPWob/OXWb6K2kz/T+/RPwdE+psWvyDat1MqrMLa50c0TTNh3rln1QH4/GfT3uidkTa7HxXB5elPd7+KW3D/eUs/uJ7ed1eH8jicbA+yffWbXln2hOXg9M2hF3859Imhfv79W/UeX+ji++tw8FpT9D7dTZpHfrr/INp94ku/gVLxE+qs1nxk+ps2ja9X9rj+7C6fl9O+ytWv235gbS/qmzO4pUlu+rf36c9mem3LYf24g9L26m9f2wbjbct90ny8bSd4LreOl+e5NHZs23p2pKr6jr/nxr/6Vru8bZlPP4v0ybmP5p2v1hq/lePlX+p+B/MnnU8Hn9grav1tbyH188/IO0X/Wl1cFjaE4aH1vkcnXa/n1YH69O2pR+q6/BPaU9MfzOLfU73a+Nna+x90n7JSNov5k/Inm3LAWm/uGxP24Z18dPa0378urTJ1aRtD9elTXLNmv8/1Ph+Hc+K/6fsWcfj8ZfWbXVr2hO7dRntY7v2/SNpj/Ur0ra/Nyb5t2nb5111+/+7LLbvb8ji1bVd/JZaR93x8/0Zbd/vk8Vfgj9cy3dJXefr0rZ1s7bpsWn3n2Ry+3tIjb847b5wTF3WeJ85LX5aHY/Hf6Ou10Lak9tp7fVX0h4Dh9f1vbHGTGuv/7Zuu+N78bdnzz5zvL0+sLcOP1Drpot/wpR16K9zd140qf0dj79vJp/nTIufVmf9tqLUz/ztjDobjz8o7fnrtDro2paP1m3Utb/T6qBrW86t8z+0F/+t7NlnjrfvJW29f6iWrduvp7Wn/XXekPZ8cFZ7Oh4/qc6mxc86brpzzQPT7oNLHTefr9tsW9q2/Ytpfzw4Mm27fVf2bFs+W+f/L2nbir9I++P4/WuZjs3ouePfZvFKt5vSJvu/u7fOl6ftM/tty7q07cZV9fz9fUkeUeO3pT0u+m3L+rq8f0rbtnw4bZ82rT2dFN/1Kf06nhX/+OxZx+PxX67b+7y059vjfea0bfqXaffx8T5z2jb9WK2/8T6z37YckuT6pmkuSNtn3pnFK6H6fWb/3Ou2pmkuqHXQ30bTzn/78R/I4t0K/T5z1vy/u8b3+8xZ8T+YPet4PP5jTdN8odbB+uzZZ3bt++VJDmia5rp6fvoXSZ6ePfvMrn3/ai3r+l786dmzz5xWBx+u8+n2636fOW2dL0nbZiWT29/1aROdSbuP/GPac+Zp7e94/LQ6nhY/rY7H4w+o2+u8JA/Pnt9Lu/b9czX2kLTt9V+k/cFi/Htp175/oo7vx2/Mnn1sv33vzlveXbfpgzPanj5hyjofkPZ4+3DaY/mItPvdUWl/KLmkrsPlNf6QUsqhNeY+afMyb63rfkn9e3DavusrvfhLktzRNM11tUwfTnJ60zS3NE2zs47r1iGllAcl2dA0zSfr1VlvSs35TLNqk1p9pZTj017N8OkkD2ya5uqkTXylbRDHPS/tzvCJfnzag3tD2pPi8fikbYC7+KvSdmQb0jbmv19K+Vwp5a/T7kTb0+5gn057id430jbST0p70C8k+WQp5a9LKQ9M+4tDk/bku2t4jk27w3Yn3P347iTz6iTrxsp037QNxn8tpXwu7cHyr1k8kZm0DunKlPaXofenrf/T0u7AfzQj/iVJTqjjPpm203pAjT8hbcPyX+t2/Om0O3rT26br0zZQO2v5ujq4Ku0B/bZeHdzVK/+s+NPTNgL/Jm1Dti7tSXXSZn4PG4v//bo+d3XbqMbfkcX94iu9+KT9defALF5+uyHtl6H79+KPTfKLdZ4X1vjvT3LMXqxDt84HJtm1F/HvyJ51Ni2+O9GYVGf3722fJHnWjDqYFX962v326LTHZ7dfPzTtieBB/fjaKP3nur7fzOhxcGfaDuQjvfn/ftoOc9J+fXgvvlvGdeN1tsQ6vDOLX0Z/PIsn63uzjfp1Nit+Up1Niz+vbseFuk2/r36m26ZPTFvPn097BclH6+dPrOX+WBY71GPT7tf3TXvi8sxeHcxq7+6b5ItN09w+YZ2Pzp5t0Ya0X8S6MnXxB9b3h6T99WlW/IW1/J9K++vMUvP/trpNn5g2mb51Rvw7M7uOJ82/268flLaj7rfv43XwtiTPT3uicGDatvA+M+rgvLRfTF9ct+m6tInNq9O2R6+u839d2pOU7seEg+vw00kOn9BW/E7aX7Pem/bY6+KntafT4j9fp799L+LH63hW/GnZs45nxe+s839okh/LYnudtPV4Wy/++9Mmhf9j2nr+j1n8YeWOtFcAJG3dd/N/fdr9tKQeB9mzff902i9gV6RNLrw0i7ci3WeJdXhQLUMyvf39h7T1uyXtyf+2Gt/vM6fFT6vjWfHbMr29/r66Lv3412d6e31bXfbv9uKvzvT2t2uvf6cu4+q0/cPTs2efuTfrPK397cd/b9rjd1b7249fqs421PmtT3tyv1SdjccvVQc/nrYf7MfPqoNuvz60F/9dmd5ndu37v6T98rqlzu85U+ps0jp8f13mrPZ0fJ1n1dl4/Kw66M41D0q7P125RB2cUN9/V9rz06TdDx5V6+CALJ6/d23LD9Xybkv7pey0OjyxTv9ERs8dv1rLc9lY/EifmdG2peuPfrCUclD9zM9lz/P9rm05vK5Tv0xPz/T2dFL8huzZZ06LPyOLCd9+Hc+af7dN+33mtG36tLTnzuN95rRtekLaL9eT+syubTk4yZH1u+K31/k+tsb3+8xuvzs0yaGllON7ddDFT2pPZ8X3+8yl4qedF02Kn1THs+LXZ88+s2tbviPJAb34M+o2Hu8zu7ZlS9q+8Z978+/269195ow6aNImMP5D9uwzp63DCVn8Pj+p/T087f711bRJqa7vSCa3v+PxS9XxrPhJdTwp/lHZ83tpVwfdcdCP/8ns+b20q4PuOOjHH5rZ33G6H2kfVEp5QNo+blYfO20dbkhb3913hqvqOjxmLL47ln8r7VV5XZ91VV2HS+t6d/EnJDlqbL99cJKUUn6klPKVtP3x82qSq/8jR3rznWrVJ7VKKUekvUTyhU3T3LQX8Sekvfztt/rxdQO+JcntTdN8Y0L8J7J4opa0B+4L0zayD0ry903T/HDaHe7laXeuN/eW0cVfWF//YY3/ZNpfIx+X9rK7P0m7A3efeWt9/bKx+O9Km0ntfunr4l9UXx+S9kD+pbQ77M+l/UWiSyb1y3Rn2gOgK9PmGv/pJL+edmc7e0Z8l/m/Jcl/yWIHV9L+Knhr2p317LRfhL+e9qDub9OuXCdntA5+NO0XvpfX+HGT4j+d9rLzhSR/lsVnrXTxr8jiNv7RtCdwj0x74v7O3rRuG70lbYe6o8a/Je2lmRtrmb4ri8/D6LZRF9+k/RK8Pm1H8rdps+aHLbEO/XX+d2m3+az4L6W9XHu8zqbFfyZtAzepzl6YxUvdk/YS42l1MC2+q4ND63bo9usu/sNZrPMzk7ylHmv3S/uLQ7/OStrOcmddhzOzWGe/mMn79ZN78d0yzsmedTZrHS5Ju59fksVbOf7XXm6jfp1Ni59WZ9PiP17n+YC0+9elWbyv/4VpEyKHpr0V4sws/mr6uLRfvrtfuboyvau+fnZG6+DlWXwWWj/+Rb348XXenjYR3m+7XljL+596Zeri/zbtbRtJe/n7tPgb0/569HdZPAleav7fUef//6Xdh35+RvxSdTwe39+vb83iVcHT6uCv03bmB6T9lf7zWfx1b1IdvDHtJdq/nzYRc20W9+3rs3jp+plpr7o7ub7/k7T73R0Z1U0/PW3b9stpv7B08eNmxf992kvP7+gdV7Pix+t4Wvytab8cjdfxtPir0iYud6S9svBRWTyWT07yp/V9F//VtFd4Hpu2vv4ui+1v18c2vfh/qvN4eS3b3/Vi++37Q9ImFL497QngwXWdL89iHc9ah2l95gvr6+9Ke8L3bWl/Jf3e7NlnToufVsfT4q9K+xyfae31R9K2yV18t43ul8nt9fOzeNtKF395pre/T87ibRE/lDYZcWjaY3+8z9ybdZ7W/vbjr8nS7e/4NppVZ3emfcbak7J3dTYev1QdnJ/2vK6LX6oO3l2HXfyWtL+YT+szu/b9ht46fzztl9lpfeb4OlybpdvTfvxSdTZpGy11rvn5tOeYD1miDrr4F6RNZn6ibvfvrXVw59h3ipPTtgm3pG2vfj/tPn1E2j5251h8SfsjxB1p26h+/Msz2meOty2fSPtF8O9qGQ/Pnuf7XZluT/vsy65Mh6ZN8sxqT/vx3fzH+8xp8bel3Q/H63hafH+b9vvMadv0irR1N63PHN+mX69/k/rM3eeCtUxvS/uD0F1pr5oZ7zP7x/7HanxXB1187kb8eJ+5VPy086Lx+Gl1PC1+R9p6He8zu/gPpj3Wuvgr0yYSp/WZz6nb8H/VaZdncb/u95ld/HgdfCI1kZY9+8xp63BTLfOs9ve2Wo5uu6zP7Pa3H79UHU+Ln1bH4/HdcTD+vbSL777rd/E31/JP+1765vr64CzWwdcyvY/t2vdfq9v0/Wnr9AGZ3sdOW4e/SZvAestY/FvH4q9Ie370trR97D9l8Xtpk7ZNv6MX//W0P9Z2dX552jpO0zSfbprm4Wn7ibNLKfepyx3XTBi326pOapVSDk6b0PrLpmneXkdfUy9J6y5Nu7YXf3zayntN0zSvGot/bdpkzpWT4tNeavjgXvyT03awV6Q9yN5RSjkubYb7wLTZ82u6ZaS9DHBzkqem3eivrtP+IW3Dfn2SbzZN8/Ia/51pG4DvmxL/mrSXJX8zyZ21TCen3YGu7MqU9vbKh9TPfl/aBNwhvXW4Pm0935bk1XWdn5d2Z76wW+e0Hc6s+BvT3uv+hrS/uGzpbaMrm6b5ai3D+rQH17beNt1R3x9by93VwffVZb26VwelVwfT4l+W9qqcT9VyXFTX57i0DePb0u4X3bb9zrQZ9ztrmb8nbad9aNqTqW6/6OK/lfYE8zVpf3X/57QH5va0J/FXZfGe4XVZbFheXdfhyiTNEuvQrfMXa1m27EX85gl1Niv+VZleZ99KklJKdyvVtDqYFt/VwS1JNvf265PSJmM+neTWGn9Q/fzf1vFf7dXZd6b9wn9jkoVefFdnt2fyfn1j2s6jX6Y/GK+zJdbhf6VtUP+qbtMtaU8GltpG43U2K35Snc3apt+sdfB/0nZCC1lsW86onzuibqOvpe0APp321/fj0naUXdvy/Wn3iy6+q4PXpO1A++3d7ralaZp/HVuHS7P4jINrJrQtz67LGN+PjqnLfNaM+P8n7cnDD6S9d/+AUso/7OX8N9aYJ8yIn1XHk+K7/forSe6qt5v32/fxOnhg2uRrVwfnpf0SMKsO/lvaL6t/kLadXRjbj66p8zg87bF4dB3/gbS3MNw81lZ0l3Lft9br03rxk9rTafGHp93frrgb8+/X8bT4n6jD8TqeFH9n2i+QJW37flWtuzuy2L5fUt/ft9bnB+vnP1Xr4K/TtuNd+35b/eviP572l+kL6vJ31W3eb9+vq5//fNpjY2NvnS9LsmsvttGVmd7+3pQkTdP8a9qT/8fVdR/vM6fFT6vjSfHH1vkfmOnt9Za0V9H8a/1ltttG09rrOyfE/1ymt7831mV3ZTqibq8rs2efudQ6T2t/x+NPqfU+rf2dNP9pdXZ93X6XNE3zsb2os/H4pepgc9qrGm6vtxQtVQdnpT2e+vE70t7iM6nP7Nr3a5Pc2lvnJ9VtManOJq3zSWn372nt6Xj8rDqbFj/ruOnOOW6oZV/quLmyfhf4x7QJnZNrvX8qi1+OrsnouePmtMf9L9TlPbbWwV29+P654+UT4kf6zOx57tikTRw8K22f8oXseb6/+9yuaZp31WW8qNbBnZneno7Hn1Ffj/eZ0+JPnVLHk+L/S2+bjveZ07bpwWkTo+N95qxt+n2Z3mfemGShaZp3NU3zI2kT2Q+s23S8z+yfe11e4/t1MOv8d1L8eJ+5N/MfPy+aFD+tjifFn1HrYFv27DP75+M3N03zI03TPCaL3xfG+8z++fhNvfhuvx7pM2fUwX+vddDtd/0+c9Y2+nymt78HJHlm0zQ/WON/su4L09rf8fhZdTwrflIdj8d3ddC1Lf3vpf1zxyubpvnBuk3Xp01cjn8v7Z87XtGLvy7tcTipj+2fv7+ut1+kVwfjfey0dbgwbft6SD2WU+M3J3lGF1/LdEva8/dLah2cl/bRRcfV9XrHWPzXkvxdb7/6WtpE1241l3Bz2h++rsriPx1Ifb0lszSr4KHwk/7S7nRvSvLysfH/M6MPij+nvr5frYh3T4j/aNqD8exp8Wkzsl9M+4vOP6TdiV+c9lf7t6btbL+Y9uTiX3rxh6a9/e+O+v7NNeYn6jIW0h5sH+zFv7buCNsmxN+cZGst07+tMR9N2yjtSntFRr9MN6Q98P46bYdwa28dbs7iLQH9+K/UuMfWMn0q7Q47Lf4LaQ/Qh6e9tP3aus63pL018Zy0JyQ76nzfnfYk4ll1/pdl8X7pq9NeqfCQLP4Hs34d7OqVf1L81rQZ3tf26uCv0x6wXR28JO2Bvy3tFVc31DI9vJbjK/Xzt9WY350Q/8Faph+o4z+ctnHYlfZS9tdk8aq9W+v2+4m6jCvS7iPT1qG/zn9e12nWOt+a9nkCV0+os6nzT/tleLzOuv36vWlPEv54iTqYFL81bSb/0xndrz+X9oT6oFonn0t7EvIHaTu9D43V2d/X8l8wIb5/bI7v1wtpk46fq9O7dZhUZ9PWodtGb037y/YX0/6ycf1ebKPxOpsWP63OZm3T1/Tq7Itp99V+23LO2DbalsXj7P1pT4z7bct4fLcOt2SxvRtpW8a26ddr/Twio+3j7rZiLP59ab9MdfX8tbTHybT4/n53Sd3+s+b/nrQnAt38/yXtrddT5z+ljifFd3Xw8l4dT2rf+9t0W9p2pKvj/16Hs+qg208fU+PekPbKxG6/eFMWL9W+oc7/r+o635T2Evh+W/HqtMf3FTX+X9K2B9Pa00nxn8risbxU/KV13Hgd7015+nU8Kf6Lab/YfbE3/7fXaV0d/GGtv8vTXkF1a9q+p6vjX067T3Tt+6T4f6318creOvfb97/I4rPSttXXr6xluibtlTBLbdMuflL7+5Fapu/qxW/Nnn3mrPhJdTwp/ot13a/O9Pb6XXX7bkx7HnJr/cy09vrdk+JntL/np/0V9+reOtyUxeeQ9PvMpdZ5Wvs7Hv+NOm1a+ztp/pPqrGsr3j9W/ml1Nil+Vh10+/UFtWxL1UHXtkyK/9cJfWa/ff+zsXX417pNx+ts1jr/c6a3p5PiJ9XZrPhJddCda/5ZHXdN2nOKaXXQPzf94bqMr6ZtJ96YxeNmvA5ekvbKlW+kPZ/qribpx4+fO47HX549+8zxc8fL07ZF29IeB9+T0fP98XPBE+pnbkx7jvc9md6eTor/kezZZ86Kf0/2rONJ8V/ubdNpfeakbXp79uwzZ23TT0zoM0fOBdOe796vrsPOtF/Sx/vM/rnX63vx/W067fx3PP6V2bPPnBZ/U9o7KMb7zL0pT7+OJ8V3dfDW7Nln9s/H/6luw++o2+ea7Nln9s/Hx+M/lj37zGl1sD1tYrTb7/p95qx1fnSmt7/vzGgdb0/bp0xrfyfFT6rjWfGT6nhS/Jfrer0to+11/3vpm5P86YRtOv69tH/uOB7/iQl97HhuoL8Od6RNUo73sdPW4bK6jfrH8v+p8V9Me6X10bUMR/bqqX8svy5t2zAp/gtJHj32/nvSHucH9dZ1S5Kj6vt/TLtflLTfL35qZu5o3smrGUmtx6bNHP9TXfEvJPmptJfSfSTtl62PJLl/jX9tFv/Fdff3rLQdS5PFZ2t8Ke0B3MXf2Yt/W634bnwXv6lW0l1Z/NeUt9adZkcvvqk715YsJrOaLP6r0m76db3xu+q8bkvbqffj70rbmF0xIX5z2oaii+3msZB2Z+9im178Hb34O+twoTd+VvyuOry5/nXxXXm+mbZB7LbFbWPl6sbfXv+2jsV35en+JsXvGCtTV65u3G1pD/y76ra5Ou1lmv39oivzZXV8F991qOctUQe7evEvT9tZdfPppt3YGz9rnW+t8Rf36mxW/B0T6mxWfLdu/TrrtvPOLP5b8aXqYDy+29eb3rRuPrf1xnf18bmx7dkdj5eNzaerm/ePbaM76ryumBB/Qxb/xWx/GVeNbdNp69ztEwu99VoqflKdTYufVmfTtmlXZ7syetx0+95Vta67X5q742zSNu2OzTvT/vrS33+7+G0T4q/I4v7b36Y7srh/9duKK2q5++Xv9rvblojvt7+f6Y2fFX9P5j9ex5Pi7+yNu6tug0nt+3gddHXcxX1ziTroH2NXZM/2/Za0ybUbJ8RvzuS2YnuvrN2+Nas9HY/vhjvGtt20+El1MCt+Uh2Px28bK8us9v2mjO6n/eNuZ2+bdvF3pv1l+Ftj4+9Ie2I2qX3/SK8OxvuDSe37+DpP6jP7x/6dvTLfVecxqc+cFj+tjsfju2lLtdddH7CQ0fWd1l5385q2jSa11zfX7TS+jcb7zGnrvFT7Ox6/VPs7Hj/tPKdrF27I4v47rc7G47vlTauDfttyY53P+LE2rX2/o36+O3fcmel9ZtcW3ZDRdn9SnzlrnbtjbVp7Oh4/qc5mxS91rvmtjO6j0+qgi+/2ufFjpN/29duW7nx2x5T4bn/v99XfyuTjoCvveNvytSnx09r3LnHfr8dZ7el4/FLt9aT5Tzov6sdv622XSX3mrG06rc+ctk1n9ZldfNcGj6/z+Dbtjr/+MbirN21aezoeP63PnBY/rQ6mxU+r4/H4pfrMfptzZxaPqWl9Zv+42Nnbpt2yx/vMaXXQ35bjfea0dZ70naXfVtzc2xbdOfKs9nc8flIdz4qfVMfj8ePtw3gd9NvIru4XevOY9L20vz3uzN71sf327sbeuEl97LR12JY213J72gRSN59tY/O/LW1/cUHaO7m+msUk2tfTJqVvrvPq+rSvpT4GJW0S74okF9T8zc+nTep9Ie33xdN6uaAT0u5v/5r2IoAyK3dU6ocAAAAAYDBW9TO1AAAAAGASSS0AAAAABkdSCwAAAIDBkdQCAAAAYHAktQAAAAAYHEktAIAVUkrZVUr5QinlK6WUL5ZSfruUcsBYzIWllE/W1yfW+C+UUhZKKV+rr99USnlCKWV7b/oXSik/OZ81AwBYeaVpmnmXAQBgTSilLDRNc0R9fXSSv0ry903TvLSOu1+SLyVZSPJTTdNc1vvs3yT5z03TXFzfP6G+f+oKrgIAwKrhSi0AgDlomubaJGcl+bVSSqmjT0/yriRvTXLmvMoGADAEkloAAHPSNM030p6PHV1HPSvJW+rfs/ZiFo8bu/3wu5apqAAAq85B8y4AAMAaV5KklPLAJN+d5BNN0zSllJ2llEc0TfPlGZ/9O7cfAgBrlSu1AADmpJTynUl2Jbk2yc8kOTLJZaWUy5McH7cgAgBMJakFADAHpZSNSV6d5E+b9j/3PCvJSU3THN80zfFJHhlJLQCAqdx+CACwcg4rpXwhycFJdib5iyR/VEo5Psm3J/lUF9g0zWWllJtKKT/SNM2np8zvcXV+nf/WNM35y1JyAIBVprQ/DAIAAADAcLj9EAAAAIDBkdQCAAAAYHAktQAAAAAYHEktAAAAAAZHUgsAAACAwZHUAgAAAGBwJLUAAAAAGBxJLQAAAAAG5/8HeHufhB98WowAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def barPlotLow(x,y):\n",
        "    plt.figure(figsize=(20,10))\n",
        "    sns.barplot(df.DATE,df.low)\n",
        "    plt.show()\n",
        "def barPlotHigh(x,y):\n",
        "    plt.figure(figsize=(20,10))\n",
        "    sns.barplot(df.DATE,df.high)\n",
        "    plt.show()\n",
        "\n",
        "barPlotHigh(df.DATE,df.high)\n",
        "barPlotLow(df.DATE,df.low)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<h1>Data Preprocessing</h1>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "pnqXc0VfDGZQ"
      },
      "outputs": [],
      "source": [
        "X_value = df[['high', 'open', 'close', 'volume','adj close', 'avg high', 'avg low', 'avg open', 'avg close','avg volume', 'avg adj close']]\n",
        "y_value = df[['low']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kKIA7Ij3DGZR",
        "outputId": "b860b601-3022-4e6b-974c-2a3591dcd1f6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "MinMaxScaler(feature_range=(-1, 1))"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_scaler = MinMaxScaler(feature_range=(-1, 1))\n",
        "y_scaler = MinMaxScaler(feature_range=(-1, 1))\n",
        "X_scaler.fit(X_value)\n",
        "y_scaler.fit(y_value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "w6wtTSTdDGZT"
      },
      "outputs": [],
      "source": [
        "X_scale_dataset = X_scaler.fit_transform(X_value)\n",
        "y_scale_dataset = y_scaler.fit_transform(y_value)\n",
        "dump(X_scaler, open('X_scaler.pkl', 'wb'))\n",
        "dump(y_scaler, open('y_scaler.pkl', 'wb'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "2FFkh9NXDGZT"
      },
      "outputs": [],
      "source": [
        "# Reshape the data\n",
        "#Set the data input steps and output steps,we use 30 days data to predict 1 day price here\n",
        "n_steps_in = 3\n",
        "n_features = X_value.shape[1]\n",
        "n_steps_out = 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "M3kBswLPDGZU"
      },
      "outputs": [],
      "source": [
        "# Get X/y dataset\n",
        "def get_X_y(X_data, y_data):\n",
        "    X = list()\n",
        "    y = list()\n",
        "    yc = list()\n",
        "\n",
        "    length = len(X_data)\n",
        "    for i in range(0, length, 1):\n",
        "        X_value = X_data[i: i + n_steps_in][:, :]\n",
        "        y_value = y_data[i + n_steps_in: i + (n_steps_in + n_steps_out)][:, 0]\n",
        "        yc_value = y_data[i: i + n_steps_in][:, :]\n",
        "        if len(X_value) == 3 and len(y_value) == 1:\n",
        "            X.append(X_value)\n",
        "            y.append(y_value)\n",
        "            yc.append(yc_value)\n",
        "\n",
        "    return np.array(X), np.array(y), np.array(yc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "nGTozT03DGZV"
      },
      "outputs": [],
      "source": [
        "# get the train test predict index\n",
        "def predict_index(dataset, X_train, n_steps_in, n_steps_out):\n",
        "    # get the predict data (remove the in_steps days)\n",
        "    train_predict_index = dataset.iloc[n_steps_in : X_train.shape[0] + n_steps_in + n_steps_out - 1, :].index\n",
        "    test_predict_index = dataset.iloc[X_train.shape[0] + n_steps_in:, :].index\n",
        "\n",
        "    return train_predict_index, test_predict_index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "WGI1l5tSDGZX"
      },
      "outputs": [],
      "source": [
        "# Split the train/test dataset\n",
        "def split_train_test(data):\n",
        "    train_size = round(len(X) * 0.7)\n",
        "    data_train = data[0:train_size]\n",
        "    data_test = data[train_size:]\n",
        "    return data_train, data_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "u84fc5mSDGZY"
      },
      "outputs": [],
      "source": [
        "# Get data and check shape\n",
        "X, y, yc = get_X_y(X_scale_dataset, y_scale_dataset)\n",
        "X_train, X_test, = split_train_test(X)\n",
        "y_train, y_test, = split_train_test(y)\n",
        "yc_train, yc_test, = split_train_test(yc)\n",
        "index_train, index_test, = predict_index(df, X_train, n_steps_in, n_steps_out)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i5Uw-AnEDGZZ",
        "outputId": "1f8d3e8e-7a1f-4c16-9f1d-9e7160966d67"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "X shape:  (1490, 3, 11)\n",
            "y shape:  (1490, 1)\n",
            "X_train shape:  (1043, 3, 11)\n",
            "y_train shape:  (1043, 1)\n",
            "y_c_train shape:  (1043, 3, 1)\n",
            "X_test shape:  (447, 3, 11)\n",
            "y_test shape:  (447, 1)\n",
            "y_c_test shape:  (447, 3, 1)\n",
            "index_train shape: (1043,)\n",
            "index_test shape: (447,)\n"
          ]
        }
      ],
      "source": [
        "#saving dataset into multiple files for ease of use\n",
        "print('X shape: ', X.shape)\n",
        "print('y shape: ', y.shape)\n",
        "print('X_train shape: ', X_train.shape)\n",
        "print('y_train shape: ', y_train.shape)\n",
        "print('y_c_train shape: ', yc_train.shape)\n",
        "print('X_test shape: ', X_test.shape)\n",
        "print('y_test shape: ', y_test.shape)\n",
        "print('y_c_test shape: ', yc_test.shape)\n",
        "print('index_train shape:', index_train.shape)\n",
        "print('index_test shape:', index_test.shape)\n",
        "\n",
        "np.save(\"X_train.npy\", X_train)\n",
        "np.save(\"y_train.npy\", y_train)\n",
        "np.save(\"X_test.npy\", X_test)\n",
        "np.save(\"y_test.npy\", y_test)\n",
        "np.save(\"yc_train.npy\", yc_train)\n",
        "np.save(\"yc_test.npy\", yc_test)\n",
        "np.save('index_train.npy', index_train)\n",
        "np.save('index_test.npy', index_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "XllMu-4ZDGZa"
      },
      "outputs": [],
      "source": [
        "# Load data\n",
        "X_train = np.load(\"X_train.npy\", allow_pickle=True)\n",
        "y_train = np.load(\"y_train.npy\", allow_pickle=True)\n",
        "X_test = np.load(\"X_test.npy\", allow_pickle=True)\n",
        "y_test = np.load(\"y_test.npy\", allow_pickle=True)\n",
        "yc_train = np.load(\"yc_train.npy\", allow_pickle=True)\n",
        "yc_test = np.load(\"yc_test.npy\", allow_pickle=True)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<h1>Using GAN and Bidirectional LSTM</h1>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "29D_4ZTDDGZb"
      },
      "outputs": [],
      "source": [
        "from keras.layers import LSTM,Bidirectional\n",
        "def make_generator_model(input_dim, output_dim, feature_size) -> tf.keras.models.Model:\n",
        "    model = Sequential()\n",
        "    model.add((LSTM(units=1024, return_sequences = True, input_shape=(input_dim, feature_size),recurrent_dropout=0.2)))\n",
        "    model.add((LSTM(units=512, return_sequences = True, recurrent_dropout=0.2)))\n",
        "    model.add((LSTM(units=256, recurrent_dropout=0.2)))\n",
        "    model.add(Dense(128))\n",
        "    model.add(Dense(64))\n",
        "    model.add(Dense(units=output_dim))\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Mhw_mYZDGZc",
        "outputId": "f267bf93-c2a0-4a6f-d203-ff6288758d7c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d_6 (Conv1D)           (None, 2, 32)             128       \n",
            "                                                                 \n",
            " conv1d_7 (Conv1D)           (None, 1, 64)             10304     \n",
            "                                                                 \n",
            " conv1d_8 (Conv1D)           (None, 1, 128)            41088     \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 220)               28160     \n",
            "                                                                 \n",
            " leaky_re_lu_11 (LeakyReLU)  (None, 220)               0         \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 220)               48400     \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 1)                 221       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 128,301\n",
            "Trainable params: 128,301\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "def make_discriminator_model():\n",
        "    cnn_net = tf.keras.Sequential()\n",
        "    cnn_net.add(Conv1D(32, input_shape=(4, 1), kernel_size=3, strides=2, padding='same', activation=LeakyReLU(alpha=0.01)))\n",
        "    cnn_net.add(Conv1D(64, kernel_size=5, strides=2, padding='same', activation=LeakyReLU(alpha=0.01)))\n",
        "    cnn_net.add(Conv1D(128, kernel_size=5, strides=2, padding='same', activation=LeakyReLU(alpha=0.01)))\n",
        "    cnn_net.add(Flatten())\n",
        "    cnn_net.add(Dense(220, use_bias=False))\n",
        "    cnn_net.add(LeakyReLU())\n",
        "    cnn_net.add(Dense(220, use_bias=False, activation='relu'))\n",
        "    cnn_net.add(Dense(1, activation='sigmoid'))\n",
        "    return cnn_net\n",
        "\n",
        "\n",
        "model = make_discriminator_model()\n",
        "print(model.summary())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "MUDfXCC1DGZd"
      },
      "outputs": [],
      "source": [
        "class GAN:\n",
        "    def __init__(self, generator, discriminator, opt):\n",
        "        self.opt = opt\n",
        "        self.lr = opt[\"lr\"]\n",
        "        self.generator = generator\n",
        "        self.discriminator = discriminator\n",
        "        self.cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
        "        self.generator_optimizer = tf.keras.optimizers.Adam(lr=self.lr)\n",
        "        self.discriminator_optimizer = tf.keras.optimizers.Adam(lr=self.lr)\n",
        "        self.batch_size = self.opt['bs']\n",
        "        self.checkpoint_dir = '../training_checkpoints'\n",
        "        self.checkpoint_prefix = os.path.join(self.checkpoint_dir, \"ckpt\")\n",
        "        self.checkpoint = tf.train.Checkpoint(generator_optimizer=self.generator_optimizer,discriminator_optimizer=self.discriminator_optimizer,generator=self.generator,discriminator=self.discriminator)\n",
        "\n",
        "    def discriminator_loss(self, real_output, fake_output):\n",
        "        real_loss = self.cross_entropy(tf.ones_like(real_output), real_output)\n",
        "        fake_loss = self.cross_entropy(tf.zeros_like(fake_output), fake_output)\n",
        "        total_loss = real_loss + fake_loss\n",
        "        return total_loss\n",
        "\n",
        "    def generator_loss(self, fake_output):\n",
        "        return self.cross_entropy(tf.ones_like(fake_output), fake_output)\n",
        "\n",
        "    @tf.function\n",
        "    def train_step(self, real_x, real_y, yc):\n",
        "        with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n",
        "            generated_data = self.generator(real_x, training=True)\n",
        "            generated_data_reshape = tf.reshape(generated_data, [generated_data.shape[0], generated_data.shape[1], 1])\n",
        "            d_fake_input = tf.concat([tf.cast(generated_data_reshape, tf.float64), yc], axis=1)\n",
        "            real_y_reshape = tf.reshape(real_y, [real_y.shape[0], real_y.shape[1], 1])\n",
        "            d_real_input = tf.concat([real_y_reshape, yc], axis=1)\n",
        "\n",
        "            real_output = self.discriminator(d_real_input, training=True)\n",
        "            fake_output = self.discriminator(d_fake_input, training=True)\n",
        "\n",
        "            gen_loss = self.generator_loss(fake_output)\n",
        "            disc_loss = self.discriminator_loss(real_output, fake_output)\n",
        "\n",
        "        gradients_of_generator = gen_tape.gradient(gen_loss, self.generator.trainable_variables)\n",
        "        gradients_of_discriminator = disc_tape.gradient(disc_loss, self.discriminator.trainable_variables)\n",
        "\n",
        "        self.generator_optimizer.apply_gradients(zip(gradients_of_generator, self.generator.trainable_variables))\n",
        "        self.discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, self.discriminator.trainable_variables))\n",
        "        return real_y, generated_data, {'d_loss': disc_loss, 'g_loss': gen_loss}\n",
        "\n",
        "    def train(self, real_x, real_y, yc, opt):\n",
        "        train_hist = {}\n",
        "        train_hist['D_losses'] = []\n",
        "        train_hist['G_losses'] = []\n",
        "        train_hist['per_epoch_times'] = []\n",
        "        train_hist['total_ptime'] = []\n",
        "\n",
        "        epochs = opt[\"epoch\"]\n",
        "        for epoch in range(epochs):\n",
        "            start = time.time()\n",
        "\n",
        "            real_price, fake_price, loss = self.train_step(real_x, real_y, yc)\n",
        "\n",
        "            G_losses = []\n",
        "            D_losses = []\n",
        "\n",
        "            Real_price = []\n",
        "            Predicted_price = []\n",
        "\n",
        "            D_losses.append(loss['d_loss'].numpy())\n",
        "            G_losses.append(loss['g_loss'].numpy())\n",
        "\n",
        "            Predicted_price.append(fake_price.numpy())\n",
        "            Real_price.append(real_price.numpy())\n",
        "\n",
        "            # Save the model every 15 epochs\n",
        "            if (epoch + 1) % 15 == 0:\n",
        "                tf.keras.models.save_model(generator, 'gen_model_3_1_%d.h5' % epoch)\n",
        "                self.checkpoint.save(file_prefix=self.checkpoint_prefix + f'-{epoch}')\n",
        "                print('epoch', epoch + 1, 'd_loss', loss['d_loss'].numpy(), 'g_loss', loss['g_loss'].numpy())\n",
        "            # For printing loss\n",
        "            epoch_end_time = time.time()\n",
        "            per_epoch_ptime = epoch_end_time - start\n",
        "            train_hist['D_losses'].append(D_losses)\n",
        "            train_hist['G_losses'].append(G_losses)\n",
        "            train_hist['per_epoch_times'].append(per_epoch_ptime)\n",
        "\n",
        "        # Reshape the predicted result & real\n",
        "        Predicted_price = np.array(Predicted_price)\n",
        "        Predicted_price = Predicted_price.reshape(Predicted_price.shape[1], Predicted_price.shape[2])\n",
        "        Real_price = np.array(Real_price)\n",
        "        Real_price = Real_price.reshape(Real_price.shape[1], Real_price.shape[2])\n",
        "\n",
        "        plt.plot(train_hist['D_losses'], label='D_loss')\n",
        "        plt.plot(train_hist['G_losses'], label='G_loss')\n",
        "        plt.xlabel('Epoch')\n",
        "        plt.ylabel('Loss')\n",
        "        plt.legend()\n",
        "        plt.show()\n",
        "\n",
        "        return Predicted_price, Real_price, np.sqrt(mean_squared_error(Real_price, Predicted_price)) / np.mean(Real_price)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kefkCbm4DGZf",
        "outputId": "7aa9ef14-f948-4e6c-f221-9a1ff4ab5a47"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n",
            "WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n",
            "c:\\Users\\rowle\\anaconda3\\lib\\site-packages\\keras\\backend.py:5676: UserWarning: \"`binary_crossentropy` received `from_logits=True`, but the `output` argument was produced by a Sigmoid activation and thus does not represent logits. Was this intended?\n",
            "  output, from_logits = _get_logits(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 15 d_loss 1.1378343 g_loss 1.1022853\n",
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 30 d_loss 1.347751 g_loss 0.73151696\n",
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 45 d_loss 1.344497 g_loss 0.7491127\n",
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 60 d_loss 1.3733221 g_loss 0.6942254\n",
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 75 d_loss 1.3657696 g_loss 0.7004842\n",
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 90 d_loss 1.3831995 g_loss 0.67770684\n",
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 105 d_loss 1.3658206 g_loss 0.69924396\n",
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 120 d_loss 1.3895699 g_loss 0.71909\n",
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 135 d_loss 1.389253 g_loss 0.69738346\n",
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 150 d_loss 1.3878703 g_loss 0.6918654\n",
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 165 d_loss 1.3864774 g_loss 0.6746757\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "if __name__ == '__main__':\n",
        "    input_dim = X_train.shape[1]\n",
        "    feature_size = X_train.shape[2]\n",
        "    output_dim = y_train.shape[1]\n",
        "\n",
        "    ## For Bayesian\n",
        "    opt = {\"lr\": 0.00016, \"epoch\": 165, 'bs': 128}\n",
        "\n",
        "    generator = make_generator_model(X_train.shape[1], output_dim, X_train.shape[2])\n",
        "    discriminator = make_discriminator_model()\n",
        "    gan = GAN(generator, discriminator, opt)\n",
        "    Predicted_price, Real_price, RMSPE = gan.train(X_train, y_train, yc_train, opt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "rlt9yphZDGZg"
      },
      "outputs": [],
      "source": [
        "# Rescale back the real dataset\n",
        "X_scaler = load(open('X_scaler.pkl', 'rb'))\n",
        "y_scaler = load(open('y_scaler.pkl', 'rb'))\n",
        "\n",
        "train_predict_index = np.load(\"index_train.npy\", allow_pickle=True)\n",
        "test_predict_index = np.load(\"index_test.npy\", allow_pickle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "yeJIjc2KDGZk"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----- predicted price ----- [[-0.93139154]\n",
            " [-0.9292765 ]\n",
            " [-0.929909  ]\n",
            " ...\n",
            " [-0.9303085 ]\n",
            " [-0.93284833]\n",
            " [-0.9312221 ]]\n",
            "----- rescaled predicted price ----- [[3002.145 ]\n",
            " [3094.6763]\n",
            " [3067.006 ]\n",
            " ...\n",
            " [3049.5266]\n",
            " [2938.4104]\n",
            " [3009.5586]]\n",
            "----- SHAPE rescaled predicted price ----- (1043, 1)\n"
          ]
        }
      ],
      "source": [
        "print(\"----- predicted price -----\", Predicted_price) \n",
        "\n",
        "rescaled_Real_price = y_scaler.inverse_transform(Real_price)\n",
        "rescaled_Predicted_price = y_scaler.inverse_transform(Predicted_price)\n",
        "\n",
        "print(\"----- rescaled predicted price -----\", rescaled_Predicted_price)\n",
        "print(\"----- SHAPE rescaled predicted price -----\", rescaled_Predicted_price.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "89ePRqtkDGZl"
      },
      "outputs": [],
      "source": [
        "predict_result = pd.DataFrame()\n",
        "for i in range(rescaled_Predicted_price.shape[0]):\n",
        "    y_predict = pd.DataFrame(rescaled_Predicted_price[i], columns=[\"predicted_price\"], index=train_predict_index[i:i+output_dim])\n",
        "    predict_result = pd.concat([predict_result, y_predict], axis=1, sort=False)\n",
        "#\n",
        "real_price = pd.DataFrame()\n",
        "for i in range(rescaled_Real_price.shape[0]):\n",
        "    y_train = pd.DataFrame(rescaled_Real_price[i], columns=[\"real_price\"], index=train_predict_index[i:i+output_dim])\n",
        "    real_price = pd.concat([real_price, y_train], axis=1, sort=False)\n",
        "\n",
        "predict_result['predicted_mean'] = predict_result.mean(axis=1)\n",
        "real_price['real_mean'] = real_price.mean(axis=1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "ZYCV6iswDGZm"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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KTZs21SltPXv2tIJfAEhMTMTVV1+NJUuW+DS/BoB//vOf+13etGnTcNJJJ/kEv3YVFRWYPXs2rr76aiQkJFjrKiI4//zzMWfOnDqtBxEREXmJ6BcRUTRhDXAI6lPzGi3Gjh2LHj16ICcnB2+//TZGjhyJU045Bf/5z38AANnZ2dixYweSk5MDfj8vLw8AMGHCBFx77bW46aab8Nxzz6Fjx45ISEjAJZdcUufHKnXp0iXgNIfDgZycHJ/Pu3Xrtt/l5eXloX///kE/z8/Ph9vtxosvvogXX3wx4DwejydmBwcjIiIiIqLAGADHiaOOOsoaBfq8887DMcccg8ceewxXXnklWrRogQ4dOqBPnz7VBp8y9e7dGwDw448/ol+/fhg9erT1mdPpRH5+fp3TlpWVFXBaSkoKOnXq5DNdKbXf5XXs2BF79uwJ+nnbtm2RkJCA++67zyoA8Mfgl4iIqH507S+rgIkoujAAjkOpqal44403cMUVV2DkyJF47LHHcNFFF+HXX39Fy5YtcdhhhwX9bnl5OZKSfA+bb775Bm63u87p2bVrFxYtWmQ1g3a73fj5559x8skn1ykQvfDCC/HSSy9h1apVAWuCW7RogTPPPBOrVq3C8ccfz2CXiIiIiChOMACOU5dffjlOOukkvPnmm/jvf/+LG2+8EV9++SUGDhyIRx55BP3794fD4cDWrVvx+++/Y9y4cUhLS8NFF12EcePGYciQIbj00kuxbNkyvP/++2jbtm2d09KlSxdce+21GD58ODp16oSPPvoImzdvxkcffVSn5Q0ZMgTff/89zj//fAwbNgxHH300cnNzMX78eHz88cdo1aoV3n77bZx11lkYNGgQbrvtNnTr1g25ublYvnw53G43Xn311TqvDxEREQECYR9gIoo6DIDj2EsvvYRBgwbh448/xpAhQzB16lS8+uqrGDVqFLZv344WLVqgb9++GDx4sDVS8h133IFdu3bhiy++wCeffIKTTjoJEyZMCGlwqmD69euHxx9/HE899RS2bNmC3r1744cffqj2CKRQtW3bFvPnz8ewYcPw6quvIi8vD126dMF5551nrcfxxx+Pv//+G8OHD8cDDzyAoqIidOrUCccffzzuvvvuOq8LERERERFFLyVxVjR34oknytKlS4N+vmHDBhx++OENmKL4ds4558DlcmHevHmNnZQmhccxERE1tsd+XoUZG7Ox/JkLGjspRBRnlFLLROTEQJ+x8yMRERERERHFBQbARERERBR2AiDeWhoSUfRjH2BqVLNmzWrsJBARERERUZxgDTARERERhZ0InwJMRNGHATARERERERHFBQbARERERBR2fA4wEUUjBsBEREREREQUFxgAExEREVH4CUeBJqLowwCYiIiIiCKC4S8RRRsGwEREREQUdgx+iSgaMQBu4kaPHg2llPVq1aoV+vfvjw8//BAulyuiv52RkQGlFEaPHm1Nu/nmm9G7d+9aLWfWrFl4/vnn4fF4wpq+559/HkqpsC4zmN69e+Pmm29ukN8iIiKKGoyCiSjKJDV2Aqhh/Pzzz+jRoweKi4vx888/4/7770d2djZeeOGFBk3HM888gwcffLBW35k1axaGDx+OYcOGISEhNstsxo4di9atWzd2MoiIiBoM+/8SUTRiABwnjj32WPTr1w8AcOGFFyI9PR3vvvtu0ADY6XQiKSkp7DWkffv2Devyol1VVRVSU1Nx3HHHNXZSiIiIGhxDYCKKNrFZnUb1dtJJJ6GkpATZ2dlWU+WRI0fi8ccfxwEHHIDU1FQUFhYCAH777TcMGDAAaWlpaNu2La6++mrs3LnTZ3nl5eW499570aFDB7Rs2RKXX345du/eXe13AzWBLisrwxNPPIG+ffsiNTUVXbt2xZVXXomsrCw8//zzGD58OAAgOTnZaspt/92hQ4eiT58+SElJQZ8+fTBixIhqzaVXrFiBM888E82aNUP37t3x4osvhlwy3bt3b/zf//0fPv30U/Tr1w/NmjXD8ccfj7/++qvauvXo0QMLFy7EaaedhubNm+Pxxx+3luHfBHr79u3497//ja5duyI1NRUHHXRQtdrx2bNnY+DAgWjVqhVatGiBQYMGYe3atSGlm4iIqDEx+CWiaMQa4Di1fft2JCYmomXLligvLwcAjBgxAieddBJGjRoFt9uNZs2a4eOPP8Y999yDW265Bc8++yxKSkrw/PPP4+yzz8bq1avRqlUrAMBdd92Fn376Cc899xxOOukkTJ8+HTfccMN+0+FwOHDBBRdg5cqVePLJJzFgwAAUFRVh6tSpKCgowO23347du3fj888/x7x585CYmGh91+VyYdCgQVi/fj2eeeYZHH300Vi0aBFefPFF5Ofn46233gIA5Obm4rzzzkPXrl3x1VdfITU1FW+88Ua1IL4ms2fPxrJlyzBixAikpqbitddew8UXX4xVq1bh0EMPteYrKirCddddh0cffRQvv/wymjdvHnT7n3zyyUhLS8Pw4cNx8MEHY9euXZg2bZo1z6RJk3DFFVdg8ODB+PbbbwEAr732Gs4880ysXr0aPXv2DDn9REREjYHNoIko2jAADsVDDwErVzZuGo49Fnj33Tp/3e12w+VyoaSkBGPGjMFvv/2Gyy67DGlpadY8Xbp0wdixY60a1tLSUgwdOhS33HILvvjiC2u+U045BYcccgg+//xzPPTQQ9i0aRO+//57jBgxAk888QQA3cy6tLQUH3/8cY3p+vbbb7Fw4UKMHz8el19+uTX9qquusv7u0aOH9btJSd5D9ocffsC8efMwe/ZsnHXWWQCAgQMHAgCGDx+OoUOHonPnznjnnXdQVlaGqVOn4sADDwQAXHDBBejVq1fI2y8rKwvz58+3vj9w4ED06tULL730Er755htrvtLSUnz77be44ooralzec889h4qKCqxatQoHHHCANf2mm26y/n7wwQdx9tlnY/z48da0c889FwcddBDeeustvFuP44GIiCjSGPsSUTRiE+g4cdhhhyE5ORnt27fHvffeixtvvNEnqAWAf/zjHz7NixcuXIji4mLceOONcLlc1qtHjx447LDDMGfOHADA4sWL4fF4cM011/gs77rrrttvuqZNm4auXbv6BL+hmjJlCnr16oXTTjvNJ30XXnghnE4nFi1aZK3HgAEDrOAVAFq0aIHLLrss5N/y/36rVq0wePBgLFy40Ge+pKQkXHrppftd3rRp03DppZf6BL92W7ZswdatW6tt+7S0NJx66qnWticiIopmjIGJKNqwBjgUTaCmbezYsejRowdatWqFXr16oVmzZtXm6datm8//s7OzAQDnn39+wGW2a9cOAJCZmQlA1yDb+f8/kLy8PHTv3n3/KxBAdnY2duzYgeTk5KDLNtN31FFHVfs8lPTVNG+XLl2wZ88en2mdO3f2aaYdTF5enlWzHYi57W+77Tbcdttt1T63B+NERETRiMEvEUUjBsBx4qijjrJGgQ7Gf8TnDh06ANDPEj7yyCOrzW/2/zUD56ysLBx00EHW51lZWftNV8eOHes8qFOHDh3Qp08fjBkzJuDn5mBb3bp1C5iWUNJX07xZWVnVgvdQR83u2LFjteDZztz2r7zySsACiJSUlJB+h4iIqDGxGTQRRRsGwBTUaaedhlatWiE9Pd2nb6q/U045BQkJCRgzZozVBxgAfvzxx/3+xoUXXogff/wREyZMCNokOTU1FQBQUVFhBd0AcNFFF+HXX39Fy5YtcdhhhwX9jVNPPRVvvPEGdu3aZQ0cVVZWhgkTJuw3faZFixb5fL+kpASTJk3C4MGDQ16G3YUXXojffvsNmZmZ1WreAeDQQw9F7969sW7dOp9tSkREFCs4ABYRRSMGwBRU69at8cYbb+C+++5DTk4OLr74YrRp0wZ79uzB7Nmzcc455+CGG27AoYceihtuuAHPPvssPB6PNQr0H3/8sd/fMB8vdP311+PJJ5/EKaecgpKSEkydOhUPPfQQDjvsMBxxxBEAgLfeegsXX3wxEhMTceKJJ+LGG2/El19+iYEDB+KRRx5B//794XA4sHXrVvz+++8YN24c0tLSMGTIEIwcORIXXnghnn/+eWsU6GAjNAfSpUsXn++/9tprKCsrwzPPPFOnbTt8+HBMmjQJp512Gp566in069cPe/bswZQpU/Dtt99CKYX//e9/uOKKK+BwOHDNNdegY8eOyMrKwoIFC3DggQfi4YcfrtNvExERNRRhQ2giijIMgKlGd911F3r27Ik33ngD33//PZxOJ7p3746zzjoLxx57rDXfJ598gpYtW+LNN9+Ew+HAeeedh++//x5nnHFGjctPTk7GtGnTMHz4cIwaNQrDhw9Hhw4dcPrpp6N9+/YAgEsvvRT33nsvRo4ciRdeeAEiAhFBcnIypk6dildffRWjRo3C9u3b0aJFC/Tt2xeDBw+2mgl37NgRM2bMwIMPPoibbroJHTp0wN133w2Xy4UXXnghpO1w9tln45xzzsFTTz2F3bt344gjjsDkyZNxyCGH1Gm79u7dG4sXL8awYcPw5JNPoqSkBN27d/cZPfqSSy7BnDlzMGLECNx+++2oqKhA165dMWDAAFx77bV1+l0iIqKGwtCXiKKRimTzFKXUEAC3Q18D1wC4BUAagJ8A9AaQAeAaESkw5n8SwG0A3AAeEJGpxvQTAIwG0BzAHwAeFBFRSqUC+BrACQDyAFwrIhk1penEE0+UpUuXBv18w4YNOPzww+u0vtQ09e7dG2eccYb1LN5YwOOYiIga233fL8ef67Ow6aWLGzspRBRnlFLLROTEQJ9F7DFISqnuAB4AcKKIHAUgEcB1AJ4AMENEDgYww/g/lFJHGJ8fCeAiACOVUuZwuh8BuBPAwcbrImP6bQAKRKQfgHcAvBap9SEiIiKiWmAVMBFFoUg/BzgJQHOlVBJ0ze9eAFcA+Mr4/CsA/zD+vgLAjyJSJSLbAaQDOFkp1Q1AaxFZKLq6+mu/75jL+gXAQBXqMLxEREREFFGMgYko2kSsD7CI7FFKvQlgJ4AKANNEZJpSqouIZBrzZCqlOhtf6Q5gkW0Ru41pTuNv/+nmd3YZy3IppYoAdACQG6HVojiUkZHR2EkgIiKKORwAi4iiUSSbQLeDrqHtA+AAAC2UUv9X01cCTJMaptf0Hf+03KmUWqqUWpqTk1NzwomIiIgoPBgDE1GUiWQT6PMBbBeRHBFxAvgNwGkAsoxmzTDes435dwPoaft+D+gm07uNv/2n+3zHaGbdBkC+f0JEZJSInCgiJ3bq1ClMq0dEREREwfAxwEQUjSIZAO8EMEAplWb0yx0IYAOA3wHcZMxzE4Dxxt+/A7hOKZWqlOoDPdjVEqO5dIlSaoCxnP/4fcdc1lUAZkoYhrXmg9splvH4JSKiaMFm0EQUbSLZB3ixUuoXAMsBuACsADAKQEsAY5RSt0EHyVcb869TSo0BsN6Y/z4RcRuLuwfexyBNNl4A8DmAb5RS6dA1v9fVN93JycmoqKhAWlpafRdF1CgqKiqQnJzc2MkgIqI4x/JYIopGEQuAAUBEngPwnN/kKuja4EDzjwAwIsD0pQCOCjC9EkYAHS6dO3fGnj170L17dzRv3hwcVJpihYigoqICe/bsQZcuXRo7OURERAyCiSjqRDQAjkWtW7cGAOzduxdOp7ORU0NUO8nJyejSpYt1HBMRETUWNn8momjEADiA1q1bM4AgIiIiqieGwEQUbSI5CBYRERERxSkRDsxIRNGHATARERERERHFBQbARERERBR2AjaBJqLowwCYiIiIiIiI4gIDYCIiIiIKO90HuLFTQUTkiwEwERERERERxQUGwEREREQUAaz+JaLowwCYiIiIiIiI4gIDYCIiIiIKO7P/L58FTETRhAEwERERERERxQUGwEREREQUdma9LyuAiSiaMAAmIiIiIiKiuMAAmIiIiIjCzuz7ywpgIoomDICJiIiIiIgoLjAAJiIiIqKw8/YBZh0wEUUPBsBEREREREQUFxgAExEREVHYWc8BbtxkEBH5YABMREREREREcYEBMBERERGFHZ8DTETRiAEwERERERERxQUGwEREREQUdt7nALMKmIiiBwNgIiIiIiIiigsMgImIiIgoYtgHmIiiCQNgIiIiIiIiigsMgImIiIgo7FjzS0TRiAEwEREREUUMA2EiiiYMgImIiIgo7Dj6MxFFIwbARERERBQxDISJKJowACYiIiKisGPTZyKKRgyAiYiIiChiGAgTUTRhAExEREREYcfAl4iiEQNgIiIiIooYxsFEFE0YABMRERFR2HHwKyKKRgyAiYiIiChihG2hiSiKMAAmIiIiorBj3EtE0YgBMBERERFFDONgIoomDICJiIiIKOwY+BJRNGIATEREREQRw6bQRBRNGAATERERUfgx8CWiKMQAmIiIiIgih4EwEUURBsBEREREFHZ8DjARRSMGwEREREQUMQyEiSiaMAAmIiIiorDj4FdEFI0YABMRERFRxDAQJqJowgCYiIiIiMKOcS8RRSMGwEREREQUdmJU/TIQJqJowgCYiIiIiIiI4gIDYCIiIiIKO7PmV9gJmIiiCANgIiIiIiIiigsMgImIiIgo7MyKX9b/ElE0YQBMREREREREcYEBMBERERGFnbcPcKMmg4jIBwNgIiIiIooYYSNoIooiDICJiIiIKPxY9UtEUYgBMBERERFFDuNgIooiDICJiIiIKOwY9xJRNGIATEREREQRw0CYiKIJA2AiIiIiCjt2ASaiaMQAmIiIiIgihoEwEUUTBsBEREREFHZ8/BERRSMGwEREREQUMQyEiSiaMAAmIiIiorBj02ciikYMgImIiIgoYhgIE1E0YQBMRERERGHHwJeIohEDYCIiIiKKGMbBRBRNGAATERERUdgx8CWiaMQAmIiIiIgiRtgWmoiiCANgIiIiIgo7Br5EFI0YABMRERFRxDAOJqJowgCYiIiIiIiI4gIDYCIiIiIiIooLDICJiIiIKOzY9JmIohEDYCIiIiKKGAbCRBRNGAATERERUdgJnwRMRFGIATARERERRQwDYSKKJgyAiYiIiCjs2PSZiKIRA2AiIiIiihgGwkQUTRgAExEREVHYid87EVE0YABMREREREREcSGiAbBSqq1S6hel1Eal1Aal1KlKqfZKqelKqS3Gezvb/E8qpdKVUpuUUoNs009QSq0xPntfKaWM6alKqZ+M6YuVUr0juT5EREREFBox2j4L20ATURSJdA3wewCmiMhhAPoD2ADgCQAzRORgADOM/0MpdQSA6wAcCeAiACOVUonGcj4CcCeAg43XRcb02wAUiEg/AO8AeC3C60NEREREREQxKmIBsFKqNYCzAHwOACLiEJFCAFcA+MqY7SsA/zD+vgLAjyJSJSLbAaQDOFkp1Q1AaxFZKLoI8Wu/75jL+gXAQLN2mIiIiIgaD/sAE1E0imQN8EEAcgB8qZRaoZT6TCnVAkAXEckEAOO9szF/dwC7bN/fbUzrbvztP93nOyLiAlAEoENkVoeIiIiIiIhiWSQD4CQAxwP4SESOA1AGo7lzEIFqbqWG6TV9x3fBSt2plFqqlFqak5NTc6qJiIiIqP6MHBm7ABNRNIlkALwbwG4RWWz8/xfogDjLaNYM4z3bNn9P2/d7ANhrTO8RYLrPd5RSSQDaAMj3T4iIjBKRE0XkxE6dOoVh1YiIiIiIiCjWRCwAFpF9AHYppQ41Jg0EsB7A7wBuMqbdBGC88ffvAK4zRnbuAz3Y1RKjmXSJUmqA0b/3P37fMZd1FYCZwqEGiYiIiBqdBPiLiKixJUV4+fcD+E4plQJgG4BboIPuMUqp2wDsBHA1AIjIOqXUGOgg2QXgPhFxG8u5B8BoAM0BTDZegB5g6xulVDp0ze91EV4fIiIiIiIiilERDYBFZCWAEwN8NDDI/CMAjAgwfSmAowJMr4QRQBMRERFR9PA+B7iRE0JEZBPp5wATERERERERRQUGwEREREQUdnwOMBFFIwbAREREREREFBcYABMRERFR2AmfA0xEUYgBMBEREREREcUFBsBEREREFHZi9P4V9gImoijCAJiIiIiIiIjiAgNgIiIiIgo79gEmomjEAJiIiIiIiIjiAgNgIiIiIgo71gATUTRiAExERERERERxgQEwEREREUUMR4EmomjCAJiIiIiIiIjiAgNgIiIiIgo7MTr/sg8wEUUTBsBEREREREQUFxgAExEREVHYseKXiKIRA2AiIiIiihg2gSaiaMIAmIiIiIjCjoEvEUUjBsBEREREFDF8DBIRRRMGwEREREQUdgx8iSgaMQAmIiIioohhU2giiiYMgImIiIgo7Bj4ElE0YgBMRERERBHDOJiIogkDYCIiIiIKOwa+RBSNGAATERERUcQI20ITURRhAExEREREYce4l4iiEQNgIiIiIooYxsFEFE0YABMRERFRBDD0JaLowwCYiIiIiCKGTaGJKJowACYiIiKisGPgS0TRiAEwEREREUUQI2Eiih4MgImIiIgo7Bj2ElE0YgBMRERERBHDptBEFE0YABMRERFR2AkjXyKKQgyAiYiIiChiGAYTUTRhAExEREREYcfAl4iiEQNgIiIiIooYtoQmomjCAJiIiIiIwo6BLxFFIwbARERERBQxHAyLiKIJA2AiIiIiCjsz8GX4S0TRhAEwERERERERxQUGwEREREQUdmbNL1tAE1E02W8ArJTqopT6XCk12fj/EUqp2yKfNCIiIiIiIqLwCaUGeDSAqQAOMP6/GcBDEUoPERERETUFYr6xCpiIokcoAXBHERkDwAMAIuIC4I5oqoiIiIiIiIjCLJQAuEwp1QFGOZ5SagCAooimioiIiIhimlT7g4io8SWFMM/DAH4H0FcpNR9AJwBXRTRVREREREQxpsLhRmKCQkoSx5klilb7DYBFZLlS6mwAhwJQADaJiDPiKSMiIiKimBWPzwG+ZfQSHNa1NZ6//MjGTgoRBRHKKND3AWgpIutEZC2AlkqpeyOfNCIiIiKi2JFdXIWckqrGTgYR1SCU9hl3iEih+R8RKQBwR8RSREREREQxLx6fAyzgqNdE0S6UADhBKaXM/yilEgGkRC5JRERERESxR0TiKuAnikWhDII1FcAYpdTH0AVbdwOYEtFUEREREVFMkzh8DrAgvmq8iWJRKAHwUAB3AbgHehCsaQA+i2SiiIiIiIhiUTwF/ESxKJRRoD0APjJeRERERET7ZQaC8VQjKhJf60sUi4IGwEqpMSJyjVJqDQKMYC8ix0Q0ZUREREREMURY/0sU9WqqAX7QeL+0IRJCRERERE2Htw9w/GDtL1H0CxoAi0imMeLz5yJyfgOmiYiIiIgo5rAJNFH0q/ExSCLiBlCulGrTQOkhIiIioibA+xzgeIsI4219iWJLKKNAVwJYo5SaDqDMnCgiD0QsVUREREREMYbPASaKfqEEwJOMFxERERFRaOKxDzDia32JYlEoj0H6SimVAuAw6HN6k4g4Ip4yIiIiIqIYovsAMwQmimb7DYCVUpcA+ATAVgAKQB+l1F0iMjnSiSMiIiKi2CRxWAXMxyARRb9QmkC/DeBcEUkHAKVUX+gm0QyAiYiIiIgMHAWaKPrVOAq0IdsMfg3bAGRHKD1ERERE1AR4nwMcXxFhfK0tUewJpQZ4nVLqDwBjoM/pqwH8rZT6FwCIyG8RTB8RERERxbB4qhEVsA8wUbQLJQBuBiALwNnG/3MAtAdwGfR5zgCYiIiIiHzEYxjI2Jco+oUyCvQtDZEQIiIiImp64iso5HOAiaJdKH2AiYiIiIhqJR6bAovEX59noljDAJiIiIiIIiaewkHdB7ixU0FENdlvAKyUSg0wrX1kkkNERERETUE8xoEibAJNFO1CqQH+TSmVbP5HKdUNwPTIJYmIiIiImop4agotYBNoomgXSgA8DsDPSqlEpVRvAFMBPBnJRBERERFRbIujuNciEp/rTRRLQhkF+lOlVAp0INwbwF0isiDC6SIiIiKiJiCe4kER1v8SRbugAbBS6mH7fwH0BLASwACl1AAReTvCaSMiIiIiihli/UNE0aqmGuBWfv8fG2Q6EREREVFAcdUkmI9BIop6QQNgERnekAkhIiIioqYhnga+IqLYEspjkKYrpdra/t9OKTU1oqkiIiIioiYifoJhPgeYKPqFMgp0JxEpNP8jIgUAOkcsRUREREQU0+I1COQgWETRL5QA2K2UOtD8j1KqF+KpKI+IiIiI6iyegmFdAxxHK0wUg/b7GCQATwOYp5Sabfz/LAB3Ri5JRERERBTL4jUEFInfdSeKFaE8B3iKUup4AAOMSUNEJDeyySIiIiKipiCeAkKBxFWNN1EsCqUGGABOg675NU2MQFqIiIiIqAmI12bArAEmin6hjAL9KoAHAaw3Xg8qpV4J9QeUUolKqRVKqYnG/9sbI0tvMd7b2eZ9UimVrpTapJQaZJt+glJqjfHZ+0opZUxPVUr9ZExfrJTqHfKaExEREVFE2IPAeIqFBYivFSaKQaEMgnUJgAtE5AsR+QLARQAG1+I3HgSwwfb/JwDMEJGDAcww/g+l1BEArgNwpPEbI5VSicZ3PoLud3yw8brImH4bgAIR6QfgHQCv1SJdREREREThwxpgoqgXSgAMAG1tf7cJdeFKqR7QwfJntslXAPjK+PsrAP+wTf9RRKpEZDuAdAAnK6W6AWgtIgtFt6f52u875rJ+ATDQrB0mIiIiosZhrwSNpwcDsQ8wUfQLJQB+BcAKpdRopdRXAJYZ00LxLoDHAXhs07qISCYAGO/mM4W7A9hlm2+3Ma278bf/dJ/viIgLQBGADv6JUErdqZRaqpRampOTE2LSiYiIiIhCp/sAx04EvGR7ftz216b4td8AWER+gB4B+jfjdaoxrUZKqUsBZIvIshDTEqjmVmqYXtN3fCeIjBKRE0XkxE6dOoWYHCIiIiKqC3sQGG/xVays75S1+3DNJwvx7eKdjZ0Uoga131GglVIzRGQggN8DTKvJ6QAuV0pdAqAZgNZKqW8BZCmluolIptG8OduYfzeAnrbv9wCw15jeI8B0+3d2K6WSoJtn5+9vnYiIiIiIwk0QOwHw7oJyAMD2nLJGTglRwwpaA6yUaqaUag+go1KqnTF6c3tjpOUD9rdgEXlSRHqISG/owa1misj/QQfSNxmz3QRgvPH37wCuM0Z27gM92NUSo5l0iVJqgNG/9z9+3zGXdZXxGzFy2SEiIiJqmnz7AMcPkVhqAK1x9ByKNzXVAN8F4CHoYHcZvM2NiwH8rx6/+SqAMUqp2wDsBHA1AIjIOqXUGOhHLbkA3CcibuM79wAYDaA5gMnGCwA+B/CNUioduub3unqki4iIiIjCLJ7qJnQNcGytb4wll6jeggbAIvIegPeUUveLyAf1+RERmQVglvF3HoCAzadFZASAEQGmLwVwVIDplTACaCIiIiKixsRgkij61dQE+iSlVFcz+FVK/UcpNV4p9b7RNJqIiIiIiGwYBBNFt5pGgf4EgAMAlFJnQTdd/hr6UUOjIp80IiIiIopF8RgEmk2fY60XMPsAU7ypqQ9wooiYIypfC2CUiPwK4Fel1MqIp4yIiIiIYl68BMPmesbL+hLFqppqgBONRwsBus/uTNtn+318EhERERHFp1irBQ2H+FtjothUUyD7A4DZSqlcABUA5gKAUqofdDNoIiIiIqIaxUsw7G0CHRtYU03xqqZRoEcopWYA6AZgmu35ugkA7m+IxBERERFR7InH4Mpc5Vh7DBK7AFO8qbEps4gsCjBtc+SSQ0RERERNSYzFg3Vm9QFu3GQQ0X7U1AeYiIiIiKjW4joIjOuVJ4p+DICJiIiIKGLipgYYsdUHmCheMQAmIiIiorCKtX6w4eB9DFL8rTtRLGEATEREREQRE2/hYLytL1GsYQBMRERERGEVj0Ggtwa4cdNBRDVjAExEREREERMvTYK9fYBjY31jJZ1E4cYAmIiIiIjCKk5iXh+xWgOs+CBgijMMgImIiIgoYmIsHqwzcz1jLQAmijcMgImIiIgovOIwCIyXpt5EsY4BMBERERFFTpzEhd4a4NhY4RhJJlHYMQAmIiIiorCKxwGWrD7AjZsMItoPBsBEREREFDHxFgzHSs0qB7+ieMUAmIiIiIjCyh4EJpWVAvfcA5SVNV6CGoJVAxwjETBRnGIATEREREQRc+g3nwAffwy8915jJyWirOcAx0j8GyvpJAo3BsBEREREFFb22CrB7TYmNu2IK1b7ACu2haY4wwCYiIiIiCLGCgibeKDF5wATxQYGwEREREQUVj6PAoqTiDBWHn9EFO8YABMRERFR5JiBYZzUAMdKI+jYSCVR+DEAJiIiIqKwkhr+11RZfYBjbHWbdrEEUXUMgImIiIgocuKmBtgYBbqR01FbsZZeovpiAExEREREYRWwFrSJB8DWc4BjpAq4ie8NoqAYABMRERFR5MRGPFhv4vce7WIlnUThxgCYiIiIiMJKAoVXTbwGmH2AiWIDA2AiIiIiipxYiwjrKVaaQBPFKwbARERERBRe9hhQPPq9qdcAx+ggWETxhgEwEREREUVeUw+AY6wTMCuqKV4xACYiIiKisPKJreIk0oqx+NeraZdLEFXDAJiIiIiIIq/J1wCLzzsRRScGwEREREQUVj4xYJzEg9Yo0I2bDCLaDwbARERERBQxyowMm3gNsIkVwETRjQEwEREREYWV/TnA1t9NPAD21gAzAiaKZgyAiYiIiChiVJxUiVqPQYqP1SWKWQyAiYiIiCis7EGg9Wfc1AATUTRjAExEREREEaPiJCKUan9ENzbVpnjFAJiIiIiIwspnEOh4GwQrxgJLxQcBU5xhAExEREREkdfEA2A+/5coNjAAJiIiIqKw8gkG4yQwNNcyTlaXKGYxACYiIiKiyGvyNcDGe+MmI2QM1CleMQAmIiIiorASCfafpsx8DFK8rC9RbGIATERERESRxxpgIooCDICJiIiIKGJUnNSIsg8wUWxgAExEREREkRMnj0GKtcCXTbUpXjEAJiIiIqKwisfYyv7831gILj3xUS5BVA0DYCIiIiKKGImTXrGx9uSnWEgjUSQwACYiIiKisIqXoNfOJwBuvGSELB73ERHAAJiIiIiIIikOR4eKhSbQMZBEoohgAExEREREYWUPrqxRoJt4xOXTB7gR0xEqM0hv4ruFqBoGwEREREQUMVZ81cQjrZjrA2y9x0BiicKIATARERERhZU9pFLm/zyeRklLY4iFoDJOKuaJqmEATEREREQRI3HSBzj2aoDNJtAxkFiiMGIATERERERhZQ+qrD7ATbwGOBZqfe08rAGmOMUAmIiIiIgiKD4irZirATZ3S+Mmg6jBMQAmIiIiorAKGFTFQlRYD+Lzd/Svq7cJdCMnhKiBMQAmIiIiosiJlybQtkgyJoJKqwY4FhJLFD4MgImIiIgorAIGgDERFdadBPk7Wnn4HGCKUwyAiYiIiChy4qYGuLFTUDuxll6icGEATERERERhFiC6avIRl70JdPSvq/fpVNGfVqJwYgBMRERERJETJzXAdrEQUlq7JRYSSxRGDICJiIiIKKzslYrWc4CbeE1jrD0GyeoDHBPhOlH4MAAmIiIiooixwqtYiArrQYL+J7o18d1CVA0DYCIiIiIKK3tMpeKkCbRPDXAMRMBi1QATxRcGwEREREQUOWbg28SrGmPtOcDeQbAaNRlEDY4BMBERERGFVcA+wE29BjjI39HKY+2kWEgtUfgwACYiIiKiiFFxUwNs/zv61zVOxiYjqoYBMBERERGFlU8fWImTANj+HOBGTEeo2ASa4hUDYCIiIiKKGKsGOAJNoNOzS7FiZ0HYl1snMfYYJKsGOCbCdaLwYQBMRERERGHl0wfYE7m2tu/+uRlPjV0b9uXWhW8f4OgPKq1RoKM/qURhxQCYiIiIiCJHIlcD7HB54HRHx+BaEmOjYFljk8VAWonCiQEwEREREYWVbw1w5PoAe8Q+mnH0iL4UVWfWUsdCbTVRODEAJiIiIqKIUREcBEtEoqYJr88gWFGSpprwKUgUrxgAExEREVFY2YPBSA6C5RGJmhpgn8cgxUBUaXXNbtxkEDU4BsBEREREFDEqgg+cjaYm0D5dgKMjSTWymkDHQmKJwogBMBERERGFlU9MFeka4OgYA8snkIyJkJI1wBSnGAATERERUcREtg9w9NRg+tYAR0eaamJ1AY7+pBKFVcQCYKVUT6XUX0qpDUqpdUqpB43p7ZVS05VSW4z3drbvPKmUSldKbVJKDbJNP0Eptcb47H2llDKmpyqlfjKmL1ZK9Y7U+hARERFR7UXyOcC6D3DYF1s30ZKOEJlNx2Ms2UT1FskaYBeAR0TkcAADANynlDoCwBMAZojIwQBmGP+H8dl1AI4EcBGAkUqpRGNZHwG4E8DBxusiY/ptAApEpB+AdwC8FsH1ISIiIqJaUhF8DnBUDYIVo6NAx0JtNVE4RSwAFpFMEVlu/F0CYAOA7gCuAPCVMdtXAP5h/H0FgB9FpEpEtgNIB3CyUqobgNYislD0Gfq133fMZf0CYKBZO0xEREREjSNgH+CIDYIV9sXWSazFkWwCTfGqQfoAG02TjwOwGEAXEckEdJAMoLMxW3cAu2xf221M62787T/d5zsi4gJQBKBDRFaCiIiIiGpNmaFWBGqA9XOAoyOC83kMUnQkqUZiNYGOgcQShVHEA2ClVEsAvwJ4SESKa5o1wDSpYXpN3/FPw51KqaVKqaU5OTn7SzIRERER1YNPUBXRPsBR+hikGAgqI/h0KqKoFtEAWCmVDB38ficivxmTs4xmzTDes43puwH0tH29B4C9xvQeAab7fEcplQSgDYB8/3SIyCgROVFETuzUqVM4Vo2IiIiIQhD5PsDhW96ibXm47IN5qHK5a/1dn8cgxUBQ6X0OcCMnhKiBRXIUaAXgcwAbRORt20e/A7jJ+PsmAONt068zRnbuAz3Y1RKjmXSJUmqAscz/+H3HXNZVAGZKtLSDISIiIopT9tyYimAfYAlzDfDGzGKs2VOEkkpXvZYTC5lRqwY4JlJLFD5JEVz26QD+DWCNUmqlMe0pAK8CGKOUug3ATgBXA4CIrFNKjQGwHnoE6ftExCx+uwfAaADNAUw2XoAOsL9RSqVD1/xeF8H1ISIiIqJaUhFsa6v7AIdveW6ru3LtFxpzzwFmE2iKUxELgEVkHgL30QWAgUG+MwLAiADTlwI4KsD0ShgBNBERERFFB3tMZdUAR6QJdHiDTXNZdWlW7TMIVpjSE0l8DjDFqwYZBZqIiIiI4pPVBzgig2CFtw+wGRS665TWWOsDbLzHQFqJwokBMBERERGFlU+tbIRrgMPZB9hTnybQPl+J/qjSm97oTytRODEAJiIiIqKIiak+wB6zCXR9+wCHKUERVffm3kSxjAEwEREREYVVwD7AEWsCzT7AdeF9PHMspJYofBgAExEREVHERPY5wJFpAu2u0yjQMdYHmINgUZxiAExEREQNLrOoAg/8sAKVTvf+Z6aY4/sc4Mg1gQ73IFhm4FuXWlHfGuDoDys5CBbFKwbARERE1OCW7SjA76v2IiOvrLGTQhEWyRpgCXMz3no1gfZZTliSE1FWuUTjJoOowTEAJiIiogZn1rTVpakpxQLvfo10H2D9Hq7l6fc6NYGWGG0CHQuJJQojBsBERETU4KzAJfyVghRlIjkKtDcADs+yw7W8WGgCTRSvGAATERFRg7MeDcvapybJtw9wBAfBCvNx5A5XABwDh3UEyyWIohoDYCIiImpwZqDhZu676ZPINYH2NuMN1/L0e30fgxQLzCCftdUUbxgAExERUYOzBhtiH+Amyec5wFZUGZnHIOn3MDWBrkff9FgLJFkDTPGKATARERE1OLfVdLVx00GRF0uDYLnrMTCUz2OQYuC4NgN2dkOgeMMAmIiIiBqc1QSaEXCT5NMHOIKPQQp3DXC4mkDHQm0wa4ApXjEAJiIiogYnYRpsiKJfJGuArT7AYYqtPfUomIm15wBbAXDjJoOowTEAJiIiogZnBhgMgJsmexPiWHwMUt2aQNueAxyW1ESWVUsdC4klCiMGwERERNTg3PUYbIhiSyw1gTb7ptdldHLfGuDoP669NcDRn1aicGIATERERA1Owhy4UHTxGQXa0xA1wOFZntRneRLwz6hlppGnIMUbBsBERETU4MwatghUClKUiWQNsLd1dXibQNfl8Vz2mtSk3buBH38MS5oixfscYKL4wgCYiIiIGpw12BCrn5okn1GgY+gxSPVpUm3/ymH/vAC4/vqorl4Nd+EBUaxgAExEREQNzqxhq0tNG8WWmBoEyxOegDolO0v/4XbXM0WRI37vRPGCATARERE1OHOwIca/TZO9OXAsDYIVrscgWVyu+iUoksJce04UKxgAExERUYNjE+j40SDPAQ5zE+i6PQYpwMQorgG2Al+egxRnGAATERFRg6vPYEMUA+x9gK0hv6O/Bthdj4KZgI8TiuIaYDO9PAMp3jAAJiIiogbntvpaMvvd1FlNoGNgEKz6PAYp4OpFcwAcua7ZRFGNATARERE1ODPAqEtfS4p+9r2aENEm0Po9fINgme9Nvw+wFQCzDpjiDANgIiIianDhHr2XopR9/4a5CbS9n27YnwNcl+UF+k4UB8CeMPefJooVDICJiIiowbnD9LgZik5mUKXstYthjrTsx074ngNcjybQgSZGcQBsYgBM8YYBMBERETW4+jxuhqKf2aw2IYI1wPZa2vA9Bsl4r0sT6FjtA9y4ySBqcAyAiYiIqMF5OAhWXEgQW9Ab9hpgWwAcpti6Pk2gAzbDjuLHIFmjQPMcpDjDAJiIiIgaXH1q2ij6mTFVJGuA7YsO10BOZouEuj0GKYAorgH2PvO4cdNB1NAYABMREVGD8z5vtZETQhGV4GmYGuBwLdo7qnR4lhfNAbBZ88tRoCneMAAmIiKiBmc1gWYNcJNk7tVkjy0AjOggWGEeBToe+gCb7wJg4kRg7drGTA5Rg0lq7AQQERFR/OFjkOJDksfWBzaig2CFd5l16gMcaGIUB8BmggUALrvM+A/PR2r6WANMREREDc5txEJ16WtJ0c9sXpvsdtonhvc3bPF02GqAzeOyTjXAsfocYJ6DFF8YABMREVGDq09TU4odyQ1UAxyuIM4bFIZlcVEdAIvfO1G8YABMREREDc7b1LSRE0IRYfUBdkeyD3CUNYGOtT7A5kjdUfyoJqJIYABMREREDc563Awj4CYtKaIBsO3vMB1H5qjkdXsMUmw+BzjZ5WjklBA1LAbAREQUkNPtYd8wihgOgtXEGbs1xRwFOjk5As8BDn8NsNSjCXSs1QCbuyPFUdW4CSFqYAyAiYioGhHBma/9hR//3tXYSaEmysx8MwBu2qwa4NTUiNYAh7sPcJ0GwQo0MYoDYFMyA2CKMwyAiYioGpdHsK+4Ervyyxs7KdREua1Ao5ETQhFhNa/12ALgWHgMUj0KZmKtBtgsNEhxVjZySogaFgNgIiKqxmlEJQ4XoxOKDGET6LiQbPaBjUgNsAT8OxzLrEuf4oB9gKM5ADbe2QSa4g0DYCIiqsbp0lkjJ6vnKELMJqZ8DFLTZMaj1nOAIxAA2xcX9gA4HvoAmzXANQTARRVOZBezhpiaFgbARERUjcOsAWYATBFSn9F2KXYkeWw1wBF9DnC4lll92bVm/24UB8BmMlNrGAX61ckbccfXSxsoRUQNgwEwEVEIPB6J6poqj0cwbd2+sKXR2wQ6eteZYpvUo6kpRT9vDXDDDIIVthpg8/Fc9Vie1e8ZiPLHIGmpjuA1vAVlDuSX8zFJ1LQwACYiCsGQMSvx6M+rGjsZQS3fWYA7v1mG5TsLwrI8s+8va4ApUqwm0Ix/m7SYGwSrXo9BMgb+sj/7OBZqgJ3Bm0C7PB643LF/kh793FRc+sHcxk4GRQkGwFSjXfnlGFOLx6CICO74eikWpOfud9680ipUOGpfMrq7oBxF5c5af4+oPnbll2NnFI+IXGacS+V1OKcCMWuAnTEwCFZJpROXfjAXm/aVNHZSqBbcYahpo+hl7lUrGExJiUAf4EgMgqXf6/QYJOMrVrNvIMoDYHMU6OABsNMtcDaBALikyoW1e4obOxkUJRgAU42u/WQhHv91dcgjwZZWuTB9fRbu/GbZfuc94aU/8c+R82udpjNe+wsD355V6+8R1YfTLXBGcVWVywhYXWGqYYmlPsB7Ciuwdk8x1mcWNXZSqBbMYIFNoJu2JE9sPge4To9BMt5jpgbYeK+pBtjtkbDdV4iiBQNgqtHeIt0vJNSLnxkoJyaokObfWMcam9xS9kehhuV0e6wgMxqZJfThKqn3Li9619nkCvO6U8Nw1yPQoOhn1S66Y6wJdD1GJ/fWAMdIAGzVABt5qsTEavPoex/PUWpaGABTSELNWFYaAXBSiAEwNW3ZxZVhK5VvbC6PRGUmwOHyoKTSaRVShSuNZuBbFQNNoM20RuP+oeCsJtDRf4hRPfiMAh3uGmDbsRPuJtB1egySUaeaHCtNoI13axCspKRq87g8EhMFoUS1wQCYQhLqxc/s0xtqDTA1XRv3FePkl2fgu8U7GzspYeFye6IyE3DTF0tw9PPTrOAvXE3VzL6/0bjO/lye8K47NQxhDXCT5m0ObHsOcCzUANenCbT/yNdAVAfAZi231QRaVc+7udyeOvWHJopmDIApJKHWrFQ6dQC8vxrgplIrSMGlZ5cCABZuy2vklISH7gMcfQGWuX29jy0Kcx/gWKgBdsVOWsmLTaDjQ3IEa4DtiwtfH2Dzve59gJNiJAD29gE2mkA7qw8w6nQLXB5hvo2aFAbAFJJQa4GqXEYNcGLNAXBdB9bhBTh2mCXGCQFKlGNRtD8KwlsLGn99gJ1hXndqGGZ5EmuXmqaAzwEOcyGiwF4DHOZBsOqSVPMxSPYm0FH8HGBz86W6jBpgt7vairt5faUmiAEwhST0JtBmH+CaD6261tRwkJvYYWYimkpreJc7uvtBmedUuAbqsh6DFAPnnDUCdhTvH6quPk1NKXYkuV3wKKX7l0ZwFGiPywMsXRqGZdb98VxWDXCsDIJlvFt9gIFqtcDOMI8vQRQNGABTSEIeBMsZWh/gumaqozkAIV9mIXJiE6kBdro9UR0MllbpTFa40uiIoWbF5jo7onj/UHVuTz1q2igGeGtD3UnJQEJCBAJg7/IO+fJD4KSTgCVL6rdMT/0LZmKmD7Cxjqku25M1/AJga5T9GD5R2XqQ/DEAppCEGnhWukLrA1z3GuDYvQDHG7P0XDWRAFiPAh29x1+ZEQDH43OAnawBjkn1qWmj2JHsdsKdmKQHWAp3E2jbsdN+7Qr9R2ZmvZZp9QH2CDBhAlBcXIv06PfkWKkBNtKbVlXhnVgtAI79GmA23yZ/DIApJKFePEIdBbqugWwsZMZJM0vRE5vIVUY3gY7em2i4a4DDPahWJFmPgGImJ6b4BBrU5HifiRvJGmDv38plBG7JyfVcpl5ou327gcsvB269NeTvmn2Sk9yx8hgknd4WVeXeidWaQBt9gGM4/8XKE/JX/YFfRAGEXgMc2nOA6/ps0WgOQMiXuY+byiBY0d78q7TSqAEOVwAcQ49BiqUBu8jLHYamphT9UtyuiNUA2wtPEpxGoFnPQafM47FZmVHzm54e8nfNQznVbWtSHM0BsJHemgJg8zx1xnBBFfOO5K+J1M1QpIU8CnSIfYDr3AQ6BmqjSKswjoWEJjAKltsjENGZhWgdsbY0zE2grX61MXDOuRgAxyRvE2gAlZXsDNzE2AeEciUl6wC4IWqAS0vDsszUcmM5tahRNpPTptKWhlgLgB0On3maQhcT3hvIHwNgCkmopWdmE+j9jQJd14sRL2KxwxwQrQnEvz7HXbQeg2WOMA+C5fY2K472JqrOJtBHLR6Zx5W43EDz5sCDDzZyiigSks0a4Ag0gbb3AVZmoFlWVq9lmsdlWnGhnlCbANhITutKIw0pKdEdABshe8uqcqBtWz0x2CBYMXx9jdb7NjUeBsAUlP3GEmrJnzkI1v7UtS8v+wDHjkpn0wlKYiEALq3S51640mdfTrSfd7H0yCbyMge/alFSoCf873+NmJr6ySquxK788v3PGEe8A0K54U5MjEwTaNspnxCuANhIeFpJoZ5Qqxpg/V2rBrhDh6h+DrCI/qdlVblOK1A9ALbGWIju+0BN7PmQYCNCz9yYhZySqoZKEjUyBsAUlD0zGfpjkIyM6H4ulHVtyswMbuwwa4BjoQnt/thvntEa0JdW6kxLuJ8D7P93NDIHv4rlDFo8MoOXtvnZ+o/U1MZLTD2d8vIMnPn6X42djKiU7HY2yGOQwt0EukUdAmCzDXTrqjI4W7QEmjWL8hpgINXt1KNWBwiARbyDP0brvS8U9kLcQN2YHC4Pbh29FP/+fHFDJosaEQNgCsrswwmEngE2v7O/+avYBLrJMwPguu7raGIv0InWwbDKzBrgMDVXthc2RXshhsuqAY7udJIvs6lpm8IcPaFZs0ZMDYWbNSKyxx25QbBsAXBSpfEon3rUANu7e6SVFuk/KitD/r69D7CjVRsgKSmqA2CPWfsLBAyA7beTWB5l335vCLQeZve97bn1az1AsYMBMAVVZQuAQ61ZMYOe/ZUUchCspq+iidYAR2srBOs5wGEKAu37zVlRBZRHb/NOh72P2saNwK5djZwiCoX1uJnCXD2hAQLg7bll+OnvnRH/HfJKdkduECz74lJKjFGb61EDbA+oW5o1wLV6DrC3CbSjZeuoD4BFgJaO4AGwT+AYwwWMvvfw6uthDdrZRJ5aQfvHAJiC8qkBdoV206pyhlYT4/O5x1Nt1MGgy4/hC3C8abJNoMvKgUWLGjE1gZVWOXHs3k1whWl725uMtXzwPuDcc8Oy3EhwWYNgeYCrrwYeeKCRU0ShMJsiti0waoDr+fzWUPxr5HwM/XVN1I7m3pR4+wC74InQIFhWwCqC1FIjUK1PDbAteS3MGuCiopC/bw2CVVUGR+u61wDP2ZyDKWsza/29uqipBtheWxqthb+hcPgE8tXXo9wYRLIpDNpJoWEATEH5BMAeT0jNgMzvuDwC7N4NbN4ccD6foOiWW0Lu+2WvARaHA8jPD+l71PAqjMIQh8sDPPMM8NlnjZyiurM3e273yAPAqacCe/Y0YoqqO3nnWoz75hH0WrcUyM4GKirqtTzzXEt2O5Hy+3hgyZKwnG/Flc6wN1W2+gA73cCWLfpFUc/MW7czm0DXoqatrgrKdea+0hmZgYmCDbBTW3mltRuM59XJG/H2tE1h+e1w86kBjtAgWM2dVUhw138QLJ8a4NJC/UcdjsvWZhPoxMQ6BcD/+WIJ7v52ea2/Zzdi0nr0fmJS0M/N63Ar/xpgW4WEy6fpcOwWZvsMgvX3EuDee32ORdYAxx8GwBSUOaAVABw48Rf9mIqpU/fzHX0Rkcoq4PzzgSuvDDifTwb466/1ewilrPYSSLnhBn3BjuGLclPmPRYqgJdeAu64o5FTVHf2m2fazD/1H7t3N1JqAjs8ezsAoPvW9UCXLsD11+/3OyKCVydvxJrd1c898xw9cfd6pJSV6IkLFlifl1a58N/vl9d61Mxjnp+Gh35aWavv7I9ZoNaiIAeoqgIyMmpd0yQiOngpKwOWLgUA/LZ8t/V85Viyr6gy5ECsqMKJZTsapyDRDDbaFxlNoIuKGqy5aEWEAuCqMLTAWLQtDye89Cemr88K+Tvz03OxYGtevX87nMwjMNkTuccgWcdQhS1IDVMT6NbFxujkxcUhp9ucq3VlGRwtWzVIE+g3p27CJ7O3Vpv+6Vx9T7j5yyWYszmn2uflRr/Xdi6jciNgE2jvejdftQI44ABg794a03P6qzPx+bzttVqHSLPnOdteciHw0Uc+62H2AWb8Gz8YAMe5zKIKXPXRAtz/w4pqGSbzgpDoceP0lx/XE5csqXF5ZtBz5fzfgE2bdH88vyH1AW+GNdVpyzzv2LHf9NovYgm//qr/iKJA5KWJ6zFjQ+iZllpxu+tdqxfIJ7O36ptnRgbw7LNheWTDU2PXYMl2nanuvXWd94MwZ34ais9xV2IEg1HWz7Rvvj4PzvjrNz1h/HifzzNyy/DRLCOTtHUrIIIqlwcfz96Kqev26UxejjeTZGZ8BqYvQVViMiQpCfjLO8rtL0t3YeLqTHw4c4v+btX+A2Gz2emk1X5N+8rLgfT0Wq2vnVkz0T7HyNCUlQF5tQsGjn1hOi77cB7w+uvAgAFYt3wzHh6zCs+MW1vndAEACgqAG28MvcVACNfBmuwprMBpr84IORj6bvEOXDdqUaMMIGYeDz2ybOtcUBC+HygtDVpAWrVjN3DQQcDy+tWy+SsLQ4HJql2FAIDF20I/hiucbh3QiNSqyW5IPB5gZ937TevnACdGqA+wXt7BubZjKAxNoFNdDnTMz9L90j2ekJdprl6bKtsgWBF+DNKHf6Xjlckbg34+a1MOhnw2t9p0M7/WwRM8ALbX+h740dtAZiYweXLQ33K5PdhTWIEXJ66vzSpEnL0JdIKZj7Jda60a4AQFZGVF9ZgXFB4MgKNYVnEljn9xOjZv2glMmBBas76qqpAyoqY1u4uwdEcBJqzaC8eqNcBTT1mZ4AqnvpEflpOBBPMiuD1IqV5uLvDBB3CWV6BzSR5u/+tboE0bXfIZ4Dttly3GhZsXov8+2zplZFSb782pm3Dw039YdxXzIta60lbCuzH4hb9eJkwADjxQNycN0XeLd+LPDaHPXyt33QWkpekg+PffgX37vBfwffvqtsyqKkxdvQeT1+4DbrsNePFFYNmyeif1+8U6s9S1OBf3/fiG94PMGvo0iXibXq1YAZxzTq22fSRZ/aBEkGCONNoYAbDHA8ycaZ0P9kKrvnk6AO66z8iotmrl89UJq/bi+x9nwTXoIqBfP+DTT60agHKHGzjmGKBzZ2t+h9sDiGDg1iWY36s/Cs6/WJeab9VBtNX0sKxE1zjfemv19Pplds1+VgCAn34Cnn9e//2f/wAHH1znjLtZQ98xxxZkBrieANAB1r33VmvOXVThxNo9xbqVi9uN5DmzAAC7C+qZERo7Fvj+e+Crr3yn33RT9W329ttA796Bg7IJE4AzztCZT6OGOpDs4kp4BMgsqtSZuP2Mr+DIzIbTLSgvd+g0hlJ7lpsbUq1Wdkklej8xCX8t3qK3+ciRPp97RNCptADd8jKBE07QE/0KLiqdbp1RLysDFi7cf9rM71U6IL16AYMGBQy6kn8do+9Nr70W8jJD4Vy1GvjHP2q+1tnt3q0Li23MWiiPAPjhhxr3t6nC4daZ+KeeAtq2rf21039/zpmDSeddi8ven6u3Ua9eoeVBPB4dQEBfn5R40LMoCwXtu+gaYABOl1tvn7Fj692Cy7wOHZm1Tf9x2mlhqQHuVbAXCeIBTjlFf+DfDHrxYn1OFhb6TBYIEj1utHRUoKoOg2B9Nnebz/Ok3TP/Ao4/Pnjey27qVOD004HffvOZfOLudVj2xpXAySfra7jBvP63dQYJgAsL0fqu23FggT6WE8xCAP9ra0UFsHIl4HKhzGEL9rdtA+ZWD7yrycwEJgVvqh0O5j2iRZXtem7bpua2uHrpJKBrV98xL8aO1fva3969wKxZkUguNQAGwFEsY18Rrp3xHQ4+vDdw+eXAVVfpzOeiRfqGnpHhvXns3AlcconOwF5yib4Y3XyzT41OIGbzvrO2LUPqcf2BV16x+mqWVbmR4nLi2lXT9MwHHuitpSksBBYsgPvSy1D54UhgyBDggQdw3biP8eSsL5HkdnkzO+bNvbwcc9buQeHiZbjs/uswauwIDEy31SgHyLB+P2kZvvjuaUhqKnDPPXA59IX56tXTvTMNGqRv0OeeqzOQgN4ugWqASkv1+lVW6uac9kxCZqZvbfX//Z8Ocr76CigpCX4TW70auOkmeHbuQoXTDZWbAwwbBowatf/Sbo9Hp2H7dh3MvvSSvtFmZQH//CeQno71GTnILqoAPv9cf6dzZ+CKKyC9esHd/1idzm7ddNBYm8zEpElAmzb4YPj1qKys8t7IA9Xyz5unM+ulpTpIGTXKW9CyYAFw1lnegGL7dty2ZCze//11LPjoFnTP2QUccoj+7MsvdYY8UCHNe+/pvuD5+cAnnwCzZwOPPaZLz+fPD1yKvndv8NL1mTP1Ddi0bRvw99+6ljs7WwcHDzwAHH448L//6Xn+/lsHH0VFwLp11rLNflDdi23n0+uv+zQJDpnDEXg/VVTo8/jhh/UxZXK7kbdiLdbuKQI+/BAYOFAHVPAdoMSsAbaUlOhgz+0Gpk+HKy8fr01+HwkLF+jj5csvrYBUCvK9hSnGNcPp9qBf3i70KcjEzH4nY+3jL+hBiu64A8jKQpvtm5HsdmLQ56/r8+n7733PudGj9TXDtg/MUnYlHuC664Dhw/VxZ7bmmDLF+32PR2fW9lcjunIlum1YBQA4fMsq73T/68ns2Xp5X3yh383g26ZVVZk+BgC0WTBHJ0MAbNhQvcBj9WrvftqzR6/HokX6+DHnzcnR2wEAnn5aZ1A9Hn1N+fprfT5s2ICfFmfgtV+XAU88oec1M1Xm9WP3bn0PmD9fX99POgno31/P59EFFdi4ERg3DskzZ6BLSS4qS8r09r/xRm+aJ0zQ9wbT6NF46IbTcdyejVBvv6nvGaecoq/n/teuzZt10D53LtCpE/DGG960HXmkPt/8LNyah1ZVZTjousv1Nn/gAf09o6DDI8DxezfomQcP1u95ecAHH1i1TA/9sAKP/bwKuPRSHdzMnQt8952+5i9YoAMRQL9/+63122++/ztUfj7w55/Au+/qbWe7vqeY17nVq3Xa+/fXQd6KFdXWoxoRn2PdVViEATtXo21FMVoOf1a3vrjxRn1Ns1+DAunZEzjsMJ9rQrdVS/HOhDdx/x2DgBtu0Pu7Xz8dDNtaYdiVO1xomZMJvPqqnjBx4v7Xw7R5M9Cihd5+5n4/+2wM/msMStZu8B7DY8bodzNwKCzU6+d0eq/p992nAwjjvn9g4T60qirHzt6HWZH9BY98r9fnX//yHkc//ACceKK+x0yapPfzl18GTq/HY51nR77wOCZ9+QDO3bYUhQccqJvo5uXpZY8bp+d3OvV6jRqlz58aavdkz168M+FNXLDFOK5OOkm/mwHw6tVYPvg6lJ17vj4ne/YEfvzROq+aFRfhko3zAABlXQ/YfwDscul0FRUhr7QKL01cjy+HvIFxXw/BqTtWAQ89qI/Ja6/1PXdtLt44D6dlrIS88II+J+64A3C7rYKUfy//Q//x99+6IMoIZM3r/wm71qKgWSu93wCdfykoAH79FS1++h6/fvsYEj1utNpsnKv2dJSV6TExjjsOeOYZOGbNwQm71+tr/JFH6rzB2rV6X9x/v94PZWWAx4M/V+/BmpXpwIAB+vz2v9ab+VynUxcCGYWvKCkBDj3U53yvZu5cfd6LAAsW4MTrB6NDWSGO2+stbJo2aRHGrdgD/P47jhh6H7oW5+LumUaXvCVLkDV3MZacdZk+lszrL4CRvyzCn0vSdYHCuecGb5mXlVW9IMo8dkV0XmvcOJ0n2bEDuPNOvW2zs/X1z94aRkT/TkGBPn5r05JifwUw6ek6vwPo+8j06dXnyc0N/fdihdnvKV5eJ5xwgkQtj0fk999FbrxR5IknpLJjJxFAsvufJHL11SL6kNevdu30+4ABIs89J9Krl+/nCQn6vVcvkSefFLn8cpH16/Xv7N0rMn++yJIlknH2IFndpa9UJSSJ49DDRbp3Fzn7bBGHQ35csE0mHHqGCCCO1GYiN98s0q2byLp1Is2a+f4eIJ4DDrD+/vCM60Xy8/X/Tz5Z5N13xdO3r+xt1aHa9xwDThVJSxMZMkQkI0Pk9ddFPv1U5MADRQCpSEoR14UXigCy6MFn5dGLHxCXSpCKiwdXW5YAIi+9JHLppSKJiSKPPKK3wezZIgsWiFx5pZ5n0CD9fv75Inl5IunpIikp+jfPPz/wclNTRUaMEKmoEPngA5HFi/W2bNlSBBDXWWfL6Xd9JlMuutH7nRtu0PPn5IjMmCGyebPI//4n8uGHItdeK5KU5J3X/Puoo0T+8x/9t1JSltJMitt1DJimysRk32nnnSficOj9vH69Xt9HHxXZudN7nOXkiHz8sYhtf9159/siffp4l/PyyyKXXSby5psir74aeHu88YY+Zs89V/9/6FC9/7p0EQGkMLWFZLZsL4//+0WRzMzq3+/VS2T0aJHJk0Xuvz/wbwAiRx+t32+/XWTYMJF33hHZuFHk3//W059/Xqfjiy/0ul59tUjz5t7vf/65yIMP+i7zwAP1+ikl0tHYtueeq/+fkOA9fw46SGTSJNnx8NNy01XPyeIeR4g72W+bu90ic+fqc/Hpp/Xx06uX3h5nnSXy3/+KDB4s8uuvIvv2ifTrp8+JJ58UOfNMvb2OO06/zGUedZTeV2VlItddJwLILyfZjvc+fUROO00cd94ln59wubxw7m0igKzodrD+/JNP9Puvv4qceKIIIFk9+4pTJUj+kMf0OQbIjj9myi1XPit/n36xd9mPPiry6afyx/nXSlbL9lKZmCQn/Pcb+WnJTpFRo3yPVfN12WX6fcgQne4ffhBp29b7+Xvvifz5p2ROmCq9hk6UG659yfrMnZzine/cc0UmTtTb5UbjPDrySH2O7tolcvDBIrNmiezYoa9pr78u0qaNOBOT5Idj9DVCunbV+/Hqq/XxtW6dtQ0E0NcFQKRnT5FJk0Qee0xk2DCZf+Ax8mffk/RnvXuLK7WZLO92qKzt219P69xZZMMGnZb77vMu7+OP9fXLf5tcdZWIeaykpXmnP/GEfk9Oto7TolbtZG2Pw7zzXHGFyCefSFX7DvLAf0aI45lnA58bzZvr42nAAJ/pe1t2kC3nXOKd9tNPInff7f1/z576+mOkb3bv48Sdmuq77MmT9TXT6dSvk0/2/bxbN30NMPdz584iubkipaUin30mUlwsn05YLtP6nSKuxCTvdcJ8DRsmg275QH4+aqBUJSaLLFump59wgnee0aNl44GHBV53++vxx71/P/usyK23ypp+/a1rqPVZjx7yV58T5MvjLxV3SmrgZR17rEh5uUhRkV6XVatETjpJ5P339Tb55huRe+/Vy73ySpETThCPeUwFeh13nMiWLSJffy3y2296O3z4oV7+sGHe+SZMEHntNZF77hGPUpLfrFXwZfbqpc+DW24R+egjkdxcufLWd+XLU6/0XrvOOUdf64cPF7njDv2bjz2mr0/Z2frasGqVyKJFIhddZC27snNX2fW/z63/jz5+sPfY7t1bX7fMYzolxXtOHXaYvgabaVRKVg19Ue69fKgIIC+98K3ICy+IAPK9ea727q2Xbe57QJ/j9nW9/HKRP//U5/+qVSK//KKvqwG2y6YzB4ncdJPv9Ouvl/JWbaW8W3fvejz0UOA8mHlPsb+mTNHvP/wgUlnpvX4AIk895TvvxIlS0Nl7X/1xykq9vZOS9DGQlyfy7rs6/zFunMg113i/e9NNsm/KX5Ldom31NAwYoM+zli113m3NGn3due02b17BfJnnz1VXyd3XPi/Dz7tDKhOTZNwZ/xSZOVN/9uWXIj/+KHmXXynfH3OhVCan6P2cnu573F7ivYZs6nCg97MDDhD5+2+R668Peow+NPhh7//N/CogMnCgPneOPFJ2t+/m+702bfQxcvfd+tpjbp8jj/TO8+23+vpi/v/tt/X5P2uWvheLePeZuf8OP1wEkDm9jpVxh58t7pQU69pb0Kxl9fS/8ooIIHu79fad/q9/iXz4oTgSk8SVYDsO/vtfvR+OOELnXWfP1nko8/M779Tb6uabdT7GXFaw87tbN+92++9/9Tqa19k2bfQ97oEH9P1r5EiRadP0feCTT/S5PXCgyDHH6PvkrFn6PJ07V2TrVpGvvhIpKdHbqaTEez8CRP74w+e8kcMP19t29GjvcTNpkr7Gzpol8uOP9Yl4GgSApSKB48GAE5vyK6oD4A0b9C5p314kIUFKe/aW4efdIV9OWa2DmoceEnnrLZ2xNg/SjrbA6PbbdQbb/P/DD/tmJtLSRPr29TnRqtJaWH9nvv2hd9ktW0pVc/3Zpg4Hyq/PfKADS78T9YVzb5ONHQ8U+de/pGjPPlnR7RCZc9Dxcsjj4/U69e5d7TsCyOoL/mnd4IuHPa8vHM2aiXSoHiC/dcaNklNc4ZMBW9Olr+zakaUvBOa8112nA8BAFxT/zJ3/67TTqk8bNEjkr7/0BeKNN0T++c/A301O1vvGPu3MM70XwN69A66XzysxUV/g3n/fd51a2TJB9kx0aakMvuldOf/W/+kLoHGBF0DfNO66y3uzT0zUfx94oEj//t4b+KGH6gtnTemyv045Ra+HfVvajyczo9mihdx0+7tyyMO/Sq+hE+WEF6frY+GHH6zgOKTXtdd690unToH3qVFIEnT/Btruxxzj3a63364LagYN0tvw4Yd1pua553Tg7Hf8Fqc0l/Wvfei7PPs5Zqy/9OwZOD22Qgdr3mOO8Z1mZhZ69fJmZo2XJyGh2jlsvv7se5L0e3Ss/OuDObogQim9r1u2FLnjDnEk6220ZcJMkYICXdhlX8YFF/j835Gkg6Oy//uP9Bo6UT6ala4LGp5/XmTAAJl771Py5hk3yuQ7nhRxuXRQAOgbNqC358cfVwsMt7TvUX3fNG8euBDk9NO9QWSwV4cOsrtnPxFA1vc8TBe23Hhj9fkGD9YFGoC+brYKEmAcfrjId99Z/y9Pba6vAf7H4C23+BYaBXpdfbXO8G7cqAuTzOknn6wzkCNG+M7/3HPVMuEOM6N14YW6QA3QmcONG/V105z3iCNEhgyRrAFnSWVikvd37MtPSRF5/HEp6Wbsg4EDpbiVPt4qex/kzZzZC5AAq5DPevXr53uOmdu1QweRM86oth2m3PmkzrgH2U7jB1ymj63OnfW0AQO85zYgRWmtRR56SPLGjJWSgRfqa55ZMGYPcANdB2bM0NfjV18VOecc2deyvbhUguwb/E8dPNrnHTeu5v3p/+rY0WfblCY3k9xzLtD7JtA9JdCrffuA04+7/zt584uZOui74ooa5/fY1/v66/X9ytweSom0br3/dKSliQwdKlvbHVD9s06ddMa+hTe/IErpwPqDD3zvTe3a6XlPPFE8xr6pSkiSe79YKDJ5spQn6bQ62rYTycqqnjal9Hnvnwb7OiYm+lw3r7rhVfnolCtl9KiJ3sqCs86qvuyzz/YGHiecoO+1J5+st9e2bdZ8Px81UASQglbt9LWtR4/qxz2gC3z8C1eN13f9B8lXC7aLfP+9vrd36eK95ga79thet1z5rLxw7m2y652ROrDbs0f/fqtW3vOkeXPxdO1q5aUq+h6sA+0Ayxvy1Fe6IKtbt4CfD7rlA5Hdu6tNL/iXN0jfdO0tImPG+M7TrZu+9mRlec9J83XssVahR42vq67yvVb5FyYCOl9zxBH6+LLne+2v887T+dQOHbzbyHiVdPdeT0qPO7Had3MOPUoW9ThSXvznw7rSwna87R38L+99DZC/+p4ouw8IcO0PlO5AL1tA73z1Vdnx5ffVj/VPPvGt+LrwQp2v29+y+/cP/pn9XnrSST4FX9arXz8r/+I85NCaf0spfcxEMQbAtldUB8AiItOn62C3rEx+WpQhvYZOlHenb/adx+nUJUpTpuhS5PR02fnBp1KRlaM/nz9fZO1a/Xdlpb64T56say2uvVYHZp9/LvLWW/L2L39bpbGrVm/XJUSHHCLSsqVsPXWgPHXhvXLIkxPlxQnrdI1n3746o7p4sci8edLr8QnS6/EJIiKSkVsqfR4bL2e8+qf0GjpRPB6PviiuWiXy009SMHSYfNd/kHw0/AsZMWm9nH735/LkoPtk1/a9ulTvyCP1xfSbb0SWLxdJT5f/DbhKDh/ys+zILROZP1+2DThX7vrHk3L4kJ9la3aJDvgnTtSH8oQJOhO1fr0umfrhB30jnjxZ5Pjjdc3rsGG6FvbTT6uf/A88IPLHH/LnGZfLra9P0jc+O49Hl+InJuqL0YgRumbplVf0drYv6/339XdGjtT/P+UUnZ6bbtLvf/+ta/cKCnRpoVk6LKK3FyDywgviyM6RE/77jdz68Rz9G4sW6dJyEek1dKL0GjpRf2ffPn3svPqqN0N41106GNi+XV9IzztPZwqeeEJkxQq9PiKyqJctAJs0SWTlSn3TfvJJKT3oYCk96GC9ji6XTkNZmd7GQ4boTGpysj4W77pLX7jff1+OenaKXPvJArn/++Vy9HNTvNuwokLXyFVV6WXccYcODN59V9cgrF3rzShPmaJv/Fu26N+dOlVk9WpdkvnCCzrI++UXPW+nTnqay6Vrb+bN09tpwQJdMrttm7cGatQofQzce6/ePjVZu1akSxfJuO2/ctu/npFDH/5Fpq/bp0tkzVYXHTvqm9WePXr6jh06Y3T55boke8AA/f8bb9QB7dixOuhevdraBzJ2rD5G9+3T5/8rr+h99txzIuPHy7fXPCDzeh0jm7Zl6VLbCRNEZs8W55FHyaRDTpNfjjrPKnC47IO5epnffaf394oVIiLy5rBP5d3TrpOFW7L150uXyr6rbpCXzrlV3r7HOIa//FLk00/F8/ffcuZL0+Stpz4RT2GhHPL0H/LypPU+m+b1KRuk19CJ8tx427XGzMR06iRSXKynT5igpzVrJgXnX2RlgOXdd+X/bn5LnnzmK+92GD9eXwPef1//7XbrY/7yy73HaNeuOtP5++/6WM7IkCHvT5Hvj7lQrnnkK72cPXt0gPr++/o7PXvq6Tk5+nhYskRfmyZOFPn2W3GfN1DOueMT+fq4S/S1raREBJCilDQZ/P4c/d0VK3TNvJmO/Hwd6LRo4Q2E335bH3OFhTp9Zo2Eef048UR9HBYU6GkOh8h338lNt70td/zzaXG5Pfq4ePRRkbFjZfQHv8i4w8+WzGdH6LRXVOjjy1RWpjMgL76o0yIi3y3aIafe84WMe/AlfT789JPelkuX6uNeRB79fK4Mun+0iIh8cuuzsrtVJ1kxfaFe/pQp+j7wyCO6tu/SS0VuvdX3erlhgw6WZ8zwpmXcOH0tSEjQQXy/fjLptMvl9n8Nk6G/rNLLBvS+27zZWtbCnkfJP57+WS/j6af19NWrdZrbtJH7/v2ynDLiTxHxu+b9+acuePR49DaYOVOf5127irz3nuS2bCeLDjqu2ind99FxcvSDP8qk1Xv1BJdLH6OLF+v/v/OOyE03SelZ+nrhSUnR6br4Yr1+11yj7wPmMbtihVQdfoScd9tH0vfRcTJlbaZ3uQUF+jr50Uc6UAT0/wcM0DVsCxfq+/nXX+uCt/XrRb75Rsa9/Kn0GjpRnhm3xptw8xwoK9Pfu+YaHYzceac4r/iHvHbWf+Tt02+QquxcPf+UKfp3fvpJH4eZmTq/8Ntv+rowZIjex2edpYPC338XEZFhF3hbCvz3ssfk7dNv0MeziK4hfP11KVq/SVYvWO1NW26u99gwz/vyctnyf3fKggOPltfOu0Xu/PpvERG58PEfZEn3I2TGA8P1fAsX6sLrcePEsWGjVM1foLftzp06MDr/fP1+8cX6fP/1V/17VVUigwbJwrc+tY6LN6du1MfB/ffr65GIeJYvl2uuf0UOemy8tb+stJqF2gkJOmBKSJCcdZuk19CJMuCeL+WOYd/p77z4ovc7F18sj178oIw66R/idLn1ts3L0+v9/ffy0Rs/SJ/Hxkufx8bL6Pnb9fcXL9YFF61a6fv0vn36HJ82Te9zo3Xanlvvkfsve1TOeX6CtU4Lt+Z6t/OuXd7CpmHDRDIypLjCIb2GTpRz7vhEpv69Tc/3xBMiV18tGZ10IJPZsr1c9dF8/dmaNSL/+IfI8OGy4t3P5YNTr5HXh34kvYZOlPnpOTpvlJOjWww88ICsXblFZhykA8YfZm3U++bOO3VhXEaGXv/ycr3sykpZOGe1TD7kVFnTpa++B+fm6qDqhx/0+n73nT63e/cWR0KiLDv8ZP3doiJde5mVpV+ffabv4w89pM+73bt9WwqMG6ePnbFj9fYfOdIbtJ57rr53X2y0bPr7b/lp9ia5/V/DZFfrTrL1vVFS9dLL8tsR58j6Tr1FAJn69Ft6v7+srzXy2GMigLx5xo36nr90qUhiorguukgOffgXufGDv/S9yUzP9On6vjN9us6XbNmit8t33+l079yp8za//abP+V9/FfnjD3l67GrpNXSi5H/4sb6m/fmnXo7p7bf19dRs2ff55zrvecUVOi9v1oCbFWiAPrYeecT7/86ddd79xht1IJ+Y6C08vesu2ZlZIGVduomrTx99f3G7RUpL5aPJa7zLMCt5zj9fz/O//3njjCjGANj2ivoA2Obzuduk19CJMsIv4+mvsMwhBz05Sb5btKPG+QIZNnaN9HlsvBx3/3cyb4sRQLtcImVl8vIf6+Xgp/+QY56f6s3k+jEv0g6XW5btyJdeQyfKlSPnS6+hE6XK6faZd+2eQuk1dKLc+90yeW78Wuu723JKg6bPnGf93iIREXln+iZr2sbMYu+Mu3bVet0lM1OXUA4dqi8YxgXm2OFT5Zw3/qr5e+bFyCbj+9/k7iuekHuHfKIvcCYzs1sb2dkiTqcUlFVJr6ET5Z//m1dtFnM7VDr9AvU9e3RGJQROl1uOeGiMHPrwL+IoK6/+G7YCjqDMjKCIiNstHo9H+jyhMyMjJq2XQ4f9EVJafBPm3P885m8vWhTa/CtW6Exndnbt0uJ2y5S1mdb2/sPMOIvoZkChLs/j0RmfOvjH/+ZJr6ET5c/1vt/fW1hupct8XfTunIDL+M/ni6XX0Ikyc0OWNe3P9fuk19CJ8g+/42vNbn2u/rREN50f8PKf8uiYlT7zPDtujfQaOtF3en6+LmAwgwkRvW8efVRkwwaZuzlHej0+QQbf9K64XW7p/YQtYDfZjye7H3/UgVkAN3+h1+3kEdOrf/jzzyLr18vOvDJZkJ5b/XPR11Bz+5nXrd9H/iyn3vOFXOy/PZcu1Rk5U1mZbiq7I4Trr9stq3cVytKMPGtSpdNl/XZRhUOW7ciXt6ZtEhF9fe41dKIs3pYnm/cVi8PlDrhYj8djXQ8/nbNVeg2dKE/8uipoMm4ytpfL7ZGrPpovvR6fIDM2hHBsrlqlM3TBTJyoM4CG/sN1k/d7v9WFdll7cqSySl87KzdvkSMfGiO9hk6U014xAmmn03cfO51yyNN/yFHP6kI0nwA4GKPg8vinfpejnhhf7WNzGT8vrfme8cXcrXLs/d/Jlqxia7meffvE7a5+fG7eV2wt97flNSx3+XKdPrc7+HEuIq/8oQuX7vl2qWQX60BOysqC3udySiqt3y8sq35vCqasyikZud77r8PllhPv+0YEEMcLLwa9x1z07hzpNXSiDgANn554hbxw3u0+841dvlt6DZ0ohzz9hxUAn/jSdOk1dKI84nc9EREZ+ssq+ffni6tND2Z+eo51vPcaOlFen7Kh2jwVDlf14+azz3RBr4i+Zl10kcj//Z/IxImSWVhhzW+d+x6PDmZmzxZPbm6N23rY2DXS+wn9+Zfztnk/cLt10F6DyWsyjVZT06zfmLrWr5B2715dUG3IKvKm9wv774ne1gPu+VJOvO+b6tcxERm3Qu+f+79fLr2GTpSzX59pffbdoh3y2/JdsmxHvhz68C9y9IM/egN6EckrrZLB78+Raz5e4JPXm7R6r5WeCodLnh67Wr5esF38VTpdcso9o+WKF6qfoz5KS3WhuWn1aquwpppFi3QzfPPcKi3VLxH5esF2na7HJ8j89Bxru136n3dETjxRPvh5ke89xO2WN+5/Sw5+ZKyMW2HUcpaVWefaJe8Z23PVqnoFghe+PVt6DZ0oa/cUSmGZQ54bv1Y+m7tt/18MZOpU3VzZvL54jAJV//xqaan+bM0akaoquf2rv+XIh8bI8N98z8kXJ6yTIYOHSNELI/QyV66s8boVjWoKgDkIVhQzByjY33Moc8uq4PYI9hVX1vo3Sqtc8CQkIj+tjffxDYmJQFoayqvcaJGSiORE5TOEfCDFFU4UlOnRRru0bgYA1R6rUVrpst7tywvl8Rtm2pzBvtejx36XUU3XrnpwiVdf1QNxJCcjv8yBgnIniiqqP7rJ53vJydUm55x6NiYfdgZWdu6nB74wtW0LQI9m+sAPK7AjL4THKXTqBCQlocTYZmVVxjN1RfDoz6uwYKt3QAJzHssBBwB9++7/NwCUO90oS01DZXIzlCOp+gxK7f/BePbPExJQ5fLAI0BaShJSEhOsR14Fs3BrHlbtKsS3i3bA5fZg2rp92FYQ4rGslB60JylA2v0deyywfDkm73PhsZ9X4bGfV+Gd6ZshIvh49lZklwT5zYQEn+cAO20DT+Hss/F3WSJe/mNDaGnt0mX/8wVg7uPF2/NxwduzkVmkB91wuqTavK4g55N5PSmzjcRsjnxZ4fAdSOzPDVlIUMD5R+j0tktLQUG572jC1rFpH9m5XTs9AMvJJ3unJSXp8+uww3QalMLarv1Q6nRDpPrxO37VXvR+YhIK/X4P116rH1sTgPnIJvt+slx1FXD44fho9lbc+13gEc7t62BebzMO6Y+9rTv7PBcUgB6t+IILvP9PS9MDCB14YMBl2+WVO3HZh/Nw5UcLsSVLP04ru9g7IFxJpQv/GrkA78/YAqfbg5JKfR3alFWCi96bi9HzMwIud/H2fAx6dw427iu2rhXljiCDw8G7zUurXHo+pazv1eiYY/SgTMEMHqyf/w7A4xHrOlpc6YTL7cHAUcvwzWI9QJj07oPS1DT9t7mNk5J89rFL6etJqcPlnQfexycFlJgIEUGBJwElkljtfDAvV/t7DnBhhQsFaW2894LERFzx8xac/PKf1ea1L6vG7Xjccfr+mpBQ43W1wjge/1izD7d/pQdlQ1pa0Puc/fwtd1bPL0xdtw/T1lV/UsBnc7fj0g/mWdu2uMKJnJbtcMyDP6LwwUet+Ur9ztENmXpAKPt98qWBd+Dzk/7hM58YT8VNSlDWuWney6ud3wDSs0uRbpwXgThcHjz68ypk5JahsNyBGz5djJcmea+9gQ6L4soA9/LbbtODngH6mjV5MvDNN8DgwT7nu/W3UkDz5sBZZ6GouXd0/ZKq6ssWCJT1t01CApCSEnTd7Gmtcnps0/z2Z7dueiAvg33U5Wy/Z7JXONzIbN0JOS3b+Y7AbzCvES1SEwEABeX69x0uD54auwZDfloFl1tQmdwMxc1a+uS51u8txto9xVi8PR9ZtrynPb9aWO7Et4t24pnxtkchmumucmNf647ITEwLsjUMLVoARxzh/f/RRwOXXRZ43lNOwdBWJ+DNaZu9323RQq+T27svXW6x0rmm28HA33/rQcBge453QgKWHnUqHEnJ3nM6Lc06F6z88jHH6MG+6ighQR8tpZUuzN+ai9ELMvDixPVB7+M1uvBCPVCheX1RSm8v//xqixb6s6OOAlJSkFtahdLUNGSW+R4jRRVO/HbUQGTecb9eZv/+TepBySHkGqmxmBe2/T1XsNC4aBXXFLQFUVLpQrPkBFQ6PdUyBGUOF9JSkuAR8TkZ7/x6KQ5o2xzPXea9KBVWOJFnBMCdWqUCqJ4ZNS84pVUun6AopADY2Bb277lqygTVUXq2fnxCUYUTIgJVi5O9zHqkTOD9lZ5dit9X7cVJfdrj3x1ahLRM+zZ7a9om/LpsN/YWVSI50Vt2VVLpQseWqT7fc3t0CVdSYs1lXOW2zFqZw4U2ad4Lpfhn/ENkHq8tUhPhdOtg2OX2BE3L9Z8usv4uKHPgremb0aNdc8wbel6dft9Ol/R5bzIA8MHMdGzLLUVqUiKKKpw465COeHXyRqQkJuDWM/pgV345Br07B7/ecxoO79YagO+zEP1vTFd/rB/N8vAFh6BZcmKd0rm7oBzd2zYPeryZN91Rc/SIsr8t34P7zu0XsGAq2HlhPfLIts8rHIGDpT0FFejcqhnat9AZtvYtUqzMkamkyrdwJhT2a4wZhPkHwF/M2w4A2JpThhN61ZxhNJnXkJquJbklVSgod8Lp9vicP4Dv+pdWutC+RQpKjfN4f4FSbdgzqLsLKnBwl1a+mcdK38yjuW0mrd4Lt0cwY2MW7jireiHAngJdILK3sMJb0FHDfjHvFcUVzqCFIPVVUuWCeQkpqXShsEKvj/mIF3sQ6w5yrfEW/Pnuo5JKJ9qmBT82KpxuKxgqrXL5zKugA5OKINdpkxnc2YO81bsDP6bLJwDdz3JrklVciV355T7ruiM/+IjF3t90B/zb9L+/0qEAXHhkV5/pewoqUFLpQkmVC62bJXsLLJq1RHGVb6FQB+MeYz/HCiuc6NAydb+FnG3TUlBY4YTbI1b68suqB8D55Q7klTmC3nsz8srwy7LdOKZHG/RsXz1wqlZYBd/rSyj39IABsCG7uBInvzzD+n+gc0wESFAKHuPeUxvmeVll254lgQJ46AKmD/9Kx3EHtrWm5dquLyLicyyWBkiredw2T9ahgJnvWbzdNsK5O3Cey17hUlThRE/zd2zbO9A+ht98/oUr9TV/ay6aJSfi0UGH+kx3+qyHp9q+M49Le+GDeY0qtRV02PNkdeX2CBKNPImZNSmscPpsryLj3GoIRca93X9/mQUyAQuRmgDWAEex8hAymH+uz8LrU/RzcGustQyitMpp1dj6/05ZlQstU5OQlKh8gtmVuwqxclehz8lSWB6gBtjvUS/WhaPSNwAOWGsD3wyStwbYVhNnu6DtK6p97XcgW3NKrd+u7QXOzFAFq3kxt1d+ac3P5bQz07CnsAIfzEzHXmM9N9tKyQMVfIyYtAH/9/ni/S7ffoP8Zdlun21aXOGbcXC4PD4ZvSXb831qos2A2Vz/tJQkpCTpS0ywFgT+QfaPf+vaocowBR2/r9qLE0f86bO8vLIqXNG/O17+59EAvIUe5v6ZtCYT5Q43fvrb+9ibYMedXW5p6M/fttuWU4qzXv8LszYHf2SZfyYowcjEuQI8TilY+sz94lsD7HvMVjjcuPaThZizJQftWniDhnYtUpDnt37VSsJDYD83zOPWf93MgpJgGb9AzIyZM8i1BPAWFBaWV1+u/Twwa3XMgoKSShf+zsjHmiDBT6h+W74bz9lqQswWB/t8ak+8aSssd1iZ90Xb8q33bxdVfyyUWTufX+YNaGsKxMwMTUmlK2DLgHCwX5dKKr33B7Mgxe1Toxt4GaX2/WLLKO/vXme/dh/7wnT8uT6r2jwVjpqDNnObBvot/3Os0nY/W7e3GPd9t7xO17DLPpiHqz5eiHLbdwvLnah0uvHc+LW45csAj6iDbyFNoIKMvFKHVUBtl2+sY2GZcW747LPqrSIA33tPoXXcBb6nmbu4XYtkFJQ7fI4x8zxcv7cYGbll1rQqlyfoPTTPuHfmlTqwYmdh0N+zs69HWQiFPPZl+Lc0WLvX9xpgP1+nrduHT+dsQ1mVC6lJCVAKPoVboTBre+33ywmr9ga8pm/OLsHb0zfjt+XeZ5/n2K7RZkusxwYditvP6BPwemAeN2YNsF4nF+Zt0ff1lqlJPi2e7MGwfd125Zej9xOT8PuqvT73g535wVu7mdfZMoe75hYdtVRQ5sC2nNJq55/Lp/Wgb/4us6gCc4z7b6XLbeVLzPuJvfAgWMGt/Xee+HU1tuaUYviEddVah83ZnIO+T/2BDZnFOOeNv7Bur25NUVju8GkV4V/gHG4ej25NuHxngXWNyy9zYPT87bj2E12oX2QrKG2KGABHsVBqgMcs3YXF23XmqC4BcFmVG11a6YDV/wJZ7nAjLTURyQkJ1gXZ7RHkllZh5a5CnPCStylYUYUD+eUOpCQloE1zXYvoH9jam91VubwXlGAZdnvJoLkNAjWdXrWrEANemYFfl/k9B7UOzGAIqP32NAsQqlyegM1XzEzChsxiLN9ZENIygwXha/d4b8SBLsTrM4uwcV/wpmQme0bj7embMWODN6NobxJc7nDj/z5fjMOf9T6n9fUpG/HSRH1xf/inlTj4af3cTjOTk5aSiBQjmAlWQ2APsgEd6ANAj3b7aRZVg1cnb8RF7+pnuK7bW4z8Mod1sxYR5JU60KFlCtq10MepFQAbNx+z4KllqreBjP/NMxD/5mehWrGzEB6BlQn05/ZItYyb2bTQbALdpbW3pDhYgZJ5Dtn3eZkV+OrPtueWGU3aqtC+hbc1QKeWqcg1Mp8VDjf2FlZYGZjaFBTZf9vcXlUuj8/xkWQUiddme5r7J1CBgMkMaPybcgO+hX/+gX1xhRNXf7wQl304L+T0mEQEp74yA18tyMDDY1ZhSUa+9ZnZ9NleeFfsV3tiL3k/rKtuojds3Frk+G0b89pSUOYNMkJpAl1caQ+Yw1sDbF4/O7RIQXGlywrAzO0vtl0lIqh0uvH6lI0+191y27FlXhuAwIUYdv6FuT8t1YVZbo9YNcP7bQJt/EZRgN/yD2zsQefktfswaU2mz71kf0qrXLjo3TnWMZ9T7Lt/f1iyE18t3IG/NuUEDKrKA3RrsMsrq7KCRzuzUCKntAqfztmGvbZtXGALau334q053uuUuY32V/jXLi1FH5vG/kxOVNb19pL35+KcN2fB7ZH9BtT2c3hFgHuoJ0AgZS9IK6ihRtJkD8b8A2r/e635f7dHcOc3yzDijw0Yt3Ivmqck4fzDu2DM0l1BW1bsK6rEH2syg6bVtHxnYbX5ACC3RK+L2aWqTfNkn/3grd1NRIvUJJQ73NW2T4XDjQQFn5ZLRRVOZBrXpEqnO+i9L9t2HC7apmuMP5q11ed+UFMexCd/F6bCtyqXG2UO3fpjs19TeofbHsj7BsBXjlxgXV9EvOtpXn9LK114ZIzuemZ+r8rlCZh3zcgrw49/78Kf67MwbV1Wta4HszbpQHv8yr3IyPO27igod/oEvUUVoVeU1EVuaRV+WbYb09dnWQVf+WUOLMnIx+Lt+dhXVGnlzwLVAKdnl2LK2sywVVY0BgbAUcy8WZQ5XFi3t8jn5mTKsmWE6lYD7EInI/McqAa4RUoSkhN1H8gFW3ORVVwZsJ9NYbkTewsr0aV1KpITdQa2Wh9gW9ORrOIqK6MbLKCwn3TmTd0ZoOZ4uxE4TApwk6gtswYYqP329MmEBLgomBnAKev24eYvloTUxDhY8yB7E6lAF6fskiqjNL3mi5N/4cruAu8xluN3bC0xClrMdGcWVVp9UX9bsQcujyCzqALDxq4FYATASTUHwJnFgR8gH6h/WCjcHt2fd+O+ElQ63dZN2sxYFle44PII2rdIQYcW+ri3AmAjg2hmzOwtGIKVgtv5ByWhMkuAg2UiAwWYZsbTLBDqbBRiAcGDwPIABWpWE2inLvW21yDYm412apVq9Bd14c5vluK0V2daN8faZF7szU6zbIGfPeNnHjO12Z5m5sbplqDnlZm58A8Eiiqcfk0FfZu4+TcpT88uCZj5DiS/zIHMoko893v1PnDmMWkP9O3ne4GtCTQADDy8Myb89wwAwLx039YC5rrllzusmutgNcBOt7eGzd4Euj5NdwMxr5892jX3qwHW7z41wCJYmlGAkbO2YuLqvdZ0+7G/y9YUeH/XZv/rmrldK31qSvfTtcj4jeySKvR+YhK+nL/d+mxvoW8Qal+uea3zD1QzcsuwYGuudf3IyC2zrs+rdxf6BAsZfuNEmAEGgIAtEWpqgl3ucFldnPw/M/fF94t3YsQfG/DChPXedSzyXpt9asvsQXKAANh+rTd3cfsWKSiq8B7PPdulochoEm0qrnBaeYtAtdWArRVVmaPaNgIC9wEuqaFJ7u6Ccqw3rr9lVS5szy2zmj0nqOotl/yvHWaeyf/a7fJ4cMMpB6Kg3Bm0sPuW0X/j3u+W+9y//QuErzj2ACOd1e+T5m/uNM6LXh3SrKAY8OZB0lISrRpe/3xJucONtJQkn/1QVOG0Cr9dtn785nqZsoqrrMqO5UZtfFpKIkqrdLc6wPdY9b8ulwVp3VEf9oKxlbsKffJz1ZtAe39zr18LwkqX9/oIALsKyvHr8t2Ysnafz70qUOVUllF4lZFXjj2FFdhVUOFzTrRspgvWV+8urJZ2e+FsQVlka13N+87W7FK4PYKUpAQUlDuwx7i2Ld9Z4NNSyN/Udftw97fLA3Y7iBUMgKOYWTtTWuXC4Pfn4cJ35lSbJ8d2k61L0FBS6UKb5slITUoIWAPcIjURSYkKS3cU4IZPF2Por6sDLqew3Imt2aU4qGNLq39d0EGwqlzYU1iBXh10LV+wDLv9xmDegAMNgmVeNHJLq/D7qr0hZ04DSc8uRWejD3ORrQZg0Dtz8NfG7Bq/69MPK0Cz9fwy702yuNK130zc8p0FuP+HFftNc6BSYzN4CFTqb+dfW5BpuxHYgyF7WvPLHPB4BNkllSgod/pkvu7+djmW7tDbv0VqEjoYzWh3BunHFqzperAM0P4s3OrNKO4rqrRuRld/vBBfzt+OPGMfdGyZ6q0BNm6SU9btw42fLcKufJ3ZsG+7UGqA9xZWWDf5016ZgQd/9O47l9sTsHYCANYZzeqCBXzm/j3Q1ufNzPyY54C9BjhQYYOIBKwZNP8WASqdHp9S/fZ+ATCgax3mGs3jzH0arItG4Joq77z2pr/22j1znm05Zej9xCT8vNTbFN1u3d4ifDx7Kxx+LS6mr8/Csh2+1wARb+2SPZPx+6q96D98Gn5b4W1GWBqgptzu0Z9X446vlwXdn3b+gZKpV4c0bxPookorw2i/5hWUO3zO7S6tm+HIA1qjfYsUzNmci78z8vGP/81HucPlDS5DqAG2B9l5ZQ4r8xtqX+5tOaU++ysYbwCcpo8t4/g2M3bm7yYnKrg9YhXwmgVtHo9Yxxrgew0ZOSsdl34wN+g5419otCW7BGt2F/k2Fd5PzUWRcZysMVrbDLcFh5lFvutvLst+HmbZanFFBJd/OA83fLoYE1frWpNz3pyF/36vrxG5ftdp/9YPy3cWomVqEhIUsHpPgAC4hibQ9uuY//3ADGDTs0uq/e6egiABcFH1PId9uYHua+3SUuAR73d7tE+DCHwK9bfbAlr7vdInvWXe39tXVFlt7IvAfYBt9y6/PNKZr/+FS96fi1mbsvH4L6tx7puzrGCza+tmyC6u8jnPc0t1wf3cx88F4G0Cba7X4GO6AdD5oUO76BYbgQJ1wFvYvs1Wo+5fmH3Tab3RpnlywPukeQ8wj52e7dOQW+pNr1nA0zwlEWkpOugq9zsvKpwuNEtO9NlGOgD2bn97cG9vXZRVUmmt4xrbMVla5UKX1s3QLi0Zq2wBsP/1yKeJfZgCYHsBx7Pj12HgW7OtfG1NTaD9VTk9EPEG/2Zt8q78cp/vBQoMzX212Ci0cnvEpym42ZVogS2vAuhzqaDMgdZGgByopVI4mfcfc90O6tgCHoE1OOOyHQU1NoHeXVCBDi1SrGMrFjEAjjLLdxZYpcLmxcq8QPqfsDoIsQcpoV1EXG4PVuwsgMcjKK1yolVqElqkJlWrySm11QCbF0F7hsSuoNyB7bllOKhTCyQlmv0TAw+C5fYIckqq0MsYCCpYE2j7xcW8iOWVOarVHJs3n9W7i/DADytwlTEoUW1VONzYU1iBE3q1A+C9kX86dxs2ZZVg9IKMGr9vb6Zqbsuicic+m7sNpVWuAKXP1TOR5Q6Xlbm646ulPp+lJCbgrav7Y9jgw32m+5caVzrd1rbzzyC6PeITmPjvc3vGzv5d+/TMokrklzts29/72apdhdbfaSmJOK1fRyQlKPy5IXDhQaAg6eTe7a2+ibWthV+9x/v7mUWVyLI1456xIdsKrDu0TEE7I8AzA14AmJ+eh1VGyWxeaRVyS6tQVuXyGwXaXsMh1qCIwyesx6B35+D3VXuxt6gS41futebp9/RkPPv72mrpFRGszzRrgPU55H+cmOfN4d28o4/mlzmwfm8xrhulBxDr3NpeA1w9E1jl8li1MT41wE7fc8x+PbH3ATYD4JzS6vsrUCn40ox8nPLyDExanYlh49ZY/VaDBcCXfzjfuhGbmdyZG3Vz/Lenb662/MJyBwa/Pw+vTt6Ip8auwRZbc9M7v1mGKz9a4DN/aZXL2i7m9nW6PXjqtzUAgEmrva1H7F01/G3OKsHKXYXILa3C+sxi7MgrC9i/1BQsUOzRrjlW7y4yCmkqcVDHlgCAl20j2uaXOXzS0LlVKhISFE7q3Q6rdhfiz/VZWLmrEGv3FFsZ+z83ZFnX6HKHG+v3FmNXfjmW7cjHM+PWwun2+GSy7RnrcocLbo9g4z59PFY63fhs7rZqrUjOe2s2Tn91Jl7+YwNen7IRO/MCF27Za4ABYIcxn9UE2jggUxIT4HR7rG21eFs+RART1u3z2fc7bL+zaFs+1u4ptoJlf/7HZG6pA5d9OM8nOKxwBr7vmMwa4DUBAs4F6XlWN5QdeWVWBt7cj4Dv8a2bs+t5tmSXWEHRdOPY2bWfga5ySqpwaNdW6Nuppc811lTTIFj2wkR7MGNvcrwhs3pTVXtwar8X7ymswMGdWyIxQQVsAm2/ZptXIvNau7tAr2dP45gwr32A3u/edAapATbSuyW7BE634OQ+7Xw+FxEs2Z6PD2duCZh2/wIA85p485d/Wy3IPjC+27N9Ghxuj8+65ZU60L5FClo304WnZt/QfcY98PS+Ha15u7ZuhpSkBOu4FRHM3pxj7fNUo6XLNlstpX+g0Tw5EV1bNwv4hA//bdSrfZpPja3Zxz0tJcnqzuN/TatwuJGWkuiThyiucCKnuAoHtNH3FHs+wLcJdBV6tG+OtBRv8+nMwgqr5aAZkJtGzdlmFfb6p6U0wGjaJnsXhK05pRj6y+qgLdvMe0eSbdDLjNzyaml3uDwBW1SaqlxuVDjd1nfMfbjLGDTOFKj1k7mvttm6ND3x6xorDxusa09BuX4CSZ9O+hrin/cREezIK6vzWCP+7DXVAHBQJ50fN68fSzPyrX1UbSRy6OtAd+M8jlUxHwArpS5SSm1SSqUrpZ5o7PTUx5asEvxr5AKM/GsrgMADNtibkRSUO3wyu7mlVZi8JhO3f/U3xtlqNPz98Pcu/HPkAvzf54tR6fSgZWoS0lISq904dQ1wktWkuSaj52egwulG306+NcDPjFuLMUYNjn9pmVkDbF5kNu0r8R2MxqcPsE7b1uxSHGYEAmbg7H8h8x9QYcXOAtw6+u+gfRVGzkrHoHfm4OdluyACnwDY4xH8+PdOANhvc2J76Wp5lW5SesfXS/HSpA14bfLGajffSz+Yh9+W+/ZbvuubZTj1lZlwuj3VLq5pqYm48oQeuO2MPmhhu+n4lxrbb1j2v9+evhlP/rYaZ77+l3UR9d/n9hore5O8Vbu8N66s4kqfwDVQ5gkAWqQkoU3zZJxyUHv8uSFwkJAZoGT7rEN0JuKub5bhkvfmhlTTZkrP8mYmMosqfB4xk55dapW+dmiRiuTEBKu01c6bqXPgqo8W4Lnf1/kEvTM2ZOsbpMONC96Z49NPbHNWKR7wq7U3a66+XbTTOn8rHPr42G27oc7cmI1z35yFW770bR5vfn5Y19bWtPwyB+742ltA0sXeBDpADbU9IAiWWS53uH2Ol/Zpvn2AAWBfke/Nt3eHtGp93h/60VsINWdzDr5dtBPDxq211tuU5bfvzWae+VZNrd4PSQGuP/YBcH4J0ve/sNyB58avxfWjFuHo56dZ080AeGlGQcAg18x4lDtcuOCILvjz4bPw9jX9AQBf2h5DNG19Fs5+YxZu/3opyqpcmLM5p9qxGiyTJUZt2HWjFiKruBJ9jMyH/Zq/u6DCp0mnWchxaJdWyMgtswpq1u0tsgIZe6a4rMqF27/6G0+PW4u3p2/GN4t2YNScbT7XVf9+/h/OTMdF787FtZ8sxGHPTMFLkzZg6C+rMWWtDg7y/DK0I2dt9TkOi8qdVm2SeR6ZAfAXRhPicocbp78609rPB3dphUqnx9r/+4orsSu/wiezDPh2TzGtz/Sd55XJG/Dz0l1Ba3c+n+dtxmxvAp1TUoWdeeV4euwavD1tk88jnAL1N/5p6S5c+sE8rNpViLPfmIURxkA3fTt7R/e3t6awZ4Yzcst9+vvf+NkiqxtPTQ5sn4ZT+3bAku351e5Fvo9B8v3MXptqvwfZmxwHGqTQ3tfXf8Cg7u2ao03zZKswwx5kB6wBNlrbmIWN5hgPG2wBsL2Z9+788oAFK+a5ax7n5xza2fpMKd0E+ppPFuLNaZutc8I83pMTlfV7TrfHGm3e398ZuvWIOcr0bts5nFtahY4tU60mxSt3FeLSD+ZaLY9O7dvBmjchQaFX+zRrX09Zuw83fbEEd3y9FEUVTmsUYHsNsH+BdfPkRHRp08znWLKnxc7MT3nv7d6xOMwgNVAer3lyYrVCsZIqFw4xxhywX1Pcxn2wwuFGVnElDmjT3GoGDehzd19xJVo3T0JPv3E83puxBYPfn4fnje4g9lpf+zVpQXouznhtJq75eCEmr8lE/xemYaVR6PPBjC34aemuai18iiuduPGzRdYxdGzPttZnZmHT7oJy637/yuQNGDlrq88yHjr/YLx33bEAdGuoQP3QdxeU77fmOlCh/tIdBZi4KhPXj1pkFYD4Kyx3orDcgZ7tmiMxQaGg3GhpZyxvwdY8nP3GLJzx2syw9LvN9htnoE9H77WrdbMkrNpdZOVtAtUA7zGeXBHLYjoAVkolAvgfgIsBHAHgeqXUETV/K3od3KUVLu9/AEbOSkd6dknAmpW1e4ox1ehUn1VcvSTonu+W488N2Xjop5UAdMmyf9Po+UYNgdkEo2f7NKMU0IlPZm+1StHKqlx6EKz9PEpnxD+Psh6JclCnFlbAvCu/HN8s2oFh49YiPbu0Wilfb6MG2OXWNcKXfjAXr03eaH2+YmcBEhMU0lISUVblQlmVC3uLKnF4V99H0+wrqsQpfdrjnnO8z75dtqPACiLGLN2NmRuz8evy3fhw5hafTKmI4KsFGdiUVWL10xtwkL6JFVY4sXFfCXJLda3zlqyaBzWxZzzKHS4s21GAJRn5SExQ+GbRDkwLcOEzawlNZu3Nsh0F1QKZFkZTE6UU+nX21jR8MDPduslml1QGbMackVuG92dswZilu+FwefC3UXPi3yRqX1ElKp1uPPzTSvyybDdO6q0LA1bZ+qs8+vMqn6bGS3foZaUm+QaUaUYm4exDOiE9uzTgjcF+8z/30E744Y4B1rrN3ZKLPYUVWJ9ZHPSCX1TuxLPj11o3/fScUpxoFGDoY867fvuKK62S3I4tdY1EsMcMtEpNwpo9RcjIK8fcLTk++2LZjgI8//t6LMnIrzbQzdSHzvL5/6HDJvv0TV+2owD5ZQ6cPOJP/LBkl9X/95Au3v25ancRpq7zHivmTdZeA7x6d5FP7aK96aUZrK/bW4Qnfl2NMvNZrwZ7wYq92WuF0zcADlQDPHmtbz/7a07qCcDb/z67uBK/r/Ie04tsj9MorXL5nCMz/LoUrNxVCJfbUy0DnV1cVa3f9fKdBUhQsDKRgczdkouJqzOx0JaxBryZ6L82ZSM5UeGus72PFerSOhWLtuVh5a5ClFW50apZEvp1bmWNbP/Dkp04pkcbnNKnPd6f4a1lenHievzniyX4Yv52bNxXbB2PwQLgk/u0B6BL3zPyytHNVoNv8q8VNNNwSNdW8Ih9ZOi8gAVJVS4P9hZVYvmOAiuz8+X8DJ/taz//yh1uzNmi+xYvttWsjlu5F0/8tgZuj1TLeD50/sHYlFWCvNIqiAjOeuMvXPzeXOwprMBHs9KRlKBw4ZFdrePHtKewAp/O0QHI9Sf3tH6zrVHosnh7Hjb7XW9X7ipE307eTNohXVpi/d5ilFbpZwQXVzrx2dzt+Gzu9mrNuc8/XAdK9lY8ZrNhEcFF787BWW/8he8W78T7M9OxdEdB0EfYfHPbyfjk3ycAAD78K92afuQBrdHHVgNsXu8cLo9VAHhQpxbIyCuzAuKOLVMwPz0v4CBH/nq2T8PZh3RChdONpRne/bBsR4FPH/MK45nJ149ahONfnO4zmn2eLRj2bw7sb82eIpzcuz0SlG9Qm1lYiW5tmqNtWrJVS26/Di7bkY+vFmQYj6DTG7FaDXB7nXG2B8CzbaPgvz8zHWe/+Rfmp+v7YaXTDYerelBy1AFtrL9FgG9sI6SbhWQllS60SEnEcQe2w9/GIHRT1+3DCxPXw5/9cmJ2OdlTUIGVuwrxyuQN2FVQjg4tU5CUmIBmyQmYsGov1u4pxlcLdyAlKQG9O/gGfb06tLDuOfZWUBNX77UKVtKzS7F2TxHOfuMvbMstQ/8e3nVqnpKILq1SfWqAF27Nw4czt1QLls38lHlPMa+1zVMSrRpg/0qICqcbzVN8A2CzS9AhXcwA2FYDbJSYzE/PhcsjOLVvB+see1T31vCIzqOe1Lu9VfBld1n/A/DNoh3IKq70qwE2WsxVOPHgTyuxu6ACSzLycc93ywEAMzdkYW9hBf5Yo/O+yzJ0/1QRQUmlEzM2ZGF+eh7en6nPR3swtz23DB6PYOmOAitvF6jpctfWzazBwKpcbivott9bK50en8IrM9/7zaId2LhPj2Zub1VzaJdWeHDgwQD0/ca8F53cu3213zdrgNu3SEHb5smYtSkHb03fhJNfnoG1e4qsa3Ol04O3p28OqdAsmIVb8/DOn74tq+zXLv/HpdmPD7dH4PGIrgGO8QA4dhtvaycDSJf/b+++w+OqzsSPf99R770XS7IkN1muuNsU02ExwZAQNvQNmyxJSHv2FwhhSSdkNxuSEAgJoQQCMb2YGIwLxrj3IkuWLEu2umT1Ls2c3x/3zmVkyQSCC169n+fR49GduaMjz6t773vOe841phxARJ4HlgDDj2xniR9cOZG1JQ18e9nuEedNelciTYgIYkFu/LDnfd332j7+sqmS8cmRPPfl2Ww53ExmXChbKppZmBfvJFuXTErmyQ0VvHuggXcPNPDqrhrm5sTRN+ghLNB/2P1bo0ICSIkKdkYIr5qSSnx4EP+78iCTUqOcEi3vwcrfJXxn2S56jys5y7RPFruOtvD7tWUMuA0v76zme5dZJb6v7aphTk4sdW29bK9scU7k4+17s762q4a06FAqjnWxKC+B/3fpeKakR/GVZ3aw9JENXFmYwg2zM3l9lzUa/n17cabiug4eun4a75c2YrA6EjJjQznS3M24pAgmpUZaZbtF9c7B7rYF2Ty2rpwjx6wT4K6jrYyJC8XjgfiIQGpae4bcg++BFcXOCXj7vRfyny/uGTEBfu9gozPn4s8+i6w8t+XIsFJW31sV5CZGsLuqjQkpkRyobecXK4q5bUE21z26cciIvfck6T14ei3fW0tGbOiwpfYbOnp5emOFMyfy+nMy2VrRwtqSRmLDAmnusg7SP/Ep1fQmwy98ZS6F6dFkfW+51V47YZ9nl4X9betRjjZ3c8v8LJIig6lq6eHt/XXMzo5l8+FmchLCnREOa38/uvrdfP25nVS1dHPLvCz2VrexMC+B2xdk0zfg4YkNh3l6YyUN7X18Y3EeZQ2dfH5mBqUNnSMuiuaNeW9yFxMawEjjABdPSuYle3S+vr2PQ42diFjJ/NqSRp7bcmTIiFReYjiT06MYlxwx5H36Bj08uKIEsBZ3+uEbRSzIi6ejb5C/bT3CufkJuAQK06M5WN/JneeP5dWdNTy7uZJLC5Kpae1xKhDGJoRz2/xsDtZ3sL5s6FQE3wTDGGtO3xW/sY4VhenRQ+4V+X5pEw+vKSPSZwQHrLlyvkm178qg3vsBv2mXCgcHuJiSHs1Nc7NYdaCBu57fxcZDx0iJChkyaulbtnrZQ+s42txDoL+LYH/XsLKqnUda2VfTjjFw45wxzsVs36CHV3fVsKn8GA0dfXxlUQ6rixsYnxxJaUMHJ+oL/+XbJSPOJa9u7WFDWRNPflDBBeMTuWpKKn94z7q/8sK8BF7cXuXEifei0vci6PLJKczJiePqhz/gmmlpvLa7xrl910+WHyDQz0VeUjivf20BtW295CSEkRIVzAdlHybiXzs/l9zEcGcOaHLU0AQ4Nixw+Gdsd9Z4L0y9fDtLRtLZN0hpQycZsSEcbe4ZUqHgm+ge6+ofdqsrr9buAXYdbRmSpADMz43n1++W8j8rD/LXzVactvUMcNmv1zmfb1JkMCvuWjjkrgEAL+2oIjjAxZKpadz/ehE9A27m5sSxqfwYWw43O8dFX7fMz+YHdjVBQVoUL++oZvqPVjIvN47JaVG4PYaS+g4ONw1Nnn9+TSHv/vTDnx8R7E9xbQcdvQPsq2534uTfF+Xwh3XlfP4PJ55GMzYhnJQoa46j72jO1VPThsTJmpJGnt1cyXsljc6xf/7YeF7ZWU1FUxfx4UG8861FTP/xSrr73czKiiU7Poz3DjZS197LFYUpzBwTw96qNl7eWc3UjCjm5MQR5O9i2bajzM+Np6imnZuOu93dz94qpq6tz7nY9o2PrRUtLJmaxuriBkpOsELvBeMTWW13Ts3KjqXP7eGRtYdYfaCBxMggjnX1kxoVjJ8Iy/fUkh5zgA2Hmpxrip+9Vez8H3ursbwJsPf4MT45kkB/l9O2z01L4xX7nHPdjHTcHmOvybCZ8ckRVBzrIiTAb9j5KjV6eMeR161PbuWr541lS8UxIoIDmJUVy+/WlHHLE1uclXiPd0VhKm/YHXjeJH1/TTu/XV3qdMjkJVp/fyEBfkOuafoHPcPuMZwVF8q60ka2V7bw3sEGLp6YRHFdh3Mt4hJr7YkVPisFf+GcTHZXWVMzQgL9SI4Kpr69j6k/eodpGdGsOUHbZ2bFkp8Uzn2v7WPz4Wae22L9PYYG+pEebY0qri6uZ05OLEeau0mKDKa5q5/I4IAhJdDbK1sBnI5o7zWEv0s42txNcV07L+2oIjzIn3Ps6UoAdywa6xxbzh+fOOJdDb59UT5v7K7huy/sHjKdrryxizf31PDi9ioaO/p47c753P/Gfuca6jery5zkNjYskIdWlfLb1WVEhwbQ1TfIJJ+OEICb5mbxgl0Z9Mu3Syiqbae1e4C5Y+NGvA4DSIoKxvvpPWFXNIYH+TM7O25Ip67vcfn7L+/ljkU53P/GyCnH9DExfOuifDYfPsbf9334GSdGDu9498ZXdGigdSzu6nc6M678rXUuz0kIo7yxi8fWlVtfN87g92sPMS0zmvuunMhDq6wBnvPHJXLeuESW760lOTKYeWPjeHlnNS6BJVPTuO+14dOxpmZ8+H94+eTkIZVV7T2DDLitKp2fvHmA2rYeegc8I3ZynE3O9gQ4DfBdIaUKmH2G2nJSJEQE8cDSQr77wm4AxiaE0Tfo4cdXF3DrE1sBawGfxo4+54RxIk9vtC4gD9S2M/VHK4c8d/XUNLLiwkiPCRnSQ+h9/aHGTtJjQpiZFcOgxzj3SAO478qJLJ2RTllDByv21RERHMAlk5K5xO418q7i+vruGnLiw/j2xfnc9fwu3B5DfHigU1Ljvbj84/uHnd+9saNvyK12vnZBLn98v5zypi6nx9b7h7q2pNE5keXZF4V5PheHb+6pdU64Xgvz4odtD/ATHry2kBv+uIkrC1MQEf7jvLE8vv4w2ypbWJgXz1VTUvnT++Us/tVa/Fwy5MQnMvx2Cd4D923zs4kODeSBpYW8U7SSmNCAYSfxi45b3CwvMXzYyDAM7dUsSIvkjT0uXvzKXB5Ze4jfrSlzDrC+c11+tfIgT26oGFZN4Pt/IGL1VGbEhrKyqJ6fvVXM+OQIvrE4j0smJfMdOxb/+7pCbnty6LzkqRnR7DraSmigH1l2+7wXQiF2AjUhJZKokABnPt8Lx5Ws3jQ3i51HWkm1exNT7GTg388dy+riBqcn1hsnm8qb+e3qUgbcxrnA8r2ImJASwdaKEPbXtA/7bNaXNVGQFulUNcSGjTwC7DvaCh+O1D956yy6+wdZ9ODaIfMPf3fDdCf5vXnuGF7aUT2kh3t8cgTnjkvgD++VO3MKd1e1sbuqjbzEcGfhqsL0aPxdLh5aVcqiB9c4nWATUiJJjwnlvn+ZyKs7q52TsPf/OtKnDA3g8ofWOx0W97yy19kuYvXg/vLtEmdbcICL3gEPSx8ZetHvO4/Kd6T18zPT+dGSAvtel8Jfvzyb/11ZyqPvWSVli8cnMjE1kt1Vbc5xw5t8gfXzN92zmOse3ciA28PB+k5cYvXUX/3wBwDMzIrhOxfn0zPg5prfb+C7L+wmLNCP3kEP6w424ucSfnJ1Ae8W1Q8bSQa4a3EeD/mM0HrNyo5lZVE9K4vqyUsM58GlU4jyKfWelhk95MR/vT066TvH+pppaSRGBrPt3guJCwukpq3HGY0F6xZV+2vamfGTlXT3uVk8IZFfXz+Vg3WdTgemv5+LxeOTnH0SI4N5/OaZzkJX9722n79sqmRMXKjTieA9rvoeB+69YgLLth0dNlo6kq9fkMd/vrhnxE6B6NCAYXNLS396Gdc+utHZ7o2Pa2ek8+L2Ku69YgKT06II8BMn+QVrTu/xnRtx4UFDYsBrfHIkwQF+LMiLZ2VRPclRwczPjR92jAArWfjCzAzSY0JIjgymqbOPl3dU0+/2DDkXwIfHCrBGdrwVH163zMvit6vLhpTGb75nMUmRwRys72BNSSNXFKYw6Pbw9v56vjgrg+e2WJcaMaGBiAgzxlhTO6ZmRHPjnDFcNTWV94/raPQmOt525CSE0dk3yLJtVnVNbFig8/f3zQvzmJcbz90v7+W5LUeYkh7FrfOzAXjw2kKnI/rfFmbz8JpDbKtoobath9iwIKd0/hsX5PLa7hr+/MFhROCV/5jPl/60maiQACalRvLi9ipe2VntHDf9XOI8vqwgmb/vq+PaGelDEuCrpqZy+1NbKanvoLShgysmp3D1tDT8/IQHV5Q4nUefm5Y2JKn59rLdH/7ux3XwpEWHcP05GTy9sZL48CCuthPgm+eO4YdLCgD4j4ZOfv7WAVYVW5Ua3t8/0O/D2zJGhQRwbn7CsI4Zr0fsMte5OXFcWpDM79aUDUt+C9Oj2GMv1rQwL95JgL0VF97j2swxMWyrbHGOtclRIbR0D3DflRN5aUfVkKosry+ck8Gzm484axLMyYnjzvNzWWIf5579tzks23aUV3ZWM29sHNdMT2dyms8IcICf03nQ2j3AmpJGxiaE0d3vpratl7gwK1mamhGNn0t4YGkh99jxYyVvsWTFhREc4MdlBck8vv4wf918hK5+NwF+woDb8P3LJ/DXLR/+/XpH5SelWgMN3iqRSwqSWb6nlkt//T5g/Q15j0kAV0xOobqlhzf31DAlPZppGdGMS46gb9DDNb/fwISUSLLjw7j+nAynwxCshNZ3rv/i8YlMyYjmuS/P4e39dTy1ocJZZTolKpirp6XZn+uHa+D43l4OYHJ6FBUPXMGcn62irr2X5XtqcQksyk8Y9hktyI1nfVkTKVHBznxj7zng/HEJTlXKufkJ7K+xqnsWj09kVXEDNW29Iya/iRFBNHT08S/2omiF6dFDzhGRIQE8cM1k3thTwwdlx5xjqndfX9efk8He6jb217SzMDd+SNXOHX/ZTqC/i11HW1ld3OCcK5Ztq3I+X7BKmr3HZN+/S1+p0SE8c/tsHlp1kNnZcdyxKIfH1pWTHBnM+rImpv7wnWHTMtM+xe0qPwvk49yK5bNKRK4DLjHG/Jv9/Y3ALGPM14973R3AHQCZmZkzKisrh73XZ01Dey+/WV3KNdPTmZ4Zw6Dbw0+WH2BhXjzTMmPoHXCzfE8t/W4PF01MYtBt+MumCsYmhBMc4Mek1EgMVq9kRVMXh5u6mJcbz1a7zOzW+dlDLmrfL23knf31TMuM5r2Djdxz+QTnBDDo9vDg2yUsyktgQkrECctGvXr63fzaLq/44qxMsuLDaOseoKS+g8zYUDaWN3FufiIxoQH8fu0hQgP9mDkmlqTIILZWtLDqQD05CWHkJ0Vw0cQk1pc1caS5G4/HWjDox0sKKK7rwCXC3upWBj2G62ZkEOjvwhjDnz+o4PLJydS29XLUnku0MD+Bzt5BJqdF8eg664SWFh1CW88A83PjmZoRzYHadnISwgjytxK3zr5BDjV0UpAWhZ9LKKnr4MXtR+kf9HBOdizljV0EB7jo6feQGRfCtooWchLCaesZIMAl3HFujvNe3vcL9nfRN2gtRBPg5+JgXQelDZ0MegxXTE6hf9BDQkQQj6wtY9qYGCKDA2jr6ae+vY/LJ6c48236Bz0cae4mNzGc3gE3T22oYMDt4bqZGawtaSA4wI+6tl5K6juckdj5ufEU1bYzPTOa4roO0qJD2H20lQkpkSydkQ5YI9JFNe1cOyPdGVX85dvFFKZHc8mkZFbsqyXQ38XOI63Myo5lXHIE//P2Qb40ZwyT7dKt3gHr5Ox7of767hreLapntt37HBkcQGigHy1d/Xx9cR6HGjsZExtGiD1XaU9VK5NSo2jt7uedonoW5SdQeayLhPAgXt1VTVVLDzGhgRxp7uar542lq8+61Yf3JFda38nuqlbm5MQ683nq263y7s9NT3c6fDYeOsarO6tZMi2VZnuBNT+XixljYnj0vUOkRAUTHuRPUW07+UkRfHFWJmCVnT6zqZKl09MpTI8a9jfh8Rh+vLyIKelWB8FVU1MZnxzBqgMN7KtuY2xiOHuqWnGJcGVhKilRwTy+/jD3XD6B1p5+7n1lH32DHhbkxnPeuARyE8Od0QVjDM9vPWqVl05M5pWdVdw8L4s1JQ1MTIni6Y0VNHX28c0L8ymp6+CN3TUMeAyDbg83zhlDXXsvF05MoqimndXFDSzMi2d9aRPtvQO4RJg3Np6D9R18Y3HekGPEziMtJEYGn7DsacW+Ol7dWc2PlkwiMTKYo83dVu90RjSfPycDYwyrixsI8HOxKD8BY4y9QmY3mbGhvFNklbl197u5dX4WEfZCMyV1Hbx7oJ4vzsqkrq2XJz44zO0LsxmfHElH7wCVx7o51NjJ+ORI9lW3ERTg4tz8BB74ezEeY4gLCyIvKZzGjj5unpfF4+sP09YzwJcX5jgj2wdq2ylt6OTc/AR+saKYgtQoRHA+b4CH15SxKC/BiXOv5q5+lm07ygXjE6lv72VsQjhbK5rZVH4Mf5eLb12U7/ycd/bXkRYT4oxY7Ktu4809tdx5/ljn9/V+xhXHukmKDGLL4WYON3U5yRBYtxeJCQtw5lJuPHSM/KRweu17Kgf4Ca/tqnF66A83dfHV88ay7mATwQEuggP8iAwOoLNvgGVbq/jOxfnsPNpKUU07S6dbx4LMuFCONnezrrSRAJeLXVWttPcM8LNrJjuLAAG8sM0q5V86PZ2gABfRIQG8vLOawrQoPAYW5MU7v9Ogx/DkBxVcODGJR9aWccH4JC4tSKa7f5AXtlVxwfhEwoL8eejdg1Q2d3NefgI1bb18bloaGbGhQzpq4cNFpNrtW+pEhvhzpLmbdQebyE0M5+JJSeQmhJMYGczKonoG3R7e2FPDL5YWsrq4wbkFTmigP3ddaJUq9vS7cRtDeJA/TZ19lNZ3MjMrhuqWHjYcOsYNszOdz+6tvbVcPjmFAjtp6Rt0c+8r+7h5XhZujyEmNJDK5i4mpUYx6PbQN+jhgb8XEx8eyKUFKcwdG8fWimYa2vucFYS9C+PlxIc7x0NfPf1uHn3vEFUtPSRHBXH7ghxCA/1oaO8jMy6U3gE3f99XS0xoIOeNS8TtMQy4PQT5u9hYfoxVBxqczsqJdjXVoNvDhNRINpQ1ccmkZJ74oIIAP+GG2WOcY0CLPSrlm+i1dQ9w/xv78RjDg9cW8of3ylmYF09WXBgr9texr7qNxRMSuWB8Ei9tr8JtDINuww2zM2nrHuDFHVUsyI1nXHIEhxo7yYkPGzKK6vEYnt1cycK8BEKD/Hh1ZzXTMmPYeaSFKenRzM6Jo3fATVlDJ0W17cSEBlLa0MHnZ1rXA+WNXfQNuJmaGU2Qvx/ljZ1UtfRggOy4MIpq27i0IIWuvkH+uvkIty3I5rVd1UQEB3DhhEQeW1dOU2cfLhHuujCPfdXtjE0IIy48iLq2Xho6epmUGuWUTosIDe3WIpHeNRu2VzZTVNtBS1e/s6pzR+8AB2o7OCcrBo+xFq87Nz/Bqbqpbu3hWGcfhenRHLM7em6cO4YXtldxbl4CIlaSdtHEJCKC/cmMDR3y/1be2ElCRNCQY8qRY938ZVMFgx7DmNhQqlp6EIG7L5tASX0HT2+sIDcxgpaufubkxLEgL56fLi+irKGT1OgQfrykgG2VLdS19xIa4MfiCYmICPuq26ht6+WiiR926B3vmU2VXDwpicSIYHoH3DyzqZLZ2XGIWJ0Yb+2tZXZOHMc6+5iSET1kde/KY12sL2si0M9FQVoUeYnhrCttJDM2lPhwqzz8pe1VXFqQYi2qZgw3zs0CrBXlm+z3dAmkRIXwzKZK3B5DdGgAnX2DfG5aGiuL6rlqSipuj+HpjZVMzYxme0ULc8fGERbkz/qyJpZMTaW5s593D9Rz09wsqlq6iQkN5KmNFWTEhLJ4QiLR9v2uw4L8qW/vdeaRF9W086uVBzlvXAIpUcHMyo4lIjgAt8fw5IYKbpiVyZqSBjp6B7h6Who7Klvpd3uYmBJJQkSQ3a4KLi1IpvJYN32DHsbEhrJifx1Lp6fzixXF1LX1cm5+AktnpLO1opm399eRnxTBoYZO/P2EWdmxbDrUjJ+fEOTvYnZ2LAkRwdYChC09zjWgr47eAUrqOli+t5amzn4W5sYzKzuWqJAA/rbtKDfPzRrxGPVZIiLbjTEzR3zuLE+A5wL3G2Musb+/G8AY8/MT7TNz5kyzbdu2Ez2tlFJKKaWUUuos9lEJ8Fm9CBawFcgTkWwRCQSuB14/w21SSimllFJKKfUZdFbPATbGDIrI14C3AT/gz8aY/f9gN6WUUkoppZRSo9BZnQADGGPeAt460+1QSimllFJKKfXZdraXQCullFJKKaWUUh+LJsBKKaWUUkoppUYFTYCVUkoppZRSSo0KmgArpZRSSimllBoVNAFWSimllFJKKTUqaAKslFJKKaWUUmpU0ARYKaWUUkoppdSooAmwUkoppZRSSqlRQRNgpZRSSimllFKjgibASimllFJKKaVGBU2AlVJKKaWUUkqNCpoAK6WUUkoppZQaFTQBVkoppZRSSik1KmgCrJRSSimllFJqVNAEWCmllFJKKaXUqCDGmDPdhtNKRBqByjPdjhHEA01nuhFq1NB4U6eTxps6nTTe1OmisaZOJ423T2aMMSZhpCdGXQL8WSUi24wxM890O9TooPGmTieNN3U6abyp00VjTZ1OGm8nj5ZAK6WUUkoppZQaFTQBVkoppZRSSik1KmgC/Nnx2JlugBpVNN7U6aTxpk4njTd1umisqdNJ4+0k0TnASimllFJKKaVGBR0BVkoppZRSSik1KmgC/BkgIpeKSImIlInI9850e9TZTUQyRGSNiBwQkf0icpe9PVZEVopIqf1vjM8+d9vxVyIil5y51quzlYj4ichOEXnT/l7jTZ0SIhItIi+KSLF9nJur8aZOFRH5ln0u3Sciz4lIsMabOllE5M8i0iAi+3y2feL4EpEZIrLXfu43IiKn+3c5m2gCfIaJiB/wMHAZMBH4oohMPLOtUme5QeA7xpgJwBzgTjumvgesMsbkAavs77Gfux6YBFwK/N6OS6U+ibuAAz7fa7ypU+UhYIUxZjwwBSvuNN7USSciacA3gJnGmALADyueNN7UyfIkVqz4+mfi6xHgDiDP/jr+PZUPTYDPvFlAmTGm3BjTDzwPLDnDbVJnMWNMrTFmh/24A+viMA0rrp6yX/YUcLX9eAnwvDGmzxhzGCjDikulPhYRSQeuAP7ks1njTZ10IhIJLAIeBzDG9BtjWtF4U6eOPxAiIv5AKFCDxps6SYwx64Dm4zZ/ovgSkRQg0hiz0ViLOz3ts48agSbAZ14acNTn+yp7m1KfmohkAdOAzUCSMaYWrCQZSLRfpjGoPq1fA/8JeHy2abypUyEHaASesEvu/yQiYWi8qVPAGFMN/DdwBKgF2owx76Dxpk6tTxpfafbj47erE9AE+MwbqUZfl+ZWn5qIhAMvAd80xrR/1EtH2KYxqD4WEbkSaDDGbP+4u4ywTeNNfVz+wHTgEWPMNKALuzzwBDTe1D/Nnnu5BMgGUoEwEfnSR+0ywjaNN3WynCi+NO4+IU2Az7wqIMPn+3Ss8hql/mkiEoCV/D5rjHnZ3lxvl8lg/9tgb9cYVJ/GfOAqEanAmsJxgYg8g8abOjWqgCpjzGb7+xexEmKNN3UqXAgcNsY0GmMGgJeBeWi8qVPrk8ZXlf34+O3qBDQBPvO2Ankiki0igViT218/w21SZzF75b/HgQPGmF/5PPU6cLP9+GbgNZ/t14tIkIhkYy2esOV0tVed3Ywxdxtj0o0xWVjHr9XGmC+h8aZOAWNMHXBURMbZmxYDRWi8qVPjCDBHRELtc+tirHU1NN7UqfSJ4ssuk+4QkTl2nN7ks48agf+ZbsBoZ4wZFJGvAW9jrS74Z2PM/jPcLHV2mw/cCOwVkV32tnuAB4BlInI71kn9OgBjzH4RWYZ1ETkI3GmMcZ/2Vqv/azTe1KnydeBZu9O4HLgVq0Nf402dVMaYzSLyIrADK352Ao8B4Wi8qZNARJ4DzgPiRaQK+C/+ufPnV7FWlA4B/m5/qRMQa7EwpZRSSimllFLq/zYtgVZKKaWUUkopNSpoAqyUUkoppZRSalTQBFgppZRSSiml1KigCbBSSimllFJKqVFBE2CllFJKKaWUUqOCJsBKKaXUpyAicSKyy/6qE5Fqn+8DP+F7rRWRmfbjt0Qk+pQ0euSf/RURuelT7H/PyWyPUkopdSrobZCUUkqpk0RE7gc6jTH/7bPN3xgz+DH3Xwt81xiz7dS08NQRkU5jTPiZbodSSin1UXQEWCmllDrJRORJEfmViKwBfiEis0Rkg4jstP8dZ78uRESeF5E9IvI3IMTnPSpEJF5EskTkgIj8UUT2i8g7IhJiv+Yce9+NIvJLEdk3QlvOE5H3RGSZiBwUkQdE5F9FZIuI7BWRsfbr7heR79qP14rIL+zXHBSRhfb2W0Tkdz7v/ab9/g8AIfao97P2c1+y998lIn8QET/760kR2Wf/7G+dsg9BKaWUGoEmwEoppdSpkQ9caIz5DlAMLDLGTAPuA35mv+arQLcxphD4KTDjBO+VBzxsjJkEtAJL7e1PAF8xxswF3B/RlinAXcBk4EYg3xgzC/gT8PUT7ONvv+abwH991C9qjPke0GOMmWqM+VcRmQB8AZhvjJlqt+1fgalAmjGmwBgz2W6/Ukopddr4n+kGKKWUUv9HvWCM8SalUcBTIpIHGCDA3r4I+A2AMWaPiOw5wXsdNsbssh9vB7Ls+cERxpgN9va/AleeYP+txphaABE5BLxjb98LnH+CfV72/XkneM2JLMZK5reKCFgj2w3AG0COiPwWWO7TDqWUUuq00ARYKaWUOjW6fB7/GFhjjPmciGQBa32e+ziLcfT5PHZjJZTyCdriu7/H53sPJ74W8L7G7fOaQYZWjwWfYF8BnjLG3D3sCZEpwCXAncDngdv+UeOVUkqpk0VLoJVSSqlTLwqoth/f4rN9HVZpMCJSABR+3Dc0xrQAHSIyx950/adv5j9UAUwVEZeIZACzfJ4bEBHvyPYq4FoRSQQQkVgRGSMi8YDLGPMS8ANg+mlos1JKKeXQEWCllFLq1HsQqwT628Bqn+2PAE/Ypc+7gC2f8H1vB/4oIl1Yo8ptn76pH+kD4DBW6fQ+YIfPc48Be0Rkhz0P+F7gHRFxAQNYI749WL+vtwN+2AixUkopdSrpbZCUUkqps5SIhBtjOu3H3wNSjDF3neFmKaWUUp9ZOgKslFJKnb2uEJG7sc7nlQwtr1ZKKaXUcXQEWCmllFJKKaXUqKCLYCmllFJKKaWUGhU0AVZKKaWUUkopNSpoAqyUUkoppZRSalTQBFgppZRSSiml1KigCbBSSimllFJKqVFBE2CllFJKKaWUUqPC/wcUtW3MYYcLfAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the predicted result\n",
        "plt.figure(figsize=(16, 8))\n",
        "plt.plot(real_price[\"real_mean\"])\n",
        "plt.plot(predict_result[\"predicted_mean\"], color = 'r')\n",
        "plt.xlabel(\"Trading minutes\")\n",
        "plt.ylabel(\"Stock price\")\n",
        "plt.legend((\"Real price\", \"Predicted price\"), loc=\"upper left\", fontsize=16)\n",
        "plt.title(\"The result of Training\", fontsize=20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "ls6H3BTmDGZn"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-- Train RMSE --  4441.7396576657775\n"
          ]
        }
      ],
      "source": [
        "# Calculate RMSE\n",
        "predicted = predict_result[\"predicted_mean\"]\n",
        "real = real_price[\"real_mean\"]\n",
        "For_MSE = pd.concat([predicted, real], axis = 1)\n",
        "RMSE = np.sqrt(mean_squared_error(predicted, real))\n",
        "print('-- Train RMSE -- ', RMSE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "Z3nFRZfzDGZp"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ]
        }
      ],
      "source": [
        "# Load test dataset/ model\n",
        "G_model = tf.keras.models.load_model('gen_model_3_1_164.h5')\n",
        "X_test = np.load(\"X_test.npy\", allow_pickle=True)\n",
        "y_test = np.load(\"y_test.npy\", allow_pickle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "Glca63c8DGZq"
      },
      "outputs": [],
      "source": [
        "def get_test_plot(X_test, y_test):\n",
        "    # Set output steps\n",
        "    output_dim = y_test.shape[1]\n",
        "\n",
        "    # Get predicted data\n",
        "    y_predicted = G_model(X_test)\n",
        "    rescaled_real_y = y_scaler.inverse_transform(y_test)\n",
        "    rescaled_predicted_y = y_scaler.inverse_transform(y_predicted)\n",
        "\n",
        "    ## Predicted price\n",
        "    predict_result = pd.DataFrame()\n",
        "    for i in range(rescaled_predicted_y.shape[0]):\n",
        "        y_predict = pd.DataFrame(rescaled_predicted_y[i], columns=[\"predicted_price\"],index=test_predict_index[i:i + output_dim])\n",
        "        predict_result = pd.concat([predict_result, y_predict], axis=1, sort=False)\n",
        "\n",
        "    ## Real price\n",
        "    real_price = pd.DataFrame()\n",
        "    for i in range(rescaled_real_y.shape[0]):\n",
        "        y_train = pd.DataFrame(rescaled_real_y[i], columns=[\"real_price\"], index=test_predict_index[i:i + output_dim])\n",
        "        real_price = pd.concat([real_price, y_train], axis=1, sort=False)\n",
        "\n",
        "    predict_result['predicted_mean'] = predict_result.mean(axis=1)\n",
        "    real_price['real_mean'] = real_price.mean(axis=1)\n",
        "\n",
        "    # Plot the predicted result\n",
        "    plt.figure(figsize=(16, 8))\n",
        "    plt.plot(real_price[\"real_mean\"])\n",
        "    plt.plot(predict_result[\"predicted_mean\"], color='r')\n",
        "    plt.xlabel(\"Trading Minutes\")\n",
        "    plt.ylabel(\"Stock price\")\n",
        "    plt.legend((\"Real price\", \"Predicted price\"), loc=\"upper left\", fontsize=16)\n",
        "    plt.title(\"The result of test\", fontsize=20)\n",
        "    plt.show()\n",
        "    plt.savefig('test_plot.png')\n",
        "    # Calculate RMSE\n",
        "    predicted = predict_result[\"predicted_mean\"]\n",
        "    real = real_price[\"real_mean\"]\n",
        "    For_MSE = pd.concat([predicted, real], axis=1)\n",
        "    RMSE = np.sqrt(mean_squared_error(predicted, real))\n",
        "    print('-- RMSE -- ', RMSE)\n",
        "\n",
        "    return predict_result, RMSE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "xfXWMhCcDGZr"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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23XZbpwr0fvvth+222w4XXXQRjj76aPTo0QP9+/fHqFGjUopPGSNHjgQAPP7449h0003xwAMPOL+LRqNYvXp1p49t2bJlaS+rqqrCwIED9QVmChgq6/0NGDAAixcvzvj7Pn36IBQK4ZxzznEGAJIx+CUiIiIiKj8MgAOouroa119/PY488kjcfvvtuOiii3DwwQfjqaeeQn19PbbccsuMt21ubkZFReLb5uGHH0Y8Hu/08SxcuBDTpk1z0qDj8TiefPJJ7LLLLp0KRA866CD89a9/xWeffZZ2JrhHjx7Yc8898dlnn2H06NEMdomIiIiIAoIBcEAdccQR2HnnnXHDDTfgd7/7HU466STcf//92H///XHhhRdi++23RyQSwbfffovnn38ezz77LOrq6nDwwQfj2Wefxfnnn4/DDjsMM2bMwK233oo+ffp0+lgGDx6M448/HhMmTMDAgQNxxx134Ouvv8Ydd9zRqfs7//zz8eijj+KAAw7AZZddhh/96EdYuXIlnnvuOdx5553o2bMnbrrpJuy1114YN24cTjvtNAwdOhQrV67EJ598gng8jmuvvbbTj4eIiIiIiIoTA+AA++tf/4px48bhzjvvxPnnn4/XXnsN1157Le6++27MmzcPPXr0wCabbIJDDz3UqZR8xhlnYOHChbjvvvtw1113Yeedd8YLL7yAn/70p50+jk033RR/+tOfcOmll+Kbb77ByJEj8dhjjzlbIHVUnz598P777+Oyyy7Dtddei1WrVmHw4MHYb7/9nMcxevRofPzxx5gwYQL+8Ic/YN26dRg4cCBGjx6Ns846q9OPhYiIiIiIipcy28sExZgxY2T69OkZf//FF19gq6226sYjCrZ99tkHsVgM7733XrvXa43G8fWyBgzrU4v+9dXddHSli+9jIiIiIgoqpdQMERmT7ndc/EglJVjDNURERERElE8MgImIiIiIiCgQuAaYCurtt9/O6Xqc+SUiIiIioq7iDDCVBkn6TkRERERE1EEMgKlEiOf/REREREREHccAmIiIiIiIiAKBATAREREREREFgm8BsFJqQ6XUW0qpL5RSnyulzrUv76eUel0p9Y39va/nNpcopeYqpb5SSo3zXL6TUmqW/btblVLKvrxaKfWEffmHSqmRfj0eKixJ8y8iIiIiIqKO8HMGOAbgQhHZCsBYAOcopbYGcDGAN0RkMwBv2D/D/t0JALYBcDCA25VSYfu+7gDwGwCb2V8H25efBmCNiGwK4GYA1/n4eIiIiIiIiKiE+RYAi8hSEfnE/ncDgC8ADANwJIAH7as9COAo+99HAnhcRNpEZB6AuQB2UUoNBdBLRKaKiAB4KOk25r7+A2B/MztM5Ynzv0RERERE1FndsgbYTk3eEcCHAAaLyFJAB8kABtlXGwZgoedmi+zLhtn/Tr484TYiEgOwDkB/Xx5EiXrggQeglHK+evbsie233x7/+Mc/EIvFfP3b8+fPh1IKDzzwgHPZqaeeipEjR3boft5++21M/MtfYFlWXo/vyiuvRHeNl4wcORKnnnpqt/wtIiIiIiJKr8LvP6CUqgfwFIDzRGR9OwFHul9IO5e3d5vkY/gNdAo1Ntpoo2yHXJaefPJJDB8+HOvXr8eTTz6J3//+91i+fDn+8pe/dOtxXH755Tj33HM7dJu3334bV0+8Ckef/oeSnQJ+5pln0KtXr0IfBhERERFRoPkaACulKqGD33+LyNP2xcuUUkNFZKmd3rzcvnwRgA09Nx8OYIl9+fA0l3tvs0gpVQGgN4DVycchIncDuBsAxowZU6IhVNfssMMO2HTTTQEABx10EObOnYtbbrklYwAcjUZRUVGR9xnSTTbZJK/3V+za2tpQXV2NHXfcsdCHQkREREQUeH5WgVYA7gXwhYjc5PnV8wBOsf99CoDnPJefYFd2HgVd7OojO026QSk11r7PXybdxtzXMQDetNcJUxY777wzGhoasHz5cidV+fbbb8ef/vQnbLDBBqiursbatWsBAE8//TTGjh2Luro69OnTB8ceeyy+//77hPtrbm7G2Wefjf79+6O+vh5HHHEEFi1alPJ306VANzU14eKLL8Ymm2yC6upqDBkyBEcffTSWLVuGK6+8EhMmTAAA7DRqIAb3rk0IypubmzF+/HiMGjUKVVVVGDVqFCZOnJiSLv3pp59izz33RE1NDYYNG4arrroKub5VRo4ciV/84he45557sOmmm6KmpgajR4/GW2+9lfLYhg8fjqlTp2L33XdHbW0t/vSnPzn3kZwCPW/ePJx88skYMmQIqqursfHGG6fMjk+ZMgX7778/evbsiR49emDcuHGYPXt2TsdNRERERESJ/JwB3gPAyQBmKaVm2pddCuBaAJOUUqcB+B7AsQAgIp8rpSYBmANdQfocEYnbt/stgAcA1AJ4xf4CdID9sFJqLvTM7wk+Pp6yMm/ePITDYdTX16O5uRkAMHHiROy88864++67EY/HUVNTgzvvvBO//e1v8atf/Qp//vOf0dDQgCuvvBJ77703/ve//6Fnz54AgDPPPBNPPPEErrjiCuy88854/fXXceKJJ2Y9jkgkggMPPBAzZ87EJZdcgrFjx2LdunV47bXXsGbNGpx++ulYtGgR7r33Xjzw9CsY2KsW/XpUAwBisRjGjRuHOXPm4PLLL8ePfvQjTJs2DVdddRVWr16NG2+8EQCwcuVK7LfffhgyZAgefPBBVFdX4/rrr08J4tszZcoUzJgxAxMnTkR1dTWuu+46/OQnP8Fnn32GLbbYwrneunXrcMIJJ+CPf/wjrr76atTW1mZ8/nfZZRfU1dVhwoQJ2GyzzbBw4UJMnjzZuc5LL72EI488EoceeigeeeQRAMB1112HPffcE//73/+w4YYbpr1vIiIiIiJKz7cAWETeQ/o1ugCwf4bbTAQwMc3l0wFsm+byVtgBtK/OOw+YOdP3P9OuHXYAbrml0zePx+OIxWJoaGjApEmT8PTTT+Pwww9HXV2dc53BgwfjmWeecWZYGxsbMX78ePzqV7/Cfffd51xv1113xeabb457770X5513Hr766is8+uijmDhxIi6++GIAOs26sbERd955Z7vH9cgjj2Dq1Kl47rnncMQRRziXH3PMMc6/hw/XGfA/2nEMhvWrx+BeNQCAxx57DO+99x6mTJmCvfbaCwCw//76rTVhwgSMHz8egwYNws0334ympia89tprzhrwAw88ECNGjMj5+Vu2bBnef/995/b7778/RowYgb/+9a94+OGHnes1NjbikUcewZFHHtnu/V1xxRVoaWnBZ599hg022MC5/JRTTnH+fe6552LvvffGc88951y27777YuONN8aNN96IW7rwfiAiIiIiCqJuqQJNhbfllluisrIS/fr1w9lnn42TTjopIagFgKOOOiohvXjq1KlYv349TjrpJMRiMedr+PDh2HLLLfHOO+8AAD788ENYloXjjjsu4f5OOCH7hPzkyZMxZMiQhOA3V6+++ipGjBiB3XffPeH4DjroIESjUUybNs15HGPHjk0ogNajRw8cfvjhOf+t5Nv37NkThx56KKZOnZpwvYqKChx22GFZ72/y5Mk47LDDEoJfr2+++QbffvttynNfV1eH3XbbzXnuiYiIiIgod75XgS4LZTDT9swzz2D48OHo2bMnRowYgZqampTrDB06NOHn5ct1fbIDDjgg7X327dsXALB06VIAegbZK/nndFatWoVhw4ZlvV46y5cvx4IFC1BZWZnxvs3xbbttSgJBTsfX3nUHDx6MxYsXJ1w2aNAghMPhrPe3atUqZ2Y7HfPcn3baaTjttNNSfh/UauZERERERF3BADggtt12W6cKdCbJFZ/799dbKj/wwAPYZpttUq5v1v+awHnZsmXYeOONnd8vW7Ys63ENGDCgQ0WdvGWr+vfvj1GjRmHSpElpr2uKbQ0dOjTtseRyfO1dd9myZSnBe65VswcMGJASPHuZ5/6aa65JOwBRVVWV098hIiIiIiIXA2DKaPfdd0fPnj0xd+7chLWpyXbddVeEQiFMmjTJWQMMAI8//njWv3HQQQfh8ccfxwsvvJAxJbm6Whe9amttAaTeufzggw/GU089hfr6emy55ZYZ/8Zuu+2G66+/HgsXLnQKRzU1NeGFF17IenzGtGnTEm7f0NCAl156CYceemjO9+F10EEH4emnn8bSpUtTZt4BYIsttsDIkSPx+eefJzynRERERETUeQyAKaNevXrh+uuvxznnnIMVK1bgJz/5CXr37o3FixdjypQp2GeffXDiiSdiiy22wIknnog///nPsCzLqQL98ssvZ/0bZnuhn//857jkkkuw6667oqGhAa+99hrOO+88bLnllth6660BAA/d/U8cfughGNS7DmPGjMFJJ52E+++/H/vvvz8uvPBCbL/99ohEIvj222/x/PPP49lnn0VdXR3OP/983H777TjooINw5ZVXOlWgM1VoTmfw4MEJt7/uuuvQ1NSEyy+/vFPP7YQJE/DSSy9h9913x6WXXopNN90UixcvxquvvopHHnkESin885//xJFHHolIJILjjjsOAwYMwLJly/DBBx9go402wgUXXNCpv01EREREFFQMgKldZ555JjbccENcf/31ePTRRxGNRjFs2DDstdde2GGHHZzr3XXXXaivr8cNN9yASCSC/fbbD48++ih+/OMft3v/lZWVmDx5MiZMmIC7774bEyZMQP/+/bHHHnugX79+AIDDDjsMp595Fp546F7cdcvfICIQEVRWVuK1117Dtddei7vvvhvz5s1Djx49sMkmm+DQQw910oQHDBiAN954A+eeey5OOeUU9O/fH2eddRZisRj+8pe/5PQ87L333thnn31w6aWXYtGiRdh6663xyiuvYPPNN+/U8zpy5Eh8+OGHuOyyy3DJJZegoaEBw4YNS6gefcghh+Cdd97BxIkTcfrpp6OlpQVDhgzB2LFjcfzxx3fq7xIRERERBZkSkezXKiNjxoyR6dOnZ/z9F198ga222qobj4hy0dAaxbyVTRjUsxpDeuc+c5sPI0eOxI9//GNnL95SwPcxEREREQWVUmqGiIxJ9ztug0QlJVjDNURERERElE8MgKkkMPAlIiIiIqKu4hpgKg0FjIDnz59fuD9ORERERER5wxlgIiIiIiIiCgQGwFRamAtNRERERESdxAA4jaBVxi4lfGWy4/uXiIiIiCg9BsBJKisr0dLSUujDoCQM6XLX0tKCysrKQh8GEREREVHRYQCcZNCgQVi8eDGam5s5k0YlRUTQ3NyMxYsXY9CgQYU+HCIiIiKiosMq0El69eoFAFiyZAmi0WiBj4aMlkgcq5oiaKquwLo6zm5mUllZicGDBzvvYyIiIiIicjEATqNXr14MIIrMK7OW4rfPf4JTdx+JK4/YqtCHQ0REREREJYgp0FQSTDK6xbR0IiIiIiLqJAbAVBJM3Mv4l4iIiIiIOosBMJUEseeAhfWgiYiIiIiokxgAU0mwOANMRERERERdxACYSoLZkspiAExERERERJ3EAJhKDCNgIiIiIiLqHAbAVBJYBIuIiIjK3UNT5+OW/35d6MMgKmsMgKkkmO2PGAATERFRuXrry+V4fc6yQh8GUVljAEwlwZkBZgo0ERERlSkBB/uJ/MYAmEqCaQtYBIuIiIjKlQirnRD5jQEwlQRhCjQRERGVOUvE6fMQkT8YAFNJYAo0ERERBQHjXyJ/MQCmkuAEvmwUiIiIqEzpFGh2doj8xACYSoIw/iUiIqIyZ4mw3gmRzxgAU0lwi2CxVSAiIqLyJAKuASbyGQNgKgncB5iIiIjKnTABmsh3DICpJDAFmoiIiMqdJRzsJ/IbA2AqCaYtYFoQERERlS2mQBP5jgEwlQamQBMREVGZE7AIFpHfGABTSbC4DzARERGVOYvbIBH5jgEwlQThDDARERGVORFhX4fIZwyAqSS4a4ALehhEREREvhGwr0PkNwbAVBKEKdBERERU5iwWwSLyHQNgKgmmKWBhCCIiIipbwqF+Ir8xAKaSwDXAREREVO4EgMXODpGvGABTSXDbAjYKREQUDCsb2wp9CNTNLBbBIvIdA2AqCSYhiI0CEREFwedL1mHnif/F3OWNhT4U6kYiHOon8hsDYCoJbhEsIiKi8reqMQIRYHVTpNCHQt1IWASLyHcMgKkkmOJXXBdDRERBYNo7tnvBwhRoIv8xAKaSwBRoIiIKEnf3AzZ8QcNXnMhfDICpJDAFmoiIgkTY8AWSJcJBDyKfMQCmksJ1MUREFATiLP0p7HFQ99JrgAt9FETljQEwlQSLPQAiIgoQ1r4IJgEH+4n8xgCYSgLXQhERUZCYIIitXrCwCBaR/xgAU0lwlkKxUSAiogDgDHBAcR9gIt8xAKaSwCrQREQUJM4MMBu+QGEKNJH/GABTSbCcYphsFIiIqPyZ1o6xULDoKtCFPgqi8sYAmEqD3QNgo0BEREFgsd0LJBEO9hP5jQEwlQRJ+QcREVH5Eq4BDiQBi2AR+Y0BMJUEYQo0EREFiMU1wIFkWUx7J/IbA2AqCW5HoMAHQkRE1I3Y7gUPB/uJ/MUAmEqCJH0nIiIqZ1wDHEzCIlhEvmMATCWBa6GIiChI2O4FkyVMeyfyGwNgKgncB5iIiILE3f6PgkSYAE3kOwbAVBKEHQEiIgoQYRGsQBLhYD+R3xgAU0lwOgBsFYiIKACYAh1Mzsw/X3ci3zAAppLAGWAiIgoSLv0JKr7uRH5jAEwlwbQDHAknIqIgsCTxOwUDZ/6J/McAmEoC9wEmIqIgYSAUTE5/p8DHQVTOGABTSeASYCIiChKLa38Cybzc7O8Q+YcBMJUUtgdERBQEpggSZ4CDhTP/RP5jAEwlgdtBEBFRkLi1Lwp6GNTNGPgS+Y8BMJUEiynQREQUIJZl1oKy4QsU9neIfMcAmEqCsx0EOwJERBQAnAEOJu56QeQ/BsBUElgEi4iIgsTNfGLDFySsAk3kPwbAVBIk6TsREVE5E27/F0jCgQ8i3zEAppLAaphERBQkrAYcTGapF1PfifzDAJhKgnAKmIiIAoSBUDBZ7O8Q+Y4BMJUEJyWosIdBRETULbgGOKCc/g5fdyK/MACmkuBUgWZHgIiIAoDFH4OJM/9E/mMATCXB4gwwEREFiMXaF4HEmX8i/zEAppLAYiBERBREnAkMFrfoZ4EPhKiMMQCmkuCmQBf4QIiIiLqBZZn9YNnwBYlbA4uvO5FfGABTaeBaKCIiChCL7V4gcdcLIv8xAKaSwHaAiIiCxCmGxFzYwPCu++XLTuQfBsBUElgMhIiIgoTFH4PH28VhCjSRfxgAU0ngdhBERBQoHPgNHO9rzZedyD8MgKkksCgEEREFiZkBZipscHhfag58EPnHtwBYKXWfUmq5Umq257IrlVKLlVIz7a9DPL+7RCk1Vyn1lVJqnOfynZRSs+zf3aqUUvbl1UqpJ+zLP1RKjfTrsVDhmXUxbA+IiCgIhNUfAychBZovO5Fv/JwBfgDAwWkuv1lEdrC/XgYApdTWAE4AsI19m9uVUmH7+ncA+A2Azewvc5+nAVgjIpsCuBnAdX49ECo84VooIiIKEM4ABw9nfYm6h28BsIi8A2B1jlc/EsDjItImIvMAzAWwi1JqKIBeIjJV9BTgQwCO8tzmQfvf/wGwv5kdpvLj7gPMxoGIiMqfOAEw270g4utO5J9CrAH+nVLqf3aKdF/7smEAFnqus8i+bJj97+TLE24jIjEA6wD09/PAqXBYBIuIiILEWfpT4OOg7sMiWETdo7sD4DsAbAJgBwBLAdxoX55u5lbauby926RQSv1GKTVdKTV9xYoVHTpgKg5MgSYioiAx7R1nAoMjcRskIvJLtwbAIrJMROIiYgG4B8Au9q8WAdjQc9XhAJbYlw9Pc3nCbZRSFQB6I0PKtYjcLSJjRGTMwIED8/VwqBtZwhRoIiIKDsti8cegYRVoou7RrQGwvabX+CkAUyH6eQAn2JWdR0EXu/pIRJYCaFBKjbXX9/4SwHOe25xi//sYAG8Ko6OyJUnfiYiIypkzA8wqWIHBFGii7lHh1x0rpR4DsA+AAUqpRQCuALCPUmoH6PP6fABnAoCIfK6UmgRgDoAYgHNEJG7f1W+hK0rXAnjF/gKAewE8rJSaCz3ze4Jfj4UKzykGwo4AEREFgMU1wIGTGPTylSfyi28BsIj8PM3F97Zz/YkAJqa5fDqAbdNc3grg2K4cI5USdgSIiCg4WAU6gDwvNcf7ifxTiCrQRB1mMQeaiIgCxKkCzXYvMJgCTdQ9GABTSeB2EEREFCRm4JflTYJDEv7N153ILwyAqSRwOwgiIgoSEwAxFTY4vIMdllXAAyEqcwyAqSQ4+wCzI0BERAFgcQ1w4HgHOzgDTOQfBsBUEtxqmGwQiIio/DkDv4U9DOpG3j4Oxz2I/MMAmEoKGwQiIgoCtwgWG77A8M4A82Un8g0DYCoJHAknIqIgcbZB4lrQwGAKNFH3YABMJcE0BBwJJyKiIODSn+DxvtYsfkbkHwbAVBLMCDjjXyIiCgJ394OCHgZ1I0lIgeYLT+QXBsBUEpwZ4AIfBxERUXcwM8CsAh0c3tearzqRfxgAU0lwt0Fik0BERAEgSd+p7HEGmKh7MACmksB+ABERBQlngIONLzuRfxgAU0lwt4Mo8IEQERF1A7P2l2uAg4Mp0ETdgwEwlQSmBRERUZAw8yl4vN0biyMfRL5hAEwlwdsMMP4lIqJyxxTo4JEM/yai/GIATCVBmBZERERBwuKPgZOQAs2Xncg3DICpJHgzgTgaTkRE5c6ZAbYKfCDUbbjci6h7MACmksAUaCIiChJn+z/mPQUIs92IugMDYCoNCSnQbBaIiKi8uWuAC3wg1G2shBngwh0HUbljAEwlgTPAREQUJE4VaDZ6gSFc7kXULRgAU0lgQ0BEREFiAl82f8EhTIEm6hYMgKkkcFSUiIiCxDR1bPOCw1vwjDP/RP5hAEwlQbguhoiIAoRrgIMnYQaYrzuRbxgAU0ng5vBERBQkkvSdyl/CYD9feSLfMACmkiAJm8OzUSAiovJmZn7Z5gVHwnIv7v9M5BsGwFQSEkdFiYiIyps4KdBs9YKCRbCIugcDYCoJCY0CR0WJiKjMiSR+p/KXWO+ELzyRXxgAU0nguhgiIgoSizPAgeN9rVn8jMg/DICpJCQUwWKjQEREZc7dBqmwx0HdR9r5iYjyhwEwlQTvqCibBCIiKndOu8dGLzC45SNR92AATKXBWxmRrQIREZU509KxzQsOYQo0UbdgAEwlgSnQREQUJKwCHTwJfR1O/RP5hgEwlYSEfYDZKBARUZnjGuDgYQo0UfdgAEwlwUocFiUiIiprZuaXTV5wJFaB5itP5BcGwFQSuDk8EREFiWnruB9scPClJuoeDICpJAiLYBERUYBYTgo027ygSBjs58tO5BsGwFQSuC6GiIgCxaRAs80LDA72E3UPBsBUEoT7ABMRUYBYLIIVOBzsJ+oeDICpJCRug8RWgYiIyptTBIttXmBYHOwn6hYMgKkkiADhkHL+TUREVM5MW8c2Lzi8LzVToIn8wwCYSoJAYMe/7AwQEVHZMwEQA6HgSJjt58tO5BsGwFQSLAGUsmeA2SoQEVFAMAAOjsT4l687kV8YAFNJEAHCiinQREQUDM4a4AIfB3Ufb9DL4mdE/mEATCVC3DXABT4SIiIiv3ENcPCwCjRR92AATCVBBLAngJkORkREZY9rgIPHYgo0UbdgAEwlwRJBiCnQREQUEKapY5sXHN4iWEyBJvIPA2AqCQI4VaCZBE1EROXOxEKcAQ6OhFearzuRbxgAU0ngPsBERBQkZjaQbV5wcAaYqHswAKaSIN4U6AIfCxERkd8spwgWW72gSCyCxdedyC8MgKkkiMAJgJkORkRE5c4UQeJMYHBIhn8TUX4xAKaS4F0DzPiXiIjKnWXZ39noBYbFFGiibsEAmEqCiCDENcBERBQwDISCgynQRN2DATCVBD0DbNYAs1EgIqLy5s4Gss0LCr7SRN0jawCslBqslLpXKfWK/fPWSqnT/D80IpclwirQREQUGCYA5gxwcCRWgeYLT+SXXGaAHwDwGoAN7J+/BnCeT8dDlJYIoLgGmIiIAoL7AAdPYgp04Y6DqNzlEgAPEJFJACwAEJEYgLivR0WURACEmQJNREQB4W6DVNjjoO7j7d/wZSfyTy4BcJNSqj/sz6JSaiyAdb4eFVEyzzZI7AwQEVH5MynQbPSCwlT+Bvi6E/mpIofrXADgeQCbKKXeBzAQwDG+HhVREstbBbrAx0JEROQ3zgAHT8I+wHzdiXyTNQAWkU+UUnsD2AKAAvCViER9PzIiD+8+wBwVJSKicmcKInE7nODga03UPXKpAn0OgHoR+VxEZgOoV0qd7f+hEbmEVaCJiChALEn8TuXP27+x+MIT+SaXNcBniMha84OIrAFwhm9HRJSGAFCmDDSToImIqMyJcA1w0LAIFlH3yCUADik38oBSKgygyr9DIkol4qZAsy9ARETlzrR1bPKCg9sgEXWPXIpgvQZgklLqTujz8FkAXvX1qIg8zCi4uw0SERFReTNtHdeFBoc365kz/0T+ySUAHg/gTAC/hS6CNRnAv/w8KCIv0waYKtBcF0NEROXOclKgC3wg1G2YAk3UPXKpAm0BuMP+Iup2phFwUqALdiRERETdw0mB5kxgYCS81HzdiXyTMQBWSk0SkeOUUrOQJuYQke18PTIim2n8Q4pVoImIKBg4Axw83sEOvu5E/mlvBvhc+/th3XEgRJmYRsDZBolzwEREVOYSCyKJZycEKlcJE8Ds6xD5JmMALCJL7YrP94rIAd14TEQJTCNgZoDZJhARUbnzBkCWAGHGv2UvYR9g9nWIfNPuNkgiEgfQrJTq3U3HQ5TCKYJlN/5sFIiIqNxZSTPAVP68lZ/5khP5J5cq0K0AZimlXgfQZC4UkT/4dlREaThrgDkFTEREZU6nPetAiAO/wZCQ9s6+DpFvcgmAX7K/iArCjIgqFsEiIqKAsASoCCnERLgnbECwCDRR98hlG6QHlVJVALaE/mx+JSIR34+MyCZOESz758IdChERke9MynM4pBDj9G9gSEIKNF93Ir9kDYCVUocAuAvAtwAUgFFKqTNF5BW/D44IcANeUwWaI+FERFTO3IFftntBklj5u3DHQVTuckmBvgnAviIyFwCUUptAp0QzAKZuIUkp0JwCJiKicuYM/CoTABfuWKj7mHW/SvE1J/JTu1WgbctN8Gv7DsByn46HKIVpBFgEi4iIgsDM+IZCpvYF270gMP2dsFLs6xD5KJcZ4M+VUi8DmAQ9KHksgI+VUj8DABF52sfjI3KGws0eiOwHEBFROTPtXEWIM8BB4mz7GFLs6xD5KJcAuAbAMgB72z+vANAPwOHQoQkDYPKVGQV1R8ILeTRERET+4gxwMDn9HcXXnMhPuVSB/lV3HAhRJpKUAs1iIEREFARhbv8XKJKQAk1EfsllDTBRQTkj4ayBRUREAWB5tkHy/kzlTTwz/3zJifzDAJiKXvI2SGwUiIionDnFkLgGOFC8GW8c9CDyT9YAWClVneayfv4cDlEq0wY42yBxDpiIiMqYJM0AsyJwMHgHPviKE/knlxngp5VSleYHpdRQAK/7d0hEiUzDz7VQREQUBO72f/o7271gcItgMQWayE+5BMDPAnhSKRVWSo0E8BqAS/w8KCIvSeoIMBWMiIjKWkoKNBu+IPAOfLAKNJF/cqkCfY9Sqgo6EB4J4EwR+cDn4yJyePfFA5gKRkRE5c0t/sjMp0ARgVKAUnzNifyUMQBWSl3g/RHAhgBmAhirlBorIjf5fGxEABJTggA2CkREVN5MM1cR5gxwkAh0h1tBcbCfyEftpUD39HzVA3gGwFzPZe1SSt2nlFqulJrtuayfUup1pdQ39ve+nt9dopSaq5T6Sik1znP5TkqpWfbvblV2JSSlVLVS6gn78g/t9GwqQ8kp0GwSiIionDnbIHHgN1AsESilEFJc7kXkp4wzwCIyoYv3/QCAfwB4yHPZxQDeEJFrlVIX2z+PV0ptDeAEANsA2ADAf5VSm4tIHMAdAH4DYBqAlwEcDOAVAKcBWCMimyqlTgBwHYDju3jMVIQsz754ANfFEBFReUte+sMZ4GAQ0YP9ikWwiHyVyzZIryul+nh+7quUei3b7UTkHQCrky4+EsCD9r8fBHCU5/LHRaRNROZBzzTvYlec7iUiU0VHPQ8l3cbc138A7G9mh6m8ePfF8/5MRERUjoQzwIGkU6A52E/kt1yqQA8UkbXmBxFZA2BQJ//eYBFZat/PUs/9DAOw0HO9RfZlw+x/J1+ecBsRiQFYB6B/J4+LSoDTEWASNBERlTHTyrEKdLBYIoACQiEu9yLyUy4BcFwptZH5QSk1Avn/XKabuZV2Lm/vNql3rtRvlFLTlVLTV6xY0clDpEJJWQPMVoGIiMqYswbYCYALeTTUbUwKNBRngIl8lHUbJAD/B+A9pdQU++e9oNfkdsYypdRQEVlqpzcvty9fBF1l2hgOYIl9+fA0l3tvs0gpVQGgN1JTrgEAInI3gLsBYMyYMTyjlJjUNcCFPBoiIiJ/mXbOBMCcDwwGkwKtWASLyFdZZ4BF5FUAowE8YX/tJCJZ1wBn8DyAU+x/nwLgOc/lJ9iVnUcB2AzAR3aadINSaqy9vveXSbcx93UMgDeFw2VlybyoZg0wU8GIiKicJe8DzGAoGCxLEFL6dedLTuSfXGaAAWB36Jlf48VsN1BKPQZgHwADlFKLAFwB4FoAk5RSpwH4HsCxACAinyulJgGYAyAG4By7AjQA/Ba6onQtdPXnV+zL7wXwsFJqLvTM7wk5PhYqMZKUCsZGgYiIylnyDDAHfoNBoCtAK7AIFpGfsgbASqlrAewM4N/2RecqpfYQkUvau52I/DzDr/bPcP2JACamuXw6gG3TXN4KO4Cm8uYs+mYmGBERBQB3PwgmuwYWoPiaE/kplxngQwDsICIWACilHgTwKYB2A2CifJGkVDBWgSYionJmZnwrOAMcKJYIlJMCzdecyC+5VIEGgD6ef/f24TiIMnJSwTgSTkREAZC8DRLbveBwU6ALfSRE5SuXGeBrAHyqlHoLOjNjLwCX+npURB5OESxuB0FERAHA3Q+CSewZYF0Fmi86kV+yBsAi8phS6m3odcAKwHgR+cHvAyMyUvYBZloQERGVMdPuMQU6WCzR6c8hpTjoQeSjrCnQSqk3RGSpiDwvIs+JyA9KqTe64+CIgNTtINgoEBFROUuufcEAOBgEAtb7JPJfxhlgpVQNgDrobYz6As5nsheADbrh2IgAeGaAuQ0SEREFgLsGOPFnKm8icItgcdCDyDftpUCfCeA86GB3BtwAeD2Af/p7WEQuk/LsFsFio0BEROXLzPi6RbDY7gWBJXYRLG6DROSrjAGwiPwdwN+VUr8Xkdu68ZiIEqSsAWajQEREZczZ/YDFHwNGp0ArxVl/Ij9lXAOslNpZKTXEBL9KqV8qpZ5TSt2qlOrXfYdIQecGwBwJJyKi8ufMAJs1wIyAA8GbAs1130T+aa8I1l0AIgCglNoLwLUAHgKwDsDd/h8akWZSoLkGmIiIgoC1L4LJEkGI+wAT+a69NcBhEVlt//t4AHeLyFMAnlJKzfT9yIhsbipY4s9ERETlyGn3WAU6UETsgjtKcdCDyEftzQCHlVImQN4fwJue32XdP5goX0wjwO0giIgoCFKLYBXyaKi7CHQRrJDici8iP7UXyD4GYIpSaiWAFgDvAoBSalPoNGiibmEaAaVUlmsSEVGxWrCqCW0xC5sP7lnoQyl6zsAvA+BAsUSgFJgCTeSz9qpAT1RKvQFgKIDJ4g5FhQD8vjsOjghwq1+yCjQRUem69pUvsWx9K54+e49CH0rRMzPAFSFmPgWKXQRLKeXUPyGi/Gs3lVlEpqW57Gv/DoconaR9gNkoEBGVnOZIHM2ReKEPoyQk737AADgYBICCToG2rEIfDVH5am8NMFFRSKmGyX4AEVHJsUQQ53Y+OZHkNcCFPBjqNroKtA6COdhP5B8GwFT0UotgFe5YiIioc+IWA+BcmWfJLYLF5y0I9D7AehEwPypE/mEATEXPslsBZw0wR0WJiEpO3BLEGcjlxLR7JgBmOmww6BRou7/DjwqRbxgAU9FjNUwiotInAsTiPIHnwpkBVkyBDhK3CjRToIn8xACYil5yMRAiIio9ca4BzpkpehViFehgsVOgFVOgiXzFAJiKniRVgbbYKhARlZy4JYjx/J0b+2kKO9v/8XkLAoHYKdCKrzmRjxgAU9FzZ4Dtnwt3KERE1Em6CjQXs+bCjBOEufQnUCxLB79Ksa9D5CcGwFT0uA0SEVHp4zZIuXMyn0K6m8anLRgE9hpgpfiaE/mIATAVPdMRCDnFQNgqEBGVmrgFBsA5cmeAzc983oLAvMzK+wMR5R0DYCp6KSnQbBOIiEqOxTXAOTPrP0OsAh0oljAFmqg7MACmomdGvpXpCDACJiIqOawCnTtJWQPM5y0YdAp0SCnO+hP5iAEwFT3TBOh1MRwVJSIqRZYliLNTnxMT/IS5DVKgiNh9HTDbjchPDICp+HnWxLBRICIqTZYIRLiVXS6SZ4BZPDsYLBE3BZofEyLfMACmomeKXiml9N54nAMmIio5ZvaX64Czc2aAuQY4UAT2YD9ToIl8xQCYip4Z+Q6ZFGi2CUREJcecy7kOODvzDDEFOlgsAaAUVKEPhKjMMQCmouesAbb/Y9+JiKj0mMA3xnzerCRpDTCLYAWDiHCwn6gbMACmoidOFWgAivsAExGVIpMCzfg3O2f7PycALuDBULdSYBVoIr8xAKai520CVPIFRERUEkQ4A5wrk+lk1gAz8ykYLBEo7gNM5DsGwFT0TKcp5BTBIiKiUmNSoLkGODuT6cQ1wMEiYtc7gWLaO5GPGABT0TNtgLMPMBsFIqKS464B5jk8G/MUhVgFOlBE7HonXANM5CsGwFT0nCJY9ubw7DsREZUec+7mDHB2ZqC3IswiWEFi6QgYitluRL5iAExFz5kBhtKNAlsFIqKSwxTo3EnSDLDF5ywQBCYFmmnvRH5iAExFz3LWAOtGgVWgiYhKjyVMgc5V8hpgPmMBYadAh5gCTeQrBsBU9BJSoNkoEBGVJBMAcwY4O1Mom1Wgg0VXgTYp0HzRifzCAJiKnrv2yaRAs1EgIio1bhEsboOUjZP5ZPfS2O4Fg06B1kWw+DEh8g8DYCoZzgxwoQ+EiIg6REScWUx27LMz7ZyTAs2GLxDEzABDFfpQiMoaA2AqepZnH2AFdgSIiEqN97zNGeDsnCrQ3Ac4UMwgEbd8JPIXA2Aqem4VaB0Ec10MEVFpiXs681wDnJ15usJ2DjSfsmAwKdAhxdecyE8MgKnoiWdElEWwiIhKjzfoZRXo7MxT5BbB4nMWCJ4UaA72E/mHATAVPU8JLACKo6JERCXGG8BxT9vsTPATYi8tUCxxi2BxzIPIPzy1UtEzHSczA8wyWEREpYUzwB3jzACbNcB8zgJBIHqoX3Gwn8hPDICp+HlToMFRUSKiUuPtzHMNcA7sho77AAeLCAf7iboDA2AqeiYVTCmli2CxTSAiKikWZ4A7xDxFIbMNEoOhQLBE93U42E/kLwbAVPS8VaD1PsBsFYiISkliFWhug5SNcAY4kETE2fGChc+I/MMAmIqelZQCzY4AEVFp8c4Axxn/ZpXQ7nFP2EBxXvNCHwhRGWMATEXPqYapFFRyCvSjjwJXX12YAyMiopx4Z4BjnAHOytn9gEt/AsUS0X0dMAWayE8MgKnoeVOggaQU6EmTgPvv7/ZjIiKi3LEIVseIZ/eDkOI+wEHhFsFiCjSRnxgAU9FzmgBl74nobROamoC2tu4/KCIiyhmLYHWMCX70bCC3xAkKAaCg9wFmDjSRfxgAU/EzI+EwHQFPq9DczACYiKjIxRPWALNnnw2LPwaTJWLXO1F8xYl8xACYip6zHUS6whCcASYiKnqJVaDZtc/Gbfe4BjhQxKz7Zto7kZ8YAFPRc9dCpSkMwQCYiKjoCQPgDjEzvqYisMXnLBB0CrSp/F3ooyEqXwyAqeg51TChg+C0M8BsKYiIipZ36yOuAc7OSYFWehaYz1gw6CrQLIJF5DcGwFT0vB2BlP0Qm5v1FWKxwhwcERFllbgGmNsgZSPeIlhMhw0MsVOguQ8wkb8YAFPRs7KlQANAJNLtx0VERLmxElKgC3ggJcKMFyj7i/FvMAjEfs0ZARP5iQEwlQyzN55TDTMScWd+uQ6YiKhocQa4Y0zAG1IKoZBKzHyismVZLIJF1B0YAFPRk0wj4c3N7pUYABMRFS1vZ55rgLNzM590EJzwlE2dCsyaVZgDI9+pdDteEFFeMQCmoudWw0zaDsKkPwMMgImIipjFKtAd4hR/TDcbePbZwOWXF+S4yF8ibgp0yqz/Dz8U5JiIyhEDYCp6qfsA2xcwACYiKgmsAt0xIgKlzE9JM8Br1wINDd1/UOQ7S8zez0h8zWfPBoYOBWbOLNShEZUVBsBU9NwUaN0bsJgCTURUUryzvtzTNjuxAyFAD/4mJMQ2NAAtLQU5LvKXwB74cEc/tIUL9fclS7r9mIjKEQNgKnpuCrRdBIsp0EREJYVrgDvGslNhAXsNsLduGAPgsqW3QYLz2jtp0I2N+ntra0GOi6jcMACmouddBqMbBaZAExGVEq4B7hjLMwOcsA9wJKK/GACXJUvceifmZwAMgInyjAEwFT0zAqq3g2AVaCKiUuMNemPcBikrgTjTgCGl3ARoEwgxAC5TdhEs+7VPmQHm606UFwyAqeg5a4CVXgfsjIRzBpiIqCQkzgAX8EBKhF4DrP+dMANsil8xECpLKSnQ5hecASbKKwbAVPSc7SCQtDceA2AiopLgDXrjnAHOSkQSUqCd8QMGwGXNsl/3UEg5PwNgAEyUZwyAqei5M8C6DjRToImISktiCjTXAGdjuRnQOgWaM8CBIHBfd8DT32EKNFFeMQCmomc5a4ABeNdCcQaYiKgkCItgdUjiNkgqtRhSPA5Eo4U5OPKN2EWwkndB4gwwUX4xAKai56RA25vDC9cAExGVlDi3QeoQS9wpYIU0a4ABzgaWIUv0PsBuFeik/g4DYKK8YABMxc/TcWIKNBFR6TGzvuGQgsUAOCcJa4DNhQyAy5voYp/uPsD2PzgDTJRXDICp6AncLQGUUnp7CECPiNbX639HIgU5NiIiys7MZFWFQ5wBzoGZCQQyrAEGEgeBqSyY193ZBsn8gmuAifKKATAVPctbDROeEdGmJqBvX/1vzgATERUtU/i5qiLENcA58K4BVsp9/pxACGAwVIYEut5JSgo0Z4CJ8ooBMBU9z1IoeyTc/qG5GejTR/+bATARUdEya4ArOQOcE0sksd0D1wAHgSmC5f0ZAANgojxjAExFz5sCDYXUFOiqKgbARERFzKz7rQpzDXAudLtnZoA9VaAZAJc1M/BhZoAleQaYrzlRXjAApqIndlEIwFTDtH/R1AT06AFUVzMAJiIqYmYGuKoihJiTz0uZSMIaYKRfA8xgqOyYgQ9nDTBngIl8wQCYip63I6AU3KoQzc1AXR0DYCKiIufMAHMNcE4sSwe+gL0GODkQAhgAlyPRr7dyf9RRMANgorxiAExFL6EKNJKqQHMGmIio6JkATs8AMwDORiBO5lNKFeiePfW/GQCXHScFOuQpghWJAPG4vgJfc6K8YABMRU/E0xEIJVWB5gwwEVHRM7O+lWHOAOfCEu8McNIa4EGD9L8ZDJUdXQU6aR9g76w/Z4CJ8oIBMBU9kcQZYGdbgOZmzgATEZUASxgAd4S3GrCufeGZAWYAXLacJV+mCBaEATCRDxgAU9GzkvZDdLpOTIEmIioJJuit5hrgnCQXwXI0NgIDB+p/MwAuO5a97WPIWwTLBMB9+zIAJsoTBsBU9PRaKM/PAr0mJhbTKdDcBomIqKg5VaC5D3BOTCos7O+cAQ4OpZSz7CshAB4wgK85UZ4wAKaiJwKnJKJSSs8ANzfrCzgDTERU9Ez8xhTo3FhJux84O0c1NAD9+umCGKYdpLJgCp0p5S77SkiBHjCAM8BEeVKQAFgpNV8pNUspNVMpNd2+rJ9S6nWl1Df2976e61+ilJqrlPpKKTXOc/lO9v3MVUrdqsyCGSo75oUNKeieVFOTvoABMBFR0XOKYHEf4JxIwtIfe/eDaFS3dT17ArW1nA0sM2ZcSEE5KdCWdwZ44ED9+gsHkIi6qpAzwPuKyA4iMsb++WIAb4jIZgDesH+GUmprACcA2AbAwQBuV0qF7dvcAeA3ADazvw7uxuOnbmKJOFsC6GIgcANgVoEmIip6JgCuCofA+Dc7sx0OoAd+EwKh+nrd9jEALitmBjik4EmB9swA9++vv3MWmKjLiikF+kgAD9r/fhDAUZ7LHxeRNhGZB2AugF2UUkMB9BKRqaLPGg95bkNlRMSdAXZGwjkDTERUMkxKb2VYcQY4B4LE3Q9ERKc/A5wBLlNmXldXgbYvS14DDDAAJsqDQgXAAmCyUmqGUuo39mWDRWQpANjf7SoPGAZgoee2i+zLhtn/Tr6cyoxAEraDEEHqGuBIpGDHR0RE7bNEEFYK4ZDiGuAc6CrQdhGskN3uMQAua5azBlg56e/izXgz1b8ZABN1WUWB/u4eIrJEKTUIwOtKqS/buW66db3SzuWpd6CD7N8AwEYbbdTRY6UCS5wBTmoQmAJNRFT04hYQCukAmFWgs9NrgPW/nSrQDIDLmlnaq5Tb50kogsUUaKK8KcgMsIgssb8vB/AMgF0ALLPTmmF/X25ffRGADT03Hw5giX358DSXp/t7d4vIGBEZM9CMoFHJ0Klg3mIgYAo0EVEJsUQQUuAMcI70GmDl+RmJa4AZAJctBeVWgTave22t7u8AfN2J8qDbA2ClVA+lVE/zbwAHAZgN4HkAp9hXOwXAc/a/nwdwglKqWik1CrrY1Ud2mnSDUmqsXf35l57bUBkR73YQ9s/cBomIqHTELZ0CXZEcAK9cCRx6KLBsWeEOrghZ4q4BDpmBX84AlzXLUwTLpEBbpgiWGfQAOANMlAeFSIEeDOAZe0avAsCjIvKqUupjAJOUUqcB+B7AsQAgIp8rpSYBmAMgBuAcEYnb9/VbAA8AqAXwiv1FZYYp0EREpS1uiZ0CHUpMgZ42DXj5ZWDqVOCoowp2fMVGxM18CimkL4K1dm3hDpDyLiEF2tkHGG4AXFOjL2QATNRl3R4Ai8h3ALZPc/kqAPtnuM1EABPTXD4dwLb5PkYqLiJJ1TAzVYH2XjEoHn8cGDMG2HTTQh8JEVFGIoJwSCEcQuIM8NKl+vvy5elvGFBip4wDOhDmGuDy51SBhnIGP5wU6B493ACYrztRlxXTNkhEaQnctVBONUyzFsoEwCJALFa4gyyEaBQ46STgn/8s9JEQEbUrLoKQ0jPAcUucPU/xww/6OwPgBAI3DTakkto9kw5rlgJRWXCrQHuKYDEFmsgXDICp6FmeapgK9kj4ihV6FLyqSgfAQPDSoJcsASyLHUciKnpxSwd0FSGzttH+BWeA07K8tS+U0s9XQ4MeBa6t5QxwGXJToBVToIl8xgCYip53LRSU3SAsWwYMHqwvC2oAvNDeHnvFisIeBxFRFpYlCId0FWgAiFmW/gVngNPKuAa4Z089RcgAuPyYABhI3QfYGwDzdSfqMgbAVPTEs72z0hcwAAaARYv0dwbARFTk4uJWgQY864BNAMwq0An0Nkiak/nU0KADIYABcBnyVoFW3ss4A0yUdwyAqfh5i2CZ7SC8AXBVlf4etACYM8BEVCIsMVWgzQywHQAzBTojs/THqX2xYgUwcKC+sK5O170IWu2LMuYUwVJp9gHmGmCivGIATEXPsounAJ5UMM4AuzPAy5e7i4eIiIqQZZkiWMr5GSJMgc5ArwHWz5VTBXrpUmDoUH0FEwxxFrhsiLcIVrp9gDkDTJQ3DICp6Am82yABoWgUWLOGAbCZAY5GgfXrC3ssRETtiIte/1vhnQFev1535nv2BFat4mymhyQUf7THOJcuBYYM0RcyAC47JilCKeWkQCMe19W+uQ0SUV4xAKaiJ+Kuh1FKoU/jGv1D0ANgMwMMMA2aiIqangEGwiHd7Yhb4qY/b7edPtGvWlXAIywueg2wyXxSuuL/smWcAS5jpt6JgjsDrJqa9C/r64GKCv3FGWCiLmMATEVPzwDbjQGAvg0ZAuBIpNuPrVBembUUbfMXABtsoC9gAExERSxuCcIhhbDd64hZnvTn7bbT35kG7RBP7YuQAno2rdWzgQyAy5czA+xZ/73cLg5n+js1NQyAifKAATAVPe9+iFDgDDCAu/77BSpXrgBGj9YXMAAmoiJmajk4M8DxpBlggJWgPSSp+GPfdfbsOFOgy5ZJgQ55imBVLrYzvTbcUH+vqeFrTpQHDICp+HlSoENKoR8DYPRavQIhEWDHHfUFDICJqIiZANjZBslbAGv77fV3zgA7BG7xR6WAfg2r9S84A1y2ElKg7V5P5RI7AN5oI/29tpYzwER5wACYip5AElKg+2RKgQ5QANxntd1R5AwwEZUANwXa7ANs6QC4uhrYYgt9JQbADishBVqh/3p7Bjg5AG5u7v6DI1+IJwXamQFeslj/Y9gw/Z0p0JSLWAy4+GJg3rxCH0nRYgBMaIvF9ZYURSqxCBb0DHBdnS4KAQQyAO632k4V3Hxz/Vyw40hERSwuQCi5CrSpaty3ry7uw/OYw7v9nwLQbz1ToMud5WyDpJxB/8qli/Vgv+nnMAWacvHJJ8B11wGXXpr5OiL69x9/3H3HVUQYABP2u2EKHp62IPHChgZg9uzCHFCSxI6AQt9GzxZIQCAD4P5r7Y7i8OHAoEF6BlgksTI0db8//AF4+OFCH0VZicYtfPDtytRfTJsGLFiQejkVJRFBWOkgGABicTsFesgQPbI5aBADYA+9BtitAt2vYQ3Qq5ce8AQYAJchZwYY7qB/1eJF7vpfgCnQlJtPPtHfJ03KPAv8/ffANdcAJ5wQyEwSBsABZ1mCxWtbsGhN0pv/8suBXXYpihNtQjXMEHRHoIQD4IbWKI654wN8u6Ix8RdffAF8+mlO9zFwzXI01fTQHaKBA3UA/PzzwIgRwDff+HDUlNWKFcA//pEaAH/2GXDqqXq/Zuqw/85ZhhPv+RALV3vOUdEoMG4ccNFF7d/4q6+A+fN9PT7KjUmBNjPAlkjivrZBCIAl90wrEXFrX4SA/g2r3PRnINgB8LPPAmZ7oDKklHIG/auWLnbX/wJMgS5WX38N3HdfoY/C9cknOksyHAZuvDH9dczM73ffAX/5S/cdW5FgABxwkbgFAIjGPQ2zCPDCC7ph7cws8IsvZp6Zicf1VwckdhnsIlglHAAvXN2C6QvWYPbidYm/+NWvgJNPzuk+BqxbgeW9B+gfTAA8ebLeK3LGjDwfsU9uvx04//xCH0X+vP66/uzMmZN4+Z13Ag8+mHo55aShNQYAWNfiGUCYNg1Yvx6YOtW56Id1SZ3CFSuAPfYATj+9Ow6TsohbupZD2JsC/cMPblA3aFB5V4H+4AM9k/fQQzldXeBuhQMo9G9Y7Q4WAMENgGfOBH76U+Deewt9JHlnUqBDZg2wiA6AvTPADICL06WXAqedVjyDeJ9+Cuy6q+5T3ndf+j3Wp08HKiuBk04CbrgB+PLL7j/OAmIAHHAmADbfAeiRrO++0/82aRTpNDToYNdr5UrgyCOBK65If5tTTwX2269Dx+hNBVMK6NdU2gFw1DznMc9zvmaNHo378sucHsfQtcvwQ+9B+gcTAL/7rv7588/zfcj5J6JHJe+4o3z2b37tNf198WJgnWdwY/Jk/f1//+v+Yyo1H3wAzJqVcFGb/Xlpi3kGzsxzvWgRsGgRvli6HmOveQNzlqx3r3PBBbrRnzGjQzNveRWNAr/+dUKgHlSWCMJKocLeBslqbdPtRRBmgF9+Gdh/f31uePbZnG6it/8zKdDQAbB3BtikQmcKgK+/PvcBxiuuAK68Mrfr5uLLL4G5c7t8N0/NWISz/500oPvmm/r7zJldvv9ik1AEC0CvtiaEm5pSU6CDNuhRZO5/fx6+WOppa9atc/vC77/f7cfzztcrcN2rnuA1GtX9jdGjdVDe0gK8807qDadP11vQXXONnph6/fXuO+giwAA44EwQlhCMvfyy/l5T034APHEicPjhiSm3r7yiZyHffDO107lkCfDYY/qD2IGOjog4I+HheAx9mtfnPwBube22ACXtrPubb+rnLR7XqdDt3kEEm65YgK8Hj9I/DxyoUwnNbH2RrN1ulxlkaWsrjePNRkQHuv3765/Nazh3rjuY1N77KxZL/5kJkmhUD5799rcJF5tzU2vUc46aPBno10//e9o0LF2nO4TLG1rd3z/yiK4uvHZt4dKgH3gAuP9+4J57/Ln/aFQPGpSAuCX42cv3Y5O/jAcA1L1vD9iZPYDLNQC2LJ2FsMUWOm0/x4IzIu4McEgpDGhcnXsKtGXpGZ1bbsneqY1E9GDkhAnAU0/ldGxZD/zQQ/WsUhfN+H4N3vk6af2/CYA/+6zL919szNlfQRfB2mC9vbsDZ4CLysSXvsAzny52L3j6abf/+d57+jOw5556KWE3ePXzH3DH299iVaN9DHPm6M/16NF6q8xwODUz0LJ0ADxmjFtLpr3+fhliABxETzzhfDBN5zIaTwqAt94a2G23zB8Iy9LBLKA/8MYLL+jvCxe6HX/jvvvc9Oc33sj5cAVAdbQNmDgRG339P73/rTcArqzU37sSAP/618BOO3XLdkJRZ9DBM6P1+uvuQuekGbBk1qzZqI7H8PmQTfQFgwbpAEpEz6aUwgzwK6+4/54+Pffb/ec/xRkwz5ql0znPPFP/bNKdzezvwIHtv6533aVniLzPix8eeqhDn728mDTJXRe9Zg2wzTbAc8/pn998Uwe9LS36M7BypQ4QPB08c45yZoBXrdLvmd/+Vg9+TZuGlkjSQN711wMjRwL/+pf+Oce19XnV1gZcdZX+99tv+/M3brxRp3kX6wzzF1/odNW1axGKRnHI5Ecx9JH7sMnKhRjwzBO6+vMhh+jrDhqkC7Gst2dWrrsO+Oc/C3fs+TJ9uh6g/OMfgYMP1lkLS5ZkvZnemEG3CdWtTaiLtCYGwFVV+v2/dGnqjT/9VA8mVFTownztZdlMm6bX0/btq2eLujpYNHWqbvs//lh/3rsgErMSs9NiMXcm6/PP9c+dMXky8OqrXTo2P7hVoPXX0AY7+G9vDfDvf99tgVZZe/HFnOqnxC3BT2a/jR4L57sXPvoosMkmOuh9912dnfDee7q/0g1Mn/KjefZe4abfPnq0HijbeuvUvvy33+qZ65131m+20aNzC4Dffx/o3bswbWqeMQAOmrVrdcdx4kRg0aLUGeDGRmDKFN0pGT1az1qlK97zwQe6ghzgBsCRiE5N3GMP/fNbb7nXj8f1LMg+++iGtiOpFvEY/vjQVcBll+GUCWfpy7wBsFK6I9DZAPiZZ3QwH4slHrNP0s4A//e/unNUXZ01AI7bAeOsQZvqCwYO1N8rK/V6j2+/7b4R4pYW4G9/0+nwHfHyy8CWW+pZvFxL8C9YABx/vO5IFhuTknvWWfo1NDPAkycDo0YBP/lJ5hlgEeDuu/W//ezwL1+uZ6LOO8+/v5GspUU/J2edpc89jzyiBwcuukh/Xs85Rxdvu+MO4N//1reJRBIGRVJmgN94Qz9nhx2mB62mTkVzRHeEI3FLB8hvvQX8/Od6dDscLkxjfc89eiDwyCN1Fc58V6yOx/X6ckCvp/fDzJnARx91/vb/93865ffee7HdFx+hR4su/PeHDx5H/9df1tVHTQbPbrvp7089pYO6yy4D/vznri+RWLq0a48h2X33AQce2G4QO+XrFTjurqmIW3Y9jXBYt6k776yvkMM5z5v51Gtt0hZIgG73Dj5Yt1+WlXjjV17Rv7/7bp2OfPnlmbNL/vtfXWXrjTd0G/jXv2Y9tnY98oh5AF1uT6NxC5GYBTHH/sknuq057DB9/vj66/bvIB7Xz4/3PRSJ6HbytNOKLuPGTYHWRbAyzgCbWf/Zs3XhxZtu0n23YrJunS5CWAr++U+dzXj22VmvGv10Jm574XoceN/1+oKlS/VA7okn6gD4k0/cjJ8vv+yWrBbTp5z2nX2eMAWwNrX7iDvtlLoUyLSxY8bo76NH60GlbH3Hq6/Wg5RdPU8UAQbAxayxUY/O5tMNN+hRWRHgySedmV9nBvjll3XAawLgtrb0C+MffVSvQdpvPzcAfvdd/cG46CLdUHsbv5de0gHz2Wfr25iCQdk0NODUB67B7v97B7joIrTW99KXewNgQI+GdyYAXr1aDwjssIOuqOzn7Njq1cDGG2PIAzrYcUa2583TQetPfgJstVXWABiffIL1VXX4rpf9HJgAeMwY/WVZ3VfM4NZbgfHj3QAug9VNEbcTYwZZDj1UH2+uAfDtt+vH9sYb+rksJi+9BGy7re6obLmlDvKiUd0wHnSQTvNculTPcCb7+GMdHG+xhe64JmdO5Mt99+ljmj27+2bRn3hCn2+am3U68D33AH366JH2I4/U79Nhw3Sj+uyzwDHH6NuZ9ewAInbWSGs0rju8t92m72PMGGDsWGDGDLQ16w5hJGbp2eV4XN9XTY3+THV3ANzYqDsIe+/tVtfM9yzwq6/qoHqLLfQse1ezVxYsSAykWlr0Z/Sww9xOkYhuk66+OnsV3tmzdfBRUQHcfjt+POMNNNX1xOqfHY8jv5iCcGsL8Mtfutffc0/9GbrtNp0REYvpz3lX1qVZFvCzn+nXwVsERkS3W8nB9SWXAMcdlxpQGk1NwJ/+pIPG3XfPGIDN+G4lZny7Qg/MPP+8HhTu189NR8wpAHaTgnqusV9b7wwwoAcEFy/WszIi7iDLyy/rz8evfqWzm/72N33ddFudvP663vFhxx31c/XUU50fdIhE9Gf+6KN1B/y//+3c/Zi7swe/Ypbddpj053PP1d+zpUHfdJN+TN726dlndVCyZIm/BSOXLwd+97uEc5njrbf05/bkkxPaBNNGKrgzwFZFRWrxM/N5vP56PXjR3Jzz2vJuc/rp+j21eHH263aHpiad5p+cMfHII/p16tVLn6PXrm33bkJ/+xsAYIvp7+j7Mv2Sk04Cfvxj3fbcfbc7aJHu9c8z8zmZ9p3dL/r0U92ntestYKed9PvR+1pMn67bx6231j+PHq2Pvb3+5xdf6HPL8OH63F7qRbNEJFBfO+20k5QEyxI54ggRQOS99/Jzn4sXi9TViZxwgsiOO4rsuqvMWrRWRox/UU6+90N9nT32EBk5UiQaFZkzR//9Bx5IvJ+WFpH+/UV+/nORv/1NX2fZMpHzzhOprhZpbNS/GzpUP46PPxbp00dkk01E2tpE7rxT3+aLL/T9NTeL/OtfIrfc4n5de63IBReIDBokAsikg04WEZGb735V7tv9GH0MXgMGiJx1Vsefkyuv1MfyyScihx+uj9Evv/mNCCDNQzaQjS96Tm5+/St9uXk+5swROflkkWHD2r2b6Jid5YONfiQjxr8olmWJfPihvv1FF4l8/rn+98MP+/c4jHXrRPr1039vu+0yXm1NU5ts/n8vy2uzl+rX/P/+T9/mv//V/w6H9XugPU1NIn37imyzjb7tvffm+cF0weLFIkrp95KIfu+PHCny4ov6WJ96SmTyZP3vN990b/fWW/p9d/rp+nP5xRf6ufjjH/N/jLGYPqYddhAJhUQuuyz1OsuXi0yf3rn7j0bTX77zziJbbSWy2276HADo9/tOO+l/77yzyEcf6X8DIlOmiGy9tcghhzh38a9bnpQ3N95J1o7YRB97TY3IrbfqX06aJALIU3c+LSPGvyiPfbhA33bkSH3uEdGfqQ026NzjMpqaRGbPzv4+Nf78Z/14pk0Ticf1+fLUU9u/jWXp5yISye1vHHqoyJAhIjNn6r913XW53S6dZ57R97H99iKvv64vM+d2QOT++0XWrBHZfXf3suuvb/8+f/5zkfp6kdtvFwEkrpS8u+fh8vV/PxABpHHkJu5rZJhzYV2dyAEH6M/8L37R+cf1wAPu8d5wg3v5v/6lLxs/3r3MfF4Bkbvu0pe1tCQe44036t//4x8iAwfq92os5v7+hx9ETj1VGvr0l3XVPaThH3ek/u0ddhA58MCsh37gTW/LWQ/rz+N//nSDvp/ZsxOv1NAgUlsrcvbZ+vwPiFx8sf6c/PnP+jqWpV8rpfR1vNas0de9/HL980sv6ft4/vn0B2VZ+ryf/LoZzz+vb//ii/r9udlmWR9ne0574CMZMf5FaWy1zy8HHaTbgLY2kcpK/Vgz+eIL3R8B9HNu7LefPh94H3euli/Xz+HChdmve8IJ7vvp8MP1OUREZMIE/VpstJF+DH37iowZI7LLLtI8eoy8ttlY+ei2h+Tjb1fIU9vsKy0bDE+834svFqmqElmwQKSiQuQPfxAZMUJk3LiOPRY/zZ7tPvYzzsjPfcbjIrNmidxxh3uO6og//lEfz4knupc9+6xuc/fdV7fNgMi//5162+XLdT/222/FCoXk1c3G6uteeKE+xx17rL7e2rX6tQVEHnxQfzbPPbdTD7cjfn2//pyM+NML0njlVfoYvOe299/Xx/Tss/rnlhZ9rt9tN/c6333nts+ZnH66bn/nzNGPLVubVgQATJcM8WDBA9Lu/iqZAPjf/9YvT0WFyNixmRucaFTkhRf0B/HCC/VJIplliTz+uO4sVVWJfPON7iwBMuvdT2XE+Bfl+Ls+0J01QAegIrphr6vTJ1gR3TG75BIdbAIir74q8sEHboegTx8dtIuI3H23vvxXvxLp3Vtk1CiR+fP177791g3Y3ntPZNtt3ZOl96u6WmS//eSKS/8lR/5DDwJc8dxs2faKV1Mf47Bh+m91REuLDrAPPVT/fMst+u+a48wHyxJZtUoHe4DT8T/9Z5fJda98IdLaqoPurbfW17VfF1m9Ov19RSJiVVfL3TsfJSPGvyiRWFyfdLfbTp+gI5HUjkFTk+4wiIi88YbIrruKPPlk1x/bVVfpYz31VP195kz3d5GI05H/bkWjbH7h0/LVUSe6r+0OO+jH/uyz+ucPPmj/b911lxsgjRolcvDB+vJMgVdHWJbIl1+6z1FHmfeNGdAxz8u22+pArK1Nd4y9n62HH058r//61/ryY47Rn6Mffuj64/IyHdtJk0T23193TL3nlGnT9IAVIHL++R17Lv73Px0MJAfVJrC97TaRRx/V/+7RQ2T9epHXXtPntrfe0tc97jiRTTfV56/f/EafM2Ixkfffl5a6elnWo698t89PRK64IvG5WbRIBJDpR/9KRox/UR6d/Jl+/194oXudm27Sf7uzz+nkySIbbqjvQymRn/7U7cyms2iR7hwcf7x72c9+pt8LmcTjuqME6MefzaxZ+lhMB37vvfX75swzdTC7bl3qbd57T+TSS1OD+JUrRQYPFtliC32MppPYt6/+nG2zjf68nnCC7izedpseKN1oo/SfP8vSAWYoJPKnP+n3kv3e+vsfb5U5S9bJzXv8XGbcnGYQq6FBv/aAyMsv6w5XfX3uAw9ea9fqx7XrrjpwN++vr77S7Vo4rN+Py5fr52DIEH0e3XtvfQwXX6zbIDMg1dKir7PvvvrnJ57Qx/n44/rnpiaRXXYRqamRT/Y4WOYMHOl+vr/6yj2uM87Qr9XMmXrQLMN7af8b35bfPjJdJBaT2Xv+JHO7cNxx+v0G6EDI/M1p0xKvd9JJ+vGuXOleZgY+pkzRP0cielDTGySI6D7DMceI9Oypr3/ooamvfTSq+yqDBun7ufnmLrenv/jXNBkx/kVZ09QmMnWq/myff77+5XbbifzkJ4k3aGhwB7KHDdOPxQxGzZihz/OAyNVXi+y5pw4CcvXDD+4A7EEHZe6TiYi88oq+3iWX6L+llB6Ie/BBffkvfqEnCmbN0gNFhxwicvDB0rj3frK0Xg8qN2+ymSzoPVjW7rRr4n2bQfvjjtPn0AUL9Oc6FBJZujT3x9OeZcv0oM0nn3Tu9ua99otf6OMybWMmN90kctpp6c9bs2bpfmXfvu57u6ZGXy6S+dywbJluL264QbdF4bAzoSLTp+vBmqoq/Zldv16fGwYP1s+r1/Llur202y+rqkp2PvtB+XrL0W6b4B2Y2n57fX5pbNSDLTvu2P5jX7lSB6v//W/712vHL/41Tba49EV5ctv99TGdcIL++0ZTkzvgs3q1yF57ScrklmXp81Km9mfmTH0+NBNNf/iDfv/lMhhUQAyAPV8lEQBPm6ZP3GPHitxzj36Z7rhDdxYvvljkRz8SOeccke+/1yPJJlBONyofjerRYRN8mRmeefNEAPn+vPEyYvyL8rPb39ejWL1765OBsfvuulGdPl134gD9/bXX9O9bW/XJqKJCN8KmoZ8/X19WV6dHJpMbwa23dk9mAwfqDvrq1e5XY6PTwPziX9PkqH/qAPjK52fLtn9OEwBvvLFuDONx3SD85S/6uXj22cSGuq1Nd2i/+UbPIpqZSBF9QvXOLq5erTtyzzyjj3/SJN34TJuWvvGLRvUM7Cuv6M7XggWJMybDh4usWSNNg4bIlJE7yl9f/FzPdAPu8/nyy4kdko8/1h3uoUP1ydme0fj94X+UEeNflKa2NB3QbbbRnYCGBt0A1NXp5/iMM/RrYkbFf/MbfZ1Mmpv1SOvFF4uccoqeyXz7bT0zP3as7owccYTIihWJQcerr+rO8eabi3z1lXz35lT53+BNxBkx/eor9/mzAxhnRu/TT0WOPFK/Z048UeS550Tuu08/hp120rf705/04zjxRN2A5TIbvGyZbszN321r00H3tdeKbLmlOLORnTmZ77ZbYkfqP/9xX/OHHnIvHzhQB7rPPKOPf5999Gjr0Ue7n5vZs/Xn6eCD3cGs1lY9o/LBB4mN/fvv61m6F1/Ugyxe8+fr2ap779XvgT599HsoEnHPKddfr2//s5/p53HUKN0JAXTn0HseyOT773VHUyn99fbb+vIlS/R5qr5efxba2nRw9fvfu7f1NtBtbe7fM4MD48eL9OghK4ZuJGN/e7/c/tbc9Mdw+uliKSXH//xq+ex4+/inTnV//9Zb+rJXXtE/r1unP8uPPiry9NO6c7R0aerg4fr1TtaGbLmlPhdceKF+nHvuqT/fyeeBefP076qq9Ii6ceut4gxAfP+9/lybDtyqVTq4ANwO9quv6udtzpzUv9HYqM+fgwa5Qf3s2fpzU1/vtgdnnKE/3/G4noU1bcSBB7rvo7Y2/bcrKnQHp7VVv1/MdT/91B18AkT++ld9OxM4TZrkHteqVfrno47SvzvgAD3DKCJy002ycMAw+d0D0+TrH9bLiPEvyvMzF6d/Pa+8Up9f4nF34PAPf9CDdt995w6cTZyog89vvtG3syz9+2++0bM5m2+uO30ffeQOKF95pc5I6NtXn9uU0ue27bbT57CZM3WQVFXlvu6mo3jssYntRTyuX4ett9aP/ac/1ff3zDNy1sPT5UfnPi4tY3fXAbiX+fyFw24wmTzrb1ly0iX/llsv/qfT7t66V4aZ8Kef1vfz4x/rIP33v9edeu/MtIibHXTZZXoQ+uKLdZvUo0figNdvfqMvmzFDP+cnnKCfmx49dOf397/X93P22Yn3f/XV+vJHH9U/m/Z0t930ufXgg3VAtOuu+jLz/jdaW1Pe68ff9YGMGP+iLJ+7QJ9nNt7YHQRIzuxobdXv7VBIvxa77qrfp2vW6HPqCSfo56iiQn/eb7Bn1b19k2hUt3MTJ+o2Q0S/zk88of92XZ17jvSe272+/16f67bYQh+TiJ71BfSx7bNPxiyPOUvWySZ/fFY+vfaf0rzZFiKALD3imMQrmUFyQB+niJut99vfpr7umViWyNy5egLljjt0W/G3v+nnzrz/+/dPHLwR0Z+Rk07SA7xjx+rP+U9/qgcm/vlPfY4IhfQEx7Jl+py0886Zg2AzQAnoz6b3ffH++7rtGjhQD4Y98IA+dw4erM+VF1+s35sXXOC+d5Yt03+7rs6djTWP5bvv9ASOGezdccfEtvOMM/Txmtdt7Vp97DU1ItdcI3LggbJq/GUyYvyLcs9v7Nc0ebDo+ed1xoyIPt8ope8n3fP/4IPuhFJ1deYgOB7Xn1/v4JXHsXd+IE8feJIIIG8e/9u0/dPGzbaULzbaSpYO31jilVUijz2Wekf77aezEYw1a3Rb/s03Yg0ZIqv7DZZvPv1S/27hQt33aG8gqAgwAPZ8FXUAvGiR7sQAunPzxRf6ZPajH7kf4nBYn3RMh7OyUp+8Wlp0x6OyUneUIxEdRI0bp2930UWpJ0Y7eH5qm33lzTEHuSP2Xm++6Y6aASJ//3vqcZvRpJtvTrx8xYrMM3Rr1+rO8sMPZx21POmeafJTOwD+ywufyzbpAmAzC7n55u7J23xtuaUOiE8+2T3ZhEL6xLrddu4H2LL0iXWnnXSno6Ym8X68XxtsIHLYYTqF99FHdWfAjI4D+hh69tRff/2r7gDP1R34mWdcIALIRwcfp0+2ZtZcRJ9UAN3ROP10/RoPGqQ7Dhtu6JzQ9zv9Dhkx/kVZ25SmIT3uON1ZMcdzzDF6hNl0fles0K+zUrqhfuABHXg//rjuxJxxhp4FMYFyRUXi6GtVle7kn3uuTv8V0e+93r31TJF5zgcOFOnZU6yKCllZ20teuuqO9C/wBhvomZWTT9b3PXCg7sB533f77uv+rY8/dhuM7bZzG/6xY/VrusEGeobqwgt1Q73xxu79bL21DkJN6jag/9aECfq16N9f5Je/1J2MO+/Unf+//U2/f664Qo+gXnqpbnivvNKdwb76avfxmM7Ittsmfub239/t9G6/ffpGUUR3IgA9M3Daafr5MMdaWakHN371q8T3Y3W1fv7++ld9m+TPwFFHuaPUq1a5M0aAfn7OOMNtXB99VB/nnnvqgalbbtHPxVNP6XPK++/rBv6UU/Tz3auXPudsuqke+Lj0Un2fPXokNuhNTbnN2NuDc+Z1//Odk2XE+Bflpslfpb9+Q4OsHLKhRFVI3+aUUxKD2TVr9OVjxuj3RP/+6T/TVVV6wOqqq0R+9zsdHJiOnHfJxWOPuQFiv376sW61lX7v19Xp99GDDyYe4zffJD7n3vfe4MH6+b7+ev13ttpKP6fmNdx8c/2a/vKXeob3gAP0ZzddGmBbmz6vnnOOm2Y5eLC+n0MO0bO3Sun35iWX6L8F6M6d16xZumNsXrfBg/U5wbyfYzH3cZ91ln4cprPZu7d+DpPam32vf0vO+fcM+XZ5g4wY/6I8++mi9t8H5u94B0vNZyD5edxiC3eW3nxttJH7HLW2up+jDTd0BxxPPNE95smT3b/75pt6xrytTT8202Zce23i8T3+uPv587SBZubyqx/Wp77nv/5aP4Zjj3XTzHfZRb9/NtlEnxvMcgFARCl5/fQ/yeb/93L65yga1Z/P5cuzP59HH62PNRzWX+PGuc+F97F7n8cBA/T7ackS9zomnXSvvfS58cIL3cfkbU933VW3MfvvLzJ6tP5M7buvfj/16KEHuS64QJ9rzCD6Ntvo9/rNN8sNp/1FJu7zK4mOHKXb408/dY/BBLDjxunBux499M8m+PAyr3Ntrcgjj7ivA6DPp9dco88bw4e7j7tnT93Gb7CBOMHZu+/qc8tuu+njGTBAP+7KSv0e/L//04+tZ8/ErKZ4XPcpNt7YDazT+HzxOhkx/kV5ZdZS+eS7FXLqMVfIB//9OPFK//iHPp7f/S4x+DjrLPc1uf12/Z4YN06/p04/Xd/uH//QbdU55+jzdbrz4MYb69+/9pp+fKNG6dvvvbf7XNTX6+ftwAP1c7HNNonnt003dft1kybp93NlpW6XDztMv3bvvafPZ4B+X06e7J6bDz1U33dVlT7/JU+gvPqq+7dG2zOxv/+9bsfq6vRn9aSTdB/67bf14MtTT+nb2ksy5Pe/T11KZzKlfvlL3cb366fv67nnUl6jX935rm4bvv8+4+spb7yh7+/ss3W7bvoSN92kPweAfv7eekufk2tr9XmrZ08dC1xxhZ7YMVkxNTW6r/Pmm3qAdv16kY8+ksm7HyYCyCt7/lSOu+P99O+tA48SAWRddQ85/6yb0x/vH/+on/PDDtPH4XlfxPv0lf1Pu13ueDvDQHSRYgDs+SrqAHjdOn2yuuqqxJm5b7/Vo42TJ7snz6lT9QnVzBSK6MDGjGyZr7o6/YFPp6lJvvvNudIWqpD1tfV65Dddp3zFCv0BzjTT9uijukOeLv06D35+91Q5+nb9ob7qhc9lq8tfSb1SPK6PY+eddWfx++/1Y3nqKXcUf4MNdHD4zDO60e3ZU8/WeZ1+ur7uqFH6MU+frhu9W2/VJ+xVq/SM5Ikn6pO+CWhMAPLww/p1Ou883RkwsxMe/37uQ5kzcKQ01ffWgd9czwnFstxGoKpKn6TNa/L55yJ9+ki8R72M/NPzemR8fWvqc3HHHfpE+stf6vXBxqJFiZ3Sd991U3u8XwMH6o7LBRfoBqGhQXeyJk/Wsw3pZgbfeku/d8eN053ElhY92rrHHrLiuJNk+z88KjdmCmCmTNENXk2NHklesUJfHono1+qhh1IHb55+WgdKra3uoNEOO+jX7Ne/dmeohw7Vn5O//U2n5W+/vX5uTjxRv/bewZc5c/R9DRmSvmNgd0alokK/NqGQe/m337r3E43q9Nd33kl9XUaP1t/bS6G1LP1eCof1a3HkkXqk9dln9Sj7Bhvo4zj/fN0pnTJFd1h69XIbydNP18f03Xf6eUq2cKHuAKZLqRTRHXvv40v31aePfo+ZNLmpU/Vl4bB+LySnYObKsnRDfNddIvG4nPPvGTJi/Ity9ctzMt7klr8+LHP7DZfXLpiYfkT6z3/WHSlApy++/bae6Zs+XT+vt92m/6ZZm9yjhw40M6Xmz56tb3PmmbqjdeyxeiDLnHvSaWzUHaLbb9d/8+9/1+eQ3XZL7NjPmKHPY2ZGZZ999HtyxAj9fqiqSg3E0pkyRd/3ccfpc6P5DE2apAOuUEjf54svZr+vFStSZ63MTGb//rpje+WV+vnKMMix19/elD889oksWNkkI8a/KP+ZnmO2hVlGMmOGfu7OP18P2K1bp2ejbrtNd3CPPlr//qGH9Dk6+Tz19tt68ML7OObP1+/h9lI0FyzQ56V0szOxmO4sHndcwmt45D/ekxHjX5RZi9K0pyKJn//rr9dt1CGH6PPS4YeLnHmm/O3oC+SWy+8RWbJErnn5C9ns0gwBcEfMmqWDkwsvdAcUk1mWfo9MmqQ/0+lez1hM91PM8qXqaj14ZM7d2SxerINeE5SOHq0HZS+4QLcF3sFPQJrH7p44QCGizzs9eugJghNP1G2uWeOY7nEfdZReruH1s5/pPpJp9444Qrc5n3+uX/PNN9f3/cQTiW3Q11/rAcqzztJB/MUXu49ns810W5Luec2yvn/2Yl2T5dXZS2Xm92tkxPgX5b9zkpZuLFum+2Lp+lsPPui2A6Yfc/jhiZeZAHbcOH1++eAD3TeYPz91EGXqVB0EDxyoX99TTtGDPOnajXhct0fezB7jhx90G7Xvvu6gm3nfnHmmO+O6fLkO+oYO1e+t88/PPGDw1FO6TxaP6zbfnLdPPTV7ynWmSZfWVn2+NcH8YYfpAXePT+3X5fi7sizbEtGfc++gSnL7eeed7uu4dKluS37xC90XHzZMX2/rrfXP992n2/XkwW1AWiur5I39jpGT737fWS6Y7MkJd8j/hm4m19/wH9n9mjfSH+/rr+vPwzbb6Pf3Ndfo98iECbLwbT2od9ULn2d/3EWkvQBY6d8Hx5gxY2R6R/Yd7W7xuK4S2Vnffaergy5frqsMHnEE0KNHxqu/PGspxt/3LjYY3AevXXxQ5/+uj35+9zTELAtPnrU7Jr40B49M+x5fXHVw7ndgWboqa69e2a8biehK1gMG5Hbfra26Et6GGwL9++d0k7umfItrXvkSx+w0HDccu33qFT74QG8hsNdeqa/dZ59hwadfYO8vewIApl6yH4b2rk29DxG3hGh7IhF9/M3N+m+NGqWrd+bRB9+uxIn3fIiz9t4EF/9ky8xXzPWYk1mWrug5fHji5bGY/iwl36dludURM1m/Xr9nLEtXHK6rczdnNNau1RVe29qAM87o+HFnk+n5iMf1ViB9+iReHovp462qys/fnzVLP8Ytt9TVo1es0BVL29qAzTbT75WKitRjC4U69zpmcObD0/Ha58tw6u4jceUR26S9ztn/noGXZ/2A3+6zCcYf3M57rKVFV1Ftz+rV+lyR/NiKRWc/J8kaG3UV0K48zkgk5/fbj697E7uM7IcLDtocP77uLfzt6O1w3M4bZr9hCTrgpimYu7wRT/12d+w0om+n7mPv69/Cjhv2wS0n7Ii/vfol7nn3O3wz8ZA8H2keNDTotiPbOTWZiD5nVVam/92KFTjzxpfx+bo4/nXlsdhySJr2Ox+fBRH9Waiv7/p9LV6s+wE1NZ26+ezF63DYbe/h7pN3wpDeNTjiH+/jX78cgwO2Hpz9xkY8Dixbpl+XzTfXjyka1ec1pfTjrKvL/f7ydb7xmjtXb0124IHuLhZdEY/r/aF33jk//RfL0s9f794pv/po3mocd9dUjN6oD54+e4/s9yWi+1eNjfpzUlOj77umpv22yLL07ZIfz5o1uhL/zJn6szN4MI76vi+GbDQUMcvCkrWtePncPVPu7qoX5+CJjxfiiB02wOTPl2H6ZQdkPt40r/dXPzRg3C3v4MgdNsDfT9gx++MuEkqpGSIyJt3virSFD7CuBL8AsPHGOe1lZkTjFhqqe6BJtf9WWNHQhtqqMOqr3estWtOMSdMX4fwDNoPK9wnSQyBQ0PevlIKgg4M2oVBuwS+gO3O5Br+APontsEOHDidl66lku++e+cbbb481/UYAX76v7yOW4bnI9fWoqtJb9PjIlOhvi8Xbv2Jn30OhUGrwC2Tu2OfSUevVK/t7pk+fxG1c8i3T8xEOpwa/QP4Dth/9KPHnDTbIfpscz18igg/nrcauo/plPXfk8v5pjsQTrptRtuAX0NvVFLN8nWvz0VHswGCLCBAKKVTYnz9na5sy1Nhq70ud7f3YDkvE+WyElELRPl09e3budkqlD37N7wYNwjcDR2KRaup6O5ftODr7GJING9alm5v5qJBSTp+nwy97OJx6rrYDpU7xo2+36abuHrX5EA4D++6bv/sLhdIGv4D7mY7Gc3xllNKBr3cyo28Og2KhUPpzdN++esvMn/zEuWjN9W9ho4oQwpZyt9dM0haLo7oihKpwqP2+WIbX29xmZWMnthstUtwHOODaYlmCMdvJ936IGycnbmo++fNluPWNb7C8IfUDISK4c8q3WNvcyf0EE+4LsNsCKOU2EqUqkuNznon3dplOdsXEPN6udAapvHw8fw1OuHsaZi1el/W65j3eGs38/mnJNQCmgolbgrBSCIf0yTxe6ifydjS12QFwF87P3okYpXRAHDTm+TN7gZc7M7jvTTYK4utezMx7sZjamkjMQlVFCFUVoYzHZa5TXZn5Otn+BqAnw8oFA+CAyzU4WdnYhpWNicGsCZ5bo6mN07yVTbj2lS/xxhfLu3yMIkDIdASgSj8AtkcOO3sCjXpu19kgujuZTkxbETUYVFjrWqIJ39uTywxwi30Oyppl4KPWaBwLVzcX7O8Xu7gIQiGgwgTAJXDu6gzLEjRGuj4DLILEzKcSb/c6w/3sl+d7JZmZ5fcGwEF83YtZW7RrExh+iMbtALid2d22mIXqihCqwyFE4hY6uvzVfBaT44BSxgA44HJN52iLWogkfbDMB60lTQBsZmta89AhTUyBRsdToItM1BnV7tzjiHpy4YrpJJwJZ4ApmTl3tLUzq2tEYtlngHNOgfbRvz/8Hj/5+7uIF22uamFZliCkFMJhfS4v1xTo5mjcCVq6MiAjIs7Ab0i5lwWJu1woGI/bvL7KkwLdiSRo8lExDui3xSxUhbPPAFdXhFFVEdLL7zt4/m2zH/fqpkhJ9DtzwQA44JwUoywf5raYlfKBNz+b9MPE3+Xewc0mIRUMpT8i6gw6BGUGONc1wBQYJpjNpRPRXqaJ4aRAF/DzsKKhDY1tMQ70ZBAXQTikELZP5uU6UGDW/wJdXQPsbfeUc1mx8TMoj3YxW6rUmGdSwS1VUYyveZDlumywO0Xs2d32AuA2T5q0+bmjf8NYVSazwAyAA86ZnWsnJcKyBJG4lRLMmg5puhngtlj+RskEiWuhSr09yFoEK8fbA0AkU3GQIlKMI6ZUWM4AWQ6DIu4a4Owp0IXsKLcWQRp2MXNmgEPlPQPc2Oam9XcpBRr6+QKKdwZ4waombHHZq/hmWYMv99/VehmlJt0McJG95IHn7TMDwLL1rfjjk5+12z75SUT3zyvDIVRXhLIWwaqu0IUqO3pu8vbfyqUQFgPggPN+CDJ1SNwAJjkFOnPHtC2Ps36WeDsCqug6AR3lFvboXKPuvV0pdAyYAk3J2jowA5zLOsBme81lIQdZ3PMh3+fpWAKEQ8pZA2yVaQDc4J0B7sL52TsDHAoV5wzwwtUtiMQtLFiV/7XvpmMPBKftcKtAe9YAl/yQf3lxZoDt79O+W4X/zFiEb1c0FuR4YpZABM7sbjQuac+tkaQZ4I5+przXL5dCWAyAAy6SMJuYYeQoQ2fVXN4SSb1dmzMbkp8UaEOh+DoBHdXVgNC7HqoUAuB8ZgNQeXDeEzmMmkfaGWgDdCBlgs5CdpTbOAPcrril17SW/wxwflKg9dKfxC1Jiq0icHtZYF3lbecCEwDb3xWUM+tfph+TkpU8A2z6wYUa+DR9wITgNk2/0BTBqgqbFOiOfWYTAmDOAFM58L6pMwVTbrpi8hrgHFKg87EGGJ6OgI/7DXeXfKZAl0IAzBlgStbagQGybNsgeQvtFXINcGuG8yRpugq0grLToIOwBrgr7wURccoghYq03TPveT/SP0ttu798MDN3+uU2KdDl+TkpVd7CsSLSoeU8fh5PVdgb3KablHKLYHlvl/vfcR9fucwAVxT6AKiwcpoBzhDAOEWwfE6Bhqcj4NRFFEkZHS8VySOIHRVL6BgUf+OYKYWegqsjWQHZiqg1e4rwFXYNcP4G/cqRZe8DDABhpcp2BrghXzPAQMoa4OKbAc6+Pr+zvM9dUAZPnRlg5b7mVFy8e1JH4lbBz/tOAFwRcgZL0n1eIvZWSdWdLYJl9+PCIcU1wFQeEhqZrDPA6dcAp0tjzDRr3Bm6I6D/XQ5745nUrmgnC1h5g95YCYyMcwaYknWoCFaWbJKWIgmACz0TUOwsuwo0oDtRxRbM5Uu+ZoAtESQnPqWbNW+NxnHBEzOxdF1Lp/9WZ/mbAl1amU75YD4SCsoZ4C/Xz0mpSh6YafMxCyIXbZ4AuN0U6GjcqRSd6TrtMY97SK+aspkBZgAccLmMsmaarTGBb9ptkDpQ5CYbyzPba0bES7lJiAQsBToaz997gcpDrqPmliXOTGGmPcVN57sipAqbAp3Hc165ERG7qJM+f1eEFGIlkL3SGWYNcE1l5i1JcmGqZgNApZ3amO45m7u8EU9/uhjvfr2y03+rs0yn34/1j21BnAEWNwXazXYr3PFQKu/7MhoXt/hhoVKgzRrgcPsVns0MsLMNUgc/s20xC0oBQ3szAKYyEUn6MKeTaT1vTinQeRgVE0FKCnQpj4p2NQU6mjBrX/zPA2eAKVmuGSLmM1JTqatbppsBMynQfeoqi2IbpELNBBQz87I5KdBhhbhVnueDxrYYaipDqKuqSEiX7Cjv9n+1lbpjm76t1ZcVojBNe32ArgriGmBzdgsp5Q72F38THyjJk0Z+DgJ15HgS9/hNPylVXRF2g+ROzABXhUMY1KuaKdBUHnLZUsedzU38ULW/D3B+q0Anp4KVcqNgnufOV4H2vGYlEFTmso0NBUuuNQLM9XrWVAJIH1yaLZB61xY2AGa1c1dLJI4zHpqO7+3tcczAhT2RWd5rgFtjqK+uRFW4azPAeuBXN3i1VbrTmu79bzrehZiV8XPQJ6EKdFACYJMC7dkGqZQH+8tRcuHY9rYD7Q5RzwywKYKV7rzTFrNQXemuAc7l3NQSiSdsMVhVEcKA+mrOAFN58H4IMnXcTCfVkqQCTN20D7C3CrRyUqBLt1HoahXoSKltgxSwvRwpu1yXSJj3TK+aiozXN0swCh0At3IbJMe8lU14fc4yfDx/NQC3Ex/yrAEu2yrQbTH0rKlAVUVXA2Bxal/UtDMDbN535RYAB7EIlvmceOtfleenpHS1xRP7zB3Z0cAP6WaAkz8vZk/tqnD7s8TJLvrPZzjv8Zn6PuN6G6WB9dVY3xori3aOAXDA5bYNUvog2Ul/8nkNsHc7CPeyLt9twZjn3JL0RU2yidknMqA0AmBvyne6Ddoz3WbGgtV+HlbBLFnbguXrW33/O4UIMNr7m3FLnDVuToZIlrQxM/PTqzbzDLAJCvrUVSV0TrpboauBFpPWpC3yTMc+7FkDnGnJTalrbI2ivtoOgLvwfrQkNQW6vfd/LgHw6qYITnvgY6xuinT6uLzMez5dH6CrcslOKzduFWjlDBYxAi4uxTYDnEsAbI6xurL9WeJkS9a2YOm6Vuf61RVhDOxZDQBY2Zifc0ghMQAuY5YluOrFOfhuRWPG60TiFnpUZV44D7QXAOeQAp2PfYA9HYFyWBcT7eIMbjRuOSlxpbQGGMg9le3Vz3/A0XdMxQ/r/A8Uu9uFkz7Dpc/M9vVvzFq0Dltd/ioWr+2+yrA/rGvFVn9+FZ8tXJv290f983384825ADIvq0hm3jvtp0AnrgEu1L6Z+Vz2kc5pD3yMa17+wpf7zodJ0xfi1dlLAQCtkcQCiWZgxJy/ayrDzvPV1BbDpc/MwvrWaHcfsi8a22I6AA6HutT+CdwiWOZ83xJJvT8nBTqHdXmzF6/DG18ux+zF6zp9XF4tPq5/zMcM8IqGNuxx7Zv4ellDvg7LV+mKYDEFurgkF2cr9BpgM+ibsMVRUj8roVBWZQdSoKOW8xn3pkADwMoySINmAFzGVjS24d735uHNL5dnvE4kZqHeTi/MvAbY7XR6O6ymcW9J88HPbwq0OGuhnDXAJTwsGomnH1DI/faCqooQKsOqJEbGc0mzT7bGnqFY11IenWKvVU1t+GG9v4Hp/FVNiMQtLF7TfQHw4rUtiMQszF/VBACYsWAN/vHmN87vv1vRiHkr9e9yDRbN+9ukQKfrZJggq09tlX2bAgXAOQb1nfX18gbMWbrel/vOh/vem4d/f/g9ADcwMoMTpt6VmdWqrQo7HceZC9fi0Q+/x4wFa7r5iP3R0BpDfU0Fqiu7PgNsoqCaivykQLe0U7ejM7prG6TOBsALVjVh8dqWEgqA9XcFb19Ha2yLYfx//lc2A0WloKE1iilfr0i4LOI5vxfVDHA4c4Vn83N1ZdidAc7h3NQSiTntayQWR1U45GRjNXr2Oy9VDIDLmOl8NLeTnhSJW+hRnSUA9gYw0dRgpjVdCnQXCsLMX9mEt79yg3YRIGS/U8tha4BIzHLWdnV2BrgqHEJlOFQaRbASAv7cGol8d9SKSVNbHGub/e3EmEarKdJ9jVRrUtDzwmdL8Pc3dAAsImiOxp3f5bplkLMG2G50071/zHukt32dQhTMiVvi/F2/ZgJaInGsL+IBoeZI3HnfOQFwVL//4k4KtL5ubWU4pX1qbiuPz3pjWww9zQxwV87PAs8MsG4A2wuAG9tiTsGaTFqcz1++AmD/Ov+57FCRTfJATLEzA/veKtBmBnjm92vxxPSF+KRMBopKwdOfLMap93+Etc1uuq+phmz+XejaD9E0M8DJbaA5tuoOboPUEo07jy9izwDX2dkoTQyAqZi15BIAxyzUV2cuMJN8uflgWZ4OX9oU6C4UBrjn3e9w3hMznZ8tSTcDXLqiOQw6ZLt9ZVjpALjEZoBzHcl3O8elf5JN1hLthgA42v1BRXNS2mtLJI5oXOxOggURNyB3Z4BzqwLdy0mBTn3/mL/bq1Z/pgpRMCchM8anjlBLJF7UGRHNkbg7cJX0XnDWAHtmgJMHubpzsMZPjW16BrirRbAsT+0LUwSrvcFmAFjZ0P66vOTXp6vMe92fKtD6cYVU55cVJL8Pi11CFeiky8yMW6k8lnKwriUKEZ3VYUTibtZkW8IMcIGLYIVDqAqnX85ofq6uCHVoG6QWzznd7CNs+q6lMqjUHgbAZazFHn1vr3GKxCz0qDLBWKZ9gFPTnr0fnnzvA7ymOYJ1LVGnYJIATmvgrgEu3RDYO+jQmQ5SNG6hwswAl0Al1c6kQJfznqrNkRga22K+Dl40F2AGODmgafZ0ts1Ahum8ZdpbPJm7BthOgU43AxyJobbSs79hIQJgb2aMDx0hEdEDJ0UcAHvT5ZKzAcy53KRA11SGPcGJfm+Uw4yCiKCxVa8Bru5qFWh4ZoBNAJz2/e9etqKx/ZoJyTP0XeVnCrTpY/Soruj0ubLUMolMcx7yRMCmhTefj6YyCDxKRbp2tC1qoUe1/jxGY5Zzvi94CnQuRbDspXPey9rTaq8BFhG7CJZnBrgMBiwZAJexXGbRvCnQGYtgJaQ9pxa3SlsFugsFYdY261G3RnPcgpQq0CUQ96VlWYKYJc5JpHMzwILKcAhVYVUyKdCZTsyZuJ3j4n98HRG3xBkp9nMW2J0B7sYU6JQZYNNhi6Wku+a6RCK5CnS6AbWWaBx1VeEOv8fyqTVhBjj/f78tZsESYL1nYLCYmBT35IDDKYIlbmonANR51gDnkqlUKtpiFmKWuDPAXVoDLG4VaKcIVuYUaCD7OuB8pwS7KdD+FcGqr67o9Ge61N5b3oH95MF+dxCx9AOPUuEOzrnvn0jcQn21u9ym1efih9lE0qVAJw2UtXmCZKUUqipCWTOVYnELkbjO3IrYM93eFOhyWLLCALiM5bQGOGY5sysd2QbJfOhDKv3Il3eNX0dna02an1nv5h0JVyWeAx213FFtAIjEOv5A9BpghcqK0kmB7pklzT5ZuaZAe2ci1rX4t42AG3x2Zwq03UFL6mR7U2PNdXKd4c9lH+DmSBw1lZ4AON79DXNrmkHC/N6/SSX2DAzm2YwFazq9dZY+z3vT4PXzYV5vc79hz4ym8/4wKdBlMANsUiXNGuCu7QPstnftFsGKxZ2aElkD4LyvAfZvhtVkpPWorshpIKEtFk9pL5qTBuOKnbsNUroUaDMbWfqBR6lI1w/RGXzuBEZRzgAnV4F2ZoD1cVfncG5q9fy+NWI5a5/r7IxRzgBTUculsdMzwNm2QUpd32Y+9H3qqtpNgTZ/oyPcAFh/wLwj4U6jUKIRsHmO3bTzzq4BtlOgS2AbpDbPIEuuHcJyTYH2NqRrumMGuFtToE3Qk5QKHYk5wU2HZ4CTimCl3Qc1Ys8A24VJCjES7z1H+jEbljBw4sP75tsVjTj6jg8w5evMOwa0Jzm1NlsV6JpKdw2wyRwohwDYrNOsr6lAdUW404MhznY49s+hkEJ1RShDESwLA3tWI6RynwHO1zrSVh/XAJtZrFxToK9+6Qucct9HCZeVXBEsT6aESpoBTj6Hkv/SDc4lL2Hzcx18LpzZ3faqQNvHaH5fXZk9APaeI1qicacIVjikUFMZKov3IQPgMpZtBtjk9TuzkR2oAm0+UH1qK501Aom36XxKoBMA2+X+xZMC7UwAF3/cl5Z3VBvoXMXaaEynQFeEVEEq3nZUJBZ3i0bk2CE0gVs5nGS9vI2KnynQztqlbkxTakkatGjxHIO3GE0sbjkzgln3AbZnc9vbBqnZDoCrC5kC7fMMsPd940chrFWNkYTvHWU6ipGYZaf5JwbCbhEsfX3vNkjuOrvS/6w32jPA9dWVXSqClbAW1FZTGU5bBKs1GkePqgr0r6/G8iwBcPLgVFf5WQXatJX11eGcnsfvVzfj+9XNScdXWmuAvUWwzKy+eS+YGbfuXNYSdC1p2lG9dahJgRbPDHBh+mJOFWi7T6hUezPAIee6WWeAPZ+ZlmjcSYEG9AROOQxYMgAuY9mCCKeByTIb6R1NMh8sE9T2rquESGqQ29miMHFLnDQy810gzmiosy4m53ssLlGnsIdbRKGjInELlXa6S0mkQMct9Kw2azhzXANcYh2XXHk/i2ua/UuBbi3EDLBzvkk87zR71gA3RWJOalV1RSjrEgk3BbqdGeBoHLWeNcCFmAH2Hpcff7/Z5wDYKbDTyU6NN8XUu3WGOe6UNcCVYUTjgmjccoLnrr5Xo3ELP77uTbw8a2mX7qcrGtr0a1Nf3bUq0M4MsKf4Ra1n1tyrNRpHdWUYA+urs84A5zsgNPcXjQtieW6LnCJYVbmlQDe2xVIG/JIr0xc7JwXa/s97mTMDXGZtYjFrTmrTAH1+984AO1kQBdoGKRKzUBFSCIV01kC64NYtgqX7nVUV2bdo854jWiJxROKWE0DXVYfLYnKCAXAZy7YFgGlUqit1Zbj2UqBrKhNTK7wzwEBqx7TNs1daR2ZEvPtcOmuA08wAW0U+BTxvZRNuev3rlM69kwLdlRnguIXKUGltg2RmgHN9vCadtpwDYD9SWZP/TnfOqrmDFomp0M2RuDN7YQo5AXrfXpH29/h0q0CbfYBT3z8tkThqKwtcBMt+7OGQ8qUKtPf86kcA3NjFCrPe93VzJJaSautUgXb2tbWrGkfjzqxmYxezFdY2R7FoTQu+XLq+S/fTFe4McNeKYLkzwO5letY89f5aoxZqKkMY2LMaKxpzWwOct22QorrzDSSuGcwHb1uZy2e6oVVX1/cWiSu1FGjLGSiCWwXaSYG2H0sZzLyVipakdlREb//p7EoQjTvtlx/n/VxEPDOzgDuw7JWcAp3L4Fy6FGgTQHMGmIpec5bRXu/+Ye0FU20xy5mBSbcGON3faIvFnX05OzIj4u3cJaRAmyJY9u+KPP7Fy7OW4tY3vsHqpsRZPtMhqq9uf+up9sTsKtCVYYVoJ4podadYXFev7dnBFOgWp+JlaXRccpW4BtjPIljd31lKLjjjnRH2vo7mM+FUdm7nPWHOHbVVYVSEVNoZ4OZIDHVVFc6AW2H2AbYzYmor/UmB9jkAdtcXdu794g0wWiLxlDXBcScF2l0DbK7bnKf3qgni17cWrmNmBnp6VOs16dG4dKpqt6lxoZJSoDPNANdUhHUAnC0FOo8zwHFLBwNOHyDP5+qIvd99rsXEGtPMkCZXpi926VKgJTkFuggfy9rmSN4zAIqBCXzN+dGblQC47znAv/3fs/HusgEAVRXhrCnQ1Wmuk8x7jmiNxtEWizt/p66KM8BU5LJtAeBWjwu3OyLUFrM8ndWkFGhnBjg15cIJmjswMpYQANtFsMRTBAtOCnRxB37OjErSrIYZZDCl5Du7D3BlhR60KPY1wOb4enZw3+N8F2spFt7Pop97uprnrztngL3plWZbHEB/BhIetz3z3V5lZ8PJUrG3eMg0A+ZNgS7EZ8I8dh0A+1AEy+cU6Eznq1wlzxYkF2GLJ88AV4ZTrtvV92pj0tKZQjCz2GYGGOjc+9EbCBm1laH0Oy7E9BKAgT2rsbKxrd2AO58Boenw963LvDyhK6J2FlmuS30a06Tx53vNs9/cV85TBAvFXQQrGrew19/ewhPTFxb6UPIueQ2w6b/UVYWhFNBgT9LowdkCzgCHk2aA0/THgcQZ4GwBe0oKtOfv9KiuYBVoKm7Z9o0zH+bKsLKDqfQNZ1s0ntJZdVKg7cbP26Ca4lo9TXDcgZGxtDPA8KRAO38k57ssCNMZM4/B8O5tCHSuCrR3ZLzYU6BzSWFNxxm8KZGOS67M46oIKaztjhngbl0D7A64mW1xzDF4j2O1/bh712Z/T3izVGoqw2nPJXoGuLAp0KbD0au20peCQN7OiB/F05z0yjzMADdH3DXArVELliXOe8HMAJsBwJZo3NPJ7Np71XRGG1r9G1jKxjyGHtUVzmxLZwZExEmBdiPg2qpwhn2A7RTo+mpE44KHps7HtO9Wpb3f5EJ1XdHqZIH5EwB7a11k+0yLSNoBkEJUw+8K8aRAJ88Au9sgFddjaWiNYX1rDN+taCr0oTimfbcKT3z8fZfvJ3kNsLOWtjKEqnDIea/19um8n4uIvSuIkW7phWmfOlIEy3uuaYrEYAkSZ4C5DzAVM+/oZ7pCM6bCalVFqN1gqs2uFK2UZx9g0/jZnVhvB81cxwmaOzAytjbTGmC7MTAdgk5uV9ltTCesMalT584Ad20NcLa09WLhBPwd3AbJ2cKryEa7u8p8Jof2qemWKtDd2Ug1e16zlqSAKKH4V1NSANzeNm2eAh81leG055Jmswa4oCnQ+jH0qqkoyRlg06nu7CxsS1LaacLsQTTu7gNs9zhqqtwU6HwFKQ32uTb5nNudmtpiUApdrkpu1oJ6JoAzFsFqiegU6M0G1wMArnxhDk6576O0bX7yXt1dYTr8mZZBdVViO9d+g98WsxCzEmdKvcdUKplE7sy/WwTL8gwkAsX3WMzAw5om/wZ0O+qRaQtw4+Svu3w/ybU0vAOyyQFwzMp/Ibhc6LW5ngA4HHK2EHOu42RS2fsAd3AbJNPmJFSBLrKBmM5gAFzGnAIkaao0A0DEXj9anWWUtS1mocbuYDprgGOJjV+6Kqi5rPFLZj5ofesqnZOLJeIEvs42SEU+BWw6YY1J6Xjmucm293J7omYNcEUIsSLfB7jNmQHOfT24iJRxFWj9fhjau9bXfYBbnbTS7muknMq/0XjCzH1KANycvAa4/Rlg796FyTPAcUvQFtMp0NWmUF9BUqDdmgi+BMD28zmgviqhUGC+OOsn81AFujnNAEhcEte01iasAe5a+rVRHCnQMfSoqtDVWLuSAm1/984AV2cIgNtiugr0npsNxIzLDsA5+26CtpiVcc9gID/nVXMffTIsg+qqiL3dn5nRaq9avDfTyjsAkrwWvRDiluCHda05XddZ+43Uvo5Tqb3IAg9T+XxVEQXADa2xLp8HTNsCuOdFd9mgfl+aiQ5nn/oCDL4mF8FK15c3g8yVYf2m0n353NcAm4KdrAJNJSO5MEky0zBXVdgFlTLOAMedNXhuFWh7DXCaFGh3NqRjaa+AO+u7Yb+6xBRouzEolSJY5uSbOgNs9jbsfAp0NG6hIqx05e5inwFOKhqRy3uhLWZ5Rr1L/yTrZR7PBr1rsM7HFOhCzAB7Z1u8AVFTW2IKtLsGOIcA2FPgo7oinDJbbILuuqowqsOdH1TqKncNcEW7M9qdZV7Pwb1qfN4GqXPHntDWRONojVpOCmdLJO6sSw2nWwPsCVLiXUjtMefa5BTo975Zib//95tO329TWwwXPDEza4EpQH/ezOBmV1LynRngpG2Q0u8DbDm7NPSvr8bQ3rUA0g8E5HNG1Lzn+/bwZwbYbLtSZXfa22vrvAPNjWlmgAvZjkyavhD73PBWynKodCz7IYaUcgNgUwSrrThns50ZYB/bs45qaI06lYs7y/t+bkwqglVl12AxnzG/lgHkIrkIVnW6FGj7s2QGIHNZVuB9LGvTzQCzCjQVM+8sTLrGyU3nyFIEK6o/YNWVYXcNcHsp0M56uM5Vga6pDGFAfXVCFWgT+rqjosXN6YwlB8BOEYWuBcBV4RAqQ6WTAu0MoOSQDeA98RZqXY1fmiNxVIX1+9uvGWDvDHpTJNbuzEk+mU5mW8xK2NImeQbYrQJtlki0nwJtUptrKlOLYDV61lwWdA1wzEI4pNCj2p8U6NZoHEoBg3pWY21L/juaXZ1dSh5sbY3G0dfODmqOxjwp0InbIHmLYOn76Xynyl0DnHgfz81cjDumzO30/X62aC2e/nQxPpq3Ou3v//nWXGfNbWMk5mxxV9WFARlvKqxRWxlOO8PUGo07AwqAm22Tbi10PtNo/V4DHI1Zzgww0P6OCd6gtynNDHBbzOrS4EpXfL5kHVqjVk5rZJ19gBXcfYBFELc85/QiW3tpnvtVjcUUAKcfDOuI5qSsFsDbnwnbM8BuCjRQoAA4qQhWVboiWNHUWeKsM8DpUqDDZg2wbudKvfI3A+AylpyWlsybztFeReE2e/8vbwCTnAKdfg1w9jV+ydY2R9C7thK9aiqcKtCAWwXaqYxY5FPAmWYj8rENkpsCrTp1++6UnDKUS2cwuaBOOWmJxFBbFUafukp7piz/j890THvVVGRc/uAH72Pxrgcz2yD1s2eK1nSwCJYp8FFTEU4ZQFnn2VO40PsA60GesF0ALL+fy5ZIHHWVYfSurfS1CnRnP2/Ja35bou7r3RyJe/a1TZcCHXeqxHfl897gnHMTA+B1LVG0Rq1OB31m5jfdwENrNI4bJ3+FZz9dDEAHX+bc3pX3o6RbA5ymCFY0rte+1ngCYNPuJm8HZVmSkALd1feoadf71KYug8oHM7OVy9r+bDPAyf/uTvNXNgMA5q1szHpd72vinQF2tteq0mnwndlayy/m+U7e8rGQGvKwHMKbPdXkFMHy1M2pCDmTNLm0ZX6JpmyDlGYG2LOHL5D7NkgVIb2UI2UNsJ3lUupFShkAl7HmSNwZDU6fAp1YBCvzGmBPCrRnG6SQcgO5xDXAdgp0J04K61qiOgCurXROLpa4FRFLJQXaNMjJa4CdIlj2CaSjJ8y4pUeDK01xkAKccDvCmzJkgoNsTEelvrqiDNcAx9GjKuwMHPkRzDjrRXtWA+h6dd2c/27E3SfQrAerrgihKRJHUySGAfWJAXCuKdBm3VG6GWDz/PWqqUQ4pBAOKee81p1aY3HUVIa7VPm3PS1RvdVN79pKZz1WPplZpeT3yrcrGvHlD+uz3r45EnOC2uZIYgDcEok7Kb3mPG5mgJvaYmiLWXl5r5pzbSRuJbRHph3pbIqmEwCned7nLm+EJe77sMleAwy46+U68350q0C7l5l9gL1BknmcJgUa8M4Ap68/YQbGurp8pjVpG6R8p+ZG7d0OKp0Z4MzH6820Sl4D7B1sKYT5q/TM77xcZoDN6x5SzmCRwA3GBvWqAVBctTHM+8y7nKHQzMRDLmnnmZjBuF41Fc7z72wnZPe/zHut0DPACVWg0/Tlkwtl6eWM7R9ri51ZUlsZds5vzhpg+xxX6pWgGQCXsZZIHP2dUfjUjkVCRbt29tpri1m67HtF2LmNGVGqq0ptXFKqQHcwAO5TW4WeNRVoaNXpmyLipAO5M8A532VBZKpIGkk4gWZed52JuX5lhd4GqejXAHseb7r96dIx76V+PaqKpkHNl+ZI3JkBBvzZ0sZ81gf0qHb+pt/Mvr/mfLOqUQcNA+qrnRng/vbxrGlKLBzSXlp8QhGsinBKB8MEg6YDksv2Dn5ojVqosQcJAR8C4IgOsHvXVaGhLZb3GaBMe4xe/NT/cPFTs3I4PssJhNa2RCCChBnglBRoOyhZbb9+ZnCkK+md3nOtN/hb19K1NYor7PdyuuJjX/3QYP8NU/U/7qZAm/dCJ4pDWUlFwwD3OfO+t8yAUE1CCrR+HZKzj8x5wbwurZEuBsDRxDog+e78t5kU6I7OANv/NktBvAMx3a0tFseStS0AgHmrmrNeP6EIln2ZJeK8twfWd985PVfez93qIlgHHLfEqdrclRnglqj9nPesds6PyRltph/a26dCcLloy6UIlj2JlXCdLH3H1mgcNVU6AF6fYQa42AqydRQD4DLWHImjv33CTF89MjEFOl06rdnT102BNmuA46iuDDkNb/o1wJ2pAh1Dr9pK9KqpRNwSNEfi6YtgFfEq4LaYW3wheQY4uYhCR2dwTQBcatsgVdnBQS4Bu3kv9etRlZdUvWKi96ytcNZH+lE4pNWZAbaDim5opMy+v6azadLhBtRXOWuAe9fqWVpnDbCzRCK3Ilg1lanrlpJT0HJNs883PUgYRrUTpOS3g2pG43vXVkIk/5WO3W2Q3DXjsbiFWYvXYWVj9uJPLdEY6qorUFsZxmp7LWA/T3EkUwU6ZAfApt1Y47xPqhOOozO8z4k3+DOdNzPw0lHtzQB/tUwHwOZ9qFOgE4tgdaYqeTxpxhwAau1ZXm8g58wAV6RbA5z4XJrzar6KVrV6ztP6/vL7uTOpnVU5DCqZIEwpdzDHBCP969216B3xz7fm4jcPTc/punOXN+DhaQvwyLQFWGwHvACwcHWLk/6fSwq0ua5SSSnQbW4wBhTXvsbePs7qIlgH7D2erlTMN4MMA+qrnfOit6aJKc4G5Laln19Si2ClZtqlBMk57gNcWxlGbZUbAJs0as4AU1Gz7KIJ/dsZ/Uze0yzdB6LN84H3pk202SkVYXuNQOIaYDd1BOjYCPh6Two0oDsWIm7gm1wZsRh5ZzEyFcHKNuueidn2qCKkUBkOwRIUrLhHLtqSRkxzaSDMe7V/jyrELSn6dc4dYWaATYPpzwywef5MWqn/jVRyZ3hloxvYNLfpALiuWmeMmHNFe+umpny9AssbWpOKYKWZAW5JEwAXZBskPcJe04VZv/a0ROOo875v8lwIy7xHRNzA4dsVTWiNWjm9R5sj+vjqqsLOAIc78+YG1aYKdDikUF0Rcq7rBCld6Ng3tqafAXYC4K6mQKd5zr9MmgFuavMWwer8GmBT/8K0gwDSDzbbba3ZVxnIXATLfHbaywrrCGcG2Kf0T1Ps0TyP7bWVJgDu36PaaXNbks5JHZ01/Wjeaqe4WTYTXpiDy5+djcuenY3b3nArjs9fqdOetx/eG/NXNmcdzHVT31VCvRMzMOQGwMUTePg1A9wajeOal7/A2g7epzftuUtrgE0A3LPaqaXh7Q97A0pnO9A8D3zmIhKzUJ1cBCuHFGhL0G4RK9Pm1FSG01SB5gwwFTHzQezfTsqMCSyqKvSesukamIQA2FMFWnf49IcgeXsG9zbhnKrNeblFsOwAuCUGS8RpDLzrYoqVt+ORugZYH3lllsJjmbgp0LoIlveyYuRuwJ77DLB5r/YtYOqaX0yjYh5bRxv3nP6GZ+Qa6J7ZguZIYud6dZObAt0Uidkz3+6SCcC7N3Ti6/vlD+txyn0f4b735ielQIcyBsC9PCnQhShE0hq11wCnSVPNh2aTAm0/znyuHTcdbJPCbDq0sxavc37OFsQ127MFNZVhpxOcmAKtrxf2TGnWVoWxyn6fDKyvsf9W14pgmefHdHzjljgBUVcD4HTP+dcmAG42KdBuESxnDXAn3gvmWE2mCOCum06skm+nQHs6t3of4tTOv3NeTVO4sjPMcejXPfWz2VXms59LMbGG1hiqwiH071HlzJYmB8DptpBqz/KGNqxvjeWUzbFsfSv23WIgthraCz+sd/f8Net/991yEBrbYk46fSbeFGhAD/gL3AGqopwB9gbATdmzRXL1wbcrcdc732HK1ys6dLuEwa8urAE27ahJO2/ynAdNBp9RyBTo5H2AqytCiCS9Z3UKtNv25pJV0RK1UGN/ts25w6kC7RQtLJ73YWcwAC5TyR3S9NsgJRbBSvdhMCf/6sqkFOiYu/dgbWU4aVTarEvKfesbQAdyTXaqpNkiZX1rFBBPCrT93SriKeCEVLy29FWgK8P2Gt5Yxx6He3vP2qhiDoCTttrKZWYseaaimAp+dJUpktPHmcnzYQbYPH95WFeZK7ezqTsLziygJwW6R1WFkzpVVeEun0g+7/zjTb1lzcLVzYkp0FVhtEYTKyyva4miZ3WFE1hVFzAF2pzvgPzPhrVGE9eO5zMAbo7EIaL3GNY/6/PXbDsAzuXvtbQzA+xdA5y8r62TKm+n6zd3pQhWWxRDe+vH4G6J5B53Z1OgTQp48kz4uuYofljfirqqMBrsznFbzEpZA9ypADjpOQTSzwCbf3vXAIdCCvXVFakp0Ka2Qn1+qjabQfYae+Aj3+dpZ7eDnGaAo6ivqUCP6rBnv1wzK9y5GeAVDTqQzWV7nxUNbdigTy2G9q5JWDIwf1UTetdWYocN++ifV7a/DthJ5vIs+UpIgS7GNcCtMQyxzx353Arp0+/Xduo+E5Y/5GEGeGBPN5PKu4StKk0AnO+lL7nIpQp0xK7j472OuTyT1ohbBCv5ds4MMFOgqRh5Cwl5f/ZK+DBXpC/IZAIWk/IRcbZBcsuq11aFE9b/mDRXvW44t8q/gJuq1qfOnQFe1RhJWb8AFHcKtBkRHVBflboGuMtFsMRze/2cxIowRTgW1/suJq4Bzl56H3Abnn55StUrJi12CnRdVRhV4VDCdkH50lqAGWAnbd3uXK9qiqCmMoT66grELUFbzHIeN6BnrNIVCfp2RSNemrUUALB4bUtCCnTfuipE7EEyY11LNCFNtGBrgO2MGD+LYHlToPMZACevLzSdmlmeADhbpoJZ217rCYB711YipBKrQCfMAHsCYCddvyvbILXGsEGfWuffQOLz1JkZ4FjcciqaJz/nZv3vTiP6QkTPAgJwAmDTPnZmgNKZAfYEwKYjmm6fdG8ADOj19cmzXy3JA4tZimDF4hbe+2Zlxt+3evoGtWmWJ3RVR2aAG1v1zHt9TaWbAh0xa4Dt82AHjs/7umdbAx+NW1jTHMWA+moMqK9yMgYAHfCOHNADGw+oB5DDOmBn7beb8WZ5U6B7dd+yllw1tsUwtE8NwiGV15oWMxeuBdDx7ZXSLX/oDKeYpBlIjsQSlw16+qQmm6lQM8DZqkC3Je0VnMu5yew8kC4A5gwwFYSI4NJnZmVdm9Kc1CFtdx/gdgoqpawBdmaA3apyNZXhtFWgdZGs3Gb9gMT1fOaE8v7clYjELexoj6C6VTGLL+gzTNA7tHdtahVoe2sHpVSnOutRzwxwLiPjhXLu4zNx3hMzE7MMcswGSE5dK6cZ4GY7BVophQH1Vc5a2bz+DScANg134vMXtwQn3D0Vr87+IW9/M/k1W9UYQW1l2BkpBpCQAl1dGUY4pFAZVgnviTvf/hbVFSEcsNVgNwC2zzMmoF/p6WCamgFGvtYAL1rTjPvfn5fz57M1amaA/SuC5U2BzufacXOOGtTTnQGOW4I5S9Zj88G6474my98zAzu1lWGnA1pnz/gnVIFWiSnQ5n4HdnEGWETQ2BpzZoBN8JdrAPzGF8vw87unpVTXXt2kK1rXVYVTA2B7e6hdRvYDAKfab0oRrE6cv1bbs9X90qRAewNXtwp0YlfO7KLgZdroXFOgn5i+EL+498OM22C12evelVJ2Flh+2yHdVrrtXHvFxEzqeX11OC8p0Kvs1x3IHgC7WQzVGNizGisbI877aP6qJozsX4cN+tSgMqwwL8sMcNIEsCcFOnEGuKWDBb381NAWQ8+aSvStq8rbXsCWJZhpZoA7GgB7su7ysgbYM5CcUAU67C7NSTc41V1Si2Clru9tyzAD3F7f3Gxt560vUM0ZYCqkhrYYHv3we7z2efudVzMy07OmAlXhUIYU6OR03NQPg1v1LnE2ty3qfqBqk9b/tCbMAOeeAr3WEwCbWZ3X5ywDAOwyqj+A0tgH2HQoh/auSTkBRz0jcZ2p4ux9zSrsKoSFmPHKZubCtfhi6fqkfYBzC/hbk1J4C9Go+MUUwQL0jFu2NWGd4ewDbBrupKBiydoWTPtuNW5785u8VdhOyTiJxp0AyNAzhInrI5MzRN6fuxIHbT0E2w3vjRUNbWhoi3kCYDO77D5negbY/Rv52Abppte/xn43TMGEF+bgnRzXnzn7AFf6NwNc6wmAlze0/75Zvr416+j8218txyffr3E6MYN6ubOw365oREs0jh9vOhBA9tlTZ89IT2fJ/NwSjbn7ACfNAJvAuGdNJaoqQmjs5IxCW8xCzBJPCrS+H1NMSqn2Z5L++8VyTP1uVUpn2zzPmwysR3MknvDe+mpZA3rVVGCzwT0BAEvW6QA4JQU6bmHGgtX4xp4xzsWaZp1Bkfx8AomBq3cdrpcOgNPPAOeaWTP5c932ZkrbNeveAT2g5ccMsBl4B9DujgkNrTHU11SgvrrCGYBuTkmBzv29tXy9+/lakeWzZn4/sL4KA+urEbcEa5ojzhZII/v3QEU4hI361WWdAbacpQL2to9QEHHXxidnaRSDxla9DKV/j/wFwN+tbHRm8ju6rth89gfUV3d5H+CqcMg55za1xd0lgXZ9G/1vdzlPd88AW3aRUO/srve8Y0SSZoDd62R+H7Xaa4DTzgBXcQaYCmDpWp1mle2kbDqktZUVqKkMOethvNrskSOllF0EK7Uz7K4BTlcF2psCna4IVqhDKdDegjZmBviH9a3YckhPp9E2qUFFXPjYOXEP7V2DtpiV0GmKxi1UVrgBcEdnq2KWW0QrW3XMb1c0YtaidWl/56do3MLSdS1Yvr41JWUol/dCcySGipBy9rMspvVOXRGL6/dCXaV+bw/sWZ31c9wZyWv9kmeA59mVST9fsh6f5en9kZy2DujzQl114gywGTmu9nQezDmmNRrHknWt2GRgPYbZqawrGtqc65qAfkWD28la3xJLnQHuQvC5aE0zbn3jG+w8qi8AYKmnoE172qIWarwp0D5UgTZFpkZv1Acv/W9Ju4MXP739A9z8+tft3uf/PTMbN7/+tWcG2B0wMeeNPTcbACCXFOh4SpGzmsoQ6qrCaPamQCfNADv/trMFOruthrMdVl0VelSFU1Kgh/aqaXfW3AQmy5JebzNAtdmg+oT7A4CvlzVi88E9nfffkrWJKdDeKtAXTvoM1736Zc6PZ3VTJGH2F0i/BjhTCnTPmsqM2yA5M6LtBKyNbTFM/VZnmS1akykA9tYByX8RrKidLWVmgNtrKxvbYuhVU4Ee1RWebZCSAv4OHN/yBvd9kC1Lx8wQD+xZjQH2Z2hlY8TZAmnkgDoAwKgB9Xjn65U45O/vJnwd9c/3MXe5fv+ZT3TIU/NERNDcposI9ijC1FMz+963R2XeAuBP7NnfQT2rO50CPaxvrTMItL412uG901siMdR6nnNvESzv+7Km0j3vd9dgvYjg5VlLnXX4yWuAgcQ2KKUIVjj7QK1OgQ4lBMDVYTe7pTKsurRkpRhUZL8KFZOl9ihzthkA0yHVnZKKjCnQpnx6lR2MiafiMpBcBTp9CnRtZTihc5HpNgDw1Q8NGWdV5izVqVZ96ipRXWEqS1oYu3F/5zrONkglkAI9pLfuxDe2xdCvQjfCJq0LQKe2QXJSoEPeFOj0z8WEF+bgh3UtmHz+3h1/EF2wZK1u+Ne3xpzGqDKscp4BbolYCcUXyqUKtOmA1XlmgGcuzP8Aheno1lXqgCR5BthUJq0MK/x72gKnQEtXmIbfW7VWB7wVCT+boMeZOfIURvt+te5ojxxQ56TjAm5j7aRANybOACcHwMnLDjpizv+3d97hclVl27/X9HZ67y05J70TCBASmvSuAoq+CopiQUUsKCp+6MtrQWwoiCgKCEjvJQFSIL3npOf03uv0sr4/9l5r9p5yShIgIc/vunLlnD1zZvbsWXut9bT7aVfmoO+cV431devRPUED2BfS90U/linQnHOpHg4A155Sgh88uxvbmgewsCwz/lyCYbQNenGoO3m0SXE2eGE1GaTBIK65OxDGgc5hWE0GLChTHAFjGY/aGm+7Ofp9i+iBVgXaoFlbtEabXV2njrSthphzU6wm1fiLbnwBoCzLKcdXIup7lHuia9iHWUVp8rhwUE3JEwZwQEbhWvo9OL0qW2MAixRo5RqY1QwdTyCMlgGv3EhPhAF3QFf/C0TTnLWpvDLbKkEK9OHuJCJYE1DXX32gRxqcrQPehM8RWQ/KuRmP+TwdWwM8XhskJQXahFG17ZbY86Q7LGBscuuI1jE5npNS2/JN6HH0jPjld1Oe5QQA3HBaadzfhiMRvHugB+8d6sGUXJfMbGNqrptMgQ4o7bXEmng8OYVH1eh7ltOKfUnS5SfLjpZBpNhMWFSeIVuNBUIRuP2huPsilmFfEBajATkuC9oHfQhHOJb9+l0sq87BfdfO0+1vx0I49cQa5g6EdUEjGQE2G2BQ24F+WG2QtjUP4GuPb8PvPj1XOYcEBnBsBFg7R4ifx9qPiawj7fXSGtoOi+moRAuPB8gAPsHoHFI2ZL3jGcBBrQGcWKFRqx4XXWQ4LKZEBrARVo2RLPoAA4hTgPSHwnKS0EaNAeDOF3Zjc+NA0vNOs5ulGmmqzQxf0K83gNX/j+8U6CCMBiZTNkd9IbnpCISi6SoWo2HSntygzgM5dhukw10jGPAE45waxxJfMIw/vn0ItyyvkhHblv7ohql10AuLUYyFiWUDeIOK51XWvAXDeHB1HQKhCL557tSjOt8Ve7vwyq52/OG6+Uf1OkeC2ICJqGiOy4p+tx/hCNeJAx0tInXLZDSoRoX+3m/s9cBuNuLK+UV4fnsr7rxkBtIc5iSvNjHE/e+ymqRWQGxKrJISHRMB1rRWE5HpimynzpAWc5NIiR/TAD7KFOi9HcNgDJhRmIqcFKucb8dDtkH6ACLA/lAEnEd7vV4ypxA/f3kvntrcktAAFucsDLJENPV5wLkiNCYjwKnRdh/tgz4UptuRajPBbGRj1gB7NWuN3RLdIAnRM28gLKMvBo2d5rDoswNcmujdZBGfwWU16epfRcS2PNuBXa2DCf92xBeUDuXO2AiwMIBzFANYOAL8oTA6h30oybTLe0dcb7FhFpvkpj4PwhEeF10ei35PQJdNAWhEsEJaA1jUAMenQMc6gmQNsCxTSD5GV+ztRIbDjCyXNbkBHAzDpmmFeDTpprFEIhyhiKICnUwEi3OOA10jmJafKo0wl9UEzpU5UDsuHZM00MV4KEq3j1sDLMZItpr+DAA9oz6pXiwM4OU1uVhekxv3GWb+7E009inOmUjUAgagOIw45xj1h+G0KLoJ2rY0HzXhCIc7EIbLakKm03LUoo4dQ17Udbuxsb4P80rSkeW0ot+tZCL8dVUd/rOpCRvuOHfM/cyIL4QUm+oI84+ge8SHAU8QL+xox8zCNHz5rMoJnYtHFYES67WoAbZqStgAyHtA68z9oBH35AHVOaCLACfoPx4ngjVOBFg4XWNLK7Tv47QYT/gIMKVAn2B0qJub8SLAIuVZGBIJVaA16nHCmIpNM4oqOit9gDlXjOTYPsDa1DV/MIJENX7DviC2NQ/iK2dVovbnFyT8t/nH50kPeqrdDMaAUyuimzwZAT6eDWBVkVIYhFpRBq3TQVGBPsI2SGrvZu0xLZ5ACO1DPniD4XEFbI6GzY39+MuqOryzv1se00ZaWge8OifLRARhtII64vfnt7fh+e1tR32+b9R24sUd7cd0szYe7+zvwun3vC03SmLjn51iRYRPXuVyPBRjTBWrsBrjnCyNfW6UZTnwyYXF8AUjWFeXXOk1lhd3tOG0/307LsIpNmRaozdRBNhp0Svkanv7NqoGcFmWE/lpNpkGGL1fDEh3mOWGNBCKwBsMx0WAj6b+dl/HMCqynXBYTMhPtcUZRIkQDkGb6YMRwfJqri2gGHmXzinAK7s6Eka7xRrRNuBNmiZd3zOqnmdE3q8iBdodCKFjyIuCNBsYY0h3WMZMgfbItUZf8203i+yjEMJJVKAFNpOy0TzSjb2IALtsqgHsj4pgmY0MhWl2JYKT4HsRjhcA6BrWr6s9I36k2EzSKSsMauXaAqWZDqSq5Triurs0kV6r0YBD3comtXc0MOGMnwF3QOcEArQiWAlSoE2xBrASBdd+/95gGGYjg0v9jpKJYAXDEbyzvxvnTMtDeZYjaQq0V5MCbUuyx9BGQidDMKIVexR7E/1YXnuoFxf+fi12tQ5ixB+Cy2rWpauK87Gpc9JkU6DTHWYUptvGNYB7R/1KCr/VFE2BHgnIFkhjRSwZYwmzE2QKNJRyL48/JD+bU72njhUdQ1786e0j04Nwa7RmMpwWDHqD0gmQCE8ghCvufx9bm/oTPn7TI1tww8MbUdfjxuLyTGQ6LRj0BBEKR7C/cxhdw/6ECvjhCMcbtZ2IRLg0gFNtJgx7Q9JYLMty4J7X9+nau42FNzYC7A/rRBm1EWBAGWcfVhskMdcIJXqtCnSiXvTJRLCSOYtlK1NLVATLaGC6+dthPbbj8KOADOATDOHdH/WHxhx80RRok0xDi0V3M4t02gTy6UC0p69yLKy7oabkutA57EOLOolr64O1ytHr6/oQjnCcPS1XpivF/tN6mDIdFkzLT41ZQJQb8HhOgR6RBrAycWpbISlOB+UzHIkIlrYNUrLvDNALl4wVCZoMWxr748TXRLS3RbOAt2g2TG0DHp1YxETbINnN0XpCTyCMln4PWge9k67jiUWcZ3Pf2Gqcx5KNDf1oH/Jha5OS+SDSRHNkTeuxrQMWLWkA5f6PFUxp7HWjItuJKWpdY7IITyK2Ng2gc9gXJ4wjN+IWAxzmqDaAtgZYG9UXc4c2AtzY50am04I0uxlmo0EaHRZj9DWyXVb0jujb0mgNYKvJeNQR4BkFqQCUvrgTidopWTFqr/QPQARLGCpag/GTC0vgCYSxNkE5Seew8n26A+GkfTDrNUaf2ERlOi0wGhg8/jA6hnwoUEs4MhzmMUWwZGaDWqMsEIaHNxhJWAMcTZ9VUgidliOPAIvPKSI/o1IEK4hUW9QISZTKLQxgxoCuofgIcE6KNU59u0W9Z0oyHXCpfajbRARYM+atZoPOwB7PcS3od8dHgIWRq6sBDoWlmrqWFJsJwTDXjUNPQMlSMBjYmG2LNjf0Y9gXwvkz8lCUbpefKxZfMCw32zaTMU4AiHOOi/6wFn9ZVTehz6wlKr5pkHWHsff1QXXcbqzvRyAUUb97Zd4b0RjAjjGCAMnoGfEjx2VV5psJ1ACLPtYpagZMz6gfTX0elGc5xn2vskyHLEuRAWAhgsWECFbUALYfRa18Iv69vgn3rjiIuh73+E+OQTqeVBEszscWzDvUNYqdLYN4d3/8vBWJcNT1jOLKeYV49pYluHlZpcz6GfAE5TqVaDy+UduJrz62FRsa+jDiCyLFZpZOILHm33PVbES4kj48ETyBEByqho6BKb9rMx9lBqQpOo+NJ4K1+mAPzvntqiOe5wQd6jU41KU4MnUCVzERYNGSUruOyjZISdYprdNVrDvaNGtAjQAfR2JsRwIZwCcYWlGW3pGxvPL6yT+R91Mrny6iibEGmS4FWtPj0q8qxAHAxbMLAED279TWB1s1XrE1B3vgtBixoDRjQp/1rstn4r5r5+qOGU6ACLDSFsAkIwHaKE1s2vlkRbASt0GKvxj1GrXJyRg4Whp63bj+bxtk9Oe+lQdx10t7dM8RnusmjUHZ0u+RG5He0UA05VsVWhvPiBX958T4ah/0wq0qsI7njR8Pcb5iw/FhIIzt7erCq60BBnDMlaC9wYg0NJ0WfQQ4FFYifmVZTtluLFmEJxHi+tX16OtLvQFlI24xGqTH2G426SLATk0KtC5tTJ0fGnrdug2jEMLSOsWyXRapAq0VzRMcTQR4yBtES78X01UDOD/NNqEUaG0/1A+iD7A0gDUpw3OK02BgwL7OeGXhDs05J3N+aY2yg+priO9n2BdE17BPKiqnOyxjZpHI6L9GBMtiMsCoGlreQEiTAh0vgiU2WE7rkW+oxBybYjXHpUCn2c3SmEyUbVHX44aBAdW5KegaSWAAu6xIV9OcRacCcR+UZjrAGEOq5j21tb4Wo15cciIOlVA4gmFfSL6nwGBQSor0IlhK5kFsSqjIPtJmuvg0KY12S3xmiGDFvi5YTAacVZ2N4gwHRnyhhFE3v0YF2m6JF8HqHPahd9SPve2T1zkQ18xsNMBsStztQKw5GxuUFFmX1aSJ1oXgDYZhMihiRQ7z5KJV3SN+5KYKA3j8CLDQJ2CMSXHDhl43yrOd475XWbYDLf1KmrxwFOnbICn1zE45pyfWdDlS1h1WMoCa+4/AAPZHMy+Ek2msNGix7h5MoIjeM+qHPxTBwvJMLCzLhNVk1N23Yp0SYnNa3lezmOp73NEIsN2ECI86+BaUZcBqMkzY+S0y0RhTnHOjqgiWNoMP0ESATeMrob+7vxv1vW7saT+6WmmZ5aPO77FtkACltOOrj26V/dwTtkFKZgAH4w1gS4wB7DjGmQgfBWQAn2B0DHrlRNg9knwx9QbCYEy5GRwWY8IeeFpp9GSqcFHZ92h6ny8Y1hm5JZkOzC1Jx6u7OuRryAiPpi5i7aFeLKnKiruRkjGjMBXT8lN1x0QKxvHY+1YwKmtQ4g1gnQhWknrFvlF/UiGfqAE8dg1wvcabe6QR4NdrO7C+vk9GLut73OgY0rdXEdFebQpXS78Hc4rT5HcVjQCP33wdiG7UrCbF83pQI+bTehTRbF8wLFNamz7ECLB4L6FsGWcAH+MIsFft3weINKXovd8+6EMowlGhKpMWZzgm5SAR3vTDMQJLImrPGJOfTyt6JX4XkWnd/CAiwL0e3YaxKCORARyNyCQygBWhtSPbHO5XRfhmFEYjwMO+0LiRI5HWbzMb1Xr3I+v9mozYFGjxXuVZTtmLVkvH4PgGcH3PqIx0N/S64bAYZRS2sc+DCAcK0lUD2G7G0ARqgLUGsBx/FiGCpRrALD4FWowJ51GJYCnn51IjwMMaAzjVbpbGZKLoVH3PKIozHCjJdMQ5PHpGlQhwik0pxxFjrrVfyWwRWRxiDJpUI1Ugxq6w+2MjzIkQRnZsBBhQrrEvEEZDrxsdQ15dKyItIi1bqwStFVJTHBPx8zDnHCv2duHMKdlwWEwoVu/BRE4yXzACu2bz7w2GwTmX33WDugZpnS0TJVG/1dh1Tqw5GxuUdFqX1SSdD6OqASydLJbEfYqf3NSMHS2Dcce7h/3ITbEh22XFoCc45n6jdyQgxwGgzFFtg160D3pRljUBAzjTiWCYo2PIq1GBFm2QFGe/OyYCfKT3SSxD3qA0kJK1uxqLkZgIMDB2315hfCYS6NM6lQTiHmjp90gnXKI5bYOqWN7U51YjwCakqk6gve3DyE2xwmY2oiTToctQGwt3IHq/OKxK1D1R1mQ0Ajy+ASyEXo/EKaSlI2YeSaQCvat1CG/s6cR/t7Tozlf7nGRtkLRzuriHtH8PHJ3D8niBDOATjM6hqErlWBtnbzAMh9yQmuBJ0Djdn6CeQUz0bYNevFHbIY1Xbd8zpa2FPiXi0tkF2N02hKY+t1oDrE+Bbupzo7nfg7Oqc47q85eok+ORRPBe3dWBl3a2J3ys3x3ASrXnMADc/vRO3PvWgTFf71dv7MfPXqyNOy4VKdVNyHBcCnS0pjF2Ye0Z8eP8+9bg1F+uxE9eqI0zhHWe8THaQ9T3jKIwzQa72Zg0hW08tqmG7+HuUXgCITnpajc0rf0JDOABL0oznVIELHaMjScUIdQXGVMiSNr+mUcazY7928Yj2JQdCZxzeW3E/2LDn/0BpUBrN36xEeAG9b4RwizFGfYJX9NIhMvnxkWAtdEgjfGjEzqyJhDBMhnhD0bgDSjOiQrNhjFxBNgqBQBl65vYNkhH6BwTm5OZIgKspmCPVwccLRMxSuE/3wccAQaAmvwUKYKipWPIJzejye79hl435pakI8VqQijC5ebaYTWiTt2cFsoUaMukU6Blexw19VQEQbUp0A6NcSLe+4hrgDUiWKmaHrjDvhBSNRHgAXfUkK9tG4I/FEZ9jxuVOU7kpVrj2yCpKdBGA0OK1YQh9To093tQnGGXEW0xBp1WU0LV1JmFypo9kQiwiKDF1gADquEaDOPGRzbjR8/t1t13WlISGMAiBRoQ6Zrx13p/5whaB7w4f0YeAMVBBiSed7Uq0HaLsvn/3jO7cMkf1wKIptk3q9HNyaDNdDIZFUdorLNYzKcjMfXfgOKEFiq2AGQmghZvIIwfv1CLv63Rp2hzztEz6kduilWmNveNkQbdM+qXtb+A4tjc3TqECId0NI6FyHoRwnRAVOuESRGskMwoc1qPneL2xvo+2VKy6Qj2VDLzwmaSGSNjqa0Lsa+mPnfc+BPGcYnqdAGALKdyXbUCdrEGcNewT461xj6PGgE2yyyIfR3D0pFTkmHXiXSOhVIDrHfOJcqajNYAj50CzTnHPjXyu68jft6eDB1DPqRoM00SGMBCkVu0M5tMDbC2ft5GEWDioyAQikixEkBJaRjxhzCnWFlMx6on8gTCsKs3b7IWBYm8WWLz+MCqOnz1sW0yJcyqqQEWmwttX7GLZucDUNKgdSnQJiUFes0hJUVl6dSjM4Arsp0wGxkOdI7dUD4R9644gN8n6Y35wOo6fOnfW6RX/aUd7fjHew1j3uAvqMJMQjzicPcIQuGIsljZzEixKhOwtgY4GI7WkJhNehEszrk0es+dnodHNzThkfcbdO+p3RiI7y6UMAXajcocFwrTbUcUAeacy8jvoe5RXURZawCLWrjOYR98wTBG/SH0uwMozYy2shFjS6aHql7Hv6w6jK8/vi3uvXXGlMWo83ZOJl03FhG9tJuNRxwB5pzjuW2tE96ADHiCGPWHYtoHiJRPE5wW4wdQA6zxXMfUADdqlJYBYQB7JiSAIlLUgEQp0CGNQRONVGjHqbauW7ZBMisp0E1q+l2ZJgJcqBrAVqPWALZgxB+CLxjGcIIa4KNRgd7bPoxsl0VG5kUN8nhp0LIVjc6on9wG1RMIIZTEcE8UAQYUA7ip3xM3R3UOezGzKA0WoyGhATzgDmDAE0RVjlNG2V0agR3xN/kiBdppxqAnGDdGQmq/b63eRMIIcDCxCnSsw0Tbw3WyjPhCsKotc1JsJtl/fVhNgRbGpDDktzcP4NI/vYfvP7MLDb1uVGa7kJ9qw4AnKLOehtR7V4yDdIdFRmdbBjy6SJUYg1oBLCC6aZxTnAazkaFzePx7XaRpJ4oA28xGHOwaRYOaRukPRuJaIAHRFOiR2BRoXUQ0foyu2NsFxoBzpytqxdEIcAIDWKMCbTMbEeHAM1tbsb9zBD0jfrlmBMMcbZN0XPo1EWAg3lkcjnC0DnjkPAYo9bdSBCugjwCLTAQtu1oHEY5w7I8xRoa9SqprToo1Yes1LaFwBAOegHweoBjA4tpOKAKsfoamPk9UBVrFoLZB8miMMbs5Xtn/SFlX1web2YDqPJc0TsdiQ30fPvXAOlzz13X4/cqD8n51Wk0oy3LCbjZiX0fy9F6RZh3h8WtIc78HjEUzf4DoPaDtVx87pwkDryjdrkaAoynQgFKGJRw5k4kAezRrmnDO+UPhuKzJaDmPccw2SK0DXoyo12vvGNdoPEQp2CkacVjtGinOS4xr8b1q9+vWcQxgsaY5LMlToJ1H4bA8XiAD+DjmobX1uPD3a6VHWHiPZxSmwmhgY0eAtTdvMhVorTEmBZWUCbhWTdEQk4vFaJAL7bBXuYm1C29xhpIG/daeLr1QgNoHeN3hXhSl2yckCjEWZqMBVTmuhKl/YzHiC6K+x42GPndCo1Yo4W5q6Mee9iEEwhG4A2G8trsz7rmAkn7eMeTDsC+E5n4P2ge9uOD3a/HohiYpgmUzK3VwozoVaK5JgdYL9ry2uxNv7OnEt8+bij9ePx9VOc649CxtCrQpJh18yBvEw+81qI4TJapRlOGYVAS4e8SHYDiChl63TDk63D2qE80RGxth7NbkpYBzZYIXRmZJpl2qyiaLAL+0ox2v7u6IWzC9GgNObGDSHWZkOMxHFQEWHu7TKjOlwTVZtjYN4Lb/7sQz21on9Z5nTsmWx7RR0ZwUq64G2B8KT9ph8UZtJ1Zoshe8mkhPrAp0Y58bTotRGnnFGQ64A+Ex+7wKxHdbmeNEXbdbV8utbZkg0iIdmii0Va0JlSnQGiERfygSNcyzxk+BBpQNaSIRLIvJgAhHnDH553cOyRrsZOxpH8b0glQZwctPU95rvKhdbCsabVq3LxjGrU9sx6aGxKqngLKZP/93a/DbtxI755JFgKflK/edEEIRdA75UJRuQ0G6LWG9nMgCqMh2SieDEG7Sjk1tBDgQjsRtdv6zqRnLfrNKRny0yu02c9QBo/QJVv42kQp0NFtBMVyTOQLGQuguAHrjTxHBMkVToNW1VKQFvrijHd5gWI0AK4Zut2qk7ulQ1kBRE55mN8sx19znQUlGdC0T6ZZaASwguhktz3IiN8U2ob7SwkhPFAG2mY3YqUbDukf86BjyxjlGgKghrkuBDsSmQCc2gOeVpEvnZbrDDKfFmDQF2qZRwAWUeR9QousNvaPy+26YZHRRrGkWY7SMRlue1T7oRTDMcbHqeAcg2yABCSLACfZAoiQldk8gSsu0BnAynYZ+dwCcAzmu6HelNYYrJmAA56faYDEadBFYg0YES2k1FILLGp3TY6PZR8q6ul6cUp6JqbkpY0ZuBQ+/14D9HUproYfW1EsnpBCCm1aQInupJ6KpzyP7zsfOWy39HhSk2nSGWoZDKT0QadrVea649XF9XR9SbSZcOCsfjX0ejPr1EWAAmgiwWtM+gfVO60hOVAMcrwI9dhskUfe7uCITB7pGjmieA6Lr0WmVUQPYrFkjxfnEliklihJPqAbYEjXwtTiOcS36RwEZwMcxK/Z2IRCOSCNIRMKK0h3IclrGrAHWR4EUL7zWg885R8+IXy4Y2pY64QiXRsnutiFYjAZVgEN5vWEZAdYPn2XVOdjVOojeEb9Uh1Rq8iLYUN+H0yqzjkk/2pr8FBzsmlwEWEw+nEM2VhcMeYLy8c2N/djWNAhAaQvyzNaWhK+3qyXqkdzVOiQVrt/Z341Rv1KDwhiDy2pKrgJtYrp0zQdW16EmLwU3L60EAMwtTsfO1iHd9yYMZrMpPgX6b2vqcPcre/GrN/Zj1B9CZbYTRZOIAK860I0zf/Uufvjsbhn9XVKZpRjAPaNgTInAiQiwMIjOUI275n63PFaa6ZB9RbUq0OJ8PYGQrAN6arP+GuuNKXVjleFAcYZj0pEELc39ymZxYVkGuob9R5S+I3pY70xQN5b4PZXrccHMPHnMHmsAa+7jv7xbh3PvXT1m2xktkQjHnS/U4q6X9shxoq31i+0D3NjrRlmWU96HY0V4YhGe87NrcuENhnWCfN5gRIpfRSMV0d9lim3MYirapDWo9WflmpTBmrwUpYVNejQiEDWAA3ITE2sAA/qygM4hH3771kE8sDq5Gm37oBd7O4axuDy6qRAG0XgGsDDubObohkhsLH7z5gG8tLMdT25uTvr3O1sH0TboxQvb2xIKxCWLAFfnpQCIiryIc+kdDSA/1Y7CNHvCe184sCqynTLNXIgHuTTfk4igZMQIQAnWHupFIBTBm7Wd8m/sMY4rcc4i8qGrAU4QLQaQVLl6LETrOUCf/itEsKwmI5wWIwY8QXgCIby8swNXzy/C6VVZAIDKbCfy0vTft9jIz1RrwtMdSiR8yBPEsC+kiwCnalKgtYhxXprlQF6qdUJttfrdY9QAmw06Acja9uFxUqCj35lHYxDazPHCmK/v7sDutiFcMbdQHmOMJdUJ0NYfLyzLwNKp2Xj8ptPAmLIm1ve6sVAVvNSWnChtw8beOAdiIsAWo760QcyrZ1Rly3pn0UkCAEb9YX0NsDk+4i3UgDmHrpRABBZyU2yytrc3SbBBGMaxEWAAOsfLWBgNDCWZdjUFWhXB0rRBcvtD4By6OfRYRIC7R3w42DWK06uyUZalCHGNZZR5AiGsOdiDqxcU4dZzpsIdCGOXapiKbLcZBanY1zGcMKPIEwihe8SP5TU5MBmYbA8maO73yBI3gcloQLrdjH53AFaTAXOK0+Oceuvr+3BqZRYqc5xy3KSqbZAE0QiwXb7XWChOO42YpNWE9kEvGvs8cq6MZraJbKaxI8B7O4ZhYMBV84uUIMURlmEJO2B6Qapc+xKqQIcjqFHXCOU8458j1ilvIIyH1tTLfYdHmwJtShIBVmvRj6R91vECGcDHKX2jfunpFZELIW5SkGZDbqp13Bpgm2ax41zv7WnodaN1wCs3AFrp9PqeURnV4FwfrQEgvX6xHqEzp2QjwpX0W206IKCkgmo9VkdDdV4K2ga9k+rluluTQhProdzU2A/OlY3e5oYBbGseQGmmA587rQwb6vsTqgbuah2EgSnXrbZtCBvqlUj5xoZ++IIRuRC7rCa5+QOECnRUVCAYjoBzju4RH3a3DeHyeYUwqd/F7OI09Iz4db0pdW2QNHXbvmAYT2xqAWOKlxYAKnNcKEq3o3c0MK44w6oD3bj50a0wMobntrfiqc0tSFG9qqP+ENbV9aEo3Y5p+akyLV8Yu2dOVcZQc59HLiwlGQ7kqFGE2PHjD0awp30Y4QhHTooVz29vgzcQxnuHejHsCyoRzJh02pJMu0zX1V7LdXW9E64va+5X0haF0NJEPN6xbGlUInkTNYBFqvW506MGsLZXqlAMFazc1wVvMCwV1cdjZ+sgekf9aBv0ypQybeQjzW5GIBRB55APgVAEu9uGUZkTjUoIA7htcPxr0dynbIJFHX+dxsPsDYRk+6No+np0wxYb7dNFgINh1LYPIdtl0XntC9Pt2P7TT2CxJtVL1Nr1qRFgh5pmLRDvI7JUAGB9vZLd8f7hvqRiNq+p1/tSzeY/xaZEv8YzWnwanQTxvz8Uxsb6Pvzj/QaYjQwb6vp0G4XOIZ+c11cfUFqCdA775IZSS7IIcFmWEzazQbd5F9HLgjQbijISG8ANvaMwGRhKMh1xKdAO9f98tQcwAKTZ49VdtSUSm9WenlrF0FijVjgBExnA4jkiMvT2vmg2w0RRyk70EeCuYUXwTRinGU6llvlVtX/ydYtL8cfr5+O286uxqDwTearDTnzftW1DyE+1SeMmzW7GsDcoHUFiMy0eA5KnQJdmOpCfNrG2WiICnMh4EtdM3IOBUDQKqyUaBY/eB1qDVQhjhiNKfemAO4CfvLgHs4pS8dnTynSvlUgnQPS+Fo7ueSXpePSmU1Ga5UBVjgtbmwfQ0u/BqZWZcFqMurKZh99rwJJ73hlz/daW+gBqB4FQBA+srsPmxn45r5ZmOaQmissWzboSfYB1QQCN0cg5x/bmAZxSrhjoWqe4KC3L0dQAJ4sAC0G+HG0NsDpeKrKdE3b4l2U50djnlvW4URVohvXq3mKamongsJiOSQ3wa6po6bLqHJRnORGK8IQZI4I1B3vgD0Vwwcx8zFbL8IT4lMh8mFGYimFfKGHWmVhvq3JcKM92xgUxxPoci3AEFaXbUZRuR5eapQYoZWfN/R6cUZUldS2AaDs0gVjnhCE8Xhq0V5MCLP5vHfDCFwjja2dPAZAgAqzqWSRjX8cwKnNcsgvKnvYh3P70Tvzy1b1jnkssHUPKtS1Is8mMykTRXQA4c2q2nNcsCQxg4TB4aG09fvnaPvz6TUX3xqdZc+wW9XPGiGA5rCZwjjHrno93yAA+zlh9sAe+YBhrD/UqXj+LEdtjIsB5qYpncqz2KbERYAC6SfNdddO1vEap9bGYoum0Iv1ZLLLiBhc3kPDQx0aA55emS4XqWKMHAE6rzJrgVRibafmKV+tQAin9ZOxqG0Jhmg0pNlNcyu36uj5YTQZ87rQyHOgawbq6PiwoTcfVC4thYMCtT26PSyfZ2TqE6rwUTC9Iwa7WIWxs6EeW0yInFG00QrsJ8WsiwBaj4s0PRzjWHFQ26cs0ImFzitPV9xqUx0KajUE0bT2Cl3e2o98dwD1XzZbfU2VONMVxrChwbdsQbnlsG6bmuvDGt5cixWrClqYBLCjNkFGmLY39qMxxoTLHifpeNzjnsv53XkkGHBYjmvo92Ns+jBTV8y1ToNXzFBvJhl63NCB/dPE0DHmDOO93q3HDwxtxz2v74Q9F4DCLKKLyt8UZDrkR45yDc44fPLMLn3loI+5/93DSz6alRfUwl2UqC+VkVS8jEY6tzQMwGhgO94zqoivJaOrzyE10cYZdpgILclxRA7hnxC8zEV7Y3jahc1q5r0sqzK5S72nRvgEALp1TAAMDHlnXiFd3t6N31I9rFhbLvx9L5CaWlgEP8lKtUj1YW8MVW2+n+18jfuXU6BIAytwy6g/hjdpOXKYxPgWxBoUQVhMp0NroLwC5uXhfbe0BAOsOK5u0UX9ICrvF8vKuDswqStXVFAJAXhKjRWvMCiEYm0bZ2hMI447ndqM004HbP1GD9iGf3LS39Htw5f3v49MPrkfrgAerDvagOs8Fk4Hhjdr4kotkEWCjgWFqrl4IS6wRBek2FKbb0TXsizP6NzcOoCrHBbPREI0Ayxpg5T1E+jOgiQBr0gbre93odwfgUjdBgFCB1kf/hVEq5s+EKdCaKGJlthNPb5lYeYGWEV9QRqFE9FMIJYoxkum04N0D3fjtWwdQme3EKeUZyHZZceu5U2ExGZCXIiLAyv1Y2z6MWUXRLgRpdjMGvUFNmYc2AhytodYi5uISVRNB68yMRHhCMb5+dwBOizFhZFdcqyvmFkrDwGaKf564b4bHUoEOhvH/Xt6DWT97E0t//S4GPQH8+pq5OocSoBrA/Z64kgcACY3vOUVpWHe4FxGurEHCuBM8s7UV/e4Ant2a/HsOyBTo6N5jT/sw/u/1/fjf1/ahqd8Ns5GhIM0uDeAUq1ltWWOMqkBrnHHa/U/rgBe9owFcPrcQTku0brVnxI8nNjXDwIC8VKusa0/WclJEhvURYOV7mUj9r6Asy4Hm/mgNcDQFWhmPGQ6zDFY4jiDytupAN17cEV1TOOd4cnMLZhelYUZhKkqFENcYpUFv7elCusOMxRWZmJLjgs1sQH2vG3azUTrtRblAojRosd6WZzlRnefS7d+8gTC6R/wJDWAhhFWUoRjAnEd1GZ7c1AKzkeHSuYUo05TXKSnQ2giwMp+Jz9kyjvNbZIcJJ25Rhh2ZTgueuPk0LCxT1hhzTAQ4mbCcYG+70mO+MscJi8mA3688hGe2tuLh9xompZQu5vj8NLscY8kM4Mocp1wTtXtxg9qy0B+KoG/UjwdX18FqMuCpzS2o6xnVrTnJRLAumpWPf924GCbjxJw8xyNkAB9HHOoawRf+uQk/f3kvVh3oRpbTgsvmFmJHyyAiEY7OYS+yXVal/UKKVXr7E5HIANamPK060I0puS65iFs0zeb3tA3DajLgulNKAOgjG4AmAhyz+JmNBmnkRtNChAFjj0tvOVKEURabyjwWtW1DmF2chulqio6W9fV9WFiWIVN5h7xBLCjLQFG6HX+4bj4aet24+I9rNSlTHLtaBzGnOA2zi9OwtWkAzf0e3HhmhZwkotEIU5wIlljUtWnn7x7oRm6KVabbAUrqncnAdAqIwXAEjCkbSWFIB8IRPLKuEdV5Llx7Sgm+de5UFGcoKZBig5usDrhzyIeb/rUZmU4L/vnFU1CW5cQtyxUP58KyDEzJdQFQRCsqs52oyHZixBdC72gALf0euKwmZDjMKM10YFvzIF7Z1YEr5hWCMRZXAzy/VNlwvrKrHbtalejKFXOLMCXXBX8ojDnFadLwE17HaAq08ln8oQh6RwO4b+UhPLe9DaWZDvzh7UPY2pS4xrJ7xIevPLoFtW1D0sMsF/tJ1qXV9Yxi0BPEhbPywXm0LimWUDiC37y5Hwc6R9Dc75aL+rT8VF2NJaBEDoZ9IfhDYbx3WDFgL5yZj82NA+Mu0gDw9r5utYbLFTWANRu/kkwHLppVgMc3NuGBVfWYmuvCco2TJdoLOPH48AUVQ+5Q1wha1OuX7bIg1WbSG8CBcFxKq/g9RaOInmpXarrE5sRqUsRzLEYDvqaOu7HQpUAnMIBnFaUiL9WKlZoo4vr6PpwxJQsmA8Pqgz1xr9nS78HOlkFcOifeAM9Pje8F3D3swyfuW4M/v3MIAPD89lak2EyozFHuFZvZiPV1fajvdeMHF06T0f/19X3oHvbhM3/fIA2Ie17bj12tg7hkdiGWVGXhjdqOuI1tsggwoJSD7NcZwNHoQFG6DRGuF/Fq6HVjU0M/Lp+nfFYRAY6mV0YjwALZ31OTli8yIW48swKAItYjWu4BkBkcy6pzUJhmk61qNPZv3DhhjOGTi4qxqbEfDb1uNPS6x9Qv4Jzj5y/vwRV/fg+HukflGJtekAq72Yj/bFTSzkV97peXVmJ+STrS7RZ845wpcdG5dIcZFpMBXcNKq7f6nlGp3iweH/IGpahMSQIRrPgUaAOynBa4rCbkp9kw6g9Jh8nPX96Ds+9dpZvfASXSnpEg/RmIOo7OnJot0xsTGcpGg1J+kywF2m5R+j0/t70N80vTce70XNx95SzZAkzLovJMjPhDWHWwWx57YpNStjK7KC3u+bOK0hBSjeWKbBcqsp3S0D/cPYr9nSMwGRgeXd+ESISjpd8TV/KhLfUBlL2FEA7a3jyIVft7UJLhgNHAcNOZFfjj9fPlOHJZlXpNrRaC3WxEIBytLxdr+YKyDExT9wQNvW5c9Ie12NEyiHuuni0jiNkua9K1QqZA6yLAyr0zkR7AgvIsJzyBsHSQaFOgAeDCWfnS4HJYFKfTq7s7JtTTdlvzAG7+91Z8+6kd2KhGk3e0DGJ/5wiuW1wi3x9AQiEskbL+9v5unDstDyZVmVs4Ql0aQ3NafgoYSyzyJASwSrMcmJqrCPgJg1FkdpUm0IgRjp7iDIfOoe8PhfHstlacPyMP2S4rCtLscm+VYjMpbenU8SP+LtVmRprdPG4E2OOPKtsDwPcvmIb3f3COdLYA0X2Ntg4+mQE85AmibVDpMW82GlCTl4KmPg/ml6bDbDTgwTHKc2LpHPIhRa13lxFgjdPKaozOBxXZWgM4Rp9ALU/80zuH4Q2G8e8bF8NmMuC3bx6YUB/gsiwnllXnxDnMTiRO3DNXYYxdyBg7wBg7zBj74Ud9PkfD1LwU3LKsCk9sasaruztwVnUOFpRmYMQXQn2v0oNVSM3nptjQ5w7EpX8+uakZP3hmF7qGfRo1VuV/IZzg9oewsb4fZ9dEN8Ki2bw/pESApxWkytTD2GjuBnUzI1LjtAgjMtrnU7l5jlX0F1CMaZfVhIOajd+gJ4AdLYNYV9cblx407AuiodeNOcXpmFGQiv2dI9KbPeAOYF/HMJZUZmFuSbqcSMSkcdncQqy47Syk2sy4T1WQbh3wYsATxJzidMwpSpfe6rNrcmUNobYuaTQuBVovPOYNhLH2YA+WVefoNmU2sxHVeUqEWRDQiGiJ/1/d3Yk97cP4n9PLwRjD18+egjXfOxsGA5Mb3EQR4HCE45tPbMOoL4SHv7BICp984fRyfOH0clw1vwjZLotMxavKccoIWUOvUu9bnGEHY0o65c6WQQQjEdx0plLDnKvWUIrPazQwXDI7H+/s78bmxn7MLUmDwcDw/NdOx9rvn4M7Lpqu2ezrN+TFmQ4ZrXx2Wyv++PYhfHpRMV659UwUpNnwtce34bENTXEqsr976yDe3NOFm/+9Bd5gGKWZdqSpbVEmonqpRdT/3niGsunf2TKEt/d14TtP7dDVE7+0sx33v1uHbz25HQ29Hrmo33hmOb55zlTda4rUud7RANYe7EWm04IfXTxdvk4s2nTp1gEP9neO4LzpeVhek4NNDf0Y8gQRinCdof2lpRUY8YVwoGsEX1paEbfxV2r8El+LZ7a24olNzbj71X1KBD3DAcYYqnJdOhGTWMEZILp5+N4FNfix+pkynRb850unSQNMzCmfP71Ml0aYDJvZiBSrCT0jfgz7gtK4ETDGcN70PJlJ09LvUdq6TM/DgrKMhAawuM6XzC6IeywvVR+18wXD+PKjW3GoexS/X3kIL+5ow5t7uvDFMypk1E2IYFVmO3HBzHxU5TiRk2LF+ro+3PHcbvSM+PGvGxfjU4tK8OruDnAOLKvJkSIutW3xonCi3CKWafkp6B31y2hKpyY6IDZ9qw/24PGNTfAFw3hmawsMDPikmgUgHGRagR0AKNQYwOkyAhzAusO9GPWHsKVxABkOM75wejkYU+5Txli07Ead91NsZvz6k3MBKMYvG6MGGACuWaBk3XzziW04/3erceX970ujPpb73z2Mf77fiFF/CMPeoHQ0pdnNuGZhkVSOFcbpZXML8c8vLsab3zkLVy8ojns9xphshbSvYwQRDp1DMs1uRjjCcd+KgyjLcujGXjQFWr/J/PySctx5qTL2RSpi17APb+7pxL/WN4FzSENd0O8JJKz/BRSDc1l1DvJSbajJT24AA/HZR95gtLTEZjZi0BPEiC+E75xXjT9cNx/XLy5N+DoXzspHQZpNltYMuAP4w8qDWDo1WyfuJxBdKgBlA16e7UDLgBfBcASv7e4AY8DtF9SgvteNn75Ui3PvXY0bH9msc/xoS320/y+rVupHD3SNyHk1L9WGyzXZI/lpNmxrGsCIPxSfBadqoaw73AeHxYgaNYtrf8cIfv7yHviDYbz0jTNx7SnRa3HRrHy8vb8b/1rXGPdZtzT2I81ulpkTAFCYbsM1C4px0az8uOcnQ0QV39qjZICI+0REgi+ZHf18wrH8jf9sx0V/WDOmFkz3iA+3PLYVeWlWlGU6cNt/d2LEF8RTm1tgNxvldVP65BrQFBOJfHZrK+b8/C3U3PkGhrxBnZaFyFDTtuNxWEyoyHYmjgD3eZDhUAzQ6QWKgN83n9iOrU0DmrKCBAawSxjAdhSqvcnbh7x4a08XBjxBXKd+V6KWGoiWAKTazLIHsKAk047mcVohiXR5MR8aDSzOARkbAXaqWjuJnHbCeSQcRnNL0uCwGPHH6+bj04tK8Oy21nG7DQjaB73SDlhSlY2KbKcuA0EbmKrMduHCWflYOjVbBjMEFpMBu9sG8fjGJlx7SglOrczCl8+qxOu1nbIkaKw+wB8HTuhPxBgzArgfwEUAZgC4njE246M9q6Pju5+owZlTshEMcyyvycH80nQAikDF9uZBOennpFgRjnDZMgFQ6iF+8mItntmmpBiJXpBigyFu6nV1fQiEIzhbTX8GgIJUxaj85/sN2Ns+jFmFqch2WVGZ7YyrddjZMoiza3KwQD03LUunqgZwjNG85BgawIwxVOe5sL9zBEOeIO596wCW3PMOrrz/fXzmoY341IPr0DPil57LWjVSN6soDTMKUuEJhNHU70HPiB9f/4/ShmdpdQ5sZiPmFKfBbjbKNGtAcTZ8aWkF1h7qxY6WQZmSPLc4XdbCpNnNmJafgrOqlc/vUtPxXDYzmvrceHZrq1QRjKpAK4vbpoZ+DPtCOHta9PsQzC1Jwy6NEJYugmyMfh9zi9Pw6UUl8u9Ef8q8VBsMDDrxKGGs/X1tPTY3DuDuK2dhWn50o2e3GHHX5TNRkqkYO1PUyFZljgtV6s/1PaNoGYiKVpSp/583PU8aybEp0ICyCfWHIugY8kUXUJsZdosRp1Zkyk1srJpsSYYDxeridu9bB1CUbsf/u2IWUm1mPHDDQmQ5rbjzhVqc+r9v42cv1uJw9wj2dQzjqS0tWFKZhXZ1cRH3T1mWAxsb+ia86ADAliYlzX1BaTrKshxYfbAbtz+9E89vb8NtT+1EJMIRjnD8+Z3DSHeYsb9zBL2jfnltTq/KlhEzgTD6uoZ9WHOoF2dOyUZplgOLyzPx2IYmGRnZ1TqIzz28Eaf8ciXufGE3/KEwHlc3zufNyMOy6lwEwhG8ubdTd90AJfK+uCITOSlWXDGvKO5zFaUn7gUcCkfwtzX1sBgNWHOwB+1DPhSrn2VxeSY2NvTjsQ1NABILlwnnxdySdCzSiEstqcqSj9XkpaA8y4GvnFU14e8hy2VRU6BDsr5Ty3kz8uAJhLGhvk/Wzy2pysay6hzsaR/G+4d7Uds2hN5RPx5d34j7VhzE6VVZCTdfigHsw7ee3I7zf7caZ/92FXa2DOJX18xGqt2Mbz25A06LETeeUS7/Rsx5X1lWCaOBgTGGJZVZeL22A2/v78Z3z6/BvJJ03LKsCiYDQ6bTgjlFabhgZj5SrCZc/9AGPL6xSTrpxLVNVE94yZwCZDotuOXxbRjxBWV/SJfVJA3gO1+oxY+fr8WX/rUFz25tw/KaXCnwleOyYlp+ioz8ie+lQCM8lq46Oh9YXY/P/H0j/ucfm7ChoQ8LyzKR6bRgbnF6XPq7yOAAlGjlF04vlw42QWydsLjeZ9fkorZtGBfMzIfHH8LN/96K5j4POod84JwjFI7g/ncP47dvHcTV84uw8rZl2PmzT0jHEQB84fTofRabJTAWIuK/pz26Zggqs13ymj/x5dN0fxdVgdZHgBdXZOKq+cXyswHAxvp+fP+ZXZhdlIar5hfhpZ3tukjtgCeYUAEaAL6yrAr/unExAGB6gTCAE2/jUtR+yIOeAIZ9QQR0pSXKNc92WWVqbTLMRgM+v6Qc7x/uw46WQdz96l6M+kO485IZCcfkjMJUGFTBxDS7GeVZTrVtkRev7e7AorIMfPGMcmS7LHhsQzPy0qzY1jyI1zXp/7EiWCISfOu5U2SLprIkGWVfWVaF+l43ekb8cU65u17ai8v//D6e2tKiiDEZDZhekKpEuA/04FvnTZWOBcH3L5yG86bn4a6X9+AXr+zFzpZBcM6xsb4PK/d148sxTkWT0YB7Pz1XpgNPhFlFaTitMhN97gC0l5QxIMtp0emnXDW/CK/duhSP3rQY/lAE961QMlFEaZCgc8iHzzy0EcPeEB68YRF+d+08dAx5Mefnb+HJzS24dE6BNBQNBobSTAcOdI3gT28fwlcf3YprH1yP7z69E9PyU/Cd86rxk0tn4BzNHkVbe61lRkGq7EGrpbnPg1I10nze9Dx8/ewqbGroxycfWIcHVtUDQJIUaK0BrMxLLf1ePLq+CcUZdp0TRkSyRYZRqs0k058FJRkOtI6TXSXU3u0xJQ1arDER4GsWFsNhNuI7T+3QBaYCoQh+t+IgpuWnYIl6r/3gwml489tnoSTTgZvPqkSEAz94dpcM3gx5g3hoTT1++mJtXFS5c9iHArVEZUlVFt69fbnOOLfITAEj8lKtKMl04NGbTo2bB60mAzY3DiDNbsH3LpgGAPjqsirMLUmXzn6ryRBt85RknjmRSf7tnhgsBnCYc14PAIyxJwFcAWByVeXHEUYDw5+un49HNzThgpn5sBgNSLGacO+Kg3BZTbjt/GoAUeOiZ8SPnBSr2kN2D+xmI1betkypp1A3+8KD/8V/bkZNfgp2tw3BZTXpNqVpDjN+ceUsfPupHQCik9uXllZqVJ+VGyHDYcavrpmTcPGbkuvCJ2bkyUjozMJULChNx3JNtPlYUJOfgqe3tGLhL1YgFOG4bG4hrpxXiAFPEHe+sBuX/ek9GBjQPuST3vTZRWnIVDcWf111GO8e6MGIL4hff3KOFGD51nlT0dLvlTUtghtOK8NfV9Xhpy/WYtCjiO/U5KfAwJQJcHFFJgwGhsvmFmJdXZ/cUF69oAi7Wgfx3ad34ofP7dK3QVIn0PtWHoTJwGT0XMuc4nQ8sakFr9d24sKZ+QiGozXERgOD0aC0Q/rdtfMSpqKYjQYUZdjx19V12NjQj85hpRaxOMOO7mE/LpyZj6vmxxtFWqbmubClaQCVOUorD4vJgKe2tKCxz4Mzpyjfa5XqXfyyqmANRNNVtakzC0ozUJhmQ/uQD3NVA1hgMDB8amEx7l1xMM5zX5xhl7WMwTDHTy6dLo28WUVpePXWM7GteRCPbWjCE5ta8K/1TUhXvc0P3LAQ9608iEfWNcpN7JeXVuK2/+7A+fetRnmWEwe6RpCfasO0/BTYzEY4rSacXZODLJcFb+3pQseQD2sO9WBxeSYYY5hbnI6XdrbDZGD4nyVl+Nf6Jnz36Z2oUmukH7hhAR7f2Iy1h3oTpnXFXqP/9/Je9I76Zd39jy6Zjk89sA63P70Liysy8H+v70e6w4JLZhfgsQ3NeHF7O0b8IZxVnaO2s7Eh3WHG95/ZpV43/dT+188ugEeTEqilOMOO9XW9eOT9BmxWa2SrclxIt5vR3O/B76+dh7tf2Ys+tb8zoDjq6npGcecLtTjcPQq3P77kQmsEJeO8GXk4b0beuM+LvWYr93UhEIpgdlH8JnNJZRYcFiUFdtgXRJbTguo8FyKc4zdvHsBn/75R9/xzpuXi99fNS/he5VkOhCIcqw704NSKTJiMDN+fkYer5hfDbDTgtv/uxOdPL0e6xmDJcFpQkGbDlZr7aklVFl7a2Y5ZRan4omosl2Q68P0La2BWVfazXVa8eutS3PH8Lvz4+Vq8tKMd91w9W1dfHUtBmh1//sx8fO7hTfj0gxsw5AnI9OWKLCe+trxKMWY5x09f2gPOgbsuj/qIDQaGN759lvxdRDy0KdAWkwEuq9IfeHlNDtYc7EGEAzecqoglffu8qVJZOra3r+Bnl83ADy+apjsmPpMt5rP93zVz0DboxbySdKzY24WbH92Cs37zLgDlu0+1mVDf68bFs/NxzzWzwRjTCd4Ayjq0rDoHqw/2yPrciVCdl4L/bGpG+5AXmer3KDh3ei523/WJuPcCkqdAaxEG8J0v7Ea6w4I/f2Y++t0BPL+9DQ+/14DmPiWjo753FBfNis9GiKVGdVomjwCbse5wH0755UpNnba+tOSyuQVxa10irl9cgj++fQhX/eV9cA58eWlFnKEocFhMqM5LkddEOER/8Owu7O8cwV2XzYDVZMT/u2IWDnSO4JblVbj8z+/hV2/sR4bDgj3tQ7LMSqxpmQ4zZhSkYkFpBj69qARv7umSxlQsn5iRhwWl6djWPCjH1uyiNBRn2LH6YDeyXVb86prZ8v4Uzt/KbCc+v6Q87vXEnuz2Z3bikXWN+Pt7DTi7JgfdI37kp9pkxtPRcvNZldhQ3w/trmpKrgtzitN035HBwOT+4obTyvDv9Y3IdJrx+MZmmI0GLCzNQKrdhPcP92HQE8A/v3iKfP7DXzgF25oGYGAM155Son17lGU5sWJvF9Ye6kVVjhNmowHfPb8aXzt7iq52XyAi/bE6DTMKU/HKrg7M/tmbyHBa8K1zpyI31Yq9HcMyQGIyGvC9C6bh62dPwTf+sx3v7O+Gw2KUxq6WTI0BbDMrz/nbmnqM+kO4+4qZ0tkvPgMQNYCvmFckI8iCkkwH3t7XjS/+cxO2Ng3g4tkFmFWUhu5hH4ozHKjKdeL2p3fCZTWhOk8fNdUSGwEuy3Li51fMwu1P78StT2xHTX4Kphekornfg6Y+D/7xhUXyOmrbNJVkOnD3FbPw4xd247qHNiDHZcX7h3tlNlxjnwd/+9xCRTQ1EkH7oE+mnyfCoO4JxxNhE/uye66eHdUTMBvx4A0Lcemf3oMnEAJjDEwtb/k4RoBPdAO4CECL5vdWAKd+ROdyzMhwWnDrudFUybkl6XjvcC9+/ck5MgInIkdf/882mAwMJqMB+zqG8YsrZyE31SbTTwFgYWkG7v/MAqzY24nDPaO4ZHYBrllYHJfTf+X8Iqw91Itnt7XKVI3PnBpNBUqzm3F2TQ4+t6RM9/paGGP42+cXyd8rc1x47mtnHOUVieeiWQXY1zGC0yqzcNncAl2tVmWOE794ZS8K0uy4MsuB3W1DyHBYkOm0wGExwmhg+O+WVswpTsO/b1ys89QunZrYUHdZTbjxjArct/IgavJS8PD/nCKv318+uwClqrBSQZodj3xxsfy7s2tysfz2HGxpGsDKvV3Y1NiPU9XUcjGBtvR7ce+n5yaMVJxdk4v8VCXFNyfFCn8wLJU3AaVe9JxpuXJcJOLvnz8Fz21vxfuHe1Gdl4Kr5hdhf8cIelP9+OVVs8ZVqrxoVgEG3EHkpyrKsKdWZGJjfT9yUqzSsXHV/CJU5bh0ir0WkwGLyzOleiWgTM6XzSvEP95rSFg/du0pJXi/rhez1O9zWXUOguGIIsdvNiI3xYqpeS5cMFOfXsYYw8KyDCwsy8Cdl0zHU1ta8Py2Ntx8ViXSHGb8+JLpuGJeoazLunh2AWYUpOIXr+6DJxDCZ08tRdew0hYiHOHoHVXEUJTviaEoXRHBEAJSc0sUA/impRX44YXTYLMY8fDaBoQiHNPyU/CJGfmYlp+KO57brbsmsVTluLCwLAMD7gDmlqRLD/u8knT88KLpuPuVvVi5rwsXzcrHrz85Byk2M87dpqQlf35JuUzbtZqMeP5rZ+C13R2obRuSnmZBlsuKZHGe4gw73IEw7np5L0oy7TAbDHhNTc2dkuvC5XML0T3iw/++th8V2VHVyfs/uwA/fHY3HtvQhFCEI011tJ1SkYnzpufKlPVjzWVzC2E2GjCvNF3qFGixmY1YVp0jI0pfPEMpDZhekIrnvnY6hjxBBMIRdAx64bCYcM3C4oQbPAC4akERpua5MLsoPW6+vGp+EfLTbFhUpv9+f3bpTPhDYV3N1bnTlfKIuy6fqdvM3hwT+S7NcuCxm07FU5tb8MtX9+Gce1fDYjQgLy15evjpVdn4v6tn4+H3GuCymWRao8HA8P0Lo0ZnmsOCt/Z04pxpyR0OYg4qiYmaXDw7H4Xpdnzr3Kl4cnMLfvJCrZwrl9fkYnmN8jyjgSnthWKcedr0aEGGw4ILZubh1Ar9yMxJscr17fwZeXj6K0tQ3+OGPxTG1qYBNPV78MANC3DhOEbi7Z+ogdNq1LXRGo8fXTwdo/4QXtzRjqVTs3VzYyJDW5BMBVpLvrpmOq0m/PvGxSjLcqI004Fp+Sn4/cpDsJgUDQ2jgSXMBopFCKel2BK/Z1mmA7VtQ7h+cSnsFiO2Nw3iFNUxLQz1KxNkhCQi3WHBd86fik0NA/jy0oox5zQA+MN18+U9Nb0gFYvKMtA74sfc4jSptH7x7AJcrM5fd1w0HV98ZDOuf2iD7nXEPffbTylp9IwxLK/JxZ3qfJ4Ixhh+eNF0fPrB9TI9d25JOt77wTkJnz+zMBVnTsnG18+eEnePC+wWI+7/zAIMeYL475YW/H7lQbgDYfzmk3OSOqcmy/LqXEzNdena4zx606ljil3deu5UPLu1Ffe/W4czp2Qjy2XBzpZB+EMRpNrMuP+zC6RzH1D2E9rMPy3nT89D36gft19Qg9Or4p3xsVTluGA3G+PG/BXzitA64IXFaMDWpgF89+mdAJSMgFij22Ex4cHPLcSPntsNTzCccC+ysCwDMwpSpfZLYbodu9uGcPNZlbghRrF8WU0OtrcMyAyKb503Ne71SjIdCIQj2NjQj2XVOXhxRzuejGnFmO2y4smbT5OR1kQUptswvSBVVyZxzYIi7GwZxFObW3SdHBaXZya97oCyz053mPG9p3diwB3A1QuKcP3iUuxtH8b3n92Fxb9cCbeq2i6uwVhYTAapSZGMRWWZOGdaLs6PcUDnp9nw6E2LsV8TxXdaTcdsnB9PsBO5hxNj7FMALuCcf0n9/XMAFnPOvxnzvJsB3AwApaWlC5uamj70cz0atjb1o67HrUtxdftD+NaTOxCKRGA3K6qHBWk23HP1nKSbuYngC4axvr4Py2PqUT9OPLq+EXaLCVfPL9J5D8cjGI5gS+MAFldkHtU1FjT2uvGrN/bjO+dXy8k9Ef5QGG/UdmLVgR54A2HML03HV5ZNPGX0WBPtVXhk18AbCKO+Vy8wM1Eaet1x7XI+CIJhpXf1kDeIs6pz4mpNe0b8ePi9Btx67hQZbR1wB7BiXxfmlaSP+X1OFM457nl9P7JdFnx5aeUHdj92DHnx4Op6XDa3AAtVY66x142ntrQode0VmQiFI1h9sAfnTMuNO48RXxA7W4YwtyTtA/9eJkr3iA972ocxuyhNVx91ItE17MPz29uwq3UQMwpS8Y1z4jdzxxpfMIzVB3viHEyJnpcs8niiwznHm3s6UZblnHAaazAcwQ+f3Y2vnV01pjPyoTX1OK0yS5bOAMC7+xWF3m+fVz0p4SRAKZ+pynEiK8EY9wXDCIYjCe/JvlE/1h7qlYKFHzWcc/x3SwvS7GbMLEzDy7vaUdftxq+umT2hCHUiVu7twtyS9AlpC0yWjiEvNtb34/K5hZPaQ4zH+4d7sWJvF+66fOaE/2Zr0wD8wXCc0+nD4P53D6Msy5FQQBBQlM7f2tsFfyiMC2flxwkxHQmPbWhC17APt51ffURjt2/Uj7+trccNp5ahJNMBt1/pF56TYsWBzhFsbuzHedPzjlq01R8KY31dH1Yf7MFnFpdi6gT2BOEIj9tbvrqrA6sOdCNXVSU3GhiuXlAUV1Ki5Tdv7sepFVkyo+xoWXWgG+VZzknPT8cDjLGtnPNFCR87wQ3gJQDu4pxfoP5+BwBwzu9J9jeLFi3iW7Zs+ZDOkCAIgiAIgiAIgvgwGcsAPtGTujcDmMoYq2CMWQBcB+Clj/icCIIgCIIgCIIgiOOQE7oGmHMeYox9A8CbAIwA/sE53/MRnxZBEARBEARBEARxHHJCG8AAwDl/DcBrH/V5EARBEARBEARBEMc3J3oKNEEQBEEQBEEQBEFMCDKACYIgCIIgCIIgiJMCMoAJgiAIgiAIgiCIkwIygAmCIAiCIAiCIIiTAjKACYIgCIIgCIIgiJMCMoAJgiAIgiAIgiCIkwIygAmCIAiCIAiCIIiTAjKACYIgCIIgCIIgiJMCMoAJgiAIgiAIgiCIkwIygAmCIAiCIAiCIIiTAjKACYIgCIIgCIIgiJMCMoAJgiAIgiAIgiCIkwIygAmCIAiCIAiCIIiTAjKACYIgCIIgCIIgiJMCMoAJgiAIgiAIgiCIkwLGOf+oz+FDhTHWA6Dpoz4P4oQgG0DvR30SBHGMoXFNfByhcU18HKFxTXwc+bDGdRnnPCfRAyedAUwQE4UxtoVzvuijPg+COJbQuCY+jtC4Jj6O0LgmPo4cD+OaUqAJgiAIgiAIgiCIkwIygAmCIAiCIAiCIIiTAjKACSI5f/uoT4AgPgBoXBMfR2hcEx9HaFwTH0c+8nFNNcAEQRAEQRAEQRDESQFFgAmCIAiCIAiCIIiTAjKAiZMGxtg/GGPdjLFazbFPMcb2MMYijLFFMc+/gzF2mDF2gDF2geb4QsbYbvWxPzLG2If5OQhCS5Jx/RvG2H7G2C7G2POMsXTNYzSuieOeJOP6bnVM72CMvcUYK9Q8RuOaOO5JNK41j93OGOOMsWzNMRrXxHFPkvn6LsZYmzpf72CMXax57CMf12QAEycTjwC4MOZYLYCrAazRHmSMzQBwHYCZ6t/8hTFmVB/+K4CbAUxV/8W+JkF8mDyC+DG4AsAszvkcAAcB3AHQuCZOKB5B/Bj8Ded8Dud8HoBXAPwUoHFNnFA8ggRjkDFWAuB8AM2aYzSuiROFR5B4DN7HOZ+n/nsNOH7GNRnAxEkD53wNgP6YY/s45wcSPP0KAE9yzv2c8wYAhwEsZowVAEjlnK/nSgH9vwFc+QGfOkEkJcm4fotzHlJ/3QCgWP2ZxjVxQpBkXA9rfnUCECImNK6JE4JE41rlPgDfR3RMAzSuiROEMcZ1Io6LcU0GMEEkpghAi+b3VvVYkfpz7HGCOF65EcDr6s80rokTGsbYLxljLQA+CzUCDBrXxAkMY+xyAG2c850xD9G4Jk50vqGWrfyDMZahHjsuxjUZwASRmER1B3yM4wRx3MEY+zGAEIDHxaEET6NxTZwwcM5/zDkvgTKmv6EepnFNnJAwxhwAfoyoM0f3cIJjNK6JE4W/AqgCMA9AB4B71ePHxbgmA5ggEtMKoETzezGAdvV4cYLjBHFcwRj7HwCXAvgsj/a7o3FNfFz4D4Br1J9pXBMnKlUAKgDsZIw1Qhmj2xhj+aBxTZzAcM67OOdhznkEwEMAFqsPHRfjmgxggkjMSwCuY4xZGWMVUIrxN3HOOwCMMMZOU9XpPg/gxY/yRAkiFsbYhQB+AOByzrlH8xCNa+KEhTE2VfPr5QD2qz/TuCZOSDjnuznnuZzzcs55ORQjYAHnvBM0rokTGLWmV3AVFNFZ4DgZ16YP6oUJ4niDMfYEgOUAshljrQB+BqVo/08AcgC8yhjbwTm/gHO+hzH2XwB7oaSQfp1zHlZf6hYoind2KLWVr4MgPiKSjOs7AFgBrFC7CGzgnH+VxjVxopBkXF/MGKsBEAHQBOCrAEDjmjhRSDSuOecPJ3oujWviRCHJfL2cMTYPShpzI4CvAMfPuGbRzDiCIAiCIAiCIAiC+PhCKdAEQRAEQRAEQRDESQEZwARBEARBEARBEMRJARnABEEQBEEQBEEQxEkBGcAEQRAEQRAEQRDESQEZwARBEARBEARBEMRJARnABEEQBHGUMMayGGM71H+djLE2ze+WSb7WKsbYIvXn1xhj6cfg/MoZY5wxdrfmWDZjLMgY+7P6+1cZY58/ivf40dGeJ0EQBEF80FAbJIIgCII4hjDG7gIwyjn/reaYiXMemuDfrwJwO+d8yzE8p3IAbwMY5pzPV4/dAqU343uc828cg/cY5Zy7jvZ1CIIgCOKDhCLABEEQBPEBwBh7hDH2O8bYuwB+xRhbzBhbxxjbrv5foz7Pzhh7kjG2izH2FAC75jUa1UhtOWNsH2PsIcbYHsbYW4wxu/qcU9S/Xc8Y+w1jrDbJKXkB7BPRZQDXAviv5r3uYozdrv68ijH2K8bYJsbYQcbYUvX4F0TEWP39FcbYcsbY/wGwqxHvx9XHblD/fgdj7EHGmFH99whjrJYxtpsx9p1jdLkJgiAIYkKQAUwQBEEQHxzVAM7jnH8XwH4AZ6kR2J8C+F/1ObcA8HDO5wD4JYCFSV5rKoD7OeczAQwCuEY9/k8AX+WcLwEQHud8ngRwHWOsWH1u+xjPNXHOFwP4NoCfjfWinPMfAvByzudxzj/LGJsOxcA+g3M+T32vzwKYB6CIcz6Lcz5bPXeCIAiC+NAwfdQnQBAEQRAfY57mnAujNA3AvxhjUwFwAGb1+FkA/ggAnPNdjLFdSV6rgXO+Q/15K4BytT44hXO+Tj3+HwCXjnE+bwC4G0AXgKfGOffntO81znNjOReKIb+ZMQYoUe1uAC8DqGSM/QnAqwDemuTrEgRBEMRRQRFggiAIgvjgcGt+vhvAu5zzWQAuA2DTPDYRQQ6/5ucwFCc2m8zJcM4DUAza7wJ4doLvJ94LAELQ7x1sSAwD8C81IjyPc17DOb+Lcz4AYC6AVQC+DuDvkzl/giAIgjhayAAmCIIgiA+HNABt6s9f0BxfAyU9GIyxWQDmTPQFVYNyhDF2mnrougn82b0AfsA575vo+2hoBDCPMWZgjJUAWKx5LMgYE1HttwF8kjGWCwCMsUzGWBljLBuAgXP+LICfAFhwBOdAEARBEEcMpUATBEEQxIfDr6GkQN8G4B3N8b8C+Kea+rwDwKZJvu5NAB5ijLmhRFaHxnoy53wPgD2TfA/B+wAaAOwGUAtgm+axvwHYxRjbptYB3wngLcaYAUAQSsTXC+WzCgf8HUd4HgRBEARxRFAbJIIgCII4gWGMuTjno+rPPwRQwDn/1kd8WgRBEARxXEIRYIIgCII4sbmEMXYHlDW9Cfr0aoIgCIIgNFAEmCAIgiAIgiAIgjgpIBEsgiAIgiAIgiAI4qSADGCCIAiCIAiCIAjipIAMYIIgCIIgCIIgCOKkgAxggiAIgiAIgiAI4qSADGCCIAiCIAiCIAjipIAMYIIgCIIgCIIgCOKk4P8DLQrbnFtAk60AAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-- RMSE --  3342.164945457742\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "test_predicted, test_RMSE = get_test_plot(X_test, y_test)\n",
        "test_predicted.to_csv(\"test_predicted.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "8mICjEWmDGZs"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([-0.99529521, -0.9952007 , -0.99529904, -0.99907114, -0.99529904,\n",
              "       -0.9954342 , -0.99555827, -0.99555358, -0.99670926, -0.99670926,\n",
              "       -0.99548904])"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_test[0][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "dy1IdFkQDGZt"
      },
      "outputs": [],
      "source": [
        "predicted = np.array(predict_result)\n",
        "real_d = np.array(real_price)\n",
        "\n",
        "#array of prices of predicted and actual values\n",
        "pred_price = []\n",
        "real_pr = []\n",
        "\n",
        "for i in range(len(predicted)):\n",
        "    pred_price.append(predicted[i][i])\n",
        "    real_pr.append(real_d[i][i])\n",
        "\n",
        "actual_trend=[]\n",
        "predicted_trend=[]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "PZe2W8iDDGZt"
      },
      "outputs": [],
      "source": [
        "for i in range(1,len(pred_price)):\n",
        "    if(pred_price[i]>real_pr[i-1]):\n",
        "        predicted_trend.append(\"bullish\")\n",
        "    else:\n",
        "        predicted_trend.append(\"bearish\")\n",
        "    if(real_pr[i]>real_pr[i-1]):\n",
        "        actual_trend.append(\"bullish\")\n",
        "    else:\n",
        "        actual_trend.append(\"bearish\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "jMRrRTtfDGZw"
      },
      "outputs": [],
      "source": [
        "count = 0\n",
        "for i in range(len(actual_trend)):\n",
        "    if(actual_trend[i]==predicted_trend[i]):\n",
        "        count=count+1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "etFiC8hzDGZy"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Accuracy:  52.49520153550864\n"
          ]
        }
      ],
      "source": [
        "print(\"Model Accuracy: \", count/len(actual_trend)*100)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "base",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12 (main, Apr  4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)]"
    },
    "orig_nbformat": 4,
    "vscode": {
      "interpreter": {
        "hash": "edd6f0f815116d3cdec6e4754fb8d707371ba4a405f78d5bd7832a905472b995"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}