Projects/stock_prediction/stock_prediction_9days.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "791d53d5-f116-4315-a653-433513701c50",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import yfinance as yf\n",
    "import time\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3043535e-abaa-408a-9b51-42e1b1cd0da5",
   "metadata": {},
   "source": [
    "# 01. Loading the stock"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2664cd97-c278-4588-9b1b-74da4f2b26ce",
   "metadata": {},
   "source": [
    "* N_STEPS: number of days for window → `depth`/ `length` of input sequence which the neural network will process  \n",
    "* LOOKUP_STEPS: array which includes the number and sequence of days for the prediction:\n",
    "    * array `[1, 2, 3]` represents a forecast for three days:  \n",
    "        * 1: tomorrow\n",
    "        * 2: the day after tomorrow\n",
    "        * 3: the day after the second day  \n",
    "* STOCK: ticker-symbol/unique code of letters assigned to a publicly traded company's stock (GOOGL the ticker on NASDAQ). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b11d755-2e7d-43bb-80a1-600329e4f9c9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-02-15\n"
     ]
    }
   ],
   "source": [
    "N_STEPS = 7\n",
    "STOCK_g = 'GOOGL'\n",
    "STOCK_a = 'AAPL'\n",
    "\n",
    "date_now = time.strftime('%Y-%m-%d')\n",
    "date_2years_ago = (dt.date.today() - dt.timedelta(days=730)).strftime('%Y-%m-%d')\n",
    "date_3years_ago = (dt.date.today() - dt.timedelta(days=1095)).strftime('%Y-%m-%d')\n",
    "\n",
    "print(date_now\n",
    "     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c766f1c0-98f9-491f-9ec2-12b2b4daaf55",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Date  Apple_close\n",
      "0 2022-02-15   172.789993\n",
      "1 2022-02-16   172.550003\n",
      "2 2022-02-17   168.880005\n",
      "3 2022-02-18   167.300003\n",
      "4 2022-02-22   164.320007\n",
      "\n",
      "        Date  Google_close\n",
      "0 2021-02-16    105.535004\n",
      "1 2021-02-17    105.931000\n",
      "2 2021-02-18    105.290497\n",
      "3 2021-02-19    104.440498\n",
      "4 2021-02-22    102.712997\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# loading the apple and google stocks\n",
    "stock_google = yf.download(STOCK_g,\n",
    "                           start=date_3years_ago,\n",
    "                           end=date_now,\n",
    "                           interval='1d')\n",
    "\n",
    "stock_apple = yf.download(STOCK_a, \n",
    "                          start=date_2years_ago,\n",
    "                          end=date_now,\n",
    "                          interval='1d')\n",
    "\n",
    "# remove ne unnecessary columns → only focus on 'Close' (displays the price on the closing market that day)\n",
    "close_apple = stock_apple.drop(['Open', 'High', 'Low', 'Adj Close', 'Volume'], axis=1)\n",
    "\n",
    "close_google = stock_google.drop(['Open', 'High', 'Low', 'Adj Close', 'Volume'], axis=1)\n",
    "\n",
    "# reset the index and rename the 'Close' columns\n",
    "close_apple = close_apple.reset_index()\n",
    "close_apple.rename(columns={'Close':'Apple_close'}, \n",
    "                   inplace=True)\n",
    "\n",
    "close_google = close_google.reset_index()\n",
    "close_google.rename(columns={'Close':'Google_close'}, \n",
    "                    inplace=True)   # allows to apply to changes to the initial df\n",
    "\n",
    "# print the resulting columns\n",
    "print(close_apple.head(5))\n",
    "print()\n",
    "print(close_google.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e5d6a6-cce9-41fb-9cb6-64ef5e8be120",
   "metadata": {},
   "source": [
    "# 02. Displaying the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f5f4dc84-7029-452f-8499-57567e5774ee",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABNYAAAHACAYAAABuwuWeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAD7YklEQVR4nOzdd3QUZRcG8GeTQIAAoQQSepVeBaRKUaQoIPIhqBQLgkhRBLFgrygWQFRsqAgqSlVEURABadKkGukdQicQSkjZ74/ryzuzJdndbM/zO4czs7Ob3Ulhd+bOLRar1WoFERERERERERERuSUi0DtAREREREREREQUihhYIyIiIiIiIiIi8gADa0RERERERERERB5gYI2IiIiIiIiIiMgDDKwRERERERERERF5gIE1IiIiIiIiIiIiDzCwRkRERERERERE5AEG1oiIiIiIiIiIiDwQFegdCAaZmZk4evQoChUqBIvFEujdISIiIiIiIiKiALFarbhw4QJKly6NiIisc9IYWANw9OhRlCtXLtC7QUREREREREREQeLQoUMoW7Zslo9hYA1AoUKFAMgPrHDhwgHeGyIiIiIiIiIiCpTz58+jXLly1+JFWWFgDbhW/lm4cGEG1oiIiIiIiIiIyKV2YRxeQERERERERERE5AEG1oiIiIiIiIiIiDzAwBoREREREREREZEHGFgjIiIiIiIiIiLyQEADa2PHjkWTJk1QqFAhlCxZEt27d8eOHTtMj7FarXjxxRdRunRp5M+fH23btsX27dtNj0lNTcXw4cMRFxeHmJgYdOvWDYcPH/bnt0JERERERERERLlMQANry5Ytw9ChQ7FmzRosWrQI6enp6NChAy5evHjtMePGjcO7776L999/H+vWrUNCQgJuueUWXLhw4dpjRowYgblz52LGjBlYsWIFUlJS0KVLF2RkZATi2yIiIiIiIiIiolzAYrVarYHeCeXkyZMoWbIkli1bhtatW8NqtaJ06dIYMWIEnnzySQCSnRYfH48333wTDz30EJKTk1GiRAlMmzYNvXv3BgAcPXoU5cqVw88//4yOHTtm+7rnz59HbGwskpOTUbhwYZ9+j0REREREREREFLzciRMFVY+15ORkAECxYsUAAPv27UNSUhI6dOhw7THR0dFo06YNVq1aBQDYsGED0tLSTI8pXbo06tSpc+0xtlJTU3H+/HnTPyIiIiIiIiIiIncETWDNarVi5MiRaNWqFerUqQMASEpKAgDEx8ebHhsfH3/tvqSkJOTNmxdFixZ1+hhbY8eORWxs7LV/5cqV8/a3Q0REREREREREYS5oAmvDhg3Dli1b8O2339rdZ7FYTLetVqvdNltZPebpp59GcnLytX+HDh3yfMeJiIiIiIiIiChXCorA2vDhw/Hjjz/ijz/+QNmyZa9tT0hIAAC7zLMTJ05cy2JLSEjA1atXcfbsWaePsRUdHY3ChQub/hEREREREREREbkjoIE1q9WKYcOGYc6cOViyZAkqVapkur9SpUpISEjAokWLrm27evUqli1bhhYtWgAAGjVqhDx58pgec+zYMWzbtu3aY4iIiIiIiIiIiLwtKpAvPnToUHzzzTf44YcfUKhQoWuZabGxscifPz8sFgtGjBiB119/Hddddx2uu+46vP766yhQoADuueeea48dMGAARo0aheLFi6NYsWJ4/PHHUbduXbRv3z6Q3x4REREREREREYWxgAbWJk+eDABo27atafsXX3yB++67DwDwxBNP4PLlyxgyZAjOnj2Lpk2b4rfffkOhQoWuPX78+PGIiopCr169cPnyZdx888348ssvERkZ6a9vhYiIiIiIiIhyucxM4LPPgO+/B0aNAjp3DvQeka9ZrFarNdA7EWjnz59HbGwskpOT2W+NiIiIiIiIiDzy/fdA796yXqIEsGsXEBsb2H0i97kTJwqK4QVERERERERERKFu5069fvIk8OqrgdsX8g8G1oiIiIiIiIiIvODYMVlef70sJ04Edu+WEtFduwDWDIYfBtaIiIiIiIiIiLxABdYeeADo1AlISwNGjwZeeQWoVg349tvA7h95HwNrRERERERERDlw8SLw4osSQElPD/TeUCCpwFqpUsA77wCRkcC8efL3AQCPPx6oPSNfYWCNiIiIiIiIyEN//AHUqwe89BLw9tvAggWB3iMKJGNgrVYt4OGHzffHx/t/n8i3GFgjIiIiIiIi8sCECcBNNwF790pmEgB88UVAd4n8bPZsYPt2WbdadWAtIUGWL74IFCmiH6/up/DBwBoRERERERGRByZMkOUDDwArVsj6ggXAuXOB2iPypzVrgJ49pZdaejpw9ixw9arcV6qULIsXB957T3/N8eNAcrL/95V8h4E1IiIiIiIiIhsnTwJjxjjPMEpPBw4flvWXXwaaNZNgSno6sGeP//aTAmfZMlkePgwsXqz/VooUAfLl04/r10+y2VSwbedOv+6myRdfAO3a6b9dyjkG1oiIiIiIiIhsdO8OjB0L3HOP4/uPHAEyMoA8eXTApEwZfR+FvzVr9PqXXwJJSbKu/h5sVasmy3fflUBbIDzwALB0qSzJOxhYIyIiIiIiIrKxapUsly61v2/WLKBpU1kvVw6I+O/MWgXWjh71+e5RgFmt5sDavHnAli2yXrq0469p0kSWM2YAX33l091z6OBBvb5oEbB/v//3IRwxsEZERERERERhIy0tZz3OrFbgr7+c35+eDgwdKr2yAKBiRX2fCqgwYy38HTwoGWpRUUDNmkBqKvD663JfvXqOv+bVV4E77pD177/3z34aqdJV5ZZbgJQU/+9HuGFgjYiIiIiIiMJG585AhQrAtm2eff20adIvzejsWb2+aBFw4oS+XaGCXmcpaO6hstNq1wYGDpT1U6dk2bix46+JjgZeeknWlywBLl3y7T7aUtmXvXtLueru3cDChf7dh3DEwBoRERERERGFjb/+As6fBx56CMjMdP/rv/3Wfltiol6fNs18X/nyep2loLnDwoXA1KmyXqsW0KePZK4pquTTkTp1pHz4yhWgb19dTpqRAdx1l9zvi6myly4Bc+bI+gMPSAAa0AFC8hwDa0RERERERBQWrl7VpW2rVgGffOLe11++rLN6OnUCKleWdRVYu3BBemkZZWTodWashb/DhyUoNXu23K5VCyhZErj1VrldpAhQtarzr7dYgFGjZH3uXKB5c+nX16kT8N13wPbt9n9j3vD99xKwq1gRuPlmoH592c7AWs4xsEZERERERERh4cwZ8+2nngKOHXP965culUyicuWAn38GunWT7Rs3ynL2bAm+VasG9OsH5M0L3Huv/nr2WAt/tr/bWrVk+fDDsuzQQYJnWXn0Ufmbuu8++RtauxZYvFjfP3++13b3msmTZfnQQ0BkpO4Dt3mz918rt2FgjYiIiIiIiMLC6dOyLFoUuOEGIDlZghiu+vlnWXbuLMGRNm3k9u+/S+N3lWnUrx/w2WfSa82YnaQy1s6e9X//LPIP9Tem1Kwpy06dJPvr009de56GDYEvvpAhCK+8Atx/P/DCC3Lfb7/JMARv2bhRgnd58kgZKKADa/v3y/8T8hwDa0RERERERBQWVMZaXJyUgUZGAjNnSqP47FitOrCmyvratgUiIoAdO2SC4pkzErAbPlwyjWJjzc8RGwsUKybr27d75VuiIHPypPm2MbBaty5QuLB7zxcfDzz7LPD55xJYi4+XcmaVJekNH30ky//9T8pWAfk7VYM3evRgX8CcYGCNiIiIiIiIwoLKJipeXHpIqfK8UaMkk+iHH5x/7a5dwN69ktVz882yrUgR3Yg+LQ24804pF7UNqCkWi/TMAoDVq3P63VAwUpM/AWD0aPl78RaLRTLZAO+VaCYnA998I+uDB5vve+89ICZGAs/16gELFnjnNXMbBtaIiIiIiIgoLKiMNZU19sILEgTbtAkYNAjo3t152ZvKVmvTBihYUG+/7z6Z+PjMM8CMGUD+/FnvgwqsrVrl4TdBfnHiBPDxx+6XXKqMtUcfBcaN8/5+qaEC3gqsff01cPGilKy2bm2+r1s3yYxr2FCC0nfeaV/qStljYI2IiIiIiIjCgjFjDZCS0GefNT/GWVmosb+a0eDBUpr36qtSFpqdFi1kycBa8LJagV695HebLx9QoEDWv6/HH5chFenpOmOtRAnf7Ju3A2vLl8uyXz/HQxWqVZPsyvh4Gcyxb593Xjc3YWCNiIiIiIiIwoJtxhog/dDat9e3Fy60/7qLF6XEE9D91Yyio13fh8aNZXnoEJvCB6uff5ZhFMrly5LR6Mjhw8A77wBffSX/VGAtLs43+6YCa1u2AJmZOX++w4dled11zh8THa0Hb5w4kfPXzG0YWCMiIiIiIqKwYJuxBkjQYNEinZG2YAGQkaHvP38eaNdOeqhVqgRUr56zfShYUGe2cTJo8MnIAJ56yn779u3AX3/p26dOAXv2mANwL76oA1W+CqxVqyZ/sxcvSs+/nFL7qwJnzqihBseP5/w1cxsG1oiIiIiIiCgsOMpYU9q2lYDbkSPAnDl6+8SJwLp1sv7cc47L5dxhsUh5ISCZUBRcpk8Htm2TwRRjxkgZaJUqct/YsbJMTZWS3tq1ZVqncugQsGGDrPuqFDQqSqaLAjkvB83MlL93AChbNuvHqsAaM9bcx8AaERERERERhQVHGWtK/vzA0KGy/tZb0mcrPV0a2APAJ58A99/vnf1QAw4CFVg7fdqclUdixw5g2DBZHzMGeO01yQxbsEACoj/8ALRqJUMudu2SAJvqyde1q/m5fJWxBnivz9rJk/I3HhEBJCRk/VgG1jzHwBoRERERERGFPKtVl7E5ylgDJKiSL59kqC1bJn3VjhyRIEn//t7bFxVYu3LFe8+ZlfXrJVDUuTNQqpR8P61a+ee1Q8X06UCNGjKIomxZHWADpPxX/f5XrrTvw5c/v2SuVa2qt4VCYE2VgcbHA3nyZP3Y+HhZOgusZWZKuez58znbp3DEwBoRERERERGFvDffBBITJfPIWZ+0EiV0Vtpbb0lACgBuucW9AQXZ8WfGWlqaDGcYO1YCQklJsv2vvyTYSMIYLHvvPf07Ur74QkpEp08HRo0C7rtP3/fIIxJIe/lluR0Z6Tx46w3eDqxlVwYKZJ2xlpwsZbF16gB33JGzfQpHUYHeASIiIiIiIqKcyMiQQBkATJgAVKjg/LEjR0r5588/A/v3y7YGDby7P/4MrP3zjwQ+ChYExo2TrKybbpKg2uXL0kOMgE2bZPnTT8Btt9nfb7FI8Kh2baBPH9lWoID8fMeMkdu9e8u0zlKlpBear9SrJ8sDB4Bz56QfnCdUf7XsBhcAWQfW/vgD+PdfWV+yRIY/3HWX9//fhCoG1oiIiIiIiCikbdokgwsKFQKGDMn6sVWrAj16ALNmSdAE8H6AwJ/DC1Qz/caNgYcflpI9i0UCaykpDKwBUpKrAkMqG8wVH3xgvh0RoQcc+FKRIhIcPnBAAnmtW3v2PN7KWNu40Xz7zTcls2/nTv59ASwFJSIiIiIiohC3aJEs27VzLZPoiSfMt90JtrjCnxlrKrDWqJEsIyKAmBhZT0nx/euHgu3bJauxeHHXsreCgTfKQVWQTPVPy4oxsGZbQqwCa7GxetuRI8A773i+b+GEgTUiIiIiIiIKaWpyY/v2rj2+SRNzFpArgQd3+Gt4gdUKrF0r6yqwBkhZKMDAmrJunSwbNJBsvlCgAmuqhNUTFy7I0hgQc6ZECVmmpwNnz5rvU4G1CRN0NiYgmWvHjnm+f+GCgTUiIiIiIiIKaTt2yLJxY9e/5vvvgQ4dgEmTvL8//spYGzlSD2Bo2lRvZ2BNu3pVZ1a1axfYfXGHNzLW1ATPQoWyf2x0tA7AGctBDx+W4FlEBHDnncCpUzIwo2lT4OJF4LnnPN+/cMHAGhEREREREYWsjAzdpL1cOde/Lj4e+PVXYNgw7++TvwJrn34qy7ffBipX1ttZCqrNnAns3i2ljo88Eui9cZ0KrG3bJllknlAZa4ULu/Z4lblpDKxNmCDLpk3l7yomRsqt331Xtn/+ufSBy80YWCMiIiIiIqKQ8cMPQMOGwG+/ye2kJAmuRUbKtMZg4I/A2sWL8g8ABg4038eMNW3rVln26uVa5lawqFxZfo+pqTIkwBMqY83VwJrqszZnDrByJXD0qB7gYJuZ1qIF0LOnlCPn9l5rDKwRERERERFRyJg0SfpOdewIrF4NHDok28uUkeBaMPDHVNCTJ2UZHW0fMMrtgbWMDN2A/+BBWVasGLDd8UhEBFC3rqx7Wg7qTikooANrEycCrVoBI0ZIn8CWLYFOnewf36+fLFXwMrdiYI2IiIiIiIhCxpkzer1/f91fzZ0yUF/zR8aaCqyVLGnfkD83B9YOHJDpnw89pG8DQPnygdsnT2XXZ81qBebO1aXQttwtBVWBNWXmTFm+9prjoQ/Vqsly5077SaK5CQNrREREREREFFQOHpQsmTlz7O8z9n/avRt44glZD8bAmi+ngqqfg20wBMjdgbVVq4DkZOk/99tvOrBWoUJg98sT2QXW5s0DevQAatQAnnxST+8EJNDlaSmo0S23AG3aOH585cqSJXrxopSN5lYMrBEREREREVFQefZZCZD873/m7RkZ0lMNkKbpgA4wBWNgzZcZa+r7LlHC/r7cHFhTmXwAMHy4DviEU2DNagW++AIYNUpup6QA48bJ1M7UVNl25YoeeuBuKajRq686f3zevHpohqd94MIBA2tEREREREQUVM6d0+uqRxYgwaSMDOk/1a8fMGiQvi+3BtaYsWZ26pReVyWK0dGOA5DBrm5d+Vs/dsxc7rl0KfDAA8C+febH790LTJ4s66oMFNB/D9kx/i3lzSvPdcMNWX+NKgdVJdm5EQNrREREREREFFSMWUd9+wJdusgUQhU8S0gAoqKAt9/WmUi1avl/P53x5/ACBtbMVGCtenW9rXx5CVCFmoIFgXr1ZH31ar19+XLz4z79FHj6aVl/+WXg7Fnz4AJXv/f4eL0+cSIweHD2X6N+zgysEREREREREQUBqxX49199+88/gQULJLCQkSHbypSRZaFCwIoVwPffAzfd5P99dYYZa4GjAo5DhgDNm8t6KJaBKi1bynLlSr3tr7/0+scfAw8+CLzyClC7tgTVXnvN/YmggPlvqVIl176mYUNZLlyYewcYMLBGREREREREQePkSV0K+sor0jvqs8+Ad97RjzEGAMqWld5SjqYWBgp7rAWOylgrWVL+blq2BB5+OLD7lBMtWshy1SpZWq06sLZ2rS6HjowE3npL1idNArZskXVXBxcA5r8lFbzOTrdukqH577/A33+7/lrhJCrQO0BEREREREQESMP1xERZr1hRhhgoGRm6Wbttb6lgw6mggaMy1uLipDx4xYrA7k9Oqay7jRuBtDRg/37gzBnpG6eGGyidOgHt2wOLFwPDhsk2dwJrxYoBDRoAly6ZS2mzUrgw0LUrMHMm8PXXwPXXu/564YIZa0RERERERBRw27cDRYsCbdvK7dq1zfdHRgKtW8t6v35+3TW3+SNjjT3WHFMZa3Fxgd0Pb6lQQTLC0tNlkIfKVmvUSAYMGFkskrVmsQAXL8o2d0pBLRZg3Tpg2zYgTx7Xv65PH6BIEf13n9swY42IiIiIiIgC7uOPzRleAwfaP2bBAvnXvbvfdssjvh5eYLUyY80Rq1UH1kJxCqgjERHS7ywxUaZ+rlkj25s2dfz4Bg0k8PzVV3LbnYw1QIaCuOu224CkJMmiy42YsUZEREREREQBlZkJzJ2rbzdsKL2bbBUsCPTuHfwn8L7OWDt/Hrh6VdbZY0375Rc94KJ48cDuizdVqSLLPXt0YK1ZM+ePN/aUcyfzzFNRUcH/f9KXmLFGREREREREAbVmDXD4sASEPvkEuPHG4BpG4C5fB9ZUtlrBgo7L71Rg7cIF37x+MFq/XjKnFJU1GA5UYG3bNmDzZll3lrFme58aekC+w4w1IiIiIiIiCqjvv5dl9+7A3XfLpM9Q5uvhBVn1VwN0+d+FC1IemRvMmhXoPfCdypVlOWuW9FpLSADKl3f+eIsF6N9f1keP9v3+5XYMrBEREREREVHAZGbKREEA6NUrsPviLcaMNV8EtrLqrwYAsbGyzMjQTezD3YIFev3uuwO3H76gMtaOH5dl06bZZ3R+/jmwbBkweLBv941YCkpEREREREQBtGoVcPSoZFl16BDovfGOQoUk8GG1yvdWpox3nz+7wFqBAjJFNSMDSE7WpaHh6tAhKZOMiAB27NAZXuGialXz7az6qynGKbrkW8xYIyIiIiIiooAxloGGSwP0mBjghhtk3ZhJ5S0qsOZs8qXForPWzp3z/usHm+3bZVmrlgShIsIs0nHddUC1avq2K4E18p+A/rktX74cXbt2RenSpWGxWDBv3jzT/SkpKRg2bBjKli2L/Pnzo2bNmpg8ebLpMampqRg+fDji4uIQExODbt264fDhw378LoiIiIiIiCgrGzcCf/xhvz0jQ/fG6t3bv/vka2qq6fz5OXuezz+XUsDERL0tu4w1QAfWkpNz9vqhYP9+WVasGMi98J2ICOCBB/Ttxo0Dty9kL6CBtYsXL6J+/fp4//33Hd7/2GOPYeHChZg+fToSExPx2GOPYfjw4fjhhx+uPWbEiBGYO3cuZsyYgRUrViAlJQVdunRBhpqxS0RERERERAFz4QLQti1w003Ar7+a73v3XeDYMaBIEaB9+0Dsne907SrLxYuBS5c8f54BA4C9e4FHHtHbshteAMjPFAh8YC0lBRg+HFi+3HevceCALMM1sAYAQ4fK/5ERI8K/tDfUBDSw1rlzZ7z66qvo0aOHw/tXr16Ne++9F23btkXFihUxaNAg1K9fH+vXrwcAJCcnY8qUKXjnnXfQvn17NGzYENOnT8fWrVuxePFif34rRERERERE5MDs2RJcA4D77wdOn5b1o0eBp56S9ddeA/LmDcz++UqdOhLouXJFgms5lZKi10MpY23ECOD994E2bXz3GiqwVqGC714j0AoWBBYtAsaPD/SekK2grjxu1aoVfvzxRxw5cgRWqxV//PEHdu7ciY4dOwIANmzYgLS0NHQwdLgsXbo06tSpg1WrVjl93tTUVJw/f970j4iIiIiIiLzvq69kGREh2WkPPyxN/f/5RyaCVq8u28KNxaKz1n78MefPF/Xf6MHMTF366KzHGhA8gTX1+wdk331B/TzCObBGwSuoA2vvvfceatWqhbJlyyJv3rzo1KkTPvzwQ7Rq1QoAkJSUhLx586Jo0aKmr4uPj0dSUpLT5x07dixiY2Ov/StXrpxPvw8iIiIiIqLcKDMTWLlS1qdNk+DQzJnAN98ABw/K9ooVJQgVjlSftW+/lXLYp5927+utVr2uAmtTp0ppaMGCQMOGzr82GAJriYlAWpq+rX7n3pYbSkEpeAV9YG3NmjX48ccfsWHDBrzzzjsYMmRItmWeVqsVlizemZ9++mkkJydf+3fo0CFv7zoREREREVGud+IEcPWqBM7uvBN47jnZPmQIsHChrJcvH7j987XWrYHChaXH2rJlwBtvyM9DSUwEmjcHZsxw/PWqhBaQwNqZM8ATT8jtF18E4uKcv7ZtYO3SJeDNN4ENGzz+dty2aJH59rZt3n+N1FTJhASYsUaBEbSBtcuXL2PMmDF499130bVrV9SrVw/Dhg1D79698fbbbwMAEhIScPXqVZw9e9b0tSdOnEB8fLzT546OjkbhwoVN/4iIiHKD06d15gAREZGvHT4sy4QEIE8eYMwYCTadPy+ZawAQzgVEefMC/3Uyuuaff2RptQKDBwNr1kgPMkfUkAJAerU9+yxw6hRQu7Z5mIEjanjBuXPyWg88ID3tRo50/Phdu4CHHpLedzlltUpvvS++MG/3RWBt7155vfz5sy6NJfKVoA2spaWlIS0tDRER5l2MjIxE5n+F2Y0aNUKePHmwyBAGP3bsGLZt24YWLVr4dX+JiIhCQb9+QKtWUo5DRETka6o4SAXPoqIk4GIcVBDOGWsA0Lmz+fbGjbKcM0dPyvz3X8dfawysrVgBfPSRrH/wgQQqs2LMWHvrLeC77+T2vn2OHz96NPDJJ/o1cuLzz4GePYFNm+T2TTfJ8pVXgEaNgHvucf49u2v2bFk2bx6+JcUU3AIaWEtJScGmTZuw6b//bfv27cOmTZtw8OBBFC5cGG3atMHo0aOxdOlS7Nu3D19++SW++uor3HHHHQCA2NhYDBgwAKNGjcLvv/+Ov//+G3379kXdunXRPtxmNRMREXnBL7/I8vnnzdvffhsYONB3TYWJiCh3Uhlrxqy0uDgJrijhHljr0QOoWVPf3rBByhdHj9bbTp+WTDRbttusVqBPH9cmbKrA2vff6+mrgJRN2n7eX7oE/PabrOc0Y+34ceCxx8zbXnwRiImR19m4UXrOvfJKzl4HkJ+HGo7Qv3/On4/IEwENrK1fvx4NGzZEw/86Lo4cORINGzbE8/8d7c+YMQNNmjRBnz59UKtWLbzxxht47bXXMHjw4GvPMX78eHTv3h29evVCy5YtUaBAAcyfPx+RkZEB+Z6IiIhCgZqeBQAXL8oB92efSa8XIiIib1EZa2XLmrcbC4zCuRQUkADXP//IwAYA+OknKcfctw8oXRooVUq2O8rgMmasKYbT4WxfF5DhAVYrcP/9ktGVnm7/vL/9Bly+LOvHj7v2/M48+qi5N1zTpsCNN8prbtsmfd4AYN26nL0OAPz1l5SwFiggAUyiQIgK5Iu3bdsWVuOYExsJCQn4wrYo20a+fPkwadIkTJo0ydu7R0REFFaMU7kA6UlSuTKwdi2QkSHbjh6Vvi1ERETe4ChjDQBq1NDrtkG3cNW+vWTrHTwIfPihbBs7VgJux47Jxa1Wrcxf4yiwVq2aa6+nAmuAZAhOngz8/LMEzo4eBYxtyefN0+s5CawtWCAlp5GRcnxx5AhQp47clz+/HGPExwNPPikBseRk8366KjNTAoRTp8rtHj2AQoU832+inAjaHmtERETkXSdOmG+rK8XGYQZJSf7bHyIiCm/Hj0vJH2AfPLvjDgmwtGwJ5Mvn/30LhBIlpOfYf52N0Lw50LevDjK6mrHmaoN+44y+t98GoqOBMmXk9pEj+r70dMmiU2yPF1x19apMewWkFPT664GuXYFKlcyPi4vT0ztVvzl3ZGQADRoAtWrpMtB77/Vsn4m8IaAZa0REROQ/ahS97W1jYM32MURERJ5ITpYSQMU2Y614cclYMg4xyA3KlJGhBYmJ0lsuIgKoX1/uW7LE/vG2Qa78+V1v0N+gAXDDDUC9ekDbtrKtdGkJZhn7qK1cKT3eoqIkyHb8uJSOujsIYOtWycYrWlR6qmWlcWPgwAFg/XqgXTv3XufkSXktpUwZ95+DyJuYsUZERJRL2GajHT0qpRSrVzt/DBERkSdGjpTACSBBD+OwAqVQIcmiyo1q1pRm/gDQrZtM+Ny0Cdi+3fy4LVvMt4sXd/018uWTHmSffqq3OcpYU2Wg3bvL8soVc480V6nfd7Vq+ntzpkkTWRqPQVxlW6rat6+UnhIFCgNrREREuYRt0OzYMTmAT042byMiIsqJn34CPv9cMp6WL5dMrNwaQHNF8eLArbfKuiqdBSS4lZPAmiMqsKYy1qxWHVjr00cHxDwpB1WBtYoVs39s69ayXL5c9sEdtvvGaaAUaAysERER5RIqsKZObo4e1WWgqtyDGWtERJQTp08DAwfK+mOPyTRIyl7nzrLctElv++svySyvUEEy2gDgttty9jqlS8tSDZXYskUmhefPD3TooAcaGLPC1q8Hxo3Tg46cURPHVf+0rDRqJK95+rT7E8mN+/b669JrjSiQGFgjIiLKJVTQ7PrrZWkMrKkTH2asERFRTgwfLp83NWoAr74a6L0JHSrLS2V9AZLNBciAh40bgddeA557LmevU7myLLdvB8aPByZNktsdOgAFCgAlS8ptlRW2b59MM33ySWDRoqyfW+27K4G1vHlleAOQ/fPaUvt2zz3A00+797VEvsDAGhERUS5w6RKwZo2sN2woy2PHdGDtf//T24iIiDwxe7aUMkZEAFOnSkYSuUYF1vbvl9LIvXsl8AVIYKtOHWDMmJxPUK1WTZaHDkkfvClT5Pbtt8vSmLGWlgbcfbduGWHsy+aIylhzpRQU0Fl6zz4L7Njh2teofQN0EJAo0BhYIyIiCnPnzgEdOwIbNshJzn33yfbkZLkSbbEAd9wh286flyAcERGRrUuXgLFjJSDiaILl++/L8oknZBolua58eVmmpMjUy/79Zb1VK+/2ECtdGihY0H57ly6yLFtWlnv2AM88I+WoyunTWT+3OxlrAPDII/L9paQAX37p2tcAOrCmgoBEgcbAGhERURg7fhxo2xZYsQIoUkTKLRo3lnIPpW5dOZBWmQXss0ZERI58/LFkTS1cKL2+Fi823797tyzVxRpyXf78OlD08MOSUV6oEDBtmncnXlosOmtNKVFC/gE6q/3tt4G33pL1OnVkeeqU8+dNTpYLeYDrgbW8eYEePWR93z7XvgbQpaDMWKNgwcAaERFRGBs2DNi8GUhIAJYtkz4tFgtQqpR+jNqWkCC3GVjLvXbuBOrXl5NnIiJb27fr9StXgK5dgd9/l9upqbpUsFIl/+9bOFABqTlzZPnBB66XVbrDGJBq3x744Qd9u3Fj82OHDJFyUCDrwNq6dbIsV85xRpwz6ns29pbLDjPWKNgwsEZERBSmtm8HZs2S9YULgXr19H3duun1li1lqYJt7LOWe02ZIhPiBg92XOZFRLnb3r2ynDJFSgdVcG3LFgmMWK1ATAwQFxfY/QxVamInAPTqBfTt65vXuXxZry9apIcIAEDt2ubHvvCC/n1mVQq6dKks27Z1b1+MveVcpTLWGFijYMHAGhERUZj64gtZ9ughWUhG48YBjz4qQTXVV4UZa7R5s17/6KPA7QcRBU5qqlyU2bXL/j4VWKtRQx7TurUEab77TpfyVaokWdDkPmNA8oMPfPdzfP11ee7HH7e/LypKr6spoWq/HGWsrVkj00v/+ENut2vn3r6ojLWkJAnUZsdqZSkoBZ+o7B9CREREoejwYVm2amV/X1QUMGGCeRsz1nI3q1WX8gDuZQ8QUeixWoE+fSQoUqwYULy49Eb7+GPgn3+AWrXMpZ9Xr8okSQCoXBmIjpY+a8uXS7aaMbBGnnnsMRkaMGaMb7P+WrSQcsqiRR3f/+GHwJNPAgsWyG1ngbWTJyWQZgyIuZuxVqyYlI6mpAAHD0r/t507pX/fQw/Z95c7c0b+FgFmrFHwYGCNiIgoTJ08KUvVkDg7KrDGjLXc5/BhOaE+c0Zvc6ffDRGFnuRk4NtvZV31RlPlfIAE1/bt04GyAweAzEzJYlIBDTXJ8uBBnc1WubLPdz1s1arlvzL8rI4NHn5Y/inFi8vSthT0m2/MQbUKFdwPrFos8nXbt8sFnTx5gOrV5b6EBD3cQFEX/4oVk+AuUTBgKSgREZGXXLokV+9Xrw70ngh3A2uqFJQZa7lLcjJw663A+vVyu2pVWZ44Ye7DQ0Th5eJFWUZGAr/+6rjhvHHq5549sqxcWZcoGhvPq/uZsRZ+VMbamTNARobe/uWX5se5m62mqL+jzp3NgdlNm+wfq45RjP3oiAKNgTUiIiIvefVV4OefpeeMccJWoDBjjbKTlgb07Als3Sq3GzUCJk3SJ9gHDwZu34jIty5dkmVMDNChg3nAjcoEmjJFl4WrHozGwIcKiBw6pLPdGjTw0Q5TwBQrJkurFTh7VtY3b7YPfLnbX01p1kyWmZnmvnKq9Njo6FFZGqebEwUaA2tERERe8tRTwO23A+np0nQ4kKxW3QvF1ea+KmNt40Zg9mzf7BcFD6sVGDRIMlJiYuT3vn490KmTOQuFiMKTyliLiZGlccjNiy9KSd5ff0nPqwcekM84QLKKlIQEeZzVKtlMBQuaJ0xSeMiTByhSRNZVOejUqbJs2lQ/ztOMtaeekh6fu3dLaek338h2lQVpxIw1CkYMrBEREXlJ4cK6J8nx44Hdl3PnJMAHuJ+xBgB33inPQeFr0yYp44mMBGbOBBo21PcxsEYU/lTGWoECsqxVS993zz3AqlVA+/aS2aqmTJcvD9x/v35cRARQrpy+3a4dkDevb/ebAkP1WTt5Uv4mpk+X288+C7zzjvxTnx3uypMHaNwYqFJF/n6qVJHtqm+fkQqsMWONggmHFxAREXmRyg5To+ADRZWBFirkenPfkiXl8RcuSPbBjh3mK9EUXtQJS9Om5gwUQDckZ2CNKHwZS0EBc2CtXDl5H1i0SJrpv/++ZLg98YT9Z4pxsuRtt/l2nylwKlaUDLJ//5XsxJMnZYhFx45Aly7efS1VbnzkiPT6zJ9f38dSUApGzFgjIiLyIhVYO3lSeoUEirv91QDdwFr591/v7hMFFxX8VdP9jFS2QGKi//aHiPxLlYKqjLV27YDx44GffjL3ubrpJmDOHPl8uPlm++dRfbVuv92czUbhpXFjWa5bpzMY+/aVbDNvK15cqgAAmUxrxFJQCkYMrBEREXmRCmRlZOgGv4HgSWANkN44Q4bI+o4d3t0nCi6qXNlRYE31SPrzT8leJKLwY5uxZrEAI0a4n3X26qvAhg0SfGMZaPhq0kSWn3yiBzTdd59vXsti0Rd4nAXWmLFGwYSBNSIiIi/Km1eXxQSyHNTTwBoAVK8uS9uMtf37dYYDhT4VWHM03KJxYyn3OnkS2LnTv/tFRP5hm7Hmqeho4Prrpd8ahS+VsQbIBZf+/YE6dXz3euqijzqeAeQ4RE0KLVPGd69N5C6+/REREXlZMPRZy0lgrUYNWRoz1v75B6hUCWjZMuf7RsEhq1LQ6GigWTNZX77cfJ/KciGi0GY7vIAoK+XL62OKmBhg3Djfvl5cnCzVFFIAGDxYBifceKPngxKIfIGBNSIiIi8LhsDa/v2y9KRUQmWs7doFXL0q619/LcvNm3O8axQksspYA+TEBZByUOWVV4DYWGDFCt/uGxF5V1qa/TaVsaZKQYmyYrEAEyZIH73dux1flPEmNYX01ClZrlkjff7y5AGmTDH3ASQKNAbWiIiIvCwYAmurVslS9URxR/nyUs6algZs2SLbzp/33r5RcMgqYw0AWreWpTFj7fnngfR0mQxIRKFh2DBpU1CkiGQk33uvvL8zY43cdc89wOefAwkJvn8tFVhTGWtvvSXLvn2B667z/esTuYOBNSIiIi8LdGDt3Dlg+3ZZV03o3WGx6DLANWtkaQysOcp8oNBhtQIvvCAZiYDzjLXmzWVS7IEDwMGD5vuKFPHpLhKRl6Sl6QmOyclS4v/VV8DixfbDC4iCiTGwtmsXMHeu3H788cDtE5EzDKwRERF5WaADayoYVrWq56UaKiCnnis5Wd9nXKfQs3kz8PLL+razv5GCBaUhOSDloMaAqup9Q0TBbf16CaAVLw4kJgK9esn2n37y3vACIl9QnzOnTgHvvCMXhbp0AWrVCux+ETnCwBoREZGXqRIJ2ywff1m3TpaeZKspthlrR4/q+86d8/x5KfBUvxolNtb5Y4191tQkNoAn4kShYulSWbZtK2Wg/frJ7QULGFij4KYy1hITgS+/lHW2IaBgxcAaERGRl9WvL8uNGwPz+keOyLJyZc+f44YbpCR0zx6ZMGoMqjCwFtqME9aArBtAG/us7dunt6ekeH+/iMj7VI/Etm1ledNNQL58UuK9dq1sYykoBSMVWDt5EkhNBZo2BVq1Cuw+ETnDwBoREZGX1a8PREQASUnmTC9/SUqSZU6aC8fGAjVryvry5fo5AQbWQp0xsPbzz1k/Vp3EJCbqTEiAgTWiUKEuiqj38wIFJLgGADt36m1EwUYF1pTHH+ckUApeDKwRERF5WYECQO3asr5+vf9f//hxWeZ0apcqB501y7zdNrCWmpqz1yH/OnNGlg8+CHTunPVjixfXf8tffaW3X7jgm30jIvfs2AF06wZs2uT4fvX/vVgxva1LF/NjmLFGwcg2sJbd5xVRIDGwRkRE5AONGslywwb/v7bKLvN0cIGiAms//mjebgys/fGHNLlv0wbYvz9nr0f+oTLWjCfaWVHZLYmJehsDa0TBoU8fYP58/f/U1tmzsixaVG+77TbzY5ixRsHI+HdZsiQDwBTcGFgjIiLyATU4YPZsmWTlL1ar9zPWLl0ybzdOBV28GEhPl3LRoUNz9nrkHyqwZpsN4MyYMfb9+lgKShR4Vqu+eHP2LHD+vPn+y5eBK1dk3RhIL18eqFRJ32ZgjYIdJ4FSsGNgjYiIyAd695arq9u366lsWdm4EfjsMwlS5cSFC3IyBeQ8Y61WLclGs2XMWNuzR68vXGgeckDByd3AWkICsGIF8NFHwKJFso0Za0SBt2uX+faCBebbKlstMhIoVMh8X8OGep2ZQBTs7ror0HtAlDUG1oiIiHwgNhbo31/WJ03K/vH33w8MHAj06JGzDDdVBlqoUM6zECIjZTqoct11snQWWMvMBKZPz9lrkvdcvCjT1GypnkuuBtYAoFQp4KGHgHLl5DYDa0Tes3cv8M8/7n/d4sXm20uWmG8by0Btm74bA2vMWKNgtXEj8N57cnxEFMwYWCMiIvKRYcNk+cMPwIEDWT92yxZZzp8PbN3q+WuqMtCcZqspqhwUAFq0kKWjwNo998hyxw7Xn/uZZ4CuXYGMjBztIjnRujVQtqx9Fou7PdaMVNZLSop/S5yJwtXOnUCDBhLocje49vvvslRBsr//Nt+vgujG/moKM9YoFDRsCAwfLpPWiYIZ/0SJiIh8pFYt4OabJZNr8mTnj7t40Xx7717PX1NlrOW0v5qiAmuRkUCTJrKuAmtnz+qMiMaN9TZXvf468NNP9lkWlHNWq1zpv3pVJgAaT9jdLQU1UqXBGRm6dxMReebyZaBXL8kAvXoVePRR549VGcHqYkZGhn7vHD1allu3yvMo6v3YURDdGFjLl8/z74GIiBhYIyIi8qnhw2X56ae695lRWpp9NtvRo56/nupx5q3AWtu2kk3Rr5/Ogtu2DZg6VYJigGxXJYIqQyI7mZl6Pad95ciebdCrVy9g1Sr5uauT7ZwE1gCWgxLl1GOPAZs3A3FxkpGzeDFw5Ijjxy5ZIu/DVasC48cDzz4rFzliY4GePWV59SowbRowahRw5506g9hRxlqpUtKCoGdP72U4ExHlVlGB3gEiIqJw1qULULEisH8/8O23wAMPSIbat98Cv/wiJ1K2k9ycnVhl5+xZ4J13ZL1BgxzstEGhQrq8aO1aWe7fD9x3n35MlSr6xM3VjDXjpNHIyJzuJdkyBr2KFZMhGi1bAo0a6RJOT0pBIyKkbOziRXmNkiW9s79Euc2MGcDHH0vvs6+/Bh55RAJh27cDZcrYP944GGbkSL3erh2QJw9w/fXAH38ADz6o71u2TJaOAmsWC/D55975XoiIcjtmrBEREflQZCQwZIisT5okQY0RI6QR75w59kE1wPOMtUceka+tVk0yIbytSRPg++8lC69dO8myACR4qII0rmasMdvJt9TPNyZGAqMPPih/ixs2yPa4OCBvXs+eW2WtpaTkfD+Jcqtnn5XlmDFAhw5AnTpyu1cvfYHEyPhZ0bs30LcvMHgw8MYbsq1TJ1kWLgzUrSvraniJJ0F0IiJyHTPWiIiIfGzAAOD554FNm4CVK4H162X7oEESAPn2W/PjPclYmztX+u9EREiZpi+mvFksUl50551y22qV8tYCBSSLDXA9Y80YlElN9epuEnRgrXBhoHx5KUV++mlg4kQJfvbp4/lzFyokQzIYHCXyzNWrupemahdQuzYwezaQnAw8/rgMH1F9LQHZDsjnxscf2z/nyJEyDKZyZSn7vukmfZ+jjDUiIvIeZqwRERH5WLFi0scGABYulClwgPTBUVkGAHDddbJ0N2Pt1CngoYdk/YknzJM8fcli0QE8lRFx5YrjXnK2jIE1Vx5P7lHZLWqKJyAn3BMnSg8m49+du1TGGgNrRJ45dEguTOTLp8upa9c2P+b11823VWAtNtbxc0ZFATVrAtHRUhZqxIw1IiLfYmCNiIjID6pUkeXmzdJfLCoKqFRJJocqjRrJ0t2MtR9/lJKf6tWBF1/0yu66rVAh3SvNNmtt40bghRfMATRjUIaBNe9TP19jYM1b1HOyFJTIMyrDt2JFuUAB6FJQZd48mfKpZBdYM4qNlZYACjPWiIh8i4E1IiIiP1ATGFetkmXlytJwumZN/ZgKFWR57py5uX92VCDuxhslWyEQLBZ98mbbZ61RI+Dll80ZGMxY8y1fBtZU9su//3r29SdPykn/Cy94b5+IQokxsKZUqwbUqyeDZ26/XbaNHavvV4G1woVde42775Zl/vz+y2ImIsqtGFgjIiLyAxVYU0EnlU0QE6MfU6aMLq08fFhv/+kn4PffnT93UpIsExK8s6+eMk4GTUuTTLXMTH2/6i0HmDPWrlzxz/7lJsYea97Wo4csP/kESE93/+sXLQJ27QJmzfLufhGFCkeBtago6cO5YYMOOn/3HbB7t6y7k7EGSPby2bPSKqBGjZzvMxEROcfAGhERkR+owJpiLNP54guge3fggQd0OdDatbI8dUoaUrdv7zyIESyBNZXJlJQkPbwaNQJmztT3G4NszFjzrsxMcwmuox5r3tKrF1CihAR/f/jB/a9XmW4MqFJudeCALI2BNUAyfyMigIYNgdtuk//Xauqn+j/tamANAIoU8c0gGyIiMmNgjYiIyA9sA2tVq+r1++6TqZ4xMUCbNrJt2TJZnj6tH3funOPnDpbAmspYe+wxYMkSWd+xQ9/PwJrvDBoExMcDX38tt31ZCpovHzBwoKxPmuT+16vAGn/vlFs5yliz9cwzspw6FTh40P2MNSIi8h8G1oiIiPwgLs58W/VTs2UbWLt4Ud9n27tMCZbAmspYMw5fMJZ8GgNrHF7gXWvXSvntvfdKkNaXgTUAePhhGVaxbBmwZYt7X8uMNcrNrFZ9waFSJeePa94cuOkmyVR+6y0G1oiIghkDa0RERH5gm7HmLLDWsqWUA+3aBRw7pst/AHP2mmK1Bk9gzTh5rl49WToLrDFjzbvUSXdGBtC7NzB7ttz2RY81AChbFrjjDln/4APXvy4jA9i5U9b5e6fc6MgR4MQJCUzXrZv1Y59+WpbTp+tSbwbWiIiCDwNrREREflCwoPl2+fKOH1ekiEyFAyQbSAVMAMcZaykpeoJofHxO9zJnWreWBtwvvSTlrYC5fNVZYI2ZSzmnfs7Nm0vmmurh5KuMNQAYNkyW06aZ+7tl5cABIDVV1q9ckcAwUW6yYYMsa9WSiZ1ZadtW/g+fO6ffJxlYIyIKPgENrC1fvhxdu3ZF6dKlYbFYMG/ePLvHJCYmolu3boiNjUWhQoXQrFkzHDx48Nr9qampGD58OOLi4hATE4Nu3brhsHGUGhERURCwWMy3swp4GMtBswusqWy1ggXtg3f+1quXZKg9/7zOlFL7B7AU1FcyMnRm4/ffA7Vr6/t8GVhr3RqoXl1+f6p0OTuqDFRRQTai3EIF1ho1yv6xUVH680DxVRYqERF5LqCBtYsXL6J+/fp4//33Hd6/Z88etGrVCjVq1MDSpUuxefNmPPfcc8iXL9+1x4wYMQJz587FjBkzsGLFCqSkpKBLly7IyMjw17dBRETkVepEavny7EtBVeCqVCnf75cr1Ee0CugcO6bvS0vT6ywF9R5jkLJECenLpPjyJNxi0SW/+/a59jW2gTVmK1Ju405gDTD/fy5QAMiTx/v7REREORMVyBfv3LkzOnfu7PT+Z555BrfeeivGjRt3bVvlypWvrScnJ2PKlCmYNm0a2rdvDwCYPn06ypUrh8WLF6Njx46+23kiIiIfufFGWf7zD7B7t97uKGNNBTQC3V/NlqPAmjEAxIw171FloPnyAdHRwA036Pt8mbEG6ObrngbWLl+W8meicJSaCuzZI2WfihpcoILS2encGRg5UtZV2T8REQWXoO2xlpmZiQULFqBatWro2LEjSpYsiaZNm5rKRTds2IC0tDR06NDh2rbSpUujTp06WLVqVQD2moiIKHsR2Xz6Fi+um1r/9JPe7iiw9vPPsmzVyjv75i0qU8oYQDNm3zFjzXtUYE0FqJo21fcZkvx9omJFWe7f79rjExPNt5mxRuFszBgpzf78c7lttQJHj8p62bKuPUeNGr7/f0xERDkTtIG1EydOICUlBW+88QY6deqE3377DXfccQd69OiBZf818khKSkLevHlR1DiGDEB8fDySjE1dbKSmpuL8+fOmf0RERL52112yfPbZ7B/burUs9+zR22xLQa9eBX75Rda7dcv5/nmTo0wpY5CNwwu8xzawVrWqvq9YMd++dk4z1vi7p3Cm8gFefFHer5OT9YUEd8r3586VZa9e3tw7IiLyloCWgmYl878Ox7fffjsee+wxAECDBg2watUqfPTRR2hj28nTwGq1wmLbJdpg7NixeOmll7y7w0RERNn47DPgwQd10CwrbdoAH3xg3mabsbZqlWSBxceby/+CgbPAmtUqvblYCuo9asCFmhZosUh/vt27deajrxgDa+p368ypU/IPkCDguXP83VP4SU8HNm2S8vy9e2XboUMyPbd5c7ldtGj2E0GNOnWS5ypZ0uu7S0REXhC0GWtxcXGIiopCLWNTAgA1a9a8NhU0ISEBV69exVmbGe8nTpxAfHy80+d++umnkZycfO3foUOHvP8NEBER2YiJAW6+2bXm046Cb7aBNfXxVa9e9uWl/uaoaX5mpvQISk0FjhzR2xlcyRnbjDVA+vTdf7/vX7tCBVlevKiDZs6o3lLly0u5M8CMNQo/48YBTZoAt90mt1Ww+fXXgQMHZL10afeft1Il+QwhIqLgE2SH4VrevHnRpEkT7FBHYf/ZuXMnKvx3FNeoUSPkyZMHixYtunb/sWPHsG3bNrRo0cLpc0dHR6Nw4cKmf0RERMEkPt6+B49tKagKtPm63M8Tzprmnz8PbN5snhDKwFrOOAqs+Uu+fDpIoLJznFFloDVr6mwd/u4p3DzzjCy3bJHlgw8CcXHy/+Pdd2WbJ4E1IiIKXgEtBU1JScFuw7izffv2YdOmTShWrBjKly+P0aNHo3fv3mjdujXatWuHhQsXYv78+Vi6dCkAIDY2FgMGDMCoUaNQvHhxFCtWDI8//jjq1q17bUooERFRqKpTBzh8WN8OpcBa3rzy7+pV8/YLF4B162S9fHng4EFmLeVUIANrgDRXP3oU2L7dPDjBlgqs1aih/5b5u6dw17UrULky8PTTwOLFso2BNSKi8BLQjLX169ejYcOGaNiwIQBg5MiRaNiwIZ5//nkAwB133IGPPvoI48aNQ926dfHZZ59h9uzZaGUYfTZ+/Hh0794dvXr1QsuWLVGgQAHMnz8fkZGRAfmeiIiIvKVOHfPt8+fNmV7BHFgDHJeDnj8PrF0r66pdKrOWcsa2x5q/NWggyyVLsp4OagysMWONwpFxKIty003AkCHSV01hYI2IKLwENGOtbdu2sFqtWT7mgQcewAMPPOD0/nz58mHSpEmYNGmSt3ePiIgooGrXtt926pSeJhfsgbVChez7bt15pw6+3HijNPS+fDn7xvfkXKAz1lRg7euvgdmzZZKto8CBMbCWL5+sM2ONwkliov021Rft0UdlOijAwBoRUbgJ2h5rREREuZ3N/B4AwIkTej0UAmu29u+XQQu33gp06SLbMjOzb3xPzgVLYA2QQJnKSDS6ckX3YGPGGoWr7dvNt7/5Rq8/8oh+T7Ttn0lERKEtoBlrRERE5FzNmnr9uuuAXbuAkyf1tlAKrLVoAVSsKNNOu3eX4QzGbKUKFeR749Q792Rk6JP5QP0d1Khhvr1nj/1j9u6VAGpsrPzus8tYy8wMvkm3RNlZvlyWjzwiPdUSEvR9RYtKhu5PPwGdOwdm/4iIyDd4yEJERBSkChWSCZqbNukMB0eBteLF/b5rLjH2WLvjDikVfOghCawAQHS0vv/yZfleyT2ffw7s3Ckn7R06BGYf8uQB7r9f3961y/4xhw7JskIFKfnNKmPthx8kADd7tvf3lchXzp8HvvtO1nv1MgfVlNtvBz791PzeR0REoY+BNSIioiBWrx5Qvz5QooTcNpaCqsmKwZqxZsxkKljQ/n6LBShTRt/essX3+xRu5syR5ZNPmpuj+9vnnwNTp8r6zp3296vptur3nVXGWvfu0gS+Z0+v7yaRz3zzDXDpkmQat2gR6L0hIiJ/YmCNiIgoBJQsKUuVsZaRoXtrBWtg7YUXJHOjSBGgeXPHj/n1VxliADBjzRMqa9FRPz5/q1ZNlraBtYsXgd27ZV1lXqrAGnusUbj45BNZDhrEQSxERLkNe6wRERGFAJWxpgJryckySRMIbKZSVmJjpTQqq35ZtWsDgwcDf/7JwJonAj24wEgF1o4ckbK4woUl86xmTZ2xpgJrqhQ0VKaC7twJbNgA3HUXgyakHT8O9OsnGWp//w3kzSu3iYgod2HGGhERUQhQGWuqFFRlKhUsKCdzwSy7JvT168ty61YJwpG9jAyZtpmebt6enCzL2Fj/75OtYsWkhxogmYpXrkjAVAXVAPuMtX375HtT0tL8s6/uGjYMuOce4NtvA70nlJ09e/z3d/T228CiRcBLL8ntnj2Dt+clERH5DgNrREREIcA2Yy3YJ4K6o3p1CQ6mpEighexNngw0bQo89ZR5ezBlrAEy9bBAASnxveMOYMUK8/22GWs//CADLRQ15ECxDSQGSlKSLD/+OLD7QVmbPx+oWlV6DvrDpUvm2wMH+ud1iYgouDCwRkREFALCObAWFSUloQDLQZ0ZMUKW77yjt125AqSmynowZKwB0i/v558luLZwITB2rPl+2+EFADBlil63DaweP+6b/XSXKlldvhz499/A7ktud+4ccPWq4/vU/5Px4/2zL+p9WGnTxj+vS0REwYWBNSIiohAQHy/Lo0elXDKcAmuALgfdvBkYORJ4773A7k+wKVdOr6ssNVUGarEAhQr5fZecatMG+OknyUpTfQAV24w1W3v3mm8fPer9/fOECmACwGefBW4/cqP339cB5dOn5b2wVSvHjzVOH/ZH/z5jhuXPP7P/HhFRbsXAGhERUQioUgWIiZFyyX/+Cd/A2gcfSLbJo48Gdn+CjXF65p9/ylIF2GJjs+9j52/t2klZnjEzDZCBBgAQHW3efvGiLLdtM28/dsw3++cuY2Bt6lTzbfKdCxeARx4BHn9c/jYWLZJstXXrHJcJG/+f2P4t+YLqH7hqFdC5s+9fj4iIglOQHYYRERGRI1FR0mMLAFauDL/AWr16sjx9Wm/jIAORkmIuiRw7VgI7xsBaMLr5ZmD3bulP9uOPwJo1OqPHONAAkEmiALBsmXl7sGSsqeynvHmBU6eAefMCuju5xvHjOuvxu+/MwTRj0PXsWaBPH2DXLr1t48acvfaHHwKff+78/sxM/XdrzCglIqLch4E1IiKiENGypSzDMbCmMtaM/FHKFQqM5ZGxscDq1cCgQcE3uMCRMmWkdK9rVx0YBoDmzc2PO3xYgqqqx16PHrIMlmEWKkPtrrtk+emngduX3ERNQQaAGTPMgdaDB/X62LHAN9+Yv3bdOs9fd/t2YOhQGUagSq5tHT8ugb6ICCAhwfPXIiIKGh9/DLz8sn0fB8oWA2tEREQhwhhYU5ld4RJYK15cN7ZXVHlgbrd7tyybNAFmzgQiI4GvvgKeeUa2B3NgzZm2bYE//gDq1JHbR47IYAAAqFVLJooC0qst0KxWHeQdMkSy7n7/3X6CKXmfMbC2e7dkPirGn79xu7J4sefnht99J8vMTGDPHsePUVmXpUpJRjERUUibNw8YPBh44QUgMTHQexNyGFgjIiIKEc2ayUn93r2SUQFIQCpcqHJQ5dKlwOxHsFEn9lWqALfcAkycKLc3bJBlsJaCZsVikeBao0Zy+/BhCbQBsr1LFyBPHuknGOgpnGlper16dT3B9p9/ArM/uYkxsAbIRQVFZawlJgI7dujt/frJ387+/c6DYlmxWnVgDQDuvhv45BP7x/39tyxZBkpEIe/IEWDAAH2bgTW3MbBGREQUImJjdfBJndSFS8YaYF8OysCa2LlTltWqyXLoUF2SCIRmxpqipoQePgwsXSrr7drJ93TzzXI7kFlrKSnmQQXR0UCFCrJuLEUk31CBNUellurnP3OmLG+7Tco/P/xQZ/f++qv7r7l5s/4/B8j6Qw+ZA6yHDslABQDo1Mn91yAiChoZGXJFQvUYAcxXK8glDKwRERGFEHXCqIRTYI0Za46pjK0aNfQ21YMMCO3Amir/3bQJ2LpV1lu3lqXqyRao4/vnn5ef7ZIlelt0NFC+vKwzsOZ7KrB2991AoULm+9TP//vvZXnnnUDjxkDBgkDHjrLt55/df01jtprRgQN6/ddfZWJpgwbAmDHuvwYRUdB44w1JGY+JAe67T7YFOlU8BDGwRkREFELCObDGjDXHHAXWjH8HatJmKKpeXZarVsmydm2gZElZr1JFlqrHHCAX1Nes8c++vfKKXMgfMkRu58kjjeoZWPMfFVirUEH33VMOHpRy3O3b5Xdz++36vm7dZLl4sQTAXGUsA7V9PePEUZXR1rq1vDYRUUiaOhV49llZnzRJ+jAAzFjzAANrREREISScA2s1a0rfXIWBNRlSceqUrKtSUAAoXVqvGwNPoaZBA/Ptdu30ugqsGftk3XyzTBRVgw78QQ0uiI6WJQNr/qMCayVLmsufAWkB9Pnnst6xozlzs2ZN4LrrgKtXgYULXX+99etlEm2BAsAjj5jvM5aHqnNO4/9JIqKQM26cLEeOlGw1dQXv3385GdRNDKwRERGFkPLldV8qAChaNHD74m0WCzB5MtCihdz29lTQWbOkbCuUjhXVCXy5clKlYfTgg7J89FH/7pM3FSumA1WADC5QVGDt8GHpc3b8uJSMAs7L9bwlM1OvX70qy3z5ZKn211ga6K6VK2UiqrF/XHq6OXhD5sDaLbcADz8MvP22Dpq9847cf+ed5q+zWHQG2w8/uP566u+qSxfghhvMk4odZawxsEZEIU2NNx40SN44q1aV5fnz+qoeuYSBNSIiohBiseistfz55V+4UQEkb2es3XknMHasuWdWsHNUBqp89JFkTd1yi3/3ydsaNtTrbdro9ZIlpV+W1SpZRLNn6/vcKe/zxMmTel39HaqMNTW84PBhKRV1V3o60KqVlDCq4CgAPPGElMa6EwgKd8bAWlSUDCYYNQro2lU/Jm9eXfpp1L27LH/6yTx4ICtz5siyd2/JWvvnHz2FVwXW0tN1FiUDa0QUsi5elAAaAJQqJcvoaH0VKSUlMPsVohhYIyIiCjEqsBZOZaBGBQrI0puBNePxoa+DMt6kLiZXrGh/X2SkZLKFOhVYq1sXiIvT2y0WnbXWrZtMQ1W2b/ftPqmfO6Cz11RgrVQp+dmnpQFJSe4/tzHb7vhx4Nw5KTcdP162vfSSR7scdjIzdcJEiRLm+/73P70+dKjjAR7NmklALjkZWLbMtdfbv1/W1Xts4cL671MF1vbvl999vnzh8f+PiHKpY8dkGRNjng6jPuyMI7EpWwysERERhZjOnaVhtjHTJ5z4IrCmTpiB4G72b7UC06bpjBiVOWUbWAgnffpIT6zHH7e/r2pVWe7aJb+3666T24mJ5nJNbzMG1hR1ET8qSv8+jJltrsjMBFavNm9btMhcEspzGXHhgi7btg2ctWgB/PabDL1Q5aC2IiN1ZpsrWYDnzzt+PfU3d+CAlJ+uXKm3R/BMiohC1dGjsixVynxgxMCaR6ICvQNERBQA6enAV18Bt94KJCQEem/ITVWrAocOhVd/NSNfBNb27dPrqvIhGC1YAPTvDzRuDKxbpzN2jJlc4aZqVSm5c2T0aCm3bNECuPtuebuKiQEuX5ZAR6VKvtmnQ4fst6lzDUAymZKSsv9bysiQ4IvFAmzcCDRtKm+/AFC8uAyn+Pln89ckJkpQyZhAkBslJ8syb14d1DRypQS6e3dgyhQJrL33XtZB9XPnZJkvn/l3HR8vJckpKRLwVr2+e/d25bsgIgpSKmPNOA0JYGDNQ7zOQkSUG330ETBggNTKUEiKj5cTznCU08DajBnA9dcD06frDBRjxlowB9bWrJHl+vXSPy03ZKxlpWlTYO5cCbCVLSvZYiqDyNhM3tuyylgDJLAGZF1WnJQkgcC77pK/w2ee0UE1QGfo/fKLBNMUqxXYsMHzfQ8XKrAWG+v5c9x8swRiDx0C/v5bfrbqeW2pwJptdpwxU/KttyQIHBsLDBvm+X4REQWcylhjYM0rGFgjIgo3row8nD9flgcOSJMfoiDiLLCWkgKMGGFfSmdr6lQ5ie7XD+jYEdi7N3QCa5s36/X58xlYc0Rlavry96iGRhjZZqxltw+rVknG4fffA59+at9Af+BAyYQ6fhxYu1a2qQDOX395vu/hQgXAHPVPc1X+/PIeAADz5sn1pCJFHL+HZPV66vfyxReyHDYsZwE/IqKAUxlranCBoq4iMbDmFgbWiIjCwdq1wMMPA82by5la6dJS9+LMxYt6feZM3+8fkRtUYM34ZwpIxs/EiVIWmBVjsGPRIqBOHeDddx3fD7gWi/aXTZv0+g8/6MBaOJeCuktNjbX9+/CWw4d1z7Py5fV2Y2BNlWlmFVgzlpOOGgVs2WK+v3hxXc6o/gb79JElA2s6gyynASw1HXTCBB0YczTMwFnGGqADa4C8P40YkbN9IiIKuJxkrJ04ATRoALzxhk92LRQxsEZEFOoOHgRat5byzjVrJM3n2DE5k7twQYJtdeoAgwcDX38t92/dqr/+668Dt+9EDjjLWHM12KCCHR9+CNx0k/TjcnR/RoZM/2vZ0j6bKBBOnzaXIC5dyow1R3wdWPvwQ/nbaNMGaNdOb3dUCupqYC0lxfGgg1tv1eulS+vXUxlsuZk3SkEB4LbbZJCBsWz34EH7x2UVyKtWTa8PHsxANxGFAWcZa64E1n79VVLsx4wBVqzwzf6FGAbWiIhC3fPPy4dfo0bAt99KDRwgzXx++EGCbdu3Ax9/DPTtK0E249ngmjU5b1a0YoVuDkWUQypwYhtYy6qflZH6827UCFi8WOZ01Kljf//Bg1Kut3q1lIkFmop3V64M1KghwT7Vk4sn8povA2uXLslbJSBZScWK6fsclYJm9TepgqQPPywliYD0iOvVC1i+XG4bA2vVqsnfbGQkcOSI/FOuXgWefFIy2nwVUAw23gqsFSsmQVJApikDWQfWHGWs1aghy7x55ZoVEVHIU809bacAqQ+7K1ecf63KdrNagQcfzPqxuQQDa0REoeKtt6Quae9evS0tTTq1A8CkSdIlu0ED6fIN6LSH2rWBkSMlFUiNR6xbVzefyUnW2pEjkhbUvLlEBHLS0Xn1auC554LnzPG55+TM19hxnHwuqx5rrlCBs0KFpPF4v34StPrkE/P9Bw7or3n/fc/311vU/lSpAnTrprcXKKB/JuTbwNrXXwNnzsh5Rteu5sm7nmas3XSTrpbp2RP47jvgxhvldunS8pYNSLlhTIwOAhuvVQwZItMov/kGePFFT7+70OKtwBogP/9+/XSHBBVYW7BA3uZTU7MOrDVpArz6qly7sq2aIiIKOcePS8aaxQLUq2e+z5WMNRVYA4AdO+QNMpdjYI2IKFR89ZWcqRl7oiUmygdf4cIyPk9RR/6qdq5JE+Cdd/QYOgC45x450wDM4xPd9f33uo5u3z7ggw9kpKE7rFZg/Hg523z1Ven0HQxefVVG9qmGS+QXOQmsWa06i0gFPxTbvljGgQZ//inlf4GkMpTKlAFuv11vZxmoma8Ca1ar9PADgOHDJXPMWcaaOz3WypYFHnlErhtMnmz/uAcflGXnzrJUPQT//FM/ZsECvf7uu8CSJdl/P6HOm4G1Jk3kI1Rlrh08KIHLO+6Qt/mxY7MOrFks0uOxR4+c7wsRUcCp6pZq1aQ3s5E7gbWbbpLlm2+am8TmQgysERGFgsxMYPduWV+3Tm9XH2INGgARhrf0MmVkqTLWVAbbmDHA229LsOjJJ6Wrc0wMsGdP9qMWnVEZc0bq7NQVKSmSaTdypI5s/P67Z/viK+qMi/zCGFi7cEHHbV0pBb18Wf8Z2QbWbLOMjBlrVmvgp4Wq0sGyZSVOrgJqDKyZqXMAbwfWfv9dquYLFgQeeEC2GQNr7mSsZWTo845y5WTZrJnjoM2QIcDZsxLkAXTwR5WLAjqo3Lq1fBz06mUODIcjbwbWFPW7SE6Wn7d6bxk7FvjnH1nPyRRSIqKQoAJrDRva3+dKYE1dCRw6VK44pKdLZU0uxsAaEVEoOHJE9y8wdrV29sGoAmuKCqxFR0uDmE6d5BJ8TIy+BD9tmvv7dfGi3p8//wTmzJH1774zp4k7s3+/RBC+/16aDw0dKtuXLQt8+aUxg8+2+z35lAqs7dkDxMcD7dtLoMKVAQMq0KH+vI1sgyG2gQl1Iu9rV68CHTpIWWBmpt5uzFiLjAS6dJHb7K9m5quMtfnzZdmnjw7meNpjLSlJ/mYjI4GEhKxf12IxB3Nat5blpk0S08/M1N/rV18BjRvLoIvu3YOnat4XfBFYK1hQ/06TkqR3Ws2a8n/yt99kOwNrRBT2chpYM04UvfdeWd+xw3v7F4IYWCMiCgXG4QKHDskZAaA/GFWTHsU2sGZ720iVg373nZxduOPUKVlGR8toxTvu0CMWVc1TSorjMtPMTClH/ecfmUi0bJlkuhUtKmerjRrp7zMQjFEcBtb8SgXWzp6VH/3y5bqhvOKsctnYXy3C5ignq4w1wH+BtW++ARYtAmbPlqViDKwBEmeOj9eZTCR8FVg7flyWqlE9kH1gzVnGmjrnKFVKgmvuKFVK+q1ZrTIX5vJl/fceFyfXL0qWlIFsgwe799yhxBeBNUBalQISZJs7F7j+evP93n49IqKgkpEB/PGHrN9wg/39Kj3bWWDNajUH1ipXlnVjD+hciIE1IqJgd+IE8Prr5m3r1kngZ8MGuW17ZmDbXVllrDly001yJnf2rDTycSe6oAJrcXGSdgHIKD0A+Ogj6RgfFwcMGmT/tV9/LeWnBQvKskULOQNVUYQtW4D77/e891tOGScccdqRXzlq1P/kk+bbzmLAKtBhWwZq3BbIjDWr1VwtMWGCXrcNrKnYsqP/PrmZCqzZ9tw7elQSYFu18izh9eRJWRpLbz0tBVX9AW1b17jKWA6qvk+LRaaLlisHzJol26ZPd31abqjxVWCtfXsJkn71lQRRq1Uz38+MNSIKOwsWALVqSe+BL7+U4/fYWLkYbiu7jLXTp/XF54QEPVX07Fn5l0sxsEZEFOyeeca+59jatTKYICVFAldqjJzirBTUkchIoG9fWe/RQ84q5s51bd+MgTWle3dJCTh1SiaFpqYCn31m/7WrVsny4YeBChX09o8/lqBb3rzAwoXA4sWu7Yu3GYNptl30yaeMwQzFNoji7FfibHCBcduVK/InriYDqji0PwJrp07pXk6A/IknJgIrV+oEzawSTMlxxprVKlM8166Vn6WxP5mrHAXWjFNBjRmQ2Q0vUOcjefO6vx+ALgddtkz/7cfE6H248Ub9czhxwrPXCCabNsm1GGNptK8Ca+PGyf9DdQ2nenXz/QysEVHY+eILOdiYPFlPzLn1ViBPHvvHZhdYU1cBS5SQD7mYGEmvB4Datc3TdnIRBtaIiILd0qV6vVYtWa5bpxvCtG9vX/NmDFRFRADFi2f9GqNHm88Af/xRr58753zK5+nTsjQ+f1SUjNQDzGe+tmeg6oO5alXz9qgoKRG9+265bfz+/cl4QOGvGkECYN9TzFELEGeBtawy1lQwBJAYckaGTGJU/6388WtWwbO4OIlBA8BDD0mWlcJhBVlzFFhLSQE2btS3VUaXOxwF1oxBHePfXHY91tTbh7F81B0qY23DBl2iapv9VrKkLJ0F1i5cCPykW1c9+KBcYzH+3nwVWLNYzD9L24w19jQkorCjpiM1bCjH2YAMDnNEfXA5qtawWnVvjooV9XZVDnrsmKQD50IMrBFR+EhKksv74UZdBYqMBMaPl/V163Rzpg4d7L+mShXgpZckU+3BB3WZpjMlSpinexr7JPTrBzRpYh6aoDjKWAP0QASjf/8131Yf8s7Sc268UZYrVjjfb18yHlDk4tT2QIiONgfBbrlFX2BVnLW9yyqwliePlNIpQ4YA8+bpE3d/BtYSEnTV9J9/mh9jGycnM0eBNdvg0pw57lWRW6367cwYWDP2R3MUWDt/3vHr5DSwVr68XB/JyNDXUGyHcaj9dBRY27RJAm/Dhnn2+v6UkQFs2ybrM2fq7er/o68zyK67zrxue62HiCjkqWPujz+WdP2//gK6dXP82Kwy1tatk6w3iwV49lm9XZ2rALm2fwUP3YgoPFy6JJed27bVfcfChTp7XLBAvr+8eYEzZ+RDETCnuigWC/D88zLowLbruzN33KF/domJskxOBn75Rda3bLH/GmeBtYoVzREM43Mq6kPeWZmq+r7++gt47TXgvvuAJ57w37RQY2Dt3Dn/vCZdY/yTKlZM+pI98ojell3GmjEwZ9SggRwzTpkCfPCB/HfyZ2BNZR/Fx0u5n+3ckUmTfL8PoS6rwJrK4jp+3L58OCvJyfqtxVnGkvH1VGDNanU8REH1APQ0sAborLWff5als4w1lWlnNHCgvIV99JHnr+8vBw7o87eff5b/23v3yv/lPHnsW4Z6W8GCMhkUkPk52V2HIiIKKenpkkkGyDF3qVKOhxYoWQXWVBXJ7bebA3PGD7t27XK0u6HK48DatGnT0LJlS5QuXRoH/hurNWHCBPzwww9e2zkiIpds2gTcdpuuyVG9u8KFOjssWFCiAKouzmqVDzKVfu0NqtnMyZNS5rl4sa4lcpQW4agUFJCUG3Wmohgz1lJT9dmgs4y1atXkDDc1Va6KTZ0q0RV/pZgzYy2gbBvIFykiJ72qR64nGWsAsGSJNLl/4AG9LVAZaxaLzlrLm1f2PRQyjAItq8BaxYq6ZYw78XD1dlSwoHlIgZEx4JI/v85mc9RnLac91gAdWFu3Tu+bkbNS0MxMPTAacH/Ys78ZPxouXQJ+/VW3FW3WzD5Tzxfmz5fk6M6dff9aRER+lZQkHwxRUfqDIytZBdbWrJGl7dCDF14A6taVdPFcmnbv0Xc9efJkjBw5ErfeeivOnTuHjP9OuooUKYIJxvFWRES+dP68nJU2amTuw7Vjh16/eFGm3zgL+h8/LgGg55/34Y7mkLFzNSBlmUqNGuZapZyKidH92RITpbO6olJtjJxlrAH2jWuMGWtqTHd0tPP+bxYL8OabcnbZvz/Qs6dsf/55PY3Il5ixFlDOJjOqREhPeqwBEjSxHY4QqMAaIK0EH31UsuecZdmRmaPAmjET0JPfp6P+asq778rbmbHqxWLRvy9HfdZyWgoK6AEGiquBtb/+MvdWs51+G2yMH9kAMHu2DqzddJN/9qFKFcfD8YiIQp6qECld2rVzBnV1yTawZrUCq1fLerNm5vtq1pTKFjUVJhfyKLA2adIkfPrpp3jmmWcQafjlNG7cGFu3bvXazhERObVpk7yJT5woV2F69gQGD5b7tm+XOpJRoyTl+f77pUu4uuxv9O23crn8lVd09lWwMWasAeb0bdV13ZtUptk//+gyUMBxxlpWgTXbyIaxb5saXFCmTNZ1Nw88IEHTqVOBadMkqnLkiH/OFJmxFlDGPynjZMYCBWTpyVRQZwJVCgpIRtOECfY95Mg5FVhLS9MxdmMpqLu/z6tXdSDHUWDtscck+GNbtW7ss2bLG4G1KlXMZZC2gTVnPdY++cR8e/duz/fBH1TGmqoemj9f/z5uvjkw+0REFDaya71iy1nG2o4dcnUwKkqSGsjEo8Davn370NDBiK7o6GhcdNRogojI2157TbKeqlSRupGZM4EBA+S+pUul+/C770qmkbryMmAAsG+f+XlUgAcApk/3x567x9jAR51VGTPWfBlYmznT/PNxN7BWv7759qFDet3dD3lAfo+qi7U7zZM8ZTygYMaa32WXseZpKagjgcxYI/cZSwPV26OngbV16yS+r5KW3ZnI6uvAmsUiScmKbUmko4y1s2f1HJpSpWQZKoG1+++XfT5/Xj5aChQAmjYN7L4REYU8bwTW0tLkTRqQVGLbPsrkWWCtUqVK2LRpk932X375BbV8cZJHRGRrzx5ZTpyop2Iaz0CsVqBjR2n4n5goQZmtW4HmzSXDTdm+Xa/PmeP7/XbEapWxgI7Ozi5f1iPnVGCtWjV9RueL91z1nIsXy1K9lqNSUGc91gDpnv3oo/os79w5/T1mNxHUGVV75Y/AmjFj7fJl4JtvfP+adI3t8AIlu4w1Feu1LffMCgNroSVvXl3NktPA2tSp+m8GcO+tJavAmuprlpMea0DWGWuOAmtTp8pbV/36QN++si1UAmu1apmriFq3zvnPj4go1/NGYO2556S/WpEirg9Fy2U8CqyNHj0aQ4cOxXfffQer1Yq1a9fitddew5gxYzB69Ghv7yMRkT1VClixot5WsKDuD/bYY9If7NZb5TF//CHbjx83n4UYA2u22Wz+8sorcgYxapT9fcazPBVRiIiQ6Zg33gi0b+/9/bEdOtC7tyxPnJAS3O+/l9unTunGRI4y1vLkkRq33r11ppnKWtu5U5buDl5QZ5b+DqwBQJ8+MqI8XK1cGbjgsgPG7BxHgTVjxtrPP8tQgyVL9OArla3jikCUgjKw5jmLxb7PmrHEVr3d2CaapqcDY8fqgcqANKwH9FtYjx6u74cKrPmqxxpgvvaQ3VRQq1VPAR08GLjuOllX16GC0Zkz+iO5enXgf//T9/mrvxoRUVhTV1eM50xZUR9c6jj411+l5zEAfPaZ68+Ty0R58kX3338/0tPT8cQTT+DSpUu45557UKZMGUycOBF33XWXt/eRiMjs/Hnd80oF0pSvvwY2bgQefti8vVkzOUM5ckSCIwkJcjZkDJQcPSodn705DCA7GzZIYA0AFi2yv18FkAoUME/ZeeYZ+ecLtoG1e+8FPv1UAmmqDUBEBPD++5KWUa1a9lfBypeXs9yDB4HatXVAs3Zt9/ZNnVk6OpP1NtvAGiCZhX36+P61fclqlbN+4+jDixeBTp1kuWePHr0ZQMb/hsYgm6PhBdOnS6x99mwdWHMncOWvwNrlyzo7SvVYI8/ExMhHgTsZa1OmAGPGyLrVKvdv2SK3t26VjxXbmStZUQm0vioFBVzLWDt5UjLVdu2SFjgFC8rblOpTFqwtItPT5S0VkI+QggXlGlOpUpLZ2alTYPePiCikXLokbXDy5JHjuIoVpWWOGh5me3zvjDFj7dgxoF8/uf3ww+arH2TiUWANAAYOHIiBAwfi1KlTyMzMRElXRrcSEXnDgQOyLF7c/kyjZUvno70qVJDA2oED0nRTZdiWKCGXzTMy5GjetjwxI0Oy2apUybrRvrtSUyVolZ6uv6/jx81n3Lb91fyhWDE5YztxQr7npk3l+1YlqQBw552yLFRIspyyC0aWLy9nsAcPyvNs2ybb69Rxb98CUQp6223y8/jiC8nqCvXA2siRktby22+S9QhIypf6mf77b1AE1oxJkMb/do5KQdWf0/79OsASjBlr69bJn39Cgnu9vMiebcaaK4G1v/823161Sn4fVavK78TdLEJf91gDss5YS0iQQPPly8B99+ntffvKW6U/E3w98eSTcg4I6E4OUVHSheDIEaBu3cDtGxFRyLBa5Vj8q6+AH390/jhjy5ysGANr994rV2/q1dNv2OSQx8MLdu3aBQCIi4u7FlTbtWsX9gf7TG8iCm2rVsmbO+B+KnL58rI8eBAYP173CHj6aZ0WYGywr7z5ptTUPP64R7vs1AsvSOZWyZI6kDFzpgTyFNuJoP6irmp17ixnOo5KPS0W6TvmStaZ+tkfOiRnTOfPSzDOnfQQIDCloMWKSXAN0HVjoerqVSnPvXJFyqTV35oq7wXM01sD6I47JIb5/vvm7baloGlpukfT+vVyfBkR4V7gSgViLlww//fzNvXn06qVd2P0uZEKap07J0E1lQlYrpzrgVLj7yMn++DLHmvGwJrt8IKICCmftKUGZAd7YM14jmb8PmrVAm65xf/7Q0QUkn76CejZUwfVbr9dkgyMiU8FC7re11hVNOzaJdU0efNKv2RjpQPZ8Shj7b777sMDDzyA61Tzhv/89ddf+Oyzz7B06VJv7BsRkT1jCrJtGWh21ON//133XPvoI+Chh4BZsyToc+iQlI0qVivw+eey/u67EnB68EHP919ZvRp46y1Z//hjYP58yYobPlzOyEaOlPsCFVgbPFgGEwwZIrdLltSNfJSxY4EuXVx7PhVYmzVL97KrVs39dA5/nimqlJN8+XQW5LZtciZfpIisFyni3mRTf8vIAObOlZLjWrXMWYcpKdKUrHlzGfKhBElDpqgox4N6bUtBd+2S4Bqgs5bi492r6FaBGECCa6pHlysOH5ZKaRWHLVkSGDbM8Z/2ypWy9DSQQ5qal3LmjPRTBuRPvEgR/fu07bFmDGZmZOgyRF8E1nxRCqqCyka1aknrS6V3bz2QOZgDa6pkW2HhCxHlSmlp8oFx9ar8K1TI/ipKdtSwMUCOy+fNk/WjR3UwLTbW9St6th9cbdq4Xkaai3kUWPv777/R0kGpVbNmzTBs2LAc7xQRkUOJiXqkHqBLKF2lgju//CLL9u2BQYNkvVw5WdpmrG3ebA40PPywZK+1aePeaxudOCGp1ZmZ0rege3eJAqgAnrGztjojcvdDNqfuukv+KS1a6L5oTZvKtE93emqqmp4dO+Qf4H4ZKJB1j7XLl2WfbrpJ9i+nVKQkOlpqrqpUkb+F1aslS69uXYn+qKhOMBo3TjeVcmT3bgmgGicBBEnGmjO2paCqDNTInTJQQH7F0dFybJuc7F5g7bXXdMN4pXx5XS2tXLqU8wwp0lRg7fRp4J9/ZL15c1k6y1gzxpVPnADWrpV1VRHtLn8MLzD+LTtq+2g81/nkExnGrARzYG31ar1erx4wYEDg9oWIKCDeeUeGkWVm6m0xMdI6xZ3hXuoq0QsvSBWOYrwyYzzOy47tB9fNN7v+tbmYR6WgFosFFxwcRSQnJyPDjRqK5cuXo2vXrihdujQsFgvmqeiqAw899BAsFgsmTJhg2p6amorhw4cjLi4OMTEx6NatGw6rkbJEFF6mTTPfvv9+977emOFWoICchairN84Ca7NmybJHD0kFSE+XrLkjR9x7bSU5WQYA7NolV5EmTpTtXbsCr78u66dP68cHoseaI8YGPj17Anff7V4tW+fOEtAcN06y4O66K+uAjzNZ9Vj7/HNJgx8xwv3ndUSdxarUdxUNWblSR0jS0x2f7QaLrVtlmSePZGLmySO/NxXoPHxYl4Fef70sQySwpo4RvRFYAzzvs6YSMG+9VbKHAD3Z3ujTTyWzqWJFoEED9/ePzIyBNRWkUYE1FRi1/V0a3zYWLZLgV8mSenqmu/wxvCBPHr3u6LzIGFhTmWqK+ti4fNm3Jc6eWLVKlg89JNevXK1QIiIKC1Yr8N575qAaIMf97kxoP39e3kQBubJi+6EzdaosVfsbV9g+B0c0u8SjwNqNN96IsWPHmoJoGRkZGDt2LFq5cRn24sWLqF+/Pt63baBiY968efjrr79Q2hh1/c+IESMwd+5czJgxAytWrEBKSgq6dOniVoCPiEKEmpr51VfSJ+322937epWxBkgZo7FBuwqsGaeEWq3S8wyQ9JMvvpBMpdOns24OmpWVKyU1Oz5exlcXLarvU2fbxsBaoEpBbTVvrs8iPblyZbHIiLfRo4EPPgC+/daz6EJWKRgqEw7QDY5ywjawpjK1V66U+jPl6NGcv5avqHqrL7+U6MP58zIgQw1gSEyUwQUA8NRTsty715zaE2RsS0EDHVg7flyWw4frY0/V70tJTdWV30895d/Bw+FKBdaOH5ehEIAk1gLOf5fG/7bqLTwn/e780WMNkMr8UqWAe+6xv69qVb1umwSs3rIBfY0mWKiJpczeJMolkpOD+tjC77Ztk3OOfPnkwyk9XXrgAjJcylVr10pwrlIlx1co+veXg5CePV1/TmNgrWhRfeGVsuRRKei4cePQunVrVK9eHTf+lz//559/4vz581iyZInLz9O5c2d07tw5y8ccOXIEw4YNw6+//orbVPPo/yQnJ2PKlCmYNm0a2rdvDwCYPn06ypUrh8WLF6Njx45ufmdEFLTOnpU+UYAEdhwE2rNVs6YEd4oVA4YONd+nMnh++0330Nq+Hdi5Uz5gbrtNzuibNZPtZ8969n2os/CGDe2b/htTMJRgCaxZLFJvtXev7HugZFUKaswiPHbM/R58tpxlrP31lzlN5MgR91L2/UmVTqtIU7588k/1hZs7V5bVqgHdusnv+eJF6acXpE2PVNBE/RdUSXkFC+r/Lu5OdzQ+r7uBNfUjTkjQMz6M/4UBuRZw5Ii8bRmTP8lz6u1yyRLJyCpSRDfAV7/L06clhr9ggTTDN75t//qrLHMS2PFHjzUAmDxZrkdEOLgcXq8e8NJLcq3Gtgdb3rxSrZ6eLm+Zan8D7cgR6QtnsQA8VCfKBf75Ry6m9u8PfPZZoPcmOPz0kyxvvllfZO/QQZaLFsmFT1f6miUmytI2ZdnI3Ss8xg+uDh14NdBFHmWs1apVC1u2bEGvXr1w4sQJXLhwAf3798e///6LOp70zHEiMzMT/fr1w+jRo1HbwdS5DRs2IC0tDR3UHyGA0qVLo06dOlilcsyJKDwsWyZXumrU8CyoBsgZxi+/AF9/bf8h0batXO5PSdHp0iro0LGjvvTvrCu2q1R3dUdBC2M3biVQPdYcKVsWaN06sPuQVcbali163dF0V3fZBtaqV5eg7OXL5imanpYF+1p6us5Ys03hsh240Lu3HEipv0H1dxqE4uNlefy4ZK2pFojGa2+eVC14EljLyNA/qoQEx7Hx9HTgjTdkffRo7wRaSP+sVeVys2Y68KR+l8eOSZbX11/LQAnj/BX1FuKNwJove6wpjoJqgASnnn9eSiod3ReMfdZUkuwNN7g3vZeIQtSKFdKP9scfmbWmqJYixqsLNWpIP18AaNRIB82ysnOnLB2NiPaU8YOLI5pd5lFgDZAA1uuvv44FCxZg1qxZeP7551GsWDFv7hvefPNNREVF4ZFHHnF4f1JSEvLmzYuixlIqAPHx8UgyNji3kZqaivPnz5v+EVGQmz9flr5qoGmxAKNGyfrEiXJWpGpVjGfsrp59Z2bKZM9hw3TNGqAz1lR0wEidKV68qM/KgqXHWrBw1mPtwgVpxK94o9em7ZlxRIQuBzVGToKxr+dPP0lzJvV3apvCpUqfFTVtV6Vc2dYyBhFjYC0xUY7RS5SQCop335VAS7t27j+vJ4G1U6fkv3pEhOyD+i9s/PHNmCH7FBdnbixPOaN+1orqrwbItRf1+6xSRZKNU1LsB97GxOQsAdcfPdZyKhgDa2oIsU0hChGFq/37ZXnypPmY6cQJ4LnnvHMxNNQ4GuRlschF/Zo15SLuI49kH4hUz1Otmvf2LX9+/eHhbtudXMzlUtAtW7agTp06iIiIwBZjVoAD9erVy/GObdiwARMnTsTGjRthcbP5hdVqzfJrxo4di5deeimnu0hE/nL5sh4iYDtqz5vuuQd45hnpmfX557ojdtu2+jGqK3Z2GWvffguMHy/rf/8tgcFixbIOrBUuLGfomZkSuCldOnhKQYOFs7NE44g5QIYrnDwpja88ZZuxBkhgTQV5lWDMWFPTbgHZfxVlUIxZnwUKSD0ZEBKBNRUjPHVKyskAOS5NSAAee8zz5/UksKau4ZUoIUmwxoy1S5fkR69mkowcGRyJp+HCNrCm+qsB8jaxYYPE2+vXlwvu6jqJUbNmksjsKUeloMnJwB13AGvWyG1v9FjLiWALrKWmAosXy3qXLoHdFyLyEzXlB5A3Z3Vxb+BAyWI7edJ+vHY4S03VPxPbTLO6deU4s1YtebN8+21Jd3fGFxlrERHSAy4jQx8XUrZczlhr0KABTv13oN2gQQM0bNgQDRo0sPvX0Eu9d/7880+cOHEC5cuXR1RUFKKionDgwAGMGjUKFStWBAAkJCTg6tWrOGvT6+jEiROId3TS+p+nn34aycnJ1/4dyo1RcqJQ8uOPcuZSvjzwX19Hn8ibF3j0UVkfMkS6T5cubR4Z58rZd2amnngZESHjz9q0kQBMVqWgERESfAN0RpR6HQbWhLMea6pXhdEjj5izBd3lKLDmqG4sGANrxn0uVcq+O7vx/nr19P0hEFgrXlz+q1itwB9/yDZvdKHISWBNBftUsGfrVlkfN06y6mJj5S2FvMcYWLNYpKzQqEoVaeljsUgAzZGcfpyowNrly1LyC8hHiPq7BJixZmvZMknELl2a03GJcg2VsQYAGzfKcuVKPUXm77/9vksBtWePnCsUKuR42lKVKsA778j6k0/qn1NKink41+XLeuiaNzPWAOlTHKz9g4OUy4G1ffv2ocR/jRD27duHvXv3Yt++fXb/9qpmFznUr18/bNmyBZs2bbr2r3Tp0hg9ejR+/a/jbKNGjZAnTx4sUpMCARw7dgzbtm1DC+OlSxvR0dEoXLiw6R8RBbFp02TZr5/zRjPe8tBD5g7QN99sDkq4krG2e7ee9LNmjXxobtsG9OiRdcYaYN+kSV2JMk4wzc0cnSVarTqwZtsLYt48z1/LUWCtSRO5injddRK4A4IzsGbMSHNWRqAOmIztFlRgzdiMKshERuq+TCoLKVgCa8YLu1euAE8/LeuPPGKfNEg5Ywys1amTdWN+Z4G1nE6kNE7d3LNHrqdMnWp+DANrZuqt+tZbPZ/GSkQhxjZjzWqVgJGisqOMvvtO+o8Fcc9Xj6nyzerVnb8RDh0qI6GtVpnkvny5lIg2bqyP63btkvWiRZlZFgRcPkOtUKECLBYL0tLS8OKLLyIjIwMVKlRw+M9VKSkp14JmgATsNm3ahIMHD6J48eKoU6eO6V+ePHmQkJCA6v+lOsbGxmLAgAEYNWoUfv/9d/z999/o27cv6tate21KKBGFEKtVxkYbs1BPnAAWLpT1fv18vw+xscDDD8t6fLx0hba9H8j67FtdjatfXwIxKn1h7VrgwAH93I4YA2uZmTJJCfBO5CAcOOqxtmyZHLRFR0vw0kgFZT3hKLCWN6+kI+3YAdx1l2wLxh5rqjcfIKXNjixeLGe5d9+tt4VAxhqgA1kqsOXNwJo7bVedZazZ6tPH8/0ixwoX1mWcxv5qjjgaZhEZ6Tzg5qo8eXTWVe3awNix9o9hYE2zWtlfjShXuXABePxxfVEZkMDa/PmSsZYvn7xJXrqkJ9Eod90F/PablEL6w9y5cjHdH5VsxsCaMxYL8N578gGWkiKVL4cPyzGouvi+fLksjZUHFDBup37kyZMHc9WkvBxav349GjZseK18dOTIkWjYsCGetz2RzcL48ePRvXt39OrVCy1btkSBAgUwf/58RHIsLFFo2bVLrkw1bSq9zpQZM+QqVpMm3u0fkJWXXpIu6GvWAFWrmu9TGWtZBdY2bJDl9dfLslo1fXajztodlYIC5smg+/ZJmnd0tJ4SlNupn+OlS/J3kZGhG2vdf79EMG68EbjvPtn222/mAzp3qMCa7ZlxRIQcwKjfYTAGoYxXeG0HFyiVKtmf3YZIYM02Lu1gcLjbcpKxpvbHUQ+1yEgmnPqCxaIr57MoUgAgSci2pbhNmnin592kSbLMyJBE1h9+kOQBhT3WtB075Nw5b16A17+JgkBystSvb94ske/XXpM3sAcfdO/D0JFjxyQYpEoaAflAPH5c978dMUJ6igHmye5GtplsvjJ2LLBkiRz/+9q//8oyu/OaPHmAmTPtz0VUaa0qEe3a1au7R57xqGXrHXfcgXnz5mHkyJE5evG2bdvC6sbI3f3G+uz/5MuXD5MmTcIkdWRDRKHFapXu3q+8oseo/fmnHrVnLAP1l5gY513Q1dl3VqWgKmOtUSNZWixyZr11q76tatlsGXusbdsm6zVrysEImXvNTZokv6tNmyTg+fLLktGmruAlJgJ//SWDJEaMcP+11N+jMWPNSAVBL12SIJx6XHo68OGHUutkezDkD1arLuUsVUq+f1eFSGDNGCssV847ZZaeBNZUFbCqvHV0wbhChcAHV8JV27YSO+/QIfvHvvuunC82by4V9t4K7LRqJRXnp08DffvK77pYMZ14zYw17eefZdmmDduGEgWFN96QrKj33pPjbHXMPWWKXNBV/QzclZgIdO6sqzQAuZpx+bIc2x48KG/ITz4pFwLXr5dzgTZt5DjkzBn9dWXLev79uSo1VU9DmjFDsuR8edy9ebMsVVAxK8WKSXVBy5Y6U23/frmSs3Sp3O7WzRd7SW7yKLBWtWpVvPLKK1i1ahUaNWqEGJtLfo8Y+7UQEWXlm2+AZ5+V9Y4dJShy8aJksGVmyodtVJQuuws0lbF2+bI0ELU9Y7Za7QNrgDmwVry481F0Kljz6qu67JFloFq+fPIzOn1agp8qGvLCC/bByn79JLA2bZpngTU1+MBZYM04xfXMGR1d+egjuQK8cqX0CPG35GQgLU3W9+51vv+OhEhgzZix5q3/HjkJrGV13G+cfULeNWOGnA+58iceHS1vq4D3yxBvv918u1gx6bmmXjeQgimwps5bjYO2iSiAVqzQ69OmSTBJZYg5ayORnZQUoF07yUyrWlXKPpcvl4qU8eP1ReOnn5Zj6iFDgFmz5Nh54kS50K7eQAHJ2vK1LVv0cVNSkgSsbr7ZN6919SqwfbusuzrBpXp16d/ct6/U0+/fL21y0tKAGjV4oBEkPOoC/tlnn6FIkSLYsGEDPvnkE4wfP/7avwkTJnh5F4ko7Fit8u/SJem9AEhw7ZdfpE8AIBOCVDOWDh2cZ3j5m7FDtqMz8H37JJstb15pcq8Ya8Fq1nT+/O3by9devKjrzJo2zdEuhxWLRVL11d9DcrIccAwdav/Y3r0lgLlxo+5V56r0dCljABxPbAIcT3EFdHfuQA01UNlqhQq5F1QD9M81yANrxtkMfft65zk9Cayp9nrGwJrtYC43Ws+SmywW9//E/cFYChrowJq6PmM7SDkQVPIKS6OJAiw9XS4KqgAPIMe3v/yiSzeNxzXu2LpVgmpxccCqVRL4GTRI9x0G5ENz2DBZb9RIKg4AfaxmDKwZp2B6i6pIUNatM9/+5hu9/sQTch6i2oM4ez5Xq/ASEyUgFhvr3gFCkSLyMwQksKbKQG2v7FDAeBRYs50CapwQ6q2poEQUpk6elIZIJUpITU5SElC+vATWLBbdl+zvv3WqdHYNdPwpMlKfqTg6A1f91erVM2ezGc8kOnZ0/vydO0v207ZtcpVv5kw5ICGtXj3ztM/x4x1f0YyLk3JMwP0hBgcPylXb6GhzFMeWsSceIMFilZqf0/4knlL91TwJRodIxtrddwMDB0rs3diSMSfUt378uGstXTIy9AX9MmX09iVLgNmz9e1AB1bI/4zXXwJdBhxMGWsqsMZgM1GAnD8v5ZeFCknq7tmzcuw9frxUiNxyi/10enepCaC1atkfh9x3n2T0z54N5M+vt6srUjt3ytKXgbV58+SN8bPP9LY//5SlSqedNUuOg3bvBt56C1i0SKoQHDl7VgKFtsOznFGpuw0auD9woGJFWe7apWvrWQYaNDwKrAHAlClTUKdOHeTLlw/58uVDnTp18JnxD5SIyFZaGnDnnXK15vRp3ai0Xz999vnfMBOsX68DayqLLVgY+6xNnCg9IVQgQgXWjGWggDmlpVOnrJ8/JkaCj126AD17Bv7MLBg1by79ON54Q4KRzqh0pq+/lquzrlIHdZUqSWaaM7YHoH/8oa+EZtWHz5dUxpqzARlZUdGlS5fMk0WDTHw88MknOm7qDRUqSJP71FQ5Zs3OiRNy0T8iwtzzrUwZ8/G1ekuj3MPYISXQgdVgCaylp+sMTwbWiALgq6+kZHDcOMm+WrhQtrdpI+0yVDlhTgNrqie6o9TUmBhgwgTghhvM21VgbfduOVYzVhl4O7B2333yhjRwoNxOSgLmzJH1sWPleP38eTnQMNatG/vFGf3+u5wDzJuny0mzsnatLFX2mTtUYO233+QYs0QJVrUEEY8Ca8899xweffRRdO3aFTNnzsTMmTPRtWtXPPbYY3hW9Uoiotxj3z652uUskNCrl6SCDxgALFsmV6lUOjgg6SeKyk5btkz3JPPkw8eXjDVj770nvSOmTpVtqr+ayrxTjA1KeaadcxaL9Od48smsH9e1q/y+Dh2SvylXqezr7Kax2paC/vKLvs+fGWspKXJgfO+98n8R8CxjrVAhnX4ViP5wARQRkf1wMiMVJChVynHLxKVLgRdfBPr399YeUqgIpsCaeovydDiyN6SlSWV8RoYkFzurriciD6WnywfO8OGOgzvr18vxwYkTEsQyXjBs2dL8WG8F1lQQyBUVKsibw5Ur0iLGWIrp7cCaMSXdagWef15eo3lzoFkz4P33pXlrZqa5pYcxi85IVbFk9RglLU0qUQDJDnRX5crm2126cLhZEPEosDZ58mR8+umnGDt2LLp164Zu3bph7Nix+OSTT/DRRx95ex+JKNi9+CIwcqR8iBobuWRkSFr3zJnAjh26HG/GDEmrbtFC6rhq19ZfU6uWpH+oD74iRWTsXzBRAwzOntVn17NmyfLvv2VpG1irVk1qxBITs86AIu/Kl0+yJAH3ykHVwVF2gTVjKajVag6sXbggB7v+8N13kj351Vd6Kqo7B7WKxaIn4r7xhus9Q8KEiuFnF1hbv15fcHc2uKBNG5mpwWPe3McYWPNH3+2sqESQf/8NzOuPGyc/j5Ej5Xb58vwIJPK6QYOAl16SoNATT0iW/vjxwJgxwK+/6n5cnTrJResaNfTXOgusGSdzusOTwFpUlD7eUhcHFdt+aDllfIPu3x/49FNZf+YZWd5+u/yM9u8HPvhA75ezdldq0BWQfT/fhQulqqBkyazbwjhTubL5/IJloEHFo4+2jIwMNG7c2G57o0aNkO6vkwgiCh6q/DE52dyzoFs3aSxv9PLLsj02VvoVfP21+X6LBbjpJn27Xj33exD4miqx27hRX0lbs0Y+iFVJqKMBBe3amQ9myD/69ZPlrFkyzdUVKrBme3XQlvHKbmKiHHgZz6TPn3dvXz21Y4cs27SRiVpjx8oBtSceekiiQbt26QEOuYSqOldV6BkZMuQ1MdH8ODVzBdAJrESK8bwt0B9f6iP41Clz68TDh+WtQlWOe5vVKgnFTz4pSRqq0oploERedv68rpoApNSyb1+JZo8dC9xxBzB3rtzXu7e0FzFesG7WzPx86rjm/HnXShttqR5r7k4pMU7+ee01fQzjzYy1tDTzG+H06bL8+GP7cdEVKsjE0rfektvOstGMdfa2Bwu2VCVAnz6eXXWxWIDRo/VtT7LeyGc8Cqz17dsXkydPttv+ySefoE+fPjneKSIKMcaUDHVVbNMm3VgTkIDSu+/qK0JZMV7FMX6ABAt1tUidKSjjxskyPt58ZkWB1aqVHCBduKD/PrNiteqDI1cz1pKSpNQZkAOdAgVk3ZVy0MOHnTfFddXu3bL83/9kEMhTT5kbf7mjYEFdDuqsp0iYUoE1lbE2eTLw8MPmAb9pacDq1fq2+hMgUoLp7T8mRrLEAHPWWrduUgH1wAPef82MDEmgUR+Jqs8bwMAakdetXi1lixUryn+8OnXkAvXdd0uFxeXLMhAL0MfXVavqrzeOMQbka9QVAXey1jIz5fhHHY+4mzXfo4dMfnnvPQmqqTp6bwbWDhwwl4JaLJIQkNWQMHUc6EpgLauMtfR0XdXg6qADR3r1At5+W85BgunDhuCgK4hrpkyZgt9++w3N/otyr1mzBocOHUL//v0xUuV7A3j33XdzvpdEFNyMV3/+/BMYNUrKHo2mTQMcZLo6dM89klrdqpUcIAQb1ShUZQkp334ry+yynMi/IiLk6uDrr0tgrXfvrB+vSnajo+0b7NpSDYy++UYO1ooUkVKMVq3kbzirAQZpaRJsfuklOfD99VcZ6e4JV0tXXVWhgkxGPXBA+o7kEqrH2sGD8qszXhtQVq7Ux/mdO3ueGEjhK9jOdWrWlL/pxER5awJ014KffvLua6Wny0f4zJny1vvJJzIH5dFH5RqcNweOEBF0+4c2bSTzyqhXL93Tq0kT3eDwpZfk8/3+++2fLzJSgm1nzkg2fny8a/uxfz+weLGs581rHpftinvvlQoDVSuuBnd5M7BmDI7VrCm9elVVgzPqmP7MGTkwUO1gFOOgp3XrJMDoqN59zRp5jmLF7LME3RERIedZFHQ8Cqxt27YN1/+XsbHnvz/QEiVKoESJEtimIuIALIHOfyci37NadYPTQoUkK8gYUH/vPWlC5GpQDZBeC4MHe3c/vck4eAGQ4MvatfoqGANrwUcNxXClK/2rr8py4MDsJ2uqdCX1u586VcofYmOBo0edB9Z27ZKJr8b96dgRaN1arkK6kwZlteqDReNV6JyoUEGC5LksY61IEfnWDxyQX42jhMMFC2TZr5+0tCOypZJCbBNBAqVmTYnb+6PP2pw5ch6fJ49cb+jZU96ibr5ZzunVtQgi8hIVWGvd2v6+G2/UgbUHH9TbixcH5s93/pzFiunAmqsOHdLrH33keKpPdowBKV8E1lQ2XbduwA8/uPY1BQtKcPH4cWn3YdtD2ZixtmuXtLhxFKxTVzE6d/bsZ0NBz6Pf6h9//OHt/SCiUJSZKV28VWPRr7+Wf6VKSebM9dfrgEY4KVJESlvVWUrTpvIhuWqV3PZW1hB5j0pF+vdfOUhTB2y2VqyQcY558kgD4OwYA2BPPKEbyaorms5KQZ94QiI3xYtL81zVrHf5cuCddyS7zlXHj8sV04gIzwYWOKLqtXJZYA2QclAVWHMUF1WBNdt2LERK5cpy/hUsQSRV4rxggbQL8uXwgKVLZTl0qATVAKm2Ms4oIiIvUuWHji5gt2un1+++2/XnLF5cglCuBta++AKYMkW/pqNMOHf5IrCmMuoaNnTv6ypXlmOtPXuyDqwBksb+v//pliCKCqx16eLea1PI4FweIvJMWpoEEVRZZHS0fFjMmCFBgmHDwjOopnTvrtfLltWTJwFmrAWjcuWkd0d6un0Jr9Err8jy/vtdm0bbsKEEkrt0kWa7iupo7yxjTQ38mDVLN8ZVPvzQPF03O+oKbLlyzgOG7lKBNTXdKxcxDjAwxkUzMyVYkpgolTKeDPSi3EMlrgaDnj3l7W/HDhlKZ2wx5G0rVsjSUfIMEXlZWprug6bKPI3q1JE00lWrpKrEVe5MBj12TJo1ql6xrhw7ucLbgbVLlyR1FzAfw7siqz5rqhT0qaekoeXhw/aTTfftA7Zv58FDmGNgjYjcl5kpvRBU6gYgH8K5qfz7kUf0epEi5kakDKwFH4tF9+sztCwwWbsW+O03OfB58knXnrdoUSl/+PFHc2p/Vhlr587pkokGDeT1Zs2S0tOiReVr/vrL/DXbtwM7dzreh++/l6WjSbSeUplvuTBjrX59WdqWgl64oN/yWrWyb7NCFKwKFZK3F0A6NRjbogJyvSGnMjNlNtHWrXK7ZcucPycRZePECVlGRjpvIXHHHe73SlXPZftm4cjChebb3g6sqaqYnPrtN+lnW7Gi/qB3VVaBNZWxFhcnU1gB4I03ZKiVYjx4CJYeAeR1DKwRkXusVslGU436ldw2Gq9UKRlp3qyZpAOULy+1L61b2/dgo+CgykHVmZ8tdYWxb1/3gqORkfZB5awy1tTrly+vozP/+590+W7TRm4bR7ZfvChBwerV7UsO9u6VDDfAuxN0jaWgVqv3njcEqIy1tWvNP+7kZJaBUugaPlzeqn7/XQ+mU44fz/nz//GHrmCvVi379pRE5AUqsFaihHdrvNXAAlfeHGyn/ARrxprqRXfrre4nArgSWCtYELjrLjkHSEkBXn5ZP4ZloLkCA2tE5J7vvgMmT5YPpccf19tzW2ANkDFnq1frRjrvvw8sWwbkyxfY/SLHqleX5d69ju9XJaLGsl5PqYCZo8CaGligAn1GKuvMGFg7eVKv214ZVkMzmjYFbrrJ0721V6GC9Jm7eDHXlYNWrQrkz2+//ehRCR4ADKxR6KlQQeL3gH37yGPHcv78qiIdkGtORLnS5cuSFW9s5O9LKvDl6uROV6my0uzeHNLSJBPMyFuBtehoWXorsLZunSyzm/buiLrY6uj40RhYi4gAxo2T219+CZw9K/ergwcG1sIaA2tE5B7VG2rgQODZZ/V2b/V2IvIVNfr9yBHH96smvXFxOX8tlbHmqBRUZayp1CijGjVkaQysGdOm5swxP16VaXjrQFaJjpYyVcC+LDXMRUbqqmGjOXPk+L5SJe9W3RL5y8iRsjTG6gEJGufUwYOyHDJEht4R5SoffSTR65gYuWhWo4Z3/mMZWa06Q01RgTVvp4gmJMgyu8Da6tXA+fPmbcGSsXbqlFxwHDpUAp0bN8p2T6pKVMbaoUP2+6N6rBUsKMs2beRv4PJlufD+yCPyNVWq6Au8FJYYWCMi95w9K8sKFcydmV1pcEoUSGXLyvLwYcf3q79hb2RfqudwVEah+m6UL29/n6OMNeMgg59/NpdmqsCaN4KBttRV3bVrvf/cQc5RzPObb2R52225q50khY+mTR3PFHJ2rcEdKkHH2zF+opDw9tsSXVafz5cu2ddc59Tjj0tm2qJFepuvM9aMfcKMRowAGjXSbWGMGfPBElj79VfJFPvwQ8k4u3RJgl+eBLfi46USITPT/PMH9MXPmBhZWizSMkf54gtZdu/Og4cwx8AaEblHBR9U+aPi6khuokBRGWtHj8rBkdHVq/rgyBuBtapVZblrl/19qjzUUQNblbF2/LgOYhsDa8nJ5mbCvgysqYm/uSxjDZCsNEVV9argA8tAKZS9/rqcI1osekbJ66/nvBxUBdYcXS8gCmtWq85O27gReO45WbcNwOTUu+/KctQovS0QpaAZGcDEifK9fvSRbBswQBr2v/mm9yb7eBpYs1qBTz8FZsyQ2/ny6QktjRpJWrq7LBbgwQdlXQ0oUIyloMqDD0opqJFx6BmFJQbWiMg9KrCmggKqacuIEQHZHSKXqQPFq1ftJ12pwHBEhDkT01PVqsly717pQWKkAmuODj4LFdKZdf/8I0tjYA0w9zzzR2Bt40b77yHMdeoky+LFzRfiCxQA2rYNyC4ReUWbNnKunJIi/7Vr1JAk3vvuy9mcElUKyow1ynWSk6XsD5D/UB06yPrixfYX8SZPliaEjvqvusoYaFKlod4OrKlSUOP3ptj2GbNY5Ht+8kn7Bo454Wlgbdo0YNAgPTDg2Wdl8lDfvnrCiidGjpR9WrkS2LRJb7ctBQXkWPLee4Hp0+X28OG86pALMLBGRO5RWTQqY+2rr2TSztChgdsnIlfkzasPPm1rn4wBY29M1ipdWqIwGRnAvn3m+7IKrAG6wdf27bK0DawdOKDXfRlYq1pV9vHKFeeTVMNUo0bAn38Cmzeb46zt2nE2CYU+i0XenooWBebOlb/p337TA4bdlZmpK+wZWKNcR2V1FSkik2+aNpXl6dPmINTatdKE8LHHgI4dzc+xdq1cVHv//exfLzVVr/sqYy02Vn/YqXLQM2eANWv0sYnStKlvjkFUYM34/bpi6lTz7SpVZBLotGmOa+FdVaoU0K2brE+bprc7ylhT+vSRi6Fq6jyFNQbWiMg9tqWgBQoAN97oWWo1kb+pctDDhyW4NmeOHLSpjDVvTbeNiACuu07Wd+403+dqYG3bNlkahxcA/stYi4jQfdZyYTloq1by52IMrLVsGbj9IfKFGjX0ELvRo/VwZHecPClJJRaLfoslyjVUGajKis+TR2eeGwcYGHuurVunM52sVsloOnIEmDLF8WsYjwNUoCktDdizR9a9PbzAYjEPMNi6VRryN28OfPaZ+bG+mlbiyVTQ06flqpiRmujpDf37y/Lrr6VnW9u2sgR0jzVbFSrwHCmXYGCNiNxjWwpKFErUwe7zz0sjrf/9TxrLOusdmBOqHNQYWMvM1BO0XA2sZVUK6s1Jpo7k4gEGijGwpn4cROFk6FCgfXup+OrfX7cjcpWatVKqlMQUiHIVlbFWurTepoJsxsDawoV63WrVn/Fz5+rP2K1b7Usvja8ByPFKZqb07Nq7VwI611+f8+/DlvoeZsyQC+jqe7EdytC1q/dfG/CsFPSzz+xbV6iJnt7QqZMcbx0/Drz6KrBsmb7PUcYa5SoMrBGR61JT9ZUZbwYgiPxFpVNs2qQPvvbu9X7GGqADaxMmSIPfK1ckqKYaGTnr5eYssKbKMlQpqNWqM9a8ud9GuXiAgWI8pm/cOHD7QeQrERFyfaFIETm/d6cNUUYG8Morst6+vU92jyi42WasATrIpu47e1YHz2rVkuXmzRLFHjNGf11Ghmy3ZZzOmZoqAxI++kgyy77+2vsZa8bvYdIk6bUWFSW3Vd+4l14CfvgBaNjQ+68NuB9YS011XErrzfOVPHmAe+6R9QkTzPexT0Sux8AaEblO9VeLiAAKFw7svhB5okMHOTC69Vbg7rtl28mTOrDmzQOw//1PAl6HDgEPPyzlAE89Jffly+f8IKxmTTlYPnlSGhOrwFrdurJUGWuXLkmwDvB9xtq//0oJ6yuvSENmo40bpSmvbV+TMKGqdQDvzLUgCkZlywIffCDrL78sibYffigtoZxlsC1ZIsHmJUvkHPill/y3v0RBI6uMNXWfGmRQq5YeLb15s0S0d+yQz/Abb5TtP/0EDB5srsu2nc6pot9vvgncfrt3vx9l4ED9odetm/1/8P79dc8xX3AnsGa1Ag89JG0+EhIk4AfIG5vF4t39UuWgtpmF3n4dCjkMrBGR61S5XJEi3mnwTuRv3bvLwdCCBXrc48mT+m/bm5lfDRtKdtnEiRJ4OnEC+PhjuS+rcfQFCujShW3bdGCtfn1Z7tkj2XYqWy062nlvj5wqWRKoWFEOWkePlhLaW24xP2bIEAke3nefb/YhwEaMkD5UkyYFek+IfOvuuyVJNSNDBugNHSpDDH/91fw4qxXo3Ru4+WZJ/o2NlQqsihUDsddEAaaGIWWVsab+E3XsCNSrJ+sffijTKwGZXKmOSV57TY4VevTQz2fMWFMeeAB4/HHvfA+OdOwoEfZffgFmz5aLfkpMjO+nXBoDa9mNLH73Xbm4FxkpQ9W6dZPS21WrvL9f11+vsw4VdaGWcjWeGROR62wnghKFItVEtkQJWf7xh1z1BbxfUhkTI31Qdu+WK9BKVoE1wFwOqgJrDRrI/73LlyVLzDi4wJdXSlXWmrFhsfEgV2X7halKlaSH1LBhgd4TIt+yWPS58rp1evvvv5sfd+AA8P338vjhw+XtrV8//+0nUdD48Udg5kxZd5axZrWaA2tt28rUUKViRTk+aNPG/Nz//KPXVcZalSoyLGDcOIl6+zpLqmRJ6SsWFWWOnNeq5fsL7CqwBmTd+PHnn+XCHyDTN9XFv44dfTOm2GLRWWuAHCB88433X4dCDgNrROQ6XzR4JwoUFVhTfQMB3/1t58kj6R2KO4E1NQ2scGFdKrJ8uWTaAb4rA1VUnzWj5GS9buxWbtxORCFHvS0a2QbWVCy9VCngvfd8/xZEFLTeekuvt2ih140Za//8IyWK+fIBrVtLeeKRI7L988+B336TzPNWrczPXaCAXle9VQcM0IEkY+DJH4yBNXWM4kvG789ZOejly5Jea7VK9p+/roD16SPB0VKl9AR4yvUYWCMi13EiKIUTR2eQvpispVStqteza9blKGOtUCE5KAdkEpUqDUlI8O5+2nIUWDNOOlOZc4D0YiOikOUoSLZli1SyK7zGRvQfNeX7xx/11HFAZ6zt2AFMny7rrVvrTLWiRaW08v77dWAmTx5z1vzlyxJQOnNG9wxr1sx330t2ihTR/ZVr1/b960VH6/XUVMeP2bRJqmlKlpR+Df7qc1a2LLBhA7Biha6CoFyPgTUi0qxWuXq2cqXj+1WGDANrFA5sA2sbNvj2oNWdke/GwJo6cC9YUAfWVqxw3NfFFxo3tu/hpspSLlzQ7wuAuXSFiEKOMbBWoICembJ0qd6uukLwUIByPTXdxraNhLEs9I03ZNmxY/bPN2OGDg5ZrcDBg8Crr0pmff36UkYaKBaLNBwFfHsRUomM1OWmzjLWNmyQZZMm/s/gq1kTqFzZv69JQY2BNSLS5s+XNPNWrfQ4baMFC2SpmqgThTLbrDFf/10XKqTXjVlejlx3nVy9vnBBB6sKFZI+a4UKScml6tni68BadLT9wbzKWNu717w9MdG3+0JEPmW83lCypK5gN5aDMmON6D+qVYPtxadChew/Nzt1yv752reXfmJqUMCgQdI3DACeeSbwkyc//xz48kv/Bfiymwy6caMs/RHoI8oGA2tEpM2Yode3bTPfd/CgvmR9zz1+2yUin7E9QPVnOn92gbW8eYHq1WVdDQooVEgaCLdsKbdXrJClrwNrgH3QUQXWbCdu7d/v+30hIp8xZqzFxzsOrHGOEdF/VMZawYLm7RaLDEY6c0YmVPbrZ56qmZWICJmaA8hzAMCECcCdd3pll3Okdm3g3nv9F+BjYI1CCANrRCTOnZOMNWXJEvP9s2fLsk0b34/YJvK3qCj/vI4KUHXvnv1jbZsDq4w3VQ6qAm7+CKw98oi5l9uhQ8CYMcCQIXJbvSeoElEiCkm2gbXWreWaw549un86262SV1itwNNP64ysUGO16sCabcaaUrSo9Ef76iv3glEqsGaxAJ98Ajz6aM72NVRlFVi7eBHYvl3WGVijIMDAGhFJ2vn//qdT2gHgscd0MA0A/vxTlp07+3ffiPwhu2EC3vLrr8DHH0vPlOzYNge2Dawp/gisxcfLVLMJE+T25MnA2LGyPngwMGWKrDOwRhTSjKWgJUpIr/ImTeS2ut7GjDXyihUrpP/YyJGO248Eu8uX9QUu24y1nOrXT/qbfvMNMHCgd587lGQVWFu0SM5fKlUCypXz734ROcDAGlFudOmSZKgpEybIEXPBgvIhrgwcqA8cVNnZjTf6c0+JfKthQ1ned59/Xi8+XnqmuHIQbsxYq1hRB/8aNwby5dP3+SOwBkjaimrInJkpgb7vvpMgm7q6fuyYPtEgopBj7MGu3mZsy0HZY428YvVqva6mX4cS48XoAgW8+9xNmwLr1gF33eXd5w01ajKoo8Dajz/K8vbbA997jggMrBHlPn//LdMJK1eWBug7dwLPPSf3TZwI3H03sHmz3D57VrLWdu6UyX/58gGNGgVu34m87aefgE8/BV55JdB7Ys8YWBsyRB84Rkebp5f6K7AGADfcICUvjRtLb5Nevcz7cOmSnmJKRCHHGLNXA/mMgbWRI4G5c+U2S0EpR/76S6+rNMhQospACxTQ/1nIu1TGWmqqebvVKsdvgPSwIwoCfBcgyi2sVgkgtGwJJCXJQcyKFTIF9MoV4JZbgPvvl8fWqwe8/LKsf/IJsHKlrN9wg756RBQOSpcGHnwQyJ8/0Htir1IlGWBQpox9KYgqB42N9e++V6ggQfa1a4GqVfX2AgWkZgxgOShRmFCxgubNJeCWlGRuh8WMNfJYRgawfLm+HYqBNWcTQcl7nJWCHj8uxyIREUCLFv7fLyIHGFgjyg2SkyUTbdAgKe1UHntMgmsFC0rQzZhK/cAD8oH155/A11/Lthtu8O9+E+VmkZGSYZqYCBQpYr5PpZBUruz33UL+/I7LLlTWGgNrRCGtXTtZDhggy3z59DBiIwbWyGNvvmmejh2KgTVnE0HJe5wF1vbulWW5crzgT0GDgTWicLdli0zL+e47mXz45pvAhx/Kfbt2yfLNNyUTxahMGaBLF1lXHYs5dYfIv/Ln10MLjG68EZg+HZg61f/75AwDa0RhYeFCmVVSt67epmL5RiwFJY+sWwe88IJ5W7AE1hIT5Xh4yBD78kNbzFjzPVWbfumSebsKrAXi4iKREwysEYW74cPlA6hiRck+e+IJc9p069Yy1c+RQYPMt1WjdyIKLIsF6NPHfOYbaAysEYWFvHnl2pqRo8AaM9YIgJR03nyzvliblZQU+exKTwfuvBO47TbZ7iywtmMHULMm8NVX3tvfrEyaBBw8KEN52rXL+vOMGWu+p8YUnzhh3s7AGgUhBtaIwl1ioixnzdINz2vXBsqXl0yYzz5z3nS1UyegbFlZj4kBrrvO9/tLRKFJTQxlYI0o7DRqJPENYwxBDSqmXMxqBYYNk8qGL76Qbbt2AR9/bJ9lBACjRsn9ZcoAH32k0x6dBdYeegj491/g3nt9s/9GV69KdQcg5YWrV8ugnm3bHD+eGWu+py7YJSWZt+/ZI8sqVfy7P0RZYGCNKJxduSLNPQHJWFOiooANG+RKYFbBsshI3WSlYUO5TUTkSFycLE+fDux+EJHXRUbKYUNSksQ5du/mIESCDLfaulXW9++X5YABUglxww3A9u36sUePSj9fQDLQihXLPrCmAiiAfZ8tb1u6FDhzRoI5mzZJJPnoUWD0aMePZ8aa7yUkyFJdsLNagc6ddQYjM9YoiPAjkSicHT4sy/z57Ws24uL0laCsjBoFjBwJvP229/ePiMKHGrBw7lwg94KIfCR/fknOqV6diSL0nw8+0Ov79kmATE2S374daNIE+PxzCYh8950sW7QAbrpJHqMCa44+N86eNQ84MAbpfOHoUVk2aADUqAHMmye3Fy2yL0UEmLHmDyqwpjLWVq6UJpAKA2sURBhYIwpnhw7Jslw5x1P8XFGoEPDOO0DTpt7bLyIKP6ouLDk5sPtBRES+l5QEzJ6tb+/fDyxeDGRmAlWrArfcIpPoBwwA7rsPmDZNHnfPPfprHGWspaUBjz4qF4SvXNHb16/31Xci1GdX4cKyrFZNAoMZGbpE1IgZa75nWwqqhq8BQPHiklVIFCQYWCMKZ8bAGhGRLzFjjYgo95gyRYJgKriRlKSzvLp1k8yisWOljvirr4C//5b1O+/Uz6E+N4yBtRdfBN57z/71Hn9cAnS//CKv623nz8vS2DywZ09ZLltm/3hmrPmesRT0+HHpFw3IwIw9exjUpKDCwBpROFOloAysEZGv2QbWVq2Skyv1PkREROHh6lUZPgAATz8t1Q0AsGCBLFu0kCZ8Tz1l7lF2yy1AyZL6tm3G2tq1wBtvyPr770um2OLFMkjr/Hlg6lTg1ltlWM6gQZK1tHy5d74n24w1ALj+ellu3mz/eGas+Z4KrB0/Lv350tJkENuNN3J6CgUdBtaIwsGlS/Khc+AAsHOnbvLJjDUi8hfbUtAxY4D584Hp0wO3T0RE5F1LlsjUzMOHpV/vnXcClSrJfer93zgwa+BAvd6rl/m5jIG1K1dk+mdmJnD33cDQofL4m2+W49tly2RbXJz0Xvv0Uxk20KaNd74vRxlr9evLcs8enaGmqMAaM9Z8Jz5elmlpwOuvy/qQIYHbH6IsMLBGFKz275eDheefd3z/hQvAhAnAww/LgUlCghzIVK8uV/Iefxz45x95LANrRORrxoy1CxckYw0wN5+2deQIsG2br/eMiIi8Ze5cvT58OJAvnzmQBphvV64sWW1dugC9e5sfpwJrSUkSgPv3XzmenTTJ/LiICKB1a8lie/ll+31SE0lzwlHGWokS0ufLarX/rFKBNmas+U7evJKVCEi/PhXIJQpCDKwRBaO9eyWotny5pMQ7agY+bBjw2GOSiq9GkEdH6wOCd97R6fGNGvlnv4ko91KBtcxM4KefdA+cM2ecf03ZskDdujq7lgLjyhVpLN61K/DNN4HeGyIKZomJshw9Gnj2WVk3NpGPibGfRP/665LBXKCAeft118nF4AsXdHbzJ5/oYIojFSrYbzMOUfCUo4w1AKhXT5a25aCnT8uSGWu+VaaMXh8wQAK5REGIgTWiYGG1yhSkzp0lEHbwoGxPS5NmnYMG6d4V69ZJI1jlkUfkZPbKFQnCzZwpV9kAaRyrekQQEflK/vxAVJSsGyeoqZMPW1arXt+0yWe7RVnIyJDsj/Llgf79JSDavz/w+++B3jMiClYqsNajh2SSAUDDhvr+ChVcn0SfLx/w449yYaZAAeCFFyTAnxXb7DgA+PPP7F/LagWee073cLPlKGMNkIs/gK4CAYBdu4ClS2X9hhuyf23y3EsvSTVO+fKSVEAUpKICvQNE9J9//zVPQapRQxp0fvkl8OCDsu3TTyWANmKE3O7fH3j3Xfsrez17Ah07SnCudm1/7D0R5XYWi5wcnTolU9sUZxlrqj8NoE/OyL9Wr5YTWUCyBytVkhPUceOkrxERkdH588DRo7JuzFIzBtbKlnXvORs1krYAkZFSeZEdRxlrK1bI8XFWnyUbNgCvvirrAwboC9CKylizDaypQJ4xs/q11yRQd9ttQK1a2e8zea57d/lHFOQCeiS7fPlydO3aFaVLl4bFYsE8NaIZQFpaGp588knUrVsXMTExKF26NPr374+j6s38P6mpqRg+fDji4uIQExODbt264TAnkFEoWrlSrzdpIlfCHn7Y/nGffy69iwoUkGw0Z+nyhQoxqEZE/qXKQVV5OuA8sKamwAG6bJT8a98+WTZvLutjx8rtf/8N3D4RUfBS7w2lSplLJqtW1eupqe4/b4ECrgXVAHPpZUSEZEufPp39+9aMGXr977/t71cZa7aloKpPsQqsLV8u00kBXQpLRLleQANrFy9eRP369fH+++/b3Xfp0iVs3LgRzz33HDZu3Ig5c+Zg586d6Natm+lxI0aMwNy5czFjxgysWLECKSkp6NKlCzIyMvz1bRB5x4oVsnzmGeCvv2QSTpMm5iuCgE6Dfvpp6UtBRBQsjCckKujvrBT03Dm9fuGCz3aJsqBOFKtVkzLeatXk9sGD0iiaiMhIlYHaHpsaM8XUQAJ/iIqS6g4g63LQzExzi4KNG+0f4yxjTWXgHTokQcNBg+T2oEH6tYko1wtoYK1z58549dVX0aNHD7v7YmNjsWjRIvTq1QvVq1dHs2bNMGnSJGzYsAEH/+s9lZycjClTpuCdd95B+/bt0bBhQ0yfPh1bt27F4sWL/f3tEOWMCqy1aqV7U1gswAcfmHtVXLkiV89GjfL/PhIRZUVlrAFSkg5Ixpqxn5pizFhTJzTkXyrDX2VkxMXp3+Hu3QHZJSIKYmvXylI19Df65hugQQPgzTf9tz8JCcCNN8p6VoG11av1+x1gn7GWmakv8DjLWDt+XHpS7tghF7+d9WojolwppJqaJCcnw2KxoMh/B30bNmxAWloaOnTocO0xpUuXRp06dbBq1aoA7SWRG7Zulf5pMTHAnj1y5a15c/Nj2rUDtm+XkebKuHGS+k5EFEyMgbV77pFlWhqQkmL/WGPGGgNrgaEy1lRGhsWis9bq1ZPBOUREigpetWplf9/dd0vASr2H+NLMmRLw+uYb+8Da889Lz7eTJ/XjVRmo6s9mm7GWkqIvANlmrMXF6UmUr78uy4kT/ZuZR0RBL2QCa1euXMFTTz2Fe+65B4X/e8NLSkpC3rx5UdTmjS0+Ph5JSUlOnys1NRXnz583/aNc4OJFx6nfgfD338Ctt8qJy5QpwKVLsv2JJ+yvlAGScv+//8l669ZA797+21ciIlep96+4ODnxUj1zHPVZY8ZaYM2bJ1NAAZ2RAQDXXafXOYGNiJSzZ4Ft22TdUWDNn3r2lJL1li2lHDMyUm4fPAi88opMmu7VS4YU1K0r1R+ADB2IipKM3NWr9fOp/mp58uggmmKxmAcydO4sz01EZBASgbW0tDTcddddyMzMxIcffpjt461WKyxZjHkeO3YsYmNjr/0rZzygpPA1YIBMHgqGK/B33y1T8yIigPbtgTZtJHD2/PPOv6ZNG2D9euDnn10fY05E5E/Fisnyllvk/U3ddtRnLbdnrP35J7BuXWBee8cO4I479G3jSaPx4k5kpP/2iYiC28qVktVVrZqUQgaLggX1VFLjMf7SpTLwa9s22e8WLSQgdu+9cv+LL+rHGvurOTrGLlBAr7/yCo/DichO0AfW0tLS0KtXL+zbtw+LFi26lq0GAAkJCbh69SrOGq96Azhx4gTis3jDf/rpp5GcnHzt3yHj+GQKX6pp6Wuv+fZ1Ll2SFHXjVDxAsuUmT5bMuV27ZNuGDcCiRfLhP2tW9hORGjUyT0MiIgom990nAZunnpLbaoABM9bMNmyQ7ON27XTGsj/t2WO+bbzA+NBDej093T/7Q0TB759/ZNm4cWD3wxFVDjp5snl7pUrADz8AR49KYDBPHhkSBgC//QYcOSLrziaCKqdO6fXrr/fefhNR2AjqwJoKqu3atQuLFy9GcXWA/p9GjRohT548WLRo0bVtx44dw7Zt29CiRQunzxsdHY3ChQub/lGYM/b3yZvXt6/12GNyRcx4JWzBArlSNmQIMHiwNEmNiQHq1/ftvhAR+VPt2sCcObqxNTPWHHviCVlevCgne/527Jj5trE3Xr16wIkTsn7ihP1FIiLKnVQQKhgrfVRgzXboygcfAN26AaVK6W2VKulpnj/+KEsVWHN2TvjuuxKUmzmT2WpE5FBAA2spKSnYtGkTNm3aBADYt28fNm3ahIMHDyI9PR09e/bE+vXr8fXXXyMjIwNJSUlISkrC1f8O8mJjYzFgwACMGjUKv//+O/7++2/07dsXdevWRfv27QP4nVHQ2bpVrxuvOnnblSvAJ5/I+tixEkCbPVsyOFJTZfv06bKsXJkfzkQU3tQFsXHjZFqcMUM8t2asnTsHLFmib//+u//34ehRvX7rrfafRXFx+iKUbRCOiHInFVgrUyaw++GIbc+3/v2l3L5zZ8ePV6Xw8+bJUgXkypd3/PjevYHLl/W0ayIiGwENrK1fvx4NGzZEw//q4keOHImGDRvi+eefx+HDh/Hjjz/i8OHDaNCgAUqVKnXtn3Hi5/jx49G9e3f06tULLVu2RIECBTB//nxEsi8IGf0XvAUA7NsnH47edu6cfTPTe++VD+O0NFk2barvq1rV+/tARBRMVMbaxo3A5s3AmDH6vnDJWEtPB/budf3xxkl1gDnI5m0//iglp9266Ys7gD5BHj4cmDvX/ussFn3yfPiw7/aPiEJHMAfWSpQAatTQt7t0yXrAQvfuslyyRD6LtmyR23XrOv8anlsSURYCGlhr27YtrFar3b8vv/wSFStWdHif1WpF27Ztrz1Hvnz5MGnSJJw+fRqXLl3C/PnzOYyA7BkDa1arNG62lZmpR217Ytw4YP5887bp04GMDOk79PXXQI8e+r4qVTx/LSKiUGDTwsGUpWYMrKkyHF9LSzO/rjeMGyfv51OmuPZ4VRYbFSXLDRu8v0+AfJ7de69kbcyfD3z/vb5PZazVqeO8PYI6eVYn00SUuwVzYA0A3nkH6NgR6NtXAmtZqVYNqFlTLoz8/LOubMkqsEZElIWg7rFG5DUHD5pv9+kjfc9UIC0zU5pIV6zo+QneTz/Jsl8/yUho00ZuDxkiJ1yRkUCHDvrxDAATUbhTGWuKseQwEKWgN98s+5SU5L3n3LxZlq+9JhdSsqMCa/XqycldZiawfLn39kdJTjYH7F58EXjzTZlInZgo20qXdv71alIoA2tEudO5c1Iq/tZb8j6lysKDNbB2663AwoXAtGlA/vzZP16Vg86ZI5NDAd0flIjITQysUe6gTqJGjwaKFpXJRl26yIfw1avAr7/Kic3Bg/ZZZ644fFiudkVEAOPHS3+a33+Xng0ffCDbAfMHdoUKOf++iIiCmW3GmpKZaQ5u+Suw9uefckFF9dVJTweefhpo2BAYNcqz51QXY/btk+lz2VF9PuPigJtuknVflIMaSzhjYqRc9amn5HNP9RPKKrDGUlCi3G3qVAnEP/GEDOZKT5fj2YSEQO+Zd6hy0NmzZZBMdDRw3XUB3SUiCl0MrFHuoE7g7r4b2LNHDhKio+XK1oIFwHvv6ceqCUHuWLhQljfcoE8kIyPtyz0jIiTg9tpr0vOGiCicOctYW7zY3GssNdXcA8wXLl3S6+npshw0CHjjDWkX8O67wIUL7j+vMSg4fnz2j1cZa8WLSwYd4JsBBirTrG5dmTz6+uvAXXdJkE3JKvOkenVZ2k4tzcxkFhtRbmAsH1fHyfHxuow91DVqZH4PrFcvfL43IvI7BtYo/GVk6BO4hATJWHvzTQmyAZKp9ttv+vG//OL+CZ4KrHXqlP1jb7pJGnhzIigRhTvbjLWUFFl+9pksH35Y3+dKUCslBRgxAli92v19MQbyUlKAv/8GvvjC3JB63z73n9cYWFuxAli7NuvHGwNrqmfstm3A8ePuv7ZRejpw4oS+rTLNypQB6teXzLxvv5Wfn1KihPPn69JFPqf++suctTZ4sJSJqvYHRBR+Dh8GVq2S94ChQ/X2YC0D9UREhGTwKoMGBW5fiCjkMbBG4e/0aQmuWSzmkwjVoHTCBLkCX726lMWkpLhXlpOWBixaJOvOxnoTEeVGthlrZ85IgEuVYg4eLBkQgPNg2erVurfPmDHAxIlAixbu74sxsHb0KPDhh7LesyfQpIms79nj/vOqwFrt2rLMLmvNGFiLi5OgFwAsXer+axsNGCABL3WhR2WVqV5pytNPA7fcAjz6qG5T4EipUvrnrH5fAPDpp7J8+eWc7S8RBa/t22VZuzYwaZIulW/QIGC75BPqIntkpAw9ICLyEANrFP5UGWhcnDnFu04d8+NuvVWXZ7rSJ0dZvVpOrOLigMaNc7avREThxDZj7cwZ6duTlibBrHr1ZOALIP0obe3YIcGdZs2kB466iOEJYzbX9u0yqRmQbAxVtr93r/vPqwJrzz8vy5kzgUOHnD9eBdbi4mTprT5rv/8uP9dBg2SfnE3wi4mRLO0JE7J/TjXJes4c+/sKF87R7hJREDtzRpYlSsiF6bfekgnGEycGdr+8rV07YNYsGUKTL1+g94aIQhgDaxS+rFY5Gfjf/+S2bbNV25HaHTsCt98u6z/+KFls2dm/X5fDdOyY9dV/IqLcxjZj7fRpXQY6cKAsH35YTtx+/dW+Ub7Kmjh4EBg71hwcc5fxa5csAS5fls+BVq2AypVlu7sZa1arDqy1bg00by4Z0lkFAI0Za4AOrP3x//buOzyKag0D+LsJqaTRTIEAIUCoSlOIlIA0kSooCCIiWEHKRQUVFRBBQMQCIuqlCbarEOwKKEUphk5AuqFJCVISIJCEZO4fnyczu9kkuym7m+X9PU+emZ2dnT2bgcnuu985Z419z2105YoepJ04IZMUqN+lZcWaPdSseevWyaQLaiZtAAgOLvxxici1qWCtXDlZmkxAkyaAv7/z2lRS+vTRK46JiAqJKQC5p9WrZSKBPn302c8sgzXj7agoGUS6XTsgMFC6HW3dmv9zrFsnj3vjDbnNbqBEROYsKwDS0qQKrWxZGUgfkFCreXNZ/+EH8/1VF1BArrXqw15hGLuCKsOHywdGFazZW7F29aoeNgUF6TPK5RcAqllBVbCmulseOqTPMGqvQ4dk6e0ty/ff13+XRRkTKSpKZkzNzpYvnC5e1O8LDCz8cYnItan/65ZfjhARkVUM1sj9fP65jB+zdSvg5aVvt+ySZDIBb74JdOoks56VKSMzhaqAzDimjDWffmp+u1OnIjediOim8MAD5sFM166y/P578/2MwVpGhvl9mZn2Padl2BUUBDz4oKwXtmJNBWGenoCfH3DLLXLbWoinWFaslS8PVK0q6zt32vf8yoEDsrz9dr0SUKlZs3DHVIzdQY0VhZbng4jch/oSg8EaEZFNGKxR6aZpwG+/yWDMqsJswwZZ3n23dNVUVOWa0Zgx0v0oPFzfprqDWhtnLS1NgrsjR8w/8JUtm//sakREpLMMf1Swtno1cP26VGCdOiU/gIzDZvyiBAAuXbLvOS2DtYcfBgICZD0qSpbHjpl3dyyI6gYaFGQ+QU5ewZqm5R5jDZCqMEBmKs1PXhVtKlirXVsq+yIj5fZLL+lVdIWlgrVVq4B9+/TttsziSkSlk6pYU11BiYgoXwzWqPRJS5OKtObNpcqgTRtg2jQJxK5c0SsO+vSRWT7VB7hnn7Xt+PfcI9Vrf/6ph3FZWcCiRfIBpX9/+dm9W+4LDrY+sDMREeXWoIF01Tdq1Eiu12lpMpB048ZyjVdfjrRtC/znP+aPMXZLtIUxWPP1lW6gipqZNDPTvu6YxmANKDhYO3YMSE+XvzHqOQHbgrU33gBCQvQx6owOHpRlTIz8Tdq2TarfJk+29ZXkrW5dOW5GhswOqDBYI3JfrFgjIrILgzUqfdaulaqGhAT50BUYKF1qTp2SDxQ//ij7qa49778vs/307Wvb8UNCgLg4Wf/6axngOjYWeOQRvXpiyxb5gAQASUnsBkpElJfp06UyTA12/9hjUt1lZDLpVWuvvSZjl508qQ/oHx4OTJwITJ2qP8ae8db++gv46SdZX7AASEyUsEjx89O7ptozQYK9wdr69bJs1sx8/Ln8grXUVGDUKGDsWLn95Ze599myRZZqAO5KlYDbbrPtNRTEZJIvkwC9IhyQL7KIyD2xYo2IyC4M1si1zZkjM8EZu+aoQZoBGefszBlg6VK5bZzJUwVrnp7Arbfm/iCXH9UddMUKYPt2+dDi5wfMnGnerSYykm86iIjyM3asdNtctUpCsyeftL6fCtZUt0ajiAi5Br/wgh5C2Rqs/fWXfDkCyN+Bli2tjzumxkcrTLCmQsOCxlhTwVqbNubb69TR22rZFXXJEuDdd/XblhUkSUnyd9HTM/dxi8u4cfrvUGHFGpH7YsUaEZFdGKyR6zp6FBgxAnjxReC99/TtqsvL88/Lt+j+/jKemvrGXqlSpfDP3a2bLDdu1J+vRQvgmWeAVq30/Sw/aBARUW6enjKw/vjx+syVltq3z/s+4ziY6oOeLcHa+fPSvT85GahXD/j9dxmHzJqiBGuWFWvJydbHassrWFNjol29mruLq3E8TyB3pdiqVbKMjdXbUdx8fWWW0ZEj9W0M1ojcF4M1IiK7MFgj12WcPODZZ6U759mzMnYMkHtA5mbNzG+XKVP4565WTT4IZmdLl1NAH9z6rrv0/aZNK/xzEBGRLiBAxlJTVDVwmTLmA/2rD3oFjbF2/bpUHx84ILNurloF3Hln3vsXZ7B2/bqEZEbHjkllmYeHVM0ZGWcUVcMMKOp1qq6rloGW6i7bsaPt7S6MkBDgnXf0qnEGa0Tui11BiYjswmCNXNeKFbIsV04Ge27UCAgLA/74Q7bnVXVQHDw89A9ImzfLUgVrfftKd6R16/RtRERUdKo7KCDdRgHptulheLtiS8VadjYwaJCMCRYcLNVWERH5P3dhgjU10YEK1sqW1cdOs+wO+vPPsmzRQkIqS1WryvL4cfPt6gOuqmqzrFg7e1aWJfk30UiNRXflivnwC0TkHq5dky8HAFasERHZiMEauaZduyS4AoCVK61/ILKsWAPkg4vJBMyaVfQ2qBnbdu6UpQrRvL1lAO2SGsuGiOhm1a2bhGiBgTLJwTffAF98Yb6PLcHarFkyyL+XFxAfrw/qn5/iqFgzmfKewEAFa3ffbf1Y1arJMq9gTQVvlpViaWmy9Pe3vd1FoYI1IHdVHhGVfuqa4+lp/v+diIjyxGCNXNO4cTI+zQMPSBfPTz4xr1gA9A9BRp06yQed0aOL3oawMPPbrE4jIipZNWpIddmPP0oo1r27TD5jpLom5dcVdOVKWb72GtCunW3Prf6mqAowW6iKZuOXP+o4xhk+MzNlNmsg72BNBWfHjsmQB5cuyW1XC9b8/PS/x+wOSuR+1JcW5crZN/EXEdFNjMEauZ6UFPMPRYCMu7NlC5CYKDPKffRR3n/sAwKK542AqlhTqlcv+jGJiCh/nTvnHoPMSF2bjWORXbsmlW2qgkp9MLSlUk2xp2Lt9GkZ02zDBhkD7oEH9Pvat5fl00/r45Ft3ixf+lSoADRpYv2YqmJt6VL5Qkkds6CuoI4O1kwmvYqFwRqR++HEBUREdmOwRq5n61apVqteHYiO1rc3aQI0aAC8/z7w6KMl3w5jsObjk7uCjYiIHE8FU9u3A1lZsv7ssxJEzZsntwvzwVBd820J1l57Ta9A693bfNbSKVOApk2BGzdkZmlA7wbaqZN0r7JGVaSpirmffwZOnMhdsXblivmMo44O1gD5AgtgsEbkjjhxARGR3RiskWt5912gQwdZb97cuW0xBmu33Za7KyoRETle3boyScCVK8C+fTLI9iefyH1JSbIsSrB26pR5cGVJ06S7KiBdV2fONL+/TBn5mwFIMAYAP/0ky7y6gQK5u7wCUoVnWbGmaXqYBjgnWDNOYGCvixfl9/HGG8Du3cXbLiIqOlasERHZrYyzG0CUIy0NGDVKv33HHc5rC2BeodaihfPaQUREOk9P6Sq5bh2QkAD8+ac+O2dKilSxqdv2fDCsWVMmp0lNlYCuRg3r++3fDxw9KpXMiYnWAy3jDJ/JyTJmGiAVa3mJjpbQcN8+fdv8+VL5BgCVK0s3TE2TSrGyZWW7M4M1eyrW0tOl++xvv+nbVqyQ7rRE5DpYsUZEZDeW4JDrWLvW/LYrVazFxjqvHUREZE79fUhIAD7+WN+emqoP+g/Y98HQ2xto2FDWVRBmjapWa9s27zBLVZcdP66PGdqoUcFDCtx+u77u6SkhHiATOZQtm7sLZmam/ADOCdYsZzDNz7Zteqimfg+nTxdvu4io6FixRkRkNwZr5Dp+/FFfHznS+WFWhQr6OivWiIhch6po/vlnvZslIJVq6kNhUJB0y7RH06ayNAZrZ8/qY7kB+t+qLl3yPo6qWDtxQh9fLb9uoMqkSVKZ9swz+rAIgD47n2UXTGOXUFXB5ggqAHzmGT1oBIDs7Ly70R4+LMu77tLDxsJ0JSWiksVgjYjIbgzWyHWoDysrVgDvvOP8Mc3q1JEBqZs00WdrIyIi51PB2tGjEnp5ecltY7BWmA+FlsHa1q1SXfXQQ3L78mVg/XpZv+eevI+jgrWjR/VgrXPngp+/enXg5EkZt61/f327qryzrFhTwZqHh1TcOcqkSUDPntK9s1cvID5ett97LxARoXclUy5e1H+nNWsWbYw2IipZ7ApKRGQ3BmvkGg4dAo4ckQ9Hd93l7NYIPz/5hv2PP6RSgIiIXEOVKubdKvv1k2VqavEFa5oGzJghtz/7TJa//ipdL6OjgVq18j6O6gqalgacOyeB2J132teWXr1kHDdA/4CbV8Wav79j/075+ABffgn07Su/j/vvly/HvvkGOHPGvAL96lUZO+7dd+V2zZp6QHjtmj6GHBG5BlasERHZjcEauQbVladVK/2Dgyvw97e/KxEREZUsk0mvWvPyAh5/XNaLWrHWoIEc7+JFqTZT45cpqttjftVqgHwxU7Gifrt9e/sryoKD9eexDNYsK9YcOb6a4uUls7Hef79UDRp/JydP6uv79kl3WsUYrAESvBGRbTRNQu1Tp0ruOVixRkRkNwZr5BpsGbOGiIhIadNGlj17AlFRsp6SApw/L+uFCdZ8fMwnMDAGa9nZ+t+qgoI1QK9+A2zrBmrNyJESYLVtK7dVIGWtYs0ZypQBPvxQhk0w2rtXXzeGbIBU+/n46F9asTsokTlNy7uSc/lyqRQdOLDknp8Va0REdmOwRs537RqwZo2sM1gjIiJbDB8OvP8+MG+eVHcB8mFUVXIU9kOhsTtoRoa+/bffZDICX18gLq7g43z0kVSqRUQAvXsXri1t20p12tixctuVKtaUkBC9fYoxWDt2zPy+6GipOLQcL46IJMyvX19mPjZOTqKo98tr15bcrLoM1oiI7MZgjZxv3Trg+nWZCa1+fWe3hoiISgNfX+DJJ2UG57Jl9THGkpJkWRzB2rlz+vZFi2R5113S1bMgkZHA6tVSsRUaWri2APo4a4AeRiUny9IVgjUAGDLE/Pa+fVLhB5gHa3fdpc9eygkMyBaalvdMs+7o+HH5/7N9OzBxYu77N22Spabpk4YUp4wMdgUlIioEBmvkfGp8tS5dOEkAERHZz8MDCAqSdRWsFfZDoTFY+/tvffuXX8rS3srq4vy7VqGCLF9/XcYk/e9/5bazg7WgIJnYYdYsCQLT0oCXX5Zw7fhx2WfWLAkaFVasUUE0DejWDYiN1btGahrw/PPAkiXObVtJ+ecfff3NNyVgU9LSgF279NtffWXbMXfulP9/BY1neO6cXv0LMFgjIrIDR2Un5+P4akREVFTBwTLGmvogWrVq4Y7TsKGMa6a6QynqQ6kt46uVlKefltmqV6wANmzQtzs7WAOAdu3k59o1YPx4YOpU6RJ6+LDcX6OGecjIijUqSEaGPmHI7t1AkyYS4E6fLtseesh5bSspxirZ7Gxg6FAgIUGuSdu2yUQhgYESSK9bJ9Wrt9yS/zFHjpSu7OPHAw88ANSuLWNUtmxpvt8HH0gPEsVYLUtERPlixRo5119/AQcPyiDG7ds7uzVERFRaqUqLrCxZ1q1buOP4+MjsoNbUri0BkbNUriyVc8ePmwd8rhCsKS++CCxeLLOgfv21Pt5atWrm+1lOxEBkyVhhpYJuy8kw3I2qWGvaVLqz79wJvPWWbDt4UJYtWwLNmknwtmJFwcfculWW169Ll/YXXwRatzbvpp2ZCcydq99u0aKIL4SI6ObCYI2cS1Wr3Xmnefk5ERGRPVRXUEC6htaqVfhjGWf0NHJmtZpReLj5TKOuFKwBwKBBUk0TFqZvswzWLCdiILJkDNbUuILGiio1jp87URVr9epJ900AmDBBKj9PnJDbkZHAfffJui3dQT3+/bg3eTLw6qsyoYqm6YGbOs7p0/J/9vBh4Oefi+f1EBHdJBiskXOtWiVLdgMlIqKiMH45ExUlkxsUVl7Bmiv9rYqK0tddLVgDpOJl61bg7rslaLMcr4kVa1QQY7CmKtXS0/VtxpDNXahgrWJF+X/ToYO8zlGjzIO1Pn1k/ddfgfPn8z7etWv67/Hpp2Xsw44d5bZx9t533pHlU0/JzL3GLyqIiKhADNbcVWYmsGCBPraJqzpyRJZNmji3HUREVLoZg7XCdgNVOnaUMY1uu02fmc/fX8YlchXVq+vrrhisAdJ19ccfpWuoJQZrVBBjsKYmEjEGa2pWXHeiuoJWqiRjEr73ntz+6ScZZw6Q8SNr1gQaNZKu719/nfs4q1cDL70kQ64Acj1T10jV1V0Fa3/8IT/e3sATT5TIyyIicncM1tzVqFEy4Gn37vp4M67ozBlZhoc7tx1ERFS6hYbq60UN1qKjgVOnpOJKdbm8776iVcEVN2Ow5sp/5/PCrqBUEGsVaykp+jZXD9bOngXef1+uJbYyVqwBMq5jgwbS7VV13YyMlGXv3rJUEzwAMivyvffKlwNTpgBvv60fT00eUr++LFWw9u67snzgAfPrKBER2YzBmjtauFD+kAPA/v22T8ftaJmZ+jdzxnFYiIiI7PXiizJLYOXK8sGyqCpWlIl1WrSQQcPnzSv6MYuTCqYAffyp0oQVa1QQaxVrFy/q265dc2x77PXKK8CwYXJN2rbNtscYK9YUy7EdVbCmKmj/+ANITQUefVS+VDBOaLB+fe7jGYO1O+4APv1Ubo8aZVsbiYgoFwZr7ig2FoiJkZJuAHjzTfuPkZ4u37RZmjxZn+a8qNQHAU9PoEKF4jkmERHdnCpVAj7+WCpbYmOL99i1agF+fsV7zOJk7e+1q1PB4Lx5EgwQWSooWHPVirWrV4H4eCAhQd82e7Ztj7WsWAOAbt3M96lSRZZNm8rEBCdPAo88AsyfL+/f77oLePZZ2UfNJGoM1iIj9S+0t2yRZZMmHJaFiKgIGKy5ozp1pFz82DH5tn3LFmDfPvuO0aOHjOFw9Ki+7exZ+fbt+efNS/ELS3UDDQ3VZywiIiIi29x+uywfeMC57SgMVbEGFE+FYUkpjd1s3YUxWDt9GsjIKB3B2tix0k1z505927ffSk+NvNy4AYwZo489bAzCWrUy31eF/AEB+nhpy5fL8rXXZHy1Hj3MH2M8nskkY7YZu8w/9FCBL4uIiPLGNMNdBQTIt1FqBrMFC8zvv3BBptrOS0KCvIH59Vd9mwrCAAntjMe6dMn+NnJ8NSIiosJbuVJm1370UWe3xH7GL9ROn87/PYmzzJgBhITY3o2PipcxWMvKkkkwSkOwNneu+W0fH3mvrLplWrp2Tf4Pv/WWvs1YsWYyAa+/Luu1apk/tnlzfd3LC/jPf2T/mBjz/YzBGiATs6xfL5Wjfn6lM5wnInIhDNbc3WOPyXLuXH3w1HnzpOvll19af0xamh6UGbtnGAdfVZVsx45JSXqFCsCECfa1TQVrHF+NiIjIfiEhQIcOMqRCaRMdbX5bjS3lSsaNkzHgpkxxdktuTsZgDZBqLGO3Z1cN1oyhWKVKwMCBsq6qyow2bpTZPY0z57ZoAZQvb77f2LEyhnJ8vPn2Bx/U15s312cIrlTJfKZky2BNtTMhQd7r8704EVGRMFhzd926yVgzaWl68PXUU7J8+mnzfdW3xadP69uM40MYt6tgbeVK+aYtO1vGtrEHgzUiIqKbU9u25mHCgQNOa4pVxgo6Y1BCjqOCtcGDpXfD8ePmQ5S46uQFxpk1PT312TtXrJD3y8rs2dLN8+BBeX3ffSf3b9igz+CpeHjI70FNPKDExQGffy6zh774or7dZJJtirVgDZDhYxo2tPcVEhGRBQZr7s5kAt54Q9YXLJBvxpRq1fT1iRPljePSpeYBWmKi/o2gtWDNsqLNnq4c6ngM1oiIiG4uJhMwaBDQsaPcdrVg7eRJfb1yZee142amgrWKFc1DI8VVK9aMw6OkpADt20uXy1OnzL+wnjxZ3jcPHCgzdHbtKv8v7B13uF8/+f+jhn9RjOOm1axp98sgIiLbMVi7GbRsKQMDZ2fLuqJmDb18GZg0ScZ/eOghGWBVycoCtm+XdWOwlpQkS2OwlpEBnD9ve7sYrBEREd3c1FhQCQnApk0ySdJ99wG7dzu3XcZJn27ccF47bmZXrsiybFkZg8wy4HTFYE3T9FnvAelO7OMjoRmgdwe9dk2fAfTdd4Fy5Yq/LSNGAH/9JZMZtG9f/McnIqIcDNZuFtOm5d6mumJ+9pn59hUrzG+rb9eMwdrevTJu29695vuq6dALcv06sHatrNepY9tjiIiIyL2oYO3DD4E775QqnmXLgJkznduuP//U169fd147bmaqYq1sWcDXFxg/3vx+VwzWUlL02T+/+kpvs+oOGh8v4ZuqiCxbVsZKLClRURKqWXYtJSKiYsVg7WZRu7Z8cwXIN2eABGuaJm9mjQ4elKWXlyxVVZoxWDt0CBg+XB5fq5YMvArYFqylpgIDBkiFXNWqQLt2hXpJREREVMrdey/QrJmsh4TIwO1A7i/uHM3YZc9Vx/Jyd8ZgDQCGDAEefli/3xWDNVWtFhQE9OkDlCkjt7t0kfffhw8De/YAJ07I9ipVGHoREbkBBms3k1mzZGBU1Y0zLQ1Yt06mkff2Bp5/3nx/VTaekCCP27RJvy8gQMZFmTgR+P57ICJCthtnDj1xQt4Ebdmib0tJAVq31mc1GjKkdM5mRkREREVXubK8T0hOlq5xaiKkffvMB3p3pF9/Na/mZ8Wac1gGaz4+wKJFwDPPyG1XDDxVsHbLLebbAwKAu+6S9V9/1SvWIiMd1zYiIioxDNZuJmXKyBgP4eHyBx6QLheAfKvWpIn5/t27y7doR4/KunL0qARkK1fKTKO1aunjXqiKNU0DHnlEpga/4w6ZFOHqVaBnTxk3xd8fGD0aGDOmBF8wERERlQqVKsn7lBo1JEC5dk3/ItBWu3ebT9JUGCkp8v7FyBUDnJuBZbCm+PvL0lUq1jRNvnw+d04P1qzNwqnGOd68Wa9YY7BGROQWyji7AeQkYWFSjv7rr3L78cf1NypK/foy/plxAN/ISKlOs5yxSAVrEycCu3ZJFdzx4/r9xpmJAgOlUq5x42J7OUREROQGPD3lvceuXdIdNDratsdlZ8vQEhcuAA0byvuO556z//n/8x95/1KjBvDkk8DYsaxYc5a8gjU/P1m6SrD2+ecyxIm3twy9AuSuWAP0bs6ff65vq1Kl5NtHREQlzqkVa+vXr0f37t0REREBk8mEFRaD5muahokTJyIiIgJ+fn5o27Yt9lqMuZGeno4RI0agYsWKKFu2LHr06IGTxinSyTrjTJy1agFxcTLemeLhAdx+u1SbKa1by2C+auw1I9UVFJBunsePyzGGD5cuo0YrVjBUIyIiIuvq15elPeOsJSdLqAYAiYkSiF2+bN/z/vijVNqbTMDixXrVkStUrB05AsybB6xa5eyWOE5pqVj79ltZZmTI+GmA9WDN+J5aYcUaEZFbcGqwdvXqVdx2222YM2eO1ftnzJiBWbNmYc6cOdiyZQvCwsLQsWNHXDa8URo9ejTi4+Px+eef4/fff8eVK1fQrVs3ZGVlOepllE7GNylPPSVvIo1vAqKj5Y1L8+b6tpYt9S6klowze86YITN+XrgAzJkjXUaXL5cwLT5eH2OCiIiIyJIK1owzcxbE2uRJqrtdQY976CGptP/vf2XbiBFAq1YyEyXgGhVr990n79c6ddJ7G7i7goI1Vwg8NU16YQDA228DgwbJkCs9euTeNzAw97ArqscHERGVak7tCtqlSxd06dLF6n2apuHtt9/G+PHj0fvfKaoXL16M0NBQfPrpp3jiiSeQkpKC+fPnY8mSJejQoQMAYOnSpYiMjMTq1avRuXNnh72WUqdNG+Dnn+WN2tNPyzZj984775Sl8ds1NfOnNa1aSXh2223SfcLSvffKDxEREVF+ClOxpoK1Zs2A9HSpWjt+HKhXL//H9esHbNggYZUKcgYOlKXqcugKAc6xY/r6qFEysZRqn7sqDRVrf/0lE3d5ecmwKgWdkwULgN9+k67OCQny/pmIiEo9l528ICkpCWfOnEGnTp1ytvn4+CAuLg4b/x2Ydtu2bcjMzDTbJyIiAg0aNMjZh/Iwbpx8E/y//5l37XzzTekCOn263G7YUL8vv+6bJpMEZ9ZCNSIiIiJbqTBsxw4ZTuLQIfP7d+2SinjjrKEqWKtcWR/awjjWqzUpKRKqARKOpKQA5cvrVUWuUrGmaUBqqn57zx6gVy/nt6skpaXpr7lcOfP7XClY27RJlrffblvQedtt8oX2Rx/Jv+OgoJJtHxEROYTLBmtnzpwBAISGhpptDw0NzbnvzJkz8Pb2RjmLP7jGfaxJT09Hamqq2c9Nx9MTqFtXAjGjMWPkGzT1e/f2lm6dy5frA7ISERERlZQaNfRQa/VqGa/VqFEj6a65dKm+zVqwVlBX0C+/zL2tfXt5jwS4TsVaWhqghjj54QcJllaulC80S2u49ttvwIMPAv/8Y/3+XbskOA0NzT1emStNXnD6tCxtnWSDiIjckssGa4rJIvjRNC3XNksF7fP6668jODg45yeSA4fmLy6O3TiJiIjIMTw9gTKG0UrUgPCWNm/W143BmnpfV1DF2g8/5N42erS+7ioVa+oLYA8P4O679XDtp5/MZ10vLa5dA/r3Bz79FFi0SLYdOSKTW2ma3N62TZZNm+b+EtiVKtZUMFihgnPbQURETuWywVrYv7NWWlaeJScn51SxhYWFISMjAxcvXsxzH2teeOEFpKSk5PycsGVwWyIiIiJyjPBwfT05WR9vKyVF324MXGzpCpqdLTNr7tkDZGYCv/wi2xculEq1777Tx5gFXKdiTQVrQUHymuPigO+/l21ffZV31ZermjNHP1/798vy4YflS9z4eLltDNYsqWBN/ZtwpvPnZclgjYjopuaywVpUVBTCwsKwyjCteEZGBtatW4c7/33T07RpU3h5eZntc/r0aezZsydnH2t8fHwQFBRk9kNERERELmLhQn1m8qwsfSw04yD+KtQAgJMnZZlfV9CFC2VmzSZNgD/+kMCqQgWp+lq9Guja1Xz/kqhY27ZNxuMydmMtiDFYU9q2BSIiZP3o0eJqXcm7eBGYOlW/feCAnN+tW+X2hx/KMr9gTQ0BY/HFulOof4MVKzq3HURE5FRODdauXLmCnTt3YufOnQBkwoKdO3fi+PHjMJlMGD16NKZOnYr4+Hjs2bMHgwcPhr+/PwYMGAAACA4OxtChQ/HMM8/gl19+wY4dOzBw4EA0bNgwZ5ZQIiIiIiplWraUrp4PPii3771XJlY6eFDfR4VsWVl6iFa5MlCtmqwnJQFLluj7q2qozEyZGR0AOnXSx1SzVNwVaxcuyPNt3SphXlKS+f07dsh4cmrcLsVasAYA1avLsjQFa9OnA5cu6eHYwYPSDTQ9XW6vXCmTVezbJ7etTZylqsOuXtUf5yysWCMiIjg5WNu6dSsaN26Mxv/+0RwzZgwaN26MV155BQAwduxYjB49GsOGDUOzZs3w999/Y+XKlQgMDMw5xltvvYVevXqhb9++aNmyJfz9/fHtt9/CM683SURERERUOkybJiFbWhrw/PPA/ffr96lgbeNG4MoVICQEqFlTKtYeeki6fg4aBEyaJGN3JSfrj/3qK1l27pz3c6uKtcxMffKAoli2TMI1ZcIE8/tfegmYOxd45BF9rDGg4GDNMqBzVSdPAu+8I+tz58oyORn4/Xd9H00DXngBuHFDXq+1cZCDgmS8OcD89+kMHGONiIjg5GCtbdu20DQt18+ifwcyNZlMmDhxIk6fPo3r169j3bp1aNCggdkxfH19MXv2bJw/fx5paWn49ttvORkBERERkTuoUkVmkFy8OPfskKdPS8WSqkTr3h3w8pL1RYuAceNkfeJEYORI6XaoqLG9OnXK+7lVxRpQPN1BVWVZo0ayjI/Xq+Gys4FNm2T9559lYH9FBWuGL5YBAFFR5sd1dR98IL/H1q2Bfv30rqzLlsmyUiXz2/Xq5Z64AJBQrXx5WTd2B3YGdgUlIiK48BhrREREREQwmaTy7Isvct938iSwfLmsG2cw9/CQajc1ZtecOXpApdx2m/kkCZZUxRpQPMGamkyhb1+pxLpyBfjxR9l26JD5mGGjR+uhjbtUrKnuut26yTmNiZHbanbWkSP1wAyQYC0vrhCsaRq7ghIREQAGa0RERERUGsTFAbfear5t2TLpEurnZ71b52OPAT16WD9eft1AARl7TVXAFcc4aypYq15dKrYAvUukqlZr3hyoX1+6GI4YIdvcZYy1S5dkGRIiy+ho8/sbN5YuvEr9+nkfSwVZzuwKmpKidxFmsEZEdFNjsEZERERErs9kkvG4tm0D+vSRbTNnyvLuuwF/f+uPe/FFqWCrWhUYOFDfXlCwBhTvzKCqYisyUiYp8PICfvkFWLNGD9batAEWLJD2fvaZBId5BWvGrqDZ2UVvX0lTFXkqWFPBoFKrFjB0qH7blmDNsmJt2zaZGEFVKpYk9dxly5pXNxIR0U2HwRoRERERlQ6BgUCTJnrl2rlzsuzdO+/HNG8O7N4NbN8OtGgh28qWlUkRCmKcGbQw4dU338h4btnZ0m0VkICvenXg8cfl9vjxerAWGwvccYdM1AAATz0ls2YCuYO1qlWBMmWkbX//bX/bHE1VrKkZQVUwCEiQWL060LAhMGCAdAO98868j6W6glpWrI0YIc/zxBPF1Oh8sBsoERH9i8EaEREREZUuxi6hZcoAXbvmv3/9+hKAxMVJF88BAwAfn4KfR1UijRoFhIUBe/bY3satW4GePWUst1dfldlFPTz0QfvHj5fgbtMmIDFRtqng75VXJGQ6d04fzN8yWPPy0rtTGidmcFWWXUGNwVpkJODtLeuffALs3Zt7sgajvCrWjOFncXTfzQ+DNSIi+heDNSIiIiIqXYzBWrt2ehVUQRo0kPHL3nvPtv1VxdqaNRJyPfec7W1UYVlGBjBpkqxHREgQCMjECU8/re9frZo+mYKPj8yEqvYFcgdrAFCnjizVLKdGmmZ7Wx0hv66gVavad6y8Ji/IzNTXt22z75j2OntWlpwRlIjopsdgjYiIiIhKl+rVpTsnkH83UGtCQvRJCQpiOXbWTz8Ba9fa9lg1qUDt2npAZhkgjRunV2bFxprf17gxMGSIftueYO3oUaBSJam0cwU3bgCXL8u6CkHDwvT7bakeNLI2eYGm6d1mAb17bUk5fFiWlpMwEBHRTYfBGhERERGVLh4ewMiRMn6ammGzJKiKNaNx42yrBktKkuWQIcCGDTJZwpgx5vtUqABMnizr1l7Hww/r656eue/PK1j79lup5nr33ZIPmGyhJmAAgOBgWZpM+jZrry0/1irWLlyQmTqV9evtO6alb78FVq7M+/6DB2VZu3bRnoeIiEq9MgXvQkRERETkYqZOLfnnsKxY8/cHEhKAFSuAe+/N/7EqWKteXSYk+Okn6/uNGiWD7VubWdJYxWbs/qrExMjSMlgzTmbwxhvA8uX5t7WkqW6gZcuaVwvecw/www/A6NH2HU9VrCUlSVdbb2/g0CHzfdau1e+z15o1QI8e0tbkZL37qhGDNSIqBllZWcg0dmMnh/L29oaHR9HrzRisERERERFZo6qrAOmyOXIkMGUK8OKLQPfu5mOgWVLBmnGQ/rxYC9UAqeo6dUqCslq1ct9fv75U7/39t8w6WqWKbDd2iTx2rODnL2mWM4IqX34pbW3Y0L7jNWsmVWsnTgA1akgF4alTcl/r1hI0njsHbN4MtGlj37GvXQMee0zWMzOB336Tc22Una0HeQzWiKgQNE3DmTNncEldH8kpPDw8EBUVBe/CfAljwGCNiIiIiMga45hoFSvK5AWzZ0tws3s30KSJ9celp+tBjy3BWn7Cw/VJDSwFBUnIlJAgVVYPPSTbjcGa5QD/zmA5I6ji729/qKaOM3++VA0aq/N8faXr7apVwKefSndOe4O1SZPMf389esgsrWoCCkDObVqaBKvGSRiIiGykQrVbbrkF/v7+MBm7x5NDZGdn49SpUzh9+jSqVq1apHPAYI2IiIiIyBrLYC04WCqkdu6ULoJ5OXZMqqjKli35WSPvukuCtV9/lWDNchD/f/4p2ee3heWMoMWhVy+ZefXSJfk9ly0rIZe3t1TGffop8OGHUl1o66yxO3YAM2fK+oABcgwAePVV4Pnn9TH3VDfQqCjbJ8IgIvpXVlZWTqhWQXVtJ6eoVKkSTp06hRs3bsCrCNdzTl5ARERERGSNZbBmXOYXWJ09K8uICPNB+ktCu3ayXL1auiieP28+WcDVq1JB50x5dQUtqgYNgFatZAbV2rX18dS6d5dKuNRU4J13bD/esGFAVhbQt68EbMYPWSpMA/QZXzkjKBEVghpTzd/f38ktIdUFNCsrq0jHYbBGRERERGSNtWCtUiVZ5hesXbkiy6CgkmmXUevWMv7byZMyi+Vbb8n28HB9tk1ndwfNqytoSfHwkO6bAPD22/rz5+f8eRmTTT0mPFy616rx7/78U9/35ElZRkYWU4OJ6GbE7p/OV1zngMEaEREREZE1xuBEhUIqYDt3Lu/HXb4sy4CAEmmWGT8/oE8fWe/SRZ8ttX17GeAfcH6wVhJdQQvSu7dM7pCSArz7bsH779ghy+hofUy7li31ceuMwdqJE7JUk0UQEdFNjcEaEREREZE1YWH6+r9dd2zqCqoq1hwRrAHAoEH6enQ0sGgRsHAhoMbucfY4a2rMN0dWeHl4AC+/LOtvvSUBW35UsNa4sfn2evVkaa1ijcEaEVGJmDhxIho1auTsZtiMwRoRERERkTWqKyUAXLsmS3uCtcDAkmmXpbZtgQULgKVLZcbShx+WGStVsObIirVt23L/blQoVb++49oBAPfdB9StK11BZ8/Of9/t22VpOdOrCtb27tW3sSsoEd3kNm7cCE9PT9x9993ObopLYLBGRERERJQX1X2xUydZumLFmskEPPII8OCDEqgpqq2OCtZ+/BFo1gyoWVNm5MzOBm7cAA4ckPtVSOUonp561dqbb+bffTevirUGDWR56JAerrIrKBHd5BYsWIARI0bg999/x/Hjx53dHKdjsEZERERElJfEROCrr4AHHpDbtgRrjhxjLT+O6gqqadL1dNw4uZ2SAjzxBNCmDfDdd0BGBuDvbz4ZhKP07QvExEjVWt265l06lStX9Fk/LYO18HCZsCI7G9izR86t6lbKYI2IiommySTOzvjRNPvaevXqVfzvf//DU089hW7dumHRokU5961duxYmkwnff/89brvtNvj6+qJ58+ZITEzM2WfRokUICQnBihUrULt2bfj6+qJjx444ob60yMPChQtRt25d+Pr6ok6dOpg7d659DS9BDNaIiIiIiPJSpYpMDuDx79tmV6xYy4ujuoL+/jswZIiEkAAwbBhQtiywYQNw772yrW5d/XfoSJ6e0k02JER+D5Mm5d5n1y75ZBkeDoSGmt9nMgFqnJ+dO4G//5b14GDHdfUlIreXliZ/Mpzxk5ZmX1u/+OILxMTEICYmBgMHDsTChQuhWaRzzz33HGbOnIktW7bglltuQY8ePZCpxioFkJaWhilTpmDx4sXYsGEDUlNT8YD6AsuKjz76COPHj8eUKVOwb98+TJ06FS+//DIWL15sX+NLCIM1IiIiIiJbGbtXZmdb38fRY6zlxVHB2qFD+rq/v4xn9uefQLdu+va6dUu2Dfm5805gzRpZX74cOH3a/H7VDdRyfDVFBWv/+x/w6KOyzmo1IrpJzZ8/HwMHDgQA3H333bhy5Qp++eUXs30mTJiAjh07omHDhli8eDHOnj2L+Pj4nPszMzMxZ84cxMbGomnTpli8eDE2btyIhIQEq885efJkvPnmm+jduzeioqLQu3dv/Oc//8EHH3xQci/UDmUK3oWIiIiIiADoYVVWlnQJLFcu9z6uUrGmZjXdvl1CwJKqGDMGVYsXy/NUrQp88410o124EHj66ZJ5bls1agTExgKbNgErVgBPPaXfl9f4asbHAsDq1bL09gaGDy+hhhLRzcjfX//T4YznttWBAweQkJCA5cuXAwDKlCmDfv36YcGCBejQoUPOfrGxsTnr5cuXR0xMDPbt25ezrUyZMmjWrFnO7Tp16iAkJAT79u3DHXfcYfac586dw4kTJzB06FA89thjOdtv3LiB4OBg2xtfghisERERERHZysdHKtEuX5bB8K0Fa64yxlq3bkBQkIwN9uWXQL9+JfM8p07J8qWXZCZOxWQC7r9fflxBXJwEa7t26duuXgXWr5f1vIK1Vq0APz+ZiOGxx4AXXmDFGhEVK5NJetC7uvnz5+PGjRuoXLlyzjZN0+Dl5YWLFy/m+1iTyZTv7by2Zf9bHf7RRx+hefPmZvd5GmfvdiJ2BSUiIiIissctt8jy7Fnr97tKxVqFCsAzz8j6yy9LMFQSVLAWEVEyxy8ut94qy927ZZmQIN1EDx+WsLR1a+uPq1pVuraeOAG89x5DNSK6Kd24cQMff/wx3nzzTezcuTPnZ9euXahWrRo++eSTnH03b96cs37x4kUcPHgQderUMTvW1q1bc24fOHAAly5dMttHCQ0NReXKlfHXX3+hZs2aZj9RUVEl9Grtw4o1IiIiIiJ7hIYCR44UHKw5e4w1APjPf2TMs0OHpJvm0KEyAP/8+dKdUXVtLQo1oH9pCdYSE4E5c4AxY4DMTKB8eZm9tFKlvB9bvbpDmkhE5Kq+++47XLx4EUOHDs3VBfO+++7D/Pnz8dZbbwEAXn31VVSoUAGhoaEYP348KlasiF69euXs7+XlhREjRuDdd9+Fl5cXnn76abRo0SJXN1Bl4sSJGDlyJIKCgtClSxekp6dj69atuHjxIsaMGVNir9lWrFgjIiIiIrKHmjnS1SvWAAn3XnxR1idNAq5fBwYMACZMAJ59Fpg7V7pDFuTyZRlTzhpVsWboGuSSYmJkfLQrV4ARIyRUu+8+YP9+GX+NiIjyNH/+fHTo0MHquGZ9+vTBzp07sX37dgDAtGnTMGrUKDRt2hSnT5/GN998A29v75z9/f39MW7cOAwYMACxsbHw8/PD559/nudzP/roo/jvf/+LRYsWoWHDhoiLi8OiRYtcpmLNpFnOi3oTSk1NRXBwMFJSUhAUFOTs5hARERGRK3vqKWDePOle+eqr+nZNMx/LbPduoGFD57TR6Pp1oFYt4ORJ4J57gB9+ML+/f3/g00/zfvznn8tr9vaWSj1jYJiVJePOZWVJ5ZqrV601aSKTFXh6AjNmSEWflTF9iIhKyvXr15GUlISoqCj4+vo6uznFau3atWjXrh0uXryIkJAQq/ssWrQIo0ePxqVLlxzaNmvyOxf25ESsWCMiIiIiskdeFWvvvGM+QYArVKwBgK8v8Morsm4ZqgHAZ58BX3yh387Kkgq1S5eAgQMleLt0CUhOBlauNH/suXOyv4eHPvacKxszRsZV+/VXWWeoRkRERcRgjYiIiIjIHtaCtaQkfaIAxRXGWFMGDwZq1pT18uVz3//UU3qXzn79ZDbRcuWATz6R0Kx+fbnvm2/MH/fnn7IMDQXKlILhmwcOBDZsANq0cXZLiIjITTBYIyIiIiKyh7VgbepUIDvbfD9XqVgDAC8vGbC/cmXpxmrUtClw8aJMbKBpwLJl+n01agC//y6PBWSQf/U69+8HHn1U1uPiSv41EBGRS2vbti00TcuzGygADB482CW6gRanUvC1EhERERGRC7EM1o4eBRYt0u9T2318HN2y/HXuLOOsGVWoACxZAjRuDPz0k4w7powdC7z0klTeZWbK6zl/XqrzVq+W8cmuXQOiooD33nPsayEiInIRrFgjIiIiIrKHZbD2+uvAjRtAhw7AI4/o+7ny+F1LlwIhIcCKFUDdusC0abL9+edlecstwPTpendWLy+gTh1Zb9wYePJJCdU6dJCKNmvdS4mIiG4CDNaIiIiIiOyhgrW0NBljbOFCuT1hAnDffbLu7e2cttnqwQel+2erVnJ75Ejgjjv0+6Oicj9GjbN2+bKMpzZzJvDzz64/EygREVEJYldQIiIiIiJ7BAQAfn5SsTV6tHSTbN9eD6nWr5exzEoTDw+gWzcgIUFuWwvWGjTQ17t3zz1ZAxER0U2IwRoRERERkT1MJqlaO3oUWLVKtr3yin5/69ZOaVaRtWypr9eokft+VbEGyHhtRERExK6gRERERER2U91BARl/rLSGaUbNm+vrZax8/85gjYiIXMjRo0dhMpmwc+dOp7aDwRoRERERkb2MwVpEhGtPVGCrsmX1SrVu3XLfX6OGzBQ6YQJQvbpDm0ZERK7jzJkzGDVqFGrWrAlfX1+EhoaiVatWmDdvHtLS0pzdPIdjV1AiIiIiInsZg7XwcOe1o7ht3gwcPw40bZr7PpNJZgolIqKb1l9//YWWLVsiJCQEU6dORcOGDXHjxg0cPHgQCxYsQEREBHr06OHsZjoUK9aIiIiIiOxlWbHmLipVsh6qERERARg2bBjKlCmDrVu3om/fvqhbty4aNmyIPn364Pvvv0f37t0BAMePH0fPnj0REBCAoKAg9O3bF2fPnjU71vvvv4/o6Gh4e3sjJiYGS5YsMbt///79aNWqFXx9fVGvXj2sXr0aJpMJK1asyLN9f/75J+655x4EBAQgNDQUDz30EP75559i/z0YMVgjIiIiIrKXuwZrRETkeJoGXL3qnB9Ns7mZ58+fx8qVKzF8+HCULVvW6j4mkwmapqFXr164cOEC1q1bh1WrVuHIkSPo169fzn7x8fEYNWoUnnnmGezZswdPPPEEHnnkEaxZswYAkJ2djV69esHf3x9//PEHPvzwQ4wfPz7f9p0+fRpxcXFo1KgRtm7dip9++glnz55F3759bX6NhcGuoERERERE9mKwRkRExSUtDQgIcM5zX7kiY2za4PDhw9A0DTExMWbbK1asiOvXrwMAhg8fjg4dOmD37t1ISkpCZGQkAGDJkiWoX78+tmzZgttvvx0zZ87E4MGDMWzYMADAmDFjsHnzZsycORPt2rXDypUrceTIEaxduxZhYWEAgClTpqBjx455tu/9999HkyZNMHXq1JxtCxYsQGRkJA4ePIjatWvb/nuxAyvWiIiIiIjs5a5jrBERERXAZDFhT0JCAnbu3In69esjPT0d+/btQ2RkZE6oBgD16tVDSEgI9u3bBwDYt28fWrZsaXacli1b5tx/4MABREZG5oRqAHDHHXfk265t27ZhzZo1CAgIyPmpU6cOAODIkSOFf8EFYMUaEREREZG9WLFGRETFxd9fKsec9dw2qlmzJkwmE/bv32+2vca/M0r7+fkBADRNyxW+WdtuuY/x/ryOkZ/s7Gx0794d061MtBNegl+CMVgjIiIiIrKXMVgrV8557SAiotLPZLK5O6YzVahQAR07dsScOXMwYsSIPMdZq1evHo4fP44TJ07kVK39+eefSElJQd26dQEAdevWxe+//45BgwblPG7jxo0599epUwfHjx/H2bNnEfrv39wtW7bk274mTZpg2bJlqF69OsqUcVzcxa6gRERERET2Cg6WD0IAYDHWDBERkbuaO3cubty4gWbNmuGLL77Avn37cODAASxduhT79++Hp6cnOnTogFtvvRUPPvggtm/fjoSEBAwaNAhxcXFo1qwZAOC5557DokWLMG/ePBw6dAizZs3C8uXL8eyzzwIAOnbsiOjoaDz88MPYvXs3NmzYkDN5QV6VbMOHD8eFCxfQv39/JCQk4K+//sLKlSsxZMgQZGVlldjvhMEaEREREZG9TCYgORk4eRIICnJ2a4iIiBwiOjoaO3bsQIcOHfDCCy/gtttuQ7NmzTB79mw8++yzmDx5MkwmE1asWIFy5cqhTZs26NChA2rUqIEvvvgi5zi9evXCO++8gzfeeAP169fHBx98gIULF6Jt27YAAE9PT6xYsQJXrlzB7bffjkcffRQvvfQSAMDX19dq2yIiIrBhwwZkZWWhc+fOaNCgAUaNGoXg4GB4eJRc/GXSNDvmVnVTqampCA4ORkpKCoL4xoiIiIiIiIiISsD169eRlJSEqKioPAMism7Dhg1o1aoVDh8+jOjo6CIfL79zYU9OxDHWiIiIiIiIiIjIpcTHxyMgIAC1atXC4cOHMWrUKLRs2bJYQrXixGCNiIiIiIiIiIhcyuXLlzF27FicOHECFStWRIcOHfDmm286u1m5MFgjIiIiIiIiIiKXMmjQILNZQ12VS09ecOPGDbz00kuIioqCn58fatSogVdffRXZ2dk5+2iahokTJyIiIgJ+fn5o27Yt9u7d68RWExERERERERHRzcClg7Xp06dj3rx5mDNnDvbt24cZM2bgjTfewOzZs3P2mTFjBmbNmoU5c+Zgy5YtCAsLQ8eOHXH58mUntpyIiIiIiIiIiNydSwdrmzZtQs+ePdG1a1dUr14d9913Hzp16oStW7cCkGq1t99+G+PHj0fv3r3RoEEDLF68GGlpafj000+d3HoiIiIiIiIiotw0TXN2E256xXUOXDpYa9WqFX755RccPHgQALBr1y78/vvvuOeeewAASUlJOHPmDDp16pTzGB8fH8TFxWHjxo1OaTMRERERERERkTVeXl4AgLS0NCe3hDIyMgAAnp6eRTqOS09eMG7cOKSkpKBOnTrw9PREVlYWpkyZgv79+wMAzpw5AwAIDQ01e1xoaCiOHTuW53HT09ORnp6eczs1NbUEWk9EREREREREpPP09ERISAiSk5MBAP7+/jCZTE5u1c0nOzsb586dg7+/P8qUKVo05tLB2hdffIGlS5fi008/Rf369bFz506MHj0aERERePjhh3P2s/xHqGlavv8wX3/9dUyaNKnE2k1EREREREREZE1YWBgA5IRr5BweHh6oWrVqkYNNk+bCHXsjIyPx/PPPY/jw4TnbXnvtNSxduhT79+/HX3/9hejoaGzfvh2NGzfO2adnz54ICQnB4sWLrR7XWsVaZGQkUlJSEBQUVHIviIiIiIiIiIgIQFZWFjIzM53djJuWt7c3PDysj5CWmpqK4OBgm3Iil65YS0tLy/UiPT09kZ2dDQCIiopCWFgYVq1alROsZWRkYN26dZg+fXqex/Xx8YGPj0/JNZyIiIiIiIiIKB+enp5FHt+LnM+lg7Xu3btjypQpqFq1KurXr48dO3Zg1qxZGDJkCADpAjp69GhMnToVtWrVQq1atTB16lT4+/tjwIABTm49ERERERERERG5M5cO1mbPno2XX34Zw4YNQ3JyMiIiIvDEE0/glVdeydln7NixuHbtGoYNG4aLFy+iefPmWLlyJQIDA53YciIiIiIiIiIicncuPcaao9jTd5aIiIiIiIiIiNyX24yx5igqW0xNTXVyS4iIiIiIiIiIyJlUPmRLLRqDNQCXL18GILOQEhERERERERERXb58GcHBwfnuw66gALKzs3Hq1CkEBgbCZDLluj81NRWRkZE4ceIEu4q6AZ5P98Vz6554Xt0Lz6f74rl1Tzyv7oXn033x3Lonnlfn0TQNly9fRkREBDw8PPLdlxVrADw8PFClSpUC9wsKCuI/ZjfC8+m+eG7dE8+re+H5dF88t+6J59W98Hy6L55b98Tz6hwFVaop+cduREREREREREREZBWDNSIiIiIiIiIiokJgsGYDHx8fTJgwAT4+Ps5uChUDnk/3xXPrnnhe3QvPp/viuXVPPK/uhefTffHcuiee19KBkxcQEREREREREREVAivWiIiIiIiIiIiICoHBGhERERERERERUSEwWCMiIiIiIiIiIioEBmtERERERERERESFUGqDtddffx233347AgMDccstt6BXr144cOCA2T6apmHixImIiIiAn58f2rZti7179+bcf+HCBYwYMQIxMTHw9/dH1apVMXLkSKSkpJgdZ8qUKbjzzjvh7++PkJAQm9uYmJiIuLg4+Pn5oXLlynj11VdhnCvi9OnTGDBgAGJiYuDh4YHRo0cX6nfhDtzhfK5duxYmkynXz/79+wv3S3ET7nBuAeC9995D3bp14efnh5iYGHz88cf2/zLciKPO69GjRzF06FBERUXBz88P0dHRmDBhAjIyMgpsI6/BtnOH88lrsHXucG4BXoMtOfJva48ePVC1alX4+voiPDwcDz30EE6dOlVgG3kNtp07nE9eg61zh3ML8BpsyZHnVUlPT0ejRo1gMpmwc+fOAtvIa7CDaaVU586dtYULF2p79uzRdu7cqXXt2lWrWrWqduXKlZx9pk2bpgUGBmrLli3TEhMTtX79+mnh4eFaamqqpmmalpiYqPXu3Vv75ptvtMOHD2u//PKLVqtWLa1Pnz5mz/XKK69os2bN0saMGaMFBwfb1L6UlBQtNDRUe+CBB7TExERt2bJlWmBgoDZz5sycfZKSkrSRI0dqixcv1ho1aqSNGjWqyL+X0sodzueaNWs0ANqBAwe006dP5/zcuHGj6L+gUswdzu3cuXO1wMBA7fPPP9eOHDmiffbZZ1pAQID2zTffFP0XVEo56rz++OOP2uDBg7Wff/5ZO3LkiPb1119rt9xyi/bMM8/k2z5eg+3jDueT12Dr3OHc8hqcmyP/ts6aNUvbtGmTdvToUW3Dhg1abGysFhsbm2/7eA22jzucT16DrXOHc8trcG6OPK/KyJEjtS5dumgAtB07duTbPl6DHa/UBmuWkpOTNQDaunXrNE3TtOzsbC0sLEybNm1azj7Xr1/XgoODtXnz5uV5nP/973+at7e3lpmZmeu+hQsX2vxhfe7cuVpwcLB2/fr1nG2vv/66FhERoWVnZ+faPy4ujv+YDUrj+VRvKC5evGjTMW9WpfHcxsbGas8++6zZ40aNGqW1bNnSpue4GTjivCozZszQoqKi8m0Pr8FFUxrPJ6/BtimN55bX4II58rx+/fXXmslk0jIyMvLch9fgoimN55PXYNuUxnPLa3DBSvq8/vDDD1qdOnW0vXv32hSs8RrseKW2K6glVTJZvnx5AEBSUhLOnDmDTp065ezj4+ODuLg4bNy4Md/jBAUFoUyZMkVqz6ZNmxAXFwcfH5+cbZ07d8apU6dw9OjRIh37ZlCaz2fjxo0RHh6O9u3bY82aNUV6XndUGs9teno6fH19zR7n5+eHhIQEZGZmFun53YUjz2tKSkrO8+SF1+CiKc3nk9fg/JXGc8trcMEcdV4vXLiATz75BHfeeSe8vLzyPA6vwUVTms8nr8H5K43nltfggpXkeT179iwee+wxLFmyBP7+/ja1h9dgx3OLYE3TNIwZMwatWrVCgwYNAABnzpwBAISGhprtGxoamnOfpfPnz2Py5Ml44oknitymM2fOWH1uY9vIutJ6PsPDw/Hhhx9i2bJlWL58OWJiYtC+fXusX7++yM/vLkrrue3cuTP++9//Ytu2bdA0DVu3bsWCBQuQmZmJf/75p8htKO0ceV6PHDmC2bNn48knn8y3TbwGF15pPZ+8BhestJ5bXoPz54jzOm7cOJQtWxYVKlTA8ePH8fXXX+fbJl6DC6+0nk9egwtWWs8tr8H5K8nzqmkaBg8ejCeffBLNmjWzuU28BjueWwRrTz/9NHbv3o3PPvss130mk8nstqZpubYBQGpqKrp27Yp69ephwoQJdj1//fr1ERAQgICAAHTp0iXf57a2ncyV1vMZExODxx57DE2aNEFsbCzmzp2Lrl27YubMmXY9vzsrref25ZdfRpcuXdCiRQt4eXmhZ8+eGDx4MADA09PTrja4I0ed11OnTuHuu+/G/fffj0cffTRnO6/Bxau0nk9egwtWWs8tr8H5c8R5fe6557Bjxw6sXLkSnp6eGDRoUM554jW4eJXW88lrcMFK67nlNTh/JXleZ8+ejdTUVLzwwgt5Pj+vwa6haH2oXMCIESPwzTffYP369ahSpUrO9rCwMACSyIaHh+dsT05OzpXeXr58GXfffTcCAgIQHx+fb7msNT/88ENOGayfn1/O81umwcnJyQByJ9ekc7fz2aJFCyxdutSu53dXpfnc+vn5YcGCBfjggw9w9uzZnG9lAwMDUbFiRbva4G4cdV5PnTqFdu3aITY2Fh9++KHZfbwGFx93O5+8ButK87nlNThvjjqvFStWRMWKFVG7dm3UrVsXkZGR2Lx5M2JjY3kNLkbudj55DdaV5nPLa3DeSvq8/vrrr9i8ebNZl04AaNasGR588EEsXryY12BXUaIjuJWg7Oxsbfjw4VpERIR28OBBq/eHhYVp06dPz9mWnp6ea8DAlJQUrUWLFlpcXJx29erVfJ/T3gHRQ0JCtPT09Jxt06ZN44CBeXC386n06dNHa9eunU3P4a7c9dy2adNG69+/v03P4Y4ceV5Pnjyp1apVS3vggQdsnl2M12D7uNv5VHgNdt9zy2uw4/+2KsePH9cAaGvWrMlzH16D7eNu51PhNdh9zy2vwY45r8eOHdMSExNzfn7++WcNgPbVV19pJ06cyLN9vAY7XqkN1p566iktODhYW7t2rdmUzmlpaTn7TJs2TQsODtaWL1+uJSYmav379zeb4jY1NVVr3ry51rBhQ+3w4cN5Tg197NgxbceOHdqkSZO0gIAAbceOHdqOHTu0y5cv59m+S5cuaaGhoVr//v21xMREbfny5VpQUJDZFLeapuUcq2nTptqAAQO0HTt2aHv37i3m35brc4fz+dZbb2nx8fHawYMHtT179mjPP/+8BkBbtmxZCfzGSg93OLcHDhzQlixZoh08eFD7448/tH79+mnly5fXkpKSiv8XVko46rz+/fffWs2aNbW77rpLO3nypNk++eE12D7ucD55DbbOHc4tr8G5Oeq8/vHHH9rs2bO1HTt2aEePHtV+/fVXrVWrVlp0dLTZbHOWeA22jzucT16DrXOHc8trcG6O/HxjlJSUZNOsoLwGO16pDdYAWP1ZuHBhzj7Z2dnahAkTtLCwMM3Hx0dr06aNlpiYmHO/mhba2o/xQvHwww9b3Se/9F/TNG337t1a69atNR8fHy0sLEybOHFiroTY2nGrVatWDL+h0sUdzuf06dO16OhozdfXVytXrpzWqlUr7fvvvy+uX1Gp5Q7n9s8//9QaNWqk+fn5aUFBQVrPnj21/fv3F9evqFRy1HlduHBhnvsUhNdg27nD+eQ12Dp3OLe8BufmqPO6e/durV27dlr58uU1Hx8frXr16tqTTz6pnTx5ssA28hpsO3c4n7wGW+cO55bX4Nwc+fnGyNZgTdN4DXY0k6b9O4odERERERERERER2cwtZgUlIiIiIiIiIiJyNAZrREREREREREREhcBgjYiIiIiIiIiIqBAYrBERERERERERERUCgzUiIiIiIiIiIqJCYLBGRERERERERERUCAzWiIiIiIiIiIiICoHBGhEREdFNoG3bthg9erSzm0FERETkVhisERERERERERERFQKDNSIiIiIiIiIiokJgsEZERETkZq5evYpBgwYhICAA4eHhePPNN83uX7p0KZo1a4bAwECEhYVhwIABSE5OBgBomoaaNWti5syZZo/Zs2cPPDw8cOTIEQDAxIkTUbVqVfj4+CAiIgIjR450zIsjIiIiciEM1oiIiIjczHPPPYc1a9YgPj4eK1euxNq1a7Ft27ac+zMyMjB58mTs2rULK1asQFJSEgYPHgwAMJlMGDJkCBYuXGh2zAULFqB169aIjo7GV199hbfeegsffPABDh06hBUrVqBhw4aOfIlERERELsGkaZrm7EYQERERUfG4cuUKKlSogI8//hj9+vUDAFy4cAFVqlTB448/jrfffjvXY7Zs2YI77rgDly9fRkBAAE6fPo3IyEhs3LgRd9xxBzIzM1G5cmW88cYbePjhhzFr1ix88MEH2LNnD7y8vBz8ComIiIhcByvWiIiIiNzIkSNHkJGRgdjY2Jxt5cuXR0xMTM7tHTt2oGfPnqhWrRoCAwPRtm1bAMDx48cBAOHh4ejatSsWLFgAAPjuu+9w/fp13H///QCA+++/H9euXUONGjXw2GOPIT4+Hjdu3HDQKyQiIiJyHQzWiIiIiNxIQZ0Rrl69ik6dOiEgIABLly7Fli1bEB8fD0C6iCqPPvooPv/8c1y7dg0LFy5Ev3794O/vDwCIjIzEgQMH8N5778HPzw/Dhg1DmzZtkJmZWXIvjIiIiMgFMVgjIiIiciM1a9aEl5cXNm/enLPt4sWLOHjwIABg//79+OeffzBt2jS0bt0aderUyZm4wOiee+5B2bJl8f777+PHH3/EkCFDzO738/NDjx498O6772Lt2rXYtGkTEhMTS/bFEREREbmYMs5uABEREREVn4CAAAwdOhTPPfccKlSogNDQUIwfPx4eHvJ9atWqVeHt7Y3Zs2fjySefxJ49ezB58uRcx/H09MTgwYPxwgsvoGbNmmZdSxctWoSsrCw0b94c/v7+WLJkCfz8/FCtWjWHvU4iIiIiV8CKNSIiIiI388Ybb6BNmzbo0aMHOnTogFatWqFp06YAgEqVKmHRokX48ssvUa9ePUybNg0zZ860epyhQ4ciIyMjV7VaSEgIPvroI7Rs2RK33norfvnlF3z77beoUKFCib82IiIiIlfCWUGJiIiIyKoNGzagbdu2OHnyJEJDQ53dHCIiIiKXw2CNiIiIiMykp6fjxIkTePzxxxEeHo5PPvnE2U0iIiIicknsCkpEREREZj777DPExMQgJSUFM2bMcHZziIiIiFwWK9aIiIiIiIiIiIgKgRVrREREREREREREhcBgjYiIiIiIiIiIqBAYrBERERERERERERUCgzUiIiIiIiIiIqJCYLBGRERERERERERUCAzWiIiIiIiIiIiICoHBGhERERERERERUSEwWCMiIiIiIiIiIioEBmtERERERERERESF8H/U2v7vQ8nuTgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 5))\n",
    "plt.plot(close_apple['Date'], close_apple['Apple_close'], color='blue', label='Apple')\n",
    "plt.plot(close_google['Date'], close_google['Google_close'], color='red', label='Google')\n",
    "plt.xlabel('days')\n",
    "plt.ylabel('price')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d848949e-4a30-43e1-9fb0-b0e68bc94b93",
   "metadata": {},
   "source": [
    "# 03. Normalizing the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6309914-0ee1-4479-babb-857d8c7a7d77",
   "metadata": {},
   "source": [
    "When working with multiple time series data, such as stock prices for different stocks, it's common practice to normalize each time series separately rather than together. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "86c69ea8-5fbf-40b7-a167-e65cad5d0fd6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "13a0cb1c-5b6e-45ef-a6b5-5ee9f024038b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(502,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_apple['Apple_close'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e5359297-68ff-4a76-b592-7cb42bdcb3e6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def reshaper_2d(col):\n",
    "    col = col.values.reshape((-1, 1))\n",
    "    return col"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d36316a4-72b9-492e-9249-ceb4e1fd8c32",
   "metadata": {},
   "source": [
    "for the `MinMaxScaler()` we need the a 2 dimensional array, therefore the shape needs first to be readjusted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "41395d3d-e2ee-4c2a-aca0-958b3c92d7fa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "scaler_apple = MinMaxScaler()\n",
    "scaler_google = MinMaxScaler()\n",
    "\n",
    "apple_np = reshaper_2d(close_apple['Apple_close'])\n",
    "google_np = reshaper_2d(close_google['Google_close'])\n",
    "\n",
    "close_apple_rescaled = close_apple.copy()\n",
    "close_google_rescaled = close_google.copy()\n",
    "\n",
    "close_apple_rescaled['Apple_close'] = scaler_apple.fit_transform(apple_np)\n",
    "close_google_rescaled['Google_close'] = scaler_google.fit_transform(google_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3da8e7bd-cc0d-4697-a79b-62579446cd91",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 5))\n",
    "plt.plot(close_apple_rescaled['Date'], close_apple_rescaled['Apple_close'], color='blue', label='Apple')\n",
    "plt.plot(close_google_rescaled['Date'], close_google_rescaled['Google_close'], color='red', label='Google')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bce27be1-e229-4f56-9b40-15bf4bb07df4",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 04. Preparation of the data for the engine"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f5465f4-6b8e-4787-bdea-c6c453587e81",
   "metadata": {
    "tags": []
   },
   "source": [
    "In order to being able to train a model an stock prizes, we need to bring the data in an acceptable format:  \n",
    "* 7 `Timesteps` → list of lists, where each sublist entails 7 censequtive stock prices (1 `Feature`)  \n",
    "* for each sublist we need a `Label` (Future), in this case the prediction in #'LOOKUP_STEPS' days  \n",
    "\n",
    "<img src=\"./data_preparation.png\" width=\"500\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "647965bc-9034-4626-8ec5-fc1623016cca",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from collections import deque                             # double ended queue, elements can be added and removed efficiently form both ends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c714d7bb-0350-4808-8f9a-9e4cf0843b3d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def PrepareData(initial_df, days):\n",
    "    df = initial_df.copy()\n",
    "    df['Future'] = df.iloc[:, 1].shift(-days)             # creates a new column & shifts the 'close'-column #'DAYS' rows up → at the bootom of the 'future'-column will be #'DAYS' NaNs\n",
    "    last_sequence = np.array(df.iloc[:, 1].tail(days))       # returns the last #days from the 'close'-column\n",
    "    df.dropna(inplace=True)                               # removes the last #days rows of the df\n",
    "  \n",
    "    sequence_data = list()\n",
    "    sequences = deque(maxlen=N_STEPS)                     # deque-object is created with the maximum length of 7\n",
    "                                                                # after it reaches its `maxlen` it removes it's 1. item and adds the new one at the 7. place\n",
    "\n",
    "    for entry, target in zip(df.iloc[:, [1, 0]].values,  # selects the columns 'X_close' & 'Date'\n",
    "                             df['Future'].values):\n",
    "        sequences.append(entry)\n",
    "        if len(sequences) == N_STEPS:                               # after 'seqnces' reached length of 7\n",
    "            sequence_data.append([np.array(sequences), target])         # 'close' & 'date' (=sequnce) + target (=close #'DAYS' rows up) \n",
    "\n",
    "    # last sequence\n",
    "    last_sequence = list([seq[0] for seq in sequences]) + list(last_sequence)   # selects all 'close' entries from 'sequences'-'entry'\n",
    "    last_sequence = np.array(last_sequence).astype(np.float32)\n",
    "\n",
    "    # construct the X's and Y's\n",
    "    X = list()\n",
    "    y = list()\n",
    "    for seq, target in sequence_data:\n",
    "        X.append(seq)             # 'df[['X_close'] + ['Date']]'\n",
    "        y.append(target)          # 'Future'-values\n",
    "\n",
    "    # convert to numpy arrays\n",
    "    X = np.array(X)\n",
    "    y = np.array(y)\n",
    "\n",
    "    return df, last_sequence, X, y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "108a7a8f-ef86-46f3-a568-22ff44f671b9",
   "metadata": {},
   "source": [
    "* df['Date', 'X_close', 'Future']  \n",
    "* last_sequence: 'Close_values' for the entries without the last #'DAYS' + entries  for the last #'DAYS'  \n",
    "* X: list of lists containing 7 seqences (moving one at a time)\n",
    "* y: prdicted Value #'DAYS' days prior"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "197c4350-c393-44c8-b81e-2647b073bb0f",
   "metadata": {},
   "source": [
    "# 05. CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ca8b2905-fa29-4cad-b399-1cfc09761ff7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def GetTrainedModel(X_train, y_train):\n",
    "    model = tf.keras.Sequential([\n",
    "          tf.keras.layers.LSTM(units=60,\n",
    "                              return_sequences=True,\n",
    "                              input_shape=(N_STEPS, 1)),     # 7 time steps (consequitive days), 1 feature (closing price)\n",
    "          tf.keras.layers.Dropout(0.3),\n",
    "          tf.keras.layers.LSTM(units=120, return_sequences=False),\n",
    "          tf.keras.layers.Dropout(0.3),\n",
    "          tf.keras.layers.Dense(units=20),\n",
    "          tf.keras.layers.Dense(units=1)  \n",
    "      ])\n",
    "\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam', metrics=['acc'])\n",
    "\n",
    "    model.fit(X_train, y_train,\n",
    "              batch_size=8,\n",
    "              epochs=80, \n",
    "              verbose=0)\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e834b355-daf2-45f3-863e-b7baffcd70ee",
   "metadata": {},
   "source": [
    "\n",
    "<img src=\"./stock_3days.png\" width=\"500\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "27cff37c-b49b-44dd-bc9d-bda319a9087e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# GET PREDICTIONS\n",
    "\n",
    "def predict_future(df_name, scaler, LOOKUP_STEPS, predictions):\n",
    "    \n",
    "    for step in LOOKUP_STEPS:\n",
    "        df, last_sequence, X_train, y_train = PrepareData(df_name, step)\n",
    "        X_train = X_train[:, :, 0].astype(np.float32).reshape(-1, 7, 1)\n",
    "\n",
    "        model = GetTrainedModel(X_train, y_train)\n",
    "\n",
    "        last_sequence = last_sequence[-N_STEPS:]     # takes the last seqence with #'N_STEPS' which will be used for a prediction #'LOOKUP_STEPS' from today\n",
    "        last_sequence = last_sequence.reshape((-1, 1))  # shape: (7, 1)\n",
    "        prediction = model.predict(last_sequence)\n",
    "        predicted_price = scaler.inverse_transform(prediction)[0][0]\n",
    "\n",
    "        predictions.append(round(float(predicted_price), 2))\n",
    "        \n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1463751c-81ff-4d0d-be12-5d97d3504e4d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:27:32.089671: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2024-02-15 22:27:32.089797: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n",
      "2024-02-15 22:27:32.463441: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2024-02-15 22:27:33.518997: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:27:33.667926: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:27:33.750958: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:27:33.909604: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:27:34.053010: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, 7, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 7, 1), dtype=tf.float32, name='lstm_input'), name='lstm_input', description=\"created by layer 'lstm_input'\"), but it was called on an input with incompatible shape (None, 1, 1).\n",
      "1/1 [==============================] - 0s 334ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:28:36.615680: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:28:36.663705: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:28:36.698905: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:28:38.335897: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:28:38.481692: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:28:38.543079: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:28:38.667700: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:28:38.782878: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, 7, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 7, 1), dtype=tf.float32, name='lstm_2_input'), name='lstm_2_input', description=\"created by layer 'lstm_2_input'\"), but it was called on an input with incompatible shape (None, 1, 1).\n",
      "1/1 [==============================] - 0s 323ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:29:40.065164: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:29:40.112689: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:29:40.145396: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:29:41.480183: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:29:41.626453: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:29:41.690781: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:29:41.816793: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:29:41.935927: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, 7, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 7, 1), dtype=tf.float32, name='lstm_4_input'), name='lstm_4_input', description=\"created by layer 'lstm_4_input'\"), but it was called on an input with incompatible shape (None, 1, 1).\n",
      "1/1 [==============================] - 0s 325ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:30:43.375955: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:30:43.422889: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:30:43.458161: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:30:44.822243: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:30:44.966092: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:30:45.033574: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:30:45.170555: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:30:45.294271: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, 7, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 7, 1), dtype=tf.float32, name='lstm_6_input'), name='lstm_6_input', description=\"created by layer 'lstm_6_input'\"), but it was called on an input with incompatible shape (None, 1, 1).\n",
      "1/1 [==============================] - 0s 408ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:32:18.381573: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:32:18.429092: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:32:18.465122: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:32:19.669925: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:32:19.814398: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:32:19.881838: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:32:20.020877: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:32:20.147593: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, 7, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 7, 1), dtype=tf.float32, name='lstm_8_input'), name='lstm_8_input', description=\"created by layer 'lstm_8_input'\"), but it was called on an input with incompatible shape (None, 1, 1).\n",
      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x17f899510> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "1/1 [==============================] - 0s 466ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:33:52.999661: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:33:53.047469: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:33:53.082436: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:33:54.297331: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:33:54.441566: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:33:54.512437: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:33:54.683825: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:33:54.836026: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, 7, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 7, 1), dtype=tf.float32, name='lstm_10_input'), name='lstm_10_input', description=\"created by layer 'lstm_10_input'\"), but it was called on an input with incompatible shape (None, 1, 1).\n",
      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x2bb6d3520> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "1/1 [==============================] - 0s 469ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:35:28.738548: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:35:28.786402: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:35:28.822373: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    }
   ],
   "source": [
    "LOOKUP_STEPS = [1, 2, 3]\n",
    "predictions_apple = list()\n",
    "predictions_google = list()\n",
    "\n",
    "predictions_apple = predict_future(close_apple_rescaled, scaler_apple, LOOKUP_STEPS, predictions_apple)\n",
    "predictions_google = predict_future(close_google_rescaled, scaler_google, LOOKUP_STEPS, predictions_google)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f4cbf526-33f7-4b33-82d2-6030227973bc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:35:30.044212: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:35:30.187770: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:35:30.259906: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:35:30.434360: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:35:30.587300: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, 7, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 7, 1), dtype=tf.float32, name='lstm_12_input'), name='lstm_12_input', description=\"created by layer 'lstm_12_input'\"), but it was called on an input with incompatible shape (None, 1, 1).\n",
      "1/1 [==============================] - 0s 483ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:36:33.235427: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:36:33.284242: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:36:33.328497: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:36:34.548922: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:36:34.697260: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:36:34.779647: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:36:34.953180: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:36:35.109421: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, 7, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 7, 1), dtype=tf.float32, name='lstm_14_input'), name='lstm_14_input', description=\"created by layer 'lstm_14_input'\"), but it was called on an input with incompatible shape (None, 1, 1).\n",
      "1/1 [==============================] - 0s 471ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:37:37.452415: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:37:37.500999: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:37:37.544015: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:37:38.749648: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:37:38.899112: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:37:38.981783: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:37:39.154752: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:37:39.304565: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, 7, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 7, 1), dtype=tf.float32, name='lstm_16_input'), name='lstm_16_input', description=\"created by layer 'lstm_16_input'\"), but it was called on an input with incompatible shape (None, 1, 1).\n",
      "1/1 [==============================] - 0s 484ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:38:41.764763: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:38:41.814755: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:38:41.858628: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:38:43.205359: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:38:43.352372: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:38:43.433797: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:38:43.607197: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:38:43.760428: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, 7, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 7, 1), dtype=tf.float32, name='lstm_18_input'), name='lstm_18_input', description=\"created by layer 'lstm_18_input'\"), but it was called on an input with incompatible shape (None, 1, 1).\n",
      "1/1 [==============================] - 0s 341ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:40:18.566577: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:40:18.613968: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:40:18.654464: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:40:19.702058: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:40:19.824233: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:40:19.904284: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:40:20.044892: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:40:20.193958: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, 7, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 7, 1), dtype=tf.float32, name='lstm_20_input'), name='lstm_20_input', description=\"created by layer 'lstm_20_input'\"), but it was called on an input with incompatible shape (None, 1, 1).\n",
      "1/1 [==============================] - 0s 477ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:41:42.751173: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:41:42.799160: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:41:42.841133: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:41:44.059113: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:41:44.203998: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:41:44.280914: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:41:44.469395: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:41:44.617485: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, 7, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 7, 1), dtype=tf.float32, name='lstm_22_input'), name='lstm_22_input', description=\"created by layer 'lstm_22_input'\"), but it was called on an input with incompatible shape (None, 1, 1).\n",
      "1/1 [==============================] - 0s 493ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:43:19.308147: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:43:19.362226: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:43:19.402667: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    }
   ],
   "source": [
    "LOOKUP_STEPS = [4, 5, 6]\n",
    "\n",
    "predictions_apple = predict_future(close_apple_rescaled, scaler_apple, LOOKUP_STEPS, predictions_apple)\n",
    "predictions_google = predict_future(close_google_rescaled, scaler_google, LOOKUP_STEPS, predictions_google)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3aaf7872-4cdb-4a9a-a0fd-db2cf36c6cf2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:45:26.590460: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:45:26.737152: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:45:26.836969: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:45:27.022987: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:45:27.172536: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, 7, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 7, 1), dtype=tf.float32, name='lstm_24_input'), name='lstm_24_input', description=\"created by layer 'lstm_24_input'\"), but it was called on an input with incompatible shape (None, 1, 1).\n",
      "1/1 [==============================] - 1s 518ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:46:32.019104: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:46:32.066885: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:46:32.107762: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:46:33.169466: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:46:33.308936: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:46:33.397684: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:46:33.585341: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:46:33.787290: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, 7, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 7, 1), dtype=tf.float32, name='lstm_26_input'), name='lstm_26_input', description=\"created by layer 'lstm_26_input'\"), but it was called on an input with incompatible shape (None, 1, 1).\n",
      "1/1 [==============================] - 1s 501ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:47:28.773647: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:47:28.823103: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:47:28.878997: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:47:30.099009: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:47:30.244085: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:47:30.351583: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:47:30.586884: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:47:30.789684: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, 7, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 7, 1), dtype=tf.float32, name='lstm_28_input'), name='lstm_28_input', description=\"created by layer 'lstm_28_input'\"), but it was called on an input with incompatible shape (None, 1, 1).\n",
      "1/1 [==============================] - 1s 508ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:48:33.106738: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:48:33.154175: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:48:33.210663: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:48:34.451299: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:48:34.604822: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:48:34.714237: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:48:34.953522: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:48:35.158729: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, 7, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 7, 1), dtype=tf.float32, name='lstm_30_input'), name='lstm_30_input', description=\"created by layer 'lstm_30_input'\"), but it was called on an input with incompatible shape (None, 1, 1).\n",
      "1/1 [==============================] - 1s 523ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:50:09.254561: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:50:09.303388: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:50:09.362445: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:50:10.603200: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:50:10.751368: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:50:10.860593: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:50:11.109079: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:50:11.316924: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, 7, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 7, 1), dtype=tf.float32, name='lstm_32_input'), name='lstm_32_input', description=\"created by layer 'lstm_32_input'\"), but it was called on an input with incompatible shape (None, 1, 1).\n",
      "1/1 [==============================] - 1s 531ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:51:45.667701: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:51:45.716286: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:51:45.775580: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:51:47.017876: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:51:47.164158: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:51:47.277093: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:51:47.522555: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:51:47.729183: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, 7, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 7, 1), dtype=tf.float32, name='lstm_34_input'), name='lstm_34_input', description=\"created by layer 'lstm_34_input'\"), but it was called on an input with incompatible shape (None, 1, 1).\n",
      "1/1 [==============================] - 1s 541ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-15 22:53:22.083299: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:53:22.131466: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-02-15 22:53:22.190732: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    }
   ],
   "source": [
    "LOOKUP_STEPS = [7, 8, 9]\n",
    "\n",
    "predictions_apple = predict_future(close_apple_rescaled, scaler_apple, LOOKUP_STEPS, predictions_apple)\n",
    "predictions_google = predict_future(close_google_rescaled, scaler_google, LOOKUP_STEPS, predictions_google)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "400b8510-4dc3-4de8-b2bd-e6188793cc9d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "today = dt.date.today()\n",
    "\n",
    "# list with days from yesterday to after tomorrow\n",
    "upcoming_days = [(today + dt.timedelta(days=i)) for i in range(-1, 9)]\n",
    "\n",
    "# adding the yesterdays price on the closing market to the prediction lists\n",
    "predictions_apple.insert(0, close_apple['Apple_close'].iloc[-1])\n",
    "predictions_google.insert(0, close_google['Google_close'].iloc[-1])\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.plot(close_apple['Date'], close_apple['Apple_close'], color='blue', label='Apple')\n",
    "plt.plot(upcoming_days, predictions_apple, color='orange', label='Apple pred.', linestyle='dashed')\n",
    "plt.plot(close_google['Date'], close_google['Google_close'], color='red', label='Google')\n",
    "plt.plot(upcoming_days, predictions_google, color='green', label='Google pred.', linestyle='dashed')\n",
    "plt.xlabel('days')\n",
    "plt.ylabel('price')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "011b96bf-d11a-4219-ae2c-e2c477fb9c75",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10 (tensorflow)",
   "language": "python",
   "name": "tensorflow"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}