plt_wer_results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c57d256",
   "metadata": {},
   "source": [
    "### Extract WER from log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "66ce376c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import json\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cb734d0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "LOG_FILE = \"output1000_atco2.txt\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bd22755",
   "metadata": {},
   "source": [
    "#### Estrazione WER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c7ef7ddd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"eval_loss\": 0.9045917391777039, \"eval_wer\": 0.5461465271170314, \"eval_runtime\": 30.1434, \"eval_samples_per_second\": 1.825, \"eval_steps_per_second\": 0.133, \"epoch\": 3.17}\n",
      "{\"eval_loss\": 0.5140445232391357, \"eval_wer\": 0.28068506184586106, \"eval_runtime\": 28.8836, \"eval_samples_per_second\": 1.904, \"eval_steps_per_second\": 0.138, \"epoch\": 6.35}\n",
      "{\"eval_loss\": 0.5870081782341003, \"eval_wer\": 0.1665080875356803, \"eval_runtime\": 17.6157, \"eval_samples_per_second\": 3.122, \"eval_steps_per_second\": 0.227, \"epoch\": 9.52}\n",
      "{\"eval_loss\": 0.5955320000648499, \"eval_wer\": 0.1665080875356803, \"eval_runtime\": 17.4822, \"eval_samples_per_second\": 3.146, \"eval_steps_per_second\": 0.229, \"epoch\": 12.7}\n",
      "{\"eval_loss\": 0.5995476245880127, \"eval_wer\": 0.15413891531874405, \"eval_runtime\": 17.4826, \"eval_samples_per_second\": 3.146, \"eval_steps_per_second\": 0.229, \"epoch\": 15.87}\n",
      "{\"eval_loss\": 0.6287272572517395, \"eval_wer\": 0.1731684110371075, \"eval_runtime\": 17.237, \"eval_samples_per_second\": 3.191, \"eval_steps_per_second\": 0.232, \"epoch\": 19.05}\n",
      "{\"eval_loss\": 0.6432343125343323, \"eval_wer\": 0.16555661274976213, \"eval_runtime\": 17.5455, \"eval_samples_per_second\": 3.135, \"eval_steps_per_second\": 0.228, \"epoch\": 22.22}\n",
      "{\"eval_loss\": 0.6878654956817627, \"eval_wer\": 0.15889628924833493, \"eval_runtime\": 17.4641, \"eval_samples_per_second\": 3.149, \"eval_steps_per_second\": 0.229, \"epoch\": 25.4}\n",
      "{\"eval_loss\": 0.7162216901779175, \"eval_wer\": 0.1598477640342531, \"eval_runtime\": 17.6116, \"eval_samples_per_second\": 3.123, \"eval_steps_per_second\": 0.227, \"epoch\": 28.57}\n",
      "{\"eval_loss\": 0.722922146320343, \"eval_wer\": 0.15794481446241673, \"eval_runtime\": 17.5074, \"eval_samples_per_second\": 3.142, \"eval_steps_per_second\": 0.228, \"epoch\": 31.75}\n",
      "{\"eval_loss\": 0.7282211780548096, \"eval_wer\": 0.16270218839200762, \"eval_runtime\": 17.5143, \"eval_samples_per_second\": 3.14, \"eval_steps_per_second\": 0.228, \"epoch\": 34.92}\n",
      "{\"eval_loss\": 0.7321106195449829, \"eval_wer\": 0.15889628924833493, \"eval_runtime\": 17.5373, \"eval_samples_per_second\": 3.136, \"eval_steps_per_second\": 0.228, \"epoch\": 38.1}\n"
     ]
    }
   ],
   "source": [
    "SINGLE_STEPS = 50\n",
    "\n",
    "epoch_list = []\n",
    "wer_list = []\n",
    "\n",
    "TIMESTAMP_LENGTH = 22\n",
    "\n",
    "with open(LOG_FILE, encoding='utf-8') as file:\n",
    "    for line in file:\n",
    "        \n",
    "        if \"eval_wer\" in line:\n",
    "            # need some preprocessing on the lines\n",
    "            line = line.strip()\n",
    "            line = line.replace(\"'\", '\"')\n",
    "            \n",
    "            # remove the timestamp (first chars)\n",
    "            line = line[TIMESTAMP_LENGTH:]\n",
    "            \n",
    "            try:\n",
    "                print(line)\n",
    "                json_data = json.loads(line)\n",
    "                \n",
    "                epoch_list.append(json_data['epoch'])\n",
    "                wer_list.append(json_data['eval_wer'])\n",
    "            except:\n",
    "                print(f\"This line can't be processed: {line}\")\n",
    "                \n",
    "# steps\n",
    "steps_list = [SINGLE_STEPS * (i + 1) for i in range(len(wer_list))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ab1f1773",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>steps</th>\n",
       "      <th>epochs</th>\n",
       "      <th>wer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.546147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>6.35</td>\n",
       "      <td>0.280685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150</td>\n",
       "      <td>9.52</td>\n",
       "      <td>0.166508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200</td>\n",
       "      <td>12.70</td>\n",
       "      <td>0.166508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>250</td>\n",
       "      <td>15.87</td>\n",
       "      <td>0.154139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>300</td>\n",
       "      <td>19.05</td>\n",
       "      <td>0.173168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>350</td>\n",
       "      <td>22.22</td>\n",
       "      <td>0.165557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>400</td>\n",
       "      <td>25.40</td>\n",
       "      <td>0.158896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>450</td>\n",
       "      <td>28.57</td>\n",
       "      <td>0.159848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>500</td>\n",
       "      <td>31.75</td>\n",
       "      <td>0.157945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>550</td>\n",
       "      <td>34.92</td>\n",
       "      <td>0.162702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>600</td>\n",
       "      <td>38.10</td>\n",
       "      <td>0.158896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    steps  epochs       wer\n",
       "0      50    3.17  0.546147\n",
       "1     100    6.35  0.280685\n",
       "2     150    9.52  0.166508\n",
       "3     200   12.70  0.166508\n",
       "4     250   15.87  0.154139\n",
       "5     300   19.05  0.173168\n",
       "6     350   22.22  0.165557\n",
       "7     400   25.40  0.158896\n",
       "8     450   28.57  0.159848\n",
       "9     500   31.75  0.157945\n",
       "10    550   34.92  0.162702\n",
       "11    600   38.10  0.158896"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_res = {\n",
    "    \"steps\": steps_list,\n",
    "    \"epochs\": epoch_list,\n",
    "    \"wer\": wer_list}\n",
    "\n",
    "df_res = pd.DataFrame(dict_res)\n",
    "\n",
    "df_res.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05cffc00",
   "metadata": {},
   "source": [
    "#### Plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a15a8b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YsWMHduzYgREjRpj3mZKdXbt2mQdH39qzcjc6d+5sHvgcExMDg8GAjz76CF999ZX5h1912rRpAwDmAZ/V8fDwgFKptBjUbXLp0iUAsHh6qD5UN2A0NzcX9vb25rZycHCATqezKnf74M/aeHl5oVu3bnjrrbeq3W9KUFq3bo39+/fbFGd1Wrdu3Sjt98knn0AIga+++gpfffWV1f7PPvsMb775JlQq1R3P9eCDD2LFihXYuHEj5syZU2tZlUqFmJgYbN68GRcuXKg2ob5w4QIOHTqE4cOHW31+YmIiEhISkJCQgL///e93jO1OvLy8zGPtTMnjrW7d1rVrV6xZswZCCPzyyy9ISUnBggUL4OjoeMd6V8fWa6WoqAjfffcd5s+fb/E5Op3O5kTj119/xeHDh5GSkoJJkyaZt//xxx9WZb28vJCUlISkpCRkZ2fj22+/xZw5c5CXl4fNmzebr8EPPvigxqdPq0vsqOlhDxBJatCgQVCpVPjqq6/w22+/WTw14u7ubn4C6OzZs9Xe/rpX77zzDjw8PPD666/DaDTWWK5jx44ICQnBJ598Um0yAQDOzs7o168fNmzYYPEbpdFoxKpVq+Dv72/TYOu62LBhg0XPUHFxMTZt2oSBAweaf3i2b98eeXl5FoO1Kyoqqp1oUqPRVPsb9UMPPYRff/0VISEh1fa2mRKgmJgYFBcX49tvv7U4/ssvv7SpPkOHDsWPP/5oTnhMVq5cCScnJ5unO6iNwWDAZ599hpCQEGzfvt3q9de//hU5OTn44YcfbDrf6NGj0bVrVyxcuBC//vprtWW2bNlifrosPj4eQghMnz7d6jalwWDACy+8ACEE4uPjLfa98cYbSEhIwLx58zB//vw61bmmnrGHHnoIQghcvHix2u+1a9euVudSKBTo3r07lixZglatWllM8ljT9VMdW68VhUIBIYRVgvbRRx9ZtV9N9TTdSrz9HP/+979rjTEwMBAzZsxAbGysuZ7R0dFo1aoVjh49Wm2b9e7d29yTa2uPJEmDPUAkKTc3N/Tq1QsbN26EUqk0j/8xGTx4MJKSkgBUP/4HAAoLC81d2bfSaDRWc7nczsPDA/Hx8Xj11Vfx5Zdf4umnn66x7LJlyzBq1Cj0798fL7/8MgIDA5GdnY0tW7bgiy++AFD1iHNsbCxiYmLwyiuvwN7eHsuXL8evv/6K1atX1zim426pVCrExsZi9uzZMBqNWLRoEbRarcWkd+PGjcPrr7+OJ554Av/7v/+L8vJyLF26tNoxQl27dsWOHTuwadMm+Pn5wdXVFZ06dcKCBQuQlpaGAQMG4C9/+Qs6deqE8vJynD17FqmpqfjXv/4Ff39/TJw4EUuWLMHEiRPx1ltvITQ0FKmpqdUmW9WZP3++ebzR66+/Dk9PT3zxxRf4/vvv8c4771g9AXg3fvjhB1y6dAmLFi2q9jHtiIgIfPjhh/j444+txmhVR6VS4euvv0ZcXByioqLwwgsvICYmBs7Ozjh37hy++uorbNq0yTzZZnR0NJKSkjBr1izcd999mDFjhvlaWrZsGfbt24ekpCQMGDDA/BnvvfceXn/9dQwbNgwjR460ut7vlBiaEplFixaZe5a6deuG6OhoTJ06FX/+859x8OBBDBo0CM7OzsjJycGePXvQtWtXvPDCC/juu++wfPlyPPzww/jTn/4EIQQ2bNiAwsJCxMbGWnxOdddPdWy9Vtzc3DBo0CD885//hJeXF9q3b4+dO3fi448/RqtWrSzKmqYhWLFiBVxdXeHg4IDg4GCEhYUhJCQEc+bMgRACnp6e2LRpE9LS0iyOLyoqQkxMDMaPH4+wsDC4urriwIED2Lx5Mx599FEAVWPFPvjgA0yaNAlXr17FmDFj4O3tjStXruDw4cO4cuUKkpOTLdr9/fffx6RJk6BWq9GpUye4urrW+n1RI5Fk6DXRLV599VUBQPTu3dtq38aNGwUAYW9vL0pLS6321/YUWLt27czlTE9dHThwwOoc169fF4GBgSI0NFRUVlbWGmtGRoYYPny4cHd3FxqNRoSEhIiXX37Zoszu3bvFkCFDhLOzs3B0dBT9+/cXmzZtsihTUzw1PWk0adIk4ezsbH5vegps0aJFIjExUfj7+wt7e3vRs2dPsWXLFqu4U1NTRY8ePYSjo6P405/+JD788MNqnwLLysoS0dHRwsnJSQAQgwcPNu+7cuWK+Mtf/iKCg4OFWq0Wnp6eIjIyUsydO1eUlJSYy124cEE89thjwsXFRbi6uorHHntMpKen2/QUmBBCHDlyRIwaNUq4u7sLe3t70b17d6vjTE+BrVu3zmK7qV1q+5yHH35Y2Nvbi7y8vBrLPPHEE8LOzk7k5uaat9X03ZgUFhaKN954Q/Tq1Uu4uLgItVotAgMDxdNPPy1++uknq/IZGRlizJgxwsfHR9jZ2Qlvb2/x6KOPivT0dKuygwcPrvE6t+W/cZ1OJ6ZMmSLatGkjFAqF1ZNJn3zyiejXr5/5mg0JCRETJ040P2F2/Phx8eSTT4qQkBDh6Ogo3N3dRd++fUVKSorF59R2/VTH1mvFVM7Dw0O4urqKYcOGiV9//VUEBQVZPXWWlJQkgoODhUqlsjjP0aNHRWxsrHB1dRUeHh7i8ccfNz9dOn/+fCGEEOXl5WLatGmiW7duws3NTTg6OopOnTqJ+fPnW/3/s3PnTjFy5Ejh6ekp1Gq1aNeunRg5cqTVNRkfHy/atm0rlEqlTU+oUeNRCGHDZClE1KScPXsWwcHB+Oc//4lXXnlF6nCIiJodjgEiIiIi2WECRERERLLDW2BEREQkO+wBIiIiItlhAkRERESywwSIiIiIZIcTIVbDaDTi0qVLcHV1rfeJ64iIiKhhCCFQXFyMtm3bQqmsvY+HCVA1Ll26ZLUaNRERETUP58+fr3XxaoAJULVM05SfP38ebm5uEkdjO71ej61btyIuLg5qtVrqcCTBNmAbyL3+ANtA7vUH5NsGWq0WAQEBNi03wgSoGqbbXm5ubs0uAXJycoKbm5usLvhbsQ3YBnKvP8A2kHv9AbaBLcNXOAiaiIiIZIcJEBEREckOEyAiIiKSHSZAREREJDtMgIiIiEh2mAARERGR7EieAC1fvhzBwcFwcHBAZGQkdu/eXWPZHTt2QKFQWL2OHz9uLpOSklJtmfLy8saoDhERETUDks4DtHbtWsyaNQvLly9HdHQ0/v3vf2P48OE4evQoAgMDazzuxIkTFvPztGnTxmK/m5sbTpw4YbHNwcGhfoMnIiKiZkvSBGjx4sWYPHkypkyZAgBISkrCli1bkJycjIULF9Z4nLe3N1q1alXjfoVCAV9f3/oOl4iIiFoIyW6BVVRU4NChQ4iLi7PYHhcXh/T09FqP7dmzJ/z8/DB06FBs377dan9JSQmCgoLg7++Phx56CJmZmfUa+90yGAUyThXgm6yLyDhVAINRSB0SERGRLEnWA5Sfnw+DwQAfHx+L7T4+PsjNza32GD8/P6xYsQKRkZHQ6XT4/PPPMXToUOzYsQODBg0CAISFhSElJQVdu3aFVqvF+++/j+joaBw+fBihoaHVnlen00Gn05nfa7VaAFVTiev1+vqoLrb8dhlvph5Hrvbm5/i6aTBvRBge7OJTy5G2M8VaXzE3R2wDtoHc6w+wDeRef0C+bVCX+iqEEJJ0Q1y6dAnt2rVDeno6oqKizNvfeustfP755xYDm2szatQoKBQKfPvtt9XuNxqN6NWrFwYNGoSlS5dWWyYhIQGJiYlW27/88ks4OTnZFEdtDhco8Mnvps62W9cnqWr6Zzsa0b01e4OIiIjuRVlZGcaPH4+ioqI7ruUpWQ+Ql5cXVCqVVW9PXl6eVa9Qbfr3749Vq1bVuF+pVKJPnz44efJkjWXi4+Mxe/Zs83vTarJxcXH3vBiqwSiw8L1dAHTV7FVAAeCHy0549alBUCnvvHhbbfR6PdLS0hAbGyvLxe8AtgHANpB7/QG2gdzrD8i3DUx3cGwhWQJkb2+PyMhIpKWl4ZFHHjFvT0tLw+jRo20+T2ZmJvz8/GrcL4RAVlYWunbtWmMZjUYDjUZjtV2tVt/zhXPwVIHFbS+r+ADkFOmQeaEYUSGt7+mzTOoj7uaObcA2kHv9AbaB3OsPyK8N6lJXSZ8Cmz17NiZMmIDevXsjKioKK1asQHZ2NqZNmwagqmfm4sWLWLlyJYCqp8Tat2+PLl26oKKiAqtWrcL69euxfv168zkTExPRv39/hIaGQqvVYunSpcjKysKyZcskqWNesW3zD9lajoiIiO6dpAnQuHHjUFBQgAULFiAnJwcRERFITU1FUFAQACAnJwfZ2dnm8hUVFXjllVdw8eJFODo6okuXLvj+++8xYsQIc5nCwkJMnToVubm5cHd3R8+ePbFr1y707du30esHAN6uts0/ZGs5IiIiuneSJkAAMH36dEyfPr3afSkpKRbvX331Vbz66qu1nm/JkiVYsmRJfYV3z/oGe8LP3QG5ReWobpizAoCvuwP6Bns2dmhERESyJflSGC2dSqnA/FHhACyf/7r1/fxR4fc8AJqIiIhsxwSoEQyL8EPy073g6255m8vX3QHJT/fCsIiaB3ETERFR/ZP8FphcDIvwQ2y4Lz7POIuETUfh6azGnr8NYc8PERGRBNgD1IhUSgUejfQHAFwt1aO4XF4zdBIRETUVTIAamZuDGv4ejgCAYznFEkdDREQkT0yAJBDmWzW79PFc22esJCIiovrDBEgC4X6uAIDj7AEiIiKSBBMgCYT5sQeIiIhISkyAJBDmW9UDdOJyMQxGrgJPRETU2JgASSCotTMc1EqU6404V1AqdThERESywwRIAiqlAp18qnqB+CQYERFR42MCJBE+CUZERCQdJkAS6ezHHiAiIiKpMAGSCJ8EIyIikg4TIImYngS7cO06tFwSg4iIqFExAZJIKyd7+N1YHf5ELm+DERERNSYmQBIy9QIdz+FtMCIiosbEBEhCnW+MAzrGHiAiIqJGxQRIQuaB0OwBIiIialRMgCTU2bQkRm4xjFwSg4iIqNEwAZJQsJcz7FVKlFYYcOHadanDISIikg0mQBKyUykR6uMCADjK22BERESNhgmQxDpzQkQiIqJGxwRIYjcfheeTYERERI2FCZDE2ANERETU+JgASczUA3TuahlKdZUSR0NERCQPTIAk1tpFgzauGggBnLjM22BERESNgQlQE8BxQERERI2LCVATEM5xQERERI2KCVATEObHHiAiIqLGxASoCQjzNS2KqoUQXBKDiIiooTEBagJC2rjATqlAcXklLhZySQwiIqKGJnkCtHz5cgQHB8PBwQGRkZHYvXt3jWV37NgBhUJh9Tp+/LhFufXr1yM8PBwajQbh4eH4+uuvG7oa98TeTokO3lVLYvA2GBERUcOTNAFau3YtZs2ahblz5yIzMxMDBw7E8OHDkZ2dXetxJ06cQE5OjvkVGhpq3peRkYFx48ZhwoQJOHz4MCZMmICxY8di3759DV2de8IJEYmIiBqPpAnQ4sWLMXnyZEyZMgWdO3dGUlISAgICkJycXOtx3t7e8PX1Nb9UKpV5X1JSEmJjYxEfH4+wsDDEx8dj6NChSEpKauDa3BvTo/DHctkDRERE1NDspPrgiooKHDp0CHPmzLHYHhcXh/T09FqP7dmzJ8rLyxEeHo558+YhJibGvC8jIwMvv/yyRfkHH3yw1gRIp9NBp9OZ32u1Vb0wer0eer3e1irdk9A2TgCAY5e0d/2ZpuMaK+amiG3ANpB7/QG2gdzrD8i3DepSX8kSoPz8fBgMBvj4+Fhs9/HxQW5ubrXH+Pn5YcWKFYiMjIROp8Pnn3+OoUOHYseOHRg0aBAAIDc3t07nBICFCxciMTHRavvWrVvh5ORU16rdlaIKALDDmfwSbNyUCnvVnY6oWVpaWn2F1WyxDdgGcq8/wDaQe/0B+bVBWVmZzWUlS4BMFAqFxXshhNU2k06dOqFTp07m91FRUTh//jzeffddcwJU13MCQHx8PGbPnm1+r9VqERAQgLi4OLi5udWpPndLCIElx3bgWpkeIb2i0bWde53PodfrkZaWhtjYWKjV6gaIsuljG7AN5F5/gG0g9/oD8m0D0x0cW0iWAHl5eUGlUln1zOTl5Vn14NSmf//+WLVqlfm9r69vnc+p0Wig0WistqvV6ka9cDr7uSH9VAH+uHIdvdp73fV5GjvupohtwDaQe/0BtoHc6w/Irw3qUlfJBkHb29sjMjLSqnsuLS0NAwYMsPk8mZmZ8PPzM7+PioqyOufWrVvrdE6pmJ4EO8YnwYiIiBqUpLfAZs+ejQkTJqB3796IiorCihUrkJ2djWnTpgGoujV18eJFrFy5EkDVE17t27dHly5dUFFRgVWrVmH9+vVYv369+ZwvvfQSBg0ahEWLFmH06NH45ptvsG3bNuzZs0eSOtYFF0UlIiJqHJImQOPGjUNBQQEWLFiAnJwcREREIDU1FUFBQQCAnJwcizmBKioq8Morr+DixYtwdHREly5d8P3332PEiBHmMgMGDMCaNWswb948vPbaawgJCcHatWvRr1+/Rq9fXd06F9Cdxi0RERHR3ZN8EPT06dMxffr0avelpKRYvH/11Vfx6quv3vGcY8aMwZgxY+ojvEbVwdsFSgVwrUyPy1odfN0dpA6JiIioRZJ8KQy6yUGtwp/aVC2JwXFAREREDYcJUBNjvg3GcUBEREQNhglQE2MeCM0eICIiogbDBKiJ6ezHJ8GIiIgaGhOgJibMt+oW2KkrJdBVGiSOhoiIqGViAtTE+Lk7wM3BDpVGgT/ySqQOh4iIqEViAtTEKBQKDoQmIiJqYEyAmqBbJ0QkIiKi+scEqAm6+SQYe4CIiIgaAhOgJijMtCgqb4ERERE1CCZATVBHHxcoFEB+iQ5XinVSh0NERNTiMAFqgpzs7dC+tTMAjgMiIiJqCEyAmihOiEhERNRwmAA1UaYJEbkoKhERUf1jAtREmZ8EYw8QERFRvWMC1ESZ5gL6I68EeoNR4miIiIhaFiZATVS7Vo5w0dihwmDE6SulUodDRETUojABaqKUSsUtEyJyHBAREVF9YgLUhIXdeBKMEyISERHVLyZATZjpSTD2ABEREdUvJkBNGOcCIiIiahhMgJqwjj5VCVCuthzXSiskjoaIiKjlYALUhLk6qBHg6QiAEyISERHVJyZATVxn0zgg3gYjIiKqN0yAmrgwPw6EJiIiqm9MgJq4zua5gNgDREREVF+YADVxph6gE7nFqOSSGERERPWCCVATF+jpBEe1CrpKI84WlEkdDhERUYvABKiJUykV6MQlMYiIiOoVE6BmgBMiEhER1S8mQM0Al8QgIiKqX0yAmgHTqvBcFJWIiKh+SJ4ALV++HMHBwXBwcEBkZCR2795t03E//fQT7Ozs0KNHD4vtKSkpUCgUVq/y8vIGiL5xmHqALhZeR9F1vcTREBERNX+SJkBr167FrFmzMHfuXGRmZmLgwIEYPnw4srOzaz2uqKgIEydOxNChQ6vd7+bmhpycHIuXg4NDQ1ShUbg7qdGuVdWSGCc4HxAREdE9kzQBWrx4MSZPnowpU6agc+fOSEpKQkBAAJKTk2s97vnnn8f48eMRFRVV7X6FQgFfX1+LV3MXxifBiIiI6o2dVB9cUVGBQ4cOYc6cORbb4+LikJ6eXuNxn376KU6dOoVVq1bhzTffrLZMSUkJgoKCYDAY0KNHD7zxxhvo2bNnjefU6XTQ6XTm91ptVZKh1+uh1zeNW04dvZ3x3+PAbxeLaozJtL2pxCwFtgHbQO71B9gGcq8/IN82qEt9JUuA8vPzYTAY4OPjY7Hdx8cHubm51R5z8uRJzJkzB7t374adXfWhh4WFISUlBV27doVWq8X777+P6OhoHD58GKGhodUes3DhQiQmJlpt37p1K5ycnOpYs4ZRlq8AoMK+4+eRqj5ba9m0tLRGiakpYxuwDeRef4BtIPf6A/Jrg7Iy2ycMliwBMlEoFBbvhRBW2wDAYDBg/PjxSExMRMeOHWs8X//+/dG/f3/z++joaPTq1QsffPABli5dWu0x8fHxmD17tvm9VqtFQEAA4uLi4ObmVtcqNYiOeSX47GQ68irsMGxYHJRK6zbS6/VIS0tDbGws1Gq1BFFKj23ANpB7/QG2gdzrD8i3DUx3cGwhWQLk5eUFlUpl1duTl5dn1SsEAMXFxTh48CAyMzMxY8YMAIDRaIQQAnZ2dti6dSuGDBlidZxSqUSfPn1w8uTJGmPRaDTQaDRW29VqdZO5cEJ93WFvp0RZhQE5xXq093KusWxTilsqbAO2gdzrD7AN5F5/QH5tUJe6SjYI2t7eHpGRkVbdc2lpaRgwYIBVeTc3Nxw5cgRZWVnm17Rp09CpUydkZWWhX79+1X6OEAJZWVnw8/NrkHo0FjuVEp18OBCaiIioPkh6C2z27NmYMGECevfujaioKKxYsQLZ2dmYNm0agKpbUxcvXsTKlSuhVCoRERFhcby3tzccHBwsticmJqJ///4IDQ2FVqvF0qVLkZWVhWXLljVq3RpCmK8rjlwswrGcYgyLaN4JHRERkZQkTYDGjRuHgoICLFiwADk5OYiIiEBqaiqCgoIAADk5OXecE+h2hYWFmDp1KnJzc+Hu7o6ePXti165d6Nu3b0NUoVGF+XFJDCIiovog+SDo6dOnY/r06dXuS0lJqfXYhIQEJCQkWGxbsmQJlixZUk/RNS2duSQGERFRvZB8KQyynakHKPtqGUp0lRJHQ0RE1HwxAWpGPJ3t4eNW9bQal8QgIiK6e0yAmhnTwqgcB0RERHT3mAA1M2F+Nx6F5zggIiKiu8YEqJnpzB4gIiKie8YEqJm5tQdICCFxNERERM0TE6Bm5k9eLlCrFCjWVeLCtetSh0NERNQsMQFqZuztlOjgbVoSg+OAiIiI7gYToGbINCHi8RyOAyIiIrobTICaIfM4IPYAERER3RUmQM2QaS6gY+wBIiIiuitMgJohUw/QmYJSXK8wSBwNERFR88MEqBnydnWAl4s9hAB+v8zbYERERHXFBKiZ4pIYREREd48JUDMVduNJsGNcEoOIiKjOmAA1U2F+7AEiIiK6W0yAmqlbe4C4JAYREVHdMAFqpkJ9XKBSKlB0XY9cbbnU4RARETUrTICaKY2dCiFtnAFULYxKREREtmMC1IyZJ0TkOCAiIqI6YQLUjJmXxGAPEBERUZ0wAWrGOnNJDCIiorvCBKgZM/UAnc4vRbmeS2IQERHZiglQM+br5oBWTmoYjAJ/5JVIHQ4REVGzwQSoGVMoFOb5gI7nchwQERGRrZgANXPmNcE4DoiIiMhmTICauc43xgHxUXgiIiLbMQFq5sxzAXFJDCIiIpsxAWrmOvq4QqkArpZWIL+kQupwiIiImgUmQM2co70K7b1uLIlxmQOhiYiIbMEEqAUwTYh4IpePwhMREdmCCVALYHoU/gQfhSciIrKJ5AnQ8uXLERwcDAcHB0RGRmL37t02HffTTz/Bzs4OPXr0sNq3fv16hIeHQ6PRIDw8HF9//XU9R920hPndeBSeCRAREZFNJE2A1q5di1mzZmHu3LnIzMzEwIEDMXz4cGRnZ9d6XFFRESZOnIihQ4da7cvIyMC4ceMwYcIEHD58GBMmTMDYsWOxb9++hqqG5Ew9QKfyS1FplDgYIiKiZkDSBGjx4sWYPHkypkyZgs6dOyMpKQkBAQFITk6u9bjnn38e48ePR1RUlNW+pKQkxMbGIj4+HmFhYYiPj8fQoUORlJTUQLWQnr+HI1w1dtAbBPKuSx0NERFR02cn1QdXVFTg0KFDmDNnjsX2uLg4pKen13jcp59+ilOnTmHVqlV48803rfZnZGTg5Zdfttj24IMP1poA6XQ66HQ683uttmpSQb1eD71eb0t1JNfJ1wUHzxXiUpmi2cTcEEx1ZxvItw3kXn+AbSD3+gPybYO61FeyBCg/Px8GgwE+Pj4W2318fJCbm1vtMSdPnsScOXOwe/du2NlVH3pubm6dzgkACxcuRGJiotX2rVu3wsnJ6U5VaRIcypUAlLhUpkBaWprU4UiObcA2kHv9AbaB3OsPyK8NysrKbC4rWQJkolAoLN4LIay2AYDBYMD48eORmJiIjh071ss5TeLj4zF79mzze61Wi4CAAMTFxcHNzc2Wakiu6MB57Pn2GC6WArGxsVCr1VKHJAm9Xo+0tDS2gYzbQO71B9gGcq8/IN82MN3BsYVkCZCXlxdUKpVVz0xeXp5VDw4AFBcX4+DBg8jMzMSMGTMAAEajEUII2NnZYevWrRgyZAh8fX1tPqeJRqOBRqOx2q5Wq5vNhdOlnQcA4FKZolnF3VDYBmwDudcfYBvIvf6A/NqgLnWVbBC0vb09IiMjrbrn0tLSMGDAAKvybm5uOHLkCLKyssyvadOmoVOnTsjKykK/fv0AAFFRUVbn3Lp1a7XnbElMT4Jp9QoUlHJJDCIiotpIegts9uzZmDBhAnr37o2oqCisWLEC2dnZmDZtGoCqW1MXL17EypUroVQqERERYXG8t7c3HBwcLLa/9NJLGDRoEBYtWoTRo0fjm2++wbZt27Bnz55GrVtjc9bYIdDTEdlXr+P3y8XwbeUsdUhERERNlqQJ0Lhx41BQUIAFCxYgJycHERERSE1NRVBQEAAgJyfnjnMC3W7AgAFYs2YN5s2bh9deew0hISFYu3atuYeoJevk44rsq9dxPLcEgzpJHQ0REVHTJfkg6OnTp2P69OnV7ktJSan12ISEBCQkJFhtHzNmDMaMGVMP0TUvYb4uSDuWhxNcFJWIiKhWki+FQfWnk0/VOCAuiUFERFQ7JkAtSJhfVQJ0Mq8UlQauiUFERFQTJkAtSEArR9grBSoqjThbUCp1OERERE0WE6AWRKlUoO2NiauP5fA2GBERUU2YALUwbZ0EAOB4ru2zYRIREckNE6AWpq1zVQLEHiAiIqKaMQFqYcw9QDnsASIiIqoJE6AWxjQG6FJROYrK9NIGQ0RE1EQxAWphHO2Adq0cAHAcEBERUU2YALVAnBCRiIiodkyAWqBOvi4AgGMcB0RERFQtJkAtUNiNHqBj7AEiIiKqFhOgFijMtyoB+j23GAajkDgaIiKipocJUAsU1NoJGjslrusNyL5aJnU4RERETQ4ToBZIpVSg041eIM4HREREZI0JUAtlug3GcUBERETWmAC1UGG+bgD4JBgREVF1mAC1UGF+prmAmAARERHdjglQC9X5Rg/Q+avXUVzOJTGIiIhuxQSohfJwtoevW9WSGL9f5jggIiKiWzEBasFMt8GO5TABIiIiuhUToBaMA6GJiIiqV28JUE5ODmbMmFFfp6N60NmPi6ISERFVx64uhY8ePYrt27dDrVZj7NixaNWqFfLz8/HWW2/hX//6F4KDgxsqTroLnf2qeoBO5BbDaBRQKhUSR0RERNQ02NwD9N1336Fnz56YOXMmpk2bht69e2P79u3o3LkzsrKysG7dOhw9erQhY6U6CvZyhr1KiRJdJS4WXpc6HCIioibD5gTorbfewrRp06DVavHuu+/i9OnTmDZtGtavX4/t27fjoYceasg46S6oVUp08HYBwHFAREREt7I5ATp27BhefPFFuLi44C9/+QuUSiWSkpIwaNCghoyP7hGfBCMiIrJmcwKk1WrRqlUrAICdnR0cHR3RsWPHhoqL6olpQkTOCE1ERHRTnQdB5+bmAgCEEDhx4gRKS0stynTr1q3+oqN7ZhoIzSfBiIiIbqpTAjR06FAIIczvTeN+FAoFhBBQKBQwGAz1GyHdE9MtsLMFpSirqISTfZ2+ciIiohbJ5p+GZ86cacg4qIF4uWjg5aJBfokOv18uQY+AVlKHREREJDmbE6CgoKCGjIMaUGc/V+w+qcPxHC0TICIiItRhEPQ777yD69dvziWza9cu6HQ68/vi4mJMnz69zgEsX74cwcHBcHBwQGRkJHbv3l1j2T179iA6OhqtW7eGo6MjwsLCsGTJEosyKSkpUCgUVq/y8vI6x9ZShPmangTjQGgiIiKgDglQfHw8iotvDqR96KGHcPHiRfP7srIy/Pvf/67Th69duxazZs3C3LlzkZmZiYEDB2L48OHIzs6utryzszNmzJiBXbt24dixY5g3bx7mzZuHFStWWJRzc3NDTk6OxcvBwaFOsbUk5jXBOBCaiIgIQB1ugd06+Lm693dj8eLFmDx5MqZMmQIASEpKwpYtW5CcnIyFCxdale/Zsyd69uxpft++fXts2LABu3fvxtSpU83bFQoFfH197zm+lsL8JFiO1jxYnYiISM4keySooqIChw4dwpw5cyy2x8XFIT093aZzZGZmIj09HW+++abF9pKSEgQFBcFgMKBHjx544403LBKn2+l0OovbeVpt1a0ivV4PvV5va5UkZ4r19pgDPTSwUyqgLa/E+YIS+Lm33N6wmtpATuTeBnKvP8A2kHv9Afm2QV3qK1kClJ+fD4PBAB8fH4vtPj4+5rmGauLv748rV66gsrISCQkJ5h4kAAgLC0NKSgq6du0KrVaL999/H9HR0Th8+DBCQ0OrPd/ChQuRmJhotX3r1q1wcnK6i9pJKy0tzWpbG40KOdcVWPXddnTxuPfeu6auujaQG7m3gdzrD7AN5F5/QH5tUFZWZnPZOiVAH330EVxcqtaWqqysREpKCry8vADAYnxQXdx+O8aWWzS7d+9GSUkJ9u7dizlz5qBDhw548sknAQD9+/dH//79zWWjo6PRq1cvfPDBB1i6dGm154uPj8fs2bPN77VaLQICAhAXFwc3N7e7qpcU9Ho90tLSEBsbC7VabbFvW+kv2PRLLlz8O2HE4D9JFGHDq60N5ELubSD3+gNsA7nXH5BvG5ju4NjC5gQoMDAQ//d//2d+7+vri88//9yqjK28vLygUqmsenvy8vKseoVuFxwcDADo2rUrLl++jISEBHMCdDulUok+ffrg5MmTNZ5Po9FAo9FYbVer1c3ywqku7vC2rbDpl1z8nlfaLOtUV831u6tPcm8DudcfYBvIvf6A/NqgLnW1OQHatm0bOnTocFcBVcfe3h6RkZFIS0vDI488Yt6elpaG0aNH23weIYTF+J3q9mdlZaFr1673FG9z1/nGjNBcEoOIiKgOCVDHjh3Rrl07xMTEYMiQIYiJibnnyRFnz56NCRMmoHfv3oiKisKKFSuQnZ2NadOmAai6NXXx4kWsXLkSALBs2TIEBgYiLCwMQNW8QO+++y5mzpxpPmdiYiL69++P0NBQaLVaLF26FFlZWVi2bNk9xdrcmZ4EO32lBOV6AxzUKokjIiIiko7NCdDOnTuxc+dO7NixAy+++CLKy8sRGBhoToZiYmLQrl27On34uHHjUFBQgAULFiAnJwcRERFITU01J1Y5OTkWcwIZjUbEx8fjzJkzsLOzQ0hICN5++208//zz5jKFhYWYOnUqcnNz4e7ujp49e2LXrl3o27dvnWJrabxdNfBwUuNamR5/5JUgop271CERERFJxuYEaODAgRg4cCDmzZsHvV6PjIwM7NixAzt27MDq1auh0+nQoUMHnDhxok4BTJ8+vcYZpFNSUizez5w506K3pzpLliyxmh2aqgabh/m6IeN0AY7laJkAERGRrN3VY/BqtRqDBg1Cnz59EBUVhS1btuD//u//8Mcff9R3fFSPwvxcbyRAHAdERETyVqcEqLy8HOnp6di+fTt27NiBAwcOIDg4GIMHD0ZycjIGDx7cUHFSPeh8Y0mM47lcE4yIiOTN5gRo8ODBOHDgAEJCQjBo0CDMnDkTgwcPvuMj69R0mAZCH+OSGEREJHM2L4aanp4OLy8vxMTEYOjQoRgyZAiTn2Ym1McFSgVwrUyPK8U1Tx1ARETU0tmcABUWFmLFihVwcnLCokWL0K5dO3Tt2hUzZszAV199hStXrjRknFQPHNQqBHs5A+DK8EREJG82J0DOzs4YNmwY3n77bezbtw/5+fl455134OTkhHfeeQf+/v6IiIhoyFipHoTdchuMiIhIrmxOgG7n7OwMT09PeHp6wsPDA3Z2djh27Fh9xkYNoLPvjRmhmQAREZGM2TwI2mg04uDBg9ixYwe2b9+On376CaWlpebZoZctW4aYmJiGjJXqgWkgNJfEICIiObM5AWrVqhVKS0vh5+eH+++/H4sXL0ZMTAxCQkIaMj6qZ6ZbYH/klaCi0gh7u7vuBCQiImq2bE6A/vnPfyImJgYdO3ZsyHiogbV1d4Crgx2Kyytx6kqJuUeIiIhITmz+9f/5559n8tMCKBQK84SIHAhNRERyxfsfMhTmd2MgNMcBERGRTDEBkqHOfBSeiIhkjgmQDIX5sgeIiIjkjQmQDHX0cYVCAVwp1iG/hEtiEBGR/DABkiFnjR2CPJ0AACfYC0RERDLEBEimwvgkGBERyRgTIJkyPQl2LIc9QEREJD9MgGTq5pIY7AEiIiL5YQIkU6bJEE9eLkGlwShxNERERI2LCZBM+Xs4wtlehQqDEWfyS6UOh4iIqFExAZIppVKBTjfmAzrKgdBERCQzTIBkLMw8DogDoYmISF6YAMmYeSA0e4CIiEhmmADJWGcuiUFERDLFBEjGOt5IgHKKylFYViFxNERERI2HCZCMuTmo4e/hCIC9QEREJC9MgGSOS2IQEZEcMQGSuc43lsQ4ziUxiIhIRpgAyRyXxCAiIjliAiRzYTcGQp+4XAyDUUgcDRERUeNgAiRzQa2d4aBWolxvxLkCLolBRETyIHkCtHz5cgQHB8PBwQGRkZHYvXt3jWX37NmD6OhotG7dGo6OjggLC8OSJUusyq1fvx7h4eHQaDQIDw/H119/3ZBVaNZUSgU6+VT1Ah3jOCAiIpIJSROgtWvXYtasWZg7dy4yMzMxcOBADB8+HNnZ2dWWd3Z2xowZM7Br1y4cO3YM8+bNw7x587BixQpzmYyMDIwbNw4TJkzA4cOHMWHCBIwdOxb79u1rrGo1O6YnwTgOiIiI5ELSBGjx4sWYPHkypkyZgs6dOyMpKQkBAQFITk6utnzPnj3x5JNPokuXLmjfvj2efvppPPjggxa9RklJSYiNjUV8fDzCwsIQHx+PoUOHIikpqZFq1fyYngRjDxAREcmFnVQfXFFRgUOHDmHOnDkW2+Pi4pCenm7TOTIzM5Geno4333zTvC0jIwMvv/yyRbkHH3yw1gRIp9NBp9OZ32u1VT0her0eer3epliaAlOsdY25QxsnAMDxnKJmVd/q3G0btCRybwO51x9gG8i9/oB826Au9ZUsAcrPz4fBYICPj4/Fdh8fH+Tm5tZ6rL+/P65cuYLKykokJCRgypQp5n25ubl1PufChQuRmJhotX3r1q1wcnKypTpNSlpaWp3Kl+oBwA4XCsux/ttUOEp2VdSfurZBSyT3NpB7/QG2gdzrD8ivDcrKymwuK/mPOoVCYfFeCGG17Xa7d+9GSUkJ9u7dizlz5qBDhw548skn7/qc8fHxmD17tvm9VqtFQEAA4uLi4ObmVpfqSEqv1yMtLQ2xsbFQq9V1Onbp7zuRq9UhqHsUegd5NFCEDe9e2qClkHsbyL3+ANtA7vUH5NsGpjs4tpAsAfLy8oJKpbLqmcnLy7PqwbldcHAwAKBr1664fPkyEhISzAmQr69vnc+p0Wig0WistqvV6mZ54dxN3J393JCrvYI/rpQhqoN3A0XWeJrrd1ef5N4Gcq8/wDaQe/0B+bVBXeoq2SBoe3t7REZGWnXPpaWlYcCAATafRwhhMX4nKirK6pxbt26t0znlyDQj9DEuikpERDIg6S2w2bNnY8KECejduzeioqKwYsUKZGdnY9q0aQCqbk1dvHgRK1euBAAsW7YMgYGBCAsLA1A1L9C7776LmTNnms/50ksvYdCgQVi0aBFGjx6Nb775Btu2bcOePXsav4LNSJhpSQwuikpERDIgaQI0btw4FBQUYMGCBcjJyUFERARSU1MRFBQEAMjJybGYE8hoNCI+Ph5nzpyBnZ0dQkJC8Pbbb+P55583lxkwYADWrFmDefPm4bXXXkNISAjWrl2Lfv36NXr9mpPOpiUxcothNAoolbWPwyIiImrOJB8EPX36dEyfPr3afSkpKRbvZ86cadHbU5MxY8ZgzJgx9RGebAR7OcNepURphQEXrl1HYOvm9/QbERGRrSRfCoOaBjuVEqE+LgCAo7wNRkRELRwTIDLjkhhERCQXTIDIzLQkxnEuiUFERC0cEyAyMz0Kzx4gIiJq6ZgAkVnYjSfBzhaU4T8HzyPjVAEMRiFxVERERPVP8qfAqOk4cPYqlArAKIBXv/oFAODn7oD5o8IxLMJP4uiIiIjqD3uACACw+dccvLDqZ9ze4ZNbVI4XVv2Mzb/mSBMYERFRA2ACRDAYBRI3HUV1N7tM2xI3HeXtMCIiajGYABH2n7mKnKLyGvcLADlF5dh/5mrjBUVERNSAmAAR8oprTn7uphwREVFTxwSI4O3qUK/liIiImjomQIS+wZ7wc3dATcufKlD1NFjfYM/GDIuIiKjBMAEiqJQKzB8VDgA1JkHzR4VDxRXiiYiohWACRACAYRF+SH66F3zdLW9zaeyUSH66F+cBIiKiFoUTIZLZsAg/xIb7Yv+Zq/j1YhHeSj0GXaURXdq6Sx0aERFRvWIPEFlQKRWICmmN5wb9Cfd18AIAfLEvW+KoiIiI6hcTIKrRhKggAMDaA9ko1xskjoaIiKj+MAGiGg0N80ZbdwdcK9Pj+1+4FAYREbUcTICoRnYqJZ7qX9ULtHLvOYmjISIiqj9MgKhW4/oEwF6lxOHzhfjlQqHU4RAREdULJkBUKy8XDUZ09QUArMxgLxAREbUMTIDojiZEtQcAbDp8CddKK6QNhoiIqB4wAaI76hXYCl3aukFXacR/Dp6XOhwiIqJ7xgSI7kihUGDijUfiV+07B4NRSBwRERHRvWECRDb5n+7t4O6oxvmr17Hz9zypwyEiIronTIDIJo72Kjwe6Q+Ag6GJiKj5YwJENnv6xpxAO3+/gnMFpRJHQ0REdPeYAJHN2ns5Y3DHNhACWMWJEYmIqBljAkR1YhoM/Z+DF3C9guuDERFR88QEiOrk/k7e8PdwRNF1PTYdviR1OERERHeFCRDViUqpMI8FWrn3LITgI/FERNT8SJ4ALV++HMHBwXBwcEBkZCR2795dY9kNGzYgNjYWbdq0gZubG6KiorBlyxaLMikpKVAoFFav8vLyhq6KbIztHQB7OyV+vahF5vlCqcMhIiKqM0kToLVr12LWrFmYO3cuMjMzMXDgQAwfPhzZ2dnVlt+1axdiY2ORmpqKQ4cOISYmBqNGjUJmZqZFOTc3N+Tk5Fi8HBwcGqNKsuDpbI9R3doCAD7nI/FERNQMSZoALV68GJMnT8aUKVPQuXNnJCUlISAgAMnJydWWT0pKwquvvoo+ffogNDQU//jHPxAaGopNmzZZlFMoFPD19bV4Uf0yDYb+/pcc5JfoJI6GiIiobiRLgCoqKnDo0CHExcVZbI+Li0N6erpN5zAajSguLoanp6fF9pKSEgQFBcHf3x8PPfSQVQ8R3bvuAa3Q3d8dFQYj1h7g+mBERNS82En1wfn5+TAYDPDx8bHY7uPjg9zcXJvO8d5776G0tBRjx441bwsLC0NKSgq6du0KrVaL999/H9HR0Th8+DBCQ0OrPY9Op4NOd7MXQ6vVAgD0ej30en1dqyYZU6yNFfNTfQNw+EIRVu09h2ejAmCnknxIWaO3QVMk9zaQe/0BtoHc6w/Itw3qUl+FkOgxnkuXLqFdu3ZIT09HVFSUeftbb72Fzz//HMePH6/1+NWrV2PKlCn45ptv8MADD9RYzmg0olevXhg0aBCWLl1abZmEhAQkJiZabf/yyy/h5ORkY43kR28E5h9SobRSgcmdDOjmySfCiIhIOmVlZRg/fjyKiorg5uZWa1nJeoC8vLygUqmsenvy8vKseoVut3btWkyePBnr1q2rNfkBAKVSiT59+uDkyZM1lomPj8fs2bPN77VaLQICAhAXF3fHBmxK9Ho90tLSEBsbC7Va3Sif+bv971ix+yyOVbbBnBG9G+UzayNFGzQ1cm8DudcfYBvIvf6AfNvAdAfHFpIlQPb29oiMjERaWhoeeeQR8/a0tDSMHj26xuNWr16NZ599FqtXr8bIkSPv+DlCCGRlZaFr1641ltFoNNBoNFbb1Wp1s7xwGjPuCVHB+L89Z5F+6iqyC3UIaePSKJ97J831u6tPcm8DudcfYBvIvf6A/NqgLnWVdNDG7Nmz8dFHH+GTTz7BsWPH8PLLLyM7OxvTpk0DUNUzM3HiRHP51atXY+LEiXjvvffQv39/5ObmIjc3F0VFReYyiYmJ2LJlC06fPo2srCxMnjwZWVlZ5nNS/QrwdMLQMG8AfCSeiIiaD0kToHHjxiEpKQkLFixAjx49sGvXLqSmpiIoqOoR65ycHIs5gf7973+jsrISL774Ivz8/Myvl156yVymsLAQU6dORefOnREXF4eLFy9i165d6Nu3b6PXTy4mRLUHAKw/dAGlukppgyEiIrKBZLfATKZPn47p06dXuy8lJcXi/Y4dO+54viVLlmDJkiX1EBnZamAHL7Rv7YSzBWXYmHURT/ULkjokIiKiWkn/3DI1e8pb1gf7POMc1wcjIqImjwkQ1YvHIwPgoFbieG4xDpy9JnU4REREtWICRPXC3UmNh3u0AwCszDgrbTBERER3wASI6s2EG+uDbf41F3nacomjISIiqhkTIKo3Xdq6IzLIA5VGgdX7uT4YERE1XUyAqF6ZVon/cv856A1GiaMhIiKqHhMgqlfDInzh5WKPy1od0o5eljocIiKiajEBonqlsVPhiT6BADgYmoiImi4mQFTvxvcLhFIB7D19Fb9fLpY6HCIiIitMgKjetW3liNhwHwBcH4yIiJomJkDUICbeWB9sw88XUFyulzYYIiKi2zABogYxIKQ1Qto4o7TCgK8zL0odDhERkQUmQNQgFAoFJtxYH2wl1wcjIqImhgkQNZhHI/3hZK/CH3klyDhdIHU4REREZkyAqMG4OajxSM8b64OlczA0ERE1HUyAqEGZBkOnHbuMnKLr0gZDRER0AxMgalCdfF3RN9gTBqPAl/uypQ6HiIgIABMgagSm9cFW7z+PikquD0ZERNJjAkQN7sEuvvB21SC/RIcffs2ROhwiIiImQNTw1ColnuxbtT4YZ4YmIqKmgAkQNYrx/QJhp1Tg4LlrOHpJK3U4REQkc0yAqFH4uDngwQhfAMDne89KGwwREckeEyBqNBNvzAy9MfMSiq5zfTAiIpIOEyBqNH2DPdHJxxXX9QZ8deiC1OEQEZGMMQGiRqNQKDDhxiPxq/aeg9HI9cGIiEgaTICoUT3Ssx1cNXY4k1+KPX/kSx0OERHJFBMgalTOGjs8FukPoGqVeCIiIikwAaJG9/SNwdA/Hr+MC9fKJI6GiIjkiAkQNboO3i6I7tAaRgF8wfXBiIhIAkyASBIT+rcHAKw9cB7leoO0wRARkewwASJJPNDZG23dHXC1tAKpR7g+GBERNS4mQCQJO5US4/tVrQ/GwdBERNTYJE+Ali9fjuDgYDg4OCAyMhK7d++useyGDRsQGxuLNm3awM3NDVFRUdiyZYtVufXr1yM8PBwajQbh4eH4+uuvG7IKdJfG9QmEWqVA1vlCHLlQJHU4REQkI5ImQGvXrsWsWbMwd+5cZGZmYuDAgRg+fDiys6sfGLtr1y7ExsYiNTUVhw4dQkxMDEaNGoXMzExzmYyMDIwbNw4TJkzA4cOHMWHCBIwdOxb79u1rrGqRjdq4ajCiqx8AYGXGWWmDISIiWZE0AVq8eDEmT56MKVOmoHPnzkhKSkJAQACSk5OrLZ+UlIRXX30Vffr0QWhoKP7xj38gNDQUmzZtsigTGxuL+Ph4hIWFIT4+HkOHDkVSUlIj1YrqYuKNmaG/PXwJ10orJI6GiIjkQrIEqKKiAocOHUJcXJzF9ri4OKSnp9t0DqPRiOLiYnh6epq3ZWRkWJ3zwQcftPmc1Lh6BXog3M8Nukoj1h06L3U4REQkE3ZSfXB+fj4MBgN8fHwstvv4+CA3N9emc7z33nsoLS3F2LFjzdtyc3PrfE6dTgedTmd+r9VqAQB6vR56ffNZtdwUa3OKGQCe6uuPud8cxcqMc5jYLwAqpeKuz9Vc26A+yb0N5F5/gG0g9/oD8m2DutRXsgTIRKGw/GEnhLDaVp3Vq1cjISEB33zzDby9ve/pnAsXLkRiYqLV9q1bt8LJyemOsTQ1aWlpUodQJ/YGwFGlwoVr17F49WZ08bj3RVKbWxs0BLm3gdzrD7AN5F5/QH5tUFZm++oCkiVAXl5eUKlUVj0zeXl5Vj04t1u7di0mT56MdevW4YEHHrDY5+vrW+dzxsfHY/bs2eb3Wq0WAQEBiIuLg5ubm61Vkpxer0daWhpiY2OhVqulDqdOjtqdwKfp53DC4IP/HdHrrs/TnNugvsi9DeRef4BtIPf6A/JtA9MdHFtIlgDZ29sjMjISaWlpeOSRR8zb09LSMHr06BqPW716NZ599lmsXr0aI0eOtNofFRWFtLQ0vPzyy+ZtW7duxYABA2o8p0ajgUajsdquVqub5YXTHOOeOCAYn6afw64/8nFJW4Gg1s73dL7m2Ab1Te5tIPf6A2wDudcfkF8b1KWukj4FNnv2bHz00Uf45JNPcOzYMbz88svIzs7GtGnTAFT1zEycONFcfvXq1Zg4cSLee+899O/fH7m5ucjNzUVR0c05ZF566SVs3boVixYtwvHjx7Fo0SJs27YNs2bNauzqUR0EezljUMc2EAJYtZcTIxIRUcOSNAEaN24ckpKSsGDBAvTo0QO7du1CamoqgoKqHo3OycmxmBPo3//+NyorK/Hiiy/Cz8/P/HrppZfMZQYMGIA1a9bg008/Rbdu3ZCSkoK1a9eiX79+jV4/qpuJN1aJ/8/BC7hewfXBiIio4Ug+CHr69OmYPn16tftSUlIs3u/YscOmc44ZMwZjxoy5x8ioscWEeaNdK0dcLLyOTYcvYWyfAKlDIiKiFkrypTCITFRKBZ6+0Qu0cu9ZCHHvT4MRERFVhwkQNSnj+gTA3k6JXy9qkXm+UOpwiIiohWICRE2Kp7M9HupWtT7Y51wlnoiIGggTIGpyJka1BwB8/0sO8kt0tRcmIiK6C0yAqMnpEdAK3f3dUWEwYu0Brg9GRET1jwkQNUkTbvQCfbkvGwYjB0MTEVH9YgJETdJD3fzg4aTGxcLr+O+xy1KHQ0RELQwTIGqSHNQq8zxAn3NmaCIiqmdMgKjJerpfEBQKYPfJfJy+UiJ1OERE1IIwAaImK8DTCUM6eQNgLxAREdUvJkDUpE2IqpoZ+qtDF1BWUSlxNERE1FIwAaImbVBoG7Rv7YTi8kpszLwkdThERNRCMAGiJk156/pgGVwfTG4MRoGMUwX4JusiMk4VcEoEIqo3kq8GT3Qnj0cG4N2tJ3A8txgHz11Dn/aeUodEjWDzrzlI3HQUOUXl5m1+7g6YPyocwyL8JIyMiFoC9gBRk+fupMbo7u0AACu5PpgsbP41By+s+tki+QGA3KJyvLDqZ2z+NUeiyIiopWACRM2CaTD0D0dykKctv0Npas4MRoHETUdR3c0u07bETUd5O4yI7gkTIGoWItq5o1dgK1QaBVbv5/pgLdn+M1eten5uJQDkFJXjpTWZ+M+B8zh07hqKrusbL0AiahE4BoiajYlR7fFzdha+2HcWkUGtUFBaAW9XB/QN9oRKqZA6PAsGo8D+M1eRV1zeZGNsakp0lfg26xKSd/xhU/nvfsnBd7/cvBXWxlWDDm1cEOzlCF2eAu6nChDm1wo+bhooFGx7IrLEBIiajeFdfTFvox3yiivw9Mf7zdub2sBYDt61nRACv1wowur92fj28CWUVRhsPvbBLj4o1RnwR14JcrXluFKsw5ViHTJOA4AK61MOAQBcNXb4k7cLQto4o4O3Czq0cUEHbxcEejrBTnXvneBMdomaJyZA1GxsP56HEp31ZIimgbHJT/fC0E5eEkR2k2nw7u2jU26NkUkQoC3X45vMi1i9/zyO5mjN20PaOGNcnwB8tPsMrhTrqh0HpADg6+6A5U9FmhON4nI9Tl0pxam8Evyeq0X6r6dQonRB9rXrKNZV4vD5Qhw+X2hxHrVKgfatbyRFN14hbapejvYqm+rBZJeo+WICRM2CaWBsdQSqfigmbjqK+0MHNmpct7rT4F1TjLHhvrLsIRBCIPN8IVbvy8Z3v+Tgur6qt8feTomRXf3wZN9A9GnvAYVCgUBPJ7yw6mcoAIv2NLXa/FHhFm3o6qBGj4BW6BHQCnq9HqmVJzFixH0QChXOFlQlRn/kleCPK1V/nrpSgnK9ESfzSnAyz3qduXatHC0SI1Ny5Olsby7DZJeoeWMCRM2CrQNjx/x7HypLlVh35VCjj/soLKuwKcb9Z64iKqR14wUmsaIyPb7OvIDV+8/jxOVi8/ZQbxc82TcQj/Zqh1ZO9hbHDIvwQ/LTvax6V3zr2Ltib6dERx9XdPRxtdhuNApcLLyOU7ckRH/cSJKulelxsfA6LhZex87fr1gc5+lsXzXOqI0zUo/kMNmlJsd0SzansBSnixQwGAXUUgfVRDEBomYhr9i2R99/vaQFoASKCho2oHuw/cRldPV3h4um5f7zE0Lg4LlrWL0vG98fyYGu0ggA0Ngp8VC3thjfLwC9Aj1qTVKHRfghNty3QcbXKJUKBHg6IcDTCfffWHDXpKBEh1NXSs0J0R9XSnAqrwQXC6/jamkF9pdexf6zV2uvP6qS3dQjORjZ1Q9KJkHUCKxvyarw1Xu7kPA/XdgbWY2W+z8wtSjerg42lZs2qD1KLp1C9+7doVLZNo6jvvyRV4LlO07dsdyKXWfw8Z6z6O7vjgEhXhjQoTV6BXrAQd248TaEa6UV2JB5Eav3Z+OPW24thfm6Yny/QIzu0Q7ujrb/PqpSKhq9t6y1iwatXTToG2w543iprhJn8qsSo9QjOdh69PIdzzVzdSb+uu4wAjwc0b61MwJbO6F9a2cEtXZCUGtn+Hs4Ql0PA7FbAg4mvzc13ZK9rNXxlmwNmABRs9A32BN+7g7ILSqvdWDsrKGh2LL5D4zo0RZqdeN2/BqMAl9nXqwxRgBwslehtbM9zl+7jp+zC/FzdiE+3P4H7O2U6B3kgQEhrTGggxe6tXOvlyeUGoMQAvvOXMXq/dn44ddcVNzo7XFUq/A/3dviyX6B6O7v3uwfRXfW2CGinTsi2rnDx83BpgRIqQAqKo1VA7SvlFrtVykVaNvK4WZS5HkzOQr0dLJ5MPadNPXkgoPJ703RdT1e2/hrrZOHzv/2NwwJ84G9nfT/rzSV65EJEDULKqUC80eF12lgbGOzJcbFY7tjWIQfzl8tQ8bpAqT/kY/0UwXIK9Yh/VQB0k8VAFt/h4vGDn2DPasSohAvhPm6NrnbKAUlOqz/+QLW7D+P0/k3f7h3aeuGJ/sGYnSPtnB1aJmjD2xNyHe8cj/yinU4V1CGswWlOFdQinMFZVWvq6Uo1xtx/up1nL96HbtPWp/H183hRq9RVVIUdKMHKbC1E9xsbNumnlw0p8HkjfmDWwiBEl0l8op1uHxjmofL2nLkaXW4XKxDnrYceTf+LLVh+ojLWh06zvsBHk5qeDjbw9PJHp7OVS8PZ3u0draHxy3bTNud7VX1+stLU7oemQBRs2HLwFi9XtoZgW0dvGsafzK2dwCEEDh1pRTpp/KR/kcBMk4XoOi6Hj8ez8OPx/MAVA2+jfpTa0SFtMaAkNYI9nKWpEfFaBTIOF2A1fuzseW3XOgNVT+2nO1V+J8e7TC+byC6+rs3elyNzdaEXKNWmb/r+0Itp2gQQtSYHJ0tKEVxeSVyteXI1VYNnL+dp7M9Aj0tkyPTn272VVFs+e0yZq453GSTi+b05GR9/eAWQqBYV4k87c0k5vJtf5q212VeLFtdK9PjWpkep2HdI1kdeztlLcmSGp7OGng4q2/ud7Kv8bZuU0t2FUIILqhzG61WC3d3dxQVFcHNzU3qcGym1+uRmpqKESNGNPrtn8ZU229hTaUN7uU3RaNR4GiOtiohOlWA/WeuWv1H6OfucCMZ8kJ0h9bwc3c072uINrhSrMNXhy5gzYFsnCsoM2/v7u+OJ/oGYlT3tk1mUHdjXgMN9dusEAKFZXqcLShF9tUynM2v6jGqSpBKkV9SUevxzhoV3JWVKNCrzAPQq+PppMabD3eFEQKVBgG9wYhKo0ClwQi9QaDSWPWn3mCs2m+s+rPSYIT+Rrmq7bcfc+PvN85n+fcb5zAacb3CYFPvRUgbZ7Rx1cDJ3g6OahUc1Co42ivhqFZVvezt4KhWwtG+ap+9Evgl8xDuvy8KLo72N8qozMdq7JR1+gWiph/cpjMkP90LD3bxvZHYlOOyVoe84ht/anW4XFyOKzf+zNPqzFNA2MJVY4c2bhr4uDrA200DHzcHeLtq4H3jTx83B5zNL8WfUw7c8Vz/eroX/tTGBQUlFbhWVoGrpTdft76/VlqBgtKKWq+d2rg52FklS62c1Fhz4DyKy63ncgNu9pru+duQe0p26/Lzu2n8j0VUB1IMjK2re4lRqVSYx5pMHRQCvcGIw+cLb9wiy8fP5wqRU1SODT9fxIafLwIAgr2czb1DvQNt64G5U5JmNArs+SMfq/dnI+3oZVTeWHzUVWOHh3u2wxN9A9Clbcvv7alNQz2pplAo4HHjB0jPQA+r/SW6SpwrKEV2QRnOFpQh+2ppVZJUUIocbTlKdQaUQgGg9h9gV8v0mP7lz/cUa2OoaQxV7VRIPra/2j1KBcxJkYM5ibrtT7UKDvZVydK6g+drHV/z4peZsFMCukrb+xNcHezMCYzpzza3vfd2q0r67iTQ08mmW7LmnjSfO8cnhMB1vcEqSbqZPOlxtVSHa6V6XC27uV8IQFteCW15Jc7e8svSHT8PjT9NCBMgoiZOrVKid3tP9G7vib8MDcX1CgMOnbtm7iH65UIhzuSX4kx+Kb7clw0AaOukQiaOY2BHb/QN9rQai1Nbz0XPQA+sO3geaw6cx4Vr1837ewa2wpN9A/FQNz+b/lOWCykScheNHbq0da82AS3XG3AmT4t31u/B9kt3HvDavrUTvN0coFYpYKdUQq1SVv1dpYRaqYCdxd+VsFMpYK9Swk5Z9febx93Yr1TcOIfl/qq/39xvp1Lgt0ta/PU/h+8Y4ytxHRHU2hnX9QaU6w24XmHA9Wr+LNdX/Vmqq0ReQSHUDs4or6zaX643osJQlRAaBVBqY++TLQxGgRunhpuDHbzdHODjpoH3jV4bb9eb701/1tcAd6BhxkgqFAo42dvByd4O/h5ONh1jMApor+tRUGrdw3Tw7FVsP3HljuewdcqT+sD/xYiaGUd7Fe4L9TKPKdGW67H/9FVzD9Hx3GJcKlMgJSMbKRnZUCkV6NrOHdEdqm6ZFZTo8NKaLKvfFHOKyjFt1c9QKqp+QABVv6U+1ssfT/QNQJhv87kdLGcOahU6eLugSyuB7ZfuXH7ho90k61EN9XbFu1tO3LHn4oX7O9Tph/fN26D3WdwG1RuM5iSpvMJYlTzpDSirqLyRWN3cVl5hQFmFAb9cLMR/j+Xd8TNffygc4/sFSjadRc3jDzWYP6px5gFSKW/2XN4u41SBTQmQrVOe1AcmQETNnJuDGg+E++CB8Kp+7dxrJfjXhh9x3T0I+85cxdmCMmSdL0TW+UIs237neYqMAugd1ApP9g3CiK5+9fqbKjWeEDcBXzcNLmtrX1Pt9vmOGlNjP91p6pmqy9OJGacKbEqAOvu5ST6X1623ZHMKS3H6tyzMGDcIDhrrhKSx2frkZGNej5JPCLB8+XIEBwfDwcEBkZGR2L17d41lc3JyMH78eHTq1AlKpRKzZs2yKpOSkgKFQmH1Ki9vvG41Iim1dtGgp5fAm6PDseN/Y/DTnCH455hueLRnO3g42fYf/1/jwvBYpD+Tn2ZMqQDmjQgDcDOZMGkqU0cAN3sufN0tf/P3dXeQ/Ck14OYP7ppaSYGq28dSJpK3Mt2SHdXND6HuQvLv18SU7AJN53qUNAFau3YtZs2ahblz5yIzMxMDBw7E8OHDkZ2dXW15nU6HNm3aYO7cuejevXuN53Vzc0NOTo7Fy8Gh8brViJqSdq0c8XjvACwe1wMJo7rYdExj3oenhvNgF58mnVyYDIvww56/DcHq5/rj/Sd6YPVz/bHnb0OaRHxN8Qd3c9XUkl1Jb4EtXrwYkydPxpQpUwAASUlJ2LJlC5KTk7Fw4UKr8u3bt8f7778PAPjkk09qPK9CoYCvr2/DBE3UjHm72faLQGPeh6eG1ZBrqtWnpvx0Z30tzktN63qULAGqqKjAoUOHMGfOHIvtcXFxSE9Pv6dzl5SUICgoCAaDAT169MAbb7yBnj171lhep9NBp9OZ32u1WgBVA+mknlivLkyxNqeY6xvboPY26OnvasO4EA16+rs22zbkNVB9G/QOdANQNZDdaKiEsf7n2GsyGuIaGNrJC/eHDsTBc9eQV6yDt6sGvYM8oFIqmuS11tT/HTTU9ViX+kqWAOXn58NgMMDHx3JCAh8fH+Tm5t71ecPCwpCSkoKuXbtCq9Xi/fffR3R0NA4fPozQ0NBqj1m4cCESExOttm/duhVOTrY9/teUpKWlSR2C5NgGNbfBCF8FPtGa7n7f+luXgAAw3KcMWzb/0NDhNTheA2yDhqq/CkABgC3HGuT09Upu10BZme1zD0n+FNjts3EKIe5piv/+/fujf//+5vfR0dHo1asXPvjgAyxdurTaY+Lj4zF79mzze61Wi4CAAMTFxTW7maDT0tIQGxvbomeCrg3b4M5tMAJAr98u483U48jV3uz59HN3wNzhYXiwiw2zpDVhvAbYBnKvPyDfNjDdwbGFZAmQl5cXVCqVVW9PXl6eVa/QvVAqlejTpw9OnqxmpcEbNBoNNBqN1Xa1Wt0sL5zmGnd9YhvU3gYP9fDH8G7tmsR9+IbCa4BtIPf6A/Jrg7rUVbKnwOzt7REZGWnVPZeWloYBAwbU2+cIIZCVlQU/Pw5SI7qVadDp6B7tEBXSukUlP0REdyLpLbDZs2djwoQJ6N27N6KiorBixQpkZ2dj2rRpAKpuTV28eBErV640H5OVlQWgaqDzlStXkJWVBXt7e4SHVz2mmJiYiP79+yM0NBRarRZLly5FVlYWli1b1uj1IyIioqZJ0gRo3LhxKCgowIIFC5CTk4OIiAikpqYiKCgIQNXEh7fPCXTr01yHDh3Cl19+iaCgIJw9exYAUFhYiKlTpyI3Nxfu7u7o2bMndu3ahb59+zZavYiIiKhpk3wQ9PTp0zF9+vRq96WkpFhtE6L21XaXLFmCJUuW1EdoRERE1EJJvhQGERERUWNjAkRERESywwSIiIiIZIcJEBEREckOEyAiIiKSHSZAREREJDuSPwbfFJketa/LmiJNgV6vR1lZGbRaraymPr8V24BtIPf6A2wDudcfkG8bmH5u32nKHIAJULWKi4sBAAEBARJHQkRERHVVXFwMd3f3WssohC1pkswYjUZcunQJrq6u97QyfWMzrWJ//vz5ZrWKfX1iG7AN5F5/gG0g9/oD8m0DIQSKi4vRtm1bKJW1j/JhD1A1lEol/P39pQ7jrrm5ucnqgq8O24BtIPf6A2wDudcfkGcb3Knnx4SDoImIiEh2mAARERGR7DABakE0Gg3mz58PjUYjdSiSYRuwDeRef4BtIPf6A2wDW3AQNBEREckOe4CIiIhIdpgAERERkewwASIiIiLZYQJEREREssMEqAVISEiAQqGwePn6+kodVoPatWsXRo0ahbZt20KhUGDjxo0W+4UQSEhIQNu2beHo6Ij7778fv/32mzTBNoA71f+ZZ56xuib69+8vTbANYOHChejTpw9cXV3h7e2Nhx9+GCdOnLAo09KvAVvaoCVfB8nJyejWrZt5or+oqCj88MMP5v0t/fsH7twGLfn7rw9MgFqILl26ICcnx/w6cuSI1CE1qNLSUnTv3h0ffvhhtfvfeecdLF68GB9++CEOHDgAX19fxMbGmtd5a+7uVH8AGDZsmMU1kZqa2ogRNqydO3fixRdfxN69e5GWlobKykrExcWhtLTUXKalXwO2tAHQcq8Df39/vP322zh48CAOHjyIIUOGYPTo0eYkp6V//8Cd2wBoud9/vRDU7M2fP190795d6jAkA0B8/fXX5vdGo1H4+vqKt99+27ytvLxcuLu7i3/9618SRNiwbq+/EEJMmjRJjB49WpJ4pJCXlycAiJ07dwoh5HcNCGHdBkLI7zrw8PAQH330kSy/fxNTGwghv++/rtgD1EKcPHkSbdu2RXBwMJ544gmcPn1a6pAkc+bMGeTm5iIuLs68TaPRYPDgwUhPT5cwssa1Y8cOeHt7o2PHjnjuueeQl5cndUgNpqioCADg6ekJQJ7XwO1tYCKH68BgMGDNmjUoLS1FVFSULL//29vARA7f/93iYqgtQL9+/bBy5Up07NgRly9fxptvvokBAwbgt99+Q+vWraUOr9Hl5uYCAHx8fCy2+/j44Ny5c1KE1OiGDx+Oxx9/HEFBQThz5gxee+01DBkyBIcOHWpxM8MKITB79mzcd999iIiIACC/a6C6NgBa/nVw5MgRREVFoby8HC4uLvj6668RHh5uTnLk8P3X1AZAy//+7xUToBZg+PDh5r937doVUVFRCAkJwWeffYbZs2dLGJm0FAqFxXshhNW2lmrcuHHmv0dERKB3794ICgrC999/j0cffVTCyOrfjBkz8Msvv2DPnj1W++RyDdTUBi39OujUqROysrJQWFiI9evXY9KkSdi5c6d5vxy+/5raIDw8vMV///eKt8BaIGdnZ3Tt2hUnT56UOhRJmJ6AM/UCmOTl5Vn9RigXfn5+CAoKanHXxMyZM/Htt99i+/bt8Pf3N2+X0zVQUxtUp6VdB/b29ujQoQN69+6NhQsXonv37nj//fdl9f3X1AbVaWnf/71iAtQC6XQ6HDt2DH5+flKHIong4GD4+voiLS3NvK2iogI7d+7EgAEDJIxMOgUFBTh//nyLuSaEEJgxYwY2bNiAH3/8EcHBwRb75XAN3KkNqtPSroPbCSGg0+lk8f3XxNQG1Wnp33+dSTb8murNX//6V7Fjxw5x+vRpsXfvXvHQQw8JV1dXcfbsWalDazDFxcUiMzNTZGZmCgBi8eLFIjMzU5w7d04IIcTbb78t3N3dxYYNG8SRI0fEk08+Kfz8/IRWq5U48vpRW/2Li4vFX//6V5Geni7OnDkjtm/fLqKiokS7du1aTP1feOEF4e7uLnbs2CFycnLMr7KyMnOZln4N3KkNWvp1EB8fL3bt2iXOnDkjfvnlF/H3v/9dKJVKsXXrViFEy//+hai9DVr6918fmAC1AOPGjRN+fn5CrVaLtm3bikcffVT89ttvUofVoLZv3y4AWL0mTZokhKh6DHr+/PnC19dXaDQaMWjQIHHkyBFpg65HtdW/rKxMxMXFiTZt2gi1Wi0CAwPFpEmTRHZ2ttRh15vq6g5AfPrpp+YyLf0auFMbtPTr4NlnnxVBQUHC3t5etGnTRgwdOtSc/AjR8r9/IWpvg5b+/dcHhRBCNF5/ExEREZH0OAaIiIiIZIcJEBEREckOEyAiIiKSHSZAREREJDtMgIiIiEh2mAARERGR7DABIiIiItlhAkREVAOFQoGNGzdKHQYRNQAmQETUJD3zzDNQKBRWr2HDhkkdGhG1AHZSB0BEVJNhw4bh008/tdim0WgkioaIWhL2ABFRk6XRaODr62vx8vDwAFB1eyo5ORnDhw+Ho6MjgoODsW7dOovjjxw5giFDhsDR0RGtW7fG1KlTUVJSYlHmk08+QZcuXaDRaODn54cZM2ZY7M/Pz8cjjzwCJycnhIaG4ttvvzXvu3btGp566im0adMGjo6OCA0NtUrYiKhpYgJERM3Wa6+9hsceewyHDx/G008/jSeffBLHjh0DAJSVlWHYsGHw8PDAgQMHsG7dOmzbts0iwUlOTsaLL76IqVOn4siRI/j222/RoUMHi89ITEzE2LFj8csvv2DEiBF46qmncPXqVfPnHz16FD/88AOOHTuG5ORkeHl5NV4DENHdk3o1ViKi6kyaNEmoVCrh7Oxs8VqwYIEQomo19GnTplkc069fP/HCCy8IIYRYsWKF8PDwECUlJeb933//vVAqlSI3N1cIIUTbtm3F3Llza4wBgJg3b575fUlJiVAoFOKHH34QQggxatQo8ec//7l+KkxEjYpjgIioyYqJiUFycrLFNk9PT/Pfo6KiLPZFRUUhKysLAHDs2DF0794dzs7O5v3R0dEwGo04ceIEFAoFLl26hKFDh9YaQ7du3cx/d3Z2hqurK/Ly8gAAL7zwAh577DH8/PPPiIuLw8MPP4wBAwbcVV2JqHExASKiJsvZ2dnqltSdKBQKAIAQwvz36so4OjradD61Wm11rNFoBAAMHz4c586dw/fff49t27Zh6NChePHFF/Huu+/WKWYianwcA0REzdbevXut3oeFhQEAwsPDkZWVhdLSUvP+n376CUqlEh07doSrqyvat2+P//73v/cUQ5s2bfDMM89g1apVSEpKwooVK+7pfETUONgDRERNlk6nQ25ursU2Ozs780DjdevWoXfv3rjvvvvwxRdfYP/+/fj4448BAE899RTmz5+PSZMmISEhAVeuXMHMmTMxYcIE+Pj4AAASEhIwbdo0eHt7Y/jw4SguLsZPP/2EmTNn2hTf66+/jsjISHTp0gU6nQ7fffcdOnfuXI8tQEQNhQkQETVZmzdvhp+fn8W2Tp064fjx4wCqntBas2YNpk+fDl9fX3zxxRcIDw8HADg5OWHLli146aWX0KdPHzg5OeGxxx7D4sWLzeeaNGkSysvLsWTJErzyyivw8vLCmDFjbI7P3t4e8fHxOHv2LBwdHTFw4ECsWbOmHmpORA1NIYQQUgdBRFRXCoUCX3/9NR5++GGpQyGiZohjgIiIiEh2mAARERGR7HAMEBE1S7x7T0T3gj1AREREJDtMgIiIiEh2mAARERGR7DABIiIiItlhAkRERESywwSIiIiIZIcJEBEREckOEyAiIiKSHSZAREREJDv/D0fbtYJZXOA5AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epoch_list, wer_list, marker='o')\n",
    "plt.title(\"WER computed on ATCO2 test dataset\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"WER\")\n",
    "plt.grid(True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7133616e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steps_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f92ef20",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:pytorch20_p39_gpu_v1_clonev1_0]",
   "language": "python",
   "name": "conda-env-pytorch20_p39_gpu_v1_clonev1_0-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}