RQ1_text_quality.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('labelling/text_quality_labelling.csv', header=0)\n",
    "# delete the last row\n",
    "df = df[:-1]\n",
    "df['dataset'] = df['state_filename'].apply(lambda x: 'Internal' if 'state_2022' in x else 'ThirdParty')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>index</th>\n",
       "      <th>app_name</th>\n",
       "      <th>state_filename</th>\n",
       "      <th>textfield_id</th>\n",
       "      <th>category_matching</th>\n",
       "      <th>llm_base</th>\n",
       "      <th>droidfiller-reasoning</th>\n",
       "      <th>droidfiller-functioncall</th>\n",
       "      <th>droidfiller</th>\n",
       "      <th>dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>org.koitharu.kotatsu</td>\n",
       "      <td>state_-1399196902</td>\n",
       "      <td>searchView_198_119</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>ThirdParty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>AOL_News_Mail_Video_v6.47.2</td>\n",
       "      <td>state_-2077864795</td>\n",
       "      <td>search_edit_text_182_66</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>ThirdParty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>de.kromke.andreas.opus1musicplayer</td>\n",
       "      <td>state_1643267838</td>\n",
       "      <td>search_src_text_111_71</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>ThirdParty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Petal_Maps‚ÄìGPS_Navigation_v2.5.0.303(002)</td>\n",
       "      <td>state_-1677063389</td>\n",
       "      <td>search_src_text_174_1600</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>ThirdParty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>AutoScout24_Buy_sell_cars</td>\n",
       "      <td>state_1694299268</td>\n",
       "      <td>textinput_filter_edittext_188_301</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>ThirdParty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>127.0</td>\n",
       "      <td>Clock</td>\n",
       "      <td>state_2022-11-01_184421020236</td>\n",
       "      <td>worldclock_search_map_txt_find_158_140</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Internal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>128.0</td>\n",
       "      <td>Clock</td>\n",
       "      <td>state_2022-11-01_184455299444</td>\n",
       "      <td>hour_48_1787</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Internal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>129.0</td>\n",
       "      <td>Clock</td>\n",
       "      <td>state_2022-11-01_184455299444</td>\n",
       "      <td>minute_201_1787</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Internal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>130.0</td>\n",
       "      <td>Clock</td>\n",
       "      <td>state_2022-11-01_184455299444</td>\n",
       "      <td>second_354_1787</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Internal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>131.0</td>\n",
       "      <td>Clock</td>\n",
       "      <td>state_2022-11-01_184455299444</td>\n",
       "      <td>preset_name_48_1944</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Internal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index                                     app_name  \\\n",
       "0      1.0                         org.koitharu.kotatsu   \n",
       "1      2.0                  AOL_News_Mail_Video_v6.47.2   \n",
       "2      3.0           de.kromke.andreas.opus1musicplayer   \n",
       "3      4.0  Petal_Maps‚ÄìGPS_Navigation_v2.5.0.303(002)   \n",
       "4      5.0                    AutoScout24_Buy_sell_cars   \n",
       "..     ...                                          ...   \n",
       "115  127.0                                        Clock   \n",
       "116  128.0                                        Clock   \n",
       "117  129.0                                        Clock   \n",
       "118  130.0                                        Clock   \n",
       "119  131.0                                        Clock   \n",
       "\n",
       "                    state_filename                            textfield_id  \\\n",
       "0                state_-1399196902                      searchView_198_119   \n",
       "1                state_-2077864795                 search_edit_text_182_66   \n",
       "2                 state_1643267838                  search_src_text_111_71   \n",
       "3                state_-1677063389                search_src_text_174_1600   \n",
       "4                 state_1694299268       textinput_filter_edittext_188_301   \n",
       "..                             ...                                     ...   \n",
       "115  state_2022-11-01_184421020236  worldclock_search_map_txt_find_158_140   \n",
       "116  state_2022-11-01_184455299444                            hour_48_1787   \n",
       "117  state_2022-11-01_184455299444                         minute_201_1787   \n",
       "118  state_2022-11-01_184455299444                         second_354_1787   \n",
       "119  state_2022-11-01_184455299444                     preset_name_48_1944   \n",
       "\n",
       "     category_matching  llm_base  droidfiller-reasoning  \\\n",
       "0                 True      True                   True   \n",
       "1                 True      True                  False   \n",
       "2                 True      True                   True   \n",
       "3                 True      True                   True   \n",
       "4                 True      True                   True   \n",
       "..                 ...       ...                    ...   \n",
       "115               True      True                   True   \n",
       "116              False      True                   True   \n",
       "117              False      True                   True   \n",
       "118              False      True                   True   \n",
       "119              False      True                   True   \n",
       "\n",
       "     droidfiller-functioncall  droidfiller     dataset  \n",
       "0                        True         True  ThirdParty  \n",
       "1                        True         True  ThirdParty  \n",
       "2                        True         True  ThirdParty  \n",
       "3                        True         True  ThirdParty  \n",
       "4                        True         True  ThirdParty  \n",
       "..                        ...          ...         ...  \n",
       "115                      True         True    Internal  \n",
       "116                      True         True    Internal  \n",
       "117                      True         True    Internal  \n",
       "118                      True         True    Internal  \n",
       "119                      True         True    Internal  \n",
       "\n",
       "[120 rows x 10 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert 'O' -> True, 'X' -> False\n",
    "df = df.replace({'O': True, 'X': False})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Settings',\n",
       " 'GalaxyStore',\n",
       " 'SmartThings',\n",
       " 'Messages',\n",
       " 'Calendar',\n",
       " 'Gallery',\n",
       " 'Contacts',\n",
       " 'SamsungMembers',\n",
       " 'MyFiles',\n",
       " 'SamsungInternet',\n",
       " 'Clock']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['dataset'] == 'Internal'].app_name.unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Technique</th>\n",
       "      <th>dataset</th>\n",
       "      <th>num_true</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DroidFilller</td>\n",
       "      <td>Internal</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DroidFilller</td>\n",
       "      <td>ThirdParty</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LLM_Base</td>\n",
       "      <td>Internal</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LLM_Base</td>\n",
       "      <td>ThirdParty</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CategoryMatching</td>\n",
       "      <td>Internal</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CategoryMatching</td>\n",
       "      <td>ThirdParty</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Technique     dataset  num_true\n",
       "0      DroidFilller    Internal        39\n",
       "1      DroidFilller  ThirdParty        79\n",
       "2          LLM_Base    Internal        33\n",
       "3          LLM_Base  ThirdParty        78\n",
       "4  CategoryMatching    Internal        22\n",
       "5  CategoryMatching  ThirdParty        56"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = [] \n",
    "\n",
    "results.append({\n",
    "    'Technique': 'DroidFilller',\n",
    "    'dataset': 'Internal',\n",
    "    'num_true': df[(df['dataset'] == 'Internal') & (df['droidfiller'] == True)].shape[0],\n",
    "})\n",
    "\n",
    "results.append({\n",
    "    'Technique': 'DroidFilller',\n",
    "    'dataset': 'ThirdParty',\n",
    "    'num_true': df[(df['dataset'] == 'ThirdParty') & (df['droidfiller'] == True)].shape[0],\n",
    "})\n",
    "\n",
    "results.append({\n",
    "    'Technique': 'LLM_Base',\n",
    "    'dataset': 'Internal',\n",
    "    'num_true': df[(df['dataset'] == 'Internal') & (df['llm_base'] == True)].shape[0],\n",
    "})\n",
    "\n",
    "results.append({\n",
    "    'Technique': 'LLM_Base',\n",
    "    'dataset': 'ThirdParty',\n",
    "    'num_true': df[(df['dataset'] == 'ThirdParty') & (df['llm_base'] == True)].shape[0],\n",
    "})\n",
    "\n",
    "results.append({\n",
    "    'Technique': 'CategoryMatching',\n",
    "    'dataset': 'Internal',\n",
    "    'num_true': df[(df['dataset'] == 'Internal') & (df['category_matching'] == True)].shape[0],\n",
    "})\n",
    "\n",
    "results.append({\n",
    "    'Technique': 'CategoryMatching',\n",
    "    'dataset': 'ThirdParty',\n",
    "    'num_true': df[(df['dataset'] == 'ThirdParty') & (df['category_matching'] == True)].shape[0],\n",
    "})\n",
    "\n",
    "eval_df = pd.DataFrame(results)\n",
    "eval_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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mrFmzhoEDBwLPei3efvttrK2tU9Te6NGjGT9+PACRkZHExsbSpEkTatWqBUBERATvvPMOzZs3p0CBAgB8+OGHLFq0iLNnz9KkSRP++usvsmXLRuHChcmbNy8dO3akZMmSlChRAoB58+bRq1cvWrRoAUCRIkV4/PgxY8eOZcCAAWTJkuWVzomIiLxYspOL+vXrK7nIpJo1a8bUqVMJDg6mXLly/Pjjj0yZMiXF7fXv35+mTZsCEBUVxfnz55k2bRp9+vRh0aJF2Nvb07FjR7Zv386pU6e4dOkSZ8+eJSwszHRZo0OHDvz00080aNCAChUqUKdOHVq0aIGLiwt3797lxo0b+Pv7J+gNiYuLIzIykitXrlCqVKlXOykiIvJCyUounp+74k2ex0KSVrBgQdzd3dm+fTtPnz4lNjaW6tWrc+3atRS15+LiQrFixUyPy5QpQ0xMDJ988gnnz5+nUKFCdOzYkYiICJo1a4avry9ubm506NDBtE/x4sXZuXMnR44c4cCBA/z8888sWrSIL774gnr16gEwYsQIateunej48b0hIiKSOswe0Dlnzhxu3ryZZNmVK1dMS7LLm6VZs2bs2LGDH3/8kWbNmmFlZfZL56WMRiPwrHdh//79nDlzhhUrVtC/f398fHxwcHDgzp07pnorVqxg586d1KlTh6FDh7JlyxZq1arFtm3bcHFxwdnZmcuXL1OsWDHTnzNnzhAQEGDRuEVEJDGzB3TOnTvXdInk706ePMnatWv5/PPPLRJcemSdK2+GOt6lS5f45ZdfEmyLvzMjIiIiURmAm5tbom3Nmzdn8uTJ3Lp1iyVLlrxSTI8ePeL27dvAs2Ti/PnzzJw5E1dXV8qWLUtkZCQAmzdv5u233+b69ev4+/sTHR1NVFQUAHfv3mXu3LnY29tTvnx5Ll68SFBQEJ06dcJgMNC9e3dmzJhBwYIFqV+/PmfPnmXMmDF4e3u/0RO9iYikB8lKLt5//31OnjwJPPuF+d57772wbuXKlS0TWTpkjItL8WyZr3pcQwp7CrZs2cKWLVsSbCtUqBC+vr7cuXOH7t27J9pnxYoViW4pzpcvH56enty4cQN3d/cUxRJv0qRJTJo0CQBra2tcXFyoXbs2gwYNwmAw4ObmxogRI1i+fDkBAQHky5cPHx8fChQowOnTpwHo27cv0dHRTJgwgdu3b5MnTx4++OADevToAUCXLl3IkiULK1euZPLkyeTOnZt///vf9O/f/5ViFxGRf2Ywxvczv8SFCxfYvn07RqORuXPn8q9//SvB7JwAVlZW5MiRg6ZNm5I37+v9dW+O+C+nFyVBERERhIaGUqJEiURzL4hkFHodv5z/ga1cfXg3rcN4bTwKFKeje33urp2ZqWYYtsldCOd2A9I6jDfKP32HxktWz0Xp0qXp27cv8OxukPjbUkVERET+zuwxF/FJxp07d4iKikowEC88PJxjx47xwQev/9KBpK3WrVtz+fLll9Y5fPiwxjuIiGQCZicXwcHBDBkyhJCQkCTLDQaDkotMaMGCBURHR7+0jq2t7WuKRkRE0pLZycXUqVN58OABw4YNY+/evdjZ2dGoUSN++eUXfvnlF1asWJEacUo6V7BgwbQOQURE0gmzb0E4efIkAwYM4MMPP8THx4fw8HDat2/PggULaNKkCStXrkyNOEVERCSDMDu5iIqKonjx4sCzWRKfn7GzTZs2nDhxwlKxiYiISAZkdnJRsGBB08C94sWL8/jxY65cuQKAnZ0dDx48sGyEIiIikqGYnVw0bdqUL7/8kh07dpAvXz5KlixJQEAAZ8+eZenSpRQpUiQ14hQREZEMwuzkom/fvnh6evL9998DzxaH2rVrF++++y6HDh2iX79+Fg9SREREMo4UjbmYNWsW8+bNA6BevXps3boVf39/tm3bZlpK+00UF/ePk5mmu+PGxMTw9ddf06ZNGzw8PPDy8qJLly4cOnQo2W0YjUY2bNjAnTt3UhzH61KuXDnKlSvHzp07kyzv2rUr5cqVY/369clu89q1a/zwww/Jqnv48GHKlStnulSYFD8/P4YPH57s44uIZDRm34rq4+PDiBEj8PHxMW0rUqTIK10O2bhxI1999RWXL1+maNGi9O3bl+bNmwPPVlodP348R48eJVu2bPzrX/+iX79+WFtbp/h4KWVlZWDX/rPce/j0tR0zV45svFW3XIr2jYyMpHPnzly/fp3+/fvj4eFBREQE69ato3PnzkydOpVWrVr9YztHjx5l+PDh7N69O0VxvG62trbs2LEjUaJ77949Dh8+bHZ7w4YNo1ChQrRo0cIi8c2ePTtNXr8iIq+L2clFVFQUuXLlslgAmzZtYuTIkXz66afUq1ePH374gUGDBpE/f34qVapE165dKV68OKtXr+avv/5i5MiRWFlZpdkCVPcePiXs7pM0Oba5Zs6cydmzZ9m6dSsFChQwbR85ciSPHz9mwoQJNG7cmOzZs7+0nWQsP5Ou1KpVi7179xIZGUmWLFlM23fu3Im7uztHjx5Nw+ggZ86caXp8EZHUZvZlkU6dOhEQEMDvv/9OeHj4Kx3caDQyc+ZMOnXqRIcOHShatCi9evWidu3aHDlyhB07dnDt2jWmTp1K2bJladKkCYMGDeLrr782Lb0tSYuOjmbdunW0adMmQWIR7+OPP2bRokXY29tz7tw5evToQfXq1alUqRLe3t4sXboUeNbN36lTJwC8vb1NlxOOHz9Ohw4dcHNzo2HDhowdO5bHjx+b2g8PD2f06NHUrFkTT09PRo4cyeDBgxNcDvj999/p1KkTVatWpWbNmowYMYJ79+6Zyhs3bsyUKVPw8fGhZs2azJkzh/Lly3P1asKFl9577z2mTJlietywYUPi4uL49ddfE9Tbtm1bgh43eDZt/cKFC3n77bepVKkSnp6edOvWjb/++gt4dgnjyJEjbNiwgcaNG5vO7cyZM2nUqBFVqlShTZs2HDhwIEG7+/bto2XLllSqVIkWLVrw888/m8qevyyyfv163nrrLdPflSpVok2bNvzvf/8z61yKiKQnZicXmzZt4syZM7Rv3x5PT08qVKiQ4I+rq2uy2woNDeXq1auJuuaXLFlCjx49OHbsGBUrVsTJyclU5uXlxePHjwkKCjI39Ezl8uXL3L9/H09PzyTL8+XLh5ubG1FRUXTp0oWcOXOyevVqtm7dSrNmzZgyZQpBQUF4eHgwe/ZsANauXYuPjw/BwcF07tyZevXqsXnzZqZPn86ZM2fo0qWLqZdj2LBhHDhwgBkzZrB69WoePXqUYNzCqVOn8PPzo0yZMvz3v/9l5syZnDx5kq5duxIbG2uqt2rVKkaNGsXixYv56KOPcHZ2ZtOmTaby0NBQTpw4Qdu2bU3bsmbNSsOGDfnxxx9N28LCwjh+/DjNmjVLcB5WrFjBkiVLGD58ODt27GDu3Ln8+eefTJ48GXh2CcPDw4PmzZubBjFPnDiR1atXM2zYMLZs2UK9evXo2bMnFy9eTNDuZ599xpYtWyhevDgff/wxT54k3eN1/fp1Vq9ezbRp09iwYQNZs2Zl+PDhyT6XIiLpjdmXRVq3bm2xg4eGhgLw9OlTunbtSmBgIIULF6ZXr140btyYGzduJFraPX459+vXr1OlShWLxfKmiZ9v5PnELCnh4eGmnqP4yyP9+/dn8eLFnD17lgoVKpjacHZ2xt7eniVLllCnTh169uwJPJvv5Msvv6RJkyYcOXKEggULsmPHDhYvXkzt2rUBmDZtGsePHzcdd+nSpZQrV47PPvsMgFKlSuHv788777zD/v37adCgAQANGjQwtQHwzjvvsGnTJnr37g08G69TuXJlSpcuneB5NW/enBEjRhAVFYWdnR3bt2+nRo0aODs7J6hXtGhRpkyZQqNGjQAoVKgQzZo1Y/v27cCzSxi2trbY29vj7OzM48eP+f777/nss89MicrAgQMxGo0Jem4+/fRTatasCUCfPn346aefCAkJwc3NLdH/QXR0NGPHjqVChQoAdO7cmT59+nD79m0iIyP/8VyKiKQ3KV4V1RLiP4yHDRtG3759GTJkCDt27KB3794sW7aMiIgIcuTIkWCf+GvokZGRKT6u0Wjk6dOkB2VGRkYSFxdHbGxsgl/QQJoOwvt7LP8kPiG4e/fuS/d1cnLi/fffZ/PmzQQFBfHXX39x9uxZ4NmdJrGxscTFxQGYzsuZM2e4dOkS7u7uidq7cOECd+/eBcDNzc10bBsbGypXrozRaCQ2NpazZ89Sp06dBLGVKVMGR0dHgoODqVu3LkajkaJFiyao8+6777J06VKOHz+Om5sbmzZtonv37gnqxMXFmfbft28fjRs35ocffqBt27amevHPpUGDBpw8eZKAgABCQ0MJDQ0lJCSEvHnzmuoajUZT3CEhIURHR1O5cuUExxwwYAAAR44cAUgQt4ODA/AsiY6NjU3QXvy5LV68uKl+fJIXERHBmTNn/vFcJiW+7fDwcNMx5NnCilmzZk3rMOQ1Cw8Pz3Bjx9Iro9GIwWD4x3pmJxfx9u3bx8GDB7l9+zYDBw4kKCiIihUrUqhQoWS3Eb9KZteuXfH19QWgQoUKBAYGsmzZMuzt7RONrYhPKrJly5bS0ImOjn7pZRUbG5tEyYuVlVWafihFRUWZ9SWRJ08eXFxcOHr0qOlX+fMuXrzI9OnT+eijjxg5ciTOzs40aNCA6tWrU7FiRZo3b050dDQRERGm/4PIyEgiIiKIjY2lefPmdO3aNVG7uXLlMo0XCA8Px8bm/15i8V/oERERxMXFERMTQ0RERIL94z8AIiIiMBqNWFtbJ6hTuHBhKlWqxMaNG3n06BF37tzB29s7QZ341Vnr16/Ptm3bKFWqFIGBgcyYMcNUL/65LVu2jEWLFtGqVSuqVq3K+++/z759+9i+fbup7t/jfv5c/F38uXr+ucW/luL3eb69+Fjj4uJM9Z8/3/HHe9m5TEpkZCQxMTEJLtXIs0tm5ly6lTdDaGjoK48RlP9jZ2f3j3XMTi7Cw8Pp06cPBw8exMHBgSdPntC1a1e+++47AgMDWbVqFWXKlElWW/ny5QOgbNmyCbaXLl2an3/+mRo1anDu3LkEZbdu3Uqwb0rY2tom6kaPFxkZybVr18iSJQv29vYpPoalJec/8+/atm3LN998Q/fu3RMN6ly1ahWBgYGcOHGChw8fsn37dlOyF3/ObWxssLe3N/UWxZ+TsmXL8ueffyb4f4tPVgYOHEilSpUwGAwEBwdTr1494NkXZnBwMF5eXtjb21O+fHlOnTqV4BwHBwfz+PFjypUrh729PQaDwRTD8/71r3+xYMECrKys8Pb2Jk+ePAnK4y9j+Pj48Omnn1KhQgVq166doF58naVLl9K7d2+6detmKvvmm28ATMe1trbG2toae3t7ypQpg42NDefPn09wieP999+nefPmpksbz79+4s+fnZ0d9vb2WFlZmdqLP+fPP8f4/+ssWbIk61y+iI2NDUWLFk1wx0xml5xfXPLmKVGihHouLOTChQvJqmd2cuHv78+ZM2dYvnw51apVo1KlSgBMmTKFbt26MXPmTObMmZOstipWrEj27Nk5efIk1apVM20/d+4cRYsWpXr16mzcuJHHjx+bupYPHTpE9uzZKV++vLmhmxgMhhf2fFhZWZk+/NPTXAQpiaV3794cOHAAPz8/BgwYgKenJ/fv3+e7775j8+bNzJgxA6PRSHh4OLt27aJq1apcvHiRL774Anj269va2tp07s+dO4eLiwtdu3alQ4cOTJgwgY4dO/Lw4UPGjh1LREQEJUuWxM7OjubNmzNx4kTGjRtHnjx5WLhwITdu3DCd2y5dutC+fXsmTZpE+/btCQsLY/z48bi6ulKnTh2sra0xGAym+s9r1aoVU6ZMYcOGDUnOGRG/T8OGDTEajSxcuJBx48YlqBdfp0CBAhw8eBBvb2+srKzYtGkTu3btInfu3Kb62bNn59q1a9y+fZv8+fPj5+fHrFmzcHFxoUyZMnz//fecP3+eKVOmcPv27QTtx//7+W0GgwGDwYC1tbWp7O+xxf9duHDhfzyXL3q9xPe2packWSQt6FKY5SQ3QTc7ufjxxx8ZNGgQXl5eCa735s2bl169ejFu3Lhkt2Vvb0+3bt2YO3eu6e6FH374gQMHDrB8+XLc3d0JCAjg448/ZsiQIVy5cgV/f3+6dOmSol/ylpArR8ovx7zu42XNmpVVq1axdOlSFi1axLVr17C3t8fV1ZWVK1dSrVo1jEYjZ86cYfLkyTx+/JhChQrRrl07du/ezenTp/nggw8oW7YsDRo04OOPP2bQoEF06dKFxYsXM3PmTHx9fcmWLRu1atVi2LBhpv+X8ePHM2HCBPr164fRaKRVq1Z4eHiYfqlXqVKFxYsXExAQwLvvvouDgwNNmjRh8ODBpjovEl/3yJEj1KlT54X1smTJgre3N7t27Ury0hDA1KlTGTduHG3btiV79uxUqVKFsWPHMmbMGK5du0bBggV5//33GTZsGK1bt+a3335j0KBBWFtbM3r0aB49ekT58uX56quvKFmypCm5sKR/OpciIumNwWhmX5Gbmxvz5s2jbt26xMbGUrFiRdatW0fFihU5ePAgvXr14uTJk2YFsWzZMlatWsXNmzcpVaoU/fr1o0mTJgBcunSJsWPHcuzYMZycnEwzdMb/ujPX6dOnAahcuXKS5REREYSGhlKiRIlEv/ji4oxYWb3+btW0Om5KRUZG8uuvv+Ll5WXq9QB4++23ad26NX369HnlY/j5+eHp6cnAgQNfua30LKXn8mWvYwH/A1u5+vBuWofx2ngUKE5H9/rcXTuTmLCr/7zDG8ImdyGc2w1I6zDeKP/0HRrP7J6LMmXKsGXLFurWrZuobM+ePckeb/G8zp0707lz5yTLihUrZprQKa2l1Rd8Rkos4NmYgbFjx1KjRg169+6NtbU133//PdeuXUs0z4S5fvrpJ4KCgjhx4gRTp061UMTpV2qeSxGR1GJ2ctGrVy/69u3L/fv3adSoEQaDgaNHj7J+/XpWr17Nl19+mRpxSgZiMBj46quvmDZtGu+99x6xsbG4urqydOlSSpUq9UptL168mNDQUMaPH5/kzKNvmtQ8lyIiqcXs5KJJkyZMmzaNL7/8kn379gEwefJkXFxcGDNmjH5NCfDsluLU6HFavXq1xdtM71LrXIqIpJYUzXPRqlUrWrVqxcWLF7l//z45cuSgZMmSKR4HISIiIm+OFE+iBVCyZElLxSEiIiJvCLOTi7t37zJx4kR+/vnnJKdUNRgMBAYGWizAtKIJVyQj0+tXRNKS2cnFuHHj2Lt3Ly1atCB//vxv3KWQ+LkDnj59qolXJMOKXztHc2FIZmaV1THD3cpvKWn9vM1OLn755Rc+/fRT3nvvvdSIJ81ZW1uTM2dO0zTj2bJl05TBkmHEL8p369YtcubMma5mmRV53QxZ7LGyMrBr/1nuPUx6sco3Ua4c2Xirbrk0jcHs5MLW1pYiRYqkRizpRvwy7/EJhkhGkzNnTtPrWCSzu/fwKWF3n6R1GJmK2cnFW2+9xdatW6ldu3ZqxJMuGAwGChQoQN68eU2rVopkFLa2tuqxEJE0ZXZy4erqSkBAAJcvX6ZKlSqJphY2GAwWmd45PUhvi5eJiIhkBCka0Alw9OhRjh49mqj8TUouRERExHxmJxfBwcGpEYeIiIi8IV7pPtJHjx4REhJCVFRUguXXRUREJPNKUXJx+PBh2rVrR40aNWjVqhXnz59n8ODBTJ482dLxiYiISAZjdnLx22+/0bVrV+zt7RkyZIhpJsDy5cuzYsUKli1bZvEgRUREJOMwO7kICAjA29ublStX8p///MeUXPTs2ZNu3bqxdu1aiwcpIiIiGYfZyUVQUBBt27YFSDRzZZ06dbh69aplIhMREZEMyezkwtHRkdu3bydZdv36dRwdHV85KBEREcm4zE4uvL29mTFjBqdPnzZtMxgM3LhxgwULFtCwYUNLxiciIiIZjNnzXAwePJiTJ0/y73//m9y5cwMwaNAgbty4QYECBRg0aJDFgxQREZGMw+zkwsnJibVr17Jx40YOHTrE/fv3cXR0xM/PjzZt2miZchERkUzO7OQCwM7Ojn//+9/8+9//tnQ8IiIiksGZnVzMmTPnhWVWVlZky5aNYsWKUadOHezs7F4pOBEREcl4zE4uNm/ezI0bN4iKisLGxoacOXNy//59YmJiMBgMpnkvSpcuzYoVK3B2drZ40CIiIpJ+mX23yIABA7Czs8Pf359Tp06xf/9+Tp8+zZw5c8iVKxcBAQFs2bIFg8GAv79/asQsIiIi6ZjZycXs2bP5+OOP8fHxwcrq2e4Gg4EmTZrQv39/Zs6cSZkyZejZsyf79u2zeMAiIiKSvpmdXFy/fp1ixYolWVaoUCHTDJ358uXjwYMHrxadiIiIZDhmJxelS5d+4foh33//PSVKlADgzz//JG/evK8WnYiIiGQ4Zg/o7NevH3369MHX15emTZvi4uJCWFgYP/30E2fPnmXWrFkEBgYybdo00xokIiIiknmYnVw0bNiQJUuWMHv2bObMmUNsbCw2NjZUrVqVr7/+mmrVqrFnzx5atGjBxx9/nAohi4iISHqWokm0vLy88PLyIioqigcPHuDi4mIa3AnQuHFjGjdubLEgRUREJONIUXIRz87Ojjx58lgqFhEREXkDmD2gU0RERORllFyIiIiIRSm5EBEREYsyO7nYuHEj9+7dS7Ls9u3bLFq06JWDEhERkYzL7ORixIgRXL58OcmyoKAgZs2a9cpBiYiISMaVrLtFPvroI0JCQgAwGo306dMnyeXU79y5Q9GiRS0boYiIiGQoyUouevbsaZrye8OGDbi6uiZaSt3KyoocOXLQpk0by0cpIiIiGUaykgtPT088PT1Nj3v37k2RIkVSLSgRERHJuMwec1GrVq0XJhbh4eGMHj36lYMSERGRjMvs5GLo0KEMHjyYR48eJdh++PBhWrZsybp16ywWnIiIiGQ8ZicXkyZNYt++fbRu3ZojR44QERHBuHHj+PDDD8mVK9cLl2MXERGRzMHstUXatGmDl5cXn332GR9++CEuLi48ffqU4cOH4+fnl2ABMxEREcl8UpQJ5MmTxzTA8/bt2+TOnZtKlSq9cmIRGhqKh4cH69evN20LCgqiY8eOuLu707hxY1asWPFKxxAREZHUZXY2cOTIEVq3bs28efPo3LkzW7ZswcXFBT8/P8aMGcPjx49TFEh0dDRDhgzh6dOnpm337t2jc+fOFC1alHXr1tGnTx+mT5+ucR0iIiLpmNmXRTp16kTx4sX55ptvcHd3B+Cbb75h+fLlzJw5k71797Jv3z6zA5k9ezYODg4Jtv33v//F1taWcePGYWNjQ6lSpbh06RJfffUVbdu2NfsYIiIikvrM7rn48MMP2bRpkymxADAYDHTu3JkNGzZQoEABs4M4evQoa9asYfLkyQm2Hzt2jBo1amBj8385kJeXF3/++SdhYWFmH0dERERSn9k9F8OHD0/wODIyEjs7OwwGAyVKlOC7774zq72HDx8ydOhQRo0alSgxuXHjBmXLlk2wLW/evABcv36d3Llzmxs+8GwK8+cvv4hI5mAwGMiaNWtahyHyWoSHh2M0Gi3aptFoxGAw/GM9s5MLgIsXLzJr1iwOHjzI48ePWbt2Ld9//z0lS5bEz8/PrLbGjBmDh4cHrVq1SlQWERGRaA2TLFmyAM+SmpSKjo4mKCgoxfuLSMaUNWtWXF1d0zoMkdciNDSU8PBwi7eb1Npif2d2chEUFESHDh1wcXGhVatWfPvttwBYW1szadIkHBwc8PX1TVZbGzdu5NixY2zZsiXJcnt7e6KiohJsi08qsmXLZm7oJra2tpQuXTrF+4tIxpScX1wib4oSJUpYvOfiwoULyapndnIxZcoUKlWqxNKlS4FngzkBRo0aRWRkJCtWrEh2crFu3Tru3LlDw4YNE2wfPXo027ZtI3/+/Ny6dStBWfzjfPnymRu6icFgeKXkREREJL1LjUuAyU3QzU4uTpw4gb+/PzY2NsTGxiYo8/HxYevWrclua/r06URERCTY1rRpU/r370/r1q3ZtGkTq1evJjY2FmtrawAOHTpEiRIlcHFxMTf01yLOGIeVIfNNJJZZn7eIiCRmdnKRJUuWRAlBvPv37yfrWky8F/U+uLi4kC9fPtq2bcvixYsZOXIk3bp149SpUyxfvpyxY8eaG/ZrY2Ww4puTv3Lz8YO0DuW1yefgRIcq9dI6DBERSSfMTi7q1KnDrFmz8PT0JE+ePMCzbpInT56wdOlSateubbHgXFxcWLx4MRMnTsTX15c8efIwdOjQZF92SSs3Hz/g6sO7aR2GiIhImjA7ufjkk0947733aNasGeXLl8dgMDB58mRCQ0MxGo34+/u/UkBnz55N8NjNzY01a9a8UpsiIiLy+ph9kbxAgQJs2rSJ//znPxiNRooWLcrTp09p2bIl69evp0iRIqkRp4iIiGQQZvdcXLt2jTx58jBw4MBEZZGRkRw/fty0qJmIiIhkPmb3XHh7e79wAqpTp07RuXPnVw5KREREMq5k9VxMmTKF+/fvA8+m/pw3bx65cuVKVC8oKAhHR0eLBigiIiIZS7KSi5IlSzJ//nzg2Z0hf/zxR6JbTq2trXF0dGTEiBGWj1JEREQyjGQlF+3ataNdu3YANG7cmHnz5lG+fPlUDUxEREQyJrMHdO7Zsyc14hAREZE3hOZrFhEREYtSciEiIiIWpeRCRERELMriycXfV0oVERGRzCVFk2gFBwcnWXbq1CmLLlwmIiIiGU+y7hbZunUrMTExAFy9epVdu3YlmWD89ttvREdHWzZCERERyVCSlVycPn2ar7/+Gng2idbcuXNfWFfTf2c+jnb2GOPiMFhlviE8mfV5i4i8TLKSi8GDB9OpUyeMRiNNmjRhzpw5VKhQIUEda2trHBwccHBwSJVAJf3KamuHwcqKBz99R+y9W2kdzmtjnSsvTk0+SOswRETSnWQlF3Z2dhQqVAiA3bt3kzdvXkJCQkyzdIaFhXHmzBnq1KmTepFKuhd77xYxYVfTOgwREUljZvfn2tjY0KZNG/r27WvadubMGXr06EHHjh1NC5yJiIhI5mR2cjF16lSioqKYPn26aVuDBg1Yv3499+/f58svv7RogCIiIpKxmJ1cHDx4kCFDhuDu7p5gu6urKwMGDGDv3r2Wik1EREQyILOTi6ioKKytrZMsy5o1K0+ePHnloERERCTjMju5qFKlCsuWLUs0n0VMTAwrVqzAzc3NYsGJiIhIxmP2kuv9+/fHz88Pb29v6tevj4uLC3fv3uXAgQPcuXOHlStXpkacIiIikkGYnVy4u7uzZs0aFixYwM8//8z9+/dxdHSkWrVq9O7dO9H8FyIiIpK5mJ1cwLPBm7NmzbJ0LCIiIvIGSFFyAbBv3z4OHjzI7du3GThwIEFBQVSsWNE02ZaIiIhkTmYnF+Hh4fTp04eDBw/i4ODAkydP6Nq1K9999x2BgYGsWrWKMmXKpEasIiIikgGYfbeIv78/Z86cYfny5Rw6dAij0QjAlClTyJcvHzNnzrR4kCIiIpJxmJ1c/PjjjwwaNAgvLy8MBoNpe968eenVqxf/+9//LBqgiIiIZCxmJxcPHz584bgKJycnnj59+spBiYiISMZldnJRpkwZtmzZkmTZnj17NN5CREQkkzN7QGevXr3o27cv9+/fp1GjRhgMBo4ePcr69etZvXq1Fi4TERHJ5MxOLpo0acK0adP48ssv2bdvHwCTJ0/GxcWFMWPG0KxZM4sHKSIiIhmH2clFSEgIrVq1olWrVly8eJH79++TI0cOSpYsiZWV2VdZRERE5A1jdjbQvn17Nm7cCEDJkiXx9PSkdOnSSixEREQESEFyYWtrS65cuVIjFhEREXkDmH1ZZMCAAUydOpVHjx5Rvnx5smXLlqhOwYIFLRKciIiIZDxmJxdjxowhNjaWTz755IV1goKCXikoERERybjMTi4mTJiQGnGIiIjIG8Ls5OL69eu8/fbblCpVKjXiERERkQzO7AGdCxcu5MqVK6kRi4iIiLwBzE4uSpcuTWhoaGrEIiIiIm8Asy+LNGrUCH9/f3799VfKlSuX6G4Rg8FAnz59LBagiIiIZCxmJxdz5swB4MCBAxw4cCBRuZILERGRzM3s5CI4ODg14hAREZE3xCvN2R0SEsKJEyf466+/UtzG/fv3+fzzz6lfvz6enp588MEHHDt2zFT+22+/0aZNG6pUqUKzZs344YcfXiVkEYuxyupIXJwxrcNIE5n1eYtI8pjdcwGwdetWpkyZQlhYmGlb7ty5GTx4MO+++65ZbQ0aNIjbt2/j7++Pi4sLK1eupGvXrmzYsAGj0UiPHj3o3Lkz06ZN4+eff2bo0KE4OztTq1atlIQuYjGGLPZYWRnYtf8s9x4+TetwXptcObLxVt1yaR2GiKRjZicXe/bs4ZNPPsHLy4tBgwaRO3dubt26xebNmxkxYgQ5c+akYcOGyWrr0qVLHDhwgG+//ZaqVasC8Nlnn/Hrr7+yZcsW7ty5Q7ly5Rg4cCAApUqVIjAwkMWLFyu5kHTj3sOnhN19ktZhiIikG2YnF/Pnz6dZs2bMmDEjwfa2bdsycOBAFi5cmOzkIleuXHz11VdUrlzZtM1gMGAwGHj48CHHjh2jSZMmCfbx8vJi4sSJGI1GDAaDueGLiIhIKjM7uTh37hz9+vVLsszX15cBAwYku60cOXLQoEGDBNt27NjBpUuX+PTTT9mwYQP58+dPUJ43b17Cw8O5d+8ezs7O5oYPgNFo5OlTy3djGwwGsmbNavF2RdKj8PBwjMaMNfZC71HJTFLjPZrcH/ZmJxe5cuXiwYMHSZbdv38fOzs7c5s0OX78OCNGjKBp06Y0bNiQiIiIRO3FP46KikrxcaKjo1NlcbWsWbPi6upq8XZF0qPQ0FDCw8PTOgyz6D0qmUlqvUeT8z1vdnJRq1Yt5syZQ/Xq1RP0Kly/fp25c+dSp04dc5sE4KeffmLIkCF4enoyffp0ALJkyZIoiYh//Cq/PmxtbSldunSK938RXaaRzKREiRIZsudCJLNIjffohQsXklXP7ORi0KBBtG3blqZNm+Lh4UHu3LkJCwvj999/x8nJicGDB5sd7KpVq5g4cSLNmjVjypQppqyoQIEC3Lp1K0HdW7dukS1bNhwdHc0+TjyDwZBoZlERMY8uL4ikb6nxHk1ugm72PBd58uRhw4YN+Pn5ER4ezh9//EF4eDh+fn5s2LCBQoUKmdXet99+y/jx4+nQoQP+/v4JuluqVavGkSNHEtQ/dOgQnp6eWFm90hQdIiIikkpSNM9Frly5aNWqFZ988gkAt2/fJjAwkJw5c5rVTmhoKJMmTeKtt96iR48eCebNsLe3x8/PD19fX6ZPn46vry/79u1j+/btLF68OCVhi4iIyGtgdnJx8+ZNunXrRnh4OD/99BMAgYGB9OjRA3d3dxYsWJDsJGPHjh1ER0eza9cudu3alaDM19eXyZMnM2/ePKZNm8bXX39N4cKFmTZtmua4EBERScfMTi6mTp1KVFSUadAlQIMGDVi/fj2DBg3iyy+/ZPz48clqq2fPnvTs2fOlderXr0/9+vXNDVNERETSiNkDFw4ePMiQIUNwd3dPsN3V1ZUBAwawd+9eS8UmIiIiGZDZyUVUVBTW1tZJlmXNmpUnTzQNsoiISGZmdnJRpUoVli1bRnR0dILtMTExrFixAjc3N4sFJyIiIhmP2WMu+vfvj5+fH97e3tSvXx8XFxfu3r3LgQMHuHPnDitXrkyNOEVERCSDMDu5cHd3Z82aNSxYsICff/6Z+/fv4+joSLVq1ejduzcVKlRIjThFREQkg0jRPBeurq7MmjXL0rGIiIjIG0DTXIqIiIhFKbkQERERi1JyISIiIhaVrOTir7/+Ii4uLrVjERERkTdAspKLdu3acfz4cQBGjBjB5cuXUzUoERERybiSlVxERkZy4cIFADZs2MC9e/dSNSgRERHJuJJ1K6qXlxdjxoxh7NixALz33nsvrGswGAgMDLRMdCIiIpLhJCu5mD59Ops2beLevXvMmTOHtm3bkj9//tSOTURERDKgZCUXDg4OdOjQAYDDhw/TuXNnSpUqlaqBiYiISMZk9gyd8WuHhISEcOTIER49ekSuXLnw9PRUwiEiIiIpm/77888/Z+3atRiNRtM2g8GAr68vkyZNslhwIiIikvGYnVwsWrSIdevW0b9/f1q3bk2ePHm4desWmzZtYv78+ZQtW5YPP/wwFUIVERGRjMDs5OL777+nW7du9OrVy7StcOHC9OnTh+joaP773/8quRAREcnEzJ7++/r163h5eSVZVrNmTa5cufLKQYmIiEjGZXZyUahQIc6ePZtkWXBwMM7Ozq8clIiIiGRcZicXLVu2ZPbs2fz444+mAZ1Go5Ft27YxZ84cfHx8LB6kiIiIZBxmj7no3r07x44dY+DAgXzyySfkypWLe/fuERsbS40aNRgwYEBqxCkiIiIZhNnJhZ2dHcuWLWPfvn0cPXqUBw8e4OTkRPXq1WnQoEFqxCgiIiIZSIrmuQBo0KCBkgkRERFJxOwxFyIiIiIvo+RCRERELErJhYiIiFiUkgsRERGxqGQlF+fOnTP9u0KFCpw6dQqA2NhYKlSowJkzZ1InOhEREclwknW3iK+vLw4ODri7u2M0Gjlz5gwlS5Yka9asCVZGFREREUlWz8WRI0fw9/enYsWKAEyePJkaNWrQqlUrDAYDP/74I4cPH+bhw4epGqyIiIikf8lKLrJnz06dOnXo378/ACtWrGDLli106dIFo9HI7t276dmzJzVq1KBRo0apGrCIiIikb8m6LLJ27VqqVq1KyZIlATAYDJQqVYrixYszatQopk+fjqurK6GhoQnGZ4iIiEjmk6zkYuXKlYwZM4YcOXJgMBjYsGEDDx8+xNXVFXiWbBgMBkqWLGlKQERERCRzSlZysXnzZh49esTx48fp0aMHp06dYtu2bTx48ACDwUBAQADVqlWjfPnylC9fnrx586Z23CIiIpJOJXttEUdHR9NaIqNHj8bNzY2QkBBatGhB9uzZOXDgAEuWLOHhw4cEBQWlWsAiIiKSvpm9cFnBggWxs7MDoESJEhQsWJDevXtTpkwZAG7evGnZCEVERCRDMTu52LNnj+nfVlZWCR4D5MuX79WjEhERkQxL03+LiIiIRSm5EBEREYtSciEiIiIWpeRCRERELCpDJBdxcXHMmjWLevXq4e7uTvfu3bl8+XJahyUiIiJJyBDJxbx58/j2228ZP348q1evJi4ujm7duhEVFZXWoYmIiMjfpPvkIioqiqVLl9K/f38aNmxI+fLlmTFjBjdu3GDnzp1pHZ6IiIj8TbpPLoKDg3ny5Am1atUybcuRIweurq4cPXo0DSMTERGRpKT75OLGjRsAFChQIMH2vHnzmspEREQk/TB7hs7XLTw8HMA05Xi8LFmy8ODBA7Pbi46Oxmg0curUKYvE93cGg4Fa9vmItcuTKu2nR7ZxNpw+fZq4YjWgSFxah/PaGGxsMZw+TfE8Roq6ZE3rcF4bKysjp0+fxmg0pnUoKaL3qN6jb7rUfI9GR0djMBj+sV66Ty7s7e2BZ2Mv4v8NEBkZSdas5r9Y4k9Kck5OSjnY2f9zpTeQVVaHtA4hTWS1t03rENJEar6HUpveo5mL3qOWbfONSC7iL4fcunWLokWLmrbfunWLcuXKmd2eh4eHxWITERGRxNL9mIvy5cvj4ODA4cOHTdsePnxIYGAg1atXT8PIREREJCnpvufCzs6Ojh07Mn36dJydnSlUqBDTpk0jf/78NG3aNK3DExERkb9J98kFQP/+/YmJiWHUqFFERERQvXp1lixZgq1t5ryOJiIikp4ZjBl1yLeIiIikS+l+zIWIiIhkLEouRERExKKUXIiIiIhFKbkQERERi1JyISIiIhal5EJEREQsSsmFiIiIWFSGmERLMrbhw4dz9epVVq5cmaz6e/fupUiRIpQuXTqVI3s5c+MWSU+GDx/Ohg0bXlqnRo0aFCpUiMmTJydZfvjwYTp16sTu3bspXLhwso/t5+dnavfKlSt4e3snKLe2tsbJyYmaNWsydOhQChYsmOy2/85oNLJx40bq16+Pi4tLitsRy1LPhaQrV69epWfPnty5cyetQxHJ0EaOHMn+/ftNfwA+/fTTRNtexsPDg/3795sWkHwVs2fPNh137969zJ49m6CgIHr06PFKS4MfPXqU4cOHEx4e/soxiuWo50LSFU0YK2IZjo6OODo6JtqWJ0+eZLdhZ2dnVv2XcXJyStBWvnz56Nu3L0OGDOHs2bOUL18+Re3qMyN9Us+FvFaNGzdmyZIl9OvXDw8PD2rWrMmECROIiYlJ0H3aqVMnZs+eDUBISAjdu3fHw8ODunXrMnjwYG7fvm1q08/Pj88++4x27dpRrVo1Nm/ezPDhwxk+fDhTpkyhVq1aVKlShR49enDz5k3TfseOHaNTp054enpSqVIlmjdvzqZNm17vCRFJY0+ePGHEiBFUq1aNqlWrMnz4cJ4+fQo8uyxSrlw5rly5Ajx7/06ZMgUfHx9q1qzJkSNHiIqKYtKkSdSqVYuqVasybdo04uLiknVsa2trANM6Uf/0nhw+fDj9+/enS5cueHp6smjRIjp16gSAt7c3//3vf6lVqxZz5sxJcJzVq1dTt25dYmJiXu1kSbIpuZDXbubMmVSvXp3NmzczdOhQVq1axdatWylQoABr164FnnWhdunShZs3b9K+fXuKFSvG999/z4IFC3j8+DHvvfee6QMQYO3atXTq1Ilvv/2WevXqAbB161bu37/PqlWrWLRoEWfOnCEgIACAmzdv0rVrVypXrsyGDRvYuHEjbm5ujBw5krCwsNd+TkTSys6dO8mbNy/r169n6tSpbNu2jUWLFr2w/qpVqxg1ahSLFy/G3d2dCRMmsG3bNiZPnszq1au5ceMGx44de+kx4+LiCAoKYv78+ZQvX54SJUok+z25Y8cOateuzbp16/Dx8TH9CFm7di2tW7emdevWbN68OcHxNm7cSOvWrbGxUWf966IzLa9d3bp1Tb82ihQpwsqVKzl+/Djvvvsuzs7OwLMu1OzZs7No0SLy58/PqFGjTPsHBATg5eXF9u3badOmDQAVKlSgVatWCY7j6OjIuHHjsLW1pVSpUvj4+LBv3z4AIiMj6devH127dsVgMADw0UcfsXHjRv78809y586d6udBJD1wc3Nj4MCBABQtWpQ6derwxx9/vLB+gwYNqF27NgCPHz9m/fr1jB49mgYNGgAwadIkDh06lGi/7t27m3oqoqKiMBqNVKtWjfHjx2NlZZXs96STkxPdunUztRvfq+Ls7Iy9vT1t27Zl+fLl/P7773h4eBAaGsrvv//OhAkTXvVUiRmUXMhrV6pUqQSPHR0diY6OTrJuYGAg58+fx8PDI8H2yMhIQkJCTI+LFSuWaN+iRYuaulv/fpyiRYvSpk0bVqxYwblz5/jrr78IDg4GIDY2NmVPTCQDKl68eILHTk5OXL169YX1n3+vhYaGEh0dTeXKlU3bsmTJgqura6L9JkyYQJUqVQCwsbHBxcUFe3t7U3ly35NJvdefV7ZsWSpXrszGjRvx8PAw9YCk9d1nmY2SC3nt7OzsEm170aCsuLg4vLy8GD16dKKy5werPf8h9bLjxLtw4QLt27enYsWK1K5dm6ZNm5IrVy7atWuXnKcg8saI701Iruffa/E9DH9//yZ1+SFfvnwvTQyS+55M6r3+d23btmXGjBmMHDmSLVu2JOjpkNdDYy4kXYn/sIpXpkwZQkJCKFCgAMWKFaNYsWI4OTkxadIkzp07l+LjrF69GhcXF5YtW0b37t1p0KCB6bquRp+LJE+JEiXIkiULx48fN22LiYkx9TiYI6Xvyb9/ZgC0bNmSyMhIli1bRlhYGC1btjQ7Hnk1Si4kXcmWLRsA586d49GjR7Rv355Hjx4xZMgQgoODCQ4OZuDAgZw+fZqyZcum+Dj58+fnxo0b7Nu3j6tXr7Jz507GjBkDPLseLCL/LHv27HTs2JFZs2axc+dOQkJCGD16dIK7spIrpe/J+M+M4OBgnjx5Ajzr1XzrrbeYN28e3t7e5MiRw/wnJ69EyYWkK7ly5aJt27ZMnTqVmTNnUqRIEVatWsWTJ0/44IMP6NixI7a2tqxYscI0+DMlOnXqRPPmzRk6dCgtW7Zk/vz5DBo0iEKFCnH69GkLPiORN9vgwYNp374948aN41//+hdGo5HGjRub3U5K35Nly5alQYMGfPzxx6xZs8a0vU2bNkRERJgGfcvrZTCqD1hERN4w69evZ/bs2ezevRsrK/2Oft00oFNERN4YZ86c4eLFi8yaNYuOHTsqsUgjOusiIvLGOHHiBKNGjaJKlSr85z//SetwMi1dFhERERGLUs+FiIiIWJSSCxEREbEoJRciIiJiUUouRERExKKUXIiIiIhFKbkQERERi1JyISIiIhal5EJEREQsSsmFiIiIWNT/A/J4dyEqGtvnAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(6, 3))\n",
    "\n",
    "# bigger font \n",
    "sns.set(font_scale=2)\n",
    "\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "# pretty palette\n",
    "ax = sns.barplot(x=\"dataset\", y=\"num_true\", hue=\"Technique\", data=eval_df, palette=\"Set2\")\n",
    "# ax.set_title('Comparison on Realistic Text Generation', fontsize=15)\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('# of correct text generations')\n",
    "\n",
    "# remove legend title\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles=handles, labels=labels, title='')\n",
    "\n",
    "# save figure\n",
    "plt.savefig('/Users/greenmon/Dropbox/COINSE/DroidFiller/figures/RQ1_text_quality.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "sr-stem",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}