{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ba9e5acd-e17d-4318-9272-04c9f6706186",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e4f0b3f0-5255-46f1-822f-e455087ba315",
   "metadata": {},
   "outputs": [],
   "source": [
    "phab_path = \"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case2/051825_coref_rel_phab_comments.csv\"\n",
    "phab_df = pd.read_csv(phab_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac5e624b-08a4-4ede-bc96-cfc26c3edac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def http_relevant(text):\n",
    "    if pd.isnull(text):\n",
    "        return False\n",
    "    # expanded dictionary for relevancy\n",
    "    # http, login, SSL, TLS, certificate \n",
    "    for word in text.split():\n",
    "        if \"://\" not in word.lower():\n",
    "            #http\n",
    "            if \"http\" in word.lower():\n",
    "                return True\n",
    "            #login\n",
    "            if \"login\" in word.lower():\n",
    "                return True\n",
    "            #ssl\n",
    "            if \"ssl\" in word.lower():\n",
    "                return True\n",
    "            #tls\n",
    "            if \"tls\" in word.lower():\n",
    "                return True\n",
    "            #cert\n",
    "            if word.lower().startswith(\"cert\") and not word.lower().startswith(\"certain\"):\n",
    "                return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d5925c49-ea1d-4813-98aa-eae10d5879ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_migrated(comment_text):\n",
    "    if pd.isnull(comment_text):\n",
    "        return False\n",
    "    text = comment_text.strip()\n",
    "    if text.startswith(\"Originally from: http://sourceforge.net\"):\n",
    "        return True \n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c05f8b0d-ae4c-4cd5-8832-edb54e36ed9a",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>task_title</th>\n",
       "      <th>comment_text</th>\n",
       "      <th>date_created</th>\n",
       "      <th>speaker</th>\n",
       "      <th>meta.affil</th>\n",
       "      <th>conversation_id</th>\n",
       "      <th>comment_type</th>\n",
       "      <th>status</th>\n",
       "      <th>meta.gerrit</th>\n",
       "      <th>id</th>\n",
       "      <th>reply_to</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>is_relevant</th>\n",
       "      <th>is_migrated</th>\n",
       "      <th>text</th>\n",
       "      <th>resolved_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>time data error</td>\n",
       "      <td>After last update via SVN bot does not work, s...</td>\n",
       "      <td>1381482240</td>\n",
       "      <td>PHID-USER-wwnv7nzuscfuc2xfjwbq</td>\n",
       "      <td>False</td>\n",
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       "      <td>2013-10-11 09:04:00+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>After last update via SVN bot does not work, s...</td>\n",
       "      <td>After last update via SVN bot does not work, s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>time data error</td>\n",
       "      <td>SVN r10320 is https://gerrit.wikimedia.org/r/8...</td>\n",
       "      <td>1381484030</td>\n",
       "      <td>PHID-USER-xezsyhikbr7hjrig2ofp</td>\n",
       "      <td>False</td>\n",
       "      <td>PHID-TASK-qjt5coghg7n62wamkubq</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>118</td>\n",
       "      <td>117.0</td>\n",
       "      <td>2013-10-11 09:33:50+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>SVN r10320 is https://gerrit.wikimedia.org/r/8...</td>\n",
       "      <td>SVN r10320 is https://gerrit.wikimedia.org/r/8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>time data error</td>\n",
       "      <td>see also bug 55399</td>\n",
       "      <td>1381483747</td>\n",
       "      <td>PHID-USER-xezsyhikbr7hjrig2ofp</td>\n",
       "      <td>False</td>\n",
       "      <td>PHID-TASK-qjt5coghg7n62wamkubq</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>119</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2013-10-11 09:29:07+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>see also bug 55399</td>\n",
       "      <td>see also bug 55399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>time data error</td>\n",
       "      <td>It's a mess with these timestamps. Without tha...</td>\n",
       "      <td>1381483651</td>\n",
       "      <td>PHID-USER-xezsyhikbr7hjrig2ofp</td>\n",
       "      <td>False</td>\n",
       "      <td>PHID-TASK-qjt5coghg7n62wamkubq</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>120</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2013-10-11 09:27:31+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>It's a mess with these timestamps. Without tha...</td>\n",
       "      <td>It's a mess with these timestamps. Without tha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>time data error</td>\n",
       "      <td>When I go back from SVN revision 10320 to 1031...</td>\n",
       "      <td>1381482504</td>\n",
       "      <td>PHID-USER-wwnv7nzuscfuc2xfjwbq</td>\n",
       "      <td>False</td>\n",
       "      <td>PHID-TASK-qjt5coghg7n62wamkubq</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>121</td>\n",
       "      <td>120.0</td>\n",
       "      <td>2013-10-11 09:08:24+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>When I go back from SVN revision 10320 to 1031...</td>\n",
       "      <td>When I go back from SVN revision 10320 to 1031...</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>6510</th>\n",
       "      <td>VisualEditor:  Automatic naming scheme for ref...</td>\n",
       "      <td>Intention:\\nRe-use a reference.\\n\\n\\nActual Re...</td>\n",
       "      <td>1385163660</td>\n",
       "      <td>PHID-USER-uf3buojo4ceizjywvyn5</td>\n",
       "      <td>True</td>\n",
       "      <td>PHID-TASK-j3rfh4pmjx4pel7dk2tn</td>\n",
       "      <td>task_description</td>\n",
       "      <td>duplicate</td>\n",
       "      <td>False</td>\n",
       "      <td>155659</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-11-22 23:41:00+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Intention:\\nRe-use a reference.\\n\\n\\nActual Re...</td>\n",
       "      <td>Intention:\\nRe-use a reference.\\n\\n\\nActual Re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6511</th>\n",
       "      <td>VisualEditor:  Automatic naming scheme for ref...</td>\n",
       "      <td>Speaking as an extensive editor, I just find t...</td>\n",
       "      <td>1385399054</td>\n",
       "      <td>PHID-USER-ydswvwhh5pm4lshahjje</td>\n",
       "      <td>True</td>\n",
       "      <td>PHID-TASK-j3rfh4pmjx4pel7dk2tn</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>155661</td>\n",
       "      <td>155660.0</td>\n",
       "      <td>2013-11-25 17:04:14+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Speaking as an extensive editor, I just find t...</td>\n",
       "      <td>Speaking as an extensive editor, I just find t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6512</th>\n",
       "      <td>VisualEditor:  Automatic naming scheme for ref...</td>\n",
       "      <td>I realize that any automagic system will have ...</td>\n",
       "      <td>1385397795</td>\n",
       "      <td>PHID-USER-uf3buojo4ceizjywvyn5</td>\n",
       "      <td>True</td>\n",
       "      <td>PHID-TASK-j3rfh4pmjx4pel7dk2tn</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>155662</td>\n",
       "      <td>155661.0</td>\n",
       "      <td>2013-11-25 16:43:15+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>I realize that any automagic system will have ...</td>\n",
       "      <td>I realize that any automagic system will have ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6513</th>\n",
       "      <td>VisualEditor:  Automatic naming scheme for ref...</td>\n",
       "      <td>Why humans need to be able to remember the ref...</td>\n",
       "      <td>1385397298</td>\n",
       "      <td>PHID-USER-uf3buojo4ceizjywvyn5</td>\n",
       "      <td>True</td>\n",
       "      <td>PHID-TASK-j3rfh4pmjx4pel7dk2tn</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>155663</td>\n",
       "      <td>155662.0</td>\n",
       "      <td>2013-11-25 16:34:58+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Why humans need to be able to remember the ref...</td>\n",
       "      <td>Why humans need to be able to remember the ref...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6514</th>\n",
       "      <td>VisualEditor:  Automatic naming scheme for ref...</td>\n",
       "      <td>(In reply to comment #0)\\n&gt; The ref naming sch...</td>\n",
       "      <td>1385394470</td>\n",
       "      <td>PHID-USER-ydswvwhh5pm4lshahjje</td>\n",
       "      <td>True</td>\n",
       "      <td>PHID-TASK-j3rfh4pmjx4pel7dk2tn</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>155664</td>\n",
       "      <td>155663.0</td>\n",
       "      <td>2013-11-25 15:47:50+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>(In reply to comment #0)\\n&gt; The ref naming sch...</td>\n",
       "      <td>(In reply to comment #0)\\n&gt; The ref naming sch...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6515 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             task_title  \\\n",
       "0                                       time data error   \n",
       "1                                       time data error   \n",
       "2                                       time data error   \n",
       "3                                       time data error   \n",
       "4                                       time data error   \n",
       "...                                                 ...   \n",
       "6510  VisualEditor:  Automatic naming scheme for ref...   \n",
       "6511  VisualEditor:  Automatic naming scheme for ref...   \n",
       "6512  VisualEditor:  Automatic naming scheme for ref...   \n",
       "6513  VisualEditor:  Automatic naming scheme for ref...   \n",
       "6514  VisualEditor:  Automatic naming scheme for ref...   \n",
       "\n",
       "                                           comment_text  date_created  \\\n",
       "0     After last update via SVN bot does not work, s...    1381482240   \n",
       "1     SVN r10320 is https://gerrit.wikimedia.org/r/8...    1381484030   \n",
       "2                                    see also bug 55399    1381483747   \n",
       "3     It's a mess with these timestamps. Without tha...    1381483651   \n",
       "4     When I go back from SVN revision 10320 to 1031...    1381482504   \n",
       "...                                                 ...           ...   \n",
       "6510  Intention:\\nRe-use a reference.\\n\\n\\nActual Re...    1385163660   \n",
       "6511  Speaking as an extensive editor, I just find t...    1385399054   \n",
       "6512  I realize that any automagic system will have ...    1385397795   \n",
       "6513  Why humans need to be able to remember the ref...    1385397298   \n",
       "6514  (In reply to comment #0)\\n> The ref naming sch...    1385394470   \n",
       "\n",
       "                             speaker  meta.affil  \\\n",
       "0     PHID-USER-wwnv7nzuscfuc2xfjwbq       False   \n",
       "1     PHID-USER-xezsyhikbr7hjrig2ofp       False   \n",
       "2     PHID-USER-xezsyhikbr7hjrig2ofp       False   \n",
       "3     PHID-USER-xezsyhikbr7hjrig2ofp       False   \n",
       "4     PHID-USER-wwnv7nzuscfuc2xfjwbq       False   \n",
       "...                              ...         ...   \n",
       "6510  PHID-USER-uf3buojo4ceizjywvyn5        True   \n",
       "6511  PHID-USER-ydswvwhh5pm4lshahjje        True   \n",
       "6512  PHID-USER-uf3buojo4ceizjywvyn5        True   \n",
       "6513  PHID-USER-uf3buojo4ceizjywvyn5        True   \n",
       "6514  PHID-USER-ydswvwhh5pm4lshahjje        True   \n",
       "\n",
       "                     conversation_id      comment_type     status  \\\n",
       "0     PHID-TASK-qjt5coghg7n62wamkubq  task_description   resolved   \n",
       "1     PHID-TASK-qjt5coghg7n62wamkubq   task_subcomment        NaN   \n",
       "2     PHID-TASK-qjt5coghg7n62wamkubq   task_subcomment        NaN   \n",
       "3     PHID-TASK-qjt5coghg7n62wamkubq   task_subcomment        NaN   \n",
       "4     PHID-TASK-qjt5coghg7n62wamkubq   task_subcomment        NaN   \n",
       "...                              ...               ...        ...   \n",
       "6510  PHID-TASK-j3rfh4pmjx4pel7dk2tn  task_description  duplicate   \n",
       "6511  PHID-TASK-j3rfh4pmjx4pel7dk2tn   task_subcomment        NaN   \n",
       "6512  PHID-TASK-j3rfh4pmjx4pel7dk2tn   task_subcomment        NaN   \n",
       "6513  PHID-TASK-j3rfh4pmjx4pel7dk2tn   task_subcomment        NaN   \n",
       "6514  PHID-TASK-j3rfh4pmjx4pel7dk2tn   task_subcomment        NaN   \n",
       "\n",
       "      meta.gerrit      id  reply_to                  timestamp  is_relevant  \\\n",
       "0           False     115       NaN  2013-10-11 09:04:00+00:00         True   \n",
       "1           False     118     117.0  2013-10-11 09:33:50+00:00         True   \n",
       "2           False     119     118.0  2013-10-11 09:29:07+00:00         True   \n",
       "3           False     120     119.0  2013-10-11 09:27:31+00:00         True   \n",
       "4           False     121     120.0  2013-10-11 09:08:24+00:00         True   \n",
       "...           ...     ...       ...                        ...          ...   \n",
       "6510        False  155659       NaN  2013-11-22 23:41:00+00:00         True   \n",
       "6511        False  155661  155660.0  2013-11-25 17:04:14+00:00         True   \n",
       "6512        False  155662  155661.0  2013-11-25 16:43:15+00:00         True   \n",
       "6513        False  155663  155662.0  2013-11-25 16:34:58+00:00         True   \n",
       "6514        False  155664  155663.0  2013-11-25 15:47:50+00:00         True   \n",
       "\n",
       "      is_migrated                                               text  \\\n",
       "0           False  After last update via SVN bot does not work, s...   \n",
       "1           False  SVN r10320 is https://gerrit.wikimedia.org/r/8...   \n",
       "2           False                                 see also bug 55399   \n",
       "3           False  It's a mess with these timestamps. Without tha...   \n",
       "4           False  When I go back from SVN revision 10320 to 1031...   \n",
       "...           ...                                                ...   \n",
       "6510        False  Intention:\\nRe-use a reference.\\n\\n\\nActual Re...   \n",
       "6511        False  Speaking as an extensive editor, I just find t...   \n",
       "6512        False  I realize that any automagic system will have ...   \n",
       "6513        False  Why humans need to be able to remember the ref...   \n",
       "6514        False  (In reply to comment #0)\\n> The ref naming sch...   \n",
       "\n",
       "                                          resolved_text  \n",
       "0     After last update via SVN bot does not work, s...  \n",
       "1     SVN r10320 is https://gerrit.wikimedia.org/r/8...  \n",
       "2                                    see also bug 55399  \n",
       "3     It's a mess with these timestamps. Without tha...  \n",
       "4     When I go back from SVN revision 10320 to 1031...  \n",
       "...                                                 ...  \n",
       "6510  Intention:\\nRe-use a reference.\\n\\n\\nActual Re...  \n",
       "6511  Speaking as an extensive editor, I just find t...  \n",
       "6512  I realize that any automagic system will have ...  \n",
       "6513  Why humans need to be able to remember the ref...  \n",
       "6514  (In reply to comment #0)\\n> The ref naming sch...  \n",
       "\n",
       "[6515 rows x 16 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phab_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d449164e-1d28-4580-9eb1-f0f69978f114",
   "metadata": {},
   "outputs": [],
   "source": [
    "#find gerrit phab PHID: PHID-USER-idceizaw6elwiwm5xshb\n",
    "phab_df['isGerrit'] = phab_df['speaker'] == 'PHID-USER-idceizaw6elwiwm5xshb'\n",
    "\n",
    "#cleaning df\n",
    "#phab_df['id'] = phab_df.index + 1\n",
    "#may have to build out the reply_to column \n",
    "#phab_df['reply_to'] = phab_df.groupby('TaskPHID')['id'].shift()\n",
    "#phab_df['reply_to'] = phab_df['reply_to'].where(pd.notnull(phab_df['reply_to']), None)\n",
    "\n",
    "#phab_df = phab_df.rename(columns={\n",
    "#    'AuthorPHID': 'speaker',\n",
    "#    'TaskPHID': 'conversation_id',\n",
    "#    'WMFaffil':'meta.affil',\n",
    "#    'isGerrit': 'meta.gerrit'\n",
    "#})\n",
    "\n",
    "# after 12-1-2012 before 12-1-2013\n",
    "phab_df['timestamp'] = pd.to_datetime(phab_df['date_created'], unit='s', origin='unix', utc=True)\n",
    "#filtered_phab_df = phab_df[(phab_df['date_created'] <  1385596799) & (phab_df['date_created'] > 1315008000)]\n",
    "\n",
    "#removing headless conversations\n",
    "task_phab_df = phab_df[phab_df['comment_type']==\"task_description\"]\n",
    "headed_task_phids = task_phab_df['conversation_id'].unique()\n",
    "filtered_phab_df = phab_df[phab_df['conversation_id'].isin(headed_task_phids)]\n",
    "\n",
    "#removing gerrit comments \n",
    "#mid_comment_phab_df = filtered_phab_df[filtered_phab_df['meta.gerrit'] != True]\n",
    "\n",
    "'''\n",
    "# filter out the sourceforge migration \n",
    "# Originally from: http://sourceforge.net in the task task_summary\n",
    "migrated_conversation_ids = task_phab_df[task_phab_df['comment_text'].apply(is_migrated)]['conversation_id'].unique()\n",
    "\n",
    "#cut down to only the data that is relevant (mentions http)\n",
    "relevant_conversation_ids = task_phab_df[\n",
    "    task_phab_df['comment_text'].apply(http_relevant) |\n",
    "    task_phab_df['task_title'].apply(http_relevant)\n",
    "]['conversation_id'].unique()\n",
    "\n",
    "task_phab_df['is_relevant'] = task_phab_df['conversation_id'].isin(relevant_conversation_ids)\n",
    "mid_comment_phab_df['is_relevant'] = mid_comment_phab_df['conversation_id'].isin(relevant_conversation_ids)\n",
    "\n",
    "task_phab_df['is_migrated'] = task_phab_df['conversation_id'].isin(migrated_conversation_ids)\n",
    "mid_comment_phab_df['is_migrated'] = mid_comment_phab_df['conversation_id'].isin(migrated_conversation_ids)\n",
    "'''\n",
    "#comment_phab_df = mid_comment_phab_df[(mid_comment_phab_df['is_relevant'] == True) & (mid_comment_phab_df['is_migrated'] != True)]\n",
    "#task_phab_df = task_phab_df[(task_phab_df['is_relevant'] == True) & (task_phab_df['is_migrated'] != True)]\n",
    "comment_phab_df = filtered_phab_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "942344db-c8f5-4ed6-a757-c97f8454f18b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique conversation_ids: 1074\n",
      "Unique ids: 6515\n",
      "Unique speakers: 305\n"
     ]
    }
   ],
   "source": [
    "unique_conversation_ids = len(comment_phab_df['conversation_id'].unique())\n",
    "unique_ids = len(comment_phab_df['id'].unique())\n",
    "unique_speakers = len(comment_phab_df['speaker'].unique())\n",
    "\n",
    "print(f\"Unique conversation_ids: {unique_conversation_ids}\")\n",
    "print(f\"Unique ids: {unique_ids}\")\n",
    "print(f\"Unique speakers: {unique_speakers}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d226d781-b002-4842-a3ae-92d4851a5878",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def preprocess_text(text):\n",
    "    text = str(text)\n",
    "    text = text.replace('*', ' ')\n",
    "    text = text.replace('-', ' ')\n",
    "    text = re.sub(r'http\\S+', '', text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3ae40d24-bbe8-49c3-a3a9-70bde1b4d559",
   "metadata": {},
   "outputs": [],
   "source": [
    "comment_phab_df['processed_text'] = comment_phab_df['comment_text'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b8eddf40-1fe2-4fce-be74-b32552b40c57",
   "metadata": {},
   "outputs": [],
   "source": [
    "comment_phab_df['processed_resolved_text'] = comment_phab_df['resolved_text'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a8469b16-4ae6-4b06-bf1b-1f2f6c736cab",
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load(\"en_core_web_sm\")\n",
    "\n",
    "def extract_dependency_tree(text):\n",
    "    doc = nlp(text)\n",
    "    dependency_trees = []\n",
    "    \n",
    "    for sentence in doc.sents:\n",
    "        for token in sentence:\n",
    "            token_info = (\n",
    "                token.text, \n",
    "                token.lemma_, \n",
    "                token.dep_, \n",
    "                token.head.text, \n",
    "                list(token.ancestors), \n",
    "                list(token.subtree), \n",
    "                list(token.children)\n",
    "            )\n",
    "            dependency_trees.append(token_info)\n",
    "        \n",
    "    return dependency_trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8b9a12f9-71bf-4bc9-bcfd-c73aab4be920",
   "metadata": {},
   "outputs": [],
   "source": [
    "comment_phab_df['dependency_tree'] = comment_phab_df['processed_text'].apply(extract_dependency_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "337a528a-5667-4e1f-ac9a-37caabc03a18",
   "metadata": {},
   "outputs": [],
   "source": [
    "comment_phab_df['resolved_dependency_tree'] = comment_phab_df['processed_resolved_text'].apply(extract_dependency_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1b51f395-aaa9-4bf2-9c67-c1bc4640a89a",
   "metadata": {},
   "outputs": [],
   "source": [
    "comment_phab_df.to_csv(\"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case2/051825_coref_resolved_dep_trees.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a3f5d40b-f56e-4e31-a7f9-40b7ddb4d2a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#get VAD scores\n",
    "import numpy as np\n",
    "#https://saifmohammad.com/WebPages/nrc-vad.html\n",
    "column_headings = ['Word', 'Valence', 'Arousal', 'Domination']\n",
    "vad_lexicon = pd.read_csv('NRC-VAD-Lexicon.txt', delimiter='\\t', header=None, names=column_headings)\n",
    "vad_dict = vad_lexicon.set_index('Word').T.to_dict()\n",
    "\n",
    "def vad_scoring(dependency_tree):\n",
    "    valence = []\n",
    "    arousal = []\n",
    "    dominance = []\n",
    "    for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
    "        if lemma in vad_dict:\n",
    "            valence.append(vad_dict[lemma]['Valence'])\n",
    "            arousal.append(vad_dict[lemma]['Arousal'])\n",
    "            dominance.append(vad_dict[lemma]['Domination'])\n",
    "\n",
    "    # Compute average scores across the comment\n",
    "    avg_valence = np.mean(valence) if valence else 0\n",
    "    avg_arousal = np.mean(arousal) if arousal else 0\n",
    "    avg_dominance = np.mean(dominance) if dominance else 0\n",
    "\n",
    "    return [avg_valence, avg_arousal, avg_dominance]\n",
    "\n",
    "def dominance_prevail(dependency_tree):\n",
    "    dominant_words = 0 \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
    "        if lemma in vad_dict:\n",
    "            if vad_dict[lemma]['Domination'] >= 0.75:\n",
    "                dominant_words += 1\n",
    "            if vad_dict[lemma]['Domination'] <= 0.25:\n",
    "                dominant_words += 1\n",
    "    return dominant_words\n",
    "\n",
    "def arousal_prevail(dependency_tree):\n",
    "    arousal_words = 0 \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
    "        if lemma in vad_dict:\n",
    "            if vad_dict[lemma]['Arousal'] >= 0.75:\n",
    "                arousal_words += 1\n",
    "            if vad_dict[lemma]['Arousal'] <= 0.25:\n",
    "                arousal_words += 1\n",
    "    return arousal_words\n",
    "\n",
    "def valence_prevail(dependency_tree):\n",
    "    valence_words = 0 \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
    "        if lemma in vad_dict:\n",
    "            if vad_dict[lemma]['Valence'] >= 0.75:\n",
    "                valence_words += 1\n",
    "            if vad_dict[lemma]['Valence'] <= 0.25:\n",
    "                valence_words += 1\n",
    "    return valence_words\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "828fb57a-e152-42ef-9c60-660648898532",
   "metadata": {},
   "outputs": [],
   "source": [
    "#establishing per-comment VAD scores \n",
    "comment_phab_df['avg_vad_scores'] = comment_phab_df['dependency_tree'].apply(vad_scoring)\n",
    "comment_phab_df['dominant_wc'] = comment_phab_df['dependency_tree'].apply(dominance_prevail)\n",
    "comment_phab_df['arousal_wc'] = comment_phab_df['dependency_tree'].apply(arousal_prevail)\n",
    "comment_phab_df['valence_wc'] = comment_phab_df['dependency_tree'].apply(valence_prevail)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "27e47f6f-0257-4b70-b222-e91ef888c900",
   "metadata": {},
   "outputs": [],
   "source": [
    "comment_phab_df[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n",
    "comment_phab_df = comment_phab_df.drop(columns=['avg_vad_scores'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "09ddcbfc-b856-40ca-ad61-13577795d94b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "184ccbe6-0a7a-41b8-9b02-bc439ff975d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# expand the dependency parser \n",
    "\n",
    "#pattern = r'\\b(ve|VE|visualeditor|VisualEditor)\\b'\n",
    "#pattern = r'\\b(WMF|Foundation)\\b'\n",
    "#pattern = r'\\b(bots|scripts|gadgets)\\b'\n",
    "pattern = r'\\b(http|https)\\b'\n",
    "\n",
    "dependency_relations = []\n",
    "resolved_dependency_relations = []\n",
    "\n",
    "for index, row in comment_phab_df.iterrows():\n",
    "    text = row['comment_text']\n",
    "    timestamp = row['timestamp']\n",
    "    comment_id = row['id']\n",
    "    conversation_id = row['conversation_id']\n",
    "    WMFaffil = row['meta.affil']\n",
    "    \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in row['dependency_tree']:\n",
    "        dependency_relations.append({\n",
    "            'comment_id': comment_id,\n",
    "            'timestamp': timestamp,\n",
    "            'wmfAffil':WMFaffil,\n",
    "            'token': token,\n",
    "            'dependency': dep,\n",
    "            'head': head,\n",
    "            'depth': len(list(ancestors)), \n",
    "            'children': len(list(children)) \n",
    "        })\n",
    "   \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in row['resolved_dependency_tree']:\n",
    "        resolved_dependency_relations.append({\n",
    "            'comment_id': comment_id,\n",
    "            'timestamp': timestamp,\n",
    "            'wmfAffil':WMFaffil,\n",
    "            'token': token,\n",
    "            'dependency': dep,\n",
    "            'head': head,\n",
    "            'depth': len(list(ancestors)), \n",
    "            'children': len(list(children)) \n",
    "        })\n",
    "\n",
    "resolved_dependency_relations_df = pd.DataFrame(resolved_dependency_relations)            \n",
    "dependency_relations_df = pd.DataFrame(dependency_relations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "82498686-14f4-40c8-9e33-27b31f115b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now analysis/plotting \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.gridspec import GridSpec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5a91a59a-0d1c-48b3-93dd-b9df76ca68e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x14ca72b957f0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1333.5x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot2 = sns.lmplot(data=affective_comment_phab_df, x=\"speakers_comment\", y=\"polarized_wc\", hue=\"date_group\", col=\"meta.affil\", scatter=False, legend=False, palette=palette)\n",
    "plot2.set_axis_labels(\"Index of Speaker's Comment\", \"Count of Polarized Words\")\n",
    "plot2.set_titles(col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot2.fig.subplots_adjust(top=0.9)  # Adjust subplots to make room for the title\n",
    "plot2.add_legend(title=\"Comment publication timestamp:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2274795e-c64d-43e4-b0f5-a19b5b8ba2c8",
   "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>comment_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>wmfAffil</th>\n",
       "      <th>token</th>\n",
       "      <th>dependency</th>\n",
       "      <th>head</th>\n",
       "      <th>depth</th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>115</td>\n",
       "      <td>2013-10-11 09:04:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>use_api_login</td>\n",
       "      <td>dobj</td>\n",
       "      <td>use_api</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>157</td>\n",
       "      <td>2013-10-07 08:09:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>use_api_login</td>\n",
       "      <td>dobj</td>\n",
       "      <td>use_api</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>177</td>\n",
       "      <td>2013-10-04 17:56:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>certainly</td>\n",
       "      <td>advmod</td>\n",
       "      <td>require</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>247</td>\n",
       "      <td>2013-09-27 22:15:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Login</td>\n",
       "      <td>ROOT</td>\n",
       "      <td>Login</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>426</td>\n",
       "      <td>2013-09-01 11:26:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>HTTP</td>\n",
       "      <td>compound</td>\n",
       "      <td>login</td>\n",
       "      <td>4</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1463</th>\n",
       "      <td>45300</td>\n",
       "      <td>2013-08-01 17:35:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>certain</td>\n",
       "      <td>amod</td>\n",
       "      <td>commands</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1464</th>\n",
       "      <td>45300</td>\n",
       "      <td>2013-08-01 17:35:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>certain</td>\n",
       "      <td>amod</td>\n",
       "      <td>commands</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>45373</td>\n",
       "      <td>2013-07-27 13:30:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>certain</td>\n",
       "      <td>amod</td>\n",
       "      <td>element</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>46078</td>\n",
       "      <td>2013-06-18 21:17:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>HTTP</td>\n",
       "      <td>compound</td>\n",
       "      <td>Error</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>46086</td>\n",
       "      <td>2013-06-19 23:31:02+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>HTTP</td>\n",
       "      <td>compound</td>\n",
       "      <td>Error</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1468 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      comment_id                 timestamp  wmfAffil          token  \\\n",
       "0            115 2013-10-11 09:04:00+00:00     False  use_api_login   \n",
       "1            157 2013-10-07 08:09:00+00:00     False  use_api_login   \n",
       "2            177 2013-10-04 17:56:00+00:00     False      certainly   \n",
       "3            247 2013-09-27 22:15:00+00:00     False          Login   \n",
       "4            426 2013-09-01 11:26:00+00:00     False           HTTP   \n",
       "...          ...                       ...       ...            ...   \n",
       "1463       45300 2013-08-01 17:35:00+00:00     False        certain   \n",
       "1464       45300 2013-08-01 17:35:00+00:00     False        certain   \n",
       "1465       45373 2013-07-27 13:30:00+00:00     False        certain   \n",
       "1466       46078 2013-06-18 21:17:00+00:00     False           HTTP   \n",
       "1467       46086 2013-06-19 23:31:02+00:00     False           HTTP   \n",
       "\n",
       "     dependency      head  depth  children  \n",
       "0          dobj   use_api      1         6  \n",
       "1          dobj   use_api      1         4  \n",
       "2        advmod   require      2         1  \n",
       "3          ROOT     Login      0         4  \n",
       "4      compound     login      4         0  \n",
       "...         ...       ...    ...       ...  \n",
       "1463       amod  commands      5         0  \n",
       "1464       amod  commands      5         0  \n",
       "1465       amod   element      8         0  \n",
       "1466   compound     Error      2         0  \n",
       "1467   compound     Error      3         0  \n",
       "\n",
       "[1468 rows x 8 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resolved_dependency_relations_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d2d67d38-f005-4c94-be3c-39eb6b22686f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_44915/3534785199.py:8: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n",
      "  filtered_dependencies = dependency_relations_df[dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
      "/tmp/ipykernel_44915/3534785199.py:9: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n",
      "  resolved_filtered_dependencies = resolved_dependency_relations_df[resolved_dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
      "/tmp/ipykernel_44915/3534785199.py:24: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  filtered_dependencies['week'] = filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
      "/tmp/ipykernel_44915/3534785199.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_dependencies['week'] = filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
      "/tmp/ipykernel_44915/3534785199.py:45: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  resolved_filtered_dependencies['timestamp'] = pd.to_datetime(resolved_filtered_dependencies['timestamp'], utc=True)\n",
      "/tmp/ipykernel_44915/3534785199.py:46: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  resolved_filtered_dependencies['week'] = resolved_filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
      "/tmp/ipykernel_44915/3534785199.py:46: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  resolved_filtered_dependencies['week'] = resolved_filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n"
     ]
    },
    {
     "data": {
      "image/png": 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3LoYMGYK//vpLt8zu3bvx4osvIigoCP/73/+gUChw4cIFbNu2Df/73//Qr18/jB07FmvXrsWzzz6rt/21a9eiXbt2Rq8Xlrh//z66dOmCl156CS+//DICAwOh0WjQo0cPHD58GKNHj0bt2rVx5swZzJ8/H5cvX8aWLVt065tz7jzNbwMichECEZGNrVq1SgAg/P3338UuI5fLhWeffVb3vUOHDkJkZKSQl5enm6bRaISWLVsK1atXN9h248aNhfz8fN30uXPnCgCEX375RTetbt26QlRUVLHxdezYUdBoNLrpb731luDm5iakp6eXWL6GDRsKlSpVEu7fv6+bdvr0aUEsFgtDhw7VTdu/f78AQNi4cWOJ2xMEQdi5c6cAQLh586be9KioKAGA8Pnnn+umqVQqXQzaY7Bnzx4BgPDrr78abDs0NFQAIGzevFk3LSMjQwgKCtKrgzfffFMAIPzxxx+6aVlZWUJ4eLgQFhYmFBYWCoIgCH///bcAQFi1apXefk6dOmVWebXHv6gGDRoI3bp1K3G9tm3bCr6+vsL169f1phetw6lTpwoAhEGDBhmsr51XFAABgHD8+HHdtOvXrwtSqVTo3bu3btqnn34qABCuXbumt/78+fMFAMLdu3dLjH3YsGEGbTEyMlJ45plnhKysLN20AwcOCACE0NBQvWWVSqXe9/z8fKFevXrC888/r5uWkJAgABBef/11vWUHDx4sABCmTp2qm9a9e3dBJpMJt27d0k27cuWK4O7urneMkpKSBDc3N2HWrFl62zxz5ozg7u6uN71bt24GcQuCIPTs2VOoW7euwXRjRo8eLXh5eelNCwoKMti/Jf2AMcW1BU9PT+Hq1au6aadPnxYACF988UWJ27t27ZoAQJg+fbpw9+5dITU1VThw4IDw7LPP6p172uUqVKggPHjwQLf+L7/8YnD+Pl7ngiAIP/74owBAOHTokEFZevToobfs66+/LgAQTp8+rVdGsVgsnDt3zmDbj7eRoUOHCmKx2Gg/rj3n8vLydP1C0WMhkUiEGTNm6KYV12fk5+cLlSpVEurVqyfk5ubqpm/btk0AIHz00Ue6acOGDRMACJMmTTKIx5hdu3YJbm5ugpubm9CiRQvh3XffFX7//Xe99iIIlrVxbX/87bff6qapVCpBoVAIffv2NVlejUYjVK9eXYiOjtbrt5RKpRAeHi506tRJN01br8OHD9fbRu/evYUKFSrovl+5ckUQi8VC7969DepCu4+srCzB399fGDVqlN781NRUQS6XG0x/nDnXsgYNGgjlypXTfR8xYoQQFBQk3Lt3T2+5l156SZDL5br2rd125cqVhczMTN1yGzZsEAAICxcu1JXF2sfOkj7T0vLUrl1bUKlUuuUWLlwoABDOnDkjCIIgFBQUCOHh4UJoaKjw8OFDvW0WLd+gQYOE4OBgvbo9efKk0fZVkrFjxxr0edr2vHz5cr3p3333nSAWi/V+CwiCICxfvlwAIPz555+CIJh/7pj724CIXBcf3yMih+Tj46N7C9+DBw+wb98+DBgwAFlZWbh37x7u3buH+/fvIzo6GleuXDG4RX306NG6OwoAYMyYMXB3dy/xUZ/HjR49Wu9ugzZt2qCwsBDXr18vdp2UlBQkJCQgJiYG5cuX102vX78+OnXqZNH+i2rXrh28vb2Nru/u7o5XX31V993T0xOvvvoq7ty5gxMnTgCA7pGecuXKGd1+cHCw3t1m2kedTp06hdTUVADAb7/9hueee073uBvwqJ5Gjx6NpKQknD9/vsQyaP+38/fffzf6eElJ/P39ce7cOVy5csXo/Lt37+LQoUMYPnw4qlSpojfP2B0xr732mtn7btGiBRo3bqz7XqVKFfTs2RO///47CgsLTcYNAL/88gs0Go3Z+7x9+zbOnDmDoUOH6u4oAoCoqChERkYaLF907LWHDx8iIyMDbdq0wcmTJ3XTtW1n/Pjxeus+/j/4hYWF2LNnD3r16oXg4GDd9GrVqqFLly56y/7000/QaDQYMGCA7ry8d+8eFAoFqlevjv3795ssq7+/P27evGnweJox5cqVQ25url776dq1a7GPtlqjHyiqY8eOqFq1qu57/fr14efnh3///des9adOnYqAgAAoFAq0a9cOiYmJmDNnDvr06aO33MCBA/XOVe3dNEX3U7TO8/LycO/ePd14cUXrXWvs2LF637UDeT9+LKKiolCnTp0Sy6HRaLBlyxZ0795db1xALe05J5FIdC9nKCwsxP3793WPsBqL8XHHjx/HnTt38Prrr+uNadWtWzfUqlXLaL2PGTPG5HaBR281jY+PR48ePXD69GnMnTsX0dHRqFy5st5j4Za2cR8fH71xeTw9PfHcc8+Z1UYSEhJw5coVDB48GPfv39ftKycnBx06dMChQ4cM+pHH+7I2bdrg/v37yMzMBPDosW2NRoOPPvrI4EUZ2nravXs30tPTMWjQIL0yurm5oVmzZmadx6YUvaYLgoDNmzeje/fuEARBb5/R0dHIyMgwaB9Dhw6Fr6+v7nu/fv0QFBSka7+lcezM7TOfpDyxsbHw9PTU2zfw3zl+6tQpXLt2DW+++abBI6ZFr2lDhw7F7du39epo7dq18PLyQt++ffG0JBIJYmNj9aZt3LgRtWvXRq1atfTKqn0cWRuLuefO0/w2ICLXwMf3iMghZWdn68b1uHr1KgRBwJQpUzBlyhSjy9+5c0fvNvXq1avrzffx8UFQUJBunB9zPJ7c0P6RWNKYNNqEVc2aNQ3m1a5dG7///nuJg+8WRyKR4Pnnn8f27dt1jzRqBQcHG2yvRo0aAB69ir7owOZCMY+qVatWzSB5U3QbCoUC169fR7NmzYyWC3hU9pIegwkPD8eECRMwb948rF27Fm3atEGPHj3w8ssvm7w9f8aMGejZsydq1KiBevXqoXPnznjllVd0b2jU/pA39RhO0VjM9XhbAh4dG6VSibt370KhUBS77sCBA/H1119j5MiRmDRpEjp06IA+ffqgX79+Jb5JUduOqlWrZjCvWrVqBn/gbNu2DR9//DESEhL0xvQoWqfXr1+HWCzWS6oAhm31zp07yM3NLXbfRV25cgWCIBg9RgD0EkLFee+997Bnzx4899xzqFatGl544QUMHjwYrVq1MlhW236Llqtr165YtWoV7t+/b/AYnDX6gaIe7xOAR/2COeNUAY+SZP3794dYLIa/v79u3CZT+zHW9zx48ADTp0/HunXrDAbmNjYOy+PHomrVqhCLxQbHwpxz4+7du8jMzDR5vmk0GixcuBBLly7FtWvX9JK45jzuWFJ/WqtWLRw+fFhvmru7O5555hmT29Vq2rQpfvrpJ+Tn5+P06dP4+eefMX/+fPTr1w8JCQmoU6eOxW38mWeeMehLy5Urh3/++cdkPNqk+7Bhw4pdJiMjQy9hWVJb8fPzQ2JiIsRicYmJRu1+i3s7np+fn8nYTcnOztYlle7evYv09HSsWLECK1asMLr842368eMvEolQrVo1XfstjWNnbp/5JOUxdY5rH+01dY516tQJQUFBWLt2LTp06ACNRoMff/wRPXv21EviPanKlSvrJc+AR8f6woULRh/nBv4rq7nnztP8NiAi18CkFBE5nJs3byIjI0P3B7D2fzcnTpyI6Ohoo+sY+wP6abm5uRmdXlxip7R169YNb7/9NvLz8w1+JJqi/QPQ3D+eS8vnn3+OmJgY/PLLL9i1axfGjx+vG+OmpD8m27Zti8TERN16X3/9NebPn4/ly5dj5MiRFsdhq7c6enl54dChQ9i/fz+2b9+OnTt3Yv369Xj++eexa9euYtuYJf744w/06NEDbdu2xdKlSxEUFAQPDw+sWrXKYLBwa9NoNBCJRNixY4fRshS9y6s4tWvXxqVLl7Bt2zbs3LkTmzdvxtKlS/HRRx9h+vTpess+fPgQMplMr/46deoENzc37Ny5s8SBra3hafuE6tWro2PHjlbZz4ABA3DkyBG88847aNiwIXx8fKDRaNC5c2ez7sorbkwta54bs2fPxpQpUzB8+HDMnDkT5cuXh1gsxptvvmnRnYPmKnpnliU8PT3RtGlTNG3aFDVq1EBsbCw2btyIqVOnWtzGn6aNaI/Jp59+anSMNGvv7/H9fvfdd0aT7E/7Jkq1Wo3Lly/rEiza/b388svFJpG0/+FgLnsdu6L7tqQ81tq3m5sbBg8ejK+++gpLly7Fn3/+idu3b5t8i565jPUHGo0GkZGRmDdvntF1QkJCdMuZe+486W8DInINTEoRkcP57rvvAECXgIqIiADw6H/VzPmDDnj0P3Tt27fXfc/OzkZKSgq6du2qm/YkAx2bEhoaCgC4dOmSwbyLFy+iYsWKFt8lpdW1a1e89tprOHjwoG5wb+DRo16P3311+fJlANANTlurVi0Axt/yBfx3N1rRY/L4NkJDQ4stl3Y+YPq4RkZGIjIyEh9++CGOHDmCVq1aYfny5fj4449LXK98+fKIjY1FbGwssrOz0bZtW0ybNg0jR47UtZGzZ8+WuI0nYeyRwcuXL0Mmk+n+p7ikMovFYnTo0AEdOnTAvHnzMHv2bHzwwQfYv39/se1ZeyyNvY3p8WmbN2+GVCrF77//rnfXzapVqwy2qdFokJiYqPc//Y/XaaVKlSCVSs3ad9WqVSEIAsLDw3V31hWnpGPk7e2NgQMHYuDAgcjPz0efPn0wa9YsTJ48We+xrWvXrunuzNPy9fVFmzZt8NtvvxkkpczpB5zRw4cPsXfvXkyfPh0fffSRbnpxj7dq5xW9C+rq1avQaDRPNIB1QEAA/Pz8TJ5vmzZtQvv27fHNN9/oTU9PT9e9EAAovm0U7U8fv4vn0qVLuvnWpH0cMSUlBYBlbdxcxZVXe0eOn5+f2dc6U6pWrQqNRoPz588Xm6zR7rdSpUpW229RmzZtQm5uru6arn2TXWFhoUXX9KIEQcDVq1d1yZ7SOHbm9plPUh5TtOU5e/asyW0OHToUn3/+OX799Vfs2LEDAQEBxf4HnrViO336NDp06FBiv27pufOkvw2IyPlxTCkicij79u3DzJkzER4ervsDs1KlSmjXrh2+/PJL3R8KRd29e9dg2ooVK6BWq3Xfly1bhoKCAr0xcby9vZGenm7V+IOCgtCwYUOsWbNGb9tnz57Frl27nuqP4ZCQEERGRhqMo1JQUIAvv/xS9z0/Px9ffvklAgICdGMhVa5cGSEhITh+/LjRbd++fRs///yz7ntmZia+/fZbNGzYUPc/5127dsWxY8cQHx+vWy4nJwcrVqxAWFiY7vEQbXLs8WObmZmJgoICvWmRkZEQi8VGXyNd1OOvuffx8UG1atV06wUEBKBt27ZYuXIlkpOT9ZZ92jvb4uPj9R6Xu3HjBn755Re88MILuv/9La7MDx48MNie9g/DksocHByMevXq4dtvv0V2drZu+sGDB3HmzBm9Zd3c3CASifQejUpKStJ7AxIAXdtftGiR3vQFCxYYbK9jx47YsmULbt++rZt+9epV7NixQ2/ZPn36wM3NDdOnTzc4zoIg6NWbt7e30cfKHq9bT09P1KlTB4Ig6J3DwKOxkrRvKSuqW7du2Llzp8EYX+b0A85I2+4eP+aP12VRS5Ys0fv+xRdfAMATHQuxWIxevXrh119/NdqnaONyc3MziHHjxo0GYwAWd/40adIElSpVwvLly/XOlx07duDChQtG34Jmrv379xvtG7TjCGmTEJa0cXMVV97GjRujatWq+Oyzz/TOey1j1zpTevXqBbFYjBkzZhjcnaYtT3R0NPz8/DB79myDc+5J96t1+vRpvPnmmyhXrpxuXDM3Nzf07dsXmzdvNprYNLa/b7/9VjcmFfAo0ZWSkqJrv6Vx7CzpMy0tjymNGjVCeHg4FixYYNBOHm+H9evXR/369fH1119j8+bNeOmll5767raSDBgwALdu3cJXX31lMC83Nxc5OTkAzD93nua3ARG5Bt4pRUR2s2PHDly8eBEFBQVIS0vDvn37sHv3boSGhmLr1q16d0gsWbIErVu3RmRkJEaNGoWIiAikpaUhPj4eN2/exOnTp/W2nZ+fjw4dOmDAgAG4dOkSli5ditatW6NHjx66ZRo3boxly5bh448/RrVq1VCpUqVix9SwxKeffoouXbqgRYsWGDFiBHJzc/HFF19ALpdj2rRpT7Xtrl27YvPmzXo/ioODgzFnzhwkJSWhRo0aWL9+PRISErBixQq98U569uyJn3/+2eCOKODRGEkjRozA33//jcDAQKxcuRJpaWl6d9tMmjQJP/74I7p06YLx48ejfPnyWLNmDa5du4bNmzfrHpupWrUq/P39sXz5cvj6+sLb2xvNmjXD6dOnMW7cOPTv3x81atRAQUEBvvvuO90P+pLUqVMH7dq1Q+PGjVG+fHkcP34cmzZtwrhx43TLLFq0CK1bt0ajRo0wevRohIeHIykpCdu3b0dCQsITH/N69eohOjoa48ePh0QiwdKlSwFA79EybfLvgw8+wEsvvQQPDw90794dM2bMwKFDh9CtWzeEhobizp07WLp0KZ555hm9AeONmT17Nnr27IlWrVohNjYWDx8+xOLFi1GvXj29P7q6deuGefPmoXPnzhg8eDDu3LmDJUuWoFq1anrj2DRs2BCDBg3C0qVLkZGRgZYtW2Lv3r1G74iaNm0adu3ahVatWmHMmDEoLCzU7bvosaxatSo+/vhjTJ48GUlJSejVqxd8fX1x7do1/Pzzzxg9ejQmTpyoO0br16/HhAkT0LRpU/j4+KB79+544YUXoFAo0KpVKwQGBuLChQtYvHgxunXrpjcmyokTJ/DgwQP07NnTIN6uXbvi7bffxtGjR/XGojKnH3BGfn5+aNu2LebOnQu1Wo3KlStj165dxd4JCTy6y6xHjx7o3Lkz4uPjda+5b9CgwRPFMHv2bOzatQtRUVG618KnpKRg48aNOHz4MPz9/fHiiy9ixowZiI2NRcuWLXHmzBmsXbtWd2ejVnF9Rnh4OObMmYPY2FhERUVh0KBBSEtLw8KFCxEWFoa33nrriWIHHg30rlQq0bt3b9SqVQv5+fk4cuQI1q9fj7CwMN3gzpa0cXOVVN6vv/4aXbp0Qd26dREbG4vKlSvj1q1b2L9/P/z8/PDrr79atK9q1arhgw8+wMyZM9GmTRv06dMHEokEf//9N4KDgxEXFwc/Pz8sW7YMr7zyCho1aoSXXnoJAQEBSE5Oxvbt29GqVSssXrzY5L7++OMP5OXl6Qa1//PPP7F161bI5XL8/PPPeo8GfvLJJ9i/fz+aNWuGUaNGoU6dOnjw4AFOnjyJPXv2GCT0y5cvj9atWyM2NhZpaWlYsGABqlWrphtjUSwWW/3YWdJnWloeU8RiMZYtW4bu3bujYcOGiI2NRVBQEC5evIhz587h999/11t+6NChunZorUf3ivPKK69gw4YNeO2117B//360atUKhYWFuHjxIjZs2IDff/8dTZo0Mfvc2bdv3xP/NiAiF1Gq7/YjIjJC+7p27cfT01NQKBRCp06dhIULF+q99rmoxMREYejQoYJCoRA8PDyEypUrCy+++KKwadMmg20fPHhQGD16tFCuXDnBx8dHGDJkiHD//n297aWmpgrdunUTfH19BQBCVFSU3jYef9W59lXO+/fvN1nGPXv2CK1atRK8vLwEPz8/oXv37sL58+eNbs+S1yAfOnRIACBcunRJEIRHr2yuW7eucPz4caFFixaCVCoVQkNDhcWLFxusq31N9OOvcQ4NDRW6desm/P7770L9+vUFiUQi1KpVy2hciYmJQr9+/QR/f39BKpUKzz33nLBt2zaD5X755RehTp06gru7u+7V1P/++68wfPhwoWrVqoJUKhXKly8vtG/fXtizZ4/eutrjX9THH38sPPfcc4K/v7/g5eUl1KpVS5g1a5bB69vPnj0r9O7dWxdfzZo1hSlTpujma18FfvfuXYOYtfOKAiCMHTtW+P7774Xq1asLEolEePbZZ422gZkzZwqVK1cWxGKxAEC4du2asHfvXqFnz55CcHCw4OnpKQQHBwuDBg0SLl++rLfusGHDdO2vqHXr1gm1atUSJBKJUK9ePWHr1q1C3759hVq1aukt98033+jiq1WrlrBq1Sqj5cnNzRXGjx8vVKhQQfD29ha6d+8u3Lhxw+D15oIgCHv37hWeffZZwdPTU6hatarw9ddfC2+//bYglUoN4ty8ebPQunVrwdvbW/D29hZq1aoljB07VtdOBUEQsrOzhcGDBwv+/v4CACE0NFQQBEH48ssvhbZt2woVKlQQJBKJULVqVeGdd94RMjIy9Pbx3nvvCVWqVNF7HXpRERERwuTJkwVBsKwfMKaktvC40NBQYdiwYSVu79q1awIA4dNPP33i5R6vo5s3b+raulwuF/r37y/cvn3bYDltWc6fPy/069dP8PX1FcqVKyeMGzdOyM3NNauMxvYvCIJw/fp1YejQoUJAQIAgkUiEiIgIYezYsbpX3efl5Qlvv/22EBQUJHh5eQmtWrUS4uPjhaioKIP2bqzP0Fq/fr3w7LPPChKJRChfvrwwZMgQ4ebNm3rrDxs2TPD29i7myBrasWOHMHz4cKFWrVqCj4+P4OnpKVSrVk144403hLS0NIPlzWnj2v74ccOGDdO1d3PKe+rUKaFPnz66cyI0NFQYMGCAsHfvXt0yxfVl2rZ/7do1vekrV67UHcNy5coJUVFRwu7du/WW2b9/vxAdHS3I5XJBKpUKVatWFWJiYoTjx4+XeCy11zLtx8PDQwgICBDatm0rzJo1S7hz547R9dLS0oSxY8cKISEhgoeHh6BQKIQOHToIK1asMNj2jz/+KEyePFmoVKmS4OXlJXTr1k24fv26wTatfews6TMtKc/j11ftuV+0HQiCIBw+fFjo1KmT4OvrK3h7ewv169cXvvjiC4Nyp6SkCG5ubkKNGjWMHmtTxo4da9DnFdeeBUEQ8vPzhTlz5gh169bVtanGjRsL06dPN+i7TZ075v42ICLXJRIEO43YS0RUClavXo3Y2Fj8/fffRl9V7uwKCwtRsWJFTJ061eC11Obo0KEDgoODdeN2OSJtHTrC5UkkEmHs2LFm3SXwNGJiYpCUlIQDBw6YXLZhw4YICAjA7t27SzUmY3r16oVz586VOHZRaVCpVAgLC8OkSZPwv//9z+gyb7zxBv7444+nuiuOiBzLgQMH0L59e2zcuBH9+vWzdzgO6969ewgKCsJHH31U7FuKiYgcFceUIiJyIm5ubli0aNETv41m9uzZWL9+ve5V6+S41Gq1wTgbBw4cwOnTp9GuXbtS339ubq7e9ytXruC3336zyb4ft2rVKnh4eOC1114rdpnRo0ejd+/eRsfEISJyZatXr0ZhYSFeeeUVe4dCRGQxjilFRORknuZHZ7NmzZCfn2/FaKi03Lp1Cx07dsTLL7+M4OBgXLx4EcuXL4dCoSgxOWMtERERiImJQUREBK5fv45ly5bB09MT7777bqnv+3GvvfaayTJr39xERFRW7Nu3D+fPn8esWbPQq1evJ3qbJhGRvTEpRURE5IDKlSuHxo0b4+uvv8bdu3fh7e2Nbt264ZNPPkGFChVKff+dO3fGjz/+iNTUVEgkErRo0QKzZ89G9erVS33fRERk2owZM3DkyBG0atVK90ZNIiJnwzGliIiIiIiIiIjI5jimFBERERERERER2RyTUkREREREREREZHMcU8oIjUaD27dvw9fXFyKRyN7hEBERERERERFZhSAIyMrKQnBwMMRi+96rxKSUEbdv30ZISIi9wyAiIiIiIiIiKhU3btzAM888Y9cYmJQywtfXF8CjCvLz87NzNERERERERERE1pGZmYmQkBBd7sOe7JqUOnToED799FOcOHECKSkp+Pnnn9GrVy/d/OIenZs7dy7eeecdo/OmTZuG6dOn602rWbMmLl68aHZc2v36+fkxKUVERERERERELscRhiuy68ODOTk5aNCgAZYsWWJ0fkpKit5n5cqVEIlE6Nu3b4nbrVu3rt56hw8fLo3wiYiIiIiIiIjoCdn1TqkuXbqgS5cuxc5XKBR633/55Re0b98eERERJW7X3d3dYF0iIiIiIiIiInIc9h1m3QJpaWnYvn07RowYYXLZK1euIDg4GBERERgyZAiSk5NtECEREREREREREZnLaQY6X7NmDXx9fdGnT58Sl2vWrBlWr16NmjVrIiUlBdOnT0ebNm1w9uzZYgfxUqlUUKlUuu+ZmZlmxVRYWAi1Wm1+IYjKMA8PD7i5udk7DCIiIiIiInIQTpOUWrlyJYYMGQKpVFrickUfB6xfvz6aNWuG0NBQbNiwodi7rOLi4gwGRy+JIAhITU1Fenq62esQEeDv7w+FQuEQA+oRERERERGRfTlFUuqPP/7ApUuXsH79eovX9ff3R40aNXD16tVil5k8eTImTJig+659PWJxtAmpSpUqQSaT8Q9sIhMEQYBSqcSdO3cAAEFBQXaOiIiIiIiIiOzNKZJS33zzDRo3bowGDRpYvG52djYSExPxyiuvFLuMRCKBRCIxa3uFhYW6hFSFChUsjoeorPLy8gIA3LlzB5UqVeKjfERERERERGWcXQc6z87ORkJCAhISEgAA165dQ0JCgt7A5JmZmdi4cSNGjhxpdBsdOnTA4sWLdd8nTpyIgwcPIikpCUeOHEHv3r3h5uaGQYMGWSVm7RhSMpnMKtsjKku05w3HYiMiIiIiIiK73il1/PhxtG/fXvdd+wjdsGHDsHr1agDAunXrIAhCsUmlxMRE3Lt3T/f95s2bGDRoEO7fv4+AgAC0bt0aR48eRUBAgFVj5yN7RJbjeUNEREREVHakK9XIUhVCma+BzFMMX4kb/GUe9g6LHIhIEATB3kE4mszMTMjlcmRkZMDPz09vXl5eHq5du4bw8HCTg64TkT6eP0REREREZcOdrHysOpqGc6lK3bS6Chlimweikq+nHSOjknIetmbXx/eIzLVixQqEhIRALBZjwYIFRqdNmzYNDRs21K0TExODXr166b63a9cOb775pk3j1jpw4ABEIpFd3tiYlJQEkUike0yWiIiIiIioNKUr1QYJKQA4l6rEqqNpSFdyOA96hEmpMmD58uXw9fVFQUGBblp2djY8PDzQrl07vWW1yZPExEQAQFhYGEQiEdatW2ew3bp160IkEuketSy6fNHPM88881TxZ2ZmYty4cXjvvfdw69YtjB492ui0iRMnYu/evcVu56effsLMmTPN3q89E0lP6vFEHBERERERka1lqQoNElJa51KVyFIV2jgiclRMSpUB7du3R3Z2No4fP66b9scff0ChUOCvv/5CXl6ebvr+/ftRpUoVVK1aVTctJCQEq1at0tvm0aNHkZqaCm9vb4P9zZgxAykpKbrPqVOnnir+5ORkqNVqdOvWDUFBQZDJZEan+fj4lPhGxPLly8PX1/epYqH/5Ofn2zsEIiIiIiJyQMp8zVPNp7KDSSk7UStzkHsnFVnJ15B7Jw1qZU6p7atmzZoICgrCgQMHdNMOHDiAnj17Ijw8HEePHtWbXnTweQAYMmQIDh48iBs3buimrVy5EkOGDIG7u+FY+b6+vlAoFLqPqUHm33vvPdSoUQMymQwRERGYMmWK7u1sq1evRmRkJAAgIiJCd2fW49OSkpIMHt973OOP73333Xdo0qSJLt7Bgwfjzp07AB498qY9DuXKlYNIJEJMTAwAQKPRIC4uDuHh4fDy8kKDBg2wadMmvX399ttvqFGjBry8vNC+fXskJSWVeAwAYN68eYiMjIS3tzdCQkLw+uuvIzs7WzffWPkWLFiAsLAw3fw1a9bgl19+0d2lVrTO//33X7Rv3x4ymQwNGjRAfHy83rY2b96MunXrQiKRICwsDJ9//rne/LCwMMycORNDhw6Fn58fRo8ejfz8fIwbNw5BQUGQSqUIDQ1FXFycybISEREREZHrknmWnGowNZ/KDrYEO1ClP8SVH1bh9GczcW7xZzj92Qxc/WEVVOkPS22f7du3x/79+3Xf9+/fj3bt2iEqKko3PTc3F3/99ZdBUiowMBDR0dFYs2YNAECpVGL9+vUYPny4VWLz9fXF6tWrcf78eSxcuBBfffUV5s+fDwAYOHAg9uzZAwA4duwYUlJS0L9/f4NpISEhFu9XrVZj5syZOH36NLZs2YKkpCRd4ikkJASbN28GAFy6dAkpKSlYuHAhACAuLg7ffvstli9fjnPnzuGtt97Cyy+/jIMHDwIAbty4gT59+qB79+5ISEjAyJEjMWnSJJPxiMViLFq0COfOncOaNWuwb98+vPvuu2aXZ+LEiRgwYAA6d+6su0utZcuWuvkffPABJk6ciISEBNSoUQODBg3SPdJ54sQJDBgwAC+99BLOnDmDadOmYcqUKXqPZgLAZ599hgYNGuDUqVOYMmUKFi1ahK1bt2LDhg24dOkS1q5dq0uSERERERFR2eQrcUNdhczovLoKGXwlbjaOiByV4W0uVKrUyhwkblqLzMsX9KZnXL6AfzetRbXBsfCQGT4S97Tat2+PN998EwUFBcjNzcWpU6cQFRUFtVqN5cuXAwDi4+OhUqkMklIAMHz4cLz99tv44IMPsGnTJlStWrXYu5Lee+89fPjhh7rvs2fPxvjx44uNreiyYWFhmDhxItatW4d3330XXl5eukfyAgICoFAoAMDoNEsVTapFRERg0aJFaNq0KbKzs+Hj44Py5csDACpVqgR/f38AgEqlwuzZs7Fnzx60aNFCt+7hw4fx5ZdfIioqCsuWLUPVqlV1dxrVrFkTZ86cwZw5c0qMp+hdXGFhYfj444/x2muvYenSpWaVx8fHB15eXlCpVEaPycSJE9GtWzcAwPTp01G3bl1cvXoVtWrVwrx589ChQwdMmTIFAFCjRg2cP38en376qS5RBwDPP/883n77bd335ORkVK9eHa1bt4ZIJEJoaKhZsRIRERERkevyl3kgtnlgsW/f85d52DE6ciRMStlYQXaWQUJKK+PyBRRkZ5VKUqpdu3bIycnB33//jYcPH6JGjRoICAhAVFQUYmNjkZeXhwMHDiAiIgJVqlQxWL9bt2549dVXcejQIaxcubLEu6TeeecdvURGxYoVAQCvvfYavv/+e9107aNp69evx6JFi5CYmIjs7GwUFBTY5LWUJ06cwLRp03D69Gk8fPgQGs2j55qTk5NRp04do+tcvXoVSqUSnTp10puen5+PZ599FgBw4cIFNGvWTG++NoFVkj179iAuLg4XL15EZmYmCgoKkJeXB6VSCZnM+P8yWKJ+/fq6fwcFBQEA7ty5g1q1auHChQvo2bOn3vKtWrXCggULUFhYCDe3R/+T0aRJE71lYmJi0KlTJ9SsWROdO3fGiy++iBdeeOGpYyUiIiIiIudWydcTr7ZSIEtVCGW+BjJPMXwlbkxIkR4mpWysIC/XxPy8Euc/qWrVquGZZ57B/v378fDhQ0RFRQEAgoODERISgiNHjmD//v14/vnnja7v7u6OV155BVOnTsVff/2Fn3/+udh9VaxYEdWqVTOYPmPGDEycOFFvWnx8PIYMGYLp06cjOjoacrkc69atMxjPyNpycnIQHR2N6OhorF27FgEBAUhOTkZ0dHSJA3hrE2nbt29H5cqV9eZJJJInjicpKQkvvvgixowZg1mzZqF8+fI4fPgwRowYgfz8fMhkMojFYgiCoLeeduwtc3h4/Nf5i0QiANAl4sz1+MD2jRo1wrVr17Bjxw7s2bMHAwYMQMeOHQ3G2CIiIiIiorLHX+bBJBSViEkpG3OXepmYLy21fbdv3x4HDhzAw4cP8c477+imt23bFjt27MCxY8cwZsyYYtcfPnw4PvvsMwwcOBDlypWzeP+VKlVCpUqV9KYdOXIEoaGh+OCDD3TTrl+/bvG2LXXx4kXcv38fn3zyiW48qqJvJwQAT09PAEBh4X+vK61Tpw4kEgmSk5N1ib3H1a5dG1u3btWbVnQweWNOnDgBjUaDzz//HGLxo6HeNmzYoLdMQEAAUlNTIQiCLqmUkJBgEHPReM1Vu3Zt/Pnnn3rT/vzzT9SoUUN3l1Rx/Pz8MHDgQAwcOBD9+vVD586d8eDBA93jj0RERERERETGMCllY+4+vpDXqI0MI4/wyWvUhruPb6ntu3379hg7dizUarVeQiUqKgrjxo1Dfn6+0fGktGrXro179+5Z5VEyrerVqyM5ORnr1q1D06ZNsX379hLvwrKWKlWqwNPTE1988QVee+01nD17FjNnztRbJjQ0FCKRCNu2bUPXrl3h5eUFX19fTJw4EW+99RY0Gg1at26NjIwM/Pnnn/Dz88OwYcPw2muv4fPPP8c777yDkSNH4sSJEwYDhj+uWrVqUKvV+OKLL9C9e3f8+eefurG+tNq1a4e7d+9i7ty56NevH3bu3IkdO3boPeoYFhaG33//HZcuXUKFChUgl8vNOh5vv/02mjZtipkzZ2LgwIGIj4/H4sWLTY5nNW/ePAQFBeHZZ5+FWCzGxo0boVAodGNwERERERERERWHb9+zMQ+ZNyL6DYG8Rm296fIatRHRb0ipjCel1b59e+Tm5qJatWoIDAzUTY+KikJWVhZq1qypG2uoOBUqVICXV8l3e1miR48eeOuttzBu3Dg0bNgQR44c0Q22XZoCAgKwevVqbNy4EXXq1MEnn3yCzz77TG+ZypUrY/r06Zg0aRICAwMxbtw4AMDMmTMxZcoUxMXFoXbt2ujcuTO2b9+O8PBwAI8SXps3b8aWLVvQoEEDLF++HLNnzy4xngYNGmDevHmYM2cO6tWrh7Vr1yIuLk5vmdq1a2Pp0qVYsmQJGjRogGPHjhk8Djlq1CjUrFkTTZo0QUBAgMHdT8Vp1KgRNmzYgHXr1qFevXr46KOPMGPGDL2xwYzx9fXF3Llz0aRJEzRt2hRJSUn47bffdHd7ERERERERERVHJDw+SA0hMzMTcrkcGRkZBgNu5+Xl4dq1awgPD4f0KR61UytzUJCdhYK8PLhLpXD38S3VhBSRI7DW+UNERERERERPpqSch63x8T078ZB5MwlFRERERERERGUWn7EhIiIiIiIiIiKbY1KKiIiIiIiIiIhsjkkpIiIiIiIiIiKyOSaliIiIiIiIiIjI5piUIiIiIiIiIiIim2NSioiIiIiIiIiIbI5JKSIiIiIiIiIisjkmpYiIiIiIiIiIyOaYlCK7WbFiBUJCQiAWi7FgwQKj06ZNm4aGDRvq1omJiUGvXr1039u1a4c333zTpnFrHThwACKRCOnp6Tbfd1JSEkQiERISEmy+byIiIiIiIiJrYFKqjIiJiYFIJMInn3yiN33Lli0QiUQ2jyczMxPjxo3De++9h1u3bmH06NFGp02cOBF79+4tdjs//fQTZs6cafZ+7ZlIelKPJ+KIiIiIiIiIXAGTUmWIVCrFnDlz8PDhQ3uHguTkZKjVanTr1g1BQUGQyWRGp/n4+KBChQrFbqd8+fLw9fW1YeSuLT8/394hEBERERERURnBpJSd5KgKcDtDhcR7uUjJUCFHVVDq++zYsSMUCgXi4uJKXG7z5s2oW7cuJBIJwsLC8Pnnn+vNDwsLw+zZszF8+HD4+vqiSpUqWLFihd4y7733HmrUqAGZTIaIiAhMmTIFarUaALB69WpERkYCACIiIiASiYxOS0pKMnh873GPP7733XffoUmTJvD19YVCocDgwYNx584dAI8eeWvfvj0AoFy5chCJRIiJiQEAaDQaxMXFITw8HF5eXmjQoAE2bdqkt6/ffvsNNWrUgJeXF9q3b4+kpKQSjyMAzJs3D5GRkfD29kZISAhef/11ZGdn6+YbK9+CBQsQFhamm79mzRr88ssvEIlEEIlEOHDggG7Zf//9F+3bt4dMJkODBg0QHx+vty1z6nLmzJkYOnQo/Pz8MHr0aOTn52PcuHEICgqCVCpFaGioyTZDREREREREZCkmpezgfo4aS/5IwaStSZi+IxnvbU3C0j9ScD9HXar7dXNzw+zZs/HFF1/g5s2bRpc5ceIEBgwYgJdeeglnzpzBtGnTMGXKFKxevVpvuc8//xxNmjTBqVOn8Prrr2PMmDG4dOmSbr6vry9Wr16N8+fPY+HChfjqq68wf/58AMDAgQOxZ88eAMCxY8eQkpKC/v37G0wLCQmxuIxqtRozZ87E6dOnsWXLFiQlJekSTyEhIdi8eTMA4NKlS0hJScHChQsBAHFxcfj222+xfPlynDt3Dm+99RZefvllHDx4EABw48YN9OnTB927d0dCQgJGjhyJSZMmmYxHLBZj0aJFOHfuHNasWYN9+/bh3XffNbs8EydOxIABA9C5c2ekpKQgJSUFLVu21M3/4IMPMHHiRCQkJKBGjRoYNGgQCgoeJTjNrcvPPvsMDRo0wKlTpzBlyhQsWrQIW7duxYYNG3Dp0iWsXbtWlyQjIiIiIiIishZ3ewdQ1uSoCvBNfCrOpij1pp9JUWJlfCpebxMEb0npVUvv3r3RsGFDTJ06Fd98843B/Hnz5qFDhw6YMmUKAKBGjRo4f/48Pv30U11yBwC6du2K119/HcCju6Lmz5+P/fv3o2bNmgCADz/8ULdsWFgYJk6ciHXr1uHdd9+Fl5eX7pG8gIAAKBQKADA6zVLDhw/X/TsiIgKLFi1C06ZNkZ2dDR8fH5QvXx4AUKlSJfj7+wMAVCoVZs+ejT179qBFixa6dQ8fPowvv/wSUVFRWLZsGapWraq706hmzZo4c+YM5syZU2I8Re/iCgsLw8cff4zXXnsNS5cuNas8Pj4+8PLygkqlMnpMJk6ciG7dugEApk+fjrp16+Lq1auoVauW2XX5/PPP4+2339Z9T05ORvXq1dG6dWuIRCKEhoaaFSsRERERERGRJXinlI1l5BUaJKS0zqQokZFXWOoxzJkzB2vWrMGFCxcM5l24cAGtWrXSm9aqVStcuXIFhYX/xVa/fn3dv0UiERQKhe4xOQBYv349WrVqBYVCAR8fH3z44YdITk4uhdLoO3HiBLp3744qVarA19cXUVFRAFDivq9evQqlUolOnTrBx8dH9/n222+RmJgI4NFxadasmd562gRWSfbs2YMOHTqgcuXK8PX1xSuvvIL79+9DqTTeBixVtB6CgoIAQFcP5tZlkyZN9JaJiYlBQkICatasifHjx2PXrl1WiZWIiIiIiIioKCalbCxXrXmq+dbQtm1bREdHY/LkyU+8DQ8PD73vIpEIGs2j2OPj4zFkyBB07doV27Ztw6lTp/DBBx+U+iDaOTk5iI6Ohp+fH9auXYu///4bP//8M4CSB/DWjvG0fft2JCQk6D7nz583GFfKEklJSXjxxRdRv359bN68GSdOnMCSJUv04hGLxRAEQW897dhb5ihaD9q3KGrrwVze3t563xs1aoRr165h5syZyM3NxYABA9CvXz+LtklERERERERkCh/fszEvj5LzgKbmW8snn3yChg0b6h6306pduzb+/PNPvWl//vknatSoATc3N7O2feTIEYSGhuKDDz7QTbt+/frTB23CxYsXcf/+fXzyySe68aiOHz+ut4ynpycA6N0pVKdOHUgkEiQnJ+vurHpc7dq1sXXrVr1pR48eLTGeEydOQKPR4PPPP4dY/KheN2zYoLdMQEAAUlNTIQiCLqmUkJBgEHPReM31NHXp5+eHgQMHYuDAgejXrx86d+6MBw8e6B5/JCIiIiIiInpavFPKxuRSN0QGyYzOiwySQS41L/HztCIjIzFkyBAsWrRIb/rbb7+NvXv3YubMmbh8+TLWrFmDxYsXY+LEiWZvu3r16khOTsa6deuQmJiIRYsW6e5YKk1VqlSBp6cnvvjiC/z777/YunUrZs6cqbdMaGgoRCIRtm3bhrt37yI7Oxu+vr6YOHEi3nrrLaxZswaJiYk4efIkvvjiC6xZswYA8Nprr+HKlSt45513cOnSJfzwww8GA4Y/rlq1alCr1bp4vvvuOyxfvlxvmXbt2uHu3buYO3cuEhMTsWTJEuzYsUNvmbCwMPzzzz+4dOkS7t27Z/adVE9al/PmzcOPP/6Iixcv4vLly9i4cSMUCoVuDC4iIiIiIiIia2BSysa8Je4Y3kJhkJiKDJJhRAtFqQ5y/rgZM2YYPOrVqFEjbNiwAevWrUO9evXw0UcfYcaMGXoDY5vSo0cPvPXWWxg3bhwaNmyII0eO6AbbLk0BAQFYvXo1Nm7ciDp16uCTTz7BZ599prdM5cqVMX36dEyaNAmBgYEYN24cAGDmzJmYMmUK4uLiULt2bXTu3Bnbt29HeHg4gEcJr82bN2PLli1o0KABli9fjtmzZ5cYT4MGDTBv3jzMmTMH9erVw9q1axEXF6e3TO3atbF06VIsWbIEDRo0wLFjxwySRqNGjULNmjXRpEkTBAQEGNz9VJwnrUtfX1/MnTsXTZo0QdOmTZGUlITffvtNd7cXERERERERkTWIhMcHtCFkZmZCLpcjIyMDfn5+evPy8vJw7do1hIeHQyqVPvE+clQFyMgrRK5aAy8PMeRSN5smpIjswVrnDxERERERET2ZknIetmbXWx8OHTqE7t27Izg4GCKRCFu2bNGbHxMTA5FIpPfp3Lmzye0uWbIEYWFhkEqlaNasGY4dO1ZKJXhy3hJ3BMslqFrRC8FyCRNSRERERERERFSm2DUplZOTgwYNGujeSGZM586dkZKSovv8+OOPJW5z/fr1mDBhAqZOnYqTJ0+iQYMGiI6Oxp07d6wdPhEREREREREVI0dVgNsZKiTey0VKhgo5qgJ7h0QOxq6353Tp0gVdunQpcRmJRAKFQmH2NufNm4dRo0YhNjYWALB8+XJs374dK1euxKRJk54qXiIiIiIiIiIy7X6OGt/Ep+JsilI3LTJIhuEtFKjg7WHHyMiROPzIxQcOHEClSpVQs2ZNjBkzBvfv3y922fz8fJw4cQIdO3bUTROLxejYsSPi4+NtES4RERERERFRmZajKjBISAHAmRQlVsan8o4p0nHogYw6d+6MPn36IDw8HImJiXj//ffRpUsXxMfHw83NzWD5e/fuobCwEIGBgXrTAwMDcfHixWL3o1KpoFKpdN8zMzOtVwgiIiIiIiKiMiQjr9AgIaV1JkWJjLxCjqtMABw8KfXSSy/p/h0ZGYn69eujatWqOHDgADp06GC1/cTFxWH69OkWraPRaKy2f6KygucNEREREZHry1WX/Lvf1HwqOxw6KfW4iIgIVKxYEVevXjWalKpYsSLc3NyQlpamNz0tLa3EcakmT56MCRMm6L5nZmYiJCTE6LKenp4Qi8W4ffs2AgIC4OnpCZFI9IQlIiobBEFAfn4+7t69C7FYDE9PT3uHREREREREpcTLo+SRgkzNp7LDqZJSN2/exP379xEUFGR0vqenJxo3boy9e/eiV69eAB7dmbF3716MGzeu2O1KJBJIJBKzYhCLxQgPD0dKSgpu375tcRmIyjKZTIYqVapALOZFiIiIiIjIVcmlbogMkuGMkUf4IoNkkEsNh+OhssmuSans7GxcvXpV9/3atWtISEhA+fLlUb58eUyfPh19+/aFQqFAYmIi3n33XVSrVg3R0dG6dTp06IDevXvrkk4TJkzAsGHD0KRJEzz33HNYsGABcnJydG/jswZPT09UqVIFBQUFKCwstNp2iVyZm5sb3N3deWchEREREZGL85a4Y3gLBVbGp+olpiKDZBjRQsHxpEjHri3h+PHjaN++ve679hG6YcOGYdmyZfjnn3+wZs0apKenIzg4GC+88AJmzpypd1dTYmIi7t27p/s+cOBA3L17Fx999BFSU1PRsGFD7Ny502Dw86clEong4eEBDw++ypKIiIiIiIioqAreHni9TRAy8gqRq9bAy0MMudSNCSnSIxIEQbB3EI4mMzMTcrkcGRkZ8PPzs3c4RERERERERERW4Ug5Dw7sQkRERERERERENsekFBERERERERER2RyTUkREREREREREZHNMShERERERERERkc0xKUVERERERERERDbHpBQREREREREREdkck1JERERERERERGRzTEoREREREREREZHNuds7ACIiIiIiIiJyPTmqAmTkFSJXrYHMQww/qRu8JUxD0H/YGoiIiIiIiIjIqu7nqPFNfCrOpih10yKDZBjeQoEK3h52jIwcCR/fIyIiIiIiIiKryVEVGCSkAOBMihIr41ORoyqwU2TkaJiUIiIiIiIiIiKrycgrNEhIaZ1JUSIjr9DGEZGjYlKKiIiIiIiIiKwmV615qvlUdjApRURERERERERW4+VRcqrB1HwqO9gSiIiIiIiIiMhq5FI3RAbJjM6LDJJBLnWzcUTkqJiUIiIiIiIiIiKr8Za4Y3gLhUFiKjJIhhEtFPCWuNspMnI0bAlEREREREREZFUVvD3wepsgZOQVIletgZeHGHKpGxNSpIetgYiIiIiIiIiszlviziQUlYiP7xERERERERERkc0xKUVERERERERERDbHpBQREREREREREdkck1JERERERERERGRzTEoREREREREREZHNMSlFREREREREREQ2x6QUERERERERERHZHJNSRERERERERERkc0xKERERERERERGRzTEpRURERERERERENsekFBERERERERER2RyTUkREREREREREZHNMShERERERERERkc0xKUVERERERERERDbHpBQREREREREREdkck1JERERERERERGRzdk1KHTp0CN27d0dwcDBEIhG2bNmim6dWq/Hee+8hMjIS3t7eCA4OxtChQ3H79u0Stzlt2jSIRCK9T61atUq5JEREREREREREZAm7JqVycnLQoEEDLFmyxGCeUqnEyZMnMWXKFJw8eRI//fQTLl26hB49epjcbt26dZGSkqL7HD58uDTCJyIiIiIiIiKiJ+Ruz5136dIFXbp0MTpPLpdj9+7detMWL16M5557DsnJyahSpUqx23V3d4dCobBqrEREREREREREZD1ONaZURkYGRCIR/P39S1zuypUrCA4ORkREBIYMGYLk5OQSl1epVMjMzNT7EBERERERERFR6XGapFReXh7ee+89DBo0CH5+fsUu16xZM6xevRo7d+7EsmXLcO3aNbRp0wZZWVnFrhMXFwe5XK77hISElEYRiIiIiIiIiIjo/4kEQRDsHQQAiEQi/Pzzz+jVq5fBPLVajb59++LmzZs4cOBAiUmpx6WnpyM0NBTz5s3DiBEjjC6jUqmgUql03zMzMxESEoKMjAyL9kVERERERERE5MgyMzMhl8sdIudh1zGlzKFWqzFgwABcv34d+/bts/iA+fv7o0aNGrh69Wqxy0gkEkgkkqcNlYiIiIiIiIiIzOTQj+9pE1JXrlzBnj17UKFCBYu3kZ2djcTERAQFBZVChERERERERERE9CTsmpTKzs5GQkICEhISAADXrl1DQkICkpOToVar0a9fPxw/fhxr165FYWEhUlNTkZqaivz8fN02OnTogMWLF+u+T5w4EQcPHkRSUhKOHDmC3r17w83NDYMGDbJ18YiIiIiIiIiIqBh2fXzv+PHjaN++ve77hAkTAADDhg3DtGnTsHXrVgBAw4YN9dbbv38/2rVrBwBITEzEvXv3dPNu3ryJQYMG4f79+wgICEDr1q1x9OhRBAQElG5hiIiIiIiIiIjIbA4z0LkjcaRBv4iIiIiIiIiIrMWRch4OPaYUERERERERERG5JialiIiIiIiIiIjI5piUIiIiIiIiIiIim2NSioiIiIiIiIiIbI5JKSIiIiIiIiIisjkmpYiIiIiIiIiIyOaYlCIiIiIiIiIiIptjUoqIiIiIiIiIiGyOSSkiIiIiIiIiIrI5JqWIiIiIiIiIiMjmmJQiIiIiIiIiIiKbY1KKiIiIiIiIiIhsjkkpIiIiIiIiIiKyOSaliIiIiIiIiIjI5piUIiIiIiIiIiIim2NSioiIiIiIiIiIbI5JKSIiIiIiIiIisjn3J1lJo9Hg6tWruHPnDjQajd68tm3bWiUwIiIiIiIiIiJyXRYnpY4ePYrBgwfj+vXrEARBb55IJEJhYaHVgiMiIiIiIiIiItdkcVLqtddeQ5MmTbB9+3YEBQVBJBKVRlxEREREREREROTCLE5KXblyBZs2bUK1atVKIx4iIiIiIiIiIioDLB7ovFmzZrh69WppxEJERERERERERGWEWXdK/fPPP7p/v/HGG3j77beRmpqKyMhIeHh46C1bv35960ZIREREREREREQuRyQ8Plq5EWKxGCKRyGBgc91G/n+eqwx0npmZCblcjoyMDPj5+dk7HCIiIiIiIiIiq3CknIdZd0pdu3attOMgIiIiIiIiIqIyxKykVGhoqO7fhw4dQsuWLeHurr9qQUEBjhw5orcsERERERERERGRMRYPdN6+fXs8ePDAYHpGRgbat29vlaCIiIiIiIiIiMi1WZyU0o4d9bj79+/D29vbKkEREREREREREZFrM+vxPQDo06cPgEeDmsfExEAikejmFRYW4p9//kHLli2tHyEREREREREREbkcs5NScrkcwKM7pXx9feHl5aWb5+npiebNm2PUqFHWj5CIiIiIiIiIiFyO2UmpVatWAQDCwsIwceJEPqpHRERERERERERPTCQIgvAkK965cweXLl0CANSsWROVKlWyamD2lJmZCblcjoyMDPj5+dk7HCIiIiIiIiIiq3CknIfFA51nZWXhlVdeQeXKlREVFYWoqChUrlwZL7/8MjIyMkojRiIiIiIiIiIicjEWJ6VGjhyJv/76C9u2bUN6ejrS09Oxbds2HD9+HK+++qpF2zp06BC6d++O4OBgiEQibNmyRW++IAj46KOPEBQUBC8vL3Ts2BFXrlwxud0lS5YgLCwMUqkUzZo1w7FjxyyKi4iIiIjIWeWoCnA7Q4XEe7lIyVAhR1Vg75CIqIxif0SmmD2mlNa2bdvw+++/o3Xr1rpp0dHR+Oqrr9C5c2eLtpWTk4MGDRpg+PDhurf7FTV37lwsWrQIa9asQXh4OKZMmYLo6GicP38eUqnU6DbXr1+PCRMmYPny5WjWrBkWLFiA6OhoXLp0yaUeMSQiIiIietz9HDW+iU/F2RSlblpkkAzDWyhQwdvDjpERUVnD/ojMYfGdUhUqVNC9ia8ouVyOcuXKWbStLl264OOPP0bv3r0N5gmCgAULFuDDDz9Ez549Ub9+fXz77be4ffu2wR1VRc2bNw+jRo1CbGws6tSpg+XLl0Mmk2HlypUWxUZERERE5ExyVAUGfwACwJkUJVbGp/IOBSKyGfZHZC6Lk1IffvghJkyYgNTUVN201NRUvPPOO5gyZYrVArt27RpSU1PRsWNH3TS5XI5mzZohPj7e6Dr5+fk4ceKE3jpisRgdO3Ysdh0AUKlUyMzM1PsQERERETmTjLxCgz8Atc6kKJGRV2jjiIiorGJ/ROay+PG9ZcuW4erVq6hSpQqqVKkCAEhOToZEIsHdu3fx5Zdf6pY9efLkEwemTXoFBgbqTQ8MDNRLiBV17949FBYWGl3n4sWLxe4rLi4O06dPf+JYiYiIiIjsLVetear5RETWwv6IzGVxUqpXr16lEIZ9TZ48GRMmTNB9z8zMREhIiB0jIiIiIiKyjJdHyQ9BmJpPRGQt7I/IXBYnpaZOnVoacRhQKBQAgLS0NAQFBemmp6WloWHDhkbXqVixItzc3JCWlqY3PS0tTbc9YyQSCSQSydMHTURERERkJ3KpGyKDZDhj5JGZyCAZ5FI3O0RFRGUR+yMy1xOlJ9PT0/H1119j8uTJePDgAYBHj+rdunXLaoGFh4dDoVBg7969ummZmZn466+/0KJFC6PreHp6onHjxnrraDQa7N27t9h1iIiIiIhcgbfEHcNbKBAZJNObHhkkw4gWCnhLLP7/aCKiJ8L+iMxlcUv4559/0LFjR8jlciQlJWHUqFEoX748fvrpJyQnJ+Pbb781e1vZ2dm4evWq7vu1a9eQkJCA8uXLo0qVKnjzzTfx8ccfo3r16ggPD8eUKVMQHBys9whhhw4d0Lt3b4wbNw4AMGHCBAwbNgxNmjTBc889hwULFiAnJwexsbGWFpWIiIiIyKlU8PbA622CkJFXiFy1Bl4eYsilbvwDkIhsjv0RmcPi1jBhwgTExMRg7ty58PX11U3v2rUrBg8ebNG2jh8/jvbt2+ttGwCGDRuG1atX491330VOTg5Gjx6N9PR0tG7dGjt37oRUKtWtk5iYiHv37um+Dxw4EHfv3sVHH32E1NRUNGzYEDt37jQY/JyIiIiIyBV5S9z5Rx8ROQT2R2SKSBAEwZIV5HI5Tp48iapVq8LX1xenT59GREQErl+/jpo1ayIvL6+0YrWZzMxMyOVyZGRkwM/Pz97hEBERERERERFZhSPlPCweU0oikSAzM9Ng+uXLlxEQEGCVoIiIiIiIiIiIyLVZnJTq0aMHZsyYAbVaDQAQiURITk7Ge++9h759+1o9QCIiIiIiIiIicj0WJ6U+//xzZGdno1KlSsjNzUVUVBSqVasGX19fzJo1qzRiJCIiIiIiIiIiF2PxiGNyuRy7d+/G4cOH8c8//yA7OxuNGjVCx44dSyM+IiIiIiIiIiJyQRYPdF4WONKgX0RERERERERE1uJIOQ+L7pTSaDRYvXo1fvrpJyQlJUEkEiE8PBz9+vXDK6+8ApFIVFpxEhERERERERGRCzF7TClBENCjRw+MHDkSt27dQmRkJOrWrYvr168jJiYGvXv3Ls04iYiIiIiIiIjIhZh9p9Tq1atx6NAh7N27F+3bt9ebt2/fPvTq1Qvffvsthg4davUgiYiIiIiIiIjItZh9p9SPP/6I999/3yAhBQDPP/88Jk2ahLVr11o1OCIiIiIiIiIick1mJ6X++ecfdO7cudj5Xbp0wenTp60SFBERERERERERuTazk1IPHjxAYGBgsfMDAwPx8OFDqwRFRERERERERESuzeykVGFhIdzdix+Cys3NDQUFBVYJioiIiIiIiIiIXJvZA50LgoCYmBhIJBKj81UqldWCIiIiIiIiIiIi12Z2UmrYsGEml+Gb94iIiIiIiIiIyBxmJ6VWrVpVmnEQEREREREREVEZYvaYUkRERERERERERNbCpBQREREREREREdkck1JERERERERERGRzTEoREREREREREZHNMSlFREREREREREQ2Z/bb94q6cuUK9u/fjzt37kCj0ejN++ijj6wSGBERERERERERuS6Lk1JfffUVxowZg4oVK0KhUEAkEunmiUQiJqWIiIiIiIiIiMgki5NSH3/8MWbNmoX33nuvNOIhIiIiIiIiIqIywOIxpR4+fIj+/fuXRixERERERERERFRGWJyU6t+/P3bt2lUasRARERERERERURlh8eN71apVw5QpU3D06FFERkbCw8NDb/748eOtFhwRkbXkqAqQkVeIXLUGMg8x/KRu8JY80bseiIiI7IrXNOfFuqOyhm2eTBEJgiBYskJ4eHjxGxOJ8O+//z51UPaWmZkJuVyOjIwM+Pn52TscInpK93PU+CY+FWdTlLppkUEyDG+hQAVvjxLWJCIiciy8pjkv1h2VNWzzjsuRch4WJ6XKAkeqICJ6OjmqAiz5I0XvYqgVGSTD622C+L81RETkFHhNc16sOypr2OYdmyPlPCweU4qIyJlk5BUavRgCwJkUJTLyCm0cERER0ZPhNc15se6orGGbJ3M9UWry5s2b2Lp1K5KTk5Gfn683b968eVYJjIjIGnLVmqeaT0RE5Ch4TXNerDsqa9jmyVwWJ6X27t2LHj16ICIiAhcvXkS9evWQlJQEQRDQqFGj0oiRiOiJeXmUfEOoqflERESOgtc058W6o7KGbZ7MZXFLmDx5MiZOnIgzZ85AKpVi8+bNuHHjBqKiotC/f//SiJGI6InJpW6IDJIZnRcZJINc6mbjiIiIiJ4Mr2nOi3VHZQ3bPJnL4qTUhQsXMHToUACAu7s7cnNz4ePjgxkzZmDOnDlWD5CI6Gl4S9wxvIXC4KIYGSTDiBYKDrBIREROg9c058W6o7KGbZ7MZXFL8Pb21o0jFRQUhMTERNStWxcAcO/ePetGByAsLAzXr183mP76669jyZIlBtNXr16N2NhYvWkSiQR5eXlWj42InEMFbw+83iYIGXmFyFVr4OUhhlzqxoshERE5HV7TnBfrjsoatnkyh8WtoXnz5jh8+DBq166Nrl274u2338aZM2fw008/oXnz5lYP8O+//0Zh4X8j8589exadOnUq8VFBPz8/XLp0SfddJBJZPS4ici7eEndeAImIyCXwmua8WHdU1rDNkykWt4558+YhOzsbADB9+nRkZ2dj/fr1qF69eqm8eS8gIEDv+yeffIKqVasiKiqq2HVEIhEUCoXVYyEiIiIiIiIiIuuwOCkVERGh+7e3tzeWL19u1YBKkp+fj++//x4TJkwo8e6n7OxshIaGQqPRoFGjRpg9e7buEUMiIiIiIiIiIrI/p3oP45YtW5Ceno6YmJhil6lZsyZWrlyJX375Bd9//z00Gg1atmyJmzdvFruOSqVCZmam3oeIiIiIiIiIiEqPSBAEwdRC5cuXx+XLl1GxYkWUK1euxLuUHjx4YNUAi4qOjoanpyd+/fVXs9dRq9WoXbs2Bg0ahJkzZxpdZtq0aZg+fbrB9IyMDPj5+T1xvEREREREREREjiQzMxNyudwhch5mPb43f/58+Pr6AgAWLFhQmvEU6/r169izZw9++ukni9bz8PDAs88+i6tXrxa7zOTJkzFhwgTd98zMTISEhDxxrEREREREREREVDKzklLDhg0z+m9bWrVqFSpVqoRu3bpZtF5hYSHOnDmDrl27FruMRCKBRCJ52hCJiIiIiIiIiMhMZiWlLBljqTRu/dJoNFi1ahWGDRsGd3f9kIcOHYrKlSsjLi4OADBjxgw0b94c1apVQ3p6Oj799FNcv34dI0eOtHpcRERERERERET0ZMxKSvn7+5c4jlRRhYWFTxWQMXv27EFycjKGDx9uMC85ORli8X/jtT98+BCjRo1CamoqypUrh8aNG+PIkSOoU6eO1eMiIiIiIiIiIqInY9ZA5wcPHtT9OykpCZMmTUJMTAxatGgBAIiPj8eaNWsQFxdnt8f7rMmRBv0iIuvIURUgI68QuWoNZB5i+End4C0xKy9PRA7O1c5vVysPWZ8rtRFXKgsRkbNwpJyHWUmpojp06ICRI0di0KBBetN/+OEHrFixAgcOHLBmfHbhSBVERE/vfo4a38Sn4myKUjctMkiG4S0UqODtYcfIiOhpudr57WrlIetzpTbiSmUhInImjpTzEJteRF98fDyaNGliML1JkyY4duyYVYIiIrKWHFWBwQ9eADiTosTK+FTkqArsFBkRPS1XO79drTxkfa7URlypLERE9OQsTkqFhITgq6++Mpj+9ddfIyQkxCpBERFZS0ZeocEPXq0zKUpk5Fl/HDwisg1XO79drTxkfa7URlypLERE9OQsfmB7/vz56Nu3L3bs2IFmzZoBAI4dO4YrV65g8+bNVg+QiOhp5Ko1TzWfiByXq53frlYesj5XaiOuVBYiInpyFt8p1bVrV1y+fBndu3fHgwcP8ODBA3Tv3h2XL19G165dSyNGIqIn5uVRcjdnaj4ROS5XO79drTxkfa7URlypLERE9OSe6NUWISEhmD17trVjISKyOrnUDZFBMpwx8ohAZJAMcqmbHaIiImtwtfPb1cpD1udKbcSVykJERE/uif4L4o8//sDLL7+Mli1b4tatWwCA7777DocPH7ZqcERET8tb4o7hLRSIDJLpTY8MkmFECwVfO03kxFzt/Ha18pD1uVIbcaWyEBHRkxMJgiBYssLmzZvxyiuvYMiQIfjuu+9w/vx5REREYPHixfjtt9/w22+/lVasNuNIr0ckIuvIURUgI68QuWoNvDzEkEvd+IOXyEW42vntauUh63OlNuJKZSEichaOlPOwOCn17LPP4q233sLQoUPh6+uL06dPIyIiAqdOnUKXLl2QmppaWrHajCNVEBERERERERGRtThSzsPix/cuXbqEtm3bGkyXy+VIT0+3RkxEREREREREROTiLE5KKRQKXL161WD64cOHERERYZWgiIiIiIiIiIjItVmclBo1ahT+97//4a+//oJIJMLt27exdu1aTJw4EWPGjCmNGImIiIiIiIiIyMVYPIrgpEmToNFo0KFDByiVSrRt2xYSiQQTJ07EG2+8URoxEhERERERERGRi7F4oHOt/Px8XL16FdnZ2ahTpw58fHysHZvdONKgX0RERERERERE1uJIOY8nft+qp6cn6tSpY81YiIiIiIiIiIiojDA7KTV8+HCzllu5cuUTB0NERERERERERGWD2Ump1atXIzQ0FM8++yye8Ik/olKRoypARl4hctUayDzE8JO6wVti2U2A1tgGlQ5nqhtnitUcrlYeIqKSuFKf50plIddnq/bK84LIMZl9Fo4ZMwY//vgjrl27htjYWLz88ssoX758acZGZNL9HDW+iU/F2RSlblpkkAzDWyhQwdvDZtug0uFMdeNMsZrD1cpDRFQSV+rzXKks5Pps1V55XhA5LrG5Cy5ZsgQpKSl499138euvvyIkJAQDBgzA77//zjunyC5yVAUGFxcAOJOixMr4VOSoCmyyDSodzlQ3zhSrOVytPEREJXGlPs+VykKuz1btlecFkWMzOykFABKJBIMGDcLu3btx/vx51K1bF6+//jrCwsKQnZ1dWjESGZWRV2hwcdE6k6JERl6hTbZBpcOZ6saZYjWHq5WHiKgkrtTnuVJZyPXZqr3yvCBybBYlpfRWFIshEokgCAIKC3kik+3lqjVPNd9a26DS4Ux140yxmsPVykNEVBJX6vNcqSzk+mzVXnleEDk2i5JSKpUKP/74Izp16oQaNWrgzJkzWLx4MZKTk+Hj41NaMRIZ5eVRcvM1Nd9a26DS4Ux140yxmsPVykNEVBJX6vNcqSzk+mzVXnleEDk2s8/A119/HUFBQfjkk0/w4osv4saNG9i4cSO6du0KsZgnMtmeXOqGyCCZ0XmRQTLIpW422QaVDmeqG2eK1RyuVh4iopK4Up/nSmUh12er9srzgsixiQQzRykXi8WoUqUKnn32WYhEomKX++mnn6wWnL1kZmZCLpcjIyMDfn5+9g6HSnA/R42V8ak489ibNEa0UKC8BW/fe9ptUOlwprpxpljN4WrlISIqiSv1ea5UFnJ9tmqvPC+I9DlSzsPspFRMTEyJySitVatWPXVQ9uZIFUSm5agKkJFXiFy1Bl4eYsilbvCWuNt8G1Q6nKlunClWc7haeYiISuJKfZ4rlYVcn63aK88Lov84Us7D7KRUWeJIFUREREREREREZC2OlPPgYFBERERERERERGRzTEoREREREREREZHNMSlFREREREREREQ2x6QUERERERERERHZHJNSRERERERERERkc0xKERERERERERGRzTEpRURERERERERENsekFBERERERERER2ZxDJ6WmTZsGkUik96lVq1aJ62zcuBG1atWCVCpFZGQkfvvtNxtFS44sR1WA2xkqJN7LRUqGCjmqAnuHRP/PlerGlcpCZA08J4iInJc5fXi6Uo0bD/NwKU2JGw/zkK5U2yFSshdz2og1fgvw94Rrc7d3AKbUrVsXe/bs0X13dy8+5CNHjmDQoEGIi4vDiy++iB9++AG9evXCyZMnUa9ePVuESw7ofo4a38Sn4myKUjctMkiG4S0UqODtYcfIyJXqxpXKQmQNPCeIiJyXOX34nax8rDqahnOp/y1TVyFDbPNAVPL1tHnMZFvmtBFr/Bbg7wnX59B3SgGPklAKhUL3qVixYrHLLly4EJ07d8Y777yD2rVrY+bMmWjUqBEWL15sw4jJkeSoCgw6MQA4k6LEyvhUZtntyJXqxpXKQmQNPCeIiJyXOX14ulJtkJACgHOpSqw6msY7plycOW3EGr8F+HuibHD4pNSVK1cQHByMiIgIDBkyBMnJycUuGx8fj44dO+pNi46ORnx8fIn7UKlUyMzM1PuQa8jIKzToxLTOpCiRkVdo44hIy5XqxpXKQmQNPCeIiJyXOX14lqrQICGldS5ViSwV+3lXZk4bscZvAf6eKBscOinVrFkzrF69Gjt37sSyZctw7do1tGnTBllZWUaXT01NRWBgoN60wMBApKamlrifuLg4yOVy3SckJMRqZSD7ylVrnmo+lR5XqhtXKguRNfCcICJyXub04cr8kpcxNZ+cmzltxBq/Bfh7omxw6KRUly5d0L9/f9SvXx/R0dH47bffkJ6ejg0bNlh1P5MnT0ZGRobuc+PGDatun+zHy6PkJm5qPpUeV6obVyoLkTXwnCAicl7m9OEyz5KXMTWfnJs5bcQavwX4e6JscKpa9Pf3R40aNXD16lWj8xUKBdLS0vSmpaWlQaFQlLhdiUQCPz8/vQ+5BrnUDZFBMqPzIoNkkEvdbBwRablS3bhSWYisgecEEZHzMqcP95W4oa7C+DJ1FTL4StjPuzJz2og1fgvw90TZ4FRJqezsbCQmJiIoKMjo/BYtWmDv3r1603bv3o0WLVrYIjxyQN4SdwxvoTDozCKDZBjRQgFvicO/gNJluVLduFJZiKyB5wQRkfMypw/3l3kgtnmgQWJK+/Y9fxnfiubKzGkj1vgtwN8TZYNIEATB3kEUZ+LEiejevTtCQ0Nx+/ZtTJ06FQkJCTh//jwCAgIwdOhQVK5cGXFxcQCAI0eOICoqCp988gm6deuGdevWYfbs2Th58iTq1atn9n4zMzMhl8uRkZHBu6ZcRI6qABl5hchVa+DlIYZc6sZOzEG4Ut24UlmIrIHnBBGR8zKnD09XqpGlKoQyXwOZpxi+EjcmpMoQc9qINX4L8PeE9TlSzsOha/LmzZsYNGgQ7t+/j4CAALRu3RpHjx5FQEAAACA5ORli8X83e7Vs2RI//PADPvzwQ7z//vuoXr06tmzZYlFCilyTNltPjseV6saVykJkDTwniIiclzl9uL/Mg0moMsycNmKN3wL8PeHaHPpOKXtxpKwhEREREREREZG1OFLOw6nGlCIiIiIiIiIiItfApBQREREREREREdkck1JERERERERERGRzTEoREREREREREZHNMSlFREREREREREQ2x6QUERERERERERHZHJNSRERERERERERkc+72DoBcW46qABl5hchVayDzEMNP6gZvCZsdme9+Tj5y8jVQ5mvg7SmGzFOMCt6e9g7riaUr1chSFUKZr4HMUwxfiRv8ZR72DousxFZ9ni3OC7ZVx8VrK5Hzcqbz15liLWuscY02ZxumljGnjThKO7JGHI5SFlfDI0il5n6OGt/Ep+JsilI3LTJIhuEtFKjgzT9syLS0rHysPpqGc6n/taG6Chlimgci0Nf5ElN3svKxykh5YpsHopITlof02arPs8V5wbbquHhtJXJeznT+OlOsZY01rtHmbMPUMua0EUdpR9aIw1HK4or4+B6VihxVgcFJCwBnUpRYGZ+KHFWBnSIjZ3E/x/APbwA4l6rE6qNpuJ+Tb6fInky6Um1wYQcelWfV0TSkK9V2ioyswVZ9ni3OC7ZVx8VrK5Hzcqbz15liLWuscY02Zxumlrmfk2+yjThKO7JGHI5SFlfFpBSVioy8QoOTVutMihIZeYU2joicTU6+xuBCqHUuVYmcfI2NI3o6WarCEsuTpeI54cxs1efZ4rxgW3VcvLYSOS9nOn+dKdayxhrXaHO2YWqZnHyNyTbiKO3IGnE4SllcFR/fo1KRqy75DyNT84mUJv64znWypJSp8piaT47NVn2eLc4LtlXHxWsrkfNypvPXmWIta6xxjbbGNkz93jCnjdiqHVmjPfOcKF1MSlGp8PIo+SY8U/OJZJ4m2pCJ+Y7GVHlMzSfHZqs+zxbnBduq4+K1lch5OdP560yxljXWuEZbYxumfm+Y00Zs1Y6s0Z55TpQuHj0qFXKpGyKDZEbnRQbJIJe62TgicjbenmLUVRhvQ3UVMng72R/GvhK3EsvjK+E54cxs1efZ4rxgW3VcvLYSOS9nOn+dKdayxhrXaHO2YWoZb0+xyTbiKO3IGnE4SllclXP9VUdOw1vijuEtFAYnb2SQDCNaKPjqTDKpgrcnYpoHGlwQtW8Zq+DtXG8A85d5ILaY8sQ2D7T4Nb7kWGzV59nivGBbdVy8thI5L2c6f50p1rLGGtdoc7ZhapkK3p4m24ijtCNrxOEoZXFVIkEQBHsH4WgyMzMhl8uRkZEBPz8/e4fj1HJUBcjIK0SuWgMvDzHkUjeetGSR+zn5yMnXIDdfAy9PMbw9xU6XkCoqXalGlqoQynwNZJ5i+Erc+Ee+C7FVn2eL84Jt1XHx2krkvJzp/HWmWMsaa1yjzdmGqWXMaSOO0o6sEYejlMUaHCnnwaSUEY5UQURERERERERE1uJIOQ8+vkdERERERERERDbHpBQREREREREREdkck1JERERERERERGRzTEoREREREREREZHNMSlFREREREREREQ2x6QUERERERERERHZHJNSRERERERERERkc0xKERERERERERGRzbnbOwCip5WuVCNLVQhlvgYyTzF8JW7wl3noLZOjKkBGXiFy1RrIPMTwk7rBW8Lm7wjuZedDqdZAma+Bt6cYXh5iVPTxtHdYpYZtkZyFNdpqWWvv5lyPrKGsHVeW1/LylrVrqzMpa+25rFErc1CQnYWCvFy4S2Vw9/GBh8zbLrGYuibxmkWOgq2BnNqdrHysOpqGc6lK3bS6Chlimweiku+jH1/3c9T4Jj4VZ1P+WyYySIbhLRSo4G39jpfMl5aVj9VG6i+meSACfV3vxzPbIjkLa7TVstbezbkeWUNZO64sr+XlLWvXVmdS1tpzWaNKf4jETWuRefmCbpq8Rm1E9BsCiX85m8Zi6prEaxY5Ej6+R04rXak26EwB4FyqEquOpiFdqUaOqsCgIwSAMylKrIxPRY6qwJYhUxH3sg1/NAOP6m/10TTcy863U2Slg22RnIU12mpZa+/mXI+soawdV5b3EUvKW9aurc6krLXnskatzDFISAFAxuUL+HfTWqiVOTaLxdQ16V62YUKq6Hxes8jWmJQip5WlKjToTLXOpSqRpSpERl6hQUeodSZFiYy8wtIMkUqgVGtKrD+lWmPjiEoX2yI5C2u01bLW3s25HllDWTuuLO9/zC1vWbu2OpOy1p7LmoLsLIOElFbG5QsoyM6yWSymrkmm+gles8jW+PgeOS1lfsk/rJT5Gri7iUpcJpc/zuzGVP3lmpjvbEy1NbZFchTWaKtlrb2bcz2yhrJ2XFley+YDZe/a6kzKWnsuawryck3Mz7NRJE9/TeI1i2yNSSlyWjLPkm/0k3mK4SYuOSnl5cGbBe3FVP15mZjvbEy1NbZFchTWaKtlrb2bcz2yhrJ2XFley+YDZe/a6kzKWnsua9ylXibmS20UydNfk3jNIltjSyCn5StxQ12FzOi8ugoZfCVukEvdEBlkfJnIIBnkUrfSDJFKIPMQl1h/Mhe7ULEtkrOwRlsta+3dnOuRNZS148ry/sfc8pa1a6szKWvtuaxx9/GFvEZto/PkNWrD3cfXZrGYuiaZ6id4zSJb45WJnJa/zAOxzQMNOlXtmyP8ZR7wlrhjeAuFQYcYGSTDiBYKvo7Ujir6eCKmmPqLaR7ocq+uZlskZ2GNtlrW2rs51yNrKGvHleV9xJLylrVrqzMpa+25rPGQeSOi3xCDxJT27XseMm+bxWLqmlTRx5PXLHIoIkEQBHsHUZy4uDj89NNPuHjxIry8vNCyZUvMmTMHNWvWLHad1atXIzY2Vm+aRCJBngXP8WZmZkIulyMjIwN+fn5PHD/ZRrpSjSxVIZT5Gsg8xfCVuBl0pjmqAmTkFSJXrYGXhxhyqRs7QgdxLzsfSrUGufkaeHmKIfMQu/SPZrZFchbWaKtlrb2bcz2yhrJ2XFley8tb1q6tzqSsteeyRq3MQUF2Fgry8uAulcLdx9emCamiTF2TeM0q2xwp5+HQreHgwYMYO3YsmjZtioKCArz//vt44YUXcP78eXh7F39y+/n54dKlS7rvIlHJ4wqRc/OXeZjsQL0l7uz8HFRZ+5HMtkjOwhpttay1d3OuR9ZQ1o4ry2u5snZtdSZlrT2XNR4yb7sloR5n6prEaxY5CoduHTt37tT7vnr1alSqVAknTpxA27Zti11PJBJBoVCUdnhERERERERERPSEnGpMqYyMDABA+fLlS1wuOzsboaGhCAkJQc+ePXHu3LkSl1epVMjMzNT7EBERERERERFR6XGapJRGo8Gbb76JVq1aoV69esUuV7NmTaxcuRK//PILvv/+e2g0GrRs2RI3b94sdp24uDjI5XLdJyQkpDSKQERERERERERE/8+hBzovasyYMdixYwcOHz6MZ555xuz11Go1ateujUGDBmHmzJlGl1GpVFCpVLrvmZmZCAkJcYhBv4iIiIiIiIiIrIUDnVto3Lhx2LZtGw4dOmRRQgoAPDw88Oyzz+Lq1avFLiORSCCRSJ42TCIiIiIiIiIiMpNDP74nCALGjRuHn3/+Gfv27UN4eLjF2ygsLMSZM2cQFBRUChESEREREREREdGTcOg7pcaOHYsffvgBv/zyC3x9fZGamgoAkMvl8PLyAgAMHToUlStXRlxcHABgxowZaN68OapVq4b09HR8+umnuH79OkaOHGm3chARERERERERkT6HTkotW7YMANCuXTu96atWrUJMTAwAIDk5GWLxfzd8PXz4EKNGjUJqairKlSuHxo0b48iRI6hTp46twiYHlKMqQEZeIXLVGsg8xPCTusFb4tDNv8wwVTfWqjtrbMdRtkFkq/PmaaUr1chSFUKZr4HMUwxfiRv8ZR42j4Ocm63as63aq6Ocn86Ex8w+bHXcWb/2cTc7H7lqDZT5Gnh7iiH1ECPAx1NvGVv0i+bswxpthL9JHJfTDHRuS4406Bc9vfs5anwTn4qzKUrdtMggGYa3UKCCNzsiezJVN9aqO2tsx1G2QWSr8+Zp3cnKx6qjaTiX+l8cdRUyxDYPRCVfzxLWJPqPrdqzrdqro5yfzoTHzD5sddxZv/aRlpWP1Ub6vJjmgQj8/z7PFv2iOfuwRhvhbxJDjpTzcOgxpYieVo6qwKATA4AzKUqsjE9FjqrATpGRqbpJVxpegIrON7furNEGHGUbRLY6b55WulJt8OMPAM6lKrHqaBrSlWqbxEHOzVb9pq3aK68DluMxsw9bHXfWr33czTZMSAGP+rzVR9NwNzvfJv2iOfuwRhvhbxLHx6QUubSMvEKDTkzrTIoSGXmFNo6ItEzVTZbKOnVnjTbgKNsgstV587SyVIUGP/60zqU+ipPIFFv1m7Zqr7wOWI7HzD5sddxZv/aRq9aU2OflqjU26RfN2Yc12gh/kzg+PqxLLi1XrXmq+VR6TB17Zb516s4abcBRtkFkq/PmaZmKw9R8IsB2/aat2iuvA5bjMbMPWx131q99mPytYEafZ41+0Zy+191NVOIy5rQR/iZxfExKkUvz8ij5ZkBT86n0mDr2Mk/r1J012oCjbIPIVufN0zIVh6n5RIDt+k1btVdeByzHY2YftjrurF/7MPlbwVOMklNB1ukXzel73cQlR2JOG+FvEsfHGiCXJpe6ITJIZnReZJAMcqmbjSMiLVN14yuxTt1Zow04yjaIbHXePC1fiRvqKozHUVfxKE4iU2zVb9qqvfI6YDkeM/uw1XFn/dqHl4e4xD7Py0Nsk37RnH1Yo43wN4njY1KKXJq3xB3DWygMOrPIIBlGtFDwdbN2ZKpu/GUeVqk7a7QBR9kGka3Om6flL/NAbPNAgx+B2jfd8BXMZA5b9Zu2aq+8DliOx8w+bHXcWb/2EeDjiZhi+ryY5oEI8PG0Sb9ozj6s0Ub4m8TxiQRBEOwdhKNxpNcjknXkqAqQkVeIXLUGXh5iyKVuvNA5CFN1Y626s8Z2HGUbRLY6b55WulKNLFUhlPkayDwf/c8rf/yRpWzVnm3VXh3l/HQmPGb2Yavjzvq1j7vZ+chVa5Cbr4GXpxheHmIE+HjqLWOLftGcfVijjfA3iT5HynkwKWWEI1UQEREREREREZG1OFLOg4/vERERERERERGRzTEpRURERERERERENsekFBERERERERER2RyTUkREREREREREZHNMShERERERERERkc0xKUVERERERERERDbHpBQREREREREREdkck1JERERERERERGRz7vYOgKwvR1WAjLxC5Ko1kHmI4Sd1g7fEPlVti1jSlWpkqQqhzNdA5imGr8QN/jIPq+6DSo816s+cdmaLtuhI5x65Nmfp95wlTlfEY2859uGO6152PpRqDZT5Gnh7iuHlIUZFH097h+XyHOWccJQ4nI2p42aN38/OVDfOFGtZw1pwMfdz1PgmPhVnU5S6aZFBMgxvoUAFb9v+GLVFLHey8rHqaBrOpf63j7oKGWKbB6KSL3+sODpr1J857cwWbdGRzj1ybc7S7zlLnK6Ix95y7MMdV1pWPlYbac8xzQMRyPZcahzlnHCUOJyNqeNmjd/PzlQ3zhRrWcTH91xIjqrA4GQDgDMpSqyMT0WOqsClYklXqg1+dAPAuVQlVh1NQ7pS/dT7oNJjjfozp53Zoi060rlHrs1Z+j1nidMV8dhbjn2447qXbZiQAh6159VH03AvO99Okbk2RzknHCUOZ2PquKUrDRM0Reeb8/vZnG04CrYjx8eklAvJyCs0ONm0zqQokZFX6FKxZKkKDX6kaJ1LVSJLZbvykuWsUX/mtDNbtEVHOvfItTlLv+cscboiHnvLsQ93XEq1psT2rFRrbBxR2eAo54SjxOFsTB23LNXT/342ZxuOgu3I8fHxPReSa+LCbGq+NdkiFmV+ydswNZ/syxr1Z412Zo226EjnHrk2Z+n3nCVOV8Rjbzn24Y7LVHvNZXsuFY5yTjhKHM7G1HExeV6ZcVytsQ1bYTtyfExKuRAvj5JvfDM135psEYvMs+RtmJpP9mWN+rNGO7NGW3Skc49cm7P0e84Spyvisbcc+3DHZaq9erE9lwpHOSccJQ5nY+q4mDyvzDiu1tiGrbAdOT7WgAuRS90QGSQzOi8ySAa51M2lYvGVuKGuwvg+6ipk8JXYrrxkOWvUnzntzBZt0ZHOPXJtztLvOUucrojH3nLswx2XzENcYnuW8Y/JUuEo54SjxOFsTB03X8nT/342ZxuOgu3I8bEndyHeEncMb6EwOOkig2QY0UJh01de2iIWf5kHYpsHGvxY0b5hiK++dmzWqD9z2pkt2qIjnXvk2pyl33OWOF0Rj73l2Ic7roo+nogppj3HNA9ERR++fa80OMo54ShxOBtTx81f5vHUv5/N2YajYDtyfCJBEAR7B+FoMjMzIZfLkZGRAT8/P3uHY7EcVQEy8gqRq9bAy0MMudTNbiebLWJJV6qRpSqEMl8DmacYvhI3/uh2ItaoP3PamS3aoiOde+TanKXfc5Y4XRGPveXYhzuue9n5UKo1yM3XwMtTDJmHmAkpG3CUc8JR4nA2po6bNX4/O1PdOFOstuBIOQ8mpYxwpAoiIiIiIiIiIrIWR8p58PE9IiIiIiIiIiKyOSaliIiIiIiIiIjI5piUIiIiIiIiIiIim2NSioiIiIiIiIiIbI5JKSIiIiIiIiIisjkmpYiIiIiIiIiIyOaYlCIiIiIiIiIiIptziqTUkiVLEBYWBqlUimbNmuHYsWMlLr9x40bUqlULUqkUkZGR+O2332wUKRERERERERERmcPhk1Lr16/HhAkTMHXqVJw8eRINGjRAdHQ07ty5Y3T5I0eOYNCgQRgxYgROnTqFXr16oVevXjh79qyNIyciIiIiIiJyPOlKNW48zMOlNCVuPMxDulJt75CojBIJgiDYO4iSNGvWDE2bNsXixYsBABqNBiEhIXjjjTcwadIkg+UHDhyInJwcbNu2TTetefPmaNiwIZYvX27WPjMzMyGXy5GRkQE/Pz/rFISIiIiIiIjIzu5k5WPV0TScS1XqptVVyBDbPBCVfD3tGBnZiiPlPBz6Tqn8/HycOHECHTt21E0Ti8Xo2LEj4uPjja4THx+vtzwAREdHF7s8ERERERERUVmQrlQbJKQA4FyqEquOpvGOKbI5d3sHUJJ79+6hsLAQgYGBetMDAwNx8eJFo+ukpqYaXT41NbXY/ahUKqhUKt33zMzMp4iaiIiIiIiIyPFkqQoNElJa51KVyFIVwl/mYeOoqCxz6DulbCUuLg5yuVz3CQkJsXdIRERERERERFalzNc81Xwia3PopFTFihXh5uaGtLQ0velpaWlQKBRG11EoFBYtDwCTJ09GRkaG7nPjxo2nD56IiIiIiIjIgcg8S04BmJpPZG0O3eI8PT3RuHFj7N27VzdNo9Fg7969aNGihdF1WrRoobc8AOzevbvY5QFAIpHAz89P70NERERERETkSnwlbqirkBmdV1chg6/EzcYRUVnn0EkpAJgwYQK++uorrFmzBhcuXMCYMWOQk5OD2NhYAMDQoUMxefJk3fL/+9//sHPnTnz++ee4ePEipk2bhuPHj2PcuHH2KgIRERERERGR3fnLPBDbPNAgMaV9+x7HkyJbc+iBzgFg4MCBuHv3Lj766COkpqaiYcOG2Llzp24w8+TkZIjF/+XWWrZsiR9++AEffvgh3n//fVSvXh1btmxBvXr17FUEIiIiIiIiIodQydcTr7ZSIEtVCGW+BjJPMXwlbkxIkV2IBEEQ7B2Eo8nMzIRcLkdGRgYf5SMiIiIiIiIil+FIOQ+Hf3yPiIiIiIiIiIhcD5NSRERERERERERkc0xKERERERERERGRzTEpRURERERERERENsekFBERERERERER2RyTUkREREREREREZHNMShERERERERERkc0xKUVERERERERERDbnbu8AHJEgCACAzMxMO0dCRERERERERGQ92lyHNvdhT0xKGZGVlQUACAkJsXMkRERERERERETWl5WVBblcbtcYRIIjpMYcjEajwe3bt+Hr6wuRSGTvcBxSZmYmQkJCcOPGDfj5+dk7HLIQ68+5sf6cF+vO+bEOnR/r0Dmx3pwX6875sQ6d3+N1KAgCsrKyEBwcDLHYvqM68U4pI8RiMZ555hl7h+EU/Pz82DE5Mdafc2P9OS/WnfNjHTo/1qFzYr05L9ad82MdOr+idWjvO6S0ONA5ERERERERERHZHJNSRERERERERERkc0xK0RORSCSYOnUqJBKJvUOhJ8D6c26sP+fFunN+rEPnxzp0Tqw358W6c36sQ+fnyHXIgc6JiIiIiIiIiMjmeKcUERERERERERHZHJNSRERERERERERkc0xKERERERERERGRzTEp5ULi4uLQtGlT+Pr6olKlSujVqxcuXbqkt0xeXh7Gjh2LChUqwMfHB3379kVaWpreMuPHj0fjxo0hkUjQsGFDg/3k5eUhJiYGkZGRcHd3R69evcyK79y5c+jbty/CwsIgEomwYMECo8stWbIEYWFhkEqlaNasGY4dO2bW9p2drervwIED6NmzJ4KCguDt7Y2GDRti7dq1ZsVoqm5WrFiBdu3awc/PDyKRCOnp6RYdA2fmCvWnJQgCunTpApFIhC1btpi1bWfm7HWXlJQEkUhk9LNx40bLD4gTcvQ6PHToELp3747g4OBizytBEPDRRx8hKCgIXl5e6NixI65cuWLRcXBmtqrDS5cuoX379ggMDIRUKkVERAQ+/PBDqNVqkzHyGmjIFepNq6xd+wDnr7+yfv1z9Prjtc80W9VhUVevXoWvry/8/f3NitEW1z4mpVzIwYMHMXbsWBw9ehS7d++GWq3GCy+8gJycHN0yb731Fn799Vds3LgRBw8exO3bt9GnTx+DbQ0fPhwDBw40up/CwkJ4eXlh/Pjx6Nixo9nxKZVKRERE4JNPPoFCoTC6zPr16zFhwgRMnToVJ0+eRIMGDRAdHY07d+6YvR9nZav6O3LkCOrXr4/Nmzfjn3/+QWxsLIYOHYpt27aVGJ85daNUKtG5c2e8//77T3gUnJcr1J/WggULIBKJLDwCzsvZ6y4kJAQpKSl6n+nTp8PHxwddunR5iiPjPBy9DnNyctCgQQMsWbKk2GXmzp2LRYsWYfny5fjrr7/g7e2N6Oho5OXlmXkUnJut6tDDwwNDhw7Frl27cOnSJSxYsABfffUVpk6dWmJ8vAYa5wr1plXWrn2A89dfWb/+OXr98dpnmq3qUEutVmPQoEFo06aNWfHZ7NonkMu6c+eOAEA4ePCgIAiCkJ6eLnh4eAgbN27ULXPhwgUBgBAfH2+w/tSpU4UGDRqUuI9hw4YJPXv2tDi20NBQYf78+QbTn3vuOWHs2LG674WFhUJwcLAQFxdn8T6cnS3qT6tr165CbGxsictYUjf79+8XAAgPHz40a/+uyFnr79SpU0LlypWFlJQUAYDw888/mxWDK3HWuiuqYcOGwvDhw82KwRU5Wh0WZey80mg0gkKhED799FPdtPT0dEEikQg//vij2dt2Jbasw7feekto3bp1icvwGmgeZ603Xvsecdb6K6osX/8crf6K4rXPPKVdh++++67w8ssvC6tWrRLkcrnJeGx17eOdUi4sIyMDAFC+fHkAwIkTJ6BWq/XubqpVqxaqVKmC+Ph4u8RYVH5+Pk6cOKEXn1gsRseOHR0iPluzZf1lZGTo9mMM68Zyzlh/SqUSgwcPxpIlS4q9m7EscMa6K+rEiRNISEjAiBEjnio2Z+ZIdWiOa9euITU1VS8+uVyOZs2aldk+1lZ1ePXqVezcuRNRUVHFLsNroPmcsd547fuPM9ZfUWX9+udI9WcOXvsMlWYd7tu3Dxs3bizxzrWibHntY1LKRWk0Grz55pto1aoV6tWrBwBITU2Fp6enwfOjgYGBSE1NtUOU+u7du4fCwkIEBgbqTXeU+GzJlvW3YcMG/P3334iNjS12GdaNZZy1/t566y20bNkSPXv2fOJ4nJ2z1l1R33zzDWrXro2WLVs+cWzOzNHq0BzaGNjHPmKLOmzZsiWkUimqV6+ONm3aYMaMGcUuy2ugeZy13njte8RZ66+osnz9c7T6MwevffpKsw7v37+PmJgYrF69Gn5+fmatY8trH5NSLmrs2LE4e/Ys1q1bZ/N9Jycnw8fHR/eZPXu2zWNwdraqv/379yM2NhZfffUV6tatCwD4448/9OrP3IGY6T/OWH9bt27Fvn37in0BQVnhjHVXVG5uLn744Ycy+7/EgPPXIdmmDtevX4+TJ0/ihx9+wPbt2/HZZ58BYB0+DWesN177/uOM9VdUWb/+OXv9UenW4ahRozB48GC0bdvW6Hx716G7TfdGNjFu3Dhs27YNhw4dwjPPPKObrlAokJ+fj/T0dL1sa1pamlVvVw4ODkZCQoLuu7mPNVSsWBFubm4GbxOwdnyOzlb1d/DgQXTv3h3z58/H0KFDddObNGmiV3+BgYGQSCSsGzM5a/3t27cPiYmJBv8T07dvX7Rp0wYHDhywOEZn46x1V9SmTZugVCr1tluWOGIdmkMbQ1paGoKCgvTiM/UWHVdjqzoMCQkBANSpUweFhYUYPXo03n77bV4Dn5Cz1huvfY84a/0VVZavf45Yf+bgte8/pV2H+/btw9atW3WJREEQoNFo4O7ujhUrVmDQoEF2vfbxTikXIggCxo0bh59//hn79u1DeHi43vzGjRvDw8MDe/fu1U27dOkSkpOT0aJFC6vF4e7ujmrVquk+5ialPD090bhxY734NBoN9u7da9X4HJUt6+/AgQPo1q0b5syZg9GjR+vN+z/27jwuivr/A/hruRYWlkUQWFDkMjwQ0dS8RVPzytQyjw7FszIrMyqtLI/U1DwyDzpUzKzMNOurZaV5VKKliamlqYmUgTfnwnLs5/eHv91cd2F3dZldltfz8eChOzM78/7M5/OZGd7MfMbHx8eo/pRKZa2vG2vU9PqbMmUKfvvtN2RkZBh+AGDx4sVYs2aNTfHVNDW97m60atUq3HfffQgODrYprprOmevQGtHR0VCr1Ubx5efn48CBA7XmGOvIaxidToeysjLodDqeA21U0+utNp/7gJpffzeqjec/Z64/a/DcJ10dpqenGx3nZs6cCaVSiYyMDAwaNMjx5z6bh0Ynp/XEE08IlUoldu/eLbKzsw0/Go3GsMzjjz8uGjRoIL7//ntx8OBB0b59e9G+fXuj9Zw6dUocPnxYPPbYYyIuLk4cPnxYHD58WGi1WsMyx48fF4cPHxb9+/cXXbt2NSxTFa1Wa1guLCxMpKSkiMOHD4tTp04Zlvnkk0+EXC4XaWlp4vfffxfjx48XAQEBIicnxz47yYlJVX/ff/+9UCgUYurUqUbbuXLlSpXxWVM32dnZ4vDhw+K9994TAMTevXvF4cOHLa7bFbhC/d0MteQNRK5Sd6dOnRIymUx8/fXXdtozNYez12FBQYFhXQDEokWLxOHDh8W5c+cMy7zxxhsiICBAfPHFF+K3334TAwYMENHR0aK4uNiOe8p5SVWHH374odiwYYP4/fffxZkzZ8SGDRtEeHi4ePjhh6uMj+dA81yh3m5WW859QrhO/dXW85+z1x/PfZZJ+fv7jax9+55U5z4mpVwIALM/a9asMSxTXFwsJkyYIOrUqSMUCoUYNGiQyM7ONlpPUlKS2fWcPXvWsExkZKTZZapy9uxZs99JSkoyWu7tt98WDRo0EF5eXuKuu+4S+/fvv91dUyNIVX8jR460qh7MsVQ3r732msUyuCpXqD9zZaoNF+auUndTp04VERERoqKi4nZ2R43k7HWof03yzT8jR440LKPT6cS0adNEaGiokMvlonv37uLkyZN22kPOT6o6/OSTT8Sdd94p/Pz8hK+vr2jatKmYM2eOVb8A8RxoyhXqzVyZasO5TwjXqb/aev5z9vrjuc8yqerwZtYmpYSQ5twn+/+dQUREREREREREJBmOKUVERERERERERJJjUoqIiIiIiIiIiCTHpBQREREREREREUmOSSkiIiIiIiIiIpIck1JERERERERERCQ5JqWIiIiIiIiIiEhyTEoREREREREREZHkmJQiIiIiIiIiIiLJMSlFRERERERERESSY1KKiIiIiIiIiIgkx6QUERERERERERFJjkkpIiIiIiIiIiKSHJNSREREREREREQkOSaliIiIiIiIiIhIckxKERERERERERGR5JiUIiIiIiIiIiIiyTEpRUREREREREREkmNSiqiGSEtLg0wmw8GDB6t9W7/88gs6dOgAX19fyGQyZGRk2PT9rl27olmzZlYv//PPP8PLywvnzp0zTIuKisK9995r03ark37/S2369OkO2S4AJCcno2vXrg7ZNgDIZDJMnz7dYdu3JDU1FQ0aNIBWq7Vq+dvtw45sC46wYMECxMTEwN3dHS1atHB0OHQDR7bFqKgoJCcn2219MpkMEydOtNv67HWutnc5iWoy9gci18akFNH/019I6n+8vb0RHh6OXr16YenSpSgoKJAkjhUrViAtLU2SbZlTVlaGBx98EFevXsXixYuxbt06REZGmizXs2dPPPzww3bZ5ssvv4zhw4eb3Y69/Pvvv5g+fbrNCbaabN++fZg+fTpyc3MdHYrT+uijj7BkyZJb+m5ycjJKS0vxzjvvGKatX78ecrkcRUVFdoqw+uXn52PGjBlITEyEn58ffHx80KxZM7z44ov4999/HRLTt99+ixdeeAEdO3bEmjVrMGfOHIfEQTXXjedzNzc3hIeH45577sHu3bsdHZrDaTQaTJ8+XfJ9kZmZiVGjRiE2Nhbe3t5Qq9Xo0qULXnvttWrdrqPKW10yMzON2renpyfq1q2LDh064KWXXkJWVpajQ6SbREVFGdVZZT+OvP4nciQPRwdA5GxmzpyJ6OholJWVIScnB7t378akSZOwaNEifPnll2jevHm1bn/FihWoW7euw/4idObMGZw7dw7vvfcexo4dW+lyffv2xezZs6HT6eDmduv57YyMDOzYsQP79u275XVY499//8WMGTMQFRVVo+66eOWVVzBlypRb+u6+ffswY8YMJCcnIyAgwL6BuYiPPvoIx44dw6RJk2z+rre3N0aOHIlFixbhqaeegkwmQ+/evVFeXo4dO3ZgwIABdo31dtpCZf766y/06NEDWVlZePDBBzF+/Hh4eXnht99+w6pVq/D555/jzz//tOs2rfH999/Dzc0Nq1atgpeXl+TbJ9fQs2dPjBgxAkIInD17FitWrMDdd9+Nbdu2oU+fPo4Or0onT568rXNrVTQaDWbMmAEAkt0Ne/r0abRp0wY+Pj4YPXo0oqKikJ2djV9//RXz5s0zxFMdHFFeKQwfPhx9+/aFTqfDtWvX8Msvv2DJkiV46623sGrVKgwbNszRIdL/W7JkCQoLCw2fv/rqK3z88cdYvHgx6tata5jeoUMHR4RH5HBMShHdpE+fPmjdurXh89SpU/H999/j3nvvxX333Yc//vgDPj4+Doywel28eBEALCYx+vXrh8mTJ2P//v23dRJds2YNGjRogHbt2t3yOlxRUVERfH194eHhAQ8PHqqd1ZAhQzB//nzs2rULd999N4KCgtC2bVts27bN7kkpe7eF8vJy3H///bhw4QJ2796NTp06Gc2fPXs25s2bZ5dt6duztS5evAgfHx+7JqQ0Gg0UCoXd1udoJSUl8PLyqrbEhSuIi4vDI488Yvg8aNAgNG/eHEuWLHHKpJQQAiUlJfDx8YFcLnd0ODarqp8vXrwYhYWFyMjIMLkrWn/dQba58847jdo3AJw7dw733HMPRo4ciSZNmiAxMdFB0dGNBg4caPQ5JycHH3/8MQYOHIioqKhKv2fruZOopuKVDJEV7r77bkybNg3nzp3Dhx9+aDTvxIkTGDx4MAIDA+Ht7Y3WrVvjyy+/NFpG/2jg3r178dhjjyEoKAj+/v4YMWIErl27ZlguKioKx48fx549ewy38t78Vz2tVovJkycjODgYvr6+GDRoEC5dumRVOb7//nt07twZvr6+CAgIwIABA/DHH38Y5icnJyMpKQkA8OCDD5rdvl5cXBxiY2Oxbdu2Srd36NAhdOjQAT4+PoiOjkZqaqrJMlu2bMHdd99d6fgk3377LVq0aAFvb280bdoUmzdvNlnmr7/+woMPPojAwEAoFAq0a9fOKK7du3ejTZs2AIBRo0aZ3CZ96tQpPPDAA1Cr1fD29kb9+vUxbNgw5OXlVVo24PqjjjNmzMAdd9wBb29vBAUFoVOnTvjuu++Mljtx4gSGDBmC4OBg+Pj4oFGjRnj55ZcN8/Xjs/z+++946KGHUKdOHUOCwNzYLfoxUNavX49GjRrB29sbrVq1wt69e43W+fzzzwMAoqOjDWXOzMwEAHz33Xfo1KkTAgIC4Ofnh0aNGuGll16qsrx6GzduRNOmTeHt7Y1mzZrh888/R3JyssmF1ZtvvokOHTogKCgIPj4+aNWqFT777DOT9Wm1Wjz77LMIDg6GUqnEfffdh3/++cfstnfv3o3WrVvD29sbsbGxeOeddyod3+bDDz9Eq1at4OPjg8DAQAwbNgx///23YX7Xrl2xbds2nDt3zrB/bizD22+/jfj4eCgUCtSpUwetW7fGRx99ZLSNVq1aITAwEF988YVhWt++ffHVV19Vuv80Gk2Vx4HKVNUWtmzZgmbNmkEulyM+Ph7bt2+3uL5NmzbhyJEjePnll00SUgDg7++P2bNnG03buHGjYZ/WrVsXjzzyCM6fP2+0THJyMvz8/HDmzBn07dsXSqXS8KivTqfDkiVLEB8fD29vb4SGhuKxxx4zKr9MJsOaNWtQVFRk9pEGS/UK/Deu3aFDh9ClSxcoFApD+9ZqtXjttdfQsGFDyOVyRERE4IUXXjAZG8yWfXv+/HmMGTMG4eHhkMvliI6OxhNPPIHS0lLDMrm5uZg0aRIiIiIgl8vRsGFDzJs3DzqdrqpqAnC93ctkMnzyySd45ZVXUK9ePSgUCuTn5wMADhw4gN69e0OlUkGhUCApKQk//fST0ToKCgowadIkREVFQS6XIyQkBD179sSvv/5qtJw1dXyzZs2aoVu3bibTdTod6tWrh8GDBxtNs9QGgOsJmtdffx3169eHQqFAt27dcPz4cYv7qioJCQmoW7cuzp49azLPUj2fO3cOEyZMQKNGjeDj44OgoCA8+OCDhmPqzazp5/qxE7/55hu0bt0aPj4+hseBzY2hk5ubi2effdZQh/Xr18eIESNw+fJlAEBpaSleffVVtGrVCiqVCr6+vujcuTN27dplWEdmZiaCg4MBADNmzDD0sRvH77N0rQBUfd4y58yZM6hfv77Zx/RDQkJMpn399deGGJRKJfr162dS//pjzfnz5zFw4ED4+fkhODgYKSkpqKiosLq8tlzH/fTTT1Zdg3399ddISkqCUqmEv78/2rRpY3L+sKbf2ioyMhJpaWkoLS3F/PnzjeZZcwzSPxr45ptvYvHixYiMjISPjw+SkpJw7Ngxk+3Ze9/Z0u9tLc+7776L2NhYyOVytGnTBr/88ovZ8lR2vbZr1y7IZDJ8/vnnJt/76KOPIJPJkJ6ebjZWa9zuuVPPmr6Tk5ODUaNGoX79+pDL5QgLC8OAAQMqPZ4RVTtBREIIIdasWSMAiF9++cXs/L///lsAEIMHDzZMO3bsmFCpVKJp06Zi3rx5YtmyZaJLly5CJpOJzZs3m6w7ISFBdO7cWSxdulQ8+eSTws3NTXTp0kXodDohhBCff/65qF+/vmjcuLFYt26dWLdunfj222+N1tGyZUtx9913i7fffls899xzwt3dXQwZMsRi+b777jvh4eEh4uLixPz588WMGTNE3bp1RZ06dcTZs2eFEELs27dPvPTSSwKAePrpp422b87TTz8tEhMTTaYnJSWJ8PBwERISIiZOnCiWLl0qOnXqJACIVatWGZb7559/BACxdOlSk3VERkaKuLg4ERAQIKZMmSIWLVokEhIShJubm1FMOTk5IjQ0VCiVSvHyyy+LRYsWicTEROHm5maog5ycHDFz5kwBQIwfP96wb8+cOSO0Wq2Ijo4W4eHh4vXXXxfvv/++mDFjhmjTpo3IzMw0qcMbvfTSS0Imk4lx48aJ9957TyxcuFAMHz5cvPHGG4Zljhw5Ivz9/UVQUJCYOnWqeOedd8QLL7wgEhISDMu89tprAoBo2rSpGDBggFixYoVYvny50bwbARDNmjUTdevWFTNnzhTz5s0TkZGRwsfHRxw9etSw3eHDhwsAYvHixYYyFxYWimPHjgkvLy/RunVr8dZbb4nU1FSRkpIiunTpYrSdkSNHiqSkJKNpW7duFTKZTDRv3lwsWrRITJs2TdSpU0c0a9ZMREZGGi1bv359MWHCBLFs2TKxaNEicddddwkAYuvWrUbLPfLIIwKAeOihh8SyZcvE/fffL5o3by4AiNdee82w3K+//irkcrmIiooSb7zxhpg9e7YIDw8XiYmJJvvo9ddfFzKZTAwdOlSsWLHC0N6joqLEtWvXhBBCfPvtt6JFixaibt26hv3z+eefCyGEePfddw39/Z133hFvvfWWGDNmjHj66afFzXr06CFatWplFCcAcfjwYaPlrD0OVKaytpCYmCjCwsLErFmzxJIlS0RMTIxQKBTi8uXLVa7voYceEgBEVlZWlcvdHH+bNm3E4sWLxZQpU4SPj4/RPhXieruRy+UiNjZWjBw5UqSmpooPPvhACCHE2LFjhYeHhxg3bpxITU0VL774ovD19RVt2rQRpaWlQggh1q1bJzp37izkcrlRXxXCunoV4voxSK1Wi+DgYPHUU0+Jd955R2zZskVUVFSIe+65RygUCjFp0iTxzjvviIkTJwoPDw8xYMCAW9q358+fF+Hh4YZ1pqamimnTpokmTZoYYioqKhLNmzcXQUFB4qWXXhKpqalixIgRQiaTiWeeecbivt+1a5fhGNGiRQuxaNEiMXfuXFFUVCR27twpvLy8RPv27cXChQvF4sWLRfPmzYWXl5c4cOCAUX17eXmJyZMni/fff1/MmzdP9O/fX3z44Yc21/HNbXHmzJnCzc1NZGdnG8W9Z88eAUBs3LjRMM2aNiCEEK+88ooAIPr27SuWLVsmRo8eLcLDw0XdunXFyJEjLe4zAOLJJ580mnb16lXh7u4u2rVrZ7ScNfW8ceNGkZiYKF599VXx7rvvipdeeknUqVNHREZGiqKiIpN9aE0/j4yMFA0bNhR16tQRU6ZMEampqWLXrl2GeTeWs6CgQDRr1ky4u7uLcePGiZUrV4pZs2aJNm3aGI41ly5dEmFhYWLy5Mli5cqVYv78+aJRo0bC09PTsExhYaFYuXKlACAGDRpk6GNHjhwRQlh3rSBE1ectc8aPHy/c3d3Fzp07LdbdBx98IGQymejdu7d4++23xbx580RUVJQICAgwimHkyJHC29tbxMfHi9GjR4uVK1eKBx54QAAQK1assKq8tl7HWXMNtmbNGiGTyUSzZs3E7NmzxfLly8XYsWPFo48+aljG2n5rztmzZwUAsWDBgkqXiY2NFcHBwYbP1h6D9OtOSEgQUVFRYt68eWLGjBkiMDBQBAcHi5ycHMOy1bHvrO33tpanZcuWomHDhmLevHli/vz5om7duqJ+/fpGxxxL12s6nU5ERESIBx54wGR/9+3bV8TGxlZeaTdZsGCBAGDSnm/n3CmE9X2nQ4cOQqVSiVdeeUW8//77Ys6cOaJbt25iz549VpeByJ6YlCL6f5aSUkIIoVKpRMuWLQ2fu3fvLhISEkRJSYlhmk6nEx06dBB33HGHybpbtWpldPKYP3++ACC++OILw7T4+HiTRMCN6+jRo4fRRe2zzz4r3N3dRW5ubpXla9GihQgJCRFXrlwxTDty5Ihwc3MTI0aMMEzT//Jz4y8Rldm+fbsAIP755x+j6UlJSQKAWLhwoWGaVqs1xKDfBzt27BAAxP/+9z+TdUdGRgoAYtOmTYZpeXl5IiwszKgOJk2aJACIH374wTCtoKBAREdHi6ioKFFRUSGEEOKXX34RAMSaNWuMtnP48GGrymsuKZWYmCj69etX5fe6dOkilEqlOHfunNH0G+tQf3E/fPhwk+9XlogAIA4ePGiYdu7cOeHt7S0GDRpkmGbuokcIIRYvXiwAiEuXLlUZu7mkVEJCgqhfv74oKCgwTNu9e7cAYJKU0mg0Rp9LS0tFs2bNxN13322YlpGRIQCICRMmGC2rT5jcmJTq37+/UCgU4vz584Zpp06dEh4eHkb7KDMzU7i7u4vZs2cbrfPo0aPCw8PDaHq/fv1M4hZCiAEDBoj4+HiT6eaMHz9e+Pj4GE0LCwsz2b4txwFzKmsLXl5e4vTp04ZpR44cEQDE22+/XeX6WrZsKVQqVZXL6JWWloqQkBDRrFkzUVxcbJi+detWAUC8+uqrhmkjR44UAMSUKVOM1vHDDz8IAGL9+vVG0/XHkRunjxw5Uvj6+hotZ0u96o9BqampRsuuW7dOuLm5GR0vhBAiNTVVABA//fSTYZq1+3bEiBHCzc3N7LlD389nzZolfH19xZ9//mk0f8qUKcLd3d1iYlB/XI6JiTHqVzqdTtxxxx2iV69eRscUjUYjoqOjRc+ePQ3TVCqVSZLmRrbU8c1t8eTJk2bb3IQJE4Sfn58hZmvbwMWLF4WXl5fo16+fUbn0fzSxNik1ZswYcenSJXHx4kVx4MAB0b17d5Nzk7X1fPPxTAgh0tPTBQDDL45C2NbP9ee57du3m6z75qTUq6++KgAY/bKvp99H5eXlQqvVGs27du2aCA0NFaNHjzZMu3TpksnxVc/aa4WqzlvmHDt2TPj4+AgAokWLFuKZZ54RW7ZsMUroCXH9/B0QECDGjRtnND0nJ0eoVCqj6fpjzcyZM42WbdmypdEfCqoqr63XcZauwXJzc4VSqRRt27Y16kf69er/tbbfmmNNUmrAgAECgMjLyxNCWH8M0q/bx8fH6NruwIEDAoB49tlnq23f2dLvbS1PUFCQuHr1qmG5L774wuT605rrtalTpwq5XG50zX3x4kXh4eFhtn1VprKk1O2cO63tO9euXbPYfoikxsf3iGzg5+dneAvf1atX8f3332PIkCEoKCjA5cuXcfnyZVy5cgW9evXCqVOnTB55GD9+PDw9PQ2fn3jiCXh4eFT5qM/Nxo8fb/QIT+fOnVFRUYFz585V+p3s7GxkZGQgOTkZgYGBhunNmzdHz549bdr+jbp27QpfX1+z3/fw8MBjjz1m+Ozl5YXHHnsMFy9exKFDhwAAV65cAQDUqVPH7PrDw8MxaNAgw2f9IxCHDx9GTk4OgOuDRd51111Gjw34+flh/PjxyMzMxO+//15lGVQqFQDgm2++gUajsabYBgEBATh+/DhOnTpldv6lS5ewd+9ejB49Gg0aNDCaZ+5xs8cff9zqbbdv3x6tWrUyfG7QoAEGDBiAb775xvDYQlVxA8AXX3xh1aNDev/++y+OHj2KESNGwM/PzzA9KSkJCQkJJsvfOPbatWvXkJeXh86dOxs9LqRvO08//bTRd28eeLyiogI7duzAwIEDER4ebpjesGFDk7FhNm/eDJ1OhyFDhhj65eXLl6FWq3HHHXcYPcpSmYCAAPzzzz9mb++/WZ06dVBcXGzUfvr27Vvpo632OA7cqEePHoiNjTV8bt68Ofz9/fHXX39V+b38/HwolUqrtnHw4EFcvHgREyZMgLe3t2F6v3790LhxY7NlfeKJJ4w+b9y4ESqVCj179jSql1atWsHPz89ivdhar3K5HKNGjTKJoUmTJmjcuLHROu6++24AMFmHpX2r0+mwZcsW9O/f32gsQj19P9+4cSM6d+6MOnXqGG23R48eqKioMHr0tiojR4406lcZGRk4deoUHnroIVy5csWw3qKiInTv3h179+419PGAgAAcOHCg0jcq3kod68XFxaFFixbYsGGDYVpFRQU+++wz9O/f3xCztW1gx44dKC0tNbxAQM/WFxKsWrUKwcHBCAkJQdu2bQ2PD928Hmv60I37vaysDFeuXEHDhg0REBBg8ggkYH0/j46ORq9evSyWZdOmTUhMTDQ6J+rp95G7u7thHDadToerV6+ivLwcrVu3NhvjzW7lWsHa81Z8fDwyMjLwyCOPIDMzE2+99RYGDhyI0NBQvPfee4blvvvuO+Tm5mL48OFGbcTd3R1t27Y1e5y4OYbOnTtbPP4Bt34dV9U12HfffYeCggJMmTLFqB8B/9WTLf32VunP0fprVluPQQMHDkS9evUMn++66y60bdvW0AaqY9/Z0u9tLc/QoUONrjU7d+4MAIZ2Yu312ogRI6DVao2GItiwYQPKy8tNxve6Vbd67rS27+jHa9y9e7dVQwcQSYGj5xLZoLCw0DD2wenTpyGEwLRp0zBt2jSzy1+8eNHopH7HHXcYzffz80NYWJhNz3DffLLUn2SrOrHoT/iNGjUymdekSRN88803tzSYolwuN7zJaNy4cUbzwsPDTdYXFxcH4Poz/jcObC6EMLv+hg0bmiRvblyHWq3GuXPn0LZtW7PlAq6XvVmzZpWWITo6GpMnT8aiRYuwfv16dO7cGffddx8eeeQRQ8KqMjNnzsSAAQMQFxeHZs2aoXfv3nj00UcNb2jUX+xUtf2bY7HWzW0JuL5vNBoNLl26BLVaXel3hw4divfffx9jx47FlClT0L17d9x///0YPHhwlYMm69tRw4YNTeY1bNjQ5JeerVu34vXXX0dGRobReD031um5c+fg5uZm9AshYNpWL168iOLi4kq3faNTp05BCGF2HwEw+kWxMi+++CJ27NiBu+66Cw0bNsQ999yDhx56CB07djRZVt9+byxX3759sWbNGly5cgVBQUFGy9vjOHCjm48JwPXjgqWLTWsSV3pVHUMaN26MH3/80Wiah4cH6tevbzTt1KlTyMvLMzt+DGB5sGNb67VevXomA6WfOnUKf/zxh2GMGUsxWNq3ly5dQn5+vsU+furUKfz2228Wt3vp0iWjpLKfn59RAvjmY4Q+IT5y5MhKt52Xl4c6depg/vz5GDlyJCIiItCqVSv07dsXI0aMQExMDADb6/hmQ4cOxUsvvYTz58+jXr162L17Ny5evIihQ4caxWtNG9DHcnNdBwcHV/pHDHMGDBiAiRMnQiaTQalUIj4+3ux5zpo+VFxcjLlz52LNmjU4f/680XnL3PiD1vZza4/7Z86cwQMPPGBxubVr12LhwoU4ceIEysrKbNrOrVwr2HLeiouLw7p161BRUYHff/8dW7duxfz58zF+/HhER0ejR48ehjatTxTfzN/f3+izt7e3Sb+y5vgH3Np1nKVrsDNnzgCo+rxvS7+9Vfo3ven/8GDtMUivsmuMTz/9FED17Dtb+r2t5bG0bWuv1xo3bow2bdpg/fr1GDNmDABg/fr1aNeundnrE1vdzrnT2r4jl8sxb948PPfccwgNDUW7du1w7733YsSIEVVeOxJVJyaliKz0zz//IC8vz3DS0f8VKyUlpdK/ctrjBHUzd3d3s9MrS+xUt379+uG5555DaWmpzW/K0v+y7ui/1CxcuBDJycn44osv8O233+Lpp5/G3LlzsX//fpOLgxt16dIFZ86cMXzv/fffx+LFi5GamoqxY8faHIdUb3X08fHB3r17sWvXLmzbtg3bt2/Hhg0bcPfdd+Pbb7+ttI3Z4ocffsB9992HLl26YMWKFQgLC4OnpyfWrFljMtirvel0OshkMnz99ddmy3LjL/mVadKkCU6ePImtW7di+/bt2LRpE1asWIFXX33V5NXl165dg0KhMKq/nj17wt3dHdu3bzcMVFpdbvWY0LhxYxw+fBh///03IiIi7BqTXC43SXDqdDqEhIRg/fr1Zr9T2S8XN37flno11590Oh0SEhKwaNEis9u4eT/Y63ir0+nQs2dPvPDCC2bn65Ptbdq0Mbrr9bXXXjMakPnmMunPQwsWLECLFi3Mrlu/X4YMGYLOnTvj888/x7fffosFCxZg3rx52Lx5s13eRDd06FBMnToVGzduxKRJk/Dpp59CpVKhd+/eRvHeThuwVf369dGjRw+Ly1lTz0899RTWrFmDSZMmoX379lCpVJDJZBg2bNht3dViz+P+hx9+iOTkZAwcOBDPP/88QkJC4O7ujrlz5xqSJfZ2K/G7u7sjISEBCQkJaN++Pbp164b169ejR48ehn25bt06s78g3/wG0ts5X93KdZw9jgm29NtbdezYMYSEhBgSEdYeg6zlqH134/ZtKY89tz1ixAg888wz+Oeff6DVarF//34sW7bM5vWYczvnTlv6zqRJk9C/f39s2bIF33zzDaZNm4a5c+fi+++/R8uWLe1SFiJbMClFZKV169YBgOHkq//rsqenp1UXvcD1v2Lc+IaiwsJCZGdno2/fvoZplb2F7nbo33Zz8uRJk3knTpxA3bp1b/mVs3379sXjjz+OPXv2oGfPnobp//77r8lfVP/8808AMLzhrHHjxgBg9k1IwH9/ibtxn9y8jsjIyErLpZ8PWN6v+gvkV155Bfv27UPHjh2RmpqK119/vcrvBQYGYtSoURg1ahQKCwvRpUsXTJ8+HWPHjjW0EXNvrLld5h4Z/PPPP6FQKAwXKFWV2c3NDd27d0f37t2xaNEizJkzBy+//DJ27dpVaXvW78vTp0+bzLt52qZNm+Dt7Y1vvvnG6NXma9asMVmnTqfDmTNnjP46f3OdhoSEwNvb26ptx8bGQgiB6OhoixfaVe0jX19fDB06FEOHDkVpaSnuv/9+zJ49G1OnTjV6LOPs2bOGO/P0lEolOnfujK+++sokKWXNcUAK/fv3x8cff4wPP/wQU6dOrXLZG48hN/8V9uTJk2bfqHWz2NhY7NixAx07drylX2Rtqdeq1nHkyBF0797dLsfa4OBg+Pv7W+zjsbGxKCwstHiuWL9+PYqLiw2f9ceQqtYLXP8LuDXnobCwMEyYMAETJkzAxYsXceedd2L27Nno06fPbddxdHQ07rrrLmzYsAETJ07E5s2bMXDgQKP+b20b0G/r1KlTRvvg0qVLDvsjxmeffYaRI0di4cKFhmklJSXIzc01u7y9+3lsbKzFdvbZZ58hJiYGmzdvNmrfr732mtFylbX96rxWqIz+sdfs7GwA/7XpkJAQq6+tLKmsvLdyHWeJPv5jx45V+odJW/utrdLT03HmzBmjx8msPQbpVXaNob/2qo59Z0u/t7U8lthyvTZs2DBMnjwZH3/8MYqLi+Hp6Wl0R6i9WXvctLXvxMbG4rnnnsNzzz2HU6dOoUWLFli4cKHJW8aJpMAxpYis8P3332PWrFmIjo42/IIZEhKCrl274p133jFcTN3I3CuC3333XaPb6VeuXIny8nKjv1L7+vpWepF7q8LCwtCiRQusXbvWaN3Hjh3Dt99+e1u/DEdERCAhIcFkvJHy8nLDq62B66+qfueddxAcHGwYC6levXqIiIjAwYMHza7733//NXr1bn5+Pj744AO0aNHC8Fegvn374ueffzZ6DW9RURHeffddREVFoWnTpgBguJC+ed/m5+ejvLzcaFpCQgLc3NxMXhF/M/2YWHp+fn5o2LCh4XvBwcHo0qULVq9ejaysLKNlb/fOtvT0dKPH5f7++2988cUXuOeeewx/EayszFevXjVZn/6vtVWVOTw8HM2aNcMHH3xgeDQAAPbs2YOjR48aLevu7g6ZTGb0KFJmZia2bNlitJy+7S9dutRo+pIlS0zW16NHD2zZssVoTJzTp0/j66+/Nlr2/vvvh7u7O2bMmGGyn4UQRvXm6+tr9tGbm+vWy8sLTZs2hRDCqA8DwK+//ooOHTqYrKNfv37Yvn27yRhf1hwHpDB48GAkJCRg9uzZZl9jXVBQYHgVduvWrRESEoLU1FSjNvL111/jjz/+QL9+/Sxub8iQIaioqMCsWbNM5pWXl1s87tlSr1XFcP78eaMxbPSKi4tRVFRkcR03cnNzw8CBA/G///3P7HFMH+eQIUOQnp6Ob775xmSZ3NxcwzGoY8eO6NGjh+HHUlKqVatWiI2NxZtvvmnUJ/X056GKigqTdh4SEoLw8HBDfdqjjocOHYr9+/dj9erVuHz5sskvata2gR49esDT0xNvv/22UV3ffFyQkru7u0m7e/vttysdw8/e/fyBBx7AkSNHzL6OXh+X/th/Y5wHDhww6d8KhQKA6bmhOq8VfvjhB5NjJ/DfuIL6P0r06tUL/v7+mDNnjtnlzV1bWVJZeW/lOs6Se+65B0qlEnPnzkVJSYnRPH29WNtvb8W5c+eQnJwMLy8vPP/884bp1h6D9LZs2WI0JtTPP/+MAwcOGNpvdew7W/q9reWxxJbrtbp166JPnz748MMPsX79evTu3Rt169a1aXu2sPa4aW3f0Wg0Jm0zNjYWSqXS4nUvUXXhnVJEN/n6669x4sQJlJeX48KFC/j+++/x3XffITIyEl9++aXRHRLLly9Hp06dkJCQgHHjxiEmJgYXLlxAeno6/vnnHxw5csRo3aWlpejevTuGDBmCkydPYsWKFejUqRPuu+8+wzKtWrXCypUr8frrr6Nhw4YICQmp9PlwWyxYsAB9+vRB+/btMWbMGBQXF+Ptt9+GSqUyejzkVvTt2xebNm0yunAIDw/HvHnzkJmZibi4OGzYsAEZGRl49913jcZ+GTBgAD7//HOTO6KA67dfjxkzBr/88gtCQ0OxevVqXLhwwehumylTpuDjjz9Gnz598PTTTyMwMBBr167F2bNnsWnTJsNt0LGxsQgICEBqaiqUSiV8fX3Rtm1bHDlyBBMnTsSDDz6IuLg4lJeXY926dXB3d7c4fkfTpk3RtWtXtGrVCoGBgTh48CA+++wzTJw40bDM0qVL0alTJ9x5552GcTMyMzOxbds2ZGRk3PI+b9asGXr16oWnn34acrkcK1asAACjR8v0yb+XX34Zw4YNg6enJ/r374+ZM2di79696NevHyIjI3Hx4kWsWLEC9evXNxow3pw5c+ZgwIAB6NixI0aNGoVr165h2bJlaNasmdHFdb9+/bBo0SL07t0bDz30EC5evIjly5ejYcOG+O233wzLtWjRAsOHD8eKFSuQl5eHDh06YOfOnWbviJo+fTq+/fZbdOzYEU888QQqKioM275xX8bGxuL111/H1KlTkZmZiYEDB0KpVOLs2bP4/PPPMX78eKSkpBj20YYNGzB58mS0adMGfn5+6N+/P+655x6o1Wp07NgRoaGh+OOPP7Bs2TL069fPaHDwQ4cO4erVqxgwYIBJvH379sVzzz2H/fv3G41FZc1xQAqenp7YvHkzevTogS5dumDIkCHo2LEjPD09cfz4cXz00UeoU6cOZs+eDU9PT8ybNw+jRo1CUlIShg8fjgsXLuCtt95CVFQUnn32WYvbS0pKwmOPPYa5c+ciIyMD99xzDzw9PXHq1Cls3LgRb731FgYPHlzp922p18o8+uij+PTTT/H4449j165d6NixIyoqKnDixAl8+umn+Oabb8wOWF6VOXPm4Ntvv0VSUhLGjx+PJk2aIDs7Gxs3bsSPP/6IgIAAPP/88/jyyy9x7733Ijk5Ga1atUJRURGOHj2Kzz77DJmZmbf0S42bmxvef/999OnTB/Hx8Rg1ahTq1auH8+fPY9euXfD398f//vc/FBQUoH79+hg8eDASExPh5+eHHTt24JdffjHc+WOPOh4yZAhSUlKQkpKCwMBAk7/WW9sGgoODkZKSgrlz5+Lee+9F3759cfjwYXz99dfV+stfVe69916sW7cOKpUKTZs2RXp6Onbs2GEyZpyevfv5888/j88++wwPPvggRo8ejVatWuHq1av48ssvkZqaisTERNx7773YvHkzBg0ahH79+uHs2bNITU1F06ZNjY7PPj4+aNq0KTZs2IC4uDgEBgaiWbNmaNasWbVdK8ybNw+HDh3C/fffbxh38ddff8UHH3yAwMBAw2DW/v7+WLlyJR599FHceeedGDZsGIKDg5GVlYVt27ahY8eONj8qVVV5bb2Os8Tf3x+LFy/G2LFj0aZNGzz00EOoU6cOjhw5Ao1Gg7Vr11rdby359ddf8eGHH0Kn0yE3Nxe//PILNm3aBJlMhnXr1hn2MwCbj0ENGzZEp06d8MQTT0Cr1WLJkiUICgoyelzO3vvOln5fHcdUW67XRowYYThfmUsW2ZO1x01r+86ff/5pODY1bdoUHh4e+Pzzz3HhwgUMGzasWstCVKlqf78fUQ2hf2Wt/sfLy0uo1WrRs2dP8dZbb4n8/Hyz3ztz5owYMWKEUKvVwtPTU9SrV0/ce++94rPPPjNZ9549e8T48eNFnTp1hJ+fn3j44YeNXrssxPVXt/br108olUoBQCQlJRmt4+bXjutfFb5r1y6LZdyxY4fo2LGj8PHxEf7+/qJ///7i999/N7u+jRs3WrHXrtu7d68AIE6ePCmEuP469vj4eHHw4EHRvn174e3tLSIjI8WyZctMvvvrr78KACavaI+MjBT9+vUT33zzjWjevLmQy+WicePGZuM6c+aMGDx4sAgICBDe3t7irrvuElu3bjVZ7osvvhBNmzYVHh4eAoBYs2aN+Ouvv8To0aNFbGys8Pb2FoGBgaJbt25ix44dRt/V7/8bvf766+Kuu+4SAQEBwsfHRzRu3FjMnj3b6DXgQlx/FfagQYMM8TVq1EhMmzbNMF//au1Lly6ZxHzzq9eFuP4K8yeffFJ8+OGH4o477hByuVy0bNnSbBuYNWuWqFevnnBzczO8fnjnzp1iwIABIjw8XHh5eYnw8HAxfPhwk1crjxw50tD+bvTJJ5+Ixo0bC7lcLpo1aya+/PJL8cADD4jGjRsbLbdq1SpDfI0bNxZr1qwxW57i4mLx9NNPi6CgIOHr6yv69+8v/v77b7Ov8N65c6do2bKl8PLyErGxseL9998Xzz33nPD29jaJc9OmTaJTp07C19dX+Pr6isaNG4snn3zS0E6FEKKwsFA89NBDIiAgQAAQkZGRQggh3nnnHdGlSxcRFBQk5HK5iI2NFc8//7zh9dp6L774omjQoIHRK6NvFBMTI6ZOnSqEsO04YE5VbeFmN79OvirXrl0Tr776qkhISBAKhUJ4e3uLZs2aialTp4rs7GyjZTds2CBatmwp5HK5CAwMFA8//LDRa8OFuN5ufH19K93eu+++K1q1aiV8fHyEUqkUCQkJ4oUXXhD//vuvVeuwpl71xyBzSktLxbx580R8fLyQy+WiTp06olWrVmLGjBlG9WvLvj137pwYMWKECA4OFnK5XMTExIgnn3xSaLVawzIFBQVi6tSpomHDhsLLy0vUrVtXdOjQQbz55psmx4ybWTouHz58WNx///2G9hoZGSmGDBkidu7cKYQQQqvViueff14kJiYKpVIpfH19RWJiolixYoXJuqypY3NtUa9jx44CgBg7dmyl5bGmDVRUVIgZM2aIsLAw4ePjI7p27SqOHTtmdduurP6sXe7m7Vy7dk2MGjVK1K1bV/j5+YlevXqJEydOmCxnSz/Xn+fMMVfOK1euiIkTJ4p69eoJLy8vUb9+fTFy5Ehx+fJlIcT1V9fPmTNHREZGGs4LW7duFSNHjjQc2/T27dsnWrVqJby8vEyOtdZcK1R13jLnp59+Ek8++aRo1qyZUKlUwtPTUzRo0EAkJyeLM2fOmCy/a9cu0atXL6FSqYS3t7eIjY0VycnJ4uDBg4ZlKjtOmGufVZXXlus4a6/BvvzyS9GhQwfDPrzrrrvExx9/bLSMpX5bmbNnzxpdr3p4eIjAwEDRtm1bMXXqVHHu3Dmz37PmGKRf94IFC8TChQtFRESEkMvlonPnzuLIkSMm67T3vrOl39tanpuZu8awdL2mp9VqRZ06dYRKpRLFxcVm93dVFixYYLgm07PHuVMIy33n8uXL4sknnxSNGzcWvr6+QqVSibZt24pPP/3U5nIQ2YtMCAeNjkxUi6SlpWHUqFH45ZdfbP4rfE1QUVGBunXr4rXXXrP5ld0A0L17d4SHhxvG7XJG+jp0hkOmTCbDk08+abeBNSuTnJyMzMxM7N692+KyLVq0QHBwML777rtqjcmcgQMH4vjx42bHwKhOWq0WUVFRmDJlCp555hmzyzz11FP44YcfbuuuOCIiIilkZmYiOjoaCxYssHjnaW1WXl6O8PBw9O/fH6tWrXJ0OEQ1HseUIqLb5u7ujqVLl1b5prqqzJkzBxs2bDB66xQ5p7KyMpOxGnbv3o0jR46ga9eu1b79GweBBq4PiPrVV19Jsu2brVmzBp6ennj88ccrXWb8+PEYNGiQ2fEdiIiIqObZsmULLl26hBEjRjg6FCKXwDGliMguHn300Vv+btu2bVFaWmrHaKi6nD9/Hj169MAjjzyC8PBwnDhxAqmpqVCr1VUmZ+wlJiYGycnJiImJwblz57By5Up4eXlV+lro6vT4449bLLP+rY5ERERUsx04cAC//fYbZs2ahZYtWyIpKcnRIRG5BCaliIjIanXq1EGrVq3w/vvv49KlS/D19UW/fv3wxhtvVDrgrz317t0bH3/8MXJyciCXy9G+fXvMmTMHd9xxR7Vvm4iIiGqvlStX4sMPP0SLFi2Qlpbm6HCIXAbHlCIiIiIiIiIiIslxTCkiIiIiIiIiIpIck1JERERERERERCQ5JqWIiIiIiIiIiEhyHOjcDJ1Oh3///RdKpRIymczR4RARERERERER2YUQAgUFBQgPD4ebm2PvVWJSyox///0XERERjg6DiIiIiIiIiKha/P3336hfv75DY2BSygylUgngegX5+/s7OBoiIiIiIiIiIvvIz89HRESEIffhSA5NSu3duxcLFizAoUOHkJ2djc8//xwDBw40zK/s0bn58+fj+eefNztv+vTpmDFjhtG0Ro0a4cSJE1bHpd+uv78/k1JERERERERE5HKcYbgihz48WFRUhMTERCxfvtzs/OzsbKOf1atXQyaT4YEHHqhyvfHx8Ubf+/HHH6sjfCIiIiIiIiIiukUOvVOqT58+6NOnT6Xz1Wq10ecvvvgC3bp1Q0xMTJXr9fDwMPkuERERERERERE5D8cOs26DCxcuYNu2bRgzZozFZU+dOoXw8HDExMTg4YcfRlZWVpXLa7Va5OfnG/0QEREREREREVH1qTEDna9duxZKpRL3339/lcu1bdsWaWlpaNSoEbKzszFjxgx07twZx44dq3QQr7lz55qMQ2WNiooKlJWV2fw9otrI09MT7u7ujg6DiIiIiIiInIRMCCEcHQRwfYCtmwc6v1Hjxo3Rs2dPvP322zatNzc3F5GRkVi0aFGld1lptVpotVrDZ/1I9Hl5eWYHOhdCICcnB7m5uTbFQlTbBQQEQK1WO8WAekRERERERLVRfn4+VCpVpTkPKdWIO6V++OEHnDx5Ehs2bLD5uwEBAYiLi8Pp06crXUYul0Mul1u9Tn1CKiQkBAqFgr9gE1kghIBGo8HFixcBAGFhYQ6OiIiIiIiIiBytRiSlVq1ahVatWiExMdHm7xYWFuLMmTN49NFH7RJLRUWFISEVFBRkl3US1QY+Pj4AgIsXLyIkJISP8hEREREREdVyDh3ovLCwEBkZGcjIyAAAnD17FhkZGUYDk+fn52Pjxo0YO3as2XV0794dy5YtM3xOSUnBnj17kJmZiX379mHQoEFwd3fH8OHD7RKzfgwphUJhl/UR1Sb6fsOx2IiIiIiIiMihd0odPHgQ3bp1M3yePHkyAGDkyJFIS0sDAHzyyScQQlSaVDpz5gwuX75s+PzPP/9g+PDhuHLlCoKDg9GpUyfs378fwcHBdo2dj+wR2Y79hoiIiIio9sjVlKFAWwFNqQ4KLzco5e4IUHg6OixyIk4z0LkzqWrQr5KSEpw9exbR0dHw9vZ2UIS1z7vvvotZs2bh/PnzWLRoESZNmmQyLTc3F1u2bDHceZecnGyYBgBdu3ZFixYtsGTJEsnj3717N7p164Zr164hICBA0m1nZmYiOjoahw8fRosWLSTd9s3Yf4iIiIiIaoeLBaVYs/8CjudoDNPi1QqMaheKEKWXAyMjZxro3KGP75E0UlNToVQqUV5ebphWWFgIT09PdO3a1WjZ3bt3QyaT4cyZMwCAqKgoyGQyfPLJJybrjY+Ph0wmM9zVduPyN/7Ur1//tuLPz8/HxIkT8eKLL+L8+fMYP3682WkpKSnYuXNnpevZvHkzZs2aZfV29fuiJr1lMTk5udI3WBIREREREUkhV1NmkpACgOM5GqzZfwG5Gg7nQdcxKVULdOvWDYWFhTh48KBh2g8//AC1Wo0DBw6gpKTEMH3Xrl1o0KABYmNjDdMiIiKwZs0ao3Xu378fOTk58PX1NdnezJkzkZ2dbfg5fPjwbcWflZWFsrIy9OvXD2FhYVAoFGan+fn5VTn4fGBgIJRK5W3FQv8pLS11dAhEREREROSECrQVJgkpveM5GhRoKySOiJwVk1K1QKNGjRAWFobdu3cbpu3evRsDBgxAdHQ09u/fbzT9xnG+AODhhx/Gnj178PfffxumrV69Gg8//DA8PEyHJVMqlVCr1YYfS+N5vfjii4iLi4NCoUBMTAymTZtmGAg7LS0NCQkJAICYmBjDnVk3T8vMzMT06dOrfDyta9eumDRpkuHzunXr0Lp1a0O8Dz30EC5evAjg+iNv+v1Qp04dyGQyJCcnAwB0Oh3mzp2L6Oho+Pj4IDExEZ999pnRtr766ivExcXBx8cH3bp1Q2ZmZpX7AAAWLVqEhIQE+Pr6IiIiAhMmTEBhYaFhvrnyLVmyBFFRUYb5a9euxRdffGG4S+3GOv/rr7/QrVs3KBQKJCYmIj093WhdmzZtQnx8PORyOaKiorBw4UKj+VFRUZg1axZGjBgBf39/jB8/HqWlpZg4cSLCwsLg7e2NyMhIzJ0712JZiYiIiIjIdWlKdbc1n2oPJqUcpExThOKLOSjIOoviixdQpimq1u1169YNu3btMnzetWsXunbtiqSkJMP04uJiHDhwwCQpFRoail69emHt2rUAAI1Ggw0bNmD06NF2iU2pVCItLQ2///473nrrLbz33ntYvHgxAGDo0KHYsWMHAODnn39GdnY2HnzwQZNpERERNm+3rKwMs2bNwpEjR7BlyxZkZmYaEk8RERHYtGkTAODkyZPIzs7GW2+9BQCYO3cuPvjgA6SmpuL48eN49tln8cgjj2DPnj0AgL///hv3338/+vfvj4yMDIwdOxZTpkyxGI+bmxuWLl2K48ePY+3atfj+++/xwgsvWF2elJQUDBkyBL179zbcpdahQwfD/JdffhkpKSnIyMhAXFwchg8fbnik89ChQxgyZAiGDRuGo0ePYvr06Zg2bZrRo5kA8OabbyIxMRGHDx/GtGnTsHTpUnz55Zf49NNPcfLkSaxfv96QJCMiIiIiotpJ4VV1qsHSfKo9HPr2vdpKm3sNZz5bj/w//zBMU8U1QczghyEPqFMt2+zWrRsmTZqE8vJyFBcX4/Dhw0hKSkJZWRlSU1MBAOnp6dBqtSZJKQAYPXo0nnvuObz88sv47LPPEBsbW+ldSS+++CJeeeUVw+c5c+bg6aefrjS2G5eNiopCSkoKPvnkE7zwwgvw8fExPJIXHBwMtVoNAGan2erGpFpMTAyWLl2KNm3aoLCwEH5+fggMDAQAhISEGAYn12q1mDNnDnbs2IH27dsbvvvjjz/inXfeQVJSElauXInY2FjDnUaNGjXC0aNHMW/evCrjufEurqioKLz++ut4/PHHsWLFCqvK4+fnBx8fH2i1WrP7JCUlBf369QMAzJgxA/Hx8Th9+jQaN26MRYsWoXv37pg2bRoAIC4uDr///jsWLFhgSNQBwN13343nnnvO8DkrKwt33HEHOnXqBJlMhsjISKtiJSIiIiIi16WUuyNerTD7CF+8WgGl3N0BUZEzYnpSYmWaIpOEFADk/fkH/vpsfbXdMdW1a1cUFRXhl19+wQ8//IC4uDgEBwcjKSnJMK7U7t27ERMTgwYNGph8v1+/figsLMTevXuxevXqKu+Sev7555GRkWH4GTFiBADg8ccfh5+fn+FHb8OGDejYsSPUajX8/PzwyiuvICsry/474SaHDh1C//790aBBAyiVSiQlJQFAlds+ffo0NBoNevbsaVSWDz74wDA4/B9//IG2bdsafU+fwKrKjh070L17d9SrVw9KpRKPPvoorly5Ao3G/LPYtmrevLnh/2FhYQBgeFzxjz/+QMeOHY2W79ixI06dOoWKiv+e927durXRMsnJycjIyECjRo3w9NNP49tvv7VLrEREREREVHMFKDwxql0o4tUKo+n6t+8FKDwdFBk5G94pJbHywgKThJRe3p9/oLywAJ4K08HDb1fDhg1Rv3597Nq1C9euXTMkYMLDwxEREYF9+/Zh165duPvuu81+38PDA48++ihee+01HDhwAJ9//nml26pbty4aNmxoMn3mzJlISUkxmpaeno6HH34YM2bMQK9evaBSqfDJJ5+YjGdkb0VFRejVqxd69eqF9evXIzg4GFlZWejVq1eVA3jrx3jatm0b6tWrZzRPLpffcjyZmZm499578cQTT2D27NkIDAzEjz/+iDFjxqC0tBQKhQJubm4QQhh9Tz/2ljU8Pf878MtkMgDXx8eyxc0D29955504e/Ysvv76a+zYsQNDhgxBjx49TMbYIiIiIiKi2iVE6YXHOqpRoK2AplQHhZcblHJ3JqTICJNSEisvKbYwv6TK+bejW7du2L17N65du4bnn3/eML1Lly74+uuv8fPPP+OJJ56o9PujR4/Gm2++iaFDh6JOHdsfMwwJCUFISIjRtH379iEyMhIvv/yyYdq5c+dsXretTpw4gStXruCNN94wjEd149sJAcDLywsAjO4Uatq0KeRyObKysgyJvZs1adIEX375pdG0GweTN+fQoUPQ6XRYuHAh3Nyu38D46aefGi0THByMnJwcCCEMSaWMjAyTmG+M11pNmjTBTz/9ZDTtp59+QlxcHNzdq7611t/fH0OHDsXQoUMxePBg9O7dG1evXjU8/khERERERLVTgMKTSSiqEpNSEvPw9rEw37vatt2tWzc8+eSTKCsrM0qoJCUlYeLEiSgtLTU7npRekyZNcPnyZSgUikqXsdUdd9yBrKwsfPLJJ2jTpg22bdtW5V1Y9tKgQQN4eXnh7bffxuOPP45jx45h1qxZRstERkZCJpNh69at6Nu3L3x8fKBUKpGSkoJnn30WOp0OnTp1Ql5eHn766Sf4+/tj5MiRePzxx7Fw4UI8//zzGDt2LA4dOmQyYPjNGjZsiLKyMrz99tvo378/fvrpJ8NYX3pdu3bFpUuXMH/+fAwePBjbt2/H119/DX9/f8MyUVFR+Oabb3Dy5EkEBQVBpVJZtT+ee+45tGnTBrNmzcLQoUORnp6OZcuWWRzPatGiRQgLC0PLli3h5uaGjRs3Qq1WG8bgIiIiIiIiIqoMx5SSmIefEqq4JmbnqeKawMNPWW3b7tatG4qLi9GwYUOEhoYapiclJaGgoACNGjUyjDVUmaCgIPj4VJ1Ys8V9992HZ599FhMnTkSLFi2wb98+w2Db1Sk4OBhpaWnYuHEjmjZtijfeeANvvvmm0TL16tXDjBkzMGXKFISGhmLixIkAgFmzZmHatGmYO3cumjRpgt69e2Pbtm2Ijo4GcD3htWnTJmzZsgWJiYlITU3FnDlzqownMTERixYtwrx589CsWTOsX78ec+fONVqmSZMmWLFiBZYvX47ExET8/PPPJo9Djhs3Do0aNULr1q0RHBxscvdTZe688058+umn+OSTT9CsWTO8+uqrmDlzptEg5+YolUrMnz8frVu3Rps2bZCZmYmvvvrKcLcXERERERERUWVk4uZBagj5+flQqVTIy8szugsFAEpKSnD27FlER0fD+xbvatLmXsNfn61HnoRv3yNyBvboP0RERERERHTrqsp5SI2P7zmAPKAOGj40CuWFBSgvKYGHtzc8/JTVMsA5EREREREREZEzYlLKQTwVvkxCEREREREREVGtxYFfiIiIiIiIiIhIckxKERERERERERGR5JiUIiIiIiIiIiIiyTEpRUREREREREREkmNSioiIiIiIiIiIJMekFBERERERERERSY5JKSIiIiIiIiIikhyTUuQw7777LiIiIuDm5oYlS5aYnTZ9+nS0aNHC8J3k5GQMHDjQ8Llr166YNGmSpHHr7d69GzKZDLm5uZJvOzMzEzKZDBkZGZJvm4iIiIiIiMgemJSqJZKTkyGTyfDGG28YTd+yZQtkMpnk8eTn52PixIl48cUXcf78eYwfP97stJSUFOzcubPS9WzevBmzZs2yeruOTCTdqpsTcURERERERESugEmpWsTb2xvz5s3DtWvXHB0KsrKyUFZWhn79+iEsLAwKhcLsND8/PwQFBVW6nsDAQCiVSgkjd22lpaWODoGIiIiIiIhqCSalapEePXpArVZj7ty5VS63adMmxMfHQy6XIyoqCgsXLjSaHxUVhTlz5mD06NFQKpVo0KAB3n33XaNlXnzxRcTFxUGhUCAmJgbTpk1DWVkZACAtLQ0JCQkAgJiYGMhkMrPTMjMzTR7fu9nNj++tW7cOrVu3hlKphFqtxkMPPYSLFy8CuP7IW7du3QAAderUgUwmQ3JyMgBAp9Nh7ty5iI6Oho+PDxITE/HZZ58Zbeurr75CXFwcfHx80K1bN2RmZla5HwFg0aJFSEhIgK+vLyIiIjBhwgQUFhYa5psr35IlSxAVFWWYv3btWnzxxReQyWSQyWTYvXu3Ydm//voL3bp1g0KhQGJiItLT043WZU1dzpo1CyNGjIC/vz/Gjx+P0tJSTJw4EWFhYfD29kZkZKTFNkNERERERERkKyalHKRIW45/87Q4c7kY2XlaFGnLq32b7u7umDNnDt5++238888/Zpc5dOgQhgwZgmHDhuHo0aOYPn06pk2bhrS0NKPlFi5ciNatW+Pw4cOYMGECnnjiCZw8edIwX6lUIi0tDb///jveeustvPfee1i8eDEAYOjQodixYwcA4Oeff0Z2djYefPBBk2kRERE2l7GsrAyzZs3CkSNHsGXLFmRmZhoSTxEREdi0aRMA4OTJk8jOzsZbb70FAJg7dy4++OADpKam4vjx43j22WfxyCOPYM+ePQCAv//+G/fffz/69++PjIwMjB07FlOmTLEYj5ubG5YuXYrjx49j7dq1+P777/HCCy9YXZ6UlBQMGTIEvXv3RnZ2NrKzs9GhQwfD/JdffhkpKSnIyMhAXFwchg8fjvLy623J2rp88803kZiYiMOHD2PatGlYunQpvvzyS3z66ac4efIk1q9fb0iSEREREREREdmLh6MDqI2uFJVhVXoOjmVrDNMSwhQY3V6NIF/Pat32oEGD0KJFC7z22mtYtWqVyfxFixahe/fumDZtGgAgLi4Ov//+OxYsWGBI7gBA3759MWHCBADX74pavHgxdu3ahUaNGgEAXnnlFcOyUVFRSElJwSeffIIXXngBPj4+hkfygoODoVarAcDsNFuNHj3a8P+YmBgsXboUbdq0QWFhIfz8/BAYGAgACAkJQUBAAABAq9Vizpw52LFjB9q3b2/47o8//oh33nkHSUlJWLlyJWJjYw13GjVq1AhHjx7FvHnzqoznxru4oqKi8Prrr+Pxxx/HihUrrCqPn58ffHx8oNVqze6TlJQU9OvXDwAwY8YMxMfH4/Tp02jcuLHVdXn33XfjueeeM3zOysrCHXfcgU6dOkEmkyEyMtKqWImIiIiIiIhswTulJFakLTdJSAHA0WwNVqfnSHLH1Lx587B27Vr88ccfJvP++OMPdOzY0Whax44dcerUKVRUVBimNW/e3PB/mUwGtVpteEwOADZs2ICOHTtCrVbDz88Pr7zyCrKysqqhNMYOHTqE/v37o0GDBlAqlUhKSgKAKrd9+vRpaDQa9OzZE35+foafDz74AGfOnAFwfb+0bdvW6Hv6BFZVduzYge7du6NevXpQKpV49NFHceXKFWg0GovftcaN9RAWFgYAhnqwti5bt25ttExycjIyMjLQqFEjPP300/j222/tEisRERERERHRjZiUklheSYVJQkrvaLYGeSUVZufZU5cuXdCrVy9MnTr1ltfh6Wl8R5dMJoNOpwMApKen4+GHH0bfvn2xdetWHD58GC+//HK1D6JdVFSEXr16wd/fH+vXr8cvv/yCzz//HEDVA3jrx3jatm0bMjIyDD+///67ybhStsjMzMS9996L5s2bY9OmTTh06BCWL19uFI+bmxuEEEbf04+9ZY0b60H/FkV9PVjL19fX6POdd96Js2fPYtasWSguLsaQIUMwePBgm9ZJREREREREZAkf35NYcVnVCQNL8+3ljTfeQIsWLQyP2+k1adIEP/30k9G0n376CXFxcXB3d7dq3fv27UNkZCRefvllw7Rz587dftAWnDhxAleuXMEbb7xhGI/q4MGDRst4eXkBgNGdQk2bNoVcLkdWVpbhzqqbNWnSBF9++aXRtP3791cZz6FDh6DT6bBw4UK4uV3P/3766adGywQHByMnJwdCCENSKSMjwyTmG+O11u3Upb+/P4YOHYqhQ4di8ODB6N27N65evWp4/JGIiIiIiIjodjEpJTEfz6pvTrM0314SEhLw8MMPY+nSpUbTn3vuObRp0wazZs3C0KFDkZ6ejmXLllk9BhIA3HHHHcjKysInn3yCNm3aYNu2bYY7lqpTgwYN4OXlhbfffhuPP/44jh07hlmzZhktExkZCZlMhq1bt6Jv377w8fGBUqlESkoKnn32Weh0OnTq1Al5eXn46aef4O/vj5EjR+Lxxx/HwoUL8fzzz2Ps2LE4dOiQyYDhN2vYsCHKysrw9ttvo3///vjpp5+QmppqtEzXrl1x6dIlzJ8/H4MHD8b27dvx9ddfw9/f37BMVFQUvvnmG5w8eRJBQUFQqVRW7Y9brctFixYhLCwMLVu2hJubGzZu3Ai1Wm0Yg4uIiIiIiIjIHvj4nsRU3u5ICFOYnZcQpoDK27q7kexh5syZJo963Xnnnfj000/xySefoFmzZnj11Vcxc+ZMo4GxLbnvvvvw7LPPYuLEiWjRogX27dtnGGy7OgUHByMtLQ0bN25E06ZN8cYbb+DNN980WqZevXqYMWMGpkyZgtDQUEycOBEAMGvWLEybNg1z585FkyZN0Lt3b2zbtg3R0dEArie8Nm3ahC1btiAxMRGpqamYM2dOlfEkJiZi0aJFmDdvHpo1a4b169dj7ty5Rss0adIEK1aswPLly5GYmIiff/4ZKSkpRsuMGzcOjRo1QuvWrREcHGxy91NlbrUulUol5s+fj9atW6NNmzbIzMzEV199Zbjbi4iIiIiIiMgeZOLmAW0I+fn5UKlUyMvLM7pjBQBKSkpw9uxZREdHw9vb+5bWf6WoDKvTc3D0prfvjWmvRmA1v32PyJHs0X+IiIiIiIjo1lWV85AaH99zgCBfT0zoHIa8kgoUl+ng4+kGlbc7fOWsDiIiIiIiIiKqHRz6PM7evXvRv39/hIeHQyaTYcuWLUbzk5OTIZPJjH569+5tcb3Lly9HVFQUvL290bZtW/z888/VVIJb5yv3QLhKjti6PghXyZmQIiIiIiIiIpdSpC3Hv3lanLlcjOw8LYq05Y4OiZyMQzMhRUVFSExMxOjRo3H//febXaZ3795Ys2aN4bNcLq9ynRs2bMDkyZORmpqKtm3bYsmSJejVqxdOnjyJkJAQu8ZPRERERERERKauFJVhVXoOjt00bM3o9moEcdga+n8OvVOqT58+eP311zFo0KBKl5HL5VCr1YafOnXqVLnORYsWYdy4cRg1ahSaNm2K1NRUKBQKrF692t7hExEREREREdFNirTlJgkpADiarcHq9BzeMUUGTv86rd27dyMkJASNGjXCE088gStXrlS6bGlpKQ4dOoQePXoYprm5uaFHjx5IT0+v9HtarRb5+flGP0RERERERERku7ySCpOElN7RbA3ySiokjoiclVMnpXr37o0PPvgAO3fuxLx587Bnzx706dMHFRXmG/Dly5dRUVGB0NBQo+mhoaHIycmpdDtz586FSqUy/ERERFiMjS8tJLId+w0RERERkesrLtPd1nyqPZx6dO1hw4YZ/p+QkIDmzZsjNjYWu3fvRvfu3e22nalTp2Ly5MmGz/n5+ZUmpjw9rz/7qtFo4OPjY7cYiGoDjeb6X0v0/YiIiIiIiFyPj2fV979Ymk+1h1MnpW4WExODunXr4vTp02aTUnXr1oW7uzsuXLhgNP3ChQtQq9WVrlcul1scQF3P3d0dAQEBuHjxIgBAoVBAJpPZUAqi2kcIAY1Gg4sXLyIgIADu7u6ODomIiIiIiKqJytsdCWEKHDXzCF9CmAIqb/4+QNfVqKTUP//8gytXriAsLMzsfC8vL7Rq1Qo7d+7EwIEDAQA6nQ47d+7ExIkT7RaHPsGlT0wRkXUCAgKqTBATEREREVHN5yv3wOj2aqxOzzFKTCWEKTCmvRq+8hqViqBq5NCWUFhYiNOnTxs+nz17FhkZGQgMDERgYCBmzJiBBx54AGq1GmfOnMELL7yAhg0bolevXobvdO/eHYMGDTIknSZPnoyRI0eidevWuOuuu7BkyRIUFRVh1KhRdotbJpMhLCwMISEhKCsrs9t6iVyZp6cn75AiIiIiIqolgnw9MaFzGPJKKlBcpoOPpxtU3u5MSJERh7aGgwcPolu3bobP+nGdRo4ciZUrV+K3337D2rVrkZubi/DwcNxzzz2YNWuW0aN2Z86cweXLlw2fhw4dikuXLuHVV19FTk4OWrRoge3bt5sMfm4P7u7u/CWbiIiIiIiIyAxfuQeTUFQlmeDrsEzk5+dDpVIhLy8P/v7+jg6HiIiIiIiIiMgunCnnwSHviYiIiIiIiIhIckxKERERERERERGR5JiUIiIiIiIiIiIiyTEpRUREREREREREkmNSioiIiIiIiIiIJMekFBERERERERERSY5JKSIiIiIiIiIikpyHowMgIiIiIiIiItdTpC1HXkkFist0UHi6wd/bHb5ypiHoP2wNRERERERERGRXV4rKsCo9B8eyNYZpCWEKjG6vRpCvpwMjI2fCx/eIiIiIiIiIyG6KtOUmCSkAOJqtwer0HBRpyx0UGTkbJqWIiIiIiIiIyG7ySipMElJ6R7M1yCupkDgiclZMShERERERERGR3RSX6W5rPtUeTEoRERERERERkd34eFadarA0n2oPtgQiIiIiIiIishuVtzsSwhRm5yWEKaDydpc4InJWTEoRERERERERkd34yj0wur3aJDGVEKbAmPZq+Mo9HBQZORu2BCIiIiIiIiKyqyBfT0zoHIa8kgoUl+ng4+kGlbc7E1JkhK2BiIiIiIiIiOzOV+7BJBRViY/vERERERERERGR5JiUIiIiIiIiIiIiyTEpRUREREREREREkmNSioiIiIiIiIiIJMekFBERERERERERSY5JKSIiIiIiIiIikhyTUkREREREREREJDkmpYiIiIiIiIiISHJMShERERERERERkeSYlCIiIiIiIiIiIskxKUVERERERERERJJjUoqIiIiIiIiIiCTHpBQREREREREREUmOSSkiIiIiIiIiIpIck1JERERERERERCQ5JqWIiIiIiIiIiEhyDk1K7d27F/3790d4eDhkMhm2bNlimFdWVoYXX3wRCQkJ8PX1RXh4OEaMGIF///23ynVOnz4dMpnM6Kdx48bVXBIiIiIiIiIiIrKFQ5NSRUVFSExMxPLly03maTQa/Prrr5g2bRp+/fVXbN68GSdPnsR9991ncb3x8fHIzs42/Pz444/VET4REREREREREd0iD0duvE+fPujTp4/ZeSqVCt99953RtGXLluGuu+5CVlYWGjRoUOl6PTw8oFar7RorERERERERERHZT40aUyovLw8ymQwBAQFVLnfq1CmEh4cjJiYGDz/8MLKysqQJkIiIiIiIiIiIrOLQO6VsUVJSghdffBHDhw+Hv79/pcu1bdsWaWlpaNSoEbKzszFjxgx07twZx44dg1KpNPsdrVYLrVZr+Jyfn2/3+ImIiIiIiIiI6D81IilVVlaGIUOGQAiBlStXVrnsjY8DNm/eHG3btkVkZCQ+/fRTjBkzxux35s6dixkzZtg1ZiIiIiIiIiIiqpzTP76nT0idO3cO3333XZV3SZkTEBCAuLg4nD59utJlpk6diry8PMPP33//fbthExERERERERFRFZw6KaVPSJ06dQo7duxAUFCQzesoLCzEmTNnEBYWVukycrkc/v7+Rj9ERERERERERFR9HJqUKiwsREZGBjIyMgAAZ8+eRUZGBrKyslBWVobBgwfj4MGDWL9+PSoqKpCTk4OcnByUlpYa1tG9e3csW7bM8DklJQV79uxBZmYm9u3bh0GDBsHd3R3Dhw+XunhERERERERERFQJh44pdfDgQXTr1s3wefLkyQCAkSNHYvr06fjyyy8BAC1atDD63q5du9C1a1cAwJkzZ3D58mXDvH/++QfDhw/HlStXEBwcjE6dOmH//v0IDg6u3sIQEREREREREZHVZEII4eggnE1+fj5UKhXy8vL4KB8RERERERERuQxnynk49ZhSRERERERERETkmpiUIiIiIiIiIiIiyTEpRUREREREREREkmNSioiIiIiIiIiIJMekFBERERERERERSY5JKSIiIiIiIiIikhyTUkREREREREREJDkmpYiIiIiIiIiISHJMShERERERERERkeSYlCIiIiIiIiIiIskxKUVERERERERERJJjUoqIiIiIiIiIiCTHpBQREREREREREUmOSSkiIiIiIiIiIpIck1JERERERERERCQ5JqWIiIiIiIiIiEhyTEoREREREREREZHkPG7lSzqdDqdPn8bFixeh0+mM5nXp0sUugRERERERERERkeuyOSm1f/9+PPTQQzh37hyEEEbzZDIZKioq7BYcERERERERERG5JpuTUo8//jhat26Nbdu2ISwsDDKZrDriIiIiIiIiIiIiF2ZzUurUqVP47LPP0LBhw+qIh4iIiIiIiIiIagGbBzpv27YtTp8+XR2xEBERERERERFRLWHVnVK//fab4f9PPfUUnnvuOeTk5CAhIQGenp5GyzZv3ty+ERIRERERERERkcuRiZtHKzfDzc0NMpnMZGBzw0r+f56rDHSen58PlUqFvLw8+Pv7OzocIiIiIiIiIiK7cKach1V3Sp09e7a64yAiIiIiIiIiolrEqqRUZGSk4f979+5Fhw4d4OFh/NXy8nLs27fPaFkiIiIiIiIiIiJzbB7ovFu3brh69arJ9Ly8PHTr1s0uQRERERERERERkWuzOSmlHzvqZleuXIGvr69dgiIiIiIiIiIiItdm1eN7AHD//fcDuD6oeXJyMuRyuWFeRUUFfvvtN3To0MH+ERIRERERERERkcuxOimlUqkAXL9TSqlUwsfHxzDPy8sL7dq1w7hx4+wfIRERERERERERuRyrk1Jr1qwBAERFRSElJYWP6hERERERERER0S2TCSHErXzx4sWLOHnyJACgUaNGCAkJsWtgjpSfnw+VSoW8vDz4+/s7OhwiIiIiIiIiIrtwppyHzQOdFxQU4NFHH0W9evWQlJSEpKQk1KtXD4888gjy8vKqI0YiIiIiIiIiInIxNielxo4diwMHDmDr1q3Izc1Fbm4utm7dioMHD+Kxxx6zaV179+5F//79ER4eDplMhi1bthjNF0Lg1VdfRVhYGHx8fNCjRw+cOnXK4nqXL1+OqKgoeHt7o23btvj5559tiouIiIiIiIiIiKqXzUmprVu3YvXq1ejVqxf8/f3h7++PXr164b333sP//vc/m9ZVVFSExMRELF++3Oz8+fPnY+nSpUhNTcWBAwfg6+uLXr16oaSkpNJ1btiwAZMnT8Zrr72GX3/9FYmJiejVqxcuXrxoU2xEREREREREdOuKtOX4N0+LM5eLkZ2nRZG23NEhkZOxeqBzvaCgIMOb+G6kUqlQp04dm9bVp08f9OnTx+w8IQSWLFmCV155BQMGDAAAfPDBBwgNDcWWLVswbNgws99btGgRxo0bh1GjRgEAUlNTsW3bNqxevRpTpkyxKT4iIiIiIiIist2VojKsSs/BsWyNYVpCmAKj26sR5OvpwMjImdh8p9Qrr7yCyZMnIycnxzAtJycHzz//PKZNm2a3wM6ePYucnBz06NHDME2lUqFt27ZIT083+53S0lIcOnTI6Dtubm7o0aNHpd8hIiIiIiIiIvsp0pabJKQA4Gi2BqvTc3jHFBnYfKfUypUrcfr0aTRo0AANGjQAAGRlZUEul+PSpUt45513DMv++uuvtxyYPukVGhpqND00NNQoIXajy5cvo6Kiwux3Tpw4Uem2tFottFqt4XN+fv6thk1ERERERERUq+WVVJgkpPSOZmuQV1IBX7nN6QhyQTa3goEDB1ZDGI41d+5czJgxw9FhEBEREREREdV4xWW625pPtYfNSanXXnutOuIwoVarAQAXLlxAWFiYYfqFCxfQokULs9+pW7cu3N3dceHCBaPpFy5cMKzPnKlTp2Ly5MmGz/n5+YiIiLiN6ImIiIiIiIhqJx/PqkcKsjSfao9bagm5ubl4//33MXXqVFy9ehXA9Uf1zp8/b7fAoqOjoVarsXPnTsO0/Px8HDhwAO3btzf7HS8vL7Rq1croOzqdDjt37qz0OwAgl8sNbxLU/xARERERERGR7VTe7kgIU5idlxCmgMrbXeKIyFnZfKfUb7/9hh49ekClUiEzMxPjxo1DYGAgNm/ejKysLHzwwQdWr6uwsBCnT582fD579iwyMjIQGBiIBg0aYNKkSXj99ddxxx13IDo6GtOmTUN4eLjRI4Tdu3fHoEGDMHHiRADA5MmTMXLkSLRu3Rp33XUXlixZgqKiIsPb+IiIiIiIiIio+vjKPTC6vRqr03Nw9Ka3741pr+Z4UmRgc0uYPHkykpOTMX/+fCiVSsP0vn374qGHHrJpXQcPHkS3bt2M1g0AI0eORFpaGl544QUUFRVh/PjxyM3NRadOnbB9+3Z4e3sbvnPmzBlcvnzZ8Hno0KG4dOkSXn31VeTk5KBFixbYvn27yeDnRERERERERFQ9gnw9MaFzGPJKKlBcpoOPpxtU3u5MSJERmRBC2PIFlUqFX3/9FbGxsVAqlThy5AhiYmJw7tw5NGrUCCUlJdUVq2Ty8/OhUqmQl5fHR/mIiIiIiIiIyGU4U87D5jGl5HI58vPzTab/+eefCA4OtktQRERERERERETk2mxOSt13332YOXMmysrKAAAymQxZWVl48cUX8cADD9g9QCIiIiIiIiIicj02J6UWLlyIwsJChISEoLi4GElJSWjYsCGUSiVmz55dHTESEREREREREZGLsXmEMZVKhe+++w4//vgjfvvtNxQWFuLOO+9Ejx49qiM+IiIiIiIiIiJyQTYPdF4bONOgX0RERERERERE9uJMOQ+b7pTS6XRIS0vD5s2bkZmZCZlMhujoaAwePBiPPvooZDJZdcVJREREREREREQuxOoxpYQQuO+++zB27FicP38eCQkJiI+Px7lz55CcnIxBgwZVZ5xERERERERERORCrL5TKi0tDXv37sXOnTvRrVs3o3nff/89Bg4ciA8++AAjRoywe5BERERERERERORarL5T6uOPP8ZLL71kkpACgLvvvhtTpkzB+vXr7RocEZG9FGnL8W+eFmcuFyM7T4sibbmjQyIiIiIiIqrVrL5T6rfffsP8+fMrnd+nTx8sXbrULkEREdnTlaIyrErPwbFsjWFaQpgCo9urEeTr6cDIiIiIiIiIai+r75S6evUqQkNDK50fGhqKa9eu2SUoIiJ7KdKWmySkAOBotgar03N4xxQREREREZGDWJ2UqqiogIdH5TdWubu7o7ycv9wRkXPJK6kwSUjpHc3WIK+kQuKIiIiIiIiICLDh8T0hBJKTkyGXy83O12q1dguKiMheist0tzWfiIiIiIiIqofVSamRI0daXIZv3iMiZ+PjWfUNoZbmExERERERUfWwOim1Zs2a6oyDiKhaqLzdkRCmwFEzj/AlhCmg8nZ3QFRERERERETEWwSIyKX5yj0wur0aCWEKo+kJYQqMaa+Gr9zq3DwRERERERHZEX8bIyKXF+TriQmdw5BXUoHiMh18PN2g8nZnQoqIiIiIiMiB+BsZEdUKvnIPJqGIiIiIiIicCB/fIyIiIiIiIiIiyTEpRUREREREREREkrulZ1lOnTqFXbt24eLFi9DpdEbzXn31VbsERkRERERERERErsvmpNR7772HJ554AnXr1oVarYZMJjPMk8lkTEoREREREREREZFFNielXn/9dcyePRsvvvhidcRDRERERERERES1gM1jSl27dg0PPvhgdcRCRERERERERES1hM1JqQcffBDffvttdcRCRERERERERES1hM2P7zVs2BDTpk3D/v37kZCQAE9PT6P5Tz/9tN2CIyLK1ZShQFsBTakOCi83KOXuCFB4Wv5iNa2HiIjI0Yq05cgrqUBxmQ4KTzf4e7vDV+5h9XxyHNYN1Ta8BidLZEIIYcsXoqOjK1+ZTIa//vrrtoNytPz8fKhUKuTl5cHf39/R4RDVWhcLSrFm/wUcz9EYpsWrFRjVLhQhSi/J10NERORoV4rKsCo9B8ey/zunJYQpMLq9GkG+nhbnk+Owbqi24TW483KmnIfNSanawJkqiKi2ytWU4Z2fcoxOYnrxagUe66i26q8s9loPERGRoxVpy7H8h2yjpIZeQpgC4zqo8e6+nErnT+gcxrtyHMRS3bFuyNXwGty5OVPOw+YxpYiIpFCgrTB7EgOA4zkaFGgrJF0PERGRo+WVVJhNagDA0ezr57Sq5ueV8JznKJbqjnVDrobX4GStW0rH//PPP/jyyy+RlZWF0tJSo3mLFi2yS2BEVLtpSnW3Nd/e6yEiInK04rLbO6dZ+j5VH0v7nnVDrobX4GQtm5NSO3fuxH333YeYmBicOHECzZo1Q2ZmJoQQuPPOO6sjRiKqhRReVd/IaWm+vddDRETkaD6et3dOs/R9qj6W9j3rhlwNr8HJWja3hKlTpyIlJQVHjx6Ft7c3Nm3ahL///htJSUl48MEHqyNGIqqFlHJ3xKsVZufFqxVQyt0lXQ8REZGjqbzdkRBm/pyWEHb9nFbVfJU3z3mOYqnuWDfkangNTtayOSn1xx9/YMSIEQAADw8PFBcXw8/PDzNnzsS8efPsHiAR1U4BCk+MahdqcjLTv7HD2oER7bUeIiIiR/OVe2B0e7VJciMhTIEx7a8PGlzVfA6k7TiW6o51Q66G1+BkLZuPfr6+voZxpMLCwnDmzBnEx8cDAC5fvmzf6IioVgtReuGxjmoUaCugKdVB4eUGpdzd5pOYvdZDRETkaEG+npjQOQx5JRUoLtPBx9MNKm93Q1LD0nxyHNYN1Ta8Bidr2HwEbNeuHX788Uc0adIEffv2xXPPPYejR49i8+bNaNeund0DjIqKwrlz50ymT5gwAcuXLzeZnpaWhlGjRhlNk8vlKCkpsXtsRFT9AhSedjlx2Ws9REREjuYr96gykWFpPjkO64ZqG16DkyU2HxEXLVqEwsJCAMCMGTNQWFiIDRs24I477qiWN+/98ssvqKj473WRx44dQ8+ePascv8rf3x8nT540fJbJZHaPi4iIiIiIiIiIbp3NSamYmBjD/319fZGammrXgG4WHBxs9PmNN95AbGwskpKSKv2OTCaDWq2u1riIiIiIiIiIiOjW1aj3MJaWluLDDz/E6NGjq7z7qbCwEJGRkYiIiMCAAQNw/PjxKter1WqRn59v9ENERERERERERNXHqqRUYGCgYRDzOnXqIDAwsNKf6rRlyxbk5uYiOTm50mUaNWqE1atX44svvsCHH34InU6HDh064J9//qn0O3PnzoVKpTL8REREVEP0RERERERERESkJxNCCEsLrV27FsOGDYNcLsfatWurXHbkyJF2C+5mvXr1gpeXF/73v/9Z/Z2ysjI0adIEw4cPx6xZs8wuo9VqodVqDZ/z8/MRERGBvLw8+Pv733bcRERERERERETOID8/HyqVyilyHlaNKXVjoqk6k05VOXfuHHbs2IHNmzfb9D1PT0+0bNkSp0+frnQZuVwOuVx+uyESEREREREREZGVrEpK2TLGUnVl2dasWYOQkBD069fPpu9VVFTg6NGj6Nu3b7XERUREREREREREtrMqKRUQEFDlwOI3qqiouK2AzNHpdFizZg1GjhwJDw/jkEeMGIF69eph7ty5AICZM2eiXbt2aNiwIXJzc7FgwQKcO3cOY8eOtXtcRERERERERER0a6xKSu3atcvw/8zMTEyZMgXJyclo3749ACA9PR1r1641JIbsbceOHcjKysLo0aNN5mVlZcHN7b/x2q9du4Zx48YhJycHderUQatWrbBv3z40bdq0WmIjouqVqylDgbYCmlIdFF5uUMrdEaDwtHk9Rdpy5JVUoLhMB4WnG/y93eErt+oQSEROztX6t6uVh+zPldqIK5WFbg3bAFHtZtVA5zfq3r07xo4di+HDhxtN/+ijj/Duu+9i9+7d9ozPIZxp0C+i2uxiQSnW7L+A4zkaw7R4tQKj2oUiROll9XquFJVhVXoOjmX/t56EMAVGt1cjyNf2BBcROQ9X69+uVh6yP1dqI65UFro1bANEjuFMOQ83y4sYS09PR+vWrU2mt27dGj///LNdgiIiytWUmSSkAOB4jgZr9l9ArqbMqvUUactNLnYA4Gi2BqvTc1CkLbdbzEQkLVfr365WHrI/V2ojrlQWujVsA0QE3EJSKiIiAu+9957J9Pfffx8RERF2CYqIqEBbYZKQ0jueo0GB1rrx6/JKKkwudvSOZmuQV2L/cfCISBqu1r9drTxkf67URlypLHRr2AaICLByTKkbLV68GA888AC+/vprtG3bFgDw888/49SpU9i0aZPdAySi2klTqrut+XrFZVUvZ2k+ETkvV+vfrlYesj9XaiOuVBa6NWwDRATcwp1Sffv2xZ9//on+/fvj6tWruHr1Kvr3748///wTffv2rY4YiagWUnhVfXiyNF/Px7Pq5SzNJyLn5Wr929XKQ/bnSm3ElcpCt4ZtgIiAW7hTCrj+CN+cOXPsHQsRkYFS7o54tcLsI3zxagWUcner1qPydkdCmAJHzdwenhCmgMrbuvUQkfNxtf7tauUh+3OlNuJKZaFbwzZARMAt3CkFAD/88AMeeeQRdOjQAefPnwcArFu3Dj/++KNdgyOi2itA4YlR7UIRr1YYTde/fS9AYd0bWXzlHhjdXo2EMOP1JIQpMKa9mq8cJqrBXK1/u1p5yP5cqY24Ulno1rANEBEAyIQQwpYvbNq0CY8++igefvhhrFu3Dr///jtiYmKwbNkyfPXVV/jqq6+qK1bJONPrEYlqu1xNGQq0FdCU6qDwcoNS7m51QupGRdpy5JVUoLhMBx9PN6i83XmxQ+QiXK1/u1p5yP5cqY24Ulno1rANEEnPmXIeNielWrZsiWeffRYjRoyAUqnEkSNHEBMTg8OHD6NPnz7Iycmprlgl40wVRERERERERERkL86U87D58b2TJ0+iS5cuJtNVKhVyc3PtERMREREREREREbk4m5NSarUap0+fNpn+448/IiYmxi5BERERERERERGRa7M5KTVu3Dg888wzOHDgAGQyGf7991+sX78eKSkpeOKJJ6ojRiIiIiIiIiIicjE2jyA3ZcoU6HQ6dO/eHRqNBl26dIFcLkdKSgqeeuqp6oiRiIiIiIiIiIhcjM0DneuVlpbi9OnTKCwsRNOmTeHn52fv2BzGmQb9IiIiIiIiIiKyF2fKedzyuza9vLzQtGlTe8ZCRERERERERES1hNVJqdGjR1u13OrVq285GCIiIiIiIiIiqh2sTkqlpaUhMjISLVu2xC0+8UdERERERERERATAhqTUE088gY8//hhnz57FqFGj8MgjjyAwMLA6YyOySpG2HHklFSgu00Hh6QZ/b3f4ym17MtUe6yDnJkUdsx2RK2F7Jqq5XK3/ulp5yDFcqR25UlmIbBroXKvVYvPmzVi9ejX27duHfv36YcyYMbjnnnsgk8mqM05JOdOgX1S1K0VlWJWeg2PZGsO0hDAFRrdXI8jXU7J1kHOToo7ZjsiVsD0T1Vyu1n9drTzkGK7UjlypLOQ4zpTzcLNlYblcjuHDh+O7777D77//jvj4eEyYMAFRUVEoLCysrhiJzCrSlpsckAHgaLYGq9NzUKQtl2Qd5NykqGO2I3IlbM9ENZer9V9XKw85hiu1I1cqC5GeTUkpoy+6uUEmk0EIgYqKCnvGRGSVvJIKkwOy3tFsDfJKLLdLe6yDnJsUdcx2RK6E7Zmo5nK1/utq5SHHcKV25EplIdKzKSml1Wrx8ccfo2fPnoiLi8PRo0exbNkyZGVlwc/Pr7piJDKruEx3W/PttQ5yblLUMdsRuRK2Z6Kay9X6r6uVhxzDldqRK5WFSM/q0dAmTJiATz75BBERERg9ejQ+/vhj1K1btzpjI6qSj2fVOVVL8+21DnJuUtQx2xG5ErZnoprL1fqvq5WHHMOV2pErlYVIz+qkVGpqKho0aICYmBjs2bMHe/bsMbvc5s2b7RYcUVVU3u5ICFPgqJlbWBPCFFB5u0uyDnJuUtQx2xG5ErZnoprL1fqvq5WHHMOV2pErlYVIz+pU6ogRI9CtWzcEBARApVJV+kMkFV+5B0a3VyMhTGE0PSFMgTHt1Va9FtUe6yDnJkUdsx2RK2F7Jqq5XK3/ulp5yDFcqR25UlmI9GRCCOHoIJyNM70ekSwr0pYjr6QCxWU6+Hi6QeXtbvMB2R7rIOcmRR2zHZErYXsmqrlcrf+6WnnIMVypHblSWcgxnCnnwaSUGc5UQURERERERERE9uJMOQ+OhEZERERERERERJJjUoqIiIiIiIiIiCTHpBQREREREREREUmOSSkiIiIiIiIiIpIck1JERERERERERCQ5JqWIiIiIiIiIiEhyTEoREREREREREZHknDopNX36dMhkMqOfxo0bV/mdjRs3onHjxvD29kZCQgK++uoriaIlIiIiIiIiIiJreTg6AEvi4+OxY8cOw2cPj8pD3rdvH4YPH465c+fi3nvvxUcffYSBAwfi119/RbNmzaQIl5xUkbYceSUVKC7TQeHpBn9vd/jKnb751wquVDeuVBYie2CfICJybbmaMhRoK6Ap1UHh5Qal3B0BCk9Hh0VWsMc5WqrzPK8nXJvT16SHhwfUarVVy7711lvo3bs3nn/+eQDArFmz8N1332HZsmVITU2tzjDJiV0pKsOq9Bwcy9YYpiWEKTC6vRpBvjxpOpIr1Y0rlYXIHtgniIhc28WCUqzZfwHHc/47zserFRjVLhQhSi8HRkaW2OMcLdV5ntcTrs+pH98DgFOnTiE8PBwxMTF4+OGHkZWVVemy6enp6NGjh9G0Xr16IT09vbrDJCdVpC03OYgBwNFsDVan56BIW+6gyMiV6saVykJkD+wTRESuLVdTZpKQAoDjORqs2X8BuZoyB0VGltjjHC3VeZ7XE7WDUyel2rZti7S0NGzfvh0rV67E2bNn0blzZxQUFJhdPicnB6GhoUbTQkNDkZOTU+V2tFot8vPzjX7INeSVVJgcxPSOZmuQV1IhcUSk50p140plIbIH9gkiItdWoK0wSUjpHc/RoEDL47yzssc5WqrzPK8nagenfnyvT58+hv83b94cbdu2RWRkJD799FOMGTPGbtuZO3cuZsyYYbf1kfMoLtPd1nyqPq5UN65UFiJ7YJ8gInJtmtKqj+OW5pPj2OMcLdV5ntcTtYNT3yl1s4CAAMTFxeH06dNm56vValy4cMFo2oULFyyOSTV16lTk5eUZfv7++2+7xUyO5eNZdRO3NJ+qjyvVjSuVhcge2CeIiFybwqvq47il+eQ49jhHS3We5/VE7VCjarGwsBBnzpxBWFiY2fnt27fHzp07jaZ99913aN++fZXrlcvl8Pf3N/oh16DydkdCmMLsvIQwBVTe7hJHRHquVDeuVBYie2CfICJybUq5O+LV5o/z8WoFlHIe552VPc7RUp3neT1ROzh1UiolJQV79uxBZmYm9u3bh0GDBsHd3R3Dhw8HAIwYMQJTp041LP/MM89g+/btWLhwIU6cOIHp06fj4MGDmDhxoqOKQA7mK/fA6PZqk4NZQpgCY9qr+SpRB3KlunGlshDZA/sEEZFrC1B4YlS7UJPElP7tewEKvhXNWdnjHC3VeZ7XE7WDTAghHB1EZYYNG4a9e/fiypUrCA4ORqdOnTB79mzExsYCALp27YqoqCikpaUZvrNx40a88soryMzMxB133IH58+ejb9++Nm03Pz8fKpUKeXl5vGvKRRRpy5FXUoHiMh18PN2g8nbnQcxJuFLduFJZiOyBfYKIyLXlaspQoK2AplQHhZcblHJ3JqRqCHuco6U6z/N6wv6cKefh1EkpR3GmCiIiIiIiIiIishdnynk49eN7RERERERERETkmpiUIiIiIiIiIiIiyTEpRUREREREREREkmNSioiIiIiIiIiIJMekFBERERERERERSY5JKSIiIiIiIiIikhyTUkREREREREREJDkPRwdArq1IW468kgoUl+mg8HSDv7c7fOVsdvQfS20kV1OGAm0FNKU6KLzcoJS7I0Dh6cCIbw/7hGuTqn6vFJWiqFQHTakOvl5uUHi5IcjXy67bYFt1Xqyb6sH96rxcqW5qUllc7RpMCmWaIpQXFqC8pBge3gp4+PnBU+Fr9+3Yox1Zcy1hqQ1YE4eztHl7xOEsZXE13INUba4UlWFVeg6OZWsM0xLCFBjdXo0gX57QyHIbuVhQijX7L+B4zn/z49UKjGoXihClfX8BlwL7hGuTqn4vFJQizUy/SG4XilA79Qu2VefFuqke3K/Oy5XqpiaVxdWuwaSgzb2GM5+tR/6ffximqeKaIGbww5AH1LHbduzRjqy5lrDUBqyJw1navD3icJayuCI+vkfVokhbbtJpAeBotgar03NQpC13UGTkLCy1kStFpidCADieo8Ga/ReQqymTMtzbxj7h2qSq3ytFpheRwPV+kbb/Aq4Uld72NthWnRfrpnpwvzovV6qbmlSWXE2ZS12DSaFMU2SSkAKAvD//wF+frUeZpsgu27FHO7LmWsJSG7hSVGoxDmdp8/aIw1nK4qqYlKJqkVdSYdJp9Y5ma5BXUiFxRORsLLWRolKdyYlQ73iOBgXamtWG2Cdcm1T1a6lfFJXqbnsbbKvOi3VTPbhfnZcr1U1NKkuBtsKlrsGkUF5YYJKQ0sv78w+UFxbYZTv2aEfWXEtYagNFpTqLcThLm7dHHM5SFlfFx/eoWhSXVf2LkaX55PosthELv1xr7PDLt01/EkoAACHTSURBVJTYJ1ybVPVrqd1b6jfWYFt1Xqyb6sH96rxcqW5qUlksnWtq2jWYFMpLii3ML7HLduzRjuxxLWFpGWvikKrN22Of1aT+WxMxKUXVwsez6pvwLM0n12exjXhVPV9hYb6zYZ9wbVLVr6V2b6nfWINt1XmxbqoH96vzcqW6qUllsXSuqWnXYFLw8PaxMN/bLtuxRzuy5lpCZikOS+uwIg6p2rw99llN6r81EfceVQuVtzsSwhRm5yWEKaDydpc4InI2ltqIr5cb4tXm58erFVDKa1YbYp9wbVLVr6V+4WuHXxTYVp0X66Z6cL86L1eqm5pUFqXc3aWuwaTg4aeEKq6J2XmquCbw8FPaZTv2aEfWXEtYagO+Xm4W43CWNm+POJylLK6KSSmqFr5yD4xurzbpvAlhCoxpr+arM8liGwny9cKodqEmJ0T9Wz9q2iuJ2Sdcm1T1G+TrheRK+kVyu1CTVznfCrZV58W6qR7cr87LleqmJpUlQOHpUtdgUvBU+CJm8MMmiSn92/c8Fb522Y492pE11xKW2kCQr5fFOJylzdsjDmcpi6uSCSGEo4NwNvn5+VCpVMjLy4O/v7+jw6nRirTlyCupQHGZDj6eblB5u7PTkhFLbSRXU4YCbQU0pToo/v8vNzX5Yoh9wrVJVb9XikpRVKpDcakOPl5u8PVys0tC6kZsq86LdVM9uF+dlyvVTU0qi6tdg0mhTFOE8sIClJeUwMPbGx5+SrslpG5kj3ZkzbWEpTZgTRzO0ubtEYezlMUenCnnwaSUGc5UQURERERERERE9uJMOQ8+vkdERERERERERJJjUoqIiIiIiIiIiCTHpBQREREREREREUmOSSkiIiIiIiIiIpIck1JERERERERERCQ5JqWIiIiIiIiIiEhyTEoREREREREREZHkmJQiIiIiIiIiIiLJeTg6AKLbdaWoFEWlOmhKdfD1coPCyw1Bvl5GyxRpy5FXUoHiMh0Unm7w93aHr5zN3xlYU3/24CxtIFdThgJtBTSlOii83KCUuyNA4Sl5HNZwln1GjmGP+q9tbUiq/l3b9qtU5XWW/WqPOC4XlkJT9t+51cfTDXX97H9ulYqz1I091KSy1KRYyZSlc1JNOmexLbo21iTVaBcKSpG2/wKO52gM0+LVCiS3C0Wo8vrF15WiMqxKz8Gx7P+WSQhTYHR7NYJ8nTMZUFtYU3/24Cxt4GJBKdaYKe+odqEIsWN57cFZ9hk5hj3qv7a1Ian6d23br1KV11n2qz3ikOrcKhVnqRt7qEllqUmxkilL56SadM5iW3R9fHyPaqwrRaYXXQBwPEeDtP0Xrt+Boy03OYgBwNFsDVan56BIWy5lyHQDa+rPHpylDeRqykxO/sD18q7ZfwG5mjJJ4rCGs+wzcgx71H9ta0NS9e/atl+lKq+z7Fd7xHG5sOpz6+VC+5xbpeIsdWMPNaksNSlWMmXpnHS50DQhdeN8ZzpnsS3WDkxKUY1VVKozOZjqHc/RoKhUh7ySCpODmN7RbA3ySiqqM0SqgjX1Zw/O0gYKtBVVlrdA6zxt0Vn2GTmGPeq/trUhqfp3bduvUpXXWfarPeLQlFV9btWU2efcKhVnqRt7qEllqUmxkilL5yRLxwlnOmexLdYOfHyPaiyNhaRFcakOHu6yqpepYRdnrsSa+rMHS3UsVRuwVF5L86XkLPuMHMMe9V/b2pBU/bu27Vepyuss+9UecUh1bpWKs9SNPdSkstSkWMnU7Z6TnOmcxbZYOzApRTWWwqvqG/18vNzg4VZ1UsrHkzcLOoo19WcPlupYqjZgqbyW5kvJWfYZOYY96r+2tSGp+ndt269SlddZ9qs94pDq3CoVZ6kbe6hJZalJsZKp2z0nOdM5i22xdmAtUo3l6+WGeLXC7Lx4tQK+Xm5QebsjIcz8MglhCqi83aszRKqCNfVnD87SBpRy9yrLq5Q7T1t0ln1GjmGP+q9tbUiq/l3b9qtU5XWW/WqPOBSeVZ9bFTXsFzhnqRt7qEllqUmxkilL5yRLxwlnOmexLdYONevMRHSDIF8vJLcLNTmo6t8wE+TrBV+5B0a3V5sczBLCFBjTXs1XiTqQNfVnD87SBgIUnhhVSXlHtQutllfw3ipn2WfkGPao/9rWhqTq37Vtv0pVXmfZr/aIo65f1efWun416+17zlI39lCTylKTYiVTls5Jdf28asw5i22xdpAJIYSjg6jM3LlzsXnzZpw4cQI+Pj7o0KED5s2bh0aNGlX6nbS0NIwaNcpomlwuR0lJidXbzc/Ph0qlQl5eHvz9/W85fpLGlaJSFJXqUFyqg4+XG3y93EwSGkXacuSVVKC4TAcfz+t3UPEg5hysqT97cJY2kKspQ4G2AppSHRReblDK3Z0qIXUjZ9ln5Bj2qP/a1oak6t+1bb9KVV5n2a/2iONyYSk0Zf+dWxWebjUuIXUjZ6kbe6hJZalJsZIpS+ekmnTOYlu0P2fKeTh1Uqp3794YNmwY2rRpg/Lycrz00ks4duwYfv/9d/j6+pr9TlpaGp555hmcPHnSME0mkyE0NNTq7TpTBRERERERERER2Ysz5TycOr24fft2o89paWkICQnBoUOH0KVLl0q/J5PJoFarqzs8IiIiIiIiIiK6RTVqTKm8vDwAQGBgYJXLFRYWIjIyEhERERgwYACOHz8uRXhERERERERERGSlGpOU0ul0mDRpEjp27IhmzZpVulyjRo2wevVqfPHFF/jwww+h0+nQoUMH/PPPP5V+R6vVIj8/3+iHiIiIiIiIiIiqj1OPKXWjJ554Al9//TV+/PFH1K9f3+rvlZWVoUmTJhg+fDhmzZpldpnp06djxowZJtOd4flKIiIiIiIiIiJ7caYxpWrEnVITJ07E1q1bsWvXLpsSUgDg6emJli1b4vTp05UuM3XqVOTl5Rl+/v7779sNmYiIiIiIiIiIquDUA50LIfDUU0/h888/x+7duxEdHW3zOioqKnD06FH07du30mXkcjnkcvnthEpERERERERERDZw6qTUk08+iY8++ghffPEFlEolcnJyAAAqlQo+Pj4AgBEjRqBevXqYO3cuAGDmzJlo164dGjZsiNzcXCxYsADnzp3D2LFjHVYOIiIiIiIiIiIy5tRJqZUrVwIAunbtajR9zZo1SE5OBgBkZWXBze2/pxCvXbuGcePGIScnB3Xq1EGrVq2wb98+NG3aVKqwiYiIiIiIiIjIghoz0LmUnGnQL7KsSFuOvJIKFJfpoPB0g7+3O3zlHjYvQ45xpagURaU6aEp18PVyg8LLDUG+Xob59qo7e6zHWdZBZKkdOUs7y9WUoUBbAU2pDgovNyjl7ghQeEoeB9VsUrVnqdqrs/TPmoT7zDGk2u+sX8e4VFiK4rL/rsG9Pd0Q7OdltIwUx0VrtmGPNsJrEmPOlPNgb6ca7UpRGVal5+BYtsYwLSFMgdHt1Qjy9bR6GXKMCwWlSNt/Acdz/qubeLUCye1CEar0slvd2WM9zrIOIkvtyFna2cWCUqwx079HtQtFiNKrim8S/Ueq9ixVe3WW/lmTcJ85hlT7nfXrGJauwQFpjovWbMMebYTXJM6tRrx9j8icIm25yQEKAI5ma7A6PQdF2nKrliHHuFJkejIEgOM5GqTtv4DLhaV2qTt7tAFnWQeRpXaUqzG9cLtxvlTtLFdTZnLxB1zv32v2X0CupkySOKhmk+q4KVV75XnAdtxnjiHVfmf9OsalwqqvwS8VlkpyXLRmG/ZoI7wmcX5MSlGNlVdSYXKA0juarUFeSYVVy5BjFJXqTE4OesdzNNCU6exSd/ZoA86yDiJL7ahA6xztrEBbUWX/LtCyvZNlUh03pWqvPA/YjvvMMaTa76xfxyguq/oavLhMJ8lx0Zpt2KON8JrE+fHxPaqxist0tzXf2mWoemhKLdSfpflW1p0U7USqdRBZaicW+5VE7cxSHJbmEwHSHTelaq88D9iO+8wxpNrvrF/HuN1rcGvWYY84NKU6eLjLqlzGmjbCaxLnx6QU1Vg+nlXf6GdpvrXLUPVQeFmoP0vzraw7KdqJVOsgstROLPYridqZpTgszScCpDtuStVeeR6wHfeZY0i131m/jmHNNXjVqSD7HBetOfa6u1UdiTVthNckzo81QDWWytsdCWEKs/MSwhRQebtbtQw5hq+XG+LV5usmXq2AwtPNLnVnjzbgLOsgstSOlHLnaGdKuXuV/VspZ3sny6Q6bkrVXnkesB33mWNItd9Zv47h41n1NbiPp5skx0VrtmGPNsJrEufHpBTVWL5yD4xurzY5UCWEKTCmvRq+cg+rliHHCPL1QnK7UJOThP7NH3X9vOxSd/ZoA86yDiJL7ShA4ekU7SxA4YlRlfTvUe1Ca/UrmMl6Uh03pWqvPA/YjvvMMaTa76xfxwj2q/oaPNjPS5LjojXbsEcb4TWJ85MJIYSjg3A2+fn5UKlUyMvLg7+/v6PDIQuKtOXIK6lAcZkOPp5uUHm7mxygrFmGHONKUSmKSnUoLtXBx8sNvl5uCPL979Ws9qo7e6zHWdZBZKkdOUs7y9WUoUBbAU2pDgqv63955cUf2Uqq9ixVe3WW/lmTcJ85hlT7nfXrGJcKS1Fc9t81uI+nG4L9vIyWkeK4aM027NFGeE1izJlyHkxKmeFMFUREREREREREZC/OlPPg43tERERERERERCQ5JqWIiIiIiIiIiEhyTEoREREREREREZHkmJQiIiIiIiIiIiLJMSlFRERERERERESSY1KKiIiIiIiIiIgkx6QUERERERERERFJzsPRAZD9FWnLkVdSgeIyHRSebvD3doev3DFVLUUsuZoyFGgroCnVQeHlBqXcHQEKT7tug6qPPerPmnYmRVt0pr5Hrq2mHPdqSpyuiPvedjyGO6/LhaXQlOmgKdXB18sNPp5uqOvn5eiwXJ5UfcLSdtg3b4099qsr1U1NirW2YS24mCtFZViVnoNj2RrDtIQwBUa3VyPIV9qLUSliuVhQijX7L+B4zn/biFcrMKpdKEKUvFhxdvaoP2vamRRt0Zn6Hrm2mnLcqylxuiLue9vxGO68LhSUIs1Me05uF4pQtudqI1WfsLQd9s1bY4/96kp1U5NirY34+J4LKdKWm3Q2ADiarcHq9BwUactdKpZcTZnJRTcAHM/RYM3+C8jVlN32Nqj62KP+rGlnUrRFZ+p75NpqynGvpsTpirjvbcdjuPO6XGiakAKut+e0/RdwubDUQZG5Nqn6hKXt5GpMEwnVEYerscd+daW64THe+TEp5ULySipMOpve0WwN8koqXCqWAm2FyUWK3vEcDQq00pWXbGeP+rOmnUnRFp2p75FrqynHvZoSpyvivrcdj+HOS1Omq7I9a8p0EkdUO0jVJyxtp0DLvnkr7LFfXalueIx3fnx8z4UUWzgxW5pvT1LEoimteh2W5pNj2aP+7NHO7NEWnanvkWurKce9mhKnK+K+tx2P4c7LUnstZnuuFlL1CUvrsVj/7JtmSbFfa1Ld8Bjv/JiUciE+nlXf+GZpvj1JEYvCq+p1WJpPjmWP+rNHO7NHW3SmvkeuraYc92pKnK6I+952PIY7L0vt1YftuVpI1Scsrcdi/bNvmiXFfq1JdcNjvPNjDbgQlbc7EsIUZuclhCmg8nZ3qViUcnfEq81vI16tgFIuXXnJdvaoP2vamRRt0Zn6Hrm2mnLcqylxuiLue9vxGO68FJ5uVbZnBX+ZrBZS9QlL21HK2TdvhT32qyvVDY/xzo9HchfiK/fA6PZqk06XEKbAmPZqSV95KUUsAQpPjGoXanKxon/DEF997dzsUX/WtDMp2qIz9T1ybTXluFdT4nRF3Pe24zHcedX180JyJe05uV0o6vrx7XvVQao+YWk7AQpP9s1bYI/96kp1w2O885MJIYSjg3A2+fn5UKlUyMvLg7+/v6PDsVmRthx5JRUoLtPBx9MNKm93h3U2KWLJ1ZShQFsBTakOCi83KOXuvOiuQexRf9a0MynaojP1PXJtNeW4V1PidEXc97bjMdx5XS4shaZMh+JSHXy83KDwdGNCSgJS9QlL22HfvDX22K+uVDc1KVYpOFPOg0kpM5ypgoiIiIiIiIiI7MWZch58fI+IiIiIiIiIiCTHpBQREREREREREUmOSSkiIiIiIiIiIpIck1JERERERERERCQ5JqWIiIiIiIiIiEhyTEoREREREREREZHkmJQiIiIiIiIiIiLJMSlFRERERERERESSqxFJqeXLlyMqKgre3t5o27Ytfv755yqX37hxIxo3bgxvb28kJCTgq6++kihSIiIiIiIiIueWqynD39dKcPKCBn9fK0GupszRIVEt5fRJqQ0bNmDy5Ml47bXX8OuvvyIxMRG9evXCxYsXzS6/b98+DB8+HGPGjMHhw4cxcOBADBw4EMeOHZM4ciIiIiIiIiLncrGgFO/8lIOXt57D7G//xstbz+Gdn3JwsaDU0aFRLSQTQghHB1GVtm3bok2bNli2bBkAQKfTISIiAk899RSmTJlisvzQoUNRVFSErVu3Gqa1a9cOLVq0QGrq/7V3b7FR1H8fxz9b2i6UnlS020I5RYwCSmMR/q1i+cdGRIIFuSCgaSlGPJSgoDFCkEYuKB4hGhIfCIELxdZqQMRIJC0t5rGglG2IYJpCUEjoFiH2EAql6f6eC5+uXSntFurszu77lcwFs7+d+e58Qr/wZZj9JKBztra2KikpSS0tLUpMTBycDwIAAAAAQBA1t3fqf/7XoxOe9utem+SK0wsPu5QcFxOEymClUJp5hPSdUteuXVNtba1yc3N9+6KiopSbm6uamppe31NTU+O3XpJmzZp1w/WS1NHRodbWVr8NAAAAAIBw0tbR1etASpJOeNrV1tFlcUWIdCE9lLp48aK6urqUkpLitz8lJUUej6fX93g8ngGtl6SSkhIlJSX5tvT09FsvHgAAAACAENJ+zXtLrwODLaSHUlZZvXq1WlpafNu5c+eCXRIAAAAAAIMqLrbvEUB/rwODLTrYBfRlxIgRGjJkiJqamvz2NzU1yeVy9foel8s1oPWS5HQ65XQ6b71gAAAAAABCVIJziCa54m74TKkE55AgVIVIFtJj0NjYWGVmZqqiosK3z+v1qqKiQllZWb2+Jysry2+9JB04cOCG6wEAAAAAiATJcTEq/E+KJrni/PZPcsWp8D8pPOQclgvpO6UkadWqVSooKNDUqVM1bdo0bd68WZcvX1ZhYaEkKT8/XyNHjlRJSYkk6ZVXXlFOTo4++OADzZkzR6WlpTp69Ki2bt0azI8BAAAAAEDQ3ZUQqxcedqmto0vt17yKi41SgnMIAykERcgPpRYuXKg//vhD69atk8fjUUZGhvbv3+97mPnZs2cVFfX3DV/Z2dnatWuX1q5dqzVr1mjChAnas2ePJk+eHKyPAAAAAABAyEiOi2EIhZDgMMaYYBcRalpbW5WUlKSWlhYlJiYGuxwAAAAAAIBBEUozj5B+phQAAAAAAADCE0MpAAAAAAAAWI6hFAAAAAAAACzHUAoAAAAAAACWYygFAAAAAAAAyzGUAgAAAAAAgOUYSgEAAAAAAMBy0cEuIBQZYyRJra2tQa4EAAAAAABg8HTPOrpnH8HEUKoXbW1tkqT09PQgVwIAAAAAADD42tralJSUFNQaHCYURmMhxuv16vz580pISJDD4Qh2OSGptbVV6enpOnfunBITE4NdDgaI/OyN/OyL7OyPDO2PDO2J3OyL7OyPDO3vnxkaY9TW1qa0tDRFRQX3qU7cKdWLqKgojRo1Kthl2EJiYiI/mGyM/OyN/OyL7OyPDO2PDO2J3OyL7OyPDO2vZ4bBvkOqGw86BwAAAAAAgOUYSgEAAAAAAMByDKVwU5xOp4qLi+V0OoNdCm4C+dkb+dkX2dkfGdofGdoTudkX2dkfGdpfKGfIg84BAAAAAABgOe6UAgAAAAAAgOUYSgEAAAAAAMByDKUAAAAAAABgOYZSAAAAAAAAsBxDqTBSUlKihx56SAkJCbrrrrs0b9481dfX+625evWqioqKdMcddyg+Pl4LFixQU1OT35oVK1YoMzNTTqdTGRkZ153n6tWrWrJkie6//35FR0dr3rx5AdV34sQJLViwQGPHjpXD4dDmzZt7XbdlyxaNHTtWQ4cO1fTp0/XTTz8FdHy7syq/qqoq5eXlKTU1VcOHD1dGRoY+++yzgGrsL5utW7dq5syZSkxMlMPhUHNz84CugZ2FQ37djDGaPXu2HA6H9uzZE9Cx7czu2f32229yOBy9buXl5QO/IDYU6hkeOnRIc+fOVVpa2g1/XxljtG7dOqWmpmrYsGHKzc1VQ0PDgK6DnVmVYX19vf773/8qJSVFQ4cO1fjx47V27Vp1dnb2WyM98HrhkFu3SOt9kv3zi/T+F+r50fv6Z1WGPZ06dUoJCQlKTk4OqEYreh9DqTBSXV2toqIiHT58WAcOHFBnZ6cef/xxXb582bdm5cqV+uabb1ReXq7q6mqdP39eTz/99HXHWrp0qRYuXNjrebq6ujRs2DCtWLFCubm5AdfX3t6u8ePHa+PGjXK5XL2uKSsr06pVq1RcXKxjx45pypQpmjVrli5cuBDweezKqvx+/PFHPfDAA/rqq690/PhxFRYWKj8/X/v27euzvkCyaW9v1xNPPKE1a9bc5FWwr3DIr9vmzZvlcDgGeAXsy+7Zpaenq7Gx0W97++23FR8fr9mzZ9/ClbGPUM/w8uXLmjJlirZs2XLDNe+++64++ugjffLJJzpy5IiGDx+uWbNm6erVqwFeBXuzKsOYmBjl5+fr+++/V319vTZv3qxt27apuLi4z/rogb0Lh9y6RVrvk+yfX6T3v1DPj97XP6sy7NbZ2alFixZpxowZAdVnWe8zCFsXLlwwkkx1dbUxxpjm5mYTExNjysvLfWt+/fVXI8nU1NRc9/7i4mIzZcqUPs9RUFBg8vLyBlzbmDFjzKZNm67bP23aNFNUVOT7dVdXl0lLSzMlJSUDPofdWZFftyeffNIUFhb2uWYg2Rw8eNBIMn/++WdA5w9Hds3P7XabkSNHmsbGRiPJ7N69O6Aawolds+spIyPDLF26NKAawlGoZdhTb7+vvF6vcblc5r333vPta25uNk6n03z++ecBHzucWJnhypUrzSOPPNLnGnpgYOyaG73vL3bNr6dI7n+hll9P9L7A/NsZvvHGG+bZZ581O3bsMElJSf3WY1Xv406pMNbS0iJJuv322yVJtbW16uzs9Lu76d5779Xo0aNVU1MTlBp7unbtmmpra/3qi4qKUm5ubkjUZzUr82tpafGdpzdkM3B2zK+9vV2LFy/Wli1bbng3YySwY3Y91dbWqq6uTs8999wt1WZnoZRhIM6cOSOPx+NXX1JSkqZPnx6xP2OtyvDUqVPav3+/cnJybriGHhg4O+ZG7/ubHfPrKdL7XyjlFwh63/X+zQwrKytVXl7e551rPVnZ+xhKhSmv16tXX31VDz/8sCZPnixJ8ng8io2Nve7/j6akpMjj8QShSn8XL15UV1eXUlJS/PaHSn1WsjK/L774Qj///LMKCwtvuIZsBsau+a1cuVLZ2dnKy8u76Xrszq7Z9bR9+3bdd999ys7Ovuna7CzUMgxEdw38jP2LFRlmZ2dr6NChmjBhgmbMmKH169ffcC09MDB2zY3e9xe75tdTJPe/UMsvEPQ+f/9mhpcuXdKSJUu0c+dOJSYmBvQeK3sfQ6kwVVRUpF9++UWlpaWWn/vs2bOKj4/3bRs2bLC8BruzKr+DBw+qsLBQ27Zt06RJkyRJP/zwg19+gT6IGX+zY3579+5VZWXlDb+AIFLYMbuerly5ol27dkXsvxJL9s8Q1mRYVlamY8eOadeuXfr222/1/vvvSyLDW2HH3Oh9f7Njfj1Fev+ze374dzN8/vnntXjxYj366KO9vh7sDKMtPRsssXz5cu3bt0+HDh3SqFGjfPtdLpeuXbum5uZmv2lrU1PToN6unJaWprq6Ot+vA/1vDSNGjNCQIUOu+zaBwa4v1FmVX3V1tebOnatNmzYpPz/ft3/q1Kl++aWkpMjpdJJNgOyaX2VlpU6fPn3dv8QsWLBAM2bMUFVV1YBrtBu7ZtfTl19+qfb2dr/jRpJQzDAQ3TU0NTUpNTXVr77+vkUn3FiVYXp6uiRp4sSJ6urq0rJly/Taa6/RA2+SXXOj9/3Frvn1FMn9LxTzCwS972//doaVlZXau3evb5BojJHX61V0dLS2bt2qRYsWBbX3cadUGDHGaPny5dq9e7cqKys1btw4v9czMzMVExOjiooK3776+nqdPXtWWVlZg1ZHdHS07r77bt8W6FAqNjZWmZmZfvV5vV5VVFQMan2hysr8qqqqNGfOHL3zzjtatmyZ32vDhg3zyy8hISHiswmE3fN78803dfz4cdXV1fk2Sdq0aZN27NgxoPrsxu7Z9bR9+3Y99dRTuvPOOwdUl92FcoaBGDdunFwul199ra2tOnLkSMT8jA3mn2G8Xq86Ozvl9XrpgQNk99wiufdJ9s+vp0jsf6GcXyDofdZlWFNT4/dzbv369UpISFBdXZ3mz58f/N434EejI2S99NJLJikpyVRVVZnGxkbf1t7e7lvz4osvmtGjR5vKykpz9OhRk5WVZbKysvyO09DQYNxut3nhhRfMPffcY9xut3G73aajo8O35sSJE8btdpu5c+eamTNn+tb0paOjw7cuNTXVvP7668btdpuGhgbfmtLSUuN0Os3OnTvNyZMnzbJly0xycrLxeDyDc5FCmFX5VVZWmri4OLN69Wq/81y6dKnP+gLJprGx0bjdbrNt2zYjyRw6dMi43e5+jx0OwiG/f1KEfANRuGTX0NBgHA6H+e677wbpythHqGfY1tbmO5Yk8+GHHxq3221+//1335qNGzea5ORk8/XXX5vjx4+bvLw8M27cOHPlypVBvFKhy6oMP/30U1NWVmZOnjxpTp8+bcrKykxaWpp55pln+qyPHti7cMjtnyKl9xkTPvlFav8L9fzoff2z8u/vPQX67XtW9T6GUmFEUq/bjh07fGuuXLliXn75ZXPbbbeZuLg4M3/+fNPY2Oh3nJycnF6Pc+bMGd+aMWPG9LqmL2fOnOn1PTk5OX7rPv74YzN69GgTGxtrpk2bZg4fPnyrl8YWrMqvoKAgoBx60182xcXF/X6GcBUO+fX2mSLhD+bhkt3q1atNenq66erqupXLYUuhnmH31yT/cysoKPCt8Xq95q233jIpKSnG6XSaxx57zNTX1w/SFQp9VmVYWlpqHnzwQRMfH2+GDx9uJk6caDZs2BDQX4DogdcLh9x6+0yR0PuMCZ/8IrX/hXp+9L7+WZXhPwU6lDLGmt7n+P+LAQAAAAAAAFiGZ0oBAAAAAADAcgylAAAAAAAAYDmGUgAAAAAAALAcQykAAAAAAABYjqEUAAAAAAAALMdQCgAAAAAAAJZjKAUAAAAAAADLMZQCAAAAAACA5RhKAQAAAAAAwHIMpQAAAAAAAGA5hlIAAAAAAACwHEMpAAAAAAAAWO7/AP1sAf5Kcp+hAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pattern = r'\\b(ve|VE|visualeditor|VisualEditor)\\b'\n",
    "#pattern = r'\\b(contributor|community|volunteer)\\b'\n",
    "#pattern = r'\\b(WMF|Foundation|Wikimedia)\\b'\n",
    "pattern = r'\\b(bots|scripts|gadgets)\\b'\n",
    "#pattern = r'\\b(http|https)\\b'\n",
    "#pattern = r'\\b(auth)\\b'\n",
    "\n",
    "filtered_dependencies = dependency_relations_df[dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
    "resolved_filtered_dependencies = resolved_dependency_relations_df[resolved_dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "gs = GridSpec(2, 1, height_ratios=[6, 6])\n",
    "\n",
    "# Main plot: Token depth by timestamp\n",
    "'''\n",
    "ax0 = plt.subplot(gs[0])\n",
    "sns.scatterplot(data=filtered_dependencies, x='timestamp', y='dependency', hue='wmfAffil', style='dependency', markers=True, s=100, ax=ax0)\n",
    "ax0.set_title('VE Depth by Timestamp w/o URLS')\n",
    "ax0.set_xlabel('')\n",
    "ax0.set_ylabel('Dependency Type')\n",
    "ax0.legend().set_visible(False)\n",
    "'''\n",
    "# Calculate the median depth over time\n",
    "filtered_dependencies['week'] = filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
    "median_depth = filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "wmf_filtered_dependencies = filtered_dependencies[filtered_dependencies['wmfAffil'] == True]\n",
    "#wmf_median_depth = wmf_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "other_filtered_dependencies = filtered_dependencies[filtered_dependencies['wmfAffil'] != True]\n",
    "#other_median_depth = other_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "# Plot the median depth over time\n",
    "ax0 = plt.subplot(gs[0])\n",
    "#sns.lineplot(data=median_depth, x='week', y='depth', ax=ax0, color='black', label='Median Depth', marker='o')\n",
    "sns.scatterplot(data=wmf_filtered_dependencies, x='week', y='depth', ax=ax0, color='#c7756a', label='WMF-affiliated authors', marker='o')\n",
    "#sns.lineplot(data=wmf_median_depth, x='week', y='depth', ax=ax0, color='#c7756a', label='WMF-affiliated authors', marker='x')\n",
    "sns.scatterplot(data=other_filtered_dependencies, x='week', y='depth', ax=ax0, color='#5da2d8', label='Nonaffiliated authors', marker='o')\n",
    "#sns.lineplot(data=other_median_depth, x='week', y='depth', ax=ax0, color='#5da2d8', label='Nonaffiliated authors', marker='x')\n",
    "ax0.set_title(f'Depth of {pattern} in Phabricator Sentence Dependency Trees')\n",
    "ax0.set_ylabel('Median Depth')\n",
    "ax0.set_xlabel('')\n",
    "\n",
    "# Calculate the median depth over time\n",
    "resolved_filtered_dependencies['timestamp'] = pd.to_datetime(resolved_filtered_dependencies['timestamp'], utc=True)\n",
    "resolved_filtered_dependencies['week'] = resolved_filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
    "resolved_median_depth = resolved_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "resolved_wmf_filtered_dependencies = resolved_filtered_dependencies[resolved_filtered_dependencies['wmfAffil'] == True]\n",
    "#resolved_wmf_median_depth = resolved_wmf_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "resolved_other_filtered_dependencies = resolved_filtered_dependencies[resolved_filtered_dependencies['wmfAffil'] != True]\n",
    "#resolved_other_median_depth = resolved_other_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "# Plot the median depth over time\n",
    "ax1 = plt.subplot(gs[1])\n",
    "#sns.lineplot(data=resolved_median_depth, x='week', y='depth', ax=ax1, color='black', label='Median Depth', marker='o')\n",
    "sns.scatterplot(data=resolved_wmf_filtered_dependencies, x='week', y='depth', ax=ax1, color='#c7756a', label='WMF-affiliated authors', marker='o')\n",
    "#sns.lineplot(data=resolved_wmf_median_depth, x='week', y='depth', ax=ax1, color='#c7756a', label='WMF-affiliated authors', marker='x')\n",
    "sns.scatterplot(data=resolved_other_filtered_dependencies, x='week', y='depth', ax=ax1, color='#5da2d8', label='Nonaffiliated authors', marker='o')\n",
    "#sns.lineplot(data=resolved_other_median_depth, x='week', y='depth', ax=ax1, color='#5da2d8', label='Nonaffiliated authors', marker='x')\n",
    "ax1.set_title(f'Depth of {pattern} in Coreference-resolved Phabricator Sentence Dependency Trees')\n",
    "ax1.set_ylabel('Median Depth')\n",
    "ax1.set_xlabel('')\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.show()\n",
    "\n",
    "#plt.savefig('031625_VE_depth_fig.png')"
   ]
  }
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