{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate figures for ICLP-26"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load blocksworld data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_spent_in_target_episode</th>\n",
       "      <th>arg_blocks_world_reversed_stack_order</th>\n",
       "      <th>behavior_policy_return_cumulative</th>\n",
       "      <th>arg_plan_for_new_states</th>\n",
       "      <th>arg_control_algorithm</th>\n",
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       "      <th>episode_id</th>\n",
       "      <th>arg_blocks_world_size</th>\n",
       "      <th>arg_db_file</th>\n",
       "      <th>arg_episodes</th>\n",
       "      <th>...</th>\n",
       "      <th>behavior_policy_return</th>\n",
       "      <th>target_policy_return</th>\n",
       "      <th>target_policy_return_cumulative</th>\n",
       "      <th>arg_carcass</th>\n",
       "      <th>time_spent_in_behavior_episode</th>\n",
       "      <th>arg_test_target_policy</th>\n",
       "      <th>naive_return</th>\n",
       "      <th>behavior_policy_is_positive</th>\n",
       "      <th>behavior_policy_is_successful</th>\n",
       "      <th>env</th>\n",
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       "  </thead>\n",
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       "      <td>20</td>\n",
       "      <td>fix-rng-15.csv</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</td>\n",
       "      <td>-61.0</td>\n",
       "      <td>-61.0</td>\n",
       "      <td>-183.0</td>\n",
       "      <td>blocksworld_stackordered.lp</td>\n",
       "      <td>0.279837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62</td>\n",
       "      <td>False</td>\n",
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       "      <td>20-blocks-world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.244308</td>\n",
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       "      <td>3000</td>\n",
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       "      <td>-61.0</td>\n",
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       "      <td>62</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>20</td>\n",
       "      <td>fix-rng-15.csv</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</td>\n",
       "      <td>-61.0</td>\n",
       "      <td>-61.0</td>\n",
       "      <td>-305.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>62</td>\n",
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       "      <td>20-blocks-world</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   time_spent_in_target_episode  arg_blocks_world_reversed_stack_order  \\\n",
       "0                      0.262752                                  False   \n",
       "1                      0.234189                                  False   \n",
       "2                      0.267949                                  False   \n",
       "3                      0.244308                                  False   \n",
       "4                      0.291141                                  False   \n",
       "\n",
       "   behavior_policy_return_cumulative  arg_plan_for_new_states  \\\n",
       "0                              -61.0                    False   \n",
       "1                             -122.0                    False   \n",
       "2                             -183.0                    False   \n",
       "3                             -244.0                    False   \n",
       "4                             -305.0                    False   \n",
       "\n",
       "  arg_control_algorithm  arg_planning_horizon  episode_id  \\\n",
       "0            q_learning                     4           1   \n",
       "1            q_learning                     4           2   \n",
       "2            q_learning                     4           3   \n",
       "3            q_learning                     4           4   \n",
       "4            q_learning                     4           5   \n",
       "\n",
       "   arg_blocks_world_size     arg_db_file  arg_episodes  ...  \\\n",
       "0                     20  fix-rng-15.csv          3000  ...   \n",
       "1                     20  fix-rng-15.csv          3000  ...   \n",
       "2                     20  fix-rng-15.csv          3000  ...   \n",
       "3                     20  fix-rng-15.csv          3000  ...   \n",
       "4                     20  fix-rng-15.csv          3000  ...   \n",
       "\n",
       "  behavior_policy_return  target_policy_return  \\\n",
       "0                  -61.0                 -61.0   \n",
       "1                  -61.0                 -61.0   \n",
       "2                  -61.0                 -61.0   \n",
       "3                  -61.0                 -61.0   \n",
       "4                  -61.0                 -61.0   \n",
       "\n",
       "   target_policy_return_cumulative                  arg_carcass  \\\n",
       "0                            -61.0  blocksworld_stackordered.lp   \n",
       "1                           -122.0  blocksworld_stackordered.lp   \n",
       "2                           -183.0  blocksworld_stackordered.lp   \n",
       "3                           -244.0  blocksworld_stackordered.lp   \n",
       "4                           -305.0  blocksworld_stackordered.lp   \n",
       "\n",
       "   time_spent_in_behavior_episode  arg_test_target_policy  naive_return  \\\n",
       "0                        0.256439                     NaN            62   \n",
       "1                        0.255649                     NaN            62   \n",
       "2                        0.279837                     NaN            62   \n",
       "3                        0.263622                     NaN            62   \n",
       "4                        0.283632                     NaN            62   \n",
       "\n",
       "   behavior_policy_is_positive  behavior_policy_is_successful              env  \n",
       "0                        False                          False  20-blocks-world  \n",
       "1                        False                          False  20-blocks-world  \n",
       "2                        False                          False  20-blocks-world  \n",
       "3                        False                          False  20-blocks-world  \n",
       "4                        False                          False  20-blocks-world  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bw = pd.concat([\n",
    "    pd.read_csv('data/exp11.csv', index_col=0),\n",
    "    pd.read_csv('data/bw-20-concrete.csv', index_col=0),\n",
    "    pd.read_csv('data/bw-20-prolog.csv', index_col=0),\n",
    "])\n",
    "\n",
    "def naive_return(blocks_world_size):\n",
    "    return 100 - (2*blocks_world_size - 2)\n",
    "\n",
    "df_bw['naive_return'] = df_bw['arg_blocks_world_size'].map(naive_return)\n",
    "df_bw['behavior_policy_is_positive'] = df_bw['behavior_policy_return'] > 0\n",
    "df_bw['behavior_policy_is_successful'] = df_bw['behavior_policy_return'] >= df_bw['naive_return']\n",
    "\n",
    "df_bw['env'] = df_bw['arg_blocks_world_size'].map(lambda n: f'{n}-blocks-world')\n",
    "\n",
    "df_bw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>time_spent_in_target_episode</th>\n",
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       "    <tr>\n",
       "      <th>bw-prolog-carcass-02.csv</th>\n",
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       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fix-rng-concrete-16.csv</th>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
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       "      <td>3000</td>\n",
       "      <td>...</td>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fix-rng-concrete-18.csv</th>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</td>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fix-rng-concrete-19.csv</th>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</td>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          time_spent_in_target_episode  \\\n",
       "arg_db_file                                              \n",
       "bw-prolog-carcass-00.csv                             0   \n",
       "bw-prolog-carcass-01.csv                             0   \n",
       "bw-prolog-carcass-02.csv                             0   \n",
       "bw-prolog-carcass-03.csv                             0   \n",
       "bw-prolog-carcass-04.csv                             0   \n",
       "...                                                ...   \n",
       "fix-rng-concrete-15.csv                              0   \n",
       "fix-rng-concrete-16.csv                              0   \n",
       "fix-rng-concrete-17.csv                              0   \n",
       "fix-rng-concrete-18.csv                              0   \n",
       "fix-rng-concrete-19.csv                              0   \n",
       "\n",
       "                          arg_blocks_world_reversed_stack_order  \\\n",
       "arg_db_file                                                       \n",
       "bw-prolog-carcass-00.csv                                   3000   \n",
       "bw-prolog-carcass-01.csv                                   3000   \n",
       "bw-prolog-carcass-02.csv                                   3000   \n",
       "bw-prolog-carcass-03.csv                                   3000   \n",
       "bw-prolog-carcass-04.csv                                   3000   \n",
       "...                                                         ...   \n",
       "fix-rng-concrete-15.csv                                    3000   \n",
       "fix-rng-concrete-16.csv                                    3000   \n",
       "fix-rng-concrete-17.csv                                    3000   \n",
       "fix-rng-concrete-18.csv                                    3000   \n",
       "fix-rng-concrete-19.csv                                    3000   \n",
       "\n",
       "                          behavior_policy_return_cumulative  \\\n",
       "arg_db_file                                                   \n",
       "bw-prolog-carcass-00.csv                               3000   \n",
       "bw-prolog-carcass-01.csv                               3000   \n",
       "bw-prolog-carcass-02.csv                               3000   \n",
       "bw-prolog-carcass-03.csv                               3000   \n",
       "bw-prolog-carcass-04.csv                               3000   \n",
       "...                                                     ...   \n",
       "fix-rng-concrete-15.csv                                3000   \n",
       "fix-rng-concrete-16.csv                                3000   \n",
       "fix-rng-concrete-17.csv                                3000   \n",
       "fix-rng-concrete-18.csv                                3000   \n",
       "fix-rng-concrete-19.csv                                3000   \n",
       "\n",
       "                          arg_plan_for_new_states  arg_control_algorithm  \\\n",
       "arg_db_file                                                                \n",
       "bw-prolog-carcass-00.csv                     3000                   3000   \n",
       "bw-prolog-carcass-01.csv                     3000                   3000   \n",
       "bw-prolog-carcass-02.csv                     3000                   3000   \n",
       "bw-prolog-carcass-03.csv                     3000                   3000   \n",
       "bw-prolog-carcass-04.csv                     3000                   3000   \n",
       "...                                           ...                    ...   \n",
       "fix-rng-concrete-15.csv                      3000                   3000   \n",
       "fix-rng-concrete-16.csv                      3000                   3000   \n",
       "fix-rng-concrete-17.csv                      3000                   3000   \n",
       "fix-rng-concrete-18.csv                      3000                   3000   \n",
       "fix-rng-concrete-19.csv                      3000                   3000   \n",
       "\n",
       "                          arg_planning_horizon  episode_id  \\\n",
       "arg_db_file                                                  \n",
       "bw-prolog-carcass-00.csv                  3000        3000   \n",
       "bw-prolog-carcass-01.csv                  3000        3000   \n",
       "bw-prolog-carcass-02.csv                  3000        3000   \n",
       "bw-prolog-carcass-03.csv                  3000        3000   \n",
       "bw-prolog-carcass-04.csv                  3000        3000   \n",
       "...                                        ...         ...   \n",
       "fix-rng-concrete-15.csv                   3000        3000   \n",
       "fix-rng-concrete-16.csv                   3000        3000   \n",
       "fix-rng-concrete-17.csv                   3000        3000   \n",
       "fix-rng-concrete-18.csv                   3000        3000   \n",
       "fix-rng-concrete-19.csv                   3000        3000   \n",
       "\n",
       "                          arg_blocks_world_size  arg_episodes  arg_mdp  ...  \\\n",
       "arg_db_file                                                             ...   \n",
       "bw-prolog-carcass-00.csv                   3000          3000     3000  ...   \n",
       "bw-prolog-carcass-01.csv                   3000          3000     3000  ...   \n",
       "bw-prolog-carcass-02.csv                   3000          3000     3000  ...   \n",
       "bw-prolog-carcass-03.csv                   3000          3000     3000  ...   \n",
       "bw-prolog-carcass-04.csv                   3000          3000     3000  ...   \n",
       "...                                         ...           ...      ...  ...   \n",
       "fix-rng-concrete-15.csv                    3000          3000     3000  ...   \n",
       "fix-rng-concrete-16.csv                    3000          3000     3000  ...   \n",
       "fix-rng-concrete-17.csv                    3000          3000     3000  ...   \n",
       "fix-rng-concrete-18.csv                    3000          3000     3000  ...   \n",
       "fix-rng-concrete-19.csv                    3000          3000     3000  ...   \n",
       "\n",
       "                          behavior_policy_return  target_policy_return  \\\n",
       "arg_db_file                                                              \n",
       "bw-prolog-carcass-00.csv                    3000                     0   \n",
       "bw-prolog-carcass-01.csv                    3000                     0   \n",
       "bw-prolog-carcass-02.csv                    3000                     0   \n",
       "bw-prolog-carcass-03.csv                    3000                     0   \n",
       "bw-prolog-carcass-04.csv                    3000                     0   \n",
       "...                                          ...                   ...   \n",
       "fix-rng-concrete-15.csv                     3000                     0   \n",
       "fix-rng-concrete-16.csv                     3000                     0   \n",
       "fix-rng-concrete-17.csv                     3000                     0   \n",
       "fix-rng-concrete-18.csv                     3000                     0   \n",
       "fix-rng-concrete-19.csv                     3000                     0   \n",
       "\n",
       "                          target_policy_return_cumulative  arg_carcass  \\\n",
       "arg_db_file                                                              \n",
       "bw-prolog-carcass-00.csv                                0         3000   \n",
       "bw-prolog-carcass-01.csv                                0         3000   \n",
       "bw-prolog-carcass-02.csv                                0         3000   \n",
       "bw-prolog-carcass-03.csv                                0         3000   \n",
       "bw-prolog-carcass-04.csv                                0         3000   \n",
       "...                                                   ...          ...   \n",
       "fix-rng-concrete-15.csv                                 0            0   \n",
       "fix-rng-concrete-16.csv                                 0            0   \n",
       "fix-rng-concrete-17.csv                                 0            0   \n",
       "fix-rng-concrete-18.csv                                 0            0   \n",
       "fix-rng-concrete-19.csv                                 0            0   \n",
       "\n",
       "                          time_spent_in_behavior_episode  \\\n",
       "arg_db_file                                                \n",
       "bw-prolog-carcass-00.csv                            3000   \n",
       "bw-prolog-carcass-01.csv                            3000   \n",
       "bw-prolog-carcass-02.csv                            3000   \n",
       "bw-prolog-carcass-03.csv                            3000   \n",
       "bw-prolog-carcass-04.csv                            3000   \n",
       "...                                                  ...   \n",
       "fix-rng-concrete-15.csv                             3000   \n",
       "fix-rng-concrete-16.csv                             3000   \n",
       "fix-rng-concrete-17.csv                             3000   \n",
       "fix-rng-concrete-18.csv                             3000   \n",
       "fix-rng-concrete-19.csv                             3000   \n",
       "\n",
       "                          arg_test_target_policy  naive_return  \\\n",
       "arg_db_file                                                      \n",
       "bw-prolog-carcass-00.csv                    3000          3000   \n",
       "bw-prolog-carcass-01.csv                    3000          3000   \n",
       "bw-prolog-carcass-02.csv                    3000          3000   \n",
       "bw-prolog-carcass-03.csv                    3000          3000   \n",
       "bw-prolog-carcass-04.csv                    3000          3000   \n",
       "...                                          ...           ...   \n",
       "fix-rng-concrete-15.csv                     3000          3000   \n",
       "fix-rng-concrete-16.csv                     3000          3000   \n",
       "fix-rng-concrete-17.csv                     3000          3000   \n",
       "fix-rng-concrete-18.csv                     3000          3000   \n",
       "fix-rng-concrete-19.csv                     3000          3000   \n",
       "\n",
       "                          behavior_policy_is_positive  \\\n",
       "arg_db_file                                             \n",
       "bw-prolog-carcass-00.csv                         3000   \n",
       "bw-prolog-carcass-01.csv                         3000   \n",
       "bw-prolog-carcass-02.csv                         3000   \n",
       "bw-prolog-carcass-03.csv                         3000   \n",
       "bw-prolog-carcass-04.csv                         3000   \n",
       "...                                               ...   \n",
       "fix-rng-concrete-15.csv                          3000   \n",
       "fix-rng-concrete-16.csv                          3000   \n",
       "fix-rng-concrete-17.csv                          3000   \n",
       "fix-rng-concrete-18.csv                          3000   \n",
       "fix-rng-concrete-19.csv                          3000   \n",
       "\n",
       "                          behavior_policy_is_successful   env  \n",
       "arg_db_file                                                    \n",
       "bw-prolog-carcass-00.csv                           3000  3000  \n",
       "bw-prolog-carcass-01.csv                           3000  3000  \n",
       "bw-prolog-carcass-02.csv                           3000  3000  \n",
       "bw-prolog-carcass-03.csv                           3000  3000  \n",
       "bw-prolog-carcass-04.csv                           3000  3000  \n",
       "...                                                 ...   ...  \n",
       "fix-rng-concrete-15.csv                            3000  3000  \n",
       "fix-rng-concrete-16.csv                            3000  3000  \n",
       "fix-rng-concrete-17.csv                            3000  3000  \n",
       "fix-rng-concrete-18.csv                            3000  3000  \n",
       "fix-rng-concrete-19.csv                            3000  3000  \n",
       "\n",
       "[120 rows x 28 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bw.groupby('arg_db_file').count()\n",
    "#df_bw['arg_db_file'].unique()\n",
    "\n",
    "#df_bw[df_bw['episode_id']==1].sort_values(by='arg_db_file').filter(['arg_db_file', 'arg_learning_rate', 'arg_epsilon', 'arg_carcass'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load minigrid data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_23475/1629816258.py:2: DtypeWarning: Columns (8) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  pd.read_csv('data/exp13.csv', index_col=0),\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>arg_learning_rate</th>\n",
       "      <th>arg_plan_for_new_states</th>\n",
       "      <th>arg_max_episode_length</th>\n",
       "      <th>episode_id</th>\n",
       "      <th>arg_test_target_policy</th>\n",
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       "      <th>arg_minigrid_use_alternative_reward_system</th>\n",
       "      <th>arg_qtable_input</th>\n",
       "      <th>...</th>\n",
       "      <th>arg_episodes</th>\n",
       "      <th>arg_behavior_policy</th>\n",
       "      <th>arg_qtable_output</th>\n",
       "      <th>arg_epsilon</th>\n",
       "      <th>arg_initial_q_estimate</th>\n",
       "      <th>arg_minigrid_level</th>\n",
       "      <th>arg_control_algorithm</th>\n",
       "      <th>behavior_policy_is_positive</th>\n",
       "      <th>behavior_policy_is_successful</th>\n",
       "      <th>env</th>\n",
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       "  </thead>\n",
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       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>minigrid</td>\n",
       "      <td>exp-mg/workspace3/returns.0028.csv</td>\n",
       "      <td>minigrid_restricted_actions_v2.lp</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>planning_epsilon_greedy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>MiniGrid-FourRooms-v0</td>\n",
       "      <td>q_learning</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>MiniGrid-FourRooms-v0</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.01</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>minigrid</td>\n",
       "      <td>exp-mg/workspace3/returns.0028.csv</td>\n",
       "      <td>minigrid_restricted_actions_v2.lp</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>planning_epsilon_greedy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>MiniGrid-FourRooms-v0</td>\n",
       "      <td>q_learning</td>\n",
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       "      <td>False</td>\n",
       "      <td>MiniGrid-FourRooms-v0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.01</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>minigrid</td>\n",
       "      <td>exp-mg/workspace3/returns.0028.csv</td>\n",
       "      <td>minigrid_restricted_actions_v2.lp</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>planning_epsilon_greedy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>MiniGrid-FourRooms-v0</td>\n",
       "      <td>q_learning</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>MiniGrid-FourRooms-v0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.01</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>minigrid</td>\n",
       "      <td>exp-mg/workspace3/returns.0028.csv</td>\n",
       "      <td>minigrid_restricted_actions_v2.lp</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>planning_epsilon_greedy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>MiniGrid-FourRooms-v0</td>\n",
       "      <td>q_learning</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>MiniGrid-FourRooms-v0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.01</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>minigrid</td>\n",
       "      <td>exp-mg/workspace3/returns.0028.csv</td>\n",
       "      <td>minigrid_restricted_actions_v2.lp</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>planning_epsilon_greedy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
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       "      <td>q_learning</td>\n",
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       "      <td>MiniGrid-FourRooms-v0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   arg_learning_rate  arg_plan_for_new_states  arg_max_episode_length  \\\n",
       "0               0.01                    False                     NaN   \n",
       "1               0.01                    False                     NaN   \n",
       "2               0.01                    False                     NaN   \n",
       "3               0.01                    False                     NaN   \n",
       "4               0.01                    False                     NaN   \n",
       "\n",
       "   episode_id  arg_test_target_policy   arg_mdp  \\\n",
       "0           1                   False  minigrid   \n",
       "1           2                   False  minigrid   \n",
       "2           3                   False  minigrid   \n",
       "3           4                   False  minigrid   \n",
       "4           5                   False  minigrid   \n",
       "\n",
       "                          arg_db_file                        arg_carcass  \\\n",
       "0  exp-mg/workspace3/returns.0028.csv  minigrid_restricted_actions_v2.lp   \n",
       "1  exp-mg/workspace3/returns.0028.csv  minigrid_restricted_actions_v2.lp   \n",
       "2  exp-mg/workspace3/returns.0028.csv  minigrid_restricted_actions_v2.lp   \n",
       "3  exp-mg/workspace3/returns.0028.csv  minigrid_restricted_actions_v2.lp   \n",
       "4  exp-mg/workspace3/returns.0028.csv  minigrid_restricted_actions_v2.lp   \n",
       "\n",
       "   arg_minigrid_use_alternative_reward_system  arg_qtable_input  ...  \\\n",
       "0                                       False               NaN  ...   \n",
       "1                                       False               NaN  ...   \n",
       "2                                       False               NaN  ...   \n",
       "3                                       False               NaN  ...   \n",
       "4                                       False               NaN  ...   \n",
       "\n",
       "   arg_episodes      arg_behavior_policy  arg_qtable_output  arg_epsilon  \\\n",
       "0         10000  planning_epsilon_greedy                NaN          0.2   \n",
       "1         10000  planning_epsilon_greedy                NaN          0.2   \n",
       "2         10000  planning_epsilon_greedy                NaN          0.2   \n",
       "3         10000  planning_epsilon_greedy                NaN          0.2   \n",
       "4         10000  planning_epsilon_greedy                NaN          0.2   \n",
       "\n",
       "   arg_initial_q_estimate     arg_minigrid_level  arg_control_algorithm  \\\n",
       "0                     0.3  MiniGrid-FourRooms-v0             q_learning   \n",
       "1                     0.3  MiniGrid-FourRooms-v0             q_learning   \n",
       "2                     0.3  MiniGrid-FourRooms-v0             q_learning   \n",
       "3                     0.3  MiniGrid-FourRooms-v0             q_learning   \n",
       "4                     0.3  MiniGrid-FourRooms-v0             q_learning   \n",
       "\n",
       "  behavior_policy_is_positive  behavior_policy_is_successful  \\\n",
       "0                       False                          False   \n",
       "1                       False                          False   \n",
       "2                       False                          False   \n",
       "3                       False                          False   \n",
       "4                       False                          False   \n",
       "\n",
       "                     env  \n",
       "0  MiniGrid-FourRooms-v0  \n",
       "1  MiniGrid-FourRooms-v0  \n",
       "2  MiniGrid-FourRooms-v0  \n",
       "3  MiniGrid-FourRooms-v0  \n",
       "4  MiniGrid-FourRooms-v0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mg = pd.concat([\n",
    "    pd.read_csv('data/exp13.csv', index_col=0),\n",
    "    pd.read_csv('data/mg-prolog.csv', index_col=0)\n",
    "])\n",
    "\n",
    "df_mg['behavior_policy_is_positive'] = df_mg['behavior_policy_return'] > 0\n",
    "df_mg['behavior_policy_is_successful'] = df_mg['behavior_policy_return'] > 0\n",
    "\n",
    "df_mg['env'] = df_mg['arg_minigrid_level']\n",
    "\n",
    "df_mg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>arg_learning_rate</th>\n",
       "      <th>arg_plan_for_new_states</th>\n",
       "      <th>arg_max_episode_length</th>\n",
       "      <th>episode_id</th>\n",
       "      <th>arg_test_target_policy</th>\n",
       "      <th>arg_mdp</th>\n",
       "      <th>arg_carcass</th>\n",
       "      <th>arg_minigrid_use_alternative_reward_system</th>\n",
       "      <th>arg_qtable_input</th>\n",
       "      <th>behavior_policy_return_cumulative</th>\n",
       "      <th>...</th>\n",
       "      <th>arg_episodes</th>\n",
       "      <th>arg_behavior_policy</th>\n",
       "      <th>arg_qtable_output</th>\n",
       "      <th>arg_epsilon</th>\n",
       "      <th>arg_initial_q_estimate</th>\n",
       "      <th>arg_minigrid_level</th>\n",
       "      <th>arg_control_algorithm</th>\n",
       "      <th>behavior_policy_is_positive</th>\n",
       "      <th>behavior_policy_is_successful</th>\n",
       "      <th>env</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arg_db_file</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>exp-mg/workspace/returns.0000.csv</th>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exp-mg/workspace/returns.0001.csv</th>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exp-mg/workspace/returns.0004.csv</th>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exp-mg/workspace/returns.0005.csv</th>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exp-mg/workspace/returns.0006.csv</th>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</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",
       "      <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>mg-prolog-carcass-16.csv</th>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mg-prolog-carcass-17.csv</th>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mg-prolog-carcass-18.csv</th>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mg-prolog-carcass-19.csv</th>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mg-prolog-carcass-20.csv</th>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>271 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   arg_learning_rate  arg_plan_for_new_states  \\\n",
       "arg_db_file                                                                     \n",
       "exp-mg/workspace/returns.0000.csv              10000                    10000   \n",
       "exp-mg/workspace/returns.0001.csv              10000                    10000   \n",
       "exp-mg/workspace/returns.0004.csv              10000                    10000   \n",
       "exp-mg/workspace/returns.0005.csv              10000                    10000   \n",
       "exp-mg/workspace/returns.0006.csv              10000                    10000   \n",
       "...                                              ...                      ...   \n",
       "mg-prolog-carcass-16.csv                       10000                    10000   \n",
       "mg-prolog-carcass-17.csv                       10000                    10000   \n",
       "mg-prolog-carcass-18.csv                       10000                    10000   \n",
       "mg-prolog-carcass-19.csv                       10000                    10000   \n",
       "mg-prolog-carcass-20.csv                       10000                    10000   \n",
       "\n",
       "                                   arg_max_episode_length  episode_id  \\\n",
       "arg_db_file                                                             \n",
       "exp-mg/workspace/returns.0000.csv                       0       10000   \n",
       "exp-mg/workspace/returns.0001.csv                       0       10000   \n",
       "exp-mg/workspace/returns.0004.csv                       0       10000   \n",
       "exp-mg/workspace/returns.0005.csv                       0       10000   \n",
       "exp-mg/workspace/returns.0006.csv                       0       10000   \n",
       "...                                                   ...         ...   \n",
       "mg-prolog-carcass-16.csv                                0       10000   \n",
       "mg-prolog-carcass-17.csv                                0       10000   \n",
       "mg-prolog-carcass-18.csv                                0       10000   \n",
       "mg-prolog-carcass-19.csv                                0       10000   \n",
       "mg-prolog-carcass-20.csv                                0       10000   \n",
       "\n",
       "                                   arg_test_target_policy  arg_mdp  \\\n",
       "arg_db_file                                                          \n",
       "exp-mg/workspace/returns.0000.csv                   10000    10000   \n",
       "exp-mg/workspace/returns.0001.csv                   10000    10000   \n",
       "exp-mg/workspace/returns.0004.csv                   10000    10000   \n",
       "exp-mg/workspace/returns.0005.csv                   10000    10000   \n",
       "exp-mg/workspace/returns.0006.csv                   10000    10000   \n",
       "...                                                   ...      ...   \n",
       "mg-prolog-carcass-16.csv                            10000    10000   \n",
       "mg-prolog-carcass-17.csv                            10000    10000   \n",
       "mg-prolog-carcass-18.csv                            10000    10000   \n",
       "mg-prolog-carcass-19.csv                            10000    10000   \n",
       "mg-prolog-carcass-20.csv                            10000    10000   \n",
       "\n",
       "                                   arg_carcass  \\\n",
       "arg_db_file                                      \n",
       "exp-mg/workspace/returns.0000.csv        10000   \n",
       "exp-mg/workspace/returns.0001.csv        10000   \n",
       "exp-mg/workspace/returns.0004.csv        10000   \n",
       "exp-mg/workspace/returns.0005.csv        10000   \n",
       "exp-mg/workspace/returns.0006.csv        10000   \n",
       "...                                        ...   \n",
       "mg-prolog-carcass-16.csv                 10000   \n",
       "mg-prolog-carcass-17.csv                 10000   \n",
       "mg-prolog-carcass-18.csv                 10000   \n",
       "mg-prolog-carcass-19.csv                 10000   \n",
       "mg-prolog-carcass-20.csv                 10000   \n",
       "\n",
       "                                   arg_minigrid_use_alternative_reward_system  \\\n",
       "arg_db_file                                                                     \n",
       "exp-mg/workspace/returns.0000.csv                                       10000   \n",
       "exp-mg/workspace/returns.0001.csv                                       10000   \n",
       "exp-mg/workspace/returns.0004.csv                                       10000   \n",
       "exp-mg/workspace/returns.0005.csv                                       10000   \n",
       "exp-mg/workspace/returns.0006.csv                                       10000   \n",
       "...                                                                       ...   \n",
       "mg-prolog-carcass-16.csv                                                10000   \n",
       "mg-prolog-carcass-17.csv                                                10000   \n",
       "mg-prolog-carcass-18.csv                                                10000   \n",
       "mg-prolog-carcass-19.csv                                                10000   \n",
       "mg-prolog-carcass-20.csv                                                10000   \n",
       "\n",
       "                                   arg_qtable_input  \\\n",
       "arg_db_file                                           \n",
       "exp-mg/workspace/returns.0000.csv                 0   \n",
       "exp-mg/workspace/returns.0001.csv                 0   \n",
       "exp-mg/workspace/returns.0004.csv                 0   \n",
       "exp-mg/workspace/returns.0005.csv                 0   \n",
       "exp-mg/workspace/returns.0006.csv                 0   \n",
       "...                                             ...   \n",
       "mg-prolog-carcass-16.csv                          0   \n",
       "mg-prolog-carcass-17.csv                          0   \n",
       "mg-prolog-carcass-18.csv                          0   \n",
       "mg-prolog-carcass-19.csv                          0   \n",
       "mg-prolog-carcass-20.csv                          0   \n",
       "\n",
       "                                   behavior_policy_return_cumulative  ...  \\\n",
       "arg_db_file                                                           ...   \n",
       "exp-mg/workspace/returns.0000.csv                              10000  ...   \n",
       "exp-mg/workspace/returns.0001.csv                              10000  ...   \n",
       "exp-mg/workspace/returns.0004.csv                              10000  ...   \n",
       "exp-mg/workspace/returns.0005.csv                              10000  ...   \n",
       "exp-mg/workspace/returns.0006.csv                              10000  ...   \n",
       "...                                                              ...  ...   \n",
       "mg-prolog-carcass-16.csv                                       10000  ...   \n",
       "mg-prolog-carcass-17.csv                                       10000  ...   \n",
       "mg-prolog-carcass-18.csv                                       10000  ...   \n",
       "mg-prolog-carcass-19.csv                                       10000  ...   \n",
       "mg-prolog-carcass-20.csv                                       10000  ...   \n",
       "\n",
       "                                   arg_episodes  arg_behavior_policy  \\\n",
       "arg_db_file                                                            \n",
       "exp-mg/workspace/returns.0000.csv         10000                10000   \n",
       "exp-mg/workspace/returns.0001.csv         10000                10000   \n",
       "exp-mg/workspace/returns.0004.csv         10000                10000   \n",
       "exp-mg/workspace/returns.0005.csv         10000                10000   \n",
       "exp-mg/workspace/returns.0006.csv         10000                10000   \n",
       "...                                         ...                  ...   \n",
       "mg-prolog-carcass-16.csv                  10000                10000   \n",
       "mg-prolog-carcass-17.csv                  10000                10000   \n",
       "mg-prolog-carcass-18.csv                  10000                10000   \n",
       "mg-prolog-carcass-19.csv                  10000                10000   \n",
       "mg-prolog-carcass-20.csv                  10000                10000   \n",
       "\n",
       "                                   arg_qtable_output  arg_epsilon  \\\n",
       "arg_db_file                                                         \n",
       "exp-mg/workspace/returns.0000.csv                  0        10000   \n",
       "exp-mg/workspace/returns.0001.csv                  0        10000   \n",
       "exp-mg/workspace/returns.0004.csv                  0        10000   \n",
       "exp-mg/workspace/returns.0005.csv                  0        10000   \n",
       "exp-mg/workspace/returns.0006.csv                  0        10000   \n",
       "...                                              ...          ...   \n",
       "mg-prolog-carcass-16.csv                           0        10000   \n",
       "mg-prolog-carcass-17.csv                           0        10000   \n",
       "mg-prolog-carcass-18.csv                           0        10000   \n",
       "mg-prolog-carcass-19.csv                           0        10000   \n",
       "mg-prolog-carcass-20.csv                           0        10000   \n",
       "\n",
       "                                   arg_initial_q_estimate  arg_minigrid_level  \\\n",
       "arg_db_file                                                                     \n",
       "exp-mg/workspace/returns.0000.csv                   10000               10000   \n",
       "exp-mg/workspace/returns.0001.csv                   10000               10000   \n",
       "exp-mg/workspace/returns.0004.csv                   10000               10000   \n",
       "exp-mg/workspace/returns.0005.csv                   10000               10000   \n",
       "exp-mg/workspace/returns.0006.csv                   10000               10000   \n",
       "...                                                   ...                 ...   \n",
       "mg-prolog-carcass-16.csv                            10000               10000   \n",
       "mg-prolog-carcass-17.csv                            10000               10000   \n",
       "mg-prolog-carcass-18.csv                            10000               10000   \n",
       "mg-prolog-carcass-19.csv                            10000               10000   \n",
       "mg-prolog-carcass-20.csv                            10000               10000   \n",
       "\n",
       "                                   arg_control_algorithm  \\\n",
       "arg_db_file                                                \n",
       "exp-mg/workspace/returns.0000.csv                  10000   \n",
       "exp-mg/workspace/returns.0001.csv                  10000   \n",
       "exp-mg/workspace/returns.0004.csv                  10000   \n",
       "exp-mg/workspace/returns.0005.csv                  10000   \n",
       "exp-mg/workspace/returns.0006.csv                  10000   \n",
       "...                                                  ...   \n",
       "mg-prolog-carcass-16.csv                           10000   \n",
       "mg-prolog-carcass-17.csv                           10000   \n",
       "mg-prolog-carcass-18.csv                           10000   \n",
       "mg-prolog-carcass-19.csv                           10000   \n",
       "mg-prolog-carcass-20.csv                           10000   \n",
       "\n",
       "                                   behavior_policy_is_positive  \\\n",
       "arg_db_file                                                      \n",
       "exp-mg/workspace/returns.0000.csv                        10000   \n",
       "exp-mg/workspace/returns.0001.csv                        10000   \n",
       "exp-mg/workspace/returns.0004.csv                        10000   \n",
       "exp-mg/workspace/returns.0005.csv                        10000   \n",
       "exp-mg/workspace/returns.0006.csv                        10000   \n",
       "...                                                        ...   \n",
       "mg-prolog-carcass-16.csv                                 10000   \n",
       "mg-prolog-carcass-17.csv                                 10000   \n",
       "mg-prolog-carcass-18.csv                                 10000   \n",
       "mg-prolog-carcass-19.csv                                 10000   \n",
       "mg-prolog-carcass-20.csv                                 10000   \n",
       "\n",
       "                                   behavior_policy_is_successful    env  \n",
       "arg_db_file                                                              \n",
       "exp-mg/workspace/returns.0000.csv                          10000  10000  \n",
       "exp-mg/workspace/returns.0001.csv                          10000  10000  \n",
       "exp-mg/workspace/returns.0004.csv                          10000  10000  \n",
       "exp-mg/workspace/returns.0005.csv                          10000  10000  \n",
       "exp-mg/workspace/returns.0006.csv                          10000  10000  \n",
       "...                                                          ...    ...  \n",
       "mg-prolog-carcass-16.csv                                   10000  10000  \n",
       "mg-prolog-carcass-17.csv                                   10000  10000  \n",
       "mg-prolog-carcass-18.csv                                   10000  10000  \n",
       "mg-prolog-carcass-19.csv                                   10000  10000  \n",
       "mg-prolog-carcass-20.csv                                   10000  10000  \n",
       "\n",
       "[271 rows x 25 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mg.groupby('arg_db_file').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing - Join BW and MG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df_bw, df_mg])\n",
    "df.head()\n",
    "\n",
    "df['behavior_policy_return_cumulative_normalised'] = df['behavior_policy_return_cumulative'] / df['episode_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def better_carcass_labels(old_label):\n",
    "    if old_label=='minigrid_v2.lp':\n",
    "        return 'Abstract Unrestricted (ASP)'\n",
    "    elif old_label=='minigrid_restricted_actions_v2.lp':\n",
    "        return 'Abstract (ASP)'\n",
    "    elif old_label=='prolog_minigrid_restricted_actions_v2.pl':\n",
    "        return 'Abstract (Prolog)'\n",
    "    elif old_label=='blocksworld_stackordered.lp':\n",
    "        return 'Abstract (ASP)'\n",
    "    elif old_label=='prolog_blocksworld_stackordered.pl':\n",
    "        return 'Abstract (Prolog)'\n",
    "    else:\n",
    "        return 'Concrete'\n",
    "        \n",
    "#df['arg_carcass'] = df.arg_carcass.fillna('original mdp')\n",
    "df['arg_carcass'] = df['arg_carcass'].map(better_carcass_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "whitelist=[\n",
    "    {'env': '20-blocks-world', 'arg_learning_rate': 0.01, 'arg_epsilon': 0.05},\n",
    "    {'env': 'MiniGrid-DoorKey-8x8-v0', 'arg_learning_rate':0.00001},\n",
    "    {'env': 'MiniGrid-FourRooms-v0', 'arg_learning_rate':0.001},\n",
    "    {'env': 'MiniGrid-MultiRoom-N2-S4-v0', 'arg_learning_rate':0.001},\n",
    "]\n",
    "\n",
    "def matches_whitelist_dict(row, whitelist_dict):\n",
    "    for k,v in whitelist_dict.items():\n",
    "        if row[k] != v:\n",
    "            return False\n",
    "    return True\n",
    "\n",
    "def matches_whitelist(row, whitelist):\n",
    "    for d in whitelist:\n",
    "        if matches_whitelist_dict(row, d):\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "df = df[df.apply(lambda row: matches_whitelist(row, whitelist), axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "env                          arg_carcass                \n",
       "20-blocks-world              Abstract (ASP)                  3000.0\n",
       "                             Abstract (Prolog)               3000.0\n",
       "                             Concrete                        3000.0\n",
       "MiniGrid-DoorKey-8x8-v0      Abstract (ASP)                 20000.0\n",
       "                             Abstract Unrestricted (ASP)    20000.0\n",
       "                             Concrete                       20000.0\n",
       "MiniGrid-FourRooms-v0        Abstract (ASP)                 10000.0\n",
       "                             Concrete                       10000.0\n",
       "MiniGrid-MultiRoom-N2-S4-v0  Abstract (ASP)                 10000.0\n",
       "                             Abstract (Prolog)              10000.0\n",
       "                             Concrete                       10000.0\n",
       "Name: episode_id, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "episode_length_shoulds={\n",
    "    '20-blocks-world': 3000,\n",
    "    'MiniGrid-FourRooms-v0': 10000,\n",
    "    'MiniGrid-DoorKey-8x8-v0': 20000,\n",
    "    'MiniGrid-MultiRoom-N2-S4-v0': 10000,\n",
    "}\n",
    "df['episode_length_should'] = df['env'].map(lambda x: episode_length_shoulds[x])\n",
    "\n",
    "# Remove inconsistent episode lengths\n",
    "df = df[(df['env']!='MiniGrid-MultiRoom-N2-S4-v0') | (df['episode_id'] <= 10000)]\n",
    "\n",
    "df.groupby(['env', 'arg_carcass']).count()['episode_id'] / 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2380000, 34)\n"
     ]
    },
    {
     "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>time_spent_in_target_episode</th>\n",
       "      <th>arg_blocks_world_reversed_stack_order</th>\n",
       "      <th>behavior_policy_return_cumulative</th>\n",
       "      <th>arg_plan_for_new_states</th>\n",
       "      <th>arg_control_algorithm</th>\n",
       "      <th>arg_planning_horizon</th>\n",
       "      <th>episode_id</th>\n",
       "      <th>arg_blocks_world_size</th>\n",
       "      <th>arg_db_file</th>\n",
       "      <th>arg_episodes</th>\n",
       "      <th>...</th>\n",
       "      <th>arg_test_target_policy</th>\n",
       "      <th>naive_return</th>\n",
       "      <th>behavior_policy_is_positive</th>\n",
       "      <th>behavior_policy_is_successful</th>\n",
       "      <th>env</th>\n",
       "      <th>arg_minigrid_use_alternative_reward_system</th>\n",
       "      <th>arg_minigrid_fully_observable</th>\n",
       "      <th>arg_minigrid_level</th>\n",
       "      <th>behavior_policy_return_cumulative_normalised</th>\n",
       "      <th>episode_length_should</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.262752</td>\n",
       "      <td>False</td>\n",
       "      <td>-61.0</td>\n",
       "      <td>False</td>\n",
       "      <td>q_learning</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>fix-rng-15.csv</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>20-blocks-world</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-61.0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.234189</td>\n",
       "      <td>False</td>\n",
       "      <td>-122.0</td>\n",
       "      <td>False</td>\n",
       "      <td>q_learning</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>fix-rng-15.csv</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>20-blocks-world</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-61.0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.267949</td>\n",
       "      <td>False</td>\n",
       "      <td>-183.0</td>\n",
       "      <td>False</td>\n",
       "      <td>q_learning</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>fix-rng-15.csv</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>20-blocks-world</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-61.0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.244308</td>\n",
       "      <td>False</td>\n",
       "      <td>-244.0</td>\n",
       "      <td>False</td>\n",
       "      <td>q_learning</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>20.0</td>\n",
       "      <td>fix-rng-15.csv</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>20-blocks-world</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-61.0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.291141</td>\n",
       "      <td>False</td>\n",
       "      <td>-305.0</td>\n",
       "      <td>False</td>\n",
       "      <td>q_learning</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>fix-rng-15.csv</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>20-blocks-world</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-61.0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   time_spent_in_target_episode arg_blocks_world_reversed_stack_order  \\\n",
       "0                      0.262752                                 False   \n",
       "1                      0.234189                                 False   \n",
       "2                      0.267949                                 False   \n",
       "3                      0.244308                                 False   \n",
       "4                      0.291141                                 False   \n",
       "\n",
       "   behavior_policy_return_cumulative  arg_plan_for_new_states  \\\n",
       "0                              -61.0                    False   \n",
       "1                             -122.0                    False   \n",
       "2                             -183.0                    False   \n",
       "3                             -244.0                    False   \n",
       "4                             -305.0                    False   \n",
       "\n",
       "  arg_control_algorithm  arg_planning_horizon  episode_id  \\\n",
       "0            q_learning                     4           1   \n",
       "1            q_learning                     4           2   \n",
       "2            q_learning                     4           3   \n",
       "3            q_learning                     4           4   \n",
       "4            q_learning                     4           5   \n",
       "\n",
       "   arg_blocks_world_size     arg_db_file  arg_episodes  ...  \\\n",
       "0                   20.0  fix-rng-15.csv          3000  ...   \n",
       "1                   20.0  fix-rng-15.csv          3000  ...   \n",
       "2                   20.0  fix-rng-15.csv          3000  ...   \n",
       "3                   20.0  fix-rng-15.csv          3000  ...   \n",
       "4                   20.0  fix-rng-15.csv          3000  ...   \n",
       "\n",
       "  arg_test_target_policy  naive_return  behavior_policy_is_positive  \\\n",
       "0                    NaN          62.0                        False   \n",
       "1                    NaN          62.0                        False   \n",
       "2                    NaN          62.0                        False   \n",
       "3                    NaN          62.0                        False   \n",
       "4                    NaN          62.0                        False   \n",
       "\n",
       "  behavior_policy_is_successful              env  \\\n",
       "0                         False  20-blocks-world   \n",
       "1                         False  20-blocks-world   \n",
       "2                         False  20-blocks-world   \n",
       "3                         False  20-blocks-world   \n",
       "4                         False  20-blocks-world   \n",
       "\n",
       "   arg_minigrid_use_alternative_reward_system  arg_minigrid_fully_observable  \\\n",
       "0                                         NaN                            NaN   \n",
       "1                                         NaN                            NaN   \n",
       "2                                         NaN                            NaN   \n",
       "3                                         NaN                            NaN   \n",
       "4                                         NaN                            NaN   \n",
       "\n",
       "   arg_minigrid_level  behavior_policy_return_cumulative_normalised  \\\n",
       "0                 NaN                                         -61.0   \n",
       "1                 NaN                                         -61.0   \n",
       "2                 NaN                                         -61.0   \n",
       "3                 NaN                                         -61.0   \n",
       "4                 NaN                                         -61.0   \n",
       "\n",
       "   episode_length_should  \n",
       "0                   3000  \n",
       "1                   3000  \n",
       "2                   3000  \n",
       "3                   3000  \n",
       "4                   3000  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_learning_curves(env, learning_rate, epsilon, carcass, ax, plot_color='tab:blue',\n",
    "                         y=('Return $G_0$', 'behavior_policy_return'),\n",
    "                         label=None,\n",
    "                         alpha=1.0):\n",
    "\n",
    "    idxs = (df.arg_epsilon == epsilon) \\\n",
    "             & (df.env == env) \\\n",
    "             & (df.arg_learning_rate == learning_rate) \\\n",
    "             & (df.arg_carcass == carcass)\n",
    "            \n",
    "    agg = df[idxs].groupby('episode_id')\n",
    "    \n",
    "    pr25 = agg[y[1]].quantile(0.25)\n",
    "    pr50 = agg[y[1]].quantile(0.50)\n",
    "    pr75 = agg[y[1]].quantile(0.75)\n",
    "\n",
    "    cnt = agg[y[1]].count()\n",
    "    for i in cnt:\n",
    "        assert i == 20, f'Count is {i}, must be 20 ({env}, lr={learning_rate}, ep={epsilon}'\n",
    "        \n",
    "    ax.plot(pr50, '-', label=f'$Q_2${' '+label if label else ''}', color=plot_color, alpha=alpha)\n",
    "    ax.fill_between(pr50.index, pr25, pr75, alpha=0.4*alpha, color=plot_color, label=f'IQR{' ' +label if label else ''}', linewidth=0)\n",
    "\n",
    "    ax.set_xlabel('Episode')\n",
    "    ax.set_ylabel('Return $G_0$')\n",
    "    ax.set_title(f'{env}\\n{carcass}\\n$\\\\alpha = {learning_rate}, \\\\ \\\\epsilon = {epsilon}$, $(n={cnt.median():.0f})$')\n",
    "    #ax.set_title(f'$\\\\alpha = {learning_rate}, \\\\ \\\\epsilon = {epsilon}$, $N={cnt.median():.0f}$')\n",
    "    ax.margins(0,.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_hist(env, learning_rate, epsilon, carcass, episode_range, ax, binwidth, plot_color='tab:blue'):\n",
    "\n",
    "    idxs = (df.episode_id.isin(range(*episode_range))) \\\n",
    "        & (df.arg_learning_rate == learning_rate) \\\n",
    "        & (df.arg_epsilon == epsilon) \\\n",
    "        & (df.env == env) \\\n",
    "        & (df.arg_carcass == carcass)\n",
    "    \n",
    "    g = df[idxs]\n",
    "    expected_rows = (episode_range[1] - episode_range[0]) * 20\n",
    "    assert g.shape[0] == expected_rows, f'shape is {g.shape}; {g.shape[0]} out of {expected_rows} expected rows.'\n",
    "    \n",
    "    sns.histplot(y='behavior_policy_return', \n",
    "        data=g, \n",
    "        stat='count',\n",
    "        binwidth=binwidth,\n",
    "        color=plot_color,\n",
    "        #linewidth=0,\n",
    "        ax=ax)\n",
    "\n",
    "    ax.set_title(f'{env}\\n $\\\\alpha = {learning_rate}, \\\\ \\\\epsilon = {epsilon}$\\n Episodes {episode_range[0]} - {episode_range[1]}')\n",
    "    #ax.set_title(f'Episodes {episode_range[0]} - {episode_range[1]-1}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table\n",
    "\n",
    "| Environment | Representation | NCR | nr. succ | Last 100 episodes |\n",
    "| ----------- | -------------- | --- | -------- | -------------------- |\n",
    "| ...        | ....           | ... | ...      | ...              |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def agg_ncr(g, k_episodes, n_samples):\n",
    "    \n",
    "    last_episode = g['episode_id'].max()\n",
    "    m = g[g['episode_id']==last_episode]['behavior_policy_return_cumulative_normalised']\n",
    "\n",
    "    #print(g['arg_minigrid_level'])\n",
    "    #print('k', k_episodes)\n",
    "    #print('last episode', last_episode)\n",
    "    #print(m.shape[0])\n",
    "    \n",
    "    assert last_episode==k_episodes, f'is={last_episode}, should={k_episodes} \\n Group={g}'\n",
    "    assert m.shape[0] == n_samples\n",
    "    \n",
    "    return m\n",
    "    #return f'${m.mean():4.2f} \\\\pm {m.std():4.2f}$'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def agg_posi(g):\n",
    "    episodes = g['episode_id'].unique().shape[0]\n",
    "    #last_episode = g['episode_id'].max()\n",
    "    return g.groupby('arg_db_file')['behavior_policy_is_positive'].sum() / episodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def agg_succ(g, k_episodes, n_samples):\n",
    "    \n",
    "    episodes = g['episode_id'].unique().shape[0]\n",
    "    m = g.groupby('arg_db_file')['behavior_policy_is_successful'].sum() / episodes\n",
    "\n",
    "    assert episodes==k_episodes, f'{g}'\n",
    "    assert m.shape[0] == n_samples\n",
    "    \n",
    "    return m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def subheader(label, g, include_min=False):\n",
    "    ret= {\n",
    "        #(label, 'N'):      f'{g.count():.0f}',\n",
    "        #(label, 'Median'): f'{g.median():.2f}',\n",
    "        #(label, 'Min'):    f'{g.min():.2f}',\n",
    "        (label, 'Q1'):     f'{g.quantile(0.25):.2f}',\n",
    "        (label, 'Q2'):     f'{g.quantile(0.50):.2f}',\n",
    "        (label, 'Q3'):     f'{g.quantile(0.75):.2f}',\n",
    "        #(label, 'P05'): f'{g.quantile(0.05):.2f}',\n",
    "    }\n",
    "\n",
    "    if include_min:\n",
    "        ret = { (label, 'Min'):    f'{g.min():.2f}' } | ret\n",
    "\n",
    "    return ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def agg_last_k_episodes(g, k_episodes, n_samples):\n",
    "    last_episode = g['episode_id'].max()\n",
    "\n",
    "    g = g[g.episode_id > last_episode-k_episodes]\n",
    "\n",
    "    assert g.shape[0]==k_episodes*n_samples\n",
    "    return g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'MiniGrid-DoorKey-8x8-v0', 'MiniGrid-FourRooms-v0',\n",
       "       'MiniGrid-MultiRoom-N2-S4-v0'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['arg_minigrid_level'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('quality', 'Q1'): '0.92',\n",
       " ('quality', 'Q2'): '0.96',\n",
       " ('quality', 'Q3'): '0.99'}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = df[(df['env']=='20-blocks-world') & (df['arg_learning_rate']==0.01) & (df['arg_epsilon']==0.05) & (df['arg_carcass']=='Abstract (ASP)')]\n",
    "assert g.shape[0]/3000 == 20\n",
    "#agg_last_k_episodes(g, 100, 20)\n",
    "subheader('quality', agg_succ(agg_last_k_episodes(g, 100, 20), 100, 20), False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Mean return (all)</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Successful (all)</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Successful (last 500)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>env</th>\n",
       "      <th>arg_carcass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">20-blocks-world</th>\n",
       "      <th>Abstract (ASP)</th>\n",
       "      <td>18.64</td>\n",
       "      <td>43.86</td>\n",
       "      <td>54.07</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abstract (Prolog)</th>\n",
       "      <td>10.61</td>\n",
       "      <td>37.18</td>\n",
       "      <td>46.40</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Concrete</th>\n",
       "      <td>-61.00</td>\n",
       "      <td>-61.00</td>\n",
       "      <td>-61.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MiniGrid-DoorKey-8x8-v0</th>\n",
       "      <th>Abstract (ASP)</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Concrete</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MiniGrid-FourRooms-v0</th>\n",
       "      <th>Abstract (ASP)</th>\n",
       "      <td>0.59</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Concrete</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">MiniGrid-MultiRoom-N2-S4-v0</th>\n",
       "      <th>Abstract (ASP)</th>\n",
       "      <td>0.68</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abstract (Prolog)</th>\n",
       "      <td>0.69</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Concrete</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Mean return (all)          \\\n",
       "                                                             Q1      Q2   \n",
       "env                         arg_carcass                                   \n",
       "20-blocks-world             Abstract (ASP)                18.64   43.86   \n",
       "                            Abstract (Prolog)             10.61   37.18   \n",
       "                            Concrete                     -61.00  -61.00   \n",
       "MiniGrid-DoorKey-8x8-v0     Abstract (ASP)                 0.82    0.86   \n",
       "                            Concrete                       0.01    0.01   \n",
       "MiniGrid-FourRooms-v0       Abstract (ASP)                 0.59    0.61   \n",
       "                            Concrete                       0.02    0.02   \n",
       "MiniGrid-MultiRoom-N2-S4-v0 Abstract (ASP)                 0.68    0.69   \n",
       "                            Abstract (Prolog)              0.69    0.70   \n",
       "                            Concrete                       0.01    0.01   \n",
       "\n",
       "                                                      Successful (all)        \\\n",
       "                                                   Q3               Q1    Q2   \n",
       "env                         arg_carcass                                        \n",
       "20-blocks-world             Abstract (ASP)      54.07             0.40  0.76   \n",
       "                            Abstract (Prolog)   46.40             0.38  0.61   \n",
       "                            Concrete           -61.00             0.00  0.00   \n",
       "MiniGrid-DoorKey-8x8-v0     Abstract (ASP)       0.90             0.93  0.97   \n",
       "                            Concrete             0.01             0.02  0.02   \n",
       "MiniGrid-FourRooms-v0       Abstract (ASP)       0.64             0.78  0.81   \n",
       "                            Concrete             0.02             0.03  0.03   \n",
       "MiniGrid-MultiRoom-N2-S4-v0 Abstract (ASP)       0.70             0.91  0.91   \n",
       "                            Abstract (Prolog)    0.71             0.91  0.92   \n",
       "                            Concrete             0.01             0.02  0.02   \n",
       "\n",
       "                                                    Successful (last 500)  \\\n",
       "                                                 Q3                    Q1   \n",
       "env                         arg_carcass                                     \n",
       "20-blocks-world             Abstract (ASP)     0.85                  0.93   \n",
       "                            Abstract (Prolog)  0.78                  0.93   \n",
       "                            Concrete           0.00                  0.00   \n",
       "MiniGrid-DoorKey-8x8-v0     Abstract (ASP)     0.99                  1.00   \n",
       "                            Concrete           0.03                  0.02   \n",
       "MiniGrid-FourRooms-v0       Abstract (ASP)     0.84                  1.00   \n",
       "                            Concrete           0.04                  0.03   \n",
       "MiniGrid-MultiRoom-N2-S4-v0 Abstract (ASP)     0.92                  1.00   \n",
       "                            Abstract (Prolog)  0.94                  1.00   \n",
       "                            Concrete           0.02                  0.02   \n",
       "\n",
       "                                                           \n",
       "                                                 Q2    Q3  \n",
       "env                         arg_carcass                    \n",
       "20-blocks-world             Abstract (ASP)     0.97  0.98  \n",
       "                            Abstract (Prolog)  0.94  0.94  \n",
       "                            Concrete           0.00  0.00  \n",
       "MiniGrid-DoorKey-8x8-v0     Abstract (ASP)     1.00  1.00  \n",
       "                            Concrete           0.02  0.04  \n",
       "MiniGrid-FourRooms-v0       Abstract (ASP)     1.00  1.00  \n",
       "                            Concrete           0.04  0.04  \n",
       "MiniGrid-MultiRoom-N2-S4-v0 Abstract (ASP)     1.00  1.00  \n",
       "                            Abstract (Prolog)  1.00  1.00  \n",
       "                            Concrete           0.03  0.03  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_k_episodes=500\n",
    "tab = df.groupby(['env', 'arg_learning_rate', 'arg_epsilon', 'arg_carcass']).apply(\n",
    "    lambda g: pd.Series(({\n",
    "        ('N', ''): g[g['episode_id']==1].shape[0],\n",
    "        **subheader('Mean return (all)', agg_ncr(g, int(g['episode_length_should'].median()), 20)),\n",
    "        **subheader('Successful (all)', agg_succ(g, int(g['episode_length_should'].median()), 20)),\n",
    "        **subheader(f'Successful (last {last_k_episodes})', agg_succ(agg_last_k_episodes(g, last_k_episodes, 20), last_k_episodes, 20)),\n",
    "    })),\n",
    "    include_groups=False).reset_index()\n",
    "\n",
    "tab = tab[~((tab['arg_carcass']=='Abstract Unrestricted (ASP)'))]\n",
    "tab = tab.set_index(['env', 'arg_carcass'])\n",
    "del tab['arg_learning_rate']\n",
    "del tab['arg_epsilon']\n",
    "del tab['N']\n",
    "tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lllllllllll}\n",
      "\\toprule\n",
      " &  & \\multicolumn{3}{r}{Mean return (all)} & \\multicolumn{3}{r}{Successful (all)} & \\multicolumn{3}{r}{Successful (last 500)} \\\\\n",
      " &  & Q1 & Q2 & Q3 & Q1 & Q2 & Q3 & Q1 & Q2 & Q3 \\\\\n",
      "env & arg_carcass &  &  &  &  &  &  &  &  &  \\\\\n",
      "\\midrule\n",
      "\\multirow[t]{3}{*}{20-blocks-world} & Abstract (ASP) & 18.64 & 43.86 & 54.07 & 0.40 & 0.76 & 0.85 & 0.93 & 0.97 & 0.98 \\\\\n",
      " & Abstract (Prolog) & 10.61 & 37.18 & 46.40 & 0.38 & 0.61 & 0.78 & 0.93 & 0.94 & 0.94 \\\\\n",
      " & Concrete & -61.00 & -61.00 & -61.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\\\\n",
      "\\cline{1-11}\n",
      "\\multirow[t]{2}{*}{MiniGrid-DoorKey-8x8-v0} & Abstract (ASP) & 0.82 & 0.86 & 0.90 & 0.93 & 0.97 & 0.99 & 1.00 & 1.00 & 1.00 \\\\\n",
      " & Concrete & 0.01 & 0.01 & 0.01 & 0.02 & 0.02 & 0.03 & 0.02 & 0.02 & 0.04 \\\\\n",
      "\\cline{1-11}\n",
      "\\multirow[t]{2}{*}{MiniGrid-FourRooms-v0} & Abstract (ASP) & 0.59 & 0.61 & 0.64 & 0.78 & 0.81 & 0.84 & 1.00 & 1.00 & 1.00 \\\\\n",
      " & Concrete & 0.02 & 0.02 & 0.02 & 0.03 & 0.03 & 0.04 & 0.03 & 0.04 & 0.04 \\\\\n",
      "\\cline{1-11}\n",
      "\\multirow[t]{3}{*}{MiniGrid-MultiRoom-N2-S4-v0} & Abstract (ASP) & 0.68 & 0.69 & 0.70 & 0.91 & 0.91 & 0.92 & 1.00 & 1.00 & 1.00 \\\\\n",
      " & Abstract (Prolog) & 0.69 & 0.70 & 0.71 & 0.91 & 0.92 & 0.94 & 1.00 & 1.00 & 1.00 \\\\\n",
      " & Concrete & 0.01 & 0.01 & 0.01 & 0.02 & 0.02 & 0.02 & 0.02 & 0.03 & 0.03 \\\\\n",
      "\\cline{1-11}\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(tab.to_latex())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,4), layout='constrained')\n",
    "grid_fig = gridspec.GridSpec(2, 2, figure=fig)\n",
    "\n",
    "fig_bw20 = fig.add_subfigure(grid_fig[0, 0])\n",
    "ax_bw20 = fig_bw20.add_subplot()\n",
    "plot_learning_curves(\n",
    "    env='20-blocks-world',\n",
    "    learning_rate= 0.01,\n",
    "    epsilon= 0.05,\n",
    "    carcass = 'Abstract (ASP)',\n",
    "    label = 'Abstract',\n",
    "    plot_color = 'tab:blue',\n",
    "    ax = ax_bw20)\n",
    "#plot_learning_curves(\n",
    "#    env='20-blocks-world',\n",
    "#    learning_rate= 0.01,\n",
    "#    epsilon= 0.05,\n",
    "#    carcass = 'Abstract (Prolog)',\n",
    "#    label = 'Abstract (Prolog)',\n",
    "#    plot_color = 'tab:red',\n",
    "#    ax = ax_bw20)\n",
    "ax_bw20.plot([0,3000], [naive_return(20)]*2, 'k--', label='Naive')\n",
    "ax_bw20.legend(loc='lower right')\n",
    "ax_bw20.set_yticks([-61, 100-61, naive_return(20), 80])\n",
    "ax_bw20.set_ylim(-61, 80)\n",
    "ax_bw20.set_xlim(0,3000)\n",
    "ax_bw20.set_title('20-blocks world')\n",
    "\n",
    "fig_mg_dk = fig.add_subfigure(grid_fig[0, 1])\n",
    "ax_mg_dk = fig_mg_dk.add_subplot()\n",
    "plot_learning_curves(\n",
    "    env='MiniGrid-DoorKey-8x8-v0',\n",
    "    learning_rate= 0.00001,\n",
    "    epsilon= 0.2,\n",
    "    carcass = 'Abstract (ASP)',\n",
    "    label = 'Abstract',\n",
    "    plot_color = 'tab:blue',\n",
    "    ax = ax_mg_dk)\n",
    "ax_mg_dk.set_ylim(0,1)\n",
    "ax_mg_dk.set_title('MiniGrid DoorKey $8 \\\\times 8$')\n",
    "\n",
    "fig_mg_mr =  fig.add_subfigure(grid_fig[1, 0])\n",
    "ax_mg_mr = fig_mg_mr.add_subplot()\n",
    "plot_learning_curves(\n",
    "    env='MiniGrid-MultiRoom-N2-S4-v0',\n",
    "    learning_rate= 0.001,\n",
    "    epsilon= 0.2,\n",
    "    carcass = 'Abstract (ASP)',\n",
    "    label = 'Abstract',\n",
    "    plot_color = 'tab:blue',\n",
    "    ax = ax_mg_mr)\n",
    "#plot_learning_curves(\n",
    "#    env='MiniGrid-MultiRoom-N2-S4-v0',\n",
    "#    learning_rate= 0.001,\n",
    "#    epsilon= 0.2,\n",
    "#    carcass = 'Abstract (Prolog)',\n",
    "#    label = 'Abstract (Prolog)',\n",
    "#    plot_color = 'tab:red',\n",
    "#    ax = ax_mg_mr)\n",
    "ax_mg_mr.set_ylim(0,1)\n",
    "ax_mg_mr.set_title('MiniGrid MultiRoom N2 S4')\n",
    "\n",
    "fig_mg_4r =  fig.add_subfigure(grid_fig[1, 1])\n",
    "ax_mg_4r = fig_mg_4r.add_subplot()\n",
    "plot_learning_curves(\n",
    "    env='MiniGrid-FourRooms-v0',\n",
    "    learning_rate= 0.001,\n",
    "    epsilon= 0.2,\n",
    "    carcass = 'Abstract (ASP)',\n",
    "    label = 'Abstract',\n",
    "    plot_color = 'tab:blue',\n",
    "    ax = ax_mg_4r)\n",
    "ax_mg_4r.set_ylim(0,1)\n",
    "ax_mg_4r.set_title('MiniGrid FourRooms')\n",
    "\n",
    "plt.savefig('plots/plt-learning-curves.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x250 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,2.5), layout='constrained')\n",
    "grid_fig = gridspec.GridSpec(1, 2, figure=fig)\n",
    "\n",
    "fig_bw20 = fig.add_subfigure(grid_fig[0, 0])\n",
    "ax_bw20 = fig_bw20.add_subplot()\n",
    "plot_learning_curves(\n",
    "    env='20-blocks-world',\n",
    "    learning_rate= 0.01,\n",
    "    epsilon= 0.05,\n",
    "    carcass = 'Abstract (ASP)',\n",
    "    label = 'Abstract (ASP)',\n",
    "    plot_color = 'tab:blue',\n",
    "    ax = ax_bw20,\n",
    "    alpha=0.7)\n",
    "plot_learning_curves(\n",
    "    env='20-blocks-world',\n",
    "    learning_rate= 0.01,\n",
    "    epsilon= 0.05,\n",
    "    carcass = 'Abstract (Prolog)',\n",
    "    label = 'Abstract (Prolog)',\n",
    "    plot_color = 'tab:red',\n",
    "    ax = ax_bw20,\n",
    "    alpha=0.7)\n",
    "ax_bw20.plot([0,3000], [naive_return(20)]*2, 'k--', label='Naive')\n",
    "\n",
    "ax_bw20.set_yticks([-61, 100-61, naive_return(20), 80])\n",
    "ax_bw20.set_ylim(-61, 80)\n",
    "ax_bw20.set_xlim(0,3000)\n",
    "ax_bw20.set_title('20-blocks world')\n",
    "\n",
    "fig_mg_mr =  fig.add_subfigure(grid_fig[0, 1])\n",
    "ax_mg_mr = fig_mg_mr.add_subplot()\n",
    "plot_learning_curves(\n",
    "    env='MiniGrid-MultiRoom-N2-S4-v0',\n",
    "    learning_rate= 0.001,\n",
    "    epsilon= 0.2,\n",
    "    carcass = 'Abstract (ASP)',\n",
    "    label = 'Abstract (ASP)',\n",
    "    plot_color = 'tab:blue',\n",
    "    ax = ax_mg_mr,\n",
    "    alpha=0.7)\n",
    "plot_learning_curves(\n",
    "    env='MiniGrid-MultiRoom-N2-S4-v0',\n",
    "    learning_rate= 0.001,\n",
    "    epsilon= 0.2,\n",
    "    carcass = 'Abstract (Prolog)',\n",
    "    label = 'Abstract (Prolog)',\n",
    "    plot_color = 'tab:red',\n",
    "    ax = ax_mg_mr,\n",
    "    alpha=0.7)\n",
    "ax_mg_mr.set_ylim(0,1)\n",
    "ax_mg_mr.legend(loc='lower right')\n",
    "ax_mg_mr.set_title('MiniGrid MultiRoom N2 S4')\n",
    "\n",
    "plt.savefig('plots/plt-learning-curves-prolog.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xticks_mg=[0,2000,4000,6000,8000,10000]\n",
    "xticklabels_mg = ['{:,.0f}k'.format(x/1000) for x in xticks_mg]\n",
    "xticks_mg_dk=[0,5000,10000,15000,20000]\n",
    "xticklabels_mg_dk = ['{:,.0f}k'.format(x/1000) for x in xticks_mg_dk]\n",
    "xticks_bw=[0,1000,2000,3000]\n",
    "xticklabels_bw = ['{:,.0f}k'.format(x/1000) for x in xticks_bw]\n",
    "\n",
    "fig = plt.figure(figsize=(8,4), layout='constrained')\n",
    "grid_fig = gridspec.GridSpec(2, 3, figure=fig)\n",
    "\n",
    "fig_bw20_asp = fig.add_subfigure(grid_fig[0, 0])\n",
    "ax_bw20_asp = fig_bw20_asp.add_subplot()\n",
    "plot_learning_curves(\n",
    "    env='20-blocks-world',\n",
    "    learning_rate= 0.01,\n",
    "    epsilon= 0.05,\n",
    "    carcass = 'Abstract (ASP)',\n",
    "    label = 'Abstract (ASP)',\n",
    "    plot_color = 'tab:blue',\n",
    "    ax = ax_bw20_asp)\n",
    "ax_bw20_asp.plot([0,3000], [naive_return(20)]*2, 'k--', label='Naive')\n",
    "#ax_bw20_asp.legend(loc='best')\n",
    "ax_bw20_asp.set_yticks([-61, 100-61, naive_return(20), 80])\n",
    "ax_bw20_asp.set_ylim(-61, 80)\n",
    "ax_bw20_asp.set_xlim(0,3000)\n",
    "ax_bw20_asp.set_title('20-blocks world')\n",
    "ax_bw20_asp.set_xticks(xticks_bw)\n",
    "ax_bw20_asp.set_xticklabels(xticklabels_bw)\n",
    "\n",
    "fig_bw20_pl = fig.add_subfigure(grid_fig[0, 1])\n",
    "ax_bw20_pl = fig_bw20_pl.add_subplot()\n",
    "plot_learning_curves(\n",
    "    env='20-blocks-world',\n",
    "    learning_rate= 0.01,\n",
    "    epsilon= 0.05,\n",
    "    carcass = 'Abstract (Prolog)',\n",
    "    label = 'Abstract (Prolog)',\n",
    "    plot_color = 'tab:red',\n",
    "    ax = ax_bw20_pl)\n",
    "ax_bw20_pl.plot([0,3000], [naive_return(20)]*2, 'k--', label='Naive')\n",
    "#ax_bw20_pl.legend(loc='best')\n",
    "ax_bw20_pl.set_yticks([-61, 100-61, naive_return(20), 80])\n",
    "ax_bw20_pl.set_ylim(-61, 80)\n",
    "ax_bw20_pl.set_xlim(0,3000)\n",
    "ax_bw20_pl.set_title('20-blocks world')\n",
    "ax_bw20_pl.set_xticks(xticks_bw)\n",
    "ax_bw20_pl.set_xticklabels(xticklabels_bw)\n",
    "\n",
    "fig_mg_dk = fig.add_subfigure(grid_fig[0, 2])\n",
    "ax_mg_dk = fig_mg_dk.add_subplot()\n",
    "plot_learning_curves(\n",
    "    env='MiniGrid-DoorKey-8x8-v0',\n",
    "    learning_rate= 0.00001,\n",
    "    epsilon= 0.2,\n",
    "    carcass = 'Abstract (ASP)',\n",
    "    label = 'Abstract (ASP)',\n",
    "    plot_color = 'tab:blue',\n",
    "    ax = ax_mg_dk)\n",
    "ax_mg_dk.set_ylim(0,1)\n",
    "ax_mg_dk.set_title('MiniGrid DoorKey $8 \\\\times 8$')\n",
    "ax_mg_dk.set_xticks(xticks_mg_dk)\n",
    "ax_mg_dk.set_xticklabels(xticklabels_mg_dk)\n",
    "\n",
    "fig_mg_mr_asp =  fig.add_subfigure(grid_fig[1, 0])\n",
    "ax_mg_mr_asp = fig_mg_mr_asp.add_subplot()\n",
    "plot_learning_curves(\n",
    "    env='MiniGrid-MultiRoom-N2-S4-v0',\n",
    "    learning_rate= 0.001,\n",
    "    epsilon= 0.2,\n",
    "    carcass = 'Abstract (ASP)',\n",
    "    label = 'Abstract (ASP)',\n",
    "    plot_color = 'tab:blue',\n",
    "    ax = ax_mg_mr_asp)\n",
    "ax_mg_mr_asp.set_ylim(0,1)\n",
    "ax_mg_mr_asp.set_title('MiniGrid MultiRoom N2 S4')\n",
    "ax_mg_mr_asp.set_xticks(xticks_mg)\n",
    "ax_mg_mr_asp.set_xticklabels(xticklabels_mg)\n",
    "ax_mg_mr_asp.set_xlim(0,10000)\n",
    "\n",
    "fig_mg_mr_pl =  fig.add_subfigure(grid_fig[1, 1])\n",
    "ax_mg_mr_pl = fig_mg_mr_pl.add_subplot()\n",
    "plot_learning_curves(\n",
    "    env='MiniGrid-MultiRoom-N2-S4-v0',\n",
    "    learning_rate= 0.001,\n",
    "    epsilon= 0.2,\n",
    "    carcass = 'Abstract (Prolog)',\n",
    "    label = 'Abstract (Prolog)',\n",
    "    plot_color = 'tab:red',\n",
    "    ax = ax_mg_mr_pl)\n",
    "ax_mg_mr_pl.set_ylim(0,1)\n",
    "ax_mg_mr_pl.set_title('MiniGrid MultiRoom N2 S4')\n",
    "ax_mg_mr_pl.set_xticks(xticks_mg)\n",
    "ax_mg_mr_pl.set_xticklabels(xticklabels_mg)\n",
    "ax_mg_mr_pl.set_xlim(0,10000)\n",
    "\n",
    "fig_mg_4r =  fig.add_subfigure(grid_fig[1, 2])\n",
    "ax_mg_4r = fig_mg_4r.add_subplot()\n",
    "plot_learning_curves(\n",
    "    env='MiniGrid-FourRooms-v0',\n",
    "    learning_rate= 0.001,\n",
    "    epsilon= 0.2,\n",
    "    carcass = 'Abstract (ASP)',\n",
    "    label = 'Abstract (ASP)',\n",
    "    plot_color = 'tab:blue',\n",
    "    ax = ax_mg_4r)\n",
    "ax_mg_4r.set_ylim(0,1)\n",
    "ax_mg_4r.set_title('MiniGrid FourRooms')\n",
    "ax_mg_4r.set_xticks(xticks_mg)\n",
    "ax_mg_4r.set_xticklabels(xticklabels_mg)\n",
    "ax_mg_4r.set_xlim(0,10000)\n",
    "\n",
    "#handles, labels = plt.gca().get_legend_handles_labels()\n",
    "#fig.legend(handles, labels, loc='upper center')\n",
    "plt.figlegend(\n",
    "    handles=ax_bw20_asp.get_legend_handles_labels()[0][0:2]+ax_bw20_pl.get_legend_handles_labels()[0],\n",
    "    loc='outside upper center',\n",
    "    ncols=3)\n",
    "\n",
    "plt.savefig('plots/plt-learning-curves-all.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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//ivvfOc7eelLXwrEBkVfXx833XQTr3jFK+ju7gZgeHiYnp6e5nEjIyN0dXU9p3M0DB3HcRJj5Q8MKSUQX9vkBfqHRXJt/zBJrusfLsm1/cMlubZ/+Pyupkm8ICfYe553XAeZptn0nCxcuJDu7m4eeuih5v4gCNi4cSOnnXba77SuCQkJCQkJCQkJCQm/Hi9Iz8pll13Gf/3XfzF//vxmGNjNN9/Mq171KiC29N785jdz0003sXTpUpYsWcJNN91EOp1uzmtJSEhISEhISEhISHh+84I0Vv7mb/6G//iP/+BDH/oQIyMj9PT08NrXvpZ3v/vdTZl3vOMd+L7Phz70IYrFIqeccgpf/vKXyefzv8eaJyQkJCQkJCQkJCQ8V16Qxko+n+eDH/wgH/zgB2eUEULw3ve+l/e+972/w5olJCQkJCQkJCQkJPymeEHOWUlISEhISEhISEhI+MMnMVYSEhISEhISEhISEp6XJMZKQkJCQkJCQkJCQsLzksRYSUhISEhISEhISEh4XpIYKwkJCQkJCQkJCQkJz0sSYyUhISEhISEhISEh4XlJYqwkJCQkJCQkJCQkJDwvSYyVhISEhISEhISEhITnJYmxkpCQkJCQkJCQkJDwvCQxVhISEhISEhISEhISnpckxkpCQkJCQkJCQkJCwvOSxFhJSEhISEhISEhISHhekhgrCQkJCQkJ/4vRSv2+q5CQkJAwI4mxkpCQkJCQ8L+ZKPp91yDhd4xOrnnCC4jEWElISHhBoZT+fVdhClo/v+qTMDO/aQ/C7/Laaymn/I6kIpIT7dFaP+uzMVP7f5V++U20WSuFjiJUrXZcmZFU6CCYkH0WpXrysY2/J/eLnKZPJu8/lmP7UPrBlDJkqdT824/ia6KlbMo06nvs9dJKIZVGa/2cDIVf5T0XSXXc+Y89/thr3Ojjmc4z0/ZGHzf26zCc9tjnep/M1BdaqWnLniIThr9xo0trjVaKIJLH7ZvtvjmunGd5poLoud+DsxEFM/fRr/ut/FXa+bsiMVYSEhJeUITPt5CVZITyBcNvfDT5d3jtte9P+e2GEi+aqpQHsygZsynJepJippWKZWdQNqOGkvo/eA51FMXnqSv0ctKp3FCigmBC6XbdGctRSjfrAxDWC3LDifZ49b8n19ebRVGMjlG0fT8grPerlrKp5GutKdZiRVH7flNGeV687RhFWwcBXhgbNcqbei2n41d5z7mhnFpHffy90DSigiC+vvX6zXSeGe+l+nGN/Q2Dc7KCGyk9rZHYrIvWRA1DL4qmvS91GDav/bH3WkMJV67b7O8Zz/Wr3qdhCFGEDMLjlP3J99Wz8WzvmmiGNsEsfX8MUirCWdo/3bV9LgaMfB4OwCXGSkLCNPw2Rkx/k2XO9iH4bZ/716UxqqiUphZEU0aWGiNxoVTPWtdj2/5cRpSPlX8u22aTmfJ3XeGabaTsV+n/X6Uux7a7oTDMVoY6VokKQ0arwbR9/+v21Ux1Do/xBMwmO9u5tdbNdviTFe1J+xtM7iM9aRQyiNSUfmwqx7N8/OUkRVZrjQyj5m9ZqTTlRivHK6OTZeNzTlyrkhc+a983FFytNZFUsbEySRGXk/o3lGpi5Lsur8MQLSV+JJHHKEOqochqjfa8uC+iKD5OT21z4z5RroucpJTKMDpuRF36PjKSU66BkhKiCM8PCYOw6ZVQUdw/fqRQYdRUMqOaS9WLDQRvUpv9SFIJ4jqGUnG06KK0RkmJG8jms+BHiloQEY2NNdvihZIokoSTFNKiW/c06LhNjT4KgxC//mz7NY+aFxJFkpIbMVZ2Ub7Pfz9ykK89fADp+5RL1fjeqF+vRh+qIKAWRIR+gAp8As9nvOyhpMSveVSGRwkihfR9lNKEfkjgegRhfG1CL97uux61oWFk/b6PpML1QtwgIook2vNAKQKpGBkeR1YqyPHxpmFd7htg4F//jcqDD6KVIpIa6bpIqfAjiQpDtFKM1YJm3WUUG5B+EBsWUiq8QBKVK0TlCqpaZWxwBDeQdQMy7nOtFKip75VQKmqBZHy8jFI6vtZhSLXqNq+5qhuF0vXwwghZLCL9AFU32P26t0u5HoMjJT5+x07u3NYXbwuCuB0qNia156FU3K9ayvg+bXiVGoal1riuT63mE1WqqChCSUlQqTb7oDRexgtV87lvtqdaw615lLwQGcmJZyUI43ZIiYqi5n0bVarIapUgiH97fkDgB/SP1yh7IWUvJKiXI4MQP5Lx810qoVy3eQ+rKCLyA4LxIkopgkhx8wN7+Y9f7kZKhVYKv+rGz1UQNPu26MbGqvR9VBBwdKTMv9+5i82Hx+L6BwFBFL8/ahW3aVTKUin2MpbLzWfxd4n1Oz1bQsILBD9SpG1zxv2RVFjmr2brR0pjmwKIFVxhzlw+xC9QIcS0+0KpMI3Zjz/23JbBjOXpKEJYv93XgR9JLMMgkAqlNUEU4VgOALVQYhkCISCUYAg9Y/82Rk8bfRjI+EVdSNvx9mdRpgOpSFkTfRdKhaUk2Pasx4VS41ii3pZJ90c9bMANJY41c50bxz4bbijJOrNfi0Z5lSCiJW1P2W6ZxIrJDO3xXJ/spH3K94m0RSgVoWTKuSffsw205yEymRnrNtOz4YYSP1S05+Jrfux1mFKG0mjN1D6rK7KWFRtYfs0l09aKF0yU0+iXUGpsM77fp/RRFBsrv9jaTyg1L13Vhk5n8CJJWoYIy0K5LkY6Pem08XOogoDRH/yQ3AlryJ56Kn6k8L2AFssEx5kYqY4kIyWXjnxqoo+VxvN8MpbR7Du3XCPXmieQCq9+/0/u+2P7x3M98sSjyT/eMUotkFy8qhvyoF0XV9goHT/Lfl2PyKesWMFCQxChoohQmYgwoDWfRlgWWmsqtYD2NiAMUdUqMlfANmDElRTSFiKK+OGWQdbOb6GnJUU6CnArLpGI+7Ut61CteRjpNJYhms+G7/oEQmKlHfKpuG1hEGFJSdWTZKIIVSqjgpAgiDCVIoigXPNpS8f9F3g+Ncsn7dhNBSltm0RS44cKjSQUgqGRMjmzgJIRoRsSpm0sM36+q35EW6WGmW/lticPkQuqrNv2MNb5F1A48QQytolXrtKacVBaE/kB3368l/6qZFkWLj89G1+zqovnBXjDY8hMjsh1qVYEm3qLKMNgKF8heOghoqyDXryUrhddwWg1oCVjE9V87tg9gIokQRBy6twcHQWHWw+UyViC3pEqL9JDzA1LZF72CtximdA0MAQ4PZ0E5QqitZXS0WF0GGJok7v2l9g7WGFdq+C0lfPAFuTDGiKTIZIw+tOfY+QssmecidXdhVaK0cefwOjvJxwcpHDppfjSQI4NI9o74uemUiJTyPLEoRpHxlxef/ZiDK+GHhhg6O57eWzuSaxcu4LOlGD/v/wz41WfM//p7xkvuqhcATE0SHj/feizzsaZ3wOVSmxA5fMopSl7EaYhkH5I2Y9QNZ+WXJrh0SoLOxRl1+fBz3yNxWefwrK1K4jKLurnt9FnFdjYvoLrL15NWHUZ+eltWGPD3L3kbA6PRvQOFnnyUJF3njGXW3ePs6onz5LOHJmw3FTCpdJ8b9sIp3enOGXdUuTICMaCBdSCCH9gkG9vHWF1m8Pirhx3HXa5bGGKOT0dlLTFrRv2ctH6RbSTolp1aZ3bHd+f5QrjIRjZDGbKJOtYVB5/nGD/AeyXvJRMFCDzBQb6Ryg8cj9y9Ymk58/FNxzIpzh4eIjbtw1wpKa4+tRFrGyzKVgGysxRc32k7RA8/ADhwAC5889j7M670EeOkH7JSxHpNM9896ccOvtyLr5oPY/vGYRUin0jVVbkDMJyGc+tkcpnEVJCKkXgB2hT45ZrZGyDbz12hMGa5L83HOAvncOkbAvv7PMIszkqoyXS+SypDfdT27iRIJTkli6h441voBokxkpCwu8dP5zeWFFKI0TsJjWUxjCemwIK8ahsU799DsaKFyoyzszKXPPvZzGctJ4Y/bRNgVQa85h6yzDCmsFYqfhRU9FolDdaDeisK2OTzx9JhSHElH6JZDzq64WKtA01P8KxDGqBpC0b94sbSKp+xNzWNBqNFylaTIOSF05RxtWkkVzPD8lkzXgUVWkK9f2hqntfhMZAUw7klDL8KFYCldLo+u/Q99FK4Bhg2xa1IGoqjo3+CqVqGiOR0gRR/bdSyDBiqOzTmjneQIikIgxDDGE3+0krhTCMpmE12Yj0QkXWOf46TFHUlEJIcL2ArG02yw2i+J7RUYQ4xlhRSkPgUx4dJ1PINQ0uHQQo0yKoeniG2Wx3EMUGWCE1cT0jqVA1F6eucOsoig0ax25el0jFBlO1fp0bLav4EZ4f0ZYy8JQgkpKUZTbvn0aflLwQ2zBQSuJYTjwy6XkgJZ4XkEs7yGqN0PUwXJew0B73YxThlao4nW2ExRLlVIbOfIqKG/eRaQiU7zNW8bnzmUGU1lw618a2U9SqHilToYRojkCPVQMe2z/CSXMKLGxLUX3oIdyntxBu3oyz/mRqgSTyA5QJtQcfwurpwWpvJwwiqm48uts0ooIQ3/WxhCKVyaCCgGrVJZXP4gWKUGrKVR8hJZlMqtn/jeM9z2fP0RJHRjTrWg3uf6YfL4g4dX4eOrPIUomKU8C2LfpGXR47VGL9nCyL5rVRLpZJ5XIoL4xH/nWI53rYhiCdMxjdvovylm3kX/0yzCgiKFdQdgrLMhmrSUrjFbbd8wj9IzU2LV7DOy9YQqk0RFXYBDikUjZZXWOsWMPWJllTEEhF3rHw/IA9IyX2j3lcvnY+Ka+K//hj9HUvYbdMc8WCFEYtQvT14a9Zg4UiMLL4pSqZfAarWqV6773451zIoAZsC6Uhl1KUqx7SMFHj44wYDg/sGaHQfwD78CHK3fPInHkaUWgQjI0zhoU5Os4hz+aZp/dwwj0/xG23YNduzL//EH1VD/uhexk79RTo6OKn2wZ4aNcgudY8R466nLKkg2jQI9iwAWPVStxSFeYvIPJDKsUKUSSRMmTff32dtKFpSZmweStbulZQ0wYXLGujODDCg7tGMADLNHj60CgX+f30FwOOdi8h1LDy/h9jtKfY1bKcBQWb/b7F6dkAv+Zio7m/18MaGiBrmXz7viPkC1lC1+POIyEBFkbgcUmPhZPK47k+4YaHKNUNp9BKsXnnMC2PPEFnEGGLiKcf2cL3RmzOSPusO0ngl6rMESH37hriZ/srCCFYkIE1ukz4rW9wdLRKKniS74y8jjdXd7B3MPYiPfmZL9G2YgnGS68l+MpXsAKP4MmniF79asLBEfp+8CPUmnWk3DJHRmvsnruKyniJlcs1q00PxxRUixUGopBd//yveEWX7Vu20vee96AqhznpscfZPeix6bLXsXPU49U9ksKDD+FEPqE1H58OTK2INj7G9w8UeCKzgCf3D/O2cxaycf9R1s5voVKu8YMne+k5spfvzV1Kz90/xcpmGHrJ9Wzb20/r7i0Ud/Szd3yQQ5aJ197Dd5efwB9fnuLmzcP0D5Q4UjtI56md2F6VVDYDjk2tr5/ik08jTlyLv/sZChdfRO3WHxEFEc7CZfhtLWSXZyl+5Suo4gjRhscwV6zAOvNsSsECfvaFH3C0Zwlj6TzffWQ//5/eizq4F/361xPkWvANH/3zXyDDkPLuvejAh0hSufkrmIUCR/YOIXoHuTeTIfQDbMehVKwy6CqCag1TKAw01Kr46RxFX/P4jhLLRw7SWhxi1FpFyVcUIo/iE3cy5IM9byV5c5hIasq9fbTcez9RrYaSklqtSumDf8vG697Oypbjv1O/LRJjJeEFTSgV9rN4ONQ0RoU/KUjaC+VxhokvJRAre5FUREqTtk28SOKYBkpBoBXpabwbYV1hbxgEWilCFW9P2yahVER+QMZxmuVbphErjZZNpGIlpRZEMxsrk8I4/Gh2Y6UxSt0wlvxoYuS+oZQHQYiVmRhNLnkheUNjOA7jtYCcYzYV6tFqQMWPmsZK0Q2bf5e9uM6T+6XkRdimwI8kphC4bgBZpxk25YZxaIobSpSO69o76nLSfJuaG2AZcV+mLBNd718vlLg1n3QmhRtOTG71o1jxc/0QmxBDRZSVRUvabnp2an5svARSUXJDDEMQVlyEtjBlSD6fpeiGhFKTtg1CqcmnLGpB7DkxoxDphYwbFj2FdN0YVJS9idAAFQQEkSSdzVD1Jcr18CS0Zx2EiJX8wDAxhEBrEEJjydgb0gizaXhYakFExhSUvQgh4jCVMNIUXZ8t+4e58MR5mIaNKpXwRYpa0UNHkjRgZjKMj1UotGQpP70FQytKpMi2tRJYDkIplOuhchnKpQoj0qQt65C2TcbKNZRhkrIMnEiyZ8zHDyVL3TFobSOo1aBapWw4zJvTjhdKVK2GzmRI2yYjR0dom9MRh4uo2CsSuD6eGVE00thRQCkI0ErS2t6K7/qksmncco1wdBAVhKSXLEQrRW3TZmQUEYwX0S1ZIteltHsfWR2hzjiH0cEx2nIpKkf6KLS34JarHPXiEf6wXKViCDKOQVQsMRbZBJHEMARjo2XaW9soD46Qacsiah6Wiq/jzQ/tp3ekysY9g/z1lStwB4cJXQ/ThJHBMYJUBjOIGN/4CO7GjeC6FP7279kzVGWsEjBWqtHZmiOUCndgmOLWbWxSKU4/+0SMXTtxO3oot7YyOFLENE1M38dHsWjJXNxKjWLFx2lJUw0iiuNVPv/EUcx8jQP5OMRIRZJHnjlCZ0uGI3uO8pg3ztUndvO5+/YhtWDndo/upQtpq44yf14ni4YPIAvtVAJFxUox7CpWdqYY/6+bQGsOzV9IbtkStu4eYLnax6IzT6bqW6j9B8ndczvL/Yi+7iXs2tvPzsFBWjpaaVcmFLLsGi+zZ7DC8oUR89qyjFQNum3FU4eK3PvLJ9ACRp7czEXDOwiHR9hSs3j80lcRbj7MpSfMQbseldEiu4ZKHBJ5GBlk+WgF51tfJRv51I4Msu3CazlpXgtOrYw8tI+xQDNY9ikeOMR9y8/F932Wbv45K70RAmExvGAhB0ddBoZLtBWH2HLoCHLBItbf+2N8qRguuxSUZsfOQxh33U537x4GH3sMdf3r2bJjhDlHDhJWu2gbHeTeSj/rRvZR27ef9F33sWPdBYjMdhaXBnCLw6TmnUHJytBf8tBK05USVALJTx/vJVMaoXBXH8Hc+dgjgs7xo1haYWqN3vUEqw1Bbu5yRjrnEQQROwYiNj29n2dGB0mZBmZlgNNOXs7AeJWnajlCpRnsWogWgrY9+ylZabSVYst9hxktdDJcGyBzio+5dxdrw4ioBgfvfYxDviC1fSujkeSwkrQ7sOW7d2D3LGCb79L71DOY1RJywSKeMdvpHjxM1q9SyZ3EzoN7WDAyynDNwDRgzWN30lsejMORtEYNDsDQEXYeHiLa24fCwNKS7pu/ilursdtMkfrFPehMhqNFj8ElJzDeMYeNfVX+3DzA+LyFbLl/I7K1jZZiLR5Y0Yqfbh1g/dYH6YjiEK6e/dtxbINNdx9mUa2KIyNSP/8xa7oXcXTxalZufoCUKXDOvpbO0jDberdQPXSY73UtpLdrIZff/30EgkV7nmZ/Z4F85PL1cDF+pcp5G37OMiEQGgwDcsP9jA/0skWOYh2tcPKhvbgdPTywx6E1ZVFeU0FpWH7rV0mjsDY8RIjgwD0Px98ADe5P76a1kGakrYfO/YcYlYqqEqSHN9O9YyejWrBoxGXh7k30zllKmGtheHA7Fpryf3yawy96Jb17DrN2rEaLkLilI8hUmnE3JBUFRKU4tNF2axx44FHslrnIVIoHfng3rWGN1fNbWZq3KK9YifeLX7DfE9zReRId44MEWx5gVcFkhX4Sa94qesb7OTjmMhrBtl88QU9HgYVjvXQMHKY8XkJFETlL4PsuJTfkwL0PsfK602fUO37TJMZKwvMOXVdYGwaGG8gZlfZjjZXpRqql1hiI+ouVelz0hLHiBscbK9EkYyaqj/wbQuAGEjMdjxwKEY9yNxR+qeIXtxfKep2M5kRGVxk0hpil0nh+RKYwYQiZhkYFITUlcENJT8E8bhJoEEmc+ki0G0raiA0tP1JknYmQscn1MY3YkyK1xqu3M4gUGbvunQgVlohwvZBs60QfDhRdbDMi09XRNAAcS6CU5tBojaxjUfUjso5J1Q3IOLHiHQSx8m8aIvawiHiEWGlBLYiNkd6RCous1qZSXvMjwkjRO+YytzWNgaAaxPHrnhcgTBMDTXdLhmoQUQtkPEouJUeLHqNlF8OKR3EDz8eXmnK5Rso0SMkQlcoTRCr27FixsZWxTRDx/YMS+G6AcOIYbG3buEEc995WN6qk1JS9ENsU5FVIaXgM2jvjay4VfhQbN42Ye12p4UUSbdkUaz5pz6MqDXJ1D1VU8/AwKIXQmXdASgoivt6yXGbMNKi5PtmuAkNFj4VpTS0wsE0DpTWGEHzql7spl2qkHJuTF7WRHh2jmu1Ajw8jshmMWgU6uygVKxg7tzH2/R8QFUsMvfatOMNFrK5ObBWhPZ/I8Aj8gEBauIEkiBT7jhbpas1imwa79w9w684xym7Ih0800ZGkNlpCeR5V4eG15Rh3Jc5YEW055B2T8tAIqfZWvFAyPFLGDH20ZdFXjRh2JD3KI1UaJbBT2NlcPFkWgTswhP/VL0MYkP+Tt1J5/Am8zZsxszmC7gUEpTyHH32K8PafExoa+6RTcQ8eJrtiMUGpQuQHlMtVaoQ8PV6iWwdUTQMraxMUy5RlBqk16UqZkaMR41aG9PAQJaObnCVQKAyp6Bt38VyfI55gbKTE0HAJ7QXYFgRjRXrdceYFRURfHzKMEJFk875hbt0+QskLuTFtkE7HxvT4vfdx4I77qPgRj96/lBPlGH57F+NveQe1UhXXC7CK4xR6OkBKakf6GT/Yh7lyGZVQ0jvuE4USo1rh8FAZlcmjI8kjB8bZPb6D2ugYgeXgeQF+zSMfuBx0Aw7oNGpslFPuuIes30tYaEecejq/cFvwa4fIZlO8wnUJhcHgI4+zZVBj/vIXFMeO4m3fwTNnXs2ygUG0kkgpsYcHuL/3ICUrRdgn+dOUiYXmyxuPUK0FzO+v8MfnLaZmZrhj80Ee2TfC+Y/djgC0ZXEkZVEJJBml6Dq4E/vp+9m1waKy9iSe2N7H3QMhnh5HBAFn/vgndJZKdKUExWf28cvuYY4MV7jkjlswUhYDA+MUsdBBwLJyRLpapjJ4lGrBxFUhRweK3PZ0P10Hd7LywBYsKXGe2og0DNCKw1WFEwQ8eudTnLp5G62tJlYY8dTm/Zz2wN0YngumGXvathsckRIsC5Sm7eFfojEYMwxKWnHW3l42nnxp7HnXmoGaBK2xS+Oc+8jPGFAarR/nAhGfGyHiPkGjFczt28Pcvr1ow0BJydqNd2KqCGGYaCUJK30cLcOJWoNS7FpxKq3FEeaNHiFEIA0TW0uElAghGOw9TPfAQfZojWlK5OATWGY82AMCpTUjnma+d4D5w4dBylhDVwqxeyvF5aewav/TCCEw926iKhW7AIxYtDA2QLH+DWx8QweikPLWbTQ2+lpT8TQyAtNSCEB4Llor5u9/hvkHd9DTsZuS28fhmsZQCsMw8CaF8a7a/ihtI0fprQ/SrN75GI0PadEwEEpiaMW8/v3MGzgIShEhOP/hH8ftkZIOIWg/egi14tT6/CSNQNA7WsWQEc7oEKc/eTdMng+n4p+towNUb/8Fq7QGDRQHQClcpRg5PMycI3s4rDUZERuoR1yNRoARn4W9+6iiMaxdHAkjhIh1mwowFsbvu8acngVH9wPQJwRojagp+MbXadWaI0CvUhiGQJUjtGFiSIWmPu9NaZZufZR8x1zGO+czb8+TAPQ+oSm022jzLg6MevgKzhY7Yt1JaXaXJGk9xpryRoSAUaUAwdon72Ys145XGqFPCARxv9U1GLTWZMeH+V2SGCsJzzu8UGEaAqdurAxXfOa3ZWIF9RgvSRhptD1hoDQmQjaU8kb4jm0aSKWphRKzHmYEUKyFuGFEOw5REMesm4ag4kf0jlSY154jqmecGSh5sSIeSIYrAflUPGI/WPJZ0G5QcsNmeFDWMZH1kLGUltS0QcY2CSNJGEbUvIC2KKIaTprwWayiWkxGKgHtWYcwUs1QoyBSeDUPO+PgS02pFlBI2wwUPdK2gYVCYtCWsRgu+7SnIwzLIpeyiJRmqOzjhZKufIowCHAtg0hpwiCgpkzGqx6tdQ+PHylqFY+RoMzcQh7fCyh5NlrHGUxCL6AYRJS8kCUdWTzX55DUFFIW4XgR0dnBSMXHMQUtpiaUBlEYT0j9yaZBdvSOcd6auZy6qI2xWkioNA/vHuLeXcMcHK5wxQk96CiiUnVxXZ/QsPBrHqGCgbEqwjTjkKiKxxefOkytXOVPrzqJvQNlOoyIkq84MlKlJ2tRdl2CzjSHx+KMNe2GpFRyKaQt0oZGux6BncJzA4Tt4Y2XsQst+K5PaJjxqKGOjcJi1SdwfQrEoR+hUyCfshgcLJO3AN9gtOQipGR0uIyWkuJgBTOToT2s4SmXiiMwbIejAyXu3T/OnvGQS9b0cFJeYeUskBK/GuE6Gcr9Q9hohgbH6SiYuGM1ovnzSGmDWrFCxY0ndO48MECnpZAjJcx5WWrDRXzLZa5yka5kpFjFfOghZBCyZSTg9i1jvCmdoyvXQrlWpbPmEQbj+MUyFNqpuR7fvXcHO0qKF5/UzZHKILsOj2LYFkEokUHE2GiV8aNjpJHUMIGjRNk82VIF0drB3uIYlaFR3FwLuYxDND5OOD5OtaWdLzw9gEpneEdhnMydP0GbFvve/T7S1SKh1KiRUbYfHI0f8E9/LvYaKokaGiWoVNn6n19m3+NbMS2T9W0m3vYdRK5Pea6HV/OoDo1QGa/wdH+FR4Ylp7SZXL1kDKOrDVl1GQlKmMVxzr7vuxy6U3Hn5W/kkmyNJaMl5nW1kCvkGD44gPR8pO9jSMlTT+/lmR2DLB6qsa7N5IkdfRjf/BqeI1i2bD6R6yP8gB1HRvFGxyGV5kv37uZtFyns8hjRrj2xh01qqvv3E7TbBLWA8jM78bIFbnn0MKB5ywU59vWNIf/jk0RuwMgb34xv2BwqKfB9LrznWxhacd+Fr0KIeIBlbKQIvgfaY4+S5MaHOeOpOwkjxb1XvxktFd2Hd7FbCFR/CXPXQcTqszljzxNEGoKsZk9Fo4tP4+zspXV8EGmYbHt4E/flTqb4xAZWS4mSitMe+gmiPpp+98XXE5Qq9LsSWalBqDgyIvmPX2znZacu4IEdR8m75XhOF7FCNe6GmCi0hhOeuhclBIO1iPDBjRzoC2DRarBTtI4N0jY6QAQclQIhFFa1wm7PZdFgmZ68w5ivEHZc1oID24FYEdxdlmgNT//ySc7c8wS2WwURD2gpKWOluZEUQypOe/KXIAS7xgLWGAapO36CxEDrerim1rGGTn3+klZ1vVYhpEILUFpw5lO/jOcGCaOp+F740A/BMNC6MfBUV/jqfSIA2VQCNaLuITZkFPdZ/bylUNcnq8f7V+95Kj5CgKnBlFMzT3UfPRifS08kHxFSooWB0MfMNdB6yn81sGrf5mZ75eSgBKXi2tb7s1H1ihegG97+yXMG6wOPdfdxsx90vU87hw5zYHJdGuXXmXv0IAgRZ6fSumngASAbPTe1/s0ELJMSTOhJfdaQUUiE0rQOHgE1te7NOnNMoo9GwgBgTu/ueJ8GF83hWvy911qDEhPtB6SUNO7PZh2kAmHE99dkGnWYlPq6eYfU+wFRDyGuG4ZSxsZx10gfXaP9U67BjrGA+nDtlGsBEExOPlK3RwQarTRtpeEpbajbUAgRH2NGs6eV/k2TGCsJvzMmx3DPhhtKMrbZfMk2QoTyKYtAToReRVIRqrpnQsSTs/1IgY6NlUo9Zt6vh9NIHY9ihVpRC+tZqcII3w2gLUul5mOkoSVtx/H1tYie1ixBpIjq2VVMJSgrTdGNM2bMbU03w4lqgWxOlgWoVV1SKRuJZFya2AUD1/WJgoBazUf7PrXIiDOreAGiXMPJ5PHqaSgjpaj4Ee1mHBpULNVwRN3T4wYcGTX49zt3ctqSdl6yqo3AtGmz08gwohQG5Fvz9aw28RyTxiuvOlbCsGyKtYCUlrh+RBTE2WQKGSd+kYdBnBbR9wmqNWrZNOHICLVsC7g1cFJo04yNDS8g0CZfemA/HUbEGy5tpeqGKFODV8UvtKOrFZSRZseRcSKpeGD3EBv2jvCGcxazqCPLht2DaA1PHBjhtHk58EM+c1cfR0sBb71sFbJW44ASfOX+PSzqbuGlJ8+jb7CMVArZyFoUhdxzaIQfP1Ph+pRLh+nguwG+G5LL2ASR4kv376IWKv6/l6xDjheR5QrhnHl4XoDhBESVKhU/wq95GNksoYwzl7U6BkrGufef7h/hx1sGOf/UNKWaz3/fv59z56Y5aWk3fqWCJSWuFyJkxHg54Hv7j7Iur7moQzBk26RbCviez87+ElgWG/aNsHR5htJ4FdJpvFqAaOsg8jwG+4ZheJjhoklQrBC2d2GkTIKBAcIgpOfQLlY+sBH3iiv4mttJtLWILUPesCqPK0JUNIiSIs74E0WEde/f17YM866F81FjJXJRjZCAYGQUYWdwSyY7+4vIVJpNB0fpr4RY9RFMlCCMBJ+7ZzeGW+O1y1KQzlIZGWOgrLjtiQFOHrcIf/EzundvYdGb34jb082tO8ZZc+f3MNGo868jMiz6n9lFdaDGvBS0FEuETzyCfPIJSqecVX/2FV4QT7LfOhoSRhrxha9jVCqxEiYVUgq84RH2RSm+/Mt9nOUOcNaPvg9BSOtYyAXpPHm3zDNCEwmT8dYuNp5+JS0jA2ip8JRi1daHUUf2sHHuUq7s1ARjw1QCxWpnLpuXn875j/4Uua0AokCkNFvHIoz/vhmEwZirKG4/TEvGZrwWsO+p7ciWLtY9fT9zjh7gK97LWLpvK4sOH6gPsmikEjw1EqJERPvNN3PEzHNZLV67Y6d3Lvt2bceu+Cit2Xf7Eyw/sJU0cHauFaFiBe3iB39ANZ3jiZMv46KNP0VIie9ksLXEjEKUaWIoyWV3/TciDGOFuR5yGAEn7tqIRmDo2FABQCtaxwbiP4mV4yvv/BpCSkK7roDreORYaM3l93+Xr6hX01HeywVP3xsr08JACDj6S8GlSkJ9TBZoKmUNBa/xt657sZcf3MaKw9unKM11ITRw2f3faWpMQ/WUwTQUZKWanuuGnnby1gfqym3ctmZxx6VzjdsVAdtG/PiAukImJilzzTYco1xqPSEXN+0Y5XNyGcfqpcfWRB9bt5gDFTltcpTJVWm0NVZiJ+2cYjxMU/6EJjztuaffPPUa6V9zPY9np27SHXtPTCuqpv09U/WFihO9LDq8Y9IOMemIaY5stFOI+n0kpsjpmfpyhr5Fq9nbdKx44/RKTtkwxaCa5v6EY85z/I13zK7ja6WP6Zauod7nXO/fBImxkvA7o+JFpPImVT+Kw3CIFe+G50CI+loBkcIUAo3GNuOJ2H44kRLQMeMPYtWXRHJiorOUGj+UlP0Iy4zDqSKpULo+WTiQ8Sh5IAlkHK5T8SJUGFJ0A2quj9IGliGIXJ9Ia8ZqAcWqj2mZlNyQQtpm3A2RCqpB1JzwPVj2SdXbEYSSmiFi4yKXIU9EWVrYpkEq8iHw66P0Hh5pomqNaiCxah4qlATFMp/begQsm+tOmYdWCss0qVRcUpbACxWBH/HI3kGk1jx9cJRzum2sVAo3LYjCkEhGiHSaatXFcmIDBBkxPl5l5GAfYSrD43uH2Hq0yuXL25irqtRGs1jdHZRqAcr1iAyJV/OJXI89fcN8656dqNY23rk6GysN6TRHjwzws2eGyBTylL2IYq1KpVTh+48dIpV2uH5pmgcHIrbuOcpLTl9STz0pYuVJSh7cNcDL1vUQhSEYNmEg+frGXta12xwarhAh+MnmPkqjJZYs7EJJxcGRKpHUeEGEH4ToMOJg3xgPHhjDc32Uhg27BznJbqUaKH608RBzOnKcPj/PyHgVKQy+9OB+5usaV7RrKq3daM8jclIMDhb5Ze9ezuuy2Dw+SGc+xYWrutG1IsrM4IchP9s+jFKS+3cOsi9nIsOQRw/6bBmNuHauYM3CdvzxCoZts3X/EFGUYcuRCuc5FtGRfqqlCtoPUH6A9nzGMPjaI4O8folDe5skqvn84vFDlEfH6clajOw9yMsObmBg5Tpu7zO4elU7XV4ZWfVYu+1hyJjIe37JGYHDtrXnsfjAdsq7NT9ccT7FCDIGvCXQmEE8+rqgfy9HFp/Axr1DPL6jDycKuW6RQ7elMfoOE4w5cerLKKJ3tBaPpIv4+HM33snTB3sYX3AWIgz50vaA65YLOlPwvc3jmFHExt1HuWzPM/Fz8cDDzLvyUkYqPhm3jDYt0uVxWgYOUt36BAhBv6tp+8JnGfElRyoh3P1gPUxGU/KhvxgPQCgNdnG8OSqI1uwYD6kM1Ng0NMrZOzZgeC5hVwodhJhAtm4E+EojhKRl5CjKCxD19Kygmde3F601Xf37OTxqsLg9zVAloMvbz6V9+xFaUzrq4bQbQJzAoV4BtFZIDWNuCFqzbutD3Hfedczt34eQkose+CGoxqh8fJTS8QgsSjPmQ5aJRQbTTz2GUZcBWH5ga/PAQmV84mUqNLlaiYsf+VEc7gKkArc+iq8nRoob6ZHV8UrRsYrxFMW8Pqrb2FYJJ8mKRhU0uWqRNTsfm6LJaKURwqhvmjwqLaccP1EPPbOSxzHbG8pZYyBcTfJYTKOATa+oHjNSPst5jmPGNTtmVhanVuzXV+qfNWX4FD17sgI923FTle3nXpn4PwcXrmZJ766Z++t/gGjaKHryj2Ol+HXqHxc7zbV+1uIa1oIBWj73c/+m++d/XJ6evamzlK9nvBa/XRJjJeF/zGwpdifTWOjIjybmmXhRPGnZrU9ervgRkVJUA02WeB6E0nFK0kZ8pmlIBAIvinPupywDo+4qjudXKAZKXvwgmiKOgw8lpVpANm1TCyRSw0jVj0POIk2tFiCjCL8+cbt3cJytQx5XmSZpFGYmQyg1oZTNieFaQ9mPmmsNNDJtPbF3iHKgOH9BliBUhDIAFVF1LMJaDRmE6CCgUvWpCqBaQwoDXa4Q+RGloRF29dUglaJW6ySq1ujobMUvVShZcUgVUhC5QazEhUG8gJWGkSL4pRqugh/vHGdJWnHm2iXxBQhDikOjaNcl8AJ+uWMI2zK5Y/Nh3nBCK97YODpfYLjoQrWCzKZ4dO8wT/UW6fMm3OF37y9y9jKLbzy1F6pVhIBxX6GdeO7Ff9y5Kx4Zt0K22R4PDhmIsss3HjuEiKL6ix6UluwbhG9sqCH9ADIWKEmppnm4GMcTayfNoVEXXQsYOzxOfdJRfB+F8cJdAD/f2g+GiVlfEM1WCul69Fc1gyXJUMWnr38UpWKjeLjkMVypoisQyWG27RhBW0WE66FbM9y5cxxtmQwWbdKW4PCREYYCwbqeDL4fIZSCMKBvNA7NkFFEserxjR0ha0cCzum2saMIR0vwfZSSgAmVCkjJhgE/zp0v43k8JS/ivqPwUtvg6eGAXcUQpKK/HHLOlkfZWS1B/8OUL1vKD7YM8sr5BoQBaMVQTSGEIEPAWY/fjkZQGoHanBqLBg+RrRb5pWWzYCReo+DE3U8w0jGPx3fHE+xDqfj+gXi9gVX7NmOYBnrJ+vhaGUa8QJphsPjwdgrlUYpbh2lLL6JY6MCqltnzg/sonnYKbSMhyw9uZeeas+L+AWp793N49YkYMgfURwSlZO22DTQfImD7iDdhhNS3a6DPVVO31UNBGr99DeZdv2BNrhXL91DA1pGgfq/Wg0YmDkADVz7wHfYsXU9jRH0yo77CG/GoRRrQjQF2ADrHBo75uB+voNqBzwm7nmi2f2r5M4+wH9vuY8udTm7aspoWUXz+Yz0DszOd7DTbJp373CfvmCr1bMr+DLt+TXXzt8pkZe7ZdVg9cf82jenm/x3HiS0Gm12bh856CZc9/MO6Lh6fRQgxEeIzTWeZhsCoG81T6yjqYWgTxrwQsWcrbWi8uq3Y6hgUw7ieJ3al2TFUQwtBxtC4kyLEjm3zM6vPZNX+LdgyBKV48Nw/wpSSJUf2NK3HNS0mAs3eisYQsDxr8PRoFOsHU9rTrG39pzHVMzJpTg9ao02zGbrXqJlhCBQGa/KwsyQptXRydO5SVu96slnzTWsv5PK9Gyj68UClhtiQFkxreM7Lmgy7EeE0NmnKFCwvmBhas7VmIrRqXvej3YuZO3hwot5N79bE8ZFpxSnyteaui1+DrSPOf+Sn2GGAEMQ6zKTn9Z4LXsmJu59oltu4n05sMXCV4EBVzfqcCY41LOoVasZzwbK8xf7yRAhh/9ylbFtzLmjFdQsdiiPj2Lf/NK7P+a+I6zPS+7t4/I4jMVYS/scoDZOXYlD1Cd3HZukK6nMworr7Vet4zkhL2saLJMKI5wZoDV7dKCikdX1NDgUYlL0QgYPSsVdmuOKTcUwss+5xkIrID/EMk7Rt4KCRwooXEyuWSTvt8QRoDUU3XpgwJSWlqocRSnziVKy3PTMEpsmDO45y8ZIWMOuZwapevBCI44BhMFqs4fshmCZREL/kHtk7DKbBioLBXNtB+gFCCfxMFlmtISMNYUjFDXCFwKh5KNPCrbg8tXOA7vESShpQi+dsRKGHtGyicplSKoWsuQjbRvo+BBFaRuggRHs+nlD4lSp7xkO2H6ywW8DKOS2QyuLVPG7f3ctyO8IOYmU4BCIVxelWKy6h6+O6HkapRJTq5AdbR1BSgW3HipuGvWM+Q/4I+BEiCGKXOCKOi40kilhJ0obBT/dUoaUNZARBEE/gFPWXaBiC7TAw5iHCCFIqHvEWAqFUvKq2EaAjM44Nh3idDMPg8/fsZoVRQ0Xxx7Hx4VG+D1IyVAt5vF9z0NWgbLSUjJZdMEzQ8cJgQkq2jCmkO4BQEhE2FC0FURTHpgNP7h/FDHwwLbYeLSNkPPlfR3LKYnoi8EEIdoz4bBv2SQuFFymEGffbf+0JOLXdZE2rZsuAj22Z8SJuKlbAd5QiFg173Hs0Arueu1hrTDUpHl1JROCx9d5nyHctmhhppikOaCINFz7ykxmf2Qsf/Sn7l62nmG+nUBrhyNzlrNm3iTlDhwEwfJ89y0+ma6QPQ0qOLFhBqlJqnu+Up+/jiVMu57yNt8UF3tXHWfWyz974i0mKhyb4+c9Y17WwWUFDSaYPR5nlC6hn3m9oRUtljOYZj41X4PifKw9smXRaPUWkOp2mMm2dphtN1yzs3zv9SZ+DrTKjN2C2c882qj9DWNH0FsOzbZtdZU8ZsUi8xstvWZt5lpHdBRmDI278wmq3BWPhr1EfAUZ9MnpDwWvoebYBoYLRjrl0jB6ti+v4vYZg26mXsHbzfQitj6uqIQS2afDg+S9HITDnzUcM9BMJgVCak9ptto0GTaPFy+RIu1UAOlIGCwoOKgzZVpQcXnwC+VPW0/7T706qtqh7r3Q82KA1CzKCvZV4UKMzYzEuFYNrz+TEgW3kLagok+6Cxc/WX8WAyIBWnLh/Ewt699C4llesX4C9bi6HfvwLDK0Rlo00rdhokA0DwoBMmuVXXoK4904MGZE2NMVCO20vexk9v/g+fcNlWlImSzJQW7ka68ABEKAsB7NaQmvYWa0HEGo4ocVkXNgcLemmkm0gePjy17Jm7DDG/scY6pjD1tMu5c/mh4TDW6j6EQ+3LEN39dAzlCZT9eirSbKtLbgKdLWGqL8xHNMgVIrRlh7mvv7l7H10O4ufikMIT2gx0NAMMzdtC6EV+bRNtRLRMC57569g7tAhQsvBkSEFC8pR3Cehk8IOfLavOpP1uzYSmiYIg3edPpcjy1/H5ns2svzwdlKmwK2n03/4zGuQlsPWtRcw1trNiXueBAEnFgSOZWBjsCZl43kB21WOuVGFuWtX0btlF66E/sVrmH94F1oIDMDL5Fhn+xyuSXwFSsaDOXbKhnLU9GQHS1aCZXJeq+CkDocH3RyNBPjSNBlrn8uc0SPNeSu/SxJjJeHXouFNmVjdWDS3h0qhFNjmpLS8Op6kPlL1EcQjCH4Uz8nwwtgwsU2DUMYv1FDGGaTG3bBZppCxV8aLVHPV4sbkZ8eMvTJSxQt6BXYK24zHbiIjnsAupURq3Uz763kBCIPAi+PDHS0JRERYH/3FMBgvVnlib0DrXM2KjCZQIXsHanTOaaetrYBfLCGVCaaJqyXStBBBgE6lYw9I4BP5AZhWvB6HH8Zlax2HMAEqkohKmfv6ffYb44iSh3ZScUxtGKHCiJobYNZqyCBeWVr4HgdHXUSo47l8MgKlqJbKhH7IliNlhB+iHBt/vMhu32Xb/gGGx1weF/Cnq8I4LMMQGEqxZ8ynKxuxoFhCuD5lX/LLPUV0GMYjUfUJgnGaFEXRCxG+Rz1IOzZETDM2Rhq6tZLNCYlxQ+u/0WDEWVCQMh5t16pZtpC6+bEVSqODoBlCIpSK88wD+3wfUulYiYgitGFAFGfEwTJ5eMCPlYeUhYjCel113RiJr4MWonk9kPU4+Ho90LHxpS1Rn5g7dXRQKBnXqz6Sma6VaSuPcnTOYsDAayiKk0bw9h8coGpGkJs7EYbTHIWHO49GE30nBLnKOFl3YmX0ef37aC2PsrB/LwsPTYq1/jW+HcsObG08zKw4uG3KvgVH95H2qnSOx3MYRtt76kZGvN8JPM7b+PNpy7WmmXjZPTwR37y6Pnn3V+E5qvDTH9uwQX/tEv4HHGMI/SZpdwRjgW6eY15a0O/9Zs+SswTVRuZEMTEVYkXeYG/leENoadbAk3Cwdvy+FltQqhsNvfNWNI26+869DkaHuHLNHOY/sxF78CiehCOTvGqLswaHXTWpO6caTgLYuuYc3EyOMzfdTYsjOOLF76bqyhNYvHQ+1pZNBKNjjPiKMSuDKSV2FE6Uc8zo/uKcSdv1r6Za9eGpx9mz5wgCWFowcfJZHj39RWypmJz36E85Le2zvSIw6mGBlQVL4ekHQGs6MybOm/6Yse98h6BUwTAEqbZW3ra+nQGRYcU5r0Ec3MfhbCdzvv81Mo7F2jZFBYuCqThy1R/x893jROkMr8qV6bnkLEa2PMPje3y8Qivrc1k6XvtaHttyCF8LXnTqQvK2IKy6pPbvJjp4EMc0WGnG4cwL3/NurJEyi1s6UcE5LB88wljnfNrGBvijdJ6xfCfff7KXfWvO5JyrL8DTBlatQq6jlRYb8gvb8caKPNDSivZcFndk0VGIdcVVKNugtb0VxzQYTaWwIkH66vNZsGIlnfPmYJx1KoUnnqR1yULk6hPJtLUzcsa55PIZ3EqNrFumPFZih9vC2ru/T7sjSFsGOctCGCFaabrPOJXsiSfwJwvmUbaWYQys4NQILp3TybyMwb6XXY9TLnPOwmVk8inCc5Ywv/8Ahqto72hBp7NEP/wuUaWCe9YFzF/Qzf7RGnPmzWXV6sWMBprw6Ydw0BhnnYvZ3YVwUvFw3J0/x0lnWZg1sNIK6ftoy8Y6YxWpc9fQZUaYu3aSeWIDrjIYfNF1BLkCnaUhsoUucuefSC7jcGIuS8ecDsI2j/NR6OAUTu1JsWU04IneMlWrg4wJrz11Ll+1LYpd84jyrZzcXaWnu5ViuYbGJlceY+3iVaQG+rE62pi39mRkpcrSrm6erJxP/97D+KbDq9Z2Yt36bRCCoaVr2D1nJW9cYiPWncy279zNeEsnTuBz/dmLODeTJz82REdLGuHneOSClxNoEKbF8PITeLKtkzMfv70Zfvq7IjFWEn4tGt6UOF3vxHat47S/kdI4yqDsRbTnHEarAUrpeL6KY1ENIsL6fJOKH8WrNcs4Ra5pxDHbSmqCKC4zrBsYjQnsYaSa56168UT6/cMVuvMpIt8HOxXrwloiMesTzRVHx138KFZ8lYwwLBvl+wRaY5oCWRrFzdlx5hTL5mglYGC8hh6T2HNNlGlx975x9OEa7zxnPpFfhXQe4UlC08SVQf2DFyvQ+B79Y1Va2wrY5VLsiZAR2rYJg3j0HhmB5zPgSsjErl2hdZwBScbGSUPJF9VYcQ3rqXSpj+SJMEQbBtVayP29VYbKfjxKq0ye7qvw6FCA8NzmNQr9ABGFaNNk3Jf88nANhODN6RHKgeKHB3xIEZ9bGBNaSj0jjSCaOrrZMPC0AqnBjBX95jF1w4NG6shGOFfdYGmWoZlIo1n3EojAj49TKvYsCBH3GZMmvMoQYdloNEJGoG0aE3lF4NVH5uPy4rImZfdp5Kls0FzDZuKccRsmDBOgbhhFtJVGKBY6uLDuZTCUom/uUlJ+jfbxIQa6F6INEzvwOGvT3QD0n3EVphAUs60QTcRe2IGHE/pUc21gGpz29H1Tnru1uzbO8EQ+dyW1OyUY8htK7szhmw1DBWBh/766ETLDiP6vQHtx6Nc67g+ZVltQ/BVH//NWbKwIYH7GoMWmaazMyxiMBQpfTr1KK/MGbt0QqGXyUwxhOD7yaF5aNCfg5yyDWv1eTZvH3zdZS2AakD1mlxCwd+l6Lj66jVKo2b/4RPYuXc9A10JC2yF00lQLHcztzNNy5RX4Bw/Ttm0zw31j+AqyJmStY+o3qaLe1X/ESqrsN7oZcA0Mw8C0LJ445QLSvstp6xaT7cig3CqZLU9RsEL6z7yMnrxDjwgYHymRasnT8sj9GKFPSQpSQtOVtUnPm0t7IU8gfMKBQdJCYZgGcuUa1iyfx9Yd4zx6wcu5oGUQr7CUPdsPUjYdRCrNpnOuZsXwAc655lwG0+3kzzoF+egjtGQdCq9/PX5k0LVoHnYUogzNvNYO7MWLscaHyHS2Y159HanKOIvXreWPWnrxw4i18xfRPr+HSv8g1eF4Yc+ulgwnFGxS88+lNWWQsw104NNqC6rrTsDduw99tJ/M1qfBNEl3tLC8p4vDVUmKDFnlkl88Fy8os7ing/Z8K286LSQlI+Yv6kHXagxX2smmTPL5NEbKIpuzsfI5XKVoK6SJXMguX0yoNB3drYSZHOYrricVuuQsk3nz54Flk77gfLInrKEwt5vS0WFMy8Q0c8xZ0MngUJGsaqNkDfGahfMZO7SYloFeLNui8EfX0Wq3kj20j/T69fTYmo6WNIfIYGSX0em6tLRkyaYN0tWIYMlSls/rJKhU8ZwsTtpgQbWCZdsEqSw64yClQ+7MU8mbikWtHvmF82ib08G5lTK9nVlSgUfL0kXQ0orV1kIgLOSDGSzLQGjIp22iSy8j3dNF2NqG7uomPzJA9rR1eJsfJyMEq05eTVUJcqMWc3J5ZC5PWigy+Szp1hYiUWReZ4Zw7kJybWmWHxmkrTBGt9XJ2nSI1Zpl7eJOth2xKDgGnavnkA5dovZOcLKI/j4WLptHNZ9CCpPO1gLC9xiuRVx/zmo2Le5mQTCOZVm4r7ie0sFRDrYswgskbUty0NVOceFytGFQCyO6O1sRhRZKMsBqyzNXg2xpJfBCTprfwoGqYrRnIZvOuILOsaOs+JXeWP8zEmMl4ddCaY1RH1RXx4weNla+dkyDWihpraesVBqk1KQsjVs3TKL6YnpSaUKl6wsqGnWdWDdTx1b9CEPEWY38UKFUPIIFUPFCfrlzkC29Rc5Z3sHJOd2sY6Q0gYyQ8Wp6hJPXLpEKTCAMEZUyYUsLolKmGKXqBkc9VWQYx+gOFAM806l7ASKE7xOVSgjDjr0dqTTfeuJoM2NIGIbsOupz38EyPa0RL1+ahiAOU4rCCD8MMGouutBCEEVxP05R3DVhpYqpZXPCsCgXUekM1ajumRAiNiZ8H2HbfGtXCS9oTPzTIBU7hl1E2FC4Y+7dOx4bHUEw4W1A8LVNg7GHJO7A+j6JUPVtsm6kRMePnIpm+sxJhgjU51Yw4VWB5j4h5aS/G0aDnvrfZp/UDRvDnNimjzF2GmVODgWalAKyaQBNMhBoeF0aTM60ojWikbtTqamjSVqx8sBWlvTupL9nSXNze3GQvrlLuejRONZ33uBBnlp/MZc88uOmzLlP3IEAHj31CkqWjRUFaERT5pHTX0Sl0EHaqx7Xz/8TFmUN8haMBI1BhuemIC/p3fkbrcdvkv6eJXSODeCE3u/83J0pwYg/ex+mTZrzBWbCmmWQMmtCmxOnGi84Boerko6UoDNtEChNxhRkTBBCsDhr4CtNpyPImQZlCa0WFENImXH4km2AWLGKn3etZUVGM7RlezxJmomZBALoSYsp4b3dGYMw0hhw3BzFtAk9qXhtJYVmsHsx21ecytLenVx4+nI8naNldBtLDAOxYj7nLLTZ2bGAde0mP+oNWaAluUyKVMqCE9Yg5s9j/iOPUN53gK60ScqEJfPbeTg1j7Lh4GfyZKpFyrlWXrRsPkvzgryn2FA2WXjdNeTzNhc43ZRKNZZ12Jj5PHL9qZiZFKKllXMKrWjLJrtoAdkDh1EqovUVL6MyNEJ7ewfp/XvIr1qG0d2Fk0lhnHwqLWPlOA09BuKEtaxcNperUzmyQpGyu7iyp5tCNkVbymTJigU8tS/H6Z0n05E2GKsJjHUn05W1ya9bi9XWRkqZmJkM3Zk8I7UaczrylC+/gnzvPlInrKE/3U6+sBSnuwvt1siFHumMjXAcUo7J605oZ9iTrJuXJ43k9PYWonQGhaB05Ci59hy+NFhwwdmMeRF0dhCsWoPtOKQchxbLoM0WmOZcZGuOfEua/Lwuwshk1cJO0koiWguEjkWL7ZF1LDJzurGv+yMoFnnz2mXsGqww7/Tr8YpljPYWQj8g09qC2dLGwjNzyCNHyJXHmTuvA0MplExjr1iCKsYZKd2xcTpaWynkM9i5LKZboy2fopJtoeUV11J7eAM9Z59KdfFyVgUatyuP19JBd8HAFppCaGIWsig/jZXPYrflyIcGUa5AoSWNqxVOKo2oZXEyaQzfIzt/DsE112AVx6guWUxO+7hD4xQ6Wslk06j5PSx6x1vxn36a/JlnoMfGyCxaSA0Ddc2LyXe0U+kdotCWhRUrcQIfZedItWSp1BzaT1xG8Zpr8O0Uzty5+OUa3VaI01Kg7GQQgU++o51ICFrzaUQmhZ/PYmTTZNpaaPMDzl48j5bSKOOOwxXr5lPIOFx60lxawjJ2y0L8kXEWL1lEP4pcRzuRAl9YtLWkCY8eZfkcm5aOVk5dUCUYk2TmdDM0NM65q1bRPebR1pIhnRK0tabJ5DP4oeTEuQUyrQV0Pkc+6iC9oJuz233cUJHxqqxevZDNIz4LW9Ls2N/CHNbwu/RXJ8ZKwq9FPOdENCNrGmitm56SXKRwgwgvkoxV44UDlYq9MZo4fEsqmvNYpNQEUmGZ8WR5qSYmdcdr98V/u2EUZyhq5qpU7B6IRwg3Hy5y8up4Mm+jDF9GaNPEaIyg1139wvfAdhBBgCiXkOk0hAG1hq6vYIpyHQS49UXBRCQhDJAyVoZFrYarBGEQNkf779xXairtA9UA4cchR4Hnc/P+kM6U4PpFNoNljx/uritakxVzpfjGpgEwDLJ5jze0ayxD8FBvlW3lZofHFVUSUXbxwkYIU73eMoqbG04Nzdkz6sYxwQ0DQ2kwGu1uHDtJw2oYHpMNkuNuiqmToSf62Y/lJ8fPTxMiRSPUbPL+KR6P6Phtk916DaPn2JtyMvrYdnCcrDjGWJmYoX1MG5hQ4ucNHpxU3tRTdo4dnb4qwJyhw2T8Guu3b5iy79wn72S0bc70bfgf4NSV4k6n7l2ZgRV5gz5XTZlw+/ugYAvKdY9DztD408hsO+EcAC565CdxRqxjmOw/Gm3roWN88Dmd2xSxx6Mm9RRjw03nyHjVpoJeDOI5QtPRkxJ0pgTbS9OHReUsGFcW7bacYvQszxkMBbHRkbUE0elnYz7xKClLsCJvNBek7UwpTGEQqTiDYt6GFhEbDQ4w1xGE51zE3EcfaC5067/s1aQtmzel4oVd/6t6KkuP7KbVAmEIWi1B3m6sbQF3Xfwarrz/O5imQZtVX8eiUCCzdi32Yw+xIBvPi7AMI14E9oprGKwWCGuS3ctP4V1rO2kbqaKFQWdG0N6ZIshYFLIWC3IGb2xvY8/ucdqWLCBVKWJpQVgo0HnFZTi5R0gd3IeDYtkl55Jv7eGXg4r5nTncqkfJDenpbieVFXRqg4uwmVtYRMq2WLW3F+EEmO3tyK4u7IEh7BddjbFvD1kEZWFiWhZmJkUmFLSecip2qRKvUn/eeaS72hDZNE7agdYCzqWXkSmPk28pUArByWW55MQcruujxsZoaWvhqpPgyFiVJT0ttGVslNI4QY2sVBR62kgvvJBUWytUKuTSBZRlkMmmsVsKdOTTWAvn0bJmKQhBxtPk8mlyuRSlthacMIVtC4RlkWlvZaXhsBZFYJhYaYdUPoOVyWGbAq+UxWkp4NQCcmkbK58nuvpKioHCtCTCNOjO5zBkhD23h8h20F2diFSKtGmQMjW2ZaJTNrZtEGlBPp/GsgyyZ58NR/s5dWE7nfkULR2LyFUqqFoNt+JiFfJgW5giRVjIYvq1eK6HlFi5LMJx0IYg3dVBGEbkc2nMTJpCKoVKWUgD8rkCNbEQzj0HqzVLPp9FV31kezuk0qTzFpgmLV6ElU4ReTbKthGGQaE1j5VNk7JMZDpNyrbwcnk6WzNUjvThpCyc887DGR/BKmSwlU06kKTSKQDS7W2QWoPd0Ynd1or2atiZFFmpMC44n5RpsHCtAUMDmD3dmEMDdHW14ZgGMpshk3Lg0ouRpTKeZeCkbPKFToRtk8lmqZYF2WyKqh9hpyxUIY9VyIIBVi5LNp9BpxyyrQV8LFqzaS4+oYcFnXlsVyDSaVKdBqZpYPd0k7FNnDmdjNUCMhmHTHcHqVwWYRrYloVsayWVTpHp7MARmit6CvQFBnlC8imLP7loOfduP8pLTurBti3sbAZDa+xshowQXH5CD6PVkK6edl46Px6A7rI0lXIZzxt7Tu/S3wSJsZLwa9GYqqDr/2tuB/qLHu1ZGzeUlL0oDgGrh4dJHf9TdS8KxAsNRjKelxJJ3TRoJmfGaJwT6lE6SsfzHgBqtdh4oh6lU1eypdKEoURhgFGfVKbqHhNoZlNqLvgl4wnefiOpS+jHo/gAWqGURDYNHo3w44xORBGiWiEI66FfWtetq3oMxqR5HQcqktuPxIZDQzl5ctCjoeE2swg1jQMJUlH1I/ZVNY+NRFQiPeH9aHRMw2txvPVI2T9e4xSNeSQTU7MnQqWahsRkj0S9fmHEcdp4g2PjAY81PCYbJs15GsdsE4I3LrH53uEQXzGlLcuzBvurM5QzqY7NPpguQ92zTMwFJnltNCe0GOyoHNuuOFzrrM13z1CA5px6lqQGV97/nWklLRmydsej0+7rmBSG9VyZnzFoteFwTcf3yTE0Hplnm0LuGPF8BVdO31+ttiDSTMxnmAGzPs/hWcSapAzi615nflrwVPciFg8fwg4lI8KhMyU46mpqqRy98ycCEYY65x8zuR0GOxewa8WpXPjYzwDYvPZCrliQZuwXt9NaGnnW+sxJC7QWHKxNGG4Pnf1SVo0e5KKRnYQXXMS8237GUVeB4LgsQu3OxD24ae2FnLrtwebvnpTANiB/6eWIsRGMBx/n8JzllPLtnDj4FAszAgotqEuuQKUzqGUrmNeZpf/zX8QQBqn582C4iKjEIxeWIRA67m/TjAeS7NPORGuJYRjxHLjXvBFTqXr2wthyPbvTJG2bzE0rMraJN2c+eB5iZAhNPMn46DmXsX58N6nuTsIjR4guvpwFpo01fIDU0AC+lFjdXTgvfim+F3Jdh8l399c4q9PEMgSFjEP1pLVYxVH00iUUlMS1UhTMiPFsC8p26OxswY88LGFSyeTJuCVKZ5xDet4cxMF95E9YTW2oxCtPamMwMsnMb4HxcWQ6RbqzgKp52FaGfFsKyzLJ5jJIFWJ0dmDk84hqjbauNoLBDI6K8KSJ41h4qRSdhRRtna0UUiZHBzLYjo1pWzhpB9uxsbJppJ1Chi7pXAbM2DjIWAa2beGriHQ2DY5Fyo0Qto1TMDCUwvIFBQIcJ09KBqRyOVQY0NaWbz4/rZ0tmKZBviWHcGwMxyFjBrGnCejsKCBrFnbGQdg2uTndUPMxCgXSvoudzSKUxLFN0JrsnB6QAS2tBRCadMpCpmwCL8IwJMKySNsmwrHQWuMIQdTeDoaJYxvkLBuyOSKlcDIpgiAi35pHI+KFlu142nXKju8hI5dD2DZZy0ZkMjhCgJUitOoqpmFgWBYik0EIgchkMXJZrKpHOuNgpGJDQTgOZkcHWdsG28YaH0VYBplcBgONUiY2JiKVio0VJ062Y6aylNx4AKq1LY/SYJkGUcbBMgzClgJGLhV7ldIplGmAFS/qjDRoaS1g1deAMw1BNpfBaGtBCYHIZbHNeEkDbZkIQ5DVmiidwrIMItMkY5sIIci15uJlFLIZbCShIcikbcxCPRuibZPJZeIuEQIzlSK9fFk9SkRjZbPYhRxm2iZtZ8gKm6xjYYg446mRycT3Vn34xUmncax4TnBLxiZlGdDaMvGdymeRmKQsyNpphO9h5jLkHcjruL0rF3QwL2eTzjh4GDiWiRCZevkpkJLWTJasE7cxrcHIOKQoJMbKc2FgYIB/+7d/44EHHsDzPJYuXcpHPvIR1q1bRxiGfPKTn+T+++/n8OHD5PN5zj//fP7iL/6COXN+86OV/xuZrBcep6MSb2vMS6kF8Vc+zgIWZwsLpGoaI1E9HKwx8tcI/3pOFYB4cvVk5VjK2KNgWrhhBJY9cYyUE8OsDSNi0ki6UBJf1kOsqIdYAdRXLdbNydfws/0Vzu8QtOXrHgEZ1UfsRRwSJVWsHdaV5yPlkG3jx7fLnEn5nmgsKMV9g1Ez9O1YpftAOWRZBo4zVqaRjc8zTY74WWYiG9QTRyvZTD287OA20n6N7avOnCUP/rMw5eZRrCxYFGzBW5fH2bD2VDS/HFBc1mOxpEOwf4hjPDSalAmBnLbaM9I52s/iI7t5ZvWZ+KnslH1XdhvcNRCfI2cK0Ip5AwfoGepl6wnnIC2bs4d2kjkm3r9Bx/gAc/HxzDhabrapCAuO7sdLZUn7tV+h9tMjgNb6rb4oK+itQU1qlmQN9lVVUwaY0lmWiNNyzksL9lUVrXYs1e4IhmfxvizOCoZ9mh6agwvXND1Nloi9M4aAfRXVNFbqus1xSn2LHU/kXtKaouqF9LmK+Zn4+JMvOxv7x71UIlh8+YXYjz/KffNO4tDCNc3jz+owKXM8Tuizam6Buy5+DQAXdZsszxtsOPVMuP/2ptzjp1/J0rxJx4N3USx00F4cavZVdP0bWPzMFuQz2/j26stY22pw0cpVwCoA8hasLBiMhPCNM1/FOU/dSUtlnHZHxMahYXK0eyGjbT34ToZKtoWe4kCzL3RXN7qrm4H2VWwvauZNMrjCq6/FsYz4RhJgCQPbNDCFwGhtRZx1Cdx9O2JoEMsSIGNlSwDONVdj9MxBuz5i+xZUoQ3bEvhRnOAkZVtkU5Lzuk3MrgzCczFyWfQFl8A9dzRDwi7tsbDmL6Z15cU4w0MEYUDoZPBHxjEuuRzzB9/CkHE6XccySFkGHY7BG5Y6mPWwyc6WDLlrriIqlZG5HCkUtpPCqBbp6GzFSjuks2l0Oo2RzeBGgmx7K7aqUnjFK7B6D2LmMthlFyubIR0aZFMmVpjCas/HI8ppG61tnGwqVqraW5CFLNnWPFXDxM9l6CqkGensIlUrkdcW7S0ZvPEM7Yu6sCwTM50it2wpjA5jWSaWbSEMAzPl0NmeZbw0jp3NYmRzZFM2hhGHyjld7QjLQtg2hXwaYRjYJtiOhTag07Tw7BRO6GGmU2jLIpWyCYkV7HQmHT8frS3oIECkUmS0gWHFd2E27aBQGE78XrTa27HlKGY+h4XEaikgK5Vm9i8n5SC8iHRLHh1FCMvCADK2bhoGDRohfVZ7O8p1SZk2RqQxLBOhDAxD0N6SRUwKgRV2rDo6dYNXCBEbEFE0JURQ1I0aYVoY9TYCmPlYeTdzmeY9AsR9WP87l3ZIzetBFksYjkMqjGi1Y4+EqBtBop7tCmIDHcAwjOY2yzBwLAPTMBBCkOruRhgGlmkgLSuuqxUbpVgT6rAQglQmRWAIUl2dE20UE/vNhuFlms02pwr5+LymgZFOxwkG0k6zH4DmNYxPbSMMgak1RBFmKoXZ3kY6a6MDTafjEElF1jHj90C9ZZYTl+dYE/2fT02a2FXHzucJAglCkjUttJFCOA55Q2FMGogybQvDccjVj9X2RP86KQeN0WyjYxlkUg4GmiK/O16QxkqxWOT1r38955xzDl/4whfo6Ojg8OHDtLS0AOB5Hs888ww33ngjJ5xwAqVSiX/6p3/ixhtv5Ac/+MHvufYvDJ5t7ZQ4DEzXjRU9ZXvjv2PV2IPgBg1PRywTZ+SamJgfKYVUE5PoG2mNn2NF44nojcF0RNMbQcP5MNlTIOVEgHjD63HMKL+M6oq8npRxpr6oWl95IqtUbzXiO2XNG7M+Tw9HLGxteCLqhks9o1TDy/DTvqipCE7G5NmMlbieUwa5j+mf248ELM8b02vs0xors4ytHyO/ttXg/C6TL+yNj1maN3hRj8B4cjt7K4oj81ZQKnRMqdSJLQYXdpvsLivuHZwtlmjimFZbcEX31OD9lXnBspyNKQSR7x1Xv56hQ5zt1Og5bR1f3jcR6lawBZUo9hAE9aZOzmx02tYHADhp10aeWn8JAHPTgj/qCDB3buEhaylupkDKjM+3dudjAFz06E/YsfIMlqUkxwZ3rSkY7CzHRu7cdJz4QAM7pgkBmoyXynCK403JotSYcN2ZElRCja/iydF76hmYTBHnMehwBKPB9A/Lwqxg0qokwIRnpcUWjASalAHL8kZTanVh4m9LTD1nI13r7mUnc3pvnPq3KyXobMkgfI9NHXNZ1LcHQ0lWTipnMivzE4ZTA9uABZm4rkJoWmwo2JOOb6RxBtS8BYQvezWH9h9/T/XPWcLC/r2Uc2246Rw9I0c4tGAVLTKeEO5JzcJMfH+du6ILnowzWu1ZdRrXnzIHA7iFl1PTBlc+8N040cfK1YBAnrQe1pzEq43YUJiMWrEaY+8unPWngBBsPetFvGGuIv2zH4IQhC97NX29EhXC1guvZSyEGx7/3nH1VyJ+32gx9RkQ9Z7IWHGKdmEYzbkktimwrr4G3XsE+9H7QStMI16byjEE2jSw0inSN7wZt+ZhyXgAyRCQcizQmgCITjgRsWUTqZNPoQbYlklQP79pxGFldsrBsgRCG9gZB5m2kUphLVmCuW8/9rr1pGyDFCksoUjbJqQzYBgI2yKXTeP6PqRt0tkMgWEhghr51jyZXKxE2dk0Tkc7YrhEqqVAXsWpca1MGuE4tLbkCDIZchZkUgZO6GAVsgjHRAB5PaFUZTMO1cAmn0sThhpZv49EJo1lQXtbO1nHItuSx+qsK6SWRaq1gOVXsRwbw7bj+jcW3mxvI9VaAMPEaDxMlhXL1Snk0s1rI4QAx8G2bbTSmHa2eR6YUPab19qKs0ZimqQyRpyABcA04331egjTxM6k45SzDcXdNCeMFUNAaE05F9BcjHkmhGWRMk3QE16GuDH2VLn6b+uY+k/x+E+S41i5OlYmg6lm/j6YLS31jJQCYRpkbPO4iItmWcbxbxy7acDU/5tJT/ThZOOkYbgcU3fHMGbUg0QmU6/kRJsbdbCM+LpbWpJzjumTenmGEM16CSHQlhWnCW4Yk5P6eKY6HLs8xLHEz27cPscwkMJu1k8wUS8nZU85hz15sppl4RzT5a1Zh+HJodS/A16QxsoXvvAF5s6dyz//8z83ty1cuLD5d6FQ4Oabb55yzN/8zd9w/fXX09fXx/z585/zuarVKmF4fCpO0zRJp9NT5GbCMAwyjRv7V5St1WrMtAKtEIJsNvtrybqui5pFYX022bIXEtomoVQ49ZHpIFJ4nodbq0JkNo0UX0wMoGeyOWqBRCmN73uoSfMiZBCvAB+ZglBq0pls8wEKfB85+eGQUZwOOIoQ1eqUF9iB0QphYLBgThvUahNy5RI6jLDz+brnQxIGPqpWRYQBupZGeC7Vqib049zjlpFFAEJpdo8HhMqPszfFqziB0ty126e3Itk8rpojOjLw4zU4GukwtYQgYLgedG/aNoZh0ucqDldCQj++x0wZMmfoMENdCwit+KVlWjZGwwiUETKKvTfHWiY7fTAtB6P+AlNSIqdJI9vAtCwM03pW2W7DRMuJ0TihIgJfYsmQ+WlYZNXY5E/cL4ZpoklhCsHKrOZp4TPgaTpSglNaDdIm/Lw/vu7XLU5x77CBKzUrc+D5x885aJ7XmHi5aqWIwoDVW+4nnRaoee2cmm/jsWGJVR7j+sxRvHlLyQ4c4SvRXDyl0UoQ1t0cXr2tolJERgFvXJ6jYINzzz14A/28xtmNe/V17KsGhL5qyhsyYt3OR2ldv4Jc3iCIQnwNaREvcLowDYIKtTD+UKTMiVdsqxWRNQX7j1HWx02HwI8QYkK2xYzImWALyDkggVBKvEhhCMGKgoMt4knT/bUQjWbjGVexeNddU/tMCDKWxeqCgcrl8UrjNBYsm58Gm3gOBuVSU7aBF0UEy1bhbY5TJC/OG+ytKnpbuzmrNwRi5S960UvwKiWuyuYZXfAi2g1J7cBexL7dALiRwjAmlJ2MkBQjhWNA+zVXk7ZManf/It7Z1k52fKxpqPgywg98qle+mP6+fuaFAYZhNJ8Xy0mxpiW+L0azLdx76mX4dhYNtC5ezbhTYLnwOHuOAVaalnpYVhiFCBmyIG3Qs6KNoG4Iv2y+5hsHfe49+1ouTleRK+YTSIkMp5stE5M6+TSMFatIF1q43g2xkARCEFz+YrBMCAOu6dZUJXRmHCrKpG2zIJQyDn2tZ+nrMgVhEBFEAbUwbF4LISCKIjAVnu8RaI2UEamOTrQM0Nogs2gebYtehesFlH90K4GUmI6DDAK8IMRsb0FpiZIBQmuyjoWwTBwt8Wo+1iUXo5csRucyuNUIU0ncKMI0BHnHItKxh9ELA8woJIhCCrZipBqgzj4bsfpEWDgPUwicbIpc5DIaGCgDXGFgpNJot0akIggDUqlWLC0wOzowLYPQNKm5LsIyUTIiCHxcESItQRhF5HNZhGVhOjah0CgiTGETaYUtFDU/ir0Zk5TqTMpm2HfjBCtBiCaiWq3imwamNklnUiAE8xd2o7WmVou9m5GUccivaWL48XW3LIuUZZHpbJ8iOx0iirCYUEony06e8SeqVUzTBGtCj6j5PioImu/7+CGI6yCUIuNMGO5+FCK8Gtr3MQwj9sjUv9OGYZCyJ57l56obCNvGrdVQQRCHNM8g2zBCQt+Lk7002hWGkxZvhFShEB9nmjPqHDoMEVFELpdrbvM8D1kvJwoCrGo17hel4sHEjH2cbBAp5DSZKrLZbNPoCqIofpYgLm8a2abOIWWcWXQGMplMrBvYNkEQNPXEyUeYWuN7cfiWUTeSGrJKacJpDKx0Oh0n0xFiSrnTMVkHDcOQIAiOk/EDieGYpFIp7Po7Rcoolp3cvkl/p1IprKasxJ+mHzx35m/1bwX9AuSaa67RH/nIR/R73/tefe655+qXvexl+tvf/vasxzz00EN6zZo1ulwuP6dzRFGkH3/8cZ3L5ZqR/JP/XXPNNTqKoua/bDY7rRygL7744imyXV1dM8qeeeaZU2SXLFkyo+xJJ500Rfakk06aUXbJkiVTZM8888wZZbu6unQYhk3Ziy++eEbZbDarh0o1HUWRHqt4+qqrXzyjLKA37Bls/rvsxX80q+zdT+9vyr7kla+dVfYN/3G7ftvNj+r3fuMJfeJF180q+93bHtD/fes9+uG7N+o3vOWds8q+8q8/r9/+8dv02//95/q0q94wq+x17/uPWPbjt+mzrn3brLIvufGjTdnzXnHjrLIvevs/6Ld/6i799o/fpi967ftmL/f69+o//Zdb9ds/fpu+/M3vn1X2ote+r1mHF739H2aV/ct3vqcp+44//5dZZc+69m36n2/6hX7s1jv1V/7tM7PK/slrb9D3ff8O/e2v366//h+fn1X2jS97dbMOr/ngzbPKvmb1CXrzDW/Xm294u/7/ven/zip79XkX68e+8zP92K136g2ve/OssufMX6K/9fK36Cff/w968w1vn71/FyzUm294u974xrfpB1//Np02rRllz5wzV294w9v0N1/2Fv3Nl71Ft6fSM8qe0NGlN93wtmb7ujLTv6MAvby1rSn3+H/erJe3ts0oOz+X15tveLve9LZ36c03vF2v7Zz5PdWeSseyb3+XfuzWO/Xpa0+eUTZlWvqbL3uL/pf/71/0xq98R1+4aOZ3GtCs7+Yb3q5ftHjprLLv+/gP9UM/uEN/879v16vOvHJW2du/+l392K136sduvVO/+prZ3z2f/uRX9SM/vEM/duud+k2veM2sst/81Bea5f7Ja2+YVfYr//YZ/ditd+qn3vfX+n2nnzWr7Jevfol+7NY79bbb7tF/+c73zCr71Y/+qy49+qgefmiD/uyf//nsbfuHD+uD9zyoBzc+pW/+6L/OKvvhCy7W+3/5gN53z8P6m1+9ZVbZf/6r9+vxDY/oo5u36p/953/OKvvRj3xE+0ND2j18WD/00EOzyv6/v/6A9gYGdFAs6cdvv31W2T//8z9vfrd2bdkyq+yNN96oQ8/TURTp/v7+WWXf/OY3N8stFouzyr7yFa+Y8q2dTfa3qUeEvv9b0yMaZZ9xxhkzynZ1dWnf9/Xjjz+ufd9/Vj1ich2uueaaWfttsuyrXvWqWWWLxaIu1+L6vvnNs7/f+/v7m+XeeOPs3+U9e/boKIp0UK3qP3+WZ27z5s3Ncv/2b/92VtkNGzY0ZT/60Y/OKnvXXXc1ZT/1qU/NKvujH/2oKfulL31pVtlvfetbTdlvfetb08rkcjn9+OOP6yiKfhNq/bPygvSsHD58mG9+85u89a1v5V3vehdPP/00H/7wh3Ech5e//OXHyfu+z8c+9jGuvfZa8vn8b6QOpVKJTZs2NX/P5qWoVCpTZKNpRiwa1Gq1KbLTWcoNPM+bIut5M6fuDIJgiuxsI0NRFLFp06bmCEOlMn1sPsTt3rJlKy0pg2qgKJenixyfYPfuPc2/ZysXYO/efaTSGdCaUmn2ckvlIr4SeLYxa58B9B4+TE9nF72HDjE+Nj6rbK1Ww6l7woJg5hGOWNZtes2erQ6u5z1nWd/zqJTjldP9YOaRHoDlezdxWlDlzjOuwfNml/UDn2q1ilCSsDZ7itzxsfEJj2B59usWBAFucZTeMGBgcPbMS6VSiZH+Pmygf3D29TdqIyPIeh1mu38hvocrddnWZ7nPjCOH8H/4HYYuuIwWd/ZyldJEYURxeLh5X8xcB9msQz0SeEZZKSWuWyOK4leyni0OUimq1ec2x0UpRaVaJcoXKI6XZn1PKR3Lhm3t2NUqUs4sq7WOZc1WRnuPTDvy1sQwKC5fxdylczlcqeEWWmatc/+8hUSFFuziONXWtlllz9CDDPSlcDQUps0VNkFfXz/Vcnw9KpXZr51dHKTvSHwNSs9yvw8MDJKqe45KpdKssiNDg/Rm8hiLllHOz94PowsW09t7hIwtKBbHZ5UdPDrAnt27iRAMPcv9PjI+Ru/RIwjTZGBw+kx1DQLf4+jhA2Bb9B4+PHt9x0bYt3cPbqGVI729s8r2DQywZds2iCL2HDkyq+zYyBBbt2+HXI69z1Lu0NBQ8zvXd/DgrLLDw8Ns3rIFhGBsbGxW2dHR0Wa57rOMKBeLxSnf2tn4beoRm7dsaf7+bekRs/VFFEVsqddhy5Ytz6pHTC732Z6jybLj4+Ozyj799NNYqTS2IRgdHZ1VduvWrbS3twPx/TEbzzzzTHxurRkamv3btWPHjqaH5OjR2Z+5Xbt2NT2EfX19s8ru2bOHtrY2AHqf5dnYv39/s98OHTo0q+yBAweasgcOHJhV9neF0LN+FZ+frFu3jnXr1vGtb32rue3DH/4wW7Zs4dvf/vYU2TAM+bM/+zP6+/u55ZZbnrOxIqVk06ZNLF26dIprucH/hjCwhrEynexYLSBrm3iRwkln6MqnKLohge+xvW+8uRr9sWSyE67eY8PAjqUZBqY1QRBMDQML4zAtPBdRrfC1Q/U2piwqRweQtsX1a7vIZ+OsIaTTGAcPoLNZzLYOjuzayZKuLqL2NvSeeI0Bnctz594Shyetvmyl0s2QFBmFU+trGFPmfpiOg1GPNz9O1jKnLP7XCAODyaFdcNlD32/K3HPBq2JZy44nRUbRFNljWbPvKZYMHMQ0DO66+DXPOQzskg0/wvBd7jrnWpQRK8tvXz51HMO2bL50ML63lmc1F7ZLnFvjuPvwokvRnV0A9NY0+z2DS+akSJkids+Hx3woqzWcO29Dd3agL72q+XwppSaMsUnZvBrnESecyLalp9Hvwb5SyEX3xc//3IxB/pxzsJ54DG1Z1EolWvK5ZghWRcFA1ae7JcOekQkl/4RC3P+WYeCYJv6rXofz/W/GSRmA4OWvRmnNw8OKM+/7AX1uHIKVtizWzCkgajVqs7jojw0Dmyy7ozz1vj+pxSJlmQz6GlsI0mLiGssTTsTcsb35u1GHBk+N+tx9wSvJmIK3bPsZ1BWT4OWvJvWj75OxLFTPHMILL0V95+vxfDQDojUnxeWvPhHr6U0Q+GTSGaJTz8B65mlqqQxy6bJp2+bc+j2yto3q7CK89Eo830frmd8nmfTEO80PAtR0ceoinp2RTqWbz1ZDVilNf/9R5s2bOzFXoC7bDNuIQu7sC9hfn2Pz9hOyzb44TjYM4jATy47X2TmGlJNqhm2E0AzVOg7DIGXZGP9/9t48Xo6iXht/qqp7Zs6WlewbCUsCBEhQQSCACIoiIF6MoBdcwOsFVFzuVYKAF7hoUBRfBRW9XFHwRYWgIJfNC/7QF2RHwr4JCQRC9pzk5OScMzPdvz+6qrqqurqnZ86cLamHTzgz1bV8q7pn5vvUdykWZWEqV6uo8OedUBol5FAwqrVFJhcol8uoiO80z4Pv+yhXKjLN+Ni2VvRUQ3SUfPSWy9illYExit62UfC3dmrrQMDQ2lIAKEW5rw99vA/i++js7Eb79CkoEKCyYT2CydMxptKDviDy5e/ZugV9o8eiunkTvO3bsabiYfQD96JrxetglGL65z4Lr1RCZdJk9L6xEujrA508Bdi6BVs3bEarT9EdUuwyfQrIpk2oTpgMvP0mVreOw5RZk1Fd8zZoRweqmzaBlkqgo0ah2N4O1tMDBAGqra3460OP4sAF+0ZuURxBTw/6mI+O1hJYby9YRwcq27ahF5ELD/F9hN1RUgAB3/dR4K5S1d5edPX2JeIqytUgyu7keSjy+IDQcO0Ktm8HVX6Hs+qaqEc32NH1iFKphKeffhr77rsv+vr6MuumuYGF5bIWoJ5V14bW1tYoYxgl6O3tzSR5qt5Tq26LxbWrGXVLpZL8HNRTN80NTEB17aqnbqVSsW5GlctlrFixAgsWLNA+twOFEWlZmTBhAnbbTT87c86cObj77ru1snK5jC9/+ctYtWoVfvWrXzVkVeno6JBfflkQwf15UE/dDu7z2ey6tdYiVALsRV21rEx70VJgYOXojBRKo5ODi6VWtLUHYBQo1MiP2tLSll1BIAhQatEzNqG3Nwp8jAQDpdt5YAyB5xXACkW0ej7a2tqjLB+lFtBSCWFLK6qMgpbLYATwCAPlBCpsaQUr9qGgBvApWa48vxA5+ccXkbZbnqhLGeDZv1CZ54PxjGUtSjBxQVHwEEayqHVNFP0iPP6lQQiJzhDwan/EC5U+gDGMrZaxtTV6htpak888IdGXm+cxtLUU4fOMJJWWVoR8Dee2AnOVNtTwIwcA+voKsIIPbN2KspKZhtJIgWDLHwdd/RbKR30A8H05TrilE+/8223YMnsupj/5Akp8rVo9gvZqBbTgAyGiTDvMk6mv2hnQPqoIINDWt7WgKzGUB9K28vH81jagXMZxz90JtBawSTkIk/RsBwhk3TxQ65qBriWeGmpSSSigcd3K9FnR50uQ6mkzEPLUsgCw5+ginh/diiMmMBQmvR/s8UdQ3W8h/NY20EMPR/jS86i+8yBQxlAcPx7Y0glKCHoXvisW4OBFAKKUxhRAsPBdiFWoJMh734fw6eUIFr4DlFK0KopRLbSUUnomNMo6F4YImQeCiqwbBAFKxSLaWlujH30CaAkwABRLrSiWGAr8PrW2dYB49l3iUjHqN/R8kEoZWZ/lQqGIonoYqFI1ZB5ItYKQsih1rOejyJ936nlR3Jo2rodqb1RWLBZRRBGEAIFXQLHgo7evDMLJjl8soK8nitEZ3dqCsaOiGIvymDHwoX+XVIIwygLm+2AASiLjkO8jDCjGjxsDACj3bEdPSwvo9iqKAFixgAIl6Bs1CkG1AsYYelFEa0sJKBYQhCEKxSKIx1AsFVHiAc/+LuNBp00Fe/o5lBhQ9EtoaW1FecsWsPZWoFTCqPFjMWbMaJR7toO2d6BarYKNGgU2ejQAcAWTgBUKaG9twahRozSlJ2xrQxUEHqPRIcSMgXZ0oEiIzHAVlEqJ7FYCtKUF41pbE+Xq75gK9Xc5KJVk1iYbBur3fkfTIwSJYIzVpX+pZKSZdVstz0Mz6ra0tGjkcCjqmsS3mXWLls9YLY+QZiM7lcAwxQEHHIDXXntNK1uxYgWmTZsm3wuisnLlSvzyl7+Upj2H5iBE9AMpMnfJM1BkNrABFiCoIjpJXpx7Ekm1vU9k4Qr1f0B8AnqI+JRz1f0pMDJuAXFbK+qYZH8NmLz9zFUvYr/nHoB5OGFeTFz3BhY+9Rf4fTYlLj37GwAsGMvgUeCdY3VF2/vrvSAba59ZkQf0tVdAX34R6NoKuvJVXbr164CeHox6fjlGl/Wdbvrc08iDqS0RwZ3Rmu+rj658FdhmcV8YTIM0IZoFr7r3vqgc+T75vs0DFs/wMbFEEY4bj8r7Pohw0mQAQDBzV1SO/iDQ1q7JnZXpLw/CyVOjccaO71c/Wp9KalDtjByRHUstMuvw91pRjTmGzJN1QprxPPBrIWUAgb55JcivyDBESCynZXwaCy8nVPSizFJRzg7FWiIzBcXJlAhjYIL8K3VlJiTLmlBKIuuy5/ENj4jsUUZBfD9Ka0sIKAhosQivVETbwYeA+gX4+y+IdrUJjRJ3UBrVLxRAW1tlMg9W4FmzCgWwYhGEMVCxqUJpNBdCJFERa0U4gWeWQGPCWGwVUVLVAtAzYKUgNYtTjme/v58PBweH5mJEkpVPfepTWL58Oa6++mqsXLkSt912G2688UZ84hOfABCZrc455xw888wz+N73vodqtYp169Zh3bp1g84GRyrynZ0Xpy4WpEVk5WqqLmdNvcsJSrUaH4goGFLAY8DMQwL5AY6Pr9qCVdsBBEF8CGNUMffhdU2ZQwPt93x1OSaufxOT1mf7pwLAlDUrsOvrz2tl+z3/IMZvXoM9XnuqtlyVCtjyx0HWRXEn7x7PcPpsH6MLRG8XBPAeeyj3VNjrK9KvPf6I0m+6bHPaG/vqGu1Hh9y1pxmc1HkJQjvA6LCks9ZACOiKeHMm7IciVd1rn+jFzFkN9zFgsBEUxERCTekbGVaMdTAJRxYBkddJjrpxnSJj8BSrWMhPvZbtFa4CkuyTMRoTLS4/o/ysBkb1JRCKuSqD78fuXzaZCY3XkY9BGANIZDWkbW2g4lwIQkA7OjgZiVIik0IBfksJ/sQJGPMvn0Vx/wUghQLnVry/lhYQvisrD9LzmDxnhPl+lNq3YBKK5P2SKXBrfQTSSMlAuZ8MgluLg4NDfoxIN7D99tsPV111Fa644gr8+Mc/xvTp0/GNb3wDJ5xwAoAogOnPf45Ol/7whz+stb3uuutw0EEHDbrMOxrCEKjws1YEbwjCEJUmmFReWduFUSUPE0dlmCmDKhBSkO5tCKVPsiAonLiohxxysvLmlj488XYF2zYz7NVTwfpt2zGPcbeAIEhaVpqG/nast6cZProC4myQ9eOmoKt9jHbNrwjSni4Xe/E50JdfBH35RZQ/vBj0zTdAxoxFOGZsUonP8KuV6NkO+uYbwJbO5LUwBF35WrJ8EEH/8bL+/uUXmq60BHvMi/rlmNJCMKYWWaEkPrgUqK2EZ40/czaCcbuAjR6FAXzYGwMhIIRGc2U0Chbh5aDUmDeBzYoQWTq41ZDWuHeUxs+xhVgA3PoirRYUFAGIEock2ymWFXGYom0rkPG4nEAo/tWAnzjPIjdEdUrKe/GXcpesCh8rVNxUowokkimsSjJGKQXhVhFaKoFyCwvxvCiWZMvWaIossp74LS1AVy9YCwPt6gYtFqLvWBKdG0J8X5IHxhjCoBqRFs+L0gsXC6hQErug8nuX4NjKGRa1LBlpZGWgLCCkH58xBweH5mNEkhUAOPLII3HkkUdar02fPh0vvvjiIEu04yHNtxcAQoTSMyUIwvgw+H6SlbVbevDnF9YAAD53uIhLSvZJgiAKXq1WQIgv2JKiIERECmGItV19aKcM7WGI7nIVpK8PCIFlr/WCsDLYuBB7dhCs6a5i+4CZVhqHOMywbZtFyU9Be1ec3aatewu62sdoZaO3bMAuG95E56hd0jvpijOw+Xf9UeZhLx//T1FgsgqLG4eG3h74//OHZPnWLUDHKJDVb4HVYZ0ZCLC/P6q9p2+/hWC6xQLRD/0oGL8L6MvR4Yh9QeTCZSLcZULk8ibHsyjl/UF7R+S+YyG8Qvc1dWAr0sM8aoJSkvyuICRKOiHOMZLlkbKrumrp7l78LCPPA1go5QoLBfutEnKrCTIkIeHXxF/GYtLDaHTgjarIUoOsMAoSEKAaRsSLQyQciXR+nWgR4ZJFKSihMhqFUAKfW0GkZ1mpFPdLFGLCbxjhPmNhUOUWFQLqsfhgQ88H87k72LhxnHx4oCAgPObF8yMrD6Mssrb4vnQFA2Na4DlodHI58TzQtjZUN22OxqFUWl2E5cZ8bp2rlYODQ1647QOHhlFV3L9CrrX017KyuTt7h75cDaIsY7aYFEVzIkFk8dnQ1YtbnlmLXz+8KnL5kifWxxaY17cFWNcb4JbXe9BbZyiI39czKK5CXqUPBz9+d+2KHO9+4n/l631feAijt6zXygrlXix49gEc+MQ9tuZJKNlA/HvuSl4XLnkpbpb+3f+TXl6tgnQmU4eSvl4tViMN5onizUJEGCxEuT+dcrcXn9qJCgAEu+rJQxK7/uZ2ewPQYiuUbkURTem77iFT6idOvhaVKY1iQ6SlAhGR4GRFqO2qwh/yXffQDIhOCbyWIkkLCYnH4yVSOuXUcDGetssvyQpD2NYRkRvRryATAHx+WB1TiA6VaxCRNEp5fIk05FB4cmg+tl+I4j8o465bhksYpTG54jE2hBOVKD6FRZnLCJGB6cT3o5gRbm3xWNQH5a5pEKe4kyhoXTv5mxDQ1taIaAl3MS5HibvL0dZWK1npN+l2cHDYaeDIikNjCCMrihqvAkA7Sb7Bbo0X8esgCHHtA6/h2gdeQ1CtKIpsGAfVK41CAG9v7YMkJuBEJdDJzitdAf6ytlrfLnEYYpcNb+KIh/6IvV5+rJGp5kYLAwrWgHgdRJn/tBb9o/2uJ/9s77vXSH+prmGaMrG92xLgH8K/+zb4f1wG9FoIXFasmJnaGIj0xxeehX/PnakK79QWirEFgo78Cbnqh8X6wPqjY6muSeb6FksIdtsDwcxd9XJCgKLiEtkEcuwzmiB5UVhCVJZKVpSbkahhaZL2A8P4OoSaSxUnApQCJCItBAA8DyFj0XvFABL7RvFRlAxw0WRSRqcqmSAIKQX1PU1+wmUAY7FFh3JXJ60vbrEgJLI68LgN0UcoMwop8Sl8XMbJCiUA9T2wYpGTOEHIokB4EjePyAMh8m9s0WHyL2GKlUfUpTSyyjDG3bNi8kA8j1tkGOD5IJSBeFGaZCriSri1hLa3a66RxPejbIpifCGPQlYIpfr9UtbOwcHBIQ/ct4VDbth0pDCMLCwVGVjfX7KStJIIbC/HiuN/P74Wz761ReMhmoA8nkYqlioLCoJYsed/1/eGyMtWir3dOPyh27Dg2QcAANPeHrhYi7EFgqMmceWiDhQb+GTv+eqTWPTI7fDKvaAvPAuaEQifyP4VAuBnD/i3/R7efffkV6rLZbBnn9KKqJjvls7UmY/2gcml+talXrCnn5SvZ7ZSlBgwo6UfX5ueco6EYUEJZsxCdeG7IivC5CmyPCQElUOPAMD1vbwfsYylsVujSG2yoir0uYZLKRUfTGpYKSjlrlcizoFEJIRGpCGOcVD6FsqzqkSrMS2mCEU1gxcnDpyESK5DKULfj6w2QhH3vYisaKEznKxQgpCxiCyomatEZUYlPxDlkZtUtOas6KNUKvC0zHyOlGjkUK4PjxsR43M/suhtqRSvA7e+UO7KRnhGMFrwY7KD2E0rcvfypOWGeJ7MGCaIHW1p0dy3aHs7SCGOYVEtTRpslhVHVhwcHHLCfVs4ZCKNfEhKEUZ1KvwAyGp/yYqtuaUwCEM8sLJTlcRKVqip2QUKSQkNgpJT9Dkrn0OhXNvS0V+cuXsBJ8/0MbZAoiMlamDKmhXydSMeFmM716HU240Zq/8B9szyzLreQ/frBcbJ72TDOpkWOov0AACzBNYPRw+RNg+Y3UZR7E/MvarpmkHDyvNbnb17XE4pwrHjEE6YCIwZi7A93zkItZbQjBkghMdnk2QIUux5lt6rlf6kVJdKuKevAWUM8AsIuesXoRSh53H3Khan84UiJPOkFeSQqW0oUuBdu8RpiRO2oAI/3E9xp2O+ByLIGuEWhWJREiUgSs+rkQRAuqhRYVlhLJ6T4tIl50ciS0YkPic4fN6lgh+ROCXonAJRTIhYTEEaOjoAsQZKamBaKhmMMiIrct38QmRtKRZ1ssL/Ekpj9y/xWrUemYkOKAXxCzK1sEq09BtOEgf7uZgVBweHvBixAfYOQwt5dAnPBlauhiAkV4hBrn4RAk+v6gQA7DvJdjhTmCQeoU48wiAEU9IWP7yhir9vVgRMkCD9PSPA5BLFm9v7OSkLJq9diY6uzSBhgI1jJmH9+Kna9Q6fAGEI9uxTCEeNBqAfADZn5XNYPXk2EIaY98oTmLpmBWjOs1c6PIKtGYkEdl/xDDCqxj5GpXY2MgH2yN+yK1hOxyVa9HY95oThheo++8VWI+5yJJEgK8r9MxROEILK4UdFsQWUAdX0k5VzIYNE2G0uPCadGKUkVG5N8j6RNFOQmB/T4x+IxxCGsbWAMGFZKUeWFcaAoBKzKgBhSwtITzfAPIxrYfjMbB9+oQDzqQo9D6RSAQqFOC6Gu4sx30OFWz1CHmwOQZK4Oxb1fT6XILKUVMIotoMShJSgKoLhSWSxif6KbGNEmaPH23IriRfFkvi+iDcRCj+3xpRKID3bwMRhfYSAtrej2rkldpvjge+En3Gi3g4irFUAWHt0gB4pFmMXMpEdjJ+dwqpBtPaURpnD+BgyTkh7bqMAfTFPSUBMIux5kQuZg4ODQwNwZMXBCpEQJ7NOGJ+xUgkC+Pyk4WZJ8OCr6wEAc8dPh3ZQuHT7EnEqQmFSLCVhiAAAkxaXAH/flF/BBtJ3hYMG3RfatnVibOc6rJoyB/NfeFiWz3zzZdxz+Mf0yiFA1q0BfeHZ6O3cD2iXW3q34cQNz+DPhamYvvofSdkbkjDCuEKO1kHttaSrXkc4dlyOeisTZaa70cikKkAwb58oBkUofT2KRc4kK2q8l0lW+F/GKCr87ga77wn68osIp+hEVzZD9rol3LiIOFSRJJ59QiLybFIZjVJaBkxLEif7VzN8AdwqQUGDCkJGQTw/Kqt4UcwKY0C5AuIxIOBkZdRokK2dPNCeRAccsjh7mBTL96MMh2rQPGUAqaJYLKCPEO7+RnjmrJisUIC/9kHKfSCUImDgrmnCPYuCegRhEEbllEoCKjJ4ESCKJRFvGANpLUXWC+FSJtaHEGnVoFxWmVJXxOcormFgnj433hFV3Odkse/rZcINrFAA7Ssj9CIXMtZSip4L1T3N+HAS31eyqunJCFRknQjv4ODgkAVHVhxSIVKYWq/J//hZi9UQHkW/yYqMWdE8uuwWEKIGyiPQN3l56mKqnmZfJ6gqjzp6g+4LIpvXHu3J9n5fD8oF/VwZolocLGOOe+0FtE1va0iWLExoUhyIGu+RiTxntAwHGMp49R0HgT3+cGr1qA2JT48HABLf07Aey4oUIbYoVOcvQDBpCsJdJuQT2CKaWTvP2Yhae/4dIXlWYkj7syQU3pAq9IcSEMbjJypc4edxImA0Ii8ykNyPiQClkfJeLAHbtwIgYDzjlSaU50cJEzzuNkYpwmIRpFpBseihC9zKUAn42SQM1GMIgkhe6vugYEC5L7oPfAwRyA4CeJ6HajXk7lc0XhglFoh6XiS7cOESMTN8fpQRIBCSK65XnMBATTTAGEIStQt5Mgi5RoRE1jDfS9xY8xwRcX4KAFCPocrl8lpbpQuekFV9biWRMdzAXDyKg4NDM+G+URxyQ8u1JQ0YoXYYZH/PhLRxioS6E/ugKa+TjUMARJKV+l250oKMw7QsQznR0rkRs9ooprTE/R/y2J3JikoGLZbi9kNT/O6y+FTWLSqy/lll+o1h7MZuiha2taPy7kVaWXX/A7I7sZ3RITtU7ox6jk3iZgoXKoZw8tTkmTeWqolikrwYxUjYG1B+LRmKEO3cizo1h5f8IbJgqIHYIitW6BeAUgkhY9HJ6DxuIrJc8KxgSmwHpfwskUIhDutQFWoajRWyyB2KMMW9yy8AhKDoeZEVw4tPew89X8Z7tPqRLLQgMn0hJiuERASDEJSKfhTELjJpaWsSubkR5vGMXYS7kVEwj8q+oixa/NwTAjCfrwGfHPHicaM4ExKnEybKmpL49PrEs2YSZRL3QYS7HaWgxQLPMKakk7Y9w3L8OB2zg4ODQ7PgyIpDXVCtHGEY8wURYN9MNzABQsKUS2GchthMX8xdy4kSs1Iv0valG7WsCFQAtDJoJ5ePDsvY198u31OiH1IorDKa6kOA90zyMcYnGJvHdYsjK/WuN8RBr5mjDzf9hxCE02cCHXE8UbDHPITjxme0yRdgL5U/Po5sQpHuW8WhXk6vmYxNiXRhInmEeY1SYmTCiiqJzGLcuKC1NR8n2ZxELlSQRAeghFtSeAA445m1BLkIKQMtFPkOv5E1S2bgivpkBV8L8g6F0i5OpPf432JEVihX7Ckvpx4DKURkhRLAZwyM0kgeGh3eKAL7CaPSfczzWDQHxsCYcQ4KIRFR8b3YpYsH53s80J4wxgP9I1JAKT+cka8LFFcxGWjPWBSrItaDxddBqZb5Sy6/SV4SgfM8g5jo03QbMxDHqtjvu4ODg0N/4MiKQ10Q56jIsJEwcpWqhtwtrNnZwEwPLhmbEv2JXMF4w2qgEZkQIYKgGvnaN2DyCQHrTnOgaGwtDWSGGr1qhXw9pYWglQHTWykOK6/BtBaKVgZMTHHFGlMg2KVI0OFF7Ub7UR8TioYykjF+CKDNi2t0eASTSwQlBkws1qdlDJRSYpKyYQXfRzh1err7VZbAmpKYXk9zEVOeN/P0c31cLp6ZwpejIE4UJ+CsJykySyFChFtV1M8D4bv7UVdEjqbbEuxCqnEeACJlXT3ssFAEZTSyZlDu7sQo0BYdMEhF2mEhm7AuUW65EGeHiMnxf6WWAghlUUwGOMkhUephQiMyEZGlqB7jweeMRUREKPBUHKLIIqLDOBmhHgPxfHlGifiJFfeN+h5oW6vM3hUyCsoofD8614TwzF5ximYC5sWWDkIgDxaNLSc8u5eYqxoHZMawCHiGBziJx4zaMSkjgEQmL4k09zLnBubg4NBEuJgVhwRM64mqG5k6vzzBXknM1a+xYXYUJs0bWtYvfhq9dPWKr63cUsbT63sixagBNzAgodNFQyoLsmsbxfNbsvuevfJZ7Na7CbsUCTb1hRivEIIxPpEWlmoYYJQPjPKTg44vEmzuC7FLkUDhGWAvPgegPqODuZzTW6PW0jqTZlKygIBY43pyNk4fhyQrplUnpP/PHRhDOG4XkHVrMipFEpQ/eIJMf2tb+OoBB8L7y72o7rWPvQv5Wl/v6j77KfLYv5rjMG2loQGPEfRyr0FKCYJqVMejQB/ArQgeoOTLoiRS1qlCAMzutUxhwgqDMLIqEKK2lM+E6TEp4lyklYB/wDyPogpEsRt95egsEOki5aEqSISwxHS0A72dfI7cfQuI4z782LIiXNUCRtHS4qNLkCJOUEJuTYraedy9KjopPkrsRUA72uFRoMxjR5jPgDAiJJSxiAhWeZrgAPEZJSyKG4nWJ4pNQaEYk04KaS3xKA+oLxRAunt4ojIi41xEkDshkcwRyWLy9PiqtDTFlhXhQpdMU52wq+lvGyUdGQH2Dg4ODo3CkRWHuhBoRIYTFVEe9j9mJYEQePbtrVi4qx8HYqtuYBzEoq0+vb6XVzFPt8+Hd4+neL3bFmAf/SBP4taPdo9g1hsvAABWzpin1T1phoepTz4fvSkmLSAq2PInUq9NLBK71WNbl7V+LVVhfIFgWyVEh9c/pYLY9eWUyjCUX1vTejoULTIIU87uyh9eDPryC2AZZIUAqOy7MCYqEPYE7UlEOHoMysf/U1JhI9BISNjaJovLR30AUM9PKfFkC4QYLmFQuIqdpVHlMlUFJBRAVQZFq+JFxINq5IKCIOAzk5vsGtmiIGEYK898UKJsMNgTIYsB4t18j1EElFsvSFlm9KIyboWTFHE6e6kFIFuiMhGID0S7Cx63iIiDErkLmIh/AaFxkD0RBEAQnah/5nnwGI0sKNEg8BlDhUWkwfN9hOVIbsooCj6NMhp7FKRKQIvFSC5wUkE48fC96CwWQkFoELmsMW4hQRXEo5FVh0Bag2Qsjedpz1RkCaJRymKRaYsQ3b1QZDSrAfNcFMLnXCs+L0F6VGuWg4ODQ5PgyIqDFSFXQswyjaxAWFZChCFpSnpZ1aAiXjz6+mYsmD5KsY4YGcNSTTtqAH590s0bRTEPXeh9fRVeGzMnOpcBwJjtnTi2+1WEHVRaOGa0EkxZ/wxWdQdYPWlX9ClZvfpLBgYEYXTA4R7KHBoFyWIDuchJSrs8HeZE+SMnw3v4AZC3VqVXUtyK0hBOnIRg7l4JiQALb+CfHUriR6/IGHpDoHzi4siFcUtn3IslhqD84cWKbEjUDQmBGc4lxo6NNiQmHXyOItOVOlvK26jxTKpVJiI+OlshlPAjR7hSTcLEQpiqrjh3hYh4Chp9b/iMoUIIvEKcxYt60RkmHo8BCUFA+TkkVD3UkCKKNQFAmBdZO/zIohASRAp7CBB+KjuhvIxbHAJBgJjPA9mjQH9GabQenNAwj0Z9EAqvEGUWY9z6Q0BQ9EXwfAj4BTAeKxLycYiaLIBFuQZBuTWGW14IZQDhcSsidoaAx+4w/VlgDMT3QUeNity0hOVF1iE1Y03UZybxnpAEianVTh3bwcHBoVlwjqUOqbDpQfE5jGGiXjNi6xNdCK4hPbwsg6QF0Iv6IbLdwMIA4zeuhleOs2+1MAL/T7dj91eexK7cagIABz12N1jP9oSSL94X+qJzND4+08fiGT5KWdHsTYRtlN3bsz/eaUSlHomlrmJplCxKup7YjA81HFTSZUgDYwjHjM3uKMekw1KLZezY+UnNhkW5cs8U5dITz4LnR3EHsq0So6JOxvfj+ASol7N3r6OD06NrjCpxKJTwMJFIWVabU77zT7Q5KMSEx2monmsgehwLEfUIQej5hhVI+xO9UA4a9H1OAjyf9xudKs8YhV/wZUvC15DywHghm8z+xWNeIiVfkJBI0Y+yhnkghHH5+CqRiGwQ34PHrRde0QfjbllgEUkRlhZCCfxiIQqO9yg8YfUhkZWFEkSWFcZJCXfZoszjAfWKxYmfFi/iQwjVCYJwhSOEyuxj4t4TJeBfLKr2RBBuVcphWUk8S9QYL287kTa5FslxcHBwqAOOrDjUBd0NTLWs9L/vMAyxZbs4c0PvsGpaRkQGMADbykF8ondo1EkjOApmv/ECFj7z/3DA039JXBtTIDgAm/NOAV4lIjyjC0SLTRlwWIayhL40f9jMKVpcS5Q3kS5r78BWmj5UvivCsya1Xi29LK1c6o+6cs8oSRCC1A65S1itGAERH5IlX3y4I085rCi4HqXRjj/Tc1RTvgCUx0SYpJHwvnTuQaCKww9uj9ZBkixOmkSMihSFcssKBRVEgERuVaIe9TxQQlHwqPRDY5KAsMhqIixFIq0uTwFMOQkA4S5UPLaE8PNM4HmRZUqJA6G+h4LvoeQzMN+LMnsRSALkMeHWFdUllMbnuZDogfb8iBiRog8qMn4JNzBu/aAkIl3RWS1eLL/inkcIQEutUdwLwAmNke5DEjSRUtjybChrkwmzDl+TmjErpmXFuYE5ODgMAJwbmENdMEmJ8MCKj4hsHH/7xwa88PYWc0QAQDVQgucNITb1JtKFKa9DcK0mFVPWrAQAjOraZO1lSs6UXwSAX+mLrThBMGhZcepRDfrNK0ncSVZMglItFZSQlHTXquJfW5xaiJVse3yLsBTUC8rdd7QeiaK0KdKZ55CEY8YhmLcPyLhxscJJlL7SFrCGQsg4UaoGofwL8F16ER8h4ynAvZGiHX01G5iaXCJyESOoyvf8MMSAcO8y7hBISBQ/JucTzcOjFJUwnhCR5IVn1OJuaawQncxOCT+EkVQlgQGimBDfE9YHIk+Kp74XpSoXLlfMA6lyy43IECZIWkhACwUE1SonI5wg+D6YF6URZr4PHwH6QhJZPwqFyKoSRjFrhPBzVIT1RRAnxsBYiKpfgEcISE81ak+JTAIAwg+tRBjFhIjMYWF8LopwGwN3SxPERg1gJwZZiUmTYnEiJF/MivleeR6zG9otK46sODg4NBOOrDgkkN9KEsWwRLEr/R/32bc6k0KEMVkhpvLGXz/XWU4WipchYHfsV6pZflht8Qd5sOuqF7Hv8w+B9e4D+sqLqL7joNxtm4VoF3xg+xfL06hOElsBmiAPsb8Pd5mIYNddLaNCWnbEfRbWhOxxLCTHtE4QkSGLX1ddeghJLF51/v5RUHmFnz5OaSbBk6SqhplIxBIxQkGpnCQISJQi1wicFrKpbl7MkJ3S6AB4WR+RaV5Ye2ikyeuuYvyvzwh6KpGFAJUqQAhCnukKlEYucgHg80MZPRbFpbDy9siiQaIUyYQyFL3ozBNxNgijJAow7wn4CfBReYvvowuRsh4yLzq3xfdAK5GbWMAJmgyy5y5gxPNAPR+FoIxyNQR8H4RFqYxR5VYRHtwOgB/SGN1X6nkgqETEhxIQr8oVeALG0yVTQsBKJWB7N4+RickGlWSLW1E8xglxmDwvRZ4tw8tofD1KQEAat6zEN75Gw6QlJ187BwcHh/xwZMUhE2k2CwFxKGSzXMGsApDIDUz6/BvZwF7bWtXra29qW1ZqiR3WYR0ZvWUDAIC+8CwAgD38QO62/UW7R7C9GmJ228Bac1TrhNg5zzICpD0Xmj6jddA/RUe0rhz+3khxDVX9PhYoYWVRBbJOKCkX4a5QoVnG+1Z3plmKAqcRS7FLn7JmkhgAqJry8BTdUbwMH5MBFAxARVon4AnLSjytKFaeRoRAyKue1wGAMYoKZyuCmAkrCMDjc0KipVfmj0eUVUs9t4NE8ooYDUKiYHYxP59GWb1olcDnrl0ejZRv3+PuZPLsEcID7Pvic1oIgc/iLFyEEHh+FJBOwgAePFQIiQ53pCJmpcCJBk87TCkIqUprBWUUpBJEmxuMn4sirBd8UiJmJ0on7IP4Fe5yF5MGSgDm+0BPZB1hgjQFBBBESDywwtUuJJKcybGEq51KEMQYxUJ8rZGYFYEa332JLGKOrDg4OAwAXMyKg4aahzqGcZ1QiQeJI0iaJYj+thrY408295l+acbrsLZkZnrOSWtfx4H33RQXKD+8w/kneEYrwZ4dtGasSnM5Zcbuvtzhj9/naUtSr6QMZW7u2sw2pm89oBEJoWQm+jX4i3X8/RdEc91zrmwnLSvqgY5pm9dKeUhiEpE2nojVSHAVGehNZJ+MK9nRdUEqmIx/kGSCt5GjE6KQK8ItG1TeT5GtCuIvEXEpyXWVf4VSbryn3E2KKSeveywOko9iPKIT4YW7GSUE8AsAY2gtKCetszhoHwDge/IeeF6UBpkSEsXBCNlFbBEfLzojxeMJBfiMqWL14Ge0UOE+JixVJI43ifhMRJZCxmSWZLF2Hl8zwjweyB+tAaUeX1vRpyA/RJIhubYipbUkI/F10tIi65MBJCtp9XNlIHNwcHDICUdWHKzQ49R19VbNFBxnBxtYSarVIFEGALevrlisKcbrWrIZv6v7vvAQSlDGG2E/vJX3vA/h6DH96CF7vqYBwtacKFfj+kSvlDWiWWCSHQv5KR/3T6jO3FWxGKhsQ9np50XqprDIhiWaWeelPuSxTg82YxaCD58E9o53xZeFYqoMEqv6+vxIvGDyRPRUCIuEOhFjUEajFL8igxeVsRKczFCqTV5mMlNIDiFxzIqwLDIZmwE5PiGAxxVjRqMxzOS1wvpAhFIuLAbcrYkphEEE+IvEAJTFrmvCsiL6Dn0f8DxpcaUEMh1ybFrypKWC+Z6Uvchdt6iIhyFCNsIPlIRMSRzJT6VrVrRULEplTCgIidy71LWL5BEvaJQpTLEqibTGxKMy9TMIiV3MSJy8wObGRRCTFUlQ1GetVIJqaamFNFexmqTDaOcsKw4ODgMBR1YcUpFmZTEzgom6Na0ydQsQ/121uUfJ7hVjazljzDDxIqVajR/WGgejDTeEpRKILZVz06DunNuukngHXrjaWHqwEZRsHYdor8h73w8UCjEFKJUAhNY+YktHFOwt+1DYAx0zVgbMx/OwQ8xIkpxiCZRSfhq6Iqeyq50WR0ShpP8lydPG1bZUFdpY11AcaMjdkMRryl2jBCEgnh6zEinE0QtGYtIgUvJCnAavxGlE+nUkNfWjWA+qKN3yHyKSw7irlziRXbh+ESLcrZQ0z0pwPuMpliVP4QSUcMtKyJi8T4SAB+orD5LnSfczjz8rlIBnGKORNUgq2BFZJJ6nWJv4M0CioHhBHoggT+rNIYI0RTKKAHwwxq1H8bw8L1r0KKYoXjsi7xV/IkT2MDMTGJB071KfO3Go6EAn+GiWRcbBwcEhA+4bxaFu2ILppcdV06CbbB59oxMbtyXTGhPjfeJQyDwj1dw9jK8PmAGpmSAEQZZlpcYkau6JtrRqdc36xHhN0ywV1nbxrnIteJMncyU5R+eqZUVVTtXySZOAyVNk/WDGTFQPP8rem6KYeiQmP1GZ0q9x+rx1Wup8CYl1X8t4QnEGAMqIricLywqLyQqh4IHdUCwISiA2uCUG8b0S5ZGrFEBLxeicERGMzuURJCOOj2GScFJuZSGyLyYZR2zFITIDGeFKO1HnA4DSyG2LKjKC8jF9D9QvKG2E25MS28F8ub6ex5QUzaJ/TsKkNYOCeD6XUZGFkxYCImNHqHD7YrG1KBKPyHYENMpG5nuKtYRKYiZc38R9pZRKdzOQmNBlWtvih0T+la5wA23hSOvfWVYcHByaCEdWHOqGsKyYR5oMCES/QYgN3WW9DIDXjCdY+WGl4rwW7frI+5hUF7wTwW57DkjfMrmupgspa6gowqrOkiA1ivJefdchyYEUUmQIAEEHxA64RBgaSr4hJwG8Y49HsMc80L3n60YAApC958ddzdkd4cTJqfJH/RMUPF1JjWvq8QJpLjWExJm3CNWcxSSoevCfWFgaW2E0EgMep0L4GnHlnijMRo0d0VydFMuFtED5Pgij0QntnPhQiHUl8p84c4QSHmQv3ajACURkYVDdo4SCHpEW3XpAQEA9YVmJ5IMyHigDKXj68wYAhfh0e3jRoYoQlhuixjRx0kBi6wmhlBMLyAMgRQyJIFOCqDEar6/2HSIImyhjDLStVd5DQgCPCTc9Ktdb3BfpzgbEGcfMZ4dYnhLtwyZXY0CRPoIjKw4ODs3DyNPCHAYcafEq4jyVIZEmVM5ZUVA2vZ0aYE1qiyMe+mOywkDmAR4IEAqUSqgufCc6R41PXG70HsqNW4SKlSC5NubueHan/O+us1E55gStqLrv/qlNZNf8fI68wxAA3vhxCN5xENh++wNTpwPvPEjumitGF64gq33E6yYUUUoQZZ1StuFj/sZjEOS6JeURfclWysGJcZ+K/IKo8IhtqRAXipFSLBReoewSwnf+RRvEu+5ifGHREEWtbfJ6yE9Q98RJ6cJSpJClSJkHD3KJZkIVBV6mdCZAW4svqiFkkbWB8nNGmOXXiHJLDCMAU0kalyFknkwEIC0Xyun2YB5aC54kzpKY8UVlyqDCTY34noyZkVY+lRBx6weNzTPKzYnXUZAdUAJWKsbWLgCeR+N1Ev0KGTiLF1ab6LHQ75kVCbKSTpCbhjQCPtK+Mx0cHIY1HFlxqBv2Q/wGwg1MCVpRo/rNceocd/LalTjs6f8PLT1dUXPFcsKslpUR9sOriPvSnFjh7/NLuZoHM3dFOG58oq+02BNAVdDTl4vMmBG/tuwMq90TEKCs3wuqKNTVdy8CARAcegRIayuqBx0aXTCC4OVp7PF2Old0KZjnAYe9B2z3PaWVR5cqfm1uVEvjgGIeSiwPpfzcjXjOaZRFGj0U1y6jK21c8LgJSZo6OhCdTyIyVilKsNi1F7v0it+cIGTa/SCxGxlodEo783hKYN8HhBuVbMvdtVTLirBYxFo4V85pTAJ8D4zF732mZPXiojDPk3OgqjVILipNPDekUIwXjAoCyi0ziC0f4rT4+N6o5A4y25d2g0QdSiN3O2GNUYSm4plTng3q+Zo1q+AxUErA1PvKya0go5HLGdMfGRXGB00jJgbRHjCkfthH2Hemg4PDsIYjKw51o6EDIPvDZORhLqmd19Xd/BcexqjuLZj3yhP5GqjByHWNNERQFIVAkb3GcTO8LRAcdIjyNqlQY5cJZpMsEWKYz4BRh8h4CF5QKWtVVTercMbMSKbx49G2+GMIZsyKKoVhTB6UIcwd5lDEVyjjEaNe1nKJnXlK4lPfKRdexugQIt195PXE/BVLBJSddaMak3Ex3CrAlHoEIMXI9SmKGyfxmIqSzwfQgp+JmINqbYkKo9eUKnEvBITFGahiaw1Bwff56esEoIgD1wmfoYhvYYzHiTAQz0PJZ/A9CsoYPMOnk5DI8iHkl8kFSOwqx5gez0EID7InMdkS5fFzEc9TunKBT4UfohhZi6IrEflQZOOxN0wkGPA8Toyp8ZxCkjTiMc3a4MkYmZjAgNIotgeQ6aClG6FhDUu8V+YaT3gQ4MiKg4PDIMCRFQcNxsHx1sD5tKxfA+IiJs5JCUOQoArd4tI/eFwZrhlgP0x/eKv7H2C/oMirzy16Xc/qWWfu+yifuDi1vgxFJsYV9blhRnyCZSc4nDhJIRIxbYoUS0V5F3o1CEhQlbvcMflQlULeR6kYBUir34CKji7qEkKAjlHR6DN3ldc8JkiGukZE39EmNDqdXggN/VGS1gmpCkftZXSKWlduwcd9ExrHjwhrAhMB2iLoW7WSiDWggvAwuTYqUZOnuiOaIKUsmge3hoi1BrfgEAIUSp6SKYwKdinlk5YVLzoEEeI0dxq7PXk0vsfyhUI2hHVFKvIE8BhVz5qM6mpEjsh/MSGMnw0xonxOVPcu9T6oSRmEVSthqVJJk/wU8LGZTFGsJifwDIJRFOssgvc94cKX4zvIMucBhyMrDg4OgwBHVhzqQqaBo+mD8QGDECQMgaBas0k9ICJRQI0fVvLWKrBKmZOlYYRSi71cUYDMAy/rQVoGIhKGIF7kDmRcUf4vZCyBHPMhVI7/J7neas0EYYFQJIFw7HjgyPfJa8mRIC0DALdOVKuIiZJwAYsZhVD8wrY2PVZEyKOMIywk5aOOQeWoY4DpsRubL87cEDRKpJjlmnC8m67s/KsKNf9/KMiNDLA3CA+HMDrIAwIZ4+eU8P686JT0SJT4rJZofcyYCq5kF3xpCYgMJIrSTSlIoSA64FYTRNnNhHxEZBUjKLUUZYpkQSbEafJQ1oV5SkA8t5pQFivkiUdOdVlTrT1iCBoH7Ivnhnp+vM4iYYEkZFJkec6LXBsiAtzjMkHGdDewiCiKNM/E8zIJRXz7VYsWXw+DHVNB2IRrmZqeuJZlRXkv+hhoNHw+i4ODg0MdcGTFIRVDnqa3WgGCAAgqEWmpBk0NjMlNVrZ0Yp+XHsFuK55t2tjNQJimjNBYwQnrdQNDrGiIWAKTlJCOUdFfWV+8AMJxu8A/9jj+lgBBADp2LFA04mUCI2uXMi4IUDzyvQB4euL3vBfkyKMlCZBjE90SQgmJnhG5cx4LSNo7eB3e2POlwmuzemjKs++Djt8lUuZ5HZ+fBi+zADMReK5QMH4yu7T8mLqlYhIgbR1yZlHQvpJFDDEhKwnW4nkREZR9i3gHnvmKu2RFCn18HwWxAiEAT/sbnRGpM6nIJYoAxSIfgBMcGfDN15Ur9MzzZMpgGXQv6opBAFCf8eGJJFXMo9riq9xOVYbl89HWJp8D5iUP0dTeinnLZ1lJaKCQOCL/KhY8SYJ0Egu+npTR2EKlkAf5iVHIV+Irhhf4zDI/hSQJ61ndloqhJgtDPb6Dg8MOBa92FYedGWEIqaANeiawchlACBJw16+gGruFNQEkFKnEav+wTlz/JnqKKal0hwppZEVRFGq6uGWAENiXet7eQFW9wLX+EAhmzQEdPy5LJN4ijAo5gzKvs4mTgO4gUgonTwUA0EmTgTffNBRbYzc5qEK9n5SxiBjtOhvh+g1o2XU6yqId0eco1HnZNu4Vhq4a76oLhd1jAKnG/TEW/RPxD7IxlRZCsWEvM2kpynSREXQHolYoA/nNIGqpINPof+Ky5wm5ieJCxgmYCBz348MPKQhU/zPpYlYqgVAWZTWrRm1Dws884YSGIJovCQJJYMRcSSCIQLQO1PPlWnjMiy1PlEaungpBUL9vpLuWkI/XY4xBN04Q3U1VkCSqkhDjH6J5UEZBWKg9A5E7VjUma5LsxbeMUgp5q1SoLN4EJ1HF1Nzrimue+i/Rt1Kkfh9Qaq0zaHBkxcHBoYlwlhWHBGqdRD9gZ6oYIEEQE5UwBKnw7FBNGr99W2fUXc4f1kJfT+bQuQ5uaybME6wFmkFWwtDqaQJAnrVhHZpReXhfpLgrCqctFiB+p40nLkn9mRKQmTPlNdGXkjSYK4+Kks5jC4SCHs7fH960aabE+oBqKVHc0jQF1ghgJ4gsHURpzc8VkS5XUBRPZQyPMRQKfiw3tzYUvHi3nvBzSMSamusW/Z9Gma+4mxZTzu8wA9eloMqBih6zkCA+NggBbW2Rl+I+4ngXSiAtKyITGCiL10pYmJSEA74fu4FZH7aUR1cSDBJZoKixpppOz9cOmoVLIT6yHovrG+0jQkm1sgRnjO0p8Tol5hMTXxGj5dvICiE87iZ9DdT+JDJOtB90OLLi4ODQRDiy4mBF060ojTActU0IoGJJK9wEhJkaQQwamoe66JhUIvApMLk0SD/UigJVna+cSaKRlfo/4vF+cNo8zEgP5YWyOw4gcrUTSuIB7wTa2lE94F2aO1flPUfp/UAo9voYVj94EgUjy7cHvhtBawvIQQdHbkhC6VcVSb25NmacXjadfDIekyEOPySExK5AcrJUuoGJU9OjjuO4EDmWqghzK5UoCknkTpWYv2R1sbwhoTKmRBAUQgl8z4urqqTAL0S8ilEeOxETvZgA8jI/jl+B7CZ2ryKAzBQmlWbhBibWQ7XqEIKC58nzSvR7a4ldUa9KCxJBqageuqkTAXW9qZJBTMikkRwhA7E99UaZEVAvrT7K2sQyEZ2kk2SdlFnGoxr3Oq2DZCKFIfx5H8qxHRwcdjg4NzCHmghTXifqNd3ioo/88JpeoN1v6kBH//XGuupn6RgFCuzePog/0tS0cBCYd0i1rEwoUWztBSYUaVaT+FLGtbQGhFtkZHs5CEBHjUb52BOiPp96PL40YVJiRxzCYsFloCbh4MqqmrgKAArjx2PTe49Gx8QpCBkBoR5AKharDuFjpJEY3dIjYsaFkiv1fp7uNo5/iOQlVHEDU5VoFp+bEVkcoCl2gg+oQd7y9HoSH4BJDIU0OvWc8GxdBJ5QvEV7Yq4dgEIhOu3evF98cnKulIC0tHCrSBwLA3GNRK5QlMUJDUgATtbieyfXjM/C9/mBj2bMh52TJu6bzwhKRV9eK3qMq/h61jmgCqrU0+6vmDBjIAii5yRQ6yXlEAdu6nLplWVQv/LcEKNRKpkgBjFF8r21zAzGH0Lrhguwd3BwaCbc9oeDBjMiZPBPrFcHDw3rypCH/A8rhO0dNesEitIwvYVgbgdFm7FFEew5T3ufIBulkvU62tr09wAwcZJ+8ruqICakI/H/jfyzke4fv48PXzR2s6VSFl33Wbw7TimL09haFGFi9gWiCWm6C4m/cQYnIrRXxbLC/3Cioi4mKRUtO862c1XieBgQEgXvE1jcuRSlkJ/xQRh3A2M6ATLnQHi/6shxBrHY+iHL/MhVzSv4ytQVQiTHJMp5KwqBE+mSES+J73nRIZjK2onqMb1JQpQWPSaJHEEU/8EUdy+izIUUW7S2oo26jtF0bGtsvE+wO0hyYLV+JeTPocgLQqyMpVkssywrAs664eDgsIPAfZs55MZA8oa+siUtcCJPclhz4GLPNkxa9zpQw2Vrh0ChgPIHT0D5QydmVFKUmiC0no4ONdUr/xtOnBy9GD06ofTIOu99nywLj/4Ayh88ARg1OjU7k5ZpyVAbZWyHbSdaVBEKuUJaRALhkCpuPoQf9idjVkzLgpr9SYdQ0tUT4NXyaExe5imsz/cjRVeMwzxdWfd9aZVQppRkhoRbVsxgaUSuXeKMDtGG8GtSp1ViZwifq35YoDKUOK9EsQKI9YmH1kmkX/TjOkpbjyn1lMxhggCap8VT7h4nT4kXhDMF6n2QmbKILqtKVOIJEPknLbaFEMSJEJj9WddTWlsyeGltFLKZwdZVEpm4oA9uq5WEp+9COOuGg4PDjgLnBuZgRZ7zVJpJWO55YY3SsfKXKOnIEhWSOPSh28DKfSjsthBvTNujeQIOV7S1Z1/XFH678hKy5NdAdd4+YBPGojJuF+Cee+wde4X4baEIMmYsSKUPukoohtYVSbLLLiAbNwDtHZHSxohOmigFIQHvIyJZVcumddSWGm5eRJ7cTkT64KT0yRmRZA1iW79CIXK3qkQEmxZ8INBd8kjBl/UJCFBqiWXlXVFKEco4B8KtDzydrWqR8X0g7AWjSgwIR7zxHinGxKOgLYX4w6lZlYx1IES7L5JsEQLCs7RpByQSgqLPII93IVQSE08cQglEgfWoSgJCweOKRMpiRO6JjEapn8Hd6AipKnPJhiC+KqkTSQK0WdL44Mu4bbws8kwYxkBIRSc0aWIQ/X4JIp0qq2lxqdW/cpEY77MQj0Gg3ncHBweHkQ5HVhwyYSUkTSQplWoAj1Gs2rjdwklsRCUblKeFHbdpzc5BVgS0GxUp+LnRYpyBAgL4Ptgee4L0VUGoFgUglSJFNVJeEcMQY79/3qL3gL7yArDHnrxPBlIogh54cJSByfNAUVF6iXeppYIIxOd9UAqCQG7QR+eO0PhQQJscCf8eYSmI5siU80m0GYrxPERjFgpAr6EctrQAVUVxbmkBYFgPNaKhFDMCX8RFEBKRo95eOVdRLKwaYCw24jAG4nkIy2WTaakTkPOXY6hERnFpopQgUJr43K1OrgG3vPg0fubkYYvynBVj+UlENSiNT5VPWh2M2BNj3STZUE6rtyVEYIwCgbCsiLUOOdnRybQcV2PVhujKWqjWKMBYblFmIcrqeGkTlHIY1qq64MiKg4PDDgLnBuZQF2qlNa4Hj6/chF888Bpe39idNhoSSnfO4VMVnX5i2EbNJILtI+TJdBZMn5UsVJW0QsGiTCJWhoQySkQQtaF8J14AtK0V/gHvAGuPLUOEUbDd9wCds1ukSgoXJBIHkGv9EKKlNpZzJsLFSImdSBAsyy48Z0GxtYHAMCxEshMk2/L26rkj2phUdwGT5EDJmCWWlEomxpV5eZK8mgaZpzOmhFsy+FgZ6WtjBViRQSEpxDIvppzdok40vv3qPeF9CwuQtGoRTdkWpMvnmcAE2UxYE+zTUOoQzfJjayfii+IDTpN9WihdcvwcdWrCsHakWWO0ZZVtHPFwcHDYeeHIioNEGIZWMpKXoGTWMoPlATy+ciMA4P6X12W0SRaTYcsYhg7BnN0Rjh6DYO990ytZ7mMwb5+au7bVAw8BtSlLmu5N5XuKpNIplFupGFNApPuS5CCRFStDdSRqW4JQyZQlOqA8bgWUwNQLpf5n7Hyru+YApOtVYsKAQqZ0JVSQAJVASuXedEdqbU1VhAmJ3KsgM4hZCBaAkLtgyWxj5kQ1CwDR5qm6DhFJHOIRqGLN0bqVfZv9KMRRpG8mgHoDRO8eS54+b3RrGRTGGtrbC/gsVvQ1lV/cNvOeJkiR3YUrqsvjXHjHccyUToQS88nDO1KIVV1wlhUHB4cdBI6sOCQwvJJuDSthBm5/s2NU/9r7PirvOxbVvffNUFLyraWq1BEAGDOWp4A1KxopkJHU26OypDxqb1IPJ0QjRVpWMQBs9uyo/uQpWh1QGsW8mHPQdtNTdrRVQkEUmsXHluEjhsIp+jfnqQb5gx/sZ84+JmxEnk2iE7m4D0+NxzGIjkznrASpqzvx1o17OZnYlStpdVKqWzMy8C6ocMFSNHPwdaSI5q8cAiliYsR99SgBI8rcpBy6PNbheR/m0phz8LgboBl8r5LhWlm2tP5TrFGNfDFIkp54QCyd1Uk8XHC9g4PDjgRHVhyGDlJ3zvhhtTGnGmxqTtvIe6yrRvrg5sDCHBruSVeUgRSdCkZQdqKuspNv7PJrrxVFVLwnbe0ITjoF5D1HxV5HUknnhw6KrzQCHmBP+TWr5g7i6+l7U5mF2kYRVCNERBc4IgLxJbETH9c1sk8RJbpG9E9J3M44dNCW1ja59Daio7QTbExT3sVlwt2ojAxZIhuXXjn+y8SBlF5sWVGhJBUwA/jTED8runZvxpdAuxp36TFLkH32kGa3teuphFN5PjzjEMl6+pUy1xGzEn+GHGFxcHDYMTDytDqHIUWcqGuALR796D76jU7voKJmsRo2aIJiYW7jpymuebsz3qj73YnMUikiqB2o5cqB6RDERd2ktrnfEBCu/BpEwTTnKIq72U9ix9mPc4wQfj20mYdAUg6WNCcflUXuZ0rqYhj1xB/G5PyNywBovFCC3GgWiLhrKsfXJJbthLwawQB4AgJ1YJPcpN1Nsf4W/zphrVKIiypTYt1s/eckESY5sz4zhKAgCFQ+bqTIah9XbU+0UpOzErQUPIPI1BDA+vw5ODg47LwYkWTlhhtuwPHHH48DDjgABxxwAE4++WT85S9/kdfXr1+PJUuWYNGiRdh///1xxhlnYMWKFUMn8AjCoDhdDYGf2Uj77Q9HjR5qEQDYdUpiKLQEiNMOE12BTFt2zZVGLY+PENGUcaF0ahaaLBIi3HWkrq8rk9IiwzyLFsmvZX07mjxFxHuoGqwag2OxcgnypQtmHBKpBrgbVgQ1ZsaaMEDIob41iYpwVTOtQsqrxDqItTUtBeJ5EHEilMYxNNocjJZWy1TynsWvqbyuW0uST5u4175JmhQ5zOx2NqS5pqnTIrby5LSSlbJGTdybejDCvvQcHBwcUjAiUxdPnjwZ//7v/46ZM2cCAG655RZ8/vOfxx/+8Afsvvvu+PznPw/P8/CTn/wE7e3t+OUvf4nPfOYzuP3229Ha2jrE0o8s2A6CHNJT7bkEO/oPcbDXfGDrFqC1Deyxh+pqGxKSkoQg/31LrG7HKKBzs36dUrQe8R6Ut20HWluk0mxaXeLdfE4eQnP3WVfu8ksWK7lWUqUOYhoARAvPs/eZIo08lwMx4bGLyZX1kPdDlPachkXplVWyIpResUgRoQjlWHr36vD6e6LP31xrSkB4PmJCKRDqKZU1csRvmEq25GV+boocOIxyzxFxtolxoGTmvbUROcvzmvWc6ITGIJ4WDpfXemO1rpgXLGPo/cibm4OjJIltXRhpOzMODg4ONTAiycp73/te7f1XvvIV/OY3v8GTTz4Jz/Pw5JNP4n/+53+wxx7RORv/8R//gUMOOQS33347Fi9ePBQijyiECLUjTppDTfrZS5h4sUMjZAzh3vuCbN7Uv36aJc8hhwPPPInKnHmaLuTPng3a3ZepRCbAd4tDQqReZt/XT7ZRdHF9PK48Eus1Q9OH0AdJdNK8Ws1op1osVDcwTUZViSfKVUrl0SpEqacORDxmkBRlbNuiqnqsQiYSk5BvjT7VjhARCxKEfLzYrKWeEm90iJrQsrpRJTTNIFFmM2MjKXWkDEtILenMOCnzsFJ1iDQDsLb+YXI+SRJplyqt3DpgAwTEcRYHB4cdBSOSrKioVqu466670N3djYULF6Kvrw8AUCwWZR3GGHzfx+OPP143WalWq6hWq7Ur7gAIwxCVamQ3CYIQYUgQBNGuKkUo14KEBNVqgGpQRRDEa1Ot6u81iHKhBRCCMIxOKA8RyNdcED3VccjPWxGplUPldWISon0y5XJsGYqVmtS0zObxLrxeyBWUZiMIQzDVihUECIIAJAhSxwuCQC+gAAlChLSKMAxBQj0ddRgiYXEJQ96PsjZAiCAMEQTK39ZW4JDDUO2t8PseICQUQRhE/wKKkAYIwhDVIJD9RMQ3Kg+4LGE1RBBUEVKGIAgQBCGqYQAShry/MJp/GMj7HFQDXreKoEoQMIIA0VhBECIMotdVMTLvKwx4ezGXaoBAWd/oeQoQBpDrHYpnLQiif7KvIGpfDfkaEIQkRFDlMgVivCBag4DPhb+P6kWfEUZpfD2oogqGkI9RDQKAEP4Z5GtRjdcrRADw+ZJqwPuvRmtfrSIg1aivMEA1CEHU+xgE/PsskGsdBAGqAUEQVvV5UsI/01G/YbUKWhXjKPcj9IBqNb5X/N5Vq1X+bETyRnXF8xLK79VqtRodBOr7QLWKalDV5qM+61W+hpFcgbweBgGqVQA0+m4iiO4RBZX1qlUS9c1fh6I8iL/b4n8EYQj+fAW8HUUQVFGt8sMvq1VUQ0R1qwECIsYJ+L/oNQmryriI7xNfI6bMQ8w1euarCHk/hK9/ru+SIEDI1ydvm6GE+hw47Fhw93bHxWDf0xFLVl588UWccsop6O3tRWtrK3784x9j9913R7lcxrRp0/D9738fl1xyCVpaWvDLX/4S69atw7p1Ked5ZOC5554bAOmHJ4IwRJXzgiCMYg+qYQiPEniUoLcSoqcSgFKCbX0B+qohuvpi7XddiWJzT2DvvFqFcI0RZKWzswsgBOVugu6+IN4KDEPQch9Yz/borecBXJGsBtG1kFDQcl9imHK5D91hH3rRh23btslyCkBIVqmU4QOoVCqJ9gJd23q095VK9FEJCdC1Lb1do9iybh1GKfJuXrsOvQGB17UF45VyFWtWvakXEBJRP0JBwmi227pLcp5bt24F7dPXrHv9emxd9SYm8TEIATa9/jrKQYitRYq+SojtAYGHED4FtlVCbCkSdPcBHgW2tTJs6QvQF1L0UQ8t1T70rKcob90KjwIV6mHrihXYHlBs9IGtPVUEIdC7iSGgFEGlim3lELTggwAoBhVUEWJzkWFjLyedQYAWn6DS0oruzdvgeRQBIWCMog1V+B1teLs7QLF3OwoUIG0tWLFiBcLWVvR4JfRu70GJhujp3IaeDQS9YNi0aSvGbd2InpYOYP06VEHQ4jP4mzeAdXWhx2PoXbUKfVu3oauvCp8SeB5Dd18VrS0+WgoMW0MPm9CHwto2vB60YFP3JmzsI2gp96BaDbDdK2J9pRt9r6/Cti3d6KU9eHtbAGzbBlCC1oKH7p4yNm16G9u8Iro3d6HQtQWb+7YCIbCmJwQplVAZNQrjOtdjGy2gi/TiDX909OyvH4Xi5g3oqgCbKtuxeWsvNrcyjEYZqFTw9tot6Olcg3DN23jr9TXwe7aBbemE17MNtBpga7EVbW+/iWK1ghafYJtXRFvfdrxdGIW2ah/aWIDNLaPQExCs37QWpTVvIdy4EW++/gZ6vAo6t2xHa28X2goMYWsrEIZYuWYbtmxdj20bt6Brcxc2d60H7exEdWsXurdsw6berWCrV6OnYz3e7FwfPXObNkWfcd8HSiW8uqmMLW0MQQh0FCMrzfruKlp8gheefx7lrm1Yva4da7dVsak9+lyu3VZF0YtywVWC6DurqxzAowTF3m4UOtpQ8ii6+gKUqyFafIKN2wN0FCjWb9iKYPs6bNpexVstDFt6A7T60bGy28shustRvbYCxda+AD43HLFyGV3EQ4kRoGsrNnpt6Gxn6KmE2F4OsaaV4e2uClj3NrSs6UBlSxfG0gq6WzvQUvKxZeMWFEd1oLh1MzB2bPzZ37gV1e29WNPqodrShmK1L1qbQs7EIL29QLEI9PQApVK+NsMATz/99FCL4DBAcPfWob8YsWRl9uzZuOWWW7Blyxb86U9/wrnnnotf//rX2H333fGjH/0I559/Pg488EAwxnDwwQfj8MMPb2icvffeG4W8PxIjHEEQohLElhVKCapBAJ9R+Ixie18V2/oq8ChB5/YKeipVbNlelu13aS9ifVevvfNqJXJyV8jK6NWvAoSgveTB317WyAp6e+O0rr4PBAFQrSJsawfp64366U2O5fsFtLZ4KBYKaGtrk+WMAHxDFJ5fACpleF7649/eVtTee3wXwaMkca0ZKE6YAP/1WN7ipIkIpkwD2dyKgjIPFf70aXoBpZFlhdGI3IGgrbtbzrOjow2kRydhLbvsgtHTp6EoxiBA+8yZKFcCjG8rYHu5iu5KCJ+E8BnF1t4KxrcVsLWnDI9STB5dwqbuMnpCgj7ioS3oxdQxLVjV0QGfEVQow/hdd8W2CjCqyLB5Wy8q1RDTxpYQUoZKuYItPRV4pSIQhighsizs0lHCmq4+aeFoL/motrShc91G+L6HkFIwz0M7qaB13BiUNvWg0NMNnwR4Y8N6zN51V9BRo1EulLB16za0MaBz3SZMG1VEDyi27jIZozvXo9I+Gr1tragAaCsWQNa1YUv7cygWPJRmTEd1/ERs3taLgs9QKvro3NaLUR2taG/x0VkhGEeraBs3GoU+H+O2bcTa7QFaytvh7b4ntnsFTAy3YzNh6JkwBbMKVZQ2bUelcwsoIRjVVkTfjF0xfmM7umgBm9dtQvukCdilrwsAUNrSB6+tFRg7FqM2jcY2eJjiV1H0RgF9vWBjx4KtextbegNM2WMW1mzchsmjixhLA4R9fSi1rsGuk8eAjRsPr1pEx6zpCDdsQNusGaBBBVsKbSi0taAUVtDqM3T7JbT2bkNHoQOt1TJG+8DmltHoDoAp0yaCvjEK/owZ8LoCTGmh6JkyHaXt29DR4oN2dABhCNa2FtNnTMTGt9aiZ9I07NI1HuH69WBTp2HT+k2YvvtMlNvb0Td6HCbMng4AqKxdC+L7IMUiaGsrvDc7MXNcK4IwxJjW6Lv3zY3bsOIfL2KvvfdCecs2TJ46Hm9s2o6Z4yLXsTc3bUepwMAIUK6G8BhB5/YyCozC7+lG27jRaC142LK9jJ5yFR0lH29v6cGYVh+rVq3DvntOw9otPZg4qoSN2/rQUfIQhkBXbwVdvRWMafExqsXH5u4+FL0ozoiU+7A1oGgtMFQ3b8Z6WsLsXdqwra+Krp4KJo8u4fWN3cDWLRgzcTy6N2zCeD9Eb/totLeVsPbt9egYNxZ+50Z4EybIz+Ta1evR27Udu4wqAR0dKJR7QUol0GK+752gpwe0VELQ3Z1wrRuOqFarePrpp7HvvvuCmYeaOoxouHu746Kvr29QN/NHLFkpFAqYNWsWAGDffffF008/jeuuuw6XXHIJ5s+fj1tvvRVbt25FuVzGuHHjsHjxYsyfP7/ucRhjO82HjJAQAeH2BxJGZyzwHezoH0BpCMYIKA3AKEBpbEmhlILSlLUKwwRZEWlYKaGKvzz/n5rpR8QckLg8NDIBxZNAHMhqZBOyB+WmOHabAbmJuIRmII43oMqZHNF4FJRS7awOE9Q8e4FQgAUAZTygFzLgPepTlT0amxJ9bEIIKKOgQcjvZ2RJI4jIK+VyUcpAGcAoBWMMNAAIJaCEgdE4w1R0IF9UlzLGU+UGYJQhYAy0GvD+oueCIQQQfeYYL6uGXEZeRilFSCk8RsEQlXmUgVIajQ2AMAYqnlvK+GHqURkFi+SnDNSL2tEwes6jgxz5c0kj6w9ljM87kl+dP6XRvChlfJwQhDIwFtVhQdSGMQrmIeqHj0FZRLii+izqh0VtgSj4njGGkDEwHgwffeYiCyWjFIx6ILQCxjz+GfXASCWSm/L+PLE2vC+5bnw9EdWJ2kflHgnAPBKVVyDlY3KuBJR58vuRMhZZRPk41PPk64BE56kwysA8DwFj0b3j36sBZdH98jw5BvOiWBpRh/K/jEXPDWMMnvLdzLzoPSVAFQFfmwCMUVkvkjMAC/h8aHTNo/F19R8AeJUQHgulXB5fZwAgIQOtkugeeR4YEW0BxkIpY8iUebH4N8WzjAf57JLo88IYWOiB8LXJ9a3C70c9bYYDdqbf2p0N7t7ueBjs+zkiUxfbEIahjFcR6OjowLhx47BixQo888wzOOqoo4ZIupGFWpmFh9cJ93bYs2GNQNQRJRvasnD1XwBFjDgaPhF3rfzL6KaGTOk92EmiTkbVcmKvJt+LbE7RH1mQLo0S7Z6UUA2GFwcm6oHcsj85bnxdjaOX8phy8Db6WS9mcLi6HkQr089nUclrcj62u2DE6SfKtOB5Nbjc0i6BOh5UktGRRRwll4Hlfoj7kOMzZt8XSaZvTqtvXz/7z2/6QaM54SLrHRwcdjCMSMvKFVdcgcMPPxyTJ0/Gtm3bcMcdd+CRRx7BNddcAwC48847MW7cOEydOhUvvvgivv3tb+Poo4/GokWLhljykQctdXF/Isv1qPYaP6g7CtPoB5qkb4QN9pPnbAe7Qm4o2mlKG0kql5kkmCTHEtmYAETbLqZCKDI9ERKxV4WQ2K1sxpOX6M+sT0GoLrSdyijlhCupYQgtxTMxJiDEjhlMchy1gpU4mG11FpGwXKrNlZTLdZ/zkSApSdZiHrBp70aRSS23EgdLG6KsU4OwjmVbTsTrZCN7APgBRLW++ywDNyq/Iy0ODg47CEYkWVm/fj2+/vWvY+3atejo6MDcuXNxzTXX4NBDDwUArFu3Dpdddhk2bNiACRMm4MMf/jDOPvvsIZZ65CJJWIjyummjIOUn3oosy8mI/IlusmIR8mD7BgQB0dIR2GsktEflMEdLg0ytz3r4n42cSOUZMu2xmGJqGli5e27IL/rOWHdi/iX6RVM0y9B8LS0dcLKgpUMmxvw1a0hSflFuKs+p8yFIHvmutjfORUl0ahKm6KCVuFzcE6Ud4a6KtlTPmbIiY8tCrF2CPKrt43uc+5PVj49g1jgJOTMIs7VBI8I0ox8HBweHYYIRSVa+/e1vZ17/5Cc/iU9+8pODJM2OBdshkLnbNlcUh0FDUtu1Kuh19GSWxO5BRFewZTWNecR1QrUXQ9FTFOOk4kpiiwYnbjZSpBOOuNA+75hc2HR6ubtuU6RFO5gkK7pGIAimYGPqIEnlk1B+srxaZlpmhDyirXxpkg5jworVJUFMFTmsZE+xFok7AHPdLYubkCEF9Vp5iCKvfI/k0qa3N4iFwVBTxVGWPUwhMuIsm6wOHN9wcHBw2IFiVhx2FoQ7BytqmpbSWD/6Lr++g29VvNKUqxQd1NQVU3RYe5+JXhRFXdVGuYJudzmKBSNmu9oDK/3X1HZjomZaIBRSoM1BdJ9y7xLzISSegCqXRUZhkZDxFup0tXsYy5x6X+TQSTJlswilESsbAVFjX8yusy1ydlkTgqtVzeU0/+bss6ZBo9Z1WU91DyT6+3yi5BzIwcHBYWTAkRUHCZUDqAco1qqbfwDzRMJGOnGoif4oKbEGWnOIJOHIUN6t7kzJsTXSQuJT1WPLhCKfeuJ6JttJDhgTr7QgaUVxtynH9pFqKsvRbnrctxhdTknV0UlMLqT7lrQYITnfhMVEnSixvxbz0tbGQgKMOQC6TMlKxP43pXojMLvRXc3iv1mcMqdBx2KNgmyoPkOaRYq/F65wptBpAfbaOA1uNjg4ODjsSHBkxaE+OIIxMGi2TsKVpoYD7LOu2Xb2MzS+SLm2KcjxFrfVNcnsR9epDaVaJ1hmsLM9zbUht0HUiBLDQcxmxva8qnhqI5kNEbl7yXZUWYOkgEmZzas1LTsGSUENBdhCgNJcr+zraqbKzvmsmPffMpb8m8GRkjJanrt6UMejnpZcod5xk6S/XpmTz6yDg4PDSIYjKw51o9l8ZTB+VIelR0QOmcJ+WUmylNKcXaR0Ze5ipyploRF0kmPAWDFVlEztdUbzNJcivcDCIWpvsUexJbqFwJpsgPdFCEAtBMHcfTcESbw1yV5CYU61LGWshWgTTSxhNYpi59NsTjGZS7dYKITFaJt+tpF9/qpcNjnka2IQSSTfJ4ZE9vWoDxsJIcZzRJT7pa9L4slVb2yaUA4ODg4OEo6sOGQi5Mpm2N/DVaztc/YZpry2IM4SthOagEwlPF+jZEnKtwKBnTypu9628JBUOQyFzqoYZ22xq941xgGZke5uHpqpKvsxoaglno0I2M5m0RTUjMlIqqLN37ZwQkYhtzmAEfNC4jaxbJYnwiiWurPI3KVcNy1WajdqZ1mHq6q3TBsw7b2tG4ulIfPepfCBJElFZhrlWrzCLp/ZPuUa1SoZ1+JscXXvVwzLnRkHBweHxuHIioOGMOT/BnoQh4FDE5SVbCVeBGnHldM3iWNFW003Syx1akhkaNF6LyRF6GgHnCqeUOmKNzFeJA9nFBYFmqpvJ8mVoVgbJEdTlE1ikuYKJOJ4allSbDzDxgpVMkaMGB5TDMto6tpKIiifEYU8WQietV9RNe2aMb42tyxBrf3lIErGa2k5SbEeJceI2+WpbxW4kc+0Iy0ODg47CBxZcagJcZ7KSD7Zfvj/bA+UhHX2y0lArIimbVEbRIGX1VL+9J1tu4ZPkFTiVQ8aW7uEXq9aUWTsiSmzMUYN5NP9srf7NXcuxHNRd9AJpYr8ROdlkhBAUf5rjy7XII+pIJUQKEQKACE0SRJzWE1sZ6TUpVhb+9TlTTy/DQ2TP7xdu38pREt8PuJ0yPURpdxIZc4ODg4OIxOOrDhkwuQfA8JHBvs31f2I69A0vRxrk6WPWwuI5h+m7jLHZUmiU89dSvSXY7dbFU8fL71x3K+pEMYKqLAiidKELUEZXFofeLlOuvSdeOuctHmkEQMSkxwhn9aFuG4jGiQplz605QLJdHFKZ1R51j29n9QqtQg0sRxcmTG+JCO5yHmSUNaEyXbc95WDg8NODkdWHBpCv2NYgIH1NRvGVh4rmq2Q9LO/XM2lEk1TtPJk5YTi3Y+dbvUEeKtOTFSxeF0i+knb/o4bm9PRXlOh+MdypVqhDPmEtYeo7mlmM6NPrb1FnsRgCkmpZU3RYzZ4Il6ixK9oV+N+xHqqN1XaDRQLQnIsGykyx0kjAvF8bEkX6rGk2CW01NPuHbG+NomLeX6O/fmsPboZi5ULzrLi4OCwg8GRFQeJMAwVl690bb9xolKr3UhjGAOIfikchmbc0Pi6LKryKJR/W/3YxSUuThMhffNY3Y3m/SVi5W071gR6K2vnOoT7jul2ZWtvIQ4JNyyqr5FpYYjEVtMcq4osscpNQJP6vc0aUpsDgBCeoUy5l/p6G/3Y1tlUyjMZAZHPQOY5NYZynwdJsXS58lpKrNctfaaNZbZJjKM8W4DyjKRYrKL76n6aHRwcHATcN6JDAqFCGrJ4yXCOUXHoB2iNPWer7mpR5LU6Ypc/0dCwLmQTrVgJNRRTM2uSSj5MC4Mqj9JTgvoQqstAbLXyQsihFcXKrCoiIXpmLl2opFxiPvZJyH/qLr2VFIkzNmtZR6DHXmiCmqTVmh4uhQBYS20V9fuW1Z6AgGouiLZnKseQCglXn7403pqHrOdGLUJoq28K5ODg4DCC4ciKQ10YFIKy05CgLGWiCYqGqaxo69q//lWCEvdod90hcYOaZMQcQ9PvU6wY0SVdQdN0Z2LSG13mdAEMWSxuR5pLVLIZFMcoXe7EelgUUsLpGxFKr0lQVBKY1t6Yp0petCUjtmZaPXVWAHgckjIu0YP/7USL2AlMsmq6pUK+trAD+9sMEp3+SYiJSe3nNEkezb6UNeJlNGUdVFc5mia4g4ODw04ER1YcNNjISFMISrNYTsj/58w6KahvVzVViTP+ZjZQFV+zD2v/GRpimkwpu+m2Nia50WgEUQiVet1Q+K3z1pRyale6KWBT1AWxUHtX42bi1wp9IXY1WaqyqWtIYqVYIzGCyNVefDm3NJYHpVu7qc18qV1PxGLUZTmwjirLtNtrEAmd7DQwdkoTlczqZC+j0QAhLcueg4ODw0iFIysOVoRI8oHa9CAvgXBEIzcaUTj6o6NYdonNLhO2ApsCqBam7SCbSmRiO1wfU/4VmrppOdDqU22XPzl2cpCaAc82nqZZFpKVTOIEQuS3rmmtySKaibUyxs6kchbNWnMJI0STXZU5JlkWhZ8gw0piJ7Vp8tSCGi9lW6b0IPbaH4da3M00WqWev2N5Xm39E3UBUwQQbo1pz6+Dg4PDzgRHVhyyMVi8wvGXIUNoKk+oreDp1hRVac4gJoqlwHSpSg2+zhLE0oaIcqkHctcjTemL41hsrkqWl3H/ShYwo2FiXqnyKoo3URR+tV8zGDtBgoiYh7leBvnSrEE8tke9rv5VLC8ywNvGIon2R1kcfe2i+VGNaNhnY3SfrsNrrbPiT3TrhqVenvuVKZutz+QblciYbdPcwJKknWQtRoasjuQ4ODjsGHBkxSGBmoc/orncIt9PqmMz9aExRcVUQPMGJNezAWx3DcouEiRE29Wv5W5k7FxrBhCtIUmWJ2S22EBsbMbsGvrufjQaVeahyJayAx/3QxNzgkoUU5kA0eJk1L4T7w3yYxErQTgTKXzVTmop9WppxgOURsj0OnnHMivVSVhqjJ0gswlLjL2dXTRHOBwcHBwcWXHQUDu58MggDSRDzhExgyHVUSIVtZbSKq+pF6ipmOkh5lqfVK1njJipuCbrmSl0NeVbCJ9ivdGaSpOAWUt5b1gsNHGlBYmXK2lq1QEJdCuHam3R5ECSKKn9wyhPymtagcwA92RShHh6Yi1SyIHFjJFL+c6hgKdV0UlA7foAQCjNlCvLkpOy0sn2CROXpU9zLVOFJjnrOTg4OOwccGTFQWLAlfhBYQn2QXaqn/t+KTfEop/m78+yqR8raZoCroxGjNbKn8xg4VqWjYQVQpdL69/SJzHk0iwlJknJg8jnS1kKkQLY0GNrKKpSDs2lK1Er29okXikdqGfExPfAMr48XFNxZTIJHCHaOunyG4p9DkuJzVKRcevsVh8hWlq/RlkaeUnUs11LaZPWb3IcBwcHBwcBR1YcrBCuYENjSTHHDDOuOVjRL76iK2RpLkNEeW/NhGTrMmMXPmkYMBRDqaSDK8OxtmjGX6gKs9SsE0qoPo+kIcWwOtgsM5qSDsWSos+RyP6gMROiTEwo8XFdo62Wqtk+vrkGctcfBOrxLNq0MlIJawcXmuQghQzaiIUcJ8Wy0oghwVat3hiUtH40WTJIkVqmz6H2hNINKzqZc8TFwcFhZ4cjKw4aGj+dvlasi+3iwBCPHcproj+T6UfbWi3TiEWiOKVns3as+KoKvkX1TFOEsxRmJJXYBLkS8sssWcmxNYJiDlJzrY26nEToXdncxyC5liFwkiRIG00tSSzkM8tKlbzZerltHQzrRx7lPYvIauUmMU3pSi5z9lApNbLqm9f09Uu31uhy5yZWKQTPwcHBYWeBIysOCYTyr51MuCNOBgONKSfxTnrDXaR0zFVptX85Zuw6lrlbXNd4tRVXRTRpfbDu2iuuSomdcmMXWxdBV0DVZU2mr1WvJb9WtbYKUUpLucsFT5FLraIovllb/pSPbl0jdX0U9y11zlnKvUKgEvUSpFE8L/URBBvpICmv0/rgElrq5H825drYrqW9rvdzaDPnODg4OOzEcGTFIRMqMemP1WWoMXIlrwON+NKkdlVHe5JDbcw0RqSfkaHKk5VlyZqtS5RnuB2p4f+ay5ZtAjbrQ1Ibln8IkHSxIiKwnmr1RVfSkiIJjQgOz7of6fMzyUlMKmnqWibkTSFNyCAd1tPlm2QhoFyexNKndV9jzFoS1QrOlwQszbqS9rkkKc+9KZSzqjg4OOzkcGTFQUOYeMHfNlPbt/ZV9wmUOzYGUz+xaX1pVZFUzqQCjHRFP3Mn3SAaqVvoimtR0nNJJRqKpUA0U5RuKU0d5I5Y5kCUcRNiK6RFJR8JZZWS2I0oMSm78hufGwNpAZFSKn2YCnMq6VD6Veer/tXqqQRMuTdWqxviQJnshAfJsW2ypXMqextBcLOMTllQSW0tEOOvfi2DUFou5M2a5uDg4LAzwJEVh4YQhs0jMHUHxe7sRGYQYFGt9HfGbn1aK1sviVS8mfWTBEERIn5J9auqEm8NICe1X6veOJo7lKqVynKqN0/Z4ZckycZL1HUh4AdQmqQj+p844TwxVCoRIJIcWZhe1KckdUSbcC1rhfVZSGuXu8+UthnItoyl95uWYCBvxjft2TBkUftJErkMocwBHHFxcHDYieHIikMCqruXPSx+pLMF98OfBUJ0ZUsrJ5bV00hDBrnIaGfpVCcOxtkgcftsBbWWNEkrgVDsa8hrLReEQL3E5TPKbERDllGqKLhJ0c05SS5iJWVifUyymZRR7d9KzhJd20ln9JxEV7RT2oUlydJPI8gb92KNIVL6yHTzyjUGSZzlYiWS6gXztdFfahsHBweHnRCOrDhIpFlL9LiVRjvP0Xikc6Bhg37sxGZppzma2na3CYkUcBA9xbGy4S/ddcy2Cbcp2BXNBEkS1gso8SFaPzEhSRvP3BG3K/gJ1RupMFylCARxUS0TSVJGjHJBGInV9CIqkDizmWSZPICeW1fS1HTdlcxGgJJjafNT66RYXWrBnFbaaqfWT+GbjaKej5Mt1bfd4JVBkmq47Dk4ODjsTHDfiA5NQ91cYwQH7O/oMJXOWK/iCq+pCpraoc1kYLmUKUMNxV8VLosyCJcpEmclVvRrqrdIzEMnKmlZtwTxUC0ZMakQXYkYipg4UPHapCTquKJ9mrtS8ibFM1IYYKoCrxA7lUhpc0tplye2wkpgUghWf1HTo0qraye6Wf2lGtfMzq195eyslhAODg4OOxkcWXGwQrh6WTOANZtjmL/FOwuHydRB7ApoEwfoXzeqAkp0PVSrYyjq8pJhUdDcnDTrRnJgacEx21tcbGKXpCyFVFHQEwp/9J6SpCzxmSyJYe0FUiaqFan0QFpeMogAgWIdSdm1t8J6k0SRsuaCkKrmL6VOYkzzpksCZhmriYp3liVFe9Ysrme1+06Stsz6WbJltEnt32bhc3BwcNhJ4ciKQ26MJA5BnNWmcaqSpRxZ3HrUQ+7sgfd5u9a1T/2txnwSA5ipgDU9X1PSTQ03SSbURmLTndosDkTZlU+ZmBRVnIdC4340RyyTbEkpamRSE7VoWh86aRCpkLMOabR0kTGy0ogQMKYnGrBZLWpZGATBtMWA1EN28rhe1SIV9ZAW9bHMTAwhyjIsOmlyODg4OOyM8PrTeMuWLbj//vuxZs0aEEIwYcIELFq0CKNHj26WfA6DjRAI+e/ijqTvqz/14Yj63W9AWII6JplSL0UHJ/x/BCQmrxaFT3ObMvqx9Uksr61iCQEsfRICO6MmyTeyqtiRl5YAfTD5VjGtCKsOVSZLtQUjUhY1CF0Pmo8zegmRfY/FAhA15oQrtgEUq4Z+gxJrRoT1RR07eq9muQqV+ip5SyUzxEKdlOsswRANmcQ/80b1l4QIa5Cl27CGJakZIJSABOnXhDCOdDg4ODjUj4YtKzfddBMWL16MJ598EkEQoFqt4sknn8THPvYx3HTTTc2U0WFYoh9MphEWlHYAzA6CpqgwjShC1i3fFJKSowvrfjJRlDX1apZiaylNuB5prjJm3Zg0CKtLTJyIZoXIIkiUkwMqdvrVdppyX4NoqbJakgEQAhQ8vX8ugGKdoVobYX1KWkJSiALiZUusZUqzmjAsXrZMaMk2WX2lValPuCxyrPZoLndanfh9euU098V6ndCIsY4ODg4OOzsatqxcc801+P3vf4+2tjat/Etf+hJOOukkLF68uN/COQwdBJ8Ioacq3jGpwtCjHtebgUYe64a2u2+xdKhXo9fZE6q14yysGbr1Q7EcCIYVJo1KhCb28TXrgg5N25ZKo013pIoph+Zx6ZHkIj49XrVuUKqeKh/fBdN2Qkg0H6KwD1vgvxrMH8/HIJCI+9CIl6Z4U/29+D8hifUTlpXY3U0nM8JiRIJq5lKZIupjp9RP7SeLBGV3Gi9djfur3EhCoFt4iL09pRRBNcUc4+Dg4OAg0fD+DSEE3d3diXJbmcOOhTTCkmkw6Y9PmWNI9UFs8yeQfyEFKUjsLKcobbESrPWgKeVG78Z4hkJpKngp1oLES0Mx1eIzbC5O6SJpcgkLiymnTtcUEqRJYFlDThxMa402Nq3BWeVYwmpCrcuk1WWe3qdm8aFKb7XHtb1lhEDGxfCL8f20t08tqwHdOhSXaTFU4r+M/pvhmpV10CkxzppJjG0dv/6kAA4ODg47Khq2rJx77rk49dRTseeee2LSpEkAgLfffhsvv/wylixZ0jQBHQYXoW1reuAG01+TEDXUs6YMNVwQ78s3R2FqClKUJ/VE+KjAaAOhc/Ndf1u/0LtOKPh5ts1NTmT0oXUkXLgogbZ/LUmYPlft1HuuYVPBNDRCEbWLdHtOZhSFNCuonBAaf7xsxEopoNzFSpIbYU7R1tCSDUzUJ8nXqsWDILSTPBIRl1CuYfqnRyrbQjYzkYDt/JtkJ/pbURSmVrF2obbJ+2mqadHLIGY2Gcx2WnzOMPmIOzg4OIw0NExWjjzySBx++OF46qmnsHbtWoRhiEmTJmG//fYDY6yZMjoMIZpyIKRDTRi6GVK18oEb0FIhuzSPAhn91ba9rR3UsghYyzVSYtgvLCYdQiOlWk49Yzc87obEhMCQnyiLqAbb54XKf1TlVsyDsti0EiniMdEIlTba/GrQckJt3828HaWSyOmXTTJBFOJjsARjbkkaIvpLJ3RG7SxRMuta65FYLrn2edrVKlOepSjQP16P6HT7OiEIoIODg4ND/7KBMcawcOHCZsniMIyQ53cybNKvaV0/5DtqoH0/SUlCt2mSpYbIvkLZrW03mtTYgTetCKJNvcIQEGmZsFlpLE1k2mGrOFxzjS0B1Kgn3Lji7F3ympFtS1XOrTTIIG1CZVfJnLo2THEms/UlLUNZSrdqDaIEhCVdoqJnh8TWFEVG3VxR3/2yWq6E7GZXtSwcaf0rfduyjOUT2ULGmgjtmci5hgO1R+Hg4OAwEpE7ZuUvf/kLTjjhBPn+O9/5Dn73u99h+fLl2L59+4AI5zC4CBHqlpS0eo3yhCE0zQz/H//+pf/RFCyxS1/HOQ5mb9bSOroz1ey6eYnisqT1RJS5kniUrF1yLQVwQlE3FGdTUM4oIlH0a1QhArUsK0RZEVsqW2mJUIwOhBnxIzaSRuI6iSB/kxwRAkKj/amEexalyXaJeRqXTeuVWo9mWEG82ntk9RLZJPch1mfCZr3JM1RNK4zxHFlfN2kDwcHBwWFnQ27Lym9+8xucdNJJ8v1vf/tbBEGA3t5eUEoxe/Zs3HjjjYnsYA47JpxLWHOh7F831DDhclVXH7omZnprmXUTl6xuWDmHFlVVEiKvkVg8G4nQREgqzoRGGrPvUfQl6loUbFu/XA41GxnhsSqUQGZ9okQ5lFKSA9E9kSfOR91QxWIjhdLlIwSMRJ5f0m2JkjiezHKPMoPIheWIpbgkcbIiYo709Qmzb6dp4iIEMl00vweqVYEWiyDd27X2Nr6YPDzSJB7210YT7W/2NOr/7KRZYZLZ1OSbhHzWz71zA3NwcHCQyL2d+9JLL2HBggVa2W233YZ77rkHV111FYrFIn7/+983Wz6HoYBmXTGsLY2bVfolUn8xcn/3LcqQhRPUOhW8jt4z69RDiohCQGzypbmT6QPCSpAECUi0tcjuyRg608XL6MHWp6inHAopr9HYfEA15TQjM5QgHrb1E2vCiYM4DT5uSGIxk8U8bbDKOC1DSCuTcl0Z0+w/dRKW94LYqXJlEaN6UOtxM58zfY2yG5MmHWqSSpg0YdwBKg4ODg71IrdlZd26dZg4cWLc0PNACMH06dMxffp0bN++Hddffz1OO+20ARHUYfigWbEqDjHqdbGSWcRAECI03JRS92v7LYxmDTDK65sE1cSt2VS4McFy6rpdGkk0mM0dLrHTTc0LcR9qZ7pI2nQ0H66UOQhQEhEHYTWKpiIC+bk1IiS6NITKbnSrApHzVE9Rl0q4IhfxmJVEEMZAQuW0d2V97CRQF8Jm5UnECqVYF9LK6rFzWLus1QaW2Blr34q7XiYjsX/mKFUyurGcmwiUDl5WRgcHB4dhjtzbPGPHjsWbb74p3z/wwAOYOnWqfD9v3jz84x//aK50DoMO8XNrWlB2LHoyTJQAi4Ukq47cNI9fKMpWmrLXvLkSpAerEwL4iiKWIVZUTJOT1zy5DI3a2o3FD0h1txHyJFxwLK44erdGv0IUo71pTVFd2SxdyNEIJaBZ1gVe2WOKC5VFblFGZP0soqQIYxMwRZ7aJJJ3bUtMIO9j8klsNFV3zQMeLfUH69Oe6hKmlhuZMhshWQ4ODg47G3KTlXe/+924+eab5ftCoaClKKaUolKpNFc6h+GFGowlt8VFEqHBo0DDXQHItcNbV1tjhxyoa7lT7RVS102SCZ/qUdWacmq6URk79hZOpn855dsC1976HouUVUptlzVFX4xr1hEWq3jusa1D2mKIYr1JvUnxDr0W/G0o8gSI4zxMqwZfQzMoX8C0ICVIGgBCWbJfRQ4QkfUstiRYli3RBqp7mWyXQpeTZhijzEa8s0mKahCy3cc0JBIsNIisfnQLnHMDc3BwcKgXub85zzjjDNx222345S9/ab3+xBNPYPr06c2Sy2EYYceyqoxk2KwaRsaoHG3qHEIWJoLYc7QVRUKRln1YduIFGHdfSrhv2ZlEbdSSO7UZ0f6ZEzTdgyhIegC7bBSLTgwFXd5LYblhxtkd4utaGyCWy9PIorl2UdtExrCo8wRxqwkb2VGJChHdEojQm7ST3NOsLPVYHdKtGhYulBgn/8Tr/aRpTw0hCcuKg4ODg0Nt5CYrc+fOxeWXX47vfe97OOmkk3DXXXdh9erVWLNmDe644w5cccUV+PCHPzyQsjoMAlT3rzAMU5lKMwmM1BW0TptLkYYn4SLWl7VqmxYJw8iRj0xka2/WRpHiFb/X0vUaO/Cqbh8prLGSZvPbV5U6xq9TdXfe1L2hxrzU1EZ1wpKlBae5Q0HZPU8suNqtsCboBIkYyjn1fd2aoZoDKANIRE20HXsS963Go4jrydgcZWwic5ppfVkmoBEmrbplLvKiuW4kdpNLEAnTWlPr/lnEtcqh1MubAKKZSCdd6cSsUXc4BwcHh50JdR0K+cEPfhCzZs3C0qVL8eUvf1l+0YZhiKOOOgqf/vSnB0JGh0GC4ClhhkNX/1IWN4Ey5BSA7DC5lXMocbapil3/ZuhCKUqq7QwQawEhPFOVeG9YVYzGjCvGWgxGHqWOQKYStirACV1eUfotfanjUkpQpVQSNMJPe1cTHRDo1gMCAIwlXKoIAFIsAcxy4whAPIawUgGlSopjbYterR7P06ME1YzpSrnV6Yk5yuelzjVX2iMItGKPu63FS5LSn+E+2AiszWtzoEEBMf7mazQMBHdwcHAYJqj7BPu9994b119/Pd566y289NJL2LZtG/bYYw/sueeeAyGfwzDEDsMDhjsSG9IECM0zL3S2YrdZcOS6b+lKknDrEW8ifd2iRSs753LXnykGCXOnOTSbC+VeKNaGG1amImfdgtdehrZrVmXXMi9lYnrqYiCwKN20WIxOjlcXhBAQjyYsTJKYsIisEK7oSwsFJxpFj6KsHBwjerFnPbPv5NdMdW2smQqfmVaUaC2kVZbXFy5nputZIr4jNbg/3XKiDJN4XTeEi2KO/jINeLXaOALi4ODg0BDqJisCU6dO1bKBOewYGDAe4hhOv0EJUIWuX9dUf/LoR7E2lX45jJVpUZaampYqh+DxQt2ykqIw1wOLqSFTkSSqgMk2pK0dqHTX6iCesxG0zyhBNTKt1FZK+Y0kNqWfEMDzgN5eucbCwiLGLHj20+Y9I8Oazf1It/yoB0Dmt6Jo42vzEnIyEBB4lIAymiCfiS6NeVhdvmqKlf5MEUIS2Q110tMcEpF22/t1UKuDg4ODQ/6YFYedEyHyHwTZOB9xP8x50BRiYjZJ3Q5OFqlfFixDmYosAIaFQboaWawxqqKa3qtdYJpSngVJdJQ2be35mkJYBnT5iXDZYhmB6oaFxMygBR7XQ3gQNiGEn9Ghyx1btwBI61YGebTJYJgREjJnrGdRISsJ1zFlXlZLjw1NyJCVvuQZJKbfow4chrNsDg4ODoMNR1YcEshLOgbOWNK/jomljxH746/pxEkfGKs+KPXwOnbMrd3k2+JOKKyaZQUAIUpqX7vSaAvCTtSjNTJtpcmouK6lXktra0C6NEnLit7Ao/ZsT6p1gXqRQVtLY0sIiO9JNzDBICgxyIm1c0giWGsqidS5xLwvtvujkzPTDcwkBJQSEMpkzEpMNJNteIPc1gfdWpScxkBAf5Qz6HQNIeqysDjLioODg4OEIysOVmQRkf5zlND6sq72/RBipB4MbXWPafJchOXAVp5rYEIAkiQVJM8ue84Jps+ZpLy2l2hzzbm2HlOsIRarQrGYw7M2zZLAmNVwJPmR7UT6vPdfchLbelqsI7V86mqNS6lhWclQ8pthWenHB8GaznkI4XiKg4ODgw5HVhw0hBk8Qs0R5kJQBgkDobmkb6I3ZfjoAEhlACUjltZnPVYNiy5tt/yYpckDELOgZzhLEpmIrIhy8+uTyFPnje34WDYCfhaLRSaL0k65dcUmi7Ugp7lhwFPmWqwlzRoyy7VLHWfwzq7vH1z6YgcHB4dsNBRg/+CDD+LBBx/Ehg0bEBjpKpcuXdoUwRyGBzKOWnEYYBDoa2/xAktR8KOWDelANV1Z1NfmzjmX1jj4jhRLsm0s9zBwiVFcp5JyKeRK/o2CxkUiLkJJ8rORx0qQcBWLrSSm4hq5VBEEmmzxS48Z8SOW12KEWrE0NVEP6VPibqzts4hU7lGGPxp+dM24KAcHB4edGHWTlauuugo//vGPMX/+fEyYMMF9oTrkg2M9dpiMxFoBDWhwTfxcEmJhTdbDXazljbmAZe+eZ7dNKc8dF5GUQ0D3bCL6X2Ts9qckFKhDECtaCulf4fmWLnmYY10djGT0c3rut8/BwcFhcFA3Wfntb3+LpUuX4sQTTxwAcRyGGtGBkCTjWMiMtsOBjPTzLJGRBNsszMD2rLZx2tp6BuUkwjw7Q/ZT4wYYRKfm6AkOQ+J/sqi+eIs8h2USWz+5LQOkMUV/kJVfk4M6DCwct3FwcHBoDHXHrJTLZRxwwAEDIYvDCEHeVMZNHnUIxhxg5FJemqHhJF2CktfTyIf9fT3jJcrydiWIEZCPYKSdRp97uKwBUn2oaleJOs8rRIalyHxby/o0CNpxA1m8Gh6q3z3U6N+RCQcHB4dhibp/3T/60Y/itttuGwhZHHY2cNIzGErCsKI6Q6UUpca3mEX1xJPUUzXpNmW+T3qD1WQoxt865LJZZ3JYT2oq3pmWnbTxs7vsN/ojcz11mxCL4tyrHBwcHBxU1O0G1tvbixtvvBEPPvgg5s6dC8/TuzjvvPOaJpzD0CNEODzcuxLIFmpEqTtNEDbKZjvws47do8LkhTBRM7ujvEq92iQ5sl6ngQxYySDwJlgBSNyvapWqu2eDRNlJVXJcc41S1yWRsSunRS01Nier7vD4VA4PKRwcHBwc8qJusvLiiy9i3rx5AICXXnpJu+Z2xHYAqKmLDY1HT2vcn4NOGm86zGwkA4psxTypCKvl+UfIUauuj3V2ZZISnB95edW2vAwMxLg0UTQQ4zf8PTlY3685hhkJX/WNZ+Kqo58RsA4O9SMIAvT19dWu6JCJarUKAOjp6QFj9gNzHYYnfN8fVvesbrJy/fXXD4QcDsMIoSVzV7/jVGo2DzGQv/zDVaeoZSkQ6afq4wsECMOkkaCBeyj3xBWzRqosqaETxDAENOdu1K+MDtBT0ECWMVtSgKZK1yy3rmGIZkk7XM9hIbW/FRwGEH19fXjttdcSxzI41I8wDOF5HlauXOk2s0cgxowZg8mTJw+Le1cXWSmXyzj99NNxySWXYPbs2QMlk8NwQ4aFxaE/6O8XQI3YgaQvUFNFMY0PtZ8LS4xIokqKpcjm/lSzva0uya+cm65XWrd6P4TXG/CvdCXpgC5Sk0a2rQ3JuNbMcZpZv2Z3/XDNazYsc6N50n07NB1hGGL16tVgjGHGjBmgec5NckhFGIbYvn07WlpahoXC65APYRiiu7sba9euBQBMmTJliCWqk6z4vo+XX37ZPXQ7KPp7FEpN17AUbXYwnqbhyK/y7Z/WERuQe6CsuA5kKoZEpg5ONpSREjbdvk7Y4i4slZLvwzpGVFzPUqddI+C+KV+FA/UBMMlUvUPnIZQDkIyh/xR++Pw+DSdZHGqjUqmgu7sbU6dORWtr61CLM+IRhiGCIECpVHJ64whDS0sLAGDt2rWYOHHikLuE1b1tcOKJJ2LZsmUDIYvDMERa3IqzrjSOgfjKtuvZpGad1Aq5hezfDnmt1kStNJA/dk3sO8s40a9+jV7iBGhphHZolAOnkjiMVIgYi0KhMMSSODgMPQRhL5fLQyxJAzEr5XIZN910E/72t79h/vz5kn0JuGxgIx8hhqclolkInToFQFEqa3lWWVhMrhW0GD7qgulnpr5O9EW0JGX2gyLzPNU1rAl541Nsbc1+8vSVt55tLHM8a317+9q7oDveZ2igyZ2zsowcOCuAg8Pw+hzUTVZeeukl7L333gCA1157Tbs2nCbm0Bh2VIvJyHwyGwkkyWgjzrVB7Sxj9Q2pKtS1TrCXjdQOzA7rGT1frTTrjOn/lcPFKXXEFGLQ1K/FZlqA0MCmRI3xd5TfgB1kGg4ODg47BFw2MId0mC5gTetX+JLVuJ7ZR84xRjjCegJtm6VhEeNvmLyUv6+kAp+u7APyFPrYx0nrqjavyZAwzVIjLpnB+QNh+bAPn7hYc151jjloyncTAvQdUXBwcHBwUOFSXThkInIJa4bi37/QfYckVG8oYlHEM3W+epR828C13KUGDHkIRP6q+UZsbhBKf8VKxCKZ1iFR2Mi9GMD7N9zcoHInhRtmcjs4ODjsbKjbsnLVVVdlXv/CF77QsDAOQ48w53knNsPFYBgziOMtTUJ+BSyXUpcvd3HdsR+yri0TV662tqIo0xgxrFY2nT9VnnqRQR6G1HVqBJox8pMMBweHwcBpp52GefPm4fzzzx9qUZqKTZs24dhjj8VNN92E6dOnD+rY55xzDhYuXIjPfOYzgzrucEXdZOWee+7R3lcqFaxatQqMMcycOdORlR0EaXpniHAAdxodE2kaZBrfZvWXUkyVFMM5gthJRl/2BoZrVrY4/UOTzv6oRT4aP8E+OXQ9fWXWHYGkpVnYUeJsHBwA4Pnnn8fPf/5zPPLII+js7MTkyZPx4Q9/GGeeeSZ832+ozyeeeAL//M//jEMOOQT//d//3WSJB5fs1DPWz3/+cxx55JFWopK1Jhs2bMAPf/hD/PWvf8X69esxevRozJs3D1/4whewcOFCAMCSJUvwhz/8AQDgeR4mT56M97///fjiF7+I1tZWnH322fjUpz6FxYsXo729vQkzH9mom6zccsstibKuri4sWbIERx99dDNkchgiCHevgbOQDB0ZGc40KIfTlqV+SmE/g9NrNdfiz1N6ItI/rZ7OhGtZg5YX5XXDcTUAQI1ZNUisCKXIcVJMje70HqzuXmabvLE2dfRpG2Ug4dyuHBwaw7Jly3DRRRfhlFNOwdVXX43Ro0fjsccew2WXXYYVK1bg+9//fkP93nzzzTj11FOxbNkyvPXWW5g6dWqTJa+Nvr6+QU0p3dPTg2XLluHnP/+59XrWmnzxi19EpVLBZZddhhkzZmDDhg148MEH0dnZqfVx2GGHYenSpahUKnjsscdwwQUXoLu7GxdffDHmzZuHadOm4Y9//CM+8YlPDOhcRwLqJis2tLe345xzzsGZZ56JE088sRldOgwThGG4o8SqD2ukq2f9UNzyBufHqbK0UbMyhpE6/PFMBToZzJ4lWjI+pnbL2hYEs0ZCQba5j9VtFkoMW8O6UWefZueZdW3E1EKncvtY1SKi+jjDCc2SZ8BmVdtA6eCQwCOPPIILL7wQ3/72t/GRj3xEls+cORPVahUXXHABzj77bOy222519dvd3Y0777wTy5Ytw/r16/H73//e6kFTrVZxySWX4I9//CMYYzjllFPwpS99SV6/66678OMf/xgrV65ES0sL9tprL/zkJz/BJZdcgkceeQSPPPIIrrvuOgDAvffei/POOw977LEHfN/HLbfcgj322AO//vWv8de//hU//elP8fLLL4MxhgULFuD888/HzJkz5VhBEOCaa67BTTfdhNWrV2OXXXbBySefjJUrV1rHsllO/vrXv4IxJi0heddky5YtePzxx3H99dfjwAMPBABMmzYN++23X6KfQqGACRMmAACOP/54PPzww7j33ntx8cUXAwDe+9734vbbb3dkBU0MsN+yZQu2bt3arO4chgh5iUlzgu4HBjYlgtSqMFhIjJ1BU7Rd76x6ChGoo//c0M0mOSopCMP+uRilng8SW2CsHMIW45IgbyZBaZRIWPyz6u1oIBT6pneZyuByDTl4GckGaZwBxg4yjRGPMAzRW6kO6r+wgR3CpUuX4vDDD9eIisC73/1uAMCLL75Yd7933HEHZs+ejTlz5uCEE07A73//e6t8f/jDH8AYw4033ojzzz8fv/rVr3DTTTcBiE5B/7d/+zecdNJJuOOOO3Ddddfhfe97H8IwxPnnn4+FCxfiYx/7GO6//37cf//9mDJlitbnb37zG6nAb9++HZ/5zGewbNky/PKXvwQhBJ///OcRBIGU5fvf/z6uueYanH322bjjjjvwve99D7vsskvmWCYeffRRzJ8/v+41aW1tRWtrK+655x709fXVtdalUkk7gHHffffFU089VXc/OyLqtqwINioQhiHWrVuHW2+9FYcddljTBHMYWtjISCNfoErjfkjTZAwjUWqipttPEiFBPxMRqFYQkkcM5KmU6u5m8ydL9JXPPSyV22RsVyd5Tu0xCLHLU5dbVULRt/SZtg65+s/TrjZ5qsUXc4ESoDqSPnhDi3rd+RwGBmEY4md/fRUrN3QP6rizxrfiXw+fk9sK+OKLL+K5557Dl7/8Zev1np4eAEBvby9OO+00bNiwAYwxnH322fjgBz+Y2feyZctwwgknAIjclrq7u/Hggw/ikEMO0epNmTIF3/jGN0AIwZw5c/DSSy/hV7/6FY477jisW7cOlUoF73vf+zBt2jQAwNy5c2Vb3/dRKpWklUGuw6xZ+PrXv66VHXPMMdr7b3/72zj44IPxyiuvYM8990RXVxeuu+46fPOb35TEbebMmXjnO9+ZOZaJN998ExMnTqx7TTzPw2WXXYYLL7wQv/3tb7H33nvjwAMPxLHHHot58+aljvfUU0/htttuw8EHHyzLJk2ahL6+Pqxbt06u286KusnKL3/5S+09pRTjxo3DRz7yEXzuc59rllwOQwgZu5Ki3PWfd+gd1O2WkS9hWcaIDv1GgymZ4mb9U8TycSdiJ9gZstvdpfIKZa9YszkhteM0cliOmo8mj7EzKt82Ip6osxOui0NT8fzzzwMA9tprL+v1Z599FgAwY8YMfOMb38Bee+2FDRs24CMf+QiOOOIItLa2Wtu9+uqrePrpp2UWWM/zcOyxx+Lmm29OkJX9999f+y1fsGABrr32WlSrVcybNw8HH3wwjj/+eCxatAiLFi3CMcccg9GjR2fOy2bZeP311/HDH/4QTz75JDZt2iS/41evXo0999wTr776Kvr6+qQ1qVH09vaiWCwmyvOsyTHHHIP3vOc9eOyxx/D3v/8d999/P6655hpceuml+Kd/+ifZ13333YeFCxeiUqmgUqngqKOOwoUXXiivl0olADHZ3JnR0KGQU6ZMAaW6B1kYhli9erXLWrADYuAU/SGiECNWN0jf7TaV3ci60o9dea1Z7RiQmkPkiqswm+jWkFilHwiXqQwXp/5YN7TuYitVY3ETjc87/3Akl2VpgAZvrP8hxEiS1aF+EELwr4fPQV81qF25iSgwWtd3hHAdsinXAHDDDTdg4cKF0roAAOPHj8fo0aPR2dmZSlaWLVuGSqWCww8/XJaFYQjP89DZ2VmTbAgwxnDttdfiiSeewAMPPIDrr78eP/jBD3DjjTdixowZqe1aWloSZWeeeSamTJmCSy+9FBMnTkQQBDjuuONqrkG9GDNmDLZs2ZIoz7smxWIRhx56KA499FB84QtfwPnnn48rr7xSIysHHXQQLrroInieh4kTJyaytYmA/LFjxzZlTiMZdcesHH300di0aVOifPPmzTjqqKOaIpTD0MGkD+bG9JB7czUowEjXKXJ59NS+2ODYOWI7rJm9Eh3pf1O707ejZaxJwuWqdgxFruvC46aJsTUk41q/YYvHMd3nbHWaK0Uq8ihZ/Qlyz5strCbpSilwBMRBBSEERY8N6r96Px/CperRRx9NXPvFL36Bl156CRdccIFW/vTTTyMMw9SYjUqlgltvvRVLlizBLbfcIv/deuutmDp1Km677Tat/vLlyxPvZ82aBcaYXMd3vOMdOOecc3DLLbfA9315FIbv+1rMSRo2bdqEf/zjHzjrrLNw8MEHY7fddktk2Np1111RKpXw0EMPWfvIO9bee++NV155RSurd01U7L777uju1t0JW1paMGvWLEybNs2aVvqll17C5MmTMW7cuJry7uio27KSFrfQ3d3dNEbrMPzRVNIyyAwoHC7UhWRFUtRsnLPMOmw0prU6AaHJPQxVl0/nCBRAtfbgWaKmJRSoyUHy39N4/v0kO4SznHoep0bIlK2aUj+1RS7S0LQcK7XHGqK2Q9m3g8NgYL/99sNhhx2Giy++GOVyGfPnz8eGDRtw00034fbbb8eVV16puVRt2rQJ5557Li699NLUPu+77z50dnbiox/9KDo6OrRrH/jAB7Bs2TKceuqpsmz16tVYunQpTj75ZDz33HP49a9/LeNNli9fjoceegiHHnooxo8fj+XLl2Pjxo2YM2cOgChb1vLly7Fq1Sq0trZizJgxVplGjx6NMWPG4He/+x0mTJiAt956K5GOuVgs4l/+5V9w+eWXw/d9HHDAAdi4cSNefvllLF682DqW6SkEAIsWLcIVV1yhWUvyrMmHPvQhfOlLX8JJJ52EuXPnoq2tDc888wyuueaaujf0H3/8cRx66KF1tdlRkZusLF26FECkFPzwhz/UzHPVahVPPfVUZvCQw8hCHv4w5FaWDJDhLFwzMVSa1kBtP+cw4iQuGgQi7JeFJHu8ZiQaqDlO85rU7M/8lPTbxWsAn8chO3/FsRmHEYArr7wSV111FS6//HK8/fbbqFarOOyww3D33Xdj0qRJsl5fXx++8IUv4HOf+xwOOOCA1P6WLVuGQw45JKGUA8D73/9+XH311Xj22Wexzz77AABOPPFE9PT0YPHixWCM4dRTT8XJJ5+M7du3o729HY8++ih+9atfoaurC1OnTsWSJUtwxBFHAABOP/10LFmyBB/60IfQ09ODe++91yoTpRQ/+MEPcOmll+K4447D7NmzccEFF+C0007T6p199tlgjOFHP/oR1q5diwkTJuCUU05JHcuWunju3LmYP38+7rzzTtk2z5q8/vrr2H///fGrX/0Kr7/+OiqVCiZPnozFixfjzDPPTF1vE729vfjf//3fATmEcyQiN1l57rnnAESWlZdeekkzWRUKBcybNw+nn3568yV0GDRI/d6i5zdF9d9ZCMQwQW5rg8XakRacXssw0hR5sjtptGFtK4KaAUv+rx450htYx27QZS2+3CQtOivhgFPUHRxGBFpaWvC1r30NX/va1wAAF154If72t7/JIG0g0t+WLFmCd9JKWEEAAH7ESURBVL/73TXPxLv66qtTr+2zzz5aGuTrr79evhYphsV4ALDbbrtlKt2zZ8/G7373O61M7VPFIYccgjvuuEMrM1MyU0px1lln4ayzzso1VhrOPvtsfOc738HHPvYxUEpzr8n++++Pf/u3f8vs+7LLLsu8vmzZMuy///5YsGBBLll3dOQmK+LBOe+883D++ee7QPodFNGXC7GUxYSloTNW6iEqjtPUjVw6JUm8aKwjJfBdS+Gb4x5H7mR1uh5lxmjkb6MIUN/4g4088T+pbWH//NTqop41Ee5vIxTDXfKcHyUHh0xceOGF+O///m8899xzMh3u448/jjvuuANz586V8SLf/e53tTTCDjGOOOIIrFixAmvWrEmN7RkoeJ6XiDPamVF3zIpwB3NwGFQkfrx3kl/zzF3vQVC7clhSCCW1b0eu+IncUjWM/F5lhL+2EaX6yJbdBtPfyTbfqkJquNDl8m6zTnZw6cEI5lEODk1DoVBIWBbe+c534oUXXhgiiUYmPvWpTw3JuCeffPKQjDtc0VB05WOPPYZ///d/x8knn4w1a9YAAG655RY89thjTRXOYWgQin8D5Q5mwCkXTYSxE7/X2Gg/YnShjo96w1ppyjWSvEYMOZPZrMwuLaTBWmBaJTJkTIl56TcI6jM+5M2elWotsicK6C+ZHQ7Eoz8YOZI6ODg4OGShbrJy991344wzzkCpVMJzzz2Hvr4+AMC2bdvws5/9rOkCOowcNOa60Gz6M7wtLnUrUP1K8QocOqmIWe0MU1oa9y3Rld4GXMialRK4P+l4M7zC9Pf2ilmKv3kt02pCUg6ATAiSX45MIjOMMCiWwDqgnnuTdq3/Y9hfp1ZycHBwcEigbrLy05/+FBdffDEuvfRSeF7sRXbAAQfIIHyHkYtBsaZkRfIPc5RYExULYn1ZdydZUQ2MErT5OU5I7y9qBIOnt8uoaFMm0yZL7IuZSTJqkIa6FdYccSHNuguN6Ld1tRloBdoF9Ts4ODg45ETdMSuvvfaadgqqQHt7u/W0TweH3BjGUaVz2ig2lUPsUhw8TYrIA0Ea7kAqyGndmIHquRT0uniJYXUQ55LUpznXuGxxJ0vrJ+Gi1uD9tLmQ9RO1uY5pwWlC5zksOk2boSMh9cGxNgcHBwcADVhWJkyYgNdffz1R/vjjj2PGjBlNEcphaKFyhmHMHwYVRQZMLhF4TdEfDIYANC1WIvNCHWPkVVkTCnTNMWq4SGkxLdBPryd6nbQ+GoEcp1afeeN5+rMO/UXONRg0XbjZA/Wzu3pCf7LKHRwcHBwGB3WTlZNPPhnf+ta3sHz5chBCsGbNGvzxj3/Ed77zHXziE58YCBkdhgC2Mzbia47EDBrMsz+U15oOZY+x7teYWc2t1zIHbK7Glx0XYpdFDeKXFp5EhWGgmeZNDCCKcnXZTPfFdDJGGsvZ4uDg4ODgkIq63cD+5V/+BV1dXfjkJz+J3t5enHrqqSgUCjj99NNx6qmnDoSMDoOEAecfQ8hw9h3D8PjGavRmGOijA4asuZnrn3MdNJJkMibtTWippLYl9iNATHI06Pcnxa8tjbTZXMk4AaoZlWJzt2sk+1qKGxpRff6G0CTQaAiTA4dbLAcHBweJuskKAHzlK1/BmWeeiVdeeQVhGGK33XZDW1sb1qxZg0mTJjVbRodBhjj0MeSvhY4bWVTC1F3aug+LHETuMmEQY02GHDViXZK2mvp26gksVon0QeyX+qtIEyKnSVQCQEKtTq6DKkX7hmJa7BacpoTSDwbZqGeIQSI/A5GJy3p9gPtvuF+MxNQjDg4ODgOHhm32LS0t2HfffbHffvuhu7sb//mf/4n3ve99zZTNYZijoZPsUzvL21e+evY9/OGFXLEX/Ur723hTs4vGxGggWDsxbxK7HdkaWy0U6aPUXnPleiOPUFr/zfCO6tez0ECsTaPxP7ayrJigJmMwtyV2oi0QBwcHhyFDbsvKli1bcPHFF+OBBx6A53n43Oc+h1NPPRVXXnklfvGLX2D33XfHt7/97YGU1WEQUYs7NJWoDAHGewE6h1qIGshjgcg2BAyAKpXq4sXHszwWGgcYKO2uwSxmDfWvWHKsn4KMm5Id1lPjXmuva/eZCMnJG9jUdJPB8FbpBzytd4Nwgf3DB4+8tnFQxztw9rhBHW+g8PDDD+NTn/oUHnnkEYwePXqoxWkqXn31VZx22mm4++670d7ePqhjf+c730G5XMYFF1wwqOMOJXLv911xxRV47LHH8JGPfARjxozB0qVL8a//+q94/PHH8V//9V+4+eabcdxxxw2krA47CoY4Op8SYDQLhlSGelFbccmh2RQKGdXTXcGI8i2RapmoNxWxzPqV0dYSMyIOQEwdLpNMZUhkC8apZcmpdxCj+4aQl/jU220DMgxXJX84wfJUWSq5dXToH5YsWYKzzz5bK1u9ejW+8Y1vYNGiRZg/fz6OPPJIXHrppdi0aZNW77TTTsPcuXMxd+5czJ8/H0cffTS+//3vywO/a+GJJ57AXnvthTPOOKNp8xF4+OGHMXfu3EE7FuO0007Dt771rVx1/8//+T/4xCc+YSUqxxxzDObPn481a9Ykrr3xxhv46le/ikWLFmHffffF4YcfjrPOOguvvfaarCPux9y5c7Fw4UL80z/9E/70pz/J65/97Gdx880344033mhgliMTucnKX/7yFyxduhTnnnsufvrTnyIMQ+y666647rrrcOCBBw6kjA6DCFumL9OKMjRUY2RbcupGDUKXKzsuR/DuQxFMm4Fw9z1z1M5wocpVK+OqGluSuJRGMnJ8RVkXolbQTFq7nP036i41mCmNa1pq+k+86okvyVt3oFX3WiRr2FAHR2IcGsQbb7yBk046CStWrMAVV1yBP/3pT7jooovw0EMP4ZRTTsHmzZu1+h/72Mdw//33409/+hO+9rWv4f/+3/+LK6+8MtdYN998M0499VQ88cQTeOuttwZgNrWRl1g1C2+//Tb+/Oc/46STTkpce+yxx9DX14djjjkGv//977VrfX19+MxnPoOuri5cddVVuOuuu/CDH/wAe+65J7Zu3arVXbp0Ke6//34sW7YM8+bNw5e+9CX8/e9/BwCMHz8eixYtwm9/+9uBm+QwQ26ysnbtWuy2224AgBkzZqBYLGLx4sUDJpjD8MBgGEGSP8k7GTFplnpkxlzwl+GcPVA59AiAJb0+846se5dlMKUmBM4n+kmzptRlzMmhmOdggDUMUsYlxXpkHdYyV6PLRg/pTJOlaehvd8NQER9+Ejk4NIaLL74Yvu/jF7/4BQ488EBMnToVRxxxBK699lqsWbMGP/jBD7T6pVIJEyZMwNSpU3HMMcfg0EMPxQMPPFBznO7ubtx55534+Mc/jve85z0J5VzgiSeewAknnIB9990XixcvxosvviivvfnmmzjzzDPxrne9CwsWLMCHPvQh/OUvf8GqVavwyU9+EgDwrne9C3PnzsWSJUsARBaQSy65BEuXLsVBBx2E008/HQBw7bXX4vjjj8eCBQtwxBFH4KKLLsK2bds0WR5//HGceuqp2H///fGud70LZ5xxBjo7O7FkyRI88sgjuO6666RVY9WqVdb53HnnnZg7dy4mT56cuCa8jD784Q/j5ptv1o6B+Mc//oE33ngD//Ef/4EFCxZg2rRpeMc73oGvfOUr2G+//bR+Ro0ahQkTJmC33XbDxRdfjGKxiD//+c/y+nvf+17cfvvtqfdmR0NushIEAXzfjxtSipaWlgERymEHRAbr6avYXLJC68vMModckPElqdf61bF4o71MKuYW6wppgg6cJ1NZw51nuIeZUtRpQMltoZDJBpo1yfT4mjpaDQ7s3G6whnVwGBHYvHkz7r//fnziE59AqVTSrk2YMAHHH3887rzzztSz1F544QU88cQT8LzaIc133HEHZs+ejTlz5uCEE07A73//e2u/l19+Oc4991wsW7YM48ePx1lnnYVyuQwAuOSSS9DX14df//rXuO222/Dv//7vaG1txZQpU6R156677sL999+P888/X/b5hz/8AYwx/OY3v8HFF18MIPoePf/883Hbbbfhsssuw0MPPYTLL79ctnn++efx6U9/Grvvvjt+97vf4YYbbsCRRx6JarWK888/HwsXLpRWpvvvvx9TpkyxzvvRRx/F/PnzE+VdXV246667cMIJJ+DQQw/F9u3b8fDDD8vr48aNA6UUd999N6rVas31FfB9H4wxVCoVWbbffvth9erVePPNN3P3M5KRO8A+DEMsWbIEBe733tfXh4suuihBWK666qrmSugwaDC/ZBLvuUNY2o93TStMyvWmHli3wyGp3BPoS5nQXWXK3gbXNcOEYQ4jC02hsrq3ddYASL053zKUfKLV6b9suWSpp2qGJctmNWmWe1bdy2B1mau3k8GB+9px2BGxcuVKeaSEDbvtths6OzuxceNGjB8/HgDwm9/8BsuWLUO5XEa5XAalFN/85jdrjrVs2TKccMIJAIDDDjsM3d3dePDBB3HIIYdo9T7/+c/j0EMPBQBcdtllOOKII/C///u/OPbYY/HWW2/hmGOOwdy5cwFEnjsCIih//PjxGDVqlNbnrFmz8PWvf10r+/SnPy1fz5gxA1/60pdw0UUX4aKLLgIAXHPNNZg/f758DwB77LGHfO37vrQyZeHNN9/EPvvskyi/4447MGvWLNnnsccei2XLluHd7343AGDSpEm44IILcPnll+Oqq67C/PnzcdBBB+GEE07Q5q2ir68P11xzDbq6umQ/oi8hy7Rp0zLl3RGQm6x85CMf0d6LB9Rhx4PVkGEpbJaLWP06Qz8UcWXENo+guwp0eARbK0NgqrFkmFKvNUeZyuEAVO84pA5mYmtahyz1r0GDsSS2+rTOfMPN1n6zU73Zm/RzCHudemJy6hRgAOESADg4JCE2IVVPmeOPPx5nnnkmurq68F//9V9ob2/HMccck9nPq6++iqefflpuUHueh2OPPRY333xzgqwsWLBAvh4zZgxmz56NV199FQDwyU9+EhdddBHuv/9+HHLIIXj/+9+PefPm1ZyHzbLx0EMP4Wc/+xleeeUVdHV1oVqtore3F93d3WhtbcXzzz+PD3zgAzX7roWenh4Ui8VEuUregEhPPvXUU7FlyxZJtv75n/8ZH/7wh/Hwww/jqaeewl133YWf/exn+OlPfyoJHQB89atfBWMMPT096OjowLnnnosjjjhCXhfjb9++vd/zGQnITVaWLl06kHI4DFOE8n9GeTN0+4HkBzn6nt5C0MXJyotbB4+sNK7q2/uqea0fSnTsvlVLjgFU1KVFJLaM2G09qJMNGbEiZlPxkNcZn2MrjZcxw4pRa43rZiLx/WsKj+pnJ3mfkUHJpjyMQaRl1MGhfsycOROEELzyyis4+uijE9dfffVVjBs3TrNUtLe3Y9asWQAil63jjjsON910U2Zc8rJly1CpVHD44YfLsjAM4XkeOjs7c6cqXrx4MRYtWoT77rsPDzzwAH7+85/j3HPPxWmnnZbZzvTqefPNN/G5z30Op5xyCr70pS9h9OjRePzxx3H++edL9ynTLa5RjB07NpGh7JVXXsHy5cvx9NNP43vf+54sr1ar+J//+R984hOfkGXt7e046qijcNRRR+HLX/4yzjjjjARZOe+883DIIYegvb1dWsBUdHZ2Aohcy3YGNOOoMoedDGm+roMzePO6YoRgtE8wiOfVDTgyleGchXWdBZI3K5YSs2LmAUhWbeCGkDrpkiUtcp76Mh6l4RTGdcSm1N13HXVsdRuw5AxHOLdSh50ZY8eOxaGHHoobbrgBPT092rV169bhtttuS3jKqPB9H//6r/+KH/7wh6m79pVKBbfeeiuWLFmCW265Rf679dZbMXXqVNx2221a/eXLl8vXnZ2dWLFiBebMmSPLpkyZgo9//OO46qqr8JnPfAY33nijlAVArviOZ555BtVqFUuWLMGCBQswe/ZsrF27Vqszd+5cPPjgg5lzD4LaxxrsvffeeOWVV7SyZcuW4V3vehduvfVWbU0++9nPYtmyZal9EUIwZ84cdHd3a+UTJkzArFmzrEQFAF5++WX4vq+5se3IcGTFQSIEj0sZaDLiNg05MhYi1z2ok2k0ysrUsz1y1DGNF7Gan/y6ifT+Jlh+zF7loCnX9U6SMtVDfZRYmOypGHWaaKmwpoNuqP8c5p28BHWkYBhOYUdYVoehw4UXXoi+vj6cccYZePTRR7F69Wr89a9/xemnn45dd90Vn//85zPbizPzbrjhBuv1++67D52dnfjoRz+KPffcU/v3gQ98IKGc/+QnP8GDDz6Il156CUuWLMHYsWOl1edb3/oW/t//+39444038Oyzz+Khhx6S8TbTpk0DIQT33XcfNm7cmMjspWLmzJmoVCq4/vrr8cYbb+CWW25JpPb93Oc+h6effhoXXXQRXnjhBfzjH//ADTfcgI0bN8rxli9fjlWrVmHjxo2pxGXRokV48sknJYkql8u49dZb8aEPfSixHosXL8azzz6LF154Ac8//zzOOuss3HXXXXjllVewcuVK3HTTTbj55ptx1FFHZd4TE4899hje8Y53NM1aNNyR2w3MYedCf/jK5u4+3PXs21g4YyzmTu4AAJAwx5n3g0RiRhpXCmbNBntjZWadhG5DCIhBTsKZsxG+/BIwfRqgpI7M7ihtwH7s/OeFSFmsFjU4RswPjKB8QTIa3LYhA624p65zncPWm2rNactNgfp8pJNHt9bDHSPtRPldd90Vy5Ytw1VXXYUvf/nL2LBhA8IwxPvf/35897vfrZnJtVAo4NRTT8U111yDU045BW1tbdr1ZcuW4ZBDDkFHR0ei7fvf/35cffXVePbZZ2XZV7/6VXzrW9/CihUrMG/ePPz0pz+VyZqCIMAll1yCt99+G+3t7TjssMNw3nnnAYiCyL/4xS/i+9//Ps477zyceOKJuOyyy6wy77XXXjjvvPPwX//1X7jiiivwzne+E1/96ldx7rnnyjqzZ8/GL37xC1xxxRVYvHgxSqUS9ttvP0nOTj/9dCxZsgQf+tCH0NPTg3vvvRfTp09PjHXEEUfA8zz87W9/w2GHHYY///nP2Lx5M973vvdZ78Wee+6JZcuW4eyzz8a0adPw4x//GKtWrQIhBNOmTcMXv/hFLTlAHvzP//wPzjnnnLrajGQ4suJQE3mVe1Hvry+vx5btZfzlpbWSrAx3jCsQbOwbbBpD4sRdiUtxrEj1XQfD18hKg1EvnofK0R9EsehFZCVNf7Xt0ueLjpFoO/hgbHvoIXQcdRQSe1M8lkKz4BECQigQVpMyEF2CbONFhhscJUAtbwI9rVqNyvlgXbv+koGmB7g0efxabRwZqg23Rg45YVPgp0+frpX/6Ec/wrXXXosXXngBCxculOXXX3+9tc8zzzwTZ555pvXa1VdfnSrLPvvsI89RCcMQTzzxBFpbW/He977XWv/CCy9M7QuIMomZlqA0mT/96U8nlP4TTzxRe3/ggQemHqY4e/Zs/O53v8uUBwAYY/jXf/1X/PKXv8Rhhx2GY445Bs8//3xqfdUt7oILLqjZ/4tpm4kc9913HxhjNZMg7EhwZMUhAaFDhjDiU3Lqx73lvPnDw3q6rU8IC/booHh5a4ApJQLorryYVCKohkBneXAIS7DHPLBVK2rUapKykmvn3VKUZT0h2evUst9+KO29N2hLCUmv5wxCQSg0RjFA+lqq+5nVa8wsrFMoy+663mWOWJF6YoPkralFaGr01SCSoS/1jTHgJ8zLfQDD/U/eG0cSHHY8nHPOOdLNaf/99wetN9uhg4aTTz4ZW7ZsQVdXF9rb2wd17O7ubixdujTXWTg7CnaemTo0FVmqajVIuxpixfptWL9hC3af4afUGTgcOZFhwRiGia8QdFuuD2qgfZN+KLIUrISKbZvf+F0yW9WypxB1HqaS6g/APc6RKoqklNfuOrZmqX3XnQF5QB+kHMTG2speL+FW11/RM4lUP/seIjjy4rCj4KSTThpqEXYYeJ6Hs846a0jGPvbYY4dk3KGEIysOEroRxU448pw1WEkhK29v7cWfXtoMlCt4Yo2NLvQPozvXA0gXjwLYZftmkCD/ybEDiomTgTdWAsUSYPgQh5Yc7s0CAVA9/iRg4zp4k6eoEd/8/42YYvK5psm+c6dnzbAoWK0gObpspG5qHyRv0EhjfWs9DBelOcsyVquGg4ODg4NDfXBkxaEm6gm276sEqZaVDdvK1vJKtXaqwDSwShlVL9rB79i2WZbbzjanb6wAe+TBVJ3aGxQNKx4kXHQE8OQTqM6aAzLOyEnfnh3rQ5CiEObdJR81CmFbK1C13xPrgDlhd6+KCVHNmBPz/tRwe0r0WGt335Ki1x5XYhGtjpTFdRkscpMeS1MzK1gjnozNTok8HDDgYTvEWV0cHBwcBgGOrDhoMC0n9eg9z721BX9+YU3dY27pqdTdBgAmrX0d+77wEF6evR9Wzqh94i1d8Vrm9XEFgt4gOiRyUFBqQbjv/oBB7sKx6ZlnwqnTQYo+sPue1utN0Z3S3KqyBsjlc1aXEPV114C7Vt6xTQHyuJkNduKnpt53828T+s1nd2vusHnh+IaDg4PD8IaLsHJoGu57cW2irLu3gt8++joef3NrcwZRNJ59XnwEALDHa081pWtKgGktBKMGNZymVhC0fj1saQU77AiQiZNq95yTV9RdKa92x60YVsuHsSudrtzXGzCSpWhnW2BIvel9a8lQ61ojKZi1eJpadfX1zbMk+eVoMqFpIoafRPVhpMvv4ODg0Gw4suJgRbPOhfz1wyuxZXsZj73Z1ZwOJXQB9x/DUmt6FJjKyiBr326yDA2gGB3gVJ2ze+JSMG+f6Nr+BwAw4rzNysR8k6IEp7k4JUCU/5uXcroIpWnEGYEM2W40SWtJ9D7FCY6kz5RkJDSwRd9kop8KerYbXMpYGS5m+QZNNrDfKku9OoeqQwSHDDgXMwcHB4cIzg3MIYE0ohJqdepnM9XUJgOQLpjLRwnwmdk+Ck/9vWaTYPpM0FWvN18WBdUDDwYmTETyFEKC6vz9Ud1rPsDSiVf/oJCGutPJCh99/p7yqKBmK1TEQkQsGc8aUeRytVHWnra25uk0R506uxgWSmqNuJ+GuuyfValZGMyh8xjC0uUZDs+Bg4ODw9DDkRWHGKkkZZid+Z4jI5kAAcAIAXp7atbtL0kIp80AefON7DrtHSCMJeJUmiIDSYkMyAqgsO2uG69NJb8hZa/uNMLGi7xuR7WDW5Ix/IKEEQJCKUqf+SxaR7eDlHuAILBbG/rpU9VU24yN7AwLwuPg4ODg4NB/ODcwh1TYjSdh4y5i9TTMWXVGK8Xu7cPjMQ5r7MRXjjgKaGvW4VE1CAQhIHk/3qabVl4iKCweMLIipQbL2OvkzpqVQpqSc8/qJL0/WdzaCtbeVp/1RgvAb8Dqk6fvfhCk9F397Pbp1xtbm1zVa5TnvS85eKu9/1y9DyyGgwwODg4OwwXOsuJgheAKzYpd6R/ShWgfQU9wOGGSed7gENmsslx8+qMm5WAdDXSfqfw3zcqTkzH112JRV3uLNt10i4lhhelvPM4OaNHZ8Wbk4ODgMLIwPLakHYYNQgxDt696MWTaRZN31AF7YHTdozSvn34vLUH2Ce95HftJtjRNVZrzWIpqdiHibpohUB1jNmgdGgg4pd/BYcfEaaedhm9961tDLUbTsWnTJhx88MFYtWrVgI81EGt4zjnn4Nprr21qn0MFR1YcamJ4WFccVOTyzskZ5pFoZ2lTK1UtoTXGsaf1ql2/0dTFOa9pQfuURkkErNnGYrJRPxHK66rVf3V+QC0bZpa5AbH0pAw9KKM4OIx8PP/88/jKV76CQw89FPPnz8fRRx+NK6+8EuVyzgOALXjiiSew11574YwzzmiipDEGk+zUM9bPf/5zHHnkkZg+fbosW7JkCebOnYu5c+din332wVFHHYXvfOc76O7uHiiRG8bZZ5+Nq6++Gl1dzc7GOvhwZMVBYqAtKnH/yt9Qr+FQH6x+9mmEIvtyavtEdXXHvo7AgHSClZO4NIKcJMlamtOikrCaEPvZMqlZv+w5hO1ji8tqc9OVK6VB3avY4LonCNMwdw0b3tI5OOTHsmXLsHjxYowfPx5XX3017rjjDpx99tm4/vrrsWTJkob7vfnmm3HqqafiiSeewFtvvdVEifOjr69vUMfr6emR62nisMMOw/3334977rkHX/7yl3HDDTfgO9/5jrWfwZZbxbx58zBt2jT88Y9/HDIZmoUR5PHvMFgIwyg1sUleallYBodqDGNCM0ib2nVX7K8bk61LWO6EkXY4DsDPOYzFPayeGGmTIBBCNBnTCUkjgTSN3+ya7llZMSQ2gpNHlpR7WK8lJkpfPcDq/WCGBQ1DpCX1cxgahGGIsB9WiUZAfL/uz9kjjzyCCy+8EN/+9rfxkY98RJbPnDkT1WoVF1xwAc4++2zstttudfXb3d2NO++8E8uWLcP69evx+9//Hl/4whcS9arVKi655BL88Y9/BGMMp5xyCr70pS/J63fddRd+/OMfY+XKlWhpacFee+2Fn/zkJ7jkkkvwyCOP4JFHHsF1110HALj33ntx3nnnYY899oDv+7jllluwxx574Ne//jX++te/4qc//SlefvllMMawYMECnH/++Zg5c6YcKwgCXHPNNbjpppuwevVq7LLLLjj55JOxcuVK61iq5UTgr3/9KxhjWLhwYeJaoVDAhAkTAADHH388Hn74Ydx77724+OKLcdppp1nl7uvrw3e/+13cfvvt6Orqwvz583Heeedhv/32s657nvpdXV34j//4D9x7771ob2/HZz/7Wdx7772YN28ezj//fADAe9/7Xtx+++34xCc+UfNeD2c4suJQA0nCMuCB4Xn8zmpUGa76ivb7M9AxAnW6ROXrNGdZVhe5FfVkOwIkj6jJM2buwno7trmW1awy8CB1mlJSMq01FfrDPwAD5BRD/B2uXxIcw12+nQFhGGLjf/83+l7PTknfbBRmzsC4M86oi7AsXboUhx9+uEZUBN797ncDAF588cW6ycodd9yB2bNnY86cOTjhhBPwn//5n/j85z+fkO0Pf/gDPvrRj+LGG2/EM888g29+85uYOnUqjjvuOKxduxb/9m//hq997Ws4+uijsW3bNjz22GMIwxDnn38+VqxYgT322APnnHMOAGDcuHGyz49//OP4zW9+I8922759Oz7zmc9gzz33xPbt2/HDH/4Qn//853HrrbeC8sN/v//97+Omm27Ceeedh3e84x1Yu3YtXnvtNZx66qmpY5l49NFHMX/+/FxrVCqVNDc7m9zf/e53cffdd+Oyyy7DtGnTcM011+Czn/0s/vSnP2HMmDGJPvPUv+yyy/D3v/8dP/3pTzF+/Hj86Ec/wrPPPot58+bJfvbdd1/87Gc/Q19fHwqFQq75DEc4suLQABqjKoMS+zKkO5KDp100a6SsOImGVcuMH9j4kt2VKlEx6YOWPV4NSwkhQGhzycpAHhe7gUdsjcltEGtE1gGc386YKSyXAWwHXBeHwcWLL76I5557Dl/+8pet13t6onPGent7cdppp2HDhg1gjOHss8/GBz/4wcy+ly1bhhNOOAFA5P7U3d2NBx98EIcccohWb8qUKfjGN74BQgjmzJmDl156Cb/61a9w3HHHYd26dahUKnjf+96HadOmAQDmzp0r2/q+j1KpJK0VArNmzcLXv/51reyYY47R3n/729/GwQcfjFdeeQV77rknurq6cN111+Gb3/ymJG4zZ87EO9/5zsyxTLz55puYOHFiZh0AeOqpp3Dbbbfh4IMPTpW7u7sbv/3tb7F06VIcccQRAID//M//xAMPPIBly5bhs5/9rNZnnvpdXV245ZZb8L3vfU+OvXTpUhx22GFaX5MmTUJfXx/WrVsn134kwpEVBwnh/mVTKpoSz2J2kbfLHdQlgoAMydTSna3sMRT2WIsGJW80TW5KNrNQXs6twdc3ZiMy5mjXiOKe5uE3nHTdwSAkud0K6+3X/vg77MQghGDcGWcMezew559/HgCw1157Wa8/++yzAIAZM2bgG9/4Bvbaay9s2LABH/nIR3DEEUegNeWMsFdffRVPP/00rrrqKgCA53k49thjcfPNNyfIyv7776/JvGDBAlx77bWoVquYN28eDj74YBx//PFYtGgRFi1ahGOOOQajR4/OnJfNsvH666/jhz/8IZ588kls2rRJWi5Wr16NPffcE6+++ir6+vqkNalR9Pb2olgsWq/dd999WLhwISqVCiqVCo466ihceOGFqXK//vrrKJfLOOCAA2SZ7/vYb7/98I9//MM6x1r1V61ahXK5rLmFdXR0YPbs2VpfpVIJQExYRyocWXFIoFFOUV+vzcWw4DODlhkpUtNV40O9/KHfrnymIQQkIrRS4UsnJVmrZPUyS4m3qLfvtEMAtTfcfSqXNaWZ2m2KO1nCGtlo0HvKyujxM8NDS29GVjQHh2aBEAIyzN1nhAtSmnJ9ww03YOHChdK6AADjx4/H6NGj0dnZmUpWli1bhkqlgsMPP1yWhWEIz/PQ2dlZk2wIMMZw7bXX4oknnsADDzyA66+/Hj/4wQ9w4403YsaMGantWlpaEmVnnnkmpkyZgksvvRQTJ05EEAQ47rjjaq5BvRgzZgy2bNlivXbQQQfhoosugud5mDhxInzfryk3kNzMSdsczlNfkDRbHRWdnZ0AgLFjx6aOMxLgsoE5WDHoyv+wYBvDERlKJgZAsavRXSKI3SJTrmEGyiWmP80JSSVBWW3yXMpFB+oNdq+rduNIzWJmvSiKG3YiHHZwlhaH4Q7hUvXoo48mrv3iF7/ASy+9hAsuuEArf/rppxGGIaZMmWLts1Kp4NZbb8WSJUtwyy23yH+33norpk6dittuu02rv3z58sT7WbNmgTEGIPpOeMc73oFzzjkHt9xyC3zfxz333AMgshoEQVBznps2bcI//vEPnHXWWTj44IOx2267SWVcYNddd0WpVMJDDz1k7SPvWHvvvTdeeeUV67WWlhbMmjUL06ZNSxAVG2bOnAnf9/H444/LsnK5jGeeecYaQ5Sn/owZM+D7Pp566ilZp6urCytXrtT6eumllzB58uTU2JyRAmdZcUhH0wnEwDGSpugTQ6iVNGtojUzkUiTT+kl737jrVl3Gn/7u9ufKkNVY4ErTnpJ+WGbMe5inh1xk0mrOaQ6Gq86/I8bSOOxc2G+//XDYYYfh4osvRrlcxvz587FhwwbcdNNNuP3223HllVdqrkmbNm3Cueeei0svvTS1z/vuuw+dnZ346Ec/io6ODu3aBz7wASxbtgynnnqqLFu9ejWWLl2Kk08+Gc899xx+/etfy7iN5cuX46GHHsKhhx6K8ePHY/ny5di4cSPmzJkDAJg2bRqWL1+OVatWobW11RpwDgCjR4/GmDFj8Lvf/Q4TJkzAW2+9he9///tanWKxiH/5l3/B5ZdfDt/3ccABB2Djxo14+eWXsXjxYutYIjBfxaJFi3DFFVfUZUFKQ2trKz7+8Y/ju9/9LkaPHo2pU6fimmuuQU9PDz760Y82VL+9vR0nnniirDN+/HhceeWViYyNjz/+OA499NB+yT8c4MiKQwKqGVHVW0b8yfbDBCSnz1Yi5rzugeptkDZuP5TcPBvseczg/YxvqauLlDFljJEtUCQj+N3qUtZfy03mHPnzZfVz6w8UyjqCFfyRIPtIkNFheOHKK6/EVVddhcsvvxxvv/02qtUqDjvsMNx9992YNGmSrNfX14cvfOEL+NznPqfFRJhYtmwZDjnkkARRAYD3v//9uPrqq/Hss89in332AQCceOKJ6OnpweLFi8EYw6mnnoqTTz4Z27dvR3t7Ox599FH86le/QldXF6ZOnYolS5bI4PHTTz8dS5YswYc+9CH09PTg3nvv/f/bO/O4qOrv/7/uwCAgroBbLrhOJuKelqif0tQWXDJMK9TUCjErMxVzCTM/7lZq5k7m0kfFpY+FrZ+f3zIzTc0W03JLKVQUBRFZ5/7+gLnM3Ze5w8zAeT4eOjP3vt/n/b73MnBe95zzvpJzslgsePvtt/HWW2/hscceQ9OmTTFjxgzExcXx2iUkJMDPzw/Lli3D1atXER4ejmHDhsmOJbV0sc1mQ2RkJPbt28f1dYXXXnsNLMtiypQpuH37NiIjI7Fu3TpZIaSlfWJiIt544w3Ex8dzSxenp6dzqXD5+fn48ssvsX79epfn72lIrBAccu4zT7yU/mfk5itJnXJA6EjrLBDRmlYmfIaJ2pREn7k0Nq0d+P24XaJmMqlcTvpQ9PwVCwOGsZgmctQcTVVz5ZQOpn4NzXOYTUkRNBmSA0RFIigoCJMnT8bkyZMBADNnzsTBgwe5Amug5G95YmIiunXrhkGDBinaW7Vqley+Nm3a4PTp09znTZs2ce9nz57NGw8AmjdvrugwN23aFNu2beNtc7bpzP3334/U1FTeNue5ACWiZty4cRg3bpymseRISEjAggULMHToUC76Mn/+fMU+cvOuUqUKZsyYIUrHk+un1h4oia44R5Zyc3OxYsUKDB06FECJ4GzXrh3at2+vOGdfgMQK4VvIeFeizeWyTrJrSD0E0bgxRqcaNDZ2mWCQHkzOKeU9INKFKIlsYMJVgaDYtvzcWvnzB+mIjrwhpY8adrgXChxogE4S4QIzZ87E+vXrcfLkSW5p26NHjyI1NRU2m42rF1m4cCFvGWGijF69euHChQu4cuWKbG2PJzl58iTOnTuHqKgo3Lp1C++99x4AoHfv3gBKVm9TEju+BIkVQgSL0mWMPT0RNeQyZMp3Fk4Dl8/IDq3gPqdaGJkRp0NJT0rhs+Jo6nU2vGmJokc6HHi1tkqiz+j1lVrpy4gZLfMwa2Uyd49BEIRbCQgIEEUWOnfujFOnTnloRr7JyJEjPT0FRTZs2IDz58/DarWiTZs22LJlC1dM/+STT3p4duZBYoXg4fUCRQZfdZ9U/T4VQabFt1c1b8j5FN65ZwCzI0WqTSTSmBgGrPG8qLJ6Ik21Isr1J85REi6yJFHIKWlaK2YLBxcL7KXT+gybkx5DszZ1/28F0m0EQXiKe+65B7t27fL0NMoFWrqYUMWsjCofyMyqWHAOMlPqiJduVuzj6Kpe96IqdCT3MSWRFI1ipOxVJpqjswBda5uyQnWpnSr2TFjdS4sd9cUHtJxj5bE0WFBt4bDvqmPvi7pA8+IUevsQBEFUIkisECaiU42YqF5Ez84zZMW7nASl2cg7NDKOvVRLRvrrr3pHWk2gyDjeeh5+pWkss1Cr75DUXPrmK71Zi7hRn4zsVATpdbJTcF7AwAtwdRp6nX0vOWyCIAhCBhIrBAf/yaesSt2KG8MkFIFxK67cuZX227VFSpw68F+dN7silLTsFzY30E1Tep2BkhrNBqElxUl4t95k1C6D2eOVA+6OaOix74vnjyAIwl2QWCFEyAU8WFZYeG+GqjBHmZT9cSel4zKSkRFhEwPixNmWRmeb4X+Um462acBSGmFQqTFRigAZGNdpgLIXPeLDTSEPt1h111xNtFsmUOVS30gqEARBeBMkVggfpWKIElW3SCGtiH/3XpzKoye5SPOMVDUG3+EXPWldxSkXOYoSURslZ5Jf/26gbsWIU6zHhtY5Sa0gpqXOx0xHW0s6nBYz3pJfRhAEQfgkJFaIcsIVccEK3ott+bJ00eaEumLfIvjs2gim+54aFZbQEed9ctRkuBLxkZkCA/l6G8U6HP1DewDj4syMKJM7MWsEirQQBEF4FhIrBIdzeldJupe5EsCXBYUmNDpg5eL6lN6d5wUahPvlugpfnVcSk4kiSC/85XBqNRRwGHRepVcWM57GVZZ2xui6UAx3vpVTyQxfe01LuUn08aUUModt71FyBEEQhBdAYoXg4dICXeWsRrzOpdHrZKkuTSvcoMWmC+NLlarocdiVliR2Ek7OERDZB0wKFBN/KWUdJ0JuHEE0h1FZrUxqjt7qU0svxmbuz6ZcM/mVyShCQRAEQRiDHgpJKKJUbO9NzpoXTcUjMBIfGAZg1QSR6ga5jp4543KRHQ0dDYppN0YnDJTU8PYpfDI6GMMwglUBTRAZ3vSLQgJXpudWAebl560ykfvjj+U6XnDnzuU6nrv44YcfMHLkSBw+fBg1atTw9HRM5dy5c4iLi8Pnn3+OkJAQt42TlpaG3r17Y8+ePWjdurVpdocMGYIXXngBffv2Nc2mO6HICiFCSqA4b3O896aHPJpSxeLNvoFb8270jmXUy9YunKQjKYx4kxFkiv8lx1boL56IdAqY1gc+aqpdcszdopxOp3FIxx7VcUWGzFiIwCBqAkGzfjVhLqpjaAoCCn9eSKcQ+khMTERCQgJvW3p6Ol5//XVER0cjMjISDzzwAN566y3cuHGD1y4uLg42mw02mw2RkZHo06cPlixZgoKCAk1jHzt2DK1bt8aYMWNMOx4HP/zwA2w2G7Kzs023LUVcXBzmzp2rqe0777yDp556ihMqjrk6/nXr1g1jx47FqVOn3DllwyQkJGDJkiWw2+2enoomSKwQqnDixGU7XqRuvARVp0Qpr8aDlGRqOadCiQWFZD/ufw21K4z++9baHVWl9DNGsaje0KCurhCm0N5oGZDjGroDcrYJwnNcunQJQ4YMwYULF7B06VJ88cUXSEpKwqFDhzBs2DDcvHmT137o0KE4cOAAvvjiC0yePBlbtmzB8uXLNY21c+dOPPPMMzh27Bj++ecfNxyNOlqFlVlcvnwZ//vf/zBkyBDRvs8++wwHDhzAmjVrkJ2djbFjx+LWrVuSdgoLC909VVn+9a9/4datW/j22289Ngc9kFghOHjRE7k2ni6Tr0B6R+KevHEDSs1crXVhTNZGRupq9AgCmT7KekH7pLQ6+PyyGN2yS5tw0yiC5IMq3qcq3D0jTx2yF55qooIye/ZsWK1WbNiwAffeey8aNGiAXr16ITk5GVeuXMHbb7/Nax8YGIjw8HA0aNAA/fr1Q/fu3fHdd9+pjpObm4t9+/Zh+PDh+Ne//oVdu3ZJtjt27BgGDBiAtm3bIjY2FqdPn+b2/f3334iPj0eXLl3Qvn17PProo/i///s/pKWlYcSIEQCALl26wGazITExEUBJBOTNN9/EvHnz0LVrV4wePRoAkJycjJiYGLRv3x69evVCUlISbt++zZvL0aNH8cwzz6Bdu3bo0qULxowZg6ysLCQmJuLw4cP48MMPuehIWlqa5PHs27cPNpsN9erVE+0LDQ1FeHg4oqKiMHXqVGRkZOCnn35CWloabDYbUlNTERcXh7Zt2+K///0v7HY7VqxYgZ49eyIyMhIDBw7EN998o3jeDx8+jCeeeAKRkZGIjo7G4sWLUVRUxO3PycnBpEmT0L59e0RHR+ODDz4QRY38/PzQq1cvfPrpp4pjeQskVggRDj1gaiCkAokMOcrtENUzjyQ2OkVBTChv0NbY3HwW8TNbDBlxexfZ9sIVw9w9ESO2nQWOlnQ2OTtegNHr5kWHQBC6uXnzJg4cOICnnnoKgYGBvH3h4eGIiYnBvn37ZDMdTp06hWPHjsHfX72kOTU1FU2bNkWzZs0wYMAA7Nq1S9LuokWLMHXqVKSkpCA0NBTjxo3jogpvvvkmCgoKsHnzZuzduxevvfYagoODUb9+fS6644hWTJ8+nbO5e/du+Pn54aOPPsLs2bMBlPyOmj59Ovbu3Yv58+fj0KFDWLRoEdfn999/x6hRo9CiRQts27YNW7duxQMPPIDi4mJMnz4dHTp04KJMBw4cQP369SWP+8iRI4iMjFQ9P47z7ywkFi9ejLi4OKSmpiI6OhoffvghkpOTMXXqVPz3v/9FdHQ0EhIScOHCBUmbV65cwfPPP4+2bdvi448/RlJSElJSUvD+++9zbebPn4/jx4/j/fffx4YNG/Djjz/it99+E9lq27YtfizneiyjUIE9Uf4oVe0bxKxseK/CUFmAjloRLTUSmuyDX8yvVDDBvfJ3iXSUYLUu9YmWpXDpLsFwroWRHL9MdJUdv4IQUzivhtP+VPaTo+0+6JwSvsRff/0FlmXRvHlzyf3NmzdHVlYWMjMzERoaCgD46KOPkJKSgsLCQhQWFsJisWDWrFmqY6WkpGDAgAEAgB49eiA3Nxfff/897r//fl678ePHo3v37gBKHOlevXrhyy+/xCOPPIJ//vkH/fr1g81mAwA0atSI6+coyg8NDUX16tV5Nps0aYIpU6bwto0aNYp736hRI7z88stISkpCUlISAGDdunWIjIzkPgNAy5YtufdWq5WLMinx999/o02bNoptbty4gRUrVqBq1aqIiorCnTt3AAAjR47kFbWvX78ezz33HB599FEAwOTJk/HDDz9g48aNeOONN0R2t27dinr16mHWrFlgGAbNmzfHlStXsHjxYowfPx65ubnYs2cPFi9ejPvuuw8AMG/ePPTo0UNkq27dukhPT4fdbofF4t2xCxIrhACpUnXp8nXh33BVqSFqYFIxjDkmfBupgm0Fr51VqQdR9M9kKoC1+u6SgkDNiKC3KbUWcvPVPBd5e5p6uugFlwkUk//IuME7F5kkBUAQHsER+bBardy2mJgYxMfHIycnB2vXrkVISAj69eunaOfcuXP45ZdfsGLFCgCAv78/HnnkEezcuVMkVtq3b8+9r1mzJpo2bYpz584BAEaMGIGkpCQcOHAA999/P/r27Yu7775b9TikIhuHDh3C6tWrcebMGeTk5KC4uBj5+fnIzc1FcHAwfv/9d/Tv31/Vthp5eXmoUqWK5L5evXoBKEmRi4iIwLvvvovQ0FAupcx53jk5Obh69So6duzIs9GxY0fZwvyzZ8+iQ4cOvBuHnTp1Qm5uLi5fvozs7GwUFhYiKiqK21+tWjU0bdpUZCswMBB2ux0FBQWiKJy3QWKFEEF18O5D//NCpGzo3WGgGcPAIQEdzyBxBC4UjZZ5+k77ypoo/WipCiReS0a8WcEYY2EA1l0OsoHUMqXnvmjZrnc8OT3j6rhuSA0jHUMQxmncuDEYhsGZM2fQp08f0f5z586hdu3avEhFSEgImjRpAqAkZeuxxx7Djh07EBsbKztOSkoKioqK0LNnT24by7Lw9/dHVlaW5qWKY2NjER0djf379+O7777DmjVrMHXqVMTFxSn2CwoK4n3++++/8fzzz2PYsGF4+eWXUaNGDRw9ehTTp0/n0rDMcshr1aolu0LZli1bEBISgtq1a0suaRwcHCzaJsxYYFlWNotBKs3Osc156Xkpm0KysrIQFBTk9UIFoJoVQgXpqApb+qrXVgXHiJel2sVQLpjx8d3lKQqLxRkVsSEzH74ZiQiPQedZ11Gbfo7K4ZybbUIuHU3hWLxNhHjbfER4/QQJb6RWrVro3r07tm7diry8PN6+jIwM7N27F4MHD5btb7Va8cILL+Ddd9/lUpeEFBUV4eOPP0ZiYiL27NnD/fv444/RoEED7N27l9f+xIkT3PusrCxcuHABzZo147bVr18fw4cPx4oVK/Dss89i+/bt3FwAoLi4WPW4f/31VxQXFyMxMRHt27dH06ZNcfXqVV4bm82G77//XvHYtSzle8899+DMmTOS+xo2bIjGjRtrevZKSEgI6tSpg6NHj/K2Hz9+XDaNr0WLFjh+/DhPfBw7dgxVq1ZF3bp10ahRI1itVvz888/c/pycHPz1118iW3/88Qfuuece1Xl6AyRWCA6hMBGKC69aelhtKl40VTWMuiSaU6FUs62cIhXinRrsa2+vualEBEp3REkyVU1hAobEplO9i0Q9jmm48Zq41EePbVfte2FdjjfNhSAczJw5EwUFBRgzZgyOHDmC9PR0fPPNNxg9ejQiIiIwfvx4xf6PPfYYgJL6CCn279+PrKwsPPHEE2jVqhXvX//+/ZGSksJrv3LlSnz//ff4448/kJiYiFq1anFRn7lz5+Lbb7/FpUuX8Ntvv+HQoUOco37XXXeBYRjs378fmZmZopW9nGncuDGKioqwadMmXLp0CXv27MF//vMfXpvnn38ev/zyC5KSknDq1CmcPXsWW7duRWZmJjfeiRMnkJaWhszMTFnhEh0djZ9++kmTiFJjzJgxWLt2LVJTU3Hu3DksXrwYp06d4lZCE/LUU0/h8uXLmDNnDs6ePYuvvvoKy5cvx7PPPguLxYKQkBAMGjQICxcuxKFDh/Dnn3/i9ddf5zIknDl69ChXS+TtUBoYoYrwgZBG/0AzmsUOq/jR13GHgyMyKVkPItNW92BO4kFpxSiTMpsYLV6qY05Kd/a1zIVhwFgYyaEsFgZaHp/FcK86RJzqBqfzbTR9UMKoyJyWRRc0LczgO168x+eqs96MKH987YnyERERSElJwYoVK/DKK6/g+vXrYFkWffv2xcKFC0UpVEICAgLwzDPPYN26dRg2bBiqVq3K25+SkoL7778f1apVE/Xt27cvVq1axVt96tVXX8XcuXNx4cIF3H333Xj//fcREBAAALDb7XjzzTdx+fJlhISEoEePHpg2bRqAkgLwCRMmYMmSJZg2bRoGDRqE+fPnS865devWmDZtGtauXYulS5eic+fOePXVVzF16lSuTdOmTbFhwwYsXboUsbGxCAwMRFRUFCfORo8ejcTERDz66KPIy8vD119/jYYNG4rG6tWrF/z9/XHw4EHJwnU9jBgxAjk5OZg/fz4yMzPRvHlzrFy5EhEREZLt69atizVr1mDhwoXYvn07atasiSeeeALjxo3j2iQmJuKNN95AfHw8QkJCMHbsWKSnp/PqbK5cuYLjx4/zVkvzZkisED5GxVEu6jUccqk2fBviKEnZPlEHOUOK8+DPqWRMhj93k+78lzjC4qiKq3aFTURz11rszzDKekFltS5dMIzs3QFNAk7NPBif+TaR604Q8kg58A0bNuRtX7ZsGZKTk3Hq1Cl06NCB275p0yZJm/Hx8YiPj5fct2rVKtm5tGnThnuOCsuyOHbsGIKDg/Hggw9Ktp85c6asLaBkJTFhJEhuzqNGjeKtCAYAgwYN4n2+9957RREXB02bNsW2bdsU5wOUPKPkhRdewAcffMCJla5du/KeHyOkYcOGkvstFgtefPFFvPjii5r73XvvvaLolTMhISFYsmQJ9zk3NxcrVqzA0KFDuW0ffPABBg8eLPmsGG+ExAohidrDH41EWHQ5Rt6UclbJ4C3lq6v+RYPDr2ROi1OupvB0IVAdGuowpM6HodQ2d6LnTr1GcefK2LoL7N0sTShqQVRGXnrpJS7NqV27dl6/VK238+STTyI7Oxs5OTma6lPKk5MnT+LcuXOIiorCrVu38N577wEAevfuzbUJDQ3FmDFjPDVF3ZBYIXiwrFMBvQ8LBt+5Z6wTFT9LKq1JpwlzJiCV16RHaGgQEcJBGBciMqrZVVrqYhjp9DFhO7Wp6XpWjlJfl2tEhELTRCe/HIQbiRKC4DNkyBBPT6HC4O/vz0u98jY2bNiA8+fPw2q1ok2bNtiyZQtq167N7R87dqwHZ6cfEitEGRodSYeG8WEt4x4MOGAlDpXCieRqMUphwf8sY9XRV18Fh3EYxqJDhyg4wVodZLkiDyVRwSrs56y48IR557moRGn4dSJ6THuBA27455wgCIJwN/fccw927drl6WmYCsUBCUVKIi0m2ZKyVKkFj7vTXRyvmvOUnN6JBYRcnURZMMXFlDEjSAYh5MWP5HLHsrZVoilmRy70dtdyvt20ihhj1jkgCIIgCBVIrBAiWFY6amJ2JIXRbE+qoRtUjgf9Lj21IYzglb9RpmaAcXZunV/V6zakowACAaNp3spxHs1pXBb94ku8S2IsKSHjQtSjzKzEr1mp8yx1XFrSz5TaabUjPHaDq4JJZ7CRoCEIgiCMQ2KFEFFs5wuBouKyBVsNC5ZKHUGRRm/ZsdpW83xCFwyproYlXutd3RyjoaiEM695H+NUYKoouqROssJcdC1KYNCGqq7wRoGgOaLl3mmUDeOF58gZb7yGBEEQHoDECsHh0BOFxXYUFJUJlKw7hcgvtMPOsrC7El5R7Fo51IxXuR8ab9pLNdWkG7hGyullvCbC6ITGsfSiq5hebXyJKIes2DB6MM6RMYOF77Ld3HCCvdnPdue6AWbhjXMiCILwFCRWCBFFdhb5RXYuwmJngey8QhQU2VFQbHdNsLhK5dA0AEwQNgb8ZUb0RqMBXuTDpFoKxeiFQZtK3ZxXMtMcydGXOmVsYsbaGJqOi2N6A940PU1zkUkfJAiCIEogsULwYFGW6pXvFF25U1iMgmI7iopZ2LU8xlttEE/293lKa008MbIoxCLdQKkA26X0G5klgDWvruVJT9DVgno9iwMQXgddOoIgCGPQ0sWELIWCWpWi4hKVUOSyWpHBTBHi1YKG4b1TTklyZRjlugpDp0ipJsUdzpiR+hanzyxvp5Z6E0j/7MgV5OtEtYsRm9CfqabNsJHj01qXYnJEyEQ7JCoIgiC8CxIrhCx2O5BfVMx9dogUYQG+VuQfMmnMXqX0KQRlHpKJV6K0EnEdiJJdoQnJSIhBR50vHsTzY0rVm9GHGzKM9p8LSceaN0md4wtEjGovpyiU7DdAGKkS7tL41TGj6F82/U+9o08IALPLjAjCE/z+++9Ys2YNDh8+jKysLNSrVw8DBw5EfHw8rFarbnsZGRlYtWoV9u/fjytXriA0NBStW7fGyJEjcd9997nhCMwlLi4Od999N6ZPn+7pqRAuQGKF4BBqiWKWhbMucQRaNGsVlqW/9EoYOTeOh0KKuqrbEnfRN760r6phXNdvtovaSRXvG4YTF+75WXXLal1KES7XDBsemyAIz5KSkoKkpCQMGzYMq1atQo0aNfDjjz9i/vz5uHDhApYsWaLLXlpaGoYPH47q1atj8uTJsNlsKCoqwoEDBzB79mx89tlnbjoSdQoKChAQEOCx8YnyhcQKIYtqBEVNjNhZwE9hv8mRFpfxUSdMMqrC6HgSu1rkRc950XB72shpdtRrOEcTGMbpjr0LqVkl/ZzH0tlRS+RKr3EzxIxeG3qiWT76XXGmAhwCQXAcPnwYM2fOxL///W8MHjyY2964cWMUFxdjxowZSEhIQPPmzTXbnD17NhiGwY4dOxAcHMxtb9myJYYMGcJ9LigowMKFC/Hpp58iJycHkZGRmDZtGtq2bcu1iYuLg81mQ0BAAFJSUmC1WjFs2DBMmDABAGC327Fu3Trs2LED6enpCAsLw5NPPolx48Zx/Vu2bAmr1Yo9e/agZcuW2Lx5M1iWxbp16/Cf//wHGRkZiIiIQEJCAvr374/ExEQcPnwYhw8fxocffggA+Prrr9GwYUPFfoT3QWKF4OGsH0xZ9YsTNGYJEBaVJQFM7ih1OVmS4RCF3TJFNA7hI51CpjF/RipHS67gwmXxIUyFg46HkHK9Sv/Xnv7kzp9MzadC8voZM2pK+pi2ocramTYiQbgOy7K4U3SnXMcM8g/S/d2bN28eevbsyRMqDrp16wYAOH36tGaxcvPmTXz77beYOHEiT6g4qF69Ovd+4cKF+PzzzzF//nzcddddWLduHcaOHYvPP/+cF/3YvXs3nn32WWzfvh0//fQTEhMT0bFjR3Tv3h1LlizBjh07MG3aNHTq1AlXr17F+fPneWPu3r0bw4cPx0cffcSllb/zzjv44osvkJSUhIiICBw5cgSTJ09G7dq1MX36dFy4cAEtW7bESy+9BACoXbu2ar97771X0zkiyg8SK4QszlqFNSo2nKIr5sVLvLp63r3ovetfjijVzcCEffK7pBSQNvi1MWWvsk+T1+JAuPuWvUpNkgum3IewDsk1U16DW+fiTQdaSWFZFiP2jcBPGT+V67gd6nTAxv4bNX9nTp8+jZMnT+KVV16R3J+XlwcAyM/PR1xcHK5fvw4/Pz8kJCTg4Ycfluxz8eJFsCyLZs2aKY6dm5uL//znP5g3bx569eoFAJgzZw6+++477Ny5E8OHD+fa2mw2vPjiiwCAiIgIbN68Gd9//z3atWuHDz/8ELNmzeLEVuPGjdG5c2feWE2aNMGUKVN4YycnJ2Pjxo3o0KEDAKBRo0Y4evQotm3bhiVLlsBqtSIwMBDh4eGa+5FY8T5IrBAASn4pSwoSpVQv4T7noIfDFGuHeIXsSiw2BOiuNCnnUycTaCnbyb3X9keVYdz83HCdqUxGHGhjaWwG9jNM2XdMrtDeNAGgY3U4cqKJSoSZ0UV38fvvvwMAWrduLbn/t99+A1DikL/++uto3bo1rl+/jsGDB6NXr16SkRNH5ELt+C9evIjCwkJ07NiR22a1WhEVFYWzZ8/y2tpsNt7n8PBwXL9+HefOnUNBQQEXAZIjMjKS9/nMmTPIz8/H6NGjedsLCwtlz4Ur/QjPQWKFAKBQPqJEUSEQUMXZCvgeLGvQsKfw/j9KDsR1JQptBREBV+WC8vM+tEU5RCuUKUZbRJ0V56eGYlIiw4AR/szKre6ltjqW1noaNzlDRmqWGEanHjZxgQWzbLhZDutCy1zE3xjGeCSbMBWGYbCx/0avTwMrLCwEAFSpUkVy/9atW9GhQwdepCI0NBQ1atRAVlaWpFhp0qQJGIbB2bNn0adPH9U5COfLsqxom78/3+VkGAYsy8rOW0hQUJBoDABYvXo16taty9unVHxvtB/hOUisEAAUHJTSO7uSmqOggC9WhAX1LAtNT5B0w99lg08R8VoYqfeSd+Nltgs3ywoOiboS573C9Ch3LPUleGX09DcCo2VpXS1OOSO5NLPEcJJtJHuYddyyq4d5yLFXPD/eIzbMxJtEFKEdhmEQbBU7896EI2Jx5MgRkbDYsGED/vjjD2zevJm3/ZdffgHLsqhfv76kzZo1ayI6OhpbtmxBXFycSNBkZ2ejevXqaNy4MaxWK44ePYoGDRoAKBFPv/76K0aMGKFp/hEREQgMDMShQ4fQqFEjTX0AoHnz5ggICMA///wjm7pltVphF/ghWvoR3gWJFQJA2Z0GPXf0mIICQWtBX1ZjZIUVCBoWYNz14EkPU+aoGriLLW1R8ImfBuXFJS4uDSybDqU130ri5DMWi2RTVXNC0WFRPyLxwgY6zwEvGqJeX1NuAqACCA0SFYSvERUVhR49emD27NkoLCxEZGQkrl+/jh07duDTTz/F8uXLeSlUN27cwNSpU/HWW28p2n3jjTcwfPhwxMbG4qWXXoLNZkNxcTG+++47fPTRR9i3bx+Cg4MxfPhwLFy4EDVq1ECDBg2wbt065OXl4YknntA0/ypVquC5557DokWLYLVa0bFjR2RmZuLPP/9EbGysbL+QkBCMHj0a8+bNA8uy6NSpE3JycnD8+HEEBwdj8ODBuOuuu3DixAmkpaUhODgYNWvW1NSP8C5IrBAASvw2SV2hJDZKQ89cO15btiTSwq/SL3UQJUQN73PFFCoiDBU/mGDe4a8r547xmqtrgLKoAqcDjNSD6HUU1VKx1Dcbw9VVyvQWsejKg3KTsy1U2uU4dOmobkXuZ4/EC+ELLF++HCtWrMCiRYtw+fJlFBcXo0ePHvj88895qU4FBQV48cUX8fzzz/PqTKRo1KgRdu3ahVWrVmHBggW4evUqateujTZt2iApKYlr99prr4FlWUyZMgW3b99GZGQk1q1bhxo1aiA3N1fT/BMSEuDn54dly5bh6tWrCA8Px7Bhw1T7vfLKKwgNDcXq1auRlpaGatWq4Z577kF8fDwAYPTo0UhMTMSjjz6KvLw8bulitX6Ed0FihdCOc0E9W6o87HZwhcBcUT3K3jhtl43a2FlAavWlyo7UKXHDys2sRJmFIgrpS1IpTiJnj1M/Cg65s1OvUDSvVXA5D60WzRLa9KpggcpkxOeakXqrzbZXHbj7qCSHSVRwgoKCMHnyZEyePBkAMHPmTBw8eBCBgYFcG5ZlkZiYiG7dumHQoEGa7NapUwezZs3CrFmzZNtUqVIFM2bMwIwZM3jbWacbkZs2bRL1W7lyJffeYrFg3Lhx3HNVhEj1B0pulI0YMUI25axp06bYtm2b7n6EdyFcpomopDh+p4gDKazMdsFGlgub8LcJIytgpb1FUTtXcVPNigmeDSP6oM/h1j2eNgWizVbpq0VuaV+5CciID7W71kLxw2joo2pMbmUtyYEdo+qw7woabbicRuboIiNQVHWNFtsim6QKtEICinCVmTNn4oknnsDJkye5bUePHkVqaiq++uorDBw4EAMHDsTp06c9OEuC0AZFVggAJVGPsoCIzHLFosiK0/ay8ImkdS2baEljBdSyhnQ6gor1MozgVatBV5BaRUvYRGORu/R2S2lUSi7Vp2Rf2aHLpzppxV3OuVarDE8UytkqR69Yq+Zz7yxMFQLuPH+kVwhXCAgIEEUpOnfujFOnTnloRgRhHIqsEAAUSlOU9INaNEQqSiMZuZGyQ8JFEl69iXx0Qxy9ce6rbF8plYjftMyhl1vhSm4M+X0aazqETRjGsOOoX+hZyiYgsaqalD296WolfdTbSgVHygtPOdNarxdFJwiCICoGJFYIMXLKRRRNQVmql7C4XvL5kqzsPrNhKqrWkXgEiGRKkNZ0IpktvAiD0y4tOkPTfi1pVjJiQDMGFzAwvHKWaj2J0jlwtNBum1/Ko+fk69gnsVv5GAwNQRAEQRCykFghADj0BivWKUpLGkuJF34DpxelNDFhm4qqNMxB+8P+ZDa7w3OUiojoHEe25MWMpY217JcUBApCSqNdkbMPRvs10DGOWh9TcTlFzvOQgCIIgvANSKwQAARiRFNOmPC9I7oiWhIMYqEiUYwv7ELAFJfObIeM0ehoG13aVqc4EDXT1Eqin0XiV6GOlDTd4xkSYDrmIkwXky1a0Zby5xImiVaCIAiickJiheDQHdDQu4KX1ABSq4iZgq8oH+3pO4yG5obGcCcStSzC4nVp8aMQ+dCQbiW/UyKtTEtERccp5GpaJMYwK6qlSezoFQlcN+OC0Rd1Bq1SRhAE4d2QWCEAOJWgiAreZQQJ71kqEu3kgjDlJCI8UrOi1xF10UfiZVxJLCXM2yJaGUpPXYtawbiO1C9DdSQS41sE281wpEujRoqmlNLHtEYzPITo+TGQnre6IS3n2nMHr/ojKDM3b7leDEMCiiAIwhkSKwQAdQmh/nR7uZoVlVoVbh+rIHAIDsdDIeV8ZsUohWwuEO9VSvjI4uyom+DtCf1+VYdaKFjckrLlAeSiP5JRIOlf4y6fCm/x3ssZU5c21qKnK+l5JgiC0AqJFUKMXCRE9tkqrHNoBnICxQMBlvJFUx6dzF1dg0MqWnNXrYVMBEHrnXpGKCoMTpMRvRENxB/TVTQW5StsNjaGXFOpiJfecbVkk+m6QNrPkbsxKzpBWoIgCMKzkFghAJStBGZ4IS5Ww5LE3ABqtS5mKBnvVkOM0/+eGVulTendes313JraKDvXpqW+aFiRS7hTmCSn1MkhtkR75drzrEuMpDA5Rm6/zlQ1ueiLlr6KzbW2kzpfsjZJHQAglUQQBFEKiRUCgCNhS2LpYF5qFj+ywjh0B0+oyD34UWEbF43xboGhisnOhda6czPtS45pKRmX0eDIOzUAL63MgIDgXrnidHFUhnurqfhebf4yNnRHKxQ6yGTkSa5GJmfAxEJ32XZesCqYm82YIopITxAEQbgff09PgPAOWDlRot0C70XWjmZN4uPCRSMlxbTysML9TqdF8519Jd+5LIdLM4yTeBDNRstKXUyZkNExqs72cmZKiuiVfrpKnoMi2131J5MTVfqnJjFY2WiSzrXaggCGBtbQvpy9dLc8G4ggNLLjjx3lOl5sq1hd7RMTE5GdnY2VK1dy29LT07F8+XJ88803uHnzJsLDw9G7d2+MHz8etWrV4trFxcXh8OHDAACr1Yp69erh4YcfxoQJExAQEKA4bkZGBlatWoX9+/fjypUrCA0NRevWrTFy5Ejcd999uo6hvElLS0Pv3r2xZ88etG7d2tPTIVQgsUJw6Cuid84Z05Y/Jm3K8YyWCuCMeOXDLBWVin5rZpR+6BxA15BGj4mBqgpxiBz5wIu8Ey8stPbGnxSCICoGly5dwpNPPomIiAgsXboUDRs2xJ9//olFixbh22+/xbZt21CzZk2u/dChQ/HSSy+hsLAQv/zyC6ZNmwYAmDRpkuwYaWlpGD58OKpXr47JkyfDZrOhqKgIBw4cwOzZs/HZZ5+5+zBlKSwshNVq9dj4hPlQGhhRAi97S4MrJfmkexlh4yxqNNg2uuww43wX2iuFg3E0145YBClTTp1NuTktuquuEkkRhwskGimYkllOSTaFR9DeeXzh+VDqKt4nlyKmKyTlnrau4MrxEAThlcyePRtWqxUbNmzAvffeiwYNGqBXr15ITk7GlStX8Pbbb/PaBwYGIjw8HA0aNEC/fv3QvXt3fPfdd6pjMAyDHTt2oH///mjatClatmyJZ599Ftu3b+fapaenIyEhAR06dEDHjh3x8ssv49q1a9z+5cuXY+DAgdizZw8efPBBdOrUCRMnTkROTg7Xxm63Y82aNXjooYcQGRmJf/3rX3j//fcBlIgmm82G1NRUxMXFoW3btvjvf/8LANi5cycefvhhtG3bFv3798eWLVs4m7179wYADBo0CDabDXFxcdw+pX6EZyCxQqigUAzvKJjXqgtUBYTOh0x6G+5YikmtS2lUQMb9NjS0YXdVIb1LKsNJ3ow4QsHXIO5zqBkJcaR7PEc/pSY65yUwrbuxpn4mnFfNJpQWFZDTofqnQxCVjps3b+LAgQN46qmnEBgYyNsXHh6OmJgY7Nu3D6zM3+NTp07h2LFj8PeXT7y5efMmvv32Wzz99NMIDg4W7a9evTqAkoV7Jk2ahKysLGzatAnJycm4dOkSJk6cyGt/8eJFfP3111i1ahVWr16NI0eOYO3atdz+JUuWYN26dUhISEBqaioWL16MsLAwno3FixcjLi4OqampiI6Oxvbt2/H2229j4sSJSE1Nxauvvoply5Zh9+7dAIAdO0pS+z744AMcOHAAy5cvBwDVfoRnoDQwAkBJcT3LW35YrYNwXWId6qJcoh5uGkNQR+AJymrKzavj4FvXUMAtfBaLlrmUnjs99euq5S1OwoBx3iYTMWAsEvUqjvYsr6kiakEgd6NUr8TItJQ6JYZ+krUU+DNyH9yP7lsGjqCZ6TMxBuP4zxdv2BAe56+//gLLsmjevLnk/ubNmyMrKwuZmZkIDQ0FAHz00UdISUlBYWEhCgsLYbFYMGvWLNkxLl68CJZl0axZM8W5HDx4EH/++Se++uorNGjQAACwcOFCPProo/j5558RFRUFoETUzJs3DyEhIQCAAQMG4Pvvv+ciLB9++CFmzZqFwYMHAwAaN26Mzp0788YaOXIk+vbty31euXIlEhMTuW2NGjXCmTNnsG3bNgwePBi1a9cGANSsWRPh4eGa+xGegcQKAUDd/2aFT6nnPfCRFaRdlaV9Maygr9xABhYD8xbngqM8RIymR7monBmtKWVOTpypy8kqLEMmFGBGoxpamhmOApjtfGty/jUel8x7rX0qAyZ9PbSNpcUupd4R5YjjpqRzTUdMTAzi4+ORk5ODtWvXIiQkBP369VO1ofZ76ezZs6hbty7q16/PbWvRogWqV6+Oc+fOcWLlrrvu4oQKANSpUwfXr18HAJw7dw4FBQXo1q2b4liRkZHc+8zMTKSnp2P69OmYOXMmt72oqAjVqlWTtWG0H+F+SKwQHGU17wacbq5mRb6vfAG/47HsLozvDZjsdEha82K/Rkt5iqiPK+OpbHC+Oa02jtTDLtUn4NkaFMaMu+8ahJikttTR31OYt8QxQfgOjRs3BsMwOHPmDPr06SPaf+7cOdSuXZtL1QKAkJAQNGnSBACwaNEiPPbYY9ixYwdiY6VXJWvSpAkYhsHZs2clx3BGStAIU9CkUs4cbapUqaJo34FzOprdbgcAzJkzB+3ateO1sygsE2+0H+F+6OwTAJxiIZKF88JWED9bRWRQOtIiWgVMyrYJGC3SL2803FM3YRA9TrXEsHrSfiT6M8I2MjZ5WyScYKm71C6dHTnHXHQwKjaExffC6JBTypyWVDXFbXJjqM/UXHQqgZLTas4svUk8uHUuXiT8CN+hVq1a6N69O7Zu3Yq8vDzevoyMDOzdu1cxnclqteKFF17Au+++izt37ki2qVmzJqKjo7Flyxbk5uaK9mdnZwMoSTm7fPky0tPTuX1nzpzBrVu3ZNPUhERERCAwMBCHDh3S1B4AwsLCULduXVy6dAlNmjTh/WvUqBF3nABQXFysqx/hGUisEADKnmBf+km8X7hNKq1L0ESzYJBVSq7gLrXimgPhXvdD2tkVBTwMhmz01JpoNqLcWNTHjPOny2k2O3JgRjRGZoU0I+MYSk3iUvY0DaE0ikchLUBUVGbOnImCggKMGTMGR44cQXp6Or755huMHj0aERERGD9+vGL/xx57DACwdetW2TZvvPEG7HY7YmNj8fnnn+PChQs4e/YsPvzwQzz55JMAgPvvvx8tW7bE5MmT8dtvv+Hnn3/GlClTcO+996Jt27aajqVKlSp47rnnsGjRIuzZswcXL17ETz/9xBXIyzFhwgSsWbMGGzduxPnz53H69Gns3LkTycnJAIDQ0FAEBgbi22+/xbVr13Dr1i1N/QjPQGlgBAAF115JQAieaK99JB8Je/gAjMihd3LsVe++S3tr+px5rpPsXX/nbQyj03VVzEGSRu9Pl2gFMJa/zYhPyzCMrA1D2VuM8oMqCYJwH3of0uhpIiIikJKSghUrVuCVV17B9evXwbIs+vbti4ULFyIoKEixf0BAAJ555hmsW7cOw4YNQ9WqVUVtGjVqhF27dmHVqlVYsGABrl69itq1a6NNmzZISkoCUPJ7cMmSJViyZAmeeeYZMAyDHj168OpBtJCQkAA/Pz8sW7YMV69eRXh4OIYNG6bYJzY2FoGBgVi/fj0WLVqE4OBgtGrVCiNHjgRQkno2Y8YMvPfee1i2bBk6d+6MTZs2qfYjPAOJFcIJLVX2Ek0dSxgL27Esz5OSrlkxOJcKhp4CdiUxYWSP2hi89xZGOoXJqS3L36A+llMusNSxmbXsLoOS8yz1k1XSXc4GX2EZ1gZmqAo3KxMGAOvCGNqDLaSwCMIs5s+fL9rWsGFD3vZly5YhOTkZp06dQocOHbjtmzZtkrQZHx+P+Ph4xXHr1KmDWbNmKa4cVr9+faxcuVL279aECRMwYcIE3rZRo0Zh1KhR3GeLxYJx48Zh3Lhxov4NGzbE6dOnJW3HxMQgJiZGdm6xsbGSdTlq/Yjyh8QKAcBJX2h8Gr1szYnq8scqq4IZRJim5hOukLscNhW7jKON8BooOuxKw6n0kVrhS205Ln6Bi/R8NYzF2254rV4dY2g14UJfyf5StrTUtQgjYgrn2J3PtxFOqTzxid8VBOEiL730Eu666y6cOHEC7dq1o4JxwqcgsUJwyPopjnoWybQvtiyyIrfal0JdC9eGt7RRxY+qlBQcq7TRIjok3uufjEJdhkxml7Zgh8FZaRExShEYRrjBIErd3bG6l+kWnWzLZNMpfdNcESfeIAAU4mRejy/MkfA9hgwZ4ukpEIQhSFoTAEofCsl9UBEX0ga0NWIhU3jvmkAx9Tkg5YjeqISxQVw3oWaGEa7WVQ4rP5VpEtfqS8o66y0ch3qESPcUXIjOuAlXo0CCLDq3U14RIHfh6/MnCIIwGxIrRAlasrPk6lREURepfnJRExdEitLfdC9+VosxX0Svg+jsRDuccP6r5odDCt/L1LNAsE+4nSdo5GpepFIT5NLG5Ar5tQhAufGVewqiTnJ1O/I2ecev5QfB6Zzxzj1PoElcD0GBv+mRIKXFFHwE4cxF3w8zxlD6rpTtMG08giCIigiJFYKDVcjCUnf9S2tYWOE2heb6BtBFpfnzzwiiSg6HS62bQo6MpnNn1BHW2laxnYxIUTSnUaBJCSO9c1a6BuXpmGoUQ+U1plYR4G1RUsUFLdx5PUnEEARBACCxQpSiqBXkVAxXpyJVlC/xrBapZ7M4wTgPo1O8iJ4DU4FhuH8aoxoqtpQ+S23V64Trqb1RbCeMFijaFm8X6RUVQWLEEXWbfyk3VxcFACMUZjrtasaMdDlOC5ITTxAEUZkgsUIAcOgOh8uv4cnyQsGhrHbk+/mayPAhP8nkcgrtg5rRxpX2OmEslpIhnCIi/PQd56kYOz615ZnFJly4eK6cLy1zM26dIAiCIHRDYoUAUCpU1HSDUwPGefUvlgVYO8+a84uW0ZU/qyO6c+zFNSuuYDi9S7clvk3nCgl9HWUiHLztTrUCUu3B314SYVEYU2V8QwhrD5xTvbTWb8jUL+h7CKd3SwXp5+SUQ+SmgkHRI4IgiDJIrBBiJBx9Vs75V4yUSBW/CFLKeIKnYgoMWXTcpdfiusjX72oZR8MAkBIaOiejcRBHb8mCe7lx3B2B0dKmnBxzYylqKnNzU2G5t+NLcyUIgqiMkFghAGjUCqLnrDjVoNjtEu35bzRrEQOapTLVrJiG3GpWFuc4ikKtg5MNi6ZVsoymNYkMGben1EeiJsaILYaR/7WqtEKX4tiKNUH6+6iOZ8CeqWOZgNyYJE4IQp64uDjMnTvX09MgCB4kVggAzgEOCdXiasSDdIRbkPR7eRsh71CrxAlMu3NftpPfRthWRwqVbBMwqrUekuMzjPT50JrepTJHlwSQTPqZyK5MP+4QLOrXRu694vLUPuz4k2ghKhKJiYmw2WxYs2YNb/tXX30Fm82m2c7y5cvx8ssvmz09gnAJEisEh2R0QqtQUWnH8KIs/Gey8B8SaUzZCJ1NpgIrJAYGipwZybdau+gYR5uocG4rKbTMGFdJtCgcnUvF7WrDKwkgo6JGqYbHTbjT0a/sGqKSHz7hAlWqVMHatWuRlZVl2EbNmjUREhJi4qwIwnVIrBAASiIqugIoXARGZjliKWO69IOWxhXnz7pZGVLuQPMSyVqiI2qpSVpt6cUxtqKA0WRIbFc0lMnpaXrMyLzX0r6iYPSYGMGrq/aEfWXtVHZ15gPcvn1b9l9eXp7mtnfu3NHU1ij3338/wsLCsHr1asn9N27cwKuvvoqePXuiXbt2iImJwSeffMJr45wGtmTJEgwdOlRkJyYmBsuWLeM+79y5Ew8//DDatm2L/v37Y8uWLYaPgSCkILFCAFAPoLDCRnL1K7x94lXBGMVxjEdDRFGhylasL4Ghh+vpcJzknDu5dkb6ljRiyqJJ3IIDjldtMJA4HxqPVbKVlEjROBdtg8qkdRnpq9RU+4y0dVSIGLkajdH1M1NOuHUuJGK8hpCQENl/Q4YM4bWtU6eObNuHH36Y1zYiIkKynVEsFgteffVVbN68GZcvXxbtLygoQJs2bbB69Wp88sknGDp0KKZMmYITJ05I2ouJicGJEydw8eJFbtuff/6JP/74AzExMQCA7du34+2338bEiRORmpqKV199FcuWLcPu3bsNHwdBCCGxQvAQiZKyreJtzksZ2+Ue+CgTYXEWNEKx480pXGakB4n+V2pndBBG6q3EGML0ORfHc341GoGRa+tYJcxgIbqoKeTPETeOgXQtyS6C97wmes+T1DlRaac/bZAcZYLwRR566CG0bt2aF/lwULduXYwZMwatW7dGo0aNEBcXh+joaHz22WeStlq1agWbzYa9e/dy2/bu3Yu2bduiadOmAICVK1ciMTERffv2RaNGjdC3b1+MHDkS27Ztc88BEpUSf09PgPA+GNYpTiF4toqUjGCEKWGAQG84VgOTEiaEmfCeSSJ4ZURtpB1S1XoOp1clO7w5KG0SFmy74mRrQWUVLrVIimi/rgJ8+Z99PcdaHufFSDvN0Q+X0/HMg2QZoUZOTo7sPj8/P97nq1evyra1CJZiv3DhgkvzkuO1117DyJEjMXr0aN724uJirFmzBqmpqbh69SoKCgpQUFCAoKAgWVsxMTHYuXMnxo8fD5Zl8cknn2DkyJEAgMzMTKSnp2P69OmYOXMm16eoqAjVqlVzy7ERlRMSKwQAA1lTzg+FLNkg0Uaur87tGjAtQlCemDhJscBQv/vPpeQxYhfT9Px6l5bJlRNVKu0MjMkwjPiZQnIiw0AhvsuX3IWIh9RPCOuKPYZxqb+n8IUZ+8IcKxNVq1b1eFs9dOnSBdHR0Vi6dCkef/xxbvuGDRvwwQcf4PXXX4fNZkNQUBD+/e9/o7CwUNZWTEwMlixZgt9++w15eXm4fPkyHn30UQCAvfSRBXPmzEG7du14/YTCjCBcgcQKwVGWmSX1UEjBduHDHAVlKq5PRr2J8x90X37OimuLYJnt1gijDuI9DCNea00lvsK14frxIhUlexT7q0QlxCuLqU2G4f88Cz8bgeE/p0WyrkZLmpxUe6mx9PRzIXXOjJ8wTzjfWn4eCaKiMmnSJAwaNAgRERHctqNHj6J3794YOHAggBKxceHCBTRv3lzWTr169dClSxfs3bsXeXl5uO+++xAWFgYACAsLQ926dXHp0iUMGDBA1Ff2YdIEoRMSKwSAMmdf8neL7O8bmYJ7qU4KRfeA4y6/9L6Kis84S6441cJmeuzoER56xpAyoWLTJVsu2nNYlbMg+/BDDW30TaGcomoEQbiMzWZDTEwMNm/ezG1r3LgxvvjiCxw7dgw1atRAcnIyrl27pihWgJLoyvLly1FYWIhp06bx9k2YMAFvvfUWQkJC0LNnTxQUFODXX39FdnY2Ro0a5Y5DIyohFKcjOHRFJzQV4Qv3SYVfnJc/1j48Ac4bNd0VlDDodmdetrHEU+UdzxWRrIpXtyfepFzDIhpXxo7SGJrxpKgQ9jca1XERelgjQZjDyy+/zItuJCQk4J577sGYMWMQFxeHsLAw9OnTR9VO//79cfPmTeTl5Ynax8bG4q233sLu3bsRExODuLg47N69Gw0bNjT9eIjKC0VWCABOj03R3qOsgzAVzLmN8wvV1otQLGZ3KpbX5IfLbBHV80g63ErOt6ZWnBPLimyL2yk71BL7XS3+1hMdUnPW5eyasfqZnvHcYY+EAkH4JPPnzxdtu+uuu/DLL79wn2vWrImVK1cq2tm0aZNoW/Xq1Xl2hMTExHBLGTtDaWCEWVBkhQCgRUPItHAWLFLtpQSM1CrFrNRG7bj3Xq8PobDUrlvOEWPu09O92Vf25rk50KKdKjquZquZqd80XQ9RZM/4eARBEBUREisEgNIn2Je8kw+xKD0UsmytY8iJDRbCdubhywX2mlEMRpTt5FZoYhQ6yQUPNGRWSTp1SkvRmpQxxVslWVhQr2LUua9UZEmcCub8QfsctaCptl6pkcAD1jo9BR0radcIjEXh50BhnybbjlcvcubdOhdvOlCCIAgPQmKF4JAN2UptFz7YUVRQD5moiZR90RuxvQqCYf9DxtFkBPtlyzh0rQ6l4NjKiASeby10ep36cHOWcsYltjEWRrSdJ5b0pJsx0tud5807p84dndtbGH5H0ZzFv1Ydc5UUB6VKSlZ4SdTKyJ5v0UE5PsoJOHXlJBa06qJGeJyKNoS7FGejHTWtpyfbr7zxxjkRBEF4ChIrBADhI1O0iBZhZEWruNBQuFIxdYoERvNVjHeVtqfdmKaIgN6OFokmer01Tgmp9NO6X1P4A8qOO8N7EdvXiUvnhyAIgiB8FBIrBACZ+njHDihoEbnoiUS0hN/EDVX35VHMV0mdxEp62L6JWUUW7hqDILwcKgwnCO/6HpBYIXgo/2zK1awAWmpWVHd7z/fCrWgtDVBLc1J1Fx0pQ1omItdXQ4qR5DaFNC/dS9M6+jlv0mdBfQg9c3F+VWrjDmTTruTyBL1EVHjLPAhCAT8/PwBAQUGBh2dCEJ4nNzcXAGC1Wj08E1q6mOBgBVlerLqDwcpER2SK6KUCK9x78mXMQdJpFexSVzml7aXrU3gbGabs58D5ve55MgBYp6IWhaZKJhXT0GTmJhAg0vUVGo5LSeBpwaAAKtEkOutOjIxZTkUepGsIT+Hv74/g4GBkZGTAarXCIlF/RmiHZVnk5+fDYrEY/71IlDssyyI3NxdXr15FzZo1ORHvSUisEAAcPqbUQxshECUQv+e14TY475S2LVIvMuNXQNz+e1vlpnppSbdgowmTMuvAPPmHTc/YZs/TG6q+3XVMBOHlMAyD+vXr4/z58/jrr788PR2fh2VZFBYWwmq1kljxQWrWrIl69ep5ehoASKwQpcjJBKZ0SWPx41JYMFxERe5ZK4KOzquHuUGPMD4qclz5FS4ZLHEl/cdoVzOL9BUaqy3Uy+2VcPqVVs+yWOSjQnpWspKdl6fSxgiC0EVAQABatmxJqWAmUFxcjFOnTqFFixZecXee0I7VavWqa0ZiheBQzeCRrTURrAamtJiY3FPuhf18U3eUC97g2zJOtSfO7+XaOl5Z55Qxrq+4rYSRkn0MwKWMSbVXrOlgJbYJ+qoch+KPpSCNTFmfiM+Zqv1So2rn2nGOGYuFsyc3HpzSM3jXR3Ua0nNwpM1IjeVqSo3zPF1pJ3keFPpp+dnWsl/vfAjPYbFYEBgY6Olp+DzFxcUAgMDAQK9yfAnfwycTMvfv34/Y2FhERUWha9euePHFF3n733rrLTz++OOIjIzEwIEDPTRL30L8AHoNToso9Uu4v+SFkV5mTOZ9JcLdTokL9qWfy1Hi8DIWi6RtrYJF75hybdUcQaW+FkuZSBLa4TnWCnZlj0n0mW9XTdwJ5yJ1PNy1cJMDrHRutTjgeuzr2WcmJAoIgiB8A5+LrHz++eeYOXMmJk6ciG7duoFlWfzxxx+idkOGDMGJEydw+vRpD8zSN3E855ERKxfxdsWaFWnxwTr+l9UmKuJHunUlgxG90+tyMdyDWqTOoDbnV3a/OGfKuZEmW6I2iu0l2ul1QkudftmogsRctN7lLw/MvDOvua8XHDdBEARROfApsVJUVIS5c+di8uTJiI2N5bY3a9aM127GjBkAgMzMTJfFypavksHC7pINXyDzdgEKi0tFyZ1csEGBACxgCvLA+vsDFn8wd26DDaoKAGBysoH8fLA3joApLADu3EFxruOOe6nTV1TES625ks+gqKC4dJ8/YLeX7LMXA4yl5DOCgLw70CJFTtfNwK2gkjvgl7P+H25XuQ57/UzkBFuQVtWOorz9sAM4mu4Hi38amPBbJR1Z4E71PARVyef5uvagi7CEZyoP6ucPFBdJ7ioOOAc/hf5FBScQUGwBwwCFxSyCbl6CJTcb+UXFqFLkh1sFhbz21Rgrsourgim4iaq1r4NhgDtB/qiVU3INCovssFosuJ1fhBC/QFwvBELYINxk/eBXXISLTBAybmUjxB6IrDtZqA47bhUU4u9bQbjhHwS7HaiSewvZRcGwhl4DAASH3Ebw5W9gvXkd+Xn5sPpZUGSvBuvtknPnZ6kFy60gsHY7irIvw89SCwDgXxwK+507JecxPx8oDf8DAGO1wu92dRTduMFt8y+ohaKbN1CcfR2FxdVQpfQ6WO5URXF2NgL+uojCa1fgZw/Fndw8VM0NAVtUhKLM6yi0+MMfgLWwJopv3uTs52RcQ5XimjibcwbZl9NhvVMD9jt3YAkKgv3OHdwpZhEIOxg/P9irVIGdBQL8LCi6cQP+BSXH4TgGS1AQ7LdvozgnB/n+AQiEHZZbQbDn58OSFQhLUJDsdWZZFgzDILewCMFWfxRc/wcBf12UbW+/c6dEMFUJAJuXz83XMQZbXAS22A5LQIBsf6n5sMXFYIuKYKlSRXZsJXtCu86fnc+ZGsU5OWACAmTnr2lOdjvOZ53H9YvXaYWmCgZd24oLXduKC1vMojZql9t4PiVWTp48iStXrsBisWDQoEG4du0a7r77bkydOhUtW7Y0dSzHXdYNNzYjz55nqm2vxwrA4Y9bANhL/zlvDyz9BwABpf9qlOMcAfza0PnTJSBU2OJvAMDPANBMuE+KS4BLC19cApT8t9yt/M+3VczlOr1v43hzAbjyg7htdulrhtO2NA12HbR2ev/Tz/Jz+lvls1mccbF/hnqTcsXV4yFKcNfPG+F56NpWXOjaVjgCLYF45+53yu3BkT4lVi5dugQAWLFiBRITE3HXXXchOTkZzzzzDD7//HPUrFnTtLHs9pJoyjt3v2OaTYIgCIIgCIKoCDh8ZXfjFWJl+fLlWLFihWKblJQU7qTEx8ejX79+AIB58+ahZ8+e+OyzzzBs2DDT5uTv74+2bdvSw4wIgiAIgiAIohSWZWG32+HvXz4ywivEytNPP41HHnlEsU3Dhg1x+3ZJ3kzz5s257QEBAWjUqBHS09NNnZPFYkGACznWBEEQBEEQBEG4hleIldq1a6N2bfVCncjISAQEBOD8+fPo3LkzAKCwsBB///03GjRo4O5pEgRBEARBEARRjniFWNFKSEgIhg0bhuXLl6N+/fpo0KAB1q9fDwDo378/1+6vv/5Cbm4uMjIykJeXh99//x1ASUSGoiUEQRAEQRAE4RswbHmV8ptEYWEhli5dio8//hh5eXlo164dXn/9dd5qYHFxcTh8+LCo79dff42GDRuKthMEQRAEQRAE4X34nFghCIIgCIIgCKJyQE/pIQiCIAiCIAjCKyGxQhAEQRAEQRCEV0JihSAIgiAIgiAIr4TECkEQBEEQBEEQXgmJFQm2bNmCBx98EG3btsXjjz+OH3/80dNTIlRYvnw5bDYb71/37t25/SzLYvny5YiOjkZUVBTi4uLw559/8mwUFBRgzpw56Nq1K9q3b4/4+Hhcvny5vA+lUnPkyBHEx8cjOjoaNpsNX331FW+/WdcxKysLkydPRqdOndCpUydMnjwZ2dnZbj++yozatU1MTBR9h4cOHcprQ9fWO1m9ejWGDBmCDh064L777kNCQgLOnTvHa0PfXd9Dy3Wl761vsnXrVsTExKBjx47o2LEjnnzySfzf//0ft9/bvq8kVgSkpqZi3rx5GDduHPbs2YNOnTrhueeewz///OPpqREqtGzZEgcOHOD+7d27l9u3du1aJCcnY9asWUhJSUFYWBieffZZ5OTkcG3mzp2LL7/8Em+//Ta2bt2K3NxcvPDCCyguLvbE4VRKcnNzYbPZMGvWLMn9Zl3HSZMm4dSpU1i3bh3WrVuHU6dOYcqUKW4/vsqM2rUFgB49evC+w2vWrOHtp2vrnRw+fBhPP/00tm/fjuTkZBQXF2PMmDHIzc3l2tB31/fQcl0B+t76IvXq1cNrr72GnTt3YufOnejWrRvGjx/PCRKv+76yBI8nnniCnTVrFm9b//792cWLF3toRoQWli1bxg4YMEByn91uZ7t3786uXr2a25afn8926tSJ/eijj1iWZdns7Gy2TZs27Keffsq1uXz5Mnv33Xez33zzjXsnT0jSqlUr9ssvv+Q+m3Udz5w5w7Zq1Yr96aefuDbHjx9nW7VqxZ49e9bdh0Ww4mvLsiw7depUdty4cbJ96Nr6DtevX2dbtWrFHj58mGVZ+u5WFITXlWXpe1uR6NKlC7t9+3av/L5SZMWJgoIC/Pbbb4iOjuZt7969O44fP+6hWRFa+euvvxAdHY0HH3wQEydOxKVLlwAAaWlpyMjI4F3XgIAAdOnShbuuv/76KwoLC3mpY3Xr1kXLli3p2nsJZl3H48ePo1q1amjXrh3Xpn379qhWrRpdaw9z+PBh3HfffejXrx9mzJiB69evc/vo2voOt27dAgDUqFEDAH13KwrC6+qAvre+TXFxMT799FPk5uaiQ4cOXvl99XflACsaN27cQHFxMUJDQ3nbw8LCkJGR4aFZEVqIiorCggULEBERgevXr+P999/HsGHD8Mknn3DXTuq6OtL7rl27BqvVKvolHBYWhmvXrpXPQRCKmHUdr127JrLhsEvX2nP07NkT/fv3R4MGDZCWloZ3330XI0eOxK5duxAQEEDX1kdgWRbz5s1Dp06d0KpVKwD03a0ISF1XgL63vszp06cxbNgw5OfnIzg4GO+99x5atGiBY8eOAfCu7yuJFQkYhuF9ZllWtI3wLnr16sX73L59ezz00EPYs2cPp+qlrqsaWtoQ5Yu7riN9zz3LI488wr1v1aoVIiMj8eCDD2L//v3o27evbD+6tt7Fm2++iT/++ANbt24V7aPvru8id13pe+u7NG3aFHv27EF2dja++OILTJ06FZs3b+b2e9P3ldLAnKhVqxb8/PxEiu/69esICwvz0KwIIwQHB6NVq1a4cOECwsPDAUDxuoaFhaGwsBBZWVmybQjPYtZ1DAsL46UpOMjMzJS8C0R4hjp16qBBgwa4cOECALq2vsCcOXPwv//9Dxs3bkS9evW47fTd9W3krqsU9L31HQICAtCkSRO0bdsWkyZNwt13340PP/zQK7+vJFacCAgIQJs2bfDdd9/xth88eBAdOnTw0KwIIxQUFODs2bMIDw9Hw4YNER4ezruuBQUFOHLkCHddIyMjYbVaeW2uXr2KP//8k669l2DWdezQoQNu3bqFn3/+mWtz4sQJ3Lp1i661F3Hjxg2kp6ejTp06AOjaejMsy+LNN9/EF198gY0bN6JRo0a8/fTd9U3UrqsU9L31XViWRUFBgVd+XykNTMCzzz6LKVOmIDIyEh06dMC2bduQnp6OYcOGeXpqhAILFizAAw88gPr16yMzMxPvv/8+cnJyMHjwYDAMgxEjRmD16tWIiIhAkyZNsHr1agQGBuKxxx4DAFSrVg1DhgzBggULUKtWLdSoUQMLFixAq1atcP/993v46CoPt2/fxsWLF7nPaWlp+P3331GjRg00aNDAlOvYvHlz9OjRAzNmzMCbb74JAJg5cyYeeOABNGvWrPwPupKgdG1r1KiBFStWoG/fvggPD8fff/+Nt99+G7Vq1UKfPn0A0LX1ZmbPno1PPvkEK1euRNWqVbkalWrVqiEwMNC038F0fcsXtet6+/Zt+t76KEuXLkXPnj1Rr1493L59G6mpqTh8+DDWrVvnld9XhqWkfBFbtmzB+vXrcfXqVbRq1QrTpk1Dly5dPD0tQoGJEyfiyJEjuHnzJmrVqoX27dvj5ZdfRosWLQCU3DFYsWIFtm3bhqysLLRr1w6zZs3iFQrm5+dj4cKF+OSTT5CXl4f77rsPb7zxBurXr++pw6p0/PDDDxgxYoRo++DBgzF//nzTruPNmzfx1ltv4X//+x8A4MEHH8SsWbNQvXp19x9kJUXp2iYlJWH8+PE4efIkbt26hfDwcHTt2hUvv/wy77rRtfVObDab5PZ58+bh8ccfB2De72C6vuWH2nXNy8uj762P8vrrr+PQoUO4evUqqlWrBpvNhueee45b3cvbvq8kVgiCIAiCIAiC8EqoZoUgCIIgCIIgCK+ExApBEARBEARBEF4JiRWCIAiCIAiCILwSEisEQRAEQRAEQXglJFYIgiAIgiAIgvBKSKwQBEEQBEEQBOGVkFghCIIgCIIgCMIrIbFCEARBEARBEIRXQmKFIAiC8ChpaWmw2Wz4/fff3TZGYmIiEhIS3GafIAiCcA/+np4AQRAE4dskJiZi9+7dou3R0dFYv369av/69evjwIEDqFWrljumRxAEQfgwJFYIgiAIl+nRowfmzZvH2xYQEKCpr5+fH8LDw90xLYIgCMLHoTQwgiAIwmUCAgIQHh7O+1ejRg0AgM1mw9atWzF27FhERUXhwQcfxL59+7i+wjSwrKwsTJo0Cd26dUNUVBT69u2LnTt3cu1Pnz6NESNGICoqCl27dsXMmTNx+/Ztbn9xcTHmzZuHzp07o2vXrli4cCFYluXNl2VZrF27Fr1790ZUVBQGDBiAzz77zJ2niCAIgjAAiRWCIAjC7bz77rvo168fPv74YwwYMACTJk3C2bNnZduePXsWa9euRWpqKpKSkrgUsTt37mDs2LGoUaMGUlJS8M477+DgwYOYM2cO13/Dhg3YuXMn5s6di61btyIrKwtffvklb4x33nkHu3btQlJSEj799FOMGjUKkydPxuHDh913EgiCIAjdUBoYQRAE4TL79+9Hhw4deNvGjh2L8ePHAwD69++P2NhYAMArr7yCgwcPYtOmTUhKShLZ+ueff9C6dWu0bdsWANCwYUNu3969e5Gfn48FCxYgODgYADBr1izEx8fjtddeQ1hYGDZu3Ijnn38e/fr1AwDMnj0bBw4c4Gzk5uYiOTkZGzdu5ObcqFEjHD16FNu2bcO9995r0lkhCIIgXIXECkEQBOEyXbt2FQkPRxoYAJGQad++vezqX8OHD8dLL72EkydPonv37ujTpw86duwIADh79ixsNhsnVACgY8eOsNvtOH/+PKpUqYKMjAzeeP7+/oiMjORSwc6cOYP8/HyMHj2aN25hYSFat26t/+AJgiAIt0FihSAIgnCZoKAgNGnSRFcfhmEkt/fq1Qv/7//9P+zfvx8HDx7EqFGj8PTTT2Pq1KlgWVa2n9x2IQ7Rsnr1atStW5e3T+uiAARBEET5QDUrBEEQhNv56aefeJ9PnDiBZs2aybavXbs2Hn/8cSxevBivv/46tm3bBgBo0aIFTp06hdzcXK7tsWPHYLFYEBERgWrVqiE8PJw3XlFREX777Tfuc/PmzREQEIB//vkHTZo04f2rX7++OQdMEARBmAJFVgiCIAiXKSgoQEZGBm+bn58fateuDQD47LPPEBkZiU6dOmHv3r34+eefMXfuXElb7777Ltq0aYOWLVuioKAA+/fvR/PmzQEAMTExWLZsGRITE/Hiiy8iMzMTc+bMwcCBAxEWFgYAGDFiBNauXYuIiAg0a9YMH3zwAbKzszn7ISEhGD16NObNmweWZdGpUyfk5OTg+PHjCA4OxuDBg91xigiCIAgDkFghCIIgXObbb79FdHQ0b1vTpk255YAnTJiA1NRUzJ49G+Hh4Vi8eDFatGghactqtWLp0qX4+++/ERgYiE6dOmHp0qUAStLN1q9fj7lz5+KJJ55AUFAQ+vbti8TERK7/6NGjkZGRgcTERFgsFgwZMgQPPfQQbt26xbV55ZVXEBoaitWrVyMtLQ3VqlXDPffcg/j4eLNPDUEQBOECDCtcfJ4gCIIgTMRms+G9995Dnz59PD0VgiAIwsegmhWCIAiCIAiCILwSEisEQRAEQRAEQXgllAZGEARBEARBEIRXQpEVgiAIgiAIgiC8EhIrBEEQBEEQBEF4JSRWCIIgCIIgCILwSkisEARBEARBEAThlZBYIQiCIAiCIAjCKyGxQhAEQRAEQRCEV0JihSAIgiAIgiAIr4TECkEQBEEQBEEQXsn/B8EgVvVBM8loAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,4), layout='constrained')\n",
    "grid_fig = gridspec.GridSpec(1, 1, figure=fig)\n",
    "\n",
    "fig_bw20 = fig.add_subfigure(grid_fig[0, 0])\n",
    "ax_bw20 = fig_bw20.add_subplot()\n",
    "plot_learning_curves(\n",
    "    env='20-blocks-world',\n",
    "    learning_rate= 0.01,\n",
    "    epsilon= 0.05,\n",
    "    carcass = 'Abstract (ASP)',\n",
    "    label = 'Abstract (ASP)',\n",
    "    plot_color = 'tab:blue',\n",
    "    ax = ax_bw20,\n",
    "    alpha=0.6)\n",
    "plot_learning_curves(\n",
    "    env='20-blocks-world',\n",
    "    learning_rate= 0.01,\n",
    "    epsilon= 0.05,\n",
    "    carcass = 'Abstract (Prolog)',\n",
    "    label = 'Abstract (Prolog)',\n",
    "    plot_color = 'tab:red',\n",
    "    ax = ax_bw20,\n",
    "    alpha=0.6)\n",
    "\n",
    "plot_learning_curves(\n",
    "    env='20-blocks-world',\n",
    "    learning_rate= 0.01,\n",
    "    epsilon= 0.05,\n",
    "    carcass = 'Concrete',\n",
    "    label = 'Concrete',\n",
    "    plot_color = 'tab:green',\n",
    "    ax = ax_bw20)\n",
    "\n",
    "ax_bw20.plot([0,3000], [naive_return(20)]*2, 'k--', label='Naive')\n",
    "ax_bw20.legend(loc='best')\n",
    "ax_bw20.set_yticks([-61, 100-61, naive_return(20), 80])\n",
    "ax_bw20.set_ylim(-61, 80)\n",
    "ax_bw20.set_xlim(0,3000)\n",
    "ax_bw20.set_title('20-blocks world')\n",
    "\n",
    "plt.savefig('plots/bw-20.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,4), layout='constrained')\n",
    "grid_fig = gridspec.GridSpec(1, 1, figure=fig)\n",
    "\n",
    "fig_bw20 = fig.add_subfigure(grid_fig[0, 0])\n",
    "ax_bw20 = fig_bw20.add_subplot()\n",
    "plot_learning_curves(\n",
    "    env='MiniGrid-MultiRoom-N2-S4-v0',\n",
    "    learning_rate= 0.001,\n",
    "    epsilon= 0.2,\n",
    "    carcass = 'Abstract (ASP)',\n",
    "    label = 'Abstract (ASP)',\n",
    "    plot_color = 'tab:blue',\n",
    "    ax = ax_bw20)\n",
    "plot_learning_curves(\n",
    "    env='MiniGrid-MultiRoom-N2-S4-v0',\n",
    "    learning_rate= 0.001,\n",
    "    epsilon= 0.2,\n",
    "    carcass = 'Abstract (Prolog)',\n",
    "    label = 'Abstract (Prolog)',\n",
    "    plot_color = 'tab:red',\n",
    "    ax = ax_bw20)\n",
    "\n",
    "plot_learning_curves(\n",
    "    env='MiniGrid-MultiRoom-N2-S4-v0',\n",
    "    learning_rate= 0.001,\n",
    "    epsilon= 0.2,\n",
    "    carcass = 'Concrete',\n",
    "    label = 'Concrete',\n",
    "    plot_color = 'tab:green',\n",
    "    ax = ax_bw20)\n",
    "\n",
    "#ax_bw20.plot([0,3000], [naive_return(20)]*2, 'k--', label='Naive')\n",
    "ax_bw20.legend(loc='best')\n",
    "#ax_bw20.set_yticks([-61, 100-61, naive_return(20), 80])\n",
    "#ax_bw20.set_ylim(0, 1)\n",
    "#ax_bw20.set_xlim(0,3000)\n",
    "#ax_bw20.set_title('20-blocks world')\n",
    "\n",
    "plt.savefig('plots/mg.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
