analysis.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import wandb \n",
    "import dataclasses\n",
    "import pandas as pd\n",
    "\n",
    "api = wandb.Api(timeout=100)\n",
    "\n",
    "@dataclasses.dataclass\n",
    "class Run:\n",
    "    config: dict\n",
    "    summary: dict\n",
    "\n",
    "\n",
    "def get_runs_from_sweep(sweep):\n",
    "    runs = []\n",
    "    experiment_stats = {}\n",
    "\n",
    "    for run in sweep.runs:\n",
    "        # discard non-test metrics from each run\n",
    "        summary = {k:v for k,v in run.summary.items() if \"test\" in k}\n",
    "        runs.append(Run(run.config, summary))\n",
    "\n",
    "    return runs\n",
    "\n",
    "\n",
    "def get_df_from_runs(runs):\n",
    "    df = pd.DataFrame([run.config | run.summary for run in runs])\n",
    "    return df\n",
    "\n",
    "def convert_fairshape_format(df):\n",
    "    \"\"\"Adds an experiment column to the fairshape dataframe\"\"\"\n",
    "    df[\"experiment\"] = df.apply(\n",
    "        lambda row: f\"{row['datamodule']}_{row['datamodule.attribute_difference']}\", \n",
    "        axis=1\n",
    "        )\n",
    "    return df\n",
    "\n",
    "def drop_unused_columns(df, mode: str):\n",
    "    if mode == \"predict_attr\":\n",
    "        for col in df.columns:\n",
    "            if \"test\" in col and \"auc\" not in col:\n",
    "                df.drop(col, axis=1, inplace=True)\n",
    "    \n",
    "    elif mode == \"predict_disease\":\n",
    "        for col in df.columns:\n",
    "            # drop all test metrics except accuracy \n",
    "            if \"test\" in col and \"accuracy\" not in col and \"auc\" not in col:\n",
    "                df.drop(col, axis=1, inplace=True)\n",
    "\n",
    "    else:\n",
    "        raise ValueError(f\"Invalid mode: {mode}\")\n",
    "\n",
    "    return df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get sweeps from wandb. We assume the sweeps are completed and no runs have crashed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(110, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rng</th>\n",
       "      <th>model</th>\n",
       "      <th>experiment</th>\n",
       "      <th>model.predict_attr</th>\n",
       "      <th>datamodule.label_noise</th>\n",
       "      <th>test/auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>baseline</td>\n",
       "      <td>fitzpatrick17k_skin</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0.895062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>baseline</td>\n",
       "      <td>fitzpatrick17k_skin</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0.889488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>baseline</td>\n",
       "      <td>fitzpatrick17k_skin</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0.901396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>baseline</td>\n",
       "      <td>fitzpatrick17k_skin</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0.864893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>baseline</td>\n",
       "      <td>fitzpatrick17k_skin</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0.890657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rng     model           experiment  model.predict_attr  \\\n",
       "0    9  baseline  fitzpatrick17k_skin                True   \n",
       "1    8  baseline  fitzpatrick17k_skin                True   \n",
       "2    7  baseline  fitzpatrick17k_skin                True   \n",
       "3    6  baseline  fitzpatrick17k_skin                True   \n",
       "4    5  baseline  fitzpatrick17k_skin                True   \n",
       "\n",
       "   datamodule.label_noise  test/auc  \n",
       "0                       0  0.895062  \n",
       "1                       0  0.889488  \n",
       "2                       0  0.901396  \n",
       "3                       0  0.864893  \n",
       "4                       0  0.890657  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_attr_sweep_id = \"<USERNAME>/subgroup_separability/<SWEEP_ID>\"\n",
    "predict_attr_sweep = api.sweep(predict_attr_sweep_id)\n",
    "predict_attr_runs = get_runs_from_sweep(predict_attr_sweep)\n",
    "\n",
    "predict_attr_df = get_df_from_runs(predict_attr_runs)\n",
    "predict_attr_df = drop_unused_columns(predict_attr_df, mode=\"predict_attr\")\n",
    "print(predict_attr_df.shape)\n",
    "predict_attr_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(220, 9)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rng</th>\n",
       "      <th>model</th>\n",
       "      <th>experiment</th>\n",
       "      <th>model.predict_attr</th>\n",
       "      <th>datamodule.label_noise</th>\n",
       "      <th>test/accuracy_group_1</th>\n",
       "      <th>split_test/accuracy</th>\n",
       "      <th>split_test/auc</th>\n",
       "      <th>test/accuracy_group_0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>baseline</td>\n",
       "      <td>fitzpatrick17k_skin</td>\n",
       "      <td>False</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.921212</td>\n",
       "      <td>0.808989</td>\n",
       "      <td>0.854915</td>\n",
       "      <td>0.893406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>baseline</td>\n",
       "      <td>fitzpatrick17k_skin</td>\n",
       "      <td>False</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.937374</td>\n",
       "      <td>0.785268</td>\n",
       "      <td>0.821667</td>\n",
       "      <td>0.890696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>baseline</td>\n",
       "      <td>fitzpatrick17k_skin</td>\n",
       "      <td>False</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.939394</td>\n",
       "      <td>0.793383</td>\n",
       "      <td>0.847700</td>\n",
       "      <td>0.892502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>baseline</td>\n",
       "      <td>fitzpatrick17k_skin</td>\n",
       "      <td>False</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.907071</td>\n",
       "      <td>0.816479</td>\n",
       "      <td>0.857215</td>\n",
       "      <td>0.877145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>baseline</td>\n",
       "      <td>fitzpatrick17k_skin</td>\n",
       "      <td>False</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.931313</td>\n",
       "      <td>0.807116</td>\n",
       "      <td>0.861531</td>\n",
       "      <td>0.888889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rng     model           experiment  model.predict_attr  \\\n",
       "0    9  baseline  fitzpatrick17k_skin               False   \n",
       "1    8  baseline  fitzpatrick17k_skin               False   \n",
       "2    7  baseline  fitzpatrick17k_skin               False   \n",
       "3    6  baseline  fitzpatrick17k_skin               False   \n",
       "4    5  baseline  fitzpatrick17k_skin               False   \n",
       "\n",
       "   datamodule.label_noise  test/accuracy_group_1  split_test/accuracy  \\\n",
       "0                    0.25               0.921212             0.808989   \n",
       "1                    0.25               0.937374             0.785268   \n",
       "2                    0.25               0.939394             0.793383   \n",
       "3                    0.25               0.907071             0.816479   \n",
       "4                    0.25               0.931313             0.807116   \n",
       "\n",
       "   split_test/auc  test/accuracy_group_0  \n",
       "0        0.854915               0.893406  \n",
       "1        0.821667               0.890696  \n",
       "2        0.847700               0.892502  \n",
       "3        0.857215               0.877145  \n",
       "4        0.861531               0.888889  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_disease_sweep_id = \"<USERNAME>/subgroup_separability/<SWEEP_ID>\"\n",
    "predict_disease_sweep = api.sweep(predict_disease_sweep_id)\n",
    "predict_disease_runs = get_runs_from_sweep(predict_disease_sweep)\n",
    "\n",
    "predict_disease_df = get_df_from_runs(predict_disease_runs)\n",
    "predict_disease_df = drop_unused_columns(predict_disease_df, mode=\"predict_disease\")\n",
    "print(predict_disease_df.shape)\n",
    "predict_disease_df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyse the predict_attr results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iIpIcCwYRERFJjgWDiIiIJMeCQURERJKTvWAsWrQI3t7esLGxQb169XDy5Mlc7z9v3jxUrFgRtra28PLywsiRI/H06VMTpSUiIqK8kLVgbNiwASEhIZgyZQrOnj2LmjVrIiAgAPfu3TN6/3Xr1mH8+PGYMmUKrly5guXLl2PDhg349NNPTZyciIiIciPreTDmzJmDgQMHom/fvgCAJUuWICwsDKGhoRg/frzB/Y8ePYpGjRohMDAQAODt7Y3u3bvjxIkTJs1NRESGNm6+n+/HpqRq9T4uyHN17eSW78eSdGSbwUhPT8eZM2fg7+//PIxaDX9/fxw7dszoYxo2bIgzZ87odqPcunULu3btQps2bXL8OmlpaXj06JHejYiIiF4v2WYw7t+/j6ysLHh4eOiNe3h44OrVq0YfExgYiPv376Nx48YQQiAzMxODBw/OdRfJzJkzMW3aNEmzExERUe5kX+T5Kg4ePIgvv/wS33//Pc6ePYstW7YgLCwMM2bMyPExEyZMQFJSku4WExNjwsRERESFk2wzGG5ubrCwsEB8fLzeeHx8PDw9PY0+ZtKkSejZsycGDBgAAKhevTpSUlIwaNAgfPbZZ1CrDfuStbU1rK2tpf8GiIiIKEeyzWBYWVmhdu3aiIiI0I1ptVpERESgQYMGRh+TmppqUCIsLCwAAEKI1xeWiIiIXomsR5GEhISgd+/eqFOnDurWrYt58+YhJSVFd1RJr169ULJkScycORMA0K5dO8yZMwe+vr6oV68eoqKiMGnSJLRr105XNIiIiEh+shaMbt26ISEhAZMnT0ZcXBxq1aqFPXv26BZ+RkdH681YTJw4ESqVChMnTsQ///wDjUaDdu3a4YsvvpDrWyAiIiIjZC0YABAUFISgoCCj2w4ePKj3uaWlJaZMmYIpU6aYIBkRERHll1kdRUJERETmgQWDiIiIJMeCQURERJJjwSAiIiLJsWAQERGR5GQ/ioSIiAqn7VsmIzklEQCQkvxAN56S/ABrVwfBwd4VHTpOlyseFRALBhERySI5JRHJjxMMxoXQGh0n88JdJERERCQ5zmAQEZEsHOxddR+npvwLIbQAAJVKDTv7onrbyfywYBARkSy4vuLNxl0kREREJDkWDCIiIpIcCwYRERFJjgWDiIiIJMeCQURERJJjwSAiIiLJsWAQERGR5FgwiIiISHIsGERERCQ5nsmTiOgN82jLPGhTkqBNfqgb0yY/xMPV06C2d4ZTxxGyZaPCgwWDiOgNo01JgvZxov6g0BqOEb1G3EVCREREkuMMBhHRG0Zt7wwA0KY8Av7/CqVQqaG2d9JtI3rdWDCIiN4wXGNBSsBdJERERCS5fBWMU6dO4cSJEwbjJ06cwOnTpwscioiIiMxbvgrG0KFDERMTYzD+zz//YOjQoQUORUREROYtXwXj8uXL8PPzMxj39fXF5cuXCxyKiIiIzFu+Coa1tTXi4+MNxmNjY2FpyXWjREREhV2+CkbLli0xYcIEJCUl6cYePnyITz/9FO+9955k4YiIiMg85Wu64ZtvvsE777yDMmXKwNfXFwBw/vx5eHh4YPXq1ZIGJCIiIvOTr4JRsmRJXLhwAWvXrkVkZCRsbW3Rt29fdO/eHUWKFJE6IxEREZmZfC+YsLe3x6BBg6TMQkRERG+IfBWMVatW5bq9V69e+QpDREREb4Z8FYzg4GC9zzMyMpCamgorKyvY2dmxYBARERVy+TqK5N9//9W7JScn49q1a2jcuDF+/vlnqTMSERGRmZHsWiQVKlTArFmzDGY3iIiIqPCR9GJnlpaWuHv3rpRPSURERGYoX2swfv31V73PhRCIjY3FwoUL0ahRI0mCERERkfnKV8H44IMP9D5XqVTQaDRo3rw5vv32WylyERERkRnLV8HQarVS5yAiIqI3iKRrMIiIiIiAApzJ8++//8avv/6K6OhopKen622bM2dOgYMREREpRXBwMBISEpCYmIisrCwAgIWFBVxdXaHRaDB//nyZEypPvgpGREQE2rdvDx8fH1y9ehXVqlXDX3/9BSEE/Pz8pM5IREQkq4SEBMTHx+uNabVagzF6Ll+7SCZMmIDRo0fj4sWLsLGxwebNmxETE4OmTZuiS5cuUmckIiIiM5OvgnHlyhXd6cAtLS3x5MkTODg4YPr06fjqq68kDUhERCQ3jUYDDw8PqNXPXzbVajU8PDyg0WhkTKZc+dpFYm9vr1t3Ubx4cdy8eRNVq1YFANy/f1+6dERERArwbI1FYGCgbreIRqPBunXr5IylaPkqGPXr18fhw4dRuXJltGnTBqNGjcLFixexZcsW1K9fX+qMREREZGbyVTDmzJmD5ORkAMC0adOQnJyMDRs2oEKFCjyChIiIiF6tYNy6dQs+Pj7w8fHRjdnb22PJkiWSByMiIiLz9UqLPGvUqIFq1arh008/xYkTJ15XJiIiIjJzr1Qw7t+/j5kzZ+LevXvo0KEDihcvjoEDB2LHjh14+vTp68pIREREZuaVCoaNjQ3atWuHZcuWITY2Fps3b0axYsUwbtw4uLm54YMPPkBoaCgSEhJeV14iIiIyA/m+FolKpULDhg0xa9YsXL58GefOnUOTJk2wYsUKlCpVCosWLZIyJxEREZmRfB1F8vvvv6Nhw4awtHz+8AoVKiA4OBhvv/02qlatisTERMlCEhERkXnJV8Fo1qwZYmNj4e7urjeelJSEZs2aISsrC8WKFZMkIBEREZmffO0iEUJApVIZjD948AD29vYFDkVERETm7ZVmMDp27Agge/1Fnz59YG1trduWlZWFCxcuoGHDhtImJCIiktDV7/N/BdSMx1l6HxfkuSp94pHvx5qDVyoYzs7OALJnMBwdHWFra6vbZmVlhfr162PgwIHSJiQiIiKz80oF46effgIAeHt7Y/To0dwdQkREREblaw3G2LFj9dZg3LlzB/PmzcPevXslC0ZERETmK18Fo0OHDli1ahUA4OHDh6hbty6+/fZbdOjQAYsXL5Y0IBEREZmffBWMs2fPokmTJgCATZs2wdPTE3fu3MGqVauwYMECSQMSERGR+clXwUhNTYWjoyMAYO/evejYsSPUajXq16+PO3fuSBqQiIiIzE++Ckb58uWxbds2xMTEIDw8HC1btgQA3Lt3D05OTpIGJCIiIvOTr4IxefJkjB49Gt7e3qhbty4aNGgAIHs2w9fXV9KAREREZH7ydarwzp07o3HjxoiNjUXNmjV14y1atMCHH34oWTgiIiIyT/kqGADg6emJ5ORk7Nu3D++88w5sbW3x9ttvGz2FOBERkTn79uBUPHySiIdPnl/I8+GTREzaPRwutq4Y9e5U+cIpVL52kTx48AAtWrTAW2+9hTZt2iA2NhYA0L9/f4waNUrSgERERHJ7+CQRian3oRVa3ZhWaJGYel+vdNBz+SoYI0eORJEiRRAdHQ07OzvdeLdu3bBnzx7JwhEREZF5ytcukr179yI8PBylSpXSG69QoQIPUyUiojeOi60rAODR04e6WQy1Sg0nGxfdNtKXr4KRkpKiN3PxTGJiot4VVvNq0aJF+PrrrxEXF4eaNWviu+++Q926dY3e991338WhQ4cMxtu0aYOwsLBX/tpEREQvwzUWry5fu0iaNGmiO1U4kH35dq1Wi9mzZ6NZs2av9FwbNmxASEgIpkyZgrNnz6JmzZoICAjAvXv3jN5/y5YtiI2N1d0uXboECwsLdOnSJT/fChEREb0G+ZrBmD17Nlq0aIHTp08jPT0dY8eOxZ9//onExEQcOXLklZ5rzpw5GDhwIPr27QsAWLJkCcLCwhAaGorx48cb3N/VVX8qav369bCzs2PBICIiUpB8zWA4OTnhypUraNy4MTp06ICUlBR07NgR586dQ5EiRfL8POnp6Thz5gz8/f2fB1Kr4e/vj2PHjuXpOZYvX46PPvqIl44nIiJSkHzNYJQtWxaxsbH47LPP9MYfPHiAUqVKISsrK0/Pc//+fWRlZcHDw0Nv3MPDA1evXn3p40+ePIlLly5h+fLlOd4nLS0NaWlpus8fPXqUp2xERETmJjg4GAkJCUhMTNS9FltYWMDV1RUajQbz5883WZZ8zWAIIYyOJycnw8bGpkCBXsXy5ctRvXr1HBeEAsDMmTPh7Oysu3l5eZksHxERkSklJCQgPj4eGRkZ0Gq10Gq1yMjIQHx8PBISEkya5ZVmMEJCQgBkL+qcPHmy3pEkWVlZOHHiBGrVqpXn53Nzc4OFhQXi4+P1xuPj4+Hp6ZnrY1NSUrB+/XpMnz491/tNmDBBlxvInsFgySAiInq9XqlgnDt3DkD2DMbFixdhZWWl22ZlZYWaNWti9OjReX4+Kysr1K5dGxEREfjggw8AAFqtFhEREQgKCsr1sb/88gvS0tLQo0ePXO9nbW2dr0NniYiIzI1GowGQPZOh1f7/+TrUamg0Gt02U3mlgnHgwAEAQN++fTF//nxJLs0eEhKC3r17o06dOqhbty7mzZuHlJQU3VElvXr1QsmSJTFz5ky9xy1fvhwffPABihUrVuAMREREb4JnaywCAwN1ewc0Gg3WrVtn8iz5WuT5008/SRagW7duSEhIwOTJkxEXF4datWphz549uoWf0dHRUKv1l4pcu3YNhw8fxt69eyXLQURERNLJ99VUpRQUFJTjLpGDBw8ajFWsWDHHhaZEREQkv3wdRUJERESUGxYMIiIikhwLBhEREUmOBYOIiIgkx4JBREREkmPBICIiIsmxYBAREZHkWDCIiIhIciwYREREJDkWDCIiIpIcCwYRERFJjgWDiIiIJMeCQURERJJjwSAiIiLJsWAQERGR5FgwiIiISHIsGERERCQ5FgwiIiKSHAsGERERSY4Fg4iIiCTHgkFERESSY8EgIiIiybFgEBERkeRYMIiIiEhyLBhEREQkORYMIiIikhwLBhEREUmOBYOIiIgkx4JBREREkmPBICIiIsmxYBAREZHkWDCIiIhIciwYREREJDkWDCIiIpIcCwYRERFJjgWDiIiIJMeCQURERJJjwSAiIiLJsWAQERGR5FgwiIiISHIsGERERCQ5FgwiIiKSnKXcAYiIiMhQ/LyT+X5s1qM0vY8L8lweI+rm63GcwSAiIiLJsWAQERGR5FgwiIiISHIsGERERCQ5FgwiIiKSHAsGERERSY4Fg4iIiCTHgkFERESSY8EgIiIiybFgEBERkeRYMIiIiEhyLBhEREQkORYMIiIikhwLBhEREUmOBYOIiIgkx4JBREREkmPBICIiIsmxYBAREZHkWDCIiIhIciwYREREJDkWDCIiIpIcCwYRERFJjgWDiIiIJMeCQURERJJjwSAiIiLJsWAQERGR5FgwiIiISHIsGERERCQ5FgwiIiKSnOwFY9GiRfD29oaNjQ3q1auHkydP5nr/hw8fYujQoShevDisra3x1ltvYdeuXSZKS0RERHlhKecX37BhA0JCQrBkyRLUq1cP8+bNQ0BAAK5duwZ3d3eD+6enp+O9996Du7s7Nm3ahJIlS+LOnTtwcXExfXgiIiLKkawFY86cORg4cCD69u0LAFiyZAnCwsIQGhqK8ePHG9w/NDQUiYmJOHr0KIoUKQIA8Pb2NmVkIiIiygPZdpGkp6fjzJkz8Pf3fx5GrYa/vz+OHTtm9DG//vorGjRogKFDh8LDwwPVqlXDl19+iaysrBy/TlpaGh49eqR3IyIiotdLtoJx//59ZGVlwcPDQ2/cw8MDcXFxRh9z69YtbNq0CVlZWdi1axcmTZqEb7/9Fp9//nmOX2fmzJlwdnbW3by8vCT9PoiIiMiQ7Is8X4VWq4W7uzt++OEH1K5dG926dcNnn32GJUuW5PiYCRMmICkpSXeLiYkxYWIiIqLCSbY1GG5ubrCwsEB8fLzeeHx8PDw9PY0+pnjx4ihSpAgsLCx0Y5UrV0ZcXBzS09NhZWVl8Bhra2tYW1tLG56IiIhyJdsMhpWVFWrXro2IiAjdmFarRUREBBo0aGD0MY0aNUJUVBS0Wq1u7Pr16yhevLjRckFERETykHUXSUhICH788UesXLkSV65cwZAhQ5CSkqI7qqRXr16YMGGC7v5DhgxBYmIigoODcf36dYSFheHLL7/E0KFD5foWiIiIyAhZD1Pt1q0bEhISMHnyZMTFxaFWrVrYs2ePbuFndHQ01OrnHcjLywvh4eEYOXIkatSogZIlSyI4OBjjxo2T61sgIiIiI2QtGAAQFBSEoKAgo9sOHjxoMNagQQMcP378NaciIiKigjCro0iIiIjIPLBgEBERkeRYMIiIiEhyLBhEREQkOdkXeRIREZE0ph5egsSnSUh8mqQbS3yahOG/fQVXG2dMbTzYZFlYMIiIiN4QiU+TcP/JQ70xrRAGY6bAXSREREQkOc5gEBERvSFcbZwBAA/THkMrsi+roVap4WLtqNtmKiwYREREbwhTrrF4Ge4iISIiIsmxYBAREZHkWDCIiIhIciwYREREJDkWDCIiIpIcCwYRERFJjgWDiIiIJMeCQURERJJjwSAiIiLJsWAQERGR5FgwiIiISHIsGERERCQ5FgwiIiKSHAsGERERSY4Fg4iIiCTHgkFERESSY8EgIiIiybFgEBERkeRYMIiIiEhyLBhEREQkORYMIiIikhwLBhEREUmOBYOIiIgkx4JBREREkmPBICIiIsmxYBAREZHkWDCIiIhIciwYREREJDkWDCIiIpIcCwYRERFJjgWDiIiIJMeCQURERJJjwSAiIiLJsWAQERGR5FgwiIiISHIsGERERCQ5FgwiIiKSHAsGERERSY4Fg4iIiCTHgkFERESSY8EgIiIiybFgEBERkeRYMIiIiEhyLBhEREQkORYMIiIikhwLBhEREUmOBYOIiIgkx4JBREREkmPBICIiIsmxYBAREZHkWDCIiIhIciwYREREJDkWDCIiIpIcCwYRERFJjgWDiIiIJMeCQURERJJjwSAiIiLJsWAQERGR5FgwiIiISHIsGERERCQ5FgwiIiKSHAsGERERSU4RBWPRokXw9vaGjY0N6tWrh5MnT+Z43xUrVkClUundbGxsTJiWiIiIXkb2grFhwwaEhIRgypQpOHv2LGrWrImAgADcu3cvx8c4OTkhNjZWd7tz544JExMREdHLyF4w5syZg4EDB6Jv376oUqUKlixZAjs7O4SGhub4GJVKBU9PT93Nw8PDhImJiIjoZWQtGOnp6Thz5gz8/f11Y2q1Gv7+/jh27FiOj0tOTkaZMmXg5eWFDh064M8//zRFXCIiIsojWQvG/fv3kZWVZTAD4eHhgbi4OKOPqVixIkJDQ7F9+3asWbMGWq0WDRs2xN9//230/mlpaXj06JHejYiIiF4v2XeRvKoGDRqgV69eqFWrFpo2bYotW7ZAo9Fg6dKlRu8/c+ZMODs7625eXl4mTkxERFT4yFow3NzcYGFhgfj4eL3x+Ph4eHp65uk5ihQpAl9fX0RFRRndPmHCBCQlJeluMTExBc5NREREuZO1YFhZWaF27dqIiIjQjWm1WkRERKBBgwZ5eo6srCxcvHgRxYsXN7rd2toaTk5OejciIiJ6vSzlDhASEoLevXujTp06qFu3LubNm4eUlBT07dsXANCrVy+ULFkSM2fOBABMnz4d9evXR/ny5fHw4UN8/fXXuHPnDgYMGCDnt0FEREQvkL1gdOvWDQkJCZg8eTLi4uJQq1Yt7NmzR7fwMzo6Gmr184mWf//9FwMHDkRcXByKFi2K2rVr4+jRo6hSpYpc3wIRERH9h+wFAwCCgoIQFBRkdNvBgwf1Pp87dy7mzp1rglRERESUX2Z3FAkREREpHwsGERERSY4Fg4iIiCTHgkFERESSY8EgIiIiybFgEBERkeRYMIiIiEhyLBhEREQkORYMIiIikhwLBhEREUmOBYOIiIgkx4JBREREkmPBICIiIsmxYBAREZHkWDCIiIhIciwYREREJDkWDCIiIpIcCwYRERFJjgWDiIiIJMeCQURERJJjwSAiIiLJsWAQERGR5FgwiIiISHIsGERERCQ5FgwiIiKSHAsGERERSY4Fg4iIiCTHgkFERESSY8EgIiIiybFgEBERkeRYMIiIiEhyLBhEREQkORYMIiIikhwLBhEREUnOUu4ApiaEAAA8evRIb/zxkydyxNFj/Z9Mxjx+kmGCJC/335/ff6U+yTRRkty9LOfTVPPICQCZqekmSJK7vOTMSJX//xLw8qwZqakmSpK7l+dMNlGS3OXl7z419bEJkrzco0dWuW5PfqKUnLa5bn/8VBl/97Yv/N0/+3fw7LU0NyqRl3u9Qf7++294eXnJHYOIiMhsxcTEoFSpUrnep9AVDK1Wi7t378LR0REqlUqS53z06BG8vLwQExMDJycnSZ7zdTGXrMwpPXPJypzSMpecgPlkLcw5hRB4/PgxSpQoAbU691UWhW4XiVqtfmnryi8nJydF/2N7kblkZU7pmUtW5pSWueQEzCdrYc3p7Oycp/txkScRERFJjgWDiIiIJMeCIQFra2tMmTIF1tbWckd5KXPJypzSM5eszCktc8kJmE9W5sybQrfIk4iIiF4/zmAQERGR5FgwiIiISHIsGERERCQ5FgwiIiKSHAsGERERSa7QncmzMLp69SoqVapkdFt4eDgCAgJMnOjNEBUVhZs3b+Kdd96Bra0thBCSnX6+MPrjjz+wdOlS3Lx5E5s2bULJkiWxevVqlC1bFo0bN5Y7HgAgIiICERERuHfvHrRard620NBQmVK93NOnT2FjYyN3jJcyl5yUN5zBKICEhIQct128eNGESXLn5+eHRYsW6Y2lpaUhKCgIHTp0kClVztLT03Ht2jVkZirjSqf/9eDBA/j7++Ott95CmzZtEBsbCwDo378/Ro0aJXM6Qzdv3sTEiRPRvXt33Lt3DwCwe/du/PnnnzIne27z5s0ICAiAra0tzp07h7S0NABAUlISvvzyS5nTZZs2bRpatmyJiIgI3L9/H//++6/eTWm0Wi1mzJiBkiVLwsHBAbdu3QIATJo0CcuXL5c53XPmknPFihVGxzMzMzFhwgTThsmFonIKyjcPDw+xc+dOg/Gvv/5a2NjYyJDIuA0bNghXV1fRunVrERcXJ86dOycqV64sKlasKE6ePCl3PJ2UlBTRr18/YWFhISwsLMTNmzeFEEIEBQWJmTNnypzuuZ49e4qAgAARExMjHBwcdDn37NkjqlSpInM6fQcPHhS2trbC399fWFlZ6bLOnDlTdOrUSeZ0z9WqVUusXLlSCCH0fqZnz54VHh4eckbT8fT0FKtWrZI7Rp5NmzZN+Pj4iDVr1ghbW1vdz3T9+vWifv36Mqd7zlxyOjo6is6dO4vExETd2NWrV4Wfn58oU6aMfMH+Q0k5WTAK4KuvvhLW1tZi8ODBIjU1Vfz999+iefPmQqPRiC1btsgdT09MTIzw9/cXxYoVEzY2NmLw4MEiJSVF7lh6hg8fLmrXri3++OMPYW9vr/tFs23bNlGrVi2Z0z3n4eEhzp8/L4TQfzG8efOmsLe3lzOagfr164tvv/1WCKGf9cSJE6JkyZJyRtNja2srbt++LYQw/JlaW1vLmOw5V1dXERUVJXeMPCtXrpz47bffhBD6P9MrV64IFxcXOaPpMZecUVFRon79+qJkyZJi7969YuHChcLOzk4EBgaKhw8fyh1PR0k5uQajAMaOHYv33nsPPXv2RI0aNZCYmIh69erhwoUL8PT0lDuegfT0dGRlZSErKwvFixdX3L7Obdu2YcOGDahfv77eWoaqVavi5s2bMibTl5KSAjs7O4PxxMRExZ06+OLFi1i3bp3BuLu7O+7fvy9DIuM8PT0RFRUFb29vvfHDhw/Dx8dHnlD/MWDAAKxbtw6TJk2SO0qe/PPPPyhfvrzBuFarRUZGhgyJjDOXnOXKlcORI0cwYsQItGrVChYWFli5ciW6d+8udzQ9SsrJNRgFVL58eVSrVg1//fUXHj16hG7duimuXKxfvx7Vq1eHs7Mzrl+/jrCwMPzwww9o0qSJbn+nEiQkJMDd3d1gPCUlRVGLJ5s0aYJVq1bpPlepVNBqtZg9ezaaNWsmYzJDLi4uujUiLzp37hxKliwpQyLjBg4ciODgYJw4cQIqlQp3797F2rVrMXr0aAwZMkTueACyFyDOmTMHTZs2xbBhwxASEqJ3U5oqVargjz/+MBjftGkTfH19ZUhknLnkBICwsDCsX78eDRo0gIuLC5YvX467d+/KHcuAYnKadL7kDXP48GHh7e0t/Pz8xOXLl8WPP/4oHB0dRdeuXfX2f8nNzs5OfP/993pjiYmJokuXLsLR0VGmVIaaNGkiFixYIITIniq9deuWECJ7DUZAQICc0fRcvHhRuLu7i1atWgkrKyvRuXNnUblyZeHh4aG4KfRRo0aJxo0bi9jYWOHo6Chu3LghDh8+LHx8fMTUqVPljqej1WrF559/Luzt7YVKpRIqlUrY2NiIiRMnyh1N5913383x1qxZM7njGdi2bZtwdnYWs2bNEnZ2duLrr78WAwYMEFZWVmLv3r1yx9Mxl5yDBg0S1tbW4ptvvhFarVbExsaK1q1bC1dXV7Fhwwa54+koKScLRgFYWVmJcePGifT0dN3Yi/u/lOLq1as5blPSorU//vhDODg4iMGDBwsbGxsRHBws3nvvPWFvby9Onz4tdzw9Dx8+FJ9//rno0qWLaN26tfjss8/E3bt35Y5lIC0tTQwYMEBYWloKlUolihQpItRqtejRo4fIzMyUO56BtLQ08eeff4oTJ06Ix48fyx3H7P3+++/C399faDQaYWtrKxo1aiTCw8PljmXAHHJWrVpVt/bqRQsXLlTU2isl5eTVVAvg0KFDaNq0qcG4VqvFF198oah9tZmZmTh48CBu3ryJwMBAODo64u7du3BycoKDg4Pc8XRu3ryJWbNmITIyEsnJyfDz88O4ceNQvXp1uaOZtejoaFy6dAnJycnw9fVFhQoV5I5EZFbS0tJyXGN17do1VKxY0cSJjFNSThYMCSj9hEt37txBq1atEB0djbS0NFy/fh0+Pj4IDg5GWloalixZIndEs/LTTz/BwcEBXbp00Rv/5ZdfkJqait69e8uUzHx9+OGHRv/PqFQq2NjYoHz58ggMDDT5L/GOHTtixYoVcHJyQseOHXO975YtW0yUiuRy8+ZN/PTTT7h58ybmz58Pd3d37N69G6VLl0bVqlXljqejlJw8iqQAHjx4gK5du+LAgQNQqVS4ceMGfHx80L9/f7i6uuKbb76ROyIAIDg4GHXq1EFkZCSKFSumG//www8xcOBAGZPp27VrFywsLAzOLBoeHg6tVovWrVvLlEzfzJkzsXTpUoNxd3d3DBo0SFEFI6fFhy++cHfo0AGurq4mTqbP2dkZ27Ztg4uLC2rXrg0AOHv2LB4+fIiWLVtiw4YN+OqrrxAREYFGjRqZNNez4uPs7GyyryuFokWLvrS09enTB3379pUh3XPmkvPQoUNo3bo1GjVqhN9//x1ffPEF3N3dERkZieXLl2PTpk2y5ntGUTlNukPmDWMuJ1xydXXVrcN4Meft27eFra2tnNH0VK9eXYSFhRmM7969W9SoUUOGRMZZW1vrztnwotu3byvqBGtCZC9MdHJyEvb29sLPz0/4+fkJBwcH4ezsLOrVqydcXFxE0aJFxZ9//ilrznHjxokhQ4aIrKws3VhWVpYICgoSEyZMEFqtVgwaNEg0atRIxpTmZc6cOaJYsWKiR48eYsGCBWLBggWiR48ews3NTXzxxRdiwIABwtraWvzwww/MmQfmck4ZJeVkwSgAcznhkouLi+4F5MWcf/zxh3B3d5czmh4bG5scX7jt7OxMHygHXl5eYvv27Qbj27ZtU9QvGiGEmDt3rujYsaNISkrSjT18+FB07txZzJs3T6SkpIgOHTqIli1byphSCDc3N3Ht2jWD8WvXrolixYoJIYS4cOGCcHZ2NnGy59atW5fjttGjR5swSd507NhRLF682GB8yZIlomPHjkIIIRYsWCCqVatm6mh6zCWnvb297si2/75RU8rJ4IRQVk6eB6MAzOWESy1btsS8efN0n6tUKiQnJ2PKlClo06aNfMH+w9nZ2eh5OaKiomBvby9DIuO6d++O4cOH48CBA7oTl+3fvx/BwcH46KOP5I6n5+uvv8aMGTPg5OSkG3N2dsbUqVMxe/Zs2NnZYfLkyThz5oyMKbMXIV+9etVg/OrVq8jKygIA2NjYyLq2aciQIdi9e7fB+MiRI7FmzRoZEuUuPDwc/v7+BuMtWrRAeHg4AKBNmzaynwvHXHKayzlllJSTBaMAzOWES99++y2OHDmCKlWq4OnTpwgMDIS3tzf++ecffPXVV3LH0+nQoQNGjBihd9bOqKgojBo1Cu3bt5cxmb4ZM2agXr16aNGiBWxtbWFra4uWLVuiefPmirkw1zNJSUm6C5y9KCEhAY8ePQKQ/QspPT3d1NH09OzZE/3798fcuXNx+PBhHD58GHPnzkX//v3Rq1cvANn7luVcSLd27Vp0794dhw8f1o0NGzYMGzduxIEDB2TLlRNXV1fs2LHDYHzHjh26NTcpKSlwdHQ0dTQ95pLzo48+wrhx4xAXF6f7XX/kyBGMHj1a929UCRSV06TzJW8YczrhUkZGhlizZo0YM2aMGDJkiPjxxx9Famqq3LH0PHz4UNSvX19YWloKb29v4e3tLSwtLUWzZs3Ev//+K3c8A9euXRMbN24UO3bsEH/99ZfccYwKDAwUZcuWFVu2bBExMTEiJiZGbNmyRfj4+IgePXoIIYT4+eefRe3atWXNmZmZKT7//HPh6empO9GWp6en+OKLL3Tn67hz546IiYmRNefatWtF0aJFxenTp8WQIUNEiRIljO7aUYIffvhBWFhYiHbt2okZM2aIGTNmiPbt2wtLS0uxbNkyIYQQ33zzjejatStz5oG5nFNGSTl5mGoBJSUlYeHChXrnbRg6dCiKFy8udzSzJITAvn37EBkZCVtbW9SoUQPvvPOO3LHMVnJyMkaOHIlVq1YhMzMTAGBpaYnevXtj7ty5sLe3x/nz5wEAtWrVki/oC57NrLy4W0dJvv/+e4SEhECj0eDAgQNGr6OhFEeOHMHChQtx7do1AEDFihUxbNgwNGzYUOZk+swlJwDExMTg4sWLij+njBJysmAUAitXroSbmxvatm0LIPsibT/88AOqVKmCn3/+GWXKlJE5ofn5+++/8euvvyI6Otpg98KcOXNkSpWz5ORk3T5sHx8fRZ1cTclyOsz3l19+gZ+fH8qVK6cbU+LfO70+WVlZuHjxIsqUKYOiRYvKHSdHcuZkwSiAPXv2wMHBAY0bNwYALFq0CD/++COqVKmCRYsWKeYfXcWKFbF48WI0b94cx44dQ4sWLTBv3jzs3LkTlpaWijpBUEpKCg4dOmT0hXv48OEypdIXERGB9u3bw8fHB1evXtVd7E4IAT8/P+zfv1/uiGZp06ZN2Lhxo9G/+7Nnz8qSKa9rqVQqlaL/3p8+fWrwM1XiDJGSc44YMQLVq1dH//79kZWVhaZNm+Lo0aOws7PDzp078e6778odEYDCcpp0h8wbplq1arrzNly4cEFYWVmJCRMmiPr164s+ffrInO45W1tbcefOHSGEEGPHjhU9e/YUQghx6dIl4ebmJmc0PWfPnhWenp7CyclJWFhYCI1GI1QqlbC3txdly5aVO57O22+/LSZPniyEeH4Y2OPHj0X79u0NLiqnBKdOnRJjxowR3bp1Ex9++KHeTSnmz58vHBwcRFBQkLCyshIff/yx8Pf3F87OzuLTTz+VO55ZSklJEUOHDhUajUao1WqDm1KYS86SJUuKU6dOCSGE2Lp1qyhevLi4du2amDhxomjYsKHM6Z5TUk4WjAKwt7fXnbdhypQpolOnTkIIIc6cOSM8PDxkTKZPo9GIs2fPCiGEqFWrlu4CZ1FRUYo6X0fTpk3FwIEDRVZWlu6FOzo6Wrzzzjti8+bNcsfTcXBw0C3idXFxEZcuXRJCCHH+/HlRpkwZGZMZ+vnnn0WRIkXE+++/L6ysrMT7778v3nrrLeHs7KyoElyxYkXdeSZePHZ/0qRJYujQoXJGy1FSUpLYunWruHLlitxRjPrkk09E5cqVxaZNm4Stra0IDQ0VM2bMEKVKlRJr1qyRO56OueS0trbWLTIeOHCgCA4OFkIIcevWLUVdlVpJOVkwCuDFMyA2atRILF26VAihvDNkBgYGCj8/P9G/f39hZ2cn7t+/L4QQYvv27aJq1aoyp3vO2dlZd8ZRZ2dncfnyZSGEEMePHxcVK1aUM5oeDw8PXbbKlSvrTrp1/vx5RRU2IbLPjrpw4UIhxPMXbq1WKwYOHKibhVECW1tb3ZE4Go1GdwK769evC1dXVzmj6XTp0kV89913QgghUlNTRYUKFUSRIkWEpaWl2LRpk8zpDHl5eYkDBw4IIYRwdHQUN27cEEJkX0G5devWMibTZy45S5cuLcLDw0VmZqbw8vISO3fuFEJkzwS7uLjInO45JeXkeTAKoHHjxggJCcGMGTNw8uRJ3SLK69evo1SpUjKne27RokVo0KABEhISsHnzZt31SM6cOYPu3bvLnO65IkWKQK3O/ifp7u6O6OhoANknhoqJiZEzmp769evrzoXQpk0bjBo1Cl988QX69euH+vXry5xO382bN3X/Lq2srJCSkgKVSoWRI0fihx9+kDndc56enkhMTAQAlC5dGsePHwcA3L59G0Ihy8R+//13NGnSBACwdetWCCHw8OFDLFiwAJ9//rnM6QwlJibCx8cHQPY6hmc/38aNG+P333+XM5oec8nZt29fdO3aFdWqVYNKpdKdHOzEiROoVKmSzOmeU1JOXuysABYuXIhPPvkEmzZtwuLFi3VnSdu9ezdatWolc7rnXFxcsHDhQoPxadOm6X3+ySefYPr06XBzczNVND2+vr44deoUKlSogKZNm2Ly5Mm4f/8+Vq9ejWrVqsmSyZg5c+YgOTkZQPbPMDk5GRs2bECFChUUdyRB0aJF8fjxYwBAyZIlcenSJVSvXh0PHz5EamqqzOmea968OX799Vf4+vqib9++GDlyJDZt2oTTp0+/9CqmppKUlKQ78dOePXvQqVMn2NnZoW3bthgzZozM6Qz5+Pjg9u3bKF26NCpVqoSNGzeibt262LFjB1xcXOSOp2MuOadOnYpq1aohJiYGXbp00Z2t2cLCAuPHj5c53XOKymnS+ZJCaubMmYo8UdR/OTo66vZ9y+HUqVNi//79Qggh4uPjRUBAgHB0dBR+fn66KXO5ZWZmikOHDpnF36cQQnTv3l134aPp06cLjUYjBgwYIMqUKaOoRZ5ZWVkiIyND9/nPP/8shg0bJhYsWCDS0tJkTPZchQoVxIYNG0RycrLQaDQiIiJCCJG9a+zZ9VKUZM6cOWL+/PlCCCH27dsnbGxshLW1tVCr1WLevHkyp3vOXHLmVbVq1UR0dLTcMV7KFDl5mKoJODk54fz587ppQKVydHREZGSkLDmFEIiJiYG7uztsbGxM/vVfhY2NDa5cuYKyZcvKHeWlEhMT8fTpU5QoUUJ3GvujR4+iQoUKmDhxomIOpc4rOWfZvv/+ewQHB8PBwQFlypTB2bNnoVar8d1332HLli2KPF34i+7cuYMzZ86gfPnyqFGjhtxxcmQuOXMi5+/RV2GKnCwYJsB/cC+n1WphY2ODP//8U7FnxnumTp06+Oqrr9CiRQu5o0hm1qxZGDx4sKKmpI2Ru6yfOXMG0dHReO+993QnKwsLC4OLiwsaNWokS6aCql69Onbt2gUvLy+5o+TKXHLy9/1zXORJiqBWq1GhQgU8ePBA7igv9fnnn2P06NHYuXMnYmNj8ejRI72bOfryyy91i+uUTO73Q7Vr18aHH36odybUtm3b6pULJycn2a/8+Sr++usvZGRkyB3jpcwlJz3HRZ6kGLNmzcKYMWOwePFiRS3q/K9nl7hv37693uXDhRBQqVS6y4ubE7lfuN8k/FkSZWPBIMXo1asXUlNTUbNmTVhZWcHW1lZvu1LeYSt9XzsRkRKwYBQC0dHR8PLy0nu3DTxfWFm6dGkAQI8ePWQ97/+8efNk+9qvomnTpnm6n9yH/RIRyYkFwwSaNGli8G7clMqWLYvY2Fi4u7vrjScmJqJs2bK6Kf3FixfLEU+nd+/eebqfuSxIXLNmDUaPHs2CQVSILF26FB4eHnLHeClT5GTBKIBdu3bBwsICAQEBeuPh4eHQarVo3bq17n5yerY24L+Sk5MVf0ioMV9++SW6du2q+ILBffF5Zy6zbHlh7P8ambf79+8jNDQUx44dQ1xcHIDss882bNgQffr0gUaj0d03MDBQrpg6ebkqtSlysmAUwPjx4zFr1iyDcSEExo8frysYcgkJCQGQ/Qtv0qRJsLOz023LysrCiRMnUKtWLZnS5R9fuKXHWTbpmNu/T77jzt2pU6cQEBAAOzs7+Pv746233gIAxMfHY8GCBZg1axbCw8NRp04dk2cz5ty5c2jTpg1SU1ORkpICV1dX3L9/H3Z2dnB3d9cVDFNgwSiAGzduoEqVKgbjlSpVQlRUlAyJ9J07dw5A9i+8ixcvwsrKSrfNysoKNWvWxOjRo+WKRybAWTbpXLp0Kcejm7Zt24YPPvgAQPalAp5dNkBuSnknmxNzmBkYNmwYunTpgiVLlhidYRs8eDCGDRuGY8eOyZLvv0aOHIl27dphyZIlcHZ2xvHjx1GkSBH06NEDwcHBJs3CE20VgKenJ9atW4fmzZvrjf/2228IDAzEvXv3ZEqmr2/fvliwYAEcHR3ljiIJnsgm72rUqIFZs2bpDq19Zs+ePRg3bhwiIyNlSpbt2Szb/PnzMXDgQKOzbBYWFjhy5IhcEXVKliyJw4cPG5zBdfPmzejVqxdSUlJkSmbcy97Jyn2ujv/ODDybnYiPj0dERARSU1MVMTNga2uLc+fO5XihsKtXr8LX1xdPnjwxcTLjXFxccOLECVSsWBEuLi44duwYKleujBMnTqB37964evWqybJwBqMAOnTogBEjRmDr1q0oV64cACAqKgqjRo1C+/btZU6XLSMjA6tXr8aoUaMUfW4Jej04yyadAQMGwN/fH0eOHIGnpycAYMOGDejXrx9WrFghbzgjlPRO1hhzmRnw9PTEyZMncywYJ0+eVNQuJmNXpa5cubI8V6V+rVc6ecM9fPhQ1K9fX1haWgpvb2/h7e0tLC0tRbNmzRR1MayyZcsq5mJhUnBwcJD1omx37twRWq3WYFyr1Yo7d+7oPh88eLBISEgwZTQDHh4euotyvWjfvn1Co9HIkMi4Pn36iEePHskd46WCgoJE1apVxYMHD8TatWuFra2t2LRpk9yxjHJ2dhZXr17VfXz58mUhhBDHjx8XFStWlDOaEEIIGxsbceXKlRy3X7lyRdjY2JgwkXELFy4U1tbWYvjw4WL79u3i+PHj4vjx42L79u1i+PDhwtbWVixatEjumDrvvfeeWLt2rRBCiAEDBoi6deuKNWvWiICAAFG3bl2TZmHBKCCtVivCw8PF7NmzxXfffScOHTokdyQDy5YtE23atBEPHjyQO4okWrduLe7evSvb11er1SI+Pt5g/P79+0KtVsuQKGeDBg0S1atXF1FRUbqxGzduiBo1aoj+/fvLmOy59PR0YWFhIS5evCh3lDwJDAwUFSpUEHZ2dmLbtm1yx8mRm5ubuH79uhAi+0qwe/bsEUJkv3Db2dnJGU0IIYS3t7dYuXJljttXrlwpypQpY7pAuVi/fr2oV6+esLS0FCqVSqhUKmFpaSnq1asnNmzYIHc8PUq6KjXXYBQCvr6+iIqKQkZGBsqUKQN7e3u97WfPnpUpmb68LkiUm1qtRnx8vN4CNCD7KpBVqlRR1L74pKQktGrVCqdPn0apUqUAAH///TeaNGmCLVu2KOZQXx8fH2zduhU1a9aUO4qeX3/91WAsIyMDI0eORMuWLfV2hSplt+gzLVu2RJ8+fRAYGIiBAwfiwoULGD58OFavXo1///0XJ06ckDXfokWLMGrUKHz88cdo0aKFwRqMH3/8Ed988w0++eQTWXO+KCMjA/fv3wcAuLm5oUiRIjInUjYWjFe0YMECDBo0CDY2NliwYEGu9zXl4UC5mTZtWq7bp0yZYqIkueOCxNdDCIF9+/YhMjIStra2qFGjBt555x25Y+lZvnw5tmzZgtWrV8PV1VXuODrP9mW/jBKvQXP69Gk8fvwYzZo1w71799CrVy8cPXoUFSpUQGhoqCLK3IYNGzB37lycOXNG9/OzsLBA7dq1ERISgq5du8qc0FBaWhoAwNraWuYkyseC8YrKli2L06dPo1ixYgaryV+kUqlkX6VtbmxtbXHlyhV4e3vrjf/111+oWrWq7DMDzZo1AwAcOnQIDRo0MFiQ6O3tjdGjRyv+cvNKZC6zbPR6KH1mYN++fZg7dy6OHTumu2Kyk5MTGjRogJCQEPj7+8uaz9fXN88neDPl/yUeRfKKbt++bfRjpXv48CE2bdqEmzdvYsyYMXB1dcXZs2fh4eGhmGP2nZ2dcevWLYOCERUVZfCCI4dnFzlT+mG/5jjL9uwcElQ4FSlSRDdzpbRysXLlSgwYMACdO3fG3Llz9Xbl7N27F23atMHy5cvRs2dP2TIq9f8PZzAKgQsXLsDf3x/Ozs7466+/cO3aNfj4+GDixImIjo7GqlWr5I4IAPj4449x7Ngxg8N+O3XqhLfffhvLli2TOWH2Oy1bW1ucP39esYf9cpZNWsOGDUPXrl3RpEkTuaO8lFLfyeZE6TMDAPDWW28hODgYQ4cONbr9+++/x9y5c3Hjxg0TJ1M+FowCEEJg06ZNOHDgAO7duwetVqu3fcuWLTIl0+fv7w8/Pz/Mnj1b7+RPR48eRWBgIP766y+5IwLggsTCTqmzbGq1GiqVCuXKlUP//v3Ru3dv3XkwlOZl661eJPfaqxdnBgICAgxmBjZt2iT7zAAA2NjYIDIyEhUrVjS6/dq1a6hVq5ZiTrSlJCwYBRAcHIylS5eiWbNm8PDwMHjn8NNPP8mUTJ+zszPOnj2LcuXK6RWMO3fuoGLFinj69KncEXW4ILFwUvIsm1qtxr59+7Bjxw6sXbsWSUlJaN26NQYOHIg2bdrkeSEo6TOXmYHatWujRYsWmD17ttHt48aNw2+//YYzZ86YONlzrq6uuH79Otzc3FC0aNFcZ7ESExNNlosFowBcXV2xZs0ag6MelMbd3R3h4eHw9fXVKxj79u1Dv379TH92NzNnTgsSOctWcGq1GnFxcXB3d0dGRga2bt2K0NBQ/Pbbb/Dw8ECfPn3Qt29flC9fXraM5shcZgYOHjyI999/Hz4+PkZPaX7r1i2EhYXJ+kZo5cqV+Oijj2BtbY2VK1fmet/evXubKBUXeRaIs7Oz4q+HAWQfnz99+nRs3LgRQPa+9+joaIwbNw6dOnWSNRsXJL5eI0aMyHWWTSlOnTqFpUuXGoyXLFlSdxEsJShSpAi6du2Krl27Ijo6GqGhoVixYgVmzZqliMNUlfpO1piqVati+fLlOc4MhIaGGj3Nvam9++67uHTpEhYvXozjx4/rXZStdevWGDx4sMHCdFN7sTSYskC8DGcwCmDlypXYs2cPQkNDZb3U9cskJSWhc+fOuuPiS5Qogbi4ODRo0AC7du2S9QgNLkh8vTjLVnAvzmAYI4TAb7/9hvfee8/EyQwp9Z2sMeYwM2CusrKysHXrVly5cgUAUKVKFXTo0AGWlqadU2DBKIAnT57gww8/xJEjR+Dt7W1weJWSpsoB4PDhw7hw4QKSk5Ph5+eniBXa5kqpCxL/q2zZsti9e3eOF2pSigEDBuDBgwfYuHEjXF1dceHCBVhYWOCDDz7AO++8g3nz5smW7cUSTNL666+/jM4MNGjQQBEzAy/KzMzEn3/+qctZvHhxVK5cWXGH1f75559o37494uLidLufrl+/Do1Ggx07dpj06DcWjALo2rUrDhw4gM6dOxudfpZ7lTa9HkpekPhfnGUr3JTyTtacabVaTJ48GYsWLUJSUpLeNmdnZwQFBWHatGmKWezboEEDaDQarFy5EkWLFgUA/Pvvv+jTpw8SEhJw9OhRk2VhwSgAe3t7hIeHo3HjxnJHeamIiAjMnTtX94umcuXKGDFihKJmMbggUXqcZSu8lPRONjdKnxkYO3YsVqxYgRkzZhg9nHbSpEno06cPvvrqK5mTZrO1tcXp06dRtWpVvfFLly7h7bffNumiWdbYAvDy8oKTk5PcMV7q+++/R3BwMDp37ozg4GAAwPHjx9GmTRvMnTs3x8PETI0LEqXXu3dvnDlzBj169FD0z/SZxo0bm0Vhf9HNmzcxcOBA7N+/X+4oegYMGICqVavi9OnTBu9kBw0aZNJ3ssaYy8zAqlWrsHr1aoOLMHp7e2PQoEEoU6YMevXqpZiC8dZbbyE+Pt6gYNy7d8/0RzqZ5qKtb6adO3eKgIAAcfv2bbmj5KpkyZLiu+++MxhfuHChKFGihAyJjCtatKgICwuTO8ZLaTQacfbsWSGEEA4ODuLmzZtCCCH27t0rSpUqJWc0A3Z2duKPP/6QO0ae/Pbbb6Jt27bCx8dH+Pj4iLZt24p9+/bJHeulzp8/L9RqtdwxDNjY2IhLly4ZjF+8eFHY2NjIkEjfmDFjhEajEUuWLBG3b98WqampIjU1Vdy+fVssXbpUuLu7i7Fjx8odU9jZ2YkLFy7kuD0yMlLY29ubMFHuwsLCRNWqVcUvv/wiYmJiRExMjPjll19E9erVRVhYmEhKStLdXjfuIimAokWLIjU1FZmZmbCzszOY0pP7MLBnHBwccP78eYP2euPGDfj6+iI5OVmmZPq4IFF6lSpVwsaNG1GjRg25o+TqxVm2Bg0aAMieZdu0aZPss2wvO3z6n3/+wTfffKOIw1RfVLNmTcydOxfNmzfXG9+/fz+Cg4Nx8eJFmZJl8/T0xMqVKw1mBp4JDw9Hr169EB8fb+Jk+tq2bYvMzEysXbsWbm5uetvu37+Pnj17wsLCAjt37pQpob4XZ3yezVg+e5l/8XNTXAGYBaMAlH4Y2DOBgYHw9fXFmDFj9Ma/+eYbnD59GuvXr5cpmT4uSJReWFgYvvvuOyxZskRRK/L/q1SpUhg/fjyCgoL0xhctWoQvv/wS//zzj0zJsn9hFy9eXO/quS9KT09HXFyc4grGrl27MHbsWEydOhX169cHkF3apk+fjlmzZuntipJjV6+9vT2OHz+O6tWrG91+4cIFNGzYUPY3QDExMWjTpg2uXr2K6tWr663BuHjxIqpUqYKdO3fCy8tL1pzPHDp0KM/3bdq06WtMwoJRKHz++ef45ptv0KhRI713h0eOHMGoUaP0frnIeTIrLkiUHmfZCq5s2bL46quv0LVrV6Pbz58/j9q1ayuuYCjpnawx5jQzoNVqER4ebvRw2pYtW8q+TkSpWDBe0aNHj3QvyM+u/pcTpSwAze0EVi+S+2RWPOxXepxlK7jOnTujXLlyOS7ii4yMhK+vr8FRT3JT0jtZY8xtZsDcpKamIjo6Gunp6XrjptxdyoLxiiwsLBAbGwt3d3fdVRb/S853BeaMh/0WXkqeZbt8+TJSU1NRp04do9szMjJw9+5dlClTxqS53gTmNDNw8uRJHDt2TC9nw4YN8fbbb8ucTF9CQgL69u2L3bt3G91uytclFoxXdOjQITRq1AiWlpYvfYcgx7sCY54+fQobGxuj22JjY1G8eHETJzKOCxKlwVk2epES3smas3v37qFTp044cuQISpcurTfTEh0djUaNGmHz5s05nkre1P73v//hzp07mDdvHt59911s3boV8fHx+Pzzz/Htt9+ibdu2pgvz2o9TecM9efJEnDhxQuzYsUNs375d76YUlStXFufOnTMY37Rpk3BzczN9oBzwsF9pqNVqER8fL4QQQqVSCbVabXB7Nk55t27duhy3jR492oRJ8ubevXuibdu2Rv/+lfR3f+LECTFv3jwxfvx4MX78eDFv3jxx8uRJuWPpdOrUSTRo0EBcvXrVYNvVq1dFw4YNRefOnWVIZpynp6c4ceKEEEIIR0dHce3aNSGEENu3bxeNGjUyaRYWjALYvXu30Gg0QqVSGdyU9B94yJAhwtraWsyaNUsIIURycrLo3bu3sLW1FXPmzJE53XMuLi7CyspKqNVq4eDgIIoWLap3Uwp7e3tx48YNg/Hr168r4nj4gwcPioyMDN3Hud2U4smTJzluu3v3rgmT5MzZ2Vns2rXLYHzEiBHC09NThkS5CwwMFI0aNRKnTp0S9vb2Yu/evWL16tWiYsWKYufOnXLHE/Hx8aJx48ZCpVKJMmXKiLp164q6deuKMmXKCJVKJRo3bqwrynJycHDQnffGmNOnTwsHBwcTJsqdo6Oj7k1a6dKlxeHDh4UQQty6dUvY2tqaNAvP5FkAw4YNQ5cuXTB58mTdtJkSff/992jbti0GDBiAnTt3IjY2Fg4ODjh58qRiThcMQFHnj8hN+/btsXXrVoMFidu3b8f7778vU6rnXtw117RpUzx9+hQXLlwwevp1pfDz88O6detQq1YtvfHNmzdj8ODBSEhIkCfYC9auXYvu3btj586dunVCw4YNw5YtW3DgwAGZ0xnav38/tm/fjjp16kCtVqNMmTJ477334OTkhJkzZ5p2qtyITz75BFlZWbhy5YruVObPXLt2Df369cPQoUPxyy+/yJQwm7W1da67Gh8/fgxra2sTJspdxYoVce3aNXh7e6NmzZpYunQpvL29sWTJEtPvDjdpnXnDODo6iqioKLlj5ElWVpb45JNPhEqlEkWKFBF79uyRO5LZmjFjhnB2dhZt2rQRM2bMEDNmzBBt27YVLi4uYsaMGWL+/Pm6m9w4yyattWvXiqJFi4rTp0+LIUOGiBIlSuimoJVGSe9kjTGXmYFPPvlElClTRmzZskXv7JdJSUliy5YtwtvbWwQFBcmYUN/q1avFTz/9JITI/hm6ubkJlUolrK2txfr1602ahQWjAPr27SuWLVsmd4yXioqKEnXr1hWlS5cWe/fuFZ999pmwsrISY8aMEenp6bJm++9/2NxuSuHt7Z2nW9myZeWOKsqXLy8++eQTERcXJ3eUl9q5c6fw9PQUjRs3FuXKlRM1a9YUFy9elDuWgUWLFglra2tRqlQpo7vKlKJOnTq6NxLt2rUTPXv2FH///bcYO3as8PHxkTmdEMWKFct1N92BAwdEsWLFTJjIuKdPn4rBgwfrdt/a2NgIGxsboVarhZWVlRgyZIh4+vSp3DGN0mq1IiUlRZw5c0YkJCSY/OvzKJICSE1NRZcuXaDRaFC9enWDkxjJedKqFzk6OqJt27ZYsmQJXFxcAABHjx5Fr1694OjoiHPnzsmWjYf9vl5OTk44d+4cypUrJ3eUl9JqtRg2bBgWL14MS0tL7NixI8fTSJtKSEiI0fFffvkFfn5+ej/XOXPmmCpWnqxZswaZmZno06cPzpw5g1atWuHBgwewsrLCypUr0a1bN1nzDR06FGFhYZg7dy5atGihd+RTREQEQkJC8P777+O7776TNeczjx49wpkzZ/QOU61du7ZijsR60fLlyzF37lzcuHEDAFChQgWMGDECAwYMMGkOFowCWL58OQYPHgwbGxsUK1ZM78VRSYfTrV69Gj179jQYf/z4MUaMGIHly5fLkCobD/t9vfr164dGjRqhf//+ckfJ1c2bNxEYGIi4uDgsW7YMhw4dwtdff43g4GB88cUXsl26u1mzZnm6n0qlUtzVVF8khMCTJ09w9epVlC5d2uDMmXJIS0vDiBEjEBoaiszMTN2p2NPT02FpaYn+/ftj7ty5ilrfYA4mT56MOXPmYNiwYbrD6I8dO4aFCxdi5MiRmD59usmysGAUgKenJ4YPH47x48cr6oQwOUlPT8ft27dRrlw5WFoqc31vbgsS27dvL1MqfVWqVFH8gsRnOMtWuCnlnWxuzGlmwJj4+HgsXboUkydPljsKAECj0WDBggXo3r273vjPP/+MYcOG4f79+6YLY/KdMm+QokWLmsUiz9TUVNGvXz9hYWEhLCwsdJcXDwoK0i2qUwIuSJTesmXLhKWlpXBwcBBlypRR3BqRZ1atWmV0/NGjR6Jfv34mTmPcw4cPxYMHDwzGHzx4oKg1Qs9MmjRJ2Nvbi/Hjx+vOzTN+/Hjh4OAgJk2aJHe8N8b58+cV9fvJ2dlZXL9+3WD82rVrwtnZ2aRZWDAKYMSIEeKLL76QO8ZLDR8+XNSuXVv88ccfwt7eXlcwtm3bJmrVqiVzuue4IFF6Hh4e4osvvhBZWVlyR8mTtLQ0cfXqVd15PJSkVatWYtGiRQbjixcvFq1bt5YhUe7c3NyMnhxs3bp1ilg8+TJxcXFi2rRpcscQkZGRud42bNigqIIRFBQkRo4caTA+atQo8cknn5g0C3eRFMDw4cOxatUq1KxZEzVq1DCYflbKoq8yZcpgw4YNqF+/PhwdHREZGQkfHx9ERUXBz8/vpaeTNhUuSJSeq6srTp06pfif6ZMnTxAUFKS7ONv169fh4+ODYcOGoVSpUhg3bpzMCbN/lkeOHEHlypX1xq9evYpGjRrhwYMHMiUzzsXFBadOnUKFChX0xq9fv466devi4cOH8gTLo8jISPj5+cm+uPvZ4nNjL5XPxpW0CH3YsGFYtWoVvLy8UL9+fQDAiRMnEB0djV69eum9Tr3u1yhl7og3ExcvXoSvry8A4NKlS3rbjB0NIZeEhASj58lPSUlRVM7OnTvj4MGDin8xfHFBYnh4OA4dOoT27dvLviDRmN69e2PDhg349NNP5Y6Sq/HjxyMyMhIHDx5Eq1atdOP+/v6YOnWqIgpGWloaMjMzDcYzMjLw5MkTGRLlrmfPnli8eLHBi8gPP/yA//3vfzKleu7ChQu5br927ZqJkuTO1dUVs2fPRosWLYxu//PPP9GuXTsTp8rZpUuX4OfnByD7dxUAuLm5wc3NTe91yhS/+1kwCkCJZ+8zpk6dOggLC8OwYcMAPP+HtWzZMt0qYyVYuHAhunTpgj/++EPRCxJr1aqFtm3bIjw8HC4uLnjvvffQpk0b9OrVC/v27VPUgsSsrCzMnj0b4eHhip5l27Ztm26W7cVffFWrVtX9kpRb3bp18cMPPxgcNrlkyRLUrl1bplS5W758Ofbu3Wv0neyLh+DK8e+gVq1aeZoZkFvt2rVzvVruw4cPjX4PclHS6xILRiHw5ZdfonXr1rh8+TIyMzMxf/58XL58GUePHn3poaGm9PPPP2Pv3r2wsbHBwYMHDQ77VUrB+P777w0O+23YsCHOnTuHESNGyBMqB5xlk87nn38Of39/REZG6t7NRkRE4NSpU9i7d6/M6Qwp6Z2sMeYyMzB48GCkpKTkuL106dL46aefTJjIfHANRiFx8+ZNzJo1C5GRkUhOToafnx/GjRuH6tWryx1Nh4f9Fl7vvPMOunTpgmHDhsHR0REXLlxA2bJlMWzYMNy4cQN79uyROyIA4Pz58/j6669x/vx52NraokaNGpgwYYLBOgd6uYCAADRp0gQTJ040uj0yMhK+vr6KvX4OvRwLBikGFyQWXocPH0br1q3Ro0cPrFixAh9//LHeLJtSd0FQ/m3duhUpKSno0aOH0e3//vsvfv31V/Tu3dvEyUgqLBiFhFarRVRUlNETWL3zzjsypdI3cuRIaDQaxS9IDA4OxpEjRzBv3jy0atUKFy5cgI+PD7Zv346pU6cqag2GOVHiLNujR4/0TmGdG3M5MRTljxACmzZtwoEDB4z+Ht2yZYtMyZSL87qFwPHjxxEYGIg7d+4YLEZS0uFVXJBYuJUrVw4//vij3DH0FC1aVHetHBcXF14rpxAbMWIEli5dimbNmsHDw0Mxa4OUjAWjEBg8eLDuSJLixYsr9j8GFyQWbkqcZdu/fz9cXV0BKGt1/pvEXGYGVq9ejS1btqBNmzZyRzEbLBiFwI0bN7Bp0yaUL19e7ii5Mpdf4OZy2K85Ueos27ML7GVmZuLQoUPo168fSpUqJUuWN5W5zAw4OzvDx8dH7hhmhWswCoHmzZtj7NixeicwovzjgkTp1apVC2+99RamTZtmdJbN2dlZpmTPOTo64uLFi/D29pY7yhvF1dUVa9asUfzMwMqVK7Fnzx6EhobC1tZW7jhmgTMYb6gXz5I3bNgwjBo1CnFxcUZPYFWjRg1TxzNrjRs3xvnz5zFr1ixUr14de/fuhZ+fH44dO6aow37NiTnMsjVv3hyHDh1iwZCYucwMdO3aFT///DPc3d3h7e1t8Hv07NmzMiVTLs5gvKFyO38+oMxz6FPhZQ6zbEuWLMG0adPwv//9D7Vr14a9vb3e9vbt28uUzLyZy8xA165dceDAAXTu3NnorpwpU6bIlEy5WDDeUHfu3MnzfXM6BS7lTIkLEs3Ni7NsN2/exMSJEzFmzBjFzrLldvI3FvX8e/LkCT788EMcOXJE0TMD9vb2CA8PR+PGjeWOYja4i+QN9WJpmDlzJjw8PNCvXz+9+4SGhiIhIYEnhnpFSl2QaG6MXYvixX+jSptl4xklX4/evXvjzJkz6NGjh6IXeXp5efFcJ6+IMxiFgLe3N9atW4eGDRvqjZ84cQIfffQRbt++LVMy82QOCxLNgbnNsq1atQrdunWDtbW13nh6ejrWr1+PXr16yZTMvJnLzEBYWBi+++47LFmyhOtw8ogFoxCwsbHBlStXULZsWb3xW7duoUqVKnj69KlMycyTvb09IiMjFb0g0dyYwyybhYWF7qRbL3rw4AHc3d0VMctijipVqoSNGzcqYjdYbooWLYrU1FRkZmbCzs7OYFdOYmKiTMmUi7tICgEvLy8cOXLEoGAcOXIEJUqUkCmV+apXrx6ioqJYMCS0dOlSrFu3zmC8atWq+OijjxRRMHK6fPjff//NWasC+PbbbzF27FjFzwzMmzdP7ghmhwWjEBg4cCBGjBiBjIwMNG/eHED2ZabHjh2LUaNGyZzOPPCw39crLi4OxYsXNxjXaDSIjY2VIdFzvr6+UKlUUKlUaNGihd6Vc7OysnD79m1FH/2idD169EBqairKlSun6JkBXnTt1bFgFAJjxozBgwcP8MknnyA9PR1A9m6TcePGYcKECTKnMw/mtiDR3Ch5lu2DDz4AkH2p9oCAADg4OOi2WVlZwdvbG506dZIpnfkzx5mBp0+f6n6XPsMFoIa4BqMQSU5OxpUrV2Bra4sKFSoYLFajnJnbgkRzM3v2bMyePRtff/210Vk2JRThlStXolu3brCxsZE7CskgJSUF48aNw8aNG/HgwQOD7XxjYYgFg+gVmcOCRHMjhMD48eOxYMECg1m2yZMny5yOTEXJMwNDhw7FgQMHMGPGDPTs2ROLFi3CP//8g6VLl2LWrFn43//+J3dExWHBIHpFPOz39VHyLFtWVhbmzp2LjRs3Ijo62uCFUClrBcyNucwMlC5dGqtWrcK7774LJycnnD17FuXLl8fq1avx888/Y9euXXJHVJycT01HREYpeUGiuXNwcMDbb7+NatWqKapcAMC0adMwZ84cdOvWDUlJSQgJCUHHjh2hVqsxdepUueOZrbFjx2L//v1YvHgxrK2tsWzZMkybNg0lSpTAqlWr5I6nk5iYqLtmipOTk65QNm7cGL///ruc0RSLBYPoFT1bkPhfSliQSK/P2rVr8eOPP2LUqFGwtLRE9+7dsWzZMkyePBnHjx+XO57Z2rFjB77//nt06tQJlpaWaNKkCSZOnIgvv/wSa9eulTuejo+Pj2528tm5O4Ds/C4uLjImUy4WDKJX9Oyw359++gl37tzBnTt3EBoaipEjR2LgwIFyx6PX5NlhyUD2TEtSUhIA4P3330dYWJic0cyaucwM9O3bF5GRkQCA8ePHY9GiRbCxscHIkSMxZswYmdMpEw9TJXpFPOy3cCpVqhRiY2NRunRplCtXDnv37oWfnx9OnTqluN055uTZzEDp0qV1MwN169ZV3MzAyJEjdR/7+/vj6tWrOHPmDMqXL89z3+SAizyJ8knJCxJJeuPHj4eTkxM+/fRTbNiwAT169IC3tzeio6MxcuRIzJo1S+6IZmnu3LmwsLDA8OHD8dtvv6Fdu3YQQiAjIwNz5sxBcHCw3BF1IiIiEBERYfQqyqGhoTKlUi4WDCKifDh27BiOHTuGChUqoF27dnLHeWPcuXNHkTMD06ZNw/Tp01GnTh2jFzncunWrTMmUiwWDiIhkZQ4zA8WLF8fs2bPRs2dPuaOYDa7BICLKo2vXruG7777DlStXAACVK1fGsGHDULFiRZmTma+XzQwoRXp6usG5byh3nMEgIsqDzZs346OPPkKdOnXQoEEDAMDx48dx6tQprF+/ntcjySdzmRkYN24cHBwcMGnSJLmjmA0WDCKiPChXrhz+97//Yfr06XrjU6ZMwZo1a3Dz5k2Zkpm3YsWK4eTJkyhXrpzcUQyEhIToPtZqtVi5ciVq1KiBGjVqGFz1dc6cOaaOp3gsGEREeWBnZ4cLFy6gfPnyeuM3btxAzZo1kZqaKlMy86bkmYFmzZrl6X4qlQr79+9/zWnMD9dgEBHlwbvvvos//vjDoGAcPnwYTZo0kSmVefrvzMAPP/yA3377TXEzAwcOHJDta78JWDCIiPKgffv2GDduHM6cOYP69esDyF6D8csvv2DatGn49ddf9e5LOTt37pze57Vq1QIAXLp0SW9cqQs+KW+4i4SIKA/U6rxdWUGlUinmCqBEcmLBICIiIsnxYmdERHkQHR2NtLQ0g3EhBKKjo2VIRKRsLBhERHng7e0NPz8/g8NR7927h7Jly8qUiki5WDCIiPKocuXKqFu3LiIiIvTGuaeZyBALBhFRHqhUKnz//feYOHEi2rZtiwULFuhtIyJ9PEyViCgPns1SjBw5EpUqVUL37t1x8eJFTJ48WeZkRMrEo0iIiPJArVYjLi4O7u7uAIDLly+jffv2sLe3x6VLl3hoKtF/cBcJEVEeNG3aFFZWVrrPq1SpghMnTsDFxYVrMIiM4AwGERERSY4zGEREeWBhYYF79+4ZjD948AAWFhYyJCJSNhYMIqI8yGmyNy0tTW/XCRFl41EkRES5eHY4qkqlwrJly+Dg4KDblpWVhd9//x2VKlWSKx6RYnENBhFRLp6dpfPOnTsoVaqU3u4QKysreHt7Y/r06ahXr55cEYkUiQWDiCgPmjVrhi1btqBo0aJyRyEyCywYREREJDmuwSAiykFISAhmzJgBe3t7hISE5HrfOXPmmCgVkXngDAYRUQ5cXV1x/fp1uLm5oVmzZjneT6VSYf/+/SZMRqR8nMEgIsrBw4cPodVqAWQv8jx16hSKFSsmcyoi88DzYBAR5aBo0aK4ffs2AOCvv/7SlQ0iejnOYBAR5aBTp05o2rQpihcvDpVKhTp16uR41s5bt26ZOB2RsrFgEBHl4IcffkDHjh0RFRWF4cOHY+DAgXB0dJQ7FpFZ4CJPIqI86Nu3LxYsWMCCQZRHLBhEREQkOS7yJCIiIsmxYBAREZHkWDCIiIhIciwYREREJDkWDCJSnL/++gsqlQrnz5+XOwoR5RMLBhEpjpeXF2JjY1GtWjW5o2Dq1KmoVauW3DGIzA5PtEVEipKeng4rKyt4enrKHYWICoAzGESFmFarxcyZM1G2bFnY2tqiZs2a2LRpE4QQ8Pf3R0BAAJ6dKicxMRGlSpXC5MmTAQAHDx6ESqVCWFgYatSoARsbG9SvXx+XLl3S+xqHDx9GkyZNYGtrCy8vLwwfPhwpKSm67d7e3pgxYwZ69eoFJycnDBo0yGAXybOvFR4eDl9fX9ja2qJ58+a4d+8edu/ejcqVK8PJyQmBgYFITU196ff3zLPnjYiIQJ06dWBnZ4eGDRvi2rVrAIAVK1Zg2rRpiIyMhEqlgkqlwooVK17HXwXRm0cQUaH1+eefi0qVKok9e/aImzdvip9++klYW1uLgwcPir///lsULVpUzJs3TwghRJcuXUTdunVFRkaGEEKIAwcOCACicuXKYu/eveLChQvi/fffF97e3iI9PV0IIURUVJSwt7cXc+fOFdevXxdHjhwRvr6+ok+fProMZcqUEU5OTuKbb74RUVFRIioqSty+fVsAEOfOndP7WvXr1xeHDx8WZ8+eFeXLlxdNmzYVLVu2FGfPnhW///67KFasmJg1a1aevr8Xn7devXri4MGD4s8//xRNmjQRDRs2FEIIkZqaKkaNGiWqVq0qYmNjRWxsrEhNTX3tfy9EbwIWDKJC6unTp8LOzk4cPXpUb7x///6ie/fuQgghNm7cKGxsbMT48eOFvb29uH79uu5+z16c169frxt78OCBsLW1FRs2bNA916BBg/Se/48//hBqtVo8efJECJFdMD744AO9++RUMH777TfdfWbOnCkAiJs3b+rGPv74YxEQEJDn78/Y84aFhQkAunxTpkwRNWvWzO1HSURGcA0GUSEVFRWF1NRUvPfee3rj6enp8PX1BQB06dIFW7duxaxZs7B48WJUqFDB4HkaNGig+9jV1RUVK1bElStXAACRkZG4cOEC1q5dq7uPEAJarRa3b99G5cqVAQB16tTJU+YaNWroPvbw8ICdnR18fHz0xk6ePJnn78/Y8xYvXhwAcO/ePZQuXTpPuYjIEAsGUSGVnJwMAAgLC0PJkiX1tllbWwMAUlNTcebMGVhYWODGjRv5+hoff/wxhg8fbrDtxRdve3v7PD1fkSJFdB+rVCq9z5+NabVa3dcGcv/+cnpeALrnIaL8YcEgKqSqVKkCa2trREdHo2nTpkbvM2rUKKjVauzevRtt2rRB27Zt0bx5c737HD9+XFcW/v33X1y/fl03M+Hn54fLly+jfPnyr/ebMSIv319eWFlZISsrS8JkRIUDCwZRIeXo6IjRo0dj5MiR0Gq1aNy4MZKSknDkyBE4OTnBzc0NoaGhOHbsGPz8/DBmzBj07t0bFy5cQNGiRXXPM336dBQrVgweHh747LPP4Obmhg8++AAAMG7cONSvXx9BQUEYMGAA7O3tcfnyZezbtw8LFy6U9fvr3bt3np7H29sbt2/fxvnz51GqVCk4OjoazIAQkSEepkpUiM2YMQOTJk3CzJkzUblyZbRq1QphYWHw9vZG//79MXXqVPj5+QEApk2bBg8PDwwePFjvOWbNmoXg4GDUrl0bcXFx2LFjB6ysrABkr204dOgQrl+/jiZNmsDX1xeTJ09GiRIlZP3+ypYtm+fn6NSpE1q1aoVmzZpBo9Hg559/fo2Jid4cKiH+/yB3IqJXcPDgQTRr1gz//vsvXFxc5I5DRArDGQwiIiKSHAsGERERSY67SIiIiEhynMEgIiIiybFgEBERkeRYMIiIiEhyLBhEREQkORYMIiIikhwLBhEREUmOBYOIiIgkx4JBREREkmPBICIiIsn9HyZBEfV/0SMSAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axs = plt.subplots(1, 1, figsize=(6, 6))\n",
    "\n",
    "sns.barplot(\n",
    "    data=predict_attr_df,\n",
    "    x=\"experiment\",\n",
    "    y=\"test/auc\",\n",
    "    errorbar=\"sd\",\n",
    "    order=predict_attr_df.groupby(\"experiment\")\n",
    "        .agg({\"test/auc\": \"mean\"})\n",
    "        .sort_values(\"test/auc\", ascending=False)\n",
    "        .index,\n",
    "    capsize=0.1,\n",
    "    ax=axs\n",
    ")\n",
    "axs.set_ylim(0.45, 1.01)\n",
    "axs.set_xticklabels(predict_attr_df.groupby(\"experiment\")\n",
    "        .agg({\"test/auc\": \"mean\"})\n",
    "        .sort_values(\"test/auc\", ascending=False)\n",
    "        .index, rotation=90\n",
    "        )\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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 colspan=\"2\" halign=\"left\">test/auc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mimic_sex</th>\n",
       "      <td>0.986394</td>\n",
       "      <td>0.007749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chexpert_sex</th>\n",
       "      <td>0.979751</td>\n",
       "      <td>0.019790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mimic_race</th>\n",
       "      <td>0.950730</td>\n",
       "      <td>0.004963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chexpert_race</th>\n",
       "      <td>0.935971</td>\n",
       "      <td>0.005270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mimic_age</th>\n",
       "      <td>0.930576</td>\n",
       "      <td>0.002520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chexpert_age</th>\n",
       "      <td>0.920307</td>\n",
       "      <td>0.003214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fitzpatrick17k_skin</th>\n",
       "      <td>0.891253</td>\n",
       "      <td>0.010444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>papila_age</th>\n",
       "      <td>0.812474</td>\n",
       "      <td>0.046200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ham10000_age</th>\n",
       "      <td>0.803266</td>\n",
       "      <td>0.020465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ham10000_sex</th>\n",
       "      <td>0.723243</td>\n",
       "      <td>0.015004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>papila_sex</th>\n",
       "      <td>0.641984</td>\n",
       "      <td>0.056934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     test/auc          \n",
       "                         mean       std\n",
       "experiment                             \n",
       "mimic_sex            0.986394  0.007749\n",
       "chexpert_sex         0.979751  0.019790\n",
       "mimic_race           0.950730  0.004963\n",
       "chexpert_race        0.935971  0.005270\n",
       "mimic_age            0.930576  0.002520\n",
       "chexpert_age         0.920307  0.003214\n",
       "fitzpatrick17k_skin  0.891253  0.010444\n",
       "papila_age           0.812474  0.046200\n",
       "ham10000_age         0.803266  0.020465\n",
       "ham10000_sex         0.723243  0.015004\n",
       "papila_sex           0.641984  0.056934"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attr_results_agg = predict_attr_df.groupby(\"experiment\").agg(\n",
    "    {\"test/auc\": [\"mean\", \"std\"]}\n",
    ").sort_values((\"test/auc\", \"mean\"), ascending=False)\n",
    "attr_results_agg"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, analyse results for predicting disease (ERM only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>rng</th>\n",
       "      <th>model</th>\n",
       "      <th>experiment</th>\n",
       "      <th>model.predict_attr</th>\n",
       "      <th>datamodule.label_noise</th>\n",
       "      <th>test/accuracy_group_1</th>\n",
       "      <th>split_test/accuracy</th>\n",
       "      <th>split_test/auc</th>\n",
       "      <th>test/accuracy_group_0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>baseline</td>\n",
       "      <td>fitzpatrick17k_skin</td>\n",
       "      <td>False</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.921212</td>\n",
       "      <td>0.808989</td>\n",
       "      <td>0.854915</td>\n",
       "      <td>0.893406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>baseline</td>\n",
       "      <td>fitzpatrick17k_skin</td>\n",
       "      <td>False</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.937374</td>\n",
       "      <td>0.785268</td>\n",
       "      <td>0.821667</td>\n",
       "      <td>0.890696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>baseline</td>\n",
       "      <td>fitzpatrick17k_skin</td>\n",
       "      <td>False</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.939394</td>\n",
       "      <td>0.793383</td>\n",
       "      <td>0.847700</td>\n",
       "      <td>0.892502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>baseline</td>\n",
       "      <td>fitzpatrick17k_skin</td>\n",
       "      <td>False</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.907071</td>\n",
       "      <td>0.816479</td>\n",
       "      <td>0.857215</td>\n",
       "      <td>0.877145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>baseline</td>\n",
       "      <td>fitzpatrick17k_skin</td>\n",
       "      <td>False</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.931313</td>\n",
       "      <td>0.807116</td>\n",
       "      <td>0.861531</td>\n",
       "      <td>0.888889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rng     model           experiment  model.predict_attr  \\\n",
       "0    9  baseline  fitzpatrick17k_skin               False   \n",
       "1    8  baseline  fitzpatrick17k_skin               False   \n",
       "2    7  baseline  fitzpatrick17k_skin               False   \n",
       "3    6  baseline  fitzpatrick17k_skin               False   \n",
       "4    5  baseline  fitzpatrick17k_skin               False   \n",
       "\n",
       "   datamodule.label_noise  test/accuracy_group_1  split_test/accuracy  \\\n",
       "0                    0.25               0.921212             0.808989   \n",
       "1                    0.25               0.937374             0.785268   \n",
       "2                    0.25               0.939394             0.793383   \n",
       "3                    0.25               0.907071             0.816479   \n",
       "4                    0.25               0.931313             0.807116   \n",
       "\n",
       "   split_test/auc  test/accuracy_group_0  \n",
       "0        0.854915               0.893406  \n",
       "1        0.821667               0.890696  \n",
       "2        0.847700               0.892502  \n",
       "3        0.857215               0.877145  \n",
       "4        0.861531               0.888889  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disease_results_df = predict_disease_df\n",
    "disease_results_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test/accuracy_group_0_mean</th>\n",
       "      <th>test/accuracy_group_0_std</th>\n",
       "      <th>test/accuracy_group_1_mean</th>\n",
       "      <th>test/accuracy_group_1_std</th>\n",
       "      <th>split_test/auc_mean</th>\n",
       "      <th>split_test/auc_std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment</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>chexpert_age</th>\n",
       "      <td>0.831145</td>\n",
       "      <td>0.020821</td>\n",
       "      <td>0.906556</td>\n",
       "      <td>0.014347</td>\n",
       "      <td>0.853908</td>\n",
       "      <td>0.010615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chexpert_race</th>\n",
       "      <td>0.880686</td>\n",
       "      <td>0.026541</td>\n",
       "      <td>0.830384</td>\n",
       "      <td>0.042722</td>\n",
       "      <td>0.851024</td>\n",
       "      <td>0.008392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chexpert_sex</th>\n",
       "      <td>0.867673</td>\n",
       "      <td>0.028604</td>\n",
       "      <td>0.799430</td>\n",
       "      <td>0.051706</td>\n",
       "      <td>0.945462</td>\n",
       "      <td>0.014456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fitzpatrick17k_skin</th>\n",
       "      <td>0.887082</td>\n",
       "      <td>0.008125</td>\n",
       "      <td>0.925657</td>\n",
       "      <td>0.010510</td>\n",
       "      <td>0.849393</td>\n",
       "      <td>0.011024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ham10000_age</th>\n",
       "      <td>0.905947</td>\n",
       "      <td>0.023445</td>\n",
       "      <td>0.778136</td>\n",
       "      <td>0.033896</td>\n",
       "      <td>0.774660</td>\n",
       "      <td>0.010705</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     test/accuracy_group_0_mean  test/accuracy_group_0_std  \\\n",
       "experiment                                                                   \n",
       "chexpert_age                           0.831145                   0.020821   \n",
       "chexpert_race                          0.880686                   0.026541   \n",
       "chexpert_sex                           0.867673                   0.028604   \n",
       "fitzpatrick17k_skin                    0.887082                   0.008125   \n",
       "ham10000_age                           0.905947                   0.023445   \n",
       "\n",
       "                     test/accuracy_group_1_mean  test/accuracy_group_1_std  \\\n",
       "experiment                                                                   \n",
       "chexpert_age                           0.906556                   0.014347   \n",
       "chexpert_race                          0.830384                   0.042722   \n",
       "chexpert_sex                           0.799430                   0.051706   \n",
       "fitzpatrick17k_skin                    0.925657                   0.010510   \n",
       "ham10000_age                           0.778136                   0.033896   \n",
       "\n",
       "                     split_test/auc_mean  split_test/auc_std  \n",
       "experiment                                                    \n",
       "chexpert_age                    0.853908            0.010615  \n",
       "chexpert_race                   0.851024            0.008392  \n",
       "chexpert_sex                    0.945462            0.014456  \n",
       "fitzpatrick17k_skin             0.849393            0.011024  \n",
       "ham10000_age                    0.774660            0.010705  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split into noise and no noise and aggregate by experiment\n",
    "\n",
    "noise_df = disease_results_df[disease_results_df[\"datamodule.label_noise\"] > 0]\n",
    "no_noise_df = disease_results_df[disease_results_df[\"datamodule.label_noise\"] == 0]\n",
    "\n",
    "noise_df_agg = noise_df.groupby(\"experiment\").agg(\n",
    "    {\"test/accuracy_group_0\": [\"mean\", \"std\"], \"test/accuracy_group_1\": [\"mean\", \"std\"], \n",
    "     \"split_test/auc\": [\"mean\", \"std\"]\n",
    "    }\n",
    "    )\n",
    "noise_df_agg.columns = [\"_\".join(col) for col in noise_df_agg.columns]\n",
    "no_noise_df_agg = no_noise_df.groupby(\"experiment\").agg(\n",
    "    {\"test/accuracy_group_0\": [\"mean\", \"std\"], \"test/accuracy_group_1\": [\"mean\", \"std\"],\n",
    "     \"split_test/auc\": [\"mean\", \"std\"]\n",
    "     }\n",
    "    )\n",
    "\n",
    "no_noise_df_agg.columns = [\"_\".join(col) for col in no_noise_df_agg.columns]\n",
    "\n",
    "print(noise_df_agg.shape)\n",
    "noise_df_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experiment</th>\n",
       "      <th>test/accuracy_group_0_mean</th>\n",
       "      <th>test/accuracy_group_0_std</th>\n",
       "      <th>test/accuracy_group_1_mean</th>\n",
       "      <th>test/accuracy_group_1_std</th>\n",
       "      <th>split_test/auc_mean</th>\n",
       "      <th>split_test/auc_std</th>\n",
       "      <th>u_group_0</th>\n",
       "      <th>u_group_1</th>\n",
       "      <th>p_group_0</th>\n",
       "      <th>p_group_1</th>\n",
       "      <th>p_group_0_adj</th>\n",
       "      <th>p_group_1_adj</th>\n",
       "      <th>predict_attr_mean</th>\n",
       "      <th>predict_attr_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PAPILA\\nSex</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>21.564335</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>11.630166</td>\n",
       "      <td>0.623968</td>\n",
       "      <td>0.065933</td>\n",
       "      <td>30.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.940860</td>\n",
       "      <td>0.604989</td>\n",
       "      <td>1.881720</td>\n",
       "      <td>1.209978</td>\n",
       "      <td>0.641984</td>\n",
       "      <td>0.056934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HAM10000\\nSex</td>\n",
       "      <td>0.478821</td>\n",
       "      <td>3.280635</td>\n",
       "      <td>0.283225</td>\n",
       "      <td>2.820583</td>\n",
       "      <td>0.689177</td>\n",
       "      <td>0.009552</td>\n",
       "      <td>48.0</td>\n",
       "      <td>53.5</td>\n",
       "      <td>0.575030</td>\n",
       "      <td>0.410098</td>\n",
       "      <td>2.300121</td>\n",
       "      <td>1.230295</td>\n",
       "      <td>0.723243</td>\n",
       "      <td>0.015004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HAM10000\\nAge</td>\n",
       "      <td>0.152144</td>\n",
       "      <td>3.139455</td>\n",
       "      <td>-0.215054</td>\n",
       "      <td>3.898965</td>\n",
       "      <td>0.774660</td>\n",
       "      <td>0.010705</td>\n",
       "      <td>45.5</td>\n",
       "      <td>54.5</td>\n",
       "      <td>0.647696</td>\n",
       "      <td>0.381011</td>\n",
       "      <td>1.943087</td>\n",
       "      <td>1.524043</td>\n",
       "      <td>0.803266</td>\n",
       "      <td>0.020465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PAPILA\\nAge</td>\n",
       "      <td>7.619047</td>\n",
       "      <td>8.054675</td>\n",
       "      <td>2.826087</td>\n",
       "      <td>9.703112</td>\n",
       "      <td>0.748214</td>\n",
       "      <td>0.048614</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.993574</td>\n",
       "      <td>0.922354</td>\n",
       "      <td>0.993574</td>\n",
       "      <td>0.922354</td>\n",
       "      <td>0.812474</td>\n",
       "      <td>0.046200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fitzpatrick17K\\nSkin</td>\n",
       "      <td>-0.117435</td>\n",
       "      <td>0.988692</td>\n",
       "      <td>-0.666666</td>\n",
       "      <td>1.302470</td>\n",
       "      <td>0.849393</td>\n",
       "      <td>0.011024</td>\n",
       "      <td>51.5</td>\n",
       "      <td>69.5</td>\n",
       "      <td>0.469815</td>\n",
       "      <td>0.074386</td>\n",
       "      <td>2.818889</td>\n",
       "      <td>0.371929</td>\n",
       "      <td>0.891253</td>\n",
       "      <td>0.010444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CheXpert\\nAge</td>\n",
       "      <td>-0.936853</td>\n",
       "      <td>3.333917</td>\n",
       "      <td>-1.837078</td>\n",
       "      <td>2.066015</td>\n",
       "      <td>0.853908</td>\n",
       "      <td>0.010615</td>\n",
       "      <td>61.5</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.202751</td>\n",
       "      <td>0.004554</td>\n",
       "      <td>1.419258</td>\n",
       "      <td>0.031880</td>\n",
       "      <td>0.920307</td>\n",
       "      <td>0.003214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MIMIC\\nAge</td>\n",
       "      <td>-0.241780</td>\n",
       "      <td>3.320141</td>\n",
       "      <td>-2.904233</td>\n",
       "      <td>3.943333</td>\n",
       "      <td>0.835927</td>\n",
       "      <td>0.013784</td>\n",
       "      <td>51.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.484925</td>\n",
       "      <td>0.022577</td>\n",
       "      <td>2.424625</td>\n",
       "      <td>0.135464</td>\n",
       "      <td>0.930576</td>\n",
       "      <td>0.002520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CheXpert\\nRace</td>\n",
       "      <td>-1.547716</td>\n",
       "      <td>3.292378</td>\n",
       "      <td>-5.214389</td>\n",
       "      <td>4.775361</td>\n",
       "      <td>0.851024</td>\n",
       "      <td>0.008392</td>\n",
       "      <td>68.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.092938</td>\n",
       "      <td>0.002286</td>\n",
       "      <td>0.743507</td>\n",
       "      <td>0.018284</td>\n",
       "      <td>0.935971</td>\n",
       "      <td>0.005270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MIMIC\\nRace</td>\n",
       "      <td>-2.577952</td>\n",
       "      <td>3.686000</td>\n",
       "      <td>-5.525497</td>\n",
       "      <td>3.869716</td>\n",
       "      <td>0.791595</td>\n",
       "      <td>0.015356</td>\n",
       "      <td>79.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.015605</td>\n",
       "      <td>0.000853</td>\n",
       "      <td>0.156045</td>\n",
       "      <td>0.007678</td>\n",
       "      <td>0.950730</td>\n",
       "      <td>0.004963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CheXpert\\nSex</td>\n",
       "      <td>-2.589598</td>\n",
       "      <td>3.330679</td>\n",
       "      <td>-8.418053</td>\n",
       "      <td>5.688703</td>\n",
       "      <td>0.945462</td>\n",
       "      <td>0.014456</td>\n",
       "      <td>77.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.022577</td>\n",
       "      <td>0.000504</td>\n",
       "      <td>0.203196</td>\n",
       "      <td>0.005040</td>\n",
       "      <td>0.979751</td>\n",
       "      <td>0.019790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MIMIC\\nSex</td>\n",
       "      <td>-4.907307</td>\n",
       "      <td>4.327664</td>\n",
       "      <td>-10.499806</td>\n",
       "      <td>4.867825</td>\n",
       "      <td>0.933515</td>\n",
       "      <td>0.013107</td>\n",
       "      <td>87.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.002898</td>\n",
       "      <td>0.000123</td>\n",
       "      <td>0.031874</td>\n",
       "      <td>0.001354</td>\n",
       "      <td>0.986394</td>\n",
       "      <td>0.007749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              experiment  test/accuracy_group_0_mean  \\\n",
       "10           PAPILA\\nSex                   10.000001   \n",
       "5          HAM10000\\nSex                    0.478821   \n",
       "4          HAM10000\\nAge                    0.152144   \n",
       "9            PAPILA\\nAge                    7.619047   \n",
       "3   Fitzpatrick17K\\nSkin                   -0.117435   \n",
       "0          CheXpert\\nAge                   -0.936853   \n",
       "6             MIMIC\\nAge                   -0.241780   \n",
       "1         CheXpert\\nRace                   -1.547716   \n",
       "7            MIMIC\\nRace                   -2.577952   \n",
       "2          CheXpert\\nSex                   -2.589598   \n",
       "8             MIMIC\\nSex                   -4.907307   \n",
       "\n",
       "    test/accuracy_group_0_std  test/accuracy_group_1_mean  \\\n",
       "10                  21.564335                    0.142857   \n",
       "5                    3.280635                    0.283225   \n",
       "4                    3.139455                   -0.215054   \n",
       "9                    8.054675                    2.826087   \n",
       "3                    0.988692                   -0.666666   \n",
       "0                    3.333917                   -1.837078   \n",
       "6                    3.320141                   -2.904233   \n",
       "1                    3.292378                   -5.214389   \n",
       "7                    3.686000                   -5.525497   \n",
       "2                    3.330679                   -8.418053   \n",
       "8                    4.327664                  -10.499806   \n",
       "\n",
       "    test/accuracy_group_1_std  split_test/auc_mean  split_test/auc_std  \\\n",
       "10                  11.630166             0.623968            0.065933   \n",
       "5                    2.820583             0.689177            0.009552   \n",
       "4                    3.898965             0.774660            0.010705   \n",
       "9                    9.703112             0.748214            0.048614   \n",
       "3                    1.302470             0.849393            0.011024   \n",
       "0                    2.066015             0.853908            0.010615   \n",
       "6                    3.943333             0.835927            0.013784   \n",
       "1                    4.775361             0.851024            0.008392   \n",
       "7                    3.869716             0.791595            0.015356   \n",
       "2                    5.688703             0.945462            0.014456   \n",
       "8                    4.867825             0.933515            0.013107   \n",
       "\n",
       "    u_group_0  u_group_1  p_group_0  p_group_1  p_group_0_adj  p_group_1_adj  \\\n",
       "10       30.0       47.0   0.940860   0.604989       1.881720       1.209978   \n",
       "5        48.0       53.5   0.575030   0.410098       2.300121       1.230295   \n",
       "4        45.5       54.5   0.647696   0.381011       1.943087       1.524043   \n",
       "9        18.0       32.0   0.993574   0.922354       0.993574       0.922354   \n",
       "3        51.5       69.5   0.469815   0.074386       2.818889       0.371929   \n",
       "0        61.5       85.0   0.202751   0.004554       1.419258       0.031880   \n",
       "6        51.0       77.0   0.484925   0.022577       2.424625       0.135464   \n",
       "1        68.0       88.0   0.092938   0.002286       0.743507       0.018284   \n",
       "7        79.0       92.0   0.015605   0.000853       0.156045       0.007678   \n",
       "2        77.0       94.0   0.022577   0.000504       0.203196       0.005040   \n",
       "8        87.0       99.0   0.002898   0.000123       0.031874       0.001354   \n",
       "\n",
       "    predict_attr_mean  predict_attr_std  \n",
       "10           0.641984          0.056934  \n",
       "5            0.723243          0.015004  \n",
       "4            0.803266          0.020465  \n",
       "9            0.812474          0.046200  \n",
       "3            0.891253          0.010444  \n",
       "0            0.920307          0.003214  \n",
       "6            0.930576          0.002520  \n",
       "1            0.935971          0.005270  \n",
       "7            0.950730          0.004963  \n",
       "2            0.979751          0.019790  \n",
       "8            0.986394          0.007749  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subtract no_noise results from noise results to get the difference\n",
    "# we assume that the results are independent (i.e. we cannot treat results with the\n",
    "# same seed as paired), hence we use the formula for the std of the difference of two \n",
    "# independent RVs. Negative difference means results have deteriorated\n",
    "# we additionally multiply by 100 to convert accuracy to percentage points\n",
    "\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "\n",
    "diff_df = pd.DataFrame()\n",
    "diff_df[\"experiment\"] = noise_df_agg.index\n",
    "\n",
    "for col in noise_df_agg.columns:\n",
    "    if \"mean\" in col and \"split\" not in col:\n",
    "        diff_df[col] = 100 * (noise_df_agg[col].values - no_noise_df_agg[col].values)\n",
    "\n",
    "    if \"std\" in col and \"split\" not in col:\n",
    "        # std of subtracted independent RVs is sqrt of sum of squares of stds\n",
    "        diff_df[col] = 100 * np.sqrt(noise_df_agg[col].values**2 + no_noise_df_agg[col].values**2)\n",
    "\n",
    "diff_df[\"split_test/auc_mean\"] = noise_df_agg[\"split_test/auc_mean\"].values\n",
    "diff_df[\"split_test/auc_std\"] = noise_df_agg[\"split_test/auc_std\"].values\n",
    "\n",
    "# perform Mann-Whitney U test for each experiment\n",
    "u_results_group_0 = {}\n",
    "u_results_group_1 = {}\n",
    "\n",
    "for experiment in diff_df[\"experiment\"].unique():\n",
    "    u_results_group_0[experiment] = stats.mannwhitneyu(\n",
    "        no_noise_df[no_noise_df[\"experiment\"] == experiment][\"test/accuracy_group_0\"].values,\n",
    "        noise_df[noise_df[\"experiment\"] == experiment][\"test/accuracy_group_0\"].values,\n",
    "        alternative=\"greater\"\n",
    "    )\n",
    "    u_results_group_1[experiment] = stats.mannwhitneyu(\n",
    "        no_noise_df[no_noise_df[\"experiment\"] == experiment][\"test/accuracy_group_1\"].values,\n",
    "        noise_df[noise_df[\"experiment\"] == experiment][\"test/accuracy_group_1\"].values,\n",
    "        alternative=\"greater\"\n",
    "    )\n",
    "\n",
    "diff_df[\"u_group_0\"] = diff_df[\"experiment\"].map(\n",
    "    {experiment: u_results_group_0[experiment].statistic for experiment in diff_df[\"experiment\"].unique()})\n",
    "\n",
    "diff_df[\"u_group_1\"] = diff_df[\"experiment\"].map(\n",
    "    {experiment: u_results_group_1[experiment].statistic for experiment in diff_df[\"experiment\"].unique()})\n",
    "diff_df[\"p_group_0\"] = diff_df[\"experiment\"].map(\n",
    "    {experiment: u_results_group_0[experiment].pvalue for experiment in diff_df[\"experiment\"].unique()})\n",
    "diff_df[\"p_group_1\"] = diff_df[\"experiment\"].map(\n",
    "    {experiment: u_results_group_1[experiment].pvalue for experiment in diff_df[\"experiment\"].unique()})\n",
    "\n",
    "\n",
    "# adjust for multiple testing with holm-bonferroni\n",
    "# add columns with adjusted p-values, calculated by sorting p-values,\n",
    "# multiplying the smallest by the number of tests, \n",
    "# second smallest by the number of tests minus 1, etc.\n",
    "diff_df[\"p_group_0_adj\"] = diff_df.sort_values(\"p_group_0\")[\"p_group_0\"] * np.flip(np.arange(1, len(diff_df) + 1))\n",
    "diff_df[\"p_group_1_adj\"] = diff_df.sort_values(\"p_group_1\")[\"p_group_1\"] * np.flip(np.arange(1, len(diff_df) + 1))\n",
    " \n",
    "\n",
    "# add columns with predict_attr results\n",
    "diff_df[\"predict_attr_mean\"] = diff_df[\"experiment\"].map(\n",
    "    attr_results_agg[(\"test/auc\", \"mean\")]\n",
    ")\n",
    "diff_df[\"predict_attr_std\"] = diff_df[\"experiment\"].map(\n",
    "    attr_results_agg[(\"test/auc\", \"std\")]\n",
    ")\n",
    "\n",
    "# sort rows by predict_attr results\n",
    "diff_df = diff_df.sort_values(\"predict_attr_mean\", ascending=True)\n",
    "\n",
    "\n",
    "# rename experiments from snake_case to Kebab-Case, capitalize first letter and after hyphen\n",
    "diff_df[\"experiment\"] = diff_df[\"experiment\"].map(\n",
    "    {experiment: experiment.replace(\"_\", \"\\n\").title() for experiment in diff_df[\"experiment\"].unique()}\n",
    ")\n",
    "\n",
    "# rename Mimic to MIMIC, Papila to PAPILA, and Ham10000 to HAM10000\n",
    "diff_df[\"experiment\"] = diff_df[\"experiment\"].map(\n",
    "    {experiment: experiment.replace(\"Mimic\", \"MIMIC\").replace(\"Papila\", \"PAPILA\").replace(\"Ham10000\", \"HAM10000\").replace(\"Chexpert\", \"CheXpert\") for experiment in diff_df[\"experiment\"].unique()}\n",
    ")\n",
    "\n",
    "\n",
    "diff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# massage data for plotting: combine accuracy columns,\n",
    "# then create an indicator column for which accuracy group the row is for\n",
    "\n",
    "plt.rcParams['font.family'] = \"serif\"\n",
    "sns.set_context(\"poster\", font_scale=1.2)\n",
    "fig, axs = plt.subplots(1, 1, figsize=(20, 7))\n",
    "\n",
    "plot_diff_df = diff_df.melt(\n",
    "    id_vars=[\"experiment\", \"predict_attr_mean\", \"predict_attr_std\"],\n",
    "    value_vars=[\"test/accuracy_group_0_mean\", \"test/accuracy_group_1_mean\"],\n",
    "    var_name=\"group\",\n",
    "    value_name=\"accuracy\"\n",
    ")\n",
    "\n",
    "\n",
    "sns.barplot(\n",
    "    data=plot_diff_df,\n",
    "    x=\"experiment\",\n",
    "    y=\"accuracy\",\n",
    "    hue=\"group\",\n",
    "    # sort by predict_attr_mean\n",
    "    order=plot_diff_df.groupby(\"experiment\").agg({\"predict_attr_mean\": \"mean\"})\n",
    "        .sort_values(\"predict_attr_mean\", ascending=True).index,\n",
    "    ax=axs,\n",
    "    palette=\"deep\"\n",
    ")\n",
    "\n",
    "axs.set_xticklabels(plot_diff_df.groupby(\"experiment\").agg({\"predict_attr_mean\": \"mean\"})\n",
    "    .sort_values(\"predict_attr_mean\", ascending=True).index, rotation=00)\n",
    "\n",
    "\n",
    "sns.despine(fig=fig, ax=axs, trim=True)\n",
    "\n",
    "axs.set_ylabel(r\"$\\Delta $ Accuracy (pp)\")\n",
    "axs.set_xlabel(\"\")\n",
    "\n",
    "\n",
    "# add asterisks for p < 0.05\n",
    "for i, experiment in enumerate(plot_diff_df[\"experiment\"].unique()):\n",
    "    if diff_df[diff_df[\"experiment\"] == experiment][\"p_group_0_adj\"].values[0] < 0.05:\n",
    "        axs.text(\n",
    "            i - 0.25, 0.5, \"*\",\n",
    "            rotation=00, fontsize=30\n",
    "        )\n",
    "    if diff_df[diff_df[\"experiment\"] == experiment][\"p_group_1_adj\"].values[0] < 0.05:\n",
    "        axs.text(\n",
    "            i + 0.13, 0.5, \"*\",\n",
    "            rotation=00, fontsize=30\n",
    "        )\n",
    "\n",
    "\n",
    "# change legend labels to no underdiagnosis and underdiagnosis\n",
    "handles, labels = axs.get_legend_handles_labels()\n",
    "axs.legend(handles, [\"Group 0\", \"Group 1\"])\n",
    "\n",
    "# remove box around legend\n",
    "axs.get_legend().set_frame_on(False)\n",
    "\n",
    "# alternate height level of xticklabels to prevent overlapping\n",
    "for i, label in enumerate(axs.get_xticklabels()):\n",
    "    if i % 2 == 0:\n",
    "        label.set_y(-0.2)\n",
    "    else:\n",
    "        label.set_y(-0.02)\n",
    "\n",
    "\n",
    "import os\n",
    "os.makedirs(\"test_outputs\", exist_ok=True)\n",
    "plt.savefig(\"test_outputs/accuracy_group_diff.eps\", bbox_inches=\"tight\", dpi=1200, format=\"eps\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now analyse results with SPLIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": 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bsvw2caRZs2Y+cSuusmXLasSIEbrvvvus27kB8IzKlSvbzk0PHTpkTZByx8KFC7Vx40Y1b948yy0aC0ph35bz589r0KBB1j+Nbr75ZvXs2dPtenr16qXrrrvO091z6ODBg1q+fLn8/f01c+ZM221EfvjhB8XExBRYXwAUflWrVtX7779ve+6FF15wWsaXYnCDBw9Wjx49rPTff/+tr776qkD7kF8OHTpk7e/r1atnPX/48GGvT76fNWuWYmNjVatWLduEbY5DAOCbiOzA5xXWE3Rv+OCDD2wzlo8dO6ZnnnnGYf4RI0bo5MmTuu+++3THHXcURBeRB6tWrdL+/fsVEhKi6dOn216bPHmy12c+AwDg61q3bq0///xTo0aN8vo/uQ8dOmSt8DBkyBCVKlXKY3WfPXtW3bt319y5c1WpUiUtW7ZMN998s8fql6Q1a9ZYf+e0Qkxmd911l0f7AsD3PPfcc7bVGMeMGaPU1FS36nj77bclSS+++KJX75VdmLdl3LhxOn36tJV+8cUXc13XsGHDPNEll6RfVd2jRw+1atVKvXv3tl6Lj4/Xt99+W2B9AXBl6Nu3r23y1O+//64jR444zO9rMbh7773Xlp49e3aBtp9f0vf33bp101NPPWV7zdsrKaRPbBk2bJhtsuKlS5f03XffealXAABHmPAAn1dYT9C9ISQkROPHj7c99/XXX2vx4sVZ8s6ePVvfffedqlSpok8++aSguog8SP+hf88996hdu3a6+uqrrdeOHDmS7ecMAK7q16+fjhw5ovXr13u7K0C+6dOnjxo3buzxeu+55x7NmTNHjz/+uMtlxo8fr7S0NPn5+VkrbnnC4cOH1alTJ61du1b16tXT2rVr1b59e4/Vn+7kyZPW3+7+469Nmza65pprdM0113j1n5gA8k+tWrU0cOBAK71v3z63/km9cuVKrVq1SvXq1VP//v3zo4suK6zbkpCQoI8//thKh4eHq3Pnzrmur0ePHgWyzzbGaNKkSZJkrQiUeWUgT/wTbNu2bfrmm280ZswYjR8/Xr/99psSExPzXG+6o0eP6ttvv9VHH32kf//73/r666+1atWqXN8SBUDelChRIsvqbn/99ZfD/L4Wg2vZsqUt7azvhUXG/f0DDzyge++91zYpZc6cOU5XLs5PGVcaGjx4cJZVlb09GQMAkFVgzllQEExCosz5WCkxWSpRTH7lysivZImcC17h8usEPfP919zx999/a/ny5Tp16pRCQkLUsmVLdezYUcWKFctVffHx8Vq5cqUiIyN19uxZhYSEqGrVqrruuutydYuJnj176r777tO0adOs54YNG6bt27erdOnSki7f7y39PtH/+9//FBoamqu+Z3bs2DGtWbNGx48fV0pKiqpXr66uXbuqUqVKua5z69at2rJli06ePKmAgABVqlRJbdq0yfEe0e7YtWuXVq9erdOnTysoKEhVqlRRixYt1KhRIwUEBHisnby4ePGiZs2aJcke9Nm4caOVZ8KECbrxxhtz3YYxRhs2bND27dsVHR2tihUrql69err++us99j7s3btXGzdu1MmTJ5WWlqaKFSuqWbNmatu2rdevtAWKuqCgIAUFBXm7GwUmNTXVuv/q6dOnVb58eTVr1kzXXnut2/ujtLQ0bd++XTt27FBUVJQSEhJUrlw51a5dWx06dFCFChXyaSvcc/LkSS1dulSHDh1SYGCgwsLC1LRpU7Vo0cKjqwucOnVKK1eu1IkTJxQTE6OyZcuqevXqatWqlerWreuxdnxJ48aN3ZpIkZSUZN0Sonv37i7fDiIn27Zt0y233KLjx4+rffv2mj9/vipWrOiRujMrXry4de/gjL9HXFGhQoUranKVp88P4JtSjdHJS/GqHFRKAUzUcckLL7xguwr23Xff1b333uvSP83TV0R44YUXfOKcrDBuy8qVK3Xq1Ckr3aNHjzydc1WuXLlArmhesWKFIiMjFRoaqj59+kiSbrzxRlWvXl1Hjx6VJK1fv167d+/O1STG1atXa8SIEdq2bVuW10JDQ/X000/rpZde0sqVK9WlS5ds6/jzzz/VunXrbF9bunSpXn75ZYfHuTJlyujhhx/Wyy+/rPLly7vdfwC5V7ZsWVv67Nmz2eYriBicu1zte2GycuVKHThwwNrflyxZUn369LFWT0hf0Wf48OEF3rf0lSduuukmVatWTeHh4apWrZqOHTsmSVq3bp327NmjRo0a5bqNM2fOaMmSJTpy5Ij8/f1VtWpVderUSdWrV/fINnj6fw4A4PMMvCYtLc2k7D1oEifMMfHPvGfin/q/fx7PvGcSJ/5oUvYeNGlpad7uqtcsWLDASLIegwYNKpB2R48ebbUZERFhjDHm/Pnzpn///rb+pD9q1aplfvnlF7faOHHihHn44YdNqVKlsq3T39/fdO3a1axfv97t/kdHR5vKlSvb6nv88cet1++++24jyQwePNil+jK+H5m32xhjjh49am677Tbj7++fJU9AQIDp37+/OXHihMv9T0lJMf/73/9MrVq1sm1XkqlevboZN26cSUpKclhPSEhItmVHjx5tjDHm4MGDpkePHg7bWLZsmYmIiHD4enYPV99Td02YMMFIMk2bNrWeO3/+vO37U7JkSXPu3Llc1T9nzhxTp06dbLepSpUq5osvvrD1I7uHs7ZnzZplmjZt6rBsWFiYefvtt83Fixdz1X9cuXYfWmi+nn+H+fzHm7I8vp5/h9l9eJG3u+gzBg8ebBtXrkjfz06YMCHb15ctW+Zw3EZGRjqsz9VH+v44p7ZcecybN8+qy1Ge9O384osvTJUqVbLNU61aNTNt2rQc37u0tDSzZMkSM2jQIFOhQgWHbfr7+5s+ffqYHTt2OK0v8+eX0yP994kxxvTu3dtpnkuXLpnHHnvMBAYGOvwcIiMjnR4PXbF69WoTERFh/Pz8HNZVs2ZNM2LECLNlyxandeXUvqPfJjl9R7OTedszfi/zy7Rp06z25syZ43K5jGMs43fAGGOWLl1qypYtaySZW265xcTFxXm205lkPq4vWpQ/++OMv8Wy+2xc2Xekj31nv2Oyk9PY8vT5AXzb6xs2m2tm/mRe37DZ213xWen75oz7p379+tnGxqxZs3KsZ8OGDUa6fM6XmJhoq9vdfXzGMu6cq/nitri7PS+++KIt/3vvvedyO96U/ptoxIgRtudfeukl2/Y899xzbtf9v//9z/Y7pW3btub999833377rfn8889N3759rd9uS5cutbXXqlUr67F79+4sdaelpZlRo0ZZ+YsXL26GDBlivv76azNjxgzz9ttvm2bNmtl+c27fvj3X7xOuLGlpaSYt2Xn8Ny05rUjHiDPKzbmvMca0aNHCVu7XX3/NNl9+x+Byc2z6448/bOUqV67sMG/m37yuns/ltm+5lf45PvbYY9Zzmf8XcM011+RrH7KTlpZmxUi///576/nMx9Xnn38+V/VHR0ebwYMHm2LFimU5h/D39ze33XabOXbsmDHGcYzliSeecFh/fv7PAQB8GSs8eEna0SglT/9FJiraQQajtK17lLZ1j/zCw1Ts3lvlXz28YDvpA5YvX25Lt2jRwiv9uHjxorp27arNmzdn+/qhQ4fUq1cvLVq0SDfccEOO9a1cuVK9e/fW+fPnJUkdOnTQ3XffrerVq+vs2bNavHix5syZo6VLl6pjx44aO3asnn76aZf7W6FCBX366ae6++67rec+/fRT9e/fX8eOHdPMmTNVrVo1ffTRRy7VFx4erlatWkm6PIM44z3u9uzZo+uvv15paWl64okn1Lp1a/n7+2v79u2aNGmSTp48qe+++05Lly7VsmXL1KxZM6dtXbhwQXfccYeWLVsmSapYsaKGDBmiVq1aKS0tTVu3btWUKVN09OhRjRgxQt9//73mzp2rkJAQl98fSTpw4ICuvfZa23LM2alfv771Oe3cudNafrJGjRrZXo1Rs2ZNt/rhqvT7xmVcyjMkJER33HGHdS/BhIQEzZgxw+1lsV999VXrqiNJuuGGG3THHXeocuXKOnXqlH7++WcNHz5cO3futF3FUqxYMds9u7O7WikpKUlDhgzRjBkzJF2+mmXw4MFq166dihUrpl27dmnq1Kk6ePCgXnnlFc2YMUO//fabx2Yzo/Bbs+NznY05mP2L8dKa7Z+pUY3uBdqnoiQ4ONja/yclJWnXrl1O8zdt2lTlypWTdHmFnnT16tVTcHBwlvzh4Vl/24SGhrq0L922bZu1YpO/v7+qVatmvZbeZ8m+75akJ554Qh9//LG6du2q559/XlWqVNHp06f1888/67ffftOxY8d03333ae3atfr0008dtv/XX3+pW7duVrpq1aq6//77Vb9+fQUHB+vo0aNauHChFi1apB9//FELFizQtGnTdMcdd2RbX82aNa1+79u3TxcvXpR0+WrK7N6n+vXr5/geSZePDTfddJNWrVrlNF/x4sVt71vGz88VL7/8st59911Jl48Pffv21Q033KDy5csrOjpaq1at0uzZs3X48GGNGzdO48aN00cffaQnnnjCrXbSZfxt8vfff+vSpUsKCgqyrZRQvHjxXNVdED777DNJl39P9OzZM8/1fffdd7r//vut4+6XX35pu998foiIiNDOnTutdO/evTV27Fg9/PDD+d52Rhn3UydOnLCuZm7RooV1JXP6b7by5ctbeePi4rR///5ct+vp8wP4tu1nzurXw5evLP/18FHdWa+WWlTgymxXvPTSS/r++++t9DvvvKO+ffs6LZN+bjJq1Cif2pcXtm1Zs2aNLd2kSZMCbT834uLislxVnW7IkCHWbw1JmjJlit59912XV82YP3++hg8fbv1+fPHFF/XOO+/YVul45JFHtHz5ct1+++3WKkbptmzZ4rT+p556Sv/9738lSZUqVdKiRYuyLEH/wgsvaNSoUfroo4907Ngx3a7oxK8AAPDhSURBVHDDDdq+fbuqVKni0jbgynVySqLitqUqpGOgKvQsLv/i/3wv05KMzsxL0oV1KQpuFaDwQSW92NPCKzExUXv37rU95yhGmZ8xuNzKvCqNJ1e+9QZH+/vu3bvbVvTZsGGDdu3aVaDHsPSVhsqXL69evXpZzw8ZMkRjxoyx0lOmTNE777zj1upNkZGRioiIsGLrFSpU0P333682bdpIuvw5T548WW3atNHKlSttZTPGojPGPzLK7/85AIBP8/aMi6IoZXekiX/+A/uKDjk9nv/ApOyO9HbXC9z1119vm4GY8erN/JR5hYcRI0YYSeb66683Y8eONZMnTzZjx441nTp1svWvefPmOda9fv16U6JECavMmDFjss23bNkyExwcbOX73//+5/Z29O3b19a/hg0bmooVKzqdxZyTjLOEq1SpYlq3bm06duxoTp8+nSXvmTNnbO9ReHi4OXXqlMO6ExMTzdVXX23l79ixozlz5kyWfOfOnTPXXXedla9z584mNTU1S77Zs2ebGTNmmBkzZpgmTZpY+V999VXTvn17I8l069bNvP/++2bixInm1VdfNeXLl3c4AzrjrFpHV0PnhwMHDhg/Pz8TGBhooqKibK8tWrTI9hm3b9/erbo/++wzW/nPPvss23zTp083AQEB5tZbb7Xypq/w4UyfPn2s/A0aNDCHDh3KkufSpUvWqiPp+fL7qlQUHp//eJN5f0Ybh4/Pf7zJ2130GfmxwkNGma+Cz+lqxIx5XbmiJP0qbVeuIvnPf/5jq//11193mDfjvvvWW281/v7+ZvLkydnmnT59um21ojfffNNhvdu3b7fy9e3b1yQkJGSbb8GCBSYoKMhIMkFBQeavv/7KcftyuqI9s1WrVlnHu4zH/oiICPP0008bSaZJkyZm9OjRZsKECWbs2LG2q/Oza8Odz++ZZ56x8oaFhZlNmzZlm++vv/4y1apVc2nbXG3/o48+Mn5+fiY0NNSsW7fOaT8dKegVHrZu3Wq19dZbb7lVNrsVHj788EPratWXX345H3qcve3bt5uAgIAsVwzVqlXLvP3222b//v0eacfV8bBy5UoTEhJi/P39zeeff55jvZlXhsiOs7GVfn5w3XXXmffff99MmjTJvP/+++aaa65x+/wAvu+JlevMNTN/sh5PrMzd/uZKl92qCMYYc/vtt9vGxc8//+ywjq1btxo/Pz9TsWJF2+pvvrDCg7e3xd3tadCggS1/bo+TBembb74xkkzLli2zfT1jHCCn9z+jmJgY22+QG2+80Wn+r7/+OsvxzZl58+bZ8i5YsMBp/oyxrl69erm0DbhypaWkmb+fijN/P3n5cej/LprEqMsxrsQTqebQ/120Xvv7qTiTlsIqD7k59824wpokc/XVV2ebLz9jcOlyc2zKvEKto/idMYVjhYf0Pmb3W/nll1+29ePZZ5/Nt35kJ/37NXLkyCyvZf5fwPz5812uNzY21tStW9cq27p162xXRD5z5ozp3Lmzad26tW1lypziNgX5PwcA8EVMeChgqUdOuD/ZIcOkh9Qjrt8W4ErgrRP0jAGIOnXqmGLFiplPP/0027wPPfSQrY+bNzte4jQmJsbUrl3byjtgwACn/ch4kh0cHGwOHz7s1nZERUXZ/oGf/njooYfcqiejzD+aQ0JCnE5iOHXqlO3WEs62OeM/TEJDQ7OcWGR0+vRp27Z98MEHTvudMVjeqFEjU7x4cduyZOm2bt1qBe99ZcLDa6+95jAQkpaWlmV5s5yWTU936NAhU7p0aavcsGHDnOZ/9dVXs/xDw5lPPvnEylusWDGn/UpISDD169e38me8BQuKNiY8uC5z0CfjsruOHulLKBamCQ9r1qyx3ZrhxhtvzHbSW7rM+8iXXnrJaf0Zl6ksVqyYw6WG0yc8VKxY0cTHxzutM+PksrvuustpXmPcn/CQUcbfMHXr1jXFihUzo0aNMikpKbZ8Fy9etN6bvEx4+Pnnn90K8C9fvtylbXOl/fTjUtWqVfO0JHRBT3gYPny49f1y9lsnOxm/z9dff721dHZAQIBL/+T3tHfffTfL78yMjzZt2pjXXnvNbNq0KddLMLsyHubNm2dKlSplihUrZmbMmOFSva5MeMgo8/lBYGCg+e9//5tt3vvvv9/l8wP4vm3RZ2yTHdIf26KzTswu6hxNEli3bp1tTHTo0MFhHekTod99991s63b1d0i6jGU8MeHBm9vi7vZkjgUUhtsnpE8E+PDDD7N9PX1CRPqjb9++LtX73//+11Zu9erVTvOnpqaa6tWru3ycaN68uZWvc+fOOfZn/vz5trp37drl0nbgyrX/xX8mPPz9ZJzZ92ycOTU7wex71v78/pe4OMQY9yc8HD9+3FStWtVWZunSpdnmza8YXEbuHpsmTpxoK9OgQQPrNknZKQwTHtL399nFc/ft22frR3h4eJbz2fwSGxtrxUqz+w2feUKcK+f36f71r39Z5UqUKGEOHDjgMG90dLQpV66crS1ncZuC/p8DAPiiy2t8okAYY5Q8/RcpKTnnzNlJSr58G4z/v/xeUXDmzBlbOrvlsPNbZGSk7r77bo0YMSLb199++23bEogbNmxwWNcXX3yhgwcPSpL8/Pz05ptvOm178ODB1jLWcXFx1vLHrqpcubK1pGJGAwcOdKseZx599FFVrFjR4esVK1bUY489ZqW//fZb7du3L0u+48eP65NPPrHSI0aMUOXKlR3WGxYWpgcffNBKf/DBB0pNTXWpz3v27NHrr79uu+VHupYtW6phw4Yu1VMQjDGaPHmypKxLekqXv0eDBw+2PTdhwgSX6v7oo4+sJdP9/Pz0yiuvOM0/atQolSzp2tKJ8fHxtu93//79nd7OpESJEnrqqaes9Jdffqlz58651BaA7G3dujXHR8ZbPRQG0dHR6t+/v1JSUiRJ1atX17Rp06xl63NSokQJPffcc07zPPfccypVqpQkKTk5We+99162+UqXLq2IiAg9/fTTOe4bBw0aZC3x/9NPPyk+Pt6l/ubVgQMH1KVLF73//vtZltkMCgrSrbfemuc2XnzxRevv6667TjfeeKPT/BEREWrbtm2e2jTGaOTIkXrrrbdUr149rV69utAs6RoTE6Np06ZJku68806nv3Vysnr1ao0dO1bS5e/j1Vdf7ZE+uuPFF1/Up59+qqCgoGxf//PPP/Xmm2+qXbt2qlGjhp5++uksy/HmVfqtYvz8/DR37lzdc889Hq0/O5GRkbrrrrv0+OOPZ/v6mDFjXD4/gO/7eudet55HVh06dFDXrl2t9Pr167V48eIs+Xbv3q3Zs2crJCTE4fm3txWmbYmJibGl03/f+KoDBw5o1apVKlasmMOYRb9+/WxxoXnz5mWJG2UnfXl66fLtJq699lqn+f39/W3LmDuzfPly7dixw0q7chzq3r27ihUrZqXTz/lRdAWW87OlTbJ0YVWKTKbTtcAQez44d+nSJU2dOlXt27fX8ePHrefff/99denSJUv+/IzB5cahQ4f09NNP64EHHrCeCwsL09y5c33qlk/uyml/X69ePV1//fVWOioqSr/++muB9G3mzJm6ePGiWrVqZd1mIqN+/fqpdOnSVvqnn35y6TgUHR2tL774wkoPGjRIderUcZi/QoUKtnh6Tgr6fw4A4IuY8FCA0vYdlomKzlMdJipaafuPeKhHvs9XTtCffPJJh69VrlzZdh8xZ/cCHjdunPV327Ztc7z/dkBAgO0fB5MmTXKht3YDBw7Mct/gxx9/3GP/5HJ0L/KM7rzzTutvY4wt2JDuf//7n+0ema4ECW655Rbr76NHj2rJkiU5lpEu/1PAUYBauvwPhBMnTuQYBCkIy5Yt08GDB1WpUiXdfvvt2eYZMmSILag+depU65+BjqSlpdmCKun/iHCmbNmytuCeM999951Onz5tpd39POPj4zVz5kyX2sKVY8/hRfrmlzs1fu7N1uNigvPj5sWEaFv+b365U3uOZA06F0Xm8kpeTh+1atXydjctHTt21JEjR/Thhx9m+3paWpruu+8+616egYGB+u677xQWFuZyG127dlVISIjTPOXKlbPt677//nvFxcVlyVenTh0tX75cL7zwQo7tBgcHq1KlSpKkpKQkbd682eU+51XGCQmZvffeezpx4oRGjRqVq7qXL1+u7du3W+m77rrLpXI9evTIVXvS5UkoAwcO1Lhx49SiRQutXr3aaaDI10yePNn6PrkTwMpOWlqa9XdMTIx69OihrVu35qnO3BgxYoR27typ4cOHO538c+zYMX344Ydq1aqVbr311iz3UM6NTz75RIMGDVJwcLAWLlxo+y2R35ydH1StWtU2gdbZ+QF82/YzZ7X+5OlsX1t/8rS2nzlbwD0qvF566SVb+u23386S591331VaWppGjhypsmXLFlTX3FZYtiXzb56CmnCZWxMnTpQxRj179nT4+6506dK2CxeSkpKsiYSOxMTE2I6P7dq1s50/O9KqVSuX+p15wkvnzp1zLFO8eHHVq1fPSq9bt86ltnDlCgxxLUQfWI5QfnZat26d5VGvXj2VK1dOgwYN0rFjxyRdnvA0a9Ysh+c/+RWDc+ann37K0vcWLVqoSpUqql27tj788EPrd3+XLl20adMmWxy6MErf399+++0OL6LLOMlDyt+JJdm1k7n9dMHBwVmOQ9OnT8+x3unTp9vi3q7E0/v06ZNjnnTe+J8DAPgafiUVoNS1WzxTz5o/PVJPYeALJ+hlypRRu3btnOapXbu29XfmSRrp9u3bZ820lFw7CZakpk2bWn+fOHFChw4dcqlcusOHD2f5x8q2bds0ZswYt+rJjr+/v1q0aJFjvtatW9uuXli5cmWWPBmDBKGhoU5XA0iX8b2RXA8SdOrUyTYbN7Py5csrPDzcJ2ZLp08OGThwoHV1cGZ16tSxTWo5efKkfvnlF6f17ty50zYDuX379i71Jz+DPrVr17ZNaiLoU/Ss2fG5zsYcVFz8KethTJrTMsak2fKfjTmoNduZmV4YlShRQtWrV1doaGi2r7/99ttauHChlf6///s/tyemtW7d2qV8GfeJiYmJ2rhxo1vtZCfjcfDkyZN5rs8VQUFBuu666xy+HhwcrPDw8FyvoJV5ouE111zjUrk33nhD586dc2mySEbx8fHq06ePpk+frmuvvVYrVqywrkopLMaPHy9Jat68ue2qpdxo3ry5Ldh59uxZde/e3TYJpaDUqlVL48eP18mTJzVx4kTdfvvtTicq//rrr2rVqlWeJje+8cYbevzxx1WpUiUtX75cnTp1ynVd7goODs5xRY2M/8hydH4A35fTKg6s8uC6bt262Y4TK1as0Jo1a6x0ZGSkZsyYoaCgIKcTinxBYdmWzJMGspvA6Sk//PCD6tev7/Rx3333OSyf01XVGWV+Pad/gm3bts02STCnfwKlq1Klikv5Nm3aZEu7umJkuXLlrL+3bNniUhlcmVJijdISXVvNNy3RKDWu6Kz866rsVjOMjIxUqVKlVK9ePfXr108TJkzQoUOH1LdvX4f15FcMzplz585l6fuOHTsUHR2tsLAwtW3bVv/617+0du1aLV261KcuWsgNV/f3d911V5YVfaKj83YhaU4OHDig1atXq3jx4k6PWe4eh6SssXBX4rDNmzd3aTVLb/3PAQB8DRMeCohJSFTads8EQ9K275VJSPRIXb6uIE/QHalXr16Os/8zXrGRmJj9Z+OJk2DJvRNhY4wefPBBxcTEqEOHDrbX3nnnHf31118u15Wd0NBQl1bdCAwMtP0gz24bMr4/DRo0cKn93L43jRs3dilfXn388cc5Bn2ef/55h+VjY2M1e/ZsSZ4P+mR+r/Iz6FOpUqUcr6iWLi+5ljEfQZ+iJyn5kk/VA9+xZMkSvfHGG1b6jjvu0NNPP+12PVWrVnUpX+Z9orP9UVxcnCZMmKC7775bDRs2VLly5RQQECA/Pz/bI2PwIDY21u2+50b9+vWz3MrCk37//Xdb2tXjd/HixVWuXDmXb5MkSRcuXNCNN95oBRN79OjhcHKMr1qxYoX12+vRRx/Nc30VKlTQ0qVL1ahRI+u56OhodevWTTt37nS5nhtuuCHH3yuu3pKhbNmyGjx4sLXE+E8//aQHH3xQ5cuXz5I3ISFB9957r9sTHI0xeuKJJ/T6669Luhyoa9mypVt15JWnzg/g25yt7pCOVR7c8/LLL9vSb731lvX3mDFjlJKSouHDh7u1epO3FIZtyTwpMD//URQTE6P9+/c7faRfYZ2dpUuX6tChQ6pSpUqOq/Vcd911tt8cW7ZscfpbLfN2u3JuKrl+S9VTp07Z0kFBQVl+B2b3WL9+vVXm/PnzLt+iE1eOxBNpOvVtog69eUkJkc4n+qdLOJCmg29c0qlvE5V4wrUyRUF2qxmmpaXpwoUL2rdvn7777jsNGTLE6flHfsbgnBk8eHC2/U9OTtbp06e1adMmffzxx+rYsWOu2/Aly5Yt06FDhxQeHu50f1+6dGn169fPSicnJ+e4ok9O5zT169d3eizKuNJQhQoVHOa77rrrbJOb//zzzxxX2st4nCpbtqzT20OnK1WqVJbYd3a88T8HAPBF2U9VhMeZ87FSmodm4KYZmQtx8itZwjP1+bDw8HDt2bPHSuf3TM7slClTJsc8JUrk/FlkPgl+7LHHcrWUsTvvwfjx47V48WK1bNlSK1as0G233WZdeZ+UlKQHHnhAa9euzfU/Q5ytkpBZxh9RsbGxSkpKslZQiImJsQWCN27c6NISk5m5+t4U1JKiZ8+ezXEJY2dX+X7//fe6dOmS2rdvn+O9yfv27auRI0daVxDOnz9fp0+fdvgDuiCDPqdOncrXzxP4f+zdd1gUx/8H8PfRsSAiVUFQ7KDYu2LvNXbRgBo1iTHGEnuLscWuUbGDqNg1lmgsEayxJrHEBgpIR0A60m5+f/C7/d5e3TsOOPTzep57dO9mZ2fvjtuZ2c/MkE9bTEwMRo8ezY3Mc3V11bpDSeh1S7bhr+z36NSpU/j22295v+WVK1dG3bp15WYJev78ObecFGMlMyqruK93snUbIZ0x2oiPj4enpyevE2n58uXo2bNnmer4k6yLWrFiRYwdO1Ynedrb2yMoKAidOnXiloh4//49unTpguDgYEFBnuHh4WpH82gzy5q5uTn69++P/v37w9fXF6dPn8Yvv/zCm3ksPz8fs2bN4o2MViU/Px9ffvklDh48yD0XFBSEtWvXYvbs2RqXUVu6ah8Q/SZ09oa9z19jU4fW6hMS9OvXD40aNcKTJ08AAJcuXcLDhw9hb2+P/fv3w8TEROtllkpaWTiXdu3a4fr169z2ixcvlE7RXtoko6ozMzPRrFkztell62Z+fn7YvHmzwrSyA2eEBlwKbcNKB7KKRCKtg/AyMzP1eikXojtMzJBwNBfp97VbBoHlA2n38pF2Lx8VWxrBdoQJRAaa97kQvuLsgyP/I2nLjx07VuksGhLjx4/Hvn37ePtOmzZNaXohy8gpW+KZMcYt66BsOQsJkUiEcePGYeHChbyybdq0Sek+0tctoX2wQGE/bHKy6uDa0rjnQAgh+ogCHkpKjuKLqdY+5qpP8wnQhwa6kKmjhJAdzens7KzVjQGhnadhYWH48ccfYWRkBH9/f5iYmGDXrl1o2LAhMjMzARQGFmzcuFHrjhhNbmLLdiqkp6dz0bKy703FihVRs2ZNjcsjdPaB4hztqkuSTp+wsDBB07BLjwjJy8vDwYMHMX36dIVpS7LTx9TUVKtZNbSdYp0QIpz0tIf6KD8/HyNHjuQa8GZmZjh+/LhGHQTShP6GKbpmyQoMDMSYMWO44IV+/fph+fLlSpf+cXFxKfEpIov7eif9vhgbG+usziRr/PjxyMrKgpeXF4KDgxEdHY2CggJ4eXnh33//LRM3B+Li4nD69GkAhVPkCrlhLpSDgwOCgoLg6emJ0NBQAIVBIl26dMH169cFz7xRnIyNjTF8+HAMHToUP//8Mzc7AwDcuXMH0dHRqFatmtp8Nm7ciKysLDRt2hRWVlZcIO/ChQvRtWtXQTfJdKGs1CWJ9oTM7iAhmeWhYRX5mUwIn0gkwvz58zFy5EjuueXLl6N69erIzc3FpEmTBM/GVNrKwrl069YNK1eu5LaLc8kjHx8f+Pj4aLVvWloaTp06xf1f3ShZRQ4dOoS1a9cqXJZStl358eNHQXkKDVCVvqaLRCIaIUvUyk9hWgc7yEq/nw+rnsYwtqKAh6Iqzj44Uig9PZ37vT9+/DhvyUohHj9+jH/++QdNmjTRedmuXbuGd+/eAQDmzJmD+fPnq0wv268quQ5JL2WpLL0mMx0K6cMo6XsOhBCiryjgoaSYKr7Yac1MvhH3KSrJBnpxk+3YXrZsGb788stiORZjDOPGjUNmZiYWL17MVQRr1KiBlStX8qJhFy9ejEGDBgle0kD2OELJdipIvx+y703z5s1x7do1jcujb5YuXcrr0NdEaGgobt26BaAwwlabKFs/Pz+lja2S6PT58OEDgMIp5KnThxCijfnz5+PmzZvc9pYtW4rUuSH0N0zVNQsoXGd1ypQpXH49evTAmTNniu2Gv76Sfl/y8vIgFouL5T3IysrC9OnTsX79egQHB6Nbt24Qi8UICwvDN998o3ZqU32we/dubjSRNqNt1KlatSo304NkZFNsbCw6d+6M69ev86ZclVWSgU8GBgZYsmQJ/vrrL1y6dIl7/t9//xUU8JCVlYWOHTvi3LlzyM7OhoeHB+Lj45GXl4fRo0fj77//1mgGMkKUETq7g3R6muVBmGHDhmHx4sXcrDRnz56FiYkJDA0NVS73p4/0/Vzatm2LKlWqICkpCQBw5cqVIl2r09LSuNHMpqamOhvJLBlV3blzZ436Afr27cstdZWUlIRz585hyJAhculklxVJTU0VlL/QJVVtbW25/4vFYmRkZFDwPlHJyFKEii2NdBL0ULGlEYwsKdihqIq7D44UOnr0KLKyCpcg1bYN4ufnp7RPoCgzKUrPIvns2TON909MTMS5c+fwxRdfKHy9QoUK3PVHaB8sIOycSvKeAyGE6LPPq1e2FIksKwK6ml7MwACiSp9H40nSQJeQNNC1lZaWhqioKERFReH9e2EjdnRFuhEsKUtx2bJlC65fv45GjRrxptcCgO+++w5t27bltrOzszFhwgStKoWSmSKESElJ4f5foUIF3sgLCwsLXnRrcb43ZYUksnzcuHEK1/JT9mjQoAGXx9OnT/Ho0SOF+Zdkpw99noQQbZw9exbr1q3jtr/88ktMnDixSHkKvW5JX7MAyK3f+dtvv/HSzJs377MLdgDk6zay75uuzJ07Fxs2bIBIJELnzp15N5ECAwMREBBQLMfVlYKCAuzatQtA4Xqv6qbI1ZajoyOCgoJQo0YN7rno6Gh07twZYWFhOj/e2bNnsWPHDrx8+VLjfUeMGMHbFloP6dy5M/744w9YWFjAzs4O+/fv50Y9vX79GlOnTtW4LITI0mR2BwnJLA9EPQMDA8ydO5fbZowhJycHo0aN0mqWv9Kk7+diamrKC7KLi4vjBZJqaujQoXBycoKTkxNWr16tiyIC+F/bd8yYMRrtJ7s8lLIlzxo1asSrp4WEhAjKPy4uTlC6li1b8rYlsy0RoozIQAS7UaZwmm0Oi9ZGEGk4HFFkBFi0NoLTbHPYjTKl5Sx0oLj74Eghyfu8f/9+we9xbGwsb3a1wMBA5ObqdubrtLQ0nD59GiKRCOHh4YLLduzYMV4+qpbelO6HFdr2AYT1w5bkPQdCCNFnn1/PbCkRmZnCoGEdneRl0LA2RGafxxRDZaWBLkRJNYJDQkIwb948bikL2am0DAwMsG/fPl6AwY0bN+Dr66vxsT58+CAoKjU/P58XuasoErdFixbc/4WsufYpE4vF3M2b4ur0kZ2erzg7fZKSkortJhj5tJgYl9OrfEjpCQsLg7e3NxeM5+bmptV1SlZsbKygdLK/ibLXLdkZp0pqGn19I33tBoRfSzTVs2dP3vayZct415nvvvtOr+sO586dQ1RUFIDimd1BmpOTE4KCguDi4sI9FxkZic6dO+t8SZUNGzbgm2++wR9//KHxvo6OjrxtocvUdOzYEebm5tx2z5498cMPP3Dbfn5+OH78uMblIUSaprM7FHW/z9GYMWNQvXp1blskEmHevHmlWCLt6fu5/Pjjj7ylH1etWqVVPq9evcKff/4JoLBPoahBqBIhISG4ffs2zMzMMHToUI32HThwIG9U6x9//KGwrmdhYcFbcuzhw4eCBnwIXVqje/fuvO179+4J2o8xhu7du6N58+aYMWOGoH3Ip8XUwQC2I0zhsqQczGoI66I3q2kAlyXlYDvCFKYO1K2vCyXRB/e5iI2NRfPmzdG8eXO532PJ7325cuWUzoKgiL29Pbp168ZtJyUl4ezZszorM/C/mYbat28PZ2dnwfv179+f1475448/lPabSvfDpqWlcct2qvLx40dB/akUeEcIIYWoZlSCDNs21k0+7XS/TpU+0/cGulA1a9bkjfIQ2ggGgM2bN6N58+Zo2bKlyoqOWCyGj48PsrOzMX/+fKVTfNWtWxdLlizhPTd37lxurTKhxGKxoGVG/v77b+Tn/2+qvg4dOsilke4kSElJwatXrwSV4c2bN1xluixMaS3En3/+icjISDg6OqJTp04a7evl5cVb3y0wMBA5OTly6Ro0aMAbsXz//n1B+Rd3p09GRgZatWqF5s2b80Z2k89Du4bfwsrCBRXMbbmHSKS6qiISGfDSW1m4oF3D4r2hSIpXTk4Ohg0bxl3vKlSogBMnTqBcOcWBLLGxsdizZw9u3LihNm+hy+tI/yaamprKdSDIzhQhZA1OTWZFKiu0/a1ft24dunXrht69e2s1OsfIyAiBgYHcTY709HR4eXnx6hr6ZPv27QAAOzs7hVNt65qzszOCgoJ4nXURERHo0qULF3ihS0LrbNJkRzVp0rEoa/Xq1bw676RJkxAZGal1fuTzps3sDhI0y4NwxsbGmD17Nrc9aNAg3ijZskTfz6VixYrw8/Pj2oiXLl3CuXPnNM5nxowZ3Gyb48ePR7169XRSPslo3/79+8PCwkKjfc3NzXlBEgUFBThw4IDCtD4+Ptz/379/z01drwxjTPD71KFDB15AxeHDhwXtd/36dVy9ehWPHj1C/fr1Be1DPk2GFUQwMBU2S4OBqQiGFWhGB10qiT64z0VOTg4ePXqER48eyb0Pkt/7gQMHarzsT3EHlkjy0zTgxczMDMOGDeO28/PzlV6HOnbsyNsW0g/77NkzQTNdl8Q9B0IIKQso4KEEGdSqDpG9tfqEKojsrWHg6qSjEpUN+t5A18R3333H/f/evXuCphcuKCjAli1b8OjRIxgbG8PS0lJp2vXr1+POnTvw8PCQW8pC1qxZs9C0aVNuOz09HZMmTVJ/EjLOnDmjNs2pU6e4/4tEIowbN04uzcSJE2Fq+r+ZS4R2Ehw4cICrTMuONNU16fIVFBTIvb5s2TLMmjWLC6zRlqQRMGrUKI2nSHdycuI10D58+KDwMzIwMOCt5/b333+rHfmZkZEheE3VYcOGwc7OjtsW+nmeOHEC9+/fx6NHj3idRuTzUNepG8b3OYWvB/7BPcqbqb5uljez5qUf3+cU6jp1U7kPKRnSSxfJ/mbm5+dj1qxZmDVrFv755x/ea9OnT+dNBbp7926V1+xXr15h4sSJ2Ldvn9oyXbt2Denp6SrTfPjwAUFBQdz2sGHD5Dpk7O3tedtv375VmWdsbKxG68Cqu978+uuvmDVrFk6cOCE4z+LQsWNHNGrUiNs+efKk2n3EYjF+/fVX/Pnnn8jJyeF9TzTh6urKBRIAhfUq2WBOfRASEoKrV68CAL766iu5mbeKi4uLC4KCguDk9L92w9u3b9G5c2fExMTo9Fi///478vLyNNrn+vXr3P+rVq1apGU+TExMcPjwYS4oKiUlBWPGjCnSMnjk81XUWRpolgfhJk+ejMjISERGRiq9OVBW6Pu59OzZExs2bOC2vby8NJp2feHChbhw4QIAoEaNGjqbLbMoo6olhN4E8/Hx4Q2kWbZsmcp89+/fr9GAkNWrV3N9VtevX8f58+dVps/Pz+eWQ3FwcND6/MmnIz9VWL0lP4XqN7pWEn1wnzuxWMxdH7X5vRs8eDCvTX7p0iXBszeqExISgjt37sDU1JQXvCCU0OvQqFGjeG3f06dPq81bk+9Scd9zIISQsoACHkqQSCSC8eg+gImWHZ0mxjAe3YcXOfq50NcGuqYmTZoEV1dXAIUjBqRHgijj6+vL3UhRlf7FixdYvHgxjI2NFS5lIcvIyAj79u2DkdH/Fgu8dOmSxlGy27dvV3kTJz4+njcN+fDhw1GrVi25dPb29pg+fTq3vXHjRrUd8lFRUdi8eTOAwpE0deroZtkYZaTXRJONemWMYfny5Vi/fr1Wa1lLpKamcpXe4u70mTZtGsqXLw/gf+VXZd26dcjOzhZUBjMzM/z000/c9sGDB9XODpGens7t06RJE96UdYSQskfVb2ZcXBzWr1+P9evX837rDx8+zLtmTJkyBSNHjtRZmT5+/Ii1a9eqTPPLL79wyzUZGRnhxx9/lEvTuXNn3vb+/ftV5rl161aNyqnqvQMKR2GsX79eo5EbxUV65q0bN26oDfo7cOAAdwNh2rRpRTr2mDFj4OXlxW2vXr2adyNdH/j6+oIxBkNDQ0yePLlEj12jRg0EBQXxlo8IDQ1F586dddZBCBQumbF+/XrB6UNCQngBSlOnTi1yGerWrcvVCYHC7+KKFSuKnC/5vBRldgcJmuVBOCMjIzg6OsLR0ZFrk5RVZeFcfvjhB+zZswcmJiZIT0+Hp6cnNm3apHJ2pLi4OIwePZr7PbW3t8f58+d5MwUWxdWrVxEVFQUrKyv07t1bqzw6derEC+57+fIl7t69K5fOwsICO3fu5PrTrl69irlz5ypc2uLGjRuYOnUqWrduLbgcvXr14vXXeHl5KR0skJ6ejlGjRuHevXsQiUTYuXMnb7km8nnKT+F/F0XGQKUORhDJdO3lp6pfjoUIV5J9cJ+zq1evIjIyEjY2NujRo4fG+8sug1FQUMAFzBWV5PPq06cPKleurPH+HTp04M1W9+LFC4XtdBsbG95s0wcPHlQ5cCI5OZkX4K9Ocd5zIISQsoICHkqYgaM9jMcN1jzowcQYxuMGw8DRXn3aT5Q+NtA1Vb58eRw7doxrzJ44cQIzZsxQOiruwIED3FqOQ4cOxcCBAxWmKygogLe3Nz5+/Ih58+bx1gVTxcPDgxtVIDFjxgzBneA2NjaoXLkyBg0ahORk+Y69pKQkfPHFF0hLSwNQeANny5YtSvP7+eef0b59ewCF65n17NkT4eHhCtO+efMGPXv2REpKCqysrHid3MWlefPm3P9lRyRfuHCB+xyLspb70aNHkZ2dDXd3d96oWU0MGTKE12Fy+fJlREdHy6VzdnbmBf/s2bNHaWX62LFjWLFihUadPpMnT8aIESMAFH5H+/btqzToIT4+Hv369UN4eDhMTU2xe/fuzzK4i5BPiarfTMlIBZFIxE1F//LlS95MQy1atOAFO+pC69atsXLlSgQGBip8/dChQ7yAiAULFij8Lfb09OTNKrRu3Tqloy+OHz+OX375RaNyqnrvnj17hjdv3gAo2vVGV/r06YNZs2Zx26NHj5Yrs0RwcDA38qRPnz5K6zWa2L59Ozd9p1gsxpgxY/Dhw4ci56sL2dnZ3Iixfv368W7IlBRXV1cEBQWhatWq3HOvX79Gly5dEB8fr7PjzJ8/Hz/99JPaKXyDgoLQpUsXZGVlASj8O5cOeC2Kr776ije1+bJlyxTe9CJEGV3NzvC5zfLQp08fNG7cGI0bN8aOHTsAAA8fPuSea9y4sdYzyyjKW9Frixcv5p4/e/Ys79jSVL2mj+dS1PORNWHCBFy7dg3u7u7IzMzE9OnT4eDggEmTJmHz5s04evQoDh06hDVr1mDgwIFwdnbmZurr0KEDHjx4oJPlOiTn6+3tDQDIzc1FixYtFJ6/MosXL0bjxo3RpEkTrr9BYsiQIQo/r/79+8PX15drZ/7yyy/cUorHjh3Dzp07MWzYMHTu3BnNmjVTOwuErNWrV2PRokUACvszunbtit69e2Pz5s04duwY9u/fjx9++AG1atXCiRMnYGhoiB07dqB///4aHYd8elgBg/jj/7ZNqhrAaaY5bL4whdMMc5g4/K9vRJxdmJ7oRkn2wZVFcXFxCA4OFvxQVveWtIlGjBjBG3inCdnAEkmeRSE984R0IL0mRCKRXLCMsqCXVatWoUaNGgAKr31ffPGFwj74Dx8+YPDgwbC0tISNjY2gchTXPQdCCClLRExRODMpduKoOOQFXgCLUz+9scjeGsaj+3zWwQ7Sbt++ja+//hrPnj0DAFhbW2Pw4MFwc3ODvb098vPzER0djdu3b+OPP/7g1obu0KEDAgMDeaPcpO3YsYPreIiLi+M6gcuXL8/NSDBgwACu0Sud/t27d1zneuXKlVG9enW59NLu3buH/v374/37whFErq6uGDlyJOrWrQsTExOEhYXht99+4yJCe/XqhRMnTvBGi5w9e5brDMjOzsbr14Udaw0aNOBmd/j666/x9ddfyx3/4cOH+OqrrwAUVrBevHjBe71atWqwti6cRn7ZsmUYMGAA95q/vz+3JIWzszNOnjyJzp07w9TUFF9++SUaN24MAwMDPH36FP7+/tz7aG1tjWvXrqFhw4YK33+JjIwMjBgxgpuRw9zcHMOGDUO7du1gZWWFpKQk3Lx5EydPnsTHjx9hbW2N33//XW59daCw4/vhw4cACkc0StZPt7Oz401HLnuOyjx58gTNmjVDfn4+DAwMMHXqVLRs2RJhYWFYu3YtUlNT0bx5czx48EBtXrIkHVSRkZFITk6GlZUVd2NE2ecoS/p8X79+zZuNwcXFBZUqVQIgv4b9/PnzeSN0O3XqhC+++AJ2dnZISEjA+fPncenSJYwePRrdu3fnff7KAlIk8vPzMXnyZG4kp5GREQYOHIguXbrAxsYG6enpuHfvHo4cOYK0tDSUK1cOR48eRb9+/dSeL/k87LvwBZLTwpW+bmXhgvF9Til9/VMn3bktfS0CILcszJ49e3g30oVQdb2oX78+NyWjorwvXLiAvn37Aiic9eXHH39EvXr18OzZM2zYsAE5OTkYOnQojh8/DgDo0qULbykJc3NzQcsd5OfnIzMzE97e3go7PVxcXLgle/z8/HDlyhUEBgaia9euGDBgAOzt7ZGYmIhz587hjz/+4PabPHkyr1Nc1tu3b9G+fXuug0IkEqFv377o3bs3bGxs8P79e5w/fx4XL17ExIkT8ccffyAyMhJA4dSnVlZWABRfg+Lj41G7dm1u+Q1vb29069YNCQkJ2LBhA6Kjo+Ho6IiQkBCYmZkJqsMAyuslABATE4M+ffpw29IBaq6urtwUoorKyxjDggULuGuJsbExhg0bBk9PT1hZWSE+Ph6XL1/G+fPnIRaL0bJlS1y6dEluykzp77Oy40u+a9LljY+PR1xcHJde+v1V9N2Uvl4C8t9t2XoCUPh9lg4aEGLfvn2YMGECgMJZtLQZzST9NwgAz58/5zqtZD9fVWV8/fo1OnXqxOtQkz5PVd8NZQYPHozffvuN91yVKlXQv39/NG3aFNbW1jA2NkZaWhpevXqFK1eu8IJhunfvjqNHjyocSSWkDiddP+rTpw9iYmKQmZmJ0NBQLh/pevnXX3+NqlWrcvXnjIwMLngI4P9mSt7LkmwfkNKVkJ2NAb9f1Vl+Z/t2g+1nMmJb+jqrTFhYGFxcXIolbwC8OoB0e1Ud2e4wfTsXoGjno4xYLMahQ4ewe/du3LlzR+HyWQBgaGiIrl27YvLkybyRtUWl6r1QVp+T5ePjo3aGLUDx53Xjxg189913ePr0qVz68uXL49tvv8WKFStw+/Zt3qxeQt/fO3fuYOHChQgODla4j4GBAXr06IGVK1dygb+ExAV8RObTAli0NkKVASYwMJYKcshlSDqXi7S7+SjfyBD2Y81KsaSlR5O2r7q6e0n3wUn34QL8to50PREoDH7fs2eP2uOrKk9ycjLX9gT47SnZumhgYCC+/fZbbjs1NVXjYyvy4MEDrh3z4sUL5Obmonr16lzdX2j/hOSzEovFcr/b7u7uMDQ01Pg9k9TZ8/Ly8Pz5cwD8/nTZflNFpNtpaWlpvCUkzMzMULduXQDy36c3b96gU6dOiIqKAgBYWVnBx8cHjRs3hkgkwpMnTxAQEICsrCwEBQVhyJAhvP4MHx8fleXSxT0HQggpsxgpNWKxmOWHRLAc/99Y9sw1LHv6L/97zFzLcvx/Y/khEUwsFpd2UfVOQUEBCwgIYB06dGCGhoYMgMKHoaEh69GjBzt58qTaPJcsWaI0H8nD29tb6/Sy3r9/z3744QdWsWJFpfvXqlWL7dixgxUUFMjt7+fnp/b4S5YsUXjsoKAgtftKHn5+fkqP6+zszBhjLCQkhHl6eir9DIYNG8aio6PVfgYSYrGYHThwgNWtW1dpucqXL88mTZrE4uLilOajrEzqzlGV/fv3K/3MWrVqxSIiIgTnJU2bz1Hb81Xk1KlTrEaNGgrTW1lZsTVr1jCxWMz7/F1cXASf3/nz51mzZs2UlsnExISNGDGCvXnzRnCe5PPw8t0Vtvf3wcz3t55yj72/D2Yv310p7SKWKqG/5QBYUFCQxvkLvV4oy3vVqlXM1NRU4T69e/dmycnJXFoPDw+NzkfoNdfZ2Zn3ey8Wi9maNWuYlZWVwnyqVavGAgICBL0/sbGxbMSIEUrrIg4ODmzv3r1y5RByDfrjjz+Yra2twn3q1q3LHj9+zKUVUidR9R4xxlhYWFiRr5k3b95kHTt2ZCKRSOG+lStXZosXL2YfP35UuL8m3zWh5VX03RR6vZR+hIWFKT1vZZo3b86AwvqctvV5Teps6sr48uVLZm9vr/F3Q5VHjx6xxYsXs44dOzJzc3O1ZTQwMGDdunVjx48fV5mvkM9Iun6k7O9LNr2Q+rP0e1nS7QNSejJyc1m/c5dZq+Nni/zod+4yy8jNLe1TIkSQ5ORkduHCBbZ37162atUq9ssvv7CdO3eyoKAglpqaWtrFK1b//PMP2717N1u5ciXbtm0bO3fuHMvMzORev3DhAvfbXaFCBY3zj4+PZ8ePH2ebN29my5cvZ5s2bWJnz55l8fHxujwN8okQi8VMnKe6vijOE3/WfcS6rLsLrWOqokkfnNA6KADm6emp1fsjtDyydVFNyqbJQ12+QvsniuM9U1dnF0JoO03R9+n9+/fsyy+/ZEZGRkrPJyQkhDHGb+f4+/sLKltR7zkQQkhZRTM86An2MQcsNQP4mAuYmUBUqQJEZqalXawy4cOHD7h79y5iY2ORkJAAAwMDWFpaok6dOmjatCksLCxKu4gq5eXl4datWwgJCUFSUhKMjY1ha2uLpk2bwt3dvbSLJ0d2hgfpEf4RERG4c+cOYmNjUVBQAEdHR3Tp0gV2dnZaH+/Vq1d48OAB4uPjkZOTA0tLS9SvXx8tW7YstejTtLQ0BAUFITQ0FB8/fkTlypXRokUL3vTmZRFjDHfv3sWTJ0+4CPeaNWuiU6dOXJSzr68vF/nt7u6ucFSMKpGRkbh16xbi4+ORmZmJSpUqoXbt2mjVqpXcSF9CyKfh/fv3CAoKQkREBPLy8mBtbY127drBzc2tRI4vO8ODZEREXl4erl+/jrdv3yIpKQmWlpZwc3ND+/btYWCg2apv8fHxuHnzJiIiIpCbm4sqVaqgUaNGaNmypcZ5Sfv48SOCg4Px8uVLZGRkwMLCAo0bN9aqjCUpLi4ON2/eRExMDDIyMrj3tnXr1jAz+zxHpH0O8vLyEBoaitDQUERHRyM9PR05OTkoV64cVzdv1KiR3tfNyefrY34+QlPTwaB9F4kIItSqVBFmWk7XTAjRH4GBgdwU5y4uLrzRu4QQQkhRJSUl4erVq9xsDw4ODmjTpg237AUAbvZdoHCZiiFDhgjOv6zdcyCEkKKigAdCiEZUBTyQT9/KlSuxYMECAIVLX0hPP08IIfpIWcADIYQQQgj5fGRkZCAjIwPW1taC1pBfuHAhVqxYAQD44osvcPLkyeIuIiGEEMIRi8UoV64ccnJyAADBwcHw9PQs5VIRQoj+0t+hYYQQQopdamoq4uLiBK9HKlnbDgCaNm1aXMUihBBCCCGEEEJ0Zvny5XBwcMCZM2cEpb9w4QL3/x49ehRXsQghhHwmCgoKEBcXh/T0dEHp3759ywU7GBgYwMPDoziLRwghZR4FPBBCyGds8uTJcHBwwN9//602bX5+Pq5cucJtU6cPIYQQQgghhJCyZP/+/WrTXL58Gf/88w8AwNraGmPHji3uYhFCCPnEhYSEwMHBAaNHjxaUXjrwrmXLlrQUMCGEqEEBD4QQQgR1+uzbt49bN87NzY0CHgghhBBCCCGElCnnzp3Dli1blL5+//59jBkzhtv29fVFuXLlSqJohBBCPgNXrlxBbGysyjTZ2dnYvHkztz19+vTiLhYhhJR56hetI4QQ8snz9fWFp6cnhgwZovD133//natcGxsbY/fu3RCJRCVZREIIIYQQQgghpMimTZuGQ4cOYeDAgahZsyYMDAwQExODa9eu4fz582CMwcjICJs2bcLQoUNLu7iEEEI+ITk5ORg9ejR+++03VKpUSe71tLQ0jBkzBm/fvgUA9O/fH8OGDSvpYhJCSJkjYkIXbieEfLZ27NiBHTt2AACSk5MRGRkJoPDGd4MGDbh0Fy5cQNWqVUuljEQ7I0eOxNGjR7ntrl27olevXqhevTrEYjHevXuHixcvIjg4GABQvnx5BAQE4IsvviilEhNCiHqNGzfm/v/8+XPk5eUBAJycnGBlZQUAGDBgAJYtW1YaxSOEEEIIISUsKioKu3fvxokTJ/D8+XOl6UQiEfr06YMlS5agRYsWJVhCQgghn7KXL1+ifv363LaNjQ1GjRqFJk2aoGLFikhJScE///yDo0ePIjExEQDQs2dPHDt2DBYWFqVVbEIIKTMo4IEQotbSpUvx008/qU0XFhYGFxeX4i8Q0ZlXr15h165dOH36NMLCwpSmMzIywrBhw7BkyRLUrVu3BEtICCGaEzIDjbe3N/z9/Yu/MIQQQgghRK/Ex8fj6dOnePv2LVJSUpCXlwdLS0tUr14d7dq14wJkCSGEEF3Jy8vD0aNHceDAAdy4cQMfP35UmrZmzZr48ccfMWnSJBgY0Kr0hBAiBAU8EEIIAQBERkbi2bNnCA8PR2pqKsRiMSpXrgxXV1e0bdsWFSpUKO0iEkIIIYQQQgghhBBCSJmVk5ODJ0+e4PXr14iPj0dWVhbKlSsHGxsbtGjRAvXq1SvtIhJCSJlDAQ+EEEIIIYQQQgghhBBCCCGEEEIIKXNoPhxCCCGEEEIIIYQQQgghhBBCCCGElDkU8EAIIYQQQgghhBBCCCGEEEIIIYSQMocCHgghhBBCCCGEEEIIIYQQQgghhBBS5lDAAyGEEEIIIYQQQgghhBBCCCGEEELKHAp4IIQQQgghhBBCCCGEEEIIIYQQQkiZQwEPhBBCCCGEEEIIIYQQQgghhBBCCClzKOCBEEIIIYQQQgghhBBCCCGEEEIIIWUOBTwQQgghhBBCCCGEEEIIIYQQQgghpMyhgAdCCCGEEEIIIYQQQgghhBBCCCGElDkU8EAIIYQQQgghhBBCCCGEEEIIIYSQMocCHgghhBBCCCGEEEIIIYQQQgghhBBS5lDAAyGEEEIIIYQQQgghhBBCCCGEEELKHAp4IIQQQgghhBBCCCGEEEIIIYQQQkiZQwEPhBBCCCGEEEIIIYQQQgghhBBCCClzKOCBEEIIIYQQQgghhBBCCCGEEEIIIWUOBTwQQgghhBBCCCGEEEIIIYQQQgghpMwxKu0CEEIIIYQQQgghhBBCSlZ6ejru3buHuLg4JCQkICcnB5aWlrC1tYW7uztq1aoFQ0PD0i4mIYQQUqLev3+Pv//+G+Hh4UhNTUV+fj4qVqwIe3t7uLq6ok6dOqhQoUJpF5MQQogUCnggZZ6uGuiDBg3CmTNn5J739PREcHBwMZScEEIIIZ+qd+/eYcKECbh69SoAwM/PDz4+PjrJOyQkBI8ePUJUVBRMTU3h6OiIDh06wNraush5JyYm4tatW4iMjEROTg4cHR3RtGlT1KlTp8h55+Tk4Pr16wgPD0dycjIcHBxQt25dtGrVCiKRqEh5M8Zw7949vHr1CrGxsbCysoKLiws8PT1hampa5LITQgiRFx4ejho1aih93draGhEREShXrlyRjiMWi+Hm5oaXL18qfF26ze7v749x48YJypcxxv1fH88F0P58VCkoKEBAQAAOHDiAW7duIS8vT2lac3NztG3bFt27d0efPn3QsGFDQccQwsfHB/v379d4v/Lly8PKygqNGjVCt27d4OPjA0tLS52VixCinzRtL4hEIlSsWBG2trZo1qwZevXqhZEjR8LMzEyjfA4ePIixY8cCKPz9iYuL0/hGtya/5RJGRkaoXLkyqlevjjZt2mD06NFo06aN0vSdOnXC9evX1ebr7e0Nf39/rcom9Dqjja+++gp79+4FULp98ceOHcPWrVtx8+ZNlekMDAxQt25dtGjRAp07d0bXrl3h5ORUQqUkhBCiCAU8kDKpOBroNWrUgIeHBwAgLi4O8fHxxVJ2XQkODkbnzp0FpVV0k0VI50JYWBhcXFy0LCHRhtAGiqyKFSvC2toaTZo0Qe/eveHl5QVzc/NiKCEhRJ8I7fRRdbN96dKl+Omnn9Tm4ezsjPDwcA1KV6hbt274888/Ach3rgjl4uKCiIgIpa/fu3cPLVu21DhfWZMmTcLu3buVvi60g2f37t2YOXMm0tPTi1wmabdu3cKcOXNw584dudeMjY0xcOBAbNiwQauOlqioKMyYMQNnzpxBbm6u3Ott27bF6tWr0aFDB43z/vjxI5YsWYLdu3fjw4cPcq+7urpi7ty5+OqrrzTOGwD27NmD1atX482bN3KvVa5cGV999RWWLVumceemLmRlZcHBwQFpaWkAdBv4Qgghpc3ExIRrQwPy7ejExETs2LEDM2bMKNJxTpw4IRcgUL9+fZiYmAAAatWqxT1vZWXFK9Pjx4+5/0tuHJWVcwG0Px9lrly5gh9++AHPnz8HUBjIMXDgQLi7u8Pe3h75+flITEzE48ePcfHiRcTHx+PPP//En3/+iblz58LNzQ2jR4/G7NmzYWRUtC7F6tWrc+eWkZHBu447OTnBysqKl54xhrS0NERGRnKP33//HUuWLMGvv/6KL7/8skjlIYToN+nfwnfv3vHaFdKvSUh+L0JDQxEaGoqjR49izpw52L59O4YMGSL4uH5+ftz/MzMzcfz4cY2DFzT9LS8oKMD79+8RHx+P9+/f49GjR9i6dSu6dOmCffv2wdnZWe4YtWrVQkpKCgAgOTkZkZGR3Guurq5ckIbssXR9ndFGVlYWjh07xm3fuHEDb9++Rc2aNYv92BIZGRkYPnw4Ll68CAAwNTVF37590bZtWzg4OMDY2BgpKSl4+fIlLl68iBcvXnCPgIAAVKtWDVFRUSVWXkIIIQowQsqYy5cvswYNGjAADACztrZmEyZMYBs3bmSHDx9mBw4cYBs3bmQ+Pj7Mzs6OSyd5uLm5sRUrVrC8vDylx1iyZAmX3tPTs+ROTgMPHjxgHh4ezMPDg7m4uMidp6urK/f6mTNn5PZftGgR97qrqyu3X8OGDbnno6OjS+HM9NfGjRvZkiVL2JIlS9g///xTLMeYMGEC9/47OTkp/Uwlj4YNGzInJycmEol4ae3t7dnFixeLpYyEEP0h/XtQuXJl3u+A9GuKrgMSvr6+XLr69evz8rCzs+Ne6927t8bli4iI4P0+lS9fnqWnp2ucT+/evRVesySP/v37a5ynrMjISGZiYsLL18nJifc+CsmjZ8+ecuUDwPz8/IpUvrVr1zJDQ0MGgJmZmbFvvvmG+fv7M19fXzZw4EDuOFZWVuzKlSsa5X316lVmZWXF5TFw4EDm6+vL/P392TfffMPMzMwYAGZoaMjWrFmjUd4RERGsYcOGXN6NGzdm69evZ4cOHWKLFy9m9vb23GtDhw5lOTk5gvPOyclhQ4cO5V37Fi9ezA4dOsTWr1/PGjduzL3m7u7OIiIiNCq7LgQEBPC+Bx07dizxMhBCSEmRbkdLHg4ODuzjx49a5ykWi5mHh4dcvmFhYYL2l97H29tb8HH18VwY0/58GGNsy5YtzMDAgAFg5cqVYxs2bFDZL5Kbm8t2797NLC0t5cr84cMHjY6tTlBQkOB6U0ZGBtu/fz+ztbXl7bNt2zadlokQor+8vb15f//KZGRksICAAFatWjVe+rVr1wo6Tnh4uFx/W4cOHYpcfqG/5WFhYeyHH37glcHa2pq9ePFCZf5+fn68YwQFBem8bLok22YCwBYtWlQix2aMsYKCAtapUyfu2K1bt2bh4eEq9zl58iSzsLDg9qlWrVoJlZYQQogyFPBAypSSaqCXhYAHaenp6czZ2Zl3fqNGjRK8/6hRoxgANnz48GIsZdkn/R4X9caVEJo0UD58+MA2bdrEKlSowKU3NDRkZ8+eLfZyEkL0g9BOH1XCwsJ4eSxZsqRIZVq2bJnc9Xfv3r1FylO2QxwAE4lE7PHjx0XKd+rUqUUKUti3bx+rVKkSF3QwZcoUnQU87N+/n8vHxsaGPXv2TC7N4cOHuYCIihUrKkyjyLNnz1jFihW560ZgYKDCNDY2NlwZAgICBOWdnp7OGjVqxO337bffMrFYzEuTmprKWrduzaUZP368oLwZY2z8+PG8TqnU1FTe62KxmH333XdcmkaNGmkVcFMUXbp0kfuuhoaGlmgZCCGkpCgKEijqjegzZ84ozLM0Ah5K+1wY0/58fv75Z26/ChUqsAcPHgje99mzZ8za2lpvAh4kQkJCeDebTExMNHovCSFll6Zt3+joaFa1alXePn/++afa/X766SeFv9shISFFKr+mv+X+/v68fWrXrq0yULysBTx07txZ7j2uXr06KygoKJHj7969mztulSpVWFJSkqD9Ll++TAEPhBCiRwxASBmxfPlyfP/99xCLxahQoQKuX7+O6dOnq5xG0djYGF999RVu3bqlk3Wt9VWFChXkpuA+fPgwzp07p3bf06dP4/Dhw7C1tcW2bduKq4ikmFlaWmLatGm4dOkSDA0NARROgTd+/HhuGm1CCClJjDGFSydJTwmqy2MtX75c6/3j4+OxZ88erfdfsGABxo8fj9TUVPTv3x/Pnj3D0KFDtc5PWnh4OCZNmsRt+/n5wc3NTS7dyJEjMX36dABAeno6hg8fjoKCApV5FxQUYPjw4dzSG9OnT8eoUaPk0rm5ufE+t4kTJwpa3mTmzJl48uQJAKBFixbYunWr3DIsFhYWOH36NDfF6r59+3DkyBG1eR8+fBj79u0DUFgPOnXqFCwsLHhpRCIRtmzZghYtWgAAnjx5glmzZqnNW1ciIiIQFBTEe44xptWyLoQQUpbUq1ePt4zQmjVrVC5DqcqKFSsAAI0bN9ZF0TT2KZzLpUuXsGTJEm47MDAQzZs3F7y/m5sbt666PqlVqxYmT57Mbefm5uLXX38txRIRQvRV1apVsXbtWt5zc+fOVbkPYwwBAQEAgNq1a/NeK+n6vLe3N7p3785th4SEFKn9qk8iIiIQHBwMAwMDuLq6cs+/e/cO165dK5EySF/jRo8eLbekkjLdu3dHz549i6tYhBBCNEQBD6RM+FQb6LrUvXt3jB8/nvfcN998g9TUVKX7JCUl4ZtvvgEA7Nix45MOCvlctG3bFl988QW3nZiYqPCGIyGEFLebN2/izZs3sLCwgJ2dHff8rVu3EBISopNjSN8wOHnypNya2EKtX78e2dnZWt+AiI6OhqWlJfz9/XH27Fk4ODholY8iS5YsQU5ODoDC3/i+ffsqTbtw4UKYm5sDAJ4/f44DBw6ozDsgIIBbw9vc3BwLFy5UmrZv375o06YNACAnJwdLly5VmXdISAgXkAAU3uSRDXaQsLe3x9SpU7ntRYsWIT8/X2neeXl5WLRoEbf9/fffK33PRSIRLxhm7969CA0NVVl2Xdm/fz8YY3IdpPv374dYLC6RMhBCSGmws7PjtU0jIiLUXpMUuXz5Mu7fvw93d3cMGDBAl0UUrKyfS0pKCsaOHctdd3r16oX+/ftrnM+AAQPQoUMHXRevyHr06MHbvnr1aimVhBCi74YMGQJTU1Nu+8GDB4iMjFSaXtKerVSpEgIDA3mvBQQElHh9fvTo0bztkydPlujxi4ukzdS1a1cugF+iOAZLyMrPz8f9+/e57QYNGmi0v64GOhBCCCk6Cnggeu9Tb6Dr0oYNG1C1alVuOzo6GjNnzlSafsqUKYiPj4eXlxcGDx5cEkUkJYA6fQgh+kAy6mXEiBEYO3aswteKav78+dz/xWIxVq5cqXEeycnJ8PX1hYmJidaj/7t06YJnz57B29tbq/2ViY2NxcGDB7ntMWPGqExfqVIlXkDEmjVrVKaXfr1///6oVKmSyvReXl7c/w8cOIDY2FilaTds2MAFLTg4OKBr166C8w4NDcWpU6eUpj116hTevHnDbat7X7p27coF3eTn52PDhg0q0+uC9AwnS5cu5QXqRkZG4s8//yz2MhBCSGmaPXs2bzbGVatWqZ15SJYkYG3evHlKg+ZKQlk+l23btuH9+/fc9rx587TOS3rGKX3h6OjI23737l0plYQQou9MTU1Rp04d3nP//fef0vSSm+0jR45E8+bN0bJlS+61yMjIEu9ra9SoEW9bVdnLCuk20/jx4zF69GheUMrp06dVDuTThffv3/OCVzS9vrdu3RqtWrVC06ZNdV00QgghGqKAh1LExAXIT4tTmSY/LQ5MrNmF9lPzqTfQdalSpUrYsWMH77m9e/cqrISfPHkSR48ehYODA037+ImhTh/yqWKMITbrPRhjpV0UokZmZiaOHz8OoLDjQnYGIl2NiBkyZAjq1q3LbR8+fBhv377VKI9NmzYhIyMD3t7eqFatmlbl+PLLL7XeV5XTp0/z3qdevXqp3Uc6zYsXL/DixQuF6Z4/f86bEUPTvMViMX777TeF6WRf6969OwwMVDc73NzceNcvVSOWpF+rXr066tevrzJvQ0ND3hSwp0+fLvbfkRs3buDt27ewtLTEF198Ifc3UBKjlQghpDQ5OzvzAtJCQ0MFLVkkcePGDdy8eROurq4YMWJEcRRRsLJ6Lh8/fsSWLVu4bXt7e7Rv317r/Lp3716qgSdCZGVlaZQ+JiYGv//+O3799VesWLECGzduxMGDB/Hy5csi1xUKCgrw119/Yd++fVi9ejXWrl2LvXv34s6dO8jIyNAqz6ioKBw5cgSbNm3C6tWrsXfvXty8eVPrZVYI+dzILoGXnJysMF1mZiZOnDgBABg3bhzvX4mSrs8LLXtZImkzVa5cGYMGDeL+lcjOztboeqsNExMT3rb0bA9CuLu74+7duzh79qxG+4nFYty9e5e7RmzatAmBgYG8wH5CCCGaMVKfhBSH/LR4JByZgvykMJT3GASrXvMhMjDkXmfiAiT/sRKZj3+DUZUasB25DUYWdipy/DQVVwO9KA3XkJAQBAcHIyEhAZUqVUKjRo3Qpk0bGBsba5VfdnY2bty4gbCwMCQnJ6NSpUqoWrUqOnTooNUSE/3794eXlxcOHTrEPTdp0iQ8ffoU5cuXB1C4zMG3334LANi1axcqV66sVdllRUdH4/bt24iJiUF+fj4cHR3RpUsX2Nraap3n48eP8e+//yI+Ph6GhoawtbVFkyZN4O7urpMyA4U3hG7duoX379+jXLlycHBwQMOGDVG3bl0YGhqqz0DPadrpExYWhn///Rfv3r1DRkYGKlSoAHt7e7Rq1QouLi5FKktubi5u376N0NBQJCYmwsTEBNbW1nBzc0PDhg15kdxCvX79Gvfv30d8fDzEYjFsbGzg5uaGZs2aqb3BRsqOrPyPmPVgPe4lPkMra3esazET5YzM1O9ISsWJEyeQkZGB+vXro3Xr1gCAVq1a4d69ewAKO2uvXLlS5PUuDQwMMG/ePPj4+AAoHL2/atUq7N69W9D+aWlp+PXXX2FoaIg5c+aonNK0NEh3mlhZWaFGjRpq92nWrJlcHooCAmQ7ZGT3U8TV1RWWlpZISUnh8pAsjSXt/v37iIv7X1CvkLwl6aKiogAAFy5cQH5+Pm9ELVD4GV+8eFGrvCWzZcTFxeH+/fto1aqVoH21IZnFZOTIkTAzM8OoUaMwY8YMfPz4EUBh0EVKSgosLS21PkZMTAyCgoIQExMDU1NTVKtWDZ6enjpboiwtLQ3BwcGIiopCSkoKrKys4OTkhA4dOsh1uBJCiCJz587lBTmuXLkSo0ePFnTTXDIjwty5c/WiTVYWz+XGjRtISEjgtoUEIKpiZ2end0syydbdhPQ9REREwM/PDydOnFA5Qrpu3bqYN2+exjN4ZWRkYN26dfj111+V3pA0NjaGp6cnRowYgdGjR6NcuXIq87x27RoWLFiAu3fvKny9YsWKmDhxIhYsWCB47XmiXxhjQFoeIFbRX2kgAiyM9T7wSJ+lpaXxtpX9vRw/fhwZGRlo0KAB12aQ1Oezs7MBAL/99luR6/OaEFr2skQSNDJq1CiYmRX274wfPx5Hjx7lpZk8eXKxlaFy5cqoUKECF4h29OhRzJ49G25ubsVyvI8fP2Lz5s1Yv349b4CntHr16mHx4sUYOXKkwr93FxcXREREKD2Gt7c31x4NDg5G586dlaYNCwsrcl8vIYToDUZKXF5qHIvePoBFrGzKPRLPLWHignzGGGPignyWeG4x7/Xo7QNYXmpcKZe85F26dIkB4B5jx44tkeMuWbKEO6anpydjjLGUlBQ2YsQIXnkkD2dnZ3bhwgWNjhEbG8smTpzIzM3NFeZpYGDAunTpwu7evatx+RMTE5mdnR0vv++//557fdiwYQwA8/b2FpSf9Pshe96MMRYVFcX69u3LDAwM5NIYGhqyESNGsNjYWMHlz8/PZ7t27WLOzs4KjwuAOTo6sm3btrHc3Fyl+VSqVEnhvkuWLGGMMRYeHs66d++u9BhBQUHM09NT6euKHkLfUyH8/PzkyiPErl27ePu1b99e7T7Pnz9nM2fOZDVr1lR5fs2aNWNnz57V+Fzev3/Pvv/+e1ahQgWleZcrV47179+fHT58WOXnKnHixAnWoEEDpflZW1uz5cuXs8zMTI3LS/RLZl42++rWUtb07Eju8dWtpSwzL7u0i6ZXvL29eX8D2ggLC1P4e6kpyW/nmjVruOd27tzJy3vEiBFa5R0UFMQ7x7y8PObi4sI9Z2Jiwt69eycorxUrVjAAzMvLSy5vAMzPz0+rMuoqLxsbG27/du3aCdonJyeHd9zhw4crTCepCwBgIpGI5eTkCMq/bdu23H62trYK02zbto1XhitXrgjKe/78+bz9nj9/Lpfmv//+46VZsGCBoLwvX77M22/79u2C9tNGeno6K1++PAPA7t+/zz0/atQoXhl8fX21yj8iIoINGDBAYb3L2NiYjRkzhqWkpDDGmNJr5MaNG5XmHxISwoYNG8aMjY0V7mtkZMQGDx7MXrx4oVX5CSGfLkm7UdKGZoyx4cOH835DTpw4oTafe/fucW0+yfVJtk0aFhYmqEzattX08Vw0PZ958+bx0kvXy/SNtvWmWbNm8fYbNmyY2n2aNWvGu6Z5eXmxTZs2saNHj7Ldu3ezSZMm8foSRo8ezQoKCgSV5+XLl6xGjRrcvnXq1GFLly5lhw4dYgcPHmSLFy9mdevW5ZXZ0tJSaX5isZh3jiYmJszHx4ft3buXHT58mC1fvpy5ublxr1erVo09ffpUUFmJ/hAXiNnHTf+xrMl31D4+bvqPiQvEpV3kUqdN2/fjx4/M1NSUt5+ydqOkPbt27Vre86NHj9ZJm0Kba5NsH2HXrl0FpxXan6ht2bQh3WZ68OAB93xBQQFzdHRU2y7Upd69e/OOZ21tzY4cOcLEYt3+rUVFRbGGDRtyx3FxcWGLFy9mgYGBzM/Pj3377besYsWKvLa8onZ67969mYeHB6tWrRqv3FWrVmUeHh5s0aJFXNoHDx4wDw8P5uHhwfXJ2tnZcc9FR0fr9BwJIaQ00QwPJSw/LR4JhyYhPyWK93zm03MAAKveC5B8cTkyn57n75cShYRDk2DrteuzmukhODiYt92wYcNSKUdmZia6dOmCv//+W+HrERERGDBgAK5cuYJOnTqpze/GjRsYOHAgN0KydevWGDZsGBwdHZGcnIyrV6/i9OnTuHbtGtq0aYN169ZhxowZgstbpUoVbN26FcOGDeOe27p1K0aMGIHo6GgcP34c1apVw6ZNmwTlZ29vDw8PDwCFU6ZJj6J49eoVOnbsCLFYjGnTpqFx48YwMDDA06dPsX//fsTHx+Po0aO4du0agoKC1EbIpqamYvDgwQgKCgIA2NjYwMfHBx4eHhCLxXj8+DEOHDiAqKgoTJkyBceOHcOZM2fUrjsu6+3bt2jbti3i4+NVpqtVqxb3OT1//pybKtLJyUlhNHX16tU1KkdxuHz5Mm+7TZs2avfp0KEDkpKSAADm5uYYO3YsGjVqBFtbWyQlJeH27ds4ceIEHj16hAEDBmDOnDlYvXq1oPL89ddfGDBgABITEwEATZo0wfDhw+Hi4oLc3Fz8999/CAwMRFRUFM6dO4dz586hcePG+OeffxTml5ubCx8fHxw+fBhA4WgWb29vNG/eHMbGxnjx4gUOHjyI8PBwLFy4EIcPH8Yff/wht9QHKRuy8j9i2r1f8HfyS97zfye/xLR7v2Bzqzk004OeCQsLw40bN2BoaIixY8dyz48YMQI//PADb0TMhw8fijzLkJGREWbPns3NXJSbm4s1a9aoXa4pKysLGzduhEgkKtJyWcUlMTGRN+KjatWqgvYzMTFBlSpVuN/058+fK0wn/by1tbXcVJ7KODg4cP9PSEhAUlISqlSpojRvTcounbckH9nZKXSZd3E5ceIEMjMz4ebmhhYtWnDPjxs3jrt2AYWjlb7++muN8n706BG6devG1U0cHR3h7e2NBg0aICcnBw8fPsTBgwdx9+5d3Llzh7evq6srKlSoAKCwfqXI8ePHMXbsWOTk5AAAevTogQEDBsDGxgbx8fE4d+4crly5gtOnT+P3339HQEBAqU81T4iuvUrOxt7/EmFrboy2VSugmW05mBrJj4rPyRfjUUIW7sRkICE7DxPcrFHXyrwUSqzf5s+fj2PHjnHbK1aswJAhQ1TuI5kRYdasWYKvTyWhrJ3L7du3edvqloAqa0JDQ7Fr1y7ec5L6oBA2Nja4ceMG6tWrx3v+q6++ws8//4zBgwfjzp07CAwMRO3atbF06VKV+YWHh6Ndu3ZcHWzSpEnYunWr3Gygixcvxpw5c7B+/XoA4K7pikyfPh2bN28GUDh7xZUrV9CoUSNemrlz52LWrFnYtGkToqOj0alTJzx9+lSu7kP0V8HtBIhfpApKK36RioLbCTDq8Pn0DevKyZMnuTouALRs2RJOTk5y6STtWSMjI157FiiszwcGBnLbfn5+Cme8Kw7SxwWg9vqj7yRtJnd3dzRv3px73sDAAN7e3lixYgX3nJ+fH9asWVNsZZk6dSpvFsHExESMHDkSP/30EyZPnozhw4cX+Tf1w4cP6NChA8LCwgAAgwcPxqFDh2Bu/r+6o4+PD+bNm4cePXrgxYsXOHbsGAwNDeU++wsXLgAonLmwUaNGXL9BpUqV8Ndff/HybN68Of799188fvwYLVu2RMWKFXHnzh3UrFmzSOdDCCF6qbQjLj4nimZ2kH1Eb++v5vXPa6aHjh078iIVz507VyLHlZ3hYcqUKQwA69ixI1u3bh0LCAhg69atY+3ateOVz93dXW3ed+/e5UUUr1q1SmG6oKAg3mj4Xbt2aXweQ4YM4ZWvTp063GjRixcvapwfY/woYQcHB9a4cWPWpk0b9v79e7m0SUlJvPfI3t6eJSQkKM07JyeHtWzZkkvfpk0blpSUJJfuw4cPrEOHDly69u3bKxxxcfLkSXb48GF2+PBhVr9+fS79okWLWIsWLbiI6LVr1zJ/f3+2aNEiZmVlpTQCWnrGiaKM+hVKm4jsO3fuMENDQ96IlZCQELX7ValShQFgtWvXZjExMQrThIaG8kajCHkP7t+/z8zMzLh9li1bpjBCOjs7mzfyVTJ7iCKDBg3i0tWuXZtFRETIpcnKyuKNXq5duzbLyMhQW16iXxTN7CD7oJke/kdfZniQXEP79esn95qXlxcv/23btmmcv+wMD4wVjtZxcHDgnjczM2NxcarrS+vXr2cA2KBBgxTmXdTf+qLm9ddff/H2l56pSR13d3duP1NTU4VppOsijRo1Epz3d999xyvXX3/9JZemZ8+evDTJycmC8j5x4gRvP0V1pFWrVvHSnDp1SlDeiYmJvP169uwpaD9tSEaErVu3jvd8QUEBq169Oq8c//33n+B8o6KiePWU7t27s9TUVLl07969Y3Xr1mV9+vTRqB5x8uRJbtYIQ0NDduDAAYXpjhw5woyMjBhQODvIH3/8IfgcCCkLfrzxjrU5/Jx7dDr2gs28/o6dfJ3MXiVns5Ovk9nM6+9Yp2MveOl+vCFsdqFPmaJZERhjrF+/frzfo/PnzyvN4/Hjx0wkEjEbGxveTG36MMNDaZ+LpudTu3ZttddsfaFJvSkjI4MFBATIzWo5a9YsQceSzPBw8uRJleni4+O5kbblypVT2ZdRUFDAWrVqpbaPQlrnzp1V1tvPnTvHO79Lly6pzE+6/2zAgAEq0xL9Ic7MY1kz7wua3YF7zLrPxJl5pV30UqVp2zcmJoZVrVqVt8+1a9cUpl28eLHSvyOxWCw3G+2zZ880Lr+m1yZ/f3/ePrVr11Y5Q19ZmOFB8pu1YcMGuddCQ0N55bC3t2f5+fnFVhbGGJs0aRLvmNIPAwMD1rZtW7Z69WqtZ5uQ7qN3dXVlWVlZStO+ePGCN9ueqjav7LXi22+/lUuTnZ3NzZAbEBCgVfkJIaQsoMXNSwgTFyDhyBS5mR1k5adEq3k9CglHpoCJC3RZPL0VGxvL29bVmsSaePfuHXbt2oWtW7fi+vXrmDlzJsaOHYuZM2fi1q1b+Oqrr7i0z549UzoqHQDS09MxcuRILqJ41KhRmDt3rsK0nTp14iL5AWDGjBkary2+bds23iwEr1+/xvv37/HVV1+hV69eGuWlSGxsLMLCwnDmzBmFn42VlRVOnz7Nzb4QFxeHadOmKc1v/vz5uH//PoDCNdROnz6tcBYFS0tLnDp1invt1q1bvPdK4osvvsDIkSMxcuRI3lqex44dw+PHj3Hs2DFcvXoVs2bNgre3N5YtW4agoCC9WCNWUykpKdiyZQt69OiBgoL//T5s3LgRtWrVEpzPkSNHlEYtu7q64sSJE9y6rwsXLkRubq7SvDIyMjBixAhuvfJRo0Zh0aJFCtefMzMzw/79+1GnTh2V5du6dSt+++03AIXrnp4+fVrhrBrm5uY4cOAAd+4hISGYP3++yryJflE2s4MsyUwPWfkfS6hkZUfjxo01fvTp06dIx2SMYf/+/QAK196UJfucZM3OojI1NcXMmTO57Y8fP2LdunVK0+fk5HCvL1iwQCdl0LXUVP4or/LlywveVzptTk6O3G91Tk4Ob3STtnkD8mvJAtqXvbTz1oW3b98qHREmGa0kTZO/gW+++YZbC9zW1hbHjh2DhYWFXDonJyecOXNGbsYnVSIjIzFhwgRubfbZs2djzJgxCtOOGDGCmxWFMYbx48dza94S8imwNeePxs4pYLgTk4F1j+LgcykM6x7F4U5MBnIKGC+dXTn+fuR/ZK+1klkPFFm+fDkYY5g+fTrKlStX3EXTWFk6F8lMAxKSWX7KgsWLFyusq7q6usLS0hJffvklN1ujtbU1fH19sXbtWkF5N2vWDN27d0e/fv1UprO1teXqxllZWTh79qzStKdOncK9e/e47Z9++olrNyujbhZP6RnI2rdvjx49eqhMP2fOHO7/Z8+excuXqttRRD/k/R4FZORrtlN6PvIvqO5fJoWysrJw8OBBtGjRAjExMdzza9euRefOneXSM8YQEBAAoHA2B1kikahI9XlNRUREYMaMGbx2tLW1Nc6cOaNXMyBp6u3bt7h58yaMjY0VtjlcXV3RsWNHbjsuLo43A0Nx2LFjBxYuXAgjI/kJ0cViMe7cuYO5c+eiQYMGqF27NhYtWoS3b98Kyvvhw4c4efIktz1v3jzeLAyy6tWrh0GDBnHbqma36NevH6ZMmcJtb9++Hb///jsvzY8//ojnz59j9OjRcm1UQgj5lFDAQwkpyHiP/KQwneSVnxSGgoz36hN+AvShgR4WFoZhw4bxKg/Sli9fzruBK93IlbVz506Eh4cDKKwkL1u2TOWxvb29YW9vD6Dw5vH27ds1KrudnZ3CQABlHdja+Oabb5ROiwwUThMpPa3kkSNHEBoaKpcuJiaGN/34lClTYGenfIo+a2trTJgwgdvesGED70a/Kq9evcLSpUt5S35INGrUSO1N99Ly1VdfyXX4eHh4wMXFBVZWVpg2bRp3w8HZ2RknT57Ed999Jyjvdu3aYcSIEWjatKnKdO7u7tz03NHR0bh586bStDt27OCmagOAn3/+WWXexsbGmDp1qtLXs7OzeX8zI0aMULlEiqmpKaZPn85t7969Gx8+fFBZBqIfhAY7SFDQg2KPHz/W+PHixYsiHTM4OBjh4eGwsbFR2IncuXNnuLi4cNsPHz7Es2fPinRMia+//pq3tMKOHTvk6hESe/fuRWxsLHr06MGbPlOfpKen87bNzIQv3SKbVjav4sxb9jmRSCS4M07TvBXto4ypqanavHVh//79YIyhb9++vGBLCR8fH1698cCBA8jPV9/J/eTJE5w7d47b/v7772Fpaak0fd26dTF8+HDB5f7ll1+4KbXLly+vNCBXYvr06dznGhMTg0OHDgk+FiH6rm1V7dqcbR3Kzs3kkta6dWt06dKF27579y6uXr0ql+7ly5c4efIkKlWqpLT9XdrK0rnIBvepurmibyIjIxXWVd++fYv8/HyIRCKMHTsWZ86cQVRUlEZLRO3cuROXL18WVD9xdXXl/n/37l2l6bZu3cr9v0qVKoKWOu3SpYvSwRbBwcG8OvLIkSPV5tetWzfe8hmSm7ZEf4njslEQFKfVvvnX4iCOy9ZxicouVQFSY8eORXR04QBDW1tbnDhxArNmzVKYT1BQEMLDw2Fra6s0KEq2Pn/w4EFB9Xllzp49K1f2hg0bwsHBAS4uLti4cSMXlNy5c2c8fPiwzC9R5O/vD8YY+vXrp7RPubgGSygjEonw888/499//8XIkSMVBj5IhIaGYvny5ahTpw68vLzkBmzKkr5GGBoaYujQoWrL07t3b+7/d+/exevXr5WmXbduHa9/dPz48UhISAAAXLx4EVu3bkWNGjXg6+ur9riEEFKWUcBDCTGysEd5j0E6yau8xyAYWdjrJC99py8N9B9++EHpa3Z2dryK5ps3b5Sm3bZtG/f/Zs2aqR15b2hoyIvil4ya1cSYMWPkGtvff/898vLyNM5LkcGDB6tN88UXX3D/Z4zB399fLs2uXbt4I1CFNOilK39RUVH4888/1e4DFHbkf//990pfv3XrFmJjY9G2bVtB+ZWUN2/eyHX4PHnyBBEREWCMwdjYGFOmTMGVK1fw5s0b3vuuzpkzZ3DkyBFBaYV2+kh/35s0acLbT5nu3bsrfe3o0aO89ew1/Y5kZ2fj+PHjavchpUvTYAcJCnqQxxjT+CEdpKQNSSfEmDFj5NYrBgo7EXx8fBTuU1Tly5fnzSKUkZGBjRs3yqXLy8vjRkjo88wv2dn8DkxF76cysh34snkVZ96yzxVn3prkLxKJeGkV5V1U6mY4AYCaNWvC09OT246Pjxc0Wkm27iSk/iU9KkiVrKws3t9h7969Fc4cIa1y5cpo3bo1t61NHZUQfdXMthxMDeVnJFPF1FCEprb6NxuBPpG95iqaGWHlypUQi8X47rvv1P4Olaayci6SmRYliuPaV1z8/Px4ddScnByEhIRgxYoVqFixIhhjOHz4MP766y+VN6WKSrruIJlRQlZ2djb++usvbrtFixZqZ3cAgHLlyiE5OVlhUL5sEE379u3V5mdiYsJrc0uXieinvBPhgJipTaeQmCHvZLgui1OmKQqQCgsLg7m5OVxdXTF8+HD4+fkhIiICQ4YMUZqPpM49ZswYpb8tNWrU4PWzxsfH48KFC1qX/cOHD3Jlf/bsGRITE2FtbY1mzZph6tSpuHPnDq5duwZnZ2etj6UP1M2iITF06FDewMdz584hMTGx2Mvn5uaGw4cPIyYmBtu2bUPXrl2VtjkLCgoQGBgINzc33LhxQ2me0v3VDRs2lLs+K9KgQQPetqrfdDMzMwQGBnJB/gkJCZgwYQLev3+PcePGwcjICIcOHdLruhUhhOgCBTyUIKte81G+oeop89Qp37A/rHrpb+e8rulDA71ixYpqR39Kj1ZVNkVyaGgoN7sDIKzBCvArOLGxsYiIiBC0n8S7d+/w999/85578uQJVq1apVE+ihgYGKBhw4Zq0zVu3JhXOVRUCZRu0FeuXFnlyH0JTSp/0tq1a6dyCmwrKyvY29vr3fRwQUFBvE6frKws/Pfff5g9ezaMjY2Rl5eHffv24b///ivWZTmEdPq8efOG931v1aqVoLzr1q2LDx8+4MmTJ3KvadPp4+LiwguUok4f/aZtsIMEBT2UroyMDJw6dQqA6o4Lb29vnY6IkTZ16lRUrFiR2966davcEggHDhxAREQE2rVrx7vxrG9kgzw1CVSUXcJCNq/izFv2ueLMW5P8GWO8tMURRBsUFISIiAjY2dmpXB5G9u9DSNCPdN2pfPnycnUgRTw8PNSmAYA7d+4gKyuL29amjvro0SOVy1wRUpaYGhmgmZ3wpX4AoLldeZgaUfeKKl27duW1Ca5fv47bt29z22FhYTh8+DDKlSuncsCBPigr5yK77GRxLj906tQp1KpVS+XDy8tL6/xNTExQq1YtzJ8/H7///juMjIyQn5+P1atXa/0e37x5E1OmTEHLli1hY2MDU1NTiEQi3uOnn37i0iubHerx48e8a2Dt2rUFl8HCwkLhjE0PHz7kbQudhVI6r3///VdwOUjJK3j6AeJnKUXKQ/w0BQXPaBZLQHGwv1gsRmpqKkJDQ3H06FH4+PionB0uPT2dW3ZAVXtW0etFCeL39vZWWP68vDy8f/8eDx8+xJYtW9CmTRutj6FPJG0me3t73iAlWeXLl+fNWJeXl6d2Vjl116FatWpxs32oI5mx+OrVq3j//j2OHDmCUaNGKZx9+sOHD+jXr5/CQZBxcXGIivrfEjTa/J4D6n/TGzVqhF9++YXbPn/+PFq2bIn4+HgsXrz4k/n+EEKIKtQiL0EiA0NY9VmsddBD+Yb9YdVnEUQGxXcjU9+UZANdGVdXV96NGUWkIySl18SWposGK6BZo5UxhgkTJiAtLY03Cg8AVqxYgf/++09wXopUrlxZ0A0DIyMjXgSyonOQfn+EdhBo+97Uq1dPULqi2rJli9qKtvQ6m5oyNzdHgwYN8Msvv3AjK7Ozs/HDDz9g/fr1GucnFotx8eJFTJgwAY0bN0aVKlVgbGws1+kjPYpTWafPgwcPeNuadPpYWloqjDqW/o7Y2toKiogWiUS8dNTpo78YY5j1YL3WwQ4Sfye/xKwH68GYliNliNaOHTuGzMxMNGvWTGUwnIuLC2+91ISEBLk1JrVlaWnJW0YpNTWVt1xSQUEBVq9eDUB+DW59Ix24AQAfPwoP5JFNK5tXceYt+xxjTPCNcE3zVrSPMrL1M9l8oqOjBXWQqSJkRBhQOFpJ+vjnz5/nzWAkizHGCwQUMmMSADg4OAhKp4s6am5ubpGXxCFEn2i6PIW2y2B8bmSvvdJL3q1atQr5+fmYPHmyXD+APioL5yJZHlOiOEfGpqWl4c2bNyofQm8yqdOhQwdekMPWrVsRHBwseP/IyEh07doVHTt2xPbt2/HgwQOkpaWhevXq8PDw4D2kl9lU1r6QTBsuoWrJKaFk8yxXrpxcu1zRQ3oGxpSUFMHLfpKSxQpY4ewOOpB3PBysgNq+unDs2DFkZWWhRYsWcHd3V5l2yJAhvH6r33//XWV9nvyPJDhk7Nixamfo0XRZC3XXoTdv3mg143GlSpUwYsQIBAYGIj4+Hrt375ZrG6anp2Px4sVy+8r+nh87dkzQ77ls37WQa/j333+PXr16cdvh4eFo3769Xs9sSQghulR8874RhUQGhrDqvRA5kf8gP0V4Y8/Ishqsei/4rIIdgMIG+qtXr7jtkpi6SpaiznZZsutCKyJbwfn22295N2WE0uQ92LFjB65evYpGjRrh+vXr6Nu3LzdKPjc3F+PHj8edO3e0ng1A1SwJsqQb/enp6cjNzeVmUEhLS+PdiLh//77aIBNFhL43JTWFV3JyssolTgDlMyRoatSoUTh16hROnDgBAJg3bx769+8v+KbF06dP4e3tjX/++Yd7ztzcHDVq1EC5cvzped+9e8dNu1lanT4JCQnF+h0hJS8uOxH3Ep+pTyjAvcRniMtOgkM5/e8s/5RIOh+UTeUvbfz48bh27Rpv34EDB+qkHDNmzMCWLVu4WaE2bdqE6dOno3z58jh69ChCQkLQtGlTlaNJ9IFsUFdmZqbgfaVH65uamsrNWGRqagpTU1Pu2qtt3oDia6qisguZNUnbvIVQl3deXp7aa7Yq0iPC1P0NlCtXDiNGjMCePXu4Yx86dEjpCNXU1FRex5yQgD8ACkcfKSJ7zVY1O4UqdI0lnxJ3a81mgXGrUjpLL5Y1/fr1Q6NGjbggrkuXLuHhw4ewt7fH/v37YWJionRddX1TFs6lXbt2uH79Orf94sULpWvSlzULFizA7t27uZm8fvzxR7mge0Xi4+PRoUMHbubMqlWrYsOGDRg0aJDCfp2lS5fyZnlQRHYQgKoR5EJJ5ykSidCoUSOt8snMzKQpzPUQe/8RLF43sxKy+I9g7z9CZE/XoaKSBC+HhYWhcePGatNLBxTl5eXh4MGDmD59ejGV7tOQnp7OzQp5/PhxXL58WaP9Hz9+jH/++QdNmjQpjuIJUq5cOXz11Vf48ssvMWXKFK5NBwCnT59GXl4eb2Zc2WtElSpV4OjoqPFxq1SpojaNSCSCv78/3NzckJSUBKCwjZaTkyPXt0sIIZ8iCngoYUxcgOSLyzUKdgCA/JRoJF9c8dnN8KAPDXQhay8KIVvBcXZ21uomsJDgCqCwgv7jjz/CyMgI/v7+MDExwa5du9CwYUPu5sD9+/exceNGrTtiNLnhLNvoT09P5yprsu9NxYoVUbNmTY3LI3QkY3Eu91CaVq1ahdOnT6OgoAB5eXmYP38+FwChyvPnz+Hp6ckFMdSrVw8bN25Et27dFEZb+/j4qF2ru7g7fUxNTbWaqUPozR9S8uzNrdHK2l0nQQ+trN1hb66+MUh0JzQ0FLdu3QIAbN68Gbt27VKZXnbE/++//46EhATY2toWuSy2trb46quvuJkdkpKS4Ovri5kzZ2LlypUACoPC9J3siJHY2FjB+8bExHD/VzbDTq1atbiZnrTNW1n+tWrVwqVLl7jt2NhYVK5cWWd5SxNadiF5F4VkRJiBgQFGjx6tNn1ycjJv28/PT2nAg+wMZ0KvqULrabLX7Nq1a2vVIaZNICIh+upZomZLKf6XlI06lYte3/3UiUQizJ8/HyNHjuSeW758OapXr47c3FxMmjQJVatWLcUSClcWzqVbt25c3QcoDHIvLj4+PvDx8Sm2/GVZWlpi6tSpWL58OYDC2Yr++OMP3shWRWbPns0FO5iZmSE4OLjIdYKizJwlJE+RSEQzFX5iRDZmENmZ6SToQWRvDpENXX+KSro9m5iYqFUgr5+fHwU8qHH06FEuEF16GVxN+Pn5KQ14KMmZPk1MTLBjxw789ddfXLs6Ozsbr1+/5i3TLHuNGDBgAPbt21ds5YqMjOQtt/3y5UtMmzYNu3fvLrZjEkKIvqCAhxLExAVIvrAMmU/Pa7V/5tNzAPBZBT2UZAO9uMlWcJYtW4Yvv/yyWI7FGMO4ceOQmZmJxYsXcxXBGjVqYOXKlZg2bRqXdvHixRg0aJDaaZqVHUcoVdNUy743zZs35438LauWLl2KpUuXltjxatWqhVGjRuHgwYMACtdRffHiBerXr69yv2+++YYLdrC3t8fNmzeLPPVqcXX6SMpZtWpV6vT5xIhEIqxrMRPT7v1SpGUtmlrVw7oWM+nGWwmTjIYBgNevX2u8f35+Pg4ePIgZM2bopDw//vgjduzYwY2KX79+PapWrYr//vsP9erVwxdffKGT4xQna2tr2NjYcFOjyt6wVyY3N5cbzQEADRo0UJiuQYMGXMdMYmIib+YlVaQDDGxtbRWONJE9ZkxMjNJyKMtbWdkV5S2EurxdXFyK1EEmmeFELBbj8ePHGu//5MkT/P3332jatKnca7LBekKvqULPR/aavWfPHnTs2FHQvoR8qu7EaraU4p2YDAyupT6wiwDDhg3D4sWLufrC2bNnYWJiAkNDwyIt91ca9P1c2rZtiypVqnD1gitXrkAsFms9qCMtLY27iWJqagobGxudlVUb33//PdavX8/N6rVixQqVAQ9ZWVk4fvw4tz1ixAidBEDKBuympKToNE+xWIyMjAwK3v+EiAxFMB7mgtytRVvOEQCMhzlDZEht36KStGfHjRun0c1oNzc3PH/+HEBhn/WjR4/QrFmz4ijiJ0HyPu/fv19wn3hcXBwcHR25GTUCAwOxbt06QW1XoQ4dOoT09HT0798f1apVE7yfoaEhhgwZwlsyWjLzkITsNUI6GEHXMjMzMXr0aOTl5aFNmzb466+/ABS273r27ImhQ4cW27EJIUQf6GboOhEk+Y+VWgc7SGQ+PYfkP1aqT/iJkDTQJSQNdG2lpaUhKioKUVFRJb62WklWcLZs2YLr16+jUaNGWLhwIe+17777Dm3btuW2s7OzMWHCBK06+TWZAlu60V+hQgVexdTCwoI3WrE435tP3ezZs7n/M8Z4AUOKhIWF4caNG9y2rtaZLe5OH/qOfJrKGZlhc6s5aGql+ewdQGGww+ZWc1DOiEa4lCSxWIwDBw4AAK5fvw7GmKDH/fv3efmoW49TE05OTrwOlLi4OEyYMAFA4ewOupq9qbhJ3/x+8eKFoH0kHW6K8lD2PGMML18K63CVzl9I3orKpIx0R1GFChUU3oSoU6cOr8Nfm7wVlbEoQkNDcfv2bVSoUAGZmZmC/wbWrFnDy0fZ30ClSpV406LKdqIpIzszhDIlWUclpCzIyRfjUbzwdg4APIzPRE6+9u3Uz4mBgQHmzp3LbTPGkJOTg1GjRmk1y19p0vdzMTU15S2jGRcXh5s3b2qd39ChQ+Hk5AQnJyesXr1aF0UsEhsbG4wbN47bvnXrFm+GUFmhoaFccAQAnd2U9PDw4PVvhISEFDnPli1b8rZDQ0OLnCfRL4bulWHgblmkPAwaWsLQjYLtikosFiMgIAAAMGbMGI32HTt2LG9bl23asig2NhbNmzdH8+bN5QLOQ0JCcPv2bZQrV06jAQj29vbo1q0bt52UlISzZ8/qrMxA4TJJ33zzjaClkWTJLk8hu/ygvb09nJycuO3i/D3//vvvERISAi8vLwQHB/OWZpk4cSIiIyOL7diEEKIPykZv7ycgPy0OmY9/00lemY9/Q35anE7y0nefUgO9pBqsISEhmDdvHreUhXQHOVDYKbNv3z5egMGNGzfg6+ur8bE+fPggaJRhfn4+b6oyRVOPtWjRgvt/UdbQ/tw1bNiQt/b24cOH8fbtW6XpZWdN0VWnj/TnCei+0ycpKUknQRRE/2gb9EDBDqXn2rVrePfuHZydndGhQwfB+7Vo0QJ169bltp89e4aHDx/qrFxz587lLWH08eNHuLi4CFpuQF8MGDCA+39ycjLCwsLU7vPo0SOleah6XnY/Rd68ecP77VWWd8uWLWFvb69R3rLp+vTpo3BZJSMjI/Tu3btIedvb28tdp4pCMlJp8ODBGi0FMXr0aF7wTWBgIHJycuTSya7d/ebNG0GBqnFxwtoLdFOFEL5HCVnIKdAsGDyngOHvhKxiKtGnZ8yYMahevTq3LRKJysRyU4ro+7n8+OOPvKUfV61apVU+r169wp9//gmgsE9h4sSJOilfUc2aNYtX35MscaGI7IANIUtECRnkYW5ujnbt2nHbDx8+FDRQJyEhAd26dUO3bt14s6UBQPfu3Xnb9+7dU5sfUBh00717dzRv3lxnM6eR4mM81AUw0HJ2BgNR4f6kyP78809ERkbC0dERnTp10mhfLy8v3uySyurzn4ucnBw8evQIjx49knsfJL9zAwcO1HjGmpIKLHn16pXG+0gHoxsYGMgFQAD83/QXL17ILSmozK1bt7gAkqCgIJVpT5w4gX379qFGjRrYvn07TExMEBgYCHNzcwCFA9G8vLy4mTIIIeRTRAEPJcSwgg2MqtTQSV5GVWrAsELpTh1Ykj6VBnrNmjV5ozyENliBwvXQmzdvjpYtW6q8ySsWi+Hj44Ps7GzMnz9f6ZpmdevWxZIlS3jPzZ07F+/evRNcJsnxhCwz8vfffyM/P5/bVnRDTLryl5KSIriS+ebNG67yd+jQIUH7fOqkRxkVFBSo/Jsprk4fV1dX1Kjxv988od/3p0+fcp0+Fy9e5L2mbadPRkYGWrVqhebNm2PdunWC9iGlS9OgBwp2KF2SzobRo0drvJRIcXZc1KpVC8OHD+c9N3v2bIU30fXV4MGDeTfE//jjD7X7SKepX7++0mWNGjRogHr1/vc3pmneBgYGGDRokMJ0sq8JmaHr+fPniIqK4raHDBmiNK30a+/evVM7+4VYLMbVq1e5bdn3tSiKMiKsWrVq6NKlC7ednJysdLSS9BITmZmZcjNWKCJ0aY02bdqgfPny3LYmddQ5c+agefPm6NKlS5FmYSNEn9yJ0Ww5C24/DZfB+JwZGxvzZqYbNGiQoKWP9JG+n0vFihXh5+fH1dEuXbqEc+fOaZzPjBkzuN/58ePH8+oQpalGjRq8+t7Vq1eVXsekgzEBqBwYIPHkyRNB5fjuu++4/ycmJqqcaULi7Nmz+PPPP/Hnn3/KzQjSoUMHeHh4cNuHDx8WVI7r16/j6tWrePTokdqlLUnpM7A3h2Fne/UJFTDqYg8DO3Mdl+jzJLkRP2rUKI3bCE5OTrwgiQ8fPuDMmTM6LN2nQXpWSE3bTEBh+006SOLSpUtyM0jowunTpzXeR/r3vlWrVnIzPAD8a0Rubi5OnjwpKO89e/bg0aNH+O+//1QOUIuMjMSkSZNgZGSEQ4cOwcLCAkBhf8CGDRu4dDdv3sSKFSsEHZsQQsoiCngoISIDQ9iO3AYjS/koP2lGlqrXiTKydITtyG0QGRiqTPcp+ZQa6NIVnHv37gkaqVlQUIAtW7bg0aNHMDY2hqWlpdK069evx507d+Dh4SG3lIWsWbNm8aZzTk9Px6RJk9SfhAwhFflTp05x/xeJRLxpJyUmTpwIU1NTbltog/7AgQNc9LAuR2sqIl0+RRGxy5Ytw6xZs7jAmtLSoUMHtGnThtsOCAhQOm1ZSXX6/PPPP4LyPnr0KP78809cu3ZNrqNw2LBhsLOz47aFfkdOnDiB+/fv49GjR7xOI6LfhAY9ULBD6UpLS+M6BbTpuBgzZgwvSOLw4cM6HREzf/58Ln8HBweMHz9eZ3mXBAcHB977evDgQZXpU1NT8fvvv3Pb0jdgFJF+/fz582qXSpAOLBw7diwvIFXWjBkzuOCS2NhYtddG6XOrVauWymlOv/jiC7i6uircV5GrV69ysx0YGRlh+vTpKtNrQjIizMHBAV27dtV4f6FBPz4+PrxtIZ1xQjtbzc3NuSVfgMLvgpBRR2lpadixYwcePXoEBweHMrNUDCHqJGTn8bZNDUVoV7UCfmxuD/+eNfBjc3u0q1oBpjJrpsdn8fcjqk2ePBmRkZGIjIzkboKUVfp+Lj179uTd8PDy8hI8QxIALFy4EBcuXABQGGCgD8tZSJszZw5vW9ksDzVq1ICLiwu3feTIEeTm5irN9+XLl4Lb9oMGDULr1q257aVLl6oMBMzNzeWC8Rs3bswLbJRYvXo1V4+9fv06zp9XvURufn4+N/hBtg5J9JdxX0eggoYB2RWNYNRHdf8yESY1NbVI7VmAlrUQ4urVq4iMjISNjQ169Oih8f6yy2AUFBRwQee6dO/ePRw5ckRw+lu3bvHa399//73CdE2aNMHIkSO57Z9++knt8oP//PMPAgMDAQBff/01F8QgSywWY+zYsfjw4QMWL17M6w+W7Cs9M+OyZctw584d1SdGCCFlFPVKlSAjCzvYeu1SGvRQvmF/OEw6ifIN+yne39KxcH8LO4Wvf8o+lQb6pEmTuA56xpjaGxEA4Ovry90oVpX+xYsXWLx4MYyNjRUuZSHLyMgI+/bt4410vXTpksYV8+3btyMxMVHp6/Hx8bzlMoYPH45atWrJpbO3t+fdgNi4cSNiYmJUHjsqKgqbN28GUNjBUKdOHY3KrinpNa5lZ9pgjGH58uVYv3694HXQi5P0LA+5ubly64RLtG7dmpveDIDaBsOVK1fw+vVrQWWYNGkSb6SK7KwispKSkrBjxw4AhVPcOTs78143MzPDTz/9xG0fPHhQ7ejV9PR0bp8mTZrw1v0j+k9d0AMFO5S+I0eOIDs7G02aNNFqNKPsMhgfPnzAb7/9prPyubu7Izo6GpGRkXj27BkvcK2s+Omnn7hy37lzh9ehImv58uXc2tQNGjSQ63yT9eWXX3KfW1ZWlsppoH///Xf89ddfAAoDAJcuXaoy79q1a/MCTBYsWKB0GYa4uDj8+uuv3PbPP/+sciYOY2Nj/Pzzz9z2r7/+qnT5BsYYLwh0woQJqF27tsqya0JSbxo5ciRvSm2hvvjiC94yGJcvX1ZY/2nUqBH69u3Lbf/666/48OGD0nxDQkJw9OhRweWYM2cOrKysABTOjCR9vVVm2bJlSEtLg0gkwqxZswQfixB9N8HNGu2rVcCQ2pWxvqMT/hhcB2s7OmFwrcqoU9kMg2tVxtr/f359RycMqV0Z7atVwAQ369IuepliZGQER0dHODo68maZKYvKwrn88MMP2LNnD0xMTJCeng5PT09s2rSJNxOjrLi4OIwePZobDWpvb4/z58+jSpUqJVVsQTw8PNCrVy9u+/z580rbidLXq/DwcIwfPx55efLBSlFRURgyZIjgqb8NDAxw5MgR7r25ceMGpk6dqjDv7OxsjBkzBq9evYKhoSG2b9+uMM9evXrx+oC8vLxw7do1hWnT09MxatQo3Lt3DyKRCDt37uS184n+EpUzgvHA6uoTSjEeUB2icmVn1jp9dvToUWRnZ8Pd3Z23hJwmhgwZwvt7u3z5MqKjo3VVxFIVFxeH4OBgwY+7d+8qzEcyi8aIESO0nnFRtm0ruxSQrowbNw6+vr5qf/9PnDiBfv36cW3cAQMG8IIaZO3cuZPrtw4PD8eAAQPw/v17hWn//vtv9OvXD3l5eahZs6bKtvfq1atx/fp1tG/fHvPnz1eYZu/evdxghYKCAowePVrtYAdCCCmTGClxealxLHr7ABaxsin3SDy3hIkL8hljjIkL8lniucW816O3D2B5qXGlXPLSt2fPHmZiYsIAsPLly7ONGzeyvLw8peljY2PZqFGjGAAGgNnb27P//vtP7XGWLFnC7ePp6ak2vbe3N5fe29tbZdpHjx4xc3NzLv306dNZbm6uwrQBAQHM2NiYAWBDhw5Vmmd+fj5r0aIFA8AWL16strzSFi5cyJUFALO0tGQxMTFK0/v5+XFpbWxsWM2aNVm7du1YUlKSXNrExETWtm1bLr2trS2Lj49XmndeXh5r3749l97d3Z2FhYUpTBsaGsoaNGjAADArKysWERGh8jw9PT25fJcsWaIyrTI//PADl4eXlxfvtfPnz3Ov/fXXX1rlL0v6vQbAgoKCBO8rFou59wcAMzMzY3Fxin9DZs2axTvOnDlzmFgslkv37Nkz5uDgwEur7vv+4MEDZmZmxqVfuXKlwryTkpJYx44dGQBWoUIFFhoaqjTPESNGcPlVq1aN/fvvvwrTxcXFcXmampqyhw8fqiwr0V+Zednsq1tLWdOzI7nHV7eWssy87NIuml6RvhZpW8ULCwvj5aHu97JNmzYMAFu3bp1Wx2OMsd27d/OO2bNnT5Xpg4KCinSOQvMGwPz8/PQir/379/Oupc+ePZNLc+TIEWZoaMgAsIoVKypMo8izZ89YxYoVGQBmaGjIDh8+LJfmv//+Y7a2tlwZ9u/fLyjv9PR01qhRI26/KVOmyF0D0tLSuO8RADZ+/HhBeTPG2Pjx47n92rRpw1JTU3mvi8Vi9t1333FpGjVqxNLT0wXnr05KSgpXpyvKNcbLy4v3XVm1apXCdO/evWOWlpZcum7dusmdM2OMRUZGsvr167PWrVtrVI84f/489x2S/F0rumaLxWK2evVqLt3MmTO1Om9CyKehd+/ezMPDg3l4eDA7OzuurS55zsPDg0VHR+ssb8mjfv363GuLFi3i9jlz5gzv2NL7VK5cmfeavp9LUc9HkVu3bjF3d3cuD2trazZx4kS2adMmduTIEXbw4EH2yy+/sAEDBnB9LwBYhw4dWGRkpFbnrsyiRYu4sru6uvLOzcnJSaP3PTg4mLe/g4MDt+/w4cO5dGKxmI0cOZKXtlatWuynn35iR44cYQEBAWzq1KmsYsWKzNXVlVe/lv4uTJgwQWE5Xr58yWrUqMHtU7duXbZs2TIWGBjIDh06xObOncucnJy4epeQOtWiRYt45e3VqxfbtGkTO3r0KPP392fTpk3j6mmGhoZs586dmn0QpNSJC8Ts46b/WNbkO2ofHzf9x8QF8vWzz4H0b0LlypV5fxfSrwn5zZCks7Ky4voVJc/5+voKKs+ECRO4faT7eQEwFxcX3m+zJr/lyn5fNCmP5HdG8nB1dVV6nTl06BCrVKkS95DeryiPBw8ecMeUXE+qV6/OPffgwQNB5yVJ37BhQ7ljuLu7F+k9k2jcuLFc3tWqVWOTJ09m27ZtY0eOHGHHjh1jO3fuZNOmTWN16tThpfXy8mIfP35Ue5y4uDiu/17y2X/99ddsz5497Pjx4+zXX39lgwcP5tpkNWvWZCEhIXL5+Pr6cu+LkZERA8CcnZ0VvheS+oeNjQ2vzJLPonfv3kV67wghRJ9QwEMpyUuNY9E7hxQGO/y+jAt2kBAX5LPE35cVBjvsHELBDlKKq4EuXVmQ7oCQbtRKVwql00tXtKUrqrKVSIm7d+/yKhqurq5swYIFLCAggB05coStWrWKtWrVitegzcjI4OUhXVmWrmg1aNBAbSVdutJZv359hZU6yetnzpzh7St9E97Z2Zk9fPiQVaxYkVlbW7MZM2awgIAAdvDgQTZnzhze+2htbc2ePHmi9vNNT09nffr04fYzNzdnX375Jdu5cyc7fvw427FjB/Py8uJuoltbW7N79+4pzEu6sl++fHkuTzs7O15jQvYclXn8+DFXkTQwMGDTpk1jhw4dYsuXL+caBc2bNxeUlzJCGyiSRpMq/v7+vP0llV8PDw/2ww8/cOmys7N5ASEAWJMmTdgvv/zCjh07xvz8/Nj48eOZqakpa9myJevfv79G3/fbt2+zKlWqcPs0a9aM/fLLL+zIkSNs//79bNq0adzr5cqVY5cvX1Z5Xnl5ebybXEZGRmzIkCFs27Zt7NixY2zv3r1s0qRJzMLCgsvz3LlzGn0ORP9k5mWzb+4sZ03PjmTf3FlOwQ7/T/o3QVWnj6rfOenrmew1Qfr3UtIQlr7+SHcUS54TciMgOjqaS1+3bl3eMQ0MDOR+V5R1iGvzWy5LaGe7uo4A6RsZ6vIS+jsubc2aNVzHh5mZGfv222/Z/v372Y4dO9igQYO441hZWbErV65olPeVK1e4zj4AbNCgQWzHjh1s//79bMqUKdw119DQkK1Zs0ajvCMiInj1tiZNmrANGzawwMBAtnTpUl4g3ZAhQ1hOTo7gvHNyctjQoUO5/e3t7dmSJUtYYGAg27BhA2vSpAmvM0xdcKRQku9MrVq15L6LQjuMpP+OqlWrxvuuWFhYKP1e37t3j9cR6ejoyBYsWMAOHTrE/P392ZQpU5iFhQVzcHBgERERvHyFBE6ePXuWV2fy8PBgP//8Mzt06BD3mbm5uXGvjxs3TmXwMSHk0+fs7Kz2poeyIHZd5A3wg7Blg8ZVPfT9XIp6PsoUFBSwgIAA1qFDB16gm+zD0NCQ9ejRg508eVKrc1ZHNli3qO+7dB+KbJ1YmlgsZhs2bOAFc0o/jIyM2MSJE9n79+95g2GkH6oGxqSlpbFFixbx6layjw4dOrD79+8Lfq9u377NOnfuzEQikcL8DAwMWK9evdjff/8tOE+iX8RiMROn5DBx8kflj5QchcGonwuhvxdCfjNU7St0kJRsP5qq32ZNfsuFDLwrSnmKcp3R5KEuX6GDuorzPZMoKChgN2/eZD/++CNr1aoVN/hQ1cPExIQNGjSIXb16VaNj5ebmsi1btjBHR0eleVtZWbHZs2eztLQ0hXkouzYpei/U1T+cnZ21fNcIIUT/UMBDKRIX5LO81FiVafJSY+WCIUjxNNBVVRYUVQo1TS/r/fv37IcffuBGVSp61KpVi+3YsYMVFBTI7S+kQqqski476lRdBVXZcSWVopCQEKUVa0NDQzZs2DCNRsKIxWJ24MABuZtg0o/y5cuzSZMmKZ21gDHhlX1NRt3u379f6WfWqlWrIt9MEVpmQP3Pd25urlzQhOQxcOBAXtqcnBy2YMECLkhA9mFubs7mzp3LMjIylHZMqfq+JyQksKlTp7IKFSoo3NfAwID17duXvXr1SvB7df78edasWTOVjY8RI0awN2/eCM6T6DexWMxiMhM+604eWdr+lksTcj2T/s1Xd/0R0iEtO5OEuuuokA5xbWdQENrZrq4jQOiNDE1+x2XdvHmTNxuC9MPY2JgNHTqUvXv3Tqv3ITIykg0bNkxp506bNm3YjRs3tMo7Ozub/fjjj3JBOZJHzZo12a5du7TKm7HCWUJq1qypMO/KlSuzWbNmsexs3QVJqfrOCO0wEtqxqOh7HR4ezvr376/wpoehoSEbNGgQVz+Sfi04OFhQ2SIiIpi3tzdvhibZh4eHBzt+/LjQt4wQQoieSk5OZhcuXGB79+5lq1atYr/88gvbuXMnCwoKUjiL0Kfk48ePLDg4mG3dupUtX76cbd68mZ0+fZqlpKToJP/8/Hx269Yttnv3brZq1Sq2Zs0adujQIfb27Vut84yPj2fHjx9nmzdvZsuXL2ebNm1iZ8+eVTmTJiGEEM1kZ2ezJ0+esFOnTrFt27ax1atXs59//plt3LiR+fv7s3v37rGsrKwiH+eff/5hfn5+bM2aNWzVqlVs586d7Pbt2xoNAiCEEPI/IsaULKZLSBnx4cMH3L17F7GxsUhISICBgQEsLS1Rp04dNG3aFBYWFqVdRJXy8vJw69YthISEICkpCcbGxrC1tUXTpk3h7u5e2sWT4+/vj3HjxgEoXHs9PDycey0iIgJ37txBbGwsCgoK4OjoiC5dusDOzk7r47169QoPHjxAfHw8cnJyYGlpifr166Nly5altj5qWloagoKCEBoaio8fP6Jy5cpo0aIFWrRoUSrl0aXMzEzcvn0br169QlpaGiwtLVGzZk14enry1hjXVm5uLm7evInQ0FAkJSXB1NQU1atXR7t27VC1alWt8oyMjMStW7cQHx+PzMxMVKpUCbVr10arVq1gaWlZ5DITQoi+ef36NR49eoTo6GiYmJjAyckJHTp0gLV10dePT0xMxM2bNxEZGYmcnBw4OjqiWbNm3HqjRZGTk4Pg4GCEh4fjw4cPsLe3R7169dCqVSuIRKIi5c0Yw7179/Dy5UvExcWhcuXKcHFxgaenJ8zMzIpcdn0UHR2NoKAgxMTEwMTEBFWrVkXHjh1hb28PoHCNcOlr98OHD9GsWTPB+WdlZeHGjRsIDw9HUlISzMzMULVqVTRv3hy1a9fW+fkQQgghhBBCCCGEEKINCngghGhEVcADIYQQQgjRDzExMahWrRq3HR4eDmdn51IsESGEEEIIIYQQQgghumdQ2gUghBBCCCGEEKJabm4u4uLikJWVJSj98+fPuf9XqVIF1atXL66iEUIIIYQQQgghhBBSaijggRBCCCGEEEL03LVr1+Dg4IA5c+YISn/hwgXu/927dy/ysiGEEEIIIYQQQgghhOgjCngghBBCCCGEkDLixIkTamd5iI+Ph5+fH7c9ffr04i4WIYQQQgghhBBCCCGlggIeCCGEEEIIIaSMiIuLw4QJE5CTk6P09QEDBiAlJQUA8N1336Fly5YlWEJCCCGEEEIIIYQQQkqOUWkXgBCi/3bs2IEdO3YAAJKTk7nnY2Ji0LhxY277woULqFq1akkXjxBCCCHks3LkyBH89ddfGDFiBNzd3WFubo7k5GTcvXsXx48fR0ZGBgDgyy+/xIYNG0q5tIQQQgghhBBCCCGEFB8RY4yVdiEIIfpt6dKl+Omnn9SmCwsLg4uLS/EXiBBCCCHkM5OZmYkDBw4gMDAQd+/eRV5entK0jRo1woIFCzB8+PASLCEhhBBCCCGEEEIIISWPAh4IIYQQQgghpAzJyMjA48ePERoaisTERGRnZ6NChQqwt7dH69atKQCVEEIIIYQQQgghhHw2KOCBEEIIIYQQQgghhBBCCCGEEEIIIWWOQWkXgBBCCCGEEEIIIYQQQgghhBBCCCFEUxTwQAghhBBCCCGEEEIIIYQQQgghhJAyhwIeCCGEEEIIIYQQQgghhBBCCCGEEFLmUMADIYQQQgghhBBCCCGEEEIIIYQQQsocCngghBBCCCGEEEIIIYQQQgghhBBCSJlDAQ+EEEIIIYQQQgghhBBCCCGEEEIIKXMo4IEQQgghhBBCCCGEEEIIIYQQQgghZQ4FPBBCCCGEEEIIIYQQQgghhBBCCCGkzKGAB0IIIYQQQgghhBBCCCGEEEIIIYSUORTwQAghhBBCCCGEEEIIIYQQQgghhJAyhwIeCCGEEEIIIYQQQgghhBBCCCGEEFLmUMADIYQQQgghhBBCCCGEEEIIIYQQQsocCngghBBCCCGEEEIIIYQQQgghhBBCSJlDAQ+EEEIIIYQQQgghhBBCCCGEEEIIKXMo4IEQQgghhBBCCCGEEEIIIYQQQgghZQ4FPBBCCCGEEEIIIYQQQgghhBBCCCGkzDEq7QIQUlTp6em4d+8e4uLikJCQgJycHFhaWsLW1hbu7u6oVasWDA0N1eYzaNAgnDlzRu55T09PBAcHF0PJCREuPz8fz58/x5MnT5CUlIT09HSYm5ujUqVKcHFxQa1ateDi4lLaxSSEEEIIIYQQUkboqj9Fl8RiMV6/fo3Hjx8jISEBaWlpMDExgYWFBZydneHq6gpXV1cYGNAYLkIIIYQQQkghCnggZVJBQQECAgJw4MAB3Lp1C3l5eUrTmpubo23btujevTv69OmDhg0bKkxXo0YNeHh4AADi4uIQHx9fLGXXleDgYHTu3FlQWj8/P/j4+PCe8/Hxwf79+1XuFxYWRjfRS1lsbCxWrVqFwMBAJCUlqUxraWmJpk2bon379ujSpQvatGkDExOTEiopIYQQae/evcOECRNw9epVAIqvxdoKCQnBo0ePEBUVBVNTUzg6OqJDhw6wtrYuct6JiYm4desWIiMjkZOTA0dHRzRt2hR16tQpct45OTm4fv06wsPDkZycDAcHB9StWxetWrWCSCQqUt6MMdy7dw+vXr1CbGwsrKys4OLiAk9PT5iamha57JqKjo7G33//jaioKKSmpgIAKlasiKpVq8LV1RV16tSBmZlZiZeLEEJ0KTw8HDVq1FD6urW1NSIiIlCuXLkiHUcsFsPNzQ0vX75U+Lr0IAV/f3+MGzdOUL6MMe7/+ngugPbno0px9KfoQmpqKtauXQt/f39ER0erTFu+fHk0adIEbdu2RZcuXdChQ4cifzaEEELUS01Nxd9//43Q0FCkpqYiJycHFStWhLW1NVxdXVG3bl1YWlqWdjHLlISEBBw+fBh16tRB7969i+04jDEcP34c79+/h7e3NypUqFBsxyJF8+rVK2zduhVBQUF49+4dcnNzYWtri6pVq6Jly5YYMGAAunXrVtrFJEV0+/Zt3Lt3D4MGDULNmjVLuzifBkZIGXP58mXWoEEDBoABYNbW1mzChAls48aN7PDhw+zAgQNs48aNzMfHh9nZ2XHpJA83Nze2YsUKlpeXp/QYS5Ys4dJ7enqW3Mlp4MGDB8zDw4N5eHgwFxcXufN0dXXlXj9z5ozc/osWLeJed3V15fZr2LAh93x0dHQpnJn+2rhxI1uyZAlbsmQJ++eff4r9eCdPnmQWFhbcZ1OnTh02a9YstnfvXnb8+HF24MAB9ssvv7AhQ4Ywc3Nzue/A7t27i72MhJDSJft3r+zh5+enNA/pa56qh7Ozs1Zl7Nq1K5eHt7e3Vnk4OzurLNu9e/e0ylfWxIkTVR5HqF27drGKFSsK/gyEunnzJmvbtq3CshkbG7OhQ4eyd+/eaZV3ZGQkGzZsGDMxMVGYf9u2bdmNGze0yjs7O5vNnj2bVa5cWWHerq6uRbpm7d69m1eXkX5UrlyZ/fjjjyw7O1vr/IXKz89nO3fuZE2aNFH792RkZMQ8PDzYpEmT2JEjR1h8fHyxl48QQnQtOjqaazt6eHgobH+vX7++yMc5evSoXL7169fnjjthwgQu7ZkzZ3hlkr0mSL+m7+dSlPNRpiT6U7Rx8+ZNZm9vzx3H0dGRTZ06le3atYsdP36cHTx4kG3YsIGNGTOGVapUSa5cCxYs0Gl5CCH6JSwsTGXd2trammVmZhb5OAUFBaxevXpKj6OvfcQl4fLly6x3797M0NBQbVunZs2abMSIEczX15e9evWqtIuu17KysliNGjW4927Hjh3Fdqw5c+Zwx2ndunWxHUdfeXp6Cur70rbfSlf27NnDjI2NufI4ODiwOnXqMFNTU636p4h+unDhAhOJRFydPiYmprSL9EmgvwxSpmzZsoUZGBgwAKxcuXJsw4YNKhvaubm5bPfu3czS0lLu4vXhwwel+5WFgAdp6enpcjeDRo0aJXj/UaNGMQBs+PDhxVjKsk/6PdbFjStVLl26xIyMjBgAZmhoyLZv387EYrHS9AkJCWzQoEG87wAFPBDy6ZPu5Ja9mSz9mqLANwlfX18uXf369Xl52NnZca/17t1b4/JFRERwFXgArHz58iw9PV3jfHr37q0wSE/y6N+/v8Z5yoqMjJS72e/k5KTRjYTIyEjWs2dPhY3mol431q5dy3UumZmZsW+++Yb5+/szX19fNnDgQO44VlZW7MqVKxrlffXqVWZlZcXlMXDgQObr68v8/f3ZN998w8zMzLjr0Zo1azTKOyIigjVs2JDLu3Hjxmz9+vXs0KFDbPHixbybG0OHDmU5OTmC887JyWFDhw7l9re3t2eLFy9mhw4dYuvXr2eNGzfmXnN3d2cREREalV0TsbGxrEWLFtzxKlasyMaMGcM2b97MDh8+zI4ePcp8fX3ZlClTFAbwtGvXrtjKRgghJUVREKWDgwP7+PGj1nmKxWK5G/0AWFhYmKD9te3A1sdzYUz782Gs5PpTNPX48WNWoUIFLu9FixapLFd6ejqbPHkyrzwU8EDIp01fg9I+B3l5eczHx4d7PwwNDVmvXr3YqlWr2MGDB9nx48fZ3r172dy5c3ntIemHroPkPiUPHz7kvVf9+vVTmT4sLIwbjLdkyRKNjuXu7s47VmJiYhFKXvZMmDCB+1t2cnLivRfSA0cXLVpUamW8efMm1+9TpUoV3qCTjIwMNm7cOAp40DPSf4+a1Om/++473nfwxIkTxVfIzwj9ZZAy4+eff+Z+ACpUqMAePHggeN9nz54xa2vrTzbggbHCSFvZCuXZs2fV7nfq1CkGgNna2rL379+XQEnLrpIKeMjNzeVF9/7000+C9svPz+dFq1LAAyGfF29vb941QBuyI2c0bUDLWrZsmdy1ae/evUXKMygoSC5PkUjEHj9+XKR8p06dWqQghX379nEjDq2srNiUKVN0FvCwf/9+Lh8bGxv27NkzuTSHDx/mGsYVK1ZUmEaRZ8+ecbNRGBoassDAQIVpbGxsuDIEBAQIyjs9PZ01atSI2+/bb7+VC95LTU1lrVu35tKMHz9eUN6MMTZ+/Hhuv9atW7PU1FTe62KxmNeIbNSokVYBN+pkZmbyRoL169dPZeeRWCxmvr6+vFEbFPBACPkUKJs1atu2bVrneebMGYV5lkbAQ2mfC2Pan09J9qdoql27dly+48aNE7yfl5cXBTwQ8pnS16C0T9HYsWO596J27drs6dOnKtMHBwezatWqUcCDQBkZGbyb75s2bVKZXrY/RBMzZszg9mvSpElRil3m+fn58d7HoKCg0i4SY4yx7t27q/wufPz4kRvsRPSDtt8j6XZBhQoVWGRkZPEV8jNiAELKgEuXLmHJkiXcdmBgIJo3by54fzc3N+zdu7c4iqY3unfvjvHjx/Oe++abb7h1oxVJSkrCN998AwDYsWOHTtb+JkV37do1hIWFAQAMDAzw7bffCtrP0NAQq1evLs6iEUKIYIwx7N+/X+55Pz+/YjnW8uXLtd4/Pj4ee/bs0Xr/BQsWYPz48UhNTUX//v3x7NkzDB06VOv8pIWHh2PSpEnctp+fH9zc3OTSjRw5EtOnTwcApKenY/jw4SgoKFCZd0FBAYYPH4709HQAwPTp0zFq1Ci5dG5ubrzPbeLEiQgPD1db9pkzZ+LJkycAgBYtWmDr1q0QiUS8NBYWFjh9+jS3fui+fftw5MgRtXkfPnwY+/btAwBUqFABp06dgoWFBS+NSCTCli1b0KJFCwDAkydPMGvWLLV5a2rlypXcWux169bFyZMnUaVKFaXpRSIRvv76a2zfvl3nZSGEEH1Qr149mJmZcdtr1qxBXl6eVnmtWLECANC4cWNdFE1jn8K56HN/yuvXr3H79m1u+7vvvhO875o1a2BgQN2ahJBCsbGxRfqtOnfuHB4/fqzDEpV9V69exYEDBwAAJiYm+P333+Hu7q5yH09PT1y6dAkmJiYlUcQyr3z58rh//z42btyIM2fOYNq0acV2rLVr1yIwMBBbtmzBtWvXiu04RDtZWVkICgritnv06CGXxtTUFDNmzMCECRNKsmikGAwYMADXr1/HunXr8OjRIzg6OpZ2kT4J1DIgei8lJQVjx46FWCwGAPTq1Qv9+/fXOJ8BAwagQ4cOui6eXtmwYQOqVq3KbUdHR2PmzJlK00+ZMgXx8fHw8vLC4MGDS6KIRADpDh8bGxuNAlFat26NatWqFUexCCFEIzdv3sSbN29gYWEBOzs77vlbt24hJCREJ8eQvmFw8uRJ7qazptavX4/s7Gytb0BER0fD0tIS/v7+OHv2LBwcHLTKR5ElS5YgJycHANC2bVv07dtXadqFCxfC3NwcAPD8+XOuc0qZgIAAPH/+HABgbm6OhQsXKk3bt29ftGnTBgCQk5ODpUuXqsw7JCSEC0gACm/yyAY7SNjb22Pq1Knc9qJFi5Cfn68077y8PCxatIjb/v7775W+5yKRiBcMs3fvXoSGhqosuyYYY7xgkEmTJgnu3JswYQLq16+vs7IQQoi+sLOz4wXjR0REqL0mKXL58mXcv38f7u7uGDBggC6LKFhZPxd970+5desWb7tBgwaC961atSpXNyGEfJ4+haA0fSYdQNKzZ0/Url1b0H5ubm4YN25ccRXrk2Nvb48ffvih2OsHBgYGGDVqFKZOnQpLS8tiPRbR3Js3b3j9INWrV1eYbuHChUUasEP0R8eOHTFz5kzUqVOntIvyyaCAB6L3tm3bhvfv33Pb8+bN0zov6RGSn6JKlSphx44dvOf27t2Lq1evyqU9efIkjh49CgcHB/z6668lVUQiQHx8PPd/daNzFRk6dChatWoFW1tbXRaLEEI04u/vDwAYMWIExo4dq/C1opo/fz73f7FYjJUrV2qcR3JyMnx9fWFiYqL16P8uXbrg2bNn8Pb21mp/ZWJjY3Hw4EFue8yYMSrTV6pUiRcQsWbNGpXppV/v378/KlWqpDK9l5cX9/8DBw4gNjZWadoNGzZwjXUHBwd07dpVcN6hoaE4deqU0rSnTp3CmzdvuG1170vXrl25oJv8/Hxs2LBBZXpNhIWFISYmhtvW5EaNSCTCF198obOyEEKIPpk9ezaMjIy47VWrVmnctpEErM2bN09p0FxJKMvnou/9KdJtX0Dz9m///v3RqlUrODk56bJYhJAyoqwHpek76QFZmrRzAOhsxkNCPhcpKSm87XLlypVOQQgpw4zUJyGk9Hz8+BFbtmzhtu3t7dG+fXut8+vevTtEIhEYY1rnERISguDgYCQkJKBSpUpo1KgR2rRpA2NjY63yy87Oxo0bNxAWFobk5GRUqlQJVatWRYcOHbRaYqJ///7w8vLCoUOHuOcmTZqEp0+fonz58gCAxMREbpmEXbt2oXLlylqVXVZ0dDRu376NmJgY5Ofnw9HREV26dCnSjffHjx/j33//RXx8PAwNDWFra4smTZqonUJNEy9evMCtW7fw/v17lCtXDg4ODmjYsCHq1q0LQ0NDnR1HKOmRoYmJiQgLC0ONGjUE779p0yatjvv+/XvcuHEDMTExyMjIgLW1NVxcXNC+fXtuxDAhpamAiRGXlQ77chVhKKKYTX2WmZmJ48ePAwDGjx+PSpUqYd26ddzrAQEB+Pnnn4s8DfGQIUNQt25dvHr1CkDhMgdLly5FzZo1BeexadMmZGRkYOLEiVrPkPPll19qtZ86p0+f5kZkAoWjMtXp1asXTpw4AaDw+vbixQuFswg8f/6cNyOG0LwlxGIxfvvtN25pLGmS1yS6d++u9rN2c3ODo6MjoqKiABQGZg4fPlxh2pMnT3L/r169utpZEgwNDdG9e3cueOT06dPYtm2bTm44FfVGjaenJ65evapwmRJCSMnKy2NITBKjCE1FiESAdRUDGBuX3s15feHs7IwxY8ZwQY6hoaE4cuQIL8BNlRs3buDmzZtwdXXFiBEj8PPPPxdjaVUrq+eij/0psmRnRbp//z46d+4seP85c+Zgzpw5Gh83LS0NwcHBiIqKQkpKCqysrODk5IQOHTrILZFFCNFvs2fPxq5du7hg61WrVsHb21uj/jzpoLTXr18XSznLoqIMyGrSpAlatWoFAKUatEhIWSH7N0Z/N4RojgIeiF67ceMGEhISuG0hHeaq2NnZ8W4caCI1NRWTJ0/G0aNH5V5zdnaGr68vevfuLTi/uLg4LF68GAcPHkR2drbc6wYGBujUqRNWrlzJVRCF2rx5M65evcpVTMPCwjB//nxs3rwZAPDtt98iISEB3t7e6Nevn9r8li5dip9++knueWdnZ4SHhyM6OhqTJ0/GxYsX5d5fQ0NDDB06FJs2bYK9vb2g8hcUFGDfvn1YsWIFIiIiFKZxdHTEvHnzMHHiRKXBJpaWlkhNTZV7fsmSJVi6dCkiIiIwceJEXLlyReH+QUFBWLp0Ka5fvy732rhx4xROz+bt7V3kkcuyU1YtXrxYqwh1of755x/MnTsXV69eVfj3YWZmhlGjRmHZsmUK15Py9/dXO1VdWFgYXFxcAACdOnVS+J4Cunn/yKdr6cM/cSkqBL2camNZ8+6lXRyiwokTJ5CRkYH69eujdevWAIBWrVrh3r17AICoqChcuXIFPXv2LNJxDAwMMG/ePPj4+AAoHL2/atUq7N69W9D+aWlp+PXXX2FoaIg5c+YgMjKySOXRtbNnz3L/t7KyEhT81qxZM7k8FAUESOetaD9FXF1dYWlpyY08OHv2rMKAh/v37yMuLk6jvCXpJAEPFy5cQH5+Pm9ELVD4GV+8eFGrvCUBD3Fxcbh//77G9StFFN2oUbXsiKzu3buje3fNf8/y8vJw+/ZthISEIDExERUqVICdnR3atWtHS1sRooWcXIaAQ9nIzCz6jdzy5UX40sscpibUSTl37lwEBARwbYyVK1di9OjRgjpwJTef5s6dWypB6LLK4rnoU3+KMrJt32XLlqFjx47F9j6FhoZi/vz5+O233xROe29kZIT+/ftj5cqVqFevntzrwcHBagMygoKC0KlTJwCAj48P9u/frzCdp6cngoODNT4HQgifvgWlvXz5Ek+ePEF0dDSysrJgYWEBR0dHtG7dWqdLH5YEExMT5ObmAihs52iiSpUquHv3rlbHLYnBb3/99RcePHiAzMxMWFtbo3Xr1mjYsKHG+cTHx+PevXsIDw9Heno6ypcvj2rVqqFx48aClwD5XInFYty/fx/Pnz9HQkICzMzMYGtri1atWsHV1bW0i6eRgoICXL9+Hf/++y9yc3O5INNatWqVaDkYY/j333/x5MkT7u/Hzs4OzZs3V1iv0VZ8fDyuXbuGiIgIGBkZwdraGg0aNEDDhg0FD5rMysrCxYsXERISAhMTE7i5uaFLly5K7/M8ePAAd+/eRUZGBmxsbNC+fXutzikrKwv//vsvnj9/jqSkJDDGYGVlhfr166N58+ZlZtBnZmYmbt68ifDwcG4QtaOjIzw9PXW2ZI2+fK81wgjRY/PmzWMAuMeaNWtK5LhLlizhjunp6ckyMjJY06ZNeWWRfRgZGbGgoCBB+V+/fp1ZWlpy+7Zu3ZqtX7+eHT16lPn6+rIhQ4YwAwMDBoCJRCK2fv16jc/h+PHjvPIZGBiw27dvs2PHjjEArFq1auzDhw+C8vL19WUeHh7Mw8ODOTk5cXk6Ozuzly9fMltbW2Ztbc2mT5/O9u/fzw4cOMBmz57N7OzsuLQ2Njbs2bNnao+VkpLCOnfuzNvvxx9/ZAcPHmQBAQFs5syZzNbWlvf5pKSkKMyrUqVKCj+rJUuWsDdv3vDKp+gRFBTEJkyYwJ27sbEx95qTkxP3vPRj0aJFmnxMCt27d0+uLCNGjGBRUVFFzlvWpk2beN+1oUOHsp07d7IjR46wtWvXstatW3NlqFSpksLv+JkzZ7jzNzQ05H3nJM9HR0dz6SXvaa1atbi0jRo10tn7Rz5Nd+PfsRantnGPe/HvSrtIesfb25v3u6GNsLAwud9LbXh6espdt3fu3Cn3u6aNoKAg3jnm5eUxFxcX7jkTExP27p2w78eKFSsYAObl5SWXNwDm5+enVRl1lZeNjQ23f7t27QTtk5OTwzvu8OHDFaYbNmwYl0YkErGcnBxB+bdt25bbz9bWVmGabdu28cpw5coVQXnPnz+ft9/z58/l0vz333+8NAsWLBCU9+XLl3n7bd++XdB+6sTHx/PytbGxYTExMTrJW5GUlBQ2e/ZsZmFhobT+0rx5c3bx4kWleaiq+8j+3fv5+alMS8inIj29gG38NUNnj/T0gtI+pVIhaUd7enpyzw0fPpz3u3HixAm1+UjaQ46Ojtz1SbqNDoCFhYUJKpP0Pt7e3mX6XDQ9n9LqT9FETEwMrw0JgHXp0oW9evVK58c6duwYMzU15Y7To0cPtnXrVnb06FG2ZcsW1r17d1598siRI3J5PHjwgGvjmpub88rt7u7OPDw82IMHD7j0ixYtYh4eHqxBgwZcuvr16zMPDw82YcIEnZ8jIZ8L2d/oly9fcn1bAFiDBg2YWCwWlJfkb3/37t28vDX5jb5//z77+uuvWdWqVVXWnbt06cJu3bqlMi99qqtL/3Zp0q7TRn5+Ptu1axdzdnZWej6Ojo5s27ZtLDc3V2k+qvqCGWPs2bNnrFmzZgrTtG3bloWEhAgq7/Pnz1nfvn153zvZh5OTE/v222/Z/fv35faX/Z5JHs7OznJpZftphDwk/beq9pXt41X13isrm6rvoHQdSlp2djZbvXo1r79D9lGvXj0WGBgo+O9YU7LlVnZP58OHD0rLKOnjuXnzJqtdu7bCNH379mXx8fGCy6HuoaxfKScnh23cuFHlb1CtWrWYv78/KyhQ3k4ZOHCgys8yKyuLffvtt8zIyEjp39k///yj9jt35swZhZ+/g4MDO3PmDK9Mr1694t2fkH706dOHJSQkKD0fiezsbHbw4EHWq1cvZmZmprR85cuXZ1OnTmXJyckq81P3t6Ls90fVvkL7X8PDw9mYMWN4dVrph6GhIevXrx978uSJ0jyK+3tdmmiGh1IQ+/YKXj/agYK8LMH7GBqXQ51m38ChZrdiLJn+kV4rDIDaKYuLy5w5c/D333+jY8eOGDBgAGxtbZGQkIDTp09zZczPz8fUqVPx9OlTlXndu3cPPXr0QE5ODoDCqdbmzp3LS/P1118jODgY/fv3R0ZGBmbOnImKFSti4sSJgss8dOhQDBkyhJv2WSwWY9y4cfjw4QMAYM+ePYKjvb7++mt8/fXXAPij+XNzczFy5Ei4urri7NmzcktwzJkzBwMGDMDt27fx/v17dOvWDU+ePMH/tXffUVFc7R/Av3QEQUFUMGCvWLAg9hiNscQeSwS7Rl/7qwassRcMsSOKvCogIoIlwW7EbuxEiS2I2BDBjoAg9f7+4Oz8ZmZ3dmepiz6fc/Ychr1zp+zulDvPfW7FihVVLiczMxNdu3bloobbtGmDw4cPw9ramiszfPhw/PLLL+jTpw8uXLiAc+fOoVevXjh37pxSb5UdO3ZwkchLly7F/fv3uX0xZMgQvHz5Et9++y26d++OihUrIjY2Fj4+Pnj37h1Xx7Zt27i/q1evzmWcWLp0KdejuLC5uLjAxcVFED0dGhqK8PBwDB48GKNGjSqUXi8bNmzA9OnTAeSNC3bw4EGlMdbd3d2xZs0auLu748OHD+jevTuuX78uiHru06cPN8bhwoULuWj43NxcTJ8+XWk/KfbpkCFD8PDhQ0yaNAk+Pj4F2hbyectlDN53Lgv+533nMgI72UOfUrzpnMePH+P8+fMwMDDA8OHDuf//+OOPmD59OpfV6I8//sD79+8LPKySoaEhZs2axQ3VlJmZCS8vL3h7e6udLy0tDevWrYOenl6BxrMuKm/evBGMuV2lShVZ8xkbG6NChQp4+/YtgLyhK1Th/9/GxkYpU4EUfq+kV69e4e3bt6hQoYJk3dqsu7jH071795Su/Qqz7sJQqVIlNGjQgLvGeP36Ndq1awc/Pz906VK41+337t1D9+7duUwkDRs2xNChQ1GzZk2kpKTg4sWLCAkJwY0bN9CjRw/MmDEDa9asUep97OTkBAB48eKF4DtWvXp1lCtXTpCRy9ramit///59ZGZmwsHBQXBtRsjnoGxZfVSraoCnz7RL16xKtaoGKFuWht5SmDdvHsLCwrjpFStWYMCAAWrnUWREcHd3l31+Kg6lbVt0pT1FHTs7OwwYMECwX0+fPg1HR0f06dMHY8aMQdeuXQu87w4cOIAhQ4YgNzcXBgYGCAgIwLBhwwRlpk6ditDQUAwbNgyZmZlwdXVF+fLlBRnJnJ2dcevWLQB57Q1jx47l3hswYAAWL14sqHPp0qVYunQpZs+ejXv37qFnz544fPhwgbaFEKKsXr16GDhwIHcsuXfvHg4cOKDxGH3t2jWcPHkS9vb2+R6mMDU1FS4uLtx0+fLlMXLkSDRo0ADW1tZITEzEmTNncOjQIZw+fRrnzp3Dhg0bMHnyZJX16dK1eseOHQX3TX379sXq1asxbtw4pUx8BfHhwwf0798fZ86cAQBUrFgRo0aNgpOTE3JzcxEVFYWgoCA8f/4ckydPRlhYGMLDw1GuXDmtlnP//n20b9+ey1godunSJXTo0AG3b99WO8z0yZMn0bdvX6Snp8PExASDBw9G27ZtYWNjg/T0dNy+fRvBwcGIi4vD5s2bsXnzZpw/fx4dOnTg6rC1teU+u3fv3qnNNmlsbMyVTU1NRWxsLPee4v9iZcuWVZoXyMueIcXR0RHly5fH06dPuX1kbGzMXT+ouvfmfwfj4+Px5s0bVKhQAfb29ip7gcfHx6NHjx7c85Pq1atjxIgRqF+/PjIyMnD9+nUEBQXh33//hZubG/744w8EBQXp1PUg39mzZ9GtWzfu+YPYkSNH0LlzZ0RGRsLExETpff7+k/PZqvptJyYmomfPnvj7778B5GXFHj58OBo2bIjs7GzcunULQUFBePjwIUaNGoWQkBDs27eP+47I9enTJ3Tr1g0XLlzQaj6xgwcPYsCAAdwwRHwJCQkYOHAgDh48iO7du+PBgwdo27Yt174ldvToUXz33Xe4fv262uHmt23bhqlTp3LTzZo1w4ABA1CtWjXo6ekhJiYG+/fvx507d+Dt7Y3w8HCcPHkSdevWVVmf4rcCCH9TtWrVUrlf+cdt/rwPHz7Ex48fJddbLDw8HK6urly76jfffIN+/frBzs4Ob968wfHjx3H48GEcPnwYx44dg7e3t8qsrJoU9Htdoko64uJLdDbsB3bEr7nWr7NhP5T0qhc7cRTR5cuXi2W5/CjLGjVqMCMjI7Zp0yaVZX/66SfBOv7999+S9SYnJwt6obq6uqpdj+3bt3Nly5YtK7vHqkJiYiKztrZWisL66aeftKqHTxx5WK5cObWRdK9evRJE16rb5p9//pkrZ2VlxRITEyXLvn79WrBta9euVbveit7GAFi9evWYsbExCwsLUyoXFRXF9TBRF+lakF6/cty9e1eQBUT8qlixIhs9ejT7/fffWWpqqtb1R0ZGCjJWbN26VW354cOHc2WbNm0qGWGblZUliLq0sLBgsbGxSuUCAwMZANawYUOWnp6u9fqTL8vRp/8KsjsoXseeFX7Pr9JMVzI8KM6hvXr1Unpv6NChgvp9fHy0rl+c4YExxj59+sTs7Oy4/5uamqo9hzDG2Jo1axgA1q9fP5V1F/RYX9C6Ll++LJh/2rRpsudt1KgRN5+JiYnKMvxo8CZNmsiue8qUKRqvzbp16yYooyk6XmHfvn2C+Tw9PZXKeHp6CsocOHBAVt1v3rwRzNetWzdZ88mxefNmledqFxcXtm3bNtnbr86jR48E1z2TJ09mWVlZSuVu377NbG1tuXJz586VrPPOnTuC3g2dO3eWPL8fPXqUAXm9qgpjewjRRfEvsgslu0P8i+yS3pQSoyorAmOM9erVS3B8PHz4sGQdUVFRTE9Pj1WsWJF9/PhRqW7FqyQyPJT0tmi7PSXVnqKtxMREVrVqVcl733LlyrEhQ4aw4ODgfJ2Dnj17Jri3VnduZCwvK4OibJUqVVhKSopk2YEDB3JlDQwM2KVLl5TKnDlzhunr6zNbW1tZvRAJIZqpOkbfunVLcOxo1qyZxnp69+7NALD169cr1S33GJ2SksKVbdu2Lfvw4YPKcpGRkVzGWn19fRYREaG2Xl24Vr99+7ZSFh4gr6f/8uXLVbb3aSsjI4O5uLhwdbdp04a9fftWqdz79+9Zhw4duHLt27dX2VN9//79LCQkhIWEhLAGDRpw5X/55RcuM+2PP/7IfHx8WEBAAFuwYIHSOWjKlCmS6/v+/XtmY2PDgLy2eqle1CkpKdz3C5DOIMCYsK1dVRYFPnE7g7b480qt08aNGwXnNrnPI5o2bcoAsJCQEJXvv3v3jtWoUYOru3///iwtLU2pXFxcnOCz0/T8JD/kZnjIzMzkvk8hISHcZw+AbdiwgVWpUoWZmpqyMWPGMF9fX+bv7888PDxYhQoVBPWvXr1a4zrl57NNSkoSZFDu27evyuuWly9fCn5nbdu2VZkp5cKFC9y2DhgwgCvfsWNHNnPmTAbkZapatGgR8/f3Z6tXrxZkglm0aBF7//69YJ/xtyk0NJRZWloyW1tbNnv2bObv7882b97M+vfvLyhna2vL0tPTuYzr33//Pdu4cSMLDAxkCxYsUMpksWHDBrX7ydvbW+NnkZuby2WBBfKeH8l5ZiHneySF/7xKU/treHg4l1FGX1+fBQQEqCx37NgxwblD1TPN4vxeFzcKeCgBp4K75yvg4VRw95Je9WInflh/+/btYlmu+OLWzc1NsmxiYiLT09Pjym7ZskWy7G+//caV09PT05gmKzs7W9BoPWfOHK23JSgoSOnC9OzZs1rXoyC+IJCzTvxUmlLbHR8fz4yNjQUXopp4eHhw5e3t7Vl2tnTjIv8EAoCtXLlSsqzioqokAx4Yy7ux4D+0knqZmJiwvn37sr1796p8+KFKz549BRfT6tJZMaacQvz48eOSZWNjY5mFhYXgZoX/2Tx69IhZWloyU1NTtemVCGGMsU/ZWaz3sUCVAQ99ju9kn7Llfee/BOKAB1XD7mh68W8q5Vxwi+Xm5nKBfaoeRJ86dUpQv7Ozs9bbqSrggTHGVq9eLajb3d1dsg5+gAQ/7bAuBTwcP35cML+mxnm+Vq1aCeYVD1fx6dMnwftt2rSRXffs2bMF8544cUKpjDjdoNzhMo4dO6Zxm+fMmaNx+aqkp6fne5s1ycnJYd27d5c8TxsZGbEuXbqwjRs3avVQi18/v3GiXbt2alN78n9nBgYGgu+42KZNmwTrqird+atXr1jlypWZvr5+ga4hCSkNDoSnFyjY4UD4lx3IKxUkIA7ia926tWQdiiGXxPdruhLwUJLbou32lFR7Sn7ExcWx9u3ba7z3NTQ0ZF26dGH+/v4qH5KoMnnyZG5+c3NzyYeRCu/evRO0Tfj6+qota29vz5WtWbMmS05O5t5///49c3BwYHp6erKvWQghmulSUJoi4MHIyIglJCSoLasITJB7L6wL1+orV65Ue1xu1qwZW7hwIbtx40a+hh8ors5vderUYRYWFiqHFPnw4QNr2LAhV9ba2lqyjdnPz48rp6lDYVJSEtcJsDQFPCQlJTEzMzOunJw2+itXrjAgr4Oe1P0//wF6rVq11J7H79+/L+ioJ7ejg1xyAx7E+M8G6tSpw+zs7Ni///6rVO758+eCZzpyOpnk57PlD1Vau3Zttfs0ISFBMDTmrFmz1NbNPxbWrFmTGRkZMXd3d6XfxsePH7n9oqoNkb9NDRs2ZC4uLiqHWV+/fr2gbN++fZm+vj4LCgpSKvvmzRtBp2JNAW6KgIfevXurLceYcAg7qQ7QUttXVAEPz58/Z1ZWVlzZefPmqa2X3ynH2NiY3bp1S235ovxeFzfKs0h0WnJysmC6TJkyJbIeirT/qlSuXFmQGpKfekiMn7a/RYsWKlM78RkYGKBr167cdGBgoIy1FRo2bBi++eYbwf+mTZuGrKwsretSpX///hrL/PDDD9zfjDEEBAQolfHz8xOkyRkyZIjGenv06MH9/fz5c5w6dUrjPABgbm6OadOmSb5/8eJFJCQkoG3btrLqKyqNGjXC33//DR8fH9SoUUOyXEZGBsLDwzFo0CDUqVMHwcHBaut9/Pgxjh49yk0PGjRIaTgQMUdHR1StWpWbVvddrFmzpuC7fvnyZS6Va05ODoYPH47k5GR4eXkJhsYgRCwjJxub7l5GYnqqyvcT0lKw6e5lZOQop0EjeWnVtH0p0vLn19mzZ/HkyRNUrFgRvXr1Unq/U6dOqF69Ojd948YN3Llzp0DLVJgwYYJgaAVfX1/JtHfbt29HQkICunbtCmdn50JZfmFLSUkRTJuamsqeV1xWXFdR1i3+n56enuz0k9rWrWoeKeI0e6rqzi99fX388ccfkkOPZWVlISIiAtOmTUONGjXg5OSEVatWITExUVb9Bw4cEAxztWTJEqVhKvg6d+6Mli1bAsg7765Zs0ay7OTJk9GzZ09u+pdffuFSdSuMHTsWL1++xJw5c9CxY0dZ60xIadWqpXQq1OKY/3PVunVrdO7cmZu+cuUKIiIilMr9+++/2L9/P8qVKyeZ6ruklaZt0ZX2FDns7e1x/vx5BAcHo0mTJpLlsrOzERERgdGjR6N69erYsGEDcnNzJcunpaXB39+fm+7RowcsLS3VrouVlRVat27NTau797WyskJQUBB3P/3o0SNBW8OECRMQFxeHGTNmCNp2iO65fPky1q5dq/EVEhKiNG9ISIiseS9fFg7TmJGRIWu+tWvX4sWLF4J5Hzx4IGu+TZs2Ka3vn3/+KWveQ4cOFe5OLgbz588XTCvaolRZvnw5GGOYMWMGzMzM8r1MAwMDdOzYEZMmTRKkLVelR48eXJkbN26obUMGdONafe7cudi0aZPkPrp58yaWLl0KZ2dnODg4YObMmfjnn39k1f3ixQvBUJSTJ09G5cqVJcvb2NgIhhJau3YtcnLkDUcWExOD3377De3atVN6z9LSUvDdeffuHWJiYlTWw982CwsLtcssV66cYBiL0qJcuXKCdvnt27erHH6Ab+vWrQCA0aNHq7z/v3HjBjfsNpD3vVJ3XVK/fn3069ePm/by8pK7+sUmJiYG/v7+qFevntJ7X331leB64M6dO0hLkz+8vRyRkZHYu3cvN61pn9ra2gquSTds2CC7TeLRo0fo1KkTfvvtN6Uhts3MzPD999/LqicmJgYhISEqh1mfOnUqvvrqK246PDwco0ePVhqCDAAqVKgAd3d3bjoqKoob5kGVr776ijtOa6IYyh0A9uzZo7F8cVi1ahU3TL25uTlmz56ttvy4ceO45ziZmZlYuHCh7GWV9Pe6oCjggeg08Vhc6g5cRcXCwkLjwxD+wxtxo4LCw4cP8eTJE266ffv2spbv6OjI/Z2QkICnT5/Kmk/h2bNn3BhOCv/88w88PT21qkcVfX19WQ+smzZtKhhH6fz580pl+I1EVlZWaNiwocZ6+fsGgNLNo5R27drB3Nxc8n1ra2vY2trqxPhgRkZGmDRpEmJjY3Hu3DlMnjwZDg4OkuWfPHmCYcOGwdXVVXKcpVOnToExxk3n57uoaV8PHz4crq6u3PTy5ctx5coVrFy5En/99Rd69uwpGDuLEL5cxnDsWTQGndyN0NjbasuGxt7GoJO7cexZNHJ532uSF2Cm7evx48cFWqaiQXnYsGEqx8/T09PDqFGjVM5TUObm5vjvf//LTaempmLdunVK5bKysrib5Xnz5hXKsouC+JpH3XiEYuLzl7iuoqxb/L+irFub+vX09ARlC/ua0sTEBH5+fvjrr7/w/fffqw1I+OeffzB37lxUr14dU6ZMwYcPH9TWzW+orlixouBBmxR+UOiBAweQmqo6cAzI+w0qGl4zMzPh5ubG7R9fX18cOnQIrVq1wpIlSzQul5DSroqdAapVNdBcUIVqVQ1QxS5/834JxOdcVQ+hVq5cidzcXEyZMkXjQ+mSVFq2RRfaU7Shp6cHNzc3REVF4caNG/Dw8JAcOxkAXr16henTp6NLly6S59JLly4JGmLzc+8bGRkpeW8N5I2fzG94DggIwL59+xAUFITQ0FA0a9asUNpfSNHKyMhAcnKyxpeqsbY/fvwoa96MjAyleeXMl5ycrPRQNysrS9Z8qoJ8P336JGteXT9mqFISQWllypTB2bNnsX79elnla9asKVg/TXThWn3y5Mm4d+8e/vOf/6gNOI+Pj8e6devg5OSE77//Hg8ePFBbb3F2fitXrpxSWwRfp06dBNNSwSj8B/8nTpxQe34AgI0bN+L69eto0aKFrPXUFRMmTOD+TkhIwMGDByXLJiUlITQ0FHp6ehg/frzKMvx7WgMDAwwcOFDjOvA/6ytXrmj8PhW3+vXro1u3bpLv879Tubm5BW5vExPvU36AiJRBgwZxf2dkZHCBKnLMnTtX8j0vLy8kJCQIghBU6dGjh+AYyKevr690nTZlyhTJuvgdfDXt3/79++Ps2bPo3r272vUDgFq1anF/X7t2TW1gbXFIS0vDjh07uOnvvvtO472FoaGh4Ptw6NAhwXNJdUr6e11QFPBAdJqNjY1gWl1jbVGpVauW2kZrAIKDjKobGCAvkpFP3Y07nzjiTRzJqw5jDGPHjkVycrKghwIArFixAnfv3pVdlypWVlayeokYGhqiWrVq3LSqbeDvnzp16shafn73Tf369WWVK6iNGzeidu3aal+aIvIU9PT08PXXX2PTpk149uwZF0EtFXCyZ88ezJgxQ+V7hfFdfPLkicYHNFu2bOE+9+zsbAwcOBBLly6Fra1toT3gJJ+fq6/iMOLMXiyKPCWZ2UEsMT0ViyJPYeSZvbj2Kq6I15BISU1NxYEDBwAII6LFRo4cKTiv7tq1S2NvAbmmTp0q6GWxadMmpWNVUFAQnj59inbt2ul0b3Xx+VWbzEziRhdxXUVZt/h/RVm3NvUzxgRli6qXa9u2bXHkyBE8efIEXl5eaNOmjWQWpYyMDPj4+KBx48aS12Tp6em4dOkSN92uXTuN16WA8EFNZmYmIiMjJctWrFgRAQEBXL3379+Hh4cHoqOj8fPPP8PCwgK7d++GoaGhxuUS8jnIb5YGyu6g3rfffotWrVpx0+fOncNff/3FTT9+/BghISEwMzNTm2FRF5SWbSnO9pQDBw5ovPcdOnSo7PpatGgBLy8vREdHIzo6Gr/99ptSm4bCmTNnJOsujHvfzMxMjVnQlixZwmVXAoDx48djypQpMDMzQ0hIiE50piDqmZiYwNLSUuNLVecZc3NzWfOKM44BkDWfpaWlUo9aIyMjWfOp6oFuamoqa15dzgqjjq4HpfGDsF++fKmxvK5cq1erVg2+vr54+fIlAgIC0KtXL7XfkWPHjsHJyUnQA12suDu/qfoNKtja2gqyWEh1aGzWrBn3971799C/f3/JbBAAUKNGDTg7O2vMBqFrWrZsiebNm3PTvr6+kmWDgoKQlpaGLl26CB4W8/EDUxo3bqwUlKlKfj/r4qKpI4J4X0h9p/KL//upU6cOrK2tNc7j5OQkCFqSGzBkZmamNltJ2bJlYWtri7Jly6qt5+uvv1b7Pr+Tp6WlJZo2bSpZlv+sCYDG5xRy8Y/RmZmZSEpKKpR68+vy5cuC4F2p62ExfjnGGE6fPi1rvpL+XhcUtVoRnWZra4vo6Ghu+s2bN8W+DnIuSNRdMCm8evVKMD1p0iRZaXTEtNkHvr6+iIiIQJMmTXDu3Dn07NmTOxlmZmZizJgxuHTpktKNk1zqsiSI8RsMUlJSkJmZyd30iyPdr127JqsxX0zuvimum5l3795pTE8n5+ZGlaZNm6Jp06ZYsGAB/vnnH6xfvx47d+4URP1v2bIF06ZNU0pBJP4uii8g5Xrz5o3aC9Ry5cph165d+Oabb5CTk4P4+HgAeSlBK1asmK9lks9XDsvF4huncOK59I2iJtEf3mDKX4fQzb4OFjt/CwM9iussTmFhYfj48SNatGihNvtP9erV0alTJ+5i+9WrVzhy5Aj69u1b4HUoX748Jk2ahF9//RVA3g2Pt7c3fvnlFwB56f1XrVoFQDndqa4RX398+vRJ9rzisuK6irJu8f8YY4JzfmHWrWoeKeKAVHE98fHxsgJgHj58KGt5VatWhYeHBzw8PPD69WscPnwYf/zxB44fP64U2BEXF4fu3bsjKipKqZEiKipKEKhRkIBZddvXrVs3/Pe//+V6pvn4+ODw4cNIS0vDzp07JXthEPI5UmR5ePpMXopkgLI7yDV//nz06dOHm162bBmOHz8OAPD09ER2djamTp2q9KBeF5WGbSnO9pTk5GSN97729vb5qrtu3bpwd3eHu7s7YmNjsWnTJmzZskVwbj9y5AhOnjyJ7777TjCv+N5XbsplMU37zsjICLt370azZs2QmprKpR3eunWrypTARPe0adMGbdq0yde8/OyW2jAxMcHMmTPzNW/dunXzPW/Xrl0/6yFWFEFpV69eBfD/QWmKoQyKKigtMzMT4eHhCA8Px82bN5GQkKAyOwef3GH2dOla3dLSEiNHjsTIkSORnp6OiIgIhIeH4/fff8e7d+8EZT99+gQ3NzfY29ur/H0VZ+c3OfVbWlpyDxalOjS6ublhxYoVePbsGQDg6NGjOHbsGL799lsMGjQIffr00Ti0SWkxYcIELmNDREQEYmNjVQY0KLIE8LNC8CUmJuL58+fcdEHuaUeOHClr3uKg6Tslfv4g9Z3KD/E+lfv70dfXR61atbgOF+LAUCm1a9fO9/MjPqmAGAV+0JGm45p4mB05+zc+Ph67du3C2bNncffuXbx7905l5ia+lJQUWcEkReX69euCabmftbjc9evXMWbMGK3nEyvK73VhoIAHotPatWuHc+fOcdP3799XOSZ4UZLqlact8UVstWrVVI5XpImc4Aog7wLew8MDhoaGCAgIgLGxMfz8/NC4cWPuQH7t2jWsW7dOY7ohKdoEJagal1sx1rp431hYWOTrYt3Ozk5WucI4QeuSJk2aYMeOHZgwYQJ69uzJNcYwxrB3717uQZ+CeH83bNgwX5HgcnrVtm/fHnPnzhVE1PMvyAhRSExLKVCwA9+J5zGY4OiCr8w1R4yTwqPI3CLnAnrMmDGC6GJ/f/9CCXgAgJkzZ2Ljxo1cms/169djxowZMDc3R2hoKGJiYtC8eXNBekRdJA4o03QTxseP/jYxMVEKNjAxMYGJiQl3Y5LfugHVQYSq1l1OwEN+65ZDU91ZWVkaH9TkV8WKFTF69GiMHj0aSUlJ8Pf3h5eXl2C8zOfPn2PNmjVYsWKFYF7xgxovL698jV8q5yHXqlWrcPr0aW5c2qdPn8LV1RXDhw/XenmElHatWhppFfBA2R3k6dWrF5o0acIdZ06cOIEbN27A1tYWgYGBMDY2zve9aXErDduiC+0pha1WrVpYt24dJk2ahB49egjO3aGhoUoBD+J73zp16kiOQ6+OnLaP2rVrY/369fjpp5+4/9G9LyElo7iD0s6fP48xY8YIjkkWFhaoXbu2Unvow4cPuXsYpsWwnLp4rV6mTBn07t0bvXv3xpYtW/D777/j119/FQytnJ2dDXd3d0EmJKD4O78VVofGsmXL4uTJk+jfvz/u3bsHIO9zjIiIQEREBCZMmIBWrVphwIABcHNzQ5UqVWStny5yc3ODu7s7kpOTwRjD1q1ble5FL168iLt378LOzk7wm+MT39OGhYUhLCxM6/UpiY6w6mj6Tsl9hpMf4n2qzTMmftn09HSkpqZqzMxQWJ1HNe0z/jFAU1ltntkxxrBkyRJ4enoKOqBUqVIFNWrUEDwrEmf20uY4XRTy+1mLy71+/VrWfCX5vS4MFPBAdFqXLl2wcuVKbvr2bfVjuesy8cFi6dKlGDFiRJEsizGG0aNH4+PHj1i4cCGXbqtGjRpYuXKlYIzzhQsXol+/fqhdu3a+liOXul6b4n3j7OwsO82OLlu8eDEWL15cbMtzcXGBt7e3oHdBVFSUUjnx/j569CiqVq1aZOslTtM9bdo0tG/fXnZEL/ky2JpZoJt9nUIJeujuUAd2Zro77vPn6OHDh7h48SIAYMOGDfDz81NbXtzD/ciRI3j16hUqVapU4HWpVKkSfvrpJ3h7ewMA3r59iy1btuDnn3/mrinUjT2oK8Tn5YSEBNnzvnjxgvtbKjq7du3a3PE5v3VL1V+7dm2cOHGCm05ISICVlVWh1c0nd93l1F0cypcvjxkzZuCnn36Cq6srjhw5wr0XGhqqFPAgflBjZ2eXr9+J3Aa+kJAQNGvWjPuNJiQkIDc3t9ACgAkpLbTJ8kDZHeTT09PDvHnzBGN0L1++HFWrVkVmZibGjx9fah4MlIZtKc72lFGjRqkdG72w1alTB7t27RL0GJZz77tt2zaN6ZQL4s6dO4LpFStWoEuXLkW6TEKIsuIMSlOMC694eN+mTRv89ttvaNu2rcoH+N98840gGE0uXb9WNzIywuDBgzFw4EAsW7ZM0B566dIlxMfH46uvvuL+V5o7v9WtWxe3bt1CQEAAtmzZgps3b3LvMcZw5coVXLlyBfPmzcOIESOwatWqUpG9Sszc3BzDhg3D5s2bAQABAQFYvny5oDODYqiLsWPHSnamE3/WFSpUyFfWJ0XHSV1Rkh0qxftUHFiljngomg8fPmgMeCisbdXmWFWYx7WJEydymUj09fUxc+ZMzJgxQ+W1+pMnT1CjRo1CW3ZB5fezVvU5y1HaOwpTwAPRaW3btkWFChXw9u1bAMDJkycLdCGXnJzMjStjYmJSrGn1xY3TRTm+zcaNG3Hu3Dk0adJEqXf/lClTEBoayo0HnZ6ejrFjx+Ls2bNaR9Jq0yOUP95R2bJlBRdHlpaWMDU15YIidG3sn+IWHR2NM2fOwM7OTusez/3794exsTF386PqZFac30U/Pz/8/vvvaNCgARISEpCUlISPHz/C1dUVly9fprFMCcdATx/LWn6HXtXqw/vOZTz4oH3kdr1yNpjaqA1cKjloLkwKVUBAAPf3gwcPtJ4/Ozsbu3btyndaVjEPDw/4+vpymWjWrFmDKlWq4O7du6hfvz5++OGHQllOUbKxsUHFihW5KGzxA3spmZmZ3HUTID1skaOjIxfw8ObNG9nDTvADDCpVqqSy0UG8zBcvXsgaPkkcvKBqHlV1y6Gp7urVqxdr5L6FhQX27NmDGjVqcD1VYmNj8fHjR8GQYeIHNRMmTMDChQuLbL1iYmIEAUlnz56Fp6enzg8BQ0hRkJvlgbI7aGfQoEFYuHAhd71w8OBBGBsbw8DAALNnzy7htdOOrm9LaWhPefHiBQ4ePIiyZcti2LBhWs3bunVrVK1alUsrXtL3vidOnMCGDRtga2sLU1NTPHnyBLm5uRg2bBiioqJkBX8SQgpHcQWl5eTkYOzYsVywQ+PGjXHq1CmlB02FpTRcq+vr62PRokW4fPmyIAj+1q1bgoCH0t75zcjICOPGjcO4ceMQHR3NZS3gB75lZWVh+/btiIiIwPnz54u0s1lRmTBhAhfw8Pr1a+zfv5/raPf27Vvs27cP+vr6GDdunGQd4s+6T58+2LFjR9Gt9BegIMOUKrKhKqgbrvpzcObMGS7YAcjrhKxLx0xN8vtZf2mfs0LJh/8RooaJiQkmTZrETScmJuLChQv5rm/gwIFwcHCAg4MDN4Z3cXFxcRFMyx0DWlsxMTGYO3cuN5SFkZGwAU5fXx87duwQRIOdP38eW7Zs0XpZ79+/l3WQzc7OxpMnT7hpRcYJvpYtW3J/F1VK6dLi8uXLmDhxIhYtWqT1vCYmJoKoYVUns+L6LkZHR2PGjBkwNTVFaGio4OLi77//LhU9rEnxa1XJATs7DcJS5y6wLaM+wljBzswCS527ILDTIAp2KAG5ubkICgoCkDc+KmNM1uvatWuCehRDYhQGBwcHQRalxMREjB07FkBedgdd6AEjR/Pmzbm/+Sn11FGk1VRVh9T/GWP4999/ta5fTt2q1kkKPyNQ2bJlVWZhqFu3rqD3QX7qVrWOBeHr6wtfX1+tH6KULVtWaSxx8cOa4nxQ8+LFC4wdOxb6+vpo1aoV9//FixfjypUrRbZcQnSVIsuDOpTdQXv6+vqYM2cON80YQ0ZGBlxdXYt1DPLCoOvbUhraUx48eICJEydiypQp+Zqf30O0JO99X79+zWW4CAwMRHBwMNdDLi4uTu2DIEJI0Rg0aJAgs+jBgwfh5+dXqEFp58+fx6NHj7jpmTNnFlmwQ0lcqx88eBC+vr6y7xX5fvzxR8G0+D5H0flNoTR3fqtXrx4WLFiA27dv4/bt25g6darge/D06VPBcEelSePGjdG2bVtuWpHRAcjr+JKRkYEePXqoDeYoznvaL4V4n/I7mmrCL1umTBmN2R1KO34HLVNT00LraFVc8vtZi8sVZ8fvklQ6WnvJF83Dw0OQnsrT0zNf9URHR+PUqVMAoDHysCjUrFlT0Ohx9epV2fNu2LABzs7OcHFxUXtQy83NxahRo5Ceno558+apDCwA8i7ExA/T58yZw/WMkCs3N1dWWsy///4b2dnZ3HSHDh2UyvDH2kxKSkJ0dLSsdYiNjYWzszOcnZ0RHBwsa57SIjY2VrDf5OJfOFarVk3p/W+//VaQzUOb7+KIESPg7OwsGDZDlczMTLi6uiItLQ1eXl5o3LgxBg8eLEhzum7dOvz555+yl02+HPp6eujuUBd7v3PDj7Uaqy07pFYThHVxRXeHutDPx3iPpOBOnz6NZ8+eoVq1aiqP71JatmyJevXqcdN37tzBjRs3Cm295syZI0jF9unTJ1SvXh1ubm6Ftoyixh8D8927d3j8+LHGeSIjIyXrUPd/8XyqxMbGCq5DpOp2cXGBra2tVnWLy33//fcqU2IaGhqiR48eBarb1tZWEGhZUBMnTsTEiRMFjZ1yiVN5ih/WODk5CTJvFNWDGsYYRo4cibdv32Lu3Lk4evQo1wMrOzsbbm5u1DBFvkiasjdQdof8GTZsmKBhXE9Pr9QGQ+v6tpSW9pQPHz4gMTExX/MpqLr3bdOmjSBzkjb3vrNnz4azszM6d+6M3NxctWXHjBmDxMREzJgxA127dkXbtm2xYMEC7v39+/fjf//7n+xlE0IKrjiC0sRtoi1atCiUesVK6lp97dq1mDhxIo4fP671vJruc4DPs/Nbo0aNsHHjRty9e1dwXjp58mS+znO6YMKECdzf58+f5zpjKIYy5b+viq2tLRwc/r9zUlHd035JbG1tBb8xuZlWc3NzBb+1wmwX0VX847Sjo2ORBaUVFfFnJPezjokRDhktDgL+XFHAA9F5FhYW8Pf35x7QnjhxAocOHdK6npkzZ3I3qWPGjEH9+vULdT3l4PdauHr1qqwHFzk5Odi4cSMiIyNhZGSE8uXLS5Zds2YNLl26BCcnJ6WhLMTc3d0FvRtTUlIwfvx4zRshEh4errHMgQMHuL/19PQwevRopTLjxo2DiYkJNx0SEiJr+UFBQYiMjERkZGSRn6T565eTo5zedunSpXB3d+caggoqNTUVERERWs0TGRmJ1NRUbrpr165KZapVq4bevXtz02FhYSq3R+zRo0cIDg5GZGSkxpvDefPm4ebNm+jZsyemTp3K/d/b2xu1atUC8P83bK9evdK4bPJlMjEwxJSGbSQzPdiZWWByw9YwMaARukqSIjODm5ub1kMjDR8+XGVdhaF27doYPHiw4H+zZs2SHFdSF/Xv31+QjUJOQxO/TIMGDdCgQQOV5RwdHQXXQtrWra+vj379+qksJ35PkUJbnXv37uH58+fc9IABAyTL8t979uyZxuwXubm5gvOpeL8WFrnBmnz8BzUVKlQQPJQB8npctG/fnpu+fv267KE3wsLCuKBQTb2y1qxZg4iICLRq1QqLFy+GtbU1goKCuP30+PFjTJw4Ue5mEfLZUJflgbI75J+RkRFmzZrFTffr10/W0Ee6SNe3pTS1p/zxxx9alX/16pXg/Kbq3rdMmTJcli8AOHz4sNJYyKokJyfD19cXkZGRsLOzU3vdsGnTJhw+fBjNmjUTBJT88ssvgl6x06dPz1cvaUJI/hV1UJp4qF85Y6trMzywQklfqxf0PgdQHZRWGju/HThwAL/88ovGAI0aNWrAy8tL8D85zwF00aBBgwRDWfr6+uL06dN48OABHBwcBB0SpPA/6/v378s6FwPAxYsXuc/6zJkz2q/8Z4y/T2NiYgRDm0qJiooSZOv+9ttvi2TddAn/mFtUx+iiJA7elZvRh19OT08PnTt3LvR100UU8FACDIzMinW+z0G3bt2wdu1abnro0KGye/QBeTeaR48eBZB3wVHcw1kojB8/XvCwl98wImXLli1cj0F15e/fv4+FCxfCyMhI5VAWYoaGhtixY4fgwc+JEye0fti0efNmbuxpVV6+fCkYLmPw4MGoXbu2UjlbW1vMmDGDm163bp3GcbmfP3+ODRs2AMhrWOKnqisK/BRC4kwbjDEsX74ca9asKdRGjLlz5yqNuSQlNzdXMAZVw4YNJU9my5Yt4wI4Hj16JGtIk1mzZiE3NxdlypRRm3I0IiICa9euha2trdL3qWzZsti9ezf3vUtMTMSoUaOKddx0UrqYGBhiomMrle9NdGxFwQ4lLDk5Gb///jsAaD3usmIefpBESEgIN/5pYZg3bx5Xv52dHcaMGVNodRcHOzs7wX7dtWuX2vIfPnzAkSNHuGlN1xn89w8fPqxy7Gs+fmPS8OHDBT1GxWbOnMkd6xMSEjQGA/K3rXbt2vjhhx8ky/7www/c9ZR4XlUiIiK43jSGhoaC643CpPgtaOPcuXPc36oe1ADCgNkXL14I5lFn8+bNiIyMRGJioiCbitjff/+N+fPnw8LCQnCO7tSpEzw8PLhyu3fvxs6dO2Utm5DPiVQWB8ruUDD/+c9/EBcXh7i4OG5orNJK17eltLSnrFixAq9fv5Zdfv78+VzgfqVKlZTSpyvMnj0b1tbWAPI6FSxZskRj3UuXLkVycjL09PTg7u4uWe7u3bvw8PCAmZkZdu/eLcjKZGBggODgYFhaWgIA0tLS4OrqWqjXuoQQ9Yo6KI2f1Q6AxoxvWVlZWrcZ6sK1+pEjR5CVlaXVPPx7lipVqqBRo0ZKZUpD5zexgwcPYsWKFbIeOjZuLMxYqjgfFAR/fwHKHfKio6Ph7u4Od3f3Qsv6YWpqipEjR3LTO3fuxPr16wHkfYb8zJpS+Pe0mZmZ2L9/v6xlb9u2DZGRkbh7926RZVAprfj7NDc3V9DhVMrevXu5v01MTPLV+bW04R+n5WTl/Oeff7Sqn3/tJ/49Zmdnc7/HmzdvalWvgpmZmaAt8+TJkxqHtcjOzha0T/Xu3Vtl0NnniAIeSkDdFhNhXr46TM0ryX6Zl6+Oui2+7F5V06dPx7Zt22BsbIyUlBR07NgR69evV5vyPzExEW5ublixYgWAvAPc4cOHBVGJxcnc3BxhYWFc6px9+/Zh5syZkheNQUFB3LhCAwcORN++fVWWy8nJwciRI/Hp0yfMnTsXTZs2lbU+Tk5OgvRuQN4DioSEBFnzV6xYEVZWVujXrx/evXun9P7bt2/xww8/cBdYlSpVwsaNGyXrW7ZsGdeLMTk5Gd26dcOTJ09Ulo2NjUW3bt2QlJQEa2trLvChKDk7O3N/i09SR48e5T7HwrwAu3XrFrp166bxhPzy5UsMHDgQJ06cAJDXk2Xr1q2SPZmbNGnCXZwCeZ+71I1FZmYmpk2bxl2Menl5ST7kevPmDUaMGAEgb4wsVeNDubi4CBqZjh07ViyfHym9ujnURd1yNoL/1Stng672dUpojYjCnj17kJ6ejmbNmuWr4Ug8DMb79++17t2nTqNGjRAfH4+4uDjcuXNHqXGgNFiyZAm33pcuXRIENIgtX76cC5JzdHRUyqAhNmLECO5zS0tLw/LlyyXLHjlyBJcvXwaQd2O8ePFitXXXqVNHcFM2f/58yeC2xMREeHt7c9PLli1Tm4nDyMgIy5Yt46a9vb0l04MyxgRZr8aOHYs6dYrm2LF371789ddfsssHBwfjzp073DQ/IxJf3759BT1E58yZozEz06FDh7hGRg8PD8nsK2lpaXBzc0NmZiZ8fHyUMjgtW7ZMcP0zefJkSkFKvjiqsjxQdoeCMzQ0hL29Pezt7ZWy25Q2pWFbSkN7yvPnz9G5c2eNDcIpKSmYOHEitm3bBiAvs5SPj4/KdOlA3oO2nTt3cg9k1qxZgzVr1qi8LmGM4ddff8WaNWsA5N0nSw0VqkiN/+nTJ6xbt05l1ovq1asLOhfcunULs2fPVrt9hJDCVZRBaZ06dRJMBwYGqi0fEBAgyMqqia5cq8fFxXHHRTliYmKwY8cOblrqPqc0dH6TIifYnZ963traulDWld8ZD1DukHf9+nXuPFeYWQ3/85//cPeUSUlJOHToEAwNDQVZlNRp1qwZhgwZwk0vWbJE42/h5s2b2L17N4C8YTMKI2Dkc9K8eXNBVlNPT0+kpaVJlk9MTISPjw83PX36dKWgrc8R/zidmJiodnhtxhg2b96sVf3qOsgmJiZyv0dNxzZ1Zs+eDSsrKwB55wVNQ9T973//Q1xcHIC89rulS5fme9mlDiOklLl48SJr1KgRA8AAMBsbGzZu3Di2fv16tmfPHrZr1y7266+/sj59+jBjY2OuXIcOHVhcXJxkvVu2bGFOTk7MycmJVa5cmZvP3Nyc+/+CBQtUlreysuLKW1lZqSzPd+XKFVaxYkVunlq1arH58+eznTt3sj179jBPT0/WqlUr7v3u3buz1NRUQR3h4eHccurWrcuVdXR05P6/ZcsWlcu/fv06V6ZBgwbcvIrXV199xb0fHh4umNff358rV61aNXbjxg1mYWHBbGxs2MyZM9nOnTvZrl272OzZswX70cbGhv3zzz8aP9+UlBT2/fffc/OVKVOGjRgxgm3dupXt3buX+fr6sqFDhzJTU1Ou3qtXr6qsa+zYsdx2mJubc3VWrlyZ+7+qbZQSFRXFDA0NGQCmr6/P/vvf/7Lg4GC2fPlyVq5cOQaAOTs7y6pLnf379zN9fX3BZ2JgYMC6du3KlixZwgICAti+fftYUFAQ8/T0ZP369WMmJiZcWVtbW3by5ElZy/Lz8+O2CQBr164d+/XXX1lISAjbtWsXmzNnDqtevTr3vtR3WrEv7e3tGQBmYmLC/e/69etcOcX3tnHjxoLtMzY21vrzIF+Wqy+fsZYHfLjX1ZfSx/Mv1ciRIwW/q/x4/PixoI5FixapLd+mTRsGgK1evTpfy2OMsf/973+CZXbr1k1t+TNnzhRoG+XWDYD5+/vrRF2BgYFcPZUqVWJ37txRKrNnzx5mYGDAADALCwuVZVS5c+cOs7Cw4M41ISEhSmXu3r3LKlWqxK1DYGCgrLpTUlJYkyZNuPkmT57McnNzBWWSk5O57xEANmbMGFl1M8bYmDFjuPnatGnDPnz4IHg/NzeXTZkyhSvTpEkTlpKSIrt+ufifs5WVFQsLC1NbPicnh23evFlwnTpt2jS18zx79kxw7ThkyBDJbTlx4gT3mbZu3ZplZmZK1jtu3DgGgLm6ukqWiY6OFlxHtWzZUm2dhHyO4l9ks3Xeqdwr/kV2Sa+SzujRo4fSfTT/HtrJyYnFx8cXWt2KV4MGDVTed/Pvk52cnJSO0fz3dH1bCro9qhRVe0p+Xbt2jRkZGSm1SbRr147Nnz+f+fv7s3379rHg4GC2evVq5urqyp3jADBLS0sWHBwsa1kHDx4UnM+cnJzYsmXLWHBwMNu9ezdbvHgxa9iwIff+6NGjWVZWllI9is+yZs2aDADT09NTeS/Lb3dRXKOJy0u12RBC1Fu0aBEDwDp27FhkdStejx8/Vlt+4MCBgvI+Pj4qy507d46VLVtWq/vtkr5W79ixo+DYtXjxYvbp0ye185w+fZprG1Ssj7p5srKyWPv27bnyjRo1ktznDx8+ZI6OjgwAs7a2Zk+fPpW9/pr2NWOMVatWTeP9O7/dxdvbW7KuFy9esPr163Nlly1bJllW3NauTm5uruA5hLj9V/F9rFevnsr5+d+/M2fOqF2WWOfOnQXz9+/fX6v5P3z4IHiG0alTJ/bq1SuVZSMjI1mVKlUYAFazZk2WlJSk1bI04e9zbfaFnO8InzbLELchyZGUlMRq1arFzdOrVy+WnJysVO7ly5fMxcVF0H6i6TjBPxYW5FirzT7Qdply6k5MTBQce+3t7Vl0dLRSuZycHDZ9+nSla1JN54B+/fpxZefPny94b9OmTdzxU9U9hDbHqPDwcO55kZ6eHtu+fbvKcseOHeOemwFgmzZtUlsvY0X7vS5uFPBASqWcnBy2c+dO1qFDB8GNo/ileEi8f/9+jXWKL2hVvUaOHJnv8mKvX79m06dPF9ysi1+1a9dmvr6+LCcnR2l+8YlZ1UvqQCk+gap7iQ9yqi7CYmJiBAdo8WcwaNAgrRqGcnNzWVBQEKtXr57kepmbm7Px48ezxMREyXqk1knTNqoTGBgo+Zm1atVK48W2XC9fvmR+fn5s4MCB3AWeplfNmjXZ4sWL2fv377Va1p07d1jfvn0FgQ/iV7t27djp06cl61C3XvwTn5zvbUEeCpLP2y/X/mQtD/iwBdf/LOlV0Rn8Rm7+TS8A2YFd/AA+cRAcP0CsR48ejDFhwzv/fKXNg4D4+HiuvPhYr6+vr9Tov2DBAu5//Js5uduojlTdAJiDg4PS9kvhP8jQVJc2DyYUvLy8uGseU1NTNmnSJBYYGMh8fX0FN1jW1tayg94UTp48yaytrbk6+vXrx3x9fVlgYCCbPHkyd7NkYGDAvLy8tKr76dOnggcrzZo1Y2vXruUeLNjZ2XHvDRgwgGVkZMiuOyMjQ9DAaGtryxYtWsR2797N1q5dy5o1a8a916hRo0I7R4uVL19e6VxWp04dNm3aNLZ161YWGhrK9uzZwzZv3swmTJggaADU19dnP//8s1IgiCoPHjwQfK/s7OzYzJkzmb+/PwsLC2Nr165l3333Hfd+ixYt2MuXL5XqUXzn+cEoderUUfmgTfFdVQR2in/zY8eOLbT9SIiuO/7nJ7bOO5Ud/1N9Q/+Xht9AJvXS1FBYkLoB4X23nPsNxUvXt6Wg2yOlKNpTCiIpKYnt2rWLDRs2jNWoUUPWttrZ2bGff/5Z6wCUp0+fspEjRwoagsUvJycntnfvXsk61H2W/HtZOe0uch7AEULy6GJQGmOMvXnzRuk++uuvv2br1q1jYWFhzM/Pjw0ePJjp6+uzXr16CTq48e+3FQFQunStzr/PVLwqVKjARo0axTZu3Mh2797N9u7dy7Zv385mzZoluP8CwL777jv27t07jcsp7s5v/LYDfnl+AB7//l1cnr+NzZs3Z4sXL2a7du1i+/btY35+fmz8+PHM0tKSKzNkyBClADp+O4yDgwNX1sjISGObwaxZs7jy9vb2bMOGDWznzp1s6NCh3P8VDzn5bS/8NhwgrwOmNu0pYWFhgvmPHz+ucR6xxMRE1rJlS64OKysrNmHCBLZt2za2d+9e5u3tzfr3789dn9SsWZPFxMRovRxV+J81f5+L94X4d8Y/Pkh9R/id/fjlpZahKM9vXxO3IYnbj/jL4EtISGDNmzcXfCfmzp3Ldu3axQIDA9n06dNZhQoVuPe7du2qMiiCMXkdgqWOhXxyvnOK4zV/H8jphKzN/lUIDw8XfHZlypRhP/30E/P392ehoaFs1apVrHHjxszAwIAtXrxY8hyg6hxz5MgRrqypqSlbsGABCw4OZnPnzuU6qA4cOFDl+ksdo6T27++//87KlCnDzdOxY0e2fv16FhoayjZv3sz69OnD9PT0uOt4qQA88XoU9ve6JFHAAyn13r17x44ePcq2b9/OPD092a+//sq2bt3Kzpw5o9TTTxdlZmay06dPs61bt7KVK1ey3377jQUGBrLbt2+X9KqppC7q9MmTJ2z37t1szZo1zMvLi+3evVttQIIc//77LwsKCmKrV69mK1asYD4+Puz06dNKGS+K04cPH9gff/zBVq9ezZYvX858fHzYtWvXinSZ8fHx7OzZs8zf35+tXbuWLVu2jK1atYpt3ryZhYeHsydPnhR4GUlJSSw8PJxt2rSJLV++nK1du5bt27ePPXv2rBC2gJCCy87NYc9Tk1h2rnIQ2JdKToOwuPFVTE4AH/+Yr6nhXc6DAHEmCamXotFfnL1C221UR07dqs55YnIfZPBf2rpw4YIgGwL/ZWRkxAYOHJjvY3ZcXBwbNGiQyl6WQF4PgPPnz+er7vT0dObh4aEUlKN41axZk/n5+eWrbsbysoQoelmKX1ZWVszd3Z2lp6fnu35NMjMz2fHjx9mUKVNY06ZN1T48UrzMzMzY0KFDtb4hTElJYYsXL2Y2NjaSdVepUoWtXLlSMnhE3Xde/KBN03YURa86QnRVTk4uS/qQw3JyNAcoEVJa6GJ7yqtXr9jFixfZzp072fr169mKFSuYp6cn27RpE9u/fz+LiYmRFSiozsePH9mxY8fYli1b2PLly9nq1avZ7t272YMHDwppKwghhU0Xg9IUkpOT2YQJEwTZV/mv8uXLMy8vL5aVlSXZOUsRAKVr1+qRkZFs4cKF7OuvvxY8aJN66evrsy5duqgNHFOlODu/8dsOtC2fkZHB9uzZw3r37s3MzMzUzufo6MgCAwNVnrPktsOokpaWxn788UeV5Q0NDZmHhwe3TLltL3LaUzIzM5mtrS0D8u7h83suzszMZBs3bhR0BBC/rK2t2axZsyQfzOeH3I6R4t+ZnOMDv7OfNuW1CWxV15M+IyODrVu3Tm2nydq1azN/f3+VHWsV5H4v1XXyZUxeW6XieC1nH/CXp+3noXDt2jXWunVryXlatGjBLly4oPY3I3WO8fT0lDz+9+jRQxD4JWf91e3fx48fs2HDhkkuz8DAgPXs2ZNFRUWp/YyK8ntdkvQYkxhMlxBCVAgICMDo0aMB5I29/uTJk5JdIUIIIeQL8+DBA0RGRiI+Ph7GxsZwcHBAhw4dYGNjU+C637x5gwsXLiAuLg4ZGRmwt7dHixYtCmW80YyMDJw9exZPnjzB+/fvYWtri/r166NVq1bceKD5xRjD1atX8e+//yIxMRFWVlaoXr06OnbsCFNT0wKvuzY+fvyI6OhoxMbG4uXLl0hNTUVOTg4sLCxQoUIFNGzYEI6OjjA2Ns73MnJzc3H16lXcu3cPr1+/hr6+PmxsbODk5ISmTZty45QTQgghhBBCisf79+9x8eJFxMbGIi0tDVZWVqhfvz7at28PIyOjkl69AsvKysLDhw/x8OFDxMfHIyUlBRkZGTAzM0P58uVRt25dNGnSBJaWlgVaTnR0NK5fv46XL18iIyMD5cuXR4MGDeDi4gJzc/NC2pqCy8jIwO3bt3H//n28fv0aaWlpMDMzg52dHZo3b4569eoV6fJjYmJw8eJFJCYmQk9PDw4ODujUqROqVKlSpMstTLdu3cKtW7fw+vVr5OTkwNraGo0aNYKzs3OB7pe/VIwx3Lx5E1FRUVw7QeXKldGiRQs4OjqW9OrphOjoaFy5coX73VSuXBmtW7cu8O/19evXOHPmDJ4+fYqsrCzY2NigXbt2aNiwYSGtudDHjx9x7tw5PH36FO/fv4elpSXs7e3RsWNHWFlZFckySwMKeCCEaIUCHgghhBBCCCGEEEIIIYQQQgghukC/pFeAEEIIIYQQQgghhBBCCCGEEEIIIURbFPBACCGEEEIIIYQQQgghhBBCCCGEkFKHAh4IIYQQQgghhBBCCCGEEEIIIYQQUuoYlvQKEEJ0n6+vL3x9fQEA79694/7/4sULNG3alJs+evQoqlSpUtyrRwghhBBCCCGEEEIIIYQQQgj5AlHAAyFEo8TERERFRSn9PysrS/D/zMzM4lwtQgghhBBCCCGEEEIIIYQQQsgXTI8xxkp6JQghhBBCCCGEEEIIIYQQQgghhBBCtKFf0itACCGEEEIIIYQQQgghhBBCCCGEEKItCngghBBCCCGEEEIIIYQQQgghhBBCSKlDAQ+EEEIIIYQQQgghhBBCCCGEEEIIKXUo4IEQQgghhBBCCCGEEEIIIYQQQgghpQ4FPBBCCCGEEEIIIYQQQgghhBBCCCGk1KGAB0IIIYQQQgghhBBCCCGEEEIIIYSUOhTwQAghhBBCCCGEEEIIIYQQQgghhJBShwIeCCGEEEIIIYQQQgghhBBCCCGEEFLqUMADIYQQQgghhBBCCCGEEEIIIYQQQkodCngghBBCCCGEEEIIIYQQQgghhBBCSKlDAQ+EEEIIIYQQQgghhBBCCCGEEEIIKXUo4IEQQgghhBBCCCGEEEIIIYQQQgghpQ4FPBBCCCGEEEIIIYQQQgghhBBCCCGk1KGAB0IIIYQQQgghhBBCCCGEEEIIIYSUOhTwQAghhBBCCCGEEEIIIYQQQgghhJBShwIeCCGEEEIIIYQQQgghhBBCCCGEEFLqUMADIYQQQgghhBBCCCGEEEIIIYQQQkodCngghBBCCCGEEEIIIYQQQgghhBBCSKlDAQ+EEEIIIYQQQgghhBBCCCGEEEIIKXUo4IEQQgghhBBCCCGEEEIIIYQQQgghpQ4FPBBCCCGEEEIIIYQQQgghhBBCCCGk1KGAB0IIIYQQQgghhBBCCCGEEEIIIYSUOhTwQAghhBBCCCGEEEIIIYQQQgghhJBS5/8A/Q1eQtPrT9YAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(25, 10), sharey=True)\n",
    "\n",
    "\n",
    "for ax, df in zip(axs, [noise_df_agg, no_noise_df_agg]):\n",
    "    plot_df = df.reset_index().copy()\n",
    "\n",
    "    # add columns with predict_attr results\n",
    "    plot_df[\"predict_attr_mean\"] = plot_df[\"experiment\"].map(\n",
    "        attr_results_agg[(\"test/auc\", \"mean\")]\n",
    "    )\n",
    "    plot_df[\"predict_attr_std\"] = plot_df[\"experiment\"].map(\n",
    "        attr_results_agg[(\"test/auc\", \"std\")]\n",
    "    )\n",
    "\n",
    "    # rename experiments from snake_case to Kebab-Case, capitalize first letter and after hyphen\n",
    "    plot_df[\"experiment\"] = plot_df[\"experiment\"].map(\n",
    "        {experiment: experiment.replace(\"_\", \"-\").title() for experiment in plot_df[\"experiment\"].unique()}\n",
    "    )\n",
    "\n",
    "    # rename Mimic to MIMIC, Papila to PAPILA, and Ham10000 to HAM10000\n",
    "    plot_df[\"experiment\"] = plot_df[\"experiment\"].map(\n",
    "        {experiment: experiment.replace(\"Mimic\", \"MIMIC\").replace(\"Papila\", \"PAPILA\").replace(\"Ham10000\", \"HAM10000\").replace(\"Chexpert\", \"CheXpert\") for experiment in plot_df[\"experiment\"].unique()}\n",
    "    )\n",
    "\n",
    "    # get kendall tau correlation between predict_attr and split_test\n",
    "    tau, p = stats.kendalltau(\n",
    "        plot_df[\"predict_attr_mean\"],\n",
    "        plot_df[\"split_test/auc_mean\"]\n",
    "    )\n",
    "    \n",
    "    # add kendall tau correlation to plot\n",
    "    ax.text(\n",
    "        0.05, 0.95, f\"$\\\\tau$ = {tau:.3f}, p = {p:.3f}\",\n",
    "        transform=ax.transAxes,\n",
    "        fontsize=30,\n",
    "        verticalalignment=\"top\",\n",
    "        # bbox=dict(boxstyle=\"none\", facecolor=\"white\", alpha=0.5)\n",
    "    )\n",
    "\n",
    "    sns.scatterplot(\n",
    "        data=plot_df,\n",
    "        x=\"predict_attr_mean\",\n",
    "        y=\"split_test/auc_mean\",\n",
    "        hue=\"experiment\",\n",
    "        style=\"experiment\",\n",
    "        ax=ax,\n",
    "        s=270,\n",
    "    )\n",
    "\n",
    "    ax.errorbar(\n",
    "        x=plot_df[\"predict_attr_mean\"],\n",
    "        y=plot_df[\"split_test/auc_mean\"],\n",
    "        xerr=plot_df[\"predict_attr_std\"],\n",
    "        yerr=plot_df[\"split_test/auc_std\"],\n",
    "        fmt=\"none\",\n",
    "        ecolor=\"black\",\n",
    "        elinewidth=1,\n",
    "        capsize=4,\n",
    "        capthick=2,\n",
    "        alpha=0.5,\n",
    "    )\n",
    "\n",
    "    # add y=x plot\n",
    "    ax.plot(\n",
    "        [0, 1],\n",
    "        [0, 1],\n",
    "        linestyle=\"--\",\n",
    "        color=\"black\",\n",
    "        alpha=0.5,\n",
    "        linewidth=2,\n",
    "        # add to legent\n",
    "        label=\"Max Sensitive Information\"\n",
    "    )\n",
    "\n",
    "    ax.set_xlabel(\"Subgroup Separability (AUC)\")\n",
    "    ax.set_ylabel(f\"SPLIT (AUC)\")\n",
    "\n",
    "    ax.set_xlim(0.55, 1)\n",
    "    ax.set_ylim(0.5, 1)\n",
    "\n",
    "\n",
    "sns.despine(fig=fig, trim=True)\n",
    "\n",
    "# share same legend for all plots\n",
    "handles, labels = axs[0].get_legend_handles_labels()\n",
    "\n",
    "# add caption to each plot\n",
    "for ax, caption in zip(axs, [\"(a) Trained with label bias\", \"(b) Trained without label bias\"]):\n",
    "    ax.text(\n",
    "        0.05, 0.95, caption,\n",
    "        transform=ax.transAxes,\n",
    "        fontsize=30,\n",
    "        verticalalignment=\"bottom\",\n",
    "        # bbox=dict(boxstyle=\"none\", facecolor=\"white\", alpha=0.5)\n",
    "    )\n",
    "\n",
    "fig.legend(handles, labels, loc=\"lower center\", ncol=4, bbox_to_anchor=(0.5,-0.25)).set_frame_on(False)\n",
    "\n",
    "\n",
    "#turn off legend for all plots\n",
    "for ax in axs.flat:\n",
    "    ax.get_legend().remove()\n",
    "\n",
    "plt.savefig(\"test_outputs/attr_split_test.eps\", bbox_inches=\"tight\", dpi=1200, format=\"eps\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now perform ablation over label noise values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(165, 7)\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>rng</th>\n",
       "      <th>model</th>\n",
       "      <th>experiment</th>\n",
       "      <th>model.predict_attr</th>\n",
       "      <th>datamodule.label_noise</th>\n",
       "      <th>test/accuracy_group_1</th>\n",
       "      <th>test/accuracy_group_0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>baseline</td>\n",
       "      <td>fitzpatrick17k_skin</td>\n",
       "      <td>False</td>\n",
       "      <td>50.0</td>\n",
       "      <td>91.111112</td>\n",
       "      <td>87.804878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>baseline</td>\n",
       "      <td>fitzpatrick17k_skin</td>\n",
       "      <td>False</td>\n",
       "      <td>50.0</td>\n",
       "      <td>91.717172</td>\n",
       "      <td>88.527554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>baseline</td>\n",
       "      <td>fitzpatrick17k_skin</td>\n",
       "      <td>False</td>\n",
       "      <td>50.0</td>\n",
       "      <td>90.303028</td>\n",
       "      <td>88.075882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>baseline</td>\n",
       "      <td>papila_sex</td>\n",
       "      <td>False</td>\n",
       "      <td>50.0</td>\n",
       "      <td>81.428570</td>\n",
       "      <td>83.333331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>baseline</td>\n",
       "      <td>papila_sex</td>\n",
       "      <td>False</td>\n",
       "      <td>50.0</td>\n",
       "      <td>78.571427</td>\n",
       "      <td>61.111110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rng     model           experiment  model.predict_attr  \\\n",
       "0    2  baseline  fitzpatrick17k_skin               False   \n",
       "1    1  baseline  fitzpatrick17k_skin               False   \n",
       "2    0  baseline  fitzpatrick17k_skin               False   \n",
       "3    2  baseline           papila_sex               False   \n",
       "4    1  baseline           papila_sex               False   \n",
       "\n",
       "   datamodule.label_noise  test/accuracy_group_1  test/accuracy_group_0  \n",
       "0                    50.0              91.111112              87.804878  \n",
       "1                    50.0              91.717172              88.527554  \n",
       "2                    50.0              90.303028              88.075882  \n",
       "3                    50.0              81.428570              83.333331  \n",
       "4                    50.0              78.571427              61.111110  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ln_ablation_sweep_id = \"<USERNAME>/subgroup_separability/<SWEEP_ID>\"\n",
    "ln_ablation_sweep = api.sweep(ln_ablation_sweep_id)\n",
    "ln_ablation_runs = get_runs_from_sweep(ln_ablation_sweep)\n",
    "\n",
    "ln_ablation_df = get_df_from_runs(ln_ablation_runs)\n",
    "ln_ablation_df = drop_unused_columns(ln_ablation_df, mode=\"predict_disease\")\n",
    "\n",
    "# convert accuracy to %\n",
    "ln_ablation_df[\"test/accuracy_group_0\"] = ln_ablation_df[\"test/accuracy_group_0\"] * 100\n",
    "ln_ablation_df[\"test/accuracy_group_1\"] = ln_ablation_df[\"test/accuracy_group_1\"] * 100\n",
    "\n",
    "# convert ln to %\n",
    "ln_ablation_df[\"datamodule.label_noise\"] = ln_ablation_df[\"datamodule.label_noise\"] * 100\n",
    "\n",
    "print(ln_ablation_df.shape)\n",
    "ln_ablation_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>test/accuracy_group_0_mean</th>\n",
       "      <th>test/accuracy_group_0_std</th>\n",
       "      <th>test/accuracy_group_1_mean</th>\n",
       "      <th>test/accuracy_group_1_std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment</th>\n",
       "      <th>datamodule.label_noise</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">chexpert_age</th>\n",
       "      <th>0.0</th>\n",
       "      <td>85.007600</td>\n",
       "      <td>1.037194</td>\n",
       "      <td>93.152682</td>\n",
       "      <td>0.667238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.5</th>\n",
       "      <td>80.378610</td>\n",
       "      <td>0.757719</td>\n",
       "      <td>89.360150</td>\n",
       "      <td>1.205327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25.0</th>\n",
       "      <td>82.478929</td>\n",
       "      <td>2.515879</td>\n",
       "      <td>90.067043</td>\n",
       "      <td>2.482966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37.5</th>\n",
       "      <td>83.068492</td>\n",
       "      <td>3.564922</td>\n",
       "      <td>89.884708</td>\n",
       "      <td>2.510821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.0</th>\n",
       "      <td>70.296164</td>\n",
       "      <td>11.571583</td>\n",
       "      <td>48.928441</td>\n",
       "      <td>28.892733</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     test/accuracy_group_0_mean  \\\n",
       "experiment   datamodule.label_noise                               \n",
       "chexpert_age 0.0                                      85.007600   \n",
       "             12.5                                     80.378610   \n",
       "             25.0                                     82.478929   \n",
       "             37.5                                     83.068492   \n",
       "             50.0                                     70.296164   \n",
       "\n",
       "                                     test/accuracy_group_0_std  \\\n",
       "experiment   datamodule.label_noise                              \n",
       "chexpert_age 0.0                                      1.037194   \n",
       "             12.5                                     0.757719   \n",
       "             25.0                                     2.515879   \n",
       "             37.5                                     3.564922   \n",
       "             50.0                                    11.571583   \n",
       "\n",
       "                                     test/accuracy_group_1_mean  \\\n",
       "experiment   datamodule.label_noise                               \n",
       "chexpert_age 0.0                                      93.152682   \n",
       "             12.5                                     89.360150   \n",
       "             25.0                                     90.067043   \n",
       "             37.5                                     89.884708   \n",
       "             50.0                                     48.928441   \n",
       "\n",
       "                                     test/accuracy_group_1_std  \n",
       "experiment   datamodule.label_noise                             \n",
       "chexpert_age 0.0                                      0.667238  \n",
       "             12.5                                     1.205327  \n",
       "             25.0                                     2.482966  \n",
       "             37.5                                     2.510821  \n",
       "             50.0                                    28.892733  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ln_ablation_df_agg = ln_ablation_df.groupby([\"experiment\", \"datamodule.label_noise\"]).agg(\n",
    "    {\n",
    "        \"test/accuracy_group_0\": [\"mean\", \"std\"],\n",
    "        \"test/accuracy_group_1\": [\"mean\", \"std\"],\n",
    "    }\n",
    ")\n",
    "ln_ablation_df_agg.columns = [\"_\".join(col) for col in ln_ablation_df_agg.columns]\n",
    "\n",
    "ln_ablation_df_agg.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experiment</th>\n",
       "      <th>label_noise</th>\n",
       "      <th>group_0_diff</th>\n",
       "      <th>group_1_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chexpert_age</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chexpert_age</td>\n",
       "      <td>12.5</td>\n",
       "      <td>-4.628990</td>\n",
       "      <td>-3.792532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chexpert_age</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-2.528671</td>\n",
       "      <td>-3.085639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chexpert_age</td>\n",
       "      <td>37.5</td>\n",
       "      <td>-1.939108</td>\n",
       "      <td>-3.267974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chexpert_age</td>\n",
       "      <td>50.0</td>\n",
       "      <td>-14.711436</td>\n",
       "      <td>-44.224241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     experiment  label_noise  group_0_diff  group_1_diff\n",
       "0  chexpert_age          0.0      0.000000      0.000000\n",
       "1  chexpert_age         12.5     -4.628990     -3.792532\n",
       "2  chexpert_age         25.0     -2.528671     -3.085639\n",
       "3  chexpert_age         37.5     -1.939108     -3.267974\n",
       "4  chexpert_age         50.0    -14.711436    -44.224241"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# percentage point degradation compared to no label noise\n",
    "ln_ablation_diff_df = pd.DataFrame()\n",
    "ln_ablation_diff_df[\"experiment\"] = ln_ablation_df_agg.index.get_level_values(0)\n",
    "ln_ablation_diff_df[\"label_noise\"] = ln_ablation_df_agg.index.get_level_values(1)\n",
    "\n",
    "for experiment in ln_ablation_diff_df[\"experiment\"].unique():\n",
    "    for noise in ln_ablation_diff_df[\"label_noise\"].unique():\n",
    "        ln_ablation_diff_df.loc[\n",
    "            (ln_ablation_diff_df[\"experiment\"] == experiment) & (ln_ablation_diff_df[\"label_noise\"] == noise),\n",
    "            \"group_0_diff\"\n",
    "        ] = ln_ablation_df_agg.loc[\n",
    "            (experiment, noise),\n",
    "            \"test/accuracy_group_0_mean\"\n",
    "        ] - ln_ablation_df_agg.loc[\n",
    "            (experiment, 0),\n",
    "            \"test/accuracy_group_0_mean\"\n",
    "        ]\n",
    "        \n",
    "        ln_ablation_diff_df.loc[\n",
    "            (ln_ablation_diff_df[\"experiment\"] == experiment) & (ln_ablation_diff_df[\"label_noise\"] == noise),\n",
    "            \"group_1_diff\"\n",
    "        ] = ln_ablation_df_agg.loc[\n",
    "            (experiment, noise),\n",
    "            \"test/accuracy_group_1_mean\"\n",
    "        ] - ln_ablation_df_agg.loc[\n",
    "            (experiment, 0),\n",
    "            \"test/accuracy_group_1_mean\"\n",
    "        ]\n",
    "\n",
    "ln_ablation_diff_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2500x1100 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 1, figsize=(25, 11), sharex=True)\n",
    "sns.lineplot(data=ln_ablation_diff_df, x=\"label_noise\", y=\"group_0_diff\", hue=\"experiment\", style=\"experiment\", markers=True, dashes=False, ax=axs[0])\n",
    "sns.lineplot(data=ln_ablation_diff_df, x=\"label_noise\", y=\"group_1_diff\", hue=\"experiment\", style=\"experiment\", markers=True, dashes=False, ax=axs[1])\n",
    "\n",
    "# rename experiments from snake_case to Kebab-Case, capitalize first letter and after hyphen\n",
    "ln_ablation_diff_df[\"experiment\"] = ln_ablation_diff_df[\"experiment\"].map(\n",
    "    {experiment: experiment.replace(\"_\", \"-\").title() for experiment in ln_ablation_diff_df[\"experiment\"].unique()}\n",
    ")\n",
    "\n",
    "# rename Mimic to MIMIC, Papila to PAPILA, and Ham10000 to HAM10000\n",
    "ln_ablation_diff_df[\"experiment\"] = ln_ablation_diff_df[\"experiment\"].map(\n",
    "    {experiment: experiment.replace(\"Mimic\", \"MIMIC\").replace(\"Papila\", \"PAPILA\").replace(\"Ham10000\", \"HAM10000\").replace(\"Chexpert\", \"CheXpert\") for experiment in ln_ablation_diff_df[\"experiment\"].unique()}\n",
    ")\n",
    "\n",
    "axs[0].set_xlabel(\"Label Noise Intensity\")\n",
    "axs[0].set_ylabel(r\"Group 0 $\\Delta$ Acc (pp)\")\n",
    "axs[1].set_ylabel(r\"Group 1 $\\Delta$ Acc (pp)\")\n",
    "axs[1].set_xlabel(\"Label Noise Intensity (%)\")\n",
    "\n",
    "sns.despine(fig=fig, trim=False)\n",
    "\n",
    "axs[0].plot(\n",
    "    [0, 50],\n",
    "    [0, 0],\n",
    "    linestyle=\"--\",\n",
    "    color=\"black\",\n",
    "    alpha=0.5,\n",
    "    linewidth=5,\n",
    "    label=\"No Performance Degradation\"\n",
    ")\n",
    "axs[1].plot(\n",
    "    [0, 50],\n",
    "    [0, 0],\n",
    "    linestyle=\"--\",\n",
    "    color=\"black\",\n",
    "    alpha=0.5,\n",
    "    linewidth=5,\n",
    "    label=\"No Performance Degradation\"\n",
    ")\n",
    "handles, labels = axs[0].get_legend_handles_labels()\n",
    "fig.legend(handles, labels, loc=\"lower center\", ncol=4, bbox_to_anchor=(0.5,-0.25)).set_frame_on(False)\n",
    "\n",
    "#turn off legend for all plots\n",
    "for ax in axs.flat:\n",
    "    ax.get_legend().remove()\n",
    "\n",
    "plt.savefig(\"test_outputs/ln_ablation.eps\", bbox_inches=\"tight\", dpi=1200, format=\"eps\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}