diff --git "a/analysis.ipynb" "b/analysis.ipynb"
--- "a/analysis.ipynb"
+++ "b/analysis.ipynb"
@@ -9,25 +9,26 @@
      "outputs_hidden": true
     },
     "ExecuteTime": {
-     "end_time": "2024-05-01T15:23:17.507403Z",
-     "start_time": "2024-05-01T15:23:17.497406Z"
+     "end_time": "2024-05-04T18:14:38.782733Z",
+     "start_time": "2024-05-04T18:14:38.763712Z"
     }
    },
    "source": [
     "import pandas as pd\n",
-    "\n",
-    "import config"
+    "import analysis_util\n",
+    "import config\n",
+    "import seaborn as sns"
    ],
    "outputs": [],
-   "execution_count": 7
+   "execution_count": 39
   },
   {
    "cell_type": "code",
    "id": "2ac8757a17e62293",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-01T15:23:19.365525Z",
-     "start_time": "2024-05-01T15:23:19.120308Z"
+     "end_time": "2024-05-04T18:14:39.020571Z",
+     "start_time": "2024-05-04T18:14:38.825568Z"
     }
    },
    "source": [
@@ -89,48 +90,48 @@
        "4  2024-04-15T15:47:31.022477  2024-04-15T15:53:08.796895        163218.0   \n",
        "\n",
        "   edit_time  ... rel_edittime_ind_rouge2_pearson  \\\n",
-       "0   121745.0  ...                        0.281944   \n",
-       "1        NaN  ...                        0.281944   \n",
-       "2   264172.0  ...                        0.281944   \n",
-       "3        NaN  ...                        0.281944   \n",
-       "4   337774.0  ...                        0.281944   \n",
+       "0   121745.0  ...                        0.144106   \n",
+       "1        NaN  ...                        0.144106   \n",
+       "2   264172.0  ...                        0.144106   \n",
+       "3        NaN  ...                        0.144106   \n",
+       "4   337774.0  ...                        0.144106   \n",
        "\n",
        "   rel_edittime_ind_rouge2_spearman  rel_edittime_ind_rougeL_pearson  \\\n",
-       "0                          0.218822                         0.091196   \n",
-       "1                          0.218822                         0.091196   \n",
-       "2                          0.218822                         0.091196   \n",
-       "3                          0.218822                         0.091196   \n",
-       "4                          0.218822                         0.091196   \n",
+       "0                           0.23866                        -0.039658   \n",
+       "1                           0.23866                        -0.039658   \n",
+       "2                           0.23866                        -0.039658   \n",
+       "3                           0.23866                        -0.039658   \n",
+       "4                           0.23866                        -0.039658   \n",
        "\n",
        "  rel_edittime_ind_rougeL_spearman  rel_edittime_ind_bertscore_pearson  \\\n",
-       "0                         0.071344                            0.158807   \n",
-       "1                         0.071344                            0.158807   \n",
-       "2                         0.071344                            0.158807   \n",
-       "3                         0.071344                            0.158807   \n",
-       "4                         0.071344                            0.158807   \n",
+       "0                        -0.013197                           -0.100207   \n",
+       "1                        -0.013197                           -0.100207   \n",
+       "2                        -0.013197                           -0.100207   \n",
+       "3                        -0.013197                           -0.100207   \n",
+       "4                        -0.013197                           -0.100207   \n",
        "\n",
        "   rel_edittime_ind_bertscore_spearman  rel_edittime_ind_chrF_pearson  \\\n",
-       "0                             0.140481                       0.184202   \n",
-       "1                             0.140481                       0.184202   \n",
-       "2                             0.140481                       0.184202   \n",
-       "3                             0.140481                       0.184202   \n",
-       "4                             0.140481                       0.184202   \n",
+       "0                             0.032695                       0.009058   \n",
+       "1                             0.032695                       0.009058   \n",
+       "2                             0.032695                       0.009058   \n",
+       "3                             0.032695                       0.009058   \n",
+       "4                             0.032695                       0.009058   \n",
        "\n",
        "   rel_edittime_ind_chrF_spearman  rel_edittime_ind_ter_pearson  \\\n",
-       "0                        0.079802                      0.062616   \n",
-       "1                        0.079802                      0.062616   \n",
-       "2                        0.079802                      0.062616   \n",
-       "3                        0.079802                      0.062616   \n",
-       "4                        0.079802                      0.062616   \n",
+       "0                       -0.026513                     -0.019534   \n",
+       "1                       -0.026513                     -0.019534   \n",
+       "2                       -0.026513                     -0.019534   \n",
+       "3                       -0.026513                     -0.019534   \n",
+       "4                       -0.026513                     -0.019534   \n",
        "\n",
        "   rel_edittime_ind_ter_spearman  \n",
-       "0                       0.305601  \n",
-       "1                       0.305601  \n",
-       "2                       0.305601  \n",
-       "3                       0.305601  \n",
-       "4                       0.305601  \n",
+       "0                       0.270956  \n",
+       "1                       0.270956  \n",
+       "2                       0.270956  \n",
+       "3                       0.270956  \n",
+       "4                       0.270956  \n",
        "\n",
-       "[5 rows x 71 columns]"
+       "[5 rows x 87 columns]"
       ],
       "text/html": [
        "<div>\n",
@@ -188,16 +189,16 @@
        "      <td>59468.0</td>\n",
        "      <td>121745.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.281944</td>\n",
-       "      <td>0.218822</td>\n",
-       "      <td>0.091196</td>\n",
-       "      <td>0.071344</td>\n",
-       "      <td>0.158807</td>\n",
-       "      <td>0.140481</td>\n",
-       "      <td>0.184202</td>\n",
-       "      <td>0.079802</td>\n",
-       "      <td>0.062616</td>\n",
-       "      <td>0.305601</td>\n",
+       "      <td>0.144106</td>\n",
+       "      <td>0.23866</td>\n",
+       "      <td>-0.039658</td>\n",
+       "      <td>-0.013197</td>\n",
+       "      <td>-0.100207</td>\n",
+       "      <td>0.032695</td>\n",
+       "      <td>0.009058</td>\n",
+       "      <td>-0.026513</td>\n",
+       "      <td>-0.019534</td>\n",
+       "      <td>0.270956</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -212,16 +213,16 @@
        "      <td>0.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.281944</td>\n",
-       "      <td>0.218822</td>\n",
-       "      <td>0.091196</td>\n",
-       "      <td>0.071344</td>\n",
-       "      <td>0.158807</td>\n",
-       "      <td>0.140481</td>\n",
-       "      <td>0.184202</td>\n",
-       "      <td>0.079802</td>\n",
-       "      <td>0.062616</td>\n",
-       "      <td>0.305601</td>\n",
+       "      <td>0.144106</td>\n",
+       "      <td>0.23866</td>\n",
+       "      <td>-0.039658</td>\n",
+       "      <td>-0.013197</td>\n",
+       "      <td>-0.100207</td>\n",
+       "      <td>0.032695</td>\n",
+       "      <td>0.009058</td>\n",
+       "      <td>-0.026513</td>\n",
+       "      <td>-0.019534</td>\n",
+       "      <td>0.270956</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -236,16 +237,16 @@
        "      <td>133655.0</td>\n",
        "      <td>264172.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.281944</td>\n",
-       "      <td>0.218822</td>\n",
-       "      <td>0.091196</td>\n",
-       "      <td>0.071344</td>\n",
-       "      <td>0.158807</td>\n",
-       "      <td>0.140481</td>\n",
-       "      <td>0.184202</td>\n",
-       "      <td>0.079802</td>\n",
-       "      <td>0.062616</td>\n",
-       "      <td>0.305601</td>\n",
+       "      <td>0.144106</td>\n",
+       "      <td>0.23866</td>\n",
+       "      <td>-0.039658</td>\n",
+       "      <td>-0.013197</td>\n",
+       "      <td>-0.100207</td>\n",
+       "      <td>0.032695</td>\n",
+       "      <td>0.009058</td>\n",
+       "      <td>-0.026513</td>\n",
+       "      <td>-0.019534</td>\n",
+       "      <td>0.270956</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -260,16 +261,16 @@
        "      <td>0.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.281944</td>\n",
-       "      <td>0.218822</td>\n",
-       "      <td>0.091196</td>\n",
-       "      <td>0.071344</td>\n",
-       "      <td>0.158807</td>\n",
-       "      <td>0.140481</td>\n",
-       "      <td>0.184202</td>\n",
-       "      <td>0.079802</td>\n",
-       "      <td>0.062616</td>\n",
-       "      <td>0.305601</td>\n",
+       "      <td>0.144106</td>\n",
+       "      <td>0.23866</td>\n",
+       "      <td>-0.039658</td>\n",
+       "      <td>-0.013197</td>\n",
+       "      <td>-0.100207</td>\n",
+       "      <td>0.032695</td>\n",
+       "      <td>0.009058</td>\n",
+       "      <td>-0.026513</td>\n",
+       "      <td>-0.019534</td>\n",
+       "      <td>0.270956</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -284,35 +285,35 @@
        "      <td>163218.0</td>\n",
        "      <td>337774.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.281944</td>\n",
-       "      <td>0.218822</td>\n",
-       "      <td>0.091196</td>\n",
-       "      <td>0.071344</td>\n",
-       "      <td>0.158807</td>\n",
-       "      <td>0.140481</td>\n",
-       "      <td>0.184202</td>\n",
-       "      <td>0.079802</td>\n",
-       "      <td>0.062616</td>\n",
-       "      <td>0.305601</td>\n",
+       "      <td>0.144106</td>\n",
+       "      <td>0.23866</td>\n",
+       "      <td>-0.039658</td>\n",
+       "      <td>-0.013197</td>\n",
+       "      <td>-0.100207</td>\n",
+       "      <td>0.032695</td>\n",
+       "      <td>0.009058</td>\n",
+       "      <td>-0.026513</td>\n",
+       "      <td>-0.019534</td>\n",
+       "      <td>0.270956</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 71 columns</p>\n",
+       "<p>5 rows × 87 columns</p>\n",
        "</div>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 8
+   "execution_count": 40
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-01T15:11:08.418257Z",
-     "start_time": "2024-05-01T15:11:08.408943Z"
+     "end_time": "2024-05-04T18:14:39.036573Z",
+     "start_time": "2024-05-04T18:14:39.023575Z"
     }
    },
    "cell_type": "code",
@@ -322,340 +323,117 @@
     {
      "data": {
       "text/plain": [
-       "9"
+       "12"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 6
+   "execution_count": 41
   },
   {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "d19c12dd10b25c75",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-01T13:02:40.761645Z",
-     "start_time": "2024-05-01T13:02:40.740647Z"
+     "end_time": "2024-05-04T18:14:39.052579Z",
+     "start_time": "2024-05-04T18:14:39.039575Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['editdist', 'edittime']"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "cell_type": "code",
    "source": [
-    "rel_metrics = [col.split(\"_\")[0] for col in df.columns if col.endswith(\"_related\")]\n",
-    "rel_metrics"
-   ]
+    "GOLDEN = df[((df['end_to_start'] == False) & (df['start_to_end'] == False))]\n",
+    "GOLDEN_E2S = df[((df['end_to_start'] == False) & (df['start_to_end'] == False)) | ((df['end_to_start'] == True) & (df['start_to_end'] == False))]\n",
+    "ALL = df"
+   ],
+   "id": "a2559c930839bc3",
+   "outputs": [],
+   "execution_count": 42
   },
   {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "79d644cd780b28a1",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-01T13:02:44.072037Z",
-     "start_time": "2024-05-01T13:02:44.055039Z"
+     "end_time": "2024-05-04T18:14:39.881308Z",
+     "start_time": "2024-05-04T18:14:39.058715Z"
     }
    },
+   "cell_type": "code",
+   "source": "sns.scatterplot(data=GOLDEN, x='edittime_related', y='editdist_related', hue='session', palette='colorblind')",
+   "id": "5df60ac60034b274",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['gptscore-ref-1-req',\n",
-       " 'gptscore-noref-1-req',\n",
-       " 'editdist',\n",
-       " 'bleu',\n",
-       " 'meteor',\n",
-       " 'rouge1',\n",
-       " 'rouge2',\n",
-       " 'rougeL',\n",
-       " 'bertscore',\n",
-       " 'chrF',\n",
-       " 'ter']"
+       "<Axes: xlabel='edittime_related', ylabel='editdist_related'>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ind_metrics = [col.split(\"_\")[0] for col in df.columns if col.endswith(\"_independent\")]\n",
-    "ind_metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "fdc5ae636bffbc8b",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-05-01T13:03:52.623346Z",
-     "start_time": "2024-05-01T13:03:52.577076Z"
-    }
-   },
-   "outputs": [
+    },
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>hash</th>\n",
-       "      <th>editdist_related</th>\n",
-       "      <th>edittime_related</th>\n",
-       "      <th>gptscore-ref-1-req_independent</th>\n",
-       "      <th>gptscore-noref-1-req_independent</th>\n",
-       "      <th>editdist_independent</th>\n",
-       "      <th>bleu_independent</th>\n",
-       "      <th>meteor_independent</th>\n",
-       "      <th>rouge1_independent</th>\n",
-       "      <th>rouge2_independent</th>\n",
-       "      <th>rougeL_independent</th>\n",
-       "      <th>bertscore_independent</th>\n",
-       "      <th>chrF_independent</th>\n",
-       "      <th>ter_independent</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>count</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>mean</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>end_to_start</th>\n",
-       "      <th>start_to_end</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">False</th>\n",
-       "      <th>False</th>\n",
-       "      <td>43</td>\n",
-       "      <td>355.441860</td>\n",
-       "      <td>364099.0625</td>\n",
-       "      <td>7.255814</td>\n",
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-       "      <td>0.202063</td>\n",
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-       "      <td>312.732989</td>\n",
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-       "    <tr>\n",
-       "      <th>True</th>\n",
-       "      <td>129</td>\n",
-       "      <td>406.627907</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>7.217054</td>\n",
-       "      <td>8.178295</td>\n",
-       "      <td>491.069767</td>\n",
-       "      <td>0.012805</td>\n",
-       "      <td>0.224961</td>\n",
-       "      <td>0.202063</td>\n",
-       "      <td>0.040718</td>\n",
-       "      <td>0.136427</td>\n",
-       "      <td>0.780266</td>\n",
-       "      <td>32.067005</td>\n",
-       "      <td>312.732989</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">True</th>\n",
-       "      <th>False</th>\n",
-       "      <td>129</td>\n",
-       "      <td>433.899225</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>7.356589</td>\n",
-       "      <td>8.302326</td>\n",
-       "      <td>534.015504</td>\n",
-       "      <td>0.009542</td>\n",
-       "      <td>0.221893</td>\n",
-       "      <td>0.205151</td>\n",
-       "      <td>0.039033</td>\n",
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-       "      <td>317.717517</td>\n",
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-       "    <tr>\n",
-       "      <th>True</th>\n",
-       "      <td>387</td>\n",
-       "      <td>444.509044</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>7.312661</td>\n",
-       "      <td>8.276486</td>\n",
-       "      <td>534.015504</td>\n",
-       "      <td>0.009542</td>\n",
-       "      <td>0.221893</td>\n",
-       "      <td>0.205151</td>\n",
-       "      <td>0.039033</td>\n",
-       "      <td>0.134114</td>\n",
-       "      <td>0.777162</td>\n",
-       "      <td>31.753065</td>\n",
-       "      <td>317.717517</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "                           hash editdist_related edittime_related  \\\n",
-       "                          count             mean             mean   \n",
-       "end_to_start start_to_end                                           \n",
-       "False        False           43       355.441860      364099.0625   \n",
-       "             True           129       406.627907              NaN   \n",
-       "True         False          129       433.899225              NaN   \n",
-       "             True           387       444.509044              NaN   \n",
-       "\n",
-       "                          gptscore-ref-1-req_independent  \\\n",
-       "                                                    mean   \n",
-       "end_to_start start_to_end                                  \n",
-       "False        False                              7.255814   \n",
-       "             True                               7.217054   \n",
-       "True         False                              7.356589   \n",
-       "             True                               7.312661   \n",
-       "\n",
-       "                          gptscore-noref-1-req_independent  \\\n",
-       "                                                      mean   \n",
-       "end_to_start start_to_end                                    \n",
-       "False        False                                8.116279   \n",
-       "             True                                 8.178295   \n",
-       "True         False                                8.302326   \n",
-       "             True                                 8.276486   \n",
-       "\n",
-       "                          editdist_independent bleu_independent  \\\n",
-       "                                          mean             mean   \n",
-       "end_to_start start_to_end                                         \n",
-       "False        False                  491.069767         0.012805   \n",
-       "             True                   491.069767         0.012805   \n",
-       "True         False                  534.015504         0.009542   \n",
-       "             True                   534.015504         0.009542   \n",
-       "\n",
-       "                          meteor_independent rouge1_independent  \\\n",
-       "                                        mean               mean   \n",
-       "end_to_start start_to_end                                         \n",
-       "False        False                  0.224961           0.202063   \n",
-       "             True                   0.224961           0.202063   \n",
-       "True         False                  0.221893           0.205151   \n",
-       "             True                   0.221893           0.205151   \n",
-       "\n",
-       "                          rouge2_independent rougeL_independent  \\\n",
-       "                                        mean               mean   \n",
-       "end_to_start start_to_end                                         \n",
-       "False        False                  0.040718           0.136427   \n",
-       "             True                   0.040718           0.136427   \n",
-       "True         False                  0.039033           0.134114   \n",
-       "             True                   0.039033           0.134114   \n",
-       "\n",
-       "                          bertscore_independent chrF_independent  \\\n",
-       "                                           mean             mean   \n",
-       "end_to_start start_to_end                                          \n",
-       "False        False                     0.780266        32.067005   \n",
-       "             True                      0.780266        32.067005   \n",
-       "True         False                     0.777162        31.753065   \n",
-       "             True                      0.777162        31.753065   \n",
-       "\n",
-       "                          ter_independent  \n",
-       "                                     mean  \n",
-       "end_to_start start_to_end                  \n",
-       "False        False             312.732989  \n",
-       "             True              312.732989  \n",
-       "True         False             317.717517  \n",
-       "             True              317.717517  "
-      ]
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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86tatS926dZk2bZrUDX/9+nV0dXVxcHCQ4o0cORJPT08AAgICqF27tnRGj5KSEnZ2dty6dUuSx8PDA3t7exo3bsz58+fzLV9GRgbLly9n9OjRlClTpkA9XLlyhZYtW0rXlStX5uzZs5QpUwYDAwM2bNhA2bJlFeIkJiZK/58/f54xY8bg6uqaJ+309HQ8PT1p1KgR5ubmODk5sXv37gJlycXd3Z0JEybkkfvKlSu0atVKutbS0uLMmTPUrFlTcrt69SqtWrXC2tqaSZMmER8fL8WtWbMmlStXVshn0qRJ+crw5MkTAKytrSU3U1NToqOjJb9cwsLC2L9/Py4uLlK9DR8+nMGDB+dJNyEhgenTp7N//378/Pyk4cXCyC/9kJAQKlSogIaGxjvLHhAQQIUKFTAyMpL8a9euzc2bN6XrgwcP0qZNG6ytrenduzd3795l//79TJ8+nWfPnmFqasr+/fsLreO3yxkUFMTdu3cV2ludOnU4cuSIwvlNBdV/YWUG0NPT49ChQ6SkpBAVFcWFCxcwMzMrVLefGtnAkZH5xoiIS6H3thvU8jpHfZ+LmHn9QZ9tN4iIS/loeUZHR1O6dGnU1dUlt7Jly5KWliYZI+Hh4VhbWzNr1ixGjhwpvRDMzc2pUqWKFO/PP//k0aNH1KtXD4ClS5cSGBjI+vXr2bp1K4mJidKLNCIigkqVKuHn50ebNm1o3rw5q1atkuYKREdHY2hoqCCrvr4+z58/B3LmZvzxxx+sXr2an3/+ucB5JMePHychIYF+/foVqIPXr18THx9PVlYWw4YNw9HRkTFjxhAVFQVAyZIladSokRQ+Ozubbdu2SeUE8PDwoEePHvmmv27dOs6dO4ePjw8nTpygS5cueHh48PLlywJl2rdvH2lpafTq1SuPX0REBJqamkycOJEGDRowcOBAQkJCFMJs376dmTNnsn37dsLCwiTDMSIiAiMjIzZu3IiTkxNt2rRh165dBcqhr68PIOkCIDIyEsiZv/ImGzdupF69egpzkAprIzNnzqRt27a0bduWixcvUqFChQLlKCj90NBQ1NTUGDVqFI6OjvTv35/bt28XS/aC2lhu+AsXLjBz5kwGDRrEoUOHsLCwYNSoUbRs2RJXV1fKly/PxYsXadeuXaF1/HY5MzMzJRmGDBlCo0aNcHFx4fXr15IchdV/YWUGmD17Nn///Td2dnY0btwYQ0NDxo8fX6huPzWygSMj8w0Rm5zO8D0BnH4QreB+6kE0I/YEfLSenJSUFAXjBpCu09Nz8ixTpgx79+7F3d2dNWvWcPLkyTzpPH78mBkzZtCxY0fMzc1JSUlh27ZtzJ07FysrK0xNTfHy8uLKlSvcv3+f5ORkwsPD2bVrF56enri4uPDbb7+xZcuWQuVKT09HCIGvry8TJ06kTp062Nra5ttzArBnzx569OhR6AnvycnJAMyfP5+OHTuyevVq0tPTGTVqVL6TMxcvXszdu3eZMmVKgWm+Sc2aNVmwYAE2NjZUrlyZ0aNHk5GRwaNHj/INHxMTw9KlS5k3b16+p0wnJyfj7e1NnTp1WL9+PRUqVGDw4MEkJSVJYcaPH0+TJk2wsLBg1qxZHD58mMTERJKTk/nrr7+4fv06P//8MyNGjMDT0zPfOgWoVKkSNjY2LFiwgLi4OKKjo1m5ciWQ0zuWS2JiIkePHi10wu3bbaREiRJoamqiqamJgYFBoadPF5R+WFgY8fHx9OzZk3Xr1lG9enUGDRpEZGRkkbIX1sYAdu/eTYcOHejTpw/GxsY4OzvToUMHUlNTKVGiBCoqKhgYGKCpqVloHb9dztTUVADmzZvH4MGD+fnnnwkODsbZ2Rkouv4LK3Ouv4WFBTt37mTlypUEBwezfv36AnX7OVD9nJlHRkYyZ84crl69ip6eHgMHDpS6Ge/evcvs2bN58OAB3333HXPnzsXCwkKKe+TIEZYvX050dDQNGzbEw8Oj0K5hGRkZiEpMy2Pc5HLqQTRRiWmU1lbP1//foKGhIT3Qc8m9zjUKSpQoQa1atTAzMyMoKIht27bRpk0bKXxYWBhDhgyhcuXKzJ8/H8jpKcjIyKB3794KaWdnZ/Po0SNUVVVJTExkyZIlVKpUCYBnz56xc+dOhg4dWqBcmpqaxMbG8urVK4Vu99xhszeJiYnh2rVruLm5KbgPHz5cYSXPqVOnAOjZsyddunQBwNvbG0dHR27duoWdnZ0UdvHixfz6668sW7YMExOTgtSqQIsWLbh06RI//fQTDx8+5O7duwBkZWVx7do1RowYIYUdNWoUDx48oFu3bgWmr6KigpOTk/Sy9/DwoGnTppw9e5aOHTvm0UetWrXIzMzk8ePHqKiokJWVhbe3N9ra2lhaWhIUFMTu3btp3bp1nqHKDRs24OXlxcSJE6lXrx4lSpTghx9+4ObNm+jq6kphL1y4gKampkJP15uEh4czduxYhTbyNvnpYvTo0YWm7+HhQWpqqiTLnDlzuHHjBgcPHmT06NGFyl5YG4Ocdv1m+1VXV8fFxSVf2Qur47dRVc15vY8YMYImTZqgra3NggUL6NKlC1FRUSxatKjQ+i+szG3atGHRokWcO3dO6p1KSUlhzpw5jBgxQsr7c/NZpZg8eTIVK1Zk//79hISEMG3aNCpVqoSjoyMjR46kY8eO/PTTT+zcuZNRo0Zx+vRptLW1uX37NjNnzmTu3LmSRTtjxgzWrl37OYsjI/OfJz4ls1D/16mF+78v5cqVIzY2lszMTOnhFx0djaamJtHR0YSEhGBvby+Fr1atmsIcheDgYAYPHkzlypXZsGGD9HLIfbDv2LEDbW1thTz19fVJTk5GQ0NDMm4AqlatKn2FlitXLs8QzsuXLxWGFMQbS8rV1NTylO3ChQsYGRlhamqq4L5gwQLpKxqgdOnSqKmpUa1aNQU3PT09aUgMcl4sO3fuZPHixbRu3TpPfgWxbNkyfH196datG126dGH27Nk4OTkBYGFhgZ+fnxS2VKlS1K1bF01NTbZt2wZAamoqN2/e5MSJExw9ehQDAwOqVq0qxVFXV6dSpUqS7gCF3pBcPampqWFoaEj58uUV6qRq1apcvHgRQEGW3Lo0Njbm4MGDxMTEUKJECR4/foyysjIVK1aUwl64cIFmzZqhrJx38CE4OJjhw4fnaSNvk58uikpfVVVVwdBSUlKiWrVq0jBTYbIX1MYMDAyktItLYXX8Nrnpv9necuvz+fPnHD16tND6L6zMd+/epXTp0gr3Sa1atUhKSiI+Pl4atvvcfLYhqvj4eG7dusWYMWOoUqUKLVq0oFGjRvz9998cO3YMDQ0NnJ2dqV69OjNnzkRHR4cTJ04AsG3bNtq2bUuXLl2oWbMmXl5enD9/noiIiM9VHBmZL4JSWoU/TEtqfpxvHjMzM1RVVaXJu5AzAdjS0pI//viDWbNmKRgS9+7dkx7ML168YOjQoRgbG7Nx40aFh27lypVRUVEhLi4OY2NjjI2N0dXVxdPTk5iYGKytrUlLSyMsLEyK8/DhQ8ngsba25ubNm1LeQghu3LiBtbU1pUuXpmzZsty5c0eKm/vF/Ca3b99W6H3JpVy5cpJMxsbGqKqqYm5urrCXzKtXr4iNjZXkWblyJbt27WLp0qW0b9/+nXS8a9cu3NzcmDZtGu3atSMlJUUqk6ampoIsenp6nDp1ikOHDuHn54efnx8WFhb07t1b2sPFxsaG+/fvS+mnp6dLc2tyyd0TJVcPampqGBkZYW1tzdOnT6WJ4KCo9zdlKVeuHNnZ2QwdOpT79++jr6+Puro6586do1atWgr1XZCuX7x4wbBhw6hcuXKeNvI2+emiqPQHDBggDTtBTg/h/fv3qVatWpGy29jY8PTpUwUj9vr169jY2Ei6eLNNZGVl4eTklO9eT4XV8dtUrFgRQ0NDhbRDQ0NRUlKiYsWKRdZ/YWU2NDQkNjaWmJgYyf/hw4doa2v/p0ZSPpuBo6mpiZaWFvv37ycjI4OHDx9y48YNzMzMilzZEBAQoPC1V6FCBSpWrEhAQMDnKIqMzBdDOV0NWpkY5OvXysSAcrr5r5j4t2hpadGlSxfmzJnD7du3OXPmDJs2bWLgwIF06tSJ6OhovL29efToEdu3b+fYsWOMHDkSgEWLFpGdnc2CBQtITk4mOjqa6OhokpKS0NXVpWfPnsyZMwd/f39CQkJwdnYmPDwcIyMjqlWrRtOmTZkxYwZBQUFcuHCBdevW0adPHwDatGnD69evWbBgASEhISxYsICUlBTatm2LkpIS/fr1Y8WKFfz111/cuXNHmkT7JsHBwXz33XfF0sOQIUP47bffOH78OKGhobi6umJmZoaVlRWhoaH88ssvjBgxgtq1a0vljI7Of0jxbfT09Pjjjz+IiIjg2rVr0lyLt4dHcnnzJW9sbIympialSpWSjJBBgwZx8uRJduzYwaNHj5g3bx4aGho0bdpUSmPZsmX8/fff3Lp1i/nz59O7d2+0tLRo0KABVatWxcXFhdDQUI4dO4avr6+k97dRVlZGU1OTJUuW8OjRI86cOcOqVaukoSPI2YwuLCwsX13ntpHZs2fnaSPFpbD0nZyc2LJlC7///jsPHz5k3rx5JCQk0LVr1yJlr1y5Mg0bNuTHH38kKCgIX19fjhw5Ik1IHzBgAIcOHeLAgQOEh4fj6emJEAJzc/M8crxLHSspKTF48GB8fHy4fPkyQUFBzJkzhxYtWmBgYFBk/RdWZhsbG6pXr46zszPBwcFcuXIFLy8v+vfvn+98ns/FZxui0tDQwN3dHQ8PD7Zu3UpWVhbdunWjZ8+e/P7773kamb6+PsHBwUCOtV7Yyod3Ib+xy/chN50PlV5xyE6MISvpFYhsVHTKoFwi/xfX5+Rz6OVL4X10k5WVhcjZvyrfr7ai0NNSY30va0bsCeDUG3NxWpkYsL6XNXpaau+VbnGYPn06c+bMYdCgQejq6jJhwgRpCeuGDRvw9PRk27ZtVKpUCS8vL2rVqkV2djZnzpwhNTVVYT4OwLhx45gwYQIuLi54eXkxYcIEMjMzsbe3Z926dSgrKyOEYPHixcyfP58+ffqgpaVFv3796N+/P0IIdHR0WLNmDXPmzGHPnj2Ympqydu1atLS0EEIwatQokpOTmTx5MqqqqowbN4558+Yp6P/ly5eUKFGiWHpr3bo18fHxeHl58erVKxwcHFi1ahUAZ86cISsri9WrV7N69WqFeLlf4W/n8eb1woULmTNnDu3bt6dcuXL07NkTFRUV7t69W+CclbfTerNcVlZWLF++HG9vbzw9PbGwsGD9+vWSbgAGDx6Mq6srcXFxtG3blqlTpyKEQFlZmbVr1zJnzhy6detG6dKlmT59Ok5OTgXqac6cObi7u9O1a1f09fWZOXMmLVq0kMLnDnG+rWshhNRGunbtqpBmbht5s4euIApKH3KMvdTUVObPn8/Lly+xsrJi06ZN6OjoIIQoUvZFixYxa9YsevXqhYGBAQsWLMDS0hIhBPb29syePZtVq1YRHR2NhYUFq1evRkNDI0+dFFXHb5dzyJAhpKam4ubmRkpKCs2aNWPOnDn56uHtvAorM+Ss2luwYAH9+vVDW1ubTp06MX78+A/2/MiVJSsrK88zsrjPTCXxsZ5mxWDx4sVERkYyZMgQgoOD8fDwYO7cuezbt0/aVjqXn3/+mZs3b7JlyxbMzMzYvHmzwvLJfv364ejoyNixY4uVd1ZWlkJ3+ZeEtpYGldSSeLl1NKmPbwGgXqEmBgN+IUrFkISk1MITkPmiUVVVpXLlygXuT1EcEjPhVUoW8amZlNJUpYyWCrr/jXmBMjIyMqSlpRERESEtd88PGxubQlfFfbZH2t9//83evXs5f/48mpqaWFpaEhUVxerVq6lcuXKhs84LmpWupaX1znJYWloWqqDikpWVxZ07dz5YeoWR+TKMh7PqItKTJbf0yCCeLmlNNY+bqNWw+aj5vwufUi9fGu+jm9TUVMLDw9HS0ip0SXJRaAOGJd87+kdFCEFKSgpaWlr/qe7u/wKybgpG1k3+fKl6UVZWRk1Nje+++y7Psy732VkUn83ACQwMlMb9cqlVqxZr1qzB3t6+0JUNRc1KfxdUVFQ+6Iv3Q6f3NiIrk7g/NykYNxJZGcQc9aLC4DUoq7+7sfcx+dh6+ZJ5F92oqKigpKQk/b5mvoUyvi+ybgpG1k3+fGl6yZX337w7PtskY0NDQ8LDwxV6Yh4+fCjNwC9oZQPkrHx4c4Z5ZGQkkZGRCltlf61kpyWRfD//7eIBUkL+Jisl/hNKJCMjIyMj89/jsxk4Tk5OqKmpMWvWLMLCwjh79ixr1qxhwIABha5sAOjTpw8HDx7E19eXoKAgnJ2dadq0qcK5J18rSmqaqOkbF+ivWqYSymr/rd4bGRkZGRmZT81nM3BKlCjBli1biI6OpkePHnh6ejJmzBi+//57dHV1Wbt2LdevX6dbt24EBASwbt06adMoW1tb5s2bx6pVq+jTpw+lSpXKd/nm14iymgb6rQveur1sB1dUtEsV6C8jIyMjI/Mt8FnXTXz33Xds3rw5Xz8rKysOHDhQYNxu3brRrVu3jyXafxr18iaUH7iK59smQvb/Xy6npETZzrPRrJJ3kyoZGRkZGZlvDXlh6BeIinYp9BwHoWvZhtTHNxFZWWhWsUW1ZHlUtEp8bvFkZGRkZGQ+O7KB84WirKmDumY11A2rFR1YRkZGRkbmG+OzzcGRkZGRkZGRkflYyAaOjIzMJ+H06dOYmpoq/N7crfxN2rdvz/79+wHyxMn95Z4I/fvvv9O4cWOsra25cOGClEZ6ejodOnTA399fIe1bt27Ru3dvbG1tad26Nb6+vgr+f/31Fx06dMDa2pqBAwcqHOKbkZHB4sWLadiwIfXq1WPRokWF7rT6Jk+ePMHW1jaPPO+Cj48PI0aMkK4DAwP5/vvvsbW1pVevXu+0O/vcuXMZMGCAgtuzZ88YMWIE1tbWtGzZkmPHjkl+QgjWrVuHk5MTdnZ2DBo0iJCQEAV/b29v6tWrh4ODA15eXmRnZxcqw86dO2nevDl2dnYMGzZM0rW/v3+B9f7s2bNil7G4xMTEMHHiROzt7WnZsqXU9iDniJH85Bg4cKAUxt7ePo9/7jlYb7fPtLQ0XF1dsbe3p2HDhmzatElBlqLap5+fH61bt8bOzo5x48YV66yya9euUbNmzUL16ePjQ+3atbG3tycxMbHQtvDq1Sv69++PpaUlLi4u767wT4X4RsnMzBTXrl0TmZmZ/8n0vhZkvRTM++gmJSVF3L17V6SkpHxEyT4Ov/zyixg1apR48eKF9IuPj88TLjs7WzRp0kTs3btXCCEUwr948UJ4eXmJZs2aidevXwshhOjUqZOYMWOGePLkiaSX1NRUMW7cOGFiYiIuX74spf3ixQthb28vlixZIsLCwsSRI0eEpaWl+OOPP4QQQjx9+lTY2NiIjRs3igcPHohJkyaJDh06iOzsbCGEEN7e3qJBgwbi3LlzIjAwUHTu3Fl4eHgUq/zDhg3LI8+78vPPP4s+ffqI7Oxs8fLlS1G7dm0xa9YsERISIjZv3ixsbGzE06dPi0zn+vXrwtTUVPTv319yy8jIEB06dBCjR48WoaGhYufOncLc3Fzcv39fCCHEjh07RN26dcXZs2fFw4cPhaurq2jatKlITk4WQgixceNG0aRJE3H16lXx999/i4YNG4oNGzYUKMOff/4pbG1tpfTGjx8vOnbsKIQQIi0tLU+99+3bV4wdO7bA9LKzs0ViYqJUV8UlOztbfP/996Jnz57in3/+EWfPnhV16tQRJ0+eFEII8fr1awU5bt68KSwsLMTp06eFEEI8f/5cmJiYiMePHyuEy5Xj7fY5b9480bFjRxEYGChOnTolbG1txfHjx4UQRbfPP//8U5iZmYnffvtNhISEiGnTponOnTuLrKysQssXGxsroqKiCtRnXFycMDExEbt37xZPnjwpsi1s3rxZODo6ipCQEBETE/NO+i4uhT3rivvslA0c2cD5qMh6KZhvzcCZOnWqWLJkSZHh3jZw3uTx48fC0tJSXLp0SXJr1qyZQtjg4GDRqVMn0bFjxzwGxY4dO0SbNm0U0nRzcxM//PCDEEKI5cuXK7z0k5OTha2trbh8+bLIzs4Wtra2CnndunVLmJubi8TExELLdPDgQdG7d+8PauBs2LBBNG/eXKH9DBs2THh7exeaRlpammjfvr34/vvvFcp65swZUbt2bZGQkCC5jRkzRuzatUsIIUTPnj3F2rVrJb/09HRhY2MjLl68KIQQokmTJmLfvn2Sv5+fn2jWrFmBcnh4eIgJEyZI10FBQcLExCTfF+bhw4eFvb19oS/T9zVwbt++LRkouaxdu1b06tUr3/BDhw4V06ZNk64vXbokHB0dC0z/zfaZlJQkLC0tFdrAqlWrpHooqn2OHDlSODs7S34pKSnCwcFB/PnnnwXmn59e3tbnkydPFHRQVFvw8fER/fr1KzDPD8GHMHDkISoZmW+QrNRY0l/dJzXyCumv7pOVGvvR8wwNDaVKlSr5+u3atYumTZtiZ2eX5yTtN1mxYgX169enQYMGQM6GoU+fPsXV1RUnJycArly5Qt26ddm9e3ee+I0aNcp3z6zExEQAAgICsLe3l9y1tLQwNzfn1q1bvHr1iqSkJIUd001NTcnIyCAwMLBAmWNjY1m8eDHz5s3L45eens78+fOpW7cudevWZdq0acTFxUn+ISEh9OnTRxoui439v3qKiIjA3NxcYRt7U1PTIoep1q1bh6mpKY6OjgruV65coX79+ujq6kpuv/zyC99//z0Azs7OdOrUSfJTUlJCCEFCQgJRUVFERkZSp04dyb927do8ffqUFy9e5CuHnp4eV69eJTQ0lMzMTPz8/KhUqRKlSinu45WRkcHy5csZPXo0ZcqUkdz//PNPunbtirW1NZ06deLvv/8GcobK1qxZg5OTExYWFjRs2JCVK1cWqI+IiAjKlCmjsFGsqakpgYGBZGRkKIT9+++/uXr1Kj/88IPkFhISQtWqVfNN++32GRQURGZmJra2tgp6CggIIDs7u8j2GRERgZWVleSuqanJ//73P6nOk5OTcXd3l9qTm5sbaWlpherzyZMn0r3TokULpk+fXmhb8PHxwcfHh6tXr2Jqavqvhlw/NrKBIyPzjZGZEMGLY/14utWSyN0NebrVkhfH+5OZEFF05PdECEFYWBgXL16kdevWtGjRAm9vb9LT07lw4QILFixg8uTJ7N69mzt37hAZGZknjWfPnnHkyBHGjh0rue3du5fy5cvj6urK3r17Aejbty+urq75Hr5rZGSEjY2NdB0TE8PRo0epX78+ANHR0dKZd7no6+vz/PlzSpUqhZqaGlFRUZJfrpxvGh5v89NPP9G1a1dq1KiRx2/p0qUEBgayfv16tm7dSmJiIpMmTQJyjJ+RI0dSuXJl9u/fT+vWrdmzZ48Ut2zZsgqyADx//rxQWUJDQ9m5cyczZszI4xcREUH58uXx9vamUaNGdOrUiTNnzkj+9vb2lC9fXrr29fUlMzOT2rVrS/NA3tRd2bJlJZnyY8CAAVSrVo127dphZWXFnj17+OWXX/KcO3T8+HESEhLo16+f5BYcHMyYMWNo2bIlBw8epEOHDowbN46XL1/i5+fHr7/+yoIFCzhx4gTjxo3Dx8eHf/75J185ypYtS0JCAikpKQp6zMzMJCEhQSHsunXr6Nq1KxUqVFDQaUpKCgMGDKBhw4aMGDGCsLAwIG/7jI6OpnTp0qirqyvkn5aWRlxcXJHtU19fX8FgzM7OJioqSqrzWbNmcf36dX755Rc2bdrE9evXWb58eaH6rFChgjTPx9fXl5kzZxbaFoYOHcrQoUOxtbXl4sWLCsbafw3ZwJGR+YbISo0l+vQoUh+fUXBPDT9N9JnRH60n59mzZ6SkpKCurs7y5ctxcXHh8OHDeHl54evrS8eOHenSpQs1atRgwYIFaGho5Elj7969WFhYKPSglClTBhUVFUqUKKHwdV8cUlNTmTBhAmXLlpV6KXJlfBN1dXXS09NRVVWlZcuWLF26lOfPn5OQkMCiRYtQVVXN86Wfy19//cX169cVjLJcUlJS2LZtG3PnzsXKygpTU1O8vLy4cuUK9+/f56+//iIuLo45c+ZQvXp1+vXrR4sWLaT4rVq14vbt2+zZs4fMzEwuXLjA77//XqAsQgjc3d2lMr9NcnIyBw4c4PXr16xZs4YuXbowceLEfE9tDggIYNGiRQwbNgwDAwNSU1MlXb2pN0DhvME3efHiBWlpaXh7e7Nr1y7q1KnDjz/+mKfHYc+ePfTo0UPhYOa9e/diZ2fH2LFjqVKlCiNHjmTQoEEkJCRQoUIFPD09qV+/PkZGRvTp0wcDAwOCg4PzlcPa2hpDQ0M8PDxITk4mPDxc2oD2TV1GRERw+fLlPBOzHz58SHx8PGPGjOGXX35BU1OTwYMHk5iYmKd9FtS+8tNTfu2zXbt27Ny5k5s3b5KRkcGaNWuIiYkhIyOD+Ph4Tpw4gbu7O7Vr18bc3Jx58+ZRsWLFQvWpoqIi3TtlypShRIkShbYFHR0dtLW1UVNTw8DAIE95/kvI++DIyHxDZCW/yGPc5JIafpqs5BeoaJb+4PlWqlQJf39/SpUqhZKSEmZmZmRnZ/Pjjz9SqVIl+vbtK4UtXbo0lSpVypPGyZMn6d279weRJykpibFjx/Lo0SN27Ngh9fZoaGjkedGkp6dTsmRJIOcLecqUKTRp0gRtbW3GjBnD7du30dXVZc2aNaxdu1aKt379etzd3Zk9e7bCyzmXiIgIMjIy8pQpOzubR48eERERQZUqVaQjagAsLS05e/YsACYmJnh4eDB//nxmz56NmZkZffr0kYYM3h4GadGiBVlZWdLL8m1UVFTQ09Njzpw5KCsrY25uzrVr19izZw+WlpZSuJs3bzJixAgaN24s9Ta9+ZLONU5z9ailpYW7uzuHDx+W0jh69CizZ8+mVatWdOzYEYAlS5bQtGlTfv/9d9q1awfk9GBcu3YNNzc3BVnDwsIwNzdXcJs0aRLJycloa2tz+/ZtlixZQmhoKPfu3SM6Oprs7GyePXtG+/btpTgdO3Zk3rx5LF++nMmTJ1O7dm309fUZPnw4np6eCkM0J0+exMzMjO+++04h340bN5KRkYGOjg4A3t7eNGnShD/++EMqWy4FtS9AoY0U1D579erFgwcPpN6X1q1b07hxY3R1dQkPDycrK0tBL/b29tSuXZvk5ORC9fk2xW0L/3VkA0dG5hsiO63wk+az0z/eSfR6enoK19WrVyctLQ1lZWWEEAp+ampqCteRkZGEhITQvHnzfy1HYmIiw4cP5/Hjx/z6668K84LKlSvHy5cvFcK/fPkSMzMzIGeIYOvWrcTFxaGhoYEQgiVLllCpUiVsbW2lA4EBoqKiiIiIyLMUfsSIEXTp0oU+ffoAsGPHDgUjJjefXbt2FamX7t2706VLF2JiYjA0NMTLywsjIyMAaRk95Lw8p02bRmBgIHZ2Oce5ZGRkkJWVha2tLUePHsXQ0BAlJSWUlf+vY79q1arcv39fuvb392f06NE4OjqyZMkSKWy5cuWAnCG+3Pxzh60MDAyYNGkSw4YNk9IxNDTkn3/+YfTo0ZKbjo4OxsbGPH36VHK7cOECRkZGmJqaKpRbVbXgV5evry+enp707NmTVq1a4eLiIi3pNjQ0VNBLrgFjZWXF2bNnpSGkS5cuUbp0acloyZUlv/anrq6u0IuhoaGBkZFRnuHDXD3FxsaSmZkplSE6OhpNTU3JiC6sfaqoqDB79mycnZ1JS0tDT0+PHj164OjomKdt5EdB+nyb4rSFLwF5iEpG5htCWaPwg1iV1T/OQa0XLlygbt26CvMc7t27h56eHjVq1FAYBklMTFTYewZyhkQqVKiQp7v9XcnOzmb8+PE8efKE3377Lc+8GGtra65fvy5dp6SkcPfuXWlY7Mcff+TixYvo6emhpaXF+fPn0dfX57vvvkNPTw9jY2PpZ2VlxalTp/Dz85N+APPnz2fSpElUrlwZFRUV4uLipDi6urp4enoSExNDjRo1ePTokcI8kLt370r/X758mSlTpqCiooKhoSFCCEnPgIIs5cqVw9vbm6NHj0qy9O7dGwsLC/z8/DA0NMTa2prg4GCysrKkPEJDQ6XetAcPHjBmzBgaNWrE8uXLFV6o5cqVo2LFigq6u379OhUrVsTQ0BB9fX0FeVRVVTE0NCQ0NFQKn56ezpMnTyQDCeD27duSQfYmxsbGBAUFKbj17t2bkydPsmvXLsaNG4erqytdunShdOnSxMTEIIRAVVVVQQ59fX3i4uLo06cPsbGxGBgYoKqqyrlz53BwcJDSFkJw586dPLIIIWjRooXCvjm5w1zVquXdZd7MzAxVVVWFieDXr1/H0tISZWXlItvnli1bWLduHVpaWujp6fHixQvu3buHg4OD1J7e1MuZM2cUzmwsSJ9vU1Rb+FKQDRwZmW8IFW1DNI1b5uunadwSFW3DfP3+Lba2tmhoaDBr1iwePnzI+fPn8fLyYvjw4fTv35/jx4+zZ88eQkNDcXd3l+Z05BIcHEz16tX/tRx79+7F39+f+fPnU7JkSaKjo4mOjpZWLnXv3p0bN26wbt06goODmTFjBkZGRpLRoKenx7Jly3jw4AH+/v54eHgwcuRIhS/dXDQ1NRVepsbGxkCOMaCvr4+uri49e/Zkzpw5+Pv7ExISgrOzM+Hh4RgZGdGgQQMqVKjAzJkzCQ0NZf/+/Rw/flxKv2rVqvzxxx/s2LGDiIgI5s6dS3x8PF26dMm37OXKlVOQpVSpUpKMqqqqdOjQgezsbObOnUt4eDjbt2/nwoUL9OrVCwB3d3cqVKjAjBkziI2NlXSXW1d9+vTB29sbf39//P39WbJkicJmeG/Ts2dP1qxZwx9//MHDhw9xc3NDR0dHWtEDOfX+9pBQbl7Xrl1j8+bNhIeHs3btWkJCQrCzs0NPT4+///6bsLAwAgMDmTJlChkZGQXOBdLT0yM5OZnFixcTERGBr68v+/btY/jw4VKYp0+fkpSUlEcWJSUlmjZtio+PD/7+/gQHB+Ps7Ez58uVp0qRJnry0tLTo0qULc+bM4fbt25w5c4ZNmzZJeiqqfRoZGbF+/XouX75McHAwEydOpEmTJpiYmKCrq0uXLl1YsGABt2/f5s6dOyxbtox69eoVqc+3KaotfCnIQ1QyMt8QKpqlMWixhugzo0kNPy25axq3xKDF2o8y/wZyhgI2btzIwoUL6d69Ozo6OvTu3Zvhw4ejpKSEp6cny5cv59WrV3Tv3h0TExOF+C9fvsyzfPh9OHnyJNnZ2YwaNUrB3cHBgd9++w0jIyN8fHxYuHAhq1atwtbWllWrVqGkpATA5MmTmTt3Ln379kVbW5vBgwczePDg95Zn+vTpLFq0iIkTJ5KRkUGdOnVYt24dKioqqKiosHbtWmbNmkXXrl0xNTWlb9++3L59G8gxWJYvX86iRYvw8vLC2tqazZs3KwyrvAu6urps3ryZOXPm0KFDBypWrMiyZcswNzcnOjqamzdvAtC0aVOFeJ6ennTr1o1hw4YRExPD+PHjUVFRoUePHoXqJnfIav78+cTFxWFra8uWLVsUJpi/fPlSGrp5k//973/4+PiwZMkSli5dSo0aNVi9ejUGBgbMnDkTV1dXOnfujL6+Pm3btkVLS4t79+4VKMuyZcuYPXs2HTt2xMjIiJ9//llhOXZMTAxAvm3wxx9/RFVVlalTp5KYmEi9evWkOsyPGTNmMGfOHAYNGoSuri4TJkygVatWQNHts0WLFoSGhjJt2jTS0tJo0aIFM2fOlMK5urqyYMEChgwZgpqaGu3atWPy5MnSbtsF6fNtCmsLXxJK4u1B3m+ErKwsbt26hY2NTYEN8XOm97Ug66Vg3kc3qamphIWFUbVq1XwnrhY779RYspJfkJ0ej7J6KVS0DT+acfOuCCGkyaK5hoVMDrJuCkbWTf58qXop7FlX3Gen3IMjI/MNoqJZ+j9j0MjIyMh8DOQ5ODIyMjIyMjJfHbKBIyMjIyMjI/PVIRs4MjIyMjIyMl8dsoEjIyMjIyMj89UhGzgyMjIyMjIyXx2ygSMjIyMjIyPz1SEvE5d5Z7LSXpOd+goQKGvoycuNZWRkZGT+c8gGjsw7kf7qAa8uuJASdgwQaFRqhH7TZaiXqYWSitycZGRkZGT+G8hDVDLFJiP+EZF7mpASdhTI2QA77ekFInc3IvP1w88rnMx/npiYGCZOnIi9vT0tW7ZUOKDwwoULdOrUCSsrKzp37sylS5ckv+nTpzN9+vRi5TFr1ix8fHyKLdOWLVswNTVV+C1atAgAJyenPH6mpqasXLmy2Om/C/b29nnySkpKAnIOopw7dy4ODg60aNGCpUuX5jlp/H2YO3cuAwYMKDTMgAEDJJ0WpZO7d+/m8XvzsMe3EULg4+ND48aNqVOnDpMnT+bVq1eSf2FtpjhcuXKFzp07Y21tTa9evfIc0PmxKKgdpqen06FDB/z9/RXc58+fn0dv27ZtAwrX0ZMnTzA1NeXJkydFypSQkECjRo0UdPjq1Sv69++PpaUlLi4uhcb39/fPt+5NTU159uxZkXl9DuRPbpliIUQ2ScH7yE6NyeuXmULcjeXoN1mKsur7Hx8g8/UihGDcuHFkZ2ezdetWoqKicHFxQVdXF1NTU8aPH8+UKVNo3rw5p0+f5ocffuD48eNUrly52HmsX78eX19fxo8fX+w4ISEh9O3bl7Fjx0puWlpaQM7Bh2+epnzy5EmWL19O165di51+cYmKiiIhIYEzZ84obEuvra0N5LwA/f392bBhAzExMbi6ulKpUiV69+793nneuHGDnTt3UqdOnWLHKUonISEhmJmZsX79eimMqmrBr5ndu3ezd+9evL290dPTY86cOcycOZPVq1cX2mZyz24qjIiICEaMGMGIESPo0KEDGzduZOzYsZw4cQJ1dfVil/ldKagdpqWlMXXqVIKDg/PECQ0NZerUqQptS1dXFyhcR++Ct7c3L168UHA7dOgQjx49ws/Pj9KlC59qYGtry8WLFxXcJk+ejJ6eHhUrVlRwX7x4cZ68PgeygSNTLLLTE/9/z03+pD4+S3ZaHMqq5T+hVDLvS2xaMlEpicRnpKCnroWhpi6lNbQ/Wn6BgYHcvHmTM2fOULlyZWrVqsXw4cPZuHEjP/zwA7169ZIOZhwyZAirV6/mzp07xTJwEhMTcXV15fLly1SoUOGd5AoNDaVLly4YGBjk8StTpoz0f0JCAqtWrcLFxYVKlSq9Ux7FlcPAwCDf8sbFxbFv3z42b96MlZUVycnJDBkyhICAgPc2cNLT03F3d8fGxuad4hWlk9DQUKpXr56vPvPj/PnztGvXDgcHBwCGDx/O1KlTgcLbTHEMnG3btmFlZSUZGq6urnTs2JGHDx9Ss2bNdyp3cSisHYaEhDB16tQCe91CQ0MZNmxYvnorTEfF5ebNm1y+fDlP+omJiVSpUoXq1asXmYa6urpC/CNHjvDgwQNOnjypEO7atWv55vU5kIeoZIqFkoo6ytrlCvRX0dJHSeXjfRXJfDgiEuPoc24b5ge8aHDEh1r7veh7bhsRiXEfL8+ICMqUKaPwAjc1NSUwMBA7OzvpROSMjAz27t1Leno6lpaWUtjExETGjh2LpaUlHTt25PLly5LfkydPSEtLY//+/fkaCOHh4QwbNgxbW1uaNm3K1q1bJb+HDx9SpUqVIuXfuHEjBgYGdO/eXXKLiYlh8uTJ2NnZ4ejoWOSw0enTp2nXrh3W1tb06NGDK1euSH4hISFUrVo133jXr19HV1dXesEBjBw5Ek9PT+n64MGDtGnTBmtra3r37s3du3cLLc+6deswNTXF0dExXzlbt26NjY0N8+bNU+ixeZP8dBIaGlosfeaip6fHuXPniIqKIjU1laNHj2JmZgYU3mYyMjIA8PX1pU2bNlhYWFCvXj08PT0lea9cuaJgCGlpaXHmzJlCjZs306tbty5z585VKP+uXbtwcnLC1taWAQMGcP/+fcmvsHZ45coV6taty+7du/PkmZiYSFRUVIF6K0xHuZw4cYLGjRtjZ2eHu7s76enpkl96ejoeHh64ubkp9Fz5+Pjg4+PD1atXMTU1xd/fn8TERGbMmEH9+vWxsLCgTZs2nDlzJo9MGRkZLF++nNGjRysYvenp6bi5ueHu7v5Re8mKi2zgyBQLZVVNStlOLNC/lP00VDTLFOgv898gNi2ZEZf2cPrZAwX3U88eMPLSHmLTkj9KvmXLliUhIYGUlBTJ7fnz52RmZpKQkADkGCLW1tbMmjWLkSNHYmRkJIU9ffo0JiYm+Pn54ejoyPjx46V4NWvWZO3atQrhc0lLS2Po0KHo6OiwZ88e3N3dWbZsGX/88QcvX74kLi6OAwcO4OTkRNu2bdm4cWMeIyUlJYVt27YxevRolJX/75E5btw4oqOj2bZtG8uXL2f//v1s37493/IHBQXh4uLCmDFjOHToEJ06dWLEiBGEh4cDOYZBSkoKAwYMoGHDhowYMYKwsDAg50VfqVIl/Pz8aNu2LR07duSXX34hOzsbyJm/NHPmTAYNGsShQ4ewsLBg1KhRCi+5NwkNDWXnzp3MmDEjj19ISAiTJ0+mT58+7Nu3j8zMTK5fv54nXEE6CQ0N5d69e3Ts2JGmTZvi7u5OYmJivnLk6lBVVVV6OV+7do2lS5cCRbeZK1euMH/+fH744QdOnDjBnDlzOHjwIL///rukN01NTSZOnEiDBg0YOHAgISEhBcrydnpz585l7969Unpnz55l5cqVuLm5ceDAAWrXrs3AgQOJj48HCm+Hffv2xdXVVRr+fJPQ0FCUlJRYs2YNjRs3plOnThw4cKBYOsplz549LFu2jDVr1vDnn3+ydu1ayW/NmjXUrFmThg0bKsQZOnQoQ4cOlYaebG1tWbBgAWFhYWzatIkjR45gb2/PzJkz87Sl48ePk5CQQL9+/RTc16xZQ61atfLk9bmQDRyZYqNWxhS9urPyuOvWGoxmxbxfgjL/PaJSEvMYN7mcevaAqJSCX0b/BmtrawwNDfHw8CA5OZnw8HA2b94MIH2NlylThr179+Lu7s6aNWsUur4tLCyYPHky1atXx9nZGT09PY4cOVJkvhcvXuTVq1csXLiQGjVq4OTkxKxZs1BWVubhw5yJ8fr6+qxevZpRo0axevVqfv31V4U0jh07hra2tkJvQFBQEDdv3uSnn36iVq1a1KlThzlz5lCyZMl85di4cSO9evWiY8eOGBsbM3DgQBo3bszOnTuBnJ6k+Ph4xowZwy+//IKmpiaDBw8mMTFR0teuXbtYuHAhkydP5rfffmPLli1AzhyNDh060KdPH4yNjXF2dqZDhw7Si/dNhBC4u7szYcIEypYtm8d/37592NvbM3jwYKpXr46bmxuGhoZ5wuWnk4yMDCIiIsjIyGDhwoUsWLCAGzdu8OOPPxZYP0+fPkVTU5M1a9bw22+/Ub58eVxdXYGi24y2tjYLFiygVatWGBkZ0aZNG0xNTSUjJjk5GW9vb+rUqcP69eupUKECgwcPliZuv01+6dWqVUuaM7NhwwZGjRpFs2bNqFKlCpMnT6ZSpUocOnSowPIVh4cPH6KkpES1atVYt24dPXv2xM3NjdOnTxepo1xcXV2pXbs2Dg4OTJo0iV27dgE5Buvu3bvzHdLS0dFBW1sbNTU1DAwMUFdXp06dOsybNw8zMzOqVKnC0KFDiYuLIyZGce7lnj176NGjh8J8sZCQEHbt2pWv4fy5kOfgyBQbFc0ylLSbiI5pL1IenUJkZ6Jl3BJV3YqoaOl/bvFkikF8Rkqh/q8zUj9KvhoaGixfvpzJkydTu3Zt9PX1GT58OJ6entJkyhIlSlCrVi3MzMwICgpi27ZttGnTBgArKyspLWVlZczMzAgNDS0y37CwMKpWrSrlASgMqVy+fFmaXGlqasqrV6/YuXOnNB8IcibStmvXTmGybFhYGHp6egpDES1atAByvmLf/IJev349oaGhHD9+XGGIIiMjQ/rS3bhxIxkZGejo6AA5E0KbNGnCH3/8gaqqKomJiSxZsoSKFSsqyDl06FDCwsIU5uKoq6tLK2JsbW0l99q1a9OiRQuysrL4/vvv89VXaGiowvCHmppanuGQgnSipqbG5cuX0dDQQE1NDYCffvqJ7t27ExUVxcyZMxV6g27cuIGLiwvOzs40a9YMgOXLl9OsWTMCAgKwtrYutM2UK1cOTU1NVqxYQUhICPfv3yc8PJzGjRsDoKKigpOTk7RKzMPDg6ZNm3L27FmEEMyePVuSZe7cuXTq1Cnf9HLrKDQ0lMWLFyv0nqSlpfHo0aN8dVlcunTpQrNmzdDT0wNyeoIePXrEzp07adGiRaE60tfPee6+eX/UqlWLly9fEh8fz6xZs5gwYYIUrjiynDlzhj179vDw4UP++ecfAIVhupiYGK5du4abm5vkJoRg1qxZTJw4MV/D+XMhGzgy74SKhh4qGnqol/nwk/RkPj6l1PJ2kb9JSbWPtwrOysqKs2fPEh0dTenSpbl06RKlS5fm2bNnxMfHY29vL4WtVq0aN2/elK5VVFQU0srOzpZeooVR2AoeIM/KkerVqxMVFSVdp6enc+XKFUaOHKkQrrC8e/fuTdu2baXrcuXKkZWVxYgRI+jSpYtC2NwvYHV1dYU5CxoaGhgZGREVFYWBgQEaGhpUqlRJGj6rWrUqkZGRRZbRz89PIa9p06ZJ854gx8jKysrC1taWo0dzFhG8PUT3dlkL0gmgYEgC0uTVqKgoFixYQGrq/xnQr169IjIyElNTU8mtQoUKlC5dmqdPn2JtbV1gm9HR0eHChQuMGzeOLl260KhRI8aOHatgtBgYGCjMa1JXV6dSpUpERkbSt29frK2tJT99ff086Y0bN465c+dKYbKysnB1daV+/fqFlvldUVJSkoybXKpVq8bly5eL1FGu4fLmMGFu/SUmJnLz5k3u37/PokWLUFJSIiUlhdmzZ3Ps2DE2bNiQRxZnZ2du3rxJ586d6dOnDwYGBnmM4QsXLmBkZKQg07NnzxTyAorM61MgGzgyMt8Q5bR0aVXRhFP5DFO1qmhCOa1/97AuiLi4OGn4JXd1xblz53BwcOCPP/5g//79HD9+HCUlJQDu3btHtWrVpPhvTubMzMzk7t27NG3atMh8q1SpQnh4OCkpKdL8h0WLFpGRkYGpqSkbNmzgxIkTheabmZmp8IUMYGxsTFxcHJGRkdKKma1bt3L58mV++eWXPC+sqlWr8uTJE4yNjSU3Ly8vqlatSo8ePWjZsiVjx46V9ozJHZKpVq0aVapUIS0tjbCwMGkiamhoqLRyydjYWGF/l6ysLFq2bMnixYupXbu2ghze3t4KRsZvv/1GQEAA3t7eGBoaUqNGDQXDMjs7m6CgIIWJuQXpJCQkhJ49e3Lo0CGpZ+vevXuoqqpibGxMqVKlFMJnZmairq4urbyCHKMnLi4OIyOjQtsM5EwI7t69u2TUZGRk8OTJE+kFb2Njo9Bu0tPTiYiIwMjICF1d3TyGydvpZWZm8vjxY+rVqyfV4fPnzxXqcMaMGbRo0YLmzZvzvvz888/cvHlTGnKEnCHQatWqUapUqUJ1lMuDBw8kvdy+fZvy5ctTrlw5Tp06hRBCav8DBw5kwIABdOrUKY8ciYmJHDlyhD179kh1e/78eUDR6L19+7ZkIOeSm9ebDBgwoMC8PhXyHBwZmW+I0hrarHPsRauKJgrurSqasK5hr4+2VFxPT4/k5GQWL15MREQEvr6+7Nu3j+HDh9OpUyeio6Px9vbm0aNHbN++nWPHjin0EFy7do3Vq1cTGhrK/PnzycjIoEOHDkXm27BhQ8qWLYu7uzuhoaH8/vvv7Nq1i4YNG9KgQQOio6NZtGgR4eHhHD16lPXr1zN8+HApfnBwMEZGRnlWhNSoUYN69eoxc+ZM7t+/j7+/P+vWrct3VRLA4MGDOXbsGFu3buXx48ds2bKFLVu2UKVKFZSUlGjatCk+Pj74+/sTHByMs7Mz5cuXp0mTJlSrVo2mTZsyY8YMgoKC+Ouvv1i/fj19+vQBcl4khw4d4sCBA4SHh+Pp6YkQAnNz8zxylCtXDmNjY+lXqlQpNDU1MTY2RlVVlV69ehEYGMjq1at5+PAhixYtyrOJW0E6qVatGsbGxri5ufHgwQNpGKNnz555jBvI6Xnq1q0bixYt4urVqzx48IAff/wRa2trLC0tC20zkNOmcnsNgoODmTFjBi9fvpTmdA0aNIiTJ0+yY8cOHj16xLx589DQ0CjQMH47venTpxMdHS1NsB0yZAi//vorfn5+PH78mMWLF3P8+PFiLbEujGbNmnH16lU2btzI48eP2bFjB35+fgwdOrRIHeXi4eFBQEAAly5dYsWKFQwePFgyLI2Njfnf//4n1bG+vj7lyuVdEauuro6WlhanTp3iyZMnXLhwgXnz5gEoTDIODg7mu+++U4j7Zl65v8Ly+mSIb5TMzExx7do1kZmZ+Z9M72tB1kvBvI9uUlJSxN27d0VKSsq/yvtVapK4Fxsl/F+Ei3uxUeJVatK/Sq84hIaGiv79+wtra2vRvn17cfbsWcnv5s2bomfPnsLKykq0bdtWHD16VGRnZwshhHBxcRHTp08XQ4cOFebm5qJr164iMDAw3zz69+8vVqxYoeAWEhIiBg4cKCwtLUWzZs3Ejh07JL+rV6+KXr16CSsrqzx+Qgixdu1a0atXr3zzioqKEmPHjhXW1tbC0dFRrFixQpI5P44cOSJat24tzM3NRdu2bcWxY8ckv9TUVOHp6SkcHR2FtbW1GDVqlHj27Jnk//r1a/Hjjz8KGxsbUa9ePeHj46OQl6+vr2jevLmwsrISffv2FUFBQQXK8SYrVqwQ/fv3V3C7ePGiaN++vbC0tBRTpkwRo0aNUtBpYTp59uyZGDdunLC3txcODg7Cw8NDpKWlFZh/amqq+Omnn0SjRo2Eg4ODmDx5soiJiZH8C2szUVFRYujQoZL+XV1dxcyZM8WQIUOkMKdPnxatW7cWFhYWonfv3uLBgwcFypJferNnzxZDhw6Vwvz666+iWbNmwtLSUnTt2lX89ddf+aaVXzvMxcTERFy+fFnB7fTp06Jjx47C0tJStGnTRpw8ebJYOoqIiBAmJiZi+/bton79+sLe3l54eXmJrKwsKX52drZITEwU2dnZolmzZmLfvn2S39v1f/r0adGiRQthZWUl2rVrJ3x9fYWjo6M4fPiwFKZNmzZi586dBeoxl7fzelcKe9YV99mpJMQH2O/7CyQrK4tbt25hY2OTZ3z/v5De14Ksl4J5H92kpqZKE2ffXMHwNSGEIDk5GW1tbWnoSCYHWTcFI+smf75UvRT2rCvus1Oeg/MNkZycTHx8POHh4airq1O1alW0tbXR0ND43KLJyMjIyMh8UGQD5xshKSmJ06dP8/TpU8nt77//pmHDhtSsWVM2cmRkZGRkvirkScbfAEII7t+/r2Dc5HLx4sVCdxqVkZGRkZH5EpENnG+A5ORkbt++XaD/m0tMZWRkZGRkvgZkA+cbQAhBWlpagf4FbV0uIyMjIyPzpSIbON8Aampq0qZg+fFv93GQkZGRkZH5ryEbON8AGhoa1KtXT2E771xKlSr1eTdikpGRkZGR+QjIq6g+EZkJL8lKeEl2RgoqOqVRLVUBZbVPt3KpdOnSdO/enYsXLxIZGYmKigqmpqbUrl37X5+lIiMjIyMj819DNnA+AWlRITxb04+Uh1cAUFLXpmyHGZR2GoVqCYNPIoOKigqGhoa0a9eO9PR0lJSU0NLSKvIwQhkZGRkZmS8ReYjqI5Px6imPFzWXjBsAkZ5M9H43Xl/xRWRnf1J5NDU1KVmyJCVKlJCNG5nPQnp6Oh06dMDf319ye/bsGSNGjMDa2ppWrVrlObivMO7evYupqanCL/fQyunTp+fxMzU1ZeDAgUDOBPx169bh5OSEnZ0dgwYNIiQkpFhpA0RERDB48GBsbGxo164dFy9eLFTW169fM3PmTBo0aEC9evWYPn06r1+/zhMuLi6OBg0a8OTJE8lNCIGbmxsNGjSgRYsWvHr1iv79+2NpaYmLi0uReho5ciTTp08vMlxBmJqaKtTZm/j7+yucLv2hEULg7e1NvXr1cHBwwMvLi+xCnp3Tp08vsKxPnjzJt02Ymppy9epVAOLj45k6dSq2trY0btyYrVu3KqTx119/0aFDB6ytrRk4cCARERH/uowfU4dnzpyhdevW2NjYMHTo0Hy3DMnvvgTFe7Nly5YcO3ZMwf/IkSO0aNECa2trxo0bx6tXrz5KGd4H2cD5yKQ9u0tGzON8/aL95pIZ9yxfPxmZr5G0tDR++OEHgoODJbfMzExGjRqFqqoqBw4cYOjQocyaNYsHD/KeeJ4fISEhmJmZcfHiRem3ceNGAGbOnKngvnv3btTV1SUDZ9euXWzatAk3Nzf27duHkZERI0aMICUlpci0hRCMGzeOsmXLsm/fPjp37sz48ePzHE75JrNnzyYoKIh169axceNGQkNDmTVrlkKY+Ph4Ro8eTUxMjIJ7UFAQvr6+LF68mG3btnHo0CEePXqEn59fkQbO0aNHpZOhv0Q2b97MkSNHWLlyJStWrODw4cNs3rz5vdKqUKGCQn1evHiRDh06YGlpiY2NDQBTp07lyZMn7N69G1dXV7y9vblw4QKQ88IfN24c3bp1Y+/evZQpU4axY8fyXz316MaNG7i6ujJkyBD279+Puro6P/zwg0KY/O5LyHtvDhs2DGdnZ+nevH37NjNnzmT8+PHs3r2b169fM2PGjE9WtqKQP+E/MqmPbxXol/X6BdkZKZ9OGBmZ/09mUixZr6PISo5HRVsPlZKGqOqU/qh5hoSEMHXq1DwvgvPnzxMZGcnOnTvR1dWlatWqnDt3jps3bxbrizY0NJTq1atjYJB3uLdEiRKUKFFCup4+fTpt2rShRYsWAJJB1axZMwDmzJmDg4MDN27cwNHRsdC0L1++TEREBLt27UJbW5vq1avz999/s2/fPiZMmJAnfHJyMidPnmTnzp1YWFgA4OrqSr9+/UhLS0NDQ4Nr167h4uKCjo5OnvgJCQkANGjQAB0dHRITE6lSpUqRqyDj4uLw8vJSOH36S2Pr1q1MnDgRe3t7AKZNm8bPP//MsGHD3jktFRUVhfq8ceMGJ0+e5ODBg6ipqUkntp88eZLKlStjYmLClStXuHHjBo0aNcLX1xcLCwuGDh0KgKenJ46Ojly5coW6det+mAJ/QDZv3kzbtm3p3bs3SkpKzJw5k0GDBvHq1SvKlClT4H0Jee/NatWq8eeff3Lz5k1MTEzYtm0bbdu2pUuXLgB4eXnRrFkzIiIiqFy58icuaV7kHpyPjEZ5kwL9lLVKoqwqH5Eg82nJeBXB0196EzrdjEfz6hE6vSZPV/cm49W/72YvjNwXwO7du/O4169fX2Gy+9KlS/n++++BnJ6SVatW0bBhQ+zt7Rk9erRCL0loaChVqlQpMv+///6bq1evKny9Ojs706lTJ+laSUkJIYRkTBSWdkBAALVq1UJbW1tyq127Nrdu3co3vLKyMmvWrMHMzEzBPSsrS9qL6uLFi3Tv3h0fHx+FMP7+/gwYMEDKw8fHBx8fH65evVro0BHAokWL6Ny5M999910ev4MHD9KmTRusra3p3bs3d+/elfxWrlxJ/fr1qVu3Lr6+vgrxEhMT+eGHH7C1taV169bcuXOnwPwhpw7XrFmDk5MTFhYWNGzYkJUrVyr4F1THUVFRREZGUqdOHSl87dq1efr0KS9evADg2rVrdO3alfr16zN58mSpB644LFmyhF69ekmG4pUrV6hZs6bCC9rd3Z1JkyYBOfWea2gBaGlpYW5uLtV7cnIy7u7u1K1bl7p16+Lm5ibtQxYSEsKwYcOwtbXF0tKSvn37EhoaqiDPb7/9JsVdtmyZguFRWH1t3rwZJycnbG1tGTZsmDRsduXKFZycnKRwlStX5uzZs5QpU0byz+++zPV7+9785ZdfpHvzbV1UqFCBihUrEhAQUKTePwWygfOR0fifLcoFfBmXaT0ZVb0Kn1gimW+ZzKRYnm0cTlKg4hyXpDuneLZpOJlJsR8t7759++Lq6oqWlpaCe0REBOXLl8fb25tGjRrRuXNn/vjjD8l/27ZtHD58mCVLlrB792709fUZOnQoGRkZQI4Rcu/ePTp27EjTpk1xd3fP9/iRdevW0bVrVypU+L97zt7envLly0vXvr6+ZGZmUrt27SLTjo6OxtDQUCEPfX19nj9/nm/5NTU1ady4Merq6pLb1q1bMTU1lV42kydPZuzYsXlOSLa1tZWMnlOnTjF06FCGDh2Kra0tFy9exNbWNt88//77b65du8bYsWPz+F24cEH6mj906BAWFhaMGjWK9PR0du/ezdatW1m4cCFbtmxh3759CnFnz57Nw4cP2bZtG7NmzSpyuMjPz49ff/2VBQsWcOLECcaNG4ePjw///PMPUHgdR0dHAyjoumzZsgA8f/6cV69eMWrUKBo0aMDOnTupXr06J06cKFSeXK5fv86tW7cYNWqU5BYREYGRkREbN27EycmJNm3asGvXLsm/qHqfNWsW169f55dffmHTpk1cv36d5cuXk52dzejRo6lUqRIHDx5k165dZGVlsXjxYoW0Dh06xObNm1m4cCE7duzgwIEDQOH1tWvXLlauXMm0adM4cOAAOjo6TJo0idevXxMfH09WVhbDhg3D0dGRMWPGEBUVJeVX0H2Zq4s3781OnTpx5swZyf/FixfvdA98amQD5yOjpl+ZKtPPolpacaO9Ug0HUsZpDEoqap9JMplvkazXUXmMm1yS7pwi63VUvn4fk+TkZA4cOMDr169Zs2YNnTt3xtnZWeoV2LBhA87OztStW5fq1aszb9484uPjuXDhAhkZGURERJCRkcHChQtZsGABN27c4Mcff1TIIyIigsuXL0u9IPkREBDAokWLGDZsGAYGBkWmnZKSomCsAKirq5Oenl6scm/bto3jx4/j7OxcZFh1dXVKlSoF5LzcdXR00NbWRk1NDQMDgzxyQM68itmzZ+Pu7o6mpmYe/927d9OhQwf69OmDsbExzs7OdOjQgfj4ePbs2cOgQYNo1qwZZmZmzJ8/X4qXkJDA8ePHmTVrFubm5jRq1ChfA+pNKlSogKenJ/Xr18fIyIg+ffpgYGAgzfkorI5TU1MlHbypD8iZGHv8+HHKlCnDtGnTqFKlChMmTCj2cNyePXto2bKlwl5gycnJ/PXXX1y/fp2ff/6ZESNG4OnpycmTJ4HC6z0+Pp4TJ07g7u5O7dq1MTc3Z968eVSsWJHU1FR69+7N9OnT+d///oe5uTldu3ZVmNQOsHDhQmrVqkXz5s0ZNGiQZFwVVl+7d+9m8ODBtGvXjipVqkg9SLk9g4sXL6ZTp06sXr2a9PR0Ro0aVegk7Td18ea92aVLFyZOnCjdm6mpqf/qHvjYyHNwPjJKSkpoVLamqvtlMuMiyUqJR03/f6iWNERFW+9ziyfzjZGVHP+v/D8GKioq6OnpMWfOHJSVlalVqxZXrlxhz549VK9enefPnzNlyhSFjSpTU1N59OgRTk5OXL58GQ0NDdTUcj4WfvrpJ7p3705UVJT04jp58iRmZmb5DtMA3Lx5kxEjRtC4cWNpKEJNTa3QtDU0NIiLi1NIJz09XTIm3uxVqV27Nhs2bJCut2/fzvz585kxYwYNGzb8lxrMmfjavn176bpjx46UKlUKCwsLGjVqlG+csLAwevfuLV2rq6tLk5VDQ0MZN26c5Pfdd99JQ3FhYWFkZWVRs2ZNyf9Ng2LNmjWsXbtWul6/fj316tUjICCAJUuWSL1i0dHRZGdnk5SUVGgd5w6BpKeno6GhIf0POcNDISEh1KxZEyUlJQV5coep2rdvLw13VaxYkaNHjwI5E2h///13vLy8FPSioqJCVlYW3t7eaGtrY2lpSVBQELt376Z169ZoaGjkeYGnp6dTsmRJwsPDycrKwtzcXPKzt7eXytCnTx/8/PwIDAzk4cOH3L17V+qNAtDW1qZGjRrSda1ataTescLqKywsTCHPsmXL4uLiIvV+denShc6dO6OkpIS3tzeOjo7cunULOzs7CuPte9Pc3Jxr166xZ88eLC0tC9RFfr1BnwPZwPkEKCkpoVbGCLUyRp9bFJlvHBXtUv/K/2NgaGiIkpKSwsvN2NiYhw8fkpWVBcDPP/9M1apVFeLl9mi8vVFl7lyKNw2cCxcu0Lx583zz9/f3Z/To0Tg6OrJkyRIFOYpK++2v75cvX0pd9n5+fpL7mz0oGzduxMvLC2dnZwYNGpSvTO+KoaGhQn66urr07NmTly9fSoZW7ovo5MmT3Lx5s8htIt6edFpY+De/4nv37k3btm2l63LlyuHr68vChQvp2bMnrVq1wsXFRVrJVlQd585fiY6OxsjISPofkCYLvy2rmpqaZOCsW7eOzMzMPGW4desWmZmZODo6KsQ1NDSkfPnyCnOrqlatKm0BUK5cOV6+fKkQ5+XLl5iZmUmGcH4kJSXRo0cPSpcujZOTEx06dODhw4ds2rRJCvOmkQaQnZ0tpVmY/gvyK126NGpqagrzyEqXLo2enl6xhpHyuzerVq3K/fv3gYJ1kd+k/M+BPEQlI/MNoVKyHDqWrfL107FshUrJT39sh7W1NcHBwdKLDnK+SCtVqkTJkiXR19cnOjoaY2NjjI2NqVChAosXLyYsLIyQkBBsbW0V9iG5d+8eqqqqGBsbAzkvvzt37uT7tfrgwQPGjBlDo0aNWL58ucILqqi0ra2t+eeff6QhFMiZ02FtbQ0gyWtsbCwZWgcOHMDLy4sZM2a81wqggsiVKfenr6/Pb7/9xuHDh/Hz88PPzw8nJyecnJwkQ8jY2JigoCApjaysLJycnLh+/To1atRQmDj85MkTab+eatWqoaampuD/5mRXPT09BVk0NTXZuXMn48aNw9XVlS5dulC6dGliYmIQQhRZx+XKlaNixYpcv35dQc8VK1bE0NCQGjVqcPfuXYX2c+/ePen/SpUqSem+eSZfQEAA5ubmUq9QLtbW1jx9+lSaaA7w8OFDKa61tbWCLCkpKdy9exdra2sqV66MioqKgl7PnDlD165duXLlCi9evGDr1q0MHz6cBg0a8OzZMwXjLCkpSWGPmjt37lCtWrUi6+ttv9jYWOrVq8fz58+pVauWwpYLr169IjY2ttDzCd/Uxdv3ZmhoaIG6iIyMJDIyUroHPjeygSMj8w2hqlOaikM35DFydCxbUXHoho++VDw/OnToQHZ2NnPnziU8PJwdO3bw119/0bNnTwAGDx7M8uXLOXv2LI8ePWLWrFncuHGDatWqUa1aNYyNjXFzc+PBgwdcu3YNNzc3evbsKfXwPH36lKSkpHyHp9zd3alQoQIzZswgNjaW6OhooqOjSU1NLTJtBwcHKW5wcDDr1q3j9u3b9OjRI99yxsXFMW/ePLp27Ur79u2lvKKjoxVeIB+KN1/sxsbG6OjooKOjIxl+AwYM4NChQxw4cIDw8HA8PT0RQmBubk7//v3ZunUrJ0+e5MGDB8ycOVP6itfV1aVz5854eHgQEBCAv7+/woqo/ChdujR///03YWFhBAYGMmXKFDIyMqRepcLqGHKGdry9vfH398ff358lS5ZIPUDt27cnJSWFBQsW8OjRIzZu3Kjw0i2I4ODgfJfYN2jQgKpVq+Li4kJoaCjHjh3D19eXPn36ANC9e3du3LjBunXrCA4OZsaMGRgZGVG3bl10dXXp0qULCxYs4Pbt29y5c4dly5ZRr1499PT0SE5O5syZMzx58gRfX1+2b9+uMMSjrKyMi4sL9+7d4/jx42zdupXBgwcXWV8DBgzg119/5cyZM4SFhTF79myMjIwwMjJiyJAh7Nq1ixMnThAaGoqrqytmZmZYWVkVqaO3783t27dz4cIFevXqJdXLwYMH8fX1JSgoCGdnZ5o2bfqfWCIOgPhGyczMFNeuXROZmZn/yfS+FmS9FMz76CYlJUXcvXtXpKSk/Ku8MxJfidRn90RSyGWR+uyeyEh89a/Se1dMTEzE5cuXpevg4GDRr18/YWFhIVq1aiUOHjwosrOzhRA5elq6dKlwdHQU1tbWol+/fuLu3btS3GfPnolx48YJe3t74eDgIDw8PERaWprkf+vWLWFiYqLgJoQQL168ECYmJvn+9u3bV6y0Hz16JMndvn17cenSpQLLfOTIkQLzi4iIUAgbERGRx/3y5cvCxMREJCYmiuzsbLFixQrRv3//YuvcxcVFuLi4KLj5+vqK5s2bCysrK9G3b18RFBQk+W3evFk4OjqK2rVri7Vr1woHBwepzlJSUoSrq6uwtbUVTZs2FRs3bhQmJiYF5h0SEiJ69eolLC0tRdOmTcWiRYvEuHHjhJubmxCi6DrOzMwUCxcuFPb29qJu3bpi8eLFUvsQQojAwEDRo0cPYWFhIQYPHiymT5+ep6xvM2zYMOHt7Z2v3/Pnz8WoUaOElZWVaNKkidixY4eC/7lz50SrVq2ElZWVGDRokHj8+LHkl5CQIKZPny7s7OxE3bp1xdy5c6U24+PjI+rVqydsbW1F7969xd69e4Wpqal4/vy5uHz5snBwcBDr1q0TtWvXFg0aNBAbN24sVn1lZ2eLNWvWiIYNGwobGxsxcuRI8fTpU8lv69atomnTpsLKykoMHz5cREZG5lvut+9LIfLemydPnlTw37dvn2jSpImwsbER48aNE69efZhnSWHPuuI+O5WE+I9uv/iRycrK4tatW9jY2ORZkvlfSO9rQdZLwbyPblJTUwkLC6Nq1ar5roz5GhBCkJycjLa2dp45Cd86sm4KRtZN/nypeinsWVfcZ6c8RCUjIyMjIyPz1SEbODIyMjIyMjJfHbKBIyMjIyMjI/PVIe+DI1MoIjUL8ToDEZEMykooGWujpKOKkoY8n0ZGRkZG5r+LbODIFIhIySLr2iuI+b9ljCI8GaX/aaNsXlI2cmRkZGRk/rPIQ1QyBSKepygYN5L742TE68zPIJGMjIyMjEzxkA0cmXwRqVlkP0wq2P9hIiKr6MPaZGRkZGRkPgeygSNTMJmFbJGUKeCb3EFJRkZGRuZL4LMaOOnp6cydO5c6derQoEEDli5dKp3LcffuXXr27Im1tTXdu3cnMDBQIe6RI0do0aIF1tbWjBs3jlevXn2OIny9qCmjVKHgjeSUjLRQUpXtYxkZGRmZ/yaf9Q01f/58/vrrLzZu3MiSJUvYs2cPu3fvJjk5mZEjR2Jvb8/+/fuxtbVl1KhRJCcnA3D79m1mzpzJ+PHj2b17N69fv2bGjBmfsyhfHUoqSihX1wW1fHa+1FFByUAjr7uMzDvg4+PDgAEDFNzCw8PzPajPz8+P1q1bY2dnx7hx46TTpD8UI0eOZPr06QpuFy9epFOnTtja2jJ48GAePnwo+QkhWLduHU5OTtjZ2TFo0KA8J4u/TUREBIMHD8bGxoZ27dpJp1O/zaFDh/LoJS0tDQ8PDxo0aEDz5s1xd3eXnocA8fHxTJ06FVtbWxo3bszWrVuLXfa5c+fmye/atWt069YNGxsbOnfuzF9//aXgf+LECVq3bo2NjQ1Dhw5VOCAyLS0NV1dX7O3tadiwocJp2e+Dqakp/v7+Bfo/fvyYIUOGYGdnR69evTh37lyh6Tk5ObF///4C/WNiYpg4cSL29va0bNmy0LCQc0aUj49PseRPTk5m1qxZ1K1blzp16uDm5kZS0v9NBShKd7du3aJ3797Y2trSunVrfH19C5Utl9jYWPr374+lpSUuLi4A2NvbY2pqqvBLSkriyZMnmJqa8uTJk2KlnR87d+6kefPm2NnZMWzYMIUDaz8ln83AiYuLY9++fXh4eGBlZUX9+vUZOnQoAQEBHDt2DA0NDZydnalevTozZ85ER0eHEydOALBt2zbatm1Lly5dqFmzJl5eXpw/f/6zKfGrRVsFlSaGKFXWBlUlUFdG6TtdVBzLoqQtL8CT+bBERkYyatQo0tLSFNwvXLiAq6srAwYMwNfXF21tbUaMGEF29oeZA3b06FHOnz+v4BYcHMyoUaNo3rw5+/bto1atWgwaNEh6Ge3atYtNmzbh5ubGvn37MDIyYsSIEaSkpOSbhxCCcePGUbZsWfbt20fnzp0ZP348z549Uwh3+fJl3N3d88RfuXIlV65cYe3ataxYsYLr16+zdOlSyX/q1Kk8efKE3bt34+rqire3NxcuXCiy7Ddu3GDnzp0KbjExMYwePZp27dpx+PBh2rZty9ixY3n+/LkUZ+rUqQwZMoT9+/ejrq7ODz/8IMX38vIiMDCQX3/9ldmzZ7Ny5Urp2f2hSUtLY8iQIWhoaLB7924GDhzIDz/8wO3bt98rvdx6ev78OVu3bsXV1ZWffvqJU6dOfRB5Fy5cSGBgIBs3bmTLli3cvn2bn376SfIvTHfR0dGMGDECBwcHDhw4wMSJE/Hw8CjSoAM4duwY4eHh+Pn54eLiQlRUFAkJCZw5c4aLFy9KP21t7X9dxgsXLrB48WJmzZrFvn370NbWZty4cf863ffhsxk4169fR1dXFwcHB8lt5MiReHp6EhAQQO3ataVzM5SUlLCzs+PWrVtAzjH39vb2UrwKFSpQsWJFAgICPmkZvnaUlJRQ0lVF2boUKs3LodLMAGWzkrJx8xWQmZxB6oskEh/HkxqdRGZyxmeV58yZM3Tr1g11dfU8ftu2baNjx47079+f6tWr4+HhQWRkJJcuXfrX+cbFxeHl5YWlpaWC+86dO7G1tWXSpElUq1aNH3/8kRIlSnD48GEADhw4wNChQ2nWrBlVq1Zlzpw5xMXFcePGjXzzuXz5MhEREcybN4/q1aszatQobGxs2LdvnxRm5cqVjBgxIt+TmM+fP8/333+PpaUl5ubm9O7dm8uXLwMQFBTEX3/9hbe3NyYmJrRp04YePXoUKEsu6enpuLu7Y2Njo+B+48YNVFRUGD58OJUrV2b06NFoaGhIz99NmzbRqVMnevfuTbVq1Zg5cybR0dG8evWK5ORkfH19mTlzJubm5rRs2ZLhw4ezffv2QmV5X/744w9iY2NZvHgxNWrUoEOHDnTq1IktW7a8V3qBgYHcvHmTJUuWUKtWLZo1a8bw4cPZuHHjB5FXTU0NNzc3LCwsMDc3p3v37tLJ50Xp7syZM5QtW5YffviBKlWq0L59e7p06SK1ycJITEzE2NiY6tWrU6ZMGUJDQzEwMKBy5coYGBhIvw9xVtX58+dp2LChdG+MHz+e+/fvf5ZpJJ/NwImIiKBSpUr4+fnRpk0bmjdvzqpVq8jOziY6OhpDQ0OF8Pr6+tIXxIsXLwr1fxeysrI+2O9Dp/df+WUrCbLVIVtdiWyyZb18wN/76EYI8a9+6XGpPNwVyD/LLnN/9TX+WXqZh7sCSY9L/ddpF/YLDg6mT58+WFtbM3DgQF69eiX5nTt3jokTJ+Lq6irdm7l+ERERWFlZSdcaGhr873//4+bNmwghSEpKws3NDQcHBxwcHJg1axapqTllefnyJZMnT8bOzg5HR0eWLFlCdna2lNaiRYvo1KkT1atXV5D17TwBTExMpDydnZ3p2LGjgr8QgtevX+db9lu3blGrVi20tLQkNzs7Oyk9IQSXLl1iw4YNtGzZMk98PT09Tpw4QVxcHK9fv+b06dOYmZkhhMDf3x9TU1OMjIyk8G5ubkycOLHQ+li3bh0mJiY0aNBAwb1UqVLExcVx8uRJsrOzOX36NElJSdSoUQMhBFeuXKFFixZSeCMjI37//XdKly7NvXv3yMzMxMbGRqGcAQEBBbbdhIQEZsyYQf369bGwsKBNmzacPn1aQbdXrlyhVatWWFtbM2nSJOLi4qR6qlatGrq6uvnWkxCCnTt30rRpU+zs7Pjll18K1UlERARlypRR0KWpqSmBgYGkp6cjhODUqVO0atUKGxsb5s6dm6dcK1eupH79+tStW1caQsr1c3d3x87OTsrryJEjODg4IIQoUncNGzZk4cKF+eov9/9NmzbRrFkzbG1tGTp0KBEREfj4+LB27VquXbuGqakply9fJjg4mCpVqhSoB8gZhmzcuDF2dna4ubmRlpYm+V+9epVu3bphZWVFhw4dOHHihEJbvXr1KqGhoWRkZODn50elSpUoWbLkez03Cnt2FsVn+xRPTk4mPDycXbt24enpSXR0NO7u7mhpaZGSkpLnS05dXZ309Jw9WVJTUwv1fxfu3Lnz/oX4iOmpq6ujVq4M12Kfce7FQ6rplqFTJTOU4pJITUj8IHl8Sj60nr8m3lU3qqqqpKSkvNcQjXKmEhH7gkgIiVVwTwh+xaP996jc3ZRs1Q+/PC49PZ2RI0dia2vLzJkzuXr1Kt7e3lhbW5OcnCzNobt27ZoUJ3e4p3Tp0jx58kSac5Kdnc3z58+Jjo6W4gYHB7Ns2TI0NDSYNWsW3t7eTJkyhTFjxqCiosL69etJSkpixowZ6Onp0bt3b65cucKVK1fYs2cPnp6eAFIepUqV4tmzZwrzXJ4+fUrJkiVJTk7GzMxMIfzevXvJzMykVq1aCnFyiYyMpEyZMgp+JUuWJDIyUnLbsGEDkDP3Jzs7WyHshAkTmDZtGvXr1wfgu+++Y8mSJSQnJxMWFkaFChVYs2YNe/bsQV1dnb59+9KjR48C6yMsLIwdO3awa9cufH19FfKrVasWvXr1YtKkSSgrK5OVlcWcOXMoX748UVFRxMfHk5yczODBgwkODsbCwoIZM2ZgaGjI06dP0dPTIzMzk8zMnL2ydHR0SEtLIzIyktKlS+eRZe7cuTx+/JhVq1ahpaXFr7/+yqxZs6hTpw5qamoAbN++nXnz5lG6dGnmzp3L/PnzmTt3LiVKlODFixckJSVJvQ8RERHExsaSnJzMX3/9xcKFC5k1axZmZmasXLmSZ8+ekZ6enm896erq8vr1a2JiYtDS0gJy5oVlZmby4sULYmNjmTJlChMnTsTR0ZHt27dz/fp1ateuTXJyMvv27ePXX39l3rx5lCtXTmpXaWlpCvm5u7tz5MgRKlasyJAhQ0hOTi5Sd2XKlFFoQ69eveLYsWOMHDmS5ORk9u7dy6pVq5g5c6ZU1gkTJrB+/Xpev35NQEAA3t7elCpVisOHD5OcnEy/fv0IDw/H1NSUadOmYWxsTGpqKoD0bs7KysLNzY2VK1cyevRoXr58yejRoxk7diwNGjTgzp07zJgxAx0dHezs7OjevTsXL16kXbt2qKiooKWlxYYNG/IMPRdFWloaGRkZBAUFvVO8N/lsBo6qqiqJiYksWbKESpUqAfDs2TN27tyJsbFxHmMlPT1dOjJdQ0MjX//cBvkuWFpaFnrcenHJysrizp07Hyy9h4mvaHZ8NZEpryW3OQGn2e80iOZVLFH/AHl8Cj60Xr4m3kc3qamphIeHo6WlJd0P70JqdHIe4yaXhOBXkCbQLvnvx+Hf5sqVK8THx+Ph4YG2tjbm5ubcunWLmJgYhXF/DY3/m7yupaWFkpISHTp0YNmyZbRs2RILCws2bNgg9f5kZGRw5swZNm3aRN26dQHw8PDg3r17REREcPv2bc6cOYORkRGQ8zJNTk5GRUUFT09PZs+eTZkyZST958rSqVMnxo4dS6dOnWjUqBGHDx/m7t27ODg45JmnEBAQwLJlyxg2bBj/+9//8i1/ZmYmWlpaCnF1dXXJzMzMk56amhrKysoK7i9evKBSpUosWrSIpKQkvLy8WLFiBR4eHmRkZHDlyhUAfv75Zx48eICHhwflypWjdevWeWQRQuDp6cmECROoXLlynvwSExN5/vw548ePp2nTppw+fZrFixcrlD3XgKxWrRo///wzU6ZMYd++fWRnZ6OhoaEge6lSpQBQUVHJd45H/fr1GTFiBCYmJgCMGDGCAwcOkJycTIUKFQAYP348LVu2BMDNzY2hQ4cye/ZsWrRogZeXFxs3bmTUqFHcuXOHQ4cOSXo9fPgwHTt2pFevXgD89NNPNG3aFHV19XxlcXBwwNDQkKVLl0pDbzt27ABy3lnHjh3D3t6ekSNHAjnt6eLFi6ipqaGtrc3BgwcZPHgwbdq0AXLm3HTo0CGPTkaPHk3//v1ZsmQJkyZNemfdpaam4uLiQtmyZRkwYABaWlr4+fkxaNAgunbtCsCcOXPYtGkTOjo6aGlpST2fkDMxOyEhgalTp6Krq8v69esZM2YMR44ckZ4rM2fOpEGDBgBMmjSJJUuW8MMPP3DgwAFpzixAzZo1CQ0NZffu3TRs2JCnT5+SmZnJ4sWLMTY2ZvXq1bi7u+Pr66twfxeFsrIyampqfPfdd3medbnPzqL4bAaOgYEBGhoaknEDULVqVSIjI3FwcODly5cK4V++fCkNS5UrVy5ffwMDg3eWQ0VF5YO+eD9EerFpyYz7e7+CcQOQJbLpde43/unqTNUSZf5VHp+aD63nr4l30Y2KikrO3Kj//3tXslIL34E6Ky3zg4zDv01oaChVqlRBR0dHcrO0tOT8+fMK+b39v5KSEr169eLBgwf0798fgNatW9O4cWNKlCjB48ePycrKwsLCQopbp04d6tSpw/Hjx9HT01OY09KiRQsAlixZgoWFBY0bN1bIN/dv48aNGTduHBMnTiQrK4u6devSuXNnEhMTFWS8efMmI0aMoHHjxkyaNAklJSUOHTrE7NmzpTBz585FQ0ODlJQUhbi5H21v6/vt+k1MTGTWrFls2bIFKysrkpOTWbhwIf3792fixImoqKiQlZWFt7c32traWFlZcf/+ffbs2UObNm2wtbWV0q5duzYtWrQgKyuL3r17K+SVm9/GjRsRQjB+/HgALCwsuH37Nlu3bpXcevbsSZcuXYAcY8fR0ZGAgADp4/PNMmVk5Mzv0tLSYsSIEdKck1z9de3alTNnzuDr68vDhw/5559/gJyeutx0rKyspP/Nzc3JzMwkIiKCWrVqsWzZMqZPn86aNWuoWLEi/fv359dff0VJSYnQ0FCpnABlypShcuXKKCkpERkZSfv27SVZOnbsyLx58/j555+ZPHky9vb26OvrM3z4cDw9PSlRogShoaGYmZlJ6amrq0vXufmNGzdO8q9Rowba2tp57tcaNWoAsHz5cho1asS1a9eK1F2ue1JSEmPHjuXRo0fs2LFDMnzCwsKYMGGCFM7AwAAXFxdpyCm3beXWcUZGhnQ/LlmyhCZNmnDu3DmpvVhbWyvo/OXLl7x+/ZqHDx9y7tw57OzsFOSsWrUqSkpKzJkzh1atWtGpUycp7aZNm3L27FnatWtHccnV2b95d3w2A8fa2pq0tDTCwsKoWrUqAA8fPqRSpUpYW1uzfv16hBAoKSkhhODGjRuMHj1ainv9+nW6desG5HT/RkZG5ru89EskJi2Z3yOD8/VLy8okMDbyizNwZP4bqGoWfsurany8R8KbD1pAGn4oChUVFWbPno2zszNpaWno6enRo0cPHB0dC02jML+jR4/y8uVL6WGe2yN88uRJbt68CcCYMWMYNmwYCQkJ6OvrM2nSJIUPMn9/f0aPHi3N7VFWzpnS6OTkpPAs0tfX59mzZ3mWkb/50VYYDx8+JDk5mZo1a0puZmZm0lCdoaEh5cuXV/jCr1q1qrQM3c/PT3LX1NRk2rRpBAYGSi+ojIwMsrKysLW15ejRo/zzzz8KeeXmFxwcTOnSpVFTU6NatWqSX+nSpdHT0+P58+eUL1+e2NhYMjMzUVXNaUvR0dFoampSsmRJFixYIA2B5OLs7MzNmzfp3Lkzffr0wcDAgO+//14hzJsvuNx2lFu/TZo04a+//uLFixdoa2tz6NAhhXoqqN0ZGhoq6EZXVxfIMabOnj1LdHQ0pUuX5tKlS5QuXVoyBopqx2/75+ohPT2dP/74A0dHRymvsmXLoqenR2xsLOXKlStUd5Bj7A4fPpzHjx/z66+/UqVKlTz5FAd1dXWFaR4aGhoYGRkRFRUlueW25zfLpKamRmZmJh07dpTex2/n/88//yj46ejoYGxsrLCVwKfis00yrlatGk2bNmXGjBkEBQVx4cIF1q1bR58+fWjTpg2vX79mwYIFhISEsGDBAlJSUmjbti0Affr04eDBg/j6+hIUFISzszNNmzbNd/XBl0hGduETqOLT81+KKiNTFKq66pSokb9xXKJGGVR1865i+hDUqFGDR48ekZCQILndu3evWHG3bNnCunXr0NLSQk9PjxcvXnDv3j0cHByoXLkyKioqCuP0Z86coWvXrhgbGxMXF0dkZKTkt3XrVsaOHctvv/3G4cOH8fPzw8/PDycnJ5ycnKQX3pEjR1iwYAHq6uro6+uTmpqKv7+/NAz24MEDxowZQ6NGjVi+fLnCS05XVxdjY2Ppp6uri7W1Nf/884/Cy/369evF+ijLNYLeNJBy9+QxMjLC2tqap0+fKug292MRUJClXLlyeHt7c/ToUansvXv3xsLCAj8/PwwNDTE0NMxjjD18+BAjIyNUVVUxNzdX0PerV6+IjY2lUqVKmJmZoaqqKq24yi2npaUlysrKlCtXTkGexMREjhw5wrJly5g4cSItW7YkPj4eUDQUHjx4IP1/+/Zt1NTUMDIyIjQ0lEGDBiGEwNDQEGVlZc6fPy/VU40aNRSGMhITEwkPDwdyXshvyqKvr09cXBx9+vQhNjYWAwMDVFVVOXfunLTa9+30srOzFXTxtv+TJ094/TqnJ15ZWZnp06crLOt+9uwZsbGxVK9evUjdZWdnM378eJ48ecJvv/0m9QLlYmxsrCBLbGws9erVy7OfjRCCFi1aKOzvkzsn9k3D9W2d5xrRVatWJTw8XEF3v//+u7Say9DQkNDQUClueno6T548kYaJPyWfdaM/b29v/ve//9GnTx9cXFzo168fAwYMQFdXl7Vr10q9NAEBAaxbt076QrG1tWXevHmsWrWKPn36UKpUKWky19dAKXVN/qeTdzJeLnb6n76hyHwdqGqrUaWbWR4jp0SNMlTpboaqdvF6Vd6VBg0aUKFCBWbOnEloaCj79+/n2LFjxYprZGTE+vXrpdUfEydOpEmTJpiYmKCrq0uXLl1YsGABt2/f5s6dOyxbtox69epRo0YN6tWrx8yZM7l//z7+/v6sW7cOR0dHKlWqpPCA1tHRkb40AapUqcKuXbs4deoUjx49YurUqVSoUEEa0nJ3d6dChQrMmDGD2NhYoqOjiY6OztM7kYuDg4MUPjg4mHXr1nH79u1CJwLnUr58eRo1aoSbmxuBgYHcvXsXd3d32rdvT5kyZWjQoAFVq1bFxcWF0NBQjh07hq+vL3369Mk3vbeNjFKlSqGpqYmxsTGqqqr07NmTP//8ky1bthAREcGWLVu4ePEiffv2BWDIkCH89ttvHD9+nNDQUFxdXTEzM8PKygotLS26dOnCnDlzpPlPmzZtYuDAgfnKoq6ujpaWFqdOneLJkydcuHCBefPmASjMs1y2bBl///03t27dYv78+fTu3RstLS0qVapEaGgoK1as4MmTJ6xfv57r169LGxf279+f48ePs2fPHkJDQ3F3dy+wjgD09PRITk5m8eLFRERE4Ovry759+xg+fDgAvXr1IjAwkNWrV/Pw4UMWLVqksJdR//792bp1KydPnuTBgwfMnDlT6glRVVXl+++/Z+nSpVy7do3AwECmTJlC8+bNqVGjRpG627t3L/7+/syfP5+SJUtKbS4uLg7I2XDw119/5cyZM4SFhTF79myMjIzyGBZKSko0bdoUHx8f/P39CQ4OxtnZmfLly9OkSRMpnIeHBwEBAVy6dIkVK1YwePBgAPr27UtgYCDLli3j0aNHHD58mKVLl1KxYkUgZ/hyzZo1/PHHHzx8+BA3Nzd0dHRwcnIqUO8fDfGNkpmZKa5duyYyMzP/k+kdCg8Uypum5vn1P7ddxKQmfZA8PgUfWi9fE++jm5SUFHH37l2RkpLyr/LOSEoXKVGJIuFxnEiJShQZSen/Kr3i8PjxYzFw4EBhaWkpevToIX766SfRv39/hTCXL18WJiYmIjExUWRnZ0vua9asEY6OjsLe3l5Mnz5dJCQkSH4JCQli+vTpws7OTtStW1fMnTtXpKWlCSGEiIqKEmPHjhXW1tbC0dFRrFixQiHdXFxcXISLi4uC2969e0WzZs2Era2tGDt2rIiKihJCCPHixQthYmKS72/fvn0Flv/Ro0eiX79+wsLCQrRv315cunQp33ArVqzIo5e4uDgxffp0Ub9+fVG/fn3h5uYmEhMTJf/nz5+LUaNGCSsrK9GkSROxY8eOAuUoTn5nzpwRnTp1EjY2NqJr1655ZN29e7do1qyZsLKyEsOHDxeRkZGSX3JysnB2dhY2NjaiYcOGYvPmzYXmf/r0adGiRQthZWUl2rVrJ3x9fYWjo6M4fPiwEEIIExMTsWnTJtGsWTNhY2MjZsyYodD+b968Kbp27Sqsra1Ft27dxPXr1xXSP3TokHBychI2NjbCw8NDdO3atdB6Cg0NFf379xfW1taiffv24uzZswr+Fy9eFO3btxeWlpZiypQpYtSoUWLFihWS/+bNm4Wjo6OoXbu2WLt2rXBwcBCXL18WQgiRlpYmfvrpJ+Ho6ChsbW2Fi4uLQlsuTHdDhw7Nt83l1l12drZYs2aNaNiwobCxsREjR44UT58+FdnZ2cLb21v069dPSis1NVV4enoKR0dHYW1tLUaNGiWePXsmhBAiIiJCmJiYiO3bt4v69esLe3t74eXlJbKysqT4ly5dEl27dhXm5ubCyclJ/Pbbb5JfZmamWLt2rXBychJ2dnZi2LBh4vHjxwXquyAKe9YV99mpJIT4Jo9MzMrK4tatW9jY2HywVVQfMr3XGancinnKj1ePcP3lEypql8TZshk9q1pTTqvEv07/U/Gh9fI18T66SU1Nleatvc8qqi8BIQTJycnS5EyZ/0PWTcHIusmfL1UvhT3rivvslLek/Y9SUk2TxuWrc7TlcFKyMlBRUqKCVskvqoHKyMjIyMh8LmQD5z9OWU2dogPJyMjIyMj8C4QQ0uahSkpKCquovlRkA0dGRkZGRuYbJisri9TUVGmHdDU1NXR1dVFRUfmiDZ0vV3IZGRkZGRmZf0VWVhYJCQkkJSVJPTgZGRnSnjxfMrKBIyMjIyMj842SnZ1d4DmOiYmJ73Xm3X8F2cCRkZGRkZH5RinskOrMzEzZwJGRkZGRkZH58ihqZe6XvHJXnmT8lSCEIDY5A5SgjPbH2W5fRkZGRubr4s0zqd4mv8NgvyRkA+crICIuhcP/PGfLtQiUlZQYXb8KLU0MqFTq69wITkZGRkbmw6CsrEyJEiUUzjKDnANOdXR05FVUMp+PiLgUWq29zPgDgVyLiOfK4ziG7r5F9y1XeBovH8op82UyYsQIfHx8pGs/Pz9at26NnZ0d48aNIzo6Ot94s2bNUogHEBMTw8SJE7G3t6dly5YKhwwC3Lp1i969e2Nra0vr1q3x9fV9J1m3bNlCo0aNsLW1xdXVlZSUgu+7AQMGYGpqmuc3Y8aMYpUlLS2NuXPnUqdOHRo2bMimTZvyzSchIYFGjRrlKWthjBw5kunTpxc7fFZWFt7e3jg6OmJra8ukSZN4+fKl5J+r99q1a+Po6MjixYsLXZUzffr0Yue/YcOGdzrbaP78+Xl0vm3btmLHfxeKU47t27fTtGlT7OzsmDhxonSe1JvExcXRoEGDPIdlFsaAAQPytJk3EULg7e1NvXr1cHBwwMvLC8g5TbxMmTLExMQwdepUWrduzaBBg7hw4YJC/HPnztG5c2dsbW3p2LEjv//+u+SXX7s2NTWVDrANDw9n2LBh2Nra0rRpUzZs2FDscr0vsoHzBSOEYO/tZ9yPTszjdyUingsPX30GqWRkPiwXLlzA1dWVAQMG4Ovri7a2NiNGjMgz+XH9+vV5jBMhBOPGjeP58+ds3boVV1dXfvrpJ06dOgVAdHQ0I0aMwMHBgQMHDjBx4kQ8PDwUTnwujJMnT7Jy5UrmzZvHr7/+SkBAAIsXLy4wvI+PDxcvXpR+q1atQk1NTTrIsrCyAHh5eXH37l22bNnC7NmzWblyJSdOnMgTbvHixbx48aJYZQA4evQo58+fL3Z4gHXr1nHs2DGWL1+Or68v8fHxODs7S/7Tpk0jMTGR3bt38/PPP3P06NEP8lKLiIhg5cqV7xQnNDSUqVOnKui+e/fu/1qW9+HYsWN4eXkxY8YMdu3aRWRkpHTAaC7x8fGMHj2amJiYD5r35s2bOXLkCCtXrmTFihUcPnyYzZs3o6ysTFZWFuPHj0dHR4c9e/YwfPhwpkyZwu3btwEICgpi/PjxdO/eXTqFftKkSdIJ5m/q9uLFiwwfPpxKlSrRvHlzsrOzGTlyJKVLl+bAgQPMnTuX1atXSyeQfyyKPUT1Lg1q/Pjx7yWMzLvxMimdzVciCvRfezmcdmblKKkpj0TKKJKZmkRG8muy0pNR0dBGTaskqv/RXbO3bdtGx44d6d+/P5BzynGTJk24dOkSjRo1IjExEVdXVy5fvkyFChUU4gYGBnLz5k3OnDlD5cqVqVWrFsOHD2fjxo20atWKM2fOULZsWX744Qcg5xRxf39/Dh8+TNOmTYuUbevWrQwaNIhmzZoBMHfuXIYNG8aPP/6IlpZWnvB6enrS/1lZWSxbtozhw4djafn/2DvPqCiSr40/Q5AgSBRUYBFQkJxEUBEFc0DBHHcVA+awBlBJgqiIa0JXzFlEUFDEHNesIKgYUJIkQSTnSfV+4KWXdmYAUXdd//07Zw5MVXeF2z1dt6tu3WsKAI32paqqCpGRkQgJCYGxsTFMTEzw7t07HD9+HIMGDaKOi4uLw8OHD9G2bdumhYu6mYKNGzdSbWguPB4PK1euhI2NDYC62YN6ObLZbKioqGDBggVUhPaBAwciPj7+i+oQhq+vLwwNDZGfn9/sc1JTUzF9+vRmy+R7snfvXsycORMDBw4EAKxYsQJr1qwBj8eDuLg44uLi4OHhgdatv/3v8ciRI9RsJlCnhG7btg3Tp0/HzZs3UVxcjODgYMjLy6Nz585ISEjAoUOHsHnzZpw/fx52dnZUdHNtbW3cuHEDFy9eRJcuXWiyzcrKwtGjRxEaGgp5eXl8/PgRhoaG8PPzg5ycHDp27Iju3bsjPj4ezs7O37yf9TR7BufRo0fU58GDB9ixYwdOnTqFFy9e4M2bN4iOjsauXbvw8uXL79ZYhi+D9f8fBoaGsMuLkHopFEnHVuP1qUAkHV2N1Eu7wS7/vjN+Hz58wOzZs2Fubg4nJyfs2LEDPB4PAHD16lUMHDgQFhYW8Pf3p9KBuoelmZkZ9V1aWhq//PILEhMTAQDZ2dmora3FmTNnoKWlRaszKysLysrKtHQDAwMkJSWBw+GgV69eWL9+vUBbKyrqZkXZbDbWr1+PXr16wdjYGE5OTggPDwdQN8C/ePGCGiwAwMLCAhwOh3qrbYwzZ86gtLQUM2fOpNIa68ubN2/A5XJhbm5OpVlbW+PZs2fUbBabzYa3tzd8fHwaNR5tSFBQEEaMGIFOnTrR0gkhCA0NhZOTE0xMTGBvb0970Z0/fz769+8PoG45KiIiAt26dQNQZ7i6adMmSrl59+4dbty4QeWLoqKiAnPnzoWpqSmcnZ3x8OFDWn50dDSqq6sxevRogXNfvHgBNzc3WFhYYODAgYiNjaXKzM/PR8eOHZslDwCIj4/HhAkTYG5uDgsLC8ycOZM2IxYREYFBgwbBxMQEtra2lILSVD8qKirw6tUrSm4AYGNjg/Pnz1NBI+tnl4QtNTV1TQAgLy8PkydPhqmpKcaOHYvk5GQAQH5+Pj58+EAppEDd/ZOTk4OPHz8iKysLurq6kJf/O5izgYEB9TtzdXXFsmXLBNr0ue0OAGzfvh3du3dHjx49AABqamrYunUr5OTkQAhBfHw8njx50uT98LU0W8E5evQo9enSpQvGjh2LGzduYPfu3dixYweuXr2KyZMnQ1ZW9nu2l6EBqq1bwa3bLyLzZ9lpQ56ZvWFoALemEunXD6Isk/4iUpaZhPTrh8Ctqfwu9RJCMH/+fKioqCAqKgrr169HTEwMQkNDkZKSgsWLF2PChAk4ffo0uFwu9VAFABUVFdrgwufzkZ+fj+LiYgBAly5dsHv3bmhqagrUq6qqivLycppdTF5eHrhcLsrLy6GpqQkLCwsqr7CwELGxsejevTuAumWYW7duISQkBJcuXYKLiwsCAgLw6dMnlJWVoba2FmpqatT5EhISUFRURF5eXpPy2LdvH3799Vfam3pjfSkoKICSkhIkJSVp/autraVsOEJDQ2FkZAR7e/tG66/nwYMHiIuLw9y5cwXyoqOjcfjwYQQGBuLSpUuYN28eQkJCBF5it2/fjh49euDp06dCbU8mT56MYcOGQV5eHpMmTWq0PVevXoW+vj6io6PRs2dPzJ8/nxpAi4qKsGnTJvj7+wvs7CksLISbmxv09fVx5swZuLu7w8PDA2/evEFqaipYLBZCQ0Ph4OCA4cOHIyoqSmQbysvL4e7ujp49e+L8+fPYv38/MjMzsWfPHgDA48ePsXbtWvz++++4dOkS1qxZg8jISJo9iqh+ZGVlUX0ZP3487O3t4eHhgbKyMurcxYsXY+7cuUKjZDfnmkRFRWHQoEGIjo6GlpYW5s+fDx6PR9mtNbxfVVVVAdT9JlRVVVFQUABCCJWfl5dH/c709PTQpUsXKu/du3d48OAB9VupJzc3F+fPnxd6TwGAk5MTJk6cSNm8fU9aZINz5swZTJs2DRISfw+eLBYL48ePp11khu8Li8XCKLP26KImJ5Bn+4sieumq/AutYviR4VSVCSg39ZRlJoFTVSY072t5+PAhcnNzERAQAF1dXdja2sLDwwNHjhzB6dOn0bVrV0ydOhV6enrw9vamTXcPGTIEYWFhSEhIAIfDQWhoKAoLC8HhcJqs19zcHGpqaggICEBVVRXev3+PgwcPAoDA+TU1NViwYAFUVVUxbtw4AHUKR2BgICwsLKClpYXZs2eDw+EgIyMDNTU1AAS32bZq1apR52lA3Yx4Xl4exo4d27Tw/p/q6mqhdQF1MzcpKSk4efKkUINlYdTW1sLX1xc+Pj6Qlhbccdm+fXusX78e3bt3h6amJiZMmIC2bdvi3bt3tONGjBiByMhIdO/eHW5ubtTsVz1eXl44cuQIOBwOtYQlChMTEyxevBh6enpYsWIFFBUVcf78eQDAunXr4Orqis6dOwucFxsbCwUFBaxYsQK6uroYOXIkli5dipqaGqSlpYHFYkFXVxd79uzBmDFj4O3tjatXrwptQ01NDebOnYt58+ZBS0sL1tbWGDBgANVvWVlZBAYGYsCAAdDU1MSgQYNgZGREk4uoflRW1r1A+Pv7Y+bMmdi2bRvevXtHs11qjOZck379+mHy5MnQ09PDmjVrUFhYiIcPHwq9XxvePw4ODigvL0dISAjYbDZevHiByMhIob+zoqIiLFiwAFZWVujbty8tLzIyEiYmJrSZxoZs374doaGheP36tdDZ029Ji17v1dTUcOfOHejo6NDSr1y5IjCtyvB90VKUweVZdrjwOh8HHmdBXIyFOd214dRZldkmziAAj13VRP732XmXmpqKkpISWFtbU2l8Ph81NTV49+4dDA0NqXRJSUkYGBhQ38eOHYu3b99Sb/8DBw6Eg4MD5OQEFfvPkZKSwtatW7F48WJYW1tDRUUFM2bMwPr162nnV1ZWYu7cucjIyMCJEyco+5l+/frh3r172LBhA9LS0vDq1SsAdctTUlJSAAQ9wbLZbMjIyCA0NBS7d++m0vfu3UstZ12+fBkODg40m5zm9EVYXfV5ixcvxsKFC6m38obk5uZi6NCh1HdnZ2coKCjAxMQEvXr1ElqfnZ0dnj17hj/++AOpqal4/fo1CgoKBIy765ehNm7cCAcHB1y5cgUjR46k8uvf+tetW4fRo0cjOzsbe/bsoRmY1i8nNVyKFBMTg6GhIVJTU3Hnzh0kJiZi7dq1Qtuanp4OQ0ND2pbmadOmAahTch0dHSlZd+nSBRkZGQgLC4OxsbGAXPz9/eHi4oJDhw7h9evXSElJQXJyMqysrADUKS/S0tLYvn07lff+/XvarJmoftTf57NmzaIUg8DAQLi4uCA/Px/q6upC+1dPc65Jw7rr7V3S09PRrl07AHX3zOf3royMDFRUVLBlyxZ4enpi165d0NTUxOTJk3H48GFaGz59+oRp06aBEILt27cLbCO/fPkyxo8fL7IP9bZetbW1WLZsGVasWNHs5dQvpUUKzrJly7BkyRLcvHmTunlfvHiBpKQk7Nq165s2kKFptBRl4N69I8aYdQCLBSgxjv4YRCDeqvElZPFWgoax3wIulwtdXV38+eefAnmBgYG0aXEAtGUYcXFx+Pr6YsWKFaitrYWioiJGjx6Nnj17NqtuMzMz3Lhxg1riuXfvHpSUlKiloYqKCsyYMQOZmZk4fPgwzVZjy5YtiIiIwMiRI+Hi4gJfX19qe7KioiKkpKTw6dMn6OnpUf0sKSlB27Zt0a1bNwwePJgqq+HgdefOnS/ejKGuri4QALGgoADS0tKoqqpCQkICkpOTERQUBKBuxsfX1xcXLlxAaGgotV0XqBv4xowZg0+fPsHS0hLA34Pd5cuXkZCQgIiICKxbtw5jxozBgAED4OHhQRmYAsDNmzdhZGRE9UtKSgpaWlooLi5GRUUF/vrrLwwaNIgaAOttfIqLi7Fo0SJMnz6dKqt+2eTzZZn6yNYXLlxAXl4etRzC5XLB4XBgaWmJvXv30lYTPofFYgkokrq6unj48CHU1NQE5JKfn49Ro0bB2NgYPXr0wNixY3Hr1i08e/YMQN21mzdvHlxcXNCrVy/MmzcPa9asoZUvqh/1M5O6urpUXv1EQV5eXpMKTlPXRFjdhBBISkpSMi4oKKCWQOuXrerb1bt3b9y/fx8FBQVQVVVFWFgYNDQ0qLLy8/Op+o4cOQJlZWVaXR8+fEBKSorArM6nT5+QmJiIfv36UWmdOnUCh8NBRUWFQDnfihYpOP3790d0dDROnz6NtLQ0AHXGdevWrcMvv4i2CWH4vii3ZhQbhsaRlG2DNr+YoCwzSSCvzS8mkJRt813q1dHRQW5uLpSVlSkjxnv37uHMmTPQ09OjBg+gbjB4+/YtjIyMANT5mWGz2Zg1axZkZGTw8eNHvH79GuvWrWuy3pKSEsyZMwd//vkn9RC/desWZdzI5/Mxf/58ZGdn4+jRo5SiUs/Jkyfh5+dHKSopKSkA6gYNMTExmJqaIj4+Hra2tgDqfOpISEigS5cukJGRETpDU1RUhKysLNpsVnMwNDSEhIQEXrx4QSl38fHxMDU1hbq6OrX1vZ4pU6ZgypQpGD58OCQkJKiZlnqOHj1KU5Y2bdoEAJQhaVhYGObNm4cZM2YAAMrKylBYWEgpo0FBQXB1dYW7uzuAOkUxIyMDenp6qK6uxpIlS9C+fXtKgXr58iXExcWho6MDOTk5qKgILqHXG8QCdUrMq1ev0KdPHzg5OWH27NlU3pUrVyibUHV1dSQnJ+P27ds0RXnx4sUwMTFBZWUltRuonjdv3kBXV1eoXI4dOwYFBQXa7NvRo0epsiMiIjBq1Cj4+vpS7czMzISdnV2T/ejQoQPU1NTw5s0bagmn3kaoQ4cOAvL4nKauCQC8ffuW+r+srAwZGRno2LEj1NXV0aFDB8THx1MKTnx8PNWm1NRU+Pv74+DBg5QydPv2bererqqqwowZMyAmJoYjR44I3ZH27NkztG/fXqAv2dnZmD9/Pm7fvk0pcUlJSVBWVv5uyg3wFZ6MO3XqBA8PD5SWlkJOTg5iYmL/aZfODAz/C0hIt4ZO36lIv36IpuS0+cUEOn2nfret4vb29tDQ0MDy5cuxZMkSlJeXw9vbGz169MD48eNx/Phx7Nq1CwMHDqR8g9SjqamJlStXwszMDCoqKvD29kbv3r2hr6/fZL2KioqoqqpCcHAw5syZg4cPH+L06dOUk7fIyEg8evQIu3btQps2bag3WklJSSgqKkJRURE3b96EiYkJ8vPzKaWqfrZj4sSJ8PHxgb6+PtTU1ODn54exY8cK3SJez7t37yAlJSXUkLgxZGRk4OLignXr1mHDhg34+PEjDhw4gPXr1wsdqCUkJKCioiJyVqDhmzkAakarvhwlJSU8ePAAffv2RWVlJbZs2QIOh0P1fdKkSQgJCUGXLl3QoUMHbN68Gb/88gscHBwgJiaGAQMGICAgAGvXrkVVVRVWr16NyZMnN7q0GBcXh127dmHAgAE4evQoOBwOhg0bRi2h1KOiokLrs7OzM7Zt24atW7di0qRJSEhIwPXr1+Hu7g4Oh4M9e/Zg//796N+/P+7evYvo6GgcOXJEaBsUFRWRm5uLBw8eQFNTExcvXsSVK1eopRVFRUVqtkxMTAy7d+9GQUEBbflQVD9YLBamTp2K7du3Q1NTEyoqKvDz80O/fv2atYW9qWsCADExMbC0tISVlRW2bt0KbW1tSqGfMGECNm3aRC1X/fHHH3BzcwNQdz+kpqZi+/btGDVqFM6dO4f4+Hj4+fkBAHbv3o3MzEwcPXoUwN+zP9LS0tRLy7t37wReEoC6ZSljY2OsWrUKK1euRE5ODoKDg2lK6/egRQpO/Va1Q4cOoby8HJcvX8a2bdsgKysLLy+v77aexsDA8PW0kleG3iD3//eDUw3xVjKQlP2+fnDExcWxa9cuBAQEYOzYsZCVlcWgQYPg4eEBaWlp7Nq1C+vXr8euXbvQt29f2vJTv379kJqaimXLlqG2thb9+vXD6tWrm133li1b4OvrC2dnZ2hqamLbtm2UncLly5fB5/OpWYh6unXrhqNHj2LdunXw8/PD0KFDoa6ujjFjxkBcXByvX7+Gg4MDhg4dipycHPj4+IDNZmPAgAFYvnx5o+0pLCxEmzZtWvRC6OnpCW9vb/z222+Qk5PDggULMGDAgC8upzmsWrUKq1atwogRI6CiooLBgwdDRkYGr1+/BlCn4FRXV8PPzw9FRUXo2bMndu3aRS1JrVu3DuvWraNsYVxcXLB06dJG63RxcUFcXBx27twJfX197N69u1FlsZ42bdogNDQUgYGBOHnyJLS0tPDHH39QNi/btm3D9u3bsW3bNmhoaOCPP/6gZpY+Z/DgwXjy5AkWLlwIFosFU1NTeHh4UMa38+fPx8qVKzFu3DjIycmhd+/emDBhAiWXpvrh5uaG2tparFixAlVVVXBycqKUiKZo6poAdTN3kZGRCAgIgKWlJUJCQqh7bfr06SgsLMT8+fMhLi6O0aNHY+rUqQDqFJV6p5WHDh1C586dsW/fPmo25vLly6ipqcGYMWNobXJ1dcWGDRsA1C1FKSgoCLRbXFwcf/75JwICAjBu3DjIyMhgypQpAstr3xoW+Xzxuxns2LEDsbGxWLFiBZYsWYKYmBhkZmbCx8cHjo6O8PLy+h5t/abweDwkJibCwsJC6Ha8f7u8nwVGLqJpiWxqamqQnp4OHR0doTtffgYIIaiqqoKsrCwzK/wZjGxEw8hGOP9VuTT2rGvus7NF28SjoqLg7+8PR0dHSmA9e/ZEUFAQLl682JIiGRgYGBgYGBi+GS1ScAoLC2nOgupp06YNqqoa34bKwMDAwMDAwPC9aZGCY2dnh/3799PSKioqsHnzZsri+n8Zbm0VaorzUPo+CRUfUlFbXgRC+E2fyMDAwMDAwPBNaJGRsZ+fH+bPn4+ePXuitrYWc+fORW5uLjp06CDUz8X/ErzqcuQ+ikbByzsA6sybJGTaoLPzIrRW0wZLrEU6JQMDAwMDA8MX0CIFp127doiMjMSDBw+QlpYGLpcLHR0d2NvbC3g1/F9CTq41CpMfoODlX7R0bnUZkqOCYTJpDaTa/PvRbBkYGBgYGH52WqSN/PrrrygrK0P37t0xadIk/Pbbb3BwcEBJSQnNRff/GiryMsiLF25kzefUoDw39R9uEQMDAwMDw/8mzZ7B+euvv/D8+XMAwJMnTxAaGioQOfz9+/fIycn5ti38DyHGArg1FSLza4o/iMxjYGBgYGBg+HY0W8HR0dHBvn37QAgBIQRPnz6lxYthsVhUlNX/Vbg8glbyKmCXFwrNb63e8Z9tEAMDAwMDw/8ozVZwtLS0KNfWK1euxOrVq5sVzfd/icLyamh0d0X6lX0CeZKtFSHbVlvIWQwMDAwMDAzfmhbZ4Kxfvx7S0tLIz89Hbm4ucnNzkZOTg/T0dFy4cOFbt/E/Q1VVFdr8YgKtXhMg1upvz4ut1TrCYOQKSMl/v6BiLYXwuOAUZYNdkA5uSd6/3RyG/xFmzZoFT09P6vvdu3cxfPhwWFpaYtq0acjIyKAdf+nSJQwcOBAWFhZwc3OjLYXn5+dj4cKF6NatG3r16oX169ejtraWyg8JCYG1tTW6du2KigrRS8if8+zZMxgaGiI7O1sgjxACNzc3nDlzptEyPD09af38Umpra7Fq1Sp07doV9vb2OHDgAC0/KSkJ48aNg6WlJcaOHYvExMRml71mzRpMmTJFaF55eTl69epF6x8hBCEhIXBwcICNjQ0WL16MoqIiKv/Dhw9wd3eHlZUVnJycaMEtW0JjfT9z5gwV1V3UuXPnzoWZmZnIPr569QoGBga0T0Mb0sZkK6z806dPY9CgQbC0tMSYMWMQHx9Pq+/48ePo06cPrKyssHDhQpSUlAi0icvlYsSIEQgJCaHSnJycBNppYGCAHTt2AAAMDAzw6NEj0YL8jIb35JQpU4SWvXLlyibLqayshJeXF+zs7ODg4IA9e/Y0uw3/GKQFXLt2jdjZ2ZEuXboIfHr16tWSIv9xuFwuiYuLI1wu95uXx+NySE3pJ1LxMYNUFeYQdlXZN6njW8MuziX5p73J69kK5OWvIO9W6JOyp+cIt6L4m9XxreX8M9ES2VRXV5NXr16R6urq79iy78v58+eJvr4+8fDwIIQQ8vbtW2JkZES2bt1KUlNTSVBQEOnZsycpLy8nhBASHx9PjIyMSFhYGElNTSXu7u5k7NixhBBC+Hw+GTt2LJkxYwZ5+/YtefLkCenfvz/ZsGEDIYSQkpISoq+vT8LDw0l2dnaz28hms8mwYcOIvr4+ycrKouXxeDzi7+9P9PX1yenTpxstx8PDg+pnS/D39yfOzs4kKSmJXLlyhVhaWpLo6GjC5/PJp0+fiLW1NfHy8iIpKSnk4MGDxMLCguTk5DRZbnx8PDEwMCCTJ08Wmu/t7S3Qv7CwMOLg4EAePXpEkpOTyYQJE8js2bOp/LFjx5LFixeT9PR0cvXqVWJubk6uXLnyTft+8eJFQgghp0+fJo6OjrTj+Xw+qaioIHw+n1y9epWYmJiQFy9ekPz8fKHlnz17lowYMYJ8/PiR+hQVFRFCSJOy/bz827dvEzMzM3L27FmSkZFBtmzZQqysrEheXh4hhJDY2FhiZmZGLl26RJKTk8no0aPJkiVLBNq0e/duoq+vT7Zv306lFRYW0tp49OhRYm1tTd3P+vr65OHDhyLl2FAuhNDvyeLiYlrZV69eJcbGxuT58+dNXp8lS5aQAQMGkLi4OPLkyRPi6OhIDhw40OR5zaWxZ11zn50tmsHZtGkT+vfvj9jYWLRp0wYnT55EaGgoNDQ0sHjx4m+sgv33EBOXgFQbFbRuqw0Z5Q6QlJH/t5skALf8Ez4cmIlPZwPAryoFALDz3iJr63BUJF0G+fIQZQz/IQibB1LOASli1/1l8/6RektKSrBx40YqMjMAhIWFwdLSEosWLYKuri6WLVsGOTk5xMTEAAAOHDiA4cOHY/z48dDV1cXq1atRUFCAoqIipKWlITExEevXr0fnzp3RtWtXLFy4EOfPnwcAasame/fuAtGzG2Pfvn1Cl+Dz8/Px22+/4caNG2jTps3XiKJJqqqqEBERgdWrV8PY2Bj9+/fHjBkzEB4eDgCIjo6GoqIi/Pz8oKenh6lTp8La2hphYWGNlstms+Hj4wMLCwuh+XFxcXj48KFAdOvbt29jyJAh6NatG/T19TFjxgw8fPgQAFBaWorExETMmTMHHTt2RL9+/dCrVy88ePDgm/b9+PHjzTq/vLwcqqqqMDExEep1HwBSU1Ohp6eHtm3bUh8lJSUATcv28/KjoqLg4uKC4cOHQ1tbG4sXL4aqqipu374NANi7dy9mzpyJgQMHQl9fHytWrMDbt2/B4/39u3v//j2OHDmCTp060dqprKxMtU9aWho7d+6Eh4fHF93PolBUVKTKVlZWxpYtWzBjxgza71MYRUVFiI2NxZo1a6jZ0WXLlgk4AP63aZGCk5WVhRkzZkBXVxcmJiYoKChA79694evri4MHD37rNjJ8B7jFOah4Fis0Lz/sd3CLc//hFjH8U5BqLvhxxeBd/wjeXwXgXf8IflwxSDX3u9cdFBSEESNG0B7iWVlZVHRvoG7DQqdOnaglgcePH6N///5UvpaWFm7cuEE9+Pft2wdVVVVaPRUVFcjOzqaWMfr160dNy8fFxWHkyJEwMzODs7MzLl++TDs3PT0dx48fF7q09PLlS7Rv3x6nT5+GvHzzXlwqKiowd+5cmJqawtnZmVIKgL+X12xsbGBiYgJXV1dqaePNmzfgcrm0qNdWVlZISkoCn89HVlYWjI2NacEGDQwMmlym2rNnDwwMDGgR2+ths9nw9vaGj48PWrVqRctTVFTErVu3kJ+fj5qaGsTGxlLRuqWlpSEjI4MzZ86Aw+EgLS0NT58+pfKF8aV9t7a2xrNnz8Dn/+0VfvPmzbCyskKvXr1w9OhRAHXLV56ensjNzYWBgYHIZcTU1FR07NhRaF5jshVW/owZM6io6Q0pLy9HRUUFXr16RbuHbWxscP78eVr5Pj4+WLBgAZSVRZsy7N+/H23btsWoUaNo6U+ePMGAAQNgbm6ORYsWobS0lMqLi4vDhAkTqLzq6mqhZZ85cwalpaWYOXMmlVZVVQUfHx/Y2trC1tYW3t7eqK2tpZZtzc3NafIpKCgQuqT7b9EiBadNmzaUkHR0dPDmzRsAgK6u7g/VuR8RwueBV10IXm3Zv9qOmsxEkXnc4lzwq0tF5jP8dyFsHvgJJSAfa+npH2vr0r/jTM6DBw8QFxeHuXPn0tJVVVWRn59PS8vPz0dxcTHKyspQWloKHo+H6dOno2fPnpgzZw51fJs2bdCrVy/qPD6fj2PHjsHOzg7t27dHREQEAFCzAQUFBXB3d8fIkSMRExODGTNmwNPTE3FxcXVyIIQaaFRUVAT64OTkhI0bNzY6CH3O1atXoa+vj+joaPTs2RPz589HeXk5AGDZsmXg8Xg4efIkoqOjoa6uDj8/PwBAQUEBlJSUaIqGqqoqamtrUVJSIlRueXl5KC4uFtmW1NRUhIWFibSxCA0NhZGREezt7QXy5s2bBwkJCTg4OMDKygpxcXHYvHkzAEBKSgo+Pj4IDw+Hubk5Bg8eDAcHB4wZM0ZkW76m7wCQk5OD5ORkhIeH4/fff8fGjRsRFxeHIUOGYNWqVWjXrh3u3r2LIUOGiJTF69ev4ezsjD59+sDHx4ea8WtMtsLKNzY2pilLf/31FzIyMmBnZ4esrCwAdbMe48ePh729PTw8PFBW9vcYcPr0adTW1mLs2LEi5VVdXY1jx45h9uzZAg51jx8/jtWrV+P48eNIT0/H+vXrqTpnz54NW1tbREVFoVOnTrh06ZJA2YQQ7Nu3D7/++itat25NpXt5eSE+Ph5//vknDhw4gPj4eGzdupX6bTSU0YcPdW5QGrv//mlapOD07t0ba9asQUpKCmxtbXH27Fm8fPkS4eHhIqcDGQBOaQZKHq9D3pnB+BgzElVpseBWffxX2iIuryo6k8UCS0Lqn2sMwz9HLV9AuamHfKwFar9PzLTa2lr4+vrCx8cH0tLStLzBgwfj8uXLuHnzJrhcLqKiovDq1StwOBwqeO/atWvh7OyMXbt2gc1mw93dnfYmX09wcDBevXqFJUuWQFxcnFJElJWVIS8vj+PHj6NHjx6YPHkytLW1MWLECIwbNw6HDx8GAERGRoLD4TQ60HwpJiYmWLx4MfT09LBixQooKiri/PnzIISgX79+8Pb2hp6eHjp16oRJkyYhJSUFQN2A9vksSv13NpuNAQMG4Pnz5zh16hS4XC7u3LmD69evg8PhCG1HQ+Xt8xkvAEhJScHJkydFKj85OTmQlpZGaGgojh49inbt2mHVqlVUfmpqKhwdHREeHo7169fj0qVLOHfunMi2fE3fgTqlasOGDejcuTNcXV0xbNgwREZGQlpaGvLy8hAXF6eWdT6Hw+EgKysLHA4H69atQ2BgIJ4+fYrly5cDQKOybar8zMxMrFy5Es7OzjA2NkZlZSUAwN/fHzNnzsS2bdvw7t07rFixAkBd8OrNmzfD398fLBZLqLwA4MKFC5CVlcWAAQME8ubPn4/evXvDxMQEXl5eiImJQUVFBS5evAhlZWVq+XfBggVCl58ePXqEvLw82n1fWlqKS5cuwcfHB9bW1jA2Noa/vz86dOgADQ0NWFhYIDAwECUlJSgoKKCMnkXdf/8GLQrVsHr1agQGBiIpKQkjRozA5cuXMXr0aMjKyiI4OPhbt/GngFOSgtyTvcCv+dtHTk32X2jdZSJUem+CuEwjCsd3QErDBKxWsiBswejvcubDIC7PhJT4KeE0YVvVVH4L2bFjB0xMTGizLfU4ODhg3rx5WLBgAXg8HmxtbTF06FBUV1dTU/hjxoyBi4sLgDobwJ49eyIxMRFWVlZUOcHBwTh8+DC2bNkCfX19oe1IS0vDzZs3aUsfHA4HOjo6KCgowJYtW3Do0KFGBxpR+Pj4UHZDABAbW7cE3HD5TUxMDIaGhkhNTQWLxcKECRNw4cIFPH36FOnp6dTyE1A3gNcP5vXUf5eWlkb79u0REBCAtWvXwtfXF4aGhpgwYQK1o+bz5Z1+/fqBx+Nh3LhxAm0nhMDLywsLFy4UqvwQQuDh4YEVK1bA0dERALB161Y4Ojri2bNnqKqqQmRkJG7fvg1paWmYmpoiPz8fu3btwvDhwzF06FDk5tYte3fo0AGxsbFf1Xegbqmy3mYGAIyMjHDq1Cmh1+ZzWezbtw8PHz6ElJQU5c9tw4YNGDVqFPLz86Gvr9+obEWRnp6OadOmQUtLC2vXrgUASEjUDbOzZs1C3759AQCBgYFwcXFBfn4+goKCMHLkSJH3bD2XL1/GkCFDqPIa0lBpMTIyApfLRWZmJlJSUmBgYEC7n01NTQWWqS5fvgwHBwcoKipSae/fvwePx4OxsTGV1rVrV3Tt2hUAsHHjRixcuBB2dnaQl5fH77//joSEhB/KfUyLFBw5OTlqCgyoe+D4+fnRbhaGv+FzKlH8wJ+m3NRT+eYEFCzn/+MKjqRiB2gtOYesP4aAcP9+kEiq6aLd5G0Ql60zoCR8HgivFiwJabBY/7txxn4aJJsYuJvKbyGxsbH49OkTNdDUD1aXL19GQkIC5syZg+nTp6O8vBzKyspYsGABNDU1oaSkBElJSejq6lJlKSkpQVFREXl5f7s1CAgIQFhYGIKDgzFw4ECR7eByuXB2dsbs2bNp6RISErh79y6Ki4spBaDe0H7YsGGYPXu2wDmfs2jRIkyfPp36Xj+b3dDOAqhbRpOUlASfz4ebmxvKysowZMgQODk5gcPhYP78+QAAdXV1FBcXg8vlUoNaQUEBpKWlKQPnUaNGwcXFBYWFhVBTU8PGjRuhqakJoM5Qth5paWksW7YMSUlJlFLI4XDA4/FgaWmJ8+fPIyEhAcnJyQgKCgJQN4vi6+uLCxcuICgoCB8+fICBgQFVZvv27aGkpIScnBzk5ORAW1ubNpthZGSE0NBQAHV2P1wul5L1t+j758s0hBCR48/nsgAgMBDr6ekBqFt2UVdXb1S2wnj37h2mTp0KLS0t7Nu3j6qn3li74T2so6MDoG7ZKzY2FtLS0jh27BgAoKamBgkJCbh06RKlJLPZbDx+/BizZs0SWnfDe6z+vhUlC0lJSQEF586dO5TsGx7XGNra2jh79iwKCwshLy+PzMxMiImJoUOHDo2e90/SbAWn4Q3SFPVvWgx18GuKUPnutMj8ireRkFLv+g+2CGBJSEJWvxf01r9G5eubYBekobV+L0hpmUJSSQN8TiW4Ze9R9nwvuMXJkOrQA3JdJkCijTZYYi3Sixl+BKTEwFKTErpMxVKTAqS+jxJ79OhRaoAD6l6KgDo7jPPnz+PZs2dYvXo1VFRUUF1djSdPnmDUqFGQkJCAsbEx3rx5Q9lSFBUVobi4mNpFsmPHDpw8eRKbN2/GoEGDGm2Hjo4OEhISoK39t9PNAwcOgM1mY/LkybQZofz8fEyZMgV79uxp8u0aAFRUVITa7SQnJ1P/c7lcvHr1Cn369EFKSgqePHmCBw8eUEtp9buECCEwNDSEhIQEEhMTqbfmp0+fwsjICGJiYnj48CHCw8OxZcsWqKmpgRCCO3fuYPz48QBA62O9zGtqaqjvR48exbNnz7Bp0yaoq6vjypUrtOOnTJmCKVOmYPjw4VBQUECrVq2onUdA3XUoKSmBpqYmOBwO3r9/DzabTS0lpaWlUQrB5zt+3r59+8V9j4+Ph6mpKaXYZGVlobq6GjIyMgCA58+fizQa/lwWKSkpGDNmDM6dOwctLS0AwOvXryEhIQFtbe0mZfs5Hz9+hJubG7S1tbF3716aHUuHDh2gpqaGN2/eUEa59TN4HTp0EJD7smXLYG5uTjNaTk5OBpfLpc0Gfi7P+pmW58+fQ1JSEpqamujcuTNu3bpF2631+vVr2vUoKipCVlYWrK2taWVqaWlBXFwcb968oa7BtWvXsHPnTpw+fRozZsyAh4cHpfTeunULRkZG/80ZnO3btzfrOBaLxSg4Xwr5PnYPTSEm0Qqt1HTRSk2Xls7nsVGdcQUfY8cDqHsbqM68htK4TWg/+hqk2v2zyhjDt4PVShxilooChsYsNSmIWSqB1Uq8kbNbzucDXP0AoK2tjfLycqxcuRI2NjbQ19dHcHAw2rVrBwcHBwDAtGnTsHLlShgaGlL5hoaGMDMzQ2pqKv7880/MmjUL1tbWKCgooOr4fJszAEycOBFHjx7Fli1b4OrqihcvXmDz5s1Yt24d5OTkaA/n+rfiDh060Kbuv5S4uDjs2rULAwYMwNGjR8HhcDBs2DCUlpZCTEwMsbGxcHJywosXLygHb2w2GzIyMnBxcYGfnx/WrVuHjx8/4sCBA/D19QVQp6zdvHkTJ06cQK9evbB//36UlpaKfP6qq6vTvisoKEBaWpoa/D9XAiQkJKCiokKdN3LkSAQFBUFJSQkKCgoICgqCubk5TE1NoaOjg+DgYHh5eWHOnDlIT09HaGgolixZIrQtbdq0aVHfG64c1NbWwsPDAwsWLEB8fDwuX77c7F28urq60NbWhre3N1atWoWysjL4+vpizJgxUFBQ+GLZBgUFgc/nIzAwEFVVVZTtmKysLFq3bo2pU6di+/bt0NTUhIqKCvz8/NCvXz+h96i0tDQUFBRov5l3795BU1NTwC6pni1btqBdu3aQkZHB2rVrMX78eMjIyGDo0KEICQlBcHAwpk6dips3byI+Pl6gbCkpKYHZKTk5Obi4uCAwMBBr1qwBi8XCli1b4ODgADExMUhLS+OPP/7AqlWrkJKSgp07d2Ljxo3Nkv8/RbMVnBs3bnzPdvzUiEkro3XnUahMPik0X87g2xk0fgt4lXkouDwN9cpNPYRbhYLLU9Fu9HVItFYXfjLDDw9LRgJiXZXqDIo5pG5ZSkrsuyk3TWFiYgI/Pz9s2LABJSUl6N69O7Zt20a9qQ8aNAhlZWUIDg5GYWEhunXrhj///BMsFgvXr18Hj8fDrl27sGvXLlq5DWdO6tHQ0EBoaCg2bdqE/fv3Q11dHZ6enhg+fPh365+Liwvi4uKwc+dO6OvrY/fu3ZCRkYGMjAz8/Pywc+dObN68GTo6OvDy8oKHhwdevXoFS0tLrFy5En5+fvjtt98gJyeHBQsWUHYc6urq2Lp1K4KCgrBx40aYm5vj4MGDtNmDb8mqVauwdetWLF26FLW1tejRoweCg4PBYrEgLy+PQ4cOITAwEKNHj4aysjLmzJkj1N4HANq1a9eivjc0sDU0NIS6ujrGjh0LJSUlrFu3DkZGRs3qi5iYGHbt2oXAwEBMmjQJYmJicHZ2pgx/v0S2hBBcu3YNNTU1AjOI8+fPx4IFC+Dm5oba2lqsWLECVVVVcHJyonaMNYdPnz5BQUFBZP60adOwevVqFBcXY/DgwVi2bBmAOiV279698PX1hYuLC2xsbDBixAian7PCwkK0adNGqN3ZqlWrEBgYiGnTpkFSUhJDhgyhlNY1a9bA29sbrq6uUFFRgZeXF20r/I8Ai7TQo1t5eTnOnTuHjIwMzJkzB8+ePUOnTp2o6b4fHR6Ph8TERFhYWAiskX+P8jglqcgN7wV+9SdaeusuE6HiEAxx2e9n1FtYU4lPtZWo5LKh1EoG7WTkISMh/E0AAKqzbiHvtKClfj0akxPQStVYZH5DvrWcfyZaIpuamhqkp6dDR0dH6O6QnwFCCKqqqiArK9siY9+fGUY2omFkI5z/qlwae9Y199nZImOKt2/f4rfffkP79u3x9u1b/Prrr7hy5Qp+//137N69G926dWtJsT81kop66DD+PipeH0NV6jmISbWBgtUSSLWz+a7KTXp5IabdCced/DQAgJS4BBYbOWCxcS+oifCwTPiNb/Mj5J/xesvAwMDAwNBSWqTgrF27FhMmTMDChQupXRHr16+HsrIyNm7ciMjIyG/ayJ8Fbit1SJosgrLJfLSSFIe41PcN4fChqhRDruzD27K/7RJqeVwEvbiB1hKtsMLUEZJCtF9JRT1ATALgC3q2FW/dHuIygoaUDAwMDAwMPxIt2jLx4sULocZW48ePpxw1MfxNdXU1UlJSEB0djbCwk7h09SYKiisF/Dx8a9LLi2jKTUM2Jd3Ch2rh3pTFZdShZOcnJIcFlb47Id76x9kGyMDAwMDAIIwWKTjKyspIT08XSH/69KnQbZL/y7DZbCQkJODy5csoKioCm81Gbm4uTp8+jezs7O8a1PJNqWgvyWWcGlRyhStYYq1aQ950OtRdYiDV3hbirdtDpuMgdBh/DzKaff5T67gMDAwMDP+btGiJaubMmfDy8sLs2bNBCMHDhw8RFRWFw4cPi9wW+L9KVVUVEhIShOb99ddfUFNT+25+A3TkRSub0uISkBEX7chJXEYFsh0HQqqdDQi3BixJOYhLfd/oyQwMDAwMDN+KFik448ePh5qaGvbv3w9paWls3LgROjo6CAgIEBnY7H+VxgKPVVZWora2FnJycvhQVoNKNg+txMWgLt8KUhJfv+OocxtVtJdpI3Qpaqa+HdqJMDJuiLh084MKMjAwMDAw/Ci0SMHZt28fhg0bRnmeZBCNsLghDWGzJBH14gOWn3+FtMIqyLYSh7udNn7vrQcNha/bBqzZWhFXB7nD+ep+pFcUUemjO5rBw8wJ0hJMWA0GBgYGhp+TFik4oaGhjcZ7YfgbBQUFSEhI0NzU19O5sz7+el+GCcf/XsKqYvOw5a80JOSU4uQUa6jJfV1Ub0NFdfw1dD7yq8tQVFsFrdaKaCstByUp2a8ql4GBgYGB4UemRUbGw4YNw65du5CRkfHddwL912ndujX69+8vYJjbqlUr6FvaYtn510LPu5VaiKySaqF5X0oH2TawVNFE3w760FdQY5QbBgYGBoafnhYpOH/99RfOnDmDwYMHw9zcHIaGhrQPw9+Ii4tDS0sLEyZMgJWVFXR0dNCrVy+MGzcONYSFnNIakefGZ5f+gy1lYPhnCQkJwZQpU5p17OnTpzFo0CBYWlpizJgxiI+Pp/LYbDaCgoLg4OAAGxsbzJs3jxZp/OrVqzAwMKB9Fi5c2KI2Z2dnw8DAANnZ2QCAxMREDBgwAKampoiIiGj03Pfv3wsNlnjw4EH06dMH5ubmmD59OjIyMmj5x48fR58+fWBlZYVFixahtPTv50Jqairc3NxgZWUFJycnhIaGgs9vXmy7ffv2wcnJqVnHenp6CsjQwMAAv/76q9DjP5eTMJKSkjBu3DhYWlpi7NixSExMpPKcnJyE1rdjxw4AdfaLXl5esLOzg4ODA/bs2dOsfjTFrl274OnpKTJ/zZo1Td6zU6ZMoeJqfU+ac00uXbqEgQMHokePHnBzc0NOTg6Vd/36dTg4OMDc3Bx37twRKP/z3+fatWsF6qqPgF5UVITJkyfD1NQUHh4e37HXX0aLlqg2bNjwrdvxUyMpKQklJSV0794dfD6firHziVMJcTEWeHzhW8XbyX/d8hQDw8/AX3/9BX9/fwQEBMDc3BxRUVGYNWsWLly4AHV1dWzfvh3Xrl3Dpk2boKysjODgYMyfPx8RERFgsVhISUmBo6MjAgICqDKlpL7Nb2vPnj345ZdfcODAgUYDcn748AHu7u6oraVHcT937hx27tyJP/74A9ra2ggJCcHs2bNx8eJFsFgsXLhwARs3bqQ2cqxevRobNmzAtm3bUF1djVmzZqFbt26IjIxEVlYWPD09IS8vj0mTJjXa7qysLOzYsYOK5N0Uq1evxtKlS6nvOTk5mDJlikgFpykKCwsxdepUDB48GOvWrcOdO3cwbdo0xMbGokOHDoiMjKRFwL58+TK2bt0KV1dXAIC3tzdevnyJnTt3ghCCFStWQEJCQmTsq+Zw/vx5hISEiIxL9vTpU4SFhcHGxqbFdXxLmromT58+xdKlS+Hl5QUzMzNs374dv//+O8LDwwHUBdC2t7fHvHnzmuXeJTU1FUuXLqWuAQBqB3B92Kbo6GgoKSl9y25+FS1ScJobisHKygpnz579z8Sn+ieoV24AQE1OCqNN2yP8Wa7AcbKtxGHWntmWzfB9qKmpQXV1NdhsNlq1agUZGZkfNrZVVFQUXFxcqIFn8eLFuHjxIm7fvo2xY8ciKioKq1evpp5LAQEB6NWrF96/f4+OHTsiNTUV+vr6QiM3fy3l5eWwsbERiMTckGvXrsHb21to/eXl5Vi+fDl69+4NoM4Fx4gRI1BUVAQVFRXs3bsXM2fOpGwely9fDj8/P/B4PDx58gSlpaVYs2YNWrVqBV1dXUydOhUxMTFNKji+vr4wNDREfn5+s/opLy8Pefm/d116enpi0KBB6NevX7PO/5zo6GgoKirCz88P4uLi0NPTw927dxEWFoalS5fSFK/y8nLs3LkTHh4e0NDQQFFREWJjY3H48GFYW1sDAJYtW4Z169a1SMHhcrkICAhAVFSUyLGKzWbDx8cHFhYWLerv96Cpa3LgwAEMHz4c48ePR1VVFby8vPDbb7+hqKgIysrKKC8vh7W1NS2yeGOkpqZi+vTpQu/jiooKdOzYEXp6et+mc9+IFi1RNZfv6cTuZ0BOSgJBwwxhpE73gyMlIYYYt27QaPNjDjgM/20qKipw5coVnDhxApGRkThx4gSuXLmCioqK71pvSkoKJkyYAHNzc/z66680FwpxcXEYOXIkzMzM4OzsjOvXr1N5M2bMwLRp0wTKKy8vB5/PR3BwMHr06CE0H6h7MHfs2FFku65evYohQ4bA3Nwco0ePxuPHj6k8DoeDgIAAdO3aFQ4ODrh9+zaVN2XKFDx+/Bg7d+6EgYGByPJv3bqFRYsWYfXq1QJ5kyZNogbl8vJynDhxAp07d4aysjIqKirw6tUrWoRmGxsbREREQFxcHIaGhti5cydataIHzm3qOkZHR6O6uhqjR48WyHv+/Dl1jQYOHIjY2FiBYx48eIAnT57g999/p9Iak5MwsrKyYGxsTAuUaGBgQFumqmf//v1o27YtRo0aBQDUspe5uTnt3IKCAuTm5ja7H/VUVVUhOTkZp06dokIPfc6ePXtgYGCAnj17CuRdvXoVAwcOhIWFBfz9/WkzTwBw8uRJODk5wdLSElOmTKFFuWez2Vi7di1sbW1ha2uLZcuWoaSkhMr/8OEDZs+eDXNzczg5OWHHjh0C5QPCr8njx49p946mpiZu3LgBZWVlODk5IScnB6tWraKWKRv7fVZUVCA/P1/o7ygkJAQhISF48uQJDAwM8OjRI6Ey/Df4rgoOQ9P8oiSLq+7dcWN2dwQO7oKjEyzxcnkf9OyoBEkJ5vIwfFtqampw48YNZGVl0dKzsrJw48YN1NSItgn7GthsNmbNmgUtLS2cOXMGAwcOpKbKCwoK4O7ujpEjRyImJgYzZ86Er68v4uLiAADGxsa0B+tff/2FjIwM2NnZQUxMDD169KAtDx05cgRKSkowMDAAIQTp6em4e/cuBg4ciH79+mHTpk3U5og3b97Aw8MDc+bMwblz5zB8+HDMnDkT79+/B1D38L558yZ27dqFbdu24ciRI1Q9ISEhsLS0hJubG+7evSuy72vXrsX48eMblU9kZCS6du2KqKgo+Pj4gMViUdeoqKgI48ePh729PTw8PCjFrW3btrC1taXKqKmpwalTp2BnZyeynqKiImzatAn+/v4CGx8KCwvh5uYGQ0NDREVFwd3dHR4eHnjz5g3tuD179sDV1RXt27enyUKUnIShqqoqMHuUl5cn4Desuroax44dw+zZs6nZ7/rllIbnf/jwAQBQUlLS7H7U06ZNG5w8eRJdunQRmp+amoqwsDCsXLlSIC8lJQWLFy/GhAkTcPr0aXC5XJp92I0bN7Bjxw54e3sjKioK1tbW+PXXXyk7qs2bNyMpKQl79+7FkSNHUFFRgUWLFgGomyCYP38+VFRUEBUVhfXr1yMmJgahoaEC7fj8mpSVlaG0tBQ8Hg/Tp09H//79MXfuXEpmkZGRaNeuHVatWoXIyMhGf5/1MmCxWAgNDYWDgwOGDx+OqKgoAICbmxvc3NxgaWmJu3fvilQS/w2YEfQHoH0bafTppIqVfTtjkrUmdFVao9U3cPTHwPA51dXVAspNPVlZWaiu/jY79z7n/v37KCkpgZ+fH/T09DBp0iRqKv348ePo0aMHJk+eDG1tbQwfPhwjR47E4cOHBcrJzMzEypUr4ezsDGNjY4H8a9eu4cCBA1i6dClatWqF3NxcVFdXo1WrVti6dSs8PDwQExODjRs3AqibHRg7diycnZ2hra2NX3/9FQ4ODggLCwMhBBEREVi4cCFsbGxgaWmJVatWUXUpKipCUlISsrKyX7381aNHD0RFRWHs2LGYO3cusrKyUFlZCQDw9/fHzJkzsW3bNqSkpMDLy0vgfD6fD09PT1RWVsLd3V1kPevWrYOrqys6d+4skBcbGwsFBQV4eXlBV1cXI0eOxNKlS2lKb1ZWFh4+fEgzPm1KTsIYMGAAnj9/jlOnToHL5eLOnTu4fv06OBwO7bgLFy5AVlYWAwYMoNI0NDRgYWGBwMBAlJSUoKCggDI+5nA4zepHcyGEwMfHBwsWLICqqqpA/unTp9G1a1dMnToVenp68Pb2hpqaGpW/b98+uLu7w9HRER07dsTixYuhoaGBc+fOUcrbmjVrYGZmBgMDA2zcuBGPHz9GcnIyHj58iNzcXAQEBEBXVxe2trbw8PAQUB6FXZOqqioAdcr18OHDsXXrVrDZbLi7u4PP50NZWRni4uKQl5eHsrJyo79PAEhLSwOLxYKuri727NmDMWPGwNvbG1evXkXr1q0hKysLSUlJtG3bVmBG8d+kRTY4DAwM/02acuvwvdw+pKSkoGPHjpCV/dtFgampKW7fvo20tDTcvHmT9ubH4XAEpsPT09Mxbdo0aGlpYe3atQJ1XLt2DYsXL8bkyZMxZswYAHWD4aNHj6CgoAAWiwVDQ0Pw+XwsX74cK1euRGpqKi5evEh7W+VwOLC3t0dxcTGKiopoO0NNTU1F9vHcuXPw9fWlvq9Zs0akwerndOjQAR06dIChoSEeP36M6Oho9OrVCwAwa9Ys9O3bF0DdgOXq6or8/Hy0a9cOQJ0NiYeHB27duoUDBw5QytbQoUOpJZsOHTrA09MTiYmJQmUH1MnXyMiIZif4+dLg5cuXYWhoiE6dOlFpTclJlFwCAgKwdu1ayh5owoQJAssbly9fxpAhQwQcpm7cuBELFy6EnZ0d5OXl8fvvvyMhIQGtW7dGRkaGyH7MmDGDNsMiKoxOPeHh4eDxeCJte1JTU2n9lpSUpH1PTU1FcHAwNm/eTKXV1tYiIyMDWVlZ4HA4ArN7fD4fGRkZKCgoQElJCWVnVJ9XU1OD4uJiyphX2DWpX/obM2YMRowYgaqqKgQHB8Pe3h6JiYmwsrKi1dnY7xMAXFxc4OjoSM2UdunSBRkZGQgLC6Mtg/1oMAoOA8P/EE29XX3Pt6/PbfIkJes8aXO5XDg7O2P27NnUcdXV1TQDynfv3mHq1KnQ0tLCvn37BAyiY2NjsWLFCowfP15g9uDz3U16enqora2lpvBnzpwJFxcX2jENy2/Y7vo2C8PJyYlmF9KcnSkPHz6EmpoadHV1AYB6Sy4uLqYUlfo8ANDR0QFQt5zTrl07cDgcLFmyBPfu3cOePXtoA9eePXsoB6MSEhLYsWMH8vLy0L17dwB1cudwOLC0tMTevXub9LoOAHfu3KGUrc8RJSdRchk1ahRcXFxQWFgINTU1bNy4kWaszWaz8fjxY8yaNUugLm1tbZw9exaFhYWQl5dHZmYmxMTE0K5du0b7ERgY+EUzObGxsUhKSqLkyuFwwOPxYGlpSdn1iLqvAYDH42HVqlWUzOuRk5PDx491wZBPnDhBUyyAOhlFRkZCV1cXf/75p0C7Gv42hF0TJSUlSEpK0u4dJSUlKCoq0lwoNKSxfrBYLIHfka6uLh4+fCi0rB+F77pExUSdZmD4sZCRkRG5U0RLSwsyMjLfpd7OnTsjIyODsh8BgNev65xc6ujo4P3799DW1qY+t2/fRkxMDADg48ePcHNzg7a2Nvbv3y8QnPbBgwdYsWIFJk2aBG9vb1renTt3YGtrS1t6e/36NRQVFaGsrAwdHR1kZ2fT6g4PD8dff/0FJSUlqKqq4sWLF9S5r169EtlHOTk5WjnNCaK7d+9eHDp0iPrO4/Hw5s0b6OnpoUOHDlBTU6PZjtTbQnTo0AEA4OPjg3v37mHv3r0Cu1s1NDSotmhoaGDZsmWIjY1FdHQ0oqOjsXDhQqipqSE6OhomJibo2LEjkpOTaQPd4sWLsW/fPgB1A+CLFy8E3v6bkpMwuTx8+BBLliyBuLg41NTUQAihrlU9ycnJ4HK5Ar6D+Hw+3NzckJycDBUVFbRq1Qq3bt2CkZERVZeofqirq9Pa0hSbNm2iyWz8+PEwMTFBdHQ01NTU0LlzZ1q/+Xw+7Xrp6OggLy+PVmdoaCgSExOhpaUFcXFxlJSU0GSzfv16FBYWQkdHB7m5uVBWVqbys7OzsX37dmpsFXVNJCQkYGxsTGtLcXExiouLhe6aauz3CQDbtm3D1KlTaee8efOGpkD9iDC7qBgY/oeQlpaGk5OTgJKjpaUFJyen77ZVvEePHmjfvj1Wr16N1NRUnDlzBhcuXAAATJw4EUlJSdiyZQsyMjIQExODHTt2UA/ioKAg8Pl8BAYGoqqqCgUFBSgoKEBlZSW4XC5WrVoFGxsbzJw5k8orKCgAm82GpaUlpKSk4OXlhbS0NNy+fRsbN27EjBkzAABTp07FhQsXcOTIEWRmZuLQoUM4dOgQOnbsCBaLhUmTJmH79u24f/8+Xrx4gfXr139TuUycOBFnzpxBTEwM0tLS4Ofnh5qaGri4uIDFYmHq1KnYvn077t27hzdv3mDNmjXo06cP2rZti3v37uHMmTPw9PSEtrY21e+ioiKhdamoqNAGWhUVFUhISEBbWxvS0tJwdnZGSUkJNm7ciIyMDJw5cwbXr1+ndg7l5OSgsrKSthQCoEVy0tHRwc2bN3HixAlkZWVhzZo1KC0tpc2kvXv3DpqamgKzimJiYpCWlsYff/yBjIwMXLt2DTt37qRsj5rqx5fwuUKkoKAAaWlpaGtrQ0JCAmPHjkVSUhJ27dqFtLQ0BAUFUcuCQN3S2OHDhxEdHY3MzEwEBwfj4sWL0NPTg5ycHMaMGQM/Pz88evQIKSkpWLFiBd6/fw9NTU3Y29tDQ0MDy5cvR3JyMuLi4uDt7Q0ZGRlqCUrUNamv++jRo7h06RLS0tKwcuVKGBoaCnU22djvEwAcHR3x5MkT7N+/H5mZmThx4gSio6Ph5ub2xTL9RyEtwMnJiRQXFwuk5+XlETs7O+p7YWEh4fF4Laniu8PlcklcXBzhcrk/ZHk/C4xcRNMS2VRXV5NXr16R6urqr6q7urqaFBUVkby8PFJUVPTV5TWHzMxM8uuvvxJTU1MyevRosmHDBjJ58mRCCCH37t0jrq6uxNjYmDg5OZF9+/YRPp9P+Hw+MTMzI/r6+gKf7du3k4SEBKF5+vr65OHDh4QQQt6+fUumTp1KLCwsSM+ePUlISAjh8/lUu86fP08GDhxIjI2NyeDBg8mFCxeoPB6PRzZt2kS6detGunfvTo4dO0b09fVJVlYWIYSQyZMnk+3btzer/w8fPiT6+voC6REREWTAgAHE1NSUTJkyhaSkpFB5fD6f7Ny5k/To0YNYWFiQJUuWkA8fPhA+n0+8vb2F9tvR0bFZ7Tl9+rTAsU+fPiWjR48mxsbGZNCgQeTy5ctUXmJiItHX1ye1tbUCZTUlJ2HcvHmTDBo0iJibm5Nff/2V1m9CCNm9ezcZO3as0HM/fvxI3N3diYWFBenbty+JjIwkfD6fVFRUED6f32g/GsPDw4N4eHiIzN++fTt1z9Zz9+5dMnToUGJqakqWLFlC3N3daffE4cOHiaOjIzE1NSWurq7k/v37VF5VVRXx9fUl3bp1I5aWlmTWrFkkMzOTys/MzCQzZ84kZmZmxM7Ojvj5+dF+q41dE0IICQ8PJ3369CFmZmZkxowZ5MOHD1Seo6MjOX36NK0uUb9PQgi5evUqcXZ2JqampgIyFSaXr6WxZ11zn50sQpo3zXLp0iXK4CgqKgpDhgwR8Aaak5ODtLS0RrdM/ijweDwkJibCwsKC5ovhRymvJZRzavGxuhyvSvLRSkwC+gpt0V5WHtLi/17U8B9BLj8qLZFNTU0N0tPToaOj88M65vtaCCGoqqqCrKwss8z9GYxsRMPIRjj/Vbk09qxr7rOz2UbG3bp1ozlvEqYXde7cGcuWLWtukQzfkE81ldjx6i4Cn18D//+vjbS4BA7Yj8cwLUO0lmTCPjAwMDAw/O/QbAVHWVmZWlfV0NDA9OnTv6lB4qxZs6CsrEzFuXr16hV8fX3x9u1bdOrUCWvWrIGJiQl1/Pnz57F161YUFBTA3t4eAQEBzY6r8qPBq/oIfm0ZICYOMWkViEt9eYiGxwWZCHh2lZZWw+Ni0u3jSHT5HSZK7UWcycDAwMDA8PPRIiPj+fPn48mTJygsLARQ5xVx1qxZlDOhLyU2NpY2O1RVVYVZs2aha9euOHPmDCwtLeHu7k45L3r+/DlWr16N+fPnIzw8HGVlZUK9TP7o8Lk1qMl9gLwzQ5B92AjZB7ug4NJv4BSnfFE5hTWVCEi8KjSPgGD3mwfg8QXdezMwMDAwMPystEjB2blzJxYtWoTs7Gw8fvwYPj4+aN++Pa5evfrFuwzqrd0bOoa6cOECpKSksGLFCujp6WH16tVo3bo1Ll26BAA4duwYBg8eDBcXF3Tp0gUbN27E7du3RXpo/VHhFCfjQ4QT2J+e/38KQXV6LD5EOIFTltnoudUcHooq2ajl8lDL5yKrskTksW/LClArJH4JAwMDAwPDz0qLFJxTp04hJCQE5ubmOHv2LGxsbLBmzRps2LCBtrWsOQQFBWHEiBG0bW7Pnj2DtbU1ZRDFYrFgZWVFBWJ79uwZunbtSh3fvn17dOjQAc+ePWtJd/4VeLVlKL7vBxBBxYNXlYfqzOuCJwEor+EgPrsEM04lYtC+h/j93EtU17BgrSo6mrGDuh6km+HEi4GBgYGB4WehRaNeaWkpdHV1QQjBrVu3MHPmTAB1Dp2ERToVxYMHDxAXF4eYmBj4+flR6QUFBQL7+lVUVPDu3TsAdY6/Gsb7qM8X5aGxMb6kvc0pp7nl8dllqM0VvdusOj0WrbtMAlh/W4jXcvmISsrD1JOJVFpcVikin39A9Ox+uJD9mjIwrkdOQgrjdS1A+AQ8/POzOF8ql/8lWiIbHo8HQgj1+Rmp79fP2r+vgZGNaBjZCOe/Kpf6ZxyPxxN4Rjb3mdkiBadLly7Yv38/FBUVUVRUhP79+yM/Px+bN2+GhYVFs8qora2Fr68vfHx8BLaA1QfHa0irVq0o+56amppG87+Ehl4ovwXNLU9LTRbisurg15YKzRdrrYH0jEwq6iwAyLbTxuzI5wLHFlSwceR+Ic46TcO8h1HIrKyLyGuq1B67bUeiJjsfidXvW9Cbb8e3lvPPxJfKRkJCAtXV1eDz+d+pRT8G3yvw588AIxvRMLIRzn9NLrW1teBwOCKjwDeHFik4fn5+8PDwQE5ODpYuXQoNDQ0EBgYiJycH27Zta1YZO3bsgImJCRVQriFSUlICygqbzaYUIVH5LdnVZWpq+s384Lx48eKLyquyXopP14RH/m1jOgPKyjq0tL/Si1HDFT6o7b6fjSX2fXB36HwU11ZBjMWCipQsVKVaA2pCT/lHaIlc/ldoiWxqamrw/v17yMjI/NR+cKqrqyEjI/Of8tvxT8DIRjSMbITzX5WLmJgYJCUl0alTJ6F+cJrzYtjiGZyzZ8/S0pYvX/5FgfpiY2Px6dMnKoJwvcJy+fJlDBs2DJ8+faId/+nTJ2pZSl1dXWh+fXC6L0FcXPybDrxfUp6MzhC0NhiHyuS/IxmDJQYVpxBIttGG2GflNDXFWMMl0G+tAI3WCl/c7u/Nt5bzz8SXyEZcXBwsFov6/Mz8L/SxpTCyEQ0jG+H81+RS396vGTuabWQcHR1NKSH1gccafi5cuED93xyOHj2KmJgY6hwnJyc4OTkhOjoa5ubmSEhIoK0dPn36lIpIa25uTgt5/+HDB3z48IEWsfa/gERrdaj02QqNyfFQ7r0Zqv32QPO3JMgZTICYEF84OiqykJIQfsm0FGWg0vrf81jMwNAY2dnZMDAwQHZ2tkDemTNn4OTkJPLc2tpazJ07F2ZmZpgyZQqcnJxgYGAg8NmxYweAuufFpk2bYGdnh27dumHjxo205bzU1FS4ubnBysoKTk5OCA0NpeXfvXsXw4cPh6WlJaZOnYq0tDQqr6ioCJMnT4apqSk8PDya7PesWbPg6enZLBnVc/78efTr1w/m5uaYN2+eyNhSa9aswZQpU5pd7sWLF2FgYEBLy8/Px8KFC9GtWzf06tUL69evR21tLZUfEhICa2trdO3aFRUVFYiLi8PIkSNhYWGBESNG4P79+1/Ut8+5c+cOhg8fDjMzMwwfPpzmLuR7M2XKFISEhFDf79+/j2HDhsHc3By//vrrN9uVm5mZiWnTpsHS0hLOzs64deuW0OOys7NhaWmJR48eUWkcDgfBwcGwt7eHnZ0dgoKCqAjxQN1vY9WqVejatSvs7e1x4MABoWWXl5fDwcEBZ86codIIIQgJCYGDgwNsbGywePFi2r32/v17TJ8+HZaWlujTpw8VdPU/RXPjQjg6OpKioiLqf1EfJyen5hZJo2EMkPLycmJnZ0cCAgLIu3fvSEBAAOnZsyeprKwkhNTFSzE2NianTp0ir1+/JpMnTybu7u5fVN9/MRZVFZtLQu+nE9bSc7SPxPIYcjX543er92tgYlGJ5t+MRfVPk5WVJTI20ecxkRrGFCKkLgaOiYkJefHiBcnPzyeFhYXk48eP1Ofo0aPE2tqaZGdnE0II2b9/P+nduzd58uQJefDgAbG3tyf79u0jhNTF/nFyciKenp4kNTWV3Lp1i9jZ2ZFjx44RQuriVhkZGZGtW7eS1NRUEhQUROzt7UlFRQUhhJCDBw+Snj17kpSUFFJYWNhon8+fP0/09fUbjW30Oc+ePSNmZmYkKiqKerbNmjVLQDZxcXHEwMCg2fF/SktLSc+ePWmxsPh8Phk7diyZMWMGefv2LXny5Anp378/2bBhAyGEkJKSEqKvr0/Cw8NJdnY2+fTpE7G2tiZ79+4lmZmZZNeuXcTc3JwW3+hLyMjIIGZmZuTgwYMkMzOTHDhwgBgbGzcav6oxPr9vmqJhHLGcnBxiYWFB9u/fT96+fUsWLVpEhg0b1uyyRFFTU0OcnJyIu7s7efv2LYmKiiIWFhbk2bNnAsdOnz6dFkONEEI2bdpEevToQW7dukWSkpLIiBEjSEBAAJXv7+9PnJ2dSVJSErly5QqxtLQkFy9epJXL5/PJypUrib6+Pi32VFhYGHFwcCCPHj0iycnJZMKECWT27NmEkLrYYgMGDCBLly4l6enp5NatW8TKyoqcO3fuq+TxJXyLWFQtCrb5Pfg8yNmzZ8+Ii4sLFfjr5cuXtONPnz5NevfuTSwsLMi8efMo5au5/BcVHEIIKa5ik/vphWT4/kfEeOMN8tuJp+RFbimpZjdd78fyGpKQXUIOPckkl97kk4yiSsLlfd0PuCkYBUc0/6aCw+OWEi47nbBrXhAuO53wuKVfVV5TfI2Cc+bMGdKnTx+h5ZaVlRE7Ozty6tQpKq137960B3l0dDRV/u3bt4m1tTUtOGFoaCgZN24cIYSQNWvWkEmTJtHaMnjwYBIWFkYIISQkJISWL4ri4mLi4OBARo0a9UUKzvLly2nH5+bmEgMDAyoAI5/PJ8XFxWTo0KFk3LhxzVZwVq9eTcaPH09TcFJSUoi+vj4pKCig0mJiYoi9vT0hhJDs7Gyir69P1X3lyhXSrVs3WrndunUTGFCby8OHD8natWtpaTY2NiQ2NrZF5X2NgrN161aaLKuqqoilpSVN2WgJFy9eJJaWlqSsrIxK8/HxIUuWLKEdd/bsWer61NfJ5/OJpaUliYyMpI5LTEwkxsbGpKKiglRWVhJTU1NaG3fu3ClwTzx58oT07duX9OzZk/a7mD17NqXMEkLI9evXiYWFBSGEkPz8fLJo0SJSXl5O5c+bN4/4+vp+hTS+jG+h4DTbBufJkyfNnhWysbH54pmk+hAN9ZiZmSEqKkrk8SNHjsTIkSO/uJ7/OooykujeURnHJ1miisOHfCtxyLRq+jLmlFbj17AE3EwppNLaSEvg4kxb2GgqQkK8RS6RGP6D8Lj5qCwMAKf2IZUmKWWH1ireEJdQ/651X7p0CUeOHEFFRQWGDRsGLy8vKm/z5s04duwYWrdujalTp8LNzQ1nzpyhvJQbGBhg/fr1tN/9/v370bZtW4waNQpA3ZLLhw8faM8ga2tr5OTk4OPHjzA0NMTOnTsF7AUrKioAAFlZWTAzM6PSWSwW9PX1kZiYiIKCAmoZzMDAAEeOHIGtra3Qftb79/r48SMtnRCC3bt349SpU/j48SMUFRUxfvx4zJ8/H0Cdj696txsA3ceXlpYWAODgwYPQ19eHjo4OHj9+3KTMHz9+jMePH2P16tWYNWsWld62bVvs27cPqqqqArLIzs5G3759AQD9+vWDq6srRo0ahZKSEly5cgX9+/fH9evXUVlZCX19fZF1x8fHY9OmTXj16hVYLBZsbGwQGBgINTU12NraUvLjcDiUGURD+R88eBBHjx5FcXExrKys4OfnBy0tLVRUVCAwMBC3bt1CeXk5NDU1sXTpUvTo0UNkW65evYpNmzYhPz8fI0eOpG01/ty3moyMDIyNjZGYmAhbW1vk5+cjMDAQDx48QHV1NTp37gwvLy9YW1sDqFvO8ff3x9OnT6GgoAA3NzdqmUtXVxfy8vJU2QYGBrTlnuLiYgQHB+PAgQMYNmwYlV5UVITKykqa6YWBgQE4HA6SkpIgKSkJLpdL2bECdfd6/ZKrmJgY2Gw2vL294enpicDAQJo8FBUVcevWLUydOhUKCgqIjY2FoaEhAEBNTQ1bt24F8LeJyJMnT+Dr60udHxcXh3Xr1iElJQXa2tqYP38+Bg4cKFL+/wbNVnA+X+tlsVgghEBGRgaSkpIoKyuDuLg42rRpgwcPHnzzhjLQkZOShFwz42fWcHjYcCOFptwAQFkNFwP3PMTzpX3QUVn2q9rDqyoAr6YIYLEgLq0McRnVpk9i+Mfh88oElBsA4NQ+RGVhAORU10FM/MtjoTWXU6dOYcuWLeDxeFixYgV2794NDQ0N5OTkIDk5GeHh4Xjx4gV8fHxgbGyMIUOGoLy8HAcOHEBkZCRtoKiursaxY8fg7+8PMbE6Bb2goAAAaH6y6gfwvLw8mJmZ0TYj1NTU4NSpU3B0dKSOzc/Pp7U5Ly8PCgoK8Pb2RlVVFRISEhASEgIFBeHG/KL8ewF19ouHDx/G5s2boaWlhTt37sDPzw+Ojo4wNjZu0sdXWloaIiIicPbsWZw8ebJJedcPcD4+PpCUpNvotWnThraLlc/n49ixY7Czs0P79u0RERGBMWPGICIiAjo6OpCTk8OkSZOwcOFCiImJgcfjYf369dDV1RVad3l5Odzd3TF16lRs3LgRHz9+xKpVq7Bnzx6aYvv+/XsMHjwYPB4PS5cuhaZmndPSkydPYseOHQgICICRkRE2b96MRYsW4cyZMwgMDER6ejoOHDgAGRkZ7Nu3D15eXrh8+bLQtqSkpGDx4sVYvnw5evXqhcOHDyM+Ph7du3cHUHffNCb3ZcuWoU2bNjh58iRl4+Xn54eYmBjU1tbCzc0NxsbGOHXqFLKysrB06VJoaWlBVVUVBQUFIIRQBr55eXkoLi6m6tmwYQNcXV3RuXNnWv0KCgqQlJREfn4+5Rfuw4cPAOqUIhaLBSUlJZqyrqqqitraWpSUlEBZWRmhoaEwNDSk+tmQefPmYc6cOXBwcIC4uDjatm2L8PBwgeOcnJyQm5sLR0dHSoEpKCiAu7s7lixZgl69eiExMRGenp5QUVGhKYr/Ns1+bX/z5g31Wbt2LczNzREbG4uEhAQ8fvwY165dg42NDaZPn/4928vQAvLLa7H/kfDQDxW1PCTkCPfF0xwIj42avMf4cGYQco6YIuewCfKinVFb8AyEiX/1w0H4RQLKTT2c2ocgfOEGrd+KVatWwdraGt26dcOiRYuoQVpKSgobNmxA586d4erqikGDBiE8PBzS0tKQl5enHsANt4teuHABsrKyGDBgAJVWU1MDALSHfv3/n7uW4PP58PT0RGVlJdzd69w1DB48GJcvX8bNmzfB5XIRFRWFFy9egMPhoHXr1pCVlYWkpCTatm0rdNdoY/69gLoZmfXr16N79+7Q1NTEhAkT0LZtW8qJaWM+vggh8PHxgbu7u8Csiyh27twJY2Nj2NvbN3lscHAwXr16hSVLlkBcXJwKXqysrAx5eXlUVlYiKysL8+fPR0REBGbPno21a9ciNTVVaHk1NTWYO3cu5s2bBy0tLVhbW2PAgAFUX+tRVlZGZGQkfHx8EBISQikp4eHhmDp1KoYMGYKOHTvCx8cHtra2qKmpgY2NDfz9/WFoaIiOHTvCzc0NJSUlVHzEzzl9+jS6du2KqVOnQk9PD97e3jSFpjHfa4QQ9OvXD97e3tDT00OnTp0wadIkpKTUxQy8e/cuioqKsG7dOnTu3BlOTk7w8vKCmJgYHBwcUF5ejpCQELDZbLx48QKRkZHgcDgA6gyb4+PjMXfuXIE2S0hIoH///ti8eTPy8vJQXl6OoKAgSEhIgMPhiGwzUHevp6Sk4OTJkyLjNObk5EBaWhqhoaE4evQo2rVrh1WrVgkct337doSGhuL169dUKKbjx4+jR48emDx5MrS1tTFixAiMGzcOhw8fFlrXv0WLton/8ccfOHjwIPT09Kg0TU1NrFq1CpMnT8aMGTO+WQMZvp5aHl+k/xwAyCxpuQMoTmk6PkQ4Aby/Bw92fjw+nOoDjUlxEJPv2OKyGb49fH5Fk/nfczN/w+UHIyMjfPr0CWVlZdDS0oKSkhKVZ2hoKOCK4nMuX76MIUOGQKJBGJKGD3gpKSnqfwA0P1lcLhceHh64desWDhw4QM3qODg4YN68eViwYAF4PB5sbW0xYsQIagmrIbm5uRg6dCj13dnZGQoKCiL9ewGAnZ0dnj17hj/++AOpqal4/fo1CgoKqF1cjfn4Cg8PB4/Ho5bjPmfGjBm03aXh4eE4deoUYmJiREjwb4KDg3H48GFs2bJF5JLTvn37QAihltOMjY3x/PlzHDlyBO7u7gKy8Pf3h4uLCw4dOoTXr18jJSUFycnJsLKyopUrLy8PIyMjGBkZITU1FceOHcPAgQORnp4OY2Nj6jhVVVVq55qLiwuuXbuGU6dOIS0tDS9fvgRQp7TGxMTQllLWrFmD1NRUavkFACQlJWnfRcm9TZs2YLFYmDBhAi5cuICnT58iPT0dSUlJ1DVLT0+nZrjqaXiNtmzZAk9PT+zatQuampqYPHkyDh8+jJqaGvj4+MDX11ekTysvLy8sWbIEvXv3hqysLObMmYPnz59DTk4ONTU1Qttc35/Fixdj4cKFUFVVpQJV10MIgYeHB1asWEHNXm7duhWOjo549uwZbVmsPk5kbW0tli1bhhUrViAtLQ03b96kLY9xOBzo6NB9t/3btEjBYbFYyM/PR5cuXWjpGRkZ1EOF4cehdStxdGgjjdyyGqH5XTVb5jeHz61BafxmmnJTD+FUovzlISh0825R2QzfBzExua/K//r6/540Jv/vBkJSUpKWDtQNVJ8vqTSEzWbj8ePHNJsSoM5HFlA3hV6/1FG/bFWvxHA4HCxZsgT37t3Dnj17BAbcOXPmYPr06SgvL4eKigoWLVoEDQ0NgTaoqanR3GLIyclhzJgxIv17JSQkICIiAuvWrcOYMWMwYMAAeHh44Ndff6W1X5SPr/DwcCQlJcHe3h4sFgscDgc8Hg+WlpaIjY1FYGAgNYMFADExMSgtLUX//v0B/O3e3tLSEmvWrMHw4cMBAAEBAQgLC0NwcHCjNhQvX74UeOYbGhri3bt3QmWRn5+PUaNGwdjYGD169MDYsWNx69YtKmbgu3fvUFpaSlvS0NPTo+yKJBqJn7dixQokJCRgxIgR1CzYuHHjAACOjo60AVpFRQXnz58X8CPW8P4SJXdDQ0Pw+Xy4ubmhrKwMQ4YMgZOTEzgcDqXoNdZOAOjduzfu37+PgoICqKqqIiwsDBoaGnj+/DmysrKwcOFC2vEzZ86Ei4sL/P39oaKigiNHjqCkpARSUlIghOCPP/6AhoYGKioqUFxcDC6XS7WhoKAA0tLS1FJqcnIygoKCQAhBTU0NfH19ceHCBQQFBeHDhw80twHt27eHkpIScnJyoKGhgcTERPTr14/K79SpEzgcDioqKsDlcuHs7IzZs2fT2t6ULP5pWtSaiRMnYsWKFZg2bRq6dOkCQghevHiBI0eOYMGCBd+6jQxfSYc20lg/pAt+axDDqh6TdvLQVWndonJJbSlqc0X7wajJuok2VotbVDbD94ElpgxJKTuhy1SSUnZgiSl/1/rfvn2Lbt26AQCeP3+Odu3aQUZGBllZWZS3VaBuMBVl2wEAycnJ4HK5tBkhoG6g6tChA+Lj4ykFJz4+Hh06dKCWJHx8fHDv3j3s3btXwF7g/PnzePbsGVavXg0VFRXU1NTg0aNHApsggLqHuba2Ni3t6NGjND8lmzZtAlBnwwEAYWFhmDdvHjXLXVZWhsLCQmrwrffxVW9I3dDHl42NDaqrqyk5HTt2DM+ePcOmTZugpqYmMLhMnjwZzs7O1Pdnz55h+fLliI6OhoqKCoA6j/InT57E5s2bMWjQIJHyBuoUuvplmXrS0tKgqakpVBbHjh2DgoICdu/eTZNPfV9v3ryJM2fO4OLFi5R9SsPrrq2tjTdv3lA+koqLizF48GAcOnQI58+fx6lTp6jrX+8/hxACOTk5mq0WAHTu3BkJCQnUdz6fjzdv3lAK2+e+1aqrq/Hq1SvMnz8fKSkpePLkCR48eEAt2x0/fpyqr2PHjnj//j3t/g0KCgKHw8GECRPg7++PgwcPUvff7du3YWtrCzMzM1y5coXWzgEDBmDt2rXo2bMngDoHuiNGjKCWGC9evAgVFRV06tQJtbW1kJCQQGJiInUfx8fHw9TUFOrq6lTZ5P89Gbu7u2PKlCkYPnw4FBQU0KpVK6SmplIrMUVFRSgpKYGmpiays7Mxf/583L59m3ppSEpKgrKyMpSVlaGjo4OEhATaNT9w4ADYbLaA0vNv0iIFZ/78+Wjbti0iIiKom7dz587w8fGh3goYfhxYLBaGGqlj7xgzrLr4BgUVbIixgJGm7RHsbIT2bVro8l9CGuKt24NTnCw0W1xeCxD/OcMJ/FcRE2+D1ireIndRfU8DY6ButmDt2rWoqKjA9u3bKZu92tpaeHh4YMGCBYiLi8PVq1cbNaJ99+4dNDU1hdrBTJgwAZs2bUK7du0A1C2pu7m5AQDu3buHM2fOwN/fH9ra2tTsTr3NSceOHbFy5UrY2NhAX18fwcHBaN++PRwcHJrVv89nelq3rnt5qB8IlJSU8ODBA/Tt2xeVlZXYsmULOBwONdMzYcIETJkyBRYWFjA1NUVgYCD69OlD7aAihKCqqgqysrJQUFCAtLS0gGJRj6KiIhQVFanv9Qaz9cenpqbizz//xKxZs2BtbU3JAoBQr/BjxozBxIkTcejQIfTt2xfXr1/H3bt3Re52VVRURG5uLh48eABNTU1cvHgRV65coZY8hg8fjt27d2PTpk0YM2YM7t27h3PnzlGGrlOmTMH69euhr68PPT09bNmyBZqamtDV1YWMjAyuXLkCZWVlpKenw9/fH4CgnVU9Y8eOxdGjR7Fr1y4MHDgQ4eHhyM3NpfJHjRqF/fv3Y8+ePXB0dMTOnTuhqalJ7aASExNDbGwsnJyc8OLFC8pBIJvNhr29PVRVVeHj44PZs2cjIyMDJ0+exJYtW6ChoYHU1FRs374do0aNwrlz5xAfHw8/Pz+R105dXZ1SQBUVFbFlyxaoqamhuLgYAQEBmDVrFsTExCAjIwMXFxf4+flh3bp1+PjxIw4cOID169fTFM76e0ZcXBwqKiqUwjJy5EgEBQVBSUkJCgoKCAoKgrm5OUxNTcHn82FsbIxVq1Zh5cqVyMnJQXBwMKW8TJw4EUePHsWWLVvg6uqKFy9eYPPmzVi3bp1Q+f9rtGR/+uPHjwmbzRZIr62tJVevXm1Jkf84/1U/OF8Dj8cnmcVV5GVeGUn5VEHKagSv4ZdSlXGVpG2RFPqpzn34n5DLv8X/oh+c48ePk+7du5OuXbuSjRs3Eh6PR06fPk1cXV3J2rVriYWFBenTpw85deoU5c/kcz85hBCye/duMnbsWKF1cblcsm7dOtK1a1dia2tLgoODqbK8vb2Jvr6+wKdh+ZGRkcTR0ZFYWlqSuXPnkvz8fCpv+/btzfY9Q4igf6+UlBQyduxYYmpqSvr06UOCgoLIvHnziLe3N3VMYz6+Gvp6+dK2PHz4kOYHZ/fu3UJlUX+MMN9F165dI8OHDycWFhbE1dWV3Lt3T2R9XC6X+Pj4kK5duxIbGxvi5uZGDh48SKysrCg/RAkJCWTMmDHEzMyMDB48mFy7do3W19DQUGJvb08sLCzIrFmzSE5ODiGkzvljv379iJmZGRkyZAiJiIggPXv2JJGRkSL94Ny9e5cMHTqUmJqakiVLlhB3d3fKDw4hhNy6dYsMGDCAmJmZkd9++43y/0MIISdPniS9evWi+h0TE0OMjIzI06dPCSF11/XXX38lpqamxNHRkZw4cYI6NyEhgbi6uhJzc3MyevRo6hxhfO7or6KigixfvpxYW1uTXr16kd27d9OOr6qqIitWrCAWFhbE3t6eHDx4UKDM+numT58+ND84NTU1ZMOGDaRXr16kW7duZPHixTTnlXl5eWTevHnEysqK9OzZk+zatYsm23v37hFXV1dibGxMnJycyNGjR0X2qyV8Cz84LEK+PIa6oaEh7t27R03X1fPy5UtMmDABz58LRrz+0eDxeEhMTISFhcU3C7b5Lcv7r8CrLkRZQghKHq8H8P+3EksMyvYbIGc8FZCU/5+US3NoyT1TU1NDGTX+zME262cp/kuxc/4JGNmIhpGNcP6rcmnsWdfcZ2ezl6hOnDgBf39/yv9N/Rrh5zTmaIlBOITPB7fkAwiPDZakNCQV2//bTWo24jIqULD+HXKGk1CT9wQsMXFIqXeFuKwaxFrJ05xpMTAwMDAw/FM0W8GZOHEiOnfuDD6fj99++w3bt2+nObpisViQkZFp1KslgyDc0nyUPjyBTzHrwSsvgKSqNtqOCoSc2SBIyKn8281rFmJSbSAm1QaSSp2bPpiBgYGBgeEf4IuMjOvdn1+/fh0dOnT4T013/YjwqspQEL0GxTd2UWmcT++Ru3sy1Cf8AaW+8yEmKWhEycDAwMDAwNA4zVZwVq5cidWrV0NOTo6KxyKKem+HDI3DLc9H8c1QoXkFUb6Q7zoKrVSF75BgYGBgYGBgEA0TYfFfhFuYBYiw8ebXVIBXIdztOAMDAwMDA0PjNHsGp+GsDDND821gSTfuNZYl+XPukvlW1HA5+FBdjrelBeDwueiiqI52MvKQk2S8aTMwMDD8r9NsBaepZamG1LuwZmgcScUOEFdQB680XyBP+hcLSLQRdLbFUEcFpxbns17B7W44anl1nmPFWWLwseiPOV16QEW6Zd6ZGRgYGBh+Dpqt4Dx69Ij6n8/nIz4+HmpqajA0NISkpCTevHmDDx8+NNvjJwMgoaSBXxbH4H2QE/g1fwfzE1dQh8acMEjIf52CU8PhQUKMBQnxn28lMr28CJNvnwDB30t8PMKHb8Jl2KhqYaBml0bOZmBgYGD42Wm2gnP06FHq/4CAAOjp6cHHx4eKf0IIwYYNGwQCljGIhsViQbqjFXTXvkDVu7uozXkJGd1ukOloDUmVX1pcbmZxFa6+/YTTLz6gbetWmNujIzqrtoZy659jRxaHx8PO1/doyk1D1j67hm5tf4GSlOw/3DIGBgYGhh+FFr3anzlzBtOmTaMFd2OxWBg/fjyuX7/+zRr3vwBLTByt2naEYo/JUB+zHm2sXb9KuUkvqoL9jnuYGfEMl958xNH4bHQPuYutd9JQUiU8Tst/jVo+F6nlohXpzMpiVPM4/2CLGL4ENpuNYcOG0WaFs7KyMHXqVFhYWGDo0KF48OAB7ZywsDD07dsXVlZWmD59OrKysppdX2lpKZYuXQpLS0s4ODjgyJEjQo979uwZDA0NkZ2dLbKsM2fOUMEfv5SioiJMnjwZpqam8PDwAI/Hw6ZNm9CzZ09YWlpi0aJFIl8Q16xZgylTptDSGspsyJAhuHv3brPbIqy8phg+fDgMDAxon7dv335RGQ2Jjo7GwIEDYWVlhXnz5tFiYX0LuFwuRowYQcWNai6fX+O3b99i8uTJsLS0xMCBA3H+/PlmlZOdnS0gr/rPkydP8OjRI1o0b2HMmTNH4NybN282WTYAhIeHw87ODlZWVkhLS2txPwCgqqoKXl5esLW1hY2NDby9vVFZWdns8/8tWqTgqKmp4c6dOwLpV65coYLCMfzzVLG5WHM5GdmlNQJ5a6+9Q3aZYPp/ERlxSdiri440ba2iCXnG0PiHpLa2Fr///jvevXtHpRFCMG/ePKiqquL06dMYPnw4li5dSgVDvHPnDoKDg+Hl5YXTp09DVlYW8+bNa3adS5cuRXZ2NsLDw7Fq1Sps2rRJ4PnF4XDg5eUFPp//bToqhHPnziEjIwPR0dHw8PDAnj17cOHCBWzduhUREREoLS3FihUrBM57+vQpwsLCaGmfy2zEiBGYP38+LYCkKISV1xQ8Hg8ZGRk4duwY7t69S30ai/jeGHfu3MGqVaswZcoUREREQFZWFjNnzvym8j9w4ADevHnzVWXUR8c2MjLC2bNnMXPmTHh6euLFixdNntu+fXuarO7evYthw4bB1NQUFhYWzao/NTUVwcHBtDJ69uzZrLKDg4MxceJExMTEQFNTE3PmzGlRPwBg3bp1SEpKwv79+3Ho0CE8f/4cGzZsaNa5/yYtiia+bNkyLFmyBDdv3qTCzT9//hxJSUkIDRXu14Xh+/OpkoOwxByR+dEv8mDS7vtGi/4nEBcTw2Q9K2xKuoUqLn1WSozFgrfFAMgzO9AapYLNRymbjyoOQWtJFtq0EoNcq+9rq5WSkoKlS5fi8/B3Dx8+RFZWFk6ePAlZWVno6uri3r17OH36NBYuXIjbt2/D3t4ejo6OAOo2MQwfPhxFRUUC8fA+582bN7h//z4uX74MLS0t6Ovr4/Hjx3j69Cl69epFHbdv3z7IyTW+q/FrqaioQMeOHaGnpwegTmmoj1wO1EXP/v3332nnsNls+Pj4CAyIjx49oslMT08PDx48wOnTp7FgwQKRbRBVXlNkZ2eDw+HAzMwMUlJf//Jw7NgxODs7Y/LkyQDqzB569+6Ne/fu0a5LS3n//j2OHDmCTp06fVU5KSkpyMnJwaJFi9C6dWv88ssvOHHiBB4/fkxFRReFuLg4LSr706dPcfnyZZw9exaSkpJN1s1ms5GdnQ1TU1Oh0d2bKru8vBzdunWDhoYGnj592uJ+AICkpCS8vb1hYmICoC76+smTJ5s879+mRU+0/v374+zZszA0NERqairS0tJgYWGBmJgYdO/e/Vu3kaGZEBBw+aJjp1Zzfp64UNpySrg1eA6MFdtRab+0VkJsvxkwYHafNUphNQ/bEkrx++0ieN0vxpLbRdieUIrC6u97fzx+/Bi2trYIDw+npT979gxGRkaQlf3bZsrCwgKJiYkAAEVFRTx58gSpqangcrmIjo6GhoYGFSomJSUF06dPh6WlJUxNTTFx4kSkpqZSdXbp0oU2s+zj44NFixZR39PT03H8+HF4enoKtDk/Px8zZsyAhYUFXF1dkZmZSct//vw5JkyYAHNzcwwcOBCxsbFC+x4SEoKQkBA8efIEBgYGePToEebPn4/+/fsDAAoLCxEREYFu3brRztuzZw8MDAwEYv8lJiYKyMza2pqSmShElQcAcXFxGDlyJMzMzODs7IzLly9TeSkpKWjfvr1I5aasrAzLly+HlZUV7O3tERAQgJoa0TPGWVlZMDMzo75LS0vjl19+odpfVVUFHx8f2NrawtbWFt7e3qitrW12XT4+PliwYEGTCjDQ+DWuv8ciIiLA5/ORkJCAtLQ0GBkZUcecPHkSTk5OsLS0xJQpU5CcnCy0nj/++ANjx46lFNx6jh49SvVzy5Yt1AtAWloaWCxWs1ZFPi+7funrt99+w5QpU9CmTZtG+8Fms7F+/Xr06tULxsbGcHJyov1OfX19YW1tDaBO2T1//jztXv3w4QNmz54Nc3NzODk5YceOHT9EHMIWKTiEEFy5cgWnT5/GnTt3sHr1auTn52Pfvn1gs38OO4//IgrSkujXWfTgPty4nci8/xoSYuKwVtXCtUHuSHJdjmcuy3Bv2HwM0DSALBPeQiQVbD5Cn5fh+Se6jdKzTxzsfl6GCvb3W6KZOHEiVq1aBRkZGVp6QUEB1NTUaGkqKirIz69znzBlyhTo6upiyJAhMDMzw6lTp/Dnn39CXFwcfD4fs2fPhoaGBs6ePYuTJ0+Cx+MhODgYQN1Aqqmpif3798PJyQmDBg2ivXkSQqjBUEVFMPbbokWLwOfzERERgZkzZ+Lw4cNUXmFhIdzc3GBoaIioqCi4u7vDw8ND6LKIm5sb3NzcYGlpibt378LS0pLK2759O3r06IGnT5/SlKzU1FSEhYVh5cqVAuWJklleXp6g4JtZnru7O0aOHImYmBjMmDEDnp6eiIuLo86VlJSEu7s7evbsicmTJ+P58+fU+atXr0Z5eTnCwsLw559/4sWLF/D39xfZFhUVFXz8+JH6zufzkZ+fj+LiYgCAl5cX4uPj8eeff+LAgQOIj4/H1q1bm1XX6dOnUVtbi7Fjx4qsvyGNXWMNDQ38/vvv2LRpE0xMTDB+/HjMmDGDepG/ceMGduzYAW9vb0RFRcHa2hq//vorSktLaXXEx8cjMTER7u7uAvWfO3cOBw8exLp163DixAlERUUBqFNw5OTksGLFCtjb22P06NG4ffu2wPnCyq63x6pXrDt06IAlS5aI7MeePXtw69YthISE4NKlS3BxcUFAQICATZiHhwf69u2LT58+UcvEhBDMnz8fKioqiIqKwvr16xETE/NDrOa0SMHZuXMnzp07hw0bNkBKSgosFguurq64d+8eNm7c+K3byNBMFGUk8YezEWQkBcPHDzVUg47Kz7erSE1GHoaK6jBRaocOsgpNn/A/TimbL6Dc1PPsEwel31HBEUV1dTVataIrpZKSktTL0sePH1FbW4tNmzbh5MmTsLGxwfLly1FbW4uamhqMHz8enp6e+OWXX2BsbAxXV1ekpKQAqJsJuH//PuLj47Ft2zbMnDkT69evp2YnIiMjweFwhA6G7969Q0JCAtauXYvOnTtjyJAhmDBhApUfGxsLBQUFeHl5QVdXFyNHjsTSpUuFzly0bt0asrKykJSURNu2bWn9HTFiBCIjI9G9e3e4ubmhoqKCpnipqqoKlFdTUyMgs1atWol8wWyqvOPHj6NHjx6YPHkytLW1MWLECIwbN44a7NPT01FaWooxY8Zgz5490NPTw2+//YYPHz4gMzMT165dQ3BwMAwMDGBmZoaAgABERUWhvLxcaHuGDBmCsLAwJCQkgMPhIDQ0FIWFheBwOCgtLcWlS5fg4+MDa2trGBsbw9/fHx06dGiyrqKiImzZsgX+/v7NipXY1DXmcDhIS0vDuHHjEBERgZUrV2Lv3r2Ugfy+ffvg7u4OR0dHdOzYEYsXL4aGhgbOnTtHq+fUqVPo378/1NXVBdqwbt06GBkZoW/fvvjtt98oBTwtLQ01NTWwt7fHvn370Lt3b8yZM0fAbkZY2fXLVwoKClBUVGyyH126dEFgYCAsLCygpaWF2bNng8PhICMjg1bXzJkzER4eDg0NDcpm6uHDh8jNzUVAQAB0dXVha2sLDw8Pkcb8/yQtssGJiorChg0bYGNjQ91EPXv2RFBQEBYtWgQvL69v2kiG5tNFTQ5Plzhgw413uJxcACVZSfzuoIehhmpQk2MMb//XqeKIXsIEgOom8r8HUlJSKCkpoaVxOBxIS9fZUfn6+mLAgAFwdnYGUDcd36dPH1y/fp0akKKjo5GUlIS0tDS8evWKGsTFxcWp3UqysrIwNTXFmzdvEB4eDisrK2zZsgWHDh0SOhimpKRAUVERHTp0oNJMTU1x6dIlAHWDvpGREcTE/n5PnDZtGgBgxowZiI+Pp9ITEhJE9l9buy7e3MaNG+Hg4IArV66AzWaDx+Nh3LhxzZYZm82mZNZwhsja2hr9+vVrtLy0tDTcvHmTdh6Hw4GOjg4AUMtA9XZKfn5+ePr0Kc6ePQsDAwPw+XwBH2h8Ph/v37/HqVOnEBMTQ6XHxsZi7NixePv2LSZNmgQAGDhwIBwcHCAnJ4f379+Dx+PB2NiYOqdr167o2rUrbt682Whde/fuhaurK/T19YX283O5jBo1qtFrXH9fnT9/HiwWC8bGxkhJScHevXtha2tLGQFv3ryZOr+2tpamGHC5XFy/fl3oy7+srCw6d+5MfTcyMsLBgwcBAHPnzsWUKVOoZbIuXbrg5cuXOHXqFGU301jZDYmNjcXLly9F9qNfv364d+8eNmzYQP2GAAgsM9XbNG3ZsgW9evWilo5LSkqoJSyg7nrU1NSguLgYSkpKjbbte9IiBaewsFBgehQA2rRpg6qqqq9uFEPLkRAXg4GaHHaONEVJNQfiYiyoyzMGtwx1yEo2/lYr00T+90BdXZ2acann06dP1Fvoy5cvMXv2bCqvdevW0NbWRk5ODiorKzF69GgoKSnByckJw4YNQ1paGg4cOACgbsdnu3btaLYqOjo61M6T4uJiatCvt30YNmwYZs+eDW1tbQGD6IbGoQ3dZHxOYGBgozYoAHDz5k0YGRlRb95SUlLQ0tJCcXExbt26haSkJFhZWQGoUzZ4PB6srKwQEREBNTU1oTKrfy5HR0dT6dLS0li2bJnQ8iwtLREbGwsulwtnZ2eanBv2UUJCgmaEzWKxoKuri/z8fHTq1Any8vI4ffq0QB/V1dWxaNEiTJ8+nUpTU1ODuLg4fH19sWLFCtTW1kJRURGjR49Gz549GzXA5fF4IutSU1PD5cuXIS0tjePHjwOom+lKSEjApUuXEBsbKyCXp0+fNnqNX758CX19fZoCbGhoiKdPn1LtWbVqlYDtaUNZJSYmgsvlCrV7+lyx5vP5VP1iYmKUclOPrq4u7bo3VnZDXr9+3Wg/tmzZgoiICIwcORIuLi7w9fWltsqz2WzcvHkTPXv2pPqlqqoKRUVFFBcXg8vlQldXF3/++adAvfLy8o2263vToiUqOzs77N+/n5ZWUVGBzZs3pwrwUAAAWPtJREFUw9bW9ps0jOHrkG0lgQ4KMoxyw0BDoZUYzFWFDyDmqpJQ+M47qYTWa26Oly9f0hSCxMREmJubA6gbuOqNhoG/d5doamri8ePH+PjxI44cOYIZM2agR48eyM3NpQYtc3Nz5OTk0JZK0tLSoKGhgf79++PSpUuIjo5GdHQ09uzZA6DOHmH8+PHQ19dHaWkp3r9/T537+vVr6v+OHTsiOTmZNkAuXrwY+/btg7q6OrS1tamPMIKCgmgDbkVFBTIyMqCnp4dNmzZRA3J0dDTGjx8PExMTREVFoW3btrCwsBCQWXx8PCWzhnWrq6uLLC86OhpqamrQ0dHB+/fvaeddv36dmnmZMmUKLVwPn89HcnIydHV1oaOjg/LycrBYLOrcmpoabNy4EWw2GyoqKrRyJSQkcOjQIezZswcyMjJQVFTEx48f8fr1a3Tr1g1aWloQFxen2TJdu3YNrq6uTdYVHR2Ns2fPUv2stzepv7afy6WpayxMkUxPT4empiaAOmU5Ly+PVm5oaCjN2PvZs2cwNjYWaqBdWVmJnJy/d76+ePGC2nrv6ekpYC/15s0b2tb8xspuSNu2bRvtx8mTJ+Ht7Y1ly5ZhyJAhqK6uBlCn9IuJicHT0xO3bt2izs3NzUVxcTH09PSgo6OD3NxcKCsrUzLIzs7G9u3bm7VM+D1p0dPMz88Pr169Qs+ePVFbW4u5c+eid+/eyMnJYZanGBh+YORaicHdrI2AkmOuKgl3szbffau4MLp164b27dtj5cqVePfuHfbs2YOXL19i9OjRAIAxY8YgNDQUN2/eRFpaGry9vdG6dWs4OTlBUVERVVVVuHbtGrKzsxEREYHjx49Ttig9evSAjo4OPDw8kJqaigsXLiAiIgITJkyAnJwcbWCqX6bo0KEDFBUVoaenh+7du2PVqlV48+YNrl27hmPHjlHtdnZ2RklJCTZu3IiMjAycOXMG169fb/Jtup5JkyZh//79uH37Nt69e4fly5fjl19+gYODg4CCpKCgAGlpaUpBsLGxEZDZ8+fPKZl9TlPlTZw4EUlJSdiyZQsyMjIQExODzZs3UzJxcnLCoUOHcP36daSlpcHf3x/l5eVwdXWFnp4eevXqhWXLluH58+d4+fIlVq5ciaqqKmr3zudoampi7969ePjwId69e4eFCxeid+/e0NfXh5ycHFxcXBAYGIjnz5/jxYsX2LJlC+zs7Jqs65dffqH1U1paGgoKCtDQ0BDajuZc46ysLAQHByMzMxPR0dE4deoU5SRx2rRpOHz4MKKjo5GZmYng4GBcvHiRtlPq3bt3Ajun6hETE4OHhwdev36Nixcv4siRI5g6dSol85iYGERHR+P9+/fYsWMH4uPjqa31TZXdkMGDBzfaD0VFRdy8eRNZWVmIi4uj/DGx2WxISEhg3Lhx2Lx5M+Li4pCUlIQlS5agb9++6Ny5M+zt7aGhoYHly5cjOTkZcXFx8Pb2hoyMDMTFBe1B/1HIV3D//n1y7NgxcujQIXL79m3C4/G+prh/FC6XS+Li4giXy/0hy/tZYOQimpbIprq6mrx69YpUV1d/Vd3ltTySXc4h74rYJLucQ8pr/9nfrr6+Pnn48CH1PSMjg0yaNImYmJiQoUOHkuvXrxM+n08IqZPT7t27iZOTE7GysiLTp08nmZmZ1LkhISHEzs6OWFpakvHjx5PIyEhiYGBA8vLyCCGE5OXlEXd3d2JmZkZ69+5NTpw4IbRNWVlZRF9fn2RlZVFpRUVFZN68ecTc3JwMHjyYhISEEEdHRyr/6dOnZPTo0cTY2JgMGjSIXL58WWSft2/fTiZPnkx95/F4ZPfu3aRPnz7EzMyMzJkzh2qzqHP5fD6pqKggfD5fQGb37t1rTOSNtoUQQu7du0dcXV2JsbExcXJyIkePHqXy+Hw+2bVrF+nTpw8xMTEhkyZNIsnJyVR+YWEhWbJkCbG0tCQ2Njbk999/J0VFRY22ITQ0lPTs2ZN07dqVeHp6kvLyciqvvLyceHp6EisrK2Jra0vWrFlDamtrG62roWzqmTx5Mtm+fXuj7WjqGsfHx5Nx48YRCwsLMmTIEHLu3Dna+YcPHyaOjo7E1NSUuLq6kvv379Pyp0+fTjZt2iRQ78OHD0m3bt3Inj17iLW1NenRowfZv38/7ZhTp06RAQMGEBMTE+Lq6koeP37crLIJ+fs3Vi+XuLg4kf2Ii4sjw4YNI6ampqRfv35k9+7dZPTo0SQ0NJQQQkhtbS3ZsGED6dmzJ7G0tCQeHh6065WZmUlmzpxJzMzMiJ2dHfHz8/vqZ1Rjz7rmPjtZhJB/3qrwB4DH4yExMREWFhbfRMv81uX9LDByEU1LZFNTU4P09HTo6OhQBqU/G4QQVFVVQVZW9l+f4v7RYGQjGkY2wvmvyqWxZ11zn50/X5hpBgYGBgYGhv95GAWHgYGBgYGB4aeDUXAYGBgYGBgYfjoYBYeBgYGBgYHhp4NRcBgYGBgYGBh+OhgFh4GBgYGBgeGng1FwGBgYGBgYGH46GAWHgYGBgYGB4aeDUXAYGBgYGBgYfjoYBYeBgeGH5tatWxgxYgQsLS3h7OyM69evCz1u165d8PT0pKWVlZVh9erV6NGjB+zs7ODp6YmysrJm1RsSEgJra2t07doVFRUVWLt2LQwMDGifhnGLRMFmszFs2DA8evSIlp6bm4uZM2fC3Nwc/fv3x4ULF6g8Qgj27NkDJycnWFlZ4bfffqMFSySEYNOmTbCzs0O3bt2wceNG8Pn8ZvXr2bNnMDQ0RHZ2NpXWlJwOHTok0PegoCAqPywsDH379oWVlRWmT5+OrKysRtuQlJSEcePGwdLSEmPHjqUFp2yKrl27CrSlsrISAJCZmYnp06fD0tISffr0wb59+76qvM8JCQmh4jcBQFxcHEaOHAkLCwuMGDEC9+/fpx1/6dIlDBw4EBYWFnBzc6MF1qytrcWqVavQtWtX2Nvb48CBA7Rzs7KyMHXqVFhYWGDIkCG4e/dui2TUpUsXWFlZUX1is9lYs2YNbGxs0KNHD2zevFkgonp93/r27dvsOh89eiQgx/pPbm5us8v5pnxVsIj/MEwsqn8GRi6i+TdjUf3INIwp9Pr1a2JsbEwOHz5MMjIyyLFjx4ixsTF5/fo17ZyYmBhiaGhIPDw8aOmLFy8mI0eOJC9evCBJSUlk9OjRZMGCBU22oaSkhOjr65Pw8HCSnZ1NCCFk6tSpZPfu3eTjx4/Up6qqqtFyampqyLx58wRib3E4HDJs2DAye/ZskpqaSsLCwoixsTEV3+nEiRPE1taW3Lhxg6SlpZFVq1aRPn36kMrKSlJRUUH27dtHevfuTZ48eUIePHhA7O3tyb59+5rsF5vNJsOGDROIudWUnFavXk38/Pxofa+PRfTXX38RS0tLqq3z588nzs7OItvw6dMnYm1tTby8vEhKSgo5ePAgsbCwIDk5OU22Py8vj+jr65PMzExaW/h8PuFyuaR///5k6dKlJD09ndy6dYtYWVkJxI5qbnnCaBjDq74fe/fuJZmZmWTXrl3E3NycfPjwgRBSF8PKyMiIhIWFkdTUVOLu7k7Gjh1LleXv70+cnZ1JUlISuXLlCrG0tCQXL14khNT9BpydncnSpUtJSkoKCQ0NJebm5i2SUX5+PsnIyKBiRXp7e5MBAwaQZ8+ekfv37xNbW1sSFhZGK+PNmzekR48etJhcTVFbW0uT4cePH8nEiRPJ3Llzm11GQ75FLCpGwWEUnO8KIxfR/JsKTlFlLXmdX0YeZhSRN/nlpKiy9qvK+5Y0VHCCg4PJ9OnTaflubm5k8+bNhJA6RcHHx4eYmpqSAQMG0BScyspKYmhoSBITE6m0p0+fEkNDQ1JTU9NoG7Kzs6lBop5evXqRO3fuNLsf7969I8OHDyfOzs4CCs61a9eItbU1LWDhnDlzyMmTJwkhhIwZM4bs3r2bymOz2cTCwoLcuXOHVFRUkN69e5PTp09T+dHR0c0ajP78808yfvx4moLTHDmNHz+eatvnBAQE0JShN2/eEH19fVJYWCj0+H379pG+ffvS7vvGgkY25N69e6Rnz55C8/Ly8si8efNIWVkZlTZv3jzi6+vbovKE0VDBuXLlCunWrRstv1u3bpSSMm/ePOLp6UnlZWZmEkdHR1JYWEgqKyuJqakp7Z7YuXMnVfb9+/eJhYUFqayspPJ/++23JgOHCutTw99TcXExMTIyIo8ePaLyd+/eTWtnWFgYsbCwIM7Ozl+k4HxOTEwM6dq1q8j7oCm+hYLDLFExMPyPkVVSjfHHnsJo4y10D7kLw403MeHYU2SVVH/Xet+/f09bPjhy5AgA4O3bt5gyZQrMzMwwcOBAnDhxgjrH1dUVy5YtEyirvLwcAFBVVYXk5GScOnUKlpaWtGPExMQQGhoKQ0NDWjqPx6Om658/f44JEybA3NwcAwcORGxsLLKzs+Hk5AQA6NevHzw9PVFRUYH8/Hx07Nix2f19/PgxbG1tER4eLjSve/fukJP7v/buOyyKq23g8G/pICrdAgbBShARJFZsxBZjwViiJiZqYjeWFFFUjD12Y9dEo74aC7FGX000MUZNbCS22CiK2BCVIh125/uDj3ldAQWNoPDc1+UlO2d29swzh52Hc87MWKrLli5dyrvvvgvA6NGj6dixo1qm0WhQFIWHDx8SExPD7du3eeONN9TyunXrcvPmTe7evZtnfa5evcqGDRtyDOPlJ04RERF57ruVlRUnT54kPDyczMxMduzYgaOjI2XLls11/aioKNzd3fUeklijRg29YarvvvsOPz8/vLy89Ia8wsLCcHFxyXW7Dg4OzJw5E0tLSxRFISQkhJMnT1KvXr08Y/Kk7WWXZ7ePDz74gNjYWL39jouL4+eff0ZRFA4cOEBSUhLVq1cHso5xq1at1PUrVarEr7/+io2NDZcuXSIzM1OvzdatW5czZ86g0+k4c+YMr7/+OhYWFnrlzxujkJAQLC0t9WIyYMAAZsyYob7+/fffmTlzJn369MnxfkVRWLJkCb6+vvj4+DBo0KBch58yMjJYsGABgwYNwsbGJq/wvnCS4AhRgsQmp/PxljPsvxKjt/znKzH033KG2OT0F/K5aWlp9OvXj1KlSrFlyxaCgoKYP38+v/76K/3796du3brs2rWLgIAAli5dyu7duwGoUqUKNWvWVLcTGhrKn3/+ScOGDQEoU6YMmzZt0lsnm5mZGU2bNsXExERdtm7dOmrUqIGNjQ3379+nX79+uLm5sX37dgYOHEhAQACJiYkEBwcDEBwczLhx4wgPD0ej0bB8+XKaNm1Kx44d2b59+xP3uVevXgQGBmJubp6jLCoqivLlyzNnzhyaNGlCx44dOXDggFru4+ND+fLl1dfBwcFkZmZSt25dYmKyjp2Dg4NabmdnB8CdO3dyrYuiKAQFBfHJJ59ga2tboDjdu3ePuLg4tm/fjp+fH2+99RarVq1S52307t0bV1dX2rVrR+3atdmyZQtLly7N8ynPdnZ2REdH6y27c+eOmjxs2rSJxYsX8/nnn7N9+3ZKlSrFiBEjAAgPDyclJYXevXvj6+tL//79uXr1ao7P8PPzo1evXnh5edGmTZtc6/G07aWnpzNgwAAqVarEtm3baNOmjV6y6uPjw3vvvcfw4cNxd3dn6NChTJ48GVdXVxISEoiPj0er1fLRRx/RuHFjBg8erO53TEwM1tbWejG3s7MjLS2NuLg4YmJi9I4vgK2trXp8CxKjAQMGEBkZCWS1O0dHR3bs2EHbtm158803WbJkid78raVLl9K6detc47V+/Xp+/PFH5s6dy+bNm7G1taVfv35kZGTorbd3714ePnzIe++9l2fsC4MkOC+xO8kJ3EiKIyE9tairIoqJ6MS0HMlNtp+vxBCdmPZCPvfIkSM8ePCA6dOnU61aNfz8/Bg/fjy3b9/G1taWkSNHUrlyZfz8/Bg4cKBeL062Bw8e8Mknn+Dt7V2gyY/Z1q9fz969exk9ejQAe/bsoWzZsowfPx5XV1feeecdPvvsM1JTU9W/Om1sbChdujQRERFoNBpcXV1ZuXIl3bp1Y8KECezfv/+Z4pGcnMz27dtJSEhg+fLl+Pv7M3z4cM6dO5dj3TNnzjBz5kw++ugj7O3tSU3N+j549OSY/XN6eu4J6g8//EBGRgbdu3d/at0ej1NERASQdYJdtmwZAwcOZNmyZaxduxaAu3fvkpaWxpw5c9i0aRNvvPEGX3zxBWlpubel1q1bc/bsWbZs2UJmZiaHDx/ml19+UU+Smzdvpk+fPrRr147KlSsTFBRE/fr1SU1NJSIigvj4eAYPHszSpUsxMzOjT58+JCYm6n3GwoULWb58ORcvXtTrnXjck7b3xx9/EBcXx5dffkmVKlV47733aNmypfrepKQkoqKiGDZsGMHBwQwaNIipU6cSHh5OcnIyAFOnTqVDhw4sW7aM9PR0Bg4ciE6nIyUlRe/4gf4xzKs8+/gWJEampqYMGjSIxMREkpOTiYyMZNOmTcyYMYOAgAD+85//sGbNmrwbxCO+/fZbRo8eTf369alSpQqTJ08mPj6ew4cP6623ZcsWunbtipmZWb62+6IYFemni1zdSXnIzshzzDl/iJjURJqWr8IU77bUKGuPmaFxUVdPvMLiUzKfWJ6Q+uTyZ3X16lVcXFz0hmS6dOnCzJkzuXTpkl5XvVarzfHX/7179+jbty+KorBw4UIMDAr2t9mGDRuYOnUqY8eOxdfXV63T66+/rretvn37AuhdYQTg7+9PixYtsLKyAqBmzZpcu3aNjRs34u7uzttvv62u26FDByZPnvzE+hgaGmJlZcWXX36JgYEB7u7unDp1ii1btuDh4aGu9/fff9O/f3+aNm2q/oVuamoKZJ0IH/0ZwNzcnKCgIH788Ud1G3v27GH+/PmsWbMGjUZT4DjVq1ePY8eOYW1tDWQNJz148ICNGzfSp08fJk6cSOvWrenQoQMAc+fOpXnz5vzyyy9kZmYyceJEdfuTJk2iY8eOTJkyhalTpzJx4kTc3Nzo2bOnepXZ1atXcXd3V99jZ2dHQEAAAKtWrSIjI4NSpUoBMGfOHJo1a8bBgwdp3769+p7sGKalpfH5558zevRohgwZQkhIiF5sn7S97CHJR4eJPDw8OHToEJB1slcUhWHDhgHg7u7O2bNnWbdunbqsW7du+Pv7q9tu3Lgxp0+fxtTUNEcymv3azMwMU1NT4uLicpRnJwzPGiMjIyMSExOZO3cujo6OQNbVfBs3bqRfv348SVJSEnfu3GHUqFF6vzOpqalcu3ZNfX3//n1OnTrFhAkTnri9wiAJzkvmXmoiQ//Yyo7r59Vle6Iu8NONS/zebij1HZyLsHbiVVfW/Mm/8mXMXsxXgpFR7tvNzMykYcOGBAUFqcsURSEl5X/zgaKjo/nggw+ArKGTgo7pr1q1ilmzZjF69Gg+/PDDp9YpNxqNRk1usrm6unLs2DEcHBzYsWOHuvzRJC4vDg4OaDQavROFi4sLly9fVl8fP36cQYMG0bhxY+bOnYuBgQGKoqhDFzExMTg5Oak/A9jb2zNixAg++ugjve3Exsaq83uyh5bat2/PoEGDGDRoEJB3nAA1uclWpUoVdbjln3/+UbcBUKpUKZydnbl586Y6fyVb9vBYly5d8Pf35/79+zg4ODBr1ix1X550XExMTPR6NkxNTXFyciI6Opp79+5x/PhxvWSzatWqZGRkkJiYyLRp09Ter/xs79FYZTM2/t8fmP/880+OoVE3NzdCQ0OxtrbG2NgYV1dXvRhaWVlx584dypcvT2xsLJmZmer+xsTEYGZmRpkyZShXrpzebQEgK8nPPvYFjZGjoyN3797F3t5efZ3NxcWF27dv57m9bFqtFoCvv/46xxyfR+dbHT58GCcnJ2rUqPHUbb5oMkT1kolKitNLbrJlKjqGHdvGvdTc788gRH6UszSldXX7XMtaV7ennKXpC/ncypUrExkZqZe4zJw5k++//56rV6/i5OSEs7Mzzs7OnDlzRp3rkJyczMcff4yBgQHr16+nXLlyBfrc7du3M2vWLMaOHat30s+u0+XLl/VOYiNHjsz13ilff/11jkmXly5dwtXVFSMjI7Xuzs7OOea45MbT05PQ0FD1pAFZcyeyTzxXrlxh8ODBNGnShAULFuidWO3t7alYsaJeb0RISAgVK1bEwcEBW1tbvfq0atWKffv2sWPHDnbs2MHKlSsBWLlyJT169HhqnIKDg2nTpo1enC5evKievB0cHAgPD1fL0tPTuXHjBk5OTlhaWurVxdLSkmPHjjFq1CgMDQ1xcHBAURQOHz5M/fr1AXB2dubSpUvq9mJjY2nQoAFRUVG0bNmSbdu2qWXZQy6urq7cuHGDzz//XG9+z/nz57GxscHGxoZy5crp1UVRlCdur1q1aly7dk2d0J6939kcHBxyJCERERE4OTlhZGSEu7u73n48ePCA2NhYHB0dcXNzw8jISG/ScEhICB4eHhgYGODp6ck///yjl5CFhISoyWJBY3T9+nVcXFzw9PQkLS1Nb95SRESEXsKTlzJlymBra0tMTIwawwoVKjB79my97Z09exZvb++nbq8wSILzkvnlVlieZX/dv0lC+ou90kUUb9YWJnzT3TNHktO6uj3fdPfE2sIkj3c+H19fX+zs7AgKCiI8PJxffvmFTZs28fXXX5OamqouP3ToENOmTVN7DFasWMH169fVm8rFxMQQExOjd9LJS1xcHJMnT6Zz5868/fbb6ntjYmLQarV06NCBuLg4Zs2axbVr19i2bRu//PILjRs3zrGtFi1acPLkSVatWsX169f5/vvv2bFjx1O79fPSvn17dDodkyZNIjIykg0bNnD48GF1jkxQUBAVKlRg7NixxMbGqvXOPuH16NGDOXPmcPz4cY4fP87cuXPVXq7HPZ5kVKxYEYCKFSuqVwI9KU6NGjUiJiaGmTNnEhkZyZ49e/jmm2/4+OOPgaxhmOXLl3Pw4EEiIiKYMGECpUqVUq9Ee5yLiwsHDx7k+++/JyoqikmTJhEfH68O5fTu3Zu1a9dy4MABrl69ysSJE3FycqJSpUo0b96cRYsWcfz4cUJDQxk9ejTly5enWbNmeHh44ObmRmBgIGFhYRw6dIjZs2fr9S49SqPRPHF7jRo1okKFCuok823btundjLFbt278/vvvrFmzhqioKNasWcORI0fo1asXkDXc+Z///Ie9e/cSHh5OYGAgbm5u1K5dG3Nzc/z9/fnyyy85e/YsBw4cYPXq1eoxrFevnnr8Q0NDWblyJWfPnqVr164FjlFAQADlypWjWbNmuLq60rx5c8aOHculS5c4fPgwK1eupGfPnvlqt3369GHBggX8+uuvXLt2jfHjx/PXX3/p9VSFhoZStWrVfG3vhXumC9SLgZf1PjjLLx5VDFZ/lus/4+++UK4mPNs9BYqK3Acnby/DfXCORz5QLkYnFMp9cMLCwpQPPvhA8fDwUFq0aKF8//33iqIoyvnz55VevXoptWrVUnx9fZUFCxYoCQkJik6nU9q0aaNUr149x7/Hb+inKIoSEBCgt3z37t25vvfRe8D89ddfSteuXRV3d3elbdu2yk8//aQoiqJERUXluBne/v37lQ4dOigeHh566+bH4/fBUZSs++S89957Sq1atZTWrVur27t7926e9f7hhx+UxMREJSMjQ5k+fbri4+Oj1K9fX5k9e3aeN6d73OP7lp84nTx5UunevbtSu3ZtvWOnKFnteMWKFYqfn5/i7e2tfPTRR3r3D8rNwYMHlbZt2yqenp7KBx98oISFhallOp1OWb58ueLr66vUqVNHGTBggHqDu9TUVGXGjBlK48aNFU9PT2XgwIHKrVu31PddvXpVGTp0qOLt7a00btxYWbZs2RPj8qTtKUrWvWuy22zXrl2Vr776Sr1XjaJk3c+oY8eOSp06dZTOnTsrR48e1dv+5s2blRYtWii1a9dWPv74Y/UmgIqiKMnJycro0aOVOnXqKL6+vsp3332n995r166p7ePtt9/W23ZBYjRgwAAlLCxMjUNCQoLyxRdfKHXq1FEaNmyoLFq0KNcYbd26Ncd9cDIzM5V58+ap237vvfeUCxcu6K3Ttm3bHDcOfBb/xn1wNIqSyz2aSwCtVsvp06epU6dOnpczFsX2QhNicNs6C4Wch6VL5dqs9n0XS+MXM4zwIvzbcS5OniU2qamp6oTdor5C4UVRFIXk5GQsLCyeOim2pJHY5E1ik7tXNS5P+q7L73enDFG9ZMqblWZxw845ljtalGVG3Xb5Sm6SM9K5+vABv94K5dDtcK4lPiBN+2KujhFCCCFeRnIV1UumtIkZvVy9aezgwurQE0QlxdLptVo0K1+F1yytn/r+uLQU1oeH8MXJH0nXZU1gtDAyYZVvd9o5ub1SvT9CCCHEs5IE5yVUxsQMD5sKzK/fCa1Oh2EB7vlxNvYWI47v0FuWnJlOr9828FenUdS2qfgv11YIIYR4+cgQ1UuuIMlNQnoqU88cyLVMQWHxhSOky1CVEEKIEkASnGIkOTODqw8f5Fl+Kf4uKZLgCCGEKAEkwSlGShubUucJQ1AN7J0pZSSPehBCCFH8SYJTjJQyNmGcZ0sMcrkU0NTQiI9rNMDIQC7VFkIIUfxJglPMVCtjx66WH1HRooy6zMXShv1tBlLZsmDP8BFCCCFeVXIVVTFTytiUto41ONZ+BPfTktCgwc6sFBUeSXiEEEKI4k56cIohjUaDY6my1LapiIdNBUluxEtv6tSp1KhRgxo1alCzZk28vb1Zv369Wr5v3z7atGlDnTp16NevHzdv3lTLbt++zcCBA/H29sbPz481a9aoZYqiMGHCBOrUqcObb74JwP79+3nrrbfw8vKiZ8+e/PPPP89U523btuX5vKX8SEtLY8qUKTRs2FB9onpycnKO9eLi4mjUqBE3btzQW/6s+5GZmUmnTp1YtGiR3vKtW7fStm1bvLy86Natm97DPJOTkxk/fjz169fnjTfeYMKECSQlPfuDfxVFYdWqVfj5+eHj48PYsWMLtL3w8HD69eunHvPly5ej0+nU8l27dtGmTRtq165Njx49OHv2bJ7bunHjBjVq1MgR339D7969c8T5UYqiMGfOHBo0aEC9evWYNWuW3n7Ex8fz2Wef4eXlRdOmTVm3bt0TP69GjRocP35cfb1hwwZatGhBkyZNGDFiBHFxcc+8L35+fnoP8dy9ezctW7bE09OToUOH8uDB/y5wSUpKYvz48TRo0ICmTZuqD3gtbJLgCCGKXHh4OJ999hlHjhzh8OHD/Pzzz3Tp0gWAv/76i88++4y+ffuybds2TExM+PTTT9X3jhw5EgsLC7Zt20ZgYCALFixg//79QNYTv7ds2cLXX3/Nhg0bCA0N5bPPPmPgwIHs3LkTNzc3Bg4cqPeU88KyePFiTpw4wcqVK1mxYgWnTp1i3rx5euvEx8czaNAg7t+/r7f8efZj9erVek+iBvj999+ZPHkyQ4YMYceOHTRu3JgBAwaoT+aePn0658+fZ9WqVaxZs4azZ8/y1VdfPfO+b968mcWLF/Ppp5+yceNGoqOj+eyzz/L13pSUFAYMGEC5cuX44YcfmDhxImvXrmXjxo0AnDp1inHjxjFkyBD27NmDl5cX/fv3f66E7EX57rvv2L17N4sXL2bhwoX8+OOPfPfdd2r5Z599xo0bN9i8eTOBgYHMmTOHw4cP52vb//3vf5k1axZjxozhu+++4/bt20yePPlfqffZs2cZN24cw4YNY/PmzSQkJDB27Fi1fMKECZw8eZIlS5Ywb948Nm3apLdfhUUSHCFKIG1qLOkPLpN6+wTpDy6jTY0t0vqEh4fz+uuvY29vj729PXZ2dpibmwNZJ+SOHTvSo0cPXF1dGTduHDExMTx48ID4+HhOnz7N4MGDqVy5Mi1btqRJkyb8+eefAOpTx5s2bUr58uU5evQoVatWxd/fn9dee41PP/2UmJgYwsLCCn2fDx06xLvvvouHhwe1a9emZ8+eHDt2TC0/deoU77zzTq69Os+6H5GRkaxbty7H0563b9+Ov78/HTt2xNnZmZEjR2JnZ8ehQ4cAMDY2ZsKECdSqVQt3d3e6dOmi18NTUOvXr6dv3760b9+eatWq8dVXX/Hbb78RERHx1PeePHmS+Ph4Jk2ahKurK82aNaNPnz7s3r0bgHv37jFkyBA6depEpUqVGDp0KHFxcYSHhz9zfV+UdevWMXz4cHx8fGjQoAGff/45GzZsALKS8z/++IM5c+ZQvXp12rZtS9euXfnrr7/yte1vvvmG/v3706ZNG6pWrcoXX3zBlStX0Gq1z13v9evX89Zbb+Hv70/NmjWZNWsWhw4dIioqigcPHrBnzx4mTZpE3bp18fHx4fPPP2fVqlXP/bkFJQmOECVM5sMo7v73PW6u8+D2Zl9urvPg7t73yXwY9UI/9/bt2wwaNAhPT0/8/PxYvHgxWq2WxMREoqOjqVy5cq7vO3HiBK1atVJfV6pUiV9//RUbGxvMzMwwNzdn27ZtZGRkEBERwV9//YWbmxvHjx+nd+/eANSsWZNFixZhZWVFWFgYISEh6HQ6tm3bhqWlJa+99hqQNRQTFBRE/fr1qV+/PhMmTCAtLQ2A6OhoPv74Y+rUqUPnzp25fv36E/c3PT2dGTNm0KRJE9zd3fHz82Pz5s1quZWVFT/99BPx8fHEx8fz888/4+bmppYfOXKELl265DrE8bT9yEtQUBCffPIJNjb6Fxx8/PHH9O3bN8f62QnixIkTqVu3LpA1pLN7927q1aunrpfXsc1LVFQUnp6e6msHBwdsbGw4ffo0AGFhYXz00Ud4eXnh4eFBr1691ATFzc2NJUuWYGJiorfNxMREANq2bcvgwYOBrAc2rlmzBltbW6pUqfLE2Ozbt4+mTZvi7e1NUFAQ6enpatnvv/9O586d8fT0pGPHjmoCrSgKy5cvx8/Pj1q1auHr68vixYv1tnvnzh3ef/99PDw86N69u9p7Fh0dze3bt3njjTfUdevWrcvNmze5e/cuJ06coGbNmlSqVEktDwoKYsSIEerrxYsX07BhQ+rXr09wcLBeLC5cuKD3e/PGG2+we/du9eGUv/zyC/7+/nh4eODj48Onn36q18u1adMmmjdvjre3N0uXLtXbpzNnzuDj46O+rlChAhUrVuTMmTPqUN+jx7dGjRrExMS8kGHAJ5EE5yWRlqnldkIqdxPTiroqohjTpsYSs38gqdf173idGrmfmAODXlhPjqIoDBs2DFtbW7Zv386MGTP48ccfWb58OeHh4Wg0GpYvX07Tpk3p1KkTP/74IwAJCQnEx8ej1Wr56KOPaNy4MYMHD1aHTkxNTQkKCmLz5s14enry1ltv0bRpU7p164aXl5eaHBw5coR+/frRrl07mjdvTq9evahVqxazZs1i4cKFlC1bFoDx48cTEhLC0qVLWb16NSEhISxYsACAESNGoNPpCA4Opn///qxdu/aJ+7xy5Up+++03Fi1axL59+/D392fKlCncu3cPgNGjR3Pjxg01mYqPj2fixInq+0eOHMmQIUNyfVry0/YjN1u3biUtLY3u3bvnKHN3d9dLMH///XeuXbtGgwYN9NYLCAjgzTff5N69ewwdOvSpxzYvtra26jGErMQyPj6e2NhYdDodgwYNwtHRkZ07d7Jp0ya0Wi2zZ88GwN7envr166vvTU1NZcuWLTnq+ueff+Ll5cXixYsJDAykVKlSedYHYMuWLcyfP5/ly5fz+++/s2LFCiBrOHDw4MG0atWKnTt30r59e4YMGUJMTAw7duxg7dq1TJs2jX379jF06FAWLVqkNx9q+/bttG3blh07dlCpUiWGDRuGVqslJiYGyErustnZ2QFZSVFUVBROTk7qXKW2bduyadMmdd3Nmzezbt06pk+fzpo1a9i6dataFhWV9cfKgwcP6NmzJ61btyYgIICEhAQArl+/zogRI+jVqxd79+5lwYIF/PHHH2zZsgWAw4cPM23aNEaOHMnmzZs5d+6c3ry3u3fv6tU7+5jeuXMHW1tbAL3je/v2bQBiYwu3p1gSnCKmKArh95IYvfsijRYdwW/ZH3xzLJLbCalFXTVRDGmT7+ZIbrKlRu5Hm3z3hXzusWPHuHXrFlOmTMHV1ZX69esTEBDAunXriIiIQKPR4OrqysqVK+natStTp05l//796vDM1KlT6dChA8uWLSM9PZ2BAweqkzHDw8Np0aIFmzdvZsaMGezbt49du3ZhYmKinvDt7e0pVaoUsbGxxMTEEBQUxJYtW+jUqRNjx47l/v37xMfHs2/fPoKCgqhbty7u7u5MnjyZihUrEhoayt9//83UqVOpVq0a7dq1o2fPnk/c55o1azJt2jTq1KlDpUqVGDRoEBkZGVy7dg3IOslUqFCBtWvXsmrVKtLS0vI9r+VJ+5Gb+/fvM2/ePCZPnowml/tkPer69euMHTuWDh064O7urlfWv39/Nm/ejKOjI/3790en0z3x2OalXbt2rFixgvDwcL39zsjIIDU1lR49ejBmzBhee+013N3d6dy5c67DbzqdjjFjxpCUlMSAAQP0yqpVq8a2bdsYPnw4Y8aMUXuH8hIYGEjdunWpV68eI0aMUJOJH374AW9vb4YMGULlypUZMGAAH374IQkJCVSoUIEZM2bQsGFDnJyc6NmzJ/b29oSGhqrbbdmyJe+//z5VqlRh0qRJ3L9/n6NHj5KamvUd/2hPVPbP6enpJCcn88cffxASEsLXX39N//79mTFjBj/99BOQlZB9+OGHtGjRAjc3N6ZOnapuJ7snZvLkyfTv359Zs2YRFhbG6NGj1biNHz+e7t274+TkhK+vL40aNVLrHRwcTIcOHfD396datWpMnz4dU9P/Pag5NTU1Rw+aiYkJ6enpODo6UqdOHaZNm0ZcXBwxMTFqr1ZGRsYTj8G/rUgvE4+OjmbatGkcO3YMU1NT2rVrx6effoqpqSlRUVFMmDCB06dPU7FiRQIDA/H19VXf+8cffzB9+nS1q3PatGl6XXmvirB7SdRfeIS4lP8d+IE/nKVFVVs29PKmfBkzALQp91AyU9EYmmJoYV9U1RWvOF1a/JPL059c/qzCw8OJi4tThzkg60s2NTWV5s2b8+eff2JlZQVkdWeHhYWxceNGvLy8AOjWrRv+/v4AzJkzh8aNG3P69GnS0tL44YcfOHToEGZmZnh4eBAdHc2yZcvo2LFjjnpkz2d47733AJgyZQpvvfUWW7dupUGDBmi1Wr2Tuo+PDz4+PuzduxcrKysqVvzfncI9PDzYt28fkDV0kN3rBLBnzx5atmzJ0aNH+eqrr4iIiODChQsA6rDcuHHjWLNmjdqVP336dN5//32GDx+e46/jx82dOzfP/fD29qZ///7qugMHDuTKlSu88847VK9e/YnbvXr1Kn379qVSpUp6J8xs2XN35s+fT5MmTTh58uQTj21sbCxffPGF3nydv//+myFDhhAVFcXbb7+NkZERPXr0oGbNmlhaWmJhYUHPnj3ZsWMH58+fV2OX3buRLTMzk4CAAH777TdWr16Nvb293nwlOzs77OzscHNz48yZM2zatAkHBwfefvttdZ0OHTqoiVHt2rXV5a+//jr37t0jPj6eq1ev5kj0Ro4cCUCVKlU4c+YMc+fOJTw8nIsXLxITE6N3JdSj27W0tMTFxYWIiAi8vb2BrGQmO3nIHhYzNzfH0NAQrVbLnDlzsLCwwMPDg0uXLrF582batGlDeHi42ouWfWwsLCwAMDLKOrUPGDAAPz8/kpOTmTp1Kp07d1aHg01MTFi2bBmhoaGEhoYSFhZGp06dgKzf1x49eqjbtra21ju/mpqa6g3hZdc9e97crFmzGD58OA0aNKB06dJ8+umn/P3331haWlKYiizBURSF4cOHU6ZMGTZs2EB8fDyBgYEYGBgwevRohg4dSvXq1dm6dSsHDhxg2LBh/Pe//6VixYrcunWLoUOH8sknn9CkSROWLFnCkCFD2LVr11P/OnmZJKdnMnV/qF5yk+1g2H0u3k3E3jiZtOiTxB6dQEbsZYytq2PV8EvMKjTA0Ny2CGotXmUGpnkPYQAYmDy5/FllZmbi6uqaYywfoHTp0uoXcjYXFxdCQkKwtrbG2NgYV1dXtcza2horKyvu3LnDzZs3cXZ2xszMTC1//fXX8xwe+eeff9R5OQAGBgbUrFmTW7duYWz85MeYKIqi9/rR9UeMGMFHH32kvnZwcGD+/PkEBwfzzjvv4O/vz8SJE9XLyiMiIkhOTqZmzZp69dbpdNy5c+epCc6T9uODDz5gx44dalnZsmWpX78+ZmZm6qX3qamp/P333+zbt489e/YAWUMxffr0oVKlSnz77bdqTNPT0zl48CCNGzdWT1B2dnZYWVkRGxv71GM7bdo0tbcim4WFBV9//TUPHz5Eo9FgaWlJw4YNcXR0JCkpia5du2JtbY2fnx/t27cnIiKC1atXq+/PyMhg1KhRHD16lJUrV+Lt7a0en3PnzmFoaKiXlFSpUoXw8HAcHBz0YmNpaaleeWbwyIONs7dlbGyco20+Kjg4mOnTp9OtWzd1GOiDDz7QW+fxIUadToexsTHlypUDICYmBicnJ/VnyOpxdHBwoHz58mrSAlm/F0eOHMlRz2zZdbW3z/oj+NHfGxcXFyBr+Cs2NpaePXuql+n36dMnx5Drk9p7uXLl1KHWbPfu3VM/19nZmZ07d3L//n1Kly7N9evXMTAw0PsDoTAU2RBVREQEp0+fZsaMGVSrVg0fHx+GDx/O7t27OXbsGFFRUUyePJkqVaowcOBA6tSpo44xBgcHU6tWLfr160e1atWYMWMGN2/e5MSJE0W1O8/kQXIG287fzrP8/M17JF3ZQvSODqTHnEbJTCE95gx3d3Xm4cX16DJlGEsUjKGFA2bOrXItM3NuhaHFk0+sz8rFxYVbt25hY2ODs7Mzzs7O3Lhxg4ULF7J48WL69Omjt/7ly5dxcXHByMgId3d3vcuaHzx4QGxsLI6Ojjg4OBAZGan312RERIR6wnicg4NDjqtprl69ipOTE5UqVcLQ0FDvsw4cOEDnzp2pXr068fHxREZGqmUXL15Uf7a1tVX3y9nZGSMjIzZt2sSECRP4/PPPadeunXoiVRRFTWAeHXbJvoIor7rndz/MzMz06mJlZcXPP//Mrl272LFjBzt27KBWrVr06NFDvT/J3bt36devH87OzqxatUrvL20DAwPGjBnDb7/9pi67desWsbGxVKlS5YnHVqPRUK5cOb36QNZf+Nu3b6d06dJYWlpy9uxZHj58iJeXFydOnODu3busW7eOjz/+mEaNGnHr1i29E25QUBBHjx7lm2++0ZvsDFlDSo9fbv/PP//g6uqKkZGRXl2y54sAXLlyRf357NmzanLh7Oyc47L6Hj16sGfPHjZu3MjQoUMJDAzE398fa2tr7t+/r1fXR7ebkJDAtWvXcHV1pVy5clSsWFGvdyskJISKFSvi4OCAp6cnN2/eVCd6Q1YbcXR0BLKG4M6dO6eW3bhxQ51jk72NR+udPdetYsWK7Ny5kzfeeIO5c+fSq1cvateuTWRkpFrvx7edmJio1/Y9PT316n379m1u376Np6cnOp2Ofv36cfnyZWxtbTExMeG3337j9ddfL/QenCJLcOzt7fn2229zdDsmJiZy5swZXn/9db3MtW7duuoY6uMzuM3NzXF3d3/qGOvLRqMBY8O8D8HbleHBkbG5lsUdnYA26c4LqpkorgzNrLFvuTxHkmPm3Ar7liswNLN+IZ/r6+uLo6MjX3zxBZcvX+bUqVNMmDABc3Nz/Pz8OHnyJKtWreL69ets3LiRPXv20K9fPwD69u3Lf/7zH/bu3Ut4eDiBgYG4ublRu3Zt/Pz8MDY2Zvz48Vy9epVff/2V5cuX6/VuPKp79+5s2bKFHTt2EBkZyZw5c7h16xadO3fG0tISf39/pk2bxtmzZzl37hzz58+nQYMGVKlShYYNGxIYGMilS5c4cOCA3o0Ic2NlZcXBgweJiori1KlT6vyH9PR0ypcvT5MmTZgwYQLnz5/n3LlzTJgwgbfffjvHFU656datW577kZtHT+rZPV5ly5ZVT5YzZ85Ep9Mxbdo0kpOTiYmJISYmhqSkJIyMjHj33XeZN28ep06d4vz584waNYo333yTatWqPfHY5jZBGrIStMWLF3P27FnOnz/PF198Qc+ePbGyssLKyork5GQOHDjAjRs3CA4OZsOGDWoSe/ToUbZt28aYMWNwdnZW65p9o7nu3btz7Ngx1q5dy7Vr11i4cCFnz57NkUQ/bsqUKZw5c4ajR4+ycOFCdf2ePXty6tQpvvvuOyIjI1mxYgWhoaH4+PhgbW3Nn3/+ydWrV9W4ZGRk6CXcP/74I1u2bCEsLIzAwECcnZ3VCdE9e/Zkzpw5HD9+nOPHjzN37ly1B6hRo0a4uLgQEBBAeHg4//3vfwkODlbnfr3//vusW7eOn376iStXrjBu3Di1F0qj0dCnTx8WLlzI0aNHuXLlCpMmTaJly5bY29tjZWXF5cuXOXv2LFevXuWrr77i3Llzar3ff/999u7dy5YtWwgPDycoKEivF65nz57s3LmT4OBgLl26xOjRo2nevDmVKlXCwMAAMzMz5s6dy7Vr1zhw4ABLlixh0KBBT4z/i1BkQ1RlypShSZMm6mudTsf69etp0KABMTExec7QBp5a/qqwK2VCHx8nFhy+mnu5YQL3M3K/OZWiTUWbHI1x2covsIaiODIqXQmHt9ajTb6LLj0eA5OyGFo4vLDkBrK66ZctW8aUKVPo3r07FhYWtG3bloCAAMzMzPj6669ZuHAhX3/9NY6OjkybNk2df9O2bVsSEhKYPXs29+/fp169eixduhSNRkPp0qVZs2YN06ZNo2vXrtjY2DB48GDefffdXOvRrl07kpKSWLFiBXfu3MHNzY21a9eqf8kHBgYybdo0+vbti7GxMe3atWPUqFFA1ryTCRMm0KNHDypWrEjv3r317uz6uOnTp/Pll1/y9ttvU65cObp164ahoSEXL16kadOmzJ07l6+++ooBAwag0Wh48803CQgIyFc827VrR3Jycp77URCKonDgwAFSU1Np27atXtmwYcP45JNP+PTTT9FoNIwcOZLk5GRat27N+PHjgScf27z07t2bmzdv0r9/fwwMDOjUqROff/45AF5eXgwdOpRJkyaRlpZGjRo1CAoKYty4cURHR6uTbIOCgggKClK3WbFiRXbv3o27uzuLFy9m3rx5zJ07l2rVqrFq1Sp1SCgvPXv2ZPDgwWRkZNC9e3c+/PBDAF577TUWLVrE3LlzmTdvHtWqVWP58uWUK1eOwMBAAgMD6dSpE7a2trz11luYm5vr9e717t2bH374gSlTpqhXdWVPpfjoo4+4f/8+w4YNw9DQkK5du6qJlaGhIStXrmTixIm88847WFtbM2bMGPWO3J06dSI2NpYpU6aQmprKgAED9Hps+vXrR1paGgEBASQlJeHn58ekSZPUOl24cIE+ffpgamrKG2+8wdChQ9XhSh8fH2bMmMGCBQt48OABXbp00buFgZeXF5MnT2bhwoXEx8fTuHFjpkyZopZPmjSJCRMm0LlzZ2xtbRk/frzeJeuFRaM8PtBWRGbOnMmGDRv44YcfWLNmDVqtlpkzZ6rlP/zwAytWrGD//v20bNmSwYMHq3c6haxLLo2NjZk2bVq+Pk+r1XL69Gk8PDzy/CujILRaLefOnSvw9m7Ep+G3/E+uPtC/mdfH9V9jQVOF6I318ngnVOx5DCM7zzzLXwbPGpeS4Flik5qaSmRkJC4uLnrzTooTRVFISUnB3Nz8lZpTVxgkNnmT2OTuVY1LamoqV69ezTHHDv733VmnTp0nfne+FA/bnD17NmvXrmX+/PlUr14dU1PTHM/MSE9PV3cyrxncZcoU/JlLj44z/hsKuj0jIyP2fOjJ75EJBJ+LpqyZEQPrOeFkrpCSGY9h6Upoc7kBm2GpCiRpzbn6igzL/dtxLk6epc2kpKToXalRHBXF4xNeFRKbvElscveqxSUtLY2MjIwc858KosgTnClTprBx40Zmz55NmzZtgKwZ2o/f8+DevXvqsFReM7gf7ULLr6LuwclWrbw1vbwrYWigwcQwO8suR6l233Nna2uUzP81To2hGfbtNmLsUI065V7ujFx6cPL2PD045ubm0oNTAkls8iaxyd2rGhcDAwOMjY2pWrVqnj04T1OkCc7ixYvZtGkT8+bN0xv79fT0ZOXKlaSmpqo7FhISot5n4fEZ3CkpKVy4cIFhw4YVuA6Ghob/6on3ebZnmcv7DMrVxfH9v0kO30nq7eOYlvOhVLXOGJV+DY1hkeen+fZvx7k4KUhsDA0N0Wg06r/irCTs47OS2ORNYpO7Vy0u2fV9nnNHkZ0hw8PDWbp0KQMGDKBu3brq9f8A9erVo0KFCowdO5YhQ4Zw8OBBzp49y4wZMwDo0qULq1atYuXKlbRo0YIlS5bg5OSkd/vu4kJjYISxlStl646ijKJDo5GbTwshhBBPU2Rny19++QWtVsuyZcvw9fXV+2doaMjSpUuJiYnhnXfeYdeuXSxZskS9SZCTkxOLFi1i69atdO3albi4OJYsWfJKZafPQpIbIYQQIn+KrAdnwIABOZ4d8ihnZ+cn3meiWbNmNGvW7EVU7ZVyJ/khcekpGGg02JmVwsbU4ulvEq+84j7BWAhRsv0b33GvziQOoSclM4NT96IY+Ecwl+Ozhvca2ldmReOuvG5Vrtj3ZpVUJiYmGBgYcOvWLezt7TExMSl2x1pRFNLS0jAwMCh2+/a8JDZ5k9jk7lWLi6IopKenExMTg4GBQY6HehaEJDivqNCEGN7ctxyt8r8s98+YazT97xJCOo6icumn3w1VvHoMDAxwcXHh9u3b3Lp1q6ir80IoikJGRgbGxsavxBdyYZLY5E1ik7tXNS4WFha89tpres8IKyhJcApRWqaW6IdpZOoULIyNKF/G9OlvysXDjFQmn/5ZL7nJFpeewtZrZ/m0VrNXqjGL/DMxMeG1114jMzMTrVZb1NX512m1Wi5dukTVqlXlyrvHSGzyJrHJ3asYF0NDQ4yMjJ77HCYJTiG5GZ/C3N8iWHk8kuR0LTXsLZnfyZ1Gla0pY/bkpxg/7mFGGn/ejcyzfP+tKwx2a4SF0bN37YmXm0ajwdjY+KlPwH4VZSdtZmZmr8wXcmGR2ORNYpO7khwXuSynEEQ/TKPn+r9YcDiC5PSsxnY5JpF23x7n94gHBd6eiYERFS3yvmtzZUsbTA1KVkMWQgghHiUJTiGIjE3myNXcE5lRO89zOyE117K82JmVIrB2yzzLB7s1wlASHCGEECWYJDiF4I9rsXmWhd9P5mFaZoG36VvehRGvN9FbZqQx4JvG3XGVCcZCCCFKOJmDUwjKlc57MrGxoQYTw4LnmfZmlgTVac3Amg05GXMdM0NjvO2cKGdmSSnjZ5u8LIQQQhQXkuAUggavWWFiaEC6NudVT728HHGwfLbJwFam5liZmlOjrMPzVlEIIYQoVmSIqhBULGvG9r4+GBvqX/JWq3xpJrWtiYWJ5JlCCCHEv0nOrIXA1MiQFlXsuDi6Bb+F3+dGXApNXW2p7mBJxTJmT9+AEEIIIQpEEpxCYmZsiKttKVxtSxV1VYQQQohiT4aohBBCCFHsSIIjhBBCiGJHEhwhhBBCFDsyB0eoMlMy0KVr0RhoMH7CvXuEEEKIl50kOAJtupbUu0nc/CmcpOvxGJc2oXyzypStaYdxaXlgpxBCiFePDFEJkqLiubT0JA/DHqBL15J2P4XIbRe5sS+UzOSMoq6eEEIIUWCS4JRwGQ/TuL7jMig5yx78dYeMh2mFXykhhBDiOUmCU8JpUzNJu5ecZ3nS9fhCrI0QQgjx75AEp4TTGGieWG5gKtO0hBBCvHokwSnhDC2MKV3VJvdCAw2lnMoUboWEEEKIf4EkOCWckbkxr3WqgdHjTzTXQOVur2MkV1EJIYR4Bcn4g8DMzoKaQ3x4GB5L/OX7mNqYYetdARNrMwyNDYu6ekIIIUSBSYIjADC1NsfUxxxb7wpPnZcjhBBCvOxkiErokeRGCCFEcSAJjhBCCCGKHUlwhBBCCFHsSIIjhBBCiGJHEhwhhBBCFDuS4AghhBCi2JEERwghhBDFjiQ4QgghhCh2JMERQgghRLEjCY4QQgghih1JcIQQQghR7EiCI4QQQohiRxIcIYQQQhQ7kuAIIYQQotiRBEcIIYQQxY4kOEIIIYQodiTBEUIIIUSxIwmOEEIIIYodSXCEEEIIUexIgiOEEEKIYkcSHCGEEEIUO5LgCCGEEKLYkQRHCCGEEMWOJDhCCCGEKHYkwRFCCCFEsSMJjhBCCCGKHUlwhBBCCFHsSIIjhBBCiGJHEhwhhBBCFDuS4AghhBCi2JEERwghhBDFjiQ4QgghhCh2JMERQgghRLEjCY4QQgghih1JcIQQQghR7EiCI4QQQohiRxIcIYQQQhQ7kuAIIYQQotiRBEcIIYQQxY4kOEIIIYQodl7pBCctLY3AwEB8fHzw9fVl9erVRV0lIYQQQrwEjIq6As9j1qxZnD9/nrVr13Lr1i0CAgKoWLEibdu2LZL62NlZoWijyczUgmKCgbEdBoaGRVKXwpaSoSMpUwHA0liDmdHLkTvHp2lJ14KhBqzMDDDQaAr0/gytQkK6Dp0CZkZQ2qRkHE8hhHhW6VodD9MVFAXMjTWUMi6a88Erm+AkJycTHBzMN998g7u7O+7u7oSGhrJhw4YiSXAU7V1sLbcTH70TlBQMjT0wtxyCgaErxha2hV6fwqIoCreTtGy6nMjJ6HQA6pc34d3qllSwLLrmlZShIzQ2g/UXE4lK1GJprKG9qwXNncywNstfknI/RcvuiGR+iUolTatQ3cqID18vzWtlDDExfDkSOCGEeJnEJGvZFpbE4ZupZOjAw86Y3m6WOFoaYWRQsD8wn9cr+y196dIlMjMz8fLyUpfVrVuXM2fOoNPpCrUuuswYEu9/RlrSJlBSANBmnCMxdiiZ6aFkpCQUan0K090ULeOPxnL8Tjo6BXQK/Hk7nQl/xHI3WVskdVIUhXP30plxMp6oxKw6JGYobLqcxOrzD3mY/vT2EZuqZebJeP57LYU0bVbP1JW4TCb8Gcv1h0WzX0II8TK7n6Jl8rFYfo3KSm4Azt3LYNzRWO4kFf735iub4MTExGBtbY2JiYm6zM7OjrS0NOLi4gq1Lpnp4WgzruRSoiMtdSW6zMKtT2HR6hR+jUpVh6Ye9TBD4febqehyFr1wsak61vyTmGvZieh0YtOenuBEPdQS+TAzx3KdAusuJPIwXZIcIYR41MUHGdxNyfn9mqGDH0ITSc0s3M6HV3aIKiUlRS+5AdTX6enp+d6OVvv8J6r01D/y3n76WRTz5H/lc142iRkKf0Wn5VkeEp1GK6esY1KY+5+cqXtiEnM1LgNHiyd3lf59N+/9uhybQWqmgoXh8+1TdkyKY9t4HhKXvEls8iaxyV1hxSWr9z41z/KzMRkkpusw1jz/X7353ZdXNsExNTXNkchkvzYzM8v3ds6dO/dc9ShVqhSVylvlvYLGAgUNly5dIjU174P/KiprX55SxhZA7o2tlJGGe3fvAM8f54Kwca6OBsjr18hUo+X8+fNkZubsoYGsY1rKqGKe2zcz1JCUmMj1i+HPX1kKNzavEolL3iQ2eZPY5O5Fx6Vs2bKUMbbPs7yUsYaEuDgib0e+0Ho86pVNcMqVK0dsbCyZmZkYGWXtRkxMDGZmZpQpUybf2/Hw8MDwOa90UrSmpCYszbXMxPRtDAxtqVnT+rk+42XV3iSDiw8yci9ztaCSVSni7t39V+KcXymZCp72Wk7H5OzJMzEAV2szbCvUeuI2SifrCA7LPSFt5WxGRRsLKtnWea56arVazp07V6ixeRVIXPImscmbxCZ3hRmXVg91/Hoj997vdi4WvOZggnO55z8XZu/T07yyCY6bmxtGRkacPn0aHx8fAEJCQvDw8MDAIP9TiwwNDZ/7oOs0DlhYjyc5dqrecgOjGpiW6o6hSeli+wtXzRqaOprx+039ZODNSma4ljXG0DCrH+XfiHN+WRpCP3dLJh+P494j48GGGvjcpyw2ZoYYGj55iMrGXMNAj9KsOPdQb7lLGSPaVbbAxOjf25fCjM2rROKSN4lN3iQ2uSuMuJQrpeHd6qXYfCVJb7mHnTENKphi9C9+b+bHK5vgmJub4+/vz5dffsn06dO5e/cuq1evZsaMGYVeFwODUhibt6S0US0yUw+j097HyLQhhsaVMTC0w9DYtNDrVFjKmhryweuWtHMx5/jtNDQaqFfeFDtzA0qbGBTZeHi5UkZMbmjNtYRM/rmfTnkLQzztTbAxM8ToKckNgLmRAQ0rmlLTxphT0WnEp+nwLmdKxVKG+b7MXAghSpJSxga0qWzOG+VNOXknjZRMHW+UN8XBwhAr08L/3nxlExyAsWPH8uWXX/Lhhx9iaWnJJ598QuvWrYukLhqNOecuxFOnzgcl7q+H0iZZyYxLWeOirooeW3NDbM0NqVvu2RJMcyMDzC0N6FiE9/MRQohXSSljA0oZG1CpdNF/bxZ9DZ6Dubk5M2fOZObMmUVdFSGEEEK8RF7Z++AIIYQQQuRFEhwhhBBCFDuS4AghhBCi2JEERwghhBDFjiQ4QgghhCh2JMERQgghRLEjCY4QQgghih1JcIQQQghR7EiCI4QQQohiRxIcIYQQQhQ7r/SjGp6HomQ95frfehhk9naK6uGSLyuJS94kNrmTuORNYpM3iU3uimNcsvcl+zyeF43ytDWKqfT0dM6dO1fU1RBCCCHEM/Dw8MDExCTP8hKb4Oh0OjIzMzEwMECj0RR1dYQQQgiRD4qioNPpMDIywsAg75k2JTbBEUIIIUTxJZOMhRBCCFHsSIIjhBBCiGJHEhwhhBBCFDuS4AghhBCi2JEERwghhBDFjiQ4QgghhCh2JMEpgLS0NAIDA/Hx8cHX15fVq1fnue6FCxfo1q0bnp6edOnShfPnzxdiTQtXQeIyePBgatSooffv4MGDhVjbopGenk779u05fvx4nuuUpDaTLT9xKWltJjo6muHDh1OvXj2aNGnCjBkzSEtLy3XdktRmChKXktZmIiMj+eijj/Dy8qJ58+Z8++23ea5bktoMisi3yZMnKx06dFDOnz+v/Pzzz4qXl5eyd+/eHOslJSUpjRs3Vr766islLCxMmTJlitKoUSMlKSmpCGr94uU3LoqiKK1atVJ27typ3L17V/2XlpZWyDUuXKmpqcrQoUOV6tWrK8eOHct1nZLWZhQlf3FRlJLVZnQ6ndK9e3fl448/Vq5cuaKcPHlSadWqlfLVV1/lWLcktZmCxEVRSlab0Wq1SuvWrZXPPvtMuXr1qvLbb78p3t7eyq5du3KsW5LajKIoiiQ4+ZSUlKR4eHjofREvWbJEef/993OsGxwcrPj5+Sk6nU5RlKxfzlatWilbt24ttPoWloLEJS0tTXFzc1MiIiIKs4pFKjQ0VOnYsaPSoUOHJ57IS1KbUZT8x6WktZmwsDClevXqSkxMjLrsxx9/VHx9fXOsW5LaTEHiUtLaTHR0tDJixAjl4cOH6rKhQ4cqEydOzLFuSWoziqIoMkSVT5cuXSIzMxMvLy91Wd26dTlz5gw6nU5v3TNnzlC3bl31ERAajQZvb29Onz5dmFUuFAWJS0REBBqNhkqVKhV2NYvMiRMnqF+/Pps3b37ieiWpzUD+41LS2oy9vT3ffvstdnZ2essTExNzrFuS2kxB4lLS2oyDgwMLFizA0tISRVEICQnh5MmT1KtXL8e6JanNQAl+mnhBxcTEYG1trfdgLzs7O9LS0oiLi8PGxkZv3apVq+q939bWltDQ0EKrb2EpSFwiIiKwtLRk9OjRnDhxgvLly/PJJ5/QrFmzoqh6oejVq1e+1itJbQbyH5eS1mbKlClDkyZN1Nc6nY7169fToEGDHOuWpDZTkLiUtDbzKD8/P27dukWLFi1o06ZNjvKS1GZAJhnnW0pKSo6nlma/Tk9Pz9e6j69XHBQkLhEREaSmpuLr68u3335Ls2bNGDx4sDzVnZLVZgqipLeZ2bNnc+HCBUaNGpWjrCS3mSfFpSS3mYULF7J8+XIuXrzIjBkzcpSXtDYjPTj5ZGpqmqMRZL82MzPL17qPr1ccFCQuQ4YMoXfv3pQtWxaAmjVr8s8//7BlyxY8PDwKp8IvqZLUZgqiJLeZ2bNns3btWubPn0/16tVzlJfUNvO0uJTkNpO9f2lpaXz++eeMHj1aL6EpaW1GenDyqVy5csTGxpKZmakui4mJwczMjDJlyuRY9969e3rL7t27h4ODQ6HUtTAVJC4GBgbql042V1dXoqOjC6WuL7OS1GYKoqS2mSlTpvDdd98xe/bsXIcaoGS2mfzEpaS1mXv37nHgwAG9ZVWrViUjIyPHHKWS1mYkwcknNzc3jIyM9CZjhYSE4OHhgYGBfhg9PT35+++/URQFAEVR+Ouvv/D09CzMKheKgsRlzJgxjB07Vm/ZpUuXcHV1LYyqvtRKUpspiJLYZhYvXsymTZuYN28eb7/9dp7rlbQ2k9+4lLQ2c+PGDYYNG6aXwJ0/fx4bGxu9OZBQ8tqMJDj5ZG5ujr+/P19++SVnz57lwIEDrF69mg8++ADI6rVITU0FoG3btiQkJDBt2jTCwsKYNm0aKSkpvPXWW0W5Cy9EQeLi5+fHjz/+yI4dO4iMjGTx4sWEhITw/vvvF+UuFJmS2maepiS3mfDwcJYuXUr//v2pW7cuMTEx6j8ouW2mIHEpaW3Gw8MDd3d3AgMDCQsL49ChQ8yePZtBgwYBJbfNAHKjv4JITk5WRo8erdSpU0fx9fVVvvvuO7WsevXqevcSOHPmjOLv7694eHgoXbt2Vf75558iqHHhKEhctmzZorRu3VqpVauW0rlzZ+XEiRNFUOOi8fj9Xkpym3nU0+JSktrMihUrlOrVq+f6T1FKbpspaFxKUptRFEW5c+eOMnToUMXb21tp3LixsmzZMvVeNyW1zSiKomgU5f/7qoQQQgghigkZohJCCCFEsSMJjhBCCCGKHUlwhBBCCFHsSIIjhBBCiGJHEhwhhBBCFDuS4AghhBCi2JEERwghhBAvVHp6Ou3bt+f48eP5fs+JEyfo1KkTnp6edO/enUuXLhXoMyXBEUIIIcQLk5aWxqeffkpoaGi+3xMVFUX//v1p1aoVO3fupEaNGgwZMqRATz6XBEcI8dx69+7NokWLgKxnAY0ZMwbIetbNhg0b1PXS09PZsmVLru97mR0/fpwaNWrke/29e/dy//79Z/qsGzduUKNGDW7cuPFM7xfiZRIWFkb37t25fv16gd63fv16ateuzbBhw6hcuTKBgYEYGBgQERGR721IgiOE+FeNGzeOcePGAXDy5EkmT56slu3Zs4fly5errxctWkS/fv0KvY4v0s2bNxk5ciQpKSlFXRUhityJEyeoX78+mzdvzlF26tQp3nnnHWrXrk2HDh346aef9N7XunVr9bW5uTkHDhygZs2a+f5so+eruhBC6CtdurT68+NPgnn8tZWVVWFUqVDJ02+E+J9evXrlujwmJoaBAwcyatQomjRpwunTpxkzZgy2trb4+PgQFRWFmZkZw4cP59SpU1StWpWgoCCqVq2a78+WHhwhRK5u377NoEGD8PT0xM/Pj8WLF6PVagHYv38/bdq0oU6dOkyePFldDv8borpx44b6VPkaNWpw/Phxxo4dy82bN9UhmMeHtmbPns3IkSPx9PSkXbt2XLhwgfnz5+Pj40PTpk3Zu3dvvur3NNl17NixIw0bNuTatWskJCTwxRdf4O3tja+vL1OmTFGfwvy4kJAQevbsiaenJ3Xq1KF///7cvXsXgDfffFP9f9u2bWq82rVrh6enJ127duXEiRPqtjIyMpgyZYq6j4cOHcrXPgjxKtuwYQONGjXi/fffx9nZmU6dOvHuu++ydu1aAJKTk5kzZw5vvPEG33zzDRUqVKBPnz4kJSXl+zMkwRFC5KAoCsOGDcPW1pbt27czY8YMfvzxR5YvX05YWBgjR46kZ8+ebN26lczMTEJCQnJso0KFCmrycuTIEby8vAgMDKR8+fIcOXKEChUq5HjP2rVrqVevHrt27cLKyooPP/yQ+/fvs3nzZvz8/Jg4cSI6ne6J9cuvnTt3MnLkSFasWEHlypUZN24cDx8+ZOPGjSxdupRz587pDa9le/jwIQMHDqRx48bs3r2bVatWcf36dVauXAlAcHCw+n+7du24dOkSAQEBDB48mF27dtGxY0f69+9PZGQkkDVMd/DgQZYtW8bXX3/NunXr8r0PQryqIiIiOHjwIF5eXuq/9evXc+3aNQAMDQ3x8/Ojd+/euLu7M2XKFHQ6Hb/++mu+P0OGqIQQORw7doxbt24RHByMgYEBrq6uBAQEMHbsWBITE/Hx8aFPnz4ATJgwgYMHD+bYhqGhIWXLlgXA3t4eyBq+MjQ0VF8/rlatWmqXdvv27Zk+fTrjx4/HzMyM3r17s3HjRu7du0d4eHie9Rs6dGi+9tHDwwM/Pz8Arl+/zoEDBzhx4oQ6xDZlyhT8/f0ZO3as3vtSU1MZMmQIffv2RaPRUKlSJVq3bs3Zs2cBsLGxUf83MzNj1apVdO/enQ4dOgDwwQcfcPLkSTZu3EhAQADBwcEEBATwxhtvABAYGMiAAQPytQ9CvKoyMzPp0KEDgwYN0ltuZJSVltjb2+Pi4qIuNzExwdHRkdu3b+f7MyTBEULkEB4eTlxcHHXr1lWX6XQ6UlNTCQ0Nxc3NTV1ubGys9/p5ODk5qT+bmZlhZ2eHmZkZAKampkDWlVhPql9sbCzW1tZP/SxHR0f15/DwcHQ6HU2bNtVbR6fTqT0t2ezt7fH392fNmjVcvHiRsLAwLl++jLe3d66fEx4ezt69e/UmWWZkZODr60tsbCwPHjzQi5+Hh8dT6y7Eq87FxYW///4bZ2dnddnq1atJT09n0KBB1KlTh8uXL6tl6enpREVF6X1HPI0kOEKIHDIzM3F1dWXp0qU5yqZNm5ZjIq2xsfG/8rnZf71lMzDIfRT9SfV7dJLzk2QnTABarZbSpUuzdevWHOuVK1eOM2fOqK+jo6Pp0qUL7u7uNGrUiO7du/Pbb7/prfMorVZL//798ff311uenbiB/sTkfyuWQrzMevXqxX/+8x/mz59P586dOXfuHPPmzWP69OkAfPjhh7z33nvUrVuXRo0a8e2332Jqakrz5s3z/RkyB0cIkYOLiwu3bt3CxsYGZ2dnnJ2duXHjBgsXLqRKlSqcO3dOXVen0+V5h1GNRvPE1y+ifs/yGS4uLjx8+BCNRqNuLzU1lVmzZuW4sdj+/fspW7YsK1as4MMPP1Sv+MhOUh7/fBcXF27cuKFu19nZmc2bN/P7779jbW2NnZ2dXjwvXLjwDBER4tXi6OjI8uXLOXz4MO3bt2fBggXqxH8AT09PFixYwLp16+jQoQPh4eF8++23WFhY5PszpAdHCJGDr68vjo6OfPHFF4waNYqHDx8yYcIEGjVqRI8ePdiwYQPLli2jTZs2bN68mVu3buW6HXNzcwDOnz9PtWrVMDc3Jz4+nmvXrhWoq7kg9TM0NCzw9qpUqUKTJk34/PPPGT9+PIaGhkyYMIGyZctSpkwZvXWtrKy4desWf/75J05OTuzdu5eff/5ZHVrK3udLly5hbW1Nnz59eO+99/Dw8KB58+b8+uuvrFmzhrVr16LRaHjvvfdYuHAhjo6OlC5dmhkzZjxzXIR4mT065ATQqFEj9UrD3LRs2ZKWLVs+8+dJD44QIgdDQ0OWLVuGTqeje/fufPLJJzRr1ozx48fj7OzMsmXL2LNnD/7+/sTExNCsWbNct1OjRg0aN25Mjx49OHToEA0aNMDZ2ZkOHTpw8eLFF1K/ZzVr1iycnJzo06cPffv2xcXFhXnz5uVY76233qJjx44MHz6cLl26cPz4cQICAggPDyc9PR0bGxs6duzIyJEjCQ4Opk6dOsyaNYvvv/+edu3asWXLFubOnatOKh40aBD+/v6MGjWKgQMH0q1bt2feByHE/2gUuSuVEEIIIYoZ6cERQgghRLEjc3CEEMXKd999x8KFC/Ms79ChQ6438BNCFC8yRCWEKFYSEhKIjY3Ns9zS0hJbW9tCrJEQoihIgiOEEEKIYkfm4AghhBCi2JEERwghhBDFjiQ4QgghhCh2JMERQgghRLEjCY4QQgghih1JcIQQQghR7EiCI4QQQohiRxIcIYQQQhQ7/wdU4/urVub/PwAAAABJRU5ErkJggg=="
      },
-     "execution_count": 19,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
-   "source": [
-    "AGGREGATION = {\"hash\": [\"count\"]}\n",
-    "\n",
-    "for metric in rel_metrics:\n",
-    "    AGGREGATION[f\"{metric}_related\"] = [\"mean\"]\n",
-    "\n",
-    "for metric in ind_metrics:\n",
-    "    AGGREGATION[f\"{metric}_independent\"] = [\"mean\"]\n",
-    "\n",
-    "df.groupby(by=[\"end_to_start\", \"start_to_end\"]).agg(AGGREGATION)"
-   ]
+   "execution_count": 43
   },
   {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "3429b60eab154b79",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-01T13:42:57.052768Z",
-     "start_time": "2024-05-01T13:42:56.812556Z"
+     "end_time": "2024-05-04T18:14:40.101715Z",
+     "start_time": "2024-05-04T18:14:39.883572Z"
     }
    },
+   "cell_type": "code",
+   "source": [
+    "grouped = pd.concat(axis=1, objs={\n",
+    "\"Golden\": analysis_util.get_correlations_df(GOLDEN, right_side='ind'),\n",
+    "\"Golden + Backward\": analysis_util.get_correlations_df(GOLDEN_E2S, right_side='ind'),\n",
+    "\"All data\": analysis_util.get_correlations_df(ALL, right_side='ind'),\n",
+    "})\n",
+    "\n",
+    "grouped.loc['editdist']"
+   ],
+   "id": "d80811f76d0a19c2",
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "                 Golden           Golden + Backward            All data  \\\n",
+       "               spearman   pearson          spearman   pearson  spearman   \n",
+       "independent                                                               \n",
+       "bertscore     -0.365381 -0.269957         -0.378203 -0.290310 -0.343406   \n",
+       "bleu           0.236109  0.246660          0.238727  0.187200  0.338027   \n",
+       "chrF          -0.361981 -0.291860         -0.277714 -0.192040 -0.175781   \n",
+       "editdist       0.747807  0.710624          0.792768  0.797605  0.883714   \n",
+       "editdist-norm  0.575983  0.539631          0.481395  0.415058  0.444490   \n",
+       "meteor         0.099908  0.210290         -0.011062  0.172998  0.072144   \n",
+       "rouge1        -0.235297 -0.195012         -0.195539 -0.169324 -0.127710   \n",
+       "rouge2         0.297922  0.334091          0.197169  0.142017  0.293727   \n",
+       "rougeL        -0.107294 -0.059070         -0.195589 -0.168861 -0.153277   \n",
+       "ter            0.632818  0.498689          0.593238  0.456480  0.598846   \n",
+       "\n",
+       "                         \n",
+       "                pearson  \n",
+       "independent              \n",
+       "bertscore     -0.232547  \n",
+       "bleu           0.269736  \n",
+       "chrF          -0.071155  \n",
+       "editdist       0.879479  \n",
+       "editdist-norm  0.363488  \n",
+       "meteor         0.264994  \n",
+       "rouge1        -0.107605  \n",
+       "rouge2         0.201313  \n",
+       "rougeL        -0.145834  \n",
+       "ter            0.409912  "
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -679,20 +457,12 @@
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th colspan=\"2\" halign=\"left\">all</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">golden</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">+s2e</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">+e2s</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">+e2s+s2e</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Golden</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Golden + Backward</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">All data</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>spearman</th>\n",
-       "      <th>pearson</th>\n",
-       "      <th>spearman</th>\n",
-       "      <th>pearson</th>\n",
        "      <th>spearman</th>\n",
        "      <th>pearson</th>\n",
        "      <th>spearman</th>\n",
@@ -701,7 +471,6 @@
        "      <th>pearson</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>relative</th>\n",
        "      <th>independent</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
@@ -709,895 +478,236 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th rowspan=\"11\" valign=\"top\">editdist</th>\n",
        "      <th>bertscore</th>\n",
-       "      <td>-0.184962</td>\n",
-       "      <td>-0.129057</td>\n",
-       "      <td>-0.316215</td>\n",
-       "      <td>-0.254700</td>\n",
-       "      <td>-0.308494</td>\n",
-       "      <td>-0.113525</td>\n",
-       "      <td>-0.181393</td>\n",
-       "      <td>-0.165924</td>\n",
-       "      <td>-0.135421</td>\n",
-       "      <td>-0.091748</td>\n",
+       "      <td>-0.365381</td>\n",
+       "      <td>-0.269957</td>\n",
+       "      <td>-0.378203</td>\n",
+       "      <td>-0.290310</td>\n",
+       "      <td>-0.343406</td>\n",
+       "      <td>-0.232547</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>bleu</th>\n",
-       "      <td>0.260118</td>\n",
-       "      <td>0.185995</td>\n",
-       "      <td>0.269028</td>\n",
-       "      <td>0.259690</td>\n",
-       "      <td>0.512841</td>\n",
-       "      <td>0.502827</td>\n",
-       "      <td>0.109831</td>\n",
-       "      <td>0.068138</td>\n",
-       "      <td>0.229712</td>\n",
-       "      <td>0.145062</td>\n",
+       "      <td>0.236109</td>\n",
+       "      <td>0.246660</td>\n",
+       "      <td>0.238727</td>\n",
+       "      <td>0.187200</td>\n",
+       "      <td>0.338027</td>\n",
+       "      <td>0.269736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>chrF</th>\n",
-       "      <td>-0.199200</td>\n",
-       "      <td>-0.129029</td>\n",
-       "      <td>-0.343201</td>\n",
-       "      <td>-0.300656</td>\n",
-       "      <td>-0.238124</td>\n",
-       "      <td>-0.064922</td>\n",
-       "      <td>-0.233123</td>\n",
-       "      <td>-0.201726</td>\n",
-       "      <td>-0.156914</td>\n",
-       "      <td>-0.093376</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>editdist</th>\n",
-       "      <td>0.909934</td>\n",
-       "      <td>0.910641</td>\n",
-       "      <td>0.710772</td>\n",
-       "      <td>0.662808</td>\n",
-       "      <td>0.950494</td>\n",
-       "      <td>0.935064</td>\n",
-       "      <td>0.861930</td>\n",
-       "      <td>0.878118</td>\n",
-       "      <td>0.939318</td>\n",
-       "      <td>0.962305</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gptscore-noref-1-req</th>\n",
-       "      <td>0.032048</td>\n",
-       "      <td>0.055364</td>\n",
-       "      <td>0.155510</td>\n",
-       "      <td>0.048588</td>\n",
-       "      <td>0.067857</td>\n",
-       "      <td>0.047215</td>\n",
-       "      <td>-0.029048</td>\n",
-       "      <td>-0.013128</td>\n",
-       "      <td>0.012102</td>\n",
-       "      <td>0.066882</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gptscore-ref-1-req</th>\n",
-       "      <td>0.024550</td>\n",
-       "      <td>0.035295</td>\n",
-       "      <td>-0.009830</td>\n",
-       "      <td>-0.062574</td>\n",
-       "      <td>-0.015178</td>\n",
-       "      <td>-0.036001</td>\n",
-       "      <td>0.071345</td>\n",
-       "      <td>0.087584</td>\n",
-       "      <td>0.013012</td>\n",
-       "      <td>0.033618</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meteor</th>\n",
-       "      <td>0.336016</td>\n",
-       "      <td>0.371949</td>\n",
-       "      <td>0.068034</td>\n",
-       "      <td>0.173237</td>\n",
-       "      <td>0.203616</td>\n",
-       "      <td>0.425775</td>\n",
-       "      <td>0.372598</td>\n",
-       "      <td>0.360051</td>\n",
-       "      <td>0.392262</td>\n",
-       "      <td>0.401802</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rouge1</th>\n",
-       "      <td>-0.077574</td>\n",
-       "      <td>-0.043738</td>\n",
-       "      <td>-0.187349</td>\n",
-       "      <td>-0.163230</td>\n",
-       "      <td>-0.139874</td>\n",
-       "      <td>-0.065543</td>\n",
-       "      <td>-0.082093</td>\n",
-       "      <td>-0.035603</td>\n",
-       "      <td>-0.054034</td>\n",
-       "      <td>-0.030799</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rouge2</th>\n",
-       "      <td>0.414256</td>\n",
-       "      <td>0.340732</td>\n",
-       "      <td>0.276139</td>\n",
-       "      <td>0.332087</td>\n",
-       "      <td>0.523559</td>\n",
-       "      <td>0.537560</td>\n",
-       "      <td>0.323911</td>\n",
-       "      <td>0.282872</td>\n",
-       "      <td>0.433859</td>\n",
-       "      <td>0.324538</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rougeL</th>\n",
-       "      <td>0.006513</td>\n",
-       "      <td>-0.008078</td>\n",
-       "      <td>-0.041502</td>\n",
-       "      <td>-0.034867</td>\n",
-       "      <td>-0.022288</td>\n",
-       "      <td>-0.004664</td>\n",
-       "      <td>0.012409</td>\n",
-       "      <td>0.016372</td>\n",
-       "      <td>0.021983</td>\n",
-       "      <td>-0.010644</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ter</th>\n",
-       "      <td>0.618095</td>\n",
-       "      <td>0.385515</td>\n",
-       "      <td>0.575614</td>\n",
-       "      <td>0.501385</td>\n",
-       "      <td>0.774086</td>\n",
-       "      <td>0.462554</td>\n",
-       "      <td>0.529338</td>\n",
-       "      <td>0.388592</td>\n",
-       "      <td>0.591684</td>\n",
-       "      <td>0.354459</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"11\" valign=\"top\">edittime</th>\n",
-       "      <th>bertscore</th>\n",
-       "      <td>0.140481</td>\n",
-       "      <td>0.158807</td>\n",
-       "      <td>0.140481</td>\n",
-       "      <td>0.158807</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>bleu</th>\n",
-       "      <td>0.302380</td>\n",
-       "      <td>0.326167</td>\n",
-       "      <td>0.302380</td>\n",
-       "      <td>0.326167</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>chrF</th>\n",
-       "      <td>0.079802</td>\n",
-       "      <td>0.184202</td>\n",
-       "      <td>0.079802</td>\n",
-       "      <td>0.184202</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>-0.361981</td>\n",
+       "      <td>-0.291860</td>\n",
+       "      <td>-0.277714</td>\n",
+       "      <td>-0.192040</td>\n",
+       "      <td>-0.175781</td>\n",
+       "      <td>-0.071155</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>editdist</th>\n",
-       "      <td>0.252645</td>\n",
-       "      <td>0.411131</td>\n",
-       "      <td>0.252645</td>\n",
-       "      <td>0.411131</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.747807</td>\n",
+       "      <td>0.710624</td>\n",
+       "      <td>0.792768</td>\n",
+       "      <td>0.797605</td>\n",
+       "      <td>0.883714</td>\n",
+       "      <td>0.879479</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>gptscore-noref-1-req</th>\n",
-       "      <td>0.206465</td>\n",
-       "      <td>0.026235</td>\n",
-       "      <td>0.206465</td>\n",
-       "      <td>0.026235</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gptscore-ref-1-req</th>\n",
-       "      <td>0.130419</td>\n",
-       "      <td>-0.055218</td>\n",
-       "      <td>0.130419</td>\n",
-       "      <td>-0.055218</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <th>editdist-norm</th>\n",
+       "      <td>0.575983</td>\n",
+       "      <td>0.539631</td>\n",
+       "      <td>0.481395</td>\n",
+       "      <td>0.415058</td>\n",
+       "      <td>0.444490</td>\n",
+       "      <td>0.363488</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>meteor</th>\n",
-       "      <td>0.253380</td>\n",
-       "      <td>0.403564</td>\n",
-       "      <td>0.253380</td>\n",
-       "      <td>0.403564</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.099908</td>\n",
+       "      <td>0.210290</td>\n",
+       "      <td>-0.011062</td>\n",
+       "      <td>0.172998</td>\n",
+       "      <td>0.072144</td>\n",
+       "      <td>0.264994</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>rouge1</th>\n",
-       "      <td>0.155926</td>\n",
-       "      <td>0.136971</td>\n",
-       "      <td>0.155926</td>\n",
-       "      <td>0.136971</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>-0.235297</td>\n",
+       "      <td>-0.195012</td>\n",
+       "      <td>-0.195539</td>\n",
+       "      <td>-0.169324</td>\n",
+       "      <td>-0.127710</td>\n",
+       "      <td>-0.107605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>rouge2</th>\n",
-       "      <td>0.218822</td>\n",
-       "      <td>0.281944</td>\n",
-       "      <td>0.218822</td>\n",
-       "      <td>0.281944</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.297922</td>\n",
+       "      <td>0.334091</td>\n",
+       "      <td>0.197169</td>\n",
+       "      <td>0.142017</td>\n",
+       "      <td>0.293727</td>\n",
+       "      <td>0.201313</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>rougeL</th>\n",
-       "      <td>0.071344</td>\n",
-       "      <td>0.091196</td>\n",
-       "      <td>0.071344</td>\n",
-       "      <td>0.091196</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>-0.107294</td>\n",
+       "      <td>-0.059070</td>\n",
+       "      <td>-0.195589</td>\n",
+       "      <td>-0.168861</td>\n",
+       "      <td>-0.153277</td>\n",
+       "      <td>-0.145834</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ter</th>\n",
-       "      <td>0.305601</td>\n",
-       "      <td>0.062616</td>\n",
-       "      <td>0.305601</td>\n",
-       "      <td>0.062616</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.632818</td>\n",
+       "      <td>0.498689</td>\n",
+       "      <td>0.593238</td>\n",
+       "      <td>0.456480</td>\n",
+       "      <td>0.598846</td>\n",
+       "      <td>0.409912</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "                                    all              golden            \\\n",
-       "                               spearman   pearson  spearman   pearson   \n",
-       "relative independent                                                    \n",
-       "editdist bertscore            -0.184962 -0.129057 -0.316215 -0.254700   \n",
-       "         bleu                  0.260118  0.185995  0.269028  0.259690   \n",
-       "         chrF                 -0.199200 -0.129029 -0.343201 -0.300656   \n",
-       "         editdist              0.909934  0.910641  0.710772  0.662808   \n",
-       "         gptscore-noref-1-req  0.032048  0.055364  0.155510  0.048588   \n",
-       "         gptscore-ref-1-req    0.024550  0.035295 -0.009830 -0.062574   \n",
-       "         meteor                0.336016  0.371949  0.068034  0.173237   \n",
-       "         rouge1               -0.077574 -0.043738 -0.187349 -0.163230   \n",
-       "         rouge2                0.414256  0.340732  0.276139  0.332087   \n",
-       "         rougeL                0.006513 -0.008078 -0.041502 -0.034867   \n",
-       "         ter                   0.618095  0.385515  0.575614  0.501385   \n",
-       "edittime bertscore             0.140481  0.158807  0.140481  0.158807   \n",
-       "         bleu                  0.302380  0.326167  0.302380  0.326167   \n",
-       "         chrF                  0.079802  0.184202  0.079802  0.184202   \n",
-       "         editdist              0.252645  0.411131  0.252645  0.411131   \n",
-       "         gptscore-noref-1-req  0.206465  0.026235  0.206465  0.026235   \n",
-       "         gptscore-ref-1-req    0.130419 -0.055218  0.130419 -0.055218   \n",
-       "         meteor                0.253380  0.403564  0.253380  0.403564   \n",
-       "         rouge1                0.155926  0.136971  0.155926  0.136971   \n",
-       "         rouge2                0.218822  0.281944  0.218822  0.281944   \n",
-       "         rougeL                0.071344  0.091196  0.071344  0.091196   \n",
-       "         ter                   0.305601  0.062616  0.305601  0.062616   \n",
-       "\n",
-       "                                   +s2e                +e2s            \\\n",
-       "                               spearman   pearson  spearman   pearson   \n",
-       "relative independent                                                    \n",
-       "editdist bertscore            -0.308494 -0.113525 -0.181393 -0.165924   \n",
-       "         bleu                  0.512841  0.502827  0.109831  0.068138   \n",
-       "         chrF                 -0.238124 -0.064922 -0.233123 -0.201726   \n",
-       "         editdist              0.950494  0.935064  0.861930  0.878118   \n",
-       "         gptscore-noref-1-req  0.067857  0.047215 -0.029048 -0.013128   \n",
-       "         gptscore-ref-1-req   -0.015178 -0.036001  0.071345  0.087584   \n",
-       "         meteor                0.203616  0.425775  0.372598  0.360051   \n",
-       "         rouge1               -0.139874 -0.065543 -0.082093 -0.035603   \n",
-       "         rouge2                0.523559  0.537560  0.323911  0.282872   \n",
-       "         rougeL               -0.022288 -0.004664  0.012409  0.016372   \n",
-       "         ter                   0.774086  0.462554  0.529338  0.388592   \n",
-       "edittime bertscore                  NaN       NaN       NaN       NaN   \n",
-       "         bleu                       NaN       NaN       NaN       NaN   \n",
-       "         chrF                       NaN       NaN       NaN       NaN   \n",
-       "         editdist                   NaN       NaN       NaN       NaN   \n",
-       "         gptscore-noref-1-req       NaN       NaN       NaN       NaN   \n",
-       "         gptscore-ref-1-req         NaN       NaN       NaN       NaN   \n",
-       "         meteor                     NaN       NaN       NaN       NaN   \n",
-       "         rouge1                     NaN       NaN       NaN       NaN   \n",
-       "         rouge2                     NaN       NaN       NaN       NaN   \n",
-       "         rougeL                     NaN       NaN       NaN       NaN   \n",
-       "         ter                        NaN       NaN       NaN       NaN   \n",
-       "\n",
-       "                               +e2s+s2e            \n",
-       "                               spearman   pearson  \n",
-       "relative independent                               \n",
-       "editdist bertscore            -0.135421 -0.091748  \n",
-       "         bleu                  0.229712  0.145062  \n",
-       "         chrF                 -0.156914 -0.093376  \n",
-       "         editdist              0.939318  0.962305  \n",
-       "         gptscore-noref-1-req  0.012102  0.066882  \n",
-       "         gptscore-ref-1-req    0.013012  0.033618  \n",
-       "         meteor                0.392262  0.401802  \n",
-       "         rouge1               -0.054034 -0.030799  \n",
-       "         rouge2                0.433859  0.324538  \n",
-       "         rougeL                0.021983 -0.010644  \n",
-       "         ter                   0.591684  0.354459  \n",
-       "edittime bertscore                  NaN       NaN  \n",
-       "         bleu                       NaN       NaN  \n",
-       "         chrF                       NaN       NaN  \n",
-       "         editdist                   NaN       NaN  \n",
-       "         gptscore-noref-1-req       NaN       NaN  \n",
-       "         gptscore-ref-1-req         NaN       NaN  \n",
-       "         meteor                     NaN       NaN  \n",
-       "         rouge1                     NaN       NaN  \n",
-       "         rouge2                     NaN       NaN  \n",
-       "         rougeL                     NaN       NaN  \n",
-       "         ter                        NaN       NaN  "
       ]
      },
-     "execution_count": 47,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": []
+   "execution_count": 44
   },
   {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "a3531f28722fa5bc",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-01T13:49:09.514129Z",
-     "start_time": "2024-05-01T13:49:09.295101Z"
+     "end_time": "2024-05-04T18:14:40.117667Z",
+     "start_time": "2024-05-04T18:14:40.103583Z"
     }
    },
+   "cell_type": "code",
+   "source": [
+    "print(grouped.loc['editdist'].sort_values(\n",
+    "\tby=('Golden', 'pearson'), ascending=False\n",
+    ")['Golden'][['pearson', 'spearman']].to_latex(float_format=\"%.3f\"))"
+   ],
+   "id": "9fe9367bda439a0b",
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead tr th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead tr:last-of-type th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th colspan=\"2\" halign=\"left\">all</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">golden</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">+s2e</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">+e2s</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">+e2s+s2e</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>spearman</th>\n",
-       "      <th>pearson</th>\n",
-       "      <th>spearman</th>\n",
-       "      <th>pearson</th>\n",
-       "      <th>spearman</th>\n",
-       "      <th>pearson</th>\n",
-       "      <th>spearman</th>\n",
-       "      <th>pearson</th>\n",
-       "      <th>spearman</th>\n",
-       "      <th>pearson</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>relative</th>\n",
-       "      <th>independent</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"11\" valign=\"top\">editdist</th>\n",
-       "      <th>bertscore</th>\n",
-       "      <td>-0.184962</td>\n",
-       "      <td>-0.129057</td>\n",
-       "      <td>-0.316215</td>\n",
-       "      <td>-0.254700</td>\n",
-       "      <td>-0.308494</td>\n",
-       "      <td>-0.113525</td>\n",
-       "      <td>-0.181393</td>\n",
-       "      <td>-0.165924</td>\n",
-       "      <td>-0.135421</td>\n",
-       "      <td>-0.091748</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>bleu</th>\n",
-       "      <td>0.260118</td>\n",
-       "      <td>0.185995</td>\n",
-       "      <td>0.269028</td>\n",
-       "      <td>0.259690</td>\n",
-       "      <td>0.512841</td>\n",
-       "      <td>0.502827</td>\n",
-       "      <td>0.109831</td>\n",
-       "      <td>0.068138</td>\n",
-       "      <td>0.229712</td>\n",
-       "      <td>0.145062</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>chrF</th>\n",
-       "      <td>-0.199200</td>\n",
-       "      <td>-0.129029</td>\n",
-       "      <td>-0.343201</td>\n",
-       "      <td>-0.300656</td>\n",
-       "      <td>-0.238124</td>\n",
-       "      <td>-0.064922</td>\n",
-       "      <td>-0.233123</td>\n",
-       "      <td>-0.201726</td>\n",
-       "      <td>-0.156914</td>\n",
-       "      <td>-0.093376</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>editdist</th>\n",
-       "      <td>0.909934</td>\n",
-       "      <td>0.910641</td>\n",
-       "      <td>0.710772</td>\n",
-       "      <td>0.662808</td>\n",
-       "      <td>0.950494</td>\n",
-       "      <td>0.935064</td>\n",
-       "      <td>0.861930</td>\n",
-       "      <td>0.878118</td>\n",
-       "      <td>0.939318</td>\n",
-       "      <td>0.962305</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gptscore-noref-1-req</th>\n",
-       "      <td>0.032048</td>\n",
-       "      <td>0.055364</td>\n",
-       "      <td>0.155510</td>\n",
-       "      <td>0.048588</td>\n",
-       "      <td>0.067857</td>\n",
-       "      <td>0.047215</td>\n",
-       "      <td>-0.029048</td>\n",
-       "      <td>-0.013128</td>\n",
-       "      <td>0.012102</td>\n",
-       "      <td>0.066882</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gptscore-ref-1-req</th>\n",
-       "      <td>0.024550</td>\n",
-       "      <td>0.035295</td>\n",
-       "      <td>-0.009830</td>\n",
-       "      <td>-0.062574</td>\n",
-       "      <td>-0.015178</td>\n",
-       "      <td>-0.036001</td>\n",
-       "      <td>0.071345</td>\n",
-       "      <td>0.087584</td>\n",
-       "      <td>0.013012</td>\n",
-       "      <td>0.033618</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meteor</th>\n",
-       "      <td>0.336016</td>\n",
-       "      <td>0.371949</td>\n",
-       "      <td>0.068034</td>\n",
-       "      <td>0.173237</td>\n",
-       "      <td>0.203616</td>\n",
-       "      <td>0.425775</td>\n",
-       "      <td>0.372598</td>\n",
-       "      <td>0.360051</td>\n",
-       "      <td>0.392262</td>\n",
-       "      <td>0.401802</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rouge1</th>\n",
-       "      <td>-0.077574</td>\n",
-       "      <td>-0.043738</td>\n",
-       "      <td>-0.187349</td>\n",
-       "      <td>-0.163230</td>\n",
-       "      <td>-0.139874</td>\n",
-       "      <td>-0.065543</td>\n",
-       "      <td>-0.082093</td>\n",
-       "      <td>-0.035603</td>\n",
-       "      <td>-0.054034</td>\n",
-       "      <td>-0.030799</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rouge2</th>\n",
-       "      <td>0.414256</td>\n",
-       "      <td>0.340732</td>\n",
-       "      <td>0.276139</td>\n",
-       "      <td>0.332087</td>\n",
-       "      <td>0.523559</td>\n",
-       "      <td>0.537560</td>\n",
-       "      <td>0.323911</td>\n",
-       "      <td>0.282872</td>\n",
-       "      <td>0.433859</td>\n",
-       "      <td>0.324538</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rougeL</th>\n",
-       "      <td>0.006513</td>\n",
-       "      <td>-0.008078</td>\n",
-       "      <td>-0.041502</td>\n",
-       "      <td>-0.034867</td>\n",
-       "      <td>-0.022288</td>\n",
-       "      <td>-0.004664</td>\n",
-       "      <td>0.012409</td>\n",
-       "      <td>0.016372</td>\n",
-       "      <td>0.021983</td>\n",
-       "      <td>-0.010644</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ter</th>\n",
-       "      <td>0.618095</td>\n",
-       "      <td>0.385515</td>\n",
-       "      <td>0.575614</td>\n",
-       "      <td>0.501385</td>\n",
-       "      <td>0.774086</td>\n",
-       "      <td>0.462554</td>\n",
-       "      <td>0.529338</td>\n",
-       "      <td>0.388592</td>\n",
-       "      <td>0.591684</td>\n",
-       "      <td>0.354459</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"11\" valign=\"top\">edittime</th>\n",
-       "      <th>bertscore</th>\n",
-       "      <td>0.140481</td>\n",
-       "      <td>0.158807</td>\n",
-       "      <td>0.140481</td>\n",
-       "      <td>0.158807</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>bleu</th>\n",
-       "      <td>0.302380</td>\n",
-       "      <td>0.326167</td>\n",
-       "      <td>0.302380</td>\n",
-       "      <td>0.326167</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>chrF</th>\n",
-       "      <td>0.079802</td>\n",
-       "      <td>0.184202</td>\n",
-       "      <td>0.079802</td>\n",
-       "      <td>0.184202</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>editdist</th>\n",
-       "      <td>0.252645</td>\n",
-       "      <td>0.411131</td>\n",
-       "      <td>0.252645</td>\n",
-       "      <td>0.411131</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gptscore-noref-1-req</th>\n",
-       "      <td>0.206465</td>\n",
-       "      <td>0.026235</td>\n",
-       "      <td>0.206465</td>\n",
-       "      <td>0.026235</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gptscore-ref-1-req</th>\n",
-       "      <td>0.130419</td>\n",
-       "      <td>-0.055218</td>\n",
-       "      <td>0.130419</td>\n",
-       "      <td>-0.055218</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>meteor</th>\n",
-       "      <td>0.253380</td>\n",
-       "      <td>0.403564</td>\n",
-       "      <td>0.253380</td>\n",
-       "      <td>0.403564</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rouge1</th>\n",
-       "      <td>0.155926</td>\n",
-       "      <td>0.136971</td>\n",
-       "      <td>0.155926</td>\n",
-       "      <td>0.136971</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rouge2</th>\n",
-       "      <td>0.218822</td>\n",
-       "      <td>0.281944</td>\n",
-       "      <td>0.218822</td>\n",
-       "      <td>0.281944</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rougeL</th>\n",
-       "      <td>0.071344</td>\n",
-       "      <td>0.091196</td>\n",
-       "      <td>0.071344</td>\n",
-       "      <td>0.091196</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ter</th>\n",
-       "      <td>0.305601</td>\n",
-       "      <td>0.062616</td>\n",
-       "      <td>0.305601</td>\n",
-       "      <td>0.062616</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                    all              golden            \\\n",
-       "                               spearman   pearson  spearman   pearson   \n",
-       "relative independent                                                    \n",
-       "editdist bertscore            -0.184962 -0.129057 -0.316215 -0.254700   \n",
-       "         bleu                  0.260118  0.185995  0.269028  0.259690   \n",
-       "         chrF                 -0.199200 -0.129029 -0.343201 -0.300656   \n",
-       "         editdist              0.909934  0.910641  0.710772  0.662808   \n",
-       "         gptscore-noref-1-req  0.032048  0.055364  0.155510  0.048588   \n",
-       "         gptscore-ref-1-req    0.024550  0.035295 -0.009830 -0.062574   \n",
-       "         meteor                0.336016  0.371949  0.068034  0.173237   \n",
-       "         rouge1               -0.077574 -0.043738 -0.187349 -0.163230   \n",
-       "         rouge2                0.414256  0.340732  0.276139  0.332087   \n",
-       "         rougeL                0.006513 -0.008078 -0.041502 -0.034867   \n",
-       "         ter                   0.618095  0.385515  0.575614  0.501385   \n",
-       "edittime bertscore             0.140481  0.158807  0.140481  0.158807   \n",
-       "         bleu                  0.302380  0.326167  0.302380  0.326167   \n",
-       "         chrF                  0.079802  0.184202  0.079802  0.184202   \n",
-       "         editdist              0.252645  0.411131  0.252645  0.411131   \n",
-       "         gptscore-noref-1-req  0.206465  0.026235  0.206465  0.026235   \n",
-       "         gptscore-ref-1-req    0.130419 -0.055218  0.130419 -0.055218   \n",
-       "         meteor                0.253380  0.403564  0.253380  0.403564   \n",
-       "         rouge1                0.155926  0.136971  0.155926  0.136971   \n",
-       "         rouge2                0.218822  0.281944  0.218822  0.281944   \n",
-       "         rougeL                0.071344  0.091196  0.071344  0.091196   \n",
-       "         ter                   0.305601  0.062616  0.305601  0.062616   \n",
-       "\n",
-       "                                   +s2e                +e2s            \\\n",
-       "                               spearman   pearson  spearman   pearson   \n",
-       "relative independent                                                    \n",
-       "editdist bertscore            -0.308494 -0.113525 -0.181393 -0.165924   \n",
-       "         bleu                  0.512841  0.502827  0.109831  0.068138   \n",
-       "         chrF                 -0.238124 -0.064922 -0.233123 -0.201726   \n",
-       "         editdist              0.950494  0.935064  0.861930  0.878118   \n",
-       "         gptscore-noref-1-req  0.067857  0.047215 -0.029048 -0.013128   \n",
-       "         gptscore-ref-1-req   -0.015178 -0.036001  0.071345  0.087584   \n",
-       "         meteor                0.203616  0.425775  0.372598  0.360051   \n",
-       "         rouge1               -0.139874 -0.065543 -0.082093 -0.035603   \n",
-       "         rouge2                0.523559  0.537560  0.323911  0.282872   \n",
-       "         rougeL               -0.022288 -0.004664  0.012409  0.016372   \n",
-       "         ter                   0.774086  0.462554  0.529338  0.388592   \n",
-       "edittime bertscore                  NaN       NaN       NaN       NaN   \n",
-       "         bleu                       NaN       NaN       NaN       NaN   \n",
-       "         chrF                       NaN       NaN       NaN       NaN   \n",
-       "         editdist                   NaN       NaN       NaN       NaN   \n",
-       "         gptscore-noref-1-req       NaN       NaN       NaN       NaN   \n",
-       "         gptscore-ref-1-req         NaN       NaN       NaN       NaN   \n",
-       "         meteor                     NaN       NaN       NaN       NaN   \n",
-       "         rouge1                     NaN       NaN       NaN       NaN   \n",
-       "         rouge2                     NaN       NaN       NaN       NaN   \n",
-       "         rougeL                     NaN       NaN       NaN       NaN   \n",
-       "         ter                        NaN       NaN       NaN       NaN   \n",
-       "\n",
-       "                               +e2s+s2e            \n",
-       "                               spearman   pearson  \n",
-       "relative independent                               \n",
-       "editdist bertscore            -0.135421 -0.091748  \n",
-       "         bleu                  0.229712  0.145062  \n",
-       "         chrF                 -0.156914 -0.093376  \n",
-       "         editdist              0.939318  0.962305  \n",
-       "         gptscore-noref-1-req  0.012102  0.066882  \n",
-       "         gptscore-ref-1-req    0.013012  0.033618  \n",
-       "         meteor                0.392262  0.401802  \n",
-       "         rouge1               -0.054034 -0.030799  \n",
-       "         rouge2                0.433859  0.324538  \n",
-       "         rougeL                0.021983 -0.010644  \n",
-       "         ter                   0.591684  0.354459  \n",
-       "edittime bertscore                  NaN       NaN  \n",
-       "         bleu                       NaN       NaN  \n",
-       "         chrF                       NaN       NaN  \n",
-       "         editdist                   NaN       NaN  \n",
-       "         gptscore-noref-1-req       NaN       NaN  \n",
-       "         gptscore-ref-1-req         NaN       NaN  \n",
-       "         meteor                     NaN       NaN  \n",
-       "         rouge1                     NaN       NaN  \n",
-       "         rouge2                     NaN       NaN  \n",
-       "         rougeL                     NaN       NaN  \n",
-       "         ter                        NaN       NaN  "
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{lrr}\n",
+      "\\toprule\n",
+      " & pearson & spearman \\\\\n",
+      "independent &  &  \\\\\n",
+      "\\midrule\n",
+      "editdist & 0.711 & 0.748 \\\\\n",
+      "editdist-norm & 0.540 & 0.576 \\\\\n",
+      "ter & 0.499 & 0.633 \\\\\n",
+      "rouge2 & 0.334 & 0.298 \\\\\n",
+      "bleu & 0.247 & 0.236 \\\\\n",
+      "meteor & 0.210 & 0.100 \\\\\n",
+      "rougeL & -0.059 & -0.107 \\\\\n",
+      "rouge1 & -0.195 & -0.235 \\\\\n",
+      "bertscore & -0.270 & -0.365 \\\\\n",
+      "chrF & -0.292 & -0.362 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n"
+     ]
     }
    ],
-   "source": [
-    "from analysis_util import get_correlations_for_groups\n",
-    "\n",
-    "get_correlations_for_groups(df, right_side=\"ind\")"
-   ]
+   "execution_count": 45
   },
   {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-05-04T18:14:40.148607Z",
+     "start_time": "2024-05-04T18:14:40.120619Z"
+    }
+   },
    "cell_type": "code",
-   "execution_count": null,
-   "id": "d5dc33a4251baf9a",
-   "metadata": {},
-   "outputs": [],
    "source": [
-    "get_correlations_for_groups(df, right_side=\"aggr\")"
-   ]
+    "print(grouped.loc['editdist'].sort_values(\n",
+    "\tby=('Golden + Backward', 'pearson'), ascending=False\n",
+    ")['Golden + Backward'][['pearson', 'spearman']].to_latex(float_format=\"%.3f\"))"
+   ],
+   "id": "2ce7521fb2220f3",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{lrr}\n",
+      "\\toprule\n",
+      " & pearson & spearman \\\\\n",
+      "independent &  &  \\\\\n",
+      "\\midrule\n",
+      "editdist & 0.798 & 0.793 \\\\\n",
+      "ter & 0.456 & 0.593 \\\\\n",
+      "editdist-norm & 0.415 & 0.481 \\\\\n",
+      "bleu & 0.187 & 0.239 \\\\\n",
+      "meteor & 0.173 & -0.011 \\\\\n",
+      "rouge2 & 0.142 & 0.197 \\\\\n",
+      "rougeL & -0.169 & -0.196 \\\\\n",
+      "rouge1 & -0.169 & -0.196 \\\\\n",
+      "chrF & -0.192 & -0.278 \\\\\n",
+      "bertscore & -0.290 & -0.378 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n"
+     ]
+    }
+   ],
+   "execution_count": 46
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-01T15:25:18.226195Z",
-     "start_time": "2024-05-01T15:25:17.464762Z"
+     "end_time": "2024-05-04T18:14:40.164668Z",
+     "start_time": "2024-05-04T18:14:40.150577Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "plt.scatter(x=df['edittime_related'], y=df['editdist_related'])"
+    "print(grouped.loc['editdist'].sort_values(\n",
+    "\tby=('All data', 'pearson'), ascending=False\n",
+    ")['All data'][['pearson', 'spearman']].to_latex(float_format=\"%.3f\"))"
    ],
-   "id": "5df60ac60034b274",
+   "id": "763fb31812dca6f8",
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x17c179da970>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{lrr}\n",
+      "\\toprule\n",
+      " & pearson & spearman \\\\\n",
+      "independent &  &  \\\\\n",
+      "\\midrule\n",
+      "editdist & 0.879 & 0.884 \\\\\n",
+      "ter & 0.410 & 0.599 \\\\\n",
+      "editdist-norm & 0.363 & 0.444 \\\\\n",
+      "bleu & 0.270 & 0.338 \\\\\n",
+      "meteor & 0.265 & 0.072 \\\\\n",
+      "rouge2 & 0.201 & 0.294 \\\\\n",
+      "chrF & -0.071 & -0.176 \\\\\n",
+      "rouge1 & -0.108 & -0.128 \\\\\n",
+      "rougeL & -0.146 & -0.153 \\\\\n",
+      "bertscore & -0.233 & -0.343 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n"
+     ]
     }
    ],
-   "execution_count": 11
+   "execution_count": 47
   }
  ],
  "metadata": {