emnlp_rebuttal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load excel files from results/a_{a}/ROI_v2/{dataset}/{dataset}_merged_v2.xlsx\n",
    "# There are multiple sheets, all need to be loaded\n",
    "# sheets corresponds to models\n",
    "\n",
    "vals_a = [0.5, 1, 2, 5, 10]\n",
    "datasets = [\"MSC\", \"TC\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "def load_excel(a, dataset):\n",
    "    df = pd.read_excel(f\"results/a_{a}/ROI_v2/{dataset}/{dataset}_merged_v2.xlsx\", sheet_name=None)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = {}\n",
    "for a in vals_a:\n",
    "    df[a] = {}\n",
    "    for dataset in datasets:\n",
    "        df[a][dataset] = load_excel(a, dataset)\n",
    "        # Drop BLEU, METEOR, ROUGE-L\n",
    "        c = [\"BLEU\", \"Bert-f1\", \"rougeL\"]\n",
    "        c = [[f\"ZS.{x}\", f\"FS.{x}\"] for x in c]\n",
    "        c = [item for sublist in c for item in sublist]\n",
    "        df[a][dataset] = {k: v.drop(columns=c) for k, v in df[a][dataset].items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>History Signal</th>\n",
       "      <th>Persona</th>\n",
       "      <th>Prompt</th>\n",
       "      <th>ZS.BLEURT</th>\n",
       "      <th>ZS.DEB</th>\n",
       "      <th>ZS.METEOR</th>\n",
       "      <th>FS.BLEURT</th>\n",
       "      <th>FS.DEB</th>\n",
       "      <th>FS.METEOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Recent-10</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>9.6288</td>\n",
       "      <td>26.0726</td>\n",
       "      <td>4.3057</td>\n",
       "      <td>4.6110</td>\n",
       "      <td>12.8767</td>\n",
       "      <td>2.1977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Semantic-10</td>\n",
       "      <td>None</td>\n",
       "      <td>ppl</td>\n",
       "      <td>9.7584</td>\n",
       "      <td>24.7405</td>\n",
       "      <td>3.8175</td>\n",
       "      <td>4.1724</td>\n",
       "      <td>11.0250</td>\n",
       "      <td>2.0297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>None_1</td>\n",
       "      <td>None</td>\n",
       "      <td>ppl</td>\n",
       "      <td>41.2393</td>\n",
       "      <td>85.3753</td>\n",
       "      <td>14.1639</td>\n",
       "      <td>16.4870</td>\n",
       "      <td>41.7226</td>\n",
       "      <td>7.7063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Recent-2</td>\n",
       "      <td>None</td>\n",
       "      <td>ppl</td>\n",
       "      <td>20.4918</td>\n",
       "      <td>51.0849</td>\n",
       "      <td>8.4496</td>\n",
       "      <td>8.0076</td>\n",
       "      <td>20.5361</td>\n",
       "      <td>3.7961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Recent-2</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>18.1921</td>\n",
       "      <td>48.2356</td>\n",
       "      <td>8.1273</td>\n",
       "      <td>8.3377</td>\n",
       "      <td>22.8009</td>\n",
       "      <td>3.9559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   History Signal Persona  Prompt  ZS.BLEURT   ZS.DEB  ZS.METEOR  FS.BLEURT  \\\n",
       "6       Recent-10    None  normal     9.6288  26.0726     4.3057     4.6110   \n",
       "28    Semantic-10    None     ppl     9.7584  24.7405     3.8175     4.1724   \n",
       "19         None_1    None     ppl    41.2393  85.3753    14.1639    16.4870   \n",
       "24       Recent-2    None     ppl    20.4918  51.0849     8.4496     8.0076   \n",
       "7        Recent-2    None  normal    18.1921  48.2356     8.1273     8.3377   \n",
       "\n",
       "     FS.DEB  FS.METEOR  \n",
       "6   12.8767     2.1977  \n",
       "28  11.0250     2.0297  \n",
       "19  41.7226     7.7063  \n",
       "24  20.5361     3.7961  \n",
       "7   22.8009     3.9559  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[1][\"MSC\"][\"T0\"].sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overall Mean UID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-50-70114918a62b>:9: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df_mean_MSC[a][model] = df[a][\"MSC\"][model].mean()\n",
      "<ipython-input-50-70114918a62b>:10: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df_mean_TC[a][model] = df[a][\"TC\"][model].mean()\n"
     ]
    }
   ],
   "source": [
    "# new dataframe for mean performances for each model, dataset and a\n",
    "df_mean_MSC = {}\n",
    "df_mean_TC = {}\n",
    "\n",
    "for a in vals_a:\n",
    "    df_mean_MSC[a] = pd.DataFrame()\n",
    "    df_mean_TC[a] = pd.DataFrame()\n",
    "    for model in df[a][\"MSC\"].keys():\n",
    "        df_mean_MSC[a][model] = df[a][\"MSC\"][model].mean()\n",
    "        df_mean_TC[a][model] = df[a][\"TC\"][model].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to take mean of pair of rows with index ZS.{x} and FS.{x}\n",
    "def mean_ZS_FS(df):\n",
    "    df_mean = pd.DataFrame(columns=df.columns)\n",
    "    for i, row in df.iterrows():\n",
    "        if i.startswith(\"ZS\"):\n",
    "            df_mean.loc[i.replace(\"ZS.\", \"\")] = (df.loc[i] + df.loc[i.replace(\"ZS\", \"FS\")])/2\n",
    "\n",
    "    return df_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "for a in vals_a:\n",
    "    df_mean_MSC[a] = mean_ZS_FS(df_mean_MSC[a])\n",
    "    df_mean_TC[a] = mean_ZS_FS(df_mean_TC[a])\n",
    "\n",
    "    # Transpose\n",
    "    df_mean_MSC[a] = df_mean_MSC[a].T\n",
    "    df_mean_TC[a] = df_mean_TC[a].T\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BLEURT</th>\n",
       "      <th>DEB</th>\n",
       "      <th>METEOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>flanT5-XL</th>\n",
       "      <td>1.998931</td>\n",
       "      <td>2.880106</td>\n",
       "      <td>1.234471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T0</th>\n",
       "      <td>2.012413</td>\n",
       "      <td>3.227519</td>\n",
       "      <td>1.313487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tk-Instruct</th>\n",
       "      <td>2.065238</td>\n",
       "      <td>3.100557</td>\n",
       "      <td>1.240249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT-3</th>\n",
       "      <td>2.045274</td>\n",
       "      <td>3.158085</td>\n",
       "      <td>1.397500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               BLEURT       DEB    METEOR\n",
       "flanT5-XL    1.998931  2.880106  1.234471\n",
       "T0           2.012413  3.227519  1.313487\n",
       "Tk-Instruct  2.065238  3.100557  1.240249\n",
       "GPT-3        2.045274  3.158085  1.397500"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean_MSC[0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BLEURT</th>\n",
       "      <th>DEB</th>\n",
       "      <th>METEOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>flanT5-XL</th>\n",
       "      <td>12.051712</td>\n",
       "      <td>25.103638</td>\n",
       "      <td>4.596447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T0</th>\n",
       "      <td>11.952976</td>\n",
       "      <td>30.766682</td>\n",
       "      <td>5.095476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tk-Instruct</th>\n",
       "      <td>12.392876</td>\n",
       "      <td>27.995106</td>\n",
       "      <td>4.471969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT-3</th>\n",
       "      <td>12.691400</td>\n",
       "      <td>30.269091</td>\n",
       "      <td>5.930076</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                BLEURT        DEB    METEOR\n",
       "flanT5-XL    12.051712  25.103638  4.596447\n",
       "T0           11.952976  30.766682  5.095476\n",
       "Tk-Instruct  12.392876  27.995106  4.471969\n",
       "GPT-3        12.691400  30.269091  5.930076"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean_MSC[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BLEURT</th>\n",
       "      <th>DEB</th>\n",
       "      <th>METEOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>flanT5-XL</th>\n",
       "      <td>438.910988</td>\n",
       "      <td>1926.821797</td>\n",
       "      <td>63.851994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T0</th>\n",
       "      <td>422.397462</td>\n",
       "      <td>2805.048503</td>\n",
       "      <td>76.976640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tk-Instruct</th>\n",
       "      <td>447.048865</td>\n",
       "      <td>2300.560007</td>\n",
       "      <td>58.346300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT-3</th>\n",
       "      <td>489.172676</td>\n",
       "      <td>2786.306688</td>\n",
       "      <td>107.124638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 BLEURT          DEB      METEOR\n",
       "flanT5-XL    438.910988  1926.821797   63.851994\n",
       "T0           422.397462  2805.048503   76.976640\n",
       "Tk-Instruct  447.048865  2300.560007   58.346300\n",
       "GPT-3        489.172676  2786.306688  107.124638"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean_MSC[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BLEURT</th>\n",
       "      <th>DEB</th>\n",
       "      <th>METEOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>flanT5-XL</th>\n",
       "      <td>16.883860</td>\n",
       "      <td>20.648267</td>\n",
       "      <td>12.066761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T0</th>\n",
       "      <td>16.753892</td>\n",
       "      <td>21.498220</td>\n",
       "      <td>12.520922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tk-Instruct</th>\n",
       "      <td>16.873235</td>\n",
       "      <td>21.023099</td>\n",
       "      <td>11.799927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT-3</th>\n",
       "      <td>17.155292</td>\n",
       "      <td>21.514852</td>\n",
       "      <td>13.378101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                BLEURT        DEB     METEOR\n",
       "flanT5-XL    16.883860  20.648267  12.066761\n",
       "T0           16.753892  21.498220  12.520922\n",
       "Tk-Instruct  16.873235  21.023099  11.799927\n",
       "GPT-3        17.155292  21.514852  13.378101"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(df_mean_MSC[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BLEURT</th>\n",
       "      <th>DEB</th>\n",
       "      <th>METEOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>flanT5-XL</th>\n",
       "      <td>34.924982</td>\n",
       "      <td>42.614405</td>\n",
       "      <td>25.307679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T0</th>\n",
       "      <td>34.641469</td>\n",
       "      <td>44.145912</td>\n",
       "      <td>26.267699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tk-Instruct</th>\n",
       "      <td>34.867771</td>\n",
       "      <td>43.303802</td>\n",
       "      <td>24.777128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT-3</th>\n",
       "      <td>35.445611</td>\n",
       "      <td>44.197174</td>\n",
       "      <td>27.939046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                BLEURT        DEB     METEOR\n",
       "flanT5-XL    34.924982  42.614405  25.307679\n",
       "T0           34.641469  44.145912  26.267699\n",
       "Tk-Instruct  34.867771  43.303802  24.777128\n",
       "GPT-3        35.445611  44.197174  27.939046"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(df_mean_MSC[10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average across models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'flanT5-XL':    History Signal     Persona  Prompt  ZS.BLEURT   ZS.DEB  ZS.METEOR  \\\n",
       " 0            BART        None  normal    18.8919  40.9520     6.8205   \n",
       " 1            Full        None  normal     6.7660  16.2571     2.4279   \n",
       " 2          None_1        None  normal    42.0373  84.8947    15.0465   \n",
       " 3      Pegasus-CD        None  normal    18.2432  37.9448     6.2578   \n",
       " 4       PegasusFT        None  normal    18.7467  40.8501     6.8292   \n",
       " 5        Recent-1        None  normal    22.6068  47.6039     8.3718   \n",
       " 6       Recent-10        None  normal    10.3733  23.1944     3.8576   \n",
       " 7        Recent-2        None  normal    19.8968  41.5581     7.3116   \n",
       " 8        Recent-4        None  normal    15.5890  33.2533     5.7964   \n",
       " 9        Recent-8        None  normal    11.5109  25.2264     4.2745   \n",
       " 10     Semantic-1        None  normal    23.8335  44.7330     8.4821   \n",
       " 11    Semantic-10        None  normal     9.6348  20.2045     3.6591   \n",
       " 12     Semantic-2        None  normal    20.2348  39.2516     7.3843   \n",
       " 13     Semantic-4        None  normal    15.7446  34.7483     5.8706   \n",
       " 14     Semantic-8        None  normal    11.0715  23.0973     4.2235   \n",
       " 15           BART  Pegasus-CD  normal    11.8595  26.7598     4.4071   \n",
       " 16      PegasusFT  Pegasus-CD  normal    11.8595  26.7598     4.4071   \n",
       " 17           BART        None     ppl    19.1197  39.3769     7.1754   \n",
       " 18           Full        None     ppl     6.6663  16.9659     2.5289   \n",
       " 19         None_1        None     ppl    27.6915  33.9146    10.8660   \n",
       " 20     Pegasus-CD        None     ppl    18.0232  37.4092     6.3871   \n",
       " 21      PegasusFT        None     ppl    19.0543  39.2613     7.1547   \n",
       " 22       Recent-1        None     ppl    21.4730  41.6829     8.3330   \n",
       " 23      Recent-10        None     ppl    10.3341  23.4569     3.9022   \n",
       " 24       Recent-2        None     ppl    19.4992  40.7751     7.4487   \n",
       " 25       Recent-4        None     ppl    15.4707  33.2314     5.8936   \n",
       " 26       Recent-8        None     ppl    11.4409  25.5096     4.3417   \n",
       " 27     Semantic-1        None     ppl    23.3613  44.3754     8.5920   \n",
       " 28    Semantic-10        None     ppl     9.7934  19.9514     3.6849   \n",
       " 29     Semantic-2        None     ppl    20.0669  39.1288     7.4333   \n",
       " 30     Semantic-4        None     ppl    15.6449  33.8889     5.8503   \n",
       " 31     Semantic-8        None     ppl    11.2135  22.5849     4.2225   \n",
       " 32           BART  Pegasus-CD     ppl    11.9399  24.8921     4.3813   \n",
       " 33      PegasusFT  Pegasus-CD     ppl    11.9399  24.8921     4.3813   \n",
       " \n",
       "     FS.BLEURT   FS.DEB  FS.METEOR  \n",
       " 0      9.0402  20.0123     3.5308  \n",
       " 1      3.3302   7.9238     1.4350  \n",
       " 2     18.3463  46.4760     8.0078  \n",
       " 3      8.1526  18.2623     3.1661  \n",
       " 4      9.9727  19.9157     3.4881  \n",
       " 5      9.9254  19.5995     3.6977  \n",
       " 6      4.8895  10.0292     1.7801  \n",
       " 7      8.8792  16.2515     3.0988  \n",
       " 8      7.1173  13.3397     2.4694  \n",
       " 9      5.3899  10.7146     1.9150  \n",
       " 10    10.0213  18.7378     3.9817  \n",
       " 11     4.2654   9.2803     1.8337  \n",
       " 12     8.4413  16.2981     3.4432  \n",
       " 13     6.6359  14.8597     2.7109  \n",
       " 14     4.7868  10.2833     2.0701  \n",
       " 15     5.8893  14.9914     2.5421  \n",
       " 16     5.8893  14.9914     2.5421  \n",
       " 17     8.9966  23.4407     4.0470  \n",
       " 18     3.1582   7.7828     1.4189  \n",
       " 19    16.2697  29.5901     6.8839  \n",
       " 20     8.1575  22.8430     3.7933  \n",
       " 21     9.5541  19.1131     3.6351  \n",
       " 22     9.7864  20.0748     3.8817  \n",
       " 23     4.3835   9.4587     1.7796  \n",
       " 24     8.6070  17.7305     3.5433  \n",
       " 25     6.5904  13.9745     2.6975  \n",
       " 26     4.8634  10.3315     1.9701  \n",
       " 27    10.3789  18.7013     4.1946  \n",
       " 28     4.2058   9.7261     1.6966  \n",
       " 29     8.8469  17.0967     3.5245  \n",
       " 30     6.8983  16.9582     2.8995  \n",
       " 31     4.7827  10.7767     1.9221  \n",
       " 32     5.7158  14.4478     2.4768  \n",
       " 33     5.7158  14.4478     2.4768  ,\n",
       " 'T0':    History Signal     Persona  Prompt  ZS.BLEURT   ZS.DEB  ZS.METEOR  \\\n",
       " 0            BART        None  normal    18.6597  48.8057     7.3640   \n",
       " 1            Full        None  normal     6.3177  18.0074     2.9253   \n",
       " 2          None_1        None  normal    40.6261  97.8292    16.0719   \n",
       " 3      Pegasus-CD        None  normal    16.2597  46.5070     7.3395   \n",
       " 4       PegasusFT        None  normal    18.5874  48.6742     7.3869   \n",
       " 5        Recent-1        None  normal    20.9440  55.8238     9.2385   \n",
       " 6       Recent-10        None  normal     9.6288  26.0726     4.3057   \n",
       " 7        Recent-2        None  normal    18.1921  48.2356     8.1273   \n",
       " 8        Recent-4        None  normal    14.1270  37.4642     6.3697   \n",
       " 9        Recent-8        None  normal    10.5981  28.3237     4.7347   \n",
       " 10     Semantic-1        None  normal    22.3998  57.1439     9.2370   \n",
       " 11    Semantic-10        None  normal     9.0994  24.2299     3.8984   \n",
       " 12     Semantic-2        None  normal    18.8461  48.0052     7.8230   \n",
       " 13     Semantic-4        None  normal    14.4037  39.1844     6.5979   \n",
       " 14     Semantic-8        None  normal    10.4309  27.3445     4.4165   \n",
       " 15           BART  Pegasus-CD  normal    11.3632  30.6550     4.6236   \n",
       " 16      PegasusFT  Pegasus-CD  normal    11.3632  30.6550     4.6236   \n",
       " 17           BART        None     ppl    21.1814  53.0600     8.0896   \n",
       " 18           Full        None     ppl     6.5417  18.3934     3.0095   \n",
       " 19         None_1        None     ppl    41.2393  85.3753    14.1639   \n",
       " 20     Pegasus-CD        None     ppl    18.9340  49.8624     7.7096   \n",
       " 21      PegasusFT        None     ppl    20.9803  52.3789     8.0501   \n",
       " 22       Recent-1        None     ppl    23.7958  58.3663     9.7657   \n",
       " 23      Recent-10        None     ppl    10.1483  26.7640     4.3369   \n",
       " 24       Recent-2        None     ppl    20.4918  51.0849     8.4496   \n",
       " 25       Recent-4        None     ppl    15.5264  39.7171     6.5178   \n",
       " 26       Recent-8        None     ppl    11.2965  29.4508     4.7961   \n",
       " 27     Semantic-1        None     ppl    25.3683  61.1743     9.9251   \n",
       " 28    Semantic-10        None     ppl     9.7584  24.7405     3.8175   \n",
       " 29     Semantic-2        None     ppl    20.9326  52.3080     8.3528   \n",
       " 30     Semantic-4        None     ppl    15.9192  42.3273     6.7547   \n",
       " 31     Semantic-8        None     ppl    11.1609  28.3243     4.3380   \n",
       " 32           BART  Pegasus-CD     ppl    11.7314  29.4785     4.5857   \n",
       " 33      PegasusFT  Pegasus-CD     ppl    11.7314  29.4785     4.5857   \n",
       " \n",
       "     FS.BLEURT   FS.DEB  FS.METEOR  \n",
       " 0      8.6265  24.0273     3.8087  \n",
       " 1      3.2713   9.3423     1.5803  \n",
       " 2     16.2437  44.6346     8.7125  \n",
       " 3      7.9648  22.4518     3.7974  \n",
       " 4      8.4581  18.9074     3.4718  \n",
       " 5      9.3425  25.4872     4.4980  \n",
       " 6      4.6110  12.8767     2.1977  \n",
       " 7      8.3377  22.8009     3.9559  \n",
       " 8      6.6494  18.3022     3.1454  \n",
       " 9      5.0415  14.0264     2.3984  \n",
       " 10     9.8070  25.4896     4.5904  \n",
       " 11     4.2333  11.3680     2.0667  \n",
       " 12     8.4918  21.8188     3.8987  \n",
       " 13     6.8777  19.2633     3.2111  \n",
       " 14     4.7816  12.7825     2.3173  \n",
       " 15     5.5539  15.3478     2.3584  \n",
       " 16     5.5539  15.3478     2.3584  \n",
       " 17     8.4819  23.8759     3.7409  \n",
       " 18     3.3250   8.6715     1.6549  \n",
       " 19    16.4870  41.7226     7.7063  \n",
       " 20     7.8464  21.9749     3.7412  \n",
       " 21     7.8380  14.2149     3.3383  \n",
       " 22     8.8967  22.7206     4.2779  \n",
       " 23     4.5171  11.9289     2.2050  \n",
       " 24     8.0076  20.5361     3.7961  \n",
       " 25     6.4134  16.6373     3.0693  \n",
       " 26     4.9291  12.9539     2.3831  \n",
       " 27     9.3758  24.9657     4.2501  \n",
       " 28     4.1724  11.0250     2.0297  \n",
       " 29     8.1150  21.9413     3.6951  \n",
       " 30     6.6189  18.5104     3.1650  \n",
       " 31     4.6952  12.5192     2.2586  \n",
       " 32     5.3263  14.2079     2.2410  \n",
       " 33     5.3263  14.2079     2.2410  ,\n",
       " 'Tk-Instruct':    History Signal     Persona  Prompt  ZS.BLEURT   ZS.DEB  ZS.METEOR  \\\n",
       " 0            BART        None  normal    21.9906  47.3800     7.2622   \n",
       " 1            Full        None  normal     6.8558  18.8409     2.4745   \n",
       " 2          None_1        None  normal    44.2689  90.3269    14.5641   \n",
       " 3      Pegasus-CD        None  normal    17.9475  47.1488     6.2238   \n",
       " 4       PegasusFT        None  normal    22.1287  47.5824     7.3159   \n",
       " 5        Recent-1        None  normal    22.6438  54.8643     8.3360   \n",
       " 6       Recent-10        None  normal    10.2636  27.0054     3.8412   \n",
       " 7        Recent-2        None  normal    19.4760  48.2107     7.2363   \n",
       " 8        Recent-4        None  normal    15.2362  38.5902     5.7808   \n",
       " 9        Recent-8        None  normal    11.3237  29.5001     4.2750   \n",
       " 10     Semantic-1        None  normal    24.0306  50.2352     8.3021   \n",
       " 11    Semantic-10        None  normal     9.7101  22.6514     3.4763   \n",
       " 12     Semantic-2        None  normal    20.1409  44.3295     7.1909   \n",
       " 13     Semantic-4        None  normal    15.6316  40.7435     5.9834   \n",
       " 14     Semantic-8        None  normal    11.1122  25.7364     3.9610   \n",
       " 15           BART  Pegasus-CD  normal    12.8197  29.9358     4.3200   \n",
       " 16      PegasusFT  Pegasus-CD  normal    12.8197  29.9358     4.3200   \n",
       " 17           BART        None     ppl    20.5142  44.2982     6.4752   \n",
       " 18           Full        None     ppl     6.7820  17.5426     2.4059   \n",
       " 19         None_1        None     ppl    39.3614  69.6909    12.5726   \n",
       " 20     Pegasus-CD        None     ppl    16.3045  36.1342     5.3766   \n",
       " 21      PegasusFT        None     ppl    20.6216  44.5522     6.5384   \n",
       " 22       Recent-1        None     ppl    21.6052  38.1706     7.3876   \n",
       " 23      Recent-10        None     ppl     9.9475  22.9772     3.5956   \n",
       " 24       Recent-2        None     ppl    18.9453  36.6561     6.5824   \n",
       " 25       Recent-4        None     ppl    14.8526  30.6842     5.3517   \n",
       " 26       Recent-8        None     ppl    11.0157  24.8462     4.0040   \n",
       " 27     Semantic-1        None     ppl    23.9619  47.2575     8.0336   \n",
       " 28    Semantic-10        None     ppl     9.8475  21.6781     3.3753   \n",
       " 29     Semantic-2        None     ppl    20.2499  39.4564     6.9006   \n",
       " 30     Semantic-4        None     ppl    15.3071  34.5655     5.5407   \n",
       " 31     Semantic-8        None     ppl    11.2140  24.2725     3.8693   \n",
       " 32           BART  Pegasus-CD     ppl    12.2332  28.1338     4.0472   \n",
       " 33      PegasusFT  Pegasus-CD     ppl    12.2332  28.1338     4.0472   \n",
       " \n",
       "     FS.BLEURT   FS.DEB  FS.METEOR  \n",
       " 0      8.8944  22.2490     3.6383  \n",
       " 1      3.2922   8.5990     1.4243  \n",
       " 2     18.0854  47.1451     7.4467  \n",
       " 3      7.8921  20.9902     3.1949  \n",
       " 4      9.2705  19.3191     3.2642  \n",
       " 5      9.5166  24.3351     3.7051  \n",
       " 6      4.5767  11.7544     1.8634  \n",
       " 7      8.3018  21.4556     3.3721  \n",
       " 8      6.5722  17.0457     2.6847  \n",
       " 9      5.0115  12.8211     2.0276  \n",
       " 10     9.9202  21.4970     3.8591  \n",
       " 11     4.2722  10.5440     1.6969  \n",
       " 12     8.4707  19.3231     3.3783  \n",
       " 13     6.6979  18.0769     2.7717  \n",
       " 14     4.7874  11.9260     1.9141  \n",
       " 15     5.8345  14.7081     2.3935  \n",
       " 16     5.8345  14.7081     2.3935  \n",
       " 17     9.3462  24.1266     3.7818  \n",
       " 18     3.4386   9.2294     1.5018  \n",
       " 19    17.8683  36.1477     6.7656  \n",
       " 20     8.1870  21.8433     3.4968  \n",
       " 21     9.3217  19.7330     3.2466  \n",
       " 22    10.0914  21.9664     3.8210  \n",
       " 23     4.8659  12.1711     1.9925  \n",
       " 24     8.9482  19.7272     3.4723  \n",
       " 25     7.0481  16.7380     2.8202  \n",
       " 26     5.3278  13.1295     2.1674  \n",
       " 27    10.6204  20.7693     4.1477  \n",
       " 28     4.4867  11.1639     1.7813  \n",
       " 29     9.0719  18.9927     3.5746  \n",
       " 30     7.0890  18.5818     2.9187  \n",
       " 31     5.0596  12.4463     2.0112  \n",
       " 32     5.6588  14.1681     2.2993  \n",
       " 33     5.6588  14.1681     2.2993  ,\n",
       " 'GPT-3':    History Signal     Persona  Prompt  ZS.BLEURT   ZS.DEB  ZS.METEOR  \\\n",
       " 0            BART        None  normal    19.6047  49.1027     9.5443   \n",
       " 1            Full        None  normal     7.9226  19.0096     3.8167   \n",
       " 2          None_1        None  normal    36.4248  79.0142    16.8863   \n",
       " 3      Pegasus-CD        None  normal    18.5897  46.7073     8.7397   \n",
       " 4       PegasusFT        None  normal    19.4004  48.8415     9.5824   \n",
       " 5        Recent-1        None  normal    22.7044  55.1230     9.8937   \n",
       " 6       Recent-10        None  normal     9.9143  24.0454     4.3052   \n",
       " 7        Recent-2        None  normal    19.9689  47.9372     9.0084   \n",
       " 8        Recent-4        None  normal    15.7696  37.7826     7.4125   \n",
       " 9        Recent-8        None  normal    11.9543  28.5965     5.7171   \n",
       " 10     Semantic-1        None  normal    23.7634  52.3294    10.1156   \n",
       " 11    Semantic-10        None  normal    10.4179  23.3761     5.0335   \n",
       " 12     Semantic-2        None  normal    20.2201  45.1859     9.1047   \n",
       " 13     Semantic-4        None  normal    16.2120  39.6069     7.6281   \n",
       " 14     Semantic-8        None  normal    11.6654  26.1598     5.6329   \n",
       " 15           BART  Pegasus-CD  normal    12.8643  31.2431     6.3423   \n",
       " 16      PegasusFT  Pegasus-CD  normal    12.8608  31.2355     6.3668   \n",
       " \n",
       "     FS.BLEURT   FS.DEB  FS.METEOR  \n",
       " 0      9.5743  23.9859     4.6548  \n",
       " 1      3.8664   9.4844     1.8203  \n",
       " 2     19.5526  46.4488     9.1296  \n",
       " 3      8.8591  22.5950     4.2399  \n",
       " 4      9.5384  23.9019     4.6463  \n",
       " 5     10.5095  25.9416     4.7790  \n",
       " 6      5.3100  13.0340     2.4920  \n",
       " 7      9.3600  22.9922     4.2867  \n",
       " 8      7.5174  18.4476     3.5006  \n",
       " 9      5.7858  14.1859     2.7196  \n",
       " 10    10.9783  25.2734     4.9957  \n",
       " 11     5.0623  11.6865     2.4115  \n",
       " 12     9.5138  21.9670     4.3850  \n",
       " 13     7.7099  19.2909     3.6277  \n",
       " 14     5.6640  13.0822     2.6915  \n",
       " 15     6.2261  15.7559     3.0470  \n",
       " 16     6.2221  15.7792     3.0652  }"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[1]['MSC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get average across model dataframes, but only for normal prompt\n",
    "df_mean_m = {\n",
    "    \"MSC\": {},\n",
    "    \"TC\": {}\n",
    "}\n",
    "\n",
    "for a in vals_a:\n",
    "    for dataset in datasets:\n",
    "        dfs_to_mean = []\n",
    "        for model in df[a][dataset].keys():\n",
    "            df_src = df[a][dataset][model]\n",
    "            dfs_to_mean.append(df_src.loc[df_src[\"Prompt\"] == \"normal\"])\n",
    "        # Take average across collected dataframes, by match [\"History\", \"Persona\", \"Prompt\"]\n",
    "        df_mean_m[dataset][a] = pd.concat(dfs_to_mean).groupby([\"History Signal\", \"Persona\", \"Prompt\"]).mean().reset_index()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to excel, sheets = {dataset}_{a}\n",
    "with pd.ExcelWriter(\"results/ROI_v4.xlsx\") as writer:\n",
    "    for dataset in datasets:\n",
    "        for a in vals_a:\n",
    "            df_mean_m[dataset][a].to_excel(writer, sheet_name=f\"{dataset}_{a}\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rank Bump Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Rank df of \"History Signal\" for each dataset, a\n",
    "df_rank = {\n",
    "    \"MSC\": {},\n",
    "    \"TC\": {}\n",
    "}\n",
    "\n",
    "for dataset in datasets:\n",
    "    for a in vals_a:\n",
    "        df_rank[dataset][a] = df_mean_m[dataset][a].copy()\n",
    "        # Drop following rows (History Signal=BART, Persona=Pegasus_CD) and (History Signal=None_1, persona=None)\n",
    "        df_rank[dataset][a] = df_rank[dataset][a].drop(df_rank[dataset][a][(df_rank[dataset][a][\"History Signal\"] == \"BART\") & (df_rank[dataset][a][\"Persona\"] == \"Pegasus-CD\")].index)\n",
    "        df_rank[dataset][a] = df_rank[dataset][a].drop(df_rank[dataset][a][(df_rank[dataset][a][\"History Signal\"] == \"None_1\") & (df_rank[dataset][a][\"Persona\"] == \"None\")].index)\n",
    "\n",
    "        # Rename PegasusFT + Pegasus-CD to PegasusDS + BI\n",
    "        j = ((df_rank[dataset][a][\"History Signal\"] == \"PegasusFT\") & (df_rank[dataset][a][\"Persona\"] == \"Pegasus-CD\"))\n",
    "        df_rank[dataset][a].loc[j, \"History Signal\"] = \"PegasusDS + BI\"\n",
    "\n",
    "        j = (df_rank[dataset][a][\"History Signal\"] == \"PegasusFT\")\n",
    "        df_rank[dataset][a].loc[j, \"History Signal\"] = \"PegasusDS\"\n",
    "\n",
    "        j = (df_rank[dataset][a][\"History Signal\"] == \"BART\")\n",
    "        df_rank[dataset][a].loc[j, \"History Signal\"] = \"BART-D\"\n",
    "        \n",
    "        for col in [\"ZS.BLEURT\", \"ZS.DEB\", \"ZS.METEOR\"]: # df_rank[dataset][a].columns:\n",
    "            df_rank[dataset][a][col] = df_rank[dataset][a][col].rank(ascending=False)\n",
    "        # df_rank[dataset][a].columns = [\"History Signal\", \"Rank\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>History Signal</th>\n",
       "      <th>Persona</th>\n",
       "      <th>Prompt</th>\n",
       "      <th>ZS.BLEURT</th>\n",
       "      <th>ZS.DEB</th>\n",
       "      <th>ZS.METEOR</th>\n",
       "      <th>FS.BLEURT</th>\n",
       "      <th>FS.DEB</th>\n",
       "      <th>FS.METEOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BART</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>3.376844e+15</td>\n",
       "      <td>2.226752e+19</td>\n",
       "      <td>7.587911e+11</td>\n",
       "      <td>1.046955e+15</td>\n",
       "      <td>1.142758e+19</td>\n",
       "      <td>4.379493e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BART</td>\n",
       "      <td>Pegasus-CD</td>\n",
       "      <td>normal</td>\n",
       "      <td>1.719676e+15</td>\n",
       "      <td>1.331358e+19</td>\n",
       "      <td>6.604288e+11</td>\n",
       "      <td>6.219106e+14</td>\n",
       "      <td>8.118806e+18</td>\n",
       "      <td>2.726311e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Full</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>7.273078e+14</td>\n",
       "      <td>8.737253e+18</td>\n",
       "      <td>3.508983e+11</td>\n",
       "      <td>3.564888e+14</td>\n",
       "      <td>4.288025e+18</td>\n",
       "      <td>1.675998e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None_1</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>8.305859e+15</td>\n",
       "      <td>2.071262e+19</td>\n",
       "      <td>1.372693e+12</td>\n",
       "      <td>2.258441e+15</td>\n",
       "      <td>2.451649e+19</td>\n",
       "      <td>1.294865e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pegasus-CD</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>2.259750e+15</td>\n",
       "      <td>2.489723e+19</td>\n",
       "      <td>5.572965e+11</td>\n",
       "      <td>8.113443e+14</td>\n",
       "      <td>1.162830e+19</td>\n",
       "      <td>3.687659e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PegasusFT</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>3.361030e+15</td>\n",
       "      <td>2.275340e+19</td>\n",
       "      <td>8.573968e+11</td>\n",
       "      <td>1.373639e+15</td>\n",
       "      <td>5.760731e+18</td>\n",
       "      <td>3.804706e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PegasusFT</td>\n",
       "      <td>Pegasus-CD</td>\n",
       "      <td>normal</td>\n",
       "      <td>1.716438e+15</td>\n",
       "      <td>1.329169e+19</td>\n",
       "      <td>6.830281e+11</td>\n",
       "      <td>6.206700e+14</td>\n",
       "      <td>8.165599e+18</td>\n",
       "      <td>2.856973e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Recent-1</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>1.346752e+15</td>\n",
       "      <td>9.440416e+18</td>\n",
       "      <td>1.913269e+11</td>\n",
       "      <td>4.819379e+14</td>\n",
       "      <td>4.522688e+18</td>\n",
       "      <td>1.449901e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Recent-10</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>9.816997e+14</td>\n",
       "      <td>1.103619e+19</td>\n",
       "      <td>1.438926e+11</td>\n",
       "      <td>5.573655e+14</td>\n",
       "      <td>5.500676e+18</td>\n",
       "      <td>2.119795e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Recent-2</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>2.973747e+15</td>\n",
       "      <td>2.066753e+19</td>\n",
       "      <td>5.566685e+11</td>\n",
       "      <td>1.035705e+15</td>\n",
       "      <td>9.567388e+18</td>\n",
       "      <td>3.147149e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Recent-4</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>2.354663e+15</td>\n",
       "      <td>1.769575e+19</td>\n",
       "      <td>6.419388e+11</td>\n",
       "      <td>8.241924e+14</td>\n",
       "      <td>7.689815e+18</td>\n",
       "      <td>2.795878e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Recent-8</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>1.650348e+15</td>\n",
       "      <td>1.440350e+19</td>\n",
       "      <td>5.475392e+11</td>\n",
       "      <td>6.246170e+14</td>\n",
       "      <td>5.915829e+18</td>\n",
       "      <td>2.332643e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Semantic-1</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>3.591404e+15</td>\n",
       "      <td>1.156465e+19</td>\n",
       "      <td>3.297360e+11</td>\n",
       "      <td>1.186296e+15</td>\n",
       "      <td>5.929033e+18</td>\n",
       "      <td>3.602429e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Semantic-10</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>1.219408e+15</td>\n",
       "      <td>6.164331e+18</td>\n",
       "      <td>4.760155e+11</td>\n",
       "      <td>4.412909e+14</td>\n",
       "      <td>2.840842e+18</td>\n",
       "      <td>2.290672e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Semantic-2</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>2.962351e+15</td>\n",
       "      <td>1.067412e+19</td>\n",
       "      <td>4.574887e+11</td>\n",
       "      <td>9.663039e+14</td>\n",
       "      <td>5.009011e+18</td>\n",
       "      <td>3.222100e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Semantic-4</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>2.347405e+15</td>\n",
       "      <td>2.288929e+19</td>\n",
       "      <td>6.845581e+11</td>\n",
       "      <td>7.187011e+14</td>\n",
       "      <td>9.980453e+18</td>\n",
       "      <td>3.072774e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Semantic-8</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>1.461841e+15</td>\n",
       "      <td>6.948368e+18</td>\n",
       "      <td>5.245401e+11</td>\n",
       "      <td>4.946602e+14</td>\n",
       "      <td>3.209301e+18</td>\n",
       "      <td>2.505059e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   History Signal     Persona  Prompt     ZS.BLEURT        ZS.DEB  \\\n",
       "0            BART        None  normal  3.376844e+15  2.226752e+19   \n",
       "1            BART  Pegasus-CD  normal  1.719676e+15  1.331358e+19   \n",
       "2            Full        None  normal  7.273078e+14  8.737253e+18   \n",
       "3          None_1        None  normal  8.305859e+15  2.071262e+19   \n",
       "4      Pegasus-CD        None  normal  2.259750e+15  2.489723e+19   \n",
       "5       PegasusFT        None  normal  3.361030e+15  2.275340e+19   \n",
       "6       PegasusFT  Pegasus-CD  normal  1.716438e+15  1.329169e+19   \n",
       "7        Recent-1        None  normal  1.346752e+15  9.440416e+18   \n",
       "8       Recent-10        None  normal  9.816997e+14  1.103619e+19   \n",
       "9        Recent-2        None  normal  2.973747e+15  2.066753e+19   \n",
       "10       Recent-4        None  normal  2.354663e+15  1.769575e+19   \n",
       "11       Recent-8        None  normal  1.650348e+15  1.440350e+19   \n",
       "12     Semantic-1        None  normal  3.591404e+15  1.156465e+19   \n",
       "13    Semantic-10        None  normal  1.219408e+15  6.164331e+18   \n",
       "14     Semantic-2        None  normal  2.962351e+15  1.067412e+19   \n",
       "15     Semantic-4        None  normal  2.347405e+15  2.288929e+19   \n",
       "16     Semantic-8        None  normal  1.461841e+15  6.948368e+18   \n",
       "\n",
       "       ZS.METEOR     FS.BLEURT        FS.DEB     FS.METEOR  \n",
       "0   7.587911e+11  1.046955e+15  1.142758e+19  4.379493e+11  \n",
       "1   6.604288e+11  6.219106e+14  8.118806e+18  2.726311e+11  \n",
       "2   3.508983e+11  3.564888e+14  4.288025e+18  1.675998e+11  \n",
       "3   1.372693e+12  2.258441e+15  2.451649e+19  1.294865e+12  \n",
       "4   5.572965e+11  8.113443e+14  1.162830e+19  3.687659e+11  \n",
       "5   8.573968e+11  1.373639e+15  5.760731e+18  3.804706e+11  \n",
       "6   6.830281e+11  6.206700e+14  8.165599e+18  2.856973e+11  \n",
       "7   1.913269e+11  4.819379e+14  4.522688e+18  1.449901e+11  \n",
       "8   1.438926e+11  5.573655e+14  5.500676e+18  2.119795e+11  \n",
       "9   5.566685e+11  1.035705e+15  9.567388e+18  3.147149e+11  \n",
       "10  6.419388e+11  8.241924e+14  7.689815e+18  2.795878e+11  \n",
       "11  5.475392e+11  6.246170e+14  5.915829e+18  2.332643e+11  \n",
       "12  3.297360e+11  1.186296e+15  5.929033e+18  3.602429e+11  \n",
       "13  4.760155e+11  4.412909e+14  2.840842e+18  2.290672e+11  \n",
       "14  4.574887e+11  9.663039e+14  5.009011e+18  3.222100e+11  \n",
       "15  6.845581e+11  7.187011e+14  9.980453e+18  3.072774e+11  \n",
       "16  5.245401e+11  4.946602e+14  3.209301e+18  2.505059e+11  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean_m[\"MSC\"][10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>History Signal</th>\n",
       "      <th>Persona</th>\n",
       "      <th>Prompt</th>\n",
       "      <th>ZS.BLEURT</th>\n",
       "      <th>ZS.DEB</th>\n",
       "      <th>ZS.METEOR</th>\n",
       "      <th>FS.BLEURT</th>\n",
       "      <th>FS.DEB</th>\n",
       "      <th>FS.METEOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BART-D</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.501875</td>\n",
       "      <td>2.372525</td>\n",
       "      <td>0.986350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Full</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.579725</td>\n",
       "      <td>0.929125</td>\n",
       "      <td>0.390750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pegasus-CD</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.381500</td>\n",
       "      <td>2.210875</td>\n",
       "      <td>0.912650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PegasusDS</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.529450</td>\n",
       "      <td>2.267950</td>\n",
       "      <td>0.964025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PegasusDS + BI</td>\n",
       "      <td>Pegasus-CD</td>\n",
       "      <td>normal</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.980575</td>\n",
       "      <td>1.577650</td>\n",
       "      <td>0.650250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Recent-1</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.713725</td>\n",
       "      <td>2.666125</td>\n",
       "      <td>1.114750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Recent-10</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.809650</td>\n",
       "      <td>1.268675</td>\n",
       "      <td>0.529775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Recent-2</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.449825</td>\n",
       "      <td>2.238200</td>\n",
       "      <td>0.939825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Recent-4</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.159525</td>\n",
       "      <td>1.796750</td>\n",
       "      <td>0.753025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Recent-8</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.884925</td>\n",
       "      <td>1.379950</td>\n",
       "      <td>0.576875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Semantic-1</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.693125</td>\n",
       "      <td>2.525925</td>\n",
       "      <td>1.106075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Semantic-10</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.755400</td>\n",
       "      <td>1.171000</td>\n",
       "      <td>0.505425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Semantic-2</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.458400</td>\n",
       "      <td>2.195975</td>\n",
       "      <td>0.958100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Semantic-4</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.175325</td>\n",
       "      <td>1.879025</td>\n",
       "      <td>0.779700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Semantic-8</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.847700</td>\n",
       "      <td>1.313125</td>\n",
       "      <td>0.567325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    History Signal     Persona  Prompt  ZS.BLEURT  ZS.DEB  ZS.METEOR  \\\n",
       "0           BART-D        None  normal        4.0     3.0        6.0   \n",
       "2             Full        None  normal       15.0    15.0       15.0   \n",
       "4       Pegasus-CD        None  normal        7.0     7.0        7.0   \n",
       "5        PegasusDS        None  normal        5.0     4.0        5.0   \n",
       "6   PegasusDS + BI  Pegasus-CD  normal       10.0    10.0       10.0   \n",
       "7         Recent-1        None  normal        2.0     1.0        1.0   \n",
       "8        Recent-10        None  normal       13.0    13.0       13.0   \n",
       "9         Recent-2        None  normal        6.0     5.0        4.0   \n",
       "10        Recent-4        None  normal        9.0     9.0        9.0   \n",
       "11        Recent-8        None  normal       11.0    11.0       11.0   \n",
       "12      Semantic-1        None  normal        1.0     2.0        2.0   \n",
       "13     Semantic-10        None  normal       14.0    14.0       14.0   \n",
       "14      Semantic-2        None  normal        3.0     6.0        3.0   \n",
       "15      Semantic-4        None  normal        8.0     8.0        8.0   \n",
       "16      Semantic-8        None  normal       12.0    12.0       12.0   \n",
       "\n",
       "    FS.BLEURT    FS.DEB  FS.METEOR  \n",
       "0    1.501875  2.372525   0.986350  \n",
       "2    0.579725  0.929125   0.390750  \n",
       "4    1.381500  2.210875   0.912650  \n",
       "5    1.529450  2.267950   0.964025  \n",
       "6    0.980575  1.577650   0.650250  \n",
       "7    1.713725  2.666125   1.114750  \n",
       "8    0.809650  1.268675   0.529775  \n",
       "9    1.449825  2.238200   0.939825  \n",
       "10   1.159525  1.796750   0.753025  \n",
       "11   0.884925  1.379950   0.576875  \n",
       "12   1.693125  2.525925   1.106075  \n",
       "13   0.755400  1.171000   0.505425  \n",
       "14   1.458400  2.195975   0.958100  \n",
       "15   1.175325  1.879025   0.779700  \n",
       "16   0.847700  1.313125   0.567325  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rank[\"MSC\"][0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bumpplot import bumpchart\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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W1X3Yb+wfAP/gLAvmO2WPicj/VtV2Z+21QFXPTfDICFDgPPs8sOVStGvjLbdz+IXnSMbj+AIBPD4fIZ8PM5UiGYuSiEYRw0NFXX3GtaoWnY1n6Ww8S3HVUpat20hhRWXOH8JsoKqc6xri9bPdNPXkzkSwobqAIy39pCwlP+Al7Pek+W6iSZPCoJ9PvWXNolK71ZWF2XW2BwQK/B7iKTPNOMdTJn6Pj3duWzZ/lZwmiaZmPAWF4DHQVGrEZyMeD0ZhIWIYWLEYnsICKj7xiUW1b8hXW0t0zx5AMILBjONWLIb4fRTde+9sPvYp4I9E5I9UVUVkq6ruBV4C3gs8JyIbgU3O+YXYwoQ+EVmCvVT4vLNSFFbVn4vIS8Bp5/yz2CtCP8b2lfsARGQ50KSq3xSRALaLI81AOff8KvYMq0dEtgGtqnrBMXabgbEzuzlnca2pjMH5kPcDH8D2IX1ERPYBh4D7nNP+GLhNRA5gL9ltdKTifwU87Uyrn8GeRk/ED4E/d5ycGSIJEfl/2KKNdSLSJCIfmUmbCsrKeevHPwVAbHAAM5UaXWYRIZCXz/bfeNekS3m9rRc49KtfcODZp+lqOj/xUs0MsCzlWGuEH7zWyCN7myc0TgDFIT9/8OZVFAV9DGvLVZWkadEXTYDCVx7YtKiME0B1UYivPLAJFCKxJB5DUFXMMbOpB65ZPJt1rViMyDPPYOTlUXCbrTTWeBwsy172Mk3M/n5Aqf7CFxeVcQLwVVVR/YUvAorZ348mk7ZAIpm8lO36ArbR2C8ih5zXYBuGChE5jO1POgT0OQPrvcBR4D+wDRnYA+QdTp/1a+BPnfJvYvup9gE3MKq6ezOwT0T2Yvvc/3FMnZ4TkYPATqAR+LhTXgn81Dm2H3t29s+z8SbMlEW1D+pyItdGXRiOJPGcE0likEA4z4kkcRsFZeXEh4ZoOXGUttMnMVOTO+BDBYUsXbeRiuX1GMbEIoyJSJoWhy/0s/tcD33RyZ8b9Hm4qraILbXFhP1eWvqiPLKnmR37W4jEkhQEfdyzuZp3LvKIC8Pt+s89TXRE4vi9BmuXFLChuoDikJ/fe9NKgr6Zv+9zgaoSefJJ4idHNUDmwADxY8dIXrgApmlHXHj73RTde++iM05jSba20vf44/T//InRSBJTaJfM4kZdZ0XH5+w/WgX8AlinqotLeXKJcQ3UPDGRgZoqyUSctlMnaDlxjGR8cjWfPxSmes16lqxcjdc3dcd9LGmy73wvDed7GUqYk55fEPSyta6ETcuKFp3w4WKwLOWbL57OeI/u3LiEK5cVzVOtpkbs8GEiv3w2o9y3dClF77wfmUz99gZglg1UAfAc9uxKgM+o6qzo2y8nXAM1T8yGgRrGTKXoOHeGC8cOExucQBrs4PH5qV69lurV6/BlWYsfJhJLsrexlwPNfVOSgJfl+7l6eQnrqwrx5NJfX+Y8d6ydhsbetLLa0jAPXF0zPxWaAqmeHnp/9GN03N46CQQo+cD78eRWtb2hmE0D5TI1XAM1T8ymgRpGLYuupkaajx1hsHfyfcOG4aFyxSqWrk2XqHcPJth1tpujrZGcG2vHsrQ4yNXLS1lVkXdJRRmLgZa+KD/ceT6tTAQ+cvOKBSk3V9Ok96H/JNXennGs8K63EVizZh5qtTBxDdTcsyhVfC7ZEcOgvK6estrl9LW10nzsMH3trTnPtyyT1lPHaT11gvLaOjxLV3Oox+J0Fql4NlaU53FNfQnLikNveMM0TFVhkOKwj96h0dmIKhxrjXDNAgzjNPTaa1mNU3DDetc4ucw7roG6DBERiquqKa6qJtLdyYVjR+hqOs9IdNYxqEJrXPjV3hY6XmsjmJ9PYVkFgbw8nP17aRgirKvK5+rlpVQULJ7ArnOFiLC+qpBXT3ellR9dgAYq0dTE0J69GeWeoiLy3vSmeaiRi0s6roG6zCkoLWfdDbcQjfTTfOwIHedOo5aFpdAUMzg+4KU3OeoAjw0MEBsYwB8KUVhWQaiwEBB8HuGKZUVsqyuhKLTwlqoWEuurCjIMVEckTudAnPL8hWHUbUn5LzJDUBlCwdveirHIokS4XJ64BuoNQqigkNXXXEf1+it4YechXjnazEAy9zpeIhqls6mRcNDPDVcs59ar15EfWhid60KnJM9PVVGQ1r50ZeXRlgg3r5n/91BVGXjuuayx9vKuuw7fkiXzUCsXl0xc7egbhFjS5LXTXXx/dxtHzRKKVm6gqHIJnizRnQHCHuWqwiRvLe6npOUAR3/xM5qPHVk0UdTnm/VVmcq3o639s75peibEDh9O2+80jG/ZMkLbts1DjVxcsuOq+OaJS6Hiy0YklmRPYy8Hc0jFVS0Ge3uIdHWSSiQo9Fqsy09RG8oeqdvj81O1ag3Va9bhDy7ezbWXmqFEim++cAZr3O/rPdfUUFMSnqdaDUvKf4Qm06Ppu5LyyXFVfHOPu8R3mdI1EGfXuR6OTSIVFzHILyljTf1SVgfjeFtPMtSXOwWMmUzQfPQQLcePUlG/kqXrNhDKdzu18YT9XpaXhTnTmT4IOdoSmTcDpaZJ5OlnMowTQMFtb3aNk8uCwzVQlxkXeqPsOtfDqfbJN+wCrKzI45r6UpYV27Mh3bSGvvY2mo8emlSi3nb6BG2nT1JeW8fSdRvJL1lYKrX5Zl1VQYaBOt4e4c3rKvDmSHR4KckpKd+4wZWUuyxIXAN1GaCqnHWiijdPErgVhqXiBVxTX5KhKhMRipdUUbykioHuLpqPHaGrqZFsEnXn6XSeP0fn+XMUL6lm2fqNFFYscfdFAasq8vF7jbSl1XjS4mzXIKsr53a2MpGkPP+WjGSrLi4LAtdALWIsSznWFmHXuR46I/FJz5+uVDy/tIx1N9xMNNLPheNHaD9rS9Rz0dvWQm9bC/klZSxbv5HSpTVv6Bhufq/Bqoo8jrSkZ2g92hqZUwNlRaMTSsoXW+JBlzcOrkhinrgYkUTStDjY3Meexl76pxhVfEttMVtqiwn5Zx5VOxEdouXEcVpPHZ9SFPVgfiHL1m2gYvmKSVPZX66c7Rzkkb3NaWUeQ/jYHEU4V1UiTzxB/NTpjGN5N1xP+JqJEsS6jMUVScw9roGaJyYyUC19UR7e08yO/ReIxFIUBL3cs3kpb99URVt/nIbzvUSnGFX86uUlXLF0dqOKpxIJWk87UdRjky8p+oMhqtesJ6+4hGOvvuikERkiEA47aURup6CsfNbqN9fY6VGezdquvJKyeY1wHj10iIFnn8so9y1bRtH99004w831PXzXZZAeZSbtcg3U3OMaqHkil4Hafa6bzzx0gIRpEvZ78RpCPGXRO5TAtOAtGysn7RzK8/1cvbyUdVUFlzSquGWadJw7Q/Oxw8QGIhOe29/Zyek9OzEMg2B+AR6fD8s0ScZjeLw+3vrxT7Fs3YZLVtdLRfOxIzz99X/CTCXxBYIYHk9Gu45TlhHhvKYkxHuuqb2kdbsYSXm272HKUoYSKfweD195YBNXL198opiLaZdroOaeeTdQIvJZ4IOACVjAx1X1tTmuw/3AcSfbLiLy/wEvqOovpnj9e4C/ATYA16rqrsmuyWagWvqiPPhvO0Eg5POQMi0GE+ZIdtakaft/3rVtGfmBTB/SsuIQ19SXsKJ8bqOKq2XRfaGJ5qOHGejpyjgeHxri8Iv2KN7j8YAIPn8Aw9kknEraOdru/OgfkldcPGf1vlgGe3t55t/shKNen+3HEREMrxfDMEjGbb/gLZ/6HD89ka6qFIHfvXkFhZcowvnFRCkf/z00LTv78XBPEU+ZoPDf37VpwYRumgqdA3H+68MHQCDgtZdXDcDnNTBEiCbtdn3/o9dmHQROZqBExAQOYAexNIE/VNWXxxz/E+DLwBJV7XPK3gw8BpwBgsAO4LvA953L6oA+569TVd8y5n4fBv4WaALysdPAf37sMxc78yqSEJEbgHuAbaoaF5FyYD48tvdjfzEOA6jqf5vm9QeBdwFfv5hKPLynmYRpUhTyE0ua9EeTado5n8cgljQ50hJh+5jAo+Ol4nONGAZlNXWULqulr72NC8cO09vWMnK8o/EMapkjnTiqdoLFMbqOVDLBzscfomb9xjmu/cxpOnqI+OAAXp8fMzGaCFVECOYX4AsEiA0O0Ln3JUoqr6FnXITz45cwgOzgq6/OWFI+9ns4lEgRiWXum4olTf71+VNp38OFzutnu+mLJgn6PMSTo2IfQ4SSsI+Qz0NfNMEje5r5xG2rZ/KIqKpuARCRtwH/A7h1zPEPAK9j9xX/d0z5i6p6j4iEsNO9PzLmPt8BdqjqQzme+SNV/UPn3NuAh0XkNlU9MpMGLDTmW2JVjT0qiAOoaqeqXhCRq0XkVyKyW0SeEpFqABF5XkT+t4jsEpEjIrJdRB4WkRMi8sXhm4rIo861h0TkY2PKB0TkSyKyT0ReFZElInIjcC/wtyLSICKrROQ7IvKAc812EXnZuWankwkzDVU9oqrHLvbN2LH/AmG/PWYYiKeyCrt9HoPjbREMETZUF/LgDcu5b8uyeTNOYxmWqG980+1svuMuymqWA0LX+UYMz8RjIcPjpev8ubmp6CyRq12qSjw6BIAvEOT4Ky+yrqow47wjrRMvi86URFMT0b0NGeVTlZQPfw9TppXVOMHo93Axcbwtgi/L/jNLlUHHRxj2e9mxvyXjnBlQCIzseHfSuucDf4VtqDJQ1SjQACybyQNV9TngG8DHJjt3sTDfBuppoFZEjovIV0XkVhHxAf8HeEBVrwa+DXxpzDUJVb0G+Br21PiTwJXAh0WkzDnnd51rrwE+NaY8D3hVVa8CXgB+z5kOPw78uapuUdWRIGUi4gd+BPyxc81bgMlVATkQkY85xnVXKpX5w4/EUngdn1Gulddhn9Lv3FzPXVdWLdgllmGJ+ta7fwNVOzniRIhhLLo4f6lkMqfIwDJN1LIwPB7iQ4NsqM7093RG4nRMYXvAdLCiUSJPP3NRkvLh7+FAPLtxcm43pSzLC4l4Knv4LmAkJJXXECKxnN9D7/Dv1/kbbwhCziD3KPBvwBfGHHs/8EPgRWCdiGRE5BWREmANdt80U/YA6y/i+gXFvC7xqeqAiFwN3ALchm0MvohtcJ5x/CgeYOyQ5nHn3wPAIVVtARCR00At0IVtlN7pnFeL/aF3AQnspTyA3cCdk1RxHdCiqq879e2fWUttVPUb2CMc8vLyMkxQQdBLylJ8HiHk9zA4poMwxC7zeQSvYVwy38VsE8ovIL+kBMs0MVMpUvFY1oCpall4fYujTcN4fT7UsrIbKVXMVAoxDALhPIrDfqqLgrSMi3B+rDUya3m1RqKUZxHfTCdKeUHQSzRpEp/AAFnKrCpD54KA18BS8GQxUiFH8p+ydKLMxylncJyLsUt8NwDfE5Er1f7CfwB4p6paIvKfwHuAf3auu0VE9mH3U/+gqrlDuEzOZbVDft436qqqCTwPPC8iB7BnRIdU9YYclwwPOS3SvBhY2COcN2PPdG5Q1SEReR7b+QiQ1NHe0WSG7ReR/wtsBS6o6ttnco9s3LN5KT96vZGikJ88vwevISRNC69hEPDZjty+aIL3b5/RCsC8se7GWzjw7NME8/Lxh0Kk4nFMM310nozFqNu0hYq6FfNUy+lTt+kqGg/swxsIYKVSWGa6lNxMpVC12HT72wBYX12YYaCOtvZz0+qyWRG1xA4dzrrfabpRyt+xqZpvv3QG7zjDa4iMGKWhRIqbV5dnnRkuVG5aXc5LJzvT9p+JQNDrSWvX+7fXXfSzVPUVx6de4cyW1jA66PZjiyKGDdSwD2oF8KqI/FhVG8bfU0Q+Cfye8zJXv7MVuCz8TzD/Iol1gKWqJ5yiLdhv7ltF5AbnQ/YBa1X10BRvWwT0OMZpPXD9FK6JANl+aceAahHZrqqvO/6nqKr+zhTrMi3etW0Zj+xpJpo0Cfk8BJ2/YaJJE7/Hwzu3LS4DtfGW2zn8wnMk43F8gQC+YJCxY9RkPI4n38ebPvjhRbUfqmr1Wh760ucA8Hh9xAfTlXrJWJRAXj4bb7kNgLVL8vnVsY60COeRWIqmnii1pRcXQDbV08Pgr1/MKJdggII73zKtiB7XrijlW78+Q9K00nw2+QEvIb/H+R76+czd6xfVfqiraovT1Injmc3fl9P3eLBXbv4E+BtV/R9jjp8RkeVjr1HVMyLyZeAzZPFTqeq/AP8y5h7jn3krtv/ptotuwAJhvufo+cB3ReSwiOwHNgL/DXgA+Ioz7W0AbpzGPZ/EnkkdwZZ0vjqFa34I/LmI7HWcmQCoagJ4H/B/nLo8w+hsbAQReaeINAE3AD8TkaemUd8RqotCfOWBTaDQF03Y0l61Jb590QQofOWBTYuqUwAoKCvnrR//FACxwQFnZmEvgcWcTv2tH//UojJOkN6uZCKOZdlSbMuySCUTWKbJWz76yZF2DUc4H8/RixRLqGkSeerpHFHKb5tWlHJV5VjbAHdutJcDY0kT01I8huAxWNTfwzn4fQ37oBqw3RW/7awQvR94ZNy5jzjl4/ka8CYRqZ/iM9/nPPM48F+Bd18uCj5YAPug3qhMFknikT3N7NjfQiSWpCDo457N1bxzke/gtyMuPOdEXBgkEM5zIi7ctuiM01giXZ0ceuFZGp7aQTIex+vzUVa7nIq6erbdfS9FlaO+n6Ot/TxxIN3FEPAZfOyWlTOOcD7w0ktEswSCDW7cQMEdd0zrXkda+nnyoF2/gXiSIy0RjrdF8BpCaV7gsvgezvT35W7UnXtcAzVPzFXCQpe549grv6arKV0qX3fFVdRsvHLkdSJl8c0XT2co4H7jquoZBZBNnD9P36OPZZR7iosped97pxUINmVafPeVcxnxHauLgrxve+0bPkK9a6Dmnvle4nNxuWworKjIKOvvTN8sOxzhfDzjI55PhZEo5eMxhIK33jntKOUHmvuyBh++aXX5G944ucwProFycZklCssrM8oiXZ0ZKUrWZ9m0e6ZzcCSk1VRQVSLPPptdUn799VOWlA8TT5m8dqY7o3xFed5FCzhcXGaKa6BcXGaJcGERHl/6rMVMJRns7UkrqysNEx6X9sS0lJNTzIIMtqQ8cfpMRrlv2TJCW7dOo9Y2u8/1ZETIF4EbV5fluMLF5dLjGigXl1lCDIPCLGKP/s6OtNeGYWc0Hs+RlqntA59QUv7WO6edJHIwnmLvuGjrAOurCqgsyBCturjMGa6BcnGZRQoqMpf5xvuhIPsyX1NPlP7cYXaAKUjK8/OnUVubnWe6M0QbHkO4YeXiVVa6XB64BsrFZRYpLM8USkQ6OzLCOy0pDFASzgypc2ySPVGDr75KqqMjozx4xUYCq6cfgbt3KMH+pr6M8k01RRRlqZ+Ly1ziGigXl1kkv6QsIzBuMh4jGklfvhMR1ldnzqIm2rSbOH8+634nT3Ex+TffPKP6vnKqKy2yBdhKw+tWLJ40Gi6XL66BcnGZRQyPh/zSTGFBpDNz1rM+ix8qV4TziSXlU4tSPp72/lhWg3j18pKRtC8uLvOJa6BcXGaZwin6oYYjnI/naGv6bGtySXnm86bCS6c6M8rCfg/b6kpmdD8Xl9nGNVAuLrNMQQ4/VDayLfMda42k+axySspraqYVpXws57uHONs5lFF+3cqyRZdGw+Xyxf0murjMMgVlFYxPyxMbHCA+lGkQ1i7JxxgXpWE4wjlMIUr5DCI8qCq/Ppk5eyoK+di0rGja93NxuVS4BsrFZZbx+nzkFWcuk0WyLPOF/V7qy7NHONdUKrek/PbbZyQpBzjZPkDruLxUYG/K9eRKOeviMg+4BsrF5RKQ3Q+VfZkv26bdE+0R+l6eQFK+alVG+VQwLeWlLLOnysIA65YsnuSDLm8MXKnOAqR1sJUdp3bw5NknGUgOkO/L5676u7hn1T1U5VXNd/VmTF9fH/v37+fgwYPE43ECgQBXXnklmzdvpqho8S4tZWvXqqVVeGNxgsHRdO65DNSqinz8XiNts+xQVw/Hzh1i5bitSBcjKQc4dKGPnqEsAWFXZQaEvVw/r2RrK32PPUb/E09iRSIYBQUU3n0XRffdh69q8f6+LkfcdBvzRK50Gw3tDfz1y39N0kwS9AbxGl5SVopYKobP4+PzN36eLZVb5r7CF0ljYyOPP/44pmni8/kwDAPLskgmk3g8Hu69917q6i4+1fZck6tdiVgUWhpZt27dmM5c2H7fu/H5Axn3efJg60ioI00liR04xHId5K3hMd8Rj0Hxux+YsWovaVp856WzDMTTlwxrS8O8e9uyNAN1uX5eQ3v20vK5v0ITSYxQCLxeSKWwolHE76P6C18kvC17LEM33cbcM+8GSkQ+C3wQMAEL+LiqvjbHdbgfOK6qh53X/x/wgqpm2XiS9fq/BX4DSACngN9R1d6JrslmoFoHW/n4Mx8HIOgNoqpYamGIgYgQS9l+g6/f+fVFNZPq6+vj+9//PgA+n89u15gI38mkPaJ/4IEHKJhG9tf5JhKJ8NBDDwF2u8YTP38WNRNs2LCRQMA2Squ330DxkszP7nxPlJ8esH1UiTNnMPv68KC8L7+XgNi/0dD27YSuvDLj2qnScL6X17NELL9/6zIqCkaNZrZ2icjI3/Dn9eCDDy6qmVSytZXGj3wEEIyg/fvCssCwf19WLAYodd/6VtaZ1FQM1OXQn425z58BfwdUqGrmuvAcMK9LfCJyA3APsE1V4yJSDkx/x+HFcz+wAzgMoKr/bZrXPwP8paqmROQrwF8Cn5luJXac2kHSTFIQKCBpJemP92OqiSEGQW+QsDfMQGKAHad28NHNH53u7eeN/fv3Y5omfr+feDxOMpnMCP2TSqX4xS9+sahG5Y2NjUSjUbxe70inPRYrGCYVE05eaKOo2O7I+3bvzmqgLFW621NE+wcxe3tHyp8dVOq8MYzCQoLxOOzePaO6pkxl7/keTCv9fS/N89N4vIfGKbTLMAxCoRA+n49YLMb+/fu55ZZbZlSf+aDvscfsmVM4jNnfj8ZiKCBeL57iYoxgELO/n77HH6f8Yx+b9v0vo/4MEakF3gppX405Z75FEtVAp6rGAVS1U1UviMjVIvIrEdktIk+JSDWAiDwvIv9bRHaJyBER2S4iD4vICRH54vBNReRR59pDIvKxMeUDIvIlEdknIq+KyBIRuRG4F/hbEWkQkVUi8h0RecC5ZruIvOxcs1NEMob4qvq0qg6vm7wK1MzkzXjy7JMEvfbGzYHEAKba6Q8stRhKDtEV7cJUk5+d/tlMbj9v7N+/H8uyGBwcJJFIZBgnsDu/9vZMldtCpr29HWOiyOFeL4YhDA6NzpQTQ9mzKBsiLPUnMfvS4+I1m0HwegmsXHlRdW3ujWYYJ0Goy5LrKVe7LMsiFouhqvh8Pg4ePHhRdZpr+h7/KZpMkuruxnKME4CmUiOboI1QiP6fPzHTR1wW/ZnD/wb+CzCvS2zzbaCeBmpF5LiIfFVEbhURH/B/gAdU9Wrg28CXxlyTUNVrgK8BjwGfBK4EPiwiwzFmfte59hrgU2PK84BXVfUq4AXg91T1ZeBx4M9VdYuqnhp+kIj4gR8Bf+xc8xYgOkmbfhfI+g0XkY85X8ZdqVSmdHggOYDXsCe1w8ZpLIqSNJO0Rdt4+uzTdAxld7ovFPr6+jhw4AD9/f1ka+9YRGTScxYaqVRqwn1I4rWXx8YuZyZi0YwEhs5JLOk8B+OMd7flx6xdMaNQRsPEUxZt/Zmy8srCAEGfJ6N8onaZpolpmhiGQTyeGZJpoaGqJM6epffhR0i1t6O5vmOm83vzerEiOeMheod/v87f+GnWZdGfich9QLOq7sv1RswV87rEp6oDInI1cAtwG/ab90XsD+gZ50fiAVrGXPa48+8B4JCqtgCIyGmgFujC/hDf6ZxXC6xxyhPYU1+A3cCdk1RxHdCiqq879Z0wYY+z/pwCfpCjvd8AvgG2D2r88XxfPikrhc/jI+AJEE1l2kJLLfyGn5O9JznZe5Laglq2Vm5lWf6yCTvLuUJV6e7uprGxkV5nqcrr9aKqE9ZPVfF6F5eodNJ2GQaIwdjJiKqSiEYJ5KW7MhJNzRTEIuSJj0EdfR+McB4t3kIuxjPX1DOUERDWEGFZSSjr+ZO1a1jRN+xXW4ioZRE/cZLont2kOrsAbCNvWeDJNMoy3JZUCiO3HzTlGJPsz7wM+jMRCQP/FXt5b96Z9x5BVU3geeB5ETmAPYI4pKo35LhkeNhmjfn/8GuviLwZe2Rwg6oOicjzwHDAs6SOri+ZzLD9IvJ/ga3ABVV9u1P2Yez15zvGPGNa3FV/Fw+feBifx0e+Lx9BiKai6JhZdtJKsrFs48jr85HznI+cpyJcwbbKbawoWoEhcz8xtiyL9vZ2zp8/z8BAembYyspKWlpaMpaNxr62LIuamppFJZKoqanh/PnzEy7zmYZBUShMYIzx9aiZ1s5Udze0t+H3GKz0JTiadIQJPh++inI6kl625YdmNAAZiKfoig8h/nRjVF+WR1lx9o2+49tljZvxDS/1XXvttdOuz6VGk0liR44Q3bsXsz99JhRYs5rY4SPIGAMlIhh5eUjQ7iKsaJTi9zww8+cv8v4M23e+AtjnfN9qgD0icq2qts7k/hfDfIsk1gGWqp5wirYAR4C3isgNqvqKM0Veq6qHpnjbIqDH+TDXA9dP4ZoIZB2kHgOqRWS7qr7urNdGVfV3xrXjLuz12ltVNTOezRS5Z9U9/PT0T4mlYgS9QfL9+YR8IaLJKDEzRsJM4DE8rC1Zm3Ftx1AHT519iqJAEVsqt7CuZN3IcuGlxDRNWlpaOH/+PLFY5jISwJIlS2htbcU0TbxeLz6fD7/fP9LhJpNJ/H4/b3/72xeVKmzNmjVp6sTxJJNJvKEg62qWpu2HKi4qYOM19kDcikbp+X8/xAraBqTOL3QPeEGE4MaNGM5Ma/n6uhllt32soRl/VXrqjKDPw3031Wdd3hvfLq/XSzQaxTRHl5yHl/iuvAhF4WxjxWJE9+8ntn8/VjT79zCwbj2xY8fQVArD78cIh5FgcCQDsRWLIX4fRffeO6M6XC79GTCyj0FEzgLXzJeKb759UPnAd0XksIjsBzYC/w14APiKiOwDGoAbp3HPJ7FHHkeAL2OLFibjh8Cfi8heERnZoq+qCeB9wP9x6vIMo6OXsfwz9hfiGccx+bVp1HeEqrwqPn/j5wHoj/eTNJMYGAQ8AXziI+wNc1f9XeT7c4e46Yv38avzv+IHR37A3va9JMzETKoyKYlEgrNnz/LKK69w4sSJnMYJIBAIsGnTJvx+Px6PZ2QpzzTNkevuvffeRWWcAIqKirjX6cxisRimaaKqae26697704wTQKSrE7UsO0r5L9OjlBcaFks8KXw1NSPGCSZPZJiN5t4opzsyRRnXrijJaZzGtysej48s+VmWNeInXLNmTcZMeT4wIxEGXvw13d/5LkOv7cxpnAA8+fkU3/9OjLww+HyIzwciaDKJ2d8PKNVf+OLFbNa9XPqzBcO874N6o5Jroy5MHEmiPFTOsZ5jNLQ30BfPzIQ6Hr/HzxVlV3BVxVWEfZmKrekSi8U4f/48LS0taaPqXOTl5VFXV0dlZSWRSOSyjEwwUcSFwsJCdj72EGYyfaCw+Y678DS3MPD88xn3O1ZSy+tL1jM24GxB0Mvv3rQCY4qx8lSVn+xqork33Y9ZEPTy4Rvr8XomH5uObVckEkFEqKysZMmSJQQCAXw+H9ddd13W2eOlJtXdTXTPHmLHjoE1eR/mW1pNaOs2/CvqSbW10ff44/T//InRSBJvv5uie++d0DiJu1F3znEN1DwxkYGaCpZanOk7w572PVNS83nEw/rS9Wyp3EJRYPrGYHBwkMbGRtra2rLKxMdTVFREXV0dZWVlC0K8MZ8c+fXz9LQ0p5XVrlhNaNfejECwEgwQfOC9fGt3e4aw4YGra6jNIgvPxumOAR5ruJBRfufGJVw5g4jlAwMD7Nq1K+OzX758OSsvUgI/HZItLQzt3kPiTGb6kWz46+sJX70N39KlF/1s10DNPfMuknCZGYYYrCpexcqilTQPNLO3fS/nI+dznm+qyaGuQxzuOszK4pVsrdxKZXjykDl9fX00NjbS2Tm1Jejy8nJqa2spLi6ealMuewrKK9IMlFoW7c8/R10gc6m24I47CJQUUV8+kLE8d6Slf0oGysoRELYs38/GLPmnpkJ+fv6IL3EsTU1NLFu27JIq+lSV5LlzDO3eQ/JCptHNwBACa9cS3rYNb1lmdmOXxYNroBY5IkJNQQ01BTV0DHWwp30Pp3tPpyn/xqIop3pPcar3FDUFNWyt3EpNfk3aLEdV6erqorGxkb6+yZcRRYQlS5ZQW1tL/gxTQFzOFJanDwSS55tIdHSiy/LS3vfgFVeMbMhdX1WYYaBOtA9w+3pr0uW5I639dA5k+h5vWl0+5SXCbNTX19Pe3p6m6jNNk7Nnz7Ju3boZ3zcXaprET54kumfPiFR8IsTnJXjFFYS2bMGziNSgLrlxDdRlREW4grfVv42+eB8N7Q0c7T6adcPvME2RJpoiTVSEK9hasZX6wno6OzppbGxkKsuPHo+H6upqamtrCQYXtK91XskvLcUwPFiWidnXR9KZhcRTSYI+ewOup6SE/JtvGrlmZUVeRoTzRMriTOcgayZIi5EyLV45ldmZLysOsbL84lanQqEQy5Yt4/z59Jl6S0sLtbW1hMMX7+MERyp++DDRhoYMqXg2jFCQ4KbNhDZvsgPAulw2uAbqMqQoUMSttbeyvWo7+zv3c7Dz4IRqvraBNh5qfYjUQIpaTy3LfMvwSG6Vl8/no6amhqVLl+K/iAgHbxQMw0N+WTl9F5qInzo9Uj4Yj9oGymNQ8NY706JF+DwGqyryRyKcD3OkNTKhgdrX1Ecklhkt4aY1mek0ZkJdXR0tLS1pUT9UlTNnznDFFVdc1L2taJTogQMTSsXHYhTkE966leCGDRcVacNl4eIaqMuYsC/M9dXXs61yG4e6DrG/Yz+DydGZkWma9Pf3E4lERhR5ffRxwjhBvb+eukAdPhlVaAWDQWpra6mqqlp0UR/mm4Kyctpf+jU6JvjqYCxGWX4RedffgK8y0x+4obogw0Cd7RwkljSzysRjSZOdWaKVr6zIY1nx7Mws/H4/tbW1nBknUmhvb6e2tpbCwun7uMxIhGhDA7FDh7JmDx6Pp6yU8LZtBNasSdt063L54fYybwD8Hj9bK7eyqXwTx3uO83rz6zR2NDIwMJARJQAgbsU5FjvGqfgp6vx1bCzZyLoV66isrJw4OKpLTgLdvZg9PWllA/EovtoaQlu3ZL2mtiRMXsDDYHzMJllLOdE2wKaaTCXennM9xJLpS7oitu9pNqmpqaG5uZlEIn1Wfvr0abZs2TLl+6S6uhjas4f48eNTl4pv24a/vv4Nrwx9o+AaqDcQsaEY0i7UdtXiN/2cNk7Ta/XmPN8b8DJYNMiR8BE0qQQTQYqDxXNW38uFVHc3cvAgIpIm006K4r/pppydrWEI66oK2XMu3bAdae3PMFAD8RR7GtPPA9hQXUh5/uwq7LxeL/X19Rw/fjytvKenh+7ubkpLS3NcaZO8cIGhPXunLhVfscKWildXz7jOLosT10C9Aejt7aWxsZGuLidoJkKVr4ol3iV0m92cjp+mIzm6lyocDlNUVDQifLDU4nDXYY50HWFF8Qq2VW6bkkTdxU7lEHn6aQwLQv4AQ/FR34p/xQoGo4OEqch5/fqqggwD1dwTpS+apCg0uvy680wXSTN9FuIxhOtXXhqZdXV1NU1NTQwNpUf2On36NCUlJRlGdziqeHTPHpIXWpgUQwiuW0do61ZXKv4GxjVQlylTkYqLCGXeMsq8ZfSb/XSHuun39+eMDKAop3tPc7r3tC1Rr9hKTUGNu9wyAYOvvEqqw96TlBcIjhgob2Ul3pISIp0dVNTV57y+siBAaZ6f7sH05bRjrRGuXWHPVHoGExxoygy0f1VtcZoRm00Mw2DFihUcOpQeUi4SidDe3s6SJUsARyp+4gRDe/ZgdmX6x8YjPh/BKza6UnEXwDVQlx3DUcWnIxVfunQpNTU1BINB+uJ97OvYx5GuI1OSqJeHytlWuY2VxSvnJYr6QibR2Ei0oWHkdV4gRAe9GKEQfidzcH/HxEkaRYT1VQW8PE46frS1n+319kzl5VNdGVEn/F6Da+snXmq7WCoqKigoKCAyLn/SmTNnKC8qInHsGEN792JFJo/ZZ4SCBDdvJrTJlYq7jOIaqMuEVCpFS0sLTU1NEwZuHWZYKr5s2bK0GVNRoIg31byJa5ZcMyWJeme0k6fPPU1RaxFbKrawrnRuoqgvdKyhISLP/CKtLC8QBEPwr1qJOJtth/p7SSbi+Py5/UTrqwozDFTXQIKOgTiqcLwtc6/QNctLCPkvrcJNRFi5ciX79o3Ja5dIYB4/zrmXXqZgCtJvT2EBoWGp+DzE9HNZ2Lix+OaJi43FN0wikaC5uZnm5maSYyTMuRiWildXV+OZgkQ3YSY43HWYfR370iTquQh7w2yq2MSV5VcS8CzchHaXElWlf8fPSJw9m3HsdNCDWZwuxV5/062ULq2Z8J4/er2RC73pA4+rl5fQEYnT2J3uB8oLePjwjSvwe+dmRrtv3z56mprwnT6D73wjYpp4PB6WLavJqfr0lpcR2raNwOrVi0Yq7sbim3tcAzVPXKyBikajNDU1TTmqeH5+PnV1dVRUVMxIKp6yUpzoOcHe9r30xnsnPd/v8bOxbCNXVVxFnu+N9ZuOHjjAwPO/yij319XSsXQJ7WdPpZUvXbeR+s1bJ7znvvO9PHs0fTkwaVp4DcnwAd6+vpKraotnVvlpkurqovullzj7wouIpm9ZKC4uyYjJ6Fu61FbkLV++6HyXroGae9y1mAXIROkbPB4PjY2NtLe3TymqeHFxMXV1dZSWll5Uh+A1vGwo28D60vUjUdTbh3L7TxJmgob2BvZ37GddyTq2Vm4lZsZyphGpyptxDp55J9naSt9jj9H/xJOYvb1oKkVg9SoC69bjcWITGqEg+Xe8hXhnW4aBmswPBbB2SQHPH+sY42tSjrb2U1ealyaEKA77ZhStfLJ2jaSluPsuiu67DyzLjip+9iwC5IdDGT7P/v4+CgoK8Hg8+FeuILxtYUjFB3piHH21lZO720lEU/hDXlZfXcn666vIL3FDdi0k3BnUPJFrBtXY2Mjjjz+OaZr4fD4Mw8A0TeLxOJZlsXr16inlTiovL6euru6S5VlSVS4MXmBP254Jo6gP0zrYykvNL+ERD/n+fLyGl5SVIpaK4fP4+PyNn2dL5ZZLUtdLydCevbR87q/QRBIJBrEiETSRQFMpO4TRbbfjq66m8B3vILByBbGBAfY88VjaPUQMrr3/PXgmic7xWEPzSADZroE4J9oHqCgIsKpiNEDvOzZXs3aCUEgzaZcRCoHXiyaTdvvMFPm3vCnN2CSTSZqbm2FMkGIVg6LNm1l1zzvwTrI3aq5oOdXHc98/gpmy8Po9GB7BMpVUwsTjNbjtwQ1Ur8r+m3FnUHPPopJdiYjpZKw9KCI/FZHiOXru/SKyMcexWhF5zsmieUhE/nimz+nr6+Pxxx8HbF/RsHFKJBJYloVpmhw7dox4PJ71esMwqK6u5tprr2XTpk2XNAmgiLAsfxm/seo3eO+697KmZA1C9hnaQGKA584/R8JKkNQkg8lBklYSr+GlIGB3pn/98l/TOtia9fqFSrK1lZbP/RUgeAoL0XgcNU3weJBAABAizz2Lb9kyAitXABDIy8MfSg+qqmox0D15OpP1VYXO+cr5Htvv1D2YwHSiMCwpDLKm8uKjyY9vF14vGos5xslEUyaR557FHJNR1+fzUeDIwtXjJbFyJUO330br8jqSC0SVN9AT47nvHwEgEPZheOzvq8drEAjbs9Dnvn+EgZ7JRUbZGNc//UREZid67iwiImdF5IDzd1hEvigiQeeYISL/5NT/gIi8LiIrLvJ5bxaRPud92S8ivxCRSufYh0Xknye6flEZKCCqqltU9UqgG/jkHD33fuz0zdlIAX+mqhuB64FP5jJmk7F///6RmZNlWUSjUaLR6IiPyePxoKq0tbWlXefxeKitreW6665j/fr15OXN7SCvPFTOncvv5IMbPsiV5VdmBJo93nMcy7JG1H0JK0FvvJeeeA+xVIyAJ0DCTLDj1I45rffF0vfYY/bMKRDAGhrCiqZnrxWvFxSSbaOGV0QoLM/cmNvfOXnSyeEI5+2ROLGk7e8xLaV3yFZZ3rx6dgLCDrfLCAbtdOjd3ZiOcRppl2kRP3Ys7bqSqiWkNmxg8I7bSWzciIZCWJbF2Sxikfng6KutIzMnM2URG0gy1J8gPpREVUfKj70244HS2P4pAfz+7NV+VrlNVTcB1wIrga875e8DlgKbnePvBHonupGInJ3C81503pfNwOtMo99ebAZqLK8AywBEZJWIPCkiu0XkRRFZ75QvEZFHRGSf83ejU/5bIrLTsepfF7F7VBEZEJEvOee+6lx/I3Av8LfO+avGVkJVW1R1j/P/CHBkuF7T5eDBgyOS71gsllX8YBgG7e22z8Lv97NixQpuuOEGVq9ePe8pL4Yl6g9ufJCrl1yN32PLjE/2nswqPU9ZKfoT/XTHuhGEJ848MddVvij6fvZzUMXs6kqbTQwjgLe0lMhTT6eVj88PBVPzQ/k8BvVleTT1pKv2OgcSLC8LU1c2OwP2/ieexAiFUMvC7OsbMUxjEa+X+IkTgC0Vz7/1TVR+5CMsuf12GCcvb2trYyDL+zPXnNzdjtdvD/LiQyksU0EhlbBIOvEOvX4PJ3dN/llMgReB1SKSJyLfdvqbvSJyH4CIhEXkx84s5hEReU1ErnGO/auI7HJWZD4/fEMR+bJz/n4R+Tun7Dsi8sCYcwacf6tF5IUxM7pbxldQVQewjej9IlIKVAMtqrbaRVWbVDUzftYMEXv0VABM+Z6LUiThGJQ7gG85Rd8Afl9VT4jIdcBXgduBfwJ+parvdK7JF5EN2COFm1Q1KSJfBX4T+B6QB7yqqp8Vkf8J/J6qflFEHgd2qOpDk9SrHtgKvJbj+MeAjwFZ01TE4/HR8EJZgrg698A0TdauXUtVVdWUpOJzTdgX5rrq69hauZXDXYf5wZEf4Ddy74kx1SSaitKX6GN32+4FL1E3BwaI7m0g1dpqp3nIFUsvP9/2S42L5FGQZQY10N2FWhYyicLStKyMkEa90QTXLC+ZZityY0UiGEVFWENDaI7vIYaBJpMUvPWtBNasHql3XV0dFy5cSNvyoKqcPn2azZs3z1odZ0IimiIQ9pJKWOi44LRWyn5teIT4UM6I6l4R2TXm9TdU9RvjTxIRL3A38CTwWeBZVf1dxyWxU0R+AfwB0KOqG0XkSqBhzC0+q6rdTp/1SxHZDDRjz2jWq6pOwb3xQeApVf2Sc5+soxdV7ReRM8Aa4MfArx1j9kvg31V17yTPmQq3iEgDUAYMAv91qhcuNgMVchq6DHum8oyI5AM3Aj8Zs7wx3LvdDnwIQFVNoE9EHgSuBl53zg8Bw0OmBDC8zrQbuHOqFXPq8Z/An6hqZtwZuw7fwDam5OXlZahTAoEAlmXh8XjweDxpOXfAnj15vV68Xi/Lls1okjan+D1+tlRuoSpcRTQVJWklSWn2H7+lFj7Dx2str7GnbQ9XlF3B5orN5PsXTobeVHc30b17iR07BqZlbyy1LMgySDD8fiQUglQKY1zInnBRMV5/gFRi1JdoppIM9HZTUJo78ng0YdLYPYTfY5AwRw1HaZ6f3miSulloI4BRUGALPcbF2RtGfD7E70cCAYLr1qYd83q9LF++nJMnT6aVd3V10dvbmyE7n0v8IS9myiKZyLIy4fijLFMJhHJ2iylVvWaCRwz3T2DPoL4FvAzcKyKfdsqDQB1wM/CPAKp6UET2j7nPe53BrBd7VrMROAzEgG+JyA5G+6lcvA58W0R8wKOq2jDBueLUo0lE1mH3m7djG8f3qOov004W+RdgOLvm0jFt/omqfinL/V9U1Xucaz8D/E+muPy52Jb4oqq6BViO/aZ+ErsNvc4a5/DfhgnuIcB3x5y7TlX/xjmW1FFZo0kWAy62KKLB+ft9p8yHbZx+oKoPz7RxV1555cjIMxAI4PV6ERG8Xi+hUIhwOIyqsmnTppk+Yl64e8XdAJQESygKFOEzMiMGJK0kq4tXj/y/oaOBfz/y7zzb+Cw9sVlbZZgRydZW+n72M3p+8B/EDh8BxzgE1qy21XrjMAIBjMJCRAQrGqXw7XenHRcRCsoyDVFkEj/Ua05A2LL80dmoYKflONoyeebZqVJ49122XH6cwlcAb3Ex3pISME2K3vH2rNcvXbo063Lz6dOnp7Q14lKx+upKErEUambWweu3u8JUwmT1NTMOhBwd06/8kaomsN+2d48pr1PVI7lu4IgSPg3c4fhsfgYEVTWF7TN6CLgHe3YGtg/ccK41AD+Aqr4AvAl75vUdEflQjucVAPXAcee6uKo+oap/Dvx3bP97Gqr6yeH2ABfGtC2bcRrP4069psRiM1AAqOoQ8Cngz4Ah4IyIvAfsdU4Ruco59ZfYU2lExCMiRU7ZA2OUJKUisnySR0aw105R1fNjPpCvOeuq3wKOqOrfX0y7hvc5JZNJDMMgFAqRn59PKBTC6/WSSqXweDzzvlQyXe5ZdQ8+j4+4GSfgCVAcKKY4UDyyjJeyUngMD2tL0kfjlloc7T7KD4/+kCfPPDmnKr/h6Nu9Dz9C708eInE6MzVEYN168BhoKoUARjCIt7QUT1ERYhhYsRji91F0770Z107XD9UXTbK/yV4qHJs+o7IwQNDnobnXjnA+G+TfdhuYZobxlVAI8fsnbBfYop0VKzLFX319fXR2Tq5WvFSsv74KtcAy05ctPT4Dw2OMSM3XXTere/KeAv7I6ScQkeEd2S8B73XKNgLDo85C7GWwPhFZgr1UOLxCU6SqPwf+f8BwH3cWe0UIbF+5zzl/OdCmqt8E/g3YNr5izj2/ij3D6hGRbSKy1DlmAJuBc7PwHozlZuDUpGc5LEoDBeCsje4HPoDtQ/qIiOwDDgH3Oaf9MXCbiBzAXrLbqKqHgb8Cnnam1c9gT6Mn4ofAnztOzlXjjt0EPAjcPmZmlX1oOQlFRUXc6/zoh0USqoppmiPx9e69995LKh+/FFTlVfH5G21fb3+8n5SVwmf4CHvD+MRHwBPg9trbcy7nKcrpvtM8fOJhHj35KI39jZdsJK6WRezYcXp/+EP6frqDZHNzznM9+fkU3vlWjEAA/H57v5DHYyvf+vsBpfoLX8RXldnhZfND9Xd25GzXK6e6RuTkYb+HkM+DIVBTMupaONY6O7OoxOkztpFC0XgcTBNEEL9/0nYNU1lZmVVNeubMmZz+1UuNx2dQd4WduiOVNLEsRVHH72Qb99se3DDbm3W/gG009ovIIec12IahQkQOA1/E7rf6VHUfsBc4CvwHtiEDe4C8w+mzfg38qVP+TeBWp++7Adu4AbwZ2Ccie7F97v84pk7PichBYCfQCHzcKa8Efuoc2489O5tQBj5FbnH6xX3YfeWfTfVCd6PuPDFRqKOJIkksNuM0ltbB1pyRJMK+MA3tDZNGUR+mPFTOlsotrC5ePStR1DWZJHbkCNG9ezH7J+/oJRggtGkzoas2Y/b10ff44/T//InRiAtvv5uie+/N2YlblsnORx/CMtNnKVvedg/hwvTPuCMS5wevnWPsT7W5J4qlSm3pqIEqy/fz4PUXF0Io2d5O749+DNhikPixY7ZaTwRvefmk7RpLZ2cnBw4cyChfv3491fMQUeLsgU7az/aTiKXobBqg+8IglqnklwRYs30J666bOJKEzOJGXUe44FPVmDPo/QWwzlkWdHFwDdQ8MVvBYi83hpJDHOw8yIHOA8TN7BuSx1LoL2RLpR1FPZtvazKsWIzo/v3E9u/Hik6+QdMoyCe8ZQvBjRttBd9FcPD5X9Dfkb6nbdXV17Fk5eq0srERJIYRgZRp4Rmn+vvN6+uoLJj5DKDvscdINKZHBjHCYUof/K1pt1dVaWhooLe3N608EAhw3XXXzakCNZU0afjFeaxU+uyt7ooyqlZObdA3ywaqAHgOe3YlwGdUdXHts5gDFpuKz+UyJ+wLc231tWyt3MqhrkOTRlHvT/TzQtMLvN76OpvK7SjqQe/kHbQZiRBt2Efs0CF0ClHgPaUlhK++msCaNbMWfbuwvDLDQPV3tKcZqKaeoQzjBPCmtRWcbBuguTd9c/DRlsiMDVTi/PkM4wQQvnb7jIyxOOk49uzZk1Yej8dpbm6mrm62dIeT03l+IMM4GV6D8tr5UYk6eyYnUgS64BoolwWKz+NjS+UWNpVv4kSvHUV9IjVfNBVlZ+tO9rbvHYmins2nleruJrpnjy0VtyZfPfBVVxHadjX+FfWzHn17sogSqspLJzNFBYUhH5uXFeE1JMNAHWuNcPPqcgxjenVVVQZffiWj3FNURHDjjAKjALZftby8PEMc0djYSHV1dc7szbOJqtJ2NnPnR3lNPl7fwttH6DKKa6BcFjQew8P60vWsK1nHmf4zNLQ3TKjmS1pJ9nXs40DnAdaWrGVL5RZKg6UkW1rs6NtnMtV42fDX19tpIZYuna2mZFBQXo6IgY5JUxEfGiA+NEggnMepjsGMHFAAN6wsw+sxWFNZwHNHO9Ky6Q7EUzT1RKcdVSJx8iSp9kwVYfj66y56xrhy5Uq6urrSBCDJZJLGxkZWrRqvOZp9+tqjxAczZ8lLVhRmOdtlIeEaKJdFgYiwsmglKwpX0DLYwp72PTT2N+Y831KLI11HOHT6Vapbk2zsDFChkyznGEJg7VrC27bhLSub5RZk4vH6yCsuYaAnPVtuf2cHZTVhXj6VOXsqz/ezvsre+Bvye6gvD2csAR5t7Z+WgVLTZPDVzOAn3ooKAmvWTPk+ucjLy6OqqoqWlpa08ubmZmpqaggELm3UkGyzp6LKMKH8i/Mhulx6XAPlsqgQEZbmL2Vp/lI6o500tDdwovdE2uhcLQuzu5tkSwtWNMoZ4IwfqqwCrjSrWGYVpkVeF5+X4MaNhLZssaN3zyGF5RWZBqqjnTZPCV0DmYKum8Yt322oLswwUCfaB7htvYXPMzV1Y+zwEcxxQgaAvBtvmLVlzfr6etra2tIk5qZpcvbsWdatWzcrz8hGdCBBX3tmRIwl9e7saTHgGiiXRUt5qJy3LH8L11Zfy772fRzuOEisrYVUWytWPLNzbzUitBoRSqwQm8wqVvirydu8hdDmTfYepnmgoLwSThxNK+vtaGfvUGakiWUlIVaUp4vIVpTbEc4TYwQAiZTFmc7BKeWF0kSCoZ07M8p9tTX4Z1HEEAwGWbZsGefPp4swWlpaqKmpuWQR+NvOZM6eAnk+iioXRgoQl4lZtBt1XVyGyTd9bLsQ4B27YcPpJL7YxBtBe4Mmr64yefI6P6dXhjAD8zdOyyaUOHO+lf6BzFF/tnQaPo/B6iw5oI60ZA0HmUF0/36sLDH38m64cUrXT4fly5fjHZeUUVU5M0W/4HRJJU06mzKjqC+pL1x06ebfqLgzKJdFiy0Vb3Ck4ikCwFaWcaVZxQlPJ4c8bQzK6EzKCIXwVVfjKS1FDIMBa4gXm1/k9TZbor6pfNOUJOqziS8YJFRQSDRiG5SUqTT3xqC4F4pHwyGtqsxnaXH2Uf+GqkIOX0g3SOe6hogmTEL+3AIHKxplaPeejPLAmtX4lsw4Hl1OfD4fdXV1nD59Oq28o6OD/v5+Cmd5eXWhSctdpo9roFwWHamuLob27CF+/HhWqbgPDxvNJawzKzhr9HC4dIiBqkI8xcVZR86xVIzXW1+nob2BjWUb2VyxmQL/xadNnyqFFZUjBupCb5SUaSEDPSMGSgRuWpVbtFFTEiI/4GUgPhqVwrSUE+0RNtcU57xuaPceNDFuKdQQwtddN/PGTEJNTQ3Nzc0ZWaFPnTrFli1bZm1m40rLLw9cA+WyaEheuMDQnr1TloqHVqzi6m1bua66mrP9Z9nbvndKEvX9nftZW7KWrZVbKQ2Wzlb1c1JQVkHb6ZMkUhat/Y6sfGB0z9fG6kLK8nMr3QxDWFdVwO5z6fvEjrbkNlBmJEJ0/76M8uDGjXa08kuEx+Ohvr6eY+Oy8fb29tLd3U3ZLKknXWn55YFroFwWNMNRxaN79pC80DL5BTmk4iuKVrCiaAUtA7ZE/Vx/7iDNqsqx7mMc6z5GfWE925ZsoypvViNcp1FYYc+UmnqiIwFhGepDzRQ+n4/rJ5g9DbM+i4EajnBeFMrcDDv02msjaUOGEZ+X8PZrZ9iKqVNVVcX58+cZGuf7On36NKWlpbMyi2o705dR5krLFx+ugXJZkKhpEj9xgqE9ezC7uic9X3xegldcYUvFC3Ivz1XnV/OO/HfQFe2iob2B473HJ4yMfrb/LGf7z1KdV822JduoK6ibdQd7IJxHyhOgPTLaTlVFhvrYsmk1hcHJoy1UFAQoy/dnSNOPtUa4dkX6LDDV1UXsaPoMBiB01VV48i+Nmm4shmGwYsUKDh06lFY+MDBAe3s7S5Ysuaj7RwcS9HVEM8pdafniwzVQLgsKTSSIHTnC0N69WJFMBdZ4jFCQ4ObNhDZNTypeFirjjuV3sL16O/va93Gk+wgpK2eqb1oGW/jZ6Z9RFixjS+UW1pSsmZUo6mDv7WpOBTIMpS/ay/b6qS0xigjrqwozQiMdbe1ne31JmlEdfOVVGJ+MMBggtC0jZdAlo6KigsLCQvr70/1EZ86coaKiAsOY+XvrSssvH1wD5bIgsKJRovsPEDswtajinsICQlu2ENyw4aKiihf6C7ml5hauqbqGg50H2d+xf8Io6l2xLn7Z+Et2tu7kqoqr2FC2YUZR1MfS2hejJZXZea4IJglOw6G/rqogw0B1DSToiMSpLLTVickLF7L68MLXXGPntZojhgPJNjQ0pJVHo1EuXLhATU3NjO7rSssvL1wDtQBJtrbS99hj9D/x5Gh+obvvoui++6aUh2ehkq1d+be9GX99PamWFjSZewYzjKes1I4qvnr1rEUVBwh5Q2yv2s6Wii0c6T5CQ3sDA8ncM7hIIsKvm3/NrrZdLMtfxune0/yy8ZcZea4m812pKr8+2Qn56cIEv9eglCEsy8QwptbOopCPZcWhjACyR1ojVBYG7YCwr2QGhDUK8glt2pRRHou10NL6KO3tPyOViuD1FlBZ+Q6qq+4nGLz4fE4lJSWUlpbS3Z2+hHvu3Dmqqqoy9kxNhalIyy91u1xmjwWRD0pETOAAtsE8Avy2k9Z9wSAiZ7FTvwN4gIeBLzoJxwzgH4DbAQViwHtVNafcLFc+qKE9e2n53F+hiaS9ZOX1QiqFFY0ifh/VX/gi4W1bs9xxYTO+XQpYkQjW4CB4DApuux3fBEnsfEuX2sFbl19cQr6pYlomJ3tPsrd9L92x3D6w1sFWnjv/HKpKni+PfF8+ihJLxfB5fHz+xs+zpXJLzuvPdg7yyN5me3lv/7NoylaerazIZ0lhgE13vI2C0syoErnY39TLL4+kB33ND3j5yM0rSJ49S//PfpZxTcFb7iC4YUNaWW/fbo4c+a9YVgKPJ4SIF9UUphnFMPxs2PDfKS66OuNe0yUSibBr166M8vr6+qxp4ydCLWX/c00j2XGHqawvpH6T/R5eTLsmywc1rh87Azyoqr3TasQMEJH7geNOtvBc53iAXUCzqt5zqes0WywUAzWgakfyFJEfALtV9e/nuVppOAbqGlXtFJF84BtAUlV/W0Q+ALwb2yhZIlIDDKpqzvwQ2QxUsrWVxo98BLDTa1uDg/Y+Fecz0lQKUEre/36MCYQACw0rEqHnhz8EBHFGxTomJttwu4ruvQ9PfvomSv+KFbZhmocMrGDPcM71n2Nv+15aBtNVhAOJAX56+qcAeA27XYIQ8AQI+8IjPq2v3/n1rDMpVeUHrzXSEbGXFPXUHrS3nZDfw1U1xYjA8s3bWLZuQ8a1uYglTb7xwulRNaDDu7YspeCZxzMEJ56yUkre/35kjM8nFmthb8OHATAMH6nUAKqjnb7ltGvZsg/g81688KC1rZWBSHoWYzEM6pcvx+OZ+iwqGknQ0ZiZDbl6TRE+v5dkqp/m5v8HgGEM31cwjCAeTxjLsj+HrVu+k3UmNQUDNbYf+y620fjSlBswQ0TkO8AOVX1ognP+FDv/VOFiMlALcYnvRWCziOQB/we4Ejvr5N+o6mMiEga+45QfA5YCn1TVXSLyr8B2IAQ8pKp/DSAiXwbuBVLA06r66fEf6vCXS0SqgR8Bhdjvzx+o6otjK6iqAyLy+8B5ESkFqoEWdfImqGrTTBre99hjaCKJp7AQs68Pa9xmRgwDjccZ2r2H8NUXP3qdK4Z270aTKSQQSDNMw4jXi8bjxI8ds9tlCMF16wht24a39NLvQ5oIEaG+qJ76onpaBlrY276Xs/1nATjecxzLsvB7R31gihIzY8TMGAFPANMy2XFqBx/d/NGMex9ri4wYJwDySqC3ndqSMMOTxEhnO0zDQAV9HurL8zjVnr48eeq1BjZlUUPmXX9DmnECaGl9FMtK4PUWkEx2o5q+9CoimGacvt5dFBdvn3LdclFU5GdgIDYu4C90djVRVjr1fVF9XRFU0usayPMhRpRUCnp7dzkzpwCq5sg5pmm/V15vHslkP62tj1Jf/wcX2SpeATYDOCnd/wWoAIaA31PVoyKyBPgasNK55g9U9WUR+S3gU4AfeA34hKqaIjIA/CNwDxAF7gNWYfdtt4rIXwHvVtVTYyviDJjfAXwJ+NOLbdhcsqBi8YmIF7gbe5r8WeBZVb0WuA34W8dofQLoUdWNwOeAsT31Z1X1Guwvxq0isllEyoB3Aleo6mbgi5NU44PAU6q6BbgKaMh2kqr2Y0/j1wA/Bn5DRBpE5H+JyIzW4PqfeNJe/lLN3OHvIF4v8RMnZnL7eSN+4uTIzCkX4vUSP3mS0JYtlH7oQxS85S3zbpzGU51fzdtXvp33rXsf60rWcbL35MjMKRtxM07CTPCjYz/ibN/ZtA7YtJSXT6ZHMaeghPygl9K8UYPX39kxoQw+G8PpOIYR02Tg1Vcxx6sEl1bjX1GfcX17+8/weEJYVjTDOA1jGD4GBmfne+jz+ijIz1wRiEQiJFOTZzsGSCZM4kOZdc0vHhV+DA6ewMghaFG1f28eT4i29sxl0OngLKfdATzuFH0D+CNVvRr4NPBVp/yfgF+p6lXANuCQiGwA3gfc5PRBJvCbzvl5wKvO+S9gG7qXnef8uapuGW+cHP4B+C/AxEEqFyALxUCFRKQBe420EfgW8FbgL5zy54EgUAfcDPwQQFUPAvvH3Oe9IrIH2AtcAWwE+rB9Qt8SkXdhj2Am4nXgd0Tkb4BNTmrmXIhTjyZgHfCX2F+CX4rIHRkni3xMRHaJyK5UKvPHZEUi4PXaPpZcHbph5DReCxVNJGAC2bAYBkZBAeL3k3/LzRPuY1oIDEvU/R4/eb68tNQd4zHEYDA5yM/P/JwfHfsRx7qPYVomB5r76IuO63xDhdRXFDLWxZZKxEfCIE2V4QjnwxQ1nkAGB+kdSv/e5N2QPZ1GKhUBDEwz00c6pmUjS2KzQXFxcYZsX1Xp7emd0vWDvZl18fgNAnmjvyO7vtm/h/bY2P7Xbn9WvMO/X+fvY+OOD/djrcAS4BnHHXAj8BPn2NexV1zA9ln/K4Cqmqrah23YrgZed86/g9EZVgLY4fx/N1Cfq6Kj7ZJ7gHZV3T3ZuQuRhbLEF3VGCyOI/ct5t6oeG1ee9QYisgJ7dLJdVXucJbygqqZE5FrsD/oB4A+xvxgpnG+rI3LwA6jqCyLyJuwp8XdE5O9V9XtZnleA/QU57lwXB54AnhCRNuB+4Jdjr1HVb2CPpsjLy8sYFhsFBZBKgc+Hp6AAs68PNc30kyzromTV84H4/WBZME51Jx4PRiiEhEJ2u+cp5cVMKfQXYqlFnj+PaCpKNBXF0vRBqqUWfo/9eXXHuvll4y95qfkVzrcsIY86PDI6ol9RmU9NsJq+9vRwTP0d7YQLi6ZcL5/HYE1lPocu9GMkE5SctDfEdkbilOXZMwr/ihU5swV7vQWO32miAbeFYcyeLN3j8VBYVEjvuLxUg4ODFBYVEvDnfpZlWQz1Zw7a8osDaYMHu74W6UZKMAwfHo/tWlJN4fXmHCClnBWaXERVdYvjhngK+CS2O6J3fP82AQJ8V1X/MsuxpI5Op02y9N8iUgv81Hn5NWA5cK+IvB17kF8oIv+uqr81xfrMKwvFQGXjKeCPROSPVFVFZKuq7gVeAt4LPCciG4FhfWwhMAj0OWu7dwPPOyOYsKr+XEReAoZDKZ/FHqn8GHsN1wcgIsuBJlX9pogEsKfeaQbKuedXgUcdY7gNaFXVC46x20z6zG5KFN59F70P/Scenw/xevGWlWUYKDMSoei+eyn97Q9N9/bzhlomfY//NGNmNFYmbkajFL/ngbmu2kVxV/1dPHziYQoCBeT58gh7wyOGynR8HEkrycayjWnXnejopClyHq8coMK/mgrfGrxGgJtWlxM7151hoCKdHVStml5m2w3VhRy60E/x6SN4knbn3TuUJGla+Lwe8m64Pue15eV30tT0XQwjfSBkGEG8XlvEkkxGqK66n9ra35lWvSZi6dIUu3btIpFIn1nGY6WsXnVFzuvaz/bji6f71wyvwdoNNXjGzCQttWhtfRSfb+z3UJAxMzfTjLJs6Xsvqh2qOiQinwIexe4nzojIe1T1J87Ae7Oq7sMewP4B8A/OsmC+U/aYiPxvVW13fNwFqpo7NpetLi5wnn0e2DLu+F8CiMibgU8vFuMEC9tAfQF77XS/0+mfwXYOfhX4rogcBo4Ch4A+VT0hInudsvPYhgzsD+4xEQlij06GnYTfdMr3AU9iGzeANwN/LiJJYAAYawmec75gBvCIU0eASmDYoAHsBP55ug0uuu8++h5/HCsWwwjaGyvHduJ2eYCS9753zjO/Xgwl73sfkaefRpPJkXaNxYrFEL+PonvvnYfazZx7Vt3DT0//lFgqRtAbREQI+8KEvCHiZpy+eB8ew8PakrUj1yRNi5Y+eyNySuO0xA/RljjK1sorCQWq8VdU2hstxtDfmS4bnwrLikMUaYLis6MLEAp0DyZYft2WCVPah0O12Et4qTS1m9ebh4gH04zh8QRZtuz9+Hyz9z30+aC+fiMnxvlYe3piDAyYlGQJYquW0tnYh5Cu/qysLSIYKk4rq1n2fjo6nsSykng8md9D04xhGH6qqu6/6Lao6l4R2Q98ANuH9K+OiMGH7aLYB/wx8A0R+Qj2jOgPVPUV57ynnX4viT0Tm8hA/RC7//kU8EAOP9SiZEHIzKeDM9LwOfuPVgG/ANbpsJdzkfBG3wd1ubSrob2Bv375r0mYCULeEF7DS8pKEU1F8Rk+PrHlE0RT0RGJ+pnOQVr70iNlGAJX1RYT8ntZXbgSa9d5Ckhf7rz6HfcTCE8vTt6rP3iMjl0NaWX54QBv+otP5vTzJZN9NDV9j2isiY6OX6JqjiyBeTyhWd8HNR7TNHn99deJRtM3GxcWFrJt27aMJf7etiGO78yMUL/5tlqC+ZmCiEu5D8pl9lmMBqoAeA57JCLAZ1T1ifmt1fTJZaDAibjw+OP0//yJ0UgSb7+bonvvXfyRJC7DdrUOtrLj1A6ePPtkzkgSrYOt/Pr86/z0cENGCquqomBaKve20ycpiQdZbdRQati+pzXX3kjF8qlvXE319ND0f7/HgfO9aeW9K9Zx70ffTVE4u5qtvf0pBgbsFPSpVISBgaMMDp1ExIfPV8SSyndQdYkjLrS2tnLkyJGM8iuvvJKKivQMxMdebckIDFtUGWbddbm/T7FYC62tj9I2JpLEVNrlGqi5Z9EZqMuFiQyUy+XJkwdb2NPURHviKN2pc6gqHkPYWleMzzPqB+ltvUCky46pV2oUssqoYfOq61h9zdQTCfY/+SSxEyc40NTHUML2h1leH2ff/Btcv2Ep163MXOKLxztovvD/MgLJFpdcS2nJDTNp8oxQVXbt2sXAQPpernA4zPbt20cCyUYjCQ48n7nlcO21VRQvCc96vVwDNfcsFJm5i8tlTXskxtHWCCFPEctD13FF3juo9K+ltqQgzTgB+Mcs5XVb/byeOswj5x8fkahPRrKt3d57hqQlOuxZuQHLH+BoayTr3qqenpczjJPHE6S4aO6inMNoINnxDA0N0do6upyXLWNuMN+NWn454RooF5c54OWTXWl9v9/IY23hNXz6+o+yvWo7Qe+o0z6br6l7qIunTz/FD478gH0d+0iauTewDr7y8sj/y5wEfalAkN56W6zRPZhIj2ABRKNNDA2dzbhXcfF1syonnyqlpaUUFxdnlJ89exbTNHNGLa90o5ZfVrgGysXlEnO+e4gznZnLudeuKKUomMf2qu08uPFBbll2CwX+AjxeL95ApsosPjTIQHKAl5pf4nuHv8fOlp1EU+n+l0RjI8nzo8teQa+HgqCX7jVXot5Rv9OR1tHNqKpKd89LjMfrK6Sg4MoZtfliyTWLisfjNDU15YxaXlGzsDd5u0wP10C5uFxCRtJpjKMo5GNzTfHIa5/hY1PFJn5zw2/yluVvYUlhppM/MSZFetyMs6ttF98//H1ebHqR/kS/nU7j5cx0GuVLK+mvSe/sj7X2YzlqjaGhU8RjmUq4kpIbxsjM556ioqIMUQRA47lGLpzMjCtYUVuAx+d2aZcTC3kflIvLoudUx0CGrBzghlVleIzMpShDDNaWrKV47Xt5qednnLKa6LL6AHsGNZ6UleJA5wEOdh1kQ28eK1paCXvTBQJ1b3kTngtGWoTzwbjJ+Z4h6kpDdPe8PP62+P3l5Oetm3Z7Z5sVK1bQ2dmZ5jMb6IvS399MRWF6JAw3pfvlhzvccHG5RFiW8tL4gLBARUEgI6DreAorKllilHKjdzM3e6+iyigjEYti5RBJqGnS9/JLHOo8xIme40QSERTFW1lJwfq11Jdn+rWOtkaIDBwmmcjMClNaeuOC8OXk5eVRPS7VykBvnL7BTpLm6NbHospw1n1PLosb10C5uFwiDl3op3swc//4zavLJ+38g3n5I2KJEqOQ7d6NvNm7jeXeZRlBVQEKznTgG7Bnar3xPo52H+Vo91G6N9mp0zdkMYjH2/ro7Hwt89mhZYRC9ZO2b65Yvnz5iLQ8lTCJDyWx1KI70jZyzpIV7uzpcsQ1UC4ul4CkafHq6czZU01JiOVlU9ujU1Ce7n8pkDBbPKv5rQ2/xVUVV+FzUkdIyqT4cHPG9R0lHp5KNPDDYz8kYZzH60mXkA8MtnCmK9OAlpbctCBmT8MEg0FqamxDOzAmann/UDfxZMyWlle40vLLEddAubhcAhrO9zIQz0ypcvOayWdPwxSWV2aU9Xd2kO/P56ZlN/Hgxge5tupaKk5144llys57NtXa/8Z6+FXz87TqM7QnjmFqEtUUsdgFTnenB4TNy1t1SaNEzJS6ujoMw8NQ36hBVZSuSIsrLb+McUUSLi6zTCxp8vrZTJXZmiX5VBdNfaRfWJ6pYIt0dWJZJobhIegNsq1wI7U9r9NZaNI62ELc8csM1paRKEn3O+WHTQ73NtAaP0yhFpJn+Wnu8xFLCUGvggglJTdOs7Vzg8/nozBQjmq6InIo0Y+/cNHl4XOZIu4MysVllnn9bDfx5Lg9OiLcuKp8WvcJFRbhHZcHyTJTDPaMihqGdu/GSJpUhivZVLGZlcUrCfvz6LmyJuN+hUEvAa9B0hriQmwfp1K7uZA6zZEOe+mvIH8jfv/CymI8jFoKkTy8nvQZX7gwwLnGs9POOuyyOHANlIvLLBKJJWlo7M0ov2JpYVoq96kgIhl+KLCX+QDM/n6iBw6Mno9QFizj6lvfy12bH2BpfroMW0Qoy/c7GWMVC5Meq4UdFw7xStc5rOD0ck7NJb3tQyRjJmUFS9LK84oD9Pb20t2dOWN1Wfy4BsrFZRZ59XQ3qXHhyr2GcN3Kmc1Msvuh7PxQg6+9Bmb6TE18PvK2b6eusI77V9/Pu9a8i5VFK0cyy5aErIxU7gMJ4WzMx3+e+hk7Tu/gwsCFBTcjGY67Vxgqxe+EhQrm+fD67S7s9OnTWJa71He54fqgFiAtfVEe3tPMjv0XiMRSFAS93LN5Ke/atmxaPoyFxuXerkf3NtHWH8fvNVi7pIAN1QXkB3xsrSuhIDizPTpZ/VCdHSQ7OogfO55xLLRlC0beqO+pKq+Ku1bcRU+sh4b2BnYPPk7QaxFLjR2bGgwkSykBGvsbaexvZEl4CbUFtexr38dT557KmUZkLohGEvQ7KTVEhPLCai50nyGveHT5c2BggPb2dqqmkLZlKulRXBYGbrqNeSJXuo3d57r5zEMHSJgmYb8XryGkLGUokcLv8fCVBzZx9fKF6SeYiDdCu1KmkjQtLLVl5h5DeMfmav7qHRsJ+jyT3ywLlmWy89GHsMx0ReAa9UJbR1qZEQpS8qEPYfizLyXGYi2cbPx3njzXy+vtPVjYm3693iIKwsVcVVM0ooZrHWzlufPPgUKhv5CwL4ypJrFUDJ/Hx+dv/DxbKrfMqE3T5ez+TtrPjUYuV1XaBs5QUJ3+ngaDQa699lo8ntzv9XCCyaSZJOgNjiSYnEq73HQbc89lZaBExAQOYCcyNIE/VNWXxxz/E+DLwBJV7XPK3gw8hp1SPgjsAL4LfN+5rA7oc/46VfUtY+73YeBvgSYgHzgNfH7sM3ORzUC19EV58N92gkDQaxBNmiRSFsOfUNK0QOH337yK4hwJ5xYivUNJvvb8KRAyUkvA5dOuxLjgpUnTIuz38tAf3HBRM8RDv/olfe2jsfKM3l6WtHSSX5Ke0yn/lpsJbdmS9R6qSkvLfxKLNTOQMPjRvjx6rFZ6tB1voAQw2FRTRH7Ay0BigJ+e/ikAXicWn0c8hH1hgp4gcdPei/T1O79+yWccqaRJwzPnscYtZZbW+znfeSLj/NWrV1NbW5v1Xq2DrXz8mY8D4Pf4iaaimJZJwBPA7/FP2q7JDNSY/scLHAF+W1WHcp2/EBCRa4G/A5YAQ8Bu4FPAe5lh3zabXG5LfFFV3QIgIm8D/gdw65jjHwBeB94F/N8x5S+q6j0iEgL2Ao+Muc93gB2q+lCOZ/5IVf/QOfc24GERuU1VM1OCTsLDe5pJmCZFIT+RWHIk0dxYYkmTZw63sb1+8cw2Xj/bTTRpEvR5MjrxYS7HdgW8HgxRHtnTzCduWz3j5xSWV4waKFW8J88QFyPNQHkKCwhemTvyeDR6lljM3syb77dYWgieSC014ZsYlEHaEkfpjMTJD3g53nMcy7Lwe0dnYqaadvgknxL2hemP97Pj1A4+uvmjM27XVOg8P5BhnDw+gxXrlzJ0pJuurvTN0OfOnaO6uhqvN7Nr23FqB0kzSdgXpifWg6n27ytmxij0FxL0Bi+2XWP7nx8Avw/8/UxuNBeIyBLgJ8D7VfUVp+wBYDjsyKz1bTPlchZJFAIjelwRWYU9EvgrbEOVgapGgQZg2UweqKrPAd8APjaT63fsv0DY70VViSWzd+Q+j8HxtkjWYwuV422RrDOnsVyO7coPeMkL+Nixv+WinlMwRihhdHZh9PenRTYHCF93HZKlUwYnnUZ3+sB3VWkCjydIKFBFuX81G/PeTglXUxYs52TvyZGZ03iGUkOoKiFviCfPPnlR7ZoMtZS2M5lJCctr7KjlK1euzNigm0wmOX/+fNb7PXn2SbyGl95474hxGiZm2mGiZrFdLwKrRSRPRL4tIjtFZK+I3AcgImER+bGIHBaRR0TkNRG5xjn2ryKyS0QOicjnh28oIl92zt8vIn/nlH3HMSrD5ww4/1aLyAsi0iAiB0Xklix1/CTw3WHjBKCqD6lq2/gTL7ZvmymXm4EKOR/IUeDfgC+MOfZ+4IfYX5x1zughDREpAdYAL1xEHfYA67MdEJGPOV+8XalUZpSBSCyF1xBEhCyBrgEwhJyzkIVKPGXlbM8wl1u7vIYQ9Bl4DSGSJcrDdCgoK0PEAEvxnT4LQCqZIJW0N+V6y8sIrF2b8/qBwWMkEukbXJeXJMgL14Kj7hMxCGoN15W/Ha/hJeDNnqTQUouYGcNreBlIZiYMnE1624eID2W+d8NRy/Pz81myJONnzPnz54nH4xnlffE+BpIDGcYJwHC6wkna5R3+/Tp/WTtrEfECd2Mv930WeFZVrwVuA/5WRPKATwA9qroR+Bxw9ZhbfFZVrwE2A7eKyGYRKQPeCVyhqpuBL+aqpMMHgaecGd1V2APv8VyJvaQ3VXL2bZeKy81ARVV1i6quB+4CviejQ6wPAD9UVQv4T+A9Y667RUT2Ac3YH2pmcpypk7MrVtVvqOo1qnpNtiWIgqB3RKJcEPSRLXqLpeD3Lq6PLeA1sCZxdV5O7TJEKAr5ELGFIDNV8A3j8frIKynB09qKjPFbDqffyLvhBsTI/t5ZVoqenswcUYX5S1i3NNNXc7R1gOJAMfm+fEoCJSPx/sYSTUVJmknyffkzbdKUyJbSfXzU8vr6+pFAssOYpsm5c+fSypoiTaSsFGaWaPCGGOT5bNdSykpN1K7U8O/X+fvGuOMhEWkAdgGNwLeAtwJ/4ZQ/j+3nrgNuxh4wo6oHgf1j7vNeEdmD7W64AtiI7QOPAd8SkXdh+4sm4nXgd0Tkb4BNqjobyxNzHk/qcvNBjaCqr4hIOVDhzJbWAM849sqPLYr4Z+f0YR/UCuBVEfmxqjaMv6eIfBL4Pefl23M8eiu2g3Ta3LN5KT96vZGikB+/16A8P0DKVJTRXjASS3LPpqW8c+uMViHnhcF4ip8duDBhR325tEsQfB4ZWXoaSqR4//a6i35WQXEpyTPpnW58aJDiDVfgW74853WRyAFSycyOvrTkJtZ7CjnZni7UOdUxwJ11d/HYqYcpCBSQL/n0xNLTcaSsFAPJAd637n0X0aKJGSstH8v4qOWhUIilS5fS1NSUVn7hwgVqamoIh8Oc7jvNM2efYVXxKo50HcFvjPrWPOKhKFCEx7CVf9FUlHevefeMqz3sgxrGGSC/W1WPjSvPegOnD/o0sF1VexwfeFBVU46g4Q7gAeAPgduBFM5EQ0QM7L4NVX1BRN4EvAP4joj8PRAB/tp51EeBQ9gzt8em2L4Z920z5bI1UCKyHvAAXcCfAH+jqv9jzPEzIpL2y1bVMyLyZeAzZPFTqeq/AP8y5h7jn3kr9hrtbTOp87u2LeORPc1EkyYhnwdDBL939BnRpEme38fv3rJiUe0b+sgtK3j+WAeWQiiL3Ppybpff4+Gd2y7e6AY7u5Fxy1aJoSHybsqdt8my4vT27swoD4frCYVqWOG3CPiMtLBMiZTFlqI7+Lnnp8RSMYLeID7DR9IaXWpLWSkMw+CeVfdcdLtykc33lCtq+fLly2ltbWXssrmqcubMGbzVXp49/yyqytqStRzrOUbKSuE1vHjEQ3GgeMQ4xVIx/B7/bLfrKeCPROSPVFVFZKuq7gVewlbKPSciG4FNzvmFwCDQ5wys7waeF5F8IKyqPxeRl7BVdQBnsY3Mj4F7AR+A07c1qeo3RSQAbFPVPwEeGa6YiJwHdorIz1T1NafsXU7d0rjYvm2mLK41lckZ9kE1AD/Clnma2P6nR8ad+4hTPp6vAW8SkfopPvN9zjOPA/8Ve7Q0o1FGdVGIrzywCRT6ogmSpoWqvbemL5oAha88sGlRdeLgtuti22XFYnhOn8kojxXkQ0lxzut6+/ZgmuOy+YpQUnoTAF6PwZrKzDxRnX1+Pn+j7Zvvj/fby3wKlmURT9lG8uZlN48si802qYRJZ1OmH2hJfVFWY+z3+7NKyw+cPcDTx58eiYqR78/ntlq7fx1eyjPEIGkm6Y/bBvHzN35+tqXzX8A2GvtF5BCjfvGvYq/uHMb2Jx0C+lR1H/bS3lHgPxg1FgXADhHZD/wa+FOn/JvYfqp9wA3Yxg3gzcA+EdkLvA/4x/EVc8QQ7wf+TkSOicgR4G3YMy2Yxb5tplxW+6AWE7k26oK9H+qRPc3s2N9CJJakIOjjns3VvPMyiLjgtmv6DL78MkO799B66jjJ+LDBEeLXXc3aO++mbFlm55xKDdLU9F0sK11kkJ+/nsrKt428Pt89xEO705fHDBF+700r6E92suPUDp448wStQ634DB+ri1eztmQt+f58tlZu5YalN1x0+8bTcqqP84fT5eMen8GWO+rw+LKPqVOpFK+99hqJhC0cuTBwgeaBZjx5HkJ16Z9B0BskkUrwi8ZfTCuSxGxu1BURD+BT1ZijMP4FsE5VMxN0vYFxDdQ8MZGBcnEZxhwYoOf730dTJt0tzQz22B23WV1NcsNalq5ZT/2WqzOu6+x8jv7+/WllIgY1NR/C5ysaKVNVvvXrM0Ri6arS29ZXsqW2eOT1rtZd7GxNXy4MeAJ86IoPZRVSzBS1lP3PNWWo95asLGL5FWU5rrJpamrixIkTNEYaaRscVUqH6kJ48uxlvNqCWu6qvwufZ/p1nmUDVQA8hz27EuAzqvrEbNz7cuJyW+JzcbmsGNr5OpqylWeBsJOJ1zBIrrDdp/1dHRnXJJO99EcOZpQXFm5OM05g+1HXZUkHf6w13Qe0sWxjRqr5uBnnRE9mNIeLIau0XGREWj4RVdVVnI+dTzNOAImOBKrKyuKV3L3i7hkZp9lGVSOOEvAqVd3sGqfsuAbKxWWBkurpIXbk8MjrQNiRQtcsg6C9T2mwpwczld6h9/S8CjouH5Xhp7h4e9bnrK/K7Pwv9MboHRpdbQr7wqwpyUzHcbDz4KxGPs8qLa8IEcyb2KikrBS/aPwFvXm9GcfMqEmdUcdbl7815wZkl4WJa6BcXBYoQ6+8wtiNVl6fH08oTGr5qM9J1SIyJtxPPN7OwECaohmAoqJteDzhrM+pKAhQXpC5Mfdoa/rWmU3lmzLO6Yx20jJ4cZEyhhnqn5q0fDxJM8nPz/yc032n8RR48ATTFZVVeVVURCvA9WYsOlwD5eKyAEm2thI/dTqjPLR1K/jSZxP9He0j/+/uzlAI4/GEKCraOuHz1mdZ5jva0p82O6oMV2YVEezv3J9RNhPas8yegvn+rNLyYWKpGI+fepymiC30EBF8laPvz7L8ZdQW1BKNRmlpmR1D6jJ3uAbKxWWBoaoMvpwZ/cHIy6Pg2mszyiOOHyoaPU802phxvLjkOgwjewqOYbL5oXqGkrRH0vdeZZtFnek9QyRxcYEKckvLC3Pu8xpMDvLoyUdpG0r3OXnCHjx5HpYXLk/LKnz27FmyhRhzWbi4BsrFZYGRbGwk2dycUR6+9lqKqpdmlEe6OjHNVNbZk9dXSGFB7ijnwxQGfdSUZM5UjrSkz2pWFq3M2P+kKAc7M0UZ06EjR9Ty8prsYYf64n08cuIRumOZqd4Nw+D2LbdTGU7PRpxIJGjO8r66LFxcA+XisoBQVQZfyZw9eUpKCG7cQKigEK8/3V9kmSm6WvcQj2cEoaa05EbsLTeTs6E609dzvC2CNcYP5jE8XFF2RcZ5h7sOp0WbmA5qadblvfLagqz7nrqiXTx68lH6E5nXeMTDXfV3sbVuK5WVlRnHGxsbR/ZKuSx8XAPl4rKAiB8/TqqjM6M874brEcNARDLSwCsWbS3PZVzjD1SQl5c7yvl4Vlfm4xkXnn0wbtLYnR6XdLYl59ORlrcOtvLoyUcZTGbuIRwOU7SiaAUAK1asyFgeTKVSNDZmLoO6LExcA+XiskBQ02Totdcyyr1VS/CvXDnyemx+KAD1tDA0mBmAv7Tkppz+m2wEfR5WlGfuQx2v5sslOT/QcWBGkvNscfeKKzOl5ecj5/npqZ+OZL4dS9Ab5N5V97IsfzTuYTgcprq6OuPc5uZmYrFYRrnLwsM1UC4uC4TYwYOYfZmddd4N6QFhCytGZ1CKiek7S2JoKC3qfShUQyg0/SjqG6ozxRKnOgYycnVlE0t0xbq4MHhhWs8b6k/Q3zm5tPx072l+dvpnWZcR83x53L/6/gyfE9jpODye9CVOy7I4cyYztqHLwsM1UC4uCwArkWBo166Mcn/9cvw16dHQ84pLMDz2hlPLex4kjmWmSI2Jdl5SOr3Z0zD1ZXkExvl9EimL053pCrvKcCXVeZmzkwOdB6b1vFzS8sLyUcHG0e6jPHX2KSzNTGhZFCjinWveSWmwNOv9A4EANTU1GeVtbW0MDFzahIsuF49roFxcFgDRvQ1YQ+NmEiLkXX99xrmG4aGgrBwlgeUd9afEndiOeflrCAZmFpE7V4Tzoy2ZMvIryzPVgdORnE9FWr6vYx/PNj6bNjscpjxUzjtXv5NC/8QbeWtra/GN2zs2nI7DZWHjGigXl3nGGhoiundvRnlg7Rq8FRVZroDC8gosXyPI6L6eeHQQxKC05OIijGfbtHuua4ihRPoeoouVnE8kLVdVdrbs5KXmTOk82NEh7lt9H2Ff9ugYY/H5fNTVZS53dnZ20tvbO6W6uswPbmCqBUjrYCs7Tu3gybNPTisdwELHbVd2hnbtQpPjfCseg7zrrst5Tbg0jOVNT5MRHxqkIH8jPl/JjNoxTE1JiIKgl5a+KEdaIhxvixBPWTyyt4n3XFPLu5w0IsOS8/FRzg93HeaaqmsmjHI+kbTc8Aq/bv51zuXCmUQkX7Zs2Yg4Ih6P09bWRnt7Ozt37qSgoIBNmzaxefNmioqKJr+Zy5zhzqAWGA3tDXz8mY/z8ImHsdSi0F+IpRYPn3iYjz/zcRraG+a7ijPCbVd2zL4+ogczZxyhK6/EM0FnmZKTIOnLXmbSIhSYfFPuZIgIhgEP72nmYHMfqhD0GsSSJj96vZEH/20nu8/ZG2Q3lm3EM26f1VQk57mk5eV1eTzb+GxO47SqeBVvX/H2aUck93g81NfX09fXx759+2hpaUFVMQyDVCrFnj17+P73v+9K0BcYc54PSkRM4AD27O0IdtbboYmvml9E5Frg74AlwBCwG/gUdsrmvwWagHzsNMyfV9WXJ7tntnxQrYOtfPyZjwNOUjUzQcIc3VQ4/P9PbfsUJcGLGyXPJT2xHv5pzz8B9l6V8VxW7RLwG358hm9EDv31O7+ecybV/9TTxI8fTysTn4/SDz2IEc6+fJVIdNLU/B+0nT5BIjr60zGSy1m39beorF+Z9bqp0tIX5QPfeJW+aBKfJ30MW5bvJ2kqKHz/o9dSXRTil42/5Fh3eoDasmAZ71333pxCjaOvtGSo9woqg5ypaOBs/9ms12wo3cCttbdm7MGaKr29vXzzm9/Esqw0ZZ9hGITD4ZEwSA8++GDWmdRk+aDG9W1ngAdVtXdGlZ0GInI/cFxVD2c5Vgt8D7vvUuAbqpqRXXehMh9LfFFV3QIgIj8Afh/4+3mox5QQkSXAT4D3q+orTtkD2CmYAX6kqn/olN8GPCwit80kNfKOUztImkkKAgUMJYcYSGY6kOOpOI+dfIxtS7bNtElzzp62PQwlh/B7/TljoV1O7RpiCK/hJewNE0/F2XFqBx/d/NGM61MdHRnGCeyAsLmME0B398ugSiCcN2qg1IeRqiPS1XHRBurhPc1YqoR8HlJW+gA2lrTID3jpiyZ4ZE8zn7htNZvLN2cYqGHJ+dh9ScNkk5abanLYs4u2/qaM8wG2VG7hhuobZqRMHObAgQN4PJ6Me1iWRSqVwufzEYvF2L9/P7fccstMHjG2b/su8EngSzOu8NS5H9gBZBgoIAX8marucZIk7haRZ7IZs4XIfC/xvQisFpE8Efm2iOwUkb0ich+AiIRF5MciclhEHhGR10TkGufYv4rILhE5JCKfH76hiHzZOX+/iPydU/Ydx6gMnzPg/FstIi+ISIOIHBSRbN/KTwLfHTZOAKr6kKpmxJVR1eeAbwAfm8mb8eTZJwl6g6gq0VTm3hAAn+HjZO/Jmdx+3jjZe3LSPDyXW7tSVor+RD9xM84jJx8haWbu38kW0sgIhwht3ZLzmbHYBYaGbPXZcH4oACO1HMGXFtl8puzYf4Gw30vQlxkiKZY0UVXCfi879tvRwSvCFdkl5x3Zl+nG53xKWimOR4/S6slunK6vvp4bl954UcYJ4ODBgwQCgYx9UcDIAMPn83Ewy5LrDHgFWAYgIqtE5EkR2S0iL4rIeqd8idOv7XP+bnTKf8vpCxtE5OtOenhEZEBEvuSc+6pz/Y3AvcDfOuevGlsJVW1R1T3O/yPYq1aZo4YFyrwZKBHxAndjT4k/CzyrqtcCt2G/2XnAJ4AeVd0IfA4Ym9v6s6p6DbAZuFVENotIGfBO4ApV3Qx8cZJqfBB4yhn1XAU0ZDnnSuwlvamyB1if7YCIfMwxqruyzSQGkgN4De+EP0RDjLRlv8VAwkxMuixzubYL7KXA7x3+HjtbdjKUtGc8iaYmEucy/R3h7dsx/Nkjj6tqWkBYvzPLEg1ipOw+JxrpJ3mRURIisRReQ7IaKNNSUpbiNYRIbNToZo1y3ncmI15eKmHSNUZanjATHO0+ykBJB+O/9oJwa82tszarjsfjeDweAoHM3FfDGIZBPJ4ZqcLBO/z7df6yDkQdg3IH8LhT9A3gj1T1auDTwFed8n8CfqWqVwHbgEMisgF4H3CT0y+ZwG865+cBrzrnvwD8nuNOeBz4c1XdoqqnclVeROqBrUBmuJIFynws8YVEpMH5/4vAt4CXgXtF5NNOeRCoA24G/hFAVQ+KyNjEM+91viBeoBrYiD3FjQHfEpEd2NPeiXgd+LaI+IBHVbVhkvOnQk7roqrfwP6ykpeXl+H8y/flk7JS+Dw+8nx5RBKRjP0fllpZ/TgLGb/Hj6XWhJ355d6uuBlnV9suGjoaWF+yjtUvnmW8m99TVEjwisxArMMMDZ0hFhuN1ODxePEFgliRFQijxqS/s4Oymtpst5gSBUEvKUvxeQz8HoPEOCl4LGkS9HkoCI62YEXRCvJ8eWkx8oYl5zcuvXGkbKy0PGbGOdZ9jAQxKEs3qiLCHXV3sLZk6rEEJyMQCIz4n/x+/0jQWBHB7wwKLMuayIClnEFxLob7tmXYM5VnRCQfuBH4yZiB5/ADbgc+BKCqJtAnIg9iD8Rfd84PAcPT4gSjfdru/z97fx4f2VXe+ePv595atUstqaXe22335na73e223QZjbDZjHC/QxDjAACGYYQgmmQnfJEOGGRLIDyaZkIQMA05C7JDFxuAd8ALY2MFuu/d93xepte+13vv8/jhXS6mqpJJaraV9369Xv1p16i7nqErnc885n/M8wHsKbDpePX4M/J6qZtsnpylTugbVj5hP4kOqenBYec4LiMhizJPIelVtF5GHgIiqpj1Dw7uAjcDvYr4EabzRoohYQAhAVV8RkXcAHwAeEpG/ArqB/+nd6neAvZgvzFMFtu8azJdzzNy26DYeP/w4QTtIJBAhYAVIuamM+Ga9qV5umX9Lxh/9dKc11srLp18ecc/KpdKupJMk6Q6OBFNuipWzVg68Trtpju18ld59h6mKVFFXXE9RwJxfdP0NSI7pJzCZc9vbs7030eJ6+jpmZ5R1tzRdkEDdsXoOj24+RXk0RCRo5xAoF8d1ue+6hQNltmWzqnoVbzRkPpzvb93P+rr1BK1ghrW8L93HobbDJnTR7ARDjYC22Lxv0ftYVL5o3G3IxapVq9i2bduAQAUCgQHBsizzkJFKpVi7dtwjtpiqrhGRIuB5zPLAQ0DH8D5vBASzpPDHOd5L6WBn4JCj//ZMEc94L7+rqt/1HsB/DPyrqj5ecGumAdNlH9TzwBdE5AuqqiJyjapuB36Nccq9JCIrgf55hDKgF/PEMRszVfiy95RQpKo/FZFfY1x1ACcwIvNDzHxtEEBEFgJnVPXvRSQMrFXV3wOe6K+YiJwG3hSRn6jqG17ZB726ZSAiN2PWn24Zzy/hjiV38MyxZ4in4wMCNXSNI56OUxoq5TOrPzOj9g3VFdex5bwJ4xMJRLLev5TaVRQsIuWk6Ev30Zfqw7bszFGAq1TuOY0qtMbaaI21UR4uZ86Clcy64vK89+rpOUAy2ZpVXl17M6fPZk4VdrU0X1C7Prh2Lk9sO0ss5ZiwR8NmDBNph1AgyD1rM5cyVlStYEvjFhx1Bo/1LOcrZ60csJb3pHo41H4Yx3WMVb52cL01ZIe4ffHtGYkGJ4rVq1ezc+dOUqkUwWAQ27Yz1qNSqRS2bbN69eoLuo+q9onIA8CTmOm84yLyYVV9zHsYX62qO4FfAJ8D/tqbFizxyp4SkW+papOIVAGlqnpyhFt245m2VPU0sKb/De9+/wjsV9Vpa0bLx1SbJPr5M4xo7BKRvd5rMB9ujYjsw6wn7QU6vQ93O3AA+DcGxaIUeNabCvwP4L965X+PWafaCWzAiBvAO4GdIrIdM++bZb/0zBAfAf5SRA6KyH7gfZgvBcC93uLkIeC/Y0aC4xpB1RXX8dUbjd+jK9FFyjGjp5SToithnjy/euNXZ1QnDm+9doFZPykLlfGpKz9FWXgwFE/JiWaC3Zk9fmeik5fr2njiyBMc6zyWFRHcddO0t2/Kun8kMoea+uxU7r0d7Tjp8eVmAqgvj/LNjVeBQnc8RcASVBXHVeIpIz73rp9HfXlmgsOiYBGXV2aLbH+U8/PHu+hMdHGw7ZARJ4DyFBIxI7RoIMpdS+66KOIEUF5ezp133glAPB7HcYzhw3Gcgejmd95554Rs1vUesHcB92HWkD7t9T97gbu8w74I3CIiuzFTdis9d92fAC94/diLmCWMkXgE+JIYg9mSYe+9Dfg4cKvXT+0QkdsvuIGTxKTvgxoL3lNFUFXj3i/+58AyVZ1Zq+k5yLUPqh8/4sLMYrR29SR72NW8i33ndzH7p1uwY5niEa8to/Edy+l3CVSEK7im9hqWVi7Ftmw6O7fR2vpq1n3nzPkwkcgctv7kKRJ9mVsSVt50KxV1o/VrI9PQGeOJbWf58dYzNPckCAUsls4uZUV9KVXFYT5z02WEApnPuM19zTx26LGsa7139u0c+Y/WbAFe2omUpygJlvAbS35jUvbBdXZ2smvXLvbs2UMikSAcDrNq1apRI0nIKPugfCae6S5QpcBLmNGVAH+oqj+b2lpNDCMJlM+lScebr3Pql89wvvc8KXfQxXnuXVeSrMpObV4cLGb1rBWUxTYjmun6LCpaTF2dGQ0cfuM1mk9lBj6dt+IqFqy6sKmqftKOy4OvHiORylyLev9VdSyvyw7U+sThJ2jobcgoSzQKodOzMg+MpuHKDioi5dy55E5KQ9kxAKcTvkBNPtNlii8nqtqtqteq6tWquvpSESeftx5uPE56xx7qi+dwVc1qFpYtJGyH6Z1XlVOcwBhHdp5+nO3n3+RMz5nBXEgiVFUNmknKarLzIHW1XPh+qH4CtsXSAiOcQ7bl/FxXA7vO7SExfDGrNk5NkYlIPt3FyWdqmNYC5eNzqdC3dSvq7a+xxaa2qJaraldz9W0fozpanfMc0SSR9Dkc16Ghp4FdzTs52XUSK7yAUGjwnNLq7IjnPa0tuK6TVT5elhUY4RwGLeeqypnuM5w534gCTTIkmaHtUr+gsuCI5D5vTXyB8vG5yDjd3cR37coqj668kqWL1/HhpR/mNy77jaywQEWp08iQJH2uKudjLTzfeIIXT75IS6zFXKe0jEAoc++O6zr0tLVNWBv6I5xn3EOVg43Zo6j+KOenuk9xrqcB+sx5LdKAgxHN2oVl/MbSOwjb+TfN+vj4AuXjc5Hpe/NNNJ05mpFggKLrrjM/izC/bD53XX4XH7riQ1xWcRm2GyfiZE/Txe06HCvE4fbD/PDgD3nm6DOc6z2XcxTVfYF284z6iuRcb8olUI7r0NjXaAQ0boNjupm0pGmTJqqildx+3a0jpuPw8QFfoHx8Lirptjbi+w9klUdXr8YuyV5vn108m9sW3cZ7audTHZmVsVldxSYWzBxlne4+zVNHnmJbbC/t8XaGep4mch0KYHl99jRfQ2ec9t7MjcnPnXiOU12nTBr23sxRV1+4k2uuuJLi0uz9cD4+w/EFysfnItL7+uswzCkrkTDRdevynAGJxHlInmNx+WJW16ymrrgO27KJBeaiknvU0RGOc6TjCHtadtPc14yrLt2tLVl7qi6E6pIw1aXZU3IHvFFU0knyk2M/4WSX2VNaI/WQHNwIWxIsIRCyoW5aZ9fxmUb4AuXjc5FINTSQPHY8q7xo3bVYIwQszQgIa4WYXzqfa+pu4Kp5dw2ERcqiJAy2RdyJc6LrBLuad3Om/SSd7RM3zQewIodZ4kBjF73JXp488iTnegaNEMVt1ZSq2VdUFiqjPFRGMGxz3Bk5maGPTz++QPn4XARUld7XcqTTKCkhujo78nc/fX0nicVOZ5XXVL2Na+uv52MrP8bN826mPJy5oVREkCHTZik3yenu0zyy+WE2NWwaiKJ+oSyrK82KOt7U3cM/73p6wLQBoCmB1jC1OoeKcDmlQWOlL64Ic6LrRFaUcx+fXPgC5eNzEUieOEHq3Lms8uLrr0MCuUNgqipt7VkhHgkGKygtNVHOA1aAK6uv5L7l9/Hehe/NsKjLsNBDAKn2Lrad38YP9v2AV868Qmeic7xNAqA0EmRe5eAoLp6Os7/tAEebhuUva4kgarGu7DpmBY2BQ2yhqCw0EOXcx2c0fIHy8Zlg1HXp25QdO8+uqiS8PGeqMAB6ew+RTGRPyVVWbkAkM8q5JRaXV16eYVGXsmyBcrtiJt6cOuxp2cO/7f83XjjxAs1945/6W+5N8/Wl+zjQdoCkk6S5M4TrOeJVwWop4oqKy6mOVrM4YOLzFZeFsCwz/Nrfun9w47GPTx58gfLxmWAShw6RbsmOPF68YQNi5f6TU3Vob8+eEgyHaykuviLvvTIs6tfcR2V0FhkpyRJpiA9uplWUIx1HeOzQYzxz9BnOdJ8Zs5Hi8toSYukeDrQdGBCZVFro8Bx7wa5ilhatGJiGXBi4HEtsiisG190SToJD7dnp7n18huILlI/PBKLpNH1vZCcsDdbXEVq8OO953d17SaWyp9+qqt5WcKrz+vK5rFl8HVdVr6ImWoN4QqVdsZzHn+4+zdNHn+bHh3/M0Y6juOrmPG44TbGzNKX3DkYk92jpCBINRLmGDZSGBsM3hSXMsuqlxsE3hP4o5z4++fAFysdnAonv2YPTlb15tXjDhrxC47pJ2juyRS0aXUA0umBM9y+rqSUSiLCofBGra642FvWekafSmvqaeP7E8/z7gX9nX+s+0m52+KJ+jrQf4afHf8qs8mzR6+4t4V3Vt6Nd2WtsG65Yn1XWFm/jbM/ZAlrl81ZluiQs9BlCqrGRzqeeoutnz+F2d2OVllL2/tsov+sugnUzNy3Fpd6uzp/8lHRjIxIMEr7icsLLlmOXlBBatIjg3Ll5z+/s3IGTznbZVVW9bcx1Kauu4ZyXlzpkB5lfOp/LokXY9QvZ1byLvhz3GahHopOXT7/M5sbNrK5ZzazILF448cJAGpGABKgrrmNp5VIqS0oI2EraMaIbCURYWrGUQwfiDI83ES0Ncdm8udTH67OinO9u2c280nljbueFcC6e5EeNbTzV1EGX41JmW9xVW8HGuirmREKTWhefkZnW6TYuZfKl2+jbtp2G//EnaDKFFY1CIADpNG4shoSC1P/Z1yham52kbrrzVmgXqrjxOLgumk6DbVF667uo/f3fI1CdOyCs4/Rx+vTDuG5mirPikqXMrn3/mOuTSibY/NSPssrX/8aHkFCAg+0H2dG0Y1Q3X2NvI786/StssSkLl5F203Qnu0m7aSzL4pb5t9DTsZjGthBFwSKTu0ptpCHO26szJWrhVdXMXlTG0Y6jPH/i+Yz3BOGjKz9KWSg7jNLFYHNnL//1wCmSrhK1LYICKYWY4xKyhL9avoD15bkzavjpNiafKRcoEfky8FuAA7jAZ/tTq09iHe4GDnnZLBGRPwVeUdWfj/E6/w34S6BGVVtGOjaXQKUaGzn16U8DghWJoI6DplIDkQjcZBJQ6v7H/yAwK3eHNx1Jt7bQ+Gd/BghWKPsJ9VJpl9vTk7Gmouk0VijEokcfyTtCbG39FZ2dOzILxWL+vI8TDFaMq147nv8JfV0dGWXLNryDWfPmA+Cqy7GOY2xr2paxd6mfnmQPzxx7BjC2dkFQBtvVPwX4zroP0nR+CVdUXoEtNj1tcbpbYtwxdxYR26we2EGLNe9egB2wcNXlB/t+QG8q83u/pnYNN865kYvNuXiSe3ceBSBqW6RcxVElZAmWCDHHrME9evWSnCOp0QRKRBxgN2Zm6jjwcVXtmPiWZN33bob0Xzne/33gdwD16vcpVY3nOna6MaVTfCKyAbgDWKuqCRGpBqZijH038CywD0BVvzLWC4jIfOC9wKnxVqLzqafQZAq7rAw3Hsft6mL444MmErT900MUjRAqZ7rRt3ULblc3Eg7jeCknhnMptssKBCAQoPPpp6m+//6s91OpLrq6sqOcl5WuGrc4gVmHGi5QXS1NAwLVb1FfUrGEMz1n2N60nTPdZwaOPdR+CNd1CQXMn6IO+xYGrACJdII+Pcu6+t+gN+GCKj0dCVzgdF+CK0qN5b1mfim2l3XXEotV1at4oyHz+XNf6z7Wz15P0L64wWN/1NhG0lXKAhZdaYc+T5AsoDIYIGpbdKYcfnS+jQcWjmvKOaaqawBE5GHg88DXJ6j6I3E3Q/qvoYjIXOABTDr5mIj8EPgI8NAk1OuCmWqTRD3QoqoJAFVtUdVzIrJORH4lIltF5HkRqQcQkZdF5FsiskVE9ovIehF5XEQOi8jX+i8qIk965+4VkfuHlPeIyNdFZKeIbBKR2SJyI3An8BciskNElojIQyKy0TtnvYi85p3zppflNxffAv4/yNKUgun62XNm+gtwe3tzXkgCARKHZ1aomMThI3k3p/ZzKbZLioqwi4vp+mnuPJvtHa+jw5xzYgWoqLjugupVaGRzEWF+6XzuXHInG5duZEnFEgThSMcRAtbIn1dxsJgTXcdZWV8BQKw3hZM2bTnZG++/AbWLMqfuVs5aiT1sT1fSSXKo4+Jbzp9q6iBiCZ1DxAnMtE2v9zpqWzzV1DERt3sdmAvg9SnPeX3SqyKy3CufLSJPeH3LTq8vQkQ+5vU1O0Tke+Jtgiu0/8pRlwAQFZEAUARk7yCfpky1QL0AzBeRQyLyHRG5WUSCwLeBjaq6Dvg+mU8hSVW9Fvgu8BTmKWUV8EkR6c8p/dveudcCDwwpLwY2qerVwCvAZ1T1NeBp4EuqukZVj/bfSERCwKPAF71z3g1k2ZdE5C7grKruHKmxInK/J65b0ulsp5Tb3Q2BgJkmyjf1alloMpn7vWmKJpOQZ//PAJdYu0QEq6gIAgHzuQ4jkWyhp+dgVnl5+VoCgQtb5iirzs6w29PeRjqV381XW1TL+xa9j/uW34eiWCN8XtFAlIpwBb3p3oEI573tgyPItmSa7lSaitlRIsXBrHOvqMze1zUZlvPOtEOv4xJ3s+/TP0oMCnSl89rtA/1/v96/7GEx4AnKuzD9CsCDwBe8PukPgO945X8L/MrrW9YCe0VkBXAv8DZvNOYAH/WOH1P/BaCqZzHLDqeABqBTVV/I+0uaZkzpFJ+q9ojIOuAm4BaMGHwNIzgverZcG/OL7af/Q98N7FXVBgAROQbMB1oxonSPd9x84AqvPIkZCgNsBd4zShWXAQ2qutmrb1YAMREpAv47ZnpvtPY+iPmyUlxcnPVXYpWWQjqNBINY0ShODhMFrovkWMeZzkgoBK4Ltp3/oEusXVZJCWJZaCplPtdhtLe9lvUQYtsRKsrXXnC9wkVFhItKSPT1DClVelpbqKirH/HcikgFNdEa0m6alJsi7sQz9kcVBYsoDhSTdtOUBEuoLglTHrQ5G8t84DrVl+DaReXDLw/A6prVHGjLTEHSbzm/WI6+PsfFUSWpip3D7l/krZmlFMoCecU57T0c5yMqIjswI6f9mD6sBLgReGzINoP+Hcu3Av8JQFUdoFNEPg6sAzZ7x0eB/rwpY+2/EJFK4C5gMdDh1eNjqvovo507HZhym7n3wbwMvCwiuzEjor2quiHPKf2Pau6Qn/tfB0TknZiRzgZV7RORl4H+KJopHXxMcxhn+0Xkn4BrMEPlP8R8+Du9L9Q8YJuIXKeqjWO5btn7b6PjRz/GDgbN9FAgkDWqcPv6KH7bjUSuvHI8VZ8Sit/2Nnpfe21g+jIXl0y7RJBQaMAM4sZiVHx4Y8Z5sdhZ+vqyo5xXVFyHZU1MhtmymhqaT/ZklHW1NI0qUAC3LbqNxw8/Tmm4lCItIuEkcNUlZIUG1oli6RgfuuJDANSkszv8c+k0pbNyt6U6Wk198eRZzrvTDo81trGkKMzu7hi2PVhfAUoDNmFvxBhzXD46p2q8t4qp6hrvofV5TF/2ENDRvzZVAAI8rKp/nOO9Ufsvby38Ge/ldzEP5sdVtdl7/3GMYPoCNRoisgxwVbV/8WEN5snjvSKyQVVf96b8lqrq3gIvWw60e+K0HLihgHO6gVxrSweBehFZr6qbvfWnmKp+athxA3MqInICuHY0F1/Oit91F51PP40bj2NFIkg4DEPSMrjxOHZ5GbW///szat9QZOUKTu3dQ787cTiXcrskFKT8zjsHylSV9hwBYQPBMkpL80c5Hytl1bU0n8wUwa4CM+zeseQOnjn2DPF0nEggQjSQ+WART8cJ2SHuWHIHqaRDeZ+LkLn4qiUBzncnqM8RwBbMKGq4QJ3oPEFnojMrUvuF0J5K81hjG51ph5UlUfb1xEm7SsAycTbKAjZRe1CcQpawcfa4BQoAr+95AHgSM513XEQ+rKqPiXmKXe0tB/wC+Bzw1960YIlX9pSIfEtVm0SkCihV1ZMj3HKg/1LV05h+FAARuR64wRPNGGbqccsFNXASmeo1qBLgYRHZJyK7gJXAV4CNwDdFZCewA6P4hfIcZiS1H/gGkB21M5tHgC+JyPahi4yqmsTMB3/bq8uLDI7GJpxgXR31f/Y1QHG6utBUClVFUymcri5Aqf+zr82oThz8dg1tV1/fMeLxhqxrVVbcgDWKMWEs5DJK9LS24DpOjqMzqSuu46s3fhWArkQXKce0K+Wk6EqYWe6v3vhV6orraD7VTUQsaiKDa02WJRSVhjjQkL321s/i8sUUBzPX2hRlb0uhz6Gj05xM8e8NRpzAjJTeV23EL+EqxbZFxBKSrtKZMsf81fIFE7JZV1W3A7uA+zBrSJ/2+pC9mCk3gC8Ct3gzR1sxTrt9wJ8AL3h94osYM9lI5Oy/vHq8AfwI2IZZFrHwlhlmAlO+D+qtSr6NuuBFJnj6abp++rPBiAu3v5/yO++ccZ34UN7q7VJ1OXP2X0kl2zLOD4WqmTv3PkQm7nlRVdnyzOOkEpnbXVbd8l7KcohXLhp7G3n26LMDkSRKgiXctug27lhyB3XFdair7PzlaZKxNMd74mxuM4JUUhmmvKaIopDN79x0GbaVO8TTtvPb2NSQ+fwYskN8YuUnLthyfi6e5Mfn24m72YaHhKu46vKr9h660i5lAS+SxOyRI0mIv1F30vEFaooYSaB8Lk26u/fS3Jy997uu7k6KivIHkh0vB157hbazmckPF1y1hnnLJ2adr62hlyNbzgOQdF2eOduGo8rsxWUEgsY4cvc1c1lcnbtPj6Vj/PPef8bRzFHdzfNv5spZ46/jiViCJ8+3k8rRt0Uti411VdSFxy6AvkBNPlM9xefj85bAdVO0t2fPNkcic4lGF12Ue+YaKeXaDzVezh8fNLWGLIv6aIhISXBAnAAONOTPnHsxLOeHeuM8nkecSm2b++pnjUucfKYGX6B8fCaBru5dpNM9WeVVVTcWnE5jrOTaD9XV0jwh+416OxN0t2ZuCVxYFKakItO5d7S5h2T+fUWsrlmdVTbeKOe7u/t4uqkDJ0f7KoMB7quvYlZoyo3LPmPAFygfn4uM4yTo6Mg2ThUVX0YkMuei3be4ohI7kDlacFJJ+jo7LvjaTSeyR0aLa0soL88UqJSjHG3OFuZ++i3nw9ndsntM9dnS2ctzLZ1ZYZkAakNB7qurojzoi9NMwxcoH5+LTGfnFlxnWGxOEaoqL26AVLEsSnME3+1qacpxdOGkkg6tZ7NFZ85l5Sydnb1b40Bj/mk+yD2K6recj4aq8h/t3bzUlvsecyMh7q2rojgwwiZxn2mLL1A+PheRdLqHzq4dWeWlJSsIhWZlnzDBFBqXbyw0n+rGdYZFwQhazJpbwrK6bIE62dpHbyJ/EsTF5YspCZZklBViOVdVftHWzesduUdoi6NhPjy7aiCyus/Mw//kfHwuIu0db6DDMtSK2FRWFrJ//MLJuQ7V3DTudSh1Nef0Xs0CE7V8bkWUsmjmtKIqHDyff09Uf5Tz4exr20fKyR0/0FHlpy2dbO/K7YRdVhzlntmVBPNY3H1mBr5A+fhcJJLJNrq7s1P0lJWvIRDIFxR/YimpmoUMC/yajMdIjHOLQ3tjH8lhcfcQoXZhmfejsDzHKOpgY36BAlgxa0XuKOft2VHOU67ydFMH+3qy084DXF1axB015Tlj7vnMLHyB8vG5SLS3vw7D0mlYVpiK8pHijU4sdiBASWX2VOJ416HOn8heF6qcXZQRtTyXQDV2xmnvzR+tPhqIsrRyaVb5rpZdGaO9hOvy4/NtHOnLnW/v+vIS3jOrDMsXp0sCX6B8fC4C8UQjvb1HssorKq7Fti9atKyc5FqHKjQu31CMtTxbGGYvzsz5NKskTE1pdqDY/aOYJa6qyY5F2B5v50yPSabY57j8sLGN0/HcQveOylLeUVV60Wz7PpOPL1A+PhOMqtLW9h9Z5XagmLKyqye9PvnWocZKrrWnaGmI0lnZgruiPoebr6F7xLWv6mg1c0qybfd7WvbQnXZ4pKGVxkT2mpQgvGdWOddXlGS95zOz8QXKx2eCicVOEo9lbzStrLgey5r8KAal1dWYLA6DxHu6SMZzr+HkIp+1fPbispwjlqWzSxle3BlL0diVe2qun6uqs0dR+9uP8U+nTtCaynYCWggfqClnTVnRKC3wmYn4AuXjM4GoKm3tr2WVB0OVlJZOTa6rYChMUXlFVvlY7OYjWctzURoJMr8yWzRGinAO2Zbz3rTD7u4+jrdnm02CItwzu5IVJfnzjPnMbHyB8vGZQHp7D5FMZHf8lZUbJjRa+VjJFZev0HWo0azl+ci1J+rg+W6cHCnX+xlqOe9KO+zpiZF0XVp7juC4g9N7YcviQ3VVXFY0MQkefaYnfuyPaUg83kBD45M0Nf2EdLqbQKCU2toPUF93N5HI6BlRpyuXervOn3+WRKIBkSDFxVdQWrKcQKCUcHg2xUWXT2kdy6praDyaadkebR2qpz3OgU2NHNjUSKwrgR2wqJpTTPW8EkLRILMXlY14/uW1Jbx0oIn0EEGKJR1OtvZyWU3+9aIVs1bwwunX2dcTG4ir57hJ2nuPU126lCLbZuPsSmaPM+hrf7uObG0iGUsTiga4fF0ty2+oo6Rycg0sPiPjj6CmGR2dW9m+45OcO/dDVF0CgXJUXc6d+yHbd3ySjs6tU13FcfFWaJfj9CESApTu7r00ND5JPHGOqqq3T7mzrDSHUaK3o510KvdG2IajnTz9NzvY/+tzpBNp7KCFqpnqO7ipEYBw0cgCEQnaOYVotD1RJxPQSH1W0NeW7gOU2hb31VeNW5yGtktdJVwUQF1l/6/P8fTf7KDh6OjhlXwmjxk1ghIRB5MVMgAcBz6uqh2TcN+7gUNetst8x9iYVMpnVfWO8dwnHm9g//7/DkAwWGays2oaUGw7guMk2Lv3S1y58v8QDmd3ONOVRKKJvfu+BOBZrBXV1MDrS6Vd6XSnZwywsO0wrpumtfVVLlv8e1NZTQDCRUVEikuI9w41Oijdrc1U1mU653ra47z0g/0ABMM26aSLIIgFlmXjOi4HNzWw8m31o444lteXcmhYFImjzT0k0g7hHPHxdnX38UJLF1Wly2juPpzxnjhd3Fwap2qcQV+HtitcFDR/X65i2UK4KEg66fDSD/Zz5xfXjHskJSJfBn4LcAAX+KyX1XbSGN5ficifAq+oanYystznrwG+i8kengb+i6q+eXFqOzIzSqCAmKquARCRh4HPA1+fhPveDTwL5BUoTPrm/cDI8x4j0ND4JK6bJBgsw3WTpFKdmO/4II6T4Nix/0NFxfrx3mbSae/YTCrV7nXafTmPuRTbZVkBRAI0Nj7JokWfm4IaZlJaXTNMoIxRYrhAHdjUiJN2CYZtkrHsFPGBkI2qcvCNRtbdtmjEey6aVUwkaBNPDV4n5ShHm3pZOSfzT+XNjh5+1W7ELBqqoiRSR0/cjNZKbJsVJRGOt+9lReXCgtucq139YpSIpUExAlUcJBCySfSlCmpXLkRkA3AHsFZVEyJSDVx4/vixczdD+itV/coYz//fwFdV9Wcicrv3+p0TWcFCmclTfK8DcwFEZImIPCciW0XkVRFZ7pXPFpEnRGSn9+9Gr/xjIvKmiOwQke95ox9EpEdEvu4du8k7/0bgTuAvvOOXDK+IiMwDPgD8w4U0qKnpJ9i2cSSZ3EHZeXQsK0hP7+Gs8ulMb+/hUe3Vl2K7LCtCIFDC+aafTGKt8lPIfqh+4XHSSqw7hZMjl1MgbBMMBziyZfS9VLYlLJ2dPc03NMK5qvJqW/eAOPVTXbbc1Dtgc2VJlJBlcbLrZEFRznNxZGsTgZCNk3YHxAnAdZRU3FjYAyG7oHbloR5oUdUEgKq2qOo5EVknIr/y+qfnRaQeQEReFpFvicgWEdkvIutF5HEROSwiX+u/qIg86Z27V0TuH1JeUH8lIg+JyEbvnPUi8pp3zpsikivmljL4oF0OnBvvL+RCmZEC5QnKu4CnvaIHgS+o6jrgD4DveOV/C/xKVa8G1gJ7RWQFcC/wNm805gAf9Y4vBjZ5x78CfEZVX/Pu8yVVXaOqR3NU6a+B/49cipJZ7/u9L+OWdDp7T0c63Y1IwJvay35yNVi4bmKk20w7TH1H+6pdau0SAoESRAKk0yOvuUwWuSJK9LS14joOqkp7Yy/7X2ugrzOB6+b+KotAIGhh2WI6+QJYXp89qXCqzUQ4V1VebO1iU2f2Hqvy6Dzqo+WsLIkS8IK+FhLlPB/JWBqxINE3KE799EekGqVdgf6/X+/f/cPefwGYLyKHROQ7InKziASBbwMbvf7p+2TO+iRV9VrMlNpTmFmhVcAnRaQ/RtVve+deCzwwpHxM/ZWYBdJHgS9657wbyLUZ7vcwAnca+Evgj/P9Qi42M22KLyoiOzAjp/3AiyJSAtwIPDZkIbrfe3or8J8A1PT4nSLycWAdsNk7Pgr0PzIlMUNjgK3Ae0arkIjcATSp6lYReedIx6rqgxgxpbi4OMtrGwiUoprGsoLYdhjHyfXdcbGsmWWtNfV1GVmkLq122XYRIjaum5q0wLCjES0tIxiOkEoMbpZ1HIeTe0/R0xYk1m1CCNkBC3Uhlys+ELYRETNVFi2s+5hTHqEsGqQrNmjIUDWhjxqjFvt7c28YXllSzK1F63mzMXMJZ1/bPtbXrSdoj80oEYzYJHrTaA6bux00jXUdHaldaU9McqKqPSKyDrgJuAUjBl/DCM6LXn9jAw1DTut/yN4N7FXVBgAROQbMB1oxonSPd9x84AqvfKz91TKgQVU3e/XNF3vqc8Dvq+qPReQ3gX/EiNmkM9MEKqaqa0SkCHge87TxENDRvzZVAAI8rKq5ngpSOhiLxSHH70dE5gPPeC+/CywE7vTmaiNAmYj8i6p+rMD6DFBb+wHOnfuhJ1ClgI1qZtyxdDpGZcW1FzUT60RTXr6Ojo7N2Hb+6fhLp12CZYWwLDNV6zgx5s75zamp4DBEhNLqGtrOnsZ1lb7OJD3tcfq6j1BeOzhzXTWnmOZT3VjWoIlBLCEYtgmETEeeTjqsfHthn1V/hPM3j7cNlLmqPHLoPLMvr8h5ztWlRbx7VhkJZyVbz2/BGTKj0B/l/MrqsW18nnNFBYfeaCQQzDRnWAFrXO3Khfcg/DLwsojsxvRRe1V1Q55T+qcN3CE/978OeA+97wY2qGqfiLyM6WeggP6qEETkn4BrgHOqejvwCcyaOsBjXODSxYUw0wQKAO+DegB4EjOdd1xEPqyqj4l5TFmtqjuBX2CeBv7amxYs8cqeEpFvqWqTiFQBpap6coRbdgOl3r1PA2uGvf/HAN6X6Q/GI04A9XV309j4JI4Tx7YjBALFmFG8wXHihEJRli79HzNq31BV1dvZvuOTADkDpV7K7bKsEHV1d09uxUagqLyaEzsP09uRwHXMvFaitw0YFKjqeSW0nunBdVzsoD0gTP0zFOmkgx2wWHZ9XcH3HSpQaVUO9MboSjuUxUqIDhux3FBewtsrSxCRgSjn+9v2Zxyzq2UXK2etLNi+H+9NEQzZWJbgOi6Wl8RQBMJFAURkXO0aiogsA1xV7V9MXYOZ6XmviGxQ1de9Kb+lqlroPGU50O71ecuBQhKJDfRXwzgI1IvIelXd7K0/xVT1U8OOOwfcjBHaW4EpWxyekWtQAKq6HdgF3IdZQ/q0iOwE9gJ3eYd9EbjFe5LZCqz0rJd/ArwgIruAFzGLmyPxCPAlEdmeyyQxUUQi9axY8ecApFJduG4KVcV1U6RSZjS+YsWfz6hOHPx2TYd2JfpSnNjdwun9SbpbYwPiBJDo7UCHpAUJRQIs3zCHUCSAZZt1GcCYC/rMNN0tH18xJiv2rJIwtWVhUuqyt8eIE0BrW+b03s2Vpdw0LCL5aFHOR8N1laPbmggELRZdbdbh0ikH11WCERt1ddztGkYJ8LCI7PP6lpXAV4CNwDe9/mkHZkmiUJ7DjKT2A98ANhVwTs7+Ss10zL3At726vMjgaGwonwH+j3fMnwPD19omDRlvZk2fC6O4uFh78ySNi8cbaGx8kvNDIi7Mrv0AdZdAxAW/XZNLX1eShqMdtJ3rRV1F1eX03p+jTqYRoH7p2wlFyyidFaX+8nLKa6L0diQ4+EYjR7Y0kYilCUcDXH5tLcuuH1/EhVePtfD3208TG2LACIcDrF5VgyUW760uY3Vp7qCvTx55knM9mWayRWWLuP2y20e975kDbZw73DHwOhlP03Kmh64WsxZXaLtEpE9Vi/Me4DPh+AI1RYwkUD4+F4Kq0t0Wp+FIJ51N2fuzzh97k3h3y2CBCAtXr2Xl26+5aKF+2lJp/uVUM69ua2C4hW7VihruXVzD8uL8QV+PdRzjuRPPZZQJwm+t+C3Kw+V5z+tqjXHg9UbjyhhCuCjIqnfMHTBHFIIvUJPPjJ3i8/HxyWTAKv7rBg681pBTnAAixVXmBxGKysPULiqlvNq5aOJ0PpHi3xvaSFhQVpZplLEQrkjbI4oTwKLyRRlRzsFYzve07Ml7TjrpcGx7c5Y4iSVcdk3NmMTJZ2rwPyEfnxmO6yrNp7vZ86uzHN58np72kXMuRctmUVIVoW5xGZV1RQRDNt0tTSMmExwvp+NJHm1so88xa06zqgaFKCDCypIoXZ3xESOcg4lynmstan/bflJOdjxBVeXE7laSOfY0zbmigtIqPyjsTMAXKB+fGYqTcmk81smuX57m+I7mgX1M+QiEbeYtr2L9HauomF2cMYJIxmNZYZAulGN9CX7c2EZiyJpTVWUEyxKCYnFlSZSygD0Q4Xw0VlStIGBlOv6STpKD7Qezjm0500Pbuez2lFRFmJPH2u4z/ZiRNnMfn7cyqYTD+eOdNJ3sJp3MF3FkkHBRkLol5VTPL8H27NUllbPobs3MB9Xd0ky0ZGI2Fe/vifHT5k7cYetNtm0xp6qImiRErUGBPNDYPWIKDoBIIMLSyqXsa80Mibm7ZTdXzrpywPkX701xck9r1vl20GLJNTWINbWR5X0Kxx9B+fjMEOK9xiq+8xenOHe4Y1RxKioPs2RtLatvmcfsRWUD4gSFxeUbLzu6+vhJDnECqAoG+NTy+gxxAjjmRTgfjf5khkMZajnvt5S7OWIILrqqetQUIT7TC38E5eMzzentTNB4tJO2ht6cYXqGU1YdpW6JsYrn28haWl1jtm0OodAMuyPxRkcPr7Tnjj1YFw7yodlVhEX49cGWgiKcD6c6Ws2ckjlZlvPdzbuZXzqfs4fa6e3IjulYPa80b3p6n+mLL1A+PtMQVaW7NU7D0dxW8SxEqKwron5JBSWVo8c0NCnghaGW73hPF8l4jFBkZEddvvq+0t7DmzmCvgLMj4S4Z3YlYW/ktKyuhJ2nM6OSH2jsGlWgAFZXr84SqJNdJzl7romGI9lrWeGiIAtXzcoq95n++ALl4zONMFbxPhqPdo7qxgNjma6eV0LdknKiJYWnHgqEQhSVV9DX2Z5R3t3SzKx5C8ZUZ1eVn7d2sbM7t5BeXhThjpoKgkPWfpbVlWUJ1Km2PnoSaUrCI3dLi8oXURoqpTs5OFJz0/Dm6/uZG5qfcaxYwpK1vqV8puJ/aj4+0wDXVZpPdbP75bMc2TK6VdwKWNQvqeDqd81n8dU1YxKnfspqstNvjHUdylHl2ebOvOK0siTKnbWZ4gSDEc6Hojp6OngwlvOha1GqwMkSzne04Gjm2tOcKyou2v4un4uPL1A+PlOIk3JpOOpZxXc2E+8pzCq+5l3zmb+yilBk/JMgZbNyCNQY1qFSrvLE+XYO5kmXcU1ZMbdXl2PnWAcTEVbUZTsGCxEoGGY5bw1DWxjHdWiNDUbI8C3lMx9/is/HZwrot4qfP9GFkxoxzyVg1lHqLy9n1rySDDfehVBWk+3k6+1oJ51KEQiO7HaLOy6PN7VzNp5bUDdUlPC2ipIRo40vqyvljSEpOADOd8Vp601SVTzyiLDfcr737AE4NWh+ON/XRE1RLQHfUn5J4AuUj88kEu9N0Xisk5bT3bjO6I68ovIw9UvKqaovnvDONhQtIlJcMmyDrtLd2kxlXf6cSL1phx+db6cpmR3BAeCWqjKuLR89ZF1/hPOmrkzX3YGGLm68vHrU81dWXcneV8+CM/h7iafjdCW6WLv2ct9SfgngC5SPzyTQ25mg4Yixig+PDZeLsmoTVbysOr9VfCIoranNiiDR1dyUV6A6U2keO99Oeyo7hJAgvK+6jKvyRCTPxfK6Mpq6MqcVDzR2s2HJrFHbnThtU5qqopvMacH2kgZmzV1TcB18pi++QE1DetrjHNjUyJGtTSRjaULRAJevq2X5DeNLczBdeKu1a9n1s1GXi2YVnwjKZtXQfOJYRlm3tw51Lp7kR41tPNXUQZfjUmQJ8yIhlhSFKQ1kZqW1RfhATQXLisf2OS6rK+XVw80Zmt0ZS9HQGWdORX67e1dLjIYjncwurs1w8xF2aKk5SWeiM3+U886zsOsR2PMEJDohXA6r7oHVH4HyuWOqv8/FxU+3MUXkS7fRcLSTl36wHyftEgjZWLbgOjqQ7fOWj6+gfkn+9ALTlbdSu5y0SzKWRl1YsGoWpaOITb9VvH5JBZGSyZ2WinV3sf25ZzLKLMvGuuV2/tvhsyRdJWpboEpryiHputgivK+6nDkRU9egCHfPrmRRdHyi+vi2M5xszRTwq+eXc+vy2TmPTycd9rxy1vyOUXY17ybpJEEUlnciJWmurrmat819W/bJp96Ap78AThKCUbCC4KYgFQM7BHd+GxZcn/O+frqNyWdUgRIRB9iNGW3tBz6hqgU8Dk4eInICBsb5NvA48DVVjYuIBfw1JnWxAnHgN1X1+AXc753AU8BxjBOyCfgtL4X8J4FrVfV3R7pGLoHqaY/z9N/sACAQMk+oqjqwlzLt7bz/wH9ZTfEMGnH0tsf5yXd2ARAI2lnvX0rtSqdd0gkH19GBrLXLbqjL6bazgxa1C8qYfVnZBbnxLgRVZcszj5NKDNrau9MO/6f6cmJllURti6Tr0pFy6LdypL1oFhvrKqkJBfng7ErmRsZuc+9n77lOXth7PqMsGrL5zE2XYQ9bd1NVjmxtor1h8G+nobeRM91nYG4vMsc4CkN2iE+s/ARBe4jgd56FH9xjfg5G+y9o8r6DESmAjz+RcyRViECJyJeB3wIcwAU+q6pvjPgLmGBE5G7gkJc9HBH5U+AVVf15ged/GPhfwArgOlXdMuS9PwY+jWnfA6r6/MTWPpNC/ipiqroGQET+FfjPwF9dzEqNk1tUtUVESoAHge8Bn8CkOJ4DrFZVV0TmASOGThaRE6q6aJT7vaqqd3jH//+AzwP/80IacGBTI07aJVwUxPXSULvpzAeIdMrh5X87OKPss+eOdNDXlSQQtEnGcsdbuxTbZdkW6ZRDy5mejHYFwwFmX1ZG7cLSnII9mYgIZTW1tJ45NVC2rydGMNiOW1FFT9qh18mMqhewhITjcqQvwRcWzqYmdGGjvstrS3jpQBOpIaaRWNLhRGsvS4YFkG053ZMhTgA10Woa5CRO/aDdvT/KeUbsvl2PmJFTpBzSCTO957oQCJuyYBTiHbDrUbjpv465HSKyAbgDWKuqCRGpBsav3OPnbuBZYB+Aqn5ljOfvAT6I6UMHEJGVwEeAKzF96s9FZKmqjh5EcZyM1a/6KnC5iBSLyPdF5E0v7/1dACJSJCI/FJF9IvKEiLwhItd67/0/EdkiIntF5Kv9FxSRb3jH7xKRv/TKHhKRjUOO6fH+rxeRV0Rkh4jsEZGbhldQVXswInq3iFQB9UCDqtnBp6pnVLV9+HnjRcxKbilwwdc8srVpYOSU7EtniROYTq/t3MzKxNt2rhdrFGv0W6Fd4eIgi1ZXc/W75jHn8oopF6d+Softh9oTS+E4aVqSaXqcXCFfIWxbnE0kL1icAMIBO2ck8+F7ouI9KU7uzY5SHg6HuGJtHcM9Fbuad2XmuNrzhBEhNw2xdnAdQCEdh6RnFAkWwZ7Hx9uUeqBFVRMAqtqiqudEZJ2I/EpEtorI8yJSDyAiL4vIt7x+cb+IrBeRx0XksIh8rf+iIvKkd+5eEbl/SHmPiHxdRHaKyCYRmS0iNwJ3An/h9ZNLhvan3j1e8855U0SyNqOp6n5Vzc5hAncBj6hqwpuBOgJcN95fViEUPK8gIgHg/cBzwJeBX6rqb4tIBfCmiPwc+BzQrqorRWQVsGPIJb6sqm0iYgO/EJHVwFngHmC5qqp3rZH4LeB5Vf26d52cdiFV7RKR48AVwA+B//DE7BfAv6jq9kLbPQI3icgOYBZmRPbfRzvB+3LdDxAKZT9YJWNpwkUBVAenh7KuYVHQvpnphJN2Rw01cym3y3WUJetqqaqbeKv4RNC/H6pLbA4Eizg0qwgLwR06/TWEgAgVAYvuAmzyhbK8rjRLkI42mQjn4YBtopRvzx+lXKtKOHhwf0Z5R6KDM91nmF/mhT9KdEK4AmJtDE87j+O5Eq2gGUXlJiAiW4a8flBVHxzy+gXgKyJyCPg58CjwGvBt4C5VbRaRe4GvA7/tnZNU1WtF5IuYZYN1QBtwVES+paqtwG97fWcU2CwiP/bKi4FNqvplEfnfwGdU9Wsi8jTwrKr+CBhwQ4pIyKvTvaq6WUTKgNy7rHMzF9g05PUZr+yiUYhARb2OGMwI6h8xv/Q7ReQPvPIIsAB4O/A3AKq6R0R2DbnOb3oddADzpLESMwSNA/8oIs9ihqUjsRn4vogEgSdVdccIx4pXjzMisgyzBnUrRhw/rKq/yDhY5P8C/auqc4a0+TFV/XqO6w+d4vtD4H9jRm558b7MD4JZgxr+figawHUUO2BhBaycHba6YAdmVgAQO2Chrums83Eptst8jkIgaDFrzvSNpN0ZKeK1aCWnCaACltWHui52KokTyjQ+BEWoDNqkFcoCEye2C2cVEw2Z5IX9pF3lSFMPV84p5+zBPFHK5/dHKS9hbslczvaczXh/V8uuQYEKlxvxcXPs37K9B0Y3BeG8AWvTqnptvjdVtUdE1gE3AbdgxOBrwCrgRU8obKBhyGlPe//vBvaqagOAiBwD5gOtwAMi4i2eMR/z4N0KJBnsM7cC78lXN49lmNmkzV59u0Y5fsoZ0xpUP9601oeGDwMlz74FEVkM/AGwXlXbReQhIKKqaRG5DngXsBH4XYyIpPGmH8WYHEIAqvqKiLwD+ADwkIj8lar+c477lQKLgEPeeQngZ8DPROQ8Zo42Q6BU9fNDzj8xvM2j8DTw4zEcn5PL19Wy/9fnsAMW4aIAyVgaJ60MfdrTtFK7uGxgKnAmUD2/lKYTXcgIHdql0y7BDgiBkI0dsEj0pbhifW432lSiqpyIJXmjs4fT8STNJZVorxnBVIcCnE+kCSbiAwIlQMS2KLUtLBFiaYePzqmasPrYlrB0do4I5w3dzA+FaDjamXVOuDjIwisHo5RfVXNVlkCd6jo1aDlf8k7Y9gOz5jQUKwAhbzIm1QdrPzHudnjrMS8DL4vIbsza9F5V3ZDnlH7VdYf83P86IMaQ9W5gg6r2icjLmAEBQEoH5zAdxrltSET+CbgGOKeqt49w6FmMQPYzzyu7aIzXOvQ88AUR+YI3NXeNN232a+A3gZe8BbWrvOPLMNNgnSIyGzNV+LIYQ0ORqv5URH4N9G/IOIEZ6v4QM58aBBCRhcAZVf17EQkDa4EMgfKu+R3MCKtdRNYCjd5csAWsBoaO7CaCtwNHL/Qiy2+o49AbjaSTDoGQnbUTPp10CEXglo8tn1H7hpZeNzvLnTiUS7lddsBi2fV1k1yz/LiqHOyN82Znb0YkiHBREXFPoGpCQZqTaSQRQ0rLidoWRbZFwHsAjTkuIUvYOHviBArMpt3hAnW6pZf9zWlkmNtYLGHJNZlRyheVZUc5V5TdLbt5e81aCERAbDOdZ/d3fWIMEmJ5VvMwrL53XPX3ZmpcVT3sFa3BOJ/fKyIbVPV1b/ZnqaruLfCy5Zhlkz4RWQ7cUMA53Zh18eEcBOpFZL03xVeKGYB8qsC6PA38m4j8FcYkcQXwZoHnjovxzqn8GUY0donIXu81GGGoEZF9mKHtXqBTVXcC24EDwL9hhAzML/FZbyrwP4B+68zfAzeLyE5gA4Ouu3cCO0VkO8ad9zdD6vSSiOzB/MJOAZ/1ymuBZ7z3dmFGZ383znYP5SZvEXIn8HHgv13oBUsqI9zy8RUAJPpSOGkXVcVJuyT6TGdyy8dXzKhOHPx2TYd2pVxlW1cv/3imhWebO7LCFIWLBt3TIUtYHg0xu+08ISBiWdhA0lU6vS0Bf7V8AXMuwFqei/ryCOVDI5yrIudiNLRk72qZuzQ7SrklFldVX5V17IHW/aT2P2PEadn7TWE6bkwSIa/d/etOd377QjbrlgAPi2f6wixjfAUzO/RNr6/YAdw4hms+hxlJ7Qe+QeYaUD4eAb4kxsC2pL9QVZOYfvPbXl1eZHA0NoCI3CMiZzB9709E5Hnv/L2YQcM+r16fv5gOPpjgjbqecSHo7T9aglkoXOb9YnyGkG+jLpj9UAffaOTIliYSsTThaIDLr61l2fUzP+KC367JJea47OjuY1tXH31O/r5EXeXswb1EHYdl6T4uS8eJp9McWHMjT/ek6Uq7lAUs7qqtYOPsqgkXp35eO9IyEEBW2pNYDTGKwgFWzx3cxF06K8LyG+pzGk7i6Tj/vO+fSbtDQjF1NfCOvhirwt50YKILzu+F1qNm5BQph1UfNCOnEcRJ/I26k85EC1Qp8BJmdCXAH6rqzybsBpcQIwmUj8+F0p122NLZy67uGEkd3R05Kxig8vBuqlsaGDpZueTaG5i9eEne8yaatt4kD792AhIO1vFexNsUvHpeBUUhGztosermeYSj+VcnXj79Mvta95kXyT5o2E6FBLmvZOngOnkwAtd+GiKjZ/DtxxeoyWdCt6+rajeQ1+Xi4+NzcWlJptnS2cu+3hhOAQ+fc8Mh1pcXc3lRmFNtVZxtach4v7uleVIFqqo4RG1JmJZjrQPiBNDak6CoqohFq6tHFCeAq6qvMgLlutB8AFyXDhKcSfcwP+gtzSx9/5jEyWdq8IPF+vhcApyLJ3mzs5fDfaOniQe4LBrmuooS5oWDA6OKspoazg7bntnVMrYMuxNBfRJa45nTkc09Sdasri3Irj8rOstYzk+9CsnBWYpdyVYjUPVXQ+3yCa+3z8TjC5SPzwxFVTnuWcXP5EkcOBQLYXlJhOvKi3NGgDARJYSh2xriPd0k4zFCkfyRxSeSrpYY4fbsVB4JSwnOL3x27apgJWc7h1nO0910hosov/zdF1xPn8nBFygfnxmGq8oBzyrenCdp4FCCIlxVWsS1ZUWUB/P/yQdCIYorKujtyIza1dXcRPX8hRdc79FIJR2O7WgmaAvl0SCdMdM2FcGdW8Th5h4WVBcgUsk+Fp3ZQakVotsdFG4Fdtcs5u2BqQiP5zMeZtbWfR+ftzD9VvF/ONPMT5o7RhWniGWxoaKEz8yv4V2zykYUp35Kq2uyyvrzQ11MVJUTu1pIxszoqbpkcDOt1oQhanPofA+OO8q6mioc/ClWsperwrMy36tYyIF4k0nN4TMj8EdQPj7TnJjjsr2rj21dvcTc0R15ZQGba8uKuao0Ssga2zNoWXUtjUcOZZRNxjrU8CjllcUhrBbBidroLDPiiadyRzjP4Nx2aDH7ZJcHK3kzfp60uhCtgPJ5Jsp520GuqsneL+Uz/fAFysdnmtLlWcV3j8Eqfn1FCcuLI9jjTBNflmME1dvRQTqZJJAjwPFEEOtJcnJPZpTygCVUlYU5XxvMCFh7oKE7v0D1tsDRwQhmESvAslAle9OdUL104Dq7W3azqnrVqCnlfaYeX6B8fKYZLck0mzt72NcTJ3eyi0zmhkNcV1HMkmj4gjvdULSISEkp8Z6hkcWV7tZmKusnPnC16yrHtjfnjN5/5bWzOd/QkVF2rHkwwnkGThr2PTUYldxjVWgWe8trM+LvZUU595m2+ALl4zNNOOtZxY8UaBVfUmQcefMmOKpDaXXNMIGCrpaLI1AjRSlfuLKa11u780Y4z+D4y9CTPRU5a971zA2kRo5y7jNt8QXKx2cKUVWOxRK82dlbsFV8RUmE9Xms4hNBWXUtzSeOZZRdjHWorpZY/ijlq2aNGOE8Q6DajsHpzdk3KKqCy9/NVb1nsgTqZNdJOuIdVEQqJqIpPhcJX6B8fKYAx4sq/kZnLy0TaBWfCHKtQ/W0teI6DpY9MelQ+i3l5IxSXjuQGyxnhPP2PnoSaUrCAbMRd3+ONHKWDSvuhEAoZ5RzgD2te3j73LdPSHt8Lg6+zdzHZxJJui7bOget4qOJU8SyuLGihPvn1xZsFb9QIiWlBIdtzFXXpactO936eBhuKR/K3KWVlFQOrhdlRTjHc5I3dnk//CwjWsQAi98BZfXACFHO2w74lvNpji9QPj6TQJ/j8uv2bh483cwv2rroSo+cpaAsYHNrVRmfnV/D2ypLKbIn709VRHKOoiZqmm+4pbyf0llR6pdkri2JCMvrs1MbHWjshnPbBizlGVQuhPnXZxQtr1pOwMoU937Luc/0xZ/i8/G5iHQNRBXvI1VA8NbqUJDryosvyCo+EZRV19J65lRGWdcEbNjNZSkHk/DxsmtqcqbQWF5XxhvH2jLKulsb6ev6JUXDe7BgBJbfkWFNB4gEIiyrXMbe1sw8gb7lfHrjC9Q05Fw8yY8a23iqqYMux6XM9vLw1F28PDyTwVupXe+eVcr8SJjzyXRhVvFIiOvLi7lsAqziE0FpdQ2Jvj6aTx2n9fQp0qkUwVCIrpYmrnzHuyidVT3ma45kKV90Vf4o5VXFIWaXRTjfZdyNommuaPklLcV9LKgqyjx42e15o5Svql7F3ta99CR7ONR+iCMdR0g6SR458Ah3LrmTO5bcQV3x9Ml+7DPB+aAuNiLiALsxwnoc+LiqdkzCfe8GDqnqvjzv/z7wO3jhvoBPqeqIXuF8+aA2d/byXw+cIukqUdsiKJDSwTTbf7V8AevLZ15KmrdKu1SVbselN+1gi/C+6nLmRPK77S4vMo68ibaKXyhn9u/l6b/6c1wnjWUHEMtCXZdAKEQwHOG9n32AuctWjOmap/e10XC0I6u8en4pl63JnlIcyrZT7fzqoBnBLWx/nfru3YQDFtcsqEDwBH3OmsGMuXn4u+1/x6MHH8V1XQJWAEssbMsmZIUI2kG+euNXWVO7Jue5heSDEpEvA78FOIALfFZV3xixUhPM8P5KRP4UeEVVf17g+X8B/AaQBI5i+rOOi1PbUeoywwSqR1VLvJ8fxnwIX5+E+z4EPKuqP8rx3lxMuvqVqhoTkR8CP1XVh0a6Zi6BOhdPcu/OowCELaE77ZJUd8Do1D9F9NH6WZQN36g4jelKO/xrg5nWCeYYHVxK7Ro6Nkh7ceM21lVSOqRdk2EVvxC6W1v40df/B4m+3iyXXbioeGD6bOOX/6zgkVRXS4wDmxqzrhcpCXLlTXMHXHv56E2k+ftXj1HWd4YVzT8dKL9yThllkSAUzYJ1n4QRAsE29jbyqec+RU+qJ2s9qipSNZCF93vv+V7OkdRoAiUiG4C/At6pqgkRqQZCqnpuxMZNMCP1VwWe/17gl6qaFpFvAqjqH05gFQtmJpskXgfmAojIEhF5TkS2isirIrLcK58tIk+IyE7v341e+cdE5E0R2SEi3/NS1SMiPSLyde/YTd75NwJ3An/hHZ8re1sAiIpIACgCxvWF/FFj28CTeFfaIea6OGo6PRewRUh7AUNjrjtj/m3r6iXtKrbIQFuG/ruU2pXxpbAER5V9PTHAiNi6smI+M7+G22sqpqU4Aex79Zc46VTOFBvplAl55KRT7Hv1pYKul0o6HN2e21J+2ZraUcUJoDgcYHEZXN6Wec+WnoSxlK+8c0RxAnj26LOgELbDWe/F03EigQhJJ2mOGx/1QIuqJgBUtUVVz4nIOhH5ldc/PS8i9QAi8rKIfEtEtojIfhFZLyKPi8hhEfla/0VF5Env3L0icv+Q8oL6KxF5SEQ2euesF5HXvHPe9LKgZ6CqL6hqv8VyEzBvvL+QC2VGCpQnKO8CnvaKHgS+oKrrgD8AvuOV/y3wK1W9GlgL7BWRFcC9wNtUdQ1mKP5R7/hiYJN3/CvAZ1T1Ne8+X1LVNap6dGhdVPUs8JfAKaAB6FTVF8bTrqeaOgamiZJ5ojYHLOFgb2GRBqYLB3vjBHIsfg/lUm7X4b7EgFX81lll036UePC1VwmGI9iB7DUhJ5UinUwSDEc4+Nqro16r31KeiuewlC/LtJSPciHWxTcRdGIZxa09SZyFN0Hp6GtHz514jmgwSjSQQ3i9/jgaiPLciecKq1M2LwDzReSQiHxHRG4WkSDwbWCj1z99Hxg665NU1WuB7wJPAZ8HVgGfFJH+cOy/7Z17LfDAkPIx9VciEgIeBb7onfNuIPMXms1vAz8b36/jwplpAhUVkR1AIzAbeFFESoAbgce8976HeZIBuBX4fwCq6qhqJ0bY1gGbvePfBVzmHZ8E+h+ftgKLRquQiFQCdwGLgTlAsYh8LM+x93tPS1vS6ew/2C7HJSjGWpuv47OBxGgpB6YZCVcZrUu+FNtli1ARsAmKTLpV/EJI9PVh2TZWwKw9DSfZ1+sdl2P/0TCaT3Xnt5RfVp7jjDyc20Zd6jT2sD+L1tAcjkdXFXSJ/qm9SCCCJZntCogR44AVoCfVk+8Sgf6/X+/f/UPfVNUeTN9yP9CMEYPPYgTnRa+/+RMyRyT9D9m7gb2q2uCNwI4B/bGYHhCRnZjRzHzgCq98rP3VMqBBVTd79e0aMlLKwltPSwP/Osp1LxozzcUXU9U1IlIEPI952ngI6PBGQ4UgwMOq+sc53kvp4KKcQ47fj4jMB57xXn4XaAWOq2qz9/7jGMH8l+HnquqDmNEexcXFWb1xmW2RUgiJ2QfTmXKyrMkOZn1qJhG2BAdG7MwvpXYFRSiyLSKWkFKITO8BUxbhoiJcx8EOBAgVFZHoyeywVZV4TzeRktxuuX5iPUlO7W3LKg+EbJbksZTnpLcFjvySgGVRWRyipcdsrk1bYY5W3YxzvofLZ2fvlRpOSbCEtJsmaAcpC5XRnezGVZeQHaIoaNyAaTdNSTBvOo+0N9rJi6o6wMvAyyKyG9NH7VXVDXlO6Q9E6A75uf91QETeiRnpbFDVPhF5GYh4x4zaXxWCiPwTcA1wTlVv98o+CdwBvGvIPSadmSZQAHgf1APAk5jpvOMi8mFVfUyMR3e1qu4EfgF8Dvhrb1qwxCt7SkS+papNIlIFlKrqyRFu2Q2Uevc+Dazpf0NErgdu8EQzhhmRbRlPu+6qreBfG9oIWTYBEWaFArjDvhudKYeP1FfxuQWzx3OLKcFSeKSxjfJg/p76UmqXNcQIEks7fHRO1WRX7YJYduNN7P7lC9iBEoKhME44RTqRGdA1lYiz6Op1ea/husrRbfkt5aE8lvIs+qOUu4OJDPsF6mjVzSQDJRxr7iGecoiM8P0CuG3RbTx++HGCdpCQHaIqYj4Xyfi8Ynzoig8VVrdhiMgywFXV/t3Da4D9wHtFZIOqvu5N+S1V1b35rjOMcqDd6/OWAzcUcM5AfzWMg0C9iKxX1c3e+lNMVT81rB23Af8fcLOq9hVYz4vCjBQoAFXdLiK7gPswa0j/T0T+BAgCjwA7gS8CD4rIpzFPGJ/zviR/ArwgIhaQwjzljCRQjwB/74nixqHzuqr6hoj8CNiGGQ5vxxsljZWNdVU8dr6dmOMS9aaDMjo7xyViW9xXP2vGTBcB3DdnFk82d5DwDCDDuZTbFbKEjbNnlkCtvOlW9r3yEqlEgmA4TLioGDedxnVM9AvHcRDLJlxURFdzE2U1tVnXOHugnb7O7CjlNQtKqZozhu0Ew6KUl0eDBG3hbHQZ7UWLgMEI56vmjjxleMeSO3jm2DMDhggZ5iiNp+OE7BB3LLmj8PplUgJ8W0QqMH3BEcx034PA34pIOabP/WugUIF6DvjPIrIfIzCbCjgno7/qL1TVpIjc69UxinmgfjcwfE7z74AwZloSzDrXfy6wvhPKjLKZX0r4+6D8dk1nzh7czwvf+1ucdIpgOIKqEuvqxEmnEMtmybrrKauuJlxUzOr3vJ9gaNDs0Nkc4+CmhqxrFmopH6DtGOx8NKv4UHeIn4Vvw7UGXZDzq4rYuG50s9mOph38z9f+J0knSTQQJWAFSLtpYukYITt0wfugfCYWX6CmiHwCBV5kgvNeZIK0S1nAi7gw+xKIuOC3a8bQ3drCvldf4uBrr5Lo60Usi7KaWmoWLCJcNBjBYda8hSy94W2ICKmkw55fnc1y7YklrHzbHIorCnTtJXth8z9mB4K1bM5f/pv8277MIK8i8Om3L6Z0hE3R/TT2NvLs0Wd57sRz9KR6KAmWcNui20aNJOEL1OTjC9QUMZJA+fhMR9R12fOrn9OdIybf5es3ULNwMUe2NNHemP29nreiijmXVxR4I4XdP4LWI9nvLbkVnX8dD712go6+zEjw71hazbqFF2861ReoyWfmTPj7+PhMKWJZXHHdjdiB7FHK8e1bOHPwfE5xKqvOjlI+Iue25RanykUw/zpEhGV12R6A/Q3d2ef4zGh8gfLx8SmYSHEJl627Lqs80Zdm5893MXxGJhCyuWxNTeEBcHua4cgvs8uDUVgxGKV8RV22xb25O2EiS/hcMvgC5ePjMyZqFiyiZuHigdfqQkdTlERvjM7m8xnHLlo9Rkv5/kFLeQbLbofw4KipsjhEXXkk67CDjf4o6lLCFygfH58xs/ia9USKzYbW7vYQqaTpSrpbWoh7a6s1C0qpqh/Dks2xl80IajhzroGapVnFuaf5urJGcT4zF1+gfHx8xkwgGOSK699GMhakt3OoU1FpO3uaYERYcOWsvOdn0XoUzmzOLi+aBZe/K+cpy2aXZuwTBOiOpznbMVp4OZ+Zgi9QPj4+4yJSWolaC7PKXSeFbZ3BGh44Lx/JXjjwk+zy/ijldm7reHE4wIJZ2YFfD/hmiUsGX6B8fHzGjKpyYmcLRWXVhIoyp/FKKhP0tJ2i6cSxQi4EB36avd8JYPHNo0YpX57DLHG4qYd0jhBLPjMPX6B8fHzGTPOpbtobexERZs2dh1gmDl4o6lBcbvYnndixlVh318gXOpvHUl61GOZnuwWHs6SmhOCwkVo85XCidUpDyPlMEL5A+fj4jIlYd2aU8kAwRFX9HCxLqaiJ9zvBcdIpDr/5Gq7r5L5QTzMczWMpX/6BAUv5SIQCFktqsqOPH2gcRRh9ZgS+QPn4+BSM67gc3Z4dpbyovIJFV1djBzIddD1trZzeuzv7QiNZypd/IMNSPhrL67On+Y439xJP5RFGnxmDL1A+Pj4Fc+ZgnijlC8u46pbrB6znQzl7YB+dTZn7o0a0lFdfkV0+AguqiigKZaba6I9w7jOz8QXKx8enIDqb+2g82plVHikJsWBl1YD1XGR4t6IcfvM1UklP2PJZyour81rKR8K2hKU5EhYe8Dftznh8gfLx8RmVVNLh2I6WrHKxhCVrawZSaJTOqmb+yquyjkvG+ji29U000ZPfUr4iv6V8NJbXZwvUmfY+uuOpHEf7zBRmbMLCS5me9jgHNjVyZGsTyViaUDTA5etqWX5DHSWV2eFdZgp+u2YW/e06vOU8fZ1JRKBqTjHV80oIRUzXMW9ZJcXlmSk05i5fScf5BrpamjLKW0+fpKtvB+VWDkv5Ze+E0vFnU64ri1BRFMyIcK5qQh9du2hYhPPOs7DrEdjzBCQ6IVwOq+6B1R+B8rnjroPPxOOn25gi8qXbaDjayUs/2I+TdgmEbCxbcB0lnXSwAxa3fHzF2CJDTxP8ds0shrYLzAhKXWOSsCxh0dU1zF1awbLr63IGgk309bLjhZ/ipAbzNpX0naCqbz+zL7s8I8EhVYth9b0FufZG4vWjrWw61ppRVlMa5mM3DNlMfOoNePoL4CSNW9AKgpuCVAzsENz5bVhwfc7r++k2Jp8pFygR+TLwW5iU7C7wWVV9Y5LrcDdwSFX3ea//FHhFVX9e4PlrgO8CEUyq5/+iqm+OdE4ugeppj/P03+wAwA5apBIOblrp/4Rcr7NY8575hIvGNxUyFST6Uux48TQAVo5sqn67phfD2+U6LgzpJlzHRUS4+79dQ1V9timin5ZTJzj0xq8BCKa7md3+H4g6hCJF1C6+zKxVBaOw/tNjcu3lo703yUOvncgq//iGhVSXhM3I6Qf3mEI7BKlecB0IRMy/dNw74YmcI6nRBEpEHGCoZfFuVc2ukDn2k8C1qvq7IvK/gB5V/ctC2vlWYkqn+ERkA3AHsFZVEyJSDUxFCtK7gWeBfQCq+pUxnv+/ga+q6s9E5Hbv9TvHWokDmxpx0i7hoiCJWJp0Itsmm045nNzTWnjyt2nAuSMdpFMOgaA90GkPx2/X9GG0dlm2hVhwfGfLiAJVvWAR7ecbaD5+mFld2xA13+dkvI/OpiYqZteN2VI+Ev0Rzhs74xnlBxq6efsVYTOt5yQhVAx9reDVh3QcIuUQLIJ4B+x6FG76r+OpQkxV11xgM3yGMNUmiXqgRVUTAKraoqrnRGSdiPxKRLaKyPMiUg8gIi+LyLdEZIuI7BeR9SLyuIgcFpGv9V9URJ70zt0rIvcPKe8Rka+LyE4R2SQis0XkRuBO4C9EZIeILBGRh0Rko3fOehF5zTvnTRHJ9dekQP9mjHLg3Hh+GUe2NhEI2agqTp49HJZt0XZuZmXibTvXi2WP/FXz2zV9GK1dgZBNuCjIkS1NeY/pZ/Gaa6l1ThJMZzrqulub6S1ePGZL+WgszxHh/ECjF+F8zxNgBaCvbVCc+kl5ohYsgj2PT1h9ROSE9+CNiFwrIi9P2MXfAky1QL0AzBeRQyLyHRG5WUSCwLeBjaq6Dvg+8PUh5yRV9VrMlNpTwOeBVcAnRaQ/fPJve+deCzwwpLwY2KSqVwOvAJ9R1deAp4EvqeoaVT3afyMRCQGPAl/0znk3kCtU8u9hBO408JfAH+dqrIjc74nrlnQ6e4NiMpbGsgURyZvgTSwG1gVmCk7aJct5PAy/XdOHkdpl2UIoatbaErEcm2yHEeg6xfzyJJD5fU7ZJRxotAet5xPE0pEinMdaIdGdLU5gXIRg1qQSeaNQBPr/fr1/9w97P+o95O4QkScutC0+UyxQqtoDrAPuB5oxYvBZjOC8KCI7gD8B5g057Wnv/93AXlVt8EZgx4D53nsPiMhOYJNX1v+YlsRM5QFsBRaNUsVlQIOqbvbq26Wquf4qPwf8vqrOB34f+Mc87X1QVa9V1WsDgezZ1VA0gOvowM+5NEpdBiy9MwU7YKGj9NF+u6YP+dollhAuCiBijCDh0RIRJnrgwLOEo0WU1ww69BSL1rI1JBJJYz2fwHXwvBHODx0Cx8kduUJsCHlTlW4KwtmRKTzS/X+/3r8Hh70f8x5y16jqPRfSDh/DlNvMVdUBXgZeFpHdmBHRXlXdkOeU/kcud8jP/a8DIvJOzEhng6r2eUPqfq9vSgf/GhzG2X4R+SfgGuCcqt4OfAL4ovf2Y8A/jOe6l6+rZf+vz2EHLOyARbQsNCBY/SRjaa5YX8vyG+vHc4spIZV0OLKlacTMqn67pg852yVgWYMj+3TSYeXb5+S/iCoc/CkkTdDW0uoa4r3dJPp66ShZTiponI2tZ07RdPwosy+7fMLqv7yujBMtQ4LF9jZz6PQB3lmznEDjzsHREpifo1WDZak+WPuJCasLxjTV/4Qyc/ccTBFTbZJYBriqetgrWgPsB94rIhtU9XVvym+pqu4t8LLlQLsnTsuBGwo4pxvItbZ0EKgXkfWqutlbf4qp6qeGHXcOuBkjtLcChxkHy2+o49AbjaSTDoGQjYhgBwaHUemkQzBss/qW+TNqf83Vt87n5O5W1FUCw0LSgN+u6UYh7bIDFsuuHyEVxtmtJmKEh4hQNXc+J8900hNdnHHo8R1bKaupJVqad+QyJvojnKcche4GaD1KAuFE+bVcfn6PiQNoB8x6VIY4xcAOG8v7xHECM0v0M+BDE3nhtwJTPfdQAjwsIvtEZBewEvgKsBH4pjdNtwO4cQzXfA4zktoPfAMzzTcajwBfEpHtIrKkv1BVk8C9wLe9urxI7qegzwD/xzvmzzFTlmOmpDLCLR9fARirr5N2jWEi7ZLwNiDe8vEVM6qzA79db7l29TTB0ZeyigNF5VTc/DtZ+51cJ83hN0aIej5GQgGLy2tLoPN0hkgeSFbDsvd7N00b96BYxtkX7zDld357ojfrfhX4GxHZgpm18RkDU74P6q1Kvo26YPZDHXyjkSNbmkjE0oSjAS6/tpZl18/8yAR+u2YO42qXk4KtD0FvdlgkrvowVF/O4c2v05wjmeHcZVeycPWaC6+4Kse3/4Int57KKLZFuX9RM5GiEjOVt+9pY4gIl8GqD5qR0wji5G/UnXx8gZoiRhIoH58Zy+EX4cyW7PK5a2Hp+wBIp1Ls+vnPiPcMD+YqXHnzrZTXjpxFd0RcFw4/j3t2B39/ooY+J3OS6D1Ly1n19rvMFN8Y8QVq8pnqKT4fH59LhdajucWpuBqW3Drw0kQ9vzFP1PPXSSXijAvXMTmmzu3AElhaMuw6xTXsL7p2XOLkMzX4AuXj43PheJbyLCwbVt6VFaW8tKqa+Vfmjnp+dDzWcycFu38ETQcGilaUDtmyWFoPNUs525mgy49wPmPwBcrHx+fCGGYpz+CyW6CkNudpc5etpKwmO4J529nTNB0/muOMPKTisPMRaMtc15odTlMRTEPFfJh1GSCowiE/T9SMwRcoHx+fC2OYpXyAqstg3rV5TxPL4orrNmAHs8NvHt+xlb6u7OSIWSR6YMe/QueZ7OsLLF9+JVQsZGgki/2+QM0YfIHy8fEZP3ks5YSKTCDYUVJohIuKWbLuuqxy10lz5M3XR7aexzqMOPXkiAkoAstvZ8Xq9VlvtXQnaO6e2BBLPhcHX6B8fHzGh5OCfU/lDh+07AMQzh/pfCjV8xdSu2hJVnlPeyun9uzKfVJvK2z/FxP4dTiWDVfeA/VXU1EUor482xJ/0B9FzQh8gfLx8RkfR1/Kvd9p7jqoHlvoosXXrCNSkh3M5dzB/XQ2NWYWdjXA9h+YwK/DsYNmv1XNsoGiZSNFOPeZ1vgC5ePjM3Zajpi1p+EUV8OSW8Z8OTswgvX8jdcGreftJ2Hnv5mwRMMJRuDq+0yG3iEsq8sd4fxMe67EBD7TCV+gfHx8xkaiBw7+JLvcCuS0lBdKXut5PGas582HYdcPIZ3MPjlUDGs+mjMSRFEowMJZRVnlB/xpvmmPL1A+Pj6FowoHfpLHUv7OvJbyQslrPT+4jaaXH8693hWtgGs+NuK9l9dnT/Mdbuom7cysXF1vNXyB8vHxKZwzW7L2GwGjWsoLpd96HgiFBwu7GqDlIMdPttEXG7bJtrjaiFNR1YjXvay6hNCwvFyJlMuJVj/c2HTGFygfH5/C6GmCY+O3lBfKgPVcgQ4vIrmC6yqHj7fiup65oazeTOuFc2XKGVbFgMWSmuwwevsb/Gm+6YwvUD4+PqMzYCnPsS9pDJbyQpk1dz61kV5jihhCb2+SU2c7oXKhMUSEsteW8rG8Ljvf1PGWXuIpPwvGdMWPmjgNSXcm6NvWRGxXM248jRUJEF1dQ9HaWgLl4dEvME25VNtF51nY9QjseQISnRAuh1X3wOqPTHRuocllaLt6GgGB2uUwe9VgWvRxWMpHxXXh0HMsLmqmKxIgHs9cdzrXFaCi9h1UBMb2nVlQVURRyKapO87+hm4One8mkXZ5fPsZPrxuPh9cO5f68ux08T5Th59uY4rIl24jcbKL9h8dQh1FQhZYAq6iSRexhcqNSwkvnJjMo5PJpdouTr0BT3/BJL0LRsEKgpvysrOGTAK8BddPdS3HztB2iWWce+oYk4LYJvHfnDWw7pPjdu3lxEnD/qeh+SAA3T0J9hw4z0A3VVIL1VcQihZx9XtvJxgeW76tv3/lKN95+SiOqwRtC0vAtixCASFk23xz41WsW5h7PctPtzH5TEuBEhEH2I0Z4R0HPq6qHZNw37uBQ6q6L8/73wfuAJpUddWQ8irgUWARJsXzb6pq+0j3yiVQ6c4ELf+w24QNC1iQVtRxzVw8DPxc9t6F2EUT2ClcZJy+FF0vnAQBsb1ZZQGxxWunaVf171w1s0ZSnWfhB/eYnwNhSMfNVFg//XboW/47FM+a/PqNl95WeOnPzc+BkGmXDnG7OWmz3vSxx2HO1RN333QS9j4Obcczis80dHHqTAeUzTFmDG+pq2rOPJbd+A6kwLWvhs4Y931vE53xFEE7c3WjuiRM0vv7+sHvXJdzJFWIQInIl4HfwmTPdYHPquobBVVwghjej4nInwKvqOrPCzz/w8D/AlYA16lqjhwqk8N0neKLqeoaABF5GPg88PVJuO/dwLNAToECHgL+DvjnYeV/BPxCVb8hIn/kvf7Dsd68b1sT6ihWNICbcNBEtqVWUy5925sILyof6+WnjMSJTjTpIEELHbKGoQAiSNhG0y5925oou2X+lNVzzOx6xHTeVgB6uzI78X7Scdj9Q1iwYfLrN15OvQapXiO6qRy2bjtgRlVHfzFxApWKwe7HjOgPY25dKR2h+XS55UNjvtJ27gxNx48y+7LCphgf33YWR10iQRvHzXwwj6ccisMBOmNJnth2lv9yy9inLUVkA+YBdq2qJkSkGsiOhHvxuZsh/ZiqfmWM5+8BPgh8b2KrNXZmgknidWAugIgsEZHnRGSriLwqIsu98tki8oSI7PT+3eiVf0xE3hSRHSLyPRGxvfIeEfm6d+wm7/wbgTuBv/COzwoOpqqvADmCf3EX8LD388OYL8iYie1qRkIWqoom8yzc2kL6fI49KNOY9Pk+sPM85aqi8TSkXHo3N+Lma/d0I9YOWx82qcOTPbnFCcyUX9P+ya3bhdJ0wIhuPgJhiFTAnscn5n4DEcmzxQlAlr6XK27/BIFw9ui64KjnwLO7zlEcDhIJ2lnvpbz9UEWhAM/uahhD5TOoB1pUNQGgqi2qek5E1onIr7x+63kRqQcQkZdF5FsiskVE9ovIehF5XEQOi8jX+i8qIk965+4VkfuHlBfUj4nIQyKy0TtnvYi85p3zpohkWSBVdb+qHhzvL2EimdYC5QnKu4CnvaIHgS+o6jrgD4DveOV/C/xKVa8G1gJ7RWQFcC/wNm805gAf9Y4vBjZ5x78CfEZVX/Pu8yVVXaOqY0hIw2xV7f9WNwLZOw1Ne+73voxb0unsJ1M3ngZLzJRFvlkLETQ9szYXatod1YKsgNuXouc/zhI70IYby/HkPh3oPm/cbG88aERqtD8hsSE9wyJnp+Om3rkQ25hA7BAkui78XrEOE/S1pznHvSxYcQfMu3bEqOeH33gN1xn9waY7niZgCZFg9mfWP00YsITu/AkNA/1/v96/+4e9/wIwX0QOich3RORmEQkC3wY2ev3W98mcDUqq6rXAd4GnMLNFq4BPikj/vPBve+deCzwwpHxM/ZiIhDBLEV/0znk3MK3jPU3XKb6oiOzAjJz2Ay+KSAlwI/DYkDnn/keqW4H/BKCqDtApIh8H1gGbveOjQH9c/iRmCAywFXjPRFVcVVVEci7sqeqDGJGluLg46xgrEkBdBVuwwjZuzGFgAWrwIkhgWj9XZCEBMyocUaS8dqmjJE93kzrTTbCumNDCMuzSqZglyawbHafg1KbMTaqBiDEO5OvMwbw/RrfZlJOvXWJBpNxEC3eSg06+8dLbAjv/3YyghmPZsPJuqFk6UDRr3gJqFy2h6UTms2NvRxun9uxk0dVrR7xdaSRA2jNHFIcD9HpT6JYIRSHT1rSrlEbyru+mPTHJiar2iMg64CbgFowYfA0jOC96/ZANDB2i9T987wb29j/oisgxYD7QihElb7GT+cAVXvlY+7FlQIOqbvbqOwFPGBeX6SpQMVVdIyJFwPOYp4qHgI7+takCEOBhVf3jHO+ldNAd4pDj9yAi84FnvJffVdXvjnCv8yJSr6oN3vA9R4Ka0YmurqF3cyMStZCgjWVbMMQkAeAmHKIrZxFdMfLO+emE25sitq8VK2w6AQVIu5kjQUcJzB1cf1aFZEMvyYZegtVRQovKsCvCBS+ITwiq0HLICFPXuez3a5dDwy7TmQIgxsk31NWW6Ibld8Cy2yalyhNCXwsc+OmwvU1iRk39bU31wdpPjP8eXedMXL1cQV/tIFy1ESoXZb21+Jp1dLU0E+/J7FvPHdpPRd0cKmbX5b3lHavn8OjmU5RHQ5SEA4QD1oCbz7bM96ovmeYj6xeMu1neA/LLwMsishvTd+1V1XyLkP3Da3fIz/2vAyLyTsxIZ4Oq9onIy0C/dXHUfqwQROSfgGuAc6p6+3iucbGYrgIFgPeBPAA8iZnOOy4iH1bVx8T0VKtVdSfwC+BzwF9704IlXtlTIvItVW3ynHalqnoy990A6AZKvXufBtYUWNWngU8A3/D+f2qMTQWgaG0tfdvOoykHCdqIJUM6P9CUgxW2Kb1l/oxyu1mlIRJHO4xzL2ib2cuQjTpmrU0TZmozWJ/bIJVqiZFqiREoDxNaVEagJnpxhcp14PweY7Xua81/3OxV0LjXHB8ph2BRxudFKmZGGW///Zm1H6q4Fo6/an4O5tgXlIqBHYbV947v+u0nYPePMh2P/QQj5rplc3KeaqKeb2DPL19Eh637HXnztRGt5x9cO5cntp0llnKIBm2CtsXQ5ahYyiFk29yzdnyflYgsA1xVPewVrcHMAL1XRDao6uvelN9SVd1b4GXLgXavL1wO3FDAOQP92DAOAvUisl5VN3vrTzFV/VSBdZl0pv1ckapuB3YB92HWkD4tIjuBvRhzAsAXgVu8J5atwErPYvknwAsisgt4EbOIORKPAF8Ske25TBIi8u8Y08YyETkjIp/23voG8B4ROYx52vnGeNoaKA9TuXGpCesSS6OOawwTjmvWZBQqNy6dUeIE+dvVPzSUSICyWxdgF49snU93Jujb2UzvpgaS53rMdOhEkk4YUdr0/8wIYiRxAiitMxbyaKWZElPHjLqcJMQ7zDF3fntmiROY+t75bfNzvMO0Z6La1XIYdj2WW5zCJbDmY3nFqZ/Sqmrmr1qdVZ6Mxzi65Y28eZ7qy6N8c+NVoNAZS5Lyvocpx6UzlgSFb2686kI265YAD4vIPq/PWQl8BdgIfNPrt3ZglioK5TnMSGo/pl/ZVMA5OfsxVU1i1uW/7dXlRQZHYwOIyD0icgbYAPxERJ4fQ30nlGm5D+qtQL6NunDpRlwYrV2ackie7iF5ursgN58VsQktKCM0t+TC1uWSvSYI6tmthRkaohUw/3qou8pMR3WehV2PGldbosuMmlZ90IwEZpo4DWWi29W42wh/LsdjtAKu/ogR+wJQ12XvK7+kq/l81nuXrb2OuiVX5D23oTPGE9vO8uyuBrrjKUojQe5YXc89o0SSEH+j7qTjC9QUMZJAvdVRxyV1rpfEya6C3HwStAjNKyW0oBQrNIJhYTixdjj9pllHypXGYTils2H+DVCzHKxpP/kwvTizBQ6/mPu9khojegUEfR1Koq+PnS/+lHQy86HCsgOsfvdtFJVN7F5BX6AmH1+gpghfoEZHXSXd1EfiRBdOd44kdcMQSwjOLSG8oBRrpEgb3efh1OvQfAAK+f5XLoQFN0Dl4gmL2P2WQRVO/npwTWs4ZXNg9W/mXusqgNYzpzj4eva1iysquerW92HZY3hgGQVfoCYfX6CmCF+gCkdVcVrjJE50kW6Pj3q8CARmFxNeNMSirgodJ80aU658RrkuUr3UCNMoayI+eVCFI7+AM5tzv1+1GK78oAmndAEc2fIGTcePZJXPWbpiVOv5WPAFavLxBWqK8AVqfDidCSNUzX0FDX4CsyKEy85jd2xDuguIEGDZxp234IZRk+D5jIDrwsGfmnWnXNQshRV3mbBJF4iTTrHzxeeyrOcAK2+6lYq60bxRheEL1OTjC9QU4QvUheH0pkie7CLV0JvbzadprN5DBLp2IOkOrHAAuyKMVRxAcoXpCIRgzjUwb/2Y10J8huGkYf9T0Hwo9/v1q2Hp+yd0Ha+nrZXdv3why3oeikTHFfU8F75ATT6+QE0RvkBNDG48TfJUN8mz3WhawU1i9+zD7t6JONm/XytoG6EqCZq9VKFiI0pzrjF7cHwujHQS9vzY7HXKxfz1sORdF2Ut78yBvZzavSOrvHLOPJaPIep5PnyBmnx8gZoifIGaWLS3k9TO/0CPvImmRl+nIlqJtextBK+6HglNcSilS4VUzESHyBV1A2DxO2DhjRfNaKKq7Hvll3Q2NWa9N5r1vBB8gZp8fIGaInyBmiD62oxVvHE3uGlUFbc7SbojkTOoroZqSJddgxu9DMQav0XdJ5NEN+x8xMTXy8UV74V56y5+NS6i9dwXqMnHF6gpwheoC6S70cTIy2MVV1Xc3hROZwI34eBG5pEuW4uG5+Z8gi/You6TTazdiFOsI/s9sWD5B6BuVfZ7F4nWM6c5+PorWeUXaj33BWry8QVqivAFahwMWMU3ZWVdzYkIWr0Mp3QNiZYi0m3jtKj75Ken2SRuzBmRPABX3g3VFza1Nh7yWs+vWM6iNeMbyfkCNfn4AjVF+AI1BlzXiyr+uhk5jYZlmzBE86/PsIo7nQkSJ7tINxVoUa+OEl5Yhl05yVHUZwqdZ0224FxrfoEQrNpoNjlPARfDeu4L1OTjC9QU4QtUAThpOL/brDH15UpkPIxACOashXnXjmgVH9WiPgy7PER4UfnFj6I+k2g7btx6OSOSR72I5BOz/2i89LS1svulF1A3cy0yGImy5j23E4yMzbXpC9Tk4wvUFOEL1AikE3Buu4lAkGvqaDjjtIq7Cc+ifqanoCzFdnGQ0MIygnXFSL4U9m8Fmg+arMJujoC+4VIT9LW4evLrlYOzB/Zxcvf2rPLxWM99gZp8fIGaInyBykGix4jSue0FRhWvhAXXw+yrLigigaZckme7SZ7qxk0UEEU9bBNaOAFR1GciDbtMhIhc/Ua00otIXjHp1crHiNbza9ZTd/nSHGflxheoyccXqCliRIHqPGsWnvc8AYlOCJfDqntg9UcugfQNOdq19DboOD1gFR+V0jpYsMHEypvAaATqKKmGHhNFva/QKOolWOVh4ntaL7n0KFmflwShYq4JBTU83XtJjfl+ZmThnR4Mt54n+vpoPnWc1tOnsAJBoiUlLLvxJlbedCuls/KP/HyBmnx8gZoi8grUqTfg6S+Y5HDBKFhBcFNeFtOQSRS34PrJr/CFkqtdqT6Id5pEf0vfD+XzRr5G1WJjfKhcdFGjiqurpJu9KOpdI0dRdzoTxPe1giVIUcCMqFxFky5iC5UblxJeWDbiNaYlQz+vQMREiEh0mgcIsWHZkM+rfC5c9ZvTOhJHv/W8q6WFo1vfQF0Hyw4QCAYJFRWTTiawA0He+9kHmLtsRc5r+AI1+UyKQInIl4HfAhzABT6rqm9c9Btn1uFu4JCXaRcR+VPgFVX9eYHnfxj4X8AK4DpV3TLkvT8GPo1p3wOqOmoGypwC1XkWfnCP+TkYNQvQTor+zLMD0163fcM8sc4UeprhuT8yPwe8EUU6AY7XHscbrVx9b/aTuQjULDN5mCZ50V1Vcdq8KOo5LOpuIk3ftiZTTVsAQYIWErIQ20JTDihU/85VM2skNfR7GIiYTbipId/VoZ9X/Rq48p4Ljkg+Gex56UV+9a//BIA9ZC9UMBIhXFRMKmG+jxu//Gc5R1KjCZSIOMBuIAAcBz6uqh0T2IR8972bIX1bjve/D9wBNKnq5G1ImwAuPJTwKIjIBswvZ62qJkSkGpiKb/PdwLPAPgBV/coYz98DfBD43tBCEVkJfAS4EpgD/FxElqrq6IsZw9n1iHlijZSbEVO8kwFx6icdhze/Z6a4ZgqnXjMZWQPhQVEaih0w7Tq/d7BdVsCzil83ZVHFRYTArCiBWVGcLi+K+hCLeqqhF1xFgv3TjIqmHDTlIAELCdm4KYe+bU2U3TJ/StowLnY9Yh4gAiHoa8medu3/vHqa4KqNxtY/A+huawEUe9h6ZSoeJxAMEQyHiff2sO/Vl7j+7g+P5xYxVV0DICIPA58Hvn6B1S6EuxnSt+XgIeDvgH+ehLpMKJOxwlsPtKhqAkBVW1T1nIisE5FfichWEXleROoBRORlEfmWiGwRkf0isl5EHheRwyLytf6LisiT3rl7ReT+IeU9IvJ1EdkpIptEZLaI3AjcCfyFiOwQkSUi8pCIbPTOWS8ir3nnvCkiWR5lVd2vqgdztO8u4BFVTajqceAIcN24flN7nhhM3JbsIUucwEyNNe0f1+WnjKYDRnBGor9dgZBJdXHD52DZbdMm5YVdFqZodQ3FG+YYc4QlpM/3QR43n6Zd3L4UOErf1vMF2dmnBekkbPsXSHsPSPnWBMNl3uc6M8QJ4PAbr5lQRzmmh1MJM0IOhiMcfC1PcsWx8TowF8Drb57z+qtXRWS5Vz5bRJ7w+p2dXj+FiHzM64d2iMj3RMT2ygvq24ZXRFVfAQrYpzH9mAyBegGYLyKHROQ7InKziASBbwMbVXUd8H0ynzSSqnot8F3gKcyTyCrgkyIyyzvmt71zrwUeGFJeDGxS1auBV4DPqOprwNPAl1R1jaoe7b+RiISAR4Eveue8G4iNoX1zgdNDXp/xyrIQkfs94d2STuf4w090mo5aNX+mV7ELc7hNJ9JxU++RsIOm49jwu7Dklmm52A7Gah5dOYuSt5skhmKNvBamruL0JOl9/RzJsz2oM02FKtkLx1+BTf/XjJpGqmaoBCKVkOyetOpNBIm+PgKhMKFo/uy9lm2T6Mvrrg30//16/+7PdZAnKO/C9DkADwJf8PqrPwC+45X/LfArr99ZC+wVkRXAvcDbvNGYA3zUO35MfdulwEWf4lPVHhFZB9wE3IIRg69hBOdFbx+CDQzNJtf/we4G9qpqA4CIHAPmA60YUfImypkPXOGVJzHDXYCtwHtGqeIyoEFVN3v1zd56PkGo6oOYLyvFxcXZXUC43Bgi7JDZ25PIURV1BtdxZgqBiKl3LpGyAqatYpufZ0jbrHAAuzSE67rggCad3A8VqkjAwulLE9vXSuJoB6EFpYTmlU4Pi3qswwu2u3NwbWmkzytcCsFi8z0dvl44zQkXFeE6DsFwBDedJp30DDAiBLx8Ua7jEC7Ku8yU9h6c8xEVkR2YB9T9mP6tBLgReGzInqv+L/mtwH8C8JYEOkXk48A6YLN3fBRo8o4fa98247noAgUDv/yXgZdFZDdmRLRXVfMtpPQPEdwhP/e/DojIOzEjnQ2q2iciLwP9FqKUDjo/HMbZRhH5J+Aa4Jyq3j7CoWcxAtnPPK9s7Ky6B7b9YFCgrIBZkxra8aV64PL3mI2pM4XOUyb1d3DIH76IGTXZYfNzvAPWfmLKqjgeoqtr6N3ciBUNoCELUi5u0oGh03mOEpg72G434RA/3EHiRBehuSWEFpRhhadgmqynycQ0bNoPw5L8Ubvc7HcaOn1nh8zIqf8BItU34z6vZTfexO5fvkCkuIRwcQl2KIk6LnYwiB0w3UQqEeeqW9833lvEVHWNiBQBz2P6uYeAjv61qQIQ4GFV/eMc743at4nIfOAZ7+V3VfW7Y6j/tGMyTBLLAFdVD3tFazBPF+8VkQ2q+ro35bdUVfcWeNlyoN0Tp+XADQWc0w3kin9zEKgXkfWqutlbf4qp6qcKrMvTwL+JyF9hTBJXAG8WeG4mqz8COx81Bolg1HQGQ0cUqRjYlXDLl2fWfqjaFXBmq/k5mGN6JRUzQrX63smt1wVStLaWvm3njSkiaEPIxgpakHbRpCdWlhCsz34i15RL4kQXyVPdBOcUE15YdvGjqKtC52kjTK0jzATNXgWNe836U6jEPCzZQ3xNM/TzWnnTrex75SVSiQTBcJhgKHO0nkoYq/nKm265oPt4/dIDwJOY6bzjIvJhVX1MzLBotaruBH4BfA74a29asMQre0pEvqWqTSJSBZSq6skRbjnQt6nqaUwfe0kwGXMMJcDDIrJPRHYBK4GvABuBb4rITmAHZhhcKM9hRlL7gW8Amwo45xHgSyKyfehCoqomMXO+3/bq8iKDo7EBROQeETkDbAB+IiLPe+fvBX6IcdA8B3x+XA4+MKJz57fNz/GOwdGTkzSvwbw/k8QJLtl2BcrDVG5cCgpuLI063kjEElTAitiU3FCPFc7/HKiukjzTQ89r5+jb1TzqvqtxoWrSr2/7Z9j+ryOLE5iIEDd+ASIVg4aCS+DzKp1VzXs/+wAA8d4enLTJH+ak08R7TUit9372gRE36xaKqm4HdgH3YdaQPu31L3sxxiqALwK3eLNKW4GVnlX8T4AXvP7yRYzRbCRy9m39iMi/Y0wby0TkjIh8+oIbOEn4G3WniNEjSTwKex4361DhMlj1QfPEOsM6hQwu0XalOxP0bWvKG0kil0V9JAKzIiaKelXkwoLTug6c32M23fa1jn68HTTxDOeth0jZJft5dbe2sO/Vlzj42qsk+noJFxV7kSRu8SNJTDN8gZoi/Fh8bz3cvhSJk12kzhUYRb0sRHhRGYGaolHdghmkE9Cw05gfEgU47UJFQ4Lt5ne4vdXxBWry8QVqivAF6q2Lm3BInuoqPIp6UcBEUa8vGTmKerIXzmyBs1sLDLZbYTZC1602oyefEfEFavLxBWqK8AXKZ1xR1BeUEppbOiR6BSbl+unNZtRUSLDdkloTsaNm+YQG273U8QVq8vEFaorwBcqnn/4o6smTXTiFRFEPmCjqoco4VtObJqLDcKt4LioXmmC7VZdd1GC7lyq+QE0+vkBNEb5A+QynsCjqakIQdZ5F4m0ES3sJVXRhh/IImwhUX2FGTGVzLlrd3wr4AjX5+AI1RfgC5ZMPVcVpT5A40Um6tT+KuhonXufZLOODoARKYoQrurAjnrBZttnPNP96KJ6Fz4XjC9Tk4wvUFOELlE8hOB0xErv2kj52Ck2NHiIyUJIivHwB9oprkcjMCkU03fEFavKZlFBHPj4+YySdgHM7sM9spsjpxq0PkOgoJdVdgmqO9SM7CGVzSJfWk24JYO/sJbzIHrtF3cdnGuGPoKYIfwTlk5NRrOJu2iLZWUqysxR1LRPYtXyuceblCO5qFQUIF2JR9xkVfwQ1+fgCNUX4AuWTQazdbKxt2FWQVVyL6kiG1pLsqsJNju7gy2tR9ykYX6AmH1+gpghfoHwA6D4Pp16H5gP5c4ANpXKhSehYuRhEjEW9sZfkic6xWdQXlI4YI9AnG1+gJh9foKYIX6DewqhCx0kTI6/t2OjHi0D1UiNMeazi6irplhiJE504naMHnBUvynpoYRl2sR9FohB8gZp8fIGaInyBeguiCi2HzIipq2H04y0b6q4yVvGiqgJv4VnUT3aRbhnd9ScCgdoiE5y2fGYki5wqfIGafHyBmiJ8gXoL4aRNVPHTb0Bf2+jHB0KDUcXDuVKYFXjbriSJk52kzxcYRb0qQnjRBERRv0TxBWry8QVqihg93cYjsOcJSHSaVPCr7jEJDWdwmoO3XLtWfhB6m+DMm5DoGf06oeIhUcWzUpKNG7cvReJUN6mzPYVHUV9YBhGb2PbmvGlEZiqjpUfJhy9Qk48vUFNEXoE69QY8/QWTHC4YBSsIbsrLYhoyieIWXD/5Fb5Q3krtSidMYj83DUtvg/J5I18jWmnaPvsqsC+eccFNOiRPdZM8042mRnb+OZ0J4vvbwBYkaiO2Ba6iSRexhcqNS42IzTASJ7to/9Eh1FEkZIElBbfLF6jJxxeoPIiIA+weUnS3qp7Ic+wngWtV9XdF5H8BPar6lyNdP6dAdZ6FH9xjfg5GTQBQJzX4ftoLe3P3d6Bk9hhaM8X0nIcn/4v5OZBjZHAptSsdN6KLmqk9gKvvNcn+hlM628TIq142qVHFNe2SPGuC0+aKou4m0vRtawIwe6dEkJA9sI9KU+acyg8vI1Aayjp/upLuTtL+2EEAJOjtGRPAEkTEtEuh+neuyjmSGk2ghvQZAeA48HFV7ZjoduS4793AIS8bb673vw/cATSp6qoh5VXAo8Ai4ATwm6rafrHrOxZ8n2l+Yqq6ZlLvuOsR8yQeKR98Ch8epTodh5e/YTq2mcKp18w+n0AYUn25j7kU22UHTLvO781sV+Uizyq+aEqiikvAIrywjNC8UmNRP9mF0zv4IJRq6AVXB/dLqaKJNEMfZTXl0vXzk4QXlU9u5S+AxIlO3L40ErQyR5CWYBUFkaCNGzPiXHbL/PHcYqDPEJGHgc8DX5+Aqo/G3cCzQE6BAh4C/g7452HlfwT8QlW/ISJ/5L3+w4tUx3Hh79gbAyJyQkSqvZ+vFZGXJ/QGe54YzGia6M6dQsEKQtP+Cb3tRafpAFijPAtd6u0SgdrlsO6TsOY+qFo85SkvxBZCc0so3lBP0dU1A6OG9Pk+GC3qhC3muBlE3na5inojSQlZxHY1T8TtXgfmAojIEhF5TkS2isirIrLcK58tIk+IyE7v341e+cdE5E0R2SEi3xMxIUJEpEdEvu4du8k7/0bgTuAvvOOXDK+Iqr4C5HLn3AU87P38MEbophW+QOUn6n3gO0TkiYm4oIjcLyJbRGRLOp1jU2Wi03RoqqB5EtiJXVi21OlEOp4zDE8Gl2q7rACgcN39cOU9UFY/KVUbCyJCsLaIovWzKV432xgpRhNPkYKyAU8nNO3mb1f/UocluPG8G54D/X+/3r/7cx3kCcq7gKe9ogeBL6jqOuAPgO945X8L/EpVrwbWAntFZAVwL/A2bzTmAB/1ji8GNnnHvwJ8RlVf8+7zJVVdo6pHR/1FDDJbVfv3OzQC025+3Z/iy8+ET/Gp6oOYLyvFxcXZi3/hcmMcsEMQiEIqh4lCHTOlNJMIREy9R+rML7V2iQXBIiNQdqjgfUxTiYgQqIoQKA/jpl1w1JsKy7FOrYoEZtbzrQQsVHOL78B0pqtYkbzdYlpVrx3hFlER2YEZOe0HXhSREuBG4LEh1v3+L/qtwH8CUFUH6BSRjwPrgM3e8VGgyTs+iZnKA9gKvGeEuowJVVURmXaGBF+gxkaawVHnxPmA+1l1D2z7genQwqVmo6aTzAyB4/bBwreZrKgzhYU3wolfgz2CAF0q7RIxn18wakQq3gFXf2TKqjgeoqtr6N3ciBUNoGFFkw4Ms6dr0iW0uJxAdXSKajl2QovLSRzrzBRWESQgA6YJTboUra8b7y1iqrpGRIqA5zFrUA8BHWN42BXgYVX94xzvpXTQ1eaQo/8WkfnAM97L76rqd0e413kRqVfVBhGpZ1AIpw2+QI2NE5inm58BH5rwq6/+COx81LjAglGzL4YhpqH+8vd9fWbtG1r09kx34nAu5XbZYVh97+TW6wIpWltL37bzaMpBgjYybEShKQc7YFFxx2Uzaj9U+LJyWv5hN8gQF98QNOUgtlC0tvaC7qOqfSLyAPAkZjrvuIh8WFUfEzMsWq2qO4FfAJ8D/tqbFizxyp4SkW+papPntCtV1ZMj3LIb76yW/gAABENJREFUKPXufRpYU2BVnwY+AXzD+/+pMTb1ojOzxuhTz1eBvxGRLZgnmImlfK7ZDwTmybt/9OQkzWsw78+kThz8ds2wdgXKw1RuXAoKbiyNOi6qijoubiwNCpUbl84ocYLJbZeqbgd2Afdh1pA+LSI7gb0YcwLAF4FbRGQ3ZspupWcV/xPgBRHZBbwIjLZw+QjwJRHZnsskISL/jjFtLBORMyLyae+tbwDvEZHDwLu919MKfx/UFDF6JIlHYc/jkOgye2hWfdA8ic+wzi4Dv10zivFGXJju+JEkZg6+QE0Rfiw+H5+ZhS9Qk48/xefj4+PjMy3xBcrHx8fHZ1riC5SPj4+Pz7TEFygfHx8fn2mJL1A+Pj4+PtMSX6B8fHx8fKYlvs18ihARF4gVcGgAE2LpUsNv18zCbxdEVdV/qJ9EfIGa5ojIllECVM5I/HbNLPx2+UwF/tOAj4+Pj8+0xBcoHx8fH59piS9Q058Hp7oCFwm/XTMLv10+k46/BuXj4+PjMy3xR1A+Pj4+PtMSX6B8fHx8fKYlvkBNE0TkNhE5KCJHROSPcrz/SRFpFpEd3r/fmYp6Xigi8n0RaRKRPVNdl4lEROaLyEsisk9E9orIF6e6ThOFiJwQkd3e927LVNdnvOT67olIlYi8KCKHvf8rp7KOPpn4AjUN8NI9/1/g/cBK4D4RWZnj0EdVdY337x8mtZITx0PAbVNdiYtAGvhvqroSuAH4fJ7PcKZyi/e9m8l7hh4i+7v3R8AvVPUKTLr1rIdDn6nDF6jpwXXAEVU9pqpJTArnu0Y5Z0aiqq8AbVNdj4lGVRtUdZv3czewH5i56XQvQfJ89+4CHvZ+fhi4ezLr5DMyvkBND+YCp4e8PkPuzu1DIrJLRH4kIvMnp2o+Y0VEFgHXAG9McVUmCgVeEJGtInL/VFdmgpmtqg3ez43A7KmsjE8mvkDNHJ4BFqnqauBFBp/6fKYRIlIC/Bj4PVXtmur6TBBvV9W1mCnoz4vIO6a6QhcDNXtu/H030whfoKYHZ4GhI6J5XtkAqtqqqgnv5T8A6yapbj4FIiJBjDj9q6o+PtX1mShU9az3fxPwBGZK+lLhvIjUA3j/N01xfXyG4AvU9GAzcIWILBaR0P+/nTtUiSiIozD+nSxmH8GHsIkaDJstsoJV9iFMJt/ALNhcELQLgs0gBquPIfwNOwuLYNGFGeT7pbkw4YS5HC53ZoAjYL46YfkSNRMW/zg0iCQBroC3qrrsnWddkmwk2VyOgQPgP+3AnAPTNp4Ctx2z6BsLagBV9QmcAQ8siuemql6TnCeZtGmztn35BZgBJ33S/k2Sa+AJ2E7ykeS0d6Y12QGOgd2VowCHvUOtwRbw2NbdM3BXVfedM/3KD2vvAthP8g7stWcNwquOJElD8gtKkjQkC0qSNCQLSpI0JAtKkjQkC0qSNCQLSpI0JAtKkjSkLztJL9LfP4oDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sd9/mq6yk8p4N+CorR12uZ6AuPZPrm+Xh4TEpqV98hSst053kfHNTidwTR3rHDhIv/LKkcfLXTKfyvvvGZJw8JgbPQHl4eAxLpLyCqvqZrvTmAbOqiUJV6X7rbZKvlD48HGiop/Lee/GVeROgqYRnoDw8PEZEQ4lZVGdLE6mu+ATUpg9Vpfs3vyH19tslrwfnzqFywwaM8KTWA3iUwDNQHh4eI6KqfibhMrdyr/nwwQmojY1aFskXXyS9vbTTg9DiRVR88INIcCJc4nlcKJ6B8vDwGBEiUnIWde7EMQr9fNtdKrRQoOtnPyOzb3/J6+Hlyyh///sR/5Q87umBZ6A8PDxGwYz5C/H5i103mYU8rcfH7KJyTFi5HPFnniV39FjJ65G1aym75ZZJpzD0GB3ep+fh4TFi/IEAtfMXutKbDx+0zxddAqx0mvhTT5E/fbrk9dh11xK78Qb6+eb0mKJ4BsrDw2NUDC45P3vRn20mu+l88kkKLa0lr5e9771Er77aM06XCZ6B8vDwGBWR8gqmlZCcNx26uJJzs7OT+BOPY7aXCL1mCOV33E5k5cqLWgePS4tnoDw8PEZN/ZJGV1q8tfmiSc4L7e10Pv4EZtwVIQLx+6i46y7Cje46eUxtPAPl4eExaqrqGi6Z5Dzf3EznE09gpVKuaxIMUvGhDYQWLBj353pMPJ6B8vDwGDWDSc5bjx8dV8l57tQp4k893euRvD9GJEzlxo0EZ88at+d5TC48A+Xh4TEmSknOLbMwbpLz7JEjxJ95pmSIdqOsjMr77iNQV1viTo/LBe8E2ySkKZ7mia1neHbnWRKZAuVhP3evmsl962bRUDl49NbJjteuqUU8Hmfnzp3s3r2bbDZLKBRixYoVrFq1isrKyl7J+UAv582HD9KwuPGCziBl9u0j8eKvoES0BV9lJZUb78FXMbZ4VJlME03NT9Ha+lMKhQR+fzm1tR+koX4j4fBwoeE8LiVeuI0JYrBwG1tOdPAnP9lFzjSJBv34DaFgKalcgaDPx9fvX8n6edUTUOMLw2vX1OLkyZNs2rQJ0zQJBAIYhoFlWeTzeXw+Hxs2bGDu3LmkE11se/4Z1/1Lb3wv1TNnj+nZ6e3bSb76m5LX/DNqqPzQhzBiY3P62hnfwr59f4Zl5fD5Ioj4US1gmmkMI8iyZf+Dqsr1Je/1wm1ceibcQInInwOfAEzAAj6nqm9d4jpsBA460XYRkf8HeEVVfznC+z8C/HdgGXCNqm4e7p5SBqopnubBf30bBCIBH6al5E2Lnk8oWzBB4X/ct5KaMneI8clKWzLLnz2xCwRCfp/r+uXULgMI+A0MEdJ5u13f+71rptRMKh6P873vfQ+AQCCAZVlYloVhGBiGQd5ZcnvwwQeprKxk36u/dp2Bqqyt58r33jqq56oqqbfeJvXOOyWvB2Y2UHH33RihsX1HMpkmtm3/NAA+XxjLKgAmIgFEDEwzA8DaNY+UnEkNZ6BExAR2Ya9MHQMeVNXOMVV2FAzsvwbJ4wM2A2dU9e6LXafxYkKX+ETkeuBuYJ2qZkWkBpgIr44bgWeBvQCq+pejvH83cB/wrQupxBNbz5AzTSojQbJ5k860e+09kzf5Xy8d4er5U2dU/s7xDuLpPOGAj2y+tLeBy6ldIjAtGiQS8BFP53hy6xn+082LJ7Cmo2Pnzp2Ypkk4HCaXy5HN9gkUAoEAgUCAfD7Pzp07uemmm6hf0ugyUD2S82jFyGIvqSrdr75KesfOkteD8+ZSceedF+T0tan5KSwrh99fTqGQwDTTgCLiw++vwucLk8930dz8FPPn/8FYHpFW1TUAIvIo8IfA18Zc4ZGzkX791yD8EbAPGNu66AQx0SKJBqBNVbMAqtqmqmdFZL2IvCwiW0Tk5yLSACAiL4nIN0Rks4jsE5GrReQJETkkIl/tKVREnnLu3SMiD/VLT4rI10Rkh4i8KSJ1InIDsAH4GxHZLiKLROQREbnfuedqEXnduedtESkf2AhV3aeqF3xK8dmdZ4kG7TFDIlsomSfgMzjYkrjQR11SDrYkCPiG/qpdTu1Sha5MHlUlGvTz7M7JFdRvOHbv3k0gEKBQKBQZJ4B8Pk8qlcI0TXbssD2IDyY5H+nBXTVNEi+8MKhxCi1ZPC4eyVtbf4phhCkUujDNFDhrE6ompmmvZvh8EVpaf3pBz3F4A5gF4PQpzzt90qsistRJrxORJ52+ZYfTFyEin3T6mu0i8i1n9jPi/mtgRURkNvBB4F/Ho2GXkok2UL8A5ojIQRH5FxF5r4gEgH8C7lfV9cB3KB6F5FT1KuCbwNPYo5QVwKdFZLqT5zPOvVcBX+iXHgPeVNXVwCvA76vq68Am4IuqukZVeyVIIhIEfgT8kXPPbUB6rI0VkYcc47q5UHAboESmgN8QVLXU3jAAhkCucGl8no0X2YKFMYznmcutXQVTKViK3xASGfdMeDLTY5QymcygeUzTJJFIsG3bNjo6OqhfvMSV59yJY+Rzbnl4f2yP5M+TPVD6/FT4yispv+MOxOdeGh4t+XwXptmNZZVql/2DE/FTKAw6UPL3/H6dv4dKZXIMyq3Y/QrAw8DnnT7pj4F/cdL/EXjZ6VvWAXtEZBnwMeBGZzZmAg84+UfVf/Xj74H/C3sLZUoxoUt8qpoUkfXATcDN2Mbgq9gG5wXHn5YP6D8E7fnQdwF7VLUJQESOAnOAdmyjdK+Tbw6wxEnPYU+FAbYAtw9TxUagSVXfcerrPsY+ClT1YewvK7FYzGWCysN+CpYS8BlEgj66S8yiLIWgf6LHFaMj5DewFHxDGKnLsV2ZvEk44KM8HCidYZISDAbJZDIMtT+tqvj9fjo7O+ns7CQaidCdN4n6jV4/eJZZ4Nzxo8y8YlnJMqxcjq5nf0r+zJmS1yPr1hK7YXycvppmFrCwrBwi7u+ZYUScdhXw+12LJD0UnMHxYEREZDv2zGkfdh9WBtwAPNavHT2baLcAn7KfqyYQF5EHgfXAO07+CNDjeHC0/RcicjfQqqpbROR9w+WfbEy4zNz5YF4CXhKRXdgzoj2qev0gt/QMyax+/+957Xc+hNuA61U1JSIvAT2hNPPa96szGWP7ReTfgLXAWVW9ayxllOLuVTP50TsnqYwEiQV9+AwhX+gTSQCkcgV+a3ENyxoG/RFNOm5cXMNrh9sIBwYfBU/1dlmWkjOLB6iZvIVpWXz8mnkTVMOxMW/ePHbt2oV/iDhKlmXR0NAnJEil0yTFR1vHeaoiEcojIQyRQSXnVjpNfNMzFFpLO32N3XA90fWl1XSjxTRTNDU/RSQyn0RiDz5fscjC7y/vTTPNNLNmfnSsj0qr6hoRiQI/x+7LHgE6e/amRoAAj6rqn5a4Nmz/JSJzgB5Z5TeBecAGEbkLux+sEJHvq+onR1ifCWWiRRKNgKWqh5ykNdgjjztE5HpVfcNZ8rtCVfeMsNhK4LxjnJYC143gngRQqmc8ADSIyNWq+o6z/5RW1d8dYV1GxX3rZvHk1jOk8yaRgK/3r4d03iToC/InH1g6pVRhq+dUFakTB3I5tCvkMziXLF7OyhZMQv4A966bOp4Ouru7CQQCiAimaeJzltZEhGAwSC6Xo1AoICLU1dUV3VtWPZ1ERzvnkt10pFJURsJUWhbnm88WSc7NZJL4009jdpx3V0CEsve+l8jKFePSnkIhQVPTE+TznZSXLSWZ3I9lFTAMPyCOcbK/c6aZwTCC1NdvvKBnOn3PF4CnsJfzjonIR1T1MbGnRatUdQfwIvAHwN87y4JlTtrTIvINVW0VkWqgXFVPDPHI3v5LVU9h96P9+VMAZ/D+x1PFOMHE70GVAY+KyF4R2QksB/4SuB/4uojsALZjT5FHyvPYM6l9wF8Db47gnh8CXxSRbf03GVU1h70e/E9OXV6gbzbWi4jcKyKngeuBn4rIz0dR314aKiN8/f6VoBBP52yJudpS83g6Bwpfv3/llOrE4d3RrkQ2j8/ZPzQtJZM3Afjta+ZMmXaZpsnevXsJBAI0Oo5XC4UClmURCoXw+Xz4fD5CoRBr1qwhNEDu7Q+GiJTZ4zzTUjq605xo72Tbm2/07meZnZ3EH3+8tHHq9Ug+PsYpl+vg7NnHyOc77fr5y5kxw5a+2+egohhGGMvKk8/bq/fLlv2PcTmsq6rbgJ3Ax7H3kD7r9CF7gHucbH8E3OysHG0BljtS8S8Bv3D6xBewxWRDUbL/uhyY8HNQ71YGO6gL9nmoJ7ee4dmdTSQyecrDAe5e1cC9U9wzweXerp9sOU1bMkvQb3BFXTnLGsqpjoX43HsW4h9GxTgZOHz4MKdOnep9nc1maWlp4fx525j09yRRUVFBe3s7J0+eJB7v82CeSSY4d/K4q+yGxVcwu6KSqu078JcQCInfR8UHPkBw/vxxaUs220pz81OOlLwY00qDKh3nX+v1JFFX+0Hqh/EkId5B3UuOZ6AmiKEMlMfUJG9aPPzKUZca8e5VDSypm9x7ax0dHb2y8f5Eo1HWr18/5H5UZ2cnJ0+epL29HbBdHQ1U71UZfupPnETyeaLRKBWVlYRD9mKEBINU3v1BArPGZyk0nT5DS8smLMvttNYwQtTXbyAcdsezGg7PQF16Jlwk4eFxuRDwGSyaUca+pmKx577mxKQ2ULlcjn379rnSDcNg+fLlQxongKqqKqqqqkgmk5w6dYru8x10NPUp80LxONFDR6CqGgwhlUqRSqUIh8NU1tXRsHHjuDl9TaWO0dLyU2ztVTE+f5T6+nsJBWvG5VkeF5/Jv+7g4TGFKKVCPN7W3bsnNdlQVfbv30+uRIiMBQsWUF4+csNaVlbGsmXLeN/td1BdVoYhQrijg+oDh8A0yWWKl9tSIhxdsIBtJ47T3NyMZV3YMZ1k8gDNLc+WNE7+QAUzGz7iGacphmegPDzGkTnTosRCxUpF01IOtSQnqEZDc/bs2d6luf5MmzaNOXPmjKnMsvJyrly9moXZDLNOnMTnHJTIpdM952GxYjHSN9yAlpXR3d3Nvn37eOuttzh9+jSlDrEPR1fXTlrP/RzUbeQCwWpmNtxPIFA1pvZ4TByegfLwGEcMQ7iixHLevuYLOuN9UUgmkxw+fNiVHggEWLZs2QUdkK3qzhA+eJhIwE9lJEI0GEDUIp/LYlZW2sYpUiyMyWQyHDp0iDfffJNjx46VnNWVorNzM21tvy4ZmiMUqmNmw/1DHb71mMR4BsrDY5xZ1uD2S3fmfJp4Cee/E4Vpmuzbt6/kslpjY6NLQj5SVJXuN96gsHVbr+RcBMIBP5WRMIHqKoxbbkGHKD+fz3P8+HHefPNNDh06NKjLJVWlveM3dHS8VvJ6JDKbhoZ7e885eUw9PAPl4THO1JaHqI65HZseaJ48znCPHj1KMuledpw5cyYzZswYU5mqSvLll0lt3gJArHp60XWrppr88iu4csWVrFy5ksrKoT2dm6bJ6dOnefPNN9m3b19RfVUt2tp/RbxzS8l7o9EF1NXdg2FMnfAtHm48FZ+HxzgjIiytL+f1I8V7O/ubu7h6/rRx8S13IbS3t3P69GlXejQaZfHisYUFUdMk8csXyR7sc/oaLisjEAzZy3q1teSXN4Jh0HzkEIvWX0NNTQ3xeJyTJ0/S1tY2eNmqNDc309zcTE1NDbNnzySXf4vu5KGS+cvKlzKj5vaSPvc8phbeJ+jhcRFYWu9e5mtP5lzukC41uVyO/fv3u9J7JOW+MXgN13yerud+VmScAAQhVj0dc9ZM8suXguOPr7+X88rKSlauXMnVV19NfX39sMb73Llmtm37FocPvUIqlUIp3neqqFzNjJo7PON0meB9ih4eF4HKaICZVS6vWOxvmrhlvvGUlPdg5XLENz1D7vjxktdrbr4Va/ky+sclscwCrceOFuXrkahfd911zJ49u6ShVM0Db6K0kslmaGlt4eyZsySTSVSVqmnXML36vRM+Q/UYPzwD5eFxkSg1izrQnMCyJsZ7y5kzZ0pKyqurq8ckKbdSKeJPPEn+7NmS12M33kDFe26idqF72bD58EG0hEAjHA6zZMkSrrvuOhYsWEAgYIcqUc0Ar6EU1z+Xz3Gu7RxNzdPpTs7GNCfneTOPseEZKA+Pi8QVdeUYA0bzyWyBM51jjnk5ZpLJJEeOuGPZBQIBli5dOupZh5lI0PnEkxTOnXNfFKHs5puJrlsHQP3iK7CjSPSRTSWLvE0MJBgMMn/+fK6//noWLqzHH3gLJe7KJwjCWvK52Rw+fHjUEnWPyY1noDw8LhKRoI/5NVFX+kBXSBeboSTlS5cuHbWkvHD+PJ2PP455voRHcp9BxfvvILLiyt6kSFk50xrcvu+aD5eOoltc905EXmfWzCpm1Mwg2C/su2AAVyEytzdtoEQ9nb70gwGP8cMzUB4eF5FSy3yHWpPkzUsXfXsoSXlNzehc/xTOnSP+xBNYCXd5EvBT+cEPElriDv/esLjRlRZvbSYV7xz0WdlsC01NP6FQSCIilJWVMXPmTOrq6oiEy4DrECnt9LVHov7WW2+xd+/eku33mPx4MvNJSFM8zRNbz/DszrMkMgXKw37uXjWT+y6DsBTvtnYtnBEj6DeKPJznChbH2rpLepwYbwaTlMdisWEl5fnmZuJPP03Xz57HSiSQcJhAfT3BxYvxlZUV5ZVQyPZIPrO0waisqydSXkE6UTx7bDp8gEXrr3XlT6dP09LyjMsjuSCUlVWzeNGnyWajI5Kot7S00NLSwvTp05k7dy4iws6dO9m9ezfZbLYojMhwZ7M8Li1euI0JYrBwG1tOdPAnP9lFzjSJBv34DaFgKalcgaDPx9fvX8n6edUTUOML493crrZkjr1nizvmRbVlbFg9+pAPoyGXy/HOO++49mMMw2DdunVDqvZSW7fR9BdfQnN5jEgEtSzM8+fRfB58BuU330LACfluRCNUbtiAf5gDvs2HD3J02zvFdfH5WX/3RgLBvmXG0Xok7+7u5uTJk7S0tDBcfxaPxzl8+DCGYfQGYbQsi3w+j8/nY8OGDcydO7fkvV64jUvPZWWgRMQEdmHvyJrAf1bV1/td/y/YUXbrVDXupL0PeBo4hh0t91ngUeB7zm1zgbjz16aqt/Ur79PA3wCnsaMDHwW+3P+Zg1HKQDXF00Wh0Xuis/Z8Qpm8CQr/+Im11FW4JcyTlZauDF/4920gEC4R8v1yapcAPkMQEdJOu/7fD6/gN4eK1Wc+Q3joPQtLvh/jgaqya9eukqq9xYsXD6nayzc3c/KznwUEIxzGymSwurp6v4daKABK5YZ7CDTUU3nPPfinTRu2TmYhz+ZnnsQsFLt8mrdqHbMalwGQSO7n3LkXSjp99QcqaKi/d1Cnr5lMhtOnT3P27NmSar5sNtsb88rn82EYBsFgEL/fj4iQz9v1evDBB0vOpIYzUP36Hz92f/KgqnYOln+8EJGNwEEnGu/Aa3OA7wJ12K56H1bVf7jYdRovLrclvrSqrgEQkfcD/y/w3n7XPw68A9wH/Fu/9FdV9W4RiQDbgCf7lfMI8Kyq/mSQZ/5IVf+zk/dm4AkRuVlV3QF2huGJrWfImSaVkSB506IzlccaMIDI5E3++mf7uXr+1JltvHO8g45UjnDARypXWgZ8ObXLZwjTokEiAR/xdI63j3YQC/nozvblMS3lYEuCVbOrLkrdhpKUz549e8h7408/jeby+CoqXMYJQPx+NJslf/IkM/7zH+Ib4fkpnz9A7YJFNB0qPijcfPggM5c0kkjupq39pZJOX4PB6dTXb8TvL3Nd6yEcDrN48WLmzZvHmTNnOH36dK/RAXpnWD3xrSzLIpPJYBgGkUiEQCBAJpNh586d3HTTTSNq0wD69z+PAn8IfG0sBY2SjdgDa5eBAgrAf1PVrSJSDmwRkRdKGbPJyOUskqgAemVGIrIIe5bzJWxD5UJV08B2YEyhPVX118DDwENjuf/ZnWeJBu0fTyJTcBknsIPiHWyZPD7dRsLBlgSBYUKeX07tMi2lM51DVYkG/fx0VzONJcQSF+vQ7oVKyrt+9ry9rGeaWIkEpdZYjHCY3JkzIzZOPZSSnGdSCU4eeW5Ij+QNDR8e0jj1JxAI9ErUlyxZQjhsz8pbW1sxDPfnZVlW7zJoIBBg9+7do2rTILyB04+IyCIReV5EtojIqyKy1EmvE5EnRWSH83eDk/5JEXlbRLaLyLdExOekJ0Xka07eN537bwA2AH/j5F/UvxKq2qSqW53/J4B9jLF/mwguNwMVcT6k/cC/Al/pd+23gR8CrwKNIlI38GYRmQYsAV65gDpsBZaWuiAiD4nIZhHZXCrmTSJTwG8IqkphkOBthuAKKT7ZyRas/o4ESnK5tatgKtmChd8QEpk8y+rdHfmZzvH3cG6aJnv37r0gSbmVSIDfj5VKldzTkUAAo7oaLbGHOhwDJeeKYgWO0HT6hdL5L8Ajuc/nY/bs2Vx77bUsX74cy7IGNc4975dhGGSzg7qj8vf8fp2/kgNRx6DcCmxykh4GPq+q64E/Bv7FSf9H4GVVXQ2sA/aIyDLgY8CNzmzMBB5w8seAN538rwC/72wnbAK+qKprVNU9Mumr13xgLfDWYHkmG5fzEt/1wHdFZIXav7KPA/eqqiUijwMfAf7Zue8mEdmBbZz+XlWbL6AOg3bFqvow9peVWCzm+uWXh/0ULCXgMwj7ffYexgAshaB/ao0rQn4DS8E3hJG6HNuVzBYwxE95OMAMx8N5R3exYOFAc4JrFozfsubRo0cpJb6ZNWvWiCXlRnk5ms2iJc4QGcEgRmUlFArIGFwjgS05P990BsXCChzA8jeR7YZ8NkMg1LcHGY0tpHbGBzCMC+umDMOgrq6O8vJyCoUCpmm69qj6L/sNYcQLqnrVEI+KiMh27BnKPuAFESkDbgAe62ccex5wC/ApALUVIXEReRBYD7zj5I8ArU7+HPZSHsAW4PZhmt6LU4/Hgf+iqpMvONkgXG4GqhdVfUNEaoAZzmxpCfYXBiCIvYnZY6B69qAWAG+KyI9VdfvAMkXkD4Hfd17eNcij12J/OUfN3atm8qN3TlIZCVIe9mMYQq5gFTnEzJsWV8+rprZi6oQRWDt3GpuPdxAaYpnvcmhXwSwec9hLfXl+5/r5l8TD+VCS8kWLFpW4ozQVH7iTju9+r9e5aw8iglFRgYhgptNUfeT+MdWzsq6ecHmMZPYt1Nfam57oaKO6wd4fKy9fRk3NbePq9HXlypVs3bqVaDSKaZrk83ksy8Lv9/e6VMrn86xzPGCMgbSqrhGRKPBz7D2oR4DOnoHzCBDgUVX90xLX8to3pTUp0X87oohnnJffVNVvikgA2zj9QFWfGHFrJgGXrYFy1nl9QDvwX4D/rqr/b7/rx0RkXv97VPWYiPw18CeU2KdS1f8J/M9+ZQx85nux959uHkud71s3iye3niGdN4kEfJSF/H1jLSCdN4kG/Hz5niun1LmhW5bWFqkTB3K5tOt8Kle0TJk3LQwR7l5ty7GX1le4DFR7Mse5RJbaC1QvZrPZQb2UL1u2bFReymM33ED7I4+AZSH+vi7CiEYRw8DKZJBggMoNG8ZUV9U80YYmEqdai9JT8U4qa+uprr6K6ur3jLvT11WrVrFjxw7y+TyBQMD1nvRIzVetWnVBz1HVlIh8AXgKeznvmIh8RFUfE7tRq1R1B/Ai8AfA3zvLgmVO2tMi8g1VbRWRaqBcVU8M8cgEUO48+xSwpueC87xvA/tU9e8uqGETwNRaUxmenj2o7cCPgN9xps6/DTw5IO+TTvpAvgm8x1mvHQkfc555EPgz4MNjUfABNFRG+Pr9K0Ehns6RNy1UlbxpEU/nQOHr96+cUp04vHvaFfYbvUcDMs7y7K3LajnbaUeErYwGmFXlbuP+CwxkqKocOHCgpP+5hQsXjspLuaqSPXyE8ptvARTNZsE07bC4gQBmVxegNHzlqwTq60ddV9PM0Nz8FP5YGjGKDYRaFqRmXxTjBHZojw2OUc1kMpimaX9eptkbtXfDhg3jclhXVbcBO7EHug8An3W2EfYA9zjZ/gi4WUR2YS/ZLXfUdV8CfiEiO4EXgIZhHvdD4Isism2gSAK4EXgQuKWnbxSRwVZ/Jh2X1TmoqcRgB3XBPg/15NYzPLuziUQmT3k4wN2rGrj3MvC4cLm3q6Urg4jtKHZZQzlloQBBv8Hv3jifaNDPjlOd/Gp/8cyhLOTns7+1AGM4JckgnD59mkOH3MH7qqurWbVq1ag6+9zJk8Sftvf2zWSS7IEDZA8dAsPAP306FXd9gMoNG8ZknAqFbpqbnyKXsz0/dDafJdHR5wXCl19CJLiMdR/4EFJCbTdexOPxMXmS8A7qXno8AzVBDGWgPKYunakcj75+wnVEYO3cKt7XWEs6Z/LwK0dd1z+8bjZzp7sdyw5HMplky5YtLtVeIBDg6quvHpUjWFWl80c/dnko91VVMe0TH0fGEMywh3w+TnPzk+TzfR7J87lsr8NYX24ZhmkbvcYb3sP0WaMP/3Gx8QzUpedyW+Lz8JhQqqJBVs52n3naeTpOPJ0f3MN58+iFVeMhKe9P9tChkuEzYtdde0HGKZdr52zTY0XGCSAQDBEpr8SXXdFrnGBkXs493h14BsrDY5y5dsF0AgO056alvOEIJJY1uA3Y4TF4OB8PSXkPapqk3nzTle6vrSU4jFPZochkmznb9BPMgruehhFg3oJPYFjFPvzirc10d5YI5eHxrsMzUB4e40ws5GfdPLdvuv3NXZxLZFlQE3Od+erxcD5SxktS3kNmzx7MuHsWF7vh+jELFtLpUzQ3PYFlZlzXDF+Y+ob7qJ2zjkiFe9+n+Yg3i/LwDJSHx0Vh/bxpRIIDVGoKrx9pI+AzWFzrdtsz0kCGQ0nKly9fPipJOYDmcqTeeceVHpw7h+AYQsEDdHcfobn5aSzL7SnD548xs+HDhEP1iAgNi65w5Tl34jj53KAeHTzeJXgGysPjIhDy+0p6iDh6rpvT51MsK+Gb73hbivQgznR7GE5SXlY2Mn91/Ult346VcnuNiF5//ajLAkgk9tHS+lzJcBn+QAUzGz5CsF+4jBnzF+ALBIvyWWaB1qODeu3xeJfgGSgPj4vEqlmVVEQCrvTfHGpjVlXYPojdD0uVQ61Dn4m6EC/lpbBSKdJbt7nSQ0uWEKitHXV58fh2zp37RclwGcFgDTMbPkIgULyk5/MHqJu/0JW/+chB+2yUx7sWz0B5eFwk/D6D6xdOd6U3xTMca09xRQkHskN5OB/MS3kwGByRl/JSpLZssYMQ9scQYte5o9wOhapy/vxbtLe/XPJ6KFw/pEfyuhJezrOpbjrOnhlVPTwuLzwD5eFxEVlaX05NuVvu/fqRNhpL7EOd6UwTT7n3bYaSlDc2No5aUg5gdnWR3rXLlR5ZsQJfVdWIy1FVOjpe4fx5twoQIBKZS0P9vfh8g7tzipSVM22mOwpE0+EDI66Hx+WHZ6A8PC4ihiHcuMg9i2pP5jiXzDK9LOi6tr/EmajxlJT30P3WWzBA2i6BANGrhnLYXYyqRVvbC8Tj20tej8UWUVf3IQzD3c6BNCxudKV1nWvxJOfvYjwD5eFxkVlQE2PWNLcrp7eOdZRU8+1vThTFYRpvSTlAoa2N7AG3lDuyZg1GbGTOEiyrQGvrcyQSpV1Plpcvp7b2rhGHy6isrfMk5x5FeAbKw+MiIyL81mL3LCeRKZAvEaSxo9v2cA7jLynvofuNN1zRa41ImMi6tSO637JytLRsoru7tNKusnLtqMNlDCk5z7rPUnlc/ngGysPjEjCzKsKiErOlvU0JakvsUe1zZlHjLSkHyJ85Q+64O3pD9KqrMILDL8WZZoam5idJp0+VvD5t2vVUV980JtHGoJLzY0dHXZbH1MczUB4el4gbF01nYJ+dyZuYltth88HmBKdOnx5XSTnYgobk66+70n0V5YRXrBj2/kIhSVPTT8hmSgednl7zPqZNu2bM3ic8yblHfy7bgIVTmXxzM/Gnn6brZ89jJRIY5eVUfOBOKu+5Z0xhDiYL7/Z2TS8Lsbyhgj1ni0UQbckspir+fiEmOhIp3tp9jpoBwrcLkZQD5I4epdDc4kqPXnttUXBCgEymiabmp2ht/SmFQgKfL0o4PItoZD5+/wCJvBjMmHE75WVLx1Sv/tQvbuTsoQPQL5J0j+R8+uxx8HIePwM7fwi7n4RsHEKVsOJeWPXbUOlWEnpMHF64jQlisHAbqa3baPqLL6G5PEYkAn4/FApY6TQSDNDwla8SHeE+wWTCa5dNVybPo68dpzBg1pTKFYgGbQOhanH2bBMN4QLr6oqNxsqVK8es2lPL4vy//wfm+WJVnL9mOlUf+1hRDKbO+Bb27fszLCuHzxdBFQqFDkwzi4iPGTNuJRyaCYCIj9rau4jF3DOfsbLvtZc5f7ZYGFIxo44V77vtwgo++RZs+jyYOQhEwAiAlYd8GnxB2PBPMLf0GTAv3MalZ1IYKBExgV3YM7p92JFwUxNbq2JE5Dh2aGWwQ8k/AXxVVTNi7wT/PWCHIYUM8FFVPTZYeaUMVL65mZOf/SwgGGF76Nz/87EyGUCZ+/DDBOrqxqdhl4B8SwsnH3qI/u3qz+XWrp7ZTW+7vv3topnUq4fOsfl4sZHo6M4RCfqIBHy0t7eTSHThN4Q75/vxO4EMZ82axRVXuEUEIyW9Zw/JX/3alV75obsJzp/f+zqTaWLb9k8D4POFsaw8+XwnYC+xWVYBgIb6jQSD06mr+xCRyNiWHAejs6WZva+86EpffftdxKrcjnhHRPwMfO9e+/+BEgEy8467pwefLDmTGs5A9evHetioqscHyftp4CpV/c8i8t+BpKr+7Uia8W5isizxpVV1DYCI/AD4P4C/m9AaleZmVW0TkTLgYeBbwO8AHwNmAqtU1RKR2cCooxHGn34azeXxVVSgpmmH187n6T+E0GyWpi/9BdH168ejPZeE1JbNmG3tSCiElSjtKeFyaZcAEgxilJdjhMOYXV3EN22i5qGHeu+7en41u87Eyeb79lQqIwFOdHRTHzVIJOwlwIKlNHcrs8vlgiTlAJrPk3rb7RA2MHMmgXnzitKamp/CsnIEAhVYVs4xTn3fQsPwY5pZulNHmDfv9wmFxn9Q0SM5T3cVx5BqOnyQxVeNzstFLzt/aM+cwpX2v5k4WCb4wxCusI1WphN2/ghu+q9jeUJvP+YxPkxGkcSrwGIRiYnId0TkbRHZJiL3AIhIVER+LCJ7ReRJEXlLRK5yrv0vEdksIntE5Ms9BYrIXzv5d4rI3zppj4jI/f3yJJ1/G0TkFRHZLiK7ReSmgRVU1SS2Ed0oItVAA9CkajsgU9XTqjrq04VdP3veXiYCrEQCHWCcAMTvt0NwTyGyhw679jcGcrm0SwErl8M8fx4tFDAiEbqe+1lRnnDAx9Xzix3J+gzBMk3OthSHgz+dtC5YUg6Q3rULK5l0pcduvMG1n9Xa+lNnWU8pFLrA9S0Eny9ENtt0UYwTOJLzEgd3204cG7vkfPeTthFShXQnWAVAoZCGnDOeDERh9xNjrvdAROS4iNQ4/79KRF4at8LfBUwqAyUifuAD2NPkPwd+parXADcDfyMiMeA/AedVdTnwF0D/Ifefq+pVwCrgvSKySkSmA/cCV6rqKuCrw1TjE8DPnZHQamB7qUyq2gUcA5YAPwY+5Bi1/09ESm6miMhDjgHdXCgUXNetRAL8flTV7R+tB8NAS8iOJzOay4ExzFftMmuXWhZmZyeqWnLWuGZOVZGzWFWlKt/O+WRxiImWbmXW3AVjlpSDvdSY2rzFlR5atLCkOKVQSCDix7IyJT2Si/gJBKZjmm4P6OPJjHnz3ZJzyxy75Dwbt/eczBwMbJfp/N6MAGQHDXvi7/n9On8PDbgecfqA7SLy5Ngq6dGfyWKgIiKyHdgMnAS+DdwB/N9O+ktAGJgL/BbwQwBV3Q3s7FfOR0VkK7ANuBJYDsSx94S+LSL3AcPtbb0D/K6zLrxSVYdyLy1OPU4DjcCfYi/Uvygitw7MrKoPq+pVqnqVv8SMwigvh0IBEUECbi/YAFgWMoKzKpMJCQZhOInwZdgutSzM8+eREvtuAZ/Bdf0cyZrJdmJmEssyyfRb+gtHIiR9bqeyoyG9dSuaHRBbSYToddeVzO/3l2NZ+ZIGyDZOVYC6lXzjzLhLzkOVtiDCLDEQ6hloWHkIuUOhOBR6fr/O38MDrqdVdY3zd+/oK+gxkMlioPp/sJ9X1Rx25//hfulzVbW0TxVARBYAfwzc6syUfgqEVbUAXAP8BLgbeN65pYDTfkfkEARQ1VeA9wBngEdE5FODPK8cmA8cdO7LqurPVPWLwP8ANo72Taj4wJ1YabtTMCoqMEIh21j1+1PTJLS0EQn4p8xfqLERNU1XWy67dpX4TDWXw19XR+74cde1K2dWUB0LYuXSFDqbEIHqoEU8bY/mfT4fNTXTOdAydAiOoTCTSdI7drjSw8uX4a92x6sCqK39IKaZRNU9i/f7KxDxYZpp6mo/OOZ6jZT6xY2Mm5fzFffaQohSBsrnDI7yKVhx3+jLHpzefgZ7kO0xCiaLSKIUPwc+LyKfV1UVkbWqug14Dfgo8GsRWQ6sdPJXYAsT4iJSh71U+JIjaIiq6nMi8hrQsz5wHHt58MfABiAAICLzgNOq+r9FJASsA77bv2JOmf8CPKWq50VkHdCsqmcdY7eK4pndiKi85x7imzZhZTIY4TC+ymK/ZFYmg1FexsyvfW1KnRuq3LjRpU7sz+XSLi0UMONx1LSXj7RQAJ9BaMkS4j/9KeW33U64sU+FZxjCdfOrePLw7l63Q9NDFi0Zi1TOZMGcOnw+P2c7M8RTeSqjg8yqhyD19jtooXg5S/w+otdcM+g9DfUbOX36e1hWociPnkgAwwhgmhkMI0h9/cZR12e0hMvKmDZzlkty3nT4wOjPRK36bdj+Q8gmwBiwn+cLOlLzEKz62AXWuojj2P3Mz4APj2fB7wYmywyqFF/BNho7RWSP8xpswzBDRPZi7yftAeKqugN7aW8/8O/YhgygHHhWRHYCvwF65Dn/G3ufagdwPX2qu/cBO0RkG7Y67x/61enXIrIbeBt7KfJzTnot8IxzbSf2qOmfR9vgQH09DV/5KqCYXV22SMLZjzK77M3qhq98dUp14vDuaReqGJWV9owwmwWU8ptvwVdWBpaSeOEF0rt2F5UhyVZiRt9+ZMQHYZ/SbQUIh/uk0KU8nA9H4fx5Mnv3utLDq1bZdRoEv7+M6dPfA4BpZlG1UAXDCJLP2/VYtux/EA43jLpOY2HcvJxXzoJb/9IeDBQytoJPsV9nnVnqhn8a78O6Xwb+QUQ2A0OHS/ZwMSnOQY0GEfEBAef80SLgl0Cjsyw4ZRjsoC44ngk2baLruZ/1eSa46wNUbtgw5Trx/rxb2iWxGMG5cwnMmlXSEMSuv47I+vW0t7eza9cuutJ59jb1GaDmXJgmqWZBTYz6SttIVceCfOr6eaPyINH13HNkjxQLCiQUovpTD5acyfbQ0fEanZ2bKRQSJJP7SXYfQjVPKNRAfd3d1NdvvGTGCWwByfZf/NQlOa9dsHj0kvMTb8C+TdCyB1r3QSFrS8yv+ow9cxrCOHkHdS89U9FAlQO/xp5dCfAnqvqzoe+afAxloDymPlYuR9dPnyNfIkwGgG/VSvb6fOQdteb+5i46U3kQA52+kB3NaQI+Yc2cKnzOBv4D186ltmJk2xj55mY6H/uJKz12w/VDnjWzrDwnT30HyyyWck+bdj3Tpg2+LHixaT5yiKNb3y5KMwwf6+/eSCA0iq2dnY9B++HitMW3wpzh2+YZqEvPZF7iK4mqJhwFzWpVXTUVjZPH5Y8RDNoeGhYscF1TVfa+8w7dBw/27j3NqY4C4K+qJxKNUh72kzeVpnifodjXPDKxhKrS/fob7jrFYkRWrRry3mT3AZdxEjGoqBjekezFZDDJecux0uE+SqIK8RIe2CvHwb+fx0VhyhkoD4+pgvj9VNz1AUJLi/dQWgoF4qZJ4dw5skeOgCqxoJ85DbX4ymzpeU2ZHYKjKZ4h70S9PdDchVXC8/lA8idOkD/jVrlFr71m8OML2IatK+5W/JWVNeLzRYd97sXE5w9Qt8DtSaPlyKGRS867z9lLekUFB6Bs6rjXerfhGSgPj4uIGAblt91GZLU9c+m2LE71O4RtdnSQPXiQgM/HnTesx++zf5LTY0FEwLSUM5320YPurMmp80Mf41NVut9805XumzaN8LJlQ96byZwhl2tzpVdUrB66kZeI+kVXUFpyXnoZ1UVnqdnT7OEPkXtMGN4n4+FxkRERYjfdRGj9eo7msugA10FmPM6sU6eojvhZOds+WuD3GVRF7CWtlq4M2bwtANs/zDJf9uBBCufcRiZ2/XVF3spL0dVV4rxUuOGiuTMaLeGyMqpnukUMTYdHGBK+5PLe+Dq59RhfPAPl4XEJEBGaa6Zjlgg0WOcPUNZxnviTT3J1XZig3/5Z1pTZBkqV3pnT4dZk75LfQLRQIFVi9uSvryO4cOhQGIVCgu6Uez9nssyeeqgfq+Tc23+akngGysPjEtDW1saZM2fw19c7xsJeqooaBnOcfaHCuTZym55mfbV9OHZaNIjPCbXRlszRnSuQK1gcPVda/ZnZswezyz3Dil3vdgg7kK6unb2CjR58/hix2OJRtfNi0+PlfCDDzqIynZAd4CzX8EHFzPGrnMe44xkoD4+LTDabZf/+/b2v/TU1hJYsxjAMFgZDGP2MhxmPs2Dzr6jMJjEMoTrWp1w71WHPokod2rVyOVKbN7vSg/PnEZw99MFTy8rTldjtSq8oX4V97HDyMGYv56X2n8rrbZGEx6TFM1AeHhcRVWX//v2955168E2bxrJbbyUWCrnuMVIprtr/GqF4e6+aD6Azlacrned4W4p0rtgpQXrbdqzUAOeuIsQGcQjbn9LSct+ES8sHY0yS83gJIYW3/zTp8QyUh8dF5PTp03R0dLjSp0+fzry1a6nceA8SdhupuiAs3Poydd3tvXtSACc7UlhqcbCfA1mru5v0tm2uMkJXLME/Y8aQ9RtcWn7FhEvLB2NMkvOS+09zx7lmHuONZ6A8PC4SiUSCo0fdsYuCwSBLly5FRAjU11N1330YsWIHBYYI88oDzNz8CjPzfcYomS3Q0Z0rWuZLbd7sjh/mM4hdO7wboMkuLR+MUUnOs0lIDRgkiIy3zz2Pi4BnoDw8LgKmabJv3z6sEiP6pUuXEuwX+8o/fTpVH74PX2VxHKLpZUHK/LDy4NsEuzp70091pDlzPk08lceMx0nv2eN6RmTFCpc3/FJ0dW13pYXDMyeNtHwwBpWcHzrgzlxqeS9WY0fX9ZjUeAbKw+MicOTIEUr5Wpw9ezbTp093pfsqK6m878P4pvfFaBKEudVRKjRPffNxQp32LCCdNzmXyLK/uYvuN9+CAbJzCQSIXnXVsHXM57voTrlneJN99tRDw5ISkvO2VrfkvOT+kycvnwp4BsrDY5zpkZQPpKysjIVDnEfylcWouvde/HW1vWmV0QCVYT+zNEXs3FnCHedAldPn0xzce5zMQfeMIbJ2LUZ0+P2jRGKXS1ru95cRi7n3dyYjFTPqiFZUudJds6j4SffNnoGaEngGysNjHBkoKe/BMAyWLVuGzze0bNuIRKjauJHAHFthJghzp8doUFuhF21vIdLWQq5g0v3GG3Rni9V8RjRCZO2aYes5mLS8vHzlpJOWD4aIlJxFtZ083ic5L2Qh2eq+2VPwTQkmc0Tddy3xeJydO3eye/dustksoVCIFStWsGrVKipHsK8wWbnc27Vr1y6SySQiQm1tLXV1dYQcGfmiRYsoGyJIYH8kGKTy7rtJ/OIXZI8cpSzkZ3bMR3U6S4eEiHS2Ma2rDVpP0eqbRtmMvnKjV1+NEQwOUbrNSKTlzd3NPHvkWZ4//jzJfJKyQBl3zr+TuxfdTX1scsTvqpk7j+M7t2Hm+8LB9UjOZy+90l7eGzBLjPvLeLQ5xdOtZ+kyLSp8BvfUVnF/fTUzw8O/dx6XjikVD0pETGAXtmE9Bjyoqp2X4LkbgYOq6gpPKiJzsEPC12HH53xYVf9hYL6BDBYP6uTJk2zatAnTNAkEAhiGgWVZ5PN5fD4fGzZsYO7cqSePfTe0CyCXy6GqWJaFiNDY2MjChQtZuXLlqIINAqhlkfzVr8js2086b/Ls6Ry7jCpQ5cqTu4hYeaK1Ndy6rA4RwVdZwbQHHkCGmaWpKmfO/LtLvVdevowZM+4AYHvrdv7q9b8ib+YJ+8P4DT8Fq0CmkCHgC/DlG77Mmto1o2rPxeL4jq2cPbivKC0YibL+rnuQ46/Cidd7089kc/zPbC0v1r6XiM8gIJBXSJsWQUP4u6VzubqydMin4eJBTcb+ybn+fwK/h90/7QJ+V1WHONU8eZhqBiqpqmXO/x/F/lC+dgme+wjwrKq6IsCJSAPQoKpbnWCKW4CNg31ZeihloOLxON/73vcA8Pv9ZLNZTNOk5zPqUYRdddVVhIeIiDrZyGQybHa8HBglHJZeLu0a+FsyTRMR4TOf+Qw1NTVjeoaq0v2b35DevoP9rd38MD2NqkQHS87a+yxWIMi16xZRWxml/I47CDdeMWyZ6fRpmpoed6XPmvVxQqFamrub+dwLn0NV8Rk+0oU0ppoEjSCxQIysaYes+Nbt35oUM6lMd5Ktz22CAU54G6+/iennXun1IpEomDzW0sFrtTdxbNoykgULU5WQIcR8BhknlMmPVi8qOZMagYGajP3TLOA3wHJVTYvIj4HnVPWRi12v8WAqL/G9AawCcEK//09gBpACfl9V94tIHfBNoGdn+g9U9XUR+STwBSAIvAX8J1U1RSQJ/ANwN5AG7gEWARuA94rIl4APq2rvkXVVbQKanP8nRGQfMAsY0kCVYufOnZimSTgcJp1OUygUiq6LCIVCgZMnT06p2cbJkycxTRO/3+/qxOHybZfP58MwDPbt28dNN900pmeICLHf+i0kFGLBa29QezpNbVvfpr+Rz3F092EaPngToSuWjKjMwaXltjjjmcPP0J3rRgyhkO/7DhasAhYWFcEKurJdPHvkWX5v1e+NqV3jSThmS84HnoFqOrCP6f6m3td7k2lMhc7YTDrzJgXnM8ubigAxv4943uQnLR18Yd4FG95J0T85+IGIiOSBKHD2Qht3qZiSIgmxd3FvBTY5SQ8Dn1fV9cAfA//ipP8j8LKqrgbWAXtEZBnwMeBGVV0DmMADTv4Y8KaT/xXsL9LrznO+qKprSnz4/es1H1iL/aUaNbt37yYQCKCqvUtGAzEMg9bWEpu+k5jW1taSM6f+XI7tCgQChMNhdu92ixFGg4gQu+Yapt/yPq7JnSOSK3Zp1NGd4/y5DjQ1dKwoGFpanrfy7G7bzQ8P/JC8ladgFVz5MoUMmUKGiD/C88efH3ujxpmSkvOzR+lO9r0n+7szFAIRWn3lvcaph6zzOuIzeLq184LqMpn6J1U9A/wtcBJ7IB1X1V9cUAMvIVNtBhURke3YM5R9wAsiUgbcADzWb42/x3fMLcCnAFTVBOIi8iCwHnjHyR8BenrGHPCs8/8twO0jrZhTj8eB/6Kqbm+edp6HgIeAooOaPWSzWcLhMCKCYRgljVTPbGMqUSgU8PuH/qpdbu0yDKNXIJHJjM9yf3j5clbUx2g7AWa//rUzWsXbbQWmPf4ElfdsGPKAblfC7bXclDD7kp3sPv590oU06UKakM/tfqmHZD5JVbCKZD45aJ5LTY/kPNXvQDOZLppaEixeYJ87y6rSFqknVSIqsd/pOwICnYVBI/T6RaS/R96HVfXhfq8nXf8kItOwZ1oLgE6nHp9U1e8Pd+9kYKoZqLSqrhGRKPBz4A+BR4BOZ7QxEgR4VFX/tMS1vPat1ZiUeH8cUcQzzstvquo3RSSAbZx+oKpPDPZg58v8MNh7UAOvh0IhLMvC5/P1LvMN9ESgqsN29pONniWwoUQCl1O7ej4/EcE0zV5DdaGkd+4kXFFOzaxams+cQ5yv6qmauXRkA1zb0oY+/gSVG+/BX13tut+y8iQSfV4nUoUcBxKtnDGj+IN9g6GgL4ilFoaUnh1aatGZ7aQqVDUu7RoPeiTnR7b0W7zIxmnLpJg3u4pAwEdIhCPh2eRLLMeGnZlwXqHCP+isuKCqQ52AnnT9E9AOHFPVc871J7ANpmegLhaqmhKRLwBPYU+Xj4nIR1T1MbF7i1WqugN4EfgD4O+daXeZk/a0iHxDVVtFpBooV9UTQzwyAZQ7zz4FrOm54Dzv28A+Vf27C2nXihUr2Lp1a+/eRSwWcxmoTCbD6tWruW4EXqonC6rKjh07hhRAXE7t6r/sl8/nWbdu3QU/y8pkSG3ZCsCi+XWcSeTxdXVyvqya7kg5KZRD7UnWhpN0Pv44lRs2EKgrdleUTO7HMjN05TPs62rheHcHlkJl5dqifIurFrOvfR9Bw57lG2JgafH3MF1I857Z77ngdo0nNXPncWLXdgq5rD1LzHRhWUpLW5LZDZUsjYX5VrDWdV9AhIAzxkibFg/MdBv30TDJ+qdrgesco5nGXnp0x2WZpExJAwWgqttEZCfwcew12v/lbBIGgB8CO4A/Ah4Wkc9ijzj+QFXfcPL9QkQMII890hnqC/BD4H87X7r7B6zz3gg8COxypvcAf6aqz422TatWrWLHjh3k83kCThC7gZ1dIBBg/fr1RCJTx4/Y+vXr2bt3b6/EfCCXc7t8Ph+rVq264GelNm9Bs7Z6rioaYNq0clrF4ETNAgAU4WAuwIJUnmkI8SefouLuDxJ0IviqKkdaf8PO9iOcScV7w84HgzMwjOK6XzHtCg6cP4AgVIYq8Rt+zmfOY6o9yypYBXyGj2ggSnN386RQ8oHt5bx2wSLOHtgLuRRYdn2bW5PMqq9gTuU04v5y1FL8Rt+sN+ozEJFeqfn9dRdmoGDy9E+q+paI/ATYChSAbTirOFOBKSUzv5zwzkF57RopZiLB+e9/Hy30LcOdaO/mZ/kq9tZdQcXZ4xiFAtM0x63+86ycVYmIIH4fZXfcQeuMAG+dfpGDLb92lV1evhK/v085LSIsqVpCwAjwd1v+jpyZI+KPYKnF+ex58mYen+Hj5jk3Ux+rpyJYwUcbP0rQNzkOuPZKzrvOQnvfOLJxUQ375l3N47E1/Lwtjqm2kQqIUOn3kbH0gs9BeYw/noGaIAYzUHD5e1zw2jU6Ei++SGZv8UHUlAWP1F/L3s4CRj5H+Znj+PI53mu2sGpGhOnlQY4bHez2t9C9qJ5MuIN8vjjkhN9fTnn5lfb/DT/LqpexunY1FUHbq/pATxKCMKtsFldMu4KyYJ/3isZpjdw679YLbud4sf+1l+nY/kvo7juIHCsP85vrP06qcg6JgsneZJoD3RlAqA35bU8SdUN7kvAM1KXHM1ATxFAGysOjh0JHB+f//T9cyrvoVet5XBt45dA5EpkCUshTfuYEjZlmqsrOkJ+ZImnY7n9UCmTr0xhlxX1rLLaE8shMVtasZEXNCqKBoR3MmpbJk4efpDXlPg5w+7zbWTJtZOewLjbxlib2/OCvodDn/uhYpJrjv/URglV9+3IGwufmzKDMPzLfg56BuvRMyXNQHh7vFrrfeMNlnCQcIrJuHctmVjCn2jYqpl9pnpvnN7NOsCt2luZc3+An709SiHdidiV6fS2UBct53/y7+dTyT3FNwzXDGicAn+Hj9nm3EzDc+20vn36ZrlzJ0xWXnIqyINFA33umwKGyOpLJXFG+xlh4xMbJY2LwDJSHxyQl39RE7ugxV3p0/VUYoRCN9eWEg3mI7Kfd90uSgUPEK8tIRsrpyhSwVAGLvN82Vmaii7Jkmmunz+OB5Z9iTe1aAj63sRmKylAlN812e8XImTlePPGiS+03EUjXGRrqyntftwdjdESmkUp0YfU7a7euYnKGtPfowzNQHh6TEFWl+/U3XOlGWRmRVSvpyHSwufVVTps/R8InUXE6XhHOVTWQiVWQzBTI+1OoWEyXIDcYVdySDDC7NUlV2ZVjrlvjtEYWVbljRjV1N7GlZcuYyx034qeoqY7id84zHYzVgj+EqkXyvL0P1xAK0hAanXH2uPR4BsrDYxKSO36c/Fm3y7T0qkU8f+oFfrj/h+zv2E91WYCAzyAa6DsxkipYJOtm0x4qY7o/z/t81dzsq2amYR8e9p+G1AuvoGP03CEivHf2eykLuMOHbG7ZTHN385jKHTfip/H5DGprykgbAU5HpoHfPiydPN+BqrKuIjpq7/Ielx7PQHl4TDLUsuy9p57XKJ3ZTnabJ9kkOzkW71v2mx4LYghURvz0dLeWJYR0DvPn3U1j1WxqpFiZFk3XkT1ylPizz6K54n2ZkRL2h7lt3m0IxZ28qvLLE78kZ46t3Asmm4SUPUuqry3jcNkMLBFwXDeZhTySTHBFdOp4zX834xkoD49JRvbgQcz2DhSlLd3G3rY9HDp/iBNLKsAoNgh+n0FVNIjPMCgPhYlYC6g2byGUW0NNoImm6HS0vqE3fzBfQaBgz3zyp07T+fTTWGP0FTizbCbr6txeMrpyXbx6+tUxlXnBxPs8mvtDAZqmzwQjAP0OvNe3ny06qOsxefEMlIfHJEILBRJvvk5rqoVd5+zZUqqQJju9jNTMaSXvmVVZTkPoSq6bvpEqVuEjSmcqic86iwJN4SqC8+cB9uypP4XmFjqfeAIzObYjD1fVXUVdtM6VfuD8AQ6ePzimMi+I+Km+OlCGvzwK/r7ZkqEws725dy/KY3LjGSgPj0lCppBh68uPse3Ia5zoOkm23zJZx8o5MGDPpCxQxo2zbuTz6z/L/LJVhP0RZlbanbFhtdGespe12pM5spXTiS5eQSg/3fVcs72D+BOPY8bjo66zz/Bx27zbSkrPXzn9yqWXnjsGSoGtVBEK+glE+xR9c8wMEbVoPjwBxtNj1HgGysNjgknkErx25jW+v/3faHrtl644TKmGKrIzKnpfV4eruXXurTyw7AFWz1hNJBhiSa3dCddXRgj4FD8dNCf6ZNSnOlJUL76NyrvuRkqc/THjXXQ+/gSF9vZR13/SSM8LWUjah4jPEqaZEAiU187szbKkYMfSajt5nPw4hUHxuHh4BsrDY4LoyHTw4skX+f6+77Pj3A6i+09iZAco6wTOr5gDQEOsgbsW3MXHGj9GY3UjPqPP0Cyttw2UzxBmlqcRTNrTYXKm/RPvTFvE8wsJLVhAxYc2ICXikVnd3XQ+8QT55tGr8BqnNbK4arEr/ZJKz+Onew81b6XKTvOHiVbPwPD5mW4WqHGMv2WZtBw7fGnq5TFmPAPl4XGJae5u5rmjz/HD/T/kQMcBVBVfJkflIbdhSM6tYdbc5dy7+F7uXXIv8yvnl5RHz54WoTzsB5So/zx+n6AKLUnbO3xG5/P60W5UleDsWVRu3IgRcSvZNJMl/tTT5E6dcl0bChHhvXMmWHruLO8l8HMQpx6hSsQwiFVNY0mhOOJw85FDWFbpyNUekwPPQHl4XAJUlRNdJ3jy0JM8cegJjncdL7petfcM0i+SqwhMj83g5o2f566Fd9FQ1sBQiAiN9eUUCl1YZpqKsL0n1Jy0l/lSNNLSleFQqx0FN1BXS+V992GUuQ2K5vPEn3mG7JEjrmtDEfKFJlZ67ij4dlBB7zsZtpdG62pqmDPg+bl0io4zp/GYvHgGysPjImJaJgc6DvDjAz/mp0d/SlN3kyuPP5mh/Ng5wA4OWBerY2XNKq68aSMzaueP+FlL6yvIZFsAiAZ9BP0G8UyQznw9BewYR68dbsN0Qp77q6up+vB9pUPEmxZdP3uezL597mtDMJT0/JXTr4yqrFFhFqCriQLCDvq1xzFQ66dXMWPWbNdtTYcPXLw6eVwwUzZg4eXMwDAHZYEy7px/J3cvunvSBIcbC++mdt0+73aumHYFp5OnSeQSQ94/bfdp/PioK6tlRrSWgBFAgkGiV60fVT2qwhnK/G2cz/kAoSISoC2R5WRiEXWOQr0zlWfP2TirZlcB4KuooOrD9xF/5hkK59qKyjMTCVq+/nXyTc1QKGCUl1PxgTupvOceAvWDf15X1V3F6cRpWlItRekHzx9kbsVcrph2xajaNSISTWAVOEA5KZy9OSMA/gg+EVaXRzEXN9Jx5hTZVIpzJ4/RfuokhXyezc88yfL33Mzym26hfHrN+NfNY8x44TYmiMHCbWxv3c5fvf5X5M08YX8Yv+GnYBXIFDIEfAG+fMOXWVO75tJX+AJ5t7TLEINkPkkyl0REegP7Dca0hLLy9WZqIjX4pE/0ELvuWqJXXz2qurS3v8prB/ay+XSfeu9c0iLFUlbPrurdu4qFfHz6hgUE/X0LKFY2S9ezz5I/a8/w8k1NJH79KzAtxO/HKC9HgkE0k0GCARq+8lWi64pDxfcnno3z4wM/Jm/li9KDviAfueIjVIbGOf7XidfRoy/zfebY6j2AaA3ULmVZLMLdtVWoKi9//zvseflF1DIxfH7EMPD5/Rg+Hz5/gDs+9wVmNS4r+Qgv3MalZ8INlIj8OfAJ7JDHFvA5VX3rEtdhI3BQVfc6r/8f4BVV/eUI7/8b4ENADjgC/K6qdg51TykD1dzdzOde+Bxgr+enC2nyVr43PHfetH/sn1nxmfH/gV9E4tk439n9HYCS3rMvp3blzb7Pq0cu/qGFHyoK8Ae2VHxt7VpqX9lL4dSZomtGNEr1g58sqbQbDMvKcfLkd+jK5Hl8VyWqzj6Qv4ED52KsmFlJWbhvweSGRdO5dmHxmSjN5eh6/nnSe/YS3/Q0IIi/7x4jEsEoK3NCzytzv/3tIWdSBzoO8OLJF13p9bF6Ni7eiCHjuMOw88ecaT/Dv9NvGW/aQqicyQMN05kZDpJob+OHf/Un5DJpfL5+UnsRYpVVFPL29/D+P/9KyZnUcAZKRExgF/bK1DHgweH6gfFgYP9V4vp3gLuBVlVdcbHrM55M6BKfiFyP/catU9WsiNQAExE7eiPwLLAXQFX/cpT3vwD8qaoWROTrwJ8CfzLaSjx75FnyZp7yUDmJXIK0c2ajP9lClpdOvVRynX+ysrVlK9lClqA/OOhG+eXYLr/hJ1vIcvD8wd52NcQaWFu7lnkV88ifPk18gHECiF5z9aiME0AyeQDLylIWhLqyAs2JACJCZVkD01MZ2pLZIgO1+cR5Vs2uIhLs66glGKTigx8k+fLL9swpFCp6hpVOI4aBEYthdnUR37SJmoceGrROV0y7ghNdJzjcWSznbu5uZkvLFq6uH90McVAsC+Kn2Orss/USrqAhFOyNkrv31V+BgD8QQK1+Z7NUyWezBCMRMt1J9r76a67d+JGx1CStqmsARORR4A+Br42loFGykX79VwkeAf4Z+O4lqMu4MtEiiQagTVWzAKrapqpnRWS9iLwsIltE5Oci0gAgIi+JyDdEZLOI7BORq0XkCRE5JCJf7SlURJ5y7t0jIg/1S0+KyNdEZIeIvCkidSJyA7AB+BsR2S4ii0TkERG537nnahF53bnnbREpZwCq+gtV7TnA8ibg3o0dAc8ff56wP4yqkjWzJfMEjIDrBz/ZOdx5GL8x9Fjocm/X/Ir53Lfkvl6pOFAynIavspLw8uWjqoeq0tW1o/f1wum2sQwGpyMSYE51lI7uHP1XS3IFi7ePu939iM9H5vCRkuo+ACuVQk0TIxKh67mfDVmvHul5edD1k2Fz8zhKz7vPkShYfdJyAPFBMFYU8+nA668SDEfwB0OuIkzHs3sgFObA6+PiR/ANYBaA06c87/RJr4rIUie9TkSedPqWHU5fhIh80ulrtovIt0Tstd+R9l8DK6KqrwBT0rfTRBuoXwBzROSgiPyLiLxXRALAPwH3q+p64DsUj0JyqnoV8E3gaexRygrg0yLSs2bxGefeq4Av9EuPAW+q6mrgFeD3VfV1YBPwRVVdo6q92loRCQI/Av7Iuec2wD2tKeYzQMlfrog85BjXzYUSoQ6S+SR+w4+IDLr8YYgxcZ6ix0jOzA27nHM5tksQooEoASPAXQvvKtqLyh0+TKHVHTo9dv11iG90UV4zmdPkcn0eIOZX5fAZSihkPy8c8DEtFiCeLt4P2nGq05UGoMkkRmUlRtQd0E9VsVIp8PuxEkOLP8CRns8tIT1HeeHEC4MOxEZF/FSxtBwgVEHM76cx1nfWK5tKYfh8BEIhl9socZzHGj4f2dSgfgn9Pb9f56/k9NExKLdi9ysADwOfd/qkPwb+xUn/R+Blp29ZB+wRkWXAx4AbndmYCTzg5B9V/3U5MKFLfKqaFJH1wE3AzdjG4KvYBucFZ1PXB/TX5vZ86LuAParaBCAiR4E5QDu2UbrXyTcHWOKk57CnwgBbgNuHqWIj0KSq7zj1HdKxmLOfVgB+MEh7H8b+shKLxVybf2WBMgpWgYAvQFmgjHg23ruf0YOlFkHfRKyCjp2gL4il1pCd+eXULkGI+CNE/JGS19U06X7jTVd5/tpagovd3hiGI961vbhefmVBTTnt+b7tktnTopw+n6Iq2vcem5by5tF23n9l8T6SUV4OpmnPolSx0sVjMk2n0UDAzjcCGsoaWF+3ns0tm4vSE7kEr55+ldvm3Taicgaj0HmqWFoOEK5gdbmt4OshFI1imSY+v59QNEo2lQJVDJ+PYNg+0GyZJqHooNtMBWdwPBgREdmOPXPah92HlQE3AI/1O2DdM4W7BfgUgKqaQFxEHgTWA+84+SNAz0hmtP3XlGfCZebOB/MS8JKI7MKeEe1R1esHuaVnyGX1+3/Pa7+IvA97pnO9qqZE5CWgZxiV1751DpMxtl9E/g1YC5xV1buctE9j76fd2u8Zo+LO+XfyxKEnCPgCBH1BaiI1LhVUMpfk/fPfz90L7x7LIyaEVD7Fz4//3CUU6M/l1K6AEehVzHVnu/nwkg8XXc/s3VvSMWvshutHHUQvn4+TSrnDwq+ev4RfHepXJ59BOODDtBRfv1AT+5q6WD9vGjVlfcteFR+4k86fPI4vEMCIxdBMpmh5UAGzs5Pq3/nUiOt5Vf1VnEqcKik9n1M+h8bqxhGXVYQq+893kKKiKNkXqWR1efEMsPGGm9j1q1/g85cRCIXxB4JYloXh8/W+7/lshpW3vH9sdXH2oEQkCvwcuy97BOjs2ZsaAQI8qqp/WuLasP2XiMwBnnFeflNVvzmK+k86Jlok0QhYqtrzU1qDPfK4Q0SuV9U3nCW/K1R1zwiLrQTOO8ZpKXDdCO5JAKWGgweABhG5WlXfcfaf0qr6uwPacSfwfwHvVdVUiXJGxN2L7uaZo8+QKWQI++3op/1nFZlChmggyieXf3JKnRv65PJP8uqZV7HUIux3u9e5nNsV9AW5e1Gf0dVcjtTb77jyBubMJjhnzqjr0NW1s9f/XA9+fznLZy7ltePHyeb7Fr5mlIdI5QuU9wt1rmof3r1nzazetMp77iG+aRNWJoMRDmNEo5j9FKd2JF4lduONI66nIQa3zbuNxw4+5lrKffXMq9TH6sek4NRUB1sLA/eUDBqn1VA2wCnu8ptuYe8rvyafzRIIhWyJeb84UflsFp8/wPKbbh51PYrqZPc9XwCewl7OOyYiH1HVx8S2hKtUdQfwIvAHwN87y4JlTtrTIvINVW0VkWqgXFVPDPHI3v5LVU9h96OXBRO9B1UGPCoie0VkJ7Ac+EvgfuDrIrID2I49RR4pz2PPpPYBf40tWhiOHwJfFJFt/TcZVTWHvR78T05dXqBvNtaff8b+grzgbFSOadRSH6vnyzd8GYCubJctWVYlb+bpytqri1++4ctTqhMHr13925XescPewxlA7PrRfMVtLCtHIuEet1VUrCLg93NFbfGYy28YRcaph6PnujnT2beMF6ivp+ErXwUUs6sLAgF7z8Y0eyXm5TffQu7w6EQtlaFKbpo1iNfzk2Pzen6m/RQtDDBQoTLWVbnHm+XTa7jjc18AINOdxCwUUFXMQoFMt+0C6o7PfWFcDuuq6jZgJ/Bx7D2kzzp9yB7gHifbHwE3OytHW4DljlT8S8AvnD7xBWwx2VCU7L96EJH/wBZtNIrIaRH57AU38BIx4eeg3q0MdlAX3l0eF95N7bLSaTq++z1XmPXQkiVU3Dn6ZaWurp20tf26KE3Ex9y5n8Xni3CqI8VPtgz0NaeE/D6yhWJjMLMqzEevmlO0xJhvbia+aRNdz/3MDsNhWYSWLCHU2IjPUflV3ruR4OyRi1ZVlV+e/CWHzh9yXbu6/upRS8+f2fYi+zuLBWozq2fywOrBZ3eJ9jb2vvprDrz+KtlUN6FojMYbbmL5TTcPaZzEO6h7yfEM1AQxlIHyuDxJvvob0tu3FycaQvUDD+CrqhpVWarKmTM/KFLvAZSXX8mMGbf15vn2b46RyBQrRudOj3Ky3T2L27BmJotmlN4nVNPk/A9+gBkv1gn5a2up+uhHRrV3ljWz/PjAj10uoARh4+KNwzrG7SFRMHn4jeewCsVxnT64ZDXLZ4+/OyXPQF16JnqJz8PjXYHZ1UV6105XevjKK0dtnMAtLe+homJ17/9FhKX1Fa48ubxFTZlbMfna4TYsq/SAVXw+ote5t3MLra2jXuobSnr+y5O/HLH0fEdHu8s4xTBprB39Xp7H5MQzUB4el4DU22+DWbysJgE/sVH62+thoLQcIByeRSg0oyhtaYN7L6a5K8OKWW5BQnsyx96mwU9ShJYswT9jhiu9+823UHN0cZV6pOcDSeQSI/J6XrCU7W0trvTVYcEXjIyqLh6TF89AeXhcZAptbWT2u8M6RNaswYiNfsVoMGl5ReVqV1pNWYiacrfnhEzeZFaVuyN/82g7BbO0WEFEiN3gPv1hdnaS2Tu6sBxgS89L7T0eOn+IAx1Dh8HY350mnSo2pj6UNdOqB7nDYyriGSgPj4tM9xtvuqTgRiRMZN3Y/A4OJi2PRV0CLgCW1btnUfubE9y4eLorPZEpsON056DPDsyZQ6CEKCL1zjsu8cdwGGJw69xbSx7QfvXMq8Sz7rNiYO+tbe1KQab4eiNJYtVj8jLmMUnxDJSHx0Ukf/YsuePHXemR9esxRukQFoaWlssgnjoa68sHevahM5XHZxgsnOGewb197DyZfOklu8FmUVZ3N+md7j224RiL9PxMNk9LOg35YqHHOjqh0jNQlxOegfLwuEioKt2vv+5K91WUE1m5ckxlJpP7saxiEYGIj/LyKwe9pzwcYPY0t1+9fc1d3Li4xmW8MnmTLSfOD1peoK6O0BK3S6bUlq0ut0gjobG6kSXTlrjSm7ub2dy82ZW+tSsF2QT0cwM2kwwNkSiERuZ+yWNq4BkoD4+LRO7YcTsa7QCi115bFGdppKgq8X5ey3soK1uKzze0MGBpiWW+g80JpkWDLG9wK/22nTxPMut2aNxD9NprwRigwsvlSG3ZOmQ9BuM9s99T0uv5lpYtNCX7XHEmCiaHujOQLd5/8mZPlyeegfLwuAioZdH9RonZ0/RqQleM7YxOJnOKfM4dNaG/tHwwFteW4R9gUFI5k5MdKa5bNN11LW8qbx11y9h78E+bVjIsSGbXTswReDkfyEil59u7Ulho0f5TDJMrSEKVJy+/3PAMlIfHRSC7fz9mh3uZLHb99Ygxtp9dPL7dlRaOuKXlpQgHfCwosd90oLmLinCA1XOqXNd2n+nifPfgwofo1dcggeKZoBZMUm+NLSB2Q1kDV9W7nYX3SM/zpsWORMoOUJhN9l5fQxwfQKVnoC43PAPl4THOaKFA99tvu9IDM2cSnD9/TGXm83FS6eOu9MqKNSMuo9Sh3cOtSXIFi6vnVxMKFHcHliqvHWkbtDxfWYzIavfsLbP/gO0aaQysr1s/qPT8F2d3krYsyCXAif7kQ1lNHIIxiEwb0zM9Ji+egfLwGGfSO3dhJZKu9LGE0+hhMGl5NLpwxGXMnx51GaG8qRw5lyQS9HHVPPcZokMtSZrjGVd6D5F165DwgHNWqiXjXY2EwaTnqspPT7xMNp+ATN8SYiNJYpj2/tMY31uPyYtnoDw8xhErmyW1xa08Cy5cQKBhZD7mXGWOQVpeCr/PcHk4B9jfbAsO1s6toizkFm/85nAbg/nsNEIhouvdy3K5Y8fInz074rr1p5T0PGFaxPMZTrb9Bivdt3S6jk77P1Vzx/Qsj8mNZ6A8PMaR9NataGaALzkRYtcPFn9zeMYiLR+MUq6PTran6c4WCPgMrl3onkWd6khxsmPwMGeRVSsxyt1OZrvfeGNQwzYcA6XnTVk7cGd39hytXbsAR1reE7PUU/Bdlkx4RF0PN+/2sBRTjXxzM/Gnnyb+7E8ptLQggQChJYsJNS7FV1ZGeNlS/NVjc8FzIdLyUsyqilAe9hd5OLdUOdiSYO3caVw5s5KtJ85zPpUnmc2zrynBwZYEj75xnPqKEB9aPYv71s2iobLv2eL3E7v2WhK/fLHoWfmzTeSOHSe0cMGo6wm29Ly5u5m2TJz2nFNfM0dL5hRlvgrW+Z2lR38QYrUjLrcQz5La2kp65zmsTAEj7CeyagbRdbX4K91uoTwmDm8GNcnY3rqdz73wOZ449ASWWlQEK7DU4olDT/C5Fz7H9tbtE13FMXG5tiu1dRsnP/tZOn/yOFZ3NxIMgiqZvfuIb3qafGsr0WuuGXP5g0rLK9eMqbzBPJzvb7b3dXyGcOPiGpriaZ7YeobdZ+KoQsAQMnmLH71zkgf/9W22nCiuU6ixEd90txHufvMN1Bp9IELok563ZAtoz6HcQg5VpTm1l3nqSM0rZsMIlZHZE120/esuut9pRlWRiN8+UP1OM23/uovsicGd5Xpcei55PCgRMYFd2LO3fcDvXEiY9EuBiFwD/C1QB6Swo19+Afgo8DfAaezowEeBL6uq+wDMAErFg2rubuZzL3wOgLA/TM7MFYXH7vn/F9Z9gWnhqaNYOp85zz9u/UeAos3vgC9A0Aj2nnH51u3fmlIzqXxzMyc/+1lAEL8fs6OD/r8mLRQwwiHm/8d/EKgfW7uamze5HMOGI7OY2XD/mOvdlszyvTfcEcQ/fcN8psWCnO1Mcc8/v07Bsgj4+jp+nyFMjwXJFCxQ+N7vXVM0k8oePUbXT3/qKrf8tlsJL1s2prrmLeWvdv6CE+e32wndbZBPMZc0NwdD3B6Zgyx6H8wbPiJxIZ6l7V93gQB+AwoWaoH4BfEZaN4EhZrfW1lyJjVcPKgBfdsx4EFV7Rx9q0eHiGwEDjrReEtd/w5wN9Cqqiv6pVcDPwLmA8eBj6rq4C5EJoCJWOJLq+oaABH5AfB/AH83AfUYESJSBzwG/LaqvuGk3Y8d4h3gR6r6n530m4EnRORmVR21e+dnjzxL3sxTHionlU+RzLuVYNlClqcPP826urE5Gp0ItrZsJZVPEfQHKRT6eScogF/8RAIRcoUczx55lt9b9XsTV9FREn/6aaxsDsPvx0wmGTjUMwIBMHzEN22i5qGHRl3+eEjLS1FTFmJGeYhzieJ9rf3NCa5fNJ0nt50l4MMlwDAtJZ03iQb9xNM5ntx6hv90c5/Lo+CC+QRmNpA/21R0X/dbbxFasmRM3jP2d6epqlhBW+oM3dlzUMhiAHVkOZxLM89fTuMI959SW1tRU5GwD82YtkECNCsYUT8S8GGlC6S2tlJx85jOVPXv2x4F/hD42lgKGiUbgWeBkgYKeAT4Z+C7A9L/b+BFVf1rEfm/ndd/cpHqOCYmeonvVWCxiMRE5Dsi8raIbBORewBEJCoiPxaRvSLypIi8JSJXOdf+l4hsFpE9IvLlngJF5K+d/DtF5G+dtEcco9KTJ+n82yAir4jIdhHZLSJur5X2l+zRHuMEoKo/UVVXMBpV/TXwMDD63gh4/vjzhP1hVJV0obRPs4AR4HDn6ALETTSHOw/jN0p3TgUtkMglyJpZnjj8xIiD1U00ZrKbzp88jmYymN3dJZexjGgUXyxG13M/G9Mzurp2XLC0fDCWlRBL7G/uQlV5dudZKiJBgn5399CdNbFUiQb9PLuz2BDJIGIQK5EkvWvXqOuoqmzrSiFiMLfmt/AhoCY15Ag656BeyTQRD44sZEl65zkkaEBBe42T8yQ0Z7+WoEF657lR17UEbwCzAERkkYg8LyJbRORVEVnqpNc5/doO5+8GJ/2TTl+4XUS+JSI+Jz0pIl9z8r7p3H8DsAH4Gye/y6W9qr4CuNeJ4R7gUef/j2IbuknFhBkoEfEDH8CeEv858CtVvQa4GfvNjgH/CTivqsuBvwD6Rzj7c1W9ClgFvFdEVonIdOBe4EpVXQV8dZhqfAL4uTPqWQ1sL5FnBfaS3kjZCiwtdUFEHnKM6uaimYRDMp/Eb/iHPCtjiFG07DcVyJk5jBHIoTsznXxv7/d44+wbpPKTc9W3cP48iV/9mo7vPorZ2TloPjEMJBoFvx9rDK5/bGm5e0A8Wmn5YFxRV9rDeXNXhkSmgN+QkpJzS5VUzsRvCIlM3nU9MHMmwQVuUURq82as7OgGH2eyeVpy9jNCgXJmR+2fVT1957LywSi/PP0SpjV8wEQrU0AFrGyJvD1vhiFYmUF9EPp7fr/OX8mBqGNQbgU2OUkPA59X1fXAHwP/4qT/I/Cyqq4G1gF7RGQZ8DHgRqdfMoEHnPwx4E0n/yvA7zvbCZuAL6rqGlU9Muwb0UedqvaMMpqxtzAmFROxxBcRke3O/18Fvg28DmwQkT920sPAXOC3gH8AUNXdItLfn/9HnS+IH2gAlmNPcTPAt0XkWexp71C8A3xHRALAU6q6fZj8I2FQ66KqD2N/WYnFYq7Nv7JAGQWrQMAXIBaIkcgl+jaHHSy1SsbPmcwEfUEstYY0Uj3typk5trVuY+e5nTRWN7K2di2VIXf010tNvqWV9NYtZI8c7Z3VSDBou93x+YryCmCUlyMiaD6PUT56D9slpeWGn/LyFYPcMTp6PJyfGiAf39+UoDzsp2ApAZ9BOOBzhd5I5QoEDKE8HChZduz66+wQI/1mf5rJkt62jViJsPGDsbWruG7TpAp/sJry/qKRcCUtqRY2t2zm2oZrhyzPCPuxcqb9mQ1AemaLlmKEB+0WC86geDB6+rZZ2PvrL4hIGXAD8Fi/gWfPBtctwKcAVNUE4iLyIPZA/B0nfwRodfLn6OvTtgC3D1GXUaGqKiKXVpAwAiZ0D6oHsT+JD6vqgQHpJQsQkQXYI5GrVfW8iDwChFW14AgabgXuB/4z9peggDNbFHv4GQR76isi7wE+CDwiIn8HJIC/ch71e8Ae7C/M0yNs31rsL+eouXP+nTxx6AkCvgBhfxi/4Sdv5YvOknTnu7l5zs3cMHP4TeHJQnu6nZdOvUQ00BfyIWcVC0DyVp7l0/ucj5pqsrd9L/va97GwaiFra9dSGx25lHg8UFXyp06R2rKV/OnTruuhJYvJ7N2HOAZKAAmFMGKx3v0WK52m6iOjEzQMKi2PNeLzhUffkEFYWl/uMlAHWxLctbKBxzafojISJBZyGyhVOJ/K8bs3lpaP+6dPJ7y0kcy+/UXp6e3bCa9Yia9s+CW5rh6v5f3JdvHhSDU7C0ESlvPdCdmKxK0tW5lTPoeZZTMHLTO8oobka2cQX3G/Ij4D/Haa5iyiV49ZqJNW1TUiEgV+jr098AjQObDPGwLB3lL40xLX8trXGZiU6L9FZA7wjPPym6r6zSGe1SIiDaraJCIN9BnCScNkOQf1c+DzIvJ5x5KvVdVtwGvYSrlfi8hyoCeITgXQjT3iqMNeKnzJGa1EVfU5EXkNW1UHtkJlPfBj7PXaAICIzANOq+r/FpEQsE5V/wvwZE/FROQU8LaI/FRV33LS7nPqVoSIvBd7/+nmsbwJdy+6m2eOPkOmkOk1UP33bjKFDOXBcn5/1e9PKbVbfayezS22d4Ww3+5go0TJW3nS+TTd+W58ho8rprm9fCvKkc4jHOk8wuzy2aytXcvsstljdhk0EtSyyB4+THrrNgrnBt+PCDUuJXPgAJgmRlkZRjTaa6wArEwGCQao3LBhVM8fb2n5YCyuLePX+1spWH0DoFTO5MbF03l621nSeZNIwEck6COd6zNSedNCEG5dNviAIXrttWQPHUILffdpvkDqnbcpv3n4n8eOHq/lPRRylBUSrJAMddE5PNl91L7qGChF+eWJX/KxpR8j5Ct9lilQbw+Q1NQiIyUhnzPbNRGfEF13YQMhVU2JyBeAp7CX846JyEdU9TFnML5KVXcALwJ/APy9syxY5qQ9LSLfUNVWR2lXrqpu2WUfCRzRlqqeAtaMsKqbgN8B/tr5d6SD8EvGRIskevgKttHYKSJ7nNdgf7gzRGQv9n7SHiDufLjbgP3Av9NnLMqBZ52lwN8A/9VJ/9/Y+1Q7gOuxjRvA+4AdIrINe933HwZWzBFD/DbwtyJyQET2Ae/H/lIAfMzZnDwI/Bn2THBMM6j6WD1fvsHWe3Rlu8ib9uwpb+bpcuLffPmGL08p4wSDt6un/6kIVvCZFZ8ZdinvdOI0zxx5hp8c+gmHzx8uGW31QtBCgfSu3Zz//g9I/PwXQxonsGcKNf/pDzEqK+xphWWhqmg+j9nVBSgNX/nqqCXmg3otD9aMqpzhGMzDeXsyx9fvXwkK8XSOkCM1Ny3tnU3dtryWY23drnt78JWXE17hDsqY2buXwvmhlcx5S22v5f3JdrGaLnxAvT/GVaFaCETB1zeAS+aTvHzq5ZLeKzRvYbaliSyf3vtaLQWfAQJWugAK0+6/YlwO6zoD7J3Ax7H3kD7r9D97sMUJAH8E3Cwiu7CX7JY7UvEvAb9w+rEXsLcwhuKHwBfFFpi5RBIi8h/Yoo1GETktIp91Lv01cLuIHAJuc15PKi75OajR4IwqAqqacd74XwKNqjq1VAIlKHUOqofL1ePCcO1K5VPsbNvJ7rbdIxKCVIYqWVO7hsZpjYOqBEeClc2S2bWL9I6dWKnhxRlGLEZkzRrCK67ECAZtTxKbNtH13M+wEgmM8nIq7voAlRs2jNo45fNxTp1+1KXeq6v7ILGYO4rthXK4NckzO4p95gV8wkPvWUR7d5Ynt57h2Z1NnEtkUGxxxbKGcspCAUTggWvnMaO8dIdupdN0fPd7aK74swwtWUzFnXcOWqddiRTPt8WL0nztR/hc4nXbMSy2WOPpoEVTmftw8C1zb2FpdbFOKXO4k+wxu0wrWyDf1E2hJQU+wRcNjMiThAxzDspj/JnsBqoc+DX27EqAP1HVsWl2JxlDGah3Ozkzx572Pew8t5Pu/PDvUdQfZdWMVVxZc+WgyzulMJPdpHdsJ7N7j6sTLYVv2jSi69YSuuKKMZ3pGQnt7a8Qj28rSvMHKpgz+3fGRb03ENNSHn7lqGuf6c4V9SzrF2k3WzD5t9eOFy31ASyoibFx7axBy0+98w7db7rjQ1V99KME6txLaarKd8+205orVghe2fw6d2UOFqV1XXE7P+7Y7hrMBIwAH238aO+M3MqaJF87g5rFfV2wIUZkxchnpZ6BuvRMliW+kqhqQlWvUtXVqrrqcjFOHkMT9AVZW7uWB5Y9wPvmvG/Ypb9UIcWbTW/yvb3f4/Wzrw9r1Gyp+K/o+O6jpLduG9Y4+evrqPjgXUx74BOEly+/aMZpUGl5+cqLYpzA9g5xRZ3b0WuPh/MeQn4f1y5wz1aOtXW7hBb9iaxejRGNutJLRRsGOJ3Nu4wTZoG1mZOuvBU1S3nP7Pe40vNWnhdOvNArPc8ei7uMkxhCaFHVoPX2mBxMagPl8e7Gb/hZPn05H1/6ce6cf+ewKr6cmWN763a+t/d7vHTqJToznUXX8y0tdD33HOd/8O9k9uwFc+g9rOC8uVTeey9V999PaOHCiyrMgIsvLR+Mxnq3DP5Ee4rubPF5oJWzKqmIuKXlrw0RjkOCwZK+CPOnTpM76TY6W7vcg4tZmqSBAYq+yDQIlXHFtCtKimtaU61sbtmMlcqTP+0+hxacU44RmSwaMY/B8D4hj0mPIQYLqxayoHIBZ5Jn2Na6jVOJU4Pmt9TqlagvqJzP6mwdkX0nS0rFXYgQWrKE6Lq1+GcMH0p9vLCl5dtd6bbX8vGTlpdiVlWEikiArnTfzEUVDrQkWDe3z+ej32dww6LpPL+7uej+pniGI+eSLC4RawogvHwZ6e3bXQebu19/g8CcOb2Gv6tgcrjbfZh3baGE+rmqzxVRj9fzrlzxrG9ry1ZqzkSp0WLnuOI3CM53O8z1mHx4MyiPKYOIMLt8Nh9a9CE+csVHWFy1GBnsXLSlRE+1kfnJJrZ97x/YvfOXxLNx18Hn3rL9PiKrVlL94CepeP8dl9Q4AaTTJ8nn3Oq2igp3SPXxRkRYWmIWdaDZPfNYWl9OTQlRxGuH27GsQd5bn4/Yde5DtIVz58geOtT72iUtB8p8Pq5IuWda/eM/BX1Bbpt3m2uGa2bzvHjqV2S12OiF5lVgBIsPV3tMTjwD5TElmRGdwR3z7+ATyz7BipoV+MQ5LFswKT/SwuzndzDjzcMEO+39ka5cgoPnD7K3fS/tmfZeQyWhENGrr6L6d36Hsve+F1/lxHit6CpxMDcSmT3u0vLBKGWgmuMZzncX78+JCL+12F2nju4ce5sGD1URXLwYf617iTb15luoaZaWlgOrYyF8ySZXOpXFzlzrY/VcVVfs5KHQniFpdfNa5p3eJUgj5CM4d/SePTwmBs9AeUxpKkOVvGf2e3hg4Ue4ujnG/Od3M33rcfwllooAUvkURzuPsqP7IK0rZ1LxqQeIXXddyY38S0U+31nSa/mlmD31MN3xcD6Qfc1uozN/epTZ09zBEt882k5+kH09ESF2g9uRrBmPk9m7l/3dadIDXBD5RFhNJwz0sxeM2XtQA1hft56GmH1kyErl7bNNwLH8CQ4X7JAloQWVfW6NPCY93iflMaUxk0mSv3mNzA8eY/6hBGsrljOnfA5BX2k/cfnyMG1XLeTQHY28XNXMvx/+MVtatkyoF/Wurp1ur+WBinHxWj4aSnk4P9CccAkgRITfWuKeRSUyBXac6hy0/OCcOQTnusNYdL/9Dls63IZwaSxMLFFi37BqTp9z134YYnDrvFsJGAEKHcWiijcym0mG0gRmuRWLHpMXz0B5TEkK58+TePFFOr77XdLbtqF5e4PfJz7qY/WsrFnFgsr5va6VstPLaL1hCWfev4rkghm9EVhThRRvNb01Yon6eGNLy/e40i+mtHwwhvJwPpCGygiLa92d/dvHO1xnqvoTLRGO40ze5OypM670dRUxiJcwUJVuI9dDRbCCG6PXuDyW5zXPbyJbsBhf7yMeFxfPQHlMKfLNzX1S8b37BpWKG2JQE5nBuvUfpPGTf4D5wfeRmlVdcuQNxRL1X5/8NeczlyawqC0tH7DPcwmk5aUoDweYM8291Lm/qXS4kBsWTXe9ndm8xTvHS4UesgnU1hJasqQobWeknHxzc+8gA2BWKEh9wAddozNQailzWmpYHCh2ZGuEfLT5O3mn5Z1B7/WYfHgyc49Jj6qSP3nS9ip+xj3SdmH0SMXX4a+poQpYqNdytvssW1u2DitR39exj/0d+1lQuYC1tWupi12cMDkTKS0fjMb6ck4OOHh7oCXBe66Ygc8otkbTy0JcObOS3WeK3RJtP9nJmjlVg4fjuO5askcOg6UkDB/HQhEwTfJnmwjOmwvA2ooodLdCYcAhan8QYoMrLPNnklipAteHr6bFPEfCsqNS+6vDiAjbWrYxp3wOs8oG937hMXnwZlAekxa1LDIHDtL5ox8R3/TMsMZJAn5HKv4gFXfcgb+mb59ERJhVNosPLfoQH238KEumLRlcoo7tHfto/CiPH3qcpw8/zcmuk4MeRh0rEyktH4zFtWX4BxiidM50Ga0erltY7cpfsJQ3jw4+i/JVVRG+8koAdkfKUGcaVmhtRbNZW1oeC0NniYFE5Zze5dmBaMHq9bcXlADvi9yIIQZGxI8RtY1lj9fzTMG9bOkx+fAMlMekQ/N50jt3cv773yfxi19QONc2ZH4Jh4hefTXVn/qULRWvGPoQZk2khtvn3e6SqA/GmeQZnj36LI8dfIxD5w+Nmxf1iZaWlyIc8LFwRgnXR4NIyMvDAdbMrXKl7zkbp6N7cBdSsauvphAIsC/S71mq5M6cYU1FFJ8IxEsZqNnuNIfcyUTR3lOtr4Y1wRX4pxfPRrvz3bx0+qVxH3B4jD/eEp/HpMHKZGyv4jt3YqXSw+Y3ysqIrFlN5Mor7ei2o6RHon5V3VXsatvFrrZdQ3pRb0u38cKJF3ir6S3bi3p1IwGj9DLWcAwuLV8zpvLGk6UN5RxsKd53OnIuSbZgEvK7jfnV86vZdSZONt9nuFVtF0gfWl06gKARi3Fq1Voyp4q9UmhbG1fmUqCxQQxU6f0nK2eSPeE2olfNuZrz0TxN3cVnqY52HmV/+X6WTV9WsjyPyYFnoCYh+eZm4k8/TdfPnu8L3/CBO6m8555Rh2+YTAzWrvLbbiN/tonMnj1FG+WD4aueRnTdOturuO/CPQJEA1GubbiWtbVr2du+lx3ndgyp5uvKdfHK6Vd4p/kdVtasZEZ0Bi8cf2FU4VEGl5aXjlJ7KZk/PUY44KMtmWFfU4KDLQmyBYvHt57m/vVzuG/dLBoq+85BhQM+rplfzauHime6h1uTNMXTRXl7UFX2zpqLNLWhhT6ff0vSKfStt+HWGyA3YFnR8EN56dBIueNdaKF4ZisCkcXV3Bq4lR8f+HHv4COZS3Lw/EEeO/AYfp+fymDlZRHO5nJkUofbuJwZLNxGaus2mv7iS2gujxGJgN8PhQJWOo0EAzR85atE162dgBpfGKXapZmMHdjPNCm7+WYCDUPHZQs01BNZt57ggvkX1XGraZkcPH+Qba3b6Mx2Dpm3ubuZl069hF/8lIfKCflCFKwCmUKGgC/Al2/4Mmtq1xTdY1k5Tp78tku9V119I1VVxd4QJopvvXyEb758BNNSAj4DQ2xffAGfEPT5+Pr9K1k/r8+7ed60ePT14yQyxQ5mZ0+LcP96dwTkU5kcP2xqp9DcUuQ09v6OZmoLOapubCQQ31xcqao5/P/be/M4Oa7q0P97qqq3mZ5F0kgzo321FtuybEnewRjbPAN+XsCxCYkDCcFZCJDkhZcQkryQwHvkFx4J4YUHTiAmPBIDjjcM2NjExqtsa5dl7fsyi0aafXqrqvP7o2pmuqd7Nmk0i3y/n09/pudW1a17u7rvufecc8/h8l8taqufcul65WSQgDCP6LwKEiuCNu5r3cczR56hsbuR5449h+/7OJZD1I6SjCTJeJlBn1cvJt3G+DPhAkpEPgd8GPAAH/it3tTq49iGO4G9YTZLROSvgBdU9dlR1vPfgC8DM1V1SMNJKQGVa2zk6Mc+BghWPI56XrCiCJ+Rn80CSt2f/znOjImzU4wW93QLjX/914Bghao4zWbxM8Hm2GAGrVTdfgd2stj+EV24MAjeOnv2eY8ono+qcqjjEFuattDU01R0vCvbxY8O/ggIIq8LQtyJk3ASOJbTZ4j/5i3fLJiZd3Rsp6XluYK6xHKYP+9jE+a9l09De4oPfXMDHekcEbvQTF2TjJH1fFD47m9eWbA6evNEO8+8Vfw53Xn5HBbVFI7rjze3src7Db5PascONJOlLpfhA63B9RGaqbo4Xvi8F1wDi99VVH9qZwvZk4W/JbGF5HVzsGL9K+yH9z7MVzcHSbPzE1yWOWUko8lBn1dfnSMQUBfCeCYifwv8VyALHAB+XVXbzk9rh2ZCVXwicg1wG3CFqmZEpAYYvTHh3LkTeBJ4C0BV/2K0FYjIPOA9QInIliOj/fHH0WwOu7ISP53G7+goCm2qmQxn/uVBytauPdvbjDs9mzbid3QisRhepjhigzgOmsmQ2bOnv18DXMUnAhFhcdViFlUuoqG7gc3Nmzna0f9497buxfd9ok4odFFSboq0myZqRylzyki5KZ488CS/ufo3g3MmoWv5QB7ZfAJffeIRG2/AqiTjepRFHdpTWR7dfILfvbE/y++q+ko2HWktco54aX8LC2eUlY5abllE5swhe/AQl/b0271yx4+QraknVpcXG7GE/cnrzJJrKNZEROdXFggngJaeFlBw7MJhL+WmiNkx4k6cjkxHwfMaDRfQePYM8FlVdUXkb4DPAn88pi0cIRPtxVcPtKgG4YZVtUVVT4rIWhH5hYhsEpGnRaQeQESeF5G/E5GNIrJLRNaLyCMisk9EvtBbqYg8Fl67U0TuzyvvEpEvisg2EdkgIrUici1wO/C3IrJVRJaIyIMicnd4zXoReSW85vUwy28p/g747zBIuOwR0PHTpwL1F+B3d5esSBynIAL0VCCzb/+wSf56+yURh8Rlq0u6ik8UIsLs5GxuW3wb9yy/h4umXYSIsL9tf8lU84qS8TK0ZlrJeTmeOPBEn8fYYK7lVRPoWj6QJ7efpDwWIV7CISIdOkKURR2e3F7oeGBZwnVLZxRd09KZYXdeZPStA6KWOzNmUBGPsTgT2py8DLhpunc39KvtREp68GUOtA005WFFLWILij05nz36LNXx6qLtBb0TC4CEk+Cpw08VXTtCLojxTFV/pqq9utoNwOCuk+eZiRZQPwPmicheEfm6iNwgIhHga8DdqroW+Dbwxbxrsqq6DvgG8DjwCeAS4KMi0vvr+I3w2nXAp/LKy4ENqnoZ8ALwcVV9BXgC+IyqrlHVA703EpEo8H3g0+E1NwNF7mUicgdwQlWL/YYLz7s//DJudF236Ljf2RnYZlSLDOh9WNaI0pNPJjSbHXTvSh+OAyJBVPF3vnNYV/GJoiZRw80LbuZXVv4KALY1tJOGpx4tqRZ+uPeH7G3dS3vHlqJzEom5RCfQtXwgnWkXxxLikeJnlvN8XM/HsYTOdLFDy5KZSeqrileCrxw4jev5paOWi7DuosX0fZLpwBvP60qTORHup0rOAqcwmK3bliZ3qtjbM7qwCinR9q5cFwknQVmkOFpGb3R7x3LoynUVHQ9xen+/4ev+AccviPFsAL8BTFgm8wlV8alql4isBd4B3Ejw4X2B4AE9E6oEbCB/qvZE+HcHsFNVGwBE5CAwDzhN8BDvCs+bBywLy7MES1+ATcAtwzRxOdCgqm+E7S3yYxWRMuBPCdR7w/X3AeABCGxQA49bFRXgukgkgpVI4JVwosD3z8qleiKRaBR8H0p43IllBZHEHQex7b4V5GSnMlpJTaIG13dx1SXlpkruj/LVJ2pHaUm18J+HfkSd+xZ1ZXXMSMzo2381GVzL86mIO7ihc4RjC+6AdOlp1yfmWCUjRYgI1y2t4eFNhSGKOlI5dpxox54WI10iavnaZYvIvTWb3MmTfQIKoHtPE7HZ05AB6j1VJbOvrej+VtwhOre0kiMZSeL6LmVOGa7v9gUI7rUdAri+SzIyaEBZNxQmJbkQxrN8QnuaC3xvmHrPGxPuZq6qHvA88LyI7CCYQexU1eKokgG9Rgw/733v/46IvItgZnCNqvaIyPNA75Qup/1eIR5n2X8R+RfgcuAkgW52EbAt/ALOBTaLyJWq2jh4LcVUvvdW2h7+D+xIBCkrw3acotWS39ND+XXX9u3EnwqUX3cd3a+8UiR8JBJBYjFEBK+jg6pfunuCWnh23LrwVh7Z9wgVsYo+e1PKTeFp/2bRnJ9j1YxVAMTdRjJuhiMdRzjZdYJZZbXMrlwyKVzL87lt9Wy+/8ZRqhJR4o5Nl1e42k/nPFzP45evXFDy+nnTy1hUU86hlsIJ1oaDp4ksLl4ZryyPU+445K69hraH/wMy/aGT/HSW1OEWylYXCij3VAq3rdieGVtShdilHWnyn1dltJK0l8bzPWJ2jEgY/T7lpvjgsg+WvH4kTPXxTFXfF5Z9lMCedlPePcadiXaSWA74qtprVFkD7ALeIyLXqOqr4RL5IlUtDvlcmiqgNXyYK4CrR3BNJ1Bq2rUHqBeR9ar6RqivTanqrw84ry8Tm4gcBtYN58VXsuF33EH7E0/gp9NY8TgSi0GsX63hp9PYVZXM+oM/mFL7oeKrVnJ055v0eicOxE+nkWiEqttvH//GnQO3LbmNHx38EWk3TdyJUxYpI+EkyHgZetwe0m4a27IDm5W6xL3+1OU53+VE1wkO5uI0RF7lspmXkYxOjlQQH7hiDo9uPkEq5xGP2HRligVUZTzCXVcMHs/u2qUzOHy6u0BT3ZRzOXOqk3nTC1Vsl1cGjnGR+nqiC+aSPfxSwfGe/c3E4zP77BHqK5kDbUX3tJMRInWDO9kNfF4Jp3DC1OvcctuS2watYygulPFMRG4lsKffoKqlY1yNExNtg0oC3xGRt0RkO7AK+AvgbuBvRGQbsBW4dhR1PkUw89gFfInAyDccDwGfEZEtIrKkt1BVs8C9wNfCtjxD/+xlzInU1VH/118AFK+jA83lUFU0lwv2C6HU//UXppRwggu3X3XldXz+2s8D0JHpIOf1p/ywsUlGknxw2QdJRpPEvFNI3soKQMWi25rOtlPb+H+7/h//efQ/OZMePIbdeFFfleBv7r4UFLoyOWxLUFU8X/tSafzK1fNLbsDtZVZFvChL70nxaexIk8vbUDsnFqUu1q8qLF9Z7KmnREi9uafv/1xDN15Xsf0rtrQasQbfhlDqeakqOS9HRybQdn3+2s+fy2bdC2U8+z8EAu6Z0NHiG6No75gy4fug3q4MtlEXwogLTzxBx09+2h9x4X3vper226fcIJ7Phdqvxu5GnjzwZMlIErVltZzsOsmOA/9Ae09hsNu0U0t3dElRfb1R1Cc6qkFDe4pHN5/g4U3HaenKEHUsLqqtYGV9BTMr4nz8HYuLIpzn096T4zuvHsbzlQzKG+KCKHWVcRaG+6Jum1nNymSeoDvwHJ0/+gHpY3mCOlmL1K9i2q/eh5VI0PXKSfx0obB3qmOUrasd0T65oZ7XUJ+5mI26444RUBPEUALKcGHR03OExsbH6HF7aOxu5Ez6NKrQFl+DZw2ean52cjaXz7qc+RXzx3WD8kAyrscDvziIO2BP1B1rZpcMLJvPc3ua2Xq0jcPicVwCoSIiXDa3ipp4lPvnzQwCw/ay+V/xGg/R+txutNeZouYiSNYSv/hiokvWkt5b7KZfvr4Op7o4Zf1YYgTU+DPRKj6D4YKnN2p5mVPG4qrFXFqzmtqqixFnaFf6k10n+fHBH/ODPT8INgWPURT10RJzBolwnre3aTCuWjQd2xEa8zLZqirHz6T6o5b34uWgsxE7ESW+KM/tPhZ8Tqm3dpF6q9jvKDIrcd6Fk2FiMALKYDiP5HKt9PQcKiiL2THWL7yH+1bdx7radcTsoQfX0+nTPHvkWb6363vsOLWDnD98QN2xZkV9sc39YBjhfCjKog4z6pK4Urj6OtOVpV4HDD8dJ8EP6itbMguJ2GBHIRKYSTRVRvZwYYRzEYgtqR5lbwxTBSOgDIbzSEfH9qKy3qjlCSfBlfVX8murfo3r5lw31P4bADqznbx44kW++9Z3eaPxjXFNutcb4TyfnKccaB5aTa2qpMsdIgM2atcgbBmYGr69f++UFXUoWzIL4pWAoL6FnyvDO3MGv7t/I22kPomdnFr7Ag0jxwgog+E84fsZOjvfKiqvrFyNSP9PL2JHuGzmZfzKyl/hpvk3MT0+veiafNJumjca3+C7b32Xl0+8TGd2eFXbuWJbwvK6Umq+Ifd6ciyd5YznMmdaocdfvVocbunhWH6m3gH5nxKLZmJNC5wW/EwSNFAHZo8eBxSxhNiSKgwXLkZAGQznic6u3UUpNcRyqEiW3mRtWzbLpy/n3uX38t5F76W+fOj0Izk/1+ei/vOjPz/vLurL64ptZkfP9NCdKQ7b1cvmjkAA1VbG+5IdVqpFRTj0vLS/JQjt5fsFKygAsS3KrrsB9Ww01y/g/M4OvPZ2ovMqsOITHmvAcB4xAspgOA+oasmU7hXJlcNGLRcRFlUt4q5ld3HX0rtYUFk6YkP+vfac2cNDux/iJwd/QmP3qAKYjJjZVXEqE4XhjVRhT1PpFVx7zmV/TxhOSGDe9EDI5NueGtvT7G/ugq6mwEkiHydG/IprIVLs+p07cYzowsHiNhsuFIyAMhjOA6nUkZJRyysrV4+qnvpkPe9f/H7uXX4vy6ctH9bd/HDHYR7Z9wiP7nuUIx1HGMttJCLCyrpiobC7obSA2taZ6gvCClBTHmNGLMKMAdHEX97fgt9WKr37XPxuD6dmYfGx3Cmyhw6Oqv2GqYcRUAbDeaDU6imRmHfWUctnJGZw04Kb+NWVv8rqmatLpvnIp6G7oc9Ffc+ZPXj+0N52I2V5CQHV1JEuygFVOmo5vHfeDKwBAqq1J8fxQ3sYiFbOJb2/DXv6NKzyvO1H4iPRbnpe24B6Y9Mvw+TECCiDYYwJXMsPF5WPRdTyimgF18+5nvtW3cf6uvV9UbgH43T6ND8/+nP+bfe/sf3U9nN2UZ+RjDGrstgtfndDobPEru5UUdRyR4T3zKkuisWHKg1H9+MNWO15Wod7Jg0IkXn9IZCsWBci4LV3kH7zzXPqj2FyYwSUwTDGDO5avnDM7pFwEqyvW899K+/j+jnXj8hF/aUTL/W5qPcm6DsbVpRwltjd2NmnTlTVPueIguvCqOXXLy1cRcbddvxsN43t/W7zKg7phn73cbuyEruqCrE8JNLf9p6NG/GnWH40w8gxAspgGENG6lo+VkTsCKtnrj4rF/WXTrx0Vi7qy+sqGGgKa0/laAgFzLF0llPZ4pXaFWHU8rqqOMtq+wVqZSZw6jjZlsINV12uzsfrLlTfRebNRWKdBff2e1KktmwddR8MUwMjoAyGMWS0ruVjRb6L+vsWvW9YF3XXd9l+anvgon7k55xOnR7xvZIxh/kD1XT074kqtXqaE49Smxe1/LolNVihpKnINIRtUk60plCFdEex516kfhrxVcUejaktW/B7JjQrhOE8YQSUwTBGnItr+VghIiysWtjnor6wcuGQ56sqe1r38P093+cnB39CQ1fDkOf3UspZYm9TF2cyuT7X8nyuqCwUaNPKo1w8O1AV9q6gABo70nSfKcOXYjVifOk0ktdcBXbhsKW5HD0bN46o3YaphdnlNglJpxtoaHyM5uYf47qdOE4Fs2a9n/q6O4nHh54ZT2Yu9H41Nj5KJtOMZcUoL19GRXIFjlNBZeVlE9Ku+mQ99cl6TqdOs7V5K3vb9g7pdn644zCHOw5TX17P5bMuJ2bH+PHBH5dMS7F01kye291MLi8dfCrr8cyxM6hdeI8K22ZZWbGAvnrJDA4cP0nM7Vcz+r5wqqGc6RcVCiinJoEzPQ7ESVxyCalthXa+1JtvkrjsMuyq4SNLdLWm2b2hkf2bmsmmXKIJh6VrZ7Hi6jqS08ZnImEYGSbdxgQxWLqNtvZN7Nr1p/h+FttOIOKg6uJ5KSwrysqV/5PqqrUT0OJz4+3QL9Ucvu8CPr6fQ8Rm7twPs2TxH050M4HAUWLbqW28dfotXH/w6A8Q5Ex64fgLOOJQEa0gYkdwfZe0myZiR/j8tZ/nZFMte/IimvuqHHZ85i+qLqjrHdMquLq6tBPHlo0vk972aN//sZ4ZWNn51K24mkS0f/6cvKoeuzJwmvB7ejjzr99Fc4V2rtjyi6h8z3uG7FfDgXae++4uPNfHidpYtuB7ipv1sB2LG+9bSf0g4ZNMuo3xZ0oJKBHxgB0EK79DwH2q2jYO970T2Kuqxdbv/nNsYCNwQlWHzRldSkCl0w1s2fpRAGw7HmSdVRfCzY6eF6hOLl71v4nFZjFVyGSa2fnWfwPALhG5+0Lol2VFcd22guO+7+I4Fay94t8m1Qox5aZ4s+VNdrTsKBlwtivbxY8O/ggAx3KwxSbhJEg4CUSk75rPXfFVXt7Tb29ryuY4lM5w+WW12KEazhHh/nkzKbftovsAZN/6Kdte+09cXxHfJtm2mO5IHVbNEi6qDdSIkbpyyi4t9Pzrfu11el5/vbAyEabdew/OzJkl79XVmuaJr24N2hW1g9+XH8T0ExHcbOCUcfun15RcSY1EQInI54APAx7gA7+lqq8Ndc1YM3C8EpG/Al5Q1WdHeP0a4BsE2XZd4HdV9fUhLzpPTDUVX0pV1wCIyHeATwBfHIf73gk8CQwqoIBPA7uAoZP8DEFD42P4fpZIpBLfz5LLtQOFe0k8L8PBg/+b6ur1Z3ubcae17Q1yuVZsO4bvlzZmX4j9su0Yqkpj42MsXPg7E9DC0vS6qK+ZuYZdZ3ax7dS2Am++va178X2fqBOsWDz16Mp1kXJTVMWqiDtxOjIdbD3zLInojaSyHqA0ZHL4vnKmNc3MmsDmtKI8PqhwAoh2n2DOtARHTvcQS81A1CLjJEl1Z+lM56hMRIiXWNEkLl9D+s0d+D157vKqdL/6KlW3317yXrs3NOK5PrGyCG7WI5NyQcGyhVh5BCdqk+nJsee1RtbeunDUn6uIXAPcBlyhqhkRqQEmItT6neSNV6r6F6O8/v8DPq+qPxWR94X/v2ssGzhSprKTxKvAHAARWSIiT4nIJhF5UURWhOW1IvKoiGwLX9eG5b8qIq+LyFYR+Wa4+kFEukTki+G5G8LrrwVuB/42PL8oR7eIzAXeD/zzuXSoufnH2HYQr8x1uxgonAAsK0JX975zuc240929D8uKDHnOhdgv2y7Dccpoav7xOLZq5PS6qH94xYcLXNT3t+0vGanCU4+OTAeqSsJJ8LOjT3FR6C7e7nr0hFEdzpzpFxq9ruUlyaWgu4W6yjhxiRFNVwOQtYM6j53pITIniVVW/Blb0Shl69YVlWePHCV7/ETJ2+3f1IwTtfFcv084AfiekksHKk8narN/Y/PgbR6aeqBFVTMAqtqiqidFZK2I/CIcn54WkXoAEXleRP5ORDaKyC4RWS8ij4jIPhH5Qm+lIvJYeO1OEbk/r3xE45WIPCgid4fXrBeRV8JrXheRUgENlf6JdhVw8mw/kHNlSgqoUKDcBDwRFj0AfFJV1wJ/BHw9LP8H4BeqehlwBbBTRFYC9wLXhasxD/iV8PxyYEN4/gvAx1X1lfA+n1HVNap6oEST/h7475SSKIXtvj/8Mm503WIbgOt2hrYZRXWwEC4Wvl/sJTWZCdo73FftQuuXYFlxRBxc9/ynwzgX8l3U37/4/XjqYQ2yZ8tVl65cF47l0JXr6tu025Dptwe1d2TJZr0i1/Ii2k+AKpYI9VIPCK4VxwuFflvWo6FycCVP/JJLsKuKFRbdr7xc0hkkm3IRCzI9/cKpl95kxZYtgfAqjdP7+w1f9w84/jNgnojsFZGvi8gNIhIBvgbcHY5P36ZQ65NV1XUEKrXHCbRClwAfFZEZ4Tm/EV67DvhUXvmoxisRiQLfBz4dXnMzUGrH9u8TCLhjwJeBzw72gZxvppqASojIVqARqAWeEZEkcC3ww/DYNwlmMgDvBv4vgKp6qtpOINjWAm+E598ELA7PzxIsjQE2AQuHa5CI3AY0q+qm4c5V1QdUdZ2qrnOc4h+e41Sg6iIiJW01AT6WNbXSWwftHS5d+YXVL9suR8RCNbBDTQVEhAWVC6gtqyUZTQ6a6TflpujJ9ZCMJKmvihOL2ZzJ5U+olDNnUkWu5UW0HwXAy0RIuNVEbIuM3e9McboywstHW/H90nZysW3KrrqqqNxtaiZ7sDiQbCRuk+l20RL12ZFgKPQ9JZYYVCi6vb/f8PVA/kFV7SIYW+4HThEIg98iEDjPhOPNnwFz8y7rnWTvAHaqakO4AjsI9MZ3+pSIbAM2hGXLwvLRjlfLgQZVfSNsb4cGRu6B/A7wB6o6D/gD4FvD1HvemJI2KBEpA54mmG08CLT12qZGgADfUdVSs4Kc9k+9PEp8PiIyD/hR+O83gAXA7aGuNg5Uisj/U9VfHWF7+pg16/2cPPkDLCuCbVcANqqFmz5dN8W06nXE47NHW/2EUVW1lra2N7DtwdXxF06/BMuKYlmBqtbzUsyZfc/ENPAsuXXhrTyy7xGqYlXk/Bxt6baCqOQArZlWPrzow4gIUhGBpsLjXW2Zkq7lBYT5nzKnqwGhMh6h2Q8ElGcLp6sj+F1ZdjV2cPHs0p51sYsuIrVlC+6ploLy7lc3EF20CMnL5Dt7WTV7X2vEGZAZ2HIsnGhwnpv1WHX92X8HNVB9PA88LyI7CMaonap6zSCX9KoN/Lz3vf87IvIugpXONaraIyLPE4wzMILxaiSIyL8AlwMnVfV9wEcIbOoAP+QcTRfnwlQTUACED+pTwGME6rxDIvJLqvpDCfIRrFbVbcDPCWYDfx+qBZNh2eMi8neq2iwi04EKVT0yxC07gYrw3seANQOOfxYg/DL90dkIJ4D6ujtpbHwMz0tj23Ecp5xgFR/geWmi0QQXXfTnk8orbDimT7++wDtxIBdyvywrSl3dnePbsHPktiW38aODPyLtpok7cZLRZIETheu7WGJRGask6/l0JIoVMUkX2rqzzEgOsir2ctDZiJuKkesJhHkiahGxqyAHp6qj+FYQaeLVA6dZXluBYxffR0Qov+Ya2p/4UWH1ra2k39pF4pIggke6O0ckamNZgu/5WGFdIhArc/q8+GzHYvlVxVEsRoKILAd8Ve01pq4hcJx6j4hco6qvhiq/i1R15wirrQJawzFvBXD1CK7pG68GsAeoF5H1qvpGaH9KqeqvDzjvJHADgaB9NzBhxuGppuLrQ1W3ANuBXyawIX0sXAbvBO4IT/s0cGM4k9kErApdL/8M+JmIbAeeoV8lOBgPAZ8RkS2lnCTGini8npUr/ycAuVwHvp9DVfH9HLlcEEZm5cr/OaUGcTD9mmr9qiuv4/PXfh6AjkwHNjZRK4rv+2TcYJJ/47wb6cx28tjxbVgxm/Ky/lWkhVAbcwr2SBXRcRL1vHD1FGLHmDNzOjlHaK3onzt3pl22HW8ftKrI/PlE5swpKu95/XU0F3gWHtjcjBOxWHhZ4ILu5jx8X4nEbdRXMj2BDe3G+1aey2bdJPAdEXkrHFtWAX8B3A38TTg+bSUwSYyUpwhWUruALxGo+Yaj5HilgTrmXuBrYVueoX81ls/Hgf8dnvM/CVSWE8KU2gd1ITHYRl0I9kM1Nj5GU17EhdpZ76fuAoi4YPo1dWjsbuTJA0/y1OGn6Mh24Poui6sWc9G0i0hGk6jCpmw9C6atpuOMcvRYIERmRSMsLQuy7/7GdQtLJ1k8/BK5HRvpaczbs1ReAzNX8FrEY2eu0DSSiNp89NqFxAeo53rJNTbS9sOHi8rLr72GM+WLOLmvra8sm3ZpOd5FR0uwnyuWcFi6bhbLrxo6koSYjbrjjhFQE8RQAspgmIwc6zjWt4EXoNWLsS07i7gd46LKFWzb0QIol1WU9e19umf9POZUJ4rq0q0P0b01jZfL8/Kbvhh7zgJ6Vk3n314/ysCh6apF07l2QKqOfDp+8hMyBwqdI3oop3He9ciAvVixsgiXvHNOn3PESDACavyZsio+g8EwvsyrnMeamWv6/j/pBQ4NaS9DQ/okVZVRKh27YGPunsaOgdWA75M7dqZQOAHEq4gtncasyjgr64vdxzcfbaUrM3iIprJrrgGrf7Xm+sKxlhi5k4XbeMQSFl8+c1TCyTAxmCdkMBhGzJX1V1KTqCHt27R4/SujU6lTRJI56gbse9rT2IU3wK1b2xvJtAxYiFg2Tu10nBmBiu3qxTOwrULVYM5TXj80eFoQZ9o04itXBfdQaOhMkvNt3KamgqSGs5dVUzHdBIWdChgBZTAYRoxjOdyy4BYa/SqUQgHSYx1n2gC/4HTO4/DpQlV2du9RfHfAibEqYsum9dmrqhIRLptXXXT/Hcc7aOsZPINu2ZXrEcemLR2jPRMIIfWV3IlgFZWcHmf20uJ6DZMTI6AMBsOoKI9WES1bVVRe57TRLQcYGKZhd0O/N5/mPDKHWouujdRX4lQVuqRfuXA6UadwiPJVeeXA4KsoO5nEWrGahq5CL2v31Ckkl2HJ5TMRq4TThmFSYgSUwWAYFbu601TFZzItVt1XZqHMdrqxY400djcWnH/wVBcZN4g0kTnUjvYUup8LSmzVXAaSiNqsWzCtqHxPYydNHcVR2AF8XznJXNQu3uI5M3OIWIm4fobJixFQBoNhxKhqkNJdYGHVQqJh3Lxau5uI+FQnXZpSx+jO9av1XF/Z39yFn3bJHmwONunmEalKY9cWCyiAy+dPozxW7Fr+0r6WEmfDib2t9HR5ROoL3fur42nKWw6SaxhZxmDD5MAIKIPBMGKOprO0ZAMB41gOi6oWATDH6QLAsmBGVZaD7Qfx8wIe727oJHOwHe1pK6hPRIktLAOr9P6mqGNx1aIZReVHz/Rw9HRhipOOlhQN+4O9WE5tLRINNg9HbY/6ZNC+7ldeHTKrsGFyYQSUwWAYMVs6CoVCZaySS6tmkbT6V0Uzq3Kk3TRHO472lR1t6qTtaDtkCtV70apOrJriKBD5XDKniuoSqrmX9rf0CRs363Fw6yl6N0+JZRGZMxtBmVvZgW0F5bmTJ8kdGSqqmWEyYQSUwWAYEW05l/09xSlR7p53CTWJ/g20FWUe8ajPqVQLrekzAOTOpNnXlYF0f8gisXxi0zqgel5RnfnYlnBdiQ26TR1p9jZ1oaoc3nGa7IA0GU5NDXV1NmWRwvLuV19F/eGi6xsmA0ZAGQyGEbG1s6coqnmFbbM8WcYtC27pS3IoAjOrA1fwwx1HyPSk8Ltz7G3vArdfwMWmtSOOQuXQKyiAZbOS1FYW71165UALzUc7OXOyq+hYxYwEC2+9oqjcbTlNZu/eYe9pmHiMgDIYDMOS9X12dBbntru8sgxLhGnxaVw357q+8pnVgcrP9V2aThxHgZaeLs54gYu35bhEq7ogWQvO8HnARITrS6yi2lrTvPFqccJXO2Kx5PKZxBYvJlJfHJ2857XX0BJJQw2TCyOgDAbDsOzqTpMeoBZzRLi0oj8p4arpq1hcFeT+LIv5JBMe0ayNl8rRne0CN8u+TOAMEZvejlgKVUOr9/KZP6OM+dPzkiCqYp1McayluyhaxcJLa4iVRfrScQzE6+gk/eabI763YWIwAspgMAxJn2v5AFaWJyjLy9EkItww7wbKI0EYo1lVWcq6Ak+6jmwn2VwP+7I2lpMjUhG6oQ9jfxrI9cv6V1FyKoOkPHKeT0N7/76omrkVzJjTn5k3MmcO0YULi+rq2bixIASSYfIxJRMWXuicTGd5uPEMjze30eH5VNoWd8yq5u666cyOD56VdrJj+jW16O3XDxvP0JR1iVnC8vI4q5IJKhy7ZEr3hJPg3fPfzZMHnmSR2HS4oQBTn9ZcFxE7wZmKLip6gzlUld7/NBi1lXGW11Ww50Ar0tIvXBraU9RWxklWRFlwSbFbevk1V5M9cgRU8bq6yOzZTWbffk4/+B2cmhoq33srVXfcQaTu7JIVGs4PZgU1yXijvZt7tx3gew1n8IBqx8IDvtdwhnu3HeCN9qmZosP0a2qR369uX4lbgg/s6EzxcGMrriqzYqWjMsyrmMdlNauZc7qMWdFQ9eZ7ePi0Wt3s690eVTYDoqPPXnHlvGrsk2kkz2HD85WT7SmWXFE6SrlTU0Ns+UXkGhpof+Jx0m/tClzSRVDXpe3h/+Doxz5Gz+Yto26P4fwxpfJBiYgH7CBY+R0C7lPVtnG4753A3jAbb6njfwD8JkEQsh3Ar6tq6VgsIaXyQZ1MZ7l32wEAEqHqxM97PikvsAH866WLqZ9CM/OGdJZf2xHk6UmUSNtt+jW5yO9X1LI4k3MLfPdcX0k6Fo9dvmzQFWLqcBtbN7zCns4UmztsyKXBy9Be1sKyZIzPLunEnn0ZrHjfqNqmquzf1MzmHc3F4Y5q43zkjhVUJUoLzvS+/Rz9tV9DAXH6lUdWIoFdUYGfTgPK/G99q+RKarh8UAPGp13AR1S1WDc6gYjIYYKU8AA28AjwBVVNi4gF/D1BmncF0sA9qnpoApoKTD0VX0pV1wCIyHeATwBfHIf73gk8CRQJKBGZA3yKIJ18SkR+AHwIeHC0N3m48QxZX6mK2LiqtOc8cgMmEBnP54/3HuOq6uQgtUw+XmvroiXrErMtOr3S+09MvyYP+f2iRL9itgUKDzed4VMLigdydX3cI50srlpMV/Yttgl4vkvWSZNzsuzNuOzsElaP0v4E0HKsi9aGbuZMS3CqM9M3gdMyB396lFcPnObWS0qr6br+8+dg20UZfjWVQhMJrHgcr6OD9ieeoOb+s8pynj8+fQ/4beArZ1PReeZGVW0RkSTwAPBN4CME6eBnA6tV1ReRucCEqgCmsorvVWAOgIgsEZGnRGSTiLwoIivC8loReVREtoWva8PyXxWR10Vkq4h8U0TssLxLRL4YnrshvP5a4Hbgb8Pzl5RoiwMkRMQByoBiv9cR8HhzW99MvMMtFk4AjiXs6R5ycTbp2NOdxhkmgrTp1+RhuH6VWUKZY/N4c1vJ45kjHfhZn7iTYFH1POqiPqoePbEgeaGrPo+cTuFXzh5Vu9JdOY7sDCKZR22L+qownYYt+HMSIMLuxg5OdRZvJgbo+OlT2NXVxQIK8FOBC72VSNDxk5+Oql2D8CKwVETKReTb4XizRUTuABCRMhH5gYi8FY5Rr4nIuvDY/xWRjSKyU0Q+31uhiHwpPH+7iHw5LHtQRO7OO6cr/FsvIi+EY9abIvKOgQ1U1S4CIXqniEwH6oEGVfXD48dVtTj0/DgyJQVUKFBuAp4Iix4APqmqa4E/Ar4elv8D8AtVvQy4AtgpIisJZgrXhbMdD/iV8PxyYEN4/gvAx1X1lfA+n1HVNap6IL8tqnoC+DJwFGgA2lX1Z4O0+/7wi7fRLbEHo8PziUigxnD90qpXG8gMcmyykvGV0pHW+jH9mjwM1S8hUGdGBDrc4tWVn/HIHu3PojszMZOLyyJkIylcu/87vz9VxutnRq458n3lwJZm/Lx71lcncGwLrUtAaHdSDTbvlqyjsxOJRrHKip078ELDmOPgd3YWHw+P9v5+w1fJZVY4UX0vgbrvc8B/quqVwI0EE91y4HeBVlVdBfw5sDavis+p6jpgNXCDiKwWkRnAXcDFqroa+MJgjQz5MPB0OMZdBmwtdZKqdhCYS5YBPwD+ayjU/reIXD7MPc47U01AJURkK9AI1ALPhMvUa4Efhse+STATgECX+n8BVNVT1XYCwbYWeCM8/yZgcXh+lkCVB7AJWDhcg0RkGnAHsIhgeVwuIr9a6lxVfUBV16nqOscp1q5W2hY5Ddx1o4PMYD0gNsXy2cQswRvmHNOvycNQ/SqzLSwRcgqVTvHwkTnUjrr5Alm4MpmEsvaC83wrzlP7ttLU3TSiNp3Y00p3W+HKyLGEZcuno1WFNqeDp7o53lps+rEqKsB1kUQCsQtFsETCOlw3OK80bu/vN3w9MOB47/i0kWDC+i3gPcCfhOXPA3FgPnA98BCAqr4JbM+r5x4R2QxsAS4GVgHtBDahb4nIB4DhbFtvAL8uIn8JXKqqg0pdgnkHqnocWA58FvCBn4vITcPc57wyJW1QIlIGPE1gg3oQaOvV/Y4AAb6jqp8tcSyn/V4jHiU+HxGZB/wo/PcbwGngkKqeCo8/QiAw/98I29PHHbOq+V7DGaKWTaVjI65PVn3yNX1ZhdWVCRLW1JlbXJJMsKUzRWSIcdr0a/JQql+WCDFLSIYq6JTn8yuzpxdc5/fkyB0vHgcrkm3c5Fk83uL3O1s4MZrbIjx79FnuuegeIvbgeZo6WlI0HGgvKo+VR3jntfUceeMonelCjcTL+1u4Z928AnVe5Xtvpe3h/8CurMSuqsLv7kZdF4nFkHBV5adSVP/S3ZwlqYHjkAQN+KCq7hlQXrICEVlEoAVar6qtIvIgEFdVV0SuJJhQ3w38HsEE3CVcaIRODlEAVX1BRN4JvB94UES+oqr/WuJ+FQQT8b3hdRngp8BPRaSJwP7+89F+EGPFVBNQAKhqj4h8CniMQJ13SER+SVV/GH4hVqvqNoIP9neAvw/Vgsmw7HER+TtVbQ51rxWqOlSI406gIrz3MWBN7wERuQq4OhSaKYIv0Maz6dfdddP5YVMrKc8nYVtURWzIU7akPJ8K4H8vnz+l9td8oHZakXdiPqZfk4uR9CtqCXfXFgqo9IF2BppNxRZi9iHeIcKG1jIavXDi78Rp73Zo7urgxRMv8u757y7ZloFRyvvqtSQIZRR3uHZJDU/vLEySeLItzYFT3Syd1e+cUnXHHbQ/8QR+Oo0Vj2NXVRVc46fTSDRC1e23D/7hjJ6ngU+KyCdVVUXkclXdArwM3AM8JyKrgEvD8ysJHBPaRaSWQFX4fKgpKlPVn4jIy8DB8PzDBBqhHxDYyiMAIrIAOK6q/yQiMQITR4GACuv8OvBYKAyvABpV9WQo7FZTuLIbd6bOtG4A4UPeDvwygQ3pYyKyDdhJoHID+DRwo4jsIFDZrQpdxf8M+JmIbAeeoV8lOBgPAZ8JjZwFThKq+hrwMLCZQOdsEdjERs3seJSvrJgPQHvOI+srqkrWDzz6AL6yYmoNdmD69Xbol9eRJddY7PAVm5nDkgx1sRwXJ+KUiQNigR1BFVrao+w+s5sDbQeKrlVVDm1vKYpSDjDnomqS0wIniRV1FdQkiz/jVw604OfZ/yJ1ddT/9RcAxevoQHM5VBXN5fA6OgCl/q+/MNabdf+aQGhsF5Gd4f8QCIaZIvIWgT1pJ4H9ehuBam838G8EggyCCfKT4Zj1EvCHYfk/EdiptgHX0O919y5gm4hsIbC5fzWvTc+JyJvA6wSqyN8Ky2cBPwqPbSdYnf2fsfgQzpYptQ/qQqLUPqheTqazPNwURiZwfSqdMDJB7QUQmcD0a8owmn51b27CPV3orWhFbZILTiCH/hOAV06X84vTcXZqCr88CFlUnvC4fGkXUTvKvcvvpSLab/85dbSTQ9tOFbWrYkacFVfXI3m2vYOnunh8a7Hz7C2rarlkTuFKKdfYSPsTT9Dxk5/id3ZiVVRQ+b73UnX77UMKJxlmH9RoCDU6kXD/0RLgWWC5qprYS3kYATVBDCWgDIaphHsmTfemYmeHxIrpRNuehpZ9ALRmbR48WsOp8mkc9vpt/Jcv66Q87jM7OZvbl9yOJRbprhxvvnACf8A+LDticckNc4klCq0TqsoPNx7nRFthxPWKuMNHrl1IpISqcrSMsYCqAJ4jWF0J8MeqOib+7RcSU1bFZzAYJh5VJb2veKuMlXCIzC6H9uN9ZdOiHnWxHDMr5jItPq2vvKUtWI2d7DrJluYt/S7lJTYJL1xdUyScIEzHkRdItpfOtMv2421n07Xziqp2hp6Al6nqaiOcSmMElMFgOGvc5h68jmKtVHxpNZI+A7nCFc2K6hzEKlhYuYCoHQimU+2RPh+I1xtf581tB4tcygFq5lUwY/bgETlmVydYMqv4+OuHWknnhtsQYJiMGAFlMBjOCvWVzP62onK7MopTWwbtx4qOXVQ3DcuycawIi6oWIQjprEVHT+Ct6rc7vLH1TTwtXD3FyiMsuLg4SvlArlsyg4Ee3Omcx6YjExoQwXCWGAFlMBjOitzJLryeYg+7+JIwnFBbsYAqnzmP+TMSAFRGK6ktrwXgVFsUzQkcSpJ2MxztONp3TeBSPqtklPKBzEjGWFVfWVS+5WgrXRmTQXeqYQSUwWAYNer6ZA4Wb551psexZwTu3/n2pz6q5rKirl+AzE3OoTxSzun2CHo4CdlgJdWSauFM+gwAcy6aRnLa8Gnhe7l6yYyiWII5T9lw4PSI6zBMDoyAMhgMoyZ7rBM/U2zXiS8NV0/p9uCVj1hQOZclM5NE7ECAiFgsrlpEvCNGuilRcPrhjiNEqoX6JYVu4sNRGY9w2bzqovKdJzs40228uKcSRkAZDIZR4Wc9Moc7isojtWXYVeFKp9TqKTkLnChRx2LJzH5nhrgXZUHPLLrThfHxPCvHwWlbURn9VpgrF00nNkAl6KsOGkjWMDkxAspgMIyK7OEOdEAkcxGILanuLyhhfyIv/9OKXjuRKtaJFEm7DPxyCgK/L+iiyW1gS/Pos9zGIzbrFkwvKt/X1EVj+9RKf/J2xggog8EwYvy0S/ZYcUDYyJwkdnlewNcSHnxU9Quo+dPLKIvaSHMGSQeqwqpoFblcuAKrSSPTA3Xc642vjzjqeT6Xz68mGSveM/XS/hZMgIKpgRFQBoNhxGQOtKED8luJLcQW59mJsj3QXUKVVjW3761tCQvjMeR0v03IEouEzELiHszv6itXVZ49+ixZb3T2o4htcfXiYtf0Y2d6OHJ6UmViNwyCEVAGg2FEeF1Zcg3F4bmi8yuw8lcqHSeKLy6bAdH+KEG5rEeiOYNQKOxyboTFl81HBmRMbM+08+KJF0fd5otnVzKtrDiVh1lFTQ2MgDIYDCMis7+tOJ1GxCK2YICXXdtRisizP6kqh7e3EEOIRQolkT8zxvSK5cwsm1lUxZ4ze9jfun9UbbYs4bqlxSGQTnVm2NM0VA4/w2TACCiDwTAsblua3KlUUXlsURUycAPtIPufejl1tJPWhm4EqEn272/SMgedEWVvUze3zL8Fxyq2Hz1//Hk6s6MTLEtnJamriheVv7L/NJ5vVlGTGSOgDAbDkKgqmX1tReVW3CE6d0B6dDcLnY1F5/Y6SKS6shzdeaavuDePk9qCPycBIpzqzOC6Ca6fc31RNVkvy7NHnsXX4kCygyEiXF9iFdWeyrHjRPFmY8PkYUpm1L3Q6WpNs3tDI/s3NZNNuUQTDkvXzmLF1XV9SdqmIqZfUwu3PUPP5mZ6NjfhdWYRx8KpLSNSX44Vc4gtrkLsAYHvOk/CQOERq4B4VRClfPOpgijliYhNecyhY2YU8lZiuxs6uW7pSo52HuVg28GC6hq6G9jctJl1detG3Jd508tYWFPG4ZYeujI5djV0srepkwdfOUR9VZz/etkcPnDFHOqrEsNXZhg3JjwflIh8Dvgw4AE+8FthltrxbMOdwN4w2y4i8lfAC6r67AivXwN8A4gTZKH8XVV9fahrBssH1XCgnee+uwvP9XGiNpYt+J7iZj1sx+LG+1aOemf9ZMD0a2qROdJB68N78V0fPD+QOargKVhC+dpaqt67qCBpIACHXoTDLxWW1a6CVXdw7K0zNBxoK7pXZ0LYrrmCsoq4w8euX0TGy/D9Pd+nO1f4WxER7lp6F3XlI89+29yR5ss/28MzbzXh+UrEtrAEEtHADha1bf7m7ktZW2L/VHjPIfNBiYhHkFVbCMaz31PVV/KO/z7wJaBWVdvDsncBjwOHCMaPJ4HvAN8NL5sPtIevFlW9Oa++jwJ/CxwHkgRp4D+ff8+pzoQKKBG5BvgK8C5VzYhIDRBV1eLUmOe3HQ8CT6rqw2d5/c+Av1PVn4rI+4D/rqrvGuqaUgKqqzXNE1/dCgSJ2bIpF89VCD2dfDf4u+r62URL5MSZrGRTLm+9FDxSy5Gi4xdWvwTbEZyo3eeOffun10yplZTbnqHln3egKKigA4KsqqdYMZuZv30ZTtWAGHlb/x1aDxeWXfQeOmKr2L2hkYFeFvFkhIVX1vIvrx7BH3Dsl9bNZe60Mk50neCJ/U8E7cmjMlrJPcvv6UvbMRwN7Snu+sdXyLheQQJDEagpj5HxfFD47m9eWXIlNQIB1aWqyfD9fwH+VFVvyDv+GpAFvq2q/xKWvQv4I1W9TUQSBOneP6aqL4fHH2SQsSkUUOtU9ffC/28E/h24UVV3jehDmeRMtA2qnmBWkAFQ1RZVPSkia0XkFyKySUSeFpF6ABF5XkT+TkQ2isguEVkvIo+IyD4R+UJvpSLyWHjtThG5P6+8S0S+KCLbRGSDiNSKyLXA7cDfishWEVkiIg+KyN3hNetF5JXwmtfDTJgDUaA3AmYVcFYCdveGxr6ZeKbHxc36qK+oH2hNxBI8z6fxUDtu1psyr8ZD7Xiej1jS15f814XVL8XN+qS7crhZn1zGZfeGhrP5OkwY3Rub8DMemvWLhBMEqdwV6NncXHjA96Cj2EEil5jLgS2nioSTWMLiNbOoLI/2RTjPZ3dD4AwxJzmHNbPWFB3vyHaMyvX8kc0ncCyKsuuqQk/OIxGxyXoej24u4SY/eiqBvhwfYVr3JPBnwC+XukBVU8BWYM7Z3FBVnwMeAO4f7typwkQLqJ8B80Rkr4h8XURuEJEI8DXgblVdC3wb+GLeNVlVXUegUnsc+ARwCfBREendlfcb4bXrgE/llZcDG1T1MuAF4OPhcvgJ4DOqukZVD/TeSESiwPeBT4fX3AwUuzLB7xMIuGPAl4HPns2HsX9TczDzVg1UKyWwbIszJ6dWqvgzJ7uxhkm5fSH2y3N93Jzy5i9OcvpEV9EG18mGuj6ZIx10b2hAPR8Gaa/EbKyYTWr7qcIDXU3gDVhtOXEOH1By6WJBN2d5f5Ty/Ajnvext7sQN7VVX1l05qOv5vtZ9I+rfk9tPkoxH+lR6+eTC31tZ1OHJ7Wc9oUiEk9zdwD8Df5137EPAQ8CLwHIRqR14sYhMA5YRjE1ny2ZgxTlcP6mYUAGlql3AWgKJf4pAGPwWgcB5RkS2Esw45uZd9kT4dwewU1UbwhXYQaB3s8WnRGQbsCEsWxaWZwl0vACbgIXDNHE50KCqb4Tt7VDVUkllfgf4A1WdB/wB8K1SlYnI/eHqb6PrFleTTblYtiAigw58YgUD31TCc31kmG/ahdyvXMblwOZmtj9/nOYjHSVTmU8kfsYjvb+VzpdOkN7biuY8irL+hYhjIY4FluAPFDol4u+dyi6ktbE4akPFjAT1edEn8iOc95LJ+RwOIz7Yls0t828hYhVvuv3F8V/QkS0OXjuQzrSLYwnJqFPUPSe8t2MJnelciauDw72/3/A1cKWSCie5K4BbgX8V6bvTLwMPqaoP/AfwS3nXvSMcr04AT6tqCTfIEVP6wU1RJnoFhap6qvq8qv4P4PeADxIInjXh61JVfU/eJb25oP28973/O6FO92bgmnDVs4XA+AiQ036jm8dZejGKyL+EM6WfhEUfAR4J3/8QuHKQvj6gqutUdZ3jFN86mnDwvaB50TKnpL1GfbCdCX9so8J2rCLHroG8HfqV6c5xeHsL235+nJP72nAnOA2535Mjtes0XS+dIHOoA80FnRHHKlLHAYhtIfHwe+srVnzAd3hA/L1UxuFoc7F7txO1WXL5zAIHi6hjsbREuvbdjf2CpzpePajr+c+P/HxY1/OKuIPrK5YlVCei2OH9Y45FeRgJw/WVinixEAxxe3+/4euBwU5U1VeBGmCmiFxKMEl+RkQOE6ym8tV8L4Zj1cXAx0KnqyJE5BPhuLNVRGYPcuvLgQvC/gQT7GYuIssBX1V71+hrCD7c94jINar6aqjyu0hVd46w2iqgVVV7RGQFcPUIrukEStmW9gD1IrJeVd8I7U8pVf31AeedBG4AngfeDYxM5zCApWtnsevlk9iOhWUJiWQ0CMeSN1ZkUjlWXFPP5e9ZcDa3mBAU2LOhgVhi0B/+BdMv1/VxM17fRMP3fGbOL/xq5TIux3cHHm2z5ldSu7iS6MDB/jzidWTJHGnHbeopJYdwasvIneiCcAAX20JiNoSrewDN+pStz/OgUy3YoOv7cODYdPxpxaq7hZfWlHSGWV5Xya6Gwk24h051k855xMOIEyumr+BI55Gzcj2/bfVsvv/GUaoSQcqPGeWBc4XkLad6si4fWj9/0DpGSjj22MBpAhPAX6rq/8o7fkhECr7sqnpIRL4E/DEl7FSq+o/AP+bVMfCeNxBoo2485w5MEibaZSoJfE1Eqgncs/cTfMAPAP8gIlUEbfx7YKQC6ingt0VkF4GA2TCCax4C/klEPgXc3VuoqlkRuTdsY4LA/nQz0DXg+o8DXxURB0hzlkbKFVfXsfe1RtyshxPqyUWkb9HuZj2ciM2q62YTKaFHn6xcfP1sDmxq7nMAGciF1K9I1MaJWHiuT6bbxbKEmrnFKwMAL+fTcKCNxkPt1MxNUr+kmnhycCF+LqgqXmuGzOF23NNDp5uI1JfjNnSDgFUWQQY6FeQ8xBbKrpjVX9hzGnL95tkTzZX0ZGIQLez7zPkVTJ9d2hFuQRjhvCfbv7J0fWV/cxeXzAnUgSLCu+a+i6bupiLX8zea3mBuxdxBXc8/cMUcHt18glToEDFwgE/lPKK2zV1XnJWPAoQ2qPC9AB9RVU9EPgS8b8C5jxKspAZuqfkG8EcislBVD4/gnveKyPVAGYGr+gcvFA8+mAT7oN6umH1QF36/LFu46o4leDmf9uYRRM8WYVpdGfVLqkeV4nwo1FfcUz1kDnfgdQwfDVwsIVJfDrbQ/uRB1FMkGtic8BXN+ogtTLv7ImIL8lZHJzbD3qcBaO+KsedwDcSroe6SvlPiyQgXv2POkKrc5/Y0s/VoW0HZvOll3L12bkHZ2bqebzpyhj9+eAdZz6Ms6uBYgusrPVn3nPdBGcYeI6AmiMEEFAT7ofa81sj+jc1kUi6xhMPSdbNYftXUjkzwdu5Xd3uGxgPtnD7ZXdK+M5DKmgT1S6uorEkUzfRHgnpKrqGL7JEOvJ5Sfj2FiGMRnZssiEzeG0kitf0UftrFijskVs+k7IpZxfuf3noCmnaScy3e3D+LnGsHAWKrAy2WWMKq62ZTXj204G1sT/PvrxcGmxWBj12/qMg2tKFhA5ubNhfVsXzacm5acNOg92hoT/Ho5hM8ub2BznSOiniE21bXc9cwkSSMgBp/jICaIIYSUIYLl3R3jsaD7bQc6+yzUw1FWVWM+iVVTK8vL47aUAJ1fbLHO8ke7cTPDO+EYcVsovMriM6pKA76Ohpe/Uc01cH+Y9Np7QgH+dpLIFENwNyV05m9tHr49qvy4CuHaesp9KR7x7Ia1i0sXNl4vsej+x+luWfAfizglgW3sGzasqLyc8EIqPFnarlNGQxTnHh5hIWX1nDZTfOZvawaexih0NOeCVzUnztO0+EOvEFc1P2MR3pf6Cq+r21Y4WSVOSRWTid53RxiC0tEJB8N6XZId3CqtaxfOCFBDD7C1eAI1bciUnJP1K7G4gjmtmVzy4Jzcz03TG6MgDIYJoBIzGbuiumsuWk+8y+eMWyIp0xPjiM7Wtj282MFLuoFruKH+13FB8OujFK2uobkNbOJzq0oDvZ6NrQdC1zKG6v7y2JJsGycqM3iNTNHpaZcWV/sUNvSmeFUZ6aovCpWdU6u54bJzUR78RkMb2vsiEXd4ipmLazk9PEuGg60k+4a3JnBzXgc332GEztPU+0I02whMoL9Y870OLGFldjT42dl0xoKv/V44FLu59UbC1ZBC1eXdikfiuqyKPVVcRraC70N9zR2MrOi2Ia1YvoKjnYe5UDbgYLyhu4GNjVtYn3d+lHd3zB5MALKYJgEWJYwc34FNfOStDX10LC/na7WwgFaAU25uG0Z/JRLCmgUmFYVY+aMOPFYoYu+CDizygLBVDk2XoGlOL67hZ70ADVbvDJwKa8/O5PN8rqKIgG1u7GD65bOKLX/hxvm3kBTdxNducIdIBubNjKvYt6oop4bJg9GxWcwTCJEhGl15ay8rp4V19ZTNassyHLRnSN3ootsQzd+qt8rTxXOtGXYe6Cdw8e66Em5iCVE5yZJXjubstUzz6twaj95hsbjxWq0+PQZzL94RokrRsbyugqsAYKoM+1yvLVUKEyIO3FuWnATMiDSj6ry7JFnyXrDu9gbJh9GQBkMkxARoaI6zqI55Swus0mmXTQ7uOODAh3dOQ61ZjgescnMSCDnOXVJLutx8PVDReUSK2PJ+qH3Ow1HWdRhwYyyovI9JZwlepmTnMPltZcXlXdkO3jx+MijnhsmD0ZAGQyTDM35ZA630/XyCVK7zhBVmD8nyYolVdRMi2ENcDcXW3Cmx4nOr8CZHqerPcPe1xrZ+eL5i6Kuqhze1kKuva3o2NylyWH3O42EFSWcJfIjnJdife16ZpXNKirf07qHva17z7lNhvHFCCiDYZLgZ9whXcWjUZs59eWsXFpFbU2CSNwmUpMgOr8SpzpWtE9qpC7qZ8Opo520NnZDptCVu7I8Q93Ksw4VVMDimiTRAauwIML54PsHh3I9f+H4C8b1fIphBJTBMMF43TlSb52m66WTgav4MGlHYtPjLL55Hut//WIWXVNPrGw0LuqtuEOoCkdCqjPL0Z1nggSFmX6nBMf2WTy3FameN8TVIyfqWCyZWexkMTCg7ECqYlW8Y+47isqN6/nUw3jxGQwThNeRIXO4A7e5dFTxgTgz4sQWVmFPi/V5svW5qJ/oovFAO6nO4VzUWzm5v51ZCyqoW1Q1ahdw3/M5sOVUkNMq00l+qP2Fc1qJVpT3RY8YC1aUinDeUhjhvBTLpy3nSMcR43o+xTECymAYR1QV70w6EExnho4qDqGreG0ZsQVV2JWlA6BaljBzXgU1c0MX9QPtdA1Rt+/6NB5op+lQBzPmJKlfWkUiWbrugRzf00pPe7hhNk+9N3NaN9Mr01C1eET1jJT5JSKcewMinJfCuJ5fGBgVn8EwDqiv5Jq66X69ke7NzcMKpwJX8UtnDiqcCq7pdVG/NnBRr64t9oIb2KaWY53seP4E+95oKtp3NZD2Uz00HmjvL0gHAioec5lfF5ZXjY16rxfLEi6qK3aW2NUwvC0p7sS5ecHNxvV8CmNWUAbDeaQ3qnjmSAf+SKKKR8Ko4vMqsWJnlxtLRKickaByRoKejiwNB9o4c7J7cG8+VVobu2lt7A5SsS+pompWYRT1XNbj4NaWgmvIdCCiLJl7BtsO6x5jAQWwsq6yKAXH8dYUHekclYNnvwVgdnI2V9RewaamTQXlHdkOXjj+AjcvuHmsm2sYQ0w08wliqGjmo0pzMIV4O/UrfvEMInXleGfSI48qvqCS6JxkkHJ9jMn05Gg82MGpo52B/WgYyqpiVM2M03iog/2bmulpzyIC02eXUzM3SVRS0LCNebXt1M8MVWiROFz3+4FecgxRVb7zymFaRxDhvBSe7/HY/sdo6mkCoCvbxd7Wvexv248tNtPi07h14a3ctuS2IdV+Jpr5+GME1AQxmIDKHOmg9eG9I08UN0V4u/RLAc14aNoDgcSqGdhDCF+7zCG6sIpIXfnYBG4dhlzWo+lQB82HO4b05utszXB42ymUIEuw5/moHzhJWJawZGmOuthBli9s6ZdHNcvg0rsHrfNc2HDwNK8eOF1QVlMR476rFwxyRSHtmXZ+sOcHHOs8xnPHnsP3fRzLwbZsKiIV5PwcETvC56/9PGtmrSlZhxFQ48+kFFAi4gE7CFSQh4D7VLVtHO57J7BXVd8a5Pi3gduAZlW9JK98OvB9YCFwGLhHVVuHulcpAeW2Z2j55x1BsmjHQrM+eH6fo5SG75PvnIt9nqMEjCVeyqXrheMgFKUPhwurX+opvQ9Mw3xPZVfM6ksC2ItdFSW2sAqnJjGiPE9jjef6nDraSePBdrKpQtVjNu2yZ0MjAFaJ5+V7PtPkIO9550mSFXltX/JumH/VeWlvW0+Wf3n5cFH5r169oGQA2VK8dOIlPvviZwFwrP7nEbEiVMeqyXiB88c3b/lmyZXUcAJqwLi1iyDl+whSKU8cInIl8GWgFugBNgGfAu4B/hY4DiSBg8DnVfWV8WzfZB0NUqq6BkBEvgN8AvjiONz3TuBJoKSAAh4E/g/wrwPK/wT4uap+SUT+JPz/j0d7857NzainWAkHP+WiueIZrro+6T1niC2cOunRM4fbUddHIlYgjEpwIfZLbEFzPrmG7r5+OTUJYgsqC1zFJwLb6Y+ifuZkFw37+13UW4534fuKM4gbt2ULtfZ+OppyJCuq+w9UzS15/lgwWITz3Y0dzKyYOaI6drXswha76HPP+TnSXpqEk6Aj08GTB57kN1f/5tk0M3/c+h7w28BXzqai8UBEaoEfAh9S1VfDsruBXq+U76vq74XlNwKPiMiNqrprvNo4Fbz4XgXmAIjIEhF5SkQ2iciLIrIiLK8VkUdFZFv4ujYs/1UReV1EtorIN0XEDsu7ROSL4bkbwuuvBW4H/jY8f8nAhqjqC8CZEm28A/hO+P47BIJu1KS2nwrURKqDb9a0BbdpUk/KinCbemA49dUF3q9IXRnJq+opv3wWznlIeXG2WJZQM7eCS26Yw7L1tSSnxzlzsrvkyqmXRDRNZayVMyfzNAC2AxXn1217RX2xCnhPYycj1QI9feRpqmPV2FIseHtXTwknwVOHnzq3hga8CCwVkXIR+XY4Dm0RkTsARKRMRH4gIm+FY9drIrIuPPZ/RWSjiOwUkc/3VigiXwrP3y4iXw7LHgyFSu85XeHfehF5IRzL3hSR4p3LwcT/O73CCUBVH1bVpoEnqupzwAPA/WPx4YyUSS2gQoFyE/BEWPQA8ElVXQv8EfD1sPwfgF+o6mXAFcBOEVkJ3AtcF85qPOBXwvPLgQ3h+S8AHw+Xrk8An1HVNapauMNvaGpVtSF830iwXC7Vn/vDL95G1y326PLTLlgSDF6DqX1Eho00MNlQ1x/ecH5B9kuQmA22jNhVfKLodVFfdd1sLNvCiZYeGixbmB5rQizBy39elXPAOjuvw5FyUW1yVBHOB9KV6yJqR6mIVhS5nvcKLcdyivZN5eH0/n7DV8nBWkQc4L0E6r7PAf+pqlcCNxJMgMuB3wVaVXUV8OfA2rwqPqeq64DVwA0islpEZgB3ARer6mrgC8N098PA0+HYdxmwtcQ5lxCo9EbKZmDFKM4/Zyarii8hIlsJVk67gGdEJAlcC/wwb/bZq3x+N/BrAKrqAe0ich/BQ38jPD8BNIfnZwlUeRA8oFvGquGqqiJSckqnqg8QCFnKy8uLzrHiTuAKbAtWzMZPKUUhBlTPi5fX+UScYFU45GB+IfVLBIlaSMQO+jVJVksjJZ6MoL4iArmMh5sLbIRiCbEyh3JpQj0tjFZ+HtzLB9Ib4fxQS6HtdndjJ/OmD73nCyAZSeL6bp+Q6sx2oiiOOJRFgutd3yUZSQ5WhRsKjsHoHbcgWEF9C3gFuF1E/igsjwPzgeuBrwKo6psisj2vnntC4ecA9cAqArNDGviWiDxJ//g1GG8A3xaRCPCYqm4d5vyRMO5f5MkqoFKqukZEyoCnCZaiDwJtvTreESAEy9fPljiW0369gEeJz0FE5gE/Cv/9hqp+Y4h7NYlIvao2iEg9/YJwVCRWz6T7jUYkYSGOhZWMgK/50WTw0x5ll82kfF3JRdqkRHM+PdtOYcUHn2FfMP0S+lfBgJ/yKFs/tSIWLF07i10vnyRWFiFWZhFVDQRW2K9yrxnfV2bOyxvIz6P9KZ8V9RVFAmpfcyc3Lp+JM4RaEuDWhbfyyL5HiNgR4k6cqB3FV7/ALpVyU3xw2QfPtnmpgeOTBBV/UFX3DCgvWYGILCLQDq1X1VYReRCIq6obOjTcBNwN/B7BxNwl1ISJiAVEITBHiMg7gfcDD4rIV4BO4H+Et/pNYCfBJP7xEfbvcoIFw7gxWQUUAKraIyKfAh4jUOcdEpFfUtUfhg9+tapuA34O/A7w96FaMBmWPS4if6eqzaGnXYWqHhnilp2EBkJVPQasGWFTnwA+Anwp/DvSB15A2RWz6NnchOY8JBL+aPJsHJrzsKIWyetmT6l9Q8nrZpPedTpYTZQwvF/I/RJbKLuiOP3DZGbF1XXsfa0RN+vhRIPvYa8LfES7sL0usISauaGAEitQ8Y0DvRHOs3nqxd4I50tnFUecyOe2Jbfxo4M/Iu2miTtxLLGwpF+opd00UTvKbUtuG8smPw18UkQ+GWpXLlfVLcDLBJ5yz4nIKuDS8PxKoJtAC1RLoCp8PtQglanqT0TkZQKvOgi8htcCPyCwoUcARGQBcFxV/0lEYsAVqvr7wKO9DRORY8DrIvJjVX0tLPtA2LYCROQGAvvTjWP1wYyESa9TCR/mduCXCWxIHxORbQTS/47wtE8DN4rIDgKV3arQVfzPgJ+Fy+dnCJbLQ/EQ8JnQmFnkJCEi/07gtLFcRI6LyMfCQ18CbhGRfcDN4f+jxqmKMe3ui0AJvPg8P3CY8Pwgi6rCtLsvmlKDOJh+TbV+JafFufG+lUCwwddzg355ro+TOgnAwtU1ROPh/LaiFpzxsa8FEc6LVXDDRTgHqCuv4/PXBj4HHZkOcl4OVSXn5egI4wp+/trPj3WMvr8mEBrbRWRn+D8EE+6ZIvIWgT1pJ9AeTri3ALuBf6NfWFQAT4Zj2UvAH4bl/0Rgp9oGXEMg3ADeBWwTkS0EtvivDmxY6AzxIeDLIrJHRHYB/4Vgog5wb+hksRf4U4KV4LiuoCblPqi3AyaShOnXZKerNc2e1xrZv7GZTMollnBYu+Qt5lcf6xdOAPOuhKU3jVu7Drd08+iWEwVltiXc/87FQ0Y476Wxu5EnDzzJU4efoivXRTKSHPdIEqGmJ6Kq6XAy/CywXFVNgMA8jICaIIYSUAbDpOX1f4LulsKySz4IMy8atyb4vvLPLx2ke0AIqZtX1nLp3PO3j26MBVQF8BzB6kqAP1bVn45F3RcSk17FZzAYJgnZnmLhBOPmINGLZQkX1Rbbm3Y3Tp1suaraqarrVPUyVV1thFNpjIAyGAwjo/14cVl5DUSHd/Eea1aW2LTbG+HccOFgBJTBYBgZ7UeLy8Zh/1MpZlXEmFZWnGpjT+PwzhKGqYMRUAaDYWSUWkGNs3qvFxEpGfpo9wgSGRqmDkZAGQyG4XGz0FkUog2qJ2YFBbCiRKbdlq4spzozE9Aaw/nACCiDwTA8HSdAB8RKjFdCfOKiz1eXRZldHS8qn0rOEoahMQLKYDAMT/ux4rIJsj/ls7zu3CKcGyY3RkAZDIbhmUT2p3zONcK5YXJjBJTBYBga3wtUfAOpnj/+bRlAWdRhYU2xm/tu4813QWAElMFgGJrORvAG5C+LJKBsxsS0ZwArSqj59jZ14g6SvdkwdTACymAwDE1J+9Pc4ZNQjhOLZ5YTHZBLLOv6RWk5DFMPI6AMBsPQlLQ/TbyDRC8Re5AI50bNN+UxAspgMAyOaukV1ATufyrFyvriPVGHW7pJ57wSZxumCpM6YeHblvYTsP0hePNRyLRDrAouuQtWfwiqxicx3HnB9Gtq0X4CNn4LNn8X3DQ4cZi1AurXQHJyZT6eN62M8phdEOHc85V9TV3FEc4v1Od1AWLSbUwQg6bbOPoaPPFJ8LKBIdqKgJ+DXArsKNz+NZh/1fg3+Fwx/Zpa9PYr0xn0TWxQD3w3EFT3/Ouk69fze5rZcrStoGzOtAT3rMtb7Z3D8xrLdBuGkTEuAkpEPgd8GPAAH/it3hTD44WI3AnsDTPtIiJ/Bbygqs+O8PpfAv4SWAlcqaob8459FvgYQf8+papPD1dfSQHVfgK+e1fw3omDmwl+SITPyA1zmd34p1A+OTyoRkT3aXjufwbvS2VevaD6JcFA58SCVQfAfY9OrZl5/vfQzYA7YE+RFQ0imE+yfjV1pPm314oD2v7G9YuoSkQG/L5ikEsHQteJBc8sF/ZzkH6NRECZsW5sOe8qPhG5BrgNuEJVMyJSA4xPfuhC7gSeBN4CUNW/GOX1bwIfAL6ZXygiqwjSJl8MzAaeFZGLVHX0yu/tDwUCKV4FmS7IljDyumnY8QOYf82oq58wjr4Cue5wUHBLn3Mh9SvXDZYDkbLgeW7/PrzjDwevZ7Kx/aHgeVhOv5DNJ5YMhNYk69esihjTy6Oc6S5MSru3qZP1C6fn9cuG7o7+0E3ZbkhMC1ZU6baz7pcZ68ae8XCSqAdaVDUDoKotqnpSRNaKyC9EZJOIPC0i9QAi8ryI/J2IbBSRXSKyXkQeEZF9IvKF3kpF5LHw2p0icn9eeZeIfFFEtonIBhGpFZFrgduBvxWRrSKyREQeFJG7w2vWi8gr4TWvh9kuC1DVXaq6p0T/7gAeUtWMqh4C9gNXntUn9eajwY9EFXI9pc+xItC866yqnzCadweD3VBcaP3yXch0BLPyzd/tn51PdnrOwKbvBN+/bBd9q/c+whVipAzefGQiWjgoIlIygOzuhg606xRsfDDsV/eAuIJ5v7dz65cZ68aY8RBQPwPmicheEfm6iNwgIhHga8DdqroW+Dbwxbxrsqq6DvgG8DjwCeAS4KMi0qsD+o3w2nXAp/LKy4ENqnoZ8ALwcVV9BXgC+IyqrlHVA703EpEo8H3g0+E1NwOjGU3mAPluTsfDsiJE5P7wy7jRdUusJDLtwUAtMvgeE7EDtctUwk0H7R6KC7VfCPSchlf/EfY/C+lJGsi0oyEYmF9/AFKtDDo02OH304oEAniSMXDTbjLTyPRDT9Lz8jeC1dGgQ174exu6X07v7zd83T/guBnrxpjzruJT1S4RWQu8A7iR4AP6AsFDeEaCgdgGGvIueyL8uwPYqaoNACJyEJgHnCZ4UKFCmXnAsrA8S7C8BdgE3DJME5cDDar6Rtje8/arU9UHgAcgsEEVnRCrCgy2dhSiSUi3UzSD7dWZTyWceNDuoQbzC71fXg6OvQEnNkPtxTDvqiAb7USiCq2H4eiG4G8vg/VLLIiFE24/B7HiCA4TTVVZhNlVMXoa9zG7YyuVmUYAWjrjlA/Vr2hoWhq6X24oTEpixrqxZ1zczEMd5fPA8yKyg2CWsFNVBzM49E6l/bz3vf87IvIuAul/jar2iMjzQG/c/Zz2e354nGUfReRfgMuBk6r6viFOPUHwpellblg2ei65K1AH2dFA1WdHgoEtX0hlOmHFbbD81rO6xYTQ0wK7fxLYLgbjQuiXAl6m0G7j5wK37L7/PWjYHrxqlgU2t/F2NPB9aNkDR18tneNp1oqgfVbvQC7B9zFa3q/SzPXAFR8ZtyaPCN+H5re4ru05jp06UnDodHeW+TNXII0D+1UW9issO8d+mbFubBkPJ4nlgK+q+8KiNcAu4D0ico2qvhougy9S1Z0jrLYKaA0f2Arg6hFc0wkUK6hhD1AvIutV9Y1QJ5tS1V8fYVueAP5NRL5CYDhcBrw+wmsLWf0h2Pb9wF4RSQSDQb6NI5cKZnfX/8Gk8p4alvJZcOjF4H0kUXz8gupXWTCpyHYHTi7iBCumUrTsC17V8wJBNX3x+Q0f5LnQuB2OvR6q8Qah9hJo3BkI03hVMIhbeauOXArsGKy+9/y1dTR4OWjYFvQr3U6t5XOcQt1DxvXpmLGSqqa8fkXLg9VTL+fYLzPWjT3jYYNKAt8RkbdEZDuwCvgL4G7gb0RkG7AVuHYUdT5FMLvYBXwJ2DCCax4CPiMiW0RkSW+hqmaBe4GvhW15hv4ZSh8icpeIHAeuAX4sIk+H1+8EfkDgMfMU8Imz9mqpmhPsw4BAX+5lAzWMlw315wTHp9IgDm+/fqGBSSM+LRC6ZdOHrqftGGz/QbAptvHNYCUwluTScORV2PB12Pv00MIJoHI2vPvPAs829cLXJHxe2R44/FJg39v3TKgSD0IfVZdFik5v0Olw019AYnr/nq6x7ZcZ68YYs1F3ghh0oy6EO92/HxitMx3B6uKSDwQzu4keFM6Ft2u/sj1wYhOc2BgIi+GIVwU2qvrVgZr3bMl0wvE34OSW/r1mQ1E2A+ZdGaygbGfyPq90e2DPa9gaqsCLaenKsK+5C4CeyDROVl5GZ+Uy7r9hGU5Xw1n1S8xG3XHHCKgJYkgBZbgwcbOBKur46yPz5oskYO46mLO2tGp0MHrOBI4PTW8G6qzhqKwPVIw1F02aCOUl6W4J+7WzOP38ADxVftEY40hyNW3x+X39um11PctqS2m/hscIqPHHCKgJwgiotzG+B81vBYNtd8vw59uRwNFi3vpgdTUYHSeDOlv2hqrGYZi+GOZfHSQenMyCqf142K99w58LgfPJvKt4+kSEt04WTgSWzEpy+2Wzz6oZRkCNPyZYrMEw3lg21F0aqNJO7w8G31IpLXrxcoGq7sSmwOFi/tX9Luqq0HoodBU/MngdvYjArJUw72qomFwBXwtQhdMHAk/DoT6bXsSC2lVBv5IzAVjhdRcJqN4I5/HIcPvXDJMBI6AMholCJJjt1ywLHCWObggE1mCoD407gteMJVA+MxBOpVzFB2I5gU1r3pWB88NkZdSrSwfqLy+5uhwswvnepk5Wz60e44YbzgdGQBkMk4HqecGr6xQc2wBNb5W2s6gHXc3BispNB4Ny5dxA6JRS0zmxwIY1d13/ZtTJiJsNXeBfG7l9bs7a4BUtK3mKZQnL6yrZfKTQa3F3oxFQUwVjg5ogjA3KMCR9nmpbgv1LvgudDdBxorTnWqQ8SMNeXtMf8WHuepi9ZnJH6OjzcNw0sniF8crAw7FudenI+ANo7kjzvaEinI8CY4Maf4yAmiCMgDKMiK5m2PrvcPgXI3NRT0yHFe+Hi+8cneffeDNQAA9HeU3gaThrZeGm4WFQVf711SNFEc6vW1rDlYuG2Z82ACOgxh8joCYII6AMQ9J9OlB39bqK+x50NUHH8dJBdWMVUDUv3BQsI1KBTQjDqTAHUj0vcHyYseSsPQ1fO3iaVw6cLiibkYxy39ULkFHUaQTU+GME1ARhBJShJB0nA8+1ln2lXcXVh+5TwSbaXHewYqqaEzoIlBhsh3AiGFdG4gSST82ywFuxau4537q9J8e3Xz5UVP4rV89nVkVRIIVBMQJq/DFOEgbDRKMKZw4GA3hbsb2kALGgoh4WvwuSdXB63zAu6m6ei3qhG/Z5R3VkbvS9iFXsRj8GVJVFmF0d52RboYp0d0PnqASUYfwxK6gJwqygDPg+nNodrJi6moc/33Kg/rJgNZTvKn6WG1mpnjf8uWfD+dqIfA5sO9bGf+4u/IyTMYePXb8IyxqZms+soMYfI6AmCCOg3sZ4ubyo4m3Dnx+J59mThhgfu06FdqvhQwEBgfps/jXnZN8pYLxCOZ0FqazHAy8cxB8w3n3wirnMnzEyG50RUOOPEVAThBFQb0NyqSBh4YmNgXv1cMQqgo219WtG5FLdxwiCqRZQXhOo1WatGpWHXB8TFQx3lDy+9QQHTxX+5i6eXcl7Lq4b0fVGQI0/RkBNEEZAvY1Id/RHFR+pwJh3VWCPORuB0Uu2B05uhuMbz8seI1JtQb8ato7MVTw5M7CBjdJVfKzY09jJT3Y0FJRFHYv737mYiD185iEjoMYfI6AmiOHTbTwEbz4KmfYgFfwldwUJDad8Woq3Ub+W/RdoOxKo3EYSVbxqTjCA1ywb2+CtXi7IkHvstb6cSUPS66JeUQ+7Hi/u15Kbg3417xq5q/h4JGQchpzn88ALBznTnWFXQyd7mzrJuD4zk1E+uHYeH7hiDvVVg6sajYAaf4yAmiAGFVBHX4MnPhkkUYskwIoEacNzqSAV/O1fg/lXjX+Dz5W3U79yPUECPPXhovcO7yo9Y0noUj3v/A7gvhcIlWMbAnvVULQfh71PBSudeDU48f5++T4sH0G/xtBVfKz4+vP7+acXDuL5SsS2sCRIcOjYQtS2+Zu7L2XtgtIbeI2AGn+MgBoEEfGAHXlFd6rq4UHO/SiwTlV/T0T+EuhS1S8PVX9JAdV+Ar57V/A+kghmvl6OvuTVvRs0b/3S+LkKjwVdp+CpPwnelwq7cyH1y00Hwgr61V6X3RskxctHrEDVNf9qSM4an/b20ufW/mqwP2kgmQ7Y9v3gve0AEngQ+qF6cqh+WXagmpx31Zi6io8FDe0pPvTNDXSkc0UqvZnJGBnPB4Xv/uaVJVdSIxFQIvI54MOAB/jAb6nqa2PXi+ERkTuBvar6Vvj/XwEvqOqzI7z+l4C/BFYCV6rqxvPU1GEx+6AGJ6Wqa8b1jtsfCga3eFWwski30yecenHT8Po3A5XJVOHoK8Gg58TAKxEFAS7MftlO0K+mnf39sp3A6WHuekhUj2dr+xEJVm0zlpR2UW96MwhK2zeZ0H7hBIP0KxLE/Zt7ZWDLmoQ8svkEvvrEHLvImy/tepRFHdpTWR7dfILfvXHpqOsXkWuA24ArVDUjIjXAKLxbxow7gScJUrOjqn8xyuvfBD4AfHNsmzV6hrcMGvoQkcPhlw4RWSciz4/pDd58tN/dNttFkXCCQIXUvGtMb3vead4dzMCH4kLvVyQOC6+Dq38Xlt0yccJpIFVz4dK74cqPBzmqxBpdv6JlsOidcM0nYOnNk1Y4ATy5/STlsQjxSPGwl3UDW1pZ1OHJ7Q1Fx0dIPdCiqhkAVW1R1ZMislZEfiEim0TkaRGpBxCR50Xk70Rko4jsEpH1IvKIiOwTkS/0Vioij4XX7hSR+/PKu0TkiyKyTUQ2iEitiFwL3A78rYhsFZElIvKgiNwdXrNeRF4Jr3ldRIrSC6vqLlXdc7YfwlhiBNTgJMIHvFVEHh2LCkXk/vDLuNF1S3g9ZdqDH77q4BlRxS4di20y46aDdg/FhdovOxoM+ld/IhjIJ2vKi/IaWHkbXP3bgA4voPr69buB4J3MgWlDOtMujiVDJit0LKEzPainpdP7+w1f9w84/jNgnojsFZGvi8gNIhIBvgbcraprgW8DX8y7Jquq64BvAI8DnwAuAT4qIjPCc34jvHYd8Km88nJgg6peBrwAfFxVXwGeAD6jqmtU9UDvjUQkCnwf+HR4zc3ACNw7Jw6j4hucMVfxqeoDwAMQ2KCKTohVBaoUOxoMZJkSmx0LVC9TBCcetHuowfxC65flQDQZDOKWM7p9TBNJvCoIoeTnAltTrrvQU8+KBN/N3n6N4z6mc6Ui7uCGzhHxiE061+9ZmYgGz9D1lYr4oH1yQ2FSElXtEpG1wDuAGwmEwRcIBM4zYWBaG8hfoj0R/t0B7FTVBgAROQjMA04TCKXQOM08YFlYniVQ5QFsAm4Z5iNYDjSo6hthe0ewm3piMQJqdLj0rzrHPojXJXfB5u/2CyjLCWxS+aupXBcsvSWwYUwV2o/C/p8HOYsG40Lpl0jw/Oxo8D7dBld8ZMKaeFb0fg/jVcH30E0H+aimeL9uWz2b779xlKpElMq4Q8yx8Hwl6lh9ThM9WZcPrZ9/1vdQVQ94HnheRHYQrIh2qupgxtVetYGf9773f0dE3kWw0rlGVXtCs0Lv2JPTfi83j7Mcz0XkX4DLgZOq+r6zqeN8YQTU6DgMrAV+CnxwzGtf/aHAeyqXClQmTqxwVZFLgT0Nbvzc1No3NGslHN8UvC+lCrqg+xWD1feOb7vOlYHfw4F9m6L9+sAVc3h08wlSOY9ExC5S9aVyHlHb5q4rzu47KCLLAV9Vez1O1gC7gPeIyDWq+mqo8rtIVXeOsNoqoDUUTiuAq0dwTSdQZFsC9gD1IrJeVd8I7U8pVf31EbZl3DE2qNHxeeCrIrKRYMYytlTNCfYDQTBD7V09edngfwiOT6VBHEy/TL8mBfVVCf7m7ktBoT2VJef5qCo5z6c9lQWFv7n70iE36w5DEviOiLwlItuBVcBfAHcDfyMi24CtwLWjqPMpgpXULuBLwIYRXPMQ8BkR2SIiS3oLVTUL3At8LWzLM5TQBInIXSJyHLgG+LGIPD2K9o4pZh/UBDF8JInvw5uPBHaoWCVc8oFgxjrFBoUCTL+mFhdovxraUzy6+QRPbm+gM52jIh7httX13GUiSUw6jICaIEwsPoNhamEE1PhjVHwGg8FgmJQYAWUwGAyGSYkRUAaDwWCYlBgBZTAYDIZJiRFQBoPBYJiUGAFlMBgMhkmJEVAGg8FgmJQYAWUwGAyGSYkRUAaDwWCYlBgBZTAYDIZJiRFQBoPBYJiUGAFlMBgMhkmJEVAGg8FgmJSYhIWTkK7WNLs3NLJ/UzPZlEs04bB07SxWXF1HctrYJ/IdL0y/phamX4aJxqTbmCAGS7fRcKCd5767C8/1caI2li34nuJmPWzH4sb7VlK/pGoCWnxumH5NLUy/ijHpNsafYVV8IuKJyFYReVNEfigiZePRsNEgIodFZEf4ektEviAi8fCYJSL/ELZ/h4i8ISKLzvF+7xKR9vBz2S4iz4rIrPDYR0Xk/5xNvV2taZ777i4AYmURbMdCRLAdi1hZBIDnvruLrtb0uTR/3DH9Mv2aDJzvfpmxsuT9zmmsHImKL6Wqa8IKvwf8NvCVc2n0eeJGVW0RkSTwAPBN4CMEKY5nA6tV1ReRucCQmQJF5LCqLhzmfi+q6m3h+f8L+ATwP86lA7s3NOK5PrGyCOormZSL5/qQt8h1cx7PPvgWs5dWn8utxpWT+9vobs/gRGwyPW7Jc0y/Jg9vq34JgYBKODhRm0xPjj2vNbL21oVncwszVpbmrMfK0TpJvAgsFZFyEfm2iLwe5r2/I7x5mYj8IJTMj4rIayKyLjz2f0Vko4jsFJHP53XwS+H520Xky2HZgyJyd945XeHfehF5IW+W8o6BDVTVLoIvxp0iMh2oBxpU1Q+PH1fV1lH2e1BERIAK4Jzr3L+pGSdqAwTCKVconAAs2+LMyamViffMyW4se+ivmunX5OFt1S8FL+eTSQUCy4na7N/YPBa3M2PlAM5mrByxk4SIOMB7gaeAzwH/qaq/ISLVwOsi8izwO0Crqq4SkUuArXlVfE5Vz4iIDfxcRFYDJ4C7gBWqqmFdQ/Fh4GlV/WJYT8kltKp2iMghYBnwA+Cl8AH9HPh/qrplpP0egneIyFZgBsEs40+Hu0BE7gfuB4hGo0XHsymXWFnwSHzXL12HFfygphKe62NHhh7wTL8mD2/HfvX+3ixbBl01Ao6IbMz7/wFVfWDgSWasLGLUY2UvI1lBJcLKNwJHgW8B7wH+JCx/HogD84HrgYcAVPVNYHtePfeIyGZgC3AxsApoB9LAt0TkA0DPMG15A/h1EflL4FJV7RziXAnbcRxYDnwW8Ake+E1FJ4v8Yzjb2ArM7n0vIp8bpP4XVXWNqs4D/gX4/4ZpO6r6gKquU9V1jlM8N4gmHHwvWDJZTulHo36gkphK2I6FDjOWmX5NHt6O/er9vfmeEksMOm93e3+/4WugcDJjZWlGPVb2MiobVF4DBfigqu4ZUF6yAgkMbX8ErFfVVhF5EIirqisiVwI3AXcDvwe8G3AJhaeIWEAUQFVfEJF3Au8HHhSRr6jqv5a4XwWwENgbXpcBfgr8VESagDsJZgh9qOon8q4/PLDPw/AE8B+jOL8kS9fOYtfLJ/t04hmKbVC+5zNzfsW53mpcmT67nFNHO7Ese9BzTL8mD2+rfuXZoADcrMeq62ef7S3MWDk8oxorz3Yf1NPAJ0Xkk+Fy8/JwKfgycA/wnIisAi4Nz68kWNq1i0gtwfL3eQmMdGWq+hMReRk4GJ5/GFhLsOS8HYgAiMgC4Liq/pOIxIArgIIPPazz68Bj4QO+AmhU1ZPhA1xN4WxlLLgeOHCulay4uo69rzXiZj2cqE28PFJw3M16xHC4+aOrptR+ja7WNE98dStAn40tH9OvycXbuV+2Y7H8qrqxvK0ZKwsZ1Vh5tmv0vyb4ILaLyM7wfwg6O1NE3gK+AOwE2lV1G8FydTfwbwQPBwKD2ZMish14CfjDsPyfgBtEZBtwDf2eJO8CtonIFgKPk6/mtek5EXkTeJ1gef1bYfks4Efhse0EM46zcgMfwDvCZe024D7gv51rhclpcW68byUAmZ4cnuujqniuT6YnB8CN962cUoMCmH6Zfk0OJqhfZqw8h7FyTDfqhsa4iKqmRWQJ8CywXFWzY3aTC4TBNupCMNPb81oj+zc2k0m5xBIOS9fNYvlVU3unu+nX1ML0qxAZw426ZqwcGWMtoCqA5whmDAL8sar+dMxucAExlIAyGAyTjzEWUGasHAEm1NEEYQSUwTC1GEsBZRgZU8tP1GAwGAxvG4yAMhgMBsOkxAgog8FgMExKjIAyGAwGw6TECCiDwWAwTEqMF98EISI+kBrBqQ7BhrkLDdOvqYXpFyRU1UzqxxEjoCY5IrJRVddNdDvGGtOvqYXpl2EiMLMBg8FgMExKjIAyGAwGw6TECKjJT1FCtAsE06+phemXYdwxNiiDwWAwTErMCspgMBgMkxIjoAwGg8EwKTECapIgIreKyB4R2S8if1Li+EdF5FSY+GuriPzmRLTzXBGRb4tIc5gU7YJBROaJyHMi8paI7BSRT090m8YKETksIjvC793GiW7P2VLquyci00XkGRHZF/6dNpFtNBRiBNQkIExe9o8E6Z1XAb8cpoEeyPdVdU34+udxbeTY8SBw60Q34jzgAv9NVVcBVwOfGOQZTlVuDL93U3nP0IMUf/f+BPi5qi4Dfh7+b5gkGAE1ObgS2K+qB8OMmg8Bd0xwm84LqvoCcGai2zHWqGqDqm4O33cCu4A5E9sqQz6DfPfuAL4Tvv8OcOd4tskwNEZATQ7mAMfy/j9O6cHtgyKyXUQeFpF549M0w2gRkYXA5cBrE9yUsUKBn4nIJhG5f6IbM8bUqmpD+L4RqJ3IxhgKMQJq6vAjYKGqrgaeoX/WZ5hEiEgS+A/g91W1Y6LbM0Zcr6pXEKigPyEi75zoBp0PNNhzY/bdTCKMgJocnADyV0Rzw7I+VPW0qmbCf/8ZWDtObTOMEBGJEAin76nqIxPdnrFCVU+Ef5uBRwlU0hcKTSJSDxD+bZ7g9hjyMAJqcvAGsExEFolIFPgQ8ET+Cb0/opDbCWwchkmCiAjwLWCXqn5lotszVohIuYhU9L4H3gNcSB6YTwAfCd9/BHh8AttiGIARUJMAVXWB3wOeJhA8P1DVnSLyVyJye3jap0L35W3Ap4CPTkxrzw0R+XfgVWC5iBwXkY9NdJvGiOuA+4B3520FeN9EN2oMqAVeCr93rwM/VtWnJrhNZ8Ug370vAbeIyD7g5vB/wyTBhDoyGAwGw6TErKAMBoPBMCkxAspgMBgMkxIjoAwGg8EwKTECymAwGAyTEiOgDAaDwTApMQLKYDAYDJMSI6AMBoPBMCn5/wF64ZD9X/SgOAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for metric in [\"BLEURT\", \"DEB\", \"METEOR\"]:\n",
    "    for dataset in datasets:\n",
    "        df_select_merge = None \n",
    "        for a in vals_a:\n",
    "            df_select = df_rank[dataset][a][[\"History Signal\", f\"ZS.{metric}\"]]\n",
    "            df_select.set_index(\"History Signal\", inplace=True)\n",
    "            df_select.columns = [str(a)]\n",
    "            df_select = df_select.T\n",
    "            if df_select_merge is None:\n",
    "                df_select_merge = df_select\n",
    "            else:\n",
    "                df_select_merge = df_select_merge.append(df_select)\n",
    "\n",
    "        plt.figure(figsize=(6, 6))\n",
    "        \n",
    "        bumpchart(df_select_merge, show_rank_axis=False, scatter=True, holes= False,\n",
    "                line_args= {\"linewidth\": 5, \"alpha\": 0.5}, scatter_args= {\"s\": 100, \"alpha\": 0.8})\n",
    "        plt.title(f\"{dataset} ({metric} Rank)\")\n",
    "        plt.tight_layout()\n",
    "        plt.savefig(f\"results/rebuttal_plots/bumpplot_{dataset}_{metric}.pdf\", dpi=300)\n",
    "        plt.savefig(f\"results/rebuttal_plots/bumpplot_{dataset}_{metric}.png\", dpi=300)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>History Signal</th>\n",
       "      <th>BART</th>\n",
       "      <th>Full</th>\n",
       "      <th>Pegasus-CD</th>\n",
       "      <th>PegasusFT</th>\n",
       "      <th>PegasusFT</th>\n",
       "      <th>Recent-1</th>\n",
       "      <th>Recent-10</th>\n",
       "      <th>Recent-2</th>\n",
       "      <th>Recent-4</th>\n",
       "      <th>Recent-8</th>\n",
       "      <th>Semantic-1</th>\n",
       "      <th>Semantic-10</th>\n",
       "      <th>Semantic-2</th>\n",
       "      <th>Semantic-4</th>\n",
       "      <th>Semantic-8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "History Signal  BART  Full  Pegasus-CD  PegasusFT  PegasusFT  Recent-1  \\\n",
       "0.5              3.0  15.0         7.0        4.0       10.0       1.0   \n",
       "1                3.0  15.0         6.0        4.0       10.0       1.0   \n",
       "2                5.0  15.0         6.0        4.0       10.0       1.0   \n",
       "5                3.0  14.0         1.0        2.0       11.0       8.0   \n",
       "10               4.0  13.0         1.0        3.0        8.0      12.0   \n",
       "\n",
       "History Signal  Recent-10  Recent-2  Recent-4  Recent-8  Semantic-1  \\\n",
       "0.5                  13.0       5.0       9.0      11.0         2.0   \n",
       "1                    13.0       5.0       9.0      11.0         2.0   \n",
       "2                    12.0       3.0       9.0      11.0         2.0   \n",
       "5                    12.0       4.0       6.0      10.0         7.0   \n",
       "10                   10.0       5.0       6.0       7.0         9.0   \n",
       "\n",
       "History Signal  Semantic-10  Semantic-2  Semantic-4  Semantic-8  \n",
       "0.5                    14.0         6.0         8.0        12.0  \n",
       "1                      14.0         7.0         8.0        12.0  \n",
       "2                      14.0         7.0         8.0        13.0  \n",
       "5                      15.0         9.0         5.0        13.0  \n",
       "10                     15.0        11.0         2.0        14.0  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_select_merge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge into single excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR']\n",
      "['History Signal', 'Persona', 'Prompt']\n",
      "['normal' 'ppl']\n",
      "Config: 0.5, Dataset: MSC, Model: flanT5-XL\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 0.5, Dataset: MSC, Model: T0\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 0.5, Dataset: MSC, Model: Tk-Instruct\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 0.5, Dataset: TC, Model: flanT5-XL\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 0.5, Dataset: TC, Model: T0\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 0.5, Dataset: TC, Model: Tk-Instruct\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 1, Dataset: MSC, Model: flanT5-XL\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 1, Dataset: MSC, Model: T0\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 1, Dataset: MSC, Model: Tk-Instruct\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 1, Dataset: TC, Model: flanT5-XL\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 1, Dataset: TC, Model: T0\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 1, Dataset: TC, Model: Tk-Instruct\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 2, Dataset: MSC, Model: flanT5-XL\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 2, Dataset: MSC, Model: T0\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 2, Dataset: MSC, Model: Tk-Instruct\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 2, Dataset: TC, Model: flanT5-XL\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 2, Dataset: TC, Model: T0\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 2, Dataset: TC, Model: Tk-Instruct\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 5, Dataset: MSC, Model: flanT5-XL\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 5, Dataset: MSC, Model: T0\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 5, Dataset: MSC, Model: Tk-Instruct\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 5, Dataset: TC, Model: flanT5-XL\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 5, Dataset: TC, Model: T0\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 5, Dataset: TC, Model: Tk-Instruct\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 10, Dataset: MSC, Model: flanT5-XL\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 10, Dataset: MSC, Model: T0\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 10, Dataset: MSC, Model: Tk-Instruct\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 10, Dataset: TC, Model: flanT5-XL\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 10, Dataset: TC, Model: T0\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['normal' 'ppl']\n",
      "Config: 10, Dataset: TC, Model: Tk-Instruct\n",
      "Shape of df_manual: (17, 9)\n",
      "Shape of df_ppl: (17, 9)\n",
      "['dataset', 'History Signal', 'Persona', 'Prompt', 'ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR', 'Prompt_ppl', 'ZS.BLEURT_ppl', 'FS.BLEURT_ppl', 'ZS.DEB_ppl', 'FS.DEB_ppl', 'ZS.METEOR_ppl', 'FS.METEOR_ppl']\n",
      "['dataset', 'History Signal', 'Persona', 'Prompt', 'ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR', 'Prompt_ppl', 'ZS.BLEURT_ppl', 'FS.BLEURT_ppl', 'ZS.DEB_ppl', 'FS.DEB_ppl', 'ZS.METEOR_ppl', 'FS.METEOR_ppl']\n",
      "['dataset', 'History Signal', 'Persona', 'Prompt', 'ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR', 'Prompt_ppl', 'ZS.BLEURT_ppl', 'FS.BLEURT_ppl', 'ZS.DEB_ppl', 'FS.DEB_ppl', 'ZS.METEOR_ppl', 'FS.METEOR_ppl']\n",
      "['dataset', 'History Signal', 'Persona', 'Prompt', 'ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR']\n",
      "['dataset', 'History Signal', 'Persona', 'Prompt', 'ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR', 'Prompt_ppl', 'ZS.BLEURT_ppl', 'FS.BLEURT_ppl', 'ZS.DEB_ppl', 'FS.DEB_ppl', 'ZS.METEOR_ppl', 'FS.METEOR_ppl']\n",
      "['dataset', 'History Signal', 'Persona', 'Prompt', 'ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR', 'Prompt_ppl', 'ZS.BLEURT_ppl', 'FS.BLEURT_ppl', 'ZS.DEB_ppl', 'FS.DEB_ppl', 'ZS.METEOR_ppl', 'FS.METEOR_ppl']\n",
      "['dataset', 'History Signal', 'Persona', 'Prompt', 'ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR', 'Prompt_ppl', 'ZS.BLEURT_ppl', 'FS.BLEURT_ppl', 'ZS.DEB_ppl', 'FS.DEB_ppl', 'ZS.METEOR_ppl', 'FS.METEOR_ppl']\n",
      "['dataset', 'History Signal', 'Persona', 'Prompt', 'ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR']\n",
      "['dataset', 'History Signal', 'Persona', 'Prompt', 'ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR', 'Prompt_ppl', 'ZS.BLEURT_ppl', 'FS.BLEURT_ppl', 'ZS.DEB_ppl', 'FS.DEB_ppl', 'ZS.METEOR_ppl', 'FS.METEOR_ppl']\n",
      "['dataset', 'History Signal', 'Persona', 'Prompt', 'ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR', 'Prompt_ppl', 'ZS.BLEURT_ppl', 'FS.BLEURT_ppl', 'ZS.DEB_ppl', 'FS.DEB_ppl', 'ZS.METEOR_ppl', 'FS.METEOR_ppl']\n",
      "['dataset', 'History Signal', 'Persona', 'Prompt', 'ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR', 'Prompt_ppl', 'ZS.BLEURT_ppl', 'FS.BLEURT_ppl', 'ZS.DEB_ppl', 'FS.DEB_ppl', 'ZS.METEOR_ppl', 'FS.METEOR_ppl']\n",
      "['dataset', 'History Signal', 'Persona', 'Prompt', 'ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR']\n",
      "['dataset', 'History Signal', 'Persona', 'Prompt', 'ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR', 'Prompt_ppl', 'ZS.BLEURT_ppl', 'FS.BLEURT_ppl', 'ZS.DEB_ppl', 'FS.DEB_ppl', 'ZS.METEOR_ppl', 'FS.METEOR_ppl']\n",
      "['dataset', 'History Signal', 'Persona', 'Prompt', 'ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR', 'Prompt_ppl', 'ZS.BLEURT_ppl', 'FS.BLEURT_ppl', 'ZS.DEB_ppl', 'FS.DEB_ppl', 'ZS.METEOR_ppl', 'FS.METEOR_ppl']\n",
      "['dataset', 'History Signal', 'Persona', 'Prompt', 'ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR', 'Prompt_ppl', 'ZS.BLEURT_ppl', 'FS.BLEURT_ppl', 'ZS.DEB_ppl', 'FS.DEB_ppl', 'ZS.METEOR_ppl', 'FS.METEOR_ppl']\n",
      "['dataset', 'History Signal', 'Persona', 'Prompt', 'ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR']\n",
      "['dataset', 'History Signal', 'Persona', 'Prompt', 'ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR', 'Prompt_ppl', 'ZS.BLEURT_ppl', 'FS.BLEURT_ppl', 'ZS.DEB_ppl', 'FS.DEB_ppl', 'ZS.METEOR_ppl', 'FS.METEOR_ppl']\n",
      "['dataset', 'History Signal', 'Persona', 'Prompt', 'ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR', 'Prompt_ppl', 'ZS.BLEURT_ppl', 'FS.BLEURT_ppl', 'ZS.DEB_ppl', 'FS.DEB_ppl', 'ZS.METEOR_ppl', 'FS.METEOR_ppl']\n",
      "['dataset', 'History Signal', 'Persona', 'Prompt', 'ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR', 'Prompt_ppl', 'ZS.BLEURT_ppl', 'FS.BLEURT_ppl', 'ZS.DEB_ppl', 'FS.DEB_ppl', 'ZS.METEOR_ppl', 'FS.METEOR_ppl']\n",
      "['dataset', 'History Signal', 'Persona', 'Prompt', 'ZS.BLEURT', 'FS.BLEURT', 'ZS.DEB', 'FS.DEB', 'ZS.METEOR', 'FS.METEOR']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-61-8059d6607256>:38: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[a][\"TC\"][model][\"dataset\"] = \"TC\"\n"
     ]
    }
   ],
   "source": [
    "# In each dataframe df[{a}][{dataset}][{model}]\n",
    "# reorganize columns ZS.BLEURT, FS.BLEURT, ZS.DEB, FS.DEB, ZS.METEOR, FS.METEOR.\n",
    "\n",
    "cols = df[1][\"MSC\"][\"T0\"].columns\n",
    "cols = [[x, x.replace(\"ZS\", \"FS\")] for x in cols if x.startswith(\"ZS\")]\n",
    "cols = [item for sublist in cols for item in sublist]\n",
    "print(cols)\n",
    "id_cols = [x for x in df[1][\"MSC\"][\"T0\"].columns if x not in cols]\n",
    "print(id_cols)\n",
    "\n",
    "for a in vals_a:\n",
    "    for dataset in datasets:\n",
    "        for model in df[a][dataset].keys():\n",
    "            df[a][dataset][model] = df[a][dataset][model][cols + id_cols]\n",
    "\n",
    "# # merge the cols values from rows with Prompt = \"manual\" and \"ppl\" and drop the later\n",
    "id_cols.remove(\"Prompt\")\n",
    "for a in vals_a:\n",
    "    for dataset in datasets:\n",
    "        for model in df[a][dataset].keys():\n",
    "            if model == \"GPT-3\":\n",
    "                break\n",
    "            print(df[a][dataset][model][\"Prompt\"].unique())\n",
    "            df_manual = df[a][dataset][model][df[a][dataset][model][\"Prompt\"] == \"normal\"]\n",
    "            df_ppl = df[a][dataset][model][df[a][dataset][model][\"Prompt\"] == \"ppl\"]\n",
    "            print(f\"Config: {a}, Dataset: {dataset}, Model: {model}\")\n",
    "            print(f\"Shape of df_manual: {df_manual.shape}\")\n",
    "            print(f\"Shape of df_ppl: {df_ppl.shape}\")\n",
    "            df[a][dataset][model] = df_manual.merge(df_ppl, on=id_cols, how=\"left\", suffixes=(None, \"_ppl\"))\n",
    "\n",
    "# append the dataframes for datasets\n",
    "cols_ppl = [x + \"_ppl\" for x in cols]\n",
    "new_dfs = {}\n",
    "for a in vals_a:\n",
    "    new_dfs[a] = {}\n",
    "    for model in df[a][\"MSC\"].keys():\n",
    "        df[a][\"MSC\"][model][\"dataset\"] = \"MSC\"\n",
    "        df[a][\"TC\"][model][\"dataset\"] = \"TC\"\n",
    "\n",
    "        H = df[a][\"MSC\"][model].append(df[a][\"TC\"][model])\n",
    "\n",
    "        # ordering should be dataset, rest, cols\n",
    "        ordering = H.columns.tolist()\n",
    "        # dataset, History, Persona, Prompt, {cols}, Prompt_ppl, {cols_ppl}\n",
    "        ordering = [\n",
    "            \"dataset\",\n",
    "            \"History Signal\",\n",
    "            \"Persona\",\n",
    "            \"Prompt\",            \n",
    "        ] + cols\n",
    "        if model != \"GPT-3\":\n",
    "            ordering = ordering + [\"Prompt_ppl\"] + cols_ppl\n",
    "        \n",
    "        print(ordering)\n",
    "        H = H[ordering]\n",
    "\n",
    "        \n",
    "        new_dfs[a][model] = H\n",
    "        \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>History Signal</th>\n",
       "      <th>Persona</th>\n",
       "      <th>Prompt</th>\n",
       "      <th>ZS.BLEURT</th>\n",
       "      <th>FS.BLEURT</th>\n",
       "      <th>ZS.DEB</th>\n",
       "      <th>FS.DEB</th>\n",
       "      <th>ZS.METEOR</th>\n",
       "      <th>FS.METEOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MSC</td>\n",
       "      <td>BART</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>3.517179e+15</td>\n",
       "      <td>1.902175e+15</td>\n",
       "      <td>3.417113e+19</td>\n",
       "      <td>1.852403e+19</td>\n",
       "      <td>2.630606e+12</td>\n",
       "      <td>1.403480e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSC</td>\n",
       "      <td>Full</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>1.825425e+15</td>\n",
       "      <td>8.534185e+14</td>\n",
       "      <td>1.154562e+19</td>\n",
       "      <td>6.732207e+18</td>\n",
       "      <td>1.228903e+12</td>\n",
       "      <td>4.566126e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSC</td>\n",
       "      <td>None_1</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>9.806661e+15</td>\n",
       "      <td>4.287269e+15</td>\n",
       "      <td>2.262520e+19</td>\n",
       "      <td>2.454048e+19</td>\n",
       "      <td>4.496649e+12</td>\n",
       "      <td>2.111920e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MSC</td>\n",
       "      <td>Pegasus-CD</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>3.346420e+15</td>\n",
       "      <td>1.643229e+15</td>\n",
       "      <td>3.354998e+19</td>\n",
       "      <td>1.913926e+19</td>\n",
       "      <td>1.765307e+12</td>\n",
       "      <td>1.035965e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MSC</td>\n",
       "      <td>PegasusFT</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>3.470914e+15</td>\n",
       "      <td>1.812069e+15</td>\n",
       "      <td>3.549914e+19</td>\n",
       "      <td>1.769028e+19</td>\n",
       "      <td>2.999804e+12</td>\n",
       "      <td>1.362924e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MSC</td>\n",
       "      <td>Recent-1</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>1.636207e+15</td>\n",
       "      <td>8.811200e+14</td>\n",
       "      <td>1.164294e+19</td>\n",
       "      <td>7.399316e+18</td>\n",
       "      <td>4.039554e+11</td>\n",
       "      <td>3.330965e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MSC</td>\n",
       "      <td>Recent-10</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>7.142140e+14</td>\n",
       "      <td>1.178589e+15</td>\n",
       "      <td>5.029371e+18</td>\n",
       "      <td>9.357997e+18</td>\n",
       "      <td>1.702583e+11</td>\n",
       "      <td>6.108503e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MSC</td>\n",
       "      <td>Recent-2</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>3.905086e+15</td>\n",
       "      <td>1.954684e+15</td>\n",
       "      <td>2.482110e+19</td>\n",
       "      <td>1.563529e+19</td>\n",
       "      <td>1.363264e+12</td>\n",
       "      <td>7.935457e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MSC</td>\n",
       "      <td>Recent-4</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>3.469850e+15</td>\n",
       "      <td>1.610560e+15</td>\n",
       "      <td>2.162855e+19</td>\n",
       "      <td>1.275609e+19</td>\n",
       "      <td>1.826954e+12</td>\n",
       "      <td>7.721750e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MSC</td>\n",
       "      <td>Recent-8</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>2.727823e+15</td>\n",
       "      <td>1.284067e+15</td>\n",
       "      <td>1.673874e+19</td>\n",
       "      <td>1.008209e+19</td>\n",
       "      <td>1.707497e+12</td>\n",
       "      <td>6.760759e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MSC</td>\n",
       "      <td>Semantic-1</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>3.827917e+15</td>\n",
       "      <td>2.261498e+15</td>\n",
       "      <td>1.026432e+19</td>\n",
       "      <td>9.455190e+18</td>\n",
       "      <td>7.478154e+11</td>\n",
       "      <td>8.609055e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MSC</td>\n",
       "      <td>Semantic-10</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>2.432667e+15</td>\n",
       "      <td>1.123907e+15</td>\n",
       "      <td>7.870211e+18</td>\n",
       "      <td>4.831410e+18</td>\n",
       "      <td>1.686323e+12</td>\n",
       "      <td>6.762311e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MSC</td>\n",
       "      <td>Semantic-2</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>3.840237e+15</td>\n",
       "      <td>1.983770e+15</td>\n",
       "      <td>1.192791e+19</td>\n",
       "      <td>8.543714e+18</td>\n",
       "      <td>1.315778e+12</td>\n",
       "      <td>8.583541e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MSC</td>\n",
       "      <td>Semantic-4</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>3.584592e+15</td>\n",
       "      <td>1.639306e+15</td>\n",
       "      <td>2.715131e+19</td>\n",
       "      <td>1.576580e+19</td>\n",
       "      <td>1.906628e+12</td>\n",
       "      <td>8.720245e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MSC</td>\n",
       "      <td>Semantic-8</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>2.656732e+15</td>\n",
       "      <td>1.246542e+15</td>\n",
       "      <td>8.544599e+18</td>\n",
       "      <td>5.385842e+18</td>\n",
       "      <td>1.830929e+12</td>\n",
       "      <td>7.317962e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MSC</td>\n",
       "      <td>BART</td>\n",
       "      <td>Pegasus-CD</td>\n",
       "      <td>normal</td>\n",
       "      <td>2.850873e+15</td>\n",
       "      <td>1.046040e+15</td>\n",
       "      <td>2.035445e+19</td>\n",
       "      <td>1.126676e+19</td>\n",
       "      <td>2.418599e+12</td>\n",
       "      <td>8.242409e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>MSC</td>\n",
       "      <td>PegasusFT</td>\n",
       "      <td>Pegasus-CD</td>\n",
       "      <td>normal</td>\n",
       "      <td>2.837921e+15</td>\n",
       "      <td>1.041078e+15</td>\n",
       "      <td>2.026688e+19</td>\n",
       "      <td>1.145393e+19</td>\n",
       "      <td>2.508996e+12</td>\n",
       "      <td>8.765056e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TC</td>\n",
       "      <td>BART</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>1.382654e+15</td>\n",
       "      <td>6.082399e+14</td>\n",
       "      <td>1.575720e+19</td>\n",
       "      <td>4.606941e+18</td>\n",
       "      <td>2.127711e+11</td>\n",
       "      <td>6.058429e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TC</td>\n",
       "      <td>Full</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>1.245211e+15</td>\n",
       "      <td>4.843409e+14</td>\n",
       "      <td>1.389154e+19</td>\n",
       "      <td>7.301100e+18</td>\n",
       "      <td>3.380089e+11</td>\n",
       "      <td>1.079267e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TC</td>\n",
       "      <td>None_1</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>6.981980e+15</td>\n",
       "      <td>2.473568e+15</td>\n",
       "      <td>2.122592e+19</td>\n",
       "      <td>2.522973e+19</td>\n",
       "      <td>9.983113e+11</td>\n",
       "      <td>4.019260e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TC</td>\n",
       "      <td>Pegasus-CD</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>1.075599e+15</td>\n",
       "      <td>4.856661e+14</td>\n",
       "      <td>1.990909e+19</td>\n",
       "      <td>1.036517e+19</td>\n",
       "      <td>2.156933e+11</td>\n",
       "      <td>9.287147e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TC</td>\n",
       "      <td>PegasusFT</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>1.396326e+15</td>\n",
       "      <td>5.935897e+14</td>\n",
       "      <td>1.559403e+19</td>\n",
       "      <td>4.671824e+18</td>\n",
       "      <td>2.191329e+11</td>\n",
       "      <td>6.121248e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TC</td>\n",
       "      <td>Recent-1</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>1.382008e+15</td>\n",
       "      <td>6.928593e+14</td>\n",
       "      <td>1.521742e+19</td>\n",
       "      <td>9.477156e+18</td>\n",
       "      <td>1.293956e+11</td>\n",
       "      <td>8.418704e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>TC</td>\n",
       "      <td>Recent-10</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>8.929960e+14</td>\n",
       "      <td>3.622365e+14</td>\n",
       "      <td>1.039140e+19</td>\n",
       "      <td>5.884986e+18</td>\n",
       "      <td>2.772203e+11</td>\n",
       "      <td>8.158530e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TC</td>\n",
       "      <td>Recent-2</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>1.257745e+15</td>\n",
       "      <td>5.885073e+14</td>\n",
       "      <td>1.573990e+19</td>\n",
       "      <td>9.221837e+18</td>\n",
       "      <td>1.989470e+11</td>\n",
       "      <td>8.821224e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TC</td>\n",
       "      <td>Recent-4</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>1.172659e+15</td>\n",
       "      <td>4.945104e+14</td>\n",
       "      <td>1.408273e+19</td>\n",
       "      <td>8.145789e+18</td>\n",
       "      <td>2.639162e+11</td>\n",
       "      <td>8.927516e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TC</td>\n",
       "      <td>Recent-8</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>9.566745e+14</td>\n",
       "      <td>3.927814e+14</td>\n",
       "      <td>1.113938e+19</td>\n",
       "      <td>6.446354e+18</td>\n",
       "      <td>2.781487e+11</td>\n",
       "      <td>8.575197e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>TC</td>\n",
       "      <td>Semantic-1</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>1.264904e+15</td>\n",
       "      <td>6.972021e+14</td>\n",
       "      <td>1.561505e+19</td>\n",
       "      <td>1.019455e+19</td>\n",
       "      <td>9.713549e+10</td>\n",
       "      <td>8.625876e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>TC</td>\n",
       "      <td>Semantic-10</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>8.151747e+14</td>\n",
       "      <td>3.551223e+14</td>\n",
       "      <td>1.035183e+19</td>\n",
       "      <td>5.679495e+18</td>\n",
       "      <td>2.409380e+11</td>\n",
       "      <td>7.989221e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>TC</td>\n",
       "      <td>Semantic-2</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>1.241527e+15</td>\n",
       "      <td>6.143799e+14</td>\n",
       "      <td>1.663920e+19</td>\n",
       "      <td>9.748488e+18</td>\n",
       "      <td>1.857235e+11</td>\n",
       "      <td>9.093525e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>TC</td>\n",
       "      <td>Semantic-4</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>1.206921e+15</td>\n",
       "      <td>4.634366e+14</td>\n",
       "      <td>1.470875e+19</td>\n",
       "      <td>7.794072e+18</td>\n",
       "      <td>1.919489e+11</td>\n",
       "      <td>7.470457e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>TC</td>\n",
       "      <td>Semantic-8</td>\n",
       "      <td>None</td>\n",
       "      <td>normal</td>\n",
       "      <td>8.994915e+14</td>\n",
       "      <td>3.901194e+14</td>\n",
       "      <td>1.129764e+19</td>\n",
       "      <td>6.302475e+18</td>\n",
       "      <td>2.585190e+11</td>\n",
       "      <td>8.696988e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>TC</td>\n",
       "      <td>BART</td>\n",
       "      <td>Pegasus-CD</td>\n",
       "      <td>normal</td>\n",
       "      <td>2.100152e+14</td>\n",
       "      <td>8.659637e+13</td>\n",
       "      <td>5.875406e+18</td>\n",
       "      <td>1.465489e+18</td>\n",
       "      <td>2.554270e+11</td>\n",
       "      <td>5.925376e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>TC</td>\n",
       "      <td>PegasusFT</td>\n",
       "      <td>Pegasus-CD</td>\n",
       "      <td>normal</td>\n",
       "      <td>2.108179e+14</td>\n",
       "      <td>8.534682e+13</td>\n",
       "      <td>5.860220e+18</td>\n",
       "      <td>1.456670e+18</td>\n",
       "      <td>2.376074e+11</td>\n",
       "      <td>5.860488e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dataset History Signal     Persona  Prompt     ZS.BLEURT     FS.BLEURT  \\\n",
       "0      MSC           BART        None  normal  3.517179e+15  1.902175e+15   \n",
       "1      MSC           Full        None  normal  1.825425e+15  8.534185e+14   \n",
       "2      MSC         None_1        None  normal  9.806661e+15  4.287269e+15   \n",
       "3      MSC     Pegasus-CD        None  normal  3.346420e+15  1.643229e+15   \n",
       "4      MSC      PegasusFT        None  normal  3.470914e+15  1.812069e+15   \n",
       "5      MSC       Recent-1        None  normal  1.636207e+15  8.811200e+14   \n",
       "6      MSC      Recent-10        None  normal  7.142140e+14  1.178589e+15   \n",
       "7      MSC       Recent-2        None  normal  3.905086e+15  1.954684e+15   \n",
       "8      MSC       Recent-4        None  normal  3.469850e+15  1.610560e+15   \n",
       "9      MSC       Recent-8        None  normal  2.727823e+15  1.284067e+15   \n",
       "10     MSC     Semantic-1        None  normal  3.827917e+15  2.261498e+15   \n",
       "11     MSC    Semantic-10        None  normal  2.432667e+15  1.123907e+15   \n",
       "12     MSC     Semantic-2        None  normal  3.840237e+15  1.983770e+15   \n",
       "13     MSC     Semantic-4        None  normal  3.584592e+15  1.639306e+15   \n",
       "14     MSC     Semantic-8        None  normal  2.656732e+15  1.246542e+15   \n",
       "15     MSC           BART  Pegasus-CD  normal  2.850873e+15  1.046040e+15   \n",
       "16     MSC      PegasusFT  Pegasus-CD  normal  2.837921e+15  1.041078e+15   \n",
       "0       TC           BART        None  normal  1.382654e+15  6.082399e+14   \n",
       "1       TC           Full        None  normal  1.245211e+15  4.843409e+14   \n",
       "2       TC         None_1        None  normal  6.981980e+15  2.473568e+15   \n",
       "3       TC     Pegasus-CD        None  normal  1.075599e+15  4.856661e+14   \n",
       "4       TC      PegasusFT        None  normal  1.396326e+15  5.935897e+14   \n",
       "5       TC       Recent-1        None  normal  1.382008e+15  6.928593e+14   \n",
       "6       TC      Recent-10        None  normal  8.929960e+14  3.622365e+14   \n",
       "7       TC       Recent-2        None  normal  1.257745e+15  5.885073e+14   \n",
       "8       TC       Recent-4        None  normal  1.172659e+15  4.945104e+14   \n",
       "9       TC       Recent-8        None  normal  9.566745e+14  3.927814e+14   \n",
       "10      TC     Semantic-1        None  normal  1.264904e+15  6.972021e+14   \n",
       "11      TC    Semantic-10        None  normal  8.151747e+14  3.551223e+14   \n",
       "12      TC     Semantic-2        None  normal  1.241527e+15  6.143799e+14   \n",
       "13      TC     Semantic-4        None  normal  1.206921e+15  4.634366e+14   \n",
       "14      TC     Semantic-8        None  normal  8.994915e+14  3.901194e+14   \n",
       "15      TC           BART  Pegasus-CD  normal  2.100152e+14  8.659637e+13   \n",
       "16      TC      PegasusFT  Pegasus-CD  normal  2.108179e+14  8.534682e+13   \n",
       "\n",
       "          ZS.DEB        FS.DEB     ZS.METEOR     FS.METEOR  \n",
       "0   3.417113e+19  1.852403e+19  2.630606e+12  1.403480e+12  \n",
       "1   1.154562e+19  6.732207e+18  1.228903e+12  4.566126e+11  \n",
       "2   2.262520e+19  2.454048e+19  4.496649e+12  2.111920e+12  \n",
       "3   3.354998e+19  1.913926e+19  1.765307e+12  1.035965e+12  \n",
       "4   3.549914e+19  1.769028e+19  2.999804e+12  1.362924e+12  \n",
       "5   1.164294e+19  7.399316e+18  4.039554e+11  3.330965e+11  \n",
       "6   5.029371e+18  9.357997e+18  1.702583e+11  6.108503e+11  \n",
       "7   2.482110e+19  1.563529e+19  1.363264e+12  7.935457e+11  \n",
       "8   2.162855e+19  1.275609e+19  1.826954e+12  7.721750e+11  \n",
       "9   1.673874e+19  1.008209e+19  1.707497e+12  6.760759e+11  \n",
       "10  1.026432e+19  9.455190e+18  7.478154e+11  8.609055e+11  \n",
       "11  7.870211e+18  4.831410e+18  1.686323e+12  6.762311e+11  \n",
       "12  1.192791e+19  8.543714e+18  1.315778e+12  8.583541e+11  \n",
       "13  2.715131e+19  1.576580e+19  1.906628e+12  8.720245e+11  \n",
       "14  8.544599e+18  5.385842e+18  1.830929e+12  7.317962e+11  \n",
       "15  2.035445e+19  1.126676e+19  2.418599e+12  8.242409e+11  \n",
       "16  2.026688e+19  1.145393e+19  2.508996e+12  8.765056e+11  \n",
       "0   1.575720e+19  4.606941e+18  2.127711e+11  6.058429e+10  \n",
       "1   1.389154e+19  7.301100e+18  3.380089e+11  1.079267e+11  \n",
       "2   2.122592e+19  2.522973e+19  9.983113e+11  4.019260e+11  \n",
       "3   1.990909e+19  1.036517e+19  2.156933e+11  9.287147e+10  \n",
       "4   1.559403e+19  4.671824e+18  2.191329e+11  6.121248e+10  \n",
       "5   1.521742e+19  9.477156e+18  1.293956e+11  8.418704e+10  \n",
       "6   1.039140e+19  5.884986e+18  2.772203e+11  8.158530e+10  \n",
       "7   1.573990e+19  9.221837e+18  1.989470e+11  8.821224e+10  \n",
       "8   1.408273e+19  8.145789e+18  2.639162e+11  8.927516e+10  \n",
       "9   1.113938e+19  6.446354e+18  2.781487e+11  8.575197e+10  \n",
       "10  1.561505e+19  1.019455e+19  9.713549e+10  8.625876e+10  \n",
       "11  1.035183e+19  5.679495e+18  2.409380e+11  7.989221e+10  \n",
       "12  1.663920e+19  9.748488e+18  1.857235e+11  9.093525e+10  \n",
       "13  1.470875e+19  7.794072e+18  1.919489e+11  7.470457e+10  \n",
       "14  1.129764e+19  6.302475e+18  2.585190e+11  8.696988e+10  \n",
       "15  5.875406e+18  1.465489e+18  2.554270e+11  5.925376e+10  \n",
       "16  5.860220e+18  1.456670e+18  2.376074e+11  5.860488e+10  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write to a single excel. Each sheet is a model_(a={a})\n",
    "with pd.ExcelWriter(\"results/ROI_v3.xlsx\") as writer:\n",
    "    for model in df[a][\"MSC\"].keys():\n",
    "        for a in vals_a:\n",
    "            new_dfs[a][model].to_excel(writer, sheet_name=f\"{model}_a={a}\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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