{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook contains an example for how to use the `taxbrain` python package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Install conda, taxbrain, and taxcalc if in Google Colab.\n",
    "import sys\n",
    "if 'google.colab' in sys.modules and 'taxbrain' not in sys.modules:\n",
    "    # Install taxbrain and dependencies\n",
    "    !pip install taxbrain &> /dev/null\n",
    "    !pip install taxcalc &> /dev/null\n",
    "    !pip install pypandoc &> /dev/null\n",
    "    !pip install -U pandas &> /dev/null  # make sure pandas is up to date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from taxbrain import TaxBrain, differences_plot, distribution_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "reform_url = \"https://raw.githubusercontent.com/PSLmodels/Tax-Calculator/master/taxcalc/reforms/Larson2019.json\"\n",
    "start_year = 2021\n",
    "end_year = 2030"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Static Reform\n",
    "\n",
    "After importing the `TaxBrain` class from the `taxbrain` package, we initiate an instance of the class by specifying the start and end year of the anlaysis, which microdata to use, and a policy reform. Additional arguments can be used to specify econoimc assumptions and individual behavioral elasticites.\n",
    "\n",
    "Once the class has been initiated, the `run()` method will handle executing each model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tb_static = TaxBrain(start_year, end_year, microdata=\"CPS\", reform=reform_url)\n",
    "tb_static.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the calculators have been run, you can produce a number of tables, including a weighted total of a given variable each year under both current law and the user reform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combined Tax Liability Over the Budget Window\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>2023</th>\n",
       "      <th>2024</th>\n",
       "      <th>2025</th>\n",
       "      <th>2026</th>\n",
       "      <th>2027</th>\n",
       "      <th>2028</th>\n",
       "      <th>2029</th>\n",
       "      <th>2030</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Base</th>\n",
       "      <td>2.269727e+12</td>\n",
       "      <td>3.067767e+12</td>\n",
       "      <td>3.246623e+12</td>\n",
       "      <td>3.395728e+12</td>\n",
       "      <td>3.575938e+12</td>\n",
       "      <td>3.979716e+12</td>\n",
       "      <td>4.161639e+12</td>\n",
       "      <td>4.344151e+12</td>\n",
       "      <td>4.543397e+12</td>\n",
       "      <td>4.749701e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reform</th>\n",
       "      <td>2.326818e+12</td>\n",
       "      <td>3.145972e+12</td>\n",
       "      <td>3.342312e+12</td>\n",
       "      <td>3.509844e+12</td>\n",
       "      <td>3.710034e+12</td>\n",
       "      <td>4.126726e+12</td>\n",
       "      <td>4.329940e+12</td>\n",
       "      <td>4.534780e+12</td>\n",
       "      <td>4.744452e+12</td>\n",
       "      <td>4.961574e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Difference</th>\n",
       "      <td>5.709077e+10</td>\n",
       "      <td>7.820500e+10</td>\n",
       "      <td>9.568834e+10</td>\n",
       "      <td>1.141166e+11</td>\n",
       "      <td>1.340957e+11</td>\n",
       "      <td>1.470099e+11</td>\n",
       "      <td>1.683013e+11</td>\n",
       "      <td>1.906291e+11</td>\n",
       "      <td>2.010544e+11</td>\n",
       "      <td>2.118730e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    2021          2022          2023          2024  \\\n",
       "Base        2.269727e+12  3.067767e+12  3.246623e+12  3.395728e+12   \n",
       "Reform      2.326818e+12  3.145972e+12  3.342312e+12  3.509844e+12   \n",
       "Difference  5.709077e+10  7.820500e+10  9.568834e+10  1.141166e+11   \n",
       "\n",
       "                    2025          2026          2027          2028  \\\n",
       "Base        3.575938e+12  3.979716e+12  4.161639e+12  4.344151e+12   \n",
       "Reform      3.710034e+12  4.126726e+12  4.329940e+12  4.534780e+12   \n",
       "Difference  1.340957e+11  1.470099e+11  1.683013e+11  1.906291e+11   \n",
       "\n",
       "                    2029          2030  \n",
       "Base        4.543397e+12  4.749701e+12  \n",
       "Reform      4.744452e+12  4.961574e+12  \n",
       "Difference  2.010544e+11  2.118730e+11  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Combined Tax Liability Over the Budget Window\")\n",
    "tb_static.weighted_totals(\"combined\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are interested in a detailed look on the reform's effect, you can produce a differences table for a given year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Differences Table\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>tax_cut</th>\n",
       "      <th>perc_cut</th>\n",
       "      <th>tax_inc</th>\n",
       "      <th>perc_inc</th>\n",
       "      <th>mean</th>\n",
       "      <th>tot_change</th>\n",
       "      <th>share_of_change</th>\n",
       "      <th>ubi</th>\n",
       "      <th>benefit_cost_total</th>\n",
       "      <th>benefit_value_total</th>\n",
       "      <th>pc_aftertaxinc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-10n</th>\n",
       "      <td>0.102049</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035165</td>\n",
       "      <td>34.458500</td>\n",
       "      <td>8.218450</td>\n",
       "      <td>0.000839</td>\n",
       "      <td>0.001469</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0-10z</th>\n",
       "      <td>8.390627</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0-10p</th>\n",
       "      <td>12.214010</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.510201</td>\n",
       "      <td>45.113777</td>\n",
       "      <td>3.693062</td>\n",
       "      <td>0.045107</td>\n",
       "      <td>0.079009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.030271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-20</th>\n",
       "      <td>20.708304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.031974</td>\n",
       "      <td>72.589110</td>\n",
       "      <td>17.199769</td>\n",
       "      <td>0.356178</td>\n",
       "      <td>0.623880</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.049703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-30</th>\n",
       "      <td>20.706921</td>\n",
       "      <td>0.019010</td>\n",
       "      <td>0.091803</td>\n",
       "      <td>13.230015</td>\n",
       "      <td>63.891753</td>\n",
       "      <td>27.008003</td>\n",
       "      <td>0.559253</td>\n",
       "      <td>0.979585</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.047439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-40</th>\n",
       "      <td>20.707534</td>\n",
       "      <td>0.259493</td>\n",
       "      <td>1.253135</td>\n",
       "      <td>11.850040</td>\n",
       "      <td>57.225744</td>\n",
       "      <td>29.340367</td>\n",
       "      <td>0.607567</td>\n",
       "      <td>1.064212</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.036801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-50</th>\n",
       "      <td>20.706181</td>\n",
       "      <td>1.020739</td>\n",
       "      <td>4.929636</td>\n",
       "      <td>12.904516</td>\n",
       "      <td>62.322051</td>\n",
       "      <td>27.887624</td>\n",
       "      <td>0.577446</td>\n",
       "      <td>1.011453</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.014320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-60</th>\n",
       "      <td>20.708634</td>\n",
       "      <td>2.983512</td>\n",
       "      <td>14.407092</td>\n",
       "      <td>12.853635</td>\n",
       "      <td>62.068967</td>\n",
       "      <td>-36.541184</td>\n",
       "      <td>-0.756718</td>\n",
       "      <td>-1.325465</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.110734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-70</th>\n",
       "      <td>20.707540</td>\n",
       "      <td>3.810788</td>\n",
       "      <td>18.402900</td>\n",
       "      <td>12.691879</td>\n",
       "      <td>61.291099</td>\n",
       "      <td>-155.091891</td>\n",
       "      <td>-3.211572</td>\n",
       "      <td>-5.625378</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.261506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-80</th>\n",
       "      <td>20.705701</td>\n",
       "      <td>4.042144</td>\n",
       "      <td>19.521890</td>\n",
       "      <td>13.340665</td>\n",
       "      <td>64.429912</td>\n",
       "      <td>-198.758187</td>\n",
       "      <td>-4.115428</td>\n",
       "      <td>-7.208569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.271550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80-90</th>\n",
       "      <td>20.709850</td>\n",
       "      <td>4.418265</td>\n",
       "      <td>21.334121</td>\n",
       "      <td>14.297969</td>\n",
       "      <td>69.039464</td>\n",
       "      <td>-427.711584</td>\n",
       "      <td>-8.857843</td>\n",
       "      <td>-15.515367</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.416827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90-100</th>\n",
       "      <td>20.708024</td>\n",
       "      <td>1.431391</td>\n",
       "      <td>6.912254</td>\n",
       "      <td>17.352383</td>\n",
       "      <td>83.795459</td>\n",
       "      <td>3471.405278</td>\n",
       "      <td>71.885943</td>\n",
       "      <td>125.915170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.292653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALL</th>\n",
       "      <td>207.075375</td>\n",
       "      <td>17.985342</td>\n",
       "      <td>8.685408</td>\n",
       "      <td>129.098443</td>\n",
       "      <td>62.343696</td>\n",
       "      <td>275.700439</td>\n",
       "      <td>57.090772</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.321387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90-95</th>\n",
       "      <td>10.354203</td>\n",
       "      <td>1.292340</td>\n",
       "      <td>12.481311</td>\n",
       "      <td>8.241434</td>\n",
       "      <td>79.595059</td>\n",
       "      <td>-298.772462</td>\n",
       "      <td>-3.093551</td>\n",
       "      <td>-5.418653</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.256932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95-99</th>\n",
       "      <td>8.282277</td>\n",
       "      <td>0.139051</td>\n",
       "      <td>1.678898</td>\n",
       "      <td>7.283324</td>\n",
       "      <td>87.938673</td>\n",
       "      <td>318.774347</td>\n",
       "      <td>2.640177</td>\n",
       "      <td>4.624526</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.070544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Top 1%</th>\n",
       "      <td>2.071544</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.827625</td>\n",
       "      <td>88.225265</td>\n",
       "      <td>34920.477840</td>\n",
       "      <td>72.339316</td>\n",
       "      <td>126.709297</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.338110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count    tax_cut   perc_cut     tax_inc   perc_inc          mean  \\\n",
       "0-10n     0.102049   0.000000   0.000000    0.035165  34.458500      8.218450   \n",
       "0-10z     8.390627   0.000000   0.000000    0.000000   0.000000      0.000000   \n",
       "0-10p    12.214010   0.000000   0.000000    5.510201  45.113777      3.693062   \n",
       "10-20    20.708304   0.000000   0.000000   15.031974  72.589110     17.199769   \n",
       "20-30    20.706921   0.019010   0.091803   13.230015  63.891753     27.008003   \n",
       "30-40    20.707534   0.259493   1.253135   11.850040  57.225744     29.340367   \n",
       "40-50    20.706181   1.020739   4.929636   12.904516  62.322051     27.887624   \n",
       "50-60    20.708634   2.983512  14.407092   12.853635  62.068967    -36.541184   \n",
       "60-70    20.707540   3.810788  18.402900   12.691879  61.291099   -155.091891   \n",
       "70-80    20.705701   4.042144  19.521890   13.340665  64.429912   -198.758187   \n",
       "80-90    20.709850   4.418265  21.334121   14.297969  69.039464   -427.711584   \n",
       "90-100   20.708024   1.431391   6.912254   17.352383  83.795459   3471.405278   \n",
       "ALL     207.075375  17.985342   8.685408  129.098443  62.343696    275.700439   \n",
       "90-95    10.354203   1.292340  12.481311    8.241434  79.595059   -298.772462   \n",
       "95-99     8.282277   0.139051   1.678898    7.283324  87.938673    318.774347   \n",
       "Top 1%    2.071544   0.000000   0.000000    1.827625  88.225265  34920.477840   \n",
       "\n",
       "        tot_change  share_of_change  ubi  benefit_cost_total  \\\n",
       "0-10n     0.000839         0.001469  0.0                 0.0   \n",
       "0-10z     0.000000         0.000000  0.0                 0.0   \n",
       "0-10p     0.045107         0.079009  0.0                 0.0   \n",
       "10-20     0.356178         0.623880  0.0                 0.0   \n",
       "20-30     0.559253         0.979585  0.0                 0.0   \n",
       "30-40     0.607567         1.064212  0.0                 0.0   \n",
       "40-50     0.577446         1.011453  0.0                 0.0   \n",
       "50-60    -0.756718        -1.325465  0.0                 0.0   \n",
       "60-70    -3.211572        -5.625378  0.0                 0.0   \n",
       "70-80    -4.115428        -7.208569  0.0                 0.0   \n",
       "80-90    -8.857843       -15.515367  0.0                 0.0   \n",
       "90-100   71.885943       125.915170  0.0                 0.0   \n",
       "ALL      57.090772       100.000000  0.0                 0.0   \n",
       "90-95    -3.093551        -5.418653  0.0                 0.0   \n",
       "95-99     2.640177         4.624526  0.0                 0.0   \n",
       "Top 1%   72.339316       126.709297  0.0                 0.0   \n",
       "\n",
       "        benefit_value_total  pc_aftertaxinc  \n",
       "0-10n                   0.0        0.006305  \n",
       "0-10z                   0.0        0.000000  \n",
       "0-10p                   0.0       -0.030271  \n",
       "10-20                   0.0       -0.049703  \n",
       "20-30                   0.0       -0.047439  \n",
       "30-40                   0.0       -0.036801  \n",
       "40-50                   0.0       -0.014320  \n",
       "50-60                   0.0        0.110734  \n",
       "60-70                   0.0        0.261506  \n",
       "70-80                   0.0        0.271550  \n",
       "80-90                   0.0        0.416827  \n",
       "90-100                  0.0       -1.292653  \n",
       "ALL                     0.0       -0.321387  \n",
       "90-95                   0.0        0.256932  \n",
       "95-99                   0.0       -0.070544  \n",
       "Top 1%                  0.0       -4.338110  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Differences Table\")\n",
    "tb_static.differences_table(start_year, \"weighted_deciles\", \"combined\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TaxBrain comes with two (and counting) built in plots as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "differences_plot(tb_static, 'combined', figsize=(10, 8));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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44w3VqlVLLi4uysjIUOPGjS19XLp06bbXfmtbbu/TzYoXLy5nZ2dFRUXdNfeb5eQezJgxQyNGjNCLL76oSZMmqXjx4rK1tdUbb7yRbTHk1j4z5y/J7PPixYuSsl5/ZtvN15DbzzSn/slnnzk3RteuXXX58mVJktls1uOPP67Vq1frk08+kbu7e65zyovfyb345Zdf1LZtW7Vo0UJz585VmTJl5ODgoHXr1mnKlClZ7omnp6c6d+6sTz/9VF27dlWtWrX+0flz8lmcP38+y+S0t7p48WK2Kz6VKlXKsv9mmZOJZsr8jt2uPXMp28w5Sho0aJBtHnebY8Pe3t7q/YIFCxQSEnLHY6QbxcSWLVvKzs5OW7ZsyZKnp6en9u3bl+W45ORkpaSkZImXpOvXr6tr166KiIjQhg0b1KhRo7vmAQC4v1AMAYACVq1aNctqMrcqXry4TCaTtm/fnu3Enre2ZfcvmCVKlNDp06fvmEPx4sXl6el52xVTihYtesfjc+PQoUPav3+/Fi5cqH79+lnaf//9d6u4YsWKyWQyWU3ymCkuLs7qfW7v081sbW3VqlUrfffdd9muavJPfPHFF2rRooVmzZpl1Z6YmHhP/WX+8ZvTe/JvfaY5ER8fr9WrV0u6/R/Dy5Yt0+DBg3Pdd178Tu7FihUrZG9vrw0bNsjJycnSvm7dumzjN2/erFmzZqlhw4Zau3atVq9erW7duuVJLreTk9+/p6enYmNjs7SfOXNG0o37mxcy+1m1apVlJFhu7N692+q9n5/fXY85efKkWrRoIcMwtG3btmx/37Vq1dKKFSsUFxcnb29vS/vBgwcl3Vjx62bXr19XYGCgwsLC9PXXX6tVq1a5vhYAQMHjMRkAuI917NhRhmHor7/+Uv369bNsOfmX5YCAAB0/fjzLUPdbz3Px4kWlp6dne54qVarkOvdbRzVkyvxD9NY/UD/77DOr9y4uLqpfv77WrVunlJQUS3tSUpI2bNiQJf9/cp9ef/11GYahF154wepcmVJTU/XNN9/c5YqzMplMWa7zwIED2rlzZ677kqQqVarI29tbX375pVV7TEyMduzYYdWWH5/pP7Fs2TJdvXpVkyZNUlhYWJatePHid31U5nbfqbz4ndwLk8kkOzs7q0ebrl69qiVLlmSJjY2NVZ8+feTv768dO3aoc+fOGjBgQK5HJOVWQECAwsLCdOzYsdvGtGrVSpGRkfrtt9+s2hcvXiyTyaSWLVvmSS7t2rWTnZ2d/vjjj2w/p9sVhTPdGpvdyJibxcTEqEWLFkpPT9fWrVtvW4Dp0qWLTCaTFi1aZNW+cOFCFSlSRE899ZSlLXNEyNatW7V69Wq1a9cuh1cPALjfMDIEAO5jzZo108CBA/Xcc89pz549at68uVxcXBQbG6uIiAjVqlVLL7300h37GDZsmFauXKkuXbpo9OjRatiwoa5evarw8HB17NhRLVu2VM+ePbV06VK1b99er776qho2bCh7e3udPn1aYWFh6tKli7p27Zqr3DP/NXXOnDkqWrSonJyc5Ofnp6pVq6pixYoaPXq0DMOQh4eHvvnmG23evDlLHxMnTlSHDh3Url07vfrqq0pPT9d7770nV1dX/f3333l2n5o0aaJZs2Zp8ODBqlevnl566SXVqFFDqamp2rt3r+bMmaOaNWtmmavkbjp27KhJkyZp/Pjx8vf317FjxzRx4kT5+fkpLS0tV31JNx4jeOuttzRo0CA988wz6t+/vy5fvqy33npLPj4+Vo8Z5Mdn+k/MmzdPxYoV08iRI61GUWTq27evZsyYof379+vRRx/Nto/Mosa0adMUEBAgW1tb1a5dO09+J/eiQ4cOmjFjhnr37q2BAwfq4sWLmj59epYCWHp6unr16iWTyaRly5bJ1tZWCxcuVJ06ddSjRw9FRETc9nGmf2rixIn67rvv1Lx5c40ZM0a1atXS5cuXtWnTJg0fPlxVq1ZVaGioFi9erA4dOmjixIkqV66cNm7cqP/+97966aWX9Mgjj+RJLuXLl9fEiRM1duxY/fnnn3rqqadUrFgxnT17Vr/88otcXFz01ltv5cm5zp07p5YtWyo2Nlbz5s3TuXPnrOZwKVOmjGWUSI0aNTRgwACNHz9etra2atCggb7//nvNmTNHkydPtnpM5plnntF3332nsWPHytPT02rpazc3N1WvXt3yPjIyUpGRkZJujNy6cuWKVq1aJUmqXr26VSwAoAAUzLytAIDbLa2bnfnz5xuNGjUyXFxcjCJFihgVK1Y0+vbta+zZs8cSc6eVLi5dumS8+uqrhq+vr2Fvb294eXkZHTp0MI4ePWqJSU1NNaZPn248+uijhpOTk+Hq6mpUrVrVGDRokHHixAlLXLly5YwOHTpkOYe/v7/h7+9v1TZz5kzDz8/PsLW1NSQZCxYsMAzDMCIjI402bdoYRYsWNYoVK2Y8++yzRkxMjCHJGD9+vFUfa9euNWrVqmU4ODgYvr6+xjvvvGMMHTrUKFas2D3dpzvZt2+f0a9fP8PX19dwcHAwXFxcjMcee8x48803LcsC5+YeXL9+3Rg5cqRRunRpw8nJyahbt66xbt06o1+/flYrS2Su0vHee+9l6TO7ezJnzhyjUqVKhoODg/HII48Y8+fPN7p06WI89thjVnE5/Uyzc7vVZLL7jt16Pbfav3+/IckYNmzYbWOOHj1qSLKsNKJsVpO5fv268fzzzxslSpQwTCaTIcmIioqy7P+nv5O7ye7Y+fPnG1WqVDEcHR2NChUqGG+//bYxb948q9zGjh1r2NjYZFnBZseOHYadnZ3x6quv3vG8t1tNJqe/w1OnThn9+/c3vL29DXt7e6NUqVJG9+7djbNnz1piTp48afTu3dvw9PQ07O3tjSpVqhjvvfee1aoxt/ueZi4t+9VXX1m13+5/49atW2e0bNnScHNzMxwdHY1y5coZzzzzjPHDDz/c8T7kRmZOt9tu/U2lpKQY48ePt/z2H3nkEeOjjz7K0u+d+rz1vmf+hnJyfgDAv89kGLmYihsAgAKWmpqqOnXqqHTp0vr+++8LOp37wuXLl/XII48oMDBQc+bMKeh0AAAA7ns8JgMAuK8NGDBAbdq0kY+Pj+Li4jR79mwdOXJEH374YUGnViDi4uI0ZcoUtWzZUp6enjp58qQ++OADJSYm6tVXXy3o9AAAAB4IFEMAAPe1xMREjRw5UufPn5e9vb3q1q2rb7/9Vq1bty7o1AqEo6OjoqOjNXjwYP39999ydnZW48aNNXv2bNWoUaOg0wMAAHgg8JgMAAAAAAAoVFhaFwAAAAAAFCoUQwAAAAAAQKFCMQQAAAAAABQqFEPwrzIMQwkJCWKqGgAAAABAQaEYgn9VYmKizGazEhMTCzoVAAAAAEAhRTEEAAAAAAAUKhRDAAAAAABAoUIxBAAAAAAAFCoUQwAAAAAAQKFiV9AJAAAAAABwq/T0dKWmphZ0GnjA2Nvby9bW9q5xFEMAAAAAAPeVpKQknT59WoZhFHQqeMCYTCaVKVNGrq6ud4yjGAIAAAAAuG+kp6fr9OnTcnZ2VokSJWQymQo6JTwgDMPQ+fPndfr0aVWuXPmOI0QohgAAAAAA7hupqakyDEMlSpRQkSJFCjodPGBKlCih6Ohopaam3rEYwgSqAAAAAID7DiNCcC9y+r2hGAIAAAAAAAoViiEAAAAAACCLVatWqXTp0nJ1ddXFixcLOp08xZwhAAAAAID72h8JR/Ot74puVXMcW758ea1YsUKNGzfOt3zuJ6+99poWL16sVq1a5Vmf5cuX17lz52Rjc2NsRp8+fTR79mxJ0sKFCzVq1CiVKVNG69atU7ly5SRJK1as0E8//aSPP/44z/KgGAIAAAAAQAFITU2Vvb19QadxWzExMapRo0a2+xITE2Vvby8nJ6dc97t169YsBaW0tDS9+eabOnTokFavXq133nlHs2bNUnJyst59911t2bLlnq7hdnhMBgAAAACAXAoJCdHw4cPVqlUrFS1aVO3atdOlS5cs+7du3ar69evLzc1NlStX1vbt2yXdGBnx7rvvqkqVKqpevbqkG4+j1KhRQx4eHurcubPOnTsnScrIyNDTTz8tLy8veXh46Nlnn9Xff/8tSbp69ap69eqlYsWKyd3dXc2aNbOc++DBg2revLmKFSumevXqac+ePbe9jo8//lgVKlRQiRIl1LdvXyUkJEiSXF1dlZ6erooVK6pp06ZZjjt06JBKlSqlV155Rfv37/+Hd1O6ePGiypQpo5IlS6pVq1aKjo6WJE2ZMkWDBw9WsWLF/vE5bkYxBAAAAACAe7By5Up9+OGHOn/+vNLS0vTJJ59Ikv7880917dpVEyZM0KVLl7Rlyxb5+PhYjlu3bp22b9+ugwcP6pdfftHw4cO1cuVKnT17VlWrVtVLL71kiX366acVFRWlqKgoJSYmauLEiZKkRYsWKTk5WWfOnNGFCxf0zjvvSLoxYiMgIEChoaG6cOGC3njjDXXt2lXXrl3Lkv///vc/vfPOO9q4caOio6OVnJys4cOHS5KSkpIkSX/88Yd27NiR5dgmTZpo9+7dcnd3V8eOHdWgQQPNmTNHiYmJd71vgYGBKlmypLp27aqTJ09KurEk7rlz5xQbG6uwsDBVr15dv//+u7Zv367+/fvn6PPIDYohAAAAAADcgx49eqhmzZpycnJSt27dLCMkli9fri5duqhjx46ytbWVr6+vKlWqZDkuNDRUXl5ecnJy0vz58/XKK6+oZs2asre315tvvqn169crLS1NNjY26tOnj1xcXGQ2mxUaGqqIiAhJkr29vc6fP68///xTdnZ2euKJJyRJGzduVO3atdW1a1fZ2tpaCg87d+7Mkv/KlSv14osvqlq1anJxcdHUqVO1YsWKHF9/xYoVNXnyZJ08eVKTJk3S5s2bVb58eQ0YMOC2xyxbtkzR0dE6ceKEfH19FRgYKMMwZGNjow8++EABAQHasGGDRo8ereHDh2v69OlasGCBGjdurL59++rq1as5zu9OKIYAAAAAAHAPvLy8LK+dnZ0toylOnz6tChUq3Pa4MmXKWF7HxMTorbfekru7u9zd3VWmTBnZ2dkpLi5OaWlpGjZsmMqVKyc3Nzc988wzllVdgoOD1aZNG3Xt2lVlypTR1KlTLf1t2bLF0p+7u7uOHDmi2NjYLHmcOXNGvr6+lvflypVTcnKy4uPjc3UfbGxsVKVKFVWrVk1OTk6KjIy8bWzTpk3l5OQkNzc3zZgxQydOnFBUVJQkqVOnTtq3b582bNigX375RcWLF1fFihX1ySef6Mcff9QjjzyiefPm5Sq32+acJ70AAAAAAABJUtmyZS1/4GfHZDJZXpcuXVpvv/22Ll++bNmuXr2qMmXKaOnSpdq+fbt27typhIQErVq1SoZhSJIcHBw0ceJEHT9+XD/88IM++ugjbd++XaVLl1aHDh2s+ktOTlbv3r2z5FGqVCnFxMRY3sfExMjZ2VlmszlH13n16lUtXbpUrVq1UqNGjZSYmKjNmzdnOwrldvfh5nuRKSUlRRMmTNA777yjP/74Q9WqVZODg4MaNGigw4cP56jvu6EYAgAAAABAHurVq5fWrVunb7/9VhkZGTp16pT++OOPbGP79++vjz/+WAcOHJAk/f333/r6668l3Zj/w9HRUe7u7rpw4YKmT59uOS4sLEyHDx9WRkaG3NzcZGdnJ1tbW3Xs2FF79uzR+vXrlZ6erqtXr2rTpk3ZjvZ49tln9dlnn+no0aNKTk7W2LFj1bNnzxxd4/79+1W6dGnNnz9fL7zwgk6dOqUPPvjAMilsdmJiYrRz506lpqYqOTlZo0aNUrly5VS+fHmruPfff199+vSRl5eXypYtq19//VVJSUnatm1blth7xdK6AAAAAID7WkW3qgWdQq74+flp9erVGjVqlHr06CEfHx/Nnz9fFStWzBLbpEkTTZs2TcHBwYqKipKHh4e6d++uLl26qG/fvtq4caOlKPD888/rxIkTkqTY2FgNHDhQsbGxMpvNevHFFy2rvmzYsEGhoaEKCQmRvb29mjVrpiZNmmQ5d0BAgEaNGqWAgAAlJibqqaee0vvvv5+ja/T29taePXvu+DjQrRITEzVw4ED9+eefcnZ2VtOmTfX111/Lxub/xmn89ddf+vrrry1zo5QqVUpBQUHy9fVV7dq1tW7duhyf705MRuYYG+BfkJCQILPZrPj4eLm5uRV0OgAAAADuM9euXVNUVJT8/Pzk5ORU0OngAZPT7w+PyQAAAAAAgEKFYggAAAAAAChUKIYAAAAAAIBChWIIAAAAAAAoVCiGAAAAAACAQoViCAAAAAAAKFTsCjoBFE6b/wqTc4JLQaeRIxFnixR0CgAAAMADb8dRR0nS3t2nLW2XZwYWUDYo7BgZAgAAAAAAChWKIQAAAAAAoFDhMRkAAAAAwH3tu9M/5FvfAWVa5zi2fPnyWrFihRo3bpxv+fybzp49qwEDBujnn39WYmKirl27dsf4CRMmaMqUKXJ0dLS0JSUlSZKio6PVtWtXnT59WtOnT1e/fv0kSfHx8WrRooV+/PFHFS1aNP8uJpcYGQIAAAAAQAFITU0t0PPb2NioY8eOmj9/fo6PGTBggJKSkixbpmnTpunFF1/UgQMHNG7cOKWlpUm6UUAZMWLEfVUIkSiGAAAAAACQayEhIRo+fLhatWqlokWLql27drp06ZJl/9atW1W/fn25ubmpcuXK2r59u6Qbo0veffddValSRdWrV5ckrVq1SjVq1JCHh4c6d+6sc+fOSZIyMjL09NNPy8vLSx4eHnr22Wf1999/S5KuXr2qXr16qVixYnJ3d1ezZs0s5z548KCaN2+uYsWKqV69etqzZ0+211CiRAm9+OKLqlWr1j++H9HR0WrZsqV8fHxUpkwZXbx4UZGRkdq3b5/69Onzj/vPaxRDAAAAAAC4BytXrtSHH36o8+fPKy0tTZ988okk6c8//1TXrl01YcIEXbp0SVu2bJGPj4/luHXr1mn79u06ePCgfvnlFw0fPlwrV67U2bNnVbVqVb300kuW2KefflpRUVGKiopSYmKiJk6cKElatGiRkpOTdebMGV24cEHvvPOOJCkxMVEBAQEKDQ3VhQsX9MYbb6hr1653fQQmp1asWCEPDw899thjWrNmjaW9atWq2rp1q2JjY3Xx4kWVKFFCoaGh+uCDD/LkvHmNYggAAAAAAPegR48eqlmzppycnNStWzft379fkrR8+XJ16dJFHTt2lK2trXx9fVWpUiXLcaGhofLy8pKTk5Pmz5+vV155RTVr1pS9vb3efPNNrV+/XmlpabKxsVGfPn3k4uIis9ms0NBQRURESJLs7e11/vx5/fnnn7Kzs9MTTzwhSdq4caNq166trl27ytbWVoGBgSpZsqR27tz5j6+3e/fuOnr0qM6dO6dp06bpueees4w6GT16tL755hsFBATogw8+0Jo1a1SpUiW5urqqTZs2at68+W1HqBQEJlAFAAAAAOAeeHl5WV47Oztb5tA4ffq0KlSocNvjypQpY3kdExOjJUuWaOrUqZY2Ozs7xcXFydvbWyNHjtTatWt16dIlGYah4sWLS5KCg4N18uRJde3aVVeuXNHgwYM1ZswYxcTEaMuWLXJ3d7f0l5qaqtjY2Fxd29KlSzVo0CBJUp8+fTR79mzLYz2S1LZtW/Xq1Uvr169X/fr1VbJkSW3cuFHSjUd4Hn/8cW3evFnPP/+8xo8fr9KlS6tXr17atWtXrvLIL4wMAQAAAAAgD5UtW1ZRUVG33W8ymSyvS5curbfffluXL1+2bFevXlWZMmW0dOlSbd++XTt37lRCQoJWrVolwzAkSQ4ODpo4caKOHz+uH374QR999JG2b9+u0qVLq0OHDlb9JScnq3fv3rm6hqCgIMskqbNnz842xsYm+5LC22+/rUGDBsnDw0NHjhxRw4YN5efnZ5kL5X5AMQQAAAAAgDzUq1cvrVu3Tt9++60yMjJ06tQp/fHHH9nG9u/fXx9//LEOHDggSfr777/19ddfS7ox/4ejo6Pc3d114cIFTZ8+3XJcWFiYDh8+rIyMDLm5ucnOzk62trbq2LGj9uzZo/Xr1ys9PV1Xr17Vpk2bFB8fn+35r127puvXr2d5nZ3169crPj5eGRkZ2rp1q5YuXar27dtbxURFRWnr1q16/vnnJUnlypVTWFiYIiMj5ezsnMM7mP94TAYAAAAAcF8LKNO6oFPIFT8/P61evVqjRo1Sjx495OPjo/nz56tixYpZYps0aaJp06YpODhYUVFR8vDwUPfu3dWlSxf17dtXGzdulJeXl8qWLavnn39eJ06ckCTFxsZq4MCBio2Nldls1osvvqimTZtKkjZs2KDQ0FCFhITI3t5ezZo1U5MmTbLNtUiRIlavy5Urp+jo6Gxjly1bppCQEKWmpsrPz09z5sxR48aNrWKGDx+u9957zzJqZPLkyerZs6euXr2aqyV885vJyBxjA/wLEhISZDabtSpynZyLuhR0OjkScbbI3YMAAAAA3NGOo46SpL27T1vaLs8MzBJ37do1RUVFyc/PT05OTv9WenhI5PT7w2MyAAAAAACgUKEYAgAAAAAAChWKIQAAAAAAoFChGFLIbdu2TSaTSZcvX5YkLVy40Go9agAAAAAAHjYUQwqJHTt2yNbWVk899VRBpwIAAAAAQIGiGFJIzJ8/X0OGDFFERIRiYmIKOh0AAAAAAAoMxZBCIDk5WV9++aVeeukldezYUQsXLizolAAAAAAAKDAUQwqBlStXqkqVKqpSpYr69OmjBQsWyDCMgk4LAAAAAB4q5cuX165duwo6DeQAxZBCYN68eerTp48k6amnnlJSUpK2bNlSwFkBAAAAwINn7ty5qlWrllxcXOTr66t+/fopOjq6oNPKN5GRkWrTpo3MZrOqVq1qtS81NVVPP/20ihUrpsGDB1vta9++vXbv3v1vppordgWdAPLXsWPH9Msvv2jNmjWSJDs7O/Xo0UPz589X69atCzg7AAAAALi7sb/+lG99T6nXLMexkydP1qeffqp58+bpySefVFpampYtW6atW7eqf//++Zbjv+HcuXPy8vLK0m5vb6/evXurR48emj59utW+NWvWqEiRIoqLi1OrVq30888/q1GjRvr6669VqlQpNWjQ4N9KP9cYGfKQmzdvntLS0lS6dGnZ2dnJzs5Os2bN0po1a3Tp0qWCTg8AAAAAHgiXL1/W1KlTNWvWLLVv315OTk5ydXXVwIEDrQohu3btUrVq1VSsWDENHTrU0n7ixAk1b95c7u7uKlWqlMaMGWPZt3DhQrVt21YvvfSS3NzcVKNGDe3bt8+yf+fOnapZs6bc3Nz04osvyt/fXytWrJAkpaena/z48SpXrpy8vb01YsQIpaWl5eia/v77b3300UeqXbu2xo8fn21M5cqV9dxzz6lSpUpZ9kVHR8vf31+Ojo564oknFB0drWvXrmnixIl6++23c5RDQaEY8hBLS0vT4sWL9f7772vfvn2Wbf/+/SpXrpyWLl1a0CkCAAAAwANh586dSklJUceOHe8Yt379ekVEROjgwYNasWKFtm/fbtk3efJkXbhwQeHh4friiy+0bt06y76wsDC1atVKly5dUteuXTVixAhJ0vXr19WtWzeFhobq4sWLql27tnbs2GE5bsaMGdqxY4d+/fVXHT16VL/99ptmzZp12/wMw1BYWJiCgoJUoUIF/fTTT3rvvff06aef5vqeVK1aVdu2bdO1a9f0008/qVq1anrvvfcUEhKiEiVK5Lq/fxPFkIfYhg0bdOnSJQ0YMEA1a9a02p555hnNmzevoFMEAAAAgAfCxYsXVbx4cdnZ3Xm2iWHDhsnT01NlypRRixYttH//fkk3Rlg0b95cdnZ2qly5soKCghQREWE5rlatWnrmmWdka2ur3r17W47buXOnXFxcNGDAANnb22vw4MHy8fGxHDdv3jxNmTJFxYsXl7u7u0aMGKFVq1Zlm9uXX36pRx55RMOHD1fjxo31559/auXKlWrXrp1sbHJfHujUqZM8PDxUr149tWvXTu7u7tq4caP69eun4OBgNW7cWAsWLMh1v/8G5gx5iM2bN0+tW7eW2WzOsq9bt26aOnWqfvvttwLIDAAAAAAeLJ6enrpw4YLS0tLuWBC5ed4NZ2dnJSUlSZL++usvvfLKK9q5c6euXr2qlJQU9ezZ867HxcXFqWzZslbnKF26tOV1TEyM2rRpI5PJJOnGyI+b99/szJkzOnfunBo0aGB5lOefsLGx0SeffGJ53717d02bNk0LFy5UlSpVNG/ePDVu3FidOnVS8eLF/9G58hojQx5i33zzjTZu3Jjtvrp168owDA0fPlyGYcjd3V2SFBISosuXL/97SQIAAADAA6BJkyayt7e/7d9YdzNu3DiVKFFCx48fV3x8vIYNGybDMO56nLe3t06fPm3V9tdff1lely5dWtu3b9fly5d1+fJlxcfHKzIyMtu+hg0bppiYGD3++ON67bXX5OfnpwkTJujkyZP3dE0327p1q+zs7OTv768jR46oYcOGcnBwUJUqVfTHH3/84/7zGsUQAAAAAADuwt3dXWPHjtXgwYO1adMmXb9+XVeuXNHnn3+u+fPn3/X4xMREubi4yNXVVYcOHdIXX3yRo/M2adJESUlJWrBggdLS0jR79mzFxsZa9g8YMEBjx45VXFycDMNQdHS0wsPDb9uf2WzW4MGD9dtvv2nNmjU6e/as6tSpc9sJVA3D0LVr15SSkmL1+mZpaWkaM2aM3nvvPUlSuXLlFBYWpqSkJO3du1e+vr45utZ/E8UQAAAAAAByYNy4cRo/frxGjRqlYsWKqUqVKtq+fbtatWp112PffPNNbdmyRW5ubho6dKi6deuWo3M6Ojpq9erVev/99+Xh4aF9+/apQYMGcnR0lCSNHDlSDRs2VNOmTWU2m9WpUyedOnUqR33XrVtXs2bN0l9//aWuXbtmG3Py5EkVKVJE7dq10/Hjx1WkSBG1bdvWKuaTTz5R165dLY/nDBw4UDt27JCvr6+Cg4Ot5ji5X5iMnIzLAfJIQkKCzGazVkWuk3NRl4JOJ0cizhYp6BQAAACAB96Oozf+eN+7+/8e+bg8MzBL3LVr1xQVFSU/Pz85OTn9W+k9MAzDUJkyZbRu3To1aNCgoNO57+T0+8PIEAAAAAAA7mNhYWE6e/asUlJSNG3aNNnb2+uxxx4r6LQeaKwmAwAAAADAfezw4cPq1auXkpOTVaNGDa1Zs+auS/zizrh7AAAAAADcx1555RW98sorBZ3GQ4XHZAAAAAAAQKFCMQQAAAAAABQqFEMAAAAAAEChQjEEAAAAAAAUKhRDAAAAAABAoUIxBAAAAAAA5EhkZKRq1aolV1dXrVu3rqDTuWcsrQsAAAAAuK+1XLon3/oOC6qf49jy5cvr3LlzsrGxkaOjox577DENHTpUnTt3zrf87jfTp09XYGCgJk2alGVfWlqaevTooV27dunMmTOKjY2Vt7f3bftauHChnn/+eTk5OVnaIiMj5evrq0uXLunpp5/W/v37NXLkSI0ZM8ZyjmbNmumrr76Sr6/vPV8HI0MAAAAAAMihrVu3KikpSUePHlWPHj0UHBys2bNn51n/GRkZysjIyLP+8lpMTIxq1Khx2/3NmzfX6tWrc9xf69atlZSUZNkyCxxz5sxRkyZNdPLkSS1atEixsbGSpE8//VSdOnX6R4UQiZEhKCBtSreUm5tbQaeRIwFlCjoDAAAA4CFQ7///NxcjMe5nJUqU0AsvvKCrV69q3LhxeuGFF2Rra6uDBw/q5Zdf1sGDB1WhQgV99tlnql//xjVHRUXplVde0a5du+Tg4KDXX39dQ4cOVUhIiNzc3HTgwAH98ssvioyMVGJi4m37mTRpkj7//HNdunRJNWrU0GeffabatWtLkqZOnaqPP/7YUlj46quvVL16df3999965ZVX9MMPP8jV1VXjx49Xv379sr22bdu2KTQ0VFFRUapTp44+++wzValSRQEBAQoLC1NERISef/55/fXXXzKbzZbj7Ozs9Oqrr+bJ/Y2OjlbXrl1VtGhR1a1bVzExMbKzs9PixYu1Y8eOf9w/I0MAAAAAALhHnTp10sWLF3Xs2DElJiYqICBAoaGhunDhgt544w117dpV165dU1pamjp06KAGDRror7/+0rFjx9SsWTNLPytWrNCMGTOUmJgoT0/P2/YjSdWrV9eePXv0999/q02bNurbt68k6ejRo5o1a5b27t2rhIQErV69Wh4eHpKk4OBglS1bVqdOndK3336r119/Xfv3789yPRcuXFBgYKAmTJig8+fPq0OHDurSpYvS09P13Xff6YknntDChQuVlJRkVQi5Vz/99JM8PT1VvXp1qxE2VatW1datW5WQkKCDBw+qYsWKGj16tCZMmCBHR8d/fF6KIQAAAAAA3CMfHx9J0qVLl7Rx40bVrl1bXbt2la2trQIDA1WyZEnt3LlTP//8s65cuaLx48fLyclJbm5uqlevnqWfZ599VnXr1pWtre0d+5Gkbt26qUSJErKzs9OYMWN04MABJSUlyc7OTteuXVNkZKTS09NVtWpVeXt7Ky4uTtu3b9fUqVPl6OioqlWrqnfv3lqzZk2W6/n2229Vr149denSRfb29hoxYoQSExP122+/5fm98/f318GDB3X+/HktWLBAEydO1Nq1ayVJzz//vE6dOqWmTZsqNDRUf/75p86ePavGjRsrMDBQTZs21XfffXfP5+YxGQAAAAAA7lHmXBbFihXTTz/9pC1btsjd3d2yPzU1VbGxsbK1tVX58uVlMpmy7adMmf97Pj8mJua2/UjS3LlzNXPmTJ0+fVomk0mGYejixYuqVKmSZsyYobFjx+ro0aN6+umn9cEHHygmJkbJycny9PS09Jeenq6goKAseZw5c8ZqPg4bGxuVLVtWZ86cuaf7k2n79u0KCAiQJD3xxBP67rvv5OfnZ9nfqFEjDR06VGvXrlXXrl3l4uKipUuXSpIMw5C/v7/mzZunt99+W88884w6duyoJk2aqF27drKxyf04D4ohAAAAAADcow0bNqh48eKqUqWK9u7dqw4dOmQ74mLHjh2Kjo6WYRjZFkRubitduvRt+4mOjtbw4cMVHh6uxx57TNeuXZOrq6sMw5B043GY4OBgXbhwQT179tSHH36o/v37y93dXRcvXrzr9ZQqVUqbN2+2vDcMQ6dOnVKpUqVydD9u54knnlBSUtIdY25X1FiwYIEef/xxVa5cWUeOHNGLL74od3d3ubu76/z58ypZsmSu86EYggIxce8uObq6FHQaAIAH3I6j//yZ4Xuxd/fpAjkvADxsLs8MLOgU7tnFixe1bt06vfHGG3rnnXdka2urjh076vXXX9f69evVoUMHpaSkKDw8XE2aNFHDhg3l7OysyZMn67XXXtP169d14sQJq0dlMt2pn6SkJJlMJnl6eio1NVXjx4+3FEKOHTum2NhYNW3aVM7OznJ0dJStra1Kly6tBg0a6M0339To0aPl4OCgAwcOyMnJSdWrV7c6d0BAgIYMGaJvvvlGAQEB+vDDD+Xq6qrHHnssR/fl+vXrlnyuX7+ua9euWS2de7NNmzapXr16KlGihH777Td99NFHmjFjhlVMfHy8Pv30U/3444+SpHLlyiksLExms1mnT5+2Gu2SGxRDAAAAAAD3tbD7aAWaJ598UjY2NnJwcFDdunW1aNEidenSRZJkNpu1YcMGhYaGKiQkRPb29mrWrJmaNGkiOzs7bdiwQYMHD5a3t7ecnJw0duzYbIshd+qnZs2aGjhwoGrXri0XFxe98cYbcnBwkHSj+DBq1CgdPXpUjo6Oatu2rWV1l6VLl2r48OGqUKGCUlJSVLNmTX3wwQdZzl2iRAmtXbtWoaGh6tOnjx599FGtXbtWdnY5Kx9UqVJFJ0+elCSVL19ekizFkVtt3rxZffv21ZUrV1S6dGn95z//Uffu3a1ixo8fr9dee00uLjf+Mf0///mPAgMDNW7cOL333ns5zutWJuN2WQH5ICEhQWazWSO2/Y+RIQCAf4yRIQDwYMtuZMi1a9cUFRUlPz+/244oAG4np98fVpMBAAAAAACFCsUQAAAAAABQqFAMAQAAAAAAhQrFEAAAAAAAUKhQDAEAAAAAAIUKxRAAAAAAAFCoUAwBAAAAAACFCsUQAAAAAABQqFAMAQAAAAAAhYpdQScAAAAAAMCduA9bl299X54ZmOPY8uXL69y5c7KxsZGjo6Mee+wxDR06VJ07d863/B4UEyZM0JQpU+To6GhpS0pKkiRFR0era9euOn36tKZPn65+/fpJkuLj49WiRQv9+OOPKlq06L+aLyNDAAAAAADIoa1btyopKUlHjx5Vjx49FBwcrNmzZ+dZ/xkZGcrIyMiz/u7FuXPn7um4AQMGKCkpybJlmjZtml588UUdOHBA48aNU1pamqQbBZQRI0b864UQiWIIAAAAAAC5VqJECb3wwguaNGmSxo0bp/T0dEnSwYMH1bx5cxUrVkz16tXTnj17LMdERUWpQ4cO8vT0lI+Pjz766CNJUkhIiIYOHaoWLVrI1dVVMTExd+xn0qRJKleunNzc3NSkSRMdOHDAsm/q1Kny8fFR0aJFVaNGDUVGRkqS/v77b/Xu3VteXl6qUKGCFi1adNtra9mypdq1a6evvvpKKSkp//heRUdHq2XLlvLx8VGZMmV08eJFRUZGat++ferTp88/7v9eUAwBAAAAAOAederUSRcvXtSxY8eUmJiogIAAhYaG6sKFC3rjjTfUtWtXXbt2TWlpaerQoYMaNGigv/76S8eOHVOzZs0s/axYsUIzZsxQYmKiPD09b9uPJFWvXl179uzR33//rTZt2qhv376SpKNHj2rWrFnau3evEhIStHr1anl4eEiSgoODVbZsWZ06dUrffvutXn/9de3fvz/ba9q9e7d69+6tjz/+WGXLltXIkSN19OjRu96LFStWyMPDQ4899pjWrFljaa9ataq2bt2q2NhYXbx4USVKlFBoaKg++OCDe77v/9RDWQwJCQkp6BQAAAAAAIWAj4+PJOnSpUvauHGjateura5du8rW1laBgYEqWbKkdu7cqZ9//llXrlzR+PHj5eTkJDc3N9WrV8/Sz7PPPqu6devK1tb2jv1IUrdu3VSiRAnZ2dlpzJgxOnDggJKSkmRnZ6dr164pMjJS6enpqlq1qry9vRUXF6ft27dr6tSpcnR0VNWqVdW7d2+rgsXNnJ2d1a9fP/3444/66aef5ODgoNatW+uJJ564bQGle/fuOnr0qM6dO6dp06bpueees4xmGT16tL755hsFBATogw8+0Jo1a1SpUiW5urqqTZs2at68udXIl3/DQ1kMuZ01a9aoXbt2Kl68uEwmk/bt25clpnz58jKZTFqxYkWWfTVq1JDJZNLChQvzP9n7LBcAAAAAQFaxsbGSpGLFiikmJkZbtmyRu7u7ZTty5IhiY2N1+vRpy9942SlTpozl9Z36kaS5c+eqRo0aMpvN8vb2lmEYunjxoipVqqQZM2Zo7NixKlGihAYMGKCEhATFxMQoOTlZnp6elv4+++wznT179q7XV7p0aVWrVk0VKlTQ77//rvj4+GzjqlevLm9vb9nZ2alt27bq1auX1q9fL0kqWbKkNm7cqH379unJJ5/U22+/rUmTJum1117T+PHjtWjRIr3yyiu5uu//1ENTDLlw4YL69esnX19fLV++XJUqVVL37t2tnm9KTk5Ws2bN9M4779yxr7Jly2rBggVWbbt27VJcXJxcXFzyPPfU1NQCyeVO5wUAAAAA3N2GDRtUvHhxValSRaVLl1aHDh10+fJly5acnKzevXurbNmyio6OlmEY2fZzc5HkTv1ER0dr+PDhWrJkiS5fvqzY2FjZ2NhY+g0ODtbOnTt14sQJnTx5Uh9++KFKly4td3d3q/4SExPvOPHrL7/8okGDBql06dJasWKFQkNDderUKTVv3jxH98XGJvtyw9tvv61BgwbJw8NDR44cUcOGDeXn53fPk7beq4emGBIaGqqff/5ZS5YsUfv27TV37lz5+flZzcIbHBysN998U61bt75jX0FBQQoPD9epU6csbfPnz1dQUJDs7O68GnFGRoYmTpyoMmXKyNHRUXXq1NGmTZss+6Ojo2UymfTll1+qRYsWcnJy0hdffJEnucTExKhLly5ydXWVm5ubunfvblXpmzBhgurUqaP58+erQoUKcnR0lGEYio+P18CBA+Xl5SU3Nzc9+eSTtx36BAAAAACQLl68qHnz5umNN97QpEmTZGtrq44dO2rPnj1av3690tPTdfXqVW3atEnx8fFq2LChnJ2dNXnyZF2/fl0JCQn69ddfs+37Tv0kJSXJZDLJ09NTqampGj9+vKUQcuzYMW3btk0pKSlydnaWo6OjbG1tVbp0aTVo0EBvvvmmrly5orS0NP3222+WyVVv1bJlS/Xo0UOlS5fWgQMHtHHjRnXt2vWOfw+vX79e8fHxysjI0NatW7V06VK1b9/eKiYqKkpbt27V888/L0kqV66cwsLCFBkZKWdn53v5GO7Znf+yf4Ds3btXwcHB8vf314IFC9SyZUu1bNnynvoqWbKk2rVrp0WLFmncuHG6cuWKVq5cqfDwcC1evPiOx3744Yd6//339dlnn+mxxx7T/Pnz1blzZx0+fFiVK1e2xP3nP//R+++/rwULFlitw3yvuRiGocDAQLm4uCg8PFxpaWkaPHiwevTooW3btlnifv/9d3355ZdavXq1bG1tJUkdOnSQh4eHvv32W5nNZn322Wdq1aqVjh8/bplsBwAAAAAKyuWZgQWdgsWTTz4pGxsbOTg4qG7dulq0aJG6dOkiSTKbzdqwYYNCQ0MVEhIie3t7NWvWTE2aNJGdnZ02bNigwYMHy9vbW05OTho7dqzVvCGZ7tRPzZo1NXDgQNWuXVsuLi5644035ODgIEm6fv26Ro0apaNHj8rR0VFt27bVq6++KklaunSphg8frgoVKiglJUU1a9a87QSmb7/9tho2bHjb0R3ZWbZsmUJCQpSamio/Pz/NmTNHjRs3tooZPny43nvvPUu/kydPVs+ePXX16lXNnz8/x+fKCw9NMaRZs2ZasGCBHn300Tzpr3///hoxYoTGjh2rVatWqWLFiqpTp85dj5s+fbr+85//qGfPnpJurKccFhammTNn6tNPP7XEDRs2TE8//XSe5fLDDz/owIEDioqKUtmyZSVJS5YsUY0aNbR79241aNBAkpSSkqIlS5aoRIkSkm6skX3w4EGdO3fOUpSZPn261q1bp1WrVmngwIE5yhEAAAAAHnbR0dF3jaldu7a2bNmS7b4KFSpYPTmQKbu5IO/Uz/Tp0zV9+nTL+5deesnyevfu3dke4+npecfldG92axEjJ7Kb6/JWa9eutXpfv359/f7777k+V154aB6TmTFjhnr06KHQ0FAtXrxYderUuePzT3fToUMHJSUl6ccff9T8+fPVv3//ux6TkJCgM2fOWC2PJN0o1Bw5csSqrX79+nmay5EjR1S2bFlLIUS6MYFN5kQ7mcqVK2cphEjSr7/+qqSkJHl6esrV1dWyRUVF6Y8//shxjgAAAAAAPCgempEhLi4umjJliqZMmaLAwEDLmsw2Njb3NLrBzs5OwcHBGj9+vH7++ecsFaw7uXV2YMMwsrTlZvLTnOSS3Tmya7/1vBkZGfLx8bF6lCaTu7t7jnMEAAAAAOBB8dCMDLmZu7u7Bg0apICAAG3fvv2e++nfv7/Cw8PVpUsXFStW7K7xbm5uKlWqlCIiIqzad+zYoWrVqt1zHjnJpXr16oqJibGaaDUyMlLx8fF3PHfdunUVFxcnOzs7VapUyWorXrz4P8oZAAAAAID70UMzMiQ0NFSBgYGqU6eO0tPTFRYWpvDwcI0bN84S8/fffysmJkZnzpyRdGOmXUny9vaWt7d3lj6rVaumCxcu5GpW21GjRmn8+PGWeT0WLFigffv2aenSpf/o+u6WS+vWrVW7dm0FBQVp5syZlglU/f397/hITuvWrdWkSRMFBgZq2rRpqlKlis6cOaNvv/1WgYGBuXqcBwAAAACAB8FDUwzx9fXV8OHDdeLECSUnJ2vbtm3q37+/hgwZYolZv369nnvuOcv7zElOx48frwkTJmTbr6enZ67yGDp0qBISEjRixAidO3dO1atX1/r1661WkrlXd8rFZDJp3bp1GjJkiJo3by4bGxs99dRT+vjjj+/Yp8lk0rfffquxY8eqf//+On/+vLy9vdW8eXOVLFnyH+cMAAAAAPcic7lYIDdy+r0xGQ/hNywkJCTb2XhR8BISEmQ2mzVi2//k6JrzeVMAAMjOjqO3X54+P+3dfbpAzgsAD5vslsxNT0/XiRMn5OzsrBIlSmQ7NyKQHcMwdP78eV25ckWVK1eWra3tbWMfmpEhAAAAAIAHn62trcqUKaPTp0/naClb4GYmk0llypS5YyFEekiLIYwKAQAAAIAHl6urqypXrqzU1NSCTgUPGHt7+7sWQqSHtBgCAAAAAHiw2dra5uiPWuBePJRL6wIAAAAAANwOxRAAAAAAAFCoUAwBAAAAAACFCsUQAAAAAABQqFAMAQAAAAAAhQrFEAAAAAAAUKhQDAEAAAAAAIUKxRAAAAAAAFCoUAwBAAAAAACFCsUQAAAAAABQqFAMAQAAAAAAhYrJMAyjoJNA4ZGQkCCz2az4+Hi5ubkVdDoAAAAAgEKIkSEAAAAAAKBQoRgCAAAAAAAKFYohAAAAAACgUKEYAgAAAAAAChW7gk4AhdPEvbvk6OpS0GkAQBY7jjoWdAoFbu/u0wWdAgCgkLg8M7CgU0AhxcgQAAAAAABQqFAMAQAAAAAAhQrFEAAAAAAAUKhQDAEAAAAAAIUKxRAAAAAAAFCoUAwBAAAAAACFCsUQAAAAAABQqFAMAQAAAAAAhQrFEAAAAAAAUKhQDAEAAAAAAIUKxRAAAAAAAFCoUAwBAAAAAACFCsUQAAAAAABQqFAMAQAAAAAAhQrFEAAAAAAAUKhQDLmDkJCQgk4BAAAAAADkMYohuRQSEiKTyWS1NW7c2Crm+vXrGjJkiIoXLy4XFxd17txZp0+ftoq5dOmSgoODZTabZTabFRwcrMuXL1v2R0dHy2Qyad++fZa2xMREtWjRQlWrVtWpU6ey5Pbjjz+qU6dOKlWqlEwmk9atW5clpkWLFjKZTHrnnXey7Gvfvr1MJpMmTJiQq3sCAAAAAMCDhGLILS5cuKB+/frJ19dXy5cvV6VKldS9e3elpKRYYp566inFxsZatm+//daqj2HDhmnt2rVasWKFIiIilJSUpI4dOyo9Pd0S07t3b+3bt0+bNm3Spk2btG/fPgUHB982r/Pnz6tly5ZKSkpSRESEypYtmyUmOTlZjz76qD755JM7XmPZsmW1YMECq7YzZ85o69at8vHxueOxAAAAAAA86OwKOoH7TWhoqHbv3q0lS5Zo5syZGjp0qDZt2qSMjAxLjKOjo7y9vbM9Pj4+XvPmzdOSJUvUunVrSdIXX3yhsmXL6ocfflC7du105MgRbdq0Sbt27VKjRo0kSXPnzlWTJk107NgxValSxarPU6dOqU2bNvLx8dH69etVtGjRbM8dEBCggICAu15jx44d9eWXX+qnn35Ss2bNJEkLFy5U27ZtFRMTc/ebBAAAAADAA4yRIbfYu3evgoOD5e/vL7PZrJYtW2ratGlycnKyxGzbtk1eXl565JFH9MILL+jcuXOWfb/++qtSU1PVtm1bS1upUqVUs2ZN7dixQ5K0c+dOmc1mSyFEkho3biyz2WyJyXTs2DE1a9ZMVatW1aZNm25bCMkNBwcHBQUFWY0OWbhwofr37/+P+wYAAAAA4H5HMeQWzZo104IFC7Rhw4Zs9wcEBGjp0qXaunWr3n//fe3evVtPPvmkrl+/LkmKi4uTg4ODihUrZnVcyZIlFRcXZ4nx8vLK0reXl5clJlPfvn1VsWJFrV69Wo6OjnlxiZKkAQMG6Msvv1RycrJ+/PFHxcfHq0OHDnnWPwAAAAAA9yuKIbeYMWOGevToodDQUC1evFh16tTR7NmzLft79OihDh06qGbNmurUqZO+++47HT9+XBs3brxjv4ZhyGQyWd7f/Pp2MZLUpUsXRUREaPXq1Za27du3y9XV1bItXbo019dZu3ZtVa5cWatWrdL8+fMVHBwse3v7XPcDAAAAAMCDhjlDbuHi4qIpU6ZoypQpCgwMVEBAgEJDQ2VjY6OBAwdmiffx8VG5cuV04sQJSZK3t7dSUlJ06dIlq9Eh586dU9OmTS0xZ8+ezdLX+fPnVbJkSau2MWPGqHbt2goKCpJhGOrRo4fq169vtcrMrcfkVP/+/fXpp58qMjJSv/zyyz31AQAAAADAg4aRIXfg7u6uQYMGKSAgQNu3b8825uLFizp16pRlFZZ69erJ3t5emzdvtsTExsbq0KFDlmJIkyZNFB8fb1WA+PnnnxUfH2+Judm4ceM0adIkBQUFafny5SpSpIgqVapk2e51HpHevXvr4MGDqlmzpqpXr35PfQAAAAAA8KBhZMgtQkNDFRgYqDp16ig9PV1hYWEKDw/XuHHjlJSUpAkTJqhbt27y8fFRdHS0xowZo+LFi6tr166SJLPZrAEDBmjEiBHy9PSUh4eHRo4cqVq1allWl6lWrZqeeuopvfDCC/rss88kSQMHDlTHjh2zrCSTafTo0bK1tVVwcLAyMjIUFBSUJSYpKUm///675X1UVJT27dsnDw8P+fr6ZokvVqyYYmNjeTwGAAAAAFCoUAy5ha+vr4YPH64TJ04oOTlZ27ZtU//+/TVkyBClpKTo4MGDWrx4sS5fviwfHx+1bNlSK1eutBqd8cEHH8jOzk7du3fX1atX1apVKy1cuFC2traWmKVLl2ro0KGWVWc6d+6sTz755I65jRo1Sra2turXr58yMjIUHBxstX/Pnj1q2bKl5f3w4cMlSf369dPChQuz7dPd3T03twcAAAAAgAeeyTAMo6CTuF+FhITctoiAe5OQkCCz2awR2/4nR1eXgk4HALLYcTTvVu56UO3dfbqgUwAAFBKXZwYWdAoopJgzBAAAAAAAFCoUQ+6AUSEAAAAAADx8KIYAAAAAAIBChWIIAAAAAAAoVCiGAAAAAACAQoViCAAAAAAAKFQohgAAAAAAgEKFYggAAAAAAChUKIYAAAAAAIBChWIIAAAAAAAoVCiGAAAAAACAQoViCAAAAAAAKFQohgAAAAAAgEKFYggAAAAAAChUTIZhGAWdBAqPhIQEmc1mxcfHy83NraDTAQAAAAAUQowMAQAAAAAAhQrFEAAAAAAAUKhQDAEAAAAAAIUKxRAAAAAAAFCo2BV0AiicNv8VJucElzztM+JskTztDwAAAHgQ7TjqWGDn3rv7dK7iL88MzJ9EgLtgZAgAAAAAAChUKIYAAAAAAIBChWIIAAAAAAAoVCiGAAAAAACAQoViCAAAAAAAKFQohgAAAAAAgEKFYggAAAAAAChUKIYAAAAAAIBChWIIAAAAAAAoVCiGAAAAAACAQoViCAAAAAAAKFQohgAAAAAAgEKFYggAAAAAAChUKIYAAAAAAIBChWIIAAAAAAAoVB76YkhISEhBpwAAAAAAAO4jD30x5Fapqan6z3/+o1q1asnFxUWlSpVS3759debMGau469eva8iQISpevLhcXFzUuXNnnT592irm0qVLCg4OltlsltlsVnBwsC5fvmzZHx0dLZPJpH379lnaEhMT1aJFC1WtWlWnTp3Kkt+ECRNkMpmsNm9vb6sYwzA0YcIElSpVSkWKFFGLFi10+PDhXOdvMpm0bt06q3vTs2dP+fj46MCBAzm5nQAAAAAAPHAeymLIhQsX1K9fP/n6+mr58uWqVKmSunfvrpSUFF25ckW//fab3njjDf32229as2aNjh8/rs6dO1v1MWzYMK1du1YrVqxQRESEkpKS1LFjR6Wnp1tievfurX379mnTpk3atGmT9u3bp+Dg4Nvmdf78ebVs2VJJSUmKiIhQ2bJls42rUaOGYmNjLdvBgwet9r/77ruaMWOGPvnkE+3evVve3t5q06aNEhMTc5X/za5cuaLOnTtr9+7dioiIUO3ate96nwEAAAAAeBDZFXQC+SE0NFS7d+/WkiVLNHPmTA0dOlSbNm1SRkaGzGazNm/ebBX/8ccfq2HDhoqJiZGvr6/i4+M1b948LVmyRK1bt5YkffHFFypbtqx++OEHtWvXTkeOHNGmTZu0a9cuNWrUSJI0d+5cNWnSRMeOHVOVKlWsznHq1Cm1adNGPj4+Wr9+vYoWLXrb/O3s7LKMBslkGIZmzpypsWPH6umnn5YkLVq0SCVLltSyZcs0aNCgHOV/s8uXL6tjx45KSEhQRESEfHx8cnG3AQAAAAB4sDyUI0P27t2r4OBg+fv7y2w2q2XLlpo2bZqcnJyyjY+Pj5fJZJK7u7sk6ddff1Vqaqratm1riSlVqpRq1qypHTt2SJJ27twps9lsKYRIUuPGjWU2my0xmY4dO6ZmzZqpatWq2rRp0x0LIZJ04sQJlSpVSn5+furZs6f+/PNPy76oqCjFxcVZ5ebo6Ch/f3/LeXOSf6a4uDj5+/srIyND4eHhFEIAAAAAAA+9h7IY0qxZMy1YsEAbNmy4a+y1a9c0evRo9e7dW25ubpJuFAgcHBxUrFgxq9iSJUsqLi7OEuPl5ZWlPy8vL0tMpr59+6pixYpavXq1HB0d75hPo0aNtHjxYv3vf//T3LlzFRcXp6ZNm+rixYuW82bmcqfc7pZ/pldffVUpKSn64YcfssQDAAAAAPAweiiLITNmzFCPHj0UGhqqxYsXq06dOpo9e3aWuMwJQzMyMvTf//73rv0ahiGTyWR5f/Pr28VIUpcuXRQREaHVq1db2rZv3y5XV1fLtnTpUklSQECAunXrplq1aql169bauHGjpBuPwtzs1nNkd96c5NapUycdP35cn3322R2PBQAAAADgYfFQzhni4uKiKVOmaMqUKQoMDFRAQIBCQ0NlY2OjgQMHSrpRCOnevbuioqK0detWy6gQSfL29lZKSoouXbpkNVri3Llzatq0qSXm7NmzWc59/vz5LKM2xowZo9q1aysoKEiGYahHjx6qX7++1Soztx5z87XUqlVLJ06csJxXujH64+ZHWs6dO2fpIyf5Z+rTp486d+6s/v37Kz09XSNHjrzNXQUAAAAA4OHwUI4MuZm7u7sGDRqkgIAAbd++XdL/FUJOnDihH374QZ6enlbH1KtXT/b29lYTrcbGxurQoUOWYkKTJk0UHx+vX375xRLz888/Kz4+PkvBQZLGjRunSZMmKSgoSMuXL1eRIkVUqVIly3a7eUSuX7+uI0eOWAoffn5+8vb2tsotJSVF4eHhlvPmJP+b9e3bV4sWLdLo0aP17rvv3vmGAgAAAADwgHsoR4aEhoYqMDBQderUUXp6usLCwhQeHq5x48YpLS1NzzzzjH777Tdt2LBB6enplnk0PDw85ODgILPZrAEDBmjEiBHy9PSUh4eHRo4caXl0RZKqVaump556Si+88ILlEZOBAweqY8eOWVaSyTR69GjZ2toqODhYGRkZCgoKyhIzcuRIderUSb6+vjp37pwmT56shIQE9evXT9KNx2OGDRumqVOnqnLlyqpcubKmTp0qZ2dn9e7dW5JylP+tgoKCZGNjY8lt9OjR/+xDAAAAAADgPvVQFkN8fX01fPhwnThxQsnJydq2bZv69++vIUOG6NSpU1q/fr0kqU6dOlbHhYWFqUWLFpKkDz74QHZ2durevbuuXr2qVq1aaeHChbK1tbXEL126VEOHDrWs2tK5c2d98sknd8xt1KhRsrW1Vb9+/ZSRkaHg4GCr/adPn1avXr104cIFlShRQo0bN9auXbtUrlw5S8xrr72mq1evavDgwbp06ZIaNWqk77//3mp0SU7yv1WvXr1ka2uroKAgZWRkaMyYMXe8FgAAAAAAHkQmwzCMgk4iP4WEhGjhwoUFnQb+v4SEBJnNZq2KXCfnoi552nfE2SJ52h8AAADwINpx9M4rWOanvbtP5yr+8szA/EkEuIuHfs4QAAAAAACAmz30xRBGhQAAAAAAgJs99MUQAAAAAACAm1EMAQAAAAAAhQrFEAAAAAAAUKhQDAEAAAAAAIUKxRAAAAAAAFCoUAwBAAAAAACFCsUQAAAAAABQqFAMAQAAAAAAhQrFEAAAAAAAUKhQDAEAAAAAAIUKxRAAAAAAAFCoUAwBAAAAAACFiskwDKOgk0DhkZCQILPZrPj4eLm5uRV0OgAAAACAQoiRIQAAAAAAoFChGAIAAAAAAAoViiEAAAAAAKBQoRgCAAAAAAAKFbuCTgCF058JxxWbcLmg08A9ijhbpKBTAAAAwENgSr1mBZ0CCilGhgAAAAAAgEKFYggAAAAAAChUKIYAAAAAAIBChWIIAAAAAAAoVCiGAAAAAACAQoViCAAAAAAAKFQohgAAAAAAgEKFYggAAAAAAChUKIYAAAAAAIBCxe5eDjp+/Li2bdums2fPyjAMq31vvvlmniQGAAAAAACQH3JdDJk/f74GDRqkjIyMbPdTDAEAAAAAAPezXBdDJk+erPT09PzIBQAAAAAAIN/les6Qs2fPymw2a//+/UpNTVVGRobVBgAAAAAAcD/LdTGkZcuW8vDwUK1atWRra5sfOQEAAAAAAOSbXD8m8+yzz2rgwIHq0aOHgoKC5O7ubrW/efPmeZUbAAAAAABAnjMZty4Hcxc2NjYymUzZd2YyKS0tLU8SyyshISFauHBhQaeB/y8hIUFms1l7T+1WrC4XdDq4RxFnixR0CgAAAHgITKnXrKBTQCGV68dkJMkwjNtuD4I1a9aoXbt2Kl68uEwmk/bt25cl5vr16xoyZIiKFy8uFxcXde7cWadPn7aKuXTpkoKDg2U2m2U2mxUcHKzLly9b9kdHR2fpPzExUS1atFDVqlV16tSpLOd9++231aBBAxUtWlReXl4KDAzUsWPHrGIMw9CECRNUqlQpFSlSRC1atNDhw4dznb/JZNK6dess71NTU9WzZ0/5+PjowIEDd7mLAAAAAAA8mHJdDImKirrt9ueff+ZHjrl24cIF9evXT76+vlq+fLkqVaqk7t27KyUlRZKUnJysZs2a6Z133rltH8OGDdPatWu1YsUKRUREKCkpSR07drRaSad3797at2+fNm3apE2bNmnfvn0KDg6+bZ/nz59Xy5YtlZSUpIiICJUtWzZLTHh4uF5++WXt2rVLmzdvVlpamtq2bavk5GRLzLvvvqsZM2bok08+0e7du+Xt7a02bdooMTExV/nf7MqVK+rcubN2796tiIgI1a5d+/Y3GAAAAACAB1iu5wwpV65cfuSRp0JDQ7V7924tWbJEM2fO1NChQ7Vp0ybLajeZBYvo6Ohsj4+Pj9e8efO0ZMkStW7dWpL0xRdfqGzZsvrhhx/Url07HTlyRJs2bdKuXbvUqFEjSdLcuXPVpEkTHTt2TFWqVLHq89SpU2rTpo18fHy0fv16FS1aNNtzb9q0yer9ggUL5OXlpV9//VXNmzeXYRiaOXOmxo4dq6efflqStGjRIpUsWVLLli3ToEGDcpT/zS5fvqyOHTsqISFBERER8vHxyemtBgAAAADggZOjYkj//v1VqVIljRkzRv37979tnMlk0rx58/IsuXu1d+9eBQcHy9/fXwsWLFDLli3VsmXLHB//66+/KjU1VW3btrW0lSpVSjVr1tSOHTvUrl077dy5U2az2VIIkaTGjRvLbDZrx44dVsWQY8eOadSoUapbt65WrlwpR0fHHOcSHx8vSfLw8JB0Y2ROXFycVW6Ojo7y9/fXjh07NGjQoBzlnykuLk7+/v5ycXFReHi4ihUrluPcAAAAAAB4EOWoGLJw4UI1btxYY8aM0cKFC7OdQNUwjPumGNKsWTMtWLBAjz766D0dHxcXJwcHhyyFgZIlSyouLs4S4+XlleVYLy8vS0ymvn37qmnTplq9enWuliM2DEPDhw/X448/rpo1a1rOm5nLrbmdPHkyx/lnevXVV1WhQgXt3LlTzs7OOc4NAAAAAIAHVY7mDGnevLnq1q1reZ3d5u/vf98sqztjxgz16NFDoaGhWrx4serUqaPZs2f/434zCz6Z7lQUulmXLl0UERGh1atXW9q2b98uV1dXy7Z06dIsfb3yyis6cOCAli9fnmXfrefI7rw5ya1Tp046fvy4PvvsszseCwAAAADAwyJHI0O2bduW7ev7lYuLi6ZMmaIpU6YoMDBQAQEBCg0NlY2NjQYOHHjX4729vZWSkqJLly5Zja44d+6cmjZtaok5e/ZslmPPnz+fZdTGmDFjVLt2bQUFBckwDPXo0UP169e3WmXm1mOGDBmi9evX68cff1SZMmWscpNujP64eW6Pc+fOWfrISf6Z+vTpo86dO6t///5KT0/XyJEj73p/AAAAAAB4kN3T0ro3u3btmtVKJ/cbd3d3DRo0SAEBAdq+fXuOjqlXr57s7e21efNmS1tsbKwOHTpkKSY0adJE8fHx+uWXXywxP//8s+Lj47MUHCRp3LhxmjRpkoKCgrR8+XIVKVJElSpVsmyZE6oahqFXXnlFa9as0datW+Xn52fVj5+fn7y9va1yS0lJUXh4uOW8Ocn/Zn379tWiRYs0evRovfvuuzm6RwAAAAAAPKhytZrMmjVrFB4erkqVKunll1/WuHHj9P777ys9PV2tWrXSihUr7osJOENDQxUYGKg6deooPT1dYWFhCg8P17hx4yRJf//9t2JiYnTmzBlJNyY4lW6MqPD29pbZbNaAAQM0YsQIeXp6ysPDQyNHjlStWrUsq7NUq1ZNTz31lF544QXLIyYDBw5Ux44ds6wkk2n06NGytbVVcHCwMjIyFBQUlCXm5Zdf1rJly/T111+raNGiljk+zGazihQpIpPJpGHDhmnq1KmqXLmyKleurKlTp8rZ2Vm9e/e2xN4t/1sFBQXJxsbGktvo0aPv9fYDAAAAAHBfy3Ex5L///a+GDBlief/1118rLCxMhmFIkn744Qe9/vrreTI3xz/l6+ur4cOH68SJE0pOTta2bdvUv39/S/7r16/Xc889Z4nv2bOnJGn8+PGaMGGCJOmDDz6QnZ2dunfvrqtXr6pVq1ZauHCh1QSoS5cu1dChQy2rtnTu3FmffPLJHXMbNWqUbG1t1a9fP2VkZFiW+c00a9YsSVKLFi2s2hcsWKCQkBBJ0muvvaarV69q8ODBunTpkho1aqTvv//earnenOR/q169esnW1lZBQUHKyMjQmDFj7ngtAAAAAAA8iExGZjXjLurWrWs1x4V0YxLP7t27KyMjQ1999ZV8fX0VHR2dD2neu5CQEC1cuLCg08D/l5CQILPZrL2nditWlws6HdyjiLNFCjoFAAAAPASm1GtW0CmgkMrxyJATJ07I09NT+/bt0/Hjx9WqVSuVL1/estLJnj17dPr06XxLFAAAAAAAIC/kuBiSnJysWrVqqXTp0ipdurQk6xVQvLy87rtRIZIYFQIAAAAAAKzkagLV8+fPa8mSJZZ5Qs6fP6/FixdbXgMAAAAAANzvcjxniI2NjUwm0233G4Yhk8mk9PT0PEsODx/mDHk4MGcIAAAA8gJzhqCg5GpkSA7rJgAAAAAAAPetHBdDwsLC8jMPAAAAAACAf0WOiyH+/v75mQcAAAAAAMC/wqagEwAAAAAAAPg3UQwBAAAAAACFCsUQAAAAAABQqFAMAQAAAAAAhQrFEAAAAAAAUKjcUzHk2LFj6tevn6pUqaJOnTpp165dmjhxog4dOpTX+QEAAAAAAOSpHC+tm2n//v164oknlJycLMMw5OnpKScnJ02YMEHnzp3TJ598kh95AgAAAAAA5IlcF0NGjx6tpKQk1a9fX3v27JEk1alTRx4eHgoLC8vzBPFwquD2iOq4uRV0GrhHAWUKOgMAAAAAuHe5fkzmp59+UunSpbVz506r9rJly+rUqVN5lhgAAAAAAEB+yHUxJD09Xa6urrK1tbVqP3/+vDIyMvIsMQAAAAAAgPyQ62JI9erVdfz4cU2ePFmSlJCQoJEjR+rMmTOqWbNmnicIAAAAAACQl0yGYRi5OeCLL75Q3759ZTKZsuxbtGiR+vTpk2fJ4eGTkJAgs9ms+Ph4uTFnCAAAAACgAOR6AtU+ffrozJkzmjhxoq5cuSJJKlKkiN544w0KIcixzX+FyTnBpaDTKFQizhYp6BQAAAAAK1PqNSvoFFBI5boYIkmvvfaahgwZosOHD8swDNWsWVNFivCHFgAAAAAAuP/dUzFEujEapH79+nmZCwAAAAAAQL7L9QSqcXFxCgoKUqlSpWRnZydbW1vLZmd3z7UVAAAAAACAf0WuqxfPPfecvv/+e+Vy3lUAAAAAAID7Qq6LIREREbK3t9drr72mChUqZLuqDAAAAAAAwP0q18WQihUr6vr165o4cWJ+5AMAAAAAAJCvcl0M+e9//6v27dvrxRdfVMeOHeXm5ma1v3nz5nmWHAAAAAAAQF7LdTHE3t5eRYsW1dy5czV37lyrfSaTSWlpaXmWHAAAAAAAQF7LdTHk+eef15kzZ5hAFQAAAAAAPJByXQz5/fff5eLiog8++EDly5dnOV0AAAAAAPBAyXUlo1WrVoqMjNSAAQPyIx8AAAAAAIB8letiyBNPPKHNmzerffv2at++fZYJVPv27ZtnyQEAAAAAAOQ1k5HLyT9sbGxkMpmy74wJVHEXCQkJMpvNWhW5Ts5FXQo6nUIl4myRgk4BAAAAsDKlXrOCTgGF1D1N+MHkqQAAAAAA4EGV62JIVFRUfuQBAAAAAADwr8h1MaRcuXKW1xcvXpQkeXp65l1GBSAkJEQLFy4s6DQAAAAAAMC/wOZeDlq2bJkqVKggLy8veXl5qUKFClq+fHle51agTCZTttt7771niWnRokWW/T179szSz7p16yzvU1NT1bNnT/n4+OjAgQNZznv48GF169ZN5cuXl8lk0syZM7PN77///a/8/Pzk5OSkevXqafv27Vb7DcPQhAkTVKpUKRUpUkQtWrTQ4cOHrWLKly9v1b9hGBoxYoSKFi2qrVu35vBOAQAAAADwYMl1MeSbb75Rnz59FB0dLcMwZBiGoqOj1adPH23cuDE/cswXFy5cUL9+/eTr66vly5erUqVK6t69u1JSUiRJsbGxVtv8+fNlMpnUrVs3q35eeOEFq7jPPvvstue8cuWKOnfurN27dysiIkK1a9fONqZChQp655135O3tnW0/K1eu1LBhwzR27Fjt3btXTzzxhAICAhQTE2OJeffddzVjxgx98skn2r17t7y9vdWmTRslJiZm22d6eroGDBigxYsXa+vWrXryySfveg8BAAAAAHgQ5boYMm3aNElSw4YNNW3aNE2bNk2NGjWSYRiWfQ+C0NBQ/fzzz1qyZInat2+vuXPnys/PTxkZGZIkb29vq+3rr79Wy5YtVaFCBat+nJ2dreLMZnO257t8+bLatm2rv/76SxEREapYsWK2cQ0aNNB7772nnj17ytHRMduYGTNmaMCAAXr++edVrVo1zZw5U2XLltWsWbMk3RjhMXPmTI0dO1ZPP/20atasqUWLFunKlStatmxZlv6uX7+uZ599Vps3b9aPP/6oBg0a5Pg+AgAAAADwoMl1MWTfvn3y8fHR9u3bNWrUKI0aNUrh4eHy9vbWb7/9lh855ou9e/cqODhY/v7+MpvNatmypaZNmyYnJ6cssWfPntXGjRs1YMCALPuWLl2q4sWLq0aNGho5cmS2Iy/i4uLk7++vjIwMhYeHy8fH557zTklJ0a+//qq2bdtatbdt21Y7duyQdGOS27i4OKsYR0dH+fv7W2IyJSUlqUOHDjp8+LB++uknVatW7Z5zAwAAAADgQZDrCVQzMjLk4OAge3t7S5u9vb0cHBweqCV3mzVrpgULFujRRx+9a+yiRYtUtGhRPf3001btQUFB8vPzk7e3tw4dOqTXX39d+/fv1+bNm63iXn31VVWoUEE7d+6Us7PzP8r7woULSk9PV8mSJa3aS5Ysqbi4OEmy/De7mJMnT1q1TZo0SUWLFlVkZKS8vLz+UW4AAAAAADwIcj0ypFq1aoqJidGzzz6rVatWafXq1erevbtiYmIeqFEFM2bMUI8ePRQaGqrFixerTp06mj17drax8+fPV1BQUJZRIy+88IJat26tmjVrqmfPnlq1apV++OGHLCNkOnXqpOPHj1vNJxITEyNXV1fLNnXq1FzlbzKZrN4bhpGlLScxbdu2VXJycq7PDwAAAADAgyrXI0NeeeUVDRgwQGvWrNGaNWss7SaTSa+88kqeJpefXFxcNGXKFE2ZMkWBgYEKCAhQaGiobGxsNHDgQEvc9u3bdezYMa1cufKufdatW1f29vY6ceKE6tata2nv06ePOnfurP79+ys9PV0jR45UqVKltG/fPkuMh4dHjvIuXry4bG1tLaM/Mp07d84yEiRz4tW4uDirR3JujsnUqlUrDR06VF26dFF6ero+/vjjHOUBAAAAAMCDKtcjQ5577jlNnDhRTk5OltVknJycNHHiRIWEhORDivnP3d1dgwYNUkBAQJYlaufNm6d69erl6HGaw4cPKzU1Nds5Qfr27atFixZp9OjRevfdd2VnZ6dKlSpZtpwWQxwcHFSvXr0sj+Js3rxZTZs2lSTLozs3x6SkpCg8PNwSc7M2bdpow4YNmj9/vl5++eUH6nEnAAAAAAByK9cjQyRp3LhxGj58uA4fPixJqlGjxj+eC+PfFhoaqsDAQNWpU0fp6ekKCwtTeHi4xo0bZ4lJSEjQV199pffffz/L8X/88YeWLl2q9u3bq3jx4oqMjNSIESP02GOPqVmzZtmeMygoSDY2NgoODlZGRoZGjx6dJSYlJUWRkZGW13/99Zf27dsnV1dXVapUSZI0fPhwBQcHq379+mrSpInmzJmjmJgYvfjii5JujNIZNmyYpk6dqsqVK6ty5cqaOnWqnJ2d1bt372xze/LJJ7Vx40Z17NhRhmHo008/zfJIDQAAAAAAD4NcF0POnDmjCxcuyNfX17IE6+XLl/X777+rePHiKlWqVJ4nmR98fX01fPhwnThxQsnJydq2bZv69++vIUOGWGJWrFghwzDUq1evLMc7ODhoy5Yt+vDDD5WUlKSyZcuqQ4cOGj9+vGxtbW973l69esnW1lZBQUHKyMjQmDFjrPafOXNGjz32mOX99OnTNX36dPn7+2vbtm2SpB49eujixYuaOHGiYmNjVbNmTX377bcqV66c5bjXXntNV69e1eDBg3Xp0iU1atRI33//vYoWLXrb3Fq0aKFvv/1WHTp0UEZGhmbNmkVBBAAAAADw0DEZuXwmokWLFtqxY4f++OMPlS1bVpJ0+vRpVahQQc2aNVNYWFi+JJqfQkJCtHDhwoJOo1BISEiQ2WzWqsh1ci7qUtDpFCoRZ4sUdAoAAACAlSn1sh9VD+S3XM8Zsn//flWuXNlSCJGkMmXKqHLlylYTggIAAAAAANyPcl0MuXbtmuLj47O0X758WdeuXcuTpP5tjAoBAAAAAKDwyHUxxM/PT7GxsQoNDVVsbKxiY2M1fPhwxcbGqkKFCvmRIwAAAAAAQJ7JdTEkKChIhmHoo48+UpkyZVSmTBl9+OGHMplM6tOnT37kCAAAAAAAkGdyXQx57bXX1KFDBxmGYbV16NBBo0aNyo8cAQAAAAAA8kyul9a1t7fXN998o+3bt+vnn3+WJDVq1EhPPPFEnicHAAAAAACQ13JdDMn0xBNPUAABAAAAAAAPnFwXQzIyMrRgwQJt2bJFZ8+elWEYln0mk0lbtmzJ0wQBAAAAAADyUq6LIcOHD9fHH38sSVaFEOlGMQQAAAAAAOB+lutiyPLly2UYhkqVKiU/Pz/Z2d3zkzYAAAAAAAD/ulxXMtLT01WmTBmdOHFCjo6O+ZETAAAAAABAvsn10ro9e/bU1atXlZqamh/5AAAAAAAA5KtcjwxxdXVVQkKC6tSpo86dO8vd3d1q/5tvvplXuQEAAAAAAOQ5k3HrLKh3YWNjI5PJJMMwsp0wNT09Pc+Sw8MnISFBZrNZqyLXybmoS0GnU6hEnC1S0CkAAAAAVqbUa1bQKaCQynUxpHz58ndcNSYqKuofJ4WHV2YxJD4+Xm5ubgWdDgAAAACgEMr1YzLR0dH5kAYAAAAAAMC/I9cTqAIAAAAAADzIcjwy5Mknn7xrjMlk0pYtW/5RQgAAAAAAAPkpx8WQbdu23XGukNtNqAoAAAAAAHA/yXExxNfXl2IH8szmv8LknFB4VpNhJRcAAAAgK1aTQUHJcTGEiVMBAAAAAMDDgAlUAQAAAABAoUIxBAAAAAAAFCoUQwAAAAAAQKFCMQQAAAAAABQqFEMAAAAAAEChQjEEAAAAAAAUKjlaWrdChQo56sxkMumPP/74RwkBAAAAAADkpxwVQ6Kjo++432QyyTAMmUymvMgJAAAAAAAg3+SoGNKvXz/La8MwtHr1ajk6OqpFixYyDEPh4eG6cuWKunfvnm+JAgAAAAAA5IUcFUMWLFhgeT1x4kQ5ODjo6NGjKl68uCTpwoULqlKlikqVKpU/WQIAAAAAAOSRXE+g+umnn8rDw8NSCJGk4sWLy8PDQ59//nmeJgcAAAAAAJDXcjQy5GbXrl3ThQsX1KdPH3Xt2lUmk0lr167VH3/8oaJFi+ZHjgAAAAAAAHkm18WQnj17au7cuVq+fLmWL1+eZR8AAAAAAMD9LNePyXz88ccKDQ2Vg4ODDMOQYRhycHDQsGHD9NFHH+VHjgAAAAAAAHkm18UQBwcHvf/++7p48aL279+vffv26eLFi5oxY4YcHR3zI8f7QkhISEGnAAAAAAAA8kCuiyGZ4uLidOjQIUVFRcnZ2Tkvc3pghISEyGQyWW2NGze2irl+/bqGDBmi4sWLy8XFRZ07d9bp06etYkwmk9atW2d5n5qaqp49e8rHx0cHDhzIct4WLVpkOa/JZFKHDh0sMRMmTMiy39vbO0s/w4YNs2r78MMP5ejoqGXLlt3jXQEAAAAA4P6W62JIenq6nn/+eT3yyCPq06ePpk2bpiVLlsjW1lYff/xxfuRYYC5cuKB+/frJ19dXy5cvV6VKldS9e3elpKRYYp566inFxsZatm+//daqj2HDhmnt2rVasWKFIiIilJSUpI4dOyo9PT3bc165ckWdO3fW7t27FRERodq1a2eJWbNmjdU5Dx06JFtbWz377LNWcTVq1LCKO3jw4B2vd/z48Xr99de1du1a9e7dO6e3CQAAAACAB0quiyFvv/225s+fr4yMDBmGIUnq2rWr7OzstH79+jxPsCCFhobq559/1pIlS9S+fXvNnTtXfn5+ysjIsMQ4OjrK29vbsnl4eFj2xcfHa968eXr//ffVunVrPfbYY/riiy908OBB/fDDD1nOd/nyZbVt21Z//fWXIiIiVLFixWzz8vDwsDrn5s2b5ezsnKUYYmdnZxVXokSJbPszDENDhgzRhx9+qO+//17t27e/l9sFAAAAAMADIdfFkAULFsje3t7qsQ5XV1eVLVtWR44cycvcCtzevXsVHBwsf39/mc1mtWzZUtOmTZOTk5MlZtu2bfLy8tIjjzyiF154QefOnbPs+/XXX5Wamqq2bdta2kqVKqWaNWtqx44dVueKi4uTv7+/MjIyFB4eLh8fnxznOW/ePPXs2VMuLi5W7SdOnFCpUqXk5+ennj176s8//8xybFpamoKDg/XVV18pPDxcjz/+eI7PCwAAAADAgyjXS+uePn1a1atXV+fOna3aixYtqlOnTuVZYveDZs2aacGCBXr00Uez3R8QEKBnn31W5cqVU1RUlN544w09+eST+vXXX+Xo6Ki4uDg5ODioWLFiVseVLFlScXFxVm2vvvqqKlSooJ07d+ZqDpZffvlFhw4d0rx586zaGzVqpMWLF+uRRx7R2bNnNXnyZDVt2lSHDx+Wp6enJW7u3LmSpP3796tq1ao5Pi8AAAAAAA+qXI8MKV68uKKionTx4kVLW0xMjI4cOXLbxzAeVDNmzFCPHj0UGhqqxYsXq06dOpo9e7Zlf48ePdShQwfVrFlTnTp10nfffafjx49r48aNd+zXMAyZTCartk6dOun48eP67LPPLG0xMTFydXW1bFOnTs3S17x581SzZk01bNjQqj0gIEDdunVTrVq11Lp1a0tOixYtsop7/PHH5erqqnHjxiktLS1nNwYAAAAAgAdYrosh7dq1U0JCgmrVqiVJioyMVN26dZWamqqnnnoqzxMsSC4uLpoyZYpOnDihzp0766WXXtLw4cM1Z86cbON9fHxUrlw5nThxQpLk7e2tlJQUXbp0ySru3LlzKlmypFVbnz59tGDBAo0aNUrTp0+XdOORmn379lm2F1980eqYK1euaMWKFXr++edzdC21atWy5JapVq1a2rJli7Zt26bu3bsrNTX1rn0BAAAAAPAgy3UxZMqUKSpTpozlMY+EhAT9/fffKlWqlCZOnJjnCd4v3N3dNWjQIAUEBGj79u3Zxly8eFGnTp2yzPdRr1492dvba/PmzZaYzNVfmjZtmuX4vn37atGiRRo9erTeffdd2dnZqVKlSpbt5slZJenLL7/U9evX1adPn7vmf/36dR05ciTbuUjq1KmjrVu3KiIiQs8++ywFEQAAAADAQy3Xc4b4+Pho7969+uSTT7R7924ZhqGGDRvq5ZdfVvHixfMjxwITGhqqwMBA1alTR+np6QoLC1N4eLjGjRunpKQkTZgwQd26dZOPj4+io6M1ZswYFS9eXF27dpUkmc1mDRgwQCNGjJCnp6c8PDw0cuRIy6Mr2QkKCpKNjY2Cg4OVkZGh0aNH3za/efPmKTAw0GoOkEwjR45Up06d5Ovrq3Pnzmny5MlKSEhQv379su2rdu3aCgsL05NPPqlnnnlGX331lRwcHO7hrgEAAAAAcH/LdTFEkjw9PTV+/Pi8zuW+4+vrq+HDh+vEiRNKTk7Wtm3b1L9/fw0ZMkQpKSk6ePCgFi9erMuXL8vHx0ctW7bUypUrVbRoUUsfH3zwgezs7NS9e3ddvXpVrVq10sKFC2Vra3vb8/bq1Uu2trYKCgpSRkaGxowZkyXm+PHjioiI0Pfff59tH6dPn1avXr104cIFlShRQo0bN9auXbtUrly52563Ro0aCgsLU6tWrdStWzetXr2agggAAAAA4KFjMgzDuFtQbh5/efPNN/9RQverkJAQLVy4sKDTeOAlJCTIbDZrVeQ6ORd1ufsBD4mIs0UKOgUAAADgvjOlXrOCTgGFVI5GhkyYMCHL6ie387AWQwAAAAAAwMMhx4/J5GAASY4LJg8iRoUAAAAAAPBwyNFqMhkZGZZt+/btcnNz0+eff66EhAQlJCRo3rx5cnZ2vu38FQAAAAAAAPeLHM0ZcrM6deooLS1Nhw4dsmqvWbOmbGxsdODAgTxNEA8X5gwBAAAAkIk5Q1BQcr2azLFjx2QYhjZv3qw2bdpIkn744Qf9/vvvD/VjMgAAAAAA4OGQ62LIo48+ql9++UVPPfWUnJ2dZTKZlJycLElq0KBBnicIAAAAAACQl3I0Z8jN5syZo1KlSskwDCUnJyspKUmGYcjHx0dz5szJjxwBAAAAAADyTK5HhtSuXVu///67li1bpsOHD8swDNWsWVO9e/eWk5NTfuQIAAAAAACQZ3JdDJEkJycn9e/fP69zAQAAAAAAyHf3VAzZsmWLtmzZorNnz+rmxWhMJpPmzZuXZ8kBAAAAAADktVwXQ6ZMmaI333wzS7thGBRDAAAAAADAfS/XxZBZs2bJMAzZ29vLy8tLdnb3NLgEAAAAAACgQOS6kpGQkKASJUooMjJSnp6e+ZETAAAAAABAvsn10rqdOnWSvb293N3d8yEdAAAAAACA/JXrkSH16tXTqlWr1Lx5c/Xo0SNLUaRv3755lRseYm1Kt5Sbm1tBp/GvCShT0BkAAAAAADKZjJuXg8kBGxsbmUym7DszmZSWlpYnieHhlJCQILPZrPj4+EJVDAEAAAAA3D/uafbT29VPcllXAQAAAAAA+NfluhiSkZGRH3kAAAAAAAD8K3I9gSoAAAAAAMCD7J4ek/nuu++0YsUKnTlzRunp6ZZ2k8mkLVu25FlyeHht/itMzgku2e6LOFvkX84GAAAAQEGYUq9ZQaeAQirXxZClS5dmu2KMYRi3nVgVAAAAAADgfpHrx2Q+/PBDGYahihUryjAMubq6ytvbW8WKFZO/v39+5AgAAAAAAJBncl0MiYyMlIeHhw4ePChJqlGjhg4dOiTDMPTcc8/leYIAAAAAAAB5KdfFkLS0NJUvX16Ojo6ytbVVUlKSihUrplKlSumtt97KjxwBAAAAAADyTK7nDPHw8NClS5ckSV5eXoqMjNRLL72ko0ePqkgRJr4EAAAAAAD3t1yPDKlWrZpiYmJ0/vx5tWzZUhkZGZozZ44yMjLUqFGj/MgRAAAAAAAgz+R6ZMh7772nqKgoZWRkaMaMGTp79qx+/vln1a5dW5999ll+5AgAAAAAAJBncl0MqVu3rurWrWt5v3nz5jxNCAAAAAAAID/luhgiSXFxcfroo48sK8rUrl1bQ4YMkbe3d54mBwAAAAAAkNdyXQz58ccf1bFjRyUnJ1vavv32W3300UfasGGD/P398zRBAAAAAACAvJTrCVSHDBmipKQk2draqk6dOqpTp45sbW2VnJysV199NT9yBAAAAAAAyDO5Hhly/PhxOTs765dfflH16tUlSUeOHFGDBg107NixPE8QAAAAAAAgL+W6GFKlShWlpaVZCiHSjeV2y5cvLycnpzxNDgAAAAAAIK/l+jGZDz74QNHR0fr888+VlJSkpKQkzZkzR6dPn9ZHH32UHzkCAAAAAADkGZNhGEZuDrC1tc1ZxyaT0tLS7ikpPLwSEhJkNpu1KnKdnIu6ZBsTcbbIv5wVAAAAgIIwpV6zgk4BhVSuR4YYhpHj7WESEhJS0CkAAAAAAIA8kOtiyIIFC3K0zZ8/Pz/yvW8NGjRIJpNJM2fOtGq/fv26hgwZouLF/197dx6XVZn/f/x9y6qgtyLFYoIaFSooimm5hFuYG+NU4oqSNmXfUkHtm1ujWWiZmdqMZn5NzCXJcRmncVJUoMg1BJcWsXEhDSJLwS3Zzu+PHt6/7kDTJG/0vJ6Px/2Yua/rOuf6nPO4HqXvrnNub3l4eCgqKkonTpywG2OxWLR+/Xrb9+LiYvXv319+fn7av39/ubkSExNlsVjKfX766Se7cfPnz1fDhg3l7u6u8PBwffLJJ3b9HTt2VFxcnF3b3Llz5ebmppUrV17/TQAAAAAA4BZw3S9QHTp06BX7Ll68qOrVb59HHE6dOqWxY8cqJSVF3333ndLT09WyZUstX75crq6utnHr16/Xrl275O/vX+4ccXFx+te//qVVq1apbt26Gjt2rHr16qWMjIwKHzm6cOGCHnvsMWVnZys9PV133313hbXVqlWr3K/3/PIFtklJSYqLi9P8+fPVrl07LVy4UN27d9cXX3yhgICACs85ZcoUvf7661q3bp169OhxTfcIAAAAAIBbzXXvDOnSpYtOnjxZrn3Pnj1q0aJFpRRVVcTHx2vXrl1atmyZevTooUWLFqlhw4YqKyuzjTl58qSee+45rVixQi4uLnbHFxQUaPHixXrjjTfUtWtXtWjRQsuXL9eBAwe0ZcuWcvOdOXNGkZGROnny5FWDEOnn3SS+vr52n1+aPXu2hg8frieffFKNGzfWnDlzVL9+fS1YsKDcuQzD0MiRIzV37lxt3ryZIAQAAAAAcFu77jAkJSVFzZo1U1JSkiSprKxML7/8stq3b6/Dhw9XeoGOlJmZqZiYGEVERMhqtapTp0567bXXbDswysrKFBMTo+eff15NmzYtd3xGRoaKi4sVGRlpa/P391dISIi2b99uNzYvL08REREqKytTWlqa/Pz8rlrbuXPnFBgYqLvuuku9evVSZmamra+oqEgZGRl280pSZGRkuXlLSkoUExOj1atXKy0tTe3bt7+2mwMAAAAAwC3quh+TeeCBB7Rz504NHDhQ//znP3Xs2DHt2rVLhmEoOjr6j6jRYdq1a6clS5aoefPmFfa/9tprcnZ21qhRoyrsz8vLk6urq+rUqWPX7uPjo7y8PLu20aNHq1GjRtqxY4dq1Khx1bqCg4OVmJio0NBQFRYWau7cuWrXrp327dune+65R6dOnVJpaal8fHx+c95FixZJkvbt26fg4OCrzgsAAAAAwO3guneGfPrpp5o5c6bc3d2VlJSknTt3ytvbW0lJSVq1atUfUaPDzJ49W/369VN8fLzee+89hYWF6e2335b0866PuXPn2l5mej0Mwyh3TO/evZWdna2FCxfa2nJycuTp6Wn7TJ8+XdLPgdTgwYPVvHlzdejQQR988IHuvfdevfXWW3bn/PUcFc3bvn17eXp6avLkyfwUMgAAAADAFK57Z4jFYlHt2rXl4uKiixcvSvr5xZ21a9eu7NoczsPDQwkJCUpISFCfPn3UvXt3xcfHq1q1arpw4YLy8/PtXkZaWlqqsWPHas6cOTp27Jh8fX1VVFSk06dP2+0Oyc/PV9u2be3mGjx4sKKiojRs2DCVlpZq3Lhx8vf3V1ZWlm2Ml5dXhXVWq1ZN999/v+0xJW9vbzk5OZXbBZKfn19ut0hoaKjtnSbR0dFKSkoq9+4TAAAAAABuJ9e9M+Thhx/W008/rcLCQvXu3VstW7bUN998o0ceeURPPfXUH1FjlVC7dm09/fTT6t69uz755BPFxMRo//79ysrKsn38/f31/PPPa9OmTZKk8PBwubi4KDk52Xae3NxcHTx4sFwYIklDhgzR0qVLNX78eM2cOVPOzs4KCgqyfa4UhhiGoaysLNt7RlxdXRUeHm43ryQlJydXOG9YWJi2bdum9PR09e3bV8XFxb/7PgEAAAAAUNVddxiydetW1ahRQwsXLtQ///lP7dy5Uy+88IIsFosWL178R9ToMPHx8UpLS1NBQYFKS0uVkpKitLQ0hYeHq27dugoJCbH7uLi4yNfXV/fdd58kyWq1avjw4Ro7dqy2bt2qzMxMDR48WKGhoeratWuFcw4aNEjLli3TxIkT9eqrr1Y45qWXXtKmTZt05MgRZWVlafjw4crKytKIESNsY8aMGaP/+7//07vvvqsvv/xS8fHxysnJsRvzS82aNVNKSop27Nihxx9/XEVFRTd49wAAAAAAqJqu+zGZ1q1ba8WKFbaffXV2dtaMGTPUo0cPDR06tNILdKSAgACNGTNGhw8f1vnz55Wamqphw4Zp5MiR13yON998U87OzoqOjtbFixfVpUsXJSYmysnJ6YrHDBgwQE5OTho0aJDKyso0ceJEu/4zZ87oqaeeUl5enqxWq1q0aKGPP/5YrVu3to3p16+ffvjhB02bNk25ubkKCQnRxo0bFRgYeMV5mzZtqpSUFHXp0kWPPfaY1qxZI1dX12u+VgAAAAAAbgUWwzCM6zmgtLT0in+RP3v2rGrWrFkphVU1sbGxSkxMdHQZt7zCwkJZrVb944v1qlHTo8Ix6d9Vv8lVAQAAAHCEhPB2ji4BJnXNj8ls2LBB6enptiCksLBQFy5csPWvXr1aS5YsqfwKAQAAAAAAKtE17wypVq2aHnzwQX366acVfn/wwQe1e/dulZaW/nHV4pbHzhAAAAAAl7EzBI5yXS9Q/XVucp1P2AAAAAAAADjcdf+aDAAAAAAAwK2MMAQAAAAAAJjKdf20bmZmpho1alTh92+//bZyKwMAAAAAAPgDXFcYUlRUpGPHjtm+X7p0ye67xWKprLoAAAAAAAD+ENcchjz00EOEHQAAAAAA4JZ3zWFIamrqH1gGAAAAAADAzcELVAEAAAAAgKkQhgAAAAAAAFMhDAEAAAAAAKZCGAIAAAAAAEyFMAQAAAAAAJiKxTAMw9FFwDwKCwtltVpVUFCgWrVqObocAAAAAIAJsTMEAAAAAACYCmEIAAAAAAAwFcIQAAAAAABgKoQhAAAAAADAVJwdXQDMaVrmTrl5eji6DAAAAAAOlBDeztElwKTYGQIAAAAAAEyFMAQAAAAAAJgKYQgAAAAAADAVwhAAAAAAAGAqhCEAAAAAAMBUCEMAAAAAAICpEIYAAAAAAABTIQwBAAAAAACmQhgCAAAAAABMhTAEAAAAAACYCmEIAAAAAAAwFcIQAAAAAABgKoQhAAAAAADAVAhDAAAAAACAqRCGAAAAAAAAUyEMuUaxsbGOLgEAAAAAAFQCwpAbMHXqVAUHB8vDw0N16tRR165dtWvXLrsxly5d0siRI+Xt7S0PDw9FRUXpxIkTdmMsFovWr19v+15cXKz+/fvLz89P+/fvv2oNq1atksViUZ8+fcr1zZ8/Xw0bNpS7u7vCw8P1ySef2PV37NhRcXFxdm1z586Vm5ubVq5c+ds3AAAAAACAWxBhyFWcOnVKQ4cOVUBAgN5//30FBQUpOjpaRUVFkqR7771Xf/vb33TgwAGlp6erQYMGioyM1Pfff287R1xcnNatW6dVq1YpPT1d586dU69evVRaWlrhnBcuXFBUVJT27Nmj9PR0NWvW7Ir1HT9+XOPGjVOHDh3K9SUlJSkuLk6TJk1SZmamOnTooO7duysnJ+eK55syZYomTJigdevWaeDAgdd6mwAAAAAAuKUQhlxFfHy8du3apWXLlqlHjx5atGiRGjZsqLKyMknSwIED1bVrVzVq1EhNmzbV7NmzVVhYaNvNUVBQoMWLF+uNN95Q165d1aJFCy1fvlwHDhzQli1bys135swZRUZG6uTJk0pPT9fdd999xdpKS0s1aNAgvfTSS2rUqFG5/tmzZ2v48OF68skn1bhxY82ZM0f169fXggULyo01DEMjR47U3LlztXnzZvXo0eP33jIAAAAAAKo8wpCryMzMVExMjCIiImS1WtWpUye99tprcnd3Lze2qKhI77zzjqxWq5o3by5JysjIUHFxsSIjI23j/P39FRISou3bt9sdn5eXp4iICJWVlSktLU1+fn5XrW3atGm64447NHz48AprycjIsJtXkiIjI8vNW1JSopiYGK1evVppaWlq37791W8KAAAAAAC3OGdHF1CVtWvXTkuWLLGFGxX58MMP1b9/f124cEF+fn5KTk6Wt7e3pJ8DDldXV9WpU8fuGB8fH+Xl5dm1jR49Wo0aNdKOHTtUo0aNq9b16aefavHixcrKyqqw/9SpUyotLZWPj89vzrto0SJJ0r59+xQcHHzVeQEAAAAAuB2wM+QqZs+erX79+ik+Pl7vvfeewsLC9Pbbb9uN6dSpk7KysrR9+3Y98sgjio6OVn5+/lXPaxiGLBaLXVvv3r2VnZ2thQsX2tpycnLk6elp+0yfPl1nz57V4MGDtWjRIlvociW/nqOiedu3by9PT09NnjxZJSUlVz0fAAAAAAC3A3aGXIWHh4cSEhKUkJCgPn36qHv37oqPj1e1atX01FNP2cYEBQUpKChIDzzwgO655x4tXrxYEyZMkK+vr4qKinT69Gm73SH5+flq27at3VyDBw9WVFSUhg0bptLSUo0bN07+/v52uz+8vLz03//+V8eOHVPv3r1t7ZffYeLs7KxDhw6pfv36cnJyKrcLJD8/v9xukdDQUNs7TaKjo5WUlCQXF5dKuX8AAAAAAFRF7Ay5RrVr19bTTz+t7t27l/uJ2l8yDEOXLl2SJIWHh8vFxUXJycm2/tzcXB08eLBcGCJJQ4YM0dKlSzV+/HjNnDlTzs7OtqAlKChIXl5eCg4O1oEDB5SVlWX7REVF2Xao1K9fX66urgoPD7ebV5KSk5MrnDcsLEzbtm1Tenq6+vbtq+Li4t97mwAAAAAAqPLYGXIV8fHx6tOnj8LCwlRaWqqUlBSlpaVp8uTJOn/+vBISEhQVFSU/Pz/98MMPmj9/vk6cOKG+fftKkqxWq4YPH66xY8eqbt268vLy0rhx4xQaGqquXbtWOOegQYNUrVo1xcTEqKysTOPHj7frd3d3V0hIiF1b7dq1JcmufcyYMYqJiVGrVq304IMP6p133lFOTo5GjBhR4bzNmjVTSkqKOnfurMcff1yrV6+Wq6vr7711AAAAAABUWYQhVxEQEKAxY8bo8OHDOn/+vFJTUzVs2DCNHDlSxcXF+uqrr7R06VKdOnVKdevW1f33369PPvlETZs2tZ3jzTfflLOzs6Kjo3Xx4kV16dJFiYmJcnJyuuK8AwYMkJOTkwYNGqSysjJNnDjxumvv16+ffvjhB02bNk25ubkKCQnRxo0bFRgYeMVjmjZtqpSUFHXp0kWPPfaY1qxZQyACAAAAALjtWAzDMBxdxK0gNjZWiYmJji7jlldYWCir1aqxqZvk5unh6HIAAAAAOFBCeDtHlwCT4p0hAAAAAADAVAhDrhG7QgAAAAAAuD0QhgAAAAAAAFMhDAEAAAAAAKZCGAIAAAAAAEyFMAQAAAAAAJgKYQgAAAAAADAVwhAAAAAAAGAqhCEAAAAAAMBUCEMAAAAAAICpEIYAAAAAAABTIQwBAAAAAACmQhgCAAAAAABMhTAEAAAAAACYCmEIAAAAAAAwFYthGIaji4B5FBYWymq1qqCgQLVq1XJ0OQAAAAAAE2JnCAAAAAAAMBXCEAAAAAAAYCqEIQAAAAAAwFScHV0AzGla5k65eXo4ugwAAAAADpQQ3s7RJcCk2BkCAAAAAABMhTAEAAAAAACYCmEIAAAAAAAwFcIQAAAAAABgKoQhAAAAAADAVAhDAAAAAACAqRCGAAAAAAAAUyEMAQAAAAAApkIYAgAAAAAATIUwBAAAAAAAmAphCAAAAAAAMBXCEAAAAAAAYCqEIQAAAAAAwFQIQwAAAAAAgKkQhgAAAAAAAFMhDAEAAAAAAKZCGHKNYmNjHV0CAAAAAACoBIQhv1NxcbFeeOEFhYaGysPDQ/7+/hoyZIi+/fZbu3GXLl3SyJEj5e3tLQ8PD0VFRenEiRN2YywWi9avX2937v79+8vPz0/79+8vN/fatWvVqlUr1a5dWx4eHgoLC9OyZcvKjZs/f74aNmwod3d3hYeH65NPPrHr79ixo+Li4uza5s6dKzc3N61cufI67wgAAAAAALcGwpCrOHXqlIYOHaqAgAC9//77CgoKUnR0tIqKinThwgXt3btXL774ovbu3au1a9cqOztbUVFRdueIi4vTunXrtGrVKqWnp+vcuXPq1auXSktLK5zzwoULioqK0p49e5Senq5mzZqVG+Pl5aVJkyZpx44d2r9/v5544gk98cQT2rRpk21MUlKS4uLiNGnSJGVmZqpDhw7q3r27cnJyrni9U6ZM0YQJE7Ru3ToNHDjwd941AAAAAACqNmdHF1CVxcfHa8+ePVq2bJnmzJmjUaNG6aOPPlJZWZmsVquSk5Ptxr/11ltq3bq1cnJyFBAQoIKCAi1evFjLli1T165dJUnLly9X/fr1tWXLFnXr1s3u+DNnzqhXr14qLCxUenq6/Pz8KqyrY8eOdt9Hjx6tpUuXKj093XbO2bNna/jw4XryySclSXPmzNGmTZu0YMECzZgxw+54wzA0atQoLVu2TJs3b1b79u1/9z0DAAAAAKCqY2fIVWRmZiomJkYRERGyWq3q1KmTXnvtNbm7u1c4vqCgQBaLRbVr15YkZWRkqLi4WJGRkbYx/v7+CgkJ0fbt2+2OzcvLU0REhMrKypSWlnbFIOTXDMPQ1q1bdejQIT300EOSpKKiImVkZNjNK0mRkZHl5i0pKVFMTIxWr16ttLQ0ghAAAAAAwG2PnSFX0a5dOy1ZskTNmzf/zbE//fSTxo8fr4EDB6pWrVqSfg44XF1dVadOHbuxPj4+ysvLs2sbPXq0GjVqpB07dqhGjRq/OV9BQYHq1aunS5cuycnJSfPnz9fDDz8s6efHe0pLS+Xj4/Ob8y5atEiStG/fPgUHB//mvAAAAAAA3OrYGXIVs2fPVr9+/RQfH6/33ntPYWFhevvtt8uNu/zC07KyMs2fP/83z2sYhiwWi11b7969lZ2drYULF9racnJy5OnpaftMnz7d1lezZk1lZWVpz549SkhI0JgxY5Sammp3zl/PUdG87du3l6enpyZPnqySkpLfrB0AAAAAgFsdO0OuwsPDQwkJCUpISFCfPn3UvXt3xcfHq1q1anrqqack/RyEREdH6+jRo9q2bZttV4gk+fr6qqioSKdPn7bbHZKfn6+2bdvazTV48GBFRUVp2LBhKi0t1bhx4+Tv76+srCzbGC8vL9v/r1atmoKCgiRJYWFh+vLLLzVjxgx17NhR3t7ecnJyKrcLJD8/v9xukdDQUL3xxhvq2rWroqOjlZSUJBcXlxu7cQAAAAAAVGHsDLlGtWvX1tNPP63u3bvbfqL2chBy+PBhbdmyRXXr1rU7Jjw8XC4uLnYvWs3NzdXBgwfLhSGSNGTIEC1dulTjx4/XzJkz5ezsrKCgINvnl2HIrxmGoUuXLkmSXF1dFR4eXu4Fr8nJyRXOGxYWpm3btik9PV19+/ZVcXHxtd8YAAAAAABuMewMuYr4+Hj16dNHYWFhKi0tVUpKitLS0myPlDz++OPau3evPvzwQ5WWltp2Ynh5ecnV1VVWq1XDhw/X2LFjVbduXXl5eWncuHEKDQ21/brMrw0aNEjVqlVTTEyMysrKNH78+HJjZsyYoVatWunuu+9WUVGRNm7cqPfee08LFiywjRkzZoxiYmLUqlUrPfjgg3rnnXeUk5OjESNGVDhvs2bNlJKSos6dO+vxxx/X6tWr5erqWgl3EQAAAACAqoUw5CoCAgI0ZswYHT58WOfPn1dqaqqGDRumkSNH6ptvvtGGDRsk/byz4pdSUlJsP3/75ptvytnZWdHR0bp48aK6dOmixMREOTk5XXHeAQMGyMnJSYMGDVJZWZkmTpxo13/+/Hn9z//8j06cOKHq1asrODhYy5cvV79+/Wxj+vXrpx9++EHTpk1Tbm6uQkJCtHHjRgUGBl5x3qZNmyolJUVdunTRY489pjVr1hCIAAAAAABuOxbDMAxHF3EriI2NVWJioqPLuOUVFhbKarVqbOomuXl6OLocAAAAAA6UEN7O0SXApHhnCAAAAAAAMBXCkGvErhAAAAAAAG4PhCEAAAAAAMBUCEMAAAAAAICpEIYAAAAAAABTIQwBAAAAAACmQhgCAAAAAABMhTAEAAAAAACYCmEIAAAAAAAwFcIQAAAAAABgKoQhAAAAAADAVAhDAAAAAACAqRCGAAAAAAAAUyEMAQAAAAAApmIxDMNwdBEwj8LCQlmtVhUUFKhWrVqOLgcAAAAAYELsDAEAAAAAAKZCGAIAAAAAAEyFMAQAAAAAAJiKs6MLgDlNy9wpN08PR5cBAAAAwIESwts5ugSYFDtDAAAAAACAqRCGAAAAAAAAUyEMAQAAAAAApkIYAgAAAAAATIUwBAAAAAAAmAphCAAAAAAAMBXCEAAAAAAAYCqEIQAAAAAAwFQIQwAAAAAAgKkQhgAAAAAAAFMhDAEAAAAAAKZCGAIAAAAAAEyFMAQAAAAAAJgKYQgAAAAAADAVwhAAAAAAAGAqhCEAAAAAAMBUCEOuUWxsrKNLAAAAAAAAlYAw5AasXbtW3bp1k7e3tywWi7KyssqNuXTpkkaOHClvb295eHgoKipKJ06csBtjsVi0fv162/fi4mL1799ffn5+2r9/f7lzLlq0SB06dFCdOnVUp04dde3aVbt37y43bv78+WrYsKHc3d0VHh6uTz75xK6/Y8eOiouLs2ubO3eu3NzctHLlymu/EQAAAAAA3EIIQ67i1KlTGjp0qAICAvT+++8rKChI0dHRKioqkiSdP39e7dq106uvvnrFc8TFxWndunVatWqV0tPTde7cOfXq1UulpaUVjr9w4YKioqK0Z88epaenq1mzZuXGpKamasCAAUpJSdGOHTsUEBCgyMhInTx50jYmKSlJcXFxmjRpkjIzM9WhQwd1795dOTk5V6x1ypQpmjBhgtatW6eBAwde620CAAAAAOCW4uzoAqqy+Ph47dmzR8uWLdOcOXM0atQoffTRRyorK5MkxcTESJKOHTtW4fEFBQVavHixli1bpq5du0qSli9frvr162vLli3q1q2b3fgzZ86oV69eKiwsVHp6uvz8/Co874oVK+y+L1q0SP/4xz+0detWDRkyRJI0e/ZsDR8+XE8++aQkac6cOdq0aZMWLFigGTNm2B1vGIZGjRqlZcuWafPmzWrfvv113CUAAAAAAG4t7Ay5iszMTMXExCgiIkJWq1WdOnXSa6+9Jnd392s6PiMjQ8XFxYqMjLS1+fv7KyQkRNu3b7cbm5eXp4iICJWVlSktLe2KQUhFLly4oOLiYnl5eUmSioqKlJGRYTevJEVGRpabt6SkRDExMVq9erXS0tIIQgAAAAAAtz12hlxFu3bttGTJEjVv3vx3HZ+XlydXV1fVqVPHrt3Hx0d5eXl2baNHj1ajRo20Y8cO1ahR47rmGT9+vOrVq2fbfXLq1CmVlpbKx8fnN+ddtGiRJGnfvn0KDg6+rnkBAAAAALgVsTPkKmbPnq1+/fopPj5e7733nsLCwvT222/f8HkNw5DFYrFr6927t7Kzs7Vw4UJbW05Ojjw9PW2f6dOnlzvXzJkz9f7772vt2rXldqz8eo6K5m3fvr08PT01efJklZSU3OilAQAAAABQ5RGGXIWHh4cSEhJ0+PBhRUVF6ZlnntGYMWP0zjvvXNPxvr6+Kioq0unTp+3a8/Pzy+3aGDx4sJYsWaLnn39es2bNkvTzIzVZWVm2z4gRI+yOmTVrlqZPn67NmzfbvWjV29tbTk5O5XaBVDRvaGiotm7dqtTUVEVHR6u4uPiarg0AAAAAgFsVYcg1ql27tp5++ml179693E/UXkl4eLhcXFyUnJxsa8vNzdXBgwfVtm3bcuOHDBmipUuXavz48Zo5c6acnZ0VFBRk+1x+J4gkvf7663r55Zf10UcfqVWrVnbncXV1VXh4uN28kpScnFzhvGFhYdq2bZvS09PVt29fAhEAAAAAwG2Nd4ZcRXx8vPr06aOwsDCVlpYqJSVFaWlpmjx5siTpxx9/VE5Ojr799ltJ0qFDhyT9vCPE19dXVqtVw4cP19ixY1W3bl15eXlp3LhxCg0Ntb3f49cGDRqkatWqKSYmRmVlZRo/fny5MTNnztSLL76olStXqkGDBrYdIJcfp5GkMWPGKCYmRq1atdKDDz6od955Rzk5OeV2l1zWrFkzpaSkqHPnznr88ce1evVqubq63tgNBAAAAACgCiIMuYqAgACNGTNGhw8f1vnz55Wamqphw4Zp5MiRkqQNGzboiSeesI3v37+/JGnKlCmaOnWqJOnNN9+Us7OzoqOjdfHiRXXp0kWJiYlycnK64rwDBgyQk5OTBg0apLKyMk2cONGuf/78+SoqKtLjjz9u1/7Lefv166cffvhB06ZNU25urkJCQrRx40YFBgZecd6mTZsqJSVFXbp00WOPPaY1a9YQiAAAAAAAbjsWwzAMRxdxK4iNjVViYqKjy7jlFRYWymq1amzqJrl5eji6HAAAAAAOlBDeztElwKR4ZwgAAAAAADAVwpBrxK4QAAAAAABuD4QhAAAAAADAVAhDAAAAAACAqRCGAAAAAAAAUyEMAQAAAAAApkIYAgAAAAAATIUwBAAAAAAAmAphCAAAAAAAMBXCEAAAAAAAYCqEIQAAAAAAwFQIQwAAAAAAgKkQhgAAAAAAAFMhDAEAAAAAAKZiMQzDcHQRMI/CwkJZrVYVFBSoVq1aji4HAAAAAGBC7AwBAAAAAACmQhgCAAAAAABMhTAEAAAAAACYirOjC4A5TcvcKTdPD0eXAQAAAMCBEsLbOboEmBQ7QwAAAAAAgKkQhgAAAAAAAFMhDAEAAAAAAKZCGAIAAAAAAEyFMAQAAAAAAJgKYQgAAAAAADAVwhAAAAAAAGAqhCEAAAAAAMBUCEMAAAAAAICpEIYAAAAAAABTIQwBAAAAAACmQhgCAAAAAABMhTAEAAAAAACYCmEIAAAAAAAwFcIQAAAAAABgKoQhAAAAAADAVAhDriI2NlZ9+vRxaA2JiYlKTU11aA0AAAAAANxOHBqGVIWwQZKOHTsmi8WirKwsR5dyXUaPHq3w8HC5ubkpLCyswjEHDhxQRESEqlevrnr16mnatGkyDMPWn5iYqNq1a9sd8+WXX+quu+7So48+qkuXLv2BVwAAAAAAwM3HzpAqKiUlRe3atdPo0aP15z//WS1bttSCBQvsxhiGoWHDhqlfv34VnqOwsFAPP/yw/P39tWfPHr311luaNWuWZs+efcV59+zZow4dOqhbt25avXq13NzcKvW6AAAAAABwtCodhnzxxRfq0aOHPD095ePjo5iYGJ06dcrW/49//EOhoaGqXr266tatq65du+r8+fOSpNTUVLVu3VoeHh6qXbu22rVrp+PHj1c4T8OGDSVJLVq0kMViUceOHe36Z82aJT8/P9WtW1fPPvusiouLbX3Lly9Xq1atVLNmTfn6+mrgwIHKz8+39aempspisWjr1q1q1aqVatSoobZt2+rQoUNXvO4zZ87oT3/6k5o2bapx48bp9ddf14QJE8qNmzdvnp599lk1atSowvOsWLFCP/30kxITExUSEqJHH31UEydO1OzZs+12h1y2bds2de7cWU888YQWL14sJyenK9YIAAAAAMCtqsqGIbm5uYqIiFBYWJg+++wzffTRR/ruu+8UHR1t6x8wYICGDRumL7/8UqmpqXr00UdlGIZKSkrUp08fRUREaP/+/dqxY4eeeuopWSyWCufavXu3JGnLli3Kzc3V2rVrbX0pKSn673//q5SUFC1dulSJiYlKTEy09RcVFenll1/Wvn37tH79eh09elSxsbHl5pg0aZLeeOMNffbZZ3J2dtawYcOueO1ff/21zp49qylTpqh+/foKCgpS37599cwzz1zXPdyxY4ciIiLsdnd069ZN3377rY4dO2Y3dt26derZs6cmTZqk119//brmAQAAAADgVuLs6AKuZMGCBWrZsqWmT59ua3v33XdVv359ZWdn69y5cyopKdGjjz6qwMBASVJoaKgk6ccff1RBQYF69eqlu+++W5LUuHHjK851xx13SJLq1q0rX19fu746derob3/7m5ycnBQcHKyePXtq69at+stf/iJJdqFGo0aNNG/ePLVu3Vrnzp2Tp6enrS8hIUERERGSpPHjx6tnz5766aef5O7uXq6e++67T97e3ho/frwCAgLUoEGDa75vv5SXl1fuWB8fH1vf5R0x586dU9++fTVx4kSNHz/+d80FAAAAAMCtosruDMnIyFBKSoo8PT1tn+DgYEnSf//7XzVv3lxdunRRaGio+vbtq0WLFun06dOSJC8vL8XGxqpbt27q3bu35s6dq9zc3N9VR9OmTe0eF/Hz87N7DCYzM1N/+tOfFBgYqJo1a9oescnJybE7T7NmzezOIcnuPL9Us2ZNbdu2TRcuXNDf//539e7dW1FRUcrMzLzu+n+9G+by4zG/bK9evboefvhhLVq0SF9++eV1zwEAAAAAwK2kyoYhZWVl6t27t7Kysuw+hw8f1kMPPSQnJyclJyfrP//5j5o0aaK33npL9913n44ePSpJWrJkiXbs2KG2bdsqKSlJ9957r3bu3Hnddbi4uNh9t1gsKisrkySdP39ekZGR8vT01PLly7Vnzx6tW7dO0s+Pz1zpPJeDiMvnqUhoaKjWrFmjOXPm6LXXXpPValWnTp30/fffX3Ptvr6+ysvLs2u7HMBc3iEiSU5OTlq/fr3Cw8PVqVMnffHFF9c8BwAAAAAAt5oqG4a0bNlSn3/+uRo0aKCgoCC7j4eHh6SfQ4V27drppZdeUmZmplxdXW1hhPTzC1EnTJig7du3KyQkRCtXrqxwLldXV0lSaWnpddX41Vdf6dSpU3r11VfVoUMHBQcHX3G3x41o0qSJ5s+fr4KCAu3fv/+aj3vwwQf18ccf2wUzmzdvlr+/f7nHZ9zc3LR27Vq1bt1anTp10sGDByurfAAAAAAAqhSHhyEFBQXldn/k5OTo2Wef1Y8//qgBAwZo9+7dOnLkiDZv3qxhw4aptLRUu3bt0vTp0/XZZ58pJydHa9eu1ffff6/GjRvr6NGjmjBhgnbs2KHjx49r8+bNys7OvuJ7Q+68805Vr17d9pLWgoKCa6o9ICBArq6ueuutt3TkyBFt2LBBL7/88g3fk71792rq1Kk6dOiQSkpKdObMGb3++utyd3dXkyZNbOO+/vprZWVlKS8vTxcvXrTdv8vhx8CBA+Xm5qbY2FgdPHhQ69at0/Tp0zVmzJgKXybr6uqqNWvWqG3bturcubMOHDhww9cCAAAAAEBV4/AXqKampqpFixZ2bUOHDlViYqI+/fRTvfDCC+rWrZsuXbqkwMBAPfLII6pWrZpq1aqljz/+WHPmzFFhYaECAwP1xhtvqHv37vruu+/01VdfaenSpfrhhx/k5+en5557Tk8//XSFNTg7O2vevHmaNm2a/vrXv6pDhw5KTU39zdrvuOMOJSYmauLEiZo3b55atmypWbNmKSoq6obuiZ+fn7755hs98sgjOnnypJycnNS4cWOtWbPG9r4RSXryySeVlpZm+375Ph49elQNGjSQ1WpVcnKynn32WbVq1Up16tTRmDFjNGbMmCvO7eLiog8++EADBgxQ586dtXXrVrv3nQAAAAAAcKuzGJffqIkqKTExUQ0aNLC9mPVWV1hYKKvVqrGpm+Tm6eHocgAAAAA4UEJ4O0eXAJNy+GMyAAAAAAAAN5PDH5PB1cXGxjq6BAAAAAAAbivsDAEAAAAAAKZCGAIAAAAAAEyFMAQAAAAAAJgKYQgAAAAAADAVwhAAAAAAAGAqhCEAAAAAAMBUCEMAAAAAAICpEIYAAAAAAABTIQwBAAAAAACmQhgCAAAAAABMhTAEAAAAAACYCmEIAAAAAAAwFYthGIaji4B5FBYWymq1qqCgQLVq1XJ0OQAAAAAAE2JnCAAAAAAAMBXCEAAAAAAAYCqEIQAAAAAAwFQIQwAAAAAAgKkQhgAAAAAAAFMhDAEAAAAAAKZCGAIAAAAAAEyFMAQAAAAAAJgKYQgAAAAAADAVwhAAAAAAAGAqhCEAAAAAAMBUCEMAAAAAAICpEIYAAAAAAABTIQwBAAAAAACmQhgCAAAAAABMhTAEAAAAAACYCmEIAAAAAAAwFcIQAAAAAABgKoQhAAAAAADAVAhDAAAAAACAqRCGAAAAAAAAUyEMAQAAAAAApkIYAgAAAAAATIUwBAAAAAAAmAphCAAAAAAAMBXCEAAAAAAAYCrOji4A5mIYhiSpsLDQwZUAAAAAqApq1qwpi8Xi6DJgMoQhuKl++OEHSVL9+vUdXAkAAACAqiA/P1933HGHo8uAyRCG4Kby8vKSJOXk5MhqtTq4GtzOCgsLVb9+fX3zzTeqVauWo8vBbYy1hpuFtYabhbWGm+XyWnN1dXV0KTAhwhDcVNWq/fyaGqvVyr9ccVPUqlWLtYabgrWGm4W1hpuFtYabhUdk4Ai8QBUAAAAAAJgKYQgAAAAAADAVwhDcVG5ubpoyZYrc3NwcXQpuc6w13CysNdwsrDXcLKw13CysNTiSxbj8W6cAAAAAAAAmwM4QAAAAAABgKoQhAAAAAADAVAhDAAAAAACAqRCGAAAAAAAAUyEMQaWbP3++GjZsKHd3d4WHh+uTTz6x9c2aNUs+Pj7y8fHRm2++aXfcrl27FB4ertLS0ptdMqqwGTNm6P7771fNmjV15513qk+fPjp06JDdGMMwNHXqVPn7+6t69erq2LGjPv/8c7sxY8aMkZeXlwICArRq1Sq7vg8++EC9e/f+w68Ft5YZM2bIYrEoLi7O1sZaQ2U5efKkBg8erLp166pGjRoKCwtTRkaGrZ+1hspQUlKiyZMnq2HDhqpevboaNWqkadOmqayszDaGtYbf4+OPP1bv3r3l7+8vi8Wi9evX2/Vfy7q6dOmSRo4cKW9vb3l4eCgqKkonTpyw64+JiVGtWrV03333adu2bXbHz5w5UyNHjvzDrhEmYACVaNWqVYaLi4uxaNEi44svvjBGjx5teHh4GMePHzf2799vVK9e3di6dauxZcsWw93d3Thw4IBhGIZRVFRkhIWFGbt373bwFaCq6datm7FkyRLj4MGDRlZWltGzZ08jICDAOHfunG3Mq6++atSsWdNYs2aNceDAAaNfv36Gn5+fUVhYaBiGYWzYsMHw8fEx9uzZY6xcudJwd3c3Tp06ZRiGYZw+fdoICgoyjh8/7pDrQ9W0e/duo0GDBkazZs2M0aNH29pZa6gMP/74oxEYGGjExsYau3btMo4ePWps2bLF+Prrr21jWGuoDK+88opRt25d48MPPzSOHj1qrF692vD09DTmzJljG8Naw++xceNGY9KkScaaNWsMSca6devs+n9rXRmGYYwYMcKoV6+ekZycbOzdu9fo1KmT0bx5c6OkpMQwDMOYN2+e0bhxY+PgwYPG66+/btx5551GWVmZYRiGceTIEeOee+4xCgoKbto14/ZDGIJK1bp1a2PEiBF2bcHBwcb48eONpKQko02bNnZjP/jgA8MwDCMhIcEYNWrUTa0Vt6b8/HxDkpGWlmYYhmGUlZUZvr6+xquvvmob89NPPxlWq9V4++23DcMwjNdee83o16+frf/OO++0BW9/+ctfjNmzZ9/EK0BVd/bsWeOee+4xkpOTjYiICFsYwlpDZXnhhReM9u3bX7GftYbK0rNnT2PYsGF2bY8++qgxePBgwzBYa6gcvw5DrmVdnTlzxnBxcTFWrVplG3Py5EmjWrVqxkcffWQYhmE888wzxgsvvGAYhmFcuHDBkGTk5+cbhvHzfyxbu3btH31puM3xmAwqTVFRkTIyMhQZGWnXHhkZqe3btys0NFTZ2dnKycnR8ePHlZ2drZCQEH399ddKTEzUK6+84qDKcSspKCiQJHl5eUmSjh49qry8PLt15+bmpoiICG3fvl2S1Lx5c3322Wc6ffq0MjIydPHiRQUFBSk9PV179+7VqFGjbv6FoMp69tln1bNnT3Xt2tWunbWGyrJhwwa1atVKffv21Z133qkWLVpo0aJFtn7WGipL+/bttXXrVmVnZ0uS9u3bp/T0dPXo0UMSaw1/jGtZVxkZGSouLrYb4+/vr5CQELu1l56erosXL2rTpk3y8/OTt7e3li9fLnd3d/35z3++uReG2w5hCCrNqVOnVFpaKh8fH7t2Hx8f5eXlqXHjxpo+fboefvhhRUZGasaMGWrcuLFGjBihmTNnatOmTQoJCVGLFi308ccfO+gqUJUZhqExY8aoffv2CgkJkSTl5eVJ0hXXnSR169ZNgwcP1v3336/Y2FgtXbpUHh4eeuaZZ7Rw4UItWLBA9913n9q1a1fueVaYy6pVq7R3717NmDGjXB9rDZXlyJEjWrBgge655x5t2rRJI0aM0KhRo/Tee+9JYq2h8rzwwgsaMGCAgoOD5eLiohYtWiguLk4DBgyQxFrDH+Na1lVeXp5cXV1Vp06dK44ZNmyYmjdvriZNmighIUEffPCBTp8+rSlTpmjevHmaPHmygoKC1K1bN508efImXBluN86OLgC3H4vFYvfdMAxb24gRIzRixAhbX2JiomrWrKkHH3xQ9913n/bs2aMTJ06of//+Onr0qNzc3G5q7ajannvuOe3fv1/p6enl+q627iRp6tSpmjp1qt33rl27ysXFRa+88ooOHDigDz/8UEOGDLF7iSHM45tvvtHo0aO1efNmubu7X3Ecaw03qqysTK1atdL06dMlSS1atNDnn3+uBQsWaMiQIbZxrDXcqKSkJC1fvlwrV65U06ZNlZWVpbi4OPn7+2vo0KG2caw1/BF+a11V5JdjXFxc9Pe//92uPzY2VqNGjVJWVpbWr1+vffv2aebMmRo1apTWrFlTuReA2x47Q1BpvL295eTkZEtzL8vPzy+XDEs/7ySZNm2a3nrrLe3atUv33nuv7rnnHnXq1EnFxcW2LZ2AJI0cOVIbNmxQSkqK7rrrLlu7r6+vJF3zupOkr776SitWrNDLL7+s1NRUPfTQQ7rjjjsUHR2tvXv3qrCw8I+7EFRZGRkZys/PV3h4uJydneXs7Ky0tDTNmzdPzs7OtvXEWsON8vPzU5MmTezaGjdurJycHEn8cw2V5/nnn9f48ePVv39/hYaGKiYmRvHx8bbdb6w1/BGuZV35+vqqqKhIp0+fvuKYX9u2bZu++OILPffcc0pNTVWPHj3k4eGh6OhopaamVv6F4LZHGIJK4+rqqvDwcCUnJ9u1Jycnq23btuXGx8XFKT4+XnfddZdKS0tVXFxs6yspKeEndiHp5/9C8Nxzz2nt2rXatm2bGjZsaNffsGFD+fr62q27oqIipaWlVbjuDMPQU089pTfeeEOenp52a+/y//7yJwdhHl26dNGBAweUlZVl+7Rq1UqDBg1SVlaWGjVqxFpDpWjXrl25nwjPzs5WYGCgJP65hspz4cIFVatm/8d9Jycn23pgreGPcC3rKjw8XC4uLnZjcnNzdfDgwQrX3k8//aRnn31WCxculJOTU7m1x98b8Ls45LWtuG1d/mndxYsXG1988YURFxdneHh4GMeOHbMbt3nzZqN169ZGaWmpYRiGceLECcPd3d3YuHGjsXDhQqNu3brGhQsXHHEJqGKeeeYZw2q1GqmpqUZubq7t88v18eqrrxpWq9VYu3atceDAAWPAgAHlfr7tsoULFxqPPfaY7fuuXbuMWrVqGTt27DD++te/Gk2aNLkp14Vbwy9/TcYwWGuoHLt37zacnZ2NhIQE4/Dhw8aKFSuMGjVqGMuXL7eNYa2hMgwdOtSoV6+e7ad1165da3h7exv/+7//axvDWsPvcfbsWSMzM9PIzMw0JBmzZ882MjMzbT+zfC3rasSIEcZdd91lbNmyxdi7d6/RuXNnu5/W/aUJEyYYY8eOtX1PSkoyAgICjH379hnDhw83evTo8cdfNG47hCGodH//+9+NwMBAw9XV1WjZsqXtJ1Avu3DhgnHvvfcamZmZdu2LFi0yfHx8jICAAOPDDz+8iRWjKpNU4WfJkiW2MWVlZcaUKVMMX19fw83NzXjooYeMAwcOlDtXXl6eERgYaJw8edKu/aWXXjK8vLyM4OBgY9euXX/0JeEW8uswhLWGyvKvf/3LCAkJMdzc3Izg4GDjnXfesetnraEyFBYWGqNHjzYCAgIMd3d3o1GjRsakSZOMS5cu2caw1vB7pKSkVPjns6FDhxqGcW3r6uLFi8Zzzz1neHl5GdWrVzd69epl5OTklJvrwIEDRlBQkHHu3DlbW2lpqfHMM88YtWrVMu6//37j8OHDf+j14vZkMQzDcMyeFAAAAAAAgJuPd4YAAAAAAABTIQwBAAAAAACmQhgCAAAAAABMhTAEAAAAAACYCmEIAAAAAAAwFcIQAAAAAABgKoQhAAAAAADAVAhDAAAAAACAqRCGAACA3zR16lRZLBY1aNDA0aWUYxiGnn/+efn7+6tatWqyWCw6duzYDZ0zMTFRFotFFoulcooEAABVCmEIAABVXMeOHW1/MbdYLHJyclK9evXUu3dvbd++vVLnatCggSwWi6ZOnWrXftddd6lNmzZq0aJFpc5XGdavX69Zs2YpNzdXwcHBatOmjdzc3Coce/n6Ln9cXV0VEBCg4cOH69SpU7Zxd9xxh9q0aaM2bdrcrMsAAAA3kbOjCwAAANfG1dVVLVq00KVLl3Tw4EF9+OGH+uijj/Tpp5+qdevWN3TuoqIiubq6XrH/ySef1JNPPnlDc/xRPv/8c0mSt7e3Pv/882vazVGzZk01adJEp0+fVnZ2tt5991199913+vDDDyVJPXv2VM+ePf/QugEAgOOwMwQAgFuEn5+fdu7cqczMTK1fv16SVFJSopUrV9rGbNiwQe3bt5enp6eqV6+uli1b6t1337U7z+VdETNnztSjjz4qDw8PPfXUU7JYLDp+/Lgk6aWXXrJ7TKSix2RKS0s1a9YsNWnSRG5ubrJarYqMjFR6erptTGpqqu0869ev10MPPaTq1asrODjYFjxcTXp6uiIjI2W1WuXm5qbGjRtr1qxZKi0tlfTzrpkXX3xRknTq1CnbYzK/pWXLltq5c6cOHTqkLl26SJLS0tJs/RU9JnN5h05MTIymTJkiPz8/1alTR4MHD9bZs2d/c04AAFB1sDMEAIBbkGEY5dqWL1+umJgYSZKPj4/c3d2VmZmp4cOHKzc3V5MmTbIb/+KLL8rNzU1BQUFyc3NTmzZtlJmZqaKiItWrV0933XXXVWt4+umntXjxYklSUFCQfvzxRyUnJyslJUVbtmxRRESE3fjo6GjbYyqHDh3SwIEDdezYMXl5eVV4/tTUVD388MMqKSlRnTp1FBgYqK+++krPP/+8Dh06pEWLFqlJkyb6+uuvdfLkSdvOmetx+vRpff/995Kku++++5qOSUpKkru7u7y9vZWXl6cVK1YoMDBQCQkJ1zU3AABwHHaGAABwi8jNzdUDDzygFi1a6M9//rMkydnZWQMGDJAkW9jRpk0bHT9+XEePHrWNS0hI0IULF+zO16BBAx07dkz79u3T/PnztXPnTvn5+Un6+bGYnTt3aufOnRXWcuTIEduOk9GjR+vw4cM6cuSIAgMDVVJSor/+9a/ljhk5cqSys7O1atUqSdLZs2e1e/fuK17vlClTVFJSosDAQB05ckTZ2dkaPXq0JGnx4sU6cuSI5s+fb3t85/LOmSvV/EtpaWmyWCzy8vLS/v37VatWLS1ZsuQ3j5Mkd3d3ffnll/r666/VqlUrSdLWrVuv6VgAAFA1EIYAAHCLKCoq0q5du7R//37dcccd6tmzp9LS0tSmTRvl5+crJydHkvToo4/Kzc1NFotF/fv3lyRdvHjR9m6Ny2JjY227MpycnK6rls8++8y2O2XgwIGSJKvVqh49etj6f+3yrpUmTZrY2r777rsrzrFnzx5JUo8ePVS7dm27uQzDUEZGxnXV/Es1a9ZUmzZtFBYWJmdnZxUWFqp///4qLCz8zWM7d+6sevXqqVq1arrvvvt+8zoAAEDVw2MyAADcIgIDA6/pJ2Ov9edgfX19b7Ci65vvcqDh7Pz///hR0eM+v/f816Nly5ZKTU2VJP3nP/9Rjx49lJ2draSkJP3lL3+56rGXr0P6/9dyLdcBAACqDnaGAABwG7jzzjsVEBAgSVqzZo0uXbokwzBsj6RUr15dTZs2tTumopChRo0akqTz589fdb7w8HDb8StWrJAkFRQUaOPGjZJke3zkRtx///2SpH//+986c+aMJOn999+31R4eHn7Dc0j2QcalS5cq5ZwAAKBqIwwBAOA2cfkFnrt27VJgYKAaNmyodevWSfr5fSKXg46rCQ4OliTNmzdP999/v5544okKx919990aNmyYJGnu3Lm655571KhRIx0/flzOzs566aWXbvh6XnrpJTk7O+v48eNq1KiR7r33Xs2ZM0eSNHz4cDVq1Oh3n3vv3r2296/06dNH0s+BUWRk5A3XDQAAqj7CEAAAbhODBw/W+vXr1bZtW509e1Z5eXkKCwvT4sWLy/2SzJW88soreuCBB1StWjV99tlnOnDgwBXHLly4UDNnzlTjxo2Vk5Oj4uJide3aVSkpKerYseMNX0/Hjh21bds22y/KHDt2TMHBwZo5c6befvvtGzr32bNntWvXLu3bt08eHh7q3Lmz/v3vf+vee++94boBAEDVZzF4yBUAAAAAAJgIO0MAAAAAAICpEIYAAAAAAABTIQwBAAAAAACmQhgCAAAAAABMhTAEAAAAAACYCmEIAAAAAAAwFcIQAAAAAABgKoQhAAAAAADAVAhDAAAAAACAqRCGAAAAAAAAUyEMAQAAAAAApvL/AIAwftEP1LPeAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_plot(tb_static, 2021, figsize=(10, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run a partial-equlibrium dynamic simulation by initiating the TaxBrian instance exactly as you would for the static reform, but with your behavioral assumptions specified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tb_dynamic = TaxBrain(start_year, end_year, microdata=\"CPS\", reform=reform_url,\n",
    "                      behavior={\"sub\": 0.25})\n",
    "tb_dynamic.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once that finishes running, we can produce the same weighted total table as we did with the static run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Partial Equilibrium - Combined Tax Liability\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>2023</th>\n",
       "      <th>2024</th>\n",
       "      <th>2025</th>\n",
       "      <th>2026</th>\n",
       "      <th>2027</th>\n",
       "      <th>2028</th>\n",
       "      <th>2029</th>\n",
       "      <th>2030</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Base</th>\n",
       "      <td>2.269727e+12</td>\n",
       "      <td>3.067767e+12</td>\n",
       "      <td>3.246623e+12</td>\n",
       "      <td>3.395728e+12</td>\n",
       "      <td>3.575938e+12</td>\n",
       "      <td>3.979716e+12</td>\n",
       "      <td>4.161639e+12</td>\n",
       "      <td>4.344151e+12</td>\n",
       "      <td>4.543397e+12</td>\n",
       "      <td>4.749701e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reform</th>\n",
       "      <td>2.307804e+12</td>\n",
       "      <td>3.119395e+12</td>\n",
       "      <td>3.312243e+12</td>\n",
       "      <td>3.476774e+12</td>\n",
       "      <td>3.673491e+12</td>\n",
       "      <td>4.082934e+12</td>\n",
       "      <td>4.282357e+12</td>\n",
       "      <td>4.483171e+12</td>\n",
       "      <td>4.690507e+12</td>\n",
       "      <td>4.904587e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Difference</th>\n",
       "      <td>3.807725e+10</td>\n",
       "      <td>5.162814e+10</td>\n",
       "      <td>6.561941e+10</td>\n",
       "      <td>8.104634e+10</td>\n",
       "      <td>9.755272e+10</td>\n",
       "      <td>1.032176e+11</td>\n",
       "      <td>1.207186e+11</td>\n",
       "      <td>1.390202e+11</td>\n",
       "      <td>1.471100e+11</td>\n",
       "      <td>1.548863e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    2021          2022          2023          2024  \\\n",
       "Base        2.269727e+12  3.067767e+12  3.246623e+12  3.395728e+12   \n",
       "Reform      2.307804e+12  3.119395e+12  3.312243e+12  3.476774e+12   \n",
       "Difference  3.807725e+10  5.162814e+10  6.561941e+10  8.104634e+10   \n",
       "\n",
       "                    2025          2026          2027          2028  \\\n",
       "Base        3.575938e+12  3.979716e+12  4.161639e+12  4.344151e+12   \n",
       "Reform      3.673491e+12  4.082934e+12  4.282357e+12  4.483171e+12   \n",
       "Difference  9.755272e+10  1.032176e+11  1.207186e+11  1.390202e+11   \n",
       "\n",
       "                    2029          2030  \n",
       "Base        4.543397e+12  4.749701e+12  \n",
       "Reform      4.690507e+12  4.904587e+12  \n",
       "Difference  1.471100e+11  1.548863e+11  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Partial Equilibrium - Combined Tax Liability\")\n",
    "tb_dynamic.weighted_totals(\"combined\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or we can produce a distribution table to see details on the effects of the reform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution Table\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>c00100</th>\n",
       "      <th>count_StandardDed</th>\n",
       "      <th>standard</th>\n",
       "      <th>count_ItemDed</th>\n",
       "      <th>c04470</th>\n",
       "      <th>c04600</th>\n",
       "      <th>c04800</th>\n",
       "      <th>taxbc</th>\n",
       "      <th>c62100</th>\n",
       "      <th>...</th>\n",
       "      <th>othertaxes</th>\n",
       "      <th>refund</th>\n",
       "      <th>iitax</th>\n",
       "      <th>payrolltax</th>\n",
       "      <th>combined</th>\n",
       "      <th>ubi</th>\n",
       "      <th>benefit_cost_total</th>\n",
       "      <th>benefit_value_total</th>\n",
       "      <th>expanded_income</th>\n",
       "      <th>aftertax_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-10n</th>\n",
       "      <td>0.102049</td>\n",
       "      <td>-7.647952</td>\n",
       "      <td>0.018507</td>\n",
       "      <td>-5.835622</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.764826</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.354646</td>\n",
       "      <td>-0.354646</td>\n",
       "      <td>0.064998</td>\n",
       "      <td>-0.289648</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.799655</td>\n",
       "      <td>0.799655</td>\n",
       "      <td>-6.940854</td>\n",
       "      <td>-6.651206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0-10z</th>\n",
       "      <td>8.391348</td>\n",
       "      <td>-0.092134</td>\n",
       "      <td>8.391348</td>\n",
       "      <td>112.514735</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.092134</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.779859</td>\n",
       "      <td>-17.779859</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-17.779859</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.779859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0-10p</th>\n",
       "      <td>12.213289</td>\n",
       "      <td>28.286909</td>\n",
       "      <td>12.207105</td>\n",
       "      <td>167.047672</td>\n",
       "      <td>0.006184</td>\n",
       "      <td>0.035899</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.109175</td>\n",
       "      <td>0.003643</td>\n",
       "      <td>28.250938</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.404318</td>\n",
       "      <td>-28.400675</td>\n",
       "      <td>3.495389</td>\n",
       "      <td>-24.905286</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.592770</td>\n",
       "      <td>19.592770</td>\n",
       "      <td>50.389807</td>\n",
       "      <td>75.295093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-20</th>\n",
       "      <td>20.707972</td>\n",
       "      <td>207.041657</td>\n",
       "      <td>20.230779</td>\n",
       "      <td>280.157298</td>\n",
       "      <td>0.472337</td>\n",
       "      <td>7.619006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.986559</td>\n",
       "      <td>2.401345</td>\n",
       "      <td>200.049307</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.924569</td>\n",
       "      <td>-66.523628</td>\n",
       "      <td>27.626076</td>\n",
       "      <td>-38.897552</td>\n",
       "      <td>0.0</td>\n",
       "      <td>107.290602</td>\n",
       "      <td>107.290602</td>\n",
       "      <td>323.220845</td>\n",
       "      <td>362.118396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-30</th>\n",
       "      <td>20.707385</td>\n",
       "      <td>316.378323</td>\n",
       "      <td>19.648591</td>\n",
       "      <td>287.017567</td>\n",
       "      <td>1.053396</td>\n",
       "      <td>18.740597</td>\n",
       "      <td>0.0</td>\n",
       "      <td>116.225685</td>\n",
       "      <td>11.584224</td>\n",
       "      <td>299.238036</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>67.787144</td>\n",
       "      <td>-56.195886</td>\n",
       "      <td>43.577393</td>\n",
       "      <td>-12.618493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>258.441980</td>\n",
       "      <td>258.441980</td>\n",
       "      <td>590.805721</td>\n",
       "      <td>603.424214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-40</th>\n",
       "      <td>20.707088</td>\n",
       "      <td>392.642544</td>\n",
       "      <td>19.510973</td>\n",
       "      <td>297.416383</td>\n",
       "      <td>1.187950</td>\n",
       "      <td>22.114507</td>\n",
       "      <td>0.0</td>\n",
       "      <td>184.988094</td>\n",
       "      <td>19.315954</td>\n",
       "      <td>371.147034</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>67.101813</td>\n",
       "      <td>-47.772787</td>\n",
       "      <td>53.603619</td>\n",
       "      <td>5.830832</td>\n",
       "      <td>0.0</td>\n",
       "      <td>369.313918</td>\n",
       "      <td>369.313918</td>\n",
       "      <td>787.711190</td>\n",
       "      <td>781.880357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-50</th>\n",
       "      <td>20.708472</td>\n",
       "      <td>550.480439</td>\n",
       "      <td>19.029242</td>\n",
       "      <td>307.522806</td>\n",
       "      <td>1.676905</td>\n",
       "      <td>33.455448</td>\n",
       "      <td>0.0</td>\n",
       "      <td>286.992712</td>\n",
       "      <td>30.516102</td>\n",
       "      <td>519.344714</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>78.292375</td>\n",
       "      <td>-47.767104</td>\n",
       "      <td>72.879144</td>\n",
       "      <td>25.112041</td>\n",
       "      <td>0.0</td>\n",
       "      <td>404.535394</td>\n",
       "      <td>404.535394</td>\n",
       "      <td>994.589197</td>\n",
       "      <td>969.477157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-60</th>\n",
       "      <td>20.707393</td>\n",
       "      <td>735.630028</td>\n",
       "      <td>18.331876</td>\n",
       "      <td>326.411031</td>\n",
       "      <td>2.373124</td>\n",
       "      <td>49.426182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>425.521954</td>\n",
       "      <td>47.073700</td>\n",
       "      <td>691.665427</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>93.114874</td>\n",
       "      <td>-46.034925</td>\n",
       "      <td>95.833407</td>\n",
       "      <td>49.798482</td>\n",
       "      <td>0.0</td>\n",
       "      <td>466.945896</td>\n",
       "      <td>466.945896</td>\n",
       "      <td>1258.080439</td>\n",
       "      <td>1208.281958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-70</th>\n",
       "      <td>20.707515</td>\n",
       "      <td>959.511949</td>\n",
       "      <td>17.811997</td>\n",
       "      <td>357.787922</td>\n",
       "      <td>2.893239</td>\n",
       "      <td>62.644720</td>\n",
       "      <td>0.0</td>\n",
       "      <td>606.027111</td>\n",
       "      <td>72.245168</td>\n",
       "      <td>905.890709</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>112.380798</td>\n",
       "      <td>-40.126138</td>\n",
       "      <td>122.146512</td>\n",
       "      <td>82.020374</td>\n",
       "      <td>0.0</td>\n",
       "      <td>578.376040</td>\n",
       "      <td>578.376040</td>\n",
       "      <td>1603.049167</td>\n",
       "      <td>1521.028793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-80</th>\n",
       "      <td>20.707197</td>\n",
       "      <td>1365.708941</td>\n",
       "      <td>16.719155</td>\n",
       "      <td>373.852288</td>\n",
       "      <td>3.982463</td>\n",
       "      <td>98.050853</td>\n",
       "      <td>0.0</td>\n",
       "      <td>941.858952</td>\n",
       "      <td>119.044547</td>\n",
       "      <td>1288.034856</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>121.724695</td>\n",
       "      <td>-2.658703</td>\n",
       "      <td>172.536431</td>\n",
       "      <td>169.877728</td>\n",
       "      <td>0.0</td>\n",
       "      <td>634.000703</td>\n",
       "      <td>634.000703</td>\n",
       "      <td>2085.339711</td>\n",
       "      <td>1915.461983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80-90</th>\n",
       "      <td>20.707626</td>\n",
       "      <td>2093.482677</td>\n",
       "      <td>14.553142</td>\n",
       "      <td>349.813909</td>\n",
       "      <td>6.152092</td>\n",
       "      <td>179.989249</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1578.829459</td>\n",
       "      <td>218.697073</td>\n",
       "      <td>1951.769562</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004426</td>\n",
       "      <td>125.344051</td>\n",
       "      <td>93.372100</td>\n",
       "      <td>257.189635</td>\n",
       "      <td>350.561734</td>\n",
       "      <td>0.0</td>\n",
       "      <td>640.771616</td>\n",
       "      <td>640.771616</td>\n",
       "      <td>2872.252958</td>\n",
       "      <td>2521.691223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90-100</th>\n",
       "      <td>20.708041</td>\n",
       "      <td>6246.236344</td>\n",
       "      <td>9.453390</td>\n",
       "      <td>234.654561</td>\n",
       "      <td>11.254651</td>\n",
       "      <td>386.830268</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5608.201771</td>\n",
       "      <td>1197.758029</td>\n",
       "      <td>5942.795098</td>\n",
       "      <td>...</td>\n",
       "      <td>13.805670</td>\n",
       "      <td>47.686797</td>\n",
       "      <td>1165.232430</td>\n",
       "      <td>553.861208</td>\n",
       "      <td>1719.093638</td>\n",
       "      <td>0.0</td>\n",
       "      <td>512.904614</td>\n",
       "      <td>512.904614</td>\n",
       "      <td>6987.435086</td>\n",
       "      <td>5268.341448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALL</th>\n",
       "      <td>207.075375</td>\n",
       "      <td>12887.659726</td>\n",
       "      <td>175.906106</td>\n",
       "      <td>3088.360551</td>\n",
       "      <td>31.052341</td>\n",
       "      <td>858.906729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9774.741472</td>\n",
       "      <td>1718.639786</td>\n",
       "      <td>12190.328720</td>\n",
       "      <td>...</td>\n",
       "      <td>13.810095</td>\n",
       "      <td>828.895941</td>\n",
       "      <td>904.990179</td>\n",
       "      <td>1402.813813</td>\n",
       "      <td>2307.803992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3992.973188</td>\n",
       "      <td>3992.973188</td>\n",
       "      <td>17545.933268</td>\n",
       "      <td>15238.129276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90-95</th>\n",
       "      <td>10.353704</td>\n",
       "      <td>1667.324398</td>\n",
       "      <td>5.676570</td>\n",
       "      <td>140.884353</td>\n",
       "      <td>4.677134</td>\n",
       "      <td>148.216845</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1374.670598</td>\n",
       "      <td>220.454555</td>\n",
       "      <td>1553.995778</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030698</td>\n",
       "      <td>34.014434</td>\n",
       "      <td>186.471142</td>\n",
       "      <td>189.172816</td>\n",
       "      <td>375.643958</td>\n",
       "      <td>0.0</td>\n",
       "      <td>284.797859</td>\n",
       "      <td>284.797859</td>\n",
       "      <td>2039.255590</td>\n",
       "      <td>1663.611631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95-99</th>\n",
       "      <td>8.283571</td>\n",
       "      <td>2341.021395</td>\n",
       "      <td>3.296412</td>\n",
       "      <td>82.219564</td>\n",
       "      <td>4.987159</td>\n",
       "      <td>170.622619</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2075.284086</td>\n",
       "      <td>397.064195</td>\n",
       "      <td>2203.591113</td>\n",
       "      <td>...</td>\n",
       "      <td>1.300444</td>\n",
       "      <td>13.626819</td>\n",
       "      <td>384.737819</td>\n",
       "      <td>212.266995</td>\n",
       "      <td>597.004815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>192.019886</td>\n",
       "      <td>192.019886</td>\n",
       "      <td>2627.393087</td>\n",
       "      <td>2030.388273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Top 1%</th>\n",
       "      <td>2.070766</td>\n",
       "      <td>2237.890550</td>\n",
       "      <td>0.480409</td>\n",
       "      <td>11.550644</td>\n",
       "      <td>1.590357</td>\n",
       "      <td>67.990803</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2158.247087</td>\n",
       "      <td>580.239279</td>\n",
       "      <td>2185.208206</td>\n",
       "      <td>...</td>\n",
       "      <td>12.474527</td>\n",
       "      <td>0.045543</td>\n",
       "      <td>594.023468</td>\n",
       "      <td>152.421397</td>\n",
       "      <td>746.444865</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.086868</td>\n",
       "      <td>36.086868</td>\n",
       "      <td>2320.786409</td>\n",
       "      <td>1574.341544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             count        c00100  count_StandardDed     standard  \\\n",
       "0-10n     0.102049     -7.647952           0.018507    -5.835622   \n",
       "0-10z     8.391348     -0.092134           8.391348   112.514735   \n",
       "0-10p    12.213289     28.286909          12.207105   167.047672   \n",
       "10-20    20.707972    207.041657          20.230779   280.157298   \n",
       "20-30    20.707385    316.378323          19.648591   287.017567   \n",
       "30-40    20.707088    392.642544          19.510973   297.416383   \n",
       "40-50    20.708472    550.480439          19.029242   307.522806   \n",
       "50-60    20.707393    735.630028          18.331876   326.411031   \n",
       "60-70    20.707515    959.511949          17.811997   357.787922   \n",
       "70-80    20.707197   1365.708941          16.719155   373.852288   \n",
       "80-90    20.707626   2093.482677          14.553142   349.813909   \n",
       "90-100   20.708041   6246.236344           9.453390   234.654561   \n",
       "ALL     207.075375  12887.659726         175.906106  3088.360551   \n",
       "90-95    10.353704   1667.324398           5.676570   140.884353   \n",
       "95-99     8.283571   2341.021395           3.296412    82.219564   \n",
       "Top 1%    2.070766   2237.890550           0.480409    11.550644   \n",
       "\n",
       "        count_ItemDed      c04470  c04600       c04800        taxbc  \\\n",
       "0-10n        0.000000    0.000000     0.0     0.000000     0.000000   \n",
       "0-10z        0.000000    0.000000     0.0     0.000000     0.000000   \n",
       "0-10p        0.006184    0.035899     0.0     0.109175     0.003643   \n",
       "10-20        0.472337    7.619006     0.0    25.986559     2.401345   \n",
       "20-30        1.053396   18.740597     0.0   116.225685    11.584224   \n",
       "30-40        1.187950   22.114507     0.0   184.988094    19.315954   \n",
       "40-50        1.676905   33.455448     0.0   286.992712    30.516102   \n",
       "50-60        2.373124   49.426182     0.0   425.521954    47.073700   \n",
       "60-70        2.893239   62.644720     0.0   606.027111    72.245168   \n",
       "70-80        3.982463   98.050853     0.0   941.858952   119.044547   \n",
       "80-90        6.152092  179.989249     0.0  1578.829459   218.697073   \n",
       "90-100      11.254651  386.830268     0.0  5608.201771  1197.758029   \n",
       "ALL         31.052341  858.906729     0.0  9774.741472  1718.639786   \n",
       "90-95        4.677134  148.216845     0.0  1374.670598   220.454555   \n",
       "95-99        4.987159  170.622619     0.0  2075.284086   397.064195   \n",
       "Top 1%       1.590357   67.990803     0.0  2158.247087   580.239279   \n",
       "\n",
       "              c62100  ...  othertaxes      refund        iitax   payrolltax  \\\n",
       "0-10n      -7.764826  ...    0.000000    0.354646    -0.354646     0.064998   \n",
       "0-10z      -0.092134  ...    0.000000   17.779859   -17.779859     0.000000   \n",
       "0-10p      28.250938  ...    0.000000   28.404318   -28.400675     3.495389   \n",
       "10-20     200.049307  ...    0.000000   68.924569   -66.523628    27.626076   \n",
       "20-30     299.238036  ...    0.000000   67.787144   -56.195886    43.577393   \n",
       "30-40     371.147034  ...    0.000000   67.101813   -47.772787    53.603619   \n",
       "40-50     519.344714  ...    0.000000   78.292375   -47.767104    72.879144   \n",
       "50-60     691.665427  ...    0.000000   93.114874   -46.034925    95.833407   \n",
       "60-70     905.890709  ...    0.000000  112.380798   -40.126138   122.146512   \n",
       "70-80    1288.034856  ...    0.000000  121.724695    -2.658703   172.536431   \n",
       "80-90    1951.769562  ...    0.004426  125.344051    93.372100   257.189635   \n",
       "90-100   5942.795098  ...   13.805670   47.686797  1165.232430   553.861208   \n",
       "ALL     12190.328720  ...   13.810095  828.895941   904.990179  1402.813813   \n",
       "90-95    1553.995778  ...    0.030698   34.014434   186.471142   189.172816   \n",
       "95-99    2203.591113  ...    1.300444   13.626819   384.737819   212.266995   \n",
       "Top 1%   2185.208206  ...   12.474527    0.045543   594.023468   152.421397   \n",
       "\n",
       "           combined  ubi  benefit_cost_total  benefit_value_total  \\\n",
       "0-10n     -0.289648  0.0            0.799655             0.799655   \n",
       "0-10z    -17.779859  0.0            0.000000             0.000000   \n",
       "0-10p    -24.905286  0.0           19.592770            19.592770   \n",
       "10-20    -38.897552  0.0          107.290602           107.290602   \n",
       "20-30    -12.618493  0.0          258.441980           258.441980   \n",
       "30-40      5.830832  0.0          369.313918           369.313918   \n",
       "40-50     25.112041  0.0          404.535394           404.535394   \n",
       "50-60     49.798482  0.0          466.945896           466.945896   \n",
       "60-70     82.020374  0.0          578.376040           578.376040   \n",
       "70-80    169.877728  0.0          634.000703           634.000703   \n",
       "80-90    350.561734  0.0          640.771616           640.771616   \n",
       "90-100  1719.093638  0.0          512.904614           512.904614   \n",
       "ALL     2307.803992  0.0         3992.973188          3992.973188   \n",
       "90-95    375.643958  0.0          284.797859           284.797859   \n",
       "95-99    597.004815  0.0          192.019886           192.019886   \n",
       "Top 1%   746.444865  0.0           36.086868            36.086868   \n",
       "\n",
       "        expanded_income  aftertax_income  \n",
       "0-10n         -6.940854        -6.651206  \n",
       "0-10z          0.000000        17.779859  \n",
       "0-10p         50.389807        75.295093  \n",
       "10-20        323.220845       362.118396  \n",
       "20-30        590.805721       603.424214  \n",
       "30-40        787.711190       781.880357  \n",
       "40-50        994.589197       969.477157  \n",
       "50-60       1258.080439      1208.281958  \n",
       "60-70       1603.049167      1521.028793  \n",
       "70-80       2085.339711      1915.461983  \n",
       "80-90       2872.252958      2521.691223  \n",
       "90-100      6987.435086      5268.341448  \n",
       "ALL        17545.933268     15238.129276  \n",
       "90-95       2039.255590      1663.611631  \n",
       "95-99       2627.393087      2030.388273  \n",
       "Top 1%      2320.786409      1574.341544  \n",
       "\n",
       "[16 rows x 24 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Distribution Table\")\n",
    "tb_dynamic.distribution_table(start_year, \"weighted_deciles\", \"expanded_income\", \"reform\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dynamic Reform with Corporate Income Tax Incidence\n",
    "\n",
    "Now we simulate a dynamic revenue estimate while accounting for the incidence of a corporate income tax change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Corporate revenue estimate\n",
    "corp_rev = [5_000_000_000] * (end_year - start_year + 1)\n",
    "incidence_assumptions = {\n",
    "    \"Incidence\": {  # long-run incidence of corporate tax\n",
    "        \"Labor share\": 0.5,\n",
    "        \"Shareholder share\": 0.4,\n",
    "        \"All capital share\": 0.1,\n",
    "    },\n",
    "    \"Long run years\": 10,  # number of years to reach long-run incidence\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "tb_dynamic = TaxBrain(start_year, end_year, microdata=\"CPS\", reform=reform_url,\n",
    "                      behavior={\"sub\": 0.25},\n",
    "                      corp_revenue=corp_rev,\n",
    "                      corp_incidence_assumptions=incidence_assumptions)\n",
    "tb_dynamic.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Partial Equilibrium - Combined Tax Liability\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>2023</th>\n",
       "      <th>2024</th>\n",
       "      <th>2025</th>\n",
       "      <th>2026</th>\n",
       "      <th>2027</th>\n",
       "      <th>2028</th>\n",
       "      <th>2029</th>\n",
       "      <th>2030</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Base</th>\n",
       "      <td>2.269727e+12</td>\n",
       "      <td>3.067767e+12</td>\n",
       "      <td>3.246623e+12</td>\n",
       "      <td>3.395728e+12</td>\n",
       "      <td>3.575938e+12</td>\n",
       "      <td>3.979716e+12</td>\n",
       "      <td>4.161639e+12</td>\n",
       "      <td>4.344151e+12</td>\n",
       "      <td>4.543397e+12</td>\n",
       "      <td>4.749701e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reform</th>\n",
       "      <td>2.309448e+12</td>\n",
       "      <td>3.121009e+12</td>\n",
       "      <td>3.313788e+12</td>\n",
       "      <td>3.478349e+12</td>\n",
       "      <td>3.675071e+12</td>\n",
       "      <td>4.084663e+12</td>\n",
       "      <td>4.284063e+12</td>\n",
       "      <td>4.484873e+12</td>\n",
       "      <td>4.692225e+12</td>\n",
       "      <td>4.906309e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Difference</th>\n",
       "      <td>3.972134e+10</td>\n",
       "      <td>5.324191e+10</td>\n",
       "      <td>6.716455e+10</td>\n",
       "      <td>8.262134e+10</td>\n",
       "      <td>9.913307e+10</td>\n",
       "      <td>1.049468e+11</td>\n",
       "      <td>1.224239e+11</td>\n",
       "      <td>1.407223e+11</td>\n",
       "      <td>1.488282e+11</td>\n",
       "      <td>1.566081e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    2021          2022          2023          2024  \\\n",
       "Base        2.269727e+12  3.067767e+12  3.246623e+12  3.395728e+12   \n",
       "Reform      2.309448e+12  3.121009e+12  3.313788e+12  3.478349e+12   \n",
       "Difference  3.972134e+10  5.324191e+10  6.716455e+10  8.262134e+10   \n",
       "\n",
       "                    2025          2026          2027          2028  \\\n",
       "Base        3.575938e+12  3.979716e+12  4.161639e+12  4.344151e+12   \n",
       "Reform      3.675071e+12  4.084663e+12  4.284063e+12  4.484873e+12   \n",
       "Difference  9.913307e+10  1.049468e+11  1.224239e+11  1.407223e+11   \n",
       "\n",
       "                    2029          2030  \n",
       "Base        4.543397e+12  4.749701e+12  \n",
       "Reform      4.692225e+12  4.906309e+12  \n",
       "Difference  1.488282e+11  1.566081e+11  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Partial Equilibrium - Combined Tax Liability\")\n",
    "tb_dynamic.weighted_totals(\"combined\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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