forecasting-store-salesPB.ipynb

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    "# **Forecasting: Store-Sales**\n",
    "\n",
    "This project aims to forecast the sales of a company's stores in Ecuador. We work with Time Series to predictions the sales, used tools of the **Pandas**. The Model learning  is the kind Hydrid,  it be first model `RandomForestRegressor` while the second model is `LinearRegression`. \n",
    "\n"
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   "source": [
    "# **Setting** #\n",
    "\n",
    "Setup of the system  and necessary libraries."
   ]
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     "text": [
      "/kaggle/input/store-sales-time-series-forecasting/oil.csv\n",
      "/kaggle/input/store-sales-time-series-forecasting/sample_submission.csv\n",
      "/kaggle/input/store-sales-time-series-forecasting/holidays_events.csv\n",
      "/kaggle/input/store-sales-time-series-forecasting/stores.csv\n",
      "/kaggle/input/store-sales-time-series-forecasting/train.csv\n",
      "/kaggle/input/store-sales-time-series-forecasting/test.csv\n",
      "/kaggle/input/store-sales-time-series-forecasting/transactions.csv\n"
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# Setup feedback system\n",
    "from learntools.core import binder\n",
    "binder.bind(globals())\n",
    "from learntools.time_series.ex3 import *\n",
    "\n",
    " # Allows the use of display() for DataFrames\n",
    "# from IPython.core.interactiveshell import InteractiveShell\n",
    "\n",
    "# InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "\n",
    "# Setup notebook\n",
    "from pathlib import Path\n",
    "from IPython.display import display\n",
    "\n",
    "from learntools.time_series.style import *  # plot style settings\n",
    "from learntools.time_series.utils import plot_periodogram, seasonal_plot\n",
    "\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "from scipy.signal import periodogram\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from statsmodels.tsa.deterministic import CalendarFourier, DeterministicProcess\n",
    "from statsmodels.graphics.tsaplots import plot_pacf\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from lightgbm import LGBMRegressor # make for microsoft\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "from scipy.stats import t\n",
    "from sklearn.model_selection import TimeSeriesSplit, train_test_split\n",
    "from sklearn.metrics import mean_absolute_error as mae \n",
    "from sklearn.metrics import  mean_squared_log_error as msle\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "        \n",
    "        \n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\n"
   ]
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   "source": [
    "# **Get Data** #\n",
    "\n",
    "The data is in  [Kaggle Store sales](https://www.kaggle.com/c/store-sales-time-series-forecasting). \n"
   ]
  },
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    "# Paths\n",
    "comp_dir = Path('../input/store-sales-time-series-forecasting')\n",
    "\n",
    "# timeInitial\n",
    "timeInitial = '2013'\n",
    "\n",
    "# Get datas\n",
    "holidays_events = pd.read_csv(\n",
    "    comp_dir / \"holidays_events.csv\",\n",
    "    dtype={\n",
    "        'type': 'category',\n",
    "        'locale': 'category',\n",
    "        'locale_name': 'category',\n",
    "        'description': 'category',\n",
    "        'transferred': 'bool',\n",
    "    },\n",
    "    parse_dates=['date'],\n",
    "    infer_datetime_format=True,\n",
    "    index_col='date'\n",
    ")\n",
    "\n",
    "hol = holidays_events.to_period('D')\n",
    "hol = hol[hol.locale == 'National'] # I'm only taking National holiday so there's no false positive.\n",
    "hol = hol.groupby(hol.index).first() # Removing duplicated holiday at the same date \n",
    "\n",
    "store_sales = pd.read_csv(\n",
    "    comp_dir / 'train.csv',\n",
    "    usecols=['store_nbr', 'family','onpromotion', 'date', 'sales'],\n",
    "    dtype={\n",
    "        'store_nbr': 'category',\n",
    "        'family': 'category',\n",
    "        'sales': 'float32',\n",
    "        'onpromotion': 'uint32',\n",
    "    },\n",
    "    parse_dates=['date'],\n",
    "    infer_datetime_format=True,\n",
    ")\n",
    "\n",
    "oil = pd.read_csv(\n",
    "    comp_dir / 'oil.csv',\n",
    "    usecols = ['date', 'dcoilwtico'],\n",
    "    dtype={'dcoilwtico': 'float32'    },\n",
    "    parse_dates=['date'],\n",
    "    infer_datetime_format=True,\n",
    ")\n",
    "\n",
    " \n",
    "holidays = (\n",
    "    holidays_events\n",
    "    .query(\"locale in ['National', 'Regional']\")\n",
    "    .loc[timeInitial :'2017-08-15', ['description'] ]\n",
    "    .assign(description=lambda x: x.description.cat.remove_unused_categories()) # assign new columns\n",
    ")\n",
    "\n",
    "df_test = pd.read_csv(\n",
    "    comp_dir / 'test.csv',\n",
    "    dtype={\n",
    "        'store_nbr': 'category',\n",
    "        'family': 'category',\n",
    "        'onpromotion': 'uint32',\n",
    "    },\n",
    "    parse_dates=['date'],\n",
    "    infer_datetime_format=True,\n",
    ")\n",
    "# Rearray to period\n",
    "\n",
    "transactions =  pd.read_csv( comp_dir / 'transactions.csv',\n",
    "                            parse_dates = ['date'], \n",
    "                            infer_datetime_format = True, \n",
    "                            )\n",
    "\n",
    "oil['date'] = oil.date.dt.to_period('D')\n",
    "oil = oil.set_index(['date']).sort_index()\n",
    "oil=oil.squeeze()\n",
    "\n",
    "store_sales['date'] = store_sales.date.dt.to_period('D')\n",
    "store_sales = store_sales.set_index(['store_nbr', 'family', 'date']).sort_index()\n",
    "\n"
   ]
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   "source": [
    "\n",
    "#  **Exploratory Data Analysis** #"
   ]
  },
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   },
   "source": [
    "Information preliminar about  `store_sales`."
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 3000888 entries, ('1', 'AUTOMOTIVE', Period('2013-01-01', 'D')) to ('9', 'SEAFOOD', Period('2017-08-15', 'D'))\n",
      "Data columns (total 2 columns):\n",
      " #   Column       Dtype  \n",
      "---  ------       -----  \n",
      " 0   sales        float32\n",
      " 1   onpromotion  uint32 \n",
      "dtypes: float32(1), uint32(1)\n",
      "memory usage: 34.4 MB\n"
     ]
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       "None"
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>3000888.0</td>\n",
       "      <td>356.758392</td>\n",
       "      <td>1093.275879</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>195.847252</td>\n",
       "      <td>124717.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>onpromotion</th>\n",
       "      <td>3000888.0</td>\n",
       "      <td>2.602770</td>\n",
       "      <td>12.218882</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>741.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                 count        mean          std  min  25%   50%         75%  \\\n",
       "sales        3000888.0  356.758392  1093.275879  0.0  0.0  11.0  195.847252   \n",
       "onpromotion  3000888.0    2.602770    12.218882  0.0  0.0   0.0    0.000000   \n",
       "\n",
       "                  max  \n",
       "sales        124717.0  \n",
       "onpromotion     741.0  "
      ]
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   ],
   "source": [
    "display(store_sales.info())\n",
    "display( store_sales.describe().T)\n"
   ]
  },
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   "source": [
    "store_sales['sales'].head()\n",
    "\n",
    "def salesDecompose(df, share_type='sales', samples=250, period=30):\n",
    "    if samples == 'all':\n",
    "        #decomposing all time series timestamps\n",
    "        res = seasonal_decompose(df[share_type].values, period=period)\n",
    "    else:\n",
    "        #decomposing a sample of the time series\n",
    "        res = seasonal_decompose(df[share_type].values[-samples:], period=period)\n",
    "    \n",
    "    observed = res.observed\n",
    "    trend = res.trend\n",
    "    seasonal = res.seasonal\n",
    "    residual = res.resid\n",
    "    \n",
    "    #plot the complete time series\n",
    "    fig, axs = plt.subplots(4, figsize=(16,8))\n",
    "    axs[0].set_title('OBSERVED', fontsize=16)\n",
    "    axs[0].plot(observed)\n",
    "    axs[0].grid()\n",
    "    \n",
    "    #plot the trend of the time series\n",
    "    axs[1].set_title('TREND', fontsize=16)\n",
    "    axs[1].plot(trend)\n",
    "    axs[1].grid()\n",
    "    \n",
    "    #plot the seasonality of the time series. Period=24 daily seasonality | Period=24*7 weekly seasonality.\n",
    "    axs[2].set_title('SEASONALITY', fontsize=16)\n",
    "    axs[2].plot(seasonal)\n",
    "    axs[2].grid()\n",
    "    \n",
    "    #plot the noise of the time series\n",
    "    axs[3].set_title('NOISE', fontsize=16)\n",
    "    axs[3].plot(residual)\n",
    "    axs[3].scatter(y=residual, x=range(len(residual)), alpha=0.5)\n",
    "    axs[3].grid()\n",
    "    \n",
    "    plt.show()\n",
    "    "
   ]
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   "source": [
    "### Sales Decompose\n",
    "In this section shown to sales decompose in three parts;  Observed, Trend and Seasonality, in a range time of  one year. "
   ]
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XrXjn+FjJ9zUTYVk2xcGjUshgM6hpBg9lVrCrx4V3jo9jy4lT8/NJyQ6pHjuDVOChUKjAUx784TgYBtCp5DCqFUU5eH750lGw7MzMROp2cg6vYh3uLMvCzYcsl3OK1ogvDIteJYynL9WBTxw8iSSLYDRR8vFRKJSpp2SBx+FwfN3hcDQ6HI5WADcBeM3hcNwK4HUA7+Nv9mEAT5b6uygnF3F2jLMMLVqkn51UA041/JE4ookkrHzIMgBhVDqFMtPp4Tc1PdTBc9pBAj6pg4dC4TKpAMBdJoHnnjc78KuXj5blvmYT/kgCBpUCDMNAr1YUnMGzt9eNFw4OodakAcvOvBahLv6aWWwBNBBNIMoH25fbwVNlUAtr0bESHfijIgfQTHsNKBSKNOWcopXO1wB8yW63HweXyfPXKfxdlClgsIwOHn8kjlcODwM4datiTv45IiHLAJfDQzN4KLOBHmco5W/K6YOXX7Q7qcBDoaCLF3jKtVZ5Yk8/nt43UJb7mk34IzHo1ZyR36ApXOD5+UsOVOqU+Pg57QBmXshvN5/RNF6kgCIW1MudwVNtUkOnUkCnkpclZFmvkgOYea8BhUKRRjH5TfLH4XC8AeAN/t8dANaV8/4pJ5cB3sFTb9aULPC8eGAI4Vj5KxUzCfIckRYtAKgza7Cts/j+bArlZEGcO9TBc/ohOHhoixaFIrgZPWX4PLAsi57xIBim5LuadfgjcRg0vMBToINnZ7cTbx0bwzcvX4j6Ci0Abu3YNCVHWhzEwVOsw52cb5Vypqxj0kd9EbTb9AAAq0FVknCfTLIY90exoM6IA/1e+KjAQ6HMCqbSwUOZ5Qx5wrAZVKg1a0oOWX5iTz+aLFoY1IpTVuAhF3mLSOCpMWngCcUQon3LlBkOCVcuJWT5ly8fxS33vFeuQ6KcJIQMnsCpeW6mUPIlHEtgkHfdlmOt4gnF4IvE4Q3HEYmfXusAfyQBg1ok8BTQ3rOn1wMAuGF1I0waJQCUVQQpB6SVLxBNIBwr/LV18fk7TZW6sj02lmW5Fi0T5yS36tVChk4xuEMxxJMs2m0GABNuT8rUk0yy+Py/dmNnt2u6D4UyC6ECDyUrA54w6sxaWPTqkiyeI74w3jk+hmtXNMCsVc64i3S5mGjRmhB4qgzcRbZYCy+FcjJgWRY9ziCUcgbOQLToKt2hAS+ODvvKfHSUqUaYohWMzugpKYcHvdjX557uw6CcwvS5gmBZoMmihScUKznYt1fU8lpqq8xswx+OCQKPvsCQ5VFfBEo5g0qdEmYtL/DMMPdI13gQKjm3jSrG5U5atFqsurIVPj2hGKKJJKqNXMCyzaAq6X1HxKH2Ks4RdKqu32ciY4EIntwzgNeODE/3oVBmIVTgoWRl0B1CnVkDq740i+czeweRZIFreIHnVHXwCC1aopDlSl7scdHKOGUGM+aPIhRLYGVzJYDUTUkheEOxgnMWKNMP2TglkuyMrtB+/9lD+NYTB6b7MCinMF1jnINxWWMFkizgj5b2eRC3vBbqpDg44JnVQykCaQ4eX4ECT5VBDYZhYNJy9zGT1o7+SBxj/giWNJgAFDeKnLRotVj1CEQTiPOBy6VA3i9VxgkHTykFRnJ/7VWcg4eGLJ88iDA37J295wDK9EEFHkpWBj1h1FdohR7eYiu7T+7px5IGE+ZWGzgHzwyrwpQLZyACjVIGnWoi2sqi5ypPdPwwZSZDNiFnzbWl/L9QPKEYwrEkEjNsnC0lN+Kq7EyepDXoCdPQesqUQgKWlzeaAQCeYGnrlWIFHpZlcdtft+HHzx8p6fdPJ/5IfCJkmXfw5LuOHPGFBZHCRBw8ZRwlXiqkPWt1C1cUKcrBE4yBYYAmiw5AedqfRnhBppoIPLyDp9j1u+Dg4TN9TsX1+7g/gsd39+GHzx3Gnfdvx1vHRqf7kABMCDwjs1jkpUwfVOChSOINc5X4OrMGFr0K8SRb1MU1kWSxr9+Dc+ZXAQBM2lM3g2fcH4VVNEELACp1xMEzMzZNwWi8pIwVyqkJeU9s5AWeYt8j5LMdLLHqTTm5eMNxGPkw1JksRo/6Ihj3R0tumwGAeCKJ4yP+MhwV5VSixxmEUaNAi5Xb0Ja6XulxTgQsj/ny/2yN+aNwBqLY3TN78zd84ZhwXjFoFEiyQCjPrJpRXwRVfJuRQaWAjJlZ4kI3H7C8ine9FtMG5QpEYdYqUakrX8ZQhoPHoC56/S6+v6ZKHZRy5pR08HztP/vwxYf34v53urD56Cie3jszJt4R59UILWpQioAKPBRJyIj0Ot7BAxSXI+MJxcCygI3Poilni9YH/7IVv3r5aFnuqxyMB6Ip+TvARB7PTBk/fPfmDlz9+7dndM4G5eRDqsyL600wa5UlOXgAzppPmT14QzG08hvamSJGpxOOJeALxxFPsnCX4Rry3IEhXPyrzehzUcGbMkHXeBCtVj0qeNdIqeuVXmcQ9hojAGC0AAdPxygnPnaMBUp2EU0HLMsiEE2kZPAAyLuFd8wfEUQKmYyBUTOz2vuJ02sV7+ApZhCJKxhFpU4lZAwV8viGPGHJCa0jPm7tThw8Nn79PlZkm9aoLwKVQgaTVgGjRnlKTtE6POjDpYtrcfC7l2BRnWnGtERRBw+lFKjAQ5FEPCLdwrtSihEpSI8xcbKUS+CJJZJ4t2McrxyeOeFjTgmBx6RRQi5jZsz44Z7xAFzBGIJ0qhdFRI8ziBqTGhqlHM0WXVECTzSeFKqzAergmTWwLAtvOIYWK9cmMFPE6HRGRIvuUqbCEIY8ISRZYF+fp+T7mi28dWwUf9vSNd2HMaPpHg+gxaqDmXdVuEsUV3pdQcyrMcKgVhT0vj0xGhD+vXcKg8UHPaGyZL+kQ1p1ibBjJAJPHg6QeCKJ8UBUEHgAzLgBHd1jQdgMalQb1VApZMU5eIJRVOqUEy1oBYgnf3j9OD76t+0ZXx/1cVEBRFgjrvJig5ZH/RNZSEaNYka1yZWDUDSBfncIi+pNUMplqDFpZkwbMCmqOwNRROPl/4xSTm2owEORJMXBw4sWY0VcINy8sFHBL5bMWiXCsWTJ40J7nUEkkiwcQ76ixlNOBc5ANCVgGeAqT5U6ZVH92VMBeQ1n6ibudCCeSM44B1XPeBAtFs7BUazAIxZug9TBM2sIxRKIJVjBwSPe0LIsW5Z2qHIw6p9YdI+VoaJJNioHB04fgedPb5zAz19yTPdhzFhiiST6XSHewcNdy0spSMX5+2u2aGEzqApaQ3WM+qFSyMAwwN5ed9HHkItAJI7zf74Zf3+3u+z3TZw6Bk2qgycfd+d4IAqWnXChAFx7/0wKgO/ihUCGYWDTq4qcohUr2sHT5wrCG45nrH9HfBFUGzVg+L5AwYFfpCg+6ovARrKQTkEHT8cY55QjU8JqTGoMzRCBR7xOL0dR4987enHv250l3w9ldkAFHookg54QZAxQY1QLF4iiHDz89Cixgwco3fZM7LHxJItDg96S7qtcjAcisOhUGV+v1KlmTNsDuUjMFEfRbMATiuHj/9iBIU/pF32WZXH179/BT16YWZusHmdQCHpssujQ5woWHJQs/kxTB8/sgQgddRUaKOVMSgbPT1904Ia7t0zXoaUgdvAU0uqSDbJROdA/M64fU00iyWJvrxu+cHxGtbrMJAbcIcSTLJqturKsVQY9Ye7+LDrYDOqChMkTo37MqTJgTpUBe6ZI4OkeDyIUS+Cd42Nlv29B4FHL+b85gccXmfz5TM+RAThxYSa9b7vHg4Lr0WJQFT1Fq1KvgklTeIj0IL8eSV+Xj3gjKc+bVWjRKm7NN+aPooq/D6NmZols5aCDd8rN4aeE1Zo0cAdjM6JwLBaEy+Eq+ue2Hty3hQo8pwtU4KFIMuAOo9qogUIuE+XIFD8Gkgg8E9MQSrtQd4jsy/umaPFTCKFoAuFYUhiLLqayxDHz5YQsnMpxPMkki0e2957y1tGd3U68eHAYb5dhEby3z4NDg94ZFe4ajiUw5A2jmRd4Wqw6xBJswVWsFIGHjkqfNZC2AC7sM1WM3tXtwr4+D2JT0MJRKOIcgnKMjiYblYMDp4fAc3zEjwDfmtvvCk3z0cxMuvjg3FarHhqlDCqFDO5Q8ddKElbfVMkLPIVk8IwFMKdKj+WNFdjb554S1ydxau7scZXl/nudQZzgs4MCgsCj5P/O38EjJfDMpBatUJS7ZhLXo1WvLjrCoFKnLEpMzCbwjPojKc4nUnQsxcFTdQo7eE6M+sEwQBs/JazGzAV7j8yAHJ5xfwQm3gFXjhyePlcIA+7wlLRkUmYeVOChSDLoCaGugjvRqRVyGNSKoiyoxO5foZ9o0QLK4+AxaRSoNqpnRIYCEbLSM3gA7gI7Exwz8URSqM6X43j29Lnx1f/sw2tHRkq+r5lM1xi3CC7HpuipPdx0BvcMeD8Q+vjH1WzVcn/zQk/PeGFtWt4UB8/0V78o+UFeN5NGCUuaGN3Dt8LOhMl7o74IZAygksvK4uAhj3vMHzktppTs6Z2YxkSDpaXp4Z3BrXzrTamiAhFQmiw62IyqvAWeSDyBXmcQ7VUGrGiuwJg/in53+UU58rl2B2MpmT/FctdTB/HpB3cBgDBtSU8cPBoSsjz580mCgqsMqQ6emTJFi7yuxMFj1RfWfgdMFAUrdCpolDIo5Uzejy8YnXDhZTp4winCmEIu42ICiohYSCRZOAMRYUiKUaM45aZonRgNoKFCC42Se5/Wmrh9z0xo03IGolhYZwJQusATjiUw6osgkWQFcZByakMFHookg54w6s1a4f9Wg6roEDmFjBEC9sol8HSOBdBWZcAyvro13Uw4lZQZ3+McPNO/MHEGub52AGU5Hif/fhj1nZyLxb+29WDN91/Bmu+/jDXffxl/eP14yvc7Rv244rdv4W9buspaoSDtgP3u0jZFiSSLZ/bxAs8MqUQCE4t8IuyQvwvd1Kdm8Jxai8BTGbKpMBEHD38uC8cSwkKQfAamkxFfGDaDmssyKWDcdDa84Ri0/KL+dHDx7O5xQ63glnxTIRaUysf/sWPaxxN3jQehVcqFDXKFVllSyHKPMwiFjEGdWQObQQ1XMJaXG657PIgkC8yp0mNFYwUATEmbVo8zCBk/wn1nd+ZEpkIZ9UXgGPbBH4kLLVpG3sFDhB5/kQ4ek1YxY1q0ugUhkHfwGAp3aYuLgkRMzPfxiTfo4mJdOJaANxxPcfBwx6cuagquMxBFkp14HYyaVMEznkjOerG4g2+FJNTMIIFn3B+FvdYIGQOMlng84nN+H3VwnhZQgYeSAcuyGHCHUMdbFQFkVHbzxRWMoUKnFALfyubgGQuizarD8kYzOsYC024bFZxKEhk8Fr0SrmC0oLBSVyBa9qq5uK2hmHa7dIhIMVrkdIZCeeXwCFiWxSWLa6FVyfHc/sGU728+OoqDA17c9dRBXP7bt7C1Y7wsv5fY9kvdFG3rdGLEF4HNoJpRDh5xlRkA6swayGVMwUHL4s90vqNwKdMPyX0waRQp53nx+adzbPoX8SO+CKpNatiMhbW6ZMMbimM1P+L4QP/0u0Cnmj29bqxvt0KrlJdlgc+yLPaXyT3rCkTx4sHhaXeDdouCc4HSp372ukJoqNRCIZcJLoh81lFkRPqcKgPstUaoFLIpCVrucQaxqN6ESp0SO7pck//AJLhDXBHpQL9nokVLQ6ZocWu/fKZojfq41hTiqgDKN6CjHHSLWvkAwKJXIxRLIFhA9lx6UbCQjCFxHqC48ColjAHFOYyAicxG8t41aRUIRBNCAe0/u/pw/i82zxjhrVCSSRYdo4EUgYc4eIan2eUSiSfgi8RRZVDDalCXPLpdfM7vneWiHCU/qMBDycAVjCEST6KuQuTgKXJKgDsYTRE9BIGnhKpYOMaNNWyzGbCsqQIsC+yf5gU6WbRJtWhV6lRIJNmCrK0/ev4wbvvr1rIdH5Aa2FYOBw8RKcqx2cqHrvEAVrdU4gfXLcWli2txbMSfEgR8dNiHCp0Sf75tNQKRBD72j51lyRXoGuMdPCVuip7aOwCdSo4rl9XDHYzNmElaPc4gNEqZYIdXyGVoqNCWJPAEaYvWrCHFwaNXwsWfm7tFLXrkMzCdjPLTYaoM6jJl8MRQZ9ag1ao75R08/kgcjmEfVjZVoLFSW5aq+xtHR3HV79/GvjI4aMkkm+5pdor1uUJorNQJ/zeXwcFDHJFkk5zPe5e0S7XZ9FApZFhSb8Le3vKvcXr541vdUomd3WUQePh1xf4+D3yR1BYtjVIGGZNfPtuIL5IhUpD8xpnQItQ1HkCFTgkzL85MTKrKf42cPoDEVEA7oNjBIxYMSetqtVGTcnubobiMoHTByMiHQZMCjmPIj2g8iYEZ6AjMh0FvGKFYQpigBXAilkYpm/ZR6eT1shrUqDGphbbFYhGvX/tmQMs1ZeqhAg8lA3Kyrs9w8BQ5JUDUtmQSHDyTX6Sz9SML1RObDssazAAw7Tk86ePgxQhTyApwbfQ4g+gaD5Y1rJZM8FArZGWZ6kUWvuXYbE1GIsmiZzwoBOHNrzEiGk+mbAgcQz7Ya4y4ZHEtbjujBZ5QDKESJyFE45wFWcZwwePFjoyOJZJ4/sAgLlxYg/oKDeJJdsa4XMgmhFStgeJGpbuDMehUcqgUMjpFaxZBNhVGjQIWHecuSyRZdPOvf2Oldoa0aEVQZVAXHFabDV84DpNWicX1ZhwczLx+xBNJ3PXkgRkhbpXKvj43WBZY2VyBhkptWRw8xL1zOMsUS5Zl8bMXj+Tl8iGh8z3O6d0ojvmjqcG+uhIdPM6gIBhVGflpRnm8d0+M+lFr0gijxZc3VWB/v6esrceJJIs+VwhNFh1Wt1jQMRYoOogX4K5xRNTZ2+cW1i7EucMwDAxqRV7XPSLmiimX+7sciIU7gCuAAiioCCo4ePRFCDz8Gt2oUaSsK0kwcIaDp8gpX4LAQxw8vBuLiGzECVJoPszf3+3Ct584UPDxlBuxU47AMAxqTZppb9EiYqHVoEK1UVNyBk+fi2sXrTGpaYvWaQIVeCgZkMVWQ6U4g4erABTqOnAHYzBrJ1wtSrkMepV80ov0gDuE1d97GW8fy5xc1MlX+9ptBlTqVWi26Eq2LyeTbEnToEjVu0Ir7eABCptcRS6sHWUIPhTuk7/Az6sxFCQ2ZcN1Eh08A+4QookkWnmBx15rBMC5dgBuM3F02C98nYiKpU4L63NxWQjLGisQTSSLfqxvHxuDOxjD1cvrBUdbKZXhcsJVcfUpX2uy6IrK4KnQKmFQK+gUrVmENxyHRimDWiFHpV6FJMuJPj3jARjUCqxqrkTnNIsciSSLcT9p0eLcpMWKrQAn3vgjcZg0SiyqN6HXGcpwlXaNB/G3d7uF3KzZzO4eNwBgBe/gKUcGj4M/92abCBiIJvCH10/gmf2TP3/EsTLmjxTU5lJOkkkWrmBU2KwDpU1u8oVjcAaiGQ6efFplOkYDmFM9cU5e0VSBUCyBo8Plm7447A0jmkii2aLDmlauVXEX/z4pBvHztK/PA384DhnDOXcIRo0yP4HHL+HgEUaJT8118+7NJ/CXtzryum26AGXlX9tCRJT0CbNmrTLvEeSD3jAsehXqzBohCxEQO3hSn7s6sxauYKxg57zQopXm4CHrd7JGKCSkfl+fG999+hAe3t6b4sAulGA0XrIL+gR/7hJ/1gCg2qSZdgcPee6tehWqjeVp0aqv0KLFqqctWqcJVOChZPCfXX2oN2uwuN4sfM2qVyGWYPO+ABHSHTxAfn3tR4a8iCVYydYrkgfRauMWTssazSU7eP76dicu+OUbRV8wXMEojGoFVIrMjxRp2yrENUPUejJytByM+SLQKuVorNCVx8EjmkIz1XSlhRrOqzaCYYAjQ9wmo98dgj8Sx/waTuApl4hCfu/GuVYAXKZCMbx0aBhGjQKb5ttQwVciZ4LAw7JsRjUS4Bw844FoQS4jTygGk1YJnUqOYB5BmpSZgTcUE6rj5FzlDEbRzb8vWm16DLhD05p9MR6IIMlyG5cqgxoJfjNeLOR9bdIqsIR3gaa7eEjORTYBYzaxp9eNdpseFToVGit1cAdjJefWHeXPvdmmL5EiRT7XmhOi57h3mlw83nAMiSSb0mZdoVXBF4kX5ZwhjyNT4Ml9vWRZFidG/Wi3TbgKVjVzAszbx0cLPo7sxzcRrr+0wQylnMGOEoKWyXpgQa0RPc4g+lxBGNSKFGeoXi3PO4Mns0WLc48UugbNh2SSxd2bT+Dh7b153Z7kgRGKcvAESG4jyeDJP0R60B1CrUmDSl1qNuaoNwwZMyE4EZY0cJOYDgwUtk4e5deMehXXZkdeA1+YE1dI20++7pJwLIEvPbIX8SSLaKL41q5wLIH1P3wVX310X0lC/4nRAIwaRcq0NgAzwsEjbtGqNmkwHoiU5ODrcwXRWKlFU6Vu2s6xlJMLFXgoKfQ6g3j7+BhuXNMEuWziwiws/Au4gLEsC1cwJlhQCSbt5OMuSRuWVJtI55gfNoNaqCYsb6xAvzuEEV8YjiEf3jme6fqZjMNDXvQ6Q0UvHlyBqDAKPh3BwZPnhiQcSwgW2LIKPP4IbEYVKvXlGdtO2tJGfZEpz5MhbRKkRUurkqPFohMcPOTvBbyDRxDVSnycZET6xrk2AMUHLXePBzCv2iC4JAAukHK6GfNHEYwm0GzRpny9voKfJFFA0CARCvQqBW3RmkV4wzGhOk6EUVcgip7xIFqsOrTZdEiyhU9VK5RgNI4Ht3Zj0JP5GRO3HpBqcjGhoYSJYGklFtdzm59DaTk8ZIFfjvHR0wnLstjT68aKpgoAQAOfrVeKiycST6CDPydnu0YRISOfNcPxUT8aecdwoa2h5WI8MNESQTCXICr0pE0n1KsV0CrlQqt0Nsb8UfjCccwR5YI0WXRY2VyB/+zsL9u1Vnx8GqUcSxrM2FlC0DIpWJwzvwoA8G7HuLBGIxjUk18b/JE4gtFEhgtlKlu0Dg954Q7G0O0MTuoqiSWScAaiKaJAURk8fFFQKee2YcQtls/rO+gJo75Cw03vCqY6eCx6dcraHQCWFhllQNaMRKQj1wlfOAZPKCa05OXr4PnZiw4cH/HjM+fNBYCinaEj3gh84Tj+vbMP//PUgaI/Ex1jfrRXGVJESACoNWsw7J36dW0uUlu01GDZwgTEdLh8MS2aLFoM+8InvWCT73MZiSdw/zudZSlCn+5QgYeSwr939gEAblzTmPL1CYEnf7dGKJZANJ7MyKUx5eHgIQKPVBhk11gQbbYJx8GyRu7iteknr+OSX7+JW/+yFbt6CluokGpjsRUFVzAmCDnpFCqOiTNtylk9HvVzGRZWvQquYKykygcwsaALx5IITHGobucYFwQsXvTNrzHCwVeRiZNnXk15W7S6xgMwqhVYxo+qLTZoecDN2WMBCA4e1wxw8AiLfGuqg4dMkihE4PEQgUctR4A6eGYN3lBcyEaz6CZyQnpdQTRbdYJrbqomabEsiyd29+P8n2/GNx8/gHvf7sy4DWk9qDJq8nZC5IIUGIwaBWx8iGV60PIQLzSdGPXPmED0Yuh3hzDqi2BlcwUACEJKKaHxHaMBJJIs2m169DqDCEtknREhY7JzcDiWQK8ziPMXVAOYPoFHalACCdEtZOphPJHEK4eG8X9vngAANInEc5tRNen7lghm7aJcEAC4YVUjHMO+sgWC9/Ij0sl1aU1LJfb1e4re+Hn4ggUphgx7I0LAMkGvVkwakpxtEtRUtmi9e4KbuJlPYDDZeIsdPDoVF8xbyPrYFYymFD9NWiXiSTavAQVD3jBqzZqM6bYj3kiGMAZwwn2TRVvwtECSe0YwaiYET3GOSz4Onl09Lvz17U586IwW3HZGCwAUne1GAofXtVrwwHs9+MGzh4s6R58YCaQIqYRqoxrReHJaXdZjgQiUcgZGtUJ4TYttG4vEExjxRdBQoUNjpQ4sy2VKnize6xjHiu++nFFEkeL5/UP4ztOHcNXv38bBAh1nlFSowDMDCMcS+MPrx6c9tyKRZPHvHb3YNK8qZZIEMGEvLqRCQU6O6cJHPn3tJDxXarHXMRYQnBwAF0B4xdI6XL+qET+5YSlUchme2zeY8XO5IBViqepxPqRPCxNDgmfFivTH/7EDj2SxA5OLl1YpL3OLVhQ2gxqVem6q12QuqslwB2NQ8JWiyaqSpdI1HkCrVQ+ZqDK1oNaIrnFuc3F0yIc6s0ao8pWvRSuIVpseBrUCZq0S/e7CNx8sy2LAE54QePhj88yAUem9aVVmQp2ZO9ZCPg8TAg918MwmOAcPt3Cv5F2Ihwa4FtkWi144105V2PC3nzyALzy8B1VGNerNGknHzKh3IluCbPxKCXcn1x8ibC2pN2csJsmkmmA0kTK1Zraxh8+nW9HEtfmQa3spQZvEMXn50jokWemNGhHl0oXsVw8P48a7twiZd93jXM7Z6pZKGNWKop1iu3pcuOiXm/HPrT1FZXuQtU16ixaQv2tk1BfB+b/YjI/+fQd6XSH8z5WLUtYFXEB47vM+yd1rT9t4XrWsHiqFDI/yRbhS6XEGUV+hFRwkq1sqEY0ncXjQV9T9kZajZosO7fw5w8CHRBOMmslDlrMKPPxntdR1ixTvdYyDmDgmEx3I+iy9rceqVxfo4ImlxBeY83x8oWgC7mAMdWYtLDrOjU3e71LTxwjLGiqwr9+d9/EBXIFHPE1X7OAhn9MKnTIvgeepPQPQKGX42qULUG1UQ6eSF+/g4X/fd65ejA+d0YK/vN2JA/2FCZ/+SBxD3nBKwDKhlh8wM51tWk5/FFa9GgzDoIYvuI0UmcNDxByuRYt7PafakSvm0Z198IRiuCsPt9XePjfUChniCRY3/GkLnt6bXwbesWEffvXy0VldjCk3VOCZAfxnVx9+9qIDrx4ZmdbjePPYKAY9Ydy0tinje5ZSpgQUkcFDJrj0u0Ipfae+cAxj/gjaRP3pGqUcf7h1FX50/VJ8YG0zNs2z4fkDQwV90MlitL9IVdsZjMIiMUEL4FL5raJKizccw4sHh/HK4WHJ25OT+JrWSnSNBcs2OWPUH4HNqIZFXx53izsYFTZ/o1Ocw9M1FhCcBIT5tUYkklxegUMUsAyIXTKltmgF0MK7WxoqtEVVvccDUUTjSWEqnXkGOnjSBV1SnSzGwXO6ZfB0jPpPSg5VMezsdk3q1PPy2UnAxHl+Ny8KtFp1qNCpUKFTonOKJmltPjqK8xdU48lPb8TKlkpJUVvYVBnVZXLwTLRoAcDcagO6xoMpz9WQJyxs+soptJ9sdvdwC+YFddz50WZQQa2QlTQq/ciQD0o5gwsX1QDgKuHpZHPwvNcxju1dLmEsN3GpzqkyoMmiK3pU+ksHh3FsxI9vPL4fV/7ubewu0MU7HiChphMbZPK5cOcp8Gw5MYYeZxA/vn4ptvz3+bjzrLaU7+czAe7EqB8apQz15tS2WbNOiYsW1eDJPf0lDYQgpGevza3m1lTFCrnkOarQKQVXtT5N4NGrJg/gF3/WxWiUXKGs3C1aiSSLrZ1OnMu3lk32+IkAVW1KnfJlNagKzOBJc/CkBRhngxRd6ngHD8tO/AwX/iwt8CxpMKPXGcrbjcayLAY9YdSJHqeBOHhCEw6eVc2VeTlL3jw2ivVtVuj5XKZWq77o9xppCas2qXH7ma0AJkTnfJmYoJXp4BEczAUIPO8cHytr29N4ICq0/pH1WLGTtMi5nmvR4j7zJytoOZZI4pXDw6g2qrG9y4Un9vTnvP2+Pg+WNpjx9GfPwqI6E778yN68gvcf2taL37x6bFZfq8sNFXhmAA9t6wFwchVVKR7e1guLXoULF9ZkfK+YDB7inkh3tkwm8CSSLPqcIVj0KsSTbEr1lGSiiFu00rlsaR363SHszbPfOBpPCo9rsMgWLXcgltXBA3AuJiI2HOMvRNkqRUQsOWOOFdFEsuhgXzHxRBKuINc3ThxVpYgf0TjXljWvhlsUTqWDJ55IoscZFCZoEex8O9bBAS9OjPiF/wOAQi6DSaMoycFDRqQTEauhyOkzg7xoSBw8KgU3SW4mhCz3OIOoMamhUaZa6TVKOSx6Vd4LnGg8iVAsIWTwzJQR8CeDj/59B7779KHpPowMDvR7cMOftuB1R+7CgSc0kcGjVcqhVsiEqYSkda+UxXguYokkBtxhLK43QSZjMKfKINnyM+qLwKRRQKOUw6RRQCWXlSQqkyo5CQ1tsugQjSdTFtBD3rCQXXFiFgct7+l18yG63HKPYZiiz2WEo0M+tNsMwjlXalE9yrsZPKFYSpGCbJDfODqS8rNzqgxotuiKbtE6OODBojoT/njrKrgCUXzuX7sL+nkyjahSlKVH2svzbQs6PuKHXMbgulUNwvMtZjKBJ5lk8ebRUSysM6W4VQnvW9UIVzA26Wf6uf2Dk4Zo9zhDKQJPk0UHhik+F8UTjIJhuElLpKWZtPQQDBrFpCHLgoCSNiYd4EQQkp9VLg4OeOALx3HtygZolfJJW1FHsjiMrHqVIBLmAzeAJHViGzCRD+YLxyRbH8l6uNasEQQiZyCCZJLFmMT0MQI5l+XrdPGG4gjFEoKbBeCm4OpUcs7B4wrCpFFgbrUBI5PkMPY6g+gYDQj5TACXp1iKg0chY2DRqdBk0UEuYwpu9yJOOSkHz4RjJr/1z/4+D279y1Y8s7ewzoFcjPsjQli2zaAGwxTfokXEuEaLDjUmDZRy5qSNSt/a4YQ7GMN3r1mM5Y1m/PC5I1nPTfFEEgcHPFjWWIEqoxqfPm8uookk9uexlzsyxL2v3+soPij+VIMKPNPM/j6PcMKdToHHG47hlcPDuH5lg+QkKA2fpF9oiBwg3aIVjCYQy+JMGeLHd545h59cJHpeOvgR6WIHTzoXLayBUs7g+f35nWzFF+ViMnhiiSR8kXjWDB4AKb3SjiHuMXSnVYwJI94IZAzXXwyUZ3PBjbgH7+Ahi4LiBQYSEDy3mlvgZ1u0/vqVo3jrWGmTPwbcYcSTbIao12rTQyln8NLBYUQTSWGCFqEyrT+9UMiI9BbeOUQcPIVaQMlGql5kda7QqQrKdZgqpCZoEWpNmrwdPESwNeu4Fq3pGnU8HQx5wtjb557uw8jAIUw5yn7+YFluMiIROhiGgUWvgjcch1LOCK16bbapEXgG3CEkkqxQVZxbbZBs+eGm1miEY6wyqsvaokU+A2JxYcgTxuJ6M0waBY7PsKrgnl43/t+/92a9hhKi8SQO9HuEgGVCY6WupAW+Y9gHe60RWpUcDRVayaw48esjdsCQFqU3jnDXhROjfjRUaKFVydFs1aHXFSo4H45lWRwa8GJJgwmXL63D+9c0Zrh/J2M8wIXeqhUTYnehwb7Hhv1osepS7kNMlYG7JmVrIXv1yAiOjfgFV0I6m+bZUGVU52zT6nUG8akHd+Hv73ZnvU0wGseYPyJ87gBArZCj3qwt2kHl5oViuYzB8iZOTEhv0TKoFfBPMt56lN+8ExeuGLNWUfYWLZK/c0a7FS1W3aRCAXlf2wyp6z2LXp0ysnwyXIFoSj4lOQd7+KDl6/64BXc9eTDj54jAU2/WCm4zZyAGVzCKeJLN6uARgpbzbNMa9BKnUKqTzKjhcpS40F6dkFeTS3h7k18Dni0SeFpt3Gd9snOYFKO+CGwGNWQyBkq5DI2V2oLFoo5RP2RMZv4gIHYw53eNeYMXXIv97EgxHogK09mUchmselVJDh6FjEGNkQvgrq/QlrzfHPaG8cuXjyI0SWbUCwcHoVXKcc78anz3miUY80fwm1eOSd722Igf4VhSOH+Q6xZxFGeDZVkcHiQCz3hhD+QUpmSBx263N9nt9tftdvshu91+0G63f57/usVut79st9uP8X9Xln64px4Pbe+BRinDnCr9SbPMSbG/z4N4kk05AadjNagLDJEjGTyZLVpA9kUTOUlumseF9YkX3cTB0yJxUhbuX6fExrk2PHdgMK/NuLivtZjgMSJkWbJM0QLAT67iHq+DV5oj8SQGJRT5UR+n3M/LUR0tFKHqZFBNOHhKED88/GNpt+nBMNJ5GLFEEr977XjJmQGdaSPSCUq5DHOqDNjMV4LFLVoAJ6KU4lIiCz0iLDVWahGIJgq2iA9ICjzKvG3/U0mvM5iyyBdTa9bknT0iCDxaJXRq+ZSHbs8UwrEEgtEEuseDU5INUQpdOXLMCMFoAokkKzh4gAlBvqlSJ0xjabXqMeAJS1aUSyF90hCxy6e3/IymhX3aDKrSpmiF42AYwKBSpPx+cu2JxBMYD0RRZ9ZgTrVBsgVpuognkvjqo3vx7519eHuSiZFHhryIxJNY2Zy6/Gqo0BYt8Pgj3OaOnG/nVhskr1Fi0V98rSFfdwz70O8O4fiIH3P49iApJ1U+DHnDGA9Esbie2xjUmrVIsoW1DjsDUVgMmcUoIP8st2MjPsyrzl58shnVSLLSTmiWZfGnN46jsVKLK5bWSf68Qi7DdSsb8PqRkaz5aORzv6MreyU7fYQ7odWmQ9d4cetQtyhTZlGdGXIZIzlFi2WR8/og3rynYxLlN8YSSXzt0X0Ft+ak827HOOZU6VFt0uQlZI/4wqjQKTNEPJtBhbFANK81J3FAWyQdPDEcGfLh+Igfm4+OZtwfCX8nIcsA5+CZcBZlOp8Abl3cbNHlHbQ84RRKFYyMGm4KLrd20AqOIdJaJ8VmxygaKrQp7VBtNgPn1i/iPJQ+pr7Fqi/YwdM1HkRDpVZSjFUrCnMwv3WMOw/3leCKTGfcPyHwANzrOprjOc5FvyuEWrMGCt5V2FSpK7kz4Om9A/jtq8fwxYf3ZBXkk0kWLx4cxnkLqqBVybG8qQLXrWzAA1u7JcX3fXyhjIiRVoMaLVYd9vS4cx7LsDcCVzAGpZzB1k4nzeHhKYeDJw7gyw6HYxGADQA+bbfbFwH4bwCvOhyOeQBe5f9/SvLCgSG8ckg6TyUXgUgcT+7uxxVL67G43jxt0yMACBVo0jstRbVRXZCt280vYqRatIDsAk8Pv8BY32aFQsakPC/H+WpfektJOpcvqUOvM5SXHZVcGNur9BgoImQ5WyuaGItOiXHRwlYp5xYvUouJEV8Y1UY1zFolqozqskzSGvNP2IpJX2++Y9ulIGKV1aCCVa8S7Phi+lyhoi/gYtJHpIux1xoRS7CQMRMZAgSLTllSG9SEmMj9XjJ9ptDHM+gJQa2QpQidlSWKT+UgHEtgyBtGiyXzeQXIqNDCBB4T36IVjSeLqszNNsTvr8Nlmm5TLkhFs8eZ/f060ao08d4kmwZxZbPVRgSQ8l6j0gWedpsBDJM5PTB9QW8zqEtqC/WGYjCoFcImsr5CCxkz4RYlon+tWYM5VdICxnTxz209ODrMtQJNFkApBCzzE7QIjZVaOAPRopx2ZENNHJNzqgzoGA1kLPLFeSBiQWPUFxHcqa8dGUHHaABz+TYJKSdVPhzkr/Nk5D3ZlBaSIeYMRFMClgGuiKBXyfMS9aPxJLrGg5hXbcx6G5IfJVUQ2d7lwq4eNz52druwEZPitg0tkMkY/OwFh+T3yXOXK38r/XNHaLHqS3LwmPk1kFYlx58/uDrDiUQmg+XKR0r/rIvhWrS41+LggBcP7+jFP7f2FHW8ACcSbe904gzeLd5q06PHmTv3MFvOjUWvEoSbySDu3YosGTwv8/uJIW84Y70x4AnDolcJbdQA5/aYyAaSfu4AYGmjOe9R6UOCwJPq4DFpOBcVcfCQdqbhLAHAsUQSW06M4+z5VSnjyEnhrHOs8HNr+nSvNqsO3WPBgjb2ZHBHNmpM+a1/fOGYMLW3lMmEYoJRrj3OKnqM1UZ11ud4MsiIdEJjpRb9JRoKTvAOqBcODuHHLxyRvM2uHhdGfRFcsrhW+NrZ86oQjiUlXbF7+zwwahQpr8vKpgrs6nHlfG2Je+eqZfUY9UXQMUUDIWYbJQs8Dodj0OFw7OL/7QNwGEADgGsA/I2/2d8AXFvq75qpvHRoCF96ZM+kPc/pPLNvAIFoAresb0KzRYcBd3jaNkX7ej1o4QM1s7GyuQJ7+zx5V3FdwRj0/AQpMZM6eJycnbCxUouGSm2K0ryr2yXY93Jx0aIayGUMnjsweZsWuTCuaKzAkCdc8AQOUp3M1aJVybc9xBJJOIZ8OHMO506SspWOiBYQc6r0ZdlckGq3zaAWcjZKaV9yi9rvsuUKEGGmlDBPgHuO9Cq5ZG852WS0WvUZol+lrrQWLTIinVRRGiq4BUmh2RUD7jAaKrQpixuzTim4oKaLfncILAs0W7WS368zaTAeiOb1efeKHDwkWPN0CFoWi3TlGl9cLkhFM5cVm9jqUxw8/Pu9RbT5I+JqsZkJ2ehxBqGSy4RNAmn5EZ/zWJbFiC+csqCvMqpLzuARP2aVQoY6s1bY+JLqdZ1ZI2RM5HJosSyLh7b1FCQoFIM7GMUvXz6KM9qtuGFVA146OJzz87m7x41qfjqZmFJGpR/lW/8W8A6eOdV6hGKJFDcqy3J5IMTlQ87D8UQSzmAUG9otaKzU4qGtPQjFEphTzb2/ihZ4BrxgGGBhHS/wmLjHV8jrIW6JEGPW5lco6BrnRseTXDopcgWE/+mN47DqVbhxdeaQCzFNFh0+elYbHtvdLwh4Yshz5w3HcSxLcYjcJt292WrVwRWMFXVtcgejKW1VFy6qybj/TfNs0KvkeC5H+3y6W0+MOL9xP+9EeWcSF1su9vd7EIgmcEY7tx5rs+oRn6QolW1SlVWYNDv5eYmcu8Tvt4kR5FxcAnmvbOtMdWINecJCCDDJi3IFoiKXdg6Bp8GMPlcoL/f2IB8yny5mGTVKdI0FEYol0FSpFb6fzcGzu8cNfySOc+bbUr5ONvGTZR5JMeoLpwhZrTY9fJF43iHXLMuiU2Jwh5hakzovgWfLiXHEkyzqzZqScs3EkCgM8fujxqTO6ZLKBRHjCE0WHcb8xQn8hOMjfqxqrsSHz2jB/73ZgQe3ZraEPn9gCCq5DOcvqBa+toR350jl6uzv82BZoznFvbeiqQIjvkhON/khXuD5MC8oi9u0ntzTX5Yi+WykrBk8dru9FcBKAFsB1DgcDnIWHwKQmdx7ivDhM1rhDceFsOR8YFkWD27twbxqA1Y1V6LJokUiyQqBrCebvX1uIRgvGxvarYjGk9g9iV2OkG10uCkPB09jpRYKuSwldHHQE0K/O4Q1LZZJf3elXoUz51jx/P7J27RGfNyFbFmjGXE+qK4QhFa0HC1a5ER9bNgPVzCGs+dXQa2QSTp4RkULCK56HCjZcjjRN64WcjZKE3gmNvQ2g3QeBtkMjvgiJU0X6BoPoMWqTxFICGSTkd6eBZSec0NGpJPf21Ckg2fAE0JdReoGq1KnnHYHT7YqLoGEK+YzmlPcoqVXcULb6TAqXfwaHhjIrzJ6MmBZVnCg9bmCWUXr9LBhAMI0wGbR4pe42MqZMQBw4lOjRSu0ggHIcMz4I3GEY8kMB48zEC04q4XgC8dTXEsAMq41ACfwkBDOXFlo3eNBfP2x/fhnAWuAYvj1K8fgDcXwP1ctwtXLG+CPxPF6jumbe3rdWNFUkXHuLNaNCHAOVB0vxAEQ3DfiRbQ/EkcknhTcLMQt6gxyWXBVRjXOs1cLC3PyHHNCeOECz4EBD9psekFcruPPXYWMt3cGIhkOHgAw61R5OXiODXOPP91JKobktqSvMQ4PevG6YxR3bGyFVpXbnQwAnzpvLmwGNb779MGMtUGvMyicg3d0S7dp9TqDMKgVGe3z5HMubnf55UuOrJkZYtzBWEqmjBQapRwXLKzBiweHs7pkRnMEBZu0CmEC3gF+g3hsxF90+OwWXhza0M6tKdv4FqJcEwOzCVDEGZ2PyEDEGLF4opDLYFArcHTYh319HtyxsRUmjQLb01rtBj1h4f2tVshhUCtSHDzZnjtgovVlfx5tWsMeTlRPDws3aZWCkNFYqROy0bK1VW4+OgK5jMGZc1MFHoteBaNGUXC2WzyRxHggmtKKRoSafO/LHYzBF47njHrI18Hz1rFR6FVyXLGsrqgCsRTkPWQVtYxWGzUY82fP78pGJJ7AsC+c4eABijv/E06MBjC32oD/uWoxzpprw0+eP5JyPWZZFi8cGMJZ82wprZptNj10KnlGQSwST+DIkDdjH0rai3PtO48M+dBYqcWyRjOqjWps5YOWd3Y78fl/7cEnHth5WjjK0ymbwGO32w0A/gPgCw6HI+WVczgcLIBTtilueVMFNs614i9vdea9kX1waw/29Xlw51ltYBim5NF1W46P4YH3sofq5WLEF8agJ4zlOdqzAGBNqwUMA2ztzC/EyhWMSooe4l5jKbqdAWGh0WTRCRXoHV0u/jjyi3O6ZHEtusaDk6q3I74ILDqV0JJQqAqfLUxaDKmKE2V5Ya2Rm0yTtpBI8AITmSAxt9oATyhWUt4EwC0odSq5sACu1KlKyuAhIcuVehWqjFkcPPxjY1mUJFx2jQUk27OACQdPesAywIkogWiiaHFJPCKd3J9WKS+46j3gDmWMvK3QcpuGYjeo5aA3SxWXUCtskiZ/vKkZPNx7bLJxuJNxdNiH837+RskOsKmECJ11Zg0OzSAHz5g/Cn8kDnsN18KY7TUUwoYncfCYtUpY9Sq81zFe1v52qZBvkulCPhsTm6GJBb3NoEIiyRYtknpDMZjSJvxwAg/3PBHnR41JM5ELNJp980CcFFPZytU5FsA/3uvGLeubsbDOhDPmWGEzqPH0Puk2LVcgis6xQEb+DgChmts1HsDf3+3CeT9/I8MpkA3HkA/zaoxClZXk54gFMLLZnM+7Wci1RlxoOG/BRN4fEUVUCm48eKEBoIcGvEL+DsBlnKkUsrwzNFiW5Vu0MjfHZq0irylax0Z8YBjpqTwEm1HawfPw9l6oFDLctqE1r+M1qBX4f5fMx64eN57el+qG6XEGsbrVAptBjZ1d0q1QPXz2WrrwR66zE9duFn9/rxsPbO2e9HOf7uDJxuVLa+EMRCWn3SSSLMZzCTx8ixbLstjf7xGcLMW6eJ7bP4QVTRWC+2YyoYBlWa5FK21EOjDhnCHh9rkY5YsmNWn3Y9Yq8cphTrC9eFEN1rZasC1D4EktGJFi3YgvDL1ojSfFkvr8BZ5B74SQJEY8Ga3JooNBrYBeJc9aCHrz6BhWNVekXGMALiy/vYhJWuP8wBCxONYqvG/zO29ky3UUU2PiBJVoPLcw8ObRMZwxx4oW3v1VrMtGDHGBpbRomdTc56OAHFSAW3uzLARBHphY8xUbtOwMROEMRDG32gC5jME1K+rhDceFITgAdy3sd4dw0aJUb4dcxmBxvSnjPXh40IdYgsWyhtR96MI6E1QKGfb0Zm/rPDzoxYJaExiGwYZ2q7BO+cGzh6FVynF8xI8Hi9wfz2bKIvDY7XYlOHHnQYfD8Rj/5WG73V7Hf78OQO65jrOcT54zFyO+CB7b1T/pbTtG/fjBs4exaZ4NH1jD2XGLtSYTfvDcYXzriQN48eBQwT+7r5f7oC1Pm7SRjlmrxOJ6U94p5a5gTFL0yNWixbIsuseCwsa62aKDMxCFLxzDzm4XtEq5YMOeDDLu/aVJ8pGIY4aE4BYqRuQj8JAwPfLcza81otWmy7i4jQciSLJIcfAA3MkynkjirWOjRQkWY/6IYPkF+EVBiRk8ChkDvUrOB55mjsnsHAsIlfliKwUxfkx8q01ahGiy6PCz9y3DBze0ZHyPbFSLyeHpdQbR6woKDiFAPF44/88oCQwVBywD3AYkyQK+aRwn3j0ehEYpy2rpJou7fDZJYoHHoCYOntJatDY7RtE5FsALBwo/p50siAvurLk2fgLEzGhLIxu0s+dnBtWLkcrgIW7D9OrmnWe14XXHKH7z6uTV/HzpGc8UeOZUGRCOJYU8NLJxEG/6SPU2V5vWoCeUVXDxSjl4rDqM+SMIRuMY8oZhUCtg1HDBpEo5k7NQQDJFcrl8usYCJU0uuf+dTsgZBp+7YB4AbqF8xdJavHp4RLI9fA+fq5c+QQvgNqMquQw/fO4w/ufJg+gcC2Q4BbJxdNiHBSJB3apXwaxVpjzXpCDRUKmFXiUXJjaSr1cZ1Tij3QaVQiaIh4QmS2GTnFyBKPrdISF/B+DO1XXm/KcA+iJxxBJsxmQkgBPjSUEjF8dG/Gi26HLmAxrVCmiV8pSNaDLJ4vkDgzh3fhXMkzhgxLxvdRMW1Znwm1eOCl9jWRbd40G0WHRY01KJ7RIOnmSSxcEBD9qrMje3E2Hj3PF1jAXgDsYw6ovkvIYnktw0PnOONRDhXHs1dCq5ZPs8Wf9kmwRl1ioRT7JwB2M4OuzDtSsbYNGr8PaxwgWe4yN+HBr04url9cLXbAYVDOrsrhJvmHOmSV0zF9WZsKTBhN++eixlstCrh4fxn7RBE0QESBeyjBouv67VqsPcagPWtlnQMRoQBMFQNAF3MJYy2WpC4JEWnsSYdUq0WHW4f0sXPv6PHfjOUwezCixDnlDKiHSCWKghTpBqkwbDEsLGc/sHsb/fg7PnSQ9vaS1C4CHXA/F7pLGSc4Hm6+Ah55fWLIVDYKLAlesa0z0eQI8ziE3zqgSHdzlyeAQHjz7VwQPk56gWI4xIF7VolergIddCIu6TIsIukctmWyd3TdzQbs34+cX1Zhwa8Ka4kfaTHNi065VKIcPSBnNWB084lkDHqB+L6rhr0vp2C0Z8Efxp8wns6nHjf65ahE3zbPjly0dL6lqYjZRjihYD4K8ADjscjl+KvvUUgA/z//4wgCdL/V0zmY1zrVjaYMafN5/IaaGLJZL44sN7oFbK8PMblwtVsDqzNiNQOF+6xwM4OOCFSiHDVx/dV/Co7319bsgYpCyQsrG+zYrdPW5hI9M1FsD7//yu5MI1e4sWPw5SYuPtCsbgi8SFhUZTJVGaQ9jR7cSKpooMy2g2as0aLGs0C4F12SA91eSiWejz5w7GoFHKclqridiwtdMJm4HLrWm16dHrDKW8X0bTrLvkBPrYrj5c+bu3cdtft+Gvb3cWdHzkfsWLV4u+RAdPMIYKnQoMw8BmUCMcS8KfJlZ0jQcEV1ghoogYEtScq9Jy45omyYqfMC2sCCHrgfe6IWMY3LC6MeXrDRXaghxew16uelKf1qJFPhfTOSqduCekWt+AiXDFfDZJnlAMOpUcSrkMOhXJ4ClNvDrItzxtPjpa0v1MJeT1O2ueDYkkW/JEl3JBFs1kKmI2YWEig2eiKnvFsnrcddWijFaTT507B+9b3Yhfv3IsY8NSDO5gFN5wXELgSXXMkAW2eEEvtLr4sn9+vvHYfnzs7zskv+cNxTIqys2WiWvNkCcsLPAVchlarbmz0IiDp2MskPX6/7l/7cbH/rEz633kwhuO4dGdfbhyeV2Kk+nqFfWIxJOS17g9Pdx1XWpwgkzGYEmDCbVmDe7+4GqYtcq82hG6xgIY80exsC5V+J5bbUgRwMiG1GZQw2KYCJQXO3i0KjkuWliDVc2pLWQtFn3OYPB0iNU/ff1Sa8pf4CHjrSVbtES5L7k4PuzPOUEL4J6rCxZW49l9g8IaalePC8PeCK5YJj05KxtyGYMrl9fhxGhAWEt5QhOtJ2taK9HrDGEk7XXdyf++ixZmpiZolHLUmTWCQLyze6JqvitHMDJxOOXj4NEo5Th/QTVePDCU0aZ1eJA7f7Zkud4TUXZrpxPxJItljWacOceKt4+PFewsfHrvABgGuFL0vDMMwxXesjhBcgUZy2QMvn3FIgx6wvi/NzsAcJPMPvHATvz8pdRA7BFfBCaNIkMMJMXPixbVgGEYrOXDyMlENOLErDVlOnhGs2QDpfP5C+ZhQa0RHaMBPLi1Gz95Xjogl2sFy8znIw4ei14luIWqjGrBlQRwouvnHtqNTz24C0sbzLhpXbPk7+CmM4YKKoxIiWPCqPQ8heHOsSBkDCcmZ4M8x7nOIW8enRj/3sgX8YrN4Xl0Zx9uv28bQtHERAaPaM0+l88pI4HC+ULW3uIWrSqDGga1IuU8BHB7snycnORaSNpz2216mDSKFBFmexe312mVaINb2mBGiBdmCHv7PLAZVBl5cQAXtLy/34NYIolwLIG7njwgHOfRYR+S7ET+GhGUfvaiA/NrDLhxdSO+feUiBKIJ/Orloxn3fSpTDgfPRgC3ATjfbrfv4f9cDuDHAC6y2+3HAFzI//+UhWEYfOrcOegaD+Lx3dldPH98/QT29nnwo+uWptgz5TLOHVBMhe95vsJ974fXIpZI4gsP7ymoT3Nvnwfza4zCxiwXG9qtiMST2MsvaH/76jFs63Ti/i1dGbd1icZmilEr5NAoZZKLJqKskws8WXQfGfLi0IAXa/NszyJctLAGe3rdGYscMaPeMKqMapg0ChjUioInaTkD0ZzuHWBi4egJxYR2ojarHtFEMkVQGklbQNSZNNCp5HhkRx/8kTharTo8tSf35BQpxtJsz/lk8GzvcuKbj++XXDhx4h332k4ER07cXzSeRL8rhA3tVshlTNGVglwTtCaDvPdcAenFebYNWziWwMM7enHxopqMBU5DpbagCo3UiHTxsRXjLhr2hvGxv+/AQ9t6UiqFhdIr0R4jxqBWwKhW5JVj4QnFhMW9nj+PlOrgOcBv3LZ2OEtu95oqXEFO2FrZxJ2XpKb2vXZkGN975tCUHsdD23rwl7c6hP93jwegkHEbhFyFA28o08Fj0atwx8a2DOGPYRj88LqlOHOOFf/92D7JgNd0PKEYvvCv3ZIV9mxBr0RYIoLBIP8ZSmnRytLqQkgmWezsdqFzLCC5efCGYymtBkCqi1accwFM5AIFo3H8+pWj+MGzE69nOJbAoUEvqoxq4byXTjSexOFBLw4PenGsCBHwke29CEQTuOPMtpSvr2yqREOFVnKa1u5eN+bXGLO2bPzrY2fgja+ch0uX1KImz0BR8nsuFk1FATKHAYiFHIso7F4QfvjX79c3rcD/fWhNyn2JnVT5QIRgcYsWwBV4Br35natJxVxK4KnIYxpjPJFEx5gfc3NM0CLcsq4ZnlAMz/MOlmf3D0KlSA0izRfymA8Ocs+B+DO1hogD3anCzDN7B6BWyHDhokyBB+Cce8TBs7vHBZOGa8HZ2Z1d4HGHJs8hFHPF0jqMB6IZm8ktJ8aglDNZ2/CJALLlBHc+WdpgxllzbRjxRQoKUmVZFk/vHcCGNmuG66XVqs862UkQF7K4Xte3W3H50lrcvfkEdnY7+ewPFsPecIqYNeKVdtuYBIGnVnh8GqUMW/nnaYB3l0u1aOUr8Fy/qhH/+Mh6vPylc3Dnxja8cng4Y33sj8ThC8ezOHi480mTSDCoMWmE54ZlWdx8z3t4bv8gvnTRfDz2qTOzHlebTQ+WlXaYZvv8T6yRM1+3Qhw89RXSI9LFjwnIbFHvdQbx0LYePLStB4/t7keTRYtWq04yozEazyx8ShFPJPHzFx14wzGK7zx1EOP+CLRKecqerN1mgFGtECYe58vRYT/UClnK9YxhGPzv1YuxrcuJTz+4C1F+T3f5b9/C+//87qRuzuMjfmiUMqHtSyZjsKK5MmU63vYuJ9a2WiQLiEv5ooM4t3BfnxtLG8ySt1/RXIFIPImDA158/l+78bd3u/G1/+xDLJHEEV4UJgJPu00Pm0ENlgW+ftlCKOQyzK8x4rYNLXhwazce29V32oxRL8cUrbcdDgfjcDiWORyOFfyf5xwOx7jD4bjA4XDMczgcFzocjvz8v7OYixfXYlVzBb79xAEckOhxDccS+OvbHbh0cS0uW5pZrWkW5c0UwvP7B7G80Yyz5tnwvWuWYFunE398/XheP8uyLPb1uXOORxezTsjhcaJ7PIAn9w5ApZDh0Z19KQtpzrIbyzqVK1tVjJzoxS1aAPDU3gEkWWB16+QBy2IuWswtYkhfczosy2KUz7whtu7CHTzSTiUx4vBBEgjcKjGZRgjKM3AnY5mMwZcumo+vXboAr3zpHNx+ZiuODPkKdgqM+aMpLVqVuompXtn4x7vdeHBrj2RooFsk3lVJbLZ6nEEkWW6zVmvSlG4FzZFrkI2JFq3M439+/yAu+MVmyWrFU3sH4A7G8KEzWjO+11ChhSsYy1twmJjGk9miBRTnLnrr2BheOjSMrz+2Hxt+9GrKxj5fWJYVchhyUZPnqHRPKCYsTvWkRasAUcYXjqWcP0JRrrqzrtWCKD9mdSbi4sXdJosWRo1C2GyKeXrvIP76dmfO0cCl8ufNJ/DrV44Jn+euMe611SjlaKjUZnVEeMMTzqt8UClk+NMHV8OoUeL3r+W+xkTjSXzygZ14Ys8APvaPHRnXxGwh3xa9ChU6ruUnHEvgH+91Y2GdKSUImpxzpMLdAS5jwRuOI8lmjnZPJln4I9IhywC3+B/2hlMKMHOq9egeD+KCX2zGr185hnve6hSqjwcHvIglWFy3sgEAcHw089x8dJjLFwC480shJJIs/vZuF9a0VAoLY4JMxmDTPFtGnkEyyWJvrxsr08aji1EpZEILLRcomtv6z7Isnto7gHWtlgzBen6NEWP+qHANGPNHIGO417JSP+HgGfNxGxcSAqyUyzLee00iJ1U+HBzwot6syRBnas0aDHsyW4elEDIvJDJ4TFolInGuchxLJCWnqXU7g4gl2EkdPABXJGu16vDQtl4kk1wQ6dnzqlKCSPOFuJZI/pf4M7W43gSNUiZkFwLce+m5A0M4z14NQxbhr1U0Kn1ntwurWyqxorkip8BDXt8K7eQtWgDXpqVVZrZpvXtiHCubKrMWG4nr7p3jYzBrlWis1GIjH977do4cHpZlcf87nYL74eCAFx1jAVy9oj7jtu02PfpdIcnslXxGkX/9soVIsCze/+f3EIkl8ZGz2pBkU0OIR3xhyTa0OrMGNoMaq/jPrUohw4qmCmzvcsIdjOL7zx6CVilPyRy0khYtr/R95uIDa5sQT7L4d5ojc0g0RTAdct4Ut/xUG9UY8XGftROjARwZ8uF/rlqEz10wL+e1Jdt0xoe392D5/76ENxyZ63ahZTdNZGuzcefofD7vXePBnK5wAEImZ/r14/vPHsLXH9uPrz+2H7t73Lh4US0YhoFOxYWWix08P3r+MK763duTHtPLh4Yx5A1jfZsFD+/oxTP7BjPOZzIZg2VNZuztLWyYw+4eF5Y2mKFIex1uWN2I7127BK8eGcEH/u9dvO/uLQC499NkoeonRv1otxlSpl2tbKrA0WEf/JE4Bj0h9LlCgsicTrtND41Shv193Odx2BvG8RF/1pgQ0gL22Yd24cWDw7hiaR06xwJ4ZEcvDg16oVPJhes3wzB4/5pGXL28HufaJ1oDv3jhfCxtMONLj+zF9X/aIpgUTmXKOkXrdEcuY3D3batRqVPio3/bkbExev7AILzhOD50ZmZeCMAtbApt0epzBbG3zyMIRtevasDVy+vxm1ePSYpMmT8fgisYm3SCFsGsU2JhLZfD86c3TkAuY/Cz9y2DJxRLqSJ6QjGwLCQdPAAn8EgulMZTF/1mnRImjQJvHRsDwyDnglUKe40RTRYtXj4knePhDsYQS7DChbG+QitUSfIlm1NJjFohh5FfUNmJgyct0BCQXkB8dFM7PnnuHGiUclyxrB4yBpIV22zEEkk4A9G0DJ7cAgPLskKYttR70hWMwswv5sj9ijdbpJLSatOjoVJbdFDukSEfqo1qQawpBOKqSs8aYlkWv+M3p9vSAsNZlsXftnRhfo1BmKwhptCsrH7BwSPdopWP9T8d8lw+8JH1WFhnxPefPZzXaFYx44EogtFETgcPwC3w8nXwmAWBhzh48hN4wrEErvrd2/jyv/cKXzs85EWS5cZeGtQKvC6x0JsJkCB5huGCA6VGpZPrwH3vdE3NMQSi6BoPwh+JC5s5bgTshEie3cETz2hVmgyzVolb1jXj1SPDWbNSWJbFNx7fjy0nxvGNyxegQqvEnfdvTxHPszl4GIbhHDMjfvzlrQ70uUL49pULUyp7RrUCGqUsqx1+j8gqnu7U80fjYFlkhCxX6JQwqhXoHAtgxBdJ2dwsqjMjkWRhNajwx1tXgWEmhBriZLphFdfOeWIk8zkhwl+LVYen9w4UVEF89fAwep0h3LGxTfL7rTY9xvzRlByeHmcQnlAMy/O8rlcbNTldrgA3PevYiB9XLc8sTglOEv79P+aPwKJXQy5jUhw8o/4IbEZV1rZQAML7Np/1C8BVgRfVZxao6kwaRPlr32SQ21gkMnjIee2xXf248JebsfK7L+N/nz6Y0mJOChG5RqQTZDIGN61rxrZOJx7d1YdBTxhXLKud9OeksBnUqDVphOdd/JlSymVY3liRMklra+c4Rn0RXCnxGhJarNz7qd8dwtFhP1a3VGJ1cyUOD3qzivbkucg3Q0irkuP8hdV4bv+QIEp7gjHs7/fgzLmZmR0EIvKeGA0I1f4miw6tVl3OoOV9fR585+lDeP/d72JntxNP7x2AQsbgsiWZz3urTY9kFldJegFOiiaLDh8/ux0sy+K3t6zEWfM4AUrsBBnxRSTFmC9fbMcTnz4zZTO+rtWCQwNefOjebegYDeCeD61JLdbpVYjEkwhEE3k5eMS0VxlwRrsV/9rekzLwgQg8tRIuI+J8bBS1N1Ub1QhGE/BH4kJLdT6ONCEcWSTwDHnC+P4zhxFLsPjKv/dmhBaP+sOo5EPUxbRYdfBH4nkNJEkfoCGFQa1AlVGd4Qo6PuLHufYqbPnv87Hlv8/HNy9fKHwv3eH99rExdI4FJJ29Yv72bhcaKrT4+0fWYV2rBUPesGQe2PLGChwe9Obd0haJJ3BgwJt133TbhhZ864qF2N3jxqZ5VXj2c2fhk+fOwdvHx3K6eI6P+IX4CMLK5gokWc6JQwqn67IIPAq5DIvqTIKD5543O8AwDK5f2Sh5+3qzBtVGNXqdIXzinDn4/S0rsaalEr955Rh297phrzWmiE1fvXQBfnvzypTrjFmnxOOf2oifvm8Zep0hfPi+bWWZeDaToQJPmak2avDX29fCG47hv/6+I+WD+NDWXrRadThDInQK4BbirmBMMjAxGySAlFyoGIbBd69ZDKtBhS8+vGfSE8HeHEGM2djQbsWObhf+s6sPN61twtXL6zG32pAyxWuy4OFsDp7u8SBqTOqU3uRmqw6JJAt7jbHgzQjDMLhoYS3eOTEuuTghVZUqkcCTz9QgMdwmb3IBgtxmPu/gqTaqoVPJU6oXI94wjBK92YQqoxpnzrHhqQI2CWTxKr74k2PJ1r7UPR4UKrpSrjJPaELUshkzR78S0arNqkdjgW1NYo4O+yRHoOdDRZY2qM1HR3Fo0AsZkzl6cVePGwcHvPjQGa2Sm5BWYVx0fgLPgDuECp0yoyJJ2pmKyUHqc4VQY1LjrHk2/L9LFgCAYOHOl3SnXDbyzbHwigQeHV+dD0byW4T89e1OdI0H8drhESFAnGxYVjRzEwrfODIyLbbaeCKJ3792LOs5QRwkv7jejCND3oxcCSLwPLd/UPK5dAej+N2rx4qe9iZulXrDwT1PXeMBYfHclMMZ6g3HUpwx+XLbGS2QMwz+tkV6MsWfNp/Aozv78PkL5uFjZ8/BfXesQyiawJ33bxes973OIKx6laSTYG6VAYcGvfjD6ydw6eJanDkndcQuwzBY1pjdVbC71yW8D9ODj6Xa0sh9Nll02NXjRiLJprQnXLakFo9/6kw89emzcPnSOqxrtQjn4N09LtSbNbDXGmHVqyRbPw8OeGFQK/DJc7g27nym2BDu39KFerMGlyyWbqmZmPwz8Rof4x9zvufOGhNXgc811e+pPQOQyxhcLuE+XsQ7SYgoI858q9SntmjZsrS3EJbUm9Fm0+OBranvrS0nxvBM2sSwQCSOzrGAZH5gbdqo9GSSxT1vdkgKWVKhpgRyHfnG4/uhlMtw3coG/G1LF879+et4cg/Xjl+o0/R9qxuhlDO468mDUMlluEAiDydfFtebhOc9/TO1ttWCgwNeQYB5Zt8gtHwGTjba+IEGT/BRA6taKrGqpRJJFkLV+9CAF5t++prgYiMh1Plk8BCuX9kAZyCKNxycIPBe5zhYFhmfdTFm0f0vEU3b2TjXhvc6nFkdyc/sG4BSzsBqUOGDf9mGf+/sw9nzqySd11KiA2HUF4FKIZv0nPmli+Zj6zcuxHn2amGCJikcsiybNRCZcyWlXpPXtlmQZLlzyO9FghHBIhHEWwg3r29GrzOU4oAi1zupDB6yBk9x8PAFyRFfBG84RjC32pDxOKQwa5Ww6FV4wzEqtJvf9dQBRBNJ3POhNfBH4vjyI3tTzksj3ojk42yVKJhK4Q5G4QnF8mr7bxW1KwLceqDHGcSCWhPqK7Sor9CmCAvijEZvOIbj/Ofj5cPZc0CPDvvwXocTH9zQArVCjt/cvAKVOqXkc7+8qQLxJCtZSJLi8KAP0XgSqyQmKRI+uqkdb331PPzlQ2tQoVPh1vUtsBmyu3hC0QT63SEhf4dA9pC7e9zY0eWCXiVPyWpLZ0kDF7Q85o/gwa09uHp5veCaSodhGHz4zFZ85Kw2fO1SOxiGwdcuW4ARXwR7e915D96RyRi8f00TXv/KOXj4Y2cIDtZTFSrwTAEL60z4zU0rsa/PI2QvHB/xY1uXEzeta85avRIHCufLc/sHsbjelBJKV6FT4afvW45jI378cpJQqb29bqgUsoI20evbLYJ99ePnzAHDMPjg+mbs7fNgfx+30CBtMRU5HDyeUKbg0uMMoMWSeuIlz8vaAtuzCBcuqkY0zk2gSic91LjezI1GLCT0jWvTmHxhIwg8vIOHYRi0pPUNj+YYEUq4enk9useD2NeX3yZBnIdAIFO9slU3xZPSeiTEDJcog8eqV0PGcPZ7QudYAGatEpV6FRordRjyhicdN5lOIsni2IhPcgR6PmiUcmiV8gwR5U9vnECtSYOrltdjT687RTh48L1uGNUKod0iHXIB6nHm1+s96AlnjEgHJhaq7iIcPP2ukLB4WtZohk4lx7sFtjD1ZmmPSafWzPXWp4sW6biDYoGHW/zm03s+7A3jD68fR0OFFqFYQqj8HOz3oEKnRL1Zg/Ps1RjwhHF0eOpGUGdjW6cTP3/pKG6/d7uk41Dcnrm43oRwLImOtI3BsDeCCxdWI8GyKSI44cfPH8EvXj6KzY7iwqR393JhuiuaKvC6YwSjvgiC0YSwgBVPIkzHIxE2nA81Jg0uX1qHf+/ozXid44kk7n7jBC5YUI0vXMhNfLLXGvHbW1biyJAPj/LtALlaBOdU6+ELx5FgWXxDVCEVs6HNgoMDHsnXZQ/fntRQoRUW2YSJYOnMx91s0eHIELd4Fjt4ZDIGK5srhcX81Svq0TEawKFBL/+7KvnjNkhmgRwc8GJhnRGXLamDUs7knaPW6wxiy4lx3LK+OcNiTxDaHEQbGyIypVdZs1Fr1iCeZLNOVmRZFk/vG8DGubaU0b0Es1aJJotWaBUa9UeF65hFr0IwmkA4lsCYL5o1v4QgkzH48Bkt2N3jFsRLTzCGTz+4C19+ZG/KNevFg0NgWWBdW+bagITEE4H18JAXP3juMO6VcNI5A1HoVHLJwsryxgosrjfhrqsW4fnPb8LPb1yOZz67CXOqDPj8v/bgL2914NiwDw0V2pwjqsXYDGpcvKgWoVgCZ8+3FfUZJCyuN+HEqB+haALd48GUTRLJ2fnA/72L7nFuIuEFC6tz5i2SteR/dvVBLmOwvLFCeH8TQfUnLxxBrzMkjDonRZTJWtXFnD2/CjaDGo/u7AXAtWdplLKcxUbx87RUJPCcv6Aa/giXj5UOy7J4dt8gzp5XhUc+cYZwPhRPzxLTxj9+KRF2xBdBlUGd04EGcGu7iaIhdx4h7kVvKI5oPJl3O9WaFgvOmV+FX31gRUb2FTCxlgMyp3LlwyWLa2DRq/DQth7ha6QQIdWK1l5lwLxqA9aLPnM1vODSMx7E1k4nzpkvPTVLik+dOwfvdY7j6t+/jbs3n8CLB4fxhQvn46JFNfifKxfjrWNjuEfUhj6SJWuobZIR9wQywS5bkLeYFqs+RTDqd4cQS7BozyIONVTo0O8KcdEXvR6wLKBXyXMOevnHu91QKWT4wFpuonKdWYtnPrcJ37t2ScZtyWcj3/Yi0ha+MofAA3BFIHJt06rk+MQ52V08HWN+sCwyhjBU6FRor9Jjd48L27ucWNVSmfWaBXACjz8Sx11PHUQolsCnzp2T8xg/fd5cfPvKRcJnb22rRRCq8xV4CEaNsujC8WyCCjxTxEWLavDxs9vx4NYePLd/EP/a1gOFjBFs3FIU2v7R6wxiV49bsqJ2zvwq3Lq+Gfe81YGdEqMyCXv7PFhUZ8o7gwEA1rdZhMdCQrauX90IrVIubGCIMySbg8ekVcIbisEbjuHO+7ej7evPou3rz2J7lyvDVUCel2zBe5OxrtUCs1aJJyUW1emJ/CRbIN/pG8kky7tZJl/Y2PRcVoe4Yt1m06WMTOWqE7kv0pcsruU2CXm2aU08RtEULUPuCVNk2le1UZ3xfgzHEgjHksJiTi5jYNGrUsZJih0EjRVaJNn8n1NCjzOIcCxZ0ok4fRz8zm4XtnY68dFNbVjXZsF4ICoIqvFEEq8cHsalS2qzLtTNWiUqdMqU14wQjiXw+9eOYd0PXhFaigbcoYy8CoCzqBo1iqJClvvcQeFzp5TLsLbVgnc7ChN4iGg3WZWt1qxBksWktmdxi5ZcxkCrlOcVkvqTF44gnmBx7+1roVLI8PoRTuQ4OODF4noTGIbBuXbuIl5Mm9bXH9uHLz2yp+CfI7zXMQ4Zw22YP/XArowKsTMQhYUXOkmbyiFRdc0ficMfiWNNqwUXLKjBP7f1pIjHB/o9eHhHL/+7JndhHR70YsMPX4VjaCLnZXePC/NrjLhiaR2ODvvxDh9A2poWVC9VOOAcPMVtLu/Y2ApfJI5H+eMXjqfXDW84jhtWN6ZshM6zV2NFUwXuf6cLySQrTHGTgiwe/2tTW9aq3vp2K5IssLMr1cUTiiZweNCHFU0VmFNtyHDUCKPhNZmf8WarDkTvrckxcviyJXVQyBjc904X+lwhYeFNwpjFJJIsDg96sbjeDLNOiXPmV+OZfYM53TKEp3nHyjUrpAVnYMKFJ97YnBjxo9qozls4IBXxbOfoPb1u9DpDWTfFAOe8Ia1oY/xGGJhwGLiCUb5Fa/JN6PvWNMGoVuC+d7iJkb959RjcoRgi8aSwEWVZFve904U5VXqcOSfTFV2X5uAhbk2pXA9nICoZsAxwm59nP7cJd2xsE9ZJi+pN+Od/bcAVS+vw/WcP4/kDQ3m1Z4m5dT03WeiqHM9pPixuMCPJcoMo0j9TK5oqcP8da9HvDuHSX78FZyCaMjVKCvJ+6hgNYEEtF9Jt1ioxv8aAnT0ubOt0Cm04RAwl1zGpz1Q2lHIZrl1Rj9eOjMAViGLLiTGsbbVktN6IEQejpws8N61twh9eP4FH0s5Hu3rcGPCEccUybgLdvz62AT+6fmnWqWWVehU2zbPhj28cz2j7GvVFcubvSB+zMmVYQbYR6dnQquT4253rsn72xG2FhWbwAFx8wPtWN+LlQ8PCsQ16w7DqVZKCp0WvwstfOiel8Eaek6f2DiAaTxYk8Hx0Uzv+fuc6OANR/Pj5I1hYZ8JHN3HtqDeva8JFi2rwq1eOCtfN0SztbQ1kVPokDp6JwR2TO4zabHqM+CYC30nxpq0qi8BTyRWqXMEYdve4wDDAnWe14fCgVzKmwBeOcRNyl9WlnH8aKrSS748akwa1Jk3WoGVXIJpSjNvV40adWSMZlp0L4uL5nUTO3sSI9MznYGVTJbZ2OuEY9k1akF/Cr5ee3TeIy5bUYl4Rhdz/vmwB2qv02Chx/qdQgWdK+fLFdixvqsDX/rMPj+7qw8WLa3Ke1CcW4tkFnngiiR8/fwRX/u4tnPOz17NapgHgG5cvRLVRjZ8875Bsb/CFuZPQeonqVy4qdCo88emNuOuqxcLXTBolrl1Zjyf29GPIE86rRWs8EMH7734Xbx4dxe1ntuKz583F5y6Yh0+kKbn2WqMwEaYYFHIZbtvQgucPDGVY+tMT+evSqi3vdYxLVogJ3nAMSTb74xTzlUvs+MWNK1K+1mrVo9cZFE7KXG927pPxxCZhIK9Nwu4eN+QyJuWCnMvBw7IstnaMY32bFS3WzPwO0londmfZDGqMikYWd40F0cYvFMl4xr4CR6WTTay9SAcPOUaxiPKnN46jQqfEzeuaJyylvdx7Yme3C95wHBcszN073mLVZ7ia3nCM4MJfbsbPXzqKQCSOu548iEg8wQs80q9npU5V8Jj0RJLFoDucMvJyQ7sVx0f8Gb3quegcD2S0QkoxsUnK7iqMxpMIxRIp9nm9Wj7pFK3dPS48tqsfH9nUBnutEWe0W/HG0RHEEkk4hnyCYFJr1mBhnQmvHh4uqE2r1xnEw9t78fTegaKncL3X6cSSBjN+dP1SvH18DN96/IDwvXgiCW84LgidrbbMUEbSDlJjUuPOja1wBqL4zy7OwcKyLL779CFYdCosbzQLmVe5+L83OzDkDQubmIkw3Uqct4BbVN/POxTEDh5AunDAZfAU3qIFcFXBFU0V+Nu73SnnodePjEAuYzJaCQBOFOoYC+C1IyMYcIezCjyb5lXhB9ctwWfPn5f1969qroRSzqS4DQEukyWRZLGiqZKb7jQSSDm+bC1aQGoekJRFnmDRq3DWPJvwWpKMgzlVeriCsZTzaudYAMFoQmgjunpFPYa84UknlQBcQPfK5oqcYegapRz1Zk2KwHN81F9QMH2N0GIhfQ55ih+mcHGWNjGAc5J0jQfhDcdShBxybRz1ReAKRidt0QK4/Isb1zTh2X2D2HJ8DH9/tws3rW3Gpnk2/OPdbsQSSezqcWF/vwe3S0x8A7hrklzGCKIVcQMdGfJlnM/GA1HJ9qxcqBQy/PbmlXj/mkZE4sm8ApbFnDnXhmc/d1ZO0SwfyPtqT68bA+5Qxmdq07wqPP6pjag2qWHWKgXBPBs6lULYQK9umSiqrW6pxK5uF3724hFUG9VY0mASJthwTkBFzoq9FDesbkQsweLedzpxdNifsz0L4NZyBl5wEo+4ZhgG37t2Cc6aa8M3HtsvTNkCuA2kSiHDRbybqVKvws3rmnMWNX9/yyq02wz4+D92pgj2oyLhshDqKiaGdwhrziLaqaQQv2+LcfAAE2HLpC1vyBMuSBSo4h/L8wcGoVHKJB11udg0rwrPf34TPnRGC35z0wrhtWEYBresb0Y4lsTWTic3FMUXQZWEyKaUy9BUqU1pVZWiazwAhpm8uAVMiJ3kmt45mnuqKym8DbhD2N3rxtwqg+AGf1Vi0Mtju/oRiCYkB3pkY3mTWXKC5Yg3jE0/fR2/EHVt7O5xFZxbCnCi4i3rW/DWsdGMQQYnRvyQMdLPwcrmCvjCXMbdZAX5eTUGQcz99HlzCz5GgOuGeO3L56K9iCEspwNU4JlCVAoZfnfTSoDlKhw3rW3OeXsSKJzLwbOz24W7N5+AUi7DZ8+fh8c/dWbWk41ercCnz5uLbV1OyQkDbx4dQyzBFtX/vaTBDK0qdXP4qXPnggXw0xeOTFh2s4zNNGuVCMe4kbL337EOd121GF+62I4vXTQ/Y2F6zYoGvP6VcyWdEPnyyXPnoNqoxnefPpiy2B/1RaBTyQVXjXCC9oTxr209uOn/3sNXHtkreZ/AhECSz3jQhXWmjAtfq02PeJJFH2/rzHfU5bUr6zHsjWCzRNtZOls7nFhSb0qZ0kE2pVIZML3OEAY8Yaxvt0jmd0iJd1VGtZDBE44lMOAJTTh4+AtpoZO0HEM+MEx+wZXZqNRNTHBxB6N45fAIbl3fDL1aAXuNEVqlXKjsvnZkBEo5g7Pm5a4+tVh06Ba1aCWTLD77z91QKWT450fX48+3rUGPM4jfvXoc3nA86/u2QqeEq0AHz7A3jHiSTVmcnMFXL/JxgAx5wvjiw3vw2K7+vAJYa02TO9qI4CcO2NSpFJOKKndvPgGrXiVc3M+1V6FjNIBXD48gmkim5GpcuawO27tceN/d72JfnmNC//5uF5IsEEuwOQM4sxGOJbCnx431bRbcuKYJHzu7HQ/v6BU+D+Rxk/ZMtUKOGpM6pVJHcqxqjBqcMceKZY1mfOuJA/jqo3vx93e7sa3Lia9cYsf5C2pwaNCbEtyazog3jGf2DUDGcJkSiSSLjjFuWtTKpgrMqTKgsVKLvX0eqOQy4X3XlKVwEEskMeILS7bc5MsdG1vRORbAG0cnFq+vO0axpqVS0j1y2ZI6VBvV+PELR5BIslkFHqVchlvXt+QUILUqOZY3VuC9tPwpYktfwT8noVgCQ6LcFW84d4sWwF27J2u7vXp5PVgWUMgYIQ8kfcQ7kDnG+8KF1TCqFfi/N3NPvzs+4sPhQW9eAkCLVS+0aLEsixMjfskKazaIWynbJK2XDw3j7HlVOR1B5PFt73QiGk8KGTykMn18hLP257sJ/fCZLUiwLO7823ZoVXJ85eL5uGNjK4a8YTx/YAj3vtMFk0aBG1ZJu5vkMgbVRrXIweMS1kpvpLVDOgORrA6eXMhlDH5ywzL89uaV+NjZuVsMpFhcLz0WuBAaKrSo0Cnx8qFhJNnM0HKAe18+97lNeP7zmyYV9YEJ959Y4FnVXAlvOI7tXS589oJ5WN5YgSNDXrAsy7dsF/78LawzYXG9CXdvPgEAkk6sdEwaBZY0mDKeN6Vchj/cugptNj0+/ved2N3jQjLJ4rn9gzhnfmFTysxaJe67Yy0MagXuuH+bIHyO+MJFiSh1Zi0GPETgmRD9ywFp/SeB5sUwp8qAlc0V+M/OfrAsi0FPWHKCVjZMGi70PhxL4ox2a17vsXSqTRp895olGS35Z7RboVbIsNkxCk8ohmgimVUca7XpMyZypdM1FkC9WVvQ54CI551jAZg0iqxisFDMdAUFcaW9yoD2Kj1eScvhYVkW/3ivG8sbzQVloK5oqkT3eDBj7f67147DH4njgXe74Y/EMeILo88Vwsqm4jofrlhaB5bl2mDFnBgNoNmikxwxT8QkpZyZ9Pcq5TKsb7Pg0sW1KXlalPJBBZ4pptmqw+9uWYlb1zfjrLm5qxMAH4iZY+IQyV2550Nr8MWL5k86/eoDa5tQb9bg5y8dzah+v3p4GJU6pTCSsVSaLDp89Kw2PLa7H28cHYFCxgiTo9JZ3VKJpQ1m/PuTZ0hWesXIZcyk45wnQ69W4L8vW4C9fR48xlcpgMx+XlK1eHx3H775xAHYDCq8dGg46wbRVUTvuRiy4Dww4IE/EkcolsjLZnvxolpUG9VCtT4b4VgCe3rdWJ8W7K1SyGBUKyRHoL/HOwnWt1nRbNFh0BtOCYAVxDttuoOH2xz0OINg2YnHVmvWgGGkBZ7Hd/fhG4/vl8x4OTrsQ7NFlzMvYDLEDh5S9djIVwkVchmWNk5UQ147MoL1bdas42MJLVZdyhjVzvEAfJE4PnnOHJw514az5tlw4cIa/PENzt6abaFUoVMVnMFDnsMGkYNnSb0JBrUiw8mQzpYTYzjv52/g2f2D+PR5c/CrD6yY9PeRz8NQjgk7gsCT4uBRIJAjZNkbjuF1xyiuWl4vPN/n8ZXlP7zOPW+LRZNxPnHOHPz0hmXoHg/g6t+/gz+9cSLncQcicfxrey8uXVzLT+Ga2NCN+SP49IO7Jp3Ss7vHjWgiifVt3GfnAr7fm1jABaEzzVotnuxEFvPVJg0YhsEDH13PnSN39eOupw5iUZ0J71/ThPXtFrAscro6Htjag3iSxRcvnI9hbwTbu5zCe3dFcwUYhhGewyaLVggQNGuVMGuVKaIkwF1LwrEk1rQUtwAEgMuX1qHGpBYmhA15wjg86MV5WUJcVQrOTUkEkGztV/myvt2CA/2elBygPb1uNFZyFndSLBALLiSLSCoolQg8dWbNpBvvixfXQq2QYWGdSdgskN8nbtM6NOCFSi4ThGqdSoHPnD8Xrx4ZEVpdpHhqDyfmXZHFoSum1TaR5Tbmj8IbjmeEYOaCXAPTJ38C3GalzxXCWTmmGwHA4gZOkCXiyUQGD3decAxzbo8qiekwUrRY9bhgQTXCsSQ+f8E8WA1qnDu/Gq1WHX776jG8cGAIN61rznl9qDVrMOwNwxOK4cRoANevbEBDhRavH0mtpjv9UVgkRqTnA8MwuHp5fdHuiVIhE/zI+T+baKpXK/IukhHngjiclYg9TRYtPrCmCQtqjfCG4xj0hOEOxrLmLU7GDas4F49Ro5AMy07nq5cuwOeyOPvMWiX+duc6WAwq3PbXbbjnLc7xOFlbmhT1FVrcd8dajPuj+N2rxxGNJ+EKxopy3tRXaDHIhyyTMd9SIcvFYFQroJQzsBlUKYG/hXLDqkY4hn04OODFkCdUkIOHYRjheSmkPSsfNEo51vPu3pG0zMx0Wq16nBj1p1zbA5E4fvvqMUH47xoPCm7byRDaX4mDZyyAtipD1msD+Xy9e2IcrmBMyL65aFFNRjfAuyfGcXzEj9sKcO8AnIMHQEqbVs94EA9t68G6Vgt8kTge2d4rTJIsxsEDAPNrOGHquf2DKV8/PpLdHUqKplIGACnuv2Mdfn/LyqKOjzI5VOA5CZxrr8YPrlua18k310hbANjT50ZDhTYvmzPAVZU/e8E87O114zXRoiaeSOI1xwjOs1cXbKvNxafOm4sqoxrvHB9HhU6Z9US4aV4Vnv7sWVhQW1g4Vilcu6IBK5oq8JMXjggbglFfOOVioVbIYTNwxz+/xogXv3A2mixafPfpQ5IiBGmxKbZysrSBmxjy7ScOCJu7fBaKKoUMH9zQgs1HRyVDPQm7elyIJpKSI78r9Sp0jgWwt9eN/X0eQcR5r2McFr0K86oNaLZwmRTiKVgTAdoTj9lmUGHMHwHLskIFhVQ/VAoZak0ayUlaD23txT+39uCupw5mCJCO4eIDlgkW0QSXPb1uMAywTFQtWdlUgUMDXhwf8ePYiD/rxlRMi5Ubo0o28mQxIRYkvnXFQmGD3ZDNwaNVFtyiRdwh4hYthZyzRL83SdDyf3b2Q6OU4ZUvnoP/d8mCvAJByTjSfBw84pYXvSp3Bs9LB4cRjSdx9YoJd0KrTY82mx77+z3QKuUpzkS5jMH71zbh9a+ci41zrfjLWx052xMf29UHXziO/zq7DWfNtWGzY2IK19+3dOHZ/YO44/7tkn3xhK2d42AYbooJIA7Y5n6GiLtiJ1tjpS5FyBz2plZrTRolvnnFIrz0xbNxy/pm/OzGZZDLGKxoqoBKIcvaphWJJ/DPrd04316Nj2xqg1Ypx1N7B7C7xwWjWiFs5kmbVrqrk7uupH7+yO8q1E4vRsm3v751bAzHhn3YzDt5zsvRBnLL+mbBmj1ZyPdkbGi3IpFksUMkjO3pcQsVUeKoEQsuJGRZSshtqNBCxuTO3yEY1Ap8+8pFKfbyhgot1ApZyuSugwNe2GuNKS0ht29sRYtVh+89c0hy8g8XajyIDe3WvDaCbTZuAqcnGBNlJOQv8CjlMtgMKkkHz1beGbhhEndFtVGDKqNacHORNQr5fBzjQ9LzXbsAwP+7ZAFuP7NVaGGQybhpKpwbiMVtG1py/nydWYNBT0gIJV3ZXIlz7VV45/iYcL1jWZZr0cpTeJqJLK7ncniAyScj5sMVy+pw/aqGlOtMm02Pa1fU47vXLIFKIcMCPtjUMeSDW5TBVijXrKiHQsZw2Y55rEWvXdmQUbASU1+hxb8+tgFVRjV+9PwRqBUyXFjklLKFdSZ8YG0THtrWIwgExQh59WYNxgPc8I6RNNd4qTAMg0qdquSWr6uW1UOlkOHBrd1wBWM5W1SlIOvocyZpASyGc+dz7t5d3blfgw+d0QKrXoUb734XLxwYxO4eF6747Vv45ctHcfM972Hz0VF0jwfyClgGuPwkm0GF7vEJB0+2gGWAWy9plXI8u59zvZDr0EULaxBLsCmDFP7+bjcqdcqCxcelDWYwDLC3d0LE+tUrRyGXMfgdPz783nc6sbPbBaWcKdodwzAMLl9Sh/c6xjHOu/MTSW59nx6wTFDIZfj65QvwmTxbruQypqz7T0oq9JmdYTRbdOhzhrJuXvb1uQUFN1/et7oRzRYdfvHSUeF+d3a74A7GhCkL5cKgVuCrl9gBFO9qmSpkMgZ3XbUIo76I4BKQyrxps+lQa9Lg3tvXwGpQ45uXL4Jj2JcyZYAgtGgV+Vg1Sjnuu30tAOAz/9wNIP/e7FvWN0Mll+FvW7qy3mZrhxMyBlgjkV9Ua9Zg89FRXPOHd3DV79/Gxb96Ey8dHMLWDifWtVogkzGS+R0TEzMmFnTNFh0i8SS++PAeYRJSq+hC2FipzdhMJ5MsDg16YdGr8ODWnpRJCZF4Ap1jXMhjKVToVPCGY0gkWezucWN+tTFlYbWyuQLRRFJ4P+QaH0uY6MvmLvrpFXqAe+x3ntUGGZN9E1uZlg+UD0QkSxeNzmi3omMsIFmBJ+zudWF1i6Ug1wTDMPwmKfv9eiUcPDp17hatp/YOoLFSi5Vp1uRz7ZxAsajeJDnC0qhR4sbVTRgPRLOOmk4mWdy3pQvLGs1YxWfTkClc4VgCD27twfKmCoRj3NhuTxYX1Xsd41hUZxIeV41RA5VcNiHwSHz2Gyu1GHCHkODPs8Ne6cV8e5UBP7xuqSAKapRyrGyqyNpm9/TeQYz5o7hjYxt0KgUuWlSD5/cPYkeXC8uazELx4Ix2G/QqeUYwebNEq+XWDifmVRtKatECgJvXcYLN/Vu68PqRUdSZNZifo63SalDjuhUNMKoVeQkpuVjdUgmFjMFW/pwz7A1jwBMWKqc2gwomjSJV4AnHYFBL54WoFDK02fQ5F/FiPrihBZcumZhsI5MxaK8yCJO7WJbFgQFPhjNBrZDjW1cswvERv+RktQP9XnSOBfIO4CVieud4YGKCVoG5BNVGjeQI8fc6uGLN/OrJz8WL601CmDcRcip0KjAM58gUfz0f7LVGfOfqxSnBuzeuaYJRo8Ali2sndfbWmDQY8oRF4r4Z59mrEYgmsIMP5w5GE4jEk0W1aM0UyPtLJZcJE41K4Vx7NX75/hUpBTqGYfDrm1YK4i0pvhwe8sJTZIsWwJ0P/nDrKnz10gUlHzehzsyJPAtqjbh+VUPe082k+NwF8yCXMbjrqYMAigsyrhMN7xjJEhJcCk0WXcluSLNOiYsW1ghTDmsLPDe32fSYV23Ia/x4oZB1wb/5Y8v2/LVXGfDEZzbCXmvEJx7Yhffd/S5iCRZ3f3A12m0GfPRv2+EKxoSJW/lAJmmFY9x48FyPj2EYNFRqMebnrvvkM7KyuRI2gwp/eP04RrxhDLhDePnwMN6/tqngdjajRom5VQbBweMY8uGJPf24/cxW1Jg0+OimNvS5QnjgvW4sErlLi+GypbVIssBL/BSw145w7fO5Cq8fOqO1qNgPSvmhAs8MY36NEdFEEocGvRnfc/ITfyZry0pHKZfhixfNw6FBL/76NjeV4lU+b2TTJO1RxXDDqkasaq5AaxkqSeVmZXMlrl/VgL++1Ynu8QBGvZmZN7+7eRWe+uxGoYJxyeIanDnHil+8fDQjI2OyrKF8aLXp8ZcPr0Gc3xTmO6XBZlDj6hX1+M+uPnhCMYRjCfzlrY4U2//WznEsqjdJZif85qYVuPf2Nbj39jX49Qe4YLuP/WMn+t0hrOcdP1ICj5Rz4f1rm/CZ8+biuQND+OvbnbDoVSkb/oYKbUaLVrczCH8kjv93iR1XLKvDD587ghcOcJWPEyMBJJJsyQ6eSp0SLMu5jsjoZDEr+D7hJ/b0o413kExGS9pzIlWhB4CvXrIAz31+U9YKvFkkPuVLnyuEKmNmODLJ4ck2Lt0TjKFjNFCUXbfGpElpOwI42/OPnj+Mrz66V8gSEb/ehhwhy+P+CN45PoarltdnOPzI5iGXVf/s+VVgmOxTtR7d1YeO0QDu2NiaMYXr6b0DGA9E8dVL7PjzB1ejYzSATz24M+M1CMcS2N3jFtqzAG7z3mjRCkKJS3Cyid7nlVrEk6zQmjXsDaPGNHm7D8BNhZIa+z3uj+CeNzswr9qAjXybzFXL6+EKxuAY9qX0umtVcjz3+U341LmpFbQmiw59rqDwOOOJJHZ0OYXPeSlYDWpcu6Iej+3qx1vHRnGuvXrSx/udqxfjqc+eJSniFYJOpcDSRjO28u0pZCFKKqcMw2SMLveGYinTeNL5x0fW4+uXSY9mz4e5osldA3z7itT7+cKF1dg0z4ZfvXxUmEYEcNlI923phFLO4DKReJQLct7qGuMEHp1KXlCGBsC5zIYlQpa3dk4I/pOxRORiJEKOXMagQqsUzv+ltjIZ1Ao8+9lN+Mn7lk162zqzBoFoAm8dG8XcKgNMGiXOnGuFSi4T2rRIkabQkOWZBBGKGy3aktp0CsGsVaKhQis4eCqKdPAA3GTQUq/16dSYNHj+85vwg2uXlnw/t5/ZiiP80IdiHTwAF7474g2XLWCZ8KdbV+H712SO1S6UG1Y3IJbgrhGFnj/uunoxHv74GSUfgxRtNj2aLFphSEouVyOZlHbL+mbcuLoRz31+Ey5dUouHPrZB2Du1FiBCtVh16BoLCq3Zk60RSfFteWOFcH2Tyxj8/Mbl6HEGcd0ft+AnLxxBkmXxwfW5HYjZWN5UgTePjmLDD1/FdX98BwaVAp84h8sAu2hRLZosWgSiiUnHo0/GojoTWqw6PLd/EEOeML72n31YWGfKOoGOMrOgAs8M4xw7t3lJD+QCIISLLmss3HJ37YoGXLK4Bj954Qj29LrxyqFhbGi3FhQ8ly8yGYN//tcG/P6WVWW/73LwtUsXQCFn8D9PHoQvEs+4YNeaNSkXYIZh8K0rFsEdjOHBbanVVlcwmjNrKF9Wt1jw25tWYG1rZUFtC3dsbEUwmsA3H9+PC36xGd9/9jA+99BuuHg78K4eNza0SduZ68xanL+gBucvqMG1Kxvw/Oc34TtXLcKKpgpcvJjbWFQZ1VArZClTo9yhKFQKGTTKidOHWiHHVy6x49UvnYOrl9fjfasbU35XY6UOQ95wSpsbCR9d2mDGL25cjqUNZnz7yQPwR+JwDHMbnlIdPESE2t3jhicUywizqzVrUGfWgGXzc+8A3HOiVcrRNRbMWqEHuIt6rhZEIj55C8jhEY9IF7OwzgSTRpFV4CHVnkLC/AjrWi3Y2e3CI9u5yU3xRBKffWg37nmzA28dG0PXeADLGs0px6VTKRDM4uB57sAQEklWMjx2fbsFZ821pbgi0rHoVVjRVJGSqwNwveEfvncbvvroPiyqM+GKpdz915i4KVyvHRnBfe90YX6NAWfOseLMuTZ879oleOf4OB7dmTpad2+vG5F4ZmujuIWWCJ3iyn96oPiIN/9q7YZ2C5IshHajaDyJv7zVgXN//gaOj/rxxYvmC8LJ2fNtwvSr9Ne0xarPqFgvbTAjlmDxJi/+HhjwIhBNYEOOVodCuGNjG0KxBALRBM6zT57BoFXJy1bp3dBuxb4+Dz76tx349hMHMK/agCUNE5+7uVUGnBidyB/yhmM5w4LrK7QpgeGFMqdKjz5XCL5wDPv5z91iCZs8w3COUpmMweW/eQvffuIAXjgwhMt+8xYe29WP969pytsV0WTRgWG4FgKSkVBoeG+NSZPRojXgDqHHGcz7fULOgzIm9XNBcqq0SnlJbgpCs1WX1wj4Wr5Is6PbJXxOdCoF1rdbBIGYDAeYzS1abTY9tEp5yS2PhWKvNeLQgBeeUGzSUPLpgGGYsghenzhnjuDCLHRMOjCRzTLgCWedAlUK1SZNShZcsZw9r0oQZgsdrW1QK6bMBccwDM6dz63P8mlv0yjl+OF1S/HjG5YJhSezVol/fGQdfnz9Upw9P//CdqtVjyFvWJimNqnAw7c1phfTzrVX45GPn4FYIokn9wzgggXVRWeLfviMVly9vB5nz7fhhlWN+POHVqeEbd+5sU3yGAqFYbgpzVtOjONTD+5EOJbA729ZWZIriHLyoALPDMNmUGNlU4XkSL19fR4wDLdYLxSGYfDTG5ZzFr6/7UDHWKDovuR80CjlM/YkUGPS4NPnzRWcLvlUZBbVm7BxrlUY0UpwBWO8Bb30RcSlS+rw70+cWdDztrjejHVtFjyzbxBGjQI/vn4p/JE4fvXKUeztdSMaT+bsVxejlMtw+8Y2PPHpjcJmnWGYjFwod4BbzEk95iaLDr+9eSW+cXlqBbyxUotEkk0J6z044IVCxmBejQEapRzfu3aJ0D7nGPJDKWcKqrRIQS56r/GLeamKBln45yvwMAzDj48P5KzQTwZxfrgKyOHpd4VSchEIchmDDe1WvH18THKU+O4evkWhCHH48xfOw6Z5Nnzj8f1469go/vfpQ3jtyAi+e80SvPv1C/Du1y/AU585K+V9q1fJhZwrTyiGjT9+DZ//124MekJ4es8A5lUbJMU7tUKOBz66ftJxuefZq7Gvzy30hu/ocuKy37yJXT0ufOuKhXji0xtT2jrOs1dhW6cThwa9uP3MibHKN61twpqWSvzsRYcQvgtwrgWGycynabboBLHTFYxCJZdBJwoTFE/RALhw6nzbkFY1V0Ill2FrhxOvO0Zw6W/exPefPYyVzZV48QubcLkobFetkOOyJdz/V+SxiLtoUQ2qjWrc+w7n4CSOl1Lyd8QsrDNhQ7sFKrkMG/MYJlBONrRbEU+y2HJiDF+91I6nP3tWyoSPOdUGjPoiQiueNxSXDFguF3OrDWBZYOl3XsInHtgFGQMszCL0zq024vUvn4vbNrTgn9t68IkHdiKWSOKeD63B96/NvyLPjUrXoms8gI7RAOZUFX7erDZpMOaPpIjwJKcpX6cXcZJY9OoUdxbJqLMZT66IQtpMWDb13H+evRonRgN498S44OApNmR5JiCXMfjapXZ8uMDA1lJZUGvEMX46mnmGteSXk0q9Cp85fy6selVBLYYEIpYMukNT0qJVLhRyGa5f1QCVXFZwBs9UQ8KbS3nudCoFblrXLDkBKhtkDUr2C/k6eKTWmksazHj80xtx1fJ6fPGi+XkfQzpLG8345QdW4KfvW47vXbskY71087pm3HXVIlyyOD8HaC4uX1KHRJLFrh43vnvNkoJbfynTx9StcihFc8HCGvzsRYdg7yfs7XVjTpWhaNeNWafEb29eiff/+V3+95Q/DG228JGz2vDQth70uUJ5XzDuOLMNH/37Drx4cAhXLuPcAa5AdNorVz+9YRkODnhx6ZJayGUMDg168eDWHjgDUW6TKpG/UwicmJHq4KnQFraYaxA2viHB5XBwwIt5NUbhYruiqQI3rGrEX9/qRHuVHnOqDBltT4VCXps3joxAr5JLhsNduqQWnWMBrC3geWq26NA5FsBBPgdmUX3hwgmpzrtDMYz7I9jZ7cJFi2qyioXJJIt+dwiXLpG2x26aZ8NLh4bRNR7MWITs6XVhXnVx5w6lXIY/3roKN979Lj5y/w5EE0l8/Ox2fDBHwKlerUAwmgDLstjX50a/O4SBvSG8dHAYoVgCXxK5UYrhPHs1fvnyUbx5bBRXL2/AXU8dhM2gxtOfPUtyAX7egmr88Y0TqNApcd3KibHKnItiMa7+w9v4/WvH8fXLF4JlWbx9fAwLak0ZDopmiw7ecByeYAzuQCwjSJ4s7vpdIbAsi2FvOO9KqEYpx4qmCtz3Thf+/GYH2mx63Hv7GpyXpeXpy5fMx/kLq/PacJDpVb94+SiOj/jwXsc42qv0ZW0V+MkNy9A1HiyLQ6MQNs214ZfvX46Nc22SYhpZkHaM+rGyuRLecKzgfIlCuHBhDb51xUKEYwnh9+eaKFKpV+F/r1mCWze04MiQD5csriloA0Jos+lxaMCLfncIN1c3FfzzNSY1WJabwkXes++dcMKkUeQ9DKHJooVRoxBGpBOI0F5VYt5ToYjbTMROtxtWN+KBrd345IM7cfO6ZgCzu0ULAG7nq/YnE3HWVyktWrOBT5wzB3dubCtqTaJRymHVq3B81A9/JF72Fq1y8qWL5uO6lQ15TUE6mZDWypM9rY5ETWw+Oooak3rS69u6NguaLTqsbZVuj2qo0OJ3N0/t5CiNUo47ynQ+WNJgwsI6E5Y2mHDDqobJf4AyY6AOnhkIEV7EU69YlsXePk9RFXgxq1sq8b1rluDmdU3CRvt0RKOU4ztXLYZWKb3pl+L8BdVoseqEkcCHBrx4+/hYyS6TUmm16XHFsjqhYvrFC+fDoFbgmX2DWFhrKqndAOBcOb3OoOAMcRUxErWd32Tt7+MEEZZlcbDfgyVpzpevXWqHUs7gyJAvIyi2GEiL1oAnjOVNFZKZH9esaMALXzg7xfExGa02PXqcQRzo93AV+rrCj5UsiO99uxPn/vwNfOwfO1M+8+mM+CKIJdiUEelizprHVbjePpbausSyLJc/1CS94MgHo0aJe29fiyqjGlcvr8fXJgnE1KsViCdZRBNJHOStzU9/5iyct6AKepUc164obaGwuN4Em0GF14+M4tGdvTg44MV/X7Ygq9ixsqkCjZVa3LmxLWPhurTRjBtXN+LedzrxzL4BvO/ud7Gt04mLJATwJlH+kjMYzQhX1yi5KXx9rhC8oTgi8WRBFcdLltRCrZThm5cvxItfOBvnL8gu+FUbNQVV6Mj0qr++3YkdXa6ytWcRWqz6so/IzQeZjMH1qxqzOqWIm4Xk8PjC8ZSJb+VGo5Tjo5va8Znz5+Ez58/DZXmMOQe4/L2rl9cXJe4AQKtNh2MjxQUsAxDCecVB7Vs7x7GuzZp3VhLDMDh7flWGy1hw8JxkgYe003CBpxPPiVmrxP23r4OcYfCnN05wxzjLBZ7pYGHdxPW72DHps4lC1gjp1FVohGluM9XBA3DnL/HrOlPQqRT40BktuHhR6a6UQmixcNcPdzCWV1vx2lYL3vzqeTNuyEyxMAyD5z53Fn5yw7KydCpQTh5U4JmB2GuMaKjQ4lVRDs+gJ4wxfwTLCwxYluKW9c340fWTBxSe6ly4qAYH/veSvIUumYzBh89oxc5uF146OIQ7798Og1qB75Uh3K6cVOpV+BJv/yxHiGqzRYdANCFY2T1FCDwNFVosbTDj6X0DALjpQuOBaEZrU7VJg0+fzwXEliN0UdyXXkz+TDbI1LDXHCNorzJApyrctUDEgWf2DWJVcyVqTGpBPJSi3505Il1Mq1WHhgot3jo2lvL17vEgXMFYXq08uaiv0GLz/zsXv7155aS5BqRtKRBJ4OCAFw0VWixpMOOPt67G/u9cUvLED5mMwTnzq7H56Ch+9qIDa1oqJTN9CAq5DG/+v/Pw2fOlx3d+5RI71Ao5PvPP3egeD+CnNyzD5y/MtFCLQ8fdwSgqJcLVuYlxISGwtpBJUXdubMW+uy7Gf53dXtJmQgqrQY1rltfjX9t74YvEsb5M7VkznWaLDko5g9385orL4Dn1zMutoskwhYxIJxDXDhF4hjxhdI0HM3KoJuP3N6/Ez25cnvI1ch62neSNrVohh82gwtIGc8bUtGarDvd8eA3UChnUitRWS0p+tNn0UMq5a8HpIPCUAtdCyV3Di8nxoQDfunIR/uvs9pP6O806peAEb7Odnu1JDMNQcWcWcuqtck4BGIbBBQur8ciOXoRjCWiU8pIClinZKXSKy41rGvGLlxz4+AM7oVcp8O9PnFFwGN3J4Nb1zRjwhHDj6sKt+umIN7VWgxquYBQrtBUF389Vy7lJWZ1jAXTwU2akwkfv3NgGdzCWc8OeL3qVHEo5g1iCLXmigBgyKv1AvxfXrCjuOJstOnzmvLlY1VKB8xfU4A+vH8fPXnTg6LBPUtwiwb1NWQQehuGm4j27bxDxRFLY0Ozu5SZPlEPgkhotLQWxMQcicRzsTw2hLteUl/MWVOE/u/rAMMB9t6+bdAGS6/dWGzX4+Y3LcWTIi4+c1Za1lY04eLqdAbiCMcyT2Eg3VGpxaMArbJQLEXimehF1+8ZWYdRsuR08MxWFXIZrVzTgn1t7sL7Nwk/ROvU2o6S6LGMmzk+FQDadwz4u10rI38kS0p8NqfewdZpatADgKxfbsxZxVjVX4p4PrcHhQS/dwBSBUi7DnCoDjgz5YC6wbft0o140hKCQawJl+mm16eHqcaN9mt36FEohUAfPDOWChTUIx5J45zhXjd/b54FSzsxI6+TphFGjxE3rmiFjGPzh1lUz9vVQyGX4+mUL824/y4VY4NnR5cSYP4KaIkQtklv09N4BHBzwgmEg+fxplHJ84/KFRU8YEMMwjGCVLaeDh9h2gdwjvXMhkzH4yiV2nL+ACzu/ZV0z1ApZVhcPEXjqJaZoEc6aZ4MvEsdevhUOAPb0uPkWhfKOoc2Fnnc0jfoj6BwPCOGr5WTTvCqoFTLcuLoRS8sgfF+6pBZfuHB+zs2/Qa2AVa9CrzMIVyAqacNurNSi3xXCoIcIPDOnWru43owz2q2YV204rTYZ379uCda1WvCVf+9FksWUhixPF6RVuMWqL6rNy8oHI4/wwuSrh0dg0iiwqMjzm5jpcvAAwE3rmnHWvOzB32fPr8LH+RHDlMIh1/DpziKc6YjzoGZyixYlE+KOLNfkRwrlZHDqrXJOETa0W6BXyfGP97oRiXNCz4Ja04ydTHU68fXLFuDOs9okx1WfipDq55tHx/DqkWG0WPW448zWgu+nvkKLda0WPLV3AO02Pdqs+knHXZaDSp0SakV5w/nqKzRQyBjEkyyWlEm8qNSrcN3KBjy+uw9fu9QOFS/2zKnS49IldehzhWDVq3K2g22cYwPDAO8cH8PqFs6xtLvXjWWN5oLdaqWgU3PnqZ1dLrBs8SJYLsxaJV78wtk5Ba+poNmqQ/d4EO5QDBbJFi0dlz3EB3DPtEDNuz+4GpFEYroP46SiVsjx59tW4/o/bUHnWCCvEduzjaZKHWQMipqgBXBu1iqDGsPeMEa8YTx/YBC3rm8py3mDfE6qZvEocoo0G+fasLVjXBhHTZGmjr9OqRQy+lzNMogjsq3IcyuFMh1QgWeGolbIcfHiWjy+ux9vOLjQ1NuL2FRTyo9CLjttxB0A0KrkqDaq8Z9dfbDoVbjv9rUp2TaFcNWKenz7iQPod4VO2hS3q5bVlz3PRCGXobGS66kvR4WbcPvGVvxrey/++z/7safXjSFvGHIZg3tvV6DPFcyav0Oo1KuwpN6Mt4+N4XMXzEM4lsDhQS8+uunk9q0T4W5rpxMANx50KpiOgPNmiw6bj44ikWQzQpYBoJE/N+zqccOkUcy4aSRc6Prpt8Go1Ktw/x1r8dVH95WcRzUTUSlkuGldc0nZSjUmNYa8ETywtQfxJFu2NceyxgqcOceKVWVsk6XMDG5Y1YD3rW6c7sOY8dTzDp4qg5q2A84yrl5ej0AknpJzRqHMdKjAM4P5+Y3L8eWL58MfiSMQic/YdiDKqU+rVQ93KIZ7PrS6pE315Utq8Z2nDiIUS0xJ244Un71g3pTcb5tNjwTLlnVawoJaE86cY8ULB4ewrNGMn924DD987gg+/eAuqBSyvAJPz5pnwz1vdqDXGcTvXjvG5Q+VsT0tH0hg6fYuJ6x61YxqUyqVZosO7mAMALK2aAHAoUEv7dmfYbRY9Xj442dM92FMGT+8bmlJP19t0uDEiB+HBjw4315dNgHVZlDjn/+1oSz3RZlZULEiP4jTlAYszz7aqwz45hWLpvswKJSCoALPDEYuY07rUeaUmcN3rl6McDxRcgXWalBj41wb3jw6iiUNs1uw/NaVi+APx8t+vz+5YRkOD3px4cIayGQM7rvdiGv/8A6GvOG8zgeb5trwpzdO4PxfvIEkC9yxsRXnLzg5bikCyeDxhGLYNM92Sm0CxNlQUrkTZIx9IsmeVjk3lNlPjUmNlw9x0zvv2Ng2zUdDoZw6VBvVkDE0f4dCoZwcqMBDoVAmpZxtSB9c34yD/R4sa6go231OB3OqpmZkZpNFlyIi1Jo1uPf2tbjtr1uxvLFi0p9f3VqJJosWrVY9/ufKRZh3EsOVCSSDB8BJc2qdLJrFAo9Eq6JOpYBFr4IzEKXVWsqsoobPi5pXbcDGuafHlDUK5WSgkMuwqrkSy0+ym5ZCoZyeUIGHQqGcVC5eXIuLF9dO92HMKhbVm7D9mxfmNWJcrZDjra+efxKOKjvi8OzZ7tRKJ0XgydKe11iphTMQpQ4eyqyCvF9v39h6SrnuKJSZwKOfPHO6D4FCoZwmTPmYdLvdfqndbnfY7fbjdrv9v6f691EoFMqpSD7izkxBo5CD7A9PNQdPjUkDlZy7dGYbDUxyeGqoHZ8yizh/YTU+de4c3LCKhuZSKBQKhTJbmVKBx263ywH8AcBlABYBuNlut9OkKgqFQjmFkckY6JRyGNQKtFhOrRwxLhtNCxmDrOO2yZQ96uChzCZsBjW+eukCaJQza/IbhUKhUCiU/JlqB886AMcdDkeHw+GIAvgXgGum+HdSKBQKZZrRqxVYVGeaVc6jfGmy6FChU2V9bCQMu5oKPBQKhUKhUCiUk8hUZ/A0AOgV/b8PwPop/p0UCoVCmWZuWN2IedVTE0Q93Vy3sgEL67JnC509vwrnL6jGgtqTH3BNoVAoFAqFQjl9oSHLFAqFQik7X7t0wXQfwpRx7cqGnN9vs+lx7+1rT9LRUCgUCoVCoVAoHFPdotUPoEn0/0b+axQKhUKhUCgUCoVCoVAolDIx1Q6e7QDm2e32NnDCzk0Abpni30mhUCgUCoVCoVAoFAqFcloxpQKPw+GI2+32zwB4EYAcwL0Oh+Ng+u127tw5lYdBoVAoFAqFQqFQKBQKhXJKw7AsO93HQKFQKBQKhUKhUCgUCoVCKYGpzuChUCgUCoVCoVAoFAqFQqFMMVTgoVAoFAqFQqFQKBQKhUKZ5VCBh0KhUCgUCoVCoVAoFAplljPVU7ROeex2+6X4/+zdZXRc1/X38e+I0UJLZravKWaGMGPTMCcNN02bNoUU/k1KTzHUMDMzNpzYjpnZ1yzJksUMw/O8mBlpJEuyNCO0f5+1vGyNBo6uj+49d5+z94EH8RaRfto0zX90c5OkBzEMYz9QBbgAp2maMwzDSAXeAIYB+4GLTdMs6642StczDONZ4Gyg0DTNib7Hmu0XhmFY8J5jzgRqgWtN01zXHe2WrtVCP7kHuBEo8j3td6Zpfur73m+B6/Geb35qmubnXd5o6XKGYQwGXgQyAQ/wpGmaD+qcIoFa6Sf3oHOK+BiGEQMsBqLx3ie+bZrm3b4dkV8H0oC1wFWmadoNw4jG26+mAyXAJaZp7u+WxkuXaaWfPA8cB1T4nnqtaZobdN3pWlrBEwLDMMKBR4AzgPHAZYZhjO/eVkkPdIJpmlNM05zh+/ou4GvTNEcDX/u+lqPL88DpTR5rqV+cAYz2/bkJeKyL2ijd73kO7ScA9/vOKVMCbsTGA5cCE3yvedR3jZIjnxO40zTN8cAc4DZff9A5RQK11E9A5xRpYANONE1zMjAFON0wjDnAP/H2k1FAGd7AH76/y3yP3+97nhz5WuonAL8KOJ9s8D2m604XUoAnNLOA3aZp7jVN0443sn1eN7dJer7zgBd8/34B+EH3NUW6g2mai4HSJg+31C/OA140TdNjmuYKINkwjP5d0lDpVi30k5acB7xumqbNNM19wG681yg5wpmmedA/E2qaZhWwHRiIzikSoJV+0hKdU45CvvNCte/LSN8fD3Ai8Lbv8abnE/955m3gJN9qDTmCtdJPWqLrThdSgCc0A4GcgK8P0PrFUo4+HuALwzDWGoZxk++xTNM0D/r+nY93ubRIS/1C5xlp6ieGYWwyDONZwzBSfI+pnwiGYQwDpgIr0TlFWtCkn4DOKRLAMIxwwzA2AIXAl8AeoNw0TafvKYF9ob6f+L5fgTeNS45wTfuJaZr+88nffOeT+30pfKDzSZdSgEekcy0wTXMa3qWJtxmGcWzgN03T9NB6xFuOQuoX0orHgJF4l0QfBO7t1tZIj2EYRgLwDnCHaZqVgd/TOUX8muknOqdII6ZpukzTnAIMwrtqa2z3tkh6oqb9xDCMicBv8faXmUAq8Jvua+HRSwGe0OQCgwO+HuR7TAQA0zRzfX8XAu/hvVAW+Jcl+v4u7L4WSg/SUr/QeUbqmaZZ4BtUuYGnaEiZUD85ihmGEYn3pv0V0zTf9T2sc4o00lw/0TlFWmKaZjnwLTAXb0qNf3OewL5Q309830/CW2xZjhIB/eR0XyqoxzRNG/AcOp90CwV4QrMaGG0YxnDDMKLwFqP7sJvbJD2EYRjxhmEk+v8NnApswdtHrvE97Rrgg+5pofQwLfWLD4GrDcOw+ArYVQSkXchRpknO+vl4zyng7SeXGoYR7dvtZDSwqqvbJ13PV+/iGWC7aZr3BXxL5xSp11I/0TlFAhmG0dcwjGTfv2OBU/DWa/oWuND3tKbnE/955kLgG9+KQTmCtdBPdgRMKljw1mkKPJ/outNFtE16CEzTdBqG8RPgc7zbpD9rmubWbm6W9ByZwHuGYYD3d+1V0zQ/MwxjNfCmYRjXA1nAxd3YRukGhmG8BhwPpBuGcQC4G/gHzfeLT/FuK7kb79aS13V5g6VbtNBPjjcMYwredJv9wM0ApmluNQzjTWAb3t1ybjNN09UNzZauNx+4Ctjsq4cA8Dt0TpHGWuonl+mcIgH6Ay/4dkwLA940TfNjwzC2Aa8bhvFXYD3eYCG+v18yDGM33k0BLu2ORkuXa6mffGMYRl/AAmwAbvE9X9edLmTxeBRkFRERERERERHpzZSiJSIiIiIiIiLSyynAIyIiIiIiIiLSyynAIyIiIiIiIiLSyynAIyIiIiIiIiLSyynAIyIiIiIiIiLSyynAIyIiIiIiIiLSyynAIyIiIiIiIiLSyynAIyIiIiIiIiLSyynAIyIiIiIiIiLSyynAIyIiIiIiIiLSyynAIyIiIiIiIiLSy0V0dwNEREREWmIYxn5gaBuemgU8D9zd5HEHkAd8DdxjmmZOwHsfD3zb2nuapjmshefebprmwwHv9Tpwie/LE0zT/M73+HfAcb7HPb72VAD7gUXAY6Zp7m3DzyciIiLSKq3gERERkSNZJN4A0Y+ApYZhJHTQ+/7WMIyYdr7GAkQBfYGZwC+BrYZhXNlBbRIREZGjmFbwiIiISI/lX0HjZxiGJ+B7libfuyfgyz/5/owF/oc3yDMYOA94pZmPWmSa5vHtaNoA4BbggXa85gTge2AYcDNwJxADPG8YRq5pmq2tJhIRERFplVbwiIiIyBHJNE2PaZrbgXcDHh7SAW/t8v39G8MwYtvZJqdpmrtN0/wV8F/fw+HA3zugXSIiInIUU4BHREREjnSBK30KO+D9vvS9Tz/gxyG8zz8D/j3bMIyMkFolIiIiRzUFeEREROSIZBiGxTCMscD5vodqgI9aePpxhmF4mvx5oIXn1gD/8v3714ZhxAfTPtM0DwKVAQ+1pZi0iIiISLMU4BEREZEj0d2AG9iON3CyBzjLNM2OWMED8BhQAGQAt4XwPpbDP0VERETk8FRkWURERI4GsXh31GpJu4osm6ZZaxjGP4D7gV8BK9vbIMMwBgKJvi89eLdOFxEREQmKVvCIiIjIkehPQDRwOd6iyAOA9wzD6Igiy36PAweBdOD0IF5/V8C/V5imWdQhrRIREZGjkgI8IiIickQyTdNumuZrwCO+hxKAf3Tg+1tp2P0qvC2vMQwjwjCMUYZh/JuG1C4X8LuOapeIiIgcnZSiJSIiIke6vwDX4U2HutQwjH+ZprmhyXOOMwzD08xrU0zTLG/lvZ8Efg0MakM7vm3mMStwo2ma37Xh9SIiIiIt0goeEREROaKZplkM/Nv3pYWGVTcd8d424P+14yUOoAhY42vTeNM0X+6o9oiIiMjRy+LxNDdZJSIiIiIiIiIivYVW8IiIiIiIiIiI9HIK8IiIiIiIiIiI9HIK8IiIiIiIiIiI9HIK8IiIiIiIiIiI9HIK8IiIiIiIiIiI9HIK8IiIiIiIiIiI9HIK8IiIiIiIiIiI9HIK8IiIiIiIiIiI9HIK8IiIiIiIiIiI9HIR3d2AtWvXerq7DSIiIiIiIiIivcX06dMtTR/r9gAPwPTp07u7CSIiIiIiIiIiPd7atWubfVwpWiIiIiIiIiIivVyPWMEjIiIiIiIiPZ/d6WZLXgWr95Wy8UA5V8weyvxR6d3dLBFBAR4RERERERFpg9zyOi58bBkHK6wAREeEsS6rnC9/cSyJMZHd3DoRUYqWiIiIiIiItMrqcHHLS2upsjp56LKprP79ybx+0xwKqqzc+8XO7m6eiKAAj4iIiIiIyFHJ4/FQUGnF7W59Y2OPx8Pv39vC5twK7r9kCudMHkDfxGimDknhqjlDeWH5fjbmlHdNo0WkRUrREhERERGRdnlzdQ6PfLebcycP4PLZQ+ifFNvdTZI2qrY5eX1VNiv2lrA2q4yyWgezhqfy0GVTyewT0+xrXlqRxTvrDvDTk0ZzyvjMRt/75WkGn23J53fvbeaD2+YTEa41BCLdRb99IiIiIiLSZot2FvHb9zbjdHl4+NvdLPjnt/zk1XVU1Dm6u2nSCrvTzfNL93Hcv77lr59sZ19xDaeMz+SOk0ezJbeCMx9cwuKdRY1eU2t3cs+HW7n7w62cODaDO04afcj79omJ5J5zJ7A1r5LHF+0Jqm05pbXU2p1BvVZEGmgFj4iIiIiItMmO/Epue2UdozMSePvWeZTV2Hl5RRbPLt3H3qIaXrx+FukJ0d3dTGmiuNrGxY8vZ29xDXNGpPL06WOZOiSl/vtnT+rPba+s55rnVjFzaCrTh6UwPD2eR77dTVZJLdfMHcpvzhhLWJil2fc/Y2I/zp08gHu/3MnozEROm9CvzW1bsbeEq59Zxbj+ibx+01xio8JD/nlFjlYWj6f1fMvOtnbtWs/06dO7tQ0iIiIiItK6PN8OSi6Ph/dvm98oLWvRziJufmkNA5JieemG2fSJiWBvUQ3ldQ4mDUwiJT6qG1t+dHO5PVzz7CpW7S/lsSumceLYDCyWQwM1dXYXj363m8W7itmaW4HT7WFwaiz/umAyc0emHfZzrA4Xlzy5gp35Vbx1y1wmDkw67Gt25Fdy0ePLSYyO4GClldMn9OORy6e1GEgSEa+1a9cyffr0Q35RFOAREREREZFWfb29gF++tRG7080bNzd/8756fyk/en41Nocbu8vd6HtGZiILRqfzs5NH0+cI2U67zu5if0kNxdU2Zg1PJTqiY1aeuN0evt9dzKzhqcREhv6eD3y1kwe+2sU/fngMl84a0qbX1Nld7CqsYlRGAnFRbU/6KKy0ct4jSwF4+PKpjO+f1OKKnLzyOn746DI8eHjn1nl8tiWfv36ynVuPH8lvTh/b5s8UORopwCMiIiIiIoeoqHOwfE8J+4prGJQSy/D0eDISo6m1u6i2OXln3QGeW7qfcf378NBlUxmVkdDie20/WMnrq7LJTIphRHoCfWIiWJ9Tzsp9pSzbXcyglFgeu3I64/r36cKfsGPtL67hhhfXsLuwuv6x4enx3H3OeI43MkJ672qbk5+/sYEvtxVw1qT+PHzZ1GZX27TV97uKuerZlZw/dSD3XjQ5pPdqq615FVz8+HJq7C7CLDAsPZ6zjunP9QuGkxwXhcfj4cttBfz1k+2U1dh569a5jO3Xx7tT1/tbeHVlNvdfMpnzpw7q9LaK9FYK8IiIiIiISL0lu4p44KtdbMgpx3WYbbKvnTeMu84YG9KKktX7S7ntlXVUWh38/YfH9MobeI/Hw9XPrmJDdjk3HjuC4enxhFks3PuFyV5f0eJ7L54c1Cql7JJabnhxNXuKajhuTF++2VHIPeeM59r5w4Nqa0Wtg5Pu+46UuCg++Mn8dq3ECVVhlZV1WWVsP1jF+pxyFu8sIjE6gsvnDGFdVhmr95cxom88/75wEtOHpta/zuFyc8VTK9mRX8lXdx5HRmLzu3qJHO0U4BEREREREQBeXL6fez7cypDUOM6ZPICFo/sytn8iuWV17C+uobjGTkJ0OAnRkQxKie2wFTeFVVZuf3U9K/eV8t/LpnLu5AEd8r5d5bMtB7nl5XXcfc54rgsIvNicLp75fh//+dzk2nnD+eM549v1vgWVVk5/YDFuDzx6xTTmjkjjppfWsGhnEW/cPJdpAQWR2+qfn+3g8UV7+Pj2BUwYcPh6OJ1pR34l//16F59uzic9IZqfnzKaS2YMbnZL9b1F1Zz+4BJOGZ/JI5dP64bWivR8CvCIiIiIiBzlHC43f/14Gy8sz+KksRk8eNlUEqK7dmNdu9PN5U+tYGteJe/+eF6vSdeqtTs5+d5F9ImN5OPbFzQbnLjrnU28s+4A39x5PINT49r83n/8wJua9L+fLWR0ZiLgXYFz1kNLcLs9vHfbfDL7tH01S0GlleP+/S2nT+jHA5dObfPrOltBpZU+MZGH3Snr4W928Z8vdvLMNTM4aVxmSJ/pdntYuqeYYWnx7fo/EenJWgrwHHpWEmlGfoWVlXtLursZIiIiItJOVoeLTzYd5OdvbGDGX7/iheVZ3LBgOE9ePaPLgzsAURFhPHrlNPrERnDTS2sor7V3yeeW1thZsbfksOloLXnom93kVVj5yw8mNhvcAbjj5DGEh1n4zxdmm983t7yO11flcPHMwfXBHYCkuEgeu2I6pbV2TntgMR9tzGvze/736104XR5+cYrR5td0hcw+MW3aBv2mY0diZCbyh/e3UG1zBvVZdqebN9fkcOoDi7nqmVXc9uo6untxg0hnU4BHDsvl9nD9C6u55MkV/OmjrTia7IogIiIiIj3TltwKzn7oe257dR3fmYWcNC6D566byR/OHk94N25FnZEYw2NXTqegwsbtr60POuhyOB6Ph082HeT651cz629fcemTK7j5pTXU2tsXNNhdWMXTS/ZywbRBzByW2uLz+iXF8KP5w/lgQx5bciva9N4Pf7MbgNtOGHXI944ZlMTHty9kaFo8t7+2nh+/spYPN+axbE8xe4qqmw1Y7Cuu4fXVOVw+ewhD0nrnipWoiDD+fsEx5FdaufXltRRWWtv1+sIqK2f9dwm/fnsTEWEWLpg2iE0HKli9v6yTWizSM4QcsjcMYzDwIpAJeIAnTdN80DCMVOANYBiwH7jYNE39RvVCL6/IYmteJQtHp/Pc0v1sya3gkcunkdGOZaIiIiIi0nWcLjePL9rDA1/tIi0hiqeunsGJYzO6NajT1LQhKfz5vAnc9e5m/vOF2SlbYz++aC///GwH/frEcP2C4fSJjeTeL0wufXIFT18zo01FfN1uD3e9s5m4qAh+e+bh23jzcSN5dVU2//xsBy9dP7vV5+aU1vLWGm8wZmBybLPPGZWRwDu3zOXJJXt54EtvHRu/aUOS+b+zxzPVV6PH4XLz7893EBUexk9OPDRg1JtMG5LC/zv/GO75cCunPbCYv51/DGce0/+wryuutnH5UyvJLavjiaumc+r4TKwON9/sKOCpJXuZNbzlAJ1Ib9cRazKdwJ2maa4zDCMRWGsYxpfAtcDXpmn+wzCMu4C7gN90wOdJFyqssvKfz00Wjk7nxR/N4sONedz1zmbOe2Qpb948t915rNU2J1klNYzv36dLtmkUERER6U225Fbw8aaDZPaJZlBKHOP6JzIopX3jrbIaO7e9uo5le0o4Z/IA/nLeBJLjojqpxaG5dNYQNuVW8Nh3e5g4IImzJh3+Br6tduRXcv+XOzl9Qj8euWJafXDLyEzk9tfWc+5DS5k5PJXUuEgy+sRw9qT+DE2LP+R9XlmZxZqsMv5z0WTSE6IP+7lJsZH85IRR/PWT7XywIZfzpgxs8bn//XoXYWEWfnx868GYiPAwfnz8KK6aM5T8CitFVTbMgioe/W4P5z+6jDMm9sPudLNibwk1dhe3nzjqiNiB6rJZQ5g5LJU739zAj19ZxzmTB3D3OeNb/H8oqbZx+VMrOFBWy3PXzmLuyDQAYqPCuXLOUB7+djf7imsYnn7o/7PIkaDDiywbhvEB8LDvz/GmaR40DKM/8J1pmockgarIcs92x+vr+XRzPp/dsZARfRMA78DjiqdXkhQbyVu3zG1zwbe88jqufW4VOwuqGZ2RwCUzB/PDaYNIje+ZAw4RERGRrnSgrJZzHvqeslpH/WMRYRZ+fPxIbjtxFNERh9Yucbk91NqdJPq25Tbzq7jxxTXkV1j52/kTuWjG4C5rf7BsTheXPbmCHflVvPfj+Rj9Elt8bnG1jUVmEcPS45kwoE+L27bbnW7Of3Qp+RVWvvj5saQ1CQhsOlDO//t0OwWVNkpr7FTUObBY4KSxmfxo/jDmjEgjLMxCXnkdp96/mKlDknnxR7PaPEFpc7q48umVbMgp5/nrZjF/VHqj71fUOnh88R6eXLyXa+YOa/euW37VNidPLNrD00v20S8phvmj0lgwqi+njM/sUau1QuVwuXnsuz08/M1u4qLD+f2Z4zhn8gBsDje1Dicbc8pZtLOIr7YXUlnn4LlrZzKvyTEvrLKy4B/fcsnMwfzlBxO76ScR6RhdsouWYRjDgMXARCDbNM1k3+MWoMz/dZOGKcDTQy3fU8JlT63g9hNHceepjWNzG3LKueKpFQxIjuWNm+ceNkiz/WAl1z63ilqbi5uPG8FX2wvZkFNOWnwUX/7iOAV5RERE5Khmdbi44LFlZJfW8sFt8+kTG8mBsjpeXLafd9fnMjojgZ+fMgaPByqtDg6U1bIhp5yNORVU25wkx0UyNDWO3YXVxEVH8MRV04PaWru7FFZaOfuh74mJDOfhy6cyaVByo+/X2V088/1eHl+0t77obniYhQkD+vCb08ceEkC578ud/PfrXTxx1XROm9DvsJ9fUGnl5RVZvLIym9IaO/36xHD6xH7sKqxiXVY5X/z82HavXK+odXDxE8u9RZRvmsOEAX3YXVjN51vzeXLxXqpsTs6bPIA/nTeRpNjIdr13U263h7AjKKDTkt2FVfz23c3N1tJJjI5gweh0rl8wnBkt1En61Vsb+WhTHsvvOokU3X9IL9bpAR7DMBKARcDfTNN81zCM8sCAjmEYZaZpHnKVUYCnZ/J4PJz78FJKa+x89Yvjmq12v2JvCdc8u4qo8DAiwi1YHW5S4iK5aMZgLps1hH5JMewrruHr7QU8+NUu4qMjeP5HMxnbz7sV5pr9pVzy5AqumD2EP5+nKLqIiIj0HjU2J2+uycHqcHPNvKHERQVf+cDj8XDnWxt5d11us9tCf7ujkN+9t5mDFQ2FZsPDLIzrn8jUwSkMSI7lQFkt2aW1xESG85fzJtIvqfel56zLLuOmF9dQXG3nh1MHcuvxI9lbXMPyPSV8uvkghVU2Th2fyS3Hj6S4ysamAxV8vCmP/SW1XDZrCL89cyw786v4eNNBXlqRxXlTBnDfxVPa1Qarw8VnW/L5ZPNBFu0swu508/szx3HjsSOC+pnyK6xc8Ngyqm1OIsMtFFd7dww7aWwGvzzN6DVbxPckbreHjzblkVteR3REODGRYYzqm8C0oSlEtrC7mZ+ZX8VpDyzmV6cZzRa1FuktOjXAYxhGJPAx8Llpmvf5HjNRilav9eW2Am58cQ3/unASF7eytHfl3hLeW59LZHgYMZFh7CqsZtHOIsIsFvr1iSG3vA6AyYOTeeyKaQxoUjzujx9s4ZWV2fzvZwsZk9nyclwRERGRnqC0xs6z3+/jpRVZVNR5U6kGJMVw97kTOHV8ZlA1Bp9avJe/fbqdn588hp+dPLrZ51TbnJj5lSRER9InNoKUuKgW05N6syqrg0e/28Mz3+/D7vTu3BoTGcacEWncdsKoQ3awsjpc3PflTp5espcwiwWn20N0RBinjM/kb+cfE9LKmGqbk215lcwYmhLS6pjdhdXc8+FW+iZGM2dEKnNHpPfa3a2OBFc/u4rtByv5/jcnNJv2KNIbdFqAx5d+9QJQaprmHQGP/xsoCSiynGqa5q+baZgCPD2Mx+Ph7Ie+p9rm5OtfHEfEYSLhTWWX1PLKqiz2F9cwf1Q6x4/JaPEiVlpj5/h/f8vkwe3LaxYRERHpSh6Phw835nHPh1spr3Nw2vh+3HTcCFxuD394bwtmQRUnjc3gnnMntCuV56Xl+/m/D7Zy5jH9ePiyaUdFmk1b5JTW8vX2AiYMTGLyoGSiIlofj67LLuOdtQeYOSyVk8dnkhDdEXvJyJFo8c4irn52Ff++cFKvqFEl0pzODPAsAJYAmwG37+HfASuBN4EhQBbebdJLm2mYAjwdqNLqYFteJXFR4YfkLrfV51vzufmltdx70WQumD6oYxvYjGe/38efP97Gs9fO4MSxmYd/gYiIiEgXKqyy8vv3tvDltgImD07mXxdMalQI2OFy89zSfTzw1S7cHg+3nziaGxeOOGxQ4vVV2dz17mZOHpfBo1dMP+zzRSR0Ho+HMx5cAsD/frZQE8zSK3VJkeVgKMATGqvDxfe7ivliWz6r95exr7im/nszh6Vw6/EjOcHIaPOJy+32cOZ/l2Bzuvny58e2e/VOMBwuN6c9sBiPBz6+fQHxmnERERGRHqKw0sqFjy+noNLKnaeO4foFI1rcnSivvI4/f7SNz7bmMyAphtMn9uf0if2YNiQZt8c75smvtLIhu5zV+0t5Y00Ox47uy5NXT1eqiEgXemtNDr96exMvXT+LhaP7dndzRNpNAZ4jgNPl5sXlWSzdXUyN3Umt3cWugmrqHC4SoyOYOzKNSYOSmDAwiaziGp5aso/c8jqGp8dz6oRMThmXydQhKa1umfj++lzueGMDD1wyhR9MHdhlP9vyPSVc8fQKThmfyWNXTNfyZBEREel25bV2LnliBTlltbx8w+w270r1rVnIy8uzWLKrGLvL3exzEqIjOGV8Jn//4TFHZC0dkZ7M5nSx4J/fMr5/H1740azubo5IuynA08tty6vkN+9sYnNuBaMyEkiNiyIuOpwhqXGcPC6TOSPSDlnW63C5+XBDHu9vyGX5nhKcbg99E6M5e1J/zpsykMmDkupX9jhdbv779S4e/nY34/r34cOfLGg1ENQZnvl+H3/5eBt3nDyaO04e06WfLSIiIhKoxubkymdWsjW3kueum3nINtxtUWV18J1ZxO7CaiLDLUSGh5ESH8WUwcmM7JvQ5WMtEWnw8De7+M8XO/ni58dqsxfpdRTg6aVcbg8Pf7Obh77ZRXJcJH86dyJnHtOv3bmilb4BxqebDvLNjkLsLjeZfaI5ZmAyEwf2YdHOItZnl3Ph9EHcc+6EbilM5/F4+OVbm3hn3QEev3Iap0/s3+VtEBEREfF4PNz44lq+2VHAo1dM5/SJ/bq7SSLSwcpq7Mz9x9eMSE/grjPGsnB0eov3WB6Ph2qbk4JKG8lxkaQnRHdxa0UaU4CnFyqptnHHGxtYsquY86YM4J5zJpASHxXy+1bUOfh8Sz7L9hSzJa+SPUXVJERH8PcfHsPZkwZ0QMuDZ3W4uPTJFWzIKWfK4GROnZDJmRP7Myw9vlvbJSIiIkeP55bu408fbeOPZ4/nRwuGd3dzRKSTfLLpIH/9ZBsHK6xMGZzM5EFJ7CupJaukhhqbE4vFggWotnnLY/iN79+HhWPSuXzWEIam6T5Fup4CPL2Ix+Nh8a5ifvP2Jkpr7fz53AlcMnNwp1V4r7E5CbNYiI3qGfnf5bV2Xl6RxRfbCth0oAKAk8dlcuvxI5g+NLWbWyciIiK90Rdb83l77QEq6hxU1DkYlBLHHSePZuLApEbP25JbwQ8fXcaxY9J56uoZ2mFH5Ahnc7p4Z20ujy3aTXmNg2Hp8QxNiyMpNhK3x7sJTXx0BP2SoslIjCG3vI7FO4tYl13GwORYvvrFcV2yMY1IIAV4eoEqq4P3N+Tx4rL97CqsZmhaHI9cPu2QgcfRJK+8jjdW5/DC8v2U1zqYMyKVBy6ZSr+kmGafn1teR05pLXNGpHVxS0VERKSn2l9cw2kPLCYlLoohaXH0iYlkTVYp5bUOzjqmP1fPHcqg1DgSoiL4waNLqbO7+N/PFnbIymkR6T08Hk+bg7qfbcnnlpfXdvnmNCKgAE+Ptbuwmg825LJsTwkbc8pxuj1MHNiHa+YO45zJA7Srgk+t3cnrq3K49wuT+OgInrx6BlMGJwPeqPt3ZhGvrcpm0c4iPB547IppnHGMaviIiIgc7TweD1c8vZLNByr46s7jyOzjnSSqtDp4evFenv5+X6PUizALvHbjHGZrskhEWuF2ezjzv0twuNx88fPjVDRdupQCPD3Qt2Yht72yDqvDxaRBycwbmcbJ4zOZOjhZy4FbsCO/khteWENRlY2bjh3Bjvwqlu0upsbuIiMxmktmDmbxziL2Ftfwye0LGZIW191NFhERkW705pocfv32Jv76g4lcOWfoId8vrbGz8UA5+RVWDpbXMXlwMieNy+yGlopIb/PJpoPc9uo6HrpsKudM7t5apnJ0UYCnh3ljdTa/e28LY/sl8sw1M1tMOZJDlVTbuPWVdazaV8rA5FiON/py0rgMjh3dl4jwMHJKaznzv0sYkR7PW7fMO2T7eBER6bmW7i7mrTU5/OTEUYzK0La1EpqiKhsn37eIMZkJvHHTXMI0wy4iHcjt9nDaA4uxWOCznx2rc4x0mZYCPF2/F7bw0Ne7uPfLnRw7pi+PXjGtW7Yk783SEqJ57cY5FFXZyOwTfchqp8Gpcfz7wsnc8vJa/vG/HfzxnPHd1FIREWmrHfmV/P3THSzaWQTAsj0lvHnzXO2iKCH5x/92UGd38fcfTtKNl4h0uLAwCz85cRQ/e30Dn2/NV4kI6XZa2tDFHv7GG9z54dSBPHPNDAV3ghQeZqFfUkyLqWynT+zHtfOG8ezSfSzdXdzFrRMRkbY6WFHHr97ayBkPLmF9dhm/P3McH9++AKfbw+VPrSCntLa7myi91MGKOj7YkMuVc4YyKiOhu5sjIkeosycNYER6PP/63KQuoJ6XdJ1dBVU8vmgPv3xrIz94ZCln/XcJX2zNp7uzlbqDUrS60BOL9vD3/+3g/KkD+c9Fk1WIq5NZHS7OeHAJTrebz+84lrgoBdNERLpTXnkd35lF2J3eAfCBsjpeWpGFxwPXzh/Gj48fSXKcd9eirXkVXPbkCpLjonjn1nn0TYzuzqZLL/Svz3bw+KI9LPrVCQxOVU0+Eek8y3YXc/nTK7lx4XB+f1bbswc8Hg9vrT1AZZ2Dq+cOU2mJdrA5XXy08SCvr8pmTVYZABmJ0Yzsm0BRtY3dhdUcb/TlnnMmHJGrgVWDp5u43R625FXw/vo8nl26j7Mn9eeBS6YQEa5f3q6wal8pFz+xnOvmD+PucyZ0d3NERI46dqebz7fm8+aaHL7fXUzTYccPpgzgzlONZm/AN+SUc9HjyzhvindiRI4e3+wooKDSxlmT+tMnJrLdr6+zu5j3j6+ZNTyVJ66a0QktFBFp7PfvbebVVdm8fctcpg9NPezz6+wufvfeZt5bnwvAyL7x/OW8icwbld7ZTe31Vu8v5a53NrGnqIYR6fFcOmsw508dVD8Z5HC5eXF5Fvd/uROHy81/L5vKaRP6dXOrO5YCPF3I7fawcl8pH23K48ttBRRV2QizwA+mDuSfF0wiUsGdLvXHD7bw0oos3rp5LjOGHf5kKyIioTtYUcerK7N5bVUOxdU2BibHcsG0gZw3dSCpvlU6kRFhh01V/vv/tvPEor28f9t8pgxO7oKWS3fbfrCS8x5eit3lJjoijNMn9uPGhSOYODCpze/x2qpsfvvuZl6/aQ5ztN25iHSBapuT0+5fTHREGJ/+bCExkeEtPje7pJabX17LjvxK7jhpDJMGJXH3h1vJLq3lmrlDuefcCdpVuRkVtQ7+9fkOXlmZzaCUWP583gROMDJaPFaFlVZuemktmw6U8+fzmt9JsbdSgKcTFVZaWbyrmANlteSU1vH97iIKKm3ERoZz4tgMThqXwfFGBqnxUd3d1KNS/ck2Mox3b51Xv/xfREQ6lt3p5psdBby15gDf7SzC7fFw0tgMrpo7jIWj0oMqcltldXDCfxYxKCWWd2+dp0K5Rzib08V5Dy+luNrGA5dM5bOtB/lwQx5Ot4eXb5jNtCEph30Pj8fDqfcvJjI8jE9+ukA3SSLSZb7fVcyVz6zknMkD+OPZ45tNLzbzq7ji6ZXYnS4evGwqJxgZgLe8xN8/3c4Ly7P4f+cfw+Wzh3R183uskmobT3+/j5eWZ1Frd/Kj+cP5xalj2lSCo9bu5PZX1/P1jkJ+dtJofn7KmC5ocedTgKcT3f7aej7amIfFApmJMRwzKIlzJw/gpHEZqvvSQyzbU8y1z65mZEYCL18/i7QE1XIQEelIW3IruPa5VRRX28lIjOaH0wZxxewhHVL75K01Ofzq7U3cf8lkzp86qANaKz3VPz/bwWPf7eHpq2dw8vhMAAoqrVz8xHLKauy8dtMcJgxofSXPkl1FXPXMKv5z0WQunK7+IiJd6/4vd/Lwt7uJCg/j2vnDuH7BcNJ99x5b8yq48umVRIaH8eqNcw4pAO92e7j2+dWs2FvCu7fOa9fKxcD3+OXbG3lvfS4WIMxiYUxmIv++aNJhz5+t2VdcQ43NGVSb2svt9rDtYCUr95Wyal8Ji3cWY3W6OOuY/vzkxFGM7denXe/ndLn544db+WZ7IUvvOvGIqIWrAE8nsjpc5FdY6Z8cQ3REy0vxpHst2lnETS+uYUhqHK/cMJuMPjHd3SQRkSNCnd3FWQ8todbm4u8/PIaFo9M7tNac2+3h/EeXkl9p5es7j9cOlEeoNfu9dfMumj6Yf144qdH3DpTVctHjy7E73bxx89wWd8WyOV1c8sQKDpTVsvSuEzUuE5Fusbeomge+2sVHm/IAmDQwiTkj0nh9dQ7xUeG8euOcFgv/llTbOOu/3xMVEcbHP13Q7jpkf/5oG88u3cdF0weR2ScGp9vDu+sOUFZr55enGty4cESbV8PW2V18vCmPt9YcYNX+UsLDLPz30qmcNanztoPfkFPO3R9uZWNOOQBDUuOYPyqd6xcMD3lHRLfbc8SsBFaARwRYvqeE619YTWafGF65YTYDkmO7u0kiIr3e3R9s4YXlWbxyw2zmd1JxyHXZZVz42DKmDknh2WtnkhTb/sK70rlKa+y8tiqb8lo7g1PjGJQSy+RByW1aNetyezjtgcVYHS7+97OFJDZzQ7O3qJqLn1gBwPPXzTxkFtnj8XDHGxv4YEMeD102lXMmD+iYH0xEJEi7Cqr4dHM+i3cVsSGnnIHJsbxyw+zDrm5dm1XKJU+sYO7INB65YlqbgzxPL9nLXz/Zzo/mD+eP5zTs5lVaY+e3727i860FGJmJzBmRyuTByQxPjyc8zEKYxUJcVDgDkmOJiQynqMrGS8v38+KKLMprHQxPj+fC6YP4dkch63PKeeTyqZw+sWODPKU1dv75vx28sSaHvonR/PzkMZw4NoN+SZqUb44CPCI+a7NKufbZ1STFRfLajXPalD5gdbhaLZQmInK0WrSziGueXXXIYLIzfLblILe/tp7RGYm8eP2s+iXv0r1yy+t4ctEe3liTg9XhLYxsc7oBiAoP4+xJ/bl63rBWi2S/t/4AP39jI49eMY0zj2n5pmF3YTVXP7OSSquTJ6+ezryRDQHF+74w+e83u/nVaQa3nTCqw34+EZGOUG1zEhMR1uYVrm+szub3721hcGocj185HaNfYqvPf2/9AX7x5kZOn9CPRy6fdshKFY/Hwzvrcnl7bQ6bD1RQY3c1+z7pCdFUWh04XG5OHpfJ9QuGM3t4KhaLhWqbk6ufWcmmAxU8duV0TvGl0oZqya4ifvHmRspq7PxowXBuP3FUs4F+aaAAj0iATQfKueqZVcRFhfPKDbMZ0bf55X5ut4e/fbqdF5bt57JZQ/jpSaObLZYmInI02ltUzaVPriA5LpIPf7KgSwLhi3cWcdNLaxiQFMsLP5rVITV+JHhZJTX84JGlVNucnD91IDcdO4KRfRMoqraRVVLLxxvzeHvtAWrsLs6a1J8HL5lyyM2N0+Xm5PsWERsVwSe3Lzjs8vmDFXVc/cwqskpquWbeUCLDwyirtfPaqhwunjGIf14wSYWVReSIsGpfKbe9uo5qq5M7T/WuaBmeHt/oHGd3uvl/n27n+WX7mT08lRd+NOuw12OX28Puwmryyutwezy4Pd5NDXLL6sgtryMmMpyr5g5lZDP3SJVWB1c9swozv5LPfnZsi6lmgbYfrGRtVhnD0+MZnZlA34RoKq1OymrsvLwii6e/38fojAQevHQq4we0r77O0UoBHpEmtuVVcuUzKwkPs/BaM0XOHC43v3lnE++uy2XWsFTWZpcRExHG9QuGc5zRlwkDkoiJDCe3vI4Ve0rIKavlh1MHMSRNNxuHszWvgpF9E7QqSqQbWR0uXG7vGCAi3NJirZKSahufbc1nfXY5UwYnc+LYDPrERvLQN7t49vt9xESE8/rNhy9825HW7C/lR8+vJizMwsOXTWPB6M5JC5PWVdQ6OP+xpZTW2Hnn1nnN3giAd9b66SV7eeCrXZw/dSD3XjS5URDnzdU5/PqdTTx51XROndCvTZ9dXmvn1pfXsXxvCRFhFsLCLJxg9OXhy6cR2YH1n0REulthpZWfvLqeVftLAejXJ4Ypg5MZlh7PsLQ4Xludw8accn40fzh3nTGWqIjOPwfmV1g55f5FjO/fh9dunNNiYP5AWS33fbmT99bnEhh2sFho9PXVc4fyuzPH6d6gHRTgEWnGroIqLntqJQCv3zSbURnepY/VNid3vL6Br7YXcOcpY/jJiaPYW1zDvz7bwedbCwAID7OQFh9FYZWt/v0iwixcOH0Qtx4/kkEpcYSHWai1O/l2RxGfbM5jT2ENd587vtGS8qPNG6uz+c07m5k6JJlnrplJary2rRfpSh6Phwe+2sVD3+zCF98hPMzCvJFpnD2pP/NGprO3uIaNOeWs3FfCir2luNweEmMiqLI6AYiLCqfW7uLC6YP49ekGGYldnx+/v7iGm15aw+7Cau46Yyw3LhyhVRtdyOFyc82zq1i9v5SXr5/N7BFph33NI9/u5t+fm1w+ewh/+8FELBYLdqebE/7zHWkJUXxw23z9H4qINMPj8bC3uIYVe0tYvqeEbXmVZJfW4nR7SIyO4F8XTuKMVtJbO4N/TP+XH0zkqjlDD/n+Syuy+MvH2wC4bv4wLp05hLzyOnYVVFFcbSc5LpLU+ChGZSQwaVByl7b9SKAAj0gLdhdWcemT3iDPw5dPZfHOIl5akUW1zcmfz53AVXOHNXp+YaWVjQcq2JhTzoGyWiYNSmbuyDSS4yJ5/Ls9vLYqB7vLV3sgIgyPx4PD5SE9IZrYqDAKKm08fNnUNs9SHkmW7Snm6mdWMbZ/IrsKqhmQHMsL183SqieRLuJwufndu5t5a+0BzjqmP5MHe1fdlFTb+WxrPlkltfXPtVhgdEYCp47vx1mT+jO2XyJ7iqr5ZkchZn41V8wZwrQhKd31owBQY3Pyy7c28r8t+UwdkszvzhzHzGGpIb2n0+XmjTU5vLwim9nDU/npSaMViG6izu7it+9u4v0Nedx70WQuaMdW5P5t0GcNS2XSoCRqHS5eXZnNc9fN5AQjoxNbLSJyZHG63OSW15EUG0lyXNdfpzweD1c9s4r12WV8/vNjGZTSMJ5/cfl+/vjBVo43+vK3849hoDa26XAK8Ii0YndhNZc9tYKiKhsWC5wxsR+3HDcyqGjywYo6Pt2cT5XVQZ3DW7zsuDF9mT08jSqrg2ufW83m3Ar+ecEkLmzHoLi321tUzfmPLiMjMZp3fjyPnflV3PDiGiLCLLx8w2zG9lO+rUhnqrU7ufXldSzaWcRPTxrNz08e3Wi1hMfjYWteJeuyyxiVkcAxA5N6RYFDj8fDW2sOcO+XJgWVNk4dn8nPTh7d7pQxl9vDl9sK+PfnO9hTVMOYzAR2F1YTHxXBrSeM5PoFw1vdctvj8XT46hOrw8XKfaV8u6OQ73cXc83coYdMOnQ0u9PN4p1FlNbaiY0MJy4qnCGpcYzsm0BYmIW1WaX88q1N7Cuu4c5TxnD7SaPb9f4ej4dHv9vDp5sPsruwGpvTzcxhKbx581yt3hER6WVySms57YHFjM5I4KZjR3Kc0Zf31+fyh/e3cPK4TB69YlqXpIwdjRTgETmMfcU1vLfuAOdPG8TwNhQLC1aNzcktL69lya5iPvzJ/B69JLHO7uLlFVmcN3VASCkYVVYH5zz0PVVWJ+/fNr++KOqeomouf2oFEWFhfPiT+W3aSldEgnPXO5t4c00Ofzv/GC6bNaS7m9Phau1Onv1+H48v2ku1zcnC0encfOxIZgxLaTan3+PxUGt3caCsjg825PLuulzyK62M6BvPXaeP5ZTxmewurOafn+3gq+2FnDWpPw9dOrXZOgNbciu47vnVVFkdpMVHk54YzQXTBnL5rCFt3i2lqaIqG+c9/D15FVZiIsPISIwhr7yON2+Z2yErpzweD0t3l1BYZSU2MpyoiDCW7Crmgw25lNU6Dnl+QnQEozMT6rf5/deFk0JON3a5PRwoqyUtIZqE6IiQ3ktERLrHBxtyuefDrZTVOogKD8PucnPi2Aweu3JaqxMjEhoFeER6kCqrg9n/72vOntSff104ubub0yyny83NL63l6x2FjOvfhzdvnhP0bP5v393MG6uzeePmuYekT2w6UM5Fjy9n8uBkXr5+tqL8Ip3gW7OQ655bzS3HjeSuM8Z2d3M6VUWdg1dWZvHs9/sprvbWSOubGM2ApBjcHqhzuKi1OSmttWN1eNNpwyzelZYXzRjMKeMzDynS+8SiPfz9fzu4/cRR3Hmq0eh7uwqquPiJ5cRFRXDmMf0oqbazp6iajQcqGNsvkXvOncCcNtSnCeTxeLjhhTUs2V3Mfy+dwvFGBjanm7P+uwSPBz796UKS4oJfXbUuu4y/f7qd1fvLGj0eFR7GKeMzuXDGIEZnJFBnd1Fjd7G7sJpNB8rZnFvBpIFJ/Or0sQrIiIhIPafLzbrscr7clo/N6eb3Z41TcKeTKcAj0sPc9c4mPtiQx8rfn0SfHpYG4fF4+M07m3hzzQEumzWEt9bkMGdEGs9eO7PdAZilu4u54umV3HTsCH535rhmn/PBhlx+9voGLps1hP93/kQt0xfpQBW1Dk59YBFJsZF8dPuCo2bAZXW4+Hp7IXuLqjlQVkdeRR0RYRbioiKIiQwnNT6StIRo0hOiWTg6ncw+La9S9Hg83PXOZt5Yk9Oo5kxWSQ0XPb4cD/DWzXPrt4r1eDx8tiWfv36yndzyOgYmxzImM4Ex/RI565j+h125+dqqbH777mb+7+zxXL9geP3j67PLuOjx5Zw0LoPHr5ze7nNlRZ2D/3t/Cx9uzCM9IZqfnzKa+SPTqbW7qHO4GNk3vlvqOIiIiEj7KMAj0sNsOlDOuQ8v5c/nTeDqTq6p0F73fmHy0De7+emJo/jFqQZvrcnhV29v4gdTBnDfxVNa3AqxqRqbk9MeWExUeBif/mxhq1sf+gtvnj2pP3ecPOaQbetFJDg/f2MDH23M4/3b5jNxYNdtZX6ksTu9u0atySpl8qBkX3pXLeFhFt64eS5jMhMPeU2d3cWrq7LZmFPOzoIq9hbVYHe5mTsijVuOH8mxo9MPCdLsL67hzP8uYeqQZF760exDzrdPLd7L3z7dzqCUWCJ83xvRN4G5I9KYOzKN8f37NHuO3nyggh+/upaD5VZ+fMIobj52BPFahSMiItIrtRTg0ZVdpJtMGpTMxIF9eHVlNlfNGRrUqpVKq4PHv9vD4NQ4Jg9KZkxmQtD1Hvw+2pjHQ9/s5tKZg/n5KWMAuGjGYAoqrfzni530S4ptc4rHvz7bQW55HW/dPLfV4A7AL081CLdYeOb7fXy6+SDnTRnIb88c26baP263h40HyhmcGke66viIAN76Jg99s4v31udyx8mjFdwJUVREGI9fOZ3fvb+Zshrv9q5Gv0RuWDi82eAOQGxUeKMVOFVWB6+vyuGZ7/dxzbOrGJ4ez4XTB3H+1IFUWZ2s2FvCKyuziAiz8J+LJjcbqLl+wXBsThe7CqsB7//ztrxKvtlRCMAxA5P403kT6uv0VFkdvLYqm/98vpP0hKgOq+EjIiIiPU+HrOAxDONZ4Gyg0DTNib7H7gFuBIp8T/udaZqfNn2tVvDI0ezVldn87r3NvHPrPKYPbf+A+xdvbODd9bn1XydGR/DoldNYOLpvUO3JKa3lzAeXMCozgTdvntuoDoXH4+EP72/hlZXZ/OncCVwzb1iz72Fzuvhk00FeWJ7Fxpxyrps/jLvPmdDmNhRX23hy8V5eWLafgSmxvH7TnBaDPFaHi/fX5/LUkr3sKaohMtzCmcf058o5Q5kxNEWpXnLUyimt5RdvbmD1/jLOnzqQf1046ZC6MtJ97E43H2/K443VOazcV9roe4NSYvnzeRM4cWxmu96zoNLKNzsKeeCrnRRU2vjh1IF4gP9tOYjV4eYEoy/3XTyFFG35LiIi0ut1aoqWYRjHAtXAi00CPNWmaf7nMA1TgEeOWtU2J7P/9hWnT+zPvRe3r9jyZ1vyueXltfz0pNFcMG0gG3LKefTbPeSW1/HmzXMZP6B92447XG4uenw5e4qq+fSnC+t3ugrkcnt8hZcLeOyKaZwwNoMdB6vYdKAcs6CKnQXVbD9YSZXVyYi+8Vw9ZyhXzBka1I3lqn2lXPvcKvonxfD6TXPpm9h4Zc6O/Eque241ByusTBjQh6vnDmVHfhVvrz1AldXJ2H6JXDFnKOdPHahioHLEy6+w8tzSfeRXWimtsbMhuxwP8NcfTOQHUwd2d/OkFVklNXy6OZ+0hCjmjkhr9tzbHjU2Jw99s5tnvt9LTGQ450wewEXTBzFlcLKC3iIiIkeITq/BYxjGMOBjBXhE2ud3723mnbUHWPm7k9pc3LKk2sap9y+mX1IM7982vz6AcrCijh8+ugy3x8N7P57PgOTYNrfjH//bweOL9vDI5dM4a1L/Fp9XZ3dx+dMr2JJbgQULdpd3F5pE3xa6Rr9Ezp40gHkj00K+mVi5t4Rrn1vNoJRYnrx6Rv329euyy7juudXERobzn4smM39Uw2fV2p18uCGPl1ZksTWvkviocM44pj9nTerP/JHp2qVLjjilNXYufHwZOaW19E+KJTU+iiGpcfzyVIMhaaEFC6T3qqh1EB0Zdtj0WBEREel9uivAcy1QCawB7jRNs6zp6xTgkaOdmV/F6Q8u5vr5w/nD2eMP+3yPx8OtL6/jmx2FfHT7Aox+jWs/7Miv5KLHltMvKYafnzKGGUNTyGhld5iyGjv//sLk1ZXZXDZrCH//4TGHbUNpjZ1/f27SJyaCyYOTmTQoiYHJsZ0yO7x8Twk/en41dQ4Xxxt9OXZ0X/7zhUnfxGhevn52i7PdHo+HDTnlvLIym8+35lNldZIUG8mvTjO4cs7QNn22y+0hvI0FpUW6Q43NyeVPrWBHfhUvXT+bWcNTu7tJIiIiItLJuiPAkwkUAx7gL0B/0zR/1EzDFOCRo95v3t7Eu+sP8MXPj6tfpdKclXtLuP+rnazYW8pvTh/LrcePbPZ5y3YXc/NLa6myOQEYnBrLjKGpzBiWwmTf9rzVNic7DlbywNe7qLI6uWbuMH59utEjZ3sLK628tiqHV1ZmUVhlY2y/RF780axWA1eBbE4XS3YW89yyfSzdXcItx43k16cZre4G9sXWfH777mZmj0jlvoun9MjjIkc3u9PN9S+sZtmeEh6/cjqnjG9fzRYRERER6Z26PMDT1u8pwCMChVVWjv/3dywcnc4TV81o9D2328OiXUU8vWQvS3eXkJ4QzY+PH8k184a1urrE7nSzNa+CtVllrNlfxpqsMoqrbYc8b9bwVP583gTG9mtfzZ7u4HC5WbanhKlDkukTE9nu1ztdbv744VZeXZnND6YM4F8XTj4kZavO7uIvn2zj1ZXZDEuLI6u0lmlDUnj66hlBFSe1O918uDGPcf0TmTBAuxhJx6izu7j1lbV8ZxbxrwsncfGMwd3dJBERERHpIl2+TbphGP1N0zzo+/J8YEtnfZZIb5eRGMOPjx/Jf77YyfI9JcwdmUZhlbW+lkxWSS19E6P5w1njuGL2UGKjDr+aJCoijKlDUpg6JIUbFnpTlrJLa9mSW0lkuIWE6AiS46IY1z+x1xTejAwP47gxwe0QBhARHsbffjCRgcmx/Ptzk9zyOh65Ylr9Ll1bciv42evr2VNUw83HjuDOUw2+2l7AHW9s4ILHl/HMNTNbXWEVyO328PHmg/znc5Ps0loSoiN4+YbZTBmcHHT7RQAq6hxc//xq1maX8fcfHqPgjoiIiIgAHbeL1mvA8UA6UADc7ft6Ct4Urf3AzQEBn3pawSPiZXW4OOneRURHhpGRGM3KfaV4PDBjaApXzxvG6RP6qUBwB/poYx6/ensjybFRPHrlNNbuL+Nfn+8gNT6K+y6ewvxR6fXPXb2/lBteWIPV4eKOk8dww8Lhre4MVlhl5ZaX1rIuu5yx/RK59fiR3PflTkpr7Lx6wxyOGaSVPNI2RVU23lt/gNX7y0iKjSQtIYpFZhF7iqp54JKprRZEFxEREZEjU6enaAVLAR6RBp9sOshtr65jRN94zp40gLMn9WdMZuLhXyhB2ZZXyU0vreFAWR0Ap47P5J8XTGo2FSu/wso9H27ls635jO2XyGkT+uFf+DRlcDLHju5LWJiFXQVVXPvcakpr7Pz5vAlcMG0QYWEWcsvruOSJ5VRZnbx245x2b2MvR5ftByu594udfGsW4nJ7GJ4ej9XhoqTaTkxkGA9fPo1jQ1jNJiIiIiK9lwI8Ir1ESbWN1PioXpM21duV1dj5f59uZ9rQFC6dOfiwx/3zrfn8+aNt5JbXNXp8UEosZ08awCsrs4iOCOfZa2cwyVfQ2i+ntJaLHl9ORLiFT3+2MKg6QnJkc7s9PLt0H//6zCQhJoKLZgzioumDGJXhDfR6PB7cHrS7m4iIiMhRTAEeEZEOEnjetLvcfLmtgFdWZLN8bwmjMhJ47tqZLW7fvjarjIufWM4ZE/vx0GVTFcgTwBvY2ZRbwX1f7mTxziJOHpfJvy6cRGoQhb1FRERE5MjW5UWWRUSOVIFBmeiIcF863QBySmtJS4giLqrlU+v0oSn84pQx/Ptzk4Wj07lk5pCuaLL0EB6Ph2V7Sli6uxi3L05YWGVl8c4iin3pV3/9wUSumD1EwT8RERERaRcFeEREOkhLq3aauvW4kSzbU8zdH25l2pAURrezzpLT5WZTbgV9E6Lb/JnSversLt5am8MLy/azp6iG8DBLfZpVQnQEC0alc+LYDI4b07fZGlAiIiIiIoejAI+ISBcLC7Nw/8VTOOPBJdzw4hreuXUe6QnRzT63qMrG3qJqSmvsFFfbWJNVxndmERV1DsLDLJw/dSA/PXE0Q9IU6OmJPB4Pn27O52+fbCOvwsqkQUnce9FkzprUn5jI8O5unoiIiIgcQRTgERHpBhl9Ynjqmhlc/tQKrntuNa/fNIf4aO8puc7u4ott+byzLpfvdxXVp/IApMVHcfK4TI43+rIhp5yXV2Tx3vpcfnO6wU3Hjuymn0aak1Nay6/f3sTyvSWM69+H+y6ZwpwRad3dLBERERE5QqnIsohIN/pmRwE3vriWeSPTuPW4kby3PpdPNx+kxu5iYHIsP5w2kFnDU0mLjyYtIYq+CdGEBeygVFhp5XfvbeY7s4hPfroQo1/70r2kcxRWWrng8WWU1zr49eljuXzWEO18JSIiIiIdQrtoiYj0UG+uyeHXb28CvPVYzjymHz+cNohZw1IbBXNaUlpj58R7v2NMRiJv3Dwn6OK8tXYne4tqKKmxU1pjY3RGIhMHJh32dZVWB794YwOr9pUyMCWOwSmx9E+KIS3BG5RKi48iLSGa1PgoBqfEERURFlT7eouKOgeXPLGc7NJaXr1xDlMGJ3d3k0RERETkCKJdtEREeqiLZwwmPioCp9vNqeP7ERvVvtosqfFR3HX6WO56dzPvrsvlgumD2vX6apuT55fu46kl+6ioc9Q/brHATceO4BenjCE6ovk25VdYufa5VewurOYHUwdSWmNnX3ENK/aWUGl1HvL8aUOSefuWeW0KXPVGVoeLG19cw56iap69dqaCOyIiIiLSZRTgERHpAc6a1D+k1188YzBvrMnh7//bzsnjMkmKizzsa6wOF88v28/ji/ZQXuvgpLEZXDB9EH0To0mKjeS5pft4YtFeFplF/OOCSYcEK7bkVnDji2uosjp57rqZLBzdt9H37U43ZbXe4tClNXaW7Snhse/28K1ZyEnjMkP6eXsij8fDb97ZxOr9pTx46dRDjoeIiIiISGdSipaIyBFia14F5zz0PWdPGsD9l0xpseaLy+3hvfW53PuFycEKK8cbffn5yWOY3Mxqk293FPKbdzZRWGVjzohUblw4gqiIMJ5eso9FO4vISIzmuetmMmHA4VO5HC43x//7OwYkx/DWLfNC/XF7nOeX7uOej7Zx5yljuP2k0d3dHBERERE5QilFS0TkCDdhQBJ3nmrw789Nwizwn4smExHeUO/G7fbw+dZ8Hvx6Fzvyq7xbdl88mXkj01t8zxPGZvDVncfx+qpsnlu6n+tfWANAekI0d54yhivnDCUlPqpN7YsMD+OGhcP500fbWJtVyvShqaH9wD3Imv2l/PWT7Zw8LoPbThjV3c0RERERkaOQAjwiIkcQf3Dh35+b2F1u/vqDY9hXXMOO/EpeWp7FjvwqRvSN57+XTeXsY/q3qRZOn5hIbjp2JNfNH85nW/JxuT2ccUy/FuvytOaSmYP579e7eOy7vTx9zZER4CmssvLjV9YxKCWWey+ecsTWFxIRERGRnk0BHhGRI8xtJ4wiOiKMv36ynU8359c/Pjw9nvsvmcy5kwcGtWV3ZHgY50weEFLb4qIiuGbeMB74ahc7C6oYk9n7t3X/68fbqahz8OL1s0iKPXztIxERERGRzqAAj4jIEeiGhSMYmhZfH0QxMhMZlBLbI1aXXDN3GE8s2svji/Zw38VTurs5Idl8oIIPN+bxkxNGMbZfn+5ujoiIiIgcxRTgERE5Qp0yPpNTxve83apS4qO4dNZgXlyexc9PHsPg1LjublLQ/vnZDlLiIrnpuBHd3RQREREROcqFHf4pIiIiHeuW40YSHmbhkW93d3dTgrZ4ZxHf7y7m9hNH0ydGqVkiIiIi0r0U4BERkS6X2SeGy2YO5u21B8gpre3u5rSb2+3hH//bwaCUWK6YM6S7myMiIiIiogCPiIh0j1uOH0mYxcJji/a0+TVOl5uc0lqqrI5ObNnhvb8hl20HK/nVaUZQu4mJiIiIiHQ01eAREZFu0T8plktmDub11dncdsIoBibHHvIct9vDir0lvL32ACv3lZJfacXl9pASF8n7t81naFp8h7fL4/Hg9tDiTmNFVTb+8vE2pgxO5pxJoe0qJiIiIiLSURTgERGRbnPL8SN5fXU2d3+wlR+fMJJjBiZhAdbnlPPNjkI+2pjHgbI6EmMiON7IYFhaHBl9Yrj3C5MbXljDuz+eR2KI9W+qrA6+3l7Isj3F7CyoZndhNZHhFu45dwLnTh6AxdIQ6PF4PPzh/c3U2F3856JJPWJXMhERERERUIBHRES60cDkWG49biT//WY3X20vID4qnIjwMCrqHESEWZg7Mo1fnWZw2oR+xEQ2pEKNTI/nqmdX8fM3NvDkVTNaDbR4PB7yKqxszClnY045eRVWYiPDiIuKIK+8ju92FmF3ukmNj8LITOSCaQPZlFvBz17fwP825/PX8yeSnhANwIcb8/h8awF3nTGWURmJnX58RERERETayuLxeLq1AWvXrvVMnz69W9sgIiLdq6Taxsp9pSzfU4LN6eK4MRksHJPe6u5ULyzbz90fbuXGhcP57RnjGgV5KmodfL4tnxV7Sli+t4SDFVYAosLD6J8cg83hptbuJCE6glMn9OOcyf2ZOjil/j1cbg9PLdnLfV/sxGKBGcNSmD08jWeX7mNYWjzv3DqvxRQuEREREZHOtHbtWqZPn37IYFQBHhER6ZU8Hg//98EWXl6RzcLR6dx70WT6Jkbz/oZc/vLxdkpr7KTFRzFnRBozh6UwdUgKY/sntqso8q6CKl5Zmc2KvSXsyK8iOiKMT366kFEZCZ34k4mIiIiItEwBHhEROeJ4PB5eXZXNXz7eRlxUBGMyE1ixt5Qpg5O5+5zxTBmc3KiGTijKauzU2J0MSonrkPcTEREREQlGSwEe1eAREZFey2KxcMXsocwensrtr21gS24lfz5vAlfMHtrhKVQp8VGkxEd16HuKiIiIiHQUBXhERKTXG5WRyMe3L8DqcBEfrUubiIiIiBx9wrq7ASIiIh0hPMyi4I6IiIiIHLUU4BERERERERER6eUU4BERERERERER6eV6xFr2tWvXdncTRERERERERER6rW7fJl1EREREREREREKjFC0RERERERERkV5OAR4RERERERERkV6uR9Tg8TMM43TgQSAceNo0zX90c5OkFzMMYz9QBbgAp2maMwzDSAXeAIYB+4GLTdMs6642Su9gGMazwNlAoWmaE32PNduXDMOw4D2PnQnUAteaprmuO9otPVsL/eoe4EagyPe035mm+anve78Frsd7TvupaZqfd3mjpcczDGMw8CKQCXiAJ03TfFDnLAlVK33rHnTekiAZhhEDLAai8d6bvm2a5t2GYQwHXgfSgLXAVaZp2g3DiMbbD6cDJcAlpmnu75bGS4/WSt96HjgOqPA99VrTNDccKdfDHrOCxzCMcOAR4AxgPHCZYRjju7dVcgQ4wTTNKaZpzvB9fRfwtWmao4GvfV+LHM7zwOlNHmupL50BjPb9uQl4rIvaKL3P8xzarwDu9523pgTcJI0HLgUm+F7zqO+6KdKUE7jTNM3xwBzgNl//0TlLQtVS3wKdtyR4NuBE0zQnA1OA0w3DmAP8E2+/GgWU4Q0U4vu7zPf4/b7niTSnpb4F8KuAc9YG32NHxPWwxwR4gFnAbtM095qmaccbsT2vm9skR57zgBd8/34B+EH3NUV6C9M0FwOlTR5uqS+dB7xomqbHNM0VQLJhGP27pKHSq7TQr1pyHvC6aZo20zT3AbvxXjdFGjFN86B/xtE0zSpgOzAQnbMkRK30rZbovCWH5Tv3VPu+jPT98QAnAm/7Hm96zvKfy94GTvKtvBBppJW+1ZIj4nrYkwI8A4GcgK8P0PpFQ+RwPMAXhmGsNQzjJt9jmaZpHvT9Ox/vMmORYLTUl3Quk1D9xDCMTYZhPGsYRorvMfUraTfDMIYBU4GV6JwlHahJ3wKdtyQEhmGEG4axASgEvgT2AOWmaTp9TwnsO/X9yvf9CrxpXCKHaNq3TNP0n7P+5jtn3e9L+4Mj5JzVkwI8Ih1tgWma0/Aut7vNMIxjA79pmqaH1qO4Im2iviQd6DFgJN6lxAeBe7u1NdJrGYaRALwD3GGaZmXg93TOklA007d03pKQmKbpMk1zCjAI7yqvsd3bIjlSNO1bhmFMBH6Lt4/NBFKB33RfCzteTwrw5AKDA74e5HtMJCimaeb6/i4E3sN7wSjwL7Xz/V3YfS2UXq6lvqRzmQTNNM0C32DEDTxFQzqD+pW0mWEYkXhvwF8xTfNd38M6Z0nImutbOm9JRzFNsxz4FpiLNz3GvyFQYN+p71e+7yfhLbYs0qKAvnW6L93UY5qmDXiOI+yc1ZMCPKuB0YZhDDcMIwpvUbYPu7lN0ksZhhFvGEai/9/AqcAWvH3qGt/TrgE+6J4WyhGgpb70IXC1YRgWXyG3ioC0CJFWNcn1Ph/veQu8/epSwzCifTuLjAZWdXX7pOfz1aJ4BthumuZ9Ad/SOUtC0lLf0nlLQmEYRl/DMJJ9/44FTsFb3+lb4ELf05qes/znsguBb3yrEkUaaaFv7QiY7LDgre0UeM7q9dfDHrNNummaTsMwfgJ8jneb9GdN09zazc2S3isTeM8wDPD281dN0/zMMIzVwJuGYVwPZAEXd2MbpZcwDOM14Hgg3TCMA8DdwD9ovi99ind7xd14t1i8rssbLL1CC/3qeMMwpuBNn9kP3AxgmuZWwzDeBLbh3cnmNtM0Xd3QbOn55gNXAZt9dQcAfofOWRK6lvrWZTpvSQj6Ay/4dlgLA940TfNjwzC2Aa8bhvFXYD3e4CK+v18yDGM33o0KLu2ORkuv0FLf+sYwjL6ABdgA3OJ7/hFxPbR4PAp4ioiIiIiIiIj0Zj0pRUtERERERERERIKgAI+IiIiIiIiISC+nAI+IiIiIiIiISC+nAI+IiIiIiIiISC+nAI+IiIiIiIiISC+nAI+IiIiIiIiISC+nAI+IiIiIiIiISC+nAI+IiIiIiIiISC+nAI+IiIiIiIiISC+nAI+IiIiIiIiISC+nAI+IiIiIiIiISC+nAI+IiIiIiIiISC8X0d0NEBERkd7BMIxJwB+AGcBAoBooBLYAr5qm+Z7veZ7DvNVw0zT3N3nvU4HPAx56yzTNi1tox0LgLmASkAFUAvnAJuBJ0zQXNXl+BnAHcBYwAu/4Jw9YBNxvmubmJs9/HrjG92Wtr72Fvu/FAHW+72WZpjmsmfbdBDwR8NC/TdP8dTPP+w44zvflCaZpftfCz3sPcLfvyz+ZpnmPYRj7gaHNPb+JAqACGOP7+hbTNOvbZhhGLN7/vxG+h35kmuZzbXhfERER6WG0gkdEREQOyzCMBcAa4CJgOBAFpAJjgQvxBk9CcVWTr88xDKNPM+24BFgMnAkM8rUjHZgIXA4sbPL8WcBm4Ld4A0IJQAzegMZ1wDrDMG5opV1xeINJofwslxmG0V1jLitwE+APuv3dF/Dy+z8agjtfK7gjIiLSe2kFj4iIiLTFXUAk4AZ+CHwJRAOj8QZ3bM29yDRNy+He2DCMOOAHTR6OwRs4erbJ4//n+7sSOBtYDfQBDN975Ae8bx/gA7yrfAAeBv6Kd+XR5cAjvp/pccMwtpimuaKFJt5iGMa/TdM82IafZRgwv8nDg4DjgW8O9/q2arpyKHDVVHPH3DCMp4EbgRTgPuBKwzDGA7/0PaUOuLmj2iciIiJdTwEeERERaYvRvr+rgC9M06zDm760yvcnFOfjXVkD8BxwLWDBuxKmaYDH346DwFLTNN14V6kUAkuaPPdGoJ/v3+tM07w94HtPGYYxDvg5EA78Dji3mba5gFi8Aa6fteFnucLXdnxt/5Hv31fRgQGeIPwKbyBuAHCFYRgvAH/EG+AC+KNpmnu6q3EiIiISOqVoiYiISFvk+P5OAnYahvG4YRjXGIYxvAPeOzCl6QEaAkbHGYYxqIV2GMB2wzAeMAzjUsMw+jfzvqcG/PuFZr4f+NiJhmGEN/OcV3x/32QYxsBmW9/Ylb6/ncBv8AaiAH7oq3fTLUzTrAB+EvDQu8AC37/XAvd3eaNERESkQynAIyIiIm3xAA11XAbhTed5HthrGMYywzCmNPciwzA8Tf5saPL9TOBk35e7TNPcBLzj+9qCd0VMoPsC/j0G76qa14BcwzD+50uR8hsS8O/9zTRvX8C/44G0Zp7zNt4aPjF4V/m0yDCMGXhrEgF8a5pmMfCe7+s+NL9CqMv4imC/6/vSv2LKCdxgmqare1olIiIiHUUBHhERETks0zQ/Bk4CvsWbthRoLvCRYRgJh7zw8C7DmyIFDYGddwK+f2Xgk03TfBS4GFhJQ8AJvMGg04F3O7igsQe4x/fvG4DBrTw3sK3N/SxNiy93h58A5QFf32ua5obuaYqIiIh0JAV4REREpE1M0/zWNM0T8e5adRbwOODwfXsQ3kBP09dYmvyZ0uQpgUGPrYZhTMS7c9Ve32MTm64OMk3zLdM05+Ctr3MB3jQqf7BnKjDS9+/sgJcNa+ZHCkwvqwVKmnkOeFfhbMC7Y9fvm3uCL73rsoCHdvl+lhKgxvfYaYZhpLfwGV3CVyh6Y8BDn3VXW0RERKRjKcAjIiIihxW4ZblpmuWmaX5qmuateNO0/FLb+Z7jgGkBD72ENx1qMw1bd0PAypgm7Sg0TfNd0zSvBL5qph1fBDx2dTNNCAwufd1SmpJpmh7g7qZtaeJUGnbrAvja93NswJv+Bd7NLS5t4fUiIiIiIdEuWiIiItIW7xuGkQe8jjc9qhxvoeNjA56zvZ3v2VKwpKnLDcP4tW/HrNWGYXyDtzbOOryrY6YDk33PdQE7ff9+Gu824P2A6YZhPAj8zfeaS4HbA17z/1prgGmaHxqGsdb3WaH8LFfh3a69qVmGYcQ0eWy/aZo72vi+IiIicpRTgEdERETaIgpvweOmRY/9PvAVSG7EMAxPM889H/igyXuNN02zUYDIMIylwDygP976P1/iXQ1zi+9Pcx42TbMMvDtHGYZxHvAx0Bf4qe9PICfwY9M0V7TwfoHu9r1XI77aQz/wfWkD+pmmWR7w/TC8RZ4H4w3kjDZNc1eTt/lnM5/3IHBHG9olIiIiohQtERERaZP/wxtwWIN3628H3ro1G/DuLnVJO99vITDU9+81TYM7Pi8G/Nu/QuY24Am8dWQK8QZoqvCuKvox8IvANzBNcxUwEfg73pSpGrxBmP1408umm6b5VFsabJrmJ77PaeqHeOsGAXwUGNzxvc5Nw3brgT+LiIiISIexeDzNTayJiIiIiIiIiEhvoRU8IiIiIiIiIiK9nAI8IiIiIiIiIiK9nAI8IiIiIiIiIiK9nAI8IiIiIiIiIiK9nAI8IiIiIiIiIiK9nAI8IiIiIiIiIiK9nAI8IiIiIiIiIiK9nAI8IiIiIiIiIiK9XER3N2Dt2rWe7m6DiIiIiIiIiEhvMX36dEvTx7o9wAMwffr07m6CiIiIiIiIiEiPt3bt2mYfV4qWiIiIiIiIiEgvpwCP1Pt+VzF55XXd3Yxez+Px8MXWfCpqHd3dlF7P7nTz2ZaDWB2u7m5Kr1dea+fr7QW43MqKDVVueR3Ldhfj8ehYhmpXQRUbcsq7uxlHhA055ewqqOruZhwRlu0uJlfjoZB5PB6+2lZAWY29u5vS6zlc3vFQnV3joVBV1Dn4alsBTpe7u5vS6+WV17FU46EeRwEeAaCi1sFVz67k5PsW8cSiPTh00gvaltxKbnppLSfe+x1vrz2gk14IvtiWzy0vr+PU+xfzrVnY3c3p1V5Zmc31L6zhvEe+1w11iB74cieXP72Sq59dxb7imu5uTq/2+/e28INHlvLzNzZQWGXt7ub0aje9uIbTH1zCXz/eRrXN2d3N6bWqbU6ufGYlJ9+7iMe+24PdqfFQsHbkV3HDi2s46b5FvLkmB7cmGIL29fYCbnl5Hafcv4ivtxd0d3N6tddWZXPDi2s49+GlrMsu6+7m9GoPfbOLK55eyZXPrGRPUXV3N0d8FOARwBvN9nggJS6Kv/9vB+c89D2VVq1ACUZZrXemKiYynF++tZGrn12lVRNBKvOtgnK5PVz33Gp+997mbm5R71VWYycy3EJhpY3zH13K44v2dHeTeq2yWgd9YiLYkF3Oafcv5qONed3dpF6rrNZO38RoPtl0kJPuXcTq/aXd3aReq7zWQWZiNM8s3cdJ937HfgUfg1JZ58DtgdT4KP752Q7OfmiJVuQGyT8eio0M59dvb+LKZ1Zq1USQ/OMhjweuf2ENv357Yze3qPcqq7UTHmahpMbGDx9dxsPf7OruJvVaZTUOEmMi2HSggtMfWMz763O7u0mCAjziU2XzXjj+7+xx3HvRZHbkV7FkZ3E3t6p38s+cPnX1DH560miW7Cpm+8HKbm5V71Rt9R7LT3+2kAumDeK1VdlU1GmgHYxqm5OUuCi+vvM45o5I4+kle7W6LEjVNgdj+/Xh6zuPY0haHM8v29/dTeq1qm1Ojh/Tl8/uWEhEmIXXVmV3d5N6JZvThd3l5oo5Q3n7lnkUVtn4YIMCj8HwX8N/e+ZYHrx0CjsLqvlup1aQBsN/DX/8yun84pQxLNtTwpY8jYeC4T+WH9++gItnDOLNNQcoVepbUKqtTpJjI/n6zuNZODqdp7/fp9VlQaq2ORmVkcA3dx7PiPQEnlu6r7ubJCjAIz7+C0dCdCTnThlAbGS4ZlKD5D+WiTERXDpzMABrdCyDUm1zEB5moU9MBBdMH4jHg5bTBqnK5iQhJoLEmEjOmTyA4mo7+0tqu7tZvVK171hm9InhpHEZbDpQrjpRQaq2eo/liL4JzB6expr9+v0ORsM1PILpQ1MwMhNZk6XrTjCqAo7lWcf0JyE6QuOhIPmDZYkxEVyi8VBIqnzHMik2kgune4/l2iydL4Phv4YnREdwzuQBlNc6lF4UpCqbk4ToCPomRnPy+Ay25FVSa1eKcHdTgEcAqPH9MibERBAZHsbUIckaHAYpcEAzIDmWgcmxrNZFOCg1NhcJ0RFYLBamDE4mIsyiwWGQamxOEqMjAJg5LAVANy1B8vdLgJlDU3G4PGw6UNHNrep9PB4P1faGfjljWArZpbUUVKoWT3vV2LwBxvp+OSyVdVllSocJQk3ANTzCPx5S4DEo/mOZEBNBZp8YhqTG6boTpBrfjXRYmIVJg5KICg/TeChI/mMJ3nMlwGr9jgelxuYkMcZ/DU/F5fawIbu8exslCvCIV8OMVTjg/SXdllepQo1B8B+z+ICbljX7S5UOE4Qqa8NFOC4qggkDk3QRDlK11VnfJ0f2TSAlLlKDwyBVBRzL6UMVLAtWrd2Fx9NwrvQPtHUz3X7+NOvA606N3cWOfO2q1V5Nr+Ezh6ViFlQpPTgI/lUnCQH9cm1WmcZDQfBew71j9JjIcI4ZlMQaTR4GJfAaPiwtjvSEKE1qB6na6iQ+ynsspw1JwWJB/bIHUIBHgMDZv0gAZgxNwe2B9UqHabcam5PoiDAiw72/XjOGplBQaeNAmbZcba/AWRbwHsuNOeXYnEqHaa/qgGNpsViYPjRFN9JBCpyxSomPYlRGgoJlQQic3QcYP6CP0oOD5L+GB86kgtJhglHdNCgxNEXpwUGqsTmJCLMQHeEfD6UqPThI1fZDx0NKDw5OTcDKUYvFwoyhqRoPBanGl+4G3vRBIzNR1/AeQAEeAby1ToD62YGpQ5IJs2jJYjCqmgYl6pd/6oTXXtW2hhkr8KYW2ZxutuSqSGN7VTfTL/cW11BcbevGVvU+TpebOoerfsYKvP1yTVaZijS2U9PZ/cjwMKYMVnpwMKqbrOAZmBzLgKQYpQcHIbCeEcCUIcmEKz04KP6VoxaLBVB6cCiqrYdew5UeHJzAFc2g9OBgud2eQwKPSg/uGRTgEaBhQOO/aUmMiWRc/z4a0ATBXzTUb0xmIokxEQqWBcFbGDiy/uvpQzUrHaxqW+N+6R9oa9aqfepXO8YEzqSmUmV1srNQ6TDt0fRGGrz9UunB7VfVzLGcMSxV6cFBaJqiFRcVwcQBfXQND0LTCa+RfRNIVnpwUJpew5UeHLxDx0NKDw5GrcObZp3QJFim9ODu1+kBHsMw9huGsdkwjA2GYazp7M+T4FT7ioaGhVnqH5s5LJX12eU4FIVtl6ZpReFh/nQYXYTbK7AwMEDfxGiGp8droN1OHo/nkH45cWASUREq0the1b6C9InRhw4O1S/bp8bWfFBC6cHt1zRFC7zBMqUHt1+NzUlMZEOaNXj7pdKD2y8wnRUgLMzCDKUHB6XpNTxV6cFBq24ytlR6cHCapllDYLBMx7I7ddUKnhNM05ximuaMLvo8aadqm6NRKgx4o7B1Dhfb8pQO0x5VtsZLP8F7wttVWE15rb2bWtU7BRYV9JsxNIW1WZqVbg+b043D5WnUL6MjwpkyKFkpHO1Uv9ox4FgOTo0lIzFaA5p2qrIdeiyVHhycpilaoPTgYDVddQJKDw5WdTPjIaUHB6fK2tzYUunB7eV0ubE63I2OpXYPDk5zK0e1e3DPoBQtARpv++s3w58Oo1/Sdmm66gS8QQmAtTqW7eKtGxPZ6LGZw1Ipq3Wwp6imm1rV+/hTDgJnUsEbxN2aW0GdXbPSbeW/kQ6csbJYLMwcpiKN7eUPlgX2S3968FoNtNul2urEYoG4yIaAuNKDg9O01gkoPThYzR1LpQcHp+mqE1B6cDAaNpU5NPCo9OD2aW4VLmj34J6gKwI8HuALwzDWGoZxUxd8ngShuRmrfkkxDE6N1YCmnZqbsZo8OJnIcIsG2u3gdnuosTtJaGZlGWig3R7+i3BgYWDwBsucbg8bcsq7oVW9U3X94PDQfplbXkdeudJh2qrGfugKHlB6cDCqbd7C34Fp1koPDk5NM9dwpQcHp2lxf1B6cDD8adbNnStBKx7bo8o/SdNM4FHpwe3TtF6Z34xhqUoP7mZdEeBZYJrmNOAM4DbDMI7tgs+Udqq2OhrNSPvNHJrK6v2lWv7ZDk2LLAPERIZzzMAkVu4r6aZW9T71xduaHMvh6fGkxUexcp8Gh21Vv4y2ybGcNiQFiwX1y3ZoKAx86MoygFXql23W3PJu8AbLau0utuRqd5i2qrY5DjmO0JAeXFqj9OC2am7CCxrSg10aD7VZcwEef3rwKgV42szmdON0ew65hvvTg3XdabvqZurGAEwdkkKYRdfw9mjpGu5fpadxevfp9ACPaZq5vr8LgfeAWZ39mdJ+lALGwwABAABJREFUzaVoASwYnU5xtZ1NGmi3WXPLaAEWjO7LxpxySpR33iYNSz8b30hbLBYWjE5n0c4iDbTbyH8sm/bLpLhIJg1K5tsdhd3RrF6puaKCAOP69yEtPopvdCzbrMbmJDLcQnRE46HIvJHphFlQv2yHGpur2UmaBaPSAR3L9mhaGNhvweh0ymodbMjRDH9btdgvR6ezObeCwiptS90W1S1cw/3jocU7i7TisY1aSitKiI5g2pAUXcPboaaF9P8xGYlkJEbrutONOjXAYxhGvGEYif5/A6cCWzrzMyU4zaUVAZw4NoPwMAtfbstv9HhFnUM3181wuNzYnO5mj+Wp4zNxe+DrJie88lq78lSbUVVfzDb8kO+dMj6T0hr7ITWNVMS6eS0towVvv9x4oIL8ioaBtsfjoaLW0WXt6038hYETmqS7hYdZOHFsBt+ahdidDQNth8tNlVXHsjn+647FYmn0eGp8FDOGpvLFtoJGj1sdLtWLakFzxf0BjhmYRGafaL5scixrbM5G/VQatDQeOt7IICLMcki/rLRqPNQct9vT4rE8ZXwmHg98vV3jobZorri/36nj+1FR5zikmLrGQ82rauVYnjI+k615leQGpFprPNSylsaWYWEWThqXyXdmYaOdB50aD3WZzl7Bkwl8bxjGRmAV8Ilpmp918mdKEFpadZIcF8WsYal8sbVhQFNcbWPBP77huaX7urKJvUJLMwMAEwb0YUBSTKNjuSW3ghl//YqvtivK3VRLhYEBjhvTl6jwML7Y2hB4/HxrPtP+8iU78rXLSVMtLUkGb4AH4MvtDf3yme/3MfcfX2uA2IzqVgKPp07oR5XV2Sjl7U8fbeX0B5YozbUZzRVg9Tt1QiY78qvIKa2tf+zGF9fwo+dXd1XzepVqq6PZa3hYmIVTxmeyaGcRVod3oO10uTnrv0u4+0PNtzWnpX6ZFBvJ3JFpfBlwDS+rsTP/H9/w1JK9XdnEXsFfY6u5fjm2XyKDU2MbXcN35Fcy829f8fnW/EOef7SrbmVseeyYdKIjwhqNLb/ZUcC0v3ypNNdm+IssNze2PHVCPwC+DOiDzy/bz+y/f6XV981orV+eOiGTGruLZXsaxkN//WQ7p9y3WAHxLtCpAR7TNPeapjnZ92eCaZp/68zPk+B4PN5ZluZu/sAb0d5VWM3+Yu+uRS8uz6LK5jxk5kVarnUC3qW0p4zP5PvdRfWz0E8t2YvT7eGbHQWHPP9o11KKFnh32pk7Mo0vtxfg8XjweDw8+t0e3B74zizq6qb2eC0t7wYYlZHAsLS4+hl+m9PFk4v3Umt3sWKvavM0VWN3EhsZTkT4oZfPBaPSiYkMqz+WhVVW3lx9gNzyOswC7XLSVHP1OfxO8QUe/aslNuaUs2RXMSv3lVCpGcBDtJRmDXDK+H7UOVws3V0MwKdb8tlfUstX2wu1WqIZhxsP7S2uYXdhNQAvr8iiyurk6+26hjdVv1tRS+Ohcf1Yuqek/lr/1OJ9OFweTXg1o7VJmrioCBaMSufLbQX1v8+PfusfD+lYNlXdQpFl8NZ4HJWRUD/hZXe6eWLRXqwOd6NAhXhVt5BmDTBvZBrxUeH146Hiahuvrcomv9LKtjxNxHY2bZMuWB1uXG5Ps8sVoWGg/eW2AursLl5ekYXFAmuzy+pnBMWrtWg2eAfaVoebJbuKyCuv4+NNB7FYYOluXTiaai1FC7z9Mqukll2F1azJKmNjTrnvWBZ3ZTN7hdaWd/sDj8v3FFNldfDhhjwKq2zqly2osjafcgAQGxXOwtF9+co30H5xWRYOtzcNRv3yUK0FeIamxTMmM6E+PfipJXuxWMDtgZV7VbixqZZSYQDmjEglITqi/gbwqcXeY1lUZasPVIiXP826aQqm38njGsZDVoeLF5bvx2KB9dnl9YEK8fLfSLc2trQ73SzeWURBpZUPN+ZiscCy3cUKPDZR3UIxW79TJ2SSW17H9oNVrMsuY01Wma7hLWgtRQu8/XLl3lIq6hx8sjmP/Eqrt1/u0TW8qWpr82nW4C2mfpzhHQ+53R5eWp6FzZcWvFTHstMpwCOtzu4DDE6NY2y/RL7cVsA76w5QWmPn5mNHYne6D6mBcrRrLUULYPaIVBJjvANtf4rbDQuGk11a2ygVQQILAx+6ggcaBx6fXLyXlLhILp4+mNX7S1VbookamxOLBeKiWgqW9cPh8vCdWcTTS/Yxtl8ix47uqwFNM1oqwOp3yvhM8iqsrN5fxssrszh1fCbD0+M1+9eMmlZWSoD3WK7eX8aW3Ar+tyWfa+YOIzoiTP2yGdWt9Mv6gfb2ApbvKWFzbgU3HzsSQP2yiZaKqPsNSI5l4sA+fLktnw825FJc7R0POd2eQ2qgHO2q/akwLYyHZg5LITkuki+3FfD8sv243B5uXDiCvAorWSUaDwXyp7u1NLY8cWwmFot3PPT0kr30iYng0pmDNRHbjPqVZa0EeJxuD9/uKOSpxfsYnZHAiUaGzpXNqGllkga8x7Kwysaq/aW8tCKLk8dlMCojQceyCyjAI60WYPU7dUI/1mSV8th3e5g0KInbTxxFRJhFs9JNVB3mWEaGh3Hi2Ay+2l7A66tyOPOY/lw0YzAAy3XCa6ShXzYflMjsE8Pkwcm8tiqbr7YXcOWcoZw0LgOrw836bAUeA1XZnCRENT/LAjB9aAqp8VH8+3MTs6CKGxaOYMGodPYU1TQqviz+lRLN90mAk8ZmEGaBX761kfJaBzcuHMG8kWms3FuiXU6aaKkwsN+p4/vhcnv48SvrsAA3HzeCmcNSWaZZ6Ub8adat9ctTx2dSXG3nrnc3kxYfxR0nj2ZQSqyu4U0cbnYfvP1yfU45D32zm/H9+/Czk0YTFR6mm5YmWls5ChDhGw99vaOQV1ZkcfrEflwy0zse0gx/Yy1tR+3XNzGaaUNSeHNNDp9tyeeKOUPrV0hpIraxapuD2MhwwsOaHw9NGZRM38Ro7vtyJ9sOVnLDwuHMH5VOVkktB8oUeAxUdZgAz4lGJuFhFn799iZKa+zcsHAE80emsXqfJmI7mwI8cthVJ9CwA1RueR03LhxBfHQEUwYns1QDmkZa2jIw0CnjMymrdVBlc3LjwuGMzkggPSFaA5omWss59zt1fCYHyuqIDAvj6rnDmD0ijTAL6pdNVFtbXykRHmbhpLEZZJfWkpEYzbmTBzBvVBqgZclNtVYYGCAtIZoZQ1PJLq1lyuBkpg9NYf6odGrsLjYdUMHLQNXW5ov7+/l3gMoureXsSf3pnxTLvFFpmAVVFFWp4KWfP826uXplfv4doLJLa7lyzlBiIsOZPzKdFXtLVPAywOFWNEPDDlAHyuq48djhxEaFM3VIsoJlTbRW68Tv1PGZVNQ5qLQ6uWHhCEakx9OvT4yCuE20dTyUW15HeJiFa+cNY9bwNMI1EXuIapur1eMYFmbh5HGZZJfWkp4QxXlTBjJ/VDqA+mUTh1vBkxQXyezh3vHQMQOTmD08lXmj0qlzuDQR28kU4JFWCwP7+XeAGpgcyxkTvVXm541MY/OBchW8DHC4PGlo2AFq9vBUJg1KxmKxMG9kGsv2lCjvPEC1zUlUeBjRES3PSvvTtM6fOpC+idEkxUZyzMAkliso0UiNvfWLMDTsHnHNvGFERYQxrl8fUuIiNSvdhLduTMs30uCthwBw48IRWCwW5ozwBcs00G7kcIND/w5QADcsHAHAvJHegfZyFQCv15abP/8OUFERYVw1dygA80alUWl1sjVPgUe/w6VoQcMOUP36xHD2pAGAt19uO1hJWY12HvSrbmW3Ir9jx/QlOiKM6UNTmDYkpX48tHxviXYeDFBjcxJmgdjIw4+Hzpk8gMw+MSRERzB5UJKu4U20tGtwIP81/Oq5w4iJDGdMZgLpCVGa8GqitYL0fqfWX8OHe8dDw70TseqXnUsBHjlsYWDwFmJ95IppPHHV9PrdY+aNSlfByybaku6WGBPJ09fM4N8XTq5/bP6oNBW8bMJbvK3lwQzAmMxEHrl8GnedMbb+sXmj0lXwsonWCgP7nTg2g3svmsz1C4YD3pvruSPTVPCyCW+Ap/V+efnsIdx/yeT6YHhqfBTj+/fRgCaAy+2hxu46bL/86UmjeeKq6UwcmATAxAF9SIyJULAsQMM1vPV++adzJ/D8tTNJT4gGYO5Ib+BRhVgbHC7NGrzjoYcvm8ZTV88g0jcemj8qDY8H7TwYoNraepFl8O4A9cw1M7n3oobx0LxR6ZTW2NmRr50H/apaKWbrN6JvAo9dMY3fnzmu/rH5o9LZpInYRqqtjsNed44b3Zf7Lp7Mjb6JBYvFwtyR6SzVRGwj1W0YW146yzse8gfDk+IimTgwScGyTqYAj7QpRQtg6pCU+kG29+tkYiJV8DJQW4Jl4J21GpIWV/+1f1ZaN4AN2jIzAHDWpP6kxEfVfz1vZJoKXjbRWgFWv/AwCxdMH0RMwAzh3JHpKnjZRFv6ZVxUBOdPHURYQI7/vJFpKngZwF809HD9MiMxhtN8q8vAW7dj9vA0nSsDNKwcbX1l2Yi+CczzpRqA99iOyUzQNTyA/1geboZ/8uBkjhmU1OjruKhw9csAh6uj57dgdDrD0uPrv543UunBTbVl1QnAGcf0J80XwAXv2NLtgVWaiK1XY3MddoweFmbhh9MGERuwMcX8kd6J2D1Fmoj1a0u/jIkM5/ypgxrVPJo30jsRW2vXRGxnUYBH6mesDnfCayo6IlwFL5uotjpbLd7WksGpcQxOVcHLQNU2J/EtbFXbmhlDU1XwsomaII/lfP8Mvwba9Vrbjro180elY3e6WbNfeefQMLEQ3LFM086DAdp6I92ceSPTWb2/FJtTgUcIvl9Ghocxa3iqzpUBqm2uw6ZZN2dAcqx2HmyiJsjrztQhyURHhKlfBjhccf+W+CditeKxQbDjIf9E7Kp9Cjx2FgV4pE055y2ZO1IFLwPV2Nu26qQ580ao4GWgauvhV500x1/wUrN/DQ5XZLklw/0FLzXQBsDudGN3uts0k9rUzOGpRIRZ1C992lKvrCX1dXjUL4HAwsCtr+BpzryRaVgdbjZkl3dwq3qnttQzasn8kens1c6D9aptjuDHQ9p5sJG2rmhuKibSOxGrc2WDmjasaG7OkLQ4BqXE6hru43J7qLUffjVUc2YO807Eql92HgV4hGrr4Yu3tWTG0FQAFWn0qTrMrjCtmT4shUqrU7PSPm0pDNySGcNS2JZXiVODQ8BfN6b9x9JisTB9WAqbtfsT0PZ01uYkREcwtn8im3N1LCG0G+kxmQkkRkfoWPqEMkkzY5j3Gq5j6VW/GiqIFY/Th6UAsEXHEmhbKkxLZgxLocbuIqukpoNb1TsFew0HmD40hR35VdqW2ieUYzljaApbcis7uEW9U1vTrJsTGxXO+AF9dN3pRArwdIG88jo+2pjX3c1okX+JXWvF21oyIDkGgILKrpmx2pFfyaKdRV3yWcEIdrkiwMDkWADyu+hYrtpX2qO3KWxL8baWDEiOxe2B4uqu2dHkq20FPTYv2+PxhDSgGZgcS36ltcsKC7677kCPXRHYliLqrRmQFNtl50q328NLK7J6bM2fttYra47FYmGAr192hRqbk1dWZvXY4ppVIaRopcRFEhMZ1mX9Mr/Cygcbcrvks4JRbXUSF9X+NGvo+mv4zoIqvjULu+SzgtGW4v4tGZDkO5YVXXMtWLO/lLVZPXs8FMo1HLpunP7NjgJ2F/bcAtmhji0LKq1dtsPb++tzKeyi/7f28q/CDeWep6vOlW63h5dXZFFn75njoc6gAE8XeHF5Fre/tr5+QNvTtLV4W3MyEmOwWOBgFy1JfvCrXfzyrY1d8lnBONy2v63pl+QNlnXV8u67P9zK//t0e5d8VjCqglxGC9DfdywPVtR1ZJOa5fF4+Nnr63n02z2d/lnBqHO4cHuCm90H6NcnBrvTTVlt5+/CUVhl5RdvbuT1Vdmd/lnBqLIGP2MF3n7ZVefKddll/N/7W/hiW0GXfF57hZKiBd7zZVedK/+3JZ/fv7elx842NhQGbn+KlsVioX9SbJf1y1dWZvGz1zdQ0QXnk2CEEgxPT4gmPMzSZf3yoW9284s3NnTJZwWj2uYIemzZ3xfg6YprOMCfP97GXz7e1iWfFYxQ+mX92LKLbqbveH0DD3+zu0s+q71sThd2lzuka7jT7aG4pvMDj6U1du54YwOvrOyZ46FQVjRDwzW8KyZONh4o5w/vb+GzrQc7/bN6CgV4OtiSXUWU1TReNZBb7r1AZffQnWhCiWZHRYSRnhDd4QMaj8fDZ1vyDyn8mFteR1GVrcdugR3KjFW/Pv6gRMceS4fLzf82HzzkJJpbVsv+HtonIfjCwAD9+vhn/zr2WFZaHXzXZMa0os7Ro5eSh7rqpLOCZQcr6ljTZKezvHLv/1dP7Zf+JclB/44nxVJldXZ4sH93YRXbDzZeNu6/7mQV9+x+GezgsLOCZRtyyg9Jk80t8x7LHtsvbd4065jI4IZ0/fp0TrBs6e5iSpuOh3zHMqu05/bLYPtkeJiFzMToTumXX2zNP2Q1Xm5ZLWW1DirqemawrMbmCmpVGUBGH+9OUB3dL50tjofqeuw1HEJbHd5wDe/YY1lldRyygqzS6qDS6uzB50rv71B8VHD9sl9S54wt8yush+z8mue/hvfQfhnsBj1+/ZNiqLW7qLR29Hio+pCyIf7x0P7intkvO4MCPB2oyurgmmdX8cLy/Y0e9/+SZvfQAU0ohYGhcwba2w9WccvLa/nf5vxGjzccy575S1pjD37VSXx0BH1iIsjv4Bvpr7YVcOsr61ifU17/WJXvIlxUZeuR2xTWF28LeQVPx/bLV1Zkc+1zqymubpi9qb+R7qF9sq3b/raks1aWPfjVLq57fnWjgXZPP1eGuuqkfycdyz+8v4Vfv72p0WM9vl92wOxfcbWtw+tK3PryWv752Y5Gj9X3yx460PYHJYJJs4bOuYbX2p1c9cxKnv1+X6PH6/tlD70BDLaYrV+/pBjyKzv2Gr6zoIqbXlp7SKq/PyDeYycPbU4SYtq/qgy8xYHT4qM42MGrTr7ZUcitr6xjTUA6ltXhoqTGTlmtg0przwuW+dOsgx1bNlzDO7Zfvr4qh+ueW90oheigv0/20OtOQ72y4PplZ40tH/52F9c+u6rReKinX8NDqf0GnTe2/NNHW/nlW43HQz393rEzKMDTgbJKanF7Dh249PSOVRVCbi90zuzf3mJvPZP9AQNqq8NVX1Olpx7LUPKkgU5ZKr/XN4MfOAsQ+Bk5pV2zBLo9Qr35S46LJDoirMOXJO/11dkJ7H/+QXZRla1H5veGvlLCv1S+o49lDVVWZ6PUrx5/rrSFlqLVWQOavUU1jc6V0POPZaj5+/6BdkfWlai1OzlYYT30Gl7Rs49lldVJYpA3LODtlx1dV8I/HjqkX/bwY9kjr+G+605gv3S43BRU9eyb6ZDHlp2QhukfD+0PWNnoP1dCzwyW1dpdeDzBX8MTYyJJiI7ohLGlr182Gg95j2VpjZ2qHhgsq+qA1GDonGt4jd1FUcDkof9Y9tSNVzpqwqujV4fvLaohq6SmyeRhzz5XdgYFeDqQfyBzoKyhAzld7voBaI+esQppQBPT4b+g/mN1oKzhfQNPqD3xIgyhLaMF/+xfx144/IGdAwGBnMABTU9c/hlqbq+3rkTHz0r7BzKB/bLR4LAHXjxCTdHqm9g5dSX8KRqB50v/Rbig0tYjiwPX9MB0t1q7k8IqG1VWZ6NUjYM9fXbf7iQqIoyoiCDTipI6vqBtdv3vd/OTND31Gl5jcwadCgOdU1eiuWu42+2pP4/02H7ZEdfwDq4r0XAsG45ZQaUV/0f01HQ3b03C0Pplh1/Dm+mXgZ/RE6/hoV53oHOCZc31y7yK3jEeCnZsmRoXRVR4WJf2y+Jqe4+s4RpqilZnpLvZnC7yKuqotbuanTzsqdfwzqAATwfyd5zAFREFVTb8k2I98WQHoRUGBu8vaaXV2aF1cfxBh5xmZgagZw5obE4XDpcn6Nl96JwBjT8XOqeZG2nomf0ylC2U/bwDmo4OPDbTLyt6drCsOsTCwOFhFjI6uK5Erd1JQaX3ZjKnhcBjj+yXIc5YZfbp+Nm/wOMU2C/9y7vzK609MlhWbQ2+uD90zlJ5f35+Wa2j/hzk8Xh6/OxfqJM0nTHQzmpmwqu42obD5R0Q9cRrOIS26QR0Tl2Jhmt44LmyZ094OV1u6hwuEoIo/O3XqdfwskPPld7v97xjGerKUejcYFmL1/AeeCxDTSsKC7OQmRTdof3SH5SA5q/h0MOPZdCb9ER3+CY9OaV19YHv5sbpxdU9t4ZrR1OApwP5LxwFVdb64sD+k11SbGRIg8P7vtzJkl2dsz14KEWWIaCuRAfOpO5vJpqd2+hYBndydbk9/OH9zezIrzz8k9upPuUgyOJt0Dl1JRoG2o0vwmEWb12WYAc0NTYnv3hjQ6dsaR3qqhPo+KXydXZXfVDiQJOBdlKsdxAb7O94bnkdd765sVNuxEMtDAwdX1ci8Dg1nf2rP5ZB9ssNOeX86aOtnbIzQ32/DLL4d0xkOKkdXFcisGhg099x/7FsuiKlrb7cVsCj33XObig1HbBSAjq2rkRggNZ/zMprHdQ5XCTFRoYULHt1ZTZvrcnpkHY2Feqqk04Jlvl+f4ur7fWpq4HX8FBSgx/8atchxe47Ssf1y84NlnXE2NLt9vDHD7YcUpC0I9T4/s9DW1kWS1mto0Ovi82tlPAfyz4xEUEfy1q7dzzUGVta14R43YGOL6UQGJRougo31PHQwYo6fvHmhk5JeW9YdRJCv+zTsWPLwKBES9fwYI/l5gMV3PNhJ42HQkyzjgwPo28Hb9LT+Bre/Dg9J8jxUG+jAE8H8l84PJ6G2RX/hWP28FRyy+pwutp/4+52e3j02928teZAxzXWx+PxUB1CYWAIfUDz9toDPPJt45sI/y/pwYo6HL5j5j+hzhqeGnSxy4MVdby8IptPN3X8Vnn+6vzBFm8D70Db4/FuFx2MR7/bzdtrG/pJi0GJijr69YlhaHpc0AXcNuSU8+76XBbv7PjAY6iFgSH0uhL3fLi1UVC1xaBEeR3j+ieSGB384PA7s5B31h3olIF2qKtOILTZP7vTze2vrWdnQVX9Y4FBxaYry2YPT/U+J8hj+enmgzy3dD8lTXbv6QjVNifxUeGEhQVXzBZCG2iX1di5+aU1jYKqgQWp/f2y2uak0upsOJZBBsveWpPDo9/u6bRgWSh9MjE6gvio8KD75e7CKm5/bX2jm8esRquhvOdL/03M7OGpeDyNz6Pt8cKy/Ty/bH9Qrz2cUAqwQujX8GW7i/m/97c0eqy5fun/v5o9PJW8irpDdslsC4/Hw6PfdeJ4KMQiy6GmYb6/PpeHvt7V6DH/729g6mpgvwz297uwysaLy7P4pBPGQ9UdsOqkX4grHp9YtIc3VzcEVRsFJQJ+1w+WW+mbGM2IvglBF/jfdKCCd9fn8p3ZeeOhUPtlYZU1qHsQgD9/tK1RULXxSonGQYkxmQmkxEUGfQ1fvLOId9flsvFAeVCvb03DqpMQV5YFGchzuNz87PX1jXa9bG5iAbz90n8ND7ZffrrlIM8v219/H9CRQk2zBt/YMshjWVHr4OaX1jQKqu5vZmxpdbgorbGHPB7qbRTg6UBZJbUMSY0DGn5J/YGeuSPTcLo9jZbVtlVprR2n29MpUcdQi7dB6LN/760/wBOLGm4i/EGJIalxuD0NF/e88jrSE6IZnZHAgSCDZfVpIUEO0ltTZfPme3bnUvlXV2bzYsAubv6Aw5DUOPLK63D5gh155XUMSI5laGp80AXc/LWlOqNfdkSKVv+kGBwuT1A3+g6Xm+eX7efNgJsIf42tIalxh8yyDEyOY0haXNAXjubSlTpKRyzv7tcnNui6EjlltXy0MY8PNzTsAOMf0AxOja0/lt4i6jYmDkwiIToi6CBufb/shHSaamtoN38QWrBsfU4Zn28taDTQ3l9SS3JcJInREfXH8qBvYmHuyDQg+AFNQZWNapuT8tqOL5ZZFeKxtFgsIdWVWLyzmI825rE5tyGomlVS0+o1HIIfaBdUWTutWGaohYFDrSvx+dZ8XlqR1XigXRw4HvIFywL6ZbDBsso6Jzanu1OuOzanG4fL063X8PfW5/L4oj31ExP+oIT/WPqPYV55HSlxkYztl8jBirqgVv02XMM7/rrTMLEQ2oQXBD+2fG1VNs8FBFX9QYkhqXHkV1rrj1lehW88FNI1vPPGQ6HWOgFvv3R7aFTEt61cbg/PL9vHGwHBsqzA8VB541W4A5JjGZIWH/Qq3IbxUOdcwyH0seXBIMdDeeV1fLAhjw8ajYcaxun+c6K/iPrY/n1Ijovskf0y1DRrCC0Nc8OBcj7fWsA3OxrGQ9klNSTGRJAUGxlwDW88HuqJ6W6dQQGeDmJ1uMivtDJ/VDoQMPtXXkefmAjG9e8DBJd33qk3LB2QCtNQVyK4X9KSajuVVmf9ic0flGg4lt6vc8vrGJAcw9C0OJxuT1AXff+x7IxaCh21UgKCH9CU1tjZcbCqftWTPygxf1Q6TrenftYhr9xK/+RYhqTFcaCstj7w0x75nXksO2hJMgQ30C6r9QaFtja5+QPvscwtq8Pt9tQXUff3y2CPRUFF5x3LGpuT8DAL0SHOsgRbV6LUF2DbErA6yR+UmNA/qf732///NCA5liGpwa8sy+/EY1ltDy19A0Ib0JT4dhHcmtd49m9oWjwDU2IbnSsBjhmYRHxUeM/sl/bQghIQWhpmfb8M+B3fX1zL1CHJxEaGN7qGQ2jBMqvDRXmtg0qrk4pOCJaFmlYUal0JfxDd3y/9QYn6a3hZQ7+MjwrnmIFJQHAD7c687oRaUwJCrytRWmOnxu6qv3b7gxINx9LfL630T/LeSLs9waVhdsk1PIRUmPqVZUGmB5fU2NlVUFW/UizwGu72NKyyyi2vY2ByDEN9E2GhBMt6ar8MZWxZUefA7Tn0Gg7eY5lX7l0Z5C+i7p08jAu6zlZn3vP4g2VxkaH1S7vT3aiIb1s1nCsbjy0ToyM4ZlDj8ZDHQ32/DLZfFfqCZZ0R1Ag1NRhCvYZ7f7am/XJoWhyDU2MDruHe9x/brw9JsZE9tv5bR1OAp4P4f/lmD08lMtxSP6A5GDAzEPi89vCf7Iqr7R1eHKojltHW15UINu2gtvEJzz+wWdBkcHiwwsqApFiGpMYDoR3LzrphgdALA0NwQQmrw0Wt3YXd5WZXgW87b99JfUFAsKzhIuy9cDhcnkaF8dqqsAtmWUIrKujf3rv9P5v/5m9vcU39Vp9Z/qDEgD7YXW4Kq2z1RdS9QYn4oINlnbndrX9232IJIa0ohH7ZXFAiu6SWoWnx9St4PB5P/dL5UINlhVWd2y9DnbHqnxQTdF2J0mYHh7UMS4tjcGpco3Ml0DCTGsSxcLk99bO9ndkvQxHKCp7mghIHK+rq+6X/WOZV1BEVEcaYjETiggyWFVYGptR17LGsT7MOtV+GUFei6TXc+zsNM4amEB0RVv+7eLDc6uuToY+HvAGzjg2WhbrDDoReV6K0Sb/0rxhb0GTCq34VbgjHsrALJg87InUwmH7pcLmpsjpxuj3szG+8zfyCgIlYj8fDQV+wbLBv1Xgw4yH/qpPODJZ119jS3ydzSuvqA9TZvqDEpEFJuHwTrv4i6gOSvNfwvHJr/WRje3TmyjL/pjKhpFmHkoZZFvD77V8BtL+klqHpcQxOiSPXt9Le3+f9Qdxg+1V+J67gCXWDHvD2yyqrM6hdwkprvH2xuQmvwSlxja7hAAN958tga7j2NgrwdJD9xd6L8PD0eAYkN6Qd5JZbGZgcS2ZiDFERYUFFUQNzJ4PN/29JR6w6geDrSng8noCZVN+AxneM5oxIJTzM0nADWF7XaHAYzEyq/1gWVdk6vIBbVQccy1DqSpQGpCJtCQiW+YMS4O0/xTU27C43A30rJSC0gXZnpBV11LagEFzx78Bjuf2gt3ZMli8oMSjFGzg6UFZbPxD0rzpxuDxBXfQ7dUmyzdUBKyVCGND4bv6Kqmz1NxT7S2oYlhbHoJQ4bE43RdW2+lkWf788UFrX7mCZx+Pp9H4ZaopWKCkcpbUNg0O324Pd6SavvK6+XwaeK/27nw1JjQ1qd7eSGlv98e+cNExXh6S7BVtXoqzJCp4DZXW4PdT3ywMBKyUGJMUQFmZhSGpccNfwgJpqHX0s69OsQ+6XwdeV8Adx/ddwf38blu5dWVZ/LH0TXn0ToomNDA/yGh5wLDv4fNkRN9IQWl2Jpise/UXUZw5PITLc0ijdbWByTIjXcFv9Z3b0NswdUeskLsqbahHUKtxmxkNZJTUk+IIS4L2G+4uoe4Nl3snDYFaPduZ1pyMCj6Gs4AkcD2096B9b1jIkzRuUAO/5MzdgPDQ4NQ6XO7jJw04NlnXIxELw13D/xEJpjb3+/yK7tJahqd5ruMPlobDK2mRsGRt0DdfOnNQONc0aAjbpCapfevvJ9oOVOF1unC43B8rqGJoax6AU7zHzj4csFshMimZwalzQ6f+9jQI8HcT/yzPMN9BuOssSFmZhcEpsyAOajv4l7YgbaQi+rkSVzVm/bWrToERaQjT9+sSQU1pLRZ2DWruLAckx9OsTQ1R4WEjpbhD8zjItqS+yHMKxrK8rEcSS5MCL8Lb62T9vUKJ/cgwWi3dA7L+RHpAU2kyq/2agM7ZhrrY5iY4IIzI8+FNUWnwUkeGW0Ac0/sFhaQ1DU70rJcB7o+a/CA/0rTqB4JbCdm4apqNDZlkgtNk/8P6O1wclUr3LaME7KPYfy35JMQxJi8Pucrf7hrPK5qTWF7jtrJnUUNIGIcSBtu9GutbuYl9JDQfKanF78B7LlDhq7d5igrnl3iLqEeFhDE2LJ8eXUtgeBRUNEws9uV8GW1fC3y93FVZjdbjqf2+HpsUxOCWWA6W1jSYWgKBTBwN/bzq6X3ZEmjWEVleiaVAiq9GxDJhJ9R1Li8UXLAvxGt7hAZ6OmvAKMg2zzu6iznct3ZrbcA1PiI6gb0I0A5O9K8uqrN50vwHJsWQkRhMTGRbU2DK/C45lKClaEPzYMrD2nj+Im1XqTd/onxRDeJh3pX1us9fw4PtlcbWNWnsHr7S3OokIMc06KTaSmMiwoPql/0YaGvfLYb7VjuAfDzWsHB2aGspEbCeOh+zODumTEHqwbEtuBU6Xm5xSf1qRb2xZGhgsi2FoanxQNVxr7c76iecDnTHh1QFp1qGUUvAfS6vDzd7iGm+qoNvj65e+ycMqG3nldfRNiCY6IpyhvjpHwRYb700U4Okg/qBEUlwkg32zfzU2JxV1Dvonezvw0MMss/N4POwrrmFTk8rxBZXW+hN7R5/wOqJ4GwQ/++efZfn/7L13mGRXda/9nspV3RU6x8mhJrQ0EsoSKIFgRAYTDTYYMNiAsTHO4ZprbF/b19jGXNufwWAwJmcssIgKIJSl0WiCanLqnCrn8P1xzj51qrq6p7vqdFf1zH6fR49munqqq3ft2mvttX5rLafNwqHRCKVSSVdKQLkJqzEzYLUoDHe6l1yLUqnEkbEoJ6ZiFV+vWEuTAzxx0WS54Yh2fVL5uaq1hLJSwmmz0u9zcWG+fJEeCLgY8LuxW5UljXC+UOTA+fCCgNhUNKOv5WgdWZqliDU4FQa0vhJ1Kssq92WUbL7I6HyKzV0ehrTL3oW5lG5wB/zLU0Nl8gUeOz3HrOFCmsmrl3KnzcK4ofGjWTQ6FQag1+uqu6/EXCKLQwvUHRqNloMSmowWymqobt0Ia2WYS+zLRCbPwydnKzLPQiHktFlWp9mlCRmrRvpKzCez+mfu0GhE/9xu7i47h+IzLpzQjZ0esvlihYpkwfMmsvz8xEyFnH7SuJYmO4f5QpF0rmiisqw+NZTTZqFQLBGaiOmlwZs05zCWyRNN5bW1dGuPefQy18WYjKZ59NTsgq8BFeVKZmGGchTUXnr19JUolUr6vrwwnyKczHJ2Vg1KdLU5dBuuNlHPMij25TLKMM/OJnjmfLjia5MGu2P2vjRDKQHqpaXePQma3RlT/aEzswk2dXlQFIUN2uVEL9+oCJYt7Q89NxGtmGQIa+NbehtQ8ADm2PAxoSxTgxI2q4XBQKU/JJRlTptlybUsFEscOB9esF7GfWm60l6z4Y2UWSuKUv++1EphxL40BiUG/G4sCgvWUvjwF/OHHj8zVzEVMl8oMhNX13JqFZT26qCExvZkd7sTq0Wpe1/arQoWBQ6NRSuDElXq8A6PHY/DtqxEbDKr+kMxQ9mqUEJdbE/Xi1ll1lB/KwWjP1S24WVl2fn5lN5zVDxWbw/X9UZj78wyCAaD+4GPAVbg30Oh0F+v9s9sBmdnk3rEerjDzUw8w6lpdbMNGbJ/j52eo1QqVRzUmXyBv/mfED84MsGFeVVa/9Sf3IXfox5Ck9GMOr5xNmF+9s8k53DA72IukSWdK+BaQfMykWW5YWsXDx6bZiqW4excgqs3dAAw3OHhp8enGTdkBkDLpNa4/EWSOf78niM8cGyamXiG7nYnT/zJi/THJ6Np9g0HeOzMnOlNx8RaNtK8DdQD76ETMyv+d3OGtXzizBzpXIHR+RSvuWoIUPdlpepEC5Z11M6knp9L8pffPcpDJ2eIpfNcs6mDr//6zQAUi6qM9OoNHepaziXZ1tNe76+8gEabhgrU7N/KDYfYl9dv6eTwWKQiKOGyW+n1Ojk/n8Rps+J322lz2nDaLNgsSs0M/8ELYT76g2M8enqWdK7Ia583xN+/4SoA3bkR+3I0nGJLd1v9v3QV8UwBv7sxh8Zhs9Dd7qzIni+XuUSWXp8Th1UNPIoGq5u7PQzpDk2KsUiaoUA5KAFq/wnR3FZwX2iKf73/JE+fmydXKPHbd+3kAy/cAZQdmn3DAZ44O0euUGxIBVaNuRmrlatOZhNZrtoQ4OlzYQ6PRfUAx6auNjwOMSEwyXhEPeegvJZnZ5N6oELw1SfO87lHzvLsaIRSCT72pqt4lXZeiIDQvuHAKihHVae94YbVPvX3mawz8CjszqGxSEVQQpRhnp5NMBmt3JeZvNp/SzimoF6g//WBk3zr6VGOaf3Pvv2+W9i3IQCofaGcNgvBfu+qqXDNCpZNRNJ0tjmW/e+ECvfWnd08eGyaw2PRiqDEcIeHcDLHiSl1XYw2/KfHpxf4Q9l8kf/7/ee49/AE5+dSWBR4/I9fRFe7E1Bt+OauNsYjqZZVQ/X73cTS+RXbMaHQE/tyNJzi7GyS3QNeQLXhPzg8WaEcBRYtHYykcvzFPUe4/9g007EMAY+dp//0Ln29p6KZsj+0aurwxtUSRwwjpZeL0bd89NQsmXyB83NJ9o/0AzAcUIO1xl4nogyzlm85Gk7xl989ws+OzxBN59m3IcC333cLUC4NNvqWO/u89f7KCzBDOQr19ywTCp7rt3RyaDSiByU2dXlw2Cxq8nAuid9jp81hxeey4XXa1LYUNfbVodEIf/eDEI+emiOVK/Cqqwb52JuuBtReo8VS2R+6MJ9kh8lr2d7gnhTlz/Uqy7ranHhdNg6PRrhus3rX2djl0c/G83NqEFfYa92GzyV4Pt0Vz/fgsWn+5f4TPHlW9Yc+8MId/PZdO4FyYkGs5UrvZxfDjCbLfQ0qeK4c9vPsaIRDo1G29Kh+86auNj3QdWE+yVgkxa5+dQ9tMCRixZ8vVVZVwRMMBq3APwN3A3uANweDwT2r+TObRaXqRN00j52ZAyodmngmXyHRA/jB4Uk+/dBpdvS286brNlAoljhtkIhORtP0+5xa9sZkI2xCY2Ao16Su9AIosiy37lAPrafPhXWlBMCGDg+T0YwemR0UaijNoamWk3/ukTN8/akL3Lyti7tH+pmJZwgny+s9Gc2wZ9CnTkkxPctSaLh5G4i+EpkVSwhnDWuZzBb42fEZPSgB6lqOatFsj8OqX/oXc2g+9uPj3H9sipeODHDT1i6OT8b09Z5LZskVSlyrGacLqxB4bPTCAuq+rNdw+N12rtoQ4PhUXM98Cgm3yKQayzdsVgvDHe6ajvZH7jnCwQth3nTdRnb1e/XLDpQ/M2ItzZfK5xpuwAqNSeW72hzsHfJzeCxqGK/ahsdho7vdoWesxFoOBlxqsKxqLUulEr/3tYOMzqd4x/O30ON1ctywluK9vnZzhzolZYWS5qUolUqm7Ms2pw2fy1anVD5Ln8/FrgEvh7WgRJvDWhGUODeX1JvZAouWDkZSOf7gG8+SzhX4wJ07sChU7stIGkWBqzcFGAubK2mOaWpHMxpWw8oVPIWiqjrZN+zH57Lp+3JjZzkoAfDk2Xm9iTrARtGjo6qE4+h4jL+9N0S708Z7bt0KsGBf9vlc+rlhJqapTupUllXb8MNjEa2JetmGAzx6utIf2tTlIZ0r6k3RBT8+Osknf3qard3tvPn6jRRLcHqm0h/q9TnZ2OVZBRVu442BwRAsW6E/JBQ8Yi0PXlCTC8KGD3d4mE1kawTLVHV4tT/0hUfP8dUnL3Dj1i5edsUA4WSOmXjZH5qIptnZ347XaVuVfemyW7A1GGDv97uYiWdWrGydN6xlJl/kp8dmNKWEsOFu3YY7bBa6tKDmYg3+P/7j4/z46BT7R/q5ZXsXJwz+UCSVI5Mvlm242fsy3biiGRpRh+doc1i5ZlMHp2YSHJ1QA276vqzyhxRFMQTLFiYP//K7RzlwPswbrh1mz4BPHwoCNfwh01spmOVb1tdKYT6RpbPNwYjmD4lpZJu15GGfz7nAH+r31e7hWiqV+IOvH+TcbJJ33LKFfp+romqhei3NVtrHTVDa60N66kwe9nid7B7wqf7QTAKX3UKv16nbcLUtRYpBXYUrbLj5iqZWY7VLtK4HToRCoVOhUCgLfAl41Sr/zDUnmy9qRlgoeLQAz2lVpl3OsooobOXGui80RcBj59/fdh3vfP4W9XsqAjxqtnBDA6PyFsMseXe9jrYIStyyvRtFge8fnqBYKjvSor738TNz2K0K3W1qFm9jVxuxTH6BnPy+0DRXDvv5pzdfzWufNwyUxznGM2qndnUt3auQ/Wu8pwSohqNQLFU4YsthPpHFalG4eZvqHH732XGgvO+GOz1a1jPBgN+lZ/E2dS0MlhWLJe4PTXPXnn7+5nVX8sLdvUTTecLaegvDMTLkXxX5Z8wkI1xvX4k5EZQY9FEolvj+4UnA4NAINVQkrZccgLovq3tDRZI5njw7z1tv3MSHX7mX6zZ3cnomob8moTq5bnMnsDo9OkzZlw1I5Tu1tRwNpzhwPkybw0p3u+pQD3V49B48ImNls1oY6nAvOCsPj0WZjmX44F07+cO7d7Or31t5VsYqHRoz1zKTL5IvlhoOhkNjZZhiLQ+NRvWJEYqi4HXZCXjsPHM+TLZQ1IPhoqy1el/+7PgMhWKJv3rNFXzwrp0Md3j0sxLUfdnd7mRrd5vpkmazmtkGPHacNsuKL9KRVI5SCW0t/RzWyt02d1cGJXQbLoISnYvbcID/763X8KEXB9X1rkrS9PmcavPwOiftLYZuw00oDYb6bfi2nnYG/S6euRDhfEVQQn1esZaDVSq9akf7vtAUXpeNT73tWt6tBcuq92Wfz3XRsqR6MLMHD6w8Ky2UEjdv68aiwA+PTJIrlCrU4aD6Q6qKoOxbpnKFilIXUNdyz4CPj7/5al53reoPiX2ZzhWIpHL0+1bPt2ykwbJgwO+iVIKpJUpMayEafz9/R6U/JKawDnd4mIplODWT0JuoA/paGH2GUqnEfaEpXri7l7993T7u2t1HIlvQfTRhw3cP+Girc9LeUphmw/0uJqPpFfdjm0tk6Gx3MDLop1SCew9NAAbfUleHlxMLoJ6X1Z/vWDrH42fmePP1G/nfrxrh+i2dnJ01+kPq+6z7QyZfxM3cl3UnvNpVGz4RTfPU2XmcNjUoAeq+FL2hhEJvsR6uxybjjEXS/OaLdvCHL93N7gGv3pQdytMbV8O3NKvMGur3LYU/NDLo54imHN3Y6cFiUXBrPuazoxHSuaJuwxvp4breWO0AzxBw3vD3C9rXLilGw6kqpYRwaOawKGUJmjgMjRn6YrHEA6FpbtvZg9Wi1lgrSjljlSsUmU1k6PWqDo0Y62gWiUzjzdugfodGZP82dHrY0t3Gj46oF+nNC4Jlc7qEFsqOtvHAmk9kefrcPLcHewHYojnrYsKZ6M8hHG2zlRKJTKFhOTLUP7FoNpGlw2NnR187DptFX0ujo10sqVlpoxHeqPWbCBuCZYfGIszEM9wR7AHU7AKUR9gLw7FagUfTsiw+F5l8seJ3Ww5ziSwd2uUP4EdHJiuCEhs61BGgF+aSCxyaaofkwePTFEvo+3JTl4dYjWDZFcN+HFbze3So+7J55W5iLUe0tfzx0Sk9KAHqeXl4LKI3URfU+ozer12kb9tZ3pfGYNlUNIPXZSPYr06NM3NfmqWUgPp6lmXz6thfEZSIpHI8cWZeD0qAui8fE0oJ7cJut1oYCrgXjAa9LzSlq9RA3ZfVwbJ+n6tcy27iWprV3F9RlLocbXGRVjOpPo5OxCqCEn6PHa/Lpq+lcLSHOtR+E7X25ciQj14t0zoUcFcEJaZialBiQ4c6aa/eaVW1MKtEq8dbX18JYcM7NZXeg6FpcgWjUqJsw6HsK9Tq0aFepKe5dWePGuQVwUltXxaKJabjGX1fXphbefPwpUhk8igKeBzNacIqep0MBdxs721fYMONa9nvUxsFAzV7dERSamLhjl3VNlz9HmHDVWWZ+QmvhAmlMFD/xKL5ZJaAx86OXi8ue9kf0oO4WvLwiTNzC2x40hC8AVWhNxnNlG14d6WST9hw4Q+Zb8PNK1nPF0vMJDIX/2YDc8kcnR5VdQKqP+S0WejTAowbOjxMRNOcq/KHNmo9y4x3lp8dV5VUd2hrubnLQyJb0Bvli7Xcu0pKe7XM2oR96VPV4Su9j80ns3RUreWmLk85wNjhJjQRI6Y1URfU6uH6k+dUf0jsy83dbZwxBMsmoqpaf4+YomuqDTenzBrqC5YViiXCKbEvfcQyeR49NaeflaDeH6tt+HJ6uF4qyCbLJqCPBNWMbI9XbdQ2n8zR53PpPSBEsMIYhT04GmE2kdUPO5fdyqC/HKmdjmUolTQj3OEmlSusWNmxFGY0b4NyX4mlPqRj4RS/9aWnK8q45hJZHDYLbQ4rewf9emO+TVUKnvlkrvLyp49KL19IxEVaBCWGO9RgmQhKCKe6z+tSo+Q1JM2NEMs03rwNyn0llnJoDo9F+O0vH6iYXiWUEnarhd39XmKZ/IKgBKhrOVQV4IHKrPR9z02jKHCruEiLYFn1Wmr7cjWaXZqjlLj4vnzw2DT/69uHKvaCyAwMd7jxu+3EMnk2GoISwx1uCsUSsUx+QbBMVTqVP6NCoScu0sLRPm1YS7tV0ctszJQkF4sl09ay3+8mqvWVWIyvP3mBj//4eMXXjGooUD8nItgN6udUKPGq9+XC7L6q0OvRsl0iWCb+vSiF6fe5sFsVU9fSrOw+LM+h+Zf7T/CVx8v5EbGnhLwb1LUUGWlQ96VYi+p9aZwMIxR64iINC4Nl6lo6K6bGmYVZylG4+MSifKHIR+45oqtsoHyR7mpzMjLkJ5svVigloHJfDhiCZYOBykyqUOgJGw7qvhSJhVKppO9Lcdaa6VyaFXhcTl+JRCbP733tGb2JP5QVPCKTKmy42Jdqo1Ar88mc5hupl6uhgBosM+5LodATaymCZSLhNRvPUCiW9H2ZLSws8WqEWCZPu6Nxf6jcV2LxfTkRSfNbX3q6Img+l8hgtSj43LaKtRT2Vyh4FvhDNdRQQqEn1nK4Qw2WiX1ptOEimG6mP7SWNvzoeJQPfvlARUPeWc2GWy0KewbUC6AxKDFs8IeMvcnKgcfyvhRnx+07K4Nlp6vX0isCPOYPnTDFhi+j38lDJ2b4k289W+UPZehsc9Dnc9LV5tBtuAhKDHe4KZXUoGKForlTDd4YJ5oJhd7zNgYAY7BM3buTUfUz0NXuND3wqJdZm7Qvk9kC0fTi/tC3nh7lH390rOJrc3F1X+6p8IcqgxK63am24VWfUaHQE+fN5q42klXBsj6fa1nNw1eKWWXWsLypg//2wEm++Ng5/e/hZLZChau+prx+D4el/SFZotU4o8AGw9+Hta9dUoiNIoIOiqLozUMHDIedy65OMjJurPuem6q4SIPmHC7IDDgbGmm9GGY1b7tYX4l4Js87PvM43zowxiOGCSOziSydHgeKojCiHXgeQ1Ci16te1KCckQZDE9aqtexsc3Cl1ly0OlimZ6z8Lt3wVPdDagSzMlYXc2jGwil+5T8e5xtPj/LcRLnedi6hZgYA9moXwOqgRPlnLHRojMGy+0JTXDkcoLu9LBtVFHT5p9iXPe3OVXEOzcpYXayvxKHRCO/53JP858NnK1Q+c8Z9OaTuS6PhMDZnqx14VNepWqEHapZF/Z6yGkqdUqWYroZKagFAM/flYuqDHx2Z5He+9gyf/Okp/Wti7G9Hm4OONocewDE6NCKIC5UOzaYuD5FUjoj2vlQr9AC9GbV+XsbUoITVomiqFfMv0mbty6X6SvzHQ6f523tD/NejZ/WvGS/Su/q95f20zH1pDOAeHotWKPRA3ZfGYNlULEOvz6WPFDZzLUX2z5xg2dLlbn/x3aN86men+cZTZddDKHg62uy6cwhV+1I7L0UT9fL3VK5ltUIP1H0pMqmxTJ5UrqAFJcp9kszC7H252OWvUCzxgS8+zVeeuMD3D0/oX58zBni0sxLKQQlFUfTkgvHy57BZGPBXlmFWK/TU52mruPwB9GplRWDuWppld/S+EousZSKT552fVf2hh06U/SFhwxVF0W24wxCUEBc1qLThwx1uFKUqSVOl0LNrPeKqfUvRG0qMFDYLMxsDw+JBiYlIml/5j8f55tOjHB4rBx7n4qoNB/SAuCjfgHLCC8rZfSifoUY/3ajQg3KwrOxbqq+t1+dkQ8fCi3ijJLTAY6NcrAzzyFiUd//nE/zXI+cqEslzcVWFa9yXxsRCpd2ptOFQXstqhR7AFqEsmynvyx5tStWGDnPVUKLM2lTfcpG1vO+5KX77Kwf45IOn9L2QyReIZVQVrs9l19fHmFgw+kNDVUFcYw/XaoUeLFxv1bd0YrEoavLQxMCj2Qqe+WSuImFt5HMPn+H//M9zfO7hsj8k1qGjzcGOvnb9nrixq/a+rLjzLNLD9VJjtQM8jwM7gsHglmAw6ADeBHxnlX/mmnN2NonHYaVHuwxD2XgYDztQHb8nzs7pG/n+0BRXbwhUTK3Y3N1mOOw0h8brqhgpbBZmNW+DxR3tfKHI+7/wlN500tjMT6hOoGyEjeUb4qIGlWupBm9cPHRyhlKpRKFY4oFj09xuuEiD6mSenqnt0ACmyj/Nagy8VF+JeCbPOz/7hH7ZGzW8/tlEhi4tMCbKYYyXP3FRAxZk/6wWRZ/cNRvP8MyFMHcaLizlYFl5X3a3O3DYLPpI4UhqZWVQSxFL501qDLy4QzMeSfHOzz5OVmseKxrQibG/nVVrWZllKe9F477cqgUcfqatpVDo3bmrvJYbOt0LgmXCWdjQaa4RLqtOTFCWLeHQHBqN8IEvPY1FUYim8/oEg1ntIt2lf8YXBsuGO2oHJbZ0q1PZxFqKi7RxLauDk1Nafw7QGmGvwkXarIbVi/WV+PHRST5yzxGsFqXi8228SLvsVnb0qutTa18am6iDui/DyZyuvPiJlliouEh3lVV6mbwa/O73ufRSGVP3pZb9M0dZtnhfic88dJrP/PyMtpblvSDOz642J1u62/SSHGO52/ASNvz4ZEy/1N333BQdBoUeiCkearBsymB3BjXVitn70m5tvMwali7D/IvvHuHHz00t2Jfz2qhaj8Oq23CjUgLK+7J6Lbf2tPHEmXndH/rJc1PsMyj0QN2XIlimJ7xWUQ1lxp6ExftKFIolfvNLT3NUmwxV/RnXz0ot4bXJEJRQG4AvXEunzcpwh5ufn5ihWCxpCr0pbjNcpEHdl+VgWXktV0OlZ5Zv6XXaaHNYa9pwESgTigVjE9n5pMG3rGHDe73qZEeoXMsNnW5sFkW3O+FklifPzlf4QwuDZeqEMpfdysZOVWk/a2Ly0CzVyVI2fDKa5p2ffZx0vtIfArX5d/W+rFZKCIxrKWy48C2FQs+4lkNCWaYnaTL0+cs23Mzkodk2HGq3Ujg6HuX9X3gKRVFIZAu6byySiAv2ZXelgkdQsZY9lb6lUOgZ/SGR8NLvPLGyb2l2zzJzbfjiVQv3h6b4s+8cVu1OeKE/1NWmqkLF1Lpa+9LYRB1gR5+XdL6g+/6XKqsa4AmFQnng/cD3gaPAV0Kh0OHV/JnNwDh9QyCisENVDs2v376Ns7NJ/uFHx5iOZXjmQqRC2g3qBp1PqplrY1BCfPDNbDpmVvM2WLyvxF989yj3h6b581ftpbPNoY/4hHLDMUAv4TBGs6Echa12Dn/9ju08cmqOLz1+ngPnw8wnc9y+q3ItVYembITbHFbanTbDGGaz17Lxi/RifSVEBvXYZIyPvekqgIq1nE/mDMEydS03Gg47m9WiX6CN+9LtsPIrN2/mK09c4KETMzxwbJpSiYrMAGjBMkPGSjR4NDuTmisUyeTNad62WF+JZDbPOz/zBIlMgb9+7RVA2aERY3+FQdirBx4rswEijmjcl9t723nR7j7+6cfHOTkdLyv0dpTX0mlTg2XGTGqfT2se3qmpVkwKlplphBdTlk1G07zrs08QcNv5o5fuBmBMm141nxAOjfr7CYfGuC+FUsJhtehN1EENPuwe8PFn3znMfCLLfc9N0dXm4MqhsuJiQ6f6PpyeSVIslpiKpSsCPKZ+vk1qZguLOzRHx6P8xhefZu+gn/fcupXZRFa//JYdGnFeLtyXxsSC0R79wvOG6fE6+Z2vPkM2X+S+0BT7hgP66Gn1ecqZVJHJN+5LU+XdJpe75QqlBReq+0JT/Pk9R3jR7j5etW9Q35NQ7hvT0WbXSzgcVUGJsg13VTzvr9yyhUKxxB9981kKxRL3H6tU6EHZyTw9k9CTNKJce9BsZZmWWGi0rAjU8uBaTek/9/AZ/uOhM7zjli08b2OgwtEWpTCKopZ4dWuqTuM0ycVs+K/dto3RcIr/+/0Qc4ksT58PVyihoBwsm0tk9SbqfT4XQwE1UG72vjTLH1qsDPOvvneUHx2d4sOv3EuP11lhw9V+ZaoPIUo4jJ9vKK9l9b587+3beeLsPJ9/9KzWQy+70IZrpYOlUompWAanzYLPvZr+UONrqShKzYlFxWKJ3/ryAY6OR3V/qNa+BNg7tHAtLZay0r46WPbOF2zhG0+N8sCxaR7UppHW8i0rbLh2dpittC8WSySyBVPWsqvNgd2qLNiXqWyBd332CSKpHH/zC1cCZd8ymc2TzhXLNryGP9TvU6deQqVvubnLw/69/fy/+05wfDLGfVrPmNsMytFysEwLPEbS9HnLdieRLSwYplIv5trw2sGyqViad37mcbwuO3/yMtUfEvtSNP7uqt6XnQttuLGJOsDzt3dzxZCfP//vI8zGM/zkOVH636F/z1DArU0fTVSUBgOmK+3NtuGw0LcMTcR4/xeeZle/j/fevo1IKqcH6YwJLzAEy4zKMoNy1GiP3njdBr71vlv0cuFLlVXvwRMKhb4XCoV2hkKhbaFQ6C9X++c1gyuG/bzyqsGKr4lgjLFEC9RSrDdfv5FPPnhKr828o4bhADWTOhlNY7Oo/TncDis9XqepWRazJMlQ26G599A4n/n5Gd75/C285YZNDAZcVUGJcllRwOPgFfsGefHevornEFHYgSqH5i3Xb+SW7V38xT1H+PwjZ7Eo5fGigupgmcgMiOc0PftnQikM1K5J/eRPT/GT56b48Cv28PIrB/E6bbrhEGN/hSQ52O/lpq1dC4KHwwFtX1Y52r/zkiBbu9v4va8d5J6D43S3l5viCozBsglDUKLchNWcDL9ZDVhh8b4SH7nnKEcnonz8F6/WMyBiX85pRljsy5u2drFvQ4Cbtnbp/95hs9Dvc6lN1A3ZZkVR+KvXjuB2WPnQV57hx89NcvWGAB2G7AGIEo5y2UGvoVkhmLcv4xnzSrRq9ZUoFkv89lcOEEnl+NTbr9NVDGItZ/Vmtuql5a69fVy9MaA7iVB2rvurjLDDZuGjr99HOJnlT799iAe0i7Txe5w2q9YTJcFcMkuuUNLfjw1aLbtQEzVKImvevqzl0KSyBd73hafwumz8+9uuZbum0NH3pUGSDPDyKwe4bWeP3lcBDGdlld3paHPwV6+5gucmYvz5PYd55kJ4wdkggmVnZpN6YqFXD5a5TVWO6vLuBpvZQu2+ElPRNL/95QPs6vfxT2++iuFOD5OxNDktYzebyNLutOkO3quvHuK1Vw9V7K2yDa88K7f1tPO7Lwnyo6NT/O//PsxcIrvAhhvLMMXr0gOPHR5TlaNm2/BktqD3fgG139uf33OEO3f18scv281gwM2Y4QwwqnAVReEN1w7z8iur/aHa+/KW7d380o2b+PRDp/n7H4a0xELlWurDEmaTTEbSWBR09eiAz2W6P7SaCa8fHpnkUz87zdtv3swv37R5wVqqCh71/PK67Lzm6iFevLe/4jnKa1m5L9903QZesKObv/rec3z252cXJBZA7dER08o8xOVPUcoqaXNLOMzclwvV4Z9+6DQ/PDLJn75c9YcCHrt+VpZKpYp9ubPPyy3buyrUDmBUllXuyw++aCfbe9v5/a8d5DsHxujw2Nmnlf4LtnR5ODuT1JVlvQv8IXP2pbA7ZuxLi0Whz7fQt/yr7x3l2dEIH3/z1dy1W/W/q+2OsOE3bOnkqg0Bbt5e9rVtVovun/f5K/2hv3jNCO1OGx/66jP86Ogk+4b9eum/YHOXoWqhKkkD5gXL9HJWE8rd1LL6ShteKpX4na8eZC6Z5d/fdi3P26gGX/SEV7LSht+1u4+rNgS4crjsDw0EVL/S2EQd1EDY371+H7F0nj/51iEeODbFrTsqEws2ESybSRJN5cnki/p0LrOV9maWWddqpZDOFXj/F57C7bDyqbeX/aFxsS+TlQGel145wAt2dFd8lhc7K+1WS0Vp9qWKbLJsAr/1op289/btFV9brEQL4I9ftpsBv5vPP3qOHq+TPQO+iseNfSUmDTWUYH4mdSauTl4ygy3dbUzHMnztyQuA6mT/4Tee5YohP39w9y5A7aNTIbOLZyvK0z5uGG8uEI52tRrKYlH0bMM3nh7leRs7CHgqL9Kbq4JlIsvS5rTR3e4wzQincwWi6dyCn18vW7rbeeZCxCBtjfDRH4TYv7eft964CVD3llhL49hfUC++X3z3jdxoCEpAOStd7Wi77Fb+7g37GI+k+MlzU9y2s7fisgNqrXQ4mSOczDIZzVSUFYF5RngmXu6PYQZbutv4yXNThLR+RT86MskXHzvHu2/dyh3BXq3kxVJ2aITh0JRlPV4n337fLfqlTTDc6aFPK2Ex0ut18b9fuZcD58McGo0uuEhDeWJRPJMnnskb1tJc53BGU2KYsS9dditDATffeGpULzv5j5+f4aETs/yvV+xh94BP/4yKfTmvG2HVydjV7+Ob770Fn6EZuctupc/nXOBkg5rF/sALd3DPwfGaCj3QnENDUMIoSQbzLi1C1dJhwloOBtw4rBY+9/BZPaD5f/7nKKemE/z9G67Sy3mg7BzOJbIoCgS00qs7dvXy2XdcXzMoUX1WAty1p4/XPm+I/3rkXE2FnjFYJlQn/QZHeyaeXbLB9kqYiavTzqo/O/WwVZOuf/Knp8gXiqqT/bWDpHIFPv6LV+Nx2BgKqCVxItgyb1BKALz1xk38tWZLBOJcq2XDf+WWLVy3uYP/fFgkFirXcrjDECzTVSero4aajmdM2ZNQ9j3+7YGTlEol0rkCH/zyATo8Dj76+n1YLQqDATfj4bQ+6t2olAD4vf27+M0X7ah43qX25R/cvYvhDjf/9ci5BQo9qCzDVEuDnfq+MXti0UxcnbxkBlu625hLZPVG6dOxDH/w9YPsGfDpSsehgGtBiZZxX/7DG6/iDdduqHjexXxLRVH9IZtF4etPXVig0APjsAT1vBSfb9Ef0qx9mc0XCadypu7Lw6NRHjw2DcBzE1H+9vshXrS7j7ffvBnQfEttLaPpPPliSd+XdquFz7/rRm7ZXpkAXCyI67Jb+ejr9zEdz/Cjo5MLFHqgKcu0YNlkNKOv5bDJAR7RC8fMfXn/sWm9RPC+56b43CNnedfzt/DC3X343GpJnGilUA7wqHupq93Jt953C9t62iued0OHp6KJuqC73clHXjXCwQsRnrkQWaDQAzURe3Y2STpXIJzM1bDhZvuWje9Lh83Chg4P3zowqtuV/3z4LA8em+aPX7aHkSG//hkV5cGzVSrcHX1evvW+Wyr8M7tV7U1Wyx8K9nv5rbt28D+HJmoq9KA8SUvYnYW+pTn+kJl++qDfjdOm+kMiCPc39z7H8ak4H339Pgb8bt12XKhOxGo//7adPXzunTdU+BS1FHqXEzLAs0rctK2Ll10xwPVbOhc81u608bevU53JO4I9Cy7SxokIambAIBs3sVFWIpNnNJxacFDXyy/ftJkX7Ojmd7/2DF9+/By/qznZ//DGq/RJYkMdqhEulUrq2F+t4dhSvHhPH6+9ekh3Po0Md3j4k5fvARZm/qCcSRUHnnCyxb81y6FRp8/Atl5z1vJDL97J1u423vGZx/nRkUk++OUDBDwO/uq1V+hS/KEOtyHLsjzD9aqrhnjn87fgsi/MnD9vYwe/eutWgAWZLihLck9Ox5lNlFUnXpedDo/dtLU8ofVqMmtffuTVI9itCm/+5CP87PgMv//1g+we8PHbd+0EVMfYGCwThqPzIs7pm6/foDuX1bxy3yD7tcxrzX2pBcuOTapBJ10NZXLG6sS0uWv5D2+8islomjd94hEeODbN39z7HC/a3cebrlMvIb1eJ3arskCSfLG1fMctW3jjdRtqPvbrt29jZMiH1aIsUOhBeWLRlKEBK5gfeDw5HafDY7/oebUc2p02PvqGfTx5bp63/8dj3HNwjP98+CzvfP4W/RJSDpapr38ukcXvti8ZFHE7rLzz+Vt4xb7Bmo//2cv30udTy2iqFXpQzqQaS4PBkJU2SS1xYipu2p7c3uvl9/YH+c4zY/zWlw/w6YfOqE72S3frP2NIUy7q+zKR1S8si7G1u53XXj3Ei3Yv/PxaLQr/93X7cNktXL2xY8G5K4JlYl96XTY8WtZ4Q6eb6VimYuJPI5ycirOtZ6FtrIc7d/Xy5us38M/3neRvvx/ib+59jmOTcf7v6/fpv+NQwE2+WNIDnsZeJ4txw5ZOXnbFADdUJRxATbb839ftA9TSjWp/aEOHRw2WzSQqsvtgbhlmOlfg/HzStH351hs3cdvOHn7v6wf5wqPn+P2vHySWyfOPb7oKh9YvaUizO6KPYDiVu+i+fNGePl591aCe0TYyGHDzp69Q/aHaNrxchqk2UTf0jTRxYtHZ2QSFYoltvebsyw/etZNtve286z+f4AeHJ/itLx3A57Lx179Q7Q+VA7jARfflK/cN8vabN9dUGu3bEODXblP9odq+pXqmnJpJMB0v934TSnvT/SGTfMuPvGoEt93Kmz/5CD89Ps3vfu0gu/q9/M5LgkB5SMxiCp7FeON1G/iVWzbXfOxlVw7wsisHgMX3ZTyT54gWdBKqE6HAaFXf8h/euI/ZeJY3fuJhHjw2zV997yh3BHt46w0bgbLScCxSuS8v5qf/yi2beeN1G2s+9u4XbGXfhgAWBW7bWdu3PDubrKkcBXPX0uuyVfSerRe3w8o/vvEqDl6I8MufepTvPTvOfzx0hrffvFkfQCSCNcZErFGFWwunzcq7b93Kq66q7Q9d6pijn5QsoLPNwT+/5XmLPn7L9m4++47r2dXvXfCYy25lwO/SHW3jYbSx08N3nhkjmy/qTkK9nJpWJZG1HIV6cDusfPKXr+U9n3uS3//6swB85FV7K55/KOAmkS0QTeVJ51UH92JGeEefl79/41WLPv6m6zYQcNt5wc6F0WwRLBO9EPoMypWNnR6ePj+/7N9vKYTh2G6S4ehud/KFX72Rt/z7o7zrP58A4LPvuL5irQYDLp46p77+cm3v0oftLdu7F2SxjHzoriB7B/28pKpMDsrBssdOz1MqUeFob+z0mFbCYbYR3tbTzpfffRNv/uQjvPVTj+KwWfjim66qMAyqo11WSsDF9+Vrrh5e9DFFUfjb11/Jq68erChHEoi1fPTUHICuLPO77fjddlMv0j1eZ0XD3Ua4fksn//nO63nbpx/nbZ9+jO52R4WTbbGo/RKMzqEY+7sU77lt26KP2a0W/v2Xr+PYZKymEmlLdxuRVE6fKGesOQfzmtKfmIqbdlYCvGLfIFaLwge++DSPn3maYJ+X39WcbFAzb4pCxb5cTnDpT7WAdy38Hjv/9c4biGfyCy7SoF5a/vuZcSaiaexWRVd3GtVQu/p9C/7dSjkxHa9o8Nwo7719OzaLwl997znuOTjO7cEeXekI5RIM477s9S59VjpsliXtzubuNr7wqzfiW6Sfw5ZutaQ1my8uCEqAui939C20/yshkckzFkmbti8tFoW/fPUVWC0K/3r/SQDefvPmivfKGHjs97vUCTsXCeB2XMQfunFrF//5juv1RplGHDYLQ1qPjolIuqKh68ZOD5PRDOlcoWbSYiWcmlaTNGatpctu5d9+6Rre+/mn+KNvqv7Qn71iT8XvOBhwk8kXmU1kUUBV4V5EqbGtp51/fNPViz7++muG8blsFSU0Aj1YppUOVjb/9/DIydkF/6Yeyv5QY/tb0Nnm4Iu/egNv/dSjvPtzTwLw6bdfW1HqMxRw8/DJWUqlcj+ui12kb9rWxU3bFgYdBb/5wp0E+328dKR/wWNCHf7EmXkKxVJF8nCjiaPS9bU0aV9u7m7T/aFf+tRjqpL0nddXfH4qEl5VCp7FeNVVQ0s+/tevvYKXXzFQUY4k2FLtD/kqlfZm2fCT03E62xymJGkArtnUyefeeT2//KnH+OVPP0Znm4O/ed2Vuj8kyh+Fsmy2SoW7GO96wdZFH7NZLXzil67h6Hi05u+xqUudtnV4TA2W9XkrE15m+pbbe9tN6f0GcPcVA/yzReH9X3iK937+Kbb3tuuVH6Aq440N/pfrDwm15OWIVPA0kdt29lQ4fkY2aw3cjA1YQS0LKZYqm+vWy4lp9UJk5qVFODWv3DfIa68eqnCyoSyVGw2nytn9Bg9bRVG4+4qBmrWgYtrWM+fDqqNd1URzLJwmb0In9RNTcRSlXC5gBsKpuWV7Fx+4c/uCC9FgwE04mSORyRtqexu7yDtsFl65b7CmSkBtJA6PnVadwOp9aWZmYNDvMq1+H8pOzZXDfj7yqr0LLhKDfvfCEq0G96XPZWf/yEDt16OpocRaVqj0Ot2cM9E5NCvoKBBOza5+Lx99w1UL6umNayl6bDXqBPT7XXompxqRlRZrKTJKfrcdr9Nm6r4086wEeOkVA/y/X3weu/q9/OObrqpwsu1WtemvMShxMSXUctjR5+XqjR01H9vcpQbLjk3EtB4D2khhE5VlkVSO6VjG9LV8963b+PAr9nDFkJ+/NTjZgKHcTduXy1DwLIfnbexge2/tS+ymLo9eotVXoZQwby1PTpt7+QM1yPORV43wntu2cvO2rgonG4w2PK2P/e0y4cJ0684evZygms1a/zdVdbKwEfYFE3oanViFtXTZrfzrW5/Ha64e4pX7BnnbTZsrHjfuS/0i3WBGXFEU9o8MVJTBCkSw7NBohFSuULkvOzyMR9X3tFHKqhPz/KGAx8Hn33kjL9jRzXtv38aduyqTUIMBF/FMnmg6v6Ahfb0s5Q8Na8Gymja8wzw11ImpOL1eZ833s142dnn40rtvZN+wnw+/ci+7q9pEDAZqKHgatD1el527rxio6QtsqvKH+ir6ypnrW5rtD129sYPP/+oN7Or38nevv7KiMTKo+3LUoLS/mAp3OfT5XDVL3cCYiBX7stzTy1Sl/bT5a/mSvf3861uuUf2hN1b6Q1aLQr+v0h8yo9TuUkYqeFqUzd0e7nlmnFgmX2E4jNMOqnuCrJQTU3GsFqVidKQZuOxW/unNtTNMQwaHxq012DQrmr4Ym7rUUaywUHVSKJYYC6crpvrUw4npOBs6PA1nEasJeBx8/l031nxMrOV4JFUx9ne1cNmtDPhci67l9w9NkC8UGzZeJ6bjpsmRjWzs8vCd9z+/5mNDHWrZRDqnjocWY39Xiw1asEysZX+VsuzoeKzhn1EqlTg5FefVVy+dWauHqzd2cO9v3VrzsaEOt54Jno1nTbn8LYVowvrEmXldEg3qRcesEo7ZeIb5ZM40VZmR/SP97K+RIYZySSuoDk31RB2zMWaldxrUpR0eO+1Omym9EMxWOxp5+y1bePstWxZ83WW30t3u0MthjNMbVwsRLDsxGecugyLSzIlFZmf3BYqi8Id31858GtVQ+tjfNVjLbz49qvYrq+EPnZ9LNrwGJ6biWBRqloI3gtNm5R8WUYIZ/SGhglrt83LzEv5QqaSObd/a4GfzxHScoYBbL0s0C7/HzufeeUPNx0QZ5lg4tewSrUZw2CwMd3jKNrxqLc1S2p+YNj+xAKoP8u3F/KGAm/lkjmRWDZbZlqHCbYThDg9Wi7LoWpqptF8s6dYIVw4HFveHAm7uD6m9o+YTuVW/7xhteMBjr7iTiElajbJaSRpQS1BftGdhBQFUlmHOJbKLCiQkKlLB06KIBm5QedgF+7xYLQqPnZ5r+GecmIqzqcvTsAFaCRUKHpOyLBdjc7envJaGDv+ii/qjpxuXJZ9chez+xdCbjs2nKsb+ribGfWk8XPcO+sgXSzx9PtzQ8xeLJU5OJdZ8LcW+nIiktUkmjatOlkJVlrmJZfK0OawV6rO9g36tpHDhiN2VMBXLEMvkm7IvJ6KqMm45/TkaZbhDDZbFMvkF2bO9gz6eOjtPNt+YSm+1LtIXwzhlZy65BkEJLVgWy+QrsvuKoo4Sf9QEu3OyiWs5Gk6TyhXI5IumNYBdDOFoq2tZ3pddbQ56vU7TbLhtFZI0S+F12fG5bIzOp5bdY6tRRNkBVCpHt/d6sVkU0/blhk7zkzRLYbTh+pS8NdiXtWy4GMtu1r5cjSTNUojA4+h82bdc/eShZ9G1LJbgybONBSZEkqZZvqVQlnWssj/ksFkYCqj+kNNmqQgm7R30cX4uVTGcpR5EkqYZdmcqliGTLzCbyKz6fWe4w43Voqh2p8of2jPo5+lz4YZVes3yh4aqSgdX+/O93pEBnhZls8FhMxqOjjYHN2zp5H8OjV/0OTL5An/4jYOc1sYPVrMacsWL0dWmNR0Lp5bdcKxRjGtpvADuHVSn/tx7aOKizzGXyPKhrzxDJLlwxGChWOLUTPOCEmPh9IKxv6uFUI3ZLEqFobo92IvDZuF/nr34Wp6cjvOH3zhYszRuLJIilSs0YS3LWem1kn4KNUZ1FkL0P/r+4Yuv5aOnZvnbe5+r+VgzgxLFEkzGMgsm7KwGIlgGlZc/UCW/0XSeh09dPIj738+M8ZmHTtd8bDXKN5bDYMClTyyaT1y810mjiGAZsCBY9uK9fRwdj3J2trY9MfLpn53mnoNjNR87MR1XJ5B0rq4aqRpROljuV7Y2wTKAPm9lsOwle/u5PzS9rEbLf3vvczy6yP4VSRq7CdPIVsJQh0e14SaVs16MChtuOC/9bjs3bevi3kPjlEqlJZ8jmy/yR998Vi9rq6YZ/lDAY8fjsDIWTuulwasdxDWqAI22Z1e/l42dHv5nGf5QOKn6Q2HtNRspFkucXIXyjYuhN2GNqPvSZbeYriCqRuxLi6I21BXcurMHl93Cvcvw08/MJPiDrx8kV8MfmoxmiDcjSdNRLsM0qzT4Yhj9IWMw6SXasIrvL2NfPn5mjv/zP0drPtbMoASoycP5hHmT5RbDro1KByqaqIPqW8YzeX0671J879lx/v2np2o+1qwkjTF5KAM8F0cGeFqUCuew6kN690g/J6cTHJ9cuozj8dPzfPGx8/zkuakFj2XzRc7ONi5rXikWi6JHYZfbcKxRNlU4h5WO9v6Rfn56fIZYemHgxsj3nh3n609d4ImzC7Nb5+eSZPPFNXdo+nxq0zERlFiLw070jun1OiuatLY7bdy6o5vvH564qKP9pcfO8cXHznNmdqFUdDXLN5ZiWJN3X9D25ZqspRYsqw7wbO/1sr23fVnBsn//2Wn+5f6TNRUqzXZoRufXcF9q52V1D4/n7+imzWFdlqP98Z8c55M/XSTAMxXHbQgkrRXDATfZQpHTM/GKsb+rhTFYVr2WoozsYhfAXKHI3/0gxBcfO1fz8RNTcbZ2ty0YO7zaiHK3uTVKLIgyTFi4lneP9JPKFXjg2ELbbGQ8kuJf7j/Jt59ZPFi21p9v0MZ7h9dOKWEsR+/3Va/lAGdmk3qT9cV48uw8X3j0HD8+OrngsXyhyOkmJGnEBMexcEqf3mjWOOzFMJagVav07h7p5+cnZ4iklvaH7j00wdefulBTOTUaTpHOFdd8LbvbnDisFr2/41oEJcS+7G53VpSmexw2btvZw72HJygWL+IPPX6eLz1+Xh96YqRZ/lB1b6i1sOFiX1Z/vjd3t7Gr37usROynf3aaf3vgVM3AebOSNENVVQurHcCF8p2nei1v3taN12Vblm/58Z+c4JOLBHhEkma4Y42TNAE3hWKJM7MJMvmiDPBcBBngaVE2dRqMsL86k7o8R/vhU2qUdqpGqcfZ2QT5YokdfWvvHIqmY/PLGPtrBuLy19nmWKBw2T/ST7ZQrBkEMyIUAJPaKGYjx00eY7lcRNOx0TVVnaj7srdG7etL9vYzGk5x8EJkyecQa1lrXzYrKNHnd6Io5fr9tQyWVQdwAfbv7efR07PMxhfuN0GhWNIz+9M1vu/EVByv03bRaUFmI5zDc3NJIqnc2u7LKtWJy27ljl29/ODwJIUlHO2ZeIZjk3GmY5maAUq15KCt5uSp1USs5bOj6mdqLYNl1ftyuMPDFUP+i9qdgxciJLOFmmclNKd8A9S1TOUKnJpRz5jVXkunrRwsqz4vr9/SSYfHfnEbfnLxs7JZSRooT9lZi14noDZTFsGy6oD4XXv6UJTl+EOL2/Dz8ymyhWLT9uVoOMVcMot3DVS44qz0umwLFC4vGeknVyjVDIIZWdKGN+kibbEoDARcjM6rCp7V7gsFRhu+0B+6e2SAyWjmomXr5X1Zyx8yfxDKcujzOvWJRXNrUGYNRt+yhj800s/jZ+eYii1etl4slnhE7Msa33d8Mo7HoQ5dWUsGja0UkquvwoXF96XDZuFFu/v44dHJmooxwXwiy9HxKNOxTE2/6fhkrClJGqG01/2hNVjL9YwM8LQoboeVfp8Lt92Kt2qaUJ/PxTWbOi4a0X5EGzk4Fat9+QPzxliuBCGVX6vMgAiW1broXrOxgx6vc8lymFKpfJGuZTiaFZSAck2q6Buz2mzRVScL1/KuPX3YLAr3LrGWkVROH9+42L7s8NjpanCSyEpx2qz0tDvXdl921VbwgOrQFEvwoyUc7aPjUaJptf5/sWDZNhPHWC4XYYSPjEUplVa/FAYu7mjPJrI8fmbx3hJiPGu2UNQbxxo52YTyDTAEeC6on5k13Zfe2vvymfPhJac4PrLE5S+dK3B+PtmUtRwSzuEarmU5WFa5ljarhbv29PGTo1NL9kMoX1gWnpVnZxMUiqWm2Z1YOs/Z2aSqwl1lR1sEy+xWhY4qhUuP18l1mzsvqtJbai2bbcPXsjR4Q6cbi1L7rLxqOEC/z7VksKxUKi25ls0q34DyWs6uQTkrGG34Qn/lzt292K3Kkvsyls5xSLus1tyX03G8Lhs9a5yksVkt+sSitVaHL2bDSyX4weHF/aFjUzHmNdtdc19Ox9nWs/b+0IBmd54bj1FYAxUulEsHayYPR/oJJ3O6z1MLocwrlmA2UXtfNuPzLUrP1tKGr2dkgKeF2dTloc/nrHkg3T3Sz5HxKOdqlLkAJDJ5ntEyB7UzA+aPsVwuQx1q07HJaHpNLn8iWFbLcFgsCi/Z28d9zy3eD+HEVJwZTT5dK/t3YipOj9eJf5VLzWqhdpVfOyMsppbUWsuAx6H1Q1i8TOux03OIhxbbl80wHKCu5ZmZJHGTxv5eDBEsq6WG2jvoY0One0lH+xFDX46a+7JJRtjjsNHZ5tAd12Y7NLcHe3DaLEsGxIXaEWCyKoibyOQZi6Sbc2HRHBqxlqs5JU+wZQmV3t1amdZSAXGxL6PpPOlc5Zl6ajpBqdSsy596djVjX/bUCFjfPTJALJPn5ycW7w+1dHa/iUmaQHlfBtz2Ncnkbuluo9frWtQfOjYZX7S/TjpX4MC5MHCRtWxSudtsIstYOLUme9JpszIYcNc8Ky0WtWz9wWPTJLTmwdWoAwBUe7PYWna2OZpy+VLL3dLMrUEzW1D9IYtS+6z0uezcsr2b/1nCH3rizLyukFjKH1rroASoiZpzc0nCydWf/ATGkvWF+3JnXztbu9uWtuEnjf5Q6/iWTpuVHq+zbMPXQlmmJRZq7cvbdvbgcViX7ONq9C2nqnzLdK7AhflUU9ZywF/pD62FSm89IwM8LcxvvnAHv79/V83HXqKXaanNBR8/M1cx/eCJs/PkiyUCHvuimYHVGGO5HAYDbkolVYGwFlkWgD99+R5+/fZtNR+7e2RA64cwTbFY4r7QFEc0lQmUneyAx850LQXPdJwdTQpKDAZcTETSa9Y3xu2w8hevHuEtN2yq+fj+kX5OzyQITcbI5ot879nxirGMD5+cxWmz4LJbFuzLUqmkBSXW/sIC6r48PKYajrXIpG7vaed3XxLkFVcuHNup9kMY4KETaj+ERCbPt54erWhq+fDJWb1fQ/W+jCTVMZbN3JdiLddiX75gRw8fuHM7t2zvXvBYm1Prh3BI7YcwG8/wjacuVAQfjGtZ7dCc1EsO1n5f+lx2vE6bYV+ufhD5VVcP8jsv3snWGuOit/a0E+zz6oHH83NJvvPMmN5nIpsv6uNZYeFaivKNZpUGAxwei6hjf12rb/t++abN/MWrR2pOqrx5exdep013tEMTsYrSmAvzSc7PpQh47MzEswuk8s1M0ogAz+GxyJqclQDvv3M7f/TS2qPbhT8kLoBPnp2ruKQ8eXaebKGo2fDaSZo+nxOfa+2TNGItj4xF1ywo8scv3c377the87H9I/1k8kXuC01RLJa4PzSlX6ig0h9aTA3VrCTNYMDNZCzNdCyzJvvSYbPwF6++gl+6sbY/dPdIPxfmUxwei5IrFLn30HhFUvbhU7M4rBY8Dusi+zLRRBvu5uj42ikltna38Xv7g7xi3+CCx0S/zIdPzTKfyJLMqv6Q6KcGS9vweCbPeJOSNFDlW67Bnefmbd184M7tvGDHQn/IZbdyR7CX72tl6/OJ7NL+UJVveXI6TqkEO5rgD7U5bQQ89rJvKUu0lmTtb/eSZXNzjcuKYEOnh5EhH19+/DzfPzzBU+fCuOwWfvb7d9Ld7uThk7PYrQp37e6rWTLTrD4IUG46lsgW1iSaDfCyGpdowfVbOgl47PzrAyf56A9CHJ+KM+h3cf/v3oHDZuHhk7MMBdxs721foJQQYyxf+7yh1f4VajIYcJMvlmCNpJ8Ab13EmQG1TOtPvnWIj9xzhNPTCcYiaa7d1MFXf+0mFEXh4VOzXLOpg/FIekGWZTaRJdyEMZaCoYCbhKbiWovsn8WiLOpkg3pp+cSDp/jQV57hibNzhJM5XnfNMH/3+n3kC0UeOz3HS68Y4GtPXViwL09MN6d2XzDod3NodO2cQ7fDym+/OLjo4/tH+vnBkUl+40tP8+Ojk6RzRcbCKd5/5w6momlOTid4yw0b+fyj5xbsy+OTzcvug/oZD2kN9ddCwdPrdfH+O3cs+vhLRvr5+E+O84EvPs13nx2nUCyhAK/YN8gzF8KkcgVe+7whdS1jaTYapvacmIxhUSobva4VnW0OXHYLiWyBHm9tZazZBPu9BPtrO8JOm5U7d/fygyOTzH7mcX6s9YH7zvtv4crhgJ6RftkVA3z+0XPMJjIVPaaOTzUvSSOk8olsYU3OSoAbt3Yt+thgwM2+DQG++sR57g9N8fiZeZw2Cz/9vTvo9bl4+OQsVovCS/b0898HxyiVShXv/4mpWFPtDqhruVY2/O4rFveHrtvcSVebg088eIqP//gEockYfT4nD/zuHbjsVh4+OUufz8nIoJ+xSOVZWSqVOD4VX9LfWk2GteRhOldcs335izdsXPSxu/b080ffPMRffvco5+aSjIZTXLUhwDffe7PqD52c5aqNAeYS2QV2J5zMMhPPtIQ/tBb7UlEU3nv74v7Q/pF+/uX+k/zOV5/hqXPzzCdzvPqqQf7xTVdTLJZ49PQcd+3p49sHRheocJtZNgjqvhQVFWthw132pf2hl4z0891nx/nAF5/mvtAUyWyBs7NJPnjXTmbjGUKTMX7xho184dFzC33LJq/lUMCtt3mQCp6lkQqedcxLrxjglCaX/e27dpLNF/Wu5w+fmmXfcIDN3W3E0vmK8qNmjbEUCIcG1iaafTHsVgsv2aP2lrAoCr9++zbGImm+/tQFvXHbDVs76fe5FkSzmzXGUmBcy1aIZvd6XVy/uZOHTswy3OHhLTds5Imz83rm5eh4lBu3dtHrdS7I/rWC4RC0wr68ekOAQb+LHx2d5NpNnbzsigG++fQo52aTHB6LEsvkuXl7Fz3tzgXOYdPXssOwL1ugTvqFu/tw2Cx8/9AEL7tikBu3dvLvPztNPFMeof6qq9Qg7YJ9OR3HZlEqxguvJWItXXYLbsfqNmBdDi+9op9SCX54ZJK33bSZ7b3tfPwnxykWSzx8chZFQc/CLtiX03E2dnpWvZFsLcTEIlibAO5yeOkVA4STOZ48N89vvnAHAY+df/rxCUC14R0eu56FXaCGaqJSoqfdid2qBkha4fMN8LIr+jkzm2R0PsVv37WTfLHEJx4s+0MjQ3629baRzBaIG8qPSqUSJ6cTTfOHBg12pxX2pdWi8JKRfg5eiFCixHtv38ZkNMNXnziv9d+Z48atXfT5XQv6bM3Es0RSuZZYy841uEhfjM42Bzdt7eLhU7MM+F289caNHDgf5qfHZ7R+hBF1LX2tZ8Mr17L5+/KKIT8bOt38+Lkpnrexg5dfOcB3nhnj1HScI+NRIqkcN2/rotfrYrrFghJCPQpro8K9GHfu6sVlt3Dv4Qn27+3nlu1dfPqh00TTOb136ys1G75A0TwVx6JUTnpeS8S+tFuVBf1pJZXI1VnHvPP5W9g3HOD6LZ3YrRZOTsf53MNneesNmzg0GuG9t2/TGwtPxdJ6Q7hmjbEUGEfGtoLhAPijl+7mddcOc83GDhQFfn5yln++7wRXDPmZT+a4aWsX5+aSeld50XOgWWMsBUMtZoQBPvamq5mOZbhi2E86V+CHRyb5px8f5+03bwHgpm1dHJ+K8+yFcMW/a74RNjjaLZAZsFgU/utdN1Asldje62UqmuaHRyf5l/tP6AqIm7Z20eurHSxrxhhLQasFy/xuO998780EPA6GtGzaq/75IT738FnOzSXxOm08b2MAn8u24NJyYirO5u427Ks87W8xhHO4Fpm/5bCr38fXf/0mtnS309nm4NsHRvnNLx3g+4cneOTULLv6fQT7VNVKKwUlQN2Xp6YTLbEnAV68p4/Pv+sGrtoQoM1pw2pR+PsfHuPQaIRHxUVa66OgJhf8gJqkOTUT56Zti6taVhOLRWHA7+bcXLJl7M7bb97CngE/12/pxGGzcGY2wX89epa33byZZ86HedcLtuoKqKlYBq9WjjURTTc1SdPvd2FR1Iama1XudjF+f/8uXn3VENduUv2hR0/P8a/3n+SaTZ3MxDPctLWLiahaGp4rFPWzsdk2vDKx0PyLNMBH37CPyWiaK4cDZPIFfnx0in/68XHec9s2iiXVhp+fSy4YAtDMHlvQekkaRVH43DtuIFcosqPPy3Qsw4+OTvLP951k94C6RjdtU/2hagXPiek4dqvCps7m+0OtYMfbnTa+8eu34HXZ2NDp4dBohJd//Gd89qEzTMUyeBxWrtnUQWebo+Zabupqa0qSBspr2eFxNKU31XpCKnjWMU6blVu2d+vG9f13bCeVK/D+LzxFoVjipgrnsOxoN2uMpcBlt9KtNZ1shYs0gN9j57rNnVgsCoqi8Jsv3M6F+RR//M1nAWE4XAu6yjdrjKWgIsvSImvZ73dxxbB6EXHZrfzabdt45NQc/3r/Cdx2K/uGA/RpCh5j88ETU80ZYykwZllaIfsHas8T0ful1+fizddt4GtPXuDbB8bY1tNGr89Fr9dVM/vXjDGWArEvvS5bzf4jzWDvoF93DvZtCHDbzh4++dNT/PT4NNdv6cRmtdDncy2QJDdrgpZArGUrONmCazZ16q/n5VcOsrW7jX/80XGePDvPTVu7CHjsOKyWCucwXyhyeibRtNJgQB9b3ipnpaIo3LK9mzYtE/m2mzfjddn4428+y2g4xU3byjbcuC+bnaSB8nnZKvvSYbPw/B3d+nnzvju2k80Xef8XniJfLOmXP6hUlpV7GTVnLe3auQOts5Z+t53rt5T9oQ+8cAdjkTR/+I2DABX7crqFfMsBf+vZ8D6fiyuHA4Dqs//67dt44uw8/+8nx3HYLFy9MaAmaaIL/SGnzVIRaFlLhlpMWQZqI+YdWvKgx+vkF6/fxLcOjPLNp0fZ3OVhwO+mz+uqmVjY3NWGrWlJGnUt3XZrS6hwAfYM+tigBbxGhvy8aHcvn3roNA8cm+a6zapooNfrrLmW25roDw21oD/UqrSG5y0xhR19Xl46MsAzFyI4rBaet6mjpkMj6lGb1bwNytmBVsmkVnNHsJeRIR/PXIiwodPNcIenrIaKVjo0zRhjKRBNx6A1SrRq8ebrN9Ld7uSZCxGu3dyBw2ah1+dcIJVv1hhLwbA2ZUdRaMpEtOXwa7dvw6IoHBmP6tn7Xp9zQYPGZk3QEqwHI/yBF+5gLpHlwnyqYi2NZZjZfJGzc8mWWMtWye5XY7UovP/O7YQmY2TyRW7a1oWiKPR4nRVS+XNzSXKFUlODZcLutOpZ6Xfb+ZVbtvDMBbWJ5E1bu/RkSIXdabJSAsqXlla14dt62rW+UGpT7Ws3degKnoqgRAutZavuy1t3dLNvQ4BnLkQY9LvY2GnwhwxreXIqTpvDWhFoWUvU5KG6hq2i4KnmDdduoNer+kPXbOzAZbfS63WRLRSJpHL6952YjrO1p73pSRpQJ6S2Iu+5bStWi8Lhsajeo6u3RrnbySYrR1sxSVPNb9y5g3Ayx7m5pGEtK9tSiCSNXMv1gQzwXGK8/061SdlVGwO47Fb6hCS5yjnsanM09cIw1GJlB9UoisIHtEajN2mHXaVUXqWZYywFrZaVrsbtsPKeW7cC5UaZNZVlTTbCPreNNoeVDo+jaU7VxRjwu3n9tcMA3LRV7c3R51XH7GbzRaC5YywF68EIX7Opg+drjez1femtVPCcmU1QKJZaIsDTKlnUWrxy3yCbutSRwddv6QTUcbeTsYVKCbkvl+Ydt2ym3Wmju93J9t52HDYLXVVS+WaXBoPaOBRaR4Vbi/ffsR1FURV7bU6bPoK5WsHjc9lqjrJfK/SAeIuupVA1A9yoBXDLyrLKtdzWZH+onFxoTd9SqJoBPbFQ3pet4w+1O2343faWUuFW06epmsG4li6i6bw+FSqTLzQ9SSOa0rey3RGqZjCsZZWCR0/StIA4oJXXslWQPXguMXYP+PhfL9/DTk3GWEsqf7yJE7QEIijRCg3HFuOuPX38xp3buXtEnQghMlbVRviOYG9TXp9gMODm+FSspRuOvfXGTYxH0rzuGjU40eMtO9rbetqJpXNNHWMJ5SasxVLp4t/cRH7zRTuwWy3csUs1xkKlNxPPMBhw62Msm7mWXW0OHDZLy2akBX/2ij18/alR9gz4AOjR1FBiyk4rBSVaVSkBYLNa+OvXXsmh0Yiufuv1uvQR81Au32hqiVaLlRXVIuBx8Ne/cAXZfFG/KPdUOdqtkKRZD/tyR5+XD79iL5u1nmXtThtuu3XBWjY9SdPiCh5QVc0feOEOXrynD6CmgufEVJybm9QXSjAYcHPQcA61Ir94w0ZGwyndHyr3hkoT7PeSyhYYDad4/TUbmvkyGQy4SWbzF//GJvIbL9yBoii8cHfVvoxm2Njl4cxMsulJGr/bjsdhbVkVruB/vWIPX3niPFcMqS0W+nwupuPlvqPHW8Ifan0b3iq07o1QUjfveP4W/c9CKi8cmlKpxIkmjrEUXLOpg/85NNG00qbloCgKHzKMGjQGJUCMscw29bADuHZzB/PJbEs3HHM7rPyvV+zR/64reLR9eXI6AdDU2l6A67Z0kjZMnGtFer0uPvzKvfrfjVnpwYC7JYISFovC9Zs7GdEchVZlR5+XP7h7l/73Pk0qH07m6Ghz6Gu5tWftx3rrr8nnYnOXh5EhX9New3K4aVtXRdPfPp+Tn5+c0f9+YipOn8+Jz9W8i1ewz0uHx87ewdZey5dfOVjx974qqfyJ6eYnafZtUJuSi54Yrcrbbt6s/1lVnjiZNJYVTce5c1dzkzTXbOqg3+eqGEDRaiiKwm/ftVP/e1e7E4uC3pQ+ls4xEU03fV9es6mDsXCqZVW4oKp4/vTlRn+oMnnYCkkagOs2dzCfzF38G5tId7uzwh/qFcqyWJqNXZ5yj60m+paKonDd5k49cNKqbOtp5w/v3q3/vdfnpFAsMZvI0Ot1Gdayef5Qd5uTrT1tjAy29lq2AjLAcxnQZ+gr0ewxloK7rxjg7iuaG2RaKXarKpUXGatWuEgD/Npt23TJ73rBON0NWmct/+o1VzT159eDyP7pzqE2xlJM2moW//WuG5r68+tB71kWS+sBnqGAG4+jeabSalG4/3fvaNrPr5deg1TeZbc2vQ8CqJfSp//Xi5v6Guqh1+vkuYko0DpJmt0DPg5++CVNfQ31YGxK3ypJmrv29HGXpoxZL1gtCt3t5X4nIknT7LV81wu28q4XbG3qa1gpZRsu1rI1/KE/f9VIU39+PYhgmUgenpiKoyjNTx5+9h3XN/Xn10OvocVHr9fFyak4/T6XPoGwGVgsCj/50O1N+/nridYsrJSYSq+hr0SrXKTXK70+l56xkmtZP+1OGx6HtWJf2iwKm7qaM8ZyPSOCEtMiWDYdZ2Onp2ljLNcz1cqyZvdBWM8YpfKlUomT04mmJxbWK30+FzPxLIViqWWSNOsVY1N6acMbQ1WWybVsFLfDitdlq9iXFgU2d0t/aKVUB8tOTKtJmlaZXrWeEL7llMG3lJ/v9YMM8FwG9Pmc5aBEi2QG1iu92nhvKI+xHO6QRnilKIqyYC23dLdhb9IYy/VMV5sqlZ+sCEq0dtlEq9JrKMMsFkucmok3ddrgesYolZ+Ipoln8mxv8XKeVsUolRcX6R19cl/Wg1HBo6+lPC/rotfrrLA7dqvCpk7pD9WDupblfbm5q00maeqgw2PHblUqfEtpw+vDmPAqlUotocKVLB95m7oMMErlmz3Gcr3TZxjB2OwxluudXp+rQpIsDUd9WC1an61YuiXGWK5nys0uM4yGU6RzRbmWdWKUyrfC1Kf1jFEqL5M0jdHnc5LMFohn8pyYiuOyW/TJS5KV0etzlZWjWpLGJpM0dVGthmp2L6P1ipo8VJX2hWKJU9K3rBsxWXAymmE8kiaRLci1XEes2kkcDAY/HAwGR4PB4AHtv5eu1s+SLI1RKt8KYyzXM73eslRelm80Rq9Xlcpn8gXOzsqgRCOIMsxWGGO5nhFS+aloWpYcNIhRKi/XsjGMUvmTU3HanTb6fTJJUw+9hqb0J6bjbO1uxyKTNHXR63UyE8+SKxRlkqZBhIInXyhyRvpDDdHrU9Xho/MpMnmZpKkXh81CZ5uDqZi04euR1Q61/0MoFLpK++97q/yzJIvQZ5DKy6BEY/RpUvnR+RSj4ZTMSDdAn6bgOTOTpNgCEyPWM0JZJo1w46j7MiPXskGEVF7YHb/bTne7HG1aD7oNF0manjaZpKmTvqrAo/x814/Yl6PzKTVJI/2huunzuZiKZjgzqyVp5FrWTZ9WhnliOgZIG94IogxT+kPrD6mlvAwQGatT03Emomn5AW2AHs05fOTUbEuMsVzP9HpVqfwz58NA86ccrGd6vC6mYxmOt8AYy/VOr1budnwqRne7g4BHBiXqQUjlp6PqvtwulaN1I6TyU9EMx6disnyjAYQ/dHY2yYX5lLThDSDU4Y+enqVYQu7LBujxOskWijx5dg6QvmUjCAVPuTRY9tiqF1GGeWI6TsBjp6tN+kPrhdUO8Lw/GAweDAaDnw4Ggx2r/LMkiyAyVj8/OQvIPgiNIPpKPHRyBpBGuBFE9u+hkzMtMcZyPdPnczKbyPLcRKzpYyzXO0YFj9yTjdHrczKplRVJu1M/Qip/cjrOZDQj7U4DiObfj5zS/CG5lnUjbLjuW8q1rJvqtZTBsvrp87mIpHIcGo3S3e7E75H+UL30CQXPpGrDZZJm/WBr5B8Hg8EfAf01Hvpj4F+BjwAl7f8fBd7RyM+T1EfAY8dhtUgjbAK9BiMsx1g2hsj+/fzkrBxj2SC9BmVZUE4qagjRGyqWzvGKfYPNfjnrml6vk6fOhZlNZKXdaZBer5OHT8kkTaN4nTZcdukPmYFQQ/385KxM0jSI0R8a8LtodzZ0Pbus6dHW8uFTs2zvlWrmRuj1OZmOq70yX7K31nVf0qo0dIKEQqEXLef7gsHgJ4F7GvlZkvpRFHXKzmg4hcNqYaMcY1k3Qio/HcuwpVuOsWwEESybjmW4PdjT5FezvhHKsulYhpddMdDkV7O+6fW5yBaKZAuyOWOj9PnU0kGQF+lG6fW5eG5C9pRoFEVR6PO5ODubxGpR2NwlL4D10tXmwKKodmdDpxuXXfpD9dJn8IdesKO7ya9mfWNcy5fs7Wvyq1nf9PlcFIol5pM5aXfWGas5Rct4y3gNcGi1fpbk4ohMy+Zujxxj2QAOm0WvQZXZqsYQQQmQGelG6TNM1JHS7sao2JdyLRvCuC/lWjZGn5aVlkmaxhFl65s6PThs0h+qF5vVQreW9JI2vDF6DXZH+paNIdRQIPdloxjXUvqW64vV1AD+bTAYvAq1ROsM8J5V/FmSiyAcGulkN06P1ylLDkyg3WnDbbeSyhXkWjaIdGjMQ5S7gTwvG0VI5V12C0MBd5NfzfpGJmnMo0dbS3lhaRzR0FaelY3hcdjwOm3EMnm5lg1SmViQJeuN0GtcS+lbritWLcATCoV+abWeW7JyRFZafkAbp0+Tyu+QRrghVKm8kzOzSXb0ybVshK52JxYFiiXkWjaIOCvbnTb6Dc6NZOUIR3tbTzsWi2zO2AhiLXfIC0vDiISXtOGN0+d1cYio3Jcm0OtzEpvOy33ZIB0eO3arQq5Qkv5Qgwi747ZbZZJmnSHTQJcJIgq7XTZgbRihlpBZlsYRagk5xrIxrBaF7nanHGNpAmJPbpNjvRtGnpXmIdZSqk4aR6ih5L5snF6phjKNXqm0NwVFUej1uvA6bRXqZsnKEX1Ht/W2ySTNOkO2ab9MGAyohkNO2GmcwYAbiyIdGjMYDLjo88kxlmYwGHDjsFlkUKJB3A4rHR47QZn5a5gBvwtFgZ3S7jTMoJY9lTa8ccRayn3ZOIN+1R+S6vDGGQy46W530NUugxKNMhhw0e93SX+oQRw2Cz1epzwr1yFKqVRq6gt48sknS9dcc01TX8PlQCZf4GfHZ3jhbtlRvlHmE1mem4hx07auZr+Udc/5uSTzySxXDgea/VLWPaGJGFaLIrN/JvDk2XmGAm76/bJEq1F+enyafRsC+FwyiNsIpVKJHx6Z5M5dvbIHT4Nk80UePDbNC3f3ygtgg4STWY6MRbl5u5z81Cij4RQzsQz7NgSa/VLWPccnYyiK7MFjBk+fm6fP59ID45LW4sknn+Saa65ZYMhkgEcikUgkEolEIpFIJBKJZJ2wWIBHpoEkEolEIpFIJBKJRCKRSNY5MsAjkUgkEolEIpFIJBKJRLLOaYkmy08++WSzX4JEIpFIJBKJRCKRSCQSybql6T14JBKJRCKRSCQSiUQikUgkjSFLtCQSiUQikUgkEolEIpFI1jkywCORSCQSiUQikUgkEolEss5piR4865lgMLgf+BhgBf49FAr9dZNfkqSFCAaDZ4AYUADyoVDo2mAw2Al8GdgMnAHeEAqF5pv1GiVrTzAY/DTwcmAqFAqNaF+ruS+CwaCCesa8FEgCbw+FQk8143VL1pZF9smHgV8FprVv+6NQKPQ97bE/BN6Jet58IBQKfX/NX7RkzQkGgxuA/wT6gBLwiVAo9DF5pkiMLLFPPow8UyQawWDQBTwIOFHviV8LhUJ/FgwGtwBfArqAJ4FfCoVC2WAw6ETdV9cAs8AbQ6HQmaa8eMmascQ++QxwGxDRvvXtoVDogLQ7a4tU8DRAMBi0Av8M3A3sAd4cDAb3NPdVSVqQO0Kh0FWhUOha7e9/APw4FArtAH6s/V1yefEZYH/V1xbbF3cDO7T/3g386xq9Rknz+QwL9wnAP2hnylWGi9ge4E3AXu3f/ItmoySXPnngQ6FQaA9wI/A+bT/IM0ViZLF9AvJMkZTJAHeGQqF9wFXA/mAweCPwN6j7ZDswjxr4Q/v/vPb1f9C+T3Lps9g+Afhdw3lyQPuatDtriAzwNMb1wIlQKHQqFAplUSPbr2rya5K0Pq8CPqv9+bPAq5v3UiTNIBQKPQjMVX15sX3xKuA/Q6FQKRQKPQIEgsHgwJq8UElTWWSfLMargC+FQqFMKBQ6DZxAtVGSS5xQKDQuMqGhUCgGHAWGkGeKxMAS+2Qx5JlyGaKdC3Htr3btvxJwJ/A17evV54k4Z74GvFBTa0guYZbYJ4sh7c4aIgM8jTEEnDf8/QJLG0vJ5UcJ+EEwGHwyGAy+W/taXygUGtf+PIEql5ZIFtsX8pyRVPP+YDB4MBgMfjoYDHZoX5P7REIwGNwMXA08ijxTJItQtU9AnikSA8Fg0BoMBg8AU8APgZNAOBQK5bVvMe4FfZ9oj0dQy7gklzjV+yQUConz5C+18+QftBI+kOfJmiIDPBLJ6vL8UCj0PFRp4vuCweCtxgdDoVCJpSPekssQuS8kS/CvwDZUSfQ48NGmvhpJyxAMBtuBrwO/FQqFosbH5JkiEdTYJ/JMkVQQCoUKoVDoKmAYVbW1q7mvSNKKVO+TYDA4Avwh6n65DugEfr95r/DyRQZ4GmMU2GD4+7D2NYkEgFAoNKr9fwr4JqqhnBSyRO3/U817hZIWYrF9Ic8ZiU4oFJrUnKoi8EnKJRNyn1zGBINBO+ql/fOhUOgb2pflmSKpoNY+kWeKZDFCoVAYuA+4CbWkRgznMe4FfZ9oj/tRmy1LLhMM+2S/VgpaCoVCGeA/kOdJU5ABnsZ4HNgRDAa3BINBB2ozuu80+TVJWoRgMNgWDAa94s/Ai4FDqHvkbdq3vQ34dnNeoaTFWGxffAf45WAwqGgN7CKGsgvJZUZVzfprUM8UUPfJm4LBoFObdrIDeGytX59k7dH6XXwKOBoKhf7e8JA8UyQ6i+0TeaZIjASDwZ5gMBjQ/uwG7kLt13Qf8Drt26rPE3HOvA74iaYYlFzCLLJPnjMkFRTUPk3G80TanTVCjklvgFAolA8Gg+8Hvo86Jv3ToVDocJNflqR16AO+GQwGQf2sfSEUCt0bDAYfB74SDAbfCZwF3tDE1yhpAsFg8IvA7UB3MBi8APwZ8NfU3hffQx0reQJ1tOSvrPkLljSFRfbJ7cFg8CrUcpszwHsAQqHQ4WAw+BXgCOq0nPeFQqFCE162ZO25Bfgl4FmtHwLAHyHPFEkli+2TN8szRWJgAPisNjHNAnwlFArdEwwGjwBfCgaDfwE8jRosRPv/54LB4AnUoQBvasaLlqw5i+2TnwSDwR5AAQ4Av6Z9v7Q7a4hSKskgq0QikUgkEolEIpFIJBLJekaWaEkkEolEIpFIJBKJRCKRrHNkgEcikUgkEolEIpFIJBKJZJ0jAzwSiUQikUgkEolEIpFIJOscGeCRSCQSiUQikUgkEolEIlnnyACPRCKRSCQSiUQikUgkEsk6RwZ4JBKJRCKRSCQSiUQikUjWOTLAI5FIJBKJRCKRSCQSiUSyzpEBHolEIpFIJBKJRCKRSCSSdY4M8EgkEolEIpFIJBKJRCKRrHNkgEcikUgkEolEIpFIJBKJZJ0jAzwSiUQikUgkEolEIpFIJOscGeCRSCQSiUQikUgkEolEIlnn2Jr9AiQSiUQikUiWSzAY/DDwZ9pfS8C+UCj0rOHxCaAPIBQKKYave4H3Aa8FgoALmAIeAj4eCoUeWuLn/O9QKPRhw2NXAn8CXAsMAXHtuQ4BXwiFQt80fG/pIr/SllAodOaiv7hEIpFIJBLJRZAKHolEIpFIJOsVBfjfF/umYDC4DXga+D/AdYAPcADDwBuBn2kBnYsSDAafDzwBvB7Yoj1PJ7ALeB3wspX+EhKJRCKRSCRmIBU8EolEIpFI1jOvDgaDV4VCoQO1HgwGg1bgm8A27UtfA34HmAReCvwHasDnz4LB4OFQKPTVi/y8PwDsQBFVDfRDwAnsQA3uZBb7h0ZFkUQikUgkEonZyACPRCKRSCSS9UoBsKKqeF61yPe8GrhC+/ME8JZQKJTV/v6NYDA4DHxM+/ufAhcL8OzQ/h8DfhAKhVJAEnhM+08ikUgkEomkKcgSLYlEIpFIJOuVz2v/f2UwGLxmke95seHPXzQEdwSfNfz5imAw2H+Rn3le+78fOBYMBv+/YDD4tmAwuGV5L1kikUgkEolkdZABHolEIpFIJOuVx4F7tD//+SLfs9Hw5zPVD4ZCoQgwv8j31+IfUZs7g9rD5z3AZ4BTwWDw58Fg8KrF/mEwGCxV/XfgIj9LIpFIJBKJZNnIAI9EIpFIJJL1jJh09dJgMHj9av+wUCh0D/BC4D7UEjEjNwH/HQwG21f7dUgkEolEIpFUI3vwSCQSiUQiWbeEQqGngsHgt1F78NRS8Zwz/Hlz9YPBYNAPdCzy/Yv9zPuA+4LBYAC4GXgF8E7U5svDqIGeH9b4d7LJskQikUgkklVDKngkEolEIpGsd/4MtWzqJUB31WM/MPz5jcFg0FH1+C8Z/vxsKBSaWOoHBYNBn/hzKBQKh0Kh74VCoV9HLdMSdC73hUskEolEIpGYhVTwSCQSiUQiWdeEQqFngsHgN1HHllurHv4WcAgYAQaBzwWDwd8FpoC7gb8wfO9HlvHjvhUMBseALwGPAmEgCNxq+J6jK/8tJBKJRCKRSBpDBngkEolEIpFcCvwZ8BqgogwqFAoVgsHgq4HvA9uAN2j/VfPnoVDoYiPSARzAW7T/avHtUCh0sNYDwWCwVOPLrwmFQt9axs+VSCQSiUQiWRJZoiWRSCQSiWTdEwqFDgE1AzShUOgk8Dzgj4AngBiQA0aBrwAvCIVCf1br39bgT4GPac8zrj1PEjigPf8b6/4lJBKJRCKRSBpAKZVqJZMkEolEIpFIJBKJRCKRSCTrBangkUgkEolEIpFIJBKJRCJZ58gAj0QikUgkEolEIpFIJBLJOkcGeCQSiUQikUgkEolEIpFI1jkywCORSCQSiUQikUgkEolEss6RAR6JRCKRSCQSiUQikUgkknWODPBIJBKJRCKRSCQSiUQikaxzZIBHIpFIJBKJRCKRSCQSiWSdIwM8EolEIpFIJBKJRCKRSCTrHFuzX8CTTz5ZavZrkEgkEolEIpFIJBKJRCJZL1xzzTVK9deaHuABuOaaa5r9EiQSiUQikUgkEolEIpFIWp4nn3yy5tdbIsAjkUgkEolEIpFIJNUcHY9w76FJRsMphgJu9o/0sXvA3+yXJZFIJC2JDPBILhmkAyCRSCQSiURy6XB0PMInHjyN322np91BJJXjEw+e5t23bpE+nkQikdRANlmWXBIIByCSyjHgd+kOwNHxSLNfmkQikUgkEomkDu49NInfbSeezvOJn57GZlHwu+3ce2iy2S9NIpFIWhIZ4JFcEggHAOCps/P4XDbpAEgkEolEIpGsY0bDKbwuG/PJLIViiVgmj9dlYzScavZLk0gkkpZEBngklwTCATgyFuWhk7MkswXpAEgkEolEIpGsY4YCbmLpPJl8EYBcvkgsnWco4G7yK5NIJJLWRAZ4JJcEwgGIpXMAxDN56QBIJBKJRCKRrGP2j/QRSeWIpFT/Lqz9ef9IX5NfmUQikbQmssmy5JJg/0gfn3jwNHPJLABTsQx+t503Xjfc5FcmkUgkzUc2oZdIJOuR3QN+3n3rFn7va88CYLcqssHyCpBnv0Ry+SEVPJJLAuEApLIFAEqlknQAJBKJBNmEXiKRrG92D/jZ3N0GwK07e6Rvt0zE2T8dy9Dvc8qzXyK5TJAKHsklw84+H0ktwLN30C8dAIlkBcgs36WLaELvtFl48uw8127q0L8u32OJRLIeiGolWolMvsmvZP1w76FJPA4rX33iAnfu6iXY79W/Ls9+ieTSRSp4JJcMk9E0+WIJgIlousmvRiJZP0iFx6WNaEJ/ajrBz0/OMhPPyib0EolkXRHRAzyFJr+S9cNoOIWiQLZQJKy1MJBnv0Ry6SMVPJJLBqPBmpQBHsllghnKG6HwOD+fREFVwImvyyzf+mco4CaSypHJqxejdK4gm9BLJJJ1RTQtFTwrZSjg5vhkDIC0NoVMnv0SyaWPVPBILhkuzCcB2NbTxkREBngklz5mKW+EwuPZCxEOj0UBmeW7lBBTaKJp9WI0m8jKKTQSiWRdoZdoZaWCZ7nsH+nTh4+kcwV9Glmrn/1HxyP8ww+P8TtffYZ/+OExqSaWSFaIDPBILhlG59XL6DWbOmSJluSywKi8OTOTwO+243fbuffQ5IqeZyjgJpbOk8kXyRZklu9SQzShFyggm9BLJJJ1Q6lUIppSA9SXi4LHjCDH7gE/N27tBNQSN7/b3vJnvywZl0gaRwZ4JJcMo+EUXW0ONnW1EUvnSWYvDydAcvkilDePn57j6XNhoD7ljVB4pHMFcvniusnySZbP7gE/O3rbAbhyQ6ClHXyJRCIxks6Vkw+Xg29nZpDDZVe7cQwF3Hzwrp2mn/1mq21E4koB8oVS3YkrieRypuEePMFgcAPwn0AfUAI+EQqFPhYMBjuBLwObgTPAG0Kh0HyjP08iWYwL8ymGOtz0+1wATEYzbOmWbaYkly5DATfhZJZ4Jo+iKEB9ypvdA35+9QWb+faBUUqA323njdcNyyDAJUZMK9GaS2Sa/EokEolk+Yj+OwDxBhQ862Va5L2HJvE6bTx1dp5rN3fgd9v1r6/09c4n1BIt0aTaTEQgyu+2VwSiGlEJjYZTDPhd/MdDZwj2e3n+9m5ZMi6RrBAzFDx54EOhUGgPcCPwvmAwuAf4A+DHoVBoB/Bj7e+SVeZyrlsVBrvfrwZ4ZB8eyaXO/pE+pmIZiiWIpXOEk/X3Vtnc3Y42hG5VsnyS5hPLqA7+fMJ8R18ikUhWC2NwIllnDx6jKibgsbd06c9oOEW2UOTgaIQzs2p/yXqDHHNagCe8CgEeobY5NBphKpoxRW0zFHAzl1ATV3EtKSFLxiWSldFwgCcUCo2HQqGntD/HgKPAEPAq4LPat30WeHWjP+tSw+xgjDBec4ksPe2OljZeZlMqlRjTAjx9uoJHBngklza7B/x6MKdYApvFUnfmTPQ3yOaL5DQpvOTSIqY3WZYKHomkVbmcE3WLIRos+1y2uhU8IhgRz+T595+eplAstWzpz1DAzXRMPaczOTWgVW+QY15rshxJ5iiVSua9SNRAlNNm4Ymz8xybUqd1Naq22T/Sp/fRzOTXT2NoiaSVMLUHTzAY3AxcDTwK9IVCoXHtoQnUEi6Jxmo0ERPG6/Ezc3z7mbHLqm51NpElnSuqJVpCwSMDPJLLAFFfD/DSK/vrVt4YJfDJjJxScikiLkkioyuRSFoL2WC2NkLBMxhw122fRM+6c5oiJprOtWzpz/6RPma1czrV4PQrcd5nC0XSOXOTN0MBN9NxNRAllFWNqm12D/i5I9ijP9d6aAwtkbQapgV4gsFgO/B14LdCoVDU+FgoFCqh9ueRaIhgzHgkxXPjMVOCMaPhFDarwrHJGOGkagxb1XiZjZigNRRw0+600e60tWyJlszOSczEuM8vzNf/WTdK4BOXQRPLy5FyDx4Z4JFIWhHhG87EMzxwbPqyStQthUhADPhddU/REtMixyKqnczmiy1b+rN7wM/uAS8A88nGpl/NJ8u2PZwy9+zfP9LHjKY0Smbypqlt7Fb1etrV7pAl4xJJHZgS4AkGg3bU4M7nQ6HQN7QvTwaDwQHt8QFgyoyfdakgMgnPnI9w4HwYaDwYMxRw8+yFCMUSZHJFSqVSyxovsxHrNtzhAaDX52zJEi2ZnZOYzXgkjdWiNlhu5PyIGgM8l8kY2ssNEeCZT+YoFmXORSJpNYRveGwyxqHRCKVS6bJJ1C2FKCEeCLhJZPN1lRrtH+ljPpllXEuKhJOtXfqTK6i/454BX0NBjrlElh6vEzC/0fLuAT/P39ENQNREtY14j4TNkkgkK6PhAE8wGFSATwFHQ6HQ3xse+g7wNu3PbwO+3ejPupQQmYRENk+qwfpawf6RPo5NqjWwhVKJ2UT9DVfXG7qCp0Ndv36fqyVLtER2LpMv8IkHT2G1KDI7J2mIiYg6cSLgseufg3owlmgl6mxiKWld0rkC2UKR7nYnhWKp4v2WSCStgfANY+k8xVJrq0zWEhGYGPC5KJaoq9RIlP4UtOC2xUJLl/6IJGUjQZl8oUgklWNLVxuAru43E6fNCqiqG7PUNuNh9XePrkJjaInkcsCMGdK3AL8EPBsMBg9oX/sj4K+BrwSDwXcCZ4E3mPCzLhn2j/Txbw+c0jPl4WSWaDrPG68brvs57VYL88kcXW0OZhNZnDYLb71xY8saLzO5MJ/E67TpoyT7fS4eOTXb5Fe1EDH+cWwiRSZfJJzMMhhwX/bZuUZZL6NPV4PxSJpBv5t4Jt+ggqecKZMKnksPkQnd1OVhJp5hNpEl4HE0+VVJJBIj+0f6dJUvwFQsQwka8g0vBaKpHB6HFb9H9fES2Txuh3XFzzNrmCC4bzjQ0n6CULE0EuARk7O2dLfx2Jm5VRmVLppBzyUyFIolXVHcCKKMLpEtkC8UsVlNbRkrqZPV8LUvZ/99NWk4wBMKhX4GLPZpfmGjz3+psnvAzy/esIFvHRgDwO2w8qbrNzS0qb/25ChWi8IH79rJn3zrEK+7ZgO7B3xmveSWZjSc0tU7AH1+lzo+uljCYoKxMYuhgJtIKkdaU21lZHauYUTZm99tryh7azQzt16MzkQ0zb7hAKlcgbOzibqfx5gpq3dKiaR1EYqdTV0enjw7z1wiy7aeJr+oJrFePtuSy4/dA35+5ZZNfPPpUQBsVoVfuWXzZb8/Iym1D02bQ722JDMFaF/58zx5do7hDjeJTL7lVYyTJgR45rV+a1t6VAVPZBUUPCLAUyypE7u6250NP+e4obdgLJ2no00mI5rNavja4jnbnTYCbrtp/rvE5ClakpXR4Skfgr94/aaGPiAf/UGIz/78DBs63NitakDD7GZqrcyF+VRFkKTf5yJfLOlTCFqF/SN9ehM6UI3h5VJGt1qIsrfTMwl94kKjZW/rpVdSqVRiPJJmwO9iKOBmdD5V9xjUiilassnyJYdQ8GzWpPqXa6Pl9fLZlly++N3ly+xde1Y2GfFSHeIQTefwuey0OVXVTj1JiFKpxBNn5rl2Uwc+t72l+7vEM3limTyK0liAR5zz4txfFQWPNkULYDbeuF0plUqMa72oQPbhaRWErx1J5Xj89JwpvrZ4zjMzCf7rkbN6JYZsW9E4MsDTRKZihkMxkVniOxdHOKuhiRipXIHNXW18/7D6wViNWttWZYGCx6eOSm+1Rsu7B/y8+9YtiDu4RVFkpLpBRsMpbBaFB45Nc2RMHeDXaFPKew9N4nPZ+PnJGU7PJFp2ksl8Mkc2X6Tf71KzktlC3Q5cNJXHpqnd4nJM+iVHTAvgbe6+vAM89x6axOOw8oMjE8zEMy372V6vXKoBhrXEOA1xfgWf00s5eKkreJyagqeOJMT5uRRTsQzXbO7E67K1dH8XMR1zY6eHaLr+pvjzSXX/bOh0Y7Uoq5L4nY5laNPK5Wbi9d1ljMQyeRLZArv61Slira60ulwQDeCfG4/y2Jk5UxrAi+ecT2ZJ54vEs3nZVN4kZICniUwbAjz1Otsi+jkaTuGwWdg76KNLkzJeLgGeaDq3oMyp368GeFpxVPruAT+btEvW1RtauwZ8PTAUcOuBPOH0NVr2NhpOkcwWODYZ54xW9tSKRmdcq1MXCh6of1R6JJXTA6NJWaJ1yaH34OlUJw1ergGe0XCKYqnEWDjNsck40Jqf7fWICDDMJbL0+ZyXVIBhMVYjoGVslr8SFbLwB9O5Ak+cmb+kgpfRVB6f24ZHK9GqR8HzxNk5AFXB42ptBY/wXXf2eSmV1KBHPcxpPYe62pz43fZVuRdMxdLs0tpBmBHgEQ2Wd/ZpAZ4WDsRdTpSHAxUoliCZLTTsaxufE9T3WratMAcZ4GkiU7Fy8KHeUiIR/Yyl83S1ObBZLXS3awGey6BE6+h4hL/+3nMAHDgf1p2rPp9a/taKk7RAbaoN6lhJSWPsH+nTJcKJbF4vgWuk7G0o4ObQmLqXxLSOVjQ6wgns97t1BVu9F9VoOkefz4miyCbLlyLCSe72OvE4rKZI6dcjQwG3Htwa0z4rrfjZXo+IAMNPnpviZ8dnLqkAQy1WSzFzYT6JooDDZmFuBepu4Q8eGY/y8KlZMvnCugteLhYwi6Ry+NzlEq1kHZMenzg7j9dpY2efF5/L3tLKEOG76iqWOoMcQsET8Nj1HidmUiyWmIln9ddpTFzXi2iwXFbwSH+kFRAtJoQaeCKSbtjXrn7OyWhGtq0wCTOmaEnqZDqWwWpRKBRLdWdTjU17PZpEMpUrYrUopjVTa9WGlMK5EobPoih6c64dvV4sSuuVaAmEkTXT2Lbq+7Ta7B7w87yNAR45NUc4qcq433jdcEO/+117evn/HjgJQEore4qkci03yUQ0Ihzwu/TyqnpHpUfTOXq9Ljx2qxyTfgkistVel43ONod+cWz1c8Ps17d/pI+HtQmLU7EMc4ks8UxjEywlKqPhFP0+Z0Xyar0FGFaCCGjZrQrRVE6fSnfvocmG9uiFcIp+nwuLougKjOUg/EHxWY+kcjht1nUTvFyqiaveg6cOBY84Q+55ZozOdgfHJqN6YrRVmdCCHDs0FUsklWNDHc8zl8jS5rDislvxrUKAZz6ZpVAssb23HbtVMaXv5QIFTwsH4i4nRIuJew9NAFCChltMiOf84RE1CZArFGXbCpOQCp4mMhXLMBRwa1ma+g5FEf1MZgs47Vb9IhowSYrZyjXdwrkqaLXJA36Xni1UlUzOukq01qKHgHhvzDK2rfw+rQXFkhrccNutfPCunQ0bh2gqTyZfxGZRiKXVoFErGp2JSBqrRaG73UlnmwOX3VK/gieVx+ey0ea0SQXPJUgsnUNRoN1ho6vNwVwy1/Lnxmq8vt0Dfm7e2glAoVginSu05Gd7LTDb1g0F3MzEs+QKJf0MMUMd1ap9fYRi5odHJvnvZ8YBcwJao9rQCGMgdjkIf1AohMfDjWfY1xLh03kcVo5NxvC51Iar33t2Qh+goPfgWaaNEmfIVCxNNJ2n0+PgEw+eJl8stnTpz0Q0TcBjp8+rqtHr9RXnE1l9AlXAY36AR6ine70uutqczJig4BmPpLAo5eBWK79Plxvbe72ktCnA12zqMMVu7ur3kcmrzznc4bksbfFqIAM8TWQqmqHX61Sd7ToDPCL6mc0XKRRL+kW0u91pSomWMLgum4Wo1uSuVSTXwrlKZgsogMdhrXCu+v2uFZdordWFRy/RMslw3XtoEptF4b8PjhE3aZLUekL0opmOZ+qeImXkvw+O0eaw8vIrB3CaFDRaDcYjafq8TqwWBUVRGAy49bKTlRJNCwm8TSp4LkGi6TztThsWi6JfHMX5XiqVuOfgOC67paXOjXsPTeJ12TgxFdftmxmvz+0oi5e397bX/Gy3alDBLFbD1u0f6dNVs2aVy4rXOZtQ/aVWCkIOBdxMxzKcnUuSMKn/G6h91IY73HSs0DcU/qAoX8qus2y48OlOTMX5/uFJZuJZvC4bZ2eTABUlWsu1UeW+RGqp9ebuNvxuO2PhNIlsgXyhuDq/TINMRNL0+1z4PXag/gDPXDJLpxbgWY0ePKIkq8frpNvrMKcHTyRNr1dN2IKcotVKGEu7hd/dKJFUjlxB9dvPzydNeU6JDPA0lel4hh6vU3O26w/GbO/1ki0UecWVg/pF1O8x5yAXBvfhU7N85YkLpnRNNwvRnCuVK+CyW1EURXeujo5HiKfzPHM+vCLnXDgDyWyeLz9+HrfdavqFp1As6TXFZmVTRsMpUrkCs/GsHtRqlfdpLRCS3lyh1PCaZvNFvvfsBHft6WMg4CaczJoSNFoNxiMpvaE4qJ+Jet7zUqlENFUeQysVPJcesXQen0t1mDvaHMzFs/r5fnI6wemZBOPhdEudG6PhFIlMnp+dmOHMjHnNzsOpHHarwpbuNp44O7/g8VZXNpmBsHUPn5zlufGYKcGz3QN+nr+jG1DPYrfd2nCAQbzOh07M8qMjky2VvNg/0sfR8SilEmTyRcLJbMMBrXyhyEQ0zVCHW1Parcw3HO7wkMmrQYsN6ywbbvTpQC3/iaXzFQEKh9WCzaIs20aJM058v89lw+uykdZ+Rj3NmteCiWiafn85yNGQgkcrHVyNHjwiwKMmq53MmNDbTfg1VouC12m77Eq0Wjm5YCy/HTNpiI3YQ267lQtzMsBjFjLA00Smoml6tQBPI3WrQgUS0CL9AB0mSTGFwQ0nc6RyBeaTrdPhXMiRo+kcLrtFzxbu7GvjEw+exmGzkMkXV+ScC2dgPJJmKpZhNpEx/cJjVO2YZWyHAm69mV7cRGn8emE0nMJpU4+zRpr8HR2P8KGvHiCSylEsQa5QIFcota4TGEkz4C+/x8Md7rp68MQzeYol9CklMsBz6RFN5/C6VOVKl2ZzBv0uYum8nnWdjmda6twYCrj1jKHI2Jvx+kSvrms2dfDU2fkFAVwRVMgVipRKtFRQwSxGwylcdguhyZipkwIVFP3Pr7+2sV5o4nV6XTYmo2m9HKRVgpC7B/x65hkwJaA1GctQKJYY7vCoyb8VXpjHwuVLl3hf1wvCpxP9I0UT12s2BQA1OKMoyorKiPWgkXZ+uB1WYuk8vV41MdKq6hDVtjce4KlW8DQycr0WU0YFT7uTWZOmaA0G1PfH57YTTbXme7QaiOTCdCyj3+NaKbkg/Guv08a4SWeweM4rh/1MRNNk862pqltvyABPk0jnCkTTeXr0Eq36D8VwjQBPwO0wRcGjG1ztZ5yeSbRMTbeQI+cLJawWRS9POzaZwO+209HmIJMv4nEsX4UjnAGR3QmvQkBLBGJ6vU7TMhP7R/r09zueMUcav14oFEtMRtPsHVTHdNYb4BGG9fBoFKdNLVV56mwYgPkVNLpcK0qlEuOR9AIFz2wiqzuzy0UoynwuO+1Om15uILl0iBkCPJ1tTjL5Infs6iGSyullNeMmTMUwk/0jfbqCwcxzLZLK6gGe2USW0zOVF+HRcAqrBb7w2DlCEzGgdYIKZjEUcHNhTv19zEwKGNdoMtr4ZU9MPUtmC8QzeUqlUssEIc/PJTkyHmVLdxsAb7lxU8MBLZHBFj14EtmC7o8sh9Gw+u+39rTppU3rBeHTifBDSuuP1dWm9qERwY42x/IHAQgfdj6VxaKUhybcuE3tw2W2osUMsvkiM/EsfT4XbrsVu1VpQMGT0xU8fo9DHbluYlBrOpbB47DS5rRpJVqNKZ5LpRJjkZSeuFKbYbfee7Ra3Htokjanle8fnuB/np1oueSCHozZ4NeHfDT8nFpQ8HmbOiiWzCv9utyRAZ4mUZY1unS5fL2Ii70wfqAGe+ZXKO2tRXVNdySVa6ma7t0DftqcVq7d1KmXp4mMnz5VLLv8UaHVAa2JqPkXHhGQ29jpIZ0r6s3FGmH3gJ/dA2pDuplYZtGmwK0s/ayXmXiGfLHElcMBoGwsVsq9hybxuWxcmE+xvbedzjaH7hiZ8Vkym2hKlbIPGAM8dY5KF6oy0cQykZE9eC41jCVaXVpGt7vdxdtv3lQx1a/VzncRuJ2JL36urZRwUp14dO2mDoAFZVpDATdT0QylUvmz3ypBhWrqPdP3j/QxqjnS8bR5wbPR+RQOq+paGuX89bJ/pE+/SOQKJaZjrTNG978PjgHwlhs2AuYEC8TZPdTh1pUXK7E/o5qC5+ZtXUxE0ysKDrUCuwf8bOzyAOC0W9k94NfX1ScCPCtQ8AgftlgEp81KwOPQzhD1XGlFBY8IuA/4XSiKmrysZ29l8mpQtLNNXbeAtn5m9OcUTMfUVhMA3W1OsoViQ2PNw8kc6VxR92tacZz9avrRo+EUh0ajzCay+ue+lZILQrF1xVCAyWjalB5W4j589YYAoPYgkzSODPA0CXEJFQqelWZpjES0w1qM6ATwe+xk8kVTjPtwh4es9iEuUWoZ518wl8jpUwKgrMJx28XY+MKynXPdGdASEOms+RNWhPxYODFmZZAsiiqNHwy4azYFvlT7Soimwvs2qL9vvQqe0XAKm1UhWyjS3a46LMLBXmkfhLVgPFpuJi4YCqh7qt4Aj89tV7OjskTrkiOWzusKHnFeziWyWC0WiiXY2t1GJJVjS3d7M1/mAkQyeHNXm2nNzsNJddLktp52/G47T56pDPDsH+nTy6ZjLaiIFBeMd332cT745Wc4MxNf8Zm+e8DPZs0GxTPqBD0zbN1oOMXeIfXyPGWCgmf3gJ9btnfpf1cUpelBSLH+//bAKQb8Lrwu1dcw4yIqLjdCwQOVjU0vxuh8CrtV4brNqkLl3DrsaSGGUJyejqv94dKVSUzPCgcB7B7wM+B3saW7fIaIYHerBQ+gHODp85XLlOrxE0Xyt8NQogXmqpamYxl6NH+p26v+nEYaLY9pQedBzV/3uW0tVaK1Wn60OFMeOz3LgfNh7FaFRLZAodg6ikVALx3b2OmhWCoHfBphKpbBZbfoQdfz6/DMakVsF/8WyWogHB+1ybJ6OM4nsxX9NJaLOMQD7soSLfFYv9/a0GsV2bONnR6eG4+R1poaN8LR8Qj3HppkNKyOA90/0leXw1YqlZhPZvUMBajO+ScePK3XGc/EM3hddt543fCynnP3gJ+edicnpuI47BbTHUmRPdnUqcq6o6k8vd7Gn1c06p5cZHLYvYcmsVsVvnNgjJdfOaAb/XsPTZr2Oy72vhq/7rCqXRoyhVJD771A7M+dfV6cNkvdAZ6hgJuTU3EA/SJc0PbQfAM9slYL8XsbFTyprHoWfOxHx3jq7Pyy19ZYoiXHpF+aqCVa6jnZaQjwCGf8tc8b4u9+cIzjk3GuGG6dIL54fWY4koJIKseuAS8Wi0Kwr50fHJmg8NXK8+jmbV38/OQsc3G1nOuN1zXeT8YMxAXD77YzE8sQTua4/9gMN2wp6K9vuWe6aIhaAt560ya9L0kjjIZTvGRvP0fHo4sqeFZq/40VHy/c3dv04I7wLyKpHLv6vXr5hBkX0dH5FN3tTlx2a8XndLmMhdXyls1dqn9xZibBzj4THIw1RPi00XSeuUR2gYKnvY5BALOJLF3t5USgCPC0ooKnbNvV+4Dfba9r4qrYN52e8ph0wNRJWtPxDDt61aSASIzNxrNs66nv+Sa0310krrwuO9F0rPEXahKiP9vJ6TiRZI6tPR5OzyT50FcOcteevrr8WXGmuO1WZuJZ7BYFn8vObCKrqmSKpWXfX1abqViaHq+TAa1H0ngkpQfj6kWowAa0xtpSwWMOUsHTJISCRzRZhpVlaYzoAR5PZYkWmCPFFAqJu6/oJ18scWi08Ui1WRHwaDpPoVjSS2mgrMLp1mSjljoyfvN6Bilh+gSlsK7gUQ9Fs7Ip5QBP7VHhYiLNXDKrZ0nMlH6K93Umnql4X797cFR/v20WePTUHA+fmsNuxZTsh9ifg343PV5n3QGe/SN9+iWy3Wkjksrp00jmTR4tagZlR0jdR0fHI3zj6TEU1D2/krUtK3hstDmsJHMFUxsxSpqLmgXPVzRZBvXSc3Q8hstuYf/IAABHx6NNe521mNbsYiPN06uJpHIE3A512mJGHR4QqGpo6bSpSYw2p9U05ZAZiFLSHx2d5MCFiF5G/KTWL2wlZ3poIqb7ChMm9FNIZtUL+XCHm16vq2YPnnrs/9nZJFaLqlBtZHKLGaUV4oI3FkmhKHDFkF/vEVPPJbya0bA6Ih2os0RLDZqJAM9q9uFZrVKVcDKnn1GnZhJEU3msFoU2rey+nkEAc4lys2FQbR2Y856ZjUjS9fvKZUr1+IkiMVWt4Amb+DtPRdN6iZb4HDSm4FF/90HNr/G5bC0VhBMtIJ4djXBwNMK3D4wzHk6RKxTq9mfFmXJ4LEIqV+COXT34NFutKDRdsWhkOpah1+vS3x9jU/dGnrOn3YnNamHA75Kj0k1CBniaxHQ0jUWBrnannlWod1R6OJVDUdCzs2CotTXhYiqyCXdrF4AD58MNPZ84zCajab759Cg+l63uJmLCgBkNN6hBng+9OAjAdZs7V3w4hpM5LIo6uaXeni5LPbeiqCNMwTwHQwQhUloD72qGAm49gCEeN1P6ee+hScLJLF987BynZxJ6c7jPPnxO622T5IFjM4RTOeaTWY6MmTOadzySxmW3EPDY1QBPne/X7gE/V25QJaLJbAG/285779iK1aK0nILn6HiEbz49CsAXHz2nZ8Q7PA7aXTZimfyK1lbI1IWCp1RCH1UrWf+kcqrUW1fwaDZnPpHl6HiUYL+PLd1tuO1Wjk60VoBnRjuz5hIZXVHXCLlCkXgmT8CjfjY2dqrn8GQkXfGZESVa45G06UH+RhgNp7BoWc6udgc7e9vp8JT7VCz3TJ9PZJmKZXj+dnWsuRkBHuHsDwXc9PmcNRU8wv7bLAqZfHFZ59TZuSTBPi82i6IH9FeKWYklccGbiWfpanPQ5rTR2W5euc+F+aTeS62rjuTfWFjNqPs9dgIe+6pN0lrNku9IKsfVG9X+WKen1cEeYoIWUNcggLlEVg9AiOeA1lXwuO1WPQhVbw8eUVquT9HymFuiJYbF9HrNK9EaD6ewWRQ9aORz24mZPPmrEUQLiGS2QIfHjt9jZyqeZS6Rq9ufFWfKhXCKTZ0edg/4uXGbWpZ6286VKRZXu8/mdDyzQMHT8HMa+jgNd7ilgsckZICnSUzHM3S2ObFalLpkuEYiySw+l13PcEH5IDclwBNOYVFg76CPoYCbpxsM8IjDbHQ+xYX5FNl8sW4liTBgHVUBHlAj/3arUlf/lPlkVpc1n5kxN5qsOit2/TWbVQM+G8/oe2mqRpnW/pE+3VGMaj0lzOgrIQzKFx87x/2habULvuboi/G2+WKJHx2dYjyS1gI8OSai5e9pREUkJKKKotDTXr+CByBfUB2/j73pKj541072DgYIuO0t1YNHONbziSxtDivxTJ5PPHiaI+MRvC4bXqeNuOa0LndthcPnddnwaI7vpThJ61JsMr4cxCVGXBi8TvVsnE1kOToRZc+AF6tFIdjvbSkFT6lUYjqeoc1hpVjClBG8Yq8HPHZGwyk2dFYqKcVnRvysTL5Yt21eDYYCbr1HwdXDfiwWBZvVQq6gThNc7pl+bFIte7htp1pLMbFIae9KMDYI7vW5avbgEfb/noPj3B+aAi5+Tp2bTbClu41+v6vu0bwisDQ6n+KhEzMkMzlOTcf50FcOrugsKF/w8ngc6ucpk1OVno1enIvFEmPhNMOi/4jm1y13/+UKRSajaT1AtKlr9SZpifWciWf4j4fO4NAmTzY67SebVwOwewd92K2KquBJ5/TyLACPw0pyBYMA0jm12bCxRMtmtdDmsLZkD56JqDodUwS06g3w6AoeT1UPHpP8mRlDL1FQS8EUpVz6uVKOjkf4yXNTOGwW/unHxzmq+TTFUuv4I/tH+phLZMnmi9gsChs73LQ7beS0PqX1+LNDAbfqkycN/US1eNZKzuXV7rNZKpWYiqrBGDFx1RQFT7wc4NnQ4ZE9eExCBniaxFQ0o0e9RX3sbAMKHmN5FpQP9IgZJVoRVYJpt1q4akOAA+fCDT2fcJCEQiCWydetJJmvqjE2oigKHZ6VTyhLZQtk8sVyBmkmvuLXtRThZJaAx67XgJuRTclpkwvEJK1aRmH3gL+iHNCMiTTCoIQmYszEMzhsFhxWi14CFkvn6fO59AN7ZNDHXu01WjXnpVEV0Vg4rctFGynRAvXyMRgoO1agBg/DLRTgEY51Jl+kXVO/qQ5gXmuka9fHii53baOpPO1OGzarhXanKoO/1CZpXapNxpeD2A9CwSPOxiPjUcLJnN7ccPeAl6PjsZZRrMQyebL5ov76zOjDY5w6ORRwk80XsVoUfb+Lz4zxUm3WOFgz2D/SpzvVW3vb2dnbjtumunKJbH7ZZ7oI8Ny8vRubRTFFwTNqaBDc63XW7Acn7H8kldN71ix1ThWKJS7Mp9jY5WHQ7667REsElg5cCPPUuTDffmacTDZPsVRc0VkgJm0mMgXcdqv6e6TV87NRNe5MPEO2UNRLtCwWhQ7P8hMME5E0xRIMadn1TZ2eVVPwiPU8O5sknslzbCJmSsm38Ie62x1s7PRweiZOJJWrmBLb7rQRX0GJ1twiSm9fnb1tVpuJSFovz4JyD56VqljmEpXtG5w2q75nzUD4WuJybrNa6PQ46lLw6ImrZFYPaH3iwdN6KV6rKK12D/h57fMGAbBaFRRFYbjDRTJboFSqryHy/pE+JqPqNFjxu6dyBZw2CxMrUMiUfcMCF+ZTpo9Yj2XyZPJF/e464HfVragU5ApqAqWnXd3vGzo9TMUy6276XysiAzxNwhix9LvtDZWBiIkgRsxspjYeSenN3q7aEGA0nGroEi0cJHFgryTrWM1ihlvQ2eZYceBM1LuLDNJpkxU889r7JRwWMxwM8Zp396sXoVq9D6C8Xi6HxZS+EvcemsRlt/CT56Zod9rY0OHG7bAwE8/oCqG33bSR8YhakrhnwEsiU8BqUXA7rKaoiNT9qRqHHq+TuWRWz6aslNH51ALj3OlxNJTBN1s1IhzreCavy8y9Lhs+l9o3yG5TiGfyhJPZZa9tNJ3Ta77btKy0GY2WW0kxI5wfUPtSmO38tDJRg6JL0Nnm4LHTswDs0QM8PiKpXMsENISd2aONSjejD49IevjddvaP9BHVJi7Gq6ZlzSSy+n5plfUA9YLR7XXgc9mYS+TY0tPOx958FVaLwqbOtmWf6aHJGD6XjUG/iz6fy5wATziJzaLQ51OfM5EtLLiI7x/pYz6ZJZUrkMxefELZWDhFvlhiU6eHgYCr7pKAoYCbSCrHXCKL12UjXyxxbDpBscSKzoLdA35+9QWbSecKFEslPVHS0WZvaDz00fEIf/eDEACPn5nXz8qVJKl0BZU2TXFzl4exsKqSNhsRqBOX+SPjUVNKvo1TYbf2tHNqOkFUUz0LPA4bmXxx2SOaF/MTvS3W3wXUfXBsMsZoOKXbTL/bTrEE8RWqWOaTWU3JXr7q+d1205os6wGe9nIwqqvdoZfVrgRhn9O5Il5nOXF1fFJNsAqlVSM+hVn+SMCj3t1+7dZtbO1px+92kC+WmIxm6vJndw/4uT2olsoWtCDPe27bymDAvSLbI3zDB4/N8OOj6llmZp/N6oDewApfXy1EVYGxRAtWPglWshAZ4GkSRgWPyNI0ouDxVylY3HYrDqvlos1hl3PgjUfSDGoZoQ5tWtVvfunpug9I0QRZXMJLpfqbiM0vUaIFqkGfS6zM2IjnFBmkMzPmZsDE++WwWUzLpsxrmRqR6a6VORWZUEVRS6jM6GcxGk6RyRXJForcsauX67d04nfZyRVK2K1qc+uXXTlEu8tGd7uTIgo3bO3E57LpfW4aURHlCkWmYhkGAmUFT6lUf7njaDily9sFAY9dX9+VshqqEeFYx9N5vM7yJJC9g1pz8TYnxRLYrZZlr200VZbAtznNCfAYM3K9XmfTFTPC+Xng2DT/fXCMZDZvqvOz1qzEWdWbaBsCPF3tDtJaackuQ4BHfe6Vl2mtRjBPXBTE6zIjwFMeSuDQbZHXZWMuWalqnEtkGNHGfa8ki7oWXJhPccPWLv7u9fv44F07uXpjJ8E+74r64x2biBPs96IoCn0+pzklWvMp+rVJKMK/qS4X3j3g5xeeNwSwLBsgxnxv7PIw4HerKpU6bNf+kT7GI6rda3fY2NHThs1i0ZMhKzkLNnW1ky+W+IVrhvVESb1lNFA+K8V7oCjoZ6XqwyzPnukDB4SCp6uNYknt62M2+0f6CCezTMcyOG0WpmIZzs8lGy75njcMDdnarZaYzScrFTxtQmW6zFHpwrfubq9S8LjsLVWidXQ8wr89cIp4Jk+P11FWsWjTMSMrDMxUN5YG9GbyZjBdVaIF6iStehQ8o+GUPh2tzVVOXMU0PySayjfkT5npiwm7dPXGDt596xb9rIP67zLFkqoa/z+vvUI/U/pXGHgfCriZT2SZiKRJZOpXFC2GPv1Zm5Y26K8/4C6oDhpt0HriyTKtxpEBniZQLJaYMSh4oL5AhCCSzC5Q8CiKgt9jX7JEazkHXqlUYjycZsDv5uh4hAePzaAoap10Iwfk7gE/ogpm75C/7gv+XCKHQ6ulrsVKnCOB8QKwpbuN0yYHeIzvl89tM8XYit9xIODSG1hXMxFNky0U2TvoI18sLTrCdiUYGzf73XZ6vC79snh7UG0OVyqVODeb5PZgD3/3+n381WuvZO+gnwG/q2EV0UQkTamkGhooG556LoIigy+yn4LONkfdPXhEVkpRIF8omaIa2T/Sp0v525yVKqjdA37ecuMmAF6+b3DZa6sqeCoDPMllOs+LIX73p87O860Do01XzAwF3ExFM5yZTVAqwfHJeE3np5VUR4uxUmc1pit4ynaiU2s4urHToyvBdvWr5ZPPTaxsLK3x9XS1OUwL5okLxB69RKvxM0vvwaOdwbsH/Fy1IYDfba84j2bjah82m0VpaHKT2SSzeU7PJPQ1EVy1McAz58PLCn6USiVCkzG9z5wInDTKaLg8MrdPKzGpVVbnd6uXznyxxK/dtm3Jc0r0kNnY6WEo4CJXKNV1gdw94OfmbZ0AOO0WnHYrm7s95VLxFVyExM/vMk5lctVf7iPOynxBfe8G/W79rOxqX779ESVy4j3Y3K3astXow7N7wM8r9g2QL5bY0deORVGDB42qgnX/y636X9lCkbOzCb1/GBht1PKSEKKfVqehyTK0noLn3kOTOG1WSiXwOu0GFYvqg67UV5xPZhckP31uu2lTtKaiGRSFit5G3e3OupLVQwE388kc+WIJj+bPx9J5fS/H0jn9c3LwQphT04ll+RTCnv/2V57h2ESUx8/Mcn6uMQWv+Px3e53sHvDz3ju2A/CCnd117/+zswkcNovebgDUUfErCbzvH+njzGySQqlEoVRiKlafomgx9OnPPlGi5WYmniWTr99XnI6rv1+1gqfRRsvrwY9bbWSApwnMJ7Pki6WKqG89gQhBrR48oDqwS0kxxWE5Hcvw2Om5mgeeqAUd8Lu499AknW0OetqdjEfSZHL5upoUghrkEr9vI9nR+USWjjZ7Rc8UI10NlGh1eBxs7mrjzGzC1A7+xvdLra1u3MEwSpD7fM6azvo5zcm7aavanX/UhE71ItgA5dHiNq3Zt1ABTMUyzCayFReSrvaVvy+1EPJQo4IH6gvwVGc/BaIHTz19SYRq5OtPXuDrT10gX6i/obhAONYAuWJpQQZclKutpBlpNJXXHWgRLF1Jj4NaiN99MpZhLJwmls41VTGzf6SPI+NRSiW1Sefh8egC52e99OkRZ3c4meU/HjqDy750g1O9ybIW4Dk6HuGc1pvDblX03+/CfBKfy8YXHzu3ojNdvJ5EJs8nf3qKdK5gSjBPZEqHOtz4XDaTFTxlm1ndLyaVLZDMFujxOk0rXzKL0ESMUqmsahJctSFALJPn1DJ6xgnHP6gF9Pp86kWi0d5LxgbBwr+plWwwBn1mDYmtWk752bkEdqvCgN+tl4rXG3BLaYq1v3v9lWztaafdaSObL+olxcu9CIkmst0GH64RNYg4K6djGTwOKw6bRT8rO1ZQIjwWSdHd7sBlV8/wTdqo9NXqw5PNq/vlr15zBS/a3ccDx2aWXTa1GKLfXcBjZ0u3+vqLJSqaLK9UZbpkD54WUvCMhlN64rPNaVSxqK9xpQHEuUR2QX/KwAr7Di11UZ6OZ+j0OCpKwOot0do/0qef7y5bOXF11+5eQE1CjYZTtDmsHDgfJqT1EFvKpzCqiOfiWZ6biHF4LMajp+cu+m+XojrAK1TfjfjUp2cSbOr0YDEMy+n3u5iMLl+xuHvArw8NALAoiqkj1oUaU5TkiUlajdjHagVPn9eF3ao0NCp9vfhxq40M8DSBsqzRULfaVl/Uu1gsEUnlCNRoMhzwLB3gEU7FodEIT5ydp1gqLTjwRDPHwYBb//4NnR5Gwym+9tQoE5EUmXx+xR+gcCqHOLMaqeGcS2b1htK16GxzEkvnV9STRUiEOzx2tvS0kckXTZGvg+H9cpcDPKYoeJLGAI+LyRoGVkgeb9LGL5px0d494Gcw4MLrsjEVy+B32/mNF26n1+vk6LhqgI+MqYGePYNlI9PV5lzR6NfFEPLQQUMPHqgvwCOM83BViVaHRy05qyfgMRRwE06qTTinYhnuOTjOA6EpjoxFG8oqiOktv/Pi4AIVlMh4reQSVFvBs/zft5YTaJwMAaoDU69c2IxszO4BP6mcOtJ1a08b07EMr9g3ULF2xhHOY2HzmxSahTiLz8yoDU5n4tklndVyk2Wb7vyIoLjXZecTD57muwdH+cSDp/F77Lqabblnung9x6fiFEvw3Lg5DVdn4lmsFrUhdI/XaU6T5VQORalUM/X6XGrjf021JoIOXW0OBgONN5I0E3Gu7h2sDPBcvSEAwNPLGIIQ0hRaZQWP2iQ01kBQN19Q7aS47PRqCp5aZ7FRiSXswGJO+eHRCBs6PFgtSnk0b53vx/HJOAN+F9du7uLdt27RFZ9Wy8ouQkIR0m1QhDRiy4cCbqZjGU5NJ9jZq74n4qzsanMwn8wuq6T6QlUPua42B20O66pN0joyHsWiqPvoxq2dzMQz/PKnH2vIthkDsFt72vWvG3vwiCTEcgcBzCay2K1KRYkqtJ6CZyjg1oMHRhXLsKYqXsn+Ojoe4exskhPT8Yr3w3gvuJhdvdhF2TjeWtDd7iSRLehn6XLZPeDn7iv6AUjny6WbYthJNKV+Hsa1RuJiUuhSPoWw5w8em2YylsHtsNLmsOoBv3r9kelYBq/LpgdSfS7VT2hEdXJmNsFmLaApGPCrisWV3A1PTiVw2dWr/V17+kwL7oB6d3XYLHoyUKiNGpmkJeyDKJ8MTUbxOGx89+B43eeIeN+tFoXDY9GW9eNWGxngaQKijlHI3EDtbVOPgieWzlMqsaBEC1QZ9FJSTNHLYzahOg+x9MJpVuIC3e936d9/09Yutva04bRZmIxlOTeX0puiLfcDJMrRLEpj0d/5RLZCHlpNp/aYaGC9nItiOFFu8rdFy4CZVaYl3i/RM8nnMifAYxyH2edzMVljTc/OJbBZFK7drMrUG5VACqKpPHsGfHo/iN0DfnYN+HhuQg3sHNGUPLu06VmgZnnimfyKO+VXv3+i54RQ8HSLEq06JPwXqhpUCkQAsZ4+PPtH+vTLTIfHztm5JBfmU+zb4Gsoq6Arl/yuBY91eOw4bZYVXYIixh48WvAovkzneTEncGdfGzPxLFktuHp8Kl6XXHgpJ3MlgZ/QRIwTUwl+/fZt/NsvXQuUg48CEah4+nyYbz49SjbfuOJqNRBnsdhb88nsks5qLJ3HalHwOKy689OhKViGO9RykM8+fA6/286A300kmSOxgjHS4vUIpcDxqZhW7thY7f90LENnm0Pr6eIyp8lyMquPnxbo/WK09RS2uKvNyYB/eY0k10oSfnQ8itdpWxCI3tbTjtdpu2gfnqPjET754CkAfnJ0iqPjEfr8jWdiJ6JqfxvxnvtcNtVHqJEcma6h4BH78vxcktBETHfKQ5NxNnapZ7K4UNT7eTw+FWOHFtTaPeDnbbdsBmD/3v4VXYTKCh5DiZbbVrcad/9IH4fGIhRKJfYMeivKbjvbHJRKy7vcjxlK5ACem1AvS/cemliVPXl0PMq2nnZOz8R55kIEl/Z+N2Lbwik1qNvutNHd7sCrJRz8jSh44movmmqltyirW82pgSs5F8QIbqBiCMVL9qoqluX6isJmpnIFOqtKZkUgcjkqh3sPTeJz2Tg2GSOs9UESfv7R8QjPXogwl8hW/F4iaFpPGaXoKfh7L9ml+5IiEB9L5/Q+WuLvF2vQPhpOYbcqnJlNsqu/naGAm3anlUxendxUb/nSTDyr/56CoYC77l5XxWKJs7NJXbEmEGWuyz2XU9kCB86HedFu9XeqdfY2wnQsQ0+7U/8c6QH3BqowprXEsNNm1fdkm8O6oA3ISj5Hwo87eCHMT56bIppqrnq8WcgATxMod543lmipTUhXKm8Np8py1moCHjuRJWq3RXmNUCZcmE8uOPCECmDQ79anX8UzeZxWC1u72xjUIszjkfSKPkAia7e1p70hp/KiCh7DCPrlyvbmkznaNJm0iKibFeDR3y+DgscMibCYDGK3Wuj3uZiOZxZk/M7NpbRSB/VyZ9Zhd24uycbOyqDI7n4vxyfj5ApFDo9F2NjpqcjACWnrSoKatd6/B0LTtDuteg8Rl91adynHWDiFzaIsyEgJWfd8HX14dg/4uWuP+nlqd9oIuO3MJXMkMsWGsgrjWsbEGCQWKIqiZ7qWQ7GoqpNEgMejj0lfnvMsLmYOm4WU1jTV77ZzbDLBizXHtM1hZTyS5pdv2rjijJJ4/lg6V5GN+dzDZ5f1eRaOwW988SksCrqjd/3mTr51YKzCuReBimQ2T7GkOkjNVB0thmhwKhQtE5GlJxFG0znanTYURdGdn16vi3anjX6fqsCbjKpn+FDATQn41oEx5uIZCsXCRS9s+0f6GA2nCCdzDPrV6UmnpxMN1/7PxDN60LbH66wrcFtNuGrkMpTVJmI9hX3qbHcw4Hdpvb4WvwSKs2k2nqHft7oNxY+MR9k94FtwWbVYFPZtCCwZ4BGvczySxuOwki0U+cSDp8nk1M96I7ZYH5GuBZ7U5s2umqor49dEsETsyyfPzXN/aJpMvkC708p8Issmzb4EPHZcdktdqt9iscSJqTg7esuqEDGKeqUK3XJPl8oePKlcoa6JVbv6fYST6jmWLVSW3XbotrJyHavPlyNjYXVIgHZWiffa47SSzl/8M1wPR8dj7B7wce+hSTo8DnYP+Dg9k8Btt9Zt28RUWEVReG4iiltTsvzs+Iz+2vVJj8tuspxZ0H8HVBVfvljSm82bjXgPJqPpZZWK7B7ws29YtY9R7Zx6961buGazqrpeboDn3kOTtDmsFIolPNp7Id6PgMdBKlfgnmfG8bpsHB6LEKkK3ghGwykcNgs/PzmrJ+zEv/nEg6eJZXJ0VAWQRMK1ngCPCPYaP1cOmwWX3UI0nWf3gJ8rh1XlYiJboN1pW1J5NxRwc3Ja9d1HBgNcs6kDh03dTwr1N0SejmcqyjNBTZTU61OPR9Nk8kU2dVX60SKBt9zz6elz82QLRV65b3BF/265VCu2RMC9kSoM40Rp4et1tjmIpfP4XLYKXy+czOLX+pb+/+29d5wb2XkleqoKKOSMbjQ6B5JgMwzJyTPSjGYkjUTJSk4Ka1n2WrbW9vrtrvw2OOyz/d4+b7DX1vrtOuxYsi0HWZKVJVscSRM0eYbD3GQTZLNzNxrdjZxjvT9u3YsCUEAD3c04dX6/+Q3ZRBcq3Lr3u993vnPavUc0jqP7gEimfQHsToWW4LnBmA4l8LXTywCAL51cYgPUI1dpuhU/U9MToHCZ24upTfodeKfc3woAhXK1acILKTa91HHEYdIDHAng3rrHA54D5iLdtV/Qjf2hfjtShTJrIegWai4BSrgViQSlbsUXXluERVQPQuLZImt567MbYdTzmN/M7MqGjbV/WajI8i61aGWKLGnisxtQqUosCKVYjGRYImbQZW7ZL9zNdeZLFaynCkz5nmK/34ZipYq5zQwurSab2gnoc+mmTYtWk66up7CWyMNh0qNUqcKgrxfY7rEZtt2i5Xca6yr7ANhY2K7QskEOKPb77fjo/UPosRrwvYtrO9KkWUsSrQV67Eb4nUasdlhVSRUIq4zS1/UCD1HHI9NhixbdmD01tYbvnA8BqPW2CzxZYn71XQF5THY/1unxX5uL4rngOvKlCmPZOEx6xLJFvHB1E/FsoYlxouzBX5HbF778xgqmQwk8MO7CzHoaP//Xb7DPK5PYAEns7jbraDcw6XfgvYf7UJaTuNlipW2wmsqXmUU6DX76HEZ88q1jsBhIm4JPblMadpuxz2eFjuexFM9jMZqHqGuv8TPpd2CihyTDDw44oOM52E26HdPDlWYEPTYD1pOFLavtW81f8WyzZp3PTh2fagEhQFpwyKa72pYmf2IqDKOex5dPLeNqOH3dKOHVqoTLoSQmFWxIJQZdJlwKJfFvvqTucknXwGS+BI9VZOd5cZW0bO0owcMYkLUYoFHbiGI9WWCbGboG0JbOdL6MYqWKi6tJbKaJXiFdXziOk62Du58zl2M55EtV7PMpEjxUr6zL695MF2SGUm3+dchjajsFm9fnoliO5fCrT+yrY8IChEUG1K+VynnNbSExxP969hrypSpj8NBn7bOR+SedL3csSNvJ2h/PFrESz2HSb2dz9IDLhKpUKzhtZ22jGoX0GmnxplKV2Bxq6bIIEVHER0rQVpPrpcNzYiqMUqWKL51c6ljYl+N4WA06/I+PHmPjwCIKEHiu41iRrL0kjqFtRPR50ELOfDSLpWgWJ+djjGXd+MyoMQGAOjHypLz5zpeqLI6m10Vj+d9/Kth1nEyTvY2sfKWAeU6RjPvo/UNt15jjh3xYiGQIA9RugKgTMCavU4/t79n2+rSZLjQxeAZdZizHcttig1GnXtoxQNHHmJWdvUevzkbAc0SGwWsVmUPgbmEjVajTjjWJApxm/Y5amCkrCKjFej67EblSBZfXUizWsxt1eH0+ir9+ZWFLzUEax9HrX4nndlVs+naBluC5gaALVjRL+oFzpUqdFSbQvb0zTeBQVwolnGYR2WKlrcI5LTbxHJmgGie8tUQePntt0zvpd+DTT+zDH374CMZ7rDAb9CRL3mX7BQ2WDw042Pd0i3KFUPjaMXjoQhHJFNnksRTLIZIpIpZV32AT5wGyCAbDSVgMOnzj7Ao+/aVzmN9M72jDRgUE6fOym/RI5ctt++s7CbqUbgmU1tk4uS9GsyxQHnCqVxu63ZhSSmoTg0cWAH1jPob5SLbJ8cUjT+iRFs5xate8Es9BAvDSTARfPb2M12YjiGSKyBbK9RTh7SZ44jnVBCVj8GxTFJqO7YkeC/KlKt5zuA8VSdqRHk8okWeLvxr8DhNj+WwFZqGtYDVYDbqOg+cBpwnRTBFLsSw20gVUFdac1OL4J+4ehEUU8PTl9Y6O2Xh8sliT3vtrG8T9igMHq0HAi1c3cXYpjm+eDSEUz6FUqVWr//aVBVnIvIRMsYLDAw5WEZrdyILnwKjaTz4/B4BU9ej7GM0Wt1Xlo5urSlXC6cUYq0Tt5oafBrrjXgs4oO05phQaSzT4SeRKqEoS+/PPPDTM/ixwHPb0mNHvkAOtUHLLDdt0KIXxHgv+/BP34t0H+/DqbHTHgqsbqQLrze+1GZArVdpW7WsMlVxLJo0qg8dG50zyzjCGhlVEn1ylpO9xq7kpkSuhqNBrux6U8MVoFpliBQcaEub0vKhDHM9xqtdOrYhJQcDAzpNuHHdS8W10cALQksGzkcpj2G2GSS+we00YxUWWtDy9GGMbhxHFxqffYcLKNjQfrsiirHt6a8kxs6iDXWavdYPNTJExyyjo+7UdAdt/95XzEAUee3otTZ+hsYiSQUrnl6vhND734jxmN9K4Gibi2qcXY2xM2ow6HBlywGnS45vnVnByLoJvnF3Zlt5K87mT+3mg386SxjQWi2/RMtoOtMBGr5FuKN0Wkc2hrEWrwyJEq0Kgsv3nejAuV+I5zG5kIKHGht9qXthIF5rs3DmO60rjacBpYglBpZbPgNPE2ONWUYdXZiMAamOr8ZkpTTRyxQpbHxwmHQx6HpWqxNhUlNnz3ak1AIBBx3cdJ0czRZhFgWkMUtiMOpaEU7Lpt0rMKo0nqEbkLz82IR9n+7pUm6nmZzTgMiFbrLAibjeg19SoweO1GKDjuY4T0K/ORnF4gLS1+ezG69OipUjwTIcS0PEcnr68vu13RnlMOo/QOeWZy+v4/qU1rMbzeC64gelQCqUKaWdr9x5N+h34yXsGWFIyWyjvqtj07QItwXMDwYL+CpkUlVlvzzYYDUC940AjaBDbblG4up6CKPC4a9CJaxvN7huriZyqzoeSzeO2EK2fRsHSdqCJLLrx3w7FL5ErQZKanRGUYImzdIFNHvR+JPMl1SAkliVJI1ZBEnVM0O3ccgJvzEe3vWFjFr0KFy2gJhjXiE6Drki65pZQS/DU7mkyX0IsW2JU9wGXCSsq1QZaiX56OtzUc60GuoFvZPCMe63QCxy+cWYFAJo2JHRxVBvvra7ZIHBsE2E16PDqXBRZmaarvC9e6/ZaOQjDw9z0c3pft7NwA2RsW0QBHzjiRyJXAs9xuHvYifVUAUvb1ONZS+TRZ28dPPc7jAin8h2Ji9PAqU7E0iAg26EGz/FDPsxuZFCVSJV1OVarlixFs/BaRTjMejy6rwfPXA53XeGixynJFsLTIaLvcmzIgflIFpFMEf1OIxwmPdbTRSzH8uz9PLOUgM2oQzCcgqjjMea1sIpQj82AUY8FM+v1jItJv4NpAUQzRQR8zZvprUA30t+/FMZLMxGkCuVd3/BfWE7AoOPxroN9WIhm27aGJBUMHuXcHZKZcJ96dAw/ctdAHUOT53k8vr+HCaa327DlihW8MhvB4wHCCH3fXX5EMkXmVrIdSJJEtA4UDB6g5uShhhNTYXAA/uHUMhaj6iLZSRVTApdZD73AsWRENFOEQcfDIgrMVW81nms7N83JrQCUVXs9KOHUmbDRQYte+4ibbBDWkwXVax9wmrCayKNclVjxg7K2PBZx21T76VACJy6uwaDj8afPXatLtq+rVJHXUwX02ozEAlyOBSb9DrzvLiKyOuw2I1OooFQlY1rZuuB3GLclsnx1ncQ3exUMHkC2Iu6WwZMqNLEMOom3lKBjaT2Zx0o8h/EeC/76lcWmNYAxeBQFBpq8WYnnUKlKeP7qJtOP4gA2JlP5MsyiDm/b54UkAZfX0rAb1FscGLsrV8J3zoe21FWsjUUbSxrT7w8l27eMtgNt0WLVfIcRPEc2+fSat6vB0wjKWj2/HL8ujEuvVWSJReostdW8sJHKN7WJA92JeB8/5GPjxajQ8iHPifz8e5fWCAPHICAiFzkan9mk34EHxolmY0LRMnbA78BmqqYVRK8rmS+zc1e2a3caJ0fSze8VUCuEAiQZQue/rZgjiWwJs5sZ/OQ9Q4wZ98CYB3ajjrFmukWhXEEyX25K8A7uwElrIZKBQcezllEKnueYw2E7TIcS+L0Tl/HGQhQ6nie6aruc4CnJLFb6fOn8ZdQLKFWq235nlAkeOo+k8mXcPexAVZIwt5mF26zDQjQLiyjAoOMx24FhR1GOF20GHQSBe9MldwAtwXNDQResSLYIq7GW9V6J55gYcLcMHpYwUBFZpkmEdk5aM+E0xnssCPhsuLbenOAJJfJMwLYRlM3z/33sGICaDXcnoBRemhjYDoOH2Zm3SfC4zCI4DohmS2zyoPd4PalujdpYQfLaDMiXq1iMZjEfyeKV2ShWu9Qcqh27/nltFRTScyiUKvjfP5yFJEmqCyahasttZSp9u/TZDCsYPLlSc7VhJZ5DrljBUiyHqVUyUbe7zqUo+bnSmhEgfdMTPVa8Pk82eI0JnnaMtRNTYRh1PL59bhULkRqtWVJc00SPhY3vQbepLpDYDoOnVKkinMpjwNmczLQZdeC5HTB4kjn0OYw40O9kG2jqpBbPllCuoOtAKJTIqyZeKfxOEySpM5E9KgxK6eoA0Tjo1DVs0u9gLS4AUFS0eipZY5N+G8LJQl1LVKfHp3bOQy7SnvGx+wfx8YdGcGmVuLhYRR2G3Sb47ITlsS67XEiQSJC3kcGE1wKdwDP2j82oQ4+NuOxVq/UOgvEcqSSm8mW2MVRiq2rvgNOEi6tJNl7Xk4Vd3/BPrSaw32/HPp8VlarEkq1qIC1atTWCzt2N7SCNDE29IGC/34aNdAGL0WzLDdsrs5solqssweN3GqAXOPzHb0y1fdbt7mNSbtWh9G3Ksmn3blMmjSTVPtc4f8Wzxab1kuOIiDNNHm2mSUsHx3HMmnstmZfZAwJenNmsS6JIqIm0x7Lqm6WtrncrTIcS+MuX5gEAT02tNf3uSjyHXrsBJr3A1pPGaz9+yMc2IG6zWHee1JK3WyjbhejcRgN9n92IdKFctxGvViVspgvotRvgsRqwqZhXTXLl/r//5BEMOE145RpZP755ZoVdr99pwka60JTQ3OreXg2n0Gc31iWyAaDPYepeg0eNwcPafbrTLltNEHHqu4ddqmsAZfBEFcUQ2s5GRchJkYN8r99pYmOSPt/VOGFMgQNmNzOqyRvmzBfJYG4zg/gWwqSXQkl4rSJ6bUaWNHZbRJgNAnKKltFuxzxpoRRZQW7UY8EvPDLOWkkHnCaY9Z27aBXKxB2ukXEB1Bg8P7yyCYdJD44D/uGNZXAcdoVxqRc4lKskZttIFbYUBQZQl9RWolU7v9r9nfQ78KCcmKGteZ96dAwA8INpwqKNZUsYcBqJJXm21FLPhrZZu8wiWyuUzB6jnq9j9rjMIjwWEQvyetSVNmemqKqVRFu0CuUKVuM5PCw7wW6VkH59PgpJArsXAJnrx7wWZgjQLWoC6+oJnu0ILc9tZjHqsdRZpFNslYCm8++M7GDpshBXzFYC99sFLcbSdbhOLydX3lbRO1MoI1OssPGuLD7NbGQw5DKjUK5iIUrEsvudJlgNOsxtZhDLFtu+RxdlA433HO4j96ZDq/k7Cdc9wRMIBI4HAoFgIBCYCQQCv3a9v+9WxoDThIVIFpF0EROy/SNdsChLoFFIbyvQhEEj5RwAnCax7jNquLqexp5eKyZ6LaRtSRFsSZK05UYSIPTpiR5LV+0XtCeask22UzmMyq5G7jYtWgLPEWHbTAGTfgd+4ZFRxpYpVpo1hwDK4FFUkOTJpypJGJIn8fltWj43Pi9aQWqV4KHncCWcRrFSZfaMygVTkiR5YST3wWMRwXP1le6lBqbNQItqw4DTxKrY19bTkBTtNmpYjGZh1PNN/cgAWOVbFHj8/Wv1lUmrQQdRx2NTZbyvxHOY2Ugjkiky6qrNqEOxIqHPboDVoEM0W8K+XivedaAXD4972WdW4jn02AxIF8pd2XwTEdXafVGCl22at6vBQ94hcly6gT404MRH7huCSRSYrXGngVC2SFho7Vu0On+v1Bk8OmS7sDm9uJrE3cNOAMD+Pht7p6gA93QogcsypT+db23B3WozsBwjTMI/+al7UJWA+c0cJnps2EwXMNFjhSBw4DgOj+wlmmBX10kb17EhwkwslKsY77GwQPTYkAOpfJnpE+TLlbpxHs+W8MheMq5OLcSaznGrau8TB3pxcp60ZvEcuQ/brWir3ZNqVcLFlSQOD9iZlfCsCgOTIpkrNVkEt4My0HKY9OA5UjBonCvpuf3uP05DL3CwGYkTxudfXsSgy4xQPIdYptjyWW9lwQugTmQZQFurdOJkUkvSAfUV82qVtKSpMV577TUb9kimwNpIPRYReoHDapwwLSLpImbW0zi1SMYFaR8gDpQGHREDVdss7USXSdl65raIyBQrTb9LN8QmvcAsihvnbmWytFSp1on59tmNbL7oZlNOtdGSuTIbLzTQ71V5ZvFcCaWKhF6bAV6LWKcVRzcy2WKJPW+LKNRdb7/D2JS87uTeXl1PN7F3AKDPbui6wKTGNOi2RYuu7aFEDnY52ay2Bhh0grzm1dYf4s5YQK5UgZ7nMOoxwW7UwWIQYNTxbL2k73BYbil5YMyFTLGCzXRzHEHHD01QbabbJ6WnZbFvCrq23T/qhk7gWXKn2zFPCmz6ulZSUcfXJUd42RGwEwYPLSKpJQ4cclJuTS7YLUayWEvmMbeZ2RXG5bPBDYx5zTg0QLQmDTp+y1YR0paqzuBpHFvt7q9eR7Z3f/DhWhL/xFSYxYkCz+FdB/pwsN8OCcAHjvarnteGgtVIMel34LFADwCSZFMye1L5MgJ9NoRk4f9u4uRIugivaisdmWOXollUJZA2JINuSybfq7MRGHQ8jgw5634+4rFs2zhls2Fdohh0kdh6O+60xCK9mT0ObJ3gqWmLlsBxwJ5eKxwm4sq8mS52xOLuBJQhSOfl2v6IaNRtR3eLJgmV+wc6jxzsd+ADR/txsN8OnuPwjv29cJlJErZYruLhCU/b9+jiagJDbhPuGXEhX6piaZsOZ7czrmuCJxAICAD+GMB7ABwA8LFAIHDgen7nrYzjh3ys4ry311q3YNWcErprA4lli7AZdNAJzY+yxuBR35jmSxUsxbLY22vDHtlZYnaztkmIZIoolqtbJngA4B2TPrw2G+246h/NkMqTqOPhtRq2JZpIFxxa4WoFl6VGA+9zmJlts9dqaJogKlUJyXx9BWlPrxUfPOLHiNsMi4HQhGc2tmf5HM/VPy+a6Gkl8kfPYU6uNtCNhHLBzBaJcwcdQzqBR4/NUFeVpNUUajdLf38lXj/pKSszyXwZ85H2G9OlaBZDLnOTo8t0KMGo+b12A5L5cl1gx3EcPBZRtUXLaxVxbol8ji4q9JrDqQKODTvxoaMDONDvwKTfwe4l/Qyz6Uy1Tsg0bmBevrYh3xf1RdZlEVu+R1thTUUvZ8BpQqFMBDFpO1mngRBd7Nu9l1QHoxPxO5pcVCaJzaLQ8bscSuRwdT2N44f6MOgyIShT0kuVKqscn5gKo9duRJ/diOlQEjqea6r2tAtWTy/EcPeIC4cG7Bh2m/GdCyFif5kv4zd+ZJIxTqwGEYMus2zrWsRPPzQCp1kPHc9B1AksEP34QyMy04NUdWh19fghH/KlCgrlKu4adMJrFfHGQn2bEQ2o0vkynrm8DpuickXH1W9/6xLShTKODjpgN+kRz6lr+Wy1kW51T54NhpEqlHGo34FxWTRytk3ASgW9uwENtP7nx+7GOyd9eP7KZp2mDj23eLaIjVQBfQ4j/urlBaZ7dLDfjny5inShXtyVXvOvfvkcLq4m8OLMBorlZlc5Fvw1tGi1Y/AcP+RjG/94ttlGN1Ug7mhqBRGlILBSs4PnOfQ5jAglcoyZBZBWgUKJJAbLMh38Y/cPAwB+9O6BpmdNEyEXVxMIxfNdsfbo78ayJdLyqPK7dEOs13HIlsot2QKxbAlDblOdiCsAxuDpdlNOWVPpQpmNRRro0wKOstig3Ch4rPVrQCiRB8cBb8zHcajfDqOeh9Ncf71qcxt9J2c30jgri68r70/NQatZnJq6Tna6ESpVqvJzqN/gdduipWwZpy2DrdYAtyKGAci7SVkMBj0Pgefx/iN+/NxbxsBxHDsOfYfperm/jyRkVuI5Vb2VRK7E1rmVmLow6XQogf/+VBDToSSyhUrTuBjzWjG/mUG1KtU5IH7htcUtxVGL5SoyxQqcJn3LVlI6Xs2iriMXLeaI10aDxyISdhBNRK6q3J9uMB1K4Ne+eh5TK0mMuC147yHSevi2QHth34LsdqZWMFNr0aLzQiRdQFl2X6P3N5Ju1rNZiefQYzVA4EmbuN2kZ+8T1XBqBJ0TY9liHQuCsu3+04cO1TF7ErkSO+bZpXiX2pztWrQIExcgWjXESKJ9Yva1uQjuHnaxQg7FqNeC1XiurT5pKzSuSxQOkx42Q/dJwUpVwmKEMHjUQBPvrVrbaTv4lfUU/HYjDDoBNqOOaZnRMb1TjSnm/tygl1OzSu/ecbTxmErQ479jfy8++dYxBPrsONDvwE/eMwRR4FuOV4qLq0kc6ndgn4/M+Ve2+PydiOvN4LkfwEwwGJwNBoNFAF8E8MHr/J03FN28NHt6ScV5zGthQo90wZpZT8Gg4/Gtc+oCeK2QyJaYe0MjaMDRyknr2kYakkQyvpRRdG1dIWAWpxvJrV/YiR4LipUqfuHznbVfRNI1Kma/07gtBg9t0WqnwQOgLpGwICdKRB2vSqWk9H6XooKUzJcx7CFtbJDIxjqeLeGDRzvXHGLHb3he9M+tgsLjh3xYjedYcLeZbm4rq1Woavehz26sE1lejGbhMutZlbFGJ61fjCb9DvQ7TTDK1Z9wMt+24qRmkQ6QwIO29vXaDKobEqX+ghL5UgXFCkksbqYJqyyRK+FdB3sxs57GPp+trrqnFIqlG3QA+K1vqbeHKDemdAPzpZPE2U6NwQOQ8dBt+yRAhMDXU4WmZAw9fypwqkwwbAWa4Nk1Bk9OXWS5UwbUi1c3AQCP7O1BwGdjC28oTloPhtxmVu156x4vcqUKvvTGEs4uxuoEP+lmQMdzdfpP//DGMlYTedw74gLHcXhgzI0Xr27gP37jAsyigB6rWLcZoG1X7zzQi4DPjtfnY3jigA+f+chRFogqWwoAkhSl41ypk3X3sAunGxg89Fpm1tO4uJrElXCqzjo2ki7gciiJHqsBbqsBD417kMiW2OaKopONdJ3z3+uLLDH2lVNE2+rQgAN2ox5eq0G1xRYgDL90oVz3fLvF/WMubKYL+Jm/fL3peZGkeBl7e211ukeDLIlc0wtRXnMiW8LV9TRm1jO4sBIHUM9ia2TwOE1EJ6edvtaY14qUnJiMZopNm8JEG8arUhA4ki7WbTT8dhNCiTzesseN5ViWORWeX0nI40WC3ajD++7yA4CqvsNKPIdssYKT82TcX1pN4NJqoqXobePvJvMkiTIoJ6Ebq6V0TNsMRK+i8doprqylyFrWgD67EdFMEd85F4LDpEemUGbtHe025QNOE87JelB7GpjJvXLrZliRlGOJf5sRbosBkUzNGS2czMNrJcUJl0XE++/qx6N76xma/c7muY1uck7Ox5hNvPL+rMRzyJUq6gweh6mupW8rUJazp6lFqzsXrVpChbDr2rX1FctVvHItUjdO0nJ70n//yVo7JQDV49DvqkrkviyoFG7o+KHMzWS+1JKFRpkUZoPQNGeN9ViQK1UQTuXr5kql0UWrTXCjRmGrVlKA6MR1w+BRTRzI8dCI14xErsQKjcuxHOJbtIC0Ar1Hpxdj0PEc/E4jXpohYsaX5ORwK9A4VV2DR9cUJ67Ec4hnS/j2+VCdjTlhGjYnS2hh6WcfGsVD4yRBqJPbgmZarB80wVOV6uNUtbiTjiFaaJvfzOAXHhntKE6WJEmed1u1aJVZW9WYx7Klm14iV8LF1STTEFJizGtGVapJDHQDmuBRa/kbcJnYvqLTveFqPIdipdoksEzhl40OWrV+DjhNuBJOI54t4fAguc+pfJmtv2uJ7pP2jZgOJfCF1xYBAN86S9pl6ZzCATDpBcxHunMcnQ4l8BcvEWOLEyotx8r9l5LB94Gjfjw44cEPplvrOSbzJSxEsjjYb8deluBJdXRedxKud4JnAMCS4u/L8s/uCHT70jx/ZQPxXAm/9p7JugWLHseg41u6X7QCtZRUA/15okWL1oxCdHDQZYYo8ExoeTqUwP9+/hoA4LngettzmQ4l8PK1CESBRyJX7Oj8lbbeffbuRQ7pMQC0ddEC6qtfVKfivlGXqqUh0/Uxi00VpLEeKz7z0SP4s4/fIx9r+xagFDTAaJXgmfQ7MCE7a+gFrk7fhIIttIr70NsgsLbUkIhxmPSwiIJqoJXMlXBs2IX7Rl1I5EotF2dJkgiDRyXBsxLPYdRjhlkU2Pc2BnYei6GOnk+F4r7w+hL8DiPGvWZUqhLKVUI1txr0zOa2VXUPAJ6RWwWpFoXaptlq0OE7F0J4fS4Kh0kPWphqxYpxmUXEumTXAaRfu1KVmpIx9PyHZFpvq3ZBNYQSWydebUY9of+3cEpTBh6zG2lwHBGjozCLuo70DQDghaub8FoN2N9nw16fDbObaZQqVfauDbvNrBoz4DLhbft6kCmUcTGUhFOh2XEpRBIDz1xex5feWGJ26JQxce+IG9OhBDbTBVQlcm/HvRZ87sV5pjvw6Sf24c8/cS8EnsN0KIUzSzFspAo4LldQlZj0O/Dzj4wDAJ444GP3XtlGee+oC/ORbN3mj15LWk6AvTYXZYGIw6THlXAamWIFb9vXA6dZRLFcRTJfxkKDRhlNkEQzJHmzFM002bzTDdGFlQQi6SJemY3AZtTh2kYaosCz6tREj6UlgydTrLCN3XYwHUrgwnISBh3PdMuUzysYToEDmIC1BNLWadAL6LEa6tgCtOJ8djGO1UQeJrmySh1NlBXAxkCa5zl4reqivRSXQklUqhKODDlRKFfxC4+O171TtG2rUWQZIInoRK6EfKmCSKa+TcLvJAyemXWysX080AubgeiVfOrRMVwKpfDguIe1y6nR/wecJlxeS4HniHXx05fXsZ4qwGdTd/tq/N1XrkVhFgVmka5WLaWiqHqBb9oQA4QhcW0jzcaNEj55jrq2mUGhXMFXTy/j1TmyMW23KX9owo2FSAbjPURDQplgoGyQz74wy8Y0TaL12gzwWkWUKhLbvIQSefTZjewd63ea0CuzgOj10oT0Z1+sHZO2v+dkRhX9/4DThOlQAr//VBAAcHoh1nSP+xzkOVPG61Ybsw3WVlA/hgw6Xo6BOlsnJv0O/NQDQyiUq8wlqVVCRSdwkOQNNh0nl0JJDDhNuH/M05bpQr+LfsZlFrGeKqhuvMe8VpbgKZarqiw0h0nPiijDbnNT8m9C3qjObmTYc6T3djXevspfMw1pH9MBhHXTSRGCJXhUCoFGPQ+9wMGkF/BzbxlBLFuCXuCQLVbwoWPqLUtb4cRUGGZRwNxmBnt9VvTajOi1G2ExCFsmeBqT2ko4THok8+W6mHXAaWJC9lSEmN5fNZ0oummuSBLTaMoUK+izG3B1XX0DvJ4qQJRZ0so2wc10AVaDrokdQ9fhf/fuAOK5EgrlzrRPkrkyEX9v0aJVrFRxOZQibmpmfVun0OlQAv/x6xcgSST2bXyHKVtmO0LLTINH5RlRq/Ru9oY0adWSwcOs0tWv9fghH86vJGDUE91LOv++6yDRw1uXteMo6/jSarIj9iidB3/+8yfx6S+dY2z/UkWqcxx1mkW4zHpspjt3HKX3hyY0K1Wp6f60Y/Ad7rdjPpLFv/jbU+pFXPk9O9jvgNWgk5NgWoJHQxegL81CJNPRS/OVU8vwWETWu9p4HItBh1ypO+V5IhipvhhaDTpSDc+pMw+uhtMQeA6jHgsEngiPzayn2ctHFxtJQtvg88RUGC6ziFGvGXOb2S1puJIkEVFgOUDyy/T3bhGTLRUbF5hGuC0Glrihm6yHJ7xIF8pN+kSNrmRqFaTxHivGvBaWSOjqnBueF2vRahMUXlxNYdxrwaN7e2DQC00TaFRFbLpPkeCZDiVwfjmBaKbIJkOO4+RqQ/N9X47lMOQ24d0H+3B5LdVSPDuWJcGBWoJnwGlCqSLhFx4ZZxa3jYGdxyKyxZKOuZPzURTLVRwbcjIK8NFhJyb9DkaxpBl5tWdDRZYBtHyXVuI5bKYLiKSJy8+1jTSK5SoMOr7lWHJbtqfBQ8d1Kye6X3/vfgCkp7zTYJIGy1u1TvY7TE0UZrXA47W5KMx6oU7gz2oQOrKgrVYlvDiziUf3esFxHPb5rLKNZaYuwaNkXCVzJQy5zMxxiz6jRK6MRLaEpRhxhDq9GEMqX0a+VIFJT8R+T0yF2aYCAI4OOZuer9si4qFxD/7pwhq+e2ENeoHD4/t7Vc+fvjNK7TFWRTaJLNj8V188w94dVlnKkc1APEsqhQ6TDql8CSfno9jfZ8OAywSbUQfavXh+pVkU12bU4fRiDJF0EU9f3kA4kUelWrN5NwgcopkiFiJZiAKPi6tJzG1mkCtVEOizQZSZduM91pYaPHRusRm3x+A5MRWGyyJif58Ns5vE7YM9r1wJl0JJjHjMsMoiqMeGnOxZ+50keU8FEem7d3Y5jn29Vgy5CFswlW9up9pMFyDI+lcUvbb2DnnnZfbGB4/0A6gxNimYyL2qBo9R/p0s8qVqXWXa7zAhnCjg62eWMd5jwX/5scP4F28bx2o8j3i2jMVoFg9PeOAy65lQbSOOH/JhbjMDn92IiR4LeI7DUowII2+15g+5iRDwwX57UxKlER6LiFiWJJYbMR/JoFyVmA6PEnQ+MesFfO9iuK5i325TPrWSRFUC7h52NSXbv/DaIgSeg8DXClcXZbYWEVmmborkmdJ21lYMzX0+C/7m1UWIAg8OtWPu89UnOBfkavI+nwVPPj/HklM6gWuKZWgbWaeVbropaWQacBwHu0nPROs7gVkk4/CXHptQTcjR2NBmbI4NiQZO67WwEfQzv/TYBHKlCgz65oQv3UTu7bViNZFvKg7SOSucKkDHc3Cbxabk35iiZfT4IR+imSKLJZe20CKjbPNWRUslLIbO2ohpotijosHDcRxsRj2S+RJ0goBKVcLPvYWM3Xi28+eoxEo8h3S+jFJFYkw5m1EHm0GPS6HOEjytXLQqVanumke9ZhYPpAr1bZlEKL7+OK02zQf6HaoMHkmSsJ4sYEKWcFCymFtZz1McP9wHUeDx29+6iH/7D+fwG187j9/82vmWidOIrMfYqkULAM4txzEqywz0O4yIZIos2UhB3+HZzQwEnoNJVGGZyUnI7Qgtb6QKsKkktgDCjF+O5fDdC2twmPR4ZTaCr59eQTRdaCre0HP93IskWfKDS2HVPVaNja2+R7Ia9Agn8rhrwMms4D/16BgemiD7zLVkjUn36lwET19ex8XVRNukvXIejKYLiKQLuLqegUnPkwSbqeY4+ukn9uETD4+S1kLb1nIegMI8plwFz4GtgY3rn9q8Nh1KYEaOdTIt9BwvsgQPYU3v9Vm1Fq3rgBUAQ4q/D8o/uyNAX5rVRB4vX4s0ObFQTIcS+M//NI2nLq5h2G3GTEOmnB7HbtSzALRTsap4rnWLFsdxcJr1LUWWr66nMOoxs03CRK8F1zbS7OUrVyUIHAdfi5ev8fyPDbmQK1Xw6my07fkn5cWPMXgcJiTzxGmjm5a3aLa4JXsHoAFvCdWqhIUIoddTzaFG4S3K0tjquI8HevHytUhXQr4AYVMpgxezKEAnB79qyBbLeHU2gsf396LfacKKSltZTKVCxXEkAfPP//J1/JsvnkUyV4LPXh+wDjhNTSLLedmBaNBlxrsPEtbDb379gurzUG7gG9GuhYpC2aJ1YioMm0GHS6tJDLpM2OuzYcBpgijwmJI3xjQDv7e3mWZPQUWWBY5TbGybxSSnVhMwiwJ6bQY8dXEN12QWS6sx5zQTDZ5uLb5ZO1ULS/M+uxEusx7TWwR+SoQSObjM+i0Tm5R1oAR9tzfTBUyHksgVy4hmiihWqnXXbjbotqS/T4cS+M2vX0A0U0RafneV/c6LUZKU8NmNdYFlOFWAz27A0SEHIpkiknKrmt2ow1wkg1JFgsVAxKfXk3lkimUcHXJCL/BYiedgN+lx74gLB/x2eKzqwqTHhh2Y28zgr16eb/neADXWm9JNjiZ5Y9kCfnhlEzxH3gv67gCkcpUrVeB3mOAy63FlLYX5SBbfOrcKi0FgSfxUvoz9fXaIOp4lHygGnCasJfJYjhGXNadJj7VUAWvJInO5kUAS8eWqhCcO9EIUeDw9vS6LgkvseU30WBDLllTbCGlld7sMHjq/H+x3oFKVcGYpzp7X1XAamUIFk347e8d/+qER9qzNooByVcJjsvYEZbFQe/d7Rlww6AUUyvWCvwAJpD0WsS7xSGy3W7M9zy0n0GszMFp+I2uqlrxT1+ABavbP9fOphGKlipPzMfhsRlxeS+KDRwkZ+Zf/7hQAwoi9vJbEmNfS9L0AoBfIhjTgsyGeK2HSb4Oe5/DM5Q3MrKdU27Xoevj7TwVh0PEY95hbsjQo3BYRkqSuvXd5jcyhagwe+r7/YDqMSKYIk55HKtday2c6lMAffi+IP3r6KvrsRnz8weGmZLvTLBKR5EKt1evMUgJWgw5mUddkAR5KEDH1VhvRK+EMHCY97CYdkvlaG+eVcAZ6gYNVZiFmC5W6z2eLZVgMAnptxqZYhrmkJWqVbkmSsJFSt5pnG1E12235vDpFuzUUqL17DpOeuZHZjDosRrOY3UjjgN+u+nvtcN8oeTdOzkWb/o3qGr1jkjzr6bX6dYkycsJJYuXN81xT8q/PboRJL2B2I41JvwOP7+9BVSIFx810AT/z8HDLYkbNZbQDBk+HRgDRTBE6nqtziVSCCoTTOOMn7hmE1aDDyfnm+9MJBpwm5qjnU7DPhtwmzKynmxISSrTSdwHUNZ6+e2ENVoOOxVLKeWEzXVBtI1LbNO/ptWJ2M9OUFE7kSihWqpiUE8LK9aWxjbURK7EsemwGBNdS4CHhtdkoXr4WgV6AeuKUxbFqLVrk2c1uZjDmJfGfX9GCpAR9h9dTBfjtRngszfsXp1mE06xnTMtu9h0b6UKTgxbFoMuEdIFoV2YKZQTXUliO5/CdCyHEMgVUpZqd+D+eX8GTz89hPUmSpeVqVbWQTsdQoyMWPedPfv4kwAG/8vaJumfqMushCjzCyQIGnCYkciWsJwvgOMJy/+6FEC6tJlXXm1/98jlcW0/h9GIUZ5YT2EiT9YAmxhpjrntHXACaDSlaYSWeg1kUEFxLYcxLih2d7nlPTIXhd5AWwLnNjOocfXE1Ca/VwIo2+3w2XNtI12kIvhlwvRM8JwHsDQQCY4FAQATwUQDfus7fecNAF7sJL+k5Xk3k6mjBSnrbK9c2UZWIi1Er94tehftPp2JViWxJNVgFyMtaqUr44ZUNddtQ2UGLYk+PFYvRLBajWVhEATPraXhtolzlaP3y0fPvcxhxeMCBc0txzG9mWp5/Y+8uzVC/NLPRVctbLNN+gaFwW0RUZPeUxWgGI25LSw0aZYtWO+zptaBYruLnO9Qcomhs0aJVv0SupLrIvDQTQbFcxTv292LAZZJFlusDyJrYNDnn6VCCBStrCdk2GIDA19txD7hMTc+U3o8htwnpAgmgg+GU6vOoOXM1P+etBBIBwqzKlSrIFstYkfuQM8UKy7rb5UD+gnwtV8Mp9DuMbZkIA04TMoUKXBY9Cxga36WHJtxYjuYw5rXg/jEXKlUJhXIVPW1aJdwWPUoVqWPhYYpaO5V6ZYPjOBzot29Z2VOCVLm3nhvUKMxEB6SMf7oQwg+m1/HV0yvIFMpNraFWgw6litRkRUxBKzy0KuI0E2vOUqUCjgOCayksRbMYdJkgyBv0RsHPQwNOAKSdJZUv42C/A8NuEzgA94+6UalKuBJO4do6CTxpUjKVL+PQgANPHJCFcxue73QogZn1DDgA5aqEAaep5TxiEgUYdDx774FaFfnUQhxu2elvLVEviru/z458qYIHxtzY57MhVSgjFM+hVJFgN+pZq2oiV8KP3NWHA357E4Pn+CEfe+4WvYAhl5G1CV2WdX2KFQkmkYdJLyBfqsBt0SNXqqBUkWAz6tl1MaHlBhbPdCiBz74wC6B1hXAr0HveYyOJ8TOLpO3tYL8DPE9aIU16oe4dp8/6//vYMQC1yvQTB3qxFCVixRwHiDoB/Q4jyhUJ/+Jt9e1UanbBPTYj2wSp4dxSHEeGnIw12NgqRZ+tWlGE2r+yBI+1Np+eXoizz/U7jXjy+TmcX47BYyXFA7MoQOAJO8RtFlVbtL53aQ0A8F9+/DA+dHQA94y48RP3DKIqSThxMYyNdH27Ft0AzKynEU4WcKjfgWIV+ORbR1uyNADALTNL1JJ9V9ZSEHiOjReK6VAC3zkfAkAKMH6HEWZRh3ShDFsbRzDqVjfRY2l6x2hywms1MD04m5Fs8ulzVTJ4skXi4ERbEtQ2ovSYfgeplGeLJOGxEM3g0moKH71vCH12I7w2Q93n1xJ51lLRGMu4zHqIsqUw/fzTl9fx1dPLLPms/DwV71fb5DlMzU5H7dDobtkI+u5RcWh6vw06HlUJdS5WnWKixwKPRcTrKgmMZZbgIYzHyw3r0vFDPsSyRaynCmyuakz+URtq+g7QpM2vv3c/qhKQL7UukjQyqNvBIo/PrRDNFOGyiE1GEBQ2IxHwvbiahEUUMNFjxd0jLrwx39lmtRHHD/kQTuRhEYU67ZAnDvhQlsW+W4HOk2pxLY37fudbF/EbXzuPX/m7U/jepTAO+O2Y7LPBZtSx96RalYgMQgfxMUB0OIvlKhuPFPS93e9XSfAoZBbUQNZJGwrlKp66tI6VRA7zkSxemomoJ07leV2NFUQZPJJE9HMAwuABgNWGItZKPIdiuYKNVKFlQgIgTlrzkUxb1p5aTL6ZUk+cATVtS4tBwCvXIjDqeRwasMOoF7AYyyOhcBr8/CuLTOvMYdYzMfnGQjp1Vv78ywvsHGotTgUsRrIYdpnx9TOrdfMvx3HotRPjgOOHfFiJkRj76KADAsdhbjOLPb3mpvUmkSuhUpEwt5nF+eUkbAYd9vVaccBP3NaA5pjr0IADosB3nOChZgW5UgWHBmq6QZ3seekcPdFjwWoijxevbjQVRy6uJtg+AiAJnmK5ysxm3iy4rgmeYDBYBvArAJ4CMA3gy8Fg8OL1/M4bCcpScFlECDyHi6vJOlrwejKPq+EUFiJZXFhJwm3RY8xrael+YTZQPYLOxKokSWqpwUMnAL3cO9u4cS2Wq1iIZOtcJSZ6rahKxJb03DIRj7x3hFR72r18SrbGQ+NuiDoe378UxsXVuDoVs2Eip5vf78qZ93Ayj2+eXWVV7FbMoWi21BGDh35PJFMkosAeMwuoGhc0VkFq48w1HUrgtdko9EKNKt+JZlK1Kqm21DlMeizHsnWLzNxGGp/+4jn8zremIAo8rEaB3f/Vhk07q1DJVY4TU2FGH59eS2MtWQDPgVW76GKnl/UC/rWi/YQKxA26iPPRmNdC7FiLze1OtPpIdWQasRVtvBbcF+vsjelGK5UvY8hlxnQoiXKlimA4zdqzWoGORbtJj0i6qBqABtfSkAAcGXTi2kYGI/JYoNVdtTFHx1m3OjxryTwMOr5twHrAb8fltVTH1QViu741FVZJYaaByvnlOL5xZhVGvYA9vVaIAo9iRWJtN/TaLSKZi1qxeE5MhWHQ8bi6nkKP1QC/wwSHSY/ngpsYdptxdT2FxRb6TPQZ8TLD8Mp6ij2j4Foadw058e+PBzDkNmNmIwMJpDJOWzG2YoadmArDJ+t4cCA03XbzSKNDDd2gxbJFeTNpxHqygKpUY2jGssTqeT1VwP4+m5ycr+DwgB09NgPOLSXrEh53DTpwcSVRVyGd9DuQKZTRZzdC1PPgeR7vnOxBj82A12VdH5/NgDfmY/jQsQEcGnDiXQf7GNNk2FPTv5hgVum1xAJdA2jyivbOd5vkUc7vD4y5Ua5IeGM+hgfH3Xh9LoqfemAYf6gQsFai12bEqMeM1+dI4JcpEAH1/bKNrsOkx4/dPQAJzSKfpALdmOAxIJIpqr4riVwJs5sZHB1ywmogiYXGFq2EfC/URZbJd11iDB7yd2Xbp99hxKDLzAJ02oIx6DKxAD1bqqg6tHzvYhhHBh3wO0zsnuoEHnvlZ7cYzcFrE+s2AHajDlOrCRh1PO4fc3fUuu1RrHeNCIZJxdSgq2f/nZgKw2s1QC9wMOp5vO8uPwJ9NkgAPnL/UMvWoflIBqLA467B5lZJpY5OIldCpkAKV1VJYveTPt9IptiROyA95pFBwia7uJokDDWJ6Jg9sq8HB/vtuLiaYJ8PJ/PEOUxepxpjGU5mKYcSeQw4TYhnSwgl8ihVJDx9eR3JXKnu85sZokmi1CyjIGKw3TF46PNWAx0nAs/BaxVxcZXEZXSsHujvPsHDcRzuHXWpMlRW4zlwHFkbXWY9Y3xRTPodePcBHypVCQZdfVJXifEeC5uLzizG0Wc34kcOEwFyNeYQRbsWykZYDAKyHejEbZWIIKyrMi6sJHBAboG8f9SFYDjFWtS6YXhM+h3gOMK8UBa33nWAMKLp2FTDRpqwxhrfz+lQAs8FiZlBpVrFa7NRPHdlkyVrQ4l8XTEnkSuhUpVUdWLUQAu9jXMwZY0EZIOAegZPQZVtQ7ESzyHQZ4PTTJIYBh0PUcdjLUFE1RuTLnS+Ujtnu4J9SsWI/S3i4QGnCWeXEuC5WmJKbf8y5jFjfjOraBeqIJ4toViuYHYjjV/+29P49JfOYX4zXZf4WZVZ4mqgVulVScJyPIcDfjsqFQkTXjNsRh3WkwWmLRhO5iHwpIuglU7ldCiBv3xpgWnV0XOgTpVryTzy5SruHXWprg1UqoHGIPQY4z0WcBxwaiHRlHCqVCUsRLNIF8oYcBox5DKB5zkk8yTZrxZzGfUCDg3YVRM8au8OdZS2GAQMuUxtW44bQdeAe0ZcGHSacGoxjpmNDHqtIuY20vjXf38WwbUU6wgBgH2yuP7VN5kOz3XX4AkGg/8UDAb3BYPBiWAw+LvX+/tuJChLwWs1wGc3YDmWw88/Moor4QxsRh2+Px3GQjQHQILPbsCIWz2bTI9Ds80ZmV5M+w1bLSzpQhmVqqRKZ1Xq+hRK9Ra006EE/p9vX0SlKuHqekpB8ycvwaDbhDNLMbgtIsZkd4F2L5+SrTG7mYHHIiJfrqJU6YyKSSnSS9EszKKAF2c2sRjNYimWbcscim3RA0xBP7MazyGcLGDEbYbdSO5HU4tWliRL1II35b11WURWAcgVy6r9tY1IF4lFb2PwYjfpMb+ZZdTwL51cwkvXIsiXK8yB6Xj0s8AAAE+SSURBVC9fWkCpQoKZRmvzWLa+QrUSz2FPjwV3Dzsx7DZjby/5M7VNTOXLEAWOsXwsYk2MmAZ9Qy7ifER7/Km+gfJ5LEWz8FpFWNrcq3agVZBIhuhzrCXz4DnUOYq880Av8qUqroTTsjho6/YsoDYW/XYj0nJQ0RiAfu30Mib9dvznHzuMg/0OvO9IP37ynkHcI9NM1cYcS/B0qcNDkzGtqocACdKL5Wpbm2sl1GzX1UADoBev1phxhXIVxUoVTpMelWoVEz1mDLlMeHRfvVONWX6mjRVSOh99/cwy/vF8COlCGY/v76n73X0+G2vRUms9aHS8Wkvk8VMPDGHQZcaZpTge2ePFiakw7ht1gXbn7OmxslaMrZhhtMLzlj1ePL6/F2ZR13YeccntdxTxbAk8B4y4zUjly3CaRFQkiW1QaWsVQGxt7SY93jnpw/2jbjwW6MWj+3pwoN9el/C4a9CJTLFSx7CZDiUxH8nilx+fYDbvok6H+0fdSORKmFpJwGsTkSlW8J5DfViJE72id076cMBvR4+iPY2J5G/Wjk/XAEEee8re+W6gfF6FchVjXjNmN9P4rW9eRKki4d5RZ9vfv2/UjZPzUVSrEr5/KQxR4PF7P3mEJX4f30/WlcYe+Y1Uc4KnUq1CkoB//cWzTXPtheWEfK/JPR/zmjEfaU7gm0WhaQMFkHGgk8W5gdq6sRLPod9hhF7gcKifHJsG6Ifk5OE+hdaGJEmyQ0vtu9cSeZxdiuNdctur8p4mC2Uc8NsgCjy+f2kdpxeiuLSawNRKHN86t4qFSBb3jLog6viOaOz0vFUZPGF1By2ly92PHPbDLOqYVXPjBop+3iIKuLaeIYlilXOrJdvJXDKznibtJVKtHY7Oq5F0cct2VuUxdQKPQZcJ55biiGWL4DlAFHjcP+rGgX47rm1kkC9VcPyQjyUaBlzGlrGM3040jo4f8mE+QhiDIx4zFqNZPHUxjEvyvEcq+EV4reqMELtJ39LtRg2t5kgK5TjxO0zYTBfxvrv6EM+WYBGFlsWVrXD/mAdL0VxTC+9qPIdemwGijsf+Prtq6zC9vv/644dbMsnGvRYsx7IolCs4uxTH0SEnnGYRAZ+NMYfUYtt4rgiBr7XatQMxAtj6Xqu5SSlhMxCXwkurSRyU3+975Ta2U4vRrhke2WIZy/Ec3n+kv664New2wyK2F1reSBVUkwck5iRx46mFOFYTOaQLZXithGHqMouysDhJSNX0bLpL8FxtSPBQQfRRjxkmvcDkACSJMITcbe7rgNOEbLGCTzw4grtHXBj1WOC1iihWqthMF5uSLu3s7O0K1jbdJzFtmob58O37e3B1PYVhtxlG2WhD7Z0f9VqwmshhMZqFJEn4yqll/O1rC/jepTCkahXJfAnFchUvzERwbinO9lDrKusSRaZA7v+X31iGKPDY02MBOIDnebx1jwcVScLltRRS+TJ8diPOLSVQlcDWlcZ7otTgyhTr21yJ2QJpURpwmlTXBp/dyDSawqkCkdIYduFdB/tw76gLK/FcXcLJZtTh5WubkCTSNuq1kNhnX68VkAjztVVS995RNy4sJ+paEFu9OyuxHDbSBdw14MRastDymGqga0CmWIHfYYDTRIwqlmI5nFqMIVuqQAJpCaXvKR3fbzYdnu3tzDQwUDr6qNeMT3/pHAplCStx0hZDXV5sRh0MOp7Zt7ZyvyB962uwm3R17lpkcaesjjUMeUw44Hfg2DB5GdTo5itx0svutohYiGSQzJeYle9iNMsmalHH48nn5/CpR8dYAuFPnr2GTKGCe4ZdWJP7Nz9y32Dbl4+e/2e+fwUDThOeubyO12YjLNimglyAokVLXhyolape4HFuOY5UvszaPJxmsSVzKJbpTIOHLhjnZA2MYVmkbVBFZJgyotptyOm93ddrxcx6Gl85vQKPWQ+rnJig97OR0k6tuF+bjeItezzs3+1GHRYjJCn47OV1bKQKkFBLhO3zEfvhi6tk49GomxNJF+sctGi/7SN7e7CRyuP0YhzpQqUucWLW8yyQSObLmOghi+Wrs1GIAo9em0GuZpK+7msbaRwecNSN3aVYllUstgM31V9IF/COSV+Nzp+qjTm9wOP3n7qCf7oQQrFc3ZLBA9TckV66FsE7JmvuSNOhBL7w2hLOLSfwyF4va/lJ5EroV4wxtfeTtr91K7S8lshtmYw5IJ/fpdWkqjaGEsThpwi/vQMGj2wn/E+y4N9qPIeFSBaHB+zgOY7o2Rj1eHSfF15rvVMN07IoNi/WdqMO2UIFqUKZbXyVv+uzG/D0NBFpbbV5ofPFQxMefPTJV7GeKiKejaJSlfCWPV589fQy+p0m3D3sQjRThEEvQK/j5aRje0Fq+kz7HEZ279sxEF0Wfd1mOJEjrYnvOdyHJ5+fgyBf32o8D5Mo4CP3DbKgaVhOAvXYDOx9SjRU/IFa0uH8cgLlahUnpsL47lQIPEc0pehG7sRUGKl8iej6hFP4i5cKMOh4uMw6dl09NgNrT6PfdSWchNWow7fOrsKoE5igsd9hxMl4DqJAGFoch4763Fs9r+lQAslcCfORLK6up+G1injq4jpGPJaWz+T+MTf+4dQyrq6n8b1LYTy8x1O3gRv1kOSUsrp2aTWO9WQBUytkA0WDc9o2YTUKTXPtueU4uddy69+Ix4IXrm7UnUs817qlmec59NoMTJicbgrpff/5t47XjXWf3YhiRcLPPjzKjpHKlzHuteLkfEymwNswHUrg904QF6eQbBVP7yezb8+VcP+YB984s4IXZiJwmfUoliVkiyX02gwYkdtgO6Gxt2LwZItEDPrHjg02/Q69xrsGnXX3A6AFBXfT52fW0yhWqhjxqDNj6Jj+x/NrEDgiRv4f3hPAUxfXGEtT1PGwG3WIpAusnbXdfKl8T4bcxLHmUL8dXz29gvvGXDCJAg7221GpSgiupXBkyAmPVYRBx6NckeCw61VjGZ/DiPPLcRLLecx44SpJ8K4nC9hME006Ot6WotmWG2c1K+t2WIpmt2yzouPkw/cN4S3/9RlMh1KYDqWw32+v06fqBtQB7F9/8SweGvfg+CEfa2mja+Gk344vvL6ASlVibbYA0bmyGXUtXX8AIvpelYCzi3EsRrP4Zw8MAwDuG3Ph66dXMLUSx+denIfDpK9rSyyUK3Ca2sdfFFYD2fRKktT289FMEYddzpb/bjfpMLeZIRttuV3kyKATeoHDyfkYRIG08D5zeR0fPNLPYoG/eWUBObl4qty8PrLXA0lCXYsIQN6nSX/7duzNdIElVpVYiefgsxsh8BzShTKsBh1cZhFWmdlCY1yySdfXnJ46KIACJIHisxtaMnh6bcY6pms7xyuK44d8TLNu3GvGybkYdDwPDsDUSgITvVZ85L7aXBRJF2A36pgmaN35KeZsOu6MegFui9hkJLEQJa3SR4ecjJWn9s6PeS2Q5I6F70+ToofTRO4dLZ5HZMb2a3NRHB1ywiwSvTi1BM90KIG/f31J1tORcLDfjlIV+JXHJ/CD6Q04THr02gw4txyHy6zHJx4cwq997SJ6bQY4zXoWmyvvCV3DbUZdnT4rdapM5krwyMXdRqYhQBI8zwWJGczZxTiODDrZXDboNAOIYiWek0kKZAwvx3LY67PiyKADF1eT4DkeYz1W/NLjE23jrntGXHjy+VlMrSRYgpQmqOY2M7i4msC9I0SI/z989QJ4WTfoLXt6Wh5TDco1YD1dxN5eC4oVCeeXE6yFDABGPGZwHIcTU2EcP+SD3ajDl99YQqUqsfnuTofmorVLePt+H/QCh6em1tBnN+LVaxH0WA14YMyFgmyT24repsShAQemVsgCQF+OSLqA54LrZGHgiO5OIlfC519eAKAuGElpbHRjcWaRJE6olW+O2lzKdPO/eWUBf/3KIiyigGS+DLdFhNsibtnv3wgqgjrptyNfriKaKTZllhttK416AR6LCIdJh3NLCbgtIib7bLi6nkYkU1C9V8VyFalCGe42rVQUNFA/Iyd4qD7DkMus0qJV3NKik97bPb1W7PNZYREFrKeLmItkUZWkpio53RhT7YiKVN8qQcVUN1IFTK+l4DDpcWzIgUGXCT5Z94JM8EWIAs/65CkIg6d2H5QtFR6rQTX7XqhIGHabwXM1kTqaxR+QKZnHD/mQzJcx6DJhKUqsounYnQ4lcGE5gUi60JUGkRKNG5GlaA6P7uupq3qNea0wiwK+foZos2+VAKGgLjFBedNIn8G5pTg4kOpPpy0/ABCTK2J/8uxMV9dLGDztN2TjPRaIOr4jHR4acHXC4MnJAuA/vLKBV65t4gfTYQw4TYxlcnTIifEeK/SC0HTtZrlFi4onK4X3Ti3EsJkhVWy7SY+r4XTd7+7z2ZjtvJo+kxL3jhBq8Q+mw3hxZhMmvYC7R5zsHXvLHi/eLzsiddqj3YnAtxIus1gvspwrwWkWa2ww+V5XJYklE8LyO/Pew30dfVexXIFe4PA/fnAFn/7SOcxtpNhm6stvLLNN/6ef2Ieff2QMgT4b0oUKlqI5DLpM+IuXFlqOVdoSbJVdGOlGwyCQoG9uM4MRjxmCiiBqtzgxFUa/04TDAzVm0lasoPvHSLD3d68tYDGaZa0KFDqBx3iPhYmoT4cS+ONnZ1GRW3mUtHQaWMezNYFdykr92ullWA06/MVLZG4d9ZgRThbqhPDj2VLdZqERPXLi1KQXYJZd/Oh4ysmVQXrff+ahYdXn8eP3kPE6v1nTdwiGU3Ca9UynRzl/MOviqoQxjxkOkw4xmUU25DLBbRExs57pmMbOktHp+gTPzHoakgRVBy21d4a2EzYWFOjnqcZKv7M1M2bS78C/fXcA94+5UapWMey2IFussKIOQFoyNjNFljTt2yJ5Td+Tv/3kA+i1GfBnz8+yVqLpUKKWMJfn00uhJN62rwd/8GH1NkKAOnkS4fJrGxlM+u04NuzCew71QQLw4tVNNt4WZeaqGmiLVidi/NRFsJX+TiMGnCbcN+rCN86uYjqU3JbAMkDu0dOX16HjOeSK9S40q/E8S/Ds99uQL1Wb2hzPL5ONYrvkEmVZ0HX72JATAGHzZYoV/N2rpB1kdiONJ1+YhcATm/ir4XRH7VnToQROL8ZQlYDffyrYdj3eqkXLZtSz9erQALmnJlmL55tnVvB3ry3gueAGErkS5iI1JvOZpTjssqYUpJq+4bfPES0rtfY5v5OwNv7PLzczEAGZtajC4BlwmlCuSPjUI+O4Z8SFMa8FPTYDK3DycoJrLUHilFZOb+2wp9faZACzLrM6TKJQ5yTazvGKQsk+K1eBB8bdeOseD3psBixEs/iFR0br3sVIptjyfFdl1rpRz+PJ52fZfVNz4P3SyUUMukz4ww8fbesqR9/Rpy6FsRrPY3+fDX12I0bcxAFWkgCfzYBBpxGFchUbqUJbG3sqKu8wEU2vB+SWWiXreNBFWkAf3edFn8OMdIHs0VqxkWkcRJ3eJEliTpXxbBHJfIkVltXmX5/dgEyR6BEFwykcHXKyud4k68bNbmTYerYYzaJQrhKDE52A8R4r/uDDd3W0B7QbScz4f3/7EhvbK/EcOAAvzWxiM13EUxfD2EwVEM8WMeQy4yunVra1d1DqOR4ccOKxQC8CfTZM9lnRZyfFacp8urhaI0pki5W6+e5Oh5bg2SU4THocGXTiiyeX8IPpMFKFMiZ6zPDajB3R2ygOD5AqSixTxEo8B6tBwHNXNnBhJYlrGxlE0gVEZLV8qq+jlpCgL7EkEVvpqZWE7Aqhg1HP40o4BbdZhE7g2WLlMOnZBPuWCQ+cZrFrOj+dkGrWfnlVKqZFYW8+HUqA5zn8YHod6UIZB/02eG0GlgVXu1cxFWvwVqDVjbM0wSMHVENuwuBRBmOxDKmetwOzSc6XIXAcRj1mjHst4EAq9I0JLZqo0/HqrRKkNQu4IGt0DLpMyJWqsBp0eOeBXog6Hql8GYMuM/xOYxNlngQwtQWnUeB4rMeKz3z0CP78E/exiXrAaUK+RISFaeU0lS+jWK4yoTh6nAN+O6oSScxR+9s/++Esq2Jvd8JUavBspEildH/D5kPgORzw29n9bOegpQStegTXaslS6tI07DEzzZhOWn6mQwl8/cwqADDKb6eaS+Hk1u1UeoFHwGdrS92mqIk2t9+kT4cS+OZZEmgSseI09AKPhyZc4DmOiRq3unbKsLisoNhWK6Q3e2o1iXGvGU9M9sIuW+Yqf1dZgHv+ymbb+6QTeDwW6MGzl9fx/NUN3D/mhkFmoHSTpFGiE4FvJdyyrTRFPFtkSYBJvwO/+SOTAEjATo9BxScfnujpaPx87sV5VgHNlyp4eTaKTKGCwwOOpgTJiakwAj4ba2M5ILcBtRqr1Cmo12ZAMleCVeHANR/JIlusYNRr6eoetgJt5Xlw3IMHxtzYL4t7tmMFZQqkneRvXiEFiRFP89ilbX30+ilThl4LpaWPeMxwmvQ4ORdjrpU0gCPV2tp8pJOPoXS0SuSKbTeQvgbxX6D1ePqRuwZUf37/mJc4tEQyODEVho7nsJbIY6LHqiqkqTz+RqaIfb1WPBbw4qcfGsUje72q71g76AXCiqHinBRBOQmiluBRu8ZfemwcXquo+mwn/Q6IOg4eqwgqHNru3O4bc2M6lGRJoV7FJtZjFWUGTw5Osx4msb07IMWVcJJoMtHWDrOIJ5+fQ7pQZI6MS9EslqI5PDzhaXssn92IYrmK9VQBpxeJvtRKPIcBlwlHBh24tpFmrQypfLkNg4e4j3bi7hRO5lGsVNu2aDXi/lE3ZtbTSBWIMcF2NiknpsJwmUX0O01YjdfE4797YY20e1IGj6y7QlsWAcIgDa6lWOGwFahV+j9eCEHgORyWP09jqxMX1/DKtU28cHUTpYqEaxtp2OSN6lYFNpo0pQnISLrYcj0ulqvkebXT4JHbfww6HntkmYLpUALlqoRQIo/NdBEmPXE7pcmuVL4MDhziuRK+cz7E3MZsRh3mI1nWNtN43suy0K1F1KnGERspdQaPMsk80WNBOk/ahcd7CBujIsewNEnKrOE7FFkGgL29NjkJXIuHqZg2UK9V187xSgmlDuN//rG78Ls/dhd+9Yl98rpenyCMpNUTcdOhBD7/8gI4jhQFlfdNaSQxHUrgt785hZdmIhhymREMt46npkMJfP8SYbZspApwm/UolqrIlytwmkV85N4B/MQ9A3BZRJjkRP/UahIbaZrgaT5PujY+NOHBuw/4YNALbG2k9+GvP/kAzKKAV2ej+NLJJVhEAX/00WMtE1H0uYsCzzT/EjniVPlTDw6jVCHPqtX8S2PPp6fDqFQJq4nO9W6LCLdZxGa6wNazCfm91cuajJ22TU2HEvjyGyuwGYnwubLI9MMrG5BA4hiLQYeVRB4VCTg2vHVxaCso40S7UQcJHJxmEY8FvODkOJeSGnx2I+LZIospdvK9twu0Fq1dAl0QErkScsUKXGY9YpkSLq8lccDv2JLeRkEroxdWSPvINdk1wWsVwXMcNtIFxHMlFMoVpk+hlpBQ0tiG3WYE11LwWPVwma04MbWGWLaEDx2tVcY5EKesvb1WGHTEDk9C93R+Ssu0G3Uw6QUsRMjmQ0k7jGYKrD2LLtZGHY9yVWJJpl94dAxX1tKEkv+25u9hbV4dtGgZdAKsBh1jE9EAf8htJpn5dIFRxmPZ4pYVNeW9BUdECx+acEMncJgOJXGo3163uFOaJXWksBt1ZKKT763dRETo1pMFDLpM6HMYsRzLIeCzwm2pOVV85L5BvD4XbbJ8jmXqGTz0HNuNN/qcPBYDguEUs7rOlyt1bVeTfgf+24/fheeubEDgOdaGV61KkFBLWgL1bXidwCySMRJJF9jmQ42uPuAy4o0FYin/5POzHdErOY5DwGdjx12J56CTGQwPyIwC5eLb7nhEgFQEB9Ky1Op6p0MJnJgKk42B04QHxlwoVaSOBJEn/TY8Pb2+Jd18rYM2BnpubosIk15AXA4QhlwmLEZysBpENp5aXTtlL7x8LcpajxZjOdka3ChbUJpg0OvwkEmPTz+xj92Dpy6ugwMYXVatZbHx2r95dhWxbAkDDhNjs9B3jN7PrdpE64/Z/pkq4TST+1GuVKETeCQbNhkGncA01ijCqTw8FhGiju9o/DhMevQ7TDiTjCMjJxzMooAxrwUCz9XNs3S+eNu+HpxejMlMO67lWP3ci/PwO4zosRlQlYDlGBG3DiXK6HcawYFUPuk8vBNqMm3lcZj0eHCcbJrVWtIopkMJ/PkL8+ixGTAfIcyHr5xaISxNxXns81nxrXOrSMsbV0pHp8l5SkvPFit4yx4v/vFCCBdXkxj2mJHMl2ERiWVyn5y4BYCFTXKfFyIZNq8kciWMe1sniSmzpHGj0eoZt/r5qMeC+c0MiuUqm4Po2q6WEGts16LXYDXoIOqEunesE3ishroWLdI+sAieIxpk7z3c13TeateiFL9Xggi3p/BTD4zgt95/YMvzuX/UjaoEnJgiTmJKnRGPxYBrG2lYDYUt2TtKnJgK4/CAA5fXUhAFErMk82U8dXEdk7LQ8ivXIgCAh/d42x6Lfu/3Lq4hX6riwXEPLsmmGXt7bTi9GMfcZgZ+B0kEtdLgoInhZL60pTbdYhsXSjVMhxKY28ywudWkF7acW9VA55cBpwmvzEaQkxNXs/J4pQyevT4reA64vJbEj9xFBJIvhZIoV6W6Vj412I16wsxKFzDpt8Ms6lihhDg0VjGznoFex0MUeMxuZDDqITFnqxZKCjqfUn04kyjABEF1Pf7KG4RBdGYxztaVxnt6aoFoArnMIq6up2SphDBG3GbMrKfRZzfALic+1xIFFo8dG3Lggsy2vxJO42A/aWHPFss44Lc3reMnpsIYcplxZjGOk/NRHBlyMO3GJw748FjAi0yxoqrBo1wP04UyHhh3gwNQqEjotenxoWN+fP9SmDF8I2lirNGJhAGFxSAgU6zgX37hNPb22ogbWDLPbLrdFhHXZA25dno5W+H4oT78x29M4dvnV1lLHEBYQZT5pQRlxlDhfGX81e804rW5CNtHXFtPgwORX2j3blDhfKOeJE7ed1c/eJ5DsVxBrlSFqCO6fft6K7gSTsNjEbEYzeIn7hnAc8GNliyrRK7EtEyBZtax1aDDo3u9+NrpFUgA9vfZsBjNtImNyHP/42evsZ/Ra6IFwV982wTeKwuYN4Lua566SObdIzKTTjnX/89nrmJAjvmvbWQw0WPBH/+zu1WP1wr0nRx0kdbdxWgGkXQR68k8QskC9vVaIVUljHlMCCULMIsCht3mbe0xlVC+F1T/rHHf5DCRZ0njo4hsc7+T771doCV4dgm06npuKY5ipYr37/fDJgv5dhOYHZQnvKlVojT+r/4+BI4Dhl0mZEtVmEQBC5EsXp2NwqgnpXI1DR6g/iX+yT97Gf/wxgr29FoQDKcw0WPBkNtct1hR+2E66ar1dG4F5QvnMhMno8ZJNpIpMv0VOjG4zCKWYjk8MOaG0yziexfX8ZY9Hnz9zAr+j78/DZtBxxY0UeDYQvaD6TBGveYtAxyXhQQEtC8TqFkaLkVzbCKMZ0u4a3BrijC9tzRRohcEHOp34Eo4jUuhJH73Rw+xzw44TYhli5haTWLYbYbNqGeboulQAueX4yhXJWykC/it903i59463pQsoBuzAZepTleiUiVOap0kuhrP/1OPjuF/PXMNl0JJFMoV/PSDw/j6mZWmgJPnOTxxwIdvnllBvlTBciyLs0txGHQ8JuTNUifin2rwWElV6LJcAWtk8EyHEliKkuMq2zU6CWr39dnwnXOrkCRilU3vG+3h7saW0e8wwu8wYjqUxH2jLlW3A6VeViJXwpMvkP7zTjYtbouISKaIX/nCGezptbZMYnWiU6E8Z5dZD32Bw9v392A1nkc4VcBDHWz0KYNnPZXHeI+F9S4PuUxwmwldWK1nnCaWHGY98qUKe2atkn/ToQSmVpLgOGKB6rKIdc/3RvRJu82EQUdbGuO5EnPqoGhs5wwn8ujtcDNKn8Vdgw6sJfPQCxxcJj0KlSpzslOOQ6UuFN1stUui0M+P91hgEQWcWogx7bLvXQrjvlE3/ufHugvYWkGpq0DZDI1jQAk6v496LJiPZJmeWON4oNpaV8MpDDhNePbyOrxWkVVKKS09kSvBaxXhdxjx4rVNjETNSOZLOLsoz0c9NSMDyspSCi3Hs+qukxQ+eR3oprVBDWNeC16bjSDgs8mWrQ62MdnKkbKb+9sKbovINmF0blpL5uGxGpDKlzueQwdcpiYnJQA4vRhDoVzdkhlDcWzYCYHn8I8XCKuQrrcAWQNeny/CoOc7SoZT0PfqkT1eVmih8/IBvx1ffmMJL8xswmsVt2R+0vn0G2cJU/P+UTeG3SZWrLIadJheS0Iv8KhIUtsWLYBolGw1ddEET6cMnhNTYfTajRj2mLEQyWLEY0a2WOm6sELnCxr/rMSIKxBNSNE5Z24zDadJxFdOLYPnSMv2eZkFfWSo/fdNhxIwyFROgasVP+gm8PJaCjwHDDrNyBXLWIqRFnAO6ox0Jehzp06P68k8JnqtqutxuUrc9iRITWOefoa2ZzlMevYZajBhNggYdpkRyxbxwtVNLOVJoYgymT/+2dcBEC2ltUQe+XIFsUwJB+5qLlStxHMY8Zhx/xhxH1yKZTHmNjOG6p8+NwtAnR0CbF20IOw7cg82ZQMSoU0bnRLToQTOLxEmkZ7nWZy1Es/h4QmSHK1n8LRuVdoKTrOIo0NO/P1ri9hMEX2r44d8iKSLTLtFCfq8f/zuQRjlMUXf84keK1L5Mr51NgSjnsfV9TRGvRbm3Nfq3aDHvG/EDbMowGURUZUkhBLlusIS1Z45u5TAr3/tAubkokErltVWc/d0KIFcqcoMT0Y9lg4KYA780mMT+M75EN62r4d9jo73/jYxLJ3XXpqJYMBpakoePjDuxh89DZyaj+Gte714fS6KH7+7u7WGnovfYcQDYx6sxHJ45vIG+uwkljLoeEAC8uUK7EY93n3QxzQft7PHbITyvVDbN52YChNNRjleCyXzMOiFHX/v7QCtRWuXsBLPodduwJjXgnGvBYMu87Y2vQ6THiMeM6ZWEtjfZ0cqX8Kw2wy3PKEcHXTg8IAdZ5fiuChXDz7/8nxbqu50KAGjXkCuVMHUShJ2ow4Cx+HyWs3K9+MPjWy7JaIRlI74iYdHEc+V4GtwxYgqeqIprXHUa8FEjwUBn43R7qOZIqoScXZ4bTaKV2ajSOeLeG02yion5Wpn1r80oUSdzICavfeyghETy3Ym3Ky8VkptpxTSVL5cN1kfP+TDlXAK6UIZhwbsTdoZlBFrNehwfjlRp8fRSN0ccJqwniqgWCaBy+tzEUgS8PzVza61cCb9DvzOBw4CIP3X1DlJzZnj3Qf7kClW8NLMJorlKpZiOTw47mF0+u1qe3gsIjYzRUyHUuixGZo2VrTqBQBei6FOd2Mr7O+zIZkvM3eUhUiWsFrE1s4KaqBth4/s60G2WMHrc9GWbgeRTIFV4GlffCftVBflagzPqTvP0c+dmApBL3D4c0Ufertzft9d/fhnDwxjxGPFgX4HPnR0oKN+arOBPFeLqMNCJItwsoCHJjx4ZK8X4DjwnDqFl77PQy4z+uXrbjcPUnvmQacJZlHAqML6+0aBtnlSHR6q76JEoyB7OJVHn72z4JY+C6dZxOOBHphFHQoVCXajXnUcdtuexlwlChUcHXJiKZbDfCSDu4cdmA4l8Y7J3q7uRzt02/5Gx8N4jwUDTlPLlq4AS/CksafXgkimiIkea53mzU8/NIJPPToGp1nEoMuEYrmKcrWKRL6EcrUKv8PIrMlT+TJGPRZ4ZJMBgAg3RzJFnF6MtZwrK/KG8NpGetvaYgCphq8m8vjuVAiSBPjtho6eZbf3txWUmzE6NyVzZVnrrvM5dMBpwmo816Qp88q1CASewwPjzRsyNVgMOhzst6u3aMktkiuxrQXpG88tlS/jyJATR2RGCZ2XD/bbkS1W8NTFNTw47tlStJd+76mFGPb32eCy1DS4nGYRfXbSGk2fW6vWFzpvJHJbCy0vRbPgufYbNCWUTmfvnOyFXujMVa0RdL4wym1H1zaIhtoeOTlKC09PPj8Hu6m+5eKFqxvosRnaFi3o71IdN5o4uRQi7es0ofXIXi+8VhFVkOT+oQE7MoXylho89LkPucla8cZCrGmj2Ogg6JFjh8ZWWIdJz75v0GVinxlwmpAuVDDutUIn8OixGVnh84FxYpAx5rUikS9h1EOYCOFkHu851Idipaqqj0TP+6FxD0Y8ZqQLFVyLZGE16Ota+FtZcG+FPrtRocHT3sK8ESemwhiQE35RWQLCbtRhM1Wsa9HKFivIlypM36uROd4JpkMJVCUJyXwZPM8hkSvhf/9wFrGseosWvW8Okx4GfX28SY0krm2kMbVCHJweHK9naKuBHvPuERf2y8+KHlMt9n73wT4IPIevniJGKe1YVu3m7hNTYYx6zOixGeC1ipjosXQ0Fw+yvUrtelbla2sXd/vkGKVYqeLosLPp348NuaAXOLw6F8H55QSyxQoe6jBpr4TyGe312eA06bGWLCBfquJt+3rw8F4vHhr3tNR83C2oPTs631UlCWZRwFI0u+vfe6tCS/DsEmobKj/eJ9NZt7vpPdTvwIUVUtleSxbwy49N4LM/cx8+85EjGPVaWWY+VShDL3DIFCptkxwnpsIY91rQZzeC5zl88OgAHt7jxQH5ZaAZ0N0ILJW4e5jYTp9eiNX9PKqwN6f3bcxrYTRJ2jc57DbDbRExtZLEpqxJ9L1L60zXByCZ9E4mSLpwUActoDZp0sp8rlhBoVzdsoLUCDqp/MGHj+Ij9w3i6noaP/dXJ9kGYdLvQLkiwWIgukON2hmUknxMthJtdy0DLhMkibTqTIcS+OyLpGKgdKLoZlPSYzNg1GPGyfkYlmWmDK3sKfHQuAcWUcDvPRXED6bXZfeTnU/UHqsB0UwBl9eSTewdgAS1g24T7hp0MO2IToNaumm8vJZCv5NU4SZ6LF2PbyZKpxdwwG/DmaU4fhjcqLPOXYnnkMyV8O1zIXzr7CrKlSrbLPoc7QMtZRJrUw6uWgl1x7IlxgBr96zpORcVLJFunhFl8Iz1WHBxNcHcnrYS3qPv89v393Ykjkw3Le+Y9OGDR/rrqvA3CjShG8sWUa1KSOabnZZIy1MOpQp5puFkgVHXt0InoufK+9jtXKz8vFe2OC5XqliQ3+d3HtjdQKZV8lkNSpHIn7hnEDajXnU8DLnNMOiINtzJ+Rj0AsdcUJTXT7/74QkvRj1mzEWyKJWJbplRLzQJfo94zJiTxY7/9IezqFQleK3qc+V0KIHX5sha1aj10A2mQwlclnVL1pIFjHnNWIzm6goq7e5ZN/e3FbxWkbVorcRzMOh4pAtllszs9B2jWm2NjlwvzWzi8IADNmPnG7z75Oq8KPB1m3iP1QBJIgnWdhbpjWiXCDWJJLQtlqvIFitbPsNouqZrZ9QJ7PP0WfynDx1CpSrh9GIcQGvmArWETzYkeNTstBejWfQ7TUxLcSvQd8lrNTA77+3EmEoNDo9VxEaKaHDQJNiA06Rg2xCXwFA8B4dJj9OLcRwZdLRNmNHfpUmgMS/ZxCZyRDdmf58Nn3zrGI4OuXCg34EP3zsEr1XE2aU4MsXKli1a9Lmn8mXcPezEeqqAmY103dpG15XVeB4cSEKucczTz3itxGqZsKvJZ9TGll7gIfAcziySOeLMYhzlioTfev8B7PNZkSmWUZALbwcHmhM8ymOa9QJh5RcrqNLkqXxLt5vg8TmMihatYlf6OyvxnCzarGfMMr2OsNV6FS1aAInfI7LcgUHXmV6WEiemwgj02cBzwLX1NBwmPYx6AVWpuTUWaP+eF+WE/pnFGC6sJLGvz8rYgVsxJbsporgtIg4P2JEqlKHjOfzpc9dU55St5m5qQvPBI/340NGBjuMdKhisLEavxHMQdXxLxhdA2pOpK1kyV2o6Z5Mo4MigkxTQr20CAGu97gbK+5ktlDHkMsJnN2Cfz8qKOsWKtOt7zE6gTNQ7W3SV3KnQWrR2CY30vKRK+0KnODTgwD9eCOELry9C4DnmOkID3M98/woqVeDFmU0YdMKWGiiUPvf+I37kihW4ZTpiOx2A3cBdgw7oeA6nFmNskyFJUp2rQStao8Okg92kx+EBB354hbTWmPQCJEmCTuCQL1UgCjxMegHYwvp3OpRg1NUraymWdDGJArxWkWXFmXBzBy4Orb5nMZqTRTVzGHab8eTzc/jAET9OLcbwf7x9L35V0a5HtTNEnRlHh5w4NOCATuDaXgtdsJbjWbw2G2UsEbMsHAZ0r4Vz76gbz1xex0PyxK6mQXRtIwW3RWR6Eg+OuXE1nEG+XMUBv2Pb2h4ei4gLKwkksiX87FtGm/6d0skfD9RYCJ0GtTQhdGUthVyxgqoE/Pvj+9lGo1Mo2w49VgM4pJAplhHw2dgmUOSBp6bDTPPm5HwMuRLRyfJuUUmj76fbIuLCcgL7fTWWA6Wcfu8SsRqOycnRrZ71TjVsDDoSzIoCj41UAeM9VsTlKmm743TbYqLUdEEHLSzXAzRwjWWKSOXLkCTA0ZDkHXSZUJUTq30OIzbThY5btBqfRSeWo93OxcrPG79yDl96YxnnlxNwmvQsCL4Z6HQ8CDyHPb1WnF9J4HIoifce9uPX3zvZ8rgr8Rzetq8H+Yth3D/mhtUgYGY93dSCOOqx4JXZCE5MhdlcadKrr5knpsLw2shzN4vbn09PTIUZI0THc3j7fh/Tl+umXXsnoMLh1SppT53dJNoZri7fMcouWYnlWFIjXSjj3HICv/i28a7OyS8nunUCh//xg6usDVW5Ee2mRavVHAcA37+4Dp4DqhLRdGnXBjEdSuAvXlqASWY5uyzNn79v1AW3RWQV/FbsCNqilZA3Uyemwri4mmCaesMeC1szgmuprgSWd6t9D6jNFwY9j987EUSPzYiVeA4WUYDdpKtrK52PZPDUpTBG3CbEsiXmrNiOted3GHGg3w6zQYDHSthrdmPNQp4KKtPz30gV8BX53m7F4FE+d6dZhEUUkC1U6s6HrisL0Qz6HEZmjqDWCusw6fEzD48CqLXCthpbs5tpZtbx+lwUHAfcM+LG++/qxx98/wr6HCaIAl+nw6J23uAIczRZKLO2a8q4U2v/6QR9dgOmZZ3HzXRhS50kJei9GPVYcH4lgVKlig3ZSICyQGghhCZ42glXtwMdH4Muknx/ZG8P0xNVa41t+57LQsmb6SIEnkOhWMV6KgeDTtf23eg2PiKJEXKSFkNNILvbREFdvCOj07l40GWuY/CsxMh5t0q2MiadXkCxTExb1M75gXE3/uyHpD1w0m/flq5S49jmeR7vnOxtasW6UW33auc36XfAqBfw305c7qqQcDtDS/DsEna6oVLCIVeBvvj6IobcZqwlc3VuUSvxHI4OOTC/mWFCplvREemkQsVTb8QmyqgXcLDfjlMKBk+mWEGxXGWTSKv7Rvsmjw45kSmWUZIrI5vpAgs0jXqBUTzbiXw++fwco+rqBI5NcgCg43k8c3kdv/G184jJC+wLVzdxZKj7iYi2m+zvs2F6LYVemwHRTBH/9h82AAm4d8RZ93nlc3nbvh4A7bU26O8AZHJfieewEstBL3CsWrYd9sN9oy585dQyXri6AZNsWa92bRO9VizFchj3WvDAuIed+042LW65ggg06+8AOwtqnWYRPrsBwbUUc+mgdq3dQplczZcqODkfw6VQCj6biNmNNOY2M0jly3h4wo1IuoSTC1EYBB6ijscfPV3bzKiBjoPjB/vw1dPL+IdTSxh2m5ErVjC1kkDAZ0W+WMFKnLiu3DVoZ/ej3bPeyWLKcRzMooDvnA8hmS/jjz42WZdka/ed3cyDu7lp2S5qLVpFxHNkDmhs0RpSsP10AgdJ6kxbieJGBTbToQShvnNAMl/GQb99W4HobqGb8eCzG/DMZZLMFziu7SaSvjMfuW+I/UxNjNhiEBBK5PHlN5YQSRehFzjWiqBW0e93mOCxiCzRsJ35lF6nwBMWksWgUy2oXE+4LQZUZDba8UM+/PY3idiw3aSv21hvBXqvVuI5HBlyEibUc4QJFYrn2z4jJaZDCZxeiAOobe7puFQmS7pp0QLU36vPfP8KXBYRHosBuVIFgy4Tkvlyy0QdZZzYjDrkShXs7bWhWKnWfV4n8Lhv1IWnLhJW5ddOL4PjBpqOR+eNq+spvDhDrNUjadK2+2xwAz57Eu85RBjeC5EsjnaxHu1mjElBizqvzkawKruGcRxXF5s8PO7Gdy6sMUagzdA+Yab83f19tfaXg/2kXULt/Pf32ZguycvXIrh31NVxAvw/feciPvfiPH7ur07i8AD5juOHfPifT88gnCzggTG36pjfau1RG1vHhlz4+pkVVKoSXpuLYLKPOBy+7whJ8Hz73CqcZj3+1zMzqms+PSb9bo7jcHYpjlCCCMtz2J5wMUDWo810AaVKtWsGDz2fHhuZN4JrKeRKpDBAGTF1DJ50Yds6ZXR8jHkt+OGVDcSyRYUrV+f6Q5/5/hW2BksAHhp3I5Uv49xSEk8c8G35bnSzJis1Vs3i1kX1VthJvDPoMmExUs/gabdXYPOaSYdkvoRxrwUZFc0uv8OISlXC2aU4jspz/HbmlMaxTVuxbkZM1wrH5Da1s0txPL5/91rXb1VoLVq7iN2gVk+HEnhphgRjEmpq8EpqHe0P/uDRfnyggzaInVgO7xR3j7hwfjnOWhuiKur77fomaW84tYU8PGBvsohsdy21PutaS5jDpMffvLKAJ5+fg0kUkCmW8dpsFGdlkblKh7o+jaCU33tH3TDpBTx3ZROzmxnEc0TQ8OtnVuuOuZ3n4pd7jlfjefTaDLi6nsKeXiujYW4ncUeF7Z6/uolBl3pFYCWew2SfDXcPO/G2AElG7UYrjZLdQoNBJXbaOhjos2N6LYUfXtnAI3u90HVIh2+FlXgO94260Wc34pnL6/jG2VVspgtI5ssYcZsRTZdgNfDgAOTLVbjMW7d6MCtMHY9H93qQKVZwdT3NxsSLMxHMR7KoShImeiyQc53XNUk7HUpAksj1WgwCeroIFruZB69Ha2i3oIy9aKbEKsyNbQLKHnjqZObrUIPnRuLEVBg+u5G1cOz310SNbxY6GQ/ToQTTj7AbietFJ+9Mu7lzOpRgFs/hZB4VWWydtk42vj8DThMK5So+/uAIY65s5x2jx/nEgyNMz+BGs9LoRimSKWLS78CBfpI8z5cqXb1jg04y7ldlNiF1qhF4TnY17GydpALBXqvIrOLpuPRuk8HTCkyrZq8Xb9/fu2UbBP2820K0dkyi0PT56VACyVyZ/b1Yqapeu81ICmhnF+NwmPS4vJbEmaUEQok80oUyrq5nML2WgkHHI1eqbOnY2YjdiDGVODzggEUU8Mq1CElwymNU+X7NR7IYdBGNNIHnML6Fbki7d1Pt/KdDCZxejDMNmm7ir+lQAuFkAXqBw2Ikw9ZagGj6AIRxsRutsABwdMiJdKGMy2tJnF6MMQ2qfKnM1ox+h2nLNZ9+96F+co7ryQLGvRZ4rOK2YxSfw0jW7FgOqUK5KwFkej57fVYIPIeNVIFp2dB1zq0ohChlFroFHR9e5qSbZOylbpJGK/EcHGaioTTiMePuYRce3deDA/32XXk3Gr+r127ApN/OWHfbiX93Eu8QHcAs00PbKsFD57URtwV7fTboVDS7pkMJvDYbpd2B8MgmF9vVntvpNV5v3DXoAM+BtVne6dAYPLcYqH2f3ahDqlDGoX5HUzWpMQu8VUXuelR+OsU9Iy785UvzmA4lcdegk6nvb1VdUJ5zoy3kA+OGOovIdtdC6aCTfhtMegE2ox5VScIPpiN4YMwDj0XE/GYGBXMVJTnw98qL7HbdKRwmPfb7bViN5RBOFVCpSrh72NXkHrOd52LQCeixGbASz2LUY0apImFYdmLYbqZ83GthopytAk56bY/s7WE/241NCw0SdDyHid5mi0xgZ+yHgM+K5+UWv8f27TxjT+/DT94ziG9fWMVyNIeVeB6iwOO9h/3IlSq4FErioQkPXpqJwNZB65xyHLw2l8e4lzgO0YopAFgNAvrsxDkkkSt2VYXvFoz1Jgfc414rPvfi/HVbpG8WbZfCpBdg0PGIZ4vMnrvRmdDvNILngKVYllkhd6rBcyNB57uHxj1wW4iTloSdWZHeCJyYCsPvMOJSKImDAw44zSI4jttRC+KJqTCr8Br1Anx2Iww6HlfDaegFoeuKfqegx1EK7t7oCqay2j7RA2QKFfTaDPgfHz3W1XHsJuIgtRzLsWJJOJWH32GEx2qALtfaqUYJOi5/9NgAm1foZkO5qfPtQoKHztHK9qd2axX9/Nv39zI9FDUR/T29VpxaiEHgObjMIniV8akTeFgNOmxmipAkCSfnYnCa9eixGuA06TC1msQLVzdYS0o3LVrXAzqBx31jbrwyG5EdRJ0A6t+vcKoAn82Ae0acMOuJXoqo41vOKd3GNdR5ccRjxrWNDNwWEUZ9s+15q9/1Wg04OuTEyfkYHprwsjhrKUpMFT77iXvBt3CT6nbtoUK1f/PKAvKlKh4Yc7Pz2NNrxRsLMfTaDB0xPCb9DvynDx3CK7MRFMpV6HX8tlypKOhcRw0bum2hovciuJbG5bUkBJ4kmhoZPJF0EZvpImNDdAvl+LAbdVhL5PG+I36cnI91xTpSxmKiwIPjuF1xZWr3XU8o9Oy2G/9uN94ZdJmRKVaQyBGB9I1Uoa1AOz3ne0ZcLc+Zyg747ES/aa/Pinyp2vXepxE3O6ZrBbOow/4+O87IbZZ3OrQEzy0GGgjt89mQLVZgEgUYpPrFdDuJgZv1wtnlitb/9c0pHPTbsSL3kD4lB/TX+5yVSZcD/TW6MAdS1eu1GyChpk7PgQjM6tsEMK2g3CBk8mV4rSIsBh2GXCaMyE4Lu6F7NOA0YSWeQyhBWDwBnw2rify2E3ccxyHgs+KV2SgWIhl85vtXmujF16uVJlskVVGzKOBPnr3WtpVpO6AVVQAIhlPbpp9SKO+DSScg4LNiI13EPSNOiDoeOoFDOJnH++/yI5OvMMv5Ttup6Pufypfx6mwEkCRYDDpkSxUcG3Li4mqSOVhdryQt3cgZ9TwSOUJrVdvM3CngOI4lOFsxePQCD7+DOGlREcxbMcGjnO9o+8f1Cnp3EyvxHPb5iOXtEdmtZqctiCvxHEa8ZrxlwoN9fTYUShVVnR7l8XajEHIzCyoUys0YAMxHMhj1qCfQ24G266zEc0gXyjDoeGymi3h0L7FO7kasuZX2RCiRBQeiw/TZ52fxnsN9uzZHd7JWdVIwY3FZnxXxTE1HRu3a7UYd9AKHF2c2AQ54bJ8Xl9fSSBUqmOix4uJqEs9ME+2QF2c2safXclPn1YfGPXguSIogynlC+X51qxvSTVxD7+3+PjvmI1nYjDoY9UJH44r+7tEhJ84uxfHqbATHD/VhOZbF81cJa7dVcmc7GPNYYDfq8LXTKwBqwuGkhdGB9VQBo17ynnXybnAch/cc8uNPnpvBkNu8o4SfjyV4CPtiuy1UjwV68IPpMF65FoHNqGNOqcQZlFikx7LbZ/AAtfFRKFfx2RdmkS9VwXHoysFWmUg36rnrmki/FVrJmf5mLMdMMAZUDFEoOjln5fuzkSrAoBOgF7rf+9xOODrsxLfPrqJalXZ1brgVoSV4bjHQQOgte7zsZ2qL6a2aIVViOpTA18+swiwKiGWIvXkiT4KjcrV6Q7QhWk1yx4YcSOXL2Ntrw7HhPPLFCiRIMIs6GFQE+TqBMrAHRxbv+8dcTUJjO8WAy4SXZzaRyJXwK4/vwa++K7Cj402HEsyVrM9hVBWQux6blulQAs9cXm/7vTu9rprricio3zs5fuMz1gk8njhQE5NL5cvw2Y1IFyqslY3+vJNnr9wI3T3sxOlF4ixiN+qYg9X1fmfoou8w6WHUCXCZ1UXZ7yQ4zaKswaPO4AEIRXopmoXfYYSO57YtMnk9cSsEotsBHfdKi9adMgTpMWkLKox6VZ0eJXZrXb3Z6zOthNPWh7nNLN6+v6fdr7TEgMuElVgOB/x2PBtcBwdgn+xQ2OkzajUu7xt14rMvzMOgJ4YJyXx5V+foTtaqTj6vFPunWwK1a58OJVCoVLEQySJbrGBPjwVDbguqVeBKOA2rQYDbUnM444CbqpEFAH0Kl8fzy/GmIsj1nlPovd3Ta8Uvesahk10fu10vjw258Pp8FPs3M4RFlS4ybcPdAi+LwZ9ejMNm1OGvX1nA8UM+dh4/emyAfbbzd6MP/+vZGSxEsrhn2LXl51uB6ldRBk87d6V2eEyOW0jysSYWLfAcnGYR85tZVKpSVzbsrfCOyV782Q+v4dvnQnCZRcbu6wQ3MpF+KyTtqbvtcizL3Au3SrJ2Oq8F+mzMlORGtxPfaBwbcuILry3i2kYae33Nup93ErQEzy2G2zVAVwNlAgw4Tbi2kYbdpEdZ1uLptRmRKzULfu02Wk1yANh9DvisODkXgwTg2LB1R5UAGthfL6Gx6VACa/E8YnIryZGhnd+7E1NhjHotmFpNwmNpTS/e7U0LbUcEiDXodoXr2h1/0GmCXuAw3mPdteNv9Yx/5qFh/GCaVES7fYeV7z+11L4STjNL7RsRVNBF/90H+0AdXO/0Rd9t0SOWLSGRVRdZBghF+qWZTYx4Cui1GW7J6s+tEIhuB9dj3buT1tJuUWvRKiCVL2EzXWMWdIsBpwmnFmJ498Fe/PkLs/A7iE5NN+tkq3FJYwSrQbcj5zK17+vm97f6fCcuqbS1VcdzyBYr0PEcOHC4vJbEAb8Dv/T4hNzKY8O3z4eQK5G2uXYC0Ncb06EEnr28Ab3AoVSRwHPcDSnuKNGt5ECr3z065MDZ5Then4/i+EHiPKtsKd8NTIcSyBSJ+PCIx8yKUu+c7Nn2mn+w344+uwFryQKmVhOqDOpO4DaL0AucIsGzvQTMoMuMEY8ZC5EsErlS3fm4LSKurhNds27aqVrh2JATTrMem+kC9iqSSZ3iRibSb3bSfkihA2g3koLsVjFZt/Pam2GNPCYnUc8sxrUEj4Ybi9s1QFcDZQIcGXJiNZ5DtlBBsVKFjuegFzjohJ2L9HaCVpNcK42frXR9Ov3O68F4efL5OegEsrHstRnwnfNr6JftB7eLlXgOY14L/tn9w6zqsxsCyp18r99hxI8c9nfcyrSd43/8gRGYDcKuH7/dMx7vsW7r2TcesxNL7d3GTgLu2xUus4hLq0kkciVZk0do+syQ24RwKo+lWLZji/SbgZsdiG4H12O+vJPW0m5h0AmwGnSIZIpYkJ1XxrbRogUQBk8iV8JGuoRssYK37PEitI2WYLVx+bkX5+F3GPGOSR/0Ddo8txI6GUs0WWURdQAKuG/MjYDPVuc0Sa/3A0f6kciVthSAvt6gVuPUsrpPFuq93sUdJXbynjb+7mSfDeeWE/jSG0votRmwmc6zItJu4MRUGEMuE4JrKQw6zSwheSWc2fY1XF5LwmrUA8kCemyGbTOZeZ5Dr2x3D2w/ATMdSkAvCz27FSYRn3p0DG6ziDNLRKB2Nxg8OoHH3cNOPHN5A/FsadvJrTcD7CYdbLIemsNUBsd17zrYiDfjGlkok1bjP/3hNazEc3f0eNMSPLcgbscAXQ2UCTDgNOHggAOFUgVVSYJBJ1xXQbROcb3v8/VgvDhMepRlMejDA44m4ebtgD4nZSB0IxgbSmr29fjedroPu4VWz3gnz/5mv/9vxkXfZRYRlUWWnSrtWQCpbEoSaWPYbeq/husz7m/2u3QzQXWl5jYzALAjBg8A/MmzMzDqefzhh4+wFoGdgs7RfYqE6a3KFtxqLNGCgt1IGEnHhpwQeK4ueUOv120RGcvqZl4vPeeAz4ZkrgSrqAO4Gy/Kvhvr5XQogT959hqC4RTypSr29Rp3vf1tJZ7D/j4bBLlVC6glJLd7DSemwtjvs2FmPc3c5ejPuz1en4MkeEx6AWZxe9u7E1NhTPRYMLOehtVYz6pzW0SUKoTWuxsMHqU8gMOs3/U2/TsJHMdhQHbSShdE+GxG5p67E7yZ1sjpUAKffWEeHqvIWJh38njTbNI1XDco7TLHvWak82VkChXs9VluqFX7nQKl7eFjgR7s99t2pfq3Hbv23cD1/t6bdV13Aib9u2vHe6vDZSGVyli2qNqeBQBDcg98vlSt25Bq0HArgiZ4FiJygmebDJ5ShbSkvDYXxbDLjOVYdtfO8U6aowecJqTyZbxlrwcff2AYeoFvSt7catdLzznQZ8PHHxwBz3O3bIJtK1BHoAfHiI7Xfr+trZ37djDgNCFdqGB/n53pxez0fhGmrgUfu28IE976pFG3oOuS17b95MtKPIeJHiuGXCa25tHzcSl053ZDg466j/EcYJVbNHf7md1JGHSZsRzLYSWWQ79Ti0G6BS2SD7nMiGaLsMout3fqeNMSPBquGygTgLBOgAfG3Xho3I1ShWhc3KlZ0+sFGowJPIcjg07o+OYAcjtQPqdQIn/Dns31/t6bdV0abj+4zXpIErAQybZM8AwqHE5u5RYtDRoAsgGLpIuY28yiz25kbjjdYDqUwPcurbO/D7nNePL5OUyHErtyjnfSHE2TN5lCBXodr5q8udWu91ZLOO0EtAB216ADH7t/CH6Hadfb367H/aJxXa/dyHTdthvXUSetnbRPDThNyBYr+LG7BzEiJ4Xp+bgttbXRtQsJnpV4Dl6rAR88OoC7R5wAbs0WzVsFgy7i5LkSz2HAtX3HtTcr6BxxdMiJJyZ9EPib2yJ7vaG1aGm4rngz0f+uN66nINrNek63W5uchjsTNFhdiGQx3qPOdOizE/esclW6JS3SNWhQwm0RcXE1iflIBiOe7W0GTkyF4bMZIHBEN2/Sb0e6sLuiwHfKHN1pa+utdL13UjuusiW711Zztdztluzdvl+7GddRR7TtOmhtdT4vX4sCIAVaqtOzE9BnprSHv10ZZDcCgy4T0oUyMsUy3nvYf7NP57aDco6Y9NsB3NnjTUvwaNBwm+BOCsY0aLiV4DKTgLhYqbZk8FwJJ2ExENHpl2Y2cbDfpr17Gm5ZuK2kRatUqeKJA9tjGFCNlgGXCT674Y6veO4Ut1LyplPcjueshhvlCLTb92s347pKlejjzKynty1Y3O58Lq/tnoMW8OZ0cdoJqFW6JAEDWotW13izjTctwaNBw22EOyUY06DhVgJN8ACA09wcvFIHO7NsDy1J0h0tzqfh9ofHIqJYqSKSKe5IYDmRK+FHjw2wn93JFU8Nty9u5wLYbsR106EEXr4WAQB4rNt342p3Pm659Ws39Hfo99yuz+xmYFDRljXg0ubgbvFmG29agkeDBg0aNLyp4VJoC6gxeKg4n9siIpTIo89hRL5U3dVWFQ0adhNuhQ7HdgWW32wVTw23N97MBbATU2H0yk6oFlHYkRtXKySyRQDAajy/a5bmb+Zn1i2yxRL789PT6/A7jNq96xJvpvGmiSxr0KBBg4Y3NdyKiqRagoeK8416LBjxmCEKvNaqouGWhrLKPrZNBs+tJgqsQYMGddB2ysf29SDQZwOwu4LF06EE/vFCCADgUlia75bguob2mA4l8IXXlqAXiBC3JEG7/xraQmPwaNCgQYOGNzVMegGijkexXIXT3Jzgoa0qe3qt2NNLrGyTuZLWqqLhloUyabldkWXgzVXx1KDhdgVdo44MOdnPdrOd8sRUGD1WmSEk20vTn2vzw/XHiakwnGYRdpMe6XwZPTbShqfdfw2toDF4NGjQoEHDmxocx8Eta++oMXjuJDthDW8OxLIFACR5+afPXdMqvRo03MG43mvUSjwHl0XE++7y4/AASShoLNYbB8oi9lhElrzX7r+GdtASPBo0aNCg4U0PapXuNDULSGqtKhpuJ0yHEvjqqRUApFVLa6fQoOHOxvVeowacJqTyZUz0WGExkOYPTXD9xoHe/7fv78UHjvQD0O6/hvbQWrQ0aNCgQcObGtOhBFJ5ImD41dPL0AloCoy1VhUNtwtOTIXhsRogCjxcFlFrp9Cg4U2A67lGaYLrNxe1+6+HzahjDC3t/mtoBY3Bo0GDBg0a3rSgFug6nogXFsoVje2g4bYGpfN/4Gg/7h9zA9Do/Bo0aNg+NBbrzYV2/zV0C43Bo0GDBg0a3rSgFuh2ox4cgB6rAcl8WWM7aLhtQQVXlfR9jc6vQYOGnUBjsd5caPdfQzfYUYInEAj8PoD3AygCuAbgnweDwbj8b78O4JMAKgD+VTAYfGpnp6pBgwYNGjTsLqi9bKDPBpMogOM4je2g4baG1k6hQYMGDRo0vHmx0xat7wM4FAwG7wJwBcCvA0AgEDgA4KMADgI4DuBPAoGAsMPv0qBBgwYNGnYVVLyw32nCg+MeABrbQcPtDY3Or0GDBg0aNLx5sSMGTzAY/J7ir68C+An5zx8E8MVgMFgAMBcIBGYA3A/glZ18nwYNGjRo0LCb0NgOGu5EaHR+DRo0aNCg4c2J3RRZ/jkA35X/PABgSfFvy/LPNGjQoEGDhlsGGttBgwYNGjRo0KBBw52CLRk8gUDgBwD6VP7pN4PB4Dflz/wmgDKAv9vOSZw6dWo7v6ZBgwYNGjTsCh51A3ADQArZ1RROrd7kE9KgQYMGDRo0aNCgoUtsmeAJBoPvbPfvgUDgZwG8D8A7gsGgJP94BcCQ4mOD8s+acM8993AdnakGDRo0aNCgQYMGDRo0aNCgQYMGVezURes4gH8P4G3BYDCr+KdvAfhCIBD4QwD9APYCeH0n36VBgwYNGjRo0KBBgwYNGjRo0KBBHZwkSVt/qgVk8WQDgIj8o1eDweAvyv/2myC6PGUA/yYYDH5X/SgaNGjQoEGDBg0aNGjQoEGDBg0adoIdJXg0aNCgQYMGDRo0aNCgQYMGDRo03HzsqEVrtyG3fP0RAAHAZ4PB4H+9yaek4TZGIBCYB5ACUAFQDgaD9wYCATeALwEYBTAP4MPBYDB2s85Rw+2BQCDwFyBaY+vBYPCQ/DPVsRQIBDiQeey9ALIAfjYYDJ6+Geet4dZGi3H1OwB+AcCG/LHfCAaD/yT/268D+CTInPavgsHgUzf8pDXc8ggEAkMA/hqAD4AE4MlgMPhH2pylYadoM7Z+B9q8pWGbCAQCRgDPg3SF6AB8JRgM/nYgEBgD8EUAHgCnAPx0MBgsBgIBA8g4vAeki+QjwWBw/qacvIZbGm3G1l8BeBuAhPzRnw0Gg2fvlPVwN23Sd4RAICAA+GMA7wFwAMDHAoHAgZt7VhruADweDAaPBoPBe+W//xqAp4PB4F4AT8t/16BhK/wVgOMNP2s1lt4Doju2F8CnAPzpDTpHDbcf/grN4woAPiPPW0cVm6QDAD4K4KD8O38ir5saNDSiDOD/DAaDBwA8COBfyuNHm7M07BStxhagzVsato8CgLcHg8EjAI4COB4IBB4E8N9AxtUeADGQRCHk/8fkn39G/pwGDWpoNbYA4N8p5qyz8s/uiPXwlknwALgfwEwwGJwNBoNFkIztB2/yOWm48/BBAJ+X//x5AB+6eaei4XZBMBh8HkC04cetxtIHAfx1MBiUgsHgqwCcgUDAf0NOVMNthRbjqhU+COCLwWCwEAwG5wDMgKybGjTUIRgMhmjFMRgMpgBMAxiANmdp2CHajK1W0OYtDVtCnnvS8l/18n8SgLcD+Ir888Y5i85lXwHwDpl5oUFDHdqMrVa4I9bDWynBMwBgSfH3ZbRfNDRo2AoSgO8FAoFTgUDgU/LPfMFgMCT/eQ2EZqxBw3bQaixpc5mGneJXAoHA+UAg8BeBQMAl/0wbVxq6RiAQGAVwDMBr0OYsDbuIhrEFaPOWhh0gEAgIgUDgLIB1AN8HcA1APBgMluWPKMcOG1fyvydA2rg0aGhC49gKBoN0zvpdec76jNz2B9whc9atlODRoGG38dZgMHg3CN3uXwYCgUeV/xgMBiW0z+Jq0NARtLGkYRfxpwAmQKjEIQB/cFPPRsNti0AgYAXwVRAn06Ty37Q5S8NOoDK2tHlLw44QDAYrwWDwKIBBEJbX/pt7RhruFDSOrUAgcAjAr4OMsfsAuAH8h5t3hruPWynBswJgSPH3QflnGjRsC8FgcEX+/zqAr4MsGGFKtZP/v37zzlDDbY5WY0mbyzRsG8FgMCwHI1UAf45aO4M2rjR0jEAgoAfZgP9dMBj8mvxjbc7SsGOojS1t3tKwWwgGg3EAzwJ4CKQ9hhoCKccOG1fyvztAxJY1aGgJxdg6LrebSsFgsADgL3GHzVm3UoLnJIC9gUBgLBAIiCCibN+6yeek4TZFIBCwBAIBG/0zgHcBmAIZUz8jf+xnAHzz5pyhhjsArcbStwB8IhAIcLKQW0LRFqFBQ1s09Hr/KMi8BZBx9dFAIGCQnUX2Anj9Rp+fhlsfshbF5wBMB4PBP1T8kzZnadgRWo0tbd7SsBMEAoGeQCDglP9sAvAEiL7TswB+Qv5Y45xF57KfAPCMzErUoKEOLcbWZUWxgwPRdlLOWbf9enjL2KQHg8FyIBD4FQBPgdik/0UwGLx4k09Lw+0LH4CvBwIBgIzzLwSDwROBQOAkgC8HAoFPAlgA8OGbeI4abhMEAoG/B/AYAG8gEFgG8NsA/ivUx9I/gdgrzoBYLP7zG37CGm4LtBhXjwUCgaMg7TPzAP4FAASDwYuBQODLAC6BONn8y2AwWLkJp63h1sdbAPw0gAuy7gAA/Aa0OUvDztFqbH1Mm7c07AB+AJ+XHdZ4AF8OBoPfCQQClwB8MRAI/L8AzoAkFyH//28CgcAMiFHBR2/GSWu4LdBqbD0TCAR6AHAAzgL4Rfnzd8R6yEmSlvDUoEGDBg0aNGjQoEGDBg0aNGi4nXErtWhp0KBBgwYNGjRo0KBBgwYNGjRo2Aa0BI8GDRo0aNCgQYMGDRo0aNCgQcNtDi3Bo0GDBg0aNGjQoEGDBg0aNGjQcJtDS/Bo0KBBgwYNGjRo0KBBgwYNGjTc5tASPBo0aNCgQYMGDRo0aNCgQYMGDbc5tASPBg0aNGjQoEGDBg0aNGjQoEHDbQ4twaNBgwYNGjRo0KBBgwYNGjRo0HCb4/8HEUp4p5Nlj7gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "salesDecompose(store_sales, samples= 365, period = 30 )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7586b70c",
   "metadata": {
    "papermill": {
     "duration": 0.081263,
     "end_time": "2022-05-22T00:56:10.924061",
     "exception": false,
     "start_time": "2022-05-22T00:56:10.842798",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Here, we maked average sales from time initial `2013`. Then we show Partial Correlation of Lags,  noting the most  significant ones.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "38be0546",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T00:56:11.090647Z",
     "iopub.status.busy": "2022-05-22T00:56:11.089875Z",
     "iopub.status.idle": "2022-05-22T00:56:11.544845Z",
     "shell.execute_reply": "2022-05-22T00:56:11.545590Z",
     "shell.execute_reply.started": "2022-05-22T00:37:40.588898Z"
    },
    "papermill": {
     "duration": 0.532687,
     "end_time": "2022-05-22T00:56:11.545839",
     "exception": false,
     "start_time": "2022-05-22T00:56:11.013152",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 83488 entries, 0 to 83487\n",
      "Data columns (total 3 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   date          83488 non-null  datetime64[ns]\n",
      " 1   store_nbr     83488 non-null  int64         \n",
      " 2   transactions  83488 non-null  int64         \n",
      "dtypes: datetime64[ns](1), int64(2)\n",
      "memory usage: 1.9 MB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "PeriodIndex: 1684 entries, 2013-01-01 to 2017-08-15\n",
      "Freq: D\n",
      "Data columns (total 2 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   sales        1684 non-null   float32\n",
      " 1   onpromotion  1684 non-null   float64\n",
      "dtypes: float32(1), float64(1)\n",
      "memory usage: 32.9 KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>sales</th>\n",
       "      <th>onpromotion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1.409438</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>278.390808</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>202.840195</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>198.911148</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>267.873230</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sales  onpromotion\n",
       "date                               \n",
       "2013-01-01    1.409438          0.0\n",
       "2013-01-02  278.390808          0.0\n",
       "2013-01-03  202.840195          0.0\n",
       "2013-01-04  198.911148          0.0\n",
       "2013-01-05  267.873230          0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# transactions per store\n",
    "transactions.info()\n",
    "\n",
    "\n",
    "# average sales \n",
    "average_sales = (\n",
    "    store_sales\n",
    "    .groupby('date').mean()\n",
    "    .squeeze()\n",
    "    .loc[timeInitial:]\n",
    ")\n",
    "\n",
    "\n",
    "plot_pacf(average_sales.sales, lags=15)\n",
    "\n",
    "\n",
    "display(average_sales.info())\n",
    "#ax = average_sales.plot(**plot_params, alpha = 0.5,  title='Averange Sales', ylabel='Items Sold')\n",
    "#ax.legend();\n",
    "\n",
    "average_sales.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d762974e",
   "metadata": {
    "papermill": {
     "duration": 0.071967,
     "end_time": "2022-05-22T00:56:11.695427",
     "exception": false,
     "start_time": "2022-05-22T00:56:11.623460",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The price's Oil from initial time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0e803e52",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T00:56:11.848933Z",
     "iopub.status.busy": "2022-05-22T00:56:11.848183Z",
     "iopub.status.idle": "2022-05-22T00:56:12.116667Z",
     "shell.execute_reply": "2022-05-22T00:56:12.115978Z",
     "shell.execute_reply.started": "2022-05-22T00:37:41.108722Z"
    },
    "papermill": {
     "duration": 0.348769,
     "end_time": "2022-05-22T00:56:12.116813",
     "exception": false,
     "start_time": "2022-05-22T00:56:11.768044",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "oil[timeInitial:].plot()\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "92a62959",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T00:56:12.275060Z",
     "iopub.status.busy": "2022-05-22T00:56:12.274360Z",
     "iopub.status.idle": "2022-05-22T00:56:12.277883Z",
     "shell.execute_reply": "2022-05-22T00:56:12.278347Z",
     "shell.execute_reply.started": "2022-05-22T00:37:41.389080Z"
    },
    "papermill": {
     "duration": 0.088042,
     "end_time": "2022-05-22T00:56:12.278518",
     "exception": false,
     "start_time": "2022-05-22T00:56:12.190476",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "PeriodIndex: 1218 entries, 2013-01-01 to 2017-08-31\n",
      "Freq: D\n",
      "Data columns (total 1 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   dcoilwtico  1175 non-null   float32\n",
      "dtypes: float32(1)\n",
      "memory usage: 46.6 KB\n"
     ]
    }
   ],
   "source": [
    "dfOil = pd.DataFrame(oil)\n",
    "dfOil.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b69cf98a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T00:56:12.430954Z",
     "iopub.status.busy": "2022-05-22T00:56:12.430279Z",
     "iopub.status.idle": "2022-05-22T00:56:12.758504Z",
     "shell.execute_reply": "2022-05-22T00:56:12.757912Z",
     "shell.execute_reply.started": "2022-05-22T00:37:41.406690Z"
    },
    "papermill": {
     "duration": 0.405093,
     "end_time": "2022-05-22T00:56:12.758697",
     "exception": false,
     "start_time": "2022-05-22T00:56:12.353604",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "PeriodIndex: 1218 entries, 2013-01-01 to 2017-08-31\n",
      "Freq: D\n",
      "Data columns (total 2 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   dcoilwtico  1217 non-null   float32\n",
      " 1   ma_oil      1211 non-null   float64\n",
      "dtypes: float32(1), float64(1)\n",
      "memory usage: 56.1 KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfOil['ma_oil'] = dfOil['dcoilwtico'].rolling(7).mean() # calculating mean by oil ( there are NaNs  values)\n",
    "dfOil.fillna(method='ffill', inplace=True)\n",
    "dfOil.info()\n",
    "\n",
    "dfOil.plot()\n",
    "#dfOil.ma_oil[timeInitial:].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96665116",
   "metadata": {
    "papermill": {
     "duration": 0.075164,
     "end_time": "2022-05-22T00:56:12.909532",
     "exception": false,
     "start_time": "2022-05-22T00:56:12.834368",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Model with Oil** #\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "521f0561",
   "metadata": {
    "papermill": {
     "duration": 0.075169,
     "end_time": "2022-05-22T00:56:13.065464",
     "exception": false,
     "start_time": "2022-05-22T00:56:12.990295",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We started create training  data, used `CalendarFourier`. For determine seasonal we take care `DeterministicProcess`. Futher, we add the Holiday days. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5d72ef97",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T00:56:13.219974Z",
     "iopub.status.busy": "2022-05-22T00:56:13.219346Z",
     "iopub.status.idle": "2022-05-22T00:56:14.541885Z",
     "shell.execute_reply": "2022-05-22T00:56:14.541256Z",
     "shell.execute_reply.started": "2022-05-22T00:37:41.790858Z"
    },
    "papermill": {
     "duration": 1.4013,
     "end_time": "2022-05-22T00:56:14.542026",
     "exception": false,
     "start_time": "2022-05-22T00:56:13.140726",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sdate = '2017-06-15'\n",
    "edate = '2017-08-15'\n",
    "y = store_sales.unstack(['store_nbr', 'family']).loc[sdate:] \n",
    "\n",
    "\n",
    "# y = y.join( oil.rename('Oil') , on='date').fillna(method = 'bfill')\n",
    "\n",
    "# Create training data\n",
    "fourier = CalendarFourier(freq='M', order=4)\n",
    "dp = DeterministicProcess(\n",
    "    index=y.index,\n",
    "    constant=True,\n",
    "    order=1,\n",
    "    seasonal=True,\n",
    "    additional_terms=[fourier],\n",
    "    drop=True,\n",
    ")\n",
    "\n",
    "X = dp.in_sample()\n",
    "\n",
    "X1 = X.copy() \n",
    "\n",
    "#holidays = holidays.groupby(holidays.index).first() # Removing duplicated holiday at the same date"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c32ebb6",
   "metadata": {
    "papermill": {
     "duration": 0.075439,
     "end_time": "2022-05-22T00:56:14.697371",
     "exception": false,
     "start_time": "2022-05-22T00:56:14.621932",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Feature Engineering for Holidays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "040d7e2f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T00:56:14.866406Z",
     "iopub.status.busy": "2022-05-22T00:56:14.859754Z",
     "iopub.status.idle": "2022-05-22T00:56:14.991477Z",
     "shell.execute_reply": "2022-05-22T00:56:14.992011Z",
     "shell.execute_reply.started": "2022-05-22T00:37:43.487030Z"
    },
    "papermill": {
     "duration": 0.217344,
     "end_time": "2022-05-22T00:56:14.992184",
     "exception": false,
     "start_time": "2022-05-22T00:56:14.774840",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NewYear</th>\n",
       "      <th>wd</th>\n",
       "      <th>dofw_1</th>\n",
       "      <th>dofw_2</th>\n",
       "      <th>dofw_3</th>\n",
       "      <th>dofw_4</th>\n",
       "      <th>dofw_5</th>\n",
       "      <th>dofw_6</th>\n",
       "      <th>type_Additional</th>\n",
       "      <th>type_Bridge</th>\n",
       "      <th>type_Event</th>\n",
       "      <th>type_Holiday</th>\n",
       "      <th>type_Transfer</th>\n",
       "      <th>type_Work Day</th>\n",
       "      <th>school_season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>False</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-27</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-28</th>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-29</th>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-30</th>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31</th>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            NewYear  wd  dofw_1  dofw_2  dofw_3  dofw_4  dofw_5  dofw_6  \\\n",
       "2013-01-01     True   0       1       0       0       0       0       0   \n",
       "2013-01-02    False   1       0       1       0       0       0       0   \n",
       "2013-01-03    False   1       0       0       1       0       0       0   \n",
       "2013-01-04    False   1       0       0       0       1       0       0   \n",
       "2013-01-05    False   1       0       0       0       0       1       0   \n",
       "...             ...  ..     ...     ...     ...     ...     ...     ...   \n",
       "2017-08-27    False   0       0       0       0       0       0       1   \n",
       "2017-08-28    False   1       0       0       0       0       0       0   \n",
       "2017-08-29    False   1       1       0       0       0       0       0   \n",
       "2017-08-30    False   1       0       1       0       0       0       0   \n",
       "2017-08-31    False   1       0       0       1       0       0       0   \n",
       "\n",
       "            type_Additional  type_Bridge  type_Event  type_Holiday  \\\n",
       "2013-01-01                0            0           0             1   \n",
       "2013-01-02                0            0           0             0   \n",
       "2013-01-03                0            0           0             0   \n",
       "2013-01-04                0            0           0             0   \n",
       "2013-01-05                0            0           0             0   \n",
       "...                     ...          ...         ...           ...   \n",
       "2017-08-27                0            0           0             0   \n",
       "2017-08-28                0            0           0             0   \n",
       "2017-08-29                0            0           0             0   \n",
       "2017-08-30                0            0           0             0   \n",
       "2017-08-31                0            0           0             0   \n",
       "\n",
       "            type_Transfer  type_Work Day  school_season  \n",
       "2013-01-01              0              0              0  \n",
       "2013-01-02              0              0              0  \n",
       "2013-01-03              0              0              0  \n",
       "2013-01-04              0              0              0  \n",
       "2013-01-05              0              1              0  \n",
       "...                   ...            ...            ...  \n",
       "2017-08-27              0              0              1  \n",
       "2017-08-28              0              0              1  \n",
       "2017-08-29              0              0              1  \n",
       "2017-08-30              0              0              1  \n",
       "2017-08-31              0              0              1  \n",
       "\n",
       "[1704 rows x 15 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calendar = X.join(hol)\n",
    "calendar = pd.DataFrame(index = pd.date_range('2013-01-01', '2017-08-31')).to_period('D')\n",
    "\n",
    "calendar['NewYear'] = (calendar.index.dayofyear == 1)\n",
    "\n",
    "calendar['dofw'] = calendar.index.dayofweek # Weekly day\n",
    "calendar['wd'] = True\n",
    "calendar.loc[calendar.dofw > 4, 'wd'] = False # not work day\n",
    "\n",
    "calendar = calendar.join(hol) # Joining calendar with holiday dataset\n",
    "calendar['dofw'] = calendar.index.dayofweek # Weekly day\n",
    "calendar['wd'] = 1\n",
    "calendar.loc[calendar.dofw > 4, 'wd'] = 0 # If it's saturday or sunday then it's not Weekday\n",
    "calendar.loc[calendar.type == 'Work Day', 'wd'] = 1 # If it's Work Day event then it's a workday\n",
    "calendar.loc[calendar.type == 'Transfer', 'wd'] = 0 # If it's Transfer event then it's not a work day\n",
    "calendar.loc[calendar.type == 'Bridge', 'wd'] = 0 # If it's Bridge event then it's not a work day\n",
    "calendar.loc[(calendar.type == 'Holiday') & (calendar.transferred == False), 'wd'] = 0 # If it's holiday and the holiday is not transferred then it's holiday\n",
    "calendar.loc[(calendar.type == 'Holiday') & (calendar.transferred == True), 'wd'] = 1 # If it's holiday and transferred then it's not holiday\n",
    "calendar = pd.get_dummies(calendar, columns = ['dofw'], drop_first = True) # One-hot encoding (Make sure to drop one of the columns by 'drop_first = True')\n",
    "calendar = pd.get_dummies(calendar, columns = ['type']) # One-hot encoding for type holiday (No need to drop one of the columns because there's a \"No holiday\" already)\n",
    "calendar.drop(['locale', 'locale_name', 'description', 'transferred'], axis = 1, inplace = True) # Unused columns\n",
    "\n",
    "\n",
    "school_season = [] # Feature for school fluctuations\n",
    "for i, r in calendar.iterrows() :\n",
    "    if i.month in [4, 5, 8, 9] :\n",
    "        school_season.append(1)\n",
    "    else :\n",
    "        school_season.append(0)\n",
    "calendar['school_season'] = school_season\n",
    "\n",
    "calendar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "87ee9bac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T00:56:15.154210Z",
     "iopub.status.busy": "2022-05-22T00:56:15.153183Z",
     "iopub.status.idle": "2022-05-22T00:56:15.164311Z",
     "shell.execute_reply": "2022-05-22T00:56:15.164870Z",
     "shell.execute_reply.started": "2022-05-22T00:37:43.631068Z"
    },
    "papermill": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#X = X.join( oil.rename('Oil') , on='date').fillna(method = 'bfill')\n",
    "\n",
    "\n",
    "oilInitial = dfOil.loc[sdate:] \n",
    "\n",
    "\n",
    "fourier = CalendarFourier(freq='M', order=4)\n",
    "dpOil = DeterministicProcess(\n",
    "    index=oilInitial.index,\n",
    "    constant=True,\n",
    "    order=1,\n",
    "    seasonal=True,\n",
    "    additional_terms=[fourier],\n",
    "    drop=True,\n",
    ")\n",
    "\n",
    "Xoil = dpOil.in_sample()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aff49d13",
   "metadata": {
    "papermill": {
     "duration": 0.076772,
     "end_time": "2022-05-22T00:56:15.319196",
     "exception": false,
     "start_time": "2022-05-22T00:56:15.242424",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Missing Values Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "992fc6ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T00:56:15.479331Z",
     "iopub.status.busy": "2022-05-22T00:56:15.478319Z",
     "iopub.status.idle": "2022-05-22T00:56:15.483497Z",
     "shell.execute_reply": "2022-05-22T00:56:15.484376Z",
     "shell.execute_reply.started": "2022-05-22T00:37:43.653182Z"
    },
    "papermill": {
     "duration": 0.088763,
     "end_time": "2022-05-22T00:56:15.484596",
     "exception": false,
     "start_time": "2022-05-22T00:56:15.395833",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num_null:  0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "missingValueCount = X1.isnull().sum()\n",
    "print('Num_null: ' , missingValueCount.sum() )\n",
    "#X2 = X2.dropna()\n",
    "#X2 = X2.fillna(0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acb9f996",
   "metadata": {
    "papermill": {
     "duration": 0.076617,
     "end_time": "2022-05-22T00:56:15.638783",
     "exception": false,
     "start_time": "2022-05-22T00:56:15.562166",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Add new columns to train set. From Lag's graph, we consider seven Lags. While Oil, we saw that three Lags are enough."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "22cb1d58",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T00:56:15.798216Z",
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     "shell.execute_reply": "2022-05-22T00:56:16.074653Z",
     "shell.execute_reply.started": "2022-05-22T00:37:43.664854Z"
    },
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     "exception": false,
     "start_time": "2022-05-22T00:56:15.715516",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "calendar['Oil'] = dfOil['ma_oil']\n",
    "\n",
    "\n",
    "_ = plot_pacf(calendar.Oil, lags = 12) # Lagplot oil price (Feature Engineering)\n",
    "\n",
    "# first \n",
    "n_lags = 3\n",
    "for l in range(1, n_lags + 1) :\n",
    "    calendar[f'oil_lags{l}'] = calendar.Oil.shift(l)\n",
    "\n",
    "\n",
    "    \n",
    "#X2.Oil.fillna(value = oil.mean() , inplace=True ) # drop values NaNs\n",
    "#X2.fillna(method='ffill') # drop values NaNs\n",
    "\n",
    "#X2['salesLag1'] = average_sales.sales.shift(periods=1, freq='D')\n",
    "#X2['salesLag2'] = average_sales.sales.shift(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e7adeee4",
   "metadata": {
    "execution": {
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    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X1 = X1.join(calendar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "85c51829",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T00:56:16.410991Z",
     "iopub.status.busy": "2022-05-22T00:56:16.410261Z",
     "iopub.status.idle": "2022-05-22T00:56:16.417073Z",
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    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X1['Onpromotion'] = average_sales['onpromotion']\n",
    "X1['salesLag7'] = average_sales.sales.shift(7)\n",
    "\n",
    "X2 = X1.join( Xoil , on='date', rsuffix='RT').fillna(method = 'ffill')\n",
    "\n",
    "#X = X.join( oil.rename('Oil') , on='date').fillna(method = 'bfill')\n",
    "\n",
    "#X1.fillna(method='ffill') # drop values NaNs\n",
    "\n",
    "X2.dropna(how='any', axis=0, inplace=True) # drop values NaNs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a7cd4269",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T00:56:16.580715Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dateRange =  X2.index\n",
    "\n",
    "# train set and target train\n",
    "y_train = y.loc[dateRange]\n",
    "Xtrain=X2.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4239ed4",
   "metadata": {
    "papermill": {
     "duration": 0.077859,
     "end_time": "2022-05-22T00:56:16.739150",
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     "start_time": "2022-05-22T00:56:16.661291",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#  **Hybrid Model**\n",
    "\n",
    "First  model to get us seasonal information. Meanwhile, the second model will take care of training the rest of the data.\n",
    "While, second model to  training  rest of train set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd255ff6",
   "metadata": {
    "papermill": {
     "duration": 0.078229,
     "end_time": "2022-05-22T00:56:16.896815",
     "exception": false,
     "start_time": "2022-05-22T00:56:16.818586",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model Bizen's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f72085c5",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from joblib import Parallel, delayed\n",
    "import warnings\n",
    "\n",
    "# Import necessary library\n",
    "from sklearn.linear_model import Ridge, LinearRegression, ElasticNet\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "from sklearn.ensemble import VotingRegressor\n",
    "\n",
    "# SEED for reproducible result\n",
    "SEED = 5\n",
    "\n",
    "class CustomRegressor():\n",
    "    \n",
    "    def __init__(self, n_jobs=-1, verbose=0):\n",
    "        \n",
    "        self.n_jobs = n_jobs\n",
    "        self.verbose = verbose\n",
    "        \n",
    "        self.estimators_ = None\n",
    "        \n",
    "    def _estimator_(self, X, y):\n",
    "    \n",
    "        warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "        \n",
    "        if y.name[2] == 'SCHOOL AND OFFICE SUPPLIES': # Because SCHOOL AND OFFICE SUPPLIES has weird trend, we use decision tree instead.\n",
    "            r1 = ExtraTreesRegressor(n_estimators = 225, n_jobs=-1, random_state=SEED)\n",
    "            r2 = RandomForestRegressor(n_estimators = 225, n_jobs=-1, random_state=SEED)\n",
    "            b1 = BaggingRegressor(base_estimator=r1,\n",
    "                                  n_estimators=10,\n",
    "                                  n_jobs=-1,\n",
    "                                  random_state=SEED)\n",
    "            b2 = BaggingRegressor(base_estimator=r2,\n",
    "                                  n_estimators=10,\n",
    "                                  n_jobs=-1,\n",
    "                                  random_state=SEED)\n",
    "            model = VotingRegressor([('et', b1), ('rf', b2)]) # Averaging the result\n",
    "        else:\n",
    "            ridge = Ridge(fit_intercept=True, solver='auto', alpha=0.75, normalize=True, random_state=SEED)\n",
    "            svr = SVR(C = 0.2, kernel = 'rbf')\n",
    "            \n",
    "            model = VotingRegressor([('ridge', ridge), ('svr', svr)]) # Averaging result\n",
    "        model.fit(X, y)\n",
    "\n",
    "        return model\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        from tqdm.auto import tqdm\n",
    "        \n",
    "        \n",
    "        if self.verbose == 0 :\n",
    "            self.estimators_ = Parallel(n_jobs=self.n_jobs, \n",
    "                                  verbose=0,\n",
    "                                  )(delayed(self._estimator_)(X, y.iloc[:, i]) for i in range(y.shape[1]))\n",
    "        else :\n",
    "            print('Fit Progress')\n",
    "            self.estimators_ = Parallel(n_jobs=self.n_jobs, \n",
    "                                  verbose=0,\n",
    "                                  )(delayed(self._estimator_)(X, y.iloc[:, i]) for i in tqdm(range(y.shape[1])))\n",
    "        return\n",
    "    \n",
    "    def predict(self, X):\n",
    "        from tqdm.auto import tqdm\n",
    "        if self.verbose == 0 :\n",
    "            y_pred = Parallel(n_jobs=self.n_jobs, \n",
    "                              verbose=0)(delayed(e.predict)(X) for e in self.estimators_)\n",
    "        else :\n",
    "            print('Predict Progress')\n",
    "            y_pred = Parallel(n_jobs=self.n_jobs, \n",
    "                              verbose=0)(delayed(e.predict)(X) for e in tqdm(self.estimators_))\n",
    "        \n",
    "        return np.stack(y_pred, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5f5f5a29",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2022-05-22T01:06:19.139279Z",
     "shell.execute_reply": "2022-05-22T01:06:19.138645Z",
     "shell.execute_reply.started": "2022-05-22T00:37:43.966400Z"
    },
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     "duration": 601.984651,
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     "exception": false,
     "start_time": "2022-05-22T00:56:17.154781",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit Progress\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e2781e89ffde4ecd950e6d8cc8420685",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3564 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict Progress\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3b13d5929a2a4d44b8fedd008cc6b562",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3564 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %%time\n",
    "\n",
    "modelB = CustomRegressor(n_jobs=-1, verbose=1)\n",
    "modelRF = RandomForestRegressor(random_state = 42)\n",
    "modelLR = LinearRegression(fit_intercept = True, n_jobs = -1, normalize = True)\n",
    "\n",
    "# The data set is already  divided, test Train\n",
    " # argument to improvemnt considerment performance\n",
    "\n",
    "\n",
    "\n",
    "modelB.fit(Xtrain, y_train)\n",
    "y_fit = modelB.predict(Xtrain)\n",
    "\n",
    "y_resid = y_train - y_fit\n",
    "\n",
    "#model = LinearRegression(fit_intercept=False)\n",
    "# model = LGBMRegressor(random_state = 42) \"not work with  mulpli-columns\"\n",
    "\n",
    "modelRF.fit(Xtrain, y_resid)\n",
    "y_pred = pd.DataFrame(modelRF.predict(Xtrain), index=Xtrain.index, columns=y.columns)\n",
    "\n",
    "\n",
    "yLR_RF = y_pred + y_fit\n",
    "#y_pred_boosted = xgb.predict(X_test) + y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f5c32659",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T01:06:19.306399Z",
     "iopub.status.busy": "2022-05-22T01:06:19.305344Z",
     "iopub.status.idle": "2022-05-22T01:06:19.308051Z",
     "shell.execute_reply": "2022-05-22T01:06:19.307402Z",
     "shell.execute_reply.started": "2022-05-22T00:48:43.064277Z"
    },
    "papermill": {
     "duration": 0.087173,
     "end_time": "2022-05-22T01:06:19.308201",
     "exception": false,
     "start_time": "2022-05-22T01:06:19.221028",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#y_pred = pd.DataFrame(model.predict(Xtrain), index=Xtrain.index, columns=y_train.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa767103",
   "metadata": {
    "papermill": {
     "duration": 0.081624,
     "end_time": "2022-05-22T01:06:19.470544",
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     "start_time": "2022-05-22T01:06:19.388920",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "In the next figure, we see the `Produce` sales at Store 1, compare the value of sales and value prediction by the hybrid model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "be17906a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T01:06:19.637387Z",
     "iopub.status.busy": "2022-05-22T01:06:19.636341Z",
     "iopub.status.idle": "2022-05-22T01:06:20.684895Z",
     "shell.execute_reply": "2022-05-22T01:06:20.683769Z",
     "shell.execute_reply.started": "2022-05-22T00:49:36.088533Z"
    },
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     "duration": 1.132878,
     "end_time": "2022-05-22T01:06:20.685065",
     "exception": false,
     "start_time": "2022-05-22T01:06:19.552187",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalIndex(['AUTOMOTIVE', 'BABY CARE', 'BEAUTY', 'BEVERAGES', 'BOOKS',\n",
       "                  'BREAD/BAKERY', 'CELEBRATION', 'CLEANING', 'DAIRY', 'DELI',\n",
       "                  'EGGS', 'FROZEN FOODS', 'GROCERY I', 'GROCERY II',\n",
       "                  'HARDWARE', 'HOME AND KITCHEN I', 'HOME AND KITCHEN II',\n",
       "                  'HOME APPLIANCES', 'HOME CARE', 'LADIESWEAR',\n",
       "                  'LAWN AND GARDEN', 'LINGERIE', 'LIQUOR,WINE,BEER',\n",
       "                  'MAGAZINES', 'MEATS', 'PERSONAL CARE', 'PET SUPPLIES',\n",
       "                  'PLAYERS AND ELECTRONICS', 'POULTRY', 'PREPARED FOODS',\n",
       "                  'PRODUCE', 'SCHOOL AND OFFICE SUPPLIES', 'SEAFOOD'],\n",
       "                 categories=['AUTOMOTIVE', 'BABY CARE', 'BEAUTY', 'BEVERAGES', 'BOOKS', 'BREAD/BAKERY', 'CELEBRATION', 'CLEANING', ...], ordered=False, dtype='category', name='family')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "PeriodIndex: 62 entries, 2017-06-15 to 2017-08-15\n",
      "Freq: D\n",
      "Columns: 3564 entries, ('sales', '1', 'AUTOMOTIVE') to ('onpromotion', '9', 'SEAFOOD')\n",
      "dtypes: float32(1782), uint32(1782)\n",
      "memory usage: 865.7 KB\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "STORE_NBR = '1'  # 1 - 54\n",
    "FAMILY = 'PRODUCE'\n",
    "# Uncomment to see a list of product families\n",
    "display(store_sales.index.get_level_values('family').unique())\n",
    "\n",
    "ax = y_train.loc(axis=1)['sales', STORE_NBR, FAMILY].plot(**plot_params) # choose single store 1 and family equal produce. \n",
    "ax = yLR_RF.loc(axis=1)['sales', STORE_NBR, FAMILY].plot(ax=ax, color='red')\n",
    "ax.set_title(f'{FAMILY} Sales at Store {STORE_NBR}');\n",
    "\n",
    "y.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86d6bb08",
   "metadata": {
    "papermill": {
     "duration": 0.082222,
     "end_time": "2022-05-22T01:06:20.849989",
     "exception": false,
     "start_time": "2022-05-22T01:06:20.767767",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Metrics\n",
    "\n",
    "It's calculating metrics:\n",
    "\n",
    "- Mean Absolute Error MAE\n",
    "- Root Mean Squared Log Error RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fcd1f9e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T01:06:21.023013Z",
     "iopub.status.busy": "2022-05-22T01:06:21.022315Z",
     "iopub.status.idle": "2022-05-22T01:06:21.041440Z",
     "shell.execute_reply": "2022-05-22T01:06:21.042018Z",
     "shell.execute_reply.started": "2022-05-22T00:49:47.150675Z"
    },
    "papermill": {
     "duration": 0.106701,
     "end_time": "2022-05-22T01:06:21.042192",
     "exception": false,
     "start_time": "2022-05-22T01:06:20.935491",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "Y = y_train.loc(axis=1)['sales', STORE_NBR, FAMILY]\n",
    "Ypred = yLR_RF.loc(axis=1)['sales', STORE_NBR, FAMILY]\n",
    "\n",
    "yfit_lnr = pd.DataFrame(yLR_RF, index = Xtrain.index, columns = y_train.columns).clip(0.)\n",
    "\n",
    "Mae = np.round(mae(Y, Ypred), 3)  \n",
    "#Rmsle =  np.round(msle(Y, Ypred)**0.5, 3 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f31b6586",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T01:06:21.215771Z",
     "iopub.status.busy": "2022-05-22T01:06:21.214775Z",
     "iopub.status.idle": "2022-05-22T01:06:21.783294Z",
     "shell.execute_reply": "2022-05-22T01:06:21.782592Z",
     "shell.execute_reply.started": "2022-05-22T00:49:50.645525Z"
    },
    "papermill": {
     "duration": 0.658465,
     "end_time": "2022-05-22T01:06:21.783441",
     "exception": false,
     "start_time": "2022-05-22T01:06:21.124976",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "family\n",
      "AUTOMOTIVE                    0.213714\n",
      "BABY CARE                     0.098357\n",
      "BEAUTY                        0.204526\n",
      "BEVERAGES                     0.067806\n",
      "BOOKS                         0.035127\n",
      "BREAD/BAKERY                  0.052979\n",
      "CELEBRATION                   0.241397\n",
      "CLEANING                      0.115771\n",
      "DAIRY                         0.046384\n",
      "DELI                          0.058191\n",
      "EGGS                          0.117872\n",
      "FROZEN FOODS                  0.103158\n",
      "GROCERY I                     0.047407\n",
      "GROCERY II                    0.246711\n",
      "HARDWARE                      0.221076\n",
      "HOME AND KITCHEN I            0.200100\n",
      "HOME AND KITCHEN II           0.180967\n",
      "HOME APPLIANCES               0.138018\n",
      "HOME CARE                     0.067400\n",
      "LADIESWEAR                    0.191157\n",
      "LAWN AND GARDEN               0.179591\n",
      "LINGERIE                      0.278788\n",
      "LIQUOR,WINE,BEER              0.244813\n",
      "MAGAZINES                     0.209630\n",
      "MEATS                         0.071527\n",
      "PERSONAL CARE                 0.075631\n",
      "PET SUPPLIES                  0.194252\n",
      "PLAYERS AND ELECTRONICS       0.191976\n",
      "POULTRY                       0.070762\n",
      "PREPARED FOODS                0.100225\n",
      "PRODUCE                       0.051005\n",
      "SCHOOL AND OFFICE SUPPLIES    0.259849\n",
      "SEAFOOD                       0.215354\n",
      "dtype: float64\n",
      "RF+LNR RMSLE : 0.1925253294415422\n"
     ]
    }
   ],
   "source": [
    "y_pred2   = yLR_RF.stack(['store_nbr', 'family']).reset_index()\n",
    "y_target = y_train.stack(['store_nbr', 'family']).reset_index().copy()\n",
    "\n",
    "y_target['sales_pred'] = y_pred2['sales'].clip(0.) # Sales should be >= 0\n",
    "\n",
    "\n",
    "print( y_target.groupby('family').apply(lambda r: np.sqrt(msle(r['sales'], r['sales_pred'] ))) )\n",
    "print('RF+LNR RMSLE :', np.sqrt(  msle(y_train, yfit_lnr) ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f037551",
   "metadata": {
    "papermill": {
     "duration": 0.085901,
     "end_time": "2022-05-22T01:06:21.954901",
     "exception": false,
     "start_time": "2022-05-22T01:06:21.869000",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We seek, there is overfiting. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70fb69e5",
   "metadata": {
    "papermill": {
     "duration": 0.085096,
     "end_time": "2022-05-22T01:06:22.124978",
     "exception": false,
     "start_time": "2022-05-22T01:06:22.039882",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Test set\n",
    "\n",
    "The test set, building with same variables the train set, it has 16 steps.\n",
    "\n",
    "### Missing Values ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5b6c3b16",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T01:06:22.306804Z",
     "iopub.status.busy": "2022-05-22T01:06:22.305973Z",
     "iopub.status.idle": "2022-05-22T01:06:22.367234Z",
     "shell.execute_reply": "2022-05-22T01:06:22.367755Z",
     "shell.execute_reply.started": "2022-05-22T00:49:57.453444Z"
    },
    "papermill": {
     "duration": 0.154535,
     "end_time": "2022-05-22T01:06:22.367954",
     "exception": false,
     "start_time": "2022-05-22T01:06:22.213419",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_test = pd.read_csv(\n",
    "    comp_dir / 'test.csv',\n",
    "    dtype={\n",
    "        'store_nbr': 'category',\n",
    "        'family': 'category',\n",
    "        'onpromotion': 'uint32',\n",
    "    },\n",
    "    parse_dates=['date'],\n",
    "    infer_datetime_format=True,\n",
    ")\n",
    "\n",
    "df_test['date'] = df_test.date.dt.to_period('D')\n",
    "df_test = df_test.set_index(['store_nbr', 'family', 'date']).sort_index()\n",
    "\n",
    "\n",
    "average_sales_test = (\n",
    "    df_test\n",
    "    .groupby('date').mean()\n",
    "    .squeeze()\n",
    "    .loc['2017']\n",
    ")\n",
    "\n",
    "\n",
    "#df_test = df_test.loc['2017'] \n",
    "# Create features for test set\n",
    "X_test = dp.out_of_sample(steps=16)\n",
    "X_testOil = dpOil.out_of_sample(steps=16)\n",
    "\n",
    "X_test.index.name = 'date'\n",
    "\n",
    "X_test1 = X_test.copy()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "X_test1 = X_test1.join(calendar)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "759baa99",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T01:06:22.549201Z",
     "iopub.status.busy": "2022-05-22T01:06:22.548444Z",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "PeriodIndex: 56 entries, 2017-06-15 to 2017-08-31\n",
      "Freq: D\n",
      "Data columns (total 16 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   const          56 non-null     float64\n",
      " 1   trend          56 non-null     float64\n",
      " 2   s(2,7)         56 non-null     float64\n",
      " 3   s(3,7)         56 non-null     float64\n",
      " 4   s(4,7)         56 non-null     float64\n",
      " 5   s(5,7)         56 non-null     float64\n",
      " 6   s(6,7)         56 non-null     float64\n",
      " 7   s(7,7)         56 non-null     float64\n",
      " 8   sin(1,freq=M)  56 non-null     float64\n",
      " 9   cos(1,freq=M)  56 non-null     float64\n",
      " 10  sin(2,freq=M)  56 non-null     float64\n",
      " 11  cos(2,freq=M)  56 non-null     float64\n",
      " 12  sin(3,freq=M)  56 non-null     float64\n",
      " 13  cos(3,freq=M)  56 non-null     float64\n",
      " 14  sin(4,freq=M)  56 non-null     float64\n",
      " 15  cos(4,freq=M)  56 non-null     float64\n",
      "dtypes: float64(16)\n",
      "memory usage: 7.4 KB\n"
     ]
    }
   ],
   "source": [
    "Xoil.info()"
   ]
  },
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   "source": [
    "Preparation Test set."
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   "source": [
    "X_test1['Onpromotion'] = average_sales_test['onpromotion']\n",
    "X_test1['salesLag7'] = average_sales.sales.shift(periods=16, freq='D')"
   ]
  },
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>trend</th>\n",
       "      <th>s(2,7)</th>\n",
       "      <th>s(3,7)</th>\n",
       "      <th>s(4,7)</th>\n",
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       "      <th>type_Work Day</th>\n",
       "      <th>school_season</th>\n",
       "      <th>Oil</th>\n",
       "      <th>oil_lags1</th>\n",
       "      <th>oil_lags2</th>\n",
       "      <th>oil_lags3</th>\n",
       "      <th>Onpromotion</th>\n",
       "      <th>salesLag7</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>48.648572</td>\n",
       "      <td>48.934286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.137486</td>\n",
       "      <td>497.113831</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-17</th>\n",
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       "      <td>47.995714</td>\n",
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       "      <td>4.211560</td>\n",
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       "      <th>2017-08-19</th>\n",
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       "      <td>4.833895</td>\n",
       "      <td>408.568176</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-20</th>\n",
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       "<p>5 rows × 37 columns</p>\n",
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      ],
      "text/plain": [
       "            const  trend  s(2,7)  s(3,7)  s(4,7)  s(5,7)  s(6,7)  s(7,7)  \\\n",
       "date                                                                       \n",
       "2017-08-16    1.0   63.0     0.0     0.0     0.0     0.0     0.0     1.0   \n",
       "2017-08-17    1.0   64.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2017-08-18    1.0   65.0     1.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2017-08-19    1.0   66.0     0.0     1.0     0.0     0.0     0.0     0.0   \n",
       "2017-08-20    1.0   67.0     0.0     0.0     1.0     0.0     0.0     0.0   \n",
       "\n",
       "            sin(1,freq=M)  cos(1,freq=M)  ...  type_Holiday  type_Transfer  \\\n",
       "date                                      ...                                \n",
       "2017-08-16       0.101168      -0.994869  ...             0              0   \n",
       "2017-08-17      -0.101168      -0.994869  ...             0              0   \n",
       "2017-08-18      -0.299363      -0.954139  ...             0              0   \n",
       "2017-08-19      -0.485302      -0.874347  ...             0              0   \n",
       "2017-08-20      -0.651372      -0.758758  ...             0              0   \n",
       "\n",
       "            type_Work Day  school_season        Oil  oil_lags1  oil_lags2  \\\n",
       "date                                                                        \n",
       "2017-08-16              0              1  48.281429  48.648572  48.934286   \n",
       "2017-08-17              0              1  47.995714  48.281429  48.648572   \n",
       "2017-08-18              0              1  47.852857  47.995714  48.281429   \n",
       "2017-08-19              0              1        NaN  47.852857  47.995714   \n",
       "2017-08-20              0              1        NaN        NaN  47.852857   \n",
       "\n",
       "            oil_lags3  Onpromotion   salesLag7  \n",
       "date                                            \n",
       "2017-08-16        NaN    17.137486  497.113831  \n",
       "2017-08-17  48.934286     4.211560  554.729370  \n",
       "2017-08-18  48.648572     7.731201  541.364746  \n",
       "2017-08-19  48.281429     4.833895  408.568176  \n",
       "2017-08-20  47.995714     5.338384  464.520599  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test1.head()"
   ]
  },
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   "source": [
    "X_test2 = X_test1.join( Xoil , on='date', rsuffix='RT').fillna(method = 'ffill')"
   ]
  },
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.299363</td>\n",
       "      <td>-0.954139</td>\n",
       "      <td>0.571268</td>\n",
       "      <td>0.820763</td>\n",
       "      <td>-0.790776</td>\n",
       "      <td>-0.612106</td>\n",
       "      <td>0.937752</td>\n",
       "      <td>0.347305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.485302</td>\n",
       "      <td>-0.874347</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.299363</td>\n",
       "      <td>-0.954139</td>\n",
       "      <td>0.571268</td>\n",
       "      <td>0.820763</td>\n",
       "      <td>-0.790776</td>\n",
       "      <td>-0.612106</td>\n",
       "      <td>0.937752</td>\n",
       "      <td>0.347305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-20</th>\n",
       "      <td>1.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.651372</td>\n",
       "      <td>-0.758758</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.299363</td>\n",
       "      <td>-0.954139</td>\n",
       "      <td>0.571268</td>\n",
       "      <td>0.820763</td>\n",
       "      <td>-0.790776</td>\n",
       "      <td>-0.612106</td>\n",
       "      <td>0.937752</td>\n",
       "      <td>0.347305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            const  trend  s(2,7)  s(3,7)  s(4,7)  s(5,7)  s(6,7)  s(7,7)  \\\n",
       "date                                                                       \n",
       "2017-08-16    1.0   63.0     0.0     0.0     0.0     0.0     0.0     1.0   \n",
       "2017-08-17    1.0   64.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2017-08-18    1.0   65.0     1.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2017-08-19    1.0   66.0     0.0     1.0     0.0     0.0     0.0     0.0   \n",
       "2017-08-20    1.0   67.0     0.0     0.0     1.0     0.0     0.0     0.0   \n",
       "\n",
       "            sin(1,freq=M)  cos(1,freq=M)  ...  s(6,7)RT  s(7,7)RT  \\\n",
       "date                                      ...                       \n",
       "2017-08-16       0.101168      -0.994869  ...       0.0       0.0   \n",
       "2017-08-17      -0.101168      -0.994869  ...       0.0       0.0   \n",
       "2017-08-18      -0.299363      -0.954139  ...       0.0       0.0   \n",
       "2017-08-19      -0.485302      -0.874347  ...       0.0       0.0   \n",
       "2017-08-20      -0.651372      -0.758758  ...       0.0       0.0   \n",
       "\n",
       "            sin(1,freq=M)RT  cos(1,freq=M)RT  sin(2,freq=M)RT  \\\n",
       "date                                                            \n",
       "2017-08-16         0.101168        -0.994869        -0.201299   \n",
       "2017-08-17        -0.101168        -0.994869         0.201299   \n",
       "2017-08-18        -0.299363        -0.954139         0.571268   \n",
       "2017-08-19        -0.299363        -0.954139         0.571268   \n",
       "2017-08-20        -0.299363        -0.954139         0.571268   \n",
       "\n",
       "            cos(2,freq=M)RT  sin(3,freq=M)RT  cos(3,freq=M)RT  \\\n",
       "date                                                            \n",
       "2017-08-16         0.979530         0.299363        -0.954139   \n",
       "2017-08-17         0.979530        -0.299363        -0.954139   \n",
       "2017-08-18         0.820763        -0.790776        -0.612106   \n",
       "2017-08-19         0.820763        -0.790776        -0.612106   \n",
       "2017-08-20         0.820763        -0.790776        -0.612106   \n",
       "\n",
       "            sin(4,freq=M)RT  cos(4,freq=M)RT  \n",
       "date                                          \n",
       "2017-08-16        -0.394356         0.918958  \n",
       "2017-08-17         0.394356         0.918958  \n",
       "2017-08-18         0.937752         0.347305  \n",
       "2017-08-19         0.937752         0.347305  \n",
       "2017-08-20         0.937752         0.347305  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test2.fillna(method='ffill', inplace=True)\n",
    "X_test2.fillna(method='bfill', inplace=True)\n",
    "\n",
    "X_test2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7218ce62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T01:06:23.665171Z",
     "iopub.status.busy": "2022-05-22T01:06:23.664125Z",
     "iopub.status.idle": "2022-05-22T01:10:16.603841Z",
     "shell.execute_reply": "2022-05-22T01:10:16.602951Z",
     "shell.execute_reply.started": "2022-05-22T00:50:39.109240Z"
    },
    "papermill": {
     "duration": 233.035249,
     "end_time": "2022-05-22T01:10:16.604055",
     "exception": false,
     "start_time": "2022-05-22T01:06:23.568806",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict Progress\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9ffbd4b80cdd4ebeba63cf070f08298f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3564 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 3564) \n",
      "  (16, 3564)\n"
     ]
    }
   ],
   "source": [
    "y_pred1 = modelB.predict(X_test2)\n",
    "y_pred2 = modelRF.predict(X_test2)\n",
    "\n",
    "print(y_pred1.shape, '\\n ',  y_pred2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f4646f6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T01:10:16.783420Z",
     "iopub.status.busy": "2022-05-22T01:10:16.781988Z",
     "iopub.status.idle": "2022-05-22T01:10:16.785299Z",
     "shell.execute_reply": "2022-05-22T01:10:16.785890Z",
     "shell.execute_reply.started": "2022-05-22T00:54:56.534344Z"
    },
    "papermill": {
     "duration": 0.093592,
     "end_time": "2022-05-22T01:10:16.786057",
     "exception": false,
     "start_time": "2022-05-22T01:10:16.692465",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_final = y_pred2 + y_pred1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f7209c2",
   "metadata": {
    "papermill": {
     "duration": 0.092933,
     "end_time": "2022-05-22T01:10:16.977116",
     "exception": false,
     "start_time": "2022-05-22T01:10:16.884183",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Creation Submit\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c7a3ba34",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T01:10:17.154053Z",
     "iopub.status.busy": "2022-05-22T01:10:17.153430Z",
     "iopub.status.idle": "2022-05-22T01:10:17.599309Z",
     "shell.execute_reply": "2022-05-22T01:10:17.598693Z",
     "shell.execute_reply.started": "2022-05-22T00:55:00.519775Z"
    },
    "papermill": {
     "duration": 0.534755,
     "end_time": "2022-05-22T01:10:17.599462",
     "exception": false,
     "start_time": "2022-05-22T01:10:17.064707",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_submit = pd.DataFrame(y_final, index=X_test2.index, columns=y.columns)\n",
    "\n",
    "#y_submit = pd.DataFrame(model.predict(X_test2), index = X_test2.index, columns = y.columns).clip(0.)\n",
    "\n",
    "y_submit = y_submit.stack(['store_nbr', 'family'])\n",
    "y_submit = y_submit.join(df_test.id).reindex(columns=['id', 'sales'])\n",
    "y_submit.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d46adb15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T01:10:17.796919Z",
     "iopub.status.busy": "2022-05-22T01:10:17.795933Z",
     "iopub.status.idle": "2022-05-22T01:10:17.800258Z",
     "shell.execute_reply": "2022-05-22T01:10:17.799458Z",
     "shell.execute_reply.started": "2022-05-22T00:55:03.067384Z"
    },
    "papermill": {
     "duration": 0.112941,
     "end_time": "2022-05-22T01:10:17.800446",
     "exception": false,
     "start_time": "2022-05-22T01:10:17.687505",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 2022-05-22 01:10:17.793391\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from datetime import datetime\n",
    "\n",
    "now = datetime.now()\n",
    "print('\\n', now)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f038c4f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-13T00:46:44.238365Z",
     "iopub.status.busy": "2022-05-13T00:46:44.237872Z",
     "iopub.status.idle": "2022-05-13T00:46:44.251793Z",
     "shell.execute_reply": "2022-05-13T00:46:44.250084Z",
     "shell.execute_reply.started": "2022-05-13T00:46:44.238303Z"
    },
    "papermill": {
     "duration": 0.092789,
     "end_time": "2022-05-22T01:10:17.998103",
     "exception": false,
     "start_time": "2022-05-22T01:10:17.905314",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "File submit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bcd9d4e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T01:10:18.187986Z",
     "iopub.status.busy": "2022-05-22T01:10:18.187157Z",
     "iopub.status.idle": "2022-05-22T01:10:18.190478Z",
     "shell.execute_reply": "2022-05-22T01:10:18.190996Z",
     "shell.execute_reply.started": "2022-05-22T00:55:06.017168Z"
    },
    "papermill": {
     "duration": 0.09771,
     "end_time": "2022-05-22T01:10:18.191165",
     "exception": false,
     "start_time": "2022-05-22T01:10:18.093455",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain.columns == X_test2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "120463dd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T01:10:18.372897Z",
     "iopub.status.busy": "2022-05-22T01:10:18.371957Z",
     "iopub.status.idle": "2022-05-22T01:10:18.383222Z",
     "shell.execute_reply": "2022-05-22T01:10:18.382693Z",
     "shell.execute_reply.started": "2022-05-22T00:55:09.016690Z"
    },
    "papermill": {
     "duration": 0.10315,
     "end_time": "2022-05-22T01:10:18.383365",
     "exception": false,
     "start_time": "2022-05-22T01:10:18.280215",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>store_nbr</th>\n",
       "      <th>family</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2017-08-16</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>AUTOMOTIVE</th>\n",
       "      <td>3000888</td>\n",
       "      <td>3.394856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BABY CARE</th>\n",
       "      <td>3000889</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEAUTY</th>\n",
       "      <td>3000890</td>\n",
       "      <td>3.512388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEVERAGES</th>\n",
       "      <td>3000891</td>\n",
       "      <td>2415.352908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOOKS</th>\n",
       "      <td>3000892</td>\n",
       "      <td>0.097871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      id        sales\n",
       "date       store_nbr family                          \n",
       "2017-08-16 1         AUTOMOTIVE  3000888     3.394856\n",
       "                     BABY CARE   3000889     0.000000\n",
       "                     BEAUTY      3000890     3.512388\n",
       "                     BEVERAGES   3000891  2415.352908\n",
       "                     BOOKS       3000892     0.097871"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_submit.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64fd5bce",
   "metadata": {
    "papermill": {
     "duration": 0.088423,
     "end_time": "2022-05-22T01:10:18.559826",
     "exception": false,
     "start_time": "2022-05-22T01:10:18.471403",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Pre-Processing ##"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60d8bd61",
   "metadata": {
    "papermill": {
     "duration": 0.086725,
     "end_time": "2022-05-22T01:10:18.733764",
     "exception": false,
     "start_time": "2022-05-22T01:10:18.647039",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Selection only Giants or Dwarfs ###"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28158c41",
   "metadata": {
    "papermill": {
     "duration": 0.088122,
     "end_time": "2022-05-22T01:10:18.909506",
     "exception": false,
     "start_time": "2022-05-22T01:10:18.821384",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Balance ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bda9e46d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-22T01:10:19.090617Z",
     "iopub.status.busy": "2022-05-22T01:10:19.089666Z",
     "iopub.status.idle": "2022-05-22T01:10:19.093907Z",
     "shell.execute_reply": "2022-05-22T01:10:19.094412Z",
     "shell.execute_reply.started": "2022-05-22T00:48:43.118367Z"
    },
    "papermill": {
     "duration": 0.095317,
     "end_time": "2022-05-22T01:10:19.094581",
     "exception": false,
     "start_time": "2022-05-22T01:10:18.999264",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# dividir set \n",
    "\n",
    "\n",
    "\n",
    "# from sklearn.utils import resample\n",
    "# dfGiantsDownSample = resample(dfGiants, \n",
    "  #                           replace = False, # sample without replacement\n",
    "   #                          n_samples = num_of_dwarf,\n",
    "   #                          random_state =1 \n",
    " #                            )\n",
    "#dfDownSampled = pd.concat([dfGiantsDownSample , dfDwarfs] )\n",
    "\n",
    "#dfBalanced= dfDownSampled.reset_index(drop=True)\n",
    "\n",
    "# dataStar = dfBalanced.sample(frac=1).reset_index(drop=True) # shuffle\n",
    "# dfCopyStar = dataStar.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3848338b",
   "metadata": {
    "papermill": {
     "duration": 0.086406,
     "end_time": "2022-05-22T01:10:19.268117",
     "exception": false,
     "start_time": "2022-05-22T01:10:19.181711",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Visualization ##"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d210198a",
   "metadata": {
    "papermill": {
     "duration": 0.086633,
     "end_time": "2022-05-22T01:10:19.443332",
     "exception": false,
     "start_time": "2022-05-22T01:10:19.356699",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Correlation Linear ##"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6aebab4",
   "metadata": {
    "papermill": {
     "duration": 0.086624,
     "end_time": "2022-05-22T01:10:19.616683",
     "exception": false,
     "start_time": "2022-05-22T01:10:19.530059",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Data Cleaning ##"
   ]
  },
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     "exception": false,
     "start_time": "2022-05-22T01:10:19.703836",
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    "tags": []
   },
   "source": [
    "# **Data Processing** #"
   ]
  },
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    "tags": []
   },
   "source": [
    "### Train and test Set ###"
   ]
  },
  {
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   "id": "584089f4",
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    "execution": {
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   "source": [
    "## Stratified ##\n",
    "#from sklearn.model_selection import StratifiedShuffleSplit\n",
    "#split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "#for train_index, test_index in split.split(data, data[\"variableStratificada\"]):\n",
    "#strat_train_set = housing.loc[train_index]\n",
    "#strat_test_set = housing.loc[test_index]\n",
    "\n",
    "#from  sklearn.impute import SimpleImputer\n",
    "#imputer = SimpleImputer(strategy='median')\n",
    "\n",
    "#from  sklearn.preprocessing import OneHotEncoder\n",
    "#catEncoder = OneHotEncoder()\n",
    "\n",
    "## numerical ##\n",
    "#from  sklearn.pipeline import Pipeline\n",
    "#from  sklearn.preprocessing import StandardScaler\n",
    "#numerical_pipeline = Pipeline([('imputer', SimpleImputer(strategy=\"median\")),('attribs_adder', CombinedAttributesAdder()),])\n",
    "# new_variable_transformed_numerical = num_pipeline.fit_transform(varible_numerica)\n",
    "\n",
    "## numerical and categorial ##\n",
    "#from sklearn import ColumnTransformer\n",
    "#numerical_attribs = list(numerical_columns)\n",
    "#categorical_attribs = list(categorial_columns)\n",
    "#full_pipeline = ColumnTransformer([(\"num\", numerical_pipeline, numerical_attribs),(\"cat\", OneHotEncoder(), categorical_attribs),])\n",
    "#dataTotal_prepared = full_pipeline.fit_transform(dataTotal) "
   ]
  },
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    "tags": []
   },
   "source": [
    "## Target ## "
   ]
  },
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   },
   "source": [
    "## Featuring Engineering ##"
   ]
  },
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    "tags": []
   },
   "source": [
    "##  Pipeline and Featuring Scaling ##"
   ]
  },
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   "source": [
    " # **MODEL** # "
   ]
  },
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    "tags": []
   },
   "source": [
    "#### Models ####"
   ]
  },
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   "source": [
    "### stratified CV ### see pag 136  Geron's book\n",
    "#from sklearn.model_selection import StratifiedKFold\n",
    "#from sklearn.base import clone\n",
    "#skfolds = StratifiedKFold(n_splits=3, random_state=42)"
   ]
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    "tags": []
   },
   "source": [
    "# **Refinament** #"
   ]
  },
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    "tags": []
   },
   "source": [
    "# **Deploy** #"
   ]
  },
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