11_dbns.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "'''Main'''\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os, time, re\n",
    "import pickle, gzip, datetime\n",
    "\n",
    "'''Data Viz'''\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "color = sns.color_palette()\n",
    "import matplotlib as mpl\n",
    "from mpl_toolkits.axes_grid1 import Grid\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "'''Data Prep and Model Evaluation'''\n",
    "from sklearn import preprocessing as pp\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.model_selection import StratifiedKFold \n",
    "from sklearn.metrics import log_loss, accuracy_score\n",
    "from sklearn.metrics import precision_recall_curve, average_precision_score\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score, mean_squared_error\n",
    "\n",
    "'''Algos'''\n",
    "import lightgbm as lgb\n",
    "\n",
    "'''TensorFlow and Keras'''\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Activation, Dense, Dropout\n",
    "from keras.layers import BatchNormalization, Input, Lambda\n",
    "from keras.layers import Embedding, Flatten, dot\n",
    "from keras import regularizers\n",
    "from keras.losses import mse, binary_crossentropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load the datasets\n",
    "current_path = os.getcwd()\n",
    "file = os.path.sep.join(['', 'datasets', 'mnist_data', 'mnist.pkl.gz'])\n",
    "f = gzip.open(current_path+file, 'rb')\n",
    "train_set, validation_set, test_set = pickle.load(f, encoding='latin1')\n",
    "f.close()\n",
    "\n",
    "X_train, y_train = train_set[0], train_set[1]\n",
    "X_validation, y_validation = validation_set[0], validation_set[1]\n",
    "X_test, y_test = test_set[0], test_set[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train:  (50000, 784)\n",
      "Shape of y_train:  (50000,)\n",
      "Shape of X_validation:  (10000, 784)\n",
      "Shape of y_validation:  (10000,)\n",
      "Shape of X_test:  (10000, 784)\n",
      "Shape of y_test:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Verify shape of datasets\n",
    "print(\"Shape of X_train: \", X_train.shape)\n",
    "print(\"Shape of y_train: \", y_train.shape)\n",
    "print(\"Shape of X_validation: \", X_validation.shape)\n",
    "print(\"Shape of y_validation: \", y_validation.shape)\n",
    "print(\"Shape of X_test: \", X_test.shape)\n",
    "print(\"Shape of y_test: \", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create Pandas DataFrames from the datasets\n",
    "train_index = range(0,len(X_train))\n",
    "validation_index = range(len(X_train),len(X_train)+len(X_validation))\n",
    "test_index = range(len(X_train)+len(X_validation), \\\n",
    "                   len(X_train)+len(X_validation)+len(X_test))\n",
    "\n",
    "X_train = pd.DataFrame(data=X_train,index=train_index)\n",
    "y_train = pd.Series(data=y_train,index=train_index)\n",
    "\n",
    "X_validation = pd.DataFrame(data=X_validation,index=validation_index)\n",
    "y_validation = pd.Series(data=y_validation,index=validation_index)\n",
    "\n",
    "X_test = pd.DataFrame(data=X_test,index=test_index)\n",
    "y_test = pd.Series(data=y_test,index=test_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "      <th>8</th>\n",
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       "      <th>782</th>\n",
       "      <th>783</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>50000.0</td>\n",
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       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000739</td>\n",
       "      <td>0.000354</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.000009</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>std</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.022778</td>\n",
       "      <td>0.015422</td>\n",
       "      <td>0.012079</td>\n",
       "      <td>0.007217</td>\n",
       "      <td>0.007181</td>\n",
       "      <td>0.001483</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <th>25%</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.992188</td>\n",
       "      <td>0.992188</td>\n",
       "      <td>0.988281</td>\n",
       "      <td>0.988281</td>\n",
       "      <td>0.992188</td>\n",
       "      <td>0.242188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 784 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0        1        2        3        4        5        6        7    \\\n",
       "count  50000.0  50000.0  50000.0  50000.0  50000.0  50000.0  50000.0  50000.0   \n",
       "mean       0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "std        0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "min        0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "25%        0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "50%        0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "75%        0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "max        0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "           8        9     ...              774           775           776  \\\n",
       "count  50000.0  50000.0   ...     50000.000000  50000.000000  50000.000000   \n",
       "mean       0.0      0.0   ...         0.000739      0.000354      0.000204   \n",
       "std        0.0      0.0   ...         0.022778      0.015422      0.012079   \n",
       "min        0.0      0.0   ...         0.000000      0.000000      0.000000   \n",
       "25%        0.0      0.0   ...         0.000000      0.000000      0.000000   \n",
       "50%        0.0      0.0   ...         0.000000      0.000000      0.000000   \n",
       "75%        0.0      0.0   ...         0.000000      0.000000      0.000000   \n",
       "max        0.0      0.0   ...         0.992188      0.992188      0.988281   \n",
       "\n",
       "                777           778           779      780      781      782  \\\n",
       "count  50000.000000  50000.000000  50000.000000  50000.0  50000.0  50000.0   \n",
       "mean       0.000090      0.000071      0.000009      0.0      0.0      0.0   \n",
       "std        0.007217      0.007181      0.001483      0.0      0.0      0.0   \n",
       "min        0.000000      0.000000      0.000000      0.0      0.0      0.0   \n",
       "25%        0.000000      0.000000      0.000000      0.0      0.0      0.0   \n",
       "50%        0.000000      0.000000      0.000000      0.0      0.0      0.0   \n",
       "75%        0.000000      0.000000      0.000000      0.0      0.0      0.0   \n",
       "max        0.988281      0.992188      0.242188      0.0      0.0      0.0   \n",
       "\n",
       "           783  \n",
       "count  50000.0  \n",
       "mean       0.0  \n",
       "std        0.0  \n",
       "min        0.0  \n",
       "25%        0.0  \n",
       "50%        0.0  \n",
       "75%        0.0  \n",
       "max        0.0  \n",
       "\n",
       "[8 rows x 784 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe the training matrix\n",
    "X_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5\n",
       "1    0\n",
       "2    4\n",
       "3    1\n",
       "4    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the labels\n",
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def view_digit(X, y, example):\n",
    "    label = y.loc[example]\n",
    "    image = X.loc[example,:].values.reshape([28,28])\n",
    "    plt.title('Example: %d  Label: %d' % (example, label))\n",
    "    plt.imshow(image, cmap=plt.get_cmap('gray'))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "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": [
    "# View the first digit\n",
    "view_digit(X_train, y_train, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def one_hot(series):\n",
    "    label_binarizer = pp.LabelBinarizer()\n",
    "    label_binarizer.fit(range(max(series)+1))\n",
    "    return label_binarizer.transform(series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def reverse_one_hot(originalSeries, newSeries):\n",
    "    label_binarizer = pp.LabelBinarizer()\n",
    "    label_binarizer.fit(range(max(originalSeries)+1))\n",
    "    return label_binarizer.inverse_transform(newSeries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create one-hot vectors for the labels\n",
    "y_train_oneHot = one_hot(y_train)\n",
    "y_validation_oneHot = one_hot(y_validation)\n",
    "y_test_oneHot = one_hot(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 1, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show one-hot vector for example 0, which is the number 5\n",
    "y_train_oneHot[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define RBM class\n",
    "class RBM(object):\n",
    "    \n",
    "    def __init__(self, input_size, output_size, \n",
    "                 learning_rate, epochs, batchsize):\n",
    "        # Define hyperparameters\n",
    "        self._input_size = input_size\n",
    "        self._output_size = output_size\n",
    "        self.learning_rate = learning_rate\n",
    "        self.epochs = epochs\n",
    "        self.batchsize = batchsize\n",
    "        \n",
    "        # Initialize weights and biases using zero matrices\n",
    "        self.w = np.zeros([input_size, output_size], dtype=np.float32)\n",
    "        self.hb = np.zeros([output_size], dtype=np.float32)\n",
    "        self.vb = np.zeros([input_size], dtype=np.float32)\n",
    "\n",
    "    def prob_h_given_v(self, visible, w, hb):\n",
    "        return tf.nn.sigmoid(tf.matmul(visible, w) + hb)\n",
    "\n",
    "    def prob_v_given_h(self, hidden, w, vb):\n",
    "        return tf.nn.sigmoid(tf.matmul(hidden, tf.transpose(w)) + vb)\n",
    "    \n",
    "    def sample_prob(self, probs):\n",
    "        return tf.nn.relu(tf.sign(probs - tf.random_uniform(tf.shape(probs))))\n",
    "\n",
    "    def train(self, X):\n",
    "        _w = tf.placeholder(tf.float32, [self._input_size, self._output_size])\n",
    "        _hb = tf.placeholder(tf.float32, [self._output_size])\n",
    "        _vb = tf.placeholder(tf.float32, [self._input_size])\n",
    "        \n",
    "        prv_w = np.zeros([self._input_size, self._output_size], dtype=np.float32)\n",
    "        prv_hb = np.zeros([self._output_size], dtype=np.float32)\n",
    "        prv_vb = np.zeros([self._input_size], dtype=np.float32)\n",
    "        \n",
    "        cur_w = np.zeros([self._input_size, self._output_size], dtype=np.float32)\n",
    "        cur_hb = np.zeros([self._output_size], dtype=np.float32)\n",
    "        cur_vb = np.zeros([self._input_size], dtype=np.float32)\n",
    "        \n",
    "        v0 = tf.placeholder(tf.float32, [None, self._input_size])\n",
    "        h0 = self.sample_prob(self.prob_h_given_v(v0, _w, _hb))\n",
    "        v1 = self.sample_prob(self.prob_v_given_h(h0, _w, _vb))\n",
    "        h1 = self.prob_h_given_v(v1, _w, _hb)\n",
    "        \n",
    "        positive_grad = tf.matmul(tf.transpose(v0), h0)\n",
    "        negative_grad = tf.matmul(tf.transpose(v1), h1)\n",
    "        \n",
    "        update_w = _w + self.learning_rate * \\\n",
    "            (positive_grad - negative_grad) / tf.to_float(tf.shape(v0)[0])\n",
    "        update_vb = _vb +  self.learning_rate * tf.reduce_mean(v0 - v1, 0)\n",
    "        update_hb = _hb +  self.learning_rate * tf.reduce_mean(h0 - h1, 0)\n",
    "        \n",
    "        err = tf.reduce_mean(tf.square(v0 - v1))\n",
    "        \n",
    "        error_list = []\n",
    "        \n",
    "        with tf.Session() as sess:\n",
    "            sess.run(tf.global_variables_initializer())\n",
    "            \n",
    "            for epoch in range(self.epochs):\n",
    "                for start, end in zip(range(0, len(X), \\\n",
    "                        self.batchsize),range(self.batchsize,len(X), \\\n",
    "                                              self.batchsize)):\n",
    "                    batch = X[start:end]\n",
    "                    cur_w = sess.run(update_w, feed_dict={v0: batch, \\\n",
    "                                    _w: prv_w, _hb: prv_hb, _vb: prv_vb})\n",
    "                    cur_hb = sess.run(update_hb, feed_dict={v0: batch, \\\n",
    "                                    _w: prv_w, _hb: prv_hb, _vb: prv_vb})\n",
    "                    cur_vb = sess.run(update_vb, feed_dict={v0: batch, \\\n",
    "                                    _w: prv_w, _hb: prv_hb, _vb: prv_vb})\n",
    "                    prv_w = cur_w\n",
    "                    prv_hb = cur_hb\n",
    "                    prv_vb = cur_vb\n",
    "                error = sess.run(err, feed_dict={v0: X, \\\n",
    "                                _w: cur_w, _vb: cur_vb, _hb: cur_hb})\n",
    "                print ('Epoch: %d' % epoch,'reconstruction error: %f' % error)\n",
    "                error_list.append(error)\n",
    "            self.w = prv_w\n",
    "            self.hb = prv_hb\n",
    "            self.vb = prv_vb\n",
    "            return error_list\n",
    "\n",
    "    def rbm_output(self, X):\n",
    "        \n",
    "        input_X = tf.constant(X)\n",
    "        _w = tf.constant(self.w)\n",
    "        _hb = tf.constant(self.hb)\n",
    "        _vb = tf.constant(self.vb)\n",
    "        out = tf.nn.sigmoid(tf.matmul(input_X, _w) + _hb)\n",
    "        hiddenGen = self.sample_prob(self.prob_h_given_v(input_X, _w, _hb))\n",
    "        visibleGen = self.sample_prob(self.prob_v_given_h(hiddenGen, _w, _vb))\n",
    "        with tf.Session() as sess:\n",
    "            sess.run(tf.global_variables_initializer())\n",
    "            return sess.run(out), sess.run(visibleGen), sess.run(hiddenGen)\n",
    "        \n",
    "    def show_features(self, shape, suptitle, count=-1):\n",
    "        maxw = np.amax(self.w.T)\n",
    "        minw = np.amin(self.w.T)\n",
    "        count = self._output_size if count == -1 or count > \\\n",
    "                self._output_size else count\n",
    "        ncols = count if count < 14 else 14 \n",
    "        nrows = count//ncols\n",
    "        nrows = nrows if nrows > 2 else 3\n",
    "        fig = plt.figure(figsize=(ncols, nrows), dpi=100)\n",
    "        grid = Grid(fig, rect=111, nrows_ncols=(nrows, ncols), axes_pad=0.01)\n",
    "\n",
    "        for i, ax in enumerate(grid):\n",
    "            x = self.w.T[i] if i<self._input_size else np.zeros(shape)\n",
    "            x = (x.reshape(1, -1) - minw)/maxw\n",
    "            ax.imshow(x.reshape(*shape), cmap=mpl.cm.Greys)\n",
    "            ax.set_axis_off()\n",
    "\n",
    "        fig.text(0.5,1, suptitle, fontsize=20, horizontalalignment='center')\n",
    "        fig.tight_layout()\n",
    "        plt.show()\n",
    "        return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Since we are training, set input as training data\n",
    "inputX = np.array(X_train)\n",
    "inputX = inputX.astype(np.float32)\n",
    "\n",
    "# Create list to hold our RBMs\n",
    "rbm_list = []\n",
    "\n",
    "# Define the parameters of the RBMs we will train\n",
    "rbm_list.append(RBM(784,700,1.0,100,200))\n",
    "rbm_list.append(RBM(700,600,1.0,100,200))\n",
    "rbm_list.append(RBM(600,500,1.0,100,200))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[name: \"/device:CPU:0\"\n",
      "device_type: \"CPU\"\n",
      "memory_limit: 268435456\n",
      "locality {\n",
      "}\n",
      "incarnation: 11349992173038295840\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.python.client import device_lib\n",
    "print(device_lib.list_local_devices())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RBM 1\n",
      "Epoch: 0 reconstruction error: 0.075924\n",
      "Epoch: 1 reconstruction error: 0.065297\n",
      "Epoch: 2 reconstruction error: 0.059989\n",
      "Epoch: 3 reconstruction error: 0.055971\n",
      "Epoch: 4 reconstruction error: 0.052860\n",
      "Epoch: 5 reconstruction error: 0.050888\n",
      "Epoch: 6 reconstruction error: 0.049374\n",
      "Epoch: 7 reconstruction error: 0.048545\n",
      "Epoch: 8 reconstruction error: 0.047555\n",
      "Epoch: 9 reconstruction error: 0.046638\n",
      "Epoch: 10 reconstruction error: 0.045534\n",
      "Epoch: 11 reconstruction error: 0.045118\n",
      "Epoch: 12 reconstruction error: 0.044242\n",
      "Epoch: 13 reconstruction error: 0.044123\n",
      "Epoch: 14 reconstruction error: 0.044215\n",
      "Epoch: 15 reconstruction error: 0.043799\n",
      "Epoch: 16 reconstruction error: 0.043111\n",
      "Epoch: 17 reconstruction error: 0.042752\n",
      "Epoch: 18 reconstruction error: 0.042900\n",
      "Epoch: 19 reconstruction error: 0.042479\n",
      "Epoch: 20 reconstruction error: 0.042163\n",
      "Epoch: 21 reconstruction error: 0.042040\n",
      "Epoch: 22 reconstruction error: 0.042250\n",
      "Epoch: 23 reconstruction error: 0.041702\n",
      "Epoch: 24 reconstruction error: 0.041814\n",
      "Epoch: 25 reconstruction error: 0.041653\n",
      "Epoch: 26 reconstruction error: 0.041510\n",
      "Epoch: 27 reconstruction error: 0.041726\n",
      "Epoch: 28 reconstruction error: 0.041134\n",
      "Epoch: 29 reconstruction error: 0.041003\n",
      "Epoch: 30 reconstruction error: 0.040873\n",
      "Epoch: 31 reconstruction error: 0.041462\n",
      "Epoch: 32 reconstruction error: 0.040825\n",
      "Epoch: 33 reconstruction error: 0.040516\n",
      "Epoch: 34 reconstruction error: 0.040799\n",
      "Epoch: 35 reconstruction error: 0.040463\n",
      "Epoch: 36 reconstruction error: 0.040436\n",
      "Epoch: 37 reconstruction error: 0.040732\n",
      "Epoch: 38 reconstruction error: 0.041011\n",
      "Epoch: 39 reconstruction error: 0.040225\n",
      "Epoch: 40 reconstruction error: 0.040045\n",
      "Epoch: 41 reconstruction error: 0.040169\n",
      "Epoch: 42 reconstruction error: 0.040602\n",
      "Epoch: 43 reconstruction error: 0.040165\n",
      "Epoch: 44 reconstruction error: 0.040165\n",
      "Epoch: 45 reconstruction error: 0.039976\n",
      "Epoch: 46 reconstruction error: 0.039995\n",
      "Epoch: 47 reconstruction error: 0.039689\n",
      "Epoch: 48 reconstruction error: 0.040085\n",
      "Epoch: 49 reconstruction error: 0.039764\n",
      "Epoch: 50 reconstruction error: 0.039655\n",
      "Epoch: 51 reconstruction error: 0.039715\n",
      "Epoch: 52 reconstruction error: 0.039947\n",
      "Epoch: 53 reconstruction error: 0.039936\n",
      "Epoch: 54 reconstruction error: 0.039805\n",
      "Epoch: 55 reconstruction error: 0.039739\n",
      "Epoch: 56 reconstruction error: 0.039509\n",
      "Epoch: 57 reconstruction error: 0.039842\n",
      "Epoch: 58 reconstruction error: 0.039607\n",
      "Epoch: 59 reconstruction error: 0.039435\n",
      "Epoch: 60 reconstruction error: 0.039352\n",
      "Epoch: 61 reconstruction error: 0.039341\n",
      "Epoch: 62 reconstruction error: 0.039817\n",
      "Epoch: 63 reconstruction error: 0.039525\n",
      "Epoch: 64 reconstruction error: 0.039686\n",
      "Epoch: 65 reconstruction error: 0.039468\n",
      "Epoch: 66 reconstruction error: 0.039480\n",
      "Epoch: 67 reconstruction error: 0.039439\n",
      "Epoch: 68 reconstruction error: 0.039374\n",
      "Epoch: 69 reconstruction error: 0.039279\n",
      "Epoch: 70 reconstruction error: 0.039125\n",
      "Epoch: 71 reconstruction error: 0.039295\n",
      "Epoch: 72 reconstruction error: 0.039489\n",
      "Epoch: 73 reconstruction error: 0.039337\n",
      "Epoch: 74 reconstruction error: 0.039158\n",
      "Epoch: 75 reconstruction error: 0.039124\n",
      "Epoch: 76 reconstruction error: 0.039432\n",
      "Epoch: 77 reconstruction error: 0.039102\n",
      "Epoch: 78 reconstruction error: 0.038897\n",
      "Epoch: 79 reconstruction error: 0.039116\n",
      "Epoch: 80 reconstruction error: 0.039141\n",
      "Epoch: 81 reconstruction error: 0.039371\n",
      "Epoch: 82 reconstruction error: 0.039344\n",
      "Epoch: 83 reconstruction error: 0.039100\n",
      "Epoch: 84 reconstruction error: 0.038948\n",
      "Epoch: 85 reconstruction error: 0.039160\n",
      "Epoch: 86 reconstruction error: 0.038802\n",
      "Epoch: 87 reconstruction error: 0.038773\n",
      "Epoch: 88 reconstruction error: 0.038910\n",
      "Epoch: 89 reconstruction error: 0.038951\n",
      "Epoch: 90 reconstruction error: 0.038861\n",
      "Epoch: 91 reconstruction error: 0.039312\n",
      "Epoch: 92 reconstruction error: 0.038628\n",
      "Epoch: 93 reconstruction error: 0.038542\n",
      "Epoch: 94 reconstruction error: 0.038663\n",
      "Epoch: 95 reconstruction error: 0.038911\n",
      "Epoch: 96 reconstruction error: 0.038923\n",
      "Epoch: 97 reconstruction error: 0.038881\n",
      "Epoch: 98 reconstruction error: 0.038809\n",
      "Epoch: 99 reconstruction error: 0.038831\n",
      "RBM 2\n",
      "Epoch: 0 reconstruction error: 0.052228\n",
      "Epoch: 1 reconstruction error: 0.046622\n",
      "Epoch: 2 reconstruction error: 0.044128\n",
      "Epoch: 3 reconstruction error: 0.042513\n",
      "Epoch: 4 reconstruction error: 0.041625\n",
      "Epoch: 5 reconstruction error: 0.040963\n",
      "Epoch: 6 reconstruction error: 0.040256\n",
      "Epoch: 7 reconstruction error: 0.039500\n",
      "Epoch: 8 reconstruction error: 0.038791\n",
      "Epoch: 9 reconstruction error: 0.038006\n",
      "Epoch: 10 reconstruction error: 0.037184\n",
      "Epoch: 11 reconstruction error: 0.036244\n",
      "Epoch: 12 reconstruction error: 0.035632\n",
      "Epoch: 13 reconstruction error: 0.034870\n",
      "Epoch: 14 reconstruction error: 0.034091\n",
      "Epoch: 15 reconstruction error: 0.033358\n",
      "Epoch: 16 reconstruction error: 0.032604\n",
      "Epoch: 17 reconstruction error: 0.031790\n",
      "Epoch: 18 reconstruction error: 0.031277\n",
      "Epoch: 19 reconstruction error: 0.030700\n",
      "Epoch: 20 reconstruction error: 0.030144\n",
      "Epoch: 21 reconstruction error: 0.029693\n",
      "Epoch: 22 reconstruction error: 0.029348\n",
      "Epoch: 23 reconstruction error: 0.028814\n",
      "Epoch: 24 reconstruction error: 0.028560\n",
      "Epoch: 25 reconstruction error: 0.028162\n",
      "Epoch: 26 reconstruction error: 0.027893\n",
      "Epoch: 27 reconstruction error: 0.027679\n",
      "Epoch: 28 reconstruction error: 0.027295\n",
      "Epoch: 29 reconstruction error: 0.027048\n",
      "Epoch: 30 reconstruction error: 0.026843\n",
      "Epoch: 31 reconstruction error: 0.026582\n",
      "Epoch: 32 reconstruction error: 0.026382\n",
      "Epoch: 33 reconstruction error: 0.026162\n",
      "Epoch: 34 reconstruction error: 0.025954\n",
      "Epoch: 35 reconstruction error: 0.025881\n",
      "Epoch: 36 reconstruction error: 0.025736\n",
      "Epoch: 37 reconstruction error: 0.025501\n",
      "Epoch: 38 reconstruction error: 0.025407\n",
      "Epoch: 39 reconstruction error: 0.025336\n",
      "Epoch: 40 reconstruction error: 0.025164\n",
      "Epoch: 41 reconstruction error: 0.025058\n",
      "Epoch: 42 reconstruction error: 0.024929\n",
      "Epoch: 43 reconstruction error: 0.024675\n",
      "Epoch: 44 reconstruction error: 0.024557\n",
      "Epoch: 45 reconstruction error: 0.024406\n",
      "Epoch: 46 reconstruction error: 0.024336\n",
      "Epoch: 47 reconstruction error: 0.024245\n",
      "Epoch: 48 reconstruction error: 0.024152\n",
      "Epoch: 49 reconstruction error: 0.023957\n",
      "Epoch: 50 reconstruction error: 0.023859\n",
      "Epoch: 51 reconstruction error: 0.023635\n",
      "Epoch: 52 reconstruction error: 0.023531\n",
      "Epoch: 53 reconstruction error: 0.023360\n",
      "Epoch: 54 reconstruction error: 0.023360\n",
      "Epoch: 55 reconstruction error: 0.023120\n",
      "Epoch: 56 reconstruction error: 0.023078\n",
      "Epoch: 57 reconstruction error: 0.022940\n",
      "Epoch: 58 reconstruction error: 0.022829\n",
      "Epoch: 59 reconstruction error: 0.022667\n",
      "Epoch: 60 reconstruction error: 0.022599\n",
      "Epoch: 61 reconstruction error: 0.022400\n",
      "Epoch: 62 reconstruction error: 0.022336\n",
      "Epoch: 63 reconstruction error: 0.022268\n",
      "Epoch: 64 reconstruction error: 0.022297\n",
      "Epoch: 65 reconstruction error: 0.022177\n",
      "Epoch: 66 reconstruction error: 0.022115\n",
      "Epoch: 67 reconstruction error: 0.021968\n",
      "Epoch: 68 reconstruction error: 0.021837\n",
      "Epoch: 69 reconstruction error: 0.021830\n",
      "Epoch: 70 reconstruction error: 0.021765\n",
      "Epoch: 71 reconstruction error: 0.021598\n",
      "Epoch: 72 reconstruction error: 0.021565\n",
      "Epoch: 73 reconstruction error: 0.021375\n",
      "Epoch: 74 reconstruction error: 0.021288\n",
      "Epoch: 75 reconstruction error: 0.021303\n",
      "Epoch: 76 reconstruction error: 0.021150\n",
      "Epoch: 77 reconstruction error: 0.021066\n",
      "Epoch: 78 reconstruction error: 0.021002\n",
      "Epoch: 79 reconstruction error: 0.020903\n",
      "Epoch: 80 reconstruction error: 0.020824\n",
      "Epoch: 81 reconstruction error: 0.020832\n",
      "Epoch: 82 reconstruction error: 0.020803\n",
      "Epoch: 83 reconstruction error: 0.020723\n",
      "Epoch: 84 reconstruction error: 0.020639\n",
      "Epoch: 85 reconstruction error: 0.020476\n",
      "Epoch: 86 reconstruction error: 0.020526\n",
      "Epoch: 87 reconstruction error: 0.020468\n",
      "Epoch: 88 reconstruction error: 0.020374\n",
      "Epoch: 89 reconstruction error: 0.020329\n",
      "Epoch: 90 reconstruction error: 0.020289\n",
      "Epoch: 91 reconstruction error: 0.020223\n",
      "Epoch: 92 reconstruction error: 0.020238\n",
      "Epoch: 93 reconstruction error: 0.020173\n",
      "Epoch: 94 reconstruction error: 0.020052\n",
      "Epoch: 95 reconstruction error: 0.020075\n",
      "Epoch: 96 reconstruction error: 0.020035\n",
      "Epoch: 97 reconstruction error: 0.019968\n",
      "Epoch: 98 reconstruction error: 0.019949\n",
      "Epoch: 99 reconstruction error: 0.019838\n",
      "RBM 3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 reconstruction error: 0.033191\n",
      "Epoch: 1 reconstruction error: 0.027791\n",
      "Epoch: 2 reconstruction error: 0.025368\n",
      "Epoch: 3 reconstruction error: 0.023727\n",
      "Epoch: 4 reconstruction error: 0.022622\n",
      "Epoch: 5 reconstruction error: 0.022051\n",
      "Epoch: 6 reconstruction error: 0.021293\n",
      "Epoch: 7 reconstruction error: 0.020597\n",
      "Epoch: 8 reconstruction error: 0.020206\n",
      "Epoch: 9 reconstruction error: 0.019682\n",
      "Epoch: 10 reconstruction error: 0.019452\n",
      "Epoch: 11 reconstruction error: 0.019081\n",
      "Epoch: 12 reconstruction error: 0.018940\n",
      "Epoch: 13 reconstruction error: 0.018807\n",
      "Epoch: 14 reconstruction error: 0.018381\n",
      "Epoch: 15 reconstruction error: 0.018351\n",
      "Epoch: 16 reconstruction error: 0.018078\n",
      "Epoch: 17 reconstruction error: 0.018014\n",
      "Epoch: 18 reconstruction error: 0.017686\n",
      "Epoch: 19 reconstruction error: 0.017846\n",
      "Epoch: 20 reconstruction error: 0.017562\n",
      "Epoch: 21 reconstruction error: 0.017530\n",
      "Epoch: 22 reconstruction error: 0.017483\n",
      "Epoch: 23 reconstruction error: 0.017218\n",
      "Epoch: 24 reconstruction error: 0.017054\n",
      "Epoch: 25 reconstruction error: 0.016914\n",
      "Epoch: 26 reconstruction error: 0.016921\n",
      "Epoch: 27 reconstruction error: 0.016857\n",
      "Epoch: 28 reconstruction error: 0.016640\n",
      "Epoch: 29 reconstruction error: 0.016629\n",
      "Epoch: 30 reconstruction error: 0.016530\n",
      "Epoch: 31 reconstruction error: 0.016350\n",
      "Epoch: 32 reconstruction error: 0.016515\n",
      "Epoch: 33 reconstruction error: 0.016331\n",
      "Epoch: 34 reconstruction error: 0.016467\n",
      "Epoch: 35 reconstruction error: 0.016327\n",
      "Epoch: 36 reconstruction error: 0.016202\n",
      "Epoch: 37 reconstruction error: 0.016237\n",
      "Epoch: 38 reconstruction error: 0.016216\n",
      "Epoch: 39 reconstruction error: 0.016203\n",
      "Epoch: 40 reconstruction error: 0.016062\n",
      "Epoch: 41 reconstruction error: 0.015967\n",
      "Epoch: 42 reconstruction error: 0.016035\n",
      "Epoch: 43 reconstruction error: 0.015999\n",
      "Epoch: 44 reconstruction error: 0.015941\n",
      "Epoch: 45 reconstruction error: 0.015789\n",
      "Epoch: 46 reconstruction error: 0.015851\n",
      "Epoch: 47 reconstruction error: 0.015895\n",
      "Epoch: 48 reconstruction error: 0.015900\n",
      "Epoch: 49 reconstruction error: 0.015924\n",
      "Epoch: 50 reconstruction error: 0.015596\n",
      "Epoch: 51 reconstruction error: 0.015655\n",
      "Epoch: 52 reconstruction error: 0.015899\n",
      "Epoch: 53 reconstruction error: 0.015507\n",
      "Epoch: 54 reconstruction error: 0.015520\n",
      "Epoch: 55 reconstruction error: 0.015674\n",
      "Epoch: 56 reconstruction error: 0.015546\n",
      "Epoch: 57 reconstruction error: 0.015575\n",
      "Epoch: 58 reconstruction error: 0.015501\n",
      "Epoch: 59 reconstruction error: 0.015448\n",
      "Epoch: 60 reconstruction error: 0.015502\n",
      "Epoch: 61 reconstruction error: 0.015424\n",
      "Epoch: 62 reconstruction error: 0.015371\n",
      "Epoch: 63 reconstruction error: 0.015405\n",
      "Epoch: 64 reconstruction error: 0.015186\n",
      "Epoch: 65 reconstruction error: 0.015224\n",
      "Epoch: 66 reconstruction error: 0.015156\n",
      "Epoch: 67 reconstruction error: 0.015149\n",
      "Epoch: 68 reconstruction error: 0.015296\n",
      "Epoch: 69 reconstruction error: 0.015216\n",
      "Epoch: 70 reconstruction error: 0.015008\n",
      "Epoch: 71 reconstruction error: 0.015033\n",
      "Epoch: 72 reconstruction error: 0.015163\n",
      "Epoch: 73 reconstruction error: 0.014979\n",
      "Epoch: 74 reconstruction error: 0.015235\n",
      "Epoch: 75 reconstruction error: 0.014936\n",
      "Epoch: 76 reconstruction error: 0.015114\n",
      "Epoch: 77 reconstruction error: 0.015019\n",
      "Epoch: 78 reconstruction error: 0.015085\n",
      "Epoch: 79 reconstruction error: 0.015052\n",
      "Epoch: 80 reconstruction error: 0.015045\n",
      "Epoch: 81 reconstruction error: 0.014879\n",
      "Epoch: 82 reconstruction error: 0.014989\n",
      "Epoch: 83 reconstruction error: 0.014990\n",
      "Epoch: 84 reconstruction error: 0.014779\n",
      "Epoch: 85 reconstruction error: 0.014988\n",
      "Epoch: 86 reconstruction error: 0.014715\n",
      "Epoch: 87 reconstruction error: 0.014859\n",
      "Epoch: 88 reconstruction error: 0.014942\n",
      "Epoch: 89 reconstruction error: 0.014860\n",
      "Epoch: 90 reconstruction error: 0.014808\n",
      "Epoch: 91 reconstruction error: 0.014843\n",
      "Epoch: 92 reconstruction error: 0.014745\n",
      "Epoch: 93 reconstruction error: 0.014928\n",
      "Epoch: 94 reconstruction error: 0.014715\n",
      "Epoch: 95 reconstruction error: 0.014706\n",
      "Epoch: 96 reconstruction error: 0.014640\n",
      "Epoch: 97 reconstruction error: 0.014609\n",
      "Epoch: 98 reconstruction error: 0.014752\n",
      "Epoch: 99 reconstruction error: 0.014806\n"
     ]
    }
   ],
   "source": [
    "outputList = []\n",
    "error_list = []\n",
    "#For each RBM in our list\n",
    "for i in range(0,len(rbm_list)):\n",
    "    print('RBM', i+1)\n",
    "    #Train a new one\n",
    "    rbm = rbm_list[i]\n",
    "    err = rbm.train(inputX)\n",
    "    error_list.append(err)\n",
    "    #Return the output layer\n",
    "    outputX, reconstructedX, hiddenX = rbm.rbm_output(inputX)\n",
    "    outputList.append(outputX)\n",
    "    inputX = hiddenX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RBM 1\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RBM 2\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RBM 3\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": [
    "# Plot reconstruction errors\n",
    "i = 1\n",
    "for err in error_list:\n",
    "    print(\"RBM\",i)\n",
    "    pd.Series(err).plot(logy=False)\n",
    "    plt.xlabel(\"Epoch\")\n",
    "    plt.ylabel(\"Reconstruction Error\")\n",
    "    plt.show()\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RBM 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x400 with 56 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "RBM 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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pqnmVAGeRId/r5iGvO3cPFLOToU2ehXpe0+95+TR+u1THE3+7YlneUFXVVAnMbqSbQGZesbr9/dMhv+lWu7avTRcppRxWSvketRv4PYP3qD19A3tYm85vhvUhr6erhtSTJxjzetI4BvjEQCH47lLK2nM5C/2sMOx3rzD4771THR5UVfUwbZzOFSa7jtqmTsaD9vd0r+sbp7PFfLKzR9JuOM9VVSrUibOqqnoP9ZyzEvB66jiyt9hlT6COo3rI3P78EEIIYVzIRmoIIYQwOZ+l3txaEViFOtnP0dTqueWBS0spy87uzQduuvdXVXUidSKVX1MnsTmplDIrCSFPGvxXbqZyPz1pKmXoXGbR2Xxfxw28lPIE6k21E6ljnT5xyPuHvf6bIa87k7qfD9Ama2f9NFDjLjwLnyMmJox6+uC/UypaB5uskyn35iZzq01Xo94IfzfwQroZ2fsY1qbzm2F9aI7raaCw3Aa4Y1C0BnUG+m9Tu16fV0p51SCp1fxi2O9+xuC/P5/GvRSm4hlTXDPV+PMDjuleN7frap7a2UHoh48P/netUsoWU10/B59TVVV1c1VVR1dVtV9VVS+mTgToKvj3SnkbQgghhC6z8pAWQggh/LPxi6qqXGl3I3BuKeUs6gzkzwC+WEpZcxbcx3upquqnpZQTqbNRL0UdN/Ub03z7idQxJpctpbyJ+qEY2gf/+YFvWrwTOH+a7/vjhP9/L22IgMuATwPfpd6I+bMUo6WUb1G71Q7blJujdpkm/ttPZc7VZNPZlBn2u+cG+l0PM8RdfwIKu8BATXkq8EzqA4hPUW/Y3AX8RnElSylLAz/Q2+bsa891hvUhb/8NgF9N876dDfOqqm4vpaxIvaG6DXUioH+l3ljefPDv7aWULacRPmJuMN2xs6D013nN/LCzH6eeA55O7WrfG293blNV1V2llB2oQ0+8jPoZcXvajd0QQgghDMhGagghhDCLVFV1Tinlc8AbgFWpM10fOxdufaf9vSLT3Eitqur7pZSrqGMufmRQfG1VVd+bC99puvjm0WMnbEBPi1JKoU1udAWw0RSu9k+fpHy+U1XVQ6WUP1ErDBeend8+4DfU7t7/MtVFA7Xy/Pj9atPHAb+qquqnU108CRvRxpR94yDZVh9z8/f0Kod7ePJc+jzv+w/PQfsrsdIZg3+UUp5DfbDwBurN7NWoVfHbz/a3nXtIFT1TKIce1Kfnh5J6njA/7GxVVb8rpRwJHAqsXkrZlm7c13lGVVX/KKUcR72RCrDMVNeHEEII/6zEtT+EEEKYPd5Pq6Y8eC7FMfQDzsfO4nuV7OQJE/5/fnEbbdzOzWbzHs+g3ZQ5dbJN1FLKU2jVYAsKigH5solZ62cBxSpceUhoh5WoNzfnNTfY37Pbpp487ZQprlt9itdgeqpHocRQwzZn51Yfmhv11EtVVT+tquo46s277w6Kty6lLAjhD7RhvGQpZdLwBoPQF6tMeM+oMj/s7CepExtCPc/MTzWvJ6KaKl50CCGE8E9LNlJDCCGE2WDgWnv04H+fB+w5F267hv193yy+98vUG0h/oU5oM9Wm1Vynqqo/ARcN/neDUsqas3Eb3zycajPydcz6RvO85szBf58MvHE27/HNwX+fQe3aPRmvnc37zyrnUbvjA7xtFuP2iqFtWkp5FLDvkPs8ZNc/fsi19wz+u+pA5dz3mSvQJveaU66gVVq+vpTy1Ll034ZBDNXLBv/7GGYvJu/cRv21MHWf3Al42oT3jCrz3M5WVfUH4PDB/64E7Dgn95tsDEyCH2jcM+lVIYQQwj8x2UgNIYQQZp+P0G7wvGtOEsGUUlYFdhn878PM4oZDVVW/rqrqqVVVPaGqqoWqqnpg+LvmOofSKgdPKaX862QXllIePUie81wr/iVt1u1d+1S+pZQ1gA/OrS88F/kcrYrsA8MSxZRSXlZKWW9C8fHAnwd/f6yUMpOLfyllfYZvOs4Vqqq6H/jfwf+uBBw91WZqKWXRUsreE4rvtr8nO2z4EHWIjKnwsAKT9qsB2nBcDNht4oullIWA44bcY9pUVfUQdcZ1qBXVp5RSJg0bUEpZaBBj08vWLaVM6ko9GAvrD/73D9Rj5ZHmdFoF43tKKStNvKCU8jzauvkTbX8aSeajnf00bYKuA+fwXgeXUo4opSw21UWD9nvH4H//AZw1h58bQgghjCWJkRpCCCHMJlVV/ayUciy1AnFp4FVM7ur52IEKznk0dezATQb3kNLuI/Mpmcxcpaqqb5dSZgAHUyfMunFQPxdSb4Q9HliS2k15J+qNrhUZJCcaxOg7mbouVgYuL6V8HPg+taJtS+pYkX+g3sB54Xz7cUMYxDbcjVrF+Xjg7FLKV4Gv0iZReg51jMvtgRcDb6ZO7qJ7/LyUchD1xtOSwPWllA8B11K7Em8JvA24n1rd+ax5/8s4AFgbWIFadbhWKeUY4HrqdliY2n1/k8H3uwX4H3v/BdRJlRYFDi2lLEGt3n2AOgbjPsDG1NnpX8bkXGl/f7yUcih1n9LG/b1VVf1t8PdJwCHAU4FjBxuUFwyuXR14O7A4tUv+KswdjqD+HRtTxzS9fRBH+Srqw4GFqEMJbABsR30Ac5S9f2PgoFLK5cA5wM3Um6VPpO7nr6fdbP4f+62PGFVV/bWUsi/1httCwBWllI9QK9P/Rt1v3kXd9gDveIQOeEaOqqr+XEr5MPAJYJE5vN1TqMfx20spF1O3z43U/asASwAvpz7o0Bz0qaqq7u65VwghhPBPTzZSQwghhDnjcOrNoMdRq7JOniS252K0MTAno6KOj3fQ3P2K84+qqg4ppTwIfJj6Af4tg399PIy5bA94L/WG2srAmsCXJrz+a2pX1xksQBupAFVVfbOU8nLgZGpl4isH/ybjdz33+Ggp5fnA/tSbfUdNuOQB6k3o0+bKlx5CVVV/GKhgT6bOGr889ebOZHR+U1VVfyyl7EGdPOkJ1Bvhb5jwnkuBNzFF/MxBop9TgZ2p45BOjEW6FHDv4NpfDpSxXxp85vsH/8RDwB7AVsyljdSqqv5eStmGWpm8B/B84LAp3tJ3UPIoatXp+j2via8B757d7zm3GSTeew11mJOnMHNdA/wdOKiqqs/O7+834hwNvJPaDswJP6Vug0cDmw7+TcY/gP+mVaaGEEIIYQJx7Q8hhBDmgKqq7qN2yQZYjlmLZ/cP4LfUyrijgFWrqnprVVWzklhngaOqqk9Qu19/ALiaevPvb9TJue6iVmm+Hli8qqrvT3jvb6k3Ug+i3nh+iFr5eAe1UnOlqqq+xQJKVVUXU//2NwHnU29iaMP4Pmp17nuB5aqqOmGSe7yFepPvAuqN44eoVbmfBFapquq6efwzJn6fX1dVtQW1avJ/qd31/0Ddpr8GvkPtirwlPZs0VVVdQK0EPYlaSfxXajXcZdRhCjamTdw2FbsD/0mt0P0tUyTDqarqK9SKyNMHn/Uwdf0fD6w+eH2uUlXVn6uq2pP6t36WOgHbb6nr6UFqFeCx1BvhL5rw9iOo6+/j1GPmx9Tt/hD1BvGXga2qqtpxEEpggaGqquOpbd9/U4/TP1KHqPgB8HnqPvuhR+4bjiaDdj50Ltzno9Rq+D2p+991wK+o++VfgJ9TK+MPA5avqurtkyX6CyGEEAKUEX9WCyGEEEIIIYQQQgghhHlOFKkhhBBCCCGEEEIIIYQwhGykhhBCCCGEEEIIIYQQwhCykRpCCCGEEEIIIYQQQghDyEZqCCGEEEIIIYQQQgghDCEbqSGEEEIIIYQQQgghhDCEbKSGEEIIIYQQQgghhBDCELKRGkIIIYQQQgghhBBCCEPIRmoIIYQQQgghhBBCCCEMIRupIYQQQgghhBBCCCGEMIRspIYQQgghhBBCCCGEEMIQspEaQgghhBBCCCGEEEIIQ8hGagghhBBCCCGEEEIIIQwhG6khhBBCCCGEEEIIIYQwhGykhhBCCCGEEEIIIYQQwhCykRpCCCGEEEIIIYQQQghDyEZqCCGEEEIIIYQQQgghDCEbqSGEEEIIIYQQQgghhDCExzzSX6CPj33sYxXAox7V7vM+9rGPZWLZX//6VwAefvjhpuynP/0pAE94whOasn/5l3/pXP/nP/+5ee3vf/87AE996lObsrPOOguAVVZZZabPev7znz9T2a233tqUveQlLwFg//33L9P4qQssv//97yuA73znO03Z8ssvD8D3vve9puwf//hH578Av/3tbwF40pOe1JQ97nGPA6CqqpmuV93/4Q9/aMre//73A7DHHns0ZWuvvTYAP/nJT5qyvvupr2y00UYj3QYAyy+/fAWw9dZbN2VPecpTAPjxj3/clKmP//GPf2zKVOf33ntvU7bkkksC7ZjxOlddLrzwwk2Z6vpXv/pVU7byyisD8Je//KUp05jy65ZZZhkADj/88JFuh4985CMVwPrrrz/Ta257nvzkJ89Uds455wBt3wVYZJFFgNb2eD1eeOGFALziFa9oytSf//a3vzVlP//5zzv38te9DZ773OcCsNpqq410G1x00UUVdG33M5/5TABuueWWmcqcq6++GoA111yzKdM8oj7v99Xc4XPN73//ewAeeOCBpkz9+09/+lNTpjGnzwR46UtfCsCOO+440m2w/vrrVwD/9m//1pTJ3rjNUJ16vai/qs8DLLXUUkA7Xtx2/frXvwZgiSWWaMquueYaABZbbLGmTJ/7xCc+sSnT/W666aambL311gPgqKOOGuk2WHjhhSuAV77ylU2Z7M6DDz7YlGnu9TZQvfzmN79pyp73vOcB7VrGr7///vsBWHbZZZsy2ZjrrruuKVthhRWA7ppL9/PxornnmGOOGek2AFhqqaUqgHe+851NmeZS7+OyR16vN954IwCLL754UyY7rXrzsfB///d/ALzoRS9qytTWWqsC7LzzzkD/Gln38Pvst99+I90OyyyzzExjQb/d+92iiy4KdNtAc6rXy8Sx4G2gsr75xeef5ZZbbqbXNe4e//jHN2UaK4ceeuhIt8Huu+9eQVuf0NputzOaM3ye1Xs0X0B3HQ9QSls9Wu8+5znPacrURv789sIXvhDo2n/ND27L7rjjDgCOP/74kW6D/fbbb6Y2UF/XegTa/ufrzSuvvBLo1suzn/1soLX1Pm40rmTLnWc84xnN39///veBbrtoHPiaVd/lE5/4xEi3wUEHHVRNLLvvvvuA7hpG/dnHwQ033AC0cwC0c0PfvLzQQgvN9PlaB/mzseaGHXbYoSlTG/jco7XtjBkzRroNALbbbrsKur9Pda3fCe0a0m285m2tewBe8IIXAG0/9edl/a15A1q7fu211zZlz3rWs4Du3PHQQw8B3TbXdxn1NerSSy9dAWywwQZNmfr95Zdf3pTpeVp1Aa2dvueee5qyifOyjx21gWwWwGMeU29r6lka4MUvfjHQfU7RWNA4hXaP69hjj52lNogiNYQQQgghhBBCCCGEEIawQCpSdarsCgudwmhXGtpd7j51o59y6qTuF7/4BdDdlf7Zz34GdBUt2223HdA9BdIut99XZdrthq4abZSRElUKW2hPL1WP0Coh/fR5tdVWA+Cuu+5qytSmfSelv/zlL4GuouV973sf0K1vP22beF8/ufb3jDpSovopmepcylRox0rfib8rIaRU0imM1BrQPxZ08uxjRidIXs/6DD8Z8pOmUWbDDTcE4Nxzz23KXv7ylwNwySWXNGUbbbQR0FUkbbvttkCr0oa2XWS3fCzotN7Vp7rOy3Sdt8GjH/1ooD0Bha69HGXUv3/4wx82ZfIO8BN61Ye3gSu5hE6Yf/e73wFdlYTskdejxomPObWHt59YffXVm78nqmxGFamKvC9LDellaoO+vudeCrL3E8eD/+3qGalKfV3Qp2pSmXuUuF0cZaS+67NFfrIu1ZbXley6zwf6u68va57xNY3mWXklwNRrI59fxmVtBK0S9YILLmjKNtlkE6CrWpF98bqREttRO2jM+FpI6pall166KZO6ZaeddmrKJqrsoR0L7knlY2qU0VhwW69+LLsEbRu4DZD6x8eCxof6rtsjqe1cwaS54F//9V9n+m59beDz97jYI9WR23qtAX1eVp362JAt8eu0ZpSKT+qcQyrWAAAgAElEQVRWaO2R7uX38PW/POb6bJmvH8ZlXlb/OvPMM5uyrbbaCmj7PrRt5Ip1Pau5TVC9qa+7LZIngquCNdb8GUXP4V7HajdvU1fRjgPyiIW2jvy5VfXi41+KO+/Xsku6ztvgxBNPBFoPBGjVlj4OpETts0V61oP2uWEckBJVnoAAm222GdBde2q/wlWqqie/TnOC9i18XulTSKoNfa7uW99qjHl/GZexICXq7bff3pTJS2CllVZqylT33u/lPdvnvaG28P4sz7+nP/3pTZn6szwBYernBFfXz+7aKIrUEEIIIYQQQgghhBBCGEI2UkMIIYQQQgghhBBCCGEIC6Rrv5Kl7L333k2ZAie7DF2uMu5WLjcRD3J9/fXXA60LkLubSULs0mDJrd01RIlFbr755qZM91EiDei6TYwySsxy/PHHN2VykXW3DkmlXfIu9xl3eZLrp9yhXEL97W9/G4DPf/7zTdmxxx4LdOtTSb3cVVdt5Pdz94ZRR7/5He94R1P2jW98A+i6tKkdfCzI/c/bQYku5GbpgbL1Xg+OLhcQdy1RmbtAyJVBQeahK9kfZeRu4Amj1O/kQgVtnbqbs/qvB+JX2AwF7lfoAGjboy8xRp9Lm4dP0Gd4sqlxsUdyt3/Na17TlF1xxRVAdxzI3cZtgMJSuGuxXAyV1EvjAloXcnfFVJgSr0/ZHHfJ0fw0TjZIaB7dfffdm7K7774b6NaB+qS7Lalv+px62223Ae18IRcsaOve20AhZfwecuHyRHByY/Yke+Piviab+1//9V9NmdYk7k6mvt4X6sVdaWWDVD9u07WucXdmvdfbQGPC3eTkYucJEcdlPoC2TrROAfja174GdH+nbISPhb6khFoDaT2qBF4A22yzDdDaQGjXUe4CJ9fejTfeuCnTfPKjH/2oKRuXsaBQU+4u+LSnPQ3o2o2JYVygXe97UpIvfvGLQLu+9HXm6aefDnQTaMgeeRtoDeV9QOspT6AxLmNBNsJDOmk+9rlS61O1D7T930OAffnLXwbgLW95CwB77bVX89p///d/A925VS777qrcN9f462Jc2kAuw/vtt19Tpt/rIbk0JtyOyJ57OC/Nm7Idvr5S+Cq39X3rIK1B/b7qI+7qPi6hp7Q/4Xa7L9yZ6srnVLWR17PGlerKE3zqmaMvwZv3+ZNOOgmA7bffvilTciMPAzQuzwjQ7vt4st6+8B8aC32hQ7y9rrrqKqC14T6eFObiZS97WVPW97wsO+PP5lqrue0bF9d+JXB8wxve0JQpyZTb3L5kT743J/R8oPZTO0G7t+HPwfrbx4KeyX3to7Hga6PZnRPGZwSFEEIIIYQQQgghhBDCPGKBPJKTKuKEE05oyrSb/4IXvKAp06mCJwDRaaSfdCmYrHb8fVdau+euxNCpht/jjjvuALoqF522+em3K2FHmbPPPhvo1rd2+v0EXnidSinmJ12qy76Azgqe/YUvfKEp02m/J3yR0uiMM85oynbccUege0pxyimnAG0SjlFGpyonn3xyUya1oitNdbrjykcpX1yVJJWq2tCVjzoR9QQl+nw/LdMptuoZ4LWvfW3nvtBVeY8y6lseqFon7t7v1afdHmkcKTEGtOp2nV6fdtppzWvvec97gFaJDHD00UcD8KY3vakpk9JrxowZTdkxxxwDdBXyOil3xd4oIlt70UUXNWU6OXYvAanlva2uueYaANZYY42mTH1YJ85+Mq1TZz+pVNv6yafmJD/x18mrK/FWXXXVaf3GBR3NbVKCQauGcWW7VBeyNdDaD58fNScooaGr2V01KTQOXQ0jG+jKF41Nt1nuMTHKSPHgCkf9dq8z1a23gfqkJ8fUSb1O+/2+Uut5n5dtczWl+kDfePET/nFJPgjtvOleNFIcuueCxoIrWTQfuxpS/VP1Ja8FaNVLSgwDbWIXT1ylZFdf/epXmzKpxz2Zno+zUUbrC/dG0vjwtYf6tqsh1Wd9baQ5Um3gCiZ5jfQl2OxTEvkaQPbI3+vrrlFG9sXH+b777gvAgQce2JT1JViTjVp22WWbMq1/ZNOkBIZ2jvE2k0eCkno6Xsdqb7/u/PPPn+KXjQ6qDz2jQjvP9iVd876pevHnMT3Pam7QGhZgrbXWArr2RG3vz3tam/mzhP8txiUxsBSjfXbHbb+ea30d9MlPfhKAgw46qCnT2lJ7F77mkT3xZy09p/k43GKLLYD+edltm9r3qKOOGvo7F3TkVeBrfdWN15ee21x92pcgTapF9W0fC1IGuweikswq4Z1/rj/TaQy6Mtm/8yij+v7617/elMmmLLbYYk2Z7L/bEj3LrbPOOk3ZxKTM3/3ud5vXlFDKn990Xx8LsjM/+MEPmjLZRl83+3PbrBBFagghhBBCCCGEEEIIIQwhG6khhBBCCCGEEEIIIYQwhAXStV+ya0+aItcQSeOhlax7IGa5J7tkWq43CuwruTy0rqAXX3xxUybJtku89be7Sugz3LVnXFwVJLd2VwW5ZvhvlMuBy6Mls/bg2WpTuSy4a4Hck90FQe4+7g6oz5CcG1op/y677NKUbbrpptP6jaOAXAs8WP7yyy8PdIPGqy+6K6fq3F1q5GYg1wOFS4DWTdZl8hor7ial+8rNB+CGG24AuqEgxsV9Tb/XA4O/8IUvBOCCCy5oytSP5S4OrUt/X9IXBST3hFVyD/WQATvssAPQHU9ySTnggAOaMrm9u1vEG9/4xmn9xgUd2V3v86pnD/CvuvX6VtJCTyilcSBXDk+gJ9cRD4dw4YUXAl3XFAVG99ACK664ItB1N/XxNMrIFrnt1lzg863axV2cdZ27QclVR25T7gKndnR3QdksD+OidYHCN0A7Nj3gv4ffGWVUZ3Lthtbmur298847gW7CHF3nboKqZyW18PlDrlHuxq9QSH6d7JK3nxIE+Ljy10cduea5nVluueVmuk42WUlaoF1TefKjiWtUhX+Bts3d7Uzu+T5/q708dIPcfT2BpIcUGGW0NnFbq1BQPhYUrsJ/t+rP1+4T3WM9RI7a29dSmovcbV2us74uUDIsXyOPSwgw/SZ3Zf3sZz8LdENP9SW0UcJH/Rfa+tt5552BNrEqtHO2u+gqqYw/q2nt5DZK4S7crb0vRNkoot/rz2Xqwx6qRbbFQw3JnrsbrP7W/Ln55ps3r6nOfH2qZ7S+RDJ9zxLeF8alDTTPeUIhzbO+br/22muBNpwRtOt7nw9ki1SP3rZ6Nvc+LxvoIRr0t+9Z6Lv4Gm6nnXaa1m8cBfpCsGid7s8OfQmxtYb1uUP9U/Xrz7x9baNQap74U/tYWpdCuz7Tmhq6a7pRRnOb7+doDnRbrxCQ6667blOm0Ah9a1TNx95maitvA/VtH3cKFePzru7rc7qvp2aFKFJDCCGEEEIIIYQQQghhCAukIlUnA65g0GmNn+q6slRIIdB3OiY1UZ/yRYH6od0V96C22kn3EwS910+SxiWphU5wdVrm+Cm+1GB++qBTYkenAzoR1okbdBNKiUsuuQSAl73sZU3ZkUceCcDb3/72puxVr3oV0D2lkKpvHFA9eZ/VKbMnT1BfdRWYgi57XWtsqc492LZOZvwkf+uttwbg3HPPbcquvvpqoKsE1ImPTvpgfJQvDzzwANA9ZZTCbcstt2zKpA69/fbbmzK1gZ/g63RTJ6QebFvt4ydsaiNvR526uXJJSmU/UXVV3ijjyYyE+rArtXSS6fVy0003Af1KSikJXBmhceUqGyWu8zJ9lt9XqldXrvr8MMrI7nhCO/1272frrbce0D0RlnrC5w71U/V5V3bJFrnqQrbNlWI6xV9zzTWbMn0XP9V2WznKqF58zpTK19VWqg9XAEgJ5G2g9ZLqzG2R+q3bHdl5T+513XXXAV3PoImeD9CqEsYB1ZcnJNU60xUOUtZ52VRrVKmt+9rXbaDmVlcgad7RPOCvu0eLz/mjjBJH+JpcCjhXnqhO3U5rLvd5VvOyVGWu4lX7eX3LxvuaX8kG/XlCfcXtoY/VUUbrTldjy7643VCf9zbQ3Ov1onFw3nnnAV1PAr3mCuSpkrn5dUoy5c8G3r7jgPdl9Vfvm9tttx3QXS+pD/s8K5umda/biz6VpbxwPBGzlPB9z+g+Nr2PjDLqp17fqkf36NAc6PWtZ1dvP71HayS3/ZqX/R6yJ74O016J13GfLRqXPQto1Z/+m6S6dpsrrz2fF7Wm8XrV2lT93lWlmmtcYS21t9ev5m9fg2pMuSfXuCRnlt11byQ96/pzlnsWC80Pfc8JWi/5/oSv8YU8r2S/oF2H+jjSePM5aXbn5ShSQwghhBBCCCGEEEIIYQjZSA0hhBBCCCGEEEIIIYQhLJCu/QrIvM022zRlLpUWckPzAM+6zuW/kvBK6u4JAiR7dxc4BYp3mbzcGd2VS5/l321c3DhV9+5+1sd9990HdN0X9J6zzz67KZO7f18gYEnjXWKt8Ape35KCu5ReMny5F8L4SOQBLr30UqB1sYc2uLu7i8v12N1r5B7obk9yi9VrfYH+PUzDZz7zGaAredff7pag7+Kubx7ce5SR25r/XvV3T4YmV0Pvf3L19sRFci+TPfJ7yKXBP0tt5HWr8Ar/8R//0ZTJncpd2sYl4deVV14JdIOBy53K3f/63KSUSMfthtxs5GbmdkYuPj6v3HbbbUDX7UeJfM4555ymTIlePLzDuLhx6nd4sifhfVjzpvojtHXlbri33nor0M7jbovUBp5ESu3h9akyn6tli3wceOLKUUa/3dc3+p0+f6qfyiZB63rvSQ3U/2Wz3O6oTt1FSnbP20A2xsehxpqHYvL7jDpa93hyRdkQtxGqL68bJUz1MAqy+3K17RsLXn/6LF9HffrTnwZgn332aco0d/k6blyS36nfexgE9UV3DVQ/1jwArYuht5WeBeT+19cGXt99yVflyuyhkLSW9vAObptGGdkPtz2qby8T/nwkt0xfn05MZuTPaqpnL9Nc5ONLtszHi97jY67PLXQU0Vzgaxjh/Uzrce+Hqnu30xNDxfSFivLwOpqjPdyLrnM3fo0n//xxWRsJt0V9yYjUVr4eUX/1NZTeoxAuPi+rnj3xtuySPyP0JSTUvOz360sEN6qoLn2c962P9Pv75kK3R6pP2Rm3X33Pyxpjfg/1e5+T+hKqjwuaR33sy9a6jVC/83lZdsiT5ClEgPbg3P7LzqivQ3+oK7WV70kp3IsnwPU2mhXGZwSFEEIIIYQQQgghhBDCPGKBVKQqwZCf0Oi0zU9SdNJw0UUXNWV9p7/arVaAeQ/6qxMG373WqZLvfOu6vpNXP80bl9MdnZj4ib1OyfxU5q677gJaFS/AgQceCHQT8ehURnXmgYavuuqqzmdCGyzaT5qVWMpPH/QeVwH2nYSPKgpe7eoI9fu+RAlSKgKstdZaQPfERX1a48hPMPuSLOiExseC8M/X394O3p6jjAKSe6BsnQC7+kdJ0DbeeOOmbNFFFwXacQJw8sknA21/VhsD3HDDDUDXHmkseL+WQlmJlKBtK7dH49IGrmQUsk2bbbZZUyZlutuI1VZbDeiq5pVwQUmpPAmFAsH3BeH3PqB+oWQL0NorT7TmNmyU0UlznzJbCj1o7YcnIFHAf1dRSLUlNf0tt9zSvCZl70te8pKmTKfZbov66naiBwqMzzhQn3eVUJ86XusQty3q864K0P1k+32eUf25ukNjwq+TKtPVemoDf++4rI2gXUN6O2gOdlu/yiqrAHD33Xc3ZbIvriDTvL3GGmsA3XWX7ut1qfnBVXUzZswA+pPqjeMaVd4Hbg9Up/4bpW5xW6J51u25bJhUY24z1M7+7KDEIz4WdL+NNtqoKVMb+HvHpQ30TOV2uE+pq9fdc0B15f11YlIqHzdK1ONtpv7v9am/fW2r+7ky2+3gKKN1iK9XZDPkyQNtEjpXXelZzseQnsc0XuQ5Am2CWfdK6ZuXNRe5Kljt5uPgn2Fe9nrR85HbbV3n7XLWWWcB7TO0r5tU93q2gLZf+xpX6zC/r8akKybHxRZB+/vdJqsvegJYJUX26zR+fH175plnArDVVlsBXYW3+vOwpHWq3769FB9H4zIWNAf3eRNo3oV2HeRJqWTL3ONKzw4q8zaQ7fHkwhoLPidpDbDpppvO9J3mRhuMzwgKIYQQQgghhBBCCCGEecQCqUjVqaWfaEol6qcnOlXZeeedmzKpZlyZp5M1xY/0ExrtRnsckZtvvhnonl7q5MJ3wxUPyU9ZxyUm4SWXXALAbrvt1pSdeuqpAGy44YZNmRQUrjCVMsVPzHSiplMZj32nE1CP7/nqV78a6J6y6nTI45pssMEGQLdfjMtJM7T90+OI6IRGJ8zQ9kGpuaEdM2o3gJ122gloT41cxaR2Uyw9aGN8OhqXrkDSuHMlpZ/2jTLql1JEAFx++eUAnHDCCU2ZVA8eG1ZKID+N3HvvvQE48cQTgW7fVTu7wk9t6qdlH/7wh4FWBQttm6otYHzsUZ9CXvbZ1aeaE9wmq351yg/wyle+EmjrWcoaaE+k3atB49BPWS+++GKgO+akGvPrXFUzykhp5wpczYeaC6FtK4+jprnAvRl23XVXoI0DrbkE2lNqv6/Uxq5g0qn2hRde2JRpfnJFks8to0xfnECNA58r1Qb+u9U33eZLVaE1j7et1kTu8aM5x9dm+lxfL8kGeRuMU4zUvrjuGguKBwytKkLqSehfn0gp8clPfhJo5who5wRXl0nx6HNCXxywHXfccabP9PYfZdS3fb0i9ZzP1bIRUiFBa4+8T0rVojldayWAk046CeiuufRc4fO3vpOrhzUWfHxKeTPq6Le7wk7PVx4jT3XgKiHZC++bWlN+7nOfA+Btb3tb85re6+vKvnwIGgd968++OKKjjurZn2u/9a1vAbDeeus1ZbIVHvtd9ef1sskmmwCtimvllVduXtMcLC8faNvM7b+eUfw7aZz62mxc8ihILefKUdkYt9Fav/bF4XTPQXmr6fnLY3FrLep2XOt8t0WyN1qnAmyxxRadz4TxWRtB2+/WXnvtpkzree9rqn+vL7Wh27I999wTgGOPPRZonxugVVZr/QrtnOB9XM8dr3/965sy2SafE/x5Y5RR3focJxvvXmoaF/6Mqj0Lbys9A1x//fVAq4r3+33wgx9syrRn5Z4L8h7x9tYa1seCj8FZIYrUEEIIIYQQQgghhBBCGEI2UkMIIYQQQgghhBBCCGEIC6RrvyS/7jYlF0x35ZA7gkvn+5IsnHLKKQBst912QNctTbJvuSxAK+c+8sgjmzJJjl1+rfu4q3lfgpJRRAlAfvWrXzVlchPpk0e7y47azaXdcvuQ67dLqNdff32g6yKrhD3rrLNOU3bwwQcDXVeuO+64Y6bPHxd3ZsddxeQe4u4DGgvuMqJ2UGIigI9+9KMAvO51rwO6Y0d9XKEWAA444ACgdSWH1l3H3XT1/TwEgX+XUUZuNe7+pD7orkvqg16nagMPAq+6kvuO9125IHgAbrkmepB+JbnyUCP6LHfd8lALo4zq1H+vbEpfchV3/xDuliN7725rQvXs9S277/UpFzj/fLWz20h39R1l5C7l/VXzp7tnqg68rvqSfZx++ulA68b8ox/9qHlNtuXb3/52U6b2O+6445oyfYYS60HbBzy0gMbQqCPb4bZI6w8P0K826psPHJUpqYWHFNHc727p++23H9Btg74+rzZwV0dPeDTq9P2WvnlZ7eDzhOrc1ylKtCa3TV+jak7wPrz99tsD3XbQ2tPDVPW1g7tcjzJa43uIoaWWWgrot0d9CYl8va7x89a3vhXojoU3vvGNQHdulRunJ8lT3fs8pTbw9fC4tIH6utsW2WRf86j/962N+lzw9QzmKDyA2zQ9R/hYkku194u+sEfjEuJCder1rXWNu7f2PSNoHHhd3XnnnUCb8NHX+Qql5mVKcufPb3puk6s1tOPAnyl9jTXK9CXb1frDn93UBn3zgfdNhWbwUFZC48Vd0F/xilcA3VBjamc9XzvuOu3PGqOOQk64TVE7eLIp2ee+JIx97v7vfve7gW6YLrWlh5HZZZddADjmmGOasm233RaA8847rynT+PDn5Wc84xnT+o0LOurP2k+Ado3q817f/p3e6+2i8DsaC742UgLVHXbYoSnT88Shhx7alGkseLIrPc/4WJjd5+UoUkMIIYQQQgghhBBCCGEIC6QiVSoXV1jopF6vQbvL7bvXet1P4nR6LzWMJ2rQ6YOf/Jx88skzfb5e9x11JTnxhBjjEsxciQ3WXHPNpkynh35yptNFP2H7zne+A8Aqq6zSlEmZoZNMr28/YRDf/OY3ATjooIOaMr3HFQBSy3jA7L5TvFHlsssuA1qFLrQn6d6PdariJ6I6yfHxoYDXakM/1dTJkJ9OK/mFKw761E4ai366NC4n/gomrkDt0CbGUZIWaOvAT+IUaNzbT2OmL4GSxoLXt4L+u0JGuApBahk/VRsXe9SnulD/83qRjffrZOP9xHf11VcH2vr2enLFpZDCyW2VbJ6XaewoMROMjyJVyVqUlAXaunWvDNW916mUXz5PbLbZZkDbv73e+2zR1772NaA/OYmPIc0x3t7jYos0t7oCV4p59/LQmHB7/N3vfhdoExVBq+RSm/V5lnjdHnXUUUC/Et7LVN+uzvTXRx2tj1yFpXHhysO+BBJSKLndkIeD1C19XjXeNqeddhrQXefqdU/oIGWOzyfjMhaE2wOp6DyJlOZFtyU33XQT0PV40piRjXL71ed5JbW816fq2cedPtffOy5jQb/dVXey51IvQmt73CNB48QVcbIXmk/6PLH8uUxtNZVCHNpx6vPyuCRDVV/ypCnqc74+laK9r2+6MlvPVJoTfNwIr9v3vOc9QDfhsF53FaDUd66sHxdb9IMf/ACAZZddtinTs5vbeangfB9DdfXc5z63KdP6dLp7Fueeey7QHQdqt75nN1ddjksbQDu+fQ7UPsT3vve9pky255577mnKZGvkAQLtvkJfsjzNvb7u0t6RjzGNT0/kqb7hHnHj4kmrvRt/5lVf9fWQxoD3T3kY+/pWfVpq42FjQYkK/TlY7ebqYbWp939/bpwVokgNIYQQQgghhBBCCCGEIWQjNYQQQgghhBBCCCGEEIawQLr2yyXPJbxyu/Hg1JL8uguO5NYuXZecWC4h7oovd0WXAUvq6y5DcoGW9BhaF16Xe7sr2ygjFymvR0ner7322qZMEnYF3oe2PlyyrdAMV1xxBQBf/vKXm9d23nlnoNuOcod1ibzu6y4sCsr9vOc9rynz7zzqbLjhhkDXBV/uHp74QG7O7qqgxAt9CZGUpMKT8igwvLtaqU3cZURuV/fff39TtvTSSwPduvfPHWUUyN1dwRRA/L3vfW9Tpja4/vrrmzIlJHKXNtmh//iP/wDgS1/6UvOaxpbcdaFNFOJunHKL8M/aY489gK772riMBY15d8OQ7Xb3P/1eD68gV0N3UVNbKcyCu1rJLdT77xJLLNG5P7QuOz5PKcD54Ycf3pT1hQoYRTRvuguSAvm7u6XaSu0DbX37OFB/vvzyy4FuKBiFKvHEOXINdDdEze1yrYPWxdBtps/lo4x+m9uiCy64AOgmYdQayhN7yKXf51S5Mmm8eIgG1e1XvvKVpkzJFHwOdtc2oXWauzCPSxtAG+bF60vJJDw5l9aVnkhNc6m3g+rmzW9+MwDveMc7mtcUqsJdYtVufe3g41Outd4O4zIvy057uC3NlW6TZS8WX3zxpkxt4PZIdlprKK2BoR1vCtUDbcgwf3bQmszdefVM4CGx/HNHmb4EIKrn2267rSmTrfeQC7LtPqdqTGjtqrkb2vnb1wC6r8/teu7wfq41lrvtjsu8rL7sc0Kfa7364TnnnNOUKRGt90fVn8Ix7LTTTs1rF198MdANIab2c1skm+XXaT4exzlBc6/PhXLbdvuk39uXfLYvLJpCuChRNrThffy+mhv65gP/LI1T/6xxsUXQ1oOv5y+55BKgu++j63ye0DNs3/zYt6+jtZU/h6stfU7Qdd5e6ht9idJHHYVR8f0ctYGHe9Ea0RO9y6Xf+6Tmb63/3aYoYa2HAuhLJKl1mtdx33Pe7I6F8Wi5EEIIIYQQQgghhBBCmIcskEcROs3y05VFF10U6AaV1alD32ma7/TrNEGnmx/72Mea1w488ECgq67TqdGPf/zjpkwKMQ9WK8WNK8V8Z3yUUd1//vOfb8pe97rXAf2nKFKG+ut9gc6FAgJDW/d+vdpZJ9PQqpL9RFOnsa5+9feMOlJY+Om5Trt8LOjExRXbahs/9dXY2nPPPYFu4q5VV10V6CbL0KmSn3ZrLPp99f36AjyPOqpbP3k8+OCDgW6/0+mmK1k0FtxGqZ+fdNJJQFfhLQWBq8DWXXddoKv60Ph0e3PAAQcAraoJ4M4775zpulFECkUpKADOOOMMoKtGkerBT5Bln7399LrUxjrlh1bNd+GFFzZlUtlIgenX+Zg78sgjgf6kVKOOFA5ej0qQ4L9Xr7saRrbI20onwkpoqOSN0I4hr2/ZIk8aoPnY1Zi333470FV160R81FGd3XjjjU3ZxMRp0LaBqxPVBq6akZJYKjxf82g+8DEnm+5tK3xe1pjQfAPdeWXUkTrihhtuaMqkMvF20Lzpim21Q1+Sjw9+8INAt49rfPjY0X39HloL+bx8zTXXAF2PoRkzZgCw//77T/kbF3Q0t956661NmdTAXgcaM76GcZst9B7Zmauuuqp5TW2w2267NWXq274ellrGx4KeU/wzvX1HGdkDrwMpb10VpvWPew7IS0GJvwDWW289oFVxuYKoz9NBc7DbGSldfX0shfeZZ57ZlLkabZRR/3b7oLpyW6TrPNmg6tc9zvScrDXj1Vdf3bwmRa97e2i8+JiT+s7nn+WXXx7oekm4Gm2U0TrFkx5LAen9UHOvt4vawPu6vAJ33f1SmEEAACAASURBVHXXzv2htUVuz/rmkj6vENknbxf3lhh11BfdRmht6HWu5wn3ZtPrvp+jtbvs3Pnnn9+8tuOOO3Zeg1b967Zen+H2Rusz96R1eznKqA08sdSWW24JdNtAz9V9Seq8z058fpPnLMAOO+wAdO2RxkLf/p3fVzbKy9xrZVaIIjWEEEIIIYQQQgghhBCGkI3UEEIIIYQQQgghhBBCGMIC6dovObVLnSWfdvcFyaIl04ZWst6X1ELIBRZalxSXusttTol+oJUVuwuEXEhcsv3pT38aaAPujjpy53e8DdRW7r6gtpIrB7T1fOKJJwLdwPuSwXvdyt3fXYbkRioXf4DNNtsM6LqfjIvLDrQuOt4/5T7gLgVKGuVydrmRuNuHXJrl4uEhF+SW78mP5GKy1VZbNWV97tMrrrgi0HWzO/vsswH41Kc+NfyHLsCor3qQa/V3bwOFPHA3M/VFD7ytOpU7ubvtLLPMMkCb1AVa93x3D/3qV78KtAkvoD/xwitf+cpp/cYFHdkZJXSBtv95eIW+pFR97jaqoy984QtANxC83Dc9tIxchdwVR3bIr9PY8DYYlxAXE20HtP3af6MSBXq4HNWL230Fg5fN8GQAcnO7++67mzLN8z636nUP5yJXLoVFgW57jDIa4x5iqK8Nll12WaDrTq828PWQxobWWn4PuV558jrNA163Glf+XoUK8LGhhJPjgOpQ7vzQ3w5yNfekOX1jQfOJ1jtycfbrDjnkkKZMidnWWGONSe8BsM466wDdNZHc7EYd9S1P7jXV2kjhGGDqNlDb+vOH7IcS8EDb3quttlpTpvv55ysEjSeM0Tw/6mhO8P4lV8m+kGseYkL1rRAi0Nor1bfPyypz90v1dYUJgHbucDfSr3/968D4uM866q++5ul7Xl5uueWAru3Wc4UnJJKLvp4fPKGenhHcbVfrV09cK3dmTy6mcG5K1AfdRFajjNpASUmhtQ/eBnIx9vlAfbIvKbD6sNuTvoQ4KvPXNC97KAUled54442bsnGal9V3tVcAbTt4H9fzqruJa07wZzXZN4VR8HWXxpE/m/eFpdTrHvpC4RQ8tID/PcpoLLj97VsbyR75GlU2zNeomlvUpm6P9Ezgz9yaH/qSxXuZkiF6Qlufb2aFKFJDCCGEEEIIIYQQQghhCAukIlU7zq7AEr7Tr5MwP4nTqY7vPOtkRq/57rXe6yobJSDxUyPdwwM8a/fa7+enq+OAB8DWaZefKuhvrz+dQvrpmOpKQd9PP/305jWdULoiSScIfqIpnvWsZzV/S9Xnp6F33HEH0E1AMqqov/lJiU78XU2hE2KpvKDt26701XU6afOTOymx991336ZMwfxdiaFTV29fJRHw8bnttttO6zcu6FxwwQUAbL755jO95kpsqSM88LXq2U/Y9LfUwJ78RcHqvY/3Kd+lNJUyCdr+4P1C42ODDTaY4hcu+Ehp7YpQnbT7PKE+6afwqg9Xrqo/K1GP2xkl+nJbLxWxK190yuqKYp3uj2OyKSWDUsIJ6E8iJbvjfVgn/56MTjatL8mdkrG4WkJB6Y8//vimTHO1FJjQqlTd7o16/xdSh+o0H1rFg/9e2RQfB5obfI7QvC2b5NfrHq4iU5/3NZfGldsdzRu+fvAxMepIRe2JBWV7XNGiPu11o7rzsaCxovp3pYzskHsH6Xr3ztH9XHWnOclt2bisUZXoyxWhagO3uVIieV8U3gay8RoL8vrw11wBrHHk84qeXfyz9thjD6C7bh4XhbzW7vI+gnZN5ONAv7dPTecKaq1tlczO+63WUl63es7zJFZa01500UVNmTzX/PPHpQ20dpFHFLS21m2u5om+ceDrdvVhKeT6EuL43C51tTyn/HP9vVKfenvLc2HUkdrdf6/svNt+rRV9La/68DaQbdF48MSZ6uv+zK328DWUvGj1PAywzTbbAN1x1TcmRxWp/5WECNpx7v1e86yr27W+9Wdt2Zc+OyNFqtt/JQK7/PLLmzJ5aDnyFHEPUt+zGmW0dvGx4N6rQmPh4osvbsrWXnttoDsWNJerHX39qjWq1pvQroM9iZTa/oorrmjK5PXjz99Sqc4qUaSGEEIIIYQQQgghhBDCEBbIo4i+eAY6tfFTRO36H3fccU3ZySefDHTjVujkRifTfl+dmrqqSac1fuqm02TfDdfpnJ9I+GnbKKNYINdff31TppPm97znPU2ZYp766cNVV13VuR7gm9/8JtCe2LhaUW3gJwOqUz9pEK4AE66kkapvHFC9bbTRRk2Z+pirLhRzZZ999mnKzjzzTKCrmJYiWLF8/ZSu72RUbeLxbtVefqqp0x+d6kFXITXKKFZynwpLJ6DQqjNOPfXUpkzt4fZo4gm+x0uT7XOb0qdAlj3y8Sn7du655zZl+++//7R+44KO7K7bFFcTCZ1uukpJCiNXyCi2oeqxT2nqc4KU3K7w6LNRmp98bHhbjjJSLrg9UZnHn1X/U1w6gCOOOALoqrVnzJgBwIEHHgh020Dt4op52SLFsIW2z/tJssaL9w/3dhhl9Nu8HtUGbmMUO+rNb35zU3baaacB3b651157AXDMMccAXaWr5lRXE+i93ufVVm4fXQ0gvI+MOlIUeTto/vRYnFKwHHrooU3ZhRdeCHTbQe105JFHAt01qurVlTIag65CU537e/UZHmt4XGLiyVOjrw28TOrhvffeuym77LLLgK4t23PPPYF2LGy99dbNaxoLvuZS3fq8rNd9Las5y8dMnypwFNHzlvdN/V7vh1oLenw72S1X58lGqM+7Wq5P0S41ps8TqltXgul+3i/61g+jiPpw3/Oyo3XI+973vqbsAx/4ANBVsf/nf/4nAJ/5zGeArjeVbIw/I+i9HqtY6yBX1uu9Pk+MizpefdPtidrA48necsstABx22GFNmZ4hvA0+/OEPA20+l749Bn++6puXtY71Zzdd55/l+UVGHT1LeZ3LhsjLCWDppZcGus8JUpi67X7DG94AwMc+9jGgVcpDq/715zd9rnsuTIxDD60d0veA8ZmX9dt8faM+25fPSHkqoJ1PvK4+8pGPAO2+k9s5XefPDmo/v059wPMr6Huef/75Tdns5hQZj9k8hBBCCCGEEEIIIYQQ5iHZSA0hhBBCCCGEEEIIIYQhLJCu/XLF7wsY7+6ZL3/5y4HWFQdaNwR3A5EEXoH5XXIs932X5Ouz+tyD5JoOrRuKgjpDVxY+ysjdxl1E5F5w+OGHN2X3338/AOedd15TtuKKK850P91H7rLeBnIDcfcFlSkBErRuh+5q0udO524+o866664LdH9zn5uS3Nw+9KEPNWVKwOVuZEcffTTQyurdFV+JM9wtQuPIg5jLPUtJp6B1S/CECuPivqZQEko4Ae34cNck2Zk+N2K3RxPdHLxtVY9e37JRHlxe1ymMBrTuDQqiDXDTTTcB3dAQo0hfYg/Vm7v7y3XDQ33oOndHE3L3cdcQBaD38SW75a5qcgvyRFV9YRjGJdmUkk/4b9M4cPdMhcLwgP9KFOV18da3vhVox4a7/Kmve9vqOs3P0LpJu8uOxovPCT6/jzKyLd4GPpcKzQf/8z//05SpTt0uy61K7edhc5RUzNc3SsDpCcdkx9yVSi6GcpebeJ9RR/3Zwylobep2WkluPFHUrrvuCnTDVH3xi18EWlc1t/9KNOJjTHXpLtVyVfbrZLc8Wcm4hBqRPehzt/dwFFoHnXPOOU2Z5hO/TuGOZMO9HjUneB9WH/dnEoVT2mSTTZoytZG36bi0Qd8cLPviiVwUeuqQQw5pyg4++GCgfbaC1gVTSUd8TlDdexgxzdtKwget3ffnEN3Xw7WNSxg2jXsfB+rDbuvV5+UqC20/9LqQrVKy3r5naZ9P9fm+DlJ9u42TLfLrvO1HGdVLX1JBr1slGTrqqKOaMvVnr+eNN94YaMNTuEt0ny3SmsjXsWoj/05qA39O82SSo46e/f2ZSvXgdkN7N1deeWVTpj7rtlnzstYx3p+1xupLbOttrjWVj08lGvbnib6QhaOI5jv/vZojfb5Ve5xwwglNmdrA60+hSNSOngRec7uvM3UPDw+gft+3Ntp0002bstl9ThiPnY4QQgghhBBCCCGEEEKYhyyQilSdCPhpmnayPfi41FY6OYN2x9lPSHVaqV1uD9CvkxlXU+hvV3bpFM1PcnSCpNMF6J5YjDJK3uH1rRMGrwPVlQe51nV+0vCud70LaBNB3XPPPc1rSy21FNBtM53snHXWWU3Z7rvvDrTtDm0fcRWgB4QeddTHvB30W/30RP1+gw02aMrUNkrAA63qVPfzQORqVw/Cr3uo7qFVkrnyV23nqk0/fRpl1Lf9lExKRleof/7znwfaAP4Ab3vb24CujdDYUkI3PyXTKVpfoiONE2gVHn7arL7i6oxxUWf3qZul8PEg7Trl/PGPf9yUqV97shv1f9WpK7OlKPP2lr1adtllmzKpKVyBJlwt4G00yuhU2ZW9qlO3TxdddBHQ2glog/WvtdZaTZnUYzrB9jaWPfG5Rp8lxSu0c4HPHfrbVXh+8j/KqD68byrJh3vDXHPNNUBXia6EQ67UlZJXdscVW7JLntRC84EnafjgBz8ItN4T0NoiV+v1JYsZVaQQckWoryHFW97yFgA+8YlPNGXyCvGEFFoXKXGF2+2+cSc1jKu+Nf/0qbiV5AS6SXhGGdkLtz1SZLka5vTTTwdg3333bcq0nr/22mubsm222QZox4n3V40FTzarsehqeKmT3G6pPTTfQ1edOsr0KWt/+MMfAt12kfL93//935syve7KUdWb5gZvA/V/T1ilMec2SvOxK/XVLzwJnnvWjTJS33l9q168/qTactWiFPWu4Ft//fWBdt3pNk516rZIfeDVr351Uyb1t3v1aBz4e/07jzJ67vd5WR48vicg2+LPZPK0dJvvexrQXeOqHt0W6XNlwwBe+9rXAq3nrr/Xv5M/s406mivdHuh52T2ZlBBVybygVae64nGiN6DfQ3OC92et9d1bUwpiT4iqdvC9KJ+3RxnZHh/bag9vF61Rvf/p+e0b3/hGU7bLLrsArcLb9yxkz/0ZTPbIx4ye1/07qb7dRvYlN58OUaSGEEIIIYQQQgghhBDCELKRGkIIIYQQQgghhBBCCENYIHX1Sy+9NNANsCzXPHfXk/zd3Xjkoumu3pLAy/XN3WokCXapu+7niVz0ndwNV24R/l53kRhlvv3tbwNdV3HJ4V1GLRc/d0tQvSmpArSJviSn9vvKbdZdO1WnCkgPcOCBBwKw8sorN2Vyi1DSK2il+e4COqooTIG7FMhFQG6A0J8gQS4H7hIuFxDJ2d0FWS48/lnCEzVoDLpLg8aWu5uMS5gLuc56QGvxgx/8oPl7zz33BFo3TYDXv/71M71HdSR3Wnc3UJ25S6zcReTOD207uguJ3NlXWWWVpmxc7FEfqgMfB7L/cn2Fti69TlXn6useSkG4PVIbeHIZuRief/75TdlOO+0EdN1VxiWxiNzy3UVJtt5dyJVQwa9TmdsEzdFyh/Xg8AoL4slG5DKkBFPQuoW6u6Lsv7e33OxGHY1ndyFUCB13t5cN8P6qPuyuUaojzQfueqW69zlFtsvD62ht5LZfLqDuXjUuoV6g7c+33357U3bDDTcAXZt8xBFHAF1bstdeewHdsaB6l03xta/q0Puz7Napp57alMkV1+cT/S13XRifduhzp73jjjuA7hp1q622AvoTEPpYkLu/7tsXLsTbQG0qF0X/Lr4e1vOGhxfpCwMxiqiufOxrHemhRvbee++ZrlN/9f440fXYXcPldum2XHXq9am53Nc+soNu3975zncC8KUvfWn4D12Akeuwh/qSffa1v+ZIr+++dY3mAv23L4Gyh67Q355wWPbJXWX72ntcEnEqDKCHUNE6xN23tS7db7/9mjL1YQ89pTGk5zMPD6W69TbQuPK1kcK4+XjR/OJl4+JSDm07+NrzjDPOALprcu0N3HnnnTPdoy8cmuro+uuvb8qUpMvnca1hPbSYXve5Rq7m/gztIfVGGT0v+3yrZ2dvF9VfX/Jwra+gtWuyR742kj33saB7+OfLDvl8orbysTC7z8tRpIYQQgghhBBCCCGEEMIQFkhFqk7AfLdeJ2u+G63TdldM6DTBT7omqi58B1qnyn4iJwWAK9C08+1qg7PPPhvoJpxxVc0ooxMbV1NtttlmQPcEpu/UUsoYP72fqMrqU9d5EHmdKru6QqdNXsc6rfCTTw8oP+qo3jzgu04nPSi26tDVkAqUrRNraE9k1IZeb6prnRxDe7rk406nNq7G0elnn7J71NHJlv8eqfO8L+q3u2JaJ5heLzo90ymnnzbrOj8VVYB6P3XT51599dVNmZQ3Un9Dq/Z2leoo48ou9e++REI+DnQS7Ul2pGJVn+9LnvCKV7yiKdMJs5/4a1ztv//+Tdm3vvUtoDtPuCpklJHNdrWXlKh++itb5P21L2GO7LTGgasXdQ9XS8juuN3TPOF9Xu3n75XabNSRqshtxvbbbw907bbq25PcaY52LwTVaZ/iTp/hdk/KLz/ZVwI2XwOo7f17josyG1qb7Ao7rXt8HaP69LpRnbiSUtepHTxhpq7zdY1snyfJW3vttYE2uRK07e9t+POf/3xav3FBR4kp/LcpoZOPdym4/DqpRKeyR65eVBv4+JC9cg83tbMrW2SjfMy4enCU0brUEzupXrzPa13jNkC2xOtUZbrO16cq60sk6c908mJTglvoT/iopFijjvqtK9q03uubE7xvep8UGi8aBz5uNA68bfWMIJUd9CtdZbN8zezrhlFGCV69DVQH/hu13nRVrt7jz3gTbZE/P/TNyxon3t6y/b7ufeELXwh0bZZ7/Yw6Wvf7GlUJuHzdqPp1u6VnC28H7S2p/j2xoNrS21y2zxP+qr1c/arnD3928XXZKKPx7fOnbI4nXJSdcc9v2S231+qfagP3PpM9cjW3xoUnNNf48LGoOcOfE/rU99MhitQQQgghhBBCCCGEEEIYQjZSQwghhBBCCCGEEEIIYQgLpGu/5LfuxikJttwDoHVR8kQhklu7XFfvlWTbZfJyd3A3K0mJPTCuklp4GIF111238z1gfJJaSD7tLt1K6ORuq5JnuxxeLjguz5YLjrvqT3zN7yFuvvnm5u/DDjsMgOOOO64pk2TcJfLuFjfqqC+6K4bGh7sMqw/6b1f9u8uA3A41jtytSmEavN0k03dXFLWvJ/SRi6O3w7gkOvroRz8KwD777NOUySXQ3WvkKubua3rdr5M7lVxC3Fap7t19TePC3UjVLxRuA1p3RndrlPvjqKPf7q5oct9xVzG5M7mdVp16UqqJLlPunqPx5S6yus7vIdcRd+XV6/49+5KUjSJyjfJ5WXWg+RHa+nDXHrlsel9XPWv+dtdlzQk+V6tOvQ0U2kLJEQFe85rXAHDrrbc2ZXJpG3VUt7LV0IY1kIs9dJMvCtlwt8uyFWoDnw+Ejy+tk3y8aJx4ggC5uCkBE7TJQccBJRXx0AkaH54Q9dJLLwX6XQIdjQXZNA+LIXzsaKz4vKIyd2tUIjBfy/r3G2W0JuoLGeH9U2PGr9P87eNoqrGg67xd5Ebr1ynJj3+WEhC667Pby1FGa8u+JHXugnnFFVcA3XGgednnatVv33OZ5h1fi6rM+/cBBxwAdNtA13kiVbmkjzrqV/5srN/rSbg0f7o971ujq1+r/frGgbeBwlT453vYL6HXfW3rc/koI5fgvrrydbvq3teHqksPg6D7TTUfuFu6xonPB+oDPob0Hg8JM07zsuZID+3V5xKufQ13CZc98nWo5gStZ9x+9CU/0mf5npTCgfWFj/RQDJ4Ma5RRH3e73pfoTs9Fq6++elOm/uvzsuZv1amPBfXxvjCcvhei8BZaD/l7r7zyyqbMw3TOClGkhhBCCCGEEEIIIYQQwhAWSEWqThJ9V3/RRRcFusFitfPspzATT9Ng5lMdD66s04Q11lijKZOy4Ctf+UpTpiC4fafffurnysFRRvXtQfG1m9+XRMpPW3TK6e2i0x6dGHkgYLWzq091cv3Sl760KTv66KNnuq9OLlwZ4qdGo47q309upUT1vqaTHle76JTG60YJcjQGPOC+1It+IqfTpYsvvrgp22KLLYBuv9fY8rbxhGKjzCGHHAJ0T/LV7/1EUSdgniBBCl0/ldYpqOrsvPPOa17TiZirHKWc8JN8KW48gLr6gNs+KdPcvo0isvWuUPQTT6Hf6wnWNGd4v554gu99Ve3obas69ZN8KYW9X2hMeB8YF2SfldwIWlW6nwjL1rsqTONA8wq0J/BqF58TVKdKoAOtmsLVpxobroaR/ffkVeOiCpbNcKW5+qsrI9Rf3R5LXeFtMFFp4eNLf7uXgdYA7uWgxJSuItD3dK8FT8Aw6qjOXUWiNnEPJaktXLEnG+XKUbWJxoLfQ+stt18aY74GkD3y76R28DWA27BRRvZZiS6hnZd9LGgM+BwshYqPBdWR6tnXRlLxXnvttU2ZFNj+TLL55psDXdWMxtGSSy7ZlPlcPsrIA8+9mGQbXO2lNnJbojnS1ytCbebPW/qMvgSf3r9V5vZf48nVea7gH2Vki9wbUrbY15FSYLkKVa+7N6Du05eQRwmW5Y0J7TOH27gVVlihcw/ob6txSXynfuXrENGXUMv7sJ6Jfbyo76r+fF4+44wzANh6662bMj1/u93TPbzP6z6+fvB116ijdfxyyy0302vupaMx4PZFz1L+7KCxpTnY7bb+9uv7EoG95CUv6dwD2vnBx6J774wysheeMFm2x8eCvEKGPSfIXmiN6t4HmkOU5Bdg5513Brp7UhO/m3/ui170oqZMSURnlShSQwghhBBCCCGEEEIIYQgLpCJVJ1t9p8ozZsxoyvS37/RL3dgXT0onM66k61P36Top0aBVU/ipsk7xttxyy6bssssum8YvXPCR6shPbffcc0+gjQ8G7Umzn3r1xanVSZhOgDy+p8o8fpLwE1WpYe68886mTCcS3/zmN5syjxs56uhkRnFWoG2Tc845pylTe7kSQnEB/RRGpzRqI1e66jTN1QVq17XWWqspkyLM21AnsR6XpE9pMIpIFfyd73ynKVt++eWB7qmXTpS9z8qW+fjQuFC/l8LXr+uL++ynl1/72tcA2G233WZ6r9sgKWRGHSkTFa8WWpVQn6raTz518tinKJZyw0+VhfdftbOflEp540o8qZ68D7jSYJSRfT7ppJOasg022ADo2hjVm5fpdLpPUax+67EbZYN8fGkceLsoFpuPr5VWWgnonnT7dxllVAfnn39+U7bqqqsCXXWF1ji+NtLYcI8Nva73upJCigGPJejxcYWUga441Rzs42Bc4tRCq3xxpbPmPle3a83iMfE0b7o9kg1RO7hqS2PB203t4Ipf2UEfH1of+eePS1xCedWccsopTVmfOlo22etb86v3Z9lzXe/1pHG33nrrNWV6ryvx9Llut/RZruL2dhtl9Jt8baK/+7xlXBUse+B2Q9dpHev9Vvf1NlN9+7OGvHrcw0320MeQx1EcZTSP+rpP61Pvm7ItrhLVeqYvb0hf/95jjz061zgem19zx1VXXdWUaS0qhR501WWjjOqq71nL9xv68odIOaq4qND20755WWt+b4M+tbEUk24L9bzua9K+3CSjivZnXKGo9Ys/G8sOeb+XPeqL2az69WdePYf7eNJ1rn5VG7ontMbddddd15Rtuumm0/mJCzxaf3i9yI735dLxMaM1lNvziWtUqd2hHRe77rprU9Y3FhVH2vuA6tvH1uyujaJIDSGEEEIIIYQQQgghhCFkIzWEEEIIIYQQQgghhBCGsEC69guXxEuu+973vnemMnf1kAzY3Wh0H8m5Pcj/9ddfD8ASSyzRlN10001A16VBMu6+ZFff+MY3mjKX0Y8yhx12GNCfXMsDjcsdwd01FJDf3Rfk4qHkC6uttlrzmlybPbHIu971LqArkZerkPeLK664AmhdTgDuu+++YT9vZJAkfsUVV2zK5Ga2ww47NGVqJ3dL0BjwNlTSKLnZuKxdn+Xuy9dccw3QDWSvtvZ6lhuLu5u4K8Moc+ONNwLdNpArcZ97prsP6Dp3PZZ7g9rHXeBkh9yO6PWNN964KZMLnLuWqv3cnb/PFXcU+dznPgd03b+FbAq0ddqXMM37uux4XxIr2bfLL7+8KZPLlFzJoXUD6QtY733f236U0e/YZJNNmjLZov9n7z2ft6uq+/93Hn6f5YmZZGJmNGZoUqVLEZDeewdp0kVAmlEBRSmCCoQmIiBFpUiR3pVeBQRECBjLmExmkskfkAf379Hr7Ne57/3jA0yc+Zwz6/2Em31d17k+1157r7XPWe/3WpbsMPdew73GYEigsanjMr6lF8dd8H/11VdfZox/W+I8lwaEzCPy1aTZxTJJpJ1em/hozwVlS2hQ5GaF+BPbjhIublbBXnJzgXfffTfJuGRGr/j/VNErU4AdHD/xv/bxvRJTnHOWbqyQtNjrkkZ8h0vAIKf1dzFme81lL7DGKZ+QtPXruIzvZi6Stqb9PvwG0kHPNzbw3BFP+DuS1jzHskJ8mZuA9fzhFEH87JXxslSZ9eqzEU1uLEemPAVzZZsRC+yjsIcbjtG0hTNS0vycm+f6TDtl4E969wieW84kjtX4eJ+NWPesazdQIp5wJk6adNqNo/BtbvrDvYRj0lz2AVJ5n1eYe9/D4t99NiJ+Oj5yhmLubR/2V+9c6T3HvYTtQjMdS9Tn0ogzaT7FJQmZO+995s73q7zueybKS+KPnn766eG1XgkRfA7NyZNmV5eP5G+ynN9lkKYMYkGvAbnL4+GvvD4pc2EfTyzFb3m+eRZh2/I+l8/h2Z+bHfK9PkN92BJg87jLLhQKhUKhUCgUCoVCoVAoFAqFvyAWJSOVrA6F8pP2RNtFknkKi0wJtQAAIABJREFU7WwoTXFoiJO07A7Zr1/96lfDazzRNtOUDJufVJNJdbF0rmvm5VxApsYZTbI4LioOYKEmLRPgpgvHHXdckuTb3/52kjGb8Vvf+laSMYvsxhtvTDLOND/yyCNJxvO94YYbJulne+YA7OAscq+BESwUv2/fffdNkvzoRz8axmD/8llnaLAbWb2kz2ghY+29QGbbY/73lAHTyuuOf/cY28420/jGPueCCy5I0himsOp8PWfqYV1QMDtJbrnlliTjJmDYyP7Q/54yeixoMsdmpMKUcJb9+OOPT5JceOGFy1wXloaz0M8++2ySse8jW2yGBevba6CnkpgLyDA7BjMHPbav9wFqAzcBgDHhbDKAiWEGE9/hpoT4NmeS999//yRj/2j235TBWjPbinVqH43/8Pu++MUvJhk3bMP/o+xwIX8YA2bhvfnmm0nGcZmmXp5v2MXeB/5bpg5YRm5OwBqzz4VZ4SaMxx57bJLkoosuGsZgatDQ0+wUmnl5P9HwjfcnjWFhhgf+stfwZOrgzOO5ZY2Z0cK8OabCYnWjI+IrbOpebP33f//3YYyzln0P/3b8wZd5f8xFpcBat59mvVrRRzNYqxk4M9pWxBOYYGak9vwca8D7hfOrvx9mvM+kvYZJUwQMRftX9ntvvbqB1yGHHJIk+cEPfjCMcd/L/Djew9p2I8e77747ybjpMvffPi/BEvQ+9BlrymC9es2x1mwXGPCOi4ceemiS5Pvf//4wBjMPhrbnCVar/TwsPTMcew07uY5j+lyaoSbt3OH1yV5wrH799deTjOeVZwmPPfbYMMYzB+zhmICtzXzE99i/c0b1PHP2tT+aix2IfVYjYQMaoybNv7ixJmd7N8574IEHkjQFru/3iD89lVWvcarPPuwPx4EP64/md7dXKBQKhUKhUCgUCoVCoVAoFAr/x6gHqYVCoVAoFAqFQqFQKBQKhUKhsAAWpbQfurPpulBuLa9FCm5ZCZRt03op/Itkx/I1aO+WG0D1NRUcicSmm246jL3zzjtJxgXrLVOZMpg/y9SQhJgKTUMP2wDZm2nU9957b5Im73ABbijgLoCNVMcNlZAzf/KTnxzGkK7ZBnORriVNItCTv1qig51cHuH+++9PMpaW7LDDDkmaHMcNWZBk2b7IfHxdZAmWXbGPvO/mItvp+SPWbK/Zk/cCxd09L0hC8FWWQCC/QlKSNImtC51//vOfTzIuzdBrnDQXCSH73DZAbnPdddcNYxSFtwz24osvTjL2ZcipzjrrrCTjJlZ81nOHTXsNFQzs4QYkc5H5E9ssFaMMj+U5vG6fQZMu+4Ttt98+SfNFLq+Db7GPQVbuRj/ICV2GB//vODCXci/4Bzcz6zV7ovyObXXttdcmGdvgsssuS5IcffTRScbyMmTKHkPC6EZsH/vYx5KMm/7wHf4751QCCTm5z4382yVYOEN6LyDL93xQyggp7OGHHz68Rokr7wXitv0cPszlffBDLokyFzswB7YBc+SyYPx2r0XOl94L9913X5LWaNXN7zh/WbqL/+c+IGkNjNwYEnmu99Fc/BHnbp8x+W2Oe1tttVWS8dkIW/m8yZxiW5cM49zPOTVpZwA3WGNver7xg94vcyn3wjx6ff/+979PMm58x+ue71tvvTXJeB+svfbaSVpcoexL0uKs7ci5wI2OkN+67AbNpiid589OHZwVHW85C9oGnM19ju3ZgDMOc+rSddxneP0imfbewM+7MeVrr72WZOx/5uKLDJdbYa36fIkf8D00Z1T7Ms6clMmzjTj7Wp7PMww/o9hrr72SjH0UdvjMZz4zjM3lPoHYa3/EmcjnIPyA30eJCp9RvvGNbyRp51ef9bnf870adrHvYR85frMGvP4/rD+ah+UKhUKhUCgUCoVCoVAoFAqFQuEviEWZDiJr44wLmQNnEMgE9Ar77rnnnsMY2X0K85s9A3PD2QKyO85gkJG+6667hjGyz85yzg3OkrxXcxVnccjKmJnxi1/8YvSamb2wXB599NFhDGbGfvvtN4yRWSJr4b/JbLM52YO94EwKWU9nT2Ch9JoL2V5HHnlkksak3HHHHYfXmDfWddL2oJmXZJCcyeF9zjjNBcyLs2RuyAXMGAJk5+xfNtlkkySNTWfmBEW2zZqH8eWi5rBszOLGX811LyT9jLD9OXBMwFYwqZPkjjvuSNL8l5uOwOzzmoc1DMM4afuErHWS7LzzzklaQ7al/5Ypg3XVayKHLZLG5Ooxs20rMs2w5rx/8DFubAhT5uWXXx7GWN9eF3NuAokNer7GNnC8ADBkzLS7/vrrkzQfYzCPVnusuOKKScZ24X1mPRIben/nHNDbC6xZVDd+3TH4xRdfTNLmMklOOeWUJK0pmxlLvb0Am/uZZ54ZxmjC40YbNDmcU6Mv0LMBMdDzTQz22QRFlGMvvhsGmZmunK98Dc489jMwX/z9/NuMvbmAM6jvE5g/M41Yk2aYcnax72YuYQzDrEya//c88r2+Bn+T7xWJwVZg+e+bMoiB3uO9M1GvKaD9DKDxHQpOK6ZY/7Csk8a+doNn2PFuAgaTzGzBuaDni/i9Voj14rJZrIC4zBnKZ1dsYCY8rFPHYOzsBp+cWXsKxzmgd79sPw6YB7/G+ciKDppk04jKsZXGtt5P7BUrdtgDPnfx/d4fc0HvjMr6dVwkfjoGc8/leWadwwr2M7hes2Diiu8TuJ73GmdUx4kPi2KkFgqFQqFQKBQKhUKhUCgUCoXCAqgHqYVCoVAoFAqFQqFQKBQKhUKhsAAWpbQfmq4lqr1CvMg+TONGGmW5zcEHH5ykSU7ctAWKuwsyI2WzpA1atqU90JSRpifjQtpTBr/T8rOenJkC4pYZbLfddknGMpqvfe1rSZI33ngjybgxhaXkAJkaErakSbQsDeHvNJXeTTemDmQ2LkqNlMCy7ZdeeilJstFGGw1jyIstCd9iiy2SNNtYGoitve4pXu51T4H/XsF5SyUsT58y2Ocuco0EwVIFZDWWmeEbXHCeMhcUJN9mm22G15CcU0Q7aXPqucWmyK+SZlPbby57Abms5cuW3gNkfyeeeOIw9sc//jFJa3iXNB+CrTzfyD5tbyRwnk9Kl3idU7DeEhKaiu2xxx7v+RsXO1h/lj7hi7wPHn744STjhjlIcFyuh/ItPZkT78PXJK0Bm2Ms8iH7QiSObrzg5jNTRk+2yu+1DWhmQBkRj3mt8xn8iQv0Ez8sgf3DH/6QZCxnQ0rlhhhIHG0DN++cOjhffuQjHxnGek02kdZfeOGFwxgx3WdU9gI+x+VCaHRqiS3+yGPIee0Xkc35b7IPnTJYd/jcpP97r7766iRNup+0ObB/WW211ZK0WOPSXtxjeAzbOrazjyydRVbI/ktaA9ypA3mk5cs9uSWljVyKiNjr92EX4uw+++wzvIZte99l/8/6t5SWz/pezXLpKYO46HsEYoLnFjk+jaCSdg/tmE5M3WyzzZb5LuKyr4t/sn/H//t9vRIXvXu/KQJf4KalnDnsizh3Oi7TbMr3xMRo1qjveXv7hrNTzy5e5+wdv8/36VMHv88lFoiV/s0/+9nPkiRbbrnlMMb51mUx8Btcw3PF2nU8xc/Z/xOje/fLvea5Uwf+13GRs473AmcnzxXrvNfokedPfiaFvX0e5hzm0gLY0esCH+nzkv3gB0ExUguFQqFQKBQKhUKhUCgUCoVCYQEsSkYqT6OduYLtYDYFLFJYEknLGDiDw/XI2tBMKkluvPHGJONC3Tw1N7OLZgA33XTTMAazwEVtyXgeffTRC//QRQyyLS4qDvt0/fXXH8awgbOcZH7MRoGVRQYBBmXSWHXOyGFTs73ISJiZR2bp2muvHcbcEGbqWHXVVZOMM1duMgVgtFAwO2kZH+8j2BFkbVZZZZXhNTLbvUy0v//SSy9N0vZE0hpomKHjRgFTBvNtxhV+yNksWFoulE0mzpmuI444Ikljt8BCTZLlllsuybhRG3BRbtisZjXRvOeMM84YxubW/MvzDQvFTc/wPVddddUw9tBDDyUZr0fmGV9iFjGZYY9xXX8/jdtsF5gG55577jB22GGHvb8ft8gB2wLfkbRssRmfsOpgaSQtvvaaUnE9Z/thtdoXwbaHaZE0/3/AAQcMYw888ECSMXN1Ls0VYDV437P+vdfx617zxAg3n0DBw7nFTGrireeRjL7fd/nllydJdtppp2Hsf//3f5OMfeZcmq4lrRkIDRCSxoAwg5d1R8xMGvPRDBUYjzBazCL61re+lSQ57bTThjHOqMxz0vzRmWeeOYzxHT6jcqb75je/ueDvXMwgBvYarJi1yP4wG4bmasxt0uaX69mn3XbbbUma2ippe8zn0XPOOSdJctJJJw1jNAnzWXrdddd9Pz9x0YPf5hjIGjYj6ZhjjknSfEXS/JX3ATGB2O7XUJb4fIVN3dyRplT2fZxt7aMcx6YMWL6OCaxJK0BgjHpeek3osAFnHZ9duS/zOZ+YYL8HC9xnH8cC0FOaThE8b/AZhrOO2XWc+R977LFhDMain1mgDOk1iKTZjtmUwMzsm2++OUnyne98ZxhjjfgM5fv0qQPFpc84+Cj7I+zUY4T6+QHPJPA9Zj5iy57ayfvjyiuvTJJsvfXWwxj+0vvv/6Lp0WIAzWN91sc39JrCWuUBG9jPLJgr3mf//+qrryZpz0mSvj+C9e3zGj5yoSat7wfz8GKFQqFQKBQKhUKhUCgUCoVCofAXxF85g7FY8M1vfnNJMq5xADuCTHLSsvHOrpAd8JNl6haRaXYGmQy2v4vMgLNlZBhcx41sqOeQf5966qkt/T1BPPDAA0uSMduLuqVkZ5JWx9ZP//mMP0v2oVdX8P2CbJpZGO+FzTfffNI2SJJDDjlkSTLOSJIVdu1T1rSzizApnR1jX5DBd7aZ93vvsN7NJmIPOqPf2wtk+6688spJ2+Hxxx9fkoyzhzArYAEljT1x9913D2NkSO1fls5em92Fz3HWDVaTGTX4OWfuejbgeuutt96kbXDTTTctScZsbPyBs8r8ds+Va20D2Hk//vGPk4z9kZkd74VerWL+7b+JdbPrrrtO2gb777//kmRcZwh/Y0YSscDrGjh7z7omC+36dcytfRHf5bhMLT2zXXr7AFxzzTWTtsFee+21JBn7h3vuuSdJsu222w5jvWw/7CTHkrfffjtJiyW9uvQL+SLYM2Zrv1c8uOmmmyZtgyQ599xzl7EDzPfNN998GGMveG2zf7wXqJu94447Jhmv554d8PveCzCbFtoL2Pjkk0+etB2OOuqoJcnYR/TORuyFXm1N2wAb4ct8piUmLHSfwBkZVmby3ja4+OKLJ22DE088cUnSr3PXU075nqBXr5E5Wn311ZOMVQ1cr8cg8nV79we87rjM+6buj7hH8Npkjny+53zaO2/6fcQH6jQ7tvN+nwHYB44JvZj+XvfLP/jBDyZtg2OOOWZJMmakwkD3Mwt80X/+538OY8y959nzlox9Pzaw3yMefJi4zLq48MILJ22DJNl9992XJOP+BvQBgameND/ge1jm3zEB9TLPjvzsgzOq7cZ5yz4NxrjVou8VE6677rpJ22G33XZb5myEcmzXXXcdxrCBa5yjxPEZlTkixvj+rOePeJ9tgIJ0IX/E33Trrbd+IBsUI7VQKBQKhUKhUCgUCoVCoVAoFBZAPUgtFAqFQqFQKBQKhUKhUCgUCoUFsCibTVGQ9ic/+ckwRqMJU9eh4SJpShpN15JX5CEUG0YOmPQl0UgxLXOD0m1aMd9vWvH7lZ0vdkBbX3PNNYexX//610nGBeBXXHHFJE0imLQCzR7jMxQ89zxiM0uvln4tSR599NEk44LxyBYsI+pdZ6qAHm+JJrKnXpkJyxJ6DT+YJ/aM9w7N1SwFQXbidY3MwTaEMm9pS09SPUUgC3MxcApq9/zR9ttvP4yxFl1Ins9Q3NyvsU/8XfhD7wVKnVh+iBzCJQgsZZsy8Ecu44JUxoXGWXNussB82y9Q2oImeW4syPvdvId9Y7kIf4vjCd/vvTGXfcBvotlB0uKywe91XMRWjh3EaHwcsSFp8kMaBfh6vgZyRtsFv2i54lziMr7gjTfeGMZ6NsAv0DQyaXaxvAnJE/Po93M2cpzhfbbBs88+m6Q1I0uatPTDFu9f7GAvuNGRpWyAuXajC3yT4yzN71jvbjaFH1poL+DDLDXk77Md5nI+6jWwczNawBz1mhJ6L+Dj2WO2wfv1RzS6s5yXfWe7zMUfcS71PPI7/RuJ3x6j6ZH9BnuIddsr0WO/3rsupaxsF2TW3odufDJl8Dt8ZuSs6JJcSF5dAgCf4T3EdTifujQYccXvx595vs8+++wk49JXve/3Z6YMfpP3AU0F/SyC+aP0QdLWru/x8NG8D1v4Gn5/Ly7TII8GqEmL5Y4HczmfJu13WbLP3Hkv4Fd8dmcfuSwLJXe4r6YkWNL2jss08P32R9jBTbqJ/Zawz+Vejbj82muvDWO9pseUeaRpZNLm7b1KIblsA3uhd0b1OYdmm/b/2M/PLD4sipFaKBQKhUKhUCgUCoVCoVAoFAoLYFEyUu+7774kyb777juMkS3wU3vG/vVf/3UYIyNthgrv4wk1bK6kZRDMaCTb5ow0WRtnNcj6+buc9Z4yfvSjHyUZ/zbg30hBcrMryCo4m7zxxhsnSV588cUk/Uyks/1ktZ0BggXiDBA2gGG29GemDgplb7rppsMYe8BrEThLyfz7fewB/uti/mRrzM7mfbCMk8YWMMPi3XffTdKysMl8Mv5k3P17tt566yRjRiiv08Aoaexp2wB2Hpk2MwlgW9xxxx3DGE1kaGSRtD3oPca+wxbJmBkzZfA7KaCfNDa1s53Y6v777x/GaPhln0MGc2lGXtLPDJO9hImcNMaeM/oUTj/hhBOGMTM4pwzW0i677DKMETd7DCuYiknzyd4HrHt8+Kuvvjq8hi/y/iJLTQOApMVyfz8ZZjNpHDOmDObbLOwe45p94HjA3Pusw1pn/hwPYGv0CvSbRQaT2PsQZlkvRs0BzIn96/LLL59k3MyUdWy/xVnFc4MP4bz1+uuvD6/BqEERlLSzqZsq8TeZCU48cUxwLJ8y8OGeW9gwtgHrHQZM0uzivYANiBM9f2RVCEof70X2kxUR7Au/z7acMmD6rLPOOsMYc+91yHw4JrBffI/x9NNPJ2mqK1+DWOCYAByzuRex/4fF5DPAXO7V+L1m1/Waa3HG8Zmxx1xlrXPmgc2bNH/iMyufdUw48cQTl/l+4pMZmr6HmDK4J/IcEFM9t/gn36M+99xzy7wPH41azQ15WMs01kvamceKRNSknm/WhW06l3iQNB9rNSzr3jEBvPTSS8O/ian2CzBG8Rs09kzava4ZksRZfFuSbLbZZknGykOeZbhB6L333rvQz5sEiKNWM6+77rpJxr6bOfJ9LXHRcRl28Sc+8Ykk4ziOfXrKEqttiSdmG3NGtcLBTRM/CIqRWigUCoVCoVAoFAqFQqFQKBQKC6AepBYKhUKhUCgUCoVCoVAoFAqFwgJYlNJ+qNCW6yGPsbTm4YcfTpJssMEGwxjyARedp1A8VGzTvpG+We6AnMSyOCjzbkoCpduSurkUkT/wwAOTjGWr0LItcUbK4HmB3u4iwkiAKKVgyccLL7ywzPuRjrhsAw2/LOWiDIMlt14jUwdSBdZw0ubGtmF9es1CU1977bWX+exuu+2WJLn77ruH15CA9JoyuJgz1HlLidgLlnf2CkxPEb15Yf32mn24GRqSA69P5D2UuUD2kDQZwzbbbDOMUfB88803H8YoH0CJgSTZYYcdlvk75+KPensaKZQlO9jK8m8kUB//+MeHMeTI2Md7idhh+RrxgbIMSZvb7bbbbpkxlxawTGXKQJJs/09M8G9EHm4fT9MESzXxGVzDpXSwo+1OIySalCRNhtizi9fFXMqM4GPczIy1aV+E5MmSbiTillKxxtkvljNTDsGNKSj74jiDXMo+Dj/mphpziQdJW8eOy6y3Xky46qqrhjHK9Fh6xrw+8sgjScYxBEmgZaO9Rkt8v8+y7Aukbcl87NCTbDKPlsnyei8uIhdMmq1o/GUZIOdQN3JkbXMu9XfY3+AH7be8f6cM9rzjJ3vD840/t49nXXuu1ltvvSRtXfdKrvneDnvbLr0GSgcffHCS5IYbbhjG5tLcBWmq9wF+yXZhT3i9soZdBmHpkkW2D77D65fvcuM9bO/zJ7Hcfm8uNuAcYhvwbz8fYF5cxo6yF24oxbrmfY6tzLPfz/scq4nBPvdiq145kjmAMgn2B/gZ7wXujWmWnbS4aR/FvLJ3/KyJfeRSeJyn3BSXc6tLAPBZSpks/TdPGex5PxNi7m0DSuP4jMhe8ZrlLMt6dikKSiD1YoLv33hW6EbQlAhweZEPW+6lGKmFQqFQKBQKhUKhUCgUCoVCobAAFiVVhkwzT6w9ZkYLWXaKxvp9zvjAOrr88suTJIcffvjwGp915oxCzC74T1FgM2R44v7ggw8OY7DCpg6YiGQDkjaPZjyQQXDBXrIOznStssoqSVomyA1YKErsYst8v7MFZKvN+CAr53XhTMTUQRFxWLtJa3DhuSEDRmY0adlJZ6DZF2eeeWaSMVOFOXRRbFgZZniQLTJbGHs5O+eM6ZRBxvj5558fxmBPO4tI8WwzWfBHLviOPQ477LAk4+LZzBlN3JLGFlh55ZWHsX322SfJ2B89/vjjScZ7wRnAKQOf02M42PfAovPaoyGRM83YiOz+1VdfPbx20UUXJRlnL9l/blTIPJvVDYOm14Rs6sCf+Lfhi91chay9/TRMCO+D1157LUnzWfbbjDmL/+STTyYZsyE//elPJxn7J9gyTz311DBmPzdlcF5xDGY+zEZhnZrFCDvVWXfWP/vqlVdeGV6DKQ/LI2lMbq9p9prPXNjAxfvn1HiKuOwmIDArfB5lzL6EWG0b4o+wjRuJcA37cs6obrKw1lprJRnHBJgaZufNpckO6723Ph0TOM+YfQpbDJZL0nw8+8lsY1jcZt2h0lljjTWGMeKP5xt7+Gw0F5UCe9pnHtacmXjAdtlkk02SJI899tgwhh/i3sFrmfOuzwDEnd6Ym1jdfvvtScYsKcesKQN/Y6Y5MdpNNDmv9Joou1nw0vul16zI88hesmqN77D9+Dvtx3pNh6eI3r0Wv9fxgLO8m18yb54rfAVn/l6jPN9f8x1uIsV9ovchcdu2mgsTMmk+wnuBuYQZmrQ5sW1uuummJONYjYqAZnpWw7JP3DiQ/cb5IGnnIq91fKTjwFzUg/gZK7+Ze/t/1p19D/vDz/6wAY01n3jiieE1nglZkcYasPIKn+PrYvteQ7wPimKkFgqFQqFQKBQKhUKhUCgUCoXCAqgHqYVCoVAoFAqFQqFQKBQKhUKhsAAWpb4EynSv4YplSUgD3fTmvRoiIaW1dBkasGVnSHT5b9Lo4W7ugiRlxx13HMYs15oyoKFffPHFw9jee++dZCyZueKKK5IkW2655TBG4yNLOGhUAnXahfdpdOG5ZR5tF2x79NFHD2NXXnllkrFN52KDpEkwkO0ljQpvaRKUdcs0kBmYrk7RctazJdDIsyyBQFZo2Qnf5ULdSFpc9HkuUk6kSzTBS5aVYibNl1j+jQ28JrEBpQLeeeed4TVKBri4PPvN8h6kK7fddtswttdee41eS+YjIcSX9JpOeW75t+cUibL9EbZCkrXvvvsOrzF/luwDy1CQr1kmhe+bi5zfQOKHnCZpMhv7HXy7m0qwhu0TaLrDdS2HxWdZDos832sAe3hvsq8suZ2LL0Iqb/+AdMxrDp/l8i9IozwXNJ0gfroJBfvFzQr5rG3AZ5G/JU3ybv8zFxskbV2eeuqpw9g111yTZLwXLrjggiTJ6aefPozho+yPkHDiUzbccMPhNdazZXH4GQOZP2WUklbOwWtjLnbABm7CiN/37+Wc5HMQsj7LLSnfhT/izJo0X2ZpJ/NoH8X+9DmIMZ8V5mIDSni4ueJBBx2UpC+Z9Jn1gQceSNJvZoTfcExg/ffKFdkfMbeWlnKP4XsXy3qnDNaXG0AxH55bSj+5uSnwGQpZMiUuXP6CRi8u+cb9gu1NmR7HYMpV+X22x5SBz/C+Zp06HiC9d1zGV3gNM8+UAPBa5br+Lu7j3DSMcwHPPZLkJz/5SZLxWpmLDZJ2FqexeNJ8iMtcUFaEkh9JcsABByQZ3z+xV7jGrbfeOrxGzHaTbPyWr8Fnd9lll2GMppK9shlTB/6o1/DX647zis8yzJ/3DNfBp/ga7IXecweXPeKMSiPJpJVY6jXk+6CYzw4qFAqFQqFQKBQKhUKhUCgUCoW/EP6qx/ApFAqFQqFQKBQKhUKhUCgUCoVCw6LUfZ588slLknGXaqjw7poJJdgdBKHrWtKALAdJgbtxQp13d9s33ngjybgTJXIp05C5njsLM3bggQdOWl/+xBNPLEnGnZSRyljKutFGGyUZ2+Dll19OMpaI000Vyru7+SJbMLUdGY87w0ODp2t80i/lQNe4rbfeetI2SJLTTjttSZLsueeewxiUdVPckdpYNoM0zRJb1i+SNr8fOahlO7z/tNNOG8YoeeHuhnQctKSB66yxxhqTtsOpp566JBmvZ9a7pavIEZycQk5lqebSUhq/nzmzXBBZgmWISJotVeA6SEGTJk05+eSTJ22Dgw46aEky9tPMo7tmrrnmmknaWk6azJhup0mTaPZiAn7IMkS6mVtCgnTKNmAfWJrCGjn11FMnbYOVVlppSdLkyknrlmlpIN1iHROIpZbX0hUYn+FYg6/vdRj+1a9+NYxR1scxeGl5aNJizDbbbDNpG2ywwQZLknFZFebZsj7WoW3w+uuvJ2nlLJK2n9gHfr+l5EuP3XfffcMYZwDHEuJy72/6wQ9+MGkbJMknPvGJJclYNsnatp9hHduWCtqTAAAgAElEQVQfIT93Z9/38kfEeZ9x8PVIBJPmm1ySiuu5SzRnsdNOO23Sdthwww2XJOMyF6xtd+flDGMbcIZyLF7aBn4/9xWOP9jIZaU41+IDkxa3e/cOl1566aRtcMQRRyxJxj4FH2Jf37MB821/Tqflnv/ibOTrMs/csyVtX/m78Ee+x0Dqe/7550/aBsstt9ySZCwdJlbaJ+O7vV5BT77PZx2XKaeHz0/a3LpjPfbz2Yi47HsEJOnnnHPOpG3wkY98ZEmSHHjggcMYcmaXHuRc6HWNz/CZHz/TOxvxWV+XMgxGL36zJyxh5tnK1PdBkmy55ZZLkvH9KvHW50FioO3w3HPPJRmfWYgtrHHbiOv5frlnc0qYuASN4zvg/nKTTTaZuh2WJMnVV189DOCn7Wc419if40Owz0LAz9ivs8bt+4DXPfcELq/AXth9990/kA1K2l8oFAqFQqFQKBQKhUKhUCgUCgtgUTJSYaI6y0iWvZcRIFuQ9AuIL934o8cAc/YStoezyjw992d5Cu6C6HNp7kIGzE2hyFSaSQE71VlOmlq4iC/srl6jBZrz8LmkZeJglyYtq20bcD2z9XrZnqnCTFQA+8qZFNavCyxvsMEGSRprLGnr86mnnkoybhDy7LPPJhmzJ7ne8ccfP4z1Go6QaTXDg2zR1EGm0L+Ndel1x5p2dmynnXZKMs4Ys2axmdmLsL6dFYXt4T3DHuyVZrFN54IVVlghSfLoo48OYxtvvHGS8T7Ab9kuZDft4/HnMCXNFkL14MYiNDqyb+n5I/ZXr9HG1AET1YxnmkmY8UZMsF1g/9gnsO7J4pvJ9/TTTycZN8Njz5mN2YvLjHkN2PZTBr/91VdfHcZodvDMM88MY6g2HJdZz16PxGXWq89KNAch7ieNweEmIsRq24DrzPFslDQmKkzFpK1fx2DmxmdU/IbZEUuvWbPhid/2R+wnx2rmt3dG9WfNCJsyUIm5uSNnSDcWISb01n1vrrCL4y2ftV/nXsPnAs5mveZHvneZS7MpziZmBaMQ6TF1vQ9eeumlJO/dGMx2xB5ev1YoAmJ0zx95DcxlH8BEdXM0zjz2T701zPnH93TMOXHc92CcS21Hzsee2/eKy177c4nLMFGffPLJYYymnPYjzKl/d68RGzbgbORzEP7OZ1Zed7xl7nv7YI5no6QxUX3ORNXkOIEdUJUl7Rxl9i9nVBQObhL24IMPJmlNU5PGJPYZACaq90fPH83lucUdd9yRZLzPiQX25z1wvu01rmOuzADmfT01s6/Ra0DOecn77sPGhGKkFgqFQqFQKBQKhUKhUCgUCoXCAqgHqYVCoVAoFAqFQqFQKBQKhUKhsAAWpdYKqrnlgquttlqSceMcqL6WkCA1cfFzpDf33HNPkmTbbbcdXoM2bHlJr4ERsjnkREkrjA79PpmPZAcq+2abbTaMIaNxYw8o9G4ehdTDchHmnvdvvfXWw2vIT5A1J8n666+fZCx7pvGUZc9IWPzZOUkIkYW48RnrzhR3pBpes8yJ7fDWW2+NXrM0EFkcjYz8WTe2gjLfK93g4t2Wj0wZzKnlAb2GNku/lrT1bnkNe4s1/rvf/W54DRkVjXiS5N13313m+/m3r4ufs1160v8pApmxC7bzez/zmc8MY8y9SymwNi1Ro2kCMcRNpIgJjiu9mEDzHje/Iz5ZcjiXmPDiiy8mSXbfffdhDL/k+WPNW6qEDSwxpgkYDdk+97nPDa+ttdZaSfoN1hwTiPMuYo89aDKQzCcmIPN24zvWJGekpO0Dr0PihaX6yI17UjPGfL7he20D7OK9gf+aow2SJqP1XsBH2f+y9x0XmTvLBO+///4krfHOySefPLzG+yxN/Nu//dsk4zknRlveyfss8Z2LP0JGaxsg1bSsj3XnmMC5xpJZylgxP95jrHHLafFplhVSzsRjc7YBjWV32GGHYYwzpvc79nC5FySYvp/AfuwR+xns15PD+n18l89hyHtdqmwuNkCm7PJbNKHzOZIzjGMl69/31Q8//HCS1rjTZ/8XXnghybiJFPGhZwPLxvlb3EB4LjZAyr3HHnsMY6zlXvkvx2Vs4Pvau+66K0mTOlt+zL7xNdhDjgfcc/seD+m5y0DMKS4zv76nQrLvJoysS8v4KdXiObz22muTJGuvvXaS8TkXn+LSLuwFS9hpukSZt6TtH84MyThmTBnMqc/6vQbZrLve/arvHXgfc+ayIpyHXRaMs5bt2LsP75U68T3iB0ExUguFQqFQKBQKhUKhUCgUCoVCYQEsylQEzAk3NCAbafYPjFVn9mFGOnMMa4bMjNkBv/jFL5KMM0lka8jI+bM06Una02sXWqdJxtRx6623JhlnDHlybyYk2QJnlY855pgkyc033zyMweQlI+EMGxkdmiMljQ1mZjGZiNtuu20YO+igg5K0TFuS/PCHPxy9NmXAejOLiKy61x3MImdUmBMzIWBH7LfffknGzXtg+5ldDAsG5m+SPPHEE0mSP/7xj8MYc+2smplMUwb+orcXnHGH5eIMJdk5sshJy56RNXUTCtY7LNSksWHMnuE7jj322GHs4osvTtIYCsmyjfamCrKRZluRZTTrAhaA/cZDDz2UZOzjmUv8kOf705/+dJIxM5tstVku/E233HLLMLbvvvsmGWdUXXh+yqD5Iqy5pK15sy5Ym47B+BGzvLARjDJicZKsu+66ScbzDbvR5wIallx11VXD2LnnnptkHKdeeeWV9/ELFz9g/ZidDjMFpYJfx98nzX85A0+cJTtvn81ZyzaAUeb9xfnKjT7xd/aZjitTBywKnweZS7MjsIPnC0YWLJck+dSnPpWkNShxg0h8lX05zDB8VdLOnmbSw9Y3i8/2nzI491uNxBrzeZAxx2DYST4bsbeYZ/t/WE1mGuH7fD/B+dY+EuWJG6D0muJOEbDgr7vuumGMM6jnit9udRR2cUxgbbJv7NPwJW4QQ2z1+7gnMfOR+TYjygy1KYP1d8MNNwxjxGOf2/HjvldjDnxm5FzDNXzvgW+zj8F+jhP4m55ywYyyucQE1heN1pLmn3xPxrp2jOg15V111VWTtLWMYsHf5aab7CvbgPu5xx57bBjDB5qBbLXj1IHf/ehHPzqMEaM/+9nPDmOsQd9TsQd8X73JJpskafcJMFSTdt/h9cy6d6ymefpll102jP3zP/9zknGcp4Eo9+ZTBed/lJdJ8+te68QHn2VRftsG7B/8vn0G9xiOCbBOfV1s5M/CAPfZzH7tg6AYqYVCoVAoFAqFQqFQKBQKhUKhsADqQWqhUCgUCoVCoVAoFAqFQqFQKCyARSntR75puQHScMttaFKx8cYbD2NQq03hRW6MVMHSpiOPPDLJmP6OJNByRT5jyjYNSyztMWV+yjj44IOTJG+88cYwhrzJc0uxeTeBwR49iTEUazdOoFC9ZYVIzi3HRaZiSdx55503+nt9vTkA+YbnnILXHrv++uuTJAceeOAwRgkAr+OlGymss846w2vsO5pOJY2eb5o8n0F+mzSJrZuxuQzHlMGcuRwF8N5HcsyeSJqkeKONNhrGkB7gyyy1QhJu6Q8yH0slsOlXvvKVYWyvvfZKMpZI225TBpIQy5CQLVvC99xzzyVJttlmm2EM6YYLyyPxQxro8jCUrHBzEuy8wgorLPO37b///sO/WSOWbrqJwJTBb3OTA3yRyxcgt/Ha65VmICYQC4gvSbOHyyYg8bE/w1cdddRRw9iFF16YJDnssMOGMfulKQM5lPcB0kuXVUHm6UY8xHKXHsEGlEnyWkVWaLkgvtA2YE/Y7wH7MZcZmDqYczevYcylWvBbngd8g2VkSzeKsiyacgqWoVMCwI0aiBOWleMPvf4tcZ8ykO/7nImk041qeyUXOK9Y1sf+wf/3bGbfh4x39dVXH8bYHx7rNbR1qYUpA9/Qa3rmxh5IWC25ZT27ARWf5R6Me6ykLyunWYsbm3CP4XMsJZg8774HmTI4P/p8gx/3mZEzjEvucM9lyS22ZB/4jEuTYJcuwt85/hAffL/OGkDqnIz32JTBWndc5Lzk9coceG1yTurdL9OQ0/6E63otUybM80lscGMwPuN1MZcyI0krVdEr1eXnC5SDcflGSkf5LMtcY49NN910eI3Y4cZWPLc4/PDDhzHOtS43g11pWJXM54zKXFluj1/3szfmz2cYzqtuiMeaJU70ysP4mRD+341OsaNLTbFXfb71tT8IipFaKBQKhUKhUCgUCoVCoVAoFAoLYFEyUmFCuAg5GRRn0yke7CwQ2RVnQ8me8aTahclhAJDNTxqzzE+0YXmZwceTd2c65sK6IJNIg5GkzR8spGSc+QeHHHJIknGmgYww2TlnychMO1sAq9SMga9+9atJklNPPXUYO+mkk5K0zHQyzjJNHRSgdvFlMjRmrey5555J+gxE9lPSMkKsWWensa+ZL2SBXHCe7LUL98NOdZZ0Lo2OyPI6uw7MJnXzLQA72/4IRhisAa97/u2MP5k9F60ni+Zi6b/85S+TjNkFvvaUwbrqMf6dhUdhYJ9MFtk2YB8wP74GzA43SuB7zZ5hn7ipEXYxW3wurAuyxGYtsic8B7D0vDd6LCViATHbsZM1T7OcpDHAHBNgljmeEB/si+bCuuB32G+zJv17t9hii2U+i7rBcZn1il3sT2AaOabwumMK/sasAPaOGcgfNtu/GMGZ02ch4qf3B00lvvGNbwxjxFLvBexAbPDewR/5LAbLxixgGmGgTEjavjO7fi7+iLOn5wqf4vsEWJBmfLF/ejaADW+fhg18T8L39poqmfWNvzJT2deZMvANbrhCbLXKA9a62Y0wtOyPeB2Wl5l7xFT7/1133TXJ+Mxz3333JRnHDvxRjxE1dcCg8+9hruxzeZ/3C37c65U9BIvLbF/2kGMNZyOfWbGfP0tzHjPVvMemDM6Cnm/ORt4HvWaRxBLfLzGXxBLPo3052HnnnZO05sR+nz+LTf3MZC6+KGn723EZ9Y7jMs9x7Hto+OiYgD/iGlZNsZ/MKuWZlOMPvsmNifku+0PvyymDtd1T4pn9ubSfSdp9m23AviDO2lfBgve6X2uttZb5XtaFWftcl2aQS1/ng6AYqYVCoVAoFAqFQqFQKBQKhUKhsADqQWqhUCgUCoVCoVAoFAqFQqFQKCyARSnth4ZrCRL0aFODkbm5ODA0duQdSbLBBhskaXIe06mh2lN8OGn0X1OO+beLNCNpsMzN8qEpg/m2tAa7WJZA0wM3fAEug8Dr0L0tkUWy/Nvf/nYYY75N8WY9WHpOmQZLN3vlBqaK1VZbLcm4kDu2sZyJ+bLcntctZ+J15sv7Ccq8JSbICV0wHcmKpURIWizltKRlymBeLClgzVoqwN63P8DX+H1IaZCVW17SK83Q2ws0sfIe43WvgbmAdeUSE8ybZWbEAs8pdrO0hv1EvHBMYP3bz/RiAn+T5VxIpvw+SzqnDOavV17B0k5k9o6FzNE999wzjCGpQS7uPcL+sj/B1zve48fcfIn3WQJn208ZrCtL1yypBMiZfV7h/OO5wFcgu7SfR0pluRPzbd/O3+TSI3yHv39Ofqkn9WMP2P9fcMEFScbrmHkgtidNos8+sU3xR17P9957b5Kxn9lss81G10jGJUbAXPwRv9NlLnwmAsyl5wIZZa/JznvZwA2K2AuONZQfcZzqSTZ9/p0ymNNe8xzPH2OeP8pVef6I5ZyRHJd792DMY6/skWMSZwDfG/hcPGWwNh2X8b8+MzLfvtdlnjlPJs0vEZe9b4gJLnuHb/M9GHb2fSGlFhx/HMunDNaXyway/i2jp5wFTaeSFg8sscdu3C/7bMQ824633357krGknH3l5oOUufL15gRKRTgu9p5b8NzH65Mz01133TWMIdWn7JT9NnPthpNc1+dRGlD5fpnrusyF18mUgc+2P2CufE+Fz/Z5hPmjdFGSPPPMM0ma7/EzC85aPre++OKLScb2puyUm6ezLvw3ORZ9EBQjtVAoFAqFQqFQKBQKhUKhUCgUFsCiZKTyZN7ZSJ4eO4NF1sHZFZ5Cm6n18MMPJ0k23HDD/9/vdLMKruvMGaxWN5zh6bWL6s6luQtZfj/pZz7IECTNLttuu+0wRsb4tddeG8Z+85vfJEmWX375JOPMGRlNCtL7dWdDKSxvZsvSmbtkPjZIGmPCv4nf7Iw6mbWPf/zjwxjr059lL2yyySajayV95iVsih6r1U0WgIvH+9pTBuvZGS58jrNuzJv9DAw8s2bYU2Tk3KQKZoe/q7eeae41lzleCGRzzXLpMa6ZK7NWWJP2G1//+teTtMZEzlSuvPLKSfpF+h0T8GWOP7zP2ee5+CMy9r2YYPYpfsexknVNkf0kuf7665M0v27m4yqrrJJkzK6DhWmmEXZ2oyq+375oLjaAyeL1tXTDqCT505/+lGTso8m8W3GA/dhXvgY+iP2QNMaA4zLsbp/N8G1m5s3FBklja/X8keemx3qAtfTcc88NY7CL/umf/inJOF6wt8xsxIZmZ2AH+z78kc9bju9TRk9pgA3so/AXZikxp1b63HDDDUmSfffdN0n/bOTzFXvLawDVlBmSXMdjc7EBMdXzzZr3mmOOYOQlbU94LmiYdtBBByUZ25b5dkO8ng122mmnJOOYhA2scpyLP8IGPofgF8yQfOGFF5KM2aSwTznLJMnzzz+fpLHtfRblnhw/lbQzcK+ZG2eGpMXlOdoAhYZtwNo1C5rzzLPPPjuMcSayL7/zzjuTJNtvv32S8T0CzY38zKJ3vwwT1c132Ac+G9kHTh2cQRw/sYN9CecofEWSPP3000nGzYdoXnv++ecnGTNNuT9wE8ieUpHr+dw152dHxFmzO/H1W2655TCGn7GCB1/Ga0l7HkhsNyt4aYVt0ubUtkLx0zvfWunzYRtkzyOaFwqFQqFQKBQKhUKhUCgUCoXCXxCLkpFKVqHH9HFtMJ7gm3XBk2nXPiKrc//99ydJNtpoo+G1HsOD7KozBNR8oQZSkmy99dbLfL9rYU0ZzDdM0qRlkzfeeONhrFejglo9rvkBo/fGG29Mkuy4447Da2TfPIb9nK0gw7DddtstM+aMRK8u2FRx4YUXJkm+/e1vD2NkGp1tXnHFFZOM54uMkDNxzN3xxx+fJDn77LOH18jaYKuksQu8F8hGm8VHVu7DZnQWM8gGmxGEH3I2i+yYmUawLWCIJc1ua6yxxjLf1WMA4wed4aPmi9nhsMUMZ7mnDNgOZq9cc801SVrWOGnr1KwVfPu77747jLHuyULbFnfccUeS5LLLLhvGqHPk9Y1Nbe+dd945yTgmzKVuNnNrhgrzstVWWw1j+KAec8g+4/Of/3yS5Ktf/WqS5LTTThtewwZmS+yyyy7LjMGkOfTQQ4cxWE1mgL355pvv5ycuevTqTpO9d10pYrBrcMHaNTuSMep8uU4btausFOG85HiADzRbjxhsG8wpLsO0Za0lbe97bvA9PZWHx2AlUavQa/zJJ59Mktx9993D2EUXXZRk7I9g4bjmGNcxG7NXU3eK4Lf77Imvdb1I2Feel169YOIISirqBifJLbfckiTZa6+9hjG+1+dczv9WxPE32Qb+m6cMWFmuNcj691mUMd/TwRwyg/rEE09Mkjz22GNJ2jknaay7Sy+9dBjD71ttwv2jFSWcgxyXfSabMogJVmqgBrENOKN7HeL//Vlqmd58881JxmpDati61jmx39+Fj3H9evar79Uci6YM/InvB6jVSF3SpM3RFltsMYxxbnetzYMPPjhJu1/eYYcdhtfwMVbsYkfbgOv6byL22Aavvvrq+/mJkwBsRSuUOKP6uc+qq66aZPw8B6avz4qopt56660k4zMOfsuxGj/jMezK86ckOeecc5KMz0dmYU4ZrEvPAX6Xs4xhn8y9Bez5pDF6WcdmGzN/vsfizOvrYj8/M2GvmKXqusMfBMVILRQKhUKhUCgUCoVCoVAoFAqFBVAPUguFQqFQKBQKhUKhUCgUCoVCYQEsSmk/MmXL86H1WsrE65a0Qg223AaasOnsYKWVVkoyLmALldgyFOjKpodDCXZTDRdYnzKgTEOBT5qM3rIpqOyWi2ADy0WQRlFs23Tqww47LMmYnk0zJMtrkZRfcsklwxhyT8t0TP2eOk4++eQk4/VJsXbLmZYuyJw0an1PYkVDBb9GkyRLRpCCWM7LXuwVx/ae6UnNpwgkUV6z2MM2AD0pt4vuI+lEHuVyJb3PMqc07EmSl156Kcl4rWM3/529v2+KoKyIpWL7779/kn5MsD9ivu23kAdus802ScZzhnzTUhyaHz3wwAPDGHbbbLPNhrE5xwT8jstO0PTMMpqevAkbuDA/zV2uuOKKJK18TtKajViWRome2267bRhjPXCNpJXA+POf/zyMIduaOvDDXq+cl+xv2QcuR8Jn3cALW9GQwvJCpLHIopLkhBNOSJLcddddwxjyaEsEkcBZttsrSzBVIBNnTSZtz3vOsYPnnL1giT1NXyw1B/ioCy64YBijjMBTTz01jCFpdgkF9qwbgbmM1ZSBz7H/Z1/09oLXn88z4JFHHknSGmJ4Hj/3uc8lGUvyzzjjjCRj/09ZJErGJK0pjMtszMUGxALL6PmdC8VlbGC70JiHElRu1INf32+//YYx7hOeeOKJYazXiI114eu50cuUwZz6PM7e8HxjK/9ufJHLRvE+Sl384Q9/GF6j9Ivvg5H2W+7PfbUbDvNZx2U3PZoyOGe7NA4+vWcDl3VhH9gu3M9y/+14T1kZ+xNKVVEmKWnnXUvGOUdTosdjcwDrzs9/GPPzH3yDx7CD7xMoO4jfd5kLmrHZpzD2ve99bxgjJrjUFfvNe4t7jKmD84/v1fD/vbjrMf7thl+UMuRM6fdzXrIfYY/ZLnzW9y5cx37zw94nFCO1UCgUCoVCoVAoFAqFQqFQKBQWwKJkpJJddNbr8ccfTzLOnpDld3aHTK+fMpMZgiFjxh2ZHmcwyOCYUdl7ks4Td2fz/JkpA7bQEUccMYxRUN4sITIB/t00wNlpp52GsV133TVJy9yZudhjET/00ENJxkw+XnfxblhMZjr1WB1TBcwGsx7ItLigNmxhMwPIcP72t78dxlj7NLdwhqbXKArmpZmu2Au2S9KyzGa4ziXjT3bYmcrjjjsuyTgLTzOjX/7yl8MYReU9f/gNbOVsM7BPwaf1GtmZIcO/PeY1MmXQ0MmZytdffz3JmDlERthjMEI9F4ccckiStl7NDmO+7aNouuc1jR0df955551lvmsuDb9gQHgO8PswpJOWkXbhdtjUZl/DqqPhhPcIcFb79ttvTzKeT3yRfT6sSv+dc4oJyZhxRxbdMZiGCZ5vWCg+QzG/xI+eosG4/PLLk/Tn0+x4ruu/cy5no6T5eDfehClkJgQsVf923me/T6NH5nwhfwQjuOdb3MAC/2a7OuZPGZxDzQJ79tlnk4wbZjJvXs+cdTxGbGFt2/f0mHtnnnlmkmS99dYbxrCpz63EIqtD5rIXaMZCPE3afZnXGQxuN3ykmZfXK2udc6fvCbCB7QKLuOePfAbgemaGz8UGwGvZcwp+97vfLTMGY5TGyUmzG2oC25H1bxtwBrbPIi5zHktanDdjfy5xGdWGWYWciXq/0Uxd7qHd8BSWNizGXqNG2wDlYu+71lxzzeHfKBMcl+dig6StQbPcWXcwG5N2/+vnOj0VD43XYBW7YSGwHVDloNRKmg/zdfGDVhnOpQEhe8E+iHXstcYa9HO+e++9N8mYKcz6xffYzxBv7cuJ7fb1wDGYZxY+P3xYBWcxUguFQqFQKBQKhUKhUCgUCoVCYQHUg9RCoVAoFAqFQqFQKBQKhUKhUFgAi1Laj/TMNGDov5ZDQbd2IX3ovJYYI2dGGkiB8qTJdk11R5ZpejGUbsuroC67jEBPDjdF7LnnnknGTY4ojIwkJ2lFzd1UAkm/5wK7nHLKKUnGJQN4H3L+pDVYcKMWpKUuLYB00eUaek3FpgrkZpbmIfe3tB5K+n333TeMrbPOOknG0mPmCTkO85y0MgmW5CL5seyffUm5Db+vV8x56kBu5t/DOraEj9e9P5B1+LPIMVnblq8hW7BcBD/nayBzsF3Yqy5BMBcbUJ7Fsg7k9rvvvvswxlzZB/Bv+2lkzszPP/zDPwyvMY+UMkmSFVZYIclYrsL7LMnhOo5TjkVTRq80DuvQhfSZb0vr8dOOCewDfJblUPh4xx+k427Uwvv8N62++upJxg2/7AOnjJ5/IG66MSSlbrzmadbo9ci8/PGPf0zSGuMkTQpnG/Ad3l/EZft+ZGz+rBuSTR00WOlJ/XxGZB4sPWPd2w74EM6yluGyLygbkrQGGp5T5tryc/7t8+1c/BG/w3PLOdPnJcZccgEfYr9AHKbMhaWYyEP5b9L2im2A7XsNv2yXudiAxk+OgUgm3WSHOaKshcc8F3yGPeKzET7KTXa4nv0RJZBc5grf5PPQXGzAWveap5yX54AY7BIsvN6TOBNHKauUtHJKPodx3u3dq7l5Kt/vODGXezXOpy6/tfnmmycZ36/ye136CV9uWTF+hHm0L7r66quTjMtccdbq2cDPR7CBz6dzeWaRtLVtO9BoqHdGdakR/HnvfEQpHRoyJ62siM+jlFnqNdDzma13lp5LM07WWK9UYE8670aBrF/PC6UvOM/3mlwbxFn7tF4jZuLP/8X6n8fdRaFQKBQKhUKhUCgUCoVCoVAo/AWxKFNyPMl2tp8xP1Ema9NrqALjKGnZBwoMm3FH1ogsdNIycma0UBTYmX3+Fo/B4jj00EPf8zcudsA6POGEE4ax7373u0nGNiDj6KLJS2f2k5ZhvuKKK5Ikd9555/Aan91ll12GMWzvAtE0EDArDBYlWb0k+elPf5pk3OxqqiCD42wzbGHbgfmlOHbSmBp+H4WV99hjjyStqViSfP7zn08yZkTHFTIAACAASURBVIKTRes1anADLK7r7OeKK674vn7jYgdZMrOEyHL20GtIZJbS8ssvnyR58sknk4wz9BTnN8MD+5mJwXf4s2bHAvu6KYP17d97wAEHJBmvb5hAzv6SMXaBf/zGZz/72dH/J80+sFCTtv/s02D92f/T6MgNffy9UwZz/8Ybbwxj/E6zrfDdZJKTlkU2Q4XC/Kx5swNQjTirjD8zw4M1b1YY+8SMqN122y1Jv7HblMC6dlaeuOjfxvnH7CP8mFkrMOxg7Zl9hKLB880+9JrHFzluoF7x2czs1KkDlomZL6x3zw1+32OwXLw+OWviU/wasdXsLeKDz6jsBftI/BVMmSS55JJLkiRf+9rXFvydixn4Bjd8xJ97L7DuvBb5rOeZeWMvWJFAwwv7NNY7zKQk2XbbbZOMmTecicwOZH9MHcyt5xGfYxvgQ3yOxIfZR3Fewac4jrM3zAAjLvuc02MHMvf2UXfffXeS5KKLLlrwdy5mYAPf88Ku8/y9F0PR6xW2KfvK9x40RPJ8s2/MHsZnOS6j/uR85bGpg7nv3SN4bxA33ASSfeAYwT0uTNTvf//7w2vHHntskjHrnX/bLtjKNuDfPleZXTx1wFqHtZu0Rnh+boA/MtOXuOyYik2wl1WY++67b5LWXCnpq034rO8B2Vt77733MHbrrbcmGTd+myKYMytm2ef2Efhkny+JD1aK4LdY28T4pPm+V155ZRiDSe/v4m/yuRm/5fXv5ycfBMVILRQKhUKhUCgUCoVCoVAoFAqFBVAPUguFQqFQKBQKhUKhUCgUCoVCYQEsamm/pSHQdE3XRVZouSXyP1O2kS3QEMmUY6jBl1122TBGEeeNN954GOtJtJBIeMyU/SkDCQBy/qRRoS31QEZjiQZUaUt2kBKcd955ScZF55Hg2GZIRZEX+ntNz3755ZeTjEsL/PznP39fv3EKQJpjiY73AEAKZflkr9ERewqJiRs1QLGnUH3SKPMHHnjgMPbSSy8lSdZaa61h7I477kgyLsbNvrQUa4pg/iznZx49t8gR7A9Ar/g4pRHefPPN4TX2HQ0bkrbvkEslTabiEgD4qxdffHEY6/0tU0QvJuAPLGWloY4bi1BonNeSJidHYml/xJxa8kcM+cIXvrDM+yzdQn7iGIPcf+pgbXoeezGBMiuWGiL3dwF61itx1KVAiAmWtG266abLvK/XXIbmVW4qiRxu6mCtWSaGDbwPiJuOy8ypfT7Xo3GbYzDytNtuu20YIw74u7Cp5Wz4J6RuSWvQNAfwmy31Zg06PuNXfEbFH3lu8O3YyKVBkCN7fpGQ4seSZkt/PzI4l0iiNNDUgU+21I9zkueAZjiWNLMHvI6RFSKTtSQW/2VJPufQLbfcchgjJjjuslYcq102YMrgd1oeyT7wmXWVVVZJMo4JzLP9EfuJ+G2ZLSVJ7Pvw/94H2NEx4aabbkrSYkgyvmeYMtj3Lu2Eb7GPQertMkasa0v7WfecZ33u5P7i17/+9TBGCSQ37GT/uZQDJYF8NrJfmjKYM98j9M5Ga6+9dpKxDfD53kNcj7OjS7Nwb2wbcK/diwdeA88//3yS8RnO93tTB+VvkPMn/b2A/Nt26JWEwg7MueeKMgJutN07IwPvBe4bXb6ShupTB/PsezDihG2AL7GMv9dEmbniTOSYwD2Ez7m9coTYyA3HHPsB16GMzPtFMVILhUKhUCgUCoVCoVAoFAqFQmEB/NVibL5wyCGHLEnG7Dbgv5dMpjM5PN0mU2mQQX799deHMd5n9iSsDGfLyDSZKQArwNlnnrgfdthhjao2QRx//PFLknG2v7dWyByQnTGceSSjRkaaRjtJawJgpunKK6+cZNyoBbbveuutN4y98MILScbNTmBu7LzzzpO2QZL86Ec/WpK0+Uhag5BekzUXbiY75gyb5zgZZ9PISnsvMJfO3PG9tjn2NbsAbLzxxpO2wxFHHLEkGWcZe01fmCs3zSFL76wbDF2YFW5OhT2ckeO7zHaHscpaSJK///u/TzK2HxnPL33pS5O2wZ577rkkaQz4pGUUnXn0+gfYxaxg5gVf78L9PVYRhdHNdqcwec/3mFHMZ88444xJ22CvvfZakiT777//MIbP8L4ngwzLMWmsFq91mHawCNwchH/bBsTeXkzw3sRXeW8Su6YeE9ZYY40lSWNUJI3haAYdv9c+mjhgG3Be4bNmjHGuwq8kLaY7brD/zEBgzZsZyN64/vrrJ22DJDnssMOWJGPmQs8f4bvNSmLO7Y9Y51yDRmxJm3M3m2IMdUjS9oDnnP1pHwW+/vWvT9oOK6200pJkzOTpnVHx9X4N32C1DPcFq666apKxP8JmbliCDXrMG5pTJU01ZUYx6+Hqq6+etA322WefJcn4bML685mH84zXJgxhn1do3IKqgYZQSbOL/RH2c6zhfOo9h13cbAp/ePHFF0/aBsQE36vx273m+LcbPrKuHRPYJ/4s4J7XzC72ge+rsYH3EGcA2wX/9L3vfW/SNthiiy2WJOMGpZyDrFojHviZBWvS98tL36d5HvE3Vo/wfMJxgxhs309s6rFfL7nkkknbIEkuuOCCZe4TmH8z5ImzVgbwLMj+hb3AvFnxyVnJcRm1lP0W6iCre0899dQk4/sV7LnttttO2g7XX3/9kmT83OGBBx5IMm6kxev2ydx7Oc5iI+bWigTWrseICb4nwed4DH/k7+J9O+644weyQTFSC4VCoVAoFAqFQqFQKBQKhUJhASzKGqmw78wSIiPgp9dkIQ899NBhjLohfkK9xx57JEluv/32JGP2CtlqZxVgeLjeCE+qnflkzDUJ11133ff1Gxc7yOA6E0aG0pkdMmf+3dR8dP0PMmbMrTP2ZNhsF+y33HLLDWNkkR5//PFhjAyDa284Uzd19DLp/D5ndsn0m2GBbTw3Tz31VJJWP9BzhW2cQWXOXU+K73JNJP4+swv8vVMGdY2cyYdtYUYo9fC22267YQz2nJkYjMHidc0X5t7vZ8zfxVgve+q1YmbklME6dZ05/JGZRtjo+OOPH8ao3+vPUuv0hz/8YZIxA5j5c20wPuuMKvvP+5D39TKkU8enPvWpJOOYQFw2GwUmhOvRUevLGf3ddtstScvem32Kbe1jmFOrQpj7d955Z5n3ed7nYgP8q2Mrc+V9T4x0jdkf//jHScbnqgsvvDBJY0h4zcM6pTZh0mxvxQ828Gd5n2uKURN6DmBO7FOIt56HSy+9NEnypS99aRijDhfxNmmsykcffTTJuA4wtvac81lqTyb92pT8fV7/i1GF9mEAk8TziA28F2C9nX322cPYGWeckWTMzLruuuuSJN/61rdG10/anNoGfNZ1conbvifgHOQ6bq7JPWVwhnH9WWxAHEjauj7qqKOGMXyU/dGtt96aJDn55JOTJJtvvvnwGuwtM775rOeTfeB1Ttyx35xLTOB+2b+NOXKshu37la98ZRiDKebPnn766UmSb3/720nGzGJs65iADXq10z3HfIdZgHO5X+acbWY2NvBcPfbYY0mSe++9dxj7xje+kWRsq4svvjhJcsoppyTp11y2IpG90btfdozibOR7sxtuuCFJcskllyz8Qxc5OB/ZH+H/7aeZa98noDYzS3TnnXdOkjz44INJ+gpE+x78zBZbbDGMMdcnnHDCMMbf55jQU3NOEbDWURUkrY642dGO24CYa3UVz46Iwd4L+Hg/v4Ppav/PmGtB49d69/UfFMVILRQKhUKhUCgUCoVCoVAoFAqFBVAPUguFQqFQKBQKhUKhUCgUCoVCYQEsSmk/lGlLOKA9uwD2BhtskKRJ9pNGA7ZU88Ybbxxdzw0YkCua3gs92zR9ZG5uEIDc1JIGX2fKQM5siTFyKdOzkS188YtfHMYOP/zwJGNp1EMPPZSkSaNMsYaC7aL9ULv9XcilTBlHPvSHP/xhGLNUaOpAGtNrVIB0I2nr8utf//ow9rWvfS3JeB9BsceWlvEjffB1ocRbvnDBBRckGcuu+Dsti/C1p4we3Z/f9tGPfnQYw0cde+yxw9iRRx6ZZCwJWVpSZnkh+8lyoP/6r/9KMt4zFFOn2VrS1oglE72GZFME/txzwHzb5yL7/pd/+ZdhjLIUljideOKJSZrvtjScRj6Wu/Fvr2nkXPZHSIssr3VJhikDX2sb4Csst8ePIJVNWikRg7IK7A3PGbHVEjT+bRuw184999xhjDXvxkkuKTBl4I8tb2IfWMqEnz/nnHOGMfaBfdF3vvOdJK28iOWFyHa9D5AG9s5Gbi6ADXx+sIxt6mAN9vaCmw3SmM3+hXlyTKfcC7a0HYi9xAGj11zPjWB6Use5xGXOiJ5HfITPPMy3m+R94hOfSDL2Ochosan9B5JZ+/Je2SVs5PMS/tDfNZeyR5TusIyfNeySRdxn+bzCOvS92nHHHZekX36Cde2542z2yiuvDGOsB5/NaKZkG8xlHyCldQNCfLb9E/fLP/vZz4Yx7tEcE84///wk7VzqeyvOnbYt6DU59f1bLyb0fNoUwf2y13JvH3AOOumkk4Yx7t3sx5Ccs9bt57GBx7gntH9i7m0X9qmfgey6667v5ydOAvh6n0W4P/A5Bt/w5S9/eRjDht4Ln/vc55IkO+64Y5JxXKakhs+W7DeX9nruueeSjMvwcBZzWbAPKytfbKBkl8s6cU+6UIkhfLKfQXAvTOzAFn6f7+2ws/ciz4RcKowzkf2Rv/eDYB6WKxQKhUKhUCgUCoVCoVAoFAqFvyAWJSOVp8LOKpC18VP7559/PkmyxhprDGNk9j/2sY8NYzRkITMBazVpGTF/F1kbXxc4q/FehW6nDubKzYhgoZjxecUVVyRp7KKkNR4577zzhrGDDz44ScvYmNFCBsgMVuxsW5H5d9aADLMzHW+//fb7+o1TAHPidf/0008nGTNCL7vssiStsU7SMtW8P2nsOdaus80wkJwZJVvjpkWwC5xt7jU6Wn755d/Xb1zswEeYwQAbxQXCd9hhhyTjhgrf/e53k4ybVJA9I1Ps15g/Zz5Zz2RAk+bTXnrppWEMGzgD3mMsTRGsSWeL8U2eP3yPC8vTPMSF5clMcl1ni2EB2B/ht4455phhjIYYzqzyb8+72ZpTRm9eaKzjfX/LLbckSdZff/1hjEL+66233jCG3dhXZklwXe859ouzytdee22SMdsRv+S4PBeVAvHO+wD2m30GbDrHb5ij//Zv/zaMbbPNNknafrHipheX/+Zv/ibJeH3DyPDZBxs4lvhMNhfYDvx+n0V++ctfJhmzfljbbkhEnO3NOfHCtuE8sOeeew5jNFPCRkmzg8+tjttTBgw32wAfYZYJzW3cwMsMYUAs58xpVin7DmZj0mxw0EEHDWP4o97ZyHHCzTSmDNa6bdC7V6OxlJsLwRp28yHOjGZ0ARiS3hvEBzPLXn311STtXjBp9rANzNyeMlinZmHDuPPvpeGXfQZzhJ9KGqMM321/gg16Tde8BthLVk6wh7wu5uKLOBv5zMG8WXF2zz33JBk3JMQ3O6ausMIKSRqrzqxS3uc4wzz7fTS0ctzABmaQ21dOHZxDPTeozsz+pCm51yL3CVZP0SCTveX3YwerB3mfz134K7PhWfe+f1x11VXfz09c9MD/9hr4+jxotjzAn1tVyZxjRze27T1vwx9ef/31w1ivYXSPEVvNpgqFQqFQKBQKhUKhUCgUCoVC4S+EepBaKBQKhUKhUCgUCoVCoVAoFAoLYFFK+5F1WJ6JvNZSAIprmyLMZzwGpZr/mlZPwVnTvqFbu4A51GRfl7/T9GLLK6aMhx9+OMm4QP/ee++dpMn5kyZreuutt4axBx54IMlYBovkgIZSN9xww/Aa9HnLQ5EquOA/0s911llnGKO4tuU+G2644fv5iZPApz/96STJG2+8MYzRKME0dBp8QatPmszE0mKo9UhR/JplBoC1bXkKsgVLQriepSVzafBCGQqksUkrpG1J+F133bXMZ3slACihgLTgP/7jP4bXLAkBa665ZpKxDa655ppl3sdn3WDKJTSmDEoZuKj4yy+/nGTc7ImmIJaUnXXWWUnGchEkmkg9LMliLffWt+cd+ZCbWmBL9mjSl7BMEfhn+x1Kg3i+KXFhn8zrlvsQqylFYQkhc9+Ly5amI5XzdyGR8pgluVMGa9iyVGSzPtfwe302QS7oz3KeWvq/hn0X329pJzHqzTffHMYopeB4YP81dSANd4MXYgI+KGlzY2k969dnRcoVMf+eK/aO/RGfRa6btGYyLqnzzDPPJBmXVfBenTKQZ2600UbD2JVXXplkXF6H327pMTHSpUOQwGIrn2mZU8sz8U2O+z3Jfq80w1xswFxZ3oq/8Hwj6ffvZo07fnI+ZR94Pok/jgl8x3333TeMMd++f8Qf2UdaZjpl4He9DyjxtfHGGw9j+CLLW7mX8vkHGzFn9lOs/54NemV4HIP5XttgLmcj7mF9n8b9gu+DPvvZzyYZr2vOUJxxk2Xvl/F1SZMpu8Qd8+jmXcyzZe7sV9vbNpo6uPenrF3SSodYyo1M3PGbBlA+KzKHPGfwnLMH3Pyud0bmzOvvx06+T5gbPAe98gacZ+wD7FcAr+NffJbqPZ946qmnkrTzkL/Xz5O4X/6/eGZXjNRCoVAoFAqFQqFQKBQKhUKhUFgAizIlR7bAT6p5auxsGpkAMybIQrpwMxk2spx+jWyas6dkSF2sHJYBDNZkWRZBMh8W3iGHHJJkbIOrr746ScvOJC3bcuuttw5jNLBwEXka5ZAZ2HLLLYfXyDTYtmSKzD6CPXnJJZcMY1/84heTjO0ylwLmSVuDzp6TxTLDjuyOGUCw45yZZ68wR86wscecoekV7+ZvIVuatOyT9xHZn17TtimB3+mMOywXMyR7zed43cwwbMD7zULlO2666aZhjCy2m+zg05w9JQvrMTcJmzLIVHpuWVf2G6xNZ3+Zb/syYgf7wHuJ99nPwXJ0RpN95Wwo+9A+yPtkyiAGeh5Zh25wxtw7U49/MFuaWMmcmRmBDzILnGYx/i7Yr2bcnXTSSUnGTWC8bqaMpZtQJC1Weq7w67///e+HMRi/Zgoszb62n8KX+7rsAzM+aFzhz/L3mYHg2DR1wO5yrKQJY88fWSnCPrLfR92E37APxw4+T8Ek8/kIH+bGa8y/14tj+ZRBXPTZCGWOz/isdzcwwof5s0uf592cqtcEjFjtOMEa/81vfjOMsWcdE6y+mjJQg/Riq9lxzJ/Pm71YubQN/H5sZTYf7HrvA+xt23JP6b0xF4b8VlttlWRsA5rbOVZyDvKZkPsm+2n8OGOeb/wd7PukKaZ8Fua6vh8mRptRZhXKlPHuu+8mGbPOsYfXcO9+ivnuNevCBm7cybo2C5w953Ms39FTYnnMZ4Spg3veHvvfjZXZ+94L/gwgZuCrfA/GXrAfQW3o7+J9bqbKmc0qk7k8t+ipm1jjViTgL3xewtf43mHphlJWQxGDfQ7jXrHn330fwF702ejDKkWKkVooFAqFQqFQKBQKhUKhUCgUCgugHqQWCoVCoVAoFAqFQqFQKBQKhcICWJTSfmjypuFC4TUlnmLYpuPyb8scoA4jxXRBW2SIprojSeHvSJpEwsWBkSW4WLolb1MGv9dzxRwh5UuSH/7wh0nGTamgSn/yk59c5npIcXpFm3uFySkAnSR33313kmTbbbcdxrjOiy++OIy5yPDUgbTAsnKkS6azM+YSB+wBU9yhs0Oht6Sntxeg1Vu2c/HFFydJrr322mGMxj8uim5p3JTRk+fT1OLII48cxijD4AZU2M1SPubZEtulscceewz/5n298gqWK9JYytdFajJ1IGt1mQok8//4j/84jCHVcUMd7Ga5///8z/8kaTIRS2yApcjMs2Wc2MDyNKRTc9wHrCvLKLGBG+zQ/MJ2wbdbckucZf4ca/guy9fYQ2+//fYwxhnh5z//+TB23HHHJRn7p3322ef9/MRFD+bbsinWvJuuIZ90AwvihfcBUjhKLvSK/VtKxT5wiQaa7XgPEYNpypSMJVRzgRsFEmct2X/yySeTtFIASfMblrhylu35I/aCZYXEaDduYW/RbCFpJZp+9atfDWNzaYiKfNL3CewLj7Fn7M97jYawBxJP24A9Yxki3+FzK+/rxZ8HH3xwGKM5zdRBnOs1sLNUFvmwm19yBu01YWEP9fyRYwjf5fI57EnKiSXtHGapu/fTlNGTKSO9p8lX0ta8JeTcI9gGlHLpxWXgueMewvcI+Kxnn312GOMs6hIlc2m6xvz5mQFloHwfDFz2Aj/SswH7ylLnXpNa4oDtwrpwKbwvfOELSVoz6STZfvvtF/p5k8Ftt92WpJWbSNret//HXzh+9ppCYQfOUS4jyb7wWYw5dzkMbGffg80pTZgk559//vv6jYsd7GnPo+9TAfNmH4ANemUOmEffB3Mf53jPnrFtie3eR/i+1VZbbRhzw9QPgmKkFgqFQqFQKBQKhUKhUCgUCoXCAliUjFQyZ2Y+kkV3Foasl58ykxUzK4DMGuxFZ+dhOLkoNk+ozT7lb+k1+HnllVeGMZgFUweZF7NtybLYBrvttluSMVMMVq6zz2TKsJWvAcPJmX2yybbt2muvnWTMFODvNBvHjKWpg2yaM1xkbcxyZE7M+mGtmp0Nw4tsmrPIZGO22267YYwsmjNxZDBp7JC07JP3jLOuUwbMCrN3jzrqqCTjrBtMVLMzyGiaJYpN+azZxsy39wK2t5/hM96f/H3+O+eS8Se7aPYnPtnrDEaXM5T4GmekYY4SE5wxff3115MkO++88zDmhjsApqk/y9ybGcD1pg7YdW4UCOvUjCDs4nXN62Yysnbx8WSXk7YPzF7BL9F0MGnr28xLfOHWW289jM0lJnC+cAO/j3/840n6Tc2s/GCfeK6WjsuOrbBWnNmH6WF2Bd9vlh/XMaPYDRimjhdeeCFJO5MkLc7aHzFf3gusc/sj5pi4bNYoa/eNN94YxojBfh8NdbBH0vyRm7GZUT5lsLbsU2Cy2JfAYPFcwVDxuRUbsBccR1GbOP6w3r1nsJ9ZM1zHa2UuDV5YVz7rMx8+47PWvQ+WViQkzZbY4M477xxeYy/1mh/5zMVZy6wmbGB/1IvpUwTsU68vzuH+jUszrv2652/puGxFW695HfcQPgtzft1kk02GMdaFz6w+004ZNBLy/Q/nFDco7TEW2Se9uMx9l22A+s/zjQLEikT22t577z2MYQMrJKzmnDo4ky+//PLLvGY74Dd8D82ZxrGS8y3z7/u4s846K0lyzDHHDGPssV4Dql5cPuWUU4Yx9gJNFKcKnqU5tvYaeTkWANZ0T21LvPWzOu69/JyIWOPmXqwLqxnYC77H6P1N7wfFSC0UCoVCoVAoFAqFQqFQKBQKhQWwKBmpZBOcrSJTaTYp9V+cOV6afZq07ABPuddaa63hNcb8fmrImMnH0+1f/OIXw9hhhx2WZFyTk8z11EGtkdNPP30Yg2nqOoHUMDUDgHlzLR6ylrzmrCRZH9uAbIHriGF7Z7/JrDlD6kzI1PHWW28lSR555JFhjDo3zo6xjr0XYGV7Xvk3mRdn6Ndff/0kY0bN0tmgpM2155z32TZzYUM+8MADSZIdd9xxGGM9r7feesMY9aDMDIMVbIZKjzEB8F/ONmMrMzfIrrp+IfXbnGX9sBm2xQbWpP0ryoHLL798GGNvmCUK+9RzSgYTWzhj+pnPfCbJmHXH+jf76cYbbxy9lrRanK7RZHbslLHqqqsmSb73ve8NY7vsskuScayEjWJfRJzw2NK+yCoS/Ijry2IrZ6Spz2n1CjW0HQfmEhOYW+qFJ80urPOkzZXnG8a8YyqMl96ZZ6eddkrSPxv12KeuzYlSxd81l3rNSatxecEFFwxj7H37X/a+fT1r1fPK6+wF243zsGsOY1/7ORQlZr4QH6yk8utTBmcjs3+Y76effnoY22ijjZKMaz3CwPMY/8YGXuM9NRQ2sFqHc7CZYXy/95Zr/08ZnB/NIPrTn/6UZLy+8VuevxVWWCHJ2EfwGeaW+OLv6PkjKwG5t+AeJkn222+/JOMY06uLPkXAwLrvvvuGMRQLvleDaWf2Fix2r2H+jQ18fmEN+xrYwD6LM7D90+abb55kXDd1m222eT8/cdGD9f3qq68OY8Rj+xHm0nOFP/bY0udT22CLLbZIMrYZNrAvvOWWW5Ik++677zDWq0Hp5yFTB2xdg7Mffilp694+gDjrOtisX2zo+6lzzz03yTjeYwcrPXlm9OMf/3gY4/xmtY/Pb1MG8+HnAzCw7esZ83MC4kmvpipz6+dKvh8DPeUVz65sP2zai1MfFMVILRQKhUKhUCgUCoVCoVAoFAqFBVAPUguFQqFQKBQKhUKhUCgUCoVCYQEsSmk/xfAt/4aGu+GGGw5j0NNdVJb3mfYOddhSaADl1zTgn//850mSAw44YBhDsrPxxhsPY++8806SMV3Zkocpg9/uuf1//+//JUkee+yxYezQQw9Nkvz5z38exiiUbdkU9Gmu4cLPvM/lAZDguKkGtG//TZR/sGzQ8oapg8YuyDKTRk+3XA9KuuUL2MGycuR/SMsss2FtH3300cMY0hvvBQrUW77GfnOh7rnsBZrWWK6BDX7zm98sM2bpQU/auTS8XpGrez9hU9ub9W75OTawRMS+acrAH1jCzTy7YDtrzr+bmOB5fu6555I0mRS+PEk++clPJhnLS1jr9lE0o/I+oHyAJXV+fcrAh++xxx7DGHJL4mjSpGpuCvXuu+8maf7fYzRocMzG3o8//vgyY/Yxu+66a5Kx37v//vuTJOuss84H+HXTAFIm+3TWd69Av+eUdei4TBxebrnlkowbTyDFtI/jO5ArJ63ZiWMwMlM3G7GvmjqQ/+2///7DGP7I+50zi2MH77Nt8E2Ub/E5kz3gaxALLBvtNQLDXpaS2zdNlK5MwQAAIABJREFUGexv+3Xm1uW2GHOTLcok2AZIAvEvLnHEnLopD2vAJXe4j7CMH/lor6zJ1MFvsv/Fx/u8gj+yBJ+16bMRDdU+9alPJRnLZpHoeh45J/k+gVi++uqrL/M+l/B5rzPZlMB5z+Vr8Asu3cU+sK16Un3KJ1E2zf4f23oe8Xf+LqTTvldjD1E6KRk3JJsyWEs+nzK3jgecYXyO7J1P2QfEWTcmvOOOO5K0s2uS/PrXv07SyrMlrfQg56yk+X7H6rmcT5Nkq622SjJ+1sP8+uzZKwnCWnTpKBoR8dzJUnxKvnk9cz031uQe3qV5OB+5catLCkwZrG2X+eO+yNJ57GJfwtxb7k+5Bu7LfPbFZi4jwGd9D9hrfso52L7P1/4gKEZqoVAoFAqFQqFQKBQKhUKhUCgsgEXJSOVJsTMIMLvcMAQ2hbMrPI12EWEylBQ3J9uTtMynG8SQ2XPW+Cc/+UmSZOWVVx7GaITh7LP/PWWQWTMLAhu4ADyvw35MWqbs3nvvHcbIPt9www1JGvMuacWunb084ogjkowLqF9yySVJxjaAeWA2jrMOUwcsCmdKyKaZMcfcmXny0EMPJWmNMZKWET322GOTJGefffbwGllVjz355JNJxhnUs846K8mYDQMr0HvGNpkyyGw5m9ZjBcOesA+goLaZLzB6aVRlNiQZUAP/5r14xhlnJEl22GGHYQxGkjPbc/FHZCq9vvH1XofYwOwMGMVu0MO8kTnuNf9wDHn77beTjBuOwVhynOLvtL3NJpgy+L1uVoMN3LCAOTATAka9m0XCouD9veZQVoqghDDT9Zvf/GaSMUuWmD6XtW/893//d5LxXOGXzJbAj/h9e+21V5LWJC1pTC2u6/lmDfcayZjVxFnB8Zv1YNaXG71MHfgZ/HvS/IB9Cew5x+8DDzwwSXLNNdcMY8wNDAvbCHaXgd3MDL744ouTNKZ80lhgc2zGyVyZEYoNzEgifpsRhMLm2muvHcawFawiM9/ZF2apcp9ilirMfL8P/28bzAWcNXr3avYl3KvZB3D+eeKJJ4axQw45JEnbG5yVkhZ7zWKEneemIzSf3H333YcxYoGbjVg5NGXAIHWDS+Ky/Q7nGfsn7OZzO6o14okZ7ijU7P/5Lu8vbOpzEHHC+8B7d8rAF9i3wrTz2Yjzj2M192coaZLGsL7zzjuTJCuttNLwGqxLx1ueX5jBzd4xw5J7Z9/LfNgGO4sRnGO8FlnjKGeSPhsSFcjDDz88jHGPzZ7wmRImqq+LosdKFezg+zfY8raXfeiUccwxxyRpjN2k7XnfL7/X+dx+gzjM/Pm5B/7NKkzWu8+tfMasX+Ky788+rEqhGKmFQqFQKBQKhUKhUCgUCoVCobAA6kFqoVAoFAqFQqFQKBQKhUKhUCgsgEUp7Yceb9ozUiY3SoAeb7rwX//1Xy/zPmjWyEWQaSZNJuiCs8gXTANmzHRkSgW4eP9c5MzIZyyRojHRdtttN4whZbJ8APmCCwZvu+22SZKf/vSnScZzi2zcEp/TTjstSXLmmWcOY8h+LEVArjLH8gpJW0+WGb/++utJxrJu5AVex7zusZdeeilJk7aZEo9My9JwpA+W9p944olJmqwqaXvQkpVec7cpwvJNgN94+umnhzFkay4/gmTKkjKk+uwdS6WR61gidNVVVyUZN9A477zzkrRC3Enza/ilOYF1askH82dpJfCax0YuAbDffvslSS644IIkY9kN6xaJYtJvEoAM5KmnnhrGkF1ZNjgXSSc2sLSG0giWrxEPHb8Zc0w4/fTTkzQZOk0ekybV9DwigbOU6stf/nKSVmYnSZ5//vkk84wJ/A77aObUvoC162YKlHrxejzppJOSJI888sgy1yWme83jqyjLkDR5pqW8yKUct+Zig6T5iIUaKuL//T7ips+KO+20U5LkO9/5TpKxjfBNlp0hd3NDhYMPPjjJeC9YggvmckalhJPP/8RqJJ5Ji8eWLyOZ9ZrE/1A2x43XWPeOEzRp8VkWv2XJcq8Ejf89ZTC39husL+Y4SdZee+0kYxu89dZbScYxgfljvt0wEH/kmLB0iZ6kNUu1rSg9Zn84F3+0/PLLJxnvA2K0z+PsCfsizjW+X77ooouStPVvGxDHfb/H/ZhL+VC6zaXZ+PtcbqB3tp4iWMM+GxEjXM6INelz7KuvvppkLO1eddVVk7Q94nvu3v011/UaoFm3GxVjUxr3JPM5nybt3OH1efvttydJ9tlnn2GMUiM+o9JUzc8Xzj///CTJPffck2R8T8t+sj/ivu3KK68cxijl45I7nHk993OJy5Tg8u/hDOkx/IB9D/Pre2jmj3O9z//4DzdM416bZ4G+Xs9+/pt8z/dBMI8nHYVCoVAoFAqFQqFQKBQKhUKh8BfEokyLknl3toon/WZ2kZH2GFkxs5Qo3EyR7T333HN4jczQ3/3d3w1jZE1vvvnmYQxGqlkBFCD2988F/E4/oSez5SLXZN6dVcAuzv5+73vfS9Jsasbd/8feewXbWlXp++/vou/6qrur7OqyqxQV46GBQ5YoHHI45CRJDpIVPGRBogkxoWRQyZJzzkhOggKKAVNb3e1FX/RV3/G/er75fGfPZoP1t2p/q8Z7c07Ntfdae80x5xhzfuN9xyCbvemmmw5jZFLJ1iUtk2qWC1k5swJmCawtZ73IQJtZ3cvskgXqZdhonubfg93ibN66666bZJxVpZD3euutN4y9/PLLSZK11157GJsV1kUPZIrdPAFbOetFxtPZY3wOLArY9kljdTmbRiMdMwnYM2Sdk+bznJWeFeCPnLXHZ9v/4q9sA5h6K6+88jAGc5TsJs3tkmYfGK9Ja4zx+9//fhjDphSpT5oftK1mpakFNjCjhHn2vODbveZpMuhmXcuXL0/SYsi22247vEaGGRZl0ljEZKaTlk12cxfHHTBrNnBcxs97vrGR/QisFe+Xiy66aPS++PukNRnxmQdbmYXN5/rzzQicRfT8UY+dih3c1IL46vj45S9/OUmLqY7t+CM3RKX5CPsqaT7Nv0sDpVkELBOfb9gL9v/YyOfGXkMKYgD7xE1Lsa1jK01VzXzkDGV7ExPMfpoVMKeOCfhfnw85Z9r3cO7xPNNkDdUVKrSksensj2hgZH8E7PtYF7bVrIA97nnkfGg1AWw9+wTOsb7/MuewFrk/G463MOC9BmiA5d/F35m16f9PGaxJ26B3DsEGZtzRUNhNb772ta8laSocN/zCj6AuTBrb0Wy93tmI+8qs3pexg/f+ZpttlmTMWuQc4zjBed8sbhQ72NUqhT322CPJuEEwjW153pG0Z1JmbPMsyraZFbC27etRs/k5EX7IZyj2jNUCl112WZLm462iJS77vIlalBiSNAXPzTffPIyt+FzQf9O7RTFSC4VCoVAoFAqFQqFQKBQKhUJhHtSD1EKhUCgUCoVCoVAoFAqFQqFQmAcLUntLAV7TdZHKmC58//33J0lWXXXVYQx6tum6J510UpLWhMUSrNdeey1Jn+KNVCppVHFLQ5AquNmU5aNTBpJhy/VoXOC5okHU8ccfP4whS1h//fWHMejyy5YtSzKWJvK+nltkz1Dlk2Y/S8+hYpsi76Y8UwcSHUtGetJtGkA9/PDDwxhye4NmX0itXPSc+afgf9LkIcj5kyaLsBQF6vzRRx89jB177LH/9xebEDxHgH1hf4REx3uBplCeP+RMjFlK8p//+Z9JxrJy1oDLMCDv8RjrwnvLkqApAxmg/Tpy7Z4NLFFGfmhftvHGGydJ9ttvvyRj2Q9r/gMf+MAwdsMNNyQZyxXxg14fxAnLuvw3TxmsOcvk+b9jwnHHHZck+cEPfjCM0WDNMh5stPrqqycZS9WICfb/zz//fJKxtAcZkWM65TFYC8nYllMGMjKaeSRt7nvlLOx32ENuNIFd8N9ey8yZJejsEzewQBZqyS3rwhJGPmMWQIkcmp0lbf7tj6644ookyXnnnTeM0fzCZVloLsJ55oMf/ODwGn7d+w47bL755sMYDd9ompE0iZz90azsBfy65Xg9G7BnaOiVtLJdzLvfB/mr3xcbuKEaJRQsaeae4rjLeck2mJW9QCkD+1/OqvZHjz76aJLxfPdKgnDWwe9bgtw7s+LnaCblv8W+h9/xHnLjqymD9efGc8RZ24BmyzSaTZq03z7hi1/84uj9ew3r3NCOO4ebplIKyU0JsZ/vb7NyV+Pc7vnG99oX4Zt9d2INu3kwzaXYSzzrSNoZ1E3AKHdkSTv/d1xmXdgX+TwwdfDsqFday3bAH7nUFD7CpWI4oxKrXTqBn/NZjHXgZ0e9hm7EFu/ZWYnLv/nNb5KMG6ThZ2wDfL0bBXKu9LxgP2Km1y7/9x2a9e4Yy/vttNNOc8b87OivLb9TjNRCoVAoFAqFQqFQKBQKhUKhUJgHC5qRamZVr4ERDDoXjCeD4AYusERhuPp9//3f/z1JK96ftKypi/sfeuihSRrbMmkZn8svv3zO2NTRy5wxf2TYk5ZFue2224axxYsXJ2mZtqRlbcg++H2feOKJJOOmLdjbzDyK+8MO8/uZffnII48kSW688cb5v+gCRy+DSNFqiionLTv51FNPDWNkcr773e8OYzR44f3MEPvEJz6RpGXVkpal855hr5x22mlzfvdLX/rSMAbbaerAzzjrBWvXPuL8889Pkpx99tnDGFl6fFrSGARkzsyGhGXjn2fuzaBgf7iYPwy8Z599dhgzK2PKcKYfkPH3vMCeIwOatPXqbCPMov/4j/9IMm4OgsLADRgo5m/WPHY+5ZRThrFegwH7wSkD//v0008PY6wvsx7I1LsIP/7/9ttvH8ZoLsV800QvSc4555wkrdmC38MMSRgF11133TDGecBZbZoW0rhtqsDne83zfT1GZt/rFXYqzN6ksYHxHWb7Pv7440mabzfsn1A5mB1JjNpoo42GMTf7mTrY81aHEEu9F4iBDz300DBGHKFZadLWPvHELEfYK1Yf8H+zxfbdd98kyVlnnTWMcbZCzZM0Nu2RRx45/xddwMAPeF3B+vH6ZH+4SR3z7ZgAM5JzrucbW3kvENvNXIX1R2M8v59jmFVsUwZ+xmdGGD4+L3EvM1OM+G1GEnPK+1mJdeuttyYZ+3DsYbbXBRdckCT5/Oc/P4xxzzMjalaaoXK/NfuT+bO6jLmyz0KZQ7O7pCmqOJe6MTANXHxfRnnofcg9w008uY88+OCDw9isMFJptmmVAbHXDEeeD9g/0WTZ9oMlSmNn1AZJU/rYx6DK9T6gkSSqq6SxAM2s9N8ydaCCRAmYtHXsvcCesWqpxzrlvAXL0s3vOFM6JuDjPae77LJLkvEzEu4JPm/5DjJl4Bt8/qfpnf05c+XnBLDVrVLg55gzx2Xez76H/5tpymeYDQ8cv80ufjcoRmqhUCgUCoVCoVAoFAqFQqFQKMyD/7cQGUvnnnvuW8mYBUcdlk022WQYc3YTkGE2M5SsAnUxnGHjabezRtQwdPaSbIUzr/yuGQhk+5YvX95+eYK48sor30rG80hGwGwJMlw///nPhzGyys62kCVY8d+kzaPtQhbJNY3IKjib37MB/999990nbYMkefLJJ99KxhkuWKe9OpDUX0sa48sZHJhbBx54YJJxHeJ3uhfInJnt1APZ0fXXX3/Sdjj77LPfSsZzQGbLWUR8qdnw//Iv/5KkX0MShoXZk8A/77W94uv23/zffyc49thjJ22DY489do4/ghnpbD1zZaYWzBjbChvBOnUsYW7te1jLnlt+14xiXrddyHieeeaZk7ZBLyZQu2uzzTYbxojBZPGTxgZwTCDzv+aaayZp7OCkzb3PAOy5Xlw2M7wXE2DW77PPPpO2weabb/5WMq7VeMcddyRp/j5pNoDRkrT42mNBwAqzugcbmB3A+cb+iRrAjsu9fYA9fvzjH0/aBklywgknzDmjwsBwfVNYwmaqM8f2R9TbxFc5ZvfiMnvBdnin/oj/n3HGGZO2wx577PFWMma5ULN/gw02GMawgX08c+Cz7IoKN8/ZOz0bvec970kyZsO8XUy44oorJm2Dgw46aM4+wBdbucbcM2dJm1Or0/hd5tHsSX7e9obp5H1AvVTf1d7OH11wwQWTtsGyZcvmxGV8gFnBxATmLGlr3TEBNQH3LbO2YfHad7HWbQOUij6H9eIy95ap2+CAAw54KxnPC+ob1z7lvux1zZr0fjn33HOTJKeeemqSMWubuGx7YwOfWYlDPgP09gF/0+WXXz5pGyTJFVdc8VYyVpOhJrDiifn3cwv8hs9W9957b5LG+PVZiL3Q81+2wzvdC8SpXXfdddJ2uOmmm+Y8VMSf+5zOGrRyjP3j50PMG2d9v4YKyGM+w4Lenbh3r+Z3ly5d+q5sUIzUQqFQKBQKhUKhUCgUCoVCoVCYB/UgtVAoFAqFQqFQKBQKhUKhUCgU5sGCrLYNffqll14axmhgZCCPcdFwZB+WLEPfpomUiw5Da7cUB1mVi9A++eSTScaUcejElgf1aMVTBHPgwuAUcrZdmD/mNmk2oAB30qj0ND2wjAHZpWnfULxdPPsLX/jC6N+k2dllAXolH6YK1vYLL7wwjCGltJSG9bvTTjsNY0gOLLlEZvLf//3fScZ2QMJjKQ8SBVPjmXNsn7Q9a+nDrIE5S9oas5SHObI0BOmB5+qoo45KMpY+g57k4+1kCfY9vUZHswKkgy4Ez9q1/8dG7JGkNbVwTGBd8+/73ve+4TXWv+2NHR0TkMAhW0mSv/zlL6O/N5mdJjvEhF4jL69RfICb4VGaxPJ9SjIg97QkmphgmTJz6vmkXI8/i/ebleL9Bmvea5O17rWJfNPrGjl4r4QCMkE3yyBms6aTvg2I273P/8d//MdhzLF86mCNu7Gf1yDg+/fKwtiG/BxnU59niB2W5PbOqM8880yScWMpPsP+aFbsgMzePoLmRwbz5jll/vy7K8r3LdnsyaKJCZ5Pyi5ZRkrJAJdr6MkKpwj2gc+YSOp9tuR8agk59wRLXrER8+09hT/yXY117X1w3333JRk3MqLZlGPCrO0DziNJW19e83xf+2R+l/lJmsSZmE2ptqStZd/Veg1faPi14447DmP8jv+mWTkb4QvcvMhrHTDfjgdInL2HaKjGmdUNjbCBS1f07su9ZleUCLANeveLqYLvRYmXpO0F+2Ts5OZCvXJI+Df2hG1KXPZe4PPd+JNnR74XcqbqlT+cOljPnhfuurYBa9slKrirGdyviJ9+PkHM9rMQ5tHP4tgLLkeIPbxn/lobFCO1UCgUCoVCoVAoFAqFQqFQKBTmwYJkpD722GNJki222GIYI2vizCNMJGfMXnnllST9JkX87muvvTa8RtbZTCcaxDi7zc85e8oTcv+cixdPGWQLPvWpTw1jfDczwMjYOBNHhtI/B8jUvPrqq8MYGdJeIWKzhw8++OA570e2jcLeSbL99tu/zTebFshcuaEOcCaR7L/XNvvC2RrWL5l+ZzWxpTM0ZP/Nzuhlu1988cUkY5s72zdl0NRovfXWG8Z6RdsBLL1kbA/A/PFvLztt1t9HP/rRJGM/B6vJe4bM3d133z2MrbXWWm/zzaaDN998M8k4o8neNwOdMX4+aQ0XHBMocM6+McueTKVtAFNv5ZVXHsbwc7YB/yeDnYyZsFMGrPiNN954GCOD7OYJZIy9N2BB2j/Q8AtWkW1ADDYbshfvsQuMkKT5LzOX3HRjymDNmflPjHQ2HX9Ns4QkWX311ZOMs/5PPPFEkmTttddOkjz//PPDa6hvHCNWfC1pdrZte2odN5qZOmAAuakR39/+iL1vxhe/67jMHoCJ/bOf/Wx4Db/ueEpM8jrgfb3vOG9ZEWFm5JTB+cONOFnbtgE/Zz9NnHBMYI6wxeuvvz68hv/w+8JusSKB2O/5Jr6beWM28pSBP7INOFuaOcT8cbdLmorNfgOfjU/z+Qk7uvEOc2rGHmvA51jWvJUqZp5NGTDe3PCReOi43GPgMge2AXZjbzgmYBevb1jdNI1Mkn322SfJ2OezRsyY9717yiDerbTSSsMY69s26DWowxfYBpx7OJ8+99xzw2vYwL6I85XPx5xfOUv58x23fBefOvDJ3gvMq/0RDaD8vAZ2qJmM3L2Iy+y1pJ1DicVJe9bkO+D666+fZPzsiPUyi3GZefazm949mfn2GR8f77MRZ0jmzGd91rP3Au/rOyB/i/8m9oyfKbqR4btBMVILhUKhUCgUCoVCoVAoFAqFQmEe1IPUQqFQKBQKhUKhUCgUCoVCoVCYBwtS2o/MzLRnZLCmCL/88stJmsw1aTRuSyuh1lPM2Q0VKB7vn0caYrk6FHsXUKeZkqUmvWK5UwSSMRdNRhrisV/96ldJxkWTmSMXNYdyj2zK9Oxf//rXSca0aijblisid7O0lM/fd999h7FZajYF1d30c2CpAtJ6lwBAKtArxcBesBQFiY7XMD9naSzyK0tssbkbxnidTBnrrLNOkrHvYV1aGojs2zIcZCJe7/iaXqM0pAe2N/7LzQTYi5aq8X74z1kC8iT7A3y37cLetwQHmQ8ynaTNOdIepD5J8z2eW+QntuNFF12UJPnc5z43jCFJsYzOsscpA7mSm5ohUbMvQt5EI6ikzbdlfcRtZOKWNiF3c6ME1oBLJSCXso+jsL2bjSDNmjqQJ3tNsdZ6Deo233zzYYz9Yh+NLJmz1Lrrrju8xrnGcRnf733I3+I5Zg1Ywjgr+yBpsjD7g94ZFUm/13HPd7Ontt566yTJI488MrzG3nJjK2Krpf3EGpdTQN7mz5qV8xElJyzF5M7gvfCLX/wiyThWsxZ9F8AGKzYYSZoE2j6KuGz5Mj6tF5e9Z2alKSc28PdhvToG4v/333//YYyyIm5Uy/xdc801SZLDDjtseI295LsGa913MM5QbixCXHazsFm5q6266qpJ+hJy74NbbrklSZMaJ21eHJf5XSTfLmlF/OmV7fH5lL3kcxV/n0vFzEpMwKfaxxAPfQ+iXIhtwLz5vMIax35uuua1DrhX+2zEecl/E/amrFIyjmGzAu9zziC2A3PucyN3LpfQ4X222WabJON7Bc+HXFaE93ATw4ceeihJsu222w5j+CbHH5+hpwxipH0EMdpjxAc/UyN+9xrIU47Nkv3VVlstybhUDuvd8Ru/5QbP3JN7JSjfLYqRWigUCoVCoVAoFAqFQqFQKBQK82BBMlLJrjz44IPD2OLFi5OMs+0wjJwlfuqpp5KMszAwHmm24MwdBfpd8JZMw2WXXTaMffOb30wyZorR0IGMdzLORE8ZZJCdueLJvYvmU9zarADm2xlh7HHDDTckSZYtWza8BrPRWe1zzz03ybiAOf83A+B973vfnL/zry0YvBDBnLhpyIYbbphknM0l+2I73H///UnGmU4yPmSBvBewOUyBpNnNhfmZc2fQyKw5EzgrxbNd+BowL2ZcPfDAA0kaGytpzdoefvjhYYxMNb7EDCGy9R4ji+dMPhlr2ABJy157L7jQ/JRBTLBPgeHDa0nLyO+3337DGJlPzx/rHuaEm+ewhl2EH/bKT3/602HsM5/5TJKxcoJ4YtaAGTRTBn7fzEMYRHzvpDUI6TVd87zAsNtoo42SjJtN0QyBBldJYw97fxF/zH7iXNBjzk4dxDavZb6bme34GzeLRCFiX06mfpdddkky9h3ECBp4Ju385T3HPJv9xP7y3vj/I/O/UNBrNEc89tywFj0P+CjbkLFLL700yZhNQayxwoe9YHbGxz/+8Tl/E77HfssNSaYMWL4+B3GO99mDc6P9FncL/xwsImyFEiVpihzfSWAu2c/gI30OIn73ml9MHax1fx/uQz6HsyZ93uSM40Zs/H+PPfZIMm5GRBNI+znUP24uQ6zpMaJ8fpiVmMB82E9jD9/ViJs+E3J+dFzGF3H34B6RtDu07yO8r/cX5wEUbYb/Tqvapgxs4DWFf/d6xT95HbI27R+YI+KA77zEAcdq9gn7JmmsS8dl1kWPGTgLwE+7iRTPaRw/8U34lKTNp1mLPAuCVYoCNml3hquuumoY4/kPLPGknYPN2Mbm9n1+tjVlsLbmW2M9JRXMUt+ZiMvE2R122GF4jXPNdtttN4xdf/31SfrNrswA5v7tO7QbR74bFCO1UCgUCoVCoVAoFAqFQqFQKBTmQT1ILRQKhUKhUCgUCoVCoVAoFAqFebAgtVbIBU844YRh7L777ksylvEgQXDRcApjm1aMRAd6vOnUFMM27RtJm+nZUI4tlXvmmWeSjKWGPRnwFMHcWloDRd1yqOeffz5Jv+SC5clIPbbccsskrQRD0gqdW77w2c9+ds7fhN3cXAzpgyUssyQhRD6/fPnyYQw5k2X8/N/lEWg04iLbrG1kOHfeeefwGpR5S9pYz95PyLMsP4Qe77Xhv2/K4Lu7KDZSNUt5brvttiTJ4YcfPow99thjc96P4u7YigYuHuuVRbBv4f+2LfM9K+VFDGQd+JukycwcE2gAaB+ABNR7A7kPsis3FsSXIJVNxqViAD5yzz33HMZYA45JsxYTXAaBAvBer9jFcmLm0n5k++23H72H5T+9wv+sb5fXQT5n6TTyLq+Lnv2mCM4hbtrywx/+MMm4aQRSV8u8kTrZVosWLUrSGjnanyB/czMF9pVtQDy2hBG51izaIGn+6POf//wwdv755yfpS809X8QRNx+imUivCQWlGFxKpyfPZ3+6MST7ws1kZsUfcV60DLDXjI2zjuePs5HtQrNIbGBJM40pLCFnPdunIelfunTpMIYM2n5zVmzAPqDpR9Kk9Y63rEk3w2G+vQ+4c+FTbB98is9LvIel6ax5y8Y5n7rp26zYgDmlRE7Syis4JrA2Pd/4Gcde5K3EZUvIWfO77bbbMNZrJIOk33cJSiW5AdWsxARs4DXHHNgGxFJ/b2zg9cg9mXjvfcD/LUPmPSxTxj+XXPi8AAAgAElEQVRyzkpaE0Ofl2blnpa086B9Ld/Ve/9HP/pRkuTEE08cxih54XVMGT18jptN7bTTTknG53/m0vuJ/eNG0JQI8HOLWdkLwM9peP7j+zJ+yNJ+nim4TAh3KWIITfOSVmbwjjvuGMbYb77T8XyvV4bNa+Wv3QuzEUkKhUKhUCgUCoVCoVAoFAqFQuFviP/np++FQqFQKBQKhUKhUCgUCoVCoVCYiwWpgV60aNFbyVjODA3YdGEkUpbsvPHGG0nGHSOR3iNbc8c8OqkhR0waJd7UYKRUpt/zfu5YDPbbb79Jt4TcZJNN3krGkjDo6rYB8gLbAEmD5bVID5gz/zw2sIyTB/yWyP7kJz9JMqbI8zdZRgQ9+/zzz5+0DZLkuOOOm2MH5B62AxInzyuSQNsB6QllF7wXkKestNJKwxhyHEtL+F3LubCru7IilZj6Xvi7v/u7t5LksssuG8aQCHjd8X+XqGAt4peSuaUvbEekoO54fsABByRpMoakSdgvuOCCYQzbW9qJv9p7770nbYM99tjjrWQsk2LNeQzpiKUhSNpcagQJCf7D+4YSIt5z/K7lQcCfxd9kiTSykssuu2zSNrj44ovfSsZyPfzCn/70p2EM6Z7nG39uWSaSGmK7bUCnTq95/Bn7J0muu+66JMlWW201jGFT2wUbLFq0aNI2+OMf//hWknzve98bxpDXWhqGL/I6xC/YR78demVdmFOXOemVdQH2bcjYzj777EnbIEmWLFnyVpJ88pOfHMZYv8gLkybZ91qkDJK7A9M9mL1gG2FDl5/i/Ry/8XPeM/yuO3XzO6eeeuqk7XDDDTe8lYylgcypu1dTDsbrmBhtqSF24ee8dplHlxF48MEHkyRLliwZxjjL+gzF+3mt3HPPPUmSQw45ZNI2OOigg95Kkg022GAY23XXXZMkt9566zDGOd774PXXX08yLqGDz+HnLH2l/Ah7Kkn+/u//Psm4ZABnM8r3JM0Gth+/s/vuu0/aBhdeeOFbydg/EJc9L/gYn085b1qKTPxmzRNf/HP2/0ht/XO77757kvF9GZt6z1EOZpdddpm0DXbccce3kuT0008fxjjr+MyIj/E+ePzxx5OMzzX4a+KB/Tz3OZ97OS95z+23335JxucC7Ocx3ucTn/jEpG2Q9PcCcdl7n7Oppd74C/xS0sqk9e63vWcfTzzxRJJxSbfTTjttzs/x+fZR7Nmdd9550nZYvnz5W8nYR+BzHCsp7+hSI6xFniElbc+sGBuSdva0vfF5lGVIkrvuuivJ+BkTcBkg9tE555zzrmxQ0v5CoVAoFAqFQqFQKBQKhUKhUJgHC5KRChP1xhtvHMZohOPMDE+SzXzhKbizz7AzyG46g01GrFfc9oMf/OAw1mNnwDZzhm9WGh3BxjKTjiy72b58d2dqmAOz9bAVP+fXekwjsnieW5iotgGfZeaN2ZNTB8zEV155Zc6Y2bqwJzw3NBfxnlmxwZGzpWSBvBdoskDm2p/bs4ObjjkDN2XARKXJV9KaIrjJAdlFs+7Ivjt7DIsC9ozZQvyu99P111+fZFz0Hzv2bECTgGR2CskzfzB+ksa8clYZ395rXOTMI//vMdphbjiu9PxRr8kCmX7b2+8zZcBENQuapoBmpJKpt13YB7YBfoSmVG5q9OijjyZJ9tprr2EMe8PITxoT1TG9x9hwfJgyaGhkcK7x98X3mhFE5r/nM2C4u1EdccMxgn1itt7bNcYzo2xW4kHS2IVe46xtzxcsLfsj5sF7Advh9z2nrHfihl+3LVFGeYw95qZjsxITmD/7ZHz3RRddNIyhPnBMhXVEU5dkblMLw58Btt566yRj/w6br3f/cPOqnrJhiqD57kknnTSMsQ633XbbYYyGW2Yp0RDJc8V9jDnzmQdljhu+cN60fVgXjgmczey3ZqXZFGys3l3N8wJTC9ZV0vyI5wJfxDz6PsVn2BfRbMfv27sv835moNl/ThkwUZ999tlhbM0110wytgEx1Xc32Nz+OV7HT3gtX3311UnGTY64Q+y7777DWK8ZHvHe7zcr59Ok+W7fwfBHbjLu+Qe//vWvk/TvCbyf77eoCtZdd91hbJNNNkkyVmsS7z3nxGD7o1l5dsR38jkdG7gpFL7d7GjuEVY4oPAhjvvn+b/jKevd9sZH9tQrfs7nhqnvBrMRSQqFQqFQKBQKhUKhUCgUCoVC4W+IepBaKBQKhUKhUCgUCoVCoVAoFArzYEFyiZ9++ukkySmnnDKM/epXv0rSGkclTYJs2TEFhS2Xffnll5O04rb777//nPew/Bm6sCne0LMtS6Bxj+Wmph1PGUg4Fi9ePIwxB5Z6QKO2bAp6tIs7v/baa0ma1M8UaiSjtgF28XwiHXFzL+xsGves2CBp0sBeUwtL9JgvS6GYa+RuSZMjQIW3fZEvWLaD9MZ7AdmOZQmzvBeQgVh6zBy5qQR7wHsBGyB3TpL77rsvSZOJWGaCzAapbdIkP5Z7YivPMU0baMqQzI6ME3kHcsqkrTVLYpBwW8qKrKMXE5Ay95rabbTRRsMYsjj/XK9hCXuNciUr/n1TBv550aJFwxjz4cLuvYaC+GwX16d0z9KlS5OMYyuSfktx8De2wTPPPDPnb6KkCYXok9mRcRIPXKoAKabnqvd98eWOs8QBSjTcfvvtw2s9yR8+yLblfV3wn3OB38PlM6YO1tYuu+wyjLEW7euxk2WvzLnnC3/FOcaxhnji/UFjKe8F/s9rSZPD2R/NSpkLYEks0k43jyVGumQRje7cIGennXZK0iSeLmFy2GGHJRlLd4knPhv1mh1S1sFxYlbORsyzzyt897vvvnsYwx/YJyN/9bwcf/zxSZIjjzwyybiMGOXdNt1002GM86vPXJQ2crwnfs/nI6cI7saOgcw3pSaS5rNdZgR/7nIWzBVnJJ+DkMPax/Uar1Kux+djmoT98pe/HMZm5XxKKSI3EOTOZjk4d2c30+FsxJwl7X5MiT3uz0m/xB0yaseDc845J0nzXUkrreHz8azsg6SdtX1f5p7gtca5xHbgTudnTLzOmcnPLbAb8SJp5/9eE2KfAbh32G/Oyj2BmOoyLjyj8z2Bc6vLe+CHPC80TePZhZ/18BkuA7rddtslGe9F+yvAfdklCP7akjuzs4MKhUKhUCgUCoVCoVAoFAqFQuFvhAX5CJws+hNPPDGM8fTa2R0yKWYoXnHFFUmSI444YhiDFbbzzjsnGWclyb69973vHcZeffXVJGO23uOPPz56r6Q9vTbrAmbe1EEm0U//Yamut956wxjZZGdqsJEzn7BTybq4OQnZGWeVYRU5W4CNXnjhhWGMdeHMktkAUwdZLM8lDAyvtV5zD7LNXu9krWFRmM0N/B6wwzfeeONhjKyqi9uzF2gIkyTPP//823+5iYA94HlkDTrDhg2ciYP9YzYpxa0Z89q95pprkjRmRtKydN4fsAtgIyT9DKlZCrMArykyyGb34I/MfGTezMTDH2EfN+jife1Hek1g+D9Kh6TPNiN2TB1kjldZZZVhrMdOJwNvNhHzYT+GT+F9r7rqquG1JUuWJBkzZchSuykhzWJuu+22YcyF6sGLL774f742JbDmnDmHXWL/xM+Z7XXGGWckSU488cRhjHhMIxyzXPi/FQr4He8DWGZWoPC7ZlmYKTl1cEa96667hjHiYq9hgWMqe8BMIOYL1pZZozSXtP//t3/7tyRjX8851OxT7GQbeq9OGW+++WaS8XfDLvYznDUdJ/jdD3/4w8PYzTffnKQxktZaa63hNRgvPvvCPjLLkvdYf/31hzHWgxuG0pRk6oDBfu211w5jNJHy3AIz5jhDsZaT5ptY/z5f0SDMbG32Bne2pPlDVCxJuzuYFfbSSy8lSfbee++3/Y4LHfgR39WIBb2Gdj6bXHjhhUmSE044YRjDz3B2pMlX0uKy7YgKzk1bOKe56Q/3Qp/NDjzwwCTJ4YcfPv8XXcCgsZp9AWvY7ETOOn5mQfMoM0fx0ewRs+7xd1aG3nDDDUkaey9pd4gdd9xxzs95rXB+tc+aKh566KEk43ttLy5zLrId+DmrbYjLxG/HVta2m03BxvT5DH/o83BP4WaW8JSxYqO0pK1F39/4vrYB88I5KEkefPDBJI1ZDEs7aUxtP6vjeZLPvuwnM1OJD2bEmo38blCM1EKhUCgUCoVCoVAoFAqFQqFQmAf1ILVQKBQKhUKhUCgUCoVCoVAoFObBgpT2Q8G2bJXC5JZbQntfc801h7EtttgiyVhGhryBItdQf5NWSNvFbaEEmzpP8WLLcO+4444k4+LnLl4/ZdDYwA0OaAplKjRU7bXXXnsYswQNUOiZeabYf9Lm2bT53/3ud0nGchGkEqboI2FxaYZZampBiQOkaEmTM7mkBNJkzw20d8vt2QvIpHrF4F2kGdmC9wKyOdPkkb5ttdVWw9islLlAEmWp2GabbZZkXHoCWY/9CzIRS01YnzSuoOh1kpx11llJxn6GfeHGAcizbFvexz6Iv33qYF1bkoTcxpJNZJz2L/gSl4VBfoJPcSF61jy/lzT5nPcB//ce4u+zfM2+ccpAmnTllVcOY5/5zGeSjGXKN910U5JxIx58imWFzDlxeddddx1ew1aWWTG3K6+88jDGvrJtKUey6qqrDmOW5E4ZnE28D/bZZ58kyeWXXz6M9eTgNNPxvIBeCRIkhF/5yleGMRpiIEdMWox26YxeWQDbfupAamkJOf7Ic8j6tMwNKZulfOwFzrc+d/Ga/TpyQcvX8Ef2+cQsy+e8V6YMzh8uH8E5ySUqOBtadsvZ3WuS//MenL2S5sP98+wFl9TYfvvtk4ybXxx99NFz/vZZuScQ59wMhzsVzUGSFiN7cdFrHX9P8zs3/ELS6bta707HuZMyD0nzg7YVMvWpg32ALDZpsnw3R8MXWMJNYxafY7kjEGPOPPPM4TUaeHkv4fdtx09/+tNJxtJ+/J194UUXXfSOvuNCB3Pv84pL3YCf//znScblkY455pgk4znF5zN/njP8kmMw/t02IKafe+65wxj3RO8NlwOYOngW5NKB3Iv83IKGsi4d8oc//CFJv4Ej7+dSUzz36cVg+xnuIvZb+EueayVjm0wZlHRxrPze976XZFz2jhjtsmqcp/y8g7sf69RlXHje4AbL3J39HIj39XNB4reb6bkR3rtBMVILhUKhUCgUCoVCoVAoFAqFQmEeLEhGKk+UXfiVJ8nOtu+1115JxgX3KQDvjDTMPTJEZtLxNNzNAMhwm00BA8CZT5gxzn6QsZs6eILv7AwFez0vZn4Bfsc2gGlBdsGZHbLznlsydmaFkTV15obsqTOqvUzgVMH3Yl0nbb7MMoFF54wKmSHbgawcduvtBWc/yeo4u8NesB3Yi2aM95puTBHYwPNNtt5rjSya/RZ7wRlK2Ez8nNc9zRB6jXo81mO/UozbLACzWKcM1rwzmr3C7sxpr1lLLybAVDGjHfs4o8r7em5ZF95D+CMzE2aFAUYM3nPPPYcxvqdjwtKlS+f8LiyKXkwgZpqtZHYjwJ/5DICtzPDABmaqmbU8ZcBOt4959NFHkySrr776MNZrIkhTF9uAeTGjGLCHjj322GGMhjlmYeAL7YuwnxvJwMaZBeA3fD5iL3jdsRY9X/gN/xxzzbw5tvbiPQqpl19+eRhjP/l3idtmQDqOTBn4XTN7Wdv+vjSghS2aJMuXL5/zfsw9jGLbFrYS/ybN55n1DdOJ81DSmrm4SZ7ZUVMGMdJzBTvPzbpgpFrt5BgJiJXnnXfe6P2TNmeO1U8++WSSsfoJNp+bqnG2feyxx4YxKwmnDObbMRj2mxt+wbyy8o95690R8B2+0+K77M9o4Gi1Dmxk/FTS9pUZaLOiUuD5ge8/nBVtF2zl+eNc5cZgvA/x2Wcjzjw+h6FMc9w/7rjjkrQmk0nbpz4PWY01dbDnfU/o2aEXA4mbvqtxT+B3e/cPr3vewzGBvWD2KyxZK1pmJS4T78z4x9d779Okzvcn/IHnlJ+Dieo541mE74DYxXdznp84JtCgyp/lM+y7QTFSC4VCoVAoFAqFQqFQKBQKhUJhHtSD1EKhUCgUCoVCoVAoFAqFQqFQmAcLUtoPxdmycSjWpsQjb7UMEGmI5T7Q7qHtQo1PmkzQkmjewzJO6MKWaULfttTQvzNlQHc2lR25gWU8yJWgXyeNUu1yCbyO3NvUd6QFbrCDZMeNG5AuW9rD+1maaJn/1IHcw3uBguKWICCvdBMSZD1uksRaxUa9vWBbQoX3Gufzr7322mFs7733TjKm7vvvmzJYx5Y44Y8sM6CZi+WW/I6LwCMTYR27ADavPfPMM8MYtvKeYW4t0WF/2kd5PUwZzNEnPvGJYcxzD5gPSiQkzYdZ/sE6RfJhqSWSNr8HNrU8CImcJVmUUpiVZiIG82eZGWvOMmXigwvG87ueFyQ97C/bgDn1z/Melucga7ff2WOPPZKM99WsNF1zPAbEPvtb7OGYyhy42dQbb7wxes1+ive1XVzoH6yxxhpJmlQqSe68884kraC/328WwFmy14TRMlnk+G6cSlkj2xJfQ0ywr8c2nH+S5K677koy9mnIzm+99dZh7Pjjj08ybkzpfTFlsMa23XbbYQxpq9fxDjvskGTcnIVGeF7PxBZs6vIw3DsobeGf951k2bJlScbnKt7H68L/nzJY8z6H0Ehlyy23HMaQYLokCefSe++9dxjbeuutk7QYbKk0/sPzjVzdtuLcaxk/cd6lRnrnhykCH+B7Ed/N88ea9LkGya1LI6x4R+jFYJfT692Nubf5s7jDuKRDrwTNFPHVr341SZPTJ21N+r7M+vYZBlu5YQ5Nk97ubOR7HWVzfM7hbk5D6KQ1ePN5aZaaM9Mw2f6gdw/tlcbh7O5nOEjNe5J95s3vj5/zPYGfc/zm73SDuFnxR3w3msEnrfkcsv+k/zyHM+T1118/jLkMZDJ+vsM6dhkBngv6bMZnOU7ht1xSwevm3WB2TraFQqFQKBQKhUKhUCgUCoVCofA3woJkpMJQNNOUjLoz62SzKKydtIYLPSYj7DCzZ8iOfeQjHxnGYBE4c//BD34wyTjDRvbHmbhZaWpB9tfZQ7KbzmYxR36qT5bFmUwYFFtttdXovZI2j85MMGYb8BnOoJHZd2OjWWK+sFZ7e8HMUda7Mz6MuZj4TTfdlKRlRp0lY427sVWPBcYa33HHHYexWbYD8+11T4bYfgYW/MMPPzyMsd69Zr/2ta8laU1caBSWNCbqOuusM4yRFXX2HqaZ9xh+yBm8WfFHPTYDGVzPCxl8+yMymM4msw9ojOT1zRp2sXLs7Pdgn3h/9fbBrNgAJpBtQAbX7AiaL2633XbDGMxHN5dYMSPtuSWjbzvyc17zO++8c5JxZh8bmK03KzbosUdgGvkcwpiZLDCAfV5iXnoqB+xtlgtZfjP+aAhp9iu/6xgwKzZIGuvBzBPihJu54AfM4mb/eL5uvvnmJI096cZSrHc3DoTlZBYL/uhzn/vcMMZe8D6albgME9oNjLDHc889N4wxl1/84heHMRhcNIxNmr/gzHnfffcNr22yySZJkkWLFg1j+DzvuwcffDDJ+AzFXvBenJW9gP/394EJ57MR698+mX2wxRZbDGMwujiLPv7448NrsPTM8AO+0z300ENJxoxv/Jb3kO9yswCzoJlbx0UY646fMMB6zdlQmdkGMICtasDH+RzGGjDzGhv0GjtPHQceeGCS8b2WvWFVBuvfzHbYdJ5TlAQ0ijJbFVY1DTyTdgZ1POBZCI2okv7ZaFbiQdLOnvbJxGXvd8Yuv/zyYYwGhP5dzq29xsC9z4KN6bMsc25FI2M+F8xKTOCM7+/Dcwk3D991112TjNcs8dMMeXwZ/oPmXUmy3377JRmfabHH008/PYxhb6umuN95z/61Db9mZwcVCoVCoVAoFAqFQqFQKBQKhcLfCAsyHQT7ztkqGETUP0va03/qVyQtI23WDLWMfvKTnyRJVlttteE1amTsu+++wxj1rHq1QJ39plaJWQlmCU4ZsCr43knLMPj7ku03Y5KMo2uz7L777klapsbvgX3MjiRD6vqDZOCc7Ycl6Oy3s7BTB+xCM4DIPDs7RkbyU5/61DAGA8l1wKhVAivyiCOOGF6DuWcmBqxT16ViDzgDDfPSdp2VWmxkMmFJJM0GzqaxVvfff/85Y0888cQw9s1vfjNJqyX20ksvDa/BUnLGev31108yrvnC7zjDBnPMbCbvyymDWGDmBGvS9YPxUc4Is4ecbSSTiZ8z+5T5M7OM93BMgmH08Y9/fBjj7zNz0DFjymA+qLOVtBq8ZkHjp3vMPLO6P/axjyVp2WoYFEnL3t9www3DGGwYM4AvvfTSJMluu+02jBG3bYNZicsw6eyL+J5mVeOHfTbCHz/77LPDGLZkvziO4uO8b9hf9kXEATNkYOOY+cIZYBbg7wX4zmZT9BhX2Ob2228fxvBHMPLM5uAcRYxN+vYinpiBhC8zq3hWarGhAHHdTerbea4AjLyksbB6NdOsTgOsbddQ4z18buUMxdkraf7KZyjvnykDNY39NN8dNlfSmO4vvvjiMEYtU59XiAGwvVz7jjOPVQ383yxH/KEZYPhG+0jfLaYMWF6bbbbZMEZ9WjPuUHSYGc3c+7581FFHJUlOOumkJMkJJ5wwvAbb/eCDDx7GsJnXN/cK91GAhec7Asz+qYPzI2zopM2t6/rjbxyX8c22wbe//e0kLVb7nM/ziZVWWmkYY837mQXs+Q033HAYg13ss9Gs1KlNmv+3n6ZWv8+X2OHII48cxmCw20fxO8Rlx3Pu1fZf+LRe3LdKrccMnpX7MnfOPffccxi74IILkrQzf9LuAl5/+HP3V8Aup59+epKxr++x7Hk+5LMR+813Ev4Ws+b9zOrdoBiphUKhUCgUCoVCoVAoFAqFQqEwD+pBaqFQKBQKhUKhUCgUCoVCoVAozIMFKe2nILglSNtss02SMV0X+rYlGsgL3DTh0UcfTTKmFQNks5bSIrNywXqo3ZYIQuN20wDTt6cMCl/bBkgPLK3pSfiwgSUNyFspCrzeeusNr0G3drH0XXbZJUlrwuD3NY0bWrYLSbv486yg17SjtxcssWW+LDVERnvIIYfM+QykIpYq7LTTTkmSs846axijQYM/Hzt4L5iCP2Ug77AsDGmlJeHsD0ucsBUF4pPkkksuSdKa8VhasMoqqyQZyzw23njjJOOmVKecckqSsUwXKZsLevvvmzLY05aoIqO0/8dfeW2yd+wjVvxd24C9ZD9y2mmnJRmXbaCkgKUpvJ/Li1jGNWXgz+2ngW1AGRJL8NkTlpTxf6TItgHz5zVPPPaaJk64PAa/YxvYL04Z+AfHA2TCnj/AOvfvuDQD/pqYbt+FpL/3WT5fsb7tH4khfj9knm6qMVUgobREm/97jbMXbBvm07Jl5gsJp30VP+eYQPMjNzYkTvWacdpH/bUNFRYa8L+2AedQrzvmwH4LH28pLKW/sI8lyMyf55b38D2B0mPed+wtrwH/fVPG/fffn2TcxMnlW1aEm3txNvLaRJrr8l0AiadtwOd+97vfHcYoR+XzJ7HAMcnlsqYM4p3vRcxH747gMxTr0HOBZBkfY1x55ZVJmp2S1rDz1FNPHcaQ8l599dXDGFJfygEls3NfZm4/+clPDmPsg97ZyH4Hf+M4i7SfMmu33HLL8Bp3CZev4lkEPixp6//OO++c8/faP/m+N3W45BFgXnu+2yVY2AsuZ/j6668nSbbddtsk/ZJrjsuUlKE8RtJKK/huTKkYN2mdlbhMqTPvfcoQ2m+w7uwDKAfjsoX47mXLliUZN5xnvu+4445hjHl2vKXUgp9jnHPOOaO/Ixmvh3eDYqQWCoVCoVAoFAqFQqFQKBQKhcI8WJCMVDLHziiSwXE2i6wCxZeTln0wk2XNNddM0rKRzuQwZobFNddckyT53//932Gs19iEz3eW0wWjpwwyJRQoT5J77703yTjrQ3aTJhhJy0iYOUSGmcYAnqcek4ZGDGYbYAMzC8gG/cM//MMw5ozF1AHLxyzgn/3sZ0nGmWVYQWaywP7dYosthjFsw++6GRF28JzTRMANHdgrLuYPq9jsu1nZC7/+9a+TtKxv0rJeLviObzI7+4EHHkjSspdJa4yDTzOTgOy1bfDggw8mSU488cRhjMz2GmusMYzRRMAshFlpNvXII48kGTdTw6+7ODw+2z4C+5lhQRYS3+21ClPGMeHkk09OMvZpvO6fY895v7jw/ZSB32Hek9YUxI2G8MnOwBNzrVKAsQHTqGcD7wMYd55P5h6mZtLOAGaCzEpMIM6ZUcL37BXtNzsM/8B8J82H4/sdi/l/jz3nueXnnNknbpnp5Bg9daBysj/Cn3vdw4Tw/qDBSq/pCu/heWM9ey/QNKTXtMjnW2zuz/e5dsp48sknk7SmKgZ+KWkMaO8P5sDzssEGGyRpc28/wzw7nmI/N1FjL/jucsUVVyRpzXGTZJ111pnv600CfE+f+1A9mX2KUsR24WxEbE2aXyEuuzES/sMNwlC6wRhLGoPJcZl9irJwxb9lyug1QiYeuskp50L7lp/+9KdJxo2qsBu+yDEE9ljvjmBWMq+76Rt2MfPS9p0yuC/73PfMM88kGZ9FYZOa+cb/3WDn0EMPTdKaIfls1IsHNBp0TOF17xfmu/cMZBbwwgsvJGm+PGn3p7333nsY455s1RK+2+sYe/7yl79M0o/LBsxIK27xQ74XckZwnJiVMyprzA23+O5WUHKf7s2p1zE+h33k5x48+/N+Im6b1coZlkbP/vvcLPivRTFSC4VCoVAoFAqFQqFQKBQKhUJhHtSD1EKhUCgUCoVCoVAoFAqFQqFQmAcLUtpPkw9T4mkm4YZRNJAwNR2qtOnsSBSQT5lyDLWbIrdJk0qYXoxc0b+LXMVyOFLfV/QAACAASURBVP/OlIHkyRIAvq8letCsaRiSNPmrbQC1Guq2CytTINuyCGSClikjgbYNoH1bfujXpw7o/rYDtnEDFeYJiX3SJP09O1CKweuVIvOWOyCJ6u0Fl9lA4u6/c1b2AhIBS/CRrlpOhe9xsXIKnNtHIT1gnl0egPfz3CK1snT2v/7rv5KMy5rwc573WSlgvuWWWyYZ2wAZj6UhfF8Xlqc8if0Csqenn3569DNJsw/SrKQ1/LI8B8mbJYT4sF6h86kDv+89jqTNa46Y4BIw2Mi+CFx44YVJWjOKpMk+DeSwvZjgZlP8nGOC5UBTxmOPPZZkXCKhF1OZb88BccMNJCkRwO963eJv3EzBcw/4DPs4zm5uGjMrDXaSJt2zHA+psEsnsO5cmoe58V7AX7HHLCsn1iAvTNo5uLcXbAf8lffsrOyFffbZJ0ny29/+dhjjjP+Rj3xkGGMu7aeRAnpeViyR4fIV3Ens05Bnel3zfm68hl+zvWclJnz6059O0poQJU3m71iJz6EZV9Ka3tkf4S+Qh7osFeW+7FO4t9lW+C3OSElyzz33JBmvgVlphvryyy8n6ZdIsw2I0fYtSPrtM1inX/rSl5I0GyctXrikFGdVxx+ayrh8Amcs/02zsg/wD/Yn+CDbBT/vMiPI9i0VR+b/uc99Lsm4dMWxxx6bZNywivOs4wFxxmUBOAPM6n2ZuOwSLMRKYkPS9oJLdbEvPB/Ed+7VbtTJXY2GVEk7l7lRLecnrw3ivNfGrJRYYO59X+We5ZI2fF/bhWcafvbH+zGPvecethl+vddc0jGYMzLPPZLxvnw3KEZqoVAoFAqFQqFQKBQKhUKhUCjMgwWZioCpBVMxacWDXWAZRpDHyOo4a0nGjiyAMzQ83Xamgc83+4kMtn+XJiYudn/00UcnSQ466KD5v+gCBhl1M7s23XTTJOMsCnPkjDDZLj/dp1HITTfdlGScxTnssMOSjDMDZC2drYD1ahYI2WxngMzWmTrIOrpIM9/PdiBbs9pqqw1j2MFNkmiGw2tnn3328Nrll1+epGWTkza/brLGfjM7gwYDrJGkFbKfOsjCM3dJsnjx4iRjhgN+gyYYSfMbbgJG9gx/tP/++w+v/fCHP0wyzjaTTfMY72smIL7MPurPf/7zO/iGCx/MH01ekmSjjTZKMs7kwpC0j4DxayYG/orGSZ4zstQbbrjhMMY+8M+R+fRn4bfMspmVZlOw/2+55ZZhDMaQGQ6wUByDWfNWfuBTdt555yStoVjSGriYcU0Gmb/DcHPJ2267LUlr6ubPnzpoVnH//fcPYzSycwxmbdo/wPIyW4O1zjybfQp7wPEAxrVZAfg9r/NeY6ltttlmvq83GdCsyGdElAuOy8y5YwKMPbOH8CGcaf/whz/MeV+fxdgLPSaGm0nByjDzxmelKQNfYvYnrDj7I2KC9wf3BBpRJY21SPMJ3wm4i/Be/xeYW9uWz+AekozZxVPGVVddlSS5+uqrhzGauvhexv/NFMbnuGkp/gefc+eddw6v4bfMcsSOn/zkJ4cxlCRmhrOvzJKiYdvSpUvn/6ILGKx/n08585h9zhxYcYY9zN5lnr/1rW8lSb7//e8Prx1yyCFJxooRmvN4bokP3gfEB4/5/DwLcLzFDzse4I8dPzkr+hzLPYBGdTT6TNpeclzmc81qxRfZ73F2crPKfffdN8m4YfRUwdr+3e9+N4y9//3vTzK2A3vG53nmy+dLbIht3NiWde+zPv/3vuP9HL+J92bF9861UwRnIjNwOfP0Gp/5zMl8WJ12ySWXjH7XcYX7lpsIEk98BuC+bPUiDd1QxPl33y2KkVooFAqFQqFQKBQKhUKhUCgUCvOgHqQWCoVCoVAoFAqFQqFQKBQKhcI8WJB6NyjQlmsgCzA1lyLClnpAK3YTGAr9IzU35Rd68X333TeMIWlzg4AVKd5Jo4JbSnXwwQe/o++40IEcwN+NubcNoLdbGgKl28XHKdYM9X3XXXcdXkPqYfkCslAo4Umjdpsezu/673TDpamDveByBT078Lq/O/IBS+x5P2S0y5cvH15DFuEyAhSVt4yE5g7YPumvF8smpgzkA26Ghj+ynBUpoOXLSGgs62DNIkGjAHzSGlh4PyEn2WWXXYYxJIaW0CLNsaSNhkxTB2UNkPMnzZd4H1BmxfOHnMoSJ+Qhr732WpJxE0Pe12URsL3tCOyP+CzLiNyIb8rABltttdUwxtxbboP83D6DmOC5IJaef/75c94DWPrH/y2nZi95DRCXXXLBZQamDNaVS6hYzgeYI88p5x7LyfBf+BFLm/BFPhvtt99+Sca2xQaWcmFb+zakot7DU0XPD+A3POfICpnLpElnvT7ZW/gjN7/j/SzPf+KJJ5K0ZjFJs6tLKvF3uvGC98+UwTz6nI4f8FpkLi3BxJf4nvChD30oSYvLli9THsllkvhdy3RpeueSAYcffniSVsIqmZ3Ga8Q+SzGZZ5edYO3eeuutwxhz4PJdnBkpueN1yx7CTkkr9eOSP72Yzh7yGcDvM2UQ79y0FL/uNb/KKqskGcuekdW6ARS2ev7555MkBxxwwPAadwRKeSXJkiVLkozPXNjREmfivPeBy4NNGfhvx8Xe2YhzpH+u1+TozDPPTNLm1M298O+O1eedd16ScUnB3tmI8m2OG5deeuk7+o5TQK/5NfNrO1DSwnEZX+O4jL8gFrhZEnZwmRbOYr1mbD6Dcn/z3YF4QmPdqYLY61IFfF+fTfAzPT98xx13DP+nZAjl3Xz2Yh85BvMMAjl/0sqa+KzAWXbzzTcfxvws8d2gGKmFQqFQKBQKhUKhUCgUCoVCoTAPFiQjlUZEZuGRJXDDFTJrzriQ5XRBfbJyFO0nK5Mkm2yySZLkU5/61DDGk3JnK8hMUIg+aRmkHtts6iDzaBYEMEuILI6zadjKc0ExbObbTD4KBTsTw+eaUUfmwowKmDTO8Lv5wNRB0wmYLcmY7Qawl5shkBEyM5is3Be+8IUkY4YAWWk3boFRdOONNw5j2NCZOxqemJHqZg1TBv7ITeX4nvZHZNvMoiDj6Owc+wef4wZhsDO87z7/+c8nGTffI5PvjCZj9ofbb7/9O/qOCx1kHs22Jfvs/UDm0fPNnNof8bs0DfN7kJU0w4OsKAqGpMWVXoMB28VMjSmDeGi2HGvZ/hcWNs0Yk8bEsA2Y06OOOipJ3wZmyuCL3FSDz7333nuHsW233TbJeB/wt5shOUUQZx0/OX9YAcA895qe9eIH+8qF/2G87LPPPnM+y3PLmrfvZ3+ZgQAjahaAH/DeB70mkG42yLr37zKvzJHnEkaL4ziN1GBPJm0v3HDDDcMYTEGfmcxInjJYn173KJh8DmJeiA1JmwP7LRhBa621VpJxYxHisZmPNFC1KgQmEneNpKnUjjzyyGHsrLPOeidfccGDNem5hY3ue0JPmdNrwvjRj340SVNR+fy/3nrrJRk3S8LeZoWhiLAvY6/ZH5ldPGUQ+7beeuthjDhh/4+NPO89lQJ3Du7fPvtzVjVLFbghLfvPCrljjjkmyZgFPiuN7x577LEkycYbbzyMsf6tGMEGrPMkeeWVV5KMzzr4FO4Xbs6MrRxnmFvf57hf+J7G2cixxPeVqePhhx9OMlbs9M6o2MEsUZSWjsucc5g3q2axEQ1Xk2ZX2wsf5b3Ifd4Medt/yuDMQfPGpLE+3dCMs/ibb745jHFudUNUnllwH3Ns5y7iueP5j1nJNPvcY489hjH2jxsaEjveLYqRWigUCoVCoVAoFAqFQqFQKBQK82BBMlKpQ+EadWQ5ndEky+laqmR3nIEnm0Cdi9133314jexmL0Pjegpk8VxvkgyDn6hPnfECYEG4xg0sK7Pw7r///iTjbDtZG9epIgN//PHHJxlnh3qsPRgfzhAw3/6b+Axn1WBpXHbZZfN/0QUOvp9ZtuwFZ+vJcB166KHDGJkW7wUyMrfcckuSMQuYvWA7MLbddtsNY72aM3yGM9BmakwZMIhc1wgbmDl69913J0k+85nPDGMvv/xykuarkpadg2Hqusv834wWftd1d2AsOTsHYBQkfebAFMEcmNHYq5kMW/F73/veMHbOOeckGfuj73znO0lavS6zBnpKA37XzHsyms6ewo7y7zo+TBmwR/x9mQ8z7mBFMMdJy9DbBigM+F3HBN7X8ZQ1bxYBjIFtttlmGOMzrr/++mGMvQOLbKog3rrOKGvOrLC3Qy+mw9rA1yVtffvn+Qyfl/g5MzmIDWbDmKE2ddx+++1J2tky6Z9jYLnAIE2a3/eZhfe56aabkvTPR2aNcR6wmoG9YNYFe9V71n/flIHfcExAfeBYzf6w0obzrc9V+DeYjFbEYQ/PN3YxQ5K9YvuxP1z3zWzXKQMfb9Y6e//CCy8cxpYtW5ZkvA6JkY6p3M3OOOOMJGOb4T/s51Al9hRbvVrQ9mVmiE0ZMLPNwGVO77rrrmEMhpbP5TC6/LsbbrhhklZr1kxX9pyVQdjUdsS2Psf2GLGz0s+C7+bzjc+lgHhgJiJz5d/l3os/cy1ZPsvzjY+zrdiHZq7yGd6H1HWe+tkoaYzQnioQtmrSzpAnnXTSMIZyzL/LsyXqBZvFjt8385H5tb16/X2IO44ds1A7PkkuueSSJONzOvdg+278r5+zMfeO35xJeR7osycxwXGc3/X+QPXsOwk2+upXvzqMXXfdde/oO66IYqQWCoVCoVAoFAqFQqFQKBQKhcI8qAephUKhUCgUCoVCoVAoFAqFQqEwDxaktB+quWUBUNctW33Pe96TpNGukyaNNZ0dqTfSQNOpaYLhMWQOH/nIR4YxZNRuQIK8wRJnF76fMqA9W3aM3N7SjM022yxJ8sgjjwxj//RP/5RkLF846KCDkjRpjeWu2MB0bj6jJ2dzYWFetw1caHrqQHrjuUQuufLKKw9j7IULLrhgGGP/2IbIz6HYW/aPTN0yXajz3k9vvPFGkrGkgb1guVRP2jJFWHoAkMYgf0qaDMTNPmwjgEwdWZNlgBQmtw2YU9uK4tmUQUnaGrCE5MEHH0wylshNEcjXvJbx516byMWvuOKKYYz59e/uvPPOSZoc2bIqJCk9yab9zLPPPptkLPdkrbjkwqyUe6FQ/JprrjmMEe+8R4444ogkbX6SJpt1WRjWMHZB/pO0Rgku78PvskeSJoW2LIqYgI2T8R6bMijF4mZEzK39LT6g15TKY/wOMdXSV6RRbhrGzxHjkxbL3WyE/eQ9Z5n/1MH69HomJthH4EvcKJC1aOkZZXg4Y/kc+XalZVwu4cc//nGSZMcddxzG2Jf+m95pCYiFDtaTz4OsbZfcYS3aVsQMy5yZU8cCgGTU51b2WC8u03wzaWVjfvWrXw1js9JYhO/p0jisU8dF5t53tSVLliQZy/Lx5/gc+3/iqN+D+5jLu1188cVJkp122mkYo9mg95Xj+5TB3cB3BOIDZaSSFhe9Xntn9JtvvjlJuwP6TE9ZEJ+5sK39E2UdXPKBu4zfz+tmymAt26f3mjvSrNprjzOoy0Gdfvrpo/f1nDHf9lN8Vq9kgGME9rOtZkHSD7gn+KxBXPYdCL+Pr0ja/cngrobfoNRC0mxJY9Sk2dKfT4mpT3/608MY92XbhvM1pTWmCp6fuYQQc8TdIEmuvfbaJK05lcd8Z8J3E2/9HpTr9JmGBssuLQP8WTwLcVOsv7bs0Wx4sUKhUCgUCoVCoVAoFAqFQqFQ+BtiQTJSyRY4W0a22BlkigcvWrRoGCPb5uKzZH/IUps9Q+YOVkfSsmRrrbXWMEaxWmcQyPLTSCNJ1l577Xf0HRc6ePpvhgpZFGdbYNU5E0b2xiwg5p7f9XzzGc7IAWcQKEDvn8MGZinRSGAWgB28F5g7s3WfeOKJJOOmRsz/+eefP4x98YtfTNIaNHguyab2GB7OHPO7zrSyL1y0fr311ntH33Ghg+/kAvvMvRtdkFG0D4BpZL9BsWwyoLCQkpZ5dkFt9pbZ8L/5zW+S9G1gNqvZg1MG8+K1SfbZ80eW3mxImBDeGxTzJ1vtecRH9RrvmOGB37dt+X+PNT910HzFvog9YZYja9OMdexhhiJMJNa3WVzEELMEiNUnn3zyMEaW34w7bGCG5KwwX3qszl4DCdacf56zkWM6zGnOVWa6kp33Woblu/feew9j7ImeGucDH/jA8H/OULMAWEHeC7BPvRdgsNj3sM7NtmN98r5mTxLH7UfYMzQvSZJVVlllzt/ZaxhmBuCU0WOP2A8AWEJuFMtateoMGxBP3HSEtWs/gj3Mfn3uuefmfD42gOmXJFdeeWX/S00M+HivefyuWUX4ITNHsYsZcTR/hD3m88tf/vKXJGPGFn7L587FixcnGTPFsCnNqVZ8nymDfeDYis/w+ZRGK9/85jeHMVjs/jn/PxkzWLk7myHJ/1E/ecz+kb3pv7O3X6cIvq/vU9jAd+P77rsvyfi+SpMj/y4KG97Dvq7X0BRf5AY/+Dv7LM5GZqT6+cnUwbMex0piqe9UnN2tXn366aeTjNVkKz47sh04b/mM2rur9VTSrHvH5VlpArnqqqsmGSsjOUPee++9wxgNCH/0ox8NY8QJ/y4+HhWUGwnDmnejU5qd+76OGsQNurEpjNckefTRR5OM7xjvBLNxuygUCoVCoVAoFAqFQqFQKBQKhb8h6kFqoVAoFAqFQqFQKBQKhUKhUCjMgwUp7Uem4YYGyNcsh0L+YVov8g/TuJEjQGe3DKQngYMK/9RTTw1jyBpd1Jb/b7DBBsOYafxTBrIXNwC55ZZbkozlrUgJLDNA5tNrdIEswTZDbmCZB5IGis8nrXmS6fCsC/9ur2j0VEHjNYqOJ01mZvkfEijLcmjMRkmEZG6xcUudkCPYbuwFvy+f22sE5uZKvaYNUwR+yBKZY445JslYAsD8WeqNzMG2ojQDkilLDvFptgG+6fe///0whqTOReuRUSFbX/Fzpwx8uOUxNJ2whJzvawkh8+eSLswV79eToFlegv9yUz1KKFiaiE3tD/03TxnMs2VhNFYjPiateaDXHhJv+wzkPvgOfJ3fr9cYyXvu1VdfTTKWK2MD78Ne2ZgpAp/ak/q5lAFyJcvye9J7JIbMlWWXPXzjG99IMm7kgqzRvoi9Zp/l89zU8eEPfzjJuIwKDe623XbbYQxZmiWX7B/7BdY2drDvoeSOz6jMuX0fZ95NNtlkGHvssceSjOO3z79TBtI9+26+Gw1mkyb1tu/h7O4yUPh94rPP/5x53UyGPea55ezpEhm872677TaM0Xxsiy22mP+LLmD84Ac/SJJ89rOfHcYoZ7HGGmsMYzSP8vxxt7j00kuHMeaS9X3iiScOryH7dykffIpLXPRKuCHldbm2WTmf4k987yFueq8fcsghScZSVu4NXq/4LCTnnlvKHVnaz897fT/++ONJ2vk3aTZw7HKJtykDf0MzyKTdce3naVLo5wT8n1JV/l38lM9B+BPHA+K2m3FTTsb7i9JuLrnjc9LU0Tsfca7ceOONhzEk4TwvStr9zecUYgZnG9uBefNe4Cxrm3Me9hhxx7FjVspP8YzOMnrKR7hcDmXv7KfxZY7pzCljvXMQ5cSSVprBZWG22mqrJOPzKGvFMYGmku8Ws2G5QqFQKBQKhUKhUCgUCoVCoVD4G2JBMlI//vGPJxkzWsi4mA1D1h5WStJYGc508QSbDIKLw5MlcIHhj33sY0nG7FeyfX5SDhuAzF3SL/g/Rfzyl79MMmaOLl26NMmYhUE2zY1ZyKI5s0MWGRv455lTs4b4XGdUyQaZrUEm1WvFTTemDtgt/n4wDr0XenPDHJplxF4gq/mhD31oeI2fc8YHFjBNxZKWZXbGCZawM63zsZumArKSXp8XXHBBkvG8wPh64403hjGYSM76shewgZseMGdmkjHm+WbPOMOGv3KTL4psTx3MgdcUvtaNXID9Ofbr+SNs4HXL3PqziCfOYDPPZnOQYfY+dHyYMmB6OibATHRDCr67/Qi/Y8YEvoe9YSY1zCWYTEnLHDsmEKt9BlhttdXm/E3eT1MGa7LH1LVahvm2L4JFafXGimxpM8awI+y5JPn617+eJLn++uuHMda8m0uyn+z3bPupg7k2o4QGXF6fMCYcq7GNGbzEFubL/gib2M/AKPJn8XM0nkwa28NMEH/ulAHz0Wcj1rPZKL0zIiwhs1FoFNVrLLhiI6qknWG9F2DioVhJ2n7z3cF3kCnjoIMOStL/bvY9xM/bbrttGNtll13m/BzsRs6TRx555PAaZxmfWa+99tokyQ477DCMXXPNNUnGqhTs7f1iZuaUgSLGPoO17vMK+8C+iHn22YhzLL7IewQf88orrwxjsKp9FiVOmdVNYx83ZzaTbMrYbrvtkozjJ77fcZl94rMJzHrPH7G6p9bsnYWJKZ5v7mnHHXfcMMZ+2XfffYcxr4epA6ap52azzTZL0lfv2R8Qg/1sYsX7svcJtvb80bzQ78u+s4/kbGW7ep1MGShj7H/x6zRkTpo9zMTmd2kYlTR/31PCsu7NQOas4/eFAezzKHcC38+s5nk3KEZqoVAoFAqFQqFQKBQKhUKhUCjMg3qQWigUCoVCoVAoFAqFQqFQKBQK82BBSvuRI5iGCz3akhikrO9973uHMei/LgAMzRpKsOVsSOR6zRtc/Pe8885LMm6qQeFoGm4kjVK+zjrrzP9FFzBefPHFJE0SkzS5LEWZk36jCSjylpPxf2jzlr5iZ1PbGTNFHlq41wVFhC1r8edOHW+++WaSsYSwtxeQinge3JQLsLeQKHifIDPw/FLo2fLpXpMFpIZeB5bBzQIsQUNybAkh0hw3AFlRspk0mQFlSCzTxR6WG1BWxFKJu+++O8lYvoDU3NJON3yYMlibltEgrfE6xLe7gDk+2X4Bv8LP2R/xWS4RgmzZ+4V9aEnzP//zPycZrxXkPlMHc2Q/jVzbRdotVwLYyrGD9+nFBGIwpUWSfsmWW2+9Ncn4DMDn21Z8luPZFIE/9voGvWYFlsH2Gqvhl1jDfg/sYbnTfffdl2Qs7ez5GCRxX/nKV4axE044of+lJgj8v2VmNG70GZF46/Mle8ENqIibSOB6DR/tZ/h5x1jOAC6pwd5yU8lZ8UeUmHKTnV133TXJuEkp9qBcVdLm0uVy2AvMn8uBYA83KGIf7b///sMY5R0cv1kjNGZK2jnNjQqnCM4wbtqFP+AOkbQ4wZ0paXPp8xJ+mjXvpsHMlW2w0UYbzRnDHj5zcW6w1B1J9dTBWcbNBDmje69jA84oSVuH/l3mivlxHAVuVsTn258xz14D/C2W7fr8PGVwtrTf5nv6WQAN1hwjiAe9OcV39GzgNc9+6d0zLEc/9NBDk4wb3FJKcRaA77b/5X7g+zBlQnqNah0/OW8Rl20H5tylrlgHfg+eU/FeSbL++usnGZeu8t8yZeBL/HyCmOAzPOvSe4bnDG6cyjrHfj4HcdZ3SQ38lpvkcQf0sz/2m59tPPTQQ0nac6V3imKkFgqFQqFQKBQKhUKhUCgUCoXCPFiQjFSy7BQOTtoTZWcayFa6iPyK2bSkPd2GQeRGVDRSWHvtted8vhlMe+yxR5J+MwYKTSez01Dh/e9/f5Jx9hL2m1mHZBOcvecJvzNhK2YfXFgb1oQzY2RUyQQlLYvTY4+5cPysFG1OWsbYdiAz44Y6ZGnm2wvYsFd4nsyZ2U68nwtwkzlzFhub2DZmgEwZr732WpJWvD1pTW6c9cIfmUUBE8g2gJWHn3FDKLKnbmyFDcx8pxGGM9DMvVl3zz//fJJkv/32m/+LLmDQ1GvllVcexljz9kesaxebh7lkG8BiIsPs7D7NEMywwO//4he/GMZYDz1/ZIakG5JNGcRDM8D4f68poDPHZKLtz/EzzkiDiy++OMl4H9DYxPO9/fbbJxkz87CBs880o3JDqymC795jn3oM3+y4iF+wL19RveG9hH16LAwz6bbZZpskyWOPPTaMMfcnnXRS9++bOjhD2kew5x1T8ef205yPzNjAN8FkMcMPVpJtg3/zvoMZY5Yse8HKBZo0zQp8Jr/llluSjH09SiY3j4Xp7t8lzrJO8UFJY5raBuwjnwFgGJmljQ2WLVs2jLlx2JRBbPW5k/k20we/b0bqo48+mmTsp2FuExPcjAhGI7+XND/0r//6r8MYvs/sU/ym9xxsZO52UwV+1w2EYFv1mm76fMp91f6BuWSN2j6cZRyzsb3VcJzTfBbFBr7Xmyk/ZXB+9NmIM7p9NPPiM8zbnY2Ivc8+++zwGnvCCjXuZBtssMEwxv4zOxJ/Z/Yj8WDqZ6OksYD9PIf16bjHMyHvBWKuYwf3bvaCmyXREOz+++8fxrbeeuvRe/nnfF8mJjhWuwnblMH8LV68eBh78MEHkzSFbdLO7D5fcobx/OFD2DO+q/HzPscSl7m3J82OVmj1nhPttttu8329LmbnZFsoFAqFQqFQKBQKhUKhUCgUCn8jLEhGKlm0M844Yxj7xje+kWTMZOTpsrNjZFqcheHJM0w+P71ea6215vx8r/beFVdckWTMNNprr72SjJk3MDmnDrJornFDPRvXWGNOnVWApWG7ML9k+10rj/l2xojPcF2Re++9N8k4E0dGx9lv19WbOvh+zkiS0XVGhUy755y1aPbDinUdnX1jbfs9yEY7g0odEWdfyQBSAyhp9aumDtYs7M6kffeTTz55GDv77LOTtFp1Sb/uDXsBG7i+JFn9HmMFX5X0axhjP7MLzAqfMvAvL7300jDGPneGkgyzGWAwLOyjsAF+xnWO8C89Jp7ZFLBTvQ/Yr7aL33vKwAetttpqwxhKBCsSWNe2AWvT63rFmOA4unz58iR9GzhWf+ELX5jzN332s59Nktxxxx3D2NRZR6BXq5Fzitmn+ICe/7F/WrG2mlkxsCbMJCXem4EG28D2Y19ZQWQ22NTBucQsGdnyuwAAIABJREFUR84xjpXMl/cCPsTn0BVjgs8wzLWVVMyvWWCwy3xmWrJkyZz3mxXFDt/TrB7m+8477xzGYIfaJ+OzzVo08y5pDHjDyiAYNWZ4c2e58sorhzHYZWbNmwk7ZcC+7q1Ns3JZmygAk6ZYcr1I1iY+ze/LGd/vix+0HWEiLV26dBiDNdyrpT11wFDcc889h7GzzjoryZgNyVnf/p84YR+/oi/ya7DrHMdZy/YrxG0ziqk7eMwxxwxjl1122Tv6jgsdzIv9DgxPq5hY672zkRmL+CJssO666w6vbbjhhknG9zT2nJ+PoFqwXfBV9FNIxvXOp45VVlklSf+ewDklaWvRe4Gf87yuqAAya5f5tSKZn/O9izjl+qCcs/70pz8NY7NSq5Y59fkbP+O7KX7GPpm1/fjjjw9jsLjpO9Hr4eLzKHcR7xnm9oEHHhjG+Fyfl+65554k47v2O0ExUguFQqFQKBQKhUKhUCgUCoVCYR7Ug9RCoVAoFAqFQqFQKBQKhUKhUJgHC1Jfguz7O9/5zjAGdd3SGsZMmUYuZZkOBdF5X0s6kCJbagPt2pITCsVbfv7MM88kaXTyZCy5mzKQS5kezXxbRoNczxJCZDyWYDLnSG8tVaeIvOcOmr3p4bvvvnuScQF16PUUV0/G62HqYF1a/o0dLCFkzM3OmBuXYkBqw/uZ1k6BZzeb4v223XbbYQwbei/QhGHjjTcexrxOZgFuaMDcnnPOOcMYsg7bgPIKltwA5ECWKiAD9xpGPupC2djIjS6Q6FhCOytNLfhObiLF3vjpT386jFFiwpJwJJWWueGbsKn3FyUciBtJk+Nawo7fcmF0iqm7ZIb94JSB7KYn4fZep2GI/Q5x2fJWJG/rrLPOnM9ComvpEzJSS2m//vWvJxlLyPn/gQceOGds6mAfMD+G5U3EW0tpaUThOMv8sv5tW0oFeL55X9uWz3U8okmYJYSz0lgkaTHQzdCYE/sZXnfpIX6uJy3GrsjYeq8l7Yzqcy4SOK914oj9oSWkUwZxs7cXvO5Yl73SUS4/RCMR7OdGJDQR+fKXvzyMcSayNJbGI/jApMlIHat754EpgpIpPqezrr/61a/O+Tn7Hpo13nfffcPYoYcemqSdU21b5P5bbrnlMMa9jMY+SfNlTz311DCG76FJaDKOLVMG8+z1xTnJaxNZrX0WZxPHb844nDGvuuqq4TXmmaZqSTtDWc7MOZamPkk7p1166aXDmM8DUwY+wyWGOJ+6XA53BPtgzka2C/NC6RbfKZAku2QAzzZ8n+NZieMy/3eD21kp9ZK0mODnAdjBcm3s4HlA/u37GHcA5tyxvdc0kmcTbqqH7TzPxHeXCpiVJpD4eO991p3vQr5PAxoJ+nkcdyret9cAnWdxSXtut+uuuw5jlDpxc1Bs5dJr3HHeLYqRWigUCoVCoVAoFAqFQqFQKBQK82BBMlLJFjsbCSvCzKHXX389ybggOY1CeLKdtOzP008/nWTcRIoshRs1wAAzK4xGR85S03zJGYxednyKIKtsBhjfDWZQ0rKXzpjR7OP8888fxrAHrInFixcPr5ERcEaGjM0BBxwwjMF69WeRSXXTBf/NUwcF+Z1JZ85XX331YYwMvpnBsIvMSnz44YeTJBdffHGSZO+99x5eg8XhvUAmziyXF154IUmyaNGiYYyMk1k2ZrZOGWSM3WgIn+O1yHy74Rxr2kW2n3zyySSNxWufhp8zg4k1AIMjSR555JEkY3b2pptummTs3/z/KYN1ZV+PP3L2l58zU+tLX/rS6N+kZUbJdprVil93Jp81b/YkGVJnVmF+mbE3KzYgfm6yySbDGL7Ic4UNzG6ErWI/cthhhyVpDBUY2knz52YAE5eJIUnys5/9LMnYx/FZjt9+nylj/fXXT5I88cQTwxjry8xR5goWatKaKHgu8GmsW+8b/LfjKXY2YwBGdu/sY+bSLIH1ZjYK5xjHQM6G/rntt98+SXLLLbcMY8wxDBnHEOxr1hj+f6eddprzWcSVpO0FN4zxWWnK2GKLLZKMFQGsy5tuumkYs98H+H2f3YnlrHuzeK+77rokY9Y39nMjLxrQuokODSwM+8Ypgya8Xq+cXY466qhhjDOl2T8obGyD0047LUlbo/vvv//wGjH7+9///jAGo8trmlhtlirx2PPea8Q3RfB93bQFn2G79M7o3CGee+65YYymPDDK9ttvv+G1nu/gHu5zLAxkN9Ah3jvGmIU5ZfCMwd8HG/jegA18JoQ1bAUs/przjdmnnH9s71tvvTXJ2FY8CznppJOGMeKQ/06fG6YOGLm+ezLXZiNyPvL65NmO4yf/59xqW/K7fm5BXLZKGZscffTRc37Xja1m5b6MGsPsaNaqm3Myp2aY4pusXGCMOOH55lmUz/+c+4899thh7Mc//nGSMWMc1rL9Eedq33HeCYqRWigUCoVCoVAoFAqFQqFQKBQK86AepBYKhUKhUCgUCoVCoVAoFAqFwjxYkNJ+5DkuyNyj9UL5dQFbKPCmriMx+e53v5tkLC/8n//5nyRjyQLFoV1YGNkzTZiSRgW3rNHSnykDmZ7lTUgQXKAfabGLhkOj9px++9vfTpLcfvvtScYSG6TTPdmICzQjLbVM7bXXXksyltxadjh1ID9ijpJGY7esG3q6pQJIzkyxR0Z79913J2lzmjS5gWXJSLLYJ0mTTFmujvSNZj/J7OwFbOA5wA9Zbs9atKSY1y3VP+KII5Ikd955Z5KxP2LtWm6zxhprJBn7GeQTtjd+0I2OZkXGyfewhAMf5fnGX3le8D1ej5QMQZ5pP4esxM0TiD8uao58x5J01oU/f1b8ETHQ3415sywJuY1/jlhuG1Du5bzzzksyll4hdXZDEOKtm4BRhoGSJUmTJi5dunTO+00dL730UpKxRBb/4RiBBM0+gz1kKRPrlZhNWZ6knXU830gDHYORj1oySmyyhHFWyh4lbb37jIqPd6kPbOPz0eOPP55k7JuRYSI5995hXm1LZGl33XXXMMa5jCZ4SZt/22tW4jK+wdJ+zv0ue0QjJPuXnoyS2IJPccymrALlc5LkgQceSDKWptM4ydJ+ml70YszUwVnDPoL15yY7lE3wGqZ8l/3RySefnKT5D/ZD0tat/Tp2oVRVkhx++OFJxuXduG9YRjorwE/b/3NutxSZufIZij1k38xdipJJjsE9u1AqxlJzGkX7jkIzN98fZ8UXsYa9r7kv+HyKrewLKNfjGMFcEpfxNUlb/7YZDe2uvfbaYYxma27ySLkGr5VZOZ8mLd55jePHPcZZ0s+OsI3nlWbb2JJ7c5IsWbIkyfgMQHxwOYVLLrkkyThOcI7yOpiVMyrr2A0X8f/HHHPMMHbuuecmGZ8b2SuOz5xReS7BvTlpDR8pqZO0u4CbXbHHvD+JRY7zvju/GxQjtVAoFAqFQqFQKBQKhUKhUCgU5sGCTEXwhNqsC7IJZjKSQfDPkT1zQwUYYDzl3m677eb8PAW7k2TLLbdM0jI6SWMZOKPKk/dZyS4bZE/MKKHhkbPPMOc8LzDzXMz9Rz/6UZJkpZVWStKKaCeN2ejGPTD5XASdLJ0zRjAznGGbJbzdXnAmETs4u0P2zA1HttlmmyTJmWeemWTcrAIGttczrDuyyUlriOGmVLBfzeKeFbAX3GgOuJmKGboAZpH91tlnn52k+RTblrk3I5V95IYl2J5MW9LY225KMivo+SPWmueWZlQ9xruZ1ldffXWSlq139pSfMwP5Pe95z5z3gHmJv0uaH/TPzQqwgf0vGX1ndYm9Vm+wxs0AIGsPC8nM7B6zgELxMMySxh7z3oRZbybIrADmi/08WXT7B2zFuk1a4wQzI9gvFNd3U0M+y7EdZqOZFPwtjiXEe8coM8+mDuaw18TMzTiZX7NNmDv7KBoj0JjCLMueT6ExCeefpPlDs1mJSbbXrMFMuN4aY81++ctfHsZgLdoGNMOheY73DvYgdifJjjvumCT5/e9/P4zhG2m2k7R9N4usYObbMYGYSpPMpH13xwRe506QtLnE7/s8SXMqs31hHruxCKx9M8Vo0OOYPitNIPEPPjMy354/4oTX4Ysvvphk7EdefvnlJG2eb7zxxuE1bOu5penbqaeeOoxtvPHGc/4m7hI+K8wKeudTzjpm17FPrEZgnu0zUOlwpnfTZd7XzyxgQvrMw/3MyioremcRnEV6cdm24XXvBe5N/jkUnOwdng0l7a7muwN3NZrwJa25sG3+29/+Nkn/Tjl19M6NKAFQribtnN47zxuwWGlEuGzZsuE1zjr333//MIZtzYbnHOS/Cfv53PzXohiphUKhUCgUCoVCoVAoFAqFQqEwD+pBaqFQKBQKhUKhUCgUCoVCoVAozIMFKe2nkPVHP/rRYQwKtqU40N3POOOMYQzZADLApNGxe02koHGvtdZawxhSTUuG+F03P4LSbXq4/+YpgyZESMiSNge2Ac0Ott9++2EMGZkbfkGNR562aNGi4TUo1jStSJpEwQ0CgKUSyBxeffXVYYzGDbMA5tKlC5AI2A408XIhd+jstsPpp5+epEltXbiZ9e6Cy8h83DgAyZuLo/N+ps67ydWUgb+wbAa5jPc+5RLcsIXfsXRqzz33TNJkr/Y9SM49t3w+jfSSJhnt+bLf/OY3w5ibf00ZlFCg8VbS1pr3AZJAiu8nyYUXXphk7JuZX+QlNL5IWpkK2xvZj6XprG+XZmCvWfpm208ZyFEtR0K2ahv88Y9/TDIueUADEs8FMh98m6WglHbBPkmzh9cAvsjyHHymG+ltvfXW7+g7LnTQ8NHNDNxwCGCrxx57bBjDZ1jqx3rFT9ifIJNzyRJ+3qUFwK233jr8nzXi85IlvFMH6545TfpnVOSXPh/RkMiNCnfZZZckbe26YRRyZ/se/JV9D3byWQE7WXrdK0EzRSC3tz+wnA9QVmHnnXcexrgfuGnOzTffnKRJA2k0kjRfv8MOOwxjixcvTtLWQtLOt70GGi515XIAUwZnUZd56jXyIt66ZMWf//znJONmjUjvkYfa/7PXeg0+7Wf4fO8hJKAuXTIr5Xfwyb2zoM+nBx54YJLxfqE0gqXQnJPYLzSOSpr01vdrfFaviZRl0tjWe9S2nDKQG1911VXDGOvL8WD58uVJ2jk/aXPpch/ESuT+tiN3PTdYZozSbUnzQV7n/C0uozQr59Oklarwcwv8hueQZw4uuUPZCvx60vwPscDN2zhf+s6Lf+MMnLT7hEsicp/2Xc2+bspgvfn+xLrzXuBO57jI2cXPG771rW8lafPjMi6sY5fc4Rmgz2aODyuiJ/d/tyhGaqFQKBQKhUKhUCgUCoVCoVAozIMFyUglG+OsLhl1Z+VhgDkrD0PlnnvuGcYohk2hZT+pJpvnpggUNXe2gEK3p5xyyjDG30LznaRl55YsWTLv91zIYA5cHJ5MIoWDk2SzzTZLMi4gzu/CvEjafMDsss2ef/75JGMGAHBjDJon7b333nNed3bOBeWnDphtztDA0iWjn7QskLMrZBqdCSNbT5M1isInyde+9rUk44wPGXx/1iGHHJIkOffcc4exXgbaLIUpo9fQDEaQ2RQ0CnHDC/wRTLKkZdawgTN3jDkrCrwXaIxx4oknDmOwLp555plhDIbmXnvt9bbfcaGDbLLnChaFs4ioBFzAnHhihjQxgL1B1jhphcvtw/ksM00feuihJMnBBx88jPUaW81KcwV8h9kMMA9tFxpMbLjhhsMYmX/bgPXM/JjRQjF+24VstpvpwGghhiSN/WSfOStF/Wma4vMN6DXa6TWPMlt7xffxPMGusDKHufUawAZmAPN3mlk/K/sgaXGupxwwI48zjc+IML6sMCCOwGSxn8GXLf3/2HuvYN2qKn3/7dvfVV90dVdXKaiYyoA0SeAQJAlIjgcEFAEDEkWQRlDBQJAGFUQFMYDkJAhKzkHJ0SOIrZShqvvCvu6787961nzW3rPZQBX132vVeG84zG/v79vfHHOOMeaa7zvGbrsNY8QkMzxQZlkRwZxbmWBW4JSBL3G+BwP4Ax/4wDDGd7ffgH1kJh77gzMBDNVknK+Cp556Ksm4iQj+33kQzEi/h5t3ThnMo1nOxFszD7GV9wZr2PkKdmNOvb9uvfXWJOO4zF6yP4I1ZpUa7Cef6VAGTR00RfYe5xxqliPnZY+x/lnLSWsKRf7uOA6zy+dC9o3Zfccee2yS5MILLxzG8Dv2P5wVpw72uH0MZzH7B9Ym+U3SfL99Akxh8lM/syBmb7XVVsMYips777xzGIOBTJ6atP3iZkj8LTRpmzLY01YacHbw+YnzmFmg/PvBBx8cxjbZZJMkbS/4rMF7+NkDdvJn7b333kmS733ve8MYZwyfzeeimsJHuOkmzyyc19Bc3M8g8CVWCJJLcja2Ap2xNddccxgjh+X3kvZ874477lj0d/qcYOXOa0ExUguFQqFQKBQKhUKhUCgUCoVCYQn8Q4/F8P83zjnnnNXJmB3Bk2ezI2Bb+dYS+CkzTAFu4vydYQ75STTsGt/2U8fBzDxe9/txI/W5z32u/fIEccABB6xOxvMCq8K3iHxf33Jy+wwzKWm3kNxMYLuk3Rj5s7j5sQ3+9V//Ncn4FqdnA5jMP/vZzyZtg6S/F6h755ti5tPrk1tjswBgbm255ZZJxqwB9oLtRv0v15d5tXuBfx9xxBGTtsOXvvSl1cm4hi8sCtc3xQ+5Zho3YazdpN3EcWtpX4UNXPMRJr1Zjtws+2a7ZwN82QknnDBpG+y5556rkzGr59lnn00yrreMP7JyAWYAbNWkxQSYNGbLsdbNVoL15H3wav0Rdr7yyisnbYNHHnlkdTJmWFCba+eddx7GsAH2SdrtsO0HO4ifd60u5tnMrl5cpp6z91zPBjB0VqxYMWkbHH/88auT8RzAaPEtPt/d7B9yJ69hYi6MC/t+3sN+x0wL4L9l4e8afO6ZZ545aRskyXHHHbc6Ga/Pnj8iLtu/MOf2RzCJYKW43iHz36v5ZVviy8zOe6X86D/+4z8mbYerrrpqdTL+buQzjrd8X7OvmWczZIjDF110UZIxY5F59Hzzuf58WHneJ709A/bbb79J2+Doo49enYzZcSjWXCcQG/TqZtu/wGJij3h/9XxKr1Yzv4vPT/r7YLPNNkuSvOlNb5q0Dc4///zVyTiPJC90XWry06uvvnoYYx+4LwJMO3pNWFmGrXwewbfZjsRycl2/bpuxNw499NBJ2+Chhx5anYxzRs6wHiMvdc6IX3fsBZyrreTp5UYw4e2fXm1uxL833HDDSdsgSc4777zVyfj8BBvd9U17dmA+vY945oGvsiqxd07gdedEqKo4x/l124FzysEHHzxpO5x++umrk7FKDX/hvIbva39AXuM6/tiKveB4yp7x3mGevT/4Hduvt9+w88qVK1+TDYqRWigUCoVCoVAoFAqFQqFQKBQKS6AepBYKhUKhUCgUCoVCoVAoFAqFwhJYls2mkGZYqkaDBBcRhu7uwtdQg2lqlLTi8UipLGOAfm85FPJDN+5BImc6MNKVnhxu6kCi9MgjjwxjUKVNW4fW7vIKSD9N40Zmw5wiD0+avMryN97D84nkhAY+/lxLRntyn6kCOrsL59N0y9J+5BsufcFe8NqGus665/895p9HduK9wPsa2Np7wYXXpwwkk/ZHzLP3AqUObAN8GaVJkjZ/66+/fpJxoWwk5P55/JtliDT62n333Rd9vks5WG41ZeDDPS+vBMvP8SGW8VBygTVqGzDP9l/4PvuWa6+9Nkm/GYP3gffOlMFaQiqVJN/5zneSjKX9NNGhaUXSpDWWqLE3kFxZzoNdbAN+3jYgzvt3WSOWXM0N/m7MqWV9rOGeL7IfIeZSesHSJ2TpllK5hA/ARm5shQ1ercR5asB/eD2zZv092fv2yeQvbnxDcxHyGUtysZebN2B/+xbKA7jpDLF8jvkR352GR0nzJc498BFu5kFTIzfZoRHFV77ylSSthFLS8lXLxcmRnQ8RE0499dRhjJIPc/RHNFPzWqb0l3Mjcijn8/hsS16ZZ/xQr/mHy4KRF9hvUb7KMR1beR/Q8OXggw9+pa+47MF3cm7U2+OcV1euXPmKv4tN2UM+IzD3lvuT6ziu9MpjkDf4vO79NGWQb7L/k+aHve/xFX4GQUz1+Zcx1j8l8ZImdbbfIT91PLjiiiuSJLvuuuswRg7sv2ku+WnS1pab+5K32h/hc9wkjzl0uTvmidzK5QGI1UvFZRqVn3zyycMY/tIlMuYSl5kPfHPSzqZuDEnpED/ToyyMf5ffodkmTQeTtu+8F5hHxwRKymy66abDGHuhV8rhtaIYqYVCoVAoFAqFQqFQKBQKhUKhsASWJSOVQvEuFssTbbMuuE3jKXbS2BEwBpLGJOO2gBtLf5ZvSrnBXm+99YYxbux8ewrLYC6sLwP2LrcGyWIWV/LKDXa4BU7aDRtzZYYlhaF9K4ptfXvJ7VyvcYIZsV43U8fDDz+cJNluu+2GsY022ijJ+Dtzu292BnPnvYBNuDFz8X9ulmGmJu2m533ve98wxg2Rb5P5W8xmheU0dfzyl79Mkuy///7DGHNqG8D+MhueW1DbAL/BDf6DDz44vIYvc8MSGMhml332s59NMrYft3NutGF/NWU8/vjjScb+iCY7ZqgA32iyhm0D1jpr+MknnxxeY569D/AzZor5bwG9Rhf+3CmDtW6Gyq9//eskYzYKjdB8208M9lxcd911SZKjjjoqydh3YR/7euLDv/3bvw1jsAfMCgBzmXejt9Yp6u/ciBjon+/9LusVv+05g1nZY5Wamcq/X3755WFs1apVScassDnZg3yHRhJJW++OCcRbs7hhsXo+nOckzd8lyZvf/OYk4z1GnuumGjSQtM/HD910003DmNnjU8a9996bJNl6662HMeKt1ye5rPPWp59+etH7feELX0jSci6zIZlHx1v8nH0PrBk3WoKB/MQTTwxjblw5ZcDaNdOHeGv2EfawUgQGnm21zjrrJEkeeOCBJOO8E/YwSp4kueSSS0afmTS2nz9/2223TTJu2GY28pRBQycrAvD/ZmXhi7/5zW8OY4ceemiS8RmWfJdmXA899NDwGuvWjD9YxGbrbbLJJknGPosYs8EGGwxjL7zwwqv5isserDWzfVnXbnyKL3Ijnl5TR55VEG/NlOOsB6M6aexXK6HIB5zH8n7Otebii5K2Lll/SZtX+398xGc+85lh7PTTT08yjssoFzjT2WdwVnvuueeGMc4HfgbxqU99Kkm/sZVZqD21zxTx5S9/OUly2WWXDWPkPL0mYH7ug118fkPZdv/99ycZ56PY1MogbE8MSVoc8bqg6Z4bNr/3ve99Vd9xIYqRWigUCoVCoVAoFAqFQqFQKBQKS6AepBYKhUKhUCgUCoVCoVAoFAqFwhJYllorKL+mRyOtsXwNGjeNoJImCafgedKKyUJhtwzElGAATd9yLOjx/pug2yNHmROQTJpujhzcRZuht1uisdZaayUZ07iRu2Ery76RovnnkR5YqkCphZ6Mc45NjpImY7IUhPVpWQB2sHwTWY/3Au+DfGePPfYYXqOAvCUm2N9SNfYPksOkSR622WabYawnJZ0itthiiyTjYvDAaw2pOWVIkuZfkJYlTZaJLNpNMChCzmtJ22+WNOD7LDXncy0rn0uZC0odeE3hUzwvSGS9X4gJ9i/sDWKDJYc938PrLl2BTMvSLX7O8qC5+CP8guV62MASfCSxLtrPXLnJwZFHHpmkFeH/1re+NbxGPHGTHkouuNEFJQMsF2SN2KbOG6aMXnkb1qnXPHIpNzNAEuj8hznCt7lBGCWQsF3Sml4gJ02aP3P5GfbkHMseJc2neM7x0240Qc7pPJO16PiJTZBrOo5SRsClknpllvg5fz6/89GPfnQYm4s/sqR/IRwn8M+2FRI+S5+Ze2zh2Ers8L5DGuh1f9ZZZyVJjj322GGM2OXSPPZNU8aHPvShJON5QYLpsh74DZeYoIGwS4Igk2XuHWuAS7MR7y3JJD54jpH0W5o7F9/E2nQpA/yNzwhnnHFGknEJAPyDZfmUovj85z+fJPnhD384vEYZKp8p8EXOjWhS7L1BLPL5zc2/pgzWWs/v2B+Tm1vOzO+6ZBF5Va85FZ/hJqeUgrF0mc91nCFuu2TGXBp+Je0M1ovL/p7kjXvuuecwhr14fpE0OT45lsu98Bn+eSTp9ofYy/4fn+fyVz5HTBmnnXZakvFcUQLJeSM+yjk+a9oldJDes579HpRecLNZmm7at2APmjQnzf/3mj2/VszjdFEoFAqFQqFQKBQKhUKhUCgUCm8gliUjlaf/ZoBxq+Bi4dz2+2k0zDnf+FBYHpaLWZbcxF100UXDGEW2zSyD8efbJYpsu5izbyKmDJ7m+wk9LBOzimDQ+ZYThqmZYtwsczsGiyVp9naDBW6af/e73y3623zLym2oWTZzKdqctFtcN/MC3gswTDfeeONFP+e9wG0wN5K2AzdDNHFIWmMZF39mbXh/wrowW8x/35TBvJx55pnD2AEHHDB6LWlslL333nsYo6GOb71Y57fcckuSMVuVdX/NNdcMYzS1uPzyy4exffbZJ8nYR7EvPDYXRio+x2uZMbPuYLy7eQu3+W7Cws/BZHGDGHyUb7Dx8R7Dv9nn91gAPVbNFMF8m22LX/I6+/nPf54k2XHHHYcx5t62Irb84he/SDJe3zAjXTCeHMBsCpgx9kU9tuBc2EfA34fYZ9YtrCKPMUceg8l7zz33JBmzT2la98lPfnIY+/a3v51kzJ6AaeEGJDCdzFIyq2bqILaZ0YhvMBMCNkqP1Uvu5Pc7+uijk4zzHvLbu+66axgjJmy44YbDGGw+709yIe+PubAhgRVSxEDngOQ8ZgThmy699NJFv4uvci5FsxHnYVtttVWSMYv7ggsuSDJe9+SnJo+mAAAgAElEQVREzlvnAtY3TaeS1vTMzOfrr78+yZg5yjr0z7F28T3eI9jUTbtY//Y97C/HavaO/ab35JRBroMPT9p5zN8RP+5mpJwb7DOIucccc0yS8V5irTsPI24Tc5Jk3333TTKOwcR+nx/tP6cMvpMbx3Im8vdl/nyegn3nJrUwFmE7msWLisq+iHPIxz72sWGMRmxmfBP7zznnnGHssMMOexXfcBogjjqfx0f5PMpzn14zOyvRyO3JaXfZZZfhtd5zBvyQ1SPY0AqtHot7Luxs5sVxGX/h3JP1bkU38dgxlfeBweo4zrnM+6OnFOR19k7Szt1XXHHFMOYG868FxUgtFAqFQqFQKBQKhUKhUCgUCoUlUA9SC4VCoVAoFAqFQqFQKBQKhUJhCSxLaT+SPyQaSZNiWkLYawCFhNYymg9/+MNJGjXYUnzkmQcddNAwZpkDgIJtyjhFvk1h7v3uFIENLFtlrixLQLZEmYWk0bddBgFKNVIOSz6gwbvYNvPoz0ee9v3vf38Yo6j/HBuLJE1674ZEN998c5KxHVauXJkkefLJJ4cxpJS2A5If9gJF4ZPWVGnTTTcdxnrrGYnCRhttNIwhK7Q8YS5yWiQIluezF/x9kUxZykMxc5e52HXXXZMkK1asSDKWqiFVdhF0pBIU1k5aCRNLU5BP2B/OzR+5DMJDDz2UZCyxQZ7jtckceL8gu8IP+T2wlf2ISwUA1sVOO+00jFEWwyUz5rIP8N1uLMh69fzRIM/fG3myYyX7ifXvAvPYhz2y8P0AMYEyO0mL1f6b5hITWJuWRCJZc6xE1urmF8Rg50Y0hmFuHT+IG70C/ZQ6SlossY+j0YB/znK3qYPvbOksjSnsZ8hbXdKCZgnOWZgb4qgbqCJDpLFP0uzwxz/+cRgjN7WPRKbrmDCXvcB6//GPfzyMkQc5BuNDLJ299tprk4zlf/h4yl24MRH+y2XEgBtxAksE8ZduduicbMrAn/fWpvc7ccK5Do1WLMHHn9Ns0/JlJLfOgygL4NJiwDEB2/dK80wdzJEl+3w373vmz2XQaPjl2EFpKnIYmlQlycEHH5xk3CTnxBNPHH1m0spjOD9ln1jOPJeYQDz2HmdOnYc8++yzi8Yo0+LciLMw+SkNd5Lmp1xKAdtbpoydaYqdtLzqs5/97DA2l/w0ad/Z/oA5d1xmXzh+4vftIzjfsU/se55//vkk4/mj1Ij3E3mBG2wT3/03zSUu4wdOOeWUYYx/+1xEYzSXhcEfeC/wb55nOKcl93W8xx4uGUMe6vP6HXfckWRsl14JxVeDeViuUCgUCoVCoVAoFAqFQqFQKBTeQPzDXJojFQqFQqFQKBQKhUKhUCgUCoXCG4VlqfvcaaedVifJZpttNoxB2V61atUwtvnmmycZd0Wme51/jg7KUH0t7Uf2aQkE1Hk6GiZNkuIOd7zfww8/PIxBAT/++OMbN3mC+MQnPrE6GcsHoLybdo2Er9eZ2jRpqNVImvzzyBf8Wdjj9ttvH8bofmf5BH+Tu7Ex9o1vfGPSNkiSPffcc3WS7LXXXsMY6425T5J11103yVhSgKQNuXHSqPP8nH8e2Zo7/PLzvY6DlivyPpaN8rv77bffpO1w4YUXrk7GsgAknfYlyARd/gMf4bl6JSC/QjqaJH/729+SjDulIv3pdWC2XAVZ6P777z9pG7z97W9fnbR1nrS15vWKxMk2wC7MY9LkVLxG6YAkefrpp5O0rsxJiwn2fb/5zW+SJG9729uGMX8uICZdeeWVk7bBpptuujoZzwtzb2kN+8TlW5A3WUrL2mTMcnX8if06cij/3BprrJFkLBFE+mP5IWOnnHLKpG1wxRVXrE6aLCppMkHve+bFMjWkle5ei89nL9k+Pd91+OGHJ2ldmZMmjerFZe8XZGxrrrnmpG2QJOecc86i/AhfbLme5eSAveL94XlaCMoneT2zP373u98NY8jJnVvxc+ROSVsnBx988KTtcPjhh69Ommw8aX7GEnx8gyWBd955Z5Jxx+CFUnP7cubRe+ell15KMs4LKAdAObGk5bx/+tOfhjEktkceeeSkbXDqqaeuTsYlhPDP9iXEZedLzAs2S1q5F2zgPcLPE0+Tdi50/OG853yJv8Vyf373xBNPnLQNVq1atToZ73u6VLskEXHCklf2hPcGe4f5th2xh339Y489lmQcqxnbZ599hjFs6n3w3ve+N0my7rrrTtoGZ5xxxupkXFaFfUCZiqR1ffec8mzBPnphXLZ9OFe7e3nv/E3Jk8985jPDGDZ1eQX2xNprrz1pGyTJ9ddfvzoZ+xnOVJTUSdq+8PmXOOG5fKXzMlJ8j1F20Od15tplLvgd+zfyrAMPPHDSdvjWt761Okne/e53D2P42t4Z1vNNuTbL7Snxid9yXOY5kcsi3HrrrUnGvo9/O1fg/Rx/OMttv/32r8kGy/JBKg9QXUeEZMWHVg6/djIc7Py7JLZ33313kvEhhETXdS54iOFDe6/2BZvMD3z9t0wZPPh89NFHhzHmw98Rx2yHwL99AOOQQXDxAyASe9cb43Xq7SX9uqnYwHV/5lIXMmkHVj9YY/3aNtQl8dx85zvfSTI+LHBgI1CTcCWtnprry+C0nCT1gCPzPppLDSqSdPsNHL5r5nCI8kGaeXNgZ07ZH7YZyZRrFfJQxDWje8AGPKCaE/A9JOhJW9cOzsy9a/EQMH1Zxs9hMx6sJs2fO2Djj3pB1+uc93UNvF6SO0XwANWXKhxqfTAgBjtOcOnjB0bYg/XvwzDJpy+L2Bu+oMP/e76xlR9izSUm4IN6dWrtH3prjtd7BwXmz3nTVVddlSTZf//9h7ELLrggyXhv4PPtx3qxei51IZO27v1QH//LA7akPWh1TGD9eq6xQ+8hK+vediOeuG43Nuk9lH388ceHf/tvmTJY974EI97aH/nfgEOtbcC/mVvPEzHGeSuHPdtlu+22S9LfC35Q0vubpgi+GzUxk/aQzWuYeOwLL173nOLvGXNs5ZzgOMH+8mUqtrINsK3r2c6lPifrz3PFWcp7g3jrn8NneR9g05dffjnJ+IEdPsa+fOONNx69lrSeGUvtA1/0TRn0sLj88suHMR4i+zyFb7FdeN0kFOzBQ1Y/WCKf9dxhW+dcPEC1DYjVrqc7p7jM/Dr/5/tzjkra84LenPdqleKv7at4MMvFTdJqz3qP+dJhIXwJNxd1OA9QnQfhD3wRyR7wxQ7naa9J7LLwvJC085ifHe24445JxmfuhQ/EPebL8Ncbl6tGaqFQKBQKhUKhUCgUCoVCoVAoLIF6kFooFAqFQqFQKBQKhUKhUCgUCktgWerdoGWvXLlyGEOWb0p6r7Ya1OG11157GPv5z3+eJPnnf/7nJGNJh6WDCz+/R+N2fR6o4kggkrG0ecqARm1ZAlInyyShZZuejWyc2iRJo9JDtzalHWlKT45lW0H3tqSBEgSufzgXGWeS/OEPf1g0xpql3ETSKOumxFMzzfK/5557LkmrA+l98slPfjLJmBKP9NljfJb3DvR7f/5cpP189/vvv38YY58zP0mThFguwjr2XPA6EmlLzqm76f3Rq+2Mz6HGlGFpp/+WKQNfvPXWWw9jSMlY50mTNrkUBlJAy0rYB9jRaxnZNL4laXJB24rSGpaqITe19HouEkJqlO66667D2LPPPptkLA3En1PbMWnyJpcI+f3vf58keeaZZ5KM64sxZ5ZoIQW1ra655pok45qEc7YBc3DjjTcOYyeeeGKSsY9hXbsMAj7F0jVqbJJDUbcqaRItx1bs6PnELzn3IU9y6ZieZG6qYL4s/8ZPuw5jr95mr2YzcZN8y3Iz6q4hoU2aHVw+iXIY/pvIhch9/XdOHcj5fCZgvXl9Mt8uy8Lvev6QIrKPXJMWP2Q/RzzxGJ/lPIi/z/aeyzmB77THHnsMY5Ql6p3ViCFJ8xGeP6TM5JjOT7GVSwawr3xWw1aeY/ak60jP5ZxAzwSXssEelryyJl0GjdzI80f5JMod9c7LvT4Ans9eGT1iv2PSXGICOc+xxx47jOFvXI6QeGCJMWOW5ZM7UbLCc0vprl6fEdsKX+izBGdzr5W5+KKkxWVyyiT50Ic+lGRcTqFX/gYfZb+FL6O8he32rne9a9F7kAv5/fl3r3/IXEpbGOxpr09yEudG+I1ejur6pvwcz/F22mmn4TXij9cw+8hxhfOe9yLx3aXhXm9MmIcXKxQKhUKhUCgUCoVCoVAoFAqFNxDL8krunnvuWTTGTUCv+7XHeOLt219ucLhNM1PmvvvuS9K66SXtSbafTsNEcuc3bvN6xc+nDthWLtDMzYGZXdy2eMw3YIDbUGzlbrMwJj3fzKnZEzAu/HO9otFm7k0dBxxwQJLk3nvvHca4PfY8c3PpG15uu1xgn9fpGutbUNiVHuPW7cUXXxzGuOX+7W9/O4zRyIT9lIxvjqYM1qxvD5l7GBRJY8L5NhJmpH0U/8YW7qDJ+5rRwufaz2y00UZJxk0e+F2PYeepg86nbqiDz/EtPHNvNumqVauSjJtwMb/cfLoZC8xIGJP+DH8W9jZrEhaCb0PnwgDju912223DGGvTt/jYwOoNbvvdTRtWy5ZbbplkzCLgNtudTrGV/RONkLitTtq6cCx2PjBlwJA44ogjhjFyEjMkYGY4RsBi/ehHPzqM4YNgXJgxcN555yUZNyuErec4Q7w9/fTTh7FvfetbScZ7w7acOljjZmbBJnVMYL07JvSaruCP+Ln//M//HF7Dh7tpBWyjHhMDlnjS4oSZwUs1LZwKLrnkkiSNcZQ0v2vWei9H7THD8C/Yxw0fyS/N8LOPB+S17ky+ww47JBmvFcfoKYMmODfffPMwxpq3wgx/4fmDJbTFFlsMY8RtbGC/ffHFFydJDj/88GGsp1wjn/JncXaxIsIszCmD72FFB76gx7ByTIDxa3+DTfEtPWWJc2GUh1aG0jTGKgniRO/8OHXAdPY5FP+x6aabLvp55/I0KMKfJW2uUB3+9Kc/HV7jWYXteNNNNyVJ9t5772GM/IcmwklTNfS6z88BnIM333zzYQwmqlUZ5JeOn9jEbF1Yi8yX1/jHP/7xJGPb9Jp2wsa0zyfW2B85PkwZxD4rYfEhbsyFD/HZGEXm9ttvP4zxjIc4ce211w6v4eO9nzgLWNGOna3k2m+//Rb9nZwzXyvms4MKhUKhUCgUCoVCoVAoFAqFQuENQj1ILRQKhUKhUCgUCoVCoVAoFAqFJbAspf1IYSzXgLoLdTtJ7rrrrtHPJ01WZVkf0gfovW4YAoX4uuuuG8YokI3cLWnyBVPBkU24sYlp4VMGMljLc5DsmIKOlMFzio16TQ+Q2FheBj3b8nFsZjkbP+ei0dDlLeOxdGTqQJax7rrrDmOsQTc5o9nKHXfcMYxBnbdMnLlBUuDi/8jhvMaR/Pg9kAlaUvfFL34xSfKxj31sGJtLiQXm0aUM1l9//ST9ZnWWyrxSsyckC/ZVzJn3HfvNzduQmCALSpKf/OQnScYya/vLKQO5mecWeYxLI9DAyzJ+/u2i5vgX7OfGIuwr/zzyE+8D4pP9HGuk59+mDmxgeRNSTPsMire7LAx+rNegDn9uiU2vieDCJhhJkzBayoWkjtILybjMwJSBVNs2YO2y9pMmqXcOs/vuuycZx2/iMmv5+eefH15DCufmJJR/cVMqYslXv/rVYQwpnKVrPXntVME6di5CCQ+vNea6V9rFYG54X++TfffdN0nyq1/9atH7GuRgLueCrNTyTseWKYOcx+UjkBJbUszcOi6yzn3GwB/RuMV+hvdDepi0eOySFZRrcPkRSsTYH7rEyZTRKzHBnLqkDT7EsZA5sP2wQa+B0XHHHbfo57GVzx+UJ3FTKvylpeT+nSmD/e75Jm5azkye4pIGZ511VpLxGZo5R6LbKwXiPIj17f2Fj3MpPEr40NQt6ZfHmCLIMS2ZpxEszcCS1rD2hBNOGMaQKjvfZD+R81imjG9xXOYc4BygF48oz+QYNJfyCkmTjvs78f17zed8hiZv7eUnnMssIT/33HOTjJsqYRPvhV4OwL60veZiB76bz57koX4mwFq0jyKPd35LPMFfb7XVVsNrzLObcm+22WZJxj7qrW99a5JxOYzbb789ybj0hs8MrwXFSC0UCoVCoVAoFAqFQqFQKBQKhSWwLBmpsLh8c87Nv5/qU9DaT/K5kfDNEDcC3Ba44C3vu+eeew5j3DD7No2n52aFwY7yE/W5FG7mCb9ZQtwEmy3RuzmG9WgbLCwK71sfGL1mhXH7YNvCCvDNErerZmjMiZEKG/Lpp59e9Jpvwrhd+eAHPziM9QrN834wp2H1JeMbUXDqqacmSU455ZRh7OSTT06SnH322cPYoYceuuhvMttjyuAWjRvmpH1P39bDgPCtJf7CDBnWL+vUPqPHcO01RcAfudA5e8X7bi63nPgesz/Z875xh/HiZggwX8wOxu/DZjX7iJtUs+G5wbb6ACbBdtttN4zhL82GmQMDL+n7f8bMNKLxgmPHihUrkoz3AcwK9pJZxD1Vw9Zbb51kPLe9n0NR4pgwFxuwn3sx2H6HxlNWHOCP7R/YJ/g4M+WYW/88zSrsV2BKbrPNNsMYsceMTceaqQNfYt9MvmiWFewIGusk/UZH5D7sCee+NP60bdhHZm7gB/35MFG9F+bCSOV79Jqb2i4w1HfcccdhjMZgtgFzz9zaZzDPMMKTxnix70HB41yBvWq/NZcclTlyQyJYqj4rvfvd704yVuy97W1vSzK2AX6I//IzST8HgOXrNf3CCy8kGbPxsYF/bi7+CF/rnAeVk/cBc+AcnYaw9ue8D/5mqfPeQw89lKQ1fkya/WCCJY0la/vNpeEX37f33Xwmwxf1GtL6XMs846f83MM/BzgPeD5vuOGGJMlHPvKRYYwcYI6+KGnfz3uBPW/GNooEN9fsqZbwTQtVzUlfWcLP+0zHfrNt8IM9W04dPf/PGrcv6cVt5qM3f7yHz9y9zyIP9h578MEHk4yVvfgmn0ler0phHk/9CoVCoVAoFAqFQqFQKBQKhULhDUQ9SC0UCoVCoVAoFAqFQqFQKBQKhSWwLKX9yDVMP4eqbcovhWEtt4Qa7AYkSDSRKrjgLVRjS1MoDm0a8p///OckY3o4DRf8WXOhySN19RwgDzMdHUmn5w+5j6VRzBFyGkvNoFO7sQhSql4R+ZtvvnkYW2+99ZKM18pc5MxJm19L+KCuI+dIkg022CDJWE5PAzUXYj7++OOTtH1kqRPzZjnOSSedlGQsv8IOlBNIWsFmmpEkyYYbbvhqvuJk4HXPXLngO1IP2wrZgBtKISFEHuWi2IAmaklrvGMgZbD92Ks9ScXUgUzP0ils4PlGLoK8L2nzZykOdkFSawky72EZIkX//R7IdokNSds7/t1Xajg2JSCxcUF24rLj4q677pokefbZZ4cxpK4u7s86Rb5pG2BnpDtJ8swzzyQZx9jHH3980e/uvPPOScZNSeZiA+Rnjnf4D8c94q1lnPgHl2FY2ADKTXKQiDtW8/Peh9jPsaf3N/lvnjpYxy5fwZ53zkJJHq9j/IvLifB+zGsvh3GsIZd1XCYvsD8izlsO570yZTAfLglimR7Ye++9k4xjAjmMS18Qo9kfXvfIMn12YG/ZVuwf24AGVPZBc4nLlDKwBJ/v6e/LXHofkP94XbNOObPZZ7CvnM/yu5aJEqOdi5IfOyb08q4pgnXYk3/bn2MPn9Vo1mLfjU8h1ji2ExPcSGbbbbdNMt4bxHbLyjmPeO27HM2Uwfd1WZWebJuGaUj8kzZvBx544DCG9Jxcx3bEF3kfEL8dl12aCrDm7SedP08dPDuiUVrS1rPLUfiMu/DnaJ6WtAarvbJI+H18YNLyAc8v+9K+jHViHzmX5xas2V4pIs8Le98lNJk/xwnWO3HZOS3r3T6N/MZ5GJ/rczVnSq//11v2qBiphUKhUCgUCoVCoVAoFAqFQqGwBJYlI5VmFb5l5Fasx9S6+OKLhzFun11I/IknnkjSCjf7tpgn4G501Lt9pgGVx7id8GfNpdmUb+MB7CMzcLGRb3qZA78HNzrcIPduOXvNpnz7wE2DC/nP2QZJuy3xd2IN0mwtSe66664kjSWXNGaWC29zY8ceMyODG0zfyGFDsxxpPOV9xG2aC6vPpZA2N2e+PWTdmbFNQxGv4x67BWYMDRjMxIaRAVMgaXvA88mtfu/WucfQnzq4oTTLvbc2f/e73yUZ30ayh8xAwYfBurD/4jPcRJAbbM/tSy+9NHqPpN18mkU8l6ZrzIsZbdzgeh/gU7yGFzLuksYYojGSWS7MqZnc3HT7BhkmvBkb7E2vlbnsg//6r/9KMm5cgF/yzT5sgNNOO20Y+9KXvpRkfOsOm4L1feuttw6v7bDDDknGe4l4b2YBtnduREw3e3hOcRkGnlkk+H2zP1nTXp+sS7Nm+Dl8WU+xYz9HzIDtmLR1b3v1WPvOqaYMYqvnllzHe4F1Z5YL+aLZ2ddff32SxqKzn2PuzaDrKdxgGZt91LPLXPZCr5EW9nCOD1vbsRBf4vhJ4yLUfr/+9a+H12giaAYyc+r9wuea8Y0NbO+5xATg+SbXsa/HZ3v+2BP+3c033zxJct999yVJ7rzzzuE1Ggo6thLT7VeI91dcccUwhg3MnJ3LPmDNOx7gF+6+++5hDD/C2SxpccANhVHz8NzBr62zzjpJxnZkX9mOW265ZZJxXkVcdr42p31APmM7LGwYlbTnSM539tprryRjxRVxE7/vGIL/dwzugbnu5aj+O+fCSOV79HyyWdT44nvvvXcYI+c0s5vne/vuu2+SMSsY/+Ezdy8fpUmqx/CH3guv96w2Dy9WKBQKhUKhUCgUCoVCoVAoFApvIJYlIxUmi58ew8SgrkHSblJWrlw5jHG76XpIK1asSNJqtGy//fbDa7COfFvMbb+finMz6loMe+yxR5Ixy8a3P1MGN/u+VWA+zILDBr4J47ayVxPkW9/6VpLkqKOOGl7j1sdsLzORwJVXXpkkOfHEE4cxbjjMFPC6mTq42fVN2O67755kzCaFpfvAAw8sGvMtPCxS7OYab9yYuY4ht3O+qaHWDAzMpLFjfcPsm+cpA9/Qq/fneWG+ezVKzW6BicrNmhlM7KNDDjlkGPvZz3626G+C6WVmGH+fb5h9ezdlwCDyd8PHr7322sMY65l4kbT5NfMXdQI32GZP4lNcUwlmh2MC7+s1z+2m48nrrbuz3MD3cExgj/s7YiurR9785jcnGd/KE5f5ObN4icueb9eEBtjK+wuWqhUtf/nLX5b6epMALAjnIfgY1/3CF5x88snDGLbyPMJ4gcm90047Da/BgjF7hs/tsVgce7bbbrsk4z3Uq185VbDe7OthgTGXSWPB9L57L2fifc2SwIeb8YWyxOsetY/zUT7XvzuXHBV4L+AP8C1JW6tWLZETmSED44W8ykxJ2Kqu+85+M9OJn3NMYE/Zpv6bpwzmyGsK9qlzS/JI+55ejr/11lsnaaoo2JFJy+vNUqX2qf0MZ4EXX3xxGHv/+98/es1/59TR88WcqbzmiQmOwT2VB/XgYUriy5N2Bvv6178+jJGHOT++6aabkozz44MPPnjRGMxLK9mmiFWrViUZr2m+JzVkk2YrK6CIy9RFTdp8oKZdd911h9fYa/RySRr71H6H3NaqENa/fZYVi1MHfsBrkTyw18+CZzhJs4399MI901MAOt6bBQ9Qrtn38O8eg3nqwJf4TPWrX/0qSVMVJG2+YQInbe69Pz796U8nSR599NEkLWdNkquvvjpJeyaSNIWW/SL7yLY64IADkozPaq/3nFCM1EKhUCgUCoVCoVAoFAqFQqFQWAL1ILVQKBQKhUKhUCgUCoVCoVAoFJbAspT2QzXvyYRNu0Ya1StebVnJHXfckaQvgYBObSno4YcfnqQ1Z0gandgyaWRTlohY6jVl+HsCJIQuNI4N/PPYwHIb6N6nnnpqkrFUHRmnqe1bbLFFknGh7l122SVJcuONNw5j2OXvf//7MGZJ59Txzne+c/TfpDVS8Pwi/0OynPQbTbB/mCOvXSQgvVIAO+644zAGxX6jjTYaxpD7es9aSjFlIFu2TP6VCoO7RAWw30ImQikAJDhJk2VefvnlwxhSBTe7Qo5gSZ1lXHMD82cZEgXGPbfYyo2iWJP2zewdZOCWfNDowut3n332SZL84Ac/GMawle3N3+IyApaIThnEVNsAWb7LU/RswH5xw69nnnkmSSuTsd566w2v8RmOy/gbSzvf8pa3JBnHduKyS5/MxQb4Dq/lXrzFV/RioX+X5i74fvs45ttNBSnRcPTRRw9jxx13XJJxHsb7uLyP9+nUQQ7k9cwa7JW08f5gfVpeCZC02W7Y6MMf/vAwxudapsv7Od4Ty11awH/LlIENvJ6ZN5c3wJf0Gg2xnpNWqoi1a5/xsY99LMnY1x9xxBFJxtJEpOmWcTL3lrU7h54DbAPmu3dW85zit+wjfvGLXyRpZRZcuoLSL37fr371q0mSr3zlK8MY69+lM7Cpy83MJV9irdkX4Qsso+d155G8bl/005/+NElb3z6rsQ9c1ov1fe655w5jxIde0x/b1OeVKYOSH/azrEOXJ8IGthVx22c8SqUh9/c80ujQ/ox9Q/mEpJVzefzxx4cxmhE7t7W0eepg/p3HcEbqlTxzzgT8HILYS0NU7xP8F6XakrY/XGaJ5swusYDP83qZix3IyV0CjPJrvZIS9lGsc8fvhbJ8NxtnvTsu8+yIkgBJK+HgBt2sAT8D6ZVueDUoRmqhUCgUCoVCoVAoFAqFQqFQKCyBZclIhYXi20tuwNyQgqfHvt3lhsE3EhSe5zU/KeeWwrd0F1xwQZLW2CFpN0j+fJhkvn0wQ2DK4BYH1lfSboJdKL/HPGGu/LvMPTcHnm9s4NsZmKi+Vejb+fMAACAASURBVOa2ghu5JPnv//7vJOPb5bk0d0laMeVPfOITwxiFzV3Mmb3iG5Xf/va3ScZsAW5JvQcANvF+4t9PPfXUMMatkm/uuLHjZjQZ39RNGb1be250vd9hBnhe9t577yRjFjXvh/8wixe4OQg32j3Wndc9zAEzNsyMmTK4VTdrkZtJmI1JsxEF3pPGELPfgPnFPrCfwT5mDcAQtg24UfXtKXbz7fNcisj3mOiXXHJJkmT//fcfxvDT/t40pKApQtL8A+vbMQQ4TrAGPNb7OWKN7dLzd1ME38OMReat18jFzCHWrm/l119//SRtznqx03EZpvXnPve5YYy5N/OGf3tfzampBQoYzw3/pklXknz84x9PMm4MQty2j8dHwZC0LWER2Tb4ph6bo6fQuvDCC4cxmixMHbALnWdeeumlScZNRPADnj/m3r/79re/PUnbC7YPsL2//e1vJxk3oOrlw+wPx+K55KjkMOR/SZs3GkYlbU7NACamusEdyifmxz6jd1aj8azzMPYB9kzafjKLr2ffKYLv4bwPFeYOO+wwjJH7e05hhJqVtd9++yVpZzWv+d5ZDdu6gTD7wD/HmdI2mEtM+P73v59kzILje3JGTZqNnPMTo31eItdizP6ip5Bj31iFwz4gxiftHGfFbo+VOVX88Y9/TDLe+zRNQ2mWtLXo/KjHOuXZEXO5VPPeiy++OMmYIc/vWCmC/W1X509TBgommKlJm1M/n4BtTSOqpOU6frZBAy9Yrb1c3n7mrrvuSpLstttui153nOB9fIZ+vc/vipFaKBQKhUKhUCgUCoVCoVAoFApLoB6kFgqFQqFQKBQKhUKhUCgUCoXCEliWnG5kaaY901TI1Gpo8pZxIh2xBPPll19O0or5+jVoyJbKIZuyFAcp7y9/+cth7JBDDknSbzYydSDp7tGoezawjBO5lOeZOWWeLZtFBnXrrbcOYx/5yEdG75+0IuVuDIOsyhKROUkVkKW5ODjyZuTJSaOkuxELe8GFypFDY5v7779/eO2DH/xgkrEMESq+5xSavEtfXHbZZUnGMpJXasg0JfA9ek2kLMdgDeJvkuTmm29OMt4z22+/fZLkuuuuSzKW4yC5cVmTV5IlW9KAnLxXEH3qWGuttZKMpcJIOCwXYb94bdJcxbZiXh544IEkTcKTtGZubnREWQCXyUAO578J2aH9Ua/5zBSBr/V6ZL97r7MOHVOR6liKzNolBj/22GPDaxtssEGS5JFHHhnGkIC6bALlLLBZ0qTrtsFcSlywvpwbMR/+jr0SR8yL4zI/9/Wvfz1JawaZJH/5y18WfRYNxOxjiP2Oy+9///sXfVav2edUQex1WSfyS5fGQWrokhbEB88Hv4uU85577ln0mW7SQl7Um1OXBdhmm22SJIcddtgwNhc74JOdG330ox9d9HPMlRuSrrHGGknG8RG5Jz/v98XnYc+kSQ3dJAQb9Zr8eB/N5Zzwjne8I0k/R7H/xzd5beLPPVfEV2JBr1SVzxpIeB0TsKPlvey5XimmucB5yHbbbZekn4c4lyeH6ZUsAvY7+D3L84lJvZjwhz/8YRgjprvsxVzkzJRwcbzdeOONk7QzQNL8jZ8j7LzzzqPXkhZ7+e/uu+8+vMY+cMkAGlW5nBE28N7k9V6uMAdwvvV3Zm7cmJd81c3OmAfnsuwLbOM1zr6w3bC/x8hz7cvwR7bDXM7LPP+xDfhulCVM2nfn2V7Szglek8Tta6+9NknbL0mLqU8++eQwxlnOcZmzu5sN8ln2d6+34dc8TtmFQqFQKBQKhUKhUCgUCoVCofAGYlleRfD02GwU2HJmYHE7ZgYANwEuVsvTf27Rvve97w2vUazcxbZ7rCJ+d6+99hrGKGLsgvVnnnlmkuTkk09e8nsuZ1DA3I2lmFPfylBA2Tcw/NusMJhDsO/MuIBF0LtpcCFg4KLR3LL6ltO3clPHtttumyT5zW9+M4zB9vFtFjYxI5Q1TVHnpM0Tt6QnnXTS8BqNA970pjcNY9jh9ttvH8ZgC2PTpN1++lbttttuS9KK108VzIHXGOvO/giWCQzIpN1Q+uadwvQw7z3fvIeZG9jZzRF6bArWgPei2QRTBj75+uuvH8ZgW9kGzJvXIbebnjPYMh/60IeSJDfddNPwGo1KzKhh7s3coPGg1wWsC7Pz5tJYhP0Okzppc2UmBj7e7F3ip+Ms8ZWi9Nw4J8nHPvaxJONYwzyawUSMsW2JU95DqB3OOOOMJb/ncgbrz+xdN48C+GOK9ydtDZsZwd4hXzn++OOH104//fQk4xygx65jn6yzzjrDGM1OiBXJfBqLJG3vOy5vscUWScb+yLkhgOnuOYQpB8uox2ihoUjS8ieakiSN2eF9B9vbfmsujdeYK+c3MIzsj2CW9tg/NE9LGquR84R9+CabbJJkHE/ZC2Y5khd4jKZLbko1l7gMy8trnpy9d1azDViTziNZu+TwjuOsbysQ8SluQIX9bFtsQHxOWmPUU045ZcnvuZzBPDsu02jF89drVIifMVOLeYYNadXavvvum2TMCubzn3jiiWEM1RyNYpKWu7m5mFmCUwb+xmo0YqB9ET5/q622GsawkXMjGh8RP9y86JhjjkmSvPjii8MYvgi2atLOyT57kJt5zM9Kpg7WpecSFU2P8emchLzSSgTyJ9a2fRV+zuxJ7GuGfK8ZKHmt/6a5NGfGBlbMMlded7zuBoTEBCs/8Fe77LJLkuTZZ58dXtt1112TjJsdshd6a8DnCeziZ0zkc34W9WpQjNRCoVAoFAqFQqFQKBQKhUKhUFgC9SC1UCgUCoVCoVAoFAqFQqFQKBSWwLKU9kO/tRSGQtqWcECFtvQMqbmpwRSWR05z9NFHD68hOfzJT34yjCFzOPLII4cx6PmWSG2++eZJxlRjZIpTBxRs0/6hTLtRFM0WTKOGMu3i59DmocgjqU0avf6GG24YxpAEYk+/r6UpyE7mQotfCOaN0hZJkwNYMsJcu0EOe8UFtfld1vZ3v/vd4bW77747SXLnnXcOY8ijLBFFPkIR76RfbsDF/ucArzvkTJZgIuvwHPBzlrkxL/yu9xPSEZenoOQCjR2S5vOQmCZNFueC2XORlTOnyPmT9t3sZ5AzufkQc2o5GiUUHnzwwSTjPdKzC/IPxx9saqn5xRdfnGRc3sGNAqYM5gw5f9J8t21AkwXL3HrSTtY4a/mAAw5Y9JplyMRlGgokLT54v5x//vlJWsONZLz/pgzWmuX8rEmvTSSBlqlhI0tekaURRympkDT7WX6Jj/H65t/IyJOWB3kP3XfffUnGMqypgjzQcZm9YJ/L/FsSSGkFl2pBCoh9Lf3m5y2xZT95L+C3LDWkTJZt7uaGUwY5qn13by8Qb52jEgvc1JFzB9JxyzOJJ72mtPY9+CM3kyJP87pwfJoy+J720+wD733k4vYRrHHnK6xr9ovfg59380bk585F8fXeB5wdnAMceuihr+o7LnfwPZHzJ20OvA84y1lyy/y6PB4SZOKyZa74PUuSmVOXduF9fV7mTGc59VzyU3yHz8s9X0S+7pjK3rHEmDm99NJLk4xzI+ben0WJl0033XQYoxkrJWf8GY4vDz30UJLx846pgrlBym34vEyctZ9mz7g0znPPPZekPSfyvJFP2W690mLEDJcAoyyk/RZnET/3miLYC54X5sp5ON/dcZn46bVNnGfufYZgzGUu1l133SRjn8ZneAz0mrG9VhQjtVAoFAqFQqFQKBQKhUKhUCgUlsCyZKTSGMLF9XuMkr///e9JxrfE3HB5jBtMnjb7SfmTTz6ZJNl+++2HMYrfusEOtwRuLsBNoG9U99lnn6W+3iQA6xCmYTK+0QEwLnyzD3PItz3cQsISeuaZZ4bXuLEz24wbJW5uknabYAYYDLS5MI4WAtaK5xKmhG/HYKPQgCFpTBazJmFPUwzejUdOO+20JOM1zudyM5c0Fo5vk7GN912v0cYUwe2xb7N8ywxY455vbiY9hn+BTWGmKTCbAv8Fuyhpc2t2AXAzBrM3pgzW5IoVKxa9Zltwk9+73e/dHOOPzJ58+umnkzQlQ9JsTwOBpPkes/N6ReTdEGbK4FbX+5p12mPhEZ+TFiO9HokTa665ZpI27/639xw+yz/H3PvzicHec47vUwbr1owLvqf3AbfsZlfwuy64T0zfeuutk4zVPcRZNwwh9phphB8zUwCWoMf4jDngsMMOSzJWMpnBCIjfZtaxFxzTYSii8LESh33nmEC+xc8nLVcyM4z81sxV+6spg++71lprDWOsS883/t/zDUPGuTuvw6b+2te+Nrz2yU9+Msl43/G7sMaS1vwCdU/S1r3ZmGbRThko/6w+6tkAf+ScFT/kfUNs4bVf//rXw2uwlBxbyQfst/h8x3sUcI4JzpXnAPt1zkOeK+xhlQL5uplYxASUA94jrGH/PP7EjUCJt14DMMs8ZkXVlEF+ihon6Z+XidHOV1iTPTbcIYcckmSc8/AeXt+c092wk9zW+edGG22UZHyWMIt16ug1lCLntO9hDr3+yEf9Hqx9YohjJ2NWY8Fcdd7VU6XAxvTe8jOUKeOpp55K0pihSfMz3vvkOL0mpM4byVuxlZ9FMLc+F/JMw80iiR3eY+RTboDVa9z6alCM1EKhUCgUCoVCoVAoFAqFQqFQWALLkpHK02vfmvD02DeK1A78/ve/P4ydffbZScY3+twicCPgmgjcXv7Lv/zLMMZNg+t4coPk+hX+DNCrwTBFcEvsuh7MFQyipN3EffzjHx/GqLHpeT7hhBOSJOecc06SMcsFO5sdiQ3MfuIWyTcY//u//5tkzNbo3XBMFdzgmHHLfHkvUHvWbF3m07dA1PWFybj++usPrzFvvtmGUeN1z17wZwHf2Plzpwz2tG3AraWZPi+88EKSPvPRgMXBfvr973+/6Gc8d9x4+pYT9Bintsvrrfmy3MDaNMuRPc8tcJL86le/SpJcccUVwxh1gF2X6rzzzkuSfPGLX0wy9j3cTHu+8fWuN8XfBCMvaX7LjKO5+CPWpOcRG/g7XnbZZUnGNa9vuummJOOYfsEFFyRJ/v3f/z3JuF4ULDzXSsL2roeNL/LexLfZj7l29JSBL3B9TeKAvy/z4ljNGiZmJsmqVauSJGuvvXaS8T7o1Z9lffsWHz/meD/3uHzssccmGbOAWdvOmWCHEhsMM1Rg9GHfHjvFn4XN//CHPwxjzPUvf/nLYaxXv9Zs4imDPe/5Zg4cF/E9KN2S5Mwzz0wyrpmJ0oZadeSsSWO+OLayP1auXDmM4SO33HLLRT/3T//0T8OYmZZTBr7Wfp311ct93J/iqquuSjJmih1//PFJGhvYrKaFzKSkza33C3vIcQp/ZOaqzyBzQC8mGPgKn2tR4jgmcP5mzH4b25pFzB7aZZddhjF8leMyf5//NvwiTMmpgpy/92zB+eHNN9+cZJzfH3XUUUnGNlio9HQ+ib/zmmdubQPmfoMNNlj0c5wXk3FOO3Wg9nMNanyT1zGqA88N+aqZxI8//niS5itsI2CbL/xMw/4f29hHulbolMH37D078lyhCPjCF74wjF133XWLfhdmdU+VyD7xWQ1/ZKUKtrf92At+9tfL014NipFaKBQKhUKhUCgUCoVCoVAoFApLoB6kFgqFQqFQKBQKhUKhUCgUCoXCEliW0n6afFiqhvzDzWygW1tag1TZ8qW//e1vSfrFgaH1WhaBbNdSKuS4LjaPDM6ywbnQs5HUm+YOLdtyMijyyPmTNn+W//30pz8dvUevmY5p19jP5RN6xcqRVFgm1ys4PVW46DJAamlZAI1v3DSHOeyVw4B+73nDlpZy/vWvf00ynvN77rknSbLDDjsMY0gVHnvssVf1vaYE1p3XJ9/XBa0ZcxMDpICWNCBbwA+5HAh2sW2xn8sr3HfffaOfT5p/s9S9J/GaIpBze23iGxwnaFiHbDxpc+DC7khze/JlJCH2/0hDvA+ef/75JG3fJMmb3vSmJK2pWzIff4TP9nyz5uzPDz/88CRNxpa0OfDvfvrTn07S7GMZII1fvA+IRcTipDW4OOKII4YxYpflzL2yGFME8cDzzTrtNfawzJvmIc6NaFaEf3ITDHIjvy/2sy+88cYbkyQ777zzMEZMdzO8ufiipEnmvbdZd5YQkiu5fAu57GabbTaMsabxc5aY7b333knG65l81fkZcQV5Y9L2lPMI23jKQF5suT1z73i77bbbJkmOO+64YYzc3Q2oXnzxxSRtzdqOfIbzUT7fa4DcyFJl4rvL0lBKY+rArzrXZ226TAjr7/LLLx/GmFP/Lk29mDP7Gc6Fls32mszS7MRNPDm3Oab3GnVOEaxJ5zfklo63C8uHJP2GSLfcckuS1oTIfhu7OCYQT3yGRnbuOWa/2H4uPTNlsNa8vvi3y51R2oKYmbT5tc/Ch+PzXc6o1yQH3+VyFY888kiSscSZuEwZk6QvV58qXEZhIRwnKPHBWv+/wJ5in/QaFvb2kOeU+fffxp7x+81lLxATPN/kHPZHrFXHBM5hXtvk+Pgex2V8ikv08IzQ5QEuueSSJMl+++03jPF+PB9Mxufp14L5ZLaFQqFQKBQKhUKhUCgUCoVCofAGYVkyUnnKbOYET+v9pJ+Cyb5h46aHW/yk3YDxhNzsI27RfDPAjc/+++8/jP3whz9MMr7N5zbUt3NmrE4ZPOF3UXHYVn76/9BDDyVJdtttt2HspZdeSjIu+MyNGjcNvpWEQefbCmzALWrSCj/753o30nNpppC0teXbHRjQZjLCivAtDPPlYv577LFHksac9i02NjHrgmYvvumE9c2NZ9Js4iYKPTbtFNG7sYUp58ZS+BD7o948swdgbJipAkPYPor1fNtttw1j3Cx7L7IX7KPs16YM9oGZeDBB/X1hgrqJGnN/++23D2PEhyeffHL0M0ljUfQaecHSSJp/sw/i326SYabGlIGPsS9ayGhMGvvOaw9mHj+fNBYHe8T7hvVvH4KNYC0lbb7NBGHMe7Nnyymi13STG3jPH0X4Dfy6WfSw3JkzM3fxe2YH9JjFNDD0PoClYUaxbT91sLYcW2HD33333cMY6oQdd9xxGHvmmWeSjNlaMCF4PzfUIceyzZlX5wAwX6wY4nccJ3qNMKYI1qDzPdan4wRsX/sA8pnvfOc7wxhNX2g26P3EZzkusz8cv2k+aX8EbKve/pwiyN0dE3psUtY8zUSSllu62Q0+nvnuNXzs5ZVuoISKqhcTvIfmkp+y/r0PiL0eY117XvAtO+200zC2zz77JGnnDJ9vyWUcE3jdzK5HH300yTgPY42YKWxF3JRB/uM1ha8wY5Ec3spaVHxutkkz1IMOOihJ8vTTTw+v9dQQ7EMrc2CdOu6yD9gjSb+x4VTB2vY+Z92Zyci//byGPNSsRJ47kes7jrK3bAf2hfNhziT2/6wJqwfnwpBnjTkGMy/2Rw888ECSsf8nX3Jehf14P88T+83+iM+3UnC99dZLMt6L/Ns2fb3P74qRWigUCoVCoVAoFAqFQqFQKBQKS6AepBYKhUKhUCgUCoVCoVAoFAqFwhJYltL+TTbZJEny3HPPDWPIDCz1eN/73pdkLCFEKmBZTk9CC5AlmPYNLfyKK64YxpCmmH6P1MR0YEt/pgzkF5bm/fjHP04yLpuANNBzCzXe8j8o2D1JE3R8S06Q67pAM7a3fB0KvdfFXGyQtKYT73//+4ex3XffPUly6aWXDmMUJ/fPsX9MnYcWD8X+6quvHl5D0mMgO/mf//mfYQxJlhsmIBd1A425NBdBKmD5Wq+xCLInrz98mZuHIEPgfS13YM947phv7w8a+Wy99dbDGHKhntRk6mBeLM24//77k4zLf+C3LEdjzdt3L5T/uTkUElnbFpvSyCJpPs37YNWqVYt+12UDpoxeiYKLLrooybi0C77IMjekypY8sf57svJeY0jm1I0a2F8GDRo873Mp93LNNdckST7xiU8MY8jO3va2tw1jrP8PfOADwxhrd4MNNhjGWNfM9x133DG8RhkL+x32oWX8SOI8hv9yowv7z6kDP2S53oMPPphk3GwQ/0tzC8PrnTnmfb3XGHNcIc916SXWwa677jqMXXXVVUnGZS4sJ5wyWLP+buQrSOyT5o88fzRh22qrrYYx/Abr2PJb8lHvBd7PZRj+9Kc/JRnblnzVnz8XOS1nK8dlfLZl25RS+PrXvz6M8bpzlIXNY30GJM47tnL2e/jhh4cx9qSbXbEnvV/nUvaIBrLO/c8555wkycYbbzyM0QDNfvjDH/5wknF+v7DBjuNobx+Qb1qyTzNUj/Fz9oVzyU95FuHz6o9+9KMkyUc+8pFhjHzd35s8yY2qkejzjIESVEnzN463yJjPPvvsYQw72z9xDnGpNp/Tpw7yb+yRtLjoMwExwc838N32ZQvPsP55mnEa5JnOfSml4z2GH3KZnbnkqPhax0DKDNpHUXrC841d/DyJHIdznH04fqhXFsy2IufxnmFvUbIyef0xYR5POgqFQqFQKBQKhUKhUCgUCoVC4Q3EsmSkcjPgm2ZuIf2kmif4fkLNDYJvH7hF6zUm4jbGt24wTH27REMFCnb7d3079/LLLy/x7aYBbko8LzTfMquU+fZtGjcRvoHnxgAbmBWBHW0fbECjgKQxBHwjzW2CCxD7b5k6YBSZZQgTzwwk1jh7J2m3cjT/StqtPnO93377Da8xv7x/0tgtnlP2oO3L/JvFMRdGKkWufXPG/PlmmZ9zoWz8gW8bmRfew00w+DmzmijiD6MgaUwDF/TGzjR2S+bT1AIGsNkUsBHtk2Fy2R8wR7YBbCZ897ve9a5FP2+/jp3NsoFt5wZrNJxBweDfnTqYI+/7Aw88MMm40USPqeXbYUAzEmK1m4bxHvYn5ANuYsh6+M1vfjOM4eNsPzObpgwabXnfszc8x701DCPbDBRexxeR5yRtv/iz+F3vOfaB/X0vpltJMnUQj51n8m/nNow5BsMesi9ZyBJ13gXzxfsDRpHjMjmWfT7+6Nprrx3GzFidMphHxwQUMWb/kC96HdOwxQwU5o847zwTH27mC6xSxxpyXxivSWN2Ow7MpQEh38nfjXXqeIuvtw3YQ57nhYxHx2XsjDoxaT7N5y5YTX7f3lltLs2maGzp+T7hhBOSjH0u8+FmOrBDHTs4U/X2ATbwmif+OO/dd999k4z9Yy+Phmn5b//2b0t/0WUM5tGxlVjtZjasPysBt99++yTjWMmcM7bzzjsv+kyf9dhffmbB2RF2cNLyKj8zMXNw6jDjEeDre89/HFOJJ+95z3uGMZSWrFnntMB+BPv2VLPOZfk571mrtaYMbOC4zHnVuUmv2RM5pOd0YVz2+zJ/bmxILLJP4/M9x3yu14DPMa8F83jSUSgUCoVCoVAoFAqFQqFQKBQKbyDqQWqhUCgUCoVCoVAoFAqFQqFQKCyBZSntR85tKjRSGMsSKBZrGSxSHcsHkCJDZ7dUAYq1pUBQwE05Rr7229/+dhiD5m2ZgwvXThlI9yzZQYq2xhprDGPIOSwV6Ek4mCPkmZZ89JpNYVuvAajaps1D23cBcyj6cwDf2fIL5E4UmU+Su+66K8m4+RDz74LvrH3o8ZYQst6RCiVtXm1L9ptlV/x9PUr+1AH135ICywEAc4XUKek3qmJsocQ/aRIF+zl+zn5uYXOSpEmaPTaXxmtIpv7whz8MY8wVzbiSfkkS5sDzgn/Df9ie+CavX/wQssGkNZex70NaZdmufdiUQSyw32FOXYYHGbPLkeAXbAMkmpRx6TV8tDynF5d7zV2Q3Pr9XO5iymAfeF5Yf97r+CI3wsR+jr3MJQX9HYOBZcjY1Gseu1heS2xwSQVL5qYOfI+lnKxBr09k+f455ss+fmHTU0v9b7nlliTj+eMznAuRd9GI0O/j5kY0ZOrJIKcE4qHl4vj9XoMcr2P2ivcCuWwvR+XfjtX8vKXmSJXdAJGY8eUvf3kYO/nkk1/Vd1zueP7555OM1zLz4byTuffe6MUE/FrPh/dKIWFb/9wNN9yQZNxsipIld95556KxqQOfbNksa9NzQNzuNSN1zopPIW/pNZZyTsPecEzweRpwNvcacDPEKeP3v/99knG8ZQ7IMZNWlshSfdsDcP4lLtsXsV/c7Ah/57PvWWedlSQ56KCDhjFs5Lhs/zl18F2cz/OdvT/4t/NG7ICcP1l8VrOvAl7PvIfjd28vcB703ppLU9peU3fyJfsIZPZ+xsS8OM5ypuvFZezhPYQ9/Ezo0UcfTTKW7rMvPvrRjw5jl1122RLfro9ipBYKhUKhUCgUCoVCoVAoFAqFwhJYllcR3G76docnz2Zi8BTazCFucnxDurBYv5+AczNjliVPyn2DzU2Sb5d6TEo3YZgy+B4uAt5j4HID5DnlBsa3D9gDW2GnpDVMsA24wXAxZphnnm9uJ2zv11sweDmCYvou5A4Ly7djMFFvu+22YWzbbbdNMr5x5MaH333qqaeG12AQvPDCC8MYTC7fqsEk8O/yvr6dm0uTHfa8WYbcrvvmjFv9Huuix4JnfrzGuXWzbbmRc2OR9773vUn6TXz8N82l4dc999yTpK3ppO19F9hnbn0j3WPSv/Wtb03S/MzDDz88vAZ7zM3csIFvmtkbjhN8PkyCZMwknjJQitDoJmk28O0vLBjfEsNSMnsYphY2sN+GMbDOOusMY8QM2xaliNc8f5NvxufSdI3cxPGO7+45YJ26CD+vm4Wx8JbfzXfYV9dff/0wRgMN+34UDN5ffBb7LBnHsKkDn7L22msPY6wx+3p+zj6Kdd9jSLJnHGvYM35f7OVcDJ/jtUG+ZUXEXBQ7zLdz1J7CjLmyT2Z+zdRdmKM+/vjjw2ucIVasWDGMcSbwvqPZlXMf/qZjjjlmGPN6mDKIlWafrrnmmknGMaHHtOaMYWYdax0f73m69957nyjMsgAAIABJREFUk7ScOEk23HDDJOMz4HbbbbdoDBt4v7pp4ZSBX/W+Z57tc8ljvTbJa8zyJT6wb/weNC6y8pIY7aab2MVnj4WNDZPxOXDK4GxsBjD2cB7CM41eYyGzfBfa4KKLLhpeY27ZD0ly5JFHJhnH70996lNJxmcAbMAe/b/+lqmCtd1bYz5Tsd6tFGEevGZf6fzE2e7Pf/7zMEY8cXNJzmq9te4cudcMa4rg+cFWW201jDH3jss9NRv+3mPsKfy5/TrsYfZE0uzoPHdhA72k7YUf/OAHw5hj1mvBPE7ZhUKhUCgUCoVCoVAoFAqFQqHwBmJZMlK5wfGTZ1hbvsXv1fXitt+3aPwO7+EaC9xQ9uo5+ubujjvuSDK+Ud10002TjFmoc6nF9swzzyQZ3yBssMEGScbsHm5gPH/cdrl+FzfM3PD4BhTmqu3Cz/k9uAF65JFHhjFun6lRk4wZClMHt1muzQubwjf5sN6YD79uJuPCejy+3ec1M84W1idJ2o2T2Rzcdrt+omuoThnM39NPPz2MwcIy2xA2hevEss7tj/Ar+C3vnR5zDjaR9wxsmaOOOmoYu/TSS5OMa3+ZOTZl8N1//vOfD2MwIawSgKFrX8JtpGMHTD38DL48abfUZvNxS93bh/Y9BxxwQJLx7fNcGGDMxwUXXDCMffjDH06S/PWvfx3GYCHaBsRF+3NYSvgd15+FaWr2KfvALIJenUT8nv2j4/aUASNojz32GMZ6t+gwHJ0bwVbpKTqwgdc8LJtPf/rTwxg/R1xKkgceeCDJeH/tvvvuSVoekSQbbbTRUl9vMmA9WX0Ao8T+HP/itX366acnSU488cRh7PLLL0+SrFy5MsmYWdSr+499HRPIb2GOJy23sm3molJ48cUXkyRXXXXVMHb00UcnGbN3mT+vbXyD9wK+Cf9Cvps0/2KGJGO2ASxH2wq2jPMwqx2mDObo1FNPHcaIgc5lYOeZ/QaL1XuIvYOfMXMONnxvf5ltDBvM+dohhxySZJwPfehDH3o1X3HZg/4IW2655TDG2nS9x/e9731JxvOHj++parCBmV349V7dVNdcPv/88xe970knnTT6O5LxeWHK4Hv4+7C+PX+sdSsEYKLaLgtraLL2k2Zb15cl9njPHXjggUmSn/3sZ4s+yznrXOrUJk1Z5pwb/2z1BvHb+aWVgf8XfH7Gl/kc3Mtl+QyrTGAmu+eDWf1TBudfzqNJUxK6Ni/PabxmUTXZbyw8Jzj/J/5477A/fCbgPfzsaIcddkgyjtWuFf1aMI+MqlAoFAqFQqFQKBQKhUKhUCgU3kDUg9RCoVAoFAqFQqFQKBQKhUKhUFgCy1LajzTQ8kho15YGIuswDZiyAKb/LqTO77rrrsNrX/va15IkG2+88TAGPdxjyGUt4+fvM43cf8uU8ZnPfCbJuFA2Nnj7298+jCFZNm2dn3PxZGjZ2MwSH+xiijUSCDewQIZoCjz0bcvR52KDpEn+XNyduXRTKOQFtgM/5yLmFG6mYZUlJsgMLEVBwn7LLbcMY0iqbUNsjeQwSf7f//t/r+o7Lnew5934hu/rhniMeR2zPywrROqBvMRSS0opGL0C6kgDb7zxxmGMz0X+m4zLDEwZrH/vA+bFTSOQ8bgJF2vcfgHfjQTHjRpefvnlJOO1zM/b93mfAGS1loj0ysZMEaz1j3zkI8MYc2sZDf7f35sSMf459hWlGWwf3tfvQey3FI2SAi74z17zZ1k+OmUgE6Z5V9K3gaXNgLm0xA3fgwTcMjWaf9mPI092I0lyI5cBIoeyz7QPnDqYN0tcgUtC8XNef1/4wheSjH0EcmhyHOevyJFdqoLXXbIC/297YWuXnHJ8mjKQZtNoM2m+xxLb97znPUnGZXjYKz5jEAOwqeeRPeb3JbY6fjPPzsOApc+9OD9FEGcPOuigYYy5Io4mTUJu+Sxr0/NHXkqObz/H3LtECD7t2WefHcbwR95DlB9x2aO5NL+jqYv9DvPsfc88uzxFL1bfdNNNSZJ99tknybhB1yWXXJJkfAbrNZE67LDDkozPj5wXbCufP6aMXhkz5tblqMidejHYzxaYF/YNZRGStq6POOKIYYyznsspXXfddUnGNmBP+tnGXHxR0nJDxzjmxvuDMecsvXI95PvYxuephx56KMm4lCDn6yeeeGIY44zhWM3fst566w1jc/FHzJnPSsRbPyvrlVJgDvwaZTUpW2ifQVkrlxbjDOjnfJSYcinDBx98MMm4xIvL77wWFCO1UCgUCoVCoVAoFAqFQqFQKBSWwLJkpHIr6YK93AT4Fh8mkm+Oudn/6U9/uuj9aIzh2wIYHrAvksa28K0Gt2491oeZS3MB7ERuxJJmA7OCATcxSbud/tGPfjSM3XPPPUnaDamLw8N08i0+t2S+RaJQsD+fWx7fPs8Jn/3sZ5MkN99886LXfAsKM8CMov333z/JuOjzr3/96yRtHfsmmps1j91///1JxgXieY8vfvGLw9htt92WZMwgngsLDLazGS00sPBtLmvRTF1uz+wj9t577yTNL/mW87HHHksyZpTBPjWTgM/wunfTi7mBguRmpMK0RsGQtGZQZgrDQqEpQ9JYEcyf7UOMcUF+7OzP55bT9iMWOU6YqTFlLGRSJ23NmVVErPTahKFy7rnnDmM0Z4MlYZYGrCPfEN9www1JxrfaMArss3gf22Uu8QEGXY+d6DwIFopv9q+44ookjWmUtLhM8yjHYD7DzQCwvZshXXzxxUmSFStWDGPEC8fvOYHv39vbH/zgB4d/0xTQqpDnn38+ydg2+HvWruM4c2n2JPmTPx/f5wYasMnM0F/YcHLq8N7Gj3sv8G/nsjCyYCom7Tzx7W9/O0lj+iWN3eLP4hyxzTbbDGOoQXxOwaaOMXNp+MW6cv7PfJv9g3rDPn7VqlVJxuxG1jXz6JyLs53PapwRrZKAief8uMcY9J6cMphHzrJJyz+efPLJYQx1gHN05tJ7g3yT+OwGLXyG814Y82aG95r54KscO+YC/IjngH1g/wBjtHf+tS9HYfPLX/4yybiZGz7IDEbe141XYfI5bvAMxGcz56pTB9/LuR9x1mucuXMuyzp3/ObczZ4x45fzslUpnNG8x/CRztlQ53h/zAXYwCo/9oLzQXJN24o5cgNV9g+qWBQmSWO9+n0vvPDCRT93zTXXJEnWX3/9YYy8ys+TXq+Kdh7RvFAoFAqFQqFQKBQKhUKhUCgU3kDUg9RCoVAoFAqFQqFQKBQKhUKhUFgCy5LTjazJElnkAxtssMEwhvTeNHUKZVuCc+ihhyZpjXtMiUd+Ylo9n9trdoW8JGkNFfy7c6HJ/+M//mOSMfUcWru/L2OWVl5//fVJxnTr7373u0mSe++9d9HPM8+W2kALd3F/ZGqW19LcxZTsucg4k+QXv/hFkvEaR2p+0UUXDWMUTO5JOW0vJFA0vLB8DTsg7UmSLbfcMkly5ZVXDmPI4tzUCImVC6bPpakF0lavKxrjsE+SNs8uht1rvMb+4OfctIifsx0pAWDJCbJCS01YI/6b5mIDpFCWYSBRdRkE5s8SQiSv9hGUpTjvvPOSNGlz0ubbc7fmmmsmGUvNke/Yb2EPr4G5NFQgttkGxAfPNxJWNxdCWmNZ30477ZQkOf/885OMZVa8nxslIJtDLp00u1mu3CvFMZeYwN52/EQGZTkZr3tvHHLIIUnGdiF+sm/c/AAf54YM7AlynyRZuXJlkrGU9+mnn04y3nNzyY0MN8NhPTsuYi/POXmMYwL2Yp26mRf7zXJxciuXXUBq6P3J2nBMt8x/ykAe7xwVe7i0C3PkvUBMsF+gnMitt96apMXppJ0JvO9oHmV/hHS21yzMNpjLXmB/+6zG3ndTQHyx52/TTTdNMj6PUfqLuaLUUdLsaCktTUbs6ymd5FIKxBOfSeYSl5lnzy05pZsKkd97/pDGeg+xhom9Xqs9STLndUt58TtuxMM+cUyYS4kL1rLnhVIh7Ick2X777ZOMbcXe8XqkQRUNcSjxliQvvfRSknEM5rM835tsskmS5Mtf/vKiz/JzjF7Tn6mCteXcnecFLglCvuifY694bGGZxxdffHF4Dem4/QzlA/xZ7EWXx6PsguX+cwE2cPNYzqv2R/hi/xz+xfGTEmvsHZe4I9dxLsW6dyPmHXbYYdHn87rt8npjwjy8WKFQKBQKhUKhUCgUCoVCoVAovIFYlteiPF12EVpuKs0SpdGFn0ZTMNhPmWHdUbDZT7R5Au7bam6azT7idZrqGGaFzaWQPzcwvm3pFUbmtsXzx62X54IbNmzl17DBCy+8MIxxc0Bjo6SxmVyImNs034j731MHdjDrjRsabo6T/g0bt2Jmstx+++1JWhFzGHlJs7UZkjSbMhOcQuhmvnDrZ+bNXG78e2At+na/x0iFJQbLMWn7iFtp7ytu4pjjpDVTgm2ftJtM7yP+bZaSbTllMM9uqOVYAGAi9eziuaKI/Gc+85kkyU9+8pPhNdinZrnA3GA/JM0Gjj/Yz0zKuYA59ZqCBe3mCfzbvojmXsxtkpx++ulJWkMMNw3D35mZh4+huVHSGH9mufSa+cwFzKmZVTCBfLOPf3IOhVrAcwrza7/99ksybiICU8z7ANbe3XffPYyxD7Fx0lhKc2k4uBDYwcxHmKhmXePbzTyB1WIb4kN4XxhOSXL11VcnGec1+Bk3pcLn2Oa8bh81l6aEfCfvfVjt9vXMh1lg+AizRM8555wkySc+8Ykk4wZK5DVmIMMEdPNaGN7+XWzQi1dTBzZwfgoz3f4IP22fDNvOzFUYqJzj3DwHG7ghHr/rRjI0ynUuSuOkOfqjXkzosc57zbV6iiWadcHi8vkWe5t1v9deeyUZ+39yYDdzgxXs/HgujFRgJh1rjv8mLTY4RmAr2wfVAr7Ic8seso+Brcf5LmnxgkZ5SVO1zemM3IP9P81Mjd66w684ppLLkvOi0EyS5557Lsk41yfG8LwqafHezGR8pJVcc0Hv2RHsXeeX5CH2BzTw5byVNEUHeSiNa5P2fIh4nrTnfTvuuOMwRpMpP2PqNfF8vZiXFysUCoVCoVAoFAqFQqFQKBQKhTcA9SC1UCgUCoVCoVAoFAqFQqFQKBSWwLKU9iOVoblE0ijTpmxD9X3LW94yjK211lpJxrTis88+e/T+btJz0kknJRlTvaFqWwoEFd9ylZ4U2hKTKQOqNIV7kybftw2QLLtRCA2i3LgCuTEyB0sgKKHghi9QwC33RB7Xk7j5b+pJWKaKnXfeOUlyyimnDGPQ1A2k4Jdddtkw9s1vfjPJWD6AbAFppmnt7AEX1GZeLTWHuu91zzpA7pCM5StTBuveEkKXsgDMpX3PqlWrkozllswLkgbbhzH7NCQn9i3sT+8PpBJu3kATsKkDSZ7la0icLBdEsu+GdMhr3ayLeaNIPzK2pK15S06Qn1vKS8kM+zJihuVENKGZOljLK1asGMaYe8dPSufYBpQB8R4iFhx55JFJxrIbJITeS0ijLHum3IDXAPawtNMS0SmD7+l5xO84Bt5yyy1JWmOKJNlzzz2TjJuHIOnHPm4quN122yVppY6Str532223YYw44HIazL3/pjmVesGHOxdhrVoui528tpH9ucQU80qZBuT8SZtLlwdgjVvmTM7kPUPcsc0tT58yWNuWhuOHvO4uvfTSJGP5H81b7LsPOuigJM33OI4yfy4FQN7qz6d0iRu0se5tb5qDTh3ILrfZZpthrHdOQG6/xRZbDGPMvfNE9gt+ySUpaCzluELDHZevYv0v1dTIkuspo9cEmDzJNqDhr3NC1rN9PM0aeT+XPaJJEU1rk9bE2aXGWAPOeykp4zP0u971rlf1HZc7fvCDHyRJvvSlLw1j+ADb4LTTTkvSzmZJK9XmkjvsE2Ir5RaSZLPNNkvS7JQ0X+Uzt30VIDbRbG/h70wd5DH2EZaTA8oDumxRr3Epe4UY7AaEbuwFOB/4DN0rMcWZz581l3ILxEWXN8BH+fsefPDBSVpD7aTFAsdlnoHwjOncc88dXuMs4jXMs4of/vCHwxjPhFyOEB/psxrPD18ripFaKBQKhUKhUCgUCoVCoVAoFApLYFkyUtddd90kY7YcT/V928+Tb99o8m+zvLjVgWn6ta99bXgN9qtvF3hq7ts0GIGf//znhzGeZJutx23/gQceuNTXXNaADeyi5cyfi1zfddddSZJ99tlnGKOI7z333DOMbb755qP3M2v08ccfT9IaWSTNBr5VPuOMM5IkRx999DDGTbP/zrk0U0jaevaNC7dtNJNKWvH34447bhhjD3ATnTT2F6+x1/4v9JqnrVy5MsmY+UKTADOg5sICg2llFiosR/sebrh8mwZ8K4o/4ufN7OXm0wwKWMRmn7LezdDkd81MgEXI/psquH03mwpmg20AO8hj+HEaWCSteRpMavu0XpM2bkMdk2699dYkY38Ew9UNxNwcb8rYfffdkzTFQdJ8gPcGTAg3a+S2380nUDsQP+0vYEY6rrAGaJqUJD/72c+StMZVSfOFbhpw1VVXJWkKlKmCuGl/wn4384H4beYbvsIsRvIpGN9777338BpMJJh6huMye82NXLj5p3lA0mdwTBU0NPjVr341jOFDzGRkvVtFwvybHYF/6TWXxJeY8ct+sxKINXHjjTcOYzCb3KzEzKcpg3VnX0tcNHsX1grMlqT5rXvvvXcYI0ainnJjvO9973tJWnPCpO0B+zn2ihnF5Ebed+S83m9TBDHSDS7xA85riKnkI0ljLFnFhI8nZvo9YPiZNcS+MsvxG9/4RpLkrLPOGsZgFKMsSVoDrKkD/+9ckH9bVUnub78DW9oNpYjD+JMjjjhieI2GbMcee+wwxnnAigPOFW7Exj6wf2StmEE4RdAcyo2HyEPcoOiTn/xkknFOyHqGmZq0ZjvMlRvnwKJ0g0/8mfche87M69/97ndJxrG4x9icKjh7eR56Z1hyyN5zH69PwB4zgxqf47MascBnQFit3nfE8kMPPXQYu+CCC17hm00H7AHbADhHhb1tRii+x8oozgDET3KvpDXNNkOeuOzmXsccc0yS8fmD30Xp4N99rShGaqFQKBQKhUKhUCgUCoVCoVAoLIF/MMNzueCkk05anYxv5WE2uJYLN9Gu/8Rtgm/q3/nOdyZpNzRmL/KE3J/FDY1vMnhS7poavO455Obiq1/96uJrkAnhqKOOWp2MWYewsTxX3HiaBceYmaHc/nKL41swbnR8G8DtjW0Ay8A3+z0bcNtz7rnnTtoGSXLVVVetTsbzSy0Vs+74/r5ZZl59U03tFvaEWS7At2lme4AeO5x/2158/sqVKydth0suuWR1Mr5Nw+d4vrGRfQRr1reWzCl2NOti4fsv/AzQq/nlNQLYi4cccsikbbD11luvTsZ+ndo6ZhrBcOjdhvpmHr9GTDCbAjv7s4gZXgPUsTUznNe9r7DLrbfeOmkbHHnkkauTsV+nFif1NJPm48264HfM/IXFQb0uxwTm0XuDm26vfdivrj/I694PMPimHhN++9vfrk7GsRIWhOcWnwHzLWlKEcdv9gG3966rhi93PMC29vPsEzM5enEZvOMd75i0DZLkwgsvXJ2MYyt2oM5p0r6//Qtr0T6emABLj5qSfg/vux5rpleLrRcTYGrvuuuuk7bD1772tdXJWDkGa9G+m/m2P+J1zyl7Bb9kH87c+rOwgfcCdcyXylHZnyeddNKkbUBMsO9BlQSrLml+o6cytHIAxhyxdSkbEDMcE2ASW7nwSueE008/fdI2uO+++xbFBFQerofaq12L3/GcYiPyWL8H8+yzBzmX3xfWu5WhvXMDv7PRRhtN2gYPPPDA6mTMEr3tttuS9GvHolxKms/w78KURNXpczhz5tiDHXs26LEzezZYf/31J22DJLn44otXJ+N56J3V+P5rrLHGMIZNegxTap/28hn/fC/e8vm9s3bv/T7+8Y9P2g5XXnnloud3zK3nm7Op64mzzq0WIBdCkelnEq/0/M526T076p0TsMeee+75mmxQjNRCoVAoFAqFQqFQKBQKhUKhUFgC9SC1UCgUCoVCoVAoFAqFQqFQKBSWwLJsNgVl3YXBoVhb2o/szzInJCZuSgKtGGqw5WtITkz5RSJhKnhP2gkl2fIgN92YMpCiuRA83/0973nPMAal2o0moGdb1oH9kBtYLkKTHP880hHT4Z9//vkkY3o4ck9Tu3v0+qmC7+/5feqpp5KMGwgxh5YQvuMd70iSvPDCC8MYkjbkVC7Sz3q2LIK9aJr8o48+mmQsleNvsUSoJ0mfIpgPr0XWqiWESGjc6AJ5GfKdJNlmm22SNNlOr2GU5w5piG3Vk0DwN3n9+72nDHyyZSC77bZbkrFdkKN5vvFbln8Dmhy42QF7zSUasDPlGJLkzjvvTJKss846wxiFyy3vdcOHKYM15wY7vcZ+SG88p/gF24C4jaSNhlRJK+1ieSY/571BY5F999130e96b/ZkVVMEMnv7dObN84I8002OsJVzI/IaJOUupcC6dZMEPt/zSdH+zTbbbBjjd+YYD5LmU+wPyG3sp4mbjqmUGHH8xE/zX0tn+QzLoskz7f/JqVwygJzJ+OMf/7jU15sEeg1WeiWG8Mn2R+SNjpXsFWzgue3lqMQk74UbbrghybhxKr/rvTCXHJU5cgMzr12An0Eim7Q44WZdxG38kEsGsA/8872YQPkqx2Bi+RzPavhky8XPO++8JOOGW8RD75fePuAMQRMkxwTmrBcT/B69pojkVV4fc7EBvsDPIpgX+yJkyi570Tsv0+gRWziXAf4s3qMXl4nt/jtdKmBOcbnXWIrzsp8dESfcKJx8x2cmfAjv69jaK6fX+3z8nM/m2Kn381MHa8tNpGjSRSxM2nz3YofLbwLm0c+OeuUBenGZcjMu79ZrfP56/VExUguFQqFQKBQKhUKhUCgUCoVCYQksS0YqTNQVK1YMY9wSuNAsNzK+dedptZmj3Kzx9JrmFklj5vnpNTd2vjnj376R4Cm4bzVo6DB1cHtO4f2kzYFvFbhlXLVq1TDWswE3ATzxp6h88v+1dy+tehRrGIYf/4YTHQniRHJQdBCTqCEeiOhARFDiOSA4CkScOFDQiYr+g4gQQRIRgiCiGA8oaBARHAghI/0de3R3Px2L3UH2hvU19zUK/a2sQ1V1VXX1+1bNb336TSn10W8QeDvdb3F4y9NvEvrNz67jzW+/Sb/nnnuSLN8AU1/9doWopa4vIgJ4G9NlyRtUIjiS+S0ebzeT5IUXXkiS/Pnnn9M1yrzf5o0O/NlF/B395mx0QBSfj9p9++yzz5LM0RzdpxHB1BHA1FFHtFy8eDFJcvz48ekakVD9/TqqZpfRT/eG/KNIXaKBOTwnmeug+wXaP4cI/vTTT9Nn9F/9lpO3z53NcPDgwSTLqDTqvg/o2cobf8bZRx99dLpGO+02xxtpDgNLkrvvvjvJcvykbdJ3E+mezPX48ccfT9c40Grfvn3Ttf379y9+Zv9OHVHckVC7jL68s0Io0x4PuE+6bTJWd9/CXIgxpSOGGXM6wp6opy7vw4cP/+P3HEXIdN3vOvqjLl/acUegU+YdRUdf0uVBnTCP6vGe/r+jNOij+mdx8Fu3Df7vs88+O107e/bsdf2Ne925c+eSJCdPnpyu0cd3f0QkavcHRK30YR/UAfdYR+4yr+r65mf0vcAhMX3wD+3i6tWr0zWy43Ydzz5dBkQf9bhM/9tjIZ/3fUC0PO2/n+2IyutrfN+OvOdn9L3BWNMZDt2v7TLKkefcJHn//feTLMubeWw//9IO+zng66+/TpI88MADSZZlxhy0+3XugxtvvHG6xv/p5zKeIU6dOjVd60zQXca8r6Og77333iTLcXmUJUhf3nXQ0ZNJcuXKlenfzGf7WYu1kH7eYEzv+4A+rZ+/+7DWXcdzUf9NrGGMDtjqtZuOdATPANRrP0vTb3W0MF/f9cAzfGeVjp4p9+LB7/8GUe7dH3F/9LMx7feVV16ZrpFhNjpMmTGm+y8yGLqfIbK7xyTGk74XaQP9nHLixIm1P2/IiFRJkiRJkiRJWuFCqiRJkiRJkiSt2JO5VqQmdRoN/+6UAkKISbNK5lSGm2++ebrGJv2EuJPOmcwpCB1yTKj26ICKDjkmjb9D8julaJcRot7psPxtHR492vKAMupUD+qPkPdRmfVWCoTQdyh9p/Ve+3Vdp1s53CWZ23a3K/6+brNs6t6px7TtThOkPr/99tskyxSICxcuJFkeGkK6w1133TVd602kr/1ZnUq+lVQF/qZu96QSdAo3qYOdrkF/1G2bz0nz6Xokba3bMP+3y5M66p/P7ze6tutIKR4dZDHa1oMUy2ROrekxgXRP0mVHh9916hTpIow5yZy+OUrD7d+z63eX0bf0ARKMkd3OfvzxxyTLAxUo007Lp0wZG2699dbpM1LMO9WGcuTAhiS55ZZbkozHhp4XdJrpLqMMun8YjcuUUZcB/U6XFWn57777bpLkzJkz02ekn3XZjQ4Woe/v8Z6f2+P3Vg4WSeYxsMvym2++STJvY5HM90f3B7TtPjST+hwdqPDFF18kWR5gxByr64H+7a+//pqusU1Wz1u3MiawxUi3zz6QC9TRpUuXpmvHjh1Lktx0003TNb4P9dKHuTD+9JhAun+XLeNJp/hyX2zxAEK22+o6YHzoOT6fHz16dLr2/fffJ1n2G/RRbIVB/57Maf+9LQLjfW/DwO/UW/PQD7HtQF/bdYwJ/fdQ9p3aTxl1yivttbcDIaX/5ZdfTpK88cYb02fMf3qrHMbv3nqELQD6uZDDQTvFeSvbvfA39RjHfL3n7WybcP/990/XGFO7LBgHXn311STJW2+9NX1G39F1y7Yh3T+Rtt79DvXy+OOPT9e2NC4/9NBDSdbHOMq3D+Kij+p+i7LjsKLeupGBFLxOAAAHeklEQVTy735mdKAn8+B+bubZYYuHTdHGekygj+h64VDAJ598crpG/99bFFKm9F9dB5T9aJvDvp94Nu/tXOiv+uf/23HZiFRJkiRJkiRJWrEnXwexAt0H3PA2viMAWJnuN2G8kekNsjkUhw2bn3/++ekzIif6IBc2s+3IFyLyepWdN4F96M5tt912XX/jXkd59Bt+oiu6rHhD2W922Mi6o1l5e8Pbr35zxhvSn3/+ebpGFGC/PeV3GR2q0Yc59NuMXcdb5n5zNdq4mXuGtzzJ/Bao64F7iv/7wQcfTJ8988wzScYH73Q0zK+//ppkuVE8UdxbiUIdIeIomSO5ui3Sprte+Ly/jkgv3nL2PUY5dpQLURyffPLJdG10qAZv0zh0JEkeeeSR6/vj9jj6aaJNkvlAqY66I9L60KFD0zXKqN8I048TBUldJPNbyz6chDf+HLSWJJ9//nmSZeQSbaDvuVGU1C6ibXZEKuXYkegPPvhgkmXkCf1XH7hCJMYoOp5+vd8Qf/fdd0mWBzU89dRTSZb9/+iwqdFBAruI8XB0cE4fcEPkb9cBc6KOZKG9nj59OsmyfigzIrqT5MMPP0wyR/Ql84b/3RcRKdDjwVaij5L5XiCqOhkfLsL4TQR8Mkdsd1QX8yjG7z6sgsMyerzl3x1NRCRlR78SadZZCluJzmae1+VCG+s5KlkeTzzxxHSNMbLLinqjTHu8J4Ks7w/qj2jVZI5w7TGBeVhHKXUU5i5j3tn3Pm2+D+RkDPzll1+ma4zHnfn3999/J0nuvPPOf3w90aR93/DMQHZPMveNfR8yJ+P79+++62jD/bzMQUPdvjn8peenjMt9MCzXXnvttSTLZzAi9Hp+SgR8ly39Ttct/6evbSU6nv6m1wyYR3YdEP3Waxbnz59PsoxSpYzefvvtJMv+hHb70UcfTdeY5/fP78N+rv09ewzYUlQkf0sfmkm2ardZohs7Eps+qscO+m7KvMd75lvdnul7eo48wnjc89utHJLNuPz7779P15j/9Podc5yOMKV8e85JZDXrdz2XYc7TGQnMc3tM4uD6nvvyf3tcHh0ifT2MSJUkSZIkSZKkFS6kSpIkSZIkSdKKPZlrRZoBGwcncyh8pxl89dVXSZZpe6R+9teRlk8IcacB8nUdis2m/Z2ORaram2++OV1jA+gOTd7KwSKEuR85cmS69uWXXyZZpuWT0tlh1KTgdMoAIdOkGhKmnczlvX///uka5dipgdRBbw786aefJlmGjHcI/1Z0OitpTF2+pJR1egBbVHSbJO2WlJr+HqQxdD0QJk86fzKn95Ayl8zph6ODlrbiwIED//VzUg66/Y0OZ7n28Kq+n0abXZNC3l9HPXcZk2Ly2GOPTde2krZDn90HRlF+ndL89NNPJ1n2L4wJnYJDWgntuw+RIiWrxxUOWuoDS0gp7W0EfvvttyTL8WQrKTuMwaPDzLoO0GVAKnSnTZEuxRjTW7twWFi33/vuuy/JnEKdzGmFL7744nTt3LlzSZYHyWxlXO4DOMEYOdrCpedQzGf6PqBPIdWvU5v4vl23zz333OKzZE477HuOLTO6rWylL0rmfrfbM/PWThm+Nl08SR5++OF/fD/SD0n57jRZfkb/LPRYw+/UaXGj7RS2ciAq7e6OO+6Yrl2+fDnJ+NCVHoPZRqTrhRRc5qhsUZXMfc6oT+ltrUhN7LkCKY59IOpW5qj8vT1v4W/rZzC+jnEgGad/33777UnmFNlu83xdjyudmgueE7rt0x66f9vamHDq1Knp2h9//JFkWQbvvfdekuSll16artG399hx7Tz2nXfemT7j8KO+lzhMp6/RB5FSm8zbNIzS/XcdfUavBTBn7bJ9/fXXkywP1mQ86DGVOmBc5iDgZJ7jshVbMpdjb3NF/8hWMsm8JVn3j1upg2Rug73V1Ah9fc9JWEPoeuDa6AAj2nHXL//ufovfqdcouD+28mzQaHcnT56crrF21PNBtsnpPpl6G40J9DM//PDD9Blz2h6X+8Bt8HzX82fWMbqt/Nt7YTt3kCRJkiRJkiT9n9yw5cNhJEmSJEmSJOl/wYhUSZIkSZIkSVrhQqokSZIkSZIkrXAhVZIkSZIkSZJWuJAqSZIkSZIkSStcSJUkSZIkSZKkFS6kSpIkSZIkSdIKF1IlSZIkSZIkaYULqZIkSZIkSZK0woVUSZIkSZIkSVrhQqokSZIkSZIkrXAhVZIkSZIkSZJWuJAqSZIkSZIkSStcSJUkSZIkSZKkFS6kSpIkSZIkSdIKF1IlSZIkSZIkaYULqZIkSZIkSZK0woVUSZIkSZIkSVrhQqokSZIkSZIkrXAhVZIkSZIkSZJWuJAqSZIkSZIkSStcSJUkSZIkSZKkFS6kSpIkSZIkSdIKF1IlSZIkSZIkaYULqZIkSZIkSZK0woVUSZIkSZIkSVrhQqokSZIkSZIkrXAhVZIkSZIkSZJWuJAqSZIkSZIkSStcSJUkSZIkSZKkFS6kSpIkSZIkSdIKF1IlSZIkSZIkaYULqZIkSZIkSZK0woVUSZIkSZIkSVrxH5CFmXC0npMPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1400x400 with 56 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "RBM 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x400 with 56 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "# Examine Feature Detectors\n",
    "rbm_shapes = [(28,28),(35,20),(30,20)]\n",
    "for i in range(0,len(rbm_list)):\n",
    "    rbm = rbm_list[i]\n",
    "    print(\"RBM\",i)\n",
    "    print(rbm.show_features(rbm_shapes[i], \\\n",
    "                            \"RBM learned features from MNIST\", 56))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RBM 1\n",
      "Image generated by RBM\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original image\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image generated by RBM\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original image\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image generated by RBM\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original image\n"
     ]
    },
    {
     "data": {
      "image/png": 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IWiFpRUuVmlmh6h72fzyjtBfwKPD9iLhX0ijgTSCAa6icGnyjzjp82G/WZoWd8wNI2g14APhVRPxogPZDgQci4ot11uPwm7VZo+Gve9ivyk+o3gasrg5+9kZgn9OBVYMt0szK08i7/X8GPA48R+VSH8BVwCxgMpXD/g3Axdmbg3nrcs9v1maFHvYXxeE3a7/CDvvNbOfk8JslyuE3S5TDb5Yoh98sUQ6/WaIcfrNEOfxmiXL4zRLl8JslyuE3S5TDb5Yoh98sUQ6/WaI6PUT3m8DLVY/3z6Z1o26trVvrAtfWrCJrG9fojB39Pv9nNi6tiIgppRWQo1tr69a6wLU1q6zafNhvliiH3yxRZYd/Xsnbz9OttXVrXeDamlVKbaWe85tZecru+c2sJA6/WaJKCb+kGZJelLRO0pVl1FCLpA2SnpP0TNnjC2ZjIG6RtKpq2khJSyWtzf4OOEZiSbVdLenVbN89I+mUkmobK+k/JK2W9Lyky7Pppe67nLpK2W8dP+eXtAvwEnAS0AssB2ZFxAsdLaQGSRuAKRFR+gdCJB0PvAvc0TcUmqR/BLZGxHXZC+eIiPhOl9R2NYMctr1NtdUaVv58Stx3RQ53X4Qyev6jgXURsT4ifgf8DJhZQh1dLyIeA7b2mzwTWJDdX0DlP0/H1aitK0TEpoh4Oru/DegbVr7UfZdTVynKCP8Y4JWqx72UuAMGEMDDklZK6im7mAGM6hsWLft7YMn19Fd32PZO6jesfNfsu2aGuy9aGeEfaCihbrreeFxEHAmcDFyaHd5aY24GDqMyhuMm4IdlFpMNK78I+FZEvFNmLdUGqKuU/VZG+HuBsVWPDwZeK6GOAUXEa9nfLcBiKqcp3WRz3wjJ2d8tJdfzsYjYHBEfRcQO4BZK3HfZsPKLgIURcW82ufR9N1BdZe23MsK/HJggabyk4cBZwJIS6vgMSXtmb8QgaU9gOt039PgSYHZ2fzZwf4m1fEq3DNtea1h5St533TbcfSmf8MsuZfwY2AW4PSK+3/EiBiDpD6j09lD5uvNdZdYm6W5gKpWvfG4G5gD3AfcAhwAbgTMiouNvvNWobSqDHLa9TbXVGlZ+GSXuuyKHuy+kHn+81yxN/oSfWaIcfrNEOfxmiXL4zRLl8JslyuE3S5TDb5ao/wdQa0BbNGqPDgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image generated by RBM\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original image\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image generated by RBM\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original image\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image generated by RBM\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original image\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image generated by RBM\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original image\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image generated by RBM\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original image\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image generated by RBM\n"
     ]
    },
    {
     "data": {
      "image/png": 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xLnrcAIso4Ook1yfZ3Hcxi1i/v1u07ufRPdez0LLdts/Sgm7l52bbraa7+0nrI/yLdSU0T9cbf7Gqngk8H3hNd3ir0bwXeBKDPhx3A+/qs5iuW/mPAa+vqrv7rGXYInX1st36CP8u4Nihx48H7uyhjkVV1Z3dz73A5QyaKfNkz/4ekrufe3uu58eqak9V3V9VDwAfoMdt13Ur/zHgQ1X18W5y79tusbr62m59hP864IQkT0jyCOBlwFU91PEQSQ7rTsSQ5DDgecxf1+NXAWd242cCV/ZYy4PMS7ftB+pWnp633bx1d9/LN/y6Sxl/CRwCXFRVb515EYtI8kQGe3sY3O58aZ+1JfkwsInBLZ97gPOAK4DLgOOAbwMvqaqZn3g7QG2bWGG37VOq7UDdyn+JHrfdJLu7n0g9fr1XapPf8JMaZfilRhl+qVGGX2qU4ZcaZfilRhl+qVH/DygyDWRcXmwVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original image\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image generated by RBM\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original image\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": [
    "# View generated images from the first RBM\n",
    "inputX = np.array(X_train)\n",
    "rbmOne = rbm_list[0]\n",
    "\n",
    "print('RBM 1')\n",
    "outputX_rbmOne, reconstructedX_rbmOne, hiddenX_rbmOne = \\\n",
    "                            rbmOne.rbm_output(inputX)\n",
    "reconstructedX_rbmOne = pd.DataFrame(data=reconstructedX_rbmOne, \\\n",
    "                                     index=X_train.index)\n",
    "for j in range(0,10):\n",
    "    example = j\n",
    "    print(\"Image generated by RBM\")\n",
    "    view_digit(reconstructedX_rbmOne, y_train, example)\n",
    "    print(\"Original image\")\n",
    "    view_digit(X_train, y_train, example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class DBN(object):\n",
    "    def __init__(self, original_input_size, input_size, output_size, \n",
    "                 learning_rate, epochs, batchsize, rbmOne, rbmTwo, rbmThree):\n",
    "        # Define hyperparameters\n",
    "        self._original_input_size = original_input_size\n",
    "        self._input_size = input_size\n",
    "        self._output_size = output_size\n",
    "        self.learning_rate = learning_rate\n",
    "        self.epochs = epochs\n",
    "        self.batchsize = batchsize\n",
    "        self.rbmOne = rbmOne\n",
    "        self.rbmTwo = rbmTwo\n",
    "        self.rbmThree = rbmThree\n",
    "    \n",
    "        self.w = np.zeros([input_size, output_size], \"float\")\n",
    "        self.hb = np.zeros([output_size], \"float\")\n",
    "        self.vb = np.zeros([input_size], \"float\")\n",
    "    \n",
    "    def prob_h_given_v(self, visible, w, hb):\n",
    "        return tf.nn.sigmoid(tf.matmul(visible, w) + hb)\n",
    "\n",
    "    def prob_v_given_h(self, hidden, w, vb):\n",
    "        return tf.nn.sigmoid(tf.matmul(hidden, tf.transpose(w)) + vb)\n",
    "    \n",
    "    def sample_prob(self, probs):\n",
    "        return tf.nn.relu(tf.sign(probs - tf.random_uniform(tf.shape(probs))))\n",
    "\n",
    "    def train(self, X):\n",
    "        _w = tf.placeholder(\"float\", [self._input_size, self._output_size])\n",
    "        _hb = tf.placeholder(\"float\", [self._output_size])\n",
    "        _vb = tf.placeholder(\"float\", [self._input_size])\n",
    "        \n",
    "        prv_w = np.zeros([self._input_size, self._output_size], \"float\")\n",
    "        prv_hb = np.zeros([self._output_size], \"float\")\n",
    "        prv_vb = np.zeros([self._input_size], \"float\")\n",
    "        \n",
    "        cur_w = np.zeros([self._input_size, self._output_size], \"float\")\n",
    "        cur_hb = np.zeros([self._output_size], \"float\")\n",
    "        cur_vb = np.zeros([self._input_size], \"float\")\n",
    "        \n",
    "        v0 = tf.placeholder(\"float\", [None, self._original_input_size])\n",
    "\n",
    "        forwardOne = tf.nn.relu(tf.sign(tf.nn.sigmoid(tf.matmul(v0, \\\n",
    "                        self.rbmOne.w) + self.rbmOne.hb) - tf.random_uniform( \\\n",
    "                        tf.shape(tf.nn.sigmoid(tf.matmul(v0, self.rbmOne.w) + \\\n",
    "                        self.rbmOne.hb)))))\n",
    "        forwardTwo = tf.nn.relu(tf.sign(tf.nn.sigmoid(tf.matmul(forwardOne, \\\n",
    "                        self.rbmTwo.w) + self.rbmTwo.hb) - tf.random_uniform( \\\n",
    "                        tf.shape(tf.nn.sigmoid(tf.matmul(forwardOne, \\\n",
    "                        self.rbmTwo.w) + self.rbmTwo.hb)))))\n",
    "        forward = tf.nn.relu(tf.sign(tf.nn.sigmoid(tf.matmul(forwardTwo, \\\n",
    "                        self.rbmThree.w) + self.rbmThree.hb) - \\\n",
    "                        tf.random_uniform(tf.shape(tf.nn.sigmoid(tf.matmul( \\\n",
    "                        forwardTwo, self.rbmThree.w) + self.rbmThree.hb)))))\n",
    "        h0 = self.sample_prob(self.prob_h_given_v(forward, _w, _hb))\n",
    "        v1 = self.sample_prob(self.prob_v_given_h(h0, _w, _vb))\n",
    "        h1 = self.prob_h_given_v(v1, _w, _hb)\n",
    "        \n",
    "        positive_grad = tf.matmul(tf.transpose(forward), h0)\n",
    "        negative_grad = tf.matmul(tf.transpose(v1), h1)\n",
    "        \n",
    "        update_w = _w + self.learning_rate * (positive_grad - negative_grad) / \\\n",
    "                        tf.to_float(tf.shape(forward)[0])\n",
    "        update_vb = _vb +  self.learning_rate * tf.reduce_mean(forward - v1, 0)\n",
    "        update_hb = _hb +  self.learning_rate * tf.reduce_mean(h0 - h1, 0)\n",
    "        \n",
    "        backwardOne = tf.nn.relu(tf.sign(tf.nn.sigmoid(tf.matmul(v1, \\\n",
    "                            self.rbmThree.w.T) + self.rbmThree.vb) - \\\n",
    "                            tf.random_uniform(tf.shape(tf.nn.sigmoid( \\\n",
    "                            tf.matmul(v1, self.rbmThree.w.T) + \\\n",
    "                            self.rbmThree.vb)))))\n",
    "        backwardTwo = tf.nn.relu(tf.sign(tf.nn.sigmoid(tf.matmul(backwardOne, \\\n",
    "                            self.rbmTwo.w.T) + self.rbmTwo.vb) - \\\n",
    "                            tf.random_uniform(tf.shape(tf.nn.sigmoid( \\\n",
    "                            tf.matmul(backwardOne, self.rbmTwo.w.T) + \\\n",
    "                            self.rbmTwo.vb)))))\n",
    "        backward = tf.nn.relu(tf.sign(tf.nn.sigmoid(tf.matmul(backwardTwo, \\\n",
    "                            self.rbmOne.w.T) + self.rbmOne.vb) - \\\n",
    "                            tf.random_uniform(tf.shape(tf.nn.sigmoid( \\\n",
    "                            tf.matmul(backwardTwo, self.rbmOne.w.T) + \\\n",
    "                            self.rbmOne.vb)))))\n",
    "        \n",
    "        err = tf.reduce_mean(tf.square(v0 - backward))\n",
    "        error_list = []\n",
    "        \n",
    "        with tf.Session() as sess:\n",
    "            sess.run(tf.global_variables_initializer())\n",
    "\n",
    "            for epoch in range(self.epochs):\n",
    "                for start, end in zip(range(0, len(X), self.batchsize), \\\n",
    "                        range(self.batchsize,len(X), self.batchsize)):\n",
    "                    batch = X[start:end]\n",
    "                    cur_w = sess.run(update_w, feed_dict={v0: batch, _w: \\\n",
    "                                        prv_w, _hb: prv_hb, _vb: prv_vb})\n",
    "                    cur_hb = sess.run(update_hb, feed_dict={v0: batch, _w: \\\n",
    "                                        prv_w, _hb: prv_hb, _vb: prv_vb})\n",
    "                    cur_vb = sess.run(update_vb, feed_dict={v0: batch, _w: \\\n",
    "                                        prv_w, _hb: prv_hb, _vb: prv_vb})\n",
    "                    prv_w = cur_w\n",
    "                    prv_hb = cur_hb\n",
    "                    prv_vb = cur_vb\n",
    "                error = sess.run(err, feed_dict={v0: X, _w: cur_w, _vb: \\\n",
    "                                    cur_vb, _hb: cur_hb})\n",
    "                print ('Epoch: %d' % epoch,'reconstruction error: %f' % error)\n",
    "                error_list.append(error)\n",
    "            self.w = prv_w\n",
    "            self.hb = prv_hb\n",
    "            self.vb = prv_vb\n",
    "            return error_list\n",
    "        \n",
    "    def dbn_output(self, X):\n",
    "\n",
    "        input_X = tf.constant(X)\n",
    "        forwardOne = tf.nn.sigmoid(tf.matmul(input_X, self.rbmOne.w) + \\\n",
    "                                   self.rbmOne.hb)\n",
    "        forwardTwo = tf.nn.sigmoid(tf.matmul(forwardOne, self.rbmTwo.w) + \\\n",
    "                                   self.rbmTwo.hb)\n",
    "        forward = tf.nn.sigmoid(tf.matmul(forwardTwo, self.rbmThree.w) + \\\n",
    "                                self.rbmThree.hb)\n",
    "\n",
    "        _w = tf.constant(self.w)\n",
    "        _hb = tf.constant(self.hb)\n",
    "        _vb = tf.constant(self.vb)\n",
    "\n",
    "        out = tf.nn.sigmoid(tf.matmul(forward, _w) + _hb)\n",
    "        hiddenGen = self.sample_prob(self.prob_h_given_v(forward, _w, _hb))\n",
    "        visibleGen = self.sample_prob(self.prob_v_given_h(hiddenGen, _w, _vb))\n",
    "\n",
    "        backwardTwo = tf.nn.sigmoid(tf.matmul(visibleGen, self.rbmThree.w.T) + \\\n",
    "                                    self.rbmThree.vb)\n",
    "        backwardOne = tf.nn.sigmoid(tf.matmul(backwardTwo, self.rbmTwo.w.T) + \\\n",
    "                                    self.rbmTwo.vb)\n",
    "        backward = tf.nn.sigmoid(tf.matmul(backwardOne, self.rbmOne.w.T) + \\\n",
    "                                 self.rbmOne.vb)\n",
    "\n",
    "        with tf.Session() as sess:\n",
    "            sess.run(tf.global_variables_initializer())\n",
    "            return sess.run(out), sess.run(backward)\n",
    "\n",
    "    def show_features(self, shape, suptitle, count=-1):\n",
    "        maxw = np.amax(self.w.T)\n",
    "        minw = np.amin(self.w.T)\n",
    "        count = self._output_size if count == -1 or count > \\\n",
    "                self._output_size else count\n",
    "        ncols = count if count < 14 else 14 \n",
    "        nrows = count//ncols\n",
    "        nrows = nrows if nrows > 2 else 3\n",
    "        fig = plt.figure(figsize=(ncols, nrows), dpi=100)\n",
    "        grid = Grid(fig, rect=111, nrows_ncols=(nrows, ncols), axes_pad=0.01)\n",
    "\n",
    "        for i, ax in enumerate(grid):\n",
    "            x = self.w.T[i] if i<self._input_size else np.zeros(shape)\n",
    "            x = (x.reshape(1, -1) - minw)/maxw\n",
    "            ax.imshow(x.reshape(*shape), cmap=mpl.cm.Greys)\n",
    "            ax.set_axis_off()\n",
    "\n",
    "        fig.text(0.5,1, suptitle, fontsize=20, horizontalalignment='center')\n",
    "        fig.tight_layout()\n",
    "        plt.show()\n",
    "        return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Instantiate DBN Class\n",
    "dbn = DBN(784, 500, 500, 1.0, 50, 200, rbm_list[0], rbm_list[1], rbm_list[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 reconstruction error: 0.088572\n",
      "Epoch: 1 reconstruction error: 0.080590\n",
      "Epoch: 2 reconstruction error: 0.077392\n",
      "Epoch: 3 reconstruction error: 0.075738\n",
      "Epoch: 4 reconstruction error: 0.074367\n",
      "Epoch: 5 reconstruction error: 0.073488\n",
      "Epoch: 6 reconstruction error: 0.072320\n",
      "Epoch: 7 reconstruction error: 0.071389\n",
      "Epoch: 8 reconstruction error: 0.070810\n",
      "Epoch: 9 reconstruction error: 0.069974\n",
      "Epoch: 10 reconstruction error: 0.069511\n",
      "Epoch: 11 reconstruction error: 0.068688\n",
      "Epoch: 12 reconstruction error: 0.068287\n",
      "Epoch: 13 reconstruction error: 0.067713\n",
      "Epoch: 14 reconstruction error: 0.067391\n",
      "Epoch: 15 reconstruction error: 0.066967\n",
      "Epoch: 16 reconstruction error: 0.066607\n",
      "Epoch: 17 reconstruction error: 0.066464\n",
      "Epoch: 18 reconstruction error: 0.065762\n",
      "Epoch: 19 reconstruction error: 0.065435\n",
      "Epoch: 20 reconstruction error: 0.065429\n",
      "Epoch: 21 reconstruction error: 0.065055\n",
      "Epoch: 22 reconstruction error: 0.064917\n",
      "Epoch: 23 reconstruction error: 0.064649\n",
      "Epoch: 24 reconstruction error: 0.064566\n",
      "Epoch: 25 reconstruction error: 0.064200\n",
      "Epoch: 26 reconstruction error: 0.064212\n",
      "Epoch: 27 reconstruction error: 0.063923\n",
      "Epoch: 28 reconstruction error: 0.063953\n",
      "Epoch: 29 reconstruction error: 0.063800\n",
      "Epoch: 30 reconstruction error: 0.063532\n",
      "Epoch: 31 reconstruction error: 0.063618\n",
      "Epoch: 32 reconstruction error: 0.063340\n",
      "Epoch: 33 reconstruction error: 0.063429\n",
      "Epoch: 34 reconstruction error: 0.063180\n",
      "Epoch: 35 reconstruction error: 0.063158\n",
      "Epoch: 36 reconstruction error: 0.063028\n",
      "Epoch: 37 reconstruction error: 0.062841\n",
      "Epoch: 38 reconstruction error: 0.062753\n",
      "Epoch: 39 reconstruction error: 0.062717\n",
      "Epoch: 40 reconstruction error: 0.062532\n",
      "Epoch: 41 reconstruction error: 0.062563\n",
      "Epoch: 42 reconstruction error: 0.062572\n",
      "Epoch: 43 reconstruction error: 0.062345\n",
      "Epoch: 44 reconstruction error: 0.062375\n",
      "Epoch: 45 reconstruction error: 0.062225\n",
      "Epoch: 46 reconstruction error: 0.062079\n",
      "Epoch: 47 reconstruction error: 0.062052\n",
      "Epoch: 48 reconstruction error: 0.062197\n",
      "Epoch: 49 reconstruction error: 0.062108\n"
     ]
    }
   ],
   "source": [
    "inputX = np.array(X_train)\n",
    "error_list = []\n",
    "error_list = dbn.train(inputX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DBN\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": [
    "# Plot reconstruction errors\n",
    "print(\"DBN\")\n",
    "pd.Series(error_list).plot(logy=False)\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Reconstruction Error\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x400 with 56 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(dbn.show_features((25,20),\"DBN learned features from MNIST\", 56))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run  0\n",
      "Run  1\n",
      "Run  2\n",
      "Run  3\n",
      "Run  4\n",
      "Run  5\n",
      "Run  6\n",
      "Run  7\n",
      "Run  8\n",
      "Run  9\n",
      "Run  10\n",
      "Run  11\n",
      "Run  12\n",
      "Run  13\n",
      "Run  14\n",
      "Run  15\n",
      "Run  16\n",
      "Run  17\n",
      "Run  18\n",
      "Run  19\n"
     ]
    }
   ],
   "source": [
    "# Generate images and store them\n",
    "inputXReduced = X_train.loc[:4999]\n",
    "for i in range(0,20):\n",
    "    print(\"Run \",i)\n",
    "    finalOutput_DBN, reconstructedOutput_DBN = dbn.dbn_output(inputXReduced)\n",
    "    if i==0:\n",
    "        generatedImages = finalOutput_DBN\n",
    "    else:\n",
    "        generatedImages = np.append(generatedImages, finalOutput_DBN, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Generate a vector of labels for the generated images\n",
    "for i in range(0,20):\n",
    "    if i==0:\n",
    "        labels = y_train.loc[:4999]\n",
    "    else:\n",
    "        labels = np.append(labels,y_train.loc[:4999])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Generate images based on the validation set\n",
    "inputValidation = np.array(X_validation)\n",
    "finalOutput_DBN_validation, reconstructedOutput_DBN_validation = \\\n",
    "    dbn.dbn_output(inputValidation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# View first few reconstructed images\n",
    "for i in range(0,10):\n",
    "    example = i\n",
    "    reconstructedX = pd.DataFrame(data=reconstructedOutput_DBN, \\\n",
    "                                  index=X_train[0:5000].index)\n",
    "    view_digit(reconstructedX, y_train, example)\n",
    "    view_digit(X_train, y_train, example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run  0\n",
      "Generated\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run  1\n",
      "Generated\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run  2\n",
      "Generated\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run  3\n",
      "Generated\n"
     ]
    },
    {
     "data": {
      "image/png": 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uaR4wr2/NM7OB0lTPL2kEteB/MyLuKhZvlDSpqE8CNvW0bkTMj4gZETGjFQ02s9YoDb9qXfxNwIqI+GJd6V7gouL+RUD6FCszaysqGw6RNBP4GfAUtaE+gKuo7fffCRwM/A44NyK2lPyuth17KduNSV0mesyYMcl1x40bl6xPnjw5WT/11FOT9UsuuaRhrezy2GXDkGVefvnlZD116e5bbrklue7mzZuT9V27diXruYqIpvbJS/f5I+LnQKNfdkZvGmVm7cNH+JllyuE3y5TDb5Yph98sUw6/WaYcfrNMZXPp7o6OjmT9wAMPTNY/8YlPNKyddNJJfWrTbscff3yyPnbs2GR9r732algrO+W27HTihx9+OFlPHWMAsH79+oY1T6FdLff8Zply+M0y5fCbZcrhN8uUw2+WKYffLFMOv1mmSs/nb+nGBvB8/rJx/BNOOCFZv+aaa5L1mTNnNqyVXQugbKy9v5f2fvHFFxvWFi1alFy3rP7II48k611dXcm6Db5mz+d3z2+WKYffLFMOv1mmHH6zTDn8Zply+M0y5fCbZWqPOZ+/7Bruv/3tb5P122+/PVl//fXXG9b222+/5Lqp8+0Bbr311mR948aNyfqqVasa1tatW5dct+ycek9zvedyz2+WKYffLFMOv1mmHH6zTDn8Zply+M0y5fCbZar0fH5JU4HbgAOBXcD8iPiKpOuAucDuSdSvioj7S36XB43NBliz5/M3E/5JwKSI+KWkfYAngNnAecArEfGvzTbK4TcbeM2Gv/QIv4joBDqL+12SVgAH9a95Zla1Xu3zS5oGnAQ8Viy6TNKvJd0s6YAG68yTtETSkn611Mxaqulr+EkaCzwCfDYi7pI0EXgBCODT1HYNPlTyO/yx32yAtWyfH0DSCGAhsCgivthDfRqwMCKSM046/GYDr2UX8FTt0rQ3ASvqg198EbjbOcCy3jbSzKrTzLf9M4GfAU9RG+oDuAqYA0yn9rF/LfDh4svB1O9yz282wFr6sb9VHH6zgefr9ptZksNvlimH3yxTDr9Zphx+s0w5/GaZcvjNMuXwm2XK4TfLlMNvlimH3yxTDr9Zphx+s0w5/GaZGuwpul8Anqt7PL5Y1o7atW3t2i5w2/qqlW07pNknDur5/G/ZuLQkImZU1oCEdm1bu7YL3La+qqpt/thvlimH3yxTVYd/fsXbT2nXtrVru8Bt66tK2lbpPr+ZVafqnt/MKuLwm2WqkvBLmiVplaTVkq6sog2NSFor6SlJS6ueX7CYA3GTpGV1y8ZJekDSb4qfPc6RWFHbrpO0vnjvlkp6X0Vtmyrpp5JWSHpa0seK5ZW+d4l2VfK+Dfo+v6QO4BngPcA64HFgTkQsH9SGNCBpLTAjIio/IETSqcArwG27p0KT9C/Aloi4ofjDeUBE/FObtO06ejlt+wC1rdG08hdT4XvXyunuW6GKnv8UYHVErImI7cB3gLMraEfbi4jFwJZui88GFhT3F1D7zzPoGrStLUREZ0T8srjfBeyeVr7S9y7RrkpUEf6DgOfrHq+jwjegBwH8WNITkuZV3ZgeTNw9LVrxc0LF7emudNr2wdRtWvm2ee/6Mt19q1UR/p6mEmqn8cZ3RcTJwJnApcXHW2vO14DDqc3h2AncWGVjimnlvw98PCK2VtmWej20q5L3rYrwrwOm1j2eAmyooB09iogNxc9NwN3UdlPaycbdMyQXPzdV3J7/FxEbI2JnROwCvkGF710xrfz3gW9GxF3F4srfu57aVdX7VkX4HweOlHSopJHABcC9FbTjLSSNKb6IQdIY4L2039Tj9wIXFfcvAu6psC1v0i7TtjeaVp6K37t2m+6+kiP8iqGMLwMdwM0R8dlBb0QPJB1GrbeH2unO36qybZK+DZxG7ZTPjcC1wA+AO4GDgd8B50bEoH/x1qBtp9HLadsHqG2NppV/jArfu1ZOd9+S9vjwXrM8+Qg/s0w5/GaZcvjNMuXwm2XK4TfLlMNvlimH3yxT/weMjz76q0AEbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run  4\n",
      "Generated\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run  5\n",
      "Generated\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run  6\n",
      "Generated\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run  7\n",
      "Generated\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run  8\n",
      "Generated\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run  9\n",
      "Generated\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": [
    "# Generate the first examp times\n",
    "inputXReduced = X_train.loc[:0]\n",
    "for i in range(0,10):\n",
    "    example = 0\n",
    "    print(\"Run \",i)\n",
    "    finalOutput_DBN_fives, reconstructedOutput_DBN_fives = \\\n",
    "        dbn.dbn_output(inputXReduced)\n",
    "    reconstructedX_fives = pd.DataFrame(data=reconstructedOutput_DBN_fives, \\\n",
    "                                        index=[0])\n",
    "    print(\"Generated\")\n",
    "    view_digit(reconstructedX_fives, y_train.loc[:0], example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Supervised Only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictionColumns = ['0','1','2','3','4','5','6','7','8','9']\n",
    "\n",
    "params_lightGB = {\n",
    "    'task': 'train',\n",
    "    'application':'binary',\n",
    "    'num_class':10,\n",
    "    'boosting': 'gbdt',\n",
    "    'objective': 'multiclass',\n",
    "    'metric': 'multi_logloss',\n",
    "    'metric_freq':50,\n",
    "    'is_training_metric':False,\n",
    "    'max_depth':4,\n",
    "    'num_leaves': 31,\n",
    "    'learning_rate': 0.1,\n",
    "    'feature_fraction': 1.0,\n",
    "    'bagging_fraction': 1.0,\n",
    "    'bagging_freq': 0,\n",
    "    'bagging_seed': 2018,\n",
    "    'verbose': 0,\n",
    "    'num_threads':16\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's multi_logloss: 2.03259\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[2]\tvalid_0's multi_logloss: 1.83275\n",
      "[3]\tvalid_0's multi_logloss: 1.6722\n",
      "[4]\tvalid_0's multi_logloss: 1.54122\n",
      "[5]\tvalid_0's multi_logloss: 1.4341\n",
      "[6]\tvalid_0's multi_logloss: 1.33935\n",
      "[7]\tvalid_0's multi_logloss: 1.25721\n",
      "[8]\tvalid_0's multi_logloss: 1.18422\n",
      "[9]\tvalid_0's multi_logloss: 1.11702\n",
      "[10]\tvalid_0's multi_logloss: 1.05586\n",
      "[11]\tvalid_0's multi_logloss: 0.99914\n",
      "[12]\tvalid_0's multi_logloss: 0.947816\n",
      "[13]\tvalid_0's multi_logloss: 0.90067\n",
      "[14]\tvalid_0's multi_logloss: 0.858902\n",
      "[15]\tvalid_0's multi_logloss: 0.819287\n",
      "[16]\tvalid_0's multi_logloss: 0.784158\n",
      "[17]\tvalid_0's multi_logloss: 0.752388\n",
      "[18]\tvalid_0's multi_logloss: 0.722712\n",
      "[19]\tvalid_0's multi_logloss: 0.695435\n",
      "[20]\tvalid_0's multi_logloss: 0.669445\n",
      "[21]\tvalid_0's multi_logloss: 0.645138\n",
      "[22]\tvalid_0's multi_logloss: 0.621273\n",
      "[23]\tvalid_0's multi_logloss: 0.600193\n",
      "[24]\tvalid_0's multi_logloss: 0.580599\n",
      "[25]\tvalid_0's multi_logloss: 0.563216\n",
      "[26]\tvalid_0's multi_logloss: 0.546267\n",
      "[27]\tvalid_0's multi_logloss: 0.529911\n",
      "[28]\tvalid_0's multi_logloss: 0.514883\n",
      "[29]\tvalid_0's multi_logloss: 0.500638\n",
      "[30]\tvalid_0's multi_logloss: 0.48836\n",
      "[31]\tvalid_0's multi_logloss: 0.476436\n",
      "[32]\tvalid_0's multi_logloss: 0.465163\n",
      "[33]\tvalid_0's multi_logloss: 0.45389\n",
      "[34]\tvalid_0's multi_logloss: 0.443655\n",
      "[35]\tvalid_0's multi_logloss: 0.433623\n",
      "[36]\tvalid_0's multi_logloss: 0.424042\n",
      "[37]\tvalid_0's multi_logloss: 0.415131\n",
      "[38]\tvalid_0's multi_logloss: 0.406726\n",
      "[39]\tvalid_0's multi_logloss: 0.398983\n",
      "[40]\tvalid_0's multi_logloss: 0.39175\n",
      "[41]\tvalid_0's multi_logloss: 0.384624\n",
      "[42]\tvalid_0's multi_logloss: 0.377675\n",
      "[43]\tvalid_0's multi_logloss: 0.371457\n",
      "[44]\tvalid_0's multi_logloss: 0.365628\n",
      "[45]\tvalid_0's multi_logloss: 0.360234\n",
      "[46]\tvalid_0's multi_logloss: 0.354151\n",
      "[47]\tvalid_0's multi_logloss: 0.348435\n",
      "[48]\tvalid_0's multi_logloss: 0.343218\n",
      "[49]\tvalid_0's multi_logloss: 0.338852\n",
      "[50]\tvalid_0's multi_logloss: 0.33383\n",
      "[51]\tvalid_0's multi_logloss: 0.329117\n",
      "[52]\tvalid_0's multi_logloss: 0.324934\n",
      "[53]\tvalid_0's multi_logloss: 0.320888\n",
      "[54]\tvalid_0's multi_logloss: 0.316998\n",
      "[55]\tvalid_0's multi_logloss: 0.312385\n",
      "[56]\tvalid_0's multi_logloss: 0.309486\n",
      "[57]\tvalid_0's multi_logloss: 0.305773\n",
      "[58]\tvalid_0's multi_logloss: 0.302624\n",
      "[59]\tvalid_0's multi_logloss: 0.298703\n",
      "[60]\tvalid_0's multi_logloss: 0.295796\n",
      "[61]\tvalid_0's multi_logloss: 0.292838\n",
      "[62]\tvalid_0's multi_logloss: 0.290462\n",
      "[63]\tvalid_0's multi_logloss: 0.287436\n",
      "[64]\tvalid_0's multi_logloss: 0.284425\n",
      "[65]\tvalid_0's multi_logloss: 0.282097\n",
      "[66]\tvalid_0's multi_logloss: 0.279796\n",
      "[67]\tvalid_0's multi_logloss: 0.277192\n",
      "[68]\tvalid_0's multi_logloss: 0.274674\n",
      "[69]\tvalid_0's multi_logloss: 0.272631\n",
      "[70]\tvalid_0's multi_logloss: 0.270336\n",
      "[71]\tvalid_0's multi_logloss: 0.267971\n",
      "[72]\tvalid_0's multi_logloss: 0.265696\n",
      "[73]\tvalid_0's multi_logloss: 0.263493\n",
      "[74]\tvalid_0's multi_logloss: 0.261889\n",
      "[75]\tvalid_0's multi_logloss: 0.259622\n",
      "[76]\tvalid_0's multi_logloss: 0.257964\n",
      "[77]\tvalid_0's multi_logloss: 0.255984\n",
      "[78]\tvalid_0's multi_logloss: 0.254143\n",
      "[79]\tvalid_0's multi_logloss: 0.252739\n",
      "[80]\tvalid_0's multi_logloss: 0.250811\n",
      "[81]\tvalid_0's multi_logloss: 0.248872\n",
      "[82]\tvalid_0's multi_logloss: 0.247449\n",
      "[83]\tvalid_0's multi_logloss: 0.24618\n",
      "[84]\tvalid_0's multi_logloss: 0.244955\n",
      "[85]\tvalid_0's multi_logloss: 0.24356\n",
      "[86]\tvalid_0's multi_logloss: 0.242275\n",
      "[87]\tvalid_0's multi_logloss: 0.241079\n",
      "[88]\tvalid_0's multi_logloss: 0.23954\n",
      "[89]\tvalid_0's multi_logloss: 0.238329\n",
      "[90]\tvalid_0's multi_logloss: 0.237454\n",
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      "Early stopping, best iteration is:\n",
      "[209]\tvalid_0's multi_logloss: 0.191243\n",
      "Training Log Loss:  0.0018646953029132292\n",
      "Validation Log Loss:  0.19124276982588717\n",
      "LightGBM Gradient Boosting Log Loss:  0.19124276982588717\n"
     ]
    }
   ],
   "source": [
    "trainingScore = []\n",
    "validationScore = []\n",
    "predictionsLightGBM = pd.DataFrame(data=[], \\\n",
    "                        index=y_validation.index, \\\n",
    "                        columns=predictionColumns)\n",
    "\n",
    "lgb_train = lgb.Dataset(X_train.loc[:4999], y_train.loc[:4999])\n",
    "lgb_eval = lgb.Dataset(X_validation, y_validation, reference=lgb_train)\n",
    "gbm = lgb.train(params_lightGB, lgb_train, num_boost_round=2000,\n",
    "                   valid_sets=lgb_eval, early_stopping_rounds=200)\n",
    "    \n",
    "loglossTraining = log_loss(y_train.loc[:4999], \\\n",
    "    gbm.predict(X_train.loc[:4999], num_iteration=gbm.best_iteration))\n",
    "trainingScore.append(loglossTraining)\n",
    "    \n",
    "predictionsLightGBM.loc[X_validation.index,predictionColumns] = \\\n",
    "    gbm.predict(X_validation, num_iteration=gbm.best_iteration) \n",
    "loglossValidation = log_loss(y_validation, \n",
    "    predictionsLightGBM.loc[X_validation.index,predictionColumns])\n",
    "validationScore.append(loglossValidation)\n",
    "    \n",
    "print('Training Log Loss: ', loglossTraining)\n",
    "print('Validation Log Loss: ', loglossValidation)\n",
    "\n",
    "loglossLightGBM = log_loss(y_validation, predictionsLightGBM)\n",
    "print('LightGBM Gradient Boosting Log Loss: ', loglossLightGBM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Supervised-Only Accuracy:  0.9439\n"
     ]
    }
   ],
   "source": [
    "predictionsLightGBM_firm = np.argmax(np.array(predictionsLightGBM), axis=1)\n",
    "accuracyValidation_lightGBM = accuracy_score(np.array(y_validation), \\\n",
    "                                            predictionsLightGBM_firm)\n",
    "print(\"Supervised-Only Accuracy: \", accuracyValidation_lightGBM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Unsupervised and Supervised Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Prepare DBN-based DataFrames for LightGBM use\n",
    "generatedImagesDF = pd.DataFrame(data=generatedImages,index=range(0,100000))\n",
    "labelsDF = pd.DataFrame(data=labels,index=range(0,100000))\n",
    "\n",
    "X_train_lgb = pd.DataFrame(data=generatedImagesDF,\n",
    "                           index=generatedImagesDF.index)\n",
    "X_validation_lgb = pd.DataFrame(data=finalOutput_DBN_validation,\n",
    "                                index=X_validation.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's multi_logloss: 1.93287\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[2]\tvalid_0's multi_logloss: 1.69605\n",
      "[3]\tvalid_0's multi_logloss: 1.51402\n",
      "[4]\tvalid_0's multi_logloss: 1.36849\n",
      "[5]\tvalid_0's multi_logloss: 1.24451\n",
      "[6]\tvalid_0's multi_logloss: 1.14062\n",
      "[7]\tvalid_0's multi_logloss: 1.05092\n",
      "[8]\tvalid_0's multi_logloss: 0.973116\n",
      "[9]\tvalid_0's multi_logloss: 0.904238\n",
      "[10]\tvalid_0's multi_logloss: 0.841988\n",
      "[11]\tvalid_0's multi_logloss: 0.787594\n",
      "[12]\tvalid_0's multi_logloss: 0.737513\n",
      "[13]\tvalid_0's multi_logloss: 0.692809\n",
      "[14]\tvalid_0's multi_logloss: 0.652588\n",
      "[15]\tvalid_0's multi_logloss: 0.61596\n",
      "[16]\tvalid_0's multi_logloss: 0.582917\n",
      "[17]\tvalid_0's multi_logloss: 0.552566\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[208]\tvalid_0's multi_logloss: 0.182243\n",
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      "[314]\tvalid_0's multi_logloss: 0.213611\n",
      "[315]\tvalid_0's multi_logloss: 0.213937\n",
      "Early stopping, best iteration is:\n",
      "[115]\tvalid_0's multi_logloss: 0.163776\n",
      "Training Log Loss:  0.004145635328203315\n",
      "Validation Log Loss:  0.16377638170016542\n",
      "LightGBM Gradient Boosting Log Loss:  0.16377638170016542\n"
     ]
    }
   ],
   "source": [
    "# Train LightGBM\n",
    "trainingScore = []\n",
    "validationScore = []\n",
    "predictionsDBN = pd.DataFrame(data=[],index=y_validation.index,\n",
    "                              columns=predictionColumns)\n",
    "\n",
    "lgb_train = lgb.Dataset(X_train_lgb, labels)\n",
    "lgb_eval = lgb.Dataset(X_validation_lgb, y_validation, reference=lgb_train)\n",
    "gbm = lgb.train(params_lightGB, lgb_train, num_boost_round=2000,\n",
    "                   valid_sets=lgb_eval, early_stopping_rounds=200)\n",
    "    \n",
    "loglossTraining = log_loss(labelsDF, gbm.predict(X_train_lgb, \\\n",
    "                            num_iteration=gbm.best_iteration))\n",
    "trainingScore.append(loglossTraining)\n",
    "    \n",
    "predictionsDBN.loc[X_validation.index,predictionColumns] = \\\n",
    "    gbm.predict(X_validation_lgb, num_iteration=gbm.best_iteration) \n",
    "loglossValidation = log_loss(y_validation, \n",
    "    predictionsDBN.loc[X_validation.index,predictionColumns])\n",
    "validationScore.append(loglossValidation)\n",
    "    \n",
    "print('Training Log Loss: ', loglossTraining)\n",
    "print('Validation Log Loss: ', loglossValidation)\n",
    "\n",
    "loglossDBN = log_loss(y_validation, predictionsDBN)\n",
    "print('LightGBM Gradient Boosting Log Loss: ', loglossDBN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DBN-Based Solution Accuracy:  0.9525\n"
     ]
    }
   ],
   "source": [
    "# DBN\n",
    "predictionsDBN_firm = np.argmax(np.array(predictionsDBN), axis=1)\n",
    "accuracyValidation_DBN = accuracy_score(np.array(y_validation), \\\n",
    "                                        predictionsDBN_firm)\n",
    "print(\"DBN-Based Solution Accuracy: \", accuracyValidation_DBN)"
   ]
  }
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