how_to_ftbert_classification_malay_10fold.ipynb

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    "colab": {
      "name": "copy_of_bert_classification_malay_10fold.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "ny02hYzdvSpv"
      },
      "source": [
        "# !pip uninstall tensorflow\n",
        "# !pip install tensorflow==2.1.0\n",
        "# tf.compat.v1.disable_v2_behavior()\n",
        "# !pip install --upgrade gensim --user\n",
        "# !pip install -q pyyaml h5py  # Required to save models in HDF5 format\n",
        "# !pip install np_utils\n",
        "# !pip install --upgrade --user keras\n",
        "# import nltk\n",
        "# nltk.download('punkt')\n",
        "# !pip install bert-tensorflow\n",
        "# # !pip install bert-for-tf2\n",
        "# !pip install sentencepiece\n",
        "# !pip install unidecode\n",
        "# %tensorflow_version 1.x\n",
        "# import tensorflow as tf\n",
        "# print(tf.__version__)\n",
        "# tf.test.gpu_device_name()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMVyz3b-QEwr",
        "outputId": "c60e15aa-0988-4ebf-fc58-ab9f2ed12bf1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\")"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GHd8fAfjr3P5"
      },
      "source": [
        "import pandas as pd\n",
        "import json\n",
        "import os\n",
        "import glob\n",
        "import pickle\n",
        "#from os import path\n",
        "# import malaya"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xw6oEX0hRIOw",
        "outputId": "c90e9803-877b-41cf-93b2-7b37fe90aab8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "df_allnews_unpickled = pd.read_pickle(\"./malaya_fake_news_preprocessed_dataframe.pkl\")        #Load FakeNews Dataset from your path\n",
        "df_allnews_unpickled"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>news</th>\n",
              "      <th>tokens</th>\n",
              "      <th>rejoined</th>\n",
              "      <th>length</th>\n",
              "      <th>label</th>\n",
              "      <th>real</th>\n",
              "      <th>fake</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>demi lovato telah jujur kejam tentang pertempu...</td>\n",
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              "      <td>katie holmes tidak mengandung anak jamie foxx ...</td>\n",
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              "      <td>donald trump menegaskan bahawa serangan siber ...</td>\n",
              "      <td>[donald, trump, menegaskan, bahawa, serangan, ...</td>\n",
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              "      <td>presiden donald trump mengumumkan bahawa dia t...</td>\n",
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              "                                                    news  ... fake\n",
              "0      demi lovato telah jujur kejam tentang pertempu...  ...    0\n",
              "1      selamat pagi inilah yang perlu anda ketahui se...  ...    0\n",
              "2      tahniah untuk bintang glee lea michele pelakon...  ...    0\n",
              "3      ia adalah satu kejutan yang mengejutkan untuk ...  ...    0\n",
              "4      transkrip minggu ini dengan george stephanopou...  ...    0\n",
              "...                                                  ...  ...  ...\n",
              "37587  katie holmes tidak mengandung anak jamie foxx ...  ...    1\n",
              "37588  donald trump menegaskan bahawa serangan siber ...  ...    0\n",
              "37589  ia bukan cerita pertama yang bermula dengan pe...  ...    0\n",
              "37590  presiden donald trump mengumumkan bahawa dia t...  ...    0\n",
              "37591  jika hillary clinton dipilih sebagai presiden ...  ...    1\n",
              "\n",
              "[37592 rows x 7 columns]"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 5
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lx1TuO9GcMeH"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
        "\n",
        "import tensorflow as tf\n",
        "from __future__ import division, print_function\n",
        "import gensim\n",
        "from gensim import models\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "from tensorflow.keras.layers import Dense, Dropout, Reshape, Flatten, concatenate, Input, Conv1D, GlobalMaxPooling1D, Embedding, Bidirectional, LSTM, GlobalAveragePooling1D\n",
        "from tensorflow.keras.regularizers import l2\n",
        "from tensorflow.keras.regularizers import L1L2\n",
        "from tensorflow.keras.initializers import RandomNormal\n",
        "import tensorflow.keras.backend as K\n",
        "# from keras.layers.recurrent import LSTM\n",
        "# from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.models import Model\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import KFold\n",
        "import numpy as np\n",
        "\n",
        "# demonstration of calculating metrics for a neural network model using sklearn\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import average_precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JDDrcCkdwZ9u"
      },
      "source": [
        "import json\n",
        "import re\n",
        "import sentencepiece as spm\n",
        "import time"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mqnrqH7HwcIx",
        "outputId": "c3f27b99-4646-4c3c-ce68-19dcc04cc881",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "import bert\n",
        "from bert import run_classifier\n",
        "from bert import optimization\n",
        "from bert import tokenization\n",
        "from bert import modeling\n",
        "import numpy as np\n",
        "import json\n",
        "import tensorflow_hub as hub\n",
        "import itertools\n",
        "from unidecode import unidecode\n",
        "from tqdm import tqdm\n",
        "from collections import namedtuple"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/bert/optimization.py:87: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nv7ua0J9wexJ"
      },
      "source": [
        "from bertclassify import prepro_utils\n",
        "\n",
        "sp_model = spm.SentencePieceProcessor()\n",
        "sp_model.Load('./bert-small/sp10m.cased.v4.model')\n",
        "\n",
        "with open('./bert-small/sp10m.cased.v4.vocab') as fopen:\n",
        "    v = fopen.read().split('\\n')[:-1]\n",
        "v = [i.split('\\t') for i in v]\n",
        "v = {i[0]: i[1] for i in v}\n",
        "\n",
        "class Tokenizer:\n",
        "    def __init__(self, v):\n",
        "        self.vocab = v\n",
        "        pass\n",
        "    \n",
        "    def tokenize(self, string):\n",
        "        return prepro_utils.encode_pieces(sp_model, string, return_unicode=False, sample=False)\n",
        "    \n",
        "    def convert_tokens_to_ids(self, tokens):\n",
        "        return [sp_model.PieceToId(piece) for piece in tokens]\n",
        "    \n",
        "    def convert_ids_to_tokens(self, ids):\n",
        "        return [sp_model.IdToPiece(i) for i in ids]\n",
        "    \n",
        "tokenizer = Tokenizer(v)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jJEE6qxVFBZ3"
      },
      "source": [
        "BERT_INIT_CHKPNT = './bert-small/model.ckpt'\n",
        "BERT_CONFIG = './bert-small/bert_config.json'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AXJPq7zdFE62",
        "outputId": "0154d82c-3a82-4c0e-ede0-edd45dec2075",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "bert_config = modeling.BertConfig.from_json_file(BERT_CONFIG)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/bert/modeling.py:93: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5HklPwYLFMTH"
      },
      "source": [
        "MAX_SEQ_LENGTH = 128\n",
        "tokenizer.tokenize(df_allnews_unpickled.iloc[0,2])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ELtdj-tFwrsr",
        "outputId": "8347c0e4-c202-4531-d4a5-5664ffbd98c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "from tqdm import tqdm\n",
        "\n",
        "input_ids, input_masks, segment_ids = [], [], []\n",
        "\n",
        "for text in tqdm(df_allnews_unpickled[\"rejoined\"].tolist()):\n",
        "    tokens_a = tokenizer.tokenize(text)\n",
        "    if len(tokens_a) > MAX_SEQ_LENGTH - 2:\n",
        "        tokens_a = tokens_a[:(MAX_SEQ_LENGTH - 2)]\n",
        "    tokens = [\"<cls>\"] + tokens_a + [\"<sep>\"]\n",
        "    segment_id = [0] * len(tokens)\n",
        "    input_id = tokenizer.convert_tokens_to_ids(tokens)\n",
        "    input_mask = [1] * len(input_id)\n",
        "    padding = [0] * (MAX_SEQ_LENGTH - len(input_id))\n",
        "    input_id += padding\n",
        "    input_mask += padding\n",
        "    segment_id += padding\n",
        "    \n",
        "    input_ids.append(input_id)\n",
        "    input_masks.append(input_mask)\n",
        "    segment_ids.append(segment_id)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 37592/37592 [00:49<00:00, 757.41it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gQtjYLtMwv2l",
        "outputId": "c42dc1a0-ae1c-4367-fc4d-ad025506a3ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 149
        }
      },
      "source": [
        "idx_temp = 142\n",
        "\n",
        "print (len(input_ids))\n",
        "print (len(input_masks))\n",
        "print (len(segment_ids))\n",
        "\n",
        "print (input_ids[idx_temp])\n",
        "print (input_masks[idx_temp])\n",
        "print (segment_ids[idx_temp])\n",
        "\n",
        "input_ids = np.asarray(input_ids, dtype=np.int32)\n",
        "input_masks = np.asarray(input_masks, dtype=np.int32)\n",
        "segment_ids = np.asarray(segment_ids, dtype=np.int32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "37592\n",
            "37592\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsuDvKk6ooAx"
      },
      "source": [
        "label_names = ['real', 'fake']\n",
        "labels_list = df_allnews_unpickled[\"label\"].values\n",
        "# labels_list = df_allnews_unpickled[label_names].values\n",
        "# labels_list = sub_df_allnews[label_names].values\n",
        "# print (labels_list[42])\n",
        "# print (sequences_all[42])\n",
        "# # Zip back the news and labels into a temporary list\n",
        "all_inputs_zipped = list(zip(input_ids, input_masks, segment_ids))\n",
        "# all_newsnlabels = list(zip(input_ids, input_masks, segment_ids, labels_list))\n",
        "# print (all_newsnlabels[42][0])\n",
        "# print (all_newsnlabels[42][1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "761eTWfgw61M",
        "outputId": "c902b8be-faef-4c38-b14d-f0afac62e082",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        }
      },
      "source": [
        "# print (df_allnews_unpickled.iloc[42])\n",
        "# print (len(all_newsnlabels[42][0]))\n",
        "# print (len(all_newsnlabels[42][1]))\n",
        "# print (len(all_newsnlabels[42][2]))\n",
        "# print (all_newsnlabels[42][3])\n",
        "\n",
        "print (df_allnews_unpickled.iloc[42])\n",
        "print (len(all_inputs_zipped[42][0]))\n",
        "print (len(all_inputs_zipped[42][1]))\n",
        "print (len(all_inputs_zipped[42][2]))\n",
        "print (labels_list[42])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "news        pulau staten , n y , pernahkah ia tertanya tan...\n",
            "tokens      [pulau, staten, n, y, pernahkah, ia, tertanya,...\n",
            "rejoined    pulau staten n y pernahkah ia tertanya tanya s...\n",
            "label                                                       1\n",
            "real                                                        1\n",
            "fake                                                        0\n",
            "Name: 42, dtype: object\n",
            "128\n",
            "128\n",
            "128\n",
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHrro5cUMlTC"
      },
      "source": [
        "num_epoch = 2\n",
        "batch_size = 64\n",
        "warmup_proportion = 0.1\n",
        "num_train_steps = int(len(df_allnews_unpickled[\"rejoined\"].tolist()) / batch_size * num_epoch)\n",
        "num_warmup_steps = int(num_train_steps * warmup_proportion)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CBUPN9CJT9-H"
      },
      "source": [
        "Define FT-BERT Architecture"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QSmaNykpSG6G"
      },
      "source": [
        "class FTBERT_Model:\n",
        "    def __init__(\n",
        "        self,\n",
        "        dimension_output,\n",
        "        learning_rate = 2e-5,\n",
        "    ):\n",
        "        self.X = tf.placeholder(tf.int32, [None, None])\n",
        "        self.input_masks = tf.placeholder(tf.int32, [None, None])\n",
        "        self.segment_ids = tf.placeholder(tf.int32, [None, None])\n",
        "        self.Y = tf.placeholder(tf.int32, [None])\n",
        "        # self.X = tf.keras.layers.Input(shape=(MAX_SEQ_LENGTH,), dtype=tf.int32,\n",
        "        #                                name=\"input_word_ids\")\n",
        "        # self.masks = tf.keras.layers.Input(shape=(MAX_SEQ_LENGTH,), dtype=tf.int32,\n",
        "        #                               name=\"input_mask\")\n",
        "        # self.segments = tf.keras.layers.Input(shape=(MAX_SEQ_LENGTH,), dtype=tf.int32,\n",
        "        #                             name=\"segment_ids\")\n",
        "        # self.X = Input(shape = (None, ), dtype = \"int32\")\n",
        "        # self.masks = Input(shape = (None, ), dtype = \"int32\")\n",
        "        # self.segments = Input(shape = (None, ), dtype = \"int32\")\n",
        "\n",
        "        \n",
        "        bert_model = modeling.BertModel(\n",
        "            config=bert_config,\n",
        "            is_training=True,\n",
        "            input_ids=self.X,\n",
        "            input_mask=self.input_masks,\n",
        "            token_type_ids=self.segment_ids,\n",
        "            use_one_hot_embeddings=False)\n",
        "        \n",
        "        ####################################\n",
        "        bert_output = bert_model.get_pooled_output()\n",
        "        # bert_output = bert_model.get_sequence_output()\n",
        "        ####################################\n",
        "        \n",
        "        ####################################\n",
        "        # conv_output = Conv1D(filters=10, kernel_size=3, strides=1, activation=\"relu\")(bert_output)\n",
        "        # pool_output = Lambda(lambda x: K.max(x, axis=1), output_shape=(100, ))(conv_output)\n",
        "        \n",
        "        # convs = []\n",
        "        # filter_sizes = [3,4,5]\n",
        "        # for filter_size in filter_sizes:\n",
        "        #     l_conv = Conv1D(filters=100, kernel_size=filter_size, activation='relu')(bert_output)\n",
        "        #     l_pool = GlobalMaxPooling1D()(l_conv)\n",
        "        #     convs.append(l_pool)\n",
        "        # l_merge = concatenate(convs, axis=1)\n",
        "\n",
        "        # l_pool = GlobalAveragePooling1D()(bert_output)\n",
        "        # lstm_output = LSTM(512, go_backwards=True, dropout=0.2)(bert_output)\n",
        "\n",
        "        # l_pool = GlobalAveragePooling1D()(bert_output)\n",
        "        # l_dense1 = Dense(6, activation='relu')(l_pool)\n",
        "        # l_dense2 = Dense(6, activation='relu')(l_dense1)\n",
        "        # l_dense3 = Dense(6, activation='relu')(l_dense2)\n",
        "        # l_dense4 = Dense(6, activation='relu')(l_dense3)\n",
        "        # l_dense5 = Dense(6, activation='relu')(l_dense4)\n",
        "        ####################################\n",
        "\n",
        "        ####################################\n",
        "        x3 = Dropout(0.5)(bert_output)\n",
        "        # x3 = Dropout(0.5)(pool_output)  \n",
        "        # x3 = Dropout(0.5)(l_merge)  \n",
        "        # x3 = Dropout(0.5)(lstm_output)  \n",
        "        # x3 = Dropout(0.5)(l_dense5)  \n",
        "        ####################################  \n",
        "        \n",
        "        self.logits = Dense(len(list(label_names)), activation='softmax')(x3)\n",
        "\n",
        "\n",
        "        self.cost = tf.reduce_mean(\n",
        "            tf.nn.sparse_softmax_cross_entropy_with_logits(\n",
        "                logits = self.logits, labels = self.Y\n",
        "            )\n",
        "        )\n",
        "        \n",
        "        self.optimizer = optimization.create_optimizer(self.cost, learning_rate, \n",
        "                                                       num_train_steps, num_warmup_steps, False)\n",
        "        correct_pred = tf.equal(\n",
        "            tf.argmax(self.logits, 1, output_type = tf.int32), self.Y\n",
        "        )\n",
        "        self.accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XFCHz7UuNnWq",
        "outputId": "c3d4c79c-f436-4e3f-d92b-534744210863",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 93
        }
      },
      "source": [
        "dimension_output = 2\n",
        "learning_rate = 2e-5\n",
        "\n",
        "tf.reset_default_graph()\n",
        "sess = tf.InteractiveSession()\n",
        "# tf.initialize_all_variables().run()\n",
        "model = FTBERT_Model(\n",
        "    dimension_output,\n",
        "    learning_rate\n",
        ")\n",
        "\n",
        "# model = FTBERT(len(list(label_names)))\n",
        "\n",
        "sess.run(tf.global_variables_initializer())\n",
        "var_lists = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope = 'bert')\n",
        "saver = tf.train.Saver(var_list = var_lists)\n",
        "saver.restore(sess, BERT_INIT_CHKPNT)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/tensorflow-1.15.2/python3.6/tensorflow_core/python/client/session.py:1750: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
            "  warnings.warn('An interactive session is already active. This can '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Restoring parameters from ./bert-small/model.ckpt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AokXV_O313UT"
      },
      "source": [
        "# # Only run once before starting a new InteractiveSession() from above Code Section\n",
        "sess.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9d7PiusXNzTb"
      },
      "source": [
        "saver = tf.train.Saver()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wc35nr9dxk4J"
      },
      "source": [
        "# # model.save_weights('./bertclassify/bert_model.h5')\n",
        "save_path = saver.save(sess, \"./bertclassify/bert_training_10fold/bert_model.ckpt\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WyWNU5_Ou8ga",
        "outputId": "6de1199e-6efd-447b-d256-35104c68d4ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "# # model.load_weights('./bertclassify/bert_model.h5')\n",
        "saver.restore(sess, \"./bertclassify/bert_training_10fold/bert_model.ckpt\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Restoring parameters from ./bertclassify/bert_training_10fold/bert_model.ckpt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kI0jPjMvkwz"
      },
      "source": [
        "# weights = []\n",
        "# for layer in model.layers:\n",
        "#     weights.append(layer.get_weights()) # list of numpy arrays\n",
        "\n",
        "# print ((weights[6][0]))\n",
        "\n",
        "vars = tf.trainable_variables()\n",
        "print(vars)\n",
        "\n",
        "var = [v for v in tf.trainable_variables() if v.name == \"dense_19/kernel:0\"][0]\n",
        "# print(var)\n",
        "print(\"Weight matrix: \\n{0}\".format(sess.run(var))) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jFEdktxoSD23"
      },
      "source": [
        "K-fold Cross Validation stars here"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LD3nbtcKh6qb"
      },
      "source": [
        "num_folds = 10\n",
        "# num_epochs = 3    \n",
        "# batch_size = 50\n",
        "fold_no = 1\n",
        "kfold = KFold(n_splits=num_folds, shuffle=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lcPpQlkgykjn",
        "outputId": "05624753-bfe7-4af3-ca97-4eba65f9c6e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "# data_train, data_test = train_test_split(all_newsnlabels, test_size=0.10, random_state=42)\n",
        "# inputs = [input_ids, input_masks, segment_ids]\n",
        "inputs = np.asarray(all_inputs_zipped)\n",
        "targets = labels_list\n",
        "print (len(inputs))\n",
        "print (len(targets))\n",
        "\n",
        "arr_id = np.asarray(df_allnews_unpickled.index)\n",
        "print (len(arr_id))\n",
        "\n",
        "# x_train = np.asarray([row[0] for row in data_train])\n",
        "# y_train = np.asarray([row[1] for row in data_train])\n",
        "# x_test = np.asarray([row[0] for row in data_test])\n",
        "# y_test = np.asarray([row[1] for row in data_test])\n",
        "# print (x_train[42])\n",
        "# print (y_train[42])\n",
        "# print (inputs[39505])\n",
        "# print (targets[39505])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "37592\n",
            "37592\n",
            "37592\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qBCrlwUudN-S",
        "outputId": "9ed230a8-c3b5-4563-f3e9-6b07cbe7f982",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "fold_no = 1\n",
        "results_history = []\n",
        "#[0] list_train_acc\n",
        "#[1] list_val_acc\n",
        "#[2] list_train_loss\n",
        "#[3] list_val_loss\n",
        "\n",
        "acc_per_fold = []\n",
        "loss_per_fold = []\n",
        "\n",
        "# # Lists for metrics result\n",
        "all_accuracy = []\n",
        "all_precision = []\n",
        "all_recall = []\n",
        "all_f1 = []\n",
        "\n",
        "# # Lists for plotting Precision-Recall Curve\n",
        "all_arr_precision = []\n",
        "all_arr_recall = []\n",
        "all_thresholds = []\n",
        "all_ave_precision = []\n",
        "\n",
        "# # Lists for Confusion Matrices\n",
        "all_cf_matrices = []\n",
        "\n",
        "# # Lists for Noise Data\n",
        "all_wrong = []\n",
        "\n",
        "for train, test in kfold.split(inputs, targets):\n",
        "  saver.restore(sess, \"./bertclassify/bert_training_10fold/bert_model.ckpt\")     #Load the same initial weights for all splits\n",
        "  x_train = np.asarray(inputs[train])\n",
        "  y_train = np.asarray(targets[train])\n",
        "  x_test = np.asarray(inputs[test])\n",
        "  # y_test = np.asarray(targets[test])\n",
        "  \n",
        "  temp_arr_id = np.asarray(arr_id[test])\n",
        "\n",
        "  # data_train_val = list(zip(x_train, y_train))      #Rezip to manually split train and validation\n",
        "  # data_train_val = np.column_stack(x_train,y_train)   #Stack columns to manually split train and validation\n",
        "  data_train_val = []\n",
        "  for m in range(len(x_train)):\n",
        "    temp_list = x_train[m].tolist()\n",
        "    temp_list.append(y_train[m].tolist())\n",
        "    data_train_val.append(temp_list)\n",
        "\n",
        "  data_train, data_val = train_test_split(data_train_val, test_size=0.10, random_state=42)\n",
        "  \n",
        "  x_train_ids = np.asarray([row[0] for row in data_train_val])\n",
        "  x_train_masks = np.asarray([row[1] for row in data_train_val])\n",
        "  x_train_segments = np.asarray([row[2] for row in data_train_val])\n",
        "  y_train = np.asarray([row[3] for row in data_train_val])\n",
        "\n",
        "  # x_train_ids = np.asarray([row[0] for row in data_train])\n",
        "  # x_train_masks = np.asarray([row[1] for row in data_train])\n",
        "  # x_train_segments = np.asarray([row[2] for row in data_train])\n",
        "  # y_train = np.asarray([row[3] for row in data_train])\n",
        "\n",
        "  x_val_ids = np.asarray([row[0] for row in data_val])\n",
        "  x_val_masks = np.asarray([row[1] for row in data_val])\n",
        "  x_val_segments = np.asarray([row[2] for row in data_val])\n",
        "  y_val = np.asarray([row[3] for row in data_val])\n",
        "\n",
        "  x_test_ids = np.asarray([row[0] for row in x_test.tolist()])\n",
        "  x_test_masks = np.asarray([row[1] for row in x_test.tolist()])\n",
        "  x_test_segments = np.asarray([row[2] for row in x_test.tolist()])\n",
        "  y_test = np.asarray(targets[test])\n",
        "\n",
        "\n",
        "  # # CHECKING FOR ALL TRAIN/VALIDATE/TEST ITEMS starts\n",
        "  # print (len(x_train_ids))\n",
        "  # print (len(x_train_masks))\n",
        "  # print (len(x_train_segments))\n",
        "  # print (len(y_train))\n",
        "  # print()\n",
        "\n",
        "  # print (len(x_val_ids))\n",
        "  # print (len(x_val_masks))\n",
        "  # print (len(x_val_segments))\n",
        "  # print (len(y_val))\n",
        "  # print()\n",
        "\n",
        "  # print (len(x_test_ids))\n",
        "  # print (len(x_test_masks))\n",
        "  # print (len(x_test_segments))\n",
        "  # print (len(y_test))\n",
        "  # print()\n",
        "\n",
        "  # id_tmp = 19\n",
        "\n",
        "  # print ((x_train_ids[id_tmp]))\n",
        "  # print ((x_train_masks[id_tmp]))\n",
        "  # print ((x_train_segments[id_tmp]))\n",
        "  # print ((y_train[id_tmp]))\n",
        "  # print()\n",
        "\n",
        "  # print ((x_val_ids[id_tmp]))\n",
        "  # print ((x_val_masks[id_tmp]))\n",
        "  # print ((x_val_segments[id_tmp]))\n",
        "  # print ((y_val[id_tmp]))\n",
        "  # print()\n",
        "\n",
        "  # print ((x_test_ids[id_tmp]))\n",
        "  # print ((x_test_masks[id_tmp]))\n",
        "  # print ((x_test_segments[id_tmp]))\n",
        "  # print ((y_test[id_tmp]))\n",
        "  # print()\n",
        "  # # CHECKING FOR ALL TRAIN/VALIDATE/TEST ITEMS ends\n",
        "  \n",
        "  \n",
        "  # # Generate a print\n",
        "  print('------------------------------------------------------------------------')\n",
        "  print(f'Training for Fold {fold_no} ...')\n",
        "  \n",
        "  \n",
        "  list_train_loss = []\n",
        "  list_train_acc = []\n",
        "  list_val_loss = []\n",
        "  list_val_acc = []\n",
        "\n",
        "  #########################################\n",
        "  # # manual work for model.fit() starts here\n",
        "  # TRAIN AND VALIDATE\n",
        "  for m in range(num_epoch):\n",
        "      lasttime = time.time()\n",
        "\n",
        "      train_acc, train_loss, val_acc, val_loss = 0, 0, 0, 0\n",
        "      pbar_train = tqdm(range(0, len(x_train_ids), batch_size), desc = 'Training Minibatch Loop', position=0, leave=True)\n",
        "      for i in pbar_train:\n",
        "          index = min(i + batch_size, len(x_train_ids))\n",
        "          batch_x = x_train_ids[i: index]\n",
        "          batch_masks = x_train_masks[i: index]\n",
        "          batch_segment = x_train_segments[i: index]\n",
        "          batch_y = y_train[i: index]\n",
        "          acc, cost, _ = sess.run(\n",
        "              [model.accuracy, model.cost, model.optimizer],\n",
        "              feed_dict = {\n",
        "                  model.Y: batch_y,\n",
        "                  model.X: batch_x,\n",
        "                  model.segment_ids: batch_segment,\n",
        "                  model.input_masks: batch_masks\n",
        "              },\n",
        "          )\n",
        "          assert not np.isnan(cost)\n",
        "          train_loss += cost\n",
        "          train_acc += acc\n",
        "          pbar_train.set_postfix(cost = cost, accuracy = acc)\n",
        "      \n",
        "      pbar_val = tqdm(range(0, len(x_test_ids), batch_size), desc = 'Validation Minibatch Loop', position=0, leave=True)      #val/test\n",
        "      for i in pbar_val:\n",
        "          index = min(i + batch_size, len(x_test_ids))        #val/test \n",
        "          batch_x = x_test_ids[i: index]                      #val/test\n",
        "          batch_masks = x_test_masks[i: index]                #val/test\n",
        "          batch_segment = x_test_segments[i: index]           #val/test\n",
        "          batch_y = y_test[i: index]                          #val/test\n",
        "          acc, cost = sess.run(\n",
        "              [model.accuracy, model.cost],\n",
        "              feed_dict = {\n",
        "                  model.Y: batch_y,\n",
        "                  model.X: batch_x,\n",
        "                  model.segment_ids: batch_segment,\n",
        "                  model.input_masks: batch_masks\n",
        "              },\n",
        "          )\n",
        "          val_loss += cost\n",
        "          val_acc += acc\n",
        "          pbar_val.set_postfix(cost = cost, accuracy = acc)\n",
        "\n",
        "      \n",
        "      train_loss /= len(pbar_train)\n",
        "      train_acc /= len(pbar_train)\n",
        "      val_loss /= len(pbar_val) \n",
        "      val_acc /= len(pbar_val)\n",
        "      \n",
        "      list_train_loss.append(train_loss)\n",
        "      list_train_acc.append(train_acc)\n",
        "      list_val_loss.append(val_loss)\n",
        "      list_val_acc.append(val_acc)\n",
        "          \n",
        "      print('Time Taken:', time.time() - lasttime)\n",
        "      print('Epoch: %d, train_loss: %f, train_acc: %f, val_loss: %f, val_acc: %f\\n'\n",
        "          % (m+1, train_loss, train_acc, val_loss, val_acc)\n",
        "      )\n",
        "\n",
        "  # results_history.append(hist)\n",
        "  # results_history.append(hist.history)\n",
        "  templist = list(zip(list_train_acc, list_val_acc, list_train_loss, list_val_loss))\n",
        "  results_history.append(templist)\n",
        "  templist = []\n",
        "  # # manual work for model.fit() ends here\n",
        "  #########################################\n",
        "    \n",
        "  \n",
        "\n",
        "  #########################################\n",
        "  # # manual work for model.evaluate() starts here\n",
        "  # EVALUATE MODEL ON TEST SET\n",
        "  test_acc, test_loss = 0, 0\n",
        "\n",
        "  pbar_test = tqdm(range(0, len(x_test_ids), batch_size), desc = 'Testing Minibatch Loop', position=0, leave=True)\n",
        "  for i in pbar_test:\n",
        "      index = min(i + batch_size, len(x_test_ids))\n",
        "      batch_x = x_test_ids[i: index]\n",
        "      batch_masks = x_test_masks[i: index]\n",
        "      batch_segment = x_test_segments[i: index]\n",
        "      batch_y = y_test[i: index]\n",
        "      acc, cost = sess.run([model.accuracy, model.cost],\n",
        "            feed_dict = {\n",
        "                model.Y: batch_y,\n",
        "                model.X: batch_x,\n",
        "                model.segment_ids: batch_segment,\n",
        "                model.input_masks: batch_masks\n",
        "            },\n",
        "        )\n",
        "      test_loss += cost\n",
        "      test_acc += acc\n",
        "      pbar_test.set_postfix(cost = cost, accuracy = acc)                    \n",
        "      \n",
        "  test_loss /= len(pbar_test) \n",
        "  test_acc /= len(pbar_test)\n",
        "  print('Time Taken:', time.time() - lasttime)\n",
        "  print('test_loss: %f, test_acc: %f\\n'\n",
        "      % (test_loss, test_acc)\n",
        "  )\n",
        "  \n",
        "  print(f'Score for Fold {fold_no}: Loss of {test_loss}; Accuracy of {test_acc*100}%')\n",
        "  acc_per_fold.append(test_acc * 100)\n",
        "  loss_per_fold.append(test_loss)\n",
        "  # # manual work for model.evaluate() ends here\n",
        "  #########################################\n",
        "\n",
        "\n",
        "\n",
        "  #########################################\n",
        "  # # manual work for model.predict() starts here\n",
        "  # METRICS REQUIREMENTS CALCULATION STARTS HERE\n",
        "  yhat_probs_all = []       # yhat_probs = non-argmax/min predict_Y\n",
        "  y_probs = []              # y_probs = yhat_probs[:, 0]  #Predicted Probabilities\n",
        "  yhat_classes = []         # yhat_classes = predict_Y              #Predicted Classes\n",
        "  y_test_conv = []          # y_test_conv => y_test = real_Y        #Actual Classes\n",
        "\n",
        "  pbar_test_metrics = tqdm(range(0, len(x_test_ids), batch_size), desc = 'Testing for Metrics Minibatch Loop', position=0, leave=True)\n",
        "  for i in pbar_test_metrics:\n",
        "      index = min(i + batch_size, len(x_test_ids))\n",
        "      batch_x = x_test_ids[i: index]\n",
        "      batch_masks = x_test_masks[i: index]\n",
        "      batch_segment = x_test_segments[i: index]\n",
        "      batch_y = y_test[i: index]\n",
        "      yhat_probs = sess.run(model.logits,\n",
        "              feed_dict = {\n",
        "                  model.Y: batch_y,\n",
        "                  model.X: batch_x,\n",
        "                  model.segment_ids: batch_segment,\n",
        "                  model.input_masks: batch_masks\n",
        "              },\n",
        "            )\n",
        "      # yhat_probs_all.append(yhat_probs)\n",
        "      # y_probs = np.append(y_probs, (np.max(yhat_probs, axis=1)))\n",
        "      y_probs = np.append(y_probs, (np.asarray(yhat_probs[:, 0])))     #Consider only positive-predicted class for Precision Recall Curve\n",
        "      yhat_classes = np.append(yhat_classes, (np.argmax(yhat_probs, axis=-1)))            #formerly predict_Y\n",
        "      y_test_conv = np.append(y_test_conv, batch_y)                                       #formerly real_Y\n",
        "  \n",
        "  # # manual work for model.predict() ends here\n",
        "  #########################################\n",
        "  \n",
        "  for m in range(len(yhat_classes)):\n",
        "    if (yhat_classes[m] != y_test_conv[m]):\n",
        "      all_wrong.append(temp_arr_id[m])   \n",
        "\n",
        "\n",
        "  ## Metrics calculation\n",
        "  # accuracy: (tp + tn) / (p + n)\n",
        "  accuracy = accuracy_score(y_test_conv, yhat_classes)\n",
        "  all_accuracy.append(accuracy)\n",
        "  # precision tp / (tp + fp)\n",
        "  precision = precision_score(y_test_conv, yhat_classes)\n",
        "  all_precision.append(precision)\n",
        "  # recall: tp / (tp + fn)\n",
        "  recall = recall_score(y_test_conv, yhat_classes)\n",
        "  all_recall.append(recall)\n",
        "  # f1: 2 tp / (2 tp + fp + fn)\n",
        "  f1 = f1_score(y_test_conv, yhat_classes)\n",
        "  all_f1.append(f1)\n",
        "\n",
        "  ## Precision-Curve calculation\n",
        "  arr_precision, arr_recall, thresholds = precision_recall_curve(y_test_conv, y_probs)\n",
        "  thresholds = np.concatenate([thresholds,[thresholds[thresholds.size-1]]])\n",
        "  all_arr_precision.append(arr_precision)\n",
        "  all_arr_recall.append(arr_recall)\n",
        "  all_thresholds.append(thresholds)\n",
        "\n",
        "  ave_precision = average_precision_score(y_test_conv, y_probs)\n",
        "  all_ave_precision.append(ave_precision)\n",
        "\n",
        "  ## Confusion Matrix calculation\n",
        "  cf_matrix = confusion_matrix(y_test_conv, yhat_classes)\n",
        "  all_cf_matrices.append(cf_matrix)\n",
        "\n",
        "  # Increase fold number\n",
        "  fold_no = fold_no + 1\n",
        "####################################\n",
        "\n",
        "# == Provide average scores ==\n",
        "print('------------------------------------------------------------------------')\n",
        "print('Score per fold')\n",
        "for i in range(0, len(acc_per_fold)):\n",
        "  print('------------------------------------------------------------------------')\n",
        "  print(f'> Fold {i+1} - Loss: {loss_per_fold[i]} - Accuracy: {acc_per_fold[i]}%')\n",
        "print('------------------------------------------------------------------------')\n",
        "print('Average scores for all folds:')\n",
        "print(f'> Accuracy: {np.mean(acc_per_fold)} (+- {np.std(acc_per_fold)})')\n",
        "print(f'> Loss: {np.mean(loss_per_fold)}')\n",
        "print('------------------------------------------------------------------------')\n",
        "\n",
        "\n",
        "\n",
        "# # Create Dataframe to save all train/val history result\n",
        "df_history = pd.DataFrame(results_history)\n",
        "df_history.to_pickle(\"./bertclassify/BERTTrainHistoryDict_10fold.pkl\")\n",
        "# df_history\n",
        "\n",
        "# # Create Dataframe to save all Metrics Result\n",
        "df_allmetrics_10fold = pd.DataFrame(acc_per_fold)\n",
        "df_allmetrics_10fold.columns = ['acc_per_fold']\n",
        "df_allmetrics_10fold['loss_per_fold'] = loss_per_fold\n",
        "\n",
        "df_allmetrics_10fold['all_accuracy'] = all_accuracy\n",
        "df_allmetrics_10fold['all_precision'] = all_precision\n",
        "df_allmetrics_10fold['all_recall'] = all_recall\n",
        "df_allmetrics_10fold['all_f1'] = all_f1\n",
        "\n",
        "df_allmetrics_10fold['all_arr_precision'] = all_arr_precision\n",
        "df_allmetrics_10fold['all_arr_recall'] = all_arr_recall\n",
        "df_allmetrics_10fold['all_thresholds'] = all_thresholds\n",
        "df_allmetrics_10fold['all_ave_precision'] = all_ave_precision\n",
        "\n",
        "df_allmetrics_10fold['all_cf_matrices'] = all_cf_matrices\n",
        "\n",
        "df_allmetrics_10fold.to_pickle(\"./bertclassify/bert_all_metrics_10fold.pkl\")\n",
        "# df_allmetrics_10fold\n",
        "\n",
        "\n",
        "# # Create Dataframe to save all wrongly predicted index\n",
        "df_indices = pd.DataFrame(all_wrong)\n",
        "df_indices.columns = ['indices']\n",
        "df_indices.to_pickle(\"./bertclassify/bert_indices_wrong.pkl\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Restoring parameters from ./bertclassify/bert_training_10fold/bert_model.ckpt\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\rTraining Minibatch Loop:   0%|          | 0/529 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Training for Fold 1 ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Training Minibatch Loop: 100%|██████████| 529/529 [06:59<00:00,  1.26it/s, accuracy=0.925, cost=0.39]\n",
            "Validation Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.44it/s, accuracy=0.875, cost=0.43]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 436.6938729286194\n",
            "Epoch: 1, train_loss: 0.463205, train_acc: 0.841245, val_loss: 0.403713, val_acc: 0.904396\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Training Minibatch Loop: 100%|██████████| 529/529 [06:58<00:00,  1.26it/s, accuracy=0.95, cost=0.37]\n",
            "Validation Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.46it/s, accuracy=0.854, cost=0.447]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 435.65886187553406\n",
            "Epoch: 2, train_loss: 0.391862, train_acc: 0.918561, val_loss: 0.394547, val_acc: 0.916225\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.47it/s, accuracy=0.854, cost=0.459]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 452.6684718132019\n",
            "test_loss: 0.394228, test_acc: 0.915696\n",
            "\n",
            "Score for Fold 1: Loss of 0.3942277234489635; Accuracy of 91.5695621805676%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing for Metrics Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.45it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Restoring parameters from ./bertclassify/bert_training_10fold/bert_model.ckpt\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Training for Fold 2 ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Training Minibatch Loop: 100%|██████████| 529/529 [06:58<00:00,  1.26it/s, accuracy=0.9, cost=0.41]\n",
            "Validation Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.46it/s, accuracy=0.938, cost=0.375]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 435.6978163719177\n",
            "Epoch: 1, train_loss: 0.467966, train_acc: 0.836295, val_loss: 0.416180, val_acc: 0.893008\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 435.6128942966461\n",
            "Epoch: 2, train_loss: 0.396214, train_acc: 0.914012, val_loss: 0.403557, val_acc: 0.905720\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.47it/s, accuracy=0.917, cost=0.392]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 452.6235017776489\n",
            "test_loss: 0.403461, test_acc: 0.906427\n",
            "\n",
            "Score for Fold 2: Loss of 0.40346115271923905; Accuracy of 90.64265540090658%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing for Metrics Minibatch Loop: 100%|██████████| 59/59 [00:16<00:00,  3.49it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Restoring parameters from ./bertclassify/bert_training_10fold/bert_model.ckpt\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Training for Fold 3 ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Training Minibatch Loop: 100%|██████████| 529/529 [06:58<00:00,  1.26it/s, accuracy=0.878, cost=0.433]\n",
            "Validation Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.46it/s, accuracy=0.851, cost=0.469]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 436.003121137619\n",
            "Epoch: 1, train_loss: 0.465702, train_acc: 0.838528, val_loss: 0.418097, val_acc: 0.891808\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 435.9013509750366\n",
            "Epoch: 2, train_loss: 0.390327, train_acc: 0.919922, val_loss: 0.410033, val_acc: 0.897826\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.46it/s, accuracy=0.894, cost=0.428]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 452.9489233493805\n",
            "test_loss: 0.408600, test_acc: 0.900210\n",
            "\n",
            "Score for Fold 3: Loss of 0.40860006415237815; Accuracy of 90.02096107450582%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing for Metrics Minibatch Loop: 100%|██████████| 59/59 [00:16<00:00,  3.48it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Restoring parameters from ./bertclassify/bert_training_10fold/bert_model.ckpt\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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        },
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Training for Fold 4 ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Training Minibatch Loop: 100%|██████████| 529/529 [06:59<00:00,  1.26it/s, accuracy=0.927, cost=0.381]\n",
            "Validation Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.46it/s, accuracy=0.851, cost=0.466]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 436.3715875148773\n",
            "Epoch: 1, train_loss: 0.465235, train_acc: 0.840067, val_loss: 0.404173, val_acc: 0.904520\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 436.52965474128723\n",
            "Epoch: 2, train_loss: 0.393745, train_acc: 0.916466, val_loss: 0.395760, val_acc: 0.912296\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.46it/s, accuracy=0.915, cost=0.412]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 453.57691526412964\n",
            "test_loss: 0.396468, test_acc: 0.911428\n",
            "\n",
            "Score for Fold 4: Loss of 0.3964683650913885; Accuracy of 91.14282818163856%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing for Metrics Minibatch Loop: 100%|██████████| 59/59 [00:16<00:00,  3.47it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Restoring parameters from ./bertclassify/bert_training_10fold/bert_model.ckpt\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Training for Fold 5 ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Training Minibatch Loop: 100%|██████████| 529/529 [06:59<00:00,  1.26it/s, accuracy=0.951, cost=0.377]\n",
            "Validation Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.46it/s, accuracy=0.915, cost=0.419]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 436.76409244537354\n",
            "Epoch: 1, train_loss: 0.462256, train_acc: 0.841502, val_loss: 0.408381, val_acc: 0.901894\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
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            "Validation Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.46it/s, accuracy=0.915, cost=0.411]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 436.7942838668823\n",
            "Epoch: 2, train_loss: 0.390225, train_acc: 0.920289, val_loss: 0.399697, val_acc: 0.910104\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
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            "Testing Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.46it/s, accuracy=0.894, cost=0.432]\n",
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          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 453.8731870651245\n",
            "test_loss: 0.401126, test_acc: 0.908419\n",
            "\n",
            "Score for Fold 5: Loss of 0.40112622208514453; Accuracy of 90.841935650777%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Restoring parameters from ./bertclassify/bert_training_10fold/bert_model.ckpt\n"
          ],
          "name": "stdout"
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        },
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Training for Fold 6 ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "Training Minibatch Loop: 100%|██████████| 529/529 [06:59<00:00,  1.26it/s, accuracy=0.927, cost=0.382]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 436.95480608940125\n",
            "Epoch: 1, train_loss: 0.463011, train_acc: 0.842164, val_loss: 0.411395, val_acc: 0.899607\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 436.834335565567\n",
            "Epoch: 2, train_loss: 0.391411, train_acc: 0.918609, val_loss: 0.401086, val_acc: 0.907816\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 453.91711950302124\n",
            "test_loss: 0.401947, test_acc: 0.909141\n",
            "\n",
            "Score for Fold 6: Loss of 0.40194681589886294; Accuracy of 90.91405969555095%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing for Metrics Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.47it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Restoring parameters from ./bertclassify/bert_training_10fold/bert_model.ckpt\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Training for Fold 7 ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "Training Minibatch Loop: 100%|██████████| 529/529 [06:59<00:00,  1.26it/s, accuracy=0.902, cost=0.398]\n",
            "Validation Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.46it/s, accuracy=0.894, cost=0.427]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 436.91290760040283\n",
            "Epoch: 1, train_loss: 0.463363, train_acc: 0.839873, val_loss: 0.408272, val_acc: 0.900474\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "Validation Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.45it/s, accuracy=0.915, cost=0.403]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 436.92918133735657\n",
            "Epoch: 2, train_loss: 0.393840, train_acc: 0.917247, val_loss: 0.394744, val_acc: 0.915136\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.46it/s, accuracy=0.894, cost=0.431]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 453.97672939300537\n",
            "test_loss: 0.394675, test_acc: 0.916894\n",
            "\n",
            "Score for Fold 7: Loss of 0.39467498104451065; Accuracy of 91.68939327789565%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing for Metrics Minibatch Loop: 100%|██████████| 59/59 [00:16<00:00,  3.47it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Restoring parameters from ./bertclassify/bert_training_10fold/bert_model.ckpt\n"
          ],
          "name": "stdout"
        },
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        },
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Training for Fold 8 ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "Training Minibatch Loop: 100%|██████████| 529/529 [06:59<00:00,  1.26it/s, accuracy=0.927, cost=0.374]\n",
            "Validation Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.46it/s, accuracy=0.83, cost=0.482]\n",
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          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 436.92898082733154\n",
            "Epoch: 1, train_loss: 0.466179, train_acc: 0.837882, val_loss: 0.413865, val_acc: 0.896479\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 436.62623834609985\n",
            "Epoch: 2, train_loss: 0.391455, train_acc: 0.918993, val_loss: 0.400873, val_acc: 0.910611\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.45it/s, accuracy=0.915, cost=0.418]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 453.7255074977875\n",
            "test_loss: 0.399664, test_acc: 0.912488\n",
            "\n",
            "Score for Fold 8: Loss of 0.39966440907979417; Accuracy of 91.24876038502838%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing for Metrics Minibatch Loop: 100%|██████████| 59/59 [00:16<00:00,  3.48it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Restoring parameters from ./bertclassify/bert_training_10fold/bert_model.ckpt\n"
          ],
          "name": "stdout"
        },
        {
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        },
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Training for Fold 9 ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Training Minibatch Loop: 100%|██████████| 529/529 [06:59<00:00,  1.26it/s, accuracy=0.927, cost=0.386]\n",
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          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 436.76094794273376\n",
            "Epoch: 1, train_loss: 0.464383, train_acc: 0.840215, val_loss: 0.419522, val_acc: 0.884511\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 437.0155107975006\n",
            "Epoch: 2, train_loss: 0.392395, train_acc: 0.918133, val_loss: 0.405518, val_acc: 0.904543\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.46it/s, accuracy=0.957, cost=0.366]\n",
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          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 454.070387840271\n",
            "test_loss: 0.405116, test_acc: 0.904999\n",
            "\n",
            "Score for Fold 9: Loss of 0.4051162706593336; Accuracy of 90.49990985353115%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing for Metrics Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.47it/s]"
          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Restoring parameters from ./bertclassify/bert_training_10fold/bert_model.ckpt\n"
          ],
          "name": "stdout"
        },
        {
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        },
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Training for Fold 10 ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Training Minibatch Loop: 100%|██████████| 529/529 [06:59<00:00,  1.26it/s, accuracy=0.878, cost=0.434]\n",
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          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 437.09222626686096\n",
            "Epoch: 1, train_loss: 0.461168, train_acc: 0.843076, val_loss: 0.407774, val_acc: 0.902520\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Training Minibatch Loop: 100%|██████████| 529/529 [06:59<00:00,  1.26it/s, accuracy=0.902, cost=0.417]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 437.10631132125854\n",
            "Epoch: 2, train_loss: 0.390532, train_acc: 0.920214, val_loss: 0.393645, val_acc: 0.916556\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.45it/s, accuracy=0.957, cost=0.363]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time Taken: 454.2158191204071\n",
            "test_loss: 0.392854, test_acc: 0.917181\n",
            "\n",
            "Score for Fold 10: Loss of 0.39285371313660833; Accuracy of 91.7181301925142%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing for Metrics Minibatch Loop: 100%|██████████| 59/59 [00:17<00:00,  3.47it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Score per fold\n",
            "------------------------------------------------------------------------\n",
            "> Fold 1 - Loss: 0.3942277234489635 - Accuracy: 91.5695621805676%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 2 - Loss: 0.40346115271923905 - Accuracy: 90.64265540090658%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 3 - Loss: 0.40860006415237815 - Accuracy: 90.02096107450582%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 4 - Loss: 0.3964683650913885 - Accuracy: 91.14282818163856%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 5 - Loss: 0.40112622208514453 - Accuracy: 90.841935650777%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 6 - Loss: 0.40194681589886294 - Accuracy: 90.91405969555095%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 7 - Loss: 0.39467498104451065 - Accuracy: 91.68939327789565%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 8 - Loss: 0.39966440907979417 - Accuracy: 91.24876038502838%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 9 - Loss: 0.4051162706593336 - Accuracy: 90.49990985353115%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 10 - Loss: 0.39285371313660833 - Accuracy: 91.7181301925142%\n",
            "------------------------------------------------------------------------\n",
            "Average scores for all folds:\n",
            "> Accuracy: 91.02881958929159 (+- 0.525168895028518)\n",
            "> Loss: 0.39981397173162236\n",
            "------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0R2ySYs2DKqp"
      },
      "source": [
        "#TODO: Determine history for each fold\n",
        "\n",
        "# history_dict = hist.history\n",
        "# history_dict = results_history[0]\n",
        "# # history_dict = hist\n",
        "# history_dict.keys()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DvbznpxfRIRl"
      },
      "source": [
        "Plot Loss and Accuracy for each Fold between Training and Validation for each epoch.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tyOe8kQrRHq9",
        "outputId": "e9a867f3-1a86-4b3f-e360-aaeb768425b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "acc = []\n",
        "val_acc = []\n",
        "loss = []\n",
        "val_loss = []\n",
        "\n",
        "for i in range(len(results_history)):\n",
        "  tmp_acc, tmp_val_acc, tmp_loss, tmp_val_loss = [], [], [], []\n",
        "  for j in range(len(results_history[i])):\n",
        "    tmp_acc.append(np.asarray(results_history[i][j][0]))\n",
        "    tmp_val_acc.append(np.asarray(results_history[i][j][1]))\n",
        "    tmp_loss.append(np.asarray(results_history[i][j][2]))\n",
        "    tmp_val_loss.append(np.asarray(results_history[i][j][3]))\n",
        "  acc.append(tmp_acc)\n",
        "  val_acc.append(tmp_val_acc)\n",
        "  loss.append(tmp_loss)\n",
        "  val_loss.append(tmp_val_loss)\n",
        "\n",
        "  #[0] list_train_acc\n",
        "  #[1] list_val_acc\n",
        "  #[2] list_train_loss\n",
        "  #[3] list_val_loss\n",
        "\n",
        "\n",
        "# # Loss Plot\n",
        "epochs = range(1, len(acc[0]) + 1)\n",
        "fig_row = -(-(len(acc)) // 2)\n",
        "fig_col = 2\n",
        "plt.rcParams['figure.figsize'] = [12, 18]\n",
        "for i in range(len(loss)):\n",
        "  plt.subplot(fig_row, fig_col, i+1)\n",
        "  plt.plot(epochs, loss[i], 'r', label='Training loss')\n",
        "  plt.plot(epochs, val_loss[i], 'r--', label='Validation loss')\n",
        "  plt.title('FT-BERT Training and Validation Loss, Fold {}'.format(i+1))\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.ylabel('Loss')\n",
        "  plt.legend()\n",
        "  plt.grid(True)\n",
        "  # plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
        "  # plt.minorticks_on()\n",
        "plt.tight_layout(pad=3.0)\n",
        "plt.savefig(\"./bertclassify/bert_loss_10fold.png\", format='png', dpi=150, bbox_inches='tight')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# # Accuracy Plot\n",
        "plt.clf()   # clear figure\n",
        "for i in range(len(acc)):\n",
        "  plt.subplot(fig_row, fig_col, i+1)\n",
        "  plt.plot(epochs, acc[i], 'b', label='Training acc')\n",
        "  plt.plot(epochs, val_acc[i], 'b--', label='Validation acc')\n",
        "  plt.title('FT-BERT Training and Validation Accuracy, Fold {}'.format(i+1))\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.legend()\n",
        "  plt.grid(True)\n",
        "plt.tight_layout(pad=3.0)\n",
        "plt.savefig(\"./bertclassify/bert_accuracy_10fold.png\", format='png', dpi=150, bbox_inches='tight')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x1296 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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e9PmIVL6qjEHhbJHBOTfTOXewc+5A59xdgedGOefeDNyf7pxrF9jnr8657YHnX3DO1XHOpYTc5oazrCIi0S47Gx56CNq2hZtvhq5d4ZtvYMYMOOCArZEunlSxtWvXkpKSQkpKCs2bN6dFixYFj3Nyyv6nYvbs2Vx77bW7fI0ePXpUVnFFJMpFIgZFerC/iIjsoZwcePppuOsuWLECTjwRxo0D/Y8Z25o0acLcuf4a4OjRo0lISODGG28s2J6Xl0ft2iX/G5CamkpqauouX+Pzzz+vnMKKSNSKZAwKa4uMiIiET26uDx4HHwxXXgmtW8OHH8IHHxQGkJwc38wvAjBkyBCGDRtGt27dGDFiBF9//TXdu3enU6dO9OjRg0WLFgF+Yow+ffoAPgm65JJLSEtLo23btjzyyCMF50tISCjYPy0tjQEDBtChQwcGDRqEcw6AmTNn0qFDB7p06cK1115bcN5QGRkZHHfccXTu3JnOnTsXSZDuvfdejjjiCDp27MjIkSMBWLJkCSeffDIdO3akc+fOLF2qFRpEqlp5YlC4qUVGRCRKOAdmkJ/vr3xNnAh//gktWkD//nDUUdCzp9/3xBPh229h0yb429+gb9/Ilj3WXXcdzC2jg3R+/l7Exe3eOVNSfDeO3ZWZmcnnn39OXFwcmzZt4pNPPqF27dq8//773Hrrrbz22ms7HbNw4UI++ugjsrKyaN++PVdcccVOUxbPmTOH+fPns99++3HMMcfw2WefkZqaytChQ/n4449p06YNAwcOLLFMycnJvPfee8THx7N48WIGDhzI7NmzmTVrFm+88QZfffUV9evXZ926dQAMGjSIkSNH0q9fP7Kzs9mxY6cVGkQkTPLz4aWXYMwYWLrUx54nnoBTTvExqiopkRERiQDnYONGWLOm8LZtG5x7rt/+wAPw8cdFtzdrBnfeCaNHQ+iyGytWwDvv+JaXwAVrjjsOjjwSkpKge/cqr55UY+eeey5xgaxp48aNDB48mMWLF2Nm5ObmlnjMGWecQb169ahXrx7Jycn8+eeftGzZssg+Xbt2LXguJSWFjIwMEhISaNu2bcH0xgMHDmTSpEk7nT83N5err76auXPnEhcXx88//wzA+++/z8UXX0z9+vUBaNy4MVlZWaxYsYJ+/foBfi0YEQm/HTtg+vTCGJSSAm++CX36VH0CE6RERkRkDzkHW7f6ZGP//aFWLfjqK/jyy6KJyIYN8O67/g/+JZfAc88VPU9iYmEis2wZLF/uE5GOHf00lnPmwPnnw6GHwoQJfiaY5GS/z157FT3XmDFFH2stzMjaVctJVta2KltLpUGDBgX377jjDnr27Mnrr79ORkZGqWsN1atXr+B+XFwceXl5FdqnNA8++CDNmjVj3rx57NixQ8mJSDXinE9YRo2C77/3MWj6dOjXz8e7SFIiIyJSgk2bICOjaCKyejVcey3ssw9MmeJbTYLbgktRrFkDTZrAG2/A3//u/8g3aeKTjaQkP2alXj2fsBxxROHzTZv6/YIefdQHj7ff9sFj9mxo1w7+8Q+fzOxuNySRkmzcuJEWLVoA8FzxzLoStG/fnmXLlpGRkUHr1q155ZVXSi1Hy5YtqVWrFpMnTyY/Px+AXr16MXbsWAYNGlTQtaxx48a0bNmSGTNm0LdvX7Zv305+fn5Bq42IVI6SYtCLL1avGKRERkRqtPx8WL/eJyHff9+QI47wCcOPP8KzzxZNVNas8VeZOnWCV1+Fyy7b+XznnOMTmfr1oWVL37TetGlhQhK8KD1iBNx4IzRqVPIVq9NP97fSfPgh3H47fPGFH0D57LPwf/8HpUwyJVIhI0aMYPDgwYwfP54zzjij0s+/1157MXHiRHr37k2DBg046qijStzvyiuv5JxzzuH5558v2Begd+/ezJ07l9TUVOrWrcvpp5/O3XffzZQpUxg6dCijRo2iTp06TJs2jbZt21Z6+UViVbTEoGpWHBGR0jnnE5PatX2LSfExJGvWwMUXwzHHwOefw1lnwbp1/jivE61b++d//dUPTgxNQg4+GII9Wk46ySc1wW1JSdC4MQTHN59zjr+VplEFFzH+9FO/gFh6uk+U/vUvX6e6dSt2PhHwM4+VpHv37gXjUQDGjx8PQFpaWkE3s+LH/vjjjwX3N2/evNP+AI8++mjB/Z49e7Jw4UKcc1x11VUlTuvcrl07vv/++4LH9957b8H9kSNHFsxWFrr/hx9+WGKdRKTioi0GKZERkYgIHVcSemvfHlJTYe1aGDasaLeutWvh/vv9DFArVsCZZxaer04dn5ScfLJ/vN9+vvk7tOvWihXz6NGjIwCnnQaB/8FK1KaNv1WVr7/2wePdd/2g/ocfhssvL0ysRKLVk08+yeTJk8nJyaFTp04MHTo00kUSkWKiNQYpkRGRSrF9u080nPPTAYO/krNqlU9CggnJKafATTf5/QNLUBRx000+kalTB+bP90lIu3Z+5q2kJD/NI/gk46uvCltUEhKKzprSujU89ljRc6enrycpyd+P1Awrxc2Z4/sf/+c/vh733+/n41d3f6kphg8fzvDhwyNdDBEpQbTHICUyIlKqJUtg5cqiiUizZnDppX77GWf4KRjXrPGzagEMGADTpvn7t93mu3Y1alTYMhJMIOLj/cxb++xTtPtW8+Z++957w4IFpZctPh66dg1PvavC/Pl+KuXXXvPvz113wTXX+JnLREREwqmmxCAlMiI1XPH1SoKza4FvMZk9u2ii0rKlX5UX4IIL/KKKodLSChOZVq38uJHQRKRDh8J9Fy2Chg0Lx5UUd8MNlVbNqPHzz34O/qlTfSvSqFEwfHjFx9SIiIiUV02LQUpkRKJIcFxJcBmI77+HH34oOsZk61aYPNlvv+wyv1ZJ6HIOzZvDyy/7+x9+6AfFB5OQTp38GJWgCRP8scHtTZoUXa/k8cfLLm+wG5f4NWHGjoXnn/etSTff7Gc1C51yWUREJBxqagxSIiMSQTk5Ow92X7MGhgzx/VNfftlPeRjaYpKd7ZOVvfby24IL7QXXK2na1K++W6sW9OxZuGBi8JacXNgN7NVXyy5fKWvjyW747TcYPx6eecbPtnbddT6AJCdHumQiIlLT1fQYpERGpJLk5/vxIMHV3RMS/PiRGTOKJiJr1sBLL8FBB/muXX/7287nOvVUOPBAn7BkZRWuVxJMRoLTCd9wA1xxhX+upPVK/vKXksuqVd7Db+VKvyDmE0/4z2voULj1Vj+bmkhV6NmzJyNHjuTUU08teO6hhx5i0aJFPF5Kc2paWhoTJkwgNTWV008/nZdeeolGxfqcjB49moSEBG688cZSX3vGjBkcfPDBHHrooQCMGjWK448/npOD0wqKSFhVRQzKy/P/0wQn9WnUCLp08a83bFjh8337+kl8wkGJjEgZtmzx3beKt5gMGgRHHunnW7/0Uv/c+vWFCcZ77/lpgBcs8H84GjQo2ioSWLSaE0/0yUzotuAN/LmD41FK0rJleOsvu2/1arj3Xj9jWm6un3//9tvhgAMiXTKJNQMHDmTq1KlFEpmpU6dy3333lev4mTNnVvi1Z8yYQZ8+fQoSmbFjx1b4XCJSfnsSg5wrnJDn8899a04wGVm1Ctq29Ys9g++GHrIEFeCXPJg61Z8jPd2vPdO0aXhnQFMiIzVeSeuVBNcHWbXKD3QrnqjcfTdccokfrN6jR9Hz1a3rrzgceaSfcatTp52TkMMP9/ueeWZhN7CSHH544b4S3dat82OKHnkEtm3zKyCPGuVb1kQiYcCAAdx+++3k5ORQt25dMjIy+P333znuuOO44oor+Oabb9i2bRsDBgxgzJgxOx3funVrZs+eTVJSEnfddReTJ08mOTmZ/fffny5dugB+jZhJkyaRk5PDQQcdxJQpU5g7dy5vvvkm//vf/xg/fjyvvfYa48aNo0+fPgwYMIAPPviAG2+8kby8PI466ijuu+8+EhMTad26NYMHD+att94iNzeXadOm0SF09hAgIyODCy+8kC1btgB+4c0egT/S9957Ly+88AK1atXitNNO45577mHJkiUMGzaM1atXExcXx7Rp0zhQX0qpgUqKQbfd5v9PWbXKJxY5OX4JBPDTLH/9td8WTFbatYMvvvDbr7oK5s719838/zYh10S48EJ/UTY52ScrTZsWTZYWLSpavnD1BFEiI1Fn+3b/ZUxM9GNBpk/fORHp1ctfhdi40Q9uD52pC/yUg2lpPsl5/fXCBKR9e78qfNu2fr927WDmzKJJSuh6JYcd5q8+lKa6roQrlWfjRj9O6YEHfDfA88/3v1/F/v8SKXHM2Xnn+TUbtm4tusBr0JAh/rZmjZ/aPNSu/jFo3LgxXbt2ZdasWZx99tlMnTqV8847DzPjrrvuonHjxuTn53PSSSfx/fffc+SRR5Z4nm+//ZapU6cyd+5c8vLy6Ny5c0Ei079/fy677DIAbr/9dp5++mmuueYazjrrrILEJVR2djZDhgzhgw8+4OCDD+aiiy7iqaeeYuTIkQAkJSXx3XffMXHiRCZMmMBTTz1V5Pjk5GTee+894uPjWbx4MQMHDmT27NnMmjWLN954g6+++or69euzbt06AAYNGsTIkSPp168f2dnZ7Nixo+w3TaQaC45/Bd/jY8EC+OUXvwbMZ5/5FpgLLvAx6J57fBwK9hQBv8ZbZqa/P3cu/PijT0QOO8z/DJ3s57nn/Jia5GQ/O2lcXNGy3H57WKtabkpkJOKCq7aHJiJNm/o+leD/MVy+vHBbVhZcdJGfmcsMBg8uTFQaNfLHHnGEf5yY6OdFL95i0rYtLFzo10T588/Sy5aY6FeAFylu82b45z/9Va3166FfPxgzpvB3T6Q6CHYvCyYyTz/9NACvvvoqkyZNIi8vj5UrV7JgwYJSE5lPPvmEfv36UT/QP+Sss84q2Pbjjz9y++23s2HDBjZv3lykG1tJFi1aRJs2bTj44IMBGDx4MA8//HDB9v79+wPQpUsX/v3vf+90fG5uLldffTVz584lLi6OnwN9W95//30uvvjigjI2btyYrKwsVqxYQb9+/QCIr+5LlEvM2bEDNmwo2n2rTx+oV89fZH3llaLb1q71Fz3q1vUtL088UXiuOnV8D4CXXvL/G/Xq5ZdIaNq0sNWkWbPC/V98seyydewYnjpXNiUyUmlC1yvJzi7sMvX8837hpeJdu154wW8/4YSdFz48+eTCRGb7dn814OCDCxORzp39NjO/Ku0++/h9iq9XUqsWlNYdfOHCyqm3xJZt2/y00/fc44PLGWf4KS2Dv5MipSmrBaV+/bK3JyVVrGvG2WefzfDhw/nuu+/YunUrXbp0Yfny5UyYMIFvvvmGffbZhyFDhpBdvNm6nIYMGcKMGTPo2LEjzz33HOl72H+kXr16AMTFxZEXOm98wIMPPkizZs2YN28eO3bsUHIi1Ypz/larFvz+O3z1VdFEZP78Q2jb1icYjz/uL7QGx8wGLV8OrVv78SnffeeTkIMO8t3cmzb1F3MnTy5cePqkk/xilt26FT3PoEFVUuWIUyIju7Rkib+FJiK5uX4wGfhuEf/+t79SEIw7Bx7ojwGYMgU+/rjk1dvB/xOYm1t0e+i85jNmlF0+deGRqrB9Ozz5pB8/tXKlT7bHjYOjj450yURKl5CQQM+ePbnkkksYOHAgAJs2baJBgwY0bNiQP//8k1mzZpFWxlzrxx9/PEOGDOGWW24hLy+Pt956i6FDhwKQlZXFvvvuS25uLi+++CItWrQAIDExkazgPO8h2rdvT0ZGBkuWLCkYU3PMMceUuz4bN26kZcuW1KpVi8mTJ5Mf+C+wV69ejB07lkGDBhV0LWvcuDEtW7ZkxowZ9O3bl+3bt5Ofn1/QaiOyK87Bpk1Fk5EuXfzMpD/84C9ohW5bvRrefdcvffDJJ76bV9Dee/vvxfr1PpHp1MlPgxzaYpKcDPvu6/e/9lp/CwrGoCOOUAwKpUQmRmzf7hON0G5c55zj+z+mpzfliSeKJirr1/svb7BF48knC89Vq5afLSuYyBxyCJx9dtFEJPhFBN93s27dwnElxZ1zTvjqLbKncnN9X+Fx4/wVsuOP9+Oijj8+0iUTKZ+BAwfSr18/pgYG9HXs2Pn6NEAAACAASURBVJFOnTrRoUMH9t9//10mEp07d+b888+nY8eOJCcnc9RRRxVsGzduHN26daNp06Z069atIHm54IILuOyyy3jkkUeYPn16wf7x8fE8++yznHvuuQWD/S8ta2rGYq688krOOeccnn/+eXr37k2DwOrAvXv3Zu7cuaSmplK3bl1OP/107r77bqZMmcLQoUMZNWoUderUYdq0abQNDoKUmOOc784VF+dnJf3gg50TkQsv9IPa58zxSUJOTtFzTJniB9Jv3QpffumTj1at/PTCTZsWTm/cq5dvUQkOhK9XD9LTv6ZjxzTAn7s8SYhiUNnMhY4CimKpqalu9uzZFT4+PT29zCtS1UlwvZKGDX2CsHix73JQfMD7k0/6L9SECXDTTTufZ+VK3zIybNhSPvzwwJ3GkYwb5794P/3kE5vg8yWtVxKNoukzr2yxWvfdqXdenu9DPHYsLFvmA864cb4Zv7SkvDrbk8/czL51zoVpFYCaoaQY9NNPP3HIIYeU6/isrCwSExPDUbRqL9J1353PqTLF6t9hqLy6O+cTktBEZL/9fFffrVuLrmUS3Of22/1txYqiSxg0aOCTktGj/TjcVav8JC7FW0wOPND//xXueisGFSorBqlFphrYvt1n2cUTkb59fb/ITz6BkSOLtpY452eo6NHDT5V3+eX+XA0a+C9bUpL/coOfhWv8+KJJStOmhd23LrjgN/71r9Kno4zA33eRiNmxww+wHDPGTx/ZqZNvVTz99OgMHrHMzHoDDwNxwFPOuXuKbT8AeAZoCqwD/s85lxnYNhgIzssz3jk3ucoKLhLD8vJ8bxHw3bR+/71oMpKS4lend85fWN20qejxQ4f69dnq1fNrvQW7sx95pP/fp3t3v1+zZvDNN4UtJsV7HCYn+65jVU0xaPcokalExdcrad7cT3X3559+dqPiicrYsdC/v5/Hu6QmwgMO8IlM3boQH190ZfekpML5uvv29YlQkyYlr1fSvXvhF1dEShacivvOO/2UlIcf7sd+9e2r4BGNzCwOeAzoBWQC35jZm8650KlFJgDPO+cmm9mJwN+BC82sMXAnkAo44NvAseurthYi0W/btqItJrVq+SQD/ILR8+YVbTE59lh45x2//fLL/fTC4P8PSk72k/uA/7t8/fX+/57QFpNWrfz2uDjfklGa2rXDt9p8RSgGVYwSmTIE5+vOzy+561bPnj4RCQ7+Cs7WFXTvvX4F1C1b4O9/94lG6HolwabJQw7xM3sV79qVkOC3d+vm+3GWZu+9/U1Edp9z8N//+sUr58zx382XX/bre9SELpQxrCuwxDm3DMDMpgJnA6GJzKHA9YH7HwHBqUVOBd5zzq0LHPse0Bt4uQrKLVKt5eQUrpE2e7bvfh7aYlK7duG0wKedBm+/XfT4ww6DRx/19xctgj/+8ElIhw5Fl08A3xJRv75PUBo02Pkf+jvvDE8dq5Ji0J5RIhNw333tue++wiRl9Wo/mOvxx/0vWa9eRRcV2mcf/8Xq398nEb167ZyIBL+Mbdr4wVql/UImJfnBZSJSdZyD99+HO+7wU2S2beuntPzLXwq7NUhUawH8FvI4Eyg2QSnzgP747mf9gEQza1LKsS0qUgjnHKbLqdVWTRknvCdycoq2mKxe7f8OmvlB5v/+d9EWk/z8wq7rDz1UuB5JnTqFUwUHXXCB73ES2mLSvDlkZPjtr71WdtmCyzjURIpBlUNvVcDy5Q1ISChsLUlK8s2b4H+hPv7YJy9JSTuvVxIfD888U/q5zdQsKFKd/O9/Pnh88omfRvPJJ/3CqsXXIZIa70bgUTMbAnwMrADyyzwihJldDlwO0KxZs53WUElISCAzM5OGDRvuMpnJz88vcbriWBCpujvn2LhxI1u2bNnj9W8qYvPmzWF53bw8w8wRFwe//x7PggV7s2FDHTZsqFvwc8SIhey9dx7PP38Azz7bZqdzNGz4KQkJeXz2WSsWLEimUaMcWrfOJSUll0aNcvjww1+pVQv69Imnd2+jUaMcGjTIL/hfJ1itAw4o7AYPvtdKRkb46l7dBes9d25Dnn22Dd9/34jk5GxuvPEXTj31D2rXdnz6aaRLGR7h+syVyAQ8/vh3Zc6mEExqRCR6zZ+/N+PH+66a++7ruzf89a+F/bWlRlkB7B/yuGXguQLOud/xLTKYWQJwjnNug5mtANKKHZte/AWcc5OASeBnLSseQ3Jzc8nMzGTFihXFD91JdnZ2zC7uGMm6x8fH07FjR+pE4CpGeWdxyssr7CkSbDVJS/MtG59/7mcmDd22fr2f9rdTJ9/F6667/Hni4goHth955LG0bu17irRtW7TFpGlTOPDAY4mL869Tsj2bwjpWZ2x77LHveP31zsViUDz16rUH2ke6eGEVrs9ciYyI1Hjffuv7H8+c2ZmmTf2UmsOGlTw5htQY3wDtzKwNPoG5APhL6A5mlgSsc87tAG7Bz2AG8A5wt5kFhhVzSmD7bqlTpw5t2ux8tbsk6enpdOrUaXdfokaItbrn5xeu5r5+Pbz3nk9CQtczue46OO44+PBDP91ucf/5D5xxhp9gaPFin3x06lQ0GQEYMABOOME/V9LSCccfr/VIqoJiUPgokRGRGuuHH3zwmDHDdw297LJlPPBA24KJNKTmcs7lmdnV+KQkDnjGOTffzMYCs51zb+JbXf5uZg7fteyqwLHrzGwcPhkCGBsc+C9SXHBtt9BWkXbt/Eyjq1fDlVcW3bZ2LTzyiB//8fvvcP75/jxmflKgpk19ggN+vMno0YUtKcEkpXVrv/3kk/3fudI0aVK41IJUPcWg8FMiIyI1zsKFPvi/+iokJvr5+K+7Dr777lcSErSqd6xwzs0EZhZ7blTI/enA9OLHBbY9Q2ELjcSQHTv8+m7Bq+WvvbZzi0nPnnDFFb5FJDHRHxNq5EifyNSt66fSTU6GQw8tTEaOOspPS3zQQX57cG23uLii52nVqmbMzBVrFIOqjhIZEakxli71AePFF/0/IbfcAjfc4CfoEJHYtGMHbNhQtFWkQQM49VS//Yor4OefC7etWeO7ZE2d6rdfeils3Ojv77NP0SmC69f3V9wbNy7aYhJcMb5hQz89cUnS0/34vMMOC1vVpYopBlW9sCYyWlVZRKrCL7/A+PHw7LP+Cuj11/s1nIL9xEWk5nDOT/8b7J7zv//59UiCicqqVb51I7hWSdeufoxCqGOPLUxkfv3Vt8AcdJBfPDo5GTp3Ltz3yy99QpKUVPLMhmoxEcWgyAlbIqNVlUUk3FasgLvv9tMnm/m+6Lfc4meDEZHo4Jxv8QhtMdm4ES66yG9/6CGYObPoOifJyZCZ6bfff79fUBD8um5Nm/puXUFXXeXPF9pi0rx54fbgsaXp0KHy6io1i2JQ5IWzRUarKotIWKxaBffcAxMn+oG2l14Kt93m14QRkchyDjZv9t23atWC+fN9q0ZoorJqFbz5pu9a9eijB/Hvfxc9hxkMGuTHjKxdC5s2+fEiXbr4ZGS//Qr3nTjRv07TpiVPpX7xxeGtr8QexaDqI5yJTNhXVd7VYmS7I1YXZ4LYrXus1huit+4bN9bmlVda8frrLcjJqcUpp/zBRRf9wr77ZrN0qe+fXJZorXdliOW6y54JduUqnoicfbbvwvX22/Dww0W3bd8Oy5f72bXeestfpQaf3ARbRrZs8YlHjx5r6N695U5rmQSnCh43zt9K06pV2N8CEcAn1fffD//8p1/cc/Bgv7hyOWdZlzCI9GD/PVpVeVeLke2OWF2cCWK37rFab4i+um/YAA8+6G+bN8PAgb5f+sEH7wuUvw0/2updmWK57rKzrVt9ohAfD3/84ZOR0DEmq1fD2LGQmgrTp8N55+18jvbt4Zhj/D90a9b47lpHHFGYiATHsPz1r/4727SpHxxfXJcuG8pYeFEk8kqPQZEumYQzkQn7qsoiUrNlZfn1FiZM8IFkwAA/paVm+REpatu2nVtMOnf2icXy5XDNNUW3bd0KU6bA//0fLFlS2P2qXj2fiCQn+33Ajze5556iq7+Hdu/q29ffSpOU5G8i0UYxqPoLZyIT8VWVRSQ6bd0Kjz0G993nr/Seeaa/Ohw6gFekJsvOhtxcvwZFbq6fCrh4otK/v09AVqwonO431D33+EQmLs4vvJic7FtRgglJ8PvUpYvvlhlsRTErep527eDmm8NfZ5HqQjEoeoQtkdGqyiKyu7KzYdIkPwvMn3/66VHHjvXTp4rUZKNHH8r27YVJSlYWDB0K//qX7wI2eLAfq1KnTmGrSLDFpGlTuOsudhpjEmwxadUKvvuu9Nfeay9oqzX6RBSDolBYx8hoVWURKY+cHHjmGf/PWGYmpKX5fvnHHhvpkolUjaysOjRu7AcNBxOR4D9PcXGweLEfWN+w4c4tJnXrwq23Vn2ZRWoKxaDoFenB/iISw/LyfD/9sWMhIwN69IDJk+HEEyNdMpGq9Y9/zCtzMoYDD6y6sojECsWg6Fcr0gUQkdiTnw8vvgiHHgqXXOIHAs+aBZ9+qgAiIiLhpRhUcyiREZEqs2OHb64/8kg/W9Jee8GMGfD119C7985dZkRERCqLYlDNo0RGRMLOOb+Kd+fOcO65/vGrr8KcOX5RPQUPEREJF8WgmkuJjIiEjXN+ob1u3Xyw2LLF90f+4QcfTGrpL5CIiISJYlDNp49QRMLio4/guOPgtNP8dLJPPw0//eSb8+PiIl06ERGpyRSDYoMSGRGpVJ995gdLnniinwXm8cfh55/9gMramidRRETCSDEotiiREZFK8c03/srXscfCggXw0EOwZAkMG+bXuRAREQkXxaDYpERGRPbIvHm+73HXrj6Q3HcfLF0Kf/sbxMdHunQiIlKTKQbFNjWyiUiFLFgAd97pp7Js2BDGjfOBIzEx0iUTEZGaTjFIQImMiOymxYthzBh46SVo0ABuvx2uvx722SfSJRMRkZpOMUhCKZERkXLJyICxY+H5531/45tu8rekpEiXTEREajrFICmJEhkRKVNmJtx1l5+6slYtuOYaGDkSmjWLdMlERKSmUwySsiiREZES/fEH/P3v8MQTsGMH/PWvcNtt0KJFpEsmIiI1nWKQlIcSGREpYs0aP+vLo49CTg4MGeL7ILduHemSiYhITacYJLtjl4mMmZ0J/Nc5t6MKyiMiEbJ+PfzjH/Dww7Bli1/9eNQoOOigSJdMYplikEhsUAySiijPOjLnA4vN7D4z6xDuAolI1dq0yU9b2aaN74d8+ukwf74fUKkAItWAYpBIDaYYJHtil4mMc+7/gE7AUuA5M/vCzC43M83ULRLFtmyBe+/1wWPUKEhLg7lz4ZVX4JBDIl06EU8xSKRmUgySylCeFhmcc5uA6cBUYF+gH/CdmV0TxrKJSBhs2wbTprWkbVs/80u3bn415BkzoGPHSJdOZGeKQSI1h2KQVKZdJjJmdpaZvQ6kA3WArs6504COwA3hLZ6IVJbt22HiRN9UP3HiQRxxBHz2GcycCampkS6dSMkUg0RqBsUgCYfyzFp2DvCgc+7j0Cedc1vN7NLwFEtEKktuLkye7Psg//orHHss3HTTXK67LiXSRRMpD8UgkSimGCThVJ6uZaOBr4MPzGwvM2sN4Jz7ICylEpE9lp8PU6b4vsaXXQbNm8M778DHH0NKyoZIF0+kvEajGCQSdRSDpCqUJ5GZBoROe5kfeE5EqqEdO/xgycMPh4sugsREeOst+PJLOOUUMIt0CUV2S4VjkJn1NrNFZrbEzEaWsL2VmX1kZnPM7HszOz3wfB0zm2xmP5jZT2Z2S6XURCQGKAZJVSpPIlPbOZcTfBC4Xzd8RRKRinDOD5ZMSYELLoC4OJg+Hb79Fvr0UfCQqFWhGGRmccBjwGnAocBAMzu02G63A6865zoBFwATA8+fC9Rzzh0BdAGGBluBRKRkikESCeVJZFab2VnBB2Z2NrAmfEUSkd3hnB8sedRR0K8fZGfDiy/CvHlwzjlQq1xzE4pUWxWNQV2BJc65ZYHkZypwdrF9HLB34H5D4PeQ5xuYWW1gLyAH2FTxKojUXIpBEknlGew/DHjRzB4FDPgNuCispRKRXXIOPvgA7rjDN9m3aQPPPutXQ65dnm+2SHSoaAxqEdg3KBPoVmyf0cC7gWmcGwAnB56fjk96VgL1geHOuXUVrYBITaQYJNXBLn/VnHNLgaPNLCHweHPYSyUiZfrkEx88/vc/aNkSnngChgyBuur0KTVMmGPQQOA559w/zKw7MMXMDse35uQD+wH7AJ+Y2fvOuWWhB5vZ5cDlAM2aNSM9Pb3CBdm8efMeHR/NYrXu0Vzv779vyDPPtGHevEY0bZrN9df/Qu/ef1CnjuPTT3d9fDTXfU/Ear0hfHUvV85sZmcAhwHxFujk6JwbW+mlEZEyffWVT2Dee8/PAPPII342mPj4SJdMJHwqGINWAPuHPG4ZeC7UpUDvwPm+MLN4IAn4C/C2cy4XWGVmnwGpQJFExjk3CZgEkJqa6tLS0na7bkHp6ensyfHRLFbrHo31LjkGxRMf3x5oX+7zRGPdK0Os1hvCV/fyLIj5L+B84Bp8s/65wAGVXhIRKdWcOXDmmXD00f7+hAmwdClcc42SGKnZ9iAGfQO0M7M2ZlYXP5j/zWL7/AqcFHidQ4B4YHXg+RMDzzcAjgYW7nFlRKKUYpBUV+UZgtXDOXcRsN45NwboDhwc3mKJCMCPP/rBkp07+xWQ774bli+HG26A+vUjXTqRKlGhGOScywOuBt4BfsLPTjbfzMaGTB5wA3CZmc0DXgaGOOccfrazBDObj0+InnXOfV/pNROp5hSDpLorT9ey7MDPrWa2H7AW2Dd8RRKRRYtg9Gg/F39CAtx5JwwfDg0bRrpkIlWuwjHIOTcTmFnsuVEh9xcAx5Rw3GZ8y49ITFIMkmhRnkTmLTNrBNwPfIeflvLJsJZKJEYtWwZjx/rVkOPj4eab4cYboUmTSJdMJGIUg0SqiGKQRJsyExkzqwV84JzbALxmZv8B4p1zG8tzcjPrDTwMxAFPOefuKba9FTAZaBTYZ6RzbqaZ1QGeAjoHyvi8c+7vu1c1kejx668wfryfurJ2bbjuOh9AkpMjXTKRyNnTGCQi5aMYJNGqzETGObfDzB4DOgUebwe2l+fEIasq98LP3/+Nmb0ZaMoPCq6q/HhgxeWZQGtCVlU2s/rAAjN72TmXsVu1E6nmVq70fY4nTfKPhw2DW26B/faLbLlEqoM9iUEismuKQRLtyjPY/wMzO8eCc16Wn1ZVFinFqlV+sGTbtvCvf8HgwbB4MfzznwogIsVUNAaJSCkUg6SmKM8YmaHA9UCemWXjp790zrm9yz5MqyqLFLdunZ+28pFHYNs2uPBCGDXKBxMRKVFFY5CIFKMYJDXNLhMZ51xiGF9fqypXA7Fa96qs9+bNcUyfvj/Tp7dk69Y4evZcxeDBGbRqtY1ff/X9k6uSPvPYE611D3MMEokJGzfCgw/6W1YWnH++n5WsffnXsBSplnaZyJjZ8SU975z7eBeHalXlKBGrda+Kem/e7Jvq778f1q+H/v1hzBg4/PBmQLOwvnZZ9JnHnmit+x7EIJGYV3oMinTJRCpHebqW3RRyPx7fWvItgVWPy1CwqjI+gbkAn6CECq6q/FwpqypPCVlV+aFylFWkWti6FR5/HO69F1avhjPO8FNadu4c6ZKJRJ2KxiCRmKUYJLGiPF3Lzgx9bGb7U46kwjmXZ2bBVZXjgGeCqyoDs51zb+JXVX7SzIbjB/gPcc65wCw1zwZWVTa0qrJEie3b/ewvd98Nf/wBvXr54HH00ZEumUh0qmgMEolFikESa8rTIlNcJnBIeXbUqsoSK3Jz/fz748fDb7/B8cf7FZGPL7FTjIjsgXLHIJFYoRgksao8Y2T+iW8tAT9dcwp+dWWRmJeXBy+84K94LV/ur3o98wycdBJosliRPacYJFI6xSCJdeVpkZkdcj8PeNk591mYyiMSFXbs8Fe7Ro+Gn3/2/Y4ffRROO03BQ6SSKQaJFKMYJOKVJ5GZDmQ75/IBzCzOzOo757aGt2gi1c+OHfD663DnnTB/vp/55fXX4eyzFTxEwkQxSCRAMUikqFrl2OcDYK+Qx3sB74enOCLVk3Pwn/9Aly4wYIBvzp86FebNg759FUBEwkgxSGKeYpBIycqTyMQHBt8DBQPx64evSCLVh3Pw7rvQvTuceSZs2gSTJ8OPP/oFxWqV5xskIntCMUhilmKQSNnK8xXYYmYFM4+bWRdgW/iKJFI9/O9/fsaXU0+FlSvhySdh4UK46CKoXZH5/kSkIhSDJCYpBonsWnm+CtcB08zsd/yaLs2B88NaKpEI+uILuOMO+OAD2HdfeOwxuPRSqFcv0iUTiUmKQRJTFINEyq88C2J+Y2YdgPaBpxY553LDWyyRqvfttz54zJoFycnwwAMwbBjstdeujxWR8FAMklihGCSy+3bZtczMrgIaOOd+dM79CCSY2ZXhL5pI1fj+e+jXD1JT4auv4J57YNkyGD5cAUQk0hSDpKZTDBKpuPKMkbnMObch+MA5tx64LHxFEqkav/xSn/PPh44d4cMPYcwYv6DYzTdDgwaRLp2IBCgGSY2kGCSy58ozRibOzMw558DP4Q/UDW+xRMJnyRK/CvKLLx5F/fpw221www2wzz6RLpmIlEAxSGoUxSCRylOeROZt4BUzeyLweCgwK3xFEgmPX36BcePgueegbl0499zfePTRViQlRbpkIlIGxSCpERSDRCpfebqW3Qx8CAwL3H6g6OJkItXaihVw5ZXQrh1MmQJXXeX7Hw8btkwBRKT6UwySqKYYJBI+u0xknHM7gK+ADKArcCLwU3iLJbLn/vzTD5Y88EA///4ll/gm/YcfhubNI106ESkPxSCJVopBIuFXatcyMzsYGBi4rQFeAXDO9ayaoolUzNq1cN998OijsH27XzzsjjugTZtIl0xEyksxSKKVYpBI1SlrjMxC4BOgj3NuCYCZDa+SUolUwIYNft79Bx+ELVvgL3+BO+/0zfkiEnUUgySqKAaJVL2yEpn+wAXAR2b2NjAVv6qySLWSleWb6v/xDx9IBgyA0aPhsMMiXTIR2QOKQRIVFINEIqfUMTLOuRnOuQuADsBHwHVAspk9bmanVFUBRUqzdSvcf79vrr/jDjj+eJgzB6ZNUwARiXaKQVLdKQaJRF55Bvtvcc695Jw7E2gJzMHPIiMSEdnZ/upX27YwYgQcdRR8/TW88QakpES6dCJSmRSDpLpRDBKpPsqzjkyBwIrKkwI3kSqVkwPPPAPjx/vpLHv2hNdeg2OOiXTJRKQqKAZJJCkGiVQ/5VlHRiSi8vJ88GjfHq64Ag44AD74AD78UAFERETCSzFIpPpSIiPVVn4+vPACHHIIXHopJCXBrFnw6adw4omRLp2IiNRkikEi1Z8SGal2duzwgyWPOAIuvBAaNPB9j7/+Gnr3BtO8RSIiEiaKQSLRQ4mMVBvO+WDRqROcd55/bto0+O47OOssBQ8R2T1m1tvMFpnZEjMbWcL2Vmb2kZnNMbPvzez0kG1HmtkXZjbfzH4ws/iqLb1UNcUgkeijREYizjl4+23o2hX69vVTWr7wAvzwg5+Pv5Z+S0VkN5lZHPAYcBpwKDDQzA4tttvtwKvOuU74NWsmBo6tDbwADHPOHQakAblVVHSpYopBItFLX0+JqA8/hGOPhdNOg9Wr/YDKn36CQYMgLi7SpRORKNYVWOKcW+acy8EvqHl2sX0csHfgfkPg98D9U4DvnXPzAJxza51z+VVQZqliikEi0W23pl8WqSyffuoXEEtPhxYt4PHH4ZJLoG7dSJdMRGqIFsBvIY8zgW7F9hkNvGtm1wANgJMDzx8MODN7B2gKTHXO3Vf8BczscuBygGbNmpGenl7hwm7evHmPjo9mkaj7Dz805JlnWjN37j4kJW1n+PBfOO20ldSp4/j006opgz7z9EgXo8rFar0hfHVXIiNV6uuvYdQoeOcdaNYMHnoIhg6FePU+F5GqNxB4zjn3DzPrDkwxs8PxsfFY4ChgK/CBmX3rnPsg9GDnXMGaNqmpqS4tLa3CBUlPT2dPjo9mVVn3kmNQPeLjD8bnr1VHn3lapItR5WK13hC+uqtrmVSJuXP9YMlu3WD2bLjvPli2DP72NyUxIhIWK4D9Qx63DDwX6lLgVQDn3BdAPJCEb7352Dm3xjm3FZgJdA57iSVsFINEaiYlMhJW8+fDuef6WWA++cSviLx8Odx0E9SvH+nSiUgN9g3QzszamFld/GD+N4vt8ytwEoCZHYJPZFYD7wBHmFn9wMD/E4AFVVZyqTSKQSI1m7qWSVj8/DOMGQMvvwwJCX48zPXXQ6NGkS6ZiMQC51yemV2NT0rigGecc/PNbCww2zn3JnAD8KSZDccP/B/inHPAejN7AJ8MOWCmc+6/kamJVIRikEhsUCIjlWr5chg7FqZMgXr1YMQIf+WrSZNIl0xEYo1zbia+W1joc6NC7i8Ajinl2BfwUzBLFFEMEoktSmSkUvz2G9x1Fzz9tJ+y8tpr4eab/WBKERGRcFIMEolNYR0jo1WVa76VK33AOOggP//+5ZfD0qXwwAMKICIiEl6KQSKxLWwtMiGrKvfCzwDzjZm9GWjKDwquqvx4YMXlmUDrkFWVL3TOzTOzJmhV5Wpl9Wo/68tjj0FODgwZ4vsgH3BApEsmIiI1nWKQiEB4u5YVrKoMYGbBVZVDE5lyr6ocxnLKbli/HiZMgIcfhm3b/OrHo0b5q2EiIiLhpBgkIqHCmchoVeUoUZ66b9kSx/TpLZk2bX+2bKlNz56rGDw4gwMO2EpmJmRmVk1ZK5M+8/RIF6PKxWq9IbbrLtFv0ya/eOUDD8DGjXD++XDnnXDIIZEumYhEUqQH+2tV5WqgrLpv2QL//Cfcfz+s1Zp2LwAAIABJREFUWwd9+/opLY88MhlIrtJyVjZ95mmRLkaVi9V6Q2zXXaJX6TEo0iUTkeognIP9tapyFNu2zV/5atMGbrkFjj7ar4b8+usKICIiEl6KQSJSHuFMZLSqchTavt0PnjzwQLjhBh8wPv8c/vtf6NIl0qUTEZGaTDFIRHZH2LqWaVXl6JKbC5Mnw7hx8OuvcNxxfkXkE06IdMlERKSmUwwSkYoI6xgZrapc/eXnwzvvNOPSS2HZMujaFZ58Enr1ArNIl05ERGoyxSAR2RORHuwvEbJjB7z6KoweDYsWHUKnTvDWW3DGGQoeIiISXopBIlIZwjlGRqoh5/xgyZQUGDgQateGMWN+ZPZs6NNHAURERMJHMUhEKpMSmRjhnB8smZoK/fv7AZUvvQTz5sHxx6+hln4TREQkTBSDRCQc1LWshnMO3n/fr3z85Zd+KsvnnvOrIdfWpy9VKDc3l8zMTLKzsyNWhoYNG/LTTz9F7PUjqTx1j4+Pp2XLltSpU6eKSiU1nWKQiIST/ozUYB9/DHfc4X/uvz9MmgRDhoD+R5FIyMzMJDExkdatW2MR6j+SlZVFYmJiRF470nZVd+cca9euJTMzkzZt2lRhyaSmUgwSkXBTY24N9OWXfsaXE06An3/2qyIvXgyXXaYAIpGTnZ1NkyZNIpbESNnMjCZNmkS0xUxqBsUgEakqSmRqkO++84Mlu3eHuXNhwgRYuhSuvhrq1Yt06URQElPN6fORPaEYJCJVTYlMDfDDD37wZJcufgXku++G5cv9qsj160e6dCLVw9q1aznmmGNISUmhefPmtGjRgpSUFFJSUsjJySnz2NmzZ3Pttdfu8jV69OhRWcUViRqKQSISKRojE8UWLfJz8L/yCiQm+vvXXQcNG0a6ZCLVT5MmTfjss89ITExk9OjRJCQkcOONNxZsz8vLo3Ypo49TU1NJTU3d5Wt8/vnnlVZekepOMUhEIk0tMlFo6VIYPBgOPdQvIDZypL/6deedCiAiu2PIkCEMGzaMbt26MWLECL7++mu6d+9Op06d6NGjB4sWLQIgPT2dPn36ADB69GguueQS0tLSaNu2LY888kjB+RISEgr2T0tLY8CAAXTo0IFBgwbhnANg5syZdOjQgS5dunDttdcWnDdURkYGxx13HJ07d6Zz585FEqR7772XI444go4dOzJy5EgAlixZwsknn0zHjh3p3LkzS5cuDc8bJoJikIhUH2qRiSK//grjxvmpK2vXhuHDYcQISE6OdMlEds911/k+9JUpJQUeemj3j8vMzOTzzz8nLi6OTZs28cknn1C7dm3ef/99br31Vl577bWdjlm4cCEfffQRWVlZtG/fniuuuGKnKYvnzJnD/Pnz2W+//TjmmGP47LPPSE1NZejQoXz88ce0adOGgQMHllim5ORk3nvvPeLj41m8eDEDBw5k9uzZzJo1izfeeIOvvvqK+vXrs27dOgAGDRrEyJEj6devH9nZ2ezYsWP33wiRXVAMEpHqRolMFPj9d9/n+Mkn/eNhw+DWW2HffSNbLpGa4NxzzyUuLg6AjRs3MnjwYBYvXoyZkZubW+IxZ5xxBvXq1aNevXokJyfz559/0rJlyyL7dO3ateC5lJQUMjIySEhIoG3btgXTGw8cOJBJkybtdP7c3Fyuvvpq5s6dS1xcHD///DMA77//PhdffDH1AwMPGjduTFZWFitWrKBfv36AXwtGpDIpBolIdaVEphpbtQruuQcefxzy8uCSS+C226BVq0iXTGTPVKTlJFwaNGhQcP+OO+6gZ8+evP7662RkZJCWllbiMfVCpmCKi4sjLy+vQvuU5sEHH6RZs2bMmzePHTt2KDmRiFAMEpHqTmNkqqG1a+GWW/wKyA8/DOef7wdVPvGEAohIOG3cuJEWLVoA8Nxzz1X6+du3b8+yZcvIyMgA4JVXXim1HPvuuy+1atViypQp5OfnA9CrVy+effZZtm7dCsC6detITEykZcuWzJgxA4Dt27cXbBepCMUgEYkWSmSqkY0b/WDJNm3g3nvh7LNhwQLfH7lt20iXTqTmGzFiBLfccgudOnXarRaU8tprr72YOHEivXv3pkuXLiQmJtKwhNHRV155JZMnT6Zjx44sXLiwoNWod+/enHXWWaSmppKSksKECRMAmDJlCo888ghHHnkkPXr04I8//qj0skvNpxgkItFGXcuqgc2b4ZFH/OJh69f7+fjH/D97dx4eZXX2cfx7E5YQiCAgiKAGF0ARwhJFURQUK6KiKC5xBX0V96VuqIiIaMViVarY4gJIbXEtYgu1blErLiACVQRFocqmLAJhCSThvH+cZ5LJZCVkMpnM73Ndc2Uy53lmzplJcud+znY/HHFErGsmUjuNGjWqxMePOeaYgvkoAGPGjAGgT58+BcPMIs/96quvCu5v2bKl2PEATz75ZMH9vn37snjxYpxzXHfddSUu63zooYeycOHCgu/Hjh1bcH/48OEFq5WFH//ee++V2CaR8igGiUi8Uo9MDG3b5gNHu3Z+3PGxx/qdkV97TQFEpLZ65pln6Nq1K506dWLTpk0MGzYs1lWSBKUYJCLxTj0yMZCTAxMnwu9+B2vWwG9+A6NHQ8+esa6ZiETbLbfcwi233BLrakgCUwwSkdpCiUw12rkTJk2CMWNgxQo44QR4+WXo3TvWNRMRkdpOMUhEahsNLasGeXk+eHTo4Nff339/eOcdeP99BRAREYkuxSARqa2UyERRfj789a9w+OF+/f3mzeGf/4SPP4aTTgKzWNdQRERqK8UgEantlMhEwa5dfrJkly5w0UWQnAx//zvMmQMDBih4iIhI9CgGiUiiUCJThZyDN9+EHj1g8GAfTF56CebPh7POUvAQiaW+ffvyzjvvFHns8ccf55prrin1nD59+jB37lwABgwYwMaNG4sdM2rUqIL9XEozffp0Fi1aVPD9yJEji9VFqp6Z9TezJWa21MyGl1B+gJm9b2ZfmtlCMxtQQvkWM7ut+mpdeYpBIpJolMhUAefgrbfg6KNh4EDIzoYXXoCvvoLzzoM6epdFYi4zM5PXXnutyGPTpk0jMzOzQufPnDmTpk2bVuq1IxOZ0aNH069fv0o9l1SMmSUBTwGnAocDmWZ2eMRhI4CXnXPdgAuACRHlfwBmRbuue0oxSEQSlf687aGsLDj+eOjf3y9j+eyz8M03cMklkJQU69qJSMjgwYN566232LlzJwDLly9n1apV9O7dm2uuuYaMjAw6derEfffdV+L5aWlprFu3DoAHH3yQ9u3bc9xxx7FkyZKCY5555hmOPPJI0tPTOeecc9i2bRuzZ89mxowZ3H777XTt2pXvv/+eIUOG8OqrrwLw7rvv0q1bNzp37szll1/Ojh07Cl7vvvvuo3v37nTu3JnFixcXq9Py5cvp3bs33bt3p3v37syePbugbOzYsXTu3Jn09PSCDTSXLl1Kv379SE9Pp3v37nz//fdV8M7WWEcBS51zPzjndgLTgDMjjnHAXsH9JsCqUIGZnQUsA76uhrpWmmKQiCQyLb9cSbNnw733wnvvwX77wVNPwRVXQIMGsa6ZSHwI2/i+wHnnwbXX+o36BgwoXj5kiL+tW+eHzoTLyir79Zo1a0aPHj2YNWsWZ555JtOmTeO8887DzHjwwQdp1qwZ+fn5nHTSSSxcuJAuXbqU+DxffPEF06ZNY/78+eTl5dG9e3d69OgBwNlnn82VV14JwIgRI3juuee44YYbGDhwIKeffjqDIyqdk5PDkCFDePfdd2nfvj2XXnopTz/9NDfffDMALVq0YN68eUyYMIFx48bx7LPPFjm/ZcuWvP322yQnJ/Pdd9+RmZnJ3LlzmTVrFm+88QafffYZKSkpbNiwAYCLLrqI4cOHM2jQIHJycti1a1fZb1p8awP8FPb9CiByp5RRwL/N7AagEdAPwMwaA3cCJwOlDiszs6uAqwBatWpFVnk/hGXYsmXLbp3/1Vd78fzz7fjyy71p0WIHN930PwYMWE39+o6PP650NWJid9teWyRquyFx256o7YbotV2JzG6aO9cnMP/6F7RsCY89BsOGQcOGsa6ZiJRn8ODBTJs2rSCRee655wB4+eWXmThxInl5eaxevZpFixaVmsh89NFHDBo0iJSUFAAGDhxYUPbVV18xYsQINm7cyJYtWzjllFPKrM+SJUto164d7du3B+Cyyy7jqaeeKkhkzj77bAB69OjB66+/Xuz83Nxcrr/+eubPn09SUhLffvstAO+88w5Dhw4tqGOzZs1YtWoVK1euZNCgQQAkJydX7E2r3TKByc65R83sGGCqmR2BT3Aec85tsTImljjnJgITATIyMlyfkrLzCsrKyqIi55ccgxrQsGF7oH2lXz+WKtr22iZR2w2J2/ZEbTdEr+1KZCpo4UIYORLeeAOaNYOHH4brr4dGjWJdM5H4VNaFmZSUsstbtCi/B6Ykp512GnfffTfz5s1j27Zt9OjRg2XLljFu3DjmzJnD3nvvzZAhQ8jJydn9JweGDBnC9OnTSU9PZ/LkyXt89alB0MWblJREXl5esfLHHnuMVq1asWDBAnbt2qXkpKiVwP5h37cNHgt3BdAfwDn3iZklAy3wPTeDzewRoCmwy8xynHNPRr/aJVMMEhEpTnNkyrFokR/ukp7u/3EaPRqWLYM771QAEYk3jRs3pm/fvlx++eUFk/w3b95Mo0aNaNKkCT///DOzZpU9t/v4449n+vTpbN++nezsbN58882CsuzsbFq3bk1ubi4vvvhiweOpqalkZ2cXe64OHTqwfPlyli5dCsDUqVM54YQTKtyeTZs20bp1a+rUqcPUqVPJz88H4OSTT2bSpEls27YNgA0bNpCamkrbtm2ZPn06ADt27Cgor6XmAIeaWTszq4+fzD8j4pgfgZMAzOwwIBlY65zr7ZxLc86lAY8DD8UqiVEMEhEpnRKZUixd6idLHnEEzJoF99zjg8e998Jee5V/vojUTJmZmSxYsKAgkUlPT6dbt2507NiRCy+8kGOPPbbM87t37875559Peno6p556KkceeWRB2QMPPEDPnj059thj6dixY8HjF1xwAb///e/p1q1bkQn2ycnJTJo0iXPPPZfOnTtTp04drr766gq35dprr2XKlCmkp6ezePFiGgX/2fbv35+BAweSkZFB165dC5aHnjp1KuPHj6dLly706tWLNWvWVPi14o1zLg+4HngL+Aa/OtnXZjbazELjAW8FrjSzBcDfgCHOORebGhelGCQiUgHOuVpx69Gjh9sT77//vnPOuWXLnLv8cueSkpxr2NC52293bu3aPXrqGi/U9kSTqO12LjZtX7RoUbW/ZqTNmzfHugoxU9G2l/Q5AXNdDfg7X5NvikGVl6h/ixO13c4lbtsTtd3O7Vnby4pBmiMTWLu2Ptde65eurFPHjz0ePhz23TfWNRMRkdpOMUhEZPdFNZExs/7AE0AS8Kxz7uGI8gOAKfjJlEnAcOfczIjyRcAo51zZW2fvgXvvhbFjjwb8Esr33ANt20br1URERAopBomIVE7UEpmwXZVPxq/fP8fMZjjnFoUdFtpV+elgx+WZQFpYebXsqlyvHvTr9zMTJrQmLa3cw0VERKqMYpCISOVEs0emYFdlADML7aocnshUZFflrVGsI+CXtMzKWkJaWutov5RIQnPOUda+HBJbfiiyVDfFIBGRyonmqmUl7arcJuKYUcDFZrYC3xtzAxTZVfn+KNZPRKpRcnIy69ev1z/LNZRzjvXr12svGhERiRuxnuy/R7sqm9lVwFUArVq12qPN57Zs2bLHm9fFq0Rte6K2G2LTdjOjUaNG/PTTT+UfHCWJ3CNUkbbn5+ezdetW/ve//1VTrURERCovmolM1HdVds5NBCYCZGRkuD59+lS6sllZWezJ+fEsUdueqO2GxG17orYbErvtIiJSO0UzkSnYVRmfwFwAXBhxTGhX5cmRuyqHDjCzUcCWyCRGREREREQSV9TmyLg431VZRERERERqrqjOkQn2hJkZ8djIsPuLgGPLeY5RUamciIiIiIjELastHSBmthbYkxmqLYB1VVSdeJOobU/UdkPitj1R2w171vYDnXP7VGVlahvFoD2SqG1P1HZD4rY9UdsNUYpBtSaR2VNmNtc5lxHresRCorY9UdsNidv2RG03JHbb40Eifz6J2vZEbTckbtsTtd0QvbZHcx8ZERERERGRqFAiIyIiIiIicUeJTKGJsa5ADCVq2xO13ZC4bU/UdkNitz0eJPLnk6htT9R2Q+K2PVHbDVFqu+bIiIiIiIhI3FGPjIiIiIiIxJ2ESmTM7Hkz+8XMviql3MxsvJktNbOFZta9uusYLRVo+0VBm/9rZrPNLL266xgN5bU77LgjzSzPzAZXV92irSJtN7M+ZjbfzL42sw+qs37RUoGf9SZm9qaZLQjaPbS66xgNZra/mb1vZouCdt1UwjG19m9cPFAMUgwq4zjFIMWguBarGJRQiQwwGehfRvmpwKHB7Srg6WqoU3WZTNltXwac4JzrDDxA7RnHOZmy242ZJQFjgX9XR4Wq0WTKaLuZNQUmAAOdc52Ac6upXtE2mbI/8+uARc65dKAP8KiZ1a+GekVbHnCrc+5w4GjgOjM7POKY2vw3Lh5MRjGoNIpBikGKQfEtJjEooRIZ59yHwIYyDjkTeMF5nwJNzax19dQuuspru3NutnPu1+DbT4G21VKxKKvAZw5wA/Aa8Ev0a1R9KtD2C4HXnXM/BsfXivZXoN0OSDUzAxoHx+ZVR92iyTm32jk3L7ifDXwDtIk4rNb+jYsHikGKQaVQDEIxqDrqFk2xikEJlchUQBvgp7DvV1D8Q0gEVwCzYl2J6mBmbYBB1K4rnxXVHtjbzLLM7AszuzTWFaomTwKHAauA/wI3Oed2xbZKVcvM0oBuwGcRRfobV7Pp8/EUgxKDYpBi0B7/jau7JydL7WNmffFB5LhY16WaPA7c6Zzb5S+OJJS6QA/gJKAh8ImZfeqc+za21Yq6U4D5wInAwcDbZvaRc25zbKtVNcysMf7q7s21pU2SOBSDEopikGLQHlMiU9RKYP+w79sGjyUEM+sCPAuc6pxbH+v6VJMMYFoQQFoAA8wszzk3PbbVqhYrgPXOua3AVjP7EEgHansQGQo87Pza80vNbBnQEfg8ttXac2ZWDx9AXnTOvV7CIQn9Ny4OJPTnoxikGIRiUFyLRQzS0LKiZgCXBqsqHA1scs6tjnWlqoOZHQC8DlySAFdDCjjn2jnn0pxzacCrwLUJEkAA3gCOM7O6ZpYC9MSPaa3tfsRfAcTMWgEdgB9iWqMqEIy3fg74xjn3h1IOS9i/cXEiYT8fxSDFIBSD4lqsYlBC9ciY2d/wK0S0MLMVwH1APQDn3J+AmcAAYCmwDZ811woVaPtIoDkwIbgylOecy4hNbatOBdpda5XXdufcN2b2L2AhsAt41jlX5hKh8aACn/kDwGQz+y9g+GEd62JU3ap0LHAJ8F8zmx88djdwANT+v3HxQDFIMQjFIMUgxaAq/RtnvmdLREREREQkfmhomYiIiIiIxB0lMiIiIiIiEneUyIiIiIiISNxRIiMiIiIiInFHiYyIiIiIiMQdJTIilWBm+WY2P+w2vAqfO83M4n4JShERiQ7FIBEvofaREalC251zXWNdCRERSUiKQSKoR0akSpnZcjN7xMz+a2afm9khweNpZvaemS00s3eDXawxs1Zm9nczWxDcegVPlWRmz5jZ12b2bzNrGBx/o5ktCp5nWoyaKSIiNZBikCQaJTIildMwolv//LCyTc65zsCTwOPBY38EpjjnugAvAuODx8cDHzjn0oHuwNfB44cCTznnOgEbgXOCx4cD3YLnuTpajRMRkRpNMUgEMOdcrOsgEnfMbItzrnEJjy8HTnTO/WBm9YA1zrnmZrYOaO2cyw0eX+2ca2Fma4G2zrkdYc+RBrztnDs0+P5OoJ5zboyZ/QvYAkwHpjvntkS5qSIiUsMoBol46pERqXqulPu7Y0fY/XwK57OdBjyFv3I2x8w0z01ERMIpBknCUCIjUvXOD/v6SXB/NnBBcP8i4KPg/rvANQBmlmRmTUp7UjOrA+zvnHsfuBNoAhS7IiciIglNMUgShjJpkcppaGbzw77/l3MutPzl3ma2EH9FKzN47AZgkpndDqwFhgaP3wRMNLMr8Fe9rgFWl/KaScBfgkBjwHjn3MYqa5GIiMQLxSARNEdGpEoF45MznHPrYl0XERFJLIpBkmg0tExEREREROKOemRERERERCTuqEdGRERERETijhIZERERERGJO0pkREREREQk7iiRERERERGRuKNERkRERERE4o4SGRERERERiTtKZEREREREJO4okRERERERkbijREZEREREROKOEhkREREREYk7SmRERERERCTuKJEREREREZG4o0RGRERERETijhIZERERERGJO0pkREREREQk7iiRERERERGRuKNERkRERERE4o4SGRERERERiTtKZEREREREJO4okRERERERkbijREZEREREROKOEhkREREREYk7SmRERERERCTuKJEREREREZG4o0RGRERERETijhIZERERERGJO0pkREREREQk7iiRERERERGRuKNERkRERERE4o4SGakyZvYnM7u3qo+NJTPLMrP/i8LzTjazMcH93ma2pCLHVvK1tpjZQZU9X6qGmS03s36llPUxsxXVXSeR2kQxaLeeVzEowdTWGKREppKCH4jtwS9o6HZ32P0cM8sP+/7riPPTzMyFlf9sZhPMrF45r/FkUDYk7Pk3m9kCMzvdzA6ION6Z2daw73tH1OPrsLL8oN4F7dmd98Q5d7Vz7oGqPrYmMrMLgs/HIh6va2a/mNnpFX0u59xHzrkOVVSvYkHPOdfYOfdDVTx/Ga/5q5k1iNZr1CRmNsrMciN+z+6I8mu+b2Zrw37Xz4zm60nNpxhUnGKQYlC0XqMmiUUMCnvtE4Lf6UontlVNicyeOSP4BQ3dHgrdB64GPgkr61TKczQNju8MHANcV85rXB9W9klwblNgAjAN2Bx+fHBcethjH4U/uXOuU9ixHwHXh7cndJyZ1a3ke1RbTce/7ydEPN4fcMC/qr1GMWBmaUBvfJsHVvNrx/Jn8qWI38tHovx6NwGtnXN7AVcBfzGz1lF+Tan5FIMSl2IQikHVGIMILnI8AXwW7dfaHUpkagjn3C/A28DhlTh3FzAVaAQcWhX1Cbtad4WZ/Qi8Fzz+ipmtMbNNZvahmXUKOye8q7qPma0ws1uDq0OrzWxoJY9tbmZvBlf95pjZGDP7Txl1L6+OT5nZP80s28w+M7ODw8pPNrPFwblPAlbSazjncoCXgUsjii4F/uqcyyurHhH1LdKla2bdzGxeUL+XgOSwsr3N7B/mr87/GtxvG5Q9iP+D/mTElVNnZocE95uY2QvB+f8zsxFmVicoG2Jm/zGzccFzLzOzU0t7n8Pa+ykwGbgsol37m9nrwWutD9UnKLvSzL4J2rjIzLpH1jX4vqSfkzvNbA0wqaz3IzinmZlNMrNVQfn04PGvzOyMsOPqmdk6M+tWTnvLZGYDzV9h3mj+KuFhpRzXMGjbr2a2CDiyrOd1zi10zuWFvgXqAfvvSV1FwikGKQaFfa8YVHicYlChW4F/A4v3pI5VTYlMDWFm+wGn4H8hd/fcJGAokAv8r4qrdgJwGL5uALPwgaolMA94sYxz9wWaAG2AK4CnzGzvShz7FLA1OOYyIv5YlaC8Ol4A3A/sDSwFHgQwsxbA68AIoAXwPXBsGa8zBRhsZg2D85sAZwSPV6QexZhZffyVtqlAM+AV4JywQ+oAk4ADgQOA7cCTAM65eyh6RTP8ymnIH/Hv80H4z/ZS/M9OSE9gSdD+R4DnzKzEQBq4NGjXi8ApZtYqaEcS8A/8z2Ma/nOdFpSdC4wKzt0LfxVtfVnvS5h98e/LgfieiVLfj8BUIAXohP8cHgsefwG4OOy4AcBq59yXFaxHMWbWHvgbcDOwDzATeDP4TCPdBxwc3E6h/J9pggCZg78algXMrWxdRSIpBikGBc+hGFS2hIxBZnYgcDkwurL1ixrnnG6VuAHLgS3AxuA2PaJ8CPCfMs5Pw19ZDZ3vgNnAXmW8xkbgyrDnzwsey8X/8pxXwus44JAKtikL+L+I+h1UxvFNg2OaBN9PBsYE9/sEdaobdvwvwNG7cyyQFLSvQ1jZmLLe2wrU8dmw8gHA4uD+pcCnYWUGrAi9J6U8/3fAhcH9K4EFlXyvVgT3jwdWARZ27uzQsSU8b1fg15I+w8ifgeC93AkcHlY2DMgK+5laGlaWEpy7bymvfVzw2bQIvl8M3BLcPwZYG/6Zhp33FnBTKc9Z5Oe1hPdpJ5BcxudR8H4ArYFdwN4lHLcfkE3w+wa8CtxRwZ+pUUE9wn8v9wPuBV4OO64OsBLoE/b73C+4/wPQP+zYq0I/A+W8dj3gVOC3FamrbrX3hmIQKAaBYpBiUDXEIOAN4PzI96Qm3NQjs2fOcs41DW5nlXWg+RVSSprA2MI51xT/C/sx/hestNdo6px7Jqzs0+DcvYEZ+C7dqvZTWBuSzOxhM/vezDbjfynAXzkpyXpXOBwGYBvQeDeP3QeoG16PiPtFVLCOa0qp037hz+38b2yprxV4gcKu/UuC7yvzXoXsB6wMXjuk4AqnmaWY2Z+DLvnNwIdA0+DqU3la4P8RDr9i+j/8laqQgvfGObctuFvaZ3YZ8G/n3Lrg+79SeFVnf+B/EZ8pYWXfV6C+JVnr/JAKoNz3Y39gg3Pu18gncc6twv++nWNmTfHJQblXK8O8HPF7uQr/2RW8t84Pt/mJou9vSJGfNSp4Fds5l+ucmwX8xsyqdTy41EiKQUEbSjlXMUgxSDFoD2NQMAQu1Tn30m7Ur9ookakmzq+QUmwCY1g+bvEdAAAgAElEQVT5dnyWe3TQvbw7z70FuAa4ZE/HV5b09GH3LwTOBPrhu4bTgsfL6vbdU2vxV/3ahj1W1tyAPanj6vDnDrqzy5uHMBU4ycyOwV+9C/0hqmw9VgNtIrrSDwi7fyvQAejp/MTv4yOeN/zzirQOf/XqwIjnXllOnYoJhjKcB5xgfgz2GuAWIN3M0vF/IA+wkidD/oTvzi7JNvw/VCH7RpRHtq+s9+MnoFkQJEoyBd+1fy5+0vJuvw8RVhH23ob9/JT0vEV+1ij6GVdEXUp/D0WKUQyqNMUgxaBwiRiDTgIywt7n84GbzeyNPaxvlVAiU0OYXzbwEvzViIqO1SzgnNsAPAuMrOKqhUsFduDrlwIUC4ZVzTmXjx8zPCq48tGR4pMbq6qO/wQ6mdnZwR+/Gyn+RyyyfsuB/+DHpb7tnAtdTapsPT7BB80bzU/+Oxs4Kqw8FT8EYqOZNcOPcw33M37scUl1zcdPDn3QzFKDMa+/Bf5SwbqFOwvIx08M7hrcDsOPj74U+Bz/h/JhM2tkZslmFhrr/Sxwm5n1MO+QoC4A84ELg6uJ/Sm+Ik+kUt8P59xq/BjxCeYnZNYzs+PDzp0OdMevCPZC+JOaX9Z0yO68Ifj39jQzO8n86i634n8GZpdy7F1BvdoCN5T2pGbW0cxONT85s56ZXYwPlh/sZv1ESqUYVDLFIMWgcupR62MQfshaewrf5xnAMxSd2xQzSmRib6OZbcH/8h8DDIzo0n3Tiq4V/vcynutxYICZdYlSXV/Adz+uBBZRiUmhlXQ9/orSGvzVp7/hfzlLUuk6Bt3T5wIP4//4H4rv+i3PFPxVkPA/RJWqh3NuJ3A2fqzwBvyVj9fDDnkcaIi/svUpxZfYfAI/+fNXMxtfwkvcgJ+0+gM++P0VeL4idYtwGTDJOfejc25N6Iaf5HgR/mrUGfhx0T/ix3mfH7TxFfzk1r/ixwhPx0+eBP8H/Qz8mN+LgrKylPd+XIK/ArgYP+b95lBBcAX6NaAdYe+x+YmRzdnNn2/n3BL81bU/BvU5A7907c4SDr8f//OxDL8KzNQyntrwY6J/wV8dvgk/Vnne7tRPpBSKQeVTDCqkGFRUrY9BzrnsiPd4O7A1uHgRc1b075VIzWdmY/GT/y4r92CRMpjZSKC9c+7isMeOA65zzmXGrmYiUlMpBklVUQzac0pkpMYLuvLrA//Fr3U+E78qSnlXSkRKFQwD+BK4xDn3YazrIyI1k2KQRINiUNXQ0DKJB6n4btetwEvAo/ilAEUqxcyuxE/EnKUAIiLlUAySKqUYVHXUIyMiIrVSMFn3Cfz+Fc865x6OKD8QPz5/H/x8gIudcyvMrCvwNH6zvHzgwZq69KiISCJTIiMiIrWO+X0cvgVOxk/0nQNkOucWhR3zCvAP59wUMzsRGOqcu8T8DtnOOfed+R3vvwAOc85trP6WiIhIaTS0TEREaqOj8LuE/xCs2jMNv69GuMOB94L774fKnXPfOue+C+6vwq82tE+11FpERCqspI2C4lKLFi1cWlpapc/funUrjRo1qroKxZFEbXuithsSt+2J2m7Ys7Z/8cUX65xz8faPfBuK7ly9AugZccwC/FKzTwCDgFQza+6cK9hHxcyOwk/0LnMncMWgykvUtidquyFx256o7YboxaBak8ikpaUxd+7cSp+flZVFnz59qq5CcSRR256o7YbEbXuithv2rO1m9r+qrU2NcRvwZLDx3If4PTfyQ4Vm1hq/v8JlzrldkSeb2VXAVQCtWrVi3Lhxla7Ili1baNy4caXPj2eJ2vZEbTckbtsTtd2wZ23v27dvqTGo1iQyIiIiYVYC+4d93zZ4rEAwbOxsADNrDJwTmgdjZnvhd1q/xzlX4sZ0zrmJwESAjIwMtydJspLsPrGuRrVL1HZD4rY9UdsN0Wu75siIiEhtNAc41MzaBTtlXwDMCD/AzFqYWSgO3kWww3hw/N+BF5xzr1ZjnUVEZDdENZExs/5mtsTMlprZ8BLKDzSzd81soZllmVnb4PGuZvaJmX0dlJ0fzXqKiEjt4pzLA64H3gK+AV52zn1tZqPNbGBwWB9giZl9C7QCHgwePw84HhhiZvODW9fqbYGIiJQnakPLgqUvnyJs6UszmxG+9CUwDn/FK7T05e+AS4BtwKXhS1+a2Vu7u/Rlbm4uK1asICcnp9xjmzRpwjfffLM7T19rxLLtycnJtG3blnr16sXk9UWk9nLOzcTvwh7+2Miw+68CxXpcnHN/Af4S9QqKiMgeieYcmYKlLwHMLLT0ZXgiczjw2+D++8B08Etfhg5wzq0ys9DSl7uVyKxYsYLU1FTS0tIwszKPzc7OJjU1dXeevtaIVdudc6xfv54VK1bQrl27an99EREREYlf0RxaVtLSl20ijgktfQlhS1+GH1DRpS9LkpOTQ/PmzctNYiQ2zIzmzZtXqMdMRHafczBzJvTsCcuWpcS6OiIikkCqIwbFetWyKl36Misrq0h5kyZN2LJlS4Uqkp+fT3Z2dqUaEe9i3facnJxin1112LJlS0xetyZI1LYnSrudg3nzmjJpUju+/roJ++67nVWr8hOi7SIiElvOwXvvwb33wiefQFoa/Ppr/ai8VjQTmZgvffnNN99UeMhUNIZXrV+/npNOOgmANWvWkJSUxD77+P18Pv/8c+rXL/1DnTt3Li+88ALjx48v8zV69erF7Nmz96iesR5Wl5ycTLdu3ar9dbUMYp9YV6PaJUK7P/rIB48PPoC2beFPf4KhQxsye/aOWt92ERGJrZJjEMyevVuzQyosmolMwdKX+ATmAuDC8APMrAWwIehtqXVLXzZv3pz58+cDMGrUKBo3bsxtt91WUJ6Xl0fduiV/BBkZGWRkZJT7GnuaxIhI7fDZZz54vP02tGoFTzwBV10FycmxrpmIiNR2sYpBUZsjo6UvSzZkyBCuvvpqevbsyR133MHnn3/OMcccQ7du3ejVqxdLliwB/JXj008/HfBJ0OWXX06fPn046KCDivTShHZJDV1pHjx4MB07duSiiy7COQfAzJkz6dixIz169ODGG28seN5wy5cvp3fv3nTv3p3u3bsXSZDGjh1L586dSU9PZ/hwv4r20qVL6devH+np6XTv3p3vv9/tKUwiUgW+/BLOOAOOPhrmzYPf/x5++AFuvFFJjIiIRFesY1BU58jUpKUvb74Zgs6REuXnNyQpafees2tXePzx3a/LihUrmD17NklJSWzevJmPPvqIunXr8s4773D33Xfz2muvFTtn8eLFvP/++2RnZ9OhQweuueaaYksWf/nll3z99dfst99+HHvssXz88cdkZGQwbNgwPvzwQ9q1a0dmZmaJdWrZsiVvv/02ycnJfPfdd2RmZjJ37lxmzZrFG2+8wWeffUZKSgobNmwA4KKLLmL48OEMGjSInJwcdu0qNoVJRKLoq6/gvvvg9dehaVMYM8YHjgRdfFFERKpRTYlBsZ7sn5DOPfdckoKsadOmTVx22WV89913mBm5ubklnnPaaafRoEEDGjRoQMuWLfn5559p27ZtkWOOOuqogse6du3K8uXLady4MQcddFDB8saZmZlMnDix2PPn5uZy/fXXM3/+fJKSkvj2W78C9jvvvMPQoUNJSfGrTTRr1ozs7GxWrlzJoEGDAD/HRUSqx7ffwqhRMG0aNG4MI0fCLbf4QCIiIhJNNS0GJUwiU17PSXb29mqb8N6oUaOC+/feey99+/bl73//O8uXLy91Mm6DBg0K7iclJZGXl1epY0rz2GOP0apVKxYsWMCuXbuUnIjUMD/8AKNHw9Spvrv+jjvg9tuhefPyzxUREdkTNTUGRXMfGamATZs20aaN315n8uTJVf78HTp04IcffmD58uUAvPTSS6XWo3Xr1tSpU4epU6eSn+9XwT755JOZNGkS27ZtA2DDhg2kpqbStm1bpk+fDsCOHTsKykWkav30EwwbBh06+CtgN93kA8rDD8c+gIiISO1W02OQEpkYu+OOO7jrrrvo1q3bbvWgVFTDhg2ZMGEC/fv3p0ePHqSmptKkSZNix1177bVMmTKF9PR0Fi9eXNBr1L9/fwYOHEhGRgZdu3Zl3LhxAEydOpXx48fTpUsXevXqxZo1a6q87iKJbPVquOEGOOQQmDTJB5IffoA//MGvCCMiIhItK1f6VccOPhiefx7OPBMmT4bbbvMx6Mcf4aGHYPhwuO46uPRSOOssmDPHn//vf0ObNrDXXvDb30avngkztCzWRo0aVeLjxxxzTMF8FIAxY8YA0KdPn4JhZpHnfvXVVwX3Qxt+hh8P8OSTTxbc79u3L4sXL8Y5x3XXXVfiss6HHnooCxcuLPh+7NixBfeHDx9esFpZ+PHvvfdeiW0SkcpbuxbGjoWnnoLcXL/+/ogRcOCBsa6ZiIjUZLm5kJ/vh37l5vqkIjvb3zZv9l979vQrjP3yi5/bEll+442wfDn88Y+wc2fhc7/2mr9NngyXXQYrVsA990C9en6C/157+a+h/dX33RdOPdU/dvzx0WuzEpkE8MwzzzBlyhR27txJt27dGDZsWKyrJCIRNmyAceNg/HjYvh0uvthPojz44FjXTEREomXHjqLJREoKHHqoL5s8GX79tTDJ2LwZevWCIUN8wpKRUTQJycnx81YeeQS2boVjjy3+evfd5xMZ5+DTTwuTkObNfey57TafBA0e7ONPu3ZFE5WOHf3zHHWUf72w6dlFdOkCzz5b+H1WVhW+aWGUyCSAW265hVtuuSXW1RCREmzaBI895m+bN8P55/sVYULBQkREaqYVK2DduqKJRuPGfl8V8PNIli4tLFuxohsnnAChQTMHHQTLlhV9znPOgVeDjUluvdVf5AJo2NAnE6HZAUlJPslo2NAnGKFko1cvX77XXjBrVtEkJHQf/PCw77+vfAyqW9ffYq0GVEFEJPFs2eK77n//e3/FbdAguP9+6Nw51jUTEaldnPO9DZs3w7ZtPoEAP/Tqu+8KE5HsbJ8g3HefLx8xAt5/v2h5mzYQGomfmQn/+U/R1+revTCRmTXLP38ogahXb1eRfVauu873aoQnG2lpheX//a/voWncuOSk4fXXS29znTrQv3/p5bUlBimRERGpRtu3w4QJfh7M2rUwYIBf0rJHj1jXTESkZsnJ8T0S4cOnsrP93Iv69X2S8c47xed5zJzp//G/+27/9zY7G0L7dtev74dzge8ZeeGFwtczgwMOKExkdu3yiUSrVoXJRvgWfqNG+dcM7/HYe+/C8g8+KNqerKwFReYz33pr2e3fb7/dersqpLbFICUyIiLVYMcOeOYZv8rL6tXQr58PHsccE+uaiYhUjfx8f6W/YUOfMPzyi++9CE9CNm+GDh3qAz4JeeqpoknI5s3w4Yd+fsb48XDnncVfZ+VK/0/+hx/6f8jDezT22sv/va1bF7p18xPTI4dXOeeTlgce8BPWQ483auQfD3noobLbe9JJVfjmRVltjUFKZEREoig31y+fPGaMX4+/d2/429/ghBNiXTMREcjLK97jkZYGrVvDmjXw0kvFezxuuMGvfvXxx35lxVD51q3+Od96C37zG59onHtu8dccP95vup2d7edphCabp6X5+6EJ5L/5jZ8TEjnHo0ULXz5ihF8UJTz5CHfuuSW/fsgBB1TqLYsrtT0GKZGJor59+zJ8+HBOOeWUgscef/xxlixZwtNPP13iOX369GHcuHFkZGQwYMAA/vrXv9K0adMix4waNYrGjRtz2223lfra06dPp3379hx++OEAjBw5kuOPP55+/fpVQctEpDx5efDii37M8bJlPug/95y/ClZa0BURqYhdu/wciPx8+Prr4j0eXbr4VaU2bvQ9GpE9Hjfd5Fe+WrKk5Endf/qT37tq5Uq4+Wb/WHJyYTJxwQX+saZN/ZCkyB6P0KpbJ5wAH31UNAlJTYVPPtkM+HkZgwaV3s6uXf2tNElJu//eJYpEiUFKZKIoMzOTadOmFUlkpk2bxiOPPFKh82fOnFnp154+fTqnn356QSIzevToSj+XiFTcrl3+CuaoUfDtt35owz/+4cch16bgISIV55yf7xFKNsInbr/2GqxfXzTZSE/3Q6IA+vYtPk9k2DA/7Covzx8b6c47fSLjHLzxRtEkY//9i65cNXp08R6PI47w5V26+Lqlpvr9QiJ16uSv7pdmn338TapPosUgJTJRNHjwYEaMGMHOnTupX78+y5cvZ9WqVfTu3ZtrrrmGOXPmsH37dgYPHsz9999f7Py0tDTmzp1LixYtePDBB5kyZQotW7Zk//33p0cwK+uZZ55h4sSJ7Ny5k0MOOYSpU6cyf/58ZsyYwQcffMCYMWN47bXXeOCBBzj99NMZPHgw7777Lrfddht5eXkceeSRPPLII6SmppKWlsZll13Gm2++SW5uLq+88godIy7VLF++nEsuuYStQf/xk08+Sa9grb+xY8fyl7/8hTp16nDqqafy8MMPs3TpUq6++mrWrl1LUlISr7zyCgdrYwyphZyDv//dD3P4+mv/j8Drr/udjmtj8BBJFOvWFSYSoWSiQQMIXaOcMMH/wxjeK9K+vU80AI48EubP90lHyGmn+f06wA/TWr26sKxxY7jwwsJEJjXVD68K79E47jhf1qCBT4QiezyaNfPle+/th4eVpmlTuPfe0svr1St8LqnZEjUGJVQiE7ZQRIHzzoNrr/XL8YWWyws3ZIi/rVvnNwcKV97mPs2aNeOoo45i1qxZnHnmmUybNo3zzjsPM+PBBx+kWbNm5Ofnc9JJJ7Fw4UK6dOlS4vN88cUXTJs2jfnz55OXl0f37t0LEpmzzz6bK6+8EoARI0bw3HPPccMNNzBw4MCCxCVcTk4OQ4YM4d1336V9+/ZceumlPPvsswwfPhyAFi1aMG/ePCZMmMC4ceN4Nnw3I6Bly5a8/fbbJCcn891335GZmcncuXOZNWsWb7zxBp999hkpKSlsCBY+v+iiixg+fDiDBg0iJyeHXaFlQ0RqCefgn//0wePLL6FDB3+F8rzz/NAPEak+u3b5eRrZ2YUrPn39tV8CN7xHIz/fT/IGv9fHu+8WTVSaN/fJB/ik4u23i77O4Yf75wX/+75wYdFkIj+/8NjBg+Hkk4tORk9LKzzmP//xCclee/nJ5pF/N2bMKLvNZ5+922+T1CKJHoMSKpGJhdDwslAi89xzzwHw8ssvM3HiRPLy8li9ejWLFi0qNZH56KOPGDRoECkpKQAMHDiwoOyrr75ixIgRbNy4kS1bthQZxlaSJUuW0K5dO9q3bw/AZZddxhNPPFFQfnbwF7FHjx68XsIC5bm5uVx//fXMnz+fpKQkvv32WwDeeecdhg4dWlDHZs2akZ2dzcqVKxkUDIBNTk4u/w0TiRPO+X9uRo6Ezz7z+xJMmeL/6akJm4SJxIv8fL8pX/gcjuxsPyk5ORk++aT4Xh6bN/t/1ho29InIH/9YWBb+vHXq+J6RiROLvmajRoWJzKZNfqWt0NK6qalFl7299Va49NKiPR7hS+x++GHZV7xLWnULCi+GhvY0EdkdikFeAjW17B6UlJSyy1u0KL8HpiRnnnkmt9xyC/PmzWPbtm306NGDZcuWMW7cOObMmcPee+/NkCFDyMnJ2f0nB4YMGcL06dNJT09n8uTJZFWmkmEaBEuFJCUlkRfeDx547LHHaNWqFQsWLGDXrl1KTiQhffCBH47x0Ud+vPnEib7ntqQx5CK1UW6u/+e9bl0/oXzRouIrXw0e7DcP/Pjjwr08wo+ZMcNPNH/jjTaUtA7N99/7f84++MAnHfXqFU0mtm/3iczBB/uN/8J7PFJTCyfE33mnn1MSPg+kYcPC1/nd78puaznXB2v1sB2pmRSDCiVUIhMLjRs3pm/fvlx++eVkZmYCsHnzZho1akSTJk34+eefmTVrVpENkiIdf/zxDBkyhLvuuou8vDzefPNNhg0bBkB2djatW7cmNzeXF198kTZt2gCQmppKdvilqUCHDh1Yvnw5S5cuLZhTc+yxx1a4PZs2baJt27bUqVOHKVOmkB/0jZ988smMHj2aiy66qGBoWbNmzWjbti3Tp0/nrLPOYseOHeTn5xf02ojEm08+8cHj3Xf90qRPPgn/93+FS4WK1GQ7dhRNJPbd10/2Xr/ej62P7PEYOtTvMTFvHlxxRdFzc3J8InLGGX5oVElDszt18onM+vXw6aeFSca++/pVrer7rUTo0mUTjz5afOWr1q19+c03wy23lP57Vt4Su+rxkNpCMag4JTLVIDMzk0GDBjFt2jQA0tPT6datGx07dmT//fcvN5Ho3r07559/Punp6bRs2ZIjjzyyoOyBBx6gZ8+e7LPPPvTs2bMgebngggu48sorGT9+PK+++mrB8cnJyUyaNIlzzz23YLL/FVdcUeG2XHvttZxzzjm88MIL9O/fn0aNGgHQv39/5s+fT0ZGBvXr12fAgAE89NBDTJ06lWHDhjFy5Ejq1avHK6+8wkGKKhJnvvjCd9/PnOlX4Hn0UbjmmqJXdUWixTnfOxHZ43HIIX4i+datfj+NyB6Pyy/3tx9/9InDzp1Fn/fxx/0yvD//DMFUS8CPUEhN9Zv9HXOM/75t2+IrW4WW2O3ZE2bNKl7epIkvHzjQ30pzyCFbSpzDGqKOf0l0ikGlM+dcrOtQJTIyMtzcuXOLPPbNN99w2GGHVej87OxsUlNTo1G1Gi/Wbd+dz6kqZWVlldkTVpslatt3t90LF8J998H06X5M/B13wPXX+1WF4s2efOZm9oVzLqNqa1S7lBSDdkfv3mvJz9+nSCJy/vnw9NM+kUlK8l/D3XgjPPGEH2LVsmXxHo2hQ+GSS/z8jzFjiq9s1a2b763IzfUrW6Wm+p/t6h5fr79HiSdR264Y1KdS55YVg9QjIyIS4Ztv/Br8L7/s/+kbNcoPbwldYRaparm5dUhN9cOuQslGqLPeDP7yF3/1NXweSMuWvrxhw6KT3CM1buwnxJemXj0/zl5EagbFoIpTIiMiEvj+e78L8osv+n8O777br1ikfRQk2h5++L9lXq288MLqq4uIxIZi0O5TIiMiCe9//4MHHoDJk/3V6d/+1nfha0dqERGJNsWgyqv1iYxzDtPaiDVWbZmjJfFp5Up46CF45hk/fOfaa+GuuwpXSxIREYkWxaA9V6sTmeTkZNavX0/z5s2VzNRAzjnWr1+vvWik2v38s58z8PTTftO8K67w+1RonoCIiESbYlDVqdWJTNu2bVmxYgVr164t99icnJyE/Yc6lm1PTk6mbdu2MXltSTybNtVl+HC/C3hOjt+t+957tc+EiIhEn2JQ1avViUy9evVo165dhY7NysqiW7duUa5RzZTIbZfEsHEj/OEP8OijR7N9O2Rm+iUt27ePdc0kmsysP/AEkAQ865x7OKL8QOB5YB9gA3Cxc25FUHYZMCI4dIxzbkq1VVxEahXFoOip1YmMiCS27GwYPx7GjfOB5Pjjf2XChH3o1CnWNZNoM7Mk4CngZGAFMMfMZjjnFoUdNg54wTk3xcxOBH4HXGJmzYD7gAzAAV8E5/5ava0QkXimGBR9dWJdARGRqrZtG/z+99Cund/xvHdvmDcP7r//awWQxHEUsNQ594NzbicwDTgz4pjDgfeC+++HlZ8CvO2c2xAkL28D/auhziJSCygGVR8lMiJSa+Tk+J3ODzrIL13Zowd89hnMmOF3MZeE0gb4Kez7FcFj4RYAZwf3BwGpZta8gueKiBShGFT9NLRMROLezp3w/PMwZoxfzrJPH3j1VTjuuFjXTGq424AnzWwI8CGwEsiv6MlmdhVwFUCrVq3IysqqdEW2bNmyR+fHs0Rte6K2G2pf23NzjVmzWjN16oGsW9eArl1/5Z57ltO58ya2bYNQU2tbu3dHtNoe1URGEy1FJJry8uCFF/xGYsuXQ69e/vsTT4x1zaQGWAmEL2baNnisgHNuFUGPjJk1Bs5xzm00s5VAn4hzsyJfwDk3EZgIkJGR4fr06RN5SIVlZWWxJ+fHs0Rte6K2G2pP20uKQS+9BCeeuDewd7Hja0u7KyNabY/a0LKwiZan4schZ5rZ4RGHhSZadgFG4ydaEjbRsid+nPN9Zlb8J0JEElJ+Prz4Ihx+uF9/v0ULmDkT/vMfJTFSYA5wqJm1M7P6wAXAjPADzKyFmYXi4F34C2sAbwG/MbO9g9jzm+AxERHFoBokmnNkNNFSRKrUrl1+yFiXLnDxxdCwIUyfDp9/Dqee6ndGFgFwzuUB1+MTkG+Al51zX5vZaDMbGBzWB1hiZt8CrYAHg3M3AA/gk6E5wOjgMRFJYIpBNU80h5aVNFmyZ8QxoYmWT1CJiZYan1w1ErXtidpuiL+2OwezZzdn0qR2fP99Yw44YCsjRy7nhBPWUqcOfPBBxZ4n3tpdlRKx7c65mcDMiMdGht1/FXi1lHOfp7CHRkQSmHPw5pswciQsWAAdO/ohZIMHQx0tmxVTsZ7sv0cTLTU+uWokatsTtd0QP213Dt56ywePOXPgkENg6lTIzGxEUtLur2EZL+2OhkRuu4hIZZQegyApKda1E4ju0LIKTbR0zp3tnOsG3BM8trEi54pI7fb++37t/VNPhV9+geeeg2++8d35CiAiIhJNikHxIZqJjCZaishu+/hjP1nyxBP9KjATJsC338Lll0PdWPch12DOwY4d/gZ+SeoFC+DTT+H772NbNxGReKEYFF+i9pE45/LMLDTRMgl4PjTREpjrnJuBn2j5OzNz+KFl1wXnbjCz0ERL0ERLkVpvzhy4917fjd+qFTz+OAwbBsnJsa7Zntm1C7Zv9zs9b9sG9etD69a+7L33IDvbPx46pkMHOPlkn5jccEPh46GvgwbBNdf48444omiZczBqFNx3H6xdC127+te5/no455yYvQUiIjVebY1BtV1Uc0tNtBSR8ixY4CCUaAwAACAASURBVMcfz5gBzZvD2LFw3XXQqFH0X3vLFp8QhCcaSUl+N2bwy2muWFFYtn077Luvrx/ALbfAd98VPf/II+HZZ335IYcU7w0ZPBheecXfP+cc2LixaPmQIT6RMfOr49Sr51fGSUnxt9xcf1xyMvTtW/h4Soo/rndvX96iBbz2mn+8XTtYvbrK3z4RkbgXyxgke06dZCISE4sW+Z6DV1+FJk38hmI33uj/cQ9dAfv5Z/8PeHgisX07nHeeL581y19FC08kzGDiRF8+cqQ/JlS2aVMvWreGr7/25YMH+6tv4Q47zNcN4He/8/sChNSpA8ceW5jI/PQTrFnjE4i99vJX8Q48sPD4YcP864aSjJQUOPjgwvK33vLtDS9v3LiwfM2a0t+/evVg8uTSyxs0gLPPLvxeiYyISKGSYtBNN0FqaqxrJrtDiYyIlCsvr+gQp3339cnGqlXw3/8WJgqh8osvhmbNICvL9z6ED39atw722w9ef93/M77XXr4X5KGHfLc+wObNPpj8/vfw6KPF63POOf6cGTPgT3/yw7VCiUCzZoXHJSfDPvsU9lhs3LiO9PT9CsqvvRbOOqtoIhF+/ksv+a+h8vr1i+4T8GqJ/cmFbr+97PKjjiq7XEREqtZ338H998Nf/+p7XUaMgN/+FvbWtutxSYmMSBzbtcv/0x+eRGzbBmlpvndg7Vo/PCpynsXBBzeiTx/fpf7AA0XLtm3zycGxx8Lf/w7nn184nClk9mw45hj4979h6NDi9erTxycES5b4ZCAlxSctGzfChg0+wbj9dujc2c8TCR86lZJSuCLMpZf6eoQPnUpJKUwmHn8c/vjH0idg3n130e+zsr6lT5/CRGbgQMq0335ll4uISHxYvhxGj4YXXvAXpW6/3d9atIh1zWRPKJERqWK7dvkejPr1/eTrJUuKJgmhRCM93a8wNX588UTktNP8pO5ffvG9D5HnjxrlexOWLIHDDy9eh4kT4cor/R/uIUOKliUlwT33pAD+eRcvLtob0aZN4dCu9u3h1luLJxIHHeTLTz3VJzWRiUiTJr582DDflgcf9PNGkpLg5pth+HCfaIHvvSlNly7+VpoGDcr5MEREJKGtWFE0Bt1wQ9EYJPFNiYwkjNzcwmTBucLHP/8c1q8vmiy0agVnnunLR4/2cxXCk42ePX13NED37j7hCJXl5MAVVxRO+O7UySc34W68EZ54wj9+xx3+sQYNCpOFjh39Y/Xr+1uTJkUTifbtfXnr1vDYY8UnfHfu7Ms7d/aTzcPPrVcPsrLWAnD00fDVV6W/Z506+XkipWnVqvRgsGaNP/fPf/btvPJK30PStm3pzyciIlIVFIMSgxIZibkdO/zqUeGJRG6uX/0J/Jru331XNJFo0KAwAXj4YZ+MhJ/fpg1Mn+7LTz7Zz9XIyyt8zZ49/XkA//d/fp5HuBNPLExkXnnF/0EMTxa2bCk89qijfH3Dk4XQqldm8Le/+fqG91qElt9NTvbP1bChn0geqWlTePfd0t+7pk19D0dpkpMLe0+qy9q18Mgj8NRTfi+TIUN80peWVr31EBGRxKMYlFiUyEiJ8vP9P+F16vh5DaGVo8KThVNO8f+Af/IJfPhh8fIJE/w/0k8/DX/5S9GynBy/IpWZ3+Mi1HsR0qhRYbLw1FM+GQjXpk1hIrNsmU90QonEPvv48pBBg3xSFJ6IhJc//7xPcsITkfBVSyKTnEh/+lPZ5aEVtkpiVnuWePz1Vz8x/4knYOtWuOgivyLMIYfEumYiIlLbKQYlJiUyccS5wuFR27b5q/EpKf6Xd8GC4itHDRgA++8PCxf6ZVojE41x4/xSsx9+2IIrryx6/s6dfshRp04wZUrJV/2XL/dLzb7/Ptxzj38svNdh+3afyNSp4x9v3rxospCf7ydpn3eeHwIVfm74P/fjxvnxreFDp8Ind//5z2W/b9deW/LjWVn+a0ZGBT8AKdHmzT5wPPoobNrkP89Ro/zPloiISDQpBiU2JTJVwLmiCULo/j77+LGY27b5ZWIjJ2yffDL06gUrV/rehcjz777b7wPxxRd+Faht24rOtXjpJf8LO28e9OtXvF7/+IdPZH780fd4hBKF0NcdO/xxTZrkctRRRctSUnziAdC/P0ybVnzC9777+vLf/tZvDJicXHRp2pBhw/ytNCef7G+l0cpRNdPWrfDkk74Lf8MGPxTv/vv9IgYiIiLRpBgkoESmwPTp+/HJJ0UTiaOO8mMrnfP/aIcnIdu2weWX+4ngW7eWvIHSvff68uxsyMwsXt6okU9kcnPhs8+KDn1q1cp/BX//yiuLJxrdu/vy7t19r0h4eagHBOD00/0Vi9Kkp2/ipptKL+/Qwd9KE1rhShLD9u1+ON3DD/tFDk491f+cq2dLRESiTTFIwimRCUya1I7Nm32PQigZqF/fl5n5npDGjYturhda9rZhQ/8LFblyVKhbs3lzv4NsZKIR2isjLQ2WLi29bm3bwh/+UHr53nv7HhuRaNqxA557zg/zW7UKTjrJB49evWJdMxERqe0Ug6QkSmQCL774Gf36HUeDBiUPj3rvvdLPTUqCO+8svbxuXY3VlPiVm+vnST3wgB+meNxx8OKLSp5FRCT6FIOkLCUs+JqYGjfOK3WOh0giys/3OyAfdpgf2rjvvvDWW36FOgUQERGJJsUgqQglMiJSxK5dfiGJI46Ayy7z879mzIBPP4Xf/EbJvoiIRI9ikOyOchMZMzvDzJTwiNRyzvlNRLt2hQsu8EMmX33Vr5p3xhkKHhIbikEiiUExSCqjIsHhfOA7M3vEzDpGu0IiUr2cg5kz/aahgwb5zUpffNHvTXTOOX4fIJEYUgwSqcUUg2RPlPvj4Zy7GOgGfA9MNrNPzOwqMythwWERiRfOwTvv+BVfTjvNr8M/aZJfYe/CCwtX1ROJJcUgkdpJMUiqQoXyXOfcZuBVYBrQGhgEzDOzG6JYNxGJkoULm9C3r98facUK+POfYfFiv29SXa1lKDWMYpBI7aIYJFWl3B8XMxsIDAUOAV4AjnLO/WJmKcAi4I/RraKIVJXPPvMbtb79djf23RfGj/erwWhTU6mpFINEag/FIKlqFcl7zwEec859GP6gc26bmV0RnWqJSFWaNw9GjoR//hNatIBrrlnKuHGHkJIS65qJlEsxSCTOKQZJtFRkaNko4PPQN2bW0MzSAJxz70alViJSJb76yk+W7NEDZs/2OyL/8AOcd94KBRCJF6NQDBKJS4pBEm0VSWReAXaFfZ8fPCYiNdSSJZCZCV26wNtvw333wbJlcPfdfk1+kTiiGCQSZxSDpLpUZGhZXefcztA3zrmdZlY/inUSkUr64QcYPRqmTvVjju+8E267DZo3j3XNRCpNMUgkTigGSXWrSI/M2mCyJQBmdiawLnpVEpHd9eOPcNVV0KGD3xH55pv91a/f/U4BROKeYpBIDacYJLFSkR6Zq4EXzexJwICfgEujWisRqZDVq+Ghh2DiRP/91VfDXXfBfvvFtl4iVUgxSKSGUgySWCs3kXHOfQ8cbWaNg++3RL1WIlKmX36BsWNhwgTIy4OhQ2HECDjggFjXTKRqKQaJ1DyKQVJTVGjbITM7DegEJJsZAM650VGsl4iUYMMGGDfOr72/fTtccolf0vKgg2JdM5HoUQwSqRkUg6SmqciGmH8CUoC+wLPAYMKWwhSR6Nu0CR57zN+ys+H882HUKD8eWaQ2UwwSiT3FIKmpKjLZv5dz7lLgV+fc/cAxQPvoVktEALZs8ZMl27WD+++Hfv1g4UL4298UQCRhVDoGmVl/M1tiZkvNbHgJ5QeY2ftm9qWZLTSzAcHj9cxsipn918y+MbO7qrRFInFCMUhquookMjnB121mth+QC7SOXpVEZNs2ePRR311/993Qqxd88QW89hoccUSsaydSrSoVg8wsCXgKOBU4HMg0s8MjDhsBvOyc6wZcAEwIHj8XaOCc6wz0AIaFNuEUSQSKQRIvKpLIvGlmTYHfA/OA5cBfK/Lkuhomsnt27IA//hEOPtivvd+1K3zyCfzjH9C9e6xrJxITlY1BRwFLnXM/BPvQTPt/9u48PKry/P/4+yYgIRD2gggqUAUsYkISVEA0FGxxg6K4RCpGLCKKVtwKFhAXvhVL1aLir4CC4oKKilqhVigpKlZFBZRNtqgBN0AggMEsz++PcxKHkGVIMplM5vO6rrmYOec559zPTGZunuWcAwwqVsYBjf3nTYDtAcsbmlldoAHwE7C3MpUQiQTKQRJpyjxHxszqAEucc7uBl8zsn0Csc25PeTsO6A07G8gCPjSz15xzawOKFfaGPeb3lC0E2hPQG2ZmccBaM3vOOZd55FUUqflyc2H2bLj3XvjqKzjzTO9a/GeeGe7IRMKnMjkIaIt3qeZCWcBpxcpMAv5tZjcADYH+/vL5eI2er/HOzxnjnNtVQnzXANcAtG7dmoyMjCBrdrh9+/ZVavtIFq11r0n1zsszFi06mqefPp7vvovllFN2M3bsVhIS9pCTA1UdZk2qe3WK1npD6OpeZkPGOVdgZo8C3f3XB4GDQe67qDcMwMwKe8MCGzLqDZOolpcHTz/t3Ql561Y4/XR44gno1w/8izOJRK1K5qBgpAFznHN/M7OewFwzOxkvf+UDxwDNgLfNbHFhPguIbwYwAyAlJcWlpqZWOJCMjAwqs30ki9a614R6l5SDnnkG+vVriln3kB23JtQ9HKK13hC6ugcztWyJmV1kdsT/rSqpN6xtsTKTgN+bWRbeaMwN/vL5wH683rAvgakl9YaJRKqCAu9kya5dvevvN2sGb7wBy5d7J1OqESNSpKI5aBtwbMDrdv6yQFcDLwA4594DYoGWwOXAv5xzuc6574B3gZSKBC9SEykHSW0RzH1kRgI3A3lmloN3Z2XnnGtc9mZBqVRvmIb1q0a01j0c9S4ogLffbsmcOR3IzGxIhw77uOeeTHr33oEZ/Pe/1ROHPvPoE8F1r2gO+hA40cw64DVgLsNroAT6EugHzDGzk/AaMt/7y3+Nl5MaAqcDD1VRfUTCpqAAXnkF7rwT1qzxTtx/5RUYNEiNF4lM5TZknHPxFdx3sL1hA/zjvGdmh/WGAd+ZWWFvmIb1QyBa616d9XbO6+2aMAFWroQuXWDePLj44kbUqVP9l4DRZx59IrXuFc1Bzrk8MxsNvAnEAE8459aY2d3ACufca8AtwEwzG4M3pTndOef86WyzzWwNXsNptnNudZVUSCQMSs9BUCeYuTkiNVQwN8Qs8XRj59yycjZVb5hEPefgrbe8Ox+//753KcunnoLLL4eYmHBHJ1LzVSIH4ZxbiDdtOXDZxIDna4HeJWy3D++iMyIRTTlIartgppbdFvA8Fm/a10d4DY1SqTdMot1//wvjx8M778Bxx8HMmXDllVCvXrgjE4koFcpBItFOOUiiQTBTyy4IfG1mxxLk6Ih6wyQavfeeN3y/ZAm0aQOPPgpXXw3164c7MpHIU5kcJBKNlIMkmlRkZmQWcFJVByIS6T76CM4917sD8qefwgMPwObNcN11SiAiVUg5SKQEykESjYI5R+ZhvGlf4DV8EvHuriwiwOrV3hVgFiyA5s3hvvtg9Gho2DDckYlEPuUgkbIpB0k0C+YcmRUBz/OA55xz74YoHpGIsW4dTJoEL7wAjRvDXXfBTTd5z0WkyigHiZRAOUgkuIbMfCDHOZcPYGYxZhbnnDsQ2tBEaqZNm7y7ID/zDMTFwZ//DLfc4t1QTESqnHKQSADlIJGfBXOOzBKgQcDrBsDi0IQjUnN98QX84Q/e9ffnz/cSx9atcO+9SiAiIaQcJIJykEhJghmRifWvIgZ4VxQzs7gQxiRSo2zbBpMnw6xZ3p2Pr78exo2Do48Od2QiUUE5SKKacpBI6YJpyOw3syTn3McAZpYM/BjasETC79tvvZMmH3sM8vO9nrA//xnatQt3ZCJRRTlIopJykEj5gmnI3AS8aGbb8W5OeTRwaUijEgmjnTvh/vvhkUfg4EEYNsy7Jn+HDuGOTCQqKQdJVFEOEgleMDfE/NDMugCd/UUbnHO5oQ1LpPrt3u1dd/+hh2DfPrj8cu+SlieeGO7IRKKXcpBEC+UgkSNX7sn+ZnY90NA595lz7jOgkZldF/rQRKpHdrZ3smSHDnDPPfDb33o3E3v6aSUQkXBTDpLaTjlIpOKCuWrZCOfc7sIXzrkfgBGhC0mkeuTk1OGvf/WSx4QJcOaZ8Mkn8OKL0LVruKMTEZ9ykNRKykEilRfMOTIxZmbOOQfeNfyBo0Iblkjo5OTAP/4Bd911Oj/8AAMGeNfk79Ej3JGJSAmUg6RWUQ4SqTrBNGT+BTxvZv/wX48EFoUuJJHQ+OkneOIJbwh/2zbo3n0/r79+FL17hzsyESmDcpDUCspBIlUvmIbMn4BrgGv916vxrhojEhHy8uCpp7y5x5mZ0Ls3zJ0LZqvo3Ts13OGJSNmUgySiKQeJhE6558g45wqA94FM4FTg18C60IYlUnn5+d7JkiedBFdfDS1bwqJF8Pbb0LdvuKMTkWAoB0mkUg4SCb1SR2TMrBOQ5j92AM8DOOf09ZMaraAAXnrJu2zlunWQkACvvgoXXODdFVlEaj7lIIlUykEi1aesEZn1eD1f5zvnznDOPQzkV09YIkfOOS9ZJCXBJZd4y158ET7+GAYOVAIRiTDKQRJRlINEql9ZDZkLga+BpWY208z64d1VWaRGcQ7+9S849VT43e9g/35vOP/TT2HIEKgTzEXGRaSmUQ6SiKAcJBI+pX69nHMLnHOXAV2ApcBNQCsze8zMflNdAYqU5T//gTPOgHPOge+/964Is24dDB0KMTHhjk5EKko5SCKBcpBIeAVzsv9+59yzzrkLgHbAJ3hXkREJm3fe8U6W7NcPvvgCHnsMPv8crroK6gZzLT4RiQjKQVITKQeJ1AxHNODpnPvBOTfDOdcvVAGJlOXDD72bh/Xp4/V6/f3vsGkTXHstHKVb5InUaspBEm7KQSI1i2ZuSkRYudI7WfLUU2HFCrj/ftiyBW68EWJjwx2diIjUZspBIjWTBkClRlu71ruE5fz50LSpd0fkG2+E+PhwRyYiIrWdcpBIzaaGjNRIn38Od90Fzz0HjRrBhAlw881eIhEREQkl5SCRyKCGjNQoW7fCPffAU09B/fpw++1w223QokW4IxMRkdpOOUgksqghIzXCV1/B5Mnw+OPeJStvvBH+9Cdo3TrckYmISG2nHCQSmdSQkbD6+mv4y1/gH//wbip2zTVwxx3Qtm24IxMRkdpOOUgksqkhI2Hx/ffeVV8efRR++sm79v748XD88eGOTEREajvlIJHaQQ0ZqVY//ABTp8K0aXDggHf344kT4YQTwh2ZiIjUdspBIrWLGjJSLfbuhYceggcegD174NJLYdIk6NIl3JGJiEhtpxwkUjuF9IaYZjbAzDaY2SYzG1vC+uPMbKmZfWJmq83s3IB1p5jZe2a2xsw+NTPdcioC7d8P990HHTp41+Lv2xdWrYJ585RAREQktJSDRGq3kDVkzCwGeBQ4B/gVkGZmvypWbDzwgnOuO3AZMN3fti7wNHCtc64rkArkhipWqXo//ggPPuglj3Hj4PTTvbshv/IKnHJKuKMTkWigzrTopRwkEh1COSJzKrDJObfFOfcTMA8YVKyMAxr7z5sA2/3nvwFWO+dWATjndjrn8kMYq1SRgwe9kydPOMG7eVhCAixfDm+8AcnJ4Y5ORKKFOtOik3KQSHQJZUOmLfBVwOssf1mgScDvzSwLWAjc4C/vBDgze9PMPjaz20MYp1SB3FyYNQs6dYLRo+GXv4SMDHjrLejZM9zRiUgUUmdaFFEOEolO5pwLzY7NhgADnHN/8F9fAZzmnBsdUOZmP4a/mVlP4HHgZOBm4HqgB3AAWAKMd84tKXaMa4BrAFq3bp08b968Cse7b98+GjVqVOHtI1ll6p6fD4sXt+app9qzfXsDTjppL8OHbyU5+QfMqjjQKqbPPPrqHq31hsrVvW/fvh8551KqOKSQCjIHtQH+DTQDGgL9nXMfmdlNQDLQCvgFMM85d38Jx1AOqgLKQdEnWuserfWGEOYg51xIHkBP4M2A1+OAccXKrAGODXi9BS9xXAY8GbB8AnBbWcdLTk52lbF06dJKbR/JKlL3/Hzn5s1zrnNn58C57t2de/115woKqj6+UNFnHn2itd7OVa7uwAoXolwRqgcwBJgV8PoK4JFiZW4GbnE/56y1eDMVbgW2Ai2BOOA9oF9Zx1MOqjjloOgTrXWP1no7F7ocFMqpZR8CJ5pZBzM7ym+cvFaszJdAPwAzOwmIBb4H3gS6mVmcP1f5LD/BSJg5550smZgIl10GdevCSy/BRx/B+edT43vARCRqbAOODXjdzl8W6GrgBQDn3Ht4Oagl3lToZc65Hc65A3hTn5NCHrGUSzlIRAKFrCHjnMsDRuM1StbhnVC5xszuNrOBfrFbgBFmtgp4Dkj3G18/AA/gNYZWAh87594IVaxSPue8kyVTUuDCC70TKp991ruM5YUXKnmISI2jzrRaRDlIREoS0htiOucW4vVkBS6bGPB8LdC7lG2fxrtqjISRc7BkCUyYAP/7n3cpyzlzvLsh19XtVEWkhnLO5ZlZYWdaDPBEYWca3jSF1/A602aa2Ri8E//T/WkMP5hZYWeaAxaqMy08lINEpCz6GZBSLVvmJY9ly+DYY2HGDEhPh3r1wh2ZiEj51JkW2ZSDRKQ8oTxHRiLU//4HZ58NZ50FGzfCww97/44YoQQiIiKhpRwkIsFSQ0aKfPyxd7Jkz57evOO//Q02b/auyV+/frijExGR2kw5SESOlKaWCVu2NGTaNO9KMM2awV/+4iWOKL3UuYiIVCPlIBGpKDVkotiGDTBpEjz/fArx8d7zm26CJk3CHZmIiNR2ykEiUllqyEShzZvh7rvh6aehQQNIS/uShx8+nubNwx2ZiIjUdspBIlJVdI5MFPnyS7jmGujSBV54AcaMga1bYcSIrUogIiISUspBIlLVNCITBbZvh//7P5g503s9ahSMGwdt2oQ3LhERqf2Ug6QmyM3NJSsri5ycnLDF0KRJE9atWxe244dTMHWPjY2lXbt21DuCyxOqIVOLffcdTJkC06dDXh4MHw7jx3vX4xcREQkl5SCpSbKysoiPj6d9+/aYWVhiyM7OJj4+PizHDrfy6u6cY+fOnWRlZdGhQ4eg96uGTC20axf89a/etfd//BGGDfNuKtaxY7gjExGR2k45SGqinJycsDZipGxmRosWLfj++++PaDs1ZGqRPXvgwQfhgQdg3z647DK4807o3DnckYmISG2nHCQ1nRoxNVtFPh81ZGqBfftg2jSYOhV++AEuusi7jOXJJ4c7MhERqe2Ug0QkXHTVsgh24ICXODp0gD//GXr39u6MPH++EoiIiISWcpBI8Hbu3Env3r1JTEzk6KOPpm3btiQmJpKYmMhPP/1U5rYrVqzgxhtvLPcYvXr1qqpwI4ZGZCLQwYMwY4Z3FZhvvoHf/Ma7Jv9pp4U7MhERqe2Ug0SOXIsWLXj33XeJj49n0qRJNGrUiFtvvbVofV5eHnXrlvzf8pSUFFJSUso9xvLly6ss3kihEZkIkpvrJY8TToAbb/TmHS9bBm++qQQiIiKhpRwkUrXS09O59tprOe2007j99tv54IMP6NmzJ927d6dXr15s2LABgIyMDM4//3wAJk2axPDhw0lNTaVjx45MmzataH+NGjUqKp+amsqQIUPo0qULQ4cOxTkHwMKFC+nSpQvJycnceOONRfsNlJmZSZ8+fUhKSiIpKemQBtKUKVPo1q0bCQkJjB07FoBNmzbRv39/EhISSEpKYvPmzaF5w0qgEZkIkJfn3QH57ru9m4f17Alz5sCvfw06b01EREJJOUhqm5tugpUrq3afiYnw0ENHvl1WVhbLly8nJiaGvXv38vbbb1O3bl0WL17MHXfcwUsvvXTYNuvXr2fp0qVkZ2fTuXNnRo0addi9Vz755BPWrFnDMcccQ+/evXn33XdJSUlh5MiRLFu2jA4dOpCWllZiTK1ateKtt94iNjaWjRs3kpaWxooVK1i0aBGvvvoq77//PnFxcezatQuAoUOHMnbsWAYPHkxOTg4FBQVH/kZUkBoyNVh+Pjz/PNx1F3z+OSQnw6OPwoABSh4iIhJaykEioXfxxRcTExMDwJ49e7jyyivZuHEjZkZubm6J25x33nnUr1+f+vXr06pVK7799lvatWt3SJlTTz21aFliYiKZmZk0atSIjh07Ft2nJS0tjRkzZhy2/9zcXEaPHs3KlSuJiYnh888/B2Dx4sVcddVVxMXFAdC8eXOys7PZtm0bgwcPBrybWlYnNWRqoIICeOUV77KVa9ZAt26wYAEMHKjkISIioaUcJLVdRUZOQqVhw4ZFzydMmEDfvn155ZVXyMzMJDU1tcRt6tevX/Q8JiaGvLy8CpUpzYMPPkjr1q1ZtWoVBQUF1d44ORI6R6YGcQ5ef93r9Roy5OfesJUrYdAgJRAREQkd5SCR8NqzZw9t27YFYM6cOVW+/86dO7NlyxYyMzMBeP7550uNo02bNtSpU4e5c+eSn58PwNlnn83s2bM5cOAAALt27SI+Pp527dqxYMECAA4ePFi0vjqoIVMDOOedLHn66V6PV3Y2zJ0Ln30Gl1wCdfQpiYhIiCgHidQMt99+O+PGjaN79+5HNIISrAYNGjB9+nQGDBhAcnIy8fHxNGnS5LBy1113HU8++SQJCQmsX7++aNRowIABDBw4kJSUFBITE5k6dSoAc+fOZdq0aZxyyin06tWLb775pspjL42mloVZRgZMmADvvAPHHQezZsGwYVDsnC0REZEqpxwkUv0mTZpU4vKePXsWnY8CcO+99wKQmppaNM2s+LafffZZ0fN9hbnG0wAAIABJREFU+/YdVh7gkUceKXret29f1q9fj3OO66+/vsTLOp944omsXr266PWUKVOKno8dO7boamWB5f/zn/+UWKdQUz9LmCxfDv36Qd++sGULTJ8OGzfC1VcrgYiISGgpB4lEp5kzZ5KYmEjXrl3Zs2cPI0eODHdIlaIRmWq2YgVMnAiLFkGrVvDggzByJDRoEO7IRESktlMOEoluY8aMYcyYMeEOo8poRKaarF4Nv/sd9OgBH3wAU6Z4vWA33aQEIiIioaUcJCK1kUZkQmzdOpg0CV54AZo0gXvu8e6I3LhxuCMTEZHaTjlIRGozNWRCZNMm7yZizz4LcXEwfjzcfDM0axbuyEREpLZTDhKRaKCGTBXLzPR6vJ58Eo46Cm69FW67DVq2DHdkIiJS2ykHiUg00TkyVWTbNrjuOujUCZ55BkaP9uYfT5miBCIiIqGlHCRSs/Xt25fFixcfsuyhhx5i1KhRpW6TmprKihUrADj33HPZvXv3YWUmTZpUdD+X0ixYsIC1a9cWvZ44ceJhsUQqNWQq6dtvYcwY+OUvvevv/+EP3pD+Qw/B0UeHOzoREanNlINEIkNaWhovvfTSIcvmzZtHWlpaUNsvXLiQpk2bVujYxRsyd999N/3796/QvmoaNWQqaMcO+NOfoGNHePhhGDoUPv/cuxZ/u3bhjk5ERGoz5SCRyDJkyBDefPNNfvrpJwAyMzPZvn07ffr0YdSoUaSkpNC1a1fuvPPOErdv3749O3bsAGDy5Ml06tSJM844gw0bNhSVmTlzJj169CAhIYGLLrqIAwcOsHz5cl577TVuu+02EhMT2bx5M+np6cyfPx+AJUuW0L17d7p168bw4cM5ePBg0fHuvPNOkpKS6NatG+vXrz8spszMTPr06UNSUhJJSUksX768aN2UKVPo1q0bCQkJRTfQ3LRpE/379ychIYGkpCQ2b95c6fdV58gcod274W9/83q79u/3ksfEiXDiieGOTEREajvlIJGqEXDj+yKXXOJN0TxwAM499/D16eneY8cOGDLk0HUZGWUfr3nz5iQnJ7No0SIGDRrEvHnzuOSSSzAzJk+eTPPmzcnPz6dfv36sXr2aU045pcT9fPTRR8ybN4+VK1eSl5dHUlISycnJAFx44YWMGDECgPHjx/P4449zww03MHDgQM4//3yGFAs6JyeH9PR0lixZQqdOnRg2bBiPPfYYN910EwAtW7bk448/Zvr06UydOpVZs2Ydsn2rVq146623iI2NZePGjaSlpbFixQoWLVrEq6++yvvvv09cXBy7du0CYOjQoYwdO5bBgweTk5NDQUFB2W9aEEI6ImNmA8xsg5ltMrOxJaw/zsyWmtknZrbazM4tYf0+M7s1lHEGIzsb7r0XOnTw/j3nHPjsM5g7VwlERERCSzlIJPINGTKEefPmAYdOK3vhhRdISkqie/furFmz5pBpYMW9/fbbDB48mLi4OBo3bszAgQOL1n322Wf06dOHbt268cwzz7BmzZoy49mwYQMdOnSgU6dOAFx55ZUsW7asaP2FF14IQHJyMpmZmYdtn5uby4gRI+jWrRsXX3xxUdyLFy/mqquuIi4uDvAacdnZ2Wzbto3BgwcDEBsbW7S+MkI2ImNmMcCjwNlAFvChmb3mnAv8dMYDLzjnHjOzXwELgfYB6x8AFoUqxmDs3w+PPgr33w87d8KgQd4lLRMSwhmViIhEA+UgkdAoawQlLq7s9S1blj8CU5LzzjuPO+64g48//pgDBw6QnJzM1q1bmTp1Kh9++CHNmjUjPT2dnJycI985kJ6ezoIFC0hISGDOnDlkVCTIAPXr1wcgJiaGvLy8w9Y/+OCDtG7dmlWrVlFQUEBsbGyljlcRoRyRORXY5Jzb4pz7CZgHDCpWxgGFt+VqAmwvXGFmvwO2AmU3J0MkJ8cbuv/lL715yKeeCh9+CAsWKIGIiEhoKQeJ1D6NGjWib9++DB8+vGg0Zu/evTRs2JAmTZrw7bffsmhR2f33Z555JgsWLODHH38kOzub119/vWhddnY2bdq0ITc3l2eeeaZoeXx8PNnZ2Yftq3PnzmRmZrJp0yYA5s6dy1lnnRV0ffbs2UObNm2oU6cOc+fOJT8/H4Czzz6b2bNnc+DAAQB27dpFfHw87dq1Y8GCBQAcPHiwaH1lhPIcmbbAVwGvs4DTipWZBPzbzG4AGgL9AcysEfAnvNGcUqeVmdk1wDUArVu3rlTLc9++fWRkZJCbayxc2Iannz6eHTvq0737D4wfv5WTT97Lvn0Va4HXdIV1jzbRWm+I3rpHa70hOutuZgOAvwMxwCzn3H3F1h8HPAk09cuMdc4tLLZ+LTDJOVf29U2ryE8/weOPw+TJ3iWVf/1rePll6NWrOo4uIqGWlpbG4MGDi6aYJSQk0L17d7p06cKxxx5L7969y9w+KSmJSy+9lISEBFq1akWPHj2K1t1zzz2cdtpp/OIXv+C0004rarxcdtlljBgxgmnTphWd5A/e9K7Zs2dz8cUXk5eXR48ePbj22muDrst1113HRRddxFNPPcWAAQNo2LAhAAMGDGDlypWkpKRw1FFHce655zJu3Djmzp3LyJEjmThxIvXq1ePFF1+kY8eOQR+vRM65kDyAIXiJo/D1FcAjxcrcDNziP++JlzDqAFOBS/zlk4BbyztecnKyq4zFizPc4487d/zxzoFzZ5zh3NKlldplxFgaLRUtJlrr7Vz01j1a6+1c5eoOrHAhyhWheuA1TDYDHYGjgFXAr4qVmQGM8p//Csgstn4+8KJyUGhF6/cyWuvtXHjqvnbt2mo/ZnF79+4NdwhhE2zdS/qcyspBoRyR2QYcG/C6nb8s0NXAAADn3HtmFgu0xBu5GWJm9+P1lBWYWY5z7pFQBPrcc3DbbT3Ytg169IB//AN+8xswC8XRRESkGhRNbwYws8LpzYHnaQYzvXl/qANVDhIRqZhQNmQ+BE40sw54DZjLgMuLlfkS6AfMMbOTgFjge+dcn8ICZjYJ2BeqRgzA4sXQoEE+r70G55+v5CEiUgtEzPTmuXM7U79+HJMnf0rPnjsxg//+t0K7ikjROO0RorfeEJ66N2nSpMTzRKpTfn5+2GMIl2DrnpOTc0R/GyFryDjn8sxsNPAm3hD/E865NWZ2N94Q0WvALcBMMxuD1zOW7g8hVau//x0++OAjfv3r1Oo+tIiIhE8aMMc59zcz6wnMNbOT8Ro4Dzrn9lkZPVvOuRl409NISUlxqSXdmCIIKSnwwQcZUZuDMjIyqOh7F8mitd4QnrqvW7eORo0aUdZ3OtSys7OJj48P2/HDKZi6O+eIjY2le/fuQe83pDfEdN5JkwuLLZsY8HwtUOZZTc65SSEJLkCjRlAnpHfUERGRahYx05uVg0RCLzY2lp07d9KiRYuwNmakZM45du7cecSXcA5pQ0ZERCRMImZ6s4iEXrt27cjKyuL7778PWww5OTlhuddKTRBM3WNjY2nXrt0R7VcNGRERqXUiaXqziIRevXr16NChQ1hjyMjIOKJpU7VJqOquhoyIiNRKkTK9WUREKkazckVEREREJOKoISMiIiIiIhHHast0YDP7HviiErtoCeyoonAiTbTWPVrrDdFb92itN1Su7sc7535RlcHUNspBlRKtdY/WekP01j1a6w0hykG1piFTWWa2wjmXEu44wiFa6x6t9YborXu01huiu+6RIJo/n2ite7TWG6K37tFabwhd3TW1TEREREREIo4aMiIiIiIiEnHUkPnZjHAHEEbRWvdorTdEb92jtd4Q3XWPBNH8+URr3aO13hC9dY/WekOI6q5zZEREREREJOJoREZERERERCKOGjIiIiIiIhJxoqohY2ZPmNl3ZvZZKevNzKaZ2SYzW21mSdUdY6gEUfehfp0/NbPlZpZQ3TGGQnn1DijXw8zyzGxIdcUWasHU3cxSzWylma0xs/9WZ3yhEsTfehMze93MVvn1vqq6YwwFMzvWzJaa2Vq/Xn8soUyt/Y2LBMpBykFllFMOUg6KaOHKQVHVkAHmAAPKWH8OcKL/uAZ4rBpiqi5zKLvuW4GznHPdgHuoPSekzaHsemNmMcAU4N/VEVA1mkMZdTezpsB0YKBzritwcTXFFWpzKPszvx5Y65xLAFKBv5nZUdUQV6jlAbc4534FnA5cb2a/KlamNv/GRYI5KAeVRjlIOUg5KLKFJQdFVUPGObcM2FVGkUHAU87zP6CpmbWpnuhCq7y6O+eWO+d+8F/+D2hXLYGFWBCfOcANwEvAd6GPqPoEUffLgZedc1/65WtF/YOotwPizcyARn7ZvOqILZScc1875z72n2cD64C2xYrV2t+4SKAcpBxUCuUglIOqI7ZQClcOiqqGTBDaAl8FvM7i8A8hGlwNLAp3ENXBzNoCg6ldPZ/B6gQ0M7MMM/vIzIaFO6Bq8ghwErAd+BT4o3OuILwhVS0zaw90B94vtkq/cTWbPh+PclB0UA5SDqr0b1zdymwstY+Z9cVLImeEO5Zq8hDwJ+dcgdc5ElXqAslAP6AB8J6Z/c8593l4wwq53wIrgV8DvwTeMrO3nXN7wxtW1TCzRni9uzfVljpJ9FAOiirKQcpBlaaGzKG2AccGvG7nL4sKZnYKMAs4xzm3M9zxVJMUYJ6fQFoC55pZnnNuQXjDqhZZwE7n3H5gv5ktAxKA2p5ErgLuc95NtDaZ2VagC/BBeMOqPDOrh5dAnnHOvVxCkaj+jYsAUf35KAcpB6EcFNHCkYM0texQrwHD/KsqnA7scc59He6gqoOZHQe8DFwRBb0hRZxzHZxz7Z1z7YH5wHVRkkAAXgXOMLO6ZhYHnIY3p7W2+xKvBxAzaw10BraENaIq4M+3fhxY55x7oJRiUfsbFyGi9vNRDlIOQjkoooUrB0XViIyZPYd3hYiWZpYF3AnUA3DO/T9gIXAusAk4gNdqrhWCqPtEoAUw3e8ZynPOpYQn2qoTRL1rrfLq7pxbZ2b/AlYDBcAs51yZlwiNBEF85vcAc8zsU8DwpnXsCFO4Vak3cAXwqZmt9JfdARwHtf83LhIoBykHoRykHKQcVKW/ceaNbImIiIiIiEQOTS0TEREREZGIo4aMiIiIiIhEHDVkREREREQk4qghIyIiIiIiEUcNGRERERERiThqyIhUgJnlm9nKgMfYKtx3ezOL+EtQiohIaCgHiXii6j4yIlXoR+dcYriDEBGRqKQcJIJGZESqlJllmtn9ZvapmX1gZif4y9ub2X/MbLWZLfHvYo2ZtTazV8xslf/o5e8qxsxmmtkaM/u3mTXwy99oZmv9/cwLUzVFRKQGUg6SaKOGjEjFNCg2rH9pwLo9zrluwCPAQ/6yh4EnnXOnAM8A0/zl04D/OucSgCRgjb/8ROBR51xXYDdwkb98LNDd38+1oaqciIjUaMpBIoA558Idg0jEMbN9zrlGJSzPBH7tnNtiZvWAb5xzLcxsB9DGOZfrL//aOdfSzL4H2jnnDgbsoz3wlnPuRP/1n4B6zrl7zexfwD5gAbDAObcvxFUVEZEaRjlIxKMRGZGq50p5fiQOBjzP5+fz2c4DHsXrOfvQzHSem4iIBFIOkqihhoxI1bs04N/3/OfLgcv850OBt/3nS4BRAGYWY2ZNStupmdUBjnXOLQX+BDQBDuuRExGRqKYcJFFDLWmRimlgZisDXv/LOVd4+ctmZrYar0crzV92AzDbzG4Dvgeu8pf/EZhhZlfj9XqNAr4u5ZgxwNN+ojFgmnNud5XVSEREIoVykAg6R0akSvnzk1OcczvCHYuIiEQX5SCJNppaJiIiIiIiEUcjMiIiIiIiEnE0IiMiIiIiIhFHDRkREREREYk4asiIiIiIiEjEUUNGREREREQijhoyIiIiIiIScdSQERERERGRiKOGjIiIiIiIRBw1ZEREREREJOKoISMiIiIiIhFHDRkREREREYk4asiIiIiIiEjEUUNGREREREQijhoyIiIiIiIScdSQERERERGRiKOGjIiIiIiIRBw1ZEREREREJOKoISMiIiIiIhFHDRkREREREYk4asiIiIiIiEjEUUNGREREREQijhoyIiIiIiIScdSQERERERGRiKOGjIiIiIiIRBw1ZEREREREJOKoISMiIiIiIhFHDRkREREREYk4asiIiIiIiEjEUUNGREREREQijhoyIiIiIiIScdSQkSpjZv/PzCZUddlwMrMMM/tDCPY7x8zu9Z/3MbMNwZSt4LH2mVnHim4vVcPMMs2sfynrUs0sq7pjEqlNlIOOaL/KQVGmtuYgNWQqyP+D+NH/ghY+7gh4nmNm+QGv1xTbvr2ZuYD135rZdDOrV84xHvHXpQfsf6+ZrTKz883suGLlnZntD3jdp1gcawLW5ftxF9XnSN4T59y1zrl7qrpsTWRml/mfjxVbXtfMvjOz84Pdl3Pubedc5yqK67Ck55xr5JzbUhX7L+OYP5hZ/VAdoyYxs0lmllvse3Z7iI9Z/Lfg36E8ntR8ykGHUw5SDgrVMWqScOQg/7h/NLOt/vd5nZl1CvUxg6GGTOVc4H9BCx//V/gcuBZ4L2Bd11L20dQv3w3oCVxfzjFGB6x7z9+2KTAdmAfsDSzvl0sIWPZ24M6dc10Dyr4NjA6sT2E5M6tbwfeotlqA976fVWz5AMAB/6r2iMLAzNoDffDqPLCajx3Ov8nni30v76+GYwb+FvymGo4nNZ9yUPRSDkI5qLpykN84vRo4D2gEnA/sCOUxg6WGTA3hnPsOeAv4VQW2LQDmAg2BE6sinoDeuqvN7EvgP/7yF83sGzPbY2bLzKxrwDaBQ9WpZpZlZrf4vUNfm9lVFSzbwsxe93v9PjSze83snTJiLy/GR83sDTPLNrP3zeyXAevPNrP1/raPAFbSMZxzOcALwLBiq4YBzzrn8sqKo1i8hwzpmll3M/vYj+95IDZgXTMz+6eZfe/3QP3TzNr56ybj/aA/Uqzn1JnZCf7zJmb2lL/9F2Y23szq+OvSzewdM5vq73urmZ1T2vscUN//AXOAK4vV61gze9k/1s7CePx1I/wenWwzW2tmScVj9V+X9HfyJzP7Bphd1vvhb9PczGab2XZ//QJ/+WdmdkFAuXpmtsPMupdT3zKZ2UDzeph3m9dLeFIp5Rr4dfvBzNYCPSpzXJHKUg5SDgp4rRz0c7moz0H+53MnMMY5t9Z5NjvndlUm1qqihkwNYWbHAL/F+0Ie6bYxwFVALvBFFYd2FnASXmwAi/ASVSvgY+CZMrY9GmgCtMVryT9qZs0qUPZRYL9f5kqK/ViVoLwYLwPuApoBm4DJAGbWEngZGA+0BDYDvcs4zpPAEDNr4G/fBLjAXx5MHIcxs6PwetrmAs2BF4GLAorUAWYDxwPHAT8CjwA45/7MoT2agT2nhR7Ge5874n22w/D+dgqdBmzw638/8LiZlZhIfcP8ej0D/NbMWvv1iAH+iff32B7vc53nr7sYmORv2xivF21nWe9LgKPx3pfjgWso4/3wzQXigK54n8OD/vKngN8HlDsX+No590mQcRzGvGH254CbgF8AC4HX/c+0uDuBX/qP31L+3zTAM36y/LeZJVQ0TpGSKAcpB/n7UA4qWzTmoHb+42Qz+8pvYN5V2AANO+ecHhV4AJnAPmC3/1hQbH068E4Z27fHGwot3N4By4HGZRxjNzAiYP95/rJcvC/PJSUcxwEnBFmnDOAPxeLrWEb5pn6ZJv7rOcC9/vNUP6a6AeW/A04/krJAjF+/zgHr7i3rvQ0ixlkB688F1vvPhwH/C1hnQFbhe1LK/jcCl/vPRwCrKvheZfnPzwS2Axaw7fLCsiXsNxH4oaTPsPjfgP9e/gT8KmDdSCAj4G9qU8C6OH/bo0s59hn+Z9PSf70er8cGvCkq3wd+pgHbvQn8sZR9HvL3WsL79BMQW8bnUfR+AG2AAqBZCeWOAbLxv2/AfOD2IP+mJvlxBH4vjwEmAC8ElKsDbANSA77P/f3nW4ABAWWvKfwbKOWYvYEG/mcyDvgGb0pQ2H8L9QjPA+UgUA4C5SDloBDnIKCX/7684f8dtQc+x/8tCPejZrSmItfvnHNN/cfvyipo3hVSSjqBsaVzrineF/ZdvC9Yacdo6pybGbDuf/62zYDX8IZ0q9pXAXWIMbP7zGyzme3F+1KA13NSkp3OubyA1wfw5lYeSdlfAHUD4yj2/BBBxvhNKTEdE7hv532DSz2W7yl+Htq/wn9dkfeq0DHANv/YhYp6OM0szsz+4Q/J7wWWAU393qfytATqcWiP6Rd4PVWFit4b59wB/2lpn9mVwL+dc4XzZJ/l516dY4Evin2mBKzbHES8JfneeVMqgHLfj2OBXc65H4rvxDm3He/7dpGZNQXOIYjeygAvFPtebsf77IreW+dNt/mKQ9/fQof8rVFOL7Zz7l3n3I/OuQPOub/gJa5QfN8lsigH+XUoZVvlIOUg5aDK56Af/X/vd87tds5lAv/Aa4SHnRoy1cR5V0g57ATGgPU/4rX8T/eHl49k3/uAUcAVlZ1fWdLuA55fDgwC+uMNDbf3l5c17FtZ3+P1+rULWHZsGeUrE+PXgfv2h7PLOhZ4w8b9zKwnXu9d4Q9RReP4GmhbbCj9uIDntwCdgdOcc43xes8C9xv4eRW3A6/36vhi+95WTkyH8acyXAKcZd4c7G+AMUCCP+3pK+A4K/lkyK/whrNLcgDvP1SFji62vnj9yno/vgKa+0miJE/iDe1fjHfS8hG/D8VsJ+C9Dfj7KWm/h/ytcehnHAxHaL93UssoB1WYcpByUKBozEEb8EaAAute1udcrdSQqSHMu2zgFXi9EcHO1SzivJOuZgETqzi0QPHAQbz44oDDkmFVc87l480ZnuT3fHTh8JMbqyrGN4CuZnah/+N3I4f/iBWPLxN4B29e6lvOucLepIrG8R5e0rzRvJP/LgRODVgfj9c7stvMmuPNcw30Ld7c45Jizcc7OXSymcWb2fHAzcDTQcYW6HdAPt6JwYn+4yS8+dHDgA/wfijvM7OGZhZrZoVzvWcBt5pZsnlO8GMBWAlc7vcmDuDwK/IUV+r74Zz7Gm+O+HTzTsisZ2ZnBmy7AEgC/ojfi1nIvMuaph/JG4L33p5nZv3Mu4TtLXh/A8tLKTvOj6sdcENpOzXvcra9zewo/328Da9n890jjE+kVMpBJVMOUg4qJ45an4P8kbHngdv9z60d3lS0fx5hfCGhhkz47TazfXhf/p7AwGJDuq/bodcKf6WMfT0EnGtmp4Qo1qfwhh+3AWupwEmhFTQar0fpG7zep+fwvpwlqXCM/vD0xcB9eD/+JxLcfxafxOsFCfwhqlAczrmfgAvx5grvAi7FS6KFHsI7V2KHv8/il9j8O97Jnz+Y2bQSDnED3kmrW/CS37PAE8HEVsyVwGzn3JfOuW8KH3gnOQ7F6426AG9e9Jd487wv9ev4It7Jrc/izRFegHfyJHg/6BfgTZ0a6q8rS3nvxxV4PYDr8ea831S4wu+BfgnoQMB7bN6JkS04wr9v59wGvN61h/14LsC7dO1PJRS/C+/vYyvwb7y/69LEA48BP+D9PQ0AznHOHfF/NkVKoBxUPuWgnykHHSoachB434F9eKM+71Hxz63K2aG/VyI1n5lNwTv578pyC4uUwcwmAp2cc78PWHYGcL1zLi18kYlITaUcJFVFOajy1JCRGs8fyj8K+BTvWucL8a6KUl5PiUip/GkAnwBXOOeWhTseEamZlIMkFJSDqoamlkkkiMcbdt2PN0/zb8CrYY1IIpqZjcA7EXOREoiIlEM5SKqUclDV0YiMiIiIiIhEHI3IiIiIiIhIxCnp+toRqWXLlq59+/YV3n7//v00bNiw6gKKINFa92itN0Rv3aO13lC5un/00Uc7nHO/qOKQahXloIqL1rpHa70heuserfWG0OWgWtOQad++PStWrKjw9hkZGaSmplZdQBEkWuserfWG6K17tNYbKld3Myvrrs+CclBlRGvdo7XeEL11j9Z6Q+hykKaWiYhIrWRmA8xsg5ltMrOxJaw/3syWmNlqM8vwb/SGmSWa2XtmtsZfd2n1Ry8iIuVRQ0ZERGodM4sBHgXOwbv7d5qZ/apYsanAU865U4C7gb/4yw8Aw5xzXfFuQPqQmTWtnshFRCRYasiIiEhtdCqwyTm3xb+z9TxgULEyvwL+4z9fWrjeOfe5c26j/3w73h25dY6QiEgNE9JzZMxsAPB3IAaY5Zy7r9j644En8BLELuD3zrksM0sEHgMaA/nAZOfc80d6/NzcXLKyssjJySm3bJMmTVi3bt2RHqJWCGfdY2NjadeuHfXq1QvL8UWk1mqLd5+GQlnAacXKrAIuxMtTg4F4M2vhnNtZWMDMTsW7GeLm4gcws2uAawBat25NRkZG8fU0bNiQmJiYcoNt3Lgxn3zySfm1qoXCWff8/Hz2799POG5FsW/fvsP+ZqJFtNY9WusNoat7yBoyAcP6Z+MlkA/N7DXn3NqAYoXD+k+a2a/xhvWv4Odh/Y1mdgzwkZm96ZzbfSQxZGVlER8fT/v27TGzMstmZ2cTHx9/JLuvNcJVd+ccO3fuJCsriw4dOlT78UUk6t0KPGJm6cAyYBte5xkAZtYGmAtc6ZwrKL6xc24GMAMgJSXFFT+RdevWrcTHx9OiRQvloDKEOwdlZ2eHJQfpxO/UcIdR7aK13hC6uodyalnYh/VzcnKCSiASHmZGixYtghoxE5GK+fJLuO462LXrqHCHUt22AccGvG7nLyvinNvunLvQOdcd+LO/bDeAmTUG3gD+7Jz7X0UCUA6q2ZSDREIv1DkolA2Zkob12xYrUzisDwHD+oEFyhrWD4YSSM2mz0ckNL7+Gm64AU48EWbNgk8/bRLukKrbh8CJZtbBzI4CLgNeCyxgZi3NrDAPjsOb6oxf/hW8GQPzKxOEfuPUbluIAAAgAElEQVRqNn0+IqFRXTko3PeRqdSwfnnzk5s0aUJ2dnZQgeTn5wddtrYJd91zcnLCMmdUc1Uzwh1GtYuGev/wQz2ee+44Xn31GPLy6nDOOV9zxRVf0LDhTjIyvg93eNXGOZdnZqOBN/HO03zCObfGzO4GVjjnXgNSgb+YmcPLQdf7m18CnAm08PMTQLpzbmV11kFEJNJ89x1MmQLTp0NuLgwfDuPHw5Ytock/oWzIBDWsjz8iY2aNgIuOZFi/vPnJ69atC3rebSjm6O7cuZN+/foB8M033xATE8MvfuHNkPvggw846qjSh9lWrFjBU089xbRp08o8Rq9evVi+fHml4gz33OzY2Fi6d+9e7cfVXNXUcIdR7WpzvXftgr/+FR5+GH78EX7/e5g4EX75y2OAY2p13UvjnFsILCy2bGLA8/nAYSMuzrmngadDHmCIRUoOEpHIV3oO8tZv2RKa44ayIVM0rI/XgLkMuDywgJm1BHb5oy0hGdYPpxYtWrBypdeBN2nSJBo1asStt95atD4vL4+6dUv+CFJSUkhJSSn3GEogItFtzx548EHvkZ0Nl14Kd94JXbqEOzIJN+UgEQm1cOegkJ0j45zLAwqH9dcBLxQO65vZQL9YKrDBzD4HWgOT/eWFw/rpZrbSfySGKtbqlJ6ezrXXXstpp53G7bffzgcffEDPnj3p3r07vXr1YsOGDYDXc3z++ecDXgIaPnw4qampdOzY8ZAeskaNGhWVT01NZciQIXTp0oWhQ4cWXU5y4cKFdOnSheTkZG688cai/QbKzMykT58+JCUlkZSUdEhymjJlCt26dSMhIYGxY72bY2/atIn+/fuTkJBAUlISmzdX6BQmEamgffvg//4POnSAu+6Cfv1g1Sp47jk1YqR0ykEiUhVqSg4K6TkyNWlY/6abYGUZs5vz8xsQxKX+D5GYCA89dOSxZGVlsXz5cmJiYti7dy9vv/02devWZfHixdxxxx289NJLh22zfv16li5dSnZ2Np07d2bUqFGH3Xvlk08+Yc2aNRxzzDH07t2bd999l5SUFEaOHMmyZcvo0KEDaWlpJcbUqlUr3nrrLWJjY9m4cSNpaWmsWLGCRYsW8eqrr/L+++8TFxfHrl27ABg6dChjx45l8ODB5OTkUFBw2ClMIhICBw7AY4/BfffBjh1w3nlw992QlBTuyKQsykHKQSK1QU3LQeE+2T8qXXzxxUU3SNuzZw9XXnklGzduxMzIzc0tcZvzzjuP+vXrU79+fVq1asW3335Lu3btDilz6qmnFi1LTEwkMzOTRo0a0bFjx6Jr5KelpTFjxozD9p+bm8vo0aNZuXIlMTExfP755wAsXryYq666iri4OACaN29OdnY227ZtY/DgwYB3jouIhNbBgzBjhtcD9s030L8/3HMPnH56uCOTSKMcJCJHqqbmoKhpyJTXa5Wd/WO1nfDesGHDoucTJkygb9++vPLKK2RmZpZ6Mm79+vWLnsfExJCXl1ehMqV58MEHad26NatWraKgoECJQaSGyM2F2bPh3nvhq6/gzDPh+ee9fyVyKAeVTTlIpGaq6TkolPeRkSDs2bOHtm292+vMmTOnyvffuXNntmzZQmZmJgDPP/98qXG0adOGOnXqMHfuXPLzvatgn3322cyePZsDBw4AsGvXLuLj42nXrh0LFiwA4ODBg0XrRaRq5OXBnDnQuTOMHAlt28Jbb0FGRs1JIBL5lINEpCSRkoPUkAmz22+/nXHjxtG9e/cj6r0KVoMGDZg+fToDBgwgOTmZ+Ph4mjQ5/KZE1113HU8++SQJCQmsX7++qMduwIABDBw4kJSUFBITE5k6dSoAc+fOZdq0aZxyyin06tWLb775pspjF4lG+fnw7LPQtStcdRU0awb//CcsX+4N5ev+fVKVlINEJFDE5SDnXK14JCcnu+LWrl172LLS7N27N+iykSY7O9s551xBQYEbNWqUe+CBBw5ZH+66H8nnVJWWLl0aluPWBNFa95pc7/x85+bPd65rV+fAuZNPdu7ll50rKKia/Vem7ng3kAz773xNfigHlU45qGQ1+fco1KK17jW53pGagzQiEwVmzpxJYmIiXbt2Zc+ePYwcOTLcIYmIzzl4/XVIToYhQ7zh/Oee8y5jOXhwDez9EjlCykEiNVek56CoOdk/mo0ZM4YxY8aEOwwRCeCcN994wgT44APo2BGefBIuvxxKuUehSERSDhKpeWpLDtKIjIhINfvvf72TJX/7W/j6a5g5E9avh2HDIiuBiIhI5KlNOUgNGRGRavLee97JkqmpsHkzPPIIbNwIf/gDFLu3oIiISJWqjTlIDRkRkRD76CM491zo1QtWr4YHHvCSyPXXQ8CtN0RERKpcbc5BasiIiITI6tXwu99BSgq8/z785S+wZQuMGQMNGoQ7OhERqc2iIQepIRNCffv25c033zxk2UMPPcSoUaNK3SY1NZUVK1YAcO6557J79+7DykyaNKnoWvqlWbBgAWvXri16PXHiRBYvXnwk4YtIBa1bB5deCgkJsHQp3HUXbN0KY8dCo0bhjk6ihXKQSHSKphykhkwIpaWlMW/evEOWzZs3j7S0tKC2X7hwIU2bNq3QsYsnkbvvvpv+/ftXaF8iEpxNm7yTJU8+Gd54A+64w0seEydC48bhjk6ijXKQSHSJxhykhkwIDRkyhDfeeIOffvoJgMzMTLZv306fPn0YNWoUKSkpdO3alTvvvLPE7du3b8+OHTsAmDx5Mp06deKMM85gw4YNRWVmzpxJjx49SEhI4KKLLuLAgQMsX76c1157jdtuu43ExEQ2b95Meno68+fPB2DJkiV0796dbt26MXz4cA4ePFh0vDvvvJOkpCS6devG+vXrD4spMzOTPn36kJSURFJSEsuXLy9aN2XKFLp160ZCQgJjx44FYNOmTfTv35+EhASSkpLYvHlzFbyzIjXLF194J0t26QLz58PNN3vJY/JkaN483NFJtFIOUg6S6BDNOSjCLrJWOamphy+75BK47jo4cAAuuODw9enp3mPHDu9GQYEyMso+XvPmzTn11FNZtGgRgwYNYt68eVxyySWYGZMnT6Z58+bk5+fTr18/Vq9ezSmnnFLifj766CPmzZvHypUrycvLIykpieTkZAAuvPBCRowYAcD48eN5/PHHueGGGxg4cCDnn38+Q4oFnZOTQ3p6OkuWLKFTp04MGzaMWbNmFf3ot2zZko8//pjp06czdepUZs2adcj2rVq14q233iI2NpaNGzeSlpbGihUrWLRoEa+++irvv/8+cXFx7Nq1C4ChQ4cyduxYBg8eTE5ODgUFBWW/aSIRZNs2L1HMmuXdNOy662DcOGjTpmqP4xwcPAh793rTAuLivN+k996D7Gxv+d693vNhw+DEE+Gdd+Duu73lF10EPXpUbUxy5JSDlINEqlJ15aCaLKoaMuFQOLRfmEQef/xxAF544QVmzJhBXl4eX3/9NWvXri01ibz99tsMHjyYuLg4AAYOHFi07rPPPmP8+PHs3r2bffv28dvf/rbMeDZs2ECHDh3o1KkTAFdeeSV///vfi9ZfeOGFACQnJ/Pyyy8ftn1ubi6jR49m5cqVxMTE8PnnnwOwePFirrrqqqIYmzdvTnZ2Ntu2bWPw4MEAxMbGlv+GiUSAb7+F++6Dxx6D/Hy4+mr485/h2GMPLZeb6zUuAhsbxx4Lxx0Hu3bBnDk/N0AK1199NfzmN95JmoMG/bwuN9fb57x53tzn1ash4KcA8BJZz55eQyY/39u2SROIj6+Wt0VqIOUg5SCpfYLNQdEgqhoyZfVexcWVvb5ly/J7v0oyaNAgxowZw8cff8yBAwdITk5m69atTJ06lQ8//JBmzZqRnp5OTk7Oke8cSE9PZ8GCBSQkJDBnzhwyKhJkgPr+dfhiYmLIy8s7bP2DDz5I69atWbVqFQUFBUoMUus45zUIwDthMnC0Y/t2WL4cXn0VfvwROnWCzp29XrErrvDKDRsGN90E330HrVsfvv+//MU74XLPHrjlFm9Zw4be/OX4eNi501vWtCn06eMtL1zXuDEkJXnrU1Lgww9/Xt64sfc7Vhj7WWd5IzaFKvnTIFVAOah8ykEipdu5E+6/37v/S04OXHklTJgAHTqE7pjOeSPGe/d6eeuYY7x8k5UFixf/nCMLHzff7OXGRYu8XLd3L/z+99CvX2jii6qGTDg0atSIvn37Mnz48KITLPfu3UvDhg1p0qQJ3377LYsWLSK1pDkHvjPPPJP09HTGjRtHXl4er7/+OiNHjgQgOzubNm3akJubyzPPPEPbtm0BiI+PJzs7+7B9de7cmczMTDZt2sQJJ5zA3Llz6d27d9D12bNnz/9n777DpCrPPo5/bxYIHQUUEVAsRLBRRLGhqDFBLNgQxIYxGhNLYjSWqIhib7GhxgqoiC0oGmKJcaNGfRUTFAFF7ChERBQXVNjd+/3jOcOena3s7tnZmfl9rmsuZuacmXM/M7Pn5qmHHj160KxZMyZPnkxJSQkA++67L5dccglHHXXU2m79Tp060aNHDx5//HEOPvhgfvzxR0pKSta2mIk0FPdQsVixIrRORX8G/OMfsGRJ2Ql2zpzNWLgwjCWGMFTn00/LV1YOPhjuuy9sHzQonMDTjRkTkseee8I775RVJLp3LxuPvN56MGFC+YpG+/ah4gOhV2b58vBcQUHFY2yyCUyZUnWZO3QI8YlURzlIOUiy3zffhGu//PnPsHIlHHkkXHRRqDBUxR2KikLlI17R6N07VHz+9z/4y1/KKiip7WecEUYF/N//wbBh4bn4iMy//hUOOSTkvuOPL3u+VauQl8aMCXG1bx+O06FDmLuTFFVkGsGRRx7JIYccsnb1mH79+jFgwAD69OlDz549azyJDxw4kFGjRtGvXz823HBDdowNdp8wYQKDBw9mgw02YPDgwWsTx+jRoznxxBO56aab1k6whNC1fu+99zJy5EiKi4vZcccdOeGEE2pdlt/+9rccdthhTJkyhWHDhtG2bVsAhg0bxuzZsxk0aBAtW7Zk+PDhXH755dx33338+te/Zty4cbRo0YJHHnmEzTffvNbHk9yWmveRGj713XehIpL6P9VDD8HcueWHXm2wAdx6a9h+0EHw0kvlT7S77BJ6TSCckN95p+x4BQU9WbmyrCJjBp07h5NtqsIRrxzcdVdoVZo+PSSE/fYLyWPw4LD9f/+rumwtW8IFF1S9vaAgVHZEkqYcpBwk2SM1KsAdXn8d7r4bpk4NFZgddoDf/S6MAPj++zCkLL1H5OST4bTTwkiByoaaXXttGA3w7bchn6VGBKRuqc7ZjTYKx4lvi+fIIUPCNWlSjXQtW5Y/zu67h1tKUqMCzN2TeedGNmjQIE+tfZ8yf/58+vbtW6vXf/fdd7TP04HkmS77unxPDamwsLDaVshcVp+yFxeH/4SbhaFWn3xSvkfju+/g9NPD9smT4emny1dW3OGtt8J7jR4dKitxG20EixeH+wcdBE89FU6SqYpG377w2GNh+9VXh+7teI/HJpvA/vuH7e+9F2JNbXvttUL22qvmcq9aBRMnwlVXha78Aw8ME+f796/TR9Yk1Oc7N7M33V39P9VQDqq7TJddOajx5WLZU3Mii4thww3Dc88/H4YZl40K+Ji99+7F2LFh+yGHhDwa7xE54ogwdOyWW+Cccyoe55RTwrY1a8p6PDp2LMuDI0eG9/j++9DoF6+EdOwIm28e8mxpacjHlY0ISEJSOUg9MiJ5oLQ0tOakKhQ//hhWXn///TCPIt6a89134T/tnTuHIVY33lh+26pV4T/3nTrBzTeHCYfpTjopXDX4ww/hzTfLKhKbblq+F+LYY2G33cqfaOPbH3kEWrSAZlUsFH/22dWXOzWMKyU1f6QqP/wQutqvuCL0tvziF+Gz2Gmn6l8nIiLZafXq8hWJ4uKyVR6nT4cFC8q2ffttmPt4zTVh+4gRYQjWihWh4gBhRMELL4T7v/lNyLMpZpuyahVrKzLuIZf26lU2z/Hbb0Nl43//gwEDwr477VSWI9dfP7y2RYvQkFeV1q3L5mFWpqq8mm1UkRFpwoqLQ6UhfYndHXYIE+7efz/0eqSvfHXNNaH34LHHwhjWoqJwwky5/fYwHKOwMFQ6Ulq2DCfK3/8+VGRatw4n7d69y8/1aB6dOY47Lpy045PRO3QIY2UhXE344ourLt/w4dWXP5r3m7jVq+Gee+DSS0N3/NChYS3+eLe4iIg0Ld9+W77HY8WKkO+OOipsf+gh+Ne/ym9v1gz++c+w/YgjQoNZ3CabhJEGEBq2nnkm9Fqkej369Svbd+DA0LsRb4yLT7z/619Dvkxte+ONf5UbFfD44+Ff5aC6y/mKjLtjNTXDSsbkytDGuNWrQw9Iq1ZhDsgbb1ScBzJkSGjx+ewzOOus8pWUFStCL8fo0WF8bGXD1x9+OHQff/JJ6D2In0Tbtw/HBdhiC/jlLyuufNWmTRgEO3JkWEkktS294nD44RWvXRHXp0+yk/iSVlwcJtRPmAAffwy77hoe7713piOTXKEc1LTlYg5qylIrYEVrNPDFFzBvXsV5HqefHvLSI4+UNdbFe00++SRcU2vCBLjuuorHGTUqVCBeeSW8R3z4VapHA0J+69+/fA7t3Lls+7RpoYGvdevKe/SruJbsWttuW/5x+nsoB9VfTldkWrVqxbJly+jcubMSSRPk7ixbtqxJLJ9ZUhJacVInybZtQ1evO9x7b/lKyIoVsMceYWWOlStDpSR+Av7xx3ByGz8+XCtkyJCKx7vqqlCRKS0N1wNJVTA23DD8m7qY1ZZbhrka6Stfbbll2L733uFEWNXPu39/uOGGis8XFoaLkqy3Xn5OOC8pgQcfDL1FCxeGyYu33RaGkulUIQ1FOahpa0o5qKkrLS2fI7t3DxWDzz6D554rnwO//TYM++3dO8xxPO+88ttLS+Guu8KogOnT4dRTKx5v9OiQ71asCCtPpno6UnkwVf8cNSrkufgckQ4dyoZN3XhjuFXliCOqL3dS+VE5qOHkdEWmR48eLFq0iKVLl9a47w8//JC3J7NMlr1Vq1b06NGjXu/x5ZdhGdt4RaN9+7I1y6++OiyxGx96teOOYVlBCPM2Pv20/Hsec0xoFTELE+tSq3iklhfs0qXscY8eZRWN1L977BG2d+kCzz5bcehVu3Zlx54/v+qybbhhuFJvVXJljGtjKS0NrXMXXRQ+9+23D137Bx2k5CENTzmodrI9BzVlpaUVeztWrAjL426+eagk3H57xSV6zzoL9t039Gjst1/In/HOq8cfD/ND5swJK2eltGkTctxxx4WKTMeO4d/0la/WXz80po0YEc7D6aMKUitgnXBC+fdPt+OOZfNZsoFyUMPL6YpMixYt2KyWVwkqLCxkwIABCUfUNDV22X/8MbTspLpv58ypuPJVy5ZhiUEIf/CvvVa+orL55mVL+e2/P6QtFsRuu5VVZKZODa1G8ZNkfJWOE08MrSPxykiqxwPCylft2oXnW7Qof5yCApgxo+qytmgRkoFklnv4nv7wh0F8+GFY+ezhh+Gww1QZlOQoB9VOPpe9KvEleOfMqTi0auutQ2//ypWhRyO9ovKb34ThWYsWhQazdNdfH5an/+ab0CsQb2jr2LFsePLGG5cfnpy+BO+ee4YhUanc2Tztf5VDhlQ+KqGwcDUQGgJzuB65lnJQcnK6IiMNp7i44oTznXcOf4D//neYyxGvaKxaBfffH1574YXwwANlr12zJnTXLl8etk+YUHGyXffuZRWZpUvLelm6dw8nzC22KNv3wgvDcasa4zp7duVlSlWEqrvWB4SJf5Kd3MNEzQsvDJXd7t2bcd994WJijbXkpIjkj9S1sUpKwiRwCL3yqTwWvyhhr15h+4EHhiXn49vHjAnXsYIwoTw1pyTl1FNDBaFFi7DEb2poVWoFrI03Dvt16RIqLelL9KYupfPTn4b3ruo/0716hYswVqVt23CTyikHJU8VmTzw3XehVSZ9ZasRI8Kkt9mzO/LQQxXngcycGVasuuyyyv+zv3x5qJDMmBGGb0FZt3KHDqHC0qJFqAjsvnv54VXxcacXXwx//GP5FqH4hZdTFz+sykEH1f8zktzzz3+G5PHKK6FF8u67YdNN32CfffbMdGgi0oS4h+HD8YpESUnZsuuPPRaW4I33iHTrVrYE7/77ly3qkurJ2GuvspWxTjklzINIKSgILfG/+U143Lx52eqQqRy4885hm1lY+SqeW+M5tGXLikOj49q0CT0vVVFvQHKUgxqHKjJNjHtYi/y778JwprZtw/K7//53xZWtjj46dE++9lr4Y0mvqMycGSoQjz8erteR7s03Q0Xm889b8+ij5YdedetW1gI0ZEi4lkb6hPPWrcP2888Pk/natavYrQxh6NaJJ1Zd5gxch0xy2Msvh7+HwsLQg3fbbWFoRMuWUFioFYpEcs3y5WVL8KYqGytXhrmOECZVpy/B27x5WUXj8MNDZSGuVy/46KNw/y9/CRPaW7Qo69WIj4TbaafQYBfv9YiPKHziifJL8KZWwEqNCpg+vfryqbEuuygHNS5VZBrI6tXlKxErVoRxn5ttFk6sd95ZcWjWcceFlpz588NkunhLEITJ5sccE7aPGFH+eAUF4eTZt2/Zcobrrx9q/amTZdeuYd8hQ8KJPN4j0r592bjU/fdfwjXXVL2G7h57lE1er0yHDvX44EQayOuvw7hxoRu/a9ewWtuvf112TRsRaRpSK2C1aRP+g7906U949tmK8zzOOCPkqmnTypbgjd8WLQqNfRMmVD78acyYkCtfey1UFuJDq1ILtkBYIWvHHcvPEenUqWz7o4+Gpemruq5VTUvwbr31un9Gkn2UgzJDFZnIxx+34dVXy1c2Nt88XJSotDR0Aaf3eIwZE5YY/Oab8uuSp4wfH05wRUVh6BSEk3KqMvH11+G59dar/KKCqW7t/v3D2Mr49via5rvsEnpsqtKrV9lYXJFcM3t2SB5PPhnmRl19dVjpTeO2xcyGATcCBcBd7n5l2vZNgXuADYCvgaPdfVG07TggNaj2Unef3GiBN1ElJeVzYM+e4T/9n3xSfgneVK/IueeG4VJPPhnuxxvyUpPYt90WXn65CzfdVPF4Rx8dct7KlWFkQqqBLn0J3jFjyldE1nUJ3pEjqy+3GuukOspBmaWKTOS00wZQVFT+udRVy5s1C8O0UkvvdugQupE32CDs1759uBpr+kUHe/cO27t1Cyf2du0qH4/arRtMmlR1bO3ahSu5i0iZuXNDY8Gjj4bGgAkTwgIR7dtnOjJpCsysAJgI7AssAt4wsxnuPi+227XAFHefbGZ7A1cAx5hZJ+AiYBDgwJvRa5c3bikaRmlpaHBL79Ho0yc02C1eHOYipm8/++yw6uLLL4dRA+k5csaMMFF9zpzyw4dTOfCEE8qW4O3bt+KE81QO3X33pRxxRO8KS/CmhirXtATvoEFlq2iJNBbloKYh0YpMNrWG/elP7zJgwHZp65yXbf/ss6pfW1AQ5olUpVkzteiINJQFC8ICEQ8+GFq8LrwQ/vCH/Lywp1RrJ2Chu38IYGbTgBFAvCKzNfCH6P4LwOPR/V8Az7n719FrnwOGAQ82QtxrlZaG/OEeWn3TKxrbbhuGDhcVhRbg9O2nnRZun35afs5Gyg03hP94LV8eFnVJr2gUF4f9evSAk04q39vRsWNZA9vee5ctcV9Zg11Nw5M32GA1u+3WMJ+ZSNKUg5qWxCoy2dYatssuyxg6NKl3F5H6+uij0OI1ZUqYNPnHP4ZbfKy7SEx3IN4EtQgYnLbPW8ChhAa3Q4D2Zta5itd2Tz+AmZ0EnATQtWtXClOzt+vgrLO2ZsWK71i5sjmrVhWwcmVzfv7zJZx11gLcYZ999sS9/BXzDj10ESUlC1m9uhnPPbcjbdqU0LZtMW3alNCzZzHLly+lsPArfvihGaec0o127Upo06aYtm3Dv926/UBh4RrcwxK+lV2QL1WkAw+suG3BgnCrr6Kionp9dtkqX8sN2Vn2xYtbMWXKpjz77Ea0aFHKqFGfM3r0Z3TsuKbKyzyky8ZyN5Skyp5kj0zWt4aJSOZ99lloLb777tD7edppYbx9ajELkXo4C7jFzMYCLwKfAyXVviLG3e8A7gAYNGiQD61Ha1jr1kvp1q19uV6PnXfemKFDwwVBnngitP6WX4K3B61ahVVbFi+u7F3L/kiGDatzaIkrLCykPp9dtsrXckN2lT09B51+Opx7bgFdu24CrNuF5rKp3A0tqbInWZHJqtYw1ZILMx1Go8vXckN2lH3ZspY88MAmPPXUxrjD8OGLOfroT9hgg9XMnx9W81tX2VDupORh2T8HesYe94ieW8vdvyDkIMysHXCYu39jZp8DQ9NeW5hksBMmzK02yVfWIyIiyVm8GK64Iiy/7R7mgZ1/flhSWZqOTE/2bzKtYaolD810GI0uX8sNTbvsS5eGVV8mTgzLmo8dGy7I2qtXdyppz1gnTbncScvDsr8B9DazzQi5ZTQwJr6DmXUBvnb3UuA8wpxNgGeAy80sNVPy59F2EclxVeegTEcmlUmyIpNVrWEiklnLl8O114ZlUletCkuvjhsHW26Z6cgkG7l7sZmdSqiUFAD3uPtcM7sEmOXuMwh55gozc0Jj2inRa782swmEyhDAJamhziKSm5SDslOSFRm1holIjVasCKsnXX99WKb8iCPCkpZ9+2Y6Msl27j4TmJn23LjY/UeBR6t47T2U5SQRyVHKQdktsYqMWsNEpDorV8LNN8M114SLwx58cFjScvvtMx2ZiIjkOuWg3JDoHBm1holIuu+/h9tvD5Moly4NF9q75BJd0E5ERJKnHJRbKrnOvIhIw/vxxzB5csstw8XDtt8e/v1vmDlTCURERJKlHJSbMr1qmYjkuG86JlQAACAASURBVDVrYPLkcDHLTz+F3XeHBx5AF6AVEZHEKQflNvXIiEgiSkpgyhTo0yesv7/RRvDMM/Dii0ogIiKSLOWg/KCKjIg0qNJSmDYNttkGjjsuXIH8ySfhtdfg5z8Hs0xHKCIiuUo5KL+oIiMiDcIdpk+H/v3hyCOheXN49FF480044AAlDxERSY5yUH5SRUZE6sUd/va3MFny0EPDhMqpU+Gtt+Cww6CZzjIiIpIQ5aD8pq9XROrEHf7xD9h119DatXw53HsvzJ0bWsMKCjIdoYiI5CrlIAFVZESkDlKTJffdFxYtgr/8Bd59F8aODd35IiIiSVEOkhRVZESk1l57LSSOPfeEBQvgppvg/ffhpJOgZctMRyciIrlMOUjSqSIjIjX6z39C1/0uu8Ds2XDttfDBB3DaadCqVaajExGRXKYcJFVRB5yIVGnOHLjoorASzPrrw+WXh8TRrl2mIxMRkVynHCQ1UUVGRCp47z0YPx4eegjatw+J5IwzoGPHTEcmIiK5TjlIaksVGRFZ64MP4JJL4P77Q3f9uefCWWdBp06ZjkxERHKdcpCsqxrnyJjZgWamuTQiOezTT8NkyT594OGH4fe/h48+Ct34SiCSScpBIrlPOUjqqjbJYRTwvpldbWZ9kg5IRBrPF1/AqadC794weTKcfHJoEbvuOthww0xHJwIoB4nkLOUgqa8ah5a5+9Fm1gE4EphkZg7cCzzo7t8lHaCINLzly1tw5plw661QXAzHHw8XXACbbJLpyETKUw4SyT3KQdJQatVd7+4rgEeBaUA34BDgP2Z2WoKxiUgDW7YMzjsPxozZmRtugFGjwqTKO+5QApGmSzlIJDcoB0lDq7FHxswOAo4HtgSmADu5+5dm1gaYB9ycbIgiUl/ffgvXXw9//jMUFcFee33Frbd2ZautMh2ZSPWUg0Syn3KQJKU2q5YdBvzZ3V+MP+nuq8zshGTCEpGGUFQUrnx87bWwfDkceihcfDF89dV8ttqqa6bDE6kN5SCRLKUcJEmrzdCy8cDrqQdm1trMegG4+/OJRCUi9bJqVUgcm20G558Pu+0Wroz82GOw7baZjk5knYxHOUgkqygHSWOpTUXmEaA09rgkek5Empgff4Sbb4YttoA//hEGDIBXX4Unnwz3RbKQcpBIllAOksZWm6Flzd19deqBu682s5YJxiQi62j1arj3Xrj0Uli0CPbYI6zFP2RIpiMTqTflIJEmTjlIMqU2PTJLo8mWAJjZCOCr5EISkdoqLoZJk2CrrcL6+z16wHPPQWGhEojkDOUgkSZKOUgyrTY9MicDD5jZLYABnwHHJhqViFSrpAQeeihMmlywAAYOhIkTYb/9wCzT0Yk0KOUgkSZGOUiaitpcEPMDYGczaxc9Lko8KhGpVGkpTJ8O48bBvHlh0uT06TBihJKH5CblIJGmQzlImpra9MhgZvsD2wCtLPqluvslCcYlIjHu8NRTIXnMng19+sC0aTByJDSr1WVtRbKXcpBIZikHSVNV48/PzG4HRgGnEbr1RwKbJhyXiBCSxzPPwM47w0EHwYoVMGUKvPNOuCKyEojkOuUgkcxRDpKmrjY/wV3d/VhgubtfDOwC/DTZsESksDCs/DJsGCxZAnfeCe++C8ccAwUFmY5OpNEoB4lkgHKQZIPaVGR+iP5dZWYbA2uAbsmFJJLfXnkF9tkH9toLPvwwTKBcsAB+9Sto0SLT0Yk0OuUgkUakHCTZpDZzZJ40s/WAa4D/AA7cmWhUInlo1qww/vjvf4cNN4Trrw/LWbZunenIRDJKOUikESgHSTaqtiJjZs2A5939G+AxM3sKaOXu3zZKdCJ54O23Q/J44gno1AmuvBJOPRXats10ZCKZpRwkkjzlIMlm1Q4tc/dSYGLs8Y9KICINY/58OOII6NcPXnghrMf/0UdwzjlKICJQ/xxkZsPM7D0zW2hm51ayfRMze8HM/mtmb5vZ8Oj5FmY22czmmNl8MzuvQQok0oQoB0kuqM0cmefN7DCzdV8hXElEpKKFC8NkyW23DV34558PH38cWsQ6dMh0dCJNTp1ykJkVECpB+wFbA0ea2dZpu10APOzuA4DRwK3R8yOBn7j7dsAOwK/NrFfdiyDSdCgHSS6pzRyZXwN/AIrN7AfC8pfu7tX+3GNJZF9gEfCGmc1w93mx3VJJ5LYowcwEehFLImbWBphnZg+6+8frVjyRpuPjj2HCBJg8GVq2hDPPhLPPhi5dMh2ZSJNWpxwE7AQsdPcPAcxsGjACiOcgB1Lv0xH4IvZ8WzNrDrQGVgMrGqAsIhmjHCS5qMaKjLu3r+N7K4mIAJ9/DpddBnfdFa58fMopcN55sNFGmY5MpOmrRw7qDnwWe7wIGJy2z3jgWTM7DWgL/Cx6/lFCvloMtAHOcPev0w9gZicBJwF07dqVwsLCOoYKRUVF9Xp9NsvXsjdWuZcubcn992/KzJndMIMRI77gqKM+pVOn1bzzTuKHr5S+8/yTVNlrrMiY2R6VPe/uL9bwUiWRLJGvZU+63F9/3ZKpUzdhxoyNKS2F/fdfzNFHf8oGG/zIu++G9fgzRd95/snWstcjB9XGkcAkd7/OzHYB7jOzbQkNcSXAxsD6wEtm9o9Uw1wshjuAOwAGDRrkQ4cOrXMghYWF1Of12Sxfy550uZcsCRP3b78dSkrC8snnnw89evQAeiR23NrQd55/kip7bYaW/TF2vxXhBP8msHcDHF9JpAnI17InVe6vvoJrroGbb4bVq+HYY+HCC2GzzboT6veZp+88/2Rx2euagz4HesYe94ieizsBGAbg7q+aWSugCzAGeNrd1wBfmtm/gUHAh4g0cVXnoExHJtLwajO07MD4YzPrCdxQi/dWEpG88s03cN11cMMNsHIljBkDF10EvXtnOjKR7FWPHPQG0NvMNiPkntGE3BL3KbAPMMnM+hIqSkuj5/cmNK61BXau5TFFMkY5SPJRbVYtS7cI6FuL/dYmETNrSUgiM9L2SSURqkgixJJIBgfiiFTtu+/g0kuhV6/w77BhMGcO3H+/EohIAmqVg9y9GDgVeAaYT1hYZq6ZXWJmB0W7nQmcaGZvAQ8CY93dCQvVtDOzuYRcdq+7v51AWUTqTTlI8llt5sjcTJh8D6Hi059wdeVquXuxmaWSSAFwTyqJALPcfQYhidxpZmdExxjr7m5mE4F7oyRiKIlIE7RyJUycCFdfDcuWwUEHhXX4+/fPdGQiuaOuOQjA3WcSVsOMPzcudn8esFslrysirJ4p0mQpB4nUbo7MrNj9YuBBd/93bd5cSURy0Q8/hMmTV14J//tfaP265BLYccdMRyaSk+qcg0RykXKQSJnaVGQeBX5w9xII14cxszbuvirZ0ESaltWr4e67w1LKn38Oe+0Fjz0Gu1WoiotIA1IOEkE5SKQytZkj8zzhWi4prYF/JBOOSNOzZk1IHj/9Kfz2t2Ec8j//GW5KICKJUw6SvKYcJFK12lRkWkVDvYC1w77aJBeSSNNQUhImS269dVh/f4MN4O9/h5deCi1hItIolIMkLykHidSsNhWZlWY2MPXAzHYAvk8uJJHMKi2FRx6B7baDY46Btm3hiSfg9dfDWGSzTEcokleUgySvKAeJ1F5t5sj8HnjEzL4grCC2ETAq0ahEMsAdZsyAcePg7behb9+QTA49FJrVZaFyEWkIykGSF5SDRNZdbS6I+YaZ9QG2ip56L7pQpUhOcIennw7JY9Ys2HLL0J0/ejQUFGQ6OpH8phwkuU45SKTuaqzjm9kpQFt3f8fd3yFcJOy3yYcmkrx//hN23x2GD4elS+Gee2D+fDjqKCUQkaZAOUhymXKQSP3UprPyRHf/JvXA3ZcDJyYXkkjyXn4ZzjijH/vsA598ArfdBgsWwPHHQ/PaDLgUkcaiHCQ5RzlIpGHUpiJTYFY2tczMCoCWyYUkkpzXX4df/AKGDIFPPmnLjTfCwoVw8snQUr9qkaZIOUhyhnKQSMOqTb3/aeAhM/tL9PjXwN+TC0mk4c2eHcYfP/kkdO4MV18N2233GsOG7ZHp0ESkespBkvWUg0SSUZsemXOAfwInR7c5lL84mUiTNXcuHH44DBgQ1t6/9FL46CP44x+hVavSTIcnIjVTDpKspRwkkqwaKzLuXgr8H/AxsBOwNzA/2bBE6mfBgjBZcrvt4Nln4cILQ/I4/3xo3z7T0YlIbSkHSTZSDhJpHFUOLTOznwJHRrevgIcA3F3Xk5Um66OP4JJLYMoUaNUKzj47tHx17pzpyERkXSgHSTZSDhJpXNXNkXkXeAk4wN0XApjZGY0Slcg6+uyz0GV/zz1hycrf/Q7OOQe6ds10ZCJSR8pBkjWUg0Qyo7qKzKHAaOAFM3samEa4qrJIk7F4MVxxBfzlL+GiYiedBH/6E3TvnunIRKSelIOkyVMOEsmsKisy7v448LiZtQVGAL8HNjSz24Dp7v5sI8UoUsHSpXDVVXDrrbB6dVh7/4ILYNNNMx2ZiDQE5SBpypSDRJqG2kz2X+nuU939QKAH8F/CKjIije7rr8Nkyc02gz//OawG8+67cOedSiAiuUg5SJoS5SCRpmWdrh8bXVH5jugm0mhWrAhJ4/rrw/1Ro+Cii6Bv30xHJiKNRTlIMkU5SKRpWqeKjEhjKyqCW26Ba64JLWEHHwwXXwzbb5/pyEREJNcpB4k0barISJP0/fdw221w5ZVhLPLw4WFJyx12yHRkIiKS65SDRLJDjXNkRBrTjz/CxImwxRZw5pmh1euVV+Bvf1MCERGRZCkHiWQX9chIk7BmDUyaBBMmhPX4hwyBBx+EPffMdGQiIpLrlINEspN6ZCSjioth8mTo0yesv7/xxvDss/CvfymBiIhIspSDRLKbKjKSEaWlMG0abLstjB0LHTvCk0/Cq6/CvvuC6bJ3IiKSEOUgkdygiow0KneYPh369YMjj4TmzeGxx2DWLDjgACUPERFJjnKQSG5RRUYahXuYLDloEBx6aLgS8tSp8NZb4XEz/RJFRCQhykEiuUl/upIod3juOdhll9DatXx5mFA5d25oDSsoyHSEIiKSq5SDRHKbKjKSmBdfhKFD4ec/hy++gDvugPfeg+OOC935IiIiSVEOEsl9qshIg3vttTBZcs894f334eabw78nnggtWmQ6OhERyWXKQSL5QxUZaTBvvgn77x+68GfPhuuugw8+gFNPhZ/8JNPRiYhILlMOEsk/6lyVepszBy66KKwEs/76cPnlcNpp0K5dpiMTEZFcpxwkkr8S7ZExs2Fm9p6ZLTSzcyvZvomZvWBm/zWzt81seGzb9mb2qpnNNbM5ZtYqyVhl3b37LoweHZaxfP55GD8ePvoIzjtPCUREMk85KLcpB4lIYj0yZlYATAT2BRYBb5jZDHefF9vtAuBhd7/NzLYGZgK9zKw5cD9wjLu/ZWadgTVJxSrr5oMP4JJL4P77oXVrOPdcOOss6NQp05GJiATKQblLOUhEUpIcWrYTsNDdPwQws2nACCCeRBzoEN3vCHwR3f858La7vwXg7ssSjFNq6dNPYcIEuPfeMGHyjDPg7LNhww0zHZmISAXKQTlGOUhE0iVZkekOfBZ7vAgYnLbPeOBZMzsNaAv8LHr+p4Cb2TPABsA0d786wVilGl98EcYc33lnePyb38Cf/gTdumU2LhGRaigH5QjlIBGpSqYn+x8JTHL368xsF+A+M9s2imt3YEdgFfC8mb3p7s/HX2xmJwEnAXTt2pXCwsI6B1JUVFSv12ezqsq+fHkLpk7dhBkzNqakxNhvvyUcffQndO36I++9F9bjz2b6zgszHUajy9dyQ36XvRrKQU2AclD+ydey52u5IcGyu3siN2AX4JnY4/OA89L2mQv0jD3+ENgQGA1Mjj1/IfDH6o63ww47eH288MIL9Xp9Nksv+1dfuZ97rnubNu7NmrmPHev+wQeZiS1J+s7zT76W271+ZQdmeUK5IqmbclD2UA7KP/la9nwtt3tyOSjJVcveAHqb2WZm1jJKDDPS9vkU2AfAzPoCrYClwDPAdmbWJpp0uSflxzVLAr75JixhudlmcNVVMGIEzJsXxiNvvnmmoxMRWSfKQVlGOUhE1lViQ8vcvdjMTiUkhALgHnefa2aXEGpWM4AzgTvN7AzCpMuxUc1ruZldT0hEDsx0978lFWu++/77Ai6/HK65JiSSQw+Fiy+GbbfNdGQiInWjHJQ9lINEpK4SnSPj7jMJy1nGnxsXuz8P2K2K195PWP5SErJqFdx6K1x66WC+/RYOOCAsaTlgQKYjExGpP+Wgpk05SETqK9OT/SUDfvgB7rgDrrgCliyBQYOKuOWWTgxOX89HRESkgSkHiUhDUUUmj6xeHcYaX3opLFoEe+4JDz8MJSVvM3jw0EyHJyIiOUw5SEQaWpKT/aWJKC4OyWOrreDkk6FnT/jHP+CFF2DIkExHJyIiuUw5SESSoopMDispgalTYeut4Ze/hM6dYeZM+Pe/YZ99wCzTEYqISK5SDhKRpKkik4NKS+HRR2H77eGoo6BVK5g+Hd54A/bbT8lDRESSoxwkIo1FFZkc4g5PPgkDB8LIkSGZPPQQzJ4NBx+s5CEiIslRDhKRxqaKTA5wh2eegZ13hoMOgqIimDIF3nkHjjgCmulbFhGRhCgHiUim6PSS5QoLw2TJYcPCMpZ33QXz58Mxx0BBQaajExGRXKYcJCKZpIpMlnrllTBZcq+94KOPYOJEWLAATjgBWrTIdHQiIpLLlINEpClQRSbLzJoVJkvutlvotv/zn2HhQvjtb+EnP8l0dCIiksuUg0SkKdEFMbPE22/DuHHwxBPQqRNcdRWccgq0bZvpyEREJNcpB4lIU6SKTBM3bx6MHw+PPAIdO8Ill8DvfgcdOmQ6MhERyXXKQSLSlKki00QtXAgXXwwPPBBavC64AP7wB1h//UxHJiIiuU45SESygSoyTczHH8OECTB5MrRsCWedBWefDV26ZDoyERHJdcpBIpJNVJFpIhYtgssug7vvDmvun3oqnHsubLRRpiMTEZFcpxwkItlIFZkMW7IErrwSbr89XAX5hBPg/POhR49MRyYiIrlOOUhEspkqMhny1Vdw9dVwyy2wejUcdxxceCH06pXpyEREJNcpB4lILlBFppF98w1cdx3ccAOsXAlHHRWWtOzdO9ORiYhIrlMOEpFcoopMI1mxAm68MSSQb7+FkSPDkpZbb53pyEREJNcpB4lILlJFJmErV8LEieHiYV9/DSNGhCUt+/XLdGQiIpLrlINEJJepIpOQH34IkyevuAK+/BKGDQsXEttxx0xHJiIiuU45SETyQbNMB5BrVq+G226DLbaAM86AbbeFl1+Gv/9dCURERJKlHCQi+UQ9Mg1kzRqYMiVcSOyTT2C33eD++2GvvTIdmYiI5DrlIBHJR+qRqaeSErjvPujbF371K9hwQ3j6aXjpJSUQERFJlnKQiOQzVWTqqLQUHn4YttsOjj0W2rWDGTPg//4PfvELMMt0hCIikquUg0REVJFZZ+7w+OMwYACMGhWSxSOPwH/+AwceqOQhIiLJUQ4SESmjikwtuZdNljzkEPj+e3jgAXj7bTj8cGimT1JERBKiHCQiUpFOfTVwh+efDxMnhw+HZcvgnntg3jwYMwYKCjIdoYiI5CrlIBGRqqkiU43UZMmf/Qw++yysyf/ee3D88dBc672JiEiClINERKqnikwlXn89TJbcYw9491248UZ4/3349a+hZctMRyciIrlMOUhEpHZUkYn573/DZMnBg8PEyWuugQ8/hNNPh1atMh2diIjkMuUgEZF1o85pYO5cuOiibXjxRVhvPbjsMjjtNGjfPtORiYhIrlMOEhGpm0R7ZMxsmJm9Z2YLzezcSrZvYmYvmNl/zextMxteyfYiMzsryTjPPhtmzVqfcePgo4/gT39SAhERkcahHCQiUjeJVWTMrACYCOwHbA0caWZbp+12AfCwuw8ARgO3pm2/Hvh7UjGm3HILTJ36GhdfHFrDREQk+2VLY5pykIhI3STZI7MTsNDdP3T31cA0YETaPg50iO53BL5IbTCzg4GPgLkJxgjAZptBx47FSR9GREQaSTY1pikHiYjUTZJzZLoDn8UeLwIGp+0zHnjWzE4D2gI/AzCzdsA5wL5AlS1hZnYScBJA165dKSwsrHOwRUVF9Xp9NsvXsudruSF/y56v5Ya8LPvaxjQAM0s1ps2L7VObxrSVjRKtiIiss0xP9j8SmOTu15nZLsB9ZrYtoYLzZ3cvMrMqX+zudwB3AAwaNMiHDh1a50AKCwupz+uzWb6WPV/LDflb9nwtN+Rl2RNvTBMRkcxKsiLzOdAz9rhH9FzcCcAwAHd/1cxaAV0IyeZwM7saWA8oNbMf3P2WBOMVEZH8Uq/GNI0KaBj5WvZ8LTfkb9nztdyQXNmTrMi8AfQ2s80IFZjRwJi0fT4F9gEmmVlfoBWw1N2HpHYws/FAkSoxIiKyDhJvTNOogIaRr2XP13JD/pY9X8sNyZU9sYqMuxeb2anAM0ABcI+7zzWzS4BZ7j4DOBO408zOIIxVHuvunlRMIiKSN9SYJiKS4yxX6g1mthT4pB5v0QX4qoHCyTb5WvZ8LTfkb9nztdxQv7Jv6u4bNGQwjSFaTvkGyhrTLos3pkWrmN0JtCM0pp3t7s+mvcd4QkXm2hqOpRxUd/la9nwtN+Rv2fO13JBQDsqZikx9mdksdx+U6TgyIV/Lnq/lhvwte76WG/K77Nkgn7+ffC17vpYb8rfs+VpuSK7sSV5HRkREREREJBGqyIiIiIiISNZRRabMHZkOIIPytez5Wm7I37Lna7khv8ueDfL5+8nXsudruSF/y56v5YaEyq45MiIiIiIiknXUIyMiIiIiIllHFRkREREREck6eVWRMbN7zOxLM3uniu1mZjeZ2UIze9vMBjZ2jEmpRdmPiso8x8xeMbN+jR1jEmoqd2y/Hc2s2MwOb6zYklabspvZUDObbWZzzexfjRlfUmrxW+9oZk+a2VtRuY9v7BiTYGY9zewFM5sXlet3leyTs+e4bKAcpBxUzX7KQcpBWS1TOSivKjLAJGBYNdv3A3pHt5OA2xohpsYyierL/hGwp7tvB0wgdyakTaL6cmNmBcBVwLPV7ZeFJlFN2c1sPeBW4CB33wYY2UhxJW0S1X/npwDz3L0fMBS4zsxaNkJcSSsGznT3rYGdgVOiCz7G5fI5LhtMQjmoKspBykHKQdktIzkoryoy7v4i8HU1u4wApnjwGrCemXVrnOiSVVPZ3f0Vd18ePXwN6NEogSWsFt85wGnAY8CXyUfUeGpR9jHAX93902j/nCh/LcrtQHszM8IV3b8mnICzmrsvdvf/RPe/A+YD3dN2y9lzXDZQDlIOqoJyEMpBjRFbkjKVg/KqIlML3YHPYo8XUfFLyAcnAH/PdBCNwcy6A4eQWy2ftfVTYH0zKzSzN83s2EwH1EhuAfoCXwBzgN+5e2lmQ2pYZtYLGAD8X9omneOaNn0/gXJQflAOUg6q9zmueX1eLLnHzPYiJJHdMx1LI7kBOMfdS0PjSF5pDuwA7AO0Bl41s9fcfUFmw0rcL4DZwN7AFsBzZvaSu6/IbFgNw8zaEVp3f58rZZL8oRyUV5SDlIPqTRWZ8j4HesYe94ieywtmtj1wF7Cfuy/LdDyNZBAwLUogXYDhZlbs7o9nNqxGsQhY5u4rgZVm9iLQD8j1JHI8cKWHi2gtNLOPgD7A65kNq/7MrAUhgTzg7n+tZJe8Psdlgbz+fpSDlINQDspqmchBGlpW3gzg2GhVhZ2Bb919caaDagxmtgnwV+CYPGgNWcvdN3P3Xu7eC3gU+G2eJBCAJ4Ddzay5mbUBBhPGtOa6TwktgJhZV2Ar4MOMRtQAovHWdwPz3f36KnbL23Nclsjb70c5SDkI5aCslqkclFc9Mmb2IGGFiC5mtgi4CGgB4O63AzOB4cBCYBWh1pwTalH2cUBn4NaoZajY3QdlJtqGU4ty56yayu7u883saeBtoBS4y92rXSI0G9TiO58ATDKzOYARhnV8laFwG9JuwDHAHDObHT33J2ATyP1zXDZQDlIOQjlIOUg5qEHPcRZ6tkRERERERLKHhpaJiIiIiEjWUUVGRERERESyjioyIiIiIiKSdVSRERERERGRrKOKjIiIiIiIZB1VZETqwMxKzGx27HZuA753LzPL+iUoRUQkGcpBIkFeXUdGpAF97+79Mx2EiIjkJeUgEdQjI9KgzOxjM7vazOaY2etmtmX0fC8z+6eZvW1mz0dXscbMuprZdDN7K7rtGr1VgZndaWZzzexZM2sd7X+6mc2L3mdahoopIiJNkHKQ5BtVZETqpnVat/6o2LZv3X074Bbghui5m4HJ7r498ABwU/T8TcC/3L0fMBCYGz3fG5jo7tsA3wCHRc+fCwyI3ufkpAonIiJNmnKQCGDunukYRLKOmRW5e7tKnv8Y2NvdPzSzFsASd+9sZl8B3dx9TfT8YnfvYmZLgR7u/mPsPXoBz7l77+jxOUALd7/UzJ4GioDHgcfdvSjhooqISBOjHCQSqEdGpOF5FffXxY+x+yWUzWfbH5hIaDl7w8w0z01EROKUgyRvqCIj0vBGxf59Nbr/CjA6un8U8FJ0/3ngNwBmVmBmHat6UzNrBvR09xeAc4COQIUWORERyWvKQZI3VJMWqZvWZjY79vhpd08tf7m+mb1NaNE6MnruNOBeM/sjsBQ4Pnr+d8AdZnYCodXrN8DiKo5ZANwfJRoDbnL3bxqsRCIiki2Ug0TQHBmRBhWNTx7k7l9lOhYREckvykGSbzS0TEREREREso56ZEREREREJOuoR0ZERERERLKOKjIiIiIiIpJ1VJEREREREZGso4qMiIiIiIhkHVVkREREREQk66giIyIiIiIiWUcVGRERERERyTqq9MfWwwAAIABJREFUyIiIiIiISNZRRUZERERERLKOKjIiIiIiIpJ1VJEREREREZGso4qMiIiIiIhkHVVkREREREQk66giIyIiIiIiWUcVGRERERERyTqqyIiIiIiISNZRRUZERERERLKOKjIiIiIiIpJ1VJEREREREZGso4qMiIiIiIhkHVVkREREREQk66giIyIiIiIiWUcVGRERERERyTqqyIiIiIiISNZRRUZERERERLKOKjIiIiIiIpJ1VJEREREREZGso4qMiIiIiIhkHVVkREREREQk66giIw3GzG43swsbet9MMrNCM/tVAu87ycwuje4PMbP3arNvHY9VZGab1/X10jDM7GMz+1kV24aa2aLGjkkklygHrdP7KgflmVzNQarI1FH0g/g++gNN3f4Uu/+DmZXEHs9Ne30vM/PY9v+Z2a1m1qKGY9wSbRsbe/8VZvaWmR1gZpuk7e9mtjL2eEhaHHNj20qiuNeWZ10+E3c/2d0nNPS+TZGZjY6+H0t7vrmZfWlmB9T2vdz9JXffqoHiqpD03L2du3/YEO9fzTGXm9lPkjpGU2Jm481sTdrf2dkJHi/9bzr1d31mUseUpk85qCLlIOWgpI7RlDR2DoqO2d/MXjKzb81skTWhRgBVZOrnwOgPNHW7PHUfOBl4NbZtmyreY71o/+2AXYBTajjGqbFtr0avXQ+4FZgGrIjvH+3XL/bcS/E3d/dtYvu+BJwaL09qPzNrXsfPKFc9Tvjc90x7fhjgwNONHlEGmFkvYAihzAc18rEz+Zt8KO3v8uqkDuTun6b9TW8HlAKPJXVMyRrKQflLOQjloMbIQZGpwItAJ8Jv7rdm1qifd1VUkWki3P1L4Dlg6zq8thS4D2gL9G6IeGKtdSeY2afAP6PnHzGzJVGt/EUz2yb2mnhX9dCo1n5m1Dq02MyOr+O+nc3syajV7w0zu9TMXq4m9ppinGhmfzOz78zs/8xsi9j2fc3s3ei1twBW2THc/QfgYeDYtE3HAlPdvbi6ONLiLdela2YDzOw/UXwPAa1i29Y3s6fMbGnUAvWUmfWItl1GOKHfktZy6ma2ZXS/o5lNiV7/iZldYGbNom1jzexlM7s2eu+PzGy/qj7nWHlfAyYBx6WVq6eZ/TU61rJUPNG2E81sflTGeWY2MD3W6HFlv5NzzGwJcG91n0f0mk5mdq+ZfRFtfzx6/h0zOzC2Xwsz+8rMBtRQ3mqZ2UEWWpi/sdBK2LeK/VpHZVtuZvOAHdfhMMcCL7r7x/WJVSROOUg5KPZYOahsP+WgoBfwgLuXuPsHwMtAVY0jjUoVmSbCzDYGfkH4g1zX1xYAxwNrgE8aOLQ9gb6E2AD+TkhUGwL/AR6o5rUbAR2B7sAJwEQzW78O+04EVkb7HEfayaoSNcU4GrgYWB9YCFwGYGZdgL8CFwBdgA+A3ao5zmTgcDNrHb2+I3Bg9Hxt4qjAzFoSWtruI7R8PAIcFtulGXAvsCmwCfA9cAuAu59P+RbNeMtpys2Ez3lzwnd7LOG3kzIYeC8q/9XA3WZWaSKNHBuV6wHgF2bWNSpHAfAU4ffYi/C9Tou2jQTGR6/tQGhFW1bd5xKzEeFz2RQ4iWo+j8h9QBvCCXdD4M/R81OAo2P7DQcWu/t/axlHBWb2U+BB4PfABsBM4MnoO013EbBFdPsFNf+mU8cwwuc2uaZ9RdaFcpByUPQeykHVy9ccdANwbFTh2orQe/uPusbaoNxdtzrcgI+BIuCb6PZ42vaxwMvVvL4XoSs09XoHXgE6VHOMb4ATY+9fHD23hvDHc0Qlx3Fgy1qWqRD4VVp8m1ez/3rRPh2jx5OAS6P7Q6OYmsf2/xLYeV32BQqi8m0V23ZpdZ9tLWK8K7Z9OPBudP9Y4LXYNgMWpT6TKt7/fWBMdP9E4K06flaLovt7AF8AFnvtK6l9K3nf/sDyyr7D9N9A9FmuBraObfs1UBj7TS2MbWsTvXajKo69e/TddIkevwucEd3fBVga/05jr3sG+F0V71nu91rJ57QaaFXN97H28wC6EYZgrV/JfhsD3xH9vQGPAmfX8jc1Pooj/ne5MXAh8HBsv2bA58DQ2N/zz6L7HwLDYvuelPoN1HDsIYRzQrvaxKpb7t5QDgLlIFAOUg5qhBwE7EqodBdHn9HFtYm1MW7qkamfg919veh2cHU7WlghpbIJjF3cfT3CH+y/CX9gVR1jPXe/M7bttei16wMzCP/JaWifxcpQYGZXmtkHZraC8EcBoeWkMsvcvTj2eBXQbh333QBoHo8j7X45tYxxSRUxbRx/bw9/vVUeKzKFsq79Y6LHdfmsUjYGPo+OnbK2hdPM2pjZX6Iu+RWEMavrRa1PNekCtKB8i+knhJaqlLWfjbuviu5W9Z0dBzzr7l9Fj6dS1qrTE/gk7Tsltu2DWsRbmaUehlQANX4ePYGv3X15+pu4+xeEv7fDzGw9YD9q0VoZ83Da3+UXhO9u7WfrYbjNZ5T/fFPK/daofSv2ccBj7l60DrFK7lIOispQxWuVg5SDlIPqmYPMrBNhztUlhGGGPQm9X79dh3gTo4pMI/GwQkqFCYyx7d8Tav47R93L6/LeRcBvgGPqO76ysreP3R8DjAB+Ruga7hU9X123b30tJbQA9Ig917Oa/esT4+L4e0fd2dUdC0K38T5mtguh9S51IqprHIuB7mld6ZvE7p8JbAUMdvcOhNaz+PvGv690XxFarzZNe+/Pa4ipgmgowxHAnhbGYC8BzgD6mVk/wglyE6t8MuRnhO7syqwi/IcqZaO07enlq+7z+AzoFCWJykwmdO2PJExaXufPIc0XxD7b2O+nsvct91uj/HdcqegzH4mGlUkdKAfVmXKQclBcPuagzYESd5/i7sXuvogwTG94PeNtEKrINBEWlg08htAaUduxmmu5+9fAXcC4Bg4trj3wIyG+NkCFZNjQ3L2EMGZ4fNTy0YeKkxsbKsa/AduY2aHRye90Kp7E0uP7mDDp7UHgOXdPtSbVNY5XCUnz9Ggs6qHATrHt7QlDIL6JWkkuSnv9/wgnncpiLSFMDr3MzNqb2abAH4D7axlb3MFACWFicP/o1pcwPvpY4HXCifJKM2trZq3MLDXW+y7gLDPbwYIto1gAZgNjotbEYVRckSddlZ+Huy8mjBG/1cKEzBZmtkfstY8DA4HfEbViplhY1nTsunwghM92fzPbx8IStmcSfgOvVLHveVFcPYDTavH+hwDLgRfWMS6RGikHVU45SDmohjjyIQctCCHZGDNrZmYbAaOAt9cxvkSoIpN535hZEeGPfxfgoLQu3Set/Frh06t5rxuA4Wa2fUKxTiF0P34OzKMOk0Lr6FRCi9ISQuvTg4Q/zsrUOcaoe3okcCXh5N+b0PVbk8mEVpD4iahOcbj7auBQwljhrwkni7/GdrkBaE1o2XqNikts3kiY/LnczG6q5BCnESatfkhIflOBe2oTW5rjgHs9LA28JHUjTHI8itAadSBhXPSnhHHeo6IyPkKY3DqVMEb4ccLkSQgn9AMJY36PirZVp6bP4xhCC+C7hDHvv09tiFqgHwM2I/YZW5gY2Zl1/H27+3uE1rWbo3gOJCxdu7qS3S8m/D4+Ap4l/K5rchxwX9r5QaS+lINqphxURjmovJzPQe6+gvCbOIPQmDYbeIcwVyzjTDlRso2ZXUWY/HdcjTuLVMPMxgE/dfejY8/tDpzi7kdmLjIRaaqUg6ShKAfVnyoy0uRFXfktgTmEtc5nElZFqamlRKRK0TCA/wLHuPuLmY5HRJom5SBJgnJQw9DQMskG7QndriuBh4DrgCcyGpFkNTM7kTAR8+9KICJSA+UgaVDKQQ1HPTIiIiIiIpJ1Eu2RMbNhZvaemS00s3Mr2b6pmT1vZm+bWWG0cgJm1t/MXjWzudG2UUnGKSIiIiIi2SWxHhkLFwNaAOxLWC3iDeBId58X2+cR4Cl3n2xmewPHu/sxZvZTwrWg3jezjYE3gb7u/k1Vx+vSpYv36tWrzvGuXLmStm3b1vn12Sxfy56v5Yb8LXu+lhvqV/Y333zzK3ffoIFDyinKQXWXr2XP13JD/pY9X8sNyeWgyi4U1FB2Aha6+4cAZjaNcHGmebF9tiasIQ7h2giPA7j7gtQO7v6FmX1JuLpulRWZXr16MWvWrDoHW1hYyNChQ+v8+myWr2XP13JD/pY9X8sN9Su7mVV51WcJlIPqLl/Lnq/lhvwte76WG5LLQUkOLetOmMiUsih6Lu4twtrUEC721t7MOsd3MLOdCKuFfJBQnCIiIiIikmWS7JGpjbOAW6Krl75IuHBTSWqjmXUjXKTnOHcvTX+xmZ0EnATQtWtXCgsL6xxIUVFRvV6fzfK17PlabsjfsudruSE/yx5dmftGoAC4y92vTNu+KeFifBsQLv53tLsvMrP+wG1AB0JOuszdH2rU4EVEpEZJVmQ+B3rGHveInlvL3b8g6pExs3bAYal5MGbWAfgbcL67V3p1U3e/A7gDYNCgQV6f7jp19w3NdBiNLl/LDflb9nwtN+Rf2aN5mhOJzdM0sxnxeZrAtcCU2DzNKwhX4l4FHBufp2lmz1Q3T1NERBpfkhWZN4DeZrYZoQIzGhgT38HMugBfR70t5xFaxjCzlsB0QoJ5NMEYRUQkNzXqPM3KrFmzhkWLFvHDDz/UuG/Hjh2ZP3/+urx9zshk2Vu1akWPHj1o0aJFRo4vIvWTWEXG3YvN7FTgGUK3/j3uPtfMLgFmufsMYChwhZk5YWjZKdHLjwD2ADpHw84Axrr77KTiFRGRnFLZPM3Bafuk5mneSGyeprsvS+1Q3TzNmoY3t2vXjq5du9K9e3fMrNpgS0pKKCgoqF3Jckymyu7ufPvtt7z11lsUFRU1+vHzcbhnSr6WPV/LDcmVPdE5Mu4+E5iZ9ty42P1HgQo9Lu5+P3B/krGJiOSD1avhvvtg440TvWxYtqrXPM2ahjfPnz+fHj161FiJAfjuu+9o3759nQuSzTJZ9vbt21NUVMSgQYMa/dj5NtwzLl/Lno/lLstBLzJ06B4N/v7KbCIiOai4GCZNgq22gl/9CgoLN8x0SI2tVvM03f1Qdx8AnB89V+t5mrVRm0qMZI6+H5FkNFYOUkVGRCSHlJTA1KmwzTZw/PHQqRP87W8wbNiSTIfW2NbO04zmXY4GZsR3MLMuZpbKg5qnKSJST42dg1SRERHJAaWl8NhjsP32cNRR0LIlTJ8Os2bB8OGQbw3P7l4MpOZpzgceTs3TNLODot2GAu+Z2QKgK3BZ9HxqnuZYM5sd3fo3bgnqb9myZfTv35/+/fuz0UYb0b1797WPV69eXe1rZ82axemnn17jMXbdddeGCldEslimclCmryMjIiL14A5PPQXjxsHs2aEbf9o0GDkSmuV5U1W+z9Ps3Lkzs2eHNXLGjx9Pu3btOOuss9ZuLy4upnnzyv8bMGjQoFrNG3nllVcaJlgRyUqZzkF5nuZERLKTO8ycCQMHwkEHwZIlcPPN8M470LNnSCK77gqbbQbnn5/paKWpGDt2LCeffDKDBw/m7LPP5vXXX2eXXXZhwIAB7Lrrrrz33ntAmJR8wAEHAKES9Mtf/pKhQ4ey+eabc9NNN619v3bt2q3df+jQoRx++OH06dOHo446CncHYObMmfTp04cddtiB008/fe37xn388ccMGTKEgQMHMnDgwHIVpKuuuortttuOfv36ce655wKwcOFCfvazn9GvXz8GDhzIBx9UWFRORBLkDs88AzvvHHLQihUweXLIQaNGNV5DmnpkRESamOJiWLQIFi8OtyVLwr+HHQb9+4cKy5lnwpo1Za9ZsiRUWpo3h5Ur4d13oVs32H33MFZZMuv3vw+tlVUpKfl/9u48Tudy/+P46zKWsSVLJhmFCidpGINwqpGUVDSimhSjfpLS4qSOFkuk0tFJndI5qEhKlkLncCpl0qlOoZB9nVOW7MtYxmzX749r7pl7NgZzzz33fb+fj8c83Mv3e9+fy3B/7s/32ipyuisQN28O48adfizbtm3ju+++IywsjMOHD/PNN99QtmxZFi5cyNNPP83s2bPznbNu3ToWLVpEcnIyjRs3ZsCAAfn2Xvn5559ZvXo1F1xwAe3bt+fbb78lJiaG/v37s3jxYho0aEB8fHyBMdWuXZsvvviC8PBwNm7cSHx8PEuXLmXBggXMnTuXH374gUqVKrF//34AevXqxZAhQ4iLiyMlJYXMzHyLyomIjyQmwtCh8J//wIUXwsSJ0KcP+GM7JhUyIiIlwFo3CbJsWTh2DObOzSlUPD/9+sGdd8LatW6csbcyZdxrPP44fPUVVKwIHTvCjTe6Hpg6deCyy9yxnTrB6tW5zw/RrQukAD179szet+XQoUP06dOHjRs3Yowhzbs69nLTTTdRoUIFKlSoQO3atdm1axeRkZG5jmndunX2Y82bNycpKYkqVarQsGFDGjRoAEB8fDwTJkzI9/ppaWkMHDiQ5cuXExYWxoYNbk/ShQsX0rdvXypVqgRAjRo1SE5OZvv27cTFxQFuU0sR8b3vvnMFzFdfuZzz5ptw331QoYL/YlIhIyJyFjIzYe9eV4hUrAiNGrkelT/9KX+h8uij8OKLcOIE3HWXO79CBZcQ6tRxrwWuZ+Xtt+H8893ju3bBa6/B6NFQuzb89a/wwAPu/SQwnKrnJDn5eIntpVK5cuXs20OHDqVDhw588sknJCUlFbrHRQWvbyphYWGkp6ef0TGFefXVV4mIiGDFihVkZmaqOBEpRZYudXNgFiwofTlIhYyISAFSU10B4T28q1YtN7wL4NprYcMGd4zn+9rdd7uNv8LCYNYsOOccV4i0aeP+vOYad9y557oekzp13O28q7lUqQL33gsrV7rkMXeuW8LypZdg4EDw+h4qclYOHTpE3bp1AZg8eXKxv37jxo3ZsmULSUlJ1K9fn48++qjQOCIjIylTpgxTpkwhI8PtS9qpUydGjhxJr169soeW1ahRg8jISObMmcOtt97KiRMnyMjIyO61EZHiEQg5SIWMiISkn36CTZty95jUqgWvvOKej47OPzwrNjankGnYEOrXd8WIp+ekSRP3nDGwY0fh721MzjCwgqxdC8OHw8yZrhh67jk3x+Kcc860tSIFe/LJJ+nTpw/PP/88N910U7G/fsWKFRk/fjydO3emcuXKtGrVqsDjHnzwQW677Tbee++97GMBOnfuzPLly4mJiaF8+fJ06dKFF154galTp9K/f3+GDRtGuXLlmDlzJg0bNiz2+EVCUSDlIBUyIhLwrIX9+90Qr8aN3WOzZ7uJiN6FSkZGDJ7FjZ56Cj7/3N0uV84VI23a5Lzmn/8MKSk5RUqdOq5L3WPSpOJvx6ZNLmF88IHrsn/mGTcnpnr14n8vCS0jRowo8PG2bdtmz0cBeP755wGIjY3NHmaW99xVq1Zl3z5y5Ei+4wHeeOON7NsdOnRg3bp1WGt56KGHClzW+dJLL2XlypXZ98eMGZN9e8iQIdmrlXkf/9VXXxXYJhE5M4GYg1TIiEiplZ4Ou3fnLkbuu89NfB8/3i31+Pvv7ic11RUkJ064Ho9//QtmzMgpQlq0gLCw/YBbLvaVV1wBVKeO6zLPu1TkPfeUXDuTkmDUKNee8uXd/Jonn4Tzziu5GER8ZeLEiUyZMoXU1FRatGhB//79/R2SiHgJ5BykQkZE/GL/ftd97Vla2PPz8stuiNfYse6DNGsrimzdurmeEWOgWjU3nMtTrJx/vpswHxYGEybAO+/kPjcxcQtwIQCXX14y7TyZ7dvdBP5Jk1x7HnrI9RSdf76/IxMpPoMGDWLQoEH+DkNE8giGHKRCRkSKhbVw6JBbhatiRdi6FT7+OP/KXdOmufknc+a43hWPsDCIiIAnnnCFTJs2bplH7yKlTh2oWdMdP2CA+ylMIRuWlwq//+4mTf79725J5vvuc1349er5OzIREQl2wZSDSnGqF5HSICMjZ3nhnTtdD0iDBm7Frqeeyr2qV0qKm5vSvbsbazt4sCtqPMXI5ZfnbJh1/fVuKUdPkVKrFrk2BLzqKvcTTPbuhb/8xW1omZoKvXu7Yi1riw0RERGfCcYcpEJGJERlZsJvv+XvMbn6areh4v/+B23bujkqWSuhAm4/k0cecbfXrXNFSPv2uYsVcK9z8KBb5STv8sIAkZHuJxQcPOjm5IwbB0ePuj1khg+HSy/1d2QiIhLsgjkHqZARCTJpaa7Xw1o32X3HjtyFSufOEBPjek/q1899rjFuSFanTm4I14035hQonp9GjdyxjRrlX57YW4UK/t3ttzRITnaF39ixbthdjx4wYgQ0bervyEREJNiVRA7yrBq6a5fbF+2CC2DfPrdp5q5d7ufGG0++5cDZUCEjEgAyM90Hxc6d7nZUlHt86FBYvz738K6bboKPPnJFyYABcOCAW4XEU4h4hm9VqgTvvutWJfE8d955OXNLqlRxu8vL6Tt6FN580y1csG8fdO3qlrRs3tzfkUko6dChA0OGDOGGG27IfmzcuHGsX7+et956q8BzYmNjGTt2LDExMXTp0oUPPviAc889N9cxI0aMoEqVKgwePLjQ954zZw6NGjXisqxvL8OGDePqq6/muuuuK4aWicjJnG0OstaNxvAUIp6fpk3dxdAjR9yoi1273HGeTaGHDXPvk54OY8a47xQREXDsmO/aqkJGxI/S0nLvHr9zp1sGuF8/93zv3rBokTsmLc091q4dfPutu/3VV+5D6vzzoVUrV4y0bp3z+j/+6JYWrl499/CuxET3Z0KCr1sYWlJS3OTJF190H+433AAjR+b+nYiUlPj4eKZPn56rkJk+fTovv/xykc6fP3/+Gb/3nDlzuPnmm7MLmZEjR57xa4lI0ZwsB6Wmugubnj1hPvwQfv01d6Fy5ZWQtZUU9eu71/PWr58rZCpXdkPDmzd3hYrnp0ULd1zt2u79vLc18HzvKG4qZER8aM0aN/zKu1BJTXWbTYHbJf7TT3Ofc9FFOYXMRRdBx465h3Z5T8rzFDSFueSS4muLFC411fVejR7tlrPs0MGt2Na+vb8jk1DWo0cPnn32WVJTUylfvjxJSUns2LGDq666igEDBrBkyRKOHz9Ojx49eO655/KdX79+fZYuXUqtWrUYPXo0U6ZMoXbt2tSrV4+WLVsCbo+YCRMmkJqayiWXXMLUqVNZvnw58+bN4+uvv+b5559n9uzZjBo1iptvvpkePXrw5ZdfMnjwYNLT02nVqhUvv/wyVatWpX79+vTp04dPP/2UtLQ0Zs6cSZMmTXLFlJSUxD333MPRo0cBt/Fmu3btALeJ5vvvv0+ZMmW48cYbeemll9i0aRMPPPAAe/bsISwsjJkzZ3LxxRf7+G9epGQcO+YKlm3b3IqgM2a40RsdOsC117qh5X36uCLlwAH32JdfunOHDXOL8lSunFOIlC/vnjPGFUTez0VEuDmvnufnzSs8LmMKnhvrCypkRIrIWvdB8Pvvbvf4sDD3gfCvf+UuVHbvhj173BCtv/3NfRiAu3/++W55Q2vdf/J+/dxQMO9CJSIi5z1HjfJPW6Vo0tLgvffc7+l//3O9Ze+955KFSF5eG99nu/12ePBB94XkllvyP5+Q4H727nXj272d6gpnjRo1aN26NQsWLKBbt25Mnz6d22+/HWMMo0ePpkaNGmRkZNCxY0dWrlzJFVdcUeDrLFu2jOnTp7N8+XLS09OJjo7OLmS6d+9Ov6wrL88++yxvv/02Dz/8MF27ds0uXLylpKSQkJDAl19+SaNGjejduzeTJk1iyJAhANSqVYuffvqJ8ePHM3bsWCZNmpTr/Nq1a/PFF18QHh7Oxo0biY+PZ+nSpSxYsIC5c+fyww8/UKlSJfbv3w9Ar169GDJkCHFxcaSkpJCZmXnyvzSRUmL1aregjmf41q5d7ruD5ztF1675L4SGh7vvJdde6woYa90CPB07uu8Wf/hDzrFff+32YqtcueD379PHN+0qbipkJORlZOTfPb57dzck6+OP3VKFnvknJ064c3791RUkP/zgNl70FCFRUe7PEydc4fLEE26eimf/k7y7xxf0xUVKv4wM1y3/3HPuilZMjEsuN9xQclehRIrCM7zMU8i8nTXxbcaMGUyYMIH09HR27tzJmjVrCi1kvvnmG+Li4qhUqRIAXbt2zX5u1apVPPvssxw8eJAjR47kGsZWkPXr19OgQQMaZa0a0qdPH1577bXs57t37w5Ay5Yt+fjjj/Odn5aWxsCBA1m+fDlhYWFs2LABgIULF9K3b9/sGGvUqEFycjLbt28nLi4OgPDw8FP/hYkUM+/J8MuXn8vVV7vvAnPmwD//mTOsa/duNyF//36XR8aOhcmTc16nZk1o2NDdzsiAunXd95T9+93oi4cfdt9dPKuBTply8rguuMAnzS1xKmQkqB05Ar/8kn+J4T/9yT0/axbccYebQO+taVO39HC5cm7S+9VX52zIWKeOW5kD4M9/hqefLvz9PR86EhwyM92/mREjYO1aV7jOnesKUhUwcion60GpVOnkz9eqdWZjzLt168agQYP46aefOHbsGC1btmTr1q2MHTuWJUuWUL16dRISEkjJOxi+iBISEpgzZw5RUVFMnjyZxLMcCF8ha6nDsLAw0j0ziL28+uqrREREsGLFCjIzM1WciN8kJ7uNn717THbtct8LatSAf/zDzU/xngwPzbn7bvf/ecUKN6LDM2yrcWP3Z3q6++7x9NPw6KPuMc9CPJmZbviYdw56993QzkEqZCSgWAuHD7urGVWruuEWU6bk9Jh4CpUXXoBbb4Vly3IP5yhTxn0o3Hmn+6Bo2tTtZutdpNSpk3Ol4pZbTt5r4r2BowQva9144GHDYOVK1z0/c6a7+pW3l02kNKlSpQodOnTg3nvvJT4+HoDDhw9TuXJlqlWrxq5du1iwYAGxBY17y3K3dFyEAAAgAElEQVT11VeTkJDAU089RXp6Op9++in9+/cHIDk5mTp16pCWlsa0adOoW7cuAFWrViU5OTnfazVu3JikpCQ2bdqUPaem/WlMJjt06BCRkZGUKVOGKVOmkJG1yVWnTp0YOXIkvXr1yh5aVqNGDSIjI5kzZw633norJ06cICMjI7vXRsRbaqorOqpXd8OtNm92F67yrtz1/vuugJgxA/7v/3K/RvnybpGeGjXgwgtdL733HJMdO5ZTpYpbOmz4cPdTGO89Xqx1F82Ug/JTISOlQmamm1fiKUjq1oVmzdyclH79chcpx4+74V6DB7tu2MGD3bhQTxFy2WU5E9KiomD+/JxC5bzzcoqPxET3YaDFdKQw1sK//+2Sx9Klrvv+/fddIawiVgJFfHw8cXFxTJ8+HYCoqChatGhBkyZNqFev3ikLiejoaO644w6ioqKoXbs2rVq1yn5u1KhRtGnThvPOO482bdpkFy933nkn/fr14/XXX2fWrFnZx4eHh/Puu+/Ss2fP7Mn+9913X5Hb8uCDD3Lbbbfx3nvv0blzZypnDfDv3Lkzy5cvJyYmhvLly9OlSxdeeOEFpk6dSv/+/Rk2bBjlypVj5syZNFRXecg4fjx/IdK+vfuesGoVDByY8/iBA+6cTz5xF0I3boQhQ1xvqacQufjinMLh2mtdMeNdqFSrltMzcuON7sdbYuJBTqcTUTno1FTIiM9t25Z/U8YmTSA+3o3zrF/fPea9e/wjj7hNnCpWdBPe6tRxywLWqeOKkg4d3HH167sPH+8PD2/nnpv/g0SkKL76Cp59Fr7/3q0e9/bb7kpbWX1qSoC59dZbsdbmemyy9+B7L95Dw5KSkrJvP/PMMzzzzDP5jh8wYAADBgzI93j79u1Zs2ZNge/XsWNHfv755+z7nuLH+/1iYmIKHKZ26aWXsnLlyuz7Y8aMyb49ZMiQ7EUDvI//6quv8r2OBCZr3XeFsmXdXNR//St/oXLnnW4RjQ0b3HCtvP72N1fIVKjgLqJefrkrSjzFiGeftmuvdcPTC5sM36BB7lVEi5tyUNHor0POSGpqzjJ9//ynm/DsvSnjZZfBuHHu+ZgY9+HiYYxbhSc+3l1RuPVW14PiKVLq1HFXPcD1tKxdW3gcYWE581VEisN//uM2Gk1MdD2Db70F996b8+9dRESKj2dFUE8hUq2a248kMxMeeMB9p/AuVB56yE2ET0tzWxh41KzpCpGDB939unXdMPOICLeviXfPCbihW4sXFx5X+fL++dxXDjo9KmQkm7Vuc8WdO91ViLZt3eOvvQbffZd7Hkrjxq6bE9yks2XL3JwTz/CurPmagNtdtkKFnOdq1859ReFvfyuxJooU6scfXfL4/HOX6MaNg/79Oa1hACIikmPdOjcqw7sQqVfP9YKA6/1YuzZnw2dwC/BMn+6GcC1e7D6DIyKgUSP3p2dERpUq8PPPuSfDe6tcGZ56qmTaWRyUg86MCpkQkJ6ef/f4gwfhySfd8xMnNqB3b1ekeD5MIiLcfYAlS9yHRZ06EB3t/vTurv34Y/eBUaNGwcO7vK+YiJQ2y5e78ceffuqu6L38srvip/nAUhystZhQXU4oAOQdcieF80yGT07O2Y9k6lT3/cB71a569dyQL3DDvFasyHmN8uXh5ptzCplbboEuXXJ6SmrXzj1ca926k8fUvHnxtc9flIPOjgqZILB1q+sR8S5Ufv/dTVgLD4fHH4fXX899TpkyMGiQ60WpVesEHToUvGoXuIllJ3PhhcXfJhFfW73arRgze7Ybnvj8825uVtWq/o5MgkV4eDj79u2jZs2aKmZKIWst+/btC+klnFNS8g/dSk7O2aJg6NCclbs8k+EjI+G339zt6dPdEChPIdKwYU6RAjnfPfJOhvdMf3r++ZJoZemkHFQ8VMiUMta63pKdO93krsqVXZHywQe5i5SdO93jF1/s/hM88YQ7PywsZ57J4cOukOnZ0109ybt7fLly7py4uB3ExjbyX6NFStCGDW445PTpbmjC0KEuaWuulRS3yMhItm3bxp49e055bEpKSsh+ofZn28PDw4n07CAYBKzNmaBepoxbqvfbb3P3mOze7XZ/L1/erfr55pu5XyMszO1f4pmDmncyvPeFzjlzcr5LFOTqq33TzkCmHFS8VMiUkIyMnOWFPT/XXuu6UL//3vWaeB737B6fmAjXXOOWAPz733Mmw19+OXTqlDMPpVcvd79OHbfJUt41xf/4R/cjEsq2boUxYxrz+eeuwH/ySXcBoGZNf0cmwapcuXI0KOKyRomJibRo0cLHEZVOodz2ovBMhvcuRjp1cvudfP45vPFG7k0Zjx93n3f167vtBzzzRDyT4SMi4NgxV8jEx7sh496T4WvXzlna9/HHTx7byYoYyU05yDdUyBSDEyfgp59y73Wyc6f7gOjY0U2Kv/LK3MsLgxtb2qCBK0gqVXJrm3v3mjRp4o67/XY3+a2wkQme40Ukv99+c13277wDxkTw6KNu52XPyjUiIv5y8KD7juDdW7Jrlxv6fcUVrsfj9ttzT4YHt7JV+/au9+XXX3NPho+IcFf6Ae6/H/r0KXgyPLjXOI39SOUMKAf5lk8LGWNMZ+A1IAyYZK19Kc/zFwHvAOcB+4G7rbXbsp7rAzybdejz1topvozVm7VujGh6upvAfuIEjB+fu0jZudNt1PjYY+6Dp127nPPLlHFXNDyPXXSR21TJu0jxnocSHQ0LFxYeT6jv2ipyJnbuhBdfhH/8w/2fvv9+iI39Lz17tjv1ySIipyktLacQiYhwS+fu3OmWCvaeg7J7t1uR6vzz4ZdfXO+KR/ny7vtDfLwrZBo3dr0i3r0lno0Zwe3s3r174THVqOHbNkvhlINKhs8KGWNMGPAm0AnYBiwxxsyz1q7xOmws8J61doox5lrgReAeY0wNYDgQA1hgWda5B3wV7/PP/4GhQ3OKlGPH3Prlb73lulgff9x9wHiGdzVunFOI1Knj9lLxXl7Ye8fV884L7QltIiVpzx4YM8ZdfEhNdXsWDR3qLigkJqb6OzwpQYF6MU1KD2tdj0feTRdbtYLrr3efN9dc4x7bvz/nvDFj3NChlBQ3NNx7Mnzbtm6RnNRUt/zw118XvDM8uPmtL75Y8u2WM6ccVLJ82SPTGthkrd0CYIyZDnQDvAuZy4CstTFYBMzJun0D8IW1dn/WuV8AnYEPfRXs3r0VqF7dfTh5CpLWrd1zZcu6D6jCdo8vWxZuuslXkYlIUezfD6+84vY9On7czR0bNgwuucTfkYk/BNrFNCkZnsnwKSnuIiPApEmwfXvuQuW669yE7IwMNwQ87yrNgwe7Quacc1yxERube8NFz7LA9evD0aMFx5KY6M7XhPjgoBzkH74sZOoCv3nd3wa0yXPMCqA77opZHFDVGFOzkHPr5n0DY8z9wP0AERERJHrW8zsDzz9/hCqeQaVZrM1ZIjCYHTly5Kz+7gJVqLYbgqvtR46EMXt2JDNn1uPo0bJ06LCbPn2SuOiiY2zb5jZjyzk2eNp9ukKw7QF1MU3OXEGT4StUgG7d3PP33+/2MvGeDH/zzW7fDnBL4O7YkXsy/DnnuOfKloUpU9zEeu/hXZ5F1ipUcCuHFkarbge/Q4fcUMG//tWtFnvHHe7flGevHfEtf0/2Hwy8YYxJABYD24GMk57hxVo7AZgAEBMTY2NjY884kMTERM7m/EAWqm0P1XZDcLT9yBG3Ws/LL7svMbfeCs89B1dcURuoXeA5wdDuMxWCbff5xTTxnczMnPmhy5a5jRG9J8NXrOiG7oDrDVm8OPf5LVrkFDLHjrkRFd6T4Zs2zTl2+XK39G1hK3Ddc0+xNk2CROE5yN+RhRZfFjLbgXpe9yOzHstmrd2BSyIYY6oAt1lrDxpjtgOxec5N9GGsIhIgjh93c9deesmNRe7SBUaOhJYt/R2ZBKCzuphWnKMCQqHHLD3dcOBAOQ4cKM/Bg+Vo3dqN1PvnP89h9Ohd2c8dOFCezEyYM+c7AEaMuIyvv3YXJ8qWzaR69VQuuugYiYkrAWjfPoLLLy9H9eqpVK+eSo0aadSokUpiolvq6//+r+B4/P3XHQq/88IEcttPnCjD3LkX8OGHF3LwYHnatNlH375badz4CPv3n/zfVSC3+2z5qu2+LGSWAJcaYxrgksOdwF3eBxhjagH7rbWZwFO4SZcAnwEvGGOqZ92/Put5EQlRJ064seyjR7sFOTp2hFGj3MRZkQL4/GKaRgU4+/fDpk35V+YaPdotAzx2rJuw7j0ZHtzckUqVYOLE/7FpUwQREW6ftIgIt6jONdfEYoz7f5+e7h4/99wyGBMOhOP5FQXoX1tA/87PViC2vfAcVBMo2mYwgdju4uKrtvuskLHWphtjBuKKkjDgHWvtamPMSGCptXYe7lPoRWOMxV0Neyjr3P3GmFG4YghgpGessoiElrQ0mDzZJYzffoOrroIPP3QrBYmchC6mnQHPZPhdu9yiN5Urw+rVMGtW/pW7/vlPt4Ln+++7neC9nXOO2wulShV3zB135J4MHxGRM5SrX7+tTJt2UaExabK0+JNyUOnm0zky1tr5wPw8jw3zuj0LmFXIue+Qk1REJMSkp8O0aW7Y2JYtbhXBSZPcnguaQCunootpOQqaDL9rl1t1q3FjWLIEBg7MPRke4LPP3DHr17sVvLwnw8fE5GwzcPPNbmUv7yLFMxke4JZb3I9IIFEOCgz+nuwvIpJLZibMmOG+OK1f7ybtfvqpW+JcyUNORzBfTMvIcF+0KlRwPShz5+aeDL9rl9u0OS7OTWaPjs7/Gu+84wqZihXzT4b3DPMCV4SkphY+Gb5hQ/cjEgyUgwKLChkRKRWshU8+cctWrlrlVhWaPdutBONZvUgkmKWluQUsPIVI7dquAElJcRPWveef7NkDzz7rVkk6dgzuvtu9RrlyObu/e3pWGjRwS8N67wwfEeF6WMAVLJ9/XnhchRUwIsFEOSgwqZAREb+yFubPdzsf//yzuyr8wQdw++05Q1dEgt0dd1zJ7t25H7v3Xnj7bdfr8uOPUKOGK0quvNIVJJ55s7Vqwdq1nsnw+a8an3uum68iIvkpBwU2FTIi4hfWwsKFLnn88IP7gjZ5stsNuaw+mSTEXH/9Li655KJcmy7Wr++eMwY2bCj83DJloEmTEglTJGgoBwUH/apEpMQtXuySx+LFUK8eTJgACQkawiKh6777thIbW/jKXSJSfJSDgodG/YlIifnvf92KL9dc464w/+1vsHGjm5SsBCIiIr6kHBR8VMiIiM8tW+ZWfGnb1q2gNHYsbN7slnytUMHf0YmISDBTDgpeGlomIj7zyy9uBZhPPoHq1eGFF+Dhh90meSIiIr6kHBT8VMiISLFbt86twT9jBlSt6m4/9pjbq0JERMSXlINChwoZESk2mze7XZDff99tsjdkCAwe7JaNFRER8SXloNCjQkZEztqvv8KoUfDuu27C5KBB8OSTbglZERERX1IOCl0qZETkjO3Y4cYcT5zo7g8YAE8/DXXq+DcuEREJfspBokJGRE7b7t3w0kvw1luQnu52IH/mGbjwQn9HJiIiwU45SDxUyIhIke3bB3/5i1t7PyUF7rkHhg2Dhg39HZmIiAQ75SDJS4WMiJzSwYPw6qvu58gRuPNOt6Rl48b+jkxERIKdcpAURoWMiBQqORlef91tHnbwIHTvDs89B5df7u/IREQk2CkHyamokBGRfI4dg/HjYcwY2LsXbr7ZLWnZooW/IxMRkWCnHCRFVcbfAYhI6ZGS4q5+XXwxPPEEREfDf/8Ln36qBCIiIr6lHCSn65SFjDHmFmOMCh6RIJaaCv/4B1x6KTz6qBt3vHgxfPYZtGnj7+gklCkHiQQ/5SA5U0VJDncAG40xLxtjmvg6IBEpOenpbgOxxo3hgQegXj1YuBAWLYKrrvJ3dCKAcpBI0FIOkrN1ykLGWns30ALYDEw2xnxvjLnfGFPV59GJiE9kZMC0aXDZZW79/Zo14V//gm+/hY4dwRh/RyjiKAeJBB/lICkuRequt9YeBmYB04E6QBzwkzHmYR/GJiLFLDMTZs2C++5rxd13Q3g4fPIJLFkCXbooeUjppBwkEhyUg6S4FWWOTFdjzCdAIlAOaG2tvRGIAh73bXgiUhyshXnz3MTJnj3d/Y8+guXL4dZblTyk9FIOEgl8ykHiK0VZfvk24FVr7WLvB621x4wx9/kmLBEpDtbC55/D0KHuitfFF8N778EFFyyhY8dYf4cnUhTKQSIBSjlIfK0oQ8tGAD967hhjKhpj6gNYa7/0SVQictYSE91kyc6dYdcumDQJ1q6Fe+6BsDB/RydSZCNQDhIJOMpBUhKKUsjMBDK97mdkPSYipdB337nJkh06wNat8OabsGED3HcflCvn7+hETptykEgAUQ6SklSUQqastTbVcyfrdnnfhSQiZ2LpUrjxRmjfHlatgldfhU2b4MEHoUIFf0cncsaUg0QCgHKQ+ENRCpk9xpiunjvGmG7AXt+FJCKnY8UK6NYNWrWCH3+El16CLVvgscegYkV/Rydy1pSDREox5SDxp6JM9n8AmGaMeQMwwG9Ab59GJSKntGYNjBgBM2dCtWowcqTbEfmcc/wdmUixUg4SKYWUg6Q0OGUhY63dDFxpjKmSdf+Iz6MSkUJt2gTPPec2E6tcGZ55Bh5/HKpX93dkIsVPOUikdFEOktKkKD0yGGNuApoC4SZrsW9r7UgfxiUieSQlwahRMGUKlC8PgwfDk09CrVr+jkzEt5SDRPxPOUhKo1MWMsaYvwOVgA7AJKAHXkthiohvbdsGo0fD229DmTIwcCAMGQLnn+/vyER8TzlIxL+Ug6Q0K8pk/3bW2t7AAWvtc0BboFFRXtwY09kYs94Ys8kYM6SA5y80xiwyxvxsjFlpjOmS9Xg5Y8wUY8wvxpi1xpinTqdRIsHg99/dZMlLLnEJ5L77XJf+uHFKIBJSlINE/EA5SAJBUYaWpWT9ecwYcwGwD6hzqpOMMWHAm0AnYBuwxBgzz1q7xuuwZ4EZ1tq3jDGXAfOB+kBPoIK1tpkxphKwxhjzobU2qYjtEglYe/fCyy/DG29Aair06eN2Ra5f39+RifiFcpBICVIOkkBSlELmU2PMucBfgJ8AC0wswnmtgU3W2i0AxpjpQDfAO4lYwLO+RTVgh9fjlY0xZYGKQCpwuAjvKRKwDhyAV16B116Do0fhrrtg+HC49FJ/RybiV8pBIiVAOUgC0UkLGWNMGeBLa+1BYLYx5p9AuLX2UBFeuy5umUyPbUCbPMeMAD43xjwMVAauy3p8Fi7h7MSNjR5krd1fhPcUCTiHD7vE8corcOgQ9OzplrS87DJ/RybiX6U9Bxlj7gfuB4iIiCAxMbGILcvvyJEjZ3V+IAvVtpeWdh89Gsbs2ZHMmFGPo0fLcs01u0lISKJ+/WNs3w7btxf/e5aWtpe0UG03+K7tJy1krLWZxpg3gRZZ908AJ4rx/eOBydbaV4wxbYGpxpjLcVfSMoALgOrAN8aYhZ4rax5KIsUjVNvu73YfP16GOXPqMn36hRw+XI527fbSt28Sl1xyhN27Yfdu3723v9vuL6HabgjMtpf2HGStnQBMAIiJibGxsbFnHEhiYiJnc34gC9W2+7vdR4/Cm2/CmDGwfz907eqWVW7evDZQ26fv7e+2+0uotht81/aiDC370hhzG/CxtdaexmtvB+p53Y/MeszbfUBnAGvt98aYcKAWcBfwb2ttGrDbGPMtEAMoifhAqLbdX+1OSYG//x1efNEVK507u43EWrWqhfvn73v6nYeeAG57qc1BIoGo8Bzk78hETl9RVi3rD8wEThhjDhtjko0xRRkrvAS41BjTwBhTHrgTmJfnmF+BjgDGmD8A4cCerMevzXq8MnAlsK4I7ylSaqWmwltvwcUXw6BBcPnl8J//wIIFSiAiJ6EcJFIMlIMkGJ2yR8ZaW/VMXtham26MGQh8BoQB71hrVxtjRgJLrbXzgMeBicaYQbjJlQnWWps1lOBdY8xqwADvWmtXnkkcIv6Wlgbvvec2Evvf/6B9e3j/fejQwd+RiZR+ykEiZ0c5SIJZUTbEvLqgx621i091rrV2Pm45S+/HhnndXgO0L+C8I7jlL0UCVkYGfPCBG3O8ebO74vWPf8D110PW5uQicgrKQSJnRjlIQkFR5sg84XU7HDcJchlZ3e4ikltmJsya5ZatXLcOoqJg7ly45RYlD5EzoBwkchqUgySUFGVo2S3e940x9YBxPotIJEBZ65LF8OGwcqVbPnnmTOjeHcoUZTaaiOSjHCRSNMpBEorO5J/2NuAPxR2ISKCyNmeyZFwcHD/uxh+vXAk9eiiBiBQz5SARL8pBEsqKMkfmb7hJkOAKn+a43ZVFQpq18NVXMHQofP891K8P77wD99wDZYsyaFNETkk5SKRgykEiRZsjs9TrdjrwobX2Wx/FIxIQvvnGJY+vv4bISLcmf9++UL68vyMTCTrKQSJ5KAeJOEUpZGYBKdbaDABjTJgxppK19phvQxMpfX780SWPzz+HiAh47TW4/34ID/d3ZCJBSzlIJItykEhuRRk5+SVQ0et+RWChb8IRKZ1+/tmt+NKmDSxbBn/5C2zZAo88ogQi4mPKQRLylINEClaUHpnwrDX1Abe+vjGmkg9jEik1Vq92K8DMng3nngvPP+8SR9Uz2qJPRM6AcpCELOUgkZMrSo/MUWNMtOeOMaYlcNx3IYn434YNcNdd0KyZ68IfNgy2boVnnlECESlhykEScpSDRIqmKD0yjwEzjTE7AAOcD9zh06hE/GTrVhg5Et57z3XXP/kkPPEE1Kzp78hEQpZykIQM5SCR01OUDTGXGGOaAI2zHlpvrU3zbVgiJeu331yX/TvvQFgYPPoo/PnPbjKliPiPcpCEAuUgkTNzyqFlxpiHgMrW2lXW2lVAFWPMg74PTcT3du6Ehx+GSy6Bd9+F/v3dBMq//lUJRKQ0UA6SYKYcJHJ2ijJHpp+19qDnjrX2ANDPdyGJ+N6ePfDWWxfTsCG89Rb07g0bN8Ibb8AFF/g7OhHxohwkQUc5SKR4FGWOTJgxxlhrLbg1/AFtuSQBaf9+GDsWXn8djh+P5O673STKiy/2d2QiUgjlIAkaykEixasohcy/gY+MMf/Iut8fWOC7kESK36FDMG6c664/fBjuuAO6dFlC796t/R2aiJyccpAEPOUgEd8oytCyPwNfAQ9k/fxC7s3JREqtI0fgxRehQQMYMQI6doSVK2H6dLjwQm0MLhIAlIMkYCkHifjWKQsZa20m8AOQBLQGrgXW+jYskbNz/Li78tWwITz9NLRtC0uXwscfu3X5RSQwKAdJIFIOEikZhQ4tM8Y0AuKzfvYCHwFYazuUTGgip+/ECZg4EV54wa0Gc911bk3+tm39HZmInA7lIAlEykEiJetkc2TWAd8AN1trNwEYYwaVSFQipyktDSZPhlGj3Hr8V10FH34I11zj78hE5AwpB0nAUA4S8Y+TDS3rDuwEFhljJhpjOuJ2VRYpNdLTYcoUaNIE7r/fLVv5+efw9ddKICIBTjlISj3lIBH/KrSQsdbOsdbeCTQBFgGPAbWNMW8ZY64vqQBFCpKZ6a52NW0KCQlQrRr885/w/ffQqRMYfd0RCWjKQVKaKQeJlA5Fmex/1Fr7gbX2FiAS+Bm3ioxIibPWTZaMioK77oLy5d39ZcvgppuUPESCjXKQlCbKQSKlS1GWX85mrT1grZ1gre3oq4BECmKtu9rVsiXcdpsbj/zhh7BiBcTFKXmIhALlIPEX5SCR0um0ChmRkmYtfPGFW/HlllvcpmKTJ8OqVXDnnVBG/4JFRMRHlINESjf9F5RSyzNZ8vrrYccOmDAB1q2DPn2g7MnW2xMRETlLykEipZ8KGSl1vv/erb0fGwubNsEbb8DGjdCvH5Qr5+/oREQkmCkHiQQOFTJSangmS7ZrBytXwiuvwObN8NBDUKGCv6MTEZFgphwkEnjUOSp+98svMGwYzJkD1avDiy/CwIFQpYq/IxMRkWCnHCQSuFTIiN+sWwcjRsCMGVC1qrv92GNuPX4RERFfUg4SCXwqZKTEbd4Mzz0H06ZBxYrw1FPw+ONQo4a/IxMRkWCnHCQSPDRHRkrM//7nJks2bgwzZ8KgQbB1K4werQQiIsXPGNPZGLPeGLPJGDOkgOcvNMYsMsb8bIxZaYzp4vXcFcaY740xq40xvxhjwks2eiluykEiwUc9MuJz27fDCy/AxIlu07AHH3RXwOrU8XdkIhKsjDFhwJtAJ2AbsMQYM89au8brsGeBGdbat4wxlwHzgfrGmLLA+8A91toVxpiaQFoJN0GKiXKQSPDyaY+MroaFtt274U9/gosvduvv9+3rlrJ8/XUlEBHxudbAJmvtFmttKjAd6JbnGAuck3W7GrAj6/b1wEpr7QoAa+0+a21GCcQsxUg5SCT4+axHRlfDQte+ffCXv8Df/gYpKdC7NwwdCg0b+jsyEQkhdYHfvO5vA9rkOWYE8Lkx5mGgMnBd1uONAGuM+Qw4D5hurX3Zt+FKcVEOEgkdvhxaln01DMAY47ka5l3IFPlqmA/jlGJy8CD89a8wbhwcOQLx8TB8ODRq5O/IREQKFA9Mtta+YoxpC0w1xlyOy41/BFoBx4AvjTHLrLVfep9sjLkfuB8gIiKCxMTEMw7kyJEjZ3V+ICuuth85UpYZMyKZPTuS48fDuPba3fTpk0S9esf59Vf49dezj7U46Xee6O8wSlyotht813ZfFjK6GhYikpNdV/3Ysa6Yue02tyJM06b+jkxEQth2oJ7X/cisx7zdB3QGsNZ+nzWEuRYuXy221u4FMMbMB6KBXIWMtXYCMAEgJibGxsbGnnGwiYmJnM35gexs2154DooAIootzuKm33msv8MocdAkySEAACAASURBVKHabvBd2/092V9Xw0qBM217SkoZ5sypy/Tp9Th0qDzt2u0lISGJSy89wp49UNr/OvU7T/R3GCUuVNsNIdn2JcClxpgGuALmTuCuPMf8CnQEJhtj/gCEA3uAz4AnjTGVgFTgGuDVkgpciubYMXjzTXj5Zdi7F265xRUwLVr4OzIRKSm+LGR0NSxAnG7bU1LcxMkXXoBdu+D662HkSGjTphbu1xcY9DuP9XcYJS5U2w2h13ZrbboxZiCuKAkD3rHWrjbGjASWWmvnAY8DE40xg3BDnROstRY4YIz5K64YssB8a+2//NMSyavwHOTvyESkpPmykNHVsCCTmgrvvOPW3N+2Da65BmbNgj/+0d+RiYjkZ62dj1tExvuxYV631wDtCzn3fdyiM1JKKAeJSF4+W37ZWpsOeK6GrcWtTrbaGDPSGNM167DHgX7GmBXAh2RdDbPWHgA8V8OWAz/papj/pKfDu++6TcQGDIB69WDhQli0SAlERER8SzlIRArj0zkyuhoW2DIyYPp0N+Z440Zo2RLGj4fOnd2mYiIiIr6iHCQip+LTDTElMGVmuu76K66Au++GihVhzhxYsgRuvFEJREREfEc5SESKSoWMZLMW5s2D6Gjo2dMlk48+gp9/hm7dlDxERMR3lINE5HSpkBGshX//26340q2b28xy6lRYtQpuvx3K6F+JiIj4iHKQiJwpf+8jI362aBE88kgLVq2Ciy6CSZOgd28oV87fkYmISLBTDhKRs6HrHCHq22/h2mvdz++/hzN+PGzYAPfdpwQiIiK+pRwkIsVBPTIhZskSGDbMdePXrg2vvgp/+MMP3HDD1f4OTUREgpxykIgUJ/XIhIgVK9zY49atXSIZMwa2bIHHHoMKFTL9HZ6IiAQx5SAR8QX1yAS5NWtg+HC3lGW1ajBqFDzyCJxzjr8jk1CTlpbGtm3bSElJ8VsM1apVY+3atX57f38qStvDw8OJjIyknMb2SDFRDhIRX1IhE6Q2bnSbiH3wAVSuDM8+C3/6E1Sv7u/IJFRt27aNqlWrUr9+fYyf1lFNTk6matWqfnlvfztV26217Nu3j23bttGgQYMSjEyCkXKQiJQEFTJBJikJRo6E996D8uXhiSfcT61a/o5MQl1KSopfixg5OWMMNWvWZM+ePf4ORQKYcpCIlCQVMkFi2zYYPRreftutuT9wIAwZAuef7+/IRHKoiCnd9PuRM6UcJCL+oMn+Ae733+HRR+GSS1wC+b//g02bYNw4JRARb/v27aN9+/Y0b96c888/n7p169K8eXOaN29OamrqSc9dunQpjzzyyCnfo127dsUVrkhAUA4SEX9Sj0yA2rsXXn4Z3ngDUlOhTx8YOhTq1/d3ZCKlU82aNfn222+pWrUqI0aMoEqVKgwePDj7+fT0dMqWLfgjMSYmhpiYmFO+x3fffVds8YqUZspBIlIaqEcmwBw44CZNNmgAY8fCbbfBunXuSpgSiMjpSUhI4IEHHqBNmzY8+eST/Pjjj7Rt25YWLVrQrl071q9fD0BiYiI333wzACNGjODee+8lNjaWhg0b8vrrr2e/XpUqVbKPj42NpUePHjRp0oRevXphrQVg/vz5NGnShJYtW/LII49kv663pKQkrrrqKqKjo4mOjs5VII0ZM4ZmzZoRFRXFkCFDANi0aRPXXXcdUVFRREdHs3nzZt/8hUnIUw4SkdJEPTIB4vBheO01eOUVOHQIbr8dRoyAP/zB35GJnL7HHoPly4v3NZs3d8NZTte2bdv47rvvCAsL4/Dhw3zzzTeULVuWhQsX8vTTTzN79ux856xbt45FixaRnJxM48aNGTBgQL4li3/++WdWr17NBRdcQPv27fn222+JiYmhf//+LF68mAYNGhAfH19gTLVr1+aLL74gPDycjRs3Eh8fz9KlS1mwYAFz587lhx9+oFKlSuzfvx+AXr16MWTIEOLi4khJSSEzU/tySPFSDhKR0kiFTCl39Kjrun/5Zdi/320o9txzEBXl78hEgkPPnj0JCwsD4NChQ/Tp04eNGzdijCEtLa3Ac2666SYqVKhAhQoVqF27Nrt27SIyMjLXMa1bt85+rHnz5iQlJVGlShUaNmyYvbxxfHw8EyZMyPf6aWlpDBw4kOXLlxMWFsaGDRsAWLhwIX379qVSpUoA1KhRg+TkZLZv305cXBzg9oIRKS7KQSJSmqmQKaWOH4e//x1eegl274Ybb3RLWhZhmL5IqXcmPSe+Urly5ezbQ4cOpUOHDnzyySckJSURGxtb4DkVKlTIvh0WFkZ6evoZHVOYV199lYiICFasWEFmZqaKEylxykEiEgg0R6aUOXECxo93K8D86U9w+eXw7bcwf74SiIivHTp0iLp16wIwefLkYn/9xo0bs2XLFpKSkgD46KOPCo2jTp06lClThqlTp5KRkQFAp06dePfddzl27BgA+/fvp2rVqkRGRjJnzhwATpw4kf28yOlSDhKRQKJCppRIS4NJk6BRI3joIWjYEBYtgi+/BK3oKlIynnzySZ566ilatGhxWj0oRVWxYkXGjx9P586dadmyJVWrVqVatWr5jnvwwQeZMmUKUVFRrFu3LrvXqHPnznTt2pWYmBiaN2/O2LFjAZg6dSqvv/46V1xxBe3ateP3338v9tgluCkHiUgg0tAyP8vIgA8+cGOON2+GVq1gwgS4/nrQ3nQivjFixIgCH2/btm32fBSA559/HoDY2NjsYWZ5z121alX27SNHjuQ7HuCNN97Ivt2hQwfWrVuHtZaHHnqowGWdL730UlauXJl9f8yYMdm3hwwZkr1amffxX331VYFtEjkZ5SARCWTqkfGTzEz46CPXbd+7N1StCvPmwQ8/wA03KIGIBKuJEyfSvHlzmjZtyqFDh+jfv7+/Q5IQpBwkIsFAPTIlzFqYOxeGD4eVK6FpU5g1C+LioIzKSpGgN2jQIAYNGuTvMCREKQeJSDDRx1YJsdZNlmzVyiWM48dh2jRYscJtKKYEIiIivqIcJCLBSB9dPmYtLFwI7dvDTTfBvn3w7ruwZg3cdRdkbV8hIiJS7JSDRCSYqZDxoW++gQ4doFMn+O03tyb/+vWQkABlNahPRER8SDlIRIKdChkf+OEHt+LL1Ve7pPH667BxI/TvD+XL+zs6EREJZspBIhIqVMgUo59/hltugSuvdLf/8he3nOXDD4M25hbxrw4dOrBw4cJcj40bN44BAwYUek5sbCxLly4FoEuXLhw8eDDfMSNGjMjez6Uwc+bMYc2aNdn3hw0bli8WkbOlHCQioUaFTDFYtcpNloyOhv/8B0aPhi1bYPBgqFTJ39GJCEB8fDyzZ8/O9dj06dOJj48v0vnz58/n3HPPPaP3zlvIjBw5kuuuu+6MXkskL+UgEQlVKmTOwvr1brLkFVfAF1+45SyTkuDpp92a/CJSevTo0YPPPvuM1NRUAJKSktixYwdXXXUVAwYMICYmhqZNmzJ8+PACz69fvz579+4FYPTo0TRq1Ig//vGPrF+/PvuYiRMn0qpVK6Kiorjttts4duwY3333HfPmzeOJJ56gefPmbN68mYSEBGbNmgXAl19+SYsWLWjWrBn33nsvJ06cyH6/4cOHEx0dTbNmzVi3bl2+mJKSkrjqqquIjo4mOjqa7777Lvu5MWPG0KxZM6KiorI30Ny0aRPXXXcdUVFRREdHs3nz5mL4mxV/UQ4SkVCn6X5nYMsWGDkSpk513fV//rO78lWzpr8jEwkcXhvfZ7v9dnjwQTh2DLp0yf98QoL72bsXevTI/Vxi4snfr0aNGrRs2ZIFCxbQrVs3pk+fzu23344xhtGjR1OjRg0yMjLo2LEjK1eu5IorrijwdZYtW8b06dNZvnw56enpREdH07JlSwC6d+9Ov379AHj22Wd5++23efjhh+natSs333wzPfIEnZKSQkJCAl9++SWNGjWid+/evPXWWzz22GMA1KpVi59++onx48czduxYJk2alOv82rVr88UXXxAeHs7GjRuJj49n6dKlLFiwgLlz5/LDDz9QqVIl9u/fD0CvXr0YMmQIcXFxpKSkkJmZefK/tABnjOkMvAaEAZOstS/lef5CYApwbtYxQ6y18/M8vwYYYa09+fjBEqQcJCLiqEfmNPz6K9x/PzRu7HZEfuwx2LoVXnxRCUQkEPTo0YPp06cDuYeVzZgxg+joaFq0aMHq1atzDQPL65tvviEuLo5KlSpxzjnn0LVr1+znVq1axVVXXUWzZs2YNm0aq1evPmk869evp0GDBjRq1AiAPn36sHjx4uznu3fvDkDLli1JSkrKd35aWhr9+vWjWbNm9OzZMzvuhQsX0rdvXypljSuqUaMGycnJbN++nbi4OADCw8Oznw9Gxpgw4E3gRuAyIN4Yc1mew54FZlhrWwB3AuPzPP9XYIGvYy0q5SARkdzUI1MEO3fCCy/AhAluTf7+/V3X/QUX+DsykcB1sh6USpVO/nytWqfugSnITTfdxNNPP81PP/3EsWPHaNmyJVu3bmXs2LEsWbKE6tWrk5CQQEpKyum/OJCQkMCcOXOIiopi8uTJJJ5JkF4qVKgAQFhYGOnp6fmef/XVV4mIiGDFihVkZmYSrhnd3loDm6y1WwCMMdOBbrgeFg8LnJN1uxqww/OEMeZWYCtwtESiPQnlIBGRgqmQOYndu2HMGBg/HtLS4N574dln4cIL/R2ZiJyJKlWq0KFDB+69997s3pjDhw9TuXJlqlWrxq5du1iwYAGxBY17y3L11VeTkJDAU089RXp6Op9++in9+/cHIDk5mTp16pCWlsa0adOoW7cuAFWrViU5OTnfazVu3JikpCQ2bdrEJZdcwtSpU7nmmmuK3J5Dhw4RGRlJmTJlmDJlChkZGQB06tSJkSNH0qtXr+yhZVWrViUyMpI5c+Zw6623cuLECTIyMoK5V6Yu8JvX/W1AmzzHjAA+N8Y8DFQGrgMwxlQB/gx0AgYX9gbGmPuB+wEiIiLOqnA9cuRIvvMPHCjHhx9eyNy5F5CeXoYbb9zJPff8j4iIE2zYABs2nPHblSoFtT0UhGq7IXTbHqrtBt+13aeFTKCOT96/H8aOdWvvHz8Od98Nw4bBxReXVAQi4ivx8fHExcVlDzGLioqiRYsWNGnShHr16tG+ffuTnh8dHc0dd9xBVFQUtWvXplWrVtnPjRo1ijZt2nDeeefRpk2b7OLlzjvvpF+/frz++uvZk/zBDe9699136dmzJ+np6bRq1YoHHnigyG158MEHue2223jvvffo3LkzlStXBqBz584sX76cmJgYypcvT5cuXXjqqaeYOnUq/fv3Z9iwYZQrV46ZM2fSsGHDIr9fEIoHJltrXzHGtAWmGmMuxxU4r1prjxhjCj3ZWjsBmAAQExNjT1YAn0piYmJ2AV14DroACL5uGO+2h5JQbTeEbttDtd3gw7Zba33ygytMNgMNgfLACuCyPMdMAAZk3b4MSMrz/CxgJjD4VO/XsmVLezYWLVpkDx60dvhwa885x1pjrL3zTmvXrj2rlw0IixYt8ncIfhGq7bbWP21fs2ZNib9nXocPH/Z3CH5T1LYX9HsCllof5Qpf/QBtgc+87j8FPJXnmNVAPa/7W4DawDdAUtbPQWA/MPBk76ccdOZC9bM4VNttbei2PVTbbe3Ztf1kOciXPTIBMz75yBGYNu1CuneHAwcgLg6eew6aNfP1O4uIiI8sAS41xjQAtuMm89+V55hfgY7AZGPMH4BwYI+19irPAcaYEcARa+0bvgpUOUhE5Mz4spAJmPHJgwdfwbJlDbnyyn307buVRo2OsG/fmU0mDkShOmYzVNsN/ml7tWrVCpwnUpIyMjL8HoO/FLXtKSkpQfH/wlqbbowZCHyGGyHwjrV2tTFmJO7q3jzgcWCiMWYQ7sJaQtbVvxIVFwcLFzbkppvcssrR0SUdgYhIYPL3ZP9SMT75tddgxYqfePDBaCD01rAM1TGbodpu8E/b165dS1U/79KXnJzs9xj8pahtDw8Pp0WLFiUQke9ZN+dyfp7HhnndXgOcdFKUtXaET4LzMmIExMV5cpCIiBSVLwuZ7UA9r/uRWY95uw/oDGCt/d4YEw7UwvXc9DDGvIxbCCDTGJPiq6799u0hLe2wL15aRLxYaznZxQnxLz90RgjKQSIiZ8qXG2Jmj082xpTHjU+el+cYz/hk8o5PttbWt9bWB8YBL/hyfLKI+F54eDj79u3Tl+VSylrLvn37tBeNiIgEDJ/1yATS+GQR8b3IyEi2bdvGnj17/BZDSkpKyH5RL0rbw8PDiYyMLKGIREREzo5P58gEyvhkEfG9cuXK0aBBA7/GkJiYGDTzP05XKLddRESCky+HlomIiIiIiPiEChkREREREQk4KmRERERERCTgmGCZW2+M2QP87yxeohawt5jCCTSh2vZQbTeEbttDtd1wdm2/yFp7XnEGE2yUg85KqLY9VNsNodv2UG03+CgHBU0hc7aMMUuttTH+jsMfQrXtodpuCN22h2q7IbTbHghC+fcTqm0P1XZD6LY9VNsNvmu7hpaJiIiIiEjAUSEjIiIiIiIBR4VMjgn+DsCPQrXtodpuCN22h2q7IbTbHghC+fcTqm0P1XZD6LY9VNsNPmq75siIiIiIiEjAUY+MiIiIiIgEnJAqZIwx7xhjdhtjVhXyvDHGvG6M2WSMWWmMiS7pGH2lCG3vldXmX4wx3xljoko6Rl84Vbu9jmtljEk3xvQoqdh8rShtN8bEGmOWG2NWG2O+Lsn4fKUI/9arGWM+NcasyGp335KO0ReMMfWMMYuMMWuy2vVoAccE7WdcIFAOUg46yXHKQcpBAc1fOSikChlgMtD5JM/fCFya9XM/8FYJxFRSJnPytm8FrrHWNgNGETzjOCdz8nZjjAkDxgCfl0RAJWgyJ2m7MeZcYDzQ1VrbFOhZQnH52mRO/jt/CFhjrY0CYoFXjDHlSyAuX0sHHrfWXgZcCTxkjLkszzHB/BkXCCajHFQY5SDlIOWgwOaXHBRShYy1djGw/ySHdAPes85/gXONMXVKJjrfOlXbrbXfWWsPZN39LxBZIoH5WBF+5wAPA7OB3b6PqOQUoe13AR9ba3/NOj4o2l+EdlugqjHGAFWyjk0vidh8yVq701r7U9btZGAtUDfPYUH7GRcIlIOUgwqhHIRyUEnE5kv+ykEhVcgUQV3gN6/728j/SwgF9wEL/B1ESTDG1AXiCK4rn0XVCKhujEk0xiwzxvT2d0Al5A3gD8AO4BfgUWttpn9DKl7GmPpAC+CHPE/pM6500+/HUQ4KDcpBykFn/RlX9mxOluBjjOmASyJ/9HcsJWQc8Gdrbaa7OBJSygItgY5AReB7Y8x/rbUb/BuWz90ALAeuBS4GvjDGfGOtPezfsIqHMaYK7uruY8HSJgkdykEhRTlIOeisqZDJbTtQz+t+ZNZjIcEYcwUwCbjRWrvP3/GUkBhgelYCqQV0McakW2vn+DesErEN2GetPQocNcYsBqKAYE8ifYGXrFt7fpMxZivQBPjRv2GdPWNMOVwCmWat/biAQ0L6My4AhPTvRzlIOQjloIDmjxykoWW5zQN6Z62qcCVwyFq7099BlYT/Z+++w6Mssz6Of4+hhC69CAqsiB0pomLDjgUR1oYNsazdXWyLvoio6FrXrrtYANEVO5YFG0ssq6uggAVREaLSFEGQFlru94/zDJmEJISQyWRmfp/rmouZp819TyY5nOduZrY98BJwZgbcDdkohNAuhNA2hNAWeAG4OEMCCMArwAFmVs3MagP74H1a092P+B1AzKw50BGYndQSVYCov/XjwNchhL+XcFjG/o1LERn781EMUgxCMSilJSsGZVSLjJk9g88Q0cTM5gI3ANUBQgj/AMYDxwCzgFV41pwWylD3oUBj4OHoztD6EEK35JS24pSh3mlrc3UPIXxtZm8AnwP5wGMhhFKnCE0FZfiZ3wyMMrMvAMO7dfyapOJWpP2BM4EvzGxatO06YHtI/79xqUAxSDEIxSDFIMWgCv0bZ96yJSIiIiIikjrUtUxERERERFKOEhkREREREUk5SmRERERERCTlKJEREREREZGUo0RGRERERERSjhIZkXIwsw1mNi3uMbgCr93WzFJ+CkoREUkMxSARl1HryIhUoNUhhL2SXQgREclIikEiqEVGpEKZWa6Z3WFmX5jZJ2a2Y7S9rZn9x8w+N7OJ0SrWmFlzM3vZzKZHjx7RpbLM7FEz+8rM3jKzWtHxl5vZjOg6Y5NUTRERqYIUgyTTKJERKZ9aRZr1T4nbtyyEsAfwIHBvtO0BYHQIYU/gaeD+aPv9wLshhE5AF+CraHsH4KEQwm7AUuCP0fbBQOfoOhcmqnIiIlKlKQaJABZCSHYZRFKOma0IIdQtZnsucGgIYbaZVQcWhhAam9mvQMsQwrpo+4IQQhMzWwS0DiGsibtGW+DtEEKH6PVfgeohhOFm9gawAhgHjAshrEhwVUVEpIpRDBJxapERqXihhOdbYk3c8w0UjGc7FngIv3M22cw0zk1EROIpBknGUCIjUvFOifv3o+j5h8Cp0fPTgfej5xOBiwDMLMvMGpR0UTPbBmgTQpgE/BVoAGxyR05ERDKaYpBkDGXSIuVTy8ymxb1+I4QQm/6yoZl9jt/R6h9tuwwYaWZXA4uAgdH2PwMjzOxc/K7XRcCCEt4zC3gqCjQG3B9CWFphNRIRkVShGCSCxsiIVKiof3K3EMKvyS6LiIhkFsUgyTTqWiYiIiIiIilHLTIiIiIiIpJy1CIjIiIiIiIpR4mMiIiIiIikHCUyIiIiIiKScpTIiIiIiIhIylEiIyIiIiIiKUeJjIiIiIiIpBwlMiIiIiIiknKUyIiIiIiISMpRIiMiIiIiIilHiYyIiIiIiKQcJTIiIiIiIpJylMiIiIiIiEjKUSIjIiIiIiIpR4mMiIiIiIikHCUyIiIiIiKScpTIiIiIiIhIylEiIyIiIiIiKUeJjIiIiIiIpBwlMiIiIiIiknKUyIiIiIiISMpRIiMiIiIiIilHiYyIiIiIiKQcJTIiIiIiIpJylMiIiIiIiEjKUSIjIiIiIiIpR4mMiIiIiIikHCUyIiIiIiKScpTIiIiIiIhIylEiIyIiIiIiKUeJjFQYM/uHmV1f0ccmk5nlmNl5CbjuKDMbHj0/0My+Kcux5XyvFWbWvrznS8Uws1wzO7yEfT3NbG5ll0kklSnmbNF1FXMyTKbEHCUy5RR9QVZHv7Cxx3Vxz/PMbEPc66+KnN/WzELc/p/N7GEzq76Z93gw2nd23PV/N7PpZnacmW1f5PhgZivjXh9YpBxfxe3bEJV7Y3225DMJIVwYQri5oo+tiszs1OjnY0W2VzOzX8zsuLJeK4TwfgihYwWVa5MgGEKoG0KYXRHXL+U9fzOzmol6j6rEzIaZ2boiv2fXJPg9e5jZJ2a23Mw+N7MDEvl+UvUo5mxKMUcxJ1HvUZUkKebcbGZfmNl6MxtWzP7TzOyH6Hd9nJk1SmR5SqNEZuv0jn5hY49bY8+BC4GP4vbtVsI1to2O3wPYD7hkM+9xady+j6JztwUeBsYCv8cfHx3XKW7b+/EXDyHsFnfs+8Cl8fWJHWdm1cr5GaWrcfjnfnCR7b2AALxR6SVKAjNrCxyI1/n4Sn7vZH4nny3ye3lHot4oChCvAXfi37k7gNfMrGGi3lOqLMWczKWYg2JOZcScyCzgGuDfRXeY2W7AP4EzgebAKvzvQVIokakiQgi/AG8Du5bj3HxgDFAH6FAR5Ym7e3eumf0I/Cfa/ryZLTSzZWb2XvSFjp0T33Td08zmmtmV0d2iBWY2sJzHNjaz16K7gJPNbLiZfVBK2TdXxofM7N/R3e2PzewPcfuPMLOZ0bkPAlbce4QQ8oDngLOK7DoL+FcIYX1p5ShS3kJNvGbW2cw+i8r3LJAdt6+hmb1uZouiO1Kvm1nraN8t+B/4B4vcSQ1mtmP0vIGZPRmd/4OZDTGzbaJ9Z5vZB2Z2V3TtOWZ2dEmfc1x9/weMAgYUqVcbM3speq/FsfJE+843s6+jOs4wsy5Fyxq9Lu578lczWwiMLO3ziM5pZGYjzWx+tH9ctP1LM+sdd1x1M/vVzDpvpr6lMrPjze84LzW/a7hLCcfViur2m5nNAPYu5bI9gIUhhOdDCBtCCE8Bi4B+W1NWyWyKOYo5ca8VcwqOU8wBQgijQwgTgOXF7D4deC2E8F4IYQVwPdDPzOptTV3KS4lMFWFmrYCj8F/QLT03CxgIrAN+qOCiHQzsgpcNYAIeuJoBnwFPl3JuC6ABsB1wLvCQlXwXubRjHwJWRscMoMgfr2JsroynAjcCDfG7DrcAmFkT4CVgCNAE+B7Yv5T3GQ2caGa1ovMbAL2j7WUpxybMrAZ+520M0Ah4Hvhj3CHbACOBHYDtgdXAgwAhhP+j8B3O+DupMQ/gn3N7/Gd7Fv7didkH+Caq/x3A42ZWbGCNnBXV62ngKDNrHtUjC3gd/z62xX+uY6N9JwHDonPr43fVFpf2ucRpgX8uOwB/opTPIzIGqA3shv8c7om2PwmcEXfcMcCCEMLUMpZjE2a2E/AM8BegKTAebzmpUczhNwB/iB5HsfnvdNGfgQG7l7esIoo5ijnRNRRzSpepMac0uwHTYy9CCN8Da4GdtuKa5RdC0KMcDyAXWAEsjR7jiuw/G/iglPPb4k2jsfMD8CFQv5T3WAqcH3f99dG2dfgv08nFvE8AdixjnXKA84qUr30px28bHdMgej0KGB497xmVqVrc8b8A+27JsUBWVL+OcfuGl/bZlqGMj8XtPwaYGT0/C/hf3D4D5sY+kxKu/x1wWvT8fGB6OT+rudHzg4D5gMWd+2Hs2GKuuxfwW3E/w6LfgeizXAvsGrfvAiAn7js1K25f7ejcFiW89wHRz6ZJ9HomMCh6vh/eceiZvQAAIABJREFUalCtmPPeBP5cwjULfV+L+ZzWAtml/Dw2fh5ASyAfaFjMca3wO031o9cvANeU8Ts1LCpH/O9lK/yu1HNxx20DzAN6xv0+Hx49nw30ijv2T7HvQDHv1zh6j/5AdTwA5QP/LEt59UiPB4o5oJgDijmKOQmOOUXe+ylgWJFtE4ELi2zb+L6V/VCLzNY5IYSwbfQ4obQDzWdMKW5AY5MQwrb4L/B/8V+4kt5j2xDCo3H7/hed2xB4FW/irWg/xdUhy8xuM7Pvzex3/JcE/E5KcRaHENbHvV4F1N3CY5sC1eLLUeR5IWUs48ISytQq/trBfztLfK/IkxQ09Z8ZvS7PZxXTCpgXvXfMxjueZlbbzP4ZNdH/DrwHbBvdjdqcJvh/hOPvoP6A37mK2fjZhBBWRU9L+pkNAN4KIfwavf4XBXd52gA/FPmZErfv+zKUtziLgnexADb7ebQBloQQfit6kRDCfPz37Y9mti1wNGW4exnnuSK/l/Pxn93GzzZ495ufKPz5xhT6rlHKXe0QwmKgD3AF8DPeJ/4d/D88klkUc6I6lHCuYo5ijmLOVsacMliBt2zFq0/x3dASTolMJQk+Y8omAxrj9q/G7wTsGzU3b8m1VwAXAWdubX/L4i4f9/w0/D9Uh+NNxW2j7aU1A2+tRfhdwNZx29qUcvzWlHFB/LWj5u3S3gu8GfkwM9sPv5sX+8NU3nIsALYr0rS+fdzzK4GOwD4hhPr43bT468b/vIr6Fb+btUORa8/bTJk2EXVtOBk42LxP9kJgENDJzDrhfzC3t+IHR/6EN28XZxX+H6yYFkX2F61faZ/HT0CjKGgUZzTe1H8SPoh5iz+HIuYT99nGfX+Ku26h7xqFf8abCCG8G0LYO4TQCP/Py87AJ1tZXkljijnlppijmBMvI2POZnwFdIp73/ZATeDbrbhmuSmRqSLMpxE8E787Uda+mxuFEJYAjwFDK7ho8eoBa/Dy1QY2CY4VLYSwAe9DPCy6E7Izmw52rKgy/hvYzcz6RX8ML2fTP2pFy5cLfID3U307hBC7u1TecnyEB9HLzQcD9gO6x+2vh3eJWGo+m9UNRc7/Ge+LXFxZN+CDRW8xs3pmtgN+l/+pMpYt3gnABnyg8F7RYxe8v/RZ+H+yFwC3mVkdM8s2s1jf78eAq8ysq7kdo7IATANOi+4u9mLTGXqKKvHzCCEswPuMP2w+QLO6mR0Ud+44oAvwZ6K7mjHm05yevSUfCP7ZHmtmh5lPaXsl/h34sIRjr43K1Rq4rLQLmw/GrW5m9YG7gJ9CCEXvpIuUmWJO8RRzFHM2U45MiTnVzSwbzxOqRZ9nrBXuaaC3+XpEdYCbgJdCCGqRyVBLzWwF/sdgP+D4Ik28r1nhucNfLuVa9wLHmNmeCSrrk3hz5DxgBuUYJFpOl+J3mBbid6OewX9Zi1PuMkbN1ScBt+HBoAPeFLw5o/G7IvF/mMpVjhDCWnw2qrOBJcApeFCNuReohd/p+h+bTrl5Hz4Y9Dczu7+Yt7gMH8Q6Gw+G/wKeKEvZihgAjAwh/BhCWBh74IMeT8fvTvXG+0n/iHeDOiWq4/P4YNd/4U3R4/DBlOB/4HvjfYBPj/aVZnOfx5n4HcGZeB/4v8R2RHekXwTaEfcZmw+UbMwWfr9DCN/gd9seiMrTG5/Kdm0xh9+Ifz/mAG/h3+vSXBNd8ye8H3bfLSmbSBzFnM1TzCmgmFNYpsScR/GErT/wf9HzM6P3/Qqf7v3pqI71gIu3pOwVyQr//RKp+szsdnww4IDNHixSCjMbCuwUQjgjbtsBwCUhhP7JK5mIVBWKOVJRFHMqnhIZqfKipv0awBf43Ofj8VlSNnfnRKREUbeAqcCZIYT3kl0eEakaFHMkERRzEkNdyyQV1MObYVcCzwJ3A68ktUSS0szsfLyb1gQFFBEpQjFHKpRiTuKoRUZERERERFKOWmRERERERCTlKJEREREREZGUU9zCQRUmmpP7PiALeCyEcFuR/Tvg0/A1xaf9OyOEMNfM9gIewVcK3QDcEkJ4trT3atKkSWjbtm25y7py5Urq1KlT7vNTWabWPVPrDZlb90ytN2xd3T/99NNfQwhNK7hIaUUxqPwyte6ZWm/I3Lpnar0hgTEohJCQB568fI8vlFQDmA7sWuSY54EB0fNDgTHR852ADtHzVvgiR9uW9n5du3YNW2PSpElbdX4qy9S6Z2q9Q8jcumdqvUPYuroDU0KCYkW6PBSDyi9T656p9Q4hc+ueqfUOIXExKJFdy7oDs0IIs4MvzjMW6FPkmF2B/0TPJ8X2hxC+DSF8Fz2fjy+4o7uBIiIiIiICJHaMzHb4VHMxc6Nt8abjK8qCr1Rdz8waxx9gZt3xFp3vE1ROERERERFJMQkdI1MGVwEPmtnZwHvAPHxMDABm1hIYg3c/yy96spn9CfgTQPPmzcnJySl3QVasWLFV56eyTK17ptYbMrfumVpvyMy6V+Y4TRERqXyJTGTmAW3iXreOtm0UdRvrB2BmdYE/hhCWRq/rA/8G/i+E8L/i3iCEMAIYAdCtW7fQs2fPQvvXrVvH3LlzycvL22xhGzRoQHZ2dpkqlm6SWffs7Gxat25N9erVK/29c3JyKPqdyRSZWvdMrTdkXt3NLAt4CDgC7xEw2cxeDSHMiDvsLuDJEMJoMzsU+BtwJrAKOCuE8J2ZtQI+NbM3Y/FJRESqhkQmMpOBDmbWDk9gTgVOiz/AzJoAS6LWlmvxO2OYWQ3gZTzAvFDeAsydO5d69erRtm1bzKzUY5cvX069evXK+1YpLVl1DyGwePFi5s6dS7t27Sr9/UUyxdSpsGHD5o9LMxvHaQKYWWycZnwisytwRfR8EjAOfJxm7IAQwnwzi43TVCIjIrKFEhmDEjZGJoSwHrgUeBP4GnguhPCVmd1kZsdHh/UEvjGzb4HmwC3R9pOBg4CzzWxa9NhrS8uQl5dH48aNN5vESHKYGY0bNy5Ti5mIbLnp06FPH+jSBf7zn+bJLk5l0zhNEZEkqowYlNAxMiGE8cD4ItuGxj1/AdikxSWE8BTwVEWUQUlM1aafj0jFmzEDhg2D55+HBg3gppugS5dfk12sqkjjNKuATK17ptYbMrfumVLv3NzajBrVlnffbUadOusZOPAnOnX6gZycnyv8vZI92D+tLV68mMMOOwyAhQsXkpWVRdOmPov0J598Qo0aNUo8d8qUKTz55JPcf//9pb5Hjx49+PDDDyuu0CKSsr77Dm68Ef71L6hTB/7v/+DKK6FhQ8jJybi+ZUkfp7klMm0MU7xMrXum1hsyt+7pXu/iY1A1GjZsR07ODwmpuxKZBGrcuDHTpk0DYNiwYdStW5errrpq4/7169dTrVrxP4Ju3brRrVu3zb6HkhgRyc2Fm2+G0aOhRg246iq45hpo0iTZJUuqpI/TFBHJBMmMQYlcR0aKcfbZZ3PhhReyzz77cM011/DJJ5+w33770blzZ3r06ME333wDeNZ+3HHHAZ4EnXPOOfTs2ZP27dsXaqWpW7fuxuN79uzJiSeeyM4778zpp5+OL4YK48ePZ+edd6Zr165cfvnlG68bLzc3lwMPPJAuXbrQpUuXQgnS7bffzh577EGnTp0YPHgwALNmzeLwww+nU6dOdOnShe+/V/dxkco2dy5cdBHstBM89RRccgnMng133JHxSUyVGKcpIpLOqkIMypgWmb/8BaLGkWJt2FCLrKwtu+Zee8G99255WebOncuHH35IVlYWv//+O++//z7VqlXjnXfe4brrruPFF1/c5JyZM2cyadIkli9fTseOHbnooos2mbJ46tSpfPXVV7Rq1Yr999+f//73v3Tr1o0LLriA9957j3bt2tG/f/9iy9SsWTPefvttsrOz+e677+jfvz9TpkxhwoQJvPLKK3z88cfUrl2bJUuWAHD66aczePBg+vbtS15eHvn5m3QfF5EEWbgQbrsN/vEPyM+Hc8/1JvzWrZNdsqqlKozTFBFJN1UpBmVMIlOVnHTSSWRFWdOyZcsYMGAA3333HWbGunXrij3n2GOPpWbNmtSsWZNmzZrx888/07rIN6Z79+4bt+21117k5uZSt25d2rdvv3F64/79+zNixIhNrr9u3TouvfRSpk2bRlZWFt9+67OPvvPOOwwcOJDatWsD0KhRI5YvX868efPo27cvQMauvyNS2X791e90PfggrF0LAwbA9ddD27bJLpmIiKS7qhiDMiaR2VzLyfLlqyttLZU6depsfH799ddzyCGH8PLLL5Obm1viQKiaNWtufJ6VlcX69evLdUxJ7rnnHpo3b8706dPJz89XciJShfz2G9x9N9x3H6xcCaedBjfcAB06JLtkIiKS7rY0Bi1bBlOmeMvNwoWw++4Q91/UCqUxMkm2bNkyttvOlzYYNWpUhV+/Y8eOzJ49m9zcXACeffbZEsvRsmVLttlmG8aMGcOGaOWiI444gpEjR7Jq1SoAlixZQr169WjdujXjxo0DYM2aNRv3i0jF+f13H0DZrh3ccgscfTR8+aX3RVYSIyIiifT77z59f9u2HoMOOcRj0N//7gP7zzsPjjsOunXzbmVPP+3nzZgBhx8OZ5zhA/+j/y4mhBKZJLvmmmu49tpr6dy58xa1oJRVrVq1ePjhh+nVqxddu3alXr16NGjQYJPjLr74YkaPHk2nTp2YOXPmxlajXr16cfzxx9OtWzf22msv7rrrLgDGjBnD/fffz5577kmPHj1YuHBhhZddJFOtXAm33+4JzNChcPDBvjLyc8/Brrsmu3QiIpLq1qyBH3+ETz6BV1+FESPgvfd837x5nrw0bOgtL7//7tv33ddjUF6ej5GZMAEWLIDmzeHII6FNNOH97rvDu+/CzJmwdCk8/HDi6pExXcuSbdiwYcVu32+//TaORwEYPnw4AD179tzYzazouV9++eXG5ytWrNjkeIAHH3xw4/NDDjmEmTNnEkLgkksuKXZa5w4dOvD5559vfH377bdvfD548OCNs5XFH/+f//yn2DqJSPnk5fngyb/9DX75BXr18rthe++d7JKJiEhVFwLE1hl/5x1PSGLduxYu9FhyxRU+QL9uXSh6//xPf4LPPoNbb4VFi6BlSzj0UOjUCVq08JYX8IRl7VrYpoTmkHr14KCDElfPeEpkMsCjjz7K6NGjWbt2LZ07d+aCCy5IdpFEJM6aNfD44950P3++N9+/9BLsv3+ySyYiIskUgrdqLFzoyUOnTr79rru8m1fRROW113z/OefATz/587p1PRHZYQd/vc02Pmi/fn3f3rAhTJoEDz3kLTOHHOLdmkuKQWYFCVOyKZHJAIMGDWLQoEHJLoaIFLFuHTz5pLe6/PijB42nnvIgIiIi6WvVqsJJyMKF3kJy+eW+/8ILvetWLIEB2HNPmD7dn//7375mS4sWsP320L07dOlScP3XXvOWkebNIW6OqY0GDSqIQRdfXBCDnn46tWKQEhkRkUq2YQP8619w443w/fd+F23ECO9jXFXucomIyJZZtw6qVfO/49OmwaefFk5UcnP3ZPJkP/a88+CZZwqf36RJQSKz/faeULRo4clILGGJmTSp9LLEWm6Kk04xSImMiEglyc+HF17wwZMzZ3qgeeUV6N079YKHiEgmyM+HxYsLkpEDDoBatby15OmnCycqixf7wPh69TxRuPNOv8a223oikp1trFsH1av7IpJHHVU4UWnSpOB9r7suMXVJtxikREZEJMFC8GBxww3w+ec+68vzz0O/fiUPlhQRkcTJy/PuVEW7d110kQ9mf+45737188/eghEzYwbssouf+9FHnoTstJMPbm/Rwv/egw+qv/RSaNYMYkvz5eRMp3r1ngAcdljl1TWdY5ASGRGRBAnB79oNHepdDDp08DEwp54KWVnJLp2ISHrJy/N/s7M9ARk3btNE5bbbfEr7t96CPn0Kn1+tmreStGnj66IcfXRBa0nsERswf8EF/ihJixaJqeOWyIQYlOJ5WNV2yCGH8Oabbxbadu+993LRRReVeE7Pnj2ZMmUKAMcccwxLly7d5Jhhw4ZtXM+lJOPGjWPGjBkbXw8dOpR33nlnS4ovIuUUAkyc6AMnjz3Wuxs88YTfyTv99PQJICIiibZ+va9VMnWq/6d85kzfvmCB/4e8Z0/YeWfvvlWrFowZ4/vnzvUB88OGeXeqWbN89q6Ybt38P/XvvANffOHTDa9Z40kOQI8e8NhjPpvkZZfBSSfBgQdC7dqVWfvyyaQYpBaZBOrfvz9jx47lqKOO2rht7Nix3HHHHWU6f/z48eV+73HjxnHcccexa7R63k033VTua4lI2b3/Plx/vS8Gtt12vi7MwIFQo0aySyYiUnWEAF9/Xbi15OeffeD5iSfCsmXeZWvRooLuWuDdo4YN89aTqVO9xWTPPX2geosWBetu7bGHr6PStKmPSSmqVSv/T326ybQYpEQmgU488USGDBnC2rVrqVGjBrm5ucyfP58DDzyQiy66iMmTJ7N69WpOPPFEbrzxxk3Ob9u2LVOmTKFJkybccsstjB49mmbNmtGmTRu6du0K+BoxI0aMYO3atey4446MGTOGadOm8eqrr/Luu+8yfPhwXnzxRW6++WaOO+44TjzxRCZOnMhVV13F+vXr2XvvvbnjjjuoV68ebdu2ZcCAAbz22musW7eO559/np133rlQmXJzcznzzDNZuXIl4Atv9ujRA/BFNJ966im22WYbjj76aG677TZmzZrFhRdeyKJFi8jKyuL555/nD3/4Q4I/eZHK9/HHHjzeftsD6333+eJisb7RIiLpbsUKn1a4WTN//fjjkJtbOFk5+GA47jjf37VrQXcwgJo1vfXjxBN9jZN+/fxa8V27Yv+FaNoUvvmm5LLUqOHJSqbI1BiUUYlM3ML3G518ss+fvWqVz9pQ1Nln++PXX/0XK15OTunv16hRI7p3786ECRPo06cPY8eO5eSTT8bMuOWWW2jUqBEbNmzgsMMO4/PPP2fPPfcs9jqffvopY8eOZdq0aaxfv54uXbpsTGT69evH+eefD8CQIUN4/PHHueyyyzj++OM3Ji7x8vLyOPvss5k4cSI77bQTZ511Fo899hiDBw8GoEmTJnz22Wc8/PDD3HXXXTz22GOFzm/WrBlvv/022dnZfPfdd/Tv358pU6YwYcIEXnnlFT7++GNq167NkiVLADj99NMZPHgwffv2JS8vj/z8/NI/NJEUM3Wq9z9+/XVo3Nhnqbn44tTofiAisjlr13pLSazFBAoSkUGDYPLkgiRl5UofxB7ryX7bbb7WSXwy0rKl7zODZ58tWJSxRQto0KBg9iwzeOSRyq1rKsr0GJRRiUwyxLqXxRKZxx9/HIDnnnuOESNGsH79ehYsWMCMGTNKTGTef/99+vbtS+3oW3n88cdv3Pfll18yZMgQli5dyooVKwp1YyvON998Q7t27dhpp50AGDBgAPfdd9/G/f369QOga9euvPTSS5ucv27dOi699FKmTZtGVlYW3377LQDvvPMOAwcO3FjGRo0asXz5cubNm0ffvn0ByE732wKSUb76yrs4vPii980ePtzn/69XL9klExEpm9mzfexIfNeu/Hy45x7f368fvPxy4XM6dixIZJYu9ZaP7t0LkpH4jhyffOKJStExGbEbwXH/nZEtpBjkMiqRKa0FpXbt0vc3abL5Fpji9OnTh0GDBvHZZ5+xatUqunbtypw5c7jrrruYPHkyDRs25OyzzyYvvm11C5x99tmMGzeOTp06MWrUKHLKU8g4NWvWBCArK4v169dvsv+ee+6hefPmTJ8+nfz8fCUnknG+/db7Z48d6wNHhw71u5LbbpvskolIJgvB1zBZuNDHlpj5gO933imcqPz2mycwZnDTTTB6dME1ateGHXcseN2nD3TuXLhrV6xFBWDkyNLL1LBhxdZRFIOK0qxlCVa3bl0OOeQQzjnnHPr37w/A77//Tp06dWjQoAE///wzEyZMKPUaBx10EOPGjWP16tUsX76c1157beO+5cuX07JlS9atW8fTTz+9cXu9evVYvnz5Jtfq2LEjubm5zJo1C4AxY8aw//77l7k+y5Yto2XLlmyzzTaMGTOGDdHk6kcccQQjR45k1apVACxZsoR69erRunVrxo0bB8CaNWs27hdJNbNnezfTXXbx+fivuQbmzPGVkTM1gIhI4q1e7X9rPvrIpxP+xz88YQH/z+y++0Lbtj5j17bbeotI1LubnBy4+25PZn791Qd/H364r0APcOWVPjj8u+9g+XLvGjZ9esF7Dxjg4y7OP9+73++9t09LLJVPMah4GdUikyz9+/enb9++jB07FoBOnTrRuXNndt55Z9q0abPZRKJLly6ccsopdOrUiWbNmrF3bEoO4Oabb2afffahadOm7LPPPhuTl1NPPZXzzz+f+++/nxdeeGHj8dnZ2YwcOZKTTjpp42D/c889t8x1ufjii/njH//Ik08+Sa9evahTpw4AvXr1Ytq0aXTr1o0aNWpwzDHHcOuttzJmzBguuOAChg4dSvXq1Xn++edp3759md9PJNl++smb7J94wrtH/PnP8Ne/+mBKEZGt8euv3v0q1loSazm58UZPSEaN8hmnitp3X9hrL5+5q359b4GJbzWJdZa4/nq/VkmLHu6xR8KqJhVEMah0FuLntEth3bp1C7H1V2K+/vprdtlllzKdv3z5cuplWsfCSLLrviU/p4qUk5NDz+JmgMgAmVr3Lan3ggVw660wYoR32Tj/fLjuOr+jmYq25mduZp+GELpVbInSS3ExaEtk6u8kpE/d8/O929bChT6jVrNmvvr7Aw8U7tq1cKGvT1K/fg55eT05+uiCazRo4InIyJGw337w5Zfw2muFk5QWLfzaqbwWSLr8zLeUYlDPcp1bWgxSi4yISJxFi+D22+Ghh7z7xcCBMGRIwWrOIpI5QvAphYu2mHTp4gsmzp0LJ5xQsD82tPTBB+GSS7wL2IMPFp46eP/9fVrgFSu8ZeWjj3xf8+bePSze7rv7QzKHYtCWUSIjIoL3Kb/rLrj/fu+TfsYZPogylZc9CsHvEEdD2UQkkp/v3a1C8BaPoonKoYd6IrJypXfdKmrwYE9k6tXz1pE99yzcYhLrAb7bbr68Q2xK4Xg5OT62Yd99E1pVSRHpGIMqgxIZEcloy5bBvffC3//ud09POcVnhCmyFmyVtWYN/PADdOhQsC7Dc8/5INDZs71+l18O0SzoImlrwwYfc7JwoScoe+3l24cO9cHs8YlKr17wr3/578xZZ/nvCfg6HC1aQLeoE0vduv6fy6KLMjZu7PsbNIDx40suU3EJjEi8VI9ByZb2iUwIAdNfkiorXcZoSepZscL7rt95p7da9O3rg2Kr2uDXEPw/YI0a+XoNkybBk096kjJnjndtCQF++cX75c+ZAzNmQLt2fse4fXvva79mTbJrIlI+y5bB/PmFE5Hq1b3FBPw/fu++611yYmsuH3CAz8YF8Oabfre7RQtvITnsMNhnn4Lrv/++TxPcrJn/jhV15ZWJrZ9kplSJQVVdWicy2dnZLF68mMaNGyuZqYJCCCxevFhr0UilWr0aHn7Y+yAvWgTHHONrKXTtmrwyrVzpA3ezs+GLL3wgcCxRmT3by/zpp94vf84ceOstT1AOOcQTlvbtC2YpGjzYH0Vt5RJTIhVq9eqC8SAzZtTn668LkpRYi8qrr/r+007btNWjQ4eCRGbnnQsGyccebdsWHPvxx6WXRf9xlMpUFWNQKkvrRKZ169bMnTuXRYsWbfbYvLy8jP0PdTLrnp2dTWtNSi+VYM0aePnl7TjtNJ8N5vDDPXjst1/i33vDBh8EXLOmt6D885+FE5Wff/b1Ifr08bI98YQnJx06wJFH+vPYInQDB8I55yS+zCJbat06bxmMJSNHHeXTAz//vD/iE5VVq/x3MisLJkxoweuvezesWBeuNm0KrvvnP/t4gfhEJX7djBtvrPy6imypZMagdJbWiUz16tVp165dmY7Nycmhc+fOCS5R1ZTJdZf0t26dr8Vw883w008dOPBAeOYZOPjgin2fDRv8P2XLl/uCdfGJSm6u93++9FLff+utsP32nqAcd5z/27GjX+fww72fdEmNyGpclsq2YoV/h+MTkYULfS2Lpk3h0Ud9Wthffy183rx5PjvXjz96S2OLFn7XOZaMrF/vvzMDB+by6KOtaNLEE5+ijjyyUqopkhCVFYMyVVonMiKSudavh6ef9jtes2d7n/jLL5/OlVd2KlcyEIInEfn5/h+3+ERl9myf4//22/2Ya67xMS3t20PnztCvX0G3gY4dIS/P+/gXp6SF60QqSgieUFev7t27fvzRVwovmqj8858+6P3ll31AfLxatbyVpGlTn1XppJM2XeskNiD+yitLH2fSqNFaWrRIXH1FkqGiY5AUL6GJjJn1Au4DsoDHQgi3Fdm/A/AE0BRYApwRQpgb7RsADIkOHR5CGJ3IsopIesjP95m7hg2Db7/1ROL1170f8rvv/lamAPLSS/D114UTlQMPhDFjPNEYPNi7xrRt68lK9+6+NgT4LEfLlhU/ZSv4+UpWJBHy8goPiN91V++e+P33cPXVhZOU1av99+Tkk33/5Zd7a0jz5p6EtGxZ8D09+GCfCS8+Salbt6B18NBD/SEiFRODpOwSlsiYWRbwEHAEMBeYbGavhhBmxB12F/BkCGG0mR0K/A0408waATcA3YAAfBqd+1uiyisiqS0Ev3N8ww2+Gvbuu8OLL/pMMEUDx7vvwvTpBYnKnDm+WvKECb5/2LCCrjDt2/sMSAcdVHD+N99AkyYlJyQlJTEi5bFhg38fi7aYHHYY9O7t46722AOWLi183t13wxVX+Pf/u+/8+7z//gXJyJ57+nE9evig40aNiv9Ob7+9P0SkZFsSg6TiJLJFpjswK4QwG8DMxgIHU8X0AAAgAElEQVR9gPhEZlfgiuj5JGBc9Pwo4O0QwpLo3LeBXsAzCSyviKSgEODf//a1IqZO9a5bt9/uXV6mT/fAMmeON/PfFrUJ33GHz4JUu7YnKu3aFawbAb6vUSPfX5xmzRJfL8ksL7ywHRMmFE5Ujj/e+9WvX+93dePVr+/fw969vQvXmWcWbjFp3ty/2+D/fvFFye9ds6Y/RGTLFReDnnnGWzvV+p54iUxktgN+ins9F9inyDHTgX5497O+QD0za1zCudslrqgikkp++AGmTPH1IV55xWdKql7dZ/s680wfVP/Xv3oQadPGE5Vdd/WAA/DQQ56kNG1a/J0yTaQnle3pp3dg9erC0wfHZu6qWdO/502bFiQp8Ul2rVq+GriIVJ4Q4O23PYH5+GO/YTBqFJx+evGTVkhiJPujvgp40MzOBt4D5gEbynqymf0J+BNA8+bNydmKhRJWrFixVeenskyte6bWG6p+3Zctq86sWXVZsCA7etRiwYJshg//ksaN13LnnTsxfnwrAMwCzZqtoWPH5TRtOpMPPthAz57ZHHig0axZHtWrFyy6WtXrnUiZXPdU8PTTH3P00QeW2AXl+OMrtzwiUrJ334Xrr/fFVNu0gREj4OyzS57ERRInkYnMPCBuJnhaR9s2CiHMx1tkMLO6wB9DCEvNbB7Qs8i5OUXfIIQwAhgB0K1bt9CzZ8+ih5RZTk4OW3N+KsvUumdqvSH5dV+5Er76atOZv/7+d+jUyVeuv+oqP7Z6ddhhB7/blZ/fg+HDYeJEH6Ny9dXw5z8bNWtmA9n4vCElS3a9kykT655KE87Urr1B/ehFqriPPvIEZuJEnxDjgQd8xkp1zUyeRCYyk4EOZtYOT2BOBU6LP8DMmgBLQgj5wLV4QAF4E7jVzBpGr4+M9otICli/HmbNKpykzJ7tK3Effrg3wx92WMHxzZp5orJqlb8+8kiYNMm3bbcdTJvmzfcnn+zda+6+Gy66qGBlcJGiNOGMiFSUTz/1GDR+vGJQVZOwRCaEsN7MLsWTkizgiRDCV2Z2EzAlhPAq3uryNzMLeNeyS6Jzl5jZzXgyBHBTbOC/iCRfCL4ycdFE5bjjfD2J3FzYZZeC47OzPSlZtsxfd+7sff7bt/exAHXrFr5+bJzAF1/4qt7jxkHDhvC3v/n4l6LHixRDE86IyFb54gtPYBSDqq6EjpEJIYwHxhfZNjTu+QvACyWc+wQFLTQiUsmWL/dEJT5Z6dQJzjvP16vYLm76DTN/HZtZaYcdfM2V9u390bx54UH1DRuW3ud/5kyfAvm556BePX/+l79AgwaJqKmkKU04IyLlohiUOpI92F9EkmT9el9/Ij5RadbMW0DAV+tetKjg+Pr14Zxz/HmtWvD449Cqlc8ItsMO3uoSU726r/q9pb7/Hm680VdDrlULrr3WVwRv1Kj89RQphSacqQIyte6ZWm+ounWfNy+b0aPbMnFic2rUyOe00+Zy8sk/Ub/+eqZO3frrV9V6V4ZE1V2JjEiaCgF++60gSZkzx7cNHuz799vPpzCOycqCY48tSGTuvNOTiVirSsOGhVtVYklNRfjhB18vY9QoT4IGDfLpk5uWPnZfpDSacCZFZGrdM7XeUPXqXnwMyqJp0x2AHSrsfapavStTouquREYkha1Z43+AY4nKokXenxfgtNNg7NjCx++yS0EiM2iQD66PJSqtWxee+37AgMSXf948uPVWePRRT5IuvthbYVq2TPx7S9rThDMiUirFoNSnREakCgsBfv658DTFgwf7HaMhQ/wPcChYJoVatXx/jRo+6H7vvQsSlXbtvK9vzGmnbfp+leWXX+C22+Dhh2HDBm/dGTKkYAFAka2lCWdEpCSKQelDiYxIkq1cWXhQ/emn+xopjz0Gl18Oq1cXPv6MMzwpOeAAuOGGgiSlfXuf6Wubbfy4fv0qvy6bs3ixd1l74AGfMOCss3xO/vbtk10ySUeacEZE4ikGpR8lMiIJtmGDN1/HxqoceqhPOfzmm/5H9JdfCh/fqRP07OndwC66qHCi0rZtwaD6Xr38kQqWLvXFLu+9F1asgFNP9SSsY8dkl0xERNKdYlD6UiIjUgGWLi1IVHbbzZOQL7+Evn19DMu6dQXHPvmkJyRt2vgUxPGJSvv20LixH7f//v5IZcuXw/33w113+Wf0xz/6NJa7757skomISLpTDEp/SmREymDtWvjxx4IpivfaC379FY46yruE/Ra33vctt3gi07QpdOkCJ55YOFGJ9cHddVcfYJiOVq2Chx6C22/3pvzevX1a5dg6MyIiIomiGJQ5lMiI4APmFy3yRKV6deja1bcdcQTMmgU//QT5+X7shRfCI4/Attv6Qo/77ls4UfnDH/y45s3h2WeTV6dkyMuDf/7TVz/++Wc48ki46SbYp+gyhCIiIhVMMSjzKJGRjLFqFeTm+r/duvm2886DTz7xBGblSt927LHw+us+FWO9enDggYUTlVif2mrVYPz4Yt8q46xdC088AcOH+3iggw+GF17wCQlEREQSSTEocymRkbSRnw/z5/vg+S5dfNtNN8Fbb3mismCBb+vUCaZN8+fr1nmScthhBcnKzjsXXPPllyu3Dqlm/XoYM8Y/59xcX2Rz9Gif0CB+8UwREZGKphgkSmQkpfz+uw+e32MPf/3YY/Dii56o5Ob6XZlGjbxPLMCSJd5V7OijCwbV77RTwfVGj670KqSFDRt8sc0bb4TvvvOueA8/7LOoKXiIiEgiKQZJjBIZqVLWrfPxKNtv7123Xn/d77bEZgRbEi1Jt3w51K1b0AKz555wwgkFrSoh+B+ze+9Nbn3STX4+vPSST1s5Y4YnlOPG+exrCh4iIpJIikFSlBIZqVQheGtJ3bq+Hsonn/jMXbFE5aef/E7LzJk+FmXuXJg61ROUbt0KxqlUi765Q4f6QxIrBHjtNf+sp0/37nfPPuszssUW4BQREUkExSApiRIZqXB5ef5Hp1Ytn/HrkUcKEpU5c7w15e234fDDvUXltdc8UenRoyBRadLEr3Xhhf6Q5AjBF+4cOhQmT/YZ2caMgf79ISsr2aUTEZF0phgkm6NERrZYfr53AatZ09dSeeihgkRl9mxPTp54AgYOhGXLPJGJdfnq2dP/3XFHv1afPt4lTKqeSZPg+uvhv/+FHXbw8UhnneVjjkRERBJJMUjKQomMFGvDBr/bsXbtponKnDlw7bXeRzU/3wfbbbedJyhHHun/xmYN69zZpzUuqe+q+rRWPf/9rwePSZOgVSsfQHnuuVCjRrJLJiIi6U4xSLbEZhMZM+sN/DuEkF8J5ZFKEhsMD/DGG815++3CycoJJ/jYlWrVPGmpWdMTlF13heOO87VVwFevX73a9xdHfVdTx+TJ3nz/xhvQrBnccw9ccIF3ERRJFsUgkcygGCTlUZYWmVOAe83sReCJEMLMBJdJKtj48fDFF4UTlV139bEpAGPGtOXnn73ptn176NcPDjnE922zDSxcCA0aFN96YlZyEiOpYfp0Dx6vvgqNG8Ptt8Mll0CdOskumQigGCSS1hSDZGtsNpEJIZxhZvWB/sAoMwvASOCZEMLyRBdQNu/jj+GzzwonKrVqwYcf+v477oB33/XWk3btoHt32GefgvMfeOAzjj9+/40zgRW17baJr4NUvhkzvHvgCy94onrzzXD55VC/frJLJlJAMUgkPSkGSUUo0xiZEMLvZvYCUAv4C9AXuNrM7g8hPJDIAgp88403ucbGp8ye7WunzJjhLSIPPghPPeXTGbdtW9AFLObpp/0PQ716xV+/UaN1JSYxkn6++w5uuWUXJk70O15DhsAVV0DDhskumUjxFINE0odikFSksoyROR4YCOwIPAl0DyH8Yma1gRmAgshWmj8fpkwpnKjMnu0D3rbd1lef/9vf/NhWrTxR6d4d1qzx5OXWW70ptkWL4sekbLdd5dZHqqbcXLjpJnjySahWrQlXXw1XX10w1bVIVaQYJJIeFIMkEcpyH/6PwD0hhPfiN4YQVpnZuYkpVnpZuhSmTds0URk50hd1euUVuPhiP7ZePU9UOnaEVas8kbn4Yp9ysG1bT1yKatOmUqsjKWbuXLjlFnj8cU90L70UDjroY/r165HsoomUhWKQSApTDJJEKksiMwxYEHthZrWA5iGE3BDCxEQVLJXk5Xk3r6KJypAhPrvXBx9A795+bFYWbL+9Jytr1vi2E07wVevbtfOBbkUH1bduXbn1kfSwcKG35P3znz5N9nnnwXXX+fcpJ2dtsosnUlbDUAwSSTmKQVIZypLIPA/Ep80bom17J6REVdCGDYUTlNjjjDM8CfnmG+jateD4xo09UVm1yl/vt5+vZN++vbeeFF3MqWVLf4hUhF9/9QkeHnzQ1wEaMMDn5G/bNtklEymXjI9BIqlEMUgqU1kSmWohhI2pcwhhrZml1bJEIcBvv1Xnf/8rvOjjAQcUrE7foUPB8TVqeOvJkiX+eqed4MUXPVFp185n34jXuDEcfnjl1Ucy02+/wd13w333+SKkp5/uM8LsuGOySyayVdI+BomkA8UgSYayJDKLzOz4EMKrAGbWB/g1scWqfKedti95eQWvW7b0LmDgM2mMHl0wI1irVoUH1deq5WuviCTD77974Lj7bk+6TzoJhg0rPHOdSArLiBgkkqoUgySZypLIXAg8bWYPAgb8BJyV0FJVMjO44opv2W+/XWjf3hOW2rUL7z8rrWos6WDlSm+6v+MObx3s0wduvBE6dUp2yUQqVNrHIJFUpBgkVUFZFsT8HtjXzOpGr1ckvFRJcMQRP9Oz5y7JLobIZq1eDf/4B9x2m68ndPTRPqVlt27JLplIxcuUGCSSKhSDpCop0zKIZnYssBuQbdGUWiGEmxJYLhEpYs0an77yllt87aFDD/WVkHtoBktJc4pBIsmnGCRVUVkWxPwHUBs4BHgMOBH4JMHlEpHIunU+Ruvmm+HHH30Siqefhp49k10ykcRTDBJJLsUgqcqKWQd+Ez1CCGcBv4UQbgT2A3ZKbLFEZMMGGDMGdtkFzj8fmjeHN96A995TAJGMohgkkgSKQZIKypLIxObyWmVmrYB1QJlWPTGzXmb2jZnNMrPBxezf3swmmdlUM/vczI6Jtlc3s9Fm9oWZfW1m15a1QiKpLj8fnn0Wdt/dJ5moVw9efRU+/hiOOmrTBVNF0ly5Y5CIbDnFIEklZUlkXjOzbYE7gc+AXOBfmzvJzLKAh4CjgV2B/mZWdDK+IcBzIYTOwKnAw9H2k4CaIYQ9gK7ABWbWtgxlFUlZIcC4cbDXXnDqqT7F9wsvwKefQu/eCh6SscoVg0RkyygGSSoqdYyMmW0DTAwhLAVeNLPXgewQwrIyXLs7MCuEMDu61ligDzAj7pgA1I+eNwDmx22vY2bVgFrAWuD3slVJJLWEABMmwNChHjA6dPD+x6ecAllZyS6dSPJsZQwSkTJQDJJUVmqLTAghH29Vib1eswUBZDt8vv+YudG2eMOAM8xsLjAeuCza/gKwElgA/AjcFUJYUsb3FUkJIcA77/iML8ceC4sXw8iRMGMGnHaaAojIVsYgESmFYpCkg7JMvzzRzP4IvBRCCBX8/v2BUSGEu81sP2CMme2Ot+ZsAFoBDYH3zeydWOtOjJn9CfgTQPPmzcnJySl3QVasWLFV56eyTK17Muv9+ecNeOKJdkyfvi1Nm+YxaNAPHH30QqpXD3zwQeLfXz/zzJPCdU9kDBLJSO+/D9dfD+++C61b+7owAwdCjRrJLpnIlilLInMBcAWw3szy8JWVQwihfumnMQ9oE/e6dbQt3rlAL/yCH5lZNtAEOA14I4SwDvjFzP4LdAMKJTIhhBHACIBu3bqFnlsxjUZOTg5bc34qy9S6J6PeH3/swePtt6FFC7j/fjj//GyyszsCHSutHPqZZ54Urnt5Y5CIFFF8DILs7GSXTKR8NjvYP4RQL4SwTQihRgihfvS6LAFkMtDBzNqZWQ18MP+rRY75ETgMwMx2AbKBRdH2Q6PtdYB9gZllrZRIVTN1qg+W3Hdff37nnfD993DZZQogIqXZihikmTNFIopBkq7KsiDmQcVtDyG8V9p5IYT1ZnYp8CaQBTwRQvjKzG4CpoQQXgWuBB41s0H4AP+zQwjBzB4CRprZV/jdt5EhhM+3qGYiVcCXX8INN8BLL8G22/qKyJdd5tNZisjmlTcGxc2ceQQ+RnOymb0aQoifcCY2c+Yj0aya44G2xM2caWa1gRlm9kwIIXerKyRSiRSDJN2VpWvZ1XHPs/HxK58StZiUJoQwHg8M8duGxj2fAexfzHkr8EAikpK++QaGDfO5+OvW9dlgBg3yQCIiW6S8MUgzZ0rGUgySTLHZRCaE0Dv+tZm1Ae5NWIlEUtjs2XDTTb4acnY2/PWvcNVV0Lhxsksmkpq2IgYVN3PmPkWOGQa8ZWaXAXWAw6PtL+BJzwKgNjCouJkzNeFMxcjUuiei3vPnZ/Pkk215++3m1KiRT//+czn55J9o0GA906ZV6FttFf3MM0+i6l6WFpmi5gK7VHRBRFLZjz/C8OE+dWW1avCXv3gS06xZsksmknYqMgZt1cyZmnCmYmRq3Suy3sXHoCyaNdsB2KFC3qMi6WeeeRJV97KMkXkAb2YHnxxgL3x1ZZGMt2AB3HorjBjhc/JfcAFcdx20apXskomkh62IQQmfOVMk2RSDJNOVpUVmStzz9cAzIYT/Jqg8Iinhl1/g9tvh4Ydh3To45xwYMgS23z7ZJRNJO+WNQRtnzsQTmFPxBCVebObMUSXMnDkmbuZMdamWKkMxSMSVJZF5AcgLIWwAnwnGzGqHEFYltmgiVc+SJXDXXT73/urVcMYZPojyD39IdslE0la5YpBmzpR0pBgkUlhZEpmJ+ADIFdHrWsBbQI9EFUqkqlm2DO65xx/Ll8Mpp/iUljvvnOySiaS9cscgzZwp6UIxSKR4ZUlksqM/6oD/gY/m1RdJeytWwAMP+OJhv/0GffvCjTfCHnsku2QiGUMxSDKWYpBI6bYpwzErzaxL7IWZdQVWJ65IIsm3ejXcfTe0b+8DJ3v0gE8/9UXFFEBEKpVikGQcxSCRsilLi8xfgOfNbD7eV7gFcEpCSyWSJGvWwKOP+urHCxfC4YfDzTfDvvsmu2QiGUsxSDKGYpDIlinLgpiTzWxnoGO06ZtoSkqRtLFunc+/P3w4/PQTHHSQr4h80EHJLplIZlMMkkygGCRSPpvtWmZmlwB1QghfhhC+BOqa2cWJL5pI4q1fD6NGQceOPv/+dtvB229DTo4CiEhVoBgk6UwxSGTrlGWMzPkhhKWxFyGE34DzE1ckkcTLz4eJE5ux224wcCA0bAivvw4ffuhN+WbJLqGIRBSDJO0oBolUjLKMkckyMwshBPA5/IEaiS2WSGLk58PLL/u0lV99tSu77+6DJ084QYFDpIpSDJK0oRgkUrHK0iLzBvCsmR1mZocBzwATElsskYoVgt/t6toVTjzRm/Ovv34G06f7dJYKICJVlmKQpDzFIJHEKEsi81fgP8CF0eMLfEEykSovBHjrLdhvP+jdG37/HUaPhi+/hEMP/YVtyvIbICLJpBgkKUsxSCSxNvsrFELIBz4GcoHuwKHA14ktlsjWe/ddOPhgOOoomD/fp7ScORPOOguqlaVTpYgknWKQpCrFIJHEK/FXycx2AvpHj1+BZwFCCIdUTtFEyuejj+D662HiRGjZEh58EM47D2rWTHbJRKSsFIMkVSkGiVSe0u4JzATeB44LIcwCMLNBlVIqkXL49FMPHhMmQNOm8Pe/w4UXQi11QhFJRYpBklIUg0QqX2ldy/oBC4BJZvZoNMhSw9Gkyvn8cx8s2a0bfPwx/O1vMHs2DBqkACKSwhSDJCUoBokkT4mJTAhhXAjhVGBnYBLwF6CZmT1iZkdWVgFFSvL113DKKdCpE/znP3DjjTBnDgweDHXrJrt0IrI1FIOkqlMMEkm+sgz2XxlC+FcIoTfQGpiKzyIjkhSzZvlgyd13h3//G667zoPH0KFQv36ySyciFUkxSKoaxSCRqmOL5s2IVlQeET1EKtUPP8DNN8OoUVCjBlxxBVxzjfdFFpH0pxgkyaQYJFL1aAJAqfLmzYNbb/WpK83g4ovh2mt9NhgREZFEUgwSqbqUyEiV9fPPcNtt8MgjsGEDnHsu/N//QZs2yS6ZiIikO8UgkapPiYxUOYsXwx13+Nz7eXkwYIBPadmuXbJLJiIi6U4xSCR1KJGRKmPpUp93/957YcUK6N8fbrgBdtop2SUTEZF0pxgkknqUyEjSLV8O990Hd9/tgeTEE2HYMNhtt2SXTERE0p1ikEjqUiIjSbNqFTz0ENx+uzfl9+4NN90Ee+2V7JKJiEi6UwwSSX2bXUdGpKLl5fndr/btferK2GrIr76qACIiIomlGCSSPtQiI5Vm7Vp44gkYPtyns+zZE154AQ44INklExGRdKcYJJJ+1CIjCbd+vQePjh3hootghx1g4kSYNEkBREREEksxSCR9KZGRhNmwAZ56CnbZxeffb9IEJkyADz6AQw9NdulERCSdKQaJpD8lMlLh8vPh+edhjz3gzDOhdm0YNw4++QR69fKVkUVERBJBMUgkcyQ0kTGzXmb2jZnNMrPBxezf3swmmdlUM/vczI6J27enmX1kZl+Z2Rdmlp3IssrWCwFeeQW6dIGTT/Ztzz0HU6dCnz4KHiIikjiKQSKZJ2GJjJllAQ8BRwO7Av3NbNcihw0BngshdAZOBR6Ozq0GPAVcGELYDegJrEtUWWXrhABvvAHdu8MJJ8DKlTBmDHzxBZx0Emyjdj8REUkQxSCRzJXIX+/uwKwQwuwQwlpgLNCnyDEBqB89bwDMj54fCXweQpgOEEJYHELYkMCySjnFBksefTQsWgSPPw5ffw1nnAFZWckunYiIpDPFIJHMlsjpl7cDfop7PRfYp8gxw4C3zOwyoA5weLR9JyCY2ZtAU2BsCOGOom9gZn8C/gTQvHlzcnJyyl3YFStWbNX5qaw8df/ii/qMHNmOqVMb0qTJGgYN+oGjj15A9eqBDz5ITDkrmn7mOckuRqXL1HpDZtdd0s9//wvXX++JzHbbwSOPwDnnQI0ayS6ZiFSmZK8j0x8YFUK428z2A8aY2e5RuQ4A9gZWARPN7NMQwsT4k0MII4ARAN26dQs9e/Ysd0FycnLYmvNT2ZbUffJkDx5vvgnNm8O998IFF9QkO3snPP9MHfqZ90x2MSpdptYbMrvukj6Kj0GQrVG0IhkpkV3L5gFt4l63jrbFOxd4DiCE8BGQDTTBW2/eCyH8GkJYBYwHuiSwrLIZ06bB8cd7H+QpU+COO+D77+HPf1YAEZGqSRPOpA/FIBEpTiITmclABzNrZ2Y18MH8rxY55kfgMAAz2wVPZBYBbwJ7mFntaOD/wcCMBJZVSjBjhg+W7NwZ3n8fbr4Z5syBq6+GOv/f3v1HR1Xf+R9/vglIDKTIjy+IRJdQBVoaJr+sClLDClsUi40FNbDVQA8KKFS3/ohWJUXdU1aqflGxC7aAKTZtpUQ8QlUoLB5TUUBAQH5K1g0WFaghiMH8+Owfd5wNkMSQZDKZua/HOXOcuffOnfc7o/P2cz/3vm+nSEcnIlI3NZyJDapBItKQsJ1a5pyrMrPb8QYlccBvnXPbzWwWsME5txz4GbDAzO7Eu/A/1znngH+Y2eN4gyEHrHDOvRKuWOV0e/bAL34BL7zgFYsHH4R/+zc455xIRyYi0iihhjMAZvZVw5naB8Ua3XCmVSKWENUgEWmMsF4j45xbgXdaWO1lD9V6vgMYWs97f4d3RExa0f793hGv55/3Lpq8+27v0aNHpCMTETkjYW84Iy1PNUhEzkSkL/aXNqK0FB55xGtdGRcH06dDXp53MaWISIxqVsMZdc5sGceOHeNPf/obBQX/xIoV59KuHWRnHyAn50O6datk27ZIRxgefv/O/Zi7X/OG8OWugYzPHTwITz11Ia+8AjU1MHky/PznXjtLEZEo1tiGM6PAazgTvKD/pIYzAGb2VcMZdc5sYQcPwm23lfLKK0nU1MAtt3xVg87n5K8v9vj1Owf/5u7XvCF8uet+tz716afedH2/flBU1Id//VfYvRvmzdMgRkRighrOtGGqQSLSEjQj4zP/+AfMmQNz58Lnn3t3P/7+999mwoRTTx0XEYleajjTNqkGiUhL0kDGJ44e9W4c9vjjUFYG118P+fnwrW/B2rVfRDo8EZEWp4YzbYdqkIiEgwYyMe7zz+Hpp72bhx05Aj/8odfScvDgSEcmIiKxTjVI2orKykpKS0upqKiIWAxdunTh/fffj9jnR1Jjco+PjycpKYkOHTo0er8ayMSoL76AX/8afvlL+OQTuOoqmDULMjMjHZmIiMQ61SBpa0pLS0lMTKRv376YWURiKC8vJzExMSKfHWlfl7tzjsOHD1NaWkpycnKj96uL/WPMiRPwzDNw4YXezcNSUqC4GFasUAEREZHwUg2StqqiooLu3btHbBAjDTMzunfvfsYzZpqRiRGVlbB4sXcjsQ8/hMsvhyVLwKdd/kREpBWpBkk00CCmbWvK96MZmShXXe3dAXngQO8eMOeeC6++CuvWqYCIiEh4qQaJNM7hw4cZOnQoqampnHvuufTp04fU1FRSU1P58ssvG3zvhg0bmDFjxtd+xpAhQ1oq3KihGZkoVVMDf/oTzJwJu3ZBaiq8/DKMHg064CAiIuGkGiRyZrp3786bb75JYmIi+fn5dO7cmbvuuiu0vqqqivbt6/7f8szMTDIbcW5mcXFxi8UbLTQjE2Wcg2XLvKJx443Qvj28+CJs3AjXXKMCIiIi4aMaJNJycnNzmTJlCpdccgn33HMPb7/9NpdddhlpaWkMGTKEXbt2AbB27VquueYaAPLz85k0aRJZWVn069ePuXPnhrgQzuQAABRgSURBVPbXuXPn0PZZWVmMHTuWgQMHMmHCBLxbZMGKFSsYOHAgGRkZzJgxI7Tf2kpKShg2bBjp6emkp6efNECaPXs2KSkpBAIB8vLyANi7dy8jRowgEAiQnp7Ovn37wvMHq4NmZKKEc7ByJTz4IGzaBP37wwsveL344+IiHZ2IiMQy1SCJJXfcAZs3t+w+U1O9eyWdqdLSUoqLi4mLi+Po0aO88cYbtG/fnlWrVnH//fezdOnS096zc+dO1qxZQ3l5OQMGDGDq1KmntSx+99132b59O+eddx5Dhw7lzTffJDMzk1tvvZV169aRnJxMTk5OnTH17NmT119/nfj4ePbs2UNOTg4bNmxg5cqVvPTSS6xfv56EhASOHDkCwIQJE8jLyyM7O5uKigpqamrO/A/RRBrItHHOwerVXvF46y1IToaFC727IdczAykiItIiVINEwmvcuHHEBY8GlJWVcfPNN7Nnzx7MjMrKyjrfM3r0aDp27EjHjh3p2bMnH3/8MUlJSSdt893vfje0LDU1lZKSEjp37ky/fv1C7Y1zcnKYP3/+afuvrKzk9ttvZ/PmzcTFxbF7924AVq1axcSJE0lISACgW7dulJeXc+DAAbKzswHvXjCtST9Dbdi6dV7xWLcOkpLgP/8TJk6EM7hPkIiISJOoBkmsasrMSbh06tQp9PzBBx9k+PDhLFu2jJKSErLq6ZjRsWPH0PO4uDiqqqqatE19nnjiCXr16sWWLVuoqalp9cHJmdA1Mm3Q+vXwL/8CV1wBu3fD3LmwZw/ccosKiIiIhJdqkEhklJWV0adPHwAWLVrU4vsfMGAAH3zwASUlJQD84Q9/qDeO3r17065dOwoKCqiurgZg5MiRLFy4kOPHjwNw5MgREhMTSUpKoqioCIATJ06E1rcGDWTakE2bvIslL70U3n0X5syBfftg+nRow4NhERGJAapBIpF1zz33cN9995GWlnZGMyiNdfbZZzNv3jxGjRpFRkYGiYmJdOnS5bTtpk2bxuLFiwkEAuzcuTM0azRq1CjGjBlDZmYmqampzJkzB4CCggLmzp3L4MGDGTJkCAcPHmzx2OujU8vagPfe81pYLlsGXbvCv/+7VziCzSdERETCRjVIpHXl5+fXufyyyy4LXY8C8MgjjwCQlZUVOs3s1Pdu27Yt9PzYsWOnbQ/w9NNPh54PHz6cnTt34pzjtttuq7Ot80UXXcTWrVtDr2fPnh16npeXF+pWVnv7v/71r3XmFG6akYmgXbsgJwcCAe9iypkzYf9+uO8+FRAREQkv1SAR/1mwYAGpqakMGjSIsrIybr311kiH1CyakYmADz6AWbOgoMCbrs/Lg7vugm7dIh2ZiIjEOtUgEf+68847ufPOOyMdRovRQKYVffghPPKI17qyfXuvj/m990LPnpGOTEREYp1qkIjEGg1kWsFHH3nnHC9Y4L2eMsWbuj/vvMjGJSIisU81SERilQYyYfTJJzB7NsybB1VVXv/9Bx6ACy6IdGQiIhLrVINEJNZpIBMGR47AY4/BU0/BF1/Aj38MDz0E/fpFOjIREYl1qkEi4hfqWtaCysogPx/69vWOgv3gB7BjByxapAIiIiLhpRok0nYNHz6cVatWnbTsySefZOrUqfW+Jysriw0bNgBw9dVX89lnn522TX5+fuh+LvUpKipix44dodcPPfTQabFEKw1kWsCxY975x8nJ8ItfwMiRsHUr/P73MGBApKMTEZFYphok0vbl5OSwdOnSk5YVFhaSk5PTqPevWLGCc845p0mffepAZtasWYwYMaJJ+2prNJBphuPH4Ve/8orHz38OQ4d6d0ZeuhS+851IRyciIrFMNUgkeowdO5ZXX32VL7/8EoCSkhI++ugjhg0bxtSpU8nMzGTQoEHMnDmzzvf37duXQ4cOAfDoo4/Sv39/Lr/8cnbt2hXaZsGCBVx88cUEAgF+9KMfcfz4cYqLi1m+fDl33303qamp7Nu3j9zcXF588UUAVq9eTVpaGikpKUyaNIkTJ06EPm/mzJmkp6eTkpLCzp07T4uppKSEYcOGkZ6eTnp6OsXFxaF1s2fPJiUlhUAgELqB5t69exkxYgSBQID09HT27dvX7L+rrpFpghMnYP587wjYwYPe0a9Zs+DSSyMdmYiIxDrVIJHmq3Xj+5Drr4dp07yDBFdfffr63FzvcegQjB178rq1axv+vG7dupGRkcHKlSu59tprKSws5Prrr8fMePTRR+nWrRvV1dVceeWVbN26lcGDB9e5n40bN1JYWMjmzZupqqoiPT2djIwMAK677jomT54MwAMPPMBvfvMbpk+fzpgxY7jmmmsYe0rQFRUV5Obmsnr1avr3789NN93Es88+yx133AFAjx492LRpE/PmzWPOnDk899xzJ72/Z8+evP7668THx7Nnzx5ycnLYsGEDK1eu5KWXXmL9+vUkJCRw5MgRACZMmEBeXh7Z2dlUVFRQU1PT8B+tETQjcwYqK73iceGFMGMG9O8P69bBa6+pgIiISHipBolEt7Fjx1JYWAicfFrZH//4R9LT00lLS2P79u0nnQZ2qjfeeIPs7GwSEhL4xje+wZgxY0Lrtm3bxrBhw0hJSWHJkiVs3769wXh27dpFcnIy/fv3B+Dmm29m3bp1ofXXXXcdABkZGZSUlJz2/srKSiZPnkxKSgrjxo0Lxb1q1SomTpxIQkIC4A3iysvLOXDgANnZ2QDEx8eH1jeHZmQaoaoKfvc774jX/v1ewVi4EK68EswiHZ2IiMQy1SCRltfQDEpCQsPre/T4+hmYuowePZr777+fTZs2cfz4cTIyMti/fz9z5szhnXfeoWvXruTm5lJRUXHmOwdyc3MpKioiEAiwaNEi1jYlyFo6duwIQFxcHFVVVaetf+KJJ+jVqxdbtmyhpqaG+Pj4Zn1eU2hGpgHV1fDCCzBokNd/v2tXeOUVKC6GESNUQEREJHxUg0RiS+fOnRk+fDiTJk0KzcYcPXqUTp060aVLFz7++GNWrlzZ4D6+973vUVRUxBdffEF5eTkvv/xyaF15eTm9e/emsrKSJUuWhJYnJiZSXl5+2r4GDBhASUkJe/fuBaCgoIArrrii0fmUlZXRu3dv2rVrR0FBAdXV1QCMHDmShQsXcvz4cQCOHDlCYmIiSUlJFBUVAXDixInQ+ubQQKYONTXexZKBAEyYAGedBcuWwYYN3jmTKh4iIhIuqkEisSsnJ4ctW7aEBjKBQIC0tDQGDhzI+PHjGTp0aIPvT09P54YbbiAQCHDVVVdx8cUXh9Y9/PDDXHLJJQwdOpSBAweGlt9444089thjpKWlnXSBfXx8PAsXLmTcuHGkpKTQrl07pkyZ0uhcpk2bxuLFiwkEAuzcuZNOnToBMGrUKMaMGUNmZiapqamh9tAFBQXMnTuXwYMHM2TIEA4ePNjoz6qXcy5sD2AUsAvYC+TVsf4CYA3wLrAVuLqO9ceAu77uszIyMlxzrFmzxtXUOLd8uXOpqc6BcwMHOldY6Fx1dbN23eatWbMm0iFEhF/zds6/ufs1b+ealzuwwYWxVsTCQzWo6fz636Vf83YuMrnv2LGj1T/zVEePHo10CBHT2Nzr+p4aqkFhm5ExszjgGeAq4NtAjpl9+5TNHgD+6JxLA24E5p2y/nGg4Tm2FuAcvPNOVy69FMaMgaNH4fnnYds2uOEGaKd5KxERCRPVIBGRpgnnxf7fBfY65z4AMLNC4FqgdisGB3wj+LwL8NFXK8zsh8B+4PMwxgjA+PFQWBjgggtgwQK4+Wbo0CHcnyoiIqIaJCLSVOEcyPQB/qfW61LgklO2yQdeM7PpQCdgBICZdQbuBUYCd9X3AWZ2C3ALQK9evZrcneGb3+zJlCnVZGcf4ayzHG++2aTdRK1jx441u7NFNPJr3uDf3P2aN/g797buBz+AXr12M3t2f4JNgkREpBEi3X45B1jknPuVmV0GFJjZd/AGOE84545ZA1c1OufmA/MBMjMzXVZddzdqhKwsWLt2LU19f7Tza+5+zRv8m7tf8wZ/597WjR8P5533ER079o90KCIxzTlHQ/9fKZHlXQ5zZsI5kDkAnF/rdVJwWW0/wWsIgHPub2YWD/TAm7kZa2b/AZwD1JhZhXPu6TDGKyIiIiIxKD4+nsOHD9O9e3cNZtog5xyHDx8+43vRhHMg8w5wkZkl4w1gbgTGn7LNh8CVwCIz+xYQD3zqnBv21QZmlg8c0yBGRETOhJmNAv4/EAc855z75SnrLwAW4x0wi8PrrrnilPU7gHzn3JxWC1xEWlxSUhKlpaV8+umnEYuhoqIiIjeNbAsak3t8fDxJSUlntN+wDWScc1VmdjvwKl6B+K1zbruZzcJro7Yc+BmwwMzuxLvwP9c1ZV5JRESkllqdM0fiXaP5jpktd87VbjjzVefMZ4NdNVcAfWutb5XOmSISfh06dCA5OTmiMaxdu5a0tLSIxhAp4co9rNfIBI9srThl2UO1nu8AGrzzj3MuPyzBiYhILIuazpkiItI0kb7YX0REJByipnMm+LurnF9z92ve4N/c/Zo3hC93DWRERMSv2kTnTPB3Vzm/5u7XvMG/ufs1bwhf7jEzkNm4ceMhM/vvZuyiB3CopeKJMn7N3a95g39z92ve0Lzc/6klA2klrdo5UzWoWfyau1/zBv/m7te8IUw1KGYGMs65/9ec95vZBudcZkvFE038mrtf8wb/5u7XvMGXubdq50zVoKbza+5+zRv8m7tf84bw5d6upXcoIiISac65KuCrzpnv43Un225ms8xsTHCznwGTzWwL8HvUOVNEJKrEzIyMiIhIbeqcKSIS2zQj83/mRzqACPJr7n7NG/ybu1/zBn/nHg38/P34NXe/5g3+zd2veUOYcjfNoouIiIiISLTRjIyIiIiIiEQdXw1kzOy3ZvaJmW2rZ72Z2Vwz22tmW80svbVjDJdG5D4hmPN7ZlZsZoHWjjEcvi7vWttdbGZVZja2tWILt8bkbmZZZrbZzLab2X+1Znzh0oh/17uY2ctmtiWY98TWjjEczOx8M1tjZjuCef20jm1i9jcuGqgGqQY1sJ1qkGpQVItUDfLVQAZYRPCeAfW4Crgo+LgFeLYVYmoti2g49/3AFc65FOBhYuc8zkU0nDdmFgfMBl5rjYBa0SIayN3MzgHmAWOcc4OAca0UV7gtouHv/DZgh3MuAGQBvzKzs1ohrnCrAn7mnPs2cClwm5l9+5RtYvk3LhosQjWoPqpBqkGqQdEtIjXIVwMZ59w64EgDm1wLPO88bwHnmFnv1okuvL4ud+dcsXPuH8GXb+HdPC7qNeI7B5gOLAU+CX9EracRuY8H/uyc+zC4fUzk34i8HZBoZgZ0Dm5b1RqxhZNz7u/OuU3B5+V4LYf7nLJZzP7GRQPVINWgeqgGoRrUGrGFU6RqkK8GMo3QB/ifWq9LOf1L8IOfACsjHURrMLM+QDaxdeSzsfoDXc1srZltNLObIh1QK3ka+BbwEfAe8FPnXE1kQ2pZZtYXSAPWn7JKv3Ftm74fj2qQP6gGqQY1+zdO95GRk5jZcLwicnmkY2klTwL3OudqvIMjvtIeyMC7s/nZwN/M7C3n3O7IhhV23wc2A/8MfBN43czecM4djWxYLcPMOuMd3b0jVnIS/1AN8hXVINWgZtNA5mQHgPNrvU4KLvMFMxsMPAdc5Zw7HOl4WkkmUBgsID2Aq82syjlXFNmwWkUpcNg59znwuZmtAwJArBeRicAvg3dw32tm+4GBwNuRDav5zKwDXgFZ4pz7cx2b+Po3Lgr4+vtRDVINQjUoqkWiBunUspMtB24KdlW4FChzzv090kG1BjO7APgz8GMfHA0Jcc4lO+f6Ouf6Ai8C03xSQABeAi43s/ZmlgBcgndOa6z7EO8IIGbWCxgAfBDRiFpA8Hzr3wDvO+cer2cz3/7GRQnffj+qQapBqAZFtUjVIF/NyJjZ7/E6RPQws1JgJtABwDn3a2AFcDWwFziON2qOCY3I/SGgOzAveGSoyjmXGZloW04j8o5ZX5e7c+59M/sLsBWoAZ5zzjXYIjQaNOI7fxhYZGbvAYZ3WsehCIXbkoYCPwbeM7PNwWX3AxdA7P/GRQPVINUgVINUg1SDWvQ3zryZLRERERERkeihU8tERERERCTqaCAjIiIiIiJRRwMZERERERGJOhrIiIiIiIhI1NFARkREREREoo4GMiJNYGbVZra51iOvBffd18yivgWliIiEh2qQiMdX95ERaUFfOOdSIx2EiIj4kmqQCJqREWlRZlZiZv9hZu+Z2dtmdmFweV8z+6uZbTWz1cG7WGNmvcxsmZltCT6GBHcVZ2YLzGy7mb1mZmcHt59hZjuC+ymMUJoiItIGqQaJ32ggI9I0Z58yrX9DrXVlzrkU4GngyeCyp4DFzrnBwBJgbnD5XOC/nHMBIB3YHlx+EfCMc24Q8Bnwo+DyPCAtuJ8p4UpORETaNNUgEcCcc5GOQSTqmNkx51znOpaXAP/snPvAzDoAB51z3c3sENDbOVcZXP5351wPM/sUSHLOnai1j77A6865i4Kv7wU6OOceMbO/AMeAIqDIOXcszKmKiEgboxok4tGMjEjLc/U8PxMnaj2v5v+uZxsNPIN35OwdM9N1biIiUptqkPiGBjIiLe+GWv/8W/B5MXBj8PkE4I3g89XAVAAzizOzLvXt1MzaAec759YA9wJdgNOOyImIiK+pBolvaCQt0jRnm9nmWq//4pz7qv1lVzPbindEKye4bDqw0MzuBj4FJgaX/xSYb2Y/wTvqNRX4ez2fGQf8LlhoDJjrnPusxTISEZFooRokgq6REWlRwfOTM51zhyIdi4iI+ItqkPiNTi0TEREREZGooxkZERERERGJOpqRERERERGRqKOBjIiIiIiIRB0NZEREREREJOpoICMiIiIiIlFHAxkREREREYk6GsiIiIiIiEjU+V/ie2nJuYUjrAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x1296 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OxY3Er8-ackQ"
      },
      "source": [
        "Calculate Model Metrics\n",
        "<br> Precision, Recall, Accuracy, F1-Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hy2iGSSZxyiK",
        "outputId": "b4f72a76-d3ab-47d0-cbbc-e6ebbdfe5e81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "idx_best = 0\n",
        "best_acc = 0\n",
        "\n",
        "# == Provide average scores ==\n",
        "print('------------------------------------------------------------------------')\n",
        "print('Metrics Score per fold')\n",
        "for i in range(0, len(all_accuracy)):\n",
        "  print('------------------------------------------------------------------------')\n",
        "  print(f'> Fold {i+1}')\n",
        "  print('Accuracy: %f' % all_accuracy[i])\n",
        "  print('Precision: %f' % all_precision[i])\n",
        "  print('Recall: %f' % all_recall[i])\n",
        "  print('F1 score: %f' % all_f1[i])\n",
        "  if (all_accuracy[i] >= best_acc):\n",
        "    idx_best = i\n",
        "    best_acc = all_accuracy[i]\n",
        "print('------------------------------------------------------------------------')\n",
        "print('------------------------------------------------------------------------')\n",
        "print('Average scores for all folds:')\n",
        "print(f'> Average Accuracy: {np.mean(np.asarray(all_accuracy))}')\n",
        "print(f'> Average Precision: {np.mean(np.asarray(all_precision))}')\n",
        "print(f'> Average Recall: {np.mean(np.asarray(all_recall))}')\n",
        "print(f'> Average F1 Score: {np.mean(np.asarray(all_f1))}')\n",
        "print('------------------------------------------------------------------------')\n",
        "print('------------------------------------------------------------------------')\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Metrics Score per fold\n",
            "------------------------------------------------------------------------\n",
            "> Fold 1\n",
            "Accuracy: 0.915957\n",
            "Precision: 0.921719\n",
            "Recall: 0.955449\n",
            "F1 score: 0.938281\n",
            "------------------------------------------------------------------------\n",
            "> Fold 2\n",
            "Accuracy: 0.905319\n",
            "Precision: 0.905291\n",
            "Recall: 0.955870\n",
            "F1 score: 0.929894\n",
            "------------------------------------------------------------------------\n",
            "> Fold 3\n",
            "Accuracy: 0.900505\n",
            "Precision: 0.910930\n",
            "Recall: 0.941697\n",
            "F1 score: 0.926058\n",
            "------------------------------------------------------------------------\n",
            "> Fold 4\n",
            "Accuracy: 0.910615\n",
            "Precision: 0.915152\n",
            "Recall: 0.955696\n",
            "F1 score: 0.934985\n",
            "------------------------------------------------------------------------\n",
            "> Fold 5\n",
            "Accuracy: 0.910082\n",
            "Precision: 0.906215\n",
            "Recall: 0.965887\n",
            "F1 score: 0.935100\n",
            "------------------------------------------------------------------------\n",
            "> Fold 6\n",
            "Accuracy: 0.909284\n",
            "Precision: 0.921614\n",
            "Recall: 0.944710\n",
            "F1 score: 0.933019\n",
            "------------------------------------------------------------------------\n",
            "> Fold 7\n",
            "Accuracy: 0.914073\n",
            "Precision: 0.920869\n",
            "Recall: 0.952266\n",
            "F1 score: 0.936304\n",
            "------------------------------------------------------------------------\n",
            "> Fold 8\n",
            "Accuracy: 0.911147\n",
            "Precision: 0.906086\n",
            "Recall: 0.966346\n",
            "F1 score: 0.935246\n",
            "------------------------------------------------------------------------\n",
            "> Fold 9\n",
            "Accuracy: 0.905560\n",
            "Precision: 0.905307\n",
            "Recall: 0.956820\n",
            "F1 score: 0.930351\n",
            "------------------------------------------------------------------------\n",
            "> Fold 10\n",
            "Accuracy: 0.915137\n",
            "Precision: 0.941294\n",
            "Recall: 0.931989\n",
            "F1 score: 0.936618\n",
            "------------------------------------------------------------------------\n",
            "------------------------------------------------------------------------\n",
            "Average scores for all folds:\n",
            "> Average Accuracy: 0.9097679894494348\n",
            "> Average Precision: 0.9154476864715017\n",
            "> Average Recall: 0.952673057748928\n",
            "> Average F1 Score: 0.9335856260960474\n",
            "------------------------------------------------------------------------\n",
            "------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "omMUUs8W9pPr"
      },
      "source": [
        "Plot Precision-Recall Curve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rEoLXKNQ0a1N",
        "outputId": "826d598d-cc53-45da-c8d1-114e89f56f78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        }
      },
      "source": [
        "import matplotlib.cm as cm\n",
        "\n",
        "colors = cm.tab10(np.linspace(0, 1, len(all_arr_recall)))\n",
        "\n",
        "\n",
        "plt.clf()   # clear figure\n",
        "plt.rcParams['figure.figsize'] = [20, 12]\n",
        "\n",
        "for i in range(len(all_arr_recall)):\n",
        "  plt.plot(all_arr_recall[i], all_arr_precision[i], color=colors[i], label='FT-BERT, Fold {}, AP={:0.2f}'.format(i+1, all_ave_precision[i]))\n",
        "  # plt.plot(np.linspace(0, 1, thresholds[i].size), thresholds[i], linestyle='--', label='Threshold')\n",
        "plt.title('FT-BERT Precision-Recall')\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "# plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
        "# plt.minorticks_on()\n",
        "plt.savefig(\"./bertclassify/bert_10fold_precision_recall.png\", format='png', dpi=150, bbox_inches='tight')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cH7lzjNr97zb"
      },
      "source": [
        "Draw Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z3zGbGIn-Q3L",
        "outputId": "3244274b-f010-47b1-9c0d-7e2cdb6c8c0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 852
        }
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "plt.rcParams['figure.figsize'] = [12, 9]\n",
        "for j in range(len(all_cf_matrices)):\n",
        "  cf_matrix_best = all_cf_matrices[j]\n",
        "\n",
        "  stats_text = \"\\n\\nAccuracy={:0.3f}\\nPrecision={:0.3f}\\nRecall={:0.3f}\\nF1 Score={:0.3f}\".format(\n",
        "                  all_accuracy[j],all_precision[j],all_recall[j],all_f1[j])\n",
        "\n",
        "  group_names = ['True Neg','False Pos','False Neg','True Pos']\n",
        "\n",
        "  group_counts = [\"{0:0.0f}\".format(value) for value in\n",
        "                  cf_matrix_best.flatten()]\n",
        "\n",
        "  group_percentages = [\"{0:.2%}\".format(value) for value in\n",
        "                      cf_matrix_best.flatten()/np.sum(cf_matrix_best)]\n",
        "\n",
        "  labels = [f'{v1}\\n{v2}\\n{v3}' for v1, v2, v3 in\n",
        "            zip(group_names,group_counts,group_percentages)]\n",
        "            \n",
        "  labels = np.asarray(labels).reshape(2,2)\n",
        "  categories = ['Fake', 'Real']\n",
        "  # cmap = sns.dark_palette(\"red\")\n",
        "  # cmap = sns.cubehelix_palette(8, start=.5, rot=-.75, as_cmap=True)\n",
        "  # cmap = sns.color_palette(\"GnBu_d\")\n",
        "\n",
        "  plt.clf() \n",
        "  plt.figure()\n",
        "  svm = sns.heatmap(cf_matrix_best, annot=labels, fmt='', cmap='PuBu',xticklabels=categories,yticklabels=categories)\n",
        "\n",
        "  plt.ylabel('True Label')\n",
        "  plt.xlabel('Predicted Label' + stats_text)\n",
        "\n",
        "  # figure = svm.get_figure()    \n",
        "  # figure.savefig('./bertclassify/bert_conf_matrix.png', dpi=150)\n",
        "\n",
        "  plt.savefig(\"./bertclassify/cfpr/bert_best_10fold_conf_matrix_fold{}.png\".format(j+1), format='png', dpi=150, bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b4fEIvKdX26j"
      },
      "source": [
        "for m in range(len(all_arr_precision)):\n",
        "  df_prerec = pd.DataFrame(all_arr_precision[m])\n",
        "  df_prerec.columns = ['precision']\n",
        "  df_prerec['recall'] = all_arr_recall[m]\n",
        "  df_prerec['threshold'] = all_thresholds[m]\n",
        "  df_prerec\n",
        "  df_prerec.to_pickle(\"./bertclassify/cfpr/bert_best_10fold_precison_recall_curve_fold{}.pkl\".format(m+1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NZtuwyjDF9Kz",
        "outputId": "27f1056e-16a5-4393-bcc4-c675ee539fc8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "!jupyter nbconvert --to html ./bertclassify/bert_classification_malay_10fold.ipynb"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[NbConvertApp] Converting notebook ./bertclassify/bert_classification_malay_10fold.ipynb to html\n",
            "[NbConvertApp] Writing 963516 bytes to ./bertclassify/bert_classification_malay_10fold.html\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}