NLP/seq2seq_with_attn.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "f878f57a",
   "metadata": {
    "id": "f878f57a"
   },
   "source": [
    "# Data Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f763a976",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f763a976",
    "outputId": "35fc2798-c752-4cf1-ecf3-64d632da8919"
   },
   "outputs": [],
   "source": [
    "# !wget https://drive.google.com/uc?id=1NWYqJgeG_4883LINdEjKUr6nLQPY6Yb_ -O data.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eed42cdb",
   "metadata": {
    "id": "eed42cdb"
   },
   "source": [
    "https://huggingface.co/datasets/cointegrated/ru-paraphrase-NMT-Leipzig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2G8PZ5ibJS4F",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2G8PZ5ibJS4F",
    "outputId": "a7d58db1-780d-47d3-9d0b-7275fea3fd6d"
   },
   "outputs": [],
   "source": [
    "# !pip install youtokentome datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca30a1fa",
   "metadata": {
    "id": "ca30a1fa"
   },
   "source": [
    "paranmt_ru_leipzig.tsv"
   ]
  },
  {
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    },
    "id": "nxm1yLMV4OOo",
    "outputId": "a18c085f-7134-4c36-f388-704317af9466"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset csv (C:/Users/Kirill/.cache/huggingface/datasets/cointegrated___csv/cointegrated--ru-paraphrase-NMT-Leipzig-f187aea54ca79219/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0acd6d698e54425588c8216040fe3beb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datasets\n",
    "data = datasets.load_dataset(\n",
    "    'cointegrated/ru-paraphrase-NMT-Leipzig',\n",
    "    data_files={\"train\": \"train.csv\",\"val\": \"val.csv\",\"test\": \"test.csv\"},\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f50c6dc5",
   "metadata": {
    "id": "f50c6dc5"
   },
   "source": [
    "# Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "nLXzdJm2VI5H",
   "metadata": {
    "id": "nLXzdJm2VI5H"
   },
   "outputs": [],
   "source": [
    "from youtokentome import BPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c26b2f41",
   "metadata": {
    "id": "c26b2f41"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import nltk\n",
    "from collections import Counter\n",
    "import random\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d130811d",
   "metadata": {
    "id": "d130811d"
   },
   "outputs": [],
   "source": [
    "from IPython.display import clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "72e891e1",
   "metadata": {
    "id": "72e891e1"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.nn import functional as F\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from torch.optim.lr_scheduler import CosineAnnealingLR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7db8d570",
   "metadata": {
    "id": "7db8d570"
   },
   "outputs": [],
   "source": [
    "SEED = 1234\n",
    "\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "torch.manual_seed(SEED)\n",
    "torch.cuda.manual_seed(SEED)\n",
    "torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eea6bc4",
   "metadata": {
    "id": "6eea6bc4"
   },
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "xmpua3bpLFZD",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "xmpua3bpLFZD",
    "outputId": "a151d7e8-c9a6-45e5-c863-e5f658386653"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Во сне гадающей явится будущий муж.'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['train']['original'][100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "RkU9nsGULR0V",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "RkU9nsGULR0V",
    "outputId": "65f2b41e-e033-46d2-9316-83b4dadf598a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The future husband will appear in the dream of the diviner.'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['train']['en'][100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "xzXMk7lB4wZ2",
   "metadata": {
    "id": "xzXMk7lB4wZ2"
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame()\n",
    "df['en'] = data['train']['en']\n",
    "df['ru'] = data['train']['original']\n",
    "df = df.dropna().reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "i_AoICSzwfa2",
   "metadata": {
    "id": "i_AoICSzwfa2"
   },
   "outputs": [],
   "source": [
    "# data = pd.read_csv('data.txt', sep='\\t', names=['en', 'ru'])\n",
    "# data['en'] = data['en'].apply(str)\n",
    "# data['ru'] = data['ru'].apply(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3b3100c2-e4d3-488d-aeee-8a9090c00201",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>en</th>\n",
       "      <th>ru</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prospects for the development of new media tec...</td>\n",
       "      <td>Перспективы развития новых медиатехнологий в Р...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>But the number of TOPs per user is not limited!</td>\n",
       "      <td>А вот количество ТОП-ов от одного пользователя...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>As for the Zemstvo Council, at that time it di...</td>\n",
       "      <td>Что касается земского собора, у него в тот мом...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Use special links to create an account to take...</td>\n",
       "      <td>Используйте специальные ссылки для создания ак...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thomason, who is rarely seen by locals, has a ...</td>\n",
       "      <td>Маячник Томасон, что редко для местных жителей...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  en  \\\n",
       "0  Prospects for the development of new media tec...   \n",
       "1    But the number of TOPs per user is not limited!   \n",
       "2  As for the Zemstvo Council, at that time it di...   \n",
       "3  Use special links to create an account to take...   \n",
       "4  Thomason, who is rarely seen by locals, has a ...   \n",
       "\n",
       "                                                  ru  \n",
       "0  Перспективы развития новых медиатехнологий в Р...  \n",
       "1  А вот количество ТОП-ов от одного пользователя...  \n",
       "2  Что касается земского собора, у него в тот мом...  \n",
       "3  Используйте специальные ссылки для создания ак...  \n",
       "4  Маячник Томасон, что редко для местных жителей...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df = pd.concat([df, data], axis=0).reset_index(drop=True)\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "E6-QVSDGwO7k",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E6-QVSDGwO7k",
    "outputId": "8529cb32-1555-4cfe-9fa8-441652fb1f7a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "979994"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d0bf8a02",
   "metadata": {
    "id": "d0bf8a02"
   },
   "outputs": [],
   "source": [
    "class YandexDataset(Dataset):\n",
    "    def __init__(self, src: list, trg: list):\n",
    "        self.src = src\n",
    "        self.trg = trg\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return {\n",
    "            'src': self.src[index],\n",
    "            'trg': self.trg[index],\n",
    "        }\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.src)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "65df135c-349f-4627-890f-489ad203b722",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['en'] = df['en'].apply(\n",
    "    lambda item: item.lower()\n",
    ").apply(\n",
    "    lambda item: '<sos> ' + item + ' <eos>'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b890e631-ff25-417e-8e0b-d96659b37bce",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ru'] = df['ru'].apply(\n",
    "    lambda item: item.lower()\n",
    ").apply(\n",
    "    lambda item: '<sos> ' + item + ' <eos>'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "N4EmLpqgVf3a",
   "metadata": {
    "id": "N4EmLpqgVf3a"
   },
   "outputs": [],
   "source": [
    "df['en'].to_csv('data_en.csv', index=False)\n",
    "df['ru'].to_csv('data_ru.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "KL-NzBpjVrzK",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KL-NzBpjVrzK",
    "outputId": "5382908b-d2f9-489b-de7a-dd5dce72b200"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 36.8 s\n",
      "Wall time: 7.27 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "en_bpe = BPE.train(data=\"data_en.csv\", vocab_size=20000, model=\"en_bpe.bin\")\n",
    "ru_bpe = BPE.train(data=\"data_ru.csv\", vocab_size=45000, model=\"en_bpe.bin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "SRMB7i3bWPpb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "SRMB7i3bWPpb",
    "outputId": "e017ca79-837a-4b8a-88b9-1ad230da537b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' ▁<sos> ▁hello ▁friend ! ▁<eos>'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded = en_bpe.encode('<sos> hello friend! <eos>')\n",
    "tokenized = \"\"\n",
    "for word in encoded:\n",
    "    tokenized += \" \" + en_bpe.id_to_subword(word)\n",
    "tokenized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8BkWFtLqWn1U",
   "metadata": {
    "id": "8BkWFtLqWn1U"
   },
   "outputs": [],
   "source": [
    "src = df['en'].apply(\n",
    "    en_bpe.encode\n",
    ").values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8bc3268a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8bc3268a",
    "outputId": "7f6cfa97-cb0a-4ea9-d01a-546a248e2bfa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[290,\n",
       " 7955,\n",
       " 312,\n",
       " 247,\n",
       " 1058,\n",
       " 266,\n",
       " 558,\n",
       " 3277,\n",
       " 3744,\n",
       " 275,\n",
       " 247,\n",
       " 651,\n",
       " 1973,\n",
       " 382,\n",
       " 300,\n",
       " 4529,\n",
       " 355,\n",
       " 247,\n",
       " 18006,\n",
       " 853,\n",
       " 25,\n",
       " 3277,\n",
       " 7007,\n",
       " 252]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "src[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "VKNar8TKXGbK",
   "metadata": {
    "id": "VKNar8TKXGbK"
   },
   "outputs": [],
   "source": [
    "trg = df['ru'].apply(\n",
    "    ru_bpe.encode\n",
    ").values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "15f187dd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "15f187dd",
    "outputId": "c64aa52c-a7bc-42e4-9742-fd427d453c55"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{290: 543,\n",
       " 5836: 10365,\n",
       " 4229: 632,\n",
       " 3549: 2789,\n",
       " 434: 653,\n",
       " 1507: 8915,\n",
       " 16746: 35327,\n",
       " 1601: 660,\n",
       " 8369: 500,\n",
       " 11649: 6739,\n",
       " 3610: 16504,\n",
       " 7048: 8649,\n",
       " 346: 1746,\n",
       " 6441: 22771,\n",
       " 275: 52,\n",
       " 9909: 33585,\n",
       " 11095: 501,\n",
       " 266: 11800,\n",
       " 6175: 22972,\n",
       " 252: 11479}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(zip(src[11], trg[11]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "t2LxjUkMmxG3",
   "metadata": {
    "id": "t2LxjUkMmxG3"
   },
   "outputs": [],
   "source": [
    "src_train, src_test, trg_train, trg_test = train_test_split(\n",
    "    src[:],\n",
    "    trg[:],\n",
    "    random_state=42,\n",
    "    test_size=0.25\n",
    ")\n",
    "src_train, src_valid, trg_train, trg_valid = train_test_split(\n",
    "    src_train,\n",
    "    trg_train,\n",
    "    random_state=42,\n",
    "    test_size=0.2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5Mmnc7nznYV_",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5Mmnc7nznYV_",
    "outputId": "48d3e90c-eacf-4750-c9b3-4e0cc6611105"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(587996, 146999, 244999)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(src_train), len(src_valid), len(src_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fb6a9aaa",
   "metadata": {
    "id": "fb6a9aaa"
   },
   "outputs": [],
   "source": [
    "train_data = YandexDataset(src_train, trg_train)\n",
    "valid_data = YandexDataset(src_valid, trg_valid)\n",
    "test_data = YandexDataset(src_test, trg_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1452d28d-0b96-46a4-bd82-062634b1ecf0",
   "metadata": {
    "id": "3d2aeaee"
   },
   "outputs": [],
   "source": [
    "def collate_fn(batch):\n",
    "    max_src_len = max(len(row[\"src\"]) for row in batch)\n",
    "    max_trg_len = max(len(row[\"trg\"]) for row in batch)\n",
    "    max_len = max(max_src_len, max_trg_len)\n",
    "    src_list = torch.empty((len(batch), max_len), dtype=torch.long)\n",
    "    trg_list = torch.empty((len(batch), max_len), dtype=torch.long)\n",
    "\n",
    "    for idx, row in enumerate(batch):\n",
    "        to_pad_src = max_len - len(row[\"src\"])\n",
    "        to_pad_trg = max_len - len(row[\"trg\"])\n",
    "        _src = row['src']\n",
    "        _trg = row['trg']\n",
    "        src_list[idx] = torch.cat((torch.tensor(_src), torch.zeros(to_pad_src)))\n",
    "        trg_list[idx] = torch.cat((torch.tensor(_trg), torch.zeros(to_pad_trg)))\n",
    "\n",
    "    return {\n",
    "        'src': src_list,\n",
    "        'trg': trg_list,\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ab34fc34",
   "metadata": {
    "id": "ab34fc34"
   },
   "outputs": [],
   "source": [
    "train_loader = DataLoader(train_data, batch_size=64, shuffle=True, collate_fn=collate_fn)\n",
    "valid_loader = DataLoader(valid_data, batch_size=64, shuffle=False, collate_fn=collate_fn)\n",
    "test_loader = DataLoader(test_data, batch_size=64, shuffle=False, collate_fn=collate_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5c5b057e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5c5b057e",
    "outputId": "aa9ca65d-8423-4535-f4a8-8ff549c8aa0f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 57])\n",
      "torch.Size([64, 57])\n"
     ]
    }
   ],
   "source": [
    "for batch in train_loader:\n",
    "    print(batch['src'].shape)\n",
    "    print(batch['trg'].shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2e1d91b9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2e1d91b9",
    "outputId": "179b6a15-9a64-4285-ebe2-ef8ce2a361e0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f13c6085",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f13c6085",
    "outputId": "11c37fd4-46f4-4438-e2e2-b5e70c6134bd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 290, 2122, 2854,  ...,    0,    0,    0],\n",
       "        [ 290,  678,  301,  ...,    0,    0,    0],\n",
       "        [ 290,  247, 1743,  ...,    0,    0,    0],\n",
       "        ...,\n",
       "        [ 290, 2087,  485,  ...,    0,    0,    0],\n",
       "        [ 290,  543,  279,  ...,    0,    0,    0],\n",
       "        [ 290, 4096,  708,  ...,  419,   22,  252]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch['src']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "26c52d3d",
   "metadata": {
    "id": "26c52d3d"
   },
   "outputs": [],
   "source": [
    "def count_parameters(model):\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d5e38ff",
   "metadata": {
    "id": "7d5e38ff"
   },
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "73a178fc",
   "metadata": {
    "id": "73a178fc"
   },
   "outputs": [],
   "source": [
    "class Encoder(nn.Module):\n",
    "    def __init__(self,\n",
    "                 vocab_size: int,\n",
    "                 embed_dim: int,\n",
    "                 hidden_dim: int,\n",
    "                 dropout: float = 0.2,\n",
    "                 n_layers: int = 2,\n",
    "                 bidirectional: bool = True\n",
    "                ):\n",
    "        super().__init__()\n",
    "        self.vocab_size = vocab_size\n",
    "        self.emb_dim = embed_dim\n",
    "        self.hid_dim = hidden_dim\n",
    "        self.n_layers = n_layers\n",
    "        self.bidirectional = bidirectional\n",
    "\n",
    "        self.embedding = nn.Embedding(vocab_size, embed_dim)\n",
    "\n",
    "        self.rnn = nn.LSTM(\n",
    "            input_size=embed_dim,\n",
    "            hidden_size=hidden_dim,\n",
    "            num_layers=n_layers,\n",
    "            bidirectional=bidirectional,\n",
    "            batch_first=True,\n",
    "        )\n",
    "\n",
    "        self.dropout = nn.Dropout(p=dropout)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # x = [batch_size, seq_len]\n",
    "        embedded = self.dropout(self.embedding(x))\n",
    "\n",
    "        # embedded = [batch_size, seq_len, emb_dim]\n",
    "        out, (hidden, cell) = self.rnn(embedded)\n",
    "\n",
    "        # out = [batch_size, seq_len, hidden_dim * 2]\n",
    "        # hidden = [2 * n_layers, batch_size, hidden_dim]\n",
    "        # cell = [2 * n_layers, batch_size, hidden_dim]\n",
    "        if self.bidirectional:\n",
    "#             hidden = hidden.transpose(0, 1).reshape(-1, self.n_layers, 2, self.hid_dim)\n",
    "#             hidden = hidden.reshape(-1, self.n_layers, 2 * self.hid_dim)\n",
    "\n",
    "#             cell = cell.transpose(0, 1).reshape(-1, self.n_layers, 2, self.hid_dim)\n",
    "#             cell = cell.reshape(-1, self.n_layers, 2 * self.hid_dim)\n",
    "\n",
    "            hidden = hidden.reshape(self.n_layers, 2, -1, self.hid_dim)\n",
    "            hidden = hidden.transpose(1, 2).reshape(self.n_layers, -1, 2 * self.hid_dim)\n",
    "\n",
    "            cell = cell.reshape(self.n_layers, 2, -1, self.hid_dim)\n",
    "            cell = cell.transpose(1, 2).reshape(self.n_layers, -1, 2 * self.hid_dim)\n",
    "\n",
    "        return out, hidden, cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "vweKc9uPtgG4",
   "metadata": {
    "id": "vweKc9uPtgG4"
   },
   "outputs": [],
   "source": [
    "encoder = Encoder(\n",
    "    vocab_size=en_bpe.vocab_size(),\n",
    "    embed_dim=128,\n",
    "    hidden_dim=128,\n",
    "    n_layers=2,\n",
    "    dropout=0.2,\n",
    "    bidirectional=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ce3e2261",
   "metadata": {
    "id": "ce3e2261"
   },
   "outputs": [],
   "source": [
    "def softmax(x, temperature=10):\n",
    "    e_x = torch.exp(x / temperature)\n",
    "    return e_x / torch.sum(e_x, dim=1, keepdim=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b62782f7",
   "metadata": {
    "id": "b62782f7"
   },
   "outputs": [],
   "source": [
    "class Attention(nn.Module):\n",
    "    def __init__(self,\n",
    "                 enc_hid_dim: int,\n",
    "                 dec_hid_dim: int,\n",
    "                 temperature: int = 1,\n",
    "                ):\n",
    "        super().__init__()\n",
    "\n",
    "        self.enc_hid_dim = enc_hid_dim\n",
    "        self.dec_hid_dim = dec_hid_dim\n",
    "        self.temperature = temperature\n",
    "\n",
    "        self.attn = nn.Linear(2 * enc_hid_dim + dec_hid_dim, enc_hid_dim)\n",
    "        self.v = nn.Linear(enc_hid_dim, 1)\n",
    "\n",
    "    def forward(self, hidden, encoder_outputs):\n",
    "        # encoder_outputs = [batch_size, src_sec_len, enc_hid_dim]\n",
    "        # hidden = [2, batch size, dec_hid_dim]\n",
    "        last_hidden = hidden[-1, :, :].unsqueeze(1)\n",
    "        hiddens = last_hidden.repeat(1, encoder_outputs.shape[1], 1)\n",
    "\n",
    "        # repeat hidden and concatenate it with encoder_outputs\n",
    "        concat_h_s = torch.concat([encoder_outputs, hiddens], dim=2)\n",
    "        # concat_h_s = [src_seq_len, batch_size, 2 * enc_hid_dim]\n",
    "\n",
    "        # calculate energy\n",
    "        E = torch.tanh(self.attn(concat_h_s))\n",
    "        # get attention, using softmax function with temperature\n",
    "        a = self.v(E)\n",
    "\n",
    "        return softmax(a, temperature=self.temperature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c8d5485d",
   "metadata": {
    "id": "c8d5485d"
   },
   "outputs": [],
   "source": [
    "attn = Attention(\n",
    "    enc_hid_dim=128,\n",
    "    dec_hid_dim=128 * 2,\n",
    "    temperature=1.6,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "55e0bf5d",
   "metadata": {
    "id": "55e0bf5d"
   },
   "outputs": [],
   "source": [
    "class DecoderWithAttention(nn.Module):\n",
    "    def __init__(self,\n",
    "                 output_dim: int,\n",
    "                 enc_hid_dim: int,\n",
    "                 embed_dim: int,\n",
    "                 hidden_dim: int,\n",
    "                 n_layers: int,\n",
    "                 attention,\n",
    "                ):\n",
    "        super().__init__()\n",
    "        self.output_dim = output_dim\n",
    "        self.enc_hid_dim = enc_hid_dim\n",
    "        self.hid_dim = hidden_dim\n",
    "        self.embed_dim = embed_dim\n",
    "        self.n_layers = n_layers\n",
    "        self.attention = attention\n",
    "\n",
    "        self.embedding = nn.Embedding(output_dim, embed_dim)\n",
    "\n",
    "        self.rnn = nn.GRU(\n",
    "            input_size=embed_dim + enc_hid_dim * 2,\n",
    "            hidden_size=hidden_dim,\n",
    "            num_layers=n_layers,\n",
    "            bidirectional=False,\n",
    "            batch_first=True,\n",
    "        )\n",
    "\n",
    "        self.fc = nn.Linear(hidden_dim + embed_dim + 2 * enc_hid_dim, output_dim)\n",
    "\n",
    "    def forward(self, x, out, hidden):\n",
    "        # x = [batch_size]\n",
    "        # out = [batch_size, src_sec_len, enc_hid_dim]\n",
    "        # hidden = [n_layers * n_directions, batch_size, hidden_dim]\n",
    "        # cell = [n_layers * n_directions, batch_size, hidden_dim]\n",
    "#         print(x.shape)\n",
    "\n",
    "        x = x.unsqueeze(1)\n",
    "        embedded = self.embedding(x)\n",
    "        # embedded = [batch_size, 1, output_dim]\n",
    "\n",
    "        # get weighted sum of encoder_outputs\n",
    "        a_t = self.attention(hidden, out)\n",
    "#         print(out.shape)\n",
    "        # a_t = [batch_size, sec_len, 1]\n",
    "        w_t = torch.sum(a_t * out, dim=1, keepdim=True)\n",
    "        # w_t = [batch_size, 1, enc_hid_dim * n_directions]\n",
    "\n",
    "        output, hidden = self.rnn(torch.concat([embedded, w_t], dim=2), hidden)\n",
    "\n",
    "        predicted = self.fc(\n",
    "            torch.cat(\n",
    "                [embedded, # [batch_size, 1, emb_dim]\n",
    "                 w_t, # [batch_size, 1, enc_hid_dim]\n",
    "                 hidden[-1, :, :].unsqueeze(1) # [batch_size, 1, dec_hid_dim]\n",
    "                ], dim=2\n",
    "            ).squeeze(1)\n",
    "        )\n",
    "\n",
    "        return predicted, hidden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1b0ecee1",
   "metadata": {
    "id": "1b0ecee1"
   },
   "outputs": [],
   "source": [
    "decoder = DecoderWithAttention(\n",
    "    output_dim=ru_bpe.vocab_size(),\n",
    "    enc_hid_dim=128,\n",
    "    embed_dim=128,\n",
    "    hidden_dim=128 * 2,\n",
    "    n_layers=2,\n",
    "    attention=attn,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "60202f62",
   "metadata": {
    "id": "60202f62"
   },
   "outputs": [],
   "source": [
    "class Seq2Seq(nn.Module):\n",
    "    def __init__(self, encoder, decoder):\n",
    "        super().__init__()\n",
    "\n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "\n",
    "        if encoder.bidirectional:\n",
    "            assert encoder.hid_dim * 2 == decoder.hid_dim, \\\n",
    "                \"Hidden dimensions of encoder and decoder must be equal!\"\n",
    "        else:\n",
    "            assert encoder.hid_dim == decoder.hid_dim, \\\n",
    "                    \"Hidden dimensions of encoder and decoder must be equal!\"\n",
    "        assert encoder.n_layers == decoder.n_layers, \\\n",
    "            \"Encoder and decoder must have equal number of layers!\"\n",
    "\n",
    "\n",
    "    def forward(self, src, trg, teacher_forcing_ratio = 0.5):\n",
    "        # src = [batch_size, seq_len]\n",
    "        # trg = [batch_size, seq_len]\n",
    "\n",
    "        batch_size = trg.shape[0]\n",
    "        trg_len = trg.shape[1]\n",
    "        trg_vocab_size = self.decoder.output_dim\n",
    "        device = src.device\n",
    "\n",
    "        outputs = torch.zeros(batch_size, trg_len, trg_vocab_size).to(device)\n",
    "\n",
    "        encoder_outputs, hidden, cell = self.encoder(src)\n",
    "        x = trg[:, 0]\n",
    "\n",
    "        for t in range(1, trg_len):\n",
    "            output, hidden = self.decoder(x, encoder_outputs, hidden)\n",
    "            outputs[:, t] = output\n",
    "\n",
    "            teacher_force = random.random() < teacher_forcing_ratio\n",
    "\n",
    "            top1 = output.argmax(-1)\n",
    "\n",
    "            x = trg[:, t] if teacher_force else top1\n",
    "\n",
    "        return outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7cc42d24",
   "metadata": {
    "id": "7cc42d24"
   },
   "outputs": [],
   "source": [
    "model = Seq2Seq(encoder, decoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8d166691",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8d166691",
    "outputId": "7e03c37e-f3b0-4e80-8cd8-7a952b1b6494"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 57, 45000])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model(batch['src'], batch['trg']).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "WFOWnA5Toc5T",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WFOWnA5Toc5T",
    "outputId": "51d29bdb-5f5d-4c52-ef84-56f4862990fd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model has 38,778,057 trainable parameters\n"
     ]
    }
   ],
   "source": [
    "print(f'The model has {count_parameters(model):,} trainable parameters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "PNO3goVUqVw4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PNO3goVUqVw4",
    "outputId": "3c8b2eb9-1579-463d-83bf-edac98b9e476"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seq2Seq(\n",
       "  (encoder): Encoder(\n",
       "    (embedding): Embedding(20000, 128)\n",
       "    (rnn): LSTM(128, 128, num_layers=2, batch_first=True, bidirectional=True)\n",
       "    (dropout): Dropout(p=0.2, inplace=False)\n",
       "  )\n",
       "  (decoder): DecoderWithAttention(\n",
       "    (attention): Attention(\n",
       "      (attn): Linear(in_features=512, out_features=128, bias=True)\n",
       "      (v): Linear(in_features=128, out_features=1, bias=True)\n",
       "    )\n",
       "    (embedding): Embedding(45000, 128)\n",
       "    (rnn): GRU(384, 256, num_layers=2, batch_first=True)\n",
       "    (fc): Linear(in_features=640, out_features=45000, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "35570bbf",
   "metadata": {
    "id": "35570bbf"
   },
   "outputs": [],
   "source": [
    "epochs = 2\n",
    "PAD_IDX = 0\n",
    "optimizer = optim.Adam(model.parameters(), lr=3e-3)\n",
    "scheduler = CosineAnnealingLR(optimizer, T_max=int(len(train_loader) + 1) * epochs)\n",
    "criterion = nn.CrossEntropyLoss(ignore_index=PAD_IDX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d1b21583",
   "metadata": {
    "id": "d1b21583"
   },
   "outputs": [],
   "source": [
    "def train(model, iterator, optimizer, scheduler, criterion, clip, train_history=None, valid_history=None):\n",
    "    model.train()\n",
    "\n",
    "    epoch_loss = 0\n",
    "    history = []\n",
    "    i = 0\n",
    "    for batch in tqdm(iterator):\n",
    "\n",
    "        src = batch['src'].to(device)\n",
    "        trg = batch['trg'].to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        output = model(src, trg)\n",
    "\n",
    "        # trg = [batch_size, trg_sent_len]\n",
    "        # output = [batch_size, trg_sent_len, output_dim]\n",
    "\n",
    "        output = output[:, 1:, :].reshape(-1, ru_bpe.vocab_size())\n",
    "        trg = trg[:, 1:].reshape(-1)\n",
    "\n",
    "        # trg = [(trg_sent_len - 1) * batch size]\n",
    "        # output = [(trg_sent_len - 1) * batch size, output dim]\n",
    "\n",
    "        loss = criterion(output, trg)\n",
    "\n",
    "        loss.backward()\n",
    "\n",
    "        # Let's clip the gradient\n",
    "        torch.nn.utils.clip_grad_norm_(model.parameters(), clip)\n",
    "\n",
    "        optimizer.step()\n",
    "        scheduler.step()\n",
    "\n",
    "        epoch_loss += loss.item()\n",
    "\n",
    "        history.append(loss.cpu().data.numpy())\n",
    "        if (i + 1) % 10 == 0:\n",
    "            fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))\n",
    "\n",
    "            clear_output(True)\n",
    "            ax[0].plot(history, label='train loss')\n",
    "            ax[0].set_xlabel('Batch')\n",
    "            ax[0].set_title('Train loss')\n",
    "            if train_history is not None:\n",
    "                ax[1].plot(train_history, label='general train history')\n",
    "                ax[1].set_xlabel('Epoch')\n",
    "            if valid_history is not None:\n",
    "                ax[1].plot(valid_history, label='general valid history')\n",
    "            plt.legend()\n",
    "\n",
    "            plt.show()\n",
    "        i += 1\n",
    "\n",
    "    return epoch_loss / len(iterator)\n",
    "\n",
    "\n",
    "def evaluate(model, iterator, criterion):\n",
    "\n",
    "    model.eval()\n",
    "    epoch_loss = 0\n",
    "    history = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for batch in tqdm(iterator):\n",
    "\n",
    "            src = batch['src'].to(device)\n",
    "            trg = batch['trg'].to(device)\n",
    "\n",
    "            # trg = [batch size, trg_sent_len]\n",
    "            # src = [batch size, src_sent_len]\n",
    "\n",
    "            output = model(src, trg, 0) #turn off teacher forcing\n",
    "            # output = [batch size, trg_sent_len, output dim]\n",
    "\n",
    "            output = output[:, 1:, :].reshape(-1, ru_bpe.vocab_size())\n",
    "            trg = trg[:, 1:].reshape(-1)\n",
    "\n",
    "            # trg = [(trg_sent len - 1) * batch_size]\n",
    "            # output = [(trg_sent_len - 1) * batch_size, output_dim]\n",
    "\n",
    "            loss = criterion(output, trg)\n",
    "\n",
    "            epoch_loss += loss.item()\n",
    "\n",
    "    return epoch_loss / len(iterator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "baa96756",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 598
    },
    "id": "baa96756",
    "outputId": "ff4dce64-7f10-44b3-d4ee-148cb504d220"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CAs/HEUi87XA1O0e3T/xBkrTr1S7yt/EXHwAAACgpTCaTzd293VlGRoa8vb21ceNGqyRRksqWLWv5f+5JyUwmk2WseUZGhiRp+vTpatWqlVW53HUGBQVZPX/zzTf1zjvvaMqUKYqPj1dQUJCGDRumrKys4p1YPsebN2+eRo4cqUmTJql169YKDg7Wm2++qbVr1xZYT17nn5OT47B9YmJitHv3bi1btkxLly7VM888ozfffFMrVqwo9gR0ua+BJD3++ONq3Lixjh49qhkzZujOO+9UtWrVinUcW3j+b4wLXb6abfn/uUtXFRlK4g0AAAC4mxo1asjX11fr169X1apVJUnnz5/Xnj17dMcdd0iSmjRpouzsbJ06dUrt2rUr0nEqVaqk6OhoHThwQH379rVr35UrV6pHjx7q16+fJCknJ0d79uxR/fr1ba6jTJkyys7OLrzg/47Xpk0bPfPMM5Zt+/fvtytmZwkICNC9996re++9V4MHD1bdunW1bds2NW3aNM9zrFevnlauXGm1beXKlapTp84tNzxyi4+PV/PmzTV9+nTNnTtX7733nsPPJy8k3gAAAABKlODgYD366KN6/vnnVb58eUVEROjll1+Wl5eXZSxvnTp11LdvX/Xv398y6dnp06e1fPlyJSQkqFu3bjYd65VXXtHQoUMVGhqqLl26KDMzUxs2bNDZs2c1fPjwfPerXbu2/v3vf2vVqlUqV66c3n77bZ08edKuxLt69eo6ePCgtmzZoipVqig4ONgyaVhex5s1a5aWLFmi2NhYzZ49W+vXr1dsbKzNx3OG5ORkZWdnq1WrVgoMDNSnn36qgIAASyt09erV9eOPP+rhhx+Wn5+fKlSooBEjRqhFixZ69dVX1bt3b61evVrvvfee1WzyBXn88cc1ZMgQBQUF6f7773fm6VmwnBgAAACAEuftt99W69at1b17dyUmJqpt27aWJb9umDFjhvr3768RI0YoLi5OPXv2tGolt8Xjjz+ujz76SDNmzFB8fLzat2+v5OTkQhPaF198UU2bNlXnzp3VoUMHRUZGWi3lZYsHH3xQXbp0UceOHVWxYsUClyN76qmn9MADD6h3795q1aqVzpw5Y9X6bZSwsDBNnz5dbdu2VUJCgpYtW6b//Oc/lnH2Y8eO1aFDh1SzZk3L+t9NmzbV/PnzNW/ePDVs2FCjR4/W2LFjrSZWK0ifPn3k4+OjPn36WL0fnMlktmcxPDeVnp6u0NBQnT9/XiEhIc47zpWrShjznSRpTVInRYa65ocEAPA8rvpsKk24poAxrly5ooMHDyo2NtZlSYozXLx4UZUrV9akSZM0cOBAo8OBgW4k8uvXr1fTpk0LLZ/f74A9n0t0NTfA97tOKjtHuqt+JaNDAQAAAEqkzZs3a9euXWrZsqXOnz9vWQIs92zYKD2uXr2qM2fO6MUXX9Rtt91mU9LtKHQ1z8eR3y/pr59t1i9Hzzm03itXs/WX5A16YtYGpV+56tC6AQAAAPzhrbfeUqNGjZSYmKiLFy/qp59+UoUKFYwOCwZZuXKloqKitH79ek2bNs2lx6bFOx9D5m7S1qPn9Z+tx3Vogm0TK9gi8+of0+hfysxWiH/xpsgHAAAAcKsmTZpo48aNRocBN9KhQwcZNdKaFu98HDh9scDXzfL4ofEAAAAAABcg8baDyegAAAAAAAAeh8TbQLSaAwAAAEDJR+LtajSbAwAAAECpYnfifezYMfXr10/h4eEKCAhQfHy8NmzYUOA+KSkpatq0qfz8/FSrVi0lJyffUuaf//ynqlevLn9/f7Vq1Urr1q2zNzSno30aAAAAAGAvuxLvs2fPqm3btvL19dWiRYu0c+dOTZo0SeXKlct3n4MHD6pbt27q2LGjtmzZomHDhunxxx/XkiVLLGX+9a9/afjw4Xr55Ze1adMmNWrUSJ07d9apU6eKfmZOZqLpGgAAAABgA7sS74kTJyomJkYzZsxQy5YtFRsbq7vvvls1a9bMd59p06YpNjZWkyZNUr169TRkyBD96U9/0uTJky1l3n77bT3xxBN67LHHVL9+fU2bNk2BgYH65JNPin5mAAAAAODBOnTooGHDhjn1GAMGDFDPnj3tOmb16tU1ZcoUm+ssSh0ljV2J98KFC9W8eXM99NBDioiIUJMmTTR9+vQC91m9erUSExOttnXu3FmrV6+WJGVlZWnjxo1WZby8vJSYmGgpk1tmZqbS09OtHq5W1InRTDc1lBu0hBwAAAAA5OmLL77Qq6++6vTjrF+/Xk8++aRNZUtCkm5X4n3gwAFNnTpVtWvX1pIlSzRo0CANHTpUM2fOzHeftLQ0VapUyWpbpUqVlJ6ersuXL+u3335TdnZ2nmXS0tLyrHP8+PEKDQ21PGJiYuw5jSKjczkAAACA4rp69arRIeSrfPnyCg4OdvpxKlasqMDAQKcf52ZZWVkuPd7N7Eq8c3Jy1LRpU40bN05NmjTRk08+qSeeeELTpk1zVnx5SkpK0vnz5y2PI0eOuPT4AAAAQKllNktZF4152NFl9MKFC+rbt6+CgoIUFRWlyZMn39KNOjMzUyNHjlTlypUVFBSkVq1aKSUlxfJ6cnKywsLCtGTJEtWrV09ly5ZVly5ddOLECatjffTRR6pXr578/f1Vt25dvf/++5bXDh06JJPJpH/9619q3769/P39NWfOHJ05c0Z9+vRR5cqVFRgYqPj4eH322Wc2n9+ePXtkMpm0a9cuq+2TJ0+2DAXOzs7WwIEDFRsbq4CAAMXFxemdd94psN7c1+jUqVO69957FRAQoNjYWM2ZM8fmGN966y1FRUUpPDxcgwcPtrrhcHMrttls1pgxY1S1alX5+fkpOjpaQ4cOtcRz+PBhPffcczKZTDLd1IV4wYIFatCggfz8/FS9enVNmjTJ6vjVq1fXq6++qv79+yskJERPPvmk7rzzTg0ZMsSq3OnTp1WmTBktX77c5nOzl489haOiolS/fn2rbfXq1dOCBQvy3ScyMlInT5602nby5EmFhIQoICBA3t7e8vb2zrNMZGRknnX6+fnJz8/PntDtR/M2AAAAcKurl6Rx0cYc++/HpTJBNhUdPny4Vq5cqYULF6pSpUoaPXq0Nm3apMaNG1vKDBkyRDt37tS8efMUHR2tL7/8Ul26dNG2bdtUu3ZtSdKlS5f01ltvafbs2fLy8lK/fv00cuRISwI6Z84cjR49Wu+9956aNGmizZs364knnlBQUJAeffRRy7H+9re/adKkSWrSpIn8/f115coVNWvWTC+88IJCQkL03//+V4888ohq1qypli1bFnp+derUUfPmzTVnzhyrruFz5szRn//8Z0nXG06rVKmizz//XOHh4Vq1apWefPJJRUVFqVevXjZdxwEDBuj48eP64Ycf5Ovrq6FDh9o0CfYPP/ygqKgo/fDDD9q3b5969+6txo0b64knnril7IIFCzR58mTNmzdPDRo0UFpamrZu3Srpetf3Ro0aWRp9b9i4caN69eqlMWPGqHfv3lq1apWeeeYZhYeHa8CAAZZyb731lkaPHq2XX35ZkrR27VoNGTJEkyZNsuSUn376qSpXrqw777zTpmtSFHYl3m3bttXu3buttu3Zs0fVqlXLd5/WrVvr22+/tdq2dOlStW7dWpJUpkwZNWvWTMuXL7cMwM/JydHy5ctvuRNREtyczzPEGwAAAHC8CxcuaObMmZo7d646deokSZoxY4aio/+4YZCamqoZM2YoNTXVsn3kyJFavHixZsyYoXHjxkm63i182rRpllbkIUOGaOzYsZZ6Xn75ZU2aNEkPPPCAJCk2NlY7d+7UBx98YJV4Dxs2zFLmhpEjR1r+/9e//lVLlizR/PnzbUq8Jalv37567733LIn3nj17tHHjRn366aeSJF9fX73yyiuW8rGxsVq9erXmz59vU+K9Z88eLVq0SOvWrVOLFi0kSR9//LHq1atX6L7lypXTe++9J29vb9WtW1fdunXT8uXL80y8U1NTFRkZqcTERPn6+qpq1aqWa1C+fHl5e3srODjYqmH27bffVqdOnfTSSy9Jun4jYufOnXrzzTetEu8777xTI0aMsDyvXLmyhgwZoq+//tpyDZKTkzVgwACr1nRHsyvxfu6559SmTRuNGzdOvXr10rp16/Thhx/qww8/tJRJSkrSsWPHNGvWLEnS008/rffee0+jRo3SX/7yF33//feaP3++/vvf/1r2GT58uB599FE1b95cLVu21JQpU3Tx4kU99thjDjpNAAAAAA7hG3i95dmoY9vgwIEDunr1qlUCGxoaqri4OMvzbdu2KTs7W3Xq1LHaNzMzU+Hh4ZbngYGBVqs4RUVFWVp8L168qP3792vgwIFWCeW1a9cUGhpqVW/z5s2tnmdnZ2vcuHGaP3++jh07pqysLGVmZto17vnhhx/WyJEjtWbNGt12222aM2eOmjZtqrp161rK/POf/9Qnn3yi1NRUXb58WVlZWVat/gX59ddf5ePjo2bNmlm21a1bV2FhYYXu26BBA3l7e1ueR0VFadu2bXmWfeihhzRlyhTVqFFDXbp00T333KN7771XPj75p6u//vqrevToYbWtbdu2mjJlirKzsy3Hzn3d/f399cgjj+iTTz5Rr169tGnTJm3fvl0LFy4s9JyKw67Eu0WLFvryyy+VlJSksWPHKjY2VlOmTFHfvn0tZU6cOKHU1FTL89jYWP33v//Vc889p3feeUdVqlTRRx99pM6dO1vK9O7dW6dPn9bo0aOVlpamxo0ba/HixbdMuAYAAADAYCaTzd293VlGRoa8vb21ceNGqwRRksqWLWv5v6+vr9VrJpNJ5v+NNc/IyJAkTZ8+Xa1atbIql7vOoCDra/bmm2/qnXfe0ZQpUxQfH6+goCANGzbMrgnAIiMjdeedd2ru3Lm67bbbNHfuXA0aNMjy+rx58zRy5EhNmjRJrVu3VnBwsN58802tXbvW5mMUVV7XLScnJ8+yMTEx2r17t5YtW6alS5fqmWee0ZtvvqkVK1bcUo+9cl93SXr88cfVuHFjHT16VDNmzNCdd95ZYC9uR7Ar8Zak7t27q3v37vm+npycfMu2Dh06aPPmzQXWO2TIkBLZtRwAAACAa9WoUUO+vr5av369qlatKkk6f/689uzZozvuuEOS1KRJE2VnZ+vUqVNq165dkY5TqVIlRUdH68CBA1aNkbZYuXKlevTooX79+km6Ptx2z549t8ypVZi+fftq1KhR6tOnjw4cOKCHH37Y6hht2rTRM888Y9m2f/9+m+uuW7eurl27po0bN1q6mu/evVvnzp2zK0ZbBAQE6N5779W9996rwYMHq27dutq2bZuaNm2qMmXKKDs726p8vXr1tHLlSqttK1euVJ06dW656ZFbfHy8mjdvrunTp2vu3Ll67733HH4+udmdeOM6R6zBbWYhbwAAAMDhgoOD9eijj+r5559X+fLlFRERoZdfflleXl6Wcbx16tRR37591b9/f8ukZ6dPn9by5cuVkJCgbt262XSsV155RUOHDlVoaKi6dOmizMxMbdiwQWfPntXw4cPz3a927dr697//rVWrVqlcuXJ6++23dfLkSbsT7wceeECDBg3SoEGD1LFjR6tx7LVr19asWbO0ZMkSxcbGavbs2Vq/fr1iY2NtqjsuLk5dunTRU089palTp8rHx0fDhg1TQECAXTEWJjk5WdnZ2WrVqpUCAwP16aefKiAgwNIKXb16df344496+OGH5efnpwoVKmjEiBFq0aKFXn31VfXu3VurV6/We++9ZzWjfEEef/xxDRkyREFBQbr//vsdej55sWs5MRSfMwfs5zZ+0a96d/lelx3PXleuZuubX47r3CXj1tMDAABAyfT222+rdevW6t69uxITE9W2bVvLkl83zJgxQ/3799eIESMUFxennj17WrWS2+Lxxx/XRx99pBkzZig+Pl7t27dXcnJyocntiy++qKZNm6pz587q0KGDIiMjLZNN2yM4OFj33nuvtm7dekur+1NPPaUHHnhAvXv3VqtWrXTmzBmr1m9b3JiUrn379nrggQf05JNPKiIiwu44CxIWFqbp06erbdu2SkhI0LJly/Sf//zHMtZ+7NixOnTokGrWrKmKFStKkpo2bar58+dr3rx5atiwoUaPHq2xY8daTaxWkD59+sjHx0d9+vSxek84i8lcAppd09PTFRoaqvPnzyskJMQhdcaPWaILV65Jkg5NuH6368KVq4of850kaXXSnYoKtf9OT0bmNTV8eYkk6ecXOqpKOecsGn/83GW1mfC9JGnv613l6+1+91jGLNyh5FWHlFAlVAuH3G50OADgUM74bCrtuKaAMa5cuaKDBw8qNjbWJQmKs1y8eFGVK1fWpEmTNHDgQKPDgcFuJPLr169X06ZNCyyb3++APZ9LdDXPj4ffjsi8lvfEBe7kqy3HJEm/HD1vcCQAAAAoaTZv3qxdu3apZcuWOn/+vGUJsNwzYaN0uXr1qs6cOaMXX3xRt912W6FJt6O4XzMonOJyVramrdiv/aczjA4FAAAAcIm33npLjRo1UmJioi5evKiffvpJFSpUMDosGGjlypWKiorS+vXrNW3aNJcdlxZvF7t5hLcrO/m/vXS3pv90UBMW7bJ0nQcAAABKqiZNmmjjxo1GhwE306FDB0MmuabFOz+umwPNJTYcPmt0CAAAAABQKpF4F5GnT0m379QFDZm7SXtPXjA6FAAAAHiAEjAnM1Akjnjvk3jbwZalwCZ9t1sd3vzB7ZfIemr2Rn3zywnd//4qo0MBAACAG/P29pYkZWW59/dbwFkuXbokSfL19S1yHYzxtsH976/Uaz0bqlp4UKFl//H9PknSJz8f1PC742553YXLeBdo/+mLkq4vbwYAAADkx8fHR4GBgTp9+rR8fX3l5UXbHUoHs9msS5cu6dSpUwoLC7PchCoKEm8bbE49p0c/Wa+U5zvYvE+2m3fFMZk8v7s8AAAAnM9kMikqKkoHDx7U4cOHjQ4HcLmwsDBFRkYWqw4Sbxv9fjHT6BAAwx0/d1mf/HxQj7aprpjygUaHAwAAXKRMmTKqXbs23c1R6vj6+harpfsGEm87FDaoPutajosiAYwxcOYG/XoiXUt2pumnUXcaHQ4AAHAhLy8v+fv7Gx0G4JEYoFFEeY3V/uaX4y6PY9+pDGVey3b5cVE6/XoiXZJ05PfLBkcCAAAAeA4Sbwe6fPWPBNgV46cXb09T4tsr9Ofpa51/MAAAAABAkZB42yh3Hu0OE5N9ti5VkrTx8Fm79vv9YpZbxA8AAAAApQGJdz7yWvXLlnW83dXNkXd/9yfD4riZ515NAAAAALAdibeLmQxKN29u4D5+/oohMeRGozsAAACA0oDE20Ce1t17+7Hzum3cci3YeNToUAAAAADAY5B428iTu0U7KsH/62eblZZ+RSM+3+qYCgEAAACgFCDxdqCbE1wPa8y2ydVs1ikHAAAAAHuReNvIkxNpk8mzW+wBAAAAwJOReOejsES7qIn4V1uO3VRH8dJ5D55kHQAAAABKDRJvOzgiz036YpsDarGfJ7fYAyjZjp27rJwc/koBAICSi8TbDnwtBBzv1xPp6vbuT/ph9ymjQ4EB/r3xqNpO+F6jFvxidCgAAABOQ+JdRHm1ft/c9dsVS4V52nJkQF6enL1BO46n67EZ640OBQaYsmyPpOsJOAAAQElF4m0jZyS5uetcf+h3bTt63vEHAtzY+UtXjQ4BAAAAcCoS73wUdzy3vROfncnI1EPTVuve934u5pHzicfGchmZ1/TfX07oYua1AssdO3e5+EEZ6PeLWRr1763acOh3o0MBAAAAUMKReNvIlkS6OK3ipzMyi76zAw2bt1mD527SyM+3Fliu7YTvXRSRc7zynx2av+Go/jRttdGhAAAAACjhSLyLyNOGV9sa77Jfr09wtWh7mvOC+R8jV0M7+NtFA48OAAAAoDQh8baDuy2b7cp1vKet2K+jZz2ve7nZbNY7y/YqhRmzAQAAABiExNtJbOl27kmt5hMW7TI6hCJZsuOkJi/bowHMmA2UCDk5Zj0+c4Ne/+9Oo0Mp0Y4dO6Z+/fopPDxcAQEBio+P14YNG/Itn5KSIpPJdMsjLe2P3lNjxoy55fW6deu64nQAADCcj9EBeAp7x2+vPXjG5rL7Tl3Q1WvFS8PXHfxdc9ce1v91q6+KwX63vO5urfWSa248HPfwSeAAWNuUelbLfj0pSfq/bvUNjqZkOnv2rNq2bauOHTtq0aJFqlixovbu3aty5coVuu/u3bsVEhJieR4REWH1eoMGDbRs2TLLcx8fvoYAAEoHPvGcZHPqOZvKfb3lmJ6dt0UVyt6aLNuj1wfXJwm7cjVH0x5pVqy6UHqcvZilID8flfGh8ws8Q1Z2jtEhlHgTJ05UTEyMZsyYYdkWGxtr074REREKCwvL93UfHx9FRkYWN0QAADwO37YNtGL3KSWvOiRJ+s1Bs5qn/n7plm3f7zqlk+nuMWs63Mexc5fV5NWl6jzlR0PjMLlysgIAhVq4cKGaN2+uhx56SBEREWrSpImmT59u076NGzdWVFSU7rrrLq1cufKW1/fu3avo6GjVqFFDffv2VWpqaoH1ZWZmKj093eoBAIAnIvEupuwcs/6z9XiRujSP+Y9rxig+NXujx6+7Dcdb/r/uuszwDkgZmdc0Z+1hh90E9WQHDhzQ1KlTVbt2bS1ZskSDBg3S0KFDNXPmzHz3iYqK0rRp07RgwQItWLBAMTEx6tChgzZt2mQp06pVKyUnJ2vx4sWaOnWqDh48qHbt2unChQv51jt+/HiFhoZaHjExMQ49VwAAXIWu5nbIa0zy3LWH9dLXO+TtZdIr9zVweUy5OXLc9ObUs2pStfAxfQDg6f7vy236estxzV59WIuH3WF0OIbKyclR8+bNNW7cOElSkyZNtH37dk2bNk2PPvponvvExcUpLi7O8rxNmzbav3+/Jk+erNmzZ0uSunbtank9ISFBrVq1UrVq1TR//nwNHDgwz3qTkpI0fPhwy/P09HSSbwCAR6LFOx+2dn/9ae9vkq63fDtKx7dStGrfb8Wqw2zvbHB5uP/9VcWuoySb/uMB3flWik5duGJ0KACKafH267Nv70rLv/W1tIiKilL9+tYT19WrV6/QbuG5tWzZUvv27cv39bCwMNWpU6fAMn5+fgoJCbF6AADgiUi83dDB3y7qzx+tNToMh8vIvOaQGwL2uPn+yb5Tjv1C/fq3v+rAbxc1Zdleh9YLAEZq27atdu/ebbVtz549qlatml31bNmyRVFRUfm+npGRof379xdYBgCAkoLEu4Rx12mqdhw/r4YvL9Gz87YYFkPi27ZNInbuUpauXM22ud5rDpxlOe38FU1euken0q/ocpbtMQCeysX34mCD5557TmvWrNG4ceO0b98+zZ07Vx9++KEGDx5sKZOUlKT+/ftbnk+ZMkVff/219u3bp+3bt2vYsGH6/vvvrfYZOXKkVqxYoUOHDmnVqlW6//775e3trT59+rj0/AAAMAJjvO1wxgMm3bnxHdbdZor+6KeDkqSFW4/r3T5NDI4mf2cyMtXstWUKDyqjjS/dJbPZrEtZ2Qryc82vyoAZ67Qr7YLeWX69Ff3le+vrsba2LeMD2+XkmOXl5V6/I7DNjb8lcJ4WLVroyy+/VFJSksaOHavY2FhNmTJFffv2tZQ5ceKEVdfzrKwsjRgxQseOHVNgYKASEhK0bNkydezY0VLm6NGj6tOnj86cOaOKFSvq9ttv15o1a1SxYkWXnh8AAEYg8c6D2WzW+ctXb9ne/s2UW7bltXxXfvIaC+wuX/1d3QXcXa0/dFaSdOZiliTpuX9t0Vdbjuvboe1UP9r5Ywtzjy995T87Sbwd7D9bjyvpi216v29T3VGHL/ye5vtdp4wOoVTo3r27unfvnu/rycnJVs9HjRqlUaNGFVjnvHnzHBEaAAAeia7meXh76Z5Cy/xtwS+SbJ+IJ+tajlq+vrxYcTmL2WxWn+lr1NPFk6m5y02Hgny15bgk6aOfDxgcCRzlr59tVkbmNfX/ZJ3RoQAAAKCUoMU7D//4Pv8ZVm/4ae9vt4wDfvGr7fmWv3Dl1hZ0d3H5arbWHPjd6DBQSrnZqAgAAADA4WjxdhFnjLl2VI3fbksr0n6e0D2dnA4AAACA0exKvMeMGSOTyWT1qFu3br7lO3TocEt5k8mkbt26WcoMGDDglte7dOlS9DNyU+6cAI78fKvRIQAopejxAAAASgO7u5o3aNBAy5Yt+6MCn/yr+OKLL5SVlWV5fubMGTVq1EgPPfSQVbkuXbpoxowZlud+fn72hmUIeyb58dQvl78cPaeEKmF5vnYpK1vrDv2uNjXD5efj7drAJGVey9bTszeqba0KerxdDYfUme/Pyf0b9+3moW9JAAAAwOPYnXj7+PgoMjLSprLly5e3ej5v3jwFBgbeknj7+fnZXKc7eWbOJpvLmvJJc4rTBd0VueB9763UoQnd8nzt2XmbtezXU+rbqqpevz8+zzKXs7L1p2mrtON4+i2vFTf+LzYd0w+7T+uH3acdlniXJiXwXgLykZ1z/aftzRJq+fLUm6MAAMAz2D3Ge+/evYqOjlaNGjXUt29fq3U8C/Pxxx/r4YcfVlBQkNX2lJQURUREKC4uToMGDdKZM2cKrCczM1Pp6elWD7dXhC917j6Getmv11v856zN/z3w1ZZjeSbdedl29LySvtim32xcL/1i5jWbytkq81q2nvvXFrv3c/MfE5zkWnaORn+9XYu3F22OBFfJyTHrzkkpunNSinJyeLMCAAAYwa7Eu1WrVkpOTtbixYs1depUHTx4UO3atdOFC4UvqbVu3Tpt375djz/+uNX2Ll26aNasWVq+fLkmTpyoFStWqGvXrsrOzs6nJmn8+PEKDQ21PGJiYuw5DUMUpTWlJHxHvmbHSdz73s/6bF2qZak2V/vX+iO6lJX/+w6ut/3Yea3a95vRYeRpwaajmrX6sJ7+dKPRoRTo7KUsHT5zSYfPXNLZS1mF7wAAAACHs6uredeuXS3/T0hIUKtWrVStWjXNnz9fAwcOLHDfjz/+WPHx8WrZsqXV9ocfftjy//j4eCUkJKhmzZpKSUlRp06d8qwrKSlJw4cPtzxPT093++SbXoy223Myw5Dj/n4x/6SkBNwD8Ujd//GzJGlNUidFhvobHI21k+m29cxwJ9N/Oqi/3llLQX6sJAkAAOBKxVpOLCwsTHXq1NG+fQWve33x4kXNmzev0ORckmrUqKEKFSoUWKefn59CQkKsHu7qux1p2nfqgsuWE3P37ukl0S9HzxsdQol34vxlo0MoEaat2K/X/vur0WEAAACUOsVKvDMyMrR//35FRUUVWO7zzz9XZmam+vXrV2idR48e1ZkzZwqt0xOs3n9GT87eqMS3f6TFuwQ78JsxLfSl0b5TGVr+60mjw3A7l7OydSnLtjkP1h4oeA4Nd7Qp9awGJq/XgdP8rgEAAM9kV+I9cuRIrVixQocOHdKqVat0//33y9vbW3369JEk9e/fX0lJSbfs9/HHH6tnz54KDw+32p6RkaHnn39ea9as0aFDh7R8+XL16NFDtWrVUufOnYtxWu5h+7HitYQWNVm/cjWblu8SZPbqQ0aHkKdHPl6rK1ddOyY+8e0VGjhzgzYe/t2lx3Vn17JzVG/0YtUfvUTXsnOMDscpHnh/lZbvOqUnZm0wOhQAAIAisSvxPnr0qPr06aO4uDj16tVL4eHhWrNmjSpWrChJSk1N1YkTJ6z22b17t37++ec8u5l7e3vrl19+0X333ac6depo4MCBatasmX766SePWcu7uApKrouSOv9+MUt1X1qsRz5eV9SQ4GYmLd1jdAh5+mnvb/rX+iOGHNvWmfJLg/OXr1r+f/bS1QJKer6jZxlyAAAAPJNdM+zMmzevwNdTUlJu2RYXF5dv62tAQICWLFliTwge5ZttJwov5GCnLlyf8OnYOff/gmpLi37yyoPalXZB4x+Id8g4+VMXruQ7iVp+a61LjJ3PD7PAo7j41QIAAKUBU9s60dYj54wOwSPdnF+P+c9OSdI98VG6o07FItRlnUy3fH25JCnEn7e+K+w7laGY8gHy8/E2OhQ4UHaOWd5exb8RZjabPeImIQAAQHEVa3I1FF+Jb+xxUHPWxcxbJ44qTtXpV2ybiCq37ByzNh7+3Wpsc0Et5c6yaNsJdf/HTzr428Ui1+HsqJftPKnEt1eo1wdril1XSf49yckxa+fxdI8Zn/3V5mOq+9IiLdtZ/Enuvt91ygERAQAAuD8Sb3iEVfvdYybm93/YpwenrtagTzdatpkNSAsHzdmk7cfSNfLzrS4/tq3m/W/8Nz0/Cvbu93t1z7s/6W9fbDM6FJsM+9cWXc0263EHTHR2Jp9hHwAAACUNiTc8wuw1h62em81mpaVfcdnxb6TWM/83w/gPu0+77NgFyShiy72jGHHTQSpZ44LfXb5XkvTvjUcNjgQAAADOQuJtsAJnNS9J2YWD/d9X2/XxzweNDkPS9SRw7YEzVrNL2+pado6SvvhFX285VmD9gKd4/b87ddfbK/IcHgIAAFBakXjDEAdOZ1gtfTRk7ia79p+7NtXRIamok6ZfyzGr94drdM87P9m97xebj+mzdUf07LwtRTt4MbhLPu/6EfJwpuk/HdTeUxlasIkWfAAAgBtIvD3MlavZHt8SfuVqtu6ctMJq2ze//LH0miMTMQesQGazoszOfCbDmDGuV7NzNPrrHYYcG6VDTo5n/50CAABwJBJvD3Lwt4uq+9Jijfz8F6NDKdCVq9nqNW21/vG/sau5nbtkf5fsm124Urz9i8LD73XcYvmvxZ+R2h6zVx/SL0fPufSYKHlceSMNAADAkUi8PchHPx2QJLfvwrlg01GtO/S7Ji3dk+c35YzM4iXORRlL7a6Mmpws85prl6566esduu+9lVbbzGazJizaZTXkID8l7cZHabHlyDmN//ZXXcpivDcAACjdfIwOoLS4fNO6z45ictPmn8yrBSd1iW//6KJI4GzFSYg3HzmnaSv2Oy4YD3Vzj+zsHLO8vdzz97ooev7zj5stSffUMzASAAAAY9Hi7SKTvtuT5/aCcmdPauRL+mKbshzUiupJ510cX285pqkpfySei7efyLPc+ctXdSYj01VhuYw9PRfc9B6Tw+W4omk/17XcePisOk1K0Y97nLdE3p6TFxxSDz0fAACApyLxdpHP1jl+Fm538tm6VM1de/jWFwz+prxoW5rNZV2d2z07b4su3LQO99Of5j+ze+sJ30u63j370zV5XGdY2XrknA6czjA0hjlrD2vpTvvG0ueYzS6fPLHfR2u1//RF9f9knUuP60qePiElAADwfCTecJjTDmqVdWQCvPrAGQfWZtx9hBu9CVbsOa0Xv9r+RzyF9A+YtmK/7pyUotMXrv9szGazNqee1cVMxw99cLXJS/fo/77cdktSdTL9inr8c+UtM+e70t6TF/R/X27XE7M22LXfnW+t0DNz7Fta72a29Az47UKmrmX/0TvFGcNgnKUoPR+Onbus1uO/1z9/2Fdw3SxsBwAAnIjE24OV1FacQ2cu6fnPt+pU+hWjQ7FyMdP4CaIOnL5o9XzPyQy9+NU2XckneZqwaJcOnL5oSToWbj2u+99fpb9/uc3psTrbO8v3as7aVO05ad2yffjMJZv2d2b39aLehDp27rIWbbe9l0Z+Lly5qt1peXfvTr9yTQ9OW13sY3iKSUt2Ky39it5cstvoUAAAQClG4u0hPvxxv/67Le8xwO7GEQnN5xuPasTnW4tfkR0Kuo2xcOtxNXh5ic5cNGbd7YJ8uibVMuN9fq7+r4VzwaZjrghJUuHvA3veJgXdY8q8ZnyLbd+P1ijbDdatvnGdOr61Qp2n/Kj1h37Ps9zWI+fsrttTVxPILqE3KAEAgGch8fYA24+d17hvdxV7/Wtnu5iZrT0nLzisO3bu1l1J+nzDEb29NO+J6kqzY+cK7x1wJiPTqRNouZIjens4Mh9bue+M1h507LCG4vjtfy3u9o4xz88/f9inRq98p/nrjzikPkfLyTErxw1ufAAAAOSH5cTc2I3EIL9WVndbTix51SElrzqkBtEhTjvG8//+xWl1u9fVdLx/b8x//fcFG4+qZkRZNY4Jc11ADuIuDZqFtXibzWa3+521xdmLWZZu2n/7wnm/f0VlNpt13z9/VubVHC0Zdoe8StBybAAAoOSgxdtgxZnQ58aEWe5mx/F0p9Q7MHm9U+r1JM5abmrE51ut1lz2JGZJaeev6G8LftGvJ2x/77kyB76UdU2dJq3wyLH1D05bVaT9XHU/5GJWtrYfS9feUxlKc7N5IQAAAG4g8fZg246dNzoEl1q+61ShZU5duFKiu5y6ZJ1nDzR03mbNW39EXd/5yehQ8vTN1hM68NtFzV3recsK5jXkw2Hc9O38rYfMpwEAADwHibcbM8us2WsOa0vqOaND8Qg/7T2tlq8v1+C5RV+OyZ0N/Wyzxn27y7DjX7ji2DkGHNXt2mw2a5cdLd2OYtfkcO6aYTqRUR2+zebij/cuzpJuAAAAeWGMt8HW5TPrsCT9sOuUXrppzWYU7IMV12f2dsRyTO5o4dbjRd7XETnu73bO6O7Mxnl7kvZr2Tny8Xb8PcbSl0oX/ZxdNbbdLKnXB6t14co1/XdoO3kz3hsAALgJWrzd2N5c6xN7Ik9ITpK++KXEronuSN/8cmv32zeX7NaJ85ddcHTrn4+tP68lO9IU99Jifb2l+MuoLdt5Uh3e/EGbU88Wu67CHDt3mfdkEWRnm7X+0FntSrug1N9tW88dAADAFUi8c/GUL7ueEqcrFbVR7bN1R/TriQv6Nc2x3ZVX7DmtLUVYL7konN2geDkr2zKzdW5j/7PTuQcvREG/CU/N3qjsHLOenbel2Md5fNYGHTpzSY+5YJK/thO+z/d6F6Skd2m3ZzLKLUfOuu1kewAAoPQh8c5lpwFjRYui5bjlRodQomRey9aFK9ccVt+xc5f16CfrPHam8NyyruXk+1pGpuOuW35Gfu78ZazOXszSg1NX6dM1hwssdzkr2+mxSNL7Kfvt3uef3+9zQiTuw54bC8/9a6vNk+1tPHxWl1z0cwUAAKUTiXcu17I9o8XIlUuJnbpQ9CV6PKUR6T9bT+invb85rL4T51zR/dqz2fPeOPhb/jNrO6rzx3s/7NPGw2f1ogfPqzBzdcE3DdyOG/yBOJl+RQ9OLdqSaQAAALYi8c6F7oa3avm6+7euF7fr/ScrDzooEmO448iDovwurT1wpggzUjvm5C9lOa/l3h1/Pra4lm3d08FTz6MgRxgLDgAAXIDEOxd7xhA629JfTxodgsdo/2aKQ1usi+Nqdv7dsp3pZPoVHcqnZdid3tcF6f3hGn22vnhrXdt6E+bWK+IZ18iVsktipg0AAGAAEu9c3KnF+5ej540OwWPkNYPxLgdPlmaLcd/+qrovLda+U66fkb7VuOXq8FaKztq57Je7+XpL4cum3ZwOOio3tPV3P/Najh6fuZ7fTwN4yg0kAACA3FjHGyXS5axsffjjAZcf98Yxpyzba9l25Wq2Un+/pDqVgi3bVu5zbOv8zUlj6u+XVC6ojEPrdwZ3uskl2dfevezXU06Lw52ZTMZ2N798lQnQAACAZ6LFGyXS2G926ItNxV+72RF6f7hGd0/+UYu2/bEOdt+P1ro0hpK+zNTNTDZm9LmviJe73QkoQX7YXTpvVAAAANxA4p0L370d6z9bTxReyAm+2lx4d+XC/LjntAMikbb+by3v+RuOOKQ+VzmVfkUf/XRA5y9ddUr9jmo5ddQtBUf+7ruyS3SWE+YUyLyWrU9+PnjLbPL2/MycMRygMHn9DPmTDgAA3AFdzXNhDKFjrTv0u9EhFFn/T9YZHYKh+kxfo/2nL2rNgTOa9FDjfMvZ2sLsLEVN6nJH7am/+Z+tc/wNnQ9WHNDbS/c4vF4AAIDSisQ7l9LUJRfGcOUa7Dd8vfm44quE2rXP/tPXWzu/32VfN+Fx3/6q9Yd+V4i/r1372eOrzY4fRlDcGwhG3bQ7d8nxk+ltPHy22HUYcTWYhB0AALgrEu9cvtvBEl4ovrT0K3lun5qyXxMX73JxNNKFzGtatf+MU+rOvXyXrZPaFSfP3XrTjOLFXcPdUQq6aeceEbqXV7/ZqY9/PujQOvP6GSwp5G86w4sAAIArMMY7l8Nn8l4HGXAEI5Lu4soxS6sPOCdpdwR3HOONwjkq6S7sx8ZM6AAAwB3Q4g235S4tmZ4g48o1p9b/9Kcb833tZD6t+65UlG7izhibnpNjVvoV50xGBwAAAM9Fi3cuRk8UhT8cP298QucpFm4t/izuRbXnZIZhx3Y3T326UY3HLtUvR89Zbfe0vyq2/hk89NtFbpABAADYgMQbQKnkjIRx6c7r44mTVx2ybMvOKbmJaYe3UvTJykN5vuaqsy7ucbhvAAAAXIHEGyhhPCWPcNQs4Gazew9LeO2/Ow079rfbTti9z897f1PKbtvXsH/7u912H8NZWA4SAAC4KxLvXPjaVjIwoVLRPDh1lTYeNnbtdXt/B52xBODi7Sf07LzNupRl+9j5T9cc/iOmm0KakU+LsCscPXtZ9ja49/t4rUOOzd9SAACAP5B458a3RXi44ryFNx4+qwenrnZYLPnZd+qC3ZOQXcvOsamcra3fBc3n8PSnm/T1luO6e/KP+nqLbWuG37xsVe6bAbkjcmUL/YAZ61x2LE/EtB4AAMAVSLxzoasiSoIrV7Pdtvv1juPnlfj2j3pmzia79uv/Sd4J5F4nTu529OxlPTtvi81Jvy0O/XZRzV9bpvdT9jmkvsL+Yu04nu6Q4+TnYpb7vNdIogEAgLsi8QZc5Ni5yy45zoHfMlT3pcV6/t+/uOR49uj/yTp9ti61SPuu2p/3WuIvL9yhS1mFDy04VYxlzxyZVr7+7a86czFLbyx2n7HRxbUp9azRIQAAALg1Em/ARVy17NaHPx6UJP1741GXHM8eP+45rU/XFC3xLsg1GwYyf7m54C7jBfV2mbx0j90x5cfexuFn5mzUoALWUc8tx4BZ1M9fvnXYgHu0gVvLvMbcDwAAwBgk3rmU8aGvIuDJ3vsh7y7cZklvLtlVpDrfT9lvV3lH9bw+ezFL325L06Ltafr9YpZG/XurxizcUfCxHXNou5y7ZN94fWcp7LoP+tS+4Q0AAACOYlfiPWbMGJlMJqtH3bp18y2fnJx8S3l/f3+rMmazWaNHj1ZUVJQCAgKUmJiovXv3Fu1sHMDXm3sRgJGKmzj+tPe3vOs1S//8wb4E2mg5N2WSx85e1vwNR5W86pDbtdz+35fbb9nmjrcwv991yugQAABAKeVj7w4NGjTQsmXL/qjAp+AqQkJCtHv3H2MZc88k/MYbb+jdd9/VzJkzFRsbq5deekmdO3fWzp07b0nSAbiGu0yW5UjOWHYsP86Y5OtqjuMmeHM0e5fvM/L9tefkBZUPKiNfby9dsHNmfQAAgKKyO/H28fFRZGSkzeVNJlO+5c1ms6ZMmaIXX3xRPXr0kCTNmjVLlSpV0ldffaWHH37Y3vAAOEDXd34y9PjZOWY9MWuDS4/p7jNi55er5t78xuKidacvLe6e/KPV8/f+3MSgSAAAQGlid7/qvXv3Kjo6WjVq1FDfvn2VmlrwREkZGRmqVq2aYmJi1KNHD+3Y8cf4xIMHDyotLU2JiYmWbaGhoWrVqpVWr85/LeHMzEylp6dbPQBc9+uJ4v8+7Eq74IBIim7lvt8c3i04d+Lqyjzb0Q28BU0E98GPBxx7MDe3bOfJwgsVwNOGHwAAAM9kV+LdqlUrJScna/HixZo6daoOHjyodu3a6cKFvL+kx8XF6ZNPPtHXX3+tTz/9VDk5OWrTpo2OHr0+23JaWpokqVKlSlb7VapUyfJaXsaPH6/Q0FDLIyYmxp7TAOBgv2Vk6ofdpxw2o3bWNdd2q76Udc2lxysuV3abL6rcEZ66cEXnLmU59Bg/7/1Nw/61xfJ8z0n7bxg54kYVAABAYezqat61a1fL/xMSEtSqVStVq1ZN8+fP18CBA28p37p1a7Vu3dryvE2bNqpXr54++OADvfrqq0UOOikpScOHD7c8T09Pd1jyXQKHtgJO1/y16/M+vPVQI4MjyV9+44qnrdivCYt2KTLE9XNKHPn9ksuPaZSWry+XJB2a0M1hf2e3HLFeP/xxFw9PAAAAsJXdY7xvFhYWpjp16mjfvryX78nN19dXTZo0sZS/Mfb75MmTioqKspQ7efKkGjdunG89fn5+8vPzK3rgBfCEliTAXX2/q3jdfqXrXcBdMd76xm/6hEXXx0SnpV9xXN02/hl5Y8nuwgvl4eau5kW9VEfPlp6kHwAAwGjFWjsrIyND+/fvt0qaC5Kdna1t27ZZysfGxioyMlLLly+3lElPT9fatWutWsoBoLiy3XBS8JxCMnRn3ga8feIPun3iD06r//AZEnsAAIAb7Eq8R44cqRUrVujQoUNatWqV7r//fnl7e6tPnz6SpP79+yspKclSfuzYsfruu+904MABbdq0Sf369dPhw4f1+OOPS7o+4/mwYcP02muvaeHChdq2bZv69++v6Oho9ezZ03FnaYeCJi0CUDBHdSF2Rot37iTXqN90d5893VGSVx0yOgQAAAC3YVdX86NHj6pPnz46c+aMKlasqNtvv11r1qxRxYoVJUmpqany8vojlz979qyeeOIJpaWlqVy5cmrWrJlWrVql+vXrW8qMGjVKFy9e1JNPPqlz587p9ttv1+LFi1nDG/BAP+x27EzkjmTkIBJHJNulJWEHAAAoiexKvOfNm1fg6ykpKVbPJ0+erMmTJxe4j8lk0tixYzV27Fh7QnEaxngDRXflqhv253YDjugJUFImfryUdU3d//GzQ+rKyMx2SD0AAADOVqwx3gDgaOlXPGtpL3stLea6057uy83HdOD0RYfUNW0Fa3ADAADPQOINwK38eiLdKet478q1XvO1HLM+33DE4cdxlpvXwPbknjnZDlrrHQAAwJOQeANwOwd/c/yM2N/l0dL8/L9/cfhxJOeMJx84073XqLZ1TfKS0mUeAADAHiTeANyOp08kdvpCpm0Fi5GErtz3m5771xadv3y16JU40AsLnHMTA8Y4duyY+vXrp/DwcAUEBCg+Pl4bNuR/8yclJUUmk+mWR1paWp7lJ0yYYFnZBACA0sCuydUAwBVoFS2Y2Sz1/Wit0WFYSb9i2w0Asx0/3AtXrmrJjtI9Jt4IZ8+eVdu2bdWxY0ctWrRIFStW1N69e1WuXLlC9929e7dCQkIszyMiIm4ps379en3wwQdKSEhwaNwAALgzEm8AQLGZbFwZ3Z57Ks/9a6uW/Uri7WoTJ05UTEyMZsyYYdkWGxtr074REREKCwvL9/WMjAz17dtX06dP12uvvVbcUAEA8Bh0NQfgdjy9q7mjbD923ugQXCr3xGsk3cZYuHChmjdvroceekgRERFq0qSJpk+fbtO+jRs3VlRUlO666y6tXLnyltcHDx6sbt26KTEx0ab6MjMzlZ6ebvUAAMATkXjnQhdXwHin0m0cI+0hijoL+fD5Wx0cifNczLymB96/NdHKraC/sT3/Wfj+cL4DBw5o6tSpql27tpYsWaJBgwZp6NChmjlzZr77REVFadq0aVqwYIEWLFigmJgYdejQQZs2bbKUmTdvnjZt2qTx48fbHMv48eMVGhpqecTExBTr3AAAMApdzXMh7waM98nKg0aHUCq9uWRXkfc98Jtta3PnFJB5bytlLfzuKicnR82bN9e4ceMkSU2aNNH27ds1bdo0Pfroo3nuExcXp7i4OMvzNm3aaP/+/Zo8ebJmz56tI0eO6Nlnn9XSpUvl7+9vcyxJSUkaPny45Xl6ejrJNwDAI9HinQs9XAG4Sua1bKNDsPLPH/YbHQLcQFRUlOrXr2+1rV69ekpNTbWrnpYtW2rfvn2SpI0bN+rUqVNq2rSpfHx85OPjoxUrVujdd9+Vj4+PsrPz/l3w8/NTSEiI1QMAAE9EizcAGGTZr6eMDgG4Rdu2bbV7926rbXv27FG1atXsqmfLli2KioqSJHXq1Enbtm2zev2xxx5T3bp19cILL8jb27t4QQMA4OZIvAHAyf7vy+1GhwDY7LnnnlObNm00btw49erVS+vWrdOHH36oDz/80FImKSlJx44d06xZsyRJU6ZMUWxsrBo0aKArV67oo48+0vfff6/vvvtOkhQcHKyGDRtaHScoKEjh4eG3bAcAoCQi8c6FMd4A3J0nTwLpybGXFi1atNCXX36ppKQkjR07VrGxsZoyZYr69u1rKXPixAmrrudZWVkaMWKEjh07psDAQCUkJGjZsmXq2LGjEacAAIDbIfEGADdVEuecKOoM73Ct7t27q3v37vm+npycbPV81KhRGjVqlF3HSElJKUJkAAB4JhJvAHBTpTVFzci8plX7fjM6DAAAAIch8c6lJLYwAYC7sKWr+cDk9Vp78HfnBwMAAOAiLCeWS2ltYQLgfn6/mJXndk/urm1L5CTdAACgpCHxBgAAAADAiUi8AQAuw6zmAACgNCLxBgC4jCd3kwcAACgqEu9caI0B4O6mLNtrdAgAAACwA4k3AHiYD388YHQIRcbNTQAAUBqReAMAAAAA4EQk3gAAt3HX2yuMDgEAAMDhSLwBAC6Tk1NwX/O9pzJcFAkAAIDrkHgDAAAAAOBEJN4AAJdhbjUAAFAakXgDAFyGWc0BAEBpROKdi8lkdAQAAAAAgJKExDsXWmMAAAAAAI5E4p3Lj3tOGx0CAAAAAKAEIfHO5eylLKNDAAAAAACUICTeAAAAAAA4EYk3AAAAAABOROINAHCZH3afMjoEAAAAlyPxBgC4zJYj54wOAQAAwOVIvAEAAAAAcCISbwAAAAAAnIjEGwAAAAAAJyLxBgAAAADAiUi8czEZHQAAAAAAoEQh8QYAAAAAwIlIvAEAAAAAcCISbwAAAAAAnIjEGwAAAAAAJyLxBgAAAADAiexKvMeMGSOTyWT1qFu3br7lp0+frnbt2qlcuXIqV66cEhMTtW7dOqsyAwYMuKXOLl26FO1sHMBkYl5zAAAAAIDj+Ni7Q4MGDbRs2bI/KvDJv4qUlBT16dNHbdq0kb+/vyZOnKi7775bO3bsUOXKlS3lunTpohkzZlie+/n52RsWAAAAAABuye7E28fHR5GRkTaVnTNnjtXzjz76SAsWLNDy5cvVv39/y3Y/Pz+b63Q2s9lsdAgAAAAAgBLE7jHee/fuVXR0tGrUqKG+ffsqNTXV5n0vXbqkq1evqnz58lbbU1JSFBERobi4OA0aNEhnzpwpsJ7MzEylp6dbPQAAAAAAcEd2Jd6tWrVScnKyFi9erKlTp+rgwYNq166dLly4YNP+L7zwgqKjo5WYmGjZ1qVLF82aNUvLly/XxIkTtWLFCnXt2lXZ2dn51jN+/HiFhoZaHjExMfacBgAAAAAALmMyF6Nv9blz51StWjW9/fbbGjhwYIFlJ0yYoDfeeEMpKSlKSEjIt9yBAwdUs2ZNLVu2TJ06dcqzTGZmpjIzMy3P09PTFRMTo/PnzyskJKRoJ/M/DUYv1sWs/JN+AEDJd2hCt2LXkZ6ertDQUId8NuE6rikAwJ3Y87lUrOXEwsLCVKdOHe3bt6/Acm+99ZYmTJig7777rsCkW5Jq1KihChUqFFinn5+fQkJCrB4AAAAAALijYiXeGRkZ2r9/v6KiovIt88Ybb+jVV1/V4sWL1bx580LrPHr0qM6cOVNgnc7EcmIAAAAAAEeyK/EeOXKkVqxYoUOHDmnVqlW6//775e3trT59+kiS+vfvr6SkJEv5iRMn6qWXXtInn3yi6tWrKy0tTWlpacrIyJB0PXF//vnntWbNGh06dEjLly9Xjx49VKtWLXXu3NmBp2k70m4AAAAAgCPZlXgfPXpUffr0UVxcnHr16qXw8HCtWbNGFStWlCSlpqbqxIkTlvJTp05VVlaW/vSnPykqKsryeOuttyRJ3t7e+uWXX3TfffepTp06GjhwoJo1a6affvrJsLW8WUwMAAAAAOBIdq3jPW/evAJfT0lJsXp+6NChAssHBARoyZIl9oQAAAAAAIBHKdYYbwAAAAAAUDASbwAAAAAAnIjEOxcmVwMAAAAAOBKJNwAAAAAATkTiDQBALmYza1wAAADHIfEGAAAAAMCJSLwBAAAAAHAiEm8AAAAAAJyIxBsAAAAAACci8QYAAAAAwIlIvAEAAAAAcCISbwAAAAAAnIjEGwAAAAAAJyLxzs1kdAAAAAAAgJKExBsAAAAAACci8c6FBm8AAAAAgCOReOdiNjoAAAAAAECJQuKdG5k3AAAAAMCBSLwBAAAAAHAiEm8AAAAAAJyIxDs3ZlcDAAAAADgQiXcu5N0AAAAAAEci8QYAAAAAwIlIvAEAyMXMChcAAMCBSLxz4bsWAAAAAMCRSLwBAAAAAHAiEm8AAAAAAJyIxDsXZjUHAAAAADgSiTcAAAAAAE5E4p2LyUSbNwAAAADAcUi8AQAAAABwIhLvXMws3goAKOWOHTumfv36KTw8XAEBAYqPj9eGDRvyLZ+SkiKTyXTLIy0tzVJm6tSpSkhIUEhIiEJCQtS6dWstWrTIFacDAIDhfIwOAAAAuI+zZ8+qbdu26tixoxYtWqSKFStq7969KleuXKH77t69WyEhIZbnERERlv9XqVJFEyZMUO3atWU2mzVz5kz16NFDmzdvVoMGDZxyLgAAuAsSbwAAYDFx4kTFxMRoxowZlm2xsbE27RsREaGwsLA8X7v33nutnr/++uuaOnWq1qxZQ+INACjx6GoOAAAsFi5cqObNm+uhhx5SRESEmjRpounTp9u0b+PGjRUVFaW77rpLK1euzLdcdna25s2bp4sXL6p169b5lsvMzFR6errVAwAAT0TiDQAALA4cOKCpU6eqdu3aWrJkiQYNGqShQ4dq5syZ+e4TFRWladOmacGCBVqwYIFiYmLUoUMHbdq0yarctm3bVLZsWfn5+enpp5/Wl19+qfr16+db7/jx4xUaGmp5xMTEOOw8AQBwJZO5BMwmlp6ertDQUJ0/f95qbFlRJIxZovQr1xwUGQDAEx0Yd4+8vIq3vKQjP5tcqUyZMmrevLlWrVpl2TZ06FCtX79eq1evtrme9u3bq2rVqpo9e7ZlW1ZWllJTU3X+/Hn9+9//1kcffaQVK1bkm3xnZmYqMzPT8jw9PV0xMTEed00BACWTPZ/1tHjnwjreAIDSLCoq6pZEuF69ekpNTbWrnpYtW2rfvn1W28qUKaNatWqpWbNmGj9+vBo1aqR33nkn3zr8/Pwss6DfeAAA4IlIvAEAgEXbtm21e/duq2179uxRtWrV7Kpny5YtioqKKrBMTk6OVYs2AAAlFbOaAwAAi+eee05t2rTRuHHj1KtXL61bt04ffvihPvzwQ0uZpKQkHTt2TLNmzZIkTZkyRbGxsWrQoIGuXLmijz76SN9//72+++47q326du2qqlWr6sKFC5o7d65SUlK0ZMkSl58jAACuRuKdCz3NAQClWYsWLfTll18qKSlJY8eOVWxsrKZMmaK+fftaypw4ccKq63lWVpZGjBihY8eOKTAwUAkJCVq2bJk6duxoKXPq1Cn1799fJ06cUGhoqBISErRkyRLdddddLj0/AACMwORquTQe+53OXbrqoMgAAJ6oNE+u5s64pgAAd8LkasXg+bchAAAAAADuhMQbAIBcuAcLAAAcicQbAAAAAAAnsivxHjNmjEwmk9Wjbt26Be7z+eefq27duvL391d8fLy+/fZbq9fNZrNGjx6tqKgoBQQEKDExUXv37rX/TAAAAAAAcEN2t3g3aNBAJ06csDx+/vnnfMuuWrVKffr00cCBA7V582b17NlTPXv21Pbt2y1l3njjDb377ruaNm2a1q5dq6CgIHXu3FlXrlwp2hkVE7OaAwAAAAAcye7E28fHR5GRkZZHhQoV8i37zjvvqEuXLnr++edVr149vfrqq2ratKnee+89Sddbu6dMmaIXX3xRPXr0UEJCgmbNmqXjx4/rq6++KvJJAQAAAADgLuxOvPfu3avo6GjVqFFDffv2tVrHM7fVq1crMTHRalvnzp21evVqSdLBgweVlpZmVSY0NFStWrWylMlLZmam0tPTrR4AAAAAALgjuxLvVq1aKTk5WYsXL9bUqVN18OBBtWvXThcuXMizfFpamipVqmS1rVKlSkpLS7O8fmNbfmXyMn78eIWGhloeMTEx9pwGAAAAAAAuY1fi3bVrVz300ENKSEhQ586d9e233+rcuXOaP3++s+LLU1JSks6fP295HDlyxKXHBwAAAADAVsVaTiwsLEx16tTRvn378nw9MjJSJ0+etNp28uRJRUZGWl6/sS2/Mnnx8/NTSEiI1QMAAAAAAHdUrMQ7IyND+/fvV1RUVJ6vt27dWsuXL7fatnTpUrVu3VqSFBsbq8jISKsy6enpWrt2raWMqzGpOQAAAADAkexKvEeOHKkVK1bo0KFDWrVqle6//355e3urT58+kqT+/fsrKSnJUv7ZZ5/V4sWLNWnSJO3atUtjxozRhg0bNGTIEEmSyWTSsGHD9Nprr2nhwoXatm2b+vfvr+joaPXs2dNxZwkAAAAAgEF87Cl89OhR9enTR2fOnFHFihV1++23a82aNapYsaIkKTU1VV5ef+Tybdq00dy5c/Xiiy/q73//u2rXrq2vvvpKDRs2tJQZNWqULl68qCeffFLnzp3T7bffrsWLF8vf399BpwgAAAAAgHFMZrPZbHQQxZWenq7Q0FCdP3++2OO9m4z9TmcvXXVQZAAAT7R/3D3y9ire4CNHfjbhOq4pAMCd2PO5VKwx3gAAAAAAoGAk3rmYTEyvBgAAAABwHBLvXNrWqmB0CAAAAACAEoTEO5dKwX5GhwAAAAAAKEFIvHP5a6faRocAAAAAAChBSLxzCQ3wNToEAAAAAEAJQuINAEAuJWClTQAA4EZIvAEAAAAAcCISbwAAAAAAnIjEGwAAAAAAJyLxBgAAAADAiUi8AQAAAABwIhJvAAAAAACciMQbAAAAAAAnIvEGAAAAAMCJSLwBAAAAAHAiEm8AAAAAAJyIxDsPXRtGGh0CAAAAAKCEIPHOQ9XygUaHAAAAAAAoIUi8AQAAAABwIhLvvJiMDgAAAAAAUFKQeOfFbHQAAAAAAICSgsS7hHu+c5zRIQAAAABAqUbiXcIFlfE2OgQAAAAAKNVIvF1gSMdaRocAAAAAADAIibcLmJisDQAAAABKLRJvN/ZR/+bF7ipuIusHALsxxyYAAHAkEm877Bzb2aXHS6xfSctGtC/y/vc1inZgNAAAAACAoiDxtkNgGR+XH9NMswsAAAAAeDQSbwAAAAAAnIjEOy95DIse3LGm6+NwgPJBZYwOAQAAAABKNRJvG93fpIrRIdjNLOnOuhFGhwEAAAAApRqJtwvElAss8r7FnZScSc0BAAAAwFgk3i7wYDNjWsvNzMwGAAAAAIYj8XYBby/jmp39fIq3Dri7qFExSIn1KhkdBgAAAADYjcTbRp7aZdvIpN+RejSqrMAyJeMmAgAAAIDShcQbHsFk8tybHwAAAABKNxJvN8cwbQAAAADwbCTeeahZsazRIQAAAAAASggS7zw82LSKRnWJMzqMYitOY/l7f27isDgAAAAAoDQj8c6Dt5dJz3SoZXQYhuqeEK0SMi8bAAAAABiKxBsAAAAAACci8S4BHmpWJe8XmJgNAAAAAAxH4l2AxHoRRodgE5bZAgAAAAD3ReJdgH/2bWp0CMVLqknIAQAAAMBwxUq8J0yYIJPJpGHDhuVbpkOHDjKZTLc8unXrZikzYMCAW17v0qVLcUJzCB8v+y5P06phzgmkqIrZ1dx0U9YfGuCrfrdVVeOYsOJVWkSBZbwNOS4AAAAAFFeRE+/169frgw8+UEJCQoHlvvjiC504ccLy2L59u7y9vfXQQw9ZlevSpYtVuc8++6yooRlmVJe6t2zLb2bwLg0ibarTbOA47ZtDnz2wpV7rGa8KZf1cHke72hXUt1U1VTTg2ABKJyP/9gIAgJKnSIl3RkaG+vbtq+nTp6tcuXIFli1fvrwiIyMtj6VLlyowMPCWxNvPz8+qXGH1upotX8LyyrG/fbadw2OxldmBs6v5ehszKqHfbVU1e2ArBZTx1tDE2jbfsAAAAAAAd1GkbGrw4MHq1q2bEhMT7d73448/1sMPP6ygoCCr7SkpKYqIiFBcXJwGDRqkM2fO5FtHZmam0tPTrR7uqm5kiNEhONTwu+oYduwQf19Ne6SZYccHAAAAgKKwO/GeN2+eNm3apPHjx9t9sHXr1mn79u16/PHHrbZ36dJFs2bN0vLlyzVx4kStWLFCXbt2VXZ2dp71jB8/XqGhoZZHTEyM3bE4g8nOmdCC/X2cFEneHmyaz7JjdqgfHaI9r3V1QDQAAAAAUDrYlXgfOXJEzz77rObMmSN/f3+7D/bxxx8rPj5eLVu2tNr+8MMP67777lN8fLx69uypb775RuvXr1dKSkqe9SQlJen8+fOWx5EjR+yOxRkSqoTaVf7boY7phh7kl3cCn3s8dl73BQqatOzm8jd3tS/jw2T4AAAAAGAruzKojRs36tSpU2ratKl8fHzk4+OjFStW6N1335WPj0++LdSSdPHiRc2bN08DBw4s9Dg1atRQhQoVtG/fvjxf9/PzU0hIiNXD2QprzH61RwP5+3rr68Ftba4zpnxgsWLy9TapZfXyGnpn7TxfH3FXXKF1NIwO1QNNKuf5WmiAb7HiAwAAAADYmXh36tRJ27Zt05YtWyyP5s2bq2/fvtqyZYu8vfNvPf3888+VmZmpfv36FXqco0eP6syZM4qKirInPEP5/G/ysUZOXG6rUZVQPdzij271vZrHaP7TrVUuqIwS60XcUj40sODEuYy3l0bfW19v926c5+vDbUjcS7On7qhhdAgAAAAAPIBdiXdwcLAaNmxo9QgKClJ4eLgaNmwoSerfv7+SkpJu2ffjjz9Wz549FR4ebrU9IyNDzz//vNasWaNDhw5p+fLl6tGjh2rVqqXOnTsX49RKnjlP3KYGlf/ozn7znOUFrTl+R52KkqR+t1Wz2r5zbGc1rJx/9/jyQX8k7nYOX5ckdYv3nBsnRTGwXazRIQAAAADwAA6f3Ss1NVVeuZLA3bt36+eff9Z33313S3lvb2/98ssvmjlzps6dO6fo6GjdfffdevXVV+Xn55nrNpf181FG5jWjw7BIHtBC5y5fVfmgMlbbfZy8RFi9qGD9d9uJYtXBWroAAAAAPF2xE+/cE6DlNSFaXFyczPlkUAEBAVqyZElxw3Ar4x+I118/21xgmQbRto1LjwzxV93IYPl6eyko10RotjZCe3mZbkm6AQAAAACu4dr1rEqJwiZNiw7115PtCx4fvDrpTknXk+Ybs5/bu1xZ8f1xvKK0PNNaDQAAAABFWMe7tPvwkWaW/8dVCi5SHU93qCk/n/wnopOkqNAAy/+9vEzy8io46TaLLBcAAAAA3BGJt53ubhBp+X9hybAzdPjfRGmewBG3AlzeyA8AAAAADkbi7SCuWvM6pnyg7msULW8vkwZ1qOmSYwIASpdjx46pX79+Cg8PV0BAgOLj47Vhw4Z8y6ekpMhkMt3ySEtLs5QZP368WrRooeDgYEVERKhnz57avXu3K04HAADDkXgX0xsPJujhFjHqfFNLeHHdE19wXe/2aaI9r3VVlXIFjyUvrhD//KcAmPt4q0L3d8QYb5PNU8g5VmMnrscOAO7s7Nmzatu2rXx9fbVo0SLt3LlTkyZNUrly5Qrdd/fu3Tpx4oTlERERYXltxYoVGjx4sNasWaOlS5fq6tWruvvuu3Xx4kVnng4AAG6BydWKwSSpV4sY9WoR49B63+7VuNAy3rm6uTtjIrPWNcPzfa1NrQp21VU+qIx+v5hV3JBsVjHYT6cvZBZ5/38/3Vq1/m+RAyMCAM8wceJExcTEaMaMGZZtsbGxNu0bERGhsLCwPF9bvHix1fPk5GRFRERo48aNuuOOO4ocLwAAnoAWbzfk71vwxGuucvMs6hXK2r8c2c0TvjWtGlbEGIq0m83LteV/XAaXAyidFi5cqObNm+uhhx5SRESEmjRpounTp9u0b+PGjRUVFaW77rpLK1euLLDs+fPnJUnly5fPt0xmZqbS09OtHgAAeCISbwN4Uko3/6nW+mRAc0WE+Bsdikt50s/IKMF+dJgBSqIDBw5o6tSpql27tpYsWaJBgwZp6NChmjlzZr77REVFadq0aVqwYIEWLFigmJgYdejQQZs2bcqzfE5OjoYNG6a2bduqYcOG+dY7fvx4hYaGWh4xMY7tYQYAgKvwzbkY8msUNZegBaxbxubfElGQiGA/t1zHu1Vsea09+LvRYZQIzauX0w+7TxsdBgAHy8nJUfPmzTVu3DhJUpMmTbR9+3ZNmzZNjz76aJ77xMXFKS4uzvK8TZs22r9/vyZPnqzZs2ffUn7w4MHavn27fv755wJjSUpK0vDhwy3P09PTSb4BAB6JFu8Swt1y3Nxj0IvK0S3P9aIK74L+QJPKNi8V9wwzywMlkgGrRbqNqKgo1a9f32pbvXr1lJqaalc9LVu21L59+27ZPmTIEH3zzTf64YcfVKVKlQLr8PPzU0hIiNUDAABPROJdgJu/eEWFOq6r9c1Jcv/W1RxWr6tN69dMY3s0yPO16LAAt7kZEBZo31Jvb/dubHPZcoH2j31H/v479HajQwAkST7epffjsW3btrcs87Vnzx5Vq2bf59WWLVsUFRVleW42mzVkyBB9+eWX+v77722esA0AgJKg9H6zsIHJZNKml+7Sur93UmCZW3vlO2L+rbE9GurQhG7Fr8gAXRpGqn/r6lbb5j/VWnfVr6R3Hm7skGMUdZIzfx/XTFBndvHthWgH3gByRw2iQx16kwuA/Z577jmtWbNG48aN0759+zR37lx9+OGHGjx4sKVMUlKS+vfvb3k+ZcoUff3119q3b5+2b9+uYcOG6fvvv7faZ/Dgwfr00081d+5cBQcHKy0tTWlpabp8+bJLzw8AACMwxrsQ5YM8o0XT3vHUA9pUd0ocLWPLW8aFP3JbNSWvPKhuCdE6feGKU46XlyfvqCGvEjoreeVyATp+3nXXEkDp06JFC3355ZdKSkrS2LFjFRsbqylTpqhv376WMidOnLDqep6VlaURI0bo2LFjCgwMVEJCgpYtW6aOHTtaykydOlWS1KFDB6vjzZgxQwMGDHDqOQEAYDQSbycweimqEH8fdW8U7bLjPd85Tm8u2X3L9orBfto8+m55e5k0/ttftezXUy6J5+/31NOERbtccixXq1mxrNYfOmt0GABKuO7du6t79+75vp6cnGz1fNSoURo1alSBdZakiUcBALAXiXcJdCPZLUjz6uVcEsuNOEIC7Btn7SlMLDwGAAAAoBCM8S6BCkq6VzzfQe/9uYm6xUflW8YZ7m9S2aXHg+P1aVnV6BAAAAAAj0TibQBntJHWrBhkU7lq4UHqnhDt8u7w0WEB2vFKZ5cdzxWTnplkcvnkavYY0Ka6GlUJdVh94x+Id1hdAAAAQGlCV/NicKduxkM71VZWdo7ucXFLtiRFhtg2C3WQH283V2pTM1x/61pXdV9abHQogEcJ8HXNqggAAKD0oMXbAM5oIw3y89HL9zZQi+rlnVB7wXo2qayBt8fqw0eaufzYjuLqrve2+uavrGsNAAAAeDoSbycobTO3enuZ9FL3+rq7QaTD63ZZj3gHH+fp9jUdUk9cZLDVc3d6a7lRKCgBalQI0rR+TY0OAwAAwClIvIuhhC4VXSL8qWkVSVLTqmGGHN9RXVV9vfkVtYUn97bAdcEBvqpewba5KgAAADwN3+ohSfpHnyaGHbugGxhFHUdfu1KwNr10lz5/uk2+rcQ3J8dFvYfi6hZod7rZ40ahyMfbnaJBUbnTvBkAAACOROINSVL3hCgVsvS3xykfVKbQ9czheJXDAowOASgWd7rBBQAASgYSb0iSTCaT/tSsitFhOEV+X6Kd+eXanZcZc7b2cRVdfsyS3FL6WNvqRofgGmaz2/zeuNNcCgAAoGQg8S6GkvtV37UKuo6uanmqHx3imgM5WYi/8Uu2FTdpKcr+7pKwAQAAAHkx/ls6YKB1/9dJ6ZevavuxdLv39fd1v/tWtSsFa+Phs5bndJkFAAAAjOd+mUMpQC5kO2dfq4hgf9WKCM7ztT4tq+a7X1ylYAX7+zorLI9W3GSfmwWlk1kle8gAAAAo3Ui8i6OIGQKdYj3DuPsb5vvakufukCTd37Sy4Yni2B4NjA0gF8bHOlZpup7OHDLwfOc4p9UNAABQGBJvuJ3EehEaeHusQgN89cQdNZx2nMLyZZMNGXVEsL92vdpFo7vXd0xQhWiSx7rk/VtXz7OsLfHb6iUXnZ9UuhJNZwoq45i15EuK8KAyRocAAABKMRLvIriR/PRuHmNsIA7WtWGUJCki2M/QOG6rEa6XutfX5pfuUqUQf5v2qR1RtljHLE6O6ufjugTnT81iNPHBeJvKmh2YwVYOs+3nIFmvjw7H6Nygkt37+Hh71p/3WhWL9zsMAADgzjzrm5mbmPv4bfpqcFv1aZl34l25nGeuY9whrqIWDmmrZSPaO+0Y7evYvtSUlx1rcJd3s9YsR45VbVj5jxnXvb1M6t0i/7Hnjky2i+qvd9bSOw83VuWwAHUowtJiRnfdd0cfPNLc7n3c4b1gj9H3uq5XBQAAgKuReBdBQBlvNY4Jy7crb0SwvxYMaq0lw+5wcWTFYzKZlFAlTCFOnDQs+bEWqhxWvBsTLauXd1A0nqFepPsudVah7K29I8oFlVGPxpW18m93qkERlmnzsHwRDtC7eYzCAsuoWvkgo0MBAABwChJvJ2lWrbziIv+YLfuDR5oZGI37MJlM8rajJTsvswa21Dd/vV3VwgMdFJXjOXKSqBe719cT7WL1zV9vt+G41vJqeY8Ktb3b+M015eWLQW2KUJcx2tQMNzoEFCKgjLd+GXO3fh3bxehQAAAAHIrE20U6N4g0OgS3Ze8kYP6+3mpYObTELzx04/5EaICv/q9bfTWsHOqQer98pq3d++T3I6rqxjc/cvP1sDHPpcnN768Qf18FMDEcAAAoYfgmCrcTEmB/V3dnjwse0Ka6qpbPP8lsFBPm8GN6OemkIovQ4l0SbnI83i7W6nlgCU7unrqjhkNntXe0jkUY+w8AAODJSLzhVu5rFK37m1R2aJ01HTBbchkfL6tJznJrVq2cGlVxTIv0DUXpSs/46Py1q116kr0uDe3rYbPnta5OiuRWG15M1MePtrDaxvsWAACUdCTecCvv9mni8C7B7/ZpovubVNZ/hhQ+Rro47rBjxnZb+Hjlfx3q3jR/gCsUtfXUkbO7w3b2/rzK+Ljuo6BCWT+7VixwFDfuAAAAAEoBEm+UCAW1mFUOC9Dk3o0V7+AWaSMN6lAzz+3Oajgsas7iyEnmHI1WVvdBUgwAAEo6Em+43IQH440OwSWWDLtDr/Zo4JS6C2oNdxQ/F7aCGiGvmwK316pgQCSORx5bPD4GtMgDAICSrWR/s4ZbalOzgva+7roxpflx9uRTcZHBeqR1dace4xa5mnGLcorR/5t87ZX7/rhpUFpaJD99vJXT6p7cu5GeyaenQkFKw7V3p94H5YPKKPkvLQovCAAAYAcSbxjC05Z2Cirj47C6KpQt47C6bhhzb32FBfpqwoMJNpUvaDz6yM5x2vhioh5uWdWy7UbyNyyxdrHidCeuHn9eKcRfYYH2z9hfVGZ3ymY9yOqkO9WsWnmjwwAAACWMZ2U/KJEckf44q1Xw9fsb6vZaFTQw11JUhWlXO+8uy18PbqvlIzo4IDJrA9rGavNLd6leVP4zr99s5mMFt+iFl/XLc/uwxDp2x+auXDn+vG5ksFrXCHfZ8QAAAOBeSLxhOFdOetampn3JT99W1fTp461U1q/wFu+HmsVIkkL8ffJtMWsUE6bQIqxTbosbXefviY9UTPmAAlu13XmN59wmPOD5cwIMubOWXdc8PKh4vSKK8uNdMKh1sY5ZHB70dgQAACgSx/WfBYoovnKonu8cpyph9q1bXSsiWIfOXLJrn7vqV7KrvD2qhgdqxyudFVjGu8h1NIoJ09Yj54oVxz//3FRms+TlZdLV7Jxi1XWDK7plz/xLyzy3+zhpWII798QOKOMtXbz+/9xx3lajvNYc+L1I9VYtH6jU3/P+nWlWrbwqhwXo2LnLRaq7OFzxs2BpOwAAYCRavF2oavnriWXHuAiDI3E/bWpWUNVw+xLv8Q/E6+EWMfp6cFub97m51dEZX8OD/HyK1Zpcxrv4UZlMpmKtk5xnEuSAi9UgOv9u8HfUqaj2Dl4HvTBunHcXKNi/8B4T+SWZXRpGFrhfSR4XbuvQAhJ0AADgDMVKvCdMmCCTyaRhw4blWyY5OVkmk8nq4e/vb1XGbDZr9OjRioqKUkBAgBITE7V3797ihOaWlg1vr40vJiqmvH0JJvJWMdhPEx5MUKOYMLduvSwqZ4xBfuuhRg6v82ZRof75vlacn1GwDV39AQAAAHdV5MR7/fr1+uCDD5SQUPgsyiEhITpx4oTlcfjwYavX33jjDb377ruaNm2a1q5dq6CgIHXu3FlXrlwpanhuqYyPV76TVpVmntK+5InJ/c3XdvGwdvpTsyrFrqcgU3o3LlL9hdn40l26rQYzTUu2vQ/z63RhdIv24mHtDD0+AACAUYqUeGdkZKhv376aPn26ypUrV2h5k8mkyMhIy6NSpT/G2ZrNZk2ZMkUvvviievTooYSEBM2aNUvHjx/XV199VZTwALt50mRjktSpXoRiygfo3kbRLjmerdenXBEnBSus9jI+XronPqpIdbvKx482L9J+tSLKalq/poqrFOzgiG5VlLz79lp5z9BfFHUjbZt130iunO0eAACUHkVKvAcPHqxu3bopMTHRpvIZGRmqVq2aYmJi1KNHD+3YscPy2sGDB5WWlmZVV2hoqFq1aqXVq1fnWV9mZqbS09OtHijdXJE3uzo3L2isqb+vt1aM7Kh/9GniolgcUIeTr9+U3o0VWMZb5QJ99d6fbbgu+eRXdSqVtfmY9zeprNfvb6gNLyaqU738J+4rKOH1MkldGkYpsoBu+q6SV5jtalfQt0NpqQYAACgOuxPvefPmadOmTRo/frxN5ePi4vTJJ5/o66+/1qeffqqcnBy1adNGR48elSSlpaVJklUr+I3nN17Lbfz48QoNDbU8YmJi7D0NuIEbSybd3aDgCZ8c7dWeDSVJ79qSnLmx4kyg5m5saWMsrLW2Z5PK2jm2izaPvlvdEwrvCZBfy+bXg2/X4mHtFG1DIlzWz0d9W1VTBQ8ZQlKUmx/taldU/QImxgMAAEDh7Eq8jxw5omeffVZz5sy5ZYK0/LRu3Vr9+/dX48aN1b59e33xxReqWLGiPvjggyIFLElJSUk6f/685XHkyJEi1wXj/PB8B33z19vV1oFdWW3xyG3VtPf1robOLv9QEcdaG8XRrdWta4TbtDa6ZP/a60V1Z93r74eAMt6qGxni1sMP3vhTwXNrVA4LKFKHaX/fP5bCG5ZYW8uGt88z6W5ZveSOt2dWcwAA4Ax2Jd4bN27UqVOn1LRpU/n4+MjHx0crVqzQu+++Kx8fH2VnZxdah6+vr5o0aaJ9+/ZJkiIjr7d2njx50qrcyZMnLa/l5ufnp5CQEKtHSVHQrNAlTYi/rxpWDjXk2L5OWhvaVsVZ69sZPnviNsv/88o3bU1GwnON8W4QnffP97Mnb1PNiLy7dN/cEr3u/zopOizApmMXl1ETjxUl0evVPO9ePu1qV9DiYe3sGmsfWyHI8v+bx3MH+/uqVj4/o2mPNLO5/vx8OrCVXeUjgv00qRiz8pNQAwAAI9mVfXTq1Enbtm3Tli1bLI/mzZurb9++2rJli7y9C08msrOztW3bNkVFXZ8oKTY2VpGRkVq+fLmlTHp6utauXavWrVvbeTqer7B1dgFnaG1Hq/KNFuhHWlez2v5Mh5q3zNrftZjv54hgx9yI+r976t2yraoDlvVzt4m4qpYPtHsCs3/+uanl/943DV8o6EZEeRsS+zKF3Ny6vbZ9PV3W/r2THsyjp8ifmlVRlwaRurt+/mPs7eFuP1MAAFAy2LU4bnBwsBo2bGi1LSgoSOHh4Zbt/fv3V+XKlS1jwMeOHavbbrtNtWrV0rlz5/Tmm2/q8OHDevzxxyXJsg74a6+9ptq1ays2NlYvvfSSoqOj1bNnTwecIkqD4jZWltS2MEd1l765mll/aakT569Y1qNfMKi1dqVd0J9bVi3y8W9O8hzdMplYr5KeuKPGLdtHdamrp2ZvdOixCmPruTnix5ZXFXn9ngT7O2eN9B1jO6v2/y1yWH15vZdiygdY1qYf9e+tDjsWAACAozn8G1dqaqq8vP5o6Th79qyeeOIJpaWlqVy5cmrWrJlWrVql+vXrW8qMGjVKFy9e1JNPPqlz587p9ttv1+LFi20eRw7AuW5OeXy8vSxJtyQ1q1Zezap53pjfEH9fbX35bjV65bs8X3fkEG9nDBe/q34lLd15Uo1jwrTlyDmb9nFlW649wzloZQYAACVdsRPvlJSUAp9PnjxZkydPLrAOk8mksWPHauzYscUNx+MZNMzU47nxPFglgwuvr6uSMJNJCg3wLfB1R3PkuU3q1Uj/2XpcXRtGqemrSx1W7w3uPLkcAACApzF2hinAg9xWwzWza5dUfj7O+3PjjCTRyMm4bLkBF+Lvq76tqtk03trdFfVaO+NnxCRsAADAGZwzuA8ogf7csqrK+vmohZ1LKT3auppmrj5sU9lKIe45vKI4ycjIu+to9YEzSqxXSZtSz+VZ5ubZtG05Vu/mMUqsX0nf7zqlX0+kW+2fW+OYvGdWz53c5k7eafAtnej2DgAAnIHEG5BsWlPax9tLDzS1f/1tP1/blw57+d4G+uaXE3Yfw9mKk4QOubO2htxZW5+uyf/mw19uj7X835bEZ+L/1rG+q4CZrJc+d4dW7Dl9y+zr+ck9i7e75N32XPuWsQXfFCqsdby4S6rVqVRWe05m2L2fS5Jdd/mBAgCAUonEG5DUvk5FPdCksupHu2ZN+PzSjIrBfvm8UrLZMhGXvcl/7UrBql0puMj1ObL7enHy2dz75tVl/8fnO2rr0XPqnhCVZx2TezfShkNndU98lN5cstvqNUeuk14x2K9IiTcAAEBJR+INSPLyMunt3o2NDsOpipP8lcbGQlefc2E/nld7NlTyyoP6e7c81iQPD1TVcOt1yW+u7/4mVXR/kyo6fObiLfvevHZ3cW82FPU9VuQx3qXxjQkAADwSk6uhROja8HpLX1Soe46RLkjumbXL/K9Fs1WN4i/R5ai8xNcBE6OVxCTJlRNxPXJbNS0f0UGVHdhC7elYBQIAAHgKWrzdTElMTlyh323VVLV8oBrFhBkdik1u/jGPua++nvvXVt1Rp6Ikadlz7bVkR5r63lbVmOBu8tQdNZT6+yU18ZDrao9CkzYX/S42/t+1LQm/+kX9++WIMd5Nq5bT/A1Hi12Pn4/tczIAAADYisQbJYK3l0kd60YYHUaR3N+kiuIrh6na/7oKVw0P1BN31DAsnpsT0qR7bu3WXFo4OxFeNry9vvnluAbeNLGcOynuRGuu9lDzGHmZTBq14BdJ0t+61tXHPx/U6QuZBkcGAABAV3O342HfdWEDWxK4WhFlbZpgDI7jysnV8lIroqyGJdZRsL9v4YWRp5t/RN5eJvVqEWN5Xj08yICIAAAA8sY3fcDJuJfimUpC1+/cAuxY2s6V7Bkr3652/mu2AwAAuCsSb6CUYP4A+zjjerlyMra8RIT46+/31LWMK3e0ovbYyWuMd93IvJeCmzGgRdEOUohnOtTU4mHtnFI3AAAAiTdQStiaFJWWBL2oSeJTBo6/d4Qn76ip/q2rFXn/8KAyDowmf18PaavR3evfst3HSUMyRnWpq7qRIU6pGwAAgMQbKMFKUhLt6vkP8mudLs0TzkmSj7dr3lR+Pt6qWj6wwDIl6O0NAABKOBJvwMlIDtxT4ZOruSYOT1PU7vJPFtBTIL86q5RnzXIAAFAykHgDcCt31a8kSZbl1YzyWNvqDqvL3knNHDLDfQE9BFzde6Bn42j9rUvdfF/Pbx3vupEheufhxvr86dbOCg0AAMAlSLwBuJX/u6e+Jj4Yb3iy1at5TOGFbHBPfKRljfn8Eszc/tGniUOOnR9n5d35JfQNokPl5VW0lvIejSurRfXyRdq3ebVykiR/Xz7qAACAsXyMDgAo6VhO7DpbW3EDynird4uqTo7mjySxcUyYthw5d8sxHbWO9/t9m9m9T8PKocU/cAHhFzEHvl6tG3XBL+xnNP6BeNWKKKsHmlbR+kO/uygqAACAW5F4A3CJ+xpF67N1qWpTM9zoUKz866nbdOT3S6oVkffyVQWxtQXb3dwTH6X3U/arVWzRWpLtZdR1CgssoxF3x0mSNpB4AwAAA5F4u4myfj7KyLymO//XJRVwNFeP683N39dbXz7T1tggbnKjsdTPx7tISbcn8/f11tLn7iiwxbhWRNkC67itRnmtOfC7vL1Mys7xzBsQAAAArkLi7SZ+GtVRB367qGb/G5OIkiOv1Kao413tPrY79QsuphJ0Km6hsPdG+zoV897vf//O/EtLHTh9UckrD+lfG44UfCzm9gcAAKUcM864iXJBZUi6S5FnOtRSlXIBGpZY2+hQ4CYqlPWzev747bEK8ffRU+3zX4bLmQpLzP18vFUvKsTQGyKk8wAAwFPQ4g0YoGKwn35+4U6XHpMWY2v2dr1vWb28+t7mvEnf/n5PXZ27lKXeLa7Ppv5i9/r6+z31XNY7whE8dcw7AACAs9HiDaDU+HRgqyLvO//p1urRuLLVtvy6UBdl+arwsn76eEAL3d0g0rLNHZPuogxfyC8hf7R1NXmZpEEdahU3LAAAALdG4g2g1Li9dgXL/53ZAyC/dacZ62ztlR4Ntfu1roqtEOTwuunhAQAA3AmJNwA4mFtMauchvb5tXd+9uNziZwIAAEotEm+glLB1TLMXCUqx1SlkKS44n9HL5wEAANyMydUASLo+3nb9obPq0jCy8MIoUJ3IYM38S0tFhvgbHQoAAADcAIk3AEnXx9vCQcz5r4Pt6QrqEPF85zg9OHW1BrSpbrWd1mcAAFDakXgDAByiWbXy+nVsFwWU8XbNARkVAQAAPARjvAEYgpypZGharZzV87ySbnedNuDBplWMDgEAAJQStHgDMMSEB+P15+lr9Wyn2kaHAjvkTqL/1LSKvEwmNa0aZkg8xRFbIdDoEAAAQClB4g3AEHUjQ7TxxUSbl3ly9DhhPx/Xd/gxe8oaX3bw8jLpT83cv+W4iQfeGAAAACUHXc2BEu5GXlvdDVv3jFhbefhddfSnZlXUOCbM5ccurZw1uZo9756aFcvq26Ht1KhKqHOCKWGOHTumfv36KTw8XAEBAYqPj9eGDRvyLZ+SkiKTyXTLIy0tzVLmxx9/1L333qvo6GiZTCZ99dVXLjgTAADcAy3eQAm385UuupqTo8Ay/LpL0tBS0rXdXcdVG6l+dIgqBvtLOi+J2dbzc/bsWbVt21YdO3bUokWLVLFiRe3du1flypUrdN/du3crJCTE8jwiIsLy/4sXL6pRo0b6y1/+ogceeMApsQMA4K74Jg6UcAFlvBUgF80yDbfx8YAWenLWBo25r4HRocDDTJw4UTExMZoxY4ZlW2xsrE37RkREKCwsLM/Xunbtqq5du9oVS2ZmpjIzMy3P09PT7dofAAB3QVdzwNloeXQIWnDt06J6eW166S71aFzZ6FDgYRYuXKjmzZvroYceUkREhJo0aaLp06fbtG/jxo0VFRWlu+66SytXrix2LOPHj1doaKjlERMTU+w6AQAwAok3ALiIycV3YZwxht7V51AQI+YIKA0OHDigqVOnqnbt2lqyZIkGDRqkoUOHaubMmfnuExUVpWnTpmnBggVasGCBYmJi1KFDB23atKlYsSQlJen8+fOWx5EjR4pVHwAARqGrOQAAsMjJyVHz5s01btw4SVKTJk20fft2TZs2TY8++mie+8TFxSkuLs7yvE2bNtq/f78mT56s2bNnFzkWPz8/+fn5FXl/AADcBS3egLMxgVOJ06VBpCTp0TbVDI7EvYX4X7+32yEuopCSrlOlXIDRIbi9qKgo1a9f32pbvXr1lJqaalc9LVu21L59+xwZGgAAHosWbwCw09R+TXUpK1tBfnn/CS2J63XfYE/v7tVJnfRbRqaqhQc5LyA7jbi7ji5nZeveRtHalHrW6HDcUtu2bbV7926rbXv27FG1avbdaNqyZYuioqIcGRoAAB6LxBsA7GQymfJNuvGHID8fp16noozwDvb31cQ/JUgSiXc+nnvuObVp00bjxo1Tr169tG7dOn344Yf68MMPLWWSkpJ07NgxzZo1S5I0ZcoUxcbGqkGDBrpy5Yo++ugjff/99/ruu+8s+2RkZFi1gB88eFBbtmxR+fLlVbVqVdedIAAABuCbI+BszP+EEoS3c8nXokULffnll0pKStLYsWMVGxurKVOmqG/fvpYyJ06csOp6npWVpREjRujYsWMKDAxUQkKCli1bpo4dO1rKbNiwwer58OHDJUmPPvqokpOTnX9iAAAYiMQbAPLx4SPNNOSzzZrcq7HRoQAu1b17d3Xv3j3f13MnyqNGjdKoUaMKrLNDhw4ym0vuMAwAAArC5GpAKdW31fWunY/cxgRh+bm7QaR+HdtF3RLsG6fqTktu2SPIz9voEGwSHlRGktS+TkWDIwEAALANLd5AKfXKfQ30YLMqSqgcanQobs3byzOT6KJ48o6aWnvgd93XODrfMu6wdvY3Q29Xyu7Tur9JZaNDAQAAsAkt3oCT1ajgPjM638zH20tNq5aTjzd/BhzNU2c1Dw3w1b8HtVH/1tWNDqVAUaEB6tOyqvx982+hrxVRttB6/tSsiiSpQxwt5wAAwLmK9Y17woQJMplMGjZsWL5lpk+frnbt2qlcuXIqV66cEhMTtW7dOqsyAwYMkMlksnp06dKlOKEBbuNPzWI04q46+vzp1kaHApR4S5+7Q3Mfb2VT4h0dFqBfx3bRjAEtXBAZAAAozYrc1Xz9+vX64IMPlJCQUGC5lJQU9enTR23atJG/v78mTpyou+++Wzt27FDlyn90E+zSpYtmzJhhee7n51fU0AC34u1l0l871TY6DLiBktBt3d3PoHalYNWuFGxz+YAynjGuHQAAeLYiJd4ZGRnq27evpk+frtdee63AsnPmzLF6/tFHH2nBggVavny5+vfvb9nu5+enyMhIm46fmZmpzMxMy/P09HQ7ogcAY/RqEaN561PVsW6E0aEAAADAhYrU1Xzw4MHq1q2bEhMT7d730qVLunr1qsqXL2+1PSUlRREREYqLi9OgQYN05syZfOsYP368QkNDLY+YmBi74wAAVyvr56PvnmuvpK71jA4FAAAALmR34j1v3jxt2rRJ48ePL9IBX3jhBUVHR1sl7V26dNGsWbO0fPlyTZw4UStWrFDXrl2VnZ2dZx1JSUk6f/685XHkyJEixQLAc0SG+BsdAiT372sOAADghuzqan7kyBE9++yzWrp0qfz97f8SPGHCBM2bN08pKSlW+z/88MOW/8fHxyshIUE1a9ZUSkqKOnXqdEs9fn5+jAEHSpm76lfS0DtrKaFKmNGhFMoTYgQAAIDr2JV4b9y4UadOnVLTpk0t27Kzs/Xjjz/qvffeU2Zmpry9856o5q233tKECRO0bNmyQidkq1GjhipUqKB9+/blmXgDKH1MJpOG3x1ndBgF+vmFjko7f0X1okKMDgUAAABuxK7Eu1OnTtq2bZvVtscee0x169bVCy+8kG/S/cYbb+j111/XkiVL1Lx580KPc/ToUZ05c0ZRUVH2hAcAhqpSLlBVygUaHYZT0dMcAADAfnYl3sHBwWrYsKHVtqCgIIWHh1u29+/fX5UrV7aMAZ84caJGjx6tuXPnqnr16kpLS5MklS1bVmXLllVGRoZeeeUVPfjgg4qMjNT+/fs1atQo1apVS507d3bEOQIAAAAAYJgizWpekNTUVJ04ccLyfOrUqcrKytKf/vQnRUVFWR5vvfWWJMnb21u//PKL7rvvPtWpU0cDBw5Us2bN9NNPPzGOGwAAAADg8Yq0jvfNUlJSCnx+6NChAvcPCAjQkiVLihsGAMAFTCY6mwMAANjL4S3eAAAAAADgDyTeAACb0d4NAABgPxJvAAAAAACciMQbAGCzmPIle7k0AAAAZyDxBgAUav5TrdU9IUrjH4g3OhQAAACPU+xZzQEAJV/L2PJqGVve6DAAAAA8Ei3eAAAAAAA4EYk3AAAAAABOROINAAAAAIATkXgDAAAAAOBEJN4AAAAAADgRiTcAAAAAAE5E4g0AAAAAgBOReAMAAAAA4EQk3gAAAAAAOBGJNwAAAAAATkTiDQAAAACAE5F4AwAAAADgRCTeAAAAAAA4EYk3AAAAAABOROINAAAAAIATkXgDAAAAAOBEJN4AAAAAADgRiTcAAAAAAE7kY3QAjmA2myVJ6enpBkcCAMB1Nz6TbnxGofj4vAcAuBN7PutLROJ94cIFSVJMTIzBkQAAYO3ChQsKDQ01OowSgc97AIA7suWz3mQuAbfic3JydPz4cQUHB8tkMhW7vvT0dMXExOjIkSMKCQlxQISlF9fSsbiejsX1dCyupzWz2awLFy4oOjpaXl6M7HIER3/euyN+j+zHNbMf18x+XDP7lYZrZs9nfYlo8fby8lKVKlUcXm9ISEiJfZO4GtfSsbiejsX1dCyu5x9o6XYsZ33euyN+j+zHNbMf18x+XDP7lfRrZutnPbfgAQAAAABwIhJvAAAAAACciMQ7D35+fnr55Zfl5+dndCgej2vpWFxPx+J6OhbXEyg+fo/sxzWzH9fMflwz+3HNrJWIydUAAAAAAHBXtHgDAAAAAOBEJN4AAAAAADgRiTcAAAAAAE5E4g0AAAAAgBOReOfyz3/+U9WrV5e/v79atWqldevWGR2S4caPH68WLVooODhYERER6tmzp3bv3m1V5sqVKxo8eLDCw8NVtmxZPfjggzp58qRVmdTUVHXr1k2BgYGKiIjQ888/r2vXrlmVSUlJUdOmTeXn56datWopOTnZ2adnqAkTJshkMmnYsGGWbVxL+xw7dkz9+vVTeHi4AgICFB8frw0bNlheN5vNGj16tKKiohQQEKDExETt3bvXqo7ff/9dffv2VUhIiMLCwjRw4EBlZGRYlfnll1/Url07+fv7KyYmRm+88YZLzs+VsrOz9dJLLyk2NlYBAQGqWbOmXn31Vd08ByfXEyg+W35HcrPls+GGM2fOqEqVKjKZTDp37pwTzsD1nHHNtm7dqj59+igmJkYBAQGqV6+e3nnnHWefitPY+x32888/V926deXv76/4+Hh9++23Vq/b8vfe0znyml29elUvvPCC4uPjFRQUpOjoaPXv31/Hjx939mm4jKPfYzd7+umnZTKZNGXKFAdH7UbMsJg3b565TJky5k8++cS8Y8cO8xNPPGEOCwsznzx50ujQDNW5c2fzjBkzzNu3bzdv2bLFfM8995irVq1qzsjIsJR5+umnzTExMebly5ebN2zYYL7tttvMbdq0sbx+7do1c8OGDc2JiYnmzZs3m7/99ltzhQoVzElJSZYyBw4cMAcGBpqHDx9u3rlzp/kf//iH2dvb27x48WKXnq+rrFu3zly9enVzQkKC+dlnn7Vs51ra7vfffzdXq1bNPGDAAPPatWvNBw4cMC9ZssS8b98+S5kJEyaYQ0NDzV999ZV569at5vvuu88cGxtrvnz5sqVMly5dzI0aNTKvWbPG/NNPP5lr1apl7tOnj+X18+fPmytVqmTu27evefv27ebPPvvMHBAQYP7ggw9cer7O9vrrr5vDw8PN33zzjfngwYPmzz//3Fy2bFnzO++8YynD9QSKr7DfkbwU9tlwsx49epi7du1qlmQ+e/asE87A9ZxxzT7++GPz0KFDzSkpKeb9+/ebZ8+ebQ4ICDD/4x//cPbpOJy932FXrlxp9vb2Nr/xxhvmnTt3ml988UWzr6+vedu2bZYytvy992SOvmbnzp0zJyYmmv/1r3+Zd+3aZV69erW5ZcuW5mbNmrnytJzGGe+xG7744gtzo0aNzNHR0ebJkyc7+UyMQ+J9k5YtW5oHDx5seZ6dnW2Ojo42jx8/3sCo3M+pU6fMkswrVqwwm83X/9D4+vqaP//8c0uZX3/91SzJvHr1arPZbDZ/++23Zi8vL3NaWpqlzNSpU80hISHmzMxMs9lsNo8aNcrcoEEDq2P17t3b3LlzZ2efkstduHDBXLt2bfPSpUvN7du3tyTeXEv7vPDCC+bbb78939dzcnLMkZGR5jfffNOy7dy5c2Y/Pz/zZ599ZjabzeadO3eaJZnXr19vKbNo0SKzyWQyHzt2zGw2m83vv/++uVy5cpbre+PYcXFxjj4lQ3Xr1s38l7/8xWrbAw88YO7bt6/ZbOZ6Ao5gy+9IbrZ8Ntzw/vvvm9u3b29evnx5iUm8nX3NbvbMM8+YO3bs6LjgXcTe77C9evUyd+vWzWpbq1atzE899ZTZbLbt772nc/Q1y8u6devMksyHDx92TNAGctb1Onr0qLly5crm7du3m6tVq1aiE2+6mv9PVlaWNm7cqMTERMs2Ly8vJSYmavXq1QZG5n7Onz8vSSpfvrwkaePGjbp69arVtatbt66qVq1quXarV69WfHy8KlWqZCnTuXNnpaena8eOHZYyN9dxo0xJvP6DBw9Wt27dbjlfrqV9Fi5cqObNm+uhhx5SRESEmjRpounTp1teP3jwoNLS0qyuRWhoqFq1amV1PcPCwtS8eXNLmcTERHl5eWnt2rWWMnfccYfKlCljKdO5c2ft3r1bZ8+edfZpukybNm20fPly7dmzR9L1bpg///yzunbtKonrCTiCLb8judny2SBJO3fu1NixYzVr1ix5eZWcr3jOvGa5nT9/3vL9xlMU5TtsYd8TbPl778mccc3ycv78eZlMJoWFhTkkbqM463rl5OTokf9v7+6Doqj/OIC/D85DQEEEhFMHlEQFTPEp50YnM2YUyjSHRmWQOfsj0sCHhlAaZJA/TButtFAatcSSPJ+iTCcZBRoKRy0cHgxEKh8nEM0YRE2R+/z+8MfGIiIqy8n1fs3szN3u93Y/38/c7Xc/t3e7MTFITExESEiINsE/Rexnr/yErl69iqamJlUxAwA+Pj6oqamxUVRPH6vViqVLl2LixIkYMWIEAKCmpgYGg+G+nUrL3NXU1LSZ2+Zl7bWpr6/HrVu3tOiOTVgsFpw8eRKrV6++bxlz+Wj++OMPZGRkIDAwEDk5OVi4cCEWL16M7du3A/g3H+19rmtqatCvXz/Vcr1ej759+z5Szu1BUlIS5s6di+HDh6NHjx4YPXo0li5diujoaADMJ1Fn6MhnpK3XPGxsuH37NqKiorB27Vr4+flpErutaJWz1o4ePYpdu3YhNja2U+LuKo9zDPug/XDL/XTzvI6uszvRImet/fPPP1i+fDmioqLg5ubWOYHbiFb5ev/996HX67F48eLOD/opxMKbHklcXBxOnToFi8Vi61C6pYsXL2LJkiXIyspCz549bR1Ot2e1WjFmzBi89957GD16NGJjY/HGG2/g008/tXVo3dLu3buRlZWFr776CidPnsT27duxbt065YsMInqwpKQk6HS6dqfTp09rtv13330XQUFBmDdvnmbb6Gy2zllLp06dwsyZM5GamoqpU6d2yTbJfjU2NmL27NkQEWRkZNg6nKdSUVERNmzYgMzMTOh0OluH0yX0tg7gaeHl5QVHR8f7rhB6+fJl+Pr62iiqp0t8fDwOHDiAgoICDBw4UJnv6+uLO3fuoK6uTvXNcsvc+fr63nflw+Zct2zTVv7d3Nzg7OysRZe6XFFREWprazFmzBhlXlNTEwoKCpCeno6cnBzm8hEYjUYEBwer5gUFBWHfvn0A/s3H5cuXYTQalTaXL19GaGio0qa2tla1jrt37+LatWsPzWfLbdiDxMRE5aw3ADz77LM4f/48Vq9eDbPZzHwStSMhIQHz589vt01AQECHPiOtdWSczcvLQ1lZGfbu3QsAyt0IvLy8kJycjLS0tMfsmXZsnbNm5eXlCAsLQ2xsLFasWPFYfbGlxzmGfdB+uOV+unneg/b33ZkWOWvWXHSfP38eeXl53f5sN6BNvn788UfU1taqfqHT1NSEhIQErF+/HufOnevcTjwFeMb7/wwGA8aOHYvc3FxlntVqRW5uLkwmkw0jsz0RQXx8PLKzs5GXl4fBgwerlo8dOxY9evRQ5a6yshIXLlxQcmcymVBWVqYaOA8fPgw3NzelcDKZTKp1NLexp/yHhYWhrKwMxcXFyjRu3DhER0crj5nLjps4ceJ9t7Y7c+YM/P39AQCDBw+Gr6+vKhf19fU4fvy4Kp91dXUoKipS2uTl5cFqtWLChAlKm4KCAjQ2NiptDh8+jGHDhsHDw0Oz/nW1mzdv3ve/UEdHR1itVgDMJ1F7vL29MXz48HYng8HQoc9Iax0ZZ/ft24eSkhJlbNm6dSuAewe3cXFxGvb88dk6ZwDw66+/YsqUKTCbzVi1apV2ndXQ4xzDPuw4oSP7++5Mi5wB/xbdVVVVOHLkCDw9PbXpQBfTIl8xMTEoLS1VHRP3798fiYmJyMnJ0a4ztmTji7s9VSwWizg5OUlmZqaUl5dLbGys9OnTR3X16P+ihQsXiru7u/zwww9SXV2tTDdv3lTaLFiwQPz8/CQvL09++eUXMZlMYjKZlOXNt8CaOnWqFBcXy6FDh8Tb27vNW2AlJiZKRUWFbNy40S5vgdVay6uaizCXj+LEiROi1+tl1apVUlVVJVlZWeLi4iI7duxQ2qxZs0b69Okj3377rZSWlsrMmTPbvP3V6NGj5fjx4/LTTz9JYGCg6jY1dXV14uPjIzExMXLq1CmxWCzi4uJid7e/MpvNMmDAAOV2Yl9//bV4eXnJsmXLlDbMJ9GTe9hn5NKlSzJs2DA5fvy4Mu9hY0Nr+fn5dnNVcxFtclZWVibe3t4yb9481fFNbW1tl/atMzzsGDYmJkaSkpKU9oWFhaLX62XdunVSUVEhqampbd5O7GH7++6ss3N2584dmTFjhgwcOFCKi4tV76mWd/HorrR4j7Vm71c1Z+HdyieffCJ+fn5iMBjkueeek2PHjtk6JJsD0Oa0bds2pc2tW7fkrbfeEg8PD3FxcZFZs2ZJdXW1aj3nzp2TiIgIcXZ2Fi8vL0lISJDGxkZVm/z8fAkNDRWDwSABAQGqbdir1oU3c/lovvvuOxkxYoQ4OTnJ8OHDZfPmzarlVqtVUlJSxMfHR5ycnCQsLEwqKytVbf766y+JioqSXr16iZubm7z++uty/fp1VZuSkhKZNGmSODk5yYABA2TNmjWa962r1dfXy5IlS8TPz0969uwpAQEBkpycrDpgYD6JntzDPiNnz54VAJKfn6/M68jY0JK9Fd5a5Cw1NbXN4xt/f/8u7Fnnae8YdvLkyWI2m1Xtd+/eLUOHDhWDwSAhISFy8OBB1fKO7O+7u87MWfN7sK2p5fuyO+vs91hr9l5460T+/ycgIiIiIiIiIup0/I83ERERERERkYZYeBMRERERERFpiIU3ERERERERkYZYeBMRERERERFpiIU3ERERERERkYZYeBMRERERERFpiIU3ERERERERkYZYeBMRERERERFpiIU3ET2RzMxM9OnTx9ZhEBERkcZ0Oh2++eYbW4dB1C2x8CayE/Pnz4dOp1MmT09PhIeHo7S0tMPrWLlyJUJDQ7ULkoiIiB5L63G+eQoPD7d1aETUASy8iexIeHg4qqurUV1djdzcXOj1ekyfPt3WYREREVEnaDnON087d+60dVhE1AEsvInsiJOTE3x9feHr64vQ0FAkJSXh4sWLuHLlCgBg+fLlGDp0KFxcXBAQEICUlBQ0NjYCuPeT8bS0NJSUlCjfomdmZgIA6urq8Oabb8LHxwc9e/bEiBEjcODAAdW2c3JyEBQUhF69eikHBkRERNR5Wo7zzZOHhweAez8Dz8jIQEREBJydnREQEIC9e/eqXl9WVoYXX3wRzs7O8PT0RGxsLBoaGlRtPv/8c4SEhMDJyQlGoxHx8fGq5VevXsWsWbPg4uKCwMBA7N+/X9tOE9kJFt5EdqqhoQE7duzAkCFD4OnpCQDo3bs3MjMzUV5ejg0bNmDLli346KOPAABz5sxBQkICQkJClG/R58yZA6vVioiICBQWFmLHjh0oLy/HmjVr4OjoqGzr5s2bWLduHb788ksUFBTgwoULeOedd2zSbyIiov+qlJQUREZGoqSkBNHR0Zg7dy4qKioAADdu3MC0adPg4eGBn3/+GXv27MGRI0dUhXVGRgbi4uIQGxuLsrIy7N+/H0OGDFFtIy0tDbNnz0ZpaSleeuklREdH49q1a13aT6JuSYjILpjNZnF0dBRXV1dxdXUVAGI0GqWoqOiBr1m7dq2MHTtWeZ6amiqjRo1StcnJyREHBweprKxscx3btm0TAPLbb78p8zZu3Cg+Pj5P1iEiIiJStB7nm6dVq1aJiAgAWbBggeo1EyZMkIULF4qIyObNm8XDw0MaGhqU5QcPHhQHBwepqakREZH+/ftLcnLyA2MAICtWrFCeNzQ0CAD5/vvvO62fRPZKb9Oqn4g61ZQpU5CRkQEA+Pvvv7Fp0yZERETgxIkT8Pf3x65du/Dxxx/j999/R0NDA+7evQs3N7d211lcXIyBAwdi6NChD2zj4uKCZ555RnluNBpRW1vbOZ0iIiIiAOpxvlnfvn2VxyaTSbXMZDKhuLgYAFBRUYFRo0bB1dVVWT5x4kRYrVZUVlZCp9Phzz//RFhYWLsxjBw5Unns6uoKNzc3jvlEHcDCm8iOuLq6qn4StnXrVri7u2PLli14+eWXER0djbS0NEybNg3u7u6wWCz44IMP2l2ns7PzQ7fbo0cP1XOdTgcRebxOEBERUZtaj/OdqSPjPdD2mG+1WrUIiciu8D/eRHZMp9PBwcEBt27dwtGjR+Hv74/k5GSMGzcOgYGBOH/+vKq9wWBAU1OTat7IkSNx6dIlnDlzpitDJyIiokd07Nix+54HBQUBAIKCglBSUoIbN24oywsLC+Hg4IBhw4ahd+/eGDRoEHJzc7s0ZqL/Cp7xJrIjt2/fRk1NDYB7PzVPT09HQ0MDXnnlFdTX1+PChQuwWCwYP348Dh48iOzsbNXrBw0ahLNnzyo/L+/duzcmT56M559/HpGRkfjwww8xZMgQnD59mvcOJSIi6mItx/lmer0eXl5eAIA9e/Zg3LhxmDRpErKysnDixAl89tlnAIDo6GikpqbCbDZj5cqVuHLlChYtWoSYmBj4+PgAAFauXIkFCxagX79+iIiIwPXr11FYWIhFixZ1bUeJ7BDPeBPZkUOHDsFoNMJoNGLChAnKVUtfeOEFzJgxA2+//Tbi4+MRGhqKo0ePIiUlRfX6yMhIhIeHY8qUKfD29lbuDbpv3z6MHz8eUVFRCA4OxrJly+47M05ERETaajnON0+TJk1SlqelpcFisWDkyJH44osvsHPnTgQHBwO4dz2WnJwcXLt2DePHj8drr72GsLAwpKenK683m81Yv349Nm3ahJCQEEyfPh1VVVVd3k8ie6QT/hGTiIiIiKhb0+l0yM7OxquvvmrrUIioDTzjTURERERERKQhFt5EREREREREGuLF1YiIiIiIujn+e5To6cYz3kREREREREQaYuFNREREREREpCEW3kREREREREQaYuFNREREREREpCEW3kREREREREQaYuFNREREREREpCEW3kREREREREQaYuFNREREREREpKH/AYygwo5RFGh0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 9188/9188 [1:43:20<00:00,  1.48it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 2297/2297 [08:40<00:00,  4.41it/s]\n"
     ]
    }
   ],
   "source": [
    "train_history = []\n",
    "valid_history = []\n",
    "for epoch in range(epochs):\n",
    "    train_loss = train(model, train_loader, optimizer, scheduler, criterion, 2, train_history, valid_history)\n",
    "    valid_loss = evaluate(model, valid_loader, criterion)\n",
    "\n",
    "    train_history.append(train_loss)\n",
    "    valid_history.append(valid_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "25v0E3JbMu2V",
   "metadata": {
    "id": "25v0E3JbMu2V"
   },
   "outputs": [],
   "source": [
    "def remove_tech_tokens(tokens: list, bpe):\n",
    "    result = []\n",
    "    for token in tokens:\n",
    "       if token not in [\n",
    "           en_bpe.subword_to_id('▁<eos>'), ru_bpe.subword_to_id('▁<eos>'), en_bpe.subword_to_id('▁<sos>'), ru_bpe.subword_to_id('▁<sos>'), 0]:\n",
    "          result.append(token)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "alu8DLQxIAR-",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "alu8DLQxIAR-",
    "outputId": "2e8c75b1-1875-43ce-9360-f8b597c5a680"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['the nights here are cold, and we are not thick-skinned enough not to freeze.']\n",
      "['здесь здесь холод, холод и мы не толстые не не не не']\n",
      "\n",
      "['development, support, promotion of sites.']\n",
      "['разработ разработки разработка продвижение сайтов. сайтов. сайтов.']\n",
      "\n",
      "['this level controls the other six levels.']\n",
      "['этот уровень контролирует другой уровень']\n",
      "\n",
      "['the freedom to feel, to feel, to be sensual again, and the freedom to go beyond the mind.']\n",
      "['свобода ощутить чувствовать чувствовать чувствовать чувственным, и и свобода и выйти к ума. разума.']\n",
      "\n",
      "['the trainee flew all ten metres, climbed over the railings and went down, but after ten seconds he came out of the air again right next to the portal.']\n",
      "['в времялетел в десять метров, на на на на и и и а после десять секунд секунд вышел из воздуха рядом с порта порта порта порта порта порта порта порта порта порта порта портала.']\n",
      "\n",
      "['in the morning i go to work on the first train, but return on the last one and have to walk along this tunnel in complete darkness.']\n",
      "['утром я хожу на работу первый поезд, но но на последний и и ходить ходить по тонне тоннеле в полном одиночестве.']\n",
      "\n",
      "['and our name is ram.']\n",
      "['а наш имя -']\n",
      "\n",
      "[\"they didn't fine us, but they made a promise that we would come down the mound and step back from the stairs so as not to break it, which we did.\"]\n",
      "['они не нели, но они сделали обеща обещали, что мы спустились вверх и шаг назад, чтобы не не неть, что не']\n",
      "\n",
      "['in such cases, which individuals will be held liable in the first place?']\n",
      "['в таких случаях какие будут будут будут будут в первом месте?']\n",
      "\n",
      "['osteoporosis is a skeletal disease characterized by decreased bone mass and impaired bone microarchitectonics, accompanied by reduced bone strength and an increased risk of fracture.']\n",
      "['остеопороз – это заболевание, заболевания отличается влиятельными костным костным и и и костной костная микроцидами, фосрами, фосфо прочность костей костной увеличивая прочность костей. переломов. переломов. переломов. переломов.']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "batch = next(iter(test_loader))\n",
    "\n",
    "for idx in range(0, 10):\n",
    "    src = batch['src'][idx:idx+1, :]\n",
    "    trg = batch['trg'][idx:idx+1, :]\n",
    "    model.eval()\n",
    "    output = model(src.to(device), trg.to(device), 0)\n",
    "    output = output[:, 1:, :].reshape(-1, ru_bpe.vocab_size()).argmax(1)\n",
    "\n",
    "    original = en_bpe.decode(remove_tech_tokens(src.cpu().numpy()[0].tolist(), en_bpe))\n",
    "    generated = ru_bpe.decode(remove_tech_tokens(output.cpu().numpy().tolist(), ru_bpe))\n",
    "\n",
    "    print(original)\n",
    "    print(generated)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "EoFmbnoxrmTh",
   "metadata": {
    "id": "EoFmbnoxrmTh"
   },
   "outputs": [],
   "source": [
    "from nltk.translate.bleu_score import corpus_bleu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "bNXUN2Bhrm5e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bNXUN2Bhrm5e",
    "outputId": "ea6b51e3-f8f5-497c-fba9-7eab3cc7ff7a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 3829/3829 [14:37<00:00,  4.36it/s]\n"
     ]
    }
   ],
   "source": [
    "original_text = []\n",
    "generated_text = []\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "\n",
    "    for batch in tqdm(test_loader):\n",
    "\n",
    "        src = batch['src'].to(device)\n",
    "        trg = batch['trg'].to(device)\n",
    "\n",
    "        output = model(src.to(device), trg.to(device), 0)\n",
    "        output = output[:, 1:, :].reshape(-1, ru_bpe.vocab_size()).argmax(1)\n",
    "\n",
    "        original = ru_bpe.decode(remove_tech_tokens(trg.reshape(-1).cpu().numpy().tolist(), ru_bpe))\n",
    "        generated = ru_bpe.decode(remove_tech_tokens(output.cpu().numpy().tolist(), ru_bpe))\n",
    "\n",
    "        original_text.append(original)\n",
    "        generated_text.append(generated)"
   ]
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     "data": {
      "text/plain": [
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    "corpus_bleu([[text[0].split()] for text in original_text], [text[0].split() for text in generated_text]) * 100"
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  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "99c4ab8c-35b6-4fc1-9b13-c8da8aba46f1",
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    {
     "data": {
      "text/plain": [
       "'здесь здесь холод, холод и мы не толстые не не не не разработ разработки разработка продвижение сайтов. сайтов. сайтов. этот уровень контролирует другой уровень свобода ощутить чувствовать чувствовать'"
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     "data": {
      "text/plain": [
       "'ночи здесь холодные, а мы не настолько толстокожие, чтобы не мерзнуть. разработка, поддержка, раскрутка сайтов. этот уровень управляет остальными шестью уровнями. свободу ощущать, чувствовать, снова б'"
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