{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "cdab4af6",
      "metadata": {
        "id": "cdab4af6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c947c7c-2e74-4559-8d6e-8f726bcbe1b4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.28.1-py3-none-any.whl (7.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m71.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting huggingface-hub<1.0,>=0.11.0\n",
            "  Downloading huggingface_hub-0.13.4-py3-none-any.whl (200 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m200.1/200.1 kB\u001b[0m \u001b[31m26.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers) (3.11.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (23.0)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m76.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers) (2.27.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.9/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.5.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (1.26.15)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2.0.12)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.13.4 tokenizers-0.13.3 transformers-4.28.1\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "d8221be3",
      "metadata": {
        "id": "d8221be3"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "import json\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "\n",
        "import json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "b620c98a",
      "metadata": {
        "id": "b620c98a"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "file = open('arxivData.json')\n",
        "data = json.load(file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "72d730ad",
      "metadata": {
        "id": "72d730ad"
      },
      "outputs": [],
      "source": [
        "def trl(container):\n",
        "    return tqdm(range(len(container)))\n",
        "\n",
        "def prepared(string):\n",
        "    string = string.replace(\"'\", '\"')\n",
        "    string = string.replace('None', 'null')\n",
        "    return string"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titles = [data[i]['title'] for i in trl(data)]\n",
        "summaries = [data[i]['summary'] for i in trl(data)]\n",
        "tags = [[json.loads(prepared(data[i]['tag']))[j]['term'] for j in range(len(json.loads(prepared(data[i]['tag']))))] for i in trl(data)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "178d4cb384414bf09d3bc169f053c2d6",
            "349caef343c14390b238b4a2d98ee2a5",
            "f51995e48cbb481681f9479a8929dc4a",
            "0b2dae89c43f4703b5ef14bef89fb9eb",
            "ef788247aa5b4c3f9e33b828eb3d268d",
            "2fbebcb6032d4e12abeba4aab97526f8",
            "625e0c35ec2040728fae1034df16c1a9",
            "a761c844588346cc85342abcfeb3cb24",
            "ab2bb80581ab49e8922083ee87b86470",
            "4e2a02ad68c841d1bda0f47f19abb33a",
            "b53cc88bea8e45ffa60d5c218ac4e188",
            "3dd75426281c43a39381f38219008b10",
            "4e721ae401d143a084243e4d94ffed64",
            "51895fbafc734fba9136d4a1454c1e11",
            "e89747a6ed64405d993e932ddf219e14",
            "e764a60a310446deb7acd1c5c93b2c01",
            "1dd92e188b9e4960b019098b2add2386",
            "e74975cf464c4993829e58b0950f299b",
            "ae75b8557be346cabfcd3e8828e56014",
            "f562dd9eedb6431f96787062c6a7729b",
            "7f6a9c7b1acb44a08425d061c81d1b69",
            "cc74d58c54a44f3ea96e55a9294e3de7",
            "bc6e579196d14c328b6e03216f295442",
            "e41be6bf8f7e42ad986728f410482c2b",
            "93d100d9c81f48cdaf2847723b23c8fa",
            "93538da37dd54a4caf269a78dfb2fa31",
            "63f0d1f2ae3f4cb2872414ddf29a6a23",
            "64a8ccfa2af74410af3c0f8b07c61dba",
            "cfe9261ad31a475cb4873522028d0cbe",
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            "00902257e4bc48f2a2537459841332cc",
            "f325898d1ecf463aa1846b00c382f439",
            "d2edf2539dc34bc6af15a80f5ceffab5"
          ]
        },
        "id": "FxkFT0jraIdt",
        "outputId": "b6f4e6cc-4ee3-4027-f026-904a36face4e"
      },
      "id": "FxkFT0jraIdt",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/41000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "178d4cb384414bf09d3bc169f053c2d6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/41000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3dd75426281c43a39381f38219008b10"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/41000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bc6e579196d14c328b6e03216f295442"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "d1d31cec",
      "metadata": {
        "id": "d1d31cec",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6e9d382-95f8-4262-dfc3-25e8481d03be"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['cs.LG', 'cs.AI', 'cs.CV', 'cs.NE'],\n",
              " ['cs.CV', 'cs.AI', 'cs.LG', 'cs.NE', '68T05, 68T45', 'I.2.6; I.4.6; I.5.5'],\n",
              " ['cs.LG', 'cs.AI', 'cs.CV', 'cs.NE'],\n",
              " ['cs.LG', 'cs.AI', 'cs.CV', 'cs.NE'],\n",
              " ['cs.LG', 'cs.CV', 'cs.NE', 'stat.ML'],\n",
              " ['stat.ML', 'cs.CV', 'cs.LG', 'cs.NE'],\n",
              " ['stat.ML', 'cs.CV', 'cs.LG', 'cs.NE'],\n",
              " ['cs.CV', 'cs.LG', 'cs.NE', 'stat.ML'],\n",
              " ['stat.ML', 'cs.CV', 'cs.LG', 'cs.NE'],\n",
              " ['cs.NE', 'cs.CV', 'cs.LG', 'stat.ML']]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "tags[200:210]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "32135c29",
      "metadata": {
        "id": "32135c29"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "90abdc5b",
      "metadata": {
        "id": "90abdc5b"
      },
      "source": [
        "## Какие вообще есть классы и сколько их эту инфу хочется сохранить и загружать потом"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "9d915894",
      "metadata": {
        "id": "9d915894"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "page = requests.get(\"https://arxiv.org/category_taxonomy\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "2a68042d",
      "metadata": {
        "id": "2a68042d"
      },
      "outputs": [],
      "source": [
        "from bs4 import BeautifulSoup\n",
        "soup = BeautifulSoup(page.content, 'html.parser')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "fe7248fa",
      "metadata": {
        "id": "fe7248fa"
      },
      "outputs": [],
      "source": [
        "h4_cases = soup.find_all('h4')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "1a06bfe4",
      "metadata": {
        "id": "1a06bfe4"
      },
      "outputs": [],
      "source": [
        "tags_tags = h4_cases[1:]\n",
        "possible_tags = set()\n",
        "tag_to_id = {}\n",
        "id_to_tag = {}\n",
        "id_to_description = {}\n",
        "for i, html_tag in enumerate(tags_tags):\n",
        "    idx = html_tag.get_text().find(' ')\n",
        "    tag = html_tag.get_text()[:idx]\n",
        "    description = html_tag.get_text()[idx+2:-1]\n",
        "    possible_tags.add(tag)\n",
        "    tag_to_id[tag] = i\n",
        "    id_to_tag[i] = tag\n",
        "    id_to_description[i] = description"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "1d20c9af",
      "metadata": {
        "id": "1d20c9af"
      },
      "outputs": [],
      "source": [
        "num_tags = len(possible_tags)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "56be3b1e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "56be3b1e",
        "outputId": "f01b67eb-b52d-4a59-bc27-7e3463013ab3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "155"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "len(possible_tags)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "9a2d8fe4",
      "metadata": {
        "id": "9a2d8fe4"
      },
      "outputs": [],
      "source": [
        "with open('tags.txt', 'w') as file:\n",
        "    for i in range(num_tags):\n",
        "        file.write(str(id_to_tag[i]) + ' ' + str(id_to_description[i]) + '\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "eccf3f22",
      "metadata": {
        "id": "eccf3f22"
      },
      "outputs": [],
      "source": [
        "for i, line in enumerate(tags):\n",
        "    line = list(set(line) & possible_tags)\n",
        "    int_line = []\n",
        "    for tag in line:\n",
        "        int_line.append(tag_to_id[tag])\n",
        "    tags[i] = int_line"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "ea5b030c",
      "metadata": {
        "id": "ea5b030c"
      },
      "outputs": [],
      "source": [
        "#data = pd.read_json(\"arxivData.json\")\n",
        "#data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "207f9232",
      "metadata": {
        "id": "207f9232"
      },
      "outputs": [],
      "source": [
        "#data[[\"tag\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "f4323150",
      "metadata": {
        "id": "f4323150"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "a3c87ee3",
      "metadata": {
        "id": "a3c87ee3"
      },
      "outputs": [],
      "source": [
        "#len(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "04a97667",
      "metadata": {
        "id": "04a97667"
      },
      "outputs": [],
      "source": [
        "#x = data[[\"summary\", \"title\"]]\n",
        "#(np.array(x)).shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "b35c8f07",
      "metadata": {
        "id": "b35c8f07"
      },
      "outputs": [],
      "source": [
        "#x = x[\"summary\"].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "037f7469",
      "metadata": {
        "id": "037f7469"
      },
      "outputs": [],
      "source": [
        "#x = list(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "7e78c99d",
      "metadata": {
        "id": "7e78c99d"
      },
      "outputs": [],
      "source": [
        "#data[\"tag\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "45c6dc0f",
      "metadata": {
        "id": "45c6dc0f"
      },
      "outputs": [],
      "source": [
        "#import ast"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "407caa74",
      "metadata": {
        "id": "407caa74"
      },
      "outputs": [],
      "source": [
        "#tags = (data[\"tag\"]).apply(ast.literal_eval)\n",
        "\n",
        "#y = [[tags[row][tag_index]['term'] for tag_index in range(len(tags[row]))] for row in range(len(tags))]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "2fdeb510",
      "metadata": {
        "id": "2fdeb510"
      },
      "outputs": [],
      "source": [
        "#[len(y[row]) for row in range(len(y))]"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JzRGBjOFS8Rl"
      },
      "id": "JzRGBjOFS8Rl",
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "ebaa5f4f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ebaa5f4f",
        "outputId": "c2db6f04-5a1f-459f-df17-727b1c966ec4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForTokenClassification: ['cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight']\n",
            "- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "25a0b554",
      "metadata": {
        "id": "25a0b554"
      },
      "outputs": [],
      "source": [
        "## кол-во различных тем\n",
        "#kkk_ans = []\n",
        "#dd = [[kkk_ans.append(prepared(tags[row][tag_index]['term'])) for tag_index in range(len(tags[row]))] for row in range(len(tags))]\n",
        "#set(kkk_ans)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1eace08b",
      "metadata": {
        "id": "1eace08b"
      },
      "source": [
        "## Разделение данных на train test и подготовка батчей"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "1aaae017",
      "metadata": {
        "id": "1aaae017"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "titles_train, titles_test, summaries_train, summaries_test, tags_train, tags_test = train_test_split(titles, summaries, tags, test_size=0.1, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "2ea8d260",
      "metadata": {
        "id": "2ea8d260"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer, AutoModel, pipeline, DistilBertForSequenceClassification\n",
        "\n",
        "base_model_name = 'distilbert-base-uncased'\n",
        "base_tokenizer = AutoTokenizer.from_pretrained(base_model_name)\n",
        "#base_model = AutoModel.from_pretrained(base_model_name).to('cuda')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "cf736b38",
      "metadata": {
        "id": "cf736b38"
      },
      "outputs": [],
      "source": [
        "def multi_hot(indexes, num_classes):\n",
        "    return torch.zeros((num_classes,)).scatter_(0, torch.tensor(indexes), torch.ones((num_classes,)))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "class ArXivDataset(Dataset):\n",
        "    def __init__(self, titles, abstracts, tags, possible_tags, tokenizer):\n",
        "        super(ArXivDataset, self).__init__()\n",
        "        self. titles = titles\n",
        "        self.abstracts = abstracts\n",
        "        self.tags = tags\n",
        "        self.possible_tags = possible_tags\n",
        "        self.num_classes = len(self.possible_tags)\n",
        "        self.tokenizer = tokenizer\n",
        "        \n",
        "        self.inputs = ['Title: ' + self.titles[idx] + ' Abstract: ' + self.abstracts[idx] for idx in range(len(titles))]\n",
        "        self.inputs = self.tokenizer(self.inputs, truncation=True, padding='max_length', max_length=512, return_tensors='pt')['input_ids']\n",
        "    \n",
        "    def __len__(self):\n",
        "        return len(self.titles)\n",
        "    \n",
        "    def __getitem__(self, idx):\n",
        "        return (self.inputs[idx], multi_hot(self.tags[idx], self.num_classes) / len(self.tags[idx]))"
      ],
      "metadata": {
        "id": "065yUVS355c8"
      },
      "id": "065yUVS355c8",
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_train_ds = ArXivDataset(titles_train, summaries_train, tags_train, possible_tags, base_tokenizer)\n",
        "base_test_ds = ArXivDataset(titles_test, summaries_test, tags_test, possible_tags, base_tokenizer)\n",
        "\n",
        "base_train_dl = DataLoader(base_train_ds, shuffle=True, \n",
        "    batch_size=128, num_workers=0)\n",
        "base_test_dl = DataLoader(base_test_ds, shuffle=True, \n",
        "    batch_size=128, num_workers=0)\n",
        "\n",
        "small_ds = ArXivDataset(titles_train, summaries_train, tags_train, possible_tags, base_tokenizer)\n",
        "small_dl = DataLoader(small_ds, shuffle=True, \n",
        "    batch_size=128, num_workers=0)"
      ],
      "metadata": {
        "id": "SYQAUdgQ6ENy"
      },
      "id": "SYQAUdgQ6ENy",
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "52f5d981",
      "metadata": {
        "id": "52f5d981"
      },
      "source": [
        "## Токенизация"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "73fbb93c",
      "metadata": {
        "id": "73fbb93c"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "42582978",
      "metadata": {
        "id": "42582978"
      },
      "outputs": [],
      "source": [
        "#tokens_info = tokenizer(x, padding=True, truncation=True, return_tensors=\"pt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "5b3986b0",
      "metadata": {
        "id": "5b3986b0"
      },
      "outputs": [],
      "source": [
        "#print(tokens_info)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "1086f2dd",
      "metadata": {
        "id": "1086f2dd"
      },
      "outputs": [],
      "source": [
        "#for i in range(2):\n",
        "#    print(tokenizer.decode(tokens_info['input_ids'][i]))\n",
        "# тестик того что выведет"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ec13f592",
      "metadata": {
        "id": "ec13f592"
      },
      "source": [
        "## Модель классификации"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "c612c032",
      "metadata": {
        "id": "c612c032"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "0cd67751",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0cd67751",
        "outputId": "cb1df403-a478-4703-93e2-20f154069943"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-cased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.bias', 'vocab_projector.weight', 'vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_transform.weight', 'vocab_layer_norm.bias']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias', 'pre_classifier.bias', 'pre_classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "f5271f2d",
      "metadata": {
        "id": "f5271f2d"
      },
      "outputs": [],
      "source": [
        "#tokens_info['input_ids'].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "f8e34c00",
      "metadata": {
        "id": "f8e34c00"
      },
      "outputs": [],
      "source": [
        "\n",
        "## тестики\n",
        "# #for param in model.parameters():\n",
        "# #    param.requires_grad = False\n",
        "    \n",
        "# ## !!!\n",
        "# final_model = nn.Sequential(\n",
        "#     tokenizer,\n",
        "#     model\n",
        "# )\n",
        "# ####\n",
        "\n",
        "# for param in final_model.parameters():\n",
        "#     param.requires_grad = False\n",
        "\n",
        "# final_model.second.classifier = nn.Sequential(\n",
        "#     torch.nn.Linear(20, 30),\n",
        "# )\n",
        "\n",
        "# model\n",
        "\n",
        "# model.classifier = torch.nn.Identity()\n",
        "\n",
        "# with torch.no_grad():\n",
        "#     kk= tokens_info['input_ids'][:2]\n",
        "#     ans = model(tokens_info['input_ids'][:2])\n",
        "\n",
        "# ans.logits.shape\n",
        "\n",
        "#DataLoader(data, batch_size=batch_size, shuffle=(num_samples is None))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "57875cde",
      "metadata": {
        "id": "57875cde"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "d133a5cc",
      "metadata": {
        "id": "d133a5cc"
      },
      "outputs": [],
      "source": [
        "# for epoch in range(100):\n",
        "#     gd.zero_grad()\n",
        "#     loss = batch_loss(batch)\n",
        "#     if maximization:\n",
        "#         (-loss).backward()\n",
        "#     else:\n",
        "#         loss.backward()\n",
        "#     gd.step()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "dffa5e15",
      "metadata": {
        "id": "dffa5e15"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "78547fbe",
      "metadata": {
        "id": "78547fbe"
      },
      "source": [
        "## Проедура для обучения, валидации и отрисовки графиков(взять из прошлой дз)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_learning_process(train_loss, val_loss):    \n",
        "    plt.figure()\n",
        "    plt.title('loss by epoch')\n",
        "    plt.plot(np.arange(0, len(val_loss))+0.5, train_loss, label='train')\n",
        "    plt.plot(np.arange(0, len(val_loss))+1, val_loss, label='val')\n",
        "    plt.legend()\n",
        "    plt.grid()\n",
        "    plt.xlabel('epoch')\n",
        "    plt.ylabel('loss function')\n",
        "    plt.show()\n",
        "    "
      ],
      "metadata": {
        "id": "iGEpBdTkhJQm"
      },
      "id": "iGEpBdTkhJQm",
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "d09fb6df",
      "metadata": {
        "id": "d09fb6df"
      },
      "outputs": [],
      "source": [
        "#!g1.1\n",
        "\n",
        "device = 'cuda'\n",
        "\n",
        "import IPython\n",
        "from math import ceil\n",
        "\n",
        "\n",
        "def train_loop(model, dataloader, loss_fn, optimizer, step=0.05, history_loss=None):\n",
        "    out = display(IPython.display.Pretty('Learning...'), display_id=True)\n",
        "    \n",
        "    size = len(dataloader.dataset) \n",
        "    len_size = len(str(size))\n",
        "    batches = ceil(size / dataloader.batch_size) - 1\n",
        "        \n",
        "    train_loss = []\n",
        "    percentage = 0\n",
        "    for batch, (X, y) in enumerate(tqdm(dataloader, leave=False, desc=\"Batch #\")):\n",
        "        X, y = X.to(device), y.to(device)\n",
        "        # evaluate\n",
        "        output = F.softmax(model(X).logits, dim=1)\n",
        "        loss = loss_fn(output, y)\n",
        "        train_loss.append(loss.item())\n",
        "        \n",
        "        # backpropagation\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        # print info\n",
        "        if batch / batches > percentage or batch == batches: \n",
        "            out.update(f'[{int(percentage * size)}/{size}] Loss: {train_loss[-1]:>8f}')\n",
        "            percentage += step\n",
        "        \n",
        "    if history_loss is not None:\n",
        "        history_loss.append(np.mean(train_loss))\n",
        "\n",
        "    return {'train_loss': np.mean(train_loss)}\n",
        "        \n",
        "def test_loop(model, dataloader, loss_fn, history_loss=None, history_acc=None):\n",
        "\n",
        "    size = len(dataloader.dataset)\n",
        "    test_loss, correct = 0, 0\n",
        "    batches = ceil(size / dataloader.batch_size)\n",
        "\n",
        "    val_loss = []\n",
        "    \n",
        "    with torch.no_grad():\n",
        "        # evalute and check predictions\n",
        "        for batch, (X, y) in enumerate(tqdm(dataloader, leave=False, desc='Batch #')):\n",
        "            X, y = X.to(device), y.to(device)\n",
        "            output = F.softmax(model(X).logits, dim=1)\n",
        "            loss = loss_fn(output, y)\n",
        "            test_loss += loss.item()\n",
        "                \n",
        "            val_loss.append(loss.item())\n",
        "            \n",
        "    test_loss /= batches\n",
        "    correct /= size\n",
        "    \n",
        "    print(f\"Validation accuracy: {(100*correct):>0.1f}%, Validation loss: {test_loss:>8f} \\n\")\n",
        "    \n",
        "    if history_loss is not None:\n",
        "        history_loss.append(np.mean(val_loss))\n",
        "    \n",
        "    return {'val_loss': np.mean(val_loss)}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "53b96267",
      "metadata": {
        "id": "53b96267"
      },
      "outputs": [],
      "source": []
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        },
        "outputId": "0ab7e973-93d3-4d86-ec4b-0264d8afa758"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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zjo6OWCyWu67B7rfYRo8ezeDBg2nUqBFNmjThk08+ITExkaFDhwIwaNAgypQpw/jx4wF49tlnadOmDRMmTKBbt2789NNPbNu2jSlTpgDWTu3duzc7duxg0aJFpKWlZcxP8vb2xsnJic2bN7N161ZatmxJiRIlOHbsGG+88QaVKlXKdvRIRETkXjAMg+joaOLi4uxdil0ZhoG/vz8nT568o7UGvby88Pf3v6t1Cu0ekPr168e5c+d48803iY6OJjg4mKVLl2ZMxI6MjMy0kFPz5s2ZNWsWr7/+Oq+++ipVqlRhwYIF1K5dG4CoqCgWLlwIQHBwcKbvWrVqFffddx9ubm7Mnz+ft956i8TEREqXLk2XLl14/fXXs72NJiIici9cD0elSpXCzc2tyC5EnJ6ezuXLlylWrNitF3O8gWEYJCUlERMTA0Dp0qXvuAa7BySAUaNG3fSW2urVq7Mc69OnD3369Mm2fVBQELdb2qlOnTqsXLky13WKiIjklbS0tIxwlJOpIYVZeno6KSkpuLi45CogARlL78TExFCqVKk7vt2mvdhERETygetzjorq0jG2dL0P72YelwKSiIhIPlJUb6vZki36UAFJRERE5AYKSCIiIpJvBAUF8emnn9q7jPwxSVtEREQKrvvuu4/g4GA++eSTuz7X1q1bcXV15dq1a3df2F3QCFI+YxgGm45f4Gpq7hbGEhERya8Mw8hx4PH19c0XE9UVkPKZf8/cQf8pm5i/I8repYiIiNzWkCFDWLNmDZ9++ikmkwmTycT06dMxmUz8/vvvNGzYEGdnZ9avX8+xY8fo0aMHfn5+FCtWjMaNG7N8+fJM57vxFpvJZOLrr7+mV69euLm5UaVKlYz1DvOSAlI+0zjIG4Cv1x0nPf3W6zmJiEjhZhgGSSnX7PK63ZqC13366aeEhIQwfPhwzpw5w5kzZwgMDATglVde4b333uPAgQPUrVuXy5cv07VrV1asWMHOnTvp0qUL3bt3JzIy8pbfMXbsWPr27cuePXvo2rUrAwcOJDY29q7791Y0Bymf6ds4kE+WH+b4+URWHIyhY00/e5ckIiJ2ciU1jZpv/mGX7/7znc64Od0+Jnh6euLk5ISbm1vGxvEHDx4E4J133qFjx44Zbb29valXr17G+3HjxvHLL7+wcOHCmy4YDdZRqgEDBgDw7rvv8tlnn7Flyxa6dOlyR9eWExpBymeKOTswsFl5AKauPW7nakRERO5co0aNMr2/fPkyL7zwAjVq1MDLy4tixYpx4MCB244g1a1bN+PP7u7ueHh4ZGwnklc0gpQPDWkexNfrjrMlPJadkRepX66EvUsSERE7cHW08Oc7ne323XfL3d090/sXXniB0NBQPvzwQypXroyrqyu9e/cmJSXlludxdHTM9N5kMpGenn7X9d2KAlI+5OfhQo/gMszbfoqv151g0kAFJBGRoshkMuXoNpe9OTk5kZZ2+6evN2zYwJAhQ+jVqxdgHVEKDw/P4+rujG6x5VMjWlcE4Pd9Z4i8kGTnakRERG4uKCiIzZs3Ex4ezvnz5286ulOlShXmz5/Prl272L17N4888kiejwTdKQWkfKqqX3Huq+ZLugHfrNdcJBERyb9eeOEFLBYLNWvWxNfX96Zzij766CNKlChB8+bN6d69O507d6ZBgwb3uNqcyf/jdkXYiFYVWX3oHHO2neK5DlUp4e5k75JERESyqFq1KmFhYZmODRkyJEu7oKAgVq5cmenYyJEjM70PDw8nPT2dhIQEgGyXG4iLi7u7gnNAI0j5WEilktQK8OBKahozN0fYuxwREZEiQwEpHzOZTBlzkaZvjND2IyIiIveIAlI+17VOaQI8XTh/OZlfd2n7ERERkXtBASmfc7SYebxlBQCmrjuh7UdERETuAQWkAqBf40CKOztwNOYyqw/n7cqhIiIiooBUIBR3ceSRZuUA+GqNHvkXERHJawpIBcTQ5hVwMJvYfCKW3Sfj7F2OiIhIoaaAVED4e7rwYHAAAFPXaRRJREQkLykgFSDDW1kf+V+y9wwnY7X9iIiISF5RQCpAapT2oFUVH9IN+HbDCXuXIyIiYhNBQUF88skn9i4jEwWkAub6wpGzt54kPinVztWIiIgUTgpIBUzLyj5U9y9OUkoaM7do+xEREZG8oIBUwGTafmRDOMnXtP2IiIjYz5QpUwgICCA9PT3T8R49evD4449z7NgxevTogZ+fH8WKFaNx48YsX77cTtXmnAJSAdS9XgD+Hi7EXErm112n7V2OiIjkFcOAlET7vIyc7dzQp08fLly4wKpVqzKOxcbGsnTpUgYOHMjly5fp2rUrK1asYOfOnXTp0oXu3bsTGRmZV71mEw72LkByz7r9SBDvLjnI1LXH6dOwLCaTyd5liYiIraUmwbsB9vnuV0+Dk/ttm5UoUYL777+fWbNm0b59ewDmzZuHj48Pbdu2xWw2U69evYz248aN45dffmHhwoWMGjUqz8q/WxpBKqD6NylHMWcHjsRcZvXhc/YuR0REirCBAwfy888/k5ycDMDMmTPp378/ZrOZy5cv88ILL1CjRg28vLwoVqwYBw4c0AiS5A0PF0cGNAlk6roTTF17nLbVStm7JBERsTVHN+tIjr2+O4e6d++OYRgsXryYxo0bs27dOj7++GMAXnjhBUJDQ/nwww+pXLkyrq6u9O7dm5SUlLyq3CYUkAqwoS0qMG1DOBuPXWBfVDy1y3jauyQREbElkylHt7nszcXFhYceeoiZM2dy9OhRqlWrRoMGDQDYsGEDQ4YMoVevXgBcvnyZ8PBwO1abM7rFVoAFeLnyQN3SgLYfERER+xo4cCCLFy/m22+/ZeDAgRnHq1Spwvz589m1axe7d+/mkUceyfLEW36kgFTAPfHX9iOL9pwhKu6KnasREZGiql27dnh7e3Po0CEeeeSRjOMfffQRJUqUoHnz5nTv3p3OnTtnjC7lZ7rFVsDVLuNJi8ol2XD0At+uP8EbD9S0d0kiIlIEmc1mTp/OOl8qKCiIlStXZjo2cuTITO/z4y03jSAVAiNaVwLgpy2RxF/R9iMiIiJ3SwGpEGhdxYdqfsVJTEnjxy35+7FJERGRgkABqRAwmUwM/2v7kWkbTpByLf9PfhMREcnPFJAKiQfrBeDn4czZhGR+263tR0RERO6GAlIh4eRgZkjzCoD1kX8jh3voiIhI/qJ/f989W/ShAlIh8kjTcrg7WTgYfYl1R87buxwREckFR0dHAJKSkuxcScF3vQ+v9+md0GP+hYinqyP9Gpfj2w0nmLruOK2r+tq7JBERySGLxYKXlxcxMTEAuLm5FdmNyNPT00lJSeHq1auYzTkfyzEMg6SkJGJiYvDy8sJisdxxDQpIhczjLYP4LiycdUfOs/90PLUCtP2IiEhB4e/vD5ARkooqwzC4cuUKrq6udxQSvby8MvryTikgFTJlS7jRrU5pFu4+zdfrTvBxv2B7lyQiIjlkMpkoXbo0pUqVIjW16K5rl5qaytq1a2ndunWub5M5Ojre1cjRdQpIhdDwVhVZuPs0v+0+zYudqxHg5WrvkkREJBcsFotNfskXVBaLhWvXruHi4nJX84juhiZpF0J1ynoSUrEk19INpm8Mt3c5IiIiBY4CUiE14q+FI2dtjiThatEdphUREbkTCkiFVJuqvlQpVYzLydeYveWkvcsREREpUBSQCimz2cTwVtZRpG83nCA1TduPiIiI5JQCUiHWo34AvsWdORN/lUV7tP2IiIhITikgFWLODhaGNA8CYMraE1q+XkREJIcUkAq5gU3L4eZk4cCZBDYcvWDvckRERAoEBaRCzsvNib6NAgGYsu64nasREREpGBSQioBhLStgNsHaw+c4cCbB3uWIiIjkewpIRUCgtxv31ykNwNfrTti5GhERkfxPAamIGPHXI/8Ld0cRHX/VztWIiIjkb/kiIE2aNImgoCBcXFxo2rQpW7ZsuWX7uXPnUr16dVxcXKhTpw5LlizJ+Flqaiovv/wyderUwd3dnYCAAAYNGsTp038/5h4eHs6wYcOoUKECrq6uVKpUibfeeouUlJQ8u0Z7qxfoRZMK3qSmafsRERGR27F7QJo9ezajR4/mrbfeYseOHdSrV4/OnTsTExOTbfuNGzcyYMAAhg0bxs6dO+nZsyc9e/Zk3759ACQlJbFjxw7eeOMNduzYwfz58zl06BAPPvhgxjkOHjxIeno6X331Ffv37+fjjz/myy+/5NVXX70n12wv10eRZm6O4HLyNTtXIyIikn/ZPSB99NFHDB8+nKFDh1KzZk2+/PJL3Nzc+Pbbb7Nt/+mnn9KlSxdefPFFatSowbhx42jQoAETJ04EwNPTk9DQUPr27Uu1atVo1qwZEydOZPv27URGRgLQpUsXpk2bRqdOnahYsSIPPvggL7zwAvPnz79n120P7aqXopKvO5euXuOnLZH2LkdERCTfsmtASklJYfv27XTo0CHjmNlspkOHDoSFhWX7mbCwsEztATp37nzT9gDx8fGYTCa8vLxu2cbb2zt3F1DA/HP7kWkbwrX9iIiIyE042PPLz58/T1paGn5+fpmO+/n5cfDgwWw/Ex0dnW376OjobNtfvXqVl19+mQEDBuDh4ZFtm6NHj/L555/z4Ycf3rTW5ORkkpOTM94nJFgfl09NTSU1NfWmn8tvHqhdiv/94URU3BV+23WK7nVL5/oc16+3IF13fqR+tA31o22oH21D/WgbedmPOT2nXQNSXktNTaVv374YhsHkyZOzbRMVFUWXLl3o06cPw4cPv+m5xo8fz9ixY7McX7ZsGW5ubjar+V5o6m1iSaKFj5bswXxyJybTnZ0nNDTUtoUVUepH21A/2ob60TbUj7aRF/2YlJSUo3Z2DUg+Pj5YLBbOnj2b6fjZs2fx9/fP9jP+/v45an89HEVERLBy5cpsR49Onz5N27Ztad68OVOmTLllrWPGjGH06NEZ7xMSEggMDKRTp043HZnKr0KSUlj54VpOJaZTskYzmlXM3a3F1NRUQkND6dixI46OjnlUZeGnfrQN9aNtqB9tQ/1oG3nZj9fvAN2OXQOSk5MTDRs2ZMWKFfTs2ROA9PR0VqxYwahRo7L9TEhICCtWrOC5557LOBYaGkpISEjG++vh6MiRI6xatYqSJUtmOU9UVBRt27alYcOGTJs2DbP51tOxnJ2dcXZ2znLc0dGxwP1DUMrTkb6NApkRFsG3GyNoVc3v9h/KRkG89vxI/Wgb6kfbUD/ahvrRNvKiH3N6Prs/xTZ69GimTp3Kd999x4EDB3jqqadITExk6NChAAwaNIgxY8ZktH/22WdZunQpEyZM4ODBg7z99tts27YtI1ClpqbSu3dvtm3bxsyZM0lLSyM6Opro6OiMdY6ioqK47777KFeuHB9++CHnzp3LaFNUDGtZAZMJVh06x+Gzl+xdjoiISL5i9zlI/fr149y5c7z55ptER0cTHBzM0qVLMyZiR0ZGZhrdad68ObNmzeL111/n1VdfpUqVKixYsIDatWsD1vCzcOFCAIKDgzN916pVq7jvvvsIDQ3l6NGjHD16lLJly2ZqYxhGHl5t/lG+pDtdavnz+75opq49zv/61LN3SSIiIvmG3QMSwKhRo256S2316tVZjvXp04c+ffpk2z4oKOi2IWfIkCEMGTIkt2UWOiNaV+T3fdEs2BXFi52rUcrDxd4liYiI5At2v8Um9lO/XAkaB5XQ9iMiIiI3UEAq4q4vHPnDpggStf2IiIgIoIBU5HWo4UcFH3cSrl5jzraT9i5HREQkX1BAKuLMZhNPtKoAwDfrT3BN24+IiIgoIAk83KAs3u5OnLp4haX7i85SByIiIjejgCS4OFoYFFIegClrjxeZpQ5ERERuRgFJAHisWXmcHczsORXP5hOx9i5HRETErhSQBICSxZzp08i6aObUtcftXI2IiIh9KSBJhmEtK2IywYqDMRyN0fYjIiJSdCkgSYYKPu50qmnd4uXrdSfsXI2IiIj9KCBJJiNaWxeOnL8jiphLV+1cjYiIiH0oIEkmDct706CcFylp6XwfFmHvckREROxCAUmyuD6K9P2mCJJStP2IiIgUPQpIkkXHmv6UL+lGXFIqc7edsnc5IiIi95wCkmRhMZt4oqV1+5Gv1x8nLV0LR4qISNGigCTZ6t0wkBJujpyMvcIf2n5ERESKGAUkyZark4XHQoIA+Erbj4iISBGjgCQ3NSikPE4OZnafjGNbxEV7lyMiInLPKCDJTfkUc+bhBtbtR6Zo+xERESlCFJDklp5oZZ2svfzAWY6du2znakRERO4NBSS5pUq+xehQww/D0PYjIiJSdCggyW1dXzjy5x2nOH852c7ViIiI5D0FJLmtxkElqBfoRcq1dGZo+xERESkCFJDktkwmE09e334kLJwrKWl2rkhERCRvKSBJjnSu5U+gtysXk1KZv+u0vcsRERHJUwpIkiPW7Ueso0jTNkSg3UdERKQwU0CSHOvTqCyero5ExCaxN9Zk73JERETyjAKS5JibkwOPNSsPwKoz+qsjIiKFl37LSa4Mal4eR4uJE5dM7IiMs3c5IiIieUIBSXKlVHEXegYHAPD1+nD7FiMiIpJHFJAk1x5vbr3NtvxgDCfOJ9q5GhEREdtTQJJcq1yqGLVKpGMY8M16bWIrIiKFjwKS3JF2pdMBmLvtFBe0/YiIiBQyCkhyRyp5QJ0yHiRfS+eHTZH2LkdERMSmFJDkjphMMKxFEAAzwsK5mqrtR0REpPBQQJI71rlmKcp4uXIhMYX5O6LsXY6IiIjNKCDJHXOwmBnWsgIAX687Trr2HxERkUJCAUnuSr/GgXi4OHD8fCLLD5y1dzkiIiI2oYAkd8Xd2YFH/9p+ZOo6PfIvIiKFgwKS3LUhzYNwtJjYGn6RHZEX7V2OiIjIXVNAkrtWysOFnsFlAOtcJBERkYJOAUlsYnjrigAs3RdNxAVtPyIiIgWbApLYRFW/4txXzZd0A75df8Le5YiIiNwVBSSxmRGtrKNIc7ad4mJiip2rERERuXMKSGIzIZVKUivAgyupafywKcLe5YiIiNwxBSSxGZPJxIi/5iJ9p+1HRESkAFNAEpvqWqc0ZbxcOX85hQU7tf2IiIgUTApIYlOOFjND/9rEdqq2HxERkQJKAUlsrn+TchR3ceDYuURWHYqxdzkiIiK5poAkNlfM2YFHmpYDYMpaLRwpIiIFjwKS5ImhzSvgYDax+UQsu0/G2bscERGRXFFAkjzh7+nCg8EBAEzR9iMiIlLAKCBJnhn+18KRv+89w8nYJDtXIyIiknMKSJJnapT2oFUVH9IN+Ebbj4iISAGigCR56snWlQCYs+0kcUnafkRERAoGBSTJUy0ql6RGaQ+SUtKYuTnS3uWIiIjkiAKS5Cnr9iMVAJi+MZzka9p+RERE8j8FJMlzD9QNwN/DhXOXkvl112l7lyMiInJb+SIgTZo0iaCgIFxcXGjatClbtmy5Zfu5c+dSvXp1XFxcqFOnDkuWLMn4WWpqKi+//DJ16tTB3d2dgIAABg0axOnTmX8x//e//6V58+a4ubnh5eWVF5clf3G0mHm8ZRAAU9dq+xEREcn/7B6QZs+ezejRo3nrrbfYsWMH9erVo3PnzsTEZL9FxcaNGxkwYADDhg1j586d9OzZk549e7Jv3z4AkpKS2LFjB2+88QY7duxg/vz5HDp0iAcffDDTeVJSUujTpw9PPfVUnl+jWLcfKebswJGYy6w5fM7e5YiIiNyS3QPSRx99xPDhwxk6dCg1a9bkyy+/xM3NjW+//Tbb9p9++ildunThxRdfpEaNGowbN44GDRowceJEADw9PQkNDaVv375Uq1aNZs2aMXHiRLZv305k5N+ThMeOHcvzzz9PnTp17sl1FnUeLo4MaBIIaPsRERHJ/xzs+eUpKSls376dMWPGZBwzm8106NCBsLCwbD8TFhbG6NGjMx3r3LkzCxYsuOn3xMfHYzKZ7upWWnJyMsnJyRnvExISAOstvdTU1Ds+b0F0/Xpze92PNQ1k2oZwwo5fYGf4BWqX8ciL8gqMO+1HyUz9aBvqR9tQP9pGXvZjTs9p14B0/vx50tLS8PPzy3Tcz8+PgwcPZvuZ6OjobNtHR0dn2/7q1au8/PLLDBgwAA+PO/+FPH78eMaOHZvl+LJly3Bzc7vj8xZkoaGhuf5MsLeZbefNjJu7kcFV0/OgqoLnTvpRslI/2ob60TbUj7aRF/2YlJSznR3sGpDyWmpqKn379sUwDCZPnnxX5xozZkymkauEhAQCAwPp1KnTXQWvgig1NZXQ0FA6duyIo6Njrj4bdCaBHl9sYvdFC/Wat6GMl2seVZn/3U0/yt/Uj7ahfrQN9aNt5GU/Xr8DdDt2DUg+Pj5YLBbOnj2b6fjZs2fx9/fP9jP+/v45an89HEVERLBy5cq7DjHOzs44OztnOe7o6Fhk/yG4k2uvV64kLSv7sP7oeb7ffIo3HqiZR9UVHEX575AtqR9tQ/1oG+pH28iLfszp+e4oIK1YsYIVK1YQExNDenrm2yQ3m1ydHScnJxo2bMiKFSvo2bMnAOnp6axYsYJRo0Zl+5mQkBBWrFjBc889l3EsNDSUkJCQjPfXw9GRI0dYtWoVJUuWzPnFSZ4b3roi64+e56ctkTzTvgqervqXiIiI5C+5Dkhjx47lnXfeoVGjRpQuXRqTyXRXBYwePZrBgwfTqFEjmjRpwieffEJiYiJDhw4FYNCgQZQpU4bx48cD8Oyzz9KmTRsmTJhAt27d+Omnn9i2bRtTpkwBrOGod+/e7Nixg0WLFpGWlpYxP8nb2xsnJycAIiMjiY2NJTIykrS0NHbt2gVA5cqVKVas2F1dk9xa6yo+VPMrzqGzl5i1OZKn7qtk75JEREQyyXVA+vLLL5k+fTqPPfaYTQro168f586d48033yQ6Oprg4GCWLl2aMRE7MjISs/nv1QiaN2/OrFmzeP3113n11VepUqUKCxYsoHbt2gBERUWxcOFCAIKDgzN916pVq7jvvvsAePPNN/nuu+8yfla/fv0sbSRvmEwmhreuyAtzdzNtwwmGtayAk4PdV5wQERHJkOuAlJKSQvPmzW1axKhRo256S2316tVZjvXp04c+ffpk2z4oKAjDuP1KzdOnT2f69Om5KVNs6MF6Afzvj4OcTUhm4e7T9G5Y1t4liYiIZMj1f7Y/8cQTzJo1Ky9qkSLEycHMkObWTWynrj2eo1ArIiJyr+R6BOnq1atMmTKF5cuXU7du3SyzwT/66CObFSeF2yNNyzFx5REOnb3E2iPnaVPV194liYiIAHcQkPbs2ZMxt+f6/mfX3e2EbSlaPF0d6d+kHN+sP8HUtccVkEREJN/IdUBatWpVXtQhRdTQFkFM3xjO+qPn2X86nloBnvYuSURE5O42qz116hSnTp2yVS1SBJUt4Ua3OqUB+HrdCTtXIyIiYpXrgJSens4777yDp6cn5cuXp3z58nh5eTFu3Lgsi0aK5MTwVhUB+G33aU7HXbFzNSIiIncQkF577TUmTpzIe++9x86dO9m5cyfvvvsun3/+OW+88UZe1CiFXJ2ynoRULMm1dINpGzSKJCIi9pfrOUjfffcdX3/9NQ8++GDGsbp161KmTBn+/e9/89///temBUrRMKJ1RcKOX+DHLSd5un0VPFy0/YiIiNhPrkeQYmNjqV69epbj1atXJzY21iZFSdHTpqovVUoV43LyNX7aEmnvckREpIjLdUCqV68eEydOzHJ84sSJ1KtXzyZFSdFjNlu3HwH4dn04Kdc0n01EROwn17fYPvjgA7p168by5csJCQkBICwsjJMnT7JkyRKbFyhFR4/gAP73xyGiE66yeO9petXX9iMiImIfuR5BatOmDYcPH6ZXr17ExcURFxfHQw89xKFDh2jVqlVe1ChFhLODhSHNgwCYsvaEth8RERG7yfUIEkBAQIAmY0ueGNi0HJNWHeXAmQTWHz1PqypaXVtERO69HAWkPXv2ULt2bcxmM3v27Lll27p169qkMMnHUq9QMWYppHcGbPu0mZebE30bBTJ9YzhT1h5XQBIREbvIUUAKDg4mOjqaUqVKERwcjMlkyvb2h8lkIi0tzeZFSj5iGFjmP06dqFDSf0uBXl+B5Y4GIm9qWMsKzAgLZ92R8xw4k0CN0h42Pb+IiMjt5Og324kTJ/D19c34sxRhJhPpdfvD0ZWY982DtBR4+BtwcLLZVwR6u3F/ndIs3nOGqeuO81HfYJudW0REJCdyNEm7fPnymEwmACIiIihTpkzGNiPXX2XKlCEiIiJPi5X8wajRg60Vn8awOMGBhTD7UUi9atPvePKvR/4X7jrNmXhtPyIiIvdWrp9ia9u2bbYLQsbHx9O2bVubFCX5X7RnA9L6zgQHVzjyB/zYD1ISbXb+umW9aFrBm2vpBtM3htvsvCIiIjmR64BkGEbGaNI/XbhwAXd3d5sUJQWDUbEtPDoPHN3h+Gr4oTdcTbDZ+Uf8NYo0a1Mkl66m2uy8IiIit5Pj2bUPPfQQYJ2IPWTIEJydnTN+lpaWxp49e2jevLntK5T8LaglDFoAPzwMkRvh+17W0ORa4q5P3bZaKSr5unPsXCKzt57kiVYV775eERGRHMjxCJKnpyeenp4YhkHx4sUz3nt6euLv78+IESP44Ycf8rJWya8Cm8DghdZQFLUNvusOiefv+rRms4nhra5vP3KC1DRtPyIiIvdGjkeQpk2bBkBQUBAvvvgibm5ueVaUFEAB9WHIYpjRA6L3wvRuMOhXKO5/V6ftWb8MHy47xOn4qyzZe4YewWVsVLCIiMjN5XoO0qBBg4iKispy/MiRI4SHh9uiJimo/GrB0N+heACcOwjTukL8qbs6pYujhcEhQQBMWXtc24+IiMg9keuANGTIEDZu3Jjl+ObNmxkyZIgtapKCzKcKDF0CXuUg9hhMux8uht/VKR9tVh5XRwv7TycQduyCbeoUERG5hVwHpJ07d9KiRYssx5s1a8auXbtsUZMUdN4VrCNJ3hUhLhK+vR/OH73j05Vwd6Jvo7IATFl33FZVioiI3FSuA5LJZOLSpUtZjsfHx2ubEfmbZ1lrSPKtDpdOW0eSzv55x6d7vGUFzCZYfegch6Kz/v0TERGxpVwHpNatWzN+/PhMYSgtLY3x48fTsmVLmxYnBVxxf+vEbb86kBhjnbh9etcdnap8SXe61LZO+P5ao0giIpLHch2Q3n//fVauXEm1atUYOnQoQ4cOpVq1aqxdu5b//e9/eVGjFGTuPjDkNyjTEK7EwncPwsmtd3Sq64/8L9gVxdkE225tIiIi8k+5Dkg1a9Zkz5499O3bl5iYGC5dusSgQYM4ePAgtWvXzosapaBzLQGPLYByIZAcD9/3hPD1uT5N/XIlaBxUgtQ0bT8iIiJ5K8frIP1TQEAA7777rq1rkcLMxQMe/Rl+HAAn1li3Jek/Eyq3z9VphreqyNbw7czcFMHItpUp5nxHf4VFRERu6Y5+u8TFxbFlyxZiYmJIT8+8uvGgQYNsUpgUQk7u8MhsmDMIjiyDH/tD3xlQ7f4cn6JDDT8q+Lhz4nwic7ae5PGWFfKwYBERKapyHZB+++03Bg4cyOXLl/Hw8Mi0ca3JZFJAkltzdIV+M+Hnx+HAbzD7UXj4a6jVK0cfN5tNPNGqAq/9so9v1p9gUEh5HCy5vlMsIiJyS7n+zfKf//yHxx9/nMuXLxMXF8fFixczXrGxsXlRoxQ2Dk7QezrU6QPp12De47B7do4//nCDspR0dyIq7gq/74vOuzpFRKTIynVAioqK4plnntFebHJ3LA7Q6yuo/ygY6fDLk7B9eo4+6uJoYZC2HxERkTyU64DUuXNntm3blhe1SFFjtkD3z6HxE4ABvz0Lm7/K0UcfCymPs4OZvVHxbD6hkUsREbGtXM9B6tatGy+++CJ//vknderUwdHRMdPPH3zwQZsVJ0WA2QxdPwQHFwibCL+/BKlXoOVzt/yYt7sTfRqV5YdNkUxZe5xmFUvem3pFRKRIyHVAGj58OADvvPNOlp+ZTCZtNyK5ZzJBp/8DRzdY+wEsf8saku57xfqzmxjWsiIzN0ey8mAMR85eoopf8XtYtIiIFGa5vsWWnp5+05fCkdwxkwnavQbt37S+X/OeNSjdYn5RBR93OtX0A+DrdSfuRZUiIlJE6PloyV9a/Qe6vGf984ZPrbfcblhr659GtLZuP/LLzihiLmn7ERERsY1c32LL7tbaP7355pt3XIwIAM2eAgdnWPQ8bJkC167CA59YJ3XfoGF5bxqU82JHZBwzNkbwQudq975eEREpdHIdkH755ZdM71NTUzlx4gQODg5UqlRJAUlso9Hj1onbv46EHTMg9Sr0nGxdHuAGI1pX4l8/bOf7TRH8u20l3Jy0/YiIiNydXP8m2blzZ5ZjCQkJDBkyhF69crYaskiOBD9iDUnzh8PeOdaRpIe/sS40+Q8da/oRVNKN8AtJzN12isHNg+xTr4iIFBo2mYPk4eHB2LFjeeONN2xxOpG/1X7Iul+bxQkOLIQ5j1lHk/7BYjYxrJV1LtLX64+Tlq6FI0VE5O7YbJJ2fHw88fHxtjqdyN+qd4MBP1pHkw4vhR/7QUpipia9G5SlhJsjJ2OvsFTbj4iIyF3K9S22zz77LNN7wzA4c+YM33//Pfffn/Nd2UVypXIHGDgPZvWD46vhh94wcA44W9c+cnWy8FhIEJ+tOMKUtcfoWsc/00bKIiIiuZHrgPTxxx9nem82m/H19WXw4MGMGTPGZoWJZFGhFTz2C8zsDZEbYUZPePRncPUCYFBIeb5cc4zdp+LZGn6RJhW87VquiIgUXDkKSHv27KF27dqYzWZOnNCCfGJH5ZrC4IXwfS+I2gbfdYfHFoB7SXyKOfNwg7L8uMW6/YgCkoiI3KkczUGqX78+58+fB6BixYpcuHAhT4sSuaWA+jB4Ebj7QvQemN4NLp0F4IlWFTCZYPmBsxw7d9nOhYqISEGVo4Dk5eWVMXIUHh5O+i1WNha5J/xrw5AlULw0nDsA0+6H+FNU8i1GhxrafkRERO5OjgLSww8/TJs2bahQoQImk4lGjRpRsWLFbF8i94xvVRi6BDwDIfaYNSRdDM/YfuTnHac4dynZzkWKiEhBlKM5SFOmTOGhhx7i6NGjPPPMMwwfPpzixbVzuuQD3hVh6O/WuUgXT8C399No8EKCA73YdTKOodO3MHlgQwK93exdqYiIFCA5foqtS5cuAGzfvp1nn31WAUnyD69Aa0ia0QPOH8I0rSvvd/6BAb86sS8qge4T1/P5gPq0quJr70pFRKSAyPVCkdOmTVM4kvzHozQMWQx+dSAxhmq/92dpP0/qlfUkLimVwd9uYdKqoxiGVtkWEZHbs9lK2iJ2V8zXugRAQAO4Ekupn3sz5wEH+jcOJN2A//1xiCe/386lq6n2rlRERPI5BSQpXNy8YdCvENgMkuNxnvUw7zW8xHsP1cHJYmbZn2fpMWkDR85esnelIiKSjykgSeHj4mFdYbtCa0i5DD88TP+SR5nzrxBKe7pw/FwiPSdt4Pe9Z+xdqYiI5FMKSFI4OReDR+ZA5Y5w7QrM6kdw0iZ+e7olIRVLkpiSxlMzdzD+9wNcS9O6XiIiklmuA9J3333H4sWLM96/9NJLeHl50bx5cyIiIu6oiEmTJhEUFISLiwtNmzZly5Ytt2w/d+5cqlevjouLC3Xq1GHJkiUZP0tNTeXll1+mTp06uLu7ExAQwKBBgzh9+nSmc8TGxjJw4EA8PDzw8vJi2LBhXL6slZcLFUdX6D8Tqj8AaSkweyA+Eb/z/bAmPPnXWklfrTnO4GlbuHBZ6yWJiMjfch2Q3n33XVxdXQEICwtj0qRJfPDBB/j4+PD888/nuoDZs2czevRo3nrrLXbs2EG9evXo3LkzMTEx2bbfuHEjAwYMYNiwYezcuZOePXvSs2dP9u3bB0BSUhI7duzgjTfeYMeOHcyfP59Dhw7x4IMPZjrPwIED2b9/P6GhoSxatIi1a9cyYsSIXNcv+ZyDM/SZDrV7Q/o1mDcUh31zGdO1BpMeaYCbk4UNRy/Q/fP17D4ZZ+9qRUQkvzByydXV1YiIiDAMwzBeeukl47HHHjMMwzD27dtn+Pj45PZ0RpMmTYyRI0dmvE9LSzMCAgKM8ePHZ9u+b9++Rrdu3TIda9q0qfHkk0/e9Du2bNliABl1//nnnwZgbN26NaPN77//bphMJiMqKipHdcfHxxuAER8fn6P2hUlKSoqxYMECIyUlxd6l5FzaNcP45d+G8ZaHYbzlaRjbphuGYRiHohOMtv9bZZR/eZFR5dUlxk9bIu5ZSQWyH/Mh9aNtqB9tQ/1oG3nZjzn9/Z3jhSKvK1asGBcuXKBcuXIsW7aM0aNHA+Di4sKVK1dyda6UlBS2b9/OmDFjMo6ZzWY6dOhAWFhYtp8JCwvL+M7rOnfuzIIFC276PfHx8ZhMJry8vDLO4eXlRaNGjTLadOjQAbPZzObNm+nVq1eWcyQnJ5Oc/PdtmISEBMB6Sy81tWg9Nn79egvcdXf9CLPZEcuOafDbM6QlJ1Kh8XDmPdmEl37ex/KD53j5573siLjIG92q4+yQt1P0Cmw/5jPqR9tQP9qG+tE28rIfc3rOXAekjh078sQTT1C/fn0OHz5M165dAdi/fz9BQUG5Otf58+dJS0vDz88v03E/Pz8OHjyY7Weio6OzbR8dHZ1t+6tXr/Lyyy8zYMAAPDw8Ms5RqlSpTO0cHBzw9va+6XnGjx/P2LFjsxxftmwZbm5FcxuL0NBQe5eQe8Z91PI9Q+VzS7EsG8PBfbs46teNbl7gHGhiyUkzs7edYtPBkwytmkYJ57wvqUD2Yz6kfrQN9aNtqB9tIy/6MSkpKUftch2QJk2axOuvv87Jkyf5+eefKVmyJGDdgmTAgAG5PV2eSk1NpW/fvhiGweTJk+/qXGPGjMk0cpWQkEBgYCCdOnXKCF5FRWpqKqGhoXTs2BFHR0d7l5N7RlfS1ryHZcMEap2eTfVK5Ulv9SIPmEw8fOQ8o+fuJeJyKp8dcuXTvvVoVtE7T8oo8P2YT6gfbUP9aBvqR9vIy368fgfodnIdkLy8vJg4cWKW49mNrtyOj48PFouFs2fPZjp+9uxZ/P39s/2Mv79/jtpfD0cRERGsXLkyU4jx9/fPMgn82rVrxMbG3vR7nZ2dcXbOOpzg6OhYZP8hKNDX3vFNcHaDleOwrPsAS3oKdHibdjVL89vTnjz5/Xb+PJPAkO+283KXagxvVRGTyZQnpRTofsxH1I+2oX60DfWjbeRFP+b0fLmeZLF06VLWr1+f8X7SpEkEBwfzyCOPcPHixVydy8nJiYYNG7JixYqMY+np6axYsYKQkJBsPxMSEpKpPViH4P7Z/no4OnLkCMuXL88Y5frnOeLi4ti+fXvGsZUrV5Kenk7Tpk1zdQ1SgLV+ATqPt/55wyfw+8uQnk6gtxvz/92chxqUIS3d4N0lBxk1ayeJydfsWq6IiNw7uQ5IL774Ysbw1N69e/nPf/5D165dOXHiRJbJ0zkxevRopk6dynfffceBAwd46qmnSExMZOjQoQAMGjQo0yTuZ599lqVLlzJhwgQOHjzI22+/zbZt2xg1ahRgDUe9e/dm27ZtzJw5k7S0NKKjo4mOjiYlJQWAGjVq0KVLF4YPH86WLVvYsGEDo0aNon///gQEBOT6GqQAC/k3dPvI+uctX8GiZyE9DRdHCxP61GNcj1o4Wkws3nuGnpM2cPyc1soSESkKcn2L7cSJE9SsWROAn3/+mQceeIB3332XHTt2ZEzYzo1+/fpx7tw53nzzTaKjowkODmbp0qUZE7EjIyMxm//Occ2bN2fWrFm8/vrrvPrqq1SpUoUFCxZQu3ZtAKKioli4cCEAwcHBmb5r1apV3HfffQDMnDmTUaNG0b59e8xmMw8//DCfffZZruuXQqDxMOuikr+OhB0z4Foy9PgCk8WBx0KCqBngwVM/7OBIzGV6TNzAhL716FQr+1uxIiJSOOQ6IDk5OWXMAF++fDmDBg0CwNvbO8cTn240atSojBGgG61evTrLsT59+tCnT59s2wcFBWEYxm2/09vbm1mzZuWqTinEgh+xLir583DYMxuuXYWHvgYHJxqW92bRMy0ZNXMnW8JjGfH9dp5uV5nnOlTFYs6beUkiImJfub7F1rJlS0aPHs24cePYsmUL3bp1A+Dw4cOULVvW5gWK3DO1H4Z+34PFCf78FeYMgtSrAJQq7sLM4U0Z2iIIgM9XHmXo9K3EJaXYsWAREckruQ5IEydOxMHBgXnz5jF58mTKlCkDwO+//06XLl1sXqDIPVW9G/T/ERxc4PDv8GN/SLGOmDpazLzVvRaf9g/GxdHM2sPn6D5xPfui4u1ctIiI2Fqub7GVK1eORYsWZTn+8ccf26QgEbur0gEGzoVZ/eH4KpjZGx6ZDc7FAegRXIaqfsV58vvtRMYm8fDkjbzbqw4PN9QIqohIYXFHeymkpaXx888/83//93/83//9H7/88gtpaWm2rk3Efiq0hsfmg7MHRGyAGT3hSlzGj2uU9uC3US1pW82X5Gvp/Gfubt78dR8p19LtVrKIiNhOrgPS0aNHqVGjBoMGDWL+/PnMnz+fRx99lFq1anHs2LG8qFHEPso1g0G/gosXRG2D77pD4oWMH3u6OfLN4MY816EKADPCIhgwdRNnE67aqWAREbGVXAekZ555hkqVKnHy5El27NjBjh07iIyMpEKFCjzzzDN5UaOI/ZRpAEMWg5sPRO+B6d3g0t8ruZvNJp7rUJVvBjeiuIsD2yMu8sDn69lyItaORYuIyN3KdUBas2YNH3zwAd7ef+9PVbJkSd577z3WrFlj0+JE8gX/2jD0dyheGs4dgOldIT4qU5P2Nfz4bVRLqvsX59ylZB6ZuolpG07kaMkJERHJf3IdkJydnbl06VKW45cvX8bJyckmRYnkO75VYegS8AyEC0dh2v1wMTxTkyAfd+b/uzkP1gvgWrrB2N/+5LnZu7iSovl5IiIFTa4D0gMPPMCIESPYvHkzhmFgGAabNm3iX//6Fw8++GBe1CiSP3hXtIakEhUgLgKmdYXzRzM1cXNy4NP+wbzxQE0sZhO/7jpNry82EHEh0U5Fi4jInch1QPrss8+oVKkSISEhuLi44OLiQosWLahcuTKffvppXtQokn94lbPebvOpCglR1pGks39mamIymRjWsgIzn2iKTzEnDkZfovvn61l1MMZORYuISG7lOiB5eXnx66+/cujQIebNm8e8efM4dOgQv/zyC56ennlRo0j+4lEahiwBv9qQGGOduH1md5ZmzSqWZNHTrahfzouEq9d4/LutfLr8COnpmpckIpLf3dE6SABVqlShe/fudO/encqVK9uyJpH8r5gvDP4NAurDlVjrEgCntmVp5u/pwuwRITzWrDyGAR8vP8zwGduIv5Jqh6JFRCSncrSS9ujRo3N8wo8++uiOixEpUNy8reskzewDJzfDjB7wyBwIapGpmZODmXE9a1O3rCevLdjHioMxPDhxPZP617NT4SIicjs5Ckg7d+7M0clMJu1sLkWMiyc8Ot+6Z1v4OvjhYRjwI1Rqm6Vpn0aB1CjtwZPfbyfiQhJ9pmymT5CJrnYoW0REbi1HAWnVqlV5XYdIweVczLp32+xH4ehymNUP+s6Aalk3b65dxpNFT7fkmZ92su7IeWYcsWD5/RCvdquJo+WO73iLiIiN6d/IIrbg6Ar9Z0H1ByAtGWYPhP0Lsm1awt2J6UOb8K/WFQCYtjGCgV9v5tyl5HtYsIiI3IoCkoitODhDn+lQ+2FIvwbzhsKeOdk2tZhN/KdjFR6vmoa7s4UtJ2J54PN17Ii8eG9rFhGRbCkgidiSxREemgrBA8FIh/kjYMeMmzavV9Jg/pPNqFyqGGcTkun3VRjfb4rQFiUiInamgCRia2YLPDgRGg0DDFj4NGyectPmFX3dWTCyBV3r+JOaZvDGgn28OG8PV1O1RYmIiL0oIInkBbMZuk2AZiOt739/ETbcfKX5Ys4OTHqkAWPur47ZBPO2n6L3lxs5GZt0jwoWEZF/UkASySsmE3T+L7R6wfo+9E1Y/R7c5PaZyWTiyTaV+H5YU7zdndgXlUD3ietZd+TcPSxaRERAAUkkb5lM0P4NaPe69f3q8bD87ZuGJIAWlX347emW1C3rSVxSKoO/3cIXq49qXpKIyD2kgCRyL7R+ETq/a/3zhk9g6Su3DEllvFyZ82QI/RsHkm7AB0sP8a8ftnPpqrYoERG5FxSQRO6VkJHWeUkAm7/EsmS09Um3m3BxtPDew3UZ/1AdnCxm/th/lh6TNnDk7KV7VLCISNGlgCRyLzV+Anp8ASYz5l3f0yBiKly79QKRA5qUY86/Qijt6cLxc4n0nLSB3/eeuUcFi4gUTQpIIvda/YHw0FQMk4XAixtwmNQA1n0EV+Ju+pHgQC9+e7olIRVLkpiSxlMzdzD+9wNcS7v5CJSIiNw5BSQRe6jTm7Te33HFsQSmy2dhxVj4uBYsfRXiTmb7EZ9iznw/rAkjWlcE4Ks1xxk8bQsXLmuLEhERW1NAErETo2oXQmtO4Fr3SVCqFqRchk2T4NN68PMTcGZPls84WMy82rUGEx+pj5uThQ1HL9D98/XsORV37y9ARKQQU0ASsSPD7IBRtx88tQEG/gwV2oCRBnvnwletYEYPOLoiyxNvD9QNYMHIFlTwced0/FV6fxnG7K2RdroKEZHCRwFJJD8wmaBKBxi8EEasgdq9wWSB46vhh4fgy5aw+ydI+/sx/6p+xfl1VAs61vQj5Vo6L/+8lzHz95J8TVuUiIjcLQUkkfwmIBh6fwPP7ISmT4GjO5zdB788ab39tvFzuJoAgIeLI1892pAXOlXFZIIft0TS96tNnI67Yt9rEBEp4BSQRPKrEuXh/vdg9H5o/yYU84OEKFj2unVC97I3IOE0ZrOJUe2qMH1oEzxdHdl9Mo7un69n47Hz9r4CEZECSwFJJL9zLQGt/gPP7YUHJ4JPNUhOgI2fwSd14Jd/wdn9tKnqy6KnW1KztAcXElN47JstTF17XFuUiIjcAQUkkYLCwRkaPAb/3gQDZkP5FpB+DXb/CJObww8PExi3lZ//FcJD9cuQlm7w3yUHGPXjThKTr9m7ehGRAkUBSaSgMZuhWhcYugSeWAk1e4DJDEeXw4wHcZ3ejgk1jjCuezUczCYW7zlDz0kbOH7usr0rFxEpMBSQRAqysg2h7wx4ejs0Hg4OrnBmN6b5T/DYll6sanmA8sXSORJzmR4TN7Bsf7S9KxYRKRAUkEQKA++K0O1DeH4/tH0N3HwgPpLALe+wyjKSj0r+ikvyeUZ8v50Jyw6Rlq55SSIit6KAJFKYuJeENi/B8/vggY/BuxLm5HgeSpxNmOuzvOcwhSWr1jB0+lbiklLsXa2ISL6lgCRSGDm6QqPHYdRW6DcTApviYKTS32E1K5xf5LETL/P6p1+xPyrO3pWKiORLCkgihZnZAjUegGHL4PFlUP0BDEx0tOxgYvLrXJvSjk2/fQPpWn1bROSfFJBEiopyTaH/TEyjtpFcbzCpOFLPdIxm20cT+15trm36ClKS7F2liEi+oIAkUtT4VMa512dY/vMnm8oO46JRDO+U0zgsfYn0j2rBqnfh8jl7VykiYlcKSCJFlLl4KZo98RG7eq/nvzxORHopzFdjYc378Elt+O05uHDM3mWKiNiFApJIEde2TgUGjvo//uU1hX+nPMPu9Epw7SpsnwafN4SfBsLJLfYuU0TknlJAEhGCfNz5eVQrLHUeokfKO/RNfoN97iGAAQcXwTcd4ZtOcGARpKfbu1wRkTyngCQiALg5OfBZ/2DeeKAW2001eeDC04woPonLNfuDxQlObobZA2FiI9j2LaResXfJIiJ5RgFJRDKYTCaGtazAzCea4lPMiWXnStD8z4f4odkiUkKeAxdPiD0Gi56Hj2vDmg8gKdbeZYuI2JwCkohk0axiSRY93Yr65bxIuHqN11ecp1FYcz6rt4DEtuPAMxCSzsOq/8LHtWDJixB7wt5li4jYjAKSiGTL39OFOU+G8MHDdano407C1Wt8tOYMDZdX4Z2KM7nQ5QvwrwupSbBlCnzeAOYMhqjt9i5dROSuKSCJyE05Wsz0bRxI6Og2TB7YgDplPLmams63YadourAEo0t8xsnuP0HlDmCkw58LYGo7mNYVDi3VhG4RKbAUkETktixmE/fXKc3CUS2Y+URTWlQuybV0g/k7T9NqbjpPpI1hf4+lUG8AmB0gYgP82A++aAY7vodryfa+BBGRXFFAEpEcM5lMtKjsw8wnmvHryBbcX9sfkwmWHzhLt9mx9IsZTFj3lRghT4NTcTh/CBaOgk/qwLoJcOWivS9BRCRHFJBE5I7UC/Ri8qMNWT66DX0blcXRYmLziVgGzD5F14OdWdJpBWkd3oHiAXD5LKx4Bz6qBUvHQFykvcsXEbklBSQRuSuVfIvxQe96rH2pLU+0rICbk4UDZxL497yjtAury4/NfyOl+xdQqhakJsKmL+DTYJg3DM7stnf5IiLZUkASEZso7enK6w/UZOMr7Xi+Q1VKuDkScSGJMb8eovnvfkyuMYOkvnOgQhsw0mDfPPiqNXz3IBxdDoZh70sQEcmggCQiNuXl5sSzHaqw4ZV2vNW9JgGeLpy/nMz7fxyi6Rz4wO8DLj66HGr3BpMFTqyBHx6GyS1g149wLcXelyAiooAkInnDzcmBoS0qsOaltnzYpx6VSxXj0tVrfLH6GE2nn+d1h+c4PTgMmv0bHN0hZj8s+Bd8Wg82fAZX4+19CSJShNk9IE2aNImgoCBcXFxo2rQpW7bcetfwuXPnUr16dVxcXKhTpw5LlizJ9PP58+fTqVMnSpYsiclkYteuXVnOcezYMXr16oWvry8eHh707duXs2fP2vKyROQvjhYzvRuWZdlzrZnyWEOCA71IuZbOD5siaTXlGM/G9eXQwE3Q/k0o5geXTkPoG9atTJa9DvFR9r4EESmC7BqQZs+ezejRo3nrrbfYsWMH9erVo3PnzsTExGTbfuPGjQwYMIBhw4axc+dOevbsSc+ePdm3b19Gm8TERFq2bMn777+f7TkSExPp1KkTJpOJlStXsmHDBlJSUujevTvpWtROJM+YzSY61fLnl38358fhzWhd1Ze0dINfd52m85d7GXq0Fdt6rYEHJ4JPNUhOgI2fw6d1Yf6TEL3v9l8iImIjDvb88o8++ojhw4czdOhQAL788ksWL17Mt99+yyuvvJKl/aeffkqXLl148cUXARg3bhyhoaFMnDiRL7/8EoDHHnsMgPDw8Gy/c8OGDYSHh7Nz5048PDwA+O677yhRogQrV66kQ4cOtr5MEfkHk8lESKWShFQqyb6oeL5cc4wle8+w6tA5Vh06R6PylXiq3a+0s+zCtHEiRKyHPT9ZX5XaQ4tnrBO9TSZ7X4qIFGJ2G0FKSUlh+/btmQKJ2WymQ4cOhIWFZfuZsLCwLAGmc+fON22fneTkZEwmE87OzhnHXFxcMJvNrF+/PpdXISJ3o3YZTyY+0oCV/7mPAU3K4WQxsy3iIsNm7KDLEncWBE/h2uPLoWZPMJnh2AqY0cP69NueuZCWau9LEJFCym4jSOfPnyctLQ0/P79Mx/38/Dh48GC2n4mOjs62fXR0dI6/t1mzZri7u/Pyyy/z7rvvYhgGr7zyCmlpaZw5c+amn0tOTiY5+e/tEhISEgBITU0lNbVo/Uv6+vUWteu2NfXj38p4OvFO9+qMuq8C0zdGMGvrSQ6dvcRzs3fxoZcLw1q+SZ+WY3DdMRXzrpmYovfA/Ccwlr+N0Wg4Dmn+6se7pL+PtqF+tI287MecntOut9jswdfXl7lz5/LUU0/x2WefYTabGTBgAA0aNMBsvvmA2vjx4xk7dmyW48uWLcPNzS0vS863QkND7V1CoaB+zKw28HpdWB9tYs0ZM6firjJ20UEmOBi0Kd2GdlXqUzNuBRXPheKccAqnlW/R0eLGnvhdRHmH2Lv8Ak9/H21D/WgbedGPSUlJOWpnt4Dk4+ODxWLJ8vTY2bNn8ff3z/Yz/v7+uWp/M506deLYsWOcP38eBwcHvLy88Pf3p2LFijf9zJgxYxg9enTG+4SEBAIDA+nUqVPGXKaiIjU1ldDQUDp27Iijo6O9yymw1I+31hu4mprGzzui+Hp9OKfirrL4pIXVMT4MaPwCQ3t9gP+JBZg2fYHTxeM0iphMfd9U0ju8AxYne5df4Ojvo22oH20jL/vx+h2g27FbQHJycqJhw4asWLGCnj17ApCens6KFSsYNWpUtp8JCQlhxYoVPPfccxnHQkNDCQm5s/9q9PHxAWDlypXExMTw4IMP3rSts7NzpnlL1zk6OhbZfwiK8rXbkvrx5hwdHRnSshKPhlRg0Z4zTF59jENnL/H1+nBmhEXycMNmDOvzMGm/Pku1swuxbPsay5ld0Pc78Cxr7/ILJP19tA31o23kRT/m9Hx2vcU2evRoBg8eTKNGjWjSpAmffPIJiYmJGU+1DRo0iDJlyjB+/HgAnn32Wdq0acOECRPo1q0bP/30E9u2bWPKlCkZ54yNjSUyMpLTp08DcOjQIcA6+nR9pGnatGnUqFEDX19fwsLCePbZZ3n++eepVq3avbx8EckhB4uZnvXL0CM4gFWHYvhi1TG2RVzkxy2RzN4aST3vvkzo8CAV178AUdvgy1bw8NdQub29SxeRAsqu6yD169ePDz/8kDfffJPg4GB27drF0qVLMyZiR0ZGZpo43bx5c2bNmsWUKVOoV68e8+bNY8GCBdSuXTujzcKFC6lfvz7dunUDoH///tSvXz9jGQCwhqaePXtSo0YN3nnnHV577TU+/PDDe3TVInKnTCYT7ar7Me+p5sz9Vwjtqpci3YCdF8y0W+TGaO/PuOxdG67EWrcvWf0+aH0zEbkDJsPQDpF3IiEhAU9PT+Lj44vkHKQlS5bQtWtXDSHfBfWjbew9GcvbszewK9ZCWrqBMyl85jWbzld/tzao3BEemgJu3vYtNJ/T30fbUD/aRl72Y05/f9t9qxERkbtR3b84g6qks+zZFjzWrDw4uPBk3GP8J+VfJOMER0MxvmoFUdvtXaqIFCAKSCJSKJTzdmNcz9qsf7kd/76vEssc29Ej+R1OpPthij9F2tedSdk0FTRoLiI5oIAkIoWKb3FnXupSnQ1j2tGzSyeGOP6PP9IaYTFScVr6Age+GEBc3EV7lyki+ZwCkogUSh4ujvyrTSX+eOUBznf7hi8ch3DNMFPj3O/EfNyKL+Yt5Uz8FXuXKSL5VJFbSVtEihYXRwsDmwVxrfHHbF7djhrrn6UqJym9dwiv7PoX7sG9eLJNJSr5FrN3qSKSj2gESUSKBAeLmRbte1Di+TDifBtT3HSFSQ4fU3nX+3T5aAVP/bCdPafi7F2miOQTCkgiUqSYPErj9a+l0PwZAEY4LGam43/Zvu8AD07cwMCvN7Hh6Hm0AopI0aaAJCJFj8UBOo2Dfj+AswdNzIdY7v4GzS0H2HD0AgO/3kyPSRv4fe8Z0tIVlESKIgUkESm6anSHEauhVC080mKZ6fRfplZch4ujiT2n4nlq5g46fryGOVtPknJNK3KLFCUKSCJStJWsBE8sh3oDMBnpdDw9mT3VvuPF1n54uDhw/FwiL/28h9YfrOLrdcdJTL5m74pF5B5QQBIRcXKDnpPhgU/A4oTT0aWMPPIEmx4vxWtda+Dn4Ux0wlX+b/EBmr+3ko9CDxObmGLvqkUkDykgiYgAmEzQaCgMWwZe5eDiCdxmdGF48Y2sfakt7z9chwo+7sRfSeWzFUdo/t4K3l64n6g4raUkUhgpIImI/FNAfRixBqp0gmtX4deROC95jn7BpVg+ug1fDGxAnTKeXE1NZ/rGcNp8sIr/zNnNkbOX7F25iNiQApKIyI3cvGHAbGj3OmCCHTPgm45Y4sLpWqc0C0e14IdhTWlRuSTX0g1+3nGKjh+vZfiMbeyI1DYmIoWBApKISHbMZmj9Ijz2C7iVhOg9MKUNHFqKyWSiZRUfZj7RjAUjW9Cllj8mE4T+eZaHvthI/ylhrDl8TmspiRRgCkgiIrdSqS08uRbKNoar8fBjP1jxDqSnARAc6MWXjzUk9Pk29G1UFkeLiU3HYxn87RYe+Hw9i/ac1lpKIgWQApKIyO14loUhS6DJk9b36ybA9z3h8rmMJpVLFeOD3vVY82JbhrWsgJuThf2nExg1ayftJ6xm5uYI4pL05JtIQaGAJCKSEw5O0PUDePgbcHSHE2vhq1YQuSlTswAvV954oCYbXm7H8x2qUsLNkfALSbz2yz4a/t9y+n4VxldrjnE05rJuwYnkYw72LkBEpECp0xv868Dsx+D8IZjeDTqOg2ZPWZcK+EsJdyee7VCF4a0r8OOWk8zZepJDZy+x5UQsW07EMv73g5Qv6Ub76n60r1GKxkHeODnov1lF8gsFJBGR3PKtBsNXwm/PwL6f4Y8xcHIz9JgIzsUzNXVzcmBYywoMa1mBk7FJrDoUw/IDMWw6doGIC0l8u+EE3244QXFnB1pX9aV9jVLcV60U3u5Odro4EQEFJBGRO+NczHq7LbAp/PEa/LkAzu6Hft9DqRrZfiTQ241BIUEMCgkiMfka646cZ+XBs6w8GMP5yyks3nuGxXvPYDZBg3IlaFejFB1q+FGlVDFM/xidEpG8p4AkInKnTCZo+iQENIC5g+HCEZjaDrp/BnX73PKj7s4OdKntT5fa/qSnG+yJimfFgbOsOBDDn2cS2BZxkW0RF/lg6SECvV1pX92PdtVL0bSiN84Olnt0gSJFlwKSiMjdCmxsXQrg52FwfDXMf8J6y63zf8HB+bYfN5tNBAd6ERzoxX86VeN03BVWHoxhxYGzbDh2gZOxV5i+MZzpG8Nxd7LQqor1Vlzb6qXwKXb784tI7ikgiYjYgrsPPDofVr8Haz+ArVPh9A7o8x14BebqVAFerjzarDyPNitPUso1Nhy9wMqD1tGlmEvJLN0fzdL90ZhM1nWY2lcvRfsaflT3L65bcSI2ooAkImIrZgu0ew3KNoL5IyBqO3zVGh7+Giq3v6NTujk50LGmHx1r+pGebrD/dALLD1jnLe2NimdnZBw7I+P4cNlhyni50q56KdrVKEVIxZK4OOpWnMidUkASEbG1qp2tt9zmDIIzu+CHh+G+MdatS8x3/ii/2WyiTllP6pT15PmOVYmOv8qqQ9ZbceuPnicq7grfb4rg+00RuDpaaFnFhw41StG2WilKebjY7vpEigAFJBGRvFCiPDz+Byx9BbZPg9Xvwqkt8NBU62a4NuDv6cKAJuUY0KQcV1PT2HjsPCsOxLDyYAxn4q8S+udZQv88C0C9sp60+2vNpVoBHroVJ3IbCkgiInnF0QW6f2JdCmDR83B0ufWWW9/voExDm36Vi6OFdtX9aFfdD8Mw+PNMAisOxLDiYAy7T8ax+1Q8u0/F8/Hyw/h7uNCuRinaVy9F80o+uDrpVpzIjRSQRETyWvAA6+rbcx6D2OPwbRfo8h40ejzT6tu2YjKZqBXgSa0AT55pX4WYS1dZffAcy/+6FRedcJVZmyOZtTkSF0czLSr5cF9VH9KTbV6KSIGlgCQici/414YRq2HBv+HgIlg82roUwAMfg5N7nn51qeIu9G0cSN/GgVxNTWPT8Qt/LSMQQ1TcFVYctI40gQNzosNoX8OfDjVKUTvAE7NZt+KkaFJAEhG5V1w8od8PEDYRQt+CPbMhei/0/R58Kt+bEhwt3FfNup3J2AcNDp29xIoDMSz/M5pdJ+PYf/oS+09f4rMVR/At7kz76qVoV70ULav44OakXxlSdOhvu4jIvWQyQfOnratvzxsKMX/ClPug5ySo2eMel2Kiur8H1f09GNGyPLN/XYJjuXqsOXKBtYfPc+5SMj9tPclPW0/i5GCmeaWS1sBUw48yXq73tFaRe00BSUTEHoJaWJcCmPc4RGywLgkQMgo6vA0WR7uUVNwRutYvQ78mQaRcS2fziQt/TfQ+y8nYK6w+dI7Vh87xxq/7qe5fnA41/GhXoxTBZb10K04KHQUkERF7Ke4PgxbCirGw8TPrrbeo7dB7GniUtmtpTg5mWlXxpVUVX97qXpOjMZdZfiCGlQfPsj3iIgejL3Ew+hITVx3Fp5gTbauVon2NUrSs4ksxZ/1qkYJPf4tFROzJ4gCdxkFgE+sE7sgw+KqVNSRVaGXv6gDrrbgqfsWp4lecp+6rRGxiCmsOx7D8QAxrD53j/OUU5m4/xdztp3CymGla0Ttj+5NAbzd7ly9yRxSQRETygxrdoVRN6622s/tgxoPQ7g1o8dxdrb6dF7zdnehVvyy96pclNS2dreGx1ltxB84SfiGJdUfOs+7Ied7+7U+q+hWjfQ0/2lcvRf1yJbDoVpwUEApIIiL5RclKMCwUFv8Hds+y3no7tRV6TgZXL3tXly1Hi5nmlXxoXsmHNx6oybFzl1lxwLqx7raIixw+e5nDZy8zefUxSrg5/nUrzo9WVX3wcLHPXCuRnFBAEhHJT5zcoOcXUK4pLHkJDi2BKW2sSwGUrmvv6m6rkm8xKvkWY0TrSsQlpbDm8DlWHIhh9aEYLialMn9nFPN3RuFgNtG0ojftqvvRoUYpypfM27WgRHJLAUlEJL8xmaDhEChdz3rL7WI4fNMRuk2A+o/au7oc83JzokdwGXoEl+FaWjrbIy5aF6U8cJZj5xLZcPQCG45eYNyiP6nk6259Kq56KRqWL4GDJX/dVpSiRwFJRCS/CqgPI9bAL/+CI3/AryMhchN0/R84Fqx1iBwsZppWLEnTiiV5tWsNws8nZoSlLSdiOXYukWPnjvPV2uN4ujpyXzVf2tfwo00VXzzdCsCtuPQ0iDmAKWIT1c6sw3TKF4JC8mQrGbk3FJBERPIzN28Y8BOs/whW/Rd2fg9ndkPfGeBdwd7V3bEgH3eGtazAsJYVSLiaytrD51h5IIZVf92K+3XXaX7ddRqL2UTjoBK0r+5H+xqlqOhbzN6lW106C1HbrHPETm2DqB2QmogDUB3guwVQIgjq9oM6fe/ZSuliOwpIIiL5ndkMrV+AMg3h52EQvcc6L6nXV1DtfntXd9c8XBx5oG4AD9QNIC3dYGfkxYw1lw6fvcym47FsOh7Lf5ccoHxJN+oHelG7jCd1y3pRK8AD97xed+laMpzZ81cY2moNRnGRWds5FSc9oD5n4q4SkLgf08VwWPO+9RXQwBqWaj8MxXzztl6xCQUkEZGColJbeHIdzB0Cp7bAj/2h5Who+5p1PaVCwGI20SjIm0ZB3rxyf3UiLySx8uBZVhyMYdPxC0RcSCLiQhILdp0GrHewKvq4U6eMJ7XLeFKnjCe1ynje+WKVhgFxEdZRoVN/jRBF74G0lBsamsC3OpRtBGUbW1++1UhLS2fbkiV07dAGx2Oh1v32jq2E0zusrz9ehUrtrGGpetc836hY7lzh+CdKRKSo8CwDQxZD6Buw+UvrrbdTW6H3t1CslL2rs7lyJd0Y0qICQ1pU4HLyNbaGx7LvVDx7ouLZFxXPmfirf81fSswUmir8FZrq3C40JV+C0zv/vlV2aisknsvazq3kX0Hor0AU0ABcPLK2S0u3/q+TO9TtY31dPgf751vDUtR2OBpqfTm6W9e/qtsXKrQpNCG3sND/GyIiBY2DE9z/vnX17V+fhvB18FVr6DMdyjWzd3V5ppizA22rlaJttb+D4PnLyeyNimffqXjr/0bFczr+KsfPJXL8XCK//nOkqaQr7X3jaelygmrXDuETtxfL+QNgpGf+IrMD+Nf9RyBqBCUq3PmE62K+0PRJ6+v8Udg7xxqWLobDnp+sr2J+ULu3NSyVrqfJ3fmAApKISEFV+2Hwqw2zH4Pzh2B6N+g4Dpo9VWR+wfoUc842NO2LiudoeARXTmzG/dwuKqccJPjSUTwuX8lyjlgHP+K862IObIxvjZa4l2+Qd08J+lSGtq/CfWOso1V7ZsO++XD5LGyaZH35VLMGpTp9oET5vKlDbksBSUSkIPOtBsNXwm/Pwr558McYOLkZekwE5+L2ru7eSUu1btFyahs+p7Zy36mt3Bd7/O+fW6z/k2p25oRTNbakVmLdlSB2plcmhhJwGYgENsRR0Wdzxnym2mU8qVXGw/arfptM1hHAwCbQeTwcW2ENS4d+t4bdleOsr3LNrbfpava0PtEo94wCkohIQedcDB7+GgKbWicB/7kAzu6Hft9DqRr2ri5vxEf9/VTZqW1wZhdcu5q1XckqmeYOOZaqSVWLA1WB+y8ns+90Avui4tn71y26qLgrHD+fyPHziSzcfTrjNBV83P8KTR7U/is42Sw0OThZn0asdj9cTYADv1nD0om1ELnR+lryElTtbB1ZqtIZHF1s891yUwpIIiKFgckETUdYF5ecOxguHIGp7aD7p9ZfqgVZSpI1AGVMpN4Gl05nbefiaQ1DZf6aSF2mwS1HXUoWc6ZNVV/aVP37sfvYxJSMuUz/DE0nzidy4nwiv/0jNAWVdPtruQHPjNDkarnLa3XxgPoDra+E07B3HuyZA2f3wsFF1pezJ9TqYX0SrlzzfLeZcWGhgCQiUpgENoYn11rXSzq+GuYPt95y6/wuODjbu7rbMwy4cOwfizBuheh9YKRlbmcyg1+tvx+xL9sYvCvddVjwdnfKNjTti/p7EvjeqHhOXbxC+IUkwi8ksWjPmYy25b3d8DaZifI4QXCgN7XKeOLpeocjTR4B0OIZ6+vsfmtQ2jsXEqJgxwzry6PsX0/L9Su8o4V2ooAkIlLYuPvAo/Nh9Xuw9gPY+rX1UfY+34FXoL2ry+xKnPXR9+uP2EdtgysXs7Yr5pc5DAUE37M1hLzdnWhd1ZfW/whNF/8aaboxNEXEJhGBmZ1/HMloW/6vkaY6ZTyp+9eSA7kOTX61oONYaP8WRGyw3oL781dIOAXrP7a+/Ov8tRhlb/AobavLL7IUkERECiOzBdq9Zg0T84dbQ8hXreHhqVC5g31qSrsG5w5kXnPo/OGs7SzO1gBUtrF19fCyjcGzbL56Mq/ETULTrshYfl61hWvFA9h/JoGTsVcyFrdc/M+Rpn+EpjplPKkd4JmzPefMZqjQyvrq+iEcXmodWTqyDKL3Wl/L3oAKra1hqUb37NdrkttSQBIRKcyqdrLecpszyDqP54fecN8r0PqlvJ+7cuns36NC/9ivLIsSFf6xInUj8KtjnbhcwJRwd6Jl5ZIkHDbo2rUejo6OXExMYd/pzCNNNwtN5bzdMq0IXqfMbUKTowvU6ml9JcXC/l+sYenkJjixxvpaPBqqdbWGpcrtwVIANv7NJxSQREQKuxLl4fE/YOkrsH0arB5vDS4PTbXdo+M37ld2ahvEZ79fGWUa/ON2WSPrLcFCqoS7E62q+NKqyt8jTXFJKeyLSsgUmiJjkzJei/fePDTVLuOBl1s24dHNGxoPs74uhlvnKu2ebZ2sv3++9eVWEmo9ZA1LZRvlqxG5/EgBSUSkKHB0ge6fWJcCWPQ8HF1uveXW9zvrbazcyLRf2V+B6MweSE+9oaHJOnH4n/uV+VS13v4rwrzcnGhZxYeWVf4OhrkJTYHerllGmjKFphJB0PpFaPWCddRwzxzr03CJMbB1qvVVooI1KNXtCyUr3buLL0AUkEREipLgAVC6rnX17dhj8G0X6DIe6g26+WeSL1lvj53a+teE6pvtV+bzVxBqeOv9yiSL7EJTfFJqxu2568Ep4kISJ2OvcDL2Ckv2Rme0LVvCGprqlP17TlMJdyfrsg8B9a0rrJ9YbQ1LB36DiydgzXvWV5lGf03ufqhQj+bllgKSiEhR41cLRqyCX0daf1ku/g+WiE1YzJ2s+5LFHMx8qyzmT8DIfA6zo/WpqX/eKisRpNs2NuTp5kiLyj60qHz70HTq4hVOXbzC7/uyhqa/R5paU6JyB0i+DIeWWJ+EO7bSOkcsapv1FmzlDtZRpWpdwcnNHpedbyggiYgURS6e0Pd7CJsIoW9h3jeX9o7LcTg02jpidCPPwMy3yvzrajVnO8g2NF1JZX9U5tAUfpPQVMbr+khTA9q170KNHles85P2zLYuBXHkD+vLqZj1Cbi6faFCmyJ5W9Tuy29OmjSJoKAgXFxcaNq0KVu2bLll+7lz51K9enVcXFyoU6cOS5YsyfTz+fPn06lTJ0qWLInJZGLXrl1ZzhEdHc1jjz2Gv78/7u7uNGjQgJ9//tmWlyUikv+ZTND8aRj8G4Z7KVxTL2JKvgSOblC+JbR4Dvr9AKMPwvP7oM90CBlp3T9M4Sjf8HR1pHllH55sU4mJjzRg9Ytt2f1WJ2YNb8qY+6vzQN3SBJW0jgZFxV1h6f5o/vfHITYcPQ/F/aybG49YDSO3WucueZWDlMuw+0f4vhd8VBP+eA3O7LbOPysi7DqCNHv2bEaPHs2XX35J06ZN+eSTT+jcuTOHDh2iVKlSWdpv3LiRAQMGMH78eB544AFmzZpFz5492bFjB7Vr1wYgMTGRli1b0rdvX4YPH57t9w4aNIi4uDgWLlyIj48Ps2bNom/fvmzbto369evn6TWLiOQ7QS24NmIdOxdMpH67h3AMqAsW3WAoyDxdHWleyYfmlW4YaTp9fRJ4Ak0q3PAEo29VaPc6tH0NTm6xjirtnw+Xo60jjWETwbe6dVSpTh9rkCrMDDtq0qSJMXLkyIz3aWlpRkBAgDF+/Phs2/ft29fo1q1bpmNNmzY1nnzyySxtT5w4YQDGzp07s/zM3d3dmDFjRqZj3t7extSpU3Nce3x8vAEY8fHxOf5MYZGSkmIsWLDASElJsXcpBZr60TbUj7ahfrSNQtePqcmGcWCxYcweZBjv+BrGWx5/v7693zC2TTOMpFibf21e9mNOf3/b7T8RUlJS2L59O2PGjMk4Zjab6dChA2FhYdl+JiwsjNGjR2c61rlzZxYsWJCr727evDmzZ8+mW7dueHl5MWfOHK5evcp99913088kJyeTnJyc8T4hIQGA1NRUUlNvfLS1cLt+vUXtum1N/Wgb6kfbUD/aRuHrRxNU6mh9XU3AdPA3zPvmYorYgCliA0RswFjyIkblTqTX7oNRuYNN9vzLy37M6TntFpDOnz9PWloafn5+mY77+flx8ODBbD8THR2dbfvo6Ohs29/MnDlz6NevHyVLlsTBwQE3Nzd++eUXKleufNPPjB8/nrFjx2Y5vmzZMtzciuZM/9DQUHuXUCioH21D/Wgb6kfbKLz9WAK8R+BS7GHKXtxE2diNeF49ienQIsyHFpFiceO0V1NOeYdwwb2qdVPhu5AX/ZiUlJSjdkXyJvMbb7xBXFwcy5cvx8fHhwULFtC3b1/WrVtHnTp1sv3MmDFjMo1eJSQkEBgYSKdOnfDwKFrrfKSmphIaGkrHjh1xdNSy9XdK/Wgb6kfbUD/aRtHqx8cASD27H/O+uZj3/4zTpTMEXVhF0IVVGB5lSa/dm/TafcC3Wq7OnJf9eP0O0O3YLSD5+PhgsVg4e/ZspuNnz57F398/28/4+/vnqn12jh07xsSJE9m3bx+1atUCoF69eqxbt45Jkybx5ZdfZvs5Z2dnnJ2zDhs6OjoWgX8IsleUr92W1I+2oX60DfWjbRSpfiwbbH11egfC1/+1GOVCTAmnsGz8BMvGT6zLQtTtB3V6Q/Gc/87Oi37M6fns9pi/k5MTDRs2ZMWKFRnH0tPTWbFiBSEhIdl+JiQkJFN7sA6/3ax9dq4PrZlv2KTRYrGQnp6e4/OIiIjIP5gtULEN9JwELxy2LgtRrSuYHSB6Dyx7DT6qATN6wq4fs19vKx+x6y220aNHM3jwYBo1akSTJk345JNPSExMZOjQoYD1cfwyZcowfvx4AJ599lnatGnDhAkT6NatGz/99BPbtm1jypQpGeeMjY0lMjKS06dPA3Do0CHAOvrk7+9P9erVqVy5Mk8++SQffvghJUuWZMGCBYSGhrJo0aJ73AMiIiKFkKMr1OplfSVegD9/sY4sndwMx1dZX4tcoXo367IBldqBJX+NuNk1IPXr149z587x5ptvEh0dTXBwMEuXLs2YiB0ZGZlppKd58+bMmjWL119/nVdffZUqVaqwYMGCjDWQABYuXJgRsAD69+8PwFtvvcXbb7+No6MjS5Ys4ZVXXqF79+5cvnyZypUr891339G1a9d7dOUiIiJFhHtJaPyE9RV73Lpx7p7ZcOEo7JtnfbmVhNoPW2/D5Xbz5Dxi90nao0aNYtSoUdn+bPXq1VmO9enThz59+tz0fEOGDGHIkCG3/M4qVapo5WwREZF7zbsitHnJumL36R3WUaW98yDpPGyZYn15V8Rcqzfuyb52LdXuAUlERESKGJPJOlJUpiF0+i8cX20dVTq4CGKPY1n3AR2ANN9YaPuKXUpUQBIRERH7sThAlQ7WV/JlOLiY9N0/YTq+CqNsE7uVZffNakVEREQAcC4G9fqRNmAOf9T+FKN8C7uVooAkIiIi+U6yo9ddr8R9NxSQRERERG6ggCQiIiJyAwUkERERkRsoIImIiIjcQAFJRERE5AYKSCIiIiI3UEASERERuYECkoiIiMgNFJBEREREbqCAJCIiInIDBSQRERGRGyggiYiIiNxAAUlERETkBg72LqCgMgwDgISEBDtXcu+lpqaSlJREQkICjo6O9i6nwFI/2ob60TbUj7ahfrSNvOzH67+3r/8evxkFpDt06dIlAAIDA+1ciYiIiOTWpUuX8PT0vOnPTcbtIpRkKz09ndOnT1O8eHFMJpO9y7mnEhISCAwM5OTJk3h4eNi7nAJL/Wgb6kfbUD/ahvrRNvKyHw3D4NKlSwQEBGA233ymkUaQ7pDZbKZs2bL2LsOuPDw89C8AG1A/2ob60TbUj7ahfrSNvOrHW40cXadJ2iIiIiI3UEASERERuYECkuSas7Mzb731Fs7OzvYupUBTP9qG+tE21I+2oX60jfzQj5qkLSIiInIDjSCJiIiI3EABSUREROQGCkgiIiIiN1BAEhEREbmBApLkytq1a+nevTsBAQGYTCYWLFhg75IKnPHjx9O4cWOKFy9OqVKl6NmzJ4cOHbJ3WQXO5MmTqVu3bsZCciEhIfz+++/2LqvAe++99zCZTDz33HP2LqVAefvttzGZTJle1atXt3dZBVJUVBSPPvooJUuWxNXVlTp16rBt27Z7XocCkuRKYmIi9erVY9KkSfYupcBas2YNI0eOZNOmTYSGhpKamkqnTp1ITEy0d2kFStmyZXnvvffYvn0727Zto127dvTo0YP9+/fbu7QCa+vWrXz11VfUrVvX3qUUSLVq1eLMmTMZr/Xr19u7pALn4sWLtGjRAkdHR37//Xf+/PNPJkyYQIkSJe55LdpqRHLl/vvv5/7777d3GQXa0qVLM72fPn06pUqVYvv27bRu3dpOVRU83bt3z/T+v//9L5MnT2bTpk3UqlXLTlUVXJcvX2bgwIFMnTqV//u//7N3OQWSg4MD/v7+9i6jQHv//fcJDAxk2rRpGccqVKhgl1o0giRiZ/Hx8QB4e3vbuZKCKy0tjZ9++onExERCQkLsXU6BNHLkSLp160aHDh3sXUqBdeTIEQICAqhYsSIDBw4kMjLS3iUVOAsXLqRRo0b06dOHUqVKUb9+faZOnWqXWjSCJGJH6enpPPfcc7Ro0YLatWvbu5wCZ+/evYSEhHD16lWKFSvGL7/8Qs2aNe1dVoHz008/sWPHDrZu3WrvUgqspk2bMn36dKpVq8aZM2cYO3YsrVq1Yt++fRQvXtze5RUYx48fZ/LkyYwePZpXX32VrVu38swzz+Dk5MTgwYPvaS0KSCJ2NHLkSPbt26e5CneoWrVq7Nq1i/j4eObNm8fgwYNZs2aNQlIunDx5kmeffZbQ0FBcXFzsXU6B9c+pB3Xr1qVp06aUL1+eOXPmMGzYMDtWVrCkp6fTqFEj3n33XQDq16/Pvn37+PLLL+95QNItNhE7GTVqFIsWLWLVqlWULVvW3uUUSE5OTlSuXJmGDRsyfvx46tWrx6effmrvsgqU7du3ExMTQ4MGDXBwcMDBwYE1a9bw2Wef4eDgQFpamr1LLJC8vLyoWrUqR48etXcpBUrp0qWz/AdOjRo17HK7UiNIIveYYRg8/fTT/PLLL6xevdpuExALo/T0dJKTk+1dRoHSvn179u7dm+nY0KFDqV69Oi+//DIWi8VOlRVsly9f5tixYzz22GP2LqVAadGiRZZlTw4fPkz58uXveS0KSJIrly9fzvRfRCdOnGDXrl14e3tTrlw5O1ZWcIwcOZJZs2bx66+/Urx4caKjowHw9PTE1dXVztUVHGPGjOH++++nXLlyXLp0iVmzZrF69Wr++OMPe5dWoBQvXjzL/Dd3d3dKliypeXG58MILL9C9e3fKly/P6dOneeutt7BYLAwYMMDepRUozz//PM2bN+fdd9+lb9++bNmyhSlTpjBlypR7X4whkgurVq0ygCyvwYMH27u0AiO7/gOMadOm2bu0AuXxxx83ypcvbzg5ORm+vr5G+/btjWXLltm7rEKhTZs2xrPPPmvvMgqUfv36GaVLlzacnJyMMmXKGP369TOOHj1q77IKpN9++82oXbu24ezsbFSvXt2YMmWKXeowGYZh3PtYJiIiIpJ/aZK2iIiIyA0UkERERERuoIAkIiIicgMFJBEREZEbKCCJiIiI3EABSUREROQGCkgiIiIiN1BAEhGxkdWrV2MymYiLi7N3KSJylxSQRERERG6ggCQiIiJyAwUkESk00tPTGT9+PBUqVMDV1ZV69eoxb9484O/bX4sXL6Zu3bq4uLjQrFkz9u3bl+kcP//8M7Vq1cLZ2ZmgoCAmTJiQ6efJycm8/PLLBAYG4uzsTOXKlfnmm28ytdm+fTuNGjXCzc2N5s2bZ9mdXETyPwUkESk0xo8fz4wZM/jyyy/Zv38/zz//PI8++ihr1qzJaPPiiy8yYcIEtm7diq+vL927dyc1NRWwBpu+ffvSv39/9u7dy9tvv80bb7zB9OnTMz4/aNAgfvzxRz777DMOHDjAV199RbFixTLV8dprrzFhwgS2bduGg4MDjz/++D25fhGxHW1WKyKFQnJyMt7e3ixfvpyQkJCM40888QRJSUmMGDGCtm3b8tNPP9GvXz8AYmNjKVu2LNOnT6dv374MHDiQc+fOsWzZsozPv/TSSyxevJj9+/dz+PBhqlWrRmhoKB06dMhSw+rVq2nbti3Lly+nffv2ACxZsoRu3bpx5coVXFxc8rgXRMRWNIIkIoXC0aNHSUpKomPHjhQrVizjNWPGDI4dO5bR7p/hydvbm2rVqnHgwAEADhw4QIsWLTKdt0WLFhw5coS0tDR27dqFxWKhTZs2t6ylbt26GX8uXbo0ADExMXd9jSJy7zjYuwAREVu4fPkyAIsXL6ZMmTKZfubs7JwpJN0pV1fXHLVzdHTM+LPJZAKs86NEpODQCJKIFAo1a9bE2dmZyMhIKleunOkVGBiY0W7Tpk0Zf7548SKHDx+mRo0aANSoUYMNGzZkOu+GDRuoWrUqFouFOnXqkJ6enmlOk4gUThpBEpFCoXjx4rzwwgs8//zzpKen07JlS+Lj49mwYQMeHh6UL18egHfeeYeSJUvi5+fHa6+9ho+PDz179gTgP//5D40bN2bcuHH069ePsLAwJk6cyBdffAFAUFAQgwcP5vHHH+ezzz6jXr16REREEBMTQ9++fe116SKSBxSQRKTQGDduHL6+vowfP57jx4/j5eVFgwYNePXVVzNucb333ns8++yzHDlyhODgYH777TecnJwAaNCgAXPmzOHNN99k3LhxlC5dmnfeeYchQ4ZkfMfkyZN59dVX+fe//82FCxcoV64cr776qj0uV0TykJ5iE5Ei4foTZhcvXsTLy8ve5YhIPqc5SCIiIiI3UEASERERuYFusYmIiIjcQCNIIiIiIjdQQBIRERG5gQKSiIiIyA0UkERERERuoIAkIiIicgMFJBEREZEbKCCJiIiI3EABSUREROQGCkgiIiIiN/h/mmRM6u3u++IAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#!g1.1\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "from IPython.display import clear_output\n",
        "\n",
        "classifier = DistilBertForSequenceClassification.from_pretrained(base_model_name, num_labels=num_tags).to('cuda')\n",
        "for param in classifier.base_model.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "classifier.train()\n",
        "loss_fn = F.binary_cross_entropy\n",
        "optimizer = torch.optim.Adam(classifier.parameters(), lr=1e-4)\n",
        "epochs = 6\n",
        "\n",
        "train_loss = []\n",
        "val_loss = []\n",
        "    \n",
        "for epoch in range(epochs):\n",
        "    print(f\"Epoch {epoch+1}\\n-------------------------------\")\n",
        "    \n",
        "    train_loop(classifier, base_train_dl, loss_fn, optimizer, history_loss=train_loss)\n",
        "    test_loop(classifier, base_test_dl, loss_fn, history_loss=val_loss)\n",
        "    \n",
        "    clear_output()\n",
        "    plot_learning_process(train_loss, val_loss)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(classifier.state_dict(), './weight_model')"
      ],
      "metadata": {
        "id": "pJMF9lg16z2U"
      },
      "id": "pJMF9lg16z2U",
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier.save_pretrained('./models')"
      ],
      "metadata": {
        "id": "j8BGaz9-Ni45"
      },
      "id": "j8BGaz9-Ni45",
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "b20daefd",
      "metadata": {
        "id": "b20daefd"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "source": [
        "#!unzip arxiv-metadata-oai-snapshot.json"
      ],
      "metadata": {
        "id": "Qma98ZbbWnuN"
      },
      "id": "Qma98ZbbWnuN",
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "60478d78",
      "metadata": {
        "id": "60478d78"
      },
      "outputs": [],
      "source": [
        "# big_data = []\n",
        "# with open('arxiv-metadata-oai-snapshot.json') as big_file:\n",
        "#     for line in tqdm(big_file):\n",
        "#         big_data.append(json.loads(line))\n",
        "#         if len(big_data) >= 10**5:\n",
        "#             break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "fc91b965",
      "metadata": {
        "id": "fc91b965"
      },
      "outputs": [],
      "source": [
        "# titles = [big_data[i]['title'] for i in trl(big_data)]\n",
        "# summaries = [big_data[i]['abstract'] for i in trl(big_data)]\n",
        "# tags = [big_data[i]['categories'].split() for i in trl(big_data)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "1c812bcd",
      "metadata": {
        "id": "1c812bcd"
      },
      "outputs": [],
      "source": [
        "# for i, line in enumerate(tags):\n",
        "#     line = list(set(line) & possible_tags)\n",
        "#     int_line = []\n",
        "#     for tag in line:\n",
        "#         int_line.append(tag_to_id[tag])\n",
        "#     if len(int_line) == 0:\n",
        "#         int_line.append(155)\n",
        "#     tags[i] = int_line"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dHZZvLI5bEIf"
      },
      "id": "dHZZvLI5bEIf",
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8yfzR4NSc9KD"
      },
      "id": "8yfzR4NSc9KD",
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "dde06f2f",
      "metadata": {
        "id": "dde06f2f"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "41d8e4dd",
      "metadata": {
        "id": "41d8e4dd"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "f21e828c",
      "metadata": {
        "id": "f21e828c"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "4a6d72af",
      "metadata": {
        "id": "4a6d72af"
      },
      "source": [
        "## Обучение модельки"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wxr94-s2ocIw",
        "outputId": "3cf2624b-6647-4c6c-aa84-64de4e74a7f4"
      },
      "id": "wxr94-s2ocIw",
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-cased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.bias', 'vocab_projector.weight', 'vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_transform.weight', 'vocab_layer_norm.bias']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias', 'pre_classifier.bias', 'pre_classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-51-00abe013e263>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mclear_output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mclassifier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDistilBertForSequenceClassification\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"distilbert-base-cased\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_labels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m155\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cuda'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mparam\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequires_grad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36mto\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1894\u001b[0m             )\n\u001b[1;32m   1895\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1896\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1897\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1898\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mhalf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mto\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1143\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_floating_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_complex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_blocking\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1145\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1147\u001b[0m     def register_full_backward_pre_hook(\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    795\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    796\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 797\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    798\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    799\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    795\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    796\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 797\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    798\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    799\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    795\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    796\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 797\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    798\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    799\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    818\u001b[0m             \u001b[0;31m# `with torch.no_grad():`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    819\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 820\u001b[0;31m                 \u001b[0mparam_applied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    821\u001b[0m             \u001b[0mshould_use_set_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    822\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mshould_use_set_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m   1141\u001b[0m                 return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None,\n\u001b[1;32m   1142\u001b[0m                             non_blocking, memory_format=convert_to_format)\n\u001b[0;32m-> 1143\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_floating_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_complex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_blocking\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1145\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "946c3a01",
      "metadata": {
        "id": "946c3a01"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8d6180b5",
      "metadata": {
        "id": "8d6180b5"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "1149f4e1",
      "metadata": {
        "id": "1149f4e1"
      },
      "source": [
        "какой должен быть пайплайн на сервере: я беру данные склеиваю их + предобученныЙ токенайзер(я не буду загружать для него специальный файл т к я никак его не дообучаю) + кладу его в свою модель "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4eec6639",
      "metadata": {
        "id": "4eec6639"
      },
      "outputs": [],
      "source": [
        "x = torch.tensor([1, 2])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = x.to('cuda')"
      ],
      "metadata": {
        "id": "6tF0zxPIjGk9"
      },
      "id": "6tF0zxPIjGk9",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "id": "TgVviVWGjJE0"
      },
      "id": "TgVviVWGjJE0",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FcY0nxDdkP7Y"
      },
      "id": "FcY0nxDdkP7Y",
      "execution_count": null,
      "outputs": []
    }
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