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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxpmMU-UZuAA",
        "outputId": "3a958620-4443-4f19-821e-0187f592c62e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m270.9/270.9 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install --quiet transformers datasets evaluate accelerate"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown 1oUKScMHKFS0UfgVVbb1sPpcF_B4BNco4 -O dataset.zip\n",
        "!unzip -o dataset.zip -d dataset\n",
        "!rm dataset.zip"
      ],
      "metadata": {
        "id": "BnXcsgssaGaf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "231afa94-ceb0-4c9b-a012-ad9c50ec68a1"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1oUKScMHKFS0UfgVVbb1sPpcF_B4BNco4\n",
            "To: /content/dataset.zip\n",
            "\r  0% 0.00/503k [00:00<?, ?B/s]\r100% 503k/503k [00:00<00:00, 43.0MB/s]\n",
            "Archive:  dataset.zip\n",
            "  inflating: dataset/test.csv        \n",
            "  inflating: dataset/train.csv       \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, DataCollatorWithPadding, AutoModelForSequenceClassification, TrainingArguments, Trainer, pipeline\n",
        "import evaluate\n",
        "from datasets import load_dataset\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "RJzTvK7ScZkT"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MODEL_NAME=\"HooshvareLab/bert-fa-base-uncased\"\n",
        "MODEL_NAME=\"FacebookAI/xlm-roberta-large\"\n",
        "MODEL_NAME=\"pedramyazdipoor/persian_xlm_roberta_large\"\n",
        "MODEL_NAME=\"wietsedv/xlm-roberta-base-ft-udpos28-fa\""
      ],
      "metadata": {
        "id": "7HdyAr5HuVMK"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237,
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        "outputId": "bfdaea23-6314-4ec1-f7ef-bc71fb996a19"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/454 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "output_type": "display_data",
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              "tokenizer.json:   0%|          | 0.00/9.08M [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/239 [00:00<?, ?B/s]"
            ],
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    },
    {
      "cell_type": "code",
      "source": [
        "# def preprocess_function(dataframe):\n",
        "#     return tokenizer(dataframe[\"text\"].tolist(), padding=True, truncation=True, return_tensors=\"pt\")\n",
        "def preprocess_function(data):\n",
        "    for i in range(len(data[\"label\"])):\n",
        "      data[\"label\"][i]=label2id[data[\"label\"][i]]\n",
        "    # return tokenizer(data[\"text\"],add_special_tokens = True,\n",
        "    #                  return_token_type_ids = True,\n",
        "    #                  return_tensors = 'pt',\n",
        "    #                  padding = True,\n",
        "    #                  return_offsets_mapping = True,\n",
        "    #                  truncation = 'only_first',\n",
        "    #                  max_length = 32)\n",
        "    return tokenizer(data[\"text\"], truncation=True,padding=True,max_length=50, add_special_tokens = True)"
      ],
      "metadata": {
        "id": "fiaVrgnEdM34"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "TRAIN_FILE=\"dataset/train.csv\"\n",
        "TEST_FILE=\"dataset/test.csv\""
      ],
      "metadata": {
        "id": "NhGLpwbdoTWK"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df= pd.read_csv(TRAIN_FILE)\n",
        "# test_df= pd.read_csv(TEST_FILE)"
      ],
      "metadata": {
        "id": "agljSqNGiifs"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset('csv', data_files={'train': TRAIN_FILE,'test': TEST_FILE})"
      ],
      "metadata": {
        "id": "fXvK7jK5oGHK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        "outputId": "2ae0dd98-275e-4861-a1a7-f7887822339d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2d18166379934b1ebc07dafddbbdfee6"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id2label = {}\n",
        "label2id = {}\n",
        "#TODO: use dataset[\"train\"][\"label\"].unique()\n",
        "emotions=train_df[\"label\"].unique()\n",
        "index=0\n",
        "for emotion in emotions:\n",
        "  id2label[index]=emotion\n",
        "  label2id[emotion]=index\n",
        "  index+=1\n",
        "num_classes=len(emotions)"
      ],
      "metadata": {
        "id": "OGmvyyafeJqD"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_dataset = dataset.map(preprocess_function, batched=True)"
      ],
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      "execution_count": 11,
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        {
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              "Map:   0%|          | 0/6125 [00:00<?, ? examples/s]"
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    {
      "cell_type": "code",
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        "tokenized_dataset[\"train\"][2]"
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        },
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      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "{'text': '\" کیفیتش خیلی خوبه با شک خریدم ولی واقعا راضیم بعد از حدود 2 ماه استفاده « متاسفانه باخبر شدیم » که فردی در ایرانشهر به حداقل 41 دختر تجاوز کرده . امیدواریم با همکاری نمایندگان محترم مجلس و دستگاه قضا ، دیگه به این راحتی باخبر نشیم .',\n",
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              " 'attention_mask': [1,\n",
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              "  1]}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "WgBxUHNkeBWg"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    MODEL_NAME, num_labels=num_classes, id2label=id2label, label2id=label2id\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
          "referenced_widgets": [
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            "30e82324b4444b26beb78ebff9dbeb15",
            "66d0f8b94adf4791bdab2944b5cd11be"
          ]
        },
        "id": "Tb4vzdxUe8vk",
        "outputId": "606653d2-cfd5-44c6-f36f-88a4c97f52c7"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.37k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "12445540e14b4fa39dd0d4e3acb9d739"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.11G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "63090320097947a398c5d61bb484cc38"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of XLMRobertaForSequenceClassification were not initialized from the model checkpoint at wietsedv/xlm-roberta-base-ft-udpos28-fa and are newly initialized: ['classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight', 'classifier.dense.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = evaluate.load(\"accuracy\")\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "    return accuracy.compute(predictions=predictions, references=labels)"
      ],
      "metadata": {
        "id": "Xz2HN_Kqg7UK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "215f351f5c904ae480ed820f2eb9fecc",
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            "78038b46aae44a98903d8cc549f819de",
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            "80905547c1964e79b5b1b4af376cfff5"
          ]
        },
        "outputId": "77fdd616-b5a0-4b5d-d3e9-b0106bcabbad"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "215f351f5c904ae480ed820f2eb9fecc"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir=\"my_awesome_model\",\n",
        "    learning_rate=3e-5,\n",
        "    # max_seq_len = 256,\n",
        "    gradient_accumulation_steps = 8,\n",
        "    per_device_train_batch_size=4,\n",
        "    per_device_eval_batch_size=4,\n",
        "    num_train_epochs=2,\n",
        "    weight_decay=0.01,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    load_best_model_at_end=True,\n",
        "    push_to_hub=False,\n",
        ")\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"my_awesome_model\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    num_train_epochs=5,\n",
        "    weight_decay=0.01,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    load_best_model_at_end=True,\n",
        "    push_to_hub=False,\n",
        ")"
      ],
      "metadata": {
        "id": "RpGciXzjg8tA"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_dataset[\"train\"],\n",
        "    eval_dataset=tokenized_dataset[\"test\"],\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ],
      "metadata": {
        "id": "xL9hNiVrgJhg"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "4Jut8nzQgsMc",
        "outputId": "eb2ab80e-c0d4-4e0c-beb3-fbb7b2bb6112"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a XLMRobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1915' max='1915' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1915/1915 10:21, Epoch 5/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.578419</td>\n",
              "      <td>0.414422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.568500</td>\n",
              "      <td>1.331617</td>\n",
              "      <td>0.537793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.009300</td>\n",
              "      <td>1.249021</td>\n",
              "      <td>0.591659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.822400</td>\n",
              "      <td>1.260355</td>\n",
              "      <td>0.602085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.822400</td>\n",
              "      <td>1.287994</td>\n",
              "      <td>0.601216</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1915, training_loss=1.038665022488985, metrics={'train_runtime': 623.5079, 'train_samples_per_second': 49.117, 'train_steps_per_second': 3.071, 'total_flos': 786927520312500.0, 'train_loss': 1.038665022488985, 'epoch': 5.0})"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = pipeline(\n",
        "    \"text-classification\",\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    return_all_scores=True,\n",
        "    device=0\n",
        ")\n",
        "def predict(text):\n",
        "  return classifier(text)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V5a8-Kyqjwbb",
        "outputId": "e64bc087-5c1b-4842-e676-d478f2ed6824"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/text_classification.py:105: UserWarning: `return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text=\"امروز چه روز غم انگیزیه\""
      ],
      "metadata": {
        "id": "b1QIIkl-krIx"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l72Z6ZGVBPoM",
        "outputId": "c33cf626-fed2-4b35-933d-19d5aacd0ae9"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [0, 15199, 6310, 3351, 59396, 70000, 11558, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C1DYrMSCkxtY",
        "outputId": "1ef6affb-b9fb-4ad1-f6b1-1b277275422c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[{'label': 'SAD', 'score': 0.849018394947052},\n",
              "  {'label': 'HATE', 'score': 0.03248197212815285},\n",
              "  {'label': 'OTHER', 'score': 0.009300804696977139},\n",
              "  {'label': 'FEAR', 'score': 0.008663608692586422},\n",
              "  {'label': 'ANGRY', 'score': 0.004608952905982733},\n",
              "  {'label': 'HAPPY', 'score': 0.09027151018381119},\n",
              "  {'label': 'SURPRISE', 'score': 0.005654775071889162}]]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history=pd.DataFrame(trainer.state.log_history)"
      ],
      "metadata": {
        "id": "syrYWIIAmMPz"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_acc=history[[\"epoch\",\"eval_accuracy\"]].dropna()\n",
        "val_loss=history[[\"epoch\",\"eval_loss\"]].dropna()\n",
        "train_loss=history[[\"epoch\",\"loss\"]].dropna()\n",
        "learning_rates=history[[\"epoch\",\"learning_rate\"]].dropna()"
      ],
      "metadata": {
        "id": "ztzzKteWnZhX"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(val_acc[\"epoch\"],val_acc[\"eval_accuracy\"], label=\"Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.title(\"Val Accuracy per Epoch\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "39cdQ38DokBJ",
        "outputId": "c356cd92-6177-4815-cb66-c8bdac3ec8e4"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(val_loss[\"epoch\"],val_loss[\"eval_loss\"], label=\"Val Loss\")\n",
        "plt.plot(train_loss[\"epoch\"],train_loss[\"loss\"], label=\"Train Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Loss per Epoch\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "SgrWvFByo1XD",
        "outputId": "a67c2a1c-8531-4ec2-c350-0667a7a07b9b"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(learning_rates[\"epoch\"],learning_rates[\"learning_rate\"], label=\"Learning rate\")\n",
        "plt.xlabel(\"alpha\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.title(\"Learning rate per Epoch\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Gqwa-WfGpHnd",
        "outputId": "83ad383c-6b5e-458f-a8c5-1ca5c55e92c1"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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Nmzbh/fffR9u2baWOR0TVXLUuOefOnUO7du1UX0+ZMgUAEBwcjPXr12PdunX45ptv8Mknn+DBgwewtrZGmzZt0LVr13I93v379zFgwAA8ffoUNjY28PPzw6lTp147ziJSZ82aNYOOjg5++OEHZGRkqA5G/uabb6SORkQEHpNDREREWonXySEiIiKtxJJDREREWqnaHZOjVCrx8OFDmJqavtXFyoiIiKjqiKKIzMxMODo6qi5c+SbVruQ8fPgQTk5OUscgIiKickhMTEStWrVKtW61KzkvPz4hMTERZmZmEqchIiKi0sjIyICTk1OZPgap2pWclyMqMzMzlhwiIiINU5ZDTXjgMREREWkllhwiIiLSSiw5REREpJWq3TE5RERUdRQKBQoKCqSOQRpCT0+v1KeHlwZLDhERVThRFJGUlIS0tDSpo5AGkclkcHV1hZ6eXoXcH0sOERFVuJcFx9bWFkZGRrz4Kr3Ry4v1Pnr0CLVr166Q1wxLDhERVSiFQqEqOFZWVlLHIQ1iY2ODhw8forCwELq6um99fzzwmIiIKtTLY3CMjIwkTkKa5uWYSqFQVMj9seQQEVGl4IiKyqqiXzMsOURERKSVWHKIiIg0gIuLCxYtWiR1DI3CA4+JiIj+z9ChQ5GWlobdu3dLHaWYs2fPwtjYWOoYJVLHfcd3cipQ5O0U5OQXSh2DiIg0SGkvlmhjYyPJwdyafDFHlpwKcvlBOoatO4vuSyNxMzlT6jhERFQJLl++jA8++AAmJiaws7PD4MGDkZKSovr+gQMH4OfnBwsLC1hZWaFr166Ii4tTff/u3bsQBAFbtmzBO++8AwMDA/z+++8YOnQogoKCsGDBAjg4OMDKygoTJkwoUjD+Pa4SBAFr1qxBjx49YGRkhPr162PPnj1F8u7Zswf169eHgYEB2rVrhw0bNkAQhNdepFEQBCxfvhzdunWDsbExvv32WygUCowYMQKurq4wNDSEm5sbFi9erNpm1qxZ2LBhA/744w8IggBBEHDs2DEAQGJiIvr27QsLCwtYWlqie/fuuHv3bvn+AcqIJaeC5BYoYG6ki1uPs9BtaQS2nUuUOhIRkdoQRRE5+YWS3ERRrJDnkJaWhvbt28PT0xPnzp3DgQMHkJycjL59+6rWyc7OxpQpU3Du3DkcPnwYMpkMPXr0gFKpLHJf06ZNw+TJk3Ht2jV06tQJAHD06FHExcXh6NGj2LBhA9avX4/169e/NtPs2bPRt29fXLx4EQEBARg0aBBSU1MBAPHx8ejduzeCgoIQGxuLMWPG4IsvvijVc501axZ69OiBS5cuYfjw4VAqlahVqxa2bduGq1evYsaMGfj888+xdetWAMDUqVPRt29fdO7cGY8ePcKjR4/g6+uLgoICdOrUCaampggPD0dkZCRMTEzQuXNn5Ofnl3bXlxuPyakgLV0s8edkf3y8JQbht1Lw6faLiLrzFN8EucNIj7uZiKq35wUKNJ5xUJLHvjqnU4X8HF66dCk8PT0xd+5c1bK1a9fCyckJN2/eRIMGDdCrV68i26xduxY2Nja4evUq3N3dVcs/+ugj9OzZs8i6NWrUwNKlSyGXy9GwYUN06dIFhw8fxqhRo0rMNHToUAwYMAAAMHfuXISGhuLMmTPo3LkzVq5cCTc3N8yfPx8A4ObmhsuXL+Pbb79943MdOHAghg0bVmTZ7NmzVf/t6uqKqKgobN26FX379oWJiQkMDQ2Rl5cHe3t71Xq//fYblEol1qxZozo9fN26dbCwsMCxY8fw/vvvvzHL2+A7ORXI2kQfG4a1xqed3CATgJ3RDxC4JAI3kji+IiLSdLGxsTh69ChMTExUt4YNGwKAaiR169YtDBgwAHXq1IGZmRlcXFwAAAkJCUXuq2XLlsXuv0mTJpDL5aqvHRwc8Pjx49dmatasmeq/jY2NYWZmptrmxo0baNWqVZH1W7duXarn+qp8y5Ytg5eXF2xsbGBiYoJVq1YVe17/Fhsbi9u3b8PU1FS1zywtLZGbm1tkjFdZ+BZDBZPJBExoVw8tnWsgZPMFxD3JRvdlEZjdrQn6tnTixbGIqFoy1JXj6pxOkj12RcjKykJgYCC+//77Yt9zcHAAAAQGBsLZ2RmrV6+Go6MjlEol3N3di41mXnWW1L8/xkAQhGJjrorYpjT+nW/z5s2YOnUqFi5cCB8fH5iammL+/Pk4ffr0a+8nKysLXl5e+P3334t9z8bG5q1zvglLTiXxrmOFsBB/fLw1FiduPsFnOy7h1J1UfBPkDmN97nYiql4EQdD40X2LFi2wY8cOuLi4QEen+HN5+vQpbty4gdWrV8Pf3x8AEBERUdUxVdzc3BAWFlZk2dmzZ8t1X5GRkfD19cX48eNVy/79Toyenl6xj2No0aIFtmzZAltbW5iZmZXrsd8Gx1WVyMpEH+uHtlKNr3ZdeIDApRG4npQhdTQiIipBeno6YmJiitwSExMxYcIEpKamYsCAATh79izi4uJw8OBBDBs2DAqFAjVq1ICVlRVWrVqF27dv48iRI5gyZYpkz2PMmDG4fv06PvvsM9y8eRNbt25VHchc1qlC/fr1ce7cORw8eBA3b97EV199Vawwubi44OLFi7hx4wZSUlJQUFCAQYMGwdraGt27d0d4eDji4+Nx7NgxhISE4P79+xX1VEvEklPJXo6vNo/2gb2ZAe48yUb3pZHYdCahwo74JyKiinPs2DF4enoWuc2ePRuOjo6IjIyEQqHA+++/j6ZNm+Kjjz6ChYUFZDIZZDIZNm/ejPPnz8Pd3R0ff/yx6qBfKbi6umL79u3YuXMnmjVrhuXLl6vOrtLX1y/TfY0ZMwY9e/ZEv3794O3tjadPnxZ5VwcARo0aBTc3N7Rs2RI2NjaIjIyEkZERTpw4gdq1a6Nnz55o1KgRRowYgdzc3Cp5Z0cQq9lv2oyMDJibmyM9Pb3K3zpLzc7HlK0xOHbjCQCge3NHfNujKUw4viIiLZKbm4v4+Hi4urrCwMBA6jj0D99++y1WrFiBxET1vMzJ61475fn9zXdyqpClsR7WBrfCZ50bQi4T8EfMQ3RbEoGrDzm+IiKiivfTTz/h7NmzuHPnDn799VfMnz8fwcHBUseqMiw5VUwmEzDu3brYMroNHMwNcCclG0E/RWLjaY6viIioYt26dQvdu3dH48aN8fXXX+OTTz7BrFmzpI5VZTiuklBqdj6mbovFkesvrmkQ6OGIuT3cYWqg+4YtiYjUF8dVVF4cV2kRS2M9rBnSEp8HvBhf7Y19iMAlEbj8IF3qaERERBqPJUdiMpmA0f+pi61j2sDR3AB3n+ag5/KT+PXUPY6viEij8WcYlVVFv2ZYctSEl7Ml9of4472GtsgvVOKr3ZcxcdMFZOZq7kfcE1H19PIqvDk5ORInIU3z8srQ//x4i7fBc5fVSA1jPawJbok14fH4/sB17L/4CJcfpGPZwBZwr2kudTwiolKRy+WwsLBQfYaSkZERP9KG3kipVOLJkycwMjJ65RWly4MHHqup6IRnmLTxAh6kPYeeXIYvuzbC4DbO/EFBRBpBFEUkJSUhLS1N6iikQWQyGVxdXaGnp1fse+X5/c2So8bScvIxddtF/H0tGQAQ0NQe3/VqBjOefUVEGkKhUKCggGN3Kh09PT3IZK8+koYlpxQ0qeQAL/4a+jkiHt/9eR2FShG1LY2wbGALNK3F8RUREVUfPIVcCwmCgJH+dbBtrA9qWhgiITUHvZafxIaTd3nmAhER0Wuw5GgIz9o1EBbij/cb2yFfocTMPVcw7rdopD/n28BERESvwpKjQcyNdLFysBdmBjaGrlzAgStJ6LokHLGJaVJHIyIiUjuSlpwTJ04gMDAQjo6OEAQBu3fvLvW2kZGR0NHRQfPmzSstnzoSBAHD2rpi+1hf1KphiMTU5+i94iTWRsRzfEVERPQPkpac7OxseHh4YNmyZWXaLi0tDUOGDMF7771XScnUn4eTBfaH+KNzE3sUKETM2XcVY349j/Qcjq+IiIgANTq7ShAE7Nq1C0FBQW9ct3///qhfvz7kcjl2796NmJiYUj+Opp1d9SaiKOKXqHv4dv815CuUqFXDEEsHtkBzJwupoxEREVWYanF21bp163Dnzh3MnDmzVOvn5eUhIyOjyE2bCIKAYF8X7Bjni9qWRrj/7Dl6Lz+JNeF3OL4iIqJqTaNKzq1btzBt2jT89ttvpb7k87x582Bubq66OTk5VXJKaTStZY59IX4IaGqPQqWIb/Zfw6hfziMtJ1/qaERERJLQmJKjUCgwcOBAzJ49Gw0aNCj1dtOnT0d6errqlpiYWIkppWVmoItlA1tgTvcm0JPL8Pe1ZHQJjUB0wjOpoxEREVU5jTkmJy0tDTVq1CjyyaRKpRKiKEIul+Ovv/5C+/bt3/g42nZMTkkuP0jHhI3RuPc0BzoyAdM+aIgRfq787CsiItJIWn1MjpmZGS5duoSYmBjVbezYsXBzc0NMTAy8vb2ljqhW3GuaY98kP3Rp5vCP8dU5jq+IiKjaqJjPMi+nrKws3L59W/V1fHw8YmJiYGlpidq1a2P69Ol48OABfvnlF8hkMri7uxfZ3tbWFgYGBsWW0wumBrpYOsATPnWsMGffVfx97TECFodjycAW8HKuIXU8IiKiSiXpOznnzp2Dp6cnPD09AQBTpkyBp6cnZsyYAQB49OgREhISpIyo8QRBwIdtnLFznC9crIzwMD0X/VZGYeXxOCiVajGpJCIiqhRqc0xOVakux+S8SmZuAT7fdRl7Yx8CANo3tMXCPh6oYawncTIiIqLX0+pjcujtmRroIrR/c8zt0RR6OjIcuf4YAaHhOHc3VepoREREFY4lp5oRBAEDvWtj9/i2qGNtjEfpuei36hSWH+P4ioiItAtLTjXV2NEMeyb5oZuHIxRKEd8fuI7hG84iNZtnXxERkXZgyanGTPR1sLh/c8zr2RT6OjIcu/EEAYvDcZbjKyIi0gIsOdWcIAgY0Lo2dk9oizo2xkjKyEX/Vaew7Ohtjq+IiEijseQQAKCRgxn2TvRDD8+aUChFzD94A0PXn8XTrDypoxEREZULSw6pGOvr4Me+HvihVzPo68hw4uYTBISG4/Sdp1JHIyIiKjOWHCpCEAT0beWEPRP9UNfGGMkZeRiw+hSWHrnF8RUREWkUlhx6JTd7U+yZ6IeeLWpCKQIL/rqJ4HVnkMLxFRERaQiWHCrRi/FVc8zv3QwGujKE30pBwOJwRMVxfEVEROqPJYfeqE/LF+OrerYmeJyZh0FrTiH08C0oOL4iIiI1xpJDpdLAzhR7JrZFb69aUIrAj4duInjtGTzJ5PiKiIjUE0sOlZqRng4W9PHAgj4eMNSVI+J2CgJCw3EyLkXqaERERMWw5FCZ9faqhT0T26KBnQmeZObhwzWnsejvmxxfERGRWmHJoXKpb2eKPyb4oW/LF+OrRX/fwuCfT+NxZq7U0YiIiACw5NBbMNST44feHvixrweM9OQ4GfcUAYsjEHmb4ysiIpIeSw69tZ4tamHPRD+42ZkiJSsPH/58Gj8e4viKiIikxZJDFaKerQl2T2iL/q2cIIpA6OFbGLTmFB5ncHxFRETSYMmhCmOoJ8d3vZphUb/mMNKT49SdVASEhiP81hOpoxERUTXEkkMVLsizJvZO8kNDe1OkZOVjyNozWPjXDRQqlFJHIyKiaoQlhypFXZsX46sBrWtDFIElR25j0JrTSOb4ioiIqghLDlUaA1055vVsisX9m8NYT47T8akIWByO4zc5viIiosrHkkOVrnvzF+OrRg5meJqdj+C1ZzD/4HWOr4iIqFKx5FCVqGNjgl3jfTHIuzYAYNnROAxcfRpJ6RxfERFR5WDJoSpjoCvHtz2aYskAT5jo6+DM3RdnXx278VjqaEREpIVYcqjKBXo4Yt8kPzRxNENqdj6GrjuL7w9wfEVERBWLJYck4WJtjB3jfDG4jTMAYPmxOPRfdQoP055LnIyIiLQFSw5JxkBXjq+D3LFsYAuY6Ovg3L1n6BIajqPXOb4iIqK3x5JDkuvSzAH7Q/zgXtMMz3IKMGz9Wcz78xoKOL4iIqK3wJJDasHZ6sX4aqivCwBg5fE7HF8REdFbYckhtaGvI8esbk2wfFALmOrr4Py9ZwgIDcfha8lSRyMiIg3EkkNq54OmDtgf4o9mtcyRllOAERvOYW4Yx1dERFQ2LDmklmpbGWHbWB/V+GrViTvouzIK95/lSBuMiIg0BksOqa2X46sVH3rB1EAHFxLS0CU0An9f5fiKiIjejCWH1F5nd3uEhfjDo5Y50p8XYOQv5/DNvqvIL+T4ioiISsaSQxrBydII28b6YnhbVwDAmoh49FkZhcRUjq+IiOjVWHJIY+jpyDAjsDFWDfaCmYEOYhPT0CU0HH9dSZI6GhERqSGWHNI47zexx/4QfzR3skBGbiFG/3oec/ZyfEVEREWx5JBGcrI0wtYxPhjl/2J8tTYyHn1WnOT4ioiIVFhySGPp6cjwRZfGWD2kJcwNdRF7Px0BoeE4cJnjKyIiYskhLdCxsR32h/jBs7YFMnMLMfa385i15wryChVSRyMiIgmx5JBWqFXjxfhq9H/qAADWn7yL3sujkPCU4ysiouqKJYe0hq5chs8DGmHt0JawMNLFpQfp6BIajj8vPZI6GhERSYAlh7RO+4Z2CAvxh5dzDWTmFWLc79GY+cdljq+IiKoZlhzSSo4Whtg8ug3GvPNifLUh6h56LT+Je0+zJU5GRERVhSWHtJauXIbpHzTCuqGtUMNIF5cfZKBraAT2X+T4ioioOmDJIa3XrqEtwib7o+X/ja8mbIzGV7svI7eA4ysiIm3GkkPVgoP5i/HV+HfrAgB+PfVifBWfwvEVEZG2YsmhakNHLsN/OzfE+mGtYGmshysPMxC4JAJ7Yx9KHY2IiCoBSw5VO++62SIsxB+tXSyRlVeISZsu4Itdlzi+IiLSMiw5VC3Zmxtg4yhvTGxXD4IA/H46AT1+Ook7T7KkjkZERBWEJYeqLR25DFM7uWHDsNawMtbDtUcvxld/xDyQOhoREVUAlhyq9v7TwAZhk/3h7WqJ7HwFJm+OwfSdHF8REWk6SUvOiRMnEBgYCEdHRwiCgN27d792/Z07d6Jjx46wsbGBmZkZfHx8cPDgwaoJS1rNzswAv4/0xqT2L8ZXm84kIGhZJOI4viIi0liSlpzs7Gx4eHhg2bJlpVr/xIkT6NixI8LCwnD+/Hm0a9cOgYGBuHDhQiUnpepARy7DJ++74dfh3rA20cP1pEwELonA7gscXxERaSJBFEVR6hAAIAgCdu3ahaCgoDJt16RJE/Tr1w8zZswo1foZGRkwNzdHeno6zMzMypGUqoPHGbkI2XwBp+6kAgD6t3LCzMAmMNSTS5yMiKh6Ks/vb40+JkepVCIzMxOWlpYlrpOXl4eMjIwiN6I3sTUzwO8j22Dye/UhCMDms4kIWhaJ2485viIi0hQaXXIWLFiArKws9O3bt8R15s2bB3Nzc9XNycmpChOSJpPLBHzcsQF+G+ENaxN93EjORLelEdgZfV/qaEREVAoaW3I2btyI2bNnY+vWrbC1tS1xvenTpyM9PV11S0xMrMKUpA3a1rNG2GQ/+Na1Qk6+AlO2xuK/22PxPJ9nXxERqTONLDmbN2/GyJEjsXXrVnTo0OG16+rr68PMzKzIjaisbE0N8OsIb3zcoQEEAdh67j66L4vAreRMqaMREVEJNK7kbNq0CcOGDcOmTZvQpUsXqeNQNSKXCZjcoT5+H+kNG1N93EzOQrelkdh+nuMrIiJ1JGnJycrKQkxMDGJiYgAA8fHxiImJQUJCAoAXo6YhQ4ao1t+4cSOGDBmChQsXwtvbG0lJSUhKSkJ6eroU8ama8q1rjbAQf/jVs8bzAgWmbovF1G2xyMkvlDoaERH9g6SnkB87dgzt2rUrtjw4OBjr16/H0KFDcffuXRw7dgwA8O677+L48eMlrl8aPIWcKopCKWLZ0dtY9PdNKEWgvq0Jlg1qgQZ2plJHIyLSOuX5/a0218mpKiw5VNFO3XmKkE0X8DgzDwa6Mnzd3R19WvIsPiKiilTtrpNDpA7a1LFC2GR/+Ne3Rm6BEp9uv4gpW2OQncfxFRGRlFhyiCqAtYk+NgxrjU87uUEmADujH6Db0gjcSOLZV0REUmHJIaogMpmACe3qYdOoNrAz00fck2x0XxaBLWcTUM2mwkREaoElh6iCedexQliIP/7TwAa5BUp8tuMSpmyN5fiKiKiKseQQVQIrE32sH9oK/+3sBrlMwK4LDxC4NALXHvGz04iIqgpLDlElkckEjH+3HjaPbgN7MwPceZKNoGWR2HSG4ysioqrAkkNUyVq5WCJssj/edbNBXqES03dewkdbYpDF8RURUaViySGqApbGelgb3ArTPmgIuUzAHzEP0W1JBK4+5PiKiKiysOQQVRGZTMDYd+piy+g2cDA3wJ2UbAT9FInfT9/j+IqIqBKw5BBVsZYulggL8Uf7hrbIL1Tii12XMWnTBWTmFkgdjYhIq7DkEEmghrEe1gxpic8DGkJHJmDfxUcIXBKByw/4YbNERBWFJYdIIjKZgNH/qYstY3zgaG6Au09z0HP5Sfx6iuMrIqKKwJJDJDEv5xoIm+yPDo1ejK++2n0ZEzm+IiJ6ayw5RGrAwkgPq4e0xJddGkFHJmD/xUfoyvEVEdFbYckhUhOCIGCkfx1sHeuDmhaGuPc0Bz1/Oolfou5yfEVEVA4sOURqpkXtGggL8UfHxnbIVygx448rmLAxGhkcXxERlQlLDpEaMjfSxarBXviqa2PoygWEXUpC19AIXLrP8RURUWmx5BCpKUEQMMLPFdvG+qJWDUMkpOag1/KTWB8Zz/EVEVEpsOQQqbnmThbYP8kf7//f+GrW3qsY91s00p9zfEVE9DosOUQawNxIFysHe2Fm4Ivx1YErSei6JByxiWlSRyMiUlssOUQaQhAEDGvriu1jfeFkaYjE1OfoveIk1kZwfEVE9CosOUQaxsPJAvsm+aNzE3sUKETM2XcVY349j/Qcjq+IiP6JJYdIA5kb6mL5hy0wu1sT6Mll+OtqMgJCwxHD8RURkQpLDpGGEgQBwb4u2DHOF7UtjfAg7Tl6Lz+JNeF3OL4iIgJLDpHGa1rLHPtC/NClqQMKlSK+2X8No345j7ScfKmjERFJiiWHSAuYGehi6UBPfN39xfjq72vJ6BIageiEZ1JHIyKSDEsOkZYQBAGDfVywc7wvnK1ejK/6rojC6hMcXxFR9cSSQ6Rl3GuaY98kP3Rp9mJ89W3YNYzccA7Psjm+IqLqhSWHSAuZGuhi6QBPfBPkDj0dGQ5ff4wuoeE4f4/jKyKqPlhyiLSUIAj4sI0zdo33hau1MR6m56LfyiisPB4HpZLjKyLSfiw5RFquiaM59kxsi0APRxQqRcz78zpG/nIOqRxfEZGWY8khqgZMDXQR2r855vZoCj0dGY783/jq3N1UqaMREVUalhyiakIQBAz0ro3d49uijrUxHqXnot+qU1h+jOMrItJOLDlE1UxjRzPsmeSH7s0doVCK+P7AdQzfcJbjKyLSOiw5RNWQib4OFvVrju96NoW+jgzHbjxBwOJwnInn+IqItAdLDlE1JQgC+reujd0T2qKOjTGSMnIxYPUpLDt6m+MrItIKLDlE1VwjBzPsneiHHp41oVCKmH/wBoauP4unWXlSRyMieissOUQEY30d/NjXAz/0agYDXRlO3HyCgNBwnL7zVOpoRETlxpJDRABejK/6tnLCHxP8UNfGGMkZeRiw+hSWHrnF8RURaSSWHCIqws3eFHsn+aFXi1pQisCCv24ieN0ZpHB8RUQahiWHiIox0tPBwr4emN/7xfgq/FYKAhaHIyqO4ysi0hwsOURUoj4tnbBnoh/q25rgcWYeBq05hdDDt6Dg+IqINABLDhG9VgM7U/wxsS36eL0YX/146CaGrD2NJ5kcXxGReitzyXFxccGcOXOQkJBQGXmISA0Z6elgfh8PLOjjAUNdOSJvP0VAaDhO3k6ROhoRUYnKXHI++ugj7Ny5E3Xq1EHHjh2xefNm5OXxLzqi6qC3Vy3smdgWDexM8CQzD4N+Po1Ff9/k+IqI1JIgimK5fjpFR0dj/fr12LRpExQKBQYOHIjhw4ejRYsWFZ2xQmVkZMDc3Bzp6ekwMzOTOg6RRnqer8CsPVew5VwiAMC3rhUW9W8OW1MDiZMRkbYqz+/vcpeclwoKCvDTTz/hs88+Q0FBAZo2bYqQkBAMGzYMgiC8zV1XCpYcooqz68J9fLHrMnLyFbA20cfi/s3Rtp611LGISAuV5/d3uQ88LigowNatW9GtWzd88sknaNmyJdasWYNevXrh888/x6BBg8p710SkIXp41sKeiX5wszNFSlYePvz5NH48xPEVEamHMr+TEx0djXXr1mHTpk2QyWQYMmQIRo4ciYYNG6rWuXz5Mlq1aoXnz59XeOC3xXdyiCpeboECs/dewaYzL8ZXbepYIrS/J2zNOL4ioopRJe/ktGrVCrdu3cLy5cvx4MEDLFiwoEjBAQBXV1f079+/rHdNRBrKQFeOeT2bYXH/5jDWk+PUnVQEhIYj/NYTqaMRUTVW5ndy7t27B2dn58rKU+n4Tg5R5Yp7koUJv0fjelImBAGY2K4eJr9XHzpyXpaLiMqvSt7Jefz4MU6fPl1s+enTp3Hu3Lmy3h0RaZm6NibYPaEtBnrXhigCS47cxsA1p5GckSt1NCKqZspcciZMmIDExMRiyx88eIAJEyZUSCgi0mwGunLM7dEUoQM8Yawnx5n4VAQsDsfxmxxfEVHVKXPJuXr16iuvhePp6YmrV6+W6b5OnDiBwMBAODo6QhAE7N69+43bHDt2DC1atIC+vj7q1auH9evXl+kxiajqdPNwxL4QfzRyMMPT7HwErz2D+Qevo1ChlDoaEVUDZS45+vr6SE5OLrb80aNH0NHRKdN9ZWdnw8PDA8uWLSvV+vHx8ejSpQvatWuHmJgYfPTRRxg5ciQOHjxYpscloqrjam2MXeN98WGb2gCAZUfjMHD1aSSlc3xFRJWrzAceDxgwAI8ePcIff/wBc3NzAEBaWhqCgoJga2uLrVu3li+IIGDXrl0ICgoqcZ3PPvsM+/fvx+XLl1XL+vfvj7S0NBw4cKBUj8MDj4mks+/iQ0zbcQlZeYWwNNbDj3098K6brdSxiEgDVMmBxwsWLEBiYiKcnZ3Rrl07tGvXDq6urkhKSsLChQvLHLosoqKi0KFDhyLLOnXqhKioqEp9XCKqGF2bOWLfJD80cTRDanY+hq47i+8PcHxFRJWjzCWnZs2auHjxIn744Qc0btwYXl5eWLx4MS5dugQnJ6fKyKiSlJQEOzu7Isvs7OyQkZFR4oUH8/LykJGRUeRGRNJxsTbGjnG+GOLz4lIUy4/Fof+qU3iYpn4XDyUizVa2g2j+j7GxMUaPHl3RWSrFvHnzMHv2bKljENE/GOjKMae7O9rUscJn2y/i3L1n6BIajh/7Nke7hhxfEVHFKFfJAV6cZZWQkID8/Pwiy7t16/bWoUpib29f7KDn5ORkmJmZwdDQ8JXbTJ8+HVOmTFF9nZGRUenvOBFR6QQ0dUATRzNM2BiNyw8yMGz9WYz5Tx1M7eQGXV48kIjeUplLzp07d9CjRw9cunQJgiDg5XHLLz9xXKFQVGzCf/Dx8UFYWFiRZYcOHYKPj0+J2+jr60NfX7/SMhHR23G2ejG+mhd2HetP3sXKE3dw9m4qlgxsgZoWr/7jhYioNMr8p9LkyZPh6uqKx48fw8jICFeuXMGJEyfQsmVLHDt2rEz3lZWVhZiYGMTExAB4cYp4TEwMEhISALx4F2bIkCGq9ceOHYs7d+7gv//9L65fv46ffvoJW7duxccff1zWp0FEakRfR45Z3ZpgxYctYGqgg+iENHQJDcfha8UvV0FEVFplLjlRUVGYM2cOrK2tIZPJIJPJ4Ofnh3nz5iEkJKRM93Xu3Dl4enrC09MTADBlyhR4enpixowZAF5ce+dl4QFefPDn/v37cejQIXh4eGDhwoVYs2YNOnXqVNanQURqqLO7A/ZP8kezWuZIyynAiA3nMDfsGgp49hURlUOZr5NTo0YNREdHw9XVFXXr1sWaNWvQrl07xMXFoWnTpsjJyamsrBWC18khUn95hQp89+d1rIu8CwDwrG2BJQM8UauGkbTBiEgyVXKdHHd3d8TGxgIAvL298cMPPyAyMhJz5sxBnTp1ynp3RETF6OvIMTOwCVZ86AVTAx1cSEhDl9AIHLrK8RURlV6ZS86XX34JpfLFW8dz5sxBfHw8/P39ERYWhtDQ0AoPSETVV2d3e4SF+MOjljnSnxdg1C/n8M2+q8gv5PiKiN6szOOqV0lNTUWNGjVUZ1ipM46riDRPfqES3x+4jp8j4gEAHk4WWDrAE06WHF8RVReVPq4qKCiAjo5Okc+OAgBLS0uNKDhEpJn0dGT4qmtjrBrsBTMDHcQmvjj76q8rSVJHIyI1VqaSo6uri9q1a1fqtXCIiEryfhN77A/xR3MnC2TkFmL0r+cxe+8Vjq+I6JXKfEzOF198gc8//xypqamVkYeI6LWcLI2wdYwPRvm7AgDWRd5FnxUnkZiq3md2ElHVK/MxOZ6enrh9+zYKCgrg7OwMY2PjIt+Pjo6u0IAVjcfkEGmPv68m45NtsUh/XgBTAx3M7+2Bzu72UsciokpQnt/fZf5Yh6CgoLJuQkRUKTo0tkPYZH9M3BiNCwlpGPvbeQz1dcH0gIbQ15FLHY+IJFYhZ1dpEr6TQ6R9ChRKLDh4AytP3AEAuNc0w9IBLeBibfyGLYlIU1TJxQCJiNSNrlyG6QGNsG5oK9Qw0sXlBxnouiQCe2IfSh2NiCRU5pIjk8kgl8tLvBERSaVdQ1uETfZHaxdLZOUVImTTBUzfeQm5BTwjlKg6KvMxObt27SrydUFBAS5cuIANGzZg9uzZFRaMiKg8HMwNsXGUN0IP38KSo7ex6UwCou89w7JBnqhnayp1PCKqQhV2TM7GjRuxZcsW/PHHHxVxd5WGx+QQVR8Rt1Lw0ZYYpGTlwVBXjq+D3NHbq5bUsYioHCQ9JqdNmzY4fPhwRd0dEdFb86tvjbDJfmhbzwrPCxSYui0WU7bEIDuvUOpoRFQFKqTkPH/+HKGhoahZs2ZF3B0RUYWxNTXAL8O9MfX9BpAJwM4LDxC4NAJXH2ZIHY2IKlmZj8n59wdxiqKIzMxMGBkZ4bfffqvQcEREFUEuEzCxfX20drVCyKYLuPMkG0E/RWJG18YY5F2bn71HpKXKfEzO+vXri/xAkMlksLGxgbe3N2rUqFHhASsaj8khqt5Ss/MxdVssjlx/DADo0tQB83o1hZmBrsTJiOh1yvP7mxcDJKJqR6kU8XNEPL4/cB2FShG1LY2wdKAnmtWykDoaEZWgSg48XrduHbZt21Zs+bZt27Bhw4ay3h0RUZWTyQSM+k8dbBvrg1o1DJGQmoNey09ibUQ8qtnffURarcwlZ968ebC2ti623NbWFnPnzq2QUEREVcGzdg3sD/FH5yb2KFCImLPvKkb9ch5pOflSRyOiClDmkpOQkABXV9diy52dnZGQkFAhoYiIqoq5oS6Wf9gCc7o3gZ5chr+vJSNgcTjO30uVOhoRvaUylxxbW1tcvHix2PLY2FhYWVlVSCgioqokCAKG+Lhg53hfuFgZ4WF6LvquPIXlx+KgVHJ8RaSpylxyBgwYgJCQEBw9ehQKhQIKhQJHjhzB5MmT0b9//8rISERUJdxrmmNfiD+6eThCoRTx/YHrGLr+LFKy8qSORkTlUOazq/Lz8zF48GBs27YNOjovLrOjVCoxZMgQrFixAnp6epUStKLw7CoiehNRFLH1XCJm7rmC3AIlbE31sbi/J3zq8t1qIqlU6Snkt27dQkxMDAwNDdG0aVM4OzuX526qHEsOEZXWjaRMTNwYjVuPsyATgJD36mNS+/qQy3jxQKKqxuvklAJLDhGVRU5+IWbtuYKt5+4DAHzqWGFR/+awMzOQOBlR9VIl18np1asXvv/++2LLf/jhB/Tp06esd0dEpNaM9HTwQ28P/K+fB4z05Ii68xQBi8Nx/OYTqaMR0RuUueScOHECAQEBxZZ/8MEHOHHiRIWEIiJSNz08a2HfJD80cjDD0+x8BK89g+8PXEeBQil1NCIqQZlLTlZW1isPLtbV1UVGBj/Vl4i0Vx0bE+wa74vBbV4cg7j8WBz6rzqFB2nPJU5GRK9S5pLTtGlTbNmypdjyzZs3o3HjxhUSiohIXRnoyvF1kDt+GtQCpvo6OH/vGQIWh+PQ1WSpoxHRv+iUdYOvvvoKPXv2RFxcHNq3bw8AOHz4MDZu3Ijt27dXeEAiInUU0NQB7o7mmLQpGrH30zHql3MY3tYV0z5oCD2dMv/9SESVoMz/JwYGBmL37t24ffs2xo8fj08++QQPHjzAkSNHUK9evcrISESklmpbGWHbWF+M9HvxUTdrI+PRe8VJ3HuaLXEyIgIq4BTyjIwMbNq0CT///DPOnz8PhUJRUdkqBU8hJ6LK8PfVZEzdHou0nAKY6uvgu17N0KWZg9SxiLRGlZxC/tKJEycQHBwMR0dHLFy4EO3bt8epU6fKe3dERBqtQ2M7hIX4o6VzDWTmFWLCxmh8ufsScgvU+w8/Im1WppKTlJSE7777DvXr10efPn1gZmaGvLw87N69G9999x1atWpVWTmJiNSeo4UhNo9ugwnt6kIQgN9OJSBoWSTinmRJHY2oWip1yQkMDISbmxsuXryIRYsW4eHDh1iyZEllZiMi0jg6chk+7dQQG4a1hpWxHq4nZSJwSQR2Rt+XOhpRtVPqkvPnn39ixIgRmD17Nrp06QK5XF6ZuYiINNp/Gtjgz8n+8KljhZx8BaZsjcXUbbHIyS+UOhpRtVHqkhMREYHMzEx4eXnB29sbS5cuRUpKSmVmIyLSaLZmBvhtpDc+7tAAMgHYfv4+ui2NxPUkXjiVqCqUuuS0adMGq1evxqNHjzBmzBhs3rwZjo6OUCqVOHToEDIzMyszJxGRRpLLBEzuUB+/j2wDW1N93H6che5LI7HpTAKq2ecjE1W5tzqF/MaNG/j555/x66+/Ii0tDR07dsSePXsqMl+F4ynkRCSVp1l5mLI1VvXhnoEejpjbwx2mBroSJyNSf1V6CjkAuLm54YcffsD9+/exadOmt7krIiKtZ2Wij3VDW2HaBw0hlwnYG/sQgUsicPlButTRiLTSW18MUNPwnRwiUgfn7z1DyKYLeJD2HHpyGb7o0ghDfJwhCILU0YjUUpW/k0NEROXj5VwD+0P88H5jO+QrlJi55wrG/nYe6TkFUkcj0hosOUREErEw0sPKwV6YGdgYunIBB68kIyA0HNEJz6SORqQVWHKIiCQkCAKGtXXFjnG+qG1phAdpz9F3RRRWnYiDUlmtjiYgqnAsOUREaqBZLQvsC/FDl2YOKFSKmBt2HSM2nEVqdr7U0Yg0FksOEZGaMDPQxdIBnpjboyn0dWQ4euMJPlh8AqfvPJU6GpFGYskhIlIjgiBgoHdt7J7QFnVtjJGckYcBq09hyeFbUHB8RVQmLDlERGqokYMZ9kz0Q68WtaAUgYWHbmLI2tN4nJkrdTQijcGSQ0Skpoz1dbCwrwcW9PGAoa4ckbefImBxOCJu8XMDiUqDJYeISM319qqFvZP80NDeFClZ+Ri89jQWHLyBQoVS6mhEao0lh4hIA9SzNcHuCW0x0Ls2RBFYevQ2Bqw+hUfpz6WORqS2WHKIiDSEga4cc3s0xZIBnjDR18HZu88QsDgcR64nSx2NSC2x5BARaZhAD0fsm+SHpjXN8SynAMPXn8O3+68iv5DjK6J/krzkLFu2DC4uLjAwMIC3tzfOnDnz2vUXLVoENzc3GBoawsnJCR9//DFyc3m2ARFVLy7Wxtg+zgfD2roAAFaHx6PPyigkpuZIG4xIjUhacrZs2YIpU6Zg5syZiI6OhoeHBzp16oTHjx+/cv2NGzdi2rRpmDlzJq5du4aff/4ZW7Zsweeff17FyYmIpKevI8fMwCZYOdgLZgY6iE1MQ0BoOA5cfiR1NCK1IIiiKNnVpby9vdGqVSssXboUAKBUKuHk5IRJkyZh2rRpxdafOHEirl27hsOHD6uWffLJJzh9+jQiIiJK9Zjl+ah2IiJ1d/9ZDkI2XUB0QhoAINjHGdMDGsFAVy5tMKIKUp7f35K9k5Ofn4/z58+jQ4cO/z+MTIYOHTogKirqldv4+vri/PnzqpHWnTt3EBYWhoCAgBIfJy8vDxkZGUVuRETaplYNI2wZ44Ox79QFAGyIuodey08iPiVb4mRE0pGs5KSkpEChUMDOzq7Icjs7OyQlJb1ym4EDB2LOnDnw8/ODrq4u6tati3ffffe146p58+bB3NxcdXNycqrQ50FEpC505TJM+6Ah1g9rBUtjPVx5mIGuoeH4I+aB1NGIJCH5gcdlcezYMcydOxc//fQToqOjsXPnTuzfvx9ff/11idtMnz4d6enpqltiYmIVJiYiqnrvutkiLMQfrV0tkZ2vwOTNMfhs+0U8z1dIHY2oSulI9cDW1taQy+VITi56fYfk5GTY29u/cpuvvvoKgwcPxsiRIwEATZs2RXZ2NkaPHo0vvvgCMlnxzqavrw99ff2KfwJERGrM3twAG0d6I/TIbSw5cgtbziXiQuIzLB3YAg3sTKWOR1QlJHsnR09PD15eXkUOIlYqlTh8+DB8fHxeuU1OTk6xIiOXvzioTsLjp4mI1JKOXIYpHRvg9xHesDHVx83kLHRbGoGtZxP5M5OqBUnHVVOmTMHq1auxYcMGXLt2DePGjUN2djaGDRsGABgyZAimT5+uWj8wMBDLly/H5s2bER8fj0OHDuGrr75CYGCgquwQEVFRvvWsERbiD//61sgtUOK/Oy7i4y0xyMorlDoaUaWSbFwFAP369cOTJ08wY8YMJCUloXnz5jhw4IDqYOSEhIQi79x8+eWXEAQBX375JR48eAAbGxsEBgbi22+/leopEBFpBBtTfWwY1horTsRh4V83sTvmIS7eT8eSgZ5o4mgudTyiSiHpdXKkwOvkEFF1d+5uKiZtuoBH6bnQ05Hhq66N8aF3bQiCIHU0ohJp1HVyiIhIGi1dLBEW4o/3Gtoiv1CJr3ZfxoSN0Uh/XiB1NKIKxZJDRFQN1TDWw5rglviySyPoygWEXUpC1yXhiE1MkzoaUYVhySEiqqYEQcBI/zrYNtYXTpaGSEx9jt4rTmJN+B2efUVagSWHiKiaa+5kgX2T/BHQ1B4FChHf7L+GkRvO4Vl2vtTRiN4KSw4REcHcUBfLBrbA10Hu0NOR4fD1xwgIDcfZu6lSRyMqN5YcIiIC8GJ8NbiNM3aN90Uda2M8Ss9F/1WnsOzobSiVHF+R5mHJISKiIpo4mmPPJD8ENXeEQili/sEbCF53Bk8y86SORlQmLDlERFSMib4O/tevOX7o3QwGujKE30pBQGg4Tt5OkToaUamx5BAR0SsJgoC+LZ2wd6IfGtiZ4ElmHgb9fBo/HroJBcdXpAFYcoiI6LXq25nijwl+6N/KCaIIhB6+hYGrTyEpPVfqaESvxZJDRERvZKgnx3e9mmFx/+Yw1pPjdHwqAkLDcfTGY6mjEZWIJYeIiEqte/Oa2Bfij8YOZkjNzsewdWcx789rKFAopY5GVAxLDhERlYmrtTF2jvdFsI8zAGDl8TvouzIK95/lSJyMqCiWHCIiKjMDXTlmd3fHig9bwNRABxcS0tAlNAJ/XUmSOhqRCksOERGVW2d3B4SF+MPDyQLpzwsw+tfzmL33CvIKFVJHI2LJISKit+NkaYRtY3ww+j91AADrIu+i1/KTuJuSLXEyqu5YcoiI6K3p6cjweUAjrB3aEjWMdHH5QQa6LonA3tiHUkejaowlh4iIKkz7hnYIm+yPVi41kJVXiEmbLmD6zkvILeD4iqoeSw4REVUoB3NDbBrVBhPb1YMgAJvOJCBoWSRuP86UOhpVMyw5RERU4XTkMkzt5IZfhreGtYkeridlInBJJLafvy91NKpGWHKIiKjS+Ne3Qdhkf7StZ4XnBQpM3RaLKVtjkJ1XKHU0qgZYcoiIqFLZmhrgl+HemPp+A8gEYGf0A3RbGoFrjzKkjkZajiWHiIgqnVwmYGL7+tg0qg3szQwQ9yQbQcsisfF0AkSRn2hOlYMlh4iIqox3HSuETfbHu242yCtU4vNdlzBp0wVk5hZIHY20EEsOERFVKUtjPawNboXPAxpCRyZg38VH6LokApfup0sdjbQMSw4REVU5mUzA6P/UxdaxPqhpYYh7T3PQc3kk1kXGc3xFFYYlh4iIJNOidg2EhfijUxM7FChEzN57FaN/PY+0nHypo5EWYMkhIiJJmRvpYsWHXpjdrQn05DIcupqMLqEROH/vmdTRSMOx5BARkeQEQUCwrwt2jveFi5URHqQ9R9+VUVhxPA5KJcdXVD4sOUREpDbca5pj7yQ/BHo4QqEU8d2f1zFs/Vk8zcqTOhppIJYcIiJSK6YGugjt3xzf9WwKfR0Zjt98goDQcJy681TqaKRhWHKIiEjtCIKA/q1rY89EP9SzNUFyRh4Grj6FxX/fgoLjKyollhwiIlJbbvam2DOxLfp41YJSBP739018uOY0HmfkSh2NNABLDhERqTUjPR3M7+OBH/t6wEhPjqg7T/HB4nCcuPlE6mik5lhyiIhII/RsUQt7J/mhob0pnmbnY8jaM/jhwHUUKpRSRyM1xZJDREQao66NCXZPaIsP29QGAPx0LA79V53Cw7TnEicjdcSSQ0REGsVAV45vgppi2cAWMNXXwbl7zxAQGo6/ryZLHY3UDEsOERFppC7NHLA/xB/NapkjLacAI385h6/3XUV+IcdX9AJLDhERaazaVkbYPtYXw9u6AgB+johHnxUnkfA0R+JkpA5YcoiISKPp6cgwI7AxVg9pCXNDXcTeT0eX0HCEXXokdTSSGEsOERFphY6N7RA22R9ezjWQmVeI8b9H48vdl5BboJA6GkmEJYeIiLRGTQtDbB7dBuPerQsA+O1UAnr8dBJxT7IkTkZSYMkhIiKtoiuX4bPODbFheGtYGevh2qMMBC6JwK4L96WORlWMJYeIiLTSOw1sEDbZHz51rJCTr8DHW2Lx6bZY5OQXSh2NqghLDhERaS07MwP8NtIbH3doAJkAbDt/H92XRuJmcqbU0agKsOQQEZFWk8sETO5QH7+PbANbU33cepyFbksjsOVsAkSRn2iuzVhyiIioWvCpa4Wwyf74TwMb5BYo8dmOS/hoSwyy8ji+0lYsOUREVG1Ym+hj/dBW+KxzQ8hlAv6IeYiuoeG4/CBd6mhUCVhyiIioWpHJBIx7ty62jmkDR3MD3H2ag54/ncQvUXc5vtIyLDlERFQteTlbImyyPzo0skO+QokZf1zBuN+ikf68QOpoVEFYcoiIqNqyMNLD6iFemNG1MXTlAg5cSUKX0HBcSHgmdTSqACw5RERUrQmCgOF+rtgxzhe1LY1w/9lz9FkRhdUn7kCp5PhKk7HkEBERAWhWywL7QvzQpZkDCpUivg27hpG/nENqdr7U0aicJC85y5Ytg4uLCwwMDODt7Y0zZ868dv20tDRMmDABDg4O0NfXR4MGDRAWFlZFaYmISJuZGehi6QBPfNvDHXo6Mhy5/hgBi8NxJj5V6mhUDpKWnC1btmDKlCmYOXMmoqOj4eHhgU6dOuHx48evXD8/Px8dO3bE3bt3sX37dty4cQOrV69GzZo1qzg5ERFpK0EQMMjbGbvHt0UdG2MkZeSi/6ooLD1yCwqOrzSKIEp4vpy3tzdatWqFpUuXAgCUSiWcnJwwadIkTJs2rdj6K1aswPz583H9+nXo6uqW6zEzMjJgbm6O9PR0mJmZvVV+IiLSbtl5hfhq92XsvPAAAOBXzxo/9vOAramBxMmqn/L8/pbsnZz8/HycP38eHTp0+P9hZDJ06NABUVFRr9xmz5498PHxwYQJE2BnZwd3d3fMnTsXCoWixMfJy8tDRkZGkRsREVFpGOvr4Md+zbGgjwcMdeWIuJ2CgMURiLiVInU0KgXJSk5KSgoUCgXs7OyKLLezs0NSUtIrt7lz5w62b98OhUKBsLAwfPXVV1i4cCG++eabEh9n3rx5MDc3V92cnJwq9HkQEZH26+1VC3sntYWbnSlSsvIweO1pLPzrBgoVSqmj0WtIfuBxWSiVStja2mLVqlXw8vJCv3798MUXX2DFihUlbjN9+nSkp6erbomJiVWYmIiItEU9W1P8MbEtBrSuDVEElhy5jYGrT+NR+nOpo1EJJCs51tbWkMvlSE5OLrI8OTkZ9vb2r9zGwcEBDRo0gFwuVy1r1KgRkpKSkJ//6lP89PX1YWZmVuRGRERUHga6cszr2RShAzxhoq+DM3dTEbA4HEevv/qEGZKWZCVHT08PXl5eOHz4sGqZUqnE4cOH4ePj88pt2rZti9u3b0Op/P9vD968eRMODg7Q09Or9MxEREQA0M3DEfsm+cG9phme5RRg2PqzmBt2DQUcX6kVScdVU6ZMwerVq7FhwwZcu3YN48aNQ3Z2NoYNGwYAGDJkCKZPn65af9y4cUhNTcXkyZNx8+ZN7N+/H3PnzsWECROkegpERFRNuVgbY8c4Xwz1dQEArDpxB31WRCExNUfaYKSiI+WD9+vXD0+ePMGMGTOQlJSE5s2b48CBA6qDkRMSEiCT/f8e5uTkhIMHD+Ljjz9Gs2bNULNmTUyePBmfffaZVE+BiIiqMX0dOWZ1a4I2dazw3+2xiElMQ5fQcPzQuxk6uztIHa/ak/Q6OVLgdXKIiKgyJKbmYNKmC4hJTAMABPs4Y3pAIxjoyl+/IZWKRl0nh4iISJs4WRph21gfjPlPHQDAhqh76LX8JOJTsiVOVn2x5BAREVUQXbkM0wMaYd2wVrA01sOVhxnoGhqOP2IeSB2tWmLJISIiqmDt3GwRFuKP1q6WyM5XYPLmGEzfeRHP80u+Qj9VPJYcIiKiSmBvboCNI70R8l59CAKw6UwigpZF4lZyptTRqg2WHCIiokqiI5dhSscG+G2EN2xM9XEjORPdlkZi27lEVLPzfiTBkkNERFTJ2tazRliIP/zqWeN5gQKfbr+IT7bGIjuvUOpoWo0lh4iIqArYmOrjl+Gt8WknN8gEYOeFBwhcEoGrDzOkjqa1WHKIiIiqiEwmYEK7etg82gf2Zga4k5KNoJ8i8dupexxfVQKWHCIioirW2tUSYZP90b6hLfILlfhy92VM3HgBGbkFUkfTKiw5REREErA01sPPwS3xZZdG0JEJ2H/pEbqEhiP2/66YTG+PJYeIiEgigiBgpH8dbB/ni1o1DJGY+hy9V5zEzxHxHF9VAJYcIiIiiTV3ssD+EH984G6PAoWIr/ddxahfziMtJ1/qaBqNJYeIiEgNmBvq4qdBLfB19ybQk8vw97VkBCwOx7m7qVJH01gsOURERGpCEAQM9nHBzvG+cLU2xsP0XPRbdQo/HbsNpZLjq7JiySEiIlIz7jXNsXeSH7o3d4RCKeKHAzcQvO4MUrLypI6mUVhyiIiI1JCJvg4W9WuOH3o1g4GuDOG3UvDB4nCcjEuROprGYMkhIiJSU4IgoG8rJ+yZ6If6tiZ4kpmHQWtO43+HbkLB8dUbseQQERGpuQZ2ptgz0Q/9WjpBFIHFh29h0JpTSM7IlTqaWmPJISIi0gCGenJ837sZFvVrDmM9OU7dSUXA4nAcv/lE6mhqiyWHiIhIgwR51sTeSX5o7GCGp9n5CF57Bt/9eR0FCqXU0dQOSw4REZGGqWNjgp3jfTG4jTMAYMXxOPRbGYUHac8lTqZeWHKIiIg0kIGuHF8HueOnQS1gqq+D6IQ0BCwOx19XkqSOpjZYcoiIiDRYQFMH7A/xh0ctc6Q/L8DoX89j9t4ryCtUSB1Nciw5REREGq62lRG2jfXFSD9XAMC6yLvovTwK955mS5xMWiw5REREWkBPR4YvuzbGz8EtYWGki0sP0tElNAL7Lj6UOppkWHKIiIi0yHuN7BAW4o9WLjWQlVeIiRsv4Itdl5BbUP3GVyw5REREWsbRwhCbRrXBxHb1IAjA76cTELQsErcfZ0kdrUqx5BAREWkhHbkMUzu54ZfhrWFtoofrSZnotjQCO6PvSx2tyrDkEBERaTH/+jYIC/GHb10r5OQrMGVrLKZui0VOfqHU0SodSw4REZGWszUzwK8jvDGlYwPIBGD7+fsIXBKB60kZUkerVCw5RERE1YBcJiDkvfrYOKoN7Mz0EfckG92XRmLTmQSIonZ+ojlLDhERUTXSpo4VwkL88a6bDfIKlZi+8xJCNscgM7dA6mgVjiWHiIiomrEy0cfa4FaY/kFD6MgE7I19iK5LInD5QbrU0SoUSw4REVE1JJMJGPNOXWwZ44OaFoa49zQHPX86ifWR8VozvmLJISIiqsa8nGsgLMQf7ze2Q75CiVl7r2LMr+eRnqP54yuWHCIiomrO3EgXKwd7YVZgY+jJZfjrajICQsMRnfBM6mhvhSWHiIiIIAgChrZ1xY5xvnC2MsKDtOfouyIKK4/HQanUzPEVSw4RERGpNK1ljn2T/NC1mQMKlSLm/XkdwzecxdOsPKmjlRlLDhERERVhaqCLJQM8Ma9nU+jryHDsxhMEhIbj1J2nUkcrE5YcIiIiKkYQBAxoXRt/TGyLujbGSM7Iw8DVpxB6+BYUGjK+YskhIiKiEjW0N8PeSX7o7VULShH48dBNDFl7Go8zc6WO9kYsOURERPRaRno6WNDHAwv7eMBIT47I208RsDgc4beeSB3ttVhyiIiIqFR6edXCnol+aGhvipSsfAxZewbzD15HoUIpdbRXYskhIiKiUqtna4LdE9pioHdtiCKw7GgcBqw+hYdpz6WOVgxLDhEREZWJga4cc3s0xZIBnjDR18HZu88QEBqOw9eSpY5WBEsOERERlUughyP2h/ihaU1zpOUUYMSGc/hm31XkF6rH+Iolh4iIiMrN2coY28f5YHhbVwDAmoh49FlxEs/zFRInY8khIiKit6SvI8eMwMZYNdgL5oa6aOxoBkM9udSxoCN1ACIiItIO7zexR5Oa5rA00pM6CgCWHCIiIqpANS0MpY6gwnEVERERaSWWHCIiItJKLDlERESkldSi5CxbtgwuLi4wMDCAt7c3zpw5U6rtNm/eDEEQEBQUVLkBiYiISONIXnK2bNmCKVOmYObMmYiOjoaHhwc6deqEx48fv3a7u3fvYurUqfD396+ipERERKRJJC85P/74I0aNGoVhw4ahcePGWLFiBYyMjLB27doSt1EoFBg0aBBmz56NOnXqVGFaIiIi0hSSlpz8/HycP38eHTp0UC2TyWTo0KEDoqKiStxuzpw5sLW1xYgRI6oiJhEREWkgSa+Tk5KSAoVCATs7uyLL7ezscP369VduExERgZ9//hkxMTGleoy8vDzk5eWpvs7IyCh3XiIiItIcko+ryiIzMxODBw/G6tWrYW1tXapt5s2bB3Nzc9XNycmpklMSERGROpD0nRxra2vI5XIkJxf9aPbk5GTY29sXWz8uLg53795FYGCgaplS+eKTTnV0dHDjxg3UrVu3yDbTp0/HlClTVF9nZGSw6BAREVUDkpYcPT09eHl54fDhw6rTwJVKJQ4fPoyJEycWW79hw4a4dOlSkWVffvklMjMzsXjx4leWF319fejr61dKfiIiIlJfkn921ZQpUxAcHIyWLVuidevWWLRoEbKzszFs2DAAwJAhQ1CzZk3MmzcPBgYGcHd3L7K9hYUFABRbTkRERNWb5CWnX79+ePLkCWbMmIGkpCQ0b94cBw4cUB2MnJCQAJlMow4dIiIiIjUgiKIoSh2iKqWnp8PCwgKJiYkwMzOTOg4RERGVwstjatPS0mBubl6qbSR/J6eqZWZmAgAPPiYiItJAmZmZpS451e6dHKVSiYcPH8LU1BSCIEgdp8K9bLp8p6picb9WDu7XysN9Wzm4XyvPm/atKIrIzMyEo6NjqQ9jqXbv5MhkMtSqVUvqGJXOzMyM/wNWAu7XysH9Wnm4bysH92vled2+Le07OC/xiF4iIiLSSiw5REREpJVYcrSMvr4+Zs6cyQsgVjDu18rB/Vp5uG8rB/dr5amMfVvtDjwmIiKi6oHv5BAREZFWYskhIiIircSSQ0RERFqJJYeIiIi0EkuOBjlx4gQCAwPh6OgIQRCwe/fu165/7NgxCIJQ7JaUlFQ1gTXEvHnz0KpVK5iamsLW1hZBQUG4cePGG7fbtm0bGjZsCAMDAzRt2hRhYWFVkFZzlGe/rl+/vtjr1cDAoIoSa47ly5ejWbNmqoum+fj44M8//3ztNny9vllZ9ytfr+Xz3XffQRAEfPTRR69dryJesyw5GiQ7OxseHh5YtmxZmba7ceMGHj16pLrZ2tpWUkLNdPz4cUyYMAGnTp3CoUOHUFBQgPfffx/Z2dklbnPy5EkMGDAAI0aMwIULFxAUFISgoCBcvny5CpOrt/LsV+DF1U7/+Xq9d+9eFSXWHLVq1cJ3332H8+fP49y5c2jfvj26d++OK1euvHJ9vl5Lp6z7FeDrtazOnj2LlStXolmzZq9dr8JesyJpJADirl27XrvO0aNHRQDis2fPqiSTtnj8+LEIQDx+/HiJ6/Tt21fs0qVLkWXe3t7imDFjKjuexirNfl23bp1obm5edaG0SI0aNcQ1a9a88nt8vZbf6/YrX69lk5mZKdavX188dOiQ+M4774iTJ08ucd2Kes3ynZxqoHnz5nBwcEDHjh0RGRkpdRy1l56eDgCwtLQscZ2oqCh06NChyLJOnTohKiqqUrNpstLsVwDIysqCs7MznJyc3vhXNAEKhQKbN29GdnY2fHx8XrkOX69lV5r9CvD1WhYTJkxAly5dir0WX6WiXrPV7gM6qxMHBwesWLECLVu2RF5eHtasWYN3330Xp0+fRosWLaSOp5aUSiU++ugjtG3bFu7u7iWul5SUBDs7uyLL7OzseLxTCUq7X93c3LB27Vo0a9YM6enpWLBgAXx9fXHlypVq8cG6ZXHp0iX4+PggNzcXJiYm2LVrFxo3bvzKdfl6Lb2y7Fe+Xktv8+bNiI6OxtmzZ0u1fkW9ZllytJibmxvc3NxUX/v6+iIuLg7/+9//8Ouvv0qYTH1NmDABly9fRkREhNRRtEpp96uPj0+Rv5p9fX3RqFEjrFy5El9//XVlx9Qobm5uiImJQXp6OrZv347g4GAcP368xF/IVDpl2a98vZZOYmIiJk+ejEOHDlX5gdksOdVM69at+Qu8BBMnTsS+fftw4sSJN/4VZm9vj+Tk5CLLkpOTYW9vX5kRNVJZ9uu/6erqwtPTE7dv366kdJpLT08P9erVAwB4eXnh7NmzWLx4MVauXFlsXb5eS68s+/Xf+Hp9tfPnz+Px48dFJggKhQInTpzA0qVLkZeXB7lcXmSbinrN8picaiYmJgYODg5Sx1Aroihi4sSJ2LVrF44cOQJXV9c3buPj44PDhw8XWXbo0KHXzu6rm/Ls139TKBS4dOkSX7OloFQqkZeX98rv8fVafq/br//G1+urvffee7h06RJiYmJUt5YtW2LQoEGIiYkpVnCACnzNlv34aJJKZmameOHCBfHChQsiAPHHH38UL1y4IN67d08URVGcNm2aOHjwYNX6//vf/8Tdu3eLt27dEi9duiROnjxZlMlk4t9//y3VU1BL48aNE83NzcVjx46Jjx49Ut1ycnJU6wwePFicNm2a6uvIyEhRR0dHXLBggXjt2jVx5syZoq6urnjp0iUpnoJaKs9+nT17tnjw4EExLi5OPH/+vNi/f3/RwMBAvHLlihRPQW1NmzZNPH78uBgfHy9evHhRnDZtmigIgvjXX3+JosjXa3mVdb/y9Vp+/z67qrJesyw5GuTlKeH/vgUHB4uiKIrBwcHiO++8o1r/+++/F+vWrSsaGBiIlpaW4rvvviseOXJEmvBq7FX7FIC4bt061TrvvPOOaj+/tHXrVrFBgwainp6e2KRJE3H//v1VG1zNlWe/fvTRR2Lt2rVFPT090c7OTgwICBCjo6OrPryaGz58uOjs7Czq6emJNjY24nvvvaf6RSyKfL2WV1n3K1+v5ffvklNZr1lBFEWxbO/9EBEREak/HpNDREREWoklh4iIiLQSSw4RERFpJZYcIiIi0kosOURERKSVWHKIiIhIK7HkEBERkVZiySEijXD37l0IgoCYmJhSb7N+/XpYWFhUWiYiUm8sOURERKSVWHKIiIhIK7HkEJHaOHDgAPz8/GBhYQErKyt07doVcXFxr1z32LFjEAQB+/fvR7NmzWBgYIA2bdrg8uXLxdY9ePAgGjVqBBMTE3Tu3BmPHj1Sfe/s2bPo2LEjrK2tYW5ujnfeeQfR0dGV9hyJqOqw5BCR2sjOzsaUKVNw7tw5HD58GDKZDD169IBSqSxxm08//RQLFy7E2bNnYWNjg8DAQBQUFKi+n5OTgwULFuDXX3/FiRMnkJCQgKlTp6q+n5mZieDgYERERODUqVOoX78+AgICkJmZWanPlYgqn47UAYiIXurVq1eRr9euXQsbGxtcvXoVJiYmr9xm5syZ6NixIwBgw4YNqFWrFnbt2oW+ffsCAAoKCrBixQrUrVsXADBx4kTMmTNHtX379u2L3N+qVatgYWGB48ePo2vXrhX23Iio6vGdHCJSG7du3cKAAQNQp04dmJmZwcXFBQCQkJBQ4jY+Pj6q/7a0tISbmxuuXbumWmZkZKQqOADg4OCAx48fq75OTk7GqFGjUL9+fZibm8PMzAxZWVmvfUwi0gx8J4eI1EZgYCCcnZ2xevVqODo6QqlUwt3dHfn5+eW+T11d3SJfC4IAURRVXwcHB+Pp06dYvHgxnJ2doa+vDx8fn7d6TCJSDyw5RKQWnj59ihs3bmD16tXw9/cHAERERLxxu1OnTqF27doAgGfPnuHmzZto1KhRqR83MjISP/30EwICAgAAiYmJSElJKcczICJ1w5JDRGqhRo0asLKywqpVq+Dg4ICEhARMmzbtjdvNmTMHVlZWsLOzwxdffAFra2sEBQWV+nHr16+PX3/9FS1btkRGRgY+/fRTGBoavsUzISJ1wWNyiEgtyGQybN68GefPn4e7uzs+/vhjzJ8//43bfffdd5g8eTK8vLyQlJSEvXv3Qk9Pr9SP+/PPP+PZs2do0aIFBg8ejJCQENja2r7NUyEiNSGI/xxOExFpiGPHjqFdu3Z49uwZP7qBiF6J7+QQERGRVmLJISIiIq3EcRURERFpJb6TQ0RERFqJJYeIiIi0EksOERERaSWWHCIiItJKLDlERESklVhyiIiISCux5BAREZFWYskhIiIircSSQ0RERFrp/wEzn8BasR050QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}