chapter08/Tweet_emotion_detection_using_class_weights_Huggingface.ipynb

{
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    "<a href=\"https://colab.research.google.com/github/packtpublishing/Machine-Learning-for-Imbalanced-Data/blob/main/chapter08/Tweet_emotion_detection_using_class_weights_Huggingface.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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  {
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   "id": "E6zNyJBcEIrN",
   "metadata": {
    "id": "E6zNyJBcEIrN"
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   "source": [
    "Reference https://github.com/trhgquan/MTH089/blob/dae5a8f8f7707aa9cac1df9f7b0011d4f7a741d7/practice/TREC-classification/code/deep-learning/BERT-based-classification.ipynb\n"
   ]
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   "id": "f39166f1",
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    "id": "f39166f1",
    "outputId": "586dfb1a-47dd-4539-9e9b-9904751aa266"
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
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      "Successfully installed accelerate-0.15.0 datasets-2.8.0 dill-0.3.6 huggingface-hub-0.17.3 multiprocess-0.70.14 responses-0.18.0 tokenizers-0.13.3 transformers-4.25.1 xxhash-3.3.0\n"
     ]
    }
   ],
   "source": [
    "!pip install transformers==4.25.1 datasets==2.8.0 accelerate==0.15.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "85afed58",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-29T08:02:47.456823Z",
     "iopub.status.busy": "2022-07-29T08:02:47.456311Z",
     "iopub.status.idle": "2022-07-29T08:02:55.652551Z",
     "shell.execute_reply": "2022-07-29T08:02:55.651434Z"
    },
    "id": "85afed58",
    "papermill": {
     "duration": 8.206919,
     "end_time": "2022-07-29T08:02:55.655379",
     "exception": false,
     "start_time": "2022-07-29T08:02:47.448460",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "from sklearn import preprocessing\n",
    "\n",
    "from transformers import AutoTokenizer\n",
    "from datasets import Dataset\n",
    "from transformers import DataCollatorWithPadding\n",
    "from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer\n",
    "\n",
    "import torch\n",
    "from torch.utils.checkpoint import checkpoint\n",
    "import torch.nn as nn\n",
    "\n",
    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n",
    "os.environ[\"TRANSFORMERS_NO_ADVISORY_WARNINGS\"] = \"true\"\n",
    "\n",
    "# Suppress warnings\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "torch.manual_seed(42)\n",
    "random.seed(0)\n",
    "np.random.seed(84)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3d599054",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-29T08:02:55.684154Z",
     "iopub.status.busy": "2022-07-29T08:02:55.682730Z",
     "iopub.status.idle": "2022-07-29T08:02:55.688388Z",
     "shell.execute_reply": "2022-07-29T08:02:55.687542Z"
    },
    "id": "3d599054",
    "papermill": {
     "duration": 0.015658,
     "end_time": "2022-07-29T08:02:55.690287",
     "exception": false,
     "start_time": "2022-07-29T08:02:55.674629",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_name = \"distilbert-base-uncased\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2251a9e",
   "metadata": {
    "id": "c2251a9e",
    "papermill": {
     "duration": 0.006329,
     "end_time": "2022-07-29T08:02:55.702967",
     "exception": false,
     "start_time": "2022-07-29T08:02:55.696638",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Prepare Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e4f7efa",
   "metadata": {
    "id": "4e4f7efa",
    "papermill": {
     "duration": 0.006866,
     "end_time": "2022-07-29T08:02:55.716076",
     "exception": false,
     "start_time": "2022-07-29T08:02:55.709210",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Get Data - And apply simple normalization"
   ]
  },
  {
   "cell_type": "code",
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      "784b19f4e2b1418a9b0169b890b4ff06",
      "239f9c3b7f2246c78881fa84e0fca6a1",
      "f38d013655294e3d9c0a682fc0e2413d",
      "bd4fc7a9e7f04a709c7f5c70ffa6b98a",
      "7890a9e1b5a345c3b96107e9fd3736b8",
      "df694aee9df04860994ce75b3530ade9",
      "471cce85fc8745318e5fad7f796751cb",
      "8a9d1413d17a4863a2c26c386d285fa1",
      "059d70137ca7448a88bae03d5ac8895c",
      "5844b97e0e274690a5a4f43ebb2e0479",
      "729fb581eecc4712b87ab23444af47e8",
      "bfffbd1fc4c846eb8c447279ffcb59e0"
     ]
    },
    "id": "767f22cf",
    "outputId": "3122ab1b-aed9-4e9f-8d0c-70a4b774b036"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c285a3f556b4496998c373219d77c0ba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/5.09k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "caea9e0732594e8f85c13612fd94659a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading metadata:   0%|          | 0.00/3.34k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3fabcc66a83e4c008d79398af24bb930",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/10.6k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset trec/default to /root/.cache/huggingface/datasets/trec/default/2.0.0/f2469cab1b5fceec7249fda55360dfdbd92a7a5b545e91ea0f78ad108ffac1c2...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "155ae05745b24e77bb6475334f404967",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "01f4fd42739a41128974339d550be9e5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/336k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eba5c0e743504b38b0d5850cebcec289",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/23.4k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "33d825b6fbde4767bb2179f10633243e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/5452 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "239f9c3b7f2246c78881fa84e0fca6a1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test split:   0%|          | 0/500 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset trec downloaded and prepared to /root/.cache/huggingface/datasets/trec/default/2.0.0/f2469cab1b5fceec7249fda55360dfdbd92a7a5b545e91ea0f78ad108ffac1c2. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:datasets.builder:Found cached dataset trec (/root/.cache/huggingface/datasets/trec/default/2.0.0/f2469cab1b5fceec7249fda55360dfdbd92a7a5b545e91ea0f78ad108ffac1c2)\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "train_dataset = load_dataset(\"trec\", split=\"train\")\n",
    "test_dataset = load_dataset(\"trec\", split=\"test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dSMlhae7ZIdV",
   "metadata": {
    "id": "dSMlhae7ZIdV"
   },
   "source": [
    "# Let's make this imbalanced by removing a bunch of examples from class 0 and class 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eXA0ROSWH9ry",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "dca1bfd7cb4140a69b450eac36b0bf16",
      "3d16fee3c2584b8fa7937f13f5bb1fb2",
      "272075e7844b41aeb14ed404b1865908",
      "60efcf6837c24932a2c6135b28f0ef06",
      "8a52982574024609a6a095b1773d86dd",
      "17200088d2d6424a86ae45104222c695",
      "459236d294a14f9f998d3b97bcc5a615",
      "131ef27360684f52bb5b22432a1b5e3f",
      "a34d23a710d648e3add5d4f628c8a070",
      "92816b2dfffb4bcb89506796c0b381f0",
      "488e2d0195754ea680eb9159521fecac"
     ]
    },
    "id": "eXA0ROSWH9ry",
    "outputId": "29551c2f-9c56-4de9-c151-361bd3320d7d"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dca1bfd7cb4140a69b450eac36b0bf16",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/6 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# filter() returns rows that match a specified condition: if probability > .8 and label is 0 or 2, then take it.\n",
    "train_dataset = train_dataset.filter(\n",
    "    lambda x: random.random() > 0.8 if (x[\"coarse_label\"] in (0, 2)) else True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "DTavpMZdaGik",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DTavpMZdaGik",
    "outputId": "23a60853-03d1-4f69-b362-9d5cfa83dfcb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ABBR', 'ENTY', 'DESC', 'HUM', 'LOC', 'NUM']\n",
      "ClassLabel(names=['ABBR', 'ENTY', 'DESC', 'HUM', 'LOC', 'NUM'], id=None)\n"
     ]
    }
   ],
   "source": [
    "print(train_dataset.features[\"coarse_label\"].names)\n",
    "print(train_dataset.features[\"coarse_label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ySt5blSucKXE",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "id": "ySt5blSucKXE",
    "outputId": "b706233e-5cc3-4e9b-cc5a-138d5daae270"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "\n",
    "pd.Series(train_dataset[\"coarse_label\"]).value_counts(ascending=True).plot.barh()\n",
    "plt.title(\"Frequency of Classes\")\n",
    "plt.ylabel(\"Class\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gpjhM7lLdj9P",
   "metadata": {
    "id": "gpjhM7lLdj9P"
   },
   "source": [
    "# Print frequency of various classes: verify class imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9duyjT0bcrw0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "id": "9duyjT0bcrw0",
    "outputId": "25f5f363-52dd-4b79-a502-6e6d0e7d77e4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "_, counts = np.unique(train_dataset[\"coarse_label\"], return_counts=True)\n",
    "ax.bar(train_dataset.features[\"coarse_label\"].names, counts)\n",
    "ax.set_xticks(train_dataset.features[\"coarse_label\"].names)\n",
    "\n",
    "for p in ax.patches:\n",
    "    ax.annotate(str(p.get_height()), (p.get_x() * 1.04, p.get_height() * 1.012))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "MQQEumuaEZsc",
   "metadata": {
    "id": "MQQEumuaEZsc"
   },
   "outputs": [],
   "source": [
    "train, dev = train_dataset.train_test_split(\n",
    "    test_size=0.2, seed=42, stratify_by_column=\"coarse_label\"\n",
    ").values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "Ztgx_4WqFWQ7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ztgx_4WqFWQ7",
    "outputId": "b9a776e6-ef48-457c-ad0c-e675014f32bc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['text', 'coarse_label', 'fine_label'],\n",
       "    num_rows: 500\n",
       "})"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "WL005_hFP3Gs",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WL005_hFP3Gs",
    "outputId": "6e27ad68-f478-4edf-e2b6-4a797b002792"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ClassLabel(names=['ABBR', 'ENTY', 'DESC', 'HUM', 'LOC', 'NUM'], id=None)\n",
      "['ABBR', 'ENTY', 'DESC', 'HUM', 'LOC', 'NUM']\n"
     ]
    }
   ],
   "source": [
    "print(train.features[\"coarse_label\"])\n",
    "label_names = train.features[\"coarse_label\"].names\n",
    "print(label_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "R-8G9YClFnx2",
   "metadata": {
    "id": "R-8G9YClFnx2"
   },
   "outputs": [],
   "source": [
    "from datasets import DatasetDict\n",
    "\n",
    "dataset = DatasetDict({\"train\": train, \"dev\": dev, \"test\": test_dataset})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eff404e5",
   "metadata": {
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      "5e14b8ce81f143bb91ba7015aa811bff"
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    },
    "execution": {
     "iopub.execute_input": "2022-07-29T08:03:21.215859Z",
     "iopub.status.busy": "2022-07-29T08:03:21.214176Z",
     "iopub.status.idle": "2022-07-29T08:03:29.880762Z",
     "shell.execute_reply": "2022-07-29T08:03:29.879787Z"
    },
    "id": "eff404e5",
    "outputId": "866266fe-c49c-44f3-b587-2e0044cf14ae",
    "papermill": {
     "duration": 8.676834,
     "end_time": "2022-07-29T08:03:29.883480",
     "exception": false,
     "start_time": "2022-07-29T08:03:21.206646",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d9cf75b40cf5421a9414525e8602cc20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a4cb9486b6a046ea8b4c531d961ba7e6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fee741a12567414db2afc62964e8a8b8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8bfa2ca059274c828c0780b2cce7f1f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "tokenizer.model_max_length = 512"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "UqrAsEEP7_Wz",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UqrAsEEP7_Wz",
    "outputId": "bd74a2b4-a01c-4362-c38c-5d75bb908a2d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': [101, 2129, 2515, 1037, 4045, 10250, 19879, 4263, 2147, 1029, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer(dataset[\"train\"][0][\"text\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "zOkxrjgGKqCU",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zOkxrjgGKqCU",
    "outputId": "8b0bf448-9198-4150-cc5a-81c3b092aeed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({5: 113, 4: 81, 3: 65, 2: 138, 1: 94, 0: 9})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "Counter(test_dataset[\"coarse_label\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ed7734",
   "metadata": {
    "id": "50ed7734",
    "jp-MarkdownHeadingCollapsed": true,
    "papermill": {
     "duration": 0.010693,
     "end_time": "2022-07-29T08:03:30.151522",
     "exception": false,
     "start_time": "2022-07-29T08:03:30.140829",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Create tokenized dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ufbIirey8Bm_",
   "metadata": {
    "colab": {
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    },
    "id": "ufbIirey8Bm_",
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   },
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3c8e2c757d24770b9d0e8b831301800",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "50e400c9af7b4ea498b573e00c87e46f",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9035577e96e1456cbc9bc898f52080e2",
       "version_major": 2,
       "version_minor": 0
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       "  0%|          | 0/1 [00:00<?, ?ba/s]"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['text', 'labels', 'input_ids', 'attention_mask'],\n",
       "        num_rows: 3560\n",
       "    })\n",
       "    dev: Dataset({\n",
       "        features: ['text', 'labels', 'input_ids', 'attention_mask'],\n",
       "        num_rows: 890\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['text', 'labels', 'input_ids', 'attention_mask'],\n",
       "        num_rows: 500\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def preprocess_function(examples):\n",
    "    return tokenizer(examples[\"text\"], truncation=True)\n",
    "\n",
    "\n",
    "tokenized_dataset = dataset.map(\n",
    "    preprocess_function, batched=True, remove_columns=[\"fine_label\"]\n",
    ")\n",
    "tokenized_dataset = tokenized_dataset.rename_column(\"coarse_label\", \"labels\")\n",
    "tokenized_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "mxaRfYV9Juh1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mxaRfYV9Juh1",
    "outputId": "bdbccc0c-0903-48ab-8769-ee6f622d03e7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({1: 1000, 3: 978, 5: 717, 4: 668, 2: 183, 0: 14})\n",
      "Counter({1: 250, 3: 245, 5: 179, 4: 167, 2: 46, 0: 3})\n",
      "Counter({2: 138, 5: 113, 1: 94, 4: 81, 3: 65, 0: 9})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "print(Counter(tokenized_dataset[\"train\"][\"labels\"]))\n",
    "print(Counter(tokenized_dataset[\"dev\"][\"labels\"]))\n",
    "print(Counter(tokenized_dataset[\"test\"][\"labels\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0279743c",
   "metadata": {
    "id": "0279743c",
    "papermill": {
     "duration": 0.006805,
     "end_time": "2022-07-29T08:03:29.897684",
     "exception": false,
     "start_time": "2022-07-29T08:03:29.890879",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Create a label column and input column\n",
    "* Input is going to be [discourse_text + essay_text]\n",
    "* Label is going to be numericalised version of the three classes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a63799b0",
   "metadata": {
    "id": "a63799b0",
    "jp-MarkdownHeadingCollapsed": true,
    "papermill": {
     "duration": 0.007245,
     "end_time": "2022-07-29T08:04:45.948369",
     "exception": false,
     "start_time": "2022-07-29T08:04:45.941124",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Define Dynamic padding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "36c956c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-29T08:04:45.965143Z",
     "iopub.status.busy": "2022-07-29T08:04:45.964788Z",
     "iopub.status.idle": "2022-07-29T08:04:45.969798Z",
     "shell.execute_reply": "2022-07-29T08:04:45.968718Z"
    },
    "id": "36c956c1",
    "papermill": {
     "duration": 0.015811,
     "end_time": "2022-07-29T08:04:45.971838",
     "exception": false,
     "start_time": "2022-07-29T08:04:45.956027",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7df8b9e",
   "metadata": {
    "id": "c7df8b9e",
    "jp-MarkdownHeadingCollapsed": true,
    "papermill": {
     "duration": 0.007587,
     "end_time": "2022-07-29T08:04:45.986765",
     "exception": false,
     "start_time": "2022-07-29T08:04:45.979178",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Define model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "79556705",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 156,
     "referenced_widgets": [
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      "47ef98c1ced648d18fa9fe3183821fe0",
      "477d2210cab24007b89a2b955764f5ed"
     ]
    },
    "execution": {
     "iopub.execute_input": "2022-07-29T08:04:46.004059Z",
     "iopub.status.busy": "2022-07-29T08:04:46.002491Z",
     "iopub.status.idle": "2022-07-29T08:05:09.827878Z",
     "shell.execute_reply": "2022-07-29T08:05:09.827029Z"
    },
    "id": "79556705",
    "outputId": "1272df65-4798-4533-8896-668526e3fe9d",
    "papermill": {
     "duration": 23.835787,
     "end_time": "2022-07-29T08:05:09.829949",
     "exception": false,
     "start_time": "2022-07-29T08:04:45.994162",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "537d56355fa04b1f8c6e1b038f959368",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading pytorch_model.bin:   0%|          | 0.00/268M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_projector.weight', 'vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_projector.bias', 'vocab_transform.weight']\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-uncased and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', '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": [
    "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2853daa",
   "metadata": {
    "id": "a2853daa",
    "papermill": {
     "duration": 0.007372,
     "end_time": "2022-07-29T08:05:09.845845",
     "exception": false,
     "start_time": "2022-07-29T08:05:09.838473",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Define Training Arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d203542c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-29T08:05:09.862894Z",
     "iopub.status.busy": "2022-07-29T08:05:09.862309Z",
     "iopub.status.idle": "2022-07-29T08:05:09.932111Z",
     "shell.execute_reply": "2022-07-29T08:05:09.931049Z"
    },
    "id": "d203542c",
    "papermill": {
     "duration": 0.081008,
     "end_time": "2022-07-29T08:05:09.934334",
     "exception": false,
     "start_time": "2022-07-29T08:05:09.853326",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def trainaug(runname):\n",
    "    training_args = TrainingArguments(\n",
    "        run_name=runname,\n",
    "        output_dir=\"./results\",\n",
    "        num_train_epochs=5,\n",
    "        evaluation_strategy=\"epoch\",\n",
    "        save_strategy=\"epoch\",\n",
    "        warmup_ratio=0.1,\n",
    "        lr_scheduler_type=\"cosine\",\n",
    "        auto_find_batch_size=True,\n",
    "        gradient_accumulation_steps=4,\n",
    "        fp16=True,  # uncomment if training on GPU\n",
    "        log_level=\"error\",\n",
    "    )\n",
    "    return training_args\n",
    "\n",
    "\n",
    "training_args = trainaug(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "baab576f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-29T08:05:14.874803Z",
     "iopub.status.busy": "2022-07-29T08:05:14.874511Z",
     "iopub.status.idle": "2022-07-29T08:05:14.881277Z",
     "shell.execute_reply": "2022-07-29T08:05:14.880314Z"
    },
    "id": "baab576f",
    "papermill": {
     "duration": 0.01826,
     "end_time": "2022-07-29T08:05:14.883235",
     "exception": false,
     "start_time": "2022-07-29T08:05:14.864975",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import EvalPrediction\n",
    "from typing import Dict\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "\n",
    "def custom_compute_metrics(res: EvalPrediction) -> Dict:\n",
    "    pred = res.predictions.argmax(axis=1)\n",
    "    target = res.label_ids\n",
    "    precision = precision_score(target, pred, average=\"macro\")\n",
    "    recall = recall_score(target, pred, average=\"macro\")\n",
    "    f1 = f1_score(target, pred, average=\"macro\")\n",
    "    return {\"precision\": precision, \"recall\": recall, \"f1\": f1}\n",
    "\n",
    "\n",
    "class CustomTrainer(Trainer):\n",
    "    def compute_loss(self, model, inputs, return_outputs=False):\n",
    "        # print(inputs)\n",
    "        labels = inputs.get(\"labels\")\n",
    "        outputs = model(**inputs)\n",
    "        logits = outputs.get(\"logits\")\n",
    "        loss_fct = nn.CrossEntropyLoss()\n",
    "        loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))\n",
    "        return (loss, outputs) if return_outputs else loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e4b0b3c",
   "metadata": {
    "id": "0e4b0b3c",
    "jp-MarkdownHeadingCollapsed": true,
    "papermill": {
     "duration": 0.007466,
     "end_time": "2022-07-29T08:05:14.898352",
     "exception": false,
     "start_time": "2022-07-29T08:05:14.890886",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Define Trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d14a5602",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-29T08:05:14.915450Z",
     "iopub.status.busy": "2022-07-29T08:05:14.914799Z",
     "iopub.status.idle": "2022-07-29T08:05:15.124822Z",
     "shell.execute_reply": "2022-07-29T08:05:15.123926Z"
    },
    "id": "d14a5602",
    "papermill": {
     "duration": 0.220944,
     "end_time": "2022-07-29T08:05:15.127028",
     "exception": false,
     "start_time": "2022-07-29T08:05:14.906084",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "trainer = CustomTrainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized_dataset[\"train\"],\n",
    "    eval_dataset=tokenized_dataset[\"dev\"],\n",
    "    tokenizer=tokenizer,\n",
    "    data_collator=data_collator,\n",
    "    compute_metrics=custom_compute_metrics,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3cd9a44",
   "metadata": {
    "id": "f3cd9a44",
    "jp-MarkdownHeadingCollapsed": true,
    "papermill": {
     "duration": 0.007785,
     "end_time": "2022-07-29T08:05:15.142826",
     "exception": false,
     "start_time": "2022-07-29T08:05:15.135041",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7d3a6295",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 267
    },
    "execution": {
     "iopub.execute_input": "2022-07-29T08:05:15.159835Z",
     "iopub.status.busy": "2022-07-29T08:05:15.159519Z",
     "iopub.status.idle": "2022-07-29T09:35:25.442800Z",
     "shell.execute_reply": "2022-07-29T09:35:25.442019Z"
    },
    "id": "7d3a6295",
    "outputId": "66fb0f97-c724-4bfe-91ec-210af94594cd",
    "papermill": {
     "duration": 5410.295235,
     "end_time": "2022-07-29T09:35:25.445796",
     "exception": false,
     "start_time": "2022-07-29T08:05:15.150561",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='555' max='555' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [555/555 01:38, Epoch 4/5]\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>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.300792</td>\n",
       "      <td>0.741441</td>\n",
       "      <td>0.691397</td>\n",
       "      <td>0.703288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.244023</td>\n",
       "      <td>0.767893</td>\n",
       "      <td>0.755640</td>\n",
       "      <td>0.760697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.217956</td>\n",
       "      <td>0.951520</td>\n",
       "      <td>0.808275</td>\n",
       "      <td>0.848400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.205771</td>\n",
       "      <td>0.946781</td>\n",
       "      <td>0.828028</td>\n",
       "      <td>0.859289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.277800</td>\n",
       "      <td>0.210412</td>\n",
       "      <td>0.945396</td>\n",
       "      <td>0.824418</td>\n",
       "      <td>0.856655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=555, training_loss=0.2520319065532169, metrics={'train_runtime': 102.9475, 'train_samples_per_second': 172.904, 'train_steps_per_second': 5.391, 'total_flos': 97587828203040.0, 'train_loss': 0.2520319065532169, 'epoch': 5.0})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lV_H42iSDyyD",
   "metadata": {
    "id": "lV_H42iSDyyD"
   },
   "source": [
    "Our best F1-score is 0.766 at 6th epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "72ygfyy6JDrT",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "id": "72ygfyy6JDrT",
    "outputId": "9c3de657-7d68-43dd-9318-cf198f59e313"
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions, labels, _ = trainer.predict(tokenized_dataset[\"test\"])\n",
    "predictions = np.argmax(predictions, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "-8UHZnVVJtcv",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-8UHZnVVJtcv",
    "outputId": "7e4b69b6-d8fb-4dca-dfaa-82070ecd8821"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      1.00      0.80         6\n",
      "           1       0.94      0.87      0.90       101\n",
      "           2       0.91      0.95      0.93       132\n",
      "           3       0.97      0.97      0.97        65\n",
      "           4       0.99      0.99      0.99        81\n",
      "           5       0.99      0.97      0.98       115\n",
      "\n",
      "    accuracy                           0.95       500\n",
      "   macro avg       0.91      0.96      0.93       500\n",
      "weighted avg       0.95      0.95      0.95       500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import (\n",
    "    confusion_matrix,\n",
    "    classification_report,\n",
    "    accuracy_score,\n",
    "    precision_score,\n",
    "    recall_score,\n",
    "    f1_score,\n",
    ")\n",
    "\n",
    "print(classification_report(predictions, labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "U55GE0XiOWZJ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 582
    },
    "id": "U55GE0XiOWZJ",
    "outputId": "468b5690-fe7a-4569-c92e-6aca64da4846"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NDw8PnXSau/KuX7+uvQPK0Gvjxo0AuY72c34ntWvXNlgfKysrgz+AxtDcTaT5oTeWpp8B1KlTJ9+0devWBfLvZ/n1CwcHB+DRbfKl4csvv8TGxoYHDx7Qp08fvL29GTBgAN988412HiNTadrQ2PYDw21Y0Jmq4mpDY7bfwm7jmZmZjB49mi5durBixQquXLlCQkKCzh2DGsWxnRu7fzPGq6++qj1o0WzLS5cupVy5csX2GU8SCYTEY/Pxxx9jY2NDUlISX3zxRYHpNUdJ+V3i0NCkye/ISrMDLYihI8DCpMu744uLi6N///5ERETg5eXF7NmzOXr0KHfv3iUmJkY7i7DmzIOh21iLKisrixdeeIHAwECsrKxYv369wbMohdlJp6SkaP+fc9oEJyenfPMVtDw/cXFxgHH9JaecfaagvMb0M2P7T2lp3bo1x44do3///lhbWxMVFcXGjRuZOnUqjRo1ol69emzbts2kMo3dVnMuN9SGxrafoaDCWMZ8TmG/y7lz57J8+XIAnnvuOTZs2MDly5eJjIzUbuM527g4tnNj92/Gat68ufb/Pj4+dOnSpVjLf5JIICQem0qVKmnHCPz666+Ehobmm16z0zRm/iFNGlN/BB+39evXEx4ejoWFBfv27eO9996jVatWVKhQAVdXV+0swsVxqjw/kydP1o6nWrhwYb47OU1wohm3ZMwr5xw3OYObgr7Losw15eLiAuQfpOiTs88YW7/S6mfGXpYo6Ie1cePGbNy4kYcPH7Jnzx4+++wz2rdvj0ql4sKFC/Tp04cNGzYYXS9jt9Wcy5/0bbUoNBPIDh06lM2bNzNgwABq1qyJh4eHdhvXjKl6Ep0+fZoZM2Zo/7579y5Tp04txRqVLAmExGP14YcfYmdnR2pqKp9++mm+aTWDoxMSEggJCTGYLi0tTTsB2pM+9f/Zs2cBaNCgQa7LBDmlpaVx9erVEqvDggUL+P777wGYOnUqr7zySr7pNafcb926RVZWlsmfl/M7MTSJHKh/vPNOZGcKzeVRTRsbK2f9Lly4kG/a8+fP6+R5nHIOQs7vsTVhYWFGlefo6Ejnzp354IMP2L9/P2fPntVeHv3kk0+MrpemPYxtv5x5/tdER0dz584dAF544QWD6c6dO/e4qmSSxMREXnjhBdLS0qhcuTJvv/02oD5g2rx5cynXrmRIICQeqwoVKjBhwgRAfc355s2bBtM+88wz2v+vX7/eYLq///5bOxNzzjxPIs1RYH4DVNetW5fr0lJx+ueff7Q7tr59+xo1cF0zKPzhw4dGzR6dV/369XF1dQXUs3XnV7eijPvo1q0boB6DZUpAVa9ePe0jLfLrZ2FhYdoZsEurn7m7u2sHs+c3kH7Hjh2FKr9BgwbaAc35zdiel6Y9bt26xenTpw2m09xNaGlpSZs2bQpVxyddzjM9hrbzzMxMVq1alW85mrvmimMwuyneeustrl69ipWVFatWrWLu3LnaQdgvv/yy0UH200QCIfHYTZ8+HUdHR9LT0/MdK9SkSRMaN24MwBdffKH3rFB0dDTTp08HoHz58vTp06dkKl1MNGdXLl26pPesz927d/Xeul8czp07x9ChQ8nMzKRx48asWrUKC4uCdwHdunWjQYMGAEycOJG7d+/mmz48PJyHDx9q/7a0tNQOlN+0aRN79uzRyZOQkMB7771nyuromDBhAjY2NmRlZTF69Oh8L9PkvHRkaWmpfVzAli1b9I6PycrK4s0339TmK+gsWkmxtramSZMmAKxcuVLvGbo//vjD4B1ZiYmJ2rMVhmgGnesbOG/I8OHDtdMqvP3223ov+5w4cUI77cDzzz+vd+qJ/wVly5bVXg7++++/9ab59NNPC7wjVNM+jzPwWLt2LUuWLAFgxowZtGrVShsQOTs7ExkZyciRI4s8PutJI4GQeOy8vLx46623AAp8btCPP/6IpaUlUVFRtG3blhUrVhAWFkZ4eDh//fUXbdq00Zbx448/Ym1tXeL1L4qBAwdiaWlJRkYGvXv3ZuPGjdy7d4/Q0FCWLVtGq1atePjwIRUrVizWz9XcIRYfH4+XlxerVq1CURSDt8Ln/CHTzMvj4ODArVu3aNSoEbNnzyYwMJDo6GgiIyM5f/48K1asYMiQIVSsWFHne/3oo4/w9PREURT69evHvHnzuH37NpGRkezYsYP27dtz/fp1fHx8Cr2OPj4+zJs3D4AjR47QrFkzli1bxu3bt4mJieHmzZusXbuWwYMH68xZ9OGHH2o/e9CgQXz22Wdcv36d6OhoDh8+TJ8+fbRns958803q1atX6HoW1dixYwH1PFtDhw4lKCiIhw8fcv78ed577z1GjRqVa5qHnCIiIqhSpQr9+vVj6dKlnDt3jsjISO7fv8+RI0cYOXKk9plq+V3Wyats2bLMmjULgEOHDtGpUyd2795NZGQkwcHB/PDDD3Tr1o309HRcXFz4+uuvi9gKTy5LS0sGDhwIwIoVK3j77bc5f/48UVFRnDhxgtGjRzNr1qwC77DTTEOwfPlyTp48SXJyMhkZGWRkZJRIIBISEsJrr70GQPv27Xn//fe1y6pUqaId97Rnz57/ve/vcc/gKP636ZtZWp/o6Gid504ZetbY2rVrC3zW2A8//FBgfQpS0Ky+ppSXX1lfffWVwXWxs7NT1q9fn+8ztwozs7Qxz7PK+dL3uf/991+Bz+LSvM6ePauTP79njVlYWCi//PJLsTxrbN68eYqVlVW+9SvJZ40VdsZnRTGuD2ZmZio9e/Y0WMdBgwYpixYt0vs5xvaDrl27KgkJCbnyFtezxg4dOqR3vXI+Mys/xrSRIcWx/RpT1v3797Wz0Ot7tW/fXtm+fXu+7blnzx7tY2vyvgw9a6wghvYdGRkZ2ufjubm5GZyB/8UXX1RA/ZiNvM+ie5rJGSFRKtzc3JgyZYpRaQcPHsyVK1eYNGkStWvXxtHREXt7e6pVq8arr77KuXPneOONN0q4xsXnnXfeYcuWLXTu3BkXFxdsbW2pVKkSY8eO5cSJE9qjySdNixYtuHLlCj///DM9e/akfPny2NjYYGdnh7+/Pz179mTOnDlcv35dO7FiTq1ateLChQtMmDCBihUrYmNjQ7ly5ejfvz8BAQG8+uqrxVLPKVOmcOnSJd566y3q1KmDk5MT9vb22qfP//zzz7z55ps6+erUqcPFixeZM2cOrVq1okyZMlhbW1OhQgUGDhzIjh07WLFiRamfdbSwsGDTpk18/fXXNGjQAHt7e1xdXWnTpg1Lly5l3bp1Bm/79vf35/Dhw8yaNYsuXbpQtWpVHB0dsbGxwdfXl759+7JmzRp27dqFo6OjSfVSqVR8/fXXHDlyRPv0c1tbW1xcXGjcuDEfffQRV69e1U4N8b/M29ubEydOMHnyZCpXroy1tTXu7u60bt2aH374gb179xY4eWjnzp35999/6dOnD+XKldM703Zx+fTTTzl06BCgvqPX0Az8P/30E5UrVyY9PZ0XX3yxSHd5PklUivI/drFPCCGEEMJIckZICCGEEGZLAiEhhBBCmC0JhIQQQghhtiQQEkIIIYTZkkBICCGEEGZLAiEhhBBCmK2Sm5hAFJusrCzCwsJwdnY2+unTQgghhLlSFIX4+HgqVKhQ4KOEJBB6CoSFhRmc4EoIIYQQ+t25cwdfX99800gg9BRwdnYGoME7a7G0dSjl2jw9dkx+sp9E/yT65+K90q7CU6dnnfKlXQUhRB7xcXFUq+yn/f3MjwRCTwHN5TBLWwcs7Uyb9t6cubi4lHYVnjoOTv8bU+Y/TtLPhHhyGTOcRAZLCyGEEMJsSSAkhBBCCLMlgZAQQgghzJYEQkIIIYQwWxIICSGEEMJsSSAkhBBCCLMlgZAQQgghzJYEQkIIIYQwWxIICSGEEMJsSSAkhBBCCLMlgZAQQgghzJYEQkIIIYQwWxIICSGEEMJsSSAkhBBCCLMlgZAQQgghzJYEQkIIIYQwWxIICSGEEMJsSSAkhBBCCLMlgZAQQgghzJYEQkIIIYQwWxIICSGEEMJsSSAkhBBCCLMlgZAQQgghzJYEQkIIIYQwWxIICSGEEMJsSSAkhBBCCLNlVdoVEI9fx5peDGjiQ7WyjlhZWHA3JpndF8JZczKUzCylUGW2q+ZB7wblqV3eBRd7KxJSMgiNSeb4zWiWHQnOlfb7FxrR2L9MgWVuC7rHl/9cKVR9itumf0+zaN0Bzl+7S3p6JpX9vBj8bDMmvtgZaytLk8s7eymE+ct3c+TMdeISkvH2dKVHu7q883JPvNydDeZ7EBXH14t3sOvwBe5HxOLqbE+bxtWYPLo7DWv5FWUVi93xE5f4d88pQu48ICMjE29vN9q0qkuP7i2wMqHNrl0L5fDR8wSHhBMdFUtCQjIqCws8PV2oU7sSPZ9tiZdnGZ18d+9GsG//WYKD7xMRGUtCQhKKAm5uztSq6U+Pbs3x8ytbjGtcdNLPTCdtZjpps9xUiqIU7pdPPDZxcXG4urrS+MOtWNo5FqmsN7tUY0gzXzIyszgdEkNSWiZNK5bB2c6awDsxTFkbRFpGltHlWVmo+Oi52nSuVZaU9Ewu3I0jOikNd0cbKns6YqFS8dwPh3PleamlPxU9HPSXZ6miWx1vAD7beomdF8ILva4H3+1Y6Lw5TZ+3np9XB2BlaUH75jVwtLflwMmrxMYn06pRVf764XXs7WyMLu/vPWcY98FSMjKzaFKnIhUreHDmUgi370ZS1t2ZfxZNoYqfl06+68Hh9Hp1PhHR8VTy8aRxbX+Cw6I4fTEYK0sLls5+mT6dGhZpXbecDytSfo2Vq3aza/cJLC0tqF2rInZ2Nly8FExSUgo1qvvyf9NewMbG2qiy1m8IYPPWI3h4uFDWyw1XV0eSklIJDr5PbFwitrbWTJk0hNq1KubKF7D/LEuWbcfVxZFy5d1xdXEiLS2d0NAIIqNisbS04NVxz9G6Vd0iretz9SoUKb+GOfWz4iJtZjpzabO4uDi8PVyJjY3FxcUl37RPTSDUsGFDgoKCsLGxISwsDA8PD73pRo8ezfLly3Xet7W1xcfHhw4dOjBlyhTq1aunk2bZsmWMGTNG530rKys8PT1p1qwZ48ePp3fv3jppbt++TeXKlXXet7e3x8fHh/bt2zNp0iTq169vzOrmUlyB0DPVPfliQD2SUjN488+zXA1PAMDV3prvhjWkalkn/jx+h4X7bhhd5vu9atGzfjkOXI3gqx1XiU1O1y5TAbUruHAxLM7o8jrV8uKTfnWJT8mg/4IjJgVleRVHILQtIJDh7/yGk4MtW3+ZpD1KiYpJoO+E77l4PYw3XurMp5MGGFXevYgYmg34hKSUNL6dPozRA9oBkJmZxcRZv7P2nxM0qVORf5dNQ6VSafMpikLHEXMIuhLK0F4tWPDxcCwt1Ve2l/11iMmzV+PkYMvJDTPw9sx/o89PcQRCp05f4bsfNmBnZ8P77w6nUqVyAMTHJ/HlV39wJzSCnj1a8sKwLkaVdzcsEhsbK52zPhkZmaxeu5ddu0/g7ubMN3Nfx8Li0dX+iMgY0tMzqFDeM1e+rCyFHTv/Y/XavdjYWPPdN2/g6Ghf6PUtjkDI3PpZcZA2M505tZkpgdBTMUboxIkTBAUFAZCWlsbKlSsLzFO1alVGjRqlfXXt2pXExESWLl1KkyZN2LJli8G8jo6OufL27dsXV1dXtm7dSp8+fZg+fXq+nz1w4EBt3meeeYbIyEiWLFlCkyZN+Ouvv0xb+WI0vJU/ACv/C9EGQQCxyenM23UNgAFNfHC0Me7UaNOKZehZvxw3IhL4+O+LuYIgAAVMCoIA+jQoD8CeS+FFCoKKyzdLdwHw9qhuuU7VepRxYu67QwH4bd0BYhOSjSrvpz8DSEpJo2OLmtqdBoClpQXz3huGi5M9py8Gs/fYpVz5dh+5SNCVUFyd7Zn77lDtTgNg9IB2dGhek4SkVH5eva/Q61pctmw9AkCfXq21QRCAs7MDI0c8C8C/e06SlJRiVHk+FTz1XvqysrJk2JDOWFtbEf0wnrt3I3Mt9/IsoxMEAVhYqOjVsxVlvcqQlpbO1Wuhxq5aiZF+ZjppM9NJm+n3VARCixcvBsDHxyfX3/lp164dy5Yt0762bt3K7du3GTJkCOnp6bzyyitkZGTozevp6Zkr74YNG7h8+TLffPMNAHPmzOHcuXMGP3vu3LnavDt37uT69eu0adOGjIwMXnvtNZKTjetkxcnTyYY6FdRR8b8XH+gsP3c3lvC4FGytLGhVVf/ZtrwGNvEFYF0RxhblVNbZlqYV3QDYGnS/yOUVVdiDGE5fVI9vGtyjmc7y1o2q4uPtRmpaBrsPXzCqzG0BgQAM0lOek4MtPdurzxhu2ReYO1/23z3b18fJwVYn76Bn1eVtzZPvcYt+GM/NW/cA9F5yqlnDD3d3F9IzMgkMMv7MoyEqlUp7pGllbdrYBovsna8p45VKgvQz00mbmU7azLAnPhBKSkrizz//BOD333/HycmJc+fOceLECZPLsrOz4/PPPwcgPDycCxeM+7I1Jk+ejK+vL4qisHfvXqPzeXh48PXXXwMQGRnJ0aNHTfrc4lDDWz1gLTY5nXux+o/EL9+Lz07rVGB5Fir1GSGAwDuxuDvaMLiZL1O7V+fNLtV4tp439ib+MPWsXw5LCxXXHyRw5X68SXlLQtCVOwC4uTpQ0Uf3zAJA49r+2WkLPqsQn5jCzTsRADSq459veefylBd09U6u5Xk1yn7/xp0IEpNTC6xLSQkOVgewjo72eHmV0ZumcvZZouCQwo//AsjKymLj3wdJS0vHp4In3mXdjc67L+AM9+9H4+LiQLWqPkWqR1FJPzOdtJnppM0Me+LvGlu3bh1xcXHUq1ePTp06MXToUBYvXszixYtp3ry5yeWVK/foVL2hM0L58fb2JjQ01OS8DRo00P4/PLxoPwCFUd7VTv3ZcYYvRzyIT82VNj8VytjjYKvuPnUruDClW3Xt3xoTO6Yxc/NFTofEGFXHnvXU383WoHtGpS9pIWFRAPh6G/6B9fF2y5U23/LuPUrjW05/mZrygvOUp/nbUD7f7HyKohASFk3tquULrE9JiIiMAcDDw/A1eQ939bKIiBiTyo6MiuWvjQcASExMITj4PtEP4/Eu68brE5/HwkKlN19qajrLf98BQHJyKqGhEYQ/eIiriyNvvP489va6R6SPk/Qz00mbmU7azLAnPhDSXAYbO3as9t/FixezevVqvv32W+ztTRvkePz4cQBsbGyoVq2aSXljY2O5ckV9O3fduqbdaRIX92isjLe3t0l5i4ND9riflHTD426S0zIBcLQtuFu42D+64+fdnjU5fzeOBftuEBKVRIUydrzWoQqtq3owe0A9Xl5+itCH+V8ObOxfBh83e1IzsthVhDvFilN8kjowdLA3fAeFo4N6WXxiwZc7ExIfHdk4Grgr41F5uQNWTV4Hg/ke/ZgbU5eSkpKSBoCtreE7wmyz1yHZxCO9xMQUDh3OfUm6UsVyjBvbG18f3btSNDIyM3XylfUqw8tje1Ozhv4j0sdJ+pnppM1MJ21m2BMdCF29epWDBw9ibW3N8OHDAWjTpg21atXi8uXLrF+/nhEjRhhVVlRUFIcPH+btt98G1Je5XF1djcqbkJDAhQsXmD59OgkJCbRu3ZoePXqYtC7btm0DwMvLi9atW+ebNjU1ldTUR50sZxD1pMh57B0Rn8rUtYGkZ6rHCd2ISOS9DedYMqYZVb2ceKmVP3MKmA9IM0j60LVI4lNMP1Mn/vdV9PdmxdL3URSFhzEJXL8eyoaNB/h41hJeHNaV7t30nyF2dLBjxdL3AYiNTeB2cDib/j7I7Dl/0PPZlrww1Li714QQ/5ue6DFCS5YsAaBv3754eT064tOcHcpv0PTy5cu1AylVKhWenp7069ePlJQUli9fzpdffmkwb3BwcK68zs7OtGrViiNHjvDRRx/x77//5roVMD/3799n8eLFvPPOO9jZ2bF06dICz2LNnj0bV1dX7cvPr+gTSyVln+2xszb8ldtnnzVKTC04ENGUB/DP+fvaIEgjS4HNZ9WXuJplD4A2xNHGkvY11Nestz0hl8UAnLOPSpKS0wymSUxSL3M24vZrJ8dHRzmJKfrLfFRe7suTmrxJBvM9CpyNqUtJscs+wktNTTeYJjV7HQp7SUqlUuHu5kyL5rX5+MNRuLo48sef/xJixJgjV1cnGjaoygfTR+Dv780/O/7jzNlrhapHcZF+ZjppM9NJmxn2xJ4RysjI0M4HpAl8NEaOHMn777/PgQMHuHHjBlWrVtXJX7VqVdq1e3Q7X0JCAlevXuXcuXNMnz4dT09PevXqpfezHR0dGTRokPbvtLQ07ty5w9GjR/nmm2+ws7Pj/fffN1h3ffMJeXp6cvDgQaPmEZo+fTpTpkzR/h0XF1fkYEgzQLqss+HxP2Wd1Z3zvoHB1Dndj00hS1GwUKm4F6M/fViM+pSmh1P+k3N1reONnbUl92NTOHn7YYGf/bj4lVffPXc33HCdNMv8yhc8UNcvx/Xw0PvRuFbTHaSrKc8/T3n+5T14GJtE6P1ovWWHZudTqVRG1aWkaG5zj442fBYzKnuZp6dxZ2Tz4+hgR9MmNfl37ylOn72Gv79xl52trCxp06ouISHhnDp9lcaNqhe5LoUl/cx00mamkzYz7Ik9I7Rt2zbu37+Pj4+PzmUob29vevXqhaIo2rNGeeW9fX79+vUEBQXx999/Ex4eznPPPacdL5RX3tvnV61axcGDB7l06RKurq588MEHzJs3z2DdNfMIDR8+nA4dOmBpaUlkZCRDhgwhOlr/F5+Tra0tLi4uuV5Fde2Bet6gMg7WBgdD1yqvvrPsSo45hgxJTs8kJDoJUE/IqI/m/eQcZ4/06VVfPUh6+7n7PEmzezaoqZ4eIDo2keA8c9RonLkUAmDUdPAuTvbaGVbPXgzJt7wGecprWNMv1/K8zma/X9XPS+/tqI9LxexAJCEh2eBg6Fu31XeWVapYTu9yU2nGI8XFJT6WfMVN+pnppM1MJ21m2BMbCGkue6WkpNChQwfatWuX66WZYHHZsmVkZub/Q5tT37596devH1lZWdpb2o1VvXp13nvvPYB8L61p5hH6/fffCQgI4Pz581SoUIHLly8zfvx4kz6zuETEp2onN+xaR/f5SvV9XPF2sSM1I4tjNwq+YwAg4LL61slmlfRf+mqe/f6le4Zvha/s6UidCi5kKQrbzz05l8VAfcdDkzrqxzas23lSZ/nRsze4G/4QWxsrurU1bvB8747qKePX6ykvISmVHQfVg3qfyzO1fO/sv/85cE7v7aTrd6jLK+1p/N3dXahSWT3e6+gx3ekprly9Q3R0HNZWljRsoHsmtzAuXlLPjVLOwB0ohvPdLlS+4ib9zHTSZqaTNjPsiQyE7t27x/bt24FHg5zzvm7fvg1AWFgYO3bsMKn8KlWqAHDp0qUCUhrOGxkZSWSk/qg6r1q1arFixQpAPR3AwYMHTf7c4rDymDrKHt7SP9dcQS52Vkztrr408NfpuyTmOIPzTHVPVo5rwfyhuh1y/am7xCWn07qqB30b5r69sUvtsnSr652dzvCcFH0aqM8KnLz9kPC40ptjw5ApY7oD8N3y3QRevqN9PzomgWlz1gDwyuD2uDo9uo69dV8gLQZ9Sr8J3+uUN+GFjjjY2RBw/ArLNz56BltmZhbT5qwhNj6ZJnUq0rlV7Vz5urWpQ4OavsTGJzNtzhoyMx/d/bfsr0PsP3EFJwdbxg/rVDwrXgTP9WkDwNbtR7l9+9HEmPEJSazIvo29a5dmODg8OjN58tQV3p3+M19+9YdOeVu2HtF71iYxMZkVK3dy6/Y9HOxtadk8d5vt2HWcqCjdS3Spqen8vfkQJ05ewdLSgvbtGuikedykn5lO2sx00mb6PZFjhDRneVq2bMmxY8cMpnv33Xf56quvWLx4sd7nfxly44Z6Rlsnp4InDjSU18LCwqRb97t06ULfvn3ZvHkzH330EQEBASZ/dlEdvBbJupOhDG7my88jmnAq+CEp6Vnah64Ghcay6OCtXHmcbK2o6OGAjZVuzBybnM6MzRf5ckA93nm2JgOb+hIclYhPGXtqlFNfZlt2+DbHbuq/HGhpoaJ7drD0JA2Szql3x4a8NrQjv6wJoNuYubRvXhNHexv2n7hCbHwyLRtW4f3xfXLliUtI5lpwOClpugOGy3uVYcGM4Yz7cBmTvviTlZuP4lfenTMXHz2k8LfPRusMxlepVCz6bAy9Xv2W1duOc+zsTRrX8SckLIpTF9QPKfxp5shSf5YRQNMmNenetRm7/j3JrM+WUbdOJWxtrblwUf3Q1erVfRk4oH2uPMnJqdy7H016uu7Z3XUbAtiwcT++vmXxLlsGCwsLHj6MJzgknNTUdBzsbXnj9QG4uubennftOsGfq/+lQnlPypVzx9raitjYBELuPCAxMQVrK0vGjumFr2/pP4Fe+pnppM1MJ22m3xMZCGnG/YwaNSrfdCNHjuSrr75i69atRERE5LqzzJAtW7awefNmAPr162dSva5du8acOXMA6Nq1K46Opj0A9YsvvmDr1q3s37+fPXv20KXL479t9/s91zl3N5YBjX2o5+OKlYWKuzHJrDwWwtoToWSY+KiMk7cfMmbpSUa0rkjTSm60q+5JYmomR29Ese5kKCfyGfzcrpoHZRxsiE1O5+A1486ulYYvpw2iZcMqLFp3gONBN8nIyKSSryeTRnVn4oudsLE2bTPq37UJlXw8+WbpTo6evUHQlVC8PV0YN7g977z8LGUNTEZYvZI3h1a9z9wlO9h56DzbAoJwcbLjuU4NmTr2WaOu6z8uw1/qTvXqvvy79xTXrt8lMzOTsl5u9OnVmmd7tDDpsRYjh3fnytU7BIeEc+FiMKmpadjZ2eDrW5b69SrTpVMTnSAIYNDAjpy/cJNbt+9x5eodkpJSsLW1oaxXGdo/05AunZpQtmz+dzQ+TtLPTCdtZjppM11P3NPn9+/fT8eOHbG1teXevXu4ueW/o2ratCmnT59m7ty5TJ06Vfv0eX13jV27dk07tqhLly5s3boVO7tHp+c1T5/P766xzMxM/P392bdvn/YyGeR++vytW7eoVKmS3vqOGjWKFStW0LZtWw4dOmRUmxTX0+fNTXE8fd7cFMfT581NcTx9XghRvEx5+vwTd0ZIM0j6ueeeKzAIAvVZodOnT7N48WKmTp2qff/GjRvay1gAVlZWuLu7061bN1566SVGjBiBhYX+IVKJiYnaW/dBfRrPxcWFpk2b0rdvX958881C38n1ySefsGbNGg4fPszOnTtNnphRCCGEEMXniTsjJHTJGaHCkTNCppMzQqaTM0JCPHlMOSP0RN41JoQQQgjxOEggJIQQQgizJYGQEEIIIcyWBEJCCCGEMFsSCAkhhBDCbEkgJIQQQgizJYGQEEIIIcyWBEJCCCGEMFsSCAkhhBDCbEkgJIQQQgizJYGQEEIIIcyWBEJCCCGEMFsSCAkhhBDCbEkgJIQQQgizJYGQEEIIIcyWBEJCCCGEMFsSCAkhhBDCbEkgJIQQQgizJYGQEEIIIcyWBEJCCCGEMFsSCAkhhBDCbEkgJIQQQgizJYGQEEIIIcyWBEJCCCGEMFsSCAkhhBDCbEkgJIQQQgizJYGQEEIIIcyWBEJCCCGEMFsSCAkhhBDCbFmVdgWE8XZMfgYXF5fSrsZTw63LrNKuwlMnYtdHpV2Fp05WllLaVRBmIkuRvmasjMwso9PKGSEhhBBCmC0JhIQQQghhtiQQEkIIIYTZkkBICCGEEGZLAiEhhBBCmC0JhIQQQghhtiQQEkIIIYTZkkBICCGEEGZLAiEhhBBCmC0JhIQQQghhtiQQEkIIIYTZkkBICCGEEGZLAiEhhBBCmC0JhIQQQghhtiQQEkIIIYTZkkBICCGEEGZLAiEhhBBCmC0JhIQQQghhtiQQEkIIIYTZkkBICCGEEGZLAiEhhBBCmC0JhIQQQghhtiQQEkIIIYTZkkBICCGEEGZLAiEhhBBCmC0JhIQQQghhtiQQEkIIIYTZkkBICCGEEGbLqrQrIB6/Tf+eZtG6A5y/dpf09Ewq+3kx+NlmTHyxM9ZWliaXd/ZSCPOX7+bImevEJSTj7elKj3Z1eeflnni5OxvM9yAqjq8X72DX4Qvcj4jF1dmeNo2rMXl0dxrW8ivKKha7fu3rMK5fc+pV9cbaypJbYdGs23OOheuPkZGZZVJZ9rZWjB/Qiuc71qWKjztZisK1kEhW7w5i8eYTZGUpRpVTr4o3exa+go21JTfvRtN05A+FWbUS8/eeMyxZf5AL17P7ma8nA3s0Y/wLnQrVzwIvh/Ddin85duYGcYnJeHu40K1tPaaO7aG3n4Xej2bP0UvsO3aJwCt3eBAVh5WVJZV9POnati7jh3XE081w/ywNf+85w+L1ObZNX08GPducCYVss7OXQvhuxW6O5miz7u3qMW3ss3rbLDMzi20BgZy9fIfAy3cIvBzCw7gkLC0teHDku+JYxWInbWY62TZzUymKYtxeV5SauLg4XF1dCY+KxcXFpUhlTZ+3np9XB2BlaUH75jVwtLflwMmrxMYn06pRVf764XXs7WyMLu/vPWcY98FSMjKzaFKnIhUreHDmUgi370ZS1t2ZfxZNoYqfl06+68Hh9Hp1PhHR8VTy8aRxbX+Cw6I4fTEYK0sLls5+mT6dGhZpXd26zCpSfo0vJvZgwsBWpGdkcuDMLRKT02jfuDJlnO05ei6YAf+3kpS0DKPKKuNsx+a5o6hfrRxxiamcvBRKVlYWzWr7UsbZnr0nbzDsg1WkZ+QfXFlbWbB34SvUqeyNhYWq2AKhiF0fFbkMgA++3cCva/ZjZWlBu2bqfnbolLqftWxYhXXfTTSpn23ee4bXPlpORmYWjev441/eg8DLIdy+G4WXuzNbf5mk0896vzqf40E3sbK0oH5NXypW8CQmLpFTF4KJT0zB082Jtd9NpH4N3yKtq4VKVaT8Gu9/s4Ff1qi3zWey2+xgdpu1aliF9d+btm1u3nOGVz5apm2zihU8OHvpjnbb3PbrZJ02i41PokrXd3XKelJ/1M2tzbKK4efaXLbNuLg4fMq6ERtb8O/mU3FGqFKlSgQHBxeYbunSpYwePVonz7p16xg0aJDePF27dmXPnj3avDNnzmTWLNN/QH/77TdeeeUVKlSowPnz53FzczOYtlevXvzzzz+89NJLrFy50uTPKqxtAYH8vDoAJwdbtv4ySXvWJSomgb4TvufY2Rt88fNWPp00wKjy7kXEMHHm72RkZvHt9GGMHtAOUB8hTZz1O2v/OcErHy7j32XTUOX4sVAUhZc/WEpEdDxDe7VgwcfDsbRUX6Vd9tchJs9ezYSZK2hefwbenkUL/IqqV9uaTBjYivikVPpMWUbQtfsAuLvYs3neKFrXr8j7Yzrx8S+7jSrvm0l9qF+tHBdvhjPk/VXcjYgDwMvNkVWfDqNzs6q8O7Ijny3Zm285/zeiA/WqluPXjcd59fkWRVvJYrZ9fxC/rtmPo4Mtfy98K1c/G/D6j/wXeJMvf93OrLf6G1Xe/YhY3vzkDzIys5j33lBG9m8LqPvZm5+uZN2Ok4yfsZydi6fm6mflvVz5dNLzDOnZAndXR+37kQ/jGffBUg6fvs64D5ZyZPUH2v5XWrbtD+SXNQE4Otiy5ae3c7VZ/9d/4FjgTWb/so1P3n7eqPLuRcTy+icrycjM4pv3hjHq+Udt9vonK1m34wSvfryM3Utyb5tWVpYMerYZDWr40qCmH24uDnQYMaf4V7gYSJuZTrZN/Z6qMUJt27Zl1KhRBl/VqlXTm++DDz4gI8O4I/ZGjRrpLdvb2xuAhg0b6l3erl07evXqRVhYGBMnTjRY/q+//so///yDj48PP/74o+mNUATfLN0FwNujuuW69ORRxom57w4F4Ld1B4hNSDaqvJ/+DCApJY2OLWpqgyBQHwnNe28YLk72nL4YzN5jl3Ll233kIkFXQnF1tmfuu0NzdfTRA9rRoXlNEpJS+Xn1vkKva3GZ8uIzAHz352FtEAQQHZfMtO+2AfBK/xa4ONoWWFY5Dyf6ta8DwP/9+I82CAKIeJjIpG+2ADBhYCuc7A0fkTWuWYFJL7RjY8AFNh+8aPpKlbD5y9X97K0RXXX62Zx3BgOweP0B4ozsZ7+sVvezDs1rane0oO5nX/3fUFyc7DlzMYR9/13OlW/R52MYP6xTrh0tgKebMwtnjgDg5p0ITpy7ZfpKFrNvl2VvmyN1t82v3xkCwCKT2myfus1a1NT+oIO6zea+a7jNHO1t+WXWKF5/qQvPNKuBi5N9UVetxEibmU62Tf2eqkBo3LhxLFu2zOCrXbt2OnkcHBy4evUqixYtMuoz+vfvr7fsWrVqFbh80aJFuLu7s3r1atauXatT9q1bt5g6dSoAS5YsoUyZMoVvDBOFPYjh9EX1GbLBPZrpLG/dqCo+3m6kpmWw+/AFo8rcFhAIwCA95Tk52NKzfX0AtuwLzJ0v+++e7evj5KAbQAx6Vl3e1jz5Hrfyns40reUDwLq953SWHzt/h9DwWOxsrOjWonqB5TWuUQELCxWpaRkcCdI9w3nh5gMiHibiYGdNt5b6y7O1tmThu/2JiU/m/37YbuIalbx7D2I4czEEgIF6+kWrHP3s3yPGBXHb9gcBMKBHU51lTg62PPtMPXW6AOP7S4WybniUUe+Ewx7EGJ2vJITlaLNBetaxVaG2TXWbDeyuf9vUtFlpb2OFJW1mOtk2DXuqAqHCePvttwH45JNPSEpKKtHPKl++PAsXLgRgwoQJ3Lt3T7ssKyuL0aNHk5CQwIQJE+jevXuJ1iWvoCt3AHBzdaCij6feNI1r+2enDS2wvPjEFG7eiQCgUR3/fMs7l6e8oKt3ci3Pq1H2+zfuRJCYnFpgXUpKg2rlAIiOSyLkfozeNGeuhqnTVi9XYHmO2Wd5YhNTMHSpPypO3Ucb1Sivd/kHYzpTq6IX7/24g8iYku3PhXHuqvq7dnNxoGIFD71pGmUfiebtF/okJKZwKzS7n9XS318aZr+v+WxjRMUkEBOvPur19ijdy6+520z/ttmotl+utPmJT0zhZnabFbSNabbFp420melk2zTsfz4Q6tWrFx06dODevXt8++23Jf55Q4cOZdiwYURHRzNu3Djt+/Pnz+fAgQNUq1aNr7/+usTrkVdIWBQAvt7uBtP4eLvlSptvefcepfEtp79MTXnBecrT/G0on292PkVRCAmLLrAuJcW/nLoeoeGxBtNoLm/5lytTYHkRMYkAlHVzwtHOWme5SgV+ZV0Nlteiji8TB7Vi2+HLbNh3vsDPKw2a79annOExchU0/eKeMf3s0ffva6BMH+8y6rRG9FuNBX/sJTMzC29PF5o3qGx0vpJQ0PYA4FNW/7akzx2j2kyzrZfe9lUU0mamk23TsP/5QAhgzhz1wLWvvvqKqCjjv5DCWrBgAeXLl2f79u38+uuvXLp0iQ8++AALCwuWL1+Oo6NjwYUUs/gk9ZkVh3zGnjg6qJfFJxZ8fTgh8dGZGkcDdxg8Ki9Fb14Hg/keXS4zpi4lxTm7/kkp6QbTJCanZacteIzQqUt3telH9Gqis3xY94bas0Z5y7O3tWLBu/2JS0xl6vxtxq1AKUjQ9DM7w+3haK9elrdf6C/vURqD/cWE8gD2H7/CwlXqweifvPU8Ntale8+IZh0NrR882iZKq82eNNJmppNt0zCzCIRatmzJgAEDiIuL4/PPPy/xz3N3d9eOSZo6dSrDhg0jJSWFd955hzZt2hSYPzU1lbi4uFwv8fRLSE5jwfqjAHw8rguvPt8Cb3cnPMs4MKJXY+a80ZO09ExAfTYspxnjulLN14PpC3cQHp3w2Ov+v+Li9TBe/mAJmZlZjBvcngHddcc2CCEev9LcNp+qQGjMmDGoVCqDr5iYGIN5v/jiC6ysrFi4cKFRt+IXVa9evXjllVdISEggKCiI+vXr88knnxiVd/bs2bi6umpffn5Fn1xQc4YhKfuMhD6JSdlnNxwLvuvBKcddUokp+st8VJ6d3rxJBvM9OttkTF1KSnx2/R30XMbS0JzBiU8ybizTnBX7WbL5JPa21sx5oyeX103l2oZ3+H5qX4Ku3eOPHWcAeBj36ExY24YVeaV/C3Ydu8qa3UGFXZ3HQjP4PSnFcHtoxn3l7Rf6y3uUxmB/MbK8a7fDGfTmAmLjk3mhT0u+mDKwwM9/HDTraGj94NE28bjb7EklbWY62TYNeyrmEdJo27atwVvkAWxsDJ8mrVmzJmPHjuXXX3/lo48+YsWKFSVRxVy++eYbfvvtN0AdiOVXv5ymT5/OlClTtH/HxcUVORjyK68eHHc3/KHBNJplfuUNX3fXlpfj2nzo/Whcq/kYLM8/T3n+5T14GJtE6H3919pDs/OpVCqj6lJS7oTHAOCTPW5HHx8v9WC+O/cNjyPKKStLYep321i8+QQ9W9fE19uVhOQ0DgfeZuexq/w6XT2H08VbD7R5erethYWFCt+yrmyZNypXea5O6h1MeU9n7bLpC3dw/ka4cStZzDTfdVh22+kTZqBf6ONX/tHYg9D7D6lTTTcwvpv9WZo+rs+NkAc8/8YPRDyMZ0ivFsx//4Vc85qUJk075LttPtC0meF11Mg5XsNwmxm/rT+JpM1MJ9umYU9VIDRu3DjthImFMXPmTFauXMkff/zBtGnTaNCgQfFVTg8nJye9/y+Ira0ttrYFjzkxRYOa6hk6o2MTCb4bqffOsTOX1LdWGvN4Cxcne6r4eXHzTgRnL4ZQV08gpCmvQZ7yGtb0I/DyHe3yvM5mv1/Vz0vv7fWPS9A19V1/Hq4O+Jcro/fOscY1KgAQeO2ezrL8XLz1IFewo9Gynrqt9p26qbOsThVvg+XZ21rTrlEl4FFwVBrq5+xnYVF67045e1l9102DmgX3M2dHeyr7enErNIKzl0OoU62CTprAy9n9rKb+WWhvhDyg/+s/EB4Zx+Bnm/HDhy9iYfHknAzXzJ6rbrNIvXdBnb2kabOCZ9p1cbKniq8XN0MjOHNJf5tptrGGRnwHTyJpM9PJtmnYk7M3eAzKly/P22+/TVZWFtOnTy/t6jxWPt5uNKlTEYB1O0/qLD969gZ3wx9ia2NFt7Z1jSqzd0f1IzDW6ykvISmVHQfVc+88l+dRGb2z//7nwDm9t8ev36Eur6iP2CiqsMh4Tl2+C8DgzvV1lreq54evtyspaRnsPn6tyJ/3fMe6+HmX4fiFO7kCq/cX7sStyyy9rz5TlgFw82609r3DgSV/6deQCmXdaJw9ncIGPf3iWI5+1rVNHaPK7N1BfcDy185TOssSklLZeUh9B52mP+Z0KzSC51//gfsRsQx+thk/fjz8iQqCQL1tatpsvZ51PFaobVPdZht26d82NW1W2ttYYUmbmU62TcOerD3CY/Duu+/i4eHB9u3bOXDgQGlX57GaMkY9d9F3y3cTePnRXBjRMQlMm7MGgFcGt8c1x8yoW/cF0mLQp/Sb8L1OeRNe6IiDnQ0Bx6+wfONh7fuZmVlMm7OG2PhkmtSpSOdWtXPl69amDg1q+hIbn8y0OWvIzPHQ0mV/HWL/iSs4Odgyflin4lnxIvhm1UEA3n6hba65gtxc7Jn7dm8Aftt0nLgcd9H1bluL/5a+zqavR+iUV87DSXs5Laceraozf3IfUtIymDJ/a3GvxmM1aZS6n33/+7+5+1lsIu9+vQ6Alwe1zzUD77aAQFoP/YwBb+jOtv7aMHU/23/iCr9vOqJ9PzMzi3e/XktsfDKN6/jTqWWtXPmCw6J4/vUfuRcRy5CezZ/IIEhj8ujsbXPFbp02e+dr9eSs4/K02daAQFoO+ZT+r+s+Y+61YZ3UbXb8Cis25d423/lqjcE2e5pIm5lOtk39nqpLY8XB1dWV999/n6lTp/J///d/Jl2yetr17tiQ14Z25Jc1AXQbM5f2zWviaK/uxJoH7r0/vk+uPHEJyVwLDiclTfcW8vJeZVgwYzjjPlzGpC/+ZOXmo/iVd+fMxUcPXf3ts9E613tVKhWLPhtDr1e/ZfW24xw7e5PGdfwJCYvi1AX1Q1d/mjmy1J8zBrD98BV+/us/xg9oye4fxnHgzE0SU9LpkP3Q1WPnQvhiae5Hgbg42VLD3xM7G93Nq0ktH36fOZTzN+4TfD+G9IxM6lbxpmZFL+KTUhn+8Wou3NS9ZPY06dWhAa8M6cBva/fTc9w3PNOsBg72NhzMfrhviwZVeO/VXrnyxCWkcD34Aampuo/CKeflyvcfvcRrHy9nyper+WPLUfzKe3D2UrD2wY4/zxql08/GvLdYe4QL8NZnq/TWd3jf1rRqVLWY1r5wendoyKtDOvDr2v30eHke7ZvXwMHOlgMns7fNBlWY/lrvXHniEpLVbabngb/lvVz58ePhvPLRMibPXs3KzcfwL++e64HIv36iu20CTPtqDUGX1RPgpaWry87MzKL72HnaNN3b1mXay88WZxOYTNrMdLJt6md2gRDA66+/znfffcd///2Hg4NDaVfnsfpy2iBaNqzConUHOB50k4yMTCr5ejJpVHcmvtjJ5Hkb+ndtQiUfT75ZupOjZ28QdCUUb08Xxg1uzzsvP0tZAzODVq/kzaFV7zN3yQ52HjrPtoAgXJzseK5TQ6aOfdaocUqPy/QFO/jvfAjj+jWnRR0/rKwsuR0WzfzVh1m4/miBT4rP6dKtB6zeHUjzOr50bFoFSwsLQh/EsmD9URasO8q9yPgSXJPH54spA2nRoDJLNhzkxLlb2n721oiujH/B9H7Wr0tjKvl48O2y3fwXeINzV0Px9nBl7KBnmDqmh95+FpM9S3dqWgZr/zlhsOy2TaqXeiAEMHuqettcvP4gx4MetdnbI7sxoZBtVrGCB98u28UxTZt5uvDyoGeYNtbwtnn11n1OXbit837O96pXKmtSXUqKtJnpZNvUpVLyTljyBNI8Sb6gu8a6d+/Oiy++mCvPwYMH9T6DbPny5bkGXud8cr0+HTt2ZP/+/cyYMYOZM2caXXdNJLxv3z46duxodL6c4uLicHV1JTwqFheX0j9L8rRw6zKrtKvw1InY9VFpV+GpY/GE3H0m/vdlPfk/10+MuLg4fMq6ERtb8O/mU3VG6PDhwxw+fNjg8jJlymgDoYKMGDGCefPmce6c7sM0hRBCCGEenopA6Pbt28Wex8LCgqAg4yenCwgIMLkOoDtDsBBCCCGeHE/mLRRCCCGEEI+BBEJCCCGEMFsSCAkhhBDCbEkgJIQQQgizJYGQEEIIIcyWBEJCCCGEMFsSCAkhhBDCbEkgJIQQQgizJYGQEEIIIcyWBEJCCCGEMFsSCAkhhBDCbEkgJIQQQgizJYGQEEIIIcyWBEJCCCGEMFsSCAkhhBDCbEkgJIQQQgizJYGQEEIIIcyWBEJCCCGEMFsSCAkhhBDCbEkgJIQQQgizJYGQEEIIIcyWBEJCCCGEMFsSCAkhhBDCbEkgJIQQQgizJYGQEEIIIcyWBEJCCCGEMFsSCAkhhBDCbEkgJIQQQgizJYGQEEIIIcyWVWlXQIiScmvz9NKuwlPHq++3pV2Fp0701imlXYWnjkqlKu0qPJVUSmnX4OlhaWF8H5MzQkIIIYQwWxIICSGEEMJsSSAkhBBCCLMlgZAQQgghzJYEQkIIIYQwWxIICSGEEMJsSSAkhBBCCLMlgZAQQgghzJYEQkIIIYQwWxIICSGEEMJsSSAkhBBCCLMlgZAQQgghzJYEQkIIIYQwWxIICSGEEMJsSSAkhBBCCLMlgZAQQgghzJYEQkIIIYQwWxIICSGEEMJsSSAkhBBCCLMlgZAQQgghzJYEQkIIIYQwWxIICSGEEMJsSSAkhBBCCLMlgZAQQgghzJYEQkIIIYQwW1bGJFqxYkWRPmTkyJFFyi+EEEIIURKMCoRGjx6NSqUq9IdIICSEEEKIJ5FRgdDIkSOLFAgJIYQQQjyJjAqEli1bVsLVEI/Tpn9Ps2jdAc5fu0t6eiaV/bwY/GwzJr7YGWsrS5PLO3sphPnLd3PkzHXiEpLx9nSlR7u6vPNyT7zcnQ3mexAVx9eLd7Dr8AXuR8Ti6mxPm8bVmDy6Ow1r+RVlFYvdtoCz/L7xMJduhJGekUlFHw/6dW3Ky4M7FKrNzl25w0+r9nI86AbxCSmU9XChc+s6vDmyG55u+ttMURT+/vc06/85zsXrYcQnJmNvZ0ONyuXo3akRL/Vtg421UZv0Y9GvXQ3G9W5EvSpeWFtZcivsIesCLrNw0ykyMrNMKsve1orxfZvwfPuaVKngRlaWwrXQaFbvvcjibWfJylJ08jjYWtGrVTUaVvOmUTVvGlQri4uDLTfDHtL0lSXFtZrFatO/Z1i8Pse26evJ4GebM+HFTkXaNo+evaHeNj1c6NGuHtNeflbvtpmZmcXWgEACL9/h7OU7BF4K4WFcEpaWFkQc/a44VrHYyf7MdNLPclMpiqK7BxFPlLi4OFxdXQmPisXFxaVIZU2ft56fVwdgZWlB++Y1cLS35cDJq8TGJ9OqUVX++uF17O1sjC7v7z1nGPfBUjIys2hSpyIVK3hw5lIIt+9GUtbdmX8WTaGKn5dOvuvB4fR6dT4R0fFU8vGkcW1/gsOiOH0xGCtLC5bOfpk+nRoWaV1jEtOKlF/jkx82snTDQawsLWjdpDqO9jYcOa3eSTarX5nf576Gna3xbbY9IJC3P/2djMwsGtTyw6+8B+eu3CEkLApPN2fW/fAGlXx12+zNWSvYuu8sFhYqmtStRDlPVyIfxnPq/G3SMzJpXKciq76dYFJd8qo85IdC583pi1c6MqF/U9IzMjkQeIfElDTaN/SnjJMdR8+HMuCjDaSkZRhVVhknOzbPHkz9KmWJS0rl5OV7ZGUpNKtVnjJOduw9fZthszaSnpE7uKpXxYuDP+heli/uQCh665RiKWf6Nxv4JXvbfKZZDRwdbDmo2TYbVmFDIbbNVz5clr1t+uNfwYOzl+5ot83tv03W2TZj45Oo3OVdnbKK+wequK4wmNP+DNQHQ0VlLv0sLi6Ocp5liI0t+HezWA4f09LSiIqKwtbWFnd39+IokkqVKhEcHKz9W6VS4ejoiKurK9WrV6dp06YMGTKEFi1a6M3fsWNH9u/fX+DnzJgxg5kzZ+Z67969e3z33Xfs2LGDGzdukJqaiqenJ97e3jRr1oy2bdsyYsQILC31R86XLl3it99+Y9++fYSEhBAXF4eLiws1atSgY8eOjBw5ktq1axvfGMVkW0AgP68OwMnBlq2/TNIepUTFJNB3wvccO3uDL37eyqeTBhhV3r2IGCbOVP+gfzt9GKMHtAPU0f7EWb+z9p8TvPLhMv5dNi3Xjk9RFF7+YCkR0fEM7dWCBR8Px9JSfQPjsr8OMXn2aibMXEHz+jPw9ixa4FdUuw6eY+mGgzja27L6u9epV8MXgOiYBF6c8hMnz91i3uIdfDCxr1HlhUfGMu3LP8nIzOLzqYN58bnWgLrNpn35J5t2n+Ltz1ay6adJudps58Egtu47i7OjHau/e5061Xy0y0LCohj61o+cuRjM0vUHmfBSl2JsAdP1alWNCf2bEp+URp/31hB04wEA7i72bP5iMK3r+fL+iLZ8vLjg7RPgmze6Ur9KWS7ejmDIjI3cjYwHwKuMA6s+7k/nJpV498XWfLbicK58CUlprNx1nqAb4QTdeICrky1rZhrXtx+3bQGB/JK9bW75+e1c22a/iT9wLPAmX/yyjU/fft6o8u5FxPL6rJVkZGbxzfRhjH6+LaDuZ69/spK1/5zg1Y+WsXtp7m3TysqSwc82o35NXxrW9MPNxYH2w+cU/woXA9mfmU76mX5Fun1+5cqVtGjRAkdHR3x9fZk2bZp22caNG3nxxRe5detWkSrYtm1bRo0axciRI+nVqxc1a9YkMDCQefPm0bJlSzp27MjNmzcN5m/YsCGjRo0y+GrUqFGu9EeOHKFOnTrMmTOHGzdu0KJFCwYNGkSzZs2IiYlh0aJFjBkzhuTkZJ3PysjIYPLkydSrV49vv/2WkJAQmjdvzpAhQ2jVqhW3bt3iyy+/pF69evz4449FapfC+GbpLgDeHtUt16lajzJOzH13KAC/rTtAbILuuunz058BJKWk0bFFTe1OA9RR/bz3huHiZM/pi8HsPXYpV77dRy4SdCUUV2d75r47VLvTABg9oB0dmtckISmVn1fvK/S6FpcFf/wLwPgXO2uDIAD3Mk58OmkgACs2HiLOyDZbsv4AySlptG1aQxsEgbrNPps8CGdHO4Iu3+HAiSu58h05fR2Aft2a5gqCAPwreDC8v3oHdPribdNWsARMGaI+OPlu/XFtEAQQHZfMtIV7AHilTyNcHAo+6izn7ki/tjUA+L+f92qDIICImCQmfa/u0xP6NcXJ3jpX3tv3Y3nzu538tvUs/10KIzElvWgrVoK+XZa9bY7Us23+3xAAFq07YHQ/+/nPfSSlpNGhRU3tjxOo+9ncd4dmb5sh7D12OVc+R3tbfvlkFG+81IVnmtXAxcm+qKtWYmR/ZjrpZ/oVOhAaN24co0aN4uTJk9jb2+ucsqtRowarV69mw4YNRarguHHjWLZsGcuWLWPNmjXs2bOHqKgotm3bRvXq1dm/fz9t2rQxGHD1799fm1/fq3///tq0qampDBkyhJiYGF588UXu3r3Lnj17WLVqFZs3b+bWrVtcunSJd955R+/ZoOHDhzN//nycnJxYunQpDx48YMeOHfzxxx9s27aNe/fusXPnTho1asT169eL1C6mCnsQw+mL6jNsg3s001neulFVfLzdSE3LYPfhC0aVuS0gEIBBespzcrClZ/v6AGzZF5g7X/bfPdvXx8nBVifvoGfV5W3Nk+9xux8RQ9DlOwD069pEZ3nzBlWoULYMaekZBPx3SWe5PrsOnjNYnqODLV3b1gPUZ4BysrUx7uStu6ujUelKSnkPJ5rWLA/AugDdNjl28S6hD+Kws7GiW/MqBZbXuHo5LCxUpKZncOR8qM7yC7cjiYhJwsHOmm7NCi7vSaTeNkMAGNSjqc7yVoXZNvcHZZdnYNt8Rt3PtgaU7jZWWLI/M530M8MKFQj98ccfLFmyhHr16nHixAliY2N10tStWxdfX1/++eefIlcyL5VKRa9evTh+/DjVq1cnPDyccePGFbncQ4cOcffuXaysrPj111/1XlesVasWX331Ffb2uSPYJUuWsGbNGqytrdm1axejR4/WCZZUKhXdu3fn2LFjDB06tMj1NUXQFfUPupurAxV9PPWmaVzbPzut7g9OXvGJKdy8EwFAozr++ZZ3Lk95QVfv5FqeV6Ps92/ciSAxObXAupSUC9fuAlDGxQG/8h5609Sv6ZcrbX4SklK4fTcyO5+v3jSa9/OW17Gl+lLq5n9Pc/F67mUhYVH88fcRVCoVw3q3KrAeJalBlbKA+uxPSHic3jRnrofnSpsfRzv1WZ7YhFQMDY+IilMfvTaq5m1qdZ8Imu3DzSW/bVPdz0zdNg1uY3U02+Ydk+v7JJD9memknxlWqDFCv/76K05OTmzduhU/P8Oj4evXr8+lS8YdKRdGmTJlmD9/Pr1792bv3r2cOnWKpk11I11jhYerd9BOTk44Ohp/ZK0oCp9//jkAEyZMoGXLlvmmt7a2pnXr1vmmKW4hYVEA+HobHsPl4+2WK22+5d17lMa3nP4yNeUF5ylP87ehfL7Z+RRFISQsmtpVyxdYn5Jw5340ABXKuhlMU75sGXXae9EFlhd6/1EaQ2VWyC4vNE95bZpU5/XhXVmw8l+ee/UbmtarrB0sffLcLcqXLcOvn4+lcd1KBdajJPmXcwUgNCLeYJq72cv8yxU8XiIiNgmAsm6OONpZ61zeUqnAr6yL0eU9iQraHgAqGNiW9MnZF33L6e9nPmU15RXcb59Esj8znfQzwwp1RigwMJCWLVvmGwQBuLu7a4OLktKzZ0/tAO3du3cXqSx/f3X0GhMTY9KUAefOndOOUxo1alSR6lBS4pPURyIO9obHZThmj9mITyz4+nBC4qMjG0cDdxg8Ki9Fb14Hg/kenV42pi4lJTEp/3oCONir65qQlGIwTd7y8iszv/KmjevFtx+8hL2tDSeCbrJl7xmOnrmOoii0bVqdGpXKFViHkuacPU4nKZ/xOIkpadlpdS8j5HXqyj1t8DOiR32d5cO61NWeNTKmvCeR5rvOb9t0yl63vNuSPvE5+k5B25gx5T2JZH9mOulnhhUqEEpNTcXV1bXAdBEREQbvrCouKpWKJk3U4y0uXDDuuqYhbdq0oXHjxgCMGTOGFi1a8OGHH7Jp0yZCQw2fKjx58iQANjY2NGjQoEh1AHX7xsXF5XoJ85aekcn/zVnN5M//oFu7euxY8g4Xd3zJ3pXTGfl8O1Zv/Y9+47/lohGX6J4mCcnpLNio3r4+HtWOV59rjLebI56u9ozoXp85r3UmLT0TKJ5bi4UQ5qdQgZCPj0+Bl7wUReHixYtUrly5UBUzhaen+npnVJTu6bxZs2ahUqkMvs6ePatNa2FhwbZt2+jZsycAJ06c4PPPP+f555/Hz8+PmjVrMmfOHJ07xiIi1NdJ3d3dsbIq+owEs2fPxtXVVfsq6MybMZyzI/OkZMNz6yQmZR+pOxY8gt/J8dFRjuYI33B5dnrzJhnM9+jozJi6lBTN0YyhegIkZV/zd3KwM5gmb3n5lWmovF/+3Mu6f47TqVVtvv3gJWpWKY+9nQ2Vfb346I3+vNCnFTFxScz6cWOB9ShJ8cnqszcOdtYG02iOuOONHC8xZ9VRlmw7i72tNXPGd+byyvFcWzWR79/uTtCNcP7YfR6Ah/FP9lGnIZrvOr9tMyG7rfJuS/o45+g7BW1jxpT3JJL9memknxlWqF/tLl26sGjRIv7++2/69eunN83vv/9OaGgoQ4YMKVIFjZGVpZ5ITd8kXQ0bNtS5RT6nvPMelS9fnu3bt3PhwgU2b97M0aNHOX36NHfv3uXq1au89957/PnnnwQEBFCmTJniXA2t6dOnM2XKo0na4uLiihwMaQb73g1/aDCNZplf+YLngvLLcZ059H40rnlu6c5Znn+e8vzLe/AwNinXmJmcQrPzqVQqo+pSUjTX0u89iDGYRrPM0DXynHxyjGcIe/BQ7y2jYdrycq/3hh0nAHiuc2O9Zfft2oRVW45y8twtUtMyjL7LrLjdCVffOOHjaXgGXh8v5+y0xp3pzMpSmLpwD4u3BdKzVVV8vZxJSE7n8Lk77Dxxk1+n9QLgYnBkEWtfOvwrqL/r/LbNMM22VEH/oP2ccvbF0PsPqVNNt5/dfaB/23xayP7MdNLPDCvU3nLatGn8/vvvvPjii3z++ee5gp3o6GjWrl3LtGnTcHR05K233iq2yhoSGaneAeqbzLF///46EyYao27dutStW1f796VLl1i4cCELFiwgMDCQDz74gAULFgDg5aWeNTM6OprMzMwiXw60tbXF1rZ4xzs0yL4bKTo2keC7kXrvGjhzSX1rpTHTwbs42VPFz4ubdyI4ezGEunp2HJryGuQpr2FNPwIv39Euz+ts9vtV/bz03o76uGjW6WFcInfuRem9c0xzN0TOOYYMcXa0o5KPJ7fvRnLuSii1qlTQU576EmzdGrnbMyx7h+Jk4MhKc8SVlaUQl5Cc76MASpJm3iAPV3v8vV303jnWOPvursAccwwZ42JwpN5gp2UddVvtO3PbxNo+GerXMGbbVPezhgbuNswp57Z55lIIdarp9rOzF/Vvm08L2Z+ZTvqZYYW6NFa9enWWL19OVlYWU6dOxc/PD5VKxfLly/Hy8uL1118nIyODZcuWaQcglxRFUThz5gygvkutpNSuXZsffviBN998E4BNmzZpl2nuVEtLSyMw8MmcL8HH240mdSoCsG7nSZ3lR8/e4G74Q2xtrOjWtq7Ocn16d1RPGb9eT3kJSansyJ4z57k8U8v3zv77nwPn9N5Oun6HurzimJK+KMqXLaPdgP/+97TO8hNBNwl7EIONtZX29vaCdH+mvsHyEpNS2XNEPc6txzO5x5p5e6rH5J01tLPNnlPFycG2VOcSCotK4NSVewAM7qjbJq3q+OBb1oWUtAx2nzA8Eaqxnn+mJn5lXTh+KYzA66YFVk8K9bap3k+u33lKZ/mxwmybHRpkl2dg2zykvpzYp2PpbmOFJfsz00k/M6zQEyoOHjyYEydOMHjwYJydnVEUBUVRsLOz47nnnuPo0aMMHDiwOOuq1/bt23n4UH203L179xL/PM1naM5CATRo0EA7Fmr58uUlXofCmjJGXffvlu8m8PKjeR2iYxKYNmcNAK8Mbo9rjks2W/cF0mLQp/Sb8L1OeRNe6IiDnQ0Bx6+wfOOjxxtkZmYxbc4aYuOTaVKnIp1b5f5B7NamDg1q+hIbn8y0OWvIzPEAzmV/HWL/iSs4Odgyflin4lnxInj9pa4A/LxqL+evPhow/zA2kY/mqycLHfl8u1yXuXYeDKLLiC95acpPOuWNHdQeezsbDp+6yp9bj2rfz8zM4qP5G4hLSKZBLT/aN6+ZK1/PDuodyZJ1+zl2JvdknBev3eWbJTsA6NWxUa6ZbUvDN2uPA/D2oBY0qPporiA3ZzvmTlQ//uO3rWeJS3o0rqB362r89/MYNn0+SKe8cu6Oei+19WhehflvdiMlLYMpPxbtjtHSNnl09ra5Iu+2mci0r9YCMG5w+1z9bOu+QFoO/pT+E3WfDzf+hU442Nmw//gVlm/KvW2+o902/encqlZJrVKJk/2Z6aSf6VekgQT16tVj9erVKIpCVFQUWVlZeHp6YmHxeHbEsbGxTJ48GYBu3brlOxbIGIqiFPgwwJAQ9RG5r++jU4cqlYr333+fV155hZ9++omXXnrJ4DPQQP0ojpMnT9Kq1eOd/K53x4a8NrQjv6wJoNuYubRvXhNHexv2n7hCbHwyLRtW4f3xfXLliUtI5lpwOClpurdDl/cqw4IZwxn34TImffEnKzcfxa+8O2cuPnpI4W+fjdZpU5VKxaLPxtDr1W9Zve04x87epHEdf0LCojh1Qf2Qwp9mjiz15/KA+gzO6IHPsGzDQQZM/I42Tapjb2fDkdPX1A9drVeZqS8/mytPfEIKN+88IFVPm3l7uvL1e8N4+5OVvD93HWu3/YdvOXeCcjx09bsPh+u02Zsju3Hs7HWCLt/hhckLtQ9rvR8Rw9mLIWRmZVGzSnnee62Pzmc+btuPXefnv08zvl8Tds97kQOBISSmpNOhkfqhq8cu3OWL33M/F8zF0ZYafu7Y2eheVm5Sozy/f9CX87ciCA6PJT0ji7qVPanp50F8UhrDP/ubC7f1jw/6/YO+eLurz5BpBthW8HRm17wXHqXZeZ7fd50rrtUvlN4dG/Lq0A78umY/3cfOo33zGjjY23Ig57b5Wu9ceeISk7kW/EDvw2vLe7ny48fDeeWjZUz+YjUrNx/DP8+2+eunutsmwLQ5awjMvkSbll12ZmYW3cbO06bp3rYu7+Tp94+b7M9MJ/1Mv2IZUalSqbR3bj0OiqKwY8cOJk2axLVr1yhfvjy//fZbkcvdsmULS5Ys4fXXX6dz5846Y30CAgK0442GDRuWa9m4cePYuXMn69evp1u3bnz//fcMHz48VxmKorBv3z7ee+892rRp89gDIYAvpw2iZcMqLFp3gONBN8nIyKSSryeTRnVn4oudsLE2rUv079qESj6efLN0J0fP3iDoSijeni6MG9yed15+lrIe+jf+6pW8ObTqfeYu2cHOQ+fZFhCEi5Mdz3VqyNSxzxp1Xf9xmfHm8zStV5nfNx3i9AX1k94rVvBgwoudGTu4g8lt1rtjI/zLe7Dwj385EXSLi9fv4uXuwoj+bXlzZHe943ucHOxY98Ob/LH5CNsDArl2+z4Xrt7Fwd6GBrX8eLZ9A0Y+37ZIT54vTtN/3cd/F+8yrk8jWtSugJWlBbfvxzB/3XEWbjql86T4/FwKjmT13gs0r1WBjo0qYmmhIjQingUbT7Jg4ynuRSUYzNugaln8vXNP9WFnY0XzWo/GM+w5ddvk9SsJX04dRMsGVVi8/iDHg2492jZHdmNCobbNxlTy8eCbZbs4dvYG57Tb5jNMG2t427xy6z6nzt/WeT/nezUqFjwr+OMg+zPTST/TpVKKOPlGWloap0+fJjQ0FEVR8PX1pWnTptjYFG2HrHn6fNu2balWrRqgnl8nMjKS06dPEx2tHqHfsWNHlixZonObvubp8wXdNdakSRPtgO5Nmzbx/PPqp+66urrSpEkTypUrR2JiIlevXuXyZfWD47p27crff/+Ng4NDrrLS09OZMmUKCxYsQFEUPDw8aN68Oe7u7sTGxnL69Gnu3buHpaUl33//PRMnTjSqLeLi4nB1dSU8KlbvYz+EfjGJhm8TFfpVHqJ7+lvkL3rrlIITiVwKOvMu9JO5sowXFxdHOc8yxMYW/LtZ6EAoLS2NWbNmsXDhQp0J/5ydnZkwYQIzZ84s9N1PmkAoJ0dHR1xdXalevTrNmjVj6NChNG/eXG9+TSBUkH79+mkHPqekpHDgwAH27NnD4cOHCQ0N1c6MXbZsWZo0acILL7zA4MGD892QL168yK+//kpAQADBwcEkJCTg7OxMjRo16NKlC6NHj6Z69epGtoQEQoUlgZDpJBAynQRCppNAqHAkEDJeiQdCKSkpdO/encOHD2vPfFSqVAmA27dvExUVhUqlok2bNuzevRs7uyd7MqUnnQRChSOBkOkkEDKdBEKmk0CocCQQMp4pgVChRjXPmTOHQ4cOUb16dbZs2UJERAQnTpzgxIkTREREsHXrVmrUqMGRI0f46quvCrUSQgghhBAlrVCB0KpVq3BycmLv3r307t1bZ3mvXr3Ys2cPDg4O/PHHH0WupBBCCCFESShUIBQSEkKnTp2oUEF3JkmNChUq0LlzZ+3t5kIIIYQQT5pCBUJubm7Y2xf88Dg7Ozvc3Ap+BpMQQgghRGkoVCDUtWtXDhw4QGqq4adHp6SkcPDgQTp37lzoygkhhBBClKRCBUKfffYZ6enpvPjiizx4oPt8n8jISIYPH056ejqff/55kSsphBBCCFESjJpC8pNPPtF5r3fv3qxYsYKdO3fSvXt37YSGt27dYteuXSQnJzNy5EhWrFjBRx99VLy1FkIIIYQoBkbNI2RhYYFKpco1h0Hevw1+gEpFZmZm0Wpp5mQeocKReYRMJ/MImU7mETKdzCNUODKPkPFMmUfIqDNCM2bMKJaKCSGEEEI8SSQQEkIIIYTZKtRgaSGEEEKI/wUSCAkhhBDCbBl1acyQ1NRU9u3bx5UrV4iLi9M7kEulUsldY0IIIYR4IhU6ENq4cSOvvfYaUVFRBtMoiiKBkBBCCCGeWIUKhE6ePMnQoUMBGDZsGBcuXODcuXO89957XLt2jd27dxMXF8fLL7+Mr69vsVZYCCGEEKK4FCoQmjt3LpmZmWzcuJG+ffsyZswYzp07p51FOiIigpEjR/LPP/9w5syZYq2wEEIIIURxKdRg6cOHD1OnTh369u2rd7mXlxerV68mMTGRWbNmFamCQgghhBAlpVCBUEREBLVq1dL+bWWlPrGUkpKifc/V1ZUOHTqwffv2IlZRCCGEEKJkFCoQcnZ2JiMjQ/u3q6srAGFhYbnSWVtbc//+/SJUTwghhBCi5BQqEPL19eXOnTvavzVnh/bt26d9Lz09nWPHjuHt7V3EKgohhBBClIxCDZZu164dixYtIjY2FldXV3r37o2VlRVTpkwhJSUFf39/fv31V8LCwnjppZeKu85CCCGEEMWiUGeE+vfvj6+vL/v37wegfPnyvP/++8THx/PWW2/Rv39/tm3bRpkyZfjss8+KtcJCCCGEEMWlUGeEunTpwrVr13K9N2PGDOrXr8+6deuIjo6mdu3aTJo0CX9//2KpqBBCCCFEcSvSIzbyGjBgAAMGDCjOIoUQQgghSow8dFUIIYQQZksCISGEEEKYLaMujXXu3LnQH6BSqdizZ0+h8wshhBBClBSjAqGAgIBCf4BKpSp0XiGEEEKIkmRUIJRzokQhhBBCiP8VKkVRlNKuhMhfXFwcrq6uhEfF4uLiUtrVeWqkZWSVdhWeOjZWMmzQVG5tppZ2FZ46D4/MK+0qiP9xcXFxeHu4Ehtb8O+m7PWEEEIIYbYkEBJCCCGE2ZJASAghhBBmSwIhIYQQQpgtCYSEEEIIYbYkEBJCCCGE2ZJASAghhBBmSwIhIYQQQpgto2aWzk9sbCwnTpwgIiKCihUr0qZNm+KolxBCCCFEiSv0GaH4+HjGjRtH2bJl6dGjB8OHD2fRokXa5YsWLaJChQr8999/xVJRIYQQQojiVqhAKDk5mY4dO7JkyRLc3Nzo2bMneZ/U0adPH8LDw9m0aVNx1FMIIYQQotgVKhD65ptvOHPmDC+88AI3btxg69atOmnKlStH7dq15YGtQgghhHhiFSoQWrNmDeXKlWPx4sU4OjoaTFejRg1CQ0MLXTkhhBBCiJJUqEDoxo0btGjRAjs7u3zTOTg4EBkZWaiKCSGEEEKUtEIFQpaWlqSnpxeYLjQ0NN8zRkIIIYQQpalQgVDVqlUJDAwkIyPDYJqEhASCgoKoXbt2oSsnhBBCCFGSChUI9e3bl3v37vHZZ58ZTPPZZ58RGxvL888/X+jKCSGEEEKUpEIFQpMnT8bHx4dPP/2U/v37s2rVKgDCw8P566+/GDZsGF9//TWVKlVi/PjxxVphIYQQQojiolLyTgBkpIsXL9K3b19u3ryJSqXKtUxRFCpWrMi2bduoU6dOsVTUnMXFxeHq6kp4VCwuLi6lXZ2nRlpGVmlX4aljYyVP3TGVW5uppV2Fp87DI/NKuwrif1xcXBzeHq7Exhb8u1noR2zUqVOH8+fPs2zZMrZv387NmzfJysrCz8+Pnj178uqrr+Lg4FDY4oUQQgghSlyRnjVmZ2fH+PHj5fKXEEIIIZ5Kch5cCCGEEGZLAiEhhBBCmK1CXRqrUqWK0WlVKhU3btwozMcIIYQQQpSoQgVCt2/fLjCNSqVCURSdO8qEEEIIIZ4UhQqEbt26pff9rKwsgoOD2bp1Kz/88APTp09nzJgxRaqgEEIIIURJKVQgVLFiRYPLKleuTMeOHWnZsiUvvPACHTp0yDe9EEIIIURpKbHB0oMHD6Z27drMnj27pD5CCCGEEKJIijSPUEFq167N7t27S/IjRCFs+vc0i9Yd4Py1u6SnZ1LZz4vBzzZj4oudsbayNLm8s5dCmL98N0fOXCcuIRlvT1d6tKvLOy/3xMvd2WC+B1FxfL14B7sOX+B+RCyuzva0aVyNyaO707CWX1FWsdht3nOGpRsOcuH6XdLSM6ns68nAHs14bVinQrVZ4OUQfljxL0fP3iA+MZmyHi50a1uPKWN66G2zsAcPWbT2AOeuhnLrTgTRcYmkp2fg5e5Cs/qVeXnQM7RsWLU4VrXYSD8zXb9ODRg3sC31qlXA2tqSW6FRrNt1moWr95ORadpM6fa21owf8gzPd2lEFT9PsrIUroU8YPU/p1j812GysnQfKrDlxwm0a1KtwLJXbj3Om1+sMak+JUX6memkzXIr9CM2jNG2bVvOnj1LYmJiSX2EWSjOR2xMn7een1cHYGVpQfvmNXC0t+XAyavExifTqlFV/vrhdeztbIwu7+89Zxj3wVIyMrNoUqciFSt4cOZSCLfvRlLW3Zl/Fk2hip+XTr7rweH0enU+EdHxVPLxpHFtf4LDojh9MRgrSwuWzn6ZPp0aFmldi+sRGx9+u4Hf1u7HytKCds3UbXbolLrNWjaswpr5E01qsy17zzD+4+VkZGbRqLY//hU8CLwcQvDdKLzcndny8yQq52mz/cevMOTtBZRxdqBG5XJ4e7iQkZnFtdv3uR7yAICPXu/LG8O7Fmldi+sRG+bUz4rrERtfvN2PCUPbk56RyYFT10lMSqV902qUcXHg6NmbDJj0CylpGUaVVcbZns0/TKB+DR/iElM4eSGYrMwsmtWtSBkXB/Yev8KwaYtJz8jMlW/SiM5U9y+rt0wba0sGdW8CwPhPVrFmx6lCr2txPWLDnPpZcTGXNjPlERslFgitXLmSkSNH0rRpU06cOFESH0GlSpUIDg5m6dKljB492mC6jh07sn//fmbMmMHMmTMBmDlzJrNmzaJDhw4EBAQYzBsQEECnTp0A9TPUcsp5R9w333zD5MmTDZbz+uuvs3DhQgCqVq3K9evXC1i7R4orENoWEMjwd37DycGWrb9M0kbcUTEJ9J3wPRevh/HGS535dNIAo8q7FxFDswGfkJSSxrfThzF6QDsAMjOzmDjrd9b+c4ImdSry77JpudpKURQ6jphD0JVQhvZqwYKPh2Npqf4BXvbXISbPXo2Tgy0nN8zA27Pw61scgdD2/UGMeW8Rjg62bFr4Fg1qPmqzgW/8yKUbYUx4oTMz3+pvVHn3I2JpNeRTklPS+PrdoYzs3xZQt9lbn61k/Y6TNKrtz47FU3O12YPoOB5ExlGnWgUsLHIHK3/tOsUbn/yOoijs/2M6NSqVK/T6FkcgZG79rDgCoV7t6/HHl2OIT0yhz+sLCbp6FwB3V0c2/zCeutUq8MOqAD7+cYtR5S35dATPd2nExRv3GDJ1EXcfxADg5ebEqq/G0qxuReYt/5fPfvnH6Dr279yQpZ+NJDY+mVrPzTQ6KNOnOAIhc+tnxcGc2syUQKhQe72xY8cafA0ePJj69eszatQoVCoVb7/9dqFW4mmzdOlSg8tSUlJYtWrVY6yNft8s3QXA26O65Trt6FHGibnvDgXgt3UHiE1INqq8n/4MICkljY4tamo3AABLSwvmvTcMFyd7Tl8MZu+xS7ny7T5ykaArobg62zP33aHaDQBg9IB2dGhek4SkVH5eva/Q61pcvluubrM3R3TVBkGgbrM57wwGYMmGA8QZ2Wa/rgkgOSWN9s1raoMgULfZV+8MxcXJnrOXQgj473KufGXdXahXw1cnCAIY0L0prRtVIytL4cCJKyavY3GTfma6KSO7APDdyr3aIAggOjaRaXP/AuCVgW1xcbQrsKxyni7069QAgP/7ZqM2CAKIeJjApC/XATBhaHucHGyNruPw51oCsGH3mSIFQcVF+pnppM30K1QgtGzZMoOvDRs2cOHCBZydnfnmm28YPnx4cdf5idOsWTPOnTtn8MzXX3/9RUxMDM2bN3/MNXsk7EEMpy8GAzC4RzOd5a0bVcXH243UtAx2H75gVJnbAgIBGKSnPCcHW3q2rw/Aln2BufNl/92zfX29O+JBz6rL25on3+N270EMZy+FADCgu+46tmz4qM3+PXLRqDK37w/KLq+pzjJHB1t6tKsHPGpbY1lln8mxsS7RYX8Fkn5muvKeLjSt4w/Aul1ndJYfC7pF6P2H2Nla061N7QLLa1zLDwsLC1LTMjhy9qbO8gs37hHxMB4HOxu6tS64PACfsmXo2Kw6AL9v/c+oPCVJ+pnppM0MK1QgtHTpUoOvP/74g4CAAO7fv282Z4PGjh0LwJIlS/QuX7x4ca50pSHoyh0A3FwdqOjjqTdN49r+2WlDCywvPjGFm3ciAGiUvRM3VN65POUFXb2Ta3lejbLfv3EngsTk1ALrUlLOXVXX283FgYoVPPSm0RxVnb9acJslJKZwKzQiO5/+dW+oaTMjytPYffgCh09dw87Gmo4taxmdryRIPzNdgxo+gPrsT8i9aL1pzlxWr0uD6j4Fludorx7fEZuQrHM5XyMqRj1us1FNX6Pq+EKvZlhaWnD+WhhnLxvfN0uK9DPTSZsZVqjDx1GjRhV3PZ5qPXv2pFy5cqxevZpvv/0WO7tHp69v3brFvn37aN26NbVqld6PVEhYFAC+3u4G0/h4u+VKm2959x6l8S2nv0xNecF5ytP8bSifb3Y+RVEICYumdtXyBdanJGjWUbMe+lQwqc0e/cj5ltNfpk/ZMgDcuWe4vHe/XktyShqJyancCIng0o0wnBxs+e7Dl/Avrz9ge1ykn5nOPzvIDg2PMZhGc3nLv4LhdtWIeJgAQFl3ZxztbUhMTsu1XKVS4Zfd/4wpD+DF3uqz2U/C2SCQflYY0maGFXqM0P/93/8Vd12eWlZWVowcOZKYmBg2bNiQa9nSpUtRFKVUzwYBxCepo2oHe8N3Azg6qJfFJxZ8fTgh8VGU7mjgDoNH5aXozetgMN+jU6XG1KWkaOtpb3gchWP2svikFINpNBJzpClo3fO2WU5/7TrFmu3H2bovkEs3wvBwc+L7j4bTp1OjAutQ0qSfmc45ux5JeQKWnDTBjLMRY4ROXQzRHkWPyB7Xk9Owns20/daY8to1qUplH09SUtNZW4Q7xYqT9DPTSZsZVqhAaOXKlQYfs2Gu9F0ey8rKYvny5Tg6OjJ06FCjy0pNTSUuLi7XSwiNa7vnEH70ey7vmK2+k62GL2OnL+a1j5eRaeJcM+J/T0JSKgv+3A/AxxN68+rgdnh7OOPp5sSI51oyZ3J/0tLVg52VrIL7iyaY2n7wAjHxpfdDLkRJKVQgVK5cuSfqYapjxoxBpVIZfO3fv7/E61CzZk3atm3Lvn37tA+l3b17NyEhIQwePBhnZ8OTSuU1e/ZsXF1dtS8/v6JPLGXUUWeS5qjTvsDynBwfReyJKfrLfFRe7qNOTd4kg/keHWkYU5eSoq1nPteoNUfezg4FH1k75khT0Lobc6Tu5upI68bV+PPbCXRtU5dNu0+z7K9DBeYrSdLPTGfUkbq9/iNrQ+Ys2cWSv45gb2vNnMnPc3nLTK5tm8X304cQdPUuf2w9DsDDuPwDGxdHO/p0UA94XfmEXBYD6WeFIW1mWKHGCHXr1o0dO3aQnp6OtbV1cdfJZG3btqVaNcOzoe7YsYPw8PASr8fYsWM5fPgwS5cuZdasWdqzQ6ZeFps+fTpTpkzR/h0XF1fkYMgve+zI3fCHBtNolvmVL3jcgF+Oa7uh96NxraY7iFNTnn+e8vzLe/AwNonQ+/oHhoZm51OpVEbVpaRo1jEsx+3HeYWZ0mblH40LCr3/kDrVdDdwzVgQPxPG+qhUKob1bsG/Ry6wfX8QLw9ub3Te4ib9zHR3sseOacaH6fNo7Jj+dckrK0th6twNLP7rMD2fqYuvtxsJyakcPn2DnUcu8euMFwG4ePNevuUM7NYYBzsb7tyPJuDENaM++3GQfmY6aTPDCnVGaObMmaSmpvLKK68QHx9f3HUy2bhx4/K9pV/fIGXNGa2C5pM0Zb7JIUOG4OTkxPLly4mMjOTvv/+mevXqPPPMMyatj62tLS4uLrleRdUg++6Q6NhEgu9G6k1zJvtWcWOmNndxstfOFnr2Yki+5TXIU17D7Pl4NMvz0tyyXtXPy6R5Topb/ZxtZmDwYGD23Tz1axbcZs6O9lT29crOp3/dA7PXvX4N4+7m0dCMY4p8WLrbo/Qz02nmDfIo46jzg6HROHvdAnPMMWSMizfvM2/5HiZ/tZ6PftjCjsMXURSFlg0qAbDv+NV887/UpwUAf2w7YdK+sKRJPzOdtJlhhb59/tlnn2XFihVUqVKFF154genTp/PJJ5/ovD799NPirnOxcHR0BCjw8R8JCeo7MJycnAos08nJicGDBxMcHMzYsWNJTU1lzJgxRa9sMfDxdqNJnYoArNt5Umf50bM3uBv+EFsbK7q1rWtUmb07qqc/X6+nvISkVHYcPAfAc3mmSe+d/fc/B87pvTVy/Q51eaU9JX2Fsm7a2zj/2qW7jv8FPmqzrm3qGFVmrw4NssvTHXSamJTKrsPngUdta6yDJ9Q/aFUNPB7hcZF+ZrqwiFhOZf+QDO7eWGd5qwaV8S3nRkpqOruPXNJZbqrnuzTCr5w7x8/dJjCf26RrVylH0zr+ZGVlsWpbyTwdoLCkn5lO2swwowKhzp0789VXX2n/njlzJn/++ScAUVFRrFmzhjlz5jBz5kzta9asWdr/P4n8/bPnKbhxI98jnWvXruVKXxDNZbAtW7ZgaWn5RE01MGVMdwC+W75beyYDIDomgWlz1A9QfGVwe1ydHl2y2bovkBaDPqXfhO91ypvwQkcc7GwIOH6F5RsPa9/PzMxi2pw1xMYn06RORTq3yj1pW7c2dWhQ05fY+GSmzVmTa4Dvsr8Osf/EFZwcbBk/rFPxrHgRvD1K3WY//P6vdh4OUB9Vvfu1eobesQPb45KjzbYHBNJ26GcMfONHnfJeHdoRezsbDpy4wu9/H9G+n5mZxbtfryU2PplGtf115gNasekw14N1L++mZ2SyYtNhFq1Tj4Mb0a9NEda2eEg/M903K/YA8Pbwztp5hUA9h9XcaerHHfy24TBxOcYI9W5fj//+fJdN34/XKa+cp4veS2092tRm/ruDSElNZ8pX6/Ot0/Dss0EBJ69x577hyymlRfqZ6aTN9DNqjFBAQACVKlXS/v3xxx8/UYOlC6N9+/ZYWVkRExPD3r176dKli95069erdxadO3c2qtx27drRrFkzbt26RefOnalQoUKx1bmoendsyGtDO/LLmgC6jZlL++Y1cbS3Yf+JK9oHiL4/vk+uPHEJyVwLDiclLV2nvPJeZVgwYzjjPlzGpC/+ZOXmo/iVd+fMxUcP3Pvts9E6fUWlUrHoszH0evVbVm87zrGzN2lcx5+QsChOXVA/cO+nmSNL/bk8oD6DM25IBxat3U+vcd/wTLMaONjbcDD7IYUtGlTh3Vd75coTl5jC9ZAHpOp5DEE5L1e+//Alxs9YzrQvV7Nqy1H8yntw9lKw9qGrP88apdNmG3ed4p05a6jk40nNKuVwtLclIjqeK7fu8yAqDgsLFR9MeI5OeXY4pUH6mem2HzjPz2sPMH5Ie3b/9hYHTl4jMSWNDk2rU8bFgWOBt/ji19zPBXNxsqdGxbLY2ejuxpvU8ef3L0Zx/vo9gsOiSM/Iom618tSs5E18YgrD31vKhRuGxwdZWVowpId69vOVW44X78oWE+lnppM2069Qg6Wf1LM8pihXrhyjRo1i8eLFjB8/nm3btlGjRg3t8oyMDD799FOOHj2KnZ2dSbNkl9RDZovDl9MG0bJhFRatO8DxoJtkZGRSydeTSaO6M/HFTiY/oqF/1yZU8vHkm6U7OXr2BkFXQvH2dGHc4Pa88/KzlPXQ35GrV/Lm0Kr3mbtkBzsPnWdbQBAuTnY816khU8c+a9Q16sfl88kDaVG/Mks3HOTEuVtkZGRS0ceTN0d05bVhprdZ3y6NqejjwXfLd3Ms8Abnr4bi7eHK2IHPMHlsD8q667bZ68O7UNW/LKcvBnPy/G1i45Kws7XGp5w7vTo0YOTzbamrZ7BiaZF+Zrrp8//mv6DbjBvYlhb1K2FlZcntu1HMX7mXhasP6DwpPj+Xbtxj9T+naF6vIh2b18DS0oLQ8BgWrN7PglUB3IvMf0qOns/UxdPNiejYRLYdOFfUVSsx0s9MJ22my6inz1tYWDB69GiDj5AoLUV5+jyoxwf16dOHgIAArKysaNmyJRUrViQpKYnjx48TFhaGvb09K1asYNCgQTrlaqLcO3fu4Otb8OBWzZPsS+vp8+amOJ4+b26K4+nz5qY4nj5vborj6fNC5MeUp8+X7hMaS5mjoyP//vsvf/zxB3/++SenT5/mv//+w87OjooVKzJo0CDeeustqlatWtpVFUIIIUQJeKoDIc3EhQUJCAgwuMzS0pKRI0cycuRIkz/f1NtJO3bs+ETdgiqEEEKYO6PPgy9fvhxLS0uTX1ZWT3WsJYQQQoj/YUZHKXImQwghhBD/a4wOhJ599lnefffdkqyLEEIIIcRjZXQgVK5cOTp06FCSdRFCCCGEeKzkXlkhhBBCmC0JhIQQQghhtiQQEkIIIYTZkkBICCGEEGbLqMHSWVnyqAIhhBBC/O+RM0JCCCGEMFsSCAkhhBDCbEkgJIQQQgizJYGQEEIIIcyWBEJCCCGEMFsSCAkhhBDCbEkgJIQQQgizJYGQEEIIIcyWBEJCCCGEMFsSCAkhhBDCbEkgJIQQQgizJYGQEEIIIcyWBEJCCCGEMFsSCAkhhBDCbEkgJIQQQgizJYGQEEIIIcyWBEJCCCGEMFsSCAkhhBDCbEkgJIQQQgizJYGQEEIIIcyWVWlXQIiSYmMlcb6pMrOU0q7CU+fhkXmlXYWnjlvzN0q7Ck+lhyd+LO0q/E+SXwohhBBCmC0JhIQQQghhtiQQEkIIIYTZkkBICCGEEGZLAiEhhBBCmC0JhIQQQghhtiQQEkIIIYTZkkBICCGEEGZLAiEhhBBCmC0JhIQQQghhtiQQEkIIIYTZkkBICCGEEGZLAiEhhBBCmC0JhIQQQghhtiQQEkIIIYTZkkBICCGEEGZLAiEhhBBCmC0JhIQQQghhtiQQEkIIIYTZkkBICCGEEGZLAiEhhBBCmC0JhIQQQghhtiQQEkIIIYTZkkBICCGEEGZLAiEhhBBCmC0JhIQQQghhtiQQEkIIIYTZkkBICCGEEGbLqrQrIB6/Tf+eZtG6A5y/dpf09Ewq+3kx+NlmTHyxM9ZWliaXd/ZSCPOX7+bImevEJSTj7elKj3Z1eeflnni5OxvM9yAqjq8X72DX4Qvcj4jF1dmeNo2rMXl0dxrW8ivKKhY7aTPT/b3nDIvXH+CCps18PRn4bHMmvNCp0G32/YrdHD1zg7jEZLw9XOjerh5Txz6rt80yM7PYFhBI4OU72a8QHsYlYWlpQfiR74pjFYud9DPT9evSmHGD21Ovug/W1pbcuhPBuh0nWbhqLxmZWSaVZW9rzfgXOvF8tyZU8fMiK0vhWnA4q7cdZ/H6A2RlKXrzWVpaMPr5tgzt1YKalcthZWXJ7dBINv57mh9X7iElNb04VrXYSD/LTaUoiv5vVjwx4uLicHV1JTwqFhcXlyKVNX3een5eHYCVpQXtm9fA0d6WAyevEhufTKtGVfnrh9ext7Mxury/95xh3AdLycjMokmdilSs4MGZSyHcvhtJWXdn/lk0hSp+Xjr5rgeH0+vV+UREx1PJx5PGtf0JDovi9MVgrCwtWDr7Zfp0alikdS0u5tRmmQZ29Kb64JsN/LJG3WbPNFO32cFT2W3WsArrvjetzTbvOcOrHy0jIzOLxnX88a/gQeClO9o22/rrZJ02i41PomrXd3XKKu5AyNJCVSzlmFM/c2v+RpHya3wxZSATXuhEekYmB05cJTE5lfbNalDGxYGjZ64z4M0FRgchZVwc2PzTW9Sv4UtcQjInz98mKyuLZvUqU8bFgb3HLjFs8s+kZ2TmymdjbcXqb8fTqWUtUlLTOXn+NvGJKTStW5GyHi6cuxpKn9e+Iy4hucjr+/DEj0Uuw1z6WVxcHN4ersTGFvy7+T8bCFWqVIng4GCWLl3K6NGjjcoTFRXFggUL2L59O9evXyc+Ph53d3caNmzIgAEDGDNmDNbW1gWW899//7Fs2TIOHDjA3bt3SUpKokyZMtSuXZtu3boxcuRI/P39jV6X4gqEtgUEMvyd33BysGXrL5O0EXdUTAJ9J3zPxethvPFSZz6dNMCo8u5FxNBswCckpaTx7fRhjB7QDlAfiU+c9Ttr/zlBkzoV+XfZNFSqRz8WiqLQccQcgq6EMrRXCxZ8PBxLS/VV2mV/HWLy7NU4OdhycsMMvD2LFvgVlbm1WXEEQtv3BzLy/xbh6GDL5p/eztVmz7/+AxevhzHxxc588vbzRpV3LyKWloPUbTbvvWGMer6tuq6ZWbzxyUrW7ThB4zr+7FqSu80Sk1OZ+uVq6tfwpUFNP9xcHOg4Ys4TGQiZWz8rjkCoV4cG/DH3VeITU+jz2nyCroQC4O7qyOaf3qJudR9+WLmHj7/baFR5S74Yy/PdmnDxehhDJi3kbngMAF7uzqya9xrN6lVi3pKdfPbTllz5Zr3Zj7dGduNu+EMGv72QSzfuAeDkYMuiz8bQ45l6rNtxglc/Wl7kdS5qIGRO/cyUQEjGCGX766+/qFKlCjNmzODChQs0a9aMgQMHUq1aNfbs2cNrr71G3bp1uXTpksEykpKSePHFF2nVqhU///wz0dHRtGvXjsGDB9O4cWOCgoL46KOPqF69Ops2bXp8K5ftm6W7AHh7VLdcpx09yjgx992hAPy27gCxRh65/PRnAEkpaXRsUVO7AYD6iHvee8NwcbLn9MVg9h7L3Wa7j1wk6Eoors72zH13qHYDABg9oB0dmtckISmVn1fvK/S6FhdpM9N9uyy7zUbqttlX7wwBYPH6A0YfIf+yeh9JKWl0aFFTGwSBus2+fncoLk72nLkYwr7/LufK52hvy8+zRvH6S114plkNXJzsi7pqJUb6memmjOkOwHfLd2uDIIDo2ESmzVkDwCuD2+PiaFdgWeU8XenXpREA//f1Om0QBBARHc+kz1cBMOHFTjg52GqXWVlaMGbgMwB8/vNWbRAEkJCUyluf/0FSShoDuzelsq9n4Va0GEk/008CIWDjxo0MHjyYuLg4Jk2aRHh4ODt27GDVqlUcPHiQ27dv06NHD65du0a7du24ffu2Thnp6en06NGDP//8k/Lly/P3339z7949tm7dyh9//MHOnTuJiIhg7dq1+Pv76y2jJIU9iOH0xWAABvdoprO8daOq+Hi7kZqWwe7DF4wqc1tAIACD9JTn5GBLz/b1AdiyLzB3vuy/e7avn2unojHoWXV5W/Pke9ykzUx370EMZy6GADCwR1Od5a0K1WZB6vK662+zZ5+pB5T+uheW9DPTlfdypWndSgCs23lSZ/mxwJuE3o/Gztaabm3rFlhe4zr+WFhYkJqWzpEz13WWX7geRkR0PA52NrnKq1m5HM7Zgdb+41d08j2IiufSjTAsLCzo27mRkWtXMqSfGWb2gVBkZCRjxowhKyuLyZMn8+233+Lg4JArjY+PD5s3b6ZNmzZER0czYsQInXI+/fRTDh06RJkyZTh8+DB9+/bVSWNlZcXgwYM5c+YMHTp0KLF10ifoyh0A3FwdqOij/8ikcW3/7LShepfnFJ+Yws07EQA0qqP/Mp+mvHN5ygu6eifX8rwaZb9/404EicmpBdalpEibmS7oqrrebi4OVKygv80a1VYfiZ67alyb3QrNbrMC1v1cdhs9baSfma5BTXUfio5JJCQsSm+aM5dCstP6Flieo736xzg2PhlDo0WiYhIAaJTjTIpjjh/x6NhEvfmiY9TvN6xl/HCIkiD9zDCzD4QWLFhAbGwsXl5efPHFFwbT2djY8OOP6uuzhw4dYv/+/dpl8fHxfPedeszBxx9/TOXKlfP9TCcnJxo3blwMtTeeZmfh6+1uMI2Pt1uutPmWd+9RGt9y+svUlBecpzzN34by+WbnUxSFkLDoAutSUqTNTBdSQD0BfMoa32Z37j1aF99ybvrL034HpbfeRSH9zHT+FTwACA03XIe74Q9zpc1PxMN4AMp6uOBorztQWKVS4VfeXae8iOh47f8r+ej/nIrZ71c0sPxxkX5mmNkHQpqxOkOGDMHOLv9ryY0bN6ZePfVp+M2bN2vf37dvH3FxcahUKkaOHFlidS2K+CR1VO2gZyPXcHRQL4tPLPj6cELioyjd0cAdBo/KS9Gb18FgvkdHWcbUpaRIm5kuIUldb0P1hEd1zbuO+ZWXX5mao3ljynsSST8znXN2PZKS0wymSUxSL3N2LHhs2Knzt7VnHkb0b6OzfFjvFtp+lrO8W6GR2mB9ZP+2OvnaNqlOjUrlsvMVPFapJEk/M8ysA6H09HSCgtTjD1q0aGFUnubNmwNw8uSj69Ka/1euXBkPj6JH/ampqcTFxeV6CSGEKBkJSaks+GMvAB9P7MurQzvg7eGCp5sTI/q1Zs60waSlZwCgKLnnJprz23YAXh3Sgemv9cavnBuuzvb07dyIJV+M0eYzNAeRKH1mHQhFR0eTlaXu1N7e3kbl0aSLiIjQvqf5f9myZYulXrNnz8bV1VX78vMr+sRSxX0E5eT4KGJPTNFf5qPych8JafImGcz36EjDmLqUFGkz0zk5qOttqJ7wqK7GHCFrysuvTM2RfGkfcReW9DPTFffZDVAHNEvWH8TezoY50wZzeccXXNv1Jd9/+BJBV+7wx+ZjADyMS8qV748tx/jil60oisL/jetJ0JZPub33a5bPGUfkwwR++H0PADFx+scQPS7SzwyTmaVN9DimXZo+fTpTpkzR/h0XF1fkYMivvPpMlea6uT6aZZpr4fmWl+Pabuj9aFyr+Rgszz9Pef7lPXgYm0Toff3XfkOz8+W8Ll8apM1Mp/nsfNvsgabNCj576pdjXFDo/YfUqaa7UzTlO3gSST8z3Z3s8SmaMSj6aJblHGeWn6wshalz1rB4/UF6tq+Pbzk3EpJTOXzqGjsPXeDXT0YBcPF6mE7erxftYO0/J3iuUyMq+3iSlpHByXO32bz3LO+92is73z2dfI+T9DPDzDoQcnd3x8LCgqysLMLDw43K8+DBAwC8vB7NlKn5v2ZZUdna2mJrq3tLYVFo7pyIjk0k+G6k3rsGNHdZGDO1uYuTPVX8vLh5J4KzF0Ooq2cj0N61kae8hjX9CLx8R7s8r7PZ71f189J7a+XjIm1mugY1crRZWKTeO8fOXlLfMWLM3TzOTvZU9vXiVmgEZy+FUKdaBT3lae4OerIe/WAs6Wem09zV5FHGCf8KHnoH92ruSAq8bNrdhBdvhHHxhm6w07JhFQCd+ao0gu9G8ePKPTrvt25UNd98j4v0M8PM+tKYtbU19eur5zn477//jMpz/PhxAJo2fTRHiub/t27dIiqq4NH2pcHH240mdSoC+ufdOHr2BnfDH2JrY2XUvBsAvTuqpz9fr6e8hKRUdhw8B8BzeaZJ75399z8Hzum9NXL9DnV5pf2IDWkz01XwdqNx9q20G3ae0ll+rFBt1kBd3i79bbbz0Hmg9Ne9sKSfmS7sQQynLtwG9M+J06phFXzLuZOSmm70nDj5eb5bE/zKu3M86KZJgVWzepVo3bgaofej2X4gqMj1KArpZ4aZdSAE0K9fPwDWrVtHSkr+d52cPn2aCxfUG1XOeYI6deqEs7MziqKwYsWKkqtsEeWciTXnxhwdk5BrJlbXHDPwbt0XSItBn9Jvwvc65U14oSMOdjYEHL/C8o2Hte9nZmYxbc4aYuOTaVKnIp1b1c6Vr1ubOjSo6UtsfDLT5qwhM8eDEZf9dYj9J67g5GDL+GGdimfFi0DazHSTR2e32Yo8bRabyP99vRaAlwe1zzXT87aAQFoN+ZTnX/9Bp7zXhnXCwc6G/cevsGJT7jb7v6/Ubda4jj+dWtYqqVUqcdLPTJdzluScZxfdXB1zzZIcl+OOpd4dG/Dfug/ZtPBNnfLKebri411G5/0e7eox//0XSElNZ8rs1TrLXZ3tqVZRd3xos3qVWD5nHFlZWUz64s9cbVlapJ/pZ/bPGouIiKBatWraWaW//fZbvenS0tLo1KkTR44coXXr1hw5ciTX8g8//JDPP/+cMmXKcPr06XznEkpISODatWtGzyVUnA9dfW/uen5ZE4C1lSXtm9fE0d6G/SeuEBufTMuGVdj44xu5Hri3assxXv9kJX7l3Qna/IlOeZv+Pc24D5eRmZlFs3qV8CvvzpmLBT9w79rtcHq9+i2RDxPUD9yr409IWBSnLjx5D101pzYrroeuvj9vPb+u3Z/dZjVwsLPlwMn/b+++w6I62jaA30vvIKCAIGDXGLtijagoscSOJUVpvom+iTFGTSwxlkSNiS1vLPmMUowmijUWIokGxI7Gghp7QSkWIPQiZb4/cFfWLexKU/b+XRdXcOfMnJnJYffZOXNmnvZZqwbY/tzGjr/uO4lJX20u6Yvd8xXK++3ppqtFRcVo36Kkz86X2thR2aarADD9262IvVpyG+VJQaFsEUfpqsQA0LdbC0wL7PfCba2oTVd16TqrqE1XF08dgQljeuFJQSGiT19Ddu4TeHZsChsrM5w8fwvDPlolt+nq2291wpq5Y3EvMQWth8yVK2uAZyv8/O14XLqRgLjEFBQUFqFFI2c0re+IzOw8+M/cgEMnFLdYer2JM45snomrt5NwJz4ZObn5aOTmgNbN6uFJQSGmLN6CX/aerJD2VsSmq7pynWmz15hOzxECSub3bNiwAaNGjcLKlSshkUjw9ddfy60unZCQgMDAQBw/fhw2Njb4+eefFcr58ssvERkZiePHj6N79+748ccfMWjQILljioqKsGfPHsyYMQMTJ06s8kUVAeCbaT7o1LoB1m+LRkzsbRQWFsHdxR6f+Hrjv+/0gpGhdpfE0D7t4O5sj+XBEThx/hZir8XDwd4K40f2wPTAfqhjp/wCbOzugKO/zMLSoAOIOHoJ+6NiYWVhgkG9WmNqQD+N7lFXFfaZ9hZN9YFH6wbYsP0IYmLvyPps8ri+mPC29n02xKst3OraYWXIHzh54RYuXi/ps0CfNzA1QHWfXbvzQHYLpbTSrzV2r5inPcuL15n2Zi7bgVMXbmP8yB7waNUABgb6uBufjJWhf2DNL5EKO8Wrc+VWIraEx6Bjy/ro6dEM+vp6iH+QitWb/8LqzYeQ9Dhdab6kR+kI3nEUnds0QLd2jWBkaIAHyekI2XUMqzcfws24ipk7WlF4nSmq8SNCDRo0kJvY/Lw1a9agXbt22LZtGwIDA5GZmQlLS0t069YNtWrVQkJCAo4fP47CwkI0bNgQu3fvli2q+LysrCwEBgYiLKxk+N/JyQnt27eHlZUVUlJScPr0aaSmpsLY2Bhbt26V3ZYrS0WOCBGpU1EjQrqkokaEdElFjQjpmooYEdIV2owI1fhAqCyRkZHo2bMngJJ9x1avXo3w8HDcvHkTmZmZqFWrFlq3bo3hw4cjICAARkaq162QOnnyJIKDg3HkyBEkJCQgJycHNjY2eO211/Dmm2/C19cXzs6KM+xVYSBEVYWBkPYYCGmPgdCLYSCkOQZCNQwDIaoqDIS0x0BIewyEXgwDIc1pEwjp/FNjREREpLsYCBEREZHOYiBEREREOouBEBEREeksBkJERESksxgIERERkc5iIEREREQ6i4EQERER6SwGQkRERKSzGAgRERGRzmIgRERERDqLgRARERHpLAZCREREpLMYCBEREZHOYiBEREREOouBEBEREeksBkJERESksxgIERERkc5iIEREREQ6i4EQERER6SwGQkRERKSzGAgRERGRzmIgRERERDqLgRARERHpLAZCREREpLMYCBEREZHOYiBEREREOouBEBEREeksBkJERESkswyquwJE9PKQVHcFSCf8e3pVdVfhlVSr40fVXYVXhih6ovGxHBEiIiIincVAiIiIiHQWAyEiIiLSWQyEiIiISGcxECIiIiKdxUCIiIiIdBYDISIiItJZDISIiIhIZzEQIiIiIp3FQIiIiIh0FgMhIiIi0lkMhIiIiEhnMRAiIiIincVAiIiIiHQWAyEiIiLSWQyEiIiISGcxECIiIiKdxUCIiIiIdBYDISIiItJZDISIiIhIZzEQIiIiIp3FQIiIiIh0FgMhIiIi0lkMhIiIiEhnMRAiIiIincVAiIiIiHQWAyEiIiLSWQyEiIiISGcZVHcFqOrtPngW67dF49KNBBQUFKF+vdoY2a8D/vtObxga6Gtd3vkr97Ay9E8cP3cTGVm5cLC3xpvdW2B6YH/UtrVUme9RSga+23AAfxy7jAeP02FtaYqubRthip83WjerV54mVjj2mfZ+O3QOG7aX6jMXe/j064iJb/d64T77fuOfOHHuFjKyc+FgZwXv7q9jWkA/pX1WVFSM/VEXcP7qfVy4eh8Xrt7Dvxk50NfXw6Pj31dEEyscrzPtsc+0N8SrLcaP7IHXGzvD0FAfd+4/xrYDZ7Dml79QWFSsVVmmxoaY8HYvDOvbDg3q1UZxscCNuIfYsj8GG7ZHo7hYKM2nr68Hv2HdMHqAB5rWd4SBgT7uxidj18GzWLXpEPLyCyqiqRqRCCGU15JeGhkZGbC2tsbDlHRYWVmVq6yZy7bjxy1RMNDXQ4+OTWBuaozoM9eRnpmLzm0aYucPH8LUxEjj8n47dA7jZwejsKgY7V5zg1tdO5y7cg93E5JRx9YSv6//FA3q1VbIdzPuIQa8vxKPUzPh7myPts1dEZeYgrP/xMFAXw/BiwPxVq/W5WprRdGlPlP1pqWtWct34P+2lvTZGx1K+uzI30/7rHUDbP+fdn2259A5/GdOCAqLitH2NVe41bXD+Sv3ZX22f90UhT5Lz8xBgz6fK5RV0YGQnp6kQsrRpeusouhan9Xq+FG5y1j06QhMfLsXCgqLEH36OrJz89GjQxPYWJnhxLmbGD5ptcZBiI2VGfas/Rgtm7ggIysXZy7dRXFxMTq8Xh82Vmb46+QVjJnyIwoKi+TyGRkaYMuKCejVqRny8gtw5tJdZGbnoX0LN9Sxs8LF6/F464PvkZGV+8LtFEVPkH/xJ6Snl/25+UoHQu7u7oiLiwMAbNu2DT4+PkqP69OnDw4dOoTg4GD4+fkBAPz8/BAaGgpfX1+EhISoPEdISAj8/f3h5uaGu3fvyl6/e/cu6tevL/v3zp07MWzYMJXlDBw4EOHh4QAALy8vHDx4UMNWVlwgtD/qAt6b/hMszIyx7/8+kX1LSUnLwuCJ/8M/NxPx0bu98dUnwzUqL+lxGjoMX4CcvCdYMXMM/IZ3B1DyTfy/839G2O+n0e41NxwMmQaJ5NmHhRACPccuQey1eIwe4IHVX74Hff2Su7QhO49iyuItsDAzxpkdc+FgX77Ar7x0rc8qIhDaf/gCxn22HuZmxti7drJcnw398Af8czMRH77TGwsmq/57KS3pcTo8fEr6bPmMMfAd1g1ASZ99uGATth04jbavueLPIPk+y87Nx6ffbEGrJi5o1bQealmZwXPskpcyENK166wi6GKflTcQGuDZCpuXvo/M7Dy89cFKxF6LBwDYWptjz9qP0aKxM37YdAhffr9Lo/KCFgVgWN92+OdmIkZ9sgYJD9MAALVtLfHLsg/Q4XV3LAuKwNdr98rlmz9pCD4e1xcJD//FyMlrcOVWEgDAwswY67/2x5tvvI5tB07j/TmhL9xWbQKhGjNHaPbs2SgsLKy28wcFBalMS0hIQERERBXWRrnlwX8AACb79pUbqrWzscDSz0cDAH7aFo10DaPwtb9GISfvCXp6NJW9aQAl37iXzRgDKwtTnP0nDn+dvCKX78/j/yD2WjysLU2x9PPRsjcNAPAb3h2eHZsiKycfP26JfOG2VhT2mfZWhDzts3GKffbd9FEAgPXbozX+tvd/WyKRk/cEnh5NZUEQUNJnSz8fDSsLU5z75x4iT12Vy2duaoz/m++LD9/1whsdmsDKwrS8Tas0vM60xz7T3qf+3gCA70P/lAVBAJCano1pS7YCAP4zsgeszE3KLMvR3hpDvNoAAD77bpssCAKAx6mZ+GThLwCAie/0goWZsSzNQF8P/iPeAAAs/HGfLAgCgKycfHy8cDNy8p5ghHd71Hexf7GGaqlGBEJmZma4fv061q9fX+Xn1tfXR5s2bXDgwAE8ePBA6TGhoaEoKipCx44dq7h2zyQ+SsPZf0pGz0a+2UEhvUubhnB2qIX8J4X489hljcrcH3UBAOCjpDwLM2P079ESALA38oJ8vqf/7t+jpdwfiJRPv5Ly9j2Xr6qxz7SX+CgN5/65BwDwebO9QnrnF+qzWADACG/lfdbvjdcBVH/bXxSvM+2xz7TnVNsa7Vu4AwC2RZxRSD954TbiH6TCxNgQfbu1KLO8tq+5Qk9PD/lPCnD83E2F9Ms3E/E4NRNmJkZy5TWt7wjLp4HW4ZhrCvkepWTiyq1E6OnpYXDvNhq2rnxqRCA0efJkAMCCBQuQk5NT5ecPCAhAYWEhQkOVD+MFBwfDxMQE77zzThXX7JnYa/cBALWszeDmrDzKbtvc9emx8UrTS8vMzsPt+48BAG1ec1Vb3sXnyou9fl8u/Xltnr5+6/5jZOfml1mXysI+097F6yX1rmVlBre6yvusTfN6cseqk5mdh9vxJX1WVtulffSq4XWmPfaZ9lo1Lfm7S03Lxr3EFKXHnLty7+mxLmWWZ25aEvSlZ+ZC1QyblLQsAECbUiN25qWCxdT0bKX5UtNKXm/dTHmfVrQaEQgNGDAAnp6eSEpKwooVK6r8/O+++y6MjY0RHByskHb48GHcvHkTw4YNg42NTZXXTUp64bs42Ko8xtmhltyxastLenaMi6PyMqXlxT1XnvTfqvK5PM0nhMC9xNQy61JZ2GfaK6ueAOBcR3kblbmf9KwtLo61lJcn+39Qfe0uD15n2mOfac+1rh0AIP6h6jokPPxX7lh1Hv+bCQCoY2cFc1PFCekSiQT1nGwVynucmin73d1Z+Xncnr7upiK9otWIQAgAlixZAgD49ttvkZJS9oVfkWxtbTFkyBBcu3YNx44dk0vbsGEDgJJRo+qUmVPyTcRMyQUrZW5WkpaZXfY99azsZ99szFU8lfGsvDylec1U5nv2jUGTulQW9pn2snJK6q2qnsCzuj7fRnXlqStT+s1Uk/JeRrzOtMc+057l03rk5D5ReUx2TkmapXnZ8+n+vnRXNsI1dmhXhfQxAz1kf5uly7sTnyz7gjNuaDeFfN3aNUYTd8en+cqeq1QRakwg1KlTJwwfPhwZGRlYuHBhlZ8/MDAQgPyk6YyMDOzYsQPu7u7w8vLSuKz8/HxkZGTI/RAREb0ssnLysXrzXwCAL/87GO+P9oSDnRXsa1lg7JAuWDJtJJ4UlDzAJIT82kRLfip5gvr9UZ6Y+cFA1HOsBWtLUwzu3QZBi/xl+SpqOY+y1JhACAAWLVoEAwMDrFmzRvZYfVXp06cPXF1dERYWhuzskvubv/76K3JycuDn5yf3uGVZFi9eDGtra9lPvXrlX4yror8NWJg/+5aTnae8zGflyUf10rw5KvM9+3amSV0qC/tMexZmJfVWVU/gWV01+bYnLU9dmdJvpVX17bGi8TrTHvtMexU9igaUBDRB24/A1MQIS6aNxNUDi3Djj2/wvy/eRey1+9i85yQA4N8M+bm7m/eexKL/2wchBD4b3x+xe7/C3b++Q+iS8Uj+Nws//HwIAJCWoXwOUUWrUYFQ06ZNERAQgPz8fMyZM6dKz62npwdfX19kZWUhLCwMQMnokJ6enmztIk3NnDkT6enpsp/798s/CbSeU8m9Vuk9YGWkadL7umrLK3U/PP6B8nvOsvvNz5Xn+rQuqvLFP81X+h5zdWCfaU9ab7V99kjaxrLv/5eeFxT/QHmZ2vw/eBnxOtMe+0x795/Og5LOdVJGmlZ6bp46xcUCU5dsRbcxi/D1mr0I2XkUqzYfwtuf/ohBE/4nCxr/uZmokPe79QfQwWcB5ny/C0Hbj+DHLZEYPzsYPccugXTc4J+bSQr5KkONCoQAYN68eTAzM8PmzZsRGxur8jjpCE1Z60lK0zUZ0fH394dEIkFQUBAuX76MmJgY9O7dG25ublq0ADA2NoaVlZXcT3lJnwJITc9GXEKy0mOkTwxoshy8lYWpbIXV808fl1ZVXqvnymv99OkFafrzzj99vWG92kofR60q7DPttWxSqs8SlffZ+Sslgb0mT6ZYWZiigUtJn5XVdmkfvWp4nWmPfaY96dNzdjYWKidDS598u3BVuy/f/9xKxLLgCExZvAVzVu7CgSOXIIRAp9YNAEBhjS+puIQUrNp0CFOXbMXMZTuw44+/UVBYhC5tGqrNV9FqXCDk5OSEyZMno7i4GDNnzlR5nLm5OQDIbmOpkpVV8vifhYVFmeeuX78+evbsiaNHj8rOXd2TpKWcHWqh3WslAZmyNSROnL+FhIf/wtjIQKM1JABgYM+SJeO3KykvKycfB45cBAAMem5p+YFP//179EWlj5NuP1BSXnUv488+056zQy20ffr48faIvxXST75Qn7UCAOz4Q3mfRRy9BKD62/6ieJ1pj32mvcRHafj78l0Aytde6ty6AVwcbZGXX6Dx2kvqDOvbDvWcbBETe1urwKrD6+7o0rYR4h+kIjxa9WBGRapxgRAAfP7557Czs0N4eDiio6OVHuPqWvJmffOm4kJQpd24cUPu+LJIA5+9e/eiVq1aarfdqGqlVxUtfWGmpmXJrSpqXWoF3n2RF+Dh8xWGTPyfQnkT3+4JMxMjRMVcQ+iuZ0/LFRUVY9qSrUjPzEW719zQu3NzuXx9u76GVk1dkJ6Zi2lLtqKo1CZ/ITuP4vDpa7AwM8aEMb0qpuHlwD7T3hS/p3228bk+S8/G9O9KbhuP9+kht9LzvqgL6DTqKwz98AeF8j4Y0wtmJkY4HHMNG3fL99n0b0v6rO1rrujVqVllNanS8TrTHvtMe6VX4y49IlvL2lxuNe6MUk/GDezZCqe2fYHdayYplOdobw1nBxuF19/s/jpWznobefkF+HTxFoV0a0tTNHKro/B6h9fdEbpkPIqLi/HJol/l+rIy1cjd562trTFr1ixMnToVn332mdLRnN69ewMAYmNjcfPmTTRq1EjhmIKCAuzZs0fu+LKMGDECX3zxBbKysuDr6wsTk5dnAufAnq3xweie+L+tUejrvxQ9OjaFuakRDp++hvTMXHRq3QCzJrwllycjKxc34h4i74niJnxOtW2weu57GP9FCD5Z9Cs27TmBek62OPfPs00Kf/pacaK4RCLB+q/9MeD9FdiyPwYnz99G29dccS8xBX9fLtmkcO28cdW+lxHAPnsRAz1b4/1RnlgXdhhvBi5Dj45NYGZijOgzT/usVQPM/GCgXJ6MrFzcjHuE/CeK2+Q41bbGqi/fw3/mhGDK4i3YtOckXJ1s5TbDXLdA+QMJ077ditirJbcEpE+iFBUVwztgmewY724tMC2wX0V2gdZ4nWmPfaa98MOx+HFLJCaM6YU/g6ch+vQ1ZOc+gWfHprCxMsPJ87ew6Md9cnmsLEzRxN0RJkaGCuW1a+GGn78dj0s3EhCXmIKCwiK0aOSMpvUdkZmdh/emr8NlJfOD6jnZ4sjmmbh6Owl34pORk5uPRm4OaN2sHp4UFGLS17/g0IkrCvkqS40MhADgww8/xPfff49Tp07BzMxMIb1Dhw7w8vLCoUOH8N5772HXrl1wcnKSpefm5mLSpEmIi4tD7dq1ZY/Hl8XU1FRuc9aXzTfTfNCpdQOs3xaNmNjbKCwsgruLPT7x9cZ/3+kFI0PtLomhfdrB3dkey4MjcOL8LcRei4eDvRXGj+yB6YH9UMdO+R9/Y3cHHP1lFpYGHUDE0UvYHxULKwsTDOrVGlMD+ml0X7+qsM+0t3hqSZ9t2H4EMbF3ZH02eVxfTHxb+z4b4tUWbnXtsCLkD5y8cAsXr5f0WaDPG5gWoLrPrt95ILsdUFrp1xq7K34zrQ68zrTHPtPezGU7cOrCbYwf2QMerRrAwEAfd+OTsTL0D6z5JVJhp3h1rtxKxJbwGHRsWR89PZpBX18P8Q9SsXrzX1i9+RCSHqcrzZf0KB3BO46ic5sG6NauEYwMDfAgOR0hu45h9eZDuBn3qKKaq5Easfv8kSNH0L17d4X00NBQuSe2Su8+DwAPHjyAt7c3Ll68CBMTE3Tt2hVOTk5IT0/HiRMnkJKSAltbW+zevRtvvPGGXNnS3ef19fU13uxVupN9de0+T1SWqlq3oyapiN3niTRR3t3ndYlO7j6vzNixY9GyZUuV6Y6Ojjh16hR++OEHdO7cGRcuXMDWrVsRHR0NV1dXzJw5E5cvX1YIgoiIiKhmeKVHhHQFR4SoqnBESHscEaKqwhEhzXFEiIiIiEgDDISIiIhIZzEQIiIiIp3FQIiIiIh0FgMhIiIi0lkMhIiIiEhnMRAiIiIincVAiIiIiHQWAyEiIiLSWQyEiIiISGcxECIiIiKdxUCIiIiIdBYDISIiItJZDISIiIhIZzEQIiIiIp3FQIiIiIh0FgMhIiIi0lkMhIiIiEhnMRAiIiIincVAiIiIiHQWAyEiIiLSWQyEiIiISGcxECIiIiKdxUCIiIiIdBYDISIiItJZDISIiIhIZzEQIiIiIp3FQIiIiIh0lkF1V4DKJoQAAGRmZFRzTaimKy4W1V2FV46enqS6q0A6QhQ9qe4qvDKkfSX9/FSHgdArIDMzEwDQqH69aq4JERHRqyMzMxPW1tZqj5EITcIlqlbFxcVITEyEpaUlJJKX69tnRkYG6tWrh/v378PKyqq6q/NKYJ9pj32mPfaZ9thn2ntZ+0wIgczMTNStWxd6eupnAXFE6BWgp6cHFxeX6q6GWlZWVi/VH8GrgH2mPfaZ9thn2mOfae9l7LOyRoKkOFmaiIiIdBYDISIiItJZDISoXIyNjTF37lwYGxtXd1VeGewz7bHPtMc+0x77THs1oc84WZqIiIh0FkeEiIiISGcxECIiIiKdxUCIiIiIdBYDIR3QunVrSCQSGBsbIyUlReVxfn5+kEgkCj8mJiZo2LAhAgICcOnSJaV5Q0JClOY1NDSEk5MTBg0ahP379yvNe/fuXaV5zczM0LhxYwQGBuLixYsV0hfquLu7K63H8z8hISFK82zfvl1l2X369JHLO2/ePI3O9fzP+vXrIZFI4OzsjH///VdtewYMGACJRIL33nuvIrpHwfP9paenB0tLS7i4uKBXr16YNm0aYmJiVObv2bOnRm2eN2+eQt6kpCTMmDEDbdq0gaWlJYyMjFC3bl20bdsW//nPfxASEoKioiKV575y5Qo+/fRTtG3bFnZ2djA0NISdnR26dOmCmTNn4sqVKxXRRWWS9mHpa0oZaV+V7gvpNdSzZ0+1eaOiomR9+bzS/bxixQq15Xz44YeyYxs1aqT22KqkaR+WlpKSggULFqBz586wt7eHsbExnJyc0K9fP6xbtw4FBQUalXPq1ClMnDgRLVq0gI2NDYyMjFCnTh14enri66+/xr17916wVRXrRd+ngGefC35+fmrPIf0McHd3l3v9+ff3Xbt2qS1n4MCBsmP79OlTVtMqBBdUrOFOnz6N2NhYAMCTJ0+wadMmTJ48WW2ehg0bonv37rJ/Jycn48yZMwgODsamTZuwY8cODBo0SGlec3Nz+Pj4yP6dmZmJy5cvY9++fdi3bx9mzJiBxYsXqzz3iBEjYGFhAaDkwy4mJgZBQUHYuHEjtm7diuHDh2vc9hfVrVs3tW/0qtJmz56NoUOHwsCg7D+rNm3awNfXV+H1AwcO4OHDh2jdujXatGmjkN69e3cMGDAA4eHh+O9//4tff/1Vafnr1q3D77//DmdnZ6xatarM+pRH6f7Kzc1FcnIyzp07h6ioKCxbtgyenp4ICgpCgwYNlOZX1Vap59OOHz+OgQMHIi0tDRYWFvDw8ICDgwOysrJw8eJFrF+/HuvXr4ePj4/sWpIqLCzE9OnT8b///Q/FxcWwtbVFx44dYWdnh7S0NPz99984efIkvv32W3z//ff46KOPytU3r5Lg4GBMmTJFaVpeXh5++eWXKq5R5di5cyf8/f2RkZEBCwsLdOvWDba2trh//z4OHTqEiIgILF26FL/99huaN2+utIycnByMHz9e9vfn6OiI7t27w9raGsnJyYiJiUF0dDS++uorbN26FUOHDq3CFqqnzftUZQgKCsKwYcOUpiUkJCAiIqKKawRAUI32wQcfCADC2dlZABAtW7ZUeayvr68AIHx9fRXScnNzxahRowQA4eDgIAoKCuTSg4ODBQDh5uamtOzly5cLAEIikYjY2Fi5tDt37ggAAoC4c+eOXFpycrLo2rWrACDs7e1FTk6ORu1+EW5ubgKACA4O1jqPmZmZACDWrl2r9DgvLy+Nyvb09BQAxNy5c1Uek5iYKGxtbQUAsXXrVoX027dvCwsLCwFAREREaNwWbanrr+LiYrF//37RuHFj2TVz+/ZtuWM0aevz8vLyZNfyO++8I9LT0xWOuXLlipg+fbrSa2X06NECgLCyshLBwcGisLBQod4RERGiXbt2YvLkyRrX60Vpes0p66u5c+cKAMLT01Nt3sjISNnf1/Okr3fo0EEAEDExMUrL2Lx5swAgOnbsKACIhg0bltW0KqPN3+3OnTuFnp6eACA++eQTkZ2dLZceHx8v3nzzTQFA2NraKrwfCSHEkydPRPfu3QUA4eTkJH777TeFYwoKCkRYWJho1KiRWLFixQu2rOKU531K3edCaao+A6Tv7/r6+qJNmzbCwMBAJCUlKS1j4cKFcteZl5eXNs18Ybw1VoPl5OTIvrH8/PPPsLCwwMWLF3H69GmtyzIxMcHChQsBAA8fPsTly5e1yj9lyhS4uLhACIG//vpL43x2dnb47rvvAJSMTJ04cUKr81YV6SjbggULkJOTU6nncnJywpo1awAAEydORFJSkiytuLgYfn5+yMrKwsSJE+Ht7V2pdVFFIpFgwIABiImJQePGjfHw4UOMHz++3OUePXoUCQkJMDAwwLp165Qu6d+sWTN8++23MDU1lXs9KCgIW7duhaGhIf744w/4+flBX19fod7e3t44efIkRo8eXe76vioCAgIAlPSRMhs2bJA77lWUnJwMf39/FBcXY8qUKVixYgXMzMzkjnF2dsaePXvQtWtXpKamYuzYsQrlfPXVVzh69ChsbGxw7NgxDB48WOEYAwMDjBw5EufOnYOnp2eltUlbVfk+pUxAQAAKCwsRGhqqND04OBgmJiZ45513qrReDIRqsG3btiEjIwOvv/46evXqJXtjl76pacvR0VH2e2Fhodb5HRwcXihvq1atZL8/fPhQ6/NWhQEDBsDT0xNJSUllzrWoCKNHj8aYMWOQmpoqF2CsXLkS0dHRaNSokSyArE42NjZYuXIlAOCvv/7C33//Xa7ypP//LSwsYG5urnE+IYQskJ84cSI6deqk9nhDQ0N06dLlxSv6iunfvz8cHR2xZcsW5OXlyaXduXMHkZGR6NKlC5o1a1ZNNSy/1atXIz09HbVr18aiRYtUHmdkZCS7nXz06FEcPnxYlpaZmYnvv/8eAPDll1+ifv36as9pYWGBtm3bVkDtK0ZVv089791334WxsTGCg4MV0g4fPoybN29i2LBhsLGxqdJ6MRCqwZ7/Fif975YtW5Cbm6t1edKJr0ZGRlpPlkxPT8e1a9cAAC1atNAqb0ZGhux3aTD1MlqyZAkA4Ntvv1U7Kb2irF69Gk5OTggPD8e6detw5coVzJ49G3p6eggNDdUqUKhM/fv3h62tLQDgzz//LFdZrq6uAIC0tDStJsdevHgRt2/fBgClc7N0nYGBAcaNG4e0tDTs2LFDLi04OBhCiFd6NAgAdu/eDQAYNWoUTExM1B7btm1bvP766wCAPXv2yF6PjIxERkYGJBIJxo0bV2l1rUxV/T5Vmq2tLYYMGYJr167h2LFjcmnVOerIQKiGun79Oo4cOQJDQ0PZU0Ndu3ZFs2bNkJ6ervbJgeelpKRgz549CAwMBFBym0vTXX2zsrJw6tQpDBs2DFlZWejSpQvefPNNrdoifdqsdu3aL/W39E6dOmH48OHIyMiQjT5UJltbW6xfvx4AMHXqVIwZMwZ5eXmYPn06unbtWunn15REIkG7du0AQOtbqs/r2rWr7Bu2v78/PDw88MUXX2D37t2Ij49Xme/MmTMASoL40iOM9Iyy22PFxcWyoPpVvlVYUFAge2jEw8NDozwdO3YE8OzaKf17/fr1YWdnV8G1rBpV/T71POnnSOnrLCMjAzt27IC7uzu8vLyqvE4MhGoo6UU2ePBg1K5dW/a69M1O3e2x0NBQuccd7e3tMWTIEOTl5SE0NBTffPONyrxxcXFyeS0tLdG5c2ccP34cc+bMwcGDB5U+xqvMgwcPsGHDBkyfPh0mJiYIDg5WmPdRGfz9/dU+zp2WlqYy76JFi2BgYIA1a9YgLi6u0us6YMAA/Oc//0FWVhZiY2PRsmVLLFiwoNLPqy17e3sAUPoNdP78+Wr7+/z587Jj9fT0sH//fvTv3x9AyVORCxcuxLBhw1CvXj00bdoUS5YsURjxfPz4MYCS4LG6npZRp6xrrvTtmcrStGlTdOvWDZGRkbh79y6AkhG8e/fuYeTIkbC0tKz0OlSW1NRUFBcXA9B8VFl6nPTaKf17nTp1KriGVauq36dK69OnD1xdXREWFobs7GwAwK+//oqcnBzZo/pV7eV7R6ByKz0Z7flhxnHjxmHWrFmIjo7GrVu30LBhQ4X8zz8+n5WVhevXr+PixYuYOXMm7O3tMWDAAKXnfv7x+SdPnuD+/fs4ceIEli9fDhMTE8yaNUtl3ZXdc7e3t8eRI0fQsmVL9Q2vIGU9Pm9kZKQyrWnTpggICMC6deswZ84cbNy4sTKqKGf58uX46aefAJS8wamrX3WRfggpe5Mr6/F56W01KentwMuXL2PPnj04ceIEzp49i4SEBFy/fh0zZszAr7/+iqioqCqfa/CiyrrmpMsqVLaAgAAcO3YMwcHBmD9/vuwL1at+W+xFiBq8DWd1vE9J6enpwdfXF1999RXCwsLg7++PoKAg6OnplblWUWVhIFQD7d+/Hw8ePICzs7PCbSgHBwcMGDAAe/bsQVBQkNKh0e7duyudf7Fnzx4MHz4cgwYNwokTJ5QOMdvb2yvNe+PGDfTs2ROzZ8+GsbExpk6dqrTu0nWEioqKcP/+fRw9ehTJyckYNWoUjh07pvChWBnGjx9frj/IefPmYdOmTdi8eTOmTZtW6bdiSq+V8/y6OS+L5ORkAIpBDQAMHTpU6aKJZWnRooXcfLMrV65gzZo1WL16NS5cuIDZs2dj9erVACAbFU1NTUVRUZHC02LVraxrrmfPngqBkDSoLOsDW5sP9FGjRmHy5MkIDQ3FpEmT8Ntvv6Fx48Z44403NC7jZWRraws9PT0UFxdrHFA+evQIAORG1KW/S9NeZZq+T2l7nWkyouPv74+vv/4aQUFB8PDwQExMDPr06QM3NzctW1ExeGusBpLe9srLy4Onpye6d+8u9yO9V17W6rvPGzx4MIYMGYLi4mKtn0hq3LgxZsyYAQBqb60tXboUISEh+PnnnxEVFYVLly6hbt26uHr1KiZMmKDVOauLk5MTJk+ejOLiYsycObO6q1PthBA4d+4cAFTqqF7z5s3xww8/YNKkSQCeTY4FgPbt2wMoGaG8cOFCpdWhKkknw0tvL6iSlZUFQLMg2cLCAiNHjkRcXBwCAgKQn58Pf3//8le2mhkaGsquvVOnTmmUR/pwiPTaKf37nTt3qnyicUXT9H2qMq6z+vXro2fPnjh69Kjs3NU56shAqIZJSkpCeHg4gJL5GMeOHVP4kd7/T0xMxIEDB7QqX7o68ItsQSDNm5ycLBshKEuzZs1kw7bbtm3DkSNHtD5vdfj8889hZ2eH8PBwREdHV3d1qlV4eLhsO5CqWNdIeo7S11irVq1kt11VrWHyqpE+QXfr1i2139Zv3Lghd3xZpB9Ie/fuhb6+fo15ym7IkCEASt5Hnl8i4Hlnz56VTewvvU5Qr169YGlpCSFEld5OqiyavE9Jr5ubN2+qLas811mtWrVUrjZdFRgI1TDSUZ5OnTpBCKHy57PPPgOg/ZpCt27dAvBit2CkefX09LSa9Ozl5SV7M5ozZ47W560O1tbWsrlQ0r7WRenp6bJtG/r27at2LpAmNLnNI93fycXFRfaaRCKR/f9Yu3at2j3QgJJ5didPnixHTStfjx49YGBggLS0NLWLlEqfEO3du7dG5Xbv3h0dOnSAnZ0dhg8fjrp161ZIfavbRx99BCsrKzx+/FjtCMiTJ09ko4pdunSR28vNysoKH3/8MYCSRQnv3Lmj9pxZWVmy0dCXkSbvU9LrJjY2VmUwVFBQIFtmQNPrbMSIEXBzc4OdnR38/f3LXNKgMjEQqmGkkxvL+hYnXQNj3759ck9FqLN3717ZxS79dqWpGzduyNav6NOnj9Zr3CxatAh6eno4fPgwDh06pFXe6vLhhx/C1dUVp06demlXxK4sQgj8/vvv8PDwwI0bN+Dk5CSb0F0ee/fuxdChQ/Hnn38qva0bFRUlm280ZswYubTx48fDx8cHBQUF6Nu3L0JDQxXKkK583rVrV2zZsqXc9a1Mjo6Osr/zCRMm4Pr163LphYWFmDt3Lk6cOAETE5My9xgs7fTp00hOTkZYWFiF1rk61a5dGxs2bIBEIsHKlSvx6aefKqyunJCQgMGDB+P48eOwsbHBzz//rFDOl19+ia5duyItLQ3du3fH3r17FY4pKirCrl270L59+yp54q88ynqf6tChA7y8vCCEwHvvvSe3kj1Qsr/gxIkTERcXh9q1a8sejy+Lqakp7t69i+TkZCxbtqxC2vKiOFm6BpGuzGlsbKzwIfC8Fi1aoF27djh79iw2btwoN3n56NGjchM3s7KycOPGDdncIi8vL5WbMyYnJ8vlLf3UWFFREVxdXbF27Vqt29aiRQu899572LhxI+bOnVupa02sX78eUVFRKtO9vb01WgLe2NgYCxYsgJ+fX7UsZ19VSvdXfn4+kpOTcfbsWaSmpgIomegbFBSkciLk7t27ZbdrlWnXrp3sW3hxcTF+++03/Pbbb7C2tka7du3g6OiI7OxsXL9+HVevXgVQEmzPnj1boaxffvkFjo6OWL16Nfz8/DB16lR07NgRtra2SE9Px9mzZ5GUlAR9ff1qe4JFG99//z1u3bqFqKgotGjRAp06dYKbmxtycnIQExODxMREmJqaYuPGjS/VjvGV4auvvsKPP/6oMn3NmjXw8fHB1q1bERgYiBUrVmD9+vXo1q0batWqhYSEBBw/fhyFhYVo2LAhdu/erfSpWiMjI0RERCAwMBBhYWEYPHgwnJyc0L59e1hZWSElJQWnT59GamoqjI2Ny1x9urpp8j61adMmeHt749SpU2jQoAG6du0KJycnpKen48SJE0hJSYGtrS127NjxyjypKadKdjSjKjF27FgBQPj4+Gh0/MqVKwUA0bx5cyHEs831nv8xMDAQderUEX379hUhISGiqKhIoSzphnvP/0gkEmFtbS08PDzE119/rXSTTHWbrpZ29+5dYWxsLACIAwcOaNYpWpBuTFjWT+nNOKV5jhw5orTMoqIi0bJlS1neith0VRlp+ZGRkVrlKw9l/WVubi7q1q0rPD09xdSpU1Vu4inEs7aW9TNkyBBZntzcXBERESE+++wz0a1bN+Hm5iZMTEyEiYmJcHV1FUOHDhVbt24VxcXFaut++fJlMXnyZNG6dWthY2MjDAwMRK1atUSnTp3ErFmzxPXr1yuqm9Qqz6arUoWFhSI0NFT069dP1KlTRxgYGAgLCwvRokUL8fHHH4ubN2+qLFfax/fv39eovtINXF/GTVfL+in9t/H48WMxb9484eHhIWxtbYWhoaHsPW7t2rUiPz9fo3OfOHFCvP/++6J58+bCyspKGBgYCHt7e9GjRw+xcOFCER8fX0mt1k5FvE/l5OSIH374QfTs2VPY2dkJAwMDYWVlJdq2bStmzpypciPV0puuakr6eVJVm65KhKjBiyUQERERqcE5QkRERKSzGAgRERGRzmIgRERERDqLgRARERHpLAZCREREpLMYCBEREZHOYiBEREREOouBEBEREeksBkJERESksxgIEek4d3d3SCQSuR9jY2O4urpi9OjROHLkSHVXUWbevHmQSCSyjVWlQkJCIJFIXon9wVRR1TZ1oqKiIJFI5HZIr+o6lIefnx8kEglCQkKq5HxEyjAQIiIAQLdu3eDr6wtfX1/0798fxcXFCAsLg6enJ5YvX17d1asy0sBQ3UawRFRzcPd5IgIAjB8/Xm5EJS8vDx988AE2btyIzz77DG+99RaaNGlSfRVUY9iwYejcuTOsra2ruypE9IrhiBARKWViYoLVq1fD3NwcRUVF2LlzZ3VXSSVra2s0a9YMTk5O1V0VInrFMBAiIpUsLCzQtGlTAJC7VSSdSwQAwcHB6NKlC6ytrRVuKSUmJuLTTz9F8+bNYWZmBktLS3Ts2BGrVq1CYWGh0nPm5uZi3rx5aNy4MYyNjeHk5ARfX1/cu3dPZT3LmiOUkJCA6dOno2XLlrC0tIS5uTmaNGkCPz8/HD9+XK6MuLg4AED9+vXl5k1FRUXJlVlVbXtRBw8exKRJk9CmTRvY29vD2NgYLi4uGD16NE6fPl1m/ri4OIwbNw5OTk4wMTFBkyZNMG/ePOTm5qrMc/36dXzwwQdo2LAhTExMYG1tjR49emDTpk0V2TSiCsVbY0SkVkZGBgDA2NhYIW3SpElYs2YNunbtioEDB+L27duyACk6OhpDhw7Fv//+C3d3d/Tt2xf5+fmIiYnBpEmTsHfvXuzbtw+Ghoay8nJycuDl5YWTJ0/C3Nwc3t7eMDU1RUREBPbv34+BAwdqXf9Dhw7Bx8cHaWlpqFOnDry8vGBkZIS7d+/il19+AQB07doVjRo1gq+vL7Zv347s7GyMGDECFhYWsnIcHR1lv78sbVNnwoQJuH//Plq0aIFu3brBwMAAV69eRVhYGHbu3IktW7ZgxIgRSvPeuXMH7du3h4GBAXr06IHc3FxERkZi/vz5OHjwIA4ePAgTExO5PNu2bcO4ceOQl5eHZs2aYcCAAUhPT8epU6cwduxY/PXXXwgKCqrQNhJVCEFEOs3NzU0AEMHBwQppFy5cEHp6egKACAoKkr0OQAAQVlZW4sSJEwr5kpKShJ2dnZBIJGLNmjWiqKhIlpacnCx69+4tAIj58+fL5Zs2bZoAIJo1ayYSEhJkr2dnZ4shQ4bIzjt37ly5fMHBwQKA8PX1lXv93r17wtraWgAQM2bMEPn5+XLpDx8+FEeOHFHaH3fu3FHWXVXeNnUiIyMFAOHp6amQtmvXLpGamqr0dQMDA2FnZydycnLk0ubOnSurx5AhQ+TS79+/L5o0aSLry9JiY2OFsbGxMDExETt27JBLu3v3rmjZsqUAIEJDQ+XSfH19VV57RFWFgRCRjlMWCKWlpYn9+/eLhg0bCgCibt26IisrS5Yu/bBcsGCB0jI///xzAUB89NFHStPj4+OFoaGhqF27tiguLhZCCJGTkyMsLS0FAPH7778r5ElKShImJiZaBUKffPKJACAGDRqkQU+UKCsQquq2qaMuEFLn7bffFgDE/v375V6XBkKmpqYiKSlJId/evXtlAXBubq7s9dGjRwsAYunSpUrPFxMTIwCI9u3by73OQIheBpwjREQAAH9/f9l8GBsbGwwcOBC3bt1Cw4YNER4eDnNzc4U8Pj4+Ssvav38/AGD06NFK052dndG4cWM8fvwYN27cAACcPXsWmZmZsLe3R79+/RTyODo6wtvbW6s2HThwAADw/vvva5VPnZelbZpITEzETz/9hKlTp8qeCvTz88Ply5cBANeuXVOaz9vbW+5WoNRbb70FOzs7ZGRk4OzZswCA4uJi/P777wBU90mHDh1gYWGBc+fOIS8vryKaRlRhOEeIiACUrCPUqFEjAICRkRHq1KmDzp07o1+/fjAwUP5W4e7urvT127dvAwDeeOONMs/7+PFjNGnSBPHx8WrLBEomMGtDOvG5WbNmWuVT52VpW1nmz5+PhQsXoqCgQOUx0vlf2tTF3d0dKSkpsjalpKTIyqlXr16Z9UpJSYGzs3OZxxFVFQZCRARAcR0hTZiamip9vbi4GEDJiJGykaTS7OzstDpndXsV2rZz507MmzcPFhYWWLVqFXr37o26devC1NQUEokEs2bNwuLFiyGEeOFzSPNK+wMAfH19y8ynbNI9UXViIEREFa5evXq4ceMGPv/8c3To0EGjPNJRAnUrOmu72rOrqyuuXbuGq1evyka7yutlaZs6YWFhAICFCxcqvS0ovWWnyp07d1SmSevp4uICALC3t4epqSlyc3OxdOlS2Nvbv2CtiaoH5wgRUYXr378/gGcfyJpo3749LCwskJycjD/++EMh/eHDh0pfV0c6H+enn37SOI+RkREAqFwL6GVpmzqpqakAADc3N4W0R48e4c8//1Sb/48//sCjR48UXg8PD0dKSgosLS3Rvn17AIC+vj769u0LQLs+IXpZMBAiogo3ffp02NjYYPny5Vi2bBmePHmicMydO3fkFtozNTWVjV5MmTIFSUlJsrTc3FxMnDhR7WJ+ynz66aewtLTEnj178MUXXyjMl3n06BGOHj0q95p0pEM6ofhlbZs6zZs3BwCsW7dOrn7p6enw9fVFenq62vzK6pSYmIipU6cCKFmjqPQ6QnPnzoWRkRGmT5+O0NBQudtlUpcuXXqpVycnHVbdj60RUfVSt46QKnj6+Lw6hw8fFvb29gKAqFOnjujdu7d49913xVtvvSV7LL9Tp05yebKysoSHh4cAICwsLMSgQYPEyJEjhaOjo7CzsxPjxo3T6vF5IYSIiIiQPbru4OAghg4dKkaOHCk8PDyEoaGhQp5Vq1bJzj98+HARGBgoAgMDxdWrV6ulbeqoenz+9u3bwsbGRgAQzs7OYsSIEWLw4MHC2tpaODk5iYCAAKXnkj4+P27cOGFrayscHR3FyJEjxaBBg4S5ubkAILp06aKw/pAQQoSFhQkzMzMBQLi4uAhvb2/x7rvviv79+wsXFxcBQIwePVouDx+fp5cBR4SIqFL06NEDly9fxpw5c+Di4oLTp09j27ZtOH/+PBwcHDB37lyFW1bm5uaIjIzEnDlz4ODggIiICERHR8PLywtnzpx5oServL29cenSJUyePBk2NjY4cOAAfv/9d6SlpWHs2LGYMGGC3PETJ07E4sWL4ebmhvDwcGzYsAEbNmyQG8V5WdqmSv369XHu3Dm8++670NfXx759+3DhwgW8/fbbOHfuXJlPd9WvXx9nzpxBr169EB0djYiICDg5OeHLL7/EwYMHlU6SHzlyJC5fvowpU6bAxsYGx44dw44dO/DPP/+gUaNG+Oabb7Bw4cIKayNRRZEIUY7HBoiIiIheYRwRIiIiIp3FQIiIiIh0FgMhIiIi0lkMhIiIiEhnMRAiIiIincVAiIiIiHQWAyEiIiLSWQyEiIiISGcxECIiIiKdxUCIiIiIdBYDISIiItJZDISIiIhIZ/0/rObUxw+at68AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams.update({\"font.size\": 16})\n",
    "\n",
    "\n",
    "def plot_confusion_matrix(y_preds, y_true, labels):\n",
    "    cm = confusion_matrix(y_true, y_preds, normalize=\"true\")\n",
    "    fig, ax = plt.subplots(figsize=(6, 6))\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
    "    disp.plot(cmap=\"Blues\", values_format=\".2f\", ax=ax, colorbar=False)\n",
    "\n",
    "    plt.title(\"Normalized confusion matrix\")\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_confusion_matrix(predictions, labels, label_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6VLOJorP3fqJ",
   "metadata": {
    "id": "6VLOJorP3fqJ"
   },
   "source": [
    "# Let's get the class weights for the dataset using sklearn's `compute_class_weight` API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1YBHx2r26C_8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1YBHx2r26C_8",
    "outputId": "daf6b3bf-6f84-4f60-9fff-b7e3f52eed42"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([42.38095238,  0.59333333,  3.24225865,  0.6066803 ,  0.88822355,\n",
       "        0.82752208])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use the class_weights obtained using sklearn's compute_class_weight function\n",
    "from sklearn.utils import class_weight\n",
    "from collections import Counter\n",
    "\n",
    "y = dataset[\"train\"][\"coarse_label\"]\n",
    "class_weights = class_weight.compute_class_weight(\n",
    "    \"balanced\", classes=np.unique(y), y=np.asarray(y)\n",
    ")\n",
    "class_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "jOJA3_mxy-BE",
   "metadata": {
    "id": "jOJA3_mxy-BE"
   },
   "outputs": [],
   "source": [
    "class CustomTrainerWeighted(Trainer):\n",
    "    def compute_loss(self, model, inputs, return_outputs=False):\n",
    "        labels = inputs.get(\"labels\")\n",
    "        outputs = model(**inputs)\n",
    "        logits = outputs.get(\"logits\")\n",
    "        loss_fct = nn.CrossEntropyLoss(\n",
    "            weight=torch.from_numpy(class_weights).cuda(0).float()\n",
    "        )  # using gpu\n",
    "        loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))\n",
    "        return (loss, outputs) if return_outputs else loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "hyR6Bm3X37Aj",
   "metadata": {
    "id": "hyR6Bm3X37Aj"
   },
   "outputs": [],
   "source": [
    "modelWeighted = AutoModelForSequenceClassification.from_pretrained(\n",
    "    model_name, num_labels=6\n",
    ")\n",
    "\n",
    "trainerWeighted = CustomTrainerWeighted(\n",
    "    model=modelWeighted,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized_dataset[\"train\"],\n",
    "    eval_dataset=tokenized_dataset[\"dev\"],\n",
    "    tokenizer=tokenizer,\n",
    "    data_collator=data_collator,\n",
    "    compute_metrics=custom_compute_metrics,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "mtxQ5Km54Xaz",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 267
    },
    "id": "mtxQ5Km54Xaz",
    "outputId": "f42a8906-92a0-4a33-992f-71b99583b740"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='555' max='555' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [555/555 01:35, Epoch 4/5]\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>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.379123</td>\n",
       "      <td>0.749285</td>\n",
       "      <td>0.743340</td>\n",
       "      <td>0.745025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.320126</td>\n",
       "      <td>0.948300</td>\n",
       "      <td>0.823101</td>\n",
       "      <td>0.856919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.282939</td>\n",
       "      <td>0.946484</td>\n",
       "      <td>0.840759</td>\n",
       "      <td>0.865725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.302956</td>\n",
       "      <td>0.869508</td>\n",
       "      <td>0.837595</td>\n",
       "      <td>0.850697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.317800</td>\n",
       "      <td>0.297158</td>\n",
       "      <td>0.950919</td>\n",
       "      <td>0.839207</td>\n",
       "      <td>0.867263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=555, training_loss=0.2895190213177655, metrics={'train_runtime': 95.9037, 'train_samples_per_second': 185.603, 'train_steps_per_second': 5.787, 'total_flos': 97587828203040.0, 'train_loss': 0.2895190213177655, 'epoch': 5.0})"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainerWeighted.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "M5pjg6VQEAE8",
   "metadata": {
    "id": "M5pjg6VQEAE8"
   },
   "source": [
    "Our best F1-score is 0.773 at 7th epoch, which is a bit more than the score(=0.769) obtained without class weighting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fUjUijpz4YNl",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 243
    },
    "id": "fUjUijpz4YNl",
    "outputId": "eda5f61e-03e8-48b8-fba9-d1acdfeb148e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      1.00      0.88         7\n",
      "           1       0.93      0.94      0.93        93\n",
      "           2       0.96      0.95      0.96       140\n",
      "           3       0.98      0.98      0.98        65\n",
      "           4       0.99      0.98      0.98        82\n",
      "           5       0.98      0.98      0.98       113\n",
      "\n",
      "    accuracy                           0.96       500\n",
      "   macro avg       0.94      0.97      0.95       500\n",
      "weighted avg       0.96      0.96      0.96       500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions, labels, _ = trainerWeighted.predict(tokenized_dataset[\"test\"])\n",
    "predictions = np.argmax(predictions, axis=1)\n",
    "print(classification_report(predictions, labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8DQ0XKjnMWdm",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 582
    },
    "id": "8DQ0XKjnMWdm",
    "outputId": "976a28de-4e6c-40a3-8b44-e118262d036a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "plot_confusion_matrix(predictions, labels, label_names)"
   ]
  },
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   "execution_count": 32,
   "id": "a80sLObSWa0l",
   "metadata": {
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   },
   "outputs": [],
   "source": []
  }
 ],
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