QA_using_(fine_tuend)_BERT_and_DistilBERT.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f5vw9BwW6Q1p"
      },
      "source": [
        "## Related links for BERT-for-QA\n",
        "1. BERT uses SQuAD v1.1 dataset for training. \n",
        "2. [SQuAD Dataset Link](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/Super_Bowl_50.html?model=r-net+%20(ensemble)%20(Microsoft%20Research%20Asia)&version=1.1)\n",
        "3. [Our used model Link](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad?fbclid=IwAR0ahQVEOYymV9bbiIss2nsCJh6BXyMXAOyZERjrVXiyUL1RN1txftWYlhU)\n",
        "4. [The paper of the proposed Model](https://arxiv.org/abs/1810.04805)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uLYrrzY5-iG-"
      },
      "source": [
        "### BERT large model (uncased) whole word masking finetuned on SQuAD\n",
        "- BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data). More precisely, it was pretrained with two objectives:\n",
        "  - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. It allows the model to learn a bidirectional representation of the sentence.\n",
        "  - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not.\n",
        "- This model has the following configuration: 24-layer, 1024 hidden dimension, 16 attention heads, 336M parameters."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VHN1QC4pDLNa"
      },
      "source": [
        "### BERT I/P Format:\n",
        "- To feed BERT model, we pack both the question and the reference text into the input. \n",
        "- Tokens are separated as: **[CLS] [Question]...[Tokens] [Sep] [Context]...[Tokens]**\n",
        "- BERT also uses \"Segment Embeddings\" to differentiate the question from the reference text. These are simply two embeddings (for segments \"A\" and \"B\")\n",
        "<img src=\"https://github.com/SaiferGit/Hands-On-QA/blob/main/Snapshots/input_formatting.png\" width=700>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0dOXEDPjFKjz"
      },
      "source": [
        "### Start and End Token Classifier:\n",
        "\n",
        "- start token classification:\n",
        "\n",
        "<img src=\"https://github.com/SaiferGit/Hands-On-QA/blob/main/Snapshots/start_token_classification.png\" width=\"750\"> \n",
        "\n",
        "- end token classification:\n",
        "\n",
        "<img src=\"https://github.com/SaiferGit/Hands-On-QA/blob/main/Snapshots/end_token_classification.png\" width=\"500\" align='center'>\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "asgNV60UlGRD"
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      "source": [
        "!pip install transformers --quiet\n",
        "from IPython.display import display # for ipynb display\n",
        "from IPython.html import widgets # for ipynb display\n",
        "import torch\n",
        "import time"
      ],
      "execution_count": null,
      "outputs": []
    },
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            "8133b477b4314adbaade1e07efe5e590",
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            "042ee920718541f38d7195fc5de5ecfa",
            "add1f79214b948a99b7ed8d0d51e65c8",
            "8594d44852014e04abd11f4402691213",
            "fb0c7ef49f184c8c9167d609f87dd00f",
            "09df9a06f2a84df4bb25b866c651b897",
            "4ee331a340334bc89b84d04b02a85044",
            "2aaaf7a1ab5947899d5f25b43c5e4ffa",
            "793793e8226145c9893c40270d732a85"
          ]
        },
        "id": "FVhWwcVqra_Z",
        "outputId": "fcf89f97-e176-42d7-a5a8-84f530ed9230"
      },
      "source": [
        "# For Question Answering we use the BertForQuestionAnswering class from the transformers library.\n",
        "# This class is already pre-trained, we fine-tuned it with SQuAD Dataset v1.1, it's not outputting whether the question is \"impossible\" to answer from the text (which is part of the task in v2 of SQuAD).\n",
        "from transformers import BertForQuestionAnswering\n",
        "\n",
        "model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1340675298.0, style=ProgressStyle(descr…"
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        },
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          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j-86jvdwrc_D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164,
          "referenced_widgets": [
            "52f10c0e9d6f4f8fb924e0b1305b9a0a",
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        },
        "outputId": "6bb2a453-dfc0-427a-8994-dcd54030813b"
      },
      "source": [
        "# Load the tokenizer \n",
        "from transformers import BertTokenizer\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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          "output_type": "stream",
          "text": [
            "\n"
          ],
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        },
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          "metadata": {
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        {
          "output_type": "stream",
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            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dGmmUDWZX2Gs"
      },
      "source": [
        "### Asking a Question"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zAaACLxNre8f"
      },
      "source": [
        "question = \"What are the public holidays of Bangladesh?\"\n",
        "answer_text = \"including monitoring of sighting of the moon in accordance with the lunar Islamic calendar in order to set festival dates; as well as the charitable tradition of zakat. Public holidays include the Islamic observances of Eid-ul-Fitr, Eid-al-Adha, the Prophet's Birthday, Ashura and Shab-e-Barat.\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1F3cwQZqYR6U"
      },
      "source": [
        "We'll need to run the BERT tokenizer against both the `question` and the `answer_text`. To feed these into BERT, we actually concatenate them together and place the special [SEP] token in between."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "685jAmMSrhIv",
        "outputId": "98f6246e-3dde-45d2-d9cc-776079087dfd"
      },
      "source": [
        "# Apply the tokenizer to the input text, treating them as a text-pair.\n",
        "input_ids = tokenizer.encode(question, answer_text)\n",
        "\n",
        "print('The input has a total of {:} tokens.'.format(len(input_ids)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The input has a total of 84 tokens.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k9h11NEFY-ka"
      },
      "source": [
        "BERT only needs the token IDs, but for the purpose of inspecting the tokenizer's behavior, let's also get the token strings and display them. Printing out the tokens with their IDs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V7PC5Ez3rjfq",
        "outputId": "1c02371e-c088-4d51-eaa1-a086b0c04daa"
      },
      "source": [
        "tokens = tokenizer.convert_ids_to_tokens(input_ids)\n",
        "\n",
        "# For each token and its id...\n",
        "for token, id in zip(tokens, input_ids):\n",
        "    \n",
        "    # If this is the [SEP] token, add some space around it to make it stand out.\n",
        "    if id == tokenizer.sep_token_id:\n",
        "        print('')\n",
        "    \n",
        "    # Print the token string and its ID in two columns.\n",
        "    print('{:<10} {:>3,}'.format(token, id))\n",
        "\n",
        "    if id == tokenizer.sep_token_id:\n",
        "        print('')\n",
        "    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[CLS]      101\n",
            "what       2,054\n",
            "are        2,024\n",
            "the        1,996\n",
            "public     2,270\n",
            "holidays   11,938\n",
            "of         1,997\n",
            "bangladesh 7,269\n",
            "?          1,029\n",
            "\n",
            "[SEP]      102\n",
            "\n",
            "including  2,164\n",
            "monitoring 8,822\n",
            "of         1,997\n",
            "sighting   29,426\n",
            "of         1,997\n",
            "the        1,996\n",
            "moon       4,231\n",
            "in         1,999\n",
            "accordance 10,388\n",
            "with       2,007\n",
            "the        1,996\n",
            "lunar      11,926\n",
            "islamic    5,499\n",
            "calendar   8,094\n",
            "in         1,999\n",
            "order      2,344\n",
            "to         2,000\n",
            "set        2,275\n",
            "festival   2,782\n",
            "dates      5,246\n",
            ";          1,025\n",
            "as         2,004\n",
            "well       2,092\n",
            "as         2,004\n",
            "the        1,996\n",
            "charitable 11,128\n",
            "tradition  4,535\n",
            "of         1,997\n",
            "za         23,564\n",
            "##kat      24,498\n",
            ".          1,012\n",
            "public     2,270\n",
            "holidays   11,938\n",
            "include    2,421\n",
            "the        1,996\n",
            "islamic    5,499\n",
            "ob         27,885\n",
            "##ser      8,043\n",
            "##vances   26,711\n",
            "of         1,997\n",
            "e          1,041\n",
            "##id       3,593\n",
            "-          1,011\n",
            "ul         17,359\n",
            "-          1,011\n",
            "fit        4,906\n",
            "##r        2,099\n",
            ",          1,010\n",
            "e          1,041\n",
            "##id       3,593\n",
            "-          1,011\n",
            "al         2,632\n",
            "-          1,011\n",
            "ad         4,748\n",
            "##ha       3,270\n",
            ",          1,010\n",
            "the        1,996\n",
            "prophet    12,168\n",
            "'          1,005\n",
            "s          1,055\n",
            "birthday   5,798\n",
            ",          1,010\n",
            "ash        6,683\n",
            "##ura      4,648\n",
            "and        1,998\n",
            "sha        21,146\n",
            "##b        2,497\n",
            "-          1,011\n",
            "e          1,041\n",
            "-          1,011\n",
            "bar        3,347\n",
            "##at       4,017\n",
            ".          1,012\n",
            "\n",
            "[SEP]      102\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Dx05mExaUyy"
      },
      "source": [
        "We've concatenated the question and answer_text together, but BERT still needs a way to distinguish them.\n",
        "\n",
        "BERT has two special \"Segment\" embeddings: `Segment A` for Question tokens and `Segment B` for Contexts/answer_text tokens.\n",
        "\n",
        "Then before feeding this into with BERT layers, we first concatenate `Segment A` embeddings with Question tokens and then `Segment B` embeddings with answer_text tokens. These additions are already handeled by `transformer` library. We just need to specify '0' and '1' for each token.\n",
        "\n",
        "In the `transformers` library, huggingface likes to call these `token_type_ids`, but BERT paper uses `segment_ids` instead. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PCmufqIUrmSk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "983eb476-5ab0-4da9-ad61-bfcc7b350214"
      },
      "source": [
        "# Search the input_ids for the first instance of the `[SEP]` token.\n",
        "sep_index = input_ids.index(tokenizer.sep_token_id) # returning first index of [SEP] token\n",
        "\n",
        "# The number of segment A tokens includes the [SEP] token istelf.\n",
        "num_seg_a = sep_index + 1\n",
        "\n",
        "# The remainder are segment B.\n",
        "num_seg_b = len(input_ids) - num_seg_a\n",
        "\n",
        "# Construct the list of 0s and 1s.\n",
        "segment_ids = [0]*num_seg_a + [1]*num_seg_b\n",
        "print(segment_ids)\n",
        "\n",
        "# There should be a segment_id for every input token.\n",
        "# The assert keyword lets you test if a condition in your code returns True, if not, the program will raise an AssertionError. \n",
        "assert len(segment_ids) == len(input_ids), \"length does not match, your segment_ids length does not match with number of tokens\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ba8aeMXQdtCj"
      },
      "source": [
        "We're ready to feed our example into the model!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GtsO57d8roYP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b9bcab2-b763-42cb-d5e7-a38a7e257fc0"
      },
      "source": [
        "# Run our example through the model.\n",
        "outputs = model(torch.tensor([input_ids]), # The tokens representing our input text.\n",
        "                             token_type_ids=torch.tensor([segment_ids]), # The segment IDs to differentiate question from answer_text\n",
        "                             return_dict=True) \n",
        "\n",
        "# Span-start scores (before SoftMax)\n",
        "start_scores = outputs.start_logits \n",
        "print(start_scores)\n",
        "# Span-end scores (before SoftMax)\n",
        "end_scores = outputs.end_logits \n",
        "print(end_scores)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[-5.9127, -4.5799, -6.2805, -5.9091, -6.6841, -7.3693, -6.9432, -5.1894,\n",
            "         -8.6772, -5.9126, -6.2916, -4.7875, -8.0206, -4.9869, -8.3989, -8.2482,\n",
            "         -6.3489, -8.3123, -7.5380, -8.1773, -7.0380, -5.0207, -4.6673, -6.1177,\n",
            "         -8.3096, -8.0275, -7.6229, -6.4729, -4.0683, -6.1991, -8.1525, -7.7648,\n",
            "         -7.9924, -8.1408, -5.9551, -4.3948, -5.7733, -7.6992, -5.6835, -6.9369,\n",
            "         -5.9126,  2.3565, -2.4022,  0.4396,  4.6195,  5.3954, -0.2186, -3.2802,\n",
            "         -2.4137, -2.9397,  5.6666, -3.0419, -5.0751, -4.7666, -5.5023, -4.9921,\n",
            "         -4.9605, -3.7193, -2.0489, -5.2415, -6.4632, -5.3962, -7.2315, -5.8659,\n",
            "         -5.3283, -4.6710, -1.5938, -3.9851, -6.4203, -6.6945, -4.6286, -5.6478,\n",
            "         -1.6155, -5.6244, -5.0549, -3.4503, -5.7233, -6.8241, -6.1427, -7.0829,\n",
            "         -5.9187, -4.2400, -4.4023, -5.9126]], grad_fn=<SqueezeBackward1>)\n",
            "tensor([[-1.0389, -4.5786, -5.8303, -6.7499, -7.5687, -5.9852, -6.1440, -4.6431,\n",
            "         -6.7921, -1.0388, -7.8035, -6.3010, -7.7202, -6.4264, -7.8253, -8.0763,\n",
            "         -4.0808, -7.7710, -6.8791, -7.8981, -8.2485, -6.5338, -3.9477, -3.6808,\n",
            "         -7.8943, -7.1801, -8.0752, -7.4939, -5.0164, -3.4911, -5.5888, -7.5293,\n",
            "         -7.1128, -7.1311, -7.8551, -6.0179, -4.9693, -7.3436, -7.0932, -3.4196,\n",
            "         -1.0386, -4.9676, -3.7486, -5.2591, -3.1584, -0.8977, -3.0405, -3.1692,\n",
            "          3.3645, -0.9767, -3.0254, -2.0326, -3.9327, -2.1204, -4.0473, -2.7090,\n",
            "          0.7563,  0.1139, -3.2172, -1.9528, -3.2373, -1.7047, -4.2362, -2.7306,\n",
            "          2.3503,  1.2200, -4.5093, -3.4608, -6.5059, -3.7701,  0.6419,  0.4287,\n",
            "         -2.9122, -0.8477, -2.1598, -1.9419, -2.3344, -3.4563, -2.0552, -3.1890,\n",
            "         -1.6521,  6.4520,  4.7832, -1.0390]], grad_fn=<SqueezeBackward1>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "atnadyrxifMA"
      },
      "source": [
        "Now we can highlight the answer just by looking at the most probable start and end words."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p7u7FIYqrqnI",
        "outputId": "e674631a-db9a-41ed-e77a-e157608f5c01"
      },
      "source": [
        "# Find the tokens with the highest `start` and `end` scores.\n",
        "# torch.argmax Returns the indices of the maximum value of all elements in the input tensor.\n",
        "answer_start = torch.argmax(start_scores)\n",
        "answer_end = torch.argmax(end_scores)\n",
        "\n",
        "# Combine the tokens in the answer and print it out.\n",
        "answer = ' '.join(tokens[answer_start:answer_end+1])\n",
        "\n",
        "print('Answer: ' + answer)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Answer: e ##id - ul - fit ##r , e ##id - al - ad ##ha , the prophet ' s birthday , ash ##ura and sha ##b - e - bar ##at\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NvxUOhE3lJiW"
      },
      "source": [
        "Reconstructing any words that got broken down into subwords."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ayyx6Goyrs9E",
        "outputId": "00e27b3e-9435-4c72-a6f0-1e69b11a9e46"
      },
      "source": [
        "# Start with the first token.\n",
        "answer = tokens[answer_start]\n",
        "\n",
        "# Select the remaining answer tokens and join them with whitespace.\n",
        "for i in range(answer_start + 1, answer_end + 1):\n",
        "    \n",
        "    # If it's a subword token, then recombine it with the previous token.\n",
        "    if tokens[i][0:2] == '##':\n",
        "        answer += tokens[i][2:]\n",
        "    \n",
        "    # Otherwise, add a space then the token.\n",
        "    else:\n",
        "        answer += ' ' + tokens[i]\n",
        "\n",
        "print('Answer: \"' + answer + '\"')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Answer: \"eid - ul - fitr , eid - al - adha , the prophet ' s birthday , ashura and shab - e - barat\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uV3wqPsXnXx9"
      },
      "source": [
        "### Visualizing Scores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KIRWmakHrxBY"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Use plot styling from seaborn.\n",
        "sns.set(style='darkgrid')\n",
        "\n",
        "# Increase the plot size and font size.\n",
        "#sns.set(font_scale=1.5)\n",
        "plt.rcParams[\"figure.figsize\"] = (16,8)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zfTh4lAQry7m"
      },
      "source": [
        "# Pull the scores out of PyTorch Tensors and convert them to 1D numpy arrays.\n",
        "s_scores = start_scores.detach().numpy().flatten()\n",
        "e_scores = end_scores.detach().numpy().flatten()\n",
        "\n",
        "# We'll use the tokens as the x-axis labels. In order to do that, they all need\n",
        "# to be unique, so we'll add the token index to the end of each one.\n",
        "token_labels = []\n",
        "for (i, token) in enumerate(tokens):\n",
        "    token_labels.append('{:} - {:>2}'.format(token, i))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        },
        "id": "QWHkBKiEr1BV",
        "outputId": "05330aa4-f28f-4806-aa3f-39406383794b"
      },
      "source": [
        "# Create a barplot showing the start word score for all of the tokens.\n",
        "ax = sns.barplot(x=token_labels, y=s_scores, ci=None)\n",
        "\n",
        "# Turn the xlabels vertical.\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha=\"center\")\n",
        "\n",
        "# Turn on the vertical grid to help align words to scores.\n",
        "ax.grid(True)\n",
        "\n",
        "plt.title('Start Word Scores')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        },
        "id": "oKaaoyQUr3Ee",
        "outputId": "8b31bea7-89b6-47b0-9f12-9b4607728175"
      },
      "source": [
        "# Create a barplot showing the end word score for all of the tokens.\n",
        "ax = sns.barplot(x=token_labels, y=e_scores, ci=None)\n",
        "\n",
        "# Turn the xlabels vertical.\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha=\"center\")\n",
        "\n",
        "# Turn on the vertical grid to help align words to scores.\n",
        "ax.grid(True)\n",
        "\n",
        "plt.title('End Word Scores')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oHgAAgAKUl3dITz311Dj11FOzy07ZBQAAoCG+hxQAAIBU5OUd0rr+/Oc/t0SzAAAAFBDvkAIAAJAKgRQAAIBUCKQAAACkQiAFAAAgFQIpAAAAqRBIAQAASIVACgAAQCoEUgAAAFIhkAIAAJAKgRQAAIBUCKQAAACkoiTtAQAAHU/XbT8VW3au/TKktLRrreWKTFWUl61tzWEB0MoEUgCg1W3ZuSSG3TcrZ82M0YdGeSuNB4B0OGUXAACAVAikAAAApEIgBQAAIBUCKQAAAKkQSAEAAEiFQAoAAEAqBFIAAABSIZACAACQCoEUAACAVAikAAAApEIgBQAAIBUCKQAAAKkQSAEAAEiFQAoAAEAqBFIAAABSIZACAACQCoEUAACAVAikAAAApEIgBQAAIBUCKQAAAKkQSAEAAEiFQAoAAEAqBFIAAABSIZACAACQCoEUAACAVAikAAAApKIk7QEAAEBbtO22n47OnWu/f1Na2rXWciZTE2Vlq1tzWFBQBFIAANiMzp2L48nfLclZ881jS1tpNFCYnLILAABAKgRSAAAAUiGQAgAAkAqBFAAAgFTk5aJGy5cvj4suuij+/e9/R5cuXWLnnXeOq6++Orp165aP5gEAAChAeXmHtKioKE4++eSYNWtWPPTQQ/G5z30ufvrTn+ajaQAAAApUXgLptttuG/37988u9+nTJxYuXJiPpgEAAChQRUmSJPlssKamJk466aQYOHBgfPvb385n0wBAARl236yc62eMPrSVRgL18z2kFJrFNz1c77ruZw2uXTtlev21Zw7Py3jy8hnSjU2cODG22mqrGDt2bJO2W7p0VdTUbMjGpaVdY8mS8kZt2xK17aXNjt5/Ic4p7f4LcU5p91+Ic+ro/RfinFq7/9LSro3avr5t2+Kc2lr/hTin1u7f/bTw+y/EOeWqbcx9ev12TaltqO/i4qLYfvutN9tGXgPppEmT4p133olbb701iotdwBcAAID65S2Q/uxnP4t58+bFr371q+jSpUu+mgUAAKBA5SWQvvnmm/HLX/4yPv/5z8fRRx8dERE77bRTTJkyJR/NAwAAUIDyEki/9KUvxT/+8Y98NAUAAEAH4YOeAAAApEIgBQAAIBUCKQAAAKkQSAEAAEiFQAoAAEAqBFIAAABSIZACAACQCoEUAACAVAikAAAApEIgBQAAIBUCKQAAAKkQSAEAAEiFQAoAAEAqBFIAAABSIZACAACQipK0BwAAAO3ddtt+Oko6136vp7S0a/b3qkxNLC9b3drDgjZPIAUAgGYq6Vwcs+9cUu/6/Y8vbcXRQPvhlF0AAABSIZACAACQCoEUAACAVAikAAAApEIgBQAAIBUCKQAAAKkQSAEAAEiFQAoAAEAqBFIAAABSIZACAACQipK0BwAAQPNts+1WsUXnTrVuKy3tWmt5XaY6Vpatac1hAeQkkAIAFIAtOneKc6a9m7PmFyM+10qjAWgcp+wCAACQCoEUAACAVDhlFwAA2ES3z3w6OnWp/f5V3c8lV1fWxLIVq1tzWBQYgRQAANhEpy7F8fb1H+Ss+fx5n22l0VConLILAABAKgRSAAAAUiGQAgAAkAqfIQUAAJql22e2ik5dOtW6beMLIFVXVseyFWtae1i0AwIpAADQLJ26dIqF1y6qd32vi3q24mhoT5yyCwAAQCoEUgAAAFIhkAIAAJAKgRQAAIBUCKQAAACkQiAFAAAgFXkLpAsWLIgxY8bEoYceGmPGjIm33347X00DAABQgPIWSK+44oo49thjY9asWXHsscfG5Zdfnq+mAQDIo2223SpKS7tmfyKi1vI2226V8giBjqIkH40sXbo0XnvttZg6dWpERAwePDgmTpwYy5Yti27duuWjCwAA8mSLzp3iimkL611/1YherTgaoCPLyzukixYtih49ekSnTp0iIqJTp07RvXv3WLRoUT6aBwAAoAAVJUmSNLeRefPmxfe///2YOXNm9rZBgwbF5MmT4ytf+Uqj2kiqqqOopFOjapKqqigqqf/N3fXrG6prSu3G6xvffyaKSjo30P/HNQ3VbqirjKKSLg20+XFNTVVlFDdQu76modrG1n2SNuv+nqu2uqoyOjXQ//qahmobW9dStS3ZZkREVXVllHSqv3b9+obqmlLbEm1+kv4z1ZXRuYE219c0VLvx+sbWVlZXRpcG+l9f01DthrpMdOmU+/lkfU0+azfUVUWXTrmfT9fX5LO2Jdus+3v++q+OLp3q/3u2fn1DdS1V25Jt1v299edUE1065f5f+/qafNa2ZJt1f89Vm6lOonOnopz9r69pqHb9+qrqJEoaaHN9TVNqq6uT6JSjdv36huqaUrvx+pbov6Y6ieIctevX11QlUVySu831NfmsbYk2N65NqpIoylG7fn1DdbVra6KopP77/sbrG1vbUF1Taluizdq1ufPRhmzUlBzVuDbr/p6v/jcnL6fs9uzZMz788MOorq6OTp06RXV1dSxevDh69uzZ6DaKSjrFklvuyllTevrYWLKkPEpLu8aSW6fWXzf+xI3qbs3d5vjx2drFt15fb1338efFkiXlH29T2jU+vOWaemt7nD4h2+aimyfk7L/nGddka9+fcma9dTueOSVb9+9fjM7Z5n+cc1+29s2bhuWs/dJZM7K1824eWm/dHmc8mK37661Dcra59/iHsrUv/nJwvXX9T3u41j6d/ev6a/c/5eFsm3/+zRE5+x948sxs7azbBtVbd+i4R7J1M28/PGebR5z0aLZ2egO1wzeqvXfqYfXWHXniY9m6399xaM42jzlhVrb2tzlqv/1/dREf79PbfntIvbXjvv14ts1f3pm7/9OO39D/TXfXX3vWcRvqfv673G1+99gNtZP+kLv2+0dvqL36j/XXXj5mQ90l99a/7yMifnzkhv1/7v31194w6rFa+/TEafXXTh2xoc1BD+bu/5GhG2oPn/GdeuseHfb/NtRNPydnm48O/8WG/qdfnLv/4T/ZUDvtyvrrRly5UV39z3sf12547jti2uSctTNHXLih9oH6n3tnjjxvo7qbcrc58qwNtffX/9w/c9T4Wsf0iPt/k6P25Gybg++v/+9ORMTDozb87Rl832/rrxv97Y3q7s7d5ujjNqr9QwO1R29Ue2+OuiOzdUPum5azzYdGj8jWDr3vwXrrHhw9tNY+HXbfI/XWzhg9KFu7sdLSrpu9ff264fc9WW+b00d/MzvOEff/pd66iIhpow7I1o68//mctQ+M2jdbO+r+l+qtu39Uv2zdkfe/krPNe0ftma096v7X6627Z9Sutfbp0Q8sqLf2DyO/0OR92tja0tKu8dNpH+Tc9oIRn83O6ZYHPsxZe/rIHtna//fAknrrvjOyNFt3z/0f5WzzqFE7ZGtn3Ft/7bAjd6i1Tx/9Y/21h4/ZoUX36Qt3LM657YATum943XVb7tq9x22onffL+vf/Hqdt2PdvTMl9nL58Zo8Wnf8Hk9/Jue1nL9w5O9YPfvpm/XUXfKnWMf3gZ6/VX3v+7tk2P/z533P23+O7fTbUXl//Y7/HeRse+x/eMCd3m+d+fUPtL3I/T/U4Z8Pz1OIb63/u6372hue+xTfNytlm97MObdFjuvjm+v/uRER0P+PIetfl5ZTd7bffPnbbbbd4+OGHIyLi4Ycfjt12283nRwEAAKhXXt4hjYi48sor4+KLL46bb745ttlmm5g0aVK+mgYAAKAA5S2Q/ud//mfce2/ut2oBAABgvbx9DykAAAA0hUAKAABAKgRSAAAAUiGQAgAAkAqBFAAAgFQIpAAAAKRCIAUAACAVAikAAACpEEgBAABIhUAKAABAKgRSAAAAUiGQAgAAkAqBFAAAgFQIpAAAAKRCIAUAACAVAikAAACpEEgBAABIhUAKAABAKkrSHgAAAEBd1ZXV8dkLvpRzPe2fQAoAHVBFpioeGj2iwRqAtCxbsabWcmlp11iypDyl0dBSBFIA6IDKy9ZG3Zd1XuwB0NoEUgCgTavIVMX00d/MuR6A9kkgBQDatLrv5nonF6BwtJlAWl2ZidLTxzZYQ8uoqlwXe5zxYM71AB1VRSYTD486scEaAOhoqiurovsZRzZY06nL5qNnmwmkZeXroqamIrvsv5+ta/mKyoiozC7b/wAblJdVRHn4GwUAdS1bsbbW8ub+RhYXF8X222+92e19DykAAACpEEgBAABIhUAKAABAKgRSAAAAUiGQAgAAkAqBFAAAgFQIpAAAAKRCIAUAACAVAikAAACpEEgBAABIhUAKAABAKgRSAAAAUiGQAgAAkAqBFAAAgFSUpD0AAAAAPpnqyqroftahDda0VQIpAABAO7VsxdpNbist7RpLlpSnMJqmE0gBAGiTMpmaOGrUDg3WAO2XQAoAQJtUVrZ6k9va0zs/QMNc1AgAAIBUCKQAAACkotmn7F511VUxZ86c6NKlS2y11VZx6aWXxle/+tV8jA0AAIAC1ux3SA844IB46KGH4sEHH4zTTjstvvvd7+ZjXAAAABS4Zr9DevDBB2d/79OnT3zwwQdRU1MTxcXOBgYA6CgqMzVx+sgeDdZAmqorq6PHd/s0WEPryetVdu++++446KCDhFEAgA5mRZ0r4roaLm3RshVrai27n6avwUA6YsSIWLhw4WbXPf/889GpU6eIiJg5c2Y89NBDcffdd3+igWy//dab3FZa2vUTtdXUbRtb2xJttuX+W2P/d7R92l7b7Oj9F+KcOnr/7eU5sqVqc9V90nm19j5t7v6vrK6JB0btm7OusrqmQ91PW+I+3VKPk45yP813bSHMv7202Vq1+XicpH1fazCQTps2rcFG/vSnP8XPf/7zuOOOO2KHHXJ/eXF9li5dFTU1SXY5138rGjPBJUvKG70jGlu7fjxp9p/vNtfX1lXf/rdP237/9ql9qv/8H9O62uLfqKbU5hr7J33ub8l92tjalvi719h229v9tLG1ae/T+rbtKPfTtJ9P6yrE+1RrP/a7feZT0eOcA3LWVVdWxbIVa1vkcfJJ65rbZnFx0WbfgIzIwym7Tz31VPz4xz+OqVOnxk477dTc5gAAAArSshVray03JRAWqmYH0ksuuSQ6d+4c55xzTva2O+64I7bbbrvmNg0A0G5UZKrj3lF7NlgDwAbNDqQvvPBCPsYBANCulZetibrvc3j3A1pHdWV1fPb83XOup23K61V2AQAAWpur57Zfvp8FAACAVAikAAAApEIgBQAAIBU+QwoAAFBHdWV19DivX871NJ9ACgAAUIcLJbUOp+wCAACQCoEUAACAVAikAAAApEIgBQAAIBUCKQAAAKkQSAEAAEiFQAoAAEAqBFIAAABSUZL2AAAAqF9Fpjr+MPILOdfTNJlMTRw+Zoec64HWIZACALRh5WVronyj5dLSrrFkSXm99TSsrGx1rWX7FNLjlF0AAABSIZACAACQCoEUAACAVPgMKQAA0CqqK6vjsxfu3GANHYdACgAAtIplK9ZscpuLSnVsTtkFAAAgFd4hBQAKQkWmKqaNOqDBGgDaDoEUAMipIlMVD44emnN9W1BetjbqnvTnVECAtk0gBQByqhv0hDwA8sVnSAEAAEiFd0gBoIBUZKri4dFH5lzf0v3PGD0otf4BaF8EUgAoIGmfXpt2/+RXZaYmLhjx2QZrgPyrrqyK7md/M+f6QiCQAgCwWSvKVm9yWz7+yZDJ1MR3RpbmXA8d3bIVa2stF+o/+ARSAABaVVmdoFuoL7SBhrmoEQAAAKkQSAEAAEiFQAoAAEAqfIYUgA6rIpOJmaNOzrkeAGg5AikAHVZ5WUWUR0V22YVVgLakqrIm9h7XvcEa0lVdWRU9zv16gzVsnkAKkGcVmXXxyNDHGqwBgFyWr3A14vag7tezRDhWTSGQAuRZeVlllEdldtkfJQCAzXNRIwAAAFIhkAIAAJAKgRQAAIBU+AwpkHeVmYr47rGzGqwBAKBjE0iBvFtRlomI2t/f6MI+AADUJZACQAoqMpl4ePRxDdYA5FNVZU18+cweDdZAaxFIASAF5WUVUR61T113JgHQ0up+t2mE5x7S5aJGAAAApCJvgfTFF1+M3XbbLe666658NQkAAEABy0sgXbVqVfz0pz+NAw44IB/NASHlrEAAACAASURBVAAA0AHk5TOkP/nJT2LcuHHx9NNP56M5oI2qzFTEWcfV/3UuvsoFKEQVmeq4f1S/nOsB+GSaHUifeeaZKC8vj8MOO6xZgXT77bfe5LbS0q6fuL2mbNvY2pZosy333xr7v6Pt0/baZmNru3TeMkpLt8xLf59027a6T9va81lTagux/1x1LTGului/KdsWwnFpa/1vbMvOnWLLFjimrX0/TXuftlRtY7dLe06N3Tbt5560j2l7abOj99+UNhsMpCNGjIiFCxdudt1jjz0W1113XUydOrXRHdZn6dJVUVOTZJdzXe2rMRNcsqS80TuisbXrx5Nm//luc31tXfXtf/u07fffkvu0rqZcla8171Np79O62uLzWVNqC7H/XMejMffpfBzTprSb9uO0vbbZlvtP+37aGrX2aes+ntvL/Nta/4U4p7T731xdcXHRZt+AjGhEIJ02bVq96+bOnRtLliyJI488MiIili9fHk899VSUlZXFWWed1eBgAQA6oopMddwzatec6wE6gmadstu3b9+YM2dOdvniiy+OPfbYI8aOHdvsgUFblKlcF8NPerTBGgDIpbxsTWz8/kFT3qUAKCR5uagRdBRlKyojojK77AUEAAB8cnkNpD/5yU/y2RwAAAAFLC/fQwoAAABN5ZRdAADavUymJoYduUPO9UDbI5ACANDulZWtrrXsOg/QPjhlFwAAgFQIpAAAAKRCIAUAACAVAikAAACpEEgBAABIhavsAgBAK6nK1MSAE7o3WAMdhUAKAACtZHmdr6eJ8BU1dGztMpBWV2aidPyJOdcDAADQtrXLQLpsRUVEVGSX/VcJAACg/WmXgbQjq65cF/9xzn0N1gAAALR1Amk7s2xFZURU1rrNO8QAAEB75GtfAAAASIVACgAAQCoEUgAAAFIhkAIAAJAKgRQAAIBUCKQAAACkwte+AJCaikwmZo48L+f69qYik4mHR38753oA4GMCKQCpKS+riPKoyC4XwvcqF+KcAKClCKQAjbQusy6mjngs53oAABpPIAVopJVllRFRmV32zhcAQPO4qBEAAACpEEgBAABIhUAKAABAKgRSAAAAUuGiRgDQxlVkquLh0Uc3WAMA7Y1ACgBtXHnZ2tj4es6u8AxAoXDKLgAAAKkQSAEAAEiFQAoAAEAqBFIAAABSIZACAACQCoEUAACAVAikAAAApML3kAKkqCKzLh4d9v9yrgcAKFQCKUCKyssqozwqs8ulpV1jyZLyFEcEANB6nLILAABAKgRSAAAAUiGQAgAAkAqBFAAAgFQIpAAAAKSioK+yW12ZidLx4xusAQAAoPXlJZDeeeedcffdd0fnzp2juLg4ZsyYkY9mm23ZioqIqKh1m69UAAAAaBuaHUgff/zxeOyxx+K+++6LrbfeOj766KN8jAsAAIAC1+zPkN5+++1x1llnxdZbbx0RETvssEOzBwUAAEDha/Y7pG+99Va88sorccMNN0RlZWUcffTRcdRRRzW5ne2333qT20pLuzZ6+5aqbU4fSVUmep5xTc66pCrT6HZz1X3SOeXatiXabE4fLVGbjzZbYz81pba192lrPPaasm1r79O0j0tr9NOW71OfdNu2fEw7cv+FOKe0+y/EOaXdf3uaU1O2LcT5d+Q5pd1/U9psMJCOGDEiFi5cuNl1zz//fFRXV8eiRYvid7/7XSxfvjyOOeaY+MIXvhD9+vVr9CAiIpYuXRU1NUl2uSmf9cxHbWN22vrtmlK78WdY6x9nRRPbrK05c6qv3Xy02RL7tCX6b4n9tH5dvvtPe5/W1VqPvfX9p71P62rs/NvifmpKbVPb7LrtljFzxIU56yoymSgvq9jk9tZ+7mls/5+0rqVqC7H/QpxT2v0X4pzS7r8tz8lzX9tps6P3v7m64uKizb4BGdGIQDpt2rSc63v16hWDBw+O4uLi2H777WPfffeNV199tcmBFIDCUF5WEeWN+mccANDRNfszpIMHD47Zs2dHRMSaNWvi5Zdfjl133bXZAwMAAKCwNfszpCeccEJcdtllccQRR0RExLBhw+Ib3/hGswfW0VRXVsaOZ07JuR4AAKCQNDuQbrnlljF58uR8jKVDW7ZiXUSsyy47xQ0AACh0zT5lFwAAAD4JgRQAAIBUNPuUXahPVeW66H/awznXAwAAHZdASotZvqIyIjZcjMnnYgEAgI05ZRcAAIBUCKQAAACkwim7tCuZynUx8OSZDdYAAABtn0BKu1Lmc6kAAFAwBFIAAGjnqiprYo/TeuRcD22RQAodXGWmIk47flaDNQBA27V8xepay84io70QSKGDW1GWiYhMdtkfMAAAWour7AIAAJAKgRQAAIBUCKQAAACkwmdI/091ZSa6jz8v53oAAADyRyD9P8tWVETEhiuJFsKFXaoq18WXzprRYA0AAEAaBNICtnxFZURUZpcLIWQDAACFw2dIAQAASIV3SCFlmUxFHHPCrAZrAACg0AikkLKyskxE1L5oltOrAQDoCJyyCwAAQCoEUgAAAFIhkAIAAJAKgRQAAIBUuKgRQIGpyFTGI8N/0mBNoarIZGLmqPE51wMAbYNAClBgysvWRXmsyy7numpzRaYyHhlxZb1ttcfgWl5WEeWx4auSXLUaANougRRoNyozFXH5mPq/s7XS97U2WVPCKwBAvgmkQLuxos53tgpPAADtm4saAQAAkAqBFAAAgFQ4ZReANq8ik4mZI89qsAYAaF8EUgDavLpXzo3wGWIAKARO2QUAACAVAikAAACpcMou0KGty6yLG0Y9lnM9AAAtQyAFOrSVZZURUZldbqufS6zIVMajw3/RYA0AQHsikH4C1ZWV0eP0CTnXA+RTedm6KI/a79a21fAMANBYAuknsGzFuoiNXhh6UQgAANB0LmoEAABAKgRSAAAAUuGUXdqEqsp1sf8pD+dcDwAAFBaBlDZh+Yr2caVTAAAgfwRSaCGZynVx5In1f79lxru+AAB0cAIpTVJVuS72Hv9QgzVtQaZyXRw67pGc61tSmXd9AQAgJ4GUJql7am1E2w1aAiHkT0WmMh4ZUf/3L6+vAQBoimYH0gULFsTll18eK1eujMrKyhg0aFCcffbZ+RgbUEcmUxHfPmFWzvXtTWWmIr5/dP1zWl9DusrL1kV51D6rwD95AIDmanYgnTx5chx66KExduzYWL16dQwePDgOPPDA6N27dz7GB2ykrCwTEZnsciEEghUFOCcAABqn2d9DWlRUFOXlH794rKioiKKioujWrVuzBwYAAEBhK0qSJGlOA++//36MHz8+ysrKYuXKlXHRRRfFcccdl6/xQauYefvhOdcfcdKjrTSS/Lrtt4fUu27ctx9vxZG0vkvuPSzn+h8fWf8VkAEAaB0NnrI7YsSIWLhw4WbXPf/88/HHP/4xhg0bFieffHIsXrw4jj/++Nhjjz1izz33bNJAli5dFTU1G7JxU07ba4na9tJmR+8/H22WlnZt1Pb1bdtW92lj5tXe5tTY2rZ2TAthn+q/5dvs6P0X4pzS7r8Q55R2/4U4p47efyHOKe3+N1dXXFwU22+/9WbrGwyk06ZNy7n+zjvvjCeeeCIiIrp37x4DBgyIl156qcmBFNKSqVzX4DugvjO0fVmXWdfgO6DrMo4pAEDamn1Ro5122ilmz54dw4cPj1WrVsXLL78cAwcOzMfYoFXU/XqYCBfWae9WljmmAADtQbMD6Y9//OP44Q9/GLfffntUVVXFoEGD4sADD8zH2AAAAChgzQ6ke+yxR/zhD3/Ix1gAAADoQJr9tS8AAADwSTT7HVKgbarMVOT8apfKTEUrjgYAADYlkEKBWlGWiYhMdtlFfQAAaGucsgsAAEAqBFIAAABSIZACAACQCoEUAACAVAikAAAApEIgBQAAIBUCKQAAAKkQSAEAAEiFQAoAAEAqBFIAAABSIZACAACQCoEUAACAVAikAAAApEIgBQAAIBUCKQAAAKkQSAEAAEhFSdoDWK+4uKhRtzVl++bWtpc2O3r/hTintPsvxDml3X8hzqmj91+Ic0q7/0KcU9r9F+Kc0u6/EOfU0fsvxDml3X/dulzbFSVJkjR6BAAAAJAnTtkFAAAgFQIpAAAAqRBIAQAASIVACgAAQCoEUgAAAFIhkAIAAJAKgRQAAIBUCKQAAACkQiAFAAAgFQIpAAAAqRBIm2n16tVpDyGvVq5cWXBzimhfx2n58uVpDyE1zz//fNpDAGh1b731VtpDoMB05NcSEe3ndV9HP07rtalAunz58pg/f37Mnz+/yQfopptu2uS2hQsXxl//+teorKysdftzzz2X/b28vDwmTZoU1157baxevTp+85vfxNChQ+OCCy6IsrKyBvsdOnRogzX/+te/Yvr06fH66683WDt37tyYOnVqzJkzZ5N155xzTvzpT3+KqqqqBtv5n//5n1i4cGFERLz66qtx++23x+zZszdbu3Llyrjiiiti7733jv79+0ffvn3joIMOijvvvHOT2oULF8btt98eP/rRj+JHP/pR3H777fH+++83OJ717r///k1ue/DBB+PWW2/dZP/88pe/rLX8xhtvxJtvvhkREW+//XbccccdjQ4wjTlOERFJksSiRYuipqYmZ12u47Ry5cpG9bW+nSOOOCLGjRsX7777bgwZMiQOPvjg2G+//eJvf/tbrdpXX301fvjDH8YZZ5wR55xzTtxwww2xZMmSetueN29ePPHEE/HEE0/EvHnzGj2miM2/OHr55ZfjoYce2qTPadOm5WyrqqoqXnvttSgvL691+z//+c9Nfi655JJ466234p///GfONpvymFq9enX87//+b6xatarW7SNHjow77rgjli1b1mAbNTU18dhjj2WPyeOPPx4TJ06MP/zhD5vcVx555JE4/fTTY9iwYTFy5Mi46KKL6h3njBkzYvz48TFs2LAYNmxYjB8/PqZPnx5JkjQ4pvU295hqrKeeeiqeeeaZiPj4vvjDH/4w7r333kZte/PNN29y24oVK2ot33fffTFhwoS48847G5zTu+++G48//ngsWLBgk3WNfe5bs2ZN3HbbbTFz5syIiJg6dWqMHz8+rrvuuk1enGQymfjVr34VQ4cOjb59+0b//v3juOOOiz//+c+btLt27dqYNWtW3HHHHXHHHXfErFmzYs2aNTnHUlfd/isrK+OWW26Jyy67LJ5++ula6yZOnJizrbr7OZcHHnigUXVz5syJKVOmbDL/2bNnZ/tbuXJlTJgwIYYMGRIXXnjhJo+dSZMmxWuvvdZgX015PEU0/jG1bt26mDJlSgwePDj69u0bffv2jSFDhsSUKVOioqKiUfuh7uOpKcdp4cKFcfbZZ8e5554bS5Ysiauuuir23nvvOOaYY+K9997L1q1du3aTn1NOOSUqKipi7dq1Oce3YsWKmDt3bixdujRnXX3PexEfP+4zmUzO7dd766234tRTT43LL788VqxYEePHj4+99torxowZs8nfiab+jWrOa76IiJNPPrlWW5deemmcdNJJcffdd9eqO/vss2stN+d13+ae9zYnH6/7mnKcmvJaoinPfc19zRex+b9R7eH1+T/+8Y/s83Ymk4mbb745xo8fH9dff32jn0/qaspxWq+srCzmz58fb7zxRs5+m3usNpejKisr48MPP9zk9vWvxTfWnNfoERFFSVNe+bSQf//733HZZZfFa6+9Ft27d4+IiMWLF8fuu+8eV111VXz+859vsI2DDjqo1h+LBx98MK655pooLS2NVatWxc9+9rPYa6+9IiJixIgR2RfR5513XvTo0SPWrFkT77zzTvznf/5nDB8+PGbNmhUfffRRXHvttdk2v/71r2/S74oVK+Izn/lMRET2yeTss8+OG2+8MSIinnnmmbj00ktj7733jldeeSXOP//8GDZsWHb7ESNGxG233RbdunWLe++9N37zm9/EvvvuG3Pnzo3hw4fHuHHjsrUDBgyInj17xocffhhDhgyJUaNGxZe//OVNxnTTTTfFAw88EEVFRTFu3Lj47W9/GwMGDIiXX345Bg0aFKeffnqt+tNPPz169+4dBx54YDz00EOx3XbbxYABA+LGG2+Mr371q3HOOedERMS9994bN910U3zrW9+Knj17RkTEokWL4sknn4wzzzwzjjzyyCYfp8mTJ8ff/va32H333WPWrFkxbty4OOGEEzY5TnfeeWdMnTo1qqqqYty4cTFjxoz46le/Gi+++GIcf/zxcdxxxzX5OEV8HHpPO+20iPj4D+8pp5wSq1evjpKSkrjllluid+/eTT5Oe+yxR+y3334xevToOPjgg6NTp0717o/Ro0fHGWecEStXrozrr78+vv/978fhhx8eL7zwQvzsZz+Le+65JyIibr/99pgxY0b069cvnn322ejfv38UFxfHE088Edddd13ss88+2TZfffXVuPDCC6NLly61jtO6deti8uTJseeeezb5OE2dOjV+//vfxxe/+MV49dVX48orr4xDDjlkk+O0fv9+97vfjeLi4rjhhhvi2muvjVWrVsWyZcvihhtuiAEDBkRExK677ho77rhjraDy4YcfRo8ePaKoqCiefPLJ7O1NeUxdfvnlcd5550W3bt3i5ZdfjrPPPju22267WLZsWUyePDn222+/iIjYf//9o3fv3vHcc89lj9cBBxwQxcWb/p/u6quvjnnz5kVVVVXst99+8d///d/xzW9+M+bMmRNf/OIX4wc/+EFERFx77bXx5ptvxje+8Y144oknonfv3tGtW7f43e9+FxdddFEcdthh2TZ/+MMfxoIFC+Koo46qdZzuueee2HnnnePyyy9v8DjVPVbvvvtuXHbZZbFo0aIYOHBgnHfeebHFFltERMSYMWPij3/8Y3a766+/Pp577rmoqqqKAQMGxLx582L//feP2bNnxz777FPrRVzdF3gRETfeeGO2Zv3jb+P7wm233RaPP/54DBkyJP785z/HV77ylfje976X3X78+PFxzTXXRLdu3eLJJ5+MK664Inbffff4xz/+sckxbexz3/nnnx9VVVWxdu3a2GabbaKysjIOP/zwmD17dmQymfjpT3+arb3kkkuipKQkDjjggHj00Ufj85//fHzpS1+KX//61zFq1KjsnJ5++un4wQ9+EHvssUet4zRv3ryYOHFiHHzwwU0+ThEREyZMiLVr10bv3r3j/vvvj69//etx6aWXbrIfX3/99ZgwYUIUFxfHpEmTYtKkSfHiiy/GtttuG7feemvstttu2TbX/3NhY5deemn86Ec/ioiIAw88MHv7xveH6dOnx5QpU+Jb3/pWPPvsszFy5Mg48cQTIyJi8ODB8eCDD0ZxcXH84Ac/iK222ioGDx4cs2fPjtdeey2mTJmSbfNrX/tabLnlllFaWhojR46MoUOHxrbbbrvJmBr7eIpo2mPq/PPPj6222iqOPvro6NWrV0R8/CLtD3/4Q6xatSquv/76FjtOERGnnHJK7L///rFq1ap49NFHY/DgwTFq1Kh49NFHY86cOdkws+uuu0ZRUdFm/0lTVFQU8+fPzy5PnDgxLrvssoiI+Pvf/x5nnHFGfPazn42FCxfG5MmTY//994+Ixj/vRUTstttu8ZnPfCb7WNp1113r3R/HHXdcHHbYYVFeXh6PPvpoDB8+PIYPHx5PP/10TJ8+PfuP66b8jWrKa75cAf2www7L3ufPOeec2GmnnaJPnz7x+9//Pj796U/H9ddfHyUlJTF8+PCYPn16drvGvu5r7PNeRMu87mvKcWrsa4mIxj/35eM1X0T7fX0+ZMiQuP/++6NLly4xadKkeP/99+Pwww+PZ599NmpqauLHP/5xtrZ///7Z47Txc3JdTTlO77//flxxxRXx7LPPRlFRUWyzzTZRUVERxxxzTJx//vnRpUuXbG1LvD5/9tln47vf/W5ERHzuc5+Ln//857HzzjtHxKbPfU15jV6vpA0YM2ZMMmPGjKS6ujp7W3V1dTJ9+vTkqKOOyt42atSozf6MHDky2WOPPWq1OXTo0OSDDz5IkiRJXnjhheSggw5KZs+enSRJkgwbNixbN3jw4CRJkqSqqirZZ599kqqqqiRJkqSmpia7br3vfOc7yaWXXpq88847yXvvvZe8++67yQEHHJC89957yXvvvZet27j9sWPHJq+99lqSJEny3nvv1Vq3cf/r57d06dIkSZJk9erVm/S/ftt58+YlV199dbLPPvsko0aNSn73u98l5eXl2bpBgwYla9asST766KOkT58+yeLFi5MkSZLy8vLkiCOOSOqq28/6fV5RUZEccsgh2dsPOeSQ7Pg2tnTp0uS//uu/ssvnnHPOZn/OPvvspE+fPrW2PeKII5LKysokSZLko48+So466qjkxhtvrDXfJEmSIUOGJKtXr06WLFmS9O7dO1m0aFG277r7tLHHKUmSZPjw4dnfzzzzzGTatGlJkiTJX/7yl+S4447b7D5q6DgdcsghyR133JEMHTo02XfffZOf/OQnyZtvvrnJfqs7x4MPPrjedYcffniyZs2a7JxPPPHEJEmSZP78+cmIESM22W7u3Lmb9PXSSy8lQ4cOzS7fdddd9f7ss88+tbYdPHhw9j72z3/+Mzn00EOT+++/f5NxJkmSjB49Opk/f37y3//938k+++yTvPDCC0mSJMmrr76ajBkzJlt34403JieffHLy/vvv17sPNrcvGnpMDRkyJPv78ccfn7zyyitJkiTJv/71r1r7av12H330UXLbbbclRxxxRLLffvslkydPTv71r3/VanPQoEFJVVVVsmrVqmSvvfbK7ot169bVekwddthh2eex1atXJ2PHjk2SJEnef//9Te4nGz9mNlZdXb3JusY+pk466aTkrrvuSubNm5dcfPHFyZgxY5KVK1dusg+T5ONjWlVVlZSXlyd77rlnsnz58uy46451t912S0477bTk4osvzv706dMn+3vdfZokSTJy5Mjs42TNmjWbPPdsfJzGjBmTvPPOO0mSfHw8Nl63cbsNPfetH/e6deuSffbZJ1m3bl12n9ad0+GHH579PZPJJEcffXSSJElSVlaWHHroobXq3n777aSuBQsWJIcddlit255++ul6fwYMGFCrduPxrF27Njn99NOTSy65JKmpqam1H4877rjkiSeeSKZNm5YcdNBByYwZM5IkSZInn3wy+c53vlOrzV122SUZM2ZMMnbs2OzPV7/61WTs2LHJ8ccfv9l9miQfP+e/++67SZIkyfLly2uNbdCgQdnfN36+TJKk1vPJ+jYzmUwya9as5LTTTkv69OmTnHPOOclf/vKXpKamplabjXk8JUnTHlMb/72qa+N1Tfkb1djjVHd/fOMb36i1buP79MUXX5xMmDCh1n23vue+jff5SSedlDz33HNJkiTJa6+9lowePXqz7ed63kuSj4/T/Pnzk4kTJyb9+/dPRowYkdx1113Z54r65nTAAQds0s56Tfkb1djXfEny8X161113TXbZZZfsz/rlXXfddbPzr6mpSa688srkpJNOSioqKup93dXQ677GPu/V3Rf5et3XlOPU2NcSSdL4577GvuZLkqY9ptrL6/ONn4tGjBiRZDKZbP91/0YdfPDByY9+9KNkwIAByfDhw5M777wzKSsr22TfNeU4jR07NpkxY0ZSVlaW/Pa3v01uuOGG5KOPPkomTJiQXHnllbVqG3usmpKjRo4cmcyfPz9JkiR54IEHkoMPPji7vLnXXY19jV6fkoYja8srKyvb5K314uLiGDZsWNxyyy3Z295+++247rrr4lOf+lSt2iRJsil+49t69OgRER//5+LXv/51nHrqqXHZZZdFUVFRtq6k5ONd0KlTp+jZs2f23ayioqJN3im544474q677oqLL744rrjiithll12ipKQkdtxxx1p1G7e/atWq7H9L6tbF/2fvu8OiurqvF1VFsMYWY49KUAlWsCMRFQUsIASVYm8IsSuKRrBhiyV2xd4VRFE0KrElaDQmggRFEESNIAIqg0ib8/tjvnu/uTN3Zs4ZGMW8rOfh0bmzZ59zTy977wXwp/lVqlSBoaEhatWqBQAwMTFROj3l9LZp0wZt2rTB3LlzcenSJYSFhWHVqlX8db+RkRGqVKmCKlWqwMzMDHXq1AEAmJqa8u+rqPfNmzeoUaMGXrx4wZtMVapUSSAvlUr5/MmjZs2agrxeu3YNAQEBMDIyEsgRQnD79m2l33NytWvXxu7duzF58mQUFBQIylFfXx8mJiYwMTFBo0aNUL9+fQBArVq1BHIAfT0pIi0tDUOGDAEguz2Tv01hqScTExN4e3vD29sbcXFxCAsLw4gRI9C0aVO4urrCzc2Nly0pKUF2djYkEgnevn2Lp0+fokmTJsjOzhaYshgYGPDtvlq1ary5lrm5uZLJS35+Pjp27Kj0fp06dRKYeyxfvhxOTk5K5QdASScgaz8A0KJFC+zfvx9jxoxBYWGh0u+Lior4k9xq1arB2toaANCuXTtB+r6+vvjnn3/4U0kPDw/RvABsfaqgoID/f15eHn/L3axZM4H5E6ezdu3aGDNmDMaMGYO///4bp06dgpubG+7cucPLGhgYwMDAACYmJqhSpQpfFsbGxoJxQr6/6Onp8WbK3G2NIrh+J4+3b98qtSnaPpWVlcWfRK5YsQI7d+6El5cXQkNDlcrW0NAQBgYGMDU1RePGjfl8mJiYKN3q79mzB2vXrsWwYcP4m/Hbt28LToi5d5b/P9dPuH4jj4KCAkilUujr60MqlaJx48YAZPWh7dgnL08I4X+nr6+vpNPAwAAFBQWoVKkSJBIJ326qV68uKOfi4mL+VFgeTZs2RUlJieDZpEmT0LlzZ9GbL0WTXfnfVq5cGZs2bcKsWbMwe/ZsgdlqXl4evvvuOwDAhg0b+LnSzs4OGzduFOhcsWIFjh07htmzZ/Pt3s7O3as/4QAAIABJREFUTtT9Qr6uioqK8NVXXwEAatSoIairmjVrIjY2FpaWlqhTpw6ysrJQu3Zt5OXlKZnX6unpwdDQEP369UO/fv2QmZmJ8PBwLFu2DPn5+fxtFm1/Atj6lL6+Pp49e4ZGjRoJnqelpQnel2WOoq0nxTK1sLBQ+d2KFSvw66+/wsfHB35+fujVq5fKsU8er1+/Rrdu3QDIbs/kx2nacY/Li7m5ORYuXCjoS2vWrIGdnR3Wrl3LyxYXF6OgoAB5eXl49+4dX//5+fmCNFnmKNo1HwDUqVMHERERomsP+Rt/xbF98eLFCAkJwYQJEwT5BOjXfbTjHvdbDmW17mOpJ9q1BPfeNGMf7ZoPYOtTn8v6vFKlSnw5mpmZoaCgAIaGhiguLlYa+6tXr46AgADMnj0bV65cQVhYGNauXQtbW1u4urqie/fuANjq6e3bt3w/8fT0hKurK/z8/BAcHCywDAHo64plH1VcXMyv5YYOHYqGDRti8uTJ2LBhg9J4xbJGV4VysSGtUaMGIiMjMWjQID7jhBCcPXsW1apV4+UsLCxgamoquthW7ASAzN+F+/3XX3+N0NBQjBs3TuB/o6+vz3dMeZMOVf5Bo0aNQo8ePRAYGIguXbqILjyeP38Of39/EEKQkZGBwsJC/mpd0QdqxIgRmDBhAnx9fdGnTx8sWbIEgwYNwo0bN5QmNMW0jI2NMWjQIAwaNAjp6en8czMzMxw6dAgSiQQ1atRAaGgohgwZguvXr6Ny5cpK+fX29oazszMsLCwQFxfHmyO9fv1aMOn36NED48aNg5ubm8Ac6vjx43xnA2QTpbm5OT8hymPDhg2Cz6ampkhLS+MXo6ampti5cycmTpyIxMREXk5+4p8xY4ZAh5iPBU09AUB2djZvlqNoGiT/G5Z6kke7du3Qrl07zJ8/HxcvXkR4eLhgQ+rt7Q17e3sAwJIlSzB37lxUr14d8fHxAv8YCwsLBAYGokePHrh48SLfBwoKCpTe/6uvvsK2bdvw/fff85uMN2/e4MiRI4L6bNGiBcaPH48WLVoo5VvR7t/IyAiZmZn84UbdunWxb98+jBkzBk+fPhXIyteVk5OT4DvFQdzCwgL79+/Hxo0b4ePjo9JfhqVPde3aFStXroS/vz+sra1x/vx5DBw4EL/99ptg8yfWJqysrGBlZSUwGQSABg0a8H4sLVq04DfzN27cwBdffCFIe8KECejWrRuio6N5c06JRKL07uPGjcPgwYNhb2/P18uLFy9w5coVTJkyRSBL26cUF17jx49H5cqV4eXlpdS+pVIpv2lbvny5oFwUy9Ta2hp79uzBypUrERUVpbRw4JCYmIiuXbuCEIK8vDxkZ2ejVq1aohO4g4MDZs6ciZkzZ6Jv3768T9P169eVFge0Y1/r1q3xww8/4MOHD+jRowfmzZsHe3t73Lx5E82aNRPocHR0hJubGzp16oTffvuN38i/fv1aINemTRssWrQI7u7ugnHv2LFjSqZZTZo0wbJly5Q2RIBw8QwAX3zxBR4+fMhP+AYGBli7di3mzp0r8M+Rf3f5cRaA0oZo6NCh6NatGwIDA9GqVSv4+fmpXAykpKTA1dUVhBCkpaVBIpHwG0P5frhgwQJMmzYNnTp1Qp06deDm5gYbGxvExsYKxijFvAKyzcSECRMwYcIE3Lt3j39O258Atj41e/ZseHh4oG3btnwbevHiBR48eICgoCBejmWOoq0nQLZh5cpxx44d/POcnBylQ54+ffrAysoKwcHBOH/+vNK7cMjIyMCqVatACMHbt29RUlLC65Kvf9pxDxDWk5GREQYOHIiBAwciPT1dsA4CZGO4g4MDiouLMW3aNPj5+aF169b4888/+YMSgG2Ool3zAbKx5/Hjx/zBpjzk669Ro0a4c+cOOnfuzD+bO3cu1q1bh507dwp+R7vuox33AN2s+1jqiXYtAdCPfbRrPoCtTwGfx/p81qxZGD16NIYOHQpzc3OMHj0atra2uHXrFn95oQgjIyMMGDAAAwYMQEZGBsLDwxEcHIwLFy4AYKsnQ0NDfn384MEDPp/6+vpKB7y0dcWyjyopKeHLHwC6dOmCdevWwd/fX2mtwbpGFwXVPaqOkZKSQry8vEjnzp2Jo6MjcXR0JJ07dyaenp4kOTmZl0tPTxeYuMhD3vSDEEKOHz9O/vjjDyW5tLQ04ufnx39+9eoVbwYgj5cvX5Lff/9dZZ6lUinZuXMnmThxotJ3YWFhgj/OFC49PZ2sW7dOSf7ixYvk+++/J507dyYdOnQgjo6OZPv27eTDhw8CuQsXLqjMjzxSUlLIlClTiK+vL3nx4gVZt24dsbKyIk5OTiQ+Pl70N0lJSSQqKoqkpKSo1FtSUkLCw8PJhAkT+HqaMGECCQsLE5R/QkICbyasCEWT2Xv37omasxYUFJCDBw/yn0+dOkUkEolovpcvX64yz+rqiRAiMMWZN28eycjIIITI6srHx0cgS1tPtOYJHHJyckh2djYhRGZWfeHCBfLgwQOBTF5eHlm3bh2ZMGEC2bBhA8nPz+flORMKDq9fvybz588nVlZWgr958+aRzMxMXi4iIkKlKXFkZKTg8+XLl3kTMHlkZ2eTkJAQwbMlS5aI9tPk5GSBGbQi/vrrL7J9+3bR71j6VEFBAQkODiadOnUiffv2Ja1btyZt2rQhY8aMIWlpabzcvXv3VOZFEVlZWWTZsmVk+fLl5M2bN+To0aPE0dGRTJo0SaCzpKSEHDt2jCxZsoScPHmSN1MsKCggr1+/VtKblpZGdu3aRYKDg0lwcDDZtWuXQB+HhIQEvm0qQr5PTZs2jURHRyvJHDlyRGDeRojMLJ0zsZNHSkoK2bVrl4qSkJmlcuboYnmR/+NMZrOyssgvv/wikJVKpSQ0NJT07NmTtGvXjrRu3Zp06NCBBAYG8v2BA+3Yl5+fT/bu3Uv27dtHCgoKSHR0NJk4cSIJDg5W0kkIITdu3CC7du0iMTExanX+/PPPZNCgQaR9+/akQ4cOZNCgQWTTpk1K5bd7924SFxcnqkexTFNSUpTGQ0Jk5XL16lX+85QpU0T708uXL5XMG+Vx+PBhMnToUCXTUQ63b98W/HHj66tXrwRjLyEyk+vjx4+T5cuXk+DgYLJ7927y77//Kuncv3+/yvzIg7Y/EcLep/Ly8khUVBQJDQ0loaGhJCoqSmnuYJmjaOuJe6bqfR89eiT6HSGEnDt3TskEj8OmTZsEf9w7p6enk7lz5/JytOMeIYTMmTNHZV7EkJCQwM8zL168ILt37yYXL14UyLDMUbRrPhbk5OSImkgSQpTmOW3WferGPUJ0s+5jrSeatQSHmzdvahz7aNd8hLD1qc9pfc49nzp1Kpk0aRIJCgoSXTuwrPto6+nXX38l1tbWxNHRkVhbW/PvnJmZSRYsWCCQpa0rln3U5s2byc2bN5Xk7t+/T1xcXATPtF2jy6NcBDXikJ2djZcvXwKQnaCKXT9XoALlHUVFRaI39p8CXCQ6scAinwJEzoxS13j//j3S0tIglUrRoEED1KxZ86Ok+ynBDediZZyXl4eqVauWSTq5ublITEwUPWXVBtxtFxeA4n8Vv//+O2+SqQnv379Hfn4+ateurVLm+fPn+Pvvv+Ho6Eilk7aNsLSlsmx3utT5X8HnNO5pu+YTc3VQRF5eHlJTU9GkSRP+5r+0KOtxT9egKacKlA1evHih0S1MG7x79w5Pnz5Fs2bNyqwdl1eUK9qXWrVq8T5CFZvRzwvyobrVgYWigossWFZps+gE6HnhFOXUbUZZeLEUzTdUQV2Z1qhRQzAhKdJFsIT0pqXnUQd5Spxnz57Bx8cH/fv3R0hIiMAExN3dXem3rLQzJiYmMDc3h4WFheiijIUmAKALUw/Ql1NRUREOHjyIQ4cOobi4mKe2+Omnn0R9eGkoWvT09ASbUXnKHbEFPAvtS25uLt9+pFIpsrOz8eTJEyUZbcL0m5qaomrVqqL0QKx6ad+pNJQCtBGQAdU0EaWhPQoLC4OJiYnazSggM99PS0ujzistPRatHIssF+28NDrlx62ioiJs2LAB3t7eWLlypdporaoobwBlGorZs2ejb9++mDZtmpKJI60si05FaKIT0TTuseqUL9PCwsIyKVMO3JqvadOmePnypShFzZYtW3h/1KSkJNjb28PW1ha2traIi4vj5RYtWsTTEP3555+wt7fHnDlzeJN9eZw6dYqXTU9Ph7e3Nzp06IARI0ao7S+PHj1CbGysKN1baShCVNG+KPoVRkREqBzP1JWTIu0b7fuz0M4cPHiQikKNRZaF6pBWVhf5BMR9UMXAQjcHyPyx27Vrp3EzylFp/f333wBUU2llZmYiKCgIO3bsQHFxMYKDg+Hk5IQZM2bg1atXojpp6LlYqbzEUK5uSCvw+UIxXHRp5T61zo+VPm1IexadDx48wPTp05GRkQFbW1v8+OOP/AGPfKhulpDeq1atwt9//62Rnocl/bFjx8LOzg5WVlY4ePAgUlJSsHPnTpiZmSmF6WehnaGlPmGhCaANU09LYwTIFlBv3rzBhw8fYGpqiqKiIjg4OODKlSuoUaOG4PCElqKFlnKHRScg44EMDAyEvr4+goKCsHXrVtStWxcPHz7EwoUL+QALLGH6WfJKq5flnWh1yueZw4kTJ/gw+nPmzOGfs9BE0NIesVC5sKRvY2OjdJMuRpPAQqNFq9Pf319J5/Xr19GrVy8AQp8zlvTl+9i6deuQlJSE4cOH4+LFizAyMuJ5Q2kpbxR1BgUFQSqVYsSIETh37hyePn0qoJKhlWXRSUtTER4ejqFDhwKQtaPp06cjNjYWrVq1wrp16wRUKqwUJWVdpiwUNU5OTjh79iwA2aGFq6sr7O3tcefOHaxduxZHjx4FIDukOHPmDADAy8sLs2bNgqWlJVJSUjBz5kwBH6+joyMiIyMByMYBKysr3n/99OnT2Lt3L//utHRvLBQhtHqdnZ1x+PBhmJqaYuvWrbh+/TpPj9SqVSvMnTuXuZxY3p+FdsbS0hIGBgbo2rUrXF1dYWtrK0qhxiJLS/fFIiuftouLC/r06UOVT02ytLQvLHRznE5XV1e1ZQ/QU2lxcUM4C4LWrVvD2dkZ165dQ3x8PLZt20als1mzZoI1CousKpSLoEYV+DwgtjADZGaC8rcaYosNTk7xhk5ssSGmkzZtFp2A+AKOg/xmkVYOEF9AclB0BG/fvr0SH51ihFCArUyXL1+OBQsW8Bu9kSNHIjQ0FA0aNBCk89NPP+HAgQMwNzdHeHg4Ro8ejS1btsDc3FwpGMD169cRHh4OIyMjTJ48GVOmTIFEIoGvr6+SLG36LBFhw8LCEBYWBlNTUyQnJ2Pq1KmQSCQYNmyYUvo//vgj7O3t+fS9vb35ja58+aempvJRSu3t7REUFISJEyeK3mjt3r0bERERqFevHm7fvo0ZM2YgODgYPXr0UIowTVtOf/31F86ePYvCwkJ07doVv/32GypXroy+ffvCxcVFIHvlyhWcPn0a+fn56NGjB65evYoaNWpg1KhRcHd35zcc69atw969e5GbmwtfX19s3LgR1tbWiIuLw7JlywSLElqdALBz505ERUUhLy8Pw4YNw/Hjx9GyZUs8e/YMP/zwA78hTU5Oxvr161FSUoJu3bphz549MDAwgKWlpdKtFkteafWyvBOtzsOHD6Nv375KfNgmJiZK7WTZsmXo1auX4GaqoKBA6YYCkEWZvn//PpYsWcIHobCzs1O6UZo4cSKsrKwElhfv3r3Drl27oKenJ9iQsqRvbm6Or776ChMmTICBgQEIIRg5ciQOHz4skGvdujWVHIvOu3fvonfv3nwAGvL/InHa2toq6WRJX76P3bhxAwcPHkTVqlXRs2dPQRAS+XHgyJEj2LNnD7766itMnDgRnp6egs2TvM4///wTJ0+ehJGREVq1aqUUtI1WlkXns2fP+P/v2rULO3fuxDfffIMXL15g6tSp/OZx//79/IZ07dq16NmzJ3bu3InIyEgsW7ZMENiHVqeuyvTvv//mDyk3bNiAbdu2CTaP8htS+RvCzMxMPiBM586dBTePLFGG5W/R5A8AhgwZwm/GODkun8eOHcORI0dQq1YtvH//Hu7u7oINKSGEDzpz+/ZtHD9+HIaGhhgwYICgPFn0EkL4m7FLly7hwIEDqFq1Kry9vTFs2DDBhpS2nFjev3Xr1li5ciVOnjwJHx8ffPnll3BxcYGzszPMzMwEOps3b85z0f70009YtGgRnJ2d4eLiohQ4kVa2fv36CA8PR3x8PMLCwuDp6YlGjRrBxcUFTk5OgltDWln5tNevX4/FixdT5VOTbNWqVaGvr48xY8agfv36fLqKbii1a9fG5s2bkZWVhYiICKxZswaBgYEYPHgwXFxcBMH3OJ2jR49WqxOQtbkzZ87gw4cP6NmzJ65fvw5TU1O+rXDIyMjAzp07IZVK0bNnTz4Cu9gcTauTVVYVKjakFaDGgQMHMG7cOKVogYDQZ401/PfevXuVBjdCCDw8PJjTZtEJ0FOfsFCksFA/0Ia0ZynT9+/f8ws7X19fNGvWDN7e3ti9e7eSOSdtSG+Ajp6HJX2WiLAAPe0M7UaXhSaAUIapZyknLkqesbExGjVqxEfANjY2VmrntBQttJQ7LDo5cAT29evXR8uWLQHIbtblo4OyhOlnzSuNXpZ3otUZFhaGxYsXo02bNvDx8YGenh7CwsLg6+urVEYsNBG0tEcsVC4s6dPSJLDQaNHKnj17FkuWLEF8fDxmzpyJKlWq4Oeff+Y3VNqmTwjBhw8fQAiBgYEBb6ZuaGioRB/DQR3lDSAb35OTk/lDQvkxWLFN08qy6KSlqZCfbx4+fIiQkBDo6enB3d1d6UCVhfpCF2XKsnls27YtDhw4AE9PT3zzzTe4d+8eOnTogKSkJEG5sUQZbty4MaKjo2FnZ4fGjRsjNTUVTZs2VXIHYaF7Y6EIYdHLRSo3MTHhLX2MjIyUdNKWE8v7s9DOcDRfo0ePxujRoxEbG4tTp07h+++/R4sWLQQHjLSyLHRftLK6yCdAT/vCQjdHqxOgp9IqKSnh+/T79+95f/zi4mIlc2cWei4WWVWo2JBWgBqtWrVC//79RU0H5H0aWMJ/t23bFjk5OaI6uQ0AS9osOgF66hMWihQW6gfakPYsZVpQUCCgBhg0aBCMjY3h4+MjGHBYQnrT0vOwpN+yZUv8+uuvPI0DIOPaMjIywpIlSwQ6WWhnaDe6LDQBAF2YepZy0tPTQ3FxMQwNDQUUEYWFhUqLDVqKFhbKHRbaF/nfent7q/yOJUw/S15p9bK8E63O5s2bY9++fdixYwe8vLzw448/qgzMxUITAdDRHrFQubCmT0uPRStHK1urVi1s2LABZ86cwahRozB79uwyyeejR4/Qvn17vg1wJtAc5y0HWsobAPjw4QMmTJjAp8nplEgkSgstWlkWnbQ0FRKJBNeuXQMhBCUlJUqcwNro1FWZsmweFy1ahHnz5mHv3r2oV68evLy80KBBA1SpUkVw0BIQEIBVq1ahV69ePN3dnDlzYG1tLRgLAGDx4sXw9fXFnj17UL16dQwfPhxt2rTBy5cvBaaFLHRvLBQhtHonT54MLy8vjBkzBp06dYKfnx/69++P3377jTdvZy0nlveX72eaaGcU+6SlpSUsLS0xf/58XLp0SStZRTlVdF8ssrrIpzw00b6IjV2q6OZodQL0VFrdunWDh4cHCgsL4ebmhh9++AE2NjaIiYmBlZWVIF0Wei4WWZWgisVbgQoQWZhwMUoKQgi5e/cu/3+W8N8FBQWiYb21TZtFJyH01CcsFCks1A+0YCnTJUuWKNEREEJIdHQ0sbGx4T+zhPSmpedhSV8qlaqkSVAMH85CO0NLfcJCE0Abpp6lnBISEnhaFEWdERERgme0FC0slDsstC9bt24V1ZuUlET8/f35zyxh+lnySquX5Z20oRRISEggw4cPF7RjVdBEE6EIdbRHHDRRuWibviZ6LFY5Ftn09HQyYcIE0qVLlzLTqYi3b9+Sv/76i//MQnmjCu/fv1c5J2krKyZHS1MxatQowV96ejohREYDNmzYMK10qkNpypSFooZDamoquXz5Mvnll19Uzq+EyOhnEhISSHx8vCjVkzx+++03sm/fPrJnzx5y4cIF0bGDlu6NEHqKEBa9sbGxZObMmWTIkCHEycmJTJw4kZw9e1bl3ElbTjTvz0I7s2bNmjKXpaX7YpHVRT4Joad9YaGbY6GSoaXSkkql5NKlS+Ty5ctEKpWSuLg4EhQUREJDQ5XWIyz0XCyyqvDZBzV6//69qC9PacCZL5S1rC6gq/T/+OMPdOnSpcz1VqACugY3pOma+uRzA/mIlDulRXnOa3FxMTIzM9GgQQONsrqgiWChcvncaCoqUPYoKSlBYWEhqlSp8qmzooTPiaKmAhVQB13QvuiKSqa8olzRvsiDiwCmCfJRAzVh1KhRVHJc5NGylKVNGwBWrlxZ5umz6AQg6nNUgQp8DlCkPpHH/+pmFBDfoJdXlOe8GhoaUm1GAcDMzKzMN4NfffUVNa+oLtKvwOcFAwODcrkZBbSjqKlABcojdLFx/F/ajALleEO6e/duKjmWC15aDkgWnbSyLPyTikFqyiJ9Fp0seitQgQpUoAIVqEAFKlCBClRAW5TbDSnthoiL5EYDxUhjqtChQwdqnbSytGkDbJtB2vRZN5gcjUMFKvAxIRb8pgIVqEAFKlCBClSgAv9dlNsNqZeXF5Xczz//TK3z+PHjVHKLFi2i1kkrS5s2oJpzszTps+gEZNQlNPjjjz+o5GhNsAF682LatFl0AjK/3LKUA9g2WjNmzKCSYylT2rx+6jJlMcEPDQ2llqUtfxbTetryZyknlneiTZ+l7emiTbG8P0teafV+6rGHZZygbX8s5cSSPu37s9Tpp6wngH48Zel7LOMErawuxh5d9X1dlCkLaNPXhasUS3/61HM0bTkBuhn7WN5fF/OpLsYeFllasNQTC2jriqWfstQ/iyxQjjektESq/0W0atXqs9AJ0Pua0ppgA/TmxSx+riwmy7R+uSy+xiwbrZSUFCo5ljKlzeunLlOWm/yzZ89Sy9KWP4tpPW35s5QTyzvRps/S9nTRpljenyWvtHo/9djDMk7Qtj+WcmJJn/b9Wer0U9YTQD+esvQ9lnGCVlYXY4+u+r4uypRl80ibvi5cpVj606eeo2nLCdDN2Mfy/rqYT3Ux9rDI0rZplnpi6Se0dcXST1nqnzUWTbndkFbg8wDtBkIXfrm60Pm/nv6nficWE3xdpK8L0/pPXaYV6X8eOgH69ldRpp82fZZxglb2f33sYdk8VoznFemXd52AbuLW6KKffOo65WDw448//sj0iwpUQA5v3rxBp06dNMoZGxvjm2++odLZoUMH1K5du8zSZtEJAImJiejdu3eZyQHAzZs3MXDgQCrZAQMGoHLlyhrlWMqUNq+fukxpywiQbV4bN25MJUtb/sOHD6dOn7b8WcqJ5Z1o02dpe7poUyzvz5JXWr2feuxhGSdo2x9LObGkT/v+LHX6KesJoB9PWfoeyzhBK6uLsUdXfV8XZRoWFkZdBrTps5QpbZti6U+feo6mLSdAN2Mfy/vrYj7VxdjDIkvbplnqiaWf0NYVSz9lqX8WWQD4LHlI/f39sWHDBo3POKSkpCA5ORl9+/ZFXl4eioqKUKNGDYEMIQQnT55EamoqZs+ejefPn+PVq1eiQYNOnz6NPn36oHr16gBkhX79+nU4OzsL5CQSCUxMTKCvr4/ExEQ8fvwY9vb2MDY25mVGjRqFgwcPYvXq1Zg9ezZ1GaSlpSEtLQ0lJSX8M9pBUhGHDh3SKGNiYoKhQ4dqpb8CFahABSpQgQpUoAIVqEAFxGD4qTMA0AXcMTU1xZQpUwDINmOKePLkiejvwsPDsX37dhQVFaFv377IyMhAUFAQ9u7dK5BbsWIFsrKyEB8fj9mzZ6Nq1apYvnw5Tp48qaQzNDQUQ4YM4T/XqFEDoaGhShtSLy8vHDx4EHl5eRg7dixatWqFGzduCByts7KykJOTg5s3b2LatGlKV9xi/GFr167FiRMn0KJFC+jry6yu9fT0+A1pVFQUFi1aBCMjIyxcuFDjCenGjRthZ2enVubOnTsVG9IKVKACFahABSpQgQpUoAJlinKxIT1z5gy+//57tTIRERH44osvcOzYMaSmpsLV1ZX/Ljc3F82aNRP93b59+3Dq1Cneub958+Z4/fq1ktzt27dx+vRpftNVs2ZNFBQUUL+D/E0lB0IITExMcO7cObi5uWHatGlwcnISyPTr1w+2trYoLCyElZUVANnmkhACPT09JCQkKOm9cOECLl++DFNTU9G8bNiwAUeOHMGHDx8wffp0pKamom/fvvjyyy+Rk5ODRo0aCeQtLCw0Oh+PHj1a7fcVqEAFKlCBClSgAhWoQAUqwIpyEdSoe/fu8PX1VftnZWWF7t27Y86cOWjYsCHmzJnD//3000/YvHmzqG4jIyNUrVpV8MzAwEBJrlKlStDT0+M/S6VSlfmtU6cOfvnlF/7zxYsXRe3UCwoKUFhYiN9++w1du3YFAP5Gk8P06dNx//59dOjQAQ8fPsTDhw+RkJDA/6sqfVWbUUBm4/7111+jbdu2OHToEB49eoRp06bhzp07WLp0qZL8nj17VOpikdEGXMSw1atXl2udHwMSiQTx8fEfNU2pVIpr167pRO/Dhw+p5fPz85GSkoKkpCT+TxElJSXYuHFjWWYTALBs2TKqZ9qisLAQmZmZZabvc0J2dnaZ6NFln46IiKB6pitIJBJ+vklMTMS5c+dQWFj40dJnRV5entrAGiUlJVi4cGGZpadt3etyPNUF9YMmncnJyVTPcnNzyyxPivgUc1RpoIt6+hSQSCRUz0qD/+V56lOhrPsPjoAtAAAgAElEQVQTy3ouJiaG6tnHRLm4IQ0JCaGWadiwIdMgU6NGDaSkpPCbzYiICNSvX19JrlWrVjhz5gwIIXj+/Dl27NiBjh07iuoMCAjAlClT+AnSwMAAW7ZsUZIbOHAgunfvjiZNmqBDhw7IzMxEpUqVRHVyfpwcb5iJiYmSDNfQrKysMGPGDAwYMECgjzPZrVevHh48eIC2bduibt26At/aPn36KOktLi7G0aNHkZKSgm+++QYuLi6CzbmqvMrDzMwMlpaWaNq0Kf9MzBTbzMwMVlZW/Aad1WSZJm1tzKBZfIhZ/Hfz8/ORnp4ukP3666+V5K5du4ZFixbBwMAA0dHRiIuLw+bNm7Ft2zaBXEFBAc6cOYNnz56huLiYfz5nzhyt3klfXx/r16+n9j/28PDAtm3bBP7TU6dOVaoXfX19zJ49myqc+KFDh7BmzRrUqFGDb3d6enq4cuWKQM7AwADXr1+Hn58fVV4BICkpiQ/9bmNjgxYtWijJ3L17V+nZnTt3lPKoDor0CtOnT0dQUBCMjIwwePBg5OTkYOLEiRg7dqzSb3/99VdBv7x8+TIaNGiANm3aKMnu2bMHrq6uMDMzw+zZsxEXF4eFCxeiR48eAGTtTR3E2r4mnfKg7Sf379/HDz/8wE+QcXFxOH78OIKDg0XzpamfaNOnb968iYSEBIGli6+vr5Lc3r17MXjwYI3PxBAfHy9aT7RxBgA61w4OMTExSE5OxqhRo/D69WuV1kEs4xltn05OTsacOXOQmJgIPT09tGrVCiEhIUp9ysDAAI8ePVJTav8fNG1Pm7rXNJ76+fmpneNUxaPgsHv3bjg6Oio9t7GxEdXLWTypW/Cp0slh1qxZCA8PV/uMEAJ3d3ecP39ebf5VQd3Yo+syjYyMFH1/TesRMbcqDqrKVBudubm5SElJEYwnnTt3VqmDg7oype2nnp6eSnWv+MzOzg56enqoVasWTpw4oTFfAP08NWDAAIwaNQpDhgxRexkiBnXvT7ueyc3Nxc6dO5XG8/379/P/16ZOaecIiUSCLVu24NatWwBk/XzKlCkay0JVmwbo13wsOlnWc6tWrVJqU2LPVEFx7iuLWDTlYkOqiLS0NERHR+Orr75C3759lb6naZwcAgICMHPmTKSkpMDOzg6VK1cWrfB58+Zh5cqVyMzMhJubG+zs7DB37lzR/LVo0QLnz5/nuYOaNWsmeuvq6+sLT09PmJmZQV9fHyYmJti0aZOozmfPnmHmzJlISEiAnp4eLCwssHr1aoF57a5duwS/OXDgAP9/eR/SkJAQQefWhMWLFyM5ORkdO3bEwYMH8e+//6pd9N+4cQN37tzhN5W3bt3Ct99+i3Xr1sHX15c3p87KysLdu3f5Orxy5QratWuHqKgoODg4YPLkycwmyzRpa2MGTetDrMl/Vx60Gy1A5sd78uRJjB8/HgDQrl07UV9pf39/FBUVwdLSUhAcSwy072Rubo7Y2FhYWlqq1QfIDky4hSsgO/BRdVvSpEkTPH/+XCOVS2hoKCIjI9GwYUON6dva2mL37t0YMmSI4NBGbFF6+vRprF27lq+b7du3Y9asWfymICoqClFRUXjx4gX8/f3530kkEqWIdw8ePNCYN3mkpKTAzMwMFy5cgLW1NebPnw83NzfRDemVK1cEG9JLly4hPj4e9erVU+I8CwsLw+jRo3Hr1i1kZ2dj+fLlWLp0Kb+Ab9++Pd/WOWhq+5p0yoO2Ta1YsQI7d+7ErFmzAMja87x580TLiqafsPbpNWvWIC4uDklJSfjuu+9w5coVfszgEBcXh9jYWOTk5AgmU4lEgqKiItG8KmLDhg3YsWOH0nPaOAMAnWsHAOzYsQPXrl1DZmYmRo0aheLiYgQEBODIkSNKsiwxEWj79Pz58+Hp6clv1M+cOYP58+fj+PHjSrI2NjYICgpS6qeKh3E0bU+b8VzTeMr1t9jYWMTGxvL1EhkZSTUOqooFeerUKY2/ZdWZnZ2N7OxsFBQUIDk5mZfLzc3lD7A56OnpoUGDBnj79q2gTmmhbuzRdZmq2jyqWofRQFWZcjqvXr2KJ0+e8GuWsLAw0QOe8+fPIyQkBO/evUPdunWRlpYGc3NzqsW7ujLV1E+Li4tRVFQEqVSKDx8+COpe8fAxOjpaY14UQTtPrVu3DocOHcKWLVtgb2+PkSNHUnPbq3t/2vVMQEAAWrRogdTUVPj7++PUqVNKG1zWOqWZI+TTNzU15S0/wsLCEBAQoNFiS90hE+2aj0UnoHk99/TpU6SmpkIikQhuU8XalDoozn1lEouGlAN4e3uThIQEQgghL1++JJ06dSLjxo0jAwYMIFu3blWS9/X1JT/99BPp27cvOXv2LPHx8SGrV69WkispKSFXr14lxcXFJCkpiTx+/JgUFxdrnc+CggJCCCHv378X/VPEmDFjSHR0NJFKpRp1+/j4kBMnThCpVEqkUik5efIk8fHx0TqvLHBwcODf7d27d2To0KFq5SdNmkRevHjBf/7333/JxIkTyatXr8igQYP45z4+PuTNmzf857dv3xIvLy+Sm5tLHBwcBDpHjBhBlVfatFl0EkKIs7MzkUqlZPDgwfwzR0dHJbm+ffuS3NxcKp12dnbk+fPnVLLDhg0jhBBB+vL/5zBgwAAqfYTQv5OTkxOxsLAgDg4OxMXFhf8Tg6Ojo6CtSyQSpXLn4OPjQ9q3b0/GjRtH/Pz8+D9FuLu7U79T69at+T9zc3P+XzE4OTmRV69e8Z9fvXpFnJyc+M8JCQnk1KlTxNbWloSFhfF/v/zyC3UdqwJXJkFBQeTy5cuEEFl9sCAnJ0fpGVeX69evJ+Hh4YQQQoYMGVKarDLppG1TtO2ZELZ+QtunHR0dSVFREV/f6enpZNy4cQKZS5cukXnz5pEuXbqQefPm8X/BwcEkNjaWKh1VkG9n8nkSAzf++vv7kzt37hBCxNuKk5MTKSws1Fj23O9p6ol7TtOnxZ6p0tmnTx+lPzs7OyU5lrbHMp7Ttj93d3eSn5/Pf87Pz6caj06dOkWdF1qo0rl3717Sp08f0qZNG0F5Ojs7k+PHjyvJ+/n5kT59+pDFixeTkJAQ/o8FYmOPrstU1fhQGmiqJ1dXV8H6rLi4mLi6uirJOTk5kdevX/N5vHnzJgkMDGTKi1iZauqnmzZtEsx13F/Hjh3Jzz//zJS+GFjnqbdv35LQ0FDSq1cvMmLECHLx4kXqtMTen3Y9w42nXNkUFBSobFO0dUozR6jLJ03e1bVpljmSVichmtdzYWFhZNSoUcTKyoqMGjWK/5s0aRL59ddfNaavCjT7FU0y5eKG9NWrVzA3NwcgO3Xt2rUrNm7ciHfv3mHkyJGYNGmSQP7p06fYtGkTrly5AkdHR/Tr1w9eXl5KeuWvr8VM9RQRExODtLQ0we2ivCmeu7s7wsPD+VsIDkTFSa27uzv27duHpUuXwt3dHcOHD0fNmjVF087OzhYEanJxcRG98QVkpwwWFhaoWrUqTpw4gbi4OIwfP14pWBEtKlWqxJ9OmZmZaSSzff78Ob788kv+c4MGDfDixQvUqVNHcFOckZEhOKWtVq0aMjMzYWpqqnQaRmOyzJI2i06A3odYk/+uoizNrR8AVK1aFa9fv+bzcPv2bZiZmSnJNWrUCBKJhCoPtO/E4u/l6OiI0aNHw8PDAwBw5MgR0VsfAHB2dlb5nTy6deuGVatWYdCgQQITdDHTZha/VEBWB2L/B2Qniebm5rCzs1OigVKHJ0+e4OHDhwI/P/nbMEBmRTFu3Dg8efIEM2fOxIcPH5jyDUA0T5UrV8aOHTtw7tw5HDp0CIQQ6ts8VWDRSdumjI2NkZeXx8smJSWpdFdg6Se0fdrY2BiGhobQ09NDUVER6tWrh/T0dIFM37590bdvX9y8eVP0Nrg04OIM9OvXD4DqOAMAvWtH5cqVYWRkJHimykSNJSYCbZ9u06YN7t69y/PK/fnnn2jbtq2oTtrbGpa2xzKe046nOTk5grnIyMgIOTk5GvM9bNgwtd+rMt1VZ7KrSqe3tze8vb2xbds2pbWQGFq2bImWLVtqlFMHsbFH12UqtoaTB4tlHAdN9fT27VsUFBTwFjGFhYV4+/atkpyhoSFq167NuxR0794da9asUatbEWJlqqmfcjFUgoKCsGjRIqb0aMA6T92/fx+3b99G5cqV0bNnTxw9ehTnz5/H+vXrNaYl9v606xmuPRkZGeHNmzeoXr26yvgEtHVKM0dwqFu3LrKzs1GrVi0AsjZer149tXkG1Ldp2v7EohPQvJ4bOnQohg4dirCwMI39gwVlEYumXGxI5Sffe/fu8Sae1apVEzWFZWmctOaI8+bNw4MHD2BhYSGaJgDePIN2UdyvXz/069cPycnJOHLkCBwdHdG9e3d4eXkpTeT6+vp48uQJmjdvDkBmSqEqH0FBQThz5gweP36MPXv2wNnZGQsWLFA7MKtDRkaGwN9T8bOiPX/t2rWxbds2vjGHh4ejVq1aKCkpEQyuX3/9NQIDAwVyLVq0QGFhoVJwJxqTZZa0WXQC9D7Emvx35cGy0Zo1axbGjx+P58+fw9PTE6mpqdi6dSv/PVcfZmZmcHFxQc+ePQWTvpgPKe07denSRemZKkycOBF169blF5zff/+90maMAy1N0OnTpwHIokdzUGXaDNDxCgNA48aNsXHjRri7uwMATpw4IVr3b968weTJk5GRkYHo6GjEx8cjOjoa06ZNU5Ldv38/jh07hszMTLRr1w53795F586dlcogJCQEN2/eROvWrWFiYoKMjAzMnDmTqjzUYcWKFTh8+DBmzZqFOnXqIC0tTWDeqY0Pmyad8qBtU5MmTcLYsWPx6tUrzJs3Dzdu3FAZlIaln9D26apVqyI/Px/t27fHvHnzUKdOHZXE4z169KA6ZGABbZwBgN61o379+rh79y709PQglUqxbds2lRsPlpgImvo055tVVFSEUaNGoUmTJgBkrjWtW7dWWQby/q5ZWVl49+6dkukcS9tjGc81jaccrK2tMX78eH6sioiIgLW1tcp3ooW86W5BQQHOnj0LQ0PtlluFhYUwNjaGt7e3qEmdoruCmA9cWUDXZappcUxjtskKBwcHuLu789R4UVFRojR5xsbGIISgSZMmOHDgABo2bKhkLq0NaPvpokWLqOc9FtDOU7t378axY8fQqFEjeHp6onfv3tDT08OkSZNgb2/PnC7reqZp06Z48+YNnJyc4O7uDjMzM5V1T1unNHMEl8+aNWti8ODBvFn61atX+YM5dVDXpmn7E4tOgH49N2zYMK39osXAGotGDHpE03XYR4CbmxuWLVuG2rVrw97eHhEREbzfmYODA6KiogTys2bNwsKFCxEeHo6jR4/CzMwMjRo1wk8//aSk29nZGcnJyWjSpIngRFXRl6Z///6IjIxUOoEuCzx58gSHDx/G5cuX0bNnT9y7dw89e/YU+FRdv34dc+fOxTfffANAtuldtWqV6Mn90KFDER4ejt27d8PY2Bienp4YMmQIv7Bnxc8//6z2e8UJLiMjA8uWLeODxVhbWyMgIAA1a9ZEamoqv0iRSCTYvHkzL9elSxdMnToVVapUwbt37/jTJkBGKzNo0CC4uLgAkNnoR0ZGKp2o0KbNopPL68qVK/lFmZ2dHebPn68UodnT01Ppt3p6eqKHAWL29Oo2Wrm5ubh37x4AmS9gtWrV+O9Y6wigfydtTp5pUFxcjFOnTinp1UQxpA5hYWHYsWMHioqKcOXKFTx58kSUVxiQ+TAvXboUv//+O/T09NCtWzcsWLBA6abKx8cHY8aMwdq1axEREQGpVAonJyecO3dOSaejoyOOHz8ODw8PREREIDExEZs3bxYN2CG/gJBIJCguLi71AkITXrx4ofZ72ptIVaBtU4BsA3Hjxg0QQtCjRw9+I6MIln5C26dfv36NatWqoaSkBHv27EFubi48PT0F1hUcVB0yKPrss6KkpERjnAEAGDt2LEaNGgVbW1u1E3hmZibmzp2LP/74A3p6eujUqRPWrFkjevPKUk+a8Mcff6j9XmwBJO/v+ssvvyA9PR3Tp08X9XcFILh9UAWW8RxQP55yKCoqwtGjR/l3tLGxgZubm07WAW5ubqL+tprAzffm5uai/uGKlllbt27F5MmTS51fMXzKMnV2dsaZM2fg5OSEs2fPorCwEF5eXjh69Gip9EZHRwvyamtrqyQTExODtm3bIisrCz/++CNyc3Mxc+ZMdOvWrVRp0/bT8PBwbN++nWre0wWCgoIwcuRIUUtDLoAmC7RZz3C4e/cucnNz0bNnT5WHPDR1SjNHlCafNKDpT9ropFnPlcYvWgwLFizgY9H89ttvsLOzYwpACaB8+JDGxMSQLl26kLZt25KgoCD++c2bN8nkyZPV/vbOnTskOjqaFBUViX5/+/Zt0T9FeHl5kcLCQrVpWVtbExsbG6U/7rkiLly4QDw9PYmDgwM5ePAgkUgkhBBCioqKSJ8+fZTks7KySHR0NImOjiZZWVkq8+Ho6Ej+/vtv4u7uThITE/lnnzPE/BZYfe4+hk5d4dGjRyQvL4//nJeXx9etPJKSkqiesYDGJ3vv3r2EEEJWrlwp8EtS5580f/58MmfOHNK7d2+ybds24ujoSH788Uf+e1afbEJk/hMSiUTgR6HKh1XMD1TsGYsvB+df7ejoyPupiLWpsLAw0r9/f95vLjk5mXh7e4vqZMG7d+/I2rVrybhx44inpyf/J4bc3Fzy4MGDMtX5qUHbp69cucK3Lw5i4z4hsvaTl5fH63n06JGorzMNtGnTFy9eJN7e3sTOzo5s376dZGdnq03j/fv3/FxSGmjTpwmRjU3yY5UYaP1d//77b2Jra0t69epFCCEkNjaWLFy4UFTn5zSeKyItLU3Uh5YGT548oZI7ffo0SU9PF/jWlda/vDyB84MbOnQoycnJIVKplNjb23/iXH0csMx7/zUUFxf/p9qxrkEbY6cs/KLlwRqLRgzlwmTXxsYGv//+O/Ly8gQnBO3btxc12+JQWFjIX9sXFRWJnpZour7m/FKaNm0KHx8f9O3bV2A6IO9DyhpBLywsDOPHj0fPnj0Fzw0NDUXtvGvVqiVKy6IIf39/LFq0CDY2NmjZsiVSUlJU3j7Q4ODBgzzX2/Xr19GrVy/+u02bNomaLmrytwXYKEpYTJZp0mbVyaL3xo0b+P333wHIzP26d+8u+J4zsVIVsUwsIuy8efNw7Ngx/rOhoSHmzp2LsLAwgRxN6H/Wd6LxyeZMKVluWOLi4nD27Fk4OTlh4sSJGDFiBKZMmcJ/r+iTTTSc/AP0vMIAXah87vdFRUX87VRGRoaSSTmHKlWqoKioCObm5li9ejUaNGgg6p+3b98+nDp1ii/r5s2b4/Xr16I6WUBrtsYSUp7VFE5dm9KWooGGngeg79N+fn5o164dtm7dyt9Kr1ixQrSfGBsbw8TEBFKpFIQQtGrVCqmpqSrfQR1Y4wwAml07NPHKqQrxr6nvs/ZpFpNZWn9XlmjMrOO5Ouzbtw/e3t4ICQkRzZfYHMUCedN5qVSK4uJiLFiwQCtdM2fORFhYGLy9vbFv3z6Vcs+fP8fMmTORmJiIwMBAWFhYaOW7ri10XaYsZpu0oL1NYlnLsIJmjmaZ93SBly9fYvXq1Xj48KGgnFRZe9GChvbJwMAAJiYmKCgoUBmHQB5PnjzB1q1blepKce6hlQN0a3VQ1qCNsVMWftHyYI1FI4ZysSEFZI1O8braxMQEAwcOxNWrVwXPf/nlFyxdupQn8VU32WsacOTpHBo3bozExESVeZQ3dysqKuLNsZo3by66Gd6+fbtKXZrCI6sDF4yDQ7NmzTSaFqjDqVOn+A3pTz/9JNiQivnS0fjbAmwUJdOnT8fIkSOVTJYVQZs2i04Wvbt27cLp06cxaNAgAMDKlSsxZMgQQZh0bTZaJSUlggWcsbGxgJORJfQ/6zvR+GR///33ANhMVLjJw8DAAPn5+TAzM0NWVhb/PatPNkDHK8wSKh8ARowYAV9fX+Tk5GDTpk04ffo0pk+fLpr+4sWLUVRUhHnz5mHdunV4/vy5aJvS1QKCdrJhCSlPqxPQ3Ka0oWjQRM8jD9o+/fXXX2PYsGEYOXIktm7disaNG6ucIGkPGWigTZvmwLVpIyMjVKpUCXPnzkXPnj154vTCwkLExcXxdAuJiYmwtLQU3ZDS9H3WPr1o0SK4ubkJTGYXLVokajJL6+9aVFSkdOisyrSTZTzXBG0O2Fggf3htaGiIL774Quv+/+HDB1y8eBEvXrwQPZzg6n/q1KmYOnUqXF1d4ezsjPj4eLx+/RoDBgzAN998I+rSVJbQdZlyi+XRo0ejXbt2vNlmaUB7GMeylmEB7RxNM+/pEgEBARg4cCAePnyINWvW4MiRI2jcuHGp9dJSjjVr1gwjR45E//79Ba53YhcGXIyPYcOGqS1TGrmIiAjY2Njg2rVr/IaUM6Evr6CNsVPWftGssWjEUG42pKogtohYvXo11q9fDysrK5U3GRw0DTicP5tYpC+JRCKq8+7du5g5cybvAF1QUIB169YpkRnryjcvKysLK1aswMuXL3Ho0CE8fPgQf/31Fx8lkRXyZaxY3mLl/9dff1H52z59+lTJ/1cVevXqhXPnzuH+/fsAgG+//VbUp4g2bRadLHojIiJw9OhRvq14enrCw8NDsCHVZlFqaGiIZ8+e8bcNaWlpgkHy7Nmz2LdvH169esVvMgDZSdS4ceNK9U4sJ88fPnxAZGSk0omu2GBTvXp1vH37Fj179sT48eNRs2ZNqsh06kDDK7xt2zb8/PPP0NPT43kLAcDU1BSjR49W0jlkyBB89dVX+PXXX5Gfn4+QkBCVAQuqVq0KExMTmJiYYNmyZSrzqasFBEtAN8WowqoWUiw6NbUpRYsUjs9S3SI1NDQUYWFhfH4zMzMxduxY0Q0pbZ/W09PD8OHDUb9+fYwZMwarV69WeXMrdsigKgCTLnDx4kUcOnQIr1+/xsiRI3Hu3DlUrVoVxcXF6NevH+9fNmPGDAQEBODbb78FION7VHVjxjJO0vZplkjwgYGBmDt3Lh4/foxvv/2W93dVBEs0ZpbxXBO0OWBjQWl9teUxY8YMHDt2DFlZWUp+zfIc2A4ODmjVqhXfhz09PREREYHw8HD+UEOX0HWZyoMmoAwNaA/jWNYyLKDtpzTzni6Rk5OD4cOHY//+/Wjfvj2+/fZbuLu7l7quuTXO7du34eTkhA4dOoiuOUtKStCyZUs8efJEo06pVEoVjZpG7lNbHWgD2vWcv78/JBIJZs2axftFL168WOt0R4wYofYzDcr9hlRsEVG9enWlzZ8q0A44tOZ9gMzBe/Xq1fzi6+7du/jxxx9x5swZgZwuosIBsrDOvXr1wuHDhwHIbmhnz56t9YZUvowVy1us/GkX1iwUJQCdyTLrop7WDJpFr/z70L6bJvj6+sLDw4NfXFy7dg1Lly7lv2cN/Q/QvxPLybOvry/09fXRpk0bjSfFO3bsgIGBAaZPn44zZ85AIpGUKnIpIDspPXHiBFJTU0EIEQ0Wo02o/E6dOqFTp04qQ8Rz+P7779GiRQsMHTpUKdKyPObPn6+TBQTtZMMSUp7lQIK2TSUnJ2POnDlITEyEnp4eWrVqhZCQEJWmuOroeRRB06e5RU3Pnj3x888/w8/PTyX1xLt375QOGU6fPs1TkbFAmyjHtK4d3OaOg6WlpUqLHpbxjLZPs5jM1qlTB6GhocjPz4dUKlV5IMESjRmgH89p8TmY4nEWUStWrMD8+fNVykVFRSEhIQFTp07Fxo0b8fjxY2RmZmLLli2lDr7Dgs+hTDnQHsaxrmVoQdtPaeY9XYLbMJuYmODff//FF198ofLQkgW0tE8sgRCtrKzw8OFDjeM3jdyntjrQBrTrua5duwKQXWqURXCssjiIKhcb0qSkJJXfyZ/YcuZ29vb2OHz4MAYOHChYEIr55mkacFjN+zjI3wSoOq1jMYVjQUZGBjw8PHifQ2NjY403xZr0cVfr8v8nhODVq1dK8jT+tgAbRQktaNOmBYsPMQC0bdsW8+fPx/DhwwHI/A1YI8yJoU+fPjhw4ADvmzphwgSBXzBL6H/Wd5JPQ5NP9suXL0Wjz4qBmzD19fVLvRFVNUZwZvNivua0m9Hp06cjKCgIRkZGGDx4MHJycjBx4kTBrTeHq1ev4vr16wgPD8eKFStgb2+PYcOGoX379gK55s2b62QBQTvZsISUp9HJ2qbmz58PT09PDB48GICMX3r+/PmiUUZp6XlYIG9ybW5ujgMHDuDEiROiskFBQVi/fj2/0YqKisLevXu1arOscQYAeteOKlWqICIiQlCminOeNn2ftk+zmMzScmX37t0bzZs356MxT548uVTxEGjxOZriqduMcvjmm29Qp04d3oXH0dERDRs2xNGjRwXWIrrA51immg7jtKFbo4E2/ZSjy5OP3q0uxkpZolOnTnjz5g08PDwwbNgwGBsbo3///qXWS0v7xJWXIsTKKTY2FmFhYWjWrJlgf8D5hnJxDoqLi9XKAZ/e6kBbZGdnCyxJxNZyZX1wpE0sGkWUiw3phAkTVH4n30kVffKCgoL4z6p88zQNOKzmfYDM+ffMmTO8SdnZs2dF6VlYTOFYoNi43r17p5UDMQf5q3XFa3axW9fCwkKN/raA7FRPkXeutKBNmxYsPsSAzBRt8+bN/O1lt27dBIF6SgN15cXil8r6Tiw+2S1btsSrV69Qt25dlfpU3RJxUEcOrwoTJkzg3/vly5cwNTWFnp4e3r17hy+//JI3a9QGKSkpMDMzw4ULF2BtbY358+fDzc1NdENqYGCAPn36oE+fPnjz5g3WrVuHESNGKJWVv78/NmzYILgR5J6VFdSZrVlaWmL//v3MIeVV6WRtU+/fvxds6AYPHqySRmOFpS0AACAASURBVGXJkiVYunQpnJ2deXqeoKAgjXlVB0W/ynr16qk8wV29ejV++OEHhIaGIjY2Fps3b9b6xJg1zgBA79qxYsUKzJ49GwsXLhTcOsuDtZ4Auj4NsJnM0nJlZ2dno169evy8U1hYSEUBU1p8jqZ4tAgICOD/X6dOHQwePJg/xNAlPscy1XQYx/krlvVahrWfHjp0CGvWrEGNGjX4uVUdhVxZg4sNMGTIEHTp0gUSiYT3ZS8NmjVrJgj21bhxY0ycOFFJTr68CgoKcPv2bXz77beiG1L59i8GljgH5cXqgAW//PILAgMD0bZtWxBCEBAQgODgYD7ujK4Ojlhj0YihXPCQfiyo4y+iMe/jFtqEELx584bfcBYWFqJmzZpKC20WvlQW7Ny5E8+ePUNMTAwmT56Mw4cPw8nJCd7e3qXS+78MVT7EZW2iowr37t3D6tWr8ezZM5SUlKg18aMF7TvZ29sjJCSEyic7KSkJ48aNg7m5ueBUUX6jxXFhnjx5Em/evIG7uzsIITh58iSqV6/Ozk0lh+DgYHTq1AkODg4AgAsXLuDu3buiUatp4ejoiMjISAQHB6Nbt2747rvvMHjwYERERIjKv3nzBpGRkQgPD+fNkBVPGsUGeI4/72PgxIkT6Nq1K8/nXFagbVNz587F8OHD+Q3un3/+iZMnT5aKg1aXiImJwfLlyyGVShEaGlpqX2faOAMAMG3aNLRo0QLnzp0TuHZwkWcVwcU2UDc2sYxnNH2aFbRc2ZxfGnfT+/79e/j4+GjF16kNXF1dMXfuXMTHx2Pz5s2oXbt2uTXF+1xQUab0oO2n3333Hfbv31+mvsk0UGe9CJT+hlbbOCuvXr1CUFBQqYJ5yoNz1VHlLuLu7s5bJDo6OmL8+PGIiYnBypUryyT9soSDgwO2bNnCH6CkpqZi8uTJvA/05s2bERMTg3/++QeDBg2ChYUF9u/fX2ofafnxXXGsFxv7xVAubki5UwfFE4fff/8ddevWVWr02i621N0o0Jj3sZpj6SIqHACMHz8eZ86cwbt373Dt2jWBaZw2SEpKQq1atfhT6fPnzyMyMhKNGjXCtGnT+MHxzz//RMeOHVXSEHC3ElFRUXBwcGAys9AE2rS1Ba0PcX5+PrZs2SKgfZk0aZKouTgLFixYgClTpmjcFLK0fdp3YvHJnjNnDuzs7NRGBeQmzWvXrgloawIDA+Hi4lKqDemdO3cQGBjIfx4wYIBKU1RatGjRAuPGjcOTJ08wc+ZMtaf6vr6++PPPP9G3b18EBASgY8eOgu+PHz+OY8eOITU1VRAAJjc3t8ytBdThn3/+we7du1FUVAQbGxt07doVNjY2+OKLL0qlV1Ob4syhioqKMGrUKN78Mi0tDa1btxb8Ttd9WhMUzU319PTQokULPlBQaVwLaOMMAPSuHSzlxBITgaZPs6K4uBj379/HpUuXEBwcDACCqOEcCgsLBWMnR++ga3yupng0uHbtms77jhj+y2WqK79Y2n5ap06dj74ZBVRbJuXm5qJBgwalskwCtI+zUrduXZXUXLSbXBZXnU9ldaANKlWqJFhrNG3alD8YBXTnF8sai0YM5WJDunbtWvzwww9Kz2vXro01a9YoBQPR1WJLE2gHBDEfP02+eaxwdnYWjUKpDebNm4ctW7YAAM8DNXnyZDx69AhLly7lT4HCw8PRsWNHUdM7+Wh/jx8/hoODg8DMorSgTZsVrD7EwcHBKCkp4QeokydPIigoqNQ3P5UrVxb1nVAETdtnfScWn+yioiJq30yJRCIwv8vOzlYZuZoWhBDcvXtXcPOmLUUHh5CQENy8eROtW7eGiYkJMjIyMHPmTFHZfv36Yc2aNYIBXh7du3dHkyZNEBwcLNjQmJqaKm3IdAkuWl56ejquXr2KdevW4eXLl6Jm2DSgbVMs5lC66tO0kKcPAGR1W5agiTMA0Lt2yJdTYWEhEhISYGFhISgnbWIisPRpWnBc2V27dtXIlS0/RmRlZZW6P9PgczTF04T09HTUr18fW7du5dtEYGAgfyCga/wXy1RX5o20/ZS7oezWrRtWrVqFQYMGCeZoXfuQchtOVZZJpQXtYZz85QYhBHFxcSrN+mk3uSyuOvLB5MRorsoDuHbz3XffYevWrXB1dQUhBGFhYfjuu+94OV0dHLHGohFDudiQvn79WjTSVevWrXnzP3mU9WKLFap85DjzSkVSdEVom08/Pz+1erU1sSooKOD9hy5evIihQ4di/PjxKCkpEZwCcX6TBw4c0JhPqVSKAQMGlNmikjZtVrD6EMfFxQnMLjt06FAmBwO9evWiOtmmafus78SdiNH4ZFtZWeHRo0dUmytvb28MGTIEtra2AGQn92L+ISxYvHgxZsyYwW+WCwoKsHbt2lLprFy5soDXt169ekomm1xQKXt7exBClBb3XH4aNmyIhg0bIjIyslR5Ki0ePHiAmJgYxMTEIDMzEz169OCj6mkD2jalSPuiDrrq07TQJT0FbZwBgD7KsWI5JSUlYffu3YJn2sREYOnTtKDlyuZos7h5JiIiQm1MibLEpwwApAvMnTsX2dnZyMnJwdmzZ9GmTRvExcV91Dz818pUV36xtP1UsS9cuHCB///H9CHVhWUSQH8YJ3+5YWBggBYtWqgM8kW7yeUCpt65cwe9e/dGlSpVVFqnfSqrAxYoxheR3w/o6enx852uDo5YY9GIoVxsSHNzc1V+JxYCuqwXW6yQN90tKCjA2bNnBbeeHP/kli1bYGxszPvQnThxQvR9aMGFu4+NjUVsbCy/2ImMjISlpaXWeuU74f3793mTWgMDA4EJlyqTMQ7yHVZfXx/r168v807s4eGBI0eOaHxGC20oQt6/f8/frqiLxMyCY8eOYfv27ahatSpPWCzmQ0rT9lnfiYUvNTY2Fi4uLmoj03EYOXIkOnbsiDt37vCfS7PolUqlyMvLw+XLl/lgMc2aNStTonJVEAsqpW7zrisOYlq4urqiffv2mDFjBjp37lxqfaxtiuX9y7pPs6IszfHk4wzs2bOH923m4gyImQFr69rx9ddfK51oazOesfRpWtCWqaurKxo1asTPLcHBwUyHGqXF52SKpwn79u1Dfn4+XFxc8PTpU5w/fx4pKSnw8/ND165dtaaFY8V/qUx1Zd5I209LaxJbVtCFZRJAfxjHYoFGu8mlcdX51FYHLGBZx+ni4Og/Q/tSq1Yt/PPPP7CwsBA8/+eff1CjRg0l+bJebLFC0XTX398fbm5umDp1quD5pUuXBKYdY8eOxbBhw6h5JBUxdOhQALLNy6FDh3izQXd3d/j4+GilE5BxYR06dAj16tVDfHw8v8EpLCwUbKA5k7HCwkLExcXxUdYSExNhaWmptPk0NzdHbGxsqTbLilAcNEpKStTyRtKCdvHGDZyDBg0CIPO3LYvJltY/maXt076Tqk21mMmufEQ8Gpibm2vF5ygG+UOOj2n+CoDvx7SDvq44iGlx/Phx3Lp1C1u2bEFWVhY6dOiAbt26ldoslbZNsby/rvq0JujCHI8lzgCra4f8gaBUKkVcXJxK9w8WE1zWPq0O2pSptbU1rK2tyywPLPgcTPFo4efnB2traxgbG/OLw8GDB2PKlCmlCo7Hiv9SmeraL5alnyYmJuKPP/4AIDv4+liUL4BuLJMA+sM4lkND2k0ujatOebA60BXK+uCINhaNOpSLKLs3btxAYGAgpk6dinbt2gGQmUZu2bIFS5YsEYQPBmQnurdu3UJMTEyZLra0xbNnz+Dj46NkPuHg4IBt27YJAntMnDix1NGs+vfvj6ioKP5ms6SkBAMHDsTFixe10vfvv/9iyZIlyMjIwNixY3lfxmvXruH27dtKp/ozZsyAt7c3P/HExsZi3759SgOUs7MzkpOT0aRJE4G/ljYn77t27cKuXbsgkUhgZmbGP//w4QOcnJxKTRPBgmvXruHWrVsAZOTCiu1Tl9BF2zc3Nxfc9nEorQm8LiIHz58/Hx4eHmV6yMGCZcuWKS3gxZ45OzvjzJkzfGTdwsJCeHl54ejRox8zu8jIyMCvv/6KHTt2fFS3Bpr3/9R9WlfRBmnB9TtVUKwrT09P/v+GhoZo3Lgxxo0bV2rO1rIEa5k+efIEW7duxbNnzwSc46W5nWXB52CKR4vExETExMRg8+bNqFGjBho1aoRHjx5hw4YNsLKyKrOAVZrwXypTALx5o4WFBW/eOG7cOHTr1u2jmSEfOnQI27ZtE7i/TJo0Sck0UpcoLCz86JZJ3AGXv78/P3ewHBqqY9agBWd1MHDgQMTHx+P3339H7969P6rVwecAV1dXbNmyBXXr1kVcXBy8vb35WDSGhoZUEYnLxQ1pz549sXTpUmzZsgXLli0DIDspDgoKEj0tsbS0hKWlJQYPHswvto4dO/ZJfEilUimKi4tFT5mnT58ONzc3tG3bFoDsxrcsrvqtra0xfvx4/sY0IiKiVCfMX375pSg5e+/evUUnlsePHwtOQS0tLUV5tEpDxaEId3d3DBgwAMHBwYJTRVNTU1SvXr3M0qGBqnIpDV6+fInVq1fj4cOHAhNHxUMOXbR9+Vs/zgQ9JydHVJbFFJM2cjAL4uPj4eHhUSaHHNpALJADZ5IsD11xENMiKCgIt27dwocPH2BjY4MffvgBNjY2Hy19mvf/1H1aV+Z4gOY4AwC7a4eufG3L0ryctUxnzJiBAQMGYNiwYR9twwR8XqZ4tGjVqhVatWqF8+fP85G+x4wZg7CwMCxevFjnfu3/xTIFyodf7P79+3H69GnUrl0bgCwQmIeHx0fdkJaUlMDY2BglJSVIS0sDoPugSqX14VUXTI4G5cXqQBco64Mj2lg06lAuNqSAjD5DVdAHRXzqxZa8WZahoSG++OIL0cm0X79+6NChA2JjYwHIgkeUBeF3YGAgjh49yt+I2traws3NTWt9MTExGn1w5WWqVKmCiIgIvpGdOXNG1LyT8wV6//49AOWoliwwMzODmZmZ6Mb5Y+LJkyfYtm0b0tLSyvREPyAgAAMHDkRCQgLWrFmDI0eOoHHjxkpyum77lSpVgqurK1xcXDB+/HjRfNKaYtJGDmZBWR5ysCAqKgpRUVF48eIF/P39+ecSiUQ04i6t2ZCu0Lp1a/j4+Ii2oY8Bmvf/1H1al+Z4muIMyEOTaweL7742KEvzctYylUqlWruwlAb/ZVM8bl5u2rQpqlWrxh/y6xr/5TL91H6xVatW5TejgMzNrWrV/2vv3qOavO8/gL+TAFaFRmuZ6xSVsVqFWq11UlzBCyi3QrjVgK5wXL1UpFIv1MoGTvEcp8DRqiBVaw+eowUv4apVjrXFbaVOtzPqUdQiSlXqjiOiQMFIyP7g9zy/BAK5Ps+ThM/rn0qMPJ8+IeT5fp/PZThvxz9y5AhycnIwYsQIdqONj6ZKXG4aGiMlJQU1NTVoamrCggUL4OHhgebmZrS3t+ttlGQPuNo4MrYXzUBsImXX1AVRcXEx/Pz8BLvYAnp2qGprawH0LDRHjhwpWCyWkslkKC4uxkA/CgkJCexg21u3biEtLQ0//PADRCIRJk6ciO3bt8PLy0vn39y9exfr1q1DXV0dRCIRvL29kZ2dbVGKGRdpoKaIiIiATCaDj4+PzpvM0kYczOBgJsWxu7sbcrkcx48f13keFz/72rVsTG1aZmYmqqqq+jzXlFTUnTt3Yvr06Zykb1ljk8MU169fx7Vr17Bnzx6dOaqurq7w8/MbsD7CGmlD9oz5/w8ICNB5z6SlpSE7O5udXdobH3e9+UzHW7hwIY4dO9bncUOlHdqpur2JRCKLG2VZO73clHOamZmJRYsWWa3O3BSDIRXv/v37vM6vHAznVAi5ublQKpXsbOuSkhKMHDmS3fDl+k5lYGAgDh8+zPssVGaD6+rVq8jPz4eXlxfi4uLYDS6+NnnlcrlO1oGfnx9qa2sF76ZvjqSkJHbjaMOGDfDx8cHatWvZ63tzrVixAgEBARg9ejQ++ugjfP3115BKpVCpVIiKisLp06cNfg+buDr6y1/+YnBBtH37dvaEyeVyvkLTq6qqChkZGXj11Veh0WiQnp6OrKwsts19UlISCgsL+6RtWWvx1NXVhZMnT/ZJsTJ3FuaNGzfw+uuv6z3/TG2h9pxLLy8vKBQKdqZkfxfjmZmZWLhwIWJjYwEACoUCmZmZFjU64CIN1BROTk5YunSp1b+vs7MzgJ4FVlNTE1588UW9KZ5c/Oxrd46VSCQYP358v41OTElFNbZzsCm42OQwBtOcad68eXobrTH4mEFsD7Trapm0qd61tklJSQBMm11qbXyl4929exfNzc16/85QaQfXY3GsnV5uyjn9/vvvoVAorNrh1xiOnIqnjc8FhCOfU6HrYk+dOgUAfc5jRUUFL3cq3d3deV+MArYz21aorAMucNWNe9OmTWwvms2bN7NlNzU1NWztsyE2cXVk6oJIaDt37kRRURE8PT0BAHfu3MHKlSvZBWl2djYA0zoumiIzMxNqtRoXL15EQkICKisrLcqVN6VdNNB/ClnvX9jaO3oAEBsba/FuPhdpoKbw9/fn5MNpxowZaGlpQUJCAmJiYuDi4oLg4GCrHqM/prz+pqSicvHzz8UmhykKCgqwatUqDB06FImJibh27Ro2b97MfmBxNYPY3hhTa8sswPgc86EPF+l4xvYZAIwv7SgtLcXcuXPZD/qWlhZcuHDB4jnIXKSXG3tOtZ/HJ0dMxROaI55TW6mLFWr8S319PQBg1qxZ2LFjB8LDw3U2jvjo9GsLNbzatbp5eXm8HJMrXG0cmdqLRh+bSNm1N0x6pTZLxwWYgkmtYv7b2tqK5ORk3gbMa6eQqVQq1NXVwdvbu0+KV3R0NHJzc/HrX/8aAHD79m2sW7cOCoXC7GNzmQZqjJqaGiQnJ0MsFlvtrl9vTU1NaGtrY8fqcMXQDFV9dcHajElFbWtrQ2Njo9VSa2QyGcrKygw+xhUmvfGbb75BWVkZPv74YyxfvrzP8ftrVCNEvRyfmFrb7777TqcMo62tDe3t7Tq/I/pL1WXw1aiKC/fv32f/PFCfAVMwP3va9H0WWWKwpZc7UiqerXCkc8pVeqM56uvrcfHiRQA9G169S6S4MG/evH7/jo87s4za2lq2keaSJUvsfpyQkLjqxm1q6aU+jv+Jw4HAwEDs27cPcXFx0Gg0UCgUCAwMRGdnJzQaDX7/+99zeqHF7FBJJBJ0dHTAzc2t33QwLvRe+NbX1+Ozzz7r87w1a9Zg8eLFmDx5MoCeO3E7duww65jaA+etnQZqiszMTGzbtg0+Pj5WSRlmdiB7E4vFqK+v53QHUjtVtzeRSGTwbp6hu/LV1dXIzMyERCLB+fPnceXKFeTl5aGgoMDsmMViMRoaGnQ2Ofjszsm4dOkS5s+fj9GjR+t9r1t7BrG98PT0xJw5c3DlyhWdNB2m1lYbk6r7zTffoKGhgc2mYFI4+cJFxsOYMWMM9hmwRmmHWq22atyWdqVkGHtOrdnh1xyOlIpnKxzpnHKV3miq0tJS5Obmsu+pTz/9FOvXr7c4O8IQoe7M9uZIs22FxlU3blNLL/WhBakZmFv2n3zyic7je/fuhUgkQmFhIafHl0qlePz4Mfz9/bFs2TKMHDkSo0eP5vSYA/nNb36jt4NiQEAATp06xV6UTZ061ewuw1ylP5tKKpUiJCTEat9v+fLl7KLwp59+gqurK0QiEVpbW/HSSy9x+oFgSqpuf2MsBrp43r17N06cOMF2650yZQrbLt5c1tzkMMeoUaOwadMm/PWvf8Xy5cvR1dWld1HQ2dmJxsZGnUY1hu5IOwJja22B/0/Vzc7OxrFjx9ifr7lz5yI+Pp7zWLlMxzPUZwAwvbTD3d0dVVVV7Mzhs2fP6nTeNJU572lDTD2n1uzwaw5HSsWzFY50Tm2lLvbQoUNQKBRwd3cHADx8+BDvvfce5wtSRmpqap/rXX2PcUXoGl5HZO2NI2uUXtKC1Aym1lxauyPo/v37IZFIsGbNGpSXl6OtrQ1RUVFW+d7G0K4hZbqy6kvvYpqYzJ07t89jphKioF6foKAgfPHFFwgNDdWppTCU3tofZsGZlZWFGTNmIDQ0FABw5swZvXV4QjF3Q4D5AGVYOkzbmpsc5sjNzUV5eTmio6MhlUpx7949LFmypM/zuJpBbOsKCwuRlJSE/fv36/37jz76qM9jjx8/xtOnT9nxOSqVCo8fP+Y0ToDbMRWG+gwAYGe2Gfu7LT09HcnJyexCViKRID8/3+wYudjkM/WcNjY2Ys+ePfjqq6/w9ttvY8GCBYLVG9rKZ4wjsfdzakt1sdqfpb0/V7mmbyO5oaGB8+PaSg2vI7L2xpGp6yJ9aEHKIa46gjIpimKxmNeFKOPgwYPsn52cnDBu3Di9O2XGNDYxlb5dfTc3N0ybNg1paWmc/6LetWsXAGDz5s3sro8x6a2GXLp0CRkZGezXISEh2Ldvn0Xf05r0XVg0Nzfj7t27/TYWGD58OP773/+yr9fFixfh5uZmcSwvvPCCziYHn1544QXExcWhsbERADB27FiMHTu2z/MWLFiAN954Qydlk8+Fs1CYTRpTNt9CQ0Mhl8sRFhYGoKcOlfkzl7hMxxsyZIhO2vGECRP6zKs1tYbWy8uLjRHoSY+2JF3dnPe0IaaeU2t3+CXEmrhKbzTVuHHjsHv3brbL/vHjxznvLA8Ax44dY/+/tRtUtra28lJW4cizbW2JrWwc0YKUQ9buCGorTUAMNU9iGpvcv38fqamp7ONtbW19LspMtXjxYjx58oQ9p6WlpZBIJBg6dCgyMjIsqk80hjV2gfTRaDS4fPkyW8P1z3/+E93d3ZwcyxKLFi3Cp59+Co1Gg6ioKDz//PMICAjQO7pj/fr1WLZsGe7du4d3330Xd+7csalFtjlMqYsdNWrUgE0hHFF8fDzUajXc3d2NHlG0Zs0aTJ06Ff/4xz8AAB9++KHRbeItwWU6nqE+A0OHDjVr3I1KpYJEIoFarWYXppbWmZvynjbE1HPKRYdfQqxN6LrYzZs3Y+vWrYiMjIRIJMKsWbOwZcsWzo/7u9/9DuPHj0dWVpZOdourqyteeeUVzo9vKzW8hB/UZZdD1u4Iylyw9Yfr8Qn9jXthMCkV169fx7Vr17Bnzx6sXr2a/XumsUl/c0uN8c477+D48eM6j8XGxuLkyZMIDw9n53XZm8uXL2Pt2rVs6u/Tp0+Rm5uLN954Q+DIdDFdPcvKynDjxg2sX78eMpkMFRUVep/f2tqKf/3rXwB6mig9//zzfIZrdbGxsSgoKMCyZcvY4vywsDCjhj4PJjExMRZ10+YDV90GgZ5a2v70l1FhqLTjyJEjyMnJwYgRI9iNSWt0ujT1PT0QS87pYOvwS+zT/fv3eb2jpFarkZeXp3MtxSe1Wo1NmzZh69atvB+b2eA6fvw4+3krk8mwfft21NTU6C2XIfaLfutzyNodQYWe16edqtubSCRiF6SmNDYx1ZMnT9DS0sJ+30ePHqGtrQ1AT9qXPeru7kZ7ezvOnTunk45nab0lF1QqFYCe9Nvw8HCIxeIBf6bd3NwcrhmBtetiHZGvry/OnDljVAMwoTqtcpmOZ0omhbGlHYcOHUJlZaXVL4ZNfU8PxNRzmpeXh5iYGLz00ktW6/BLCJf4Tm+USCS4cOGCYAtSiUSCGzduCHJsW6rhJdyjBSmHtDuCajQa3LhxwyodQfWl7jJ1lEuXLsXw4cMtPoY+xs45NaexibHeffddyGQydpFz4cIFLF26FO3t7Zg+fbrZ31dIYrEYu3btwuzZs3lJg7HEzJkzERYWBrVajc2bN+PJkyd9xt9w0b3TVnBVF+toSkpK8Pnnn+O5557D0KFDB3zthe60KnQ6nrGlHe7u7pxcDBvznjaVsee0ra0NCxcuhJeXF2JiYhAcHKzTLI4QAsyZMwefffYZoqKidDIozG2maKo333wTW7Zs6XN8LsfSAbZTw0v4QSm7HGtubsb3338PwHodQXNyctDY2Mg2NCorK8MvfvEL/Pzzz3j27BnbhZErpaWlmDt3LqRSKQCgpaUFFy5cYFuQFxUVIT4+Hnv37tX775naInNdv36dbY7029/+dsD0OHuxceNGJCQk4LXXXhM6lAFpNBpcv34dHh4ecHV1hVKpxIMHD+Dt7c0+5/79+wN+D1spoDdHbW0t/vznP+PevXuYNGkSWxfLdNMlPfr7GdD32kdGRqK8vBwRERGoqKiASqVCYmIiioqKuA6zD77T8QDjSzt2796Nzs5OhIeH6yzaLL0oNOY9bQlD51StVqO6uhqlpaW4fPkygoKCeKmPI8Re6LvGsUYzRWPp64VgjXIBYx09epTtCsuUGBDHQwtSHqhUKp1ZhZbuasnlchQXF7Nfd3d3Iz4+HsXFxQgLC8OXX35p0fc3hLmA1Ea/JCwTGRmJW7duYfz48To7kHw1qjJEpVLBxcWl31mafO3UCuXOnTuYMGECAMerixVaXFwcTpw4gZiYGBw6dAhSqRTBwcGoqqoSOjReREdHIzc3V6e0Y926dX1qcK19UWhr7+mbN2/i0KFDqKio0DvXmhBChNg0JPyglF0OVVVVYevWrXj48CEAWG1EyKNHj/D06VN2l5yZ2ycSiSzuYmsu7QU34+nTpygvL8fdu3fR1dXFPm5Oym5aWhqys7P77TRsKws3c/3pT38SOoQByeVylJSU4PXXX9cZdzPQz/RPP/2E7OxsXL9+Xac2kK9dVWtau3YtFAoFkpKSUFhY6HB1sdZmyms/2DutGlvawcwsthZz3tPW1tLSgsrKSigUCrS3tyM6Ohrnzp3j/LiE2BulUqkzRmzkyJGcH9PWNq0A+86wIgOjBSmHsrOzsWvXLkybNs3imhxtzNy+0NBQAMDZs2cRHByM9vZ2Xt6s7u7uqKqqwoIFC9jjjxo1qs/zUlNTZUgdsgAAC51JREFU8ezZM7z22msWN35JSkoCALNGEdgDpmGVoU6bQikpKQFgWrOW9PR0hIWFoa6uDjk5Ofjiiy8wbtw4rkLkVGdnJ86ePYumpia93aZpgarLlNc+JycHALBkyRJMmTKF7bQ6WAQEBKCysrLf0g6uLgrNeU9bW0hICObPn48//vGPNtdRnBBbUVVVhYyMDHajLj09HVlZWQgKCuL0uPo2rRh8pgyTwYFSdjnEpKJx4fz58+wYmJkzZ/I67/DWrVtITk5m52RKJBLk5+ezKWeM0NBQTtOHmTvDvbue2iNjO23aEyaNm6kN7O7uhlwu7zO2xx6cO3cOxcXFuHz5cp96UZFIxHlHWHtj7GuvVqsRFxfHLo4Gs/5KO6Kjo1FSUoJJkyY53EVhZ2enYFk9hNiL0NBQ5Ofnw9PTE0BPCcnKlSs5L88ihE90h5QDzE72/PnzcfToUYSFhek0obBGmsO8efN4XYRq8/LyYgcUAz0jSvSNCfDw8EBbW5tFc0d7W7NmDbZs2QJnZ2fIZDI8evQIK1aswHvvvWe1YwjB2E6b9oQZwzNs2DA0NTXhxRdfhFKpFDgq8wQFBSEoKAjbtm3Dxo0bhQ7H5hn72kskEgwbNkynBGGwMVTaYQt3Mrny+eefY+XKlUKHQYhNGzJkCLsYBXrKHPjeyBEiZZgMLrQg5UDv9IYtW7ZYtTanoaEB+/bt61ObyWcdpUqlgkQigVqtZhemvbs9urm5ITY2Fv7+/jopu5aMfbl9+zbc3Nxw5swZ+Pr6YuPGjVi4cKHdL0iVSiXi4uLYr2NjY+3+rtuMGTPQ0tKChIQExMTEwMXFBcHBwUKHZRFajBrHlNfe09MTixcvRnBwsE6q+uLFi/kKV1BclXbYsrKyMrz55puorq5mF6TMnWBCiK7AwEDs27cPcXFx0Gg0UCgUCAwMRGdnJzQaDee1nEKlDJPBhRakHOB6J3vt2rUICQlBTEyM2QPMLXHkyBHk5ORgxIgRbIMhfd0ePT09dXb1rIFZgF+6dAmzZ8/G0KFDHeIiTiwWo6GhQafTphCvrTUx9b5RUVGYOXMm2traMHHiRIGjInww5bVXq9V4+eWX0dDQwGeINkMqldrtDGVz3bt3D+vWrcPNmzeRkZEBb29vdHZ2Ch0WITYpLy8PAPDJJ5/oPL53715e0vZ37tyJoqKiPinDtCAl1kQ1pHZI39gVPgUGBuLw4cOCdDtLTU1Fe3s7GhoaUFlZCbFYDLlc3mdmn725cOECNmzYgMmTJwPo2dTYsWMH3nrrLYEjM9+3336LKVOmwM3NDQDw5MkTXL16FX5+fgJHRvjQ0dGBBw8e6NRF6puZqS+t39qp/raIKe04fPgw3NzcOCntsHVxcXHYsGEDrl69iry8PIwaNQqTJ0/Gzp07hQ6NEPJ/9I31o4wGYm20ILVDmZmZWLRokd5hyXyIj483amh9R0cH8vPz8e233wIA3nrrLbz//vsWXWh1dnbib3/7G1555RV4eHjgP//5D27cuIGAgACzv6et0K7R6N1p0x5FRUWhpKSEvYve3d2N2NhY+hAbBIzNogD0X9gMhoud/poU8Tl2RSihoaGYOHEirl69ivz8fHh5ebHNra5evTqoxv4QYquYTbODBw/CyclJJ2W4q6sLKSkpAkdIHAktSO1QVFQU6uvr4enpqbOjzlcN6e7du9HZ2Ynw8HCd4/e++5Geng61Wo2FCxfqxLdt2zZe4rQnN2/exNixY9kauvb2djQ1NeHll18WODLzyWSyPneuhb67T/hhTBZFV1cXnj17hvj4eBQXF7MLs9bWViQmJuLMmTN8hUsEUFdXh1WrVsHb2xs//PADHj58iKVLl2LWrFmYNm2a0OERMujp2zRjOPqmGeEf1ZDaofT0dEGPz6RuaF8w6rv7ceXKFVRUVLBfT58+HZGRkfwEaWc+/vhjFBcXs187Oztjw4YNUCgUAkZlmeHDh6O2thZTp04FANTW1trcfFXCDXd3d4Mp/QUFBWwNlPYCxNXVFUuWLOE6RCKwyZMnw93dHXv37gUAvP322xgzZgyKiopoQUqIDXDEzt7EdtGC1A7NnDlT0OOfP3/e6Of+/PPP7CKkv8HupKexCzMqAwBcXFx0au/sUVpaGlatWsXeOa+vr2cvPoljqq+vBwDMmjULO3bsGDCLIiUlBSkpKdiyZQsyMzN5j5UIT3tz1d3dHTKZDDKZTMCICCGECIFSdu1Qa2srDhw4gLq6Ojx9+pR9nOsxISqVCi4uLv0uLHvXhu7fvx8VFRUIDw8HAJw+fRqRkZFYunQpp3Hao9jYWOzatQseHh4AgB9//BEffvihXd8hBYDHjx/j3//+N4Ce2WVSqVTgiAiXBpqN3F8NKSGEEEIGN1qQ2qEPPvgAXl5eOHXqFFJTU3Hy5En4+Phg/fr1nB6XaTTSXzMOffUE1dXV+O677wAAfn5+DtF8iAtff/01MjIyMHv2bAA9523r1q2YM2eOsIERQghHqqur2d95hBBCBi9akNohpjFMREQEKioqoFKpkJiYaFTnW2K77ty5g7///e8AejoSjx8/XuCICDEPjfwhA3nw4AF++ctf6nRsz8jIQFZWlsCREUIIEQLVkNohFxcXAD2Nb1paWiCVSqFUKgWOqq+GhgYUFBTgxx9/RFdXF/s4X92A7YlSqcSvfvUrLF68GEBPerRSqbT70S9kcNqxY4fO2BZXV9c+j5HBa8OGDVAqlXj06BEqKirg4+ODK1euCB0WIYQQgdCC1A5NmDABLS0tiIiIgFwuh5ubm03ObUtNTYVMJkN0dDQkEonQ4di0FStW6NQAd3V14f3338exY8cEjIoQ8zCzNBlisdjum3QR6yksLERHRwdiY2PR2NiI06dP4/bt21i9ejX8/PyQkJAgdIiEEEJ4RAtSO5STkwMAWLJkCaZMmYLW1lb4+/sLHFVfTk5O1MDISCqVSqcp1LBhw3QaVhFiT2jkDxnI6tWr4evrCxcXF6SkpADomVucnJyMmpoagaMjhBDCN1qQ2imlUona2loAwNSpU+HkZHsvpb+/PzWtMIF2im5zczO6u7sFjogQ89DIHzKQlJQU1NTUoKmpCQsWLICHhweam5vR3t6OxMREocMjhBDCM2pqZIeqqqqQkZGBV199FRqNBnV1dcjKykJQUJDQoemoqalBcnIyxGIxXFxc2DQ+2gHv68SJEzhw4AA7g6+srAzLly9HbGyswJERYh4a+UMMkcvlKC4uxp07d/CHP/wBfn5+qK2tRWVlpdChEUII4REtSO1QaGgo8vPz4enpCaCnO+vKlSvx5ZdfChyZrvnz52PdunXw8fGBWCxmHx8zZoyAUdmuixcvorq6GgAwZ84czJw5U+CICCGEO0ePHsWiRYsAAFFRUSgtLRU4IkIIIUKwvTxPYtCQIUPYxSjQ0+ToueeeEzAi/aRSKUJCQoQOw274+vrC19dX6DAIIYQXzGIUAPLy8gSMhBBCiJDoDqkd6ejoAAAcPHgQTk5OiIuLg0ajgUKhQFdXF9scwlYUFBRAKpUiNDQUQ4YMYR/Xbt5DerS2tuLAgQOoq6vTaWak3XmXEEIIIYQQR0MLUjsyadIkiEQi6HvJRCIR6urqBIiqf5MmTWL/zMRti3Hagg8++ABeXl44deoUUlNTcfLkSfj4+GD9+vVCh0YIIYQQQghnaEFKiA2IjIxEeXk5IiIiUFFRAZVKhcTERBQVFQkdGiGEEEIIIZwRG34KIYRrLi4uAABnZ2e0tLTA2dkZSqVS4KgIIYQQQgjhFjU1IsQGTJgwAS0tLYiIiIBcLoebmxt8fHyEDosQQgghhBBOUcouITbm8uXLaG1thb+/P5ycaM+IEEIIIYQ4LlqQEkIIIYQQQggRBNWQEkIIIYQQQggRBC1ICSGEEEIIIYQIghakhBBCCCGEEEIEQQtSQgghhBBCCCGCoAUpIYQQQgghhBBB/A/fmhfHK9dSNQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XYgTEnMQr5at"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Store the tokens and scores in a DataFrame. \n",
        "# Each token will have two rows, one for its start score and one for its end\n",
        "# score. The \"marker\" column will differentiate them. A little wacky, I know.\n",
        "scores = []\n",
        "for (i, token_label) in enumerate(token_labels):\n",
        "\n",
        "    # Add the token's start score as one row.\n",
        "    scores.append({'token_label': token_label, \n",
        "                   'score': s_scores[i],\n",
        "                   'marker': 'start'})\n",
        "    \n",
        "    # Add  the token's end score as another row.\n",
        "    scores.append({'token_label': token_label, \n",
        "                   'score': e_scores[i],\n",
        "                   'marker': 'end'})\n",
        "    \n",
        "df = pd.DataFrame(scores)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "oKWwlVKLr7n9",
        "outputId": "82b93a0d-5732-4a82-a80a-1ba37b7f4096"
      },
      "source": [
        "# Draw a grouped barplot to show start and end scores for each word.\n",
        "# The \"hue\" parameter is where we tell it which datapoints belong to which\n",
        "# of the two series.\n",
        "g = sns.catplot(x=\"token_label\", y=\"score\", hue=\"marker\", data=df,\n",
        "                kind=\"bar\", height=6, aspect=4)\n",
        "\n",
        "# Turn the xlabels vertical.\n",
        "g.set_xticklabels(g.ax.get_xticklabels(), rotation=90, ha=\"center\")\n",
        "\n",
        "# Turn on the vertical grid to help align words to scores.\n",
        "g.ax.grid(True)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1793.47x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KXF2lKban2fE"
      },
      "source": [
        "### Defining All The Codes into A Method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WMsyAms7r-BX"
      },
      "source": [
        "def answer_question(question, answer_text):\n",
        "    '''\n",
        "    Takes a `question` string and an `answer_text` string (which contains the\n",
        "    answer), and identifies the words within the `answer_text` that are the\n",
        "    answer. Prints them out.\n",
        "    '''\n",
        "    # ======== Tokenize ========\n",
        "    # Apply the tokenizer to the input text, treating them as a text-pair.\n",
        "    input_ids = tokenizer.encode(question, answer_text)\n",
        "\n",
        "    # Report how long the input sequence is.\n",
        "    print('Query has {:,} tokens.\\n'.format(len(input_ids)))\n",
        "\n",
        "    # ======== Set Segment IDs ========\n",
        "    # Search the input_ids for the first instance of the `[SEP]` token.\n",
        "    sep_index = input_ids.index(tokenizer.sep_token_id)\n",
        "\n",
        "    # The number of segment A tokens includes the [SEP] token istelf.\n",
        "    num_seg_a = sep_index + 1\n",
        "\n",
        "    # The remainder are segment B.\n",
        "    num_seg_b = len(input_ids) - num_seg_a\n",
        "\n",
        "    # Construct the list of 0s and 1s.\n",
        "    segment_ids = [0]*num_seg_a + [1]*num_seg_b\n",
        "\n",
        "    # There should be a segment_id for every input token.\n",
        "    assert len(segment_ids) == len(input_ids)\n",
        "\n",
        "    # ======== Evaluate ========\n",
        "    # Run our example through the model.\n",
        "    outputs = model(torch.tensor([input_ids]), # The tokens representing our input text.\n",
        "                    token_type_ids=torch.tensor([segment_ids]), # The segment IDs to differentiate question from answer_text\n",
        "                    return_dict=True) \n",
        "\n",
        "    start_scores = outputs.start_logits\n",
        "    end_scores = outputs.end_logits\n",
        "\n",
        "    # ======== Reconstruct Answer ========\n",
        "    # Find the tokens with the highest `start` and `end` scores.\n",
        "    answer_start = torch.argmax(start_scores)\n",
        "    answer_end = torch.argmax(end_scores)\n",
        "\n",
        "    # Get the string versions of the input tokens.\n",
        "    tokens = tokenizer.convert_ids_to_tokens(input_ids)\n",
        "\n",
        "    # Start with the first token.\n",
        "    answer = tokens[answer_start]\n",
        "\n",
        "    # Select the remaining answer tokens and join them with whitespace.\n",
        "    for i in range(answer_start + 1, answer_end + 1):\n",
        "        \n",
        "        # If it's a subword token, then recombine it with the previous token.\n",
        "        if tokens[i][0:2] == '##':\n",
        "            answer += tokens[i][2:]\n",
        "        \n",
        "        # Otherwise, add a space then the token.\n",
        "        else:\n",
        "            answer += ' ' + tokens[i]\n",
        "\n",
        "    print('Answer: \"' + answer + '\"')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ux_8Ln69sAOa",
        "outputId": "557d180c-82d0-4a73-ea66-3b1e0be515ef"
      },
      "source": [
        "import textwrap\n",
        "\n",
        "# Wrap text to 80 characters.\n",
        "wrapper = textwrap.TextWrapper(width=120) \n",
        "\n",
        "bert_abstract = \"We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pretrain deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be finetuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial taskspecific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).\"\n",
        "\n",
        "print(wrapper.fill(bert_abstract))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations\n",
            "from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is\n",
            "designed to pretrain deep bidirectional representations from unlabeled text by jointly conditioning on both left and\n",
            "right context in all layers. As a result, the pre-trained BERT model can be finetuned with just one additional output\n",
            "layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference,\n",
            "without substantial taskspecific architecture modifications. BERT is conceptually simple and empirically powerful. It\n",
            "obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to\n",
            "80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question\n",
            "answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute\n",
            "improvement).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "suzqpz0OsCwQ",
        "outputId": "ea8cb1b2-131a-4e72-87e8-672addf98ccc"
      },
      "source": [
        "question = \"What does the 'B' in BERT stand for?\"\n",
        "\n",
        "answer_question(question, bert_abstract)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Query has 258 tokens.\n",
            "\n",
            "Answer: \"bidirectional encoder representations from transformers\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q8RgaRtGsEsv",
        "outputId": "793391cf-1076-4487-ed53-fbf01e69b50e"
      },
      "source": [
        "question = \"What are some example applications of BERT?\"\n",
        "\n",
        "answer_question(question, bert_abstract)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Query has 255 tokens.\n",
            "\n",
            "Answer: \"question answering and language inference\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W6e4SOWIyia8",
        "outputId": "655e4f48-53af-4328-d4fe-6b6523e251a7"
      },
      "source": [
        "start_time = time.time()\n",
        "question = \"What are the public holidays of Bangladesh?\"\n",
        "answer_text = \"including monitoring of sighting of the moon in accordance with the lunar Islamic calendar in order to set festival dates; as well as the charitable tradition of zakat. Public holidays include the Islamic observances of Eid-ul-Fitr, Eid-al-Adha, the Prophet's Birthday, Ashura and Shab-e-Barat.\"\n",
        "\n",
        "answer_question(question, answer_text)\n",
        "print(\"--- %s seconds ---\" % (time.time() - start_time))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Query has 84 tokens.\n",
            "\n",
            "Answer: \"eid - ul - fitr , eid - al - adha , the prophet ' s birthday , ashura and shab - e - barat\"\n",
            "--- 1.264247179031372 seconds ---\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dHYsYqNJozLM"
      },
      "source": [
        "## DistilBERT implementation\n",
        "1. [DistilBERT Paper](https://arxiv.org/abs/1910.01108)\n",
        "2. Used model [Link](https://huggingface.co/distilbert-base-uncased-distilled-squad)\n",
        "3. [Huggingface All Pretrained Model List](https://huggingface.co/transformers/pretrained_models.html)\n",
        "4. DistilBERT doesn’t have token_type_ids, you don’t need to indicate which token belongs to which segment. Just separate your segments with the separation token tokenizer.sep_token (or [SEP]).\n",
        "5. All functions of DistilBERT is [here](https://huggingface.co/transformers/model_doc/distilbert.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1FJc4XVnopD0",
        "outputId": "e6bf94e5-f50f-4705-f5d6-7784c063ddc9"
      },
      "source": [
        "from transformers import DistilBertForQuestionAnswering, DistilBertTokenizer\n",
        "DistilBERTmodel = DistilBertForQuestionAnswering.from_pretrained('distilbert-base-uncased-distilled-squad')\n",
        "DistilBERTtokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased', return_token_type_ids=True)\n",
        "\n",
        "def getOutputFromDistil(Dquestion, Danswer_text):\n",
        "  # ======== Tokenize ========\n",
        "  # Apply the tokenizer to the input text, treating them as a text-pair.\n",
        "  DistilBERTinput_ids = DistilBERTtokenizer.encode(question, answer_text)\n",
        "\n",
        "  # Report how long the input sequence is.\n",
        "  print('Query has {:,} tokens.\\n'.format(len(DistilBERTinput_ids)))\n",
        "\n",
        "  # ======== Evaluate ========\n",
        "  # Run our example through the model.\n",
        "  outputs = DistilBERTmodel(torch.tensor([DistilBERTinput_ids]), # The tokens representing our input text.\n",
        "                return_dict=True) \n",
        "\n",
        "  start_scores = outputs.start_logits\n",
        "  end_scores = outputs.end_logits\n",
        "\n",
        "  # ======== Reconstruct Answer ========\n",
        "  # Find the tokens with the highest `start` and `end` scores.\n",
        "  answer_start = torch.argmax(start_scores)\n",
        "  answer_end = torch.argmax(end_scores)\n",
        "\n",
        "  # Get the string versions of the input tokens.\n",
        "  tokens = DistilBERTtokenizer.convert_ids_to_tokens(DistilBERTinput_ids)\n",
        "\n",
        "  # Start with the first token.\n",
        "  answer = tokens[answer_start]\n",
        "\n",
        "  # Select the remaining answer tokens and join them with whitespace.\n",
        "  for i in range(answer_start + 1, answer_end + 1):\n",
        "    \n",
        "    # If it's a subword token, then recombine it with the previous token.\n",
        "    if tokens[i][0:2] == '##':\n",
        "        answer += tokens[i][2:]\n",
        "    \n",
        "    # Otherwise, add a space then the token.\n",
        "    else:\n",
        "        answer += ' ' + tokens[i]\n",
        "\n",
        "  print('Answer: \"' + answer + '\"')\n",
        "\n",
        "\n",
        "question = \"What are the public holidays of Bangladesh?\"\n",
        "answer_text = \"including monitoring of sighting of the moon in accordance with the lunar Islamic calendar in order to set festival dates; as well as the charitable tradition of zakat. Public holidays include the Islamic observances of Eid-ul-Fitr, Eid-al-Adha, the Prophet's Birthday, Ashura and Shab-e-Barat.\"\n",
        "start_time = time.time()\n",
        "getOutputFromDistil(question, answer_text)\n",
        "print(\"--- %s seconds ---\" % (time.time() - start_time))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Query has 84 tokens.\n",
            "\n",
            "Answer: \"eid - ul - fitr , eid - al - adha , the prophet ' s birthday , ashura and shab - e - barat\"\n",
            "--- 0.17598509788513184 seconds ---\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k3_fiYKRq4LM",
        "outputId": "5c6bfa5a-0e91-4ad5-e22c-089c26b82b0d"
      },
      "source": [
        "question = \"Who was the founder of facebook?\"\n",
        "answer_text = \"Facebook (stylized as facebook) is an American online social media and social networking service based in Menlo Park, California, and a flagship service of the namesake company Facebook, Inc. It was founded by Mark Zuckerberg, along with fellow Harvard College students and roommates Eduardo Saverin, Andrew McCollum, Dustin Moskovitz, and Chris Hughes.\"\n",
        "\n",
        "start_time = time.time()\n",
        "getOutputFromDistil(question, answer_text)\n",
        "end_time = time.time()\n",
        "DistilBERTtime_taken = end_time - start_time\n",
        "print(\"---DistilBERT Got the answer in: %s seconds ---\" % DistilBERTtime_taken)\n",
        "\n",
        "start_time = time.time()\n",
        "answer_question(question, answer_text)\n",
        "end_time = time.time()\n",
        "BERTtime_taken = end_time - start_time\n",
        "print(\"---BERT Got the answer in: %s seconds ---\" % BERTtime_taken)\n",
        "\n",
        "\n",
        "total_time = DistilBERTtime_taken + BERTtime_taken\n",
        "faster = (BERTtime_taken - DistilBERTtime_taken)*100\n",
        "faster = faster/total_time\n",
        "print(\"==============\\n   DistilBERT is:  \")\n",
        "print(faster) \n",
        "print(\"% faster than BERT  \\n  ==============\")\n",
        "# print(\"---%d\\% faster than BERT ---\" % (faster))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Query has 83 tokens.\n",
            "\n",
            "Answer: \"mark zuckerberg\"\n",
            "---DistilBERT Got the answer in: 0.1824321746826172 seconds ---\n",
            "Query has 83 tokens.\n",
            "\n",
            "Answer: \"mark zuckerberg\"\n",
            "---BERT Got the answer in: 1.2376108169555664 seconds ---\n",
            "==============\n",
            "   DistilBERT is:  \n",
            "74.30610541274379\n",
            "% faster than BERT  \n",
            "  ==============\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L9CFu1GMryNj"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UhpDJnYLr44Q"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JmAEmzdGtRqN"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}