Document_Similarity_Finding_using_BERT.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/VinniLP/Document-Similarity-Finding-using-BERT/blob/main/Document_Similarity_Finding_using_BERT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ajHxvz4C-xb4"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KrA0Uxoz_E1l"
      },
      "source": [
        "# **#Approach 1**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f8r5bz-W-84o",
        "outputId": "8cc3c721-fce7-4e8d-ee52-e3d435d80653"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25hCollecting huggingface-hub>=0.4.0\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (2022.12.7)\n",
            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125938 sha256=87bcdb8306e3aa4e966eccf6bbb186348d6b10f7f73fcf00f6235e9e3cb5a433\n",
            "  Stored in directory: /root/.cache/pip/wheels/5e/6f/8c/d88aec621f3f542d26fac0342bef5e693335d125f4e54aeffe\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: tokenizers, sentencepiece, huggingface-hub, transformers, sentence-transformers\n",
            "Successfully installed huggingface-hub-0.12.1 sentence-transformers-2.2.2 sentencepiece-0.1.97 tokenizers-0.13.2 transformers-4.26.1\n"
          ]
        }
      ],
      "source": [
        "!pip install -U sentence-transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QEjTBGeVHEGD"
      },
      "outputs": [],
      "source": [
        "sentences_a1 = [ \n",
        "    '''\n",
        " The moon is the largest natural satellite of the Earth and the fifth largest moon in the solar system. It is the only natural satellite of the Earth and is approximately one-quarter the size of Earth. The moon is believed to have formed from debris left over after a Mars-sized object collided with Earth. The moon's gravitational pull creates tides in the oceans, which affects the Earth's rotation and has a significant impact on life on Earth.\n",
        "    '''\n",
        "    ,\n",
        "    '''\n",
        "The sun is a star located at the center of the solar system. It is a huge ball of hot gas that provides light and heat to the planets that orbit around it. The sun is one of the most important sources of energy for life on Earth, and it has been studied by astronomers for thousands of years.\n",
        "    '''\n",
        "]\n"
      ]
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a69c2000765346f8bbb166916733d139",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)\"pytorch_model.bin\";:   0%|          | 0.00/438M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e0f4972ec48e4233b5c16a3c0d86c038",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)nce_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f1f10c07131348e69c7d5b5078520ecb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5031c8b2e10d47c8b5e788b655e08b52",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)821d1/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1cf3e9a186104cb9847d9c3a9d1f1ee5",
              "version_major": 2,
              "version_minor": 0
            },
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              "Downloading (…)okenizer_config.json:   0%|          | 0.00/399 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f3b69e99e51a4f42bd4fde19adaa9306",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)8d01e821d1/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c8e15ddb5ed242e093ab9c327d07395c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)1e821d1/modules.json:   0%|          | 0.00/229 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "model_name = 'sentence-transformers/bert-base-nli-mean-tokens'\n",
        "model = SentenceTransformer(model_name)\n",
        "\n",
        "#Encoding:\n",
        "sentences_embeddings_a1 = model.encode(sentences_a1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N-qBG28a_LN-",
        "outputId": "6632e4f8-25a2-425e-d1d0-08c2c775557e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(2, 768)"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sentences_embeddings_a1.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qD5JERvs_LMf"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "#let's calculate cosine similarity for sentence 0:\n",
        "similar_value_a1 = cosine_similarity(\n",
        "    [sentences_embeddings_a1[0]],\n",
        "    sentences_embeddings_a1[1:]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f1DLJV0m_LJ-",
        "outputId": "727d54f5-ac0a-4d31-f056-7666120a8fb8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Similarity Percentage =  73.19702506065369\n"
          ]
        }
      ],
      "source": [
        "print(\"Similarity Percentage = \",similar_value_a1[0][0]*100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "33YenZYv_LHo"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mY4KHvkb_lRJ"
      },
      "source": [
        "# **#Approach 2**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PwUEy2gF_fNP",
        "outputId": "5ea0af50-f109-4997-ddd1-e920ba64889b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.8/dist-packages (4.26.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers) (4.64.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.9.0)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (0.13.2)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (1.21.6)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers) (2.25.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (23.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (0.12.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.5.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (4.0.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (1.24.3)\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P7f6tMvH_jom"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer, AutoModel\n",
        "import torch\n",
        "from sklearn.metrics.pairwise import cosine_similarity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437,
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        },
        "id": "NlVqe5H9_LFp",
        "outputId": "57827c39-af1f-4920-9fd0-8f337d977d5b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4a7170f4c3124445a3c4d7b3e838f071",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/399 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "36d112aa42b54f0b9a50a65b6fe97406",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/625 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "83ca4082deab4f5f99445ae132fb7bd7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3d679f8e02c94a0b948150cbc00e23f7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ba8733b02a224f5fa8b467c687c9f4f3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/2.00 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "101d903454b741bcbeb2393ca1746f5d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6807c4cb38c342d3a2bda3896edb89ef",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)\"pytorch_model.bin\";:   0%|          | 0.00/438M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model_name_a2 = 'sentence-transformers/bert-base-nli-mean-tokens'\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name_a2)\n",
        "model_a2 = AutoModel.from_pretrained(model_name_a2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zEPIs487lbwi"
      },
      "outputs": [],
      "source": [
        "sentences_a2 = [ \n",
        "    '''\n",
        " The moon is the largest natural satellite of the Earth and the fifth largest moon in the solar system. It is the only natural satellite of the Earth and is approximately one-quarter the size of Earth. The moon is believed to have formed from debris left over after a Mars-sized object collided with Earth. The moon's gravitational pull creates tides in the oceans, which affects the Earth's rotation and has a significant impact on life on Earth.\n",
        "    '''\n",
        "    ,\n",
        "    '''\n",
        "The sun is a star located at the center of the solar system. It is a huge ball of hot gas that provides light and heat to the planets that orbit around it. The sun is one of the most important sources of energy for life on Earth, and it has been studied by astronomers for thousands of years.\n",
        "    '''\n",
        "]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tQSUHPCCIvAB"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4rm82P1v_LDQ"
      },
      "outputs": [],
      "source": [
        "tokens_a2 = {'input_ids' : [],'attention_mask':[]}\n",
        "for sentence in sentences_a2:\n",
        "  new_tokens = tokenizer.encode_plus(sentence,max_length=128,truncation=True,padding='max_length',return_tensors='pt')\n",
        "  tokens_a2['input_ids'].append(new_tokens['input_ids'][0])\n",
        "  tokens_a2['attention_mask'].append(new_tokens['attention_mask'][0])\n",
        "\n",
        "tokens_a2['input_ids'] = torch.stack(tokens_a2['input_ids'])\n",
        "tokens_a2['attention_mask'] = torch.stack(tokens_a2['attention_mask'])\n",
        "\n",
        "outputs_a2 = model_a2(**tokens_a2) \n",
        "outputs_a2.keys()\n",
        "\n",
        "embeddings_a2 = outputs_a2.last_hidden_state\n",
        "attention_a2 = tokens_a2['attention_mask']\n",
        "\n",
        "mask_a2 = attention_a2.unsqueeze(-1).expand(embeddings_a2.shape).float()\n",
        "mask_embeddings_a2 = embeddings_a2 * mask_a2\n",
        "\n",
        "summed_a2 = torch.sum(mask_embeddings_a2,1)\n",
        "counts_a2 = torch.clamp(mask_a2.sum(1),min=1e-9)\n",
        "\n",
        "mean_pooled_a2 = summed_a2 / counts_a2\n",
        "mean_pooled_a2 = mean_pooled_a2.detach().numpy()\n",
        "\n",
        "similar_value_a2 = cosine_similarity(\n",
        "  [mean_pooled_a2[0]],\n",
        "  mean_pooled_a2[1:]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GInjiFpX_LAy",
        "outputId": "1c69c311-bffa-4347-e6c6-823a0852518c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Similarity Percentage =  73.19702506065369\n"
          ]
        }
      ],
      "source": [
        "print(\"Similarity Percentage = \",similar_value_a2[0][0]*100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HcdRakFc_K-5"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7aoTtbRg_K8W"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zh0O6SDl_63n"
      },
      "source": [
        "## **Heat Map**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vYaSs5KA_K54",
        "outputId": "a919d68a-692a-4a65-cf9b-031d4cbb6a47"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.8/dist-packages (4.26.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers) (2.25.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (23.0)\n",
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            "Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (0.12.1)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (0.13.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (1.21.6)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.9.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.5.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (4.0.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (1.24.3)\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bUCIv4WR_K3d",
        "outputId": "fdd80858-08f3-4901-eba7-4c74b655e7d6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.8/dist-packages (3.7)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.8/dist-packages (from nltk) (2022.6.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from nltk) (4.64.1)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.8/dist-packages (from nltk) (1.2.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.8/dist-packages (from nltk) (7.1.2)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading collection 'all'\n",
            "[nltk_data]    | \n",
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            "[nltk_data]    | \n",
            "[nltk_data]  Done downloading collection all\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "!pip install nltk\n",
        "import nltk\n",
        "nltk.download('all')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OngMvokIG5d8"
      },
      "outputs": [],
      "source": [
        "paras = [ \n",
        "    '''\n",
        " The moon is the largest natural satellite of the Earth and the fifth largest moon in the solar system. It is the only natural satellite of the Earth and is approximately one-quarter the size of Earth. The moon is believed to have formed from debris left over after a Mars-sized object collided with Earth. The moon's gravitational pull creates tides in the oceans, which affects the Earth's rotation and has a significant impact on life on Earth.\n",
        "    '''\n",
        "    ,\n",
        "    '''\n",
        "The sun is a star located at the center of the solar system. It is a huge ball of hot gas that provides light and heat to the planets that orbit around it. The sun is one of the most important sources of energy for life on Earth, and it has been studied by astronomers for thousands of years.\n",
        "    '''\n",
        "]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rrMvq8tH_K1d"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer, AutoModel\n",
        "import torch\n",
        "from sklearn.metrics.pairwise import cosine_similarity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NLLOPtGl_Ky7"
      },
      "outputs": [],
      "source": [
        "model_name = 'sentence-transformers/bert-base-nli-mean-tokens'\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = AutoModel.from_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d4Z0018K_Kwk"
      },
      "outputs": [],
      "source": [
        "from nltk.tokenize import sent_tokenize\n",
        "sentences=[\"\",\"\"]\n",
        "sentences[0] = sent_tokenize(paras[0])\n",
        "sentences[1] = sent_tokenize(paras[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1z5cJoLwAK7b"
      },
      "outputs": [],
      "source": [
        "def my_function_a3(sentences):\n",
        "  tokens = {'input_ids' : [],'attention_mask':[]}\n",
        "  for sentence in sentences:\n",
        "    new_tokens = tokenizer.encode_plus(sentence,max_length=128,\n",
        "                truncation=True,padding='max_length',return_tensors='pt')\n",
        "    tokens['input_ids'].append(new_tokens['input_ids'][0])\n",
        "    tokens['attention_mask'].append(new_tokens['attention_mask'][0])\n",
        "\n",
        "  tokens['input_ids'] = torch.stack(tokens['input_ids'])\n",
        "  tokens['attention_mask'] = torch.stack(tokens['attention_mask'])\n",
        "\n",
        "  outputs = model(**tokens)\n",
        "  # outputs.keys()\n",
        "\n",
        "  embeddings = outputs.last_hidden_state\n",
        "  embeddings.shape\n",
        "  attention = tokens['attention_mask']\n",
        "\n",
        "  mask = attention.unsqueeze(-1).expand(embeddings.shape).float()\n",
        "  mask_embeddings = embeddings * mask\n",
        "\n",
        "  summed = torch.sum(mask_embeddings,1)\n",
        "  counts = torch.clamp(mask.sum(1),min=1e-9)\n",
        "\n",
        "  mean_pooled = summed / counts\n",
        "  mean_pooled = mean_pooled.detach().numpy()\n",
        "   \n",
        "  return mean_pooled"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XfKKF6TjAK4S"
      },
      "outputs": [],
      "source": [
        "s1 = my_function_a3(sentences[0])\n",
        "s2 = my_function_a3(sentences[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZmmfSQshQvqd"
      },
      "outputs": [],
      "source": [
        "sentences_a2 = [ \n",
        "    '''\n",
        "    The cat is a small carnivorous mammal. It is the most popular pet in the world, and is kept by millions of people. Cats are known for their soft fur, their ability to catch mice, and their independent personalities. They are affectionate animals and can be trained to perform a variety of tricks.\n",
        "    '''\n",
        "    ,\n",
        "    '''\n",
        "Dogs are man's best friend. They are loyal and obedient, and are known for their ability to be trained as service animals. They come in many different breeds, each with its own unique personality and characteristics. Dogs are playful and energetic, and are often used as therapy animals to help people who are struggling with emotional or physical challenges.\n",
        "    '''\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qFYY3ywyAK1y",
        "outputId": "b8886615-0bc8-43c9-ff7e-a8e3b6bcfa60"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (1.21.6)\n"
          ]
        }
      ],
      "source": [
        "!pip install numpy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rkgBC5Y1AKyx"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "simm = np.zeros((len(s1),len(s2)))\n",
        "for i in range(len(s1)):\n",
        "  simm[i:,] = cosine_similarity(\n",
        "    [s1[i]],\n",
        "     s2[0:]\n",
        "  )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 779
        },
        "id": "GrjCoTv1AKw1",
        "outputId": "87a5bcb8-d60a-4576-9700-7e7018bd6cbf"
      },
      "outputs": [
        {
          "data": {
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b+smOAUmSJEkT3iAUqKqDqmorYCvgIGAqsPvgPEMBqfcOBJZK8gqaGd8XVtWJNGOFjgM7iiSN3eB6UVULAHcAFyf5LPAxmueyOwKbdleh+syOAUmSJEkT2tCbwHMDR9M87HsOMEd7yguBNyf5TFc1SurO0DViceBI4NQkp7efrQhsBPwkyV+6rFPS6Kqqr7U/PqH98w7gQmA74AdJbumqNvWXwYAkSZKkCWswQqiqFgG2T/LxqpqcZFpVrQzcBjwD2A/Y0wd/Un9V1U40y0Gvo+kQ+EOSO7utStKoa68tO9DsFyjgJcD2wK5JbuyyNvWbo4QkSZIkTWSDN6GOpNknALBoVS2W5E9Jbk7yQ5r2/s07qVBSZ4YXCSc5AXg9zdzv3YBtq2pNlw1LeozmAL6f5G7gviRfBq6m2WcidcZgQJIkSdKE1Y4HWQV4RpIDquq5NHPC/1pVuwJU1VTgUuBLHZYqaTYb3ilSVZtU1WYASd4KnAK8GHgtMKW7KiVNAJcCe1fVW5M81B5bDnDhsDrlKCFJkiRJE1obDHwaOB9YGjgB+DuwL00b/71VNTXJgx2WKWk2Gxorth+wAXALsBLwS+AY4HZg5SQXu5hc0mNRVesBh9Pch/wYWCXJC7qtSn1nMCBJkiRpwquqbYCtgSOTXF5VhwKLJNlz8HCw4xIldaCqFgJ+Bjwd+DrwF2BRYBXg+Ha8kCQ9KlU1iaaBcdCdtBlwI3B1ktu8B1GXDAYkSZIk9UpVbQocDayd5B7fBJb6q6o2BJYHLgC+kORZVfVi4HXA+5Jc0mV9kkZLVU1KMr2q5k5y79BxAwCNO+4YkCRJkjShtG/nUVXLV9Wcw8da5wG7tKHAZEMBqdd+CpwKTAV+1x5bEnjAUEDSzEoyvf3xnVW16tDxaS4y13hjMCBJkiRpwmjf/p9eVUsBXwQKHvZFnSR3Jjm//dm396SeGrzZm+RO4GrgKVX1R+CNwIcH53RYoqQRUlWT27/3Ap6Z5PdVNW9VbV1Va/gigsYbRwlJkiRJmjAGY4GqandgviQfqqq5ktzXdW2SuvffRodV1drAzUmuGQQHs7E8SSOuquYAfgLsBtwDvAtYDLgZeJP3IxpPTL4lSZIkTRhtKLAc8EngNVU13+BLeFVN6bY6SV2qqjmGFoC+taomDUZ7DN70TfLrJNe0PxsKSBqTqloQIMkDwBnA+4ATgO8DrweWAVburEDpERgMSJIkSZpQklxL8wX8OuC6tqWfJA91Wpikrj2/qnauqmOA57VjhAbdA9Oh6SjorjxJo6iqtgCurqo3toe+CHwGOCDJScALgWlJLu2qRumRGAxIkiRJGnlDc30XqqonJbkhyVbAVsDrq+qWqlrRh35SP7XjPS4AtgD2AH7Rzv6euz1l8Lav85YlzZQk3wE2Bfaqql8Aqyc5PclPq2pZYCfgUPj3/Yo0HrhjQJIkSdJIG9orMDfwJWCB9qNPJvlGe85OwJccDSL1T7tA+A1Jjq+qtYD1gI3ajz8HXAj8GNjBN3olzYyqmprkwap6Jk3ouBDwPOBXwL5J/lhVKyf503/bcSLNbgYDkiRJkkbaUDBwJHArcFb75w/A3cCHkpzdnusyUalnqmpFYDngMmB3mpDwmqraBdgZ+BtNs8AO3VUpaZRV1YXAu5J8v/39BOA1wP5JPthpcdJ/4PItSZIkSSOtDQUWB9YCtqNZ9rc9cBFwMbAJcHZ7rqGA1DNJrgKuqqoNafaP7Ns+xDu5/bM4cBM0Yz6STOuqVkmjpR1ROAfN/cacg+NJdqqqeYA7u6pN+m8MBiRJkiSNpPYL93pJzk7y93Zc0Nw033OuTHJHVf0GOLE9324BqWeGH/QnOaeqfg68FHg+sCpNgHh6kvvacwwFJI1ZOxro/qo6B3hzVd0K/AnYAJgzyafg392N3VUq/W8GA5IkSZJG1brAt6rqdOC9Sf4M0C7++1y7Z/i6JL8HuwWkPkoyraqmAgcCywL3Au8CfgDsRtNpdHJ3FUoaRUNjDBcCFklyYlXdA3wQuBZYBDi2PdcXEzQuuWNAkiRJ0siqqrmAI4EtgZOAw4BJwMbAE4GTk9zul3Kpf6pqviR3VdURwOrAh4FXA1sBbwJOBZZMcoPXCEljNbheVNU6wPHA74En0FxjfgosDNyT5N4Oy5T+K4MBSZIkSSOnqibBv7sAqupJwBE0o0EOTfL1oXNt35d6pqo2B74AvAd4MvCJdtfA4LNtk7yhuwoljbqqOoZmbNDXgY2AHYB/AsclOdf7D413BgOSJEmSRsrQm3pz0bz5Ow9wPfAjYEPgU8BFSbbvrkpJXauqLWnGBi0DfDbJe9vjc9C84bvpYASZJM2MqnoRsD1wVJLL25FlSwG7ALcn+WiX9UljYTAgSZIkaaQMzfX9OM24oLuBv9PsUDsgyX1V9ZQkVzgeROqfqpqS5KGqWoHmGvF04O3Az4GfAIsCKyXZzTd6JT0aVbUHcAhwHrBrklva43MB97f3KV5fNK5N6roASZIkSZoZ7Zft5YENk7wsyc40I0MWAvZsT/tTe66hgNQj7YO4h9pfPwDMn+R4YG3gdpqRYxsB+w7+k9lfpaRRVFX/ul4k+STwVOAW4Pyq2q+qJie5bxAGGApovDMYkCRJkjSK7gNurKr1AJJcBhwDbFBV8xsISP00eBBXVbvSdAb8uT1+R5K3Ai8AvpDkTjuKJI1Ve71IVS1QVTtV1SuBBZPsCmwHvBw4qdsqpZkzpesCJEmSJGkshnYLzJnkpqo6A3hbVZ0LnELzpfzmJHfYvi/13q3AM4HTquolg8XDSS4FLm3P8RohaawG14tPAX8FXgzcVFVnAicmWa+qngj/vl/pqE5pzOwYkCRJkjTutQ/6B1+yD21HCX0COBVYDvgBsADNHHFwPIjUOzOM+fhGkoWBk4HfVtVxVfWwlyMNDyWNVdst8CxgsSRvpxlN9lPglcDJVbVRkpvacw0FNBJcPixJkiRp3BvqFjgQWDXJDlU1H/B84AaanQIPJrnfN/Wk/mlne0+rquWAXYH5gWuBrwH3AN8E7kqyZYdlShphVfVSmlGGU4FXJ9m+ql5E81LCqwYLiKVRYceAJEmSpHGvDQXmB14BHFZVawGfBXYHXpnkriT3D87tsFRJHUgyrf3xczQdQ9cB04D3AE9IsiGwPTQhQhc1Sho9VTWp/XuuJN9M8l3gIeCJVbUEsBlwXpJbhruWpFFgx4AkSZKkkVFV7wRWBVYE3g/8AfgKsEuSP3ZZm6RuVdVTgE+1IQBVtSzwTuDuJPsPugq6rFHS6BjsK2rHkH0CeF+Sa6pqKk3o+AKaDoJNkzzgfiONGjsGJEmSJI2Sc2gWDe/VvrX3YuCvhgKSgJuBuatqz/YB3V+BY4BV26XlhgKSZsagA+AwYFobCqwIbAOcS9MtsEMbCkw2FNCosWNAkiRJ0kiqqlWAo4F9k1zubgFJVbUxzTLQe4ALgW2Bi5Mc6jVC0syqqoWBHwE7AEsAewLTgT8nOaDL2qTHymBAkiRJ0kiYcQxIVS0ILGEoIKndGzAdmAI8G1gT2Ag4K8nx7TmO+ZA0ZoN7i6p6B7AOsDjNeLKrgTNoFg7/qcMSpcfEYECSJEnSuDP8AK+qFgMeSPLPjsuSNA5V1RxJHvgv5xgeShqTod0CqwFXAXMAqwP/SHJFVe0PrJZk504LlR4jdwxIkiRJGo8KoKreA7wXuKaq9n7YCVX1SP+hpImvqpZu/14GOLZdBjr8+eTh3w0FJI3VUGfR64G7aPYInNuGAmvQdCUdCE3o2FGZ0mPm/3glSZIkjSvtm3rTq2pZ4CVJ3gRcCvyt/XwFeNgXd0k90oaCL66qK4DvAmcmebCq5hyc46JhSY9Vkn2A/wHeUFXXVNWLk1wK7J7kOjuRNOoMBiRJkiSNK0MP/DcGvlpVWwL3JTmp3SvwpapasbsKJXUpjc8BpwDLAPtV1WpJ7geoqi2r6gmdFilp5Aw6EatqSlWtUVVzJzk/yTOB44HvVtVxSW4CO5E0+gwGJEmSJI1X3wGWAg4H9muPvQ64JslVnVUlqVNDY8S+AKwGnAmcW1WfrapnA+8HHuyoPEkjaujFhFcA+wObV9VK7WfvAz4EnAqOM9TE4PJhSZIkSeNWVe0IHAycCCwBPAN4ZZJrbeGX+mfw777tGloMuDTJvVW1APBpYAHg80m+VlWTHSkkaWZV1fzANsAmwNXAxTT3H6skeXmHpUmzlMGAJEmSpHGhqqYkeaiqtgBWBhahedA3DdgOuJ6mW+BCQwGpf4ZCgeWBrwA3AWsAx9GEAbdW1TxJ7um0UEkjq6pWBlYArqK579gNWAlYHPhAkou8B9FEYTAgSZIkqXNVNX+SO6pqCvAXmkBgKvB8muWixye5rcsaJY0PVfVFmuvCVTRzv6+hCRPfD3wtiWOEJI3ZoLuoqrYGDqDpENgK+BGwB3B/kofacys+TNUE4Y4BSZIkSePBaVX1cZov4p9M8v+AI4F9gaWBc6rqKV0WKKl7VbUEMJlmB8mRwEuTbA3cCSxvKCBpZg2NHHsn8J4kb0yyNE3H4pGDUKA911BAE4bBgCRJkqTxYBdgPuBY4LlVtXCSu5P8mmbHwBuTXNFlgZK6l+RGYFdgHuA2YLmqmge4FfgMNCOHuqtQ0iiqqnmBK4Abhg7vAyxdVct0U5X0+PL/LCVJkiR1qt0tcA3wVmBrYF7gjKraFCDJbUl+0Z5b3VUqqQvtiDGqaqGqWj7JvUluAs4A3gX8DPhNkhud/S3p0UhyN3Al8IWqWr09/D/A4kmu664y6fHjjgFJkiRJnamqpwEPAM8G3pzkWe3x3YG30CwXfXX7lrCknhma/T0V+BawCvBHmk6iy4En03Qb/TxJnP8taayGFprPDczdLjB/A8315dc0nUnHJPn24FrUacHSLDal6wIkSZIk9driwEHAmsB74F8dBJ+qqs8D+9MEB5L6aceq+iXwMuCPSbaoqkOBzwOn0SwmvxhcCipp5rShwPrA0cDlVXUnzULzFYCVgBuS3NmeayigCceOAUmSJEmdqqotaJYM3wf8APhWkquq6hDgOMeDSP1UVSsD+9HsElia5trwjfazFYCPAnck2bGrGiWNnqpaHHhukm9U1THAVcD5wDOB9YFrgZOT/NbAUROZwYAkSZKk2W6ofb+AeZLcXVXr0nQITAb+AOyYZLlOC5XUqapaDtgGeAawDHAc8LMk/2g/nz/JHYaHksaive9Yn2Y3yW+BS4A9ktzfBgZPA7YELk1yQneVSo8/gwFJkiRJs11VvRo4Pcldj/DZK2nm+l6Q5HftaKGHZnuRkjpTVUsm+VtVrQisTRMWbgksCdwIXAp8D5huICBprIY7AKpqV+AY4EfA1oNrSRtIXjd4gcGOAU1Uk7ouQJIkSVK/VNXBwKpJ7qqqpapqUnt8EkCSk5J8Psnv2t8NBaQeqao5gWdV1QdoHv7fluQymtFBZwILAM8DphoKSJpJU+BfocB3gOWAacDNVbUPQJJrB9cWQwFNZAYDkiRJkmab9uH/XMDKVfVTYO32jbxJgF++JQE8BPweWAeYG1irqtZMcl+S7wNnA6ckubcdCyJJ/1VVLQMcUlVvBN4CPJjkH0leQtORtEdVfb/TIqXZyFFCkiRJkma7qjqV5o3fY2kWDN/UHp+cZFqnxUkaF6rqRTR7BZ4IPBn4JXAXsGmS13ZZm6TRVFWbA18G/g5snOT6GT5fJMkt3o+oDwwGJEmSJM02g1m9VfVm4EFgWZqloqcDn3YsiKQZVdWTgfVodg28ADg8yUkuHJb0aFTV7jT7SnYEvgvsDRwMLJbkjV3WJs1OBgOSJEmSZovhh3hVtUSSG6tqLmBTYBuaN4Nfn+SaLuuU1L3BiKChJaGTgfnbQ7d3WZukiaEdLXQUTUfSP4E3JvmLC4fVFwYDkiRJkh53g1CgqpYEjqYZDTIfcFCSs6pqKeA5Sb7eaaGSOldVyyW5tv25gEmO9JD0aA3dg0xOMm3G60pVPYlm38Bf7URSn7h8WJIkSdLjbuhL9mHAH2nGgXwEOLaqjk1yA/AN+PebwpL6o+0IoKq2AT44+D2Nae2CckmaKe3b/9Orak7gmKqaZ/i60n5+ZZK/wsPuV6QJz/9jlSRJkjRbVNVCNOOCzkgyPcmXgQ2ABapq/sGbe7bvS/0z1BHwfuDD7UO711fVh9vrgw/rJD0ag5cNdqDpCrinqqZCEwJ4z6E+MxiQJEmSNFskuQ34HrDd0Nu/d9MsFX1CZ4VJGheqai3gkiS/rKr3Ac8FVgP+X1XN0W11kkZR2y2wGM1y4UEn0oNtt4DPRdVr/gOQJEmS9LgbGg90JrAmcFZVHQJ8HTg5yfV+QZf6p6qmtH9PBv4OLFFVVwL3Aq8F9gNWTPJAd1VKGnG3AkcCW1bVaVW1WtstYCeSes3lw5IkSZJmu6p6ObAI8Gfg7MEyQFv6pX6pqlWB64FjgROT/KCq1klyYVUtCHwbOCrJaS4FlTRWQwuH5wAmJ7m3qpYAdgS2B34N7EUzXsh7D/WSwYAkSZKkx8XQl/JNgMuTXFtVk4dmiQ+faygg9UxVLUIz93tDmpFBmyW5cujzTYFXJHldNxVKGnVVdSSwPvA74ArgZGBh4O3APklu6bA8qVMGA5IkSZJmucGD/qpaGjgFeG6Sh2b8vLsKJY0HbVfAl4FFaR7YXQz8BJgObAz8JMn9dgtIGquhe5D9gOcA+wIbAKsCdyR5/9C5XlvUW87wlCRJkjTLDT303wy4YDgUmOFzST002CmS5HaacR4vBuYFXgO8AfgRsE2S+9vzfHAn6b+qqsWBKe015knA55P8JckJwNdo9gysPTjfa4v6bErXBUiSJEmamNpZvq8H5q6qrYAfJrm347Ikdax9m3d6++BuP2Bl4Pokh1TV5sBawFnAh4fON0yUNBZnAJsmua2qfgC8o6r+meScJL+tqgeAeTquURoXHCUkSZIk6XHTLv3bsf1zDnAS8KcZOwgk9cfQ/pHDgCcAFwIHJ1n5P50724uUNHKq6oXAe4F30XQivQLYB1icpnvgVmDFJJt0VqQ0jhgMSJIkSZplBsuF24XDSwFrAEcACwF70ywA3C3JhR2WKalj7eLh04DnAV8Bvp/kc1W1O/CPJKd2WqCkkVNV8wFvAXYBzkvy2qqan6YLaTlgGk334k2D+5XuqpW6ZzAgSZIkaZYYWvY3H83y0PcBhwEfaGf7UlXrJTm/yzoljQ9VdSDN7sPVk7yyPXYR8OYk53VanKSRVFXbAAcDPwb+SdOt+MskD3RZlzQeuXxYkiRJ0iwxNAP8HcCJwOXAP5OcUFULtG8C/waaEKGjMiV15BH+3V8L7AtMqqqlquojwO8MBSQ9Bj8FNgW+CkwGtgP2rqp1O61KGocMBiRJkiTNar8A7qYZIXRwe+wVwOaDN/ZcJCr1z+DffVWtW1VrAKcDq9FcL06gebv3He05Pq+QNNOS3JrkpnZk4VHA2cCKuHBY+l8cJSRJkiRplqmq1YBbgDOAZYDnA/cB3wTemOQCl4lK/TO0cPhNNDPALwWuB34FnJzkwaFzy/BQ0mMxfB2pqiWS3Nh1TdJ4YzAgSZIk6TFpw4AHaBYL75VkvapaAng98DrgXODyJIf7wE/qn6H9I3MAn6bpJJoEbAysCkwBzkpyZodlShpBQ9eXTYA/Jrl66LMpSR7qrjppfJvSdQGSJEmSRt7CwCHA04ED22O3JnlfVR05/CYwUIDBgNQjQ2Hgm4G1gTuT3AJcWVX/A7wIuKmr+iSNpqqanGRaVW0AHEZzLaGqlklynaGA9H+zY0CSJEnSY1ZVmwN7A9Np5vmelOTqqjoK+GySyzstUFLn2hDgGGBe4OAkJ7fH505yb6fFSRpZVXUm8NEk36uqtwCbANcBbxnsNpL0vxkMSJIkSXpUhmaGT04yrT22MnAQTRfB74Htkjy5yzoldW+Ged87AHsAtwGHJrmg0+IkjZyhEUKTgeOAK4FVgNuB7wIvAw5PclWHZUrjmsGAJEmSpEdl6Ev5gcATgRuAH7ULhl/UHvtNksuc8yv1z9CYj3VpFpGvBhyR5IqqWgA4AHgoybs7LVTSSBm6/5gK7AlcAmwH3Jbk3VW1EvAtYF27kaT/zGBAkiRJ0kwb6hZYA/gy8AlgCWAR4Brgu0ku67JGSeNDVf0W2Bf4JDAfcArw/iQ3Dl1LJiWZ3mmhkkbC0HVjL2CdJLvM0JX0XeA7ST423NUo6eEmdV2AJEmSpNEz9ABvJ+C9SY6jCQd+RDNGaK+qWrCr+iR1q6omtX+/FbgA+BVwC7AlsDFwUVWtOriWGApIGqs2FFgQeA3wu/ZYqmpKVa0InJDkY+1xQwHpP7BjQJIkSdKjUlXLA+cA9wIvTXJFe3wVYKEk5w+/wSepH6pqgST/bH/eCLga2B5YMMk7quqlwEuS7NJdlZJGWVUtC7wNeCnwNZrlwze2nw3GmHkPIv0fDAYkSZIkPWpVtSTwVmBz4Ns03QP3d1uVpC5V1e9plo/vnOTu9tjLgFcAnwcOpLlW/MAxH5LGanj0WHtoErAeTTiwEnAu8CFoOgi6qVIaHY4SkiRJkjRmQ+NB1qmqzYCn0HwJ3xFYGvhzVT2twxIlde/ZwP3A9VV1AECSU4ArgJcBf07yg/a4oYCkMRkaOfZu4IvAUcDNwJHt788AFjEUkMbGjgFJkiRJYzL0pt5yNG37FwKbAdsn+XVVTQWek+TsTguV1JnhDoCqWhc4Fngi8IYkP2qPT03yoN0CksZq6B5kT2B94Ms0HUirJLmzvQeZq/3ZZebSGBgMSJIkSZopVfVJ4IfAn4BjkmzYjhRaPskv23Oc6yv1zNBc70VoHtBd3x7fDjgauBXYBLjFQEDSzKqqycCXaMaR7QBMTnJIVb0aWDrJUZ0WKI2YKV0XIEmSJGl0VNUcwPXAVOBjwDvbj94KLAz8EpztK/VNGwYOHvZ/CnhSVV0G/IFmKeg3qupw4B5DAUkza3CNqaqzgLcDayVZr/34tcCJQ+d5DyKNgR0DkiRJkmZKVa1NM8/3TmBnYDLwM2DLJFfZwi/1z9CYj32ApwJ70YwaW5+me+BtM57bUamSRsiMD/qr6inAR4DraLoXnwmsm+QFHZUojSyXD0uSJEn6P7Wt+1TV1lW1EXAlcBLwD+CrNAv/vmAoIPVXGwo8AXgBcF6SB5KcRtNZtFZVvXD43K7qlDRaBqFAVb2oqvZuD7+SpntxfeB2YLf2nMmdFCmNKEcJSZIkSfqPhlr35wA+ALw2yW3A8VV1HnAfcHeSG9v/xJZkqb+eCtwBvKOq5gROSnJ9Vc0DPNhtaZJGVVVtCxwBfAM4DzgVeFuSu4fPc0yZNHPsGJAkSZL0Hw217+8J/CrJL6tq3vazy4D7hkIBdwtIPZbkApq9I0cCqwNnV9UpwIVJftJpcZJG2QLAnkkOAJYD5gFuqapDoXmJocvipFFlMCBJkiRpLC6kaddn8IZeVe0MvL/LoiSND4MHc0mu4d8jxr4B/BO4varW6LA8SSNmaIzhRjRhwMuraqUk9yR5DbAB8BfwpQTp0XL5sCRJkqT/qqqWBb5Js1fgIOBumgd/+yb5mbsFJFXVFGD64FpQVQsA6wEbAQ8lOajL+iSNhsHC4fYa8lvgZ8CcwO+AS2j2mNw84/ndVCuNLoMBSZIkSWNWVQcBOwM/AG5IcphfyKX+Gf533+4QIMk9/+HcFYB7k9w02wqUNLIGLxtU1auABZN8sqqeCzwXWJxml8kxw+GApJlnMCBJkiTpPxp6a29qkgfbY3PQvBX8UPu73QJSzww9uHs3sBDwBmDvJJ+f8ZzOipQ0sqpqGeBa4Kwkm7fH5gK2AhZI8pku65MmAncMSJIkSXqYR1riNxQKTE7ywCAUaD/zwZ/UI21gOL3tBHgp8HbgDzQjxqiq5cBrg6RHp73GXAesDyxcVVdX1dZJ7ktyMvCFwXld1imNOjsGJEmSJP3L0FvA8wK7AZOB6cDHkjzQbXWSxpOqegtwH81bvfsk2aSqFgQ+A7wlyQ2dFihppLQvH0yrqkWB+YC7k/yjqrYBjgbuAjYGbk0yrctapYlgStcFSJIkSRpXBm8OHQHcD8wBLJ/kw1W1cJJbuytN0jhzBvA2YA9gi/bY7sB9hgKSZkbbJTB42P9xYAng2qq6CzgyyYpVdRTN9cVQQJoFDAYkSZIk/Uu7T2BZYK0kG1TVWcBx7cebVdUfklzUYYmSxo9rgRuApwI7VtWcNPO/XwruGJA0dkPLzN8DPABsCKxDM07oncDuSd7enuO1RZoF3DEgSZIkaUZ3A7+qqiOA25J8q134dxDN2BBJPdQuHqeqXlhVOwM7A58C1qR5u/evwL5JrvPBnaRH6QHgnDQuAE4ElqqqrQcneG2RZg07BiRJkiQ9TJJbq+r3wLuAr7Qzww8Bfpzk9227v8vKpB6pqnmT3F1Vk2h2CHybZtzYC4DvAO9OcvfgfB/cSXqUfgt8pL3WfC3J7VW1CHBbx3VJE47LhyVJkqSeG37QX1Vr0Lz1+wDNzPDn0rTznwkcnuSfvgks9U9VXQKcB3wfWDbJMVW1ErAGzTViWeBtSa7prkpJo2ZwT1FVcya5vz22OfA8mvuQPwAkeUWHZUoTksGAJEmSJACq6p3ANsAiNHsFzgEuBqYmubc9x1BA6qGqWh74MM2Duu8mGewRmANYDVg6ybc7LFHSCKuqDwLr0nQjnQ3cCcxNMwb9irZjabKLh6VZx2BAkiRJ6rGqehbwQuB04OPAZsBKwJ7AAjRvCJ+a5PrOipQ0LrTLhVcHTqLpKtozydkIUfNiAAAgAElEQVTtZ4O3fh01JmlMhq4br6LZWXIksAswGfge8MMkN3RYojShuXxYkiRJ6rfFgSVpgoB7gGlJLkmyO/A1YCuaDgJJPVRVa1TVfFW1GfDtJL9OshJNV9FXquqXVbXA4HxDAUljNQgTgS2Bo5P8OMlOwLeA7YED2s8lPQ7sGJAkSZJ6rqrWB55G85be1cBXknyv/cy2fanHqmoL4KPA/MBuSb5VVVOTPFhVk2kWkx+d5NYu65Q0mqpqHeBwmi7Fg4buP+YGVkpyqZ1I0uPDYECSJEnqoXYu+JJJrml3C3wPKOD5wFOAm4FvJbnQL+RSv1XVq4F9gak0I8eOT5Kq2r/9+Xb3j0h6NNoRZcsD29IsMb8JOCPJbzotTOoBgwFJkiSph6pqRWA34FnAXEk2aI8vCawJbAhclOTkzoqU1KmhER4LJbm1qp5PMwP8HuD7wOuSrNxZgZJGWlWtCTwHuIBm2fCKwHOBFYD93C8gPb4MBiRJkqQeqqopwLrAGcDlwAnA19s3fxelCQZOdZmoJGhCgsF1oKp2BxYFzkzym6qakuShbiuUNAoGIwqralPgMOBC4OnAecCHgYeApyQ5t8MypV4wGJAkSZJ6qqqmAv+fvfuMtqys0rd/3VVFTpJExYA5IGIObau0kTa0ObQ5BwxtTrQBFONfxRwwI4gKKuYGRUXBHEHMIgZAEESiku73w1rH3taLTRVCrbPrXL8xGGevoGN+qWfs/cxnznlv4DfAI4HNgfcBDwT2afvJCcOTtMgkWbft2VPHIWn+Jfkk8La2n06yCcOcgZsBtzDRKK0ZK6YOQJIkSdI02p4D7De2CzmOYb7AI4DlJgUkJdkMWBdYp+2xbc9eaC9kFZGk1bXSLJIjgasl2bDtacCTknwEuAZw1GRBSkuIFQOSJEnSEpRkvbZ/XenesrF10EZtz1go958qRklr3kybj7syzCFZDhzNsFH3nrZnTRqgpLm00I5sTDg+D/gacH/g3cBJwKWBPYHrmHiU1gwrBiRJkqQlIsll2x6X5GrAvyd588zJPYACtD1j/GtSQFpiZv7dvw54OHA2sDVwD+DmwBeniUzSnAvD94xHAeu1PTDJZYEnAGcBGwPPHpMHHkyQ1gATA5IkSdISMA4b/pckdwduBDxjrA7429BQT+hJAkhydeBHbQ8br9cFrgY8JMk3gLNcLyStjvE7x5bAQ4FPjffelmQ/hhlHv1+YYWJSQFozlk0dgCRJkqQ14jzgIIZ+4VsCOyfZYSEpkOSWSTaaMkBJ01mYHZBknbY/BzZM8q4k1xw3674PXLrtmSYFJF1EVwD2Ae6d5C1Jtm97StujHWwurXnOGJAkSZKWkCQ7AesD1wduBRwK/IChlP8ebvhJS1OSS7c9Ick7GDbufsowY+B6wDrAFsAr237KNh+SVlWSa7T9WZIbAPcDXgRsD9wduAHwY2A355dIa56thCRJkqS13MxQ4c2A49r+FPjc2BLkP4EHAq8a+/ouW2nugKS1XJIrAC9Lcilgm7aPHe/vB3wMuDZwVNsjwTYfklZNkuXADkk+xDBc+MFtz05yJPBr4MbAbXF/UpqEFQOSJEnSWmwmKXBt4M3AHxhmDLy47b7jO5dqe8qUcUqaVpKtGAYLbwk8D9i37TlJNgS2b/utSQOUNLeSvAK4P0My4HVtPzHefwjwlba/9mCCtOaZGJAkSZKWgCQHMLQHAXg+cFngWOBJbb+RJLYRkpamhdZASW4JnAO8FljOkCB4PPC7ts+cMkZJ82XmYMIKhtZBJwO3BJ4CHM8w9+h+bW87YZjSkmZiQJIkSVrLJbkKsAfwYOBrwO2B9YCvA4e0fcyE4UlahJI8FrgX8HPgaW3PNYEoaVUtrBfj3JJD2u433r808DBgJ2DPtgc7t0SahokBSZIkaQlIsjWwGfA64HEM/XyfDTyn7RmW8EtLzwX9u5/doEsSYIO2Z7pGSFpVM0mBGwFvBW7OUIX0WOAM4APA+a4p0rSWTR2AJEmSpItfRgvXbU9s+wvgKIaEwAHAyWNSIP44l5aWJFuMbT4um+T9SdaB/x0sPCYI2vbM8b5rhKRVMlNZdFfgQOBfgd2ABwI3BW43rj/5B/8XktYAEwOSJEnSWibJBuOGXpMsX+nx3sD3gBe2feEE4UlaHO6S5GMMs0e+NQ4aXmfhoW09JF0MDgSuCuwJfL/tLYHzGJIDswkESROwlZAkSZK0lknyXuBHDL17zxnvXWD/XnuGS0tXktcAjwE+Cvx329+N92/NMI/kXNcHSavqgr5TJLkisKLtr5JcF/gIcNO2p/kdRJqWFQOSJEnSWiTJxgybfNcEPpDkDjCc/h27C62Yfd8f5NLSM9O+443AbYE/Aocm2TXJDYB9gc1dHyStpgAkeVySFyT5BHC9tr8an98DeOWYFFjuGiNNy4oBSZIkaS2U5HnAnRgOA/0UeFPbH0wblaSpLVQPJbkKsD7w+7Z/TnIN4NXAOcAX2r71H1UaSdLKZgYObwt8BngYsBewKfAb4DltvztljJL+3ooLf0WSJEnSPEiybBzm9yDgjsAuwFYMQ/9ek+QbwMvbnj5lnJKmMW7cnZdkM+A9wAnAlZIcAOzX9u5JNlwYOAw4cFjSKpk5/f8M4K3ABsBJwC2Ag4CvJrlh259MFKKklZgYkCRJktYSbRc28a4B7N32qCTrAr8FbgRciWHon6Sl7cXA/sAXgY8xrBn7JfkA8P6Fl2zzIWl1JFkGfBA4CngdcGDbc5N8FtjLpIC0uDhjQJIkSVpLzPQN/xawe5J7tD277dHAmQzJgrPGH+6SlpixzccWwKWAvYFXAo9u+6jxlU3anjZZgJLm2nhA4dttzwAOAa6V5K7A04DD4e++q0iamD8IJEmSpDl3ARv9nwYeCzwoyReTvAq4Ydv/gb+rLJC0hIythE4GngKcBZwG/HV8/Bfg4+N77hVIWm0LcwbGy28wJCFvDbyi7TFjy0MrkaRFwuHDkiRJ0pybGfj3QmA5cB3gA8B3gZ2BPwHfaXu0w0SlpWdm4PDlgHWAbRjWh4cDzwX+ABzR9vErbexJ0j90QevFyvdmv3e4vkiLi4kBSZIkaY7NJAVuDewOvAB4O/AA4Ahg/bZnTRmjpOnMbsQl+QTwG+B6wEvbHpTk2sDlgMMXWo1ZVSRpdSR5DkMrsv+euedaIi1ylgdKkiRJc2zm5N3dGE7+Xgr4YdsfAlcGnptk/anik7Q4JNkV+BnwJmAr4NAk6wHnt/3CQgLRjTxJF8GBwDXHeQImBaQ5sWLqACRJkiRdNCv98D4IeB5wbeDG473nAqe3/csU8Uma3lhRtHAo8MPALsCb2/4lyQOBOwCPmCxASXOv7U+S7A08NsnP2v5s6pgkXTgTA5IkSdIcSrKi7blJAjwe2IuhamAz4JFJTgJuwZgksK+vtHS1PT/JN4B3AMvbPmV89Hjg/4EnfCWtupk2htcByvDd45PA1YDXJ3lE2+NdV6TFzRkDkiRJ0hxKci9gC+CqwJZtH5tkO4ZEwL2AHwFfanuYA4elpWdm4PDfNuaSPAG4AXB54M/Airb3nTJOSfMrye7ATYDfM6wtnwSeDrx3JgEpaZEyMSBJkiTNoSRbAQcA/wI8te2bZ56t0/acyYKTtGgkeQZwG2B/4HPAlRiSin8Gjmx7uslDSavqH60XY/XAFRkqCJ4L/E/bV6zp+CStOhMDkiRJ0pxKcnPgTsCdgTOB57X9epJvAQ9u+9NJA5Q0iSQ3afutJPcFngq8BXgScDzw+rZfmjI+SfNpoQIpyUYMg8yvApwOvKjtt2feuxLwbuCebU+dJlpJF2bZhb8iSZIkabFYGCKaZEPgO213a3sz4OPAPkk+AfzIpIC0NCW5AsMA0OcyJA2f33aftrcADgHenuQtkwYpaV5db/y7G3A+cB/ga8ABSV46897VgR+bFJAWNysGJEmSpDmU5K3AlgztQA5s+6nxBN/VgJ+3PdOhf9LSk2QFcBfgRsB1gQC7t/3e+Hwz4Lrj/BGHkktaJUluDbyZoS3Z1sDebb8xPrsWsDvw6LanJlmXYc/xr5MFLOlCmRiQJEmS5sRMCf+TgRsCewGfAg4CTmL4sX5Y27MnDFPSIjDOIbkNsBNwKvBjhvXh6CnjkjS/klwf2IVh4PD3gP8GTmh7bpJjgNu3/fmUMUpadSYGJEmSpDkynsL7GPAY4FnA74GDgU8DH2j73AnDkzSxsWLgvIVKgCTXZJhFchXgbOCVbU+aMERJc2ZsY9iZdeUOwH8xVC3+gaEy6by2z7QSSZofJgYkSZKkOZNkB+A3wHuBh7Q9PclHgTe1PcQWQtLSk2R74KiZjbsVbc+deb4TsHXbj0wUoqQ5N7uuJAnweOChwHHAg9qe5XcQaX6YGJAkSZIWuSTL256X5HIMMwQOH8v2XwvcDjgcuGbb204aqKRJjHMD/h/wJ2D/tt8c7wdYPpsgWLjviV5JqyLJDYFjFiqNVl5XklwGuHrbr5gUkOaLiQFJkiRpTiT5LPDRtnuN1+sDDwCuDOzb9qcLSYQp45S0ZiXZFLgBwzyBawFfBT7V9pjx+XLgfJMBklZHkh2B3YAvAF8DftD2nPHZMoZ9Rb9zSHPKxIAkSZI0B5LcGnhJ29uM1wtVBJdue8LE4UmayMxQ8uXAh4DNGPp+/xH4EvDJtmdMGKKkOZbklgwtgzYDPg58BfiFiUZp/i2bOgBJkiRJq2Qd4NgkGyVZb0wKbAe8aBxILGkJmmnb8Urg923vALwU+DHDcNA3jW3IJGmVjYPMAU4AtgZOB/4TeBywc5JtpopN0sXDxIAkSZI0H77GkBx4KLA8yebAHsAJbc8ee/5KWoLGf/9/An4H0Pb7bV8P/Ao4ou2xU8Ynaf7MzCZ5FfCutg8EHsawl7g38LgkG0wVn6R/nokBSZIkaREae/eSwabAxsDzgXsCHwPezjD8b7fpopS0GIwtPb4I3D/JM5NceXx0DeDz8L9riiStivH7x7rAscD1k2zS9ti2Twe+DBzf9qxpo5T0z1hx4a9IkiRJmsBC795XA5cGbgfsC9wPuBRw1vjf3+YNTBGkpGnMzBnZhGGw8OFJng3cGvhMkl8D3237wySZaTkkSRdqTDieneSdDDMG7pjkBOAvwJUYqgYY1xfnDUhzyOHDkiRJ0iIzM0z0esB7gZ2A5cDLgH8H7t32O9NFKGmxSLI3cD3gYIZqomMZNu42BH7T9pyFNWXCMCXNiZnvIFcFtgWOA24PXJdh1sBmwD5t3+/BBGm+mRiQJEmSFqkkjwB2avuwmXtPAc5o+67pIpM0pZmNu8cAtwF2BZ7GcIr3y8Dn2v5kyhglzZ+ZtWU74H2MbYSA2zIkHAOc2/bUyYKUdLGxx6AkSZK0iCS5f5LLj5cfBzZJcvuZV64MXHvNRyZpsRg37tYH7gh8pO0xbZ8KvBb4V+ApkwYoaV4tnB5+HvBGhsHDf2x7HLAesEXbU8eB55LmnDMGJEmSpEUiycYM/cEfkuSzwFsYWgm9Nsn3gNOBGzGU9NvXV1radgDWAV6UZAPggLZfAb6S5Arwv6d/pwxS0vxo2yQbMXzfOBnYk6EiCeCZDMmBJ/vdQ1o72EpIkiRJWkSSXAa4MXAfhh/grwZ+BNwdOBE4se2R9vWVlp7ZZGCS5Qz9vu8DbA+cCny+7cEmDSWtjjGZeK22B4/XdwGezTCn5CFJNgUOB+7W9miTjtLawcSAJEmStMgkWQFcgaFNyJ2BnwLvtme4JIAkNwWuBhzR9ogkOwL/AVwTeFrbEycNUNJcSXIrYF3gz8C6bQ9P8myG1mSnAVsAh7Z9uUkBae1hYkCSJElaJFY+5Tu2B7k6cA/gDsDD2/5yqvgkTWehSijJ/YDHAocBjwYOAp7d9sQk12l7lBUDklZVknXbnj1+fjrwROAjwG7AZRgqk05p+7PxHdcXaS1hYkCSJEma2MLpuyRbtj3pAp5vDmzb9sgJwpO0iCT5LnB/4JEMcwO3ZKgsekXbPaeMTdL8SfId4GttnzReX5VhrsCNgLe0ffuU8Um65CybOgBJkiRpqZspyX9dkhtdwPM/mRSQlOSawPsZ5o3cte2zgN2BLwInTBmbpLl1X2CHJL9Mcu+2v2z7SOBpwMOSvGvi+CRdQkwMSJIkSRNKsmz8eytgk7bfGa8zaWCSFqNfAO8BNge+OQ4rvwFwXtt9wbVD0qpLsqLtr4C3MqwveyX5dpIbtj2k7b8Azx3fdQ9RWsv4j1qSJEma0NhCaBnwIGDrJLdKso79eyWtrO15bf8MHAcU2BP4b+AA+FtbMtcOSRdqnBVwbpK7M1QH3Ae4LPBR4JAkH06yPnAS/F11o6S1xIqpA5AkSZKWspkTeF8BNmAo6d88ybfbHjtdZJIWi/FU77kL123/kuSxwL8Cv277m/G+G3eSVslMEvHywOFtTxsPJrwsyXrAvRkqGU+cLkpJlyQrBiRJkqQJzCQElo2beQcAuwA/YUgO/FeSG04Vn6RpJVmRZLPx8sFJtph5tqzt+W0PXUgKSNKqWqnl2MeAmye5T9tzxnubA89se2KS5Ws+QklrQqwylCRJkqaT5LXA+Qw/wg8F9ge2Ap4CvK3tzycMT9JEkmwP3Ai4GXC9trca78d2QZL+WWOboMcDewE3Bt4NHAscAdyq7Q4ThidpDbBiQJIkSVrDZgYO3xnYgaFaYCfgNsDbgB3bPsOkgLSkncLQ4uNRwBFJdkyycdsm2WS2gkCSLoJrANcD3shwcPiqwKuA9wJ3A7BaQFq7WTEgSZIkTSTJXsBbgJsAtwD2APYBfg08pu2p00UnaWpJtgUexjBoeFuGVmOfA14KvLntVyYMT9Kcma04SrIOQ3LgJgzzSv4E7Nf2O1YmSUuDiQFJkiRpAkk2AbYHjgTeDry47c+TvAfYv+2nF/qITxqopDUqyfK25yXZEFgP2LDt75PcDbgdsDVwpbb/OmmgkuZWkpu1/cb4+VLATYEXMSQHHt32+Cnjk7RmrJg6AEmSJGmpGJMB6wABXgg8CzgHOBrYJ8l7gZ3aPgLApIC09LQ9b/z4bmA5sHGSHwCvBg4GNl54dyGJsOajlDRvFqoAklwGODTJl4DHtf01cFCSOwHfbHu8FQPS0uCMAUmSJGnNuSHwGGBvYKu2Z3fw38CXgSsCDwb7+kpLUZIHJNkiyXbANsBTgV0ZDvV9CHgacDJwEvxdEkGS/qGxAnFho/+abdcDfgz8MMkbx4qk+zK0KpO0RNhKSJIkSVpDkqwAng08n2G2wKeAY9oeMw4SXd72xCljlDSNcSj5+4BbAocBR7R91bhubAjcjGHewFPb/nG6SCXNmyR3ZqhA2h64ftsHjPevCLwe+D1waNsPW4kkLR0mBiRJkqQ1YKaE/1rAVsCtGH6gf59hoOjbgdu3/fGEYUqaWJIdgRcDNwKe0/aD4/1lwAZtz3D+iKTVkeSqDFVH1wce1fZ9JgAkmRiQJEmSLmEzw0T/HbhT26eO93dgOAG8PvCnti+wr6+09MwkDtcBNm97QpJ7AbsBvwJ2a/vdaaOUNI9m1pfbAXcB7gQcBTy/7c+TfAHYpe1PJw1U0hrn8GFJkiTpEjZzIu/VwBMAktwFuFzbZyZZl2EIMQyDiU0MSEvTA4H7J9kLOLDtR5PsyjAodKe23544PklzZOZgwrWAzdo+PUmAPYCDk3wbONWkgLQ0WTEgSZIkrQFJbstwIu8+SZ4F3IJh2PAngd2tEpCWpoW2QEmuAbyRoQ/4cuDrDMmBryfZou3JkwYqaW4l+Rrw+rb7JdmU4QDCCuAGwHfbnmJrIWnpWTZ1AJIkSdIS8RNgiyS/AzYBHgk8CriuSQFpSVv49/8S4ANtbw88kWEWyf5JdllICownfSVplSW5J3DymBS4PbAfcCBwvbaHtD0F/q66UdISYSshSZIkaQ1oe2ySOwC3bXvw2Et8T+BN8L+nhicNUtIaN/b+3gA4Hbj0eGr3KOAxST4CPCzJNYGnmkSUdBGcAZyQZH/g98DeDAnJnYEvTxmYpGlZMSBJkiStAePwv/PGpMByhgGAf2h7AIBJAWlpSXL9JI8HaHsW8B5gR2DnJDskuRKwLXBv4DIMFQSStLq+DHwe+C3wsrYfBO45XluJJC1hzhiQJEmSLmFjUqALf2fub9D2LPv6SkvLuBF3eeBy439bt31HknsD/86QBNgSeAvwO+AVbW85VbyS1h5J7gs8ve0tpo5F0rRMDEiSJEkXs5lhohsB6zk0VNKsJFu3PXH8fE/gHcARwKOBoxkSA+sDJzOc9n1S269PFK6kOTVWKJ6/cCghyfrA9sApbX/pwQRpaTMxIEmSJF2MZqsCkryXoRXIScC7gG+0PXXC8CQtAkm+ChwHPLPtMeO93YCnAh9hOM176nj/lm0PmyxYSXPjH80rSrKi7blTxCRp8TIxIEmSJF2MZqoFdgPWBQ4ADgJ+CPwK2Av4dttzJgxT0oSSbArswTBrZB9gt7bnJtkM+BBwdtv/mDJGSfMryTOAGwNfa/uG8V6A5SYIJC0wMSBJkiRdzJJsDhwI3IOhR/h+wPeBrwPva/ucCcOTNKHZ1h1Jrge8imHOwKvafmC8v37bv3jKV9KqmjmY8ATgwcBrgN2AFcAubb84aYCSFp1lUwcgSZIkrW3a/gl4OLAOsBnwlba/Bg4FPgzDD/ip4pM0nbbnJVk2buL9sO3ODJt3T0vyvSRXBs4e3zUpIGmVjEmBFcC1gWe0/WjbHYA3AJ9Jsu+0EUpabKwYkCRJki5BSV4C3BQ4k6E9yP0nDknSIrHy4M8kzwLe3PbMCcOSNKeSPIBhVsm3gT3b/mLm2U3bfvMfzSGQtPSYGJAkSZIuQUmuBNwSuDywd9vj/FEuLT0zbT42bnv6Ss/WmZ074hoh6aIYWxneEbgX8CPgq8CPx+8eqZuAkmaYGJAkSZL+SauziecPc2npmUkKXJ6hrcdj2/5xpXdcGyRdZEmuA1wd+CHwV+CJwBWAo4A3tj1jwvAkLUL2NZUkSZL+CeNm3vlJ1kvykiQbXdA7C5/d+JOWnpnE4UuAb7X9Y5IbJXloktuO77g2SFotSZaPf+8GvA74F+CbwAltdwXeBRxrUkDSBVkxdQCSJEnSnAtQYHdgq7ZnJNka2BQ4o+3xbvhJSrItcC3gpUkeDtyTYe04EjhkwtAkzamZGSXPBnYBdgY+2PbcJLcBftX2y2BVkqT/PysGJEmSpH/CWC1wWeBuwNOS3Ax4B/Ax4N6TBidp0Wj7e+Bg4OUM/b+fzLCRd5ck20wZm6T5lWRT4EvA6QzfO3YfHz0BuPXCeyYFJK3MigFJkiTpIkiyHkOFwO+Bk4FDgecBOwJ7AicCr01yQNvjp4tU0lRmZgusA6wPvBm4KnBU21OSvAn4fNs/OHBY0kXR9tQkGwA/AV45tiq7K3C1tvtMHJ6kRczEgCRJknTR3BFYnuSvDKf09gVuBjy/7feTPA34jUkBaWmamT+yDvAm4GrAKcB/jUmBmwFbAg8b/yee5pW0ShbaAo3ry6UZWgn9Etg1yQ4Micjdx3eXz7QckqS/iZVEkiRJ0upLslnbPyd5AUOp/svbHjI+uwXwGuBebY/3JLC09Mxs3O0B/AX4PPC+ttdIstE4j+RSY5LANULSKlnY6E+yI/Ai4ATgym3vNM442gH4bttTJg1U0qJnYkCSJEn6JyS5MnAr4D+AU4G3tP12kh3b/sANP2lpShJgM+BdwH8BrwQObvveJLsCy9vu/n/9f0jSP5Lkswwzja4NbN/2QUmuBPzZpICkVeHwYUmSJOmf0PZo4EPAC4EjgVcleWrbH4zPTQpIS0iSdZJcoYNTgE8BewDrtX3v+No9GIaFLiQQJGmVJbk8cFzbjzEcTHjF+OiJwG0mC0zSXDExIEmSJF1ESZYBtP1r26OAvYDdgC+Oz93wk5aeOwMfTvLkJOsC3wAuzzCT5FFJPggc0fbQhXZDk0YraR4dDzTJT4EvtT0iyXUYkgRfnDY0SfPCVkKSJEnSKrqgTbxx83+Zg/0kASTZCrgp8HDgXGBP4DvAkxlaC/0S+JSzBSStjpW/gyS5HEM10nYMM0xuCXyu7RscOCxpVZgYkCRJklZTkpsAlwN+vdAyKMlyoG7ySUvXwkZ/kocB/wZsyzB4+JfAq9oeO/Ou1QKSLtTsWjEOF34ysA5wDnAocD6wM3Bg28MmC1TS3DExIEmSJK2ChR/mSR4AvJShb/h1gB8Ar257wqQBSloUkmwE/JTh9O6pwGWB5wHbA/u0fc2E4UmaM2Nl4lZtT0yyF7ABcDiwEXBzYI+2351936SjpFWxYuoAJEmSpMVupR/ZNwQe1fbLSW4APBT4eJK92751uiglLRIbAd8Cjm/7V+BPSd4NPBr4KrhxJ2m13A14R5J3ARu0fTBAki2BAA8A/pYYcG2RtKocPixJkiRdiJkS/icynAJeGCr8feDFwKuBYyYJTtLkktwqyTXHDf8TgBOBQ5LcdXxlG2B522+AG3eSVl3bTwD3Yxhifo8kTx3vnwTsD9xpTBJI0mqxlZAkSZL0f1ipt+/1GZIAWwHPaXvQeN8BotISNbb5eDzwHuBOwO+AI4F7MQwg3gY4BXhe26+5Xki6KJKsy7CmPBn4LcOac02GKoJdXVskrS4TA5IkSdIqSHJ74Bdtf53kwcDzgaOAF7c9ctroJE0tyQrg7QyV+V9maO3xU4aNu9+0PcUWQpL+WWN1wLOAZwNfAW7f9hzXF0mry8SAJEmSdCGSbA68BjgDOAz4OHAO8Dpgk7aPmDA8SYtAkse2fUeS/2CoFjiJYb04vO3x00YnaZ5d0KZ/kusyVAt8K8nytudNFJ6kOWViQJIkSVoFSS7DsNm3I3Am8NG2X1n4Me6PcmlpSrIM2AT4DPCRtnsm2QZ4EHA74GVtD5syRknzZea7xYZtz5w6HhP4b+cAACAASURBVElrJxMDkiRJ0gVYOJ2X5EbAaW1/Nt7fAXgFcBngmW2/OGWckhaH8fTu04H9235mvHeNhbVDklZXkgOAA9u+f+pYJK19lk0dgCRJkrQYzZTs3xl4W5KnJ9m47RHA84Cjga9OFqCkSY1Dh0myDsA4a+QDwJ5JHjYOAjUpIGm1zKwtdwS2bPv+JBsl2TXJvZJsNHGIktYSVgxIkiRJKxk39M4fW4RsDNwCuDdwaeAg4K4MJ/jevvDuhOFKmkiSKwOHA/sApwPfB9YHHgY8ru1vJgxP0hxL8hLgF8CxwB2BmwAnAm9r+4UpY5O0drBiQJIkSZoxkxTYDvgwcC5wMLAbsDdwa+Dbbd8OYFJAWrraHs2wJhwK/ArYhWG2wE2ADScMTdL8+yLwAOCFwJfb7gScBlx9yqAkrT2sGJAkSZIuQJJ9gCPaviLJjsDlgaPaHp1knbbnWC0gLT0zQ0F3ZNiguw6w95gkIMnlgPPa/sE1QtKqWphttNK97YBz2/4uyU7A69vu+I/el6TVsWLqACRJkqTFZmwPsh3wuCQPZTixtyXwPwwn984FqwWkpWbciDtvvHwnQ2uxTYDvJtkbeHrbYxfed42QtBoCNMlzGFqSXY2hbeH+4/M7Ay+G/01QThKlpLWGFQOSJEnSSpKsD7yBYcNvBfBM4DyG1kJ3bnvKhOFJmliSJwO3aXuf8Xpr4B3Aq9p+bdLgJM2dmTaG2wPvAZ4GvAU4nmF+yRvafnnKGCWtfZwxIEmSJK2k7V+A1wEfAh7b9hjgecChbU8ZhxJLWkKSZPy7DPgK8LuFZ21PBL7HcKJXklbLTHXRLsAeDAcTjgEeDmwNvDHJFaaJTtLaylZCkiRJ0krGdiE/Bn48Xv8bw4yBe46vWHYrLTEzvbz/H/Bd4CZJPgU8CzgH2Bl4Adj7W9LqS7IusB/wA+CtwF5tj0vyNeCbbX/r2iLp4mQrIUmSJOkCJFnR9tyZ623b/t6+vtLSM9PmYwdgz7a3G+/vCdweOBQ4re1zpoxT0tohyZOAGwAHAO8Crtv2JBMDki5OJgYkSZK0pM1s+G0EbAFco+0XVn4+XYSSFoskzwXuBTyx7bfGe5sDZ7Q9e7x2zZC0SpKs2/bsJPdkqExcH/g88DPg9cCZwDfa7uPBBEkXNxMDkiRJEpDkI8APgScDr2776olDkrSIjMnDBzJUCBwPHMYwd+T4SQOTNJeSbNH25CTrAL8APgicDVyFYY7Jvm1Pm3nfagFJFysTA5IkSVqyFn5kJ7kL8Hjg3gw/xh/d9ogktwYO84SetDStvBE39gDfBHgIcFXgz8BBbQ+dKERJcyrJj4EjgU8AG7V9W5LtgO2BfwOuDjy57W8mC1LSWm3Z1AFIkiRJU5nZ8Lss8BrgCcD3x6TA1YCnM5T1S1qaApDkhUn2BX7JkETcC3gLsCHwl+nCkzTHbgacBryd4WACbX8NfA54L/AGkwKSLklWDEiSJGnJS7Ijw4/wbdteery3N/DztrvbM1xaembmj1wBOBjYmSER8FLgWsBdgBPbnj5hmJLm0Oy8gHGo+XuBrYCntf3oeH+hqtEWQpIuESYGJEmStORc0EZ/ktsCbwBOAI4Crt32duMzf5RLS1SSxwHXaftfM/d2Bf7Y9u3TRSZpniVZDjCTIHggsAdDFdJODIlHDyVIusSsmDoASZIkaU1b+KGd5FnAtsDvgC8BNwb+AzgG+O34zt9O9UlaWpJsAzwU2CjJvYEvtj2ZYc7AVRhagEjSaptJCCxve17bfYF9k7wION2kgKRLmhUDkiRJWlKSXKvtT5JcBvg8Q6/wrYBLAb8CPjs+t0pAEknWZ5grcHfgaOAsYAuGlh/H22pM0uoaqwOOaHvEzL2/O4jg2iLpkmZiQJIkSUtGkssBPwG+BfyU4fTvR5JcFrglQ8XAZsCu46lgSUtUkgDL2547Xl8V2AW4FfBp4PUMp3rPnS5KSfMiyRWBpwLfAZ4M7NT2LyvNG7BKUdIaY2JAkiRJS0aS9wN/Ar4KPB9YDty97dHj8+sAm7c9bLooJU3lH8wfCcBCBVGSWwNPA9YDHmwSUdKqSnIfYF+GdoXXafvX8X6AjdueNmV8kpYWEwOSJElaEpLcnKFt0PXaNskVgBcCNwcOAHa3ZF9a2pJcZmwP9Abg4LafnHn2d0mDJDu1/dIUcUqaL0nuDnxjXF92Y5hT8mjg/W2flOSxwOXbvnDSQCUtKSYGJEmStCQk+fL48c3AkW2PGu/fFHgGcDNg57Y/mShESRMaW4rtAmzD0FrsFm1PXemdMPyONokoaZUkWcbQOujNwF2AH7X9RZLtGA4sXAs4DnjgeN8ZR5LWCBMDkiRJWusleSTwn8A7GRIAZwPfBb7U9oTxnZ3bfm66KCVNLclVgI8D6wAvYDjh+9sxIXDDtt+ZNEBJcyvJpsD+DN9B9gc+0PbcsY0hbY9y4LCkNcnEgCRJktZq44be/wDPavuDJDsCtweuBJwIfA04rO1ZC+97Uk9aWmbnCIxVRBsyDAk9G3gr8DjgrLaPmC5KSfPoAtqQ3Qt4DPBHYB/gIJMBkqZgYkCSJElrvSSbtj11dtM/yU7ArYArA59o+/EpY5Q0jZXWhRsDBX4GnA48giGReBrwnLaneKJX0kWRZEXbcxc+A/8FPBx4ZNtvTRmbpKXJxIAkSZKWlJU2ATcFdga+0PakaSOTNIWFjf4kzwNuCqwL/AX4PPC+tmcubOiZFJC0qpJcDzgV+M3CujFWJy1re954vU3bP0wYpqQlzMSAJEmSliRbBklakGQr4Ittd0jySYZBoFsD5wPvb3vgpAFKmivjwOH3Mawjb2WYafTnlZ5jolHSlJZNHYAkSZI0hbGXeKaOQ9KicGvgDUmuD2zR9rHAh4GNgJ9OGpmkudP2/LYPAb4I7AF8PMmtk2ww89ykgKRJrZg6AEmSJGkqVgxIGn2coYXQzYBfjffWAX7f9ieTRSVp3t0QeCdwfYYEwbeTvA04uu3Zk0YmacmzlZAkSZIkaclKckXg5PGywP8wtP84E3hw2x85W0DS6kpyX2CXtv82Xl8T+BCwCXCPtkdMGZ8kWTEgSZIkSVqSkjwIeDpwInAwcHDbf02yI3CuSQFJ/4RfAn8e15Oft/1pkqcBTzApIGkxsGJAkiRJkrRkJLku8O/Am4CPAC8GNgfuAGwM/Aj4TNujp4pR0tohySsY1pVPjLeeD7y+7ceSLG973nTRSVrqTAxIkiRJkpaMJP8KPIVhpsC6be883l8fuDdwV+ANbb82XZSS5s3CRn+SLYCrARu0/XKSJwA3B84DTmr7rEkDlaSRiQFJkiRJ0pKS5BrA9sDLgJ8DL237zfHZNm3/MGV8kuZXki8AJwDbAX8CntH2x+OzheSBLcokTW7Z1AFIkiRJknRJy2Cz8fJuDBt39wAOA16Q5GVJrmBSQNLqSrJs/HsD4NS2/wncCvgC8NEkn0hyqYX3TQpIWgysGJAkSZIkrfWSbAfsCmwCXKntLcb7WwNXBx4IHNv2ZVPFKGm+JXkLsAJ4LnBK2/OTXJph7Xlh2z9PGqAkzTAxIEmSJElaEpJsDxwMnAE8ve0nx/uXAm4MfKXtX23zIWl1jBUDGwCvYFhLPgnsC/yu7bmz77m2SFosTAxIkiRJktZ6SQJsCDwYOIbhRO9ZwNOBRwC/avu26SKUNG+SpCttrCW5NsP6sjnwceDjbU+eIj5J+r+YGJAkSZIkLTlJtgAeDTwUOB64oyd5Ja2OhcRAkscDWwKnAYe0PTLJnYH/BnZp+/1JA5WkC2BiQJIkSZK0JCRZ3va8le8BW7Y94YKeS9IFWWgLlORfgNcCbwd2Aw4BjgQ+yDC3xI03SYvSsqkDkCRJkiTpkjBu+pNkE4ALSgq0Pa/tCRf0XJL+kZkKo12AZwN/Ar4LfAp4IvASYNNpopOkC2diQJIkSZK0VprZ6P9gkof+H88labUl2Rj4LPA94LHAE9vuD3wVOKjtn8f5JpK06JgYkCRJkiStdRY245L8B7B+2/cn2TzJy5M8MMlGE4coac61Pb3tPgyDzH8LvDzJbYCbAgdOGpwkXQhnDEiSJEmS1lpJXgz8EjgVuDVwLeAM4N1tPzdhaJLm3MLw4fHzNsA7gQL7tt3PuSWSFjMTA5IkSZKktVaSnYDnAesBe7Q9OMn7gK+0feekwUlaKywMIh4/b9T2jKljkqQLY2JAkiRJkrTWmN2gG68DbAOc1/bEJP8OvKztDSYLUtJaZ6F9Wd1okzQnVkwdgCRJkiRJF6OFth4vBpYD1wA+A+w3Pr8lsOv4jm0+JF1sTApImidWDEiSJEmS1goL1QJJbgi8GXgK8C7gGIaEwdvafmbKGCXNv7E6YFnb85JsBZxl+yBJ82bZ1AFIkiRJknRxmGkh9Ghgd+CywC+AhwObAC9Nsu000UmaV0lWjH8vleTyHSxUGz0GODzJjaeLUJJWn62EJEmSJElzL0lm2ni8C/g5Q9XAXm1PSvId4Kttf7/Su5L0D40tx85Nsg6wD3CNJD8Bdmv77bYvT3IS8JAkP2p71rQRS9KqsWJAkiRJkjT3Fjb6k9ym7Xfangp8HXhokvsBDwIOmTJGSXPpIUmuBTwH+FnbqwPfA96T5LVJrtD2HcCr2p61MIRYkhY7ZwxIkiRJkubazGyB2wMvBD4PfAo4CngV8BfgG20PcOCwpFWV5OrAM4E/AdsCH297wPhsO2BP4LS2D5kqRkm6qEwMSJIkSZLm1kxS4LLAe4GTgTMYhg0fDnyw7V8mDFHSHEtyReAewPWBywNvA77S9sTx+aZtTzXpKGnemBiQJEmSJM29JG8Fftd2jyRXAW4PPAk4FHh/2286W0DSqkpy2bbHJbkycCPgx8BdGYaaHw8cAfwPcP7M4HNJmhvOGJAkSZIkzbWxp/cxwGYAbX819vz+KrAxcL/xvkkBSRcqyXrATZO8kqE12Z/a/gh4PfAZhrXm1sA6JgUkzSsTA5IkSZKkuTZu+H8EuG6S3ZP8a5INgNsCLwe2T7LjpEFKmifnAz8Adhg/3zDJ9dr+pe1BwBeB/R02LGme2UpIkiRJkjTXZuYM3BTYCXgQcBzwCYbTvh9ue/0JQ5Q0J2bWk/WBDwGfBrYCrgx8HTgd2LntIyYMU5L+aSYGJEmSJElrjSQbAucAWwB/BvZiGED8mUkDkzQXFmaRJHk5cGzbNya5JsOcgRsBtwNe3vZDC0mESQOWpIvIxIAkSZIkaS4leSTw/bbf/QfPlwE3b3v4mo1M0jxLchngrcAn2757vLcOsClwXttTpoxPki4OK6YOQJIkSZKkVZVkO+DZwFHAg4FbjPeXtz1v/Lys7fnjSV6TApJW144MlUcPTHIW8OW2xwInTRuWJF18rBiQJEmSJM2VJHcCPg78Abhx2z+O95cBm7d1807SalloITRzfSngP4HtgROAHwKfaXv2RCFK0sXKxIAkSZIkaS4kCcDY//u5wDrAM4AD2z4syVOArdu+YMo4Jc2X2aRAkocwtAw6uu1nxvkCDwY2AZ5tYkDS2sLEgCRJkiRpriTZgqEy4JdJNmXoBb4T8GvgAW1/u/LpX0n6RxbajyV5PrAD8AvgPsBPgD3afjvJ5dv+zrVF0trCxIAkSZIkadFbmCGQ5OHAg4AARwPfa/uWJFcDlrX92cIm35TxSpovSbYEvsQwX2A/4DDgMgzthN7V9iXTRSdJFz+HD0uSJEmSFr0xKbAB8CLgtuPtKwEPT/L7tgfOvGtSQNIqSbJR2zOAbYEXAtcArtT2fkmuA2zJMNPk/zeHQJLmmYkBSZIkSdK8uAxDhcDR4/XRSa4A3DfJIW1PmzA2SXMmyYuA84GXtP0h8MMkVwKOHFuW3RnYsO0RMMw3mS5aSbp4LZs6AEmSJEmSVtFxAEk+mOQW471TgY1NCkhaHUmuCNwd2Gu8vm+SbYATGA7SfhrYGXjZ+Nw9NElrFWcMSJIkSZIWrYXWHQvtPpJsDjwMuCVwBeBMYNe2X1uYQzBpwJLmQpK9gZ+03SPJvwB7ALdbaEU2zhw4s+1Zzi2RtDYyMSBJkiRJWtSS3B54EkPV+5uAPwAnA1cDftH2t/b+lrSqklwXOAi4T9vDk3wW2LvtvjPPb972nVPGKUmXJBMDkiRJkqRFJ8m2wA5tP5fk/2PvvqMtq8v7j78/U2gDMkoRBStiCaKIIioiGBsajMGCaAwae5QYJVgSExLFBPvPXsBu1KhYYokCUYmowQ6RBIGoaChGuoLUmc/vj7OvXkeEmWFm9j33vl9rzTrn7P2dtZ6/zqzZn/M8z5eBNzAJAvYATmUy5uNbba8csUxJUyjJUuBFwF7ApcDWbe836/4ngWPavnWkEiVpvTMYkCRJkiTNOUkOAPZn0h2wXdsDh+s7AM8C7g88r+2J41UpaZol2RF4GnBv4D3Ah4D9gKe3ffCIpUnSemcwIEmSJEmac5JsA+wG7A4cBBwNvKXtWcP93dp+Z8QSJU2pJAGYGT82jCt7NnA1sCuTYOB495ZIms8MBiRJkiRJc9bQIXAvJiOENgdOAD7e9opRC5M09VZ98J/kaUw6lA4fsSxJ2iAMBiRJkiRJc0aSndqescq1RcCdgXsC9wO+1vZtY9Qnaf5JsqTtNatcW9R25Vg1SdL6ZjAgSZIkSZoThoXDRwMPADZue1GSzBr3sQy4B3BK2wtm35Ok6zLTHZDklsAWwGkAM4HAEEDW7xRJC4XBgCRJkiRpTkjyFGBH4IPAfm1fPnJJkuaBmRAxyWbAscBWwPeB9wNfBX5mICBpoVk0dgGSJEmSJA2+CTyCyR6B02YuziwKnXmVpLX0HOC4tncCPg/8KfBC4AFJlo5amSRtYAYDkiRJkqQ5oe1/Am8F/hc4IsmLhtnfHe77i15Ja2zoFtga2Bs4b7j2duApQIGHtr16xBIlaYNzlJAkSZIkaVSzxnwE2LLtxUl2At4ELAde3faj41YpaZol2Q14OrAr8FHgY23PHO5t1vaXLhyWtJAYDEiSJEmSRjUrGHgYsBtwLvCfbb+Z5PHAq5n8ovfkUQuVNFWu7UF/kkcyWWK+HPge8E7gGgMBSQuNwYAkSZIkaTQzD+6S/D7wSuBzwLLh9r+1/dfxqpM0rWYCx+H9c4GHAN8CPgb8Erg/sDPwV20vG61QSRqJwYAkSZIkaXRJjgKOaXt0kuXAAcAzgQPa/s/sh3ySdH1mhY4vAO7LJHj8Y+DWwLuAo4Gbtz3b7xdJC5HLhyVJkiRJc8EPgMcmuXnbi9seCZzO5CGei4clrZEhFNgY2Ad4eduvtP0z4C3AnwObtT17OOv3i6QFx2BAkiRJkjQXvAs4B/jjJE9P8lDgnsCXxy1L0rRqeyXwJeDhSbYarn0aWAzcbszaJGlsjhKSJEmSJI0iyeK2K2Z93pNJGLA7cBnw0bbHrnpOklZXkj2AQ4GTgfOB2wM7tX34qIVJ0sgMBiRJkiRJG1SSndqeMbxfxOT/piuGzxu1vcowQNK6kuR2wBOBAJcDH2h7pt8zkhYygwFJkiRJ0gaTZDFwCnAlcFDb/xyuLwVWtF05Zn2S5o9Vv1eSLGl7zchlSdKc4I4BSZIkSdIG03ZF2zsBHwNOSPL+JMvaXj0sC10ydo2Spk+SzHq/FGDW98rMPbsDJGlgMCBJkiRJ2iCS3C7JPw7jOw4Hbg1sDJyV5AUA/ppX0loKQJI/B/4hyblJDgDoMC6jjs2QpF8xGJAkSZIkrXfDL3ZXAg8AvpHkCW0vansA8BDg0UkuS3L72b/8laTVMXQGbAc8BXgJcDawGUCSbcasTZLmIoMBSZIkSdJ614kftt0DeAXwgiTHJblX22+0vSfwp21P91e9ktZEkk2Ht48F3gXsBvyi7XuS3Ah4XZLtRytQkuYggwFJkiRJ0gYx0wnQ9iNt7wJ8CfhgkiOT3KztR2afk6Trk+TvgP2Hj58GtgVeBTxjuPZEYKO2Z49QniTNWfGHGJIkSZKk9WnYKbAiyc2ABwObA59se3aSrYG3ASvaPnbUQiVNlSTLgePa7p7kmcBXgWcCfwo8G9gS+BPgwLZnJFnUduV4FUvS3GEwIEmSJEnaIJL8G/BZ4DnAucBHgXe2/XmSTdtePhMijFqopKkwBItvBe4JXNj2bsP1/YCDgH8Hftj2c4YCkvSbDAYkSZIkSetdkocDT237iCQnAu9nEhBcDDyz7XdHLVDSVEmypO01w/tzgGXA54Dntv3ptZyP+0sk6dfcMSBJkiRJ2hA2Bl6V5MnAf7Z9M/AXwCXAGaNWJmkafSbJbYb3u7fdElgJnJbkpaseNhSQpN9kMCBJkiRJWi9mLxFue3TbrwA/A26VZBfgj4GPtr00if8/lbQmngqcl+RMYG+Ato8H7gcckORfR6xNkuY8RwlJkiRJktabJMuAfYeP5wD/Bzwe2B3YrO2DxqpN0vRJsjFwj7ZfHT4/GXgxcD7wjLYnDde3aXuee0sk6doZDEiSJEmS1rlZy4TfDdwE2AT4HnA28EkmIcGythf64E7S6kry+8CngA8Ch7f93+H6y4BnAicCjwRWzuwgkCT9Nls1JUmSJEnrVJKHAW9I8gTglm0f0fYhwGeZdA/s2/ZK4CIAQwFJq6vtF4EdgALHJzlsWCz8N8AdgCuBrQwFJOm6GQxIkiRJkta1S4D/Ae4FbJNkL4C2XwJeAjwoyWYuA5W0poYOo4vbPgN4NLAX8N0kB7a9oO2j2p7r3hJJum6OEpIkSZIkrXNJtgHuDjwG2JzJeI9PAE8Ctm37rOFXvv6nVNIaSbIRcE3blcPnA4BXAP8xLCCWJF0PgwFJkiRJ0nqTZAcmv+jdF3go8FXg8cP+gUUzD/Yk6bpcW5C46n6SJDu3/S+/WyTp+hkMSJIkSZLWudkP7JIsBm4L/BFwQtsTfXAnaXXNfF8kuSvwFmDv2TsEkixxp4AkrRmDAUmSJEnSejEEAitnfuWbZFnby0YuS9KUSvJK4H/aHplko7ZXjV2TJE0rF7FIkiRJkm6wJBleb5/kT5Ls1HZF22ZgKCBpbSW5DfAHwB8lufFMKDAEkJKkNWQwIEmSJEm6QYaxQU3yAODtTJYOn5xkO4AORi1S0rQ7HzgEuAR4T5KDAGbvGJAkrT5HCUmSJEmS1okk/wr8HbAT8OC2T0qyJ3BZ25PGrU7StJm1W+BGwM2Bs4FbAncA9gduAjyz7f+OWKYkTaUlYxcgSZIkSZpuSRYBmwMnAZcDzwMeM9x+OvCN4Z4krZZZocDWwLuBHYCfAMcAn2LynbKPoYAkrR1HCUmSJEmS1srMXoG2K9v+HPgFcCzwmbZnJtkHuBvwttnnJen6tF05vH0J8J22dwPeBOwBvJxJSPBu8LtFktaGHQOSJEmSpBskySOAn7c9IsmVwIuS3BHYEnhZ2xXDHgJngUu6XknuxaTTaCmwKfBDgLbHAccl+SSwU9tTh+vOyZakNWQwIEmSJElaY7PGfGzLZNb3Tkk+BbwDeC+TPQNntL0AXBAqafUk2QH4LHBTJqHAh4CnJPkhk++UnwJ3Bq4er0pJmn4uH5YkSZIkrbUkHwNOA1YAK4Edgc8AX2h73pi1SZo+SR4EHAB8DdgXeBzwAuBW/DosOLnti2YCytGKlaQpZjAgSZIkSVorSXYG3tv2HsPn2wN/Bdwc+ArwprYXjViipCmTZDnwMuAJwOuYjCO7JsldmYQCVzMJBq4xGJCktefyYUmSJEnS2joPWJnkIIC2pzNZCnopsDuwz3ilSZpGbS9mMkroGCYh4xFDF8EP257Y9tttrxnOGgpI0lqyY0CSJEmStNpm7RZY3vbiJA9lsmPgQuBLwDOAjzH5IdrubZ8zYrmSplCSZW0vS3Jz4OnArYEfAf/S9qRRi5OkecKOAUmSJEnSahtCgU2ANyR5InA68AngAuD5wHeAfwaeDXx8tEIlTa0hFFjU9py2fw+8HbglcOW4lUnS/GHHgCRJkiRptc3qGHgG8GDgJ0yWDR/fdsVwZn/gPm2fP2KpkqZEkrRtkpu1PXfV62PWJknzlcGAJEmSJOl6zXpwt1nbXw7XtgQOBp4EfBl4RdvTk2zM5P+bV4xXsaRpk+RlwEVtXzM7FHDJsCSte44SkiRJkiRdr1m/2v2zJP+Q5PZtL2n7D8BhwN2AXw5nrzQUkLQmkgT4InDvJPeZ3SlgKCBJ657BgCRJkiRptQydAD8GlgCHJnn6cGt74GNtzxoe7knS9Zr9fdGJLwKfAl6a5J7DmcVj1SdJ85mjhCRJkiRJv9O1zfhOsjnwSOC+wK7AYuABbS92JrikNZXkQOBWwIeBGwH3Bm7R9m9GLUyS5rElYxcgSZIkSZr7kjwLuCuwI/Dmtu9L8mEmD/MyhAKLZxYQS9J1mbW3ZBNgJXAP4PZMnlXtBWyZZHvgL5nsHTBwlKR1yI4BSZIkSdK1SnJb4EImHQFfB54NLAeeD5wHvKTtieNVKGnaJXkg8NO2pyTZou0vkuwMbAPsD3yy7ZfGrVKS5h+DAUmSJEnStUryISa76b4CbNP2sOH6EuAQ4EXA/duePF6VkqbNTHdRkt2BI5l0DLwO+Cpw5syy4SR/CfwBk1FlPsCSpHXIYECSJEmSdK2GAOBgJr/aXQL8FfCNtlcM92/S9sIRS5Q0xZIcC/wzcBsmY8muBI4GvtX2giR/Cixq+84Ry5SkeclgQJIkSZL0W2bvC0hyMyYdArsCnwU+AZzd9poRS5Q0xZLsBzy/7d7D51sDb2cSQn6o7TvGq06S5r9FYxcgSZIkSZp7hjEfSbKo7bltnw/8PXBP4P3AnUctUNLUSTL7OdS/A5cneXiSLdueCfwdcAVwcJJ9x6hRkhYKOwYkSZIkSdcqSdo2yZLZ3QFJHgt8ou1VI5YnacrM+k45kkn30XbA7YHvA1cBTwf+DHgwsLLta0crVpLmuSVjFyBJkiRJBnf71AAAIABJREFUmjtmHtwBzHq9Zri3pO01bT+86llJui6zQoFbA9u3/ZckYRIG3ALYisl+gbOBJwD7jVWrJC0EBgOSJEmSJGAy5qPtyiRbMVk4fAVwKvCjthe2vebaggNJuj6zvi8eC9wjyT5tjwfenmTztpcCJDmYSUfSWSOVKkkLgqOEJEmSJEm/IckngO8BfwicBxwDHAuc0fbyMWuTNN2S7Ao8lcmekuOBN7U9f9SiJGkBcvmwJEmSJOlXkuwJbNb2MOBSJqHAI4HXA/caszZJ02cYFzTz/qbAj4HDmSwaXg58IMkTh/uLV/07kqT1w1FCkiRJkqTZtgZemWR/4My2r07ybeAFwHfHLU3SFFoErEjyPOAuwH2AdwCvBc4AHgicD9B2xfDqeAtJWs8MBiRJkiRJv9L2XwCSPAS4VZIdgccAX2l78cweglGLlDQ12q5IsjXw1LY7JzkeuGi4vrjt+0YuUZIWJEcJSZIkSZKAyfLhWR+PBU4A3gvcFjhiuO4veSWtqfsCRyfZG6DtO5JsAvxTkjuMW5okLUx2DEiSJEmSAJjpBEiStk3yt8DGwKZtVw6/7l0xbpWSptC/MdlR8jbg0OHanwAXtD1ttKokaQEzGJAkSZKkBWrmQX+SGwOLgQvbrpyZ7z2EAL8c/mAoIGlNDePHLk3yVWAv4O5JDmTSifS04YyhoyRtYHGfiyRJkiQtbEk+B3yk7buTBFja9qqx65I0nWaFjr8P3B3YAXgFECbLhi8HftD22+4tkaRxuGNAkiRJkhagJIuH1/2YBAHvTnJz4A3AoUl2GLVASVNrCAUCHAWsBK4EvsKkQ+DDbT/S9tvDWUMBSRqBHQOSJEmStIAlOZTJQuFLgTsCNwEuAr7e9kNj1iZp+sx0ACR5IPDgti8Yru8IHA78AXDftt8bs05JWujsGJAkSZKkBSbJK5NsPXz8AHAX4ADg6LZPBG4EbDNWfZKm1xAKLAdeAtwtyV2SLG37g7aPZxIWGApI0sjsGJAkSZKkBSTJIuD+wJeAdwMvBs5hMk7oyiQPBY5ou+twPvU/jpJW06yOgb2ApwLLgPcCJwCXzHyf+N0iSeOyY0CSJEmSFpC2K9t+AdiUyfigTwMvZbIUFOA2wCHwqwWiPriTdL1m9pYAN01yJ2DjoQPpc8DBwJuBHWfO+90iSeOyY0CSJEmSFrAkuwAvAO4AHNX2qJFLkjTFkhwDHA88EXhV23cm2ZJJ4Pjmtj8bsz5J0oTBgCRJkiSJJPsxGSv0uLZnjlyOpCmU5BnAPYG/YdIpsC/wc2CXtl8fzjhCSJLmAIMBSZIkSVpAkuwAnN/2imu5t1Hbq3xwJ2ltJPlT4FRgP+CKti8bdg38JbC/3yuSNHcsGbsASZIkSdL6NYzxuOPwi93DmewU+NEqZ9L2KnD2t6S1dgrwUWCTttsN1/4a+GjbziwmHq88SdIMlw9LkiRJ0vx3Y+DhSU4Dbt32RzAJA2YOGAZIWlOzFg4D0PabTHYLfDXJd5O8i0nnwLuG+4YCkjRHOEpIkiRJkuaxJNu2/VmS2wJfA05mMurjvW2/m2Rn4BZtPz9qoZKmSpLNgT8EPt72itndAEm2APYAzgF+3PayJIvbrhixZEnSLAYDkiRJkjSPDV0CPwEeA6wANgcOBu4GfAd4CPD8tsePVaOk6ZPkKOBM4HXAJsBFdgRI0vRwlJAkSZIkzW93Ak5nEg78ddtz274YeDmwMXCcoYCkNZFkEfBjYAfgu8Bus7oFfNYkSVPAjgFJkiRJWgCS3A44ErgDkw6BDw7X41JQSWtqGBf0USbh4xuBz7c9ZbgX95ZI0txmMCBJkiRJC0iS/YDXAMuAPYGznPstaW0keQZwGXCb4c83mewc+L9RC5MkXS+DAUmSJElagJI8B3hb26vGrkXS9JjVZbQRsGPbU5MsBR4J3BvYCnhR27NHLVSSdJ0MBiRJkiRpAXOEkKTVlWRx2xVJdmGyp+Ry4O7Aq9q+JcnOwO3a/suohUqSrpfBgCRJkiRJklZbkk8DxwBvBXYBjgK+0PZFM2GjewYkaW5zU7wkSZIkSZJWS5Kth7efarui7UnAY4DtkiwHCmAoIElzm8GAJEmSJEmSVkvb84H/Av5+1uWNgN2AawwEJGk6LBm7AEmSJEmSJE2V1wDvSHIW8GFgV+CdbS+d2UMwbnmSpOvjjgFJkiRJkiRdryRh8ixp5fD5PsDOwDeHkUKSpClhMCBJkiRJkqTrlGRJ22uG94uZrBFYOXJZkqS15I4BSZIkSZIk/ZYhACDJnYFnJNkEYFg6vDKJI6olaUoZDEiSJEmSJOm3zNoV8Hbg/LZXJNlxGCHETAeBJGn6GAxIkiRJkiTpWiXZF7i47YeTPBJ4PfD2JE8euTRJ0g1gMCBJkiRJkqTf5WJgZZIvA/sArwaeBdx9zKIkSTeMy4clSZIkSZL0K0nSWQ+MkuwB3A84su0lST4KfKntW1Y9K0maDi6JkSRJkiRJEgBJFg2LhbcDng5cClza9lXD/f2Bm7V9C4ChgCRNJzsGJEmSJEmS9BuSHA18E7gXcEnbJyXZnsmPTK9pe3aSxbMWFEuSpog7BiRJkiRJkvQrSe4EbNb2FcBWwFHDrYcCy9qeDWAoIEnTy2BAkiRJkiRJs10CnJnkg8Bpbb86dAu8kMkyYknSlDMYkCRJkiRJWuCSLBpeN297DvAV4E7AqUnuDBwB/HPbc2bOSpKml1/kkiRJkiRJC1iSDAuHNwPekOQWwDHAW4FtgTcD3wcOG/6KCyslacq5fFiSJEmSJGkBG4KBJnkVk90Cz551bzPgqrbXDJ8XtV05Vq2SpHXDjgFJkiRJkqQFbAgFtgDuwaRLgCQ3Gm7fA9hh1llDAUmaBwwGJEmSJEmStJjJ+KC9Adr+fLj+VmDzsYqSJK0fS8YuQJIkSZIkSRvezFigJPsBBwD/BLwjyV7Ax4H9gK+0PWVm3NCY9UqS1h07BiRJkiRJkhagWWOBHg38U9tjgTsCZwP3A04AXjCcyYavUJK0vrh8WJIkSZIkaYFKcj/gH4BzgcPafn+4vrTt1cN7uwUkaZ4xGJAkSZIkSVpAZj/oT7Ip8EjggcD/At8Fvtz2ghFLlCStZwYDkiRJkiRJC1CS5wMbAa8AbgY8DrglcDHw2rYXjlieJGk9cseAJEmSJEnSApMkwLeB2wOfBnZt+0rgQ8CPDAUkaX6zY0CSJEmSJGkBSrIIuAlwf+BpwI+ZdAqcOtx3t4AkzVN2DEiSJEmSJC0gSe6XZM+2K9ueD3wKeDewM/CwmXOGApI0fxkMSJIkSZIkLSy7AZ9IcmSSm7a9kslYof8G3gi/GjUkSZqnDAYkSZIkSZLmudkP+tu+DtgRuAw4IckRwAeB09telWSR3QKSNL+5Y0CSJEmSJGkeGx70r0xyE+CZwObAz4F3AsuB/ZiEAp8dsUxJ0gZkMCBJkiRJkjSPzSwRTvJ+4HLgFGAzYA/gJW1PWvXsSKVKkjaQJWMXIEmSJEmSpPVnCAVuCdyx7e4ASZYzGTF9AHDS7LPjVClJ2pDcMSBJkiRJkjT/nQtclORPANpeDHwYuPcQEkiSFhBHCUmSJEmSJM1Ds3YLbNv2Z0keDhwM/C/wAeAJwCVtD3GEkCQtLAYDkiRJkiRJ88ysvQJLgfcAL2t7apI7A/cH/gj4HPD6tlcbDEjSwuKOAUmSJEmSpPknQIHDgfOGUOB2wMOAb7d9468ODp0FI9UpSRqBHQOSJEmSJEnzUJItmXQFHAJsBRwEXAgsBg5pe+mI5UmSRmTHgCRJkiRJ0jzU9pIkRwEvAzYC/gI4DTgBuAVw6ojlSZJGZMeAJEmSJEnSPDNrx8DNgGXAL9uek+Qw4LZtn+ReAUlauOwYkCRJkiRJmmdmHvi3PRcmewSS7AjcBnjxcGxmD4EkaYGxY0CSJEmSJGkeSXIv4MczocCs6xsBN2p7vguHJWlhWzR2AZIkSZIkSbphkmyX5ElJ7gS8lGvpBGh7Vdvzh/eGApK0gBkMSJIkSZIkTb9Lga2BrwNbtP3pyPVIkuYwgwFJkiRJkqQp1/ZS4Cjga8DPkpyY5CCAJA9M8sxRC5QkzSnuGJAkSZIkSZpnkhwA/AWTkUI3AQ5q+60kqQ+DJGnBMxiQJEmSJEmaUkkWt12RZB/gCcC2wGFtT0qyFHgUcHXbjxkKSJJmGAxIkiRJkiRNuSQnAa8C7gY8EDgOeH3bs2adMRiQJAHuGJAkSZIkSZpKSTK8Phg4pe0H2h4KPALYBjgxyd1nzhsKSJJm2DEgSZIkSZI0pZJsDBwBPAh4C/Dxtv833LtX2xPHrE+SNDcZDEiSJEmSJE2hJIvarkyyM5PxQTsB5wBfB77S9srhnCOEJEm/wVFCkiRJkiRJUyTJ4uHtlkm2A5a2fT3wbmAT4E+AfWfOGwpIklZlx4AkSZIkSdIUSvJJ4PvAHsAZwJuA7wF/CPxH25+NWJ4kaQ6zY0CSJEmSJGlKzFo4fBBwFZO9AjcFzgOOBF4NHGcoIEm6LgYDkiRJkiRJU2LWWKCdgBcDTwA+3/bFwA+A7YCVI5UnSZoSBgOSJEmSJElTJMmtgH8CzgJuAnxuuLUYeFfbK5L4zEeS9DstGbsASZIkSZIkXbck2wA7MHmW8zfAI9uuSHIa8NokXwTu0vYLAG3tGpAk/U4GA5IkSZIkSXPfLYCHAY8BTmm7AqDtUUkuHM68BiDJ4pn7kiRdG9vKJEmSJEmS5ri23wEuAJYD5yd5bpK9hts/AX7Q9ifDWUMBSdJ1yq931kiSJEmSJGmuSvI+4OPAL4CHMPnB52XAwcDdZoIBSZKuj6OEJEmSJEmS5qgki9quTLI3sEXbTw7XTwceCGwGHNL2JzNnx6xXkjQd7BiQJEmSJEmaw5IsBt4G7Ay8GPha2yuv5Vzqgx5J0mqwY0CSJEmSJGmOGroAViQ5lslznEcDN0ry3VVHBxkKSJJWlx0DkiRJkiRJc8ysEUIbMwkEtmv7gyRPZDJC6Fzgn4elxJIkrRE7BiRJkiRJkuaYWbsC3gRsCuyS5ALgycBxwAuBS0YqT5I05RaNXYAkSZIkSZJ+LUmG1wcBdwKe1fauwDHAp4Clbf+i7Q9GLFOSNMUMBiRJkiRJkuaQWbsCbgmc2PbnSZa2fQXwEeBB41UnSZoPDAYkSZIkSZLmpn8H7pvkUW2vHq7dAVg+Yk2SpHnA5cOSJEmSJElzwMzC4eH9YmApcBfg/cB5wHeB3druOZxJfbAjSVoLLh+WJEmSJEmaQ5IcDmwP3AM4uu0dkuwLXAC8cjizuO2KEcuUJE0xOwYkSZIkSZJGNtMtkOS2wGeAxwG/ZBIE7ADs3/asMWuUJM0f7hiQJEmSJEka2cwIIeBRwAfantz2jLb7A8cCe45XnSRpvjEYkCRJkiRJGlkmtgeeDTw5yT6zbm8L3HuUwiRJ85KjhCRJkiRJkuaQJM8DngN8EzgN2A54QduLZi8oliRpbdkxIEmSJEmSNJIki4fXhyV5UpIHAG8Gdgd+BjwVOK/tRfAbI4ckSVprdgxIkiRJkiSNKMnWwH8Mf64ELgSObfuFJLsCbwU2BvZu+4vxKpUkzRcGA5IkSZIkSSNKsj+wY9tXJ7knsA9wS+DSti8azuzV9oQRy5QkzSOOEpIkSZIkSdrAZo0Q2h5YDuwN0PYbwGuA44D/njlvKCBJWpfsGJAkSZIkSRpJkm8C3wDuB9wYeGHbDwz3FrVd6cJhSdK6ZseAJEmSJEnSBpRkyfC6J/Cjts9uuwvwXOAlSc5MssPMeUMBSdK6ZjAgSZIkSZK0gSRJ22uGj48Abpfkj5Js0vbotrcD3g6cbyAgSVpfHCUkSZIkSZK0gSR5KfC6thcm2QPYH1gGfB/4atuTZp11hJAkab2wY0CSJEmSJGkDSLI5cAxwWZK3A78A/gr4AnBb4OlJHj5z3lBAkrS+2DEgSZIkSZK0ASW5OfDnwO8xWTz8RmAxcBDwr23PGLE8SdICYDAgSZIkSZK0gSW5EbAL8GjgNsBn2r5j3KokSQuFwYAkSZIkSdIIkgTYBrgv8CjgOW0vGLcqSdJCYDAgSZIkSZK0ASS5BXBB21+ucn0psHnbi1w4LEnaEFw+LEmSJEmStJ4kuXGS3ZNsCxwBbLrqmbZXt71oeG8oIEla7wwGJEmSJEmS1p9tgL2B44AbOypIkjQXGAxIkiRJkiStPz8EvsdkwfDPkvxdknsBJNk1yZ6jVidJWpDcMSBJkiRJkrQeJdkC2BLYAXgocHPgbOCPgQPafnfE8iRJC5DBgCRJkiRJ0jqWZHHbFUkeARzSdu/h+k2YjBa6I7Co7T8kSX1AI0nagAwGJEmSJEmS1pMk3wH+su2XkjwVuCXwT21Pn3VmkUuHJUkbkjsGJEmSJEmS1oMkdwBOA76R5JXAfYFdgEOSLEsSAEMBSdKGZjAgSZIkSZK0fpwDbAR8Gbiy7ZOAFwK3a3uZ44MkSWNZMnYBkiRJkiRJ88XMvoAkGwNbtH1Uki3bXpJkOfBW4Kjh7OK2K0YtWJK0INkxIEmSJEmStA4MD/qbZBfgk8Cbk/wIeNpw5PeA09v+M4ChgCRpLC4fliRJkiRJWoeSfBr4HPA2JjsF3g58ue0LkmzU9ioXDkuSxmTHgCRJkiRJ0jqSZOvh7Wfarmx7MnAgsG2SGwPXgAuHJUnjMhiQJEmSJElaR9qeD/wX8LJZlzcGdgOuMhCQJM0FBgOSJEmSJEk3QJJFs95vDLwEWJnknCSvY7Jw+B1tL0uyeKw6JUma4Y4BSZIkSZKktZQkw8Lh7YA3AucD/we8FtiOSafA6W2/M2KZkiT9BjsGJEmSJEmS1lJ//YvLQ4GLgA8xed7yYeCBwOcNBSRJc82SsQuQJEmSJEmaRkkWtV2ZZHPgNOBLbf8nyX8CdwOeCmwFHD5mnZIkrcpRQpIkSZIkSTdAkpcD+wKnAs9ve9Zw/WbAz4fdAotcPCxJmiscJSRJkiRJkrSGZpYIJzmAyR6BZzGZzPCmJAcm2artuW0vAzAUkCTNJXYMSJIkSZIkrYUki4CPA+9p+8nh2h8DzwC+BxxcH7xIkuYgdwxIkiRJkiStndsCi4HXJVnW9gNtP5DkE8Ad2jZJDAckSXONHQOSJEmSJElrYRgntDXwUOAhwOXA+9oeP2ZdkiRdH4MBSZIkSZKkGyDJxky6B/5g+PPktj8atypJkn43gwFJkiRJkqS1kGRx2xWzPm8J3LTt6SOWJUnS9TIYkCRJkiRJWgNJNm575dh1SJK0thaNXYAkSZIkSdJcN+wTIMmBwD8mOSXJgUkyXPcZiyRpaviPliRJkiRJ0vVouyLJZsDfAP8IXAFs07ZJlrVdOW6FkiStPoMBSZIkSZKk1fNY4OPATYGr2r5xCAvel+S245YmSdLqMxiQJEmSJElaPf8GXAm8A3jZcO3hwLK2PxytKkmS1tCSsQuQJEmSJEmaEucCS4Htgc2THMqki+BgmOwhaLtixPokSVotaTt2DZIkSZIkSXNOkqVtr06yM3ArYDnwEWBv4DHAmcAZbT+WJPUhiyRpShgMSJIkSZIkrWL2r/+TnAqcwK+7Bd7Q9jOrnDcYkCRNDYMBSZIkSZKkVSR5P/Bl4L+B/dsemmQrYC/gCcA2wJOAMw0EJEnTxh0DkiRJkiRJv+1jwIuAGwNfHjoILkjyaeA7wN5tfzRqhZIkrSU7BiRJkiRJkmaZGSOUZDFwIHAY8HXgsLZnDmfSto4QkiRNo0VjFyBJkiRJkjRXJNkeWJZkb+C4th8AdgMuBY5J8pokG82EAYYCkqRpZDAgSZIkSZL0a7sC7wHeChwD0Payts8CHg3cAbjFaNVJkrQOGAxIkiRJkiQN2n4W+DywFLhrkj9MctPh9vbAY9v+IElGK1KSpBvIHQOSJEmSJEmzJNkaKPAw4EnAqcDJwGuBm7f9xXjVSZJ0wxkMSJIkSZKkBS/J/YGvt/3lKteXA88dPn6z7WdnlhNv8CIlSVpHDAYkSZIkSdKCluRpwJ3aHpJk87aXDqOC0nblcGZp26vHrVSSpHXDHQOSJEmSJGmhuwewSZIjgQcP19J25cwuAUMBSdJ8YjAgSZIkSZIWuucCOwCPApYDzHQKAC4ZliTNO44SkiRJkiRJC1aSRUNnwCuBZcAmw+uRbb84bnWSJK0fBgOSJEmSJGlBmgkFhvebtr08yfbAo4G9gV8AfwlcUB+gSJLmkSVjFyBJkiRJkrShJZnZIbAE+DtgtyQXAX/b9vVJTgAeAlxkKCBJmm/cMSBJkiRJkhaimd0BLwVuDRwKnAccn+Q5bb8DvLbtiiQ+P5EkzSv+wyZJkiRJkhacoVvgRsCewKvbntr2ecDjgPskWdr2ypmzY9YqSdK6ZjAgSZIkSZIWpLY/Bz4PPHDWta8Bd2LSRSBJ0rzkjgFJkiRJkrSQ/Qfw8iQ7AV8D9gG+0/aMYQ+B+wUkSfNO/PdNkiRJkiQtZEm2AJ4PbAScBXyo7QVJFjlGSJI0HxkMSJIkSZKkBSnJEibPRq4ePi9uu2J4b7eAJGneMhiQJEmSJEnz3uwH/UmWAsvbnjdzD8AgQJK0ULh8WJIkSZIkLQQBSPIy4DDgW0kOgkkgYCggSVpIDAYkSZIkSdK8NnQLrExyZ+CBwP8DfgrMdAzcdsz6JEna0AwGJEmSJEnSvDarG+APgFcA9wF+1vZzSbYGXpxkq9EKlCRpA1sydgGSJEmSJEkbyDHAC4FdgT2Ga89jkh1cMFpVkiRtYAYDkiRJkiRpQWh7UpJTgP2AQ5NcADyUyXghkixqu3LMGiVJ2hDibh1JkiRJkjQfJVncdkWShwLbAhcCJwLLgUOAk4BT2n515uyI5UqStMEYDEiSJEmSpHlnWDjcYXfAN4CvAJcBVwDHA8e2vWLEEiVJGo3BgCRJkiRJmndmBQPPAq5ue1SSuzFZPHz74dg/tv2/8aqUJGkcBgOSJEmSJGleSrI98C3g+LaPG64tBn4fuEnbD49ZnyRJYzEYkCRJkiRJ81aS/YC/Agoc0fazw/VFbVfOdBaMWqQkSRuYwYAkSZIkSZo3Zj3w3wLYArgYWAY8ADgYuBx4PHBB25XjVSpJ0niWjF2AJEmSJEnSujD8+n/mYf+bgN8D/hs4Hfggk6XDfwb8wlBAkrSQ2TEgSZIkSZLmhVkLhw8HtgeeBuwL7Als2vZ5s84uMhyQJC1Ui8YuQJIkSZIkaV0YQoHFwGbAF9quGHYKvAHYJcm9Zp01FJAkLVgGA5IkSZIkad5ouwL4d+AZSfZLskXbnwLLgRXjVidJ0tzgKCFJkiRJkjSvJFkCPAXYEdgNuAi4su0TRi1MkqQ5wmBAkiRJkiTNO0k2BXYBNmUyMeGbbS91t4AkSQYDkiRJkiRpil3bg/6ZJcRj1SRJ0lznjgFJkiRJkjSVhgBgZZKNkxyeZBlMlhCPXZskSXPZkrELkCRJkiRJWksBCrwU2LrtZUm2AW4EXDYsHZYkSauwY0CSJEmSJE2loVvgZsDDgecl2QM4EvgE8KhRi5MkaQ5zx4AkSZIkSZoqSTZm0iFw9vD+9cBFwF2B1wHnAa8FHmfXgCRJv82OAUmSJEmSNG0eDOyeZGsm44Q+CFwI/HXbY4F9gJ8YCkiSdO3sGJAkSZIkSVMlyZZtL0nyt8D9gCPafnG4d2/gNcAj2/40yaK2K8esV5KkucZgQJIkSZIkTaUktwH2Av4Q+DnwlrbfSnLXticbCkiSdO0MBiRJkiRJ0tQadgzsCOwL7Ad8qu3rxq1KkqS5zWBAkiRJkiRNnVW7AZJsAewGXDx0C6Q+9JAk6VoZDEiSJEmSpDnv2h70JwmwqO2KkcqSJGkqGQxIkiRJkqSpkWR34ObAmW1PHq4tBuo+AUmSVs+isQuQJEmSJEm6LkNnAEkOBD4E3B94VZJXJdm27QpDAUmSVp/BgCRJkiRJmrNWGSG0G/CUts8FXggsAT6Z5M9GK1CSpCm0ZOwCJEmSJEmSfpeZUCDJs4E9gX8dbp0E/P/27idk06oO4/j3zBgIqWWJaBYpRGVUBkUZUZQZiEVREdRGqk20NaV/YBAERUkhCEUtok1QGdYmlUQlpUUhhaHmwiGqjVFKov1z+rV4n8FJ3IjzzvPOvJ/P5nnuc+7FtT7Xfc55sLqk+td20gHAickdAwAAAMCedPRugbXW66qvVmdVn56ZWzbjBxwjBADPjKOEAAAAgD3pqFLg0uqRmXlXdW31jbXWj9Zar1YKAMAzZ8cAAAAAsGettc5spwx4rLqrurH6T/X16vSZ+dgW4wHACUkxAAAAAOxpa61zqg9UF1WPVz+emV+stQ7OzOEjv9tNCQAnDsUAAAAAsKccuVtgrfX66tGZeWAz/prqy9U51VUzc9s2cwLAicodAwAAAMCeMk9+xXh59c211pVrrdNm5p7qs9Wh6s6tBQSAE5wdAwAAAMCesdY6MDP/XWsdqE6r3lx9sDq7uqV6T/WTmfnWkXe3GBcATkiKAQAAAGBPOKoUOL/6WnVF9c/q3Ori6kPVAzNzzdZCAsBJ4JRtBwAAAACoOurr/y9Vv56Zx9daF1Uvru6emRvWWs+pJ0uEbWUFgBOZYgAAAADYM9ZaF1TnV59Ya11Rfbh6YXVzdU31RP1fiQAAPEOOEgIAAAD2jLXWqdV11entfNB4VXW4+kF1+cw8ssV4AHBSUAwAAAAAe8pa68LqFdUdM/PwWuv66tGZ+YwjhADg2XOUEAAAALBnrLXWzNxX3bd5fkc7dwy8f/OKLxwB4FmyYwAAAADYU9Zap8zME0c9nzczf15rHZyZw9vMBgAnA8UAAAAAsBVHjgVaaz23ekH18pm59anz20sIACenA9sOAAAAAOxPRy36f7f6aPX9tdbVTzMPABxDigEAAADguFtrrc3vu6tTq69Uh6qbNuNvW2sd3F5CADhoQm2CAAACN0lEQVR5KQYAAACA426ePNv43Ora6pPVb2bmnrXWy6or2ykMAIBj7JRtBwAAAAD2tV+1c5TQeTNz9mbsC9XdM/OYewYA4Nhz+TAAAABw3DzdQv9a65Lquuqh6t7qwpl552ZujcULADim7BgAAAAAjpsjpcDmkuHzqj9Vt1dvqN5b/aH64+adgzNzeDtJAeDkZccAAAAAcFystV45M/evtc6pfl59uzqren71YPWzzbxdAgCwixQDAAAAwK5ba72our+dOwV+X902Mz9ca51bvaWdHQPPqz4/M3/bXlIAOPkpBgAAAIBdt9b6XvVwdWf1uepg9b6ZObSZf1V15szctb2UALA/KAYAAACAXbXWuridY4NeOzOz1npJdU11cXVD9cWnXkgMAOwexQAAAACwq9Zad2z+Xl/9bmbu3Yy/sfpU9abqspm5f0sRAWBfUQwAAAAAu2at9fHqI9V32ikA/l3dXd0+Mw9t3rlsZm7aXkoA2F8UAwAAAMCuWGut6ubq6pn57VrrourS6qXVX6pfVnfNzD+OvD8WKgBg1ykGAAAAgF2z1jpjZv5+9KL/Wuvt1VurC6qfzsyN28wIAPuNYgAAAAA4Lp5SDpxRXVbdOjN/3W4yANhfFAMAAADAceXIIADYrgPbDgAAAADsLzMzm/sHAIAtsGMAAAAAAAD2ETsGAAAAAABgH1EMAAAAAADAPqIYAAAAAACAfUQxAAAAAAAA+4hiAAAAAAAA9hHFAAAAAAAA7CP/Az4sihzTedT1AAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1080x2160 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 115200x57600 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def new_figure(): \n",
        "    fig, ax = plt.subplots(figsize=(5,5))\n",
        "    im = ax.imshow(simm)\n",
        "\n",
        "    # We want to show all ticks...\n",
        "    ax.set_xticks(np.arange(len(sentences[1])))\n",
        "    ax.set_yticks(np.arange(len(sentences[0])))\n",
        "\n",
        "    # ... and label them with the respective list entries\n",
        "    ax.set_xticklabels(sentences[1])\n",
        "    ax.set_yticklabels(sentences[0])\n",
        "\n",
        "    # Rotate the tick labels and set their alignment.\n",
        "    plt.setp(ax.get_xticklabels(), rotation=60, ha=\"right\",rotation_mode=\"anchor\")\n",
        "\n",
        "    for i in range(len(s1)):\n",
        "        for j in range(len(s2)):\n",
        "            text = ax.text(j, i,round(simm[i,j],2),ha=\"center\",weight ='bold' ,va=\"center\", color=\"w\",fontsize=10)\n",
        "            # text.set_visible(False)\n",
        "    plt.figure(figsize = (1600,800))\n",
        "    ax.set_title(\"Heat Map\",fontsize=50)\n",
        "\n",
        "    return fig\n",
        "  \n",
        "fig = new_figure()\n",
        "fig.set_size_inches(15, 30)\n",
        "# show_figure(fig)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t72bzE-QAKu4"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4hOMEqrSAKsk"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mc9RSkwhAKqO"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LT0JTVIOAKn5"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CYuugsYUAKl2"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "obEADMF9AKkB"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rle5n7kqAKho"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyNjAFbDoategBbiOy7QC/9c",
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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