Keywords_Extraction_Transformer.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v6Xda9CM9el9"
      },
      "source": [
        "## Dataset description"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset used in this project is the Medical transcription dataset. It contains sample medical transcriptions for various medical specialties.\n",
        "\n",
        "The data is in CSV format with below features:\n",
        "\n",
        "- **description**\n",
        "\n",
        "- **medical_specialty**\n",
        "\n",
        "- **sample_name**\n",
        "\n",
        "- **transcription**\n",
        "\n",
        "- **keywords**"
      ],
      "metadata": {
        "id": "i98dvBWFQq4x"
      }
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Pped-3NPaDV"
      },
      "source": [
        "## Information"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QuxYf07d62Oo"
      },
      "source": [
        "Medical transcriptions are textual records of patient-doctor interactions, medical procedures, clinical findings, and more. Extracting keywords from these transcriptions can provide valuable insights into a patient's health status, medical history, and treatment plans.\n",
        "\n",
        "* Significance:\n",
        "\n",
        "  - Data Summarization: Keyword extraction helps in summarizing lengthy medical transcriptions, making it easier for healthcare professionals to quickly understand the patient's medical history.\n",
        "\n",
        "  - Search and Retrieval: Extracted keywords can be used to index medical records, facilitating faster search and retrieval of relevant documents.\n",
        "\n",
        "  - Trend Analysis: By analyzing frequently occurring keywords, healthcare institutions can identify common ailments, treatment outcomes, and more.\n",
        "\n",
        "* Applications:\n",
        "\n",
        "  - Clinical Decision Support: Extracted keywords can be used to develop clinical decision support systems that provide real-time suggestions to healthcare professionals.\n",
        "  - Patient Monitoring: By continuously analyzing the keywords from a patient's medical transcriptions, healthcare systems can monitor the patient's health and predict potential health risks.\n",
        "  - Research: Medical researchers can use extracted keywords to identify trends, study disease outbreaks, and understand treatment efficacies.\n",
        "  - Billing and Insurance: Keywords can help in automating the medical coding process, which is essential for billing and insurance claims."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Problem Statement"
      ],
      "metadata": {
        "id": "YN15WtbfwPkB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Build a transformer model for performing keywords extraction on the medical transcription dataset.\n",
        "\n"
      ],
      "metadata": {
        "id": "R8_CoX7swR14"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gt9VG5y7FLpv"
      },
      "source": [
        "### Import required packages"
      ]
    },
    {
      "cell_type": "code",
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        "id": "qV7FBip_FLpw",
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        },
        "outputId": "e7907860-4571-45b5-aa3a-9565ccc3f5df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import re\n",
        "import random\n",
        "import string\n",
        "from string import digits\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.gridspec import GridSpec\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer, tokenizer_from_json\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "import warnings\n",
        "pd.set_option(\"display.max_colwidth\", 200)\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset\n",
        "\n",
        "read_data = pd.read_csv(\"/content/Medical_transcription_dataset.csv\")\n",
        "read_data.head()"
      ],
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              "   Unnamed: 0  \\\n",
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              "                                                         description  \\\n",
              "0   A 23-year-old white female presents with complaint of allergies.   \n",
              "1                           Consult for laparoscopic gastric bypass.   \n",
              "2                           Consult for laparoscopic gastric bypass.   \n",
              "3                                             2-D M-Mode. Doppler.     \n",
              "4                                                 2-D Echocardiogram   \n",
              "\n",
              "             medical_specialty                                sample_name  \\\n",
              "0         Allergy / Immunology                         Allergic Rhinitis    \n",
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              "2                   Bariatrics   Laparoscopic Gastric Bypass Consult - 1    \n",
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              "\n",
              "                                                                                                                                                                                             transcription  \\\n",
              "0  SUBJECTIVE:,  This 23-year-old white female presents with complaint of allergies.  She used to have allergies when she lived in Seattle but she thinks they are worse here.  In the past, she has tr...   \n",
              "1  PAST MEDICAL HISTORY:, He has difficulty climbing stairs, difficulty with airline seats, tying shoes, used to public seating, and lifting objects off the floor.  He exercises three times a week at...   \n",
              "2  HISTORY OF PRESENT ILLNESS: , I have seen ABC today.  He is a very pleasant gentleman who is 42 years old, 344 pounds.  He is 5'9\".  He has a BMI of 51.  He has been overweight for ten years since...   \n",
              "3  2-D M-MODE: , ,1.  Left atrial enlargement with left atrial diameter of 4.7 cm.,2.  Normal size right and left ventricle.,3.  Normal LV systolic function with left ventricular ejection fraction of...   \n",
              "4  1.  The left ventricular cavity size and wall thickness appear normal.  The wall motion and left ventricular systolic function appears hyperdynamic with estimated ejection fraction of 70% to 75%. ...   \n",
              "\n",
              "                                                                                                                                                                                                  keywords  \n",
              "0                                                                      allergy / immunology, allergic rhinitis, allergies, asthma, nasal sprays, rhinitis, nasal, erythematous, allegra, sprays, allergic,  \n",
              "1  bariatrics, laparoscopic gastric bypass, weight loss programs, gastric bypass, atkin's diet, weight watcher's, body weight, laparoscopic gastric, weight loss, pounds, months, weight, laparoscopic,...  \n",
              "2  bariatrics, laparoscopic gastric bypass, heart attacks, body weight, pulmonary embolism, potential complications, sleep study, weight loss, gastric bypass, anastomosis, loss, sleep, laparoscopic, ...  \n",
              "3  cardiovascular / pulmonary, 2-d m-mode, doppler, aortic valve, atrial enlargement, diastolic function, ejection fraction, mitral, mitral valve, pericardial effusion, pulmonary valve, regurgitation...  \n",
              "4  cardiovascular / pulmonary, 2-d, doppler, echocardiogram, annular, aortic root, aortic valve, atrial, atrium, calcification, cavity, ejection fraction, mitral, obliteration, outflow, regurgitation...  "
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          "metadata": {},
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QvddL7X69NiB"
      },
      "source": [
        "### Pre-processing and EDA"
      ]
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    {
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        "\n",
        "df = read_data.copy()\n",
        "df = df.drop(columns=[\"Unnamed: 0\"])\n",
        "df.head()"
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              "                                                         description  \\\n",
              "0   A 23-year-old white female presents with complaint of allergies.   \n",
              "1                           Consult for laparoscopic gastric bypass.   \n",
              "2                           Consult for laparoscopic gastric bypass.   \n",
              "3                                             2-D M-Mode. Doppler.     \n",
              "4                                                 2-D Echocardiogram   \n",
              "\n",
              "             medical_specialty                                sample_name  \\\n",
              "0         Allergy / Immunology                         Allergic Rhinitis    \n",
              "1                   Bariatrics   Laparoscopic Gastric Bypass Consult - 2    \n",
              "2                   Bariatrics   Laparoscopic Gastric Bypass Consult - 1    \n",
              "3   Cardiovascular / Pulmonary                    2-D Echocardiogram - 1    \n",
              "4   Cardiovascular / Pulmonary                    2-D Echocardiogram - 2    \n",
              "\n",
              "                                                                                                                                                                                             transcription  \\\n",
              "0  SUBJECTIVE:,  This 23-year-old white female presents with complaint of allergies.  She used to have allergies when she lived in Seattle but she thinks they are worse here.  In the past, she has tr...   \n",
              "1  PAST MEDICAL HISTORY:, He has difficulty climbing stairs, difficulty with airline seats, tying shoes, used to public seating, and lifting objects off the floor.  He exercises three times a week at...   \n",
              "2  HISTORY OF PRESENT ILLNESS: , I have seen ABC today.  He is a very pleasant gentleman who is 42 years old, 344 pounds.  He is 5'9\".  He has a BMI of 51.  He has been overweight for ten years since...   \n",
              "3  2-D M-MODE: , ,1.  Left atrial enlargement with left atrial diameter of 4.7 cm.,2.  Normal size right and left ventricle.,3.  Normal LV systolic function with left ventricular ejection fraction of...   \n",
              "4  1.  The left ventricular cavity size and wall thickness appear normal.  The wall motion and left ventricular systolic function appears hyperdynamic with estimated ejection fraction of 70% to 75%. ...   \n",
              "\n",
              "                                                                                                                                                                                                  keywords  \n",
              "0                                                                      allergy / immunology, allergic rhinitis, allergies, asthma, nasal sprays, rhinitis, nasal, erythematous, allegra, sprays, allergic,  \n",
              "1  bariatrics, laparoscopic gastric bypass, weight loss programs, gastric bypass, atkin's diet, weight watcher's, body weight, laparoscopic gastric, weight loss, pounds, months, weight, laparoscopic,...  \n",
              "2  bariatrics, laparoscopic gastric bypass, heart attacks, body weight, pulmonary embolism, potential complications, sleep study, weight loss, gastric bypass, anastomosis, loss, sleep, laparoscopic, ...  \n",
              "3  cardiovascular / pulmonary, 2-d m-mode, doppler, aortic valve, atrial enlargement, diastolic function, ejection fraction, mitral, mitral valve, pericardial effusion, pulmonary valve, regurgitation...  \n",
              "4  cardiovascular / pulmonary, 2-d, doppler, echocardiogram, annular, aortic root, aortic valve, atrial, atrium, calcification, cavity, ejection fraction, mitral, obliteration, outflow, regurgitation...  "
            ],
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              "      <th></th>\n",
              "      <th>description</th>\n",
              "      <th>medical_specialty</th>\n",
              "      <th>sample_name</th>\n",
              "      <th>transcription</th>\n",
              "      <th>keywords</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A 23-year-old white female presents with complaint of allergies.</td>\n",
              "      <td>Allergy / Immunology</td>\n",
              "      <td>Allergic Rhinitis</td>\n",
              "      <td>SUBJECTIVE:,  This 23-year-old white female presents with complaint of allergies.  She used to have allergies when she lived in Seattle but she thinks they are worse here.  In the past, she has tr...</td>\n",
              "      <td>allergy / immunology, allergic rhinitis, allergies, asthma, nasal sprays, rhinitis, nasal, erythematous, allegra, sprays, allergic,</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Consult for laparoscopic gastric bypass.</td>\n",
              "      <td>Bariatrics</td>\n",
              "      <td>Laparoscopic Gastric Bypass Consult - 2</td>\n",
              "      <td>PAST MEDICAL HISTORY:, He has difficulty climbing stairs, difficulty with airline seats, tying shoes, used to public seating, and lifting objects off the floor.  He exercises three times a week at...</td>\n",
              "      <td>bariatrics, laparoscopic gastric bypass, weight loss programs, gastric bypass, atkin's diet, weight watcher's, body weight, laparoscopic gastric, weight loss, pounds, months, weight, laparoscopic,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Consult for laparoscopic gastric bypass.</td>\n",
              "      <td>Bariatrics</td>\n",
              "      <td>Laparoscopic Gastric Bypass Consult - 1</td>\n",
              "      <td>HISTORY OF PRESENT ILLNESS: , I have seen ABC today.  He is a very pleasant gentleman who is 42 years old, 344 pounds.  He is 5'9\".  He has a BMI of 51.  He has been overweight for ten years since...</td>\n",
              "      <td>bariatrics, laparoscopic gastric bypass, heart attacks, body weight, pulmonary embolism, potential complications, sleep study, weight loss, gastric bypass, anastomosis, loss, sleep, laparoscopic, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2-D M-Mode. Doppler.</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>2-D Echocardiogram - 1</td>\n",
              "      <td>2-D M-MODE: , ,1.  Left atrial enlargement with left atrial diameter of 4.7 cm.,2.  Normal size right and left ventricle.,3.  Normal LV systolic function with left ventricular ejection fraction of...</td>\n",
              "      <td>cardiovascular / pulmonary, 2-d m-mode, doppler, aortic valve, atrial enlargement, diastolic function, ejection fraction, mitral, mitral valve, pericardial effusion, pulmonary valve, regurgitation...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2-D Echocardiogram</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>2-D Echocardiogram - 2</td>\n",
              "      <td>1.  The left ventricular cavity size and wall thickness appear normal.  The wall motion and left ventricular systolic function appears hyperdynamic with estimated ejection fraction of 70% to 75%. ...</td>\n",
              "      <td>cardiovascular / pulmonary, 2-d, doppler, echocardiogram, annular, aortic root, aortic valve, atrial, atrium, calcification, cavity, ejection fraction, mitral, obliteration, outflow, regurgitation...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- **Handle missing values**"
      ],
      "metadata": {
        "id": "VKNf8eoL6fB3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop missing values\n",
        "\n",
        "df.info()"
      ],
      "metadata": {
        "id": "llQVDnRPGasQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "726f922d-8099-480d-87b4-713cd4f0ee11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4999 entries, 0 to 4998\n",
            "Data columns (total 5 columns):\n",
            " #   Column             Non-Null Count  Dtype \n",
            "---  ------             --------------  ----- \n",
            " 0   description        4999 non-null   object\n",
            " 1   medical_specialty  4999 non-null   object\n",
            " 2   sample_name        4999 non-null   object\n",
            " 3   transcription      4966 non-null   object\n",
            " 4   keywords           3931 non-null   object\n",
            "dtypes: object(5)\n",
            "memory usage: 195.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna()\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4_fxkUaryHpy",
        "outputId": "4f8d0294-6189-4071-eea3-ff4b004b52eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 3898 entries, 0 to 4995\n",
            "Data columns (total 5 columns):\n",
            " #   Column             Non-Null Count  Dtype \n",
            "---  ------             --------------  ----- \n",
            " 0   description        3898 non-null   object\n",
            " 1   medical_specialty  3898 non-null   object\n",
            " 2   sample_name        3898 non-null   object\n",
            " 3   transcription      3898 non-null   object\n",
            " 4   keywords           3898 non-null   object\n",
            "dtypes: object(5)\n",
            "memory usage: 182.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Count of rows where keywords are ' '\n",
        "\n",
        "for col in df.columns:\n",
        "  df = df[df[col].str.strip()!=\"\"]\n",
        "df.info()"
      ],
      "metadata": {
        "id": "cCgDfhukIh-t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e77cc610-66ae-4ea7-9e7c-d9f7bcdb4294"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 3813 entries, 0 to 4995\n",
            "Data columns (total 5 columns):\n",
            " #   Column             Non-Null Count  Dtype \n",
            "---  ------             --------------  ----- \n",
            " 0   description        3813 non-null   object\n",
            " 1   medical_specialty  3813 non-null   object\n",
            " 2   sample_name        3813 non-null   object\n",
            " 3   transcription      3813 non-null   object\n",
            " 4   keywords           3813 non-null   object\n",
            "dtypes: object(5)\n",
            "memory usage: 178.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C92ji6ZV9MWs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71e89d47-433b-495b-97d8-e8714e265fcb"
      },
      "source": [
        "# Displaying the distinct categories of medical specialty\n",
        "\n",
        "speciality = df.medical_specialty.unique()\n",
        "speciality"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([' Allergy / Immunology', ' Bariatrics',\n",
              "       ' Cardiovascular / Pulmonary', ' Dentistry', ' Urology',\n",
              "       ' General Medicine', ' Surgery', ' Speech - Language',\n",
              "       ' SOAP / Chart / Progress Notes', ' Sleep Medicine',\n",
              "       ' Rheumatology', ' Radiology', ' Psychiatry / Psychology',\n",
              "       ' Podiatry', ' Physical Medicine - Rehab',\n",
              "       ' Pediatrics - Neonatal', ' Pain Management', ' Orthopedic',\n",
              "       ' Ophthalmology', ' Office Notes', ' Obstetrics / Gynecology',\n",
              "       ' Neurosurgery', ' Neurology', ' Nephrology', ' Letters',\n",
              "       ' Lab Medicine - Pathology', ' IME-QME-Work Comp etc.',\n",
              "       ' Hospice - Palliative Care', ' Hematology - Oncology',\n",
              "       ' Gastroenterology', ' ENT - Otolaryngology', ' Endocrinology',\n",
              "       ' Emergency Room Reports', ' Discharge Summary',\n",
              "       ' Diets and Nutritions', ' Dermatology',\n",
              "       ' Cosmetic / Plastic Surgery', ' Consult - History and Phy.',\n",
              "       ' Chiropractic'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Total categories\n",
        "\n",
        "len(speciality)"
      ],
      "metadata": {
        "id": "kRxuMduGSWYB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d657d06-e9a6-4cd8-f055-3fc8d616c0f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "39"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YtIjY7ji9va5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4a1e6ace-8bde-407f-b30d-0e3fb472f7c7"
      },
      "source": [
        "# Displaying the distinct categories of medical specialty and the number of records belonging to each category\n",
        "\n",
        "spc = df.groupby('medical_specialty')[\"description\"].count().reset_index()\n",
        "spc = spc.rename(columns={\"medical_specialty\":\"Specialty\", \"description\": \"Count\"})\n",
        "spc = spc.sort_values(by=\"Count\", ascending=False).reset_index(drop=True)\n",
        "spc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         Specialty  Count\n",
              "0                          Surgery   1007\n",
              "1                       Orthopedic    296\n",
              "2       Cardiovascular / Pulmonary    276\n",
              "3                        Radiology    248\n",
              "4       Consult - History and Phy.    219\n",
              "5                 Gastroenterology    192\n",
              "6                        Neurology    162\n",
              "7    SOAP / Chart / Progress Notes    140\n",
              "8                          Urology    139\n",
              "9                 General Medicine    137\n",
              "10         Obstetrics / Gynecology    128\n",
              "11            ENT - Otolaryngology     83\n",
              "12                   Ophthalmology     79\n",
              "13                    Neurosurgery     79\n",
              "14               Discharge Summary     76\n",
              "15                      Nephrology     63\n",
              "16           Hematology - Oncology     60\n",
              "17                 Pain Management     58\n",
              "18                        Podiatry     42\n",
              "19                    Office Notes     40\n",
              "20           Pediatrics - Neonatal     38\n",
              "21          Emergency Room Reports     29\n",
              "22      Cosmetic / Plastic Surgery     25\n",
              "23                     Dermatology     24\n",
              "24                       Dentistry     24\n",
              "25                         Letters     20\n",
              "26         Psychiatry / Psychology     19\n",
              "27                      Bariatrics     18\n",
              "28                  Sleep Medicine     18\n",
              "29                   Endocrinology     15\n",
              "30       Physical Medicine - Rehab     11\n",
              "31            Diets and Nutritions     10\n",
              "32        Lab Medicine - Pathology      8\n",
              "33                    Rheumatology      7\n",
              "34               Speech - Language      7\n",
              "35       Hospice - Palliative Care      5\n",
              "36          IME-QME-Work Comp etc.      4\n",
              "37                    Chiropractic      4\n",
              "38            Allergy / Immunology      3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f1a34a54-0ea2-462e-b015-35c0c82206a8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Specialty</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Surgery</td>\n",
              "      <td>1007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Orthopedic</td>\n",
              "      <td>296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Radiology</td>\n",
              "      <td>248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Consult - History and Phy.</td>\n",
              "      <td>219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Gastroenterology</td>\n",
              "      <td>192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Neurology</td>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>SOAP / Chart / Progress Notes</td>\n",
              "      <td>140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Urology</td>\n",
              "      <td>139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>General Medicine</td>\n",
              "      <td>137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Obstetrics / Gynecology</td>\n",
              "      <td>128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>ENT - Otolaryngology</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Ophthalmology</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Neurosurgery</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Discharge Summary</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Nephrology</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Hematology - Oncology</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Pain Management</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Podiatry</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Office Notes</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Pediatrics - Neonatal</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Emergency Room Reports</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Cosmetic / Plastic Surgery</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Dermatology</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Dentistry</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Letters</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Psychiatry / Psychology</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Bariatrics</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Sleep Medicine</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Endocrinology</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Physical Medicine - Rehab</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Diets and Nutritions</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Lab Medicine - Pathology</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Rheumatology</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Speech - Language</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Hospice - Palliative Care</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>IME-QME-Work Comp etc.</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Chiropractic</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Allergy / Immunology</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f1a34a54-0ea2-462e-b015-35c0c82206a8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f1a34a54-0ea2-462e-b015-35c0c82206a8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f1a34a54-0ea2-462e-b015-35c0c82206a8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-be9fe194-4342-40a3-93c3-1cf6e857ad8e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-be9fe194-4342-40a3-93c3-1cf6e857ad8e')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-be9fe194-4342-40a3-93c3-1cf6e857ad8e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qrpJDoGx-CuF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 861
        },
        "outputId": "eb081ecf-70d2-4772-bd01-2f2e55a5b2de"
      },
      "source": [
        "\n",
        "top_cnt = 14\n",
        "top_spc = spc[:top_cnt].copy()\n",
        "remaining = spc[top_cnt:][\"Count\"].sum()\n",
        "new_record = pd.DataFrame([{'Specialty':'Other Specialities', 'Count':remaining}])\n",
        "top_spc= pd.concat([top_spc, new_record], ignore_index=True)\n",
        "\n",
        "colors = sns.color_palette('pastel')[0:top_cnt+1]\n",
        "\n",
        "#create pie chart\n",
        "plt.pie(top_spc.Count, labels = top_spc.Specialty, colors = colors, autopct='%.2f', startangle=60)\n",
        "plt.show()\n",
        "\n",
        "rem_spc = spc[top_cnt:].copy()\n",
        "rem_spc[\"Percentage\"] = rem_spc.Count*100/spc.Count.sum()\n",
        "sns.barplot(data=rem_spc, y=\"Specialty\", x=\"Percentage\")\n",
        "plt.title(\"Other Specialities\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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zLiGEKFRajpW1UTmYLFXdEyHya2itGSWuSiI3K2+ogFX+nxOi3EjQKkQtZzTbWHc0hxxzVfdEiPzq6TSYUmrnR1FmCpw5iAzHEaKc1M53CiEEAFabjQ3Hc0jLkU9NUf0oNvDLqnklrkoiNQHOH63qXghRO0jQKkQttiPaSGyataq7IUSBIvU6TFm1a1hAQS7EQEJ0VfdCiJpPglYhaqmoBBNRCTImQFRPBgX0KTW7xFVJxB6D9KSq7oUQNZsErULUQrFpFrZHSy1WUX1Fqg1YzbU/y3q16ANgzKnqXghRc0nQKkQtk55jZcOxHJn8IaqtOloV1lpU4qq4LCY4vS9v2VchRMndeO8aQtRiFquNDcdzMUqZHVGNhZr0YLuxsqyXZafB+aiq7oUQNZMErULUIv+dMXIxS9I4hfl5yWymDb+dx7oFMK5XKPOnDCI2Ov/U7uP7t/HW2D6M7uLHY90Cef2xuzDmFG+Jo1+WzmHYTa58OfdZh+1//7CIN5/oxWPdAhl2kyuZ6Snl8ZRqnAY6Laa0G/ujJ+kcXIyt6l4IUfPc2O8cQtQi0RfNMvGqCFG7N3PHA2N4eeE6nn33ZyxmE7Oe7kdudqa9zfH925g9fgDNb7qDqYvXM23JBu4c+BiKqui3y5OHdvL3D4sIadQ83z5jTjYtbu7BPcMnl+tzqknUgHeGtqq7US2cPQzZ6VXdCyFqFglahagF0nOs/HMqt6q7Ue1Nnr+K2/r+H8FhTanXuAWjXvmIpLgYTh3ZbW+z/J3n6fHg4/QdNongsKYE1m/MTXfej1anv+65c7Iy+OiVkYx44X1c3D3z7e/58JP0HTaJhs07lPfTqjEidXrMOTfmsIBr2ax541st1fR75pIlS/D09KzqblSq9evXoygKKSkpxT6ma9euTJgwwf44NDSUefPmXfeYadOm0bp161L18UYnQasQNZzFamPj8VxZorUUsjPSAHB19wIg7WICJw7uwN3bl1dH3cFTvRrwxuM9ObrnnyLPtWzWRFp17kmzjt0qtM81lYtaQZusrupuVCvGbIg5WHHnj4mJYcSIEdStWxedTkf9+vUZP348SUmOtbeKE2hVtL1793Lvvffi5+eHwWAgNDSUhx56iISEhErrwy233EJsbCweHh6lPseOHTsYM2aM/bGiKKxatcqhzeTJk1m7dm2pr3Ejk6BViBpuZ4yRJBnHWmJWq5Uv33mO8JadCG7YDICEc6cB+OHTN+nSbziT56+ifkRrZo7rS9yZ44Wea+uf3xAdtYeBY6dXRtdrpAjFgNUiWdZrpSZCcgWMbz158iTt27fn2LFjrFixguPHj/PRRx+xdu1aOnXqxMWLF8v/osVgMpnybUtMTOSOO+7A29ubP/74g8OHD7N48WLq1q1LZmZmAWepGDqdjoCAABSl9L+nvr6+ODs7X7eNq6srPj4+pb7GjUyCViFqsNhUC0fiq+n9xWpu2axnOHfyEGNfW2LfZrPlBf/dBozg9nuGUD+iFYOfmUlA/XA2/vx5gedJij/Ll3Of5bHpi9DpDZXR9RonQKvGclEC1sKciwJTOY/uefLJJ9HpdPz555906dKFevXq0bt3b/766y/OnTvHiy++COTd3o6OjuaZZ55BUZR8Adsff/xBZGQkrq6u9OrVi9hYxwj7s88+IzIyEoPBQJMmTfjwww/t+06fPo2iKHz99dd06dIFg8HAl19+ma+vW7ZsITU1lc8++4w2bdrQoEEDunXrxjvvvEODBg2AK7fuf/31V1q2bInBYODmm2/mwIEDDufavHkzt912G05OToSEhPD00087BL65ubk899xzhISEoNfradSoEQsXLnS4xuXhAUlJSTz88MMEBQXh7OxMixYtWLFixXVf96uz1qGhoQAMGDAARVHsjwsaHnC919FoNDJu3DgCAwMxGAzUr1+fN99887r9qK0kaBWihjJZbDKOtZSWzZrI3s2ref7D3/D2D7Jv96wTAEDdBk0c2tcNjeBifEyB5zp9ZDdpyYlMHdaZR2/x4NFbPDiyazNrVi7g0Vs8sFpk3EZwrh6QoLUwFjPEHCq/8128eJE//viDsWPH4uTk5LAvICCAwYMH8/XXX2Oz2fj+++8JDg5mxowZxMbGOgSlWVlZzJ49m88//5yNGzdy5swZJk++MpHwyy+/5JVXXuH111/n8OHDvPHGG7z88sssXbrU4ZrPP/8848eP5/Dhw/Ts2TNffwMCAjCbzfzwww/YiigwPWXKFObMmcOOHTvw9fXlnnvusWdvT5w4Qa9evbj//vvZt28fX3/9NZs3b2bcuHH244cOHcqKFSt49913OXz4MB9//DGurq4FXisnJ4d27drx66+/cuDAAcaMGcOQIUPYvn37dft42Y4dOwBYvHgxsbGx9sfXKup1fPfdd/npp59YuXIlUVFRfPnll/YA+EZz46yhJ0Qt898ZI5lGWUGgJGw2G5/PnsTODT/zvw9/x7duqMP+OoH18fQNJO6aMlhxZ47TstNdBZ6zafuuvL58m8O2z159gsD6jbl76DOo1Df2OM5wnRbTBQlYi5KelFcKyyeo6LZFOXbsGDabjcjIyAL3R0ZGkpycTGJiIn5+fqjVatzc3AgICHBoZzKZ+Oijj2jYsCEA48aNY8aMGfb9U6dOZc6cOdx3330ANGjQgEOHDvHxxx8zbNgwe7sJEybY2xTk5ptv5oUXXuCRRx7h8ccfp2PHjnTv3p2hQ4fi7+/v0Hbq1Kn06NEDgKVLlxIcHMwPP/zAgw8+yJtvvsngwYPtE6PCw8N599136dKlCwsWLODMmTOsXLmSNWvWcOeddwIQFhZWaL+CgoIcgvSnnnqKP/74g5UrV9KxY8dCj7vM19cXAE9Pz3yv7bXP6Xqv45kzZwgPD+fWW29FURTq169f5LVrKwlahaiBzqdaOJYowwJKatmsZ9j6xzeMn/UVBhc3UpLiAXB2cUdncEJRFPoMnsAPn75OvfAW1Gvcks2/fkls9FHGvfmF/Twzn7ybtl3vocfAx3FycbOPib1M7+SMq4e3w/aUpHhSk+KJP3sSgLPHD2JwccPHPxhXD+9KePaVT6OAe7oW+U0tnvNHwc0bdE5Fty2OorKWRXF2drYHrACBgYH2iVGZmZmcOHGCkSNHMnr0aHsbs9mcbyJT+/bti7zW66+/zsSJE1m3bh3btm3jo48+4o033mDjxo20aNHC3q5Tp072v3t7exMREcHhw4eBvMlc+/btcxiCYLPZsFqtnDp1iv3796NWq+nSpUuxnr/FYuGNN95g5cqVnDt3DqPRSG5ubpFjVkuiOK/j8OHD6dGjBxEREfTq1Yu+ffty110Ff4mu7SRoFaKGMcqwgFJb991nALz5RG+H7aNe/ojb+v4fkFeaymTMYfm858lIS6ZeeAueffcn/IOvZGQSzp0iI8VxBnZR/v7+M1Z9dmUc2huP98x37dqmqcaAOVeyrMVlteQNEwhrC2WYC0SjRo1QFIXDhw8zYMCAfPsPHz6Ml5eXPRNYGK3Wsaauoij2QDgjIwOATz/9lJtuusmhnfqauwsuLi7F6rePjw8DBw5k4MCBvPHGG7Rp04bZs2fnG25QmIyMDB577DGefvrpfPvq1avH8eOFT6YsyKxZs5g/fz7z5s2jRYsWuLi4MGHCBIxGY4nOcz3FeR3btm3LqVOn+P333/nrr7948MEHufPOO/n222/LrR81hQStQtQw/50xkiXDAkpl6baMYrXrO2wSfYdNKnT/nFXXH4D4vwWr820bMPpFBox+sVjXrw3c1SpUySrkN7VkMpIh6SzUCSn9OXx8fOjRowcffvghzzzzjMO41ri4OL788kuGDh1qn3Sl0+mwlHDstb+/P3Xr1uXkyZMMHjy49J0thE6no2HDhvmqB2zdupV69eoBkJyczNGjR+3DINq2bcuhQ4do1KhRgeds0aIFVquVDRs22IcHXM+WLVvo168f//d/eV8qrVYrR48epWnTpsV+Hlqt9rqvbXFfR3d3dx566CEeeughHnjgAXr16sXFixfx9q6dd2kKI0GrEDVIXJqF4zIsQNQA4TY9ZqtkWUsj9hi41ynbMIH333+fW265hZ49e/Laa6/RoEEDDh48yJQpUwgKCuL111+3tw0NDWXjxo0MGjQIvV5PnTp1inWN6dOn8/TTT+Ph4UGvXr3Izc3lv//+Izk5mYkTJxa7r7/88gtfffUVgwYNonHjxthsNn7++Wd+++03Fi9e7NB2xowZ+Pj44O/vz4svvkidOnXo378/AM899xw333wz48aNY9SoUbi4uHDo0CHWrFnD+++/T2hoKMOGDWPEiBG8++67tGrViujoaBISEnjwwQfz9Ss8PJxvv/2Wf/75By8vL+bOnUt8fHyJgtbQ0FDWrl1L586d0ev1eHl5lfh1nDt3LoGBgbRp0waVSsU333xDQEDADbf4A0j1ACFqDKvNxvZoGRYgqr8grQZziny8lJbVCueOFt3uesLDw/nvv/8ICwvjwQcfpGHDhowZM4Zu3brx77//OmToZsyYwenTp2nYsGGRQwauNmrUKD777DMWL15MixYt6NKlC0uWLLGXqSqupk2b4uzszKRJk2jdujU333wzK1eu5LPPPmPIkCEObd966y3Gjx9Pu3btiIuL4+eff0an0wHQsmVLNmzYwNGjR7ntttto06YNr7zyCnXr1rUfv2DBAh544AHGjh1LkyZNGD16dKG1YF966SXatm1Lz5496dq1KwEBAfYAubjmzJnDmjVrCAkJoU2bNgW2Kep1dHNz4+2336Z9+/Z06NCB06dP89tvv6EqxtLStY1iK+tIbSFEpTgUZ+K/M+U3lkqIiqDYoJ3VGVOmZFnLqkHrvIyryKuh2q1bN5KTk2/IDKPIc+OF6ULUQNlGK3vPScAqqr8IvU4C1nJyLiov6yqEyCNBqxA1wM4YIyapUS+qOb0KnFNlqkR5MWZD4umq7oUQ1YcMDxCimotPt/DH4Zyq7oYQRWqtNWBLurEXUyhvKjU0uQW0+qruiRBVTzKtQlRjNpuN7adlWICo/rw0KrgoHynlzWqB2JKVFxWi1pJ3GCGqsRMXzCRny6A2Uf01tOix2WQsa0VIjoWs1KruhRBVT4JWIaopi9XG3nOmqu6GEEWqr9NiSpWPk4pU1hJYQtQG8i4jRDV1NMFMpqx8Jao5BfDN1BbZTpRNViqkXajqXghRtSRoFaIaMlls7D8vY1lF9ddUr8eULcMCKkP8yarugRBVS4JWIaqhQ3EmcmS1VlHNOalAlyzVAipLVppkW8WNTYJWIaqZHJONQ3EyllVUf01UBqxmybJWJsm2ihuZBK1CVDMHY02ykICo9ny1KqzJ8hFS2STbKm5k8o4jRDWSa7YRlSBZVlH91TcZQEpcVYk4ybaKG5QErUJUI4fjTJilLKuo5sL0WkxpErBWlWzJtooblAStQlQTFouFI/GSZRXVmxrwSpcSV1VNsq3iRiRBqxDVhPrMfzyQsZx22uMoNqnPKqqnSJ0ec45kWatadhqkJ1V1L4SoXIrNJp+OQlQ5mxX+/gCyUwCwOHkR7dmBbeYmmNBUbd+EuMRVrRCe5oTVIkFrdeBeBxq0rupeCFF5JGgVojo4fxB2f59vs03nwnnvdmy1NifTZqiCjglxRTu1E2apGFCtNOkMeqeq7oUQlUOCViGqg82fQWpsobttah1JPq35V2lDstWlEjsmRJ4ArRr/JD15C7eK6sK3PtQNr+peCFE5JGgVoqoln4N/FhWrqU1Rk+bTlP807Thn8a7gjglxRXurM6YMCVirG7UWmt4KKlmYTNwAJGgVoqrt+xli9pToEBuQ7R3OXn0HjpkDKqRbQlwWrtfinKir6m6IQgRHgk9QVfdCiIonQasQVclshL/eAYux1KcwuodwyKUD+0z1QZFMmChfWgVaZjtjNsrvVnVlcIWIm6u6F0JUPAlahahKZ3bB/l/L5VQWF1+Ou3dkhzkcq1SzE+WkldYASXLvubpr2B5cPau6F0JULKmlI0RVitldbqdSZyYSkfkr4QZ3znq2519rM3JtUgRelJ6HWoWSrEIyG9VfUowEraL2k0yrEFUlLR42fVJhp7dpnYj3bsO/tCLdKjVxRMm1VzlhSpGsfU2gKND0dtDI91RRi0nQKkRVObgaTu+o8MvY1FpSfFqwTWlLgtW9wq8naodgnQafC/qq7oYoAZmQJWo7CVqFqAoWM6x9B0w5lXZJm6Iiw7sJO7UdOGPxqbTrippHsUE7izOmLJl8VZO4ekHDdlXdCyEqjtz3EaIqxEdVasAKoNisuCUdomvcUh40raKJ5mylXl/UHBEGXZUErDv3beTpl++hx0N1ad1DYd2WVfnanIw+zPiX7+XWfh7cfI8LjzzZgdiEM8U6/+q/v6J1D4UJU/s7bG/dQynwZ8nKWeXwrCpPRjKYcqu6F0JUHJmIJURViD1UpZc3pJykY8pJ2rjVJcq1A7tNYdikXJYA9CpwTtFgqYJrZ+dk0jisFf17jmDi9Pvy7Y85f4JHn7mV/r1H8sSw6bg4u3Pi9EH02qKXOD4Xd5q5n0ymbYvb8u3762vH1eg2b/+d6XNHcudt95f+yVSRlHjwrVfVvRCiYkjQKkRls5gg8URV9wIAbfp5mqf/SKSzN6c8OrDdHIFZ3hZuaE3VBiymqvkCc2vH3tzasXeh+99f/CK3duzDM6Pftm8LqduwyPNaLBZeeHMwTwydzq79m0jPTHHYX8fbcYGO9f/+SIdW3QgODCvZE6gGkuMkaBW1lwwPEKKyJZ7IC1yrEXXWRRrF/sGg1EV0V+/ESZF7jDcib40K28Xq+bFgtVrZtO1X6gc35onne9JtoB//99RNBQ4huNbHX8zA28uPAb1HFtk2KTmezdt+pX8x2lZH2WmQm1XVvRCiYlTPdycharPYw1Xdg0KpjBkEx23ggYuf0VvZgqdKPv1uJGEWPTZb9RwmcjElgazsDBZ9/Ra3dOjFgjf/pHvnAUyafh//7d1Q6HG7D2xm1eqFvPLMp8W6zk9/LsXZ2Y07bs0/PKGmSImr6h4IUTHkPqAQlclqgYRjVd2LIinmXHwTtnGP6j9SfZqzXdWOOKtnVXdLVKBQnRbTheqbx7BarQB07dSPIfc/A0CTRq3Ze/Afvv3lI9q36pLvmMysdF6cOYRXnvkUL486xbrOj38sok/3weh1RY+Tra6S48G/5o1sEKJIErQKUZkunARzzbn1rlgteCbupQf7yPJuzG59e06a/au6W6KcqYA6mVqq16AVR14eddCoNTSs39Rhe4N6kew+sLnAY2LOn+B83GnGv3yPfZvVlhf8tuupYdXiKIcxsbv2b+J0TBQzX/y6Ap5B5cnNzPvRu1R1T4QoXxK0ClGZYo9UdQ9KRcGGy8UobiWKDh71OOTckf1mme1RW0Tq9JjSquewgMu0Wh1NIzpwOibKYXv0uaME+tcv8JgG9Zrw7Sf7Hba9v+QlsrLSeXbsfAJ8Qxz2/fD7QpqGtyOiYavy7XwVSEsCXwlaRS0jQasQlcVmzavPWsPpU8/QJvUMLVz9OO7Wgf9M4ViV6ntbWVyfswp0KWqsVd0RICs7gzPnjtsfn4s7xZHje/Bw9ybQrx7DB07h2dcfom3L2+nQqhv/7FjNxn9/5rM56+3HvDRzKH51gnh65JvodQYaNWjucA03F0+AfNszMtNYs+kbJo2ZU2HPrzKlJ0kVAVH7SNAqRGVJPgem7KruRbnRZCTQJONXGhs8OOPZga3WSIw2Wfi8pmmiMmAxV48s68Gj/zF6cjf74zkfTQTgnh7DePXZJXS/dQAvjf+IhSve5O0PnqZ+cASzp35Hm+a32o+JTTiDUoovUavXfwU2G726P1z2J1INZCaD1Qoq+T4pahFZxlWIynJsExxdX9W9qDA2rTNx3m3519aCDJtTVXdHFIOfVk1gkh6oHkGrKF9hbcHNu6p7IUT5ke9gQlSWpNNV3YMKpZiyCIzfzICkz+irbKSOKr2quySKUM8oAWttlp5U1T0QonxJplWIymAxw5+zwGqu6p5UGpuiIsMnkv807Ymx+FR1d8Q1Guq0uF7QVXU3RAUyuELEzVXdCyHKj2RaRblav349iqKQkpJS1V0p0PDhw+nfv7/9cdeuXZkwYULFXzg55oYKWAEUmxW3CwfpGreUgeYfidCcr+ouiUs0gGeGjD+u7XIywFRzKuwJUSQJWm9gFouFd955hxYtWmAwGPDy8qJ3795s2bKlWMdXWsBXgb7//nteffXVir9QLR8acD0K4JR8gptiv+Lh3K9pozkJcoOnSkXq9JhzZFjAjUCGCIjaRILWG5TNZmPQoEHMmDGD8ePHc/jwYdavX09ISAhdu3Zl1apVhR5rNBorr6MVzNvbGzc3t4q/0IXTFX+NGkCbdo4WsasYnPU5nTSH0WCp6i7dcFzVKtTJ6qruhqgkGRerugdClB8JWm9QK1eu5Ntvv2XZsmWMGjWKBg0a0KpVKz755BPuvfdeRo0aRWZmJgDTpk2jdevWfPbZZzRo0ACDwcDw4cPZsGED8+fPR1EUFEXh9OnT9vPv3LmT9u3b4+zszC233EJUlGN90gULFtCwYUN0Oh0RERF8/vnnDvsVRWHBggX07t0bJycnwsLC+Pbbbx3axMTE8OCDD+Lp6Ym3tzf9+vVz6IPFYmHixIl4enri4+PDs88+y7VDuK/NFufm5vLcc88REhKCXq+nUaNGLFy4sAyvNGA2QqrcGr+aOusC4bG/MyhtEd3Uu3Ci9nwRqu4i0GOzSJb1RpGVVtU9EKL8SNB6g1q+fDmNGzfmnnvuybdv0qRJJCUlsWbNGvu248eP89133/H999+zZ88e5s+fT6dOnRg9ejSxsbHExsYSEnJldZkXX3yROXPm8N9//6HRaBgxYoR93w8//MD48eOZNGkSBw4c4LHHHuPRRx/l77//dujHyy+/zP3338/evXsZPHgwgwYN4vDhwwCYTCZ69uyJm5sbmzZtYsuWLbi6utKrVy97JnjOnDksWbKERYsWsXnzZi5evMgPP/xw3ddl6NChrFixgnfffZfDhw/z8ccf4+rqWvIX+GoXz+QtLCDyUeWmExK3ngeSP6OX6l88lKyq7lKtFqhVY06WgPVGkpuVNw9UiNpAFhe4QR09epTIyMgC913efvToUfs2o9HIsmXL8PX1tW/T6XQ4OzsTEBCQ7xyvv/46Xbp0AeD555/n7rvvJicnB4PBwOzZsxk+fDhjx44FYOLEiWzdupXZs2fTrduVwuIDBw5k1KhRALz66qusWbOG9957jw8//JCvv/4aq9XKZ599hqLkfQgvXrwYT09P1q9fz1133cW8efP43//+x3333QfARx99xB9//HHd12TlypWsWbOGO++8E4CwsLAiXsliSDlb9nPUcoo5B7/4f7lXtYNUnxZsV7clzuJR1d2qXWwQlKvHJCWubjhZaVKvVdQOkmm9gZWk2ln9+vUdAtaitGzZ0v73wMBAABISEgA4fPgwnTt3dmjfuXNnexb1sk6dOuV7fLnN3r17OX78OG5ubri6uuLq6oq3tzc5OTmcOHGC1NRUYmNjuemmm+zHazQa2rdvX2if9+zZg1qttgfb5SYltnzPV4spVjOeibvpEbeI+62/0UCdUNVdqjUa63WYMiRgvRFlpVZ1D4QoH5JpvUE1btw4X5B42eXtjRs3tm9zcXEp0fm12ivldC5nQq3W8rtFnpGRQbt27fjyyy/z7StJcH01J6cKWsUpTYLWklKw4ZJ0hNs4QkePUA44d+CgOaToA0WBdAq4psm0txuVjGsVtYVkWm9QgwYN4tixY/z888/59s2ZMwcfHx969Ohx3XPodDoslpJ/DEZGRuYrq7VlyxaaNm3qsG3r1q35Hl8eutC2bVuOHTuGn58fjRo1cvjx8PDAw8ODwMBAtm3bZj/ebDazc+fOQvvVokULrFYrGzZsKPFzKlR2KuRmlt/5bkD61NO0i/2GR7KX015zDJWMDy6xSI0Bi1GyrDeqbAlaRS0hQesNatCgQQwYMIBhw4axcOFCTp8+zb59+3jsscf46aef+Oyzz4rMroaGhrJt2zZOnz7NhQsXip1JnTJlCkuWLGHBggUcO3aMuXPn8v333zN58mSHdt988w2LFi3i6NGjTJ06le3btzNu3DgABg8eTJ06dejXrx+bNm3i1KlTrF+/nqeffpqzZ/PGkI4fP5633nqLVatWceTIEcaOHXvdRQ9CQ0MZNmwYI0aMYNWqVfZzrly5sljPq0CpcaU/VjjQZMTRNPZnHslcym2a/WiR2SXF4alRoSTLW/2NzJQriwyI2kHeyW5QiqKwcuVKXnjhBd555x0iIiK47bbbiI6OZv369Q6rRhVm8uTJqNVqmjZtiq+vL2fOnCnWtfv378/8+fOZPXs2zZo14+OPP2bx4sV07drVod306dP56quvaNmyJcuWLWPFihX2bKyzszMbN26kXr163HfffURGRjJy5EhycnJwd3cH8qogDBkyhGHDhtGpUyfc3NwYMGDAdfu2YMECHnjgAcaOHUuTJk0YPXq0vfRXqaTHl/5YUSBVdjINYtcwKHUhd6p34KLkVHWXqrVGVj02q2RZb3QyrlXUBoqtJLNxhKgkiqLwww8/FCt4rtZ2fgNxR6q6F7WaTa3jok8rtqrakGQpY3myWiZEp8H7gr6quyGqAb9QCGxU1b0QomxkIpYQFSlNMq0VTbEY8UnYQR9lJ+k+zdihacc5i9T3UWzgn6XDVNUdEdVCrpRAFrWABK1CVBSzEbKSq7oXNwzFZsX9wn66s58cr3D2GNpzzBxY1d2qMk0MOkyJMixA5JGgVdQGErSKaqlWjFqRgLVKKIBT8jE6cYx27sEcdunIXlN9UG6cAM6ggFOKlLgSVxizq7oHQpSdBK1CVJTslKruQamEjphLdEJKvu1j7+7IB0/0zbe96/OL2HDgdL7tfdqH8+u0IfbHh2MSeW7xn2w4cBqzxUrTer58979B1PPzLMfeO9KlnaVV2lmau/hywr0DO8zhWFBX2PWqi0iNAYvpxgnSRdGslrwKAloZ4ixqMAlahagoWSlV3YNS2fHOY1iuKl92IDqBHi8tZWDnZgW2//7FQRjNV3J6SWnZtHrqQwbe2ty+7UTsRW599jNG9mjL9MHdcXfWc/BMAgZd5bwFqTMTaZz5G4307pz1as+/1mbk2rRFH1gD1dGosF6UwjAiv9wsCVpFzSZBqxAVpYYGrb4ejvV53/pmEw0DvenSIrTA9t5uzg6Pv9q4H2e9loG3XglyX1z2F33aN+btET3t2xoGVv5kKVVuGvXi1hGi/ZcE7zZstbUk1eZc9IE1SKhZj8kmWVaRX24WuHpVdS+EKD35Oi5ERakFY1qNJjNfrN/HiB5t7MvxFmXhn7sYdHtzXAw6IG/53l//O0rjuj70fHkpfoNnctPEj1n1b8HLCFcGxZSNf/w/3HvhM+5hPX7q2rFkUKhOiylN3tZFwWRcq6jp5N1NiIpSQzOtV1u19QgpGTkMv6NNsdpvjzrLgegERt3Vzr4tITWTjGwjb327iV7twvnz1aEM6BTJfW98xYb9pyqq68WiWM14Je6iZ/xC7rOupr7mQpX2pyxUQJ2M2jnkQZQPqSAgajoZHiBERamhE7GutvDPnfRu14i6Pu7Fa79mFy1C/ekYEWzfZrXmVYLod3MTnul/CwCtwwL553AMH/3+H11aNCj/jpeQYrPhmnSILhwix7MBB5w6cMgcXPSB1UhTnR5TmgwLEIWToFXUdJJpFaIi5GaApWaXdY9OSOGvvScZ1bNd0Y2BzBwjX23cz8gebR2213F3RqNW0TTE12F7ZIgvZxJTyqu75caQcor2sSt5OGcF7TTHUWpA+TUXFWiTa39VBFE2ptyq7oEQZSOZViEqQnbNX+h78Zpd+Hm4cHeHxsVq/83mg+SaLPxft1YO23VaDR3Cg4g6l+Sw/ei5JOpXYLmrstKmx9Is/SeaOHlz2rM928xNMFfTt8wIlRMWi2RZxfVZTGCz3VAli0UtI5lWISqCsWbfh7NarSz+azfD7miNRu2YwRs65zv+t2RNvmMW/rmT/jc3wcc9/2z8Kfd15utNB/h09X8cP5/E+z9v4+ftUYzt07HCnkN5UWdfpGHsnzycupA71DtxVqpXuspfq8ZyUaIQUTw1/AaQuMFVz7SBEDVdDZ+m+9eek5xJTGXENbf6Ac4kpqJSOQZJUWcvsPnQGf58dWiB5xtwS1M+GnsPb36zkac/+Y2IoDp898JD3NqsfoX0vyIoxkyC4jZwv2YrF7xbsVVpQ7LVpegDK1iIUY8JCVpF8ZhNoNFVdS+EKB3FVivWyxSimjm1HQ79UdW9EBXIpqhJq9OMHep2nLdUTfHLRnotLokSgYjia9geXD2ruhdClI5kWoWoCKaanWkVRVNsFjwS93EH+8n2Dme3vgMnzP6Vdn2NAh5pWsyVdkVRG1iMVd0DIUpPglYhKoIErTcMBRvOF4/SmaN08KjHIecO7DNX/LCHplo95lQZFiBKxixjWkUNJkGrEBXBlFPVPRBVQJd6htapZ2ju4sdx9w78Zw7HWgHzXd3VKlQX1cjYLlFSMhFL1GQStApREWr4RCxRNprMBJpk/kpjgwcxXu3ZamlKrq38VqsKR4/ZKllWUXKSaRU1mQStQlQEGR4gAFVOKvVj11JP+y9x3m3YSkvSrU5lOmddnQbzBalWKErHLGNaRQ0mQasQFcFcvWp5iqqlmLIIjN9Cf/V2kn1aslVpywWrW8lPZIO62TokWSZKy2at6h4IUXoStApREazyySDyUywmvBN20lvZTYZ3JP9p2xFjqVPs4yMMOkyJMixAlJ4UuRQ1mQStQlQI+WQQhVNsVtySDtKNg2R7NWS/oT1HzEHXPUavgEuqBksl9VHUTpJpFTWZBK1CVARJZ4hicko+QUdO0MYtiCNuHdhtbFDg4vCRGgMWo2RZRdnIW5OoySRoFaJCyCeDKBlt+jlapJ+jqbMPpzw6sN0cgRk1AF4aFSTL5KuqZcv7LqGQt2iukvfd4vL3C0WxXfrzStu8djbHP7GhXD7Xpb/nne7qv1vt2+zt7X+/tM925fGVv9vg0mMFK9hsl/ZZ8/bZrOgsGqBhxb1MQlQgCVqFqAiSzhClpM5KolHWasL0mznv1Z5/Lc1paPHAVKYSVwUHXHA58Coi4LomyLry96u2XQ64lKsCLtu1AdelP68OuK5qh+3ytqsDrssB2KVg7HIQZrPm7bNa7fvnrljMT5v/5tjZaAw6HTdHNufV4WNoHBScd5zNgmK7dOzlP61WvtuymWkrlxOdmEh4QABvDHqIu1u2BGtee9XwkQW+qm8/+ABTevcsw79LFQisiwStoqaSoFWIiiBBq7iGSaPDqNNj1OkwavXkarSYtBpyNRpMajW5ahUmtYpclQqTCnJVYDClcsvFk+S41MFFnwzWXDRGG045JvRZuWiyslBZLGC9HIhZUGwWFKv10jZLXnBW1U++kmzdsYGnb7+JDg0ewmyx8sJ3P9DvxQkcen0Gznp9gcf8c+w4Q96dy5sP3EffVi1ZvnUb9899h13TXqZ5cN4449h5sx2O+X3fAUYuXsr97dpW+HMSQlyh2Gzy6SpEufvrHcjNqOpeiDIyqbUY9XqMWj1Gre7SjwajRoNRrcaoVuX9qQKjSsn7U7FhxIpRsWK0WTBixmgzYyvBkBGdVaFPnA+BcRqSA+tiqKviuCYIbWwCftbjpBgyOeuq56w6B63Rin+uHh+jBq8cFW45NpyzzeizjGgys1GMN25hzsS0dPzGT2TD81O4PaJxgW0e+vBjMo25/DLhafu2m199g9b1Qvho2JACj+n/7gek5+Sw9tlJFdLvChUQCL3urupeCFEqkmkVoiLId8EqY1JrMOoMeVnNAgNNNUa1glGluhRsXgo0FWtesIkFo+16gab50k8hbNf8WRI2G90u+tDqSBrkXOBY2CO4mP7EHN+c5k33slvfioPxQWSnZNIu8wg9UveSobER4xdAjJea3epM0q2OC1s4WZwIMBnwzdXhnavCPUfBNduCIcuELisXJTs7LzNbC6Vm570W3i4uhbb598RJJvbs4bCtZ/NmrNq9p8D28alp/LpvP0tHPlpu/RRCFI8ErUKIKmdWay7dOr860NRi1KgxqvOCzVy1gkmlIldF3u3zAjKaJpsZa2kDzSr+ntEq3YPbjprRJcUBcCZiIGajEVN2PN7prUiMdaVN8L/sCOyE3t2Zf861JcelDc20MTRJ3k/Tk/tQbFZS3L046xdAjKueGFU2GeocTqkzOWXILPC6ik2Dn8mAX64OH6MGz1wVbtlXsrXqzCwUU81bzsBqtTJhxVd0Dm9kv81fkLjUVPzdHRd68PdwJy41tcD2S7f8g5tBz33ta+jQgAIqU5TF+vXr6datG02bNmXfvn2o1Wr7Pk9PT+bNm8fw4cPL9ZrixiVBqxAVQV37/9cyq9X5M5oaLUbt5UBTlfejUq5z6zwvq2mloEzf5UCzkNXFypLRrEbq5TjT64QO13MJ9m1JDW4nOdMDT48jWFMtaLMgO9MZtUlPO6f/2GFrj3eYgiVBy8GUehxU18PLL5N2ykECUvbR/Phhml86V7KnDzF1/Ihx1XNWlUWmzfH1tCkQr8shXpdTSA8VXCzOBBgN+OZq8cpV4ZEDLtlWDFlGtJeztdXs7sKTXyznwNnzbH7h2XI976JNWxh8800YtNpyPW+l0VRMv0+ePMmyZct49NHKzUAbjUZ0Ol2lXrOir2symdDW1N+vClb7P1mFqArq6vmGY7kUaOZqdZh0eZOB8jKal26da9SFBJo2jIolL+C8dOv8Rg40y4OHWcc9Z9zwPRnvcHs+yyeMc5ZwANS2M3mvsgrcrAZOnfYkonEUbZ328V92S5RAI/XdNMSeh2SLC3/REcW1Ay28oonI2Ych+QReKUl4pSTR8tL5L3rWIcbXjxhXHTFKFtm2opcczlSbOeGUwQmngverrVr8zAb8cvX45GrwzFFwzbHinG1Bl5Wbl601XyfTXc7Gfb6cX/bsY+P/phDs7X3dtgEeHsSnpTtsi09NI8DDI1/bTUePEhUXx9dPjCnX/lYqbcV87D/11FNMnTqVRx55BH0hk95SUlKYPHkyP/74I7m5ubRv35533nmHVq1aATB8+HBSUlJYtWqV/ZgJEyawZ88e1q9fD0DXrl1p3rw5Go2GL774ghYtWvD333+zYcMGpkyZwt69e/H29mbYsGG89tpraDR5z/fbb79l+vTpHD9+HGdnZ9q0acOPP/6Ii4sLXbt2pXXr1sybN89+3f79++Pp6cmSJUsACA0NZeTIkRw7doxVq1Zx3333sWTJEj799FNmzJhBUlISPXv25LbbbmPGjBmkpKTYz/Xjjz8yffp0Dh06RN26dRk2bBgvvviivW+KovDhhx/y+++/s3btWiZPnsyXX37J448/zuTJk+3n2bNnD23atOHYsWM0atSojP9iNZMErUJUBHX5fQO3qNV5E4GuymjmarWYNBpy1WpMmkszz6+5dW7CRq5iwYSVXFverXNLgYGm5dJPISTQLFc6q0KfWB8aHE1EMTlO1rPoXYj27IotV0FRzJgzzuXtUBTcUjQkuipkZNTDw+0krQ06duc0IdfVjG9DBWO8hsRUGzZFYZ85lH2aUOr4p9OOA/gm70dlzLuWd8oFvFMu0Iq8f9Ikbz9i6vgS46LlrJJJjq3kE7csKojV5RCrywG3glqocDe7EGDUU+dSttY9B1wuja3VZOXkZWtLfGVHNpuNp75YwQ+7drP+uck08PUt8phODcNYe+gwE+66075tzcHDdGoYlq/two2baRdan1b1QsrY0yqkqZiP/QkTJvDFF1/w3nvvOQRaVxs4cCBOTk78/vvveHh48PHHH3PHHXdw9OhRvIv4cnG1pUuX8sQTT7BlyxYAzp07R58+fRg+fDjLli3jyJEjjB49GoPBwLRp04iNjeXhhx/m7bffZsCAAaSnp7Np0yZKOg999uzZvPLKK0ydOhWALVu28PjjjzNz5kzuvfde/vrrL15++WWHYzZt2sTQoUN59913ue222zhx4gRjxuR96bl8HoBp06bx1ltvMW/ePDQaDXq9nsWLFzu8losXL+b222+/YQNWkKBViAph0eoxGpwLmAykvZLRVKkuTQi6NqPpOBlIAs3ao2uSN62PpKPKPJ9vnw04E9oPY2beIgJuruewpV/6N1aBIQk0HhpOnXaiRXNnfDTHaGnQszenARa1DXVdE/XdNJyPBdOlwy5Y3fiDTqg8bqK15hSNsveiTzltDw4VoM7FBOpcTKDNpT5cqBNAjI8PMc5azioZ5NrKZzxrmsZEmsbEUeeC92utevwvTRjzydXgYc/WmtFl5aLKyi4yW/vk58tZvnUbPz79JG5OBvu4VA8nJ5wu3cod+ulCgjy9eHPgfQCM73EHXWbOZs7qP7m7VQu+2raD/06f5pPhjpUD0rKz+WbHTuYMGljGV6KKVdDwAGdnZ6ZOncoLL7zA6NGj8bgmU71582a2b99OQkKCPRM7e/ZsVq1axbfffmsP5IojPDyct99+2/74xRdfJCQkhPfffx9FUWjSpAnnz5/nueee45VXXiE2Nhaz2cx9991H/fr1AWjRokWJn2P37t2ZNOlKxYgXX3yR3r172wPLxo0b888///DLL7/Y20yfPp3nn3+eYcOGARAWFsarr77Ks88+6xC0PvLIIw5DK4YPH84rr7zC9u3b6dixIyaTieXLlzN7tmP5tRuNBK1CVICfG9XlZO61waaVQm+bgwSatVirDA9ui7oyyaogiY3uIi3zSkSnVZ/BHi6qQEHBTe1MsjGNxAsh+PlG4ac5QFO9loO5wQDkupnxd1LIidNwIf3KL5IVFbvMDdmlbUhAQCptrAeok7wfxZTl0AcF8L0Qh++FONqSV9Y/oU4AMXV8iHFSc07JxFhOQey1TCobZ/XZnNVnF9JChZfZBX+jgTq5Grxy1Ljn2HDOtuSV98rKYcHf6wHoOtPxg33xyOEMv7UzAGeSLqK6ajLSLeGNWP7YKF76fhUvfPcD4f5+rHrqyXyTt77atgMb8PBNHcvrKVeNChwrOXLkSObMmcPMmTN54403HPbt3buXjIwMfHx8HLZnZ2dz4sSJEl2nXbt2Do8PHz5Mp06dUK76d+3cuTMZGRmcPXuWVq1acccdd9CiRQt69uzJXXfdxQMPPICXl1eJrtu+fXuHx1FRUQwYMMBhW8eOHR2C1r1797JlyxZef/11+zaLxUJOTg5ZWVk4OzsXeO66dety9913s2jRIjp27MjPP/9Mbm4uAwfW8C9NZSRBqxAVQK+q/IkBovoJyXGm90kdrmcTrtsuI6ApsblX33K2YcmKufLw0mexa6aWZC2cP6/Fx9sHtTqJIO1uzDY9Uca8W+FmjQ1NsIl6aVrOx9owX/PdKc7iwe90RuN5M200J2iYtQ9d6pkC+6Vgw/9CLP4XYmkPWBWFBN+6xHh7EeOs5hwZmGyVN1Y1WWMiWWOCQrK17535nACTAT+jDu8cNR454JpjwynbhCUrF1VmNuufn5LvuIEd2jOwQ/sCznjFmK63M6br7eXxNKpWBU5a0mg0vP766wwfPpxx48Y57MvIyCAwMNA+NvVqnp6eAKhUqny37E0FVK5wuU4Js4Ko1WrWrFnDP//8w59//sl7773Hiy++yLZt22jQoEGFXRfynvf06dO577778u0zGAzXPfeoUaMYMmQI77zzDosXL+ahhx6yB7k3KglahagAErTe2DxMWu6Jcc83yaogJmcvop06gelKlsjZ5QLWzKuyoJd2uSQoKMEKNpuNmLP+1K9/EQUb9XVbMdlu46TJ036I0d1EgJOKrDg1FzPyp+/NqNlhbswOXWOCAi7S2noA74sHUMyFVREAlc1GQMI5AhLO0QGwKiri/AI56+1NjJOKc7Z0zNcbtlLBjGorZ9RZnDFkgXsBDWwqfMxO+F8eW2tfjOFytjYbJbfoiWk1WgXPtB84cCCzZs1i+vTpDtvbtm1LXFwcGo2G0NDQAo/19fXlwIEDDtv27NlT5Ez6yMhIvvvuO2w2mz3bumXLFtzc3AgOzrsLoSgKnTt3pnPnzrzyyivUr1+fH374gYkTJ+Lr60tsbKz9fBaLhQMHDtCtW7frXjciIoIdO3Y4bLv2cdu2bYmKiirVONQ+ffrg4uLCggULWL16NRs3bizxOWobCVqFqAB6pXpWDxAVS2dV6B3nQ9jRCyjG2CLb2xQV0cF9MWc5TkEyaM84VJW1KXlxq9qs4KJzJiM3k5QUFQEBdTHo8yZrNdJvwkR3YkxXMjZmrRVtsJV6qVrOxdmwFDL05JzFm3Pcjs67E23VJ2iQsRdt+rki+6+yWakbf4668efoSN6kwbjLmVgDnLdlYKmkIHbtez+z//edJByPRWvQUr99OH1feBC/RoFXGikKSVojSVojXHqZ9v68nd9nfU/y2QvUaeDPgP8N4s5bb7q0GIOa5m36Fni9tx98gCm9e1bCMytnuoJn9pent956i549HV+bO++8k06dOtG/f3/efvttGjduzPnz5/n1118ZMGAA7du3p3v37syaNYtly5bRqVMnvvjiCw4cOECbNm2ue72xY8cyb948nnrqKcaNG0dUVBRTp05l4sSJqFQqtm3bxtq1a7nrrrvw8/Nj27ZtJCYmEhkZCeSNVZ04cSK//vorDRs2ZO7cuQ6z/wvz1FNPcfvttzN37lzuuece1q1bx++//+4wTOGVV16hb9++1KtXjwceeACVSsXevXs5cOAAr7322nXPr1arGT58OP/73/8IDw+nU6dORfaptpOgVYgK4KQyFN1I1Cpdk3xoHZWGKiP/JKvCxIb3JTOrgCDCGO34WHXlr25GPRnkLRRw+rQnEY3jUZS8ELeJbh1m213Emq+cU1HA6GmirouK9PNqUrIKHzRttGnZam7CVkMT6rkk0cqyD8/kQyjm4mUf1RYLQXExBMXFcDN5tXxj/YKJ8fYgRg9xtvRCJhaW3YmtUdwy7A7qtW6A1Wzlt7e+5ZNHZjFl/ZvonQsO1E7tOMYXTy6gz/8G0vTO1uz64V8+GfUOLqtnENgkL0M3dfd8h2Oi1u3j68mL8BtzB7v8AvHMUa4snZttRF3dl86thJqm3bt3p3v37vz555/2bYqi8Ntvv/Hiiy/y6KOPkpiYSEBAALfffjv+/v4A9OzZk5dffplnn32WnJwcRowYwdChQ9m/f/91rxcUFMRvv/3GlClTaNWqFd7e3owcOZKXXnoJAHd3dzZu3Mi8efNIS0ujfv36zJkzh969ewMwYsQI9u7dy9ChQ9FoNDzzzDNFZlkhb9zsRx99xPTp03nppZfo2bMnzzzzDO+//769Tc+ePfnll1+YMWMGM2fORKvV0qRJE0aNGlWs13LkyJG88cYblV7/trpSbCWt+SCEKNKx7Gh+Tt5Q1d0QlaBFujtdjlrQJSWX6LjU4HacpjVcU+hJp8/AkPuVw7YgVR+USxOrjK42jvlcsO8La5CFu/tJ+2OrTcXenJ4kWgrISdhAm6LlXLwNazHf+fWKiXbqo4Sm70WTUfhEsuIwqTXE+gcR4+VJjN5GnC29kHq/ZZeRlMbUlk8x9rv/0fDmJgW2Wfb4Bxizchm1bKJ92/y+MwhqVo8HZg4v8JhFI+aTm5HDEyufK/TaLhYNAUYDdXK1eOeqLy3GYMGQbUKblYOSVYWLMfS/HzxLNgFJFN/o0aM5cuQImzZtKpfzbdq0iTvuuIOYmBh7cH8jk0yrEBXAVV3yAfuiZgm+NMnKrYhJVgXJdQ8gRtMazPkrkzo7ncF6bWJTuRLg6DIUDAF6ckx5jU5HO9GiuQuKkpd9VSlWWhr+Ylf2XSRbVdecB0xeJoJcVKSdV5OaXXTglGvT8o+5Gf84NSPMLYEWpn24Jx9GsZS8ioDWYqbe+Wjqnc/LJJs0Os75BxHj5c5ZnZV4W3ohy/CWXE5aXhUCZ0/XQttE7zxOlzG9HLZFdG3OgdW7CmyfnpjK4bV7eXje6Oteu6jFGFQ2bd7SuUYddXK1eOQouGVftRhDVnbFLZ1bislEonCzZ8+mR48euLi48Pvvv7N06VI+/PDDMp83NzeXxMREpk2bxsCBAyVgvUSCViEqgJv6xp7hWZt5mLT0jXHHrxiTrApiVeuI9u+FJbvgUvpqy5l8ucfLY1ovc7M5kXOpfJrVqpCQGIy/X9SVcygm2jit5b/sO0mz5r+OSWfFqb4Vt4taziXaKG7S76TZj5PKnTj73EZ7VRQh6XtRZyYW7+ACaM1GQs+dIvTS8FmjTp8XxHq4EaO3kmBNx1aKINZqtbJq6peEdgi33+YvSHpiKq6+jjO23Op4kJ6YWmD7Hd9sRu9qoEXvdgXuL3b/FIjT5RCny4ECY2oFN7Mz/kYDfrlavHLzyntdztbmLcaQU/JsrVYLWpkkWp62b9/O22+/TXp6OmFhYbz77rvFvvV/PStWrGDkyJG0bt2aZcuWlUNPawcJWoWoAC4qJ1Qo5ZY1ElVPa1XoU4JJVoU51+hesjMLfutVqYyYChoTq9i4Omx1TdOQeNX3othYLXV88kpgXaZRcmjrtJ4dWd3ILOjXUAGzj4kQVzXJ51Wk5xT/dzXLpmejpSU4tyTcPZbmxn24JkehWMtW/kpnzKVBzEkaXKr2laszcC7gUhCrM5NozShWEPv9C8uIizrHuB9eLFN/rrX9q020HdAJraHiA790jZl0TQbHC/n+q7Hq7Nla+9K52XmLMWgvZ2uvXYzhBi+XVBFWrlxZIecdPnw4w4cPr5Bz12QStApRARRFwUXtTLols6q7IspBl4t5K1mpSzDJqiAXQ2/lYqZnoftdXc9BWgHZ22uCVqcLoG6gxmK5MjM/5myAvQTWZTolg3ZOm9mefSuFxaRGvQXX+hbcL2o5n1jyvOYxcyDHVIG41rmd9qojBKXtRZ11sYRnKZjemEPYmRNcXlA1R+/E2YC6xLi7EaMzccGake+Y719cxqG/9vLk9y/gWff6S4O6+XqQkZjmsC39Qipuvh752p7cFkXiiViGLhhb6udTnswqG+f12ZzXZxe6dK6H2ZVAox6fS0vnemg9kJvMoiaToFWICuKmkqC1pmue4U7XoxZ0F8o2AQkg27s+56wR122jVa5aBetq19zhV2x5q2OlWNLt21JSFAL8gzAYzjq0NaiSaee0je3ZN2EqJCK1qcBSJy/revG8iozckt8hyLA5sd7SBlzaEOlxlsjc/bgkR6HYym+ilSE3m0bRJ7hc8TLbyYUY/7rEuLsQozHy2QsL2L96J2O/+R8+9XyLPF/9do04tvkQt4++Up7p6MaDhLbLX1Nz24qNBLcMpW6zeuX1dCpcqsZIqsZoX4yhhbMXPaq2S0KUiQStQlQQV7UzBUcg1d9rN00i+eyFfNtvGXYH978xNN/2rV+u579vtxAXlRcwBbcIpc/zD1CvTcMCz//tc0v494u/6TftEYeAoboIznWi9wl9qSZZFcSicybauzvWnILHsea5ZhWsq/co+YNItywdKde8g5867UGTiHgUxfEXz0WVQDvDbv7LbsP1buAbDRZcQy24J2k5f6H0Q1sOm4M5rA7Gw7cL7VSHqZu6F1V2SqnPVxin7Ewanz5GY2Dshz9zYMN+3v/gWfROAZy/YCTFmoWTmzNap7zb+cuf/hiPQC/u/t+DANw28i4+fOBN1n/0O5F3tmLPj9s4u+8UA992LC+Uk57Nvl+2c88rD5f7c6hMHurCJ6UJURNI0CpEBXGrwRUEJvw2FavlSoYs7sg5Pn74bVr17VBg++P/HqFNv5sJbd8IjUHL3x/8ysePzObZda/jEeh4i3b/7/8RvesE7gGeFfkUSsXdnLeSld+JeBRrwZNxSiOmQT9yM9XXbePsEo81s7DVqPIHkC4JCtR13Jabq5CeXg939/xruburz9LGSc/O7KbXLzKlAquvifquahLPq8gylj54TbU5s87SDlza0twzhiY5+3BKOVYh5Z4W/Ja3EtHQ4TMctr/x+uO0GNqHGE0uKecvoqiuVFRo0CGc/3v/cX5/+zt+m/ktvg38eXTh+HyTt3b/uBWbDdr0v7nc+12Z3DUStIqaTeq0ClFBDmQd48+Uf6u6G+Vi1StfcmjtHv63+W2H1V4KY7VYeanpE9z32hDaD7zVvj019iLz+85gzPLJfDb0HW4fdVe1yLRqrQq9431oGHWh3AvDJza8k/O5oUW28/HcjillX4H7ApzuQHMh/+Sf0+GZZBqzHLapVDZaND9tL4GVrz/mJuzJCS/W2FWVBWwXtMReLL+PCW9VBu04iH/KPlS56UUfUI7SXT2I8Q8gxtWJs5pcUq1ZRR9UizxcpzeBuqKHTQhRXUmmVYgK4qPxrOoulAuz0czO7/+hy5hexQpYAYzZuVjMFocamVarleVPf0LXJ/oQEFF4GaLKdvtFb9ocySjzJKuCZPpHEGusX6y2ttwz19tb4FY3s55MHAOvvBJYIfj7HSnwGF/NEZrr9ezPLXpsplUN+Juo76Yh/jzklMNwl4tWV9ZwEyq3DrT0jiY8Zy+G5FMOE8iK682VG/n+30McOXsBJ52WWyJDmDn8LiKC6xTY3i0jFdvBY3zy5Tp2Hj9PdEIKzz0/jK7j7idGnUO6Na+2q9Vi5Y85P7Dr+39IS0zFw9+TDgNv484J9xb7/4HqyFNT4IwtIWoMVdFNhBClUVuC1gOrd5KTlkWHB28tuvElv76+Eg9/T8Jva2rf9vcHv6LSqLhtZPWYCtI8w51xu1xo/18c6oz8s9DLyuzkQbRLZ2y2ooMcvT4V83XGfBY0phXA9ULBQw5iYzWYzQUHbgCB2r000ccX2a/Lcp3NeDewEOBVfgGbVVGxx9yAbzT9We0/inj/Tth0JRtSs+HAaZ68+ya2zh7DmleHYTJbuOvlpWTmFJ4tz8o1ERbgxVvDehDg5UpAUjy99uxh9M4jjDiZTo90Z/Z/sJZ/l/3NgNeG8Nz6N7n7hYf4e8FvbF60pqxPu8q4qJyq9fLSw4cPp3///iU+bsmSJXh6epb5+oqisGrVqjKf53pOnz6Noijs2bOnQq9Tm0nQKkQF0am0NXpc62XbvtpIk24t8Qgo3tKPa9//hd0/bWP4Z0/b61nG7DvFpoVrGPTO6CrPVAXlOjHqsCd3/XMB3YWSLb1aXDZFIbrePZiMxXuLdXa6XpaVQmuT6tMVdIUUi48564ft2rIDV6mn3U4jXfFLU1nVNggwUb8e6Mv5Hl2i1Y0/rJ1Y7jGKg4H3kusRWqy86+oZQxl+Zxua1fejVVgAS565jzOJqew8XnjWvEPjIGaN6MmgLi3Qax2fiGfqRVocP0zSuu081D6cWQ0b8aBXKA/2vYvILi05s+dkIWet/iriS7TFYuGdd96hRYsWGAwGvLy86N27N1u2bCn3awGEhoYyb968Cjm3qBkkaBWiAtX0bOvFsxc4tukgNz3SpVjt//7oN9Z98CuPLZ9C3aZXbj+f2naUjAtpvNZxIlPqPcqUeo+SfPYCP81YwWs3Taqo7jtwM2t55JQPD25Kxz2mfKoCFCY+vA8ZmcXPaqnM1w9aUQqfOuVOwWuFpqaqyMm5/jCMMN0W6mtLNq4018WMT5gFP4/y//JhQc1OcyO+1t3HmoARJPp1xKYtfkH81EsT2bxdC1k/tZhuiQxh7d6TJB6MouWxQwR/t5rz/xxkdPMmdM90JRwvnBR9ma5R2epoPcv1fDabjUGDBjFjxgzGjx/P4cOHWb9+PSEhIXTt2rXCs5bixiRBqxAVyEeTv0h5TbLj60241nEn8o5WRbZd9+Gv/DXvJ8Z8MYmQVg0c9rW7vzOT/nqNiX++av9xD/Ck6xN9GPPl5IrqPgAaq8I9sXUYtdlEwLHYUi29WhJpQa2Jzwoodnu1OhdT5vXrwNquM9/fNU1b6L7Tpz2w2QrfDxChX0+QJvv6nbyGVW1DVddEvWDQXb8oQqnFWTz53XYrKzxHcSTwbozuIdfvk9XKhE9/p3PTejQPLVsJ/ecfuI1BtzenyePvoe03jTbjFzDh3k483rEBrY8e5J7de3h811GGnsmmW5YrjfDCoFTv5VHLO2hduXIl3377LcuWLWPUqFE0aNCAVq1a8cknn3DvvfcyatQoMjPzJgNOmzaN1q1b8/HHHxMSEoKzszMPPvggqan5K3TMnj2bwMBAfHx8ePLJJzGZ8gZSd+3alejoaJ555hkURcl3x+aPP/4gMjISV1dXevXqRWzslVXrduzYQY8ePahTpw4eHh506dKFXbt2FfrcLt/GX7lyJbfddhtOTk506NCBo0ePsmPHDtq3b4+rqyu9e/cmMfHKMsZWq5UZM2YQHByMXq+ndevWrF69+rqv44YNG+jYsSN6vZ7AwECef/55zFetZJaens7gwYNxcXEhMDCQd955h65duzJhwgQAZsyYQfPmzfOdt3Xr1rz88svXvXZNJEGrEBWovD8oKpPVamXH15toP/BW1BrHyGT50x/z65tXli9c98GvrJ71PQ/NGYlXSB3SElJIS0gh91Lmy8XblcAmwQ4/ao0Gd18P/BoFVthzuO2iN09u0xK+/3y5VwUoiNHVjzPaduRbDeA6XF3OQpFFXArf75wIKlXBb+W5uZCWVvSEq6b6tfipSz7LyuhmxjfMgq97xQ35MKNhuzmCr/QDWRc4nIu+7bBp8mexn1zwKweiE/jq2YFlvubKTQf5cv0+lk9+gF3zn2DpMwOY/cMWlq7dbW+jAHWSEmgTdZB7d+/hiV3HGBKTS9dsNxriiV65/peFylZHU7zhPcW1fPlyGjduzD333JNv36RJk0hKSmLNmitjgI8fP87KlSv5+eefWb16Nbt372bsWMfVxf7++29OnDjB33//zdKlS1myZAlLliwB4Pvvvyc4OJgZM2YQGxvrEJRmZWUxe/ZsPv/8czZu3MiZM2eYPPnKl+H09HSGDRvG5s2b2bp1K+Hh4fTp04f09OvfZZg6dSovvfQSu3btQqPR8Mgjj/Dss88yf/58Nm3axPHjx3nllVfs7efPn8+cOXOYPXs2+/bto2fPntx7770cO3aswPOfO3eOPn360KFDB/bu3cuCBQtYuHAhr732mr3NxIkT2bJlCz/99BNr1qxh06ZNDgH3iBEjOHz4MDt27LBv2717N/v27ePRRx3rDdcGUj1AiApUk4cHHNt0kORzSdz00O359l1b7/KfZeuwGM0sHfO+Q7u7Jvan56QBFd7XazXLcKfrUSv6cljJqrisKg3RdXtjySpZAKdRootcg+J6mVbFpuCmdSY1t+DJZKejnWjRwhWVUvhkM0Wx0dLwF7ty7uKipWSpU4vGhjrIRH13DefOg7kCE9lnzd6cpQs671topz5OaMZetOnnGbfgF37ZEcXGt0YSXKfsdzemLP4jL9vapQUALUL9iU5I4c1vNjHsjjYFHqMAvhfi8L0QR1vAhkKCbyAxPt7EOKk5p2RitFXNaiMqVNTRlm/QevToUSIjIwvcd3n70aNH7dtycnJYtmwZQUFBALz33nvcfffdzJkzh4CAvDsTXl5evP/++6jVapo0acLdd9/N2rVrGT16NN7e3qjVatzc3OztLzOZTHz00Uc0bJi3mMm4ceOYMeNKvd7u3bs7tP/kk0/w9PRkw4YN9O3bt9DnOHnyZHr2zCvJN378eB5++GHWrl1L586dARg5cqQ9qIa8LPFzzz3HoEGDAJg5cyZ///038+bN44MPPsh3/g8//JCQkBDef/99FEWhSZMmnD9/nueee45XXnmFzMxMli5dyvLly7njjjsAWLx4MXXrXinQHBwcTM+ePVm8eDEdOnSwt+nSpQthYWH5rlnTSdAqRAWqo/VCjQrL9cu5V0sRXVow59zSAveN/fZ/Do9f2janxOcvzTFFqZvrRJ+TetxiEkqQ6ywf58PvJSuzpNk1K+bMs0U3K+L3xy1bT6qq4KDUZoOE+GACAgougXWZSjHT2rCWndl3kmot+U24XDczAWEK2fEaktIrtvy30ablX3Mk/+ibsOqDJ9m2/Tjr3xpDgwD3cjl/Vq4JlcrxN0itUmG1Fv95KdjwTzyPf+J52gNWRSHet+6lIFbFeTIw2a63Pln58dF4oFHKfxxHScq816tXzx6wAnTq1Amr1UpUVJQ9CG3WrBlq9ZV+BgYGsn///iLP7ezsbA9YLx+XkHBl3Hp8fDwvvfQS69evJyEhAYvFQlZWFmfOXH8secuWLe1/9/fPG3LSokULh22Xr5OWlsb58+ftAe1lnTt3Zu/evQWe//Dhw3Tq1MlhqEPnzp3JyMjg7NmzJCcnYzKZ6Nixo32/h4cHERGOy0GPHj2aESNGMHfuXFQqFcuXL+edd9657nOrqSRoFaICaRQ1flofYk2JRTcWpeZm1tL3rDsBxxPKdSWr4kqu34mkTO+iG17D1TUOW0bRwxaul2kFcElQwXWG0cbFa6hTxxeN5vq/hxoll7ZO69mR3Y0Ma8nDfrPWhibIRL00LefibFgq+LvaslnPsPWPHxk/6ys21A0lWX2SoIyD+JCOkz7vC8TQOd8R5OPOm8PzSq0ZTWYOxeS9DkazhXNJ6ew5GYurQUejuj4A3NMxgte/3kg9Xw+a1fNj94lY5q76hxE92pa6ryqbjcCEcwQmnKMjeSW/4vzqEuPtlRfE2tIxYynbC1IIf51PuZ+zcePGHD58uMB9l7c3bty4ROfUah2/9CmKgrUYY9ALOu7qgHrYsGEkJSUxf/586tevj16vp1OnThiLGDJ09XkvB5bXbitO/yraPffcg16v54cffkCn02EymXjggQequlsVQoJWISpYXZ2vBK0VRGNV6JXgQ3hUEkpubNEHVIAcrxDO2poW3bAAes2ZIocGQNFBqyYXnHVOZBkLn1B1JsaPBg0uFFnEX6tk0s6wie3Zt5NdioSpooDRw0RdZxUZsWqSMysu67ruu88AePOJ3g7bX5g6k2d7N8Y9+QhnElMdsqbnL6bT5ukF9sezv9/C7O+30KV5KOvfGgHAe4/dzctfrGXsh7+QkJpJXW83HuvdnlcGdS23vqtsVurGn6Vu/FluAixqNbG+dTnr7UWMAc7bMrCUUxDrpy35F6qiDBo0iEceeYSff/4537jWOXPm4OPjQ48eV2oynzlzhvPnz9tvbW/duhWVSpUva3g9Op0Oi6Xkr8mWLVv48MMP6dOnDwAxMTFcuHChxOe5Hnd3d+rWrcuWLVvo0uVKtZUtW7Y4ZEqvFhkZyXfffYfNZrMHxVu2bMHNzY3g4GC8vLzQarXs2LGDevXyxqanpqZy9OhRbr/9yrAtjUbDsGHDWLx4MTqdjkGDBuHkVLYKGtWVBK1CVLC6Ol92FryipiiD2y560zYqA3V6+a9kVVwWrYHTPndizSndYARrdnSx2tmKEby4mw1kUXjQmpamkJMTgpOhiPJagF6VSjunf9mR3YncUsacJq0VXYiVeqlazsbZKMGd9WJbuq3wcbo/Ai4+t7F06e0Ep+2FzLwgJdTfC9svMwo9DsDNWc+8MX2YN6ZPeXb3utQWC8FxMQTHxXAzYFarifULJsbbgxg9xNnSSz3MqK7Or3w7S17Q+s033zBs2DBmzZrFHXfcQVpaGh988AE//fQT33zzDS4uV+pUGwwGhg0bxuzZs0lLS+Ppp5/mwQcfzDc+9XpCQ0PZuHEjgwYNQq/XU6dO4QtoXC08PJzPP/+c9u3bk5aWxpQpUyokqJsyZQpTp06lYcOGtG7dmsWLF7Nnzx6+/PLLAtuPHTuWefPm8dRTTzFu3DiioqKYOnUqEydORKVS4ebmxrBhw5gyZQre3t74+fkxdepUVCpVvuoJo0aNso8lrqg6udWBBK1CVLCK+MC4kUVmutH9qA19YuVNsirM2bB+5GaWbqygwSkZS3bxaqQWlWkFcElWQxFzkE6dciOyiRZFKTq/66y6QFvDTnZkt6O0Iy8VBYyeJoKcVaTHqknJqtixrtfKtBnYYG4Fzq2IcD9PU+M+XJOjUKwVcxu+PGksFkJiowm5dAPBpNYQ6x9MjJc7MXobcbYMrMX4vdArOnzLuXIAYC8JNW/ePN555x3Gjh2LwWCgU6dOrF+/Pt/YzkaNGnHffffRp08fLl68SN++ffnwww9LdM0ZM2bw2GOP0bBhQ3Jzc4s9pnbhwoWMGTOGtm3bEhISwhtvvOFQXaC8PP3006SmpjJp0iQSEhJo2rQpP/30E+Hh4QW2DwoK4rfffmPKlCm0atUKb29vRo4cyUsvvWRvM3fuXB5//HH69u2Lu7s7zz77LDExMRgMjhU0wsPDueWWW7h48SI33XRTuT+36kKxlWQktRCiVBbGf0+qpfyXCr2R1DUa6H3SCfcz8ZU+yaogF8K6cc7YsOiGhfDy3IslZUfRDQEP5+a4JV6/TinAsYYpGM3XD0hDQ3Pw9DherOsCpFhC2Zndouw3qm2gTdFyLr5isq7F5abKpr1ymLqp+1BnF39FsOrGpNFxzj8oL4jVWYm3pRe4cloDfRADfO6ogh5eMW3aNFatWiXLl5aDzMxMgoKCmDNnDiNHjrRvt9lshIeHM3bsWCZOnFiFPaxYkmkVohIE6nxJzZagtTTczBr6nvW4NMkqraq7A0CWbzjnTWUrJ6MyRRc7ECzO8AAAN8WZJK4/ES062oB7czdUquJleT3Vp2ll0LM7p3GxllYtlAImLxPBzipSYtWklWbAbDlItzrxN23BpQ1NPc8Smbsf5+SjKLaqn1BTElqzkdBzpwg9l/fYqNNz1j+IGA83zuqtJFjzgthgXdkWWhBVa/fu3Rw5coSOHTuSmppqL+XVr18/e5vExES++uor4uLiamVt1qtJ0CpEJair8+VI9qmq7kaNorZC7wQfwqMuVtkkq4KYDW6cdrsNm7H0+V6NJhtTZvEn5xU3aHVN15JUxOqxNhvEJwQRWEQJrKvV0UTRwqBjf05o2QJXwKi34lzfivtFLecSCsoNVhJF4ZA5hEPqEDz9utJeOURAyl5UOZVffaI86Iy5hMWcJCwm73GuzsDZgCDqeMrwpJpu9uzZREVFodPpaNeuHZs2bXIYz+vn50edOnX45JNP8PIq/6Eg1YkMDxCiEiSZUlia+FNVd6PG6JzsTfsjmaiLWLGmstmAUxGDSc8s2yQOD4+j2FI3Fru9q1MjPC8UPC7ualaVjaj6F4tVhqd5s/giS2Bd66ypHYdy6xbdsJh0OWouxqrIyLn+x9Da7z5l3fefceF83iSyoLBI+o18nla33FVg+//+/pGfl8wm4exJzGYTASEN6fXI03Tu87BDu/OnjvD1B68QtWszFouZoAZN+GDWTLp4XcAp5ThKTf941OjhrsmgyOKXonaQTKsQlcBH64mb2pl0S1ZVd6Vaq06TrAqSEN67zAErgMZWvFJXlxU306qyKrhqnUkrZHWsq0Wf8SUsrOgSWFcL1u7EbNNx1Fi8WdtFMRosuNW34HFRy/nEwrOu3n5BPDh2Bv4hDQEbm3/9kvlTHmLG51sIDstfbszF3Yt7Hp1C3fqNUWt17N38O5+99jju3r60uPlOAOLPnuS1MXfR5d6h3Df6RQwubpw7eZgT6oYkajrh459BO9sB/FL2o8qtXl+eis2nvgSsolapcZnWawd0Dx8+nJSUFFatWlWl/SpPoaGhTJgwgQkTJlR1V0plyZIlTJgwgZSUlKruSrXyZ8o/HMgq/gSYG0mA0Ym7TxqqzSSrgqQHtuCkuiOUsYeKYsFd9QU2S/HDVmdDPbyTmhWrbUo9K+eUpGK1jWichpNT0SWwrnUstwunTOWz+tRl+hw1F86ryCxmja2xPUJ46KnX6HLvsGK1f2VoZ1rd0pP7H89bK/7DF4eh1mh5bPpn1z1OZbPSSnua8Oy96FNOVdvfzwI17QkNCq4RKkRNVOavYDabjU8++YSbbroJV1dXPD09ad++PfPmzSMrq+KzSvPnz3dY+1cULTo6GicnJzIy8mdjTp8+jaIo9h8fHx/uuusudu/eXQU9rV1C9UFFN7rBuFg0DDpdh4c3ZeBRjQNWk4sPZwxlD1gBXF3PlyhgBbDZij933zWx+G/rp067YbPpStQXgHD9BoI15fv+nmuw4B5qoq7P9V9jq8XC1j+/ITc7k0bNiw7IbDYbB3f8TWz0MSLa3Jp3DquVvf/8QUC9Rsx6uh/jeoUyfURXdm74Of/1FBW7zWGs1A7gj4BRJPjfjE3nkq9dtVSnQVX3QIhyVeagdciQIUyYMIF+/frx999/s2fPHl5++WV+/PFH/vzzz1Kf12Qq3pu6h4cHnp6epb5ObWSxWK47pu3HH3+kW7duuLq6Ftrmr7/+IjY2lj/++IOMjAx69+59w2ROi/u7V1L19YGoqm1YVrnUVrg7zocxmy3UPXoepRSr3FQWm6LidNDdmE3l82+nU5c8s1mSoFWTDU66ImZjXWI0KqSk1itxfwAi9esI0BS9BG1J2FRg9TNRPxScdY6vd8zxA4zp6s/I27xZOnMCT89cQVBYZKHnyspIzWvf2Yt3Jj7AkEmzaX5TdwDSkhPJycrgl2VzadGpB1Pe/Yl2Xe7hvece4ciuTYWeM8HizmrrLSz3GMWhwHvI9ahfLs+7Qhjcwc23qnshRLkqU9C6cuVKvvzyS1asWMELL7xAhw4dCA0NpV+/fqxbt45u3boBsGPHDnr06EGdOnXw8PCgS5cu7Nq1y+FciqKwYMEC7r33XlxcXHj99dcBeOutt/D398fNzY2RI0eSk5PjcNzw4cPp37+//XFubi5PP/00fn5+GAwGbr31VnbsyKuFaLVaCQ4OZsGCBQ7n2L17NyqViujovNVp5s6dS4sWLXBxcSEkJISxY8c6ZCWjo6O555578PLywsXFhWbNmvHbb7/Z9x88eNBeCNjNzY3bbruNEydOANC1a9d8t/379+/P8OHDC32di+rPkiVL8PT05KeffqJp06bo9XrOnCn8g/HHH3/k3nvvLXQ/gI+PDwEBAbRv357Zs2cTHx/Ptm3bWL9+PYqiOASwe/bsQVEUTp8+XeC5pk2bRuvWrVm0aBH16tXD1dWVsWPHYrFYePvttwkICMDPz8/+b37ZmTNn6NevH66urri7u/Pggw8SHx+f77yff/45oaGheHh4MGjQINKvmryzevVqbr31Vjw9PfHx8aFv3772fwu4kln++uuv6dKlCwaDgU8++QR3d3e+/fZbh/6sWrUKFxcXh/OXhF6lI0jKz9A52Ytx2/VE7ItFyc2t6u4U6Xzje8nKKnk2sjDW7FIErSUs7+9mKf642+hoPVarW0m7hKLYaK7/izrq0i49ULhcJzOeoSYCva8EroH1G/Pq5//wysL1dLtvFJ/OGMO5k4cLPYfB2Y1XP/+HqUs2cv/jU1kx/38c3pk3+c126Ut929vvptfD46jfuCV9h02i1a29Wff9wiL7Z0HNf+Zwvtbdz18Bj5Lk1wGbtpotmxlQ/OVRhagpyhS0fvnll0RERDjUC7tMURQ8PPKWZ0lPT2fYsGFs3ryZrVu3Eh4eTp8+ffJ9+E+bNo0BAwawf/9+RowYwcqVK5k2bRpvvPEG//33H4GBgUWuoPHss8/y3XffsXTpUnbt2kWjRo3o2bMnFy9eRKVS8fDDD7N8+fJ8z6Nz587Ur5/3rVmlUvHuu+9y8OBBli5dyrp163j22Wft7Z988klyc3PZuHEj+/fvZ+bMmfas5blz57j99tvR6/WsW7eOnTt3MmLECMzm0r+xF9UfgKysLGbOnMlnn33GwYMH8fMruMxJSkoKmzdvLjJovdrl5e6MxtJnVU6cOMHvv//O6tWrWbFiBQsXLuTuu+/m7NmzbNiwgZkzZ/LSSy+xbds2IO8LRr9+/bh48SIbNmxgzZo1nDx5koceeijfeVetWsUvv/zCL7/8woYNG3jrrbfs+zMzM5k4cSL//fcfa9euRaVSMWDAgHyZ6Oeff57x48dz+PBh7rvvPgYNGsTixYsd2ixevJgHHngAN7eSf8BfFmYILvWxNV1kljtj97hy04541Gk1Y2JLSr2OXMgsn0lHAE5OSVhyS76mr7UEmVYA15SSzLFViIsv3dAVlWKhleEvPFXlX+PUqgabv4n69cCgBY1Wh39IQxpEtuHBJ6cTEt6CP78u/PNApVLhH9KQ+o1b0nvw07Tv3p9fls4BwM3TB7VaQ90GTRyOqRsaQVL82RL187zFi19tt/GV52iiAvtgdK8m/4/7S9Aqap8yVQ84duwYERFF/4/RvXt3h8effPIJnp6ebNiwgb59+9q3P/LIIw6FcQcNGsTIkSPtqz689tpr/PXXX/myrZdlZmayYMEClixZQu/evQH49NNPWbNmDQsXLmTKlCkMHjyYOXPmcObMGerVq4fVauWrr75yWDbt6kxoaGgor732Go8//rg9YD5z5gz3338/LVq0ACAs7EqR8Q8++AAPDw+++uortFotAI0bNy7yNbqeovoDebe0P/zwQ1q1anXdc/3222+0bNmSunWLV7YmJSWFV199FVdXVzp27Mjhw4VnNq7HarWyaNEi3NzcaNq0Kd26dSMqKorffvsNlUpFREQEM2fO5O+//+amm25i7dq17N+/n1OnThESkrcS0LJly2jWrBk7duygQ4cO9vMuWbLEHkgOGTKEtWvX2rO2999/v0M/Fi1ahK+vL4cOHaJ58+b27RMmTOC+++6zPx41ahS33HILsbGxBAYGkpCQwG+//cZff/1Vqud/WZghmA1p/5XpHDVNgNGJPicN1XrMakFyPesSo7Qo13M66aMxZ5f8OJutZF96nS6C1kuLqYjVsS5LSNDg6+uHVpNQ4r6pFRNtnNbxX/YdpFvL/18418WMdwMFS4KG+JQrk7RsVitmU/G/SOe1z8vsa7Q6GjRtR1z0MYc2cWeOUSeg6JXHCmJCwzZzE7bpmxASkERry348kw+imKvgboLWCbyr8dCFGmj9+vV069aN5OTkWjMksSZOZC9TprW4hQfi4+MZPXo04eHheHh44O7uTkZGRr5b2O3bt3d4fPjw4Xxr6Hbq1KnQ65w4cQKTyeSw5rFWq3UItlq3bk1kZKQ927phwwYSEhIYOHCg/Zi//vqLO+64g6CgINzc3BgyZAhJSUn2iWVPP/00r732Gp07d2bq1Kns27fPfuyePXu47bbb7AFreSiqPwA6nY6WLVsWea7iDA0AuOWWW3B1dcXLy4u9e/fy9ddf4+9f+lvboaGhDhlKf39/mjZtikqlctiWkJD3oXn48GFCQkLsAStA06ZN8fT0dAicrz3v5QDzsmPHjvHwww8TFhaGu7s7oaGhAEX+7nXs2JFmzZqxdOlSAL744gvq16/P7bffXtqXAAAvjTs+Gs8ynaOmcLFoGBTtw8ObMvCsYQGrVa3jtO9dWC3l3GtTyYcGQMnGtF7mpirZ7eozZ3yxlfJfSatk09awAecK+kf+7NVpHDi5Hh3RxJ06wMoPpnJk1yY69cy78/LxtNGs/GCqvf3PS2ZzYNs6Es6d4vypI/z+5bv88/sKOvUaZG/T+//Gs+2v71i/ajHxMSdY881H7Nn8O3fcP7rM/Y2x+PAzXVnpPYbjgb0wuQWW+Zwl4hcOqvIpdXV5SFhBP3FxeaXppk2bhqIoPP744w7HXj107HKb6/2U1S+//EKXLl1wc3PD2dmZDh065JuoXdAQN1FzlOm3unHjxhw5UvSqKsOGDWPPnj3Mnz+ff/75hz179uDj45PvdrOLS+XMyBw8eLA9aF2+fDm9evXCx8cHyBvj2LdvX1q2bMl3333Hzp07+eCDD4Art8dHjRrFyZMnGTJkCPv376d9+/a89957wJVb6YVRqVT5gv3rTfwpTn8uX7eo/+mNRiOrV68uVtD69ddfs3fvXpKTkzlx4gR9+vSx9x8cv7AUZ+LStUG8oigFbitOUfSiznv1Oe655x4uXrzIp59+yrZt2+zDD4rzuzdq1Cj7G97ixYt59NFHy+WNtYlT7Z7Rq7ZCn7g6eZOsomKr9SSrwpxt1I+c7PItY63VZWLOvFCqY23Wkg8vcsso2Rfn9HSF7OzSZRkB9Kp02jn9g74CAteUxERmPfY4Q29rw5vj+nLu2E4mz//RPrHqYnwMqUlXavvm5mSy7O1neOHhDrw2pgf//f0jj03/jK79htvbtO96L8Ofm89vn8/jxcE3sfHHpTz15pc0bn1LufU716blH3NTVhgeZmPAEFJ8W2FTl19Co1CBTYpuU0JRUVHExsY6/Fw9DM1gMLBw4UKOHTtW4PGTJ092ODY4OJgZM2Y4bCuL9957j379+tG5c2e2bdvGvn37GDRoEI8//jiTJ08u07nLS1mG2Ik8ZQpaH3nkEY4ePcqPP/6Yb5/NZiM1NW85vC1btvD000/Tp08fmjVrhl6v58KFot+8IyMj7UHGZVu3bi20fcOGDdHpdGzZssW+zWQysWPHDpo2vVKA+pFHHuHAgQPs3LmTb7/9lsGDB9v37dy5E6vVypw5c7j55ptp3Lgx58+fz3etkJAQHn/8cb7//nsmTZrEp59+CkDLli3ZtGlToYGcr6+vw/+cFouFAwcOFPqcituf4li/fj1eXl5FDiGAvOfXsGHDfLdBfH3zZqNe/Rwu18wtT5GRkcTExBATE2PfdujQIVJSUhz+La8nKSmJqKgoXnrpJe644w4iIyNJTk4udh/+7//+j+joaN59910OHTrEsGHFqwdZlCZOoeVynurolkuTrJrsO18jJlkVJKlBF5IzPcr9vC7OMUU3KoS1hMMDAJzjFVQl/JJ16lTpSmBd5qRKop3TDso7LJv4wfssO7CfXy4k8PXJ48z8YxV97u+OVp23/38LVjP6lY/t7R94fCpvf7eXzzZe4MM1Mbz82Vpu6vFAvvPefu9Qe7tXv/iXtl365mtTXk5bfPmJO/jWZwynAntgdqmg5VU1evBtVO6n9fPzIyAgwOHn6jtlERERdOvWjRdffLHA411dXR2OVavVuLm5OWwrrZiYGCZNmsSECRN44403aNq0KY0aNWLSpEnMmjWLOXPmsG3bNk6fPm2fIO7l5YWiKPZJ0NebxF2QpKQkHn74YYKCgnB2dqZFixasWLHCoU3Xrl0ZN24cEyZMoE6dOvTs2ZMRI0Y4DIuEvDjFz8+PhQsX2o97+umnefbZZ/H29iYgIIBp06Y5HHPkyBFuvfVWDAYDTZs25a+//kJRFIdb/fv376d79+44OTnh4+PDmDFjCix1eVlxXoOffvqJ8PBwDAYD3bp1Y+nSpfbMdWZmZoVMYr5amYLWBx98kIceeoiHH37YPlkqOjqaX375hTvvvJO///4bgPDwcD7//HMOHz7Mtm3bGDx4cJEZSYDx48ezaNEiFi9ezNGjR5k6dSoHDx4stL2LiwtPPPEEU6ZMYfXq1Rw6dIjRo0eTlZVlHxcLebeUb7nlFkaOHInFYnHIPDZq1AiTycR7773HyZMn+fzzz/noo48crjNhwgT++OMPTp06xa5du/j777+JjMwrvTJu3DjS0tIYNGgQ//33H8eOHePzzz8nKioKyBvf++uvv/Lrr79y5MgRnnjiievepihOf4rrp59+KtEErML6ExISwrRp0zh27Bi//vorc+bMKdM5C3LnnXfSokULBg8ezK5du9i+fTtDhw6lS5cu+W7lF8bLywsfHx8++eQTjh8/zrp165g4cWKx++Dl5cV9993HlClTuOuuuwgOLp8JFh4aNwK15Te5pzpokuXG2D1u3FyDJlkVJMsnjHOW8v/AB1BbostwdMknOqmsCi465xIdYzIppKSWbSykqyqOtk57UZfpLEUzupnxC7NQx60mDTyBbJueTeYWLHf+P/4JeIR0n+bYVOWY1Q+IAFVFv/oFe+utt/juu+/477/KHbf/7bffYjKZCsyoPvbYY7i6urJixQpCQkL47rvvgCuZ4/nz5wPXn8RdkJycHNq1a8evv/7KgQMHGDNmDEOGDGH79u0O7ZYuXWpPpn300UeMGjWK1atXOyR+fvnlF7KyshwmGi9duhQXFxe2bdvG22+/zYwZM1izZg2Ql+zq378/zs7ObNu2jU8++STfl4XMzEx69uyJl5cXO3bs4JtvvuGvv/5i3Lhxhb6ORb0Gp06d4oEHHqB///7s3buXxx57zOG6Li4uFTaJ+bIyBa2KorB8+XLmzp3LqlWr6NKlCy1btmTatGn069ePnj17ArBw4UKSk5Np27YtQ4YMsUfyRXnooYd4+eWXefbZZ2nXrh3R0dE88cQT1z3mrbfe4v7772fIkCG0bduW48eP88cff+Dl5eXQbvDgwezdu5cBAwY4BNCtWrVi7ty5zJw5k+bNm/Pll1/y5ptvOhxrsVh48skniYyMpFevXjRu3Ng+KcrHx4d169aRkZFBly5daNeuHZ9++qn9NvaIESMYNmyYPQALCwuzf/MrSHH6U1zlEbRqtVpWrFjBkSNHaNmyJTNnzuS1114r0zkLoigKP/74I15eXtx+++3ceeedhIWF8fXXXxf7HCqViq+++oqdO3fSvHlznnnmGWbNmlWifowcORKj0ciIESNK+hSuq7YMEfA3GhgR5UXvzUkYEoq3ClN1ZdG7EO3ZFVsFTCZSKWbMmaW7Q2JXim655epLfEx0tA6rtWyrXXmoz9DGcLjshcCLYNHY0ASbqB+koKmBq5UetwTwg+ouvq8zhjMB3bE4+5T9pIHFWzmtpIKDg3F1dbX/NGuW/zpt27blwQcf5LnnnquQPhTm6NGjeHh4EBiYf+ywTqcjLCyMo0ePolar8fb2Bq5kjj08POyTuGfNmkXv3r1p2rQpn376KU5OTvbs57WCgoKYPHkyrVu3JiwsjKeeeopevXqxcuVKh3bh4eG8/fbbREREEBERwS233EJERASff/65vc3ixYsZOHCgQ+30li1bMnXqVMLDwxk6dCjt27dn7dq1AKxZs4YTJ06wbNkyWrVqxa233pqvZOTy5cvJyclh2bJlNG/enO7du/P+++/z+eefO5SOvKw4r8HHH39MREQEs2bNIiIigkGDBuUr1zlq1Cj++OMPe1B+eRJzeX2G1rhlXEXp7Nq1i+7du5OYmFiuk8Rqu88//5xnnnmG8+fPo9OVX63OTEs2n8R/S+GrrVdvLhYNfc96Uvd4fI0cs3otG3A64hHSMkuWmSwud7doSF9TpnME5/QBS8l+X0zONo76lnwcrZ+fhbqBpasUcrUEc1P25jSslN9yjUkhK07DxYya+f/UZU0052hq3IfLxaMoJZ2Ap3WCO58p10zr5Vnzu3btcsiUabVae5nIq5dXP3HiBJGRkfzyyy/4+fnRpk0bTp06ZZ8Ee1lxlyu/OpD7v//7vwLvND7xxBOsWLGi0LuWrVq1IigoiN9++63AKgD79u2jVatWnD592v6cAAYMGICXlxeLFi3Kd5zFYuGNN95g5cqVnDt3DqPRSG5uLgMGDLAHrl27diU8PNw+fPCyd955h08++YTDhw8THx9PcHAw69at47bbbrMf16xZM/v8FYB+/frh4+PDokWLmD9/PvPnz+fkyZP2/WlpaXh4ePDDDz/Qv39/Jk6cyO7du+13vAFSU1PtlZtuv/12h+oBxXkNrv77ZT/99BP9+vVzeD1btWrFww8/zPPPP8/cuXP58MMPOXbsWLnMCSnfmQai2jKbzbz33nsSsBZTVlYWsbGxvPXWWzz22GPlGrACuKidqKcPIDq3bJMPKpvKBj0TfGgSlYySU8bMYTWS2KhnhQWsAFpVNGVeZ00FlDSGyVJw0hnINhZcJrAwCQlqfOv4o9Xmz8iUhJ/mEM30eg7kVnztUrPWhjbYRL00LefibFjKv3RspThiDuKIKggP3y60Ux2hbupeVNnFHItft1mFDQ1o0KBBsUo9NWzYkNGjR/P8888XmqUsiavnTLi7F3wHoHHjxqSmpnL+/Pl85RyNRiMnTpy47h3N0pg1axbz589n3rx59sV/JkyYUKxJvkOHDuX555/n33//5Z9//qFBgwb2gPWy8pioXFVGjRrFBx98wPPPP1+uk5ihHJZxFTVDx44dGTJkSFV3o8Z4++23adKkCQEBAfzvf/+rkGvUtCECnVK8GLdNT+TeWJRCaiXXRBkBTYmt0KDKhiWr9JOw7Er5nu9mLd6Srtc6c6YOtnL4iKir3U2EruT1X0tDUcDoYSKwgRUvl5o11vVaqTZn1lna8oXLcHYGPkCmdwQ2pYh/j5A2ldO5IrzyyiscPXqUr776qsznatSokf2nsGGF999/P1qttsD5FR999BGZmZk8/PDDAPYEhOWqO0TFncR9tS1bttCvXz/+7//+j1atWtmHIBSHj48P/fv3Z/HixSxZssShPn1xREREEBMT43Cb/9oJU5GRkezdu5fMzCuLmWzZssVeF/1axXkNIiIi8o1XLmiyWkVNYgbJtApRoGnTpuWbrVnewg31+VvZgdFW5hxchYrIcuOOY2AoYBxUcZxLTua5ld/x+/4DZBmNNPLzY/HI4bRvEFpg+/VHoug2c3a+7bHzZhPgcWVW/wdr/2bW738Ql5pKq3ohvDf4YTqGleyLgMnZi2inTmCquADH2TkRa1YpVhS4VinjR9cUDQmuRbe7VnqGQnZWCM7OZZlAlqe+bhsm222cNHnat/382UJ+XbiQ+DN5AX39Jk0Y/NyzdLirR4Hn+PPLL5nzxJMO27R6Pb8kXvm9TE5IYOErU9m57m8yU1Np2bEzDz41G7+QiplcVykUhYPmehxU18PLL5N2ykECUvahyklzbOcRCB6ln4FflISEhHwL+/j4+BR4987f35+JEyeWeA5BadWrV4+3336bSZMmYTAYGDJkCFqtlh9//JEXXniBSZMm2Wu+169fH0VR+OWXX+jTpw9OTk64urraJ3F7e3vbz3ftJO6rhYeH8+233/LPP//g5eXF3LlziY+PL3Z1m1GjRtG3b18sFkuJg7oePXrQsGFDhg0bxttvv016erp9gaTLGc3BgwczdepUhg0bxrRp00hMTOSpp55iyJAhBdZcv3oie2GvwWOPPcbcuXN57rnnGDlyJHv27LGXhbw6k1pRk5hBglYhqoxOpSXSKYy9WVFV3ZUC+RsN3H3KGY/ouFIvDJCcmUnn12fSLTKC3yeOx9fNlWPxCXi5FH0rPurNV3G/apKk31Xj6b7etoOJX63ko6H/x01hDZi35i96zplH1Juv4lfILcRr2RQV0cF9MWdVbEbOoDuDOavodkUqZTcNSaDx0GC2lLxs1slTrjRrqkdRyl6+rJF+E2a6c8aUd7vUN6guI6ZNI6hhQ2w2G2tWrGDaw4/wweaNhF6qxnItZ3d3Fu68ktm5+oPSZrMx/eHBqLUapq1YjrO7G9+//wGzJtzDu9/uJFepuOEflSXZ6sJfdERx7UALr2gicvZiSD6Jgq3Cs6wFZef+/fdfbr755gLbT548mQULFhS6gmV5mzBhAmFhYcyePZv58+djsVho1qwZCxYscMhkBgUFMX36dJ5//nkeffRRhg4dypIlS3jrrbewWq0MGTKE9PR02rdvX+Ak7steeuklTp48Sc+ePXF2dmbMmDH079/fXuqzKHfeeSeBgYE0a9as2CtUXqZWq1m1ahWjRo2iQ4cOhIWFMWvWLO655x4Mhrw7K87Ozvzxxx+MHz+eDh064OzszP3338/cuXMLPW9Rr0GDBg349ttvmTRpEvPnz6dTp068+OKLPPHEE+j1jpM+R44cyfLly8t9ErNMxBKiCiWZUlia+FNVd8OBs0XNPee8qHus7JOsnv/mO7YcO86mF4o/m/hypjX5g/l4OhccaNz06ht0CA3l/SGPAHnL+YZMeo6n7uzO83f3LtZ1zje+l8SsCqqVeRVvp+8wF3dM4nUE2fqgZJXu7fp8eC7JxrSiGxagXr1cvL0KLhhfUjYbHMi9i1hzwVUN7q8XyujXZtBr6NB8+/788ks+ev5/fB9T8KpiZ48dZ2S79ny87V970Gu1WhnUqDGPvvIK9947krMJNmrbJ14dVTrtlUP4tewM2pJXixBVIyMjg6CgIBYvXuywhHhpbdmyhVtvvZXjx4/TsGHDcuhh8bz++ut89NFHDjXVoeImMUumVYgq5KP1JFjnz1lj2Sa8lIe8SVZ1iIi6iKqcJln9tGcvPZs3Y+AHH7Eh6ihBXp6M7d6V0V2KXg639SszyDWbaR5Ul2n976VzeN4tXqPZzM7T0fzvquBUpVJxZ9NI/j1+olj9Sg1uR2KWb+meVAno9OnlErACZZqB4JapJbmUczDPnNHh6eGOSlW6oPdqigLN9H9htt1FouVKhywWC5t+WEVuVhaRHTsWenx2RiZDmjXHarXRqFVLHp36ij1ANRnzssE6/ZUxvCqVCq1ez8Gt/9J7+FBCXNSknFeRllN7ItcLVjdO+HbGTwLWGsFqtXLhwgXmzJmDp6dnqctQ/vDDD7i6uhIeHs7x48cZP348nTt3rvCA9cMPP6RDhw74+PiwZcsWZs2a5VD7taInMctELCGqWCuX/LfdKtvNKV6M224gcu95VOV4O+9kQiIL1q0n3N+PPyZN4IluXXn6y69YuvmfQo8J9PDgo6H/x3fjnuC7Jx8nxNubrjNns+t03tjKC+kZWKxW/K8ZBuDv4U5cWtGBVa57ADGa1pT6fnsJODsVnBUslTJ01yVBVYbZuwqxcUGlv/g1VIqVloa1eKktnDp4kH6BQfSt48e7zzzDK19+Qf0mBS9BGtwonIkfvM+0Fct57tOPsVmtPNOjJ4nnzgEQ0rgxfiHBLJo+nfTkFExGI1+/M48L585xMS7vS6FRb8GlvolgX6US/vUrT4S/5J9qijNnzuDv78/y5ctZtGgRGk3p/u3S09N58sknadKkCcOHD6dDhw4Frk5a3o4dO0a/fv1o2rQpr776KpMmTXKY/1HRk5hleIAQVcxis/JZ/HdkWsthsk4JNc5y5c5jCob4ilkYQDfqcdr/f3t3HhZV9QZw/DszDDDs+44gCoqouOGG+4aae7lluYEtZmpuae6mmbuWaWWG+1KZS6bmlpZoZiWmibiDCriCCijbnN8f/JgcWURlGex8nodH595z7z33Dsx959xzzuvtzeEJY3XLhq5dz7FLlzkyofAfaE0/nkM5OztWvxFKXGIS7iNGc3j8WBpU/LdVYcw333Ew+ixHJ36Q7360KmPO+7zKgwclc5O3t9xJxv1rRbIvd1U7FM/R2Bnjl0pyWsqTC+YjoMrN554C61GZwpQjd5twPuYaqffu8evWrexauYo5O3/MN3DV2z4jg7A6dWn+ysv0m5g9COXc8UjmDxnCxZOnUKpU1GzWDKVSiRCCGd/rp5Y0fqjiTpyS5LSyfQt0tFDSrsqTM0xK0otAtrRKUilTKZRUM/Mt0WM6pZsy4KwtL0UkFlvACuBqY00VN/0sNf6ursTezjs1Yn7qlvfm/I3saZMcLC1QKZVcf6xV9frde7g8YRDWtYqdSixgVSrTyUguwnl4n7Np0DL9+R4fX46xR4iiu2UYKR5Sz/owfhV98K1Zg4FTJlO+WlW2LC1cmmojtZqKgdWJu3hJt8y3Zg2WRhzi+ysxrD8XzUebN3Hvzh1cH5vYHiDdNAsL7wzcHcp2m2tlZzn3tvTfIYNWSTIA1c39UJbE4+osFT1iHehzKBXby9dRFPODluCKFYlOSNBbdvb6dbzsny5dZWTsFVz/P92VsZERtb292Hf634xNWq2WfVFRei2vj7vj3Yg7KTZPddznYWFxFUTRTQYuFM/3XlncfL5J51NSlKQ+8HyufTzOWJFMbc0hTP//qy+0WjLSCjdTQVZWFpf+OY2dSx7T91hbY+PgwLXzFzh3/DgNXmqf906UkOWYQTlvgZlx2QteLUwUeNkVTzIBSTJEsiOMJBkAC5UZlTU+nH5QuIFET0spoM1NeyqfSSyyQVaF8V6bVjT8aBYfbf+RHkFB/H7xEl8e+IUv+/+b6GLct99zLSmRVYOy5wJcuHsv5R0cCHB342FGBl/98iv7o86we9R7um1GtGlNv6++po63N3V9yrNw915S0tIZ0Cg4z3o8sPPiqrZk+w6rFbHPnwXrUc8ZUxmngKmrCQ8znn36qktFOAXWuHEbadcukHLl7DG5e5UvVkfz96+HmLH5ewBmv/EmDm5uDJwyGYA1H8/CPygINx8fku8m8d2iT7lx5Qpt+/0708Avm7dg7WCPk4cnl07/w+fvj6VBh5eo3bJFgXVJ12Rh7Z2FzW01cbfLTneBABc1yiLKNCRJZYEMWiXJQNSzrEbUg4uIIs7UXi/JlnrRDzC6W/IpY4N8yrN5yNuM+24z07Zup7yjAwtf7UmfBv/O7Rh/N0mvu0B6ZiYjN37DtcQkzIyNqe7pwd7RI2ju/28/x571grh5/z6Ttmwl4e49apTzZNeIYThb5+4ekGVsxmXbFoi0kry5a8lKKYIsWI9SCJ43crUUGh7y7AFnZqaCxCQv7GwLl/mnIDdu3KNvvy+Ij0/C2lpDlWq+fLz5e2q0yE63efPqVZTKfx8GJiclsXDoUBKv38DCxgbfGjVYsOcnvf6vdxIS+OKD8STduIGdizOtevXi1ffHFKo+QgXCKYNyFkbcjIMHhp3zA1O1goqO8hYu/bfIgViSZEB+vPML0Q8vF8m+/FItaHlOgaYY+6yWBZcr9eZuSu7838XJ3CIBVfL2It2nq0kbVHee71FwqqPgktmt56yJoHq1qyiVhZtE/WkkZvnw54MASjvDujILtDfVJCQa7u2xpoeaam5FO52QJBk62adVkgxIXctqz70Px3QTBpy146WIxP98wHqzQqsSD1gBTI2eP/VpLs/ZpxVAczM7m85zVoT4+KfL4FNYtqqLBJqeK/XpqLQqwCUDr3JgYoCNmWoVVHKSA7Ck/x4ZtEqSAXFU21LB9NnyNGuyVHS/4sBrhx5gezmh2AdZGboU50rEp3uVyrG1D4twftb/e96BWAAKFFiqnj+d6c1bKtIziifPvaPRGaqaFEPQ/wzSzDOx98nC2aa0w2h9fo5qjI0Mq06SVBJk0CpJBqaeRfWnKp+dycqeNw8LPKPiUGQ+fY75F02mxpoY82CEKPkbu4npXbIeFv2jc4roobllatE8Uo6JsWfJkv1UD/wAK+tBWFkPokHDqezceaLA7ZKSUnjnnRW4ug3BxHQAfpVGs2NHpG79lCnf42YSSIiVje4ntHZQkdT5WWhVAoVrBl6eYGwAA/VVSvB3McDmX0kqAfI3X5IMjIuxA14mrsSkPXngVL27NtQ787BUBlkZKqFQEFOuIxkppfOd3Mw0hqyiSyqmUxQtrQDmNxQo3BU873CGlBQFjo6V+HimHb6+LgghWLnyEJ27LOD4X9MJCMj9xCA9PZPWbWbh5GTFd98Oxd3dlpiYW9jY6Lf+BgS4s3fPWGLS63IpwwbVM2YNKkppFpk4+ihIv27EzXul9xSjkpMaM2PZ3iT9N5X+J4EkSbk0sKxRYNBaMdWC1ueVaBJulGCtyoYE35dITjF9csFiosyMJatY9lw0gZIqQ4G5sYbktNTn3pd/lTYEVLmAQpEdpc+Y0Z2ln+/jt9/O5xm0fv31Qe7cSeFwxCTU6uzbj7e3Y65yRkYqXFxscOEsNmnNiMmwfO66FoUsI4HKPQMvSyPiEiCjeN7ofKlVUNVV9mWV/rvk1zVJMkBuxo5UMM09kbtjugn9z9rRMSIRTcLzjgJ/8dxzr8GN1NyTzZcUldFDMlKKLtXpo4pyKrTnzY6VIzNTwZ3E7N/TrCwtGzYcISUljQYN8s7wtu2Hv2jQoCLvvLMSZ5d3qFptLB99tI2sLP2uD+fOJeDm/i4+FUYwLbQHyvhzRVLfopJmlYmzTxYOliXb/aSKixpTtezLKv13yaBVkgxUY6tauixZjw6yspODrPKUbuFErLo2zz0L/3OwML8KxfbeFN1EUOa3i65z5v79MVhYDsLEdABvvb2Czd8Po0oV9zzLXrx4k+++O0ZWlpYdP45i4oQuzJu/k+nTt+jK1KtXgRXhb7Br52iWLunPpUs3CW3dFovUp0v9W9wy/9/qWs5NgVEJ3ElN1QqquJR8K+uUKVOoUaNGiR/3aRw4cACFQkFSUlJpV+WF4e3tzcKFC0u7GrnIoFWSDJSdkTXVNL7/H2SFHGRVAK3SiMtu7cjKLN1WKCNRfKPehaLoglaT+wpM1EUzIMvbuzw7d67n6G9TePutFvTr/yWnT1/Ls6xWK3BysuLLL0OpXbs8PXvWZ/wHnfj8i/26Mu3aBdK9ez2qVy9HSEh1dvw4iqSkVP7ZNhN7VQk/j38ChQLSrTNw8dFiZ168v3uBbmrUqqI7RlZWFgsWLKBatWqYmppia2tLu3btiIiIKLJjwLMHvU+zXcOGDYmPj8f6/6mei0NMTAwajYbk5OR8y2zatIkWLVpga2uLRqOhUqVKDBw4kOPHjxdbvf5rZNAqSQaspWkgAf/cQvngQWlXxaDF+XbiQWrp9vVToCUz5Wqx7V8U8ZT7lmiKZD9qtRpLS2+qVWvIzJk9CQwsx6JFP+VZ1tXVGj8/F1Sqf289/v5uJCTcJT097y9kNjbm+Pm5cPFCPIGm+7BWlnbqgdwy1VrUnhmUc1VQhHGljpWpAl+nohuCIoSgV69eTJs2jWHDhhEVFcWBAwfw9PSkWbNmbNmypciOVdwyMjIwNjbGxcUFRTGmtN26dSvNmzfHwsIiz/Xvv/8+PXv2pEaNGmzbto3o6GjWrVuHj48P48aNK7Z6/dfIoFWSDJlGA9UCS7sWBi3RqwG3U+xKuxqYW8Yjsooz92fRBmsW94o2yI+JtUMIFVqtlrT0vK9DcEM/zp+/jlb777mcPZuAq6sNxsZ5B2XJyQ+5cOEGrq42GCnSqKX5GQul4XWPUSgg3SYDtwpabMyKNniq7WmMsggDsm+++YbvvvuOVatWERYWRvny5QkMDOTLL7+kU6dOhIWFkZKSorfNF198gaenJ2ZmZvTo0YO7d/+d1u3AgQPUrVsXc3NzbGxsCA4OJiYmhhUrVjB16lROnDiBQqFAoVCwYsUKAJKSkggLC8PR0RErKytatGjBiRPZ06UVtJ1CoWDp0qV06tQJc3NzZsyYkWf3gIiICJo1a4aZmRm2traEhISQmJgIwHfffUe1atXQaDTY29vTqlWrXOf7uK1bt9KpU6c81/3222/Mnj2b+fPnM3/+fBo3bky5cuWoXbs2EyZMYOfOnQBcvnwZpVLJH3/8obf9woUL8fLyQqvV6s5l37591KlTBzMzMxo2bEh0dHSu+tSqVQtTU1N8fHyYOnUqmY88iUtKSuLNN9/E2dkZU1NTqlatyvbt/2bp27RpEwEBAZiYmODt7c28efMKPP/Y2Fg6d+6MhYUFVlZW9OjRg+vX9fvvT58+HScnJywtLQkLC2Ps2LG61vJffvkFtVpNQkKC3jbDhw+ncePGBR77UTJolSRDV6UqmOf97f6/7qGtJ1dFFQCWrppCjdYKvZ8uAyvnu23oyGa5ytdorWDI+JfyLD994VvUaK1gzfcL81xvoir6hAKPKuqWVrOboFI+X9/WTz75hD///JO4uDgiI88zavQODhw4Q59XGwLQt9/njBu3UVf+7bdbcudOMsOGreHs2Xh+/DGSj2Zu453BrXRlRo1ax8GDUVy+fJPDh8/StdtCVColvXs3AECtSKW26a9oDHQ8UoZai2m5DDxdFCiLoI7u1io8bYt2op9169bh5+dHx44dc60bOXIkt2/fZs+ePbpl58+f55tvvuGHH35g165dHD9+nMGDBwOQmZlJly5daNq0KX///TdHjhzhjTfeQKFQ0LNnT0aOHElAQADx8fHEx8fTs2dPALp3786NGzfYuXMnf/75J7Vq1aJly5bcuXOnwO0gu+tA165dOXnyJAMHDsx1DpGRkbRs2ZIqVapw5MgRDh06RMeOHcnKyiI+Pp7evXszcOBAXQtzt27dCpwCLikpiUOHDuUbtK5fvx4LCwvdNXlcTguwt7c3rVq1Ijw8XG99eHg4/fv3R6n8NyQbP3488+bN448//sDIyEjvPH/99Vf69u3LsGHDOH36NF988QUrVqxgxowZAGi1Wl1XjzVr1nD69Gk+/vhjXTa8P//8kx49etCrVy9OnjzJlClTmDhxou6LweO0Wi2dO3fmzp07HDx4kD179nDx4kW992Tt2rXMmDGDWbNm8eeff1KuXDmWLl2qW9+kSRN8fHxYvXq1bllGRgZr167N8z3Mj5zySpIMnZERBNWDA/tKuyYGJUut4bJ9K7QP/40MKngH8MWsvbrXKlX+H3HzJ39PRma67nXSvdv0fDOQ1k265yq7/9Bm/o76DUf7/NOXah+UraBVIRRYGJlxN/3+M+/jzp07TJw4kVu3bmFhYYGfny+7do6ndetKAMTG3kb5SOTm6WnPT7vG8N6ItVQPHI+7uy3Dhobw/vsddGWuXrtD71eXcPt2Mo6OljRq5MdvRybj6GilK2OivEttzRGOPWhAmuE1uoICMmwz8DBXcjdOxd0Hz1ZJlQLqehVN3+NHnT17Fn9//zzX5Sw/e/asbtnDhw9ZtWoV7u7ZA+w+/fRTXnrpJebNm4exsTF3796lQ4cOVKhQQW8fABYWFhgZGeHi8m8GtUOHDvH7779z48YNTEyyZ7KYO3cuW7Zs4bvvvuONN97Ic7scr776KgMGDNC9vnjxot762bNnU6dOHZYsWaJbFhAQAMBff/1FZmYm3bp1w8srO2NetWoFp8/esWMH1atXx80t77//s2fP4uPjg9Ej8wnPnz+fSZMm6V5fu3YNa2trwsLCeOutt5g/fz4mJib89ddfnDx5kq1bt+rtc8aMGTRt2hSAsWPH8tJLL/Hw4UNMTU2ZOnUqY8eOpV+/fgD4+Pjw4YcfMmbMGCZPnszevXv5/fffiYqKws/PT1fm0bq1bNmSiRMnAuDn58fp06eZM2cO/fv3z3V++/bt4+TJk1y6dAlPz+zZQlatWkVAQADHjh0jKCiITz/9lNDQUN37MmnSJHbv3q3XBzg0NJTw8HBGjx4NwA8//MDDhw/p0aNHgdf/UTJolaSywLs8eHjC1SulXRODcdWnE2kp+i2FKqURDnaFSy9qbaXfpWDXgQ2YmprR5rGg9fqta3z82bssmfkT707IuxVWo7lD1oNnD/4Ko6iD1i92rmLpqFXExGf/Tvn4+PDGG2/QqFGjfLe5f/8+ixcvZv/+/dy9exdXV1fGjRune7zXvn17QtpO19vm4EEwNzPhs8/6A9CggS+/HZmS7zE2rB9SqPqbKW9R2/QPjj2oQ3F2ynge6cZaNF5arO6ouXrj6Sctq+qmxtK0eB6IPk1yiXLlyukCVoAGDRqg1WqJjo6madOm9O/fn5CQEFq3bk2rVq3o0aMHrq6u+e7vxIkTJCcnY29vr7f8wYMHXLhw4Yn1qVOnToHrIyMj6d4995dPgMDAQFq2bEm1atUICQmhTZs2vPLKK9ja2ua7v4K6BuRn4MCBdOrUiaNHj/Laa6/prneXLl1455132Lx5M7169WLFihU0b94cb29vve2rV/83M2LOtbxx4wblypXjxIkTRERE6FpWIXtg3cOHD0lNTSUyMhIPDw9dwPq4qKgoOnfurLcsODiYhQsXkpWVpWuRfbS8p6enLmAFqFKlCjY2NkRFRREUFER0dHSulua6deuyf/+/gyz79+/PhAkT+O2336hfvz4rVqygR48emJubP+ly6sigVZLKivoNYcsmkDMIcMunBUkpuSecj407R+uebhgbm1K9SgOGhs7E1alcofa5ZedyQpr1QqP59wNUq9UyYdbr9Os+moreAfluqzGJJbOYx8qJIk5Z4G7vyvTXx6EMzB5x/cMPP/Dee++xYcMGXYvZozIyMnjrrbews7Njzpw5ODk5ERcXh6Xlv+/DmjVr0Gqz8PdPQKW8y6lTV2ndZhbdu9cr0rrnsFDFU1Nzkj8fVCumhA5FQAEZ9hl4WqhIjFNy/2HhgkVLE0WxJRLw8/MjKioqz3U5y/MLePISHh7O0KFD2bVrFxs3bmTChAns2bOH+vXr51k+OTkZV1dXDhw4kGudjY3NE4/3pCBHo8l/kKFKpWLPnj0cPnyY3bt38+mnnzJ+/HiOHj1K+fLlc5VPT09n165dfPDBB/nu09fXl0OHDpGRkYFardadh42NDVev6g/ONDY2pm/fvoSHh9OtWzfWrVvHokWLcu0zZz/wb/eCnL7gycnJTJ06lW7duuXaztTUtMDzL01OTk507NiR8PBwypcvz86dO/P8HSiI7NMqSWWFhSXUqFXatSh1qY6+xGXkvrlUq1yPaaNW8NnMXYwfupRrCZcY+F5jUlKf3AJ68szvnL98iq7twvSWh2+chUppxKtdhxa8g4zi7RoAIETRtrR2qNuaDoEtqeJXGS8vL4YMGYKZmRl///13nuW3bNnCvXv3mD9/PjVq1MDNzY06depQqVIlXRk7OzscHBzRagNwcbFh+/ZIKlRwomnT/PsWPy8b1WVqmEaX4uy8hZNukoWFVwYejopC1TXIyxhVUXSKzUOvXr04d+4cP/zwQ6518+bNw97entatW+uWxcbGEhcXp3v922+/oVQq9d77mjVrMm7cOA4fPkzVqlVZt24dkB2kZWXpf6WoVasWCQkJGBkZUbFiRb0fBweHfLcrrOrVq7NvX/7dqRQKBcHBwUydOpXjx49jbGzM5s2b8yx74MABbG1tCQzMf0Bs7969SU5O1uuOUJCwsDD27t3LkiVLdF0VnkatWrWIjo7Ode0qVqyIUqmkevXqXL16Va+Lx6P8/f1zTW0WERGBn59frlbWnPJXrlzhypV/n/SdPn2apKQkqlTJHlNQqVIljh07prfd469zzn3jxo18+eWXVKhQgeDg4Kc6d9nSKkllSZWqcOE8JBrWROslJdPUksuWjRHpuW/mjeq20/3fz6c6Vf3r0b6PF7sPfkPXdqEF7nfLruX4lq9Gtcp1dctOn/2TdZsXsX7JXwVOpWOkfkBmys1nOJunU9TdA3JYZphyLyuZPXv28ODBA73Hko86ePAg1atX5+OPP9bdyNu1a0f//v1z3ehu31Zia+PAmrURjHivXbFORQRgb3SW6qYm/P3QuwjzhhU9oYRMh+xW19txSlLy6ZDraavCw6b4bs+9evXi22+/pV+/fsyZM4eWLVty7949PvvsM7Zt28a3336r15ppampKv379mDt3Lvfu3WPo0KH06NEDFxcXLl26pJt1wM3NjejoaM6dO0ffvn2B7MFHly5d0j2ytrS0pFWrVjRo0IAuXbowe/Zs/Pz8iIuL48cff6Rr167UqVMnz+1y+r8+ybhx46hWrRqDBw/mrbfewtjYmJ9//pnu3btz4cIF9u3bR5s2bXBycuLo0aPcvHkz3z6+27Zte2LXgAYNGjBy5EhGjhxJTEwM3bp1w9PTk/j4eJYvX45CodAbZOXv70/9+vV5//33GThw4FO3jE6aNIkOHTpQrlw5XnnlFZRKJSdOnODUqVNMnz6dpk2b0qRJE15++WXmz59PxYoVOXPmDAqFgrZt2zJy5EiCgoL48MMP6dmzJ0eOHGHx4sX5Bt2tWrWiWrVq9OnTh4ULF5KZmcngwYNp2rSprqvGu+++y6BBg6hTpw4NGzZk48aN/P3333p9aQFCQkKwsrJi+vTpTJs27anOG2RLqySVLUolNGyUPb/Of4wAYr06kZFeuI8tKwsbynn4cSXufIHlHjxI4aefN9ClrX5g+9epX7mTdIN2fcpRO8SI2iFGxF+PYf4XI2n3mreunLlZ7P9rV7yKunsAwMnLUVSsV4V69eoxY8YM5s2bl2fXAMgeSLJ3716ysrL49NNPGTRoEKtXr+arr77Ks/xXy0+QlJRK//6Fn87meTgbnaSKSdyTCxqAdNMsrLwzcHPI/XesVkG9Yhh89SiFQsE333zDBx98wIIFC6hUqRKNGzcmJiaGAwcO0KVLF73yFStWpFu3brRv3542bdpQvXp1XYBjZmbGmTNnePnll/Hz8+ONN97gnXfe4c033wTg5Zdfpm3btjRv3hxHR0fWr1+PQqFgx44dNGnShAEDBuDn50evXr2IiYnB2dk53+0Ky8/Pj927d3PixAnq1q1LgwYN2Lp1K0ZGRlhZWfHLL7/Qvn17/Pz8mDBhAvPmzaNdu3Z57qswQStkDyRbt24dx48fp0OHDvj6+tK9e3e0Wi1HjhzByspKr3xoaCjp6elPNXI+R0hICNu3b2f37t0EBQVRv359FixYoBtYBtlTWgUFBdG7d2+qVKnCmDFjdC3XtWrV4ptvvmHDhg1UrVqVSZMmMW3atDwHYUH278vWrVuxtbWlSZMmtGrVCh8fHzZu/HdmkD59+jBu3DhGjRpFrVq1uHTpEv3798fU1FRvX0qlkv79+5OVlaX7YvM0FOJpemNLkmQYfjsMZ06Xdi1K1HXfdiQ8yDs9aF5SHyTTtk853np9SoGP97f+tIIZi95i94Zr2Fj9OzAk6d5tbt2O1yv79rgQOrR6nc4hA/D2zH40am+1h4x7xZcJK4elpiLWt3yLdJ/pGenE3rrGKbs4du7axebNm/nqq6/yDFw7d+5Meno627dv17Wsrl69mlWrVulNj5Rj8ODBWFsr2Lnj7SKt85NcTm/A2XSHEj3m8zB+oOJWnJLU9OxbccPyxlR0LN1EGVK2v/76ixYtWnDz5k29PqZF4cMPP+Tbb7/NtzvOi6B169a4uLjoTXMF2QH7zZs32bZt21PvU3YPkKSyqHYQxF2Fe/dKuyYl4r5rNRIe5D/dFMD8L0bRpH5HXJ29uHk7jqWrJqNSqmjbvDcAE2b1xcnBnaGhM/W227JrOc2Du+gFrAA2Vva5lhkZqbG3c9EFrApFFpkpeacsLWra/7e0zt60mAmrP+bdDqHMC5uaZ9nlu9ey5udN/BObPSF5rQrV+PC19wnyq6krE7roPVb//K3edtbW1qxfv54JEybk2qeDgwNGRkZ6XQHKly/PrVu39AagAMTFxXH06FEWLJiLEBoUipLL6OZtfIQM0YRLGcWX0rMopWuysPHOwvqWGmWWUgasBiQzM5NPP/20SAPW5ORkLl++zOLFi5k+ffqTNygjUlNT+fzzzwkJCUGlUrF+/Xr27t2r94X27t27nDx5knXr1j1TwAqye4AklU1qNTRu9p/oJpBhbk+saV14wvCV67euMu6j3nQZWIkx03tgY2XPqk9+w87GEYD4G7HcfKzl9PKVaI6fOpSra0BhLV8/ivItBuDTepDup9XAiQVus+PgH7QaOJHK7d+m7aAp/Hz0ZL5lxy9cjU/rQXz9/V6EyOKPc5F89dNaqnnn3f8ux8FTR+jZuDO7P/yGX2ZtxcPBjfZT+nDtsfMPqdWM2PC/OLP9T/bs2YOPjw/p6el57rNGjRpcuXJFL5tVbGwsDg4OuW7q27Ztw87OjoYNG3P7tufjuyp2via/4GGUWuLHfVZaFaicM2lYQQashqRu3bq8/vrrRbrPIUOGULt2bZo1a/ZMXQMM1aNdPmrXrs0PP/zApk2baNXq38QhnTt3pk2bNrz11lt6A/2e6jiye4AklWHH/4QTx0u7FsVGKFWcr9iH1NTi7eP3rFZ+M5Af9/zE6lkjdMtUKiV21rmn4wL485/z9Boxh9Gh3WhRrzrbfj7KFxt3sW3JRCqV1+/68NOhv/hk9Q/cuZvMoB4hDOjck5CBI/n0zY+Y+c0iAssH5NvS+risrCycXgtg4RvTeb35KwDUHNoKazNLVrz3CffS7rMkcgPh4eEsWbKE+vXrM2HCBJycnBg6NLtrRUJCAq+88godOnSgd+/exMbGMmXKFHr37k1Y2L+zLmi1Wl566SXatm3LsGHDAEG1qnGoVIlPc2mfmxAKTqa1JiGzcIN3Sls1Uw2uasP8PZckQyFbWiWpLAusCQ6OpV2LYhPn29FgA1YAkXEXlVKJo5217ie/gBVgxeZ9NAkK4I0eIVT0cmVE/y4EVCzHqq379col3Epk6mfrWTAuDCOj7MfxYxcspn3tlrQMfPqBTanpD8jIysDOwka37GHGQ45E/4Hfmw2oN7wtP/74I7Nnz9bNrZmQkMCtW7d05V1cXPjss884ffo0PXr0YPbs2bkyEwEcPXqUhISERwbzKLgWl/9E88VFoRBUNdmHg8rw5zV2MVLLgFWSCkH2aZWkskypzO4m8MPmFy7pQFK5etxKMdwBNRqzW2izMrgcd4P6PUdhYqymZhUfRod2w93JPs9t/jp9kdBX9B+LNa4TwJ7DkbrXWq2WkbOWM6h7CH7e2a2v/5yLJerCNZbP+vKZ6vrByo9ws3WhZeC/2a6mvDoaMxMN3k6eXEyIYfzGj3XZeVQqVZ6zAgQGBrJq1aoCj9WgQQOOH9dv/b9zR4mzsxsmxiU7ul+pyCLQdC9/PmhDktYw22hMFAr8HxthLUlS3mTQKkllnbU11KkHv0U8uWwZkWbjzhVF1dKuRoE0xjHUqFyeOaMGUN7ThZu3k/hkzXZ6vjebXcumYmGWOxC5lXgXBxv9llgHWytu3rmre/35xl2olCr6d20JQGZWFrsj/mLzpx9havz0wc3sTYv55tBW9kz/Vm/7no3/TeNYzdufKpX9CegfzB9//EG9ekWfweryZVv8fK+jUJRs7iqVIoOamv388aAl97WG1QdcAQRqzFArDDOgliRDI/9SJOlFUNkfvHNniSqLtEYmXHZsjTbLsAKMXNJjaVa3Gu2b1sHfx4MmQVX5esZQ7iU/4MeDuTPBFMbJszGs2LyPOaMH6Cbkz8jIIuVBGu3eGI2mmxeabl788s9vLP7xazTdvArMGjR/y+fM2bSEHVPWUd27SoHH9rXxwtbWVi/rTVF68EBBckrhUuoWNbXiAbVMD2JmYL9Sviam2Khk25EkFZb8a5GkF0VwE0hMhLtJpV2T53K1Qmcephj2R5PaOIXM1Nu5lltZmFHew4mYuLwzZDnYWnMrST+t7K3EezjaZU/PdOzUOW4n3adRn/d167O0WhSK7BbZnZO+A2DQpyOp5F6BUd0G55l2EWDu90v4+LtP+XHyGmpXzD8FZY6rt+JISkrSpdEsDpcvm1M1oGSnwMphorxPbU0Evz8IJp9EVCXKycgIb+OyMUhMkgyFYd8ZJEkqPLUamreE7dsgM6O0a/NMbvs0JTHF6skFS5m5WSzaPGaGSnnwkNj4m3S1y3uO0FpVfDh8PIqB3f6dBibiryhq+menOuzaqj7BNfWns+o/biFdWtWnR7vmVLWpnH18Ew32lrZU9cp+PWDhMNzsXZjx+jgA5nz/GVPXzWPViE/xcvIkIfEGABam5lhozEl+kML0jfPp2qA9zjZOXEyIYdzKGfi4e9OwYcPnuzgFyMqCW7fL4egQXWzHKIhGeYc6mt/5PbUupfkXYqZQEmBqVoo1kKSySQatkvQisbGFRo3hwP4nlzUwqQ4+XMusWNrVKBSVNhYt8NEX39KyfnXcne25fjuJhau2oVIq6di8LgAjZy3H2cGWMaHdAOjftSW9R87lq29307xeNX44cIyTZy8zY3j2XJC2VhbYWlnoHcvISIWjnTXl3R0hJe/6XLl5DeUj/SK/3Lma9Mx0es1+U6/chJ7vMan3SFRKJScvn2H1z9+RlHIPN1tnWtVowpQ+o7lrpiajGL/0XLumxs7WDpXqTrEdoyDmyuvU1kRy7EGNYkiM+2RKcvqxGlhfBUkqA2TQKkkvGm8fqHIDTp8q7ZoUWpaJOTHWzRBphn8jVyozyEzOHgWfcCuRYR8tI+l+CnbWFtSp6sumT8Zh///BVnE37qB8JDipHVCRhePCmLdiC3PDN+Pt7sTnU97JNUdr3v4NsfbO+E5vzeOvzy37rcA9aUw0/DhlbZ7rtMo07hRzO+S1ay54lrvzhHQRxcdKdYWaGmP+elAF7ZOLF6nKJhos8+nSIUlSwWRyAUl6EWm18NMOuJ5Q2jV5IgFcrvQq91LKxuNSK6vLcG9vqRzbI7Vd9gUrRskuWmJMcvfXLWr+lRMxMSmZFLj5uZFZhRMPKxT3JdVxV6tltwBJeg5y9gBJehEpldCsJVhYPLlsKbtZMaTMBKwAakVs6R28BD6xzW4o9FqHi8vlGBuEKN2HfU5GpwkwKZ7ZEh5nq1Lhb6IpkWNJ0otKBq2S9KLSaKBlSPYALQOV7FKF+DSP0q7GUxBkpZZi0FoCwaRSq8BcXfxfIh48UJCcXDpTYD3KTR1JZeMbxXoMM4WSGhrzEvkyIEkvMhm0StKLzNY2u8XVAG+WGWa2xGgagDC8uuXHzPwG2oyHpVeBEvrEtkwvmamYLl3WIETpt7KXMz5KBePEYtm3GgU1zeTAK0kqCjJolaQXnbsHNAgu7VroEQolMR4dyMwoWzdyU3UptrJCiX1iW9wsmQNptQpu3fIskWM9SQXjQ5RT5zM9wzNSAjU0Zpgr5cArSSoKMmiVpP8Cv8pQvUZp10In3rcDKallcGL1tFIOWksoxlenKtA8Q8rYZ3EtTk1Wll2JHOtJKhnvx82o6FrSq5pqsDWSk/RIUlGRQask/VfUqgMVfEu7Ftz1qMPNVMfSrsZTMzG5T+aD4nmEXFgl2ZPCMqtkglaAK1ddSmwEf0EUCggw2Yuj6vmn/PIzMcVFbVwEtZIkKYcMWiUJWLFiBTY2NrrXU6ZMoUaNGoXe/vLlyygUCiIjI4u8bkUquDF4eZfa4dOsXLhiFEiJNRkWITNNTGlXoUQvm2VSybUQJiUpSUsrzFy1xU+hEASa7sVO9eypB3yMTWSK1nw87WdrcWnWrBnDhw/Xvfb29mbhwoWF2vbx+4VUcmTQKpUZBw4cQKFQ6H4cHR1p3749J0+eLPJjjRo1in379hX5fkudUglNmmf3cy1hWpUxMc5tycosewErgDKzlLsGQIl+YpvcAaMSfLR9+XLpT4GVQ6nIpIbpPqyUT596wFttTEWTom+lFkKwbNkyGjRogJWVFRYWFgQEBDBs2DDOnz9f5McrLTkNACqVimvX9OfxjY+Px8jICIVCweXLl4v0uMeOHeONN94oVNmePXty9uzZIj2+VDgyaJXKnOjoaOLj4/npp59IS0vjpZdeIj09j0Twz8HCwgJ7e/si3afBUKmgeStwcS3Rw16r2IkHDwwjKHlaSmU6GSkGkKhBUXIP0RUosFSW3Mj+hw8V3E/2KrHjPYmRIo1amoOYP8V3LE+1MX6mRT8XqxCCV199laFDh9K+fXt2797N6dOnWb58OaampkyfPr3Ij/m0ivoz2N3dnVWrVuktW7lyJe7uxdMi7+joiJlZ4X7fNRoNTk5OxVIPqWAyaJXKHCcnJ1xcXKhVqxbDhw/nypUrnDlzRrd+/vz5VKtWDXNzczw9PRk8eDDJycl6+1ixYgXlypXDzMyMrl27cvu2fgagxx9habVapk2bhoeHByYmJtSoUYNdu3YVWM+DBw9St25dTExMcHV1ZezYsWRmZurW379/nz59+mBubo6rqysLFizQe2Q1bdo0qlatmmu/NWrUYOLEiYW9XHkzMoKWbcCxZD5473g34k6KTYkcqzhYWF4BUdIJP/NQwo3Ulikl2yfz8mVTg5gCK4exIpnamkOYFuK6uxmpqVwMLawAGzduZMOGDWzcuJGJEydSv359ypUrR/369Zk1axbh4eF65b/66iv8/f0xNTWlcuXKLFmyRLcupyXz+++/p3nz5piZmREYGMiRI0f09nHo0CEaN26MRqPB09OToUOHkpLy7+wK3t7efPjhh/Tt2xcrKytdK+X777+Pn58fZmZm+Pj4MHHiRDIynr6PcL9+/XKdV3h4OP369ctV9tSpU7Rr1w4LCwucnZ15/fXXuXXrlm59SkoKffv2xcLCAldXV+bNm5drH493D0hKSuLNN9/E2dkZU1NTqlatyvbt24H8u5OtXr0ab29vrK2t6dWrF/fv39eV0Wq1zJw5k/Lly6PRaAgMDOS77/TTL0tPJoNWqcy6e/cuGzZsAMDY+N+bq1Kp5JNPPuGff/5h5cqV7N+/nzFjxujWHz16lNDQUIYMGUJkZCTNmzd/YkvFokWLmDdvHnPnzuXvv/8mJCSETp06ce7cuTzLX7t2jfbt2xMUFMSJEydYunQpy5cv1zvOiBEjiIiIYNu2bezZs4dff/2Vv/76S7d+4MCBREVFcezYMd2y48eP8/fffzNgwICnu1h5UauhdVuwK94W5Qd23lzVVirWYxQ3NQbQNQAQJdjSCmB+I7srTknRahXcvFX6CQceZapMpI7mKMYFXAYXIzUBpppiu1br16+nUqVKdOrUKc/1jx537dq1TJo0iRkzZhAVFcVHH33ExIkTWblypd4248ePZ9SoUURGRuLn50fv3r11X6ovXLhA27Ztefnll/n777/ZuHEjhw4dYsiQIXr7mDt3LoGBgRw/flz3RdrS0pIVK1Zw+vRpFi1axLJly1iwYMFTn3OnTp1ITEzk0KFDQHYQnZiYSMeOHfXKJSUl0aJFC2rWrMkff/zBrl27uH79Oj169NCVGT16NAcPHmTr1q3s3r2bAwcO6H3WPk6r1dKuXTsiIiJYs2YNp0+f5uOPP0alyn/qsgsXLrBlyxa2b9/O9u3bOXjwIB9//LFu/cyZM1m1ahWff/45//zzD++99x6vvfYaBw8efOpr819WNp/VSf9pHh7Z/TFzvvV36tSJypUr69Y/3rl++vTpvPXWW7rWhkWLFtG2bVtdIOvn58fhw4cLbDmdO3cu77//Pr169QJg1qxZ/PzzzyxcuJDPPvssV/klS5bg6enJ4sWLUSgUVK5cmbi4ON5//30mTZpESkoKK1euZN26dbRs2RLIbkVwc3PTO8+QkBDCw8MJCgrSlWnatCk+Pj5Pfd3yZGwMIe1gz09w62bR7PMRWcZmXLZtjkgrm/1Ys2nJSrla2pXIVsKXUZkJFsZm3E8r2vlLCxIXZ4S9nT0q1e0nFy4hZsob1DY9zrEHNcl8bJ2TkRFVizFgBTh79iyVKul/8Rs+fDhfffUVADY2Nly9mv07OnnyZObNm0e3bt0AKF++PKdPn+aLL77Qa6UcNWoUL730EgBTp04lICCA8+fPU7lyZWbOnEmfPn10n6W+vr588sknNG3alKVLl2Jqmt2i3KJFC0aOHKlXrwkTJuj+7+3tzahRo9iwYYNew0FhqNVqXnvtNb7++msaNWrE119/zWuvvYb6sQx/ixcvpmbNmnz00Ue6ZV9//TWenp6cPXsWNzc3li9fzpo1a3SftStXrtTdR/Kyd+9efv/9d6KiovDz8wN44meuVqtlxYoVWFpaAvD666+zb98+ZsyYQVpaGh999BF79+6lQYMGuv0dOnSIL774gqZNmz7Vtfkvky2tUpnz66+/8ueff7JixQr8/Pz4/PPP9dbv3buXli1b4u7ujqWlJa+//jq3b98mNTUVgKioKOrVq6e3Tc4HSV7u3btHXFwcwcH6E/QHBwcTFRWV5zZRUVE0aNBA70YWHBxMcnIyV69e5eLFi2RkZFC3bl3demtr61w3pkGDBrF+/XoePnxIeno669atY+DAgQVcnWdgYpoduDq7FO1+gSvlO5OeVrYnVje3uI42M620q/F/JT8xlGVayY+Cv3LVGWFgM0xYqq5SU3NK76bpaqQm0NSsVNKzjh8/nsjISCZNmqTr/pSSksKFCxcIDQ3FwsJC9zN9+nQuXLigt3316tV1/3d1ze7ffuNGdjrbEydOsGLFCr19hISEoNVquXTpkm67OnXq5KrXxo0bCQ4OxsXFBQsLCyZMmEBs7LM9qRg4cCDffvstCQkJfPvtt3l+9p04cYKff/5Zr645jRgXLlzgwoULpKen633m29nZ5fqsfVRkZCQeHh66gLUwvL29dQErZF/TnOt5/vx5UlNTad26tV49V61alet9kQomW1qlMqd8+fLY2NhQqVIlbty4Qc+ePfnll1+A7P5aHTp04O2332bGjBnY2dlx6NAhQkNDSU9PL3RHe0PRsWNHTExM2Lx5M8bGxmRkZPDKK68U/YHUxtldBX7eC9eKplXxZoVW3E0xL5J9lSYTo9hcrWulpaS7BwCY31KBQ8keMylJiYuLO6YmBtLC/X+2qkvUMDXh+ENf3NXG+JuYlkj3CV9fX6Kjo/WWOTo64ujoqDcgKCd4XbZsWa4v5o8/2n60xTLnHLRarW4/b775JkOHDs1Vl3Ll/u2+YW6u//d95MgR+vTpw9SpUwkJCcHa2poNGzbk2Ye0MKpVq0blypXp3bs3/v7+VK1aNde0gsnJyXTs2JFZs2bl2t7V1fWZZlbQaJ5+MN3jLcAKhULvegL8+OOPuQaSmZjIqdGehmxplcq0d955h1OnTrF582YA/vzzT7RaLfPmzaN+/fr4+fkRFxent42/vz9Hjx7VW/bbb7/lewwrKyvc3NyIiIjQWx4REUGVKlXy3Mbf358jR44ghNArb2lpiYeHBz4+PqjVar3+qnfv3s01jYqRkZFuQEJ4eDi9evV6pg/UQskZnOVd/rl3leJcmbh0wxkJ/jzEQwOYnzVHKQStxilgqi75G+ulS9YGMwXWoxyMzlBPc5cqxdwl4FG9e/cmOjqarVu3FljO2dkZNzc3Ll68SMWKFfV+ypcv/N91rVq1OH36dK59VKxYUW/8wOMOHz6Ml5cX48ePp06dOvj6+hIT83x/PwMHDuTAgQP5PmGqVasW//zzD97e3rnqam5uToUKFVCr1Xqf+YmJiQVOWVW9enWuXr1aZNNaValSBRMTE2JjY3PV0dPTMNIYlxWG94kgSU/BzMyMQYMGMXnyZLp06ULFihXJyMjg008/pWPHjkREROTqPjB06FCCg4OZO3cunTt35qeffnriTACjR49m8uTJVKhQgRo1ahAeHk5kZCRr167Ns/zgwYNZuHAh7777LkOGDCE6OprJkyczYsQIlEollpaW9OvXj9GjR2NnZ4eTkxOTJ09GqVTmuhGGhYXh7+8PkCtwLnI587gaqeH8s31gZ2qsiTFvCOmG9Xj3WZiaJpH18F5pV0NHlFLeKEuh4SEl20UiLU3B/fvlsLK6WKLHfbJaWBmV7GCxXr168f3339OrVy/GjRtHSEgIzs7OxMTEsHHjRr1W1KlTpzJ06FCsra1p27YtaWlp/PHHHyQmJjJixIhCHe/999+nfv36DBkyhLCwMMzNzTl9+jR79uxh8eLF+W7n6+tLbGwsGzZsICgoiB9//FHXoPCsBg0aRPfu3fOdzP+dd95h2bJl9O7dmzFjxmBnZ8f58+fZsGEDX331FRYWFoSGhjJ69Gjs7e1xcnJi/PjxKJX5t9k1bdqUJk2a8PLLLzN//nwqVqzImTNnUCgUtG3b9qnPwdLSklGjRvHee++h1Wpp1KgRd+/eJSIiAisrqzxnRJDyJltapTJvyJAhREVF8e233xIYGMj8+fOZNWsWVatWZe3atcycOVOvfP369Vm2bBmLFi0iMDCQ3bt36w0eyMvQoUMZMWIEI0eOpFq1auzatYtt27bh65t3WlR3d3d27NjB77//TmBgIG+99RahoaF6x5k/fz4NGjSgQ4cOtGrViuDgYN00NY/y9fWlYcOGVK5cOdcjv2KhVGZnzqpa/cllHyMUCmLKdSQj/cX4aNGYGsasATql0NIKYHm3dNo3LsdoEMJQupgogIagyPvpSrEeWaFg48aNLFy4kB07dtCyZUsqVarEwIED8fT01I2wh+wvuV999RXh4eFUq1aNpk2bsmLFiqdqaa1evToHDx7k7NmzNG7cmJo1azJp0iS9gaJ56dSpE++99x5DhgyhRo0aHD58+Lmn5zMyMsLBwSHfRBc5T8GysrJo06YN1apVY/jw4djY2OgC0zlz5tC4cWM6duxIq1ataNSoEbVr1y7wuJs2bSIoKIjevXtTpUoVxowZQ1bWs2dJ+/DDD5k4cSIzZ87E39+ftm3b8uOPPz7V+yKBQjz6/FKSpFKTkpKCu7s78+bNIzQ0VLdcCIGvry+DBw8udEtJkTl7Bn47DNrCzVEa79eBG6lFP6CrtNhbbCcj2QCSCvyfs1lz1DeLZy7QgggEZ30SyXyOm/azcnXNxNnpzJMLFis10AgUhpFqVpL+q2T3AEkqJcePH+fMmTPUrVuXu3fvMm3aNAA6d+6sK3Pz5k02bNhAQkJC0czN+rT8KoOlVfYArSdkvLnnUZMbqc4lVLHipzJ6SEbK9dKuxmNKp41BgQJLlRmJWfefXLiIxccbYW/ngJHRrScXLhYWQHNQWJfS8SVJyiGDVkkqRXPnziU6OhpjY2Nq167Nr7/+ioPDv0O1nZyccHBw4Msvv8TW1rZ0KunqBu07wb7dcD/v/p3plk7EGtWCzLLfjzWHhfkVxF3DehBVWn1aASxSjUkspTvG1WvOeHndRlHi5+8MNAGFHOEtSYZAdg+QJKlwHj6E/Xvghn7ro1ZpxPmKfXiQqs5nw7LJ3nofGXcvPblgCXIyb4zxDYtSOXaWWhDtfpvSumVUrnQXU9MrJXhEXyAIFC9G/2xJehHIv0ZJkgrH1BRC2oOf/qTccb6dXriAVUEWmcmGNUcogKBwfYuLgypDgbm6mKZbK4RLl60QoiR+zxRkB6v1ZMAqSQZG/kVKklR4KhU0bJz9o1KR6NWA2yl2pV2rImduGY/IyijtauRSmkErgGVG6T0mT0tTcO9ecU81ZQy0AEX+2ZIkSSo9sk+rJElPz68Sws6BG+fsoOTS0pcYE1UshheyQmkNxMphcVsFpfgdJSbWlKpVLVAqkoth7w5AY1AYyhRbkiQ9Tra0SpL0TBQO9lQMUmDt9OSyZY32gYHNz/p/pd3SanxfgYk6/4xIxU2rVXDjhkcx7NkfaCMDVkkycDJolSTpmamMwLs6uFd6cbr/aTS3yUorjpa85yco+XlSH2dJ6fVrBUhIMCIz0+HJBQvFBGgGitovzi+wJL3A5F+pJEnPzcETfIPA2Ky0a/L8NCaG2coKpd/SCmB5t/QH3V254oTgeadXcwFeAkVxtNxKklQcZNAqSVKR0FiCX12wKzjTo+HLkEFrQTS3QKVUPblgMbp7T8nDh88abCqBmkBLULwA37Ik6T9EBq2SJBUZlRF4VoEKtcpmq6uROpXMlJulXY18CVH63QMUQoGluvTf3EuXnmUKLAeyW1cDQPHiJMKQpP8KGbRKklTkLOygUj1w8i5bsYGFWUlOXv/0DKGlFbKzY5W29PSnmQJLBdQGQmQ6Vkkqw2TQKklSsVCqwLUi+NYDszISJ6hETGlXoWAG0NIKYHHDML6JXI7RoNU+KUOYC9ARFP5l6xuUJEm5yKBVkqRipbGAinWyZxgo5a6QBVIoMslMjivtahTIUFpaVekKzA2g/4cQFDAFlhqoB4pWoCid1LfP48CBAygUCpKSkkq7KpJkMGTQKklSsVMosmcYqNwArBxLuzZ5s7CMQ2gzS7saBdIKw6mfZWbpZcd6VMJ1IzIzH/+l8iK7ddW32I67YsUKbGxs8lynUCjYsmVLsR1bkv6rZNAqSVKJUZtC+cDsuV2NDCPm0TFWGu6sATkMpaUVwOKO4TSbx+qmwLIBWoOiscHODCCEIDPTcL58SFJZIoNWSZJKnLVTdqurg6ehdDMUZKUaftBqKH1aAUzuKjAuxexYj0pJMSI9rTHQHhTOpV0dPTmP+Xfu3Ent2rUxMTHh0KFDpKWlMXToUJycnDA1NaVRo0YcO3aswH1t2rSJgIAATExM8Pb2Zt68eXrr4+Pjeemll9BoNJQvX55169bh7e3NwoULARg4cCAdOnTQ2yYjIwMnJyeWL19epOctScXBqLQrIEnSf5PKKLufq4MnJFyApOulVxcz81toU1JLrwKFpDWAjFiPskTDbdJL7fgKhQJHR0ecnZ1RqQyn5TcvY8eOZe7cufj4+GBra8uYMWPYtGkTK1euxMvLi9mzZxMSEsL58+exs7PLtf2ff/5Jjx49mDJlCj179uTw4cMMHjwYe3t7+vfvD0Dfvn25desWBw4cQK1WM2LECG7cuKHbR1hYGE2aNCE+Ph5XV1cAtm/fTmpqKj179iyR6yBJz0O2tEqSVKpMzMCrGvjVA0v70qmDqboMtLJiGPO0PsryXullx7K1taVy5cq4ubkZfMAKMG3aNFq3bk2FChUwMTFh6dKlzJkzh3bt2lGlShWWLVuGRqPJt8Vz/vz5tGzZkokTJ+Ln50f//v0ZMmQIc+bMAeDMmTPs3buXZcuWUa9ePWrVqsVXX33FgwcPdPto2LAhlSpVYvXq1bpl4eHhdO/eHQuLsjdYTfrvkUGrJEkGQWMJPjWhQu1SmCIrvWwErVoDGyhmdhOUypK9jdjY2FC5cmW8vLwwMTGwjtEFqFOnju7/Fy5cICMjg+DgYN0ytVpN3bp1iYqKynP7qKgovfIAwcHBnDt3jqysLKKjozEyMqJWrVq69RUrVsTW1lZvm7CwMMLDwwG4fv06O3fuZODAgc99fpJUEmTQKkmSQbGwBd+g7ADW3Kb4j2dsnExm6u3iP1CRMKyWVoVQYGlUMgOeclpWvb29MTU1LZFjFsTKyoqUlBS0Wv3BcTlTVFlb63/zMjc3L6mqFahv375cvHiRI0eOsGbNGsqXL0/jxo1Lu1qSVCgyaJUkySBZ2mfP7+pTq3iDVzOzstHKCoY15VUOi4fFOxgrJ1j18vIyiGA1R6VKlcjMzCQyMlJv+V9//QWAn59fvttWqFABY2NjIiIidMsyMjI4duwYVapUyXMbf39/vfIAERER+Pn5oVKpdPU5fvy4bv358+dJTEzU28be3p4uXboQHh7OihUrGDBgQKHOV5IMgRyIJUmSQbO0y/5JToTrlyD5TtHuX5UVa0ATSRXM0Pq0AljcVEERD9hXKBTY2trqRtYbooCAANq0acPAgQOZN28ePj4+REdHM3z4cHr27Im7u3u+25qbm/P2228zevRo7OzsKFeuHLNnzyY1NZXQ0NA8txk5ciRBQUF8+OGH9OzZkyNHjrB48WKWLFkCQOXKlWnVqhVvvPEGS5cuRa1WM3LkSDQaDYrHpugICwujQ4cOZGVl0a9fv6K7KJJUzGTQKklSmWBhm/2Tlgq3r0FiHGRmPN8+lcoMMlLii6aCJUCrfc4TLgZGD8HMWENq+oMnF37SvoyMcHBwwMHBASMjw789bdy4kcmTJ/Pmm28SFxeHh4cHXbt2ZeLEiU/c9uOPP0ar1fL6669z//596tSpw08//ZSrD2qOWrVq8c033zBp0iQ+/PBDXF1dmTZtmm7mAIBVq1YRGhpKkyZNcHFxYebMmfzzzz+5Av9WrVrh6upKQEAAbm5uz3UNJKkkKYQQorQrIUmS9LS0Wrh7A25fhZSkZ9uHldUluLevSOtVrAR4pLYr7Vrkcssnk+tZiU8umA9zc3McHBywtrYu8YFdL7KrV6/i6enJ3r17admypW55cnIy7u7uhIeH061bt1KsoSQ9HcP/KitJkpQHpRJsXbJ/HqZkB6+J8ZD1FN0+1YpYDK/tsgCK//8YWFODRaKK61ZPt41SqcTGxgYHBwfMzAwze1VZs3//fpKTk6lWrRrx8fGMGTMGb29vmjRpAoBWq+XWrVvMmzcPGxsbOnXqVMo1lqSnI7/SSsVuypQp1KhRo1TrcPnyZRQKRa5BE/81BeVLL6xmzZoxfPjwIqlPUTE1z05UUKUxeAYUdsosQVbqleKuWtEziAxi+kwTFaiNCjdnq5WVFV5eXlStWpVy5crJgLUIZWRk8MEHHxAQEEDXrl1xdHTUJRoAiI2NxdnZmXXr1vH111+XiS4YkvQoGbSWYUIIvvzyS+rVq4eFhQU2NjbUqVOHhQsXkppquNl9+vfvT5cuXYpkXwUFYQqFgi1btgDg6elJfHw8VatWfeI+/+sBrkKh0P1YW1sTHBzM/v37S7tahaJUgZ1r9pRZfvXB3gOM8hncbmZ+A23Gw5KtYFFQGmDUClgq8g8+zczMcHd3JyAgQJcRSnYDKHohISGcOnWK1NRUrl+/zubNm/Hy8tKt9/b2RgjBlStX9LoLSFJZIT81yrDXX3+d4cOH07lzZ37++WciIyOZOHEiW7duZffu3aVdPYOiUqlwcXEp8ZaFjIwy9fBZJzw8nPj4eCIiInBwcKBDhw5cvHixtKv1VDQW4FE5u/W1Qh1wKAfGj4xHMVXHlF7lnoeBfmpbJuv/bZmYmODs7EzlypXx8/PD0dFR1+InSZL0LAz04096km+++Ya1a9eyfv16PvjgA4KCgvD29qZz587s37+f5s2bA9l9mKZNm4aHhwcmJibUqFGDXbt26faT06r4/fff07x5c8zMzAgMDOTIkSO6MjExMXTs2BFbW1vMzc0JCAhgx44dQN4tnVu2bMk1xUqOKVOmsHLlSrZu3aprzTtw4EDRXpw8PN56mpiYSJ8+fXB0dESj0eDr66vLElO+fHkAatasiUKhoFmzZkDhr+XGjRtp2rQppqamfPnll1hZWfHdd9/p1WfLli2Ym5tz//79POu7a9cuGjVqhI2NDfb29nTo0IELFy7kOlZB7xtkvz85j2C7du3K7duFm0TfxsYGFxcXqlatytKlS3nw4AF79uzRrddqtYwZMwY7OztcXFyYMmWKbt3AgQPp0KGD3v4yMjJwcnLKN0VlcVIowMIG3P3AvxH41gWn8mCkvFfidSkShtnQitlNJRbmFri5uVG5cmX8/f1xdXU12CmrJEkqe2TQWkatXbuWSpUq0blz51zrch7rAixatIh58+Yxd+5c/v77b0JCQujUqRPnzp3T22b8+PGMGjWKyMhI/Pz86N27N5mZ2SNa3nnnHdLS0vjll184efIks2bNeuY81aNGjaJHjx60bduW+Ph44uPjadiw4TPt63lMnDiR06dPs3PnTqKioli6dCkODg4A/P777wDs3buX+Ph4vv/+e6Dw13Ls2LEMGzaMqKgounXrRq9evXQBcY7w8HBeeeUVLC0t86xfSkoKI0aM4I8//mDfvn0olUq6du2aK/tOQe/b0aNHCQ0NZciQIURGRtK8eXOmT5/+1NdKo9EAkJ6erlu2cuVKzM3NOXr0KLNnz2batGm6oDYsLIxdu3YRH//vVFLbt28nNTWVnj17PvXxi5qZFbhWANdarfBs1Bt7/0aYOXqhUJWRVkBD+tQ2UaLw1KCqbYtxG1cq+lY06LlVJUkq22Qv7DLq3LlzVKpU6Ynl5s6dy/vvv0+vXr0AmDVrFj///DMLFy7ks88+05UbNWoUL730EgBTp04lICCA8+fPU7lyZWJjY3n55ZepVq0aAD4+Ps9cbwsLCzQaDWlpabi4uDzzfh519+7dpw6iY2NjqVmzpi4fuLe3t26do6MjkJ055tE6FvZaDh8+XG8ambCwMBo2bEh8fDyurq7cuHGDHTt2sHfv3nzr9/LLL+u9/vrrr3F0dOT06dN6/XILet8WLVpE27ZtGTNmDJCdoefw4cN6rcNPkpqayoQJE1CpVDRt2lS3vHr16kyePBkAX19fFi9ezL59+2jdujUNGzakUqVKrF69Wnfs8PBwunfv/sxfdoqLkak5Vu6VsXKvjNBm8TDpOg+TEki7e4O0ezfRZqSVdhVzEYpSbGw1VaKwM0FhZ4zS3his1fk+VZGKnkKhYPPmzUU2JkCSyhpD+s4uPYXCTK9779494uLiCA4O1lseHBxMVFSU3rLq1avr/u/q6grAjRs3ABg6dCjTp08nODiYyZMn8/fffz9v9Z9o7dq1WFhY6H5+/fXXfMtaWloSGRmZ66cgb7/9Nhs2bKBGjRqMGTOGw4cPF1j+aa5lTiCco27dugQEBLBy5UoA1qxZg5eXl24amrycO3eO3r174+Pjg5WVlS6ojo3VTzla0PsWFRVFvXr19Mo3aNCgwPPM0bt3bywsLLC0tGTTpk0sX75c71iP/j/n2DnHhexAPad1+fr16+zcuZOBAwcW6tilRaFUobFzw9anFi412+LV9HU8GnTHoUpTLD38Mba0z+5rUNpKqgoKUFirUZY3R1XbFqPWzqhDXDEKskNVwQKFjXGpB6zLli0jMDBQNxC1Zs2azJw5U6/MnTt3GD58OF5eXhgbG+Pm5sbAgQNz/S3lmDlzJiqVijlz5uRat2LFCl23JqVSiYeHBwMGDND73c/PgAEDmDBhQr7rExISePfdd/Hx8cHExARPT086duzIvn0lM4+wt7c3CxcuLHT58uXL5/vF29vbW3edzM3NqVWrFt9++20R1dSwHThwAIVCQUBAAFlZ+hnsbGxsWLFiRaH3ZQgz7xgaGbSWUX5+fpw5c6bI9vfoAImcG1HOo+iwsDAuXrzI66+/zsmTJ6lTpw6ffvopkD3X4uMBdFEMPurUqZNeAPp4IPgopVJJxYoVc/0UpF27dsTExPDee+8RFxdHy5YtGTVq1HPXG7InSn9cWFiY7sMqPDycAQMGFHjD79ixI3fu3GHZsmUcPXqUo0ePAvqP6KHg9+15LFiwgMjISBISEkhISMiV6vHxATUKhULvuH379uXixYscOXKENWvWUL58eRo3bvzc9SppanNrLN18cagcjHu9rng164dr7Q7Y+dbFzMkbI40lJd7uWRyHUymyW009NCirWKFq6IBRe1eMmjmhqm6D0sMMhZlhPZj7+uuvGT58OEOHDiUyMpKIiAjGjBlDcnKyrsydO3eoX78+e/fu5fPPP+f8+fNs2LCB8+fPExQUlOfgwq+//poxY8bw9ddf53lcKysr4uPjuXr1KsuWLWPnzp28/vrrBdY1KyuL7du35zsv6uXLl6lduzb79+9nzpw5nDx5kl27dtG8eXPeeeedp7gqT+/xz5TC+Pvvv0lMTNR7+vK4adOmER8fz/HjxwkKCqJnz575Ng48Sx0Kq7QGw168eJFVq1aVyrFfZDJoLaNeffVVzp49y9atW3OtE0Jw9+5drKyscHNzIyIiQm99REQEVapUearjeXp68tZbb/H9998zcuRIli1bBmQ/Sr9//z4pKSm6sk9q5TQ2Ns71DfRxlpaWegFoTr/KouTo6Ei/fv1Ys2YNCxcu5Msvv9TVD9Cr4/Ney9dee42YmBg++eQTTp8+XWC+79u3bxMdHc2ECRNo2bIl/v7+JCY+fbYhf39/XbCb47fffivUti4uLlSsWFHXVeJp2dvb06VLF8LDw1mxYgUDBgx4pv0YGqXKCFNbF6y9quNcvRWewT3xbtEf9/ov41S9FbYVg7Bw9cPUxgUjU4viaZlVPGNmAQWgUaFwMEHpbY6yqjWqenYYtXLG6CVX1M2cMKpth8rXEqWjCQojw749bNu2jR49ehAaGkrFihUJCAigd+/ezJgxQ1dm/PjxxMXFsXfvXtq1a0e5cuVo0qQJP/30E2q1OldAePDgQR48eMC0adO4d+9enkGWQqHAxcUFNzc32rVrx9ChQ9m7dy8PHuSfxvbw4cOo1WqCgoLyXD948GAUCgW///47L7/8Mn5+fgQEBDBixIhcf7O3bt2ia9eumJmZ4evry7Zt23TrsrKyCA0NpXz58mg0GipVqsSiRYv0ts+ZcnDGjBm4ublRqVIlmjVrpvsSn9NCWpCtW7fStm3bAmeDsLS0xMXFBT8/Pz777DM0Gg0//PADkN0S++GHH9K3b1+srKx44403ANi0aRMBAQGYmJjg7e3NvHnz9PYZHx/PSy+9hEajoXz58qxbty5XC7FCoWDp0qV06tQJc3Nz3e/D1q1bqVWrFqampvj4+DB16lRd/38hBFOmTKFcuXKYmJjg5ubG0KFDdftcsmQJvr6+mJqa4uzszCuvvFLg9QF49913mTx5Mmlp+Xcxio2NpXPnzlhYWGBlZUWPHj24fv06kN2qP3XqVE6cOKF7T3IaPpKSkggLC8PR0RErKytatGjBiRMndPs9ceIEzZs3x9LSEisrK2rXrs0ff/zxxDqXBYb11VkqtB49erB582Z69+7NhAkTaNOmDY6Ojpw8eZIFCxbw7rvv0qVLF0aPHs3kyZOpUKECNWrUIDw8nMjISNauXVvoYw0fPpx27drh5+dHYmIiP//8M/7+/gDUq1cPMzMzPvjgA4YOHcrRo0ef+PjD29ubn376iejoaOzt7bG2ti7xqXAmTZpE7dq1CQgIIC0tje3bt+vOycnJCY1Gw65du/Dw8MDU1BRra+vnupa2trZ069aN0aNH06ZNGzw8PAosa29vz5dffomrqyuxsbGMHTv2qc9x6NChBAcHM3fuXDp37sxPP/30VP1Zn1dYWBgdOnQgKytLF6QvXryYzZs3l9gjz5KgUKowtrDF2CJ3znghtGQ+TCHzYTKZD5PRZqShzUzX/YjMjOz/Z2U8sjwDkZV/65BQgkKlyG5yUCmyf4yUKEyUYKL6/79KFCaq7H9Ns//FWFnqj/OLkouLCwcPHiQmJkZvLtIcWq2WDRs20KdPn1z95zUaDYMHD2bChAncuXMHOzs7AJYvX07v3r1Rq9X07t2b5cuXP3GgqEajQavV6gKgvGzbto2OHTvmef3v3LnDrl27mDFjRp5PaR6fnWXq1KnMnj2bOXPm8Omnn9KnTx9iYmKws7NDq9Xi4eHBt99+i729PYcPH+aNN97A1dWVHj166Paxb98+rKysdIMnXV1dCQwM5I033mDQoEEFnm/O+YwYMeKJ5XIYGRmhVqv1WlTnzp3LpEmTdH3j//zzT3r06MGUKVN0rbKDBw/G3t6e/v37A9lPcG7duqVLmDBixIg8u2ZMmTKFjz/+mIULF2JkZMSvv/5K3759+eSTT2jcuDEXLlzQBcqTJ09m06ZNLFiwgA0bNhAQEEBCQoIuCPzjjz8YOnQoq1evpmHDhty5c6fA7mo5hg8fzpo1a/j000/zfIqn1Wp1AevBgwfJzMzknXfeoWfPnhw4cICePXty6tQpdu3apeuGkTPAunv37mg0Gnbu3Im1tTVffPEFLVu25OzZs9jZ2dGnTx9q1qzJ0qVLUalUREZGvjjTzQmpzMrKyhJLly4VQUFBwszMTFhZWYnatWuLRYsWidTUVF2ZKVOmCHd3d6FWq0VgYKDYuXOnbh+XLl0SgDh+/LhuWWJiogDEzz//LIQQYsiQIaJChQrCxMREODo6itdff13cunVLV37z5s2iYsWKQqPRiA4dOogvv/xSPPqrNXnyZBEYGKh7fePGDdG6dWthYWGhd5xnER4eLqytrfNcB4jNmzfneZ4ffvih8Pf3FxqNRtjZ2YnOnTuLixcv6rZdtmyZ8PT0FEqlUjRt2lQI8WzX8lH79u0TgPjmm2+eeF579uwR/v7+wsTERFSvXl0cOHCgwPMRIvf7JoQQy5cvFx4eHkKj0YiOHTuKuXPn5nu98rpueWnatKkYNmyY3rLOnTuLfv366S3TarXCy8tLtG/fXrds8uTJwsvLq8DjS9nXLisjTWRlpIuszAyhzcoU2qwsodVqS7tqBiMuLk7Ur19fAMLPz0/069dPbNy4UWRlZQkhhEhISBCAWLBgQZ7bf//99wIQR48eFUIIcffuXaHRaERkZKQQQojjx48LCwsLcf/+fd02j3/enD17Vvj5+Yk6deoUWFdfX1+xffv2PNcdPXpUAOL7779/4jkDYsKECbrXycnJAtD7HHrcO++8I15++WXd6379+glnZ2eRlpamV87Lyyvfa/Woq1evCmNjY5GYmJhvmUf3lZaWJj766CMB6K6Bl5eX6NKli942r776qmjdurXestGjR4sqVaoIIYSIiooSgDh27Jhu/blz53K9x4AYPny43n5atmwpPvroI71lq1evFq6urkIIIebNmyf8/PxEenp6rnPZtGmTsLKyEvfu3cv3fB/1888/C0AkJiaKzz//XNjZ2YmkpCQhhBDW1tYiPDxcCCHE7t27hUqlErGxsbpt//nnHwGI33//XQiR+94phBC//vqrsLKyEg8fPtRbXqFCBfHFF18IIYSwtLQUK1asKFR9yxoZtEpSCVm1apWwt7fPdbN4Ud2/f19YWVmJTZs2lXZVpBfYyZMnxWeffSb69OkjTE1NRevWrUVWVpYuaJ0/f36e2z0etH7++eeiatWqemUCAgLEV199pXsdHh4uAGFubi40Go1QKBSicePGIjo6Ot/6nT59Wpibm4sHDx7kuf633357qqD18S+9VlZWYuXKlbrXixcvFrVq1RIODg7C3NxcqNVqERQUpFvfr18/0apVq1z7LmzQumTJEtGiRYsCy3h5eQljY2Nhbm4uVCqVsLa2Fh9//LHe+unTp+ttU7NmTTFlyhS9ZVu2bBFqtVpkZmaKLVu2CCMjI92Xkhy2tra5gtY1a9bolXFwcBCmpqbC3Nxc92NqaioAkZKSImJjY4Wnp6fw8PAQYWFh4vvvvxcZGRlCCCHu3bsnqlWrJhwcHMRrr70m1qxZI1JSUvI990eD1oyMDOHr6yvGjRsnhNAPWhctWiS8vb1zbW9jY6N7P/MKWhcvXiyUSqXeuZibmwulUinGjBmj287IyEi0bNlSzJw5U5w/fz7f+pY1ht1pSZJeAKmpqVy4cIGPP/6YN998U9dn9kWl1Wq5ceMGH374ITY2NvkOPpGkolC1alUGDx7MmjVr2LNnD3v27OHgwYM4OjpiY2OTa3aPHFFRUSgUCt2gzeXLl/PPP/9gZGSk+zl9+nSuAVk5s5WcOnWKlJQUfvnlF/z8/PKt37Zt22jdunW+c9f6+vqiUCgKPbC2oEGQGzZsYNSoUYSGhrJ7924iIyMZMGBAroFOeXVDKKxt27YV6m969OjRREZGcvXqVRITE3n//feLrA5P8vi+k5OTmTp1qt7g3pMnT3Lu3DlMTU3x9PQkOjqaJUuW6LqONGnShIyMDCwtLfnrr79Yv349rq6uTJo0icDAQJKSkp5YDyMjI2bMmMGiRYuIi4srknNLTk7G1dU112w50dHRjB49GsjuHvHPP//w0ksvsX//fqpUqcLmzZuL5PilTQatklTMZs+eTeXKlXFxcWHcuHGlXZ1iFxsbi7OzM+vWrePrr78u8dS50n9XzqDIlJQUlEolPXr0YN26dSQkJOiVe/DgAUuWLCEkJAQ7OztOnjzJH3/8wYEDB/QCgQMHDnDkyBG9gDJnthIfH59CDRDdunVrnklgctjZ2RESEsJnn32mN6A1R2GCoxwRERE0bNiQwYMHU7NmTSpWrKiXSa8ghRkgm5yczM8//1zg+eRwcHCgYsWKuLi4FKovtb+/f54DXf38/FCpVFSqVInMzEyOHz+uW3/+/PlCDVKtVasW0dHRec4yo1Rmh0EajYaOHTvyySef6N73kydPAtnBZ6tWrZg9ezZ///03ly9fZv/+/U88LmT3Pw0ICGDq1Km5zvfKlStcuXJFt+z06dMkJSXpfo/zek9q1apFQkICRkZGuc4lJ0EOZM8w9N5777F79266deuWK8FNWSXvJpJUzKZMmaKX5vRF5+3tXah5hCXpebz99tu4ubnRokULPDw8iI+PZ/r06Tg6OurmI/7oo490SS9mz55N1apVuXTpEhMmTCAjI0OXFGT58uXUrVs3z7mTg4KCWL58eZ7ztj7JjRs3+OOPP/RG+Ofls88+Izg4mLp16zJt2jSqV69OZmYme/bsYenSpfm2Fj/O19eXVatW8dNPP1G+fHlWr17NsWPHdKmpC+Lt7c0vv/xCr169MDEx0QuAcuzatQs/Pz+9ZCxFZeTIkQQFBfHhhx/Ss2dPjhw5wuLFi1myZAkAlStXplWrVrzxxhssXboUtVrNyJEj0Wg0TwyKJ02aRIcOHShXrhyvvPIKSqWSEydOcOrUKaZPn86KFSvIysrSDSxes2YNGo0GLy8vtm/fzsWLF2nSpAm2trbs2LEDrVZbqOQ+OT7++GNCQkL0lrVq1Ypq1arRp08fFi5cSGZmJoMHD6Zp06Z6SW8uXbpEZGQkHh4eWFpa0qpVKxo0aECXLl2YPXs2fn5+xMXF8eOPP9K1a1cCAgIYPXo0r7zyCuXLl+fq1ascO3YsV8KaMqu0+ydIkiRJ0tP67rvvRPv27YWrq6swNjYWbm5u4uWXXxZ///23XrmbN2+Kd999V3h6egq1Wi2cnZ1F//79RUxMjBAie6CQvb29mD17dp7HmTVrlnBychLp6ekFDvzMy1dffSWCg4MLVTYuLk688847uv6g7u7uolOnTnoDK8ljkOSj/SQfPnwo+vfvL6ytrYWNjY14++23xdixY/X6Rfbr10907tw51/GPHDkiqlevLkxMTER+ocFrr70mxo8f/8RzeVL/2PzWf/fdd6JKlSpCrVaLcuXKiTlz5uitj4uLE+3atRMmJibCy8tLrFu3Tjg5OYnPP/9cVyavaySEELt27RINGzYUGo1GWFlZibp164ovv/xSCJE9mLhevXrCyspKmJubi/r164u9e/cKIbIHPjVt2lTY2toKjUYjqlevLjZu3JjvuT3ap/VRbdq0EYDuvRJCiJiYGNGpUydhbm4uLC0tRffu3UVCQoJu/cOHD8XLL78sbGxs9La9d++eePfdd4Wbm5tQq9XC09NT9OnTR8TGxoq0tDTRq1cv4enpqfu7GDJkSL59qssahRCySUSSJEkqu5o1a0aNGjWeKqNTSejUqRONGjXSpTMuyzIzM3F2dmbnzp3UrVu3tKsDwNWrV/H09GTv3r20bNmytKsjlQDZp1WSpBfS5cuXUSgUT0x2IeWvKFNS/hc1atSI3r17l3Y1isSdO3d477338k2QUBL279/Ptm3buHTpEocPH6ZXr154e3sXmBJberHIoFWSSpgQgmXLltGgQQOsrKywsLAgICCAYcOGcf78+SI7ztPmEi9uMogsu0orJWVxpvcsCWPGjMHT07O0q1EknJycmDBhQqkmqMjIyOCDDz4gICCArl274ujoqEs0IP03yKBVkkqQEIJXX32VoUOH0r59e3bv3s3p06dZvnw5pqamTJ8+vUTrk5WVpZsupywprXzi/1WFSUn5pNSSOelDHzV8+HCaNWume92sWTOGDBnC8OHDcXBw0A1eOXjwIHXr1sXExARXV1fGjh1bYAaqxMRE+vbti62tLWZmZrRr145z587plVm2bBmenp6YmZnRtWtX5s+fr8s+dfnyZZRKZa7UlwsXLsTLy6tM/s28CEJCQjh16hSpqalcv36dzZs355kNTXpxyaBVkkrQxo0b2bBhAxs3bmTixInUr1+fcuXKUb9+fWbNmqU3LcmxY8do3bo1Dg4OWFtb07RpU/766y/delFAvuz8comvWLECGxsbtm3bRpUqVTAxMSE2NrZQN/lDhw7RuHFjNBoNnp6eDB06VG+KHm9vbz766CMGDhyIpaUl5cqV48svv9StzxnBXLNmTRQKhV6w8tVXX+Hv74+pqSmVK1fWjRiGf1toN27cSNOmTTE1NWXt2rVotVqmTZuGh4cHJiYm1KhR44lpap8U/Ny/f58+ffpgbm6Oq6srCxYsoFmzZgwfPhyAadOmUbVq1Vz7rVGjBhMnTizw2GXZ8OHDyczM5NNPP823TPfu3blx4wY7d+7kzz//pFatWrRs2ZI7d+481bFWrlyJsbExERERfP7551y7do327dsTFBTEiRMnWLp0KcuXLy/wC17//v11o/aPHDmCEIL27dvrvuxERETw1ltvMWzYMCIjI2ndurUuRz1k/y63atUq1zRB4eHh9O/fXzdNkiRJJaw0R4FJ0n9Np06dRKVKlQpVdt++fWL16tUiKipKnD59WoSGhgpnZ2ddOsFvv/1WWFlZiR07doiYmBhx9OhR3WjY27dvCw8PDzFt2jQRHx8v4uPjhRDZGX3UarVo2LChiIiIEGfOnBEpKSmiU6dOwt/fX/zyyy8iMjJShISEiIoVK+rSGp4/f16Ym5uLBQsWiLNnz4qIiAhRs2ZN0b9/f119vby8hJ2dnfjss8/EuXPnxMyZM4VSqRRnzpwRQgjx+++/C0Ds3btXxMfHi9u3bwshhFizZo1wdXUVmzZtEhcvXhSbNm0SdnZ2ujSEOSlrvb29dWXi4uLE/PnzhZWVlVi/fr04c+aMGDNmjFCr1eLs2bN62+Wkur169aowMzMTgwcPFlFRUWLz5s3CwcFBTJ48WXcOYWFhwsvLS+zdu1ecPHlSdO3aVVhaWurS1l65ckUolUpdmkUhhPjrr7+EQqEQFy5cKPTvQVlR2JSUhUktmdeo9WHDhunSJAuRnSK4Zs2aemU++OADUalSJb0Utp999pmwsLDQZUd6NLXw2bNnBSAiIiJ05W/duiU0Go0um1TPnj3FSy+9pHecPn366M0MsHHjRmFra6s7pz///FMoFApx6dKlJ1w1SZKKiwxaJakEVa5cWXTq1Elv2bBhw3Sp+Nzd3fPdNisrS1haWooffvhBCFFwvmwh8p5WJicNZU5+dSEKd5MPDQ0Vb7zxht6+fv31V6FUKnVTqXh5eYnXXntNt16r1QonJyexdOlSIUTuIDJHhQoVxLp16/SWffjhh6JBgwZ62y1cuFCvjJubm5gxY4besqCgIDF48OA8j/ek4OfevXtCrVaLb7/9Vrc+KSlJmJmZ6QIiIYRo166dePvtt3Wv3333XdGsWTPxIipsSsrCpJYsbNAaFhamV6Zr1656X46EECIyMlIAummrHg1at27dKoyMjERmZqbeNjVq1BBTp07N9f8cixYt0gta09LShIODg1i/fr0QIvt9flL6UkmSipd8xiFJpWz8+PFERkYyadIkkpOTdcuvX7/OoEGD8PX1xdraGisrK5KTk4mNjQWyH8c+ePAAHx8fBg0axObNmwvs55fD2NiY6tWr615HRUVhZGREvXr1dMvs7e2pVKmSblLzEydOsGLFCiwsLHQ/ISEhaLVaLl26pNvu0f0qFApcXFy4ceNGvnVJSUnhwoULhIaG6u17+vTpuTL55Ey4DXDv3j3i4uIIDg7WKxMcHFxg2s4GDRroDSQJDg4mOTmZq1evcvHiRTIyMvSm87G2ts41ifigQYNYv349Dx8+JD09nXXr1jFw4MB8z/FFUVBKysKkllQqlbmSTuTVN7k403s+DWNjY/r27Ut4ePh/6n2WJEMmM2JJUgny9fUlOjpab5mjoyOOjo44OTnpLe/Xrx+3b99m0aJFeHl5YWJiQoMGDXQjqnPyZe/du5c9e/YwePBg5syZw8GDBwscTVuYDDKPS05O5s0339T1mX1UuXLldP8vKC96fvuF7EExjwbNACqVSu+1oQQzHTt2xMTEhM2bN2NsbExGRgavvPJKaVerRHTv3p05c+bkSkn5aGrJ/LIlOTo6curUKb1lkZGRTxz57e/vz6ZNmxBC6H5vIyIisLS0xMPDI8/ymZmZHD16lIYNGwJw+/ZtoqOjdekxK1WqxLFjx/S2e/w1QFhYGFWrVmXJkiVkZmbSrVu3AusqSVLxki2tklSCevfuTXR0NFu3bn1i2YiICN0sAwEBAZiYmHDr1i29MgXlyy5MLnHQv8nnePwmX6tWLU6fPp1n7m5jY+NCnXtOuUfr5OzsjJubGxcvXsy134JST1pZWeHm5pZnrvKcOud1njmDch4tnxP8+Pj4oFar9YKXu3fvcvbsWb39GBkZ0a9fP8LDwwkPD6dXr16FykH/ovj444/5+uuv9QbhPZpacvfu3Vy+fJnDhw8zfvx43Qj8Fi1a8Mcff7Bq1SrOnTvH5MmTcwWxeRk8eDBXrlzh3Xff5cyZM2zdupXJkyczYsSIPAdE+fr60rlzZwYNGsShQ4c4ceIEr732Gu7u7nTu3BnIng1hx44dzJ8/n3PnzvHFF1+wc+fOXF/m/P39qV+/Pu+//z69e/f+T73PkmSIZNAqSSWoV69evPLKK/Tq1Ytp06Zx9OhRLl++zMGDB9m4caNe66Kvry+rV68mKiqKo0eP0qdPH72b5ooVK1i+fDmnTp3i4sWLevmy4d9c4teuXcsV7D6qMDf5999/n8OHDzNkyBAiIyM5d+4cW7duZciQIYU+dycnJzQaDbt27eL69evcvXsXgKlTpzJz5kw++eQTzp49y8mTJwkPD2f+/PkF7m/06NHMmjWLjRs3Eh0dzdixY4mMjGTYsGF5ln9S8GNpaUm/fv0YPXo0P//8M//88w+hoaEolcpcwUxYWBj79+9n165d/7lHxi1atKBFixZ6XVEUCgU7duygSZMmDBgwAD8/P3r16kVMTAzOzs5A9nRFEydOZMyYMQQFBXH//n369u37xOO5u7uzY8cOfv/9dwIDA3nrrbcIDQ1lwoQJ+W4THh5O7dq16dChAw0aNEAIwY4dO3StusHBwXz++efMnz+fwMBAdu3axXvvvYepqWmufYWGhpKenv6fe58lySCVbpdaSfrvycrKEp9//rmoV6+eMDc3F8bGxsLHx0cMGjRInD59Wlfur7/+EnXq1BGmpqbC19dXfPvtt3qDqwrKly1E3rnE88udfufOHfH6668La2trodFoREhIiG4Ufo7ff/9dtG7dWlhYWAhzc3NRvXp1vYFQeQ38CgwM1Budv2zZMuHp6SmUSqXeAJy1a9eKGjVqCGNjY2FrayuaNGkivv/+eyFE/gO4srKyxJQpU4S7u7tQq9UiMDBQ7Ny5U7c+r+0OHDgggoKChLGxsXBxcRHvv/++yMjI0K2/d++eePXVV4WZmZlwcXER8+fPF3Xr1hVjx47Ndc0aN24sAgICci2XyqawsDDRqFGjXMunTZsmqlWrVgo1kiTpcQohHusZL0mSJAHZA8Xc3d2ZN28eoaGhuuVCCHx9fRk8eDAjRowoxRpKz2ru3Lm0bt0ac3Nzdu7cyciRI1myZAlhYWFAdn/ry5cv07JlS6ZPn86gQYNKucaSJMmBWJIkSf93/Phxzpw5Q926dbl79y7Tpk0D0HWTALh58yYbNmwgISGBAQMGlFZVpef0+++/M3v2bO7fv4+Pjw+ffPKJLmAFGDJkCOvXr6dLly6ya4AkGQjZ0ipJkvR/x48fJywsjOjoaIyNjalduzbz58+nWrVqujIKhQIHBwcWLVrEq6++Woq1lSRJ+m+RQaskSZIkSZJk8OTsAZIkSZIkSZLBk0GrJEmSJEmSZPBk0CpJkiRJkiQZPBm0SpIkSZIkSQZPBq2SJEmSJEmSwZNBqyRJkiRJkmTwZNAqSZIkSZIkGTwZtEqSJEmSJEkGTwatkiRJkiRJksGTQaskSZIkSZJk8GTQKkmSJEmSJBk8GbRKkiRJkiRJBk8GrZIkSZIkSZLBk0GrJEmSJEmSZPBk0CpJkiRJkiQZPBm0SpIkSZIkSQZPBq2SJEmSJEmSwZNBqyRJkiRJkmTwZNAqSZIkSZIkGTwZtEqSJEmSJEkGTwatkiRJkiRJksGTQaskSZIkSZJk8GTQKkmSJEmSJBk8GbRKkiRJkiRJBk8GrZIkSZIkSZLB+x++MER3znhNYQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Pre-process `transcription` and `keywords` text\n",
        "\n"
      ],
      "metadata": {
        "id": "YxjnEkqScZEJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert transcription and keywords text to lowercase\n",
        "\n",
        "df.transcription = df.transcription.str.lower()\n",
        "df.keywords = df.keywords.str.lower()\n",
        "df.head()"
      ],
      "metadata": {
        "id": "2hrcz_N2X_4o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "bbb2fd3a-ee96-4285-c566-8b549425ca74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                         description  \\\n",
              "0   A 23-year-old white female presents with complaint of allergies.   \n",
              "1                           Consult for laparoscopic gastric bypass.   \n",
              "2                           Consult for laparoscopic gastric bypass.   \n",
              "3                                             2-D M-Mode. Doppler.     \n",
              "4                                                 2-D Echocardiogram   \n",
              "\n",
              "             medical_specialty                                sample_name  \\\n",
              "0         Allergy / Immunology                         Allergic Rhinitis    \n",
              "1                   Bariatrics   Laparoscopic Gastric Bypass Consult - 2    \n",
              "2                   Bariatrics   Laparoscopic Gastric Bypass Consult - 1    \n",
              "3   Cardiovascular / Pulmonary                    2-D Echocardiogram - 1    \n",
              "4   Cardiovascular / Pulmonary                    2-D Echocardiogram - 2    \n",
              "\n",
              "                                                                                                                                                                                             transcription  \\\n",
              "0  subjective:,  this 23-year-old white female presents with complaint of allergies.  she used to have allergies when she lived in seattle but she thinks they are worse here.  in the past, she has tr...   \n",
              "1  past medical history:, he has difficulty climbing stairs, difficulty with airline seats, tying shoes, used to public seating, and lifting objects off the floor.  he exercises three times a week at...   \n",
              "2  history of present illness: , i have seen abc today.  he is a very pleasant gentleman who is 42 years old, 344 pounds.  he is 5'9\".  he has a bmi of 51.  he has been overweight for ten years since...   \n",
              "3  2-d m-mode: , ,1.  left atrial enlargement with left atrial diameter of 4.7 cm.,2.  normal size right and left ventricle.,3.  normal lv systolic function with left ventricular ejection fraction of...   \n",
              "4  1.  the left ventricular cavity size and wall thickness appear normal.  the wall motion and left ventricular systolic function appears hyperdynamic with estimated ejection fraction of 70% to 75%. ...   \n",
              "\n",
              "                                                                                                                                                                                                  keywords  \n",
              "0                                                                      allergy / immunology, allergic rhinitis, allergies, asthma, nasal sprays, rhinitis, nasal, erythematous, allegra, sprays, allergic,  \n",
              "1  bariatrics, laparoscopic gastric bypass, weight loss programs, gastric bypass, atkin's diet, weight watcher's, body weight, laparoscopic gastric, weight loss, pounds, months, weight, laparoscopic,...  \n",
              "2  bariatrics, laparoscopic gastric bypass, heart attacks, body weight, pulmonary embolism, potential complications, sleep study, weight loss, gastric bypass, anastomosis, loss, sleep, laparoscopic, ...  \n",
              "3  cardiovascular / pulmonary, 2-d m-mode, doppler, aortic valve, atrial enlargement, diastolic function, ejection fraction, mitral, mitral valve, pericardial effusion, pulmonary valve, regurgitation...  \n",
              "4  cardiovascular / pulmonary, 2-d, doppler, echocardiogram, annular, aortic root, aortic valve, atrial, atrium, calcification, cavity, ejection fraction, mitral, obliteration, outflow, regurgitation...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9984ac9d-fde3-4b67-b9f3-a62999796b6a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>description</th>\n",
              "      <th>medical_specialty</th>\n",
              "      <th>sample_name</th>\n",
              "      <th>transcription</th>\n",
              "      <th>keywords</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A 23-year-old white female presents with complaint of allergies.</td>\n",
              "      <td>Allergy / Immunology</td>\n",
              "      <td>Allergic Rhinitis</td>\n",
              "      <td>subjective:,  this 23-year-old white female presents with complaint of allergies.  she used to have allergies when she lived in seattle but she thinks they are worse here.  in the past, she has tr...</td>\n",
              "      <td>allergy / immunology, allergic rhinitis, allergies, asthma, nasal sprays, rhinitis, nasal, erythematous, allegra, sprays, allergic,</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Consult for laparoscopic gastric bypass.</td>\n",
              "      <td>Bariatrics</td>\n",
              "      <td>Laparoscopic Gastric Bypass Consult - 2</td>\n",
              "      <td>past medical history:, he has difficulty climbing stairs, difficulty with airline seats, tying shoes, used to public seating, and lifting objects off the floor.  he exercises three times a week at...</td>\n",
              "      <td>bariatrics, laparoscopic gastric bypass, weight loss programs, gastric bypass, atkin's diet, weight watcher's, body weight, laparoscopic gastric, weight loss, pounds, months, weight, laparoscopic,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Consult for laparoscopic gastric bypass.</td>\n",
              "      <td>Bariatrics</td>\n",
              "      <td>Laparoscopic Gastric Bypass Consult - 1</td>\n",
              "      <td>history of present illness: , i have seen abc today.  he is a very pleasant gentleman who is 42 years old, 344 pounds.  he is 5'9\".  he has a bmi of 51.  he has been overweight for ten years since...</td>\n",
              "      <td>bariatrics, laparoscopic gastric bypass, heart attacks, body weight, pulmonary embolism, potential complications, sleep study, weight loss, gastric bypass, anastomosis, loss, sleep, laparoscopic, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2-D M-Mode. Doppler.</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>2-D Echocardiogram - 1</td>\n",
              "      <td>2-d m-mode: , ,1.  left atrial enlargement with left atrial diameter of 4.7 cm.,2.  normal size right and left ventricle.,3.  normal lv systolic function with left ventricular ejection fraction of...</td>\n",
              "      <td>cardiovascular / pulmonary, 2-d m-mode, doppler, aortic valve, atrial enlargement, diastolic function, ejection fraction, mitral, mitral valve, pericardial effusion, pulmonary valve, regurgitation...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2-D Echocardiogram</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>2-D Echocardiogram - 2</td>\n",
              "      <td>1.  the left ventricular cavity size and wall thickness appear normal.  the wall motion and left ventricular systolic function appears hyperdynamic with estimated ejection fraction of 70% to 75%. ...</td>\n",
              "      <td>cardiovascular / pulmonary, 2-d, doppler, echocardiogram, annular, aortic root, aortic valve, atrial, atrium, calcification, cavity, ejection fraction, mitral, obliteration, outflow, regurgitation...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9984ac9d-fde3-4b67-b9f3-a62999796b6a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-9984ac9d-fde3-4b67-b9f3-a62999796b6a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9984ac9d-fde3-4b67-b9f3-a62999796b6a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
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              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7437d91f-5d8d-4ac9-b394-1e1f8ec6f589 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove quotes from transcription and keywords text\n",
        "\n",
        "df.transcription = df.transcription.str.replace('\"', '').str.replace(\"'\", \"\").str.replace(\"`\", \"\")\n",
        "df.keywords = df.keywords.str.replace('\"', '').str.replace(\"'\", \"\").str.replace(\"`\", \"\")\n",
        "df.head()"
      ],
      "metadata": {
        "id": "y3xdgaWoYO15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "480505de-cb75-4253-a879-6141ac35f332"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                         description  \\\n",
              "0   A 23-year-old white female presents with complaint of allergies.   \n",
              "1                           Consult for laparoscopic gastric bypass.   \n",
              "2                           Consult for laparoscopic gastric bypass.   \n",
              "3                                             2-D M-Mode. Doppler.     \n",
              "4                                                 2-D Echocardiogram   \n",
              "\n",
              "             medical_specialty                                sample_name  \\\n",
              "0         Allergy / Immunology                         Allergic Rhinitis    \n",
              "1                   Bariatrics   Laparoscopic Gastric Bypass Consult - 2    \n",
              "2                   Bariatrics   Laparoscopic Gastric Bypass Consult - 1    \n",
              "3   Cardiovascular / Pulmonary                    2-D Echocardiogram - 1    \n",
              "4   Cardiovascular / Pulmonary                    2-D Echocardiogram - 2    \n",
              "\n",
              "                                                                                                                                                                                             transcription  \\\n",
              "0  subjective:,  this 23-year-old white female presents with complaint of allergies.  she used to have allergies when she lived in seattle but she thinks they are worse here.  in the past, she has tr...   \n",
              "1  past medical history:, he has difficulty climbing stairs, difficulty with airline seats, tying shoes, used to public seating, and lifting objects off the floor.  he exercises three times a week at...   \n",
              "2  history of present illness: , i have seen abc today.  he is a very pleasant gentleman who is 42 years old, 344 pounds.  he is 59.  he has a bmi of 51.  he has been overweight for ten years since t...   \n",
              "3  2-d m-mode: , ,1.  left atrial enlargement with left atrial diameter of 4.7 cm.,2.  normal size right and left ventricle.,3.  normal lv systolic function with left ventricular ejection fraction of...   \n",
              "4  1.  the left ventricular cavity size and wall thickness appear normal.  the wall motion and left ventricular systolic function appears hyperdynamic with estimated ejection fraction of 70% to 75%. ...   \n",
              "\n",
              "                                                                                                                                                                                                  keywords  \n",
              "0                                                                      allergy / immunology, allergic rhinitis, allergies, asthma, nasal sprays, rhinitis, nasal, erythematous, allegra, sprays, allergic,  \n",
              "1  bariatrics, laparoscopic gastric bypass, weight loss programs, gastric bypass, atkins diet, weight watchers, body weight, laparoscopic gastric, weight loss, pounds, months, weight, laparoscopic, b...  \n",
              "2  bariatrics, laparoscopic gastric bypass, heart attacks, body weight, pulmonary embolism, potential complications, sleep study, weight loss, gastric bypass, anastomosis, loss, sleep, laparoscopic, ...  \n",
              "3  cardiovascular / pulmonary, 2-d m-mode, doppler, aortic valve, atrial enlargement, diastolic function, ejection fraction, mitral, mitral valve, pericardial effusion, pulmonary valve, regurgitation...  \n",
              "4  cardiovascular / pulmonary, 2-d, doppler, echocardiogram, annular, aortic root, aortic valve, atrial, atrium, calcification, cavity, ejection fraction, mitral, obliteration, outflow, regurgitation...  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>description</th>\n",
              "      <th>medical_specialty</th>\n",
              "      <th>sample_name</th>\n",
              "      <th>transcription</th>\n",
              "      <th>keywords</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A 23-year-old white female presents with complaint of allergies.</td>\n",
              "      <td>Allergy / Immunology</td>\n",
              "      <td>Allergic Rhinitis</td>\n",
              "      <td>subjective:,  this 23-year-old white female presents with complaint of allergies.  she used to have allergies when she lived in seattle but she thinks they are worse here.  in the past, she has tr...</td>\n",
              "      <td>allergy / immunology, allergic rhinitis, allergies, asthma, nasal sprays, rhinitis, nasal, erythematous, allegra, sprays, allergic,</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Consult for laparoscopic gastric bypass.</td>\n",
              "      <td>Bariatrics</td>\n",
              "      <td>Laparoscopic Gastric Bypass Consult - 2</td>\n",
              "      <td>past medical history:, he has difficulty climbing stairs, difficulty with airline seats, tying shoes, used to public seating, and lifting objects off the floor.  he exercises three times a week at...</td>\n",
              "      <td>bariatrics, laparoscopic gastric bypass, weight loss programs, gastric bypass, atkins diet, weight watchers, body weight, laparoscopic gastric, weight loss, pounds, months, weight, laparoscopic, b...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Consult for laparoscopic gastric bypass.</td>\n",
              "      <td>Bariatrics</td>\n",
              "      <td>Laparoscopic Gastric Bypass Consult - 1</td>\n",
              "      <td>history of present illness: , i have seen abc today.  he is a very pleasant gentleman who is 42 years old, 344 pounds.  he is 59.  he has a bmi of 51.  he has been overweight for ten years since t...</td>\n",
              "      <td>bariatrics, laparoscopic gastric bypass, heart attacks, body weight, pulmonary embolism, potential complications, sleep study, weight loss, gastric bypass, anastomosis, loss, sleep, laparoscopic, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2-D M-Mode. Doppler.</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>2-D Echocardiogram - 1</td>\n",
              "      <td>2-d m-mode: , ,1.  left atrial enlargement with left atrial diameter of 4.7 cm.,2.  normal size right and left ventricle.,3.  normal lv systolic function with left ventricular ejection fraction of...</td>\n",
              "      <td>cardiovascular / pulmonary, 2-d m-mode, doppler, aortic valve, atrial enlargement, diastolic function, ejection fraction, mitral, mitral valve, pericardial effusion, pulmonary valve, regurgitation...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2-D Echocardiogram</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>2-D Echocardiogram - 2</td>\n",
              "      <td>1.  the left ventricular cavity size and wall thickness appear normal.  the wall motion and left ventricular systolic function appears hyperdynamic with estimated ejection fraction of 70% to 75%. ...</td>\n",
              "      <td>cardiovascular / pulmonary, 2-d, doppler, echocardiogram, annular, aortic root, aortic valve, atrial, atrium, calcification, cavity, ejection fraction, mitral, obliteration, outflow, regurgitation...</td>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-788eb0f3-b202-4b1c-b56d-cecc6af1a144');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    fill: #1967D2;\n",
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              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
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              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
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              "</div>\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove punctuations\n",
        "\n",
        "df.transcription = df.transcription.str.replace('[^\\w\\s]','')\n",
        "df.keywords = df.keywords.str.replace('[^\\w\\s]','')\n",
        "df.head()"
      ],
      "metadata": {
        "id": "G5JWyMHmYcIL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "926af9a2-2669-411c-a86f-7a104f70a392"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                         description  \\\n",
              "0   A 23-year-old white female presents with complaint of allergies.   \n",
              "1                           Consult for laparoscopic gastric bypass.   \n",
              "2                           Consult for laparoscopic gastric bypass.   \n",
              "3                                             2-D M-Mode. Doppler.     \n",
              "4                                                 2-D Echocardiogram   \n",
              "\n",
              "             medical_specialty                                sample_name  \\\n",
              "0         Allergy / Immunology                         Allergic Rhinitis    \n",
              "1                   Bariatrics   Laparoscopic Gastric Bypass Consult - 2    \n",
              "2                   Bariatrics   Laparoscopic Gastric Bypass Consult - 1    \n",
              "3   Cardiovascular / Pulmonary                    2-D Echocardiogram - 1    \n",
              "4   Cardiovascular / Pulmonary                    2-D Echocardiogram - 2    \n",
              "\n",
              "                                                                                                                                                                                             transcription  \\\n",
              "0  subjective  this 23yearold white female presents with complaint of allergies  she used to have allergies when she lived in seattle but she thinks they are worse here  in the past she has tried cla...   \n",
              "1  past medical history he has difficulty climbing stairs difficulty with airline seats tying shoes used to public seating and lifting objects off the floor  he exercises three times a week at home a...   \n",
              "2  history of present illness  i have seen abc today  he is a very pleasant gentleman who is 42 years old 344 pounds  he is 59  he has a bmi of 51  he has been overweight for ten years since the age ...   \n",
              "3  2d mmode  1  left atrial enlargement with left atrial diameter of 47 cm2  normal size right and left ventricle3  normal lv systolic function with left ventricular ejection fraction of 514  normal ...   \n",
              "4  1  the left ventricular cavity size and wall thickness appear normal  the wall motion and left ventricular systolic function appears hyperdynamic with estimated ejection fraction of 70 to 75  ther...   \n",
              "\n",
              "                                                                                                                                                                                                  keywords  \n",
              "0                                                                                  allergy  immunology allergic rhinitis allergies asthma nasal sprays rhinitis nasal erythematous allegra sprays allergic  \n",
              "1  bariatrics laparoscopic gastric bypass weight loss programs gastric bypass atkins diet weight watchers body weight laparoscopic gastric weight loss pounds months weight laparoscopic band loss diet...  \n",
              "2  bariatrics laparoscopic gastric bypass heart attacks body weight pulmonary embolism potential complications sleep study weight loss gastric bypass anastomosis loss sleep laparoscopic gastric bypas...  \n",
              "3  cardiovascular  pulmonary 2d mmode doppler aortic valve atrial enlargement diastolic function ejection fraction mitral mitral valve pericardial effusion pulmonary valve regurgitation systolic func...  \n",
              "4  cardiovascular  pulmonary 2d doppler echocardiogram annular aortic root aortic valve atrial atrium calcification cavity ejection fraction mitral obliteration outflow regurgitation relaxation patte...  "
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        "# Remove digits from transcription and keywords sentences\n",
        "\n",
        "df.transcription = df.transcription.str.replace('[^\\D\\s]','')\n",
        "df.keywords = df.keywords.str.replace('[^\\D\\s]','')\n",
        "df.head()"
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              "                                                         description  \\\n",
              "0   A 23-year-old white female presents with complaint of allergies.   \n",
              "1                           Consult for laparoscopic gastric bypass.   \n",
              "2                           Consult for laparoscopic gastric bypass.   \n",
              "3                                             2-D M-Mode. Doppler.     \n",
              "4                                                 2-D Echocardiogram   \n",
              "\n",
              "             medical_specialty                                sample_name  \\\n",
              "0         Allergy / Immunology                         Allergic Rhinitis    \n",
              "1                   Bariatrics   Laparoscopic Gastric Bypass Consult - 2    \n",
              "2                   Bariatrics   Laparoscopic Gastric Bypass Consult - 1    \n",
              "3   Cardiovascular / Pulmonary                    2-D Echocardiogram - 1    \n",
              "4   Cardiovascular / Pulmonary                    2-D Echocardiogram - 2    \n",
              "\n",
              "                                                                                                                                                                                             transcription  \\\n",
              "0  subjective  this yearold white female presents with complaint of allergies  she used to have allergies when she lived in seattle but she thinks they are worse here  in the past she has tried clari...   \n",
              "1  past medical history he has difficulty climbing stairs difficulty with airline seats tying shoes used to public seating and lifting objects off the floor  he exercises three times a week at home a...   \n",
              "2  history of present illness  i have seen abc today  he is a very pleasant gentleman who is  years old  pounds  he is   he has a bmi of   he has been overweight for ten years since the age of  at hi...   \n",
              "3  d mmode    left atrial enlargement with left atrial diameter of  cm  normal size right and left ventricle  normal lv systolic function with left ventricular ejection fraction of   normal lv diasto...   \n",
              "4    the left ventricular cavity size and wall thickness appear normal  the wall motion and left ventricular systolic function appears hyperdynamic with estimated ejection fraction of  to   there is ...   \n",
              "\n",
              "                                                                                                                                                                                                  keywords  \n",
              "0                                                                                  allergy  immunology allergic rhinitis allergies asthma nasal sprays rhinitis nasal erythematous allegra sprays allergic  \n",
              "1  bariatrics laparoscopic gastric bypass weight loss programs gastric bypass atkins diet weight watchers body weight laparoscopic gastric weight loss pounds months weight laparoscopic band loss diet...  \n",
              "2  bariatrics laparoscopic gastric bypass heart attacks body weight pulmonary embolism potential complications sleep study weight loss gastric bypass anastomosis loss sleep laparoscopic gastric bypas...  \n",
              "3  cardiovascular  pulmonary d mmode doppler aortic valve atrial enlargement diastolic function ejection fraction mitral mitral valve pericardial effusion pulmonary valve regurgitation systolic funct...  \n",
              "4  cardiovascular  pulmonary d doppler echocardiogram annular aortic root aortic valve atrial atrium calcification cavity ejection fraction mitral obliteration outflow regurgitation relaxation patter...  "
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          "metadata": {},
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      ]
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    {
      "cell_type": "code",
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        "# Remove extra spaces\n",
        "\n",
        "df.transcription = df.transcription.str.replace('  ',' ').str.strip()\n",
        "df.keywords = df.keywords.str.replace('  ',' ').str.strip()\n",
        "df.head()"
      ],
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        "id": "Y1OyJG10Wh-B",
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          "height": 432
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        "outputId": "1c7554aa-e9df-4be4-c75b-dd555618b653"
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      "execution_count": null,
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          "data": {
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              "                                                         description  \\\n",
              "0   A 23-year-old white female presents with complaint of allergies.   \n",
              "1                           Consult for laparoscopic gastric bypass.   \n",
              "2                           Consult for laparoscopic gastric bypass.   \n",
              "3                                             2-D M-Mode. Doppler.     \n",
              "4                                                 2-D Echocardiogram   \n",
              "\n",
              "             medical_specialty                                sample_name  \\\n",
              "0         Allergy / Immunology                         Allergic Rhinitis    \n",
              "1                   Bariatrics   Laparoscopic Gastric Bypass Consult - 2    \n",
              "2                   Bariatrics   Laparoscopic Gastric Bypass Consult - 1    \n",
              "3   Cardiovascular / Pulmonary                    2-D Echocardiogram - 1    \n",
              "4   Cardiovascular / Pulmonary                    2-D Echocardiogram - 2    \n",
              "\n",
              "                                                                                                                                                                                             transcription  \\\n",
              "0  subjective this yearold white female presents with complaint of allergies she used to have allergies when she lived in seattle but she thinks they are worse here in the past she has tried claritin...   \n",
              "1  past medical history he has difficulty climbing stairs difficulty with airline seats tying shoes used to public seating and lifting objects off the floor he exercises three times a week at home an...   \n",
              "2  history of present illness i have seen abc today he is a very pleasant gentleman who is years old pounds he is  he has a bmi of  he has been overweight for ten years since the age of at his highes...   \n",
              "3  d mmode  left atrial enlargement with left atrial diameter of cm normal size right and left ventricle normal lv systolic function with left ventricular ejection fraction of  normal lv diastolic fu...   \n",
              "4  the left ventricular cavity size and wall thickness appear normal the wall motion and left ventricular systolic function appears hyperdynamic with estimated ejection fraction of to  there is nearc...   \n",
              "\n",
              "                                                                                                                                                                                                  keywords  \n",
              "0                                                                                   allergy immunology allergic rhinitis allergies asthma nasal sprays rhinitis nasal erythematous allegra sprays allergic  \n",
              "1  bariatrics laparoscopic gastric bypass weight loss programs gastric bypass atkins diet weight watchers body weight laparoscopic gastric weight loss pounds months weight laparoscopic band loss diet...  \n",
              "2  bariatrics laparoscopic gastric bypass heart attacks body weight pulmonary embolism potential complications sleep study weight loss gastric bypass anastomosis loss sleep laparoscopic gastric bypas...  \n",
              "3  cardiovascular pulmonary d mmode doppler aortic valve atrial enlargement diastolic function ejection fraction mitral mitral valve pericardial effusion pulmonary valve regurgitation systolic functi...  \n",
              "4  cardiovascular pulmonary d doppler echocardiogram annular aortic root aortic valve atrial atrium calcification cavity ejection fraction mitral obliteration outflow regurgitation relaxation pattern...  "
            ],
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              "      <td>A 23-year-old white female presents with complaint of allergies.</td>\n",
              "      <td>Allergy / Immunology</td>\n",
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              "      <td>allergy immunology allergic rhinitis allergies asthma nasal sprays rhinitis nasal erythematous allegra sprays allergic</td>\n",
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              "      <th>1</th>\n",
              "      <td>Consult for laparoscopic gastric bypass.</td>\n",
              "      <td>Bariatrics</td>\n",
              "      <td>Laparoscopic Gastric Bypass Consult - 2</td>\n",
              "      <td>past medical history he has difficulty climbing stairs difficulty with airline seats tying shoes used to public seating and lifting objects off the floor he exercises three times a week at home an...</td>\n",
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              "      <td>Consult for laparoscopic gastric bypass.</td>\n",
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              "      <td>2-D Echocardiogram - 2</td>\n",
              "      <td>the left ventricular cavity size and wall thickness appear normal the wall motion and left ventricular systolic function appears hyperdynamic with estimated ejection fraction of to  there is nearc...</td>\n",
              "      <td>cardiovascular pulmonary d doppler echocardiogram annular aortic root aortic valve atrial atrium calcification cavity ejection fraction mitral obliteration outflow regurgitation relaxation pattern...</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to remove the stopwords\n",
        "\n",
        "def remove_stopwords(text):\n",
        "\n",
        "    stop_words = stopwords.words('english')\n",
        "    word_tokens = word_tokenize(text)\n",
        "    filtered_sentence = [w for w in word_tokens if not w.lower() in stop_words]\n",
        "    return \" \".join(filtered_sentence)"
      ],
      "metadata": {
        "id": "WJrIowuVVmDb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tx1sxCTDNN_w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "333ff627-bbed-44fe-c70d-7ae5aa3ee94d"
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          "data": {
            "text/plain": [
              "                                                         description  \\\n",
              "0   A 23-year-old white female presents with complaint of allergies.   \n",
              "1                           Consult for laparoscopic gastric bypass.   \n",
              "2                           Consult for laparoscopic gastric bypass.   \n",
              "3                                             2-D M-Mode. Doppler.     \n",
              "4                                                 2-D Echocardiogram   \n",
              "\n",
              "             medical_specialty                                sample_name  \\\n",
              "0         Allergy / Immunology                         Allergic Rhinitis    \n",
              "1                   Bariatrics   Laparoscopic Gastric Bypass Consult - 2    \n",
              "2                   Bariatrics   Laparoscopic Gastric Bypass Consult - 1    \n",
              "3   Cardiovascular / Pulmonary                    2-D Echocardiogram - 1    \n",
              "4   Cardiovascular / Pulmonary                    2-D Echocardiogram - 2    \n",
              "\n",
              "                                                                                                                                                                                             transcription  \\\n",
              "0  subjective yearold white female presents complaint allergies used allergies lived seattle thinks worse past tried claritin zyrtec worked short time seemed lose effectiveness used allegra also used...   \n",
              "1  past medical history difficulty climbing stairs difficulty airline seats tying shoes used public seating lifting objects floor exercises three times week home cardio difficulty walking two blocks ...   \n",
              "2  history present illness seen abc today pleasant gentleman years old pounds bmi overweight ten years since age highest pounds lowest pursuing surgical attempts weight loss feel good get healthy beg...   \n",
              "3  mmode left atrial enlargement left atrial diameter cm normal size right left ventricle normal lv systolic function left ventricular ejection fraction normal lv diastolic function pericardial effus...   \n",
              "4  left ventricular cavity size wall thickness appear normal wall motion left ventricular systolic function appears hyperdynamic estimated ejection fraction nearcavity obliteration seen also appears ...   \n",
              "\n",
              "                                                                                                                                                                                                  keywords  \n",
              "0                                                                                   allergy immunology allergic rhinitis allergies asthma nasal sprays rhinitis nasal erythematous allegra sprays allergic  \n",
              "1  bariatrics laparoscopic gastric bypass weight loss programs gastric bypass atkins diet weight watchers body weight laparoscopic gastric weight loss pounds months weight laparoscopic band loss diet...  \n",
              "2  bariatrics laparoscopic gastric bypass heart attacks body weight pulmonary embolism potential complications sleep study weight loss gastric bypass anastomosis loss sleep laparoscopic gastric bypas...  \n",
              "3  cardiovascular pulmonary mmode doppler aortic valve atrial enlargement diastolic function ejection fraction mitral mitral valve pericardial effusion pulmonary valve regurgitation systolic function...  \n",
              "4  cardiovascular pulmonary doppler echocardiogram annular aortic root aortic valve atrial atrium calcification cavity ejection fraction mitral obliteration outflow regurgitation relaxation pattern s...  "
            ],
            "text/html": [
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              "      <th>description</th>\n",
              "      <th>medical_specialty</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A 23-year-old white female presents with complaint of allergies.</td>\n",
              "      <td>Allergy / Immunology</td>\n",
              "      <td>Allergic Rhinitis</td>\n",
              "      <td>subjective yearold white female presents complaint allergies used allergies lived seattle thinks worse past tried claritin zyrtec worked short time seemed lose effectiveness used allegra also used...</td>\n",
              "      <td>allergy immunology allergic rhinitis allergies asthma nasal sprays rhinitis nasal erythematous allegra sprays allergic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Consult for laparoscopic gastric bypass.</td>\n",
              "      <td>Bariatrics</td>\n",
              "      <td>Laparoscopic Gastric Bypass Consult - 2</td>\n",
              "      <td>past medical history difficulty climbing stairs difficulty airline seats tying shoes used public seating lifting objects floor exercises three times week home cardio difficulty walking two blocks ...</td>\n",
              "      <td>bariatrics laparoscopic gastric bypass weight loss programs gastric bypass atkins diet weight watchers body weight laparoscopic gastric weight loss pounds months weight laparoscopic band loss diet...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Consult for laparoscopic gastric bypass.</td>\n",
              "      <td>Bariatrics</td>\n",
              "      <td>Laparoscopic Gastric Bypass Consult - 1</td>\n",
              "      <td>history present illness seen abc today pleasant gentleman years old pounds bmi overweight ten years since age highest pounds lowest pursuing surgical attempts weight loss feel good get healthy beg...</td>\n",
              "      <td>bariatrics laparoscopic gastric bypass heart attacks body weight pulmonary embolism potential complications sleep study weight loss gastric bypass anastomosis loss sleep laparoscopic gastric bypas...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2-D M-Mode. Doppler.</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>2-D Echocardiogram - 1</td>\n",
              "      <td>mmode left atrial enlargement left atrial diameter cm normal size right left ventricle normal lv systolic function left ventricular ejection fraction normal lv diastolic function pericardial effus...</td>\n",
              "      <td>cardiovascular pulmonary mmode doppler aortic valve atrial enlargement diastolic function ejection fraction mitral mitral valve pericardial effusion pulmonary valve regurgitation systolic function...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2-D Echocardiogram</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>2-D Echocardiogram - 2</td>\n",
              "      <td>left ventricular cavity size wall thickness appear normal wall motion left ventricular systolic function appears hyperdynamic estimated ejection fraction nearcavity obliteration seen also appears ...</td>\n",
              "      <td>cardiovascular pulmonary doppler echocardiogram annular aortic root aortic valve atrial atrium calcification cavity ejection fraction mitral obliteration outflow regurgitation relaxation pattern s...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6419c717-3f54-43b7-a5e5-af98469fa50a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6419c717-3f54-43b7-a5e5-af98469fa50a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6419c717-3f54-43b7-a5e5-af98469fa50a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-fde4f3f1-167b-40f2-b1d2-7d20f95788ba\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fde4f3f1-167b-40f2-b1d2-7d20f95788ba')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fde4f3f1-167b-40f2-b1d2-7d20f95788ba button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# Remove stopwords from transcriptions\n",
        "\n",
        "df.transcription = df.transcription.apply(remove_stopwords)\n",
        "df.keywords = df.keywords.apply(remove_stopwords)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p65AHp4ZNN_x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "455b7393-0cff-423e-9702-454ae8cf6421"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='transcription', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Visualize the distribution of word counts\n",
        "\n",
        "transcription_word_count = df.transcription.str.count(' ') + 1\n",
        "sns.histplot(x= transcription_word_count )"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "keywords_word_count = df.keywords.str.count(' ') + 1\n",
        "sns.histplot(x= keywords_word_count )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "o42VyBV3C1Bp",
        "outputId": "762c6384-0501-47ec-bdc2-dd152890b845"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='keywords', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fix the maximum length of the transcript\n",
        "# Fix the maximum keywords length\n",
        "\n",
        "max_len_transcript = 250\n",
        "max_len_keywords = 30"
      ],
      "metadata": {
        "id": "FBvPpAp9KJtO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Add 'start' and 'end' to keywords text\n",
        "\n",
        "df.keywords = df.keywords.map(\"[start] {} [end]\".format)"
      ],
      "metadata": {
        "id": "W8W-aL32JIlL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "YtBwjFpbD-Q5",
        "outputId": "a87bdf09-942f-40dd-9548-3add09350bcf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                         description  \\\n",
              "0   A 23-year-old white female presents with complaint of allergies.   \n",
              "1                           Consult for laparoscopic gastric bypass.   \n",
              "2                           Consult for laparoscopic gastric bypass.   \n",
              "3                                             2-D M-Mode. Doppler.     \n",
              "4                                                 2-D Echocardiogram   \n",
              "\n",
              "             medical_specialty                                sample_name  \\\n",
              "0         Allergy / Immunology                         Allergic Rhinitis    \n",
              "1                   Bariatrics   Laparoscopic Gastric Bypass Consult - 2    \n",
              "2                   Bariatrics   Laparoscopic Gastric Bypass Consult - 1    \n",
              "3   Cardiovascular / Pulmonary                    2-D Echocardiogram - 1    \n",
              "4   Cardiovascular / Pulmonary                    2-D Echocardiogram - 2    \n",
              "\n",
              "                                                                                                                                                                                             transcription  \\\n",
              "0  subjective yearold white female presents complaint allergies used allergies lived seattle thinks worse past tried claritin zyrtec worked short time seemed lose effectiveness used allegra also used...   \n",
              "1  past medical history difficulty climbing stairs difficulty airline seats tying shoes used public seating lifting objects floor exercises three times week home cardio difficulty walking two blocks ...   \n",
              "2  history present illness seen abc today pleasant gentleman years old pounds bmi overweight ten years since age highest pounds lowest pursuing surgical attempts weight loss feel good get healthy beg...   \n",
              "3  mmode left atrial enlargement left atrial diameter cm normal size right left ventricle normal lv systolic function left ventricular ejection fraction normal lv diastolic function pericardial effus...   \n",
              "4  left ventricular cavity size wall thickness appear normal wall motion left ventricular systolic function appears hyperdynamic estimated ejection fraction nearcavity obliteration seen also appears ...   \n",
              "\n",
              "                                                                                                                                                                                                  keywords  \n",
              "0                                                                     [start] allergy immunology allergic rhinitis allergies asthma nasal sprays rhinitis nasal erythematous allegra sprays allergic [end]  \n",
              "1  [start] bariatrics laparoscopic gastric bypass weight loss programs gastric bypass atkins diet weight watchers body weight laparoscopic gastric weight loss pounds months weight laparoscopic band l...  \n",
              "2  [start] bariatrics laparoscopic gastric bypass heart attacks body weight pulmonary embolism potential complications sleep study weight loss gastric bypass anastomosis loss sleep laparoscopic gastr...  \n",
              "3  [start] cardiovascular pulmonary mmode doppler aortic valve atrial enlargement diastolic function ejection fraction mitral mitral valve pericardial effusion pulmonary valve regurgitation systolic ...  \n",
              "4  [start] cardiovascular pulmonary doppler echocardiogram annular aortic root aortic valve atrial atrium calcification cavity ejection fraction mitral obliteration outflow regurgitation relaxation p...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f8ad78bc-a05e-4411-9e71-c9f42abe1595\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>description</th>\n",
              "      <th>medical_specialty</th>\n",
              "      <th>sample_name</th>\n",
              "      <th>transcription</th>\n",
              "      <th>keywords</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A 23-year-old white female presents with complaint of allergies.</td>\n",
              "      <td>Allergy / Immunology</td>\n",
              "      <td>Allergic Rhinitis</td>\n",
              "      <td>subjective yearold white female presents complaint allergies used allergies lived seattle thinks worse past tried claritin zyrtec worked short time seemed lose effectiveness used allegra also used...</td>\n",
              "      <td>[start] allergy immunology allergic rhinitis allergies asthma nasal sprays rhinitis nasal erythematous allegra sprays allergic [end]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Consult for laparoscopic gastric bypass.</td>\n",
              "      <td>Bariatrics</td>\n",
              "      <td>Laparoscopic Gastric Bypass Consult - 2</td>\n",
              "      <td>past medical history difficulty climbing stairs difficulty airline seats tying shoes used public seating lifting objects floor exercises three times week home cardio difficulty walking two blocks ...</td>\n",
              "      <td>[start] bariatrics laparoscopic gastric bypass weight loss programs gastric bypass atkins diet weight watchers body weight laparoscopic gastric weight loss pounds months weight laparoscopic band l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Consult for laparoscopic gastric bypass.</td>\n",
              "      <td>Bariatrics</td>\n",
              "      <td>Laparoscopic Gastric Bypass Consult - 1</td>\n",
              "      <td>history present illness seen abc today pleasant gentleman years old pounds bmi overweight ten years since age highest pounds lowest pursuing surgical attempts weight loss feel good get healthy beg...</td>\n",
              "      <td>[start] bariatrics laparoscopic gastric bypass heart attacks body weight pulmonary embolism potential complications sleep study weight loss gastric bypass anastomosis loss sleep laparoscopic gastr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2-D M-Mode. Doppler.</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>2-D Echocardiogram - 1</td>\n",
              "      <td>mmode left atrial enlargement left atrial diameter cm normal size right left ventricle normal lv systolic function left ventricular ejection fraction normal lv diastolic function pericardial effus...</td>\n",
              "      <td>[start] cardiovascular pulmonary mmode doppler aortic valve atrial enlargement diastolic function ejection fraction mitral mitral valve pericardial effusion pulmonary valve regurgitation systolic ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2-D Echocardiogram</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>2-D Echocardiogram - 2</td>\n",
              "      <td>left ventricular cavity size wall thickness appear normal wall motion left ventricular systolic function appears hyperdynamic estimated ejection fraction nearcavity obliteration seen also appears ...</td>\n",
              "      <td>[start] cardiovascular pulmonary doppler echocardiogram annular aortic root aortic valve atrial atrium calcification cavity ejection fraction mitral obliteration outflow regurgitation relaxation p...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f8ad78bc-a05e-4411-9e71-c9f42abe1595')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f8ad78bc-a05e-4411-9e71-c9f42abe1595 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f8ad78bc-a05e-4411-9e71-c9f42abe1595');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f877b16f-8d72-4168-b3a1-067296372c50\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f877b16f-8d72-4168-b3a1-067296372c50')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f877b16f-8d72-4168-b3a1-067296372c50 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajaAr2XgeCii"
      },
      "source": [
        "### Split data into training and testing set\n",
        "\n",
        "- test_size=0.1\n",
        "- random_state=0\n",
        "- shuffle=True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pb-27FbLeCij",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8fdb9189-30a9-4886-c257-5d0e222c2e16"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3431 382\n"
          ]
        }
      ],
      "source": [
        "# YOUR CODE HERE\n",
        "idx = np.array(df.index)\n",
        "np.random.seed(42)\n",
        "np.random.shuffle(idx)\n",
        "total = len(idx)\n",
        "train_cnt = int(total*0.9)\n",
        "train_idx = idx[0:train_cnt]\n",
        "test_idx = idx[train_cnt:]\n",
        "train_idx, test_idx\n",
        "\n",
        "X_train = df.loc[train_idx].transcription.values\n",
        "y_train = df.loc[train_idx].keywords.values\n",
        "X_test = df.loc[test_idx].transcription.values\n",
        "y_test = df.loc[test_idx].keywords.values\n",
        "\n",
        "print(len(X_train), len(X_test), len(y_train), len(y_test))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(X_train), len(X_test), len(y_train), len(y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kaYHp4-DJC6B",
        "outputId": "b8bcdbd4-fcc2-4201-dabe-b6e3363df4d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3431 382 3431 382\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Tokenization and padding"
      ],
      "metadata": {
        "id": "I1QlCkyn6JKM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L5TPQusbeCil",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "038a225a-8030-416d-9209-28fb6015765b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26808"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# Instantiate tokenizer for transcripts\n",
        "x_tokenizer = Tokenizer(5000)\n",
        "\n",
        "# Fit on training data\n",
        "\n",
        "x_tokenizer.fit_on_texts(X_train)\n",
        "# Convert transcript sequences into integer sequences for both train and val set\n",
        "\n",
        "x_train_token = x_tokenizer.texts_to_sequences(X_train)\n",
        "x_test_token = x_tokenizer.texts_to_sequences(X_test)\n",
        "# Add zero padding upto maximum length\n",
        "\n",
        "x_train_pad = pad_sequences(x_train_token, padding='post', maxlen=max_len_transcript, truncating='post')\n",
        "x_test_pad = pad_sequences(x_test_token, padding='post', maxlen=max_len_transcript, truncating='post')\n",
        "# x vocab size\n",
        "x_voc_size = len(x_tokenizer.word_index) +1\n",
        "x_voc_size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "20Ppbw4XeCim",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4bfbef35-c03c-4647-fcb4-f4aeaace88f7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5881"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# Instantiate tokenizer for keywords\n",
        "y_tokenizer = Tokenizer(5000)\n",
        "\n",
        "# Fit on training data\n",
        "\n",
        "y_tokenizer.fit_on_texts(y_train)\n",
        "# Convert keywords sequences into integer sequences for train and val set\n",
        "\n",
        "y_train_token = y_tokenizer.texts_to_sequences(y_train)\n",
        "y_test_token = y_tokenizer.texts_to_sequences(y_test)\n",
        "# Add zero padding upto maximum length\n",
        "\n",
        "y_train_pad = pad_sequences(y_train_token, padding='post', maxlen=max_len_keywords+1, truncating='post')\n",
        "y_test_pad = pad_sequences(y_test_token, padding='post', maxlen=max_len_keywords+1, truncating='post')\n",
        "# y vocab size\n",
        "y_voc_size = len(y_tokenizer.word_index) +1\n",
        "y_voc_size"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(y_train_pad), print(y_train_pad.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fgpMjlMqJj7_",
        "outputId": "61fae911-4753-4995-8d72-6591a15bd484"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(3431, 31)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(numpy.ndarray, None)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_tokenizer.word_index['end'])"
      ],
      "metadata": {
        "id": "uPGFUtc85ZgC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24e4ac48-55a2-487b-fc7c-3251551cf7a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_pad"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ncgc5cJw-sN7",
        "outputId": "1e9076d1-e8b1-48c1-a1db-a01586fd4daa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[   2,  577,  517, ...,    0,    0,    0],\n",
              "       [   2,  213,  234, ..., 1904,   30,    1],\n",
              "       [   2,    3,  152, ...,    0,    0,    0],\n",
              "       ...,\n",
              "       [   2,   24,  387, ...,    0,    0,    0],\n",
              "       [   2,   17,    6, ...,    0,    0,    0],\n",
              "       [   2,  102,  122, ...,    0,    0,    0]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace the last token with the token index of 'end' for long sequences\n",
        "\n",
        "# Apply on Train keywords set\n",
        "\n",
        "y_train_pad[:,-1][y_train_pad[:,-1]!=0] = y_tokenizer.word_index['end']\n",
        "# Apply on Validation keywords set\n",
        "\n",
        "y_test_pad[:,-1][y_test_pad[:,-1]!=0] = y_tokenizer.word_index['end']"
      ],
      "metadata": {
        "id": "LNtAVcci5nY6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Positional Embedding\n",
        "\n"
      ],
      "metadata": {
        "id": "z804TTWnnpG5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class PositionalEmbedding(layers.Layer):\n",
        "\n",
        "    def __init__(self, sequence_length, input_dim, output_dim, **kwargs):\n",
        "\n",
        "        super().__init__(**kwargs)\n",
        "        self.token_embeddings = layers.Embedding(\n",
        "            input_dim=input_dim, output_dim=output_dim)\n",
        "        self.position_embeddings = layers.Embedding(\n",
        "            input_dim=sequence_length, output_dim=output_dim)\n",
        "        self.sequence_length = sequence_length\n",
        "        self.input_dim = input_dim\n",
        "        self.output_dim = output_dim\n",
        "\n",
        "    def call(self, inputs):\n",
        "\n",
        "        length = tf.shape(inputs)[-1]\n",
        "        positions = tf.range(start=0, limit=length, delta=1)\n",
        "        embedded_tokens = self.token_embeddings(inputs)\n",
        "        embedded_positions = self.position_embeddings(positions)\n",
        "        return embedded_tokens + embedded_positions\n",
        "\n",
        "    def compute_mask(self, inputs, mask=None):\n",
        "\n",
        "        return tf.math.not_equal(inputs, 0)\n",
        "\n",
        "    def get_config(self):\n",
        "\n",
        "        config = super().get_config()\n",
        "        config.update({\n",
        "            \"output_dim\": self.output_dim,\n",
        "            \"sequence_length\": self.sequence_length,\n",
        "            \"input_dim\": self.input_dim,\n",
        "        })\n",
        "        return config\n"
      ],
      "metadata": {
        "id": "EJFVFHXcnrMv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Encoder Block\n"
      ],
      "metadata": {
        "id": "Nv61oM1lrOBa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class TransformerEncoder(layers.Layer):\n",
        "\n",
        "    def __init__(self, embed_dim, dense_dim, num_heads, **kwargs):\n",
        "\n",
        "        super().__init__(**kwargs)\n",
        "        self.embed_dim = embed_dim\n",
        "        self.dense_dim = dense_dim\n",
        "        self.num_heads = num_heads\n",
        "        self.attention = layers.MultiHeadAttention(\n",
        "            num_heads=num_heads, key_dim=embed_dim)\n",
        "        self.dense_proj = keras.Sequential(\n",
        "            [layers.Dense(dense_dim, activation=\"relu\"),\n",
        "             layers.Dense(embed_dim),\n",
        "             layers.Dropout(0.1)]\n",
        "        )\n",
        "        self.layernorm_1 = layers.LayerNormalization()\n",
        "        self.layernorm_2 = layers.LayerNormalization()\n",
        "\n",
        "    def call(self, inputs, mask=None):\n",
        "\n",
        "        if mask is not None:\n",
        "            mask = mask[:, tf.newaxis, :]\n",
        "        attention_output = self.attention(\n",
        "            inputs, inputs)\n",
        "        proj_input_pre = layers.add([inputs, attention_output])\n",
        "        proj_input = self.layernorm_1(proj_input_pre)\n",
        "        proj_output = self.dense_proj(proj_input)\n",
        "        proj_output_post = layers.add([proj_input ,proj_output])\n",
        "        return self.layernorm_2(proj_output_post)\n",
        "\n",
        "    def get_config(self):\n",
        "\n",
        "        config = super().get_config()\n",
        "        config.update({\n",
        "            \"embed_dim\": self.embed_dim,\n",
        "            \"num_heads\": self.num_heads,\n",
        "            \"dense_dim\": self.dense_dim,\n",
        "        })\n",
        "        return config\n"
      ],
      "metadata": {
        "id": "Qjs4oJvlrRI1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Decoder Block\n",
        "\n"
      ],
      "metadata": {
        "id": "KbAVK3wyuRf1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class TransformerDecoder(layers.Layer):\n",
        "\n",
        "    def __init__(self, embed_dim, dense_dim, num_heads, **kwargs):\n",
        "\n",
        "        super().__init__(**kwargs)\n",
        "        self.embed_dim = embed_dim\n",
        "        self.dense_dim = dense_dim\n",
        "        self.num_heads = num_heads\n",
        "        self.attention_1 = layers.MultiHeadAttention(\n",
        "            num_heads=num_heads, key_dim=embed_dim)\n",
        "        self.attention_2 = layers.MultiHeadAttention(\n",
        "            num_heads=num_heads, key_dim=embed_dim)\n",
        "        self.dense_proj = keras.Sequential(\n",
        "            [layers.Dense(dense_dim, activation=\"relu\"),\n",
        "             layers.Dense(embed_dim),\n",
        "             layers.Dropout(0.2)]\n",
        "        )\n",
        "        self.layernorm_1 = layers.LayerNormalization()\n",
        "        self.layernorm_2 = layers.LayerNormalization()\n",
        "        self.layernorm_3 = layers.LayerNormalization()\n",
        "        self.supports_masking = True\n",
        "\n",
        "    def get_config(self):\n",
        "\n",
        "        config = super().get_config()\n",
        "        config.update({\n",
        "            \"embed_dim\": self.embed_dim,\n",
        "            \"num_heads\": self.num_heads,\n",
        "            \"dense_dim\": self.dense_dim,\n",
        "        })\n",
        "        return config\n",
        "\n",
        "    def call(self, inputs, encoder_outputs, mask=None): # two inputs: decoder i/p and encoder o/p\n",
        "\n",
        "        attention_output_1 = self.attention_1(\n",
        "            query=inputs,\n",
        "            value=inputs,\n",
        "            key=inputs,\n",
        "            use_causal_mask = True)\n",
        "        attention_output_1 = self.layernorm_1(layers.add([inputs, attention_output_1]))\n",
        "        attention_output_2 = self.attention_2(\n",
        "            query=attention_output_1,\n",
        "            value=encoder_outputs,\n",
        "            key=encoder_outputs\n",
        "        )\n",
        "        attention_output_2 = self.layernorm_2(layers.add([attention_output_1, attention_output_2]))\n",
        "        proj_output = self.dense_proj(attention_output_2)\n",
        "        return self.layernorm_3(layers.add([attention_output_2, proj_output]))\n"
      ],
      "metadata": {
        "id": "6hXHINBbuTW4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Build Transformer model\n"
      ],
      "metadata": {
        "id": "AiaXYTIpw2k9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create transformer model\n",
        "\n",
        "embed_dim = 256\n",
        "dense_dim = 2048\n",
        "num_heads = 8\n",
        "\n",
        "encoder_inputs = keras.Input(shape=(None,), dtype=\"int64\", name=\"Transcript\")\n",
        "\n",
        "\n",
        "x = PositionalEmbedding(max_len_transcript, x_voc_size, embed_dim)(encoder_inputs)\n",
        "x = layers.Dropout(0.1)(x)\n",
        "for _ in range(2):\n",
        "  x = TransformerEncoder(embed_dim, dense_dim, num_heads)(x)\n",
        "encoder_outputs = x\n",
        "\n",
        "\n",
        "decoder_inputs = keras.Input(shape=(None,), dtype=\"int64\", name=\"Keywords\")\n",
        "\n",
        "\n",
        "y = PositionalEmbedding(max_len_keywords, y_voc_size, embed_dim)(decoder_inputs)\n",
        "y = layers.Dropout(0.1)(y)\n",
        "for _ in range(4):\n",
        "  y = TransformerDecoder(embed_dim, dense_dim, num_heads)(y, encoder_outputs)\n",
        "decoder_outputs = layers.Dense(y_voc_size)(y)\n",
        "\n",
        "transformer = keras.Model([encoder_inputs, decoder_inputs], decoder_outputs)\n",
        "transformer.summary()"
      ],
      "metadata": {
        "id": "UmHaiKyXw6oK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3f14c31-b8d2-4595-ee53-13c9d1f3879f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " Transcript (InputLayer)        [(None, None)]       0           []                               \n",
            "                                                                                                  \n",
            " positional_embedding_4 (Positi  (None, None, 256)   6926848     ['Transcript[0][0]']             \n",
            " onalEmbedding)                                                                                   \n",
            "                                                                                                  \n",
            " Keywords (InputLayer)          [(None, None)]       0           []                               \n",
            "                                                                                                  \n",
            " dropout_20 (Dropout)           (None, None, 256)    0           ['positional_embedding_4[0][0]'] \n",
            "                                                                                                  \n",
            " positional_embedding_5 (Positi  (None, None, 256)   1513216     ['Keywords[0][0]']               \n",
            " onalEmbedding)                                                                                   \n",
            "                                                                                                  \n",
            " transformer_encoder_8 (Transfo  (None, None, 256)   3155456     ['dropout_20[0][0]']             \n",
            " rmerEncoder)                                                                                     \n",
            "                                                                                                  \n",
            " dropout_23 (Dropout)           (None, None, 256)    0           ['positional_embedding_5[0][0]'] \n",
            "                                                                                                  \n",
            " transformer_encoder_9 (Transfo  (None, None, 256)   3155456     ['transformer_encoder_8[0][0]']  \n",
            " rmerEncoder)                                                                                     \n",
            "                                                                                                  \n",
            " transformer_decoder_8 (Transfo  (None, None, 256)   5259520     ['dropout_23[0][0]',             \n",
            " rmerDecoder)                                                     'transformer_encoder_9[0][0]']  \n",
            "                                                                                                  \n",
            " transformer_decoder_9 (Transfo  (None, None, 256)   5259520     ['transformer_decoder_8[0][0]',  \n",
            " rmerDecoder)                                                     'transformer_encoder_9[0][0]']  \n",
            "                                                                                                  \n",
            " transformer_decoder_10 (Transf  (None, None, 256)   5259520     ['transformer_decoder_9[0][0]',  \n",
            " ormerDecoder)                                                    'transformer_encoder_9[0][0]']  \n",
            "                                                                                                  \n",
            " transformer_decoder_11 (Transf  (None, None, 256)   5259520     ['transformer_decoder_10[0][0]', \n",
            " ormerDecoder)                                                    'transformer_encoder_9[0][0]']  \n",
            "                                                                                                  \n",
            " dense_46 (Dense)               (None, None, 5881)   1511417     ['transformer_decoder_11[0][0]'] \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 37,300,473\n",
            "Trainable params: 37,300,473\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Compilation and Training [1 Mark]"
      ],
      "metadata": {
        "id": "DeBvxrOqAohp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n",
        "\n",
        "\n",
        "    def __init__(self, d_model, warmup_steps=4000):\n",
        "      super().__init__()\n",
        "\n",
        "      self.d_model = d_model\n",
        "      self.d_model = tf.cast(self.d_model, tf.float32)\n",
        "\n",
        "      self.warmup_steps = warmup_steps\n",
        "\n",
        "    def __call__(self, step):\n",
        "      step = tf.cast(step, dtype=tf.float32)\n",
        "      arg1 = tf.math.rsqrt(step)\n",
        "      arg2 = step * (self.warmup_steps ** -1.5)\n",
        "\n",
        "      return tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2)\n"
      ],
      "metadata": {
        "id": "fe7-5dDu1wfz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Instantiate the Adam optimizer with custom learning rate\n",
        "\n",
        "learning_rate = CustomSchedule(embed_dim)\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate, beta_1=0.9, beta_2=0.999,\n",
        "                                     epsilon=1e-6)\n",
        "\n",
        "plt.plot(learning_rate(tf.range(40000, dtype=tf.float32)))\n",
        "plt.ylabel('Learning Rate')\n",
        "plt.xlabel('Train Step')"
      ],
      "metadata": {
        "id": "x2-tkPW21y-r",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "b151cb42-dad5-45bf-aad2-d6611e3203b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Train Step')"
            ]
          },
          "metadata": {},
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Exercise 14: Set up the loss and metrics**\n"
      ],
      "metadata": {
        "id": "mMQ8zocJBWDj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def masked_loss(label, pred):\n",
        "\n",
        "    mask = label != 0\n",
        "    loss_object = tf.keras.losses.SparseCategoricalCrossentropy(\n",
        "      from_logits=True, reduction='none')\n",
        "    loss = loss_object(label, pred)\n",
        "\n",
        "    mask = tf.cast(mask, dtype=loss.dtype)\n",
        "    loss *= mask\n",
        "\n",
        "    loss = tf.reduce_sum(loss)/tf.reduce_sum(mask)\n",
        "    return loss\n",
        "\n",
        "\n",
        "def masked_accuracy(label, pred):\n",
        "\n",
        "    pred = tf.argmax(pred, axis=2)\n",
        "    label = tf.cast(label, pred.dtype)\n",
        "    match = label == pred\n",
        "\n",
        "    mask = label != 0\n",
        "\n",
        "    match = match & mask\n",
        "\n",
        "    match = tf.cast(match, dtype=tf.float32)\n",
        "    mask = tf.cast(mask, dtype=tf.float32)\n",
        "    return tf.reduce_sum(match)/tf.reduce_sum(mask)\n"
      ],
      "metadata": {
        "id": "ts9shHt718CF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile\n",
        "\n",
        "\n",
        "transformer.compile(\n",
        "    loss=masked_loss,\n",
        "    optimizer=optimizer,\n",
        "    metrics=[masked_accuracy])"
      ],
      "metadata": {
        "id": "9pgKetun1_9U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train\n",
        "\n",
        "\n",
        "transformer.fit([x_train_pad, y_train_pad[:,:-1]], y_train_pad[:,1:], batch_size=64, epochs=50, validation_split=0.1)"
      ],
      "metadata": {
        "id": "EIeka1thAUo4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07fd0ba3-4f56-4deb-8e18-3f4215b35e50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "49/49 [==============================] - 66s 723ms/step - loss: 8.6564 - masked_accuracy: 0.0164 - val_loss: 8.5033 - val_masked_accuracy: 0.0614\n",
            "Epoch 2/50\n",
            "49/49 [==============================] - 32s 661ms/step - loss: 8.3977 - masked_accuracy: 0.0499 - val_loss: 8.2457 - val_masked_accuracy: 0.0614\n",
            "Epoch 3/50\n",
            "49/49 [==============================] - 35s 725ms/step - loss: 8.1453 - masked_accuracy: 0.0597 - val_loss: 7.9866 - val_masked_accuracy: 0.0614\n",
            "Epoch 4/50\n",
            "49/49 [==============================] - 31s 644ms/step - loss: 7.8726 - masked_accuracy: 0.0614 - val_loss: 7.7080 - val_masked_accuracy: 0.0719\n",
            "Epoch 5/50\n",
            "49/49 [==============================] - 32s 648ms/step - loss: 7.5927 - masked_accuracy: 0.0725 - val_loss: 7.4420 - val_masked_accuracy: 0.0801\n",
            "Epoch 6/50\n",
            "49/49 [==============================] - 31s 639ms/step - loss: 7.3310 - masked_accuracy: 0.0803 - val_loss: 7.1974 - val_masked_accuracy: 0.0836\n",
            "Epoch 7/50\n",
            "49/49 [==============================] - 32s 654ms/step - loss: 7.0822 - masked_accuracy: 0.0923 - val_loss: 6.9555 - val_masked_accuracy: 0.0982\n",
            "Epoch 8/50\n",
            "49/49 [==============================] - 32s 650ms/step - loss: 6.8094 - masked_accuracy: 0.1183 - val_loss: 6.6725 - val_masked_accuracy: 0.1297\n",
            "Epoch 9/50\n",
            "49/49 [==============================] - 33s 667ms/step - loss: 6.4981 - masked_accuracy: 0.1355 - val_loss: 6.3684 - val_masked_accuracy: 0.1541\n",
            "Epoch 10/50\n",
            "49/49 [==============================] - 32s 658ms/step - loss: 6.1527 - masked_accuracy: 0.1583 - val_loss: 6.0595 - val_masked_accuracy: 0.1672\n",
            "Epoch 11/50\n",
            "49/49 [==============================] - 32s 659ms/step - loss: 5.7966 - masked_accuracy: 0.1877 - val_loss: 5.7503 - val_masked_accuracy: 0.2000\n",
            "Epoch 12/50\n",
            "49/49 [==============================] - 33s 678ms/step - loss: 5.4369 - masked_accuracy: 0.2228 - val_loss: 5.4358 - val_masked_accuracy: 0.2254\n",
            "Epoch 13/50\n",
            "49/49 [==============================] - 32s 649ms/step - loss: 5.0770 - masked_accuracy: 0.2591 - val_loss: 5.1460 - val_masked_accuracy: 0.2553\n",
            "Epoch 14/50\n",
            "49/49 [==============================] - 32s 651ms/step - loss: 4.7165 - masked_accuracy: 0.2991 - val_loss: 4.8706 - val_masked_accuracy: 0.2765\n",
            "Epoch 15/50\n",
            "49/49 [==============================] - 32s 655ms/step - loss: 4.3715 - masked_accuracy: 0.3358 - val_loss: 4.6281 - val_masked_accuracy: 0.2861\n",
            "Epoch 16/50\n",
            "49/49 [==============================] - 32s 648ms/step - loss: 4.0194 - masked_accuracy: 0.3781 - val_loss: 4.3657 - val_masked_accuracy: 0.3222\n",
            "Epoch 17/50\n",
            "49/49 [==============================] - 33s 677ms/step - loss: 3.6906 - masked_accuracy: 0.4165 - val_loss: 4.0844 - val_masked_accuracy: 0.3490\n",
            "Epoch 18/50\n",
            "49/49 [==============================] - 32s 651ms/step - loss: 3.3328 - masked_accuracy: 0.4604 - val_loss: 3.8195 - val_masked_accuracy: 0.3772\n",
            "Epoch 19/50\n",
            "49/49 [==============================] - 32s 652ms/step - loss: 3.0207 - masked_accuracy: 0.4981 - val_loss: 3.5966 - val_masked_accuracy: 0.4015\n",
            "Epoch 20/50\n",
            "49/49 [==============================] - 33s 678ms/step - loss: 2.7019 - masked_accuracy: 0.5390 - val_loss: 3.3858 - val_masked_accuracy: 0.4256\n",
            "Epoch 21/50\n",
            "49/49 [==============================] - 33s 677ms/step - loss: 2.3952 - masked_accuracy: 0.5816 - val_loss: 3.1333 - val_masked_accuracy: 0.4551\n",
            "Epoch 22/50\n",
            "49/49 [==============================] - 33s 677ms/step - loss: 2.1065 - masked_accuracy: 0.6220 - val_loss: 2.9737 - val_masked_accuracy: 0.4731\n",
            "Epoch 23/50\n",
            "49/49 [==============================] - 32s 654ms/step - loss: 1.8306 - masked_accuracy: 0.6671 - val_loss: 2.7252 - val_masked_accuracy: 0.5135\n",
            "Epoch 24/50\n",
            "49/49 [==============================] - 33s 683ms/step - loss: 1.5457 - masked_accuracy: 0.7174 - val_loss: 2.5291 - val_masked_accuracy: 0.5463\n",
            "Epoch 25/50\n",
            "49/49 [==============================] - 32s 645ms/step - loss: 1.3182 - masked_accuracy: 0.7588 - val_loss: 2.3437 - val_masked_accuracy: 0.5723\n",
            "Epoch 26/50\n",
            "49/49 [==============================] - 32s 649ms/step - loss: 1.0923 - masked_accuracy: 0.8039 - val_loss: 2.1543 - val_masked_accuracy: 0.6115\n",
            "Epoch 27/50\n",
            "49/49 [==============================] - 33s 671ms/step - loss: 0.9046 - masked_accuracy: 0.8403 - val_loss: 1.9898 - val_masked_accuracy: 0.6360\n",
            "Epoch 28/50\n",
            "49/49 [==============================] - 32s 647ms/step - loss: 0.7322 - masked_accuracy: 0.8735 - val_loss: 1.8401 - val_masked_accuracy: 0.6752\n",
            "Epoch 29/50\n",
            "49/49 [==============================] - 32s 652ms/step - loss: 0.6189 - masked_accuracy: 0.8940 - val_loss: 1.7090 - val_masked_accuracy: 0.7072\n",
            "Epoch 30/50\n",
            "49/49 [==============================] - 33s 678ms/step - loss: 0.4973 - masked_accuracy: 0.9146 - val_loss: 1.5931 - val_masked_accuracy: 0.7310\n",
            "Epoch 31/50\n",
            "49/49 [==============================] - 33s 676ms/step - loss: 0.4103 - masked_accuracy: 0.9274 - val_loss: 1.5122 - val_masked_accuracy: 0.7562\n",
            "Epoch 32/50\n",
            "49/49 [==============================] - 33s 678ms/step - loss: 0.3436 - masked_accuracy: 0.9392 - val_loss: 1.4645 - val_masked_accuracy: 0.7703\n",
            "Epoch 33/50\n",
            "49/49 [==============================] - 33s 678ms/step - loss: 0.3042 - masked_accuracy: 0.9453 - val_loss: 1.4253 - val_masked_accuracy: 0.7780\n",
            "Epoch 34/50\n",
            "49/49 [==============================] - 32s 652ms/step - loss: 0.2568 - masked_accuracy: 0.9521 - val_loss: 1.3828 - val_masked_accuracy: 0.7839\n",
            "Epoch 35/50\n",
            "49/49 [==============================] - 32s 647ms/step - loss: 0.2213 - masked_accuracy: 0.9560 - val_loss: 1.3467 - val_masked_accuracy: 0.7950\n",
            "Epoch 36/50\n",
            "49/49 [==============================] - 32s 648ms/step - loss: 0.1964 - masked_accuracy: 0.9590 - val_loss: 1.3349 - val_masked_accuracy: 0.7998\n",
            "Epoch 37/50\n",
            "49/49 [==============================] - 33s 675ms/step - loss: 0.1743 - masked_accuracy: 0.9619 - val_loss: 1.3306 - val_masked_accuracy: 0.8007\n",
            "Epoch 38/50\n",
            "49/49 [==============================] - 33s 677ms/step - loss: 0.1488 - masked_accuracy: 0.9643 - val_loss: 1.3115 - val_masked_accuracy: 0.8029\n",
            "Epoch 39/50\n",
            "49/49 [==============================] - 33s 675ms/step - loss: 0.1373 - masked_accuracy: 0.9667 - val_loss: 1.3154 - val_masked_accuracy: 0.8019\n",
            "Epoch 40/50\n",
            "49/49 [==============================] - 33s 670ms/step - loss: 0.1299 - masked_accuracy: 0.9671 - val_loss: 1.3218 - val_masked_accuracy: 0.8042\n",
            "Epoch 41/50\n",
            "49/49 [==============================] - 33s 670ms/step - loss: 0.1279 - masked_accuracy: 0.9662 - val_loss: 1.3106 - val_masked_accuracy: 0.8054\n",
            "Epoch 42/50\n",
            "49/49 [==============================] - 33s 677ms/step - loss: 0.1262 - masked_accuracy: 0.9666 - val_loss: 1.3352 - val_masked_accuracy: 0.8033\n",
            "Epoch 43/50\n",
            "49/49 [==============================] - 33s 673ms/step - loss: 0.1175 - masked_accuracy: 0.9670 - val_loss: 1.3372 - val_masked_accuracy: 0.8011\n",
            "Epoch 44/50\n",
            "49/49 [==============================] - 33s 683ms/step - loss: 0.1192 - masked_accuracy: 0.9664 - val_loss: 1.3417 - val_masked_accuracy: 0.8022\n",
            "Epoch 45/50\n",
            "49/49 [==============================] - 32s 647ms/step - loss: 0.1271 - masked_accuracy: 0.9650 - val_loss: 1.3687 - val_masked_accuracy: 0.8000\n",
            "Epoch 46/50\n",
            "49/49 [==============================] - 32s 650ms/step - loss: 0.1380 - masked_accuracy: 0.9628 - val_loss: 1.3837 - val_masked_accuracy: 0.7957\n",
            "Epoch 47/50\n",
            "49/49 [==============================] - 32s 648ms/step - loss: 0.1564 - masked_accuracy: 0.9603 - val_loss: 1.4141 - val_masked_accuracy: 0.7902\n",
            "Epoch 48/50\n",
            "49/49 [==============================] - 32s 645ms/step - loss: 0.1775 - masked_accuracy: 0.9554 - val_loss: 1.4043 - val_masked_accuracy: 0.7870\n",
            "Epoch 49/50\n",
            "49/49 [==============================] - 32s 648ms/step - loss: 0.1758 - masked_accuracy: 0.9553 - val_loss: 1.4122 - val_masked_accuracy: 0.7911\n",
            "Epoch 50/50\n",
            "49/49 [==============================] - 33s 681ms/step - loss: 0.1578 - masked_accuracy: 0.9574 - val_loss: 1.4497 - val_masked_accuracy: 0.7891\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7c08653a8fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transformer.evaluate([x_test_pad, y_test_pad[:,:-1]], y_test_pad[:,1:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5VaEA4noXaBj",
        "outputId": "e3f4a116-4579-4d92-c510-f4d8d8c0e43b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12/12 [==============================] - 2s 122ms/step - loss: 1.4764 - masked_accuracy: 0.7677\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.4763755798339844, 0.7677227854728699]"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cxKpqCbzSW6z"
      },
      "source": [
        "## Run inference\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_keywords(sentence, transformer=transformer):\n",
        "\n",
        "    \"\"\" Takes an input sentence, and transformer. Returns extracted keywords. \"\"\"\n",
        "\n",
        "    # Convert input sentence into integer sequence (Note that tokenizer.texts_to_sequences() take list of text as input)\n",
        "    ip_tokens = x_tokenizer.texts_to_sequences([sentence])\n",
        "\n",
        "    # Add zero padding upto maximum length transcription\n",
        "    ip_tok_seq = pad_sequences(ip_tokens, padding='post', maxlen=max_len_transcript, truncating='post')\n",
        "\n",
        "    # Create a decoder sequence with 'start' token index\n",
        "    dec_tok_seq = np.array(y_tokenizer.word_index['start'])\n",
        "\n",
        "    # Variable to store the output text string\n",
        "    keyword_sentence = ''\n",
        "\n",
        "    for i in range(max_len_keywords):\n",
        "\n",
        "        # Get output logits from transformer\n",
        "        pred = transformer([ip_tok_seq.reshape(1,-1), dec_tok_seq.reshape(1, -1)], training=False)\n",
        "        pred = pred[:, -1:, :]\n",
        "\n",
        "        # Select the index with max value from 'pred' to get the output token index\n",
        "        token = np.argmax(pred[0])\n",
        "\n",
        "        # Convert output token to word\n",
        "        word = y_tokenizer.index_word[token]\n",
        "\n",
        "        # End loop is word is 'end'\n",
        "\n",
        "        if word == \"end\":\n",
        "          break\n",
        "\n",
        "        # Append 'token' to dec_tok_seq\n",
        "        dec_tok_seq = np.append(dec_tok_seq, token)\n",
        "\n",
        "        # Append 'word' to keyword sentence\n",
        "\n",
        "        keyword_sentence = keyword_sentence + \" \" + word\n",
        "\n",
        "    return keyword_sentence.strip()\n"
      ],
      "metadata": {
        "id": "lC282zeaCdtx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict keywords for a sample input\n",
        "random_index = np.random.choice(test_idx)\n",
        "input_sentence = read_data.loc[random_index].transcription\n",
        "keywords = df.loc[random_index].keywords\n",
        "pred = extract_keywords(input_sentence)\n",
        "\n",
        "print(f\"Index:{random_index}\\nTranscript: \\n{input_sentence} \\n\\nKeywords: \\n{keywords} \\n\\nPredicted Keywords: \\n{pred}\")"
      ],
      "metadata": {
        "id": "QBf2v9SocE0V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f0476c8-6085-4df1-f1e2-6667fd845d3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index:4735\n",
            "Transcript: \n",
            "PREOPERATIVE DIAGNOSIS:,  Posterior mediastinal mass with possible neural foraminal involvement.,POSTOPERATIVE DIAGNOSIS: , Posterior mediastinal mass with possible neural foraminal involvement (benign nerve sheath tumor by frozen section).,OPERATION PERFORMED:,  Left thoracotomy with resection of posterior mediastinal mass.,INDICATIONS FOR PROCEDURE:  ,The patient is a 23-year-old woman who recently presented with a posterior mediastinal mass and on CT and MRI there were some evidence of potential widening of one of the neural foramina.  For this reason, Dr. X and I agreed to operate on this patient together.  Please note that two surgeons were required for this case due to the complexity of it.  The indications and risks of the procedure were explained and the patient gave her informed consent.,DESCRIPTION OF PROCEDURE: , The patient was brought to the operating suite and placed in the supine position.  General endotracheal anesthesia was given with a double lumen tube.  The patient was positioned for a left thoracotomy.  All pressure points were carefully padded.  The patient was prepped and draped in usual sterile fashion.  A muscle sparing incision was created several centimeters anterior to the tip of the scapula.  The serratus and latissimus muscles were retracted.  The intercostal space was opened.  We then created a thoracoscopy port inferiorly through which we placed a camera for lighting and for visualization.  Through our small anterior thoracotomy and with the video-assisted scope placed inferiorly we had good visualization of the posterior mediastinum mass.  This was in the upper portion of the mediastinum just posterior to the subclavian artery and aorta.  The lung was deflated and allowed to retract anteriorly.  With a combination of blunt and sharp dissection and with attention paid to hemostasis, we were able to completely resect the posterior mediastinal mass.  We began by opening the tumor and taking a very wide large biopsy.  This was sent for frozen section, which revealed a benign nerve sheath tumor.  Then, using the occluder device Dr. X was able to _____ the inferior portions of the mass.  This left the external surface of the mass much more malleable and easier to retract.  Using a bipolar cautery and endoscopic scissors we were then able to completely resect it.  Once the tumor was resected, it was then sent for permanent sections.  The entire hemithorax was copiously irrigated and hemostasis was complete.  In order to prevent any lymph leak, we used 2 cc of Evicel and sprayed this directly on to the raw surface of the pleural space.  A single chest tube was inserted through our thoracoscopy port and tunneled up one interspace.  The wounds were then closed in multiple layers.  A #2 Vicryl was used to approximate the ribs.  The muscles of the chest wall were allowed to return to their normal anatomic position.  A 19 Blake was placed in the subcutaneous tissues.  Subcutaneous tissues and skin were closed with running absorbable sutures.  The patient was then rolled in the supine position where she was awakened from general endotracheal anesthesia and taken to the recovery room in stable condition. \n",
            "\n",
            "Keywords: \n",
            "[start] cardiovascular pulmonary posterior mediastinal mass neural foraminal nerve sheath tumor frozen section thoracotomy mediastinal mass foraminal neural sheath mediastinal [end] \n",
            "\n",
            "Predicted Keywords: \n",
            "surgery posterior mediastinal mass neural foraminal nerve sheath tumor frozen section thoracotomy mediastinal mass foraminal neural sheath mediastinal\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DXQYvbik134w"
      },
      "source": [
        "## Gradio Implementation [OPTIONAL]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f9LGFxtQjwxv"
      },
      "source": [
        "Gradio is an open-source python library that allows us to quickly create easy-to-use, customizable UI components for our ML model, any API, or any arbitrary function in just a few lines of code. We can integrate the GUI directly into the Python notebook, or we can share the link with anyone."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wl0ngdnKjKHH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1652d719-4819-46f4-93f9-df4711267510"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.1/20.1 MB\u001b[0m \u001b[31m27.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.8/65.8 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m298.2/298.2 kB\u001b[0m \u001b[31m28.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.4/75.4 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.9/139.9 kB\u001b[0m \u001b[31m19.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.7/45.7 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.5/59.5 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.9/129.9 kB\u001b[0m \u001b[31m18.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.0/67.0 kB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m74.5/74.5 kB\u001b[0m \u001b[31m11.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip -qq install gradio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3XwCJSdDDVVU"
      },
      "outputs": [],
      "source": [
        "import gradio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oy_0yXHI7fs4"
      },
      "outputs": [],
      "source": [
        "# Input from user\n",
        "in_transcript = gradio.inputs.Textbox(lines=10, placeholder=None, default=\"transcription\", label='Enter Transcription Text')\n",
        "\n",
        "# Output prediction\n",
        "out_keywords = gradio.outputs.Textbox(type=\"text\", label='Extracted Keywords')"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "b0gHDfuGDsop"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Gradio interface to generate UI link\n",
        "iface = gradio.Interface(fn = extract_keywords,\n",
        "                         inputs = [in_transcript],\n",
        "                         outputs = [out_keywords])\n",
        "\n",
        "iface.launch(share = True)"
      ],
      "metadata": {
        "id": "roRnk53KToWu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "outputId": "978ba55a-ef06-44ec-9d0d-def09132ec1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://4fce437ac3615877df.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://4fce437ac3615877df.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j2eQkcvnvntC"
      },
      "source": [
        "Click on the link generated above to see UI."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}