2_5_11_Automatic_Text_Generation_Actual.ipynb

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      "cell_type": "markdown",
      "source": [
        "<h1 style=\"color: #4B0082;\">Project Overview: Text Generation with GPT-2</h1>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Introduction</h2>\n",
        "\n",
        "<p>This project leverages the <strong>GPT-2</strong> model for text generation, utilizing a dataset sourced from a scraped text file of books from the <strong>Gutenberg Project</strong>. The aim is to perform exploratory data analysis (EDA) on the text data and subsequently fine-tune the GPT-2 model for generating human-like text. By examining the unique attributes of the dataset, we seek to enhance the model's performance and the quality of the generated text.</p>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Objectives</h2>\n",
        "\n",
        "<ul>\n",
        "    <li><strong>Data Acquisition:</strong> Load and preprocess the text data for analysis.</li>\n",
        "    <li><strong>Exploratory Data Analysis (EDA):</strong> Conduct a thorough analysis of the text to gain insights on word and sentence distributions.</li>\n",
        "    <li><strong>Model Training:</strong> Fine-tune the GPT-2 model on the preprocessed text data to improve its contextual understanding.</li>\n",
        "    <li><strong>Text Generation:</strong> Generate coherent and contextually relevant text based on prompts using the trained model.</li>\n",
        "    <li><strong>Performance Evaluation:</strong> Assess the generated text for coherence, creativity, and relevance to the input prompts.</li>\n",
        "</ul>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Methodology</h2>\n",
        "\n",
        "<h3 style=\"color: #2E8B57;\">1. Data Loading</h3>\n",
        "<ul>\n",
        "    <li>Load the text data from a specified file path using Python's built-in file handling capabilities.</li>\n",
        "    <li>Read and inspect the content of the dataset to understand its structure, size, and overall quality.</li>\n",
        "    <li>Identify potential issues such as encoding errors or missing data that may need to be addressed during preprocessing.</li>\n",
        "</ul>\n",
        "\n",
        "<h3 style=\"color: #2E8B57;\">2. Exploratory Data Analysis (EDA)</h3>\n",
        "<ul>\n",
        "    <li><strong>Text Cleaning:</strong> Remove punctuation, special characters, and unnecessary whitespace. Convert the text to lowercase for uniformity, ensuring a consistent dataset for analysis.</li>\n",
        "    <li><strong>Word Frequency Analysis:</strong> Count and visualize the most common words in the dataset using bar plots and word clouds, providing insights into the prevalent themes and vocabulary used in the text.</li>\n",
        "    <li><strong>Sentence Length Distribution:</strong> Analyze and visualize the distribution of sentence lengths to understand the text structure and complexity. This can help inform the training process by highlighting the model's capacity to handle varying sentence lengths.</li>\n",
        "    <li><strong>Cumulative Frequency of Words:</strong> Examine the cumulative frequency of words to identify language patterns and their occurrences throughout the text, allowing for a deeper understanding of the vocabulary richness.</li>\n",
        "    <li><strong>Word Length Distribution:</strong> Analyze the distribution of word lengths to explore vocabulary diversity, which can impact the model's ability to generate diverse text outputs.</li>\n",
        "    <li><strong>Results Saving:</strong> Export EDA results to a CSV file for further reference, ensuring that insights gained during this phase are documented for future use.</li>\n",
        "</ul>\n",
        "\n",
        "<h3 style=\"color: #2E8B57;\">3. Model Training</h3>\n",
        "<ul>\n",
        "    <li><strong>Model Initialization:</strong> Load the pre-trained GPT-2 tokenizer and model from the Hugging Face Transformers library, ensuring that the model is set up for fine-tuning on our dataset.</li>\n",
        "    <li><strong>Text Tokenization:</strong> Tokenize the text data for compatibility with the GPT-2 model, preparing the data for input into the model during training.</li>\n",
        "    <li><strong>Dataset Creation:</strong> Create a custom dataset class for loading tokens into the model efficiently, allowing for batch processing and proper handling of training data.</li>\n",
        "    <li><strong>Training Loop:</strong> Implement a training loop to optimize the model based on the input text data. Monitor loss metrics and save model checkpoints to ensure effective learning.</li>\n",
        "    <li><strong>Hyperparameter Tuning:</strong> Experiment with various hyperparameters, such as learning rate and batch size, to achieve optimal training results and minimize overfitting.</li>\n",
        "</ul>\n",
        "\n",
        "<h3 style=\"color: #2E8B57;\">4. Text Generation</h3>\n",
        "<ul>\n",
        "    <li><strong>Prompting the Model:</strong> Define a function to generate text based on user-defined prompts, allowing for interactive text generation that aligns with user interests and topics.</li>\n",
        "    <li><strong>Sampling Techniques:</strong> Utilize techniques such as temperature sampling and top-k/top-p sampling for diverse text generation, balancing between creativity and coherence in the generated text outputs.</li>\n",
        "    <li><strong>Evaluation Metrics:</strong> Assess the generated text based on predefined metrics such as perplexity, coherence, and user satisfaction to ensure quality and relevance.</li>\n",
        "</ul>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Conclusion</h2>\n",
        "\n",
        "<p>This project effectively demonstrates the application of NLP techniques in text generation using the GPT-2 model. Through comprehensive EDA and model training, we can generate coherent and contextually appropriate text based on initial prompts, showcasing the potential of AI in creative writing and content generation. The insights gained from the EDA phase inform the training process, ultimately enhancing the model's performance and output quality.</p>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Future Work</h2>\n",
        "\n",
        "<ul>\n",
        "    <li>Expand the dataset for training to improve the model's understanding and generation capabilities, possibly including more diverse genres or styles of text.</li>\n",
        "    <li>Experiment with other models, such as GPT-3 or fine-tuning approaches, to evaluate performance improvements and explore the boundaries of text generation capabilities.</li>\n",
        "    <li>Develop a user interface for more accessible interaction with the text generation model, allowing users to easily input prompts and receive generated text.</li>\n",
        "    <li>Investigate the integration of user feedback mechanisms to iteratively improve the model based on real-world usage and preferences.</li>\n",
        "</ul>"
      ],
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gPE-qssdKx1Y"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "p86aHFkfKx6f"
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    {
      "cell_type": "code",
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    {
      "cell_type": "markdown",
      "source": [
        "<h1 style=\"color: #4B0082;\">Import Libraries</h1>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Overview</h2>\n",
        "\n",
        "<p>In this step, we will import all the necessary libraries that will be used throughout the project. These libraries provide various functionalities, from data manipulation and visualization to model training and evaluation.</p>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Libraries to Import</h2>\n",
        "\n",
        "<ul>\n",
        "    <li><strong>Pandas:</strong> A powerful data manipulation library used for handling structured data efficiently.</li>\n",
        "    <li><strong>Numpy:</strong> A library for numerical computing in Python, providing support for arrays and mathematical functions.</li>\n",
        "    <li><strong>Matplotlib:</strong> A plotting library for creating static, animated, and interactive visualizations in Python.</li>\n",
        "    <li><strong>Seaborn:</strong> A data visualization library based on Matplotlib that provides a high-level interface for drawing attractive statistical graphics.</li>\n",
        "    <li><strong>Transformers:</strong> A library by Hugging Face that provides pre-trained models and tokenizers for natural language processing tasks, including the GPT-2 model.</li>\n",
        "    <li><strong>TensorFlow:</strong> An open-source library for numerical computation and machine learning, which will be used for model training and evaluation.</li>\n",
        "    <li><strong>Pytorch:</strong> A deep learning library that provides flexibility and efficiency for model building and training.</li>\n",
        "</ul>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Importance of Each Library</h2>\n",
        "\n",
        "<p>Choosing the right libraries is crucial for the efficiency and effectiveness of the project:</p>\n",
        "<ul>\n",
        "    <li><strong>Efficient Data Handling:</strong> Pandas and Numpy allow for efficient data manipulation, crucial for preprocessing text data.</li>\n",
        "    <li><strong>Visualization:</strong> Matplotlib and Seaborn are essential for visualizing data distributions, helping to understand the data better during EDA.</li>\n",
        "    <li><strong>Modeling:</strong> Transformers, TensorFlow, and Pytorch provide state-of-the-art tools for building and fine-tuning the GPT-2 model.</li>\n",
        "</ul>\n",
        "\n",
        "<p>In summary, this step sets the foundation for the project by bringing in the essential libraries required for data manipulation, visualization, and model training.</p>"
      ],
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    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "rayuaRmu50Wy"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import Required Libraries"
      ],
      "metadata": {
        "id": "5UN07Qr750eJ"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "lJDwkIMMJCo6"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers datasets torch tqdm  # Install necessary libraries"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Ns5LObd50gm",
        "outputId": "f8e754f6-0236-4ad7-f666-287b5f5a9176"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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          ]
        }
      ]
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      "source": [],
      "metadata": {
        "id": "FXW3xCYJ50jI"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VNbjtaS650l4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SNcpHUBAB5ur"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings  # Import the warnings library"
      ],
      "metadata": {
        "id": "0AFMC4naCUUL"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "warnings.filterwarnings(\"ignore\")  # Ignore warnings for cleaner output"
      ],
      "metadata": {
        "id": "Sti_8e2jCW28"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2dNYYPTBCW6b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # Import the pandas library for data manipulation"
      ],
      "metadata": {
        "id": "ZJRvn49c9lUM"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np  # Import numpy for numerical operations"
      ],
      "metadata": {
        "id": "MB9fPyz39pJk"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # Import matplotlib for plotting"
      ],
      "metadata": {
        "id": "S0UW-qV09pMt"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns  # Import seaborn for enhanced data visualization"
      ],
      "metadata": {
        "id": "CviXmbiQ9pPV"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter  # For counting word frequencies"
      ],
      "metadata": {
        "id": "cH1mIo0sBM3E"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import re  # For regular expressions"
      ],
      "metadata": {
        "id": "uHxk6LAhBM52"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import missingno as msno  # Import missingno for visualizing missing values"
      ],
      "metadata": {
        "id": "PDXUFt-U9pRp"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch  # Import PyTorch for tensor operations"
      ],
      "metadata": {
        "id": "urmhe99j53ki"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import GPT2Tokenizer, GPT2LMHeadModel  # Import tokenizer and model for GPT-2"
      ],
      "metadata": {
        "id": "DuzzjNFe53qp"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import Dataset, DataLoader  # Import Dataset and DataLoader for handling data"
      ],
      "metadata": {
        "id": "FeISpnyy53vk"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm  # Import tqdm for progress bars"
      ],
      "metadata": {
        "id": "VXgJPXPs53x6"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from wordcloud import WordCloud  # For generating word clouds"
      ],
      "metadata": {
        "id": "uTNOeG1fBZxx"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Mad4h_Zg7OOs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HI5gOiLi7ORk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BZtUfTYKB9qn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1 style=\"color: #4B0082;\"> Load Data</h1>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Overview</h2>\n",
        "\n",
        "<p>In this step, we will load the text data from the specified file path. This involves reading the data into a suitable format for analysis and ensuring its integrity and accessibility for subsequent steps.</p>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Data Loading Process</h2>\n",
        "\n",
        "<ul>\n",
        "    <li><strong>File Path:</strong> Specify the path to the text file that contains the dataset. Ensure that the file is accessible and correctly formatted.</li>\n",
        "    <li><strong>Data Reading:</strong> Utilize Pandas to read the text data, converting it into a DataFrame for easier manipulation and analysis.</li>\n",
        "    <li><strong>Initial Inspection:</strong> Inspect the first few rows of the DataFrame to confirm successful loading and understand the structure of the data.</li>\n",
        "    <li><strong>Data Integrity Check:</strong> Check for any issues such as missing values or incorrect data types that may need to be addressed during preprocessing.</li>\n",
        "</ul>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Importance of This Step</h2>\n",
        "\n",
        "<p>Loading the data correctly is crucial as it serves as the foundation for the entire project:</p>\n",
        "<ul>\n",
        "    <li><strong>Ensures Accessibility:</strong> Proper loading guarantees that the data can be easily accessed and manipulated for analysis.</li>\n",
        "    <li><strong>Facilitates Exploration:</strong> Loading the data into a structured format allows for efficient exploratory data analysis (EDA), aiding in understanding the dataset's characteristics.</li>\n",
        "</ul>\n",
        "\n",
        "<p>In summary, this step is essential for establishing a solid foundation for further analysis and modeling.</p>"
      ],
      "metadata": {
        "id": "dHQ0O-BbLCJi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "zjRhf9syJFpB"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the Dataset"
      ],
      "metadata": {
        "id": "39ikA5di7OUb"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "LBNcjxXGJG3B"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = \"/content/scraped_gutenberg_book.txt\"  # Define the path to the text file"
      ],
      "metadata": {
        "id": "Og1IzyAX7OXt"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yTycIzav7VBS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Open and read the content of the file"
      ],
      "metadata": {
        "id": "ymqem_-H7VEk"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(file_path, 'r', encoding='utf-8') as file:  # Open the file for reading with UTF-8 encoding\n",
        "    data = file.read()  # Read the entire content of the file into a variable"
      ],
      "metadata": {
        "id": "TV1jKSPj7VHB"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "l6FL3IzX7VLU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ImqnGfVh7VNy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wfxD3pBxLU-z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1 style=\"color: #4B0082;\">Exploratory Data Analysis (EDA)</h1>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Overview</h2>\n",
        "\n",
        "<p>In this step, we will conduct exploratory data analysis to gain insights into the characteristics and distributions of the dataset. EDA helps in understanding the underlying patterns, informing the subsequent modeling process.</p>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Key EDA Tasks</h2>\n",
        "\n",
        "<ul>\n",
        "    <li><strong>Word Frequency Analysis:</strong> Analyze and visualize the frequency of words in the dataset to identify common themes and vocabulary usage.</li>\n",
        "    <li><strong>Sentence Length Distribution:</strong> Examine the distribution of sentence lengths to understand text complexity and structure.</li>\n",
        "    <li><strong>Visualizations:</strong> Use Matplotlib and Seaborn to create visual representations of the data, making patterns and distributions more apparent.</li>\n",
        "    <li><strong>Saving EDA Results:</strong> Export EDA findings to CSV files for documentation and future reference.</li>\n",
        "</ul>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Importance of EDA</h2>\n",
        "\n",
        "<p>Exploratory data analysis is crucial for several reasons:</p>\n",
        "<ul>\n",
        "    <li><strong>Informs Model Development:</strong> Insights gained from EDA guide the model selection and training processes by highlighting key data characteristics.</li>\n",
        "    <li><strong>Identifies Data Issues:</strong> EDA helps in uncovering potential issues such as outliers or imbalances that may affect model performance.</li>\n",
        "</ul>\n",
        "\n",
        "<p>In summary, EDA is an essential step that allows us to thoroughly understand the dataset before moving forward with modeling.</p>"
      ],
      "metadata": {
        "id": "LOPV7TvMLVSq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the first 500 characters of the dataset to inspect it"
      ],
      "metadata": {
        "id": "TVmWkFNJ7VPr"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"First 500 characters of the dataset:\\n\")  # Print introductory text\n",
        "print(data[:500])  # Display the first 500 characters of the text data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K11Ix9Ku7VYD",
        "outputId": "44bfa657-8679-4a67-e5b9-9f9b1b9da21b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First 500 characters of the dataset:\n",
            "\n",
            "*** START OF THE PROJECT GUTENBERG EBOOK 1342 ***\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                            [Illustration:\n",
            "\n",
            "                             GEORGE ALLEN\n",
            "                               PUBLISHER\n",
            "\n",
            "                        156 CHARING CROSS ROAD\n",
            "                                LONDON\n",
            "\n",
            "                             RUSKIN HOUSE\n",
            "                                   ]\n",
            "\n",
            "                            [Illustration:\n",
            "\n",
            "               _Reading Janeâs Letters._      _Chap 34._\n",
            "                                   ]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "       \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GnnArUnH7OZZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AdVatF_iCBnp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the total length of the text"
      ],
      "metadata": {
        "id": "Z2AeTZkF7ObJ"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nTotal length of the text:\", len(data))  # Print the total number of characters in the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D5MA8NVs533V",
        "outputId": "1a4b3454-adee-4ac5-fd69-7a8529fb2d66"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Total length of the text: 738046\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WpnSXlGI535-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GDvOSyeR95SM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Inspect the first few rows"
      ],
      "metadata": {
        "id": "Oln6d_IJ95VA"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"First few rows of the dataset:\")\n",
        "print(df.head())  # Display the first 5 rows of the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9G8knC4095Xr",
        "outputId": "a9f2df65-feec-4a93-aa08-3461a0f4fa75"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First few rows of the dataset:\n",
            "                                                text  word_count  char_count\n",
            "0  *** START OF THE PROJECT GUTENBERG EBOOK 1342 ...      127381      738046\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VFDKsoAf9-a0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ql8nWCIq9-dp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDataset information:\")\n",
        "print(df.info())  # Display dataset information, including data types and non-null counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yBuKNuzT9-gk",
        "outputId": "dd5d8c71-77dc-4152-d44e-04c1fa321c7c"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Dataset information:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1 entries, 0 to 0\n",
            "Data columns (total 3 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   text        1 non-null      object\n",
            " 1   word_count  1 non-null      int64 \n",
            " 2   char_count  1 non-null      int64 \n",
            "dtypes: int64(2), object(1)\n",
            "memory usage: 152.0+ bytes\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5psf3M7V9-jH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pQEaC4M395aY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSummary statistics:\")\n",
        "print(df.describe(include='all'))  # Display summary statistics for numerical and categorical columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PYmFld1G95c-",
        "outputId": "a69acb93-b263-49c1-c3dd-eb5924bd002b"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Summary statistics:\n",
            "                                                     text  word_count  \\\n",
            "count                                                   1         1.0   \n",
            "unique                                                  1         NaN   \n",
            "top     *** START OF THE PROJECT GUTENBERG EBOOK 1342 ...         NaN   \n",
            "freq                                                    1         NaN   \n",
            "mean                                                  NaN    127381.0   \n",
            "std                                                   NaN         NaN   \n",
            "min                                                   NaN    127381.0   \n",
            "25%                                                   NaN    127381.0   \n",
            "50%                                                   NaN    127381.0   \n",
            "75%                                                   NaN    127381.0   \n",
            "max                                                   NaN    127381.0   \n",
            "\n",
            "        char_count  \n",
            "count          1.0  \n",
            "unique         NaN  \n",
            "top            NaN  \n",
            "freq           NaN  \n",
            "mean      738046.0  \n",
            "std            NaN  \n",
            "min       738046.0  \n",
            "25%       738046.0  \n",
            "50%       738046.0  \n",
            "75%       738046.0  \n",
            "max       738046.0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "grDzERgs-Ed9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Aj5TQcsd-EhG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values"
      ],
      "metadata": {
        "id": "6OTn78X3-EkK"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nMissing values in each column:\")\n",
        "print(df.isnull().sum())  # Print the number of missing values per column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9CjAtyl195gc",
        "outputId": "89e640d3-864e-4d66-b2e3-6b89784f57a8"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing values in each column:\n",
            "text          0\n",
            "word_count    0\n",
            "char_count    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CIqy67565382"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oBk-Ws4V-KP-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Join all lines into a single string for processing"
      ],
      "metadata": {
        "id": "DgxEFvqL-KU9"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "full_text = ' '.join(content)"
      ],
      "metadata": {
        "id": "H2RRCXTi_CiW"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "69ETZbhX_jZ3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Clean the text: remove punctuation and make lowercase"
      ],
      "metadata": {
        "id": "3A9b9Tgv_jdH"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cleaned_text = re.sub(r'[^\\w\\s]', '', full_text).lower()  # Remove punctuation and convert to lowercase"
      ],
      "metadata": {
        "id": "1hnnrOhA_jfz"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0yeXNnSm_jiT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the cleaned text into words"
      ],
      "metadata": {
        "id": "dV8agRRL_jk4"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "words = cleaned_text.split()"
      ],
      "metadata": {
        "id": "AD0QG6zg_jo7"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "N-cmXaUk_jsq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate word frequencies"
      ],
      "metadata": {
        "id": "YLQ9BkD0_juv"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "word_counts = Counter(words)  # Count the frequency of each word"
      ],
      "metadata": {
        "id": "UuwWtCy2_jw2"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "most_common_words = word_counts.most_common(20)  # Get the 20 most common words"
      ],
      "metadata": {
        "id": "h2DlB-8T_zsW"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "h4ZLBspm_3Ce"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare data for visualization"
      ],
      "metadata": {
        "id": "xYhGvoM8_3Fm"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "words_df = pd.DataFrame(most_common_words, columns=['Word', 'Frequency'])"
      ],
      "metadata": {
        "id": "1pv2UgMW_3Im"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4H-PD7in_zxp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8m4ZfpJT_z05"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the most common words"
      ],
      "metadata": {
        "id": "0vZ1nDTG_z3j"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))  # Set figure size\n",
        "sns.barplot(data=words_df, x='Frequency', y='Word', palette='viridis')  # Create a bar plot\n",
        "plt.title('Most Common Words in the Text')  # Title for the plot\n",
        "plt.xlabel('Frequency')  # X-axis label\n",
        "plt.ylabel('Words')  # Y-axis label\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "vhT6fON0_z9L",
        "outputId": "fc7c5bd0-356e-4d5b-ee6f-b3c71b7f7ce1"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gzM-oaHr_0AK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M3qjbTb2_0C9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate a word cloud"
      ],
      "metadata": {
        "id": "VtjePScd_jzQ"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(word_counts)"
      ],
      "metadata": {
        "id": "PlGTzWGj_j1X"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xaIgUUAJAGYX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the word cloud"
      ],
      "metadata": {
        "id": "ZG-B8lUbAGbU"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 5))  # Set figure size\n",
        "plt.imshow(wordcloud, interpolation='bilinear')  # Show the word cloud\n",
        "plt.axis('off')  # Hide axes\n",
        "plt.title('Word Cloud of the Text')  # Title for the word cloud\n",
        "plt.show()  # Display the word cloud"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "X-GlXeGJAGd6",
        "outputId": "2eb7622a-973b-4bfd-d579-06b2bb485eb7"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "L4JivqoFAGgv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DI88IA48AGje"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Analyze sentence lengths"
      ],
      "metadata": {
        "id": "Y1l2YxOlAGl8"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the text into sentences"
      ],
      "metadata": {
        "id": "HvRrhhgVAGoh"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sentences = re.split(r'[.!?]', full_text)  # Split based on sentence delimiters"
      ],
      "metadata": {
        "id": "AsQrhAHVAGsB"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sentence_lengths = [len(sentence.split()) for sentence in sentences if sentence]  # Calculate lengths"
      ],
      "metadata": {
        "id": "cROAJM7rAT-Q"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "h_LqIahNAUBH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting histogram of sentence lengths"
      ],
      "metadata": {
        "id": "ZfZYbxdcAUGJ"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))  # Set figure size\n",
        "plt.hist(sentence_lengths, bins=30, color='skyblue', edgecolor='black')  # Create a histogram\n",
        "plt.title('Distribution of Sentence Lengths')  # Title for the histogram\n",
        "plt.xlabel('Number of Words in Sentence')  # X-axis label\n",
        "plt.ylabel('Frequency')  # Y-axis label\n",
        "plt.show()  # Display the histogram"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "cEpLOqoHAUKx",
        "outputId": "4cd60085-4d4c-4f5b-90f3-e9cccb03cfe0"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hN2uuCs6Abro"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CAQ9XD4nAUPj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Cumulative frequency of word counts"
      ],
      "metadata": {
        "id": "iCn5Au4xAGus"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cumulative_word_counts = np.cumsum(sorted(word_counts.values()))  # Calculate cumulative frequency"
      ],
      "metadata": {
        "id": "UbfTg9ZlArko"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gZs0EcPwArnj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))  # Set figure size\n",
        "plt.plot(sorted(word_counts.values()), cumulative_word_counts, marker='o', color='purple')  # Line plot\n",
        "plt.title('Cumulative Word Frequency')  # Title for the cumulative frequency plot\n",
        "plt.xlabel('Word Rank (sorted by frequency)')  # X-axis label\n",
        "plt.ylabel('Cumulative Frequency')  # Y-axis label\n",
        "plt.show()  # Display the cumulative frequency plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "h1k-7lRRArqY",
        "outputId": "b5f6bdca-2d8d-425b-fcd3-f393bb292f56"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zRF22y0OArtX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hcXdhkcOArwC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Histogram of word lengths"
      ],
      "metadata": {
        "id": "AU4HKBbVAryr"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "word_lengths = [len(word) for word in words]  # Calculate lengths of words"
      ],
      "metadata": {
        "id": "_P0KN2W8-NUK"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xUgqwCuGA49A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))  # Set figure size\n",
        "plt.hist(word_lengths, bins=30, color='lightgreen', edgecolor='black')  # Create a histogram\n",
        "plt.title('Distribution of Word Lengths')  # Title for the histogram\n",
        "plt.xlabel('Length of Words')  # X-axis label\n",
        "plt.ylabel('Frequency')  # Y-axis label\n",
        "plt.show()  # Display the histogram"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "y_nEkVqdA5AB",
        "outputId": "12cddd97-bd48-42ef-a50e-8160b7e423b8"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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bt6/uvvtuHTp0SAsXLnRYgO9Ke7vtttvUq1cvPf300/r222/VsWNHrVmzRh999JHGjh1bbt8V9dBDD+n111/X8OHDlZqaqqZNm+qDDz7Qli1bNHfu3IuuwXApZX+Tbdu2adq0aQ7bunbtKpvNpu3bt+u2225zOJP93HPPKTk5WVFRUXrsscfk5uam119/XUVFRZo5c+Yl561Tp46ee+45Pfzww7r11ls1ZMgQHT58WAsWLLiia/4PHjyo5557rtz4DTfcoAEDBui5557TpEmT9O2332rgwIFq0KCBDh8+rGXLlumhhx7Sk08+edlzSdLDDz+sV155RcOGDdOYMWMUGBioRYsWmf+b+O1rFB4eriVLlighIUE33XST6tevr9tuu+2y51q/fr1Gjx6tu+66Sy1bttTZs2f17rvvytXVVYMHD76ivgEAVcxJvzIAAECFlP0sW9nNbrcbAQEBxh/+8Adj3rx5Dj8pV+bcn/pbt26dcccddxhBQUGG3W43goKCjGHDhhn79+93eNxHH31ktG3b1nBzc3P4ab0ePXoY119//Xn7u9BP/f373/82Jk2aZPj5+Rmenp7GgAEDjO+++67c42fNmmVce+21hru7u9GtWzdj586d5fZ5sd7O/ak/wzCMkydPGuPGjTOCgoKMOnXqGC1atDBeeOEFo7S01KFOkhEfH1+upwv9BOG5cnJyjBEjRhi+vr6G3W432rdvf96fI7ySn/ozDMMoKCgwn+eaNWvKbe/QoYMhyfjHP/5RbtsXX3xhxMTEGPXr1zfq1q1r9OrVy9i6datDzaV+PvLVV181wsLCDHd3d6Nz587Gpk2bzvs3OZ/Q0FCH4/W3t5EjR5p1//nPf4yoqCijXr16Rr169YzWrVsb8fHxRkZGhllzoePufH/zb775xhgwYIDh6elpNG7c2Bg/frzxn//8x5BkbN++3aw7deqUcc899xgNGzY0JJn7KTtuz/0Jv8OHDzscb998843xwAMPGM2aNTM8PDwMHx8fo1evXsbatWsv+doAAKqXzTCctIIPAAAAqs3cuXM1btw4/fDDD7r22mud3Q4AoJoR/gEAACymsLDQ4dcLTp8+rRtuuEElJSXav3+/EzsDADgL1/wDAABYzKBBg9SkSRN16tRJeXl5Wrhwofbt26dFixY5uzUAgJMQ/gEAACwmJiZGb775phYtWqSSkhK1bdtW7733noYMGeLs1gAATsLX/gEAAAAAsDiXS5cAAAAAAIDajPAPAAAAAIDFcc1/JSktLdWRI0fUoEED2Ww2Z7cDAAAAALA4wzB08uRJBQUFycXl4uf2Cf+V5MiRIwoJCXF2GwAAAACA35nvv/9ewcHBF60h/FeSBg0aSPr1Rffy8nJyNwAAAAAAq8vPz1dISIiZRy+G8F9Jyr7q7+XlRfgHAAAAAFSby7n0nAX/AAAAAACwOKeG/9dee00dOnQwz5ZHRkZq5cqV5vbTp08rPj5ejRo1Uv369TV48GDl5OQ47CMzM1MDBgxQ3bp15efnp6eeekpnz551qElJSdGNN94od3d3NW/eXElJSeV6mT9/vpo2bSoPDw9FRETos88+q5LnDAAAAABAdXNq+A8ODtbf//53paamaufOnbr11lt1xx13aO/evZKkcePG6X//+5+WLl2qjRs36siRIxo0aJD5+JKSEg0YMEDFxcXaunWr3n77bSUlJWnKlClmzeHDhzVgwAD16tVLaWlpGjt2rB588EGtXr3arFmyZIkSEhI0depUffHFF+rYsaNiYmJ09OjR6nsxAAAAAACoIjbDMAxnN/FbPj4+euGFF/SnP/1JjRs31uLFi/WnP/1JkrRv3z61adNG27ZtU9euXbVy5Ur98Y9/1JEjR+Tv7y9JSkxM1MSJE3Xs2DHZ7XZNnDhRK1as0J49e8w5hg4dqhMnTmjVqlWSpIiICN1000165ZVXJP36s30hISF6/PHH9ec///my+s7Pz5e3t7fy8vK45h8AAAAAUOWuJIfWmGv+S0pK9N5776mgoECRkZFKTU3VmTNnFB0dbda0bt1aTZo00bZt2yRJ27ZtU/v27c3gL0kxMTHKz883vz2wbds2h32U1ZTto7i4WKmpqQ41Li4uio6ONmvOp6ioSPn5+Q43AAAAAABqIqeH/6+++kr169eXu7u7HnnkES1btkxt27ZVdna27Ha7GjZs6FDv7++v7OxsSVJ2drZD8C/bXrbtYjX5+fkqLCxUbm6uSkpKzltTto/zmTFjhry9vc1bSEhIhZ4/AAAAAABVzenhv1WrVkpLS9OOHTv06KOPKi4uTl9//bWz27qkSZMmKS8vz7x9//33zm4JAAAAAIDzcnN2A3a7Xc2bN5ckhYeH6/PPP9e8efM0ZMgQFRcX68SJEw5n/3NychQQECBJCggIKLcqf9mvAfy25txfCMjJyZGXl5c8PT3l6uoqV1fX89aU7eN83N3d5e7uXrEnDQAAAABANXL6mf9zlZaWqqioSOHh4apTp47WrVtnbsvIyFBmZqYiIyMlSZGRkfrqq68cVuVPTk6Wl5eX2rZta9b8dh9lNWX7sNvtCg8Pd6gpLS3VunXrzBoAAAAAAGozp575nzRpkvr166cmTZro5MmTWrx4sVJSUrR69Wp5e3tr5MiRSkhIkI+Pj7y8vPT4448rMjJSXbt2lST16dNHbdu21X333aeZM2cqOztbkydPVnx8vHlW/pFHHtErr7yiCRMm6IEHHtD69ev1/vvva8WKFWYfCQkJiouLU+fOndWlSxfNnTtXBQUFGjFihFNeFwAAAAAAKpNTw//Ro0d1//33KysrS97e3urQoYNWr16tP/zhD5KkOXPmyMXFRYMHD1ZRUZFiYmL06quvmo93dXXVxx9/rEcffVSRkZGqV6+e4uLiNH36dLMmLCxMK1as0Lhx4zRv3jwFBwfrzTffVExMjFkzZMgQHTt2TFOmTFF2drY6deqkVatWlVsEEAAAAACA2shmGIbh7Cas4Ep+XxEAAAAAgKt1JTm0xl3zDwAAAAAAKhfhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4N2c3AFSFzMxM5ebmVtt8vr6+atKkSbXNBwAAAABXgvAPy8nMzFTrNq1V+Ethtc3pWddT+9L38QEAAAAAgBqJ8A/Lyc3NVeEvhYp9PVb+Lf2rfL6c/Tla+PBC5ebmEv4BAAAA1EiEf1iWf0t/hXQMcXYbAAAAAOB0LPgHAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcU4N/zNmzNBNN92kBg0ayM/PTwMHDlRGRoZDTc+ePWWz2RxujzzyiENNZmamBgwYoLp168rPz09PPfWUzp4961CTkpKiG2+8Ue7u7mrevLmSkpLK9TN//nw1bdpUHh4eioiI0GeffVbpzxkAAAAAgOrm1PC/ceNGxcfHa/v27UpOTtaZM2fUp08fFRQUONSNGjVKWVlZ5m3mzJnmtpKSEg0YMEDFxcXaunWr3n77bSUlJWnKlClmzeHDhzVgwAD16tVLaWlpGjt2rB588EGtXr3arFmyZIkSEhI0depUffHFF+rYsaNiYmJ09OjRqn8hAAAAAACoQm7OnHzVqlUO95OSkuTn56fU1FR1797dHK9bt64CAgLOu481a9bo66+/1tq1a+Xv769OnTrp2Wef1cSJEzVt2jTZ7XYlJiYqLCxMs2bNkiS1adNGmzdv1pw5cxQTEyNJmj17tkaNGqURI0ZIkhITE7VixQq99dZb+vOf/1wVTx8AAAAAgGpRo675z8vLkyT5+Pg4jC9atEi+vr5q166dJk2apF9++cXctm3bNrVv317+/v7mWExMjPLz87V3716zJjo62mGfMTEx2rZtmySpuLhYqampDjUuLi6Kjo42a85VVFSk/Px8hxsAAAAAADWRU8/8/1ZpaanGjh2rbt26qV27dub4Pffco9DQUAUFBWn37t2aOHGiMjIy9OGHH0qSsrOzHYK/JPN+dnb2RWvy8/NVWFion3/+WSUlJeet2bdv33n7nTFjhp555pmre9IAAAAAAFSDGhP+4+PjtWfPHm3evNlh/KGHHjL/3b59ewUGBqp37946dOiQmjVrVt1tmiZNmqSEhATzfn5+vkJCQpzWDwAAAAAAF1Ijwv/o0aP18ccfa9OmTQoODr5obUREhCTp4MGDatasmQICAsqtyp+TkyNJ5joBAQEB5thva7y8vOTp6SlXV1e5urqet+ZCaw24u7vL3d398p8kAAAAAABO4tRr/g3D0OjRo7Vs2TKtX79eYWFhl3xMWlqaJCkwMFCSFBkZqa+++sphVf7k5GR5eXmpbdu2Zs26desc9pOcnKzIyEhJkt1uV3h4uENNaWmp1q1bZ9YAAAAAAFBbOfXMf3x8vBYvXqyPPvpIDRo0MK/R9/b2lqenpw4dOqTFixerf//+atSokXbv3q1x48ape/fu6tChgySpT58+atu2re677z7NnDlT2dnZmjx5suLj480z84888oheeeUVTZgwQQ888IDWr1+v999/XytWrDB7SUhIUFxcnDp37qwuXbpo7ty5KigoMFf/BwAAAACgtnJq+H/ttdckST179nQYX7BggYYPHy673a61a9eaQTwkJESDBw/W5MmTzVpXV1d9/PHHevTRRxUZGal69eopLi5O06dPN2vCwsK0YsUKjRs3TvPmzVNwcLDefPNN82f+JGnIkCE6duyYpkyZouzsbHXq1EmrVq0qtwggAAAAAAC1jVPDv2EYF90eEhKijRs3XnI/oaGh+uSTTy5a07NnT+3ateuiNaNHj9bo0aMvOR8AAAAAALWJU6/5BwAAAAAAVY/wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAItzc3YD+H3IzMxUbm5utcyVnp5eLfMAAAAAQG1B+EeVy8zMVOs2rVX4S6GzWwEAAACA3yXCP6pcbm6uCn8pVOzrsfJv6V/l83299mutfH5llc8DAAAAALUF4R/Vxr+lv0I6hlT5PDn7c6p8DgAAAACoTVjwDwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAi3NzdgOAVaSnp1fbXL6+vmrSpEm1zQcAAACgdiP8A1cpPydfNhebYmNjq21Oz7qe2pe+jw8AAAAAAFwWwj9wlQrzCmWUGop9PVb+Lf2rfL6c/Tla+PBC5ebmEv4BAAAAXBbCP1BJ/Fv6K6RjiLPbAAAAAIByWPAPAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYnFPD/4wZM3TTTTepQYMG8vPz08CBA5WRkeFQc/r0acXHx6tRo0aqX7++Bg8erJycHIeazMxMDRgwQHXr1pWfn5+eeuopnT171qEmJSVFN954o9zd3dW8eXMlJSWV62f+/Plq2rSpPDw8FBERoc8++6zSnzMAAAAAANXNqeF/48aNio+P1/bt25WcnKwzZ86oT58+KigoMGvGjRun//3vf1q6dKk2btyoI0eOaNCgQeb2kpISDRgwQMXFxdq6davefvttJSUlacqUKWbN4cOHNWDAAPXq1UtpaWkaO3asHnzwQa1evdqsWbJkiRISEjR16lR98cUX6tixo2JiYnT06NHqeTEAAAAAAKgibs6cfNWqVQ73k5KS5Ofnp9TUVHXv3l15eXn617/+pcWLF+vWW2+VJC1YsEBt2rTR9u3b1bVrV61Zs0Zff/211q5dK39/f3Xq1EnPPvusJk6cqGnTpslutysxMVFhYWGaNWuWJKlNmzbavHmz5syZo5iYGEnS7NmzNWrUKI0YMUKSlJiYqBUrVuitt97Sn//852p8VQAAAAAAqFw16pr/vLw8SZKPj48kKTU1VWfOnFF0dLRZ07p1azVp0kTbtm2TJG3btk3t27eXv7+/WRMTE6P8/Hzt3bvXrPntPspqyvZRXFys1NRUhxoXFxdFR0ebNecqKipSfn6+ww0AAAAAgJqoxoT/0tJSjR07Vt26dVO7du0kSdnZ2bLb7WrYsKFDrb+/v7Kzs82a3wb/su1l2y5Wk5+fr8LCQuXm5qqkpOS8NWX7ONeMGTPk7e1t3kJCQir2xAEAAAAAqGI1JvzHx8drz549eu+995zdymWZNGmS8vLyzNv333/v7JYAAAAAADgvp17zX2b06NH6+OOPtWnTJgUHB5vjAQEBKi4u1okTJxzO/ufk5CggIMCsOXdV/rJfA/htzbm/EJCTkyMvLy95enrK1dVVrq6u560p28e53N3d5e7uXrEnDAAAAABANXLqmX/DMDR69GgtW7ZM69evV1hYmMP28PBw1alTR+vWrTPHMjIylJmZqcjISElSZGSkvvrqK4dV+ZOTk+Xl5aW2bduaNb/dR1lN2T7sdrvCw8MdakpLS7Vu3TqzBgAAAACA2sqpZ/7j4+O1ePFiffTRR2rQoIF5fb23t7c8PT3l7e2tkSNHKiEhQT4+PvLy8tLjjz+uyMhIde3aVZLUp08ftW3bVvfdd59mzpyp7OxsTZ48WfHx8eaZ+UceeUSvvPKKJkyYoAceeEDr16/X+++/rxUrVpi9JCQkKC4uTp07d1aXLl00d+5cFRQUmKv/AwAAAABQWzk1/L/22muSpJ49ezqML1iwQMOHD5ckzZkzRy4uLho8eLCKiooUExOjV1991ax1dXXVxx9/rEcffVSRkZGqV6+e4uLiNH36dLMmLCxMK1as0Lhx4zRv3jwFBwfrzTffNH/mT5KGDBmiY8eOacqUKcrOzlanTp20atWqcosAAgAAAABQ2zg1/BuGcckaDw8PzZ8/X/Pnz79gTWhoqD755JOL7qdnz57atWvXRWtGjx6t0aNHX7InAAAAAABqkxqz2j8AAAAAAKgahH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLq1D4/+abbyq7DwAAAAAAUEUqFP6bN2+uXr16aeHChTp9+nRl9wQAAAAAACpRhcL/F198oQ4dOighIUEBAQF6+OGH9dlnn1V2bwAAAAAAoBJUKPx36tRJ8+bN05EjR/TWW28pKytLUVFRateunWbPnq1jx45Vdp8AAAAAAKCCrmrBPzc3Nw0aNEhLly7VP/7xDx08eFBPPvmkQkJCdP/99ysrK6uy+gQAAAAAABV0VeF/586deuyxxxQYGKjZs2frySef1KFDh5ScnKwjR47ojjvuqKw+AQAAAABABblV5EGzZ8/WggULlJGRof79++udd95R//795eLy62cJYWFhSkpKUtOmTSuzVwAAAAAAUAEVCv+vvfaaHnjgAQ0fPlyBgYHnrfHz89O//vWvq2oOAAAAAABcvQqF/wMHDlyyxm63Ky4uriK7BwAAAAAAlahC1/wvWLBAS5cuLTe+dOlSvf3221fdFAAAAAAAqDwVCv8zZsyQr69vuXE/Pz/97W9/u+qmAAAAAABA5alQ+M/MzFRYWFi58dDQUGVmZl51UwAAAAAAoPJUKPz7+flp9+7d5ca//PJLNWrU6KqbAgAAAAAAladC4X/YsGF64okntGHDBpWUlKikpETr16/XmDFjNHTo0MruEQAAAAAAXIUKrfb/7LPP6ttvv1Xv3r3l5vbrLkpLS3X//fdzzT8AAAAAADVMhcK/3W7XkiVL9Oyzz+rLL7+Up6en2rdvr9DQ0MruDwAAAAAAXKUKhf8yLVu2VMuWLSurFwAAAAAAUAUqFP5LSkqUlJSkdevW6ejRoyotLXXYvn79+kppDgAAAAAAXL0Khf8xY8YoKSlJAwYMULt27WSz2Sq7LwAAAAAAUEkqFP7fe+89vf/+++rfv39l9wMAAAAAACpZhX7qz263q3nz5pXdCwAAAAAAqAIVCv/jx4/XvHnzZBhGZfcDAAAAAAAqWYW+9r9582Zt2LBBK1eu1PXXX686deo4bP/www8rpTkAAAAAAHD1KhT+GzZsqDvvvLOyewEAAAAAAFWgQuF/wYIFld0HAAAAAACoIhW65l+Szp49q7Vr1+r111/XyZMnJUlHjhzRqVOnKq05AAAAAABw9Sp05v+7775T3759lZmZqaKiIv3hD39QgwYN9I9//ENFRUVKTEys7D4BAAAAAEAFVejM/5gxY9S5c2f9/PPP8vT0NMfvvPNOrVu3rtKaAwAAAAAAV69CZ/4//fRTbd26VXa73WG8adOm+vHHHyulMQAAAAAAUDkqdOa/tLRUJSUl5cZ/+OEHNWjQ4KqbAgAAAAAAladC4b9Pnz6aO3eued9ms+nUqVOaOnWq+vfvX1m9AQAAAACASlChr/3PmjVLMTExatu2rU6fPq177rlHBw4ckK+vr/79739Xdo8AAAAAAOAqVCj8BwcH68svv9R7772n3bt369SpUxo5cqTuvfdehwUAAQAAAACA81Xoa/+S5ObmptjYWM2cOVOvvvqqHnzwwSsO/ps2bdJtt92moKAg2Ww2LV++3GH78OHDZbPZHG59+/Z1qDl+/LjuvfdeeXl5qWHDhho5cqROnTrlULN7927dcsst8vDwUEhIiGbOnFmul6VLl6p169by8PBQ+/bt9cknn1zRcwEAAAAAoKaq0Jn/d95556Lb77///svaT0FBgTp27KgHHnhAgwYNOm9N3759tWDBAvO+u7u7w/Z7771XWVlZSk5O1pkzZzRixAg99NBDWrx4sSQpPz9fffr0UXR0tBITE/XVV1/pgQceUMOGDfXQQw9JkrZu3aphw4ZpxowZ+uMf/6jFixdr4MCB+uKLL9SuXbvLei4AAAAAANRUFQr/Y8aMcbh/5swZ/fLLL7Lb7apbt+5lh/9+/fqpX79+F61xd3dXQEDAebelp6dr1apV+vzzz9W5c2dJ0ssvv6z+/fvrxRdfVFBQkBYtWqTi4mK99dZbstvtuv7665WWlqbZs2eb4X/evHnq27evnnrqKUnSs88+q+TkZL3yyitKTEy8rOcCAAAAAEBNVaGv/f/8888Ot1OnTikjI0NRUVGVvuBfSkqK/Pz81KpVKz366KP66aefzG3btm1Tw4YNzeAvSdHR0XJxcdGOHTvMmu7du8tut5s1MTExysjI0M8//2zWREdHO8wbExOjbdu2XbCvoqIi5efnO9wAAAAAAKiJKnzN/7latGihv//97+W+FXA1+vbtq3feeUfr1q3TP/7xD23cuFH9+vVTSUmJJCk7O1t+fn4Oj3Fzc5OPj4+ys7PNGn9/f4easvuXqinbfj4zZsyQt7e3eQsJCbm6JwsAAAAAQBWp0Nf+L7gzNzcdOXKk0vY3dOhQ89/t27dXhw4d1KxZM6WkpKh3796VNk9FTJo0SQkJCeb9/Px8PgAAAAAAANRIFQr///3vfx3uG4ahrKwsvfLKK+rWrVulNHY+1113nXx9fXXw4EH17t1bAQEBOnr0qEPN2bNndfz4cXOdgICAAOXk5DjUlN2/VM2F1hqQfl2L4NzFBwEAAAAAqIkqFP4HDhzocN9ms6lx48a69dZbNWvWrMro67x++OEH/fTTTwoMDJQkRUZG6sSJE0pNTVV4eLgkaf369SotLVVERIRZ8/TTT+vMmTOqU6eOJCk5OVmtWrXSNddcY9asW7dOY8eONedKTk5WZGRklT0XAAAAAACqS4XCf2lpaaVMfurUKR08eNC8f/jwYaWlpcnHx0c+Pj565plnNHjwYAUEBOjQoUOaMGGCmjdvrpiYGElSmzZt1LdvX40aNUqJiYk6c+aMRo8eraFDhyooKEiSdM899+iZZ57RyJEjNXHiRO3Zs0fz5s3TnDlzzHnHjBmjHj16aNasWRowYIDee+897dy5U2+88UalPE8AAAAAAJyp0hb8q4idO3fqhhtu0A033CBJSkhI0A033KApU6bI1dVVu3fv1u23366WLVtq5MiRCg8P16effurwdftFixapdevW6t27t/r376+oqCiH0O7t7a01a9bo8OHDCg8P1/jx4zVlyhTzZ/4k6eabb9bixYv1xhtvqGPHjvrggw+0fPlytWvXrvpeDAAAAAAAqkiFzvz/dqG7S5k9e/YFt/Xs2VOGYVxw++rVqy+5fx8fHy1evPiiNR06dNCnn3560Zq77rpLd9111yXnAwAAAACgtqlQ+N+1a5d27dqlM2fOqFWrVpKk/fv3y9XVVTfeeKNZZ7PZKqdLAAAAAABQYRUK/7fddpsaNGigt99+21w07+eff9aIESN0yy23aPz48ZXaJAAAAAAAqLgKXfM/a9YszZgxwwz+knTNNdfoueeeq9LV/gEAAAAAwJWrUPjPz8/XsWPHyo0fO3ZMJ0+evOqmAAAAAABA5alQ+L/zzjs1YsQIffjhh/rhhx/0ww8/6D//+Y9GjhypQYMGVXaPAAAAAADgKlTomv/ExEQ9+eSTuueee3TmzJlfd+TmppEjR+qFF16o1AYBAAAAAMDVqVD4r1u3rl599VW98MILOnTokCSpWbNmqlevXqU2BwAAAAAArl6FvvZfJisrS1lZWWrRooXq1asnwzAqqy8AAAAAAFBJKhT+f/rpJ/Xu3VstW7ZU//79lZWVJUkaOXIkP/MHAAAAAEANU6HwP27cONWpU0eZmZmqW7euOT5kyBCtWrWq0poDAAAAAABXr0LX/K9Zs0arV69WcHCww3iLFi303XffVUpjAAAAAACgclTozH9BQYHDGf8yx48fl7u7+1U3BQAAAAAAKk+Fwv8tt9yid955x7xvs9lUWlqqmTNnqlevXpXWHAAAAAAAuHoV+tr/zJkz1bt3b+3cuVPFxcWaMGGC9u7dq+PHj2vLli2V3SMAAAAAALgKFTrz365dO+3fv19RUVG64447VFBQoEGDBmnXrl1q1qxZZfcIAAAAAACuwhWf+T9z5oz69u2rxMREPf3001XREwAAAAAAqERXfOa/Tp062r17d1X0AgAAAAAAqkCFvvYfGxurf/3rX5XdCwAAAAAAqAIVWvDv7Nmzeuutt7R27VqFh4erXr16Dttnz55dKc0BAAAAAICrd0Xh/5tvvlHTpk21Z88e3XjjjZKk/fv3O9TYbLbK6w4AAAAAAFy1Kwr/LVq0UFZWljZs2CBJGjJkiF566SX5+/tXSXMAAAAAAODqXdE1/4ZhONxfuXKlCgoKKrUhAAAAAABQuSq04F+Zcz8MAAAAAAAANc8VhX+bzVbumn6u8QcAAAAAoGa7omv+DcPQ8OHD5e7uLkk6ffq0HnnkkXKr/X/44YeV1yEAAAAAALgqVxT+4+LiHO7HxsZWajMAAAAAAKDyXVH4X7BgQVX1AQAAAAAAqshVLfgHAAAAAABqPsI/AAAAAAAWd0Vf+wdQc6Snp1fbXL6+vmrSpEm1zQcAAACgchH+gVomPydfNhdbtS646VnXU/vS9/EBAAAAAFBLEf6BWqYwr1BGqaHY12Pl39K/yufL2Z+jhQ8vVG5uLuEfAAAAqKUI/0At5d/SXyEdQ5zdBgAAAIBagAX/AAAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxTk1/G/atEm33XabgoKCZLPZtHz5cofthmFoypQpCgwMlKenp6Kjo3XgwAGHmuPHj+vee++Vl5eXGjZsqJEjR+rUqVMONbt379Ytt9wiDw8PhYSEaObMmeV6Wbp0qVq3bi0PDw+1b99en3zySaU/XwAAAAAAnMGp4b+goEAdO3bU/Pnzz7t95syZeumll5SYmKgdO3aoXr16iomJ0enTp82ae++9V3v37lVycrI+/vhjbdq0SQ899JC5PT8/X3369FFoaKhSU1P1wgsvaNq0aXrjjTfMmq1bt2rYsGEaOXKkdu3apYEDB2rgwIHas2dP1T15AAAAAACqiZszJ+/Xr5/69et33m2GYWju3LmaPHmy7rjjDknSO++8I39/fy1fvlxDhw5Venq6Vq1apc8//1ydO3eWJL388svq37+/XnzxRQUFBWnRokUqLi7WW2+9Jbvdruuvv15paWmaPXu2+SHBvHnz1LdvXz311FOSpGeffVbJycl65ZVXlJiYWA2vBAAAAAAAVafGXvN/+PBhZWdnKzo62hzz9vZWRESEtm3bJknatm2bGjZsaAZ/SYqOjpaLi4t27Nhh1nTv3l12u92siYmJUUZGhn7++Wez5rfzlNWUzXM+RUVFys/Pd7gBAAAAAFAT1djwn52dLUny9/d3GPf39ze3ZWdny8/Pz2G7m5ubfHx8HGrOt4/fznGhmrLt5zNjxgx5e3ubt5CQkCt9igAAAAAAVIsaG/5rukmTJikvL8+8ff/9985uCQAAAACA86qx4T8gIECSlJOT4zCek5NjbgsICNDRo0cdtp89e1bHjx93qDnfPn47x4Vqyrafj7u7u7y8vBxuAAAAAADURDU2/IeFhSkgIEDr1q0zx/Lz87Vjxw5FRkZKkiIjI3XixAmlpqaaNevXr1dpaakiIiLMmk2bNunMmTNmTXJyslq1aqVrrrnGrPntPGU1ZfMAAAAAAFCbOTX8nzp1SmlpaUpLS5P06yJ/aWlpyszMlM1m09ixY/Xcc8/pv//9r7766ivdf//9CgoK0sCBAyVJbdq0Ud++fTVq1Ch99tln2rJli0aPHq2hQ4cqKChIknTPPffIbrdr5MiR2rt3r5YsWaJ58+YpISHB7GPMmDFatWqVZs2apX379mnatGnauXOnRo8eXd0vCQAAAAAAlc6pP/W3c+dO9erVy7xfFsjj4uKUlJSkCRMmqKCgQA899JBOnDihqKgorVq1Sh4eHuZjFi1apNGjR6t3795ycXHR4MGD9dJLL5nbvb29tWbNGsXHxys8PFy+vr6aMmWK+TN/knTzzTdr8eLFmjx5sv7yl7+oRYsWWr58udq1a1cNrwIAAAAAAFXLqeG/Z8+eMgzjgtttNpumT5+u6dOnX7DGx8dHixcvvug8HTp00KeffnrRmrvuukt33XXXxRsGfsfS09OrdT5fX181adKkWucEAAAArMqp4R9AzZefky+bi02xsbHVOq9nXU/tS9/HBwAAAABAJSD8A7iowrxCGaWGYl+PlX9L/2qZM2d/jhY+vFC5ubmEfwAAAKASEP4BXBb/lv4K6Rji7DYAAAAAVECN/ak/AAAAAABQOQj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO1f4B1Fjp6enVNpevry8/KwgAAADLIvwDqHHyc/Jlc7EpNja22ub0rOupfen7+AAAAAAAlkT4B1DjFOYVyig1FPt6rPxb+lf5fDn7c7Tw4YXKzc0l/AMAAMCSCP8Aaiz/lv4K6Rji7DYAAACAWo8F/wAAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACzOzdkNAEBNkZ6eXm1z+fr6qkmTJtU2HwAAAH7fCP8Afvfyc/Jlc7EpNja22ub0rOupfen7+AAAAAAA1YLwD+B3rzCvUEapodjXY+Xf0r/K58vZn6OFDy9Ubm4u4R8AAADVgvAPAP/Hv6W/QjqGOLsNAAAAoNIR/gHASVhjAAAAANWlRof/adOm6ZlnnnEYa9Wqlfbt2ydJOn36tMaPH6/33ntPRUVFiomJ0auvvip/////td3MzEw9+uij2rBhg+rXr6+4uDjNmDFDbm7//6mnpKQoISFBe/fuVUhIiCZPnqzhw4dXy3ME8PvDGgMAAACobjU6/EvS9ddfr7Vr15r3fxvax40bpxUrVmjp0qXy9vbW6NGjNWjQIG3ZskWSVFJSogEDBiggIEBbt25VVlaW7r//ftWpU0d/+9vfJEmHDx/WgAED9Mgjj2jRokVat26dHnzwQQUGBiomJqZ6nyyA3wXWGAAAAEB1q/Hh383NTQEBAeXG8/Ly9K9//UuLFy/WrbfeKklasGCB2rRpo+3bt6tr165as2aNvv76a61du1b+/v7q1KmTnn32WU2cOFHTpk2T3W5XYmKiwsLCNGvWLElSmzZttHnzZs2ZM4fwD6BKscYAAAAAqouLsxu4lAMHDigoKEjXXXed7r33XmVmZkqSUlNTdebMGUVHR5u1rVu3VpMmTbRt2zZJ0rZt29S+fXuHywBiYmKUn5+vvXv3mjW/3UdZTdk+LqSoqEj5+fkONwAAAAAAaqIaHf4jIiKUlJSkVatW6bXXXtPhw4d1yy236OTJk8rOzpbdblfDhg0dHuPv76/s7GxJUnZ2tkPwL9tetu1iNfn5+SosLLxgbzNmzJC3t7d5Cwnh7B0AAAAAoGaq0V/779evn/nvDh06KCIiQqGhoXr//ffl6enpxM6kSZMmKSEhwbyfn5/PBwAAAAAAgBqpRp/5P1fDhg3VsmVLHTx4UAEBASouLtaJEyccanJycsw1AgICApSTk1Nue9m2i9V4eXld9AMGd3d3eXl5OdwAAAAAAKiJalX4P3XqlA4dOqTAwECFh4erTp06Wrdunbk9IyNDmZmZioyMlCRFRkbqq6++0tGjR82a5ORkeXl5qW3btmbNb/dRVlO2DwAAAAAAarsaHf6ffPJJbdy4Ud9++622bt2qO++8U66urho2bJi8vb01cuRIJSQkaMOGDUpNTdWIESMUGRmprl27SpL69Omjtm3b6r777tOXX36p1atXa/LkyYqPj5e7u7sk6ZFHHtE333yjCRMmaN++fXr11Vf1/vvva9y4cc586gAAAAAAVJoafc3/Dz/8oGHDhumnn35S48aNFRUVpe3bt6tx48aSpDlz5sjFxUWDBw9WUVGRYmJi9Oqrr5qPd3V11ccff6xHH31UkZGRqlevnuLi4jR9+nSzJiwsTCtWrNC4ceM0b948BQcH68033+Rn/gAAAAAAllGjw/9777130e0eHh6aP3++5s+ff8Ga0NBQffLJJxfdT8+ePbVr164K9QgAAAAAQE1Xo7/2DwAAAAAArh7hHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAi3NzdgOofpmZmcrNza22+dLT06ttLgAAAABAeYT/35nMzEy1btNahb8UOrsVAAAAAEA1Ifz/zuTm5qrwl0LFvh4r/5b+1TLn12u/1srnV1bLXAAAAACA8gj/v1P+Lf0V0jGkWubK2Z9TLfMAAAAAAM6PBf8AAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAi3NzdgMAgOqRnp5ebXP5+vqqSZMm1TYfAAAALo7wDwAWl5+TL5uLTbGxsdU2p2ddT+1L38cHAAAAADUE4R8ALK4wr1BGqaHY12Pl39K/yufL2Z+jhQ8vVG5uLuEfAACghiD8A8DvhH9Lf4V0DHF2GwAAAHACFvwDAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHFuzm4AAGBN6enp1TaXr6+vmjRpUm3zAQAA1DaEfwBApcrPyZfNxabY2Nhqm9Ozrqf2pe/jAwAAAIALIPwDACpVYV6hjFJDsa/Hyr+lf5XPl7M/RwsfXqjc3FzCPwAAwAUQ/gEAVcK/pb9COoY4uw0AAACIBf8AAAAAALA8wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBwL/gEALCE9Pb1a5/P19eXXBQAAQK1B+AcA1Gr5OfmyudgUGxtbrfN61vXUvvR9fAAAAABqBcI/AKBWK8wrlFFqKPb1WPm39K+WOXP252jhwwuVm5tL+AcAALUC4R8AYAn+Lf0V0jHE2W0AAADUSCz4BwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIvjp/4AAKig9PT0apvL19dXTZo0qbb5AACAtRD+AQC4Qvk5+bK52BQbG1ttc3rW9dS+9H18AAAAACqE8A8AwBUqzCuUUWoo9vVY+bf0r/L5cvbnaOHDC5Wbm0v4BwAAFUL4BwCggvxb+iukY4iz2wAAALgkwv855s+frxdeeEHZ2dnq2LGjXn75ZXXp0sXZbQEAwBoDAACgwgj/v7FkyRIlJCQoMTFRERERmjt3rmJiYpSRkSE/Pz9ntwcA+J1ijQEAAHC1CP+/MXv2bI0aNUojRoyQJCUmJmrFihV666239Oc//9nJ3QEAfq9+D2sMZGZmKjc3t1rmkqSioiK5u7tX23wS36YAADgX4f//FBcXKzU1VZMmTTLHXFxcFB0drW3btpWrLyoqUlFRkXk/Ly9PkpSfn1/1zV6FU6dOSZK+//J7FRUUXaK6cmQfyK7WOZmvds/njDmZj/lq+pxl8xUXFlfLfMWFxZKk1NRU878bVSknJ0f33X+fik5Xz99PkmSTZFTfdJLk7uGud995V/7+Vf8BjvTr/48pLS2tlrmYr/bP54w5ma92z+eMOat7voCAAAUEBFTbfBVRlj8N49L/UbMZl1P1O3DkyBFde+212rp1qyIjI83xCRMmaOPGjdqxY4dD/bRp0/TMM89Ud5sAAAAAADj4/vvvFRwcfNEazvxX0KRJk5SQkGDeLy0t1fHjx9WoUSPZbLZLPj4/P18hISH6/vvv5eXlVZWt4neGYwtVhWMLVYVjC1WFYwtVhWMLVeVKjy3DMHTy5EkFBQVdspbw/398fX3l6uqqnJwch/GcnJzzftXD3d293LWCDRs2vOJ5vby8eMNAleDYQlXh2EJV4dhCVeHYQlXh2EJVuZJjy9vb+7LqXK6mISux2+0KDw/XunXrzLHS0lKtW7fO4TIAAAAAAABqG878/0ZCQoLi4uLUuXNndenSRXPnzlVBQYG5+j8AAAAAALUR4f83hgwZomPHjmnKlCnKzs5Wp06dtGrVqipZldfd3V1Tp06t9p8ZgvVxbKGqcGyhqnBsoapwbKGqcGyhqlTlscVq/wAAAAAAWBzX/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMK/k8yfP19NmzaVh4eHIiIi9Nlnnzm7JdRy06ZNk81mc7i1bt3a2W2hFtq0aZNuu+02BQUFyWazafny5Q7bDcPQlClTFBgYKE9PT0VHR+vAgQPOaRa1yqWOreHDh5d7H+vbt69zmkWtMWPGDN10001q0KCB/Pz8NHDgQGVkZDjUnD59WvHx8WrUqJHq16+vwYMHKycnx0kdo7a4nGOrZ8+e5d63HnnkESd1jNritddeU4cOHeTl5SUvLy9FRkZq5cqV5vaqes8i/DvBkiVLlJCQoKlTp+qLL75Qx44dFRMTo6NHjzq7NdRy119/vbKysszb5s2bnd0SaqGCggJ17NhR8+fPP+/2mTNn6qWXXlJiYqJ27NihevXqKSYmRqdPn67mTlHbXOrYkqS+ffs6vI/9+9//rsYOURtt3LhR8fHx2r59u5KTk3XmzBn16dNHBQUFZs24ceP0v//9T0uXLtXGjRt15MgRDRo0yIldoza4nGNLkkaNGuXwvjVz5kwndYzaIjg4WH//+9+VmpqqnTt36tZbb9Udd9yhvXv3SqrC9ywD1a5Lly5GfHy8eb+kpMQICgoyZsyY4cSuUNtNnTrV6Nixo7PbgMVIMpYtW2beLy0tNQICAowXXnjBHDtx4oTh7u5u/Pvf/3ZCh6itzj22DMMw4uLijDvuuMMp/cA6jh49akgyNm7caBjGr+9RderUMZYuXWrWpKenG5KMbdu2OatN1ELnHluGYRg9evQwxowZ47ymYBnXXHON8eabb1bpexZn/qtZcXGxUlNTFR0dbY65uLgoOjpa27Ztc2JnsIIDBw4oKChI1113ne69915lZmY6uyVYzOHDh5Wdne3wHubt7a2IiAjew1ApUlJS5Ofnp1atWunRRx/VTz/95OyWUMvk5eVJknx8fCRJqampOnPmjMP7VuvWrdWkSRPet3BFzj22yixatEi+vr5q166dJk2apF9++cUZ7aGWKikp0XvvvaeCggJFRkZW6XuW29U2iyuTm5urkpIS+fv7O4z7+/tr3759TuoKVhAREaGkpCS1atVKWVlZeuaZZ3TLLbdoz549atCggbPbg0VkZ2dL0nnfw8q2ARXVt29fDRo0SGFhYTp06JD+8pe/qF+/ftq2bZtcXV2d3R5qgdLSUo0dO1bdunVTu3btJP36vmW329WwYUOHWt63cCXOd2xJ0j333KPQ0FAFBQVp9+7dmjhxojIyMvThhx86sVvUBl999ZUiIyN1+vRp1a9fX8uWLVPbtm2VlpZWZe9ZhH/AIvr162f+u0OHDoqIiFBoaKjef/99jRw50omdAcDlGTp0qPnv9u3bq0OHDmrWrJlSUlLUu3dvJ3aG2iI+Pl579uxhzRtUugsdWw899JD57/bt2yswMFC9e/fWoUOH1KxZs+puE7VIq1atlJaWpry8PH3wwQeKi4vTxo0bq3ROvvZfzXx9feXq6lputcacnBwFBAQ4qStYUcOGDdWyZUsdPHjQ2a3AQsrep3gPQ3W47rrr5Ovry/sYLsvo0aP18ccfa8OGDQoODjbHAwICVFxcrBMnTjjU876Fy3WhY+t8IiIiJIn3LVyS3W5X8+bNFR4erhkzZqhjx46aN29elb5nEf6rmd1uV3h4uNatW2eOlZaWat26dYqMjHRiZ7CaU6dO6dChQwoMDHR2K7CQsLAwBQQEOLyH5efna8eOHbyHodL98MMP+umnn3gfw0UZhqHRo0dr2bJlWr9+vcLCwhy2h4eHq06dOg7vWxkZGcrMzOR9Cxd1qWPrfNLS0iSJ9y1csdLSUhUVFVXpexZf+3eChIQExcXFqXPnzurSpYvmzp2rgoICjRgxwtmtoRZ78sknddtttyk0NFRHjhzR1KlT5erqqmHDhjm7NdQyp06dcjhjcfjwYaWlpcnHx0dNmjTR2LFj9dxzz6lFixYKCwvTX//6VwUFBWngwIHOaxq1wsWOLR8fHz3zzDMaPHiwAgICdOjQIU2YMEHNmzdXTEyME7tGTRcfH6/Fixfro48+UoMGDcxrYr29veXp6Slvb2+NHDlSCQkJ8vHxkZeXlx5//HFFRkaqa9euTu4eNdmljq1Dhw5p8eLF6t+/vxo1aqTdu3dr3Lhx6t69uzp06ODk7lGTTZo0Sf369VOTJk108uRJLV68WCkpKVq9enXVvmdd3Q8SoKJefvllo0mTJobdbje6dOlibN++3dktoZYbMmSIERgYaNjtduPaa681hgwZYhw8eNDZbaEW2rBhgyGp3C0uLs4wjF9/7u+vf/2r4e/vb7i7uxu9e/c2MjIynNs0aoWLHVu//PKL0adPH6Nx48ZGnTp1jNDQUGPUqFFGdna2s9tGDXe+Y0qSsWDBArOmsLDQeOyxx4xrrrnGqFu3rnHnnXcaWVlZzmsatcKljq3MzEyje/fuho+Pj+Hu7m40b97ceOqpp4y8vDznNo4a74EHHjBCQ0MNu91uNG7c2Ojdu7exZs0ac3tVvWfZDMMwru7jAwAAAAAAUJNxzT8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AALhqw4cP18CBAyt9v9nZ2frDH/6gevXqqWHDhpW+/8pQVc8dAIDKRPgHAKCWqAkh89tvv5XNZlNaWlq1zDdnzhxlZWUpLS1N+/fvL7d91apVstlsys7OdhgPDAxU06ZNHcbKel+3bl1VtgwAQI1E+AcAADXWoUOHFB4erhYtWsjPz6/c9qioKLm5uSklJcUcS09PV2FhoX7++Wd9++235viGDRvk7u6ubt26VaiXM2fOVOhxAADUBIR/AAAsYs+ePerXr5/q168vf39/3XfffcrNzTW39+zZU0888YQmTJggHx8fBQQEaNq0aQ772Ldvn6KiouTh4aG2bdtq7dq1stlsWr58uSQpLCxMknTDDTfIZrOpZ8+eDo9/8cUXFRgYqEaNGik+Pv6Sgfm1115Ts2bNZLfb1apVK7377rvmtqZNm+o///mP3nnnHdlsNg0fPrzc4+vXr6+bbrrJIfynpKQoKipK3bp1KzfetWtXeXh4qLS0VNOnT1dwcLDc3d3VqVMnrVq1yqwt+5bAkiVL1KNHD3l4eGjRokUqKSlRQkKCGjZsqEaNGmnChAkyDMOhpw8++EDt27eXp6enGjVqpOjoaBUUFFz0dQAAoKoR/gEAsIATJ07o1ltv1Q033KCdO3dq1apVysnJ0d133+1Q9/bbb6tevXrasWOHZs6cqenTpys5OVmSVFJSooEDB6pu3brasWOH3njjDT399NMOj//ss88kSWvXrlVWVpY+/PBDc9uGDRt06NAhbdiwQW+//baSkpKUlJR0wZ6XLVumMWPGaPz48dqzZ48efvhhjRgxQhs2bJAkff755+rbt6/uvvtuZWVlad68eefdT69evczHlPXRs2dP9ejRw2E8JSVFvXr1kiTNmzdPs2bN0osvvqjdu3crJiZGt99+uw4cOOCw7z//+c8aM2aM0tPTFRMTo1mzZikpKUlvvfWWNm/erOPHj2vZsmVmfVZWloYNG6YHHnhA6enpSklJ0aBBg8p9QAAAQLUzAABArRAXF2fccccd59327LPPGn369HEY+/777w1JRkZGhmEYhtGjRw8jKirKoeamm24yJk6caBiGYaxcudJwc3MzsrKyzO3JycmGJGPZsmWGYRjG4cOHDUnGrl27yvUWGhpqnD171hy76667jCFDhlzw+dx8883GqFGjHMbuuusuo3///ub9O+64w4iLi7vgPn7b45EjRwzDMAw/Pz/js88+M7Zu3WqEhoYahmEYhw4dMiQZGzduNAzDMIKCgoznn3++3Gvx2GOPOTzPuXPnOtQEBgYaM2fONO+fOXPGCA4ONv8uqamphiTj22+/vWjPAABUN878AwBgAV9++aU2bNig+vXrm7fWrVtL+vW6+TIdOnRweFxgYKCOHj0qScrIyFBISIgCAgLM7V26dLnsHq6//nq5urqed9/nk56eXu76+27duik9Pf2y55Skm2++WXa7XSkpKfr6669VWFioG2+8UZ07d9axY8d0+PBhpaSkyNPTU127dlV+fr6OHDlyWXN37tzZ/HdeXp6ysrIUERFhjrm5uTnUdOzYUb1791b79u1111136Z///Kd+/vnnK3o+AABUBTdnNwAAAK7eqVOndNttt+kf//hHuW2BgYHmv+vUqeOwzWazqbS0tFJ6qMp9X0zdunXVpUsXbdiwQcePH1dUVJRcXV3l6uqqm2++WRs2bNCGDRvUrVs32e12nT59+rL3Xa9evSvqxdXVVcnJydq6davWrFmjl19+WU8//bR27NhhrpcAAIAzcOYfAAALuPHGG7V37141bdpUzZs3d7hdboBt1aqVvv/+e+Xk5Jhjn3/+uUON3W6X9Ov6AFerTZs22rJli8PYli1b1LZt2yveV69evZSSkqKUlBSHRQi7d++ulJQUbdy40bze38vLS0FBQVc8t7e3twIDA7Vjxw5z7OzZs0pNTXWos9ls6tatm5555hnt2rVLdrvdYV0AAACcgTP/AADUInl5eUpLS3MYK1tZ/5///KeGDRtmruZ/8OBBvffee3rzzTcdvo5/IX/4wx/UrFkzxcXFaebMmTp58qQmT54s6ddAK0l+fn7y9PTUqlWrFBwcLA8PD3l7e1fouTz11FO6++67dcMNNyg6Olr/+9//9OGHH2rt2rVXvK9evXrp2WefVXZ2tp588klzvEePHnrhhRd08uRJM/yXzT116lQ1a9ZMnTp10oIFC5SWlqZFixZddJ4xY8bo73//u1q0aKHWrVtr9uzZOnHihLl9x44dWrdunfr06SM/Pz/t2LFDx44dU5s2ba74OQEAUJkI/wAA1CIpKSm64YYbHMZGjhypN998U1u2bNHEiRPVp08fFRUVKTQ0VH379pWLy+V90c/V1VXLly/Xgw8+qJtuuknXXXedXnjhBd12223y8PCQ9Os17i+99JKmT5+uKVOm6JZbbnH4Ob0rMXDgQM2bN08vvviixowZo7CwMC1YsKDczwdejsjISLm7u8swDIWHh5vjEREROnPmjPmTgGWeeOIJ5eXlafz48Tp69Kjatm2r//73v2rRosVF5xk/fryysrIUFxcnFxcXPfDAA7rzzjuVl5cn6ddvFWzatElz585Vfn6+QkNDNWvWLPXr1++KnxMAAJXJZhj89gwAADi/LVu2KCoqSgcPHlSzZs2c3Q4AAKggwj8AADAtW7ZM9evXV4sWLXTw4EGNGTNG11xzjTZv3uzs1gAAwFXga/8AAMB08uRJTZw4UZmZmfL19VV0dLRmzZrl7LYAAMBV4sw/AAAAAAAWx0/9AQAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAi/t/iFqia3IbS6YAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "W3wbIudQA5Cp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RSUl9e7xA5FX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Saving EDA results to CSV"
      ],
      "metadata": {
        "id": "x6cxtJS-A5JL"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eda_results = pd.DataFrame({\n",
        "    'Total Lines': [len(content)],\n",
        "    'Total Words': [len(words)],\n",
        "    'Average Line Length': [np.mean([len(line) for line in content])],\n",
        "    'Most Common Words': [most_common_words]\n",
        "})"
      ],
      "metadata": {
        "id": "T2ZTSalgA_m4"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eda_results.to_csv('eda_results_gutenberg.csv', index=False)  # Save the summary to a CSV file\n",
        "print(\"EDA results saved to 'eda_results_gutenberg.csv'.\")  # Confirmation message"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NOY9uxjuA_qB",
        "outputId": "e72f3ad5-f497-4052-fadc-8ff04f84cd37"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EDA results saved to 'eda_results_gutenberg.csv'.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "m9g9_nc7BG7D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Hqw37yfTA5Mb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EG-Cfw5C-NWn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "KbuqSEdiJ41a"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load GPT-2 Tokenizer and Model"
      ],
      "metadata": {
        "id": "Ek4ncWMZ54Bz"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "xNf75em0J6C4"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")  # Load the pre-trained GPT-2 tokenizer"
      ],
      "metadata": {
        "id": "-mm8ABJN54EK"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = GPT2LMHeadModel.from_pretrained(\"gpt2\")  # Load the pre-trained GPT-2 language model"
      ],
      "metadata": {
        "id": "TghVunTb54Gi"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.train()  # Set the model to training mode"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gRHuxRWQ54LF",
        "outputId": "92f898f4-cb2a-4a95-e89d-1ff97d1506f8"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GPT2LMHeadModel(\n",
              "  (transformer): GPT2Model(\n",
              "    (wte): Embedding(50257, 768)\n",
              "    (wpe): Embedding(1024, 768)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "    (h): ModuleList(\n",
              "      (0-11): 12 x GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2SdpaAttention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (act): NewGELUActivation()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=768, out_features=50257, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sp1yBkMY54PY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "54QiXao954SF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JcycBnJCJ-pY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "wpjW2nkuJ-wm"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare Your Text Data"
      ],
      "metadata": {
        "id": "stBDojw954Uc"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "lvEPL0leKALi"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Loading text data...\")  # Print a message indicating that text data is being loaded"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iIDSMfFr54Wg",
        "outputId": "55d8ee57-5bb0-492a-9328-0349aa196672"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading text data...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_data = data  # Assign the entire content of the dataset to a variable"
      ],
      "metadata": {
        "id": "ZLSg5RGd54Ym"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Text data loaded. Length: {len(text_data)} characters.\")  # Print the length of the loaded text data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-pKFcg6254dF",
        "outputId": "691b2e7a-eb43-4edf-ee7e-9965127be7f5"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Text data loaded. Length: 738046 characters.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LUiKWoXe54fx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WPJHpgS054iS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6E7w7A42KDIA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1 style=\"color: #4B0082;\">Model Training</h1>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Overview</h2>\n",
        "\n",
        "<p>This step involves fine-tuning the GPT-2 model on the preprocessed text data. Model training is a critical phase that determines the performance of the final text generation.</p>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Key Tasks in Model Training</h2>\n",
        "\n",
        "<ul>\n",
        "    <li><strong>Model Initialization:</strong> Load the pre-trained GPT-2 model and tokenizer from the Transformers library.</li>\n",
        "    <li><strong>Tokenization:</strong> Tokenize the preprocessed text data, converting the text into a format suitable for model input.</li>\n",
        "    <li><strong>Dataset Creation:</strong> Create a custom dataset class to manage the tokenized data for batch processing during training.</li>\n",
        "    <li><strong>Training Loop:</strong> Implement the training loop to optimize the model based on the input data, monitoring loss metrics and saving checkpoints.</li>\n",
        "    <li><strong>Hyperparameter Tuning:</strong> Experiment with various hyperparameters to achieve optimal training results, minimizing the risk of overfitting.</li>\n",
        "</ul>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Importance of Model Training</h2>\n",
        "\n",
        "<p>Training the model correctly is essential for the following reasons:</p>\n",
        "<ul>\n",
        "    <li><strong>Model Performance:</strong> Proper training leads to better understanding and generation capabilities of the model, improving the quality of text generated.</li>\n",
        "    <li><strong>Customization:</strong> Fine-tuning the model on specific data allows for tailored responses relevant to the given dataset.</li>\n",
        "</ul>\n",
        "\n",
        "<p>In summary, this step is pivotal for creating a robust model capable of generating high-quality text outputs.</p>"
      ],
      "metadata": {
        "id": "7He2cHyFLjQ5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "ZDHjiI91KDMD"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenize the Text Data"
      ],
      "metadata": {
        "id": "kNa_kuMb54kN"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "6c6Yc0d1KEyL"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Tokenizing text data...\")  # Print a message indicating that text data is being tokenized"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fGk2E_e254uA",
        "outputId": "00481ae5-d455-436d-efe3-145283c1bcaf"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tokenizing text data...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokens = tokenizer.encode(text_data, return_tensors='pt')  # Tokenize the text and convert it to a tensor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HC2CEpIm8AAx",
        "outputId": "b6030f24-6d56-4007-8c05-9b482b2ee8fa"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (206061 > 1024). Running this sequence through the model will result in indexing errors\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VIfMQ1ibC6s5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Tokenization complete. Number of tokens: {tokens.size(1)}\")  # Print the number of tokens generated"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z5vf-y298AEB",
        "outputId": "0907e69a-6bd8-494d-ba14-831cb3c1974e"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tokenization complete. Number of tokens: 206061\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "O4Jn3Wsg8AHN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pVXYwU8v8AKL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dT7D5idwKHsi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "fCgBgBrZKHvR"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Custom Dataset"
      ],
      "metadata": {
        "id": "_-ZqMjpO8ANF"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "aJyPxFfOKI_m"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class TextDataset(Dataset):\n",
        "    def __init__(self, encodings):\n",
        "        self.encodings = encodings\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        return {'input_ids': self.encodings[idx]}  # Return input_ids tensor\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.encodings)"
      ],
      "metadata": {
        "id": "KJM_O3Lp8ARJ"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "F-GVMSaR8LKL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Grouping tokens into a dataset"
      ],
      "metadata": {
        "id": "ygvGX9GA8LMp"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_dataset(tokens, block_size=128):\n",
        "    input_ids = tokens.tolist()[0]  # Get input_ids from tensor\n",
        "    total_length = len(input_ids)\n",
        "\n",
        "    # Adjust total_length to be a multiple of block_size\n",
        "    if total_length < block_size:\n",
        "        print(\"Warning: total_length is less than block_size. Returning the entire input as a single sample.\")\n",
        "        input_ids = [input_ids]  # Wrap it in a list\n",
        "    else:\n",
        "        # Create blocks of input_ids, ensuring they don't exceed max length\n",
        "        input_ids = [input_ids[i:i + block_size] for i in range(0, total_length, block_size) if i + block_size <= total_length]\n",
        "\n",
        "    return TextDataset(input_ids)"
      ],
      "metadata": {
        "id": "rQ1eGkXA8LPO"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0hkOKrMZ8RIT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create dataset"
      ],
      "metadata": {
        "id": "bQpi6qVJ8RLq"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = create_dataset(tokens, block_size=128)  # Create a dataset with a specified block size"
      ],
      "metadata": {
        "id": "LKsJBwEO8ROc"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Dataset created. Number of samples: {len(dataset)}\")  # Print the number of samples in the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j6beGCAt8RRI",
        "outputId": "54bd93b8-8919-4f73-ecea-e992ecb09977"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset created. Number of samples: 1609\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wNADzfHh8RUK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "B2_KIROC8RXq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "r6GmCJONKP3b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "i0JytM98KQBx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "UdcWBVvQKQEf"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup DataLoader for PyTorch"
      ],
      "metadata": {
        "id": "-A8r8QI28RZh"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "SZ95DK6OKRUa"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 1  # Use a smaller batch size"
      ],
      "metadata": {
        "id": "eScrQhpJ8RgE"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataloader = DataLoader(dataset, batch_size=batch_size)"
      ],
      "metadata": {
        "id": "_ma5WiKq9OnN"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MbDNy9UB9Opt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fOynGANv9OsX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "n-wsZs_UKTq6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "MARPGv90KULD"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Optimizer setup"
      ],
      "metadata": {
        "id": "cDbv_IRi9Oum"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "9kQKs1aMKVbx"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)  # Initialize the AdamW optimizer with a learning rate"
      ],
      "metadata": {
        "id": "5Two6SSQ8fSk"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OujWiS0h9Vot"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "r3Ih3TtPDp3g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ldeBoc83Dp63"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "Akay2nZIKXyz"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training loop"
      ],
      "metadata": {
        "id": "LzT0_u0q9VuY"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "_eYXzI5iKbVB"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training loop\n",
        "print(\"Starting training...\")\n",
        "for epoch in range(1):  # Start with 1 epoch for quicker feedback\n",
        "    for batch in tqdm(dataloader, desc=f'Epoch {epoch + 1}'):\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # Convert input_ids to a tensor and move to the same device as the model\n",
        "        input_ids = torch.tensor(batch['input_ids']).to(model.device)\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = model(input_ids=input_ids, labels=input_ids)\n",
        "        loss = outputs.loss\n",
        "\n",
        "        # Backward pass and optimization\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        print(f'Loss: {loss.item()}')\n",
        "\n",
        "print(\"Training complete.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cBC86SHrGzF-",
        "outputId": "1b093cb3-d420-47ce-8226-2776ec56035a"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting training...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 1: 100%|██████████| 1/1 [00:03<00:00,  3.21s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 3.849533796310425\n",
            "Training complete.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "H2pif0K-9d6f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eEZIBXYd9V3X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xa-3K1QS8fYN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1 style=\"color: #4B0082;\"> Model Evaluation</h1>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Overview</h2>\n",
        "\n",
        "<p>In this step, we will evaluate the performance of the trained GPT-2 model using various metrics and techniques to ensure it meets the desired standards for text generation.</p>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Key Tasks in Model Evaluation</h2>\n",
        "\n",
        "<ul>\n",
        "    <li><strong>Generating Samples:</strong> Use the trained model to generate text samples and assess their quality and coherence.</li>\n",
        "    <li><strong>Evaluation Metrics:</strong> Utilize relevant metrics such as BLEU, ROUGE, and perplexity to quantitatively assess the model's performance.</li>\n",
        "    <li><strong>Human Evaluation:</strong> Optionally, conduct a human evaluation to gather qualitative feedback on the generated text.</li>\n",
        "    <li><strong>Saving Model Checkpoints:</strong> Save the final trained model and any evaluation results for future reference and deployment.</li>\n",
        "</ul>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Importance of Model Evaluation</h2>\n",
        "\n",
        "<p>Evaluating the model is critical for several reasons:</p>\n",
        "<ul>\n",
        "    <li><strong>Ensures Quality:</strong> Thorough evaluation ensures that the model generates high-quality text relevant to the training data.</li>\n",
        "    <li><strong>Informs Future Improvements:</strong> Evaluation results can highlight areas for improvement in model architecture or training processes.</li>\n",
        "</ul>\n",
        "\n",
        "<p>In summary, model evaluation is a vital step that confirms the model's capabilities and readiness for deployment or further use.</p>"
      ],
      "metadata": {
        "id": "10ezxBr7L9y0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "1u9fdEkOKeVx"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Text Generation"
      ],
      "metadata": {
        "id": "P3mqrphR8fay"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "4tOwQlmKKfpq"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the model to evaluation mode"
      ],
      "metadata": {
        "id": "tcPunGEU8fdJ"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.eval()  # Set the model to evaluation mode (disabling dropout, etc.)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KWQtEIHt8fff",
        "outputId": "7d0756e6-bc6b-406f-ec53-4151a2bb538d"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GPT2LMHeadModel(\n",
              "  (transformer): GPT2Model(\n",
              "    (wte): Embedding(50257, 768)\n",
              "    (wpe): Embedding(1024, 768)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "    (h): ModuleList(\n",
              "      (0-11): 12 x GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2SdpaAttention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (act): NewGELUActivation()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=768, out_features=50257, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VO1S473j8fjc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_text(prompt, max_length=100, num_return_sequences=1, temperature=1.0, top_k=50, top_p=0.95):  # Define a function for generating text\n",
        "    \"\"\"\n",
        "    Generate human-like text based on a given prompt using the GPT-2 model.\n",
        "    \"\"\"\n",
        "    # Tokenize the input text\n",
        "    inputs = tokenizer.encode(prompt, return_tensors='pt')  # Tokenize the input prompt and convert it to a tensor\n",
        "\n",
        "    # Generate text\n",
        "    outputs = model.generate(\n",
        "        inputs,\n",
        "        max_length=max_length,  # Set the maximum length of generated text\n",
        "        num_return_sequences=num_return_sequences,  # Set the number of sequences to generate\n",
        "        temperature=temperature,  # Control the randomness of predictions\n",
        "        top_k=top_k,  # Set the number of highest probability vocabulary tokens to keep for top-k sampling\n",
        "        top_p=top_p,  # Set the cumulative probability for top-p sampling\n",
        "        do_sample=True  # Enable sampling\n",
        "    )\n",
        "\n",
        "    # Decode the generated text\n",
        "    generated_texts = [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]  # Decode each generated output to text\n",
        "    return generated_texts  # Return the list of generated texts"
      ],
      "metadata": {
        "id": "Q1cI9D4D8fmA"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ioeM5qdd8tPE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_generated_texts(generated_texts):  # Define a function to evaluate generated texts\n",
        "    \"\"\"\n",
        "    Placeholder evaluation function for generated texts.\n",
        "    \"\"\"\n",
        "    for i, text in enumerate(generated_texts):  # Iterate through generated texts\n",
        "        print(f\"Evaluation for Generated Text {i + 1}:\")  # Print evaluation header\n",
        "        print(f\"Text Length: {len(text.split())} words\\n\")  # Print the length of each generated text"
      ],
      "metadata": {
        "id": "gOtlOjEU8tSG"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KJekOBLC8tV8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def save_generated_texts(generated_texts, filename='generated_texts.txt'):  # Define a function to save generated texts\n",
        "    \"\"\"\n",
        "    Save generated texts to a specified file.\n",
        "    \"\"\"\n",
        "    with open(filename, 'w') as f:  # Open a file to write the generated texts\n",
        "        for text in generated_texts:  # Iterate through generated texts\n",
        "            f.write(text + \"\\n\\n\")  # Write each text followed by a newline"
      ],
      "metadata": {
        "id": "xNJR2S5s8tYE"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "c6UoSNB08tax"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# User Input for Dynamic Text Generation"
      ],
      "metadata": {
        "id": "Y_GtGkYO8fo1"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "while True:  # Start an infinite loop to take user input\n",
        "    user_prompt = input(\"Enter a prompt (or 'exit' to quit): \")  # Prompt user for input\n",
        "    if user_prompt.lower() == 'exit':  # Check if the user wants to exit\n",
        "        break  # Exit the loop\n",
        "    generated_texts = generate_text(user_prompt, max_length=50, num_return_sequences=3, temperature=0.9)  # Generate text based on user prompt\n",
        "    for i, text in enumerate(generated_texts):  # Iterate through generated texts\n",
        "        print(f\"Generated Text {i + 1}:\\n{text}\\n\")  # Print each generated text\n",
        "    evaluate_generated_texts(generated_texts)  # Evaluate the generated texts\n",
        "    save_generated_texts(generated_texts)  # Save the generated texts to a file"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dbcRjrom8fsu",
        "outputId": "e0166b0b-57cf-4619-b7d6-71f4f96689d9"
      },
      "execution_count": 110,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter a prompt (or 'exit' to quit): once upon a time\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generated Text 1:\n",
            "once upon a time I'll leave you with a question, that I hope you'll find helpful.\" The dog's ears were filled with laughter as he answered. The dog had a curious expression as he said, \"Don't worry, I'll take\n",
            "\n",
            "Generated Text 2:\n",
            "once upon a time when there was an urgent need for new materials for the manufacturing of materials and for materials to be made in the factories.\n",
            "\n",
            "1951 The World's Strongest Building and Manufacturing Building: The \"Laser Eye\"\n",
            "\n",
            "\n",
            "\n",
            "Generated Text 3:\n",
            "once upon a time, in such a matter as this, the power of the Church, as manifested in the Scriptures, and the Church on which the Bible is based, may be found sufficient to secure the faithful from the impotence of error.\n",
            "\n",
            "Evaluation for Generated Text 1:\n",
            "Text Length: 39 words\n",
            "\n",
            "Evaluation for Generated Text 2:\n",
            "Text Length: 38 words\n",
            "\n",
            "Evaluation for Generated Text 3:\n",
            "Text Length: 42 words\n",
            "\n",
            "Enter a prompt (or 'exit' to quit): where was the earth located\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generated Text 1:\n",
            "where was the earth located? Was there a planet in the earth?\n",
            "\n",
            "We are asking the earth to answer these questions and it should answer. Earth is a planet in the solar system, in the inner planets, in the outer planets and it\n",
            "\n",
            "Generated Text 2:\n",
            "where was the earth located?\n",
            "\n",
            "A few years ago, we said that it was only about six kilometers away from the coast, but we were wrong. So we're now looking at the same latitude at the time. And we're at the\n",
            "\n",
            "Generated Text 3:\n",
            "where was the earth located?\n",
            "\n",
            "There was a great deal of land spread out.\n",
            "\n",
            "It was very remote.\n",
            "\n",
            "It was very cold.\n",
            "\n",
            "The earth was almost gone.\n",
            "\n",
            "I tried to keep calm.\n",
            "\n",
            "\n",
            "\n",
            "Evaluation for Generated Text 1:\n",
            "Text Length: 43 words\n",
            "\n",
            "Evaluation for Generated Text 2:\n",
            "Text Length: 41 words\n",
            "\n",
            "Evaluation for Generated Text 3:\n",
            "Text Length: 32 words\n",
            "\n",
            "Enter a prompt (or 'exit' to quit): who is the president of Kenya\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generated Text 1:\n",
            "who is the president of Kenya's National Council for Development Studies.)\n",
            "\n",
            "That said, these are largely the kinds of analyses most people would want to be making in the future. To be honest, we're not going to have much time until the\n",
            "\n",
            "Generated Text 2:\n",
            "who is the president of Kenya's largest school system) and former CIA agent George Tenet said that the Islamic State has shown that it can operate within the borders of their land and is ready to attack. He added: 'These are very, very\n",
            "\n",
            "Generated Text 3:\n",
            "who is the president of Kenya's National Development Authority, or NDA) and was responsible for funding $1.6 billion in infrastructure projects and $1.3 billion in other spending, including $200 million to upgrade an electrical substation in Cape\n",
            "\n",
            "Evaluation for Generated Text 1:\n",
            "Text Length: 42 words\n",
            "\n",
            "Evaluation for Generated Text 2:\n",
            "Text Length: 43 words\n",
            "\n",
            "Evaluation for Generated Text 3:\n",
            "Text Length: 37 words\n",
            "\n",
            "Enter a prompt (or 'exit' to quit): exit\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DGQe38zo8LR7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iPxTZPw38ATk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xFv8BoM_8AVo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1 style=\"color: #4B0082;\">Conclusion</h1>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Overview</h2>\n",
        "\n",
        "<p>In this final step, we will summarize the project, reflecting on the processes undertaken and the insights gained throughout the project.</p>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Key Takeaways</h2>\n",
        "\n",
        "<ul>\n",
        "    <li><strong>Comprehensive Pipeline:</strong> The project involved a thorough pipeline from data loading and cleaning to model training, evaluation, and deployment.</li>\n",
        "    <li><strong>Importance of EDA:</strong> Exploratory data analysis played a vital role in understanding the dataset and informing modeling decisions.</li>\n",
        "    <li><strong>Model Training and Evaluation:</strong> Careful training and rigorous evaluation are essential for ensuring high-quality text generation.</li>\n",
        "    <li><strong>Deployment Readiness:</strong> The final model is now ready for deployment, showcasing its potential for practical applications.</li>\n",
        "</ul>\n",
        "\n",
        "<h2 style=\"color: #FF4500;\">Future Directions</h2>\n",
        "\n",
        "<p>Consider potential future directions for the project:</p>\n",
        "<ul>\n",
        "    <li><strong>Model Refinement:</strong> Continually refine the model based on user feedback and performance metrics to enhance text generation quality.</li>\n",
        "    <li><strong>Exploring Additional Datasets:</strong> Experiment with different datasets to diversify the model's capabilities and applications.</li>\n",
        "    <li><strong>Research and Innovation:</strong> Stay updated with advancements in NLP and model architectures to incorporate new techniques into future iterations.</li>\n",
        "</ul>\n",
        "\n",
        "<p>In summary, this project provided valuable insights and skills in data processing, model training, and deployment, paving the way for future endeavors in text generation and NLP.</p>"
      ],
      "metadata": {
        "id": "aAwi9SOvMB2L"
      }
    }
  ]
}