2_8_15_Synthetic_Financial_Sentiment_Data.ipynb

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        "# 🧠 **Project Overview: Synthetic Sentiment Data Generation using GANs**\n",
        "\n",
        "In this project, we’ll be working on generating **synthetic sentiment data** using a **Generative Adversarial Network (GAN)**. The goal is to create realistic financial sentiment data by training the GAN model on existing sentiment-labeled data. This synthetic data can be useful for various applications, such as training models for sentiment analysis or enhancing financial datasets.\n",
        "\n",
        "## 🧹 **Step 1: Data Loading and Exploration**\n",
        "- **Dataset**: The dataset consists of financial phrases and their corresponding sentiment labels (positive, neutral, negative).\n",
        "- We start by **loading the dataset** and inspecting the first few rows to understand its structure.\n",
        "- Key operations include:\n",
        "  - Checking for **missing values** and **duplicates**.\n",
        "  - Displaying general **statistics** of the dataset (mean, std, etc.).\n",
        "\n",
        "## ✨ **Step 2: Text Preprocessing**\n",
        "- Text data is **cleaned** by:\n",
        "  - Removing special characters with regular expressions (e.g., `[^a-zA-Z\\s]`).\n",
        "  - Converting all text to **lowercase** for uniformity.\n",
        "- **Text normalization** ensures we have clean, readable data for model training.\n",
        "\n",
        "## 🔠 **Step 3: Sentiment Encoding**\n",
        "- The sentiment labels are **encoded** into numerical values:\n",
        "  - `positive` → `1`\n",
        "  - `neutral` → `0`\n",
        "  - `negative` → `-1`\n",
        "- This encoding is essential for training machine learning models, as they require numerical inputs.\n",
        "\n",
        "## 🧑‍💻 **Step 4: Feature Extraction**\n",
        "- **Text features** are extracted using the **CountVectorizer**.\n",
        "  - Converts the cleaned phrases into a **bag-of-words** representation.\n",
        "  - We limit the features to the **5000 most frequent words** to keep the data manageable.\n",
        "- This step results in a sparse matrix that is converted to a DataFrame for better inspection.\n",
        "\n",
        "## 🤖 **Step 5: Building the GAN Model**\n",
        "- A **GAN** consists of two main components:\n",
        "  1. **Generator**: Takes random noise as input and generates synthetic data.\n",
        "  2. **Discriminator**: Differentiates between real and fake data.\n",
        "- Both components are defined using the **Keras Sequential API**:\n",
        "  - The **generator** learns to generate realistic synthetic sentiment data.\n",
        "  - The **discriminator** gets better at distinguishing real from synthetic data.\n",
        "  \n",
        "## 🚀 **Step 6: Training the GAN**\n",
        "- **Training Process**:\n",
        "  - The **discriminator** is trained on both real and fake data.\n",
        "  - The **generator** is trained to fool the discriminator into thinking its synthetic data is real.\n",
        "  - This process continues for several **epochs**.\n",
        "- **Loss functions**:\n",
        "  - **Discriminator loss**: Measures how well the discriminator can classify real and fake data.\n",
        "  - **Generator loss**: Measures how well the generator can fool the discriminator.\n",
        "\n",
        "## 🧬 **Step 7: Generating Synthetic Data**\n",
        "- After training, we use the **generator** to produce synthetic sentiment data.\n",
        "- The generated data is then **saved** as a CSV file for further use or analysis.\n",
        "\n",
        "## 📊 **Step 8: Data Visualization**\n",
        "- **Distributions**: We compare the distributions of original and synthetic data using **histograms**.\n",
        "- **Correlation Heatmap**: A heatmap is used to visualize the relationships between features in the synthetic data.\n",
        "  \n",
        "## 🚨 **Key Points to Remember**\n",
        "- The **generator** and **discriminator** are trained simultaneously in an adversarial setup.\n",
        "- The goal of training is to make the **generator** better at producing realistic data and the **discriminator** better at identifying fake data.\n",
        "- After training, the generator can produce **new, synthetic data** that mimics the distribution of real data.\n",
        "\n",
        "---\n",
        "\n",
        "✨ **Congratulations!** You’ve now built and trained a **GAN** to generate **synthetic sentiment data**. This is a great foundation for generating other types of synthetic datasets for various machine learning applications. Keep experimenting and improving the model to make the generated data even more realistic! 💡"
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      "cell_type": "markdown",
      "source": [
        "# 📥 **Step 1: Data Loading and Exploration**\n",
        "\n",
        "In this step, we begin by loading the sentiment dataset and exploring its basic structure.\n",
        "\n",
        "- **Load Data**: The dataset is loaded using Pandas from a specified CSV file. The data is expected to contain two columns: one for financial phrases and another for sentiment labels.\n",
        "  \n",
        "- **Inspect Data**:\n",
        "  - `head()`: Shows the first few rows to give a quick preview of the data.\n",
        "  - `info()`: Provides an overview of the data types, non-null counts, and memory usage.\n",
        "  - `describe()`: Displays statistical summaries of the dataset, including counts, means, and standard deviations for numerical columns.\n",
        "  \n",
        "- **Check for Missing Data**:\n",
        "  - `isnull().sum()`: Identifies if there are any missing values in the dataset. Handling missing data is crucial as it can introduce bias or errors in the model.\n",
        "  \n",
        "- **Check for Duplicate Data**:\n",
        "  - `duplicated().sum()`: Helps identify if there are any duplicate rows that need to be removed. Duplicates can affect the model training, so we remove them to avoid biasing the results."
      ],
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    {
      "cell_type": "code",
      "source": [
        "# Load dataset with specified encoding"
      ],
      "metadata": {
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      "execution_count": 258,
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    },
    {
      "cell_type": "code",
      "source": [
        "file_path = \"/content/all-data.csv\""
      ],
      "metadata": {
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      "execution_count": 259,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "data = pd.read_csv(file_path, header=None, names=[\"phrase\", \"sentiment\"], encoding=\"latin1\")\n"
      ],
      "metadata": {
        "id": "p8ziIvt3sLqo"
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      "execution_count": 260,
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      "cell_type": "code",
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    {
      "cell_type": "code",
      "source": [
        "# Display first 5 rows of the dataset"
      ],
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      },
      "execution_count": 261,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"First 5 rows of the dataset:\")\n",
        "print(data.head(), \"\\n\")"
      ],
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          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "8ecf8505-2665-4df8-8026-d677c1811446"
      },
      "execution_count": 262,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First 5 rows of the dataset:\n",
            "     phrase                                          sentiment\n",
            "0   neutral  According to Gran , the company has no plans t...\n",
            "1   neutral  Technopolis plans to develop in stages an area...\n",
            "2  negative  The international electronic industry company ...\n",
            "3  positive  With the new production plant the company woul...\n",
            "4  positive  According to the company 's updated strategy f... \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GsJWF-uqtfdd"
      },
      "execution_count": 262,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ylfecbXptfgu"
      },
      "execution_count": 262,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display dataset information (types, non-null counts, etc.)"
      ],
      "metadata": {
        "id": "E1k2R7uhtfj8"
      },
      "execution_count": 263,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Dataset Information:\")\n",
        "print(data.info(), \"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xp-zRaUcsL4L",
        "outputId": "5ca8e949-f2ee-4121-e1d2-5dbf61b3c904"
      },
      "execution_count": 264,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset Information:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4846 entries, 0 to 4845\n",
            "Data columns (total 2 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   phrase     4846 non-null   object\n",
            " 1   sentiment  4846 non-null   object\n",
            "dtypes: object(2)\n",
            "memory usage: 75.8+ KB\n",
            "None \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lvepUVXCti7S"
      },
      "execution_count": 264,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cSqGHhknti-f"
      },
      "execution_count": 264,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display dataset statistics"
      ],
      "metadata": {
        "id": "YImlB47ztjBY"
      },
      "execution_count": 265,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Dataset Statistics:\")\n",
        "print(data.describe(include='all'), \"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L02W6SDVsL6n",
        "outputId": "1bbf9cbd-3beb-444d-8672-89803e73d494"
      },
      "execution_count": 266,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset Statistics:\n",
            "         phrase                                          sentiment\n",
            "count      4846                                               4846\n",
            "unique        3                                               4838\n",
            "top     neutral  TELECOMWORLDWIRE-7 April 2006-TJ Group Plc sel...\n",
            "freq       2879                                                  2 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kbG1IXHMtrxB"
      },
      "execution_count": 266,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "A5MYOJxHtr0U"
      },
      "execution_count": 266,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values"
      ],
      "metadata": {
        "id": "8rTeLUaTtr3R"
      },
      "execution_count": 267,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Missing Values in the dataset:\")\n",
        "print(data.isnull().sum(), \"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C6WEULqStr5u",
        "outputId": "d475ddfe-7808-4a36-b87b-9e7f507082cc"
      },
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing Values in the dataset:\n",
            "phrase       0\n",
            "sentiment    0\n",
            "dtype: int64 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tsdeZPUotzY4"
      },
      "execution_count": 268,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jhcsn7lctzb0"
      },
      "execution_count": 268,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for duplicate rows"
      ],
      "metadata": {
        "id": "WUVpeSjutzfL"
      },
      "execution_count": 269,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "duplicates = data.duplicated().sum()"
      ],
      "metadata": {
        "id": "6EKBwTsdtzi4"
      },
      "execution_count": 270,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Number of duplicate rows: {duplicates}\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W68Hf4zltzmh",
        "outputId": "b60e40f1-4dae-474a-9a92-40c5b4fe9d5f"
      },
      "execution_count": 271,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of duplicate rows: 6\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hmUqsefFt7Xb"
      },
      "execution_count": 271,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "R9wkxW-ot7bH"
      },
      "execution_count": 271,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove duplicate rows"
      ],
      "metadata": {
        "id": "Ispv9_XFt7eT"
      },
      "execution_count": 272,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.drop_duplicates()"
      ],
      "metadata": {
        "id": "7qRondPEt7iN"
      },
      "execution_count": 273,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Number of rows after removing duplicates: {data.shape[0]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aePO_OFBtzpb",
        "outputId": "bcc3193b-2c35-4b39-9ce7-c6d6bf31f369"
      },
      "execution_count": 274,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of rows after removing duplicates: 4840\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6XxtkdO9uBwa"
      },
      "execution_count": 274,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Verify unique sentiment labels in the dataset"
      ],
      "metadata": {
        "id": "vvkMJV9zuBzj"
      },
      "execution_count": 275,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Unique Sentiment Labels:\", data['sentiment'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3sAW0pWauB2j",
        "outputId": "5cbef008-f48f-489b-ee67-8bc320f40056"
      },
      "execution_count": 276,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique Sentiment Labels: ['According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .'\n",
            " 'Technopolis plans to develop in stages an area of no less than 100,000 square meters in order to host companies working in computer technologies and telecommunications , the statement said .'\n",
            " 'The international electronic industry company Elcoteq has laid off tens of employees from its Tallinn facility ; contrary to earlier layoffs the company contracted the ranks of its office workers , the daily Postimees reported .'\n",
            " ...\n",
            " 'Operating profit fell to EUR 35.4 mn from EUR 68.8 mn in 2007 , including vessel sales gain of EUR 12.3 mn .'\n",
            " 'Net sales of the Paper segment decreased to EUR 221.6 mn in the second quarter of 2009 from EUR 241.1 mn in the second quarter of 2008 , while operating profit excluding non-recurring items rose to EUR 8.0 mn from EUR 7.6 mn .'\n",
            " 'Sales in Finland decreased by 10.5 % in January , while sales outside Finland dropped by 17 % .']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CRkffqOxuB5n"
      },
      "execution_count": 276,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jq6uUJrN1H3B"
      },
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      "cell_type": "code",
      "source": [],
      "metadata": {
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      },
      "execution_count": null,
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    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the distribution of the data (features)"
      ],
      "metadata": {
        "id": "jLCnm7xdz7n0"
      },
      "execution_count": 363,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 8))\n",
        "for i, column in enumerate(data.columns):\n",
        "    plt.subplot(len(data.columns) // 3 + 1, 3, i + 1)\n",
        "    sns.histplot(data[column], kde=True)\n",
        "    plt.title(f'Distribution of {column}')\n",
        "plt.tight_layout()\n",
        "plt.show()  # Ensure the plot is shown"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "oiVOnekX1Jb5",
        "outputId": "016745a2-50ca-4e38-ef46-c993054fff9a"
      },
      "execution_count": 364,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 4 Axes>"
            ],
            "image/png": 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39h0V/y1fG0aPHl1utJtcLhfXmSouLsbt27fFqaJ1kUNTxMKUAcjJydF7w3vY0KFD0aNHD7z11ltwcXHBsGHDsGHDhmq9ITdv3rxai/q1bdtW77FMJkObNm2qnEtcG65evQo3N7dyr0fZ1K6rV6/q7X/wA3+ZZs2alVvvqKLrtG3bttxCd5Vd53E8zs/p4sWLOHv2LJycnPS2p556CkDpHO/aaN+jPHzesqLGw1PBbG1t9a518eJFZGdnw9nZuVwbcnJy6i1/ALh58yaysrKwYsWKcrmU/cHxqHyaNWsGAGI+ycnJACBO5ahI2e9Gu3btyh3z8fHBrVu3kJubi5s3byIvL6/cv6WKnnvx4kUIgoC2bduWa8v58+fFdpRd++FzmpqaolWrVpXmTFTf2K/9zZD7tcfNqTrvx1evXkXr1q3LrRvTpk2bctdITU0VvwSwtraGk5OTOAUvOzu7Rm0ZMGAAbG1tsX79enHf+vXrERAQIPbFly5dgiAI+Pjjj8u155NPPtFrDxHVHvYdf2PfUfHf8rXBy8ur3L6SkhIsXLgQbdu2hVwuh6OjI5ycnHDq1Kka9zekj2tMNXDXrl1DdnZ2hX+QlbGwsMD+/fuxZ88ebN26Fdu3b8f69evRr18/7Ny5E8bGxo+8TnXmQD+uh/+oLFNcXPxYOdWGyq4jPLQoYH14nJ9TSUkJ/Pz8sGDBggrP8XBhqK7aV9l5K9r/4LVKSkrg7OyMNWvWVPj8h+ea1+XPp+yPkDfeeAOjR4+uMMbf37/e8nkSJSUlkMlk+OOPPyrM8eG58UQNGfu1J9MQ36celVNN3o8fpbi4GM899xzu3LmD6dOnw9vbG1ZWVrh+/TrGjBnzRKMlBg8ejF9//RXffvstMjIycPDgQXz++ediTNm5P/jgg3IjXMtU9ftNRNXHvuPJNPa+o6rXuLoq+h34/PPP8fHHH+PNN9/E3LlzYW9vDyMjI0yaNKnG/Q3pY2GqgStbqLOyP3zKGBkZoX///ujfvz8WLFiAzz//HB999BH27NmDoKCgSv+x1tTFixf1HguCgEuXLun9YdmsWTNkZWWVe+7Vq1f1Rm9UJzdPT0/s2rUL9+7d0/uG4MKFC+Lx2uDp6YlTp06hpKRE7xuCJ73Oo35OrVu3xsmTJ9G/f/9a+5nV9s++Kq1bt8auXbvQo0ePWuvYa5q/k5MTbGxsUFxcjKCgoFrJpWy48ZkzZyo9Z9nvRlJSUrljFy5cgKOjI6ysrGBubg4LC4ty/5Yqem7r1q0hCAK8vLzEb+yruvbFixfF6Z8AUFhYiJSUFHTs2PERLSSqe+zX9Bl6v/Y4qvN+7OnpiXPnzkEQBL3X8eE7pJ4+fRp//fUXVq1apXcDjpiYmHLnrO7vytChQ7Fq1SrExsbi/PnzEARBnMYHQPxZm5qa1lr/QkRVY9+hj32HvrJZDg+/zhWN6KrJ78CmTZvQt29f/PDDD3r7s7KyxJtL0ZPhVL4GbPfu3Zg7dy68vLwwYsSISuPu3LlTbl9AQAAAQKfTAQCsrKwAlP/HWlM//fST3hzvTZs2IT09HQMHDhT3tW7dGocOHUJBQYG4b8uWLeVuoVqd3AYNGoTi4mIsXbpUb//ChQshk8n0rv8kBg0aBI1GozeUv6ioCEuWLIG1tXW5u/U8jsf5Ob322mu4fv06/vWvf5WLzcvLQ25ubrWvW9s/+6q89tprKC4uxty5c8sdKyoqqlEONc3f2NgYoaGh+Pnnn/Vu713m5s2b1c6lU6dO8PLywqJFi8rlU/btjqurKwICArBq1Sq9mDNnzmDnzp0YNGiQmJ9arcbmzZuRmpoqxp0/fx47duzQO/eQIUNgbGyM2bNnl/tmSxAE3L59GwDQuXNnODk5Yfny5Xr/7qKjo+vl50/0KOzXyjPkfu1xVef9WK1W4/r16/jtt9/Effn5+eX6xbJv2h98TxQEAYsXLy53/ur+rgQFBcHe3h7r16/H+vXr8cwzz+hN7XB2dkafPn3w3XffIT09vcr2ENGTY99RHvsO/ffasi+P9+/fL+4rLi7GihUryj3Pysqq2tPvjI2Ny/0NvnHjRq4nWIs4YqqB+OOPP3DhwgUUFRUhIyMDu3fvRkxMDDw9PfHbb7/B3Ny80ufOmTMH+/fvR0hICDw9PZGZmYlvv/0W7u7u6NmzJ4DSf6x2dnZYvnw5bGxsYGVlha5du1Y4h/Zx2Nvbo2fPnhg7diwyMjKwaNEitGnTRm9x0rfeegubNm3CgAED8NprryE5ORmrV6/WW+Suurm98MIL6Nu3Lz766CNcuXIFHTt2xM6dO/Hf//4XkyZNKnfumho/fjy+++47jBkzBgkJCWjZsiU2bdqEgwcPYtGiRVXOb6/M4/ycRo4ciQ0bNmDChAnYs2cPevTogeLiYly4cAEbNmzAjh070Llz52pdNzAwEEDp7VGHDRsGU1NTvPDCC2LnV5t69+6Nt99+G/PmzUNiYiKCg4NhamqKixcvYuPGjVi8eDFeeeWVap0zICAAxsbG+Oc//4ns7GzI5XL069cPzs7Oj3zuF198gT179qBr164YN24cfH19cefOHRw/fhy7du2q8A+YqhgZGWHZsmV44YUXEBAQgLFjx8LV1RUXLlzA2bNnxYLSl19+iYEDB0KlUiEsLAx5eXlYsmQJbG1tMWvWLPF8s2fPxvbt2/Hss8/i//7v/8SOvn379jh16pQY17p1a3z66aeIjIzElStXMHjwYNjY2CAlJQW//vorxo8fjw8++ACmpqb49NNP8fbbb6Nfv34YOnQoUlJSsHLlSq4xRfWO/Vrj79eq43Hfj99++20sXboUw4cPx3vvvQdXV1esWbNG/H0p+6bb29sbrVu3xgcffIDr169DoVDg559/rnC9lLJ+8N1334VarYaxsTGGDRtWaa6mpqYYMmQI1q1bh9zcXHz11VflYqKiotCzZ0/4+flh3LhxaNWqFTIyMhAfH49r167h5MmTT/yaETVF7DvYdzzocfuO9u3bo1u3boiMjMSdO3dgb2+PdevWoaioqNw5AwMDsX79ekyZMgVdunSBtbU1XnjhhSrzeP755zFnzhyMHTsW3bt3x+nTp7FmzRr+fV2b6vq2f1S1sltblm1mZmaCUqkUnnvuOWHx4sV6t+As8/CtUWNjY4WXXnpJcHNzE8zMzAQ3Nzdh+PDhwl9//aX3vP/+97+Cr6+vYGJioncby969ewvt27evML/Kbo36n//8R4iMjBScnZ0FCwsLISQkRO/WzGW+/vproXnz5oJcLhd69OghHDt2rNw5q8rt4VujCoIg3Lt3T5g8ebLg5uYmmJqaCm3bthW+/PJLoaSkRC8OgBAeHl4up8pu2fqwjIwMYezYsYKjo6NgZmYm+Pn5VXjrz8e9Nerj/pwKCgqEf/7zn0L79u0FuVwuNGvWTAgMDBRmz54tZGdn16h9c+fOFZo3by4YGRnp3Ur14djKbtVb9jv38O13R48eLVhZWZXLYcWKFUJgYKBgYWEh2NjYCH5+fsK0adOEGzdu6OVZ0etW0e/Hv/71L6FVq1aCsbFxuVu+PkpGRoYQHh4ueHh4CKampoJSqRT69+8vrFixQowp+73euHGj3nMru+XrgQMHhOeee06wsbERrKysBH9//3K32921a5fQo0cPwcLCQlAoFMILL7wgnDt3rlx++/btEwIDAwUzMzOhVatWwvLly8v9Gy/z888/Cz179hSsrKwEKysrwdvbWwgPDxeSkpL04r799lvBy8tLkMvlQufOnYX9+/dX+LoS1QX2a1Xn1pj6term9Djvx4IgCJcvXxZCQkIECwsLwcnJSXj//feFn3/+WQAgHDp0SIw7d+6cEBQUJFhbWwuOjo7CuHHjxNuNP9iuoqIi4Z133hGcnJwEmUym97sGQPjkk0/K5R8TEyMAEGQymZCWllZh25OTk4VRo0YJSqVSMDU1FZo3by48//zzwqZNmx7jlSOiB7HvqDo39h2P7juSk5OFoKAgQS6XCy4uLsKHH34ovpc/+NkhJydHeP311wU7OzsBgPi6VvZ5QBAEIT8/X3j//fcFV1dXwcLCQujRo4cQHx9f7mdY2WcHejSZIEi8qi8RERERURUWLVqEyZMn49q1a2jevLnU6RAREVEtYmGKiIiIiBqMvLw8vRto5Ofn4+mnn0ZxcTH++usvCTMjIiKiusA1pojI4OTk5CAnJ6fKGCcnp3q7BS8REdWeIUOGoEWLFggICEB2djZWr16NCxcuYM2aNVKnRkRERHWAhSkiMjhfffUVZs+eXWVMSkoKWrZsWT8JERFRrVGr1fj++++xZs0aFBcXw9fXF+vWrcPQoUOlTo2IiIjqAKfyEZHBuXz5Mi5fvlxlTM+ePau8cwsRERERERFJj4UpIiIiIiIiIiKShJHUCRAREREREdWGZcuWwd/fHwqFAgqFAiqVCn/88Yd4PD8/H+Hh4XBwcIC1tTVCQ0ORkZGhd47U1FSEhITA0tISzs7OmDp1KoqKivRi9u7di06dOkEul6NNmzaIjo6uj+YRETVKXGOqFpSUlODGjRuwsbGBTCaTOh0iololCALu3bsHNzc3GBnx+4zqYP9ARI1ZQ+wf3N3d8cUXX6Bt27YQBAGrVq3CSy+9hBMnTqB9+/aYPHkytm7dio0bN8LW1hYREREYMmQIDh48CAAoLi5GSEgIlEol4uLikJ6ejlGjRsHU1BSff/45gNJ1LENCQjBhwgSsWbMGsbGxeOutt+Dq6gq1Wv3YubKPIKLGrFp9hEBPLC0tTQDAjRs3bo16S0tLk/rt1uCwf+DGjVtT2Bp6/9CsWTPh+++/F7KysgRTU1Nh48aN4rHz588LAIT4+HhBEARh27ZtgpGRkaDRaMSYZcuWCQqFQtDpdIIgCMK0adOE9u3b611j6NChglqtrlZe7CO4cePWFLbH6SM4YqoW2NjYAADS0tKgUCgkzoaIqHZptVp4eHiI73X0+Ng/EFFj1tD7h+LiYmzcuBG5ublQqVRISEhAYWEhgoKCxBhvb2+0aNEC8fHx6NatG+Lj4+Hn5wcXFxcxRq1WY+LEiTh79iyefvppxMfH652jLGbSpElV5qPT6aDT6cTHwv+W+mUfQUSNUXX6CBamakHZ0NuyuexERI0RpxlUH/sHImoKGlr/cPr0aahUKuTn58Pa2hq//vorfH19kZiYCDMzM9jZ2enFu7i4QKPRAAA0Go1eUarseNmxqmK0Wi3y8vJgYWFRYV7z5s3D7Nmzy+1nH0FEjdnj9BENYzI4ERERERFRLWjXrh0SExNx+PBhTJw4EaNHj8a5c+ekTguRkZHIzs4Wt7S0NKlTIiJqEDhiioiIiIiIGg0zMzO0adMGABAYGIijR49i8eLFGDp0KAoKCpCVlaU3aiojIwNKpRIAoFQqceTIEb3zld2178GYh+/kl5GRAYVCUeloKQCQy+WQy+VP3D4iosaGI6aIiIiIiKjRKikpgU6nQ2BgIExNTREbGyseS0pKQmpqKlQqFQBApVLh9OnTyMzMFGNiYmKgUCjg6+srxjx4jrKYsnMQEVH1cMQUERERERE1CpGRkRg4cCBatGiBe/fuYe3atdi7dy927NgBW1tbhIWFYcqUKbC3t4dCocA777wDlUqFbt26AQCCg4Ph6+uLkSNHYv78+dBoNJgxYwbCw8PF0U4TJkzA0qVLMW3aNLz55pvYvXs3NmzYgK1bt0rZdCIig8XCFBERERERNQqZmZkYNWoU0tPTYWtrC39/f+zYsQPPPfccAGDhwoUwMjJCaGgodDod1Go1vv32W/H5xsbG2LJlCyZOnAiVSgUrKyuMHj0ac+bMEWO8vLywdetWTJ48GYsXL4a7uzu+//57qNXqem8vEVFjIBPK7lNKNabVamFra4vs7GzeUYOIGh2+x9UcXzsiasz4Hvdk+PoRUWNWnfc4rjFFRERERERERESSYGGKiIiIiIiIiIgkwcIUERERERERERFJgoUpIiIiIiIiIiKSBAtTREREREREREQkCROpEyAiIqLyUlNTcevWrVo9p6OjI1q0aFGr5yQiIiKihsMQ/4ZkYYqIiKiBSU1NhbePD/Lu36/V81pYWuLC+fMsThERERE1Qob6NyQLU0RERA3MrVu3kHf/PkZM/xIuLVrXyjkzUpOx5p9TcevWLRamiIiIiBohQ/0bkoUpIiKiBsqlRWu4t20vdRpEREREZEAM7W9ILn5ORERERERERESSYGGKiIiIiIiIiIgkwcIUERERERERERFJgoUpIiIiIiIiIiKSBAtTREREREREREQkCRamiIiIiIiIiIhIEixMERERERERERGRJFiYIiIiIiIiIiIiSbAwRUREREREREREkmBhioiIDNoXX3wBmUyGSZMmifvy8/MRHh4OBwcHWFtbIzQ0FBkZGXrPS01NRUhICCwtLeHs7IypU6eiqKhIL2bv3r3o1KkT5HI52rRpg+jo6HpoERERERFR08HCFBERGayjR4/iu+++g7+/v97+yZMn4/fff8fGjRuxb98+3LhxA0OGDBGPFxcXIyQkBAUFBYiLi8OqVasQHR2NmTNnijEpKSkICQlB3759kZiYiEmTJuGtt97Cjh076q19RERERESNHQtTRERkkHJycjBixAj861//QrNmzcT92dnZ+OGHH7BgwQL069cPgYGBWLlyJeLi4nDo0CEAwM6dO3Hu3DmsXr0aAQEBGDhwIObOnYuoqCgUFBQAAJYvXw4vLy98/fXX8PHxQUREBF555RUsXLhQkvYSERERETVGLEwREZFBCg8PR0hICIKCgvT2JyQkoLCwUG+/t7c3WrRogfj4eABAfHw8/Pz84OLiIsao1WpotVqcPXtWjHn43Gq1WjwHERERERE9OROpEyAiIqqudevW4fjx4zh69Gi5YxqNBmZmZrCzs9Pb7+LiAo1GI8Y8WJQqO152rKoYrVaLvLw8WFhYlLu2TqeDTqcTH2u12uo3joiIiIioCeGIKSIiMihpaWl47733sGbNGpibm0udjp558+bB1tZW3Dw8PKROiYiIiIioQWNhioiIDEpCQgIyMzPRqVMnmJiYwMTEBPv27cM333wDExMTuLi4oKCgAFlZWXrPy8jIgFKpBAAolcpyd+kre/yoGIVCUeFoKQCIjIxEdna2uKWlpdVGk4mIiIiIGi0WpoiIyKD0798fp0+fRmJiorh17twZI0aMEP/f1NQUsbGx4nOSkpKQmpoKlUoFAFCpVDh9+jQyMzPFmJiYGCgUCvj6+ooxD56jLKbsHBWRy+VQKBR6GxERERERVY5rTBERkUGxsbFBhw4d9PZZWVnBwcFB3B8WFoYpU6bA3t4eCoUC77zzDlQqFbp16wYACA4Ohq+vL0aOHIn58+dDo9FgxowZCA8Ph1wuBwBMmDABS5cuxbRp0/Dmm29i9+7d2LBhA7Zu3Vq/DSYiIiIiasRYmCIiokZn4cKFMDIyQmhoKHQ6HdRqNb799lvxuLGxMbZs2YKJEydCpVLBysoKo0ePxpw5c8QYLy8vbN26FZMnT8bixYvh7u6O77//Hmq1WoomERERERE1SixMERGRwdu7d6/eY3Nzc0RFRSEqKqrS53h6emLbtm1VnrdPnz44ceJEbaRIREREREQVMLg1pqKiotCyZUuYm5uja9euOHLkSJXxGzduhLe3N8zNzeHn51flh5AJEyZAJpNh0aJFtZw1ERERERERERE9zKAKU+vXr8eUKVPwySef4Pjx4+jYsSPUarXe4rUPiouLw/DhwxEWFoYTJ05g8ODBGDx4MM6cOVMu9tdff8WhQ4fg5uZW180gIiIiIiIiIiIYWGFqwYIFGDduHMaOHQtfX18sX74clpaW+PHHHyuMX7x4MQYMGICpU6fCx8cHc+fORadOnbB06VK9uOvXr+Odd97BmjVrYGpqWh9NISIiIiIiIiJq8gymMFVQUICEhAQEBQWJ+4yMjBAUFIT4+PgKnxMfH68XDwBqtVovvqSkBCNHjsTUqVPRvn37ukmeiIiIiIiIiIjKMZjFz2/duoXi4mK4uLjo7XdxccGFCxcqfI5Go6kwXqPRiI//+c9/wsTEBO++++5j56LT6aDT6cTHWq32sZ9LRERERERERESlDGbEVF1ISEjA4sWLER0dDZlM9tjPmzdvHmxtbcXNw8OjDrMkIiIiIiIiImqcDKYw5ejoCGNjY2RkZOjtz8jIgFKprPA5SqWyyvg///wTmZmZaNGiBUxMTGBiYoKrV6/i/fffR8uWLSvNJTIyEtnZ2eKWlpb2ZI0jIiIiIiIiImqCDKYwZWZmhsDAQMTGxor7SkpKEBsbC5VKVeFzVCqVXjwAxMTEiPEjR47EqVOnkJiYKG5ubm6YOnUqduzYUWkucrkcCoVCbyMiIiIiIiIiouoxmDWmAGDKlCkYPXo0OnfujGeeeQaLFi1Cbm4uxo4dCwAYNWoUmjdvjnnz5gEA3nvvPfTu3Rtff/01QkJCsG7dOhw7dgwrVqwAADg4OMDBwUHvGqamplAqlWjXrl39No6IiIiIiIiIqIkxqMLU0KFDcfPmTcycORMajQYBAQHYvn27uMB5amoqjIz+HgTWvXt3rF27FjNmzMCHH36Itm3bYvPmzejQoYNUTSAiIiIiIiIiov8xqMIUAERERCAiIqLCY3v37i2379VXX8Wrr7762Oe/cuVKDTMjIiIiIiIiIqLqMJg1poiIiIiIiKoyb948dOnSBTY2NnB2dsbgwYORlJSkF9OnTx/IZDK9bcKECXoxqampCAkJgaWlJZydnTF16lQUFRXpxezduxedOnWCXC5HmzZtEB0dXdfNIyJqlFiYIiIiIiKiRmHfvn0IDw/HoUOHEBMTg8LCQgQHByM3N1cvbty4cUhPTxe3+fPni8eKi4sREhKCgoICxMXFYdWqVYiOjsbMmTPFmJSUFISEhKBv375ITEzEpEmT8NZbb1V5AyUiIqqYwU3lIyIiIiIiqsj27dv1HkdHR8PZ2RkJCQno1auXuN/S0hJKpbLCc+zcuRPnzp3Drl274OLigoCAAMydOxfTp0/HrFmzYGZmhuXLl8PLywtff/01AMDHxwcHDhzAwoULoVar666BRESNEEdMERERERFRo5SdnQ0AsLe319u/Zs0aODo6okOHDoiMjMT9+/fFY/Hx8fDz8xNvsAQAarUaWq0WZ8+eFWOCgoL0zqlWqxEfH19XTSEiarQ4YoqIiIiIiBqdkpISTJo0CT169NC7K/frr78OT09PuLm54dSpU5g+fTqSkpLwyy+/AAA0Go1eUQqA+Fij0VQZo9VqkZeXBwsLi3L56HQ66HQ68bFWq62dhhIRGTgWpoiIiIiIqNEJDw/HmTNncODAAb3948ePF//fz88Prq6u6N+/P5KTk9G6des6y2fevHmYPXt2nZ2fiMhQcSofERERERE1KhEREdiyZQv27NkDd3f3KmO7du0KALh06RIAQKlUIiMjQy+m7HHZulSVxSgUigpHSwFAZGQksrOzxS0tLa36DSMiaoRYmCIiIiIiokZBEARERETg119/xe7du+Hl5fXI5yQmJgIAXF1dAQAqlQqnT59GZmamGBMTEwOFQgFfX18xJjY2Vu88MTExUKlUlV5HLpdDoVDobURExMIUERERERE1EuHh4Vi9ejXWrl0LGxsbaDQaaDQa5OXlAQCSk5Mxd+5cJCQk4MqVK/jtt98watQo9OrVC/7+/gCA4OBg+Pr6YuTIkTh58iR27NiBGTNmIDw8HHK5HAAwYcIEXL58GdOmTcOFCxfw7bffYsOGDZg8ebJkbSciMlQsTBERERERUaOwbNkyZGdno0+fPnB1dRW39evXAwDMzMywa9cuBAcHw9vbG++//z5CQ0Px+++/i+cwNjbGli1bYGxsDJVKhTfeeAOjRo3CnDlzxBgvLy9s3boVMTEx6NixI77++mt8//33UKvV9d5mIiJDx8XPiYiIiIioURAEocrjHh4e2Ldv3yPP4+npiW3btlUZ06dPH5w4caJa+RERUXkcMUVERERERERERJJgYYqIiIiIiIiIiCTBwhQRERmUZcuWwd/fX7yjkUqlwh9//CEe79OnD2Qymd42YcIEvXOkpqYiJCQElpaWcHZ2xtSpU1FUVKQXs3fvXnTq1AlyuRxt2rRBdHR0fTSPiIiIiKhJ4RpTRERkUNzd3fHFF1+gbdu2EAQBq1atwksvvYQTJ06gffv2AIBx48bpLVJraWkp/n9xcTFCQkKgVCoRFxeH9PR0jBo1Cqampvj8888BACkpKQgJCcGECROwZs0axMbG4q233oKrqysXtiUiIiIiqkUsTBERkUF54YUX9B5/9tlnWLZsGQ4dOiQWpiwtLaFUKit8/s6dO3Hu3Dns2rULLi4uCAgIwNy5czF9+nTMmjULZmZmWL58Oby8vPD1118DAHx8fHDgwAEsXLiQhSkiIiIiolrEqXxERGSwiouLsW7dOuTm5kKlUon716xZA0dHR3To0AGRkZG4f/++eCw+Ph5+fn5wcXER96nVami1Wpw9e1aMCQoK0ruWWq1GfHx8HbeIiIiIiKhp4YgpIiIyOKdPn4ZKpUJ+fj6sra3x66+/wtfXFwDw+uuvw9PTE25ubjh16hSmT5+OpKQk/PLLLwAAjUajV5QCID7WaDRVxmi1WuTl5cHCwqLCvHQ6HXQ6nfhYq9XWToOJiIiIiBopFqaIiMjgtGvXDomJicjOzsamTZswevRo7Nu3D76+vhg/frwY5+fnB1dXV/Tv3x/Jyclo3bp1neY1b948zJ49u06vQURERETUmHAqHxERGRwzMzO0adMGgYGBmDdvHjp27IjFixdXGNu1a1cAwKVLlwAASqUSGRkZejFlj8vWpaosRqFQVDpaCgAiIyORnZ0tbmlpaTVrIBERERFRE8HCFBERGbySkhK9KXQPSkxMBAC4uroCAFQqFU6fPo3MzEwxJiYmBgqFQpwOqFKpEBsbq3eemJgYvXWsKiKXy6FQKPQ2IiIiIiKqHKfyERGRQYmMjMTAgQPRokUL3Lt3D2vXrsXevXuxY8cOJCcnY+3atRg0aBAcHBxw6tQpTJ48Gb169YK/vz8AIDg4GL6+vhg5ciTmz58PjUaDGTNmIDw8HHK5HAAwYcIELF26FNOmTcObb76J3bt3Y8OGDdi6dauUTSciIiIianRYmCIiIoOSmZmJUaNGIT09Hba2tvD398eOHTvw3HPPIS0tDbt27cKiRYuQm5sLDw8PhIaGYsaMGeLzjY2NsWXLFkycOBEqlQpWVlYYPXo05syZI8Z4eXlh69atmDx5MhYvXgx3d3d8//33UKvVUjSZiIiIiKjRYmGKiIgMyg8//FDpMQ8PD+zbt++R5/D09MS2bduqjOnTpw9OnDhR7fyIiIiIiOjxcY0pIiIiIiIiIiKSBAtTREREREREREQkCRamiIiIiIiIiIhIEixMERERERERERGRJFiYIiIiIiIiIiIiSbAwRUREREREREREkmBhioiIiIiIiIiIJMHCFBERERERERERSYKFKSIiIiIiIiIikgQLU0REREREREREJAkWpoiIiIiIiIiISBIsTBERERERERERkSRYmCIiIiIiIiIiIkmwMEVERERERERERJJgYYqIiIiIiIiIiCTBwhQREREREREREUmChSkiIiIiIiIiIpIEC1NERERERERERCQJFqaIiIiIiIiIiEgSLEwREREREREREZEkWJgiIiIiIiIiIiJJsDBFRERERERERESSMLjCVFRUFFq2bAlzc3N07doVR44cqTJ+48aN8Pb2hrm5Ofz8/LBt2zbxWGFhIaZPnw4/Pz9YWVnBzc0No0aNwo0bN+q6GURERERERERETZ5BFabWr1+PKVOm4JNPPsHx48fRsWNHqNVqZGZmVhgfFxeH4cOHIywsDCdOnMDgwYMxePBgnDlzBgBw//59HD9+HB9//DGOHz+OX375BUlJSXjxxRfrs1lERERERERERE2SQRWmFixYgHHjxmHs2LHw9fXF8uXLYWlpiR9//LHC+MWLF2PAgAGYOnUqfHx8MHfuXHTq1AlLly4FANja2iImJgavvfYa2rVrh27dumHp0qVISEhAampqfTaNiIge07Jly+Dv7w+FQgGFQgGVSoU//vhDPJ6fn4/w8HA4ODjA2toaoaGhyMjI0DtHamoqQkJCYGlpCWdnZ0ydOhVFRUV6MXv37kWnTp0gl8vRpk0bREdH10fziIiIiIiaFIMpTBUUFCAhIQFBQUHiPiMjIwQFBSE+Pr7C58THx+vFA4Bara40HgCys7Mhk8lgZ2dXK3kTEVHtcnd3xxdffIGEhAQcO3YM/fr1w0svvYSzZ88CACZPnozff/8dGzduxL59+3Djxg0MGTJEfH5xcTFCQkJQUFCAuLg4rFq1CtHR0Zg5c6YYk5KSgpCQEPTt2xeJiYmYNGkS3nrrLezYsaPe20tERERE1JiZSJ3A47p16xaKi4vh4uKit9/FxQUXLlyo8DkajabCeI1GU2F8fn4+pk+fjuHDh0OhUFSai06ng06nEx9rtdrHbQYRET2hF154Qe/xZ599hmXLluHQoUNwd3fHDz/8gLVr16Jfv34AgJUrV8LHxweHDh1Ct27dsHPnTpw7dw67du2Ci4sLAgICMHfuXEyfPh2zZs2CmZkZli9fDi8vL3z99dcAAB8fHxw4cAALFy6EWq2u9zYTERERETVWBjNiqq4VFhbitddegyAIWLZsWZWx8+bNg62trbh5eHjUU5ZERPSg4uJirFu3Drm5uVCpVEhISEBhYaHeaFlvb2+0aNFCHC0bHx8PPz8/vS8u1Go1tFqtOOqqJiNuiYiIiIio+gymMOXo6AhjY+Ny64RkZGRAqVRW+BylUvlY8WVFqatXryImJqbK0VIAEBkZiezsbHFLS0urQYuIiKimTp8+DWtra8jlckyYMAG//vorfH19odFoYGZmVm469oOjZSsbTVt2rKoYrVaLvLy8SvPS6XTQarV6GxER1Z958+ahS5cusLGxgbOzMwYPHoykpCS9GK5FSETUsBhMYcrMzAyBgYGIjY0V95WUlCA2NhYqlarC56hUKr14AIiJidGLLytKXbx4Ebt27YKDg8Mjc5HL5eKiu2UbERHVn3bt2iExMRGHDx/GxIkTMXr0aJw7d07qtDiilohIYvv27UN4eDgOHTqEmJgYFBYWIjg4GLm5uWIM1yIkImpYDGaNKQCYMmUKRo8ejc6dO+OZZ57BokWLkJubi7FjxwIARo0ahebNm2PevHkAgPfeew+9e/fG119/jZCQEKxbtw7Hjh3DihUrAJQWpV555RUcP34cW7ZsQXFxsfhtub29PczMzKRpKBERVcnMzAxt2rQBAAQGBuLo0aNYvHgxhg4dioKCAmRlZemNmnpwtKxSqcSRI0f0zlf2TfmDMRWNuFUoFLCwsKg0r8jISEyZMkV8rNVqWZwiIqpH27dv13scHR0NZ2dnJCQkoFevXsjOzuZahEREDYzBjJgCgKFDh+Krr77CzJkzERAQgMTERGzfvl2cbpGamor09HQxvnv37li7di1WrFiBjh07YtOmTdi8eTM6dOgAALh+/Tp+++03XLt2DQEBAXB1dRW3uLg4SdpIRETVV1JSAp1Oh8DAQJiamuqNlk1KSkJqaqo4WlalUuH06dPIzMwUY8qmcfv6+ooxjxpxWxGOqCUialiys7MBlH7pDEDStQg53ZuIqGIGNWIKACIiIhAREVHhsb1795bb9+qrr+LVV1+tML5ly5YQBKE20yMiojoWGRmJgQMHokWLFrh37x7Wrl2LvXv3YseOHbC1tUVYWBimTJkCe3t7KBQKvPPOO1CpVOjWrRsAIDg4GL6+vhg5ciTmz58PjUaDGTNmIDw8HHK5HAAwYcIELF26FNOmTcObb76J3bt3Y8OGDdi6dauUTSciomooKSnBpEmT0KNHD/GL6fpai7Ci0bXz5s3D7Nmza6VtRESNicEVpoiIqGnLzMzEqFGjkJ6eDltbW/j7+2PHjh147rnnAAALFy6EkZERQkNDodPpoFar8e2334rPNzY2xpYtWzBx4kSoVCpYWVlh9OjRmDNnjhjj5eWFrVu3YvLkyVi8eDHc3d3x/fffc3oGEZEBCQ8Px5kzZ3DgwAGpUwHA6d5ERJVhYYqIiAzKDz/8UOVxc3NzREVFISoqqtIYT09PbNu2rcrz9OnTBydOnKhRjkREJK2IiAhs2bIF+/fvh7u7u7hfqVRKthahXC4XR+YSEdHfWJgiIiIiIqJGQRAEvPPOO/j111+xd+9eeHl56R1/cC3C0NBQABWvRfjZZ58hMzMTzs7OACpei/DhLzgeZy3C2pKamopbt27V2vkcHR3RokWLWjsfEVF1sDBFRERERESNQnh4ONauXYv//ve/sLGxEdeEsrW1hYWFRaNYizA1NRXePj7Iu3+/1s5pYWmJC+fPszhFRJJgYYqIiIiIiBqFZcuWASidjv2glStXYsyYMQAMfy3CW7duIe/+fYyY/iVcWrR+4vNlpCZjzT+n4tatWyxMEZEkWJgiIiIiIqJG4XHuuN1Y1iJ0adEa7m3bS3Z9IqLaYiR1AkRERERERERE1DSxMEVERERERERERJJgYYqIiIiIiIiIiCTBwhQREREREREREUmChSkiIiIiIiIiIpIEC1NERERERERERCQJFqaIiIiIiIiIiEgSLEwREREREREREZEkWJgiIiIiIiIiIiJJsDBFRERERERERESSYGGKiIiIiIiIiIgkwcIUERERERERERFJgoUpIiIiIiIiIiKSBAtTREREREREREQkCRamiIiIiIiIiIhIEixMERERERERERGRJFiYIiIiIiIiIiIiSbAwRUREREREREREkmBhioiIiIiIiIiIJMHCFBERERERERERSYKFKSIiMijz5s1Dly5dYGNjA2dnZwwePBhJSUl6MX369IFMJtPbJkyYoBeTmpqKkJAQWFpawtnZGVOnTkVRUZFezN69e9GpUyfI5XK0adMG0dHRdd08IiIiIqImhYUpIiIyKPv27UN4eDgOHTqEmJgYFBYWIjg4GLm5uXpx48aNQ3p6urjNnz9fPFZcXIyQkBAUFBQgLi4Oq1atQnR0NGbOnCnGpKSkICQkBH379kViYiImTZqEt956Czt27Ki3thIRERERNXYmUidARERUHdu3b9d7HB0dDWdnZyQkJKBXr17ifktLSyiVygrPsXPnTpw7dw67du2Ci4sLAgICMHfuXEyfPh2zZs2CmZkZli9fDi8vL3z99dcAAB8fHxw4cAALFy6EWq2uuwYSERERETUhHDFFREQGLTs7GwBgb2+vt3/NmjVwdHREhw4dEBkZifv374vH4uPj4efnBxcXF3GfWq2GVqvF2bNnxZigoCC9c6rVasTHx9dVU4iIiIiImhyOmCIiIoNVUlKCSZMmoUePHujQoYO4//XXX4enpyfc3Nxw6tQpTJ8+HUlJSfjll18AABqNRq8oBUB8rNFoqozRarXIy8uDhYVFuXx0Oh10Op34WKvV1k5DiYiIiIgaKRamiIjIYIWHh+PMmTM4cOCA3v7x48eL/+/n5wdXV1f0798fycnJaN26dZ3lM2/ePMyePbvOzk9ERERE1NjUaCpfq1atcPv27XL7s7Ky0KpVqydOioiIGqfa7D8iIiKwZcsW7NmzB+7u7lXGdu3aFQBw6dIlAIBSqURGRoZeTNnjsnWpKotRKBQVjpYCgMjISGRnZ4tbWlpatdpERNRU8fMFEVHTVaPC1JUrV1BcXFxuv06nw/Xr1584KSIiapxqo/8QBAERERH49ddfsXv3bnh5eT3yOYmJiQAAV1dXAIBKpcLp06eRmZkpxsTExEChUMDX11eMiY2N1TtPTEwMVCpVpdeRy+VQKBR6GxERPRo/XxARNV3Vmsr322+/if+/Y8cO2Nraio+Li4sRGxuLli1b1lpyRETUONRm/xEeHo61a9fiv//9L2xsbMQ1oWxtbWFhYYHk5GSsXbsWgwYNgoODA06dOoXJkyejV69e8Pf3BwAEBwfD19cXI0eOxPz586HRaDBjxgyEh4dDLpcDACZMmIClS5di2rRpePPNN7F7925s2LABW7duraVXhYiI+PmCiIiqVZgaPHgwAEAmk2H06NF6x0xNTdGyZUvxttpERERlarP/WLZsGQCgT58+evtXrlyJMWPGwMzMDLt27cKiRYuQm5sLDw8PhIaGYsaMGWKssbExtmzZgokTJ0KlUsHKygqjR4/GnDlzxBgvLy9s3boVkydPxuLFi+Hu7o7vv/8earW6Bq8AERFVhJ8viIioWoWpkpISAKV/rB89ehSOjo51khQRETUutdl/CIJQ5XEPDw/s27fvkefx9PTEtm3bqozp06cPTpw4Ua38iIjo8fHzBRER1eiufCkpKbWdBxERNQHsP4iIqCLsH4iImq4aFaYAIDY2FrGxscjMzBS/6Sjz448/PnFiRETUOLH/ICKiirB/ICJqmmpUmJo9ezbmzJmDzp07w9XVFTKZrLbzIiKiRoj9BxERVYT9AxFR01WjwtTy5csRHR2NkSNH1nY+RETUiLH/ICKiirB/ICJquoxq8qSCggJ07969tnMhIqJGjv0HERFVhP0DEVHTVaPC1FtvvYW1a9fWdi5ERNTIsf8gIqKKsH8gImq6ajSVLz8/HytWrMCuXbvg7+8PU1NTveMLFiyoleSIiKhxYf9BREQVYf9ARNR01agwderUKQQEBAAAzpw5o3eMCxUSEVFl2H8QEVFF2D8QETVdNSpM7dmzp7bzICKiJoD9BxERVYT9AxFR01WjNaakFBUVhZYtW8Lc3Bxdu3bFkSNHqozfuHEjvL29YW5uDj8/P2zbtk3vuCAImDlzJlxdXWFhYYGgoCBcvHixLptARERERERERESo4Yipvn37Vjmkdvfu3TVOqCrr16/HlClTsHz5cnTt2hWLFi2CWq1GUlISnJ2dy8XHxcVh+PDhmDdvHp5//nmsXbsWgwcPxvHjx9GhQwcAwPz58/HNN99g1apV8PLywscffwy1Wo1z587B3Ny8TtpBRNRUSdV/EBFRw1Zb/cP+/fvx5ZdfIiEhAenp6fj1118xePBg8fiYMWOwatUqveeo1Wps375dfHznzh288847+P3332FkZITQ0FAsXrwY1tbWYsypU6cQHh6Oo0ePwsnJCe+88w6mTZv2mK0lIqIH1WjEVEBAADp27Chuvr6+KCgowPHjx+Hn51fbOYoWLFiAcePGYezYsfD19cXy5cthaWmJH3/8scL4xYsXY8CAAZg6dSp8fHwwd+5cdOrUCUuXLgVQOlpq0aJFmDFjBl566SX4+/vjp59+wo0bN7B58+Y6awcRUVMlVf9BREQNW231D7m5uejYsSOioqIqjRkwYADS09PF7T//+Y/e8REjRuDs2bOIiYnBli1bsH//fowfP148rtVqERwcDE9PTyQkJODLL7/ErFmzsGLFiuo3nIiIajZiauHChRXunzVrFnJycp4oocoUFBQgISEBkZGR4j4jIyMEBQUhPj6+wufEx8djypQpevvUarVYdEpJSYFGo0FQUJB43NbWFl27dkV8fDyGDRtW4Xl1Oh10Op34WKvV1rRZYodIRFQfXF1d4erqKtn1peg/iIio4aut/mHgwIEYOHBglTFyuRxKpbLCY+fPn8f27dtx9OhRdO7cGQCwZMkSDBo0CF999RXc3NywZs0aFBQU4Mcff4SZmRnat2+PxMRELFiwQK+ARUREj6dGhanKvPHGG3jmmWfw1Vdf1eZpAQC3bt1CcXExXFxc9Pa7uLjgwoULFT5Ho9FUGK/RaMTjZfsqi6nIvHnzMHv27Gq3oSLfffddrZ2LiOhRPvnkE8yaNUvqNMqpy/6DiIgMV130D3v37oWzszOaNWuGfv364dNPP4WDgwOA0i+27ezsxKIUAAQFBcHIyAiHDx/Gyy+/jPj4ePTq1QtmZmZijFqtxj//+U/cvXsXzZo1q/C6tfnlNhFRY1Krhan4+PgmsS5TZGSk3kgsrVYLDw+PGp3r7bffxosvvlhbqRERVUnK0VJVaSr9BxERVU9t9w8DBgzAkCFD4OXlheTkZHz44YcYOHAg4uPjYWxsDI1GU27tWhMTE9jb2+t9ue3l5aUXU/ZFt0ajqbQwVZtfbhMRNSY1KkwNGTJE77EgCEhPT8exY8fw8ccf10piD3N0dISxsTEyMjL09mdkZFQ6FFepVFYZX/bfjIwMvQ9rGRkZCAgIqDQXuVwOuVxek2aUI/W0GiKi+iRF/0FERA1fffUPDy7V4efnB39/f7Ru3Rp79+5F//79a+06FanNL7eJiBqTGi1+bmtrq7fZ29ujT58+2LZtGz755JPazhEAYGZmhsDAQMTGxor7SkpKEBsbC5VKVeFzVCqVXjwAxMTEiPFeXl5QKpV6MVqtFocPH670nEREVHNS9B9ERNTwSdU/tGrVCo6Ojrh06RKA0i+uMzMz9WKKiopw584dvS+3K/ryu+xYZeRyORQKhd5GREQ1HDG1cuXK2s7jsUyZMgWjR49G586d8cwzz2DRokXIzc3F2LFjAQCjRo1C8+bNMW/ePADAe++9h969e+Prr79GSEgI1q1bh2PHjol3zJDJZJg0aRI+/fRTtG3bFl5eXvj444/h5uamd1tZIiKqHVL1H0RE1LBJ1T9cu3YNt2/fFmcwqFQqZGVlISEhAYGBgQCA3bt3o6SkBF27dhVjPvroIxQWFsLU1BRA6Zff7dq1q3QaHxERVe6J1phKSEjA+fPnAQDt27fH008/XStJVWbo0KG4efMmZs6cCY1Gg4CAAGzfvl2c052amgojo78HgXXv3h1r167FjBkz8OGHH6Jt27bYvHkzOnToIMZMmzYNubm5GD9+PLKystCzZ09s376da50QEdWh+u4/iIjIMDxp/5CTkyOOfgJK78KdmJgIe3t72NvbY/bs2QgNDYVSqURycjKmTZuGNm3aQK1WAwB8fHwwYMAAjBs3DsuXL0dhYSEiIiIwbNgwuLm5AQBef/11zJ49G2FhYZg+fTrOnDmDxYsXV3pnQSIiqlqNClOZmZkYNmwY9u7dCzs7OwBAVlYW+vbti3Xr1sHJyak2c9QTERGBiIiICo/t3bu33L5XX30Vr776aqXnk8lkmDNnDubMmVNbKRIRUSWk7D+IiKjhqq3+4dixY+jbt6/4uGxNp9GjR2PZsmU4deoUVq1ahaysLLi5uSE4OBhz587VWz92zZo1iIiIQP/+/WFkZITQ0FB888034nFbW1vs3LkT4eHhCAwMhKOjI2bOnInx48fXwitBRNT01Kgw9c477+DevXs4e/YsfHx8AADnzp3D6NGj8e677+I///lPrSZJRESNA/sPIiKqSG31D3369IEgCJUe37FjxyPPYW9vj7Vr11YZ4+/vjz///POxciIioqrVqDC1fft27Nq1S+w0AMDX1xdRUVEIDg6uteSIiKhxYf9BREQVYf9ARNR01eiufCUlJeJCfw8yNTVFSUnJEydFRESNE/sPIiKqCPsHIqKmq0aFqX79+uG9997DjRs3xH3Xr1/H5MmT0b9//1pLjoiIGhf2H0REVBH2D0RETVeNClNLly6FVqtFy5Yt0bp1a7Ru3RpeXl7QarVYsmRJbedIRESNRG30H/PmzUOXLl1gY2MDZ2dnDB48GElJSXox+fn5CA8Ph4ODA6ytrREaGoqMjAy9mNTUVISEhMDS0hLOzs6YOnUqioqK9GL27t2LTp06QS6Xo02bNoiOjn6i9hMRUcX4+YKIqOmq0RpTHh4eOH78OHbt2oULFy4AKL21alBQUK0mR0REjUtt9B/79u1DeHg4unTpgqKiInz44YcIDg7GuXPnYGVlBQCYPHkytm7dio0bN8LW1hYREREYMmQIDh48CAAoLi5GSEgIlEol4uLikJ6ejlGjRsHU1BSff/45gNJbjIeEhGDChAlYs2YNYmNj8dZbb8HV1VW8rTgREdUOfr4gImq6qlWY2r17NyIiInDo0CEoFAo899xzeO655wAA2dnZaN++PZYvX45nn322TpIlIiLDVJv9x/bt2/UeR0dHw9nZGQkJCejVqxeys7Pxww8/YO3atejXrx8AYOXKlfDx8cGhQ4fQrVs37Ny5E+fOncOuXbvg4uKCgIAAzJ07F9OnT8esWbNgZmaG5cuXw8vLC19//TWA0g9IBw4cwMKFC1mYIiKqJfx8QURE1ZrKt2jRIowbNw4KhaLcMVtbW7z99ttYsGBBrSVHRESNQ132H9nZ2QBKb+8NAAkJCSgsLNT7lt3b2xstWrRAfHw8ACA+Ph5+fn5wcXERY9RqNbRaLc6ePSvGPPxNvVqtFs9BRERPjp8viIioWoWpkydPYsCAAZUeDw4ORkJCwhMnRUREjUtd9R8lJSWYNGkSevTogQ4dOgAANBoNzMzMYGdnpxfr4uICjUYjxjxYlCo7XnasqhitVou8vLwK89HpdNBqtXpbTey7eh8OIVNw474MxSVCjc5BRGQI+PmCiIiqVZjKyMio8DauZUxMTHDz5s0nToqIiBqXuuo/wsPDcebMGaxbt+5J0qs18+bNg62trbh5eHjU6Dx7UvJg3aEf4m+ZYv2xNOQVFNdypkREDQM/XxARUbUKU82bN8eZM2cqPX7q1Cm4uro+cVJERNS41EX/ERERgS1btmDPnj1wd3cX9yuVShQUFCArK0svPiMjA0qlUox5+C59ZY8fFaNQKGBhYVFhTpGRkcjOzha3tLS0arWpzPAONtAe+y9MjQTcvKfDLyeuQVfE4hQRNT78fEFERNUqTA0aNAgff/wx8vPzyx3Ly8vDJ598gueff77WkiMiosahNvsPQRAQERGBX3/9Fbt374aXl5fe8cDAQJiamiI2Nlbcl5SUhNTUVKhUKgCASqXC6dOnkZmZKcbExMRAoVDA19dXjHnwHGUxZeeoiFwuh0Kh0Ntqop2jGe7G/gt9XAphaWaMWzkFOJR8p0bnIiJqyPj5goiIqnVXvhkzZuCXX37BU089hYiICLRr1w4AcOHCBURFRaG4uBgfffRRnSRKRESGqzb7j/DwcKxduxb//e9/YWNjI64JZWtrCwsLC9ja2iIsLAxTpkyBvb09FAoF3nnnHahUKnTr1g1A6Zolvr6+GDlyJObPnw+NRoMZM2YgPDwccrkcADBhwgQsXboU06ZNw5tvvondu3djw4YN2Lp1ax28QhVTmALBvi7YnHgDJ69lwcfNBs425vV2fSKiusbPF0REVK3ClIuLC+Li4jBx4kRERkZCEEoXZJXJZFCr1YiKiiq3UCwREVFt9h/Lli0DAPTp00dv/8qVKzFmzBgAwMKFC2FkZITQ0FDodDqo1Wp8++23YqyxsTG2bNmCiRMnQqVSwcrKCqNHj8acOXPEGC8vL2zduhWTJ0/G4sWL4e7uju+//x5qtfoJXonq83SwwlPO1vgrMwcHLt7CkE7uj34SEZGB4OcLIiKqVmEKADw9PbFt2zbcvXsXly5dgiAIaNu2LZo1a1YX+RERUSNRW/1H2YeWqpibmyMqKgpRUVGPzKcqffr0wYkTJ6qVX13o0cYRF2/mIO1uHm7e08HJRi51SkREtYafL4iImrZqF6bKNGvWDF26dKnNXIiIqAlg/1F9CgtTtHUqHTV1Iu0ugn2VUqdERFTr2D8QETVN1Vr8nIiIiKTxdIvSkQNJmnu4X1AkcTZERERERLWDhSkiIiIDoLQ1h7ONHCUC8FdGjtTpEBERERHVChamiIiIDIS30gZA6agpIiIiIqLGgIUpIiIiA/GUiw1kADTafGTdL5A6HSIiIiKiJ8bCFBERkYGwkpvAw94SAJCUwVFTRERERGT4WJgiIiIyIE+5WAMALt/MlTgTIiIiIqInx8IUERGRAWnpYAUAyLynQ46Od+cjIiIiIsPGwhQREZEBsZKbQKkwBwCk3OKoKSIiIiIybCxMERERGRgvx9JRUyxMEREREZGhY2GKiIjIwJQVplLv3EdRcYnE2RARERER1RwLU0RERAbG0doMVnJjFJcISM/OlzodIiIiIqIaY2GKiIjIwMhkMng0swRQOmqKiIiIiMhQsTBFRERkgFrYlxam0u6yMEVEREREhouFKSIiIgNUNmIqQ6tDfmGxxNkQEREREdUMC1NEREQGyNrcBPaWZgA4aoqIiIiIDBcLU0RERAbK3d4CAHDjLhdAJyIiIiLDxMIUERGRgXKz/V9hKjtP4kyIiIiIiGqGhSkiIiID5WZnDgC4maNDQVGJxNkQEREREVUfC1NEREQGysbcFDbmJhAEQKPldD4iIiIiMjwsTBERERkwV9vSUVM3sjidj4iIiIgMDwtTREREBszNrnSdqfRsjpgiIiIiIsPDwhQREZEBK1sAPT07DyUlgsTZEBERERFVDwtTREREBszB2gxmxkYoLBZwK1cndTpERERERNXCwhQRERmc/fv344UXXoCbmxtkMhk2b96sd3zMmDGQyWR624ABA/Ri7ty5gxEjRkChUMDOzg5hYWHIycnRizl16hSeffZZmJubw8PDA/Pnz6/rplWbkUz2wDpTnM5HRERERIaFhSkiIjI4ubm56NixI6KioiqNGTBgANLT08XtP//5j97xESNG4OzZs4iJicGWLVuwf/9+jB8/Xjyu1WoRHBwMT09PJCQk4Msvv8SsWbOwYsWKOmtXTYnrTHEBdCIiIiIyMCxMERGRwRk4cCA+/fRTvPzyy5XGyOVyKJVKcWvWrJl47Pz589i+fTu+//57dO3aFT179sSSJUuwbt063LhxAwCwZs0aFBQU4Mcff0T79u0xbNgwvPvuu1iwYEGdt6+6xBFT2fkQBK4zRURN16NG1AqCgJkzZ8LV1RUWFhYICgrCxYsX9WIay4haIiJDwcIUERE1Snv37oWzszPatWuHiRMn4vbt2+Kx+Ph42NnZoXPnzuK+oKAgGBkZ4fDhw2JMr169YGZmJsao1WokJSXh7t279deQx6C0NYeRDMjRFeFefpHU6RARSeZRI2rnz5+Pb775BsuXL8fhw4dhZWUFtVqN/Py/p0I3phG1RESGwETqBIiIiGrbgAEDMGTIEHh5eSE5ORkffvghBg4ciPj4eBgbG0Oj0cDZ2VnvOSYmJrC3t4dGowEAaDQaeHl56cW4uLiIxx4cgVVGp9NBp/t7AXKtVlvbTauQqbERnGzkyNDqcCM7DwoL03q5LhFRQzNw4EAMHDiwwmOCIGDRokWYMWMGXnrpJQDATz/9BBcXF2zevBnDhg0TR9QePXpU/PJiyZIlGDRoEL766iu4ubnpjag1MzND+/btkZiYiAULFugVsIiI6PFwxBQRETU6w4YNw4svvgg/Pz8MHjwYW7ZswdGjR7F37946ve68efNga2srbh4eHnV6vQcpFaXT+TK0vDMfEVFFUlJSoNFoEBQUJO6ztbVF165dER8fD6DxjaglIjIEBlOYepy53g/Lz89HeHg4HBwcYG1tjdDQUGRkZIjHT548ieHDh8PDwwMWFhbw8fHB4sWL67opRERUz1q1agVHR0dcunQJAKBUKpGZmakXU1RUhDt37kCpVIoxD/YZAMTHZTEPi4yMRHZ2trilpaXVdlMq5SIWpnhnPiKiipSNiC0b/VrGxcVFb7Ts44yoregcD16jIjqdDlqtVm8jIiIDKkw9aq53RSZPnozff/8dGzduxL59+3Djxg0MGTJEPJ6QkABnZ2esXr0aZ8+exUcffYTIyEgsXbq0rptDRET16Nq1a7h9+zZcXV0BACqVCllZWUhISBBjdu/ejZKSEnTt2lWM2b9/PwoLC8WYmJgYtGvXrsJpfEDpgusKhUJvqy9lI6Zu3tOhuIQLoBMRNTRSjqolImrIDKIw9Th3T3pYdnY2fvjhByxYsAD9+vVDYGAgVq5cibi4OBw6dAgA8Oabb2Lx4sXo3bs3WrVqhTfeeANjx47FL7/8Up/NIyKiasrJyUFiYiISExMBlE7PSExMRGpqKnJycjB16lQcOnQIV65cQWxsLF566SW0adMGarUaAODj44MBAwZg3LhxOHLkCA4ePIiIiAgMGzYMbm5uAIDXX38dZmZmCAsLw9mzZ7F+/XosXrwYU6ZMkarZVbKzNIWZiRGKSgTcyS2QOh0ioganbLRrRaNhHxwtWxcjagFpR9USETVkBlGYepy53g9LSEhAYWGh3hxyb29vtGjRQpxDXpHs7GzY29vXXvJERFTrjh07hqeffhpPP/00AGDKlCl4+umnMXPmTBgbG+PUqVN48cUX8dRTTyEsLAyBgYH4888/IZfLxXOsWbMG3t7e6N+/PwYNGoSePXvq3VHJ1tYWO3fuREpKCgIDA/H+++9j5syZDXZhW5lMBhdFafs0nM5HRFSOl5cXlEolYmNjxX1arRaHDx+GSqUCUHcjagFpR9USETVkBnFXvseZ613Rc8zMzGBnZ6e3/8E55A+Li4vD+vXrsXXr1irzkequS0REVKpPnz4QhMqnq+3YseOR57C3t8fatWurjPH398eff/5Z7fykolSYI+1OHjK0+fBrbit1OkRE9S4nJ0dcTxD4e0Stvb09WrRogUmTJuHTTz9F27Zt4eXlhY8//hhubm4YPHgwAP0RtcuXL0dhYWGFI2pnz56NsLAwTJ8+HWfOnMHixYuxcOFCKZpMRGTwJB0x9Y9//AMymazK7cKFC/WSy5kzZ/DSSy/hk08+QXBwcJWxnB9OREQNUdkC6BwxRURNVVUjagFg2rRpeOeddzB+/Hh06dIFOTk52L59O8zNzcVzNLYRtUREDZ2kI6bef/99jBkzpsqYVq1aPdZc74cplUoUFBQgKytLb9TUg3PIy5w7dw79+/fH+PHjMWPGjEfmHRkZqbfGiFarZXGKiIgkV1aYupNTgMLiEpgaG8SMfSKiWvOoEbUymQxz5szBnDlzKo1pjCNqiYgaMkkLU05OTnBycnpk3INzvQMDAwGUn+v9sMDAQJiamiI2NhahoaEAgKSkJKSmpopzyAHg7Nmz6NevH0aPHo3PPvvssfKWy+V665QQERE1BNZyE1jLTZCjK0KmVofmzSykTomIiIiIqEoG8VXq49w96fr16/D29saRI0cAlA6xDQsLw5QpU7Bnzx4kJCRg7NixUKlU6NatG4DS6Xt9+/ZFcHAwpkyZAo1GA41Gg5s3b0rWViIioidRtgB6BqfzEREREZEBMIjFz4HSud4RERHo378/jIyMEBoaim+++UY8XlhYiKSkJNy/f1/ct3DhQjFWp9NBrVbj22+/FY9v2rQJN2/exOrVq7F69Wpxv6enJ65cuVIv7SIiIqpNLgpzJN/M5TpTRERERFQj6dl5uJiZg/SsfGTnmsL5tcqnP9cGgylMPWqud8uWLcvNJzc3N0dUVBSioqIqfM6sWbMwa9as2kyTiIhIUsr/rTPFEVNEREREVB23cnTY99dNXLub98BeGUwUznV6XYOYykdERESPx/l/U/m0+UW4X1AkcTZERERE1NAJgoDEtCysO5KGa3fzYCQDvJU2GNBeib4uhchY92GdXt9gRkwRERHRo8lNjNHM0hR37xciQ6uDlyO7eiIiIiKqWIkgYP9fN3HyWjYAwMvRCn2ecoLCwhQAcO2egOKcO3WaA0dMERERNTIunM5HRERERI8gCAL2XMgUi1LPtnXEC/6uYlGqvrAwRURE1MiwMEVEREREjxKXfBtnbmghAzCgvRKdWjSDTCar9zxYmCIiImpkXP63zlSGVlfuxiBERERERBc0Why7ehcA0M/HGe2UNpLlwsIUERFRI+NkLYeRDMgrLMY9HRdAJyIiIqK/3crRIfZ8JgCgS8tm6OBmK2k+LEwRERE1MibGRnCwKhs1xel8RERERFSqqLgE289oUFQiwNPBEt1aOUidEgtTREREjVHZdL5MrU7iTIiIiIiooYhLvo3buQWwNDNGsK8LjCRYU+phLEwRERE1QlwAnYiIiIgelKnNR2JaFgAgyMcFlmYm0ib0PyxMERERNUJiYeoeF0AnIiIiaupKBAG7kzIhAHjKxRpejlZSpyRiYYqIiKgRsrcyg7GRDAVFJcjKK5Q6HSIiIiKS0Onr2cjQ6mBmYoRebZ2kTkcPC1NERESNkLGRDM42XACdiIiIqKnL1RUh7tJtAED3Vg6wkjeMKXxlWJgiIiJqpFxsytaZ4gLoRERERE3VweRbKCgugbONHH7utlKnUw4LU0RERI2Us4IjpoiIiIiasls5OpxPvwcA6NvOuUHche9hLEwRERE1UmULoN+8p0MJ1z8nIiIianLikkun8LVxtobS1lzibCrGwhQREVEj1czSFGbGRigqEXCvsOF9O0ZEREREded6Vh5SbuVCJgO6t3aQOp1KsTBFRETUSMlkMnE6350CFqaIiIiImgpBEHDw0i0AQHtXBZpZmkmcUeVYmCIiImrEyqbz3WVhioiIiKjJSLmdi/TsfJgYydC1VcMdLQWwMEVERNSoudiUjpi6q2NhioiIiKgpEATg2JW7AICOHnawlptInFHVWJgiIiKDs3//frzwwgtwc3ODTCbD5s2b9Y4LgoCZM2fC1dUVFhYWCAoKwsWLF/Vi7ty5gxEjRkChUMDOzg5hYWHIycnRizl16hSeffZZmJubw8PDA/Pnz6/rptW6shFT2YUywNhU4myIiIiIqK7d1smQnp0PYyMZnvawkzqdR2JhioiIDE5ubi46duyIqKioCo/Pnz8f33zzDZYvX47Dhw/DysoKarUa+fn5YsyIESNw9uxZxMTEYMuWLdi/fz/Gjx8vHtdqtQgODoanpycSEhLw5ZdfYtasWVixYkWdt6822ZibwMLUGAJkMHP2kjodIiIiIqpjSVpjAICP0gZWDXy0FAA0/AyJiIgeMnDgQAwcOLDCY4IgYNGiRZgxYwZeeuklAMBPP/0EFxcXbN68GcOGDcP58+exfft2HD16FJ07dwYALFmyBIMGDcJXX30FNzc3rFmzBgUFBfjxxx9hZmaG9u3bIzExEQsWLNArYDV0ZQugX719H2bKtlKnQ0RERER1yNSxBTT5pWOQOnk2kzibx8MRU0RE1KikpKRAo9EgKChI3Gdra4uuXbsiPj4eABAfHw87OzuxKAUAQUFBMDIywuHDh8WYXr16wczs7zuYqNVqJCUl4e7duxVeW6fTQavV6m0NQdl0PrkrC1NEREREjZnimSEAgDZO1g36TnwPYmGKiIgaFY1GAwBwcXHR2+/i4iIe02g0cHZ21jtuYmICe3t7vZiKzvHgNR42b9482NraipuHh8eTN6gWuChKF0A3Y2GKiIiIqNG6db8YVr59AACBLQ1jtBTAwhQREVGtiYyMRHZ2trilpaVJnRIAwMWmdMSUqYMH8gpLJM6GiIiIiOrC73/lQmZsAid5CZT/GzFvCFiYIiKiRkWpVAIAMjIy9PZnZGSIx5RKJTIzM/WOFxUV4c6dO3oxFZ3jwWs8TC6XQ6FQ6G0NgZXcBBbGAmQyI1y+Wyh1OkRERERUy7LvFyLm8n0AwFOKYomzqR4WpoiIqFHx8vKCUqlEbGysuE+r1eLw4cNQqVQAAJVKhaysLCQkJIgxu3fvRklJCbp27SrG7N+/H4WFfxdyYmJi0K5dOzRrZjhDo8s0MxMAABfvsDBFRERE1NisPnwV+UUCCjJT4GIuSJ1OtbAwRUREBicnJweJiYlITEwEULrgeWJiIlJTUyGTyTBp0iR8+umn+O2333D69GmMGjUKbm5uGDx4MADAx8cHAwYMwLhx43DkyBEcPHgQERERGDZsGNzc3AAAr7/+OszMzBAWFoazZ89i/fr1WLx4MaZMmSJRq5+MvVnpFL5LHDFFRGTQjqfnQ97cG/cKgRLBsD58ElHdyC8sxsqDKQAA7eGfIZNJnFA1mUidABERUXUdO3YMffv2FR+XFYtGjx6N6OhoTJs2Dbm5uRg/fjyysrLQs2dPbN++Hebmf8+1X7NmDSIiItC/f38YGRkhNDQU33zzjXjc1tYWO3fuRHh4OAIDA+Ho6IiZM2di/Pjx9dfQWmQnL/3wkswRU0REBquwuASf/nkXyje+ws50YE9mMprbWaCd0gZtnW1gbGRgn0aJqFb8fPwabuUUwNHSGFcv/AngXalTqhYWpoiIyOD06dMHQhXfEstkMsyZMwdz5sypNMbe3h5r166t8jr+/v74888/a5xnQ2JvJkAQSpCRC9y8p4OTjVzqlIiIqJryC4vRxt4U55OvwtzeFYXFwJXb93Hl9n0cvHQbXVvZo72rAjJDGy5BRDVWXCLgX/svAwBefMoKCSWGtb4UwKl8RERETYKpEVB48yoA4HjqXYmzISKimrAxN8X8IEfcWDEeL7kX4vVnWuAZL3tYmhkjR1eE2POZ2JhwDdl5HB1L1FTsOKvBldv3YWdpiqBWFlKnUyMsTBERETURuhsXAADHr7IwRURk6GQywMlGDlUrB4zt3hLPtnGEqbEM6dn5WHs4Fck3c6ROkYjqmCAI+G5fMgBgVDdPmJsYZonHMLMmIiKiatNdPw+AI6aIiBobE2MjdPJshje6ecLV1hwFxSXYciqdX0QQNXLxl2/j5LVsyE2MMLp7S6nTqTEWpoiIiJoI3fXSEVMnr2WjoKhE4myIiKi2KcxN8Uond/g3twUA/HnpFo6k3JE4KyKqK9/tK11b6rXOHnCwNtz1Q1mYIiIiaiKK7t6AjZkMBUUlOHsjW+p0iIioDhgZydCnnRO6t3YAUDqi4tgVFqeIGptzN7TY99dNGMmAcc+2kjqdJ8LCFBERURPSzsEMAHA8NUvaRIiIqM7IZDJ0aWkPVavS4tTB5Nucxk3UyKzYX7q21CA/V7RwsJQ4myfDwhQREVET0s7xf4UprjtCRNToPeNlj25e9gCAPy/ewsXMexJnRES1Ie3Offx+Kh0AMKF3a4mzeXIsTBERETUh7RxMAXABdCJqumbNmgWZTKa3eXt7i8fz8/MRHh4OBwcHWFtbIzQ0FBkZGXrnSE1NRUhICCwtLeHs7IypU6eiqKiovpvyWLq2ckCAhx0AYOfZDGRq86VNiIie2A8HUlBcIqBnG0d0+N+acoaMhSkiIqImpI29KYyNSm8nfiMrT+p0iIgk0b59e6Snp4vbgQMHxGOTJ0/G77//jo0bN2Lfvn24ceMGhgwZIh4vLi5GSEgICgoKEBcXh1WrViE6OhozZ86UoimP5dk2jvB0sERRiYDfT6UjR9cwi2hE9Gh3cwuw/mgaAODt3oa9tlQZFqaIiIiaEHMTI/i42gAAEjidj4iaKBMTEyiVSnFzdHQEAGRnZ+OHH37AggUL0K9fPwQGBmLlypWIi4vDoUOHAAA7d+7EuXPnsHr1agQEBGDgwIGYO3cuoqKiUFBQIGWzKmVkJMPADkrYW5ohR1eErafSUVwiSJ0WEdXAT/FXkVdYjPZuCvRs4yh1OrWChSkiIqImJrBFMwCczkdETdfFixfh5uaGVq1aYcSIEUhNTQUAJCQkoLCwEEFBQWKst7c3WrRogfj4eABAfHw8/Pz84OLiIsao1WpotVqcPXu20mvqdDpotVq9rT7JTYzxQkdXyE2MoNHmIy75Vr1en4ieXF5BMVbFXwEAvN27NWQymbQJ1RIWpoiIiJqYTp6lhaljV1iYIqKmp2vXroiOjsb27duxbNkypKSk4Nlnn8W9e/eg0WhgZmYGOzs7vee4uLhAo9EAADQajV5Rqux42bHKzJs3D7a2tuLm4eFRuw17DHaWZgjyKc31eGoWUm7l1nsORFRzGxPScCe3AB72FhjUQSl1OrXGROoEiIiIqH519Sq9ffjZG9nIziuErYWpxBkREdWfgQMHiv/v7++Prl27wtPTExs2bICFhUWdXTcyMhJTpkwRH2u1WkmKU22crdHR3RYnr2Vj5zkN+jrVewpEVANFxSVYsf8yAGDcs61gYtx4xhk1npYQERHRY1HamsPL0QolAnA05Y7U6RARScrOzg5PPfUULl26BKVSiYKCAmRlZenFZGRkQKksHZ2gVCrL3aWv7HFZTEXkcjkUCoXeJpWebR3hbCNHfmEJjt02AdA4pgMRNWbbzmhw7W4e7K3M8Gpg/Re16xILU0RERE1Qt1alo6YOXb4tcSZERNLKyclBcnIyXF1dERgYCFNTU8TGxorHk5KSkJqaCpVKBQBQqVQ4ffo0MjMzxZiYmBgoFAr4+vrWe/41YWJkhIEdlDAxkuGmzgg2gS9InRIRVUEQBCzfmwwAGK1qCQszY4kzql0sTBERETVB3VrZAwDiWZgioibmgw8+wL59+3DlyhXExcXh5ZdfhrGxMYYPHw5bW1uEhYVhypQp2LNnDxISEjB27FioVCp069YNABAcHAxfX1+MHDkSJ0+exI4dOzBjxgyEh4dDLpdL3LrHZ2dphmfblt7Ry673aFzTFkmcERFV5s+Lt3AuXQsLU2OMUnlKnU6tM5jC1J07dzBixAgoFArY2dkhLCwMOTk5VT4nPz8f4eHhcHBwgLW1NUJDQ8sNuy1z+/ZtuLu7QyaTlRu6S0RE1Nio/jdi6ly6Ftn3CyXOhoio/ly7dg3Dhw9Hu3bt8Nprr8HBwQGHDh2Ck1PpYksLFy7E888/j9DQUPTq1QtKpRK//PKL+HxjY2Ns2bIFxsbGUKlUeOONNzBq1CjMmTNHqibVmF9zW7iYl8DIVI5vjmShqLhE6pSIqAJRey4BAIZ28UAzKzOJs6l9BrP4+YgRI5Ceno6YmBgUFhZi7NixGD9+PNauXVvpcyZPnoytW7di48aNsLW1RUREBIYMGYKDBw+Wiw0LC4O/vz+uX79el80gIiJqEJwV5mjlZIXLN3NxOOU2gts3nju7EBFVZd26dVUeNzc3R1RUFKKioiqN8fT0xLZt22o7tXonk8kQaF+E3y8X4NIda3y7Nxnv9m8rdVpE9IBjV+7gcModmBrL8HbvVlKnUycMYsTU+fPnsX37dnz//ffo2rUrevbsiSVLlmDdunW4ceNGhc/Jzs7GDz/8gAULFqBfv34IDAzEypUrERcXh0OHDunFLlu2DFlZWfjggw/qozlEREQNgkpcZ4oLoBMRNVUWJsDdmOUAgG9iL+LM9WyJMyKiB5WNlgrt5A5X27q7c6iUDKIwFR8fDzs7O3Tu3FncFxQUBCMjIxw+fLjC5yQkJKCwsBBBQUHiPm9vb7Ro0QLx8fHivnPnzmHOnDn46aefYGRkEC8HERFRrShbAJ3rTBERNW255/aim7s5ikoETF6fiPzCYqlTIiIAZ29kY0/STRjJgLd7t5Y6nTpjEFP5NBoNnJ2d9faZmJjA3t4eGo2m0ueYmZnBzs5Ob7+Li4v4HJ1Oh+HDh+PLL79EixYtcPny5cfKR6fTQafTiY+1Wm01WkNERNQwlBWmLmi0yLpfADvLxrdmAVFjlZqailu3btXa+RwdHdGiRYtaOx8ZngmBtriUVYKLmTlYHHsR0wd4S50SUZP37Z7SO/GF+LvBy9FK4mzqjqSFqX/84x/45z//WWXM+fPn6+z6kZGR8PHxwRtvvFGt582bNw+zZ8+uo6yIiIjqh5ONHG2drXExMweHLt/BgA5cZ4rIEKSmpsLbxwd59+/X2jktLC1x4fx5FqeaMIXcCJ8O9sOE1Qn4bl8y1O2VCPCwkzotoiYr+WYOtp1JBwD8X5/GO1oKkLgw9f7772PMmDFVxrRq1QpKpRKZmZl6+4uKinDnzh0olRX/Ea1UKlFQUICsrCy9UVMZGRnic3bv3o3Tp09j06ZNAABBEACUfmP00UcfVVp8ioyMxJQpU8THWq0WHh4eVbaDiIioIerWygEXM3MQn3yLhSkiA3Hr1i3k3b+PEdO/hEuLJ/+wkpGajDX/nIpbt26xMNXEDeigxIsd3fDbyRv4YONJbHmnJ8xNjaVOi6hJWrY3GYIABPk4w8dVIXU6dUrSwpSTk5N4W9aqqFQqZGVlISEhAYGBgQBKi0olJSXo2rVrhc8JDAyEqakpYmNjERoaCgBISkpCamoqVCoVAODnn39GXl6e+JyjR4/izTffxJ9//onWrSvv5OVyOeRy+WO3k4iIqKHq0cYR/z50FX9erL0pQURUP1xatIZ72/ZSp0GNzOwX2yMu+TYuZeZg4a6/EDnQR+qUiJqca3fvY/OJ6wCA/+vbRuJs6p5BrPbt4+ODAQMGYNy4cThy5AgOHjyIiIgIDBs2DG5ubgCA69evw9vbG0eOHAEA2NraIiwsDFOmTMGePXuQkJCAsWPHQqVSoVu3bgCA1q1bo0OHDuLm5eUlXu/hNa2IiMhwzJo1CzKZTG/z9v57rYz8/HyEh4fDwcEB1tbWCA0NRUZGht45UlNTERISAktLSzg7O2Pq1KkoKiqq76bUuR5tHGBiJMPlW7lIvV1704KIiMgwNbMyw+cvdwAA/Gv/ZRxPvStxRkRNz/J9ySgqEdC9tQM6tWgmdTp1ziAKUwCwZs0aeHt7o3///hg0aBB69uyJFStWiMcLCwuRlJSE+w/MtV+4cCGef/55hIaGolevXlAqlfjll1+kSJ+IiOpZ+/btkZ6eLm4HDhwQj02ePBm///47Nm7ciH379uHGjRsYMmSIeLy4uBghISEoKChAXFwcVq1ahejoaMycOVOKptQpG3NTBHqW/sGz76/MR0QTEVFTENxeicEBbigRgKkbT/IufUT1KO3Ofaw/mgYAeKdfW4mzqR8GcVc+ALC3t8fatWsrPd6yZUtxjagy5ubmiIqKQlRU1GNdo0+fPuXOQUREhsnExKTCdQizs7Pxww8/YO3atejXrx8AYOXKlfDx8cGhQ4fQrVs37Ny5E+fOncOuXbvg4uKCgIAAzJ07F9OnT8esWbNgZta47l7Xu50TDqfcwd6kmxipail1OkRE1ADMerE9DibfRvLNXCyI+QsfDuKUPqL6sGT3RRQWC+jRxgGq1g5Sp1MvDGbEFBERUXVcvHgRbm5uaNWqFUaMGIHU1FQAQEJCAgoLCxEUFCTGent7o0WLFoiPjwcAxMfHw8/PDy4uLmKMWq2GVqvF2bNnK72mTqeDVqvV2wxB76dK13uMS77Nb8WJiAgAYGdphnkv+wEA/vXnZSRc5ZQ+orp2+WYOfj5eurbU+8HtJM6m/rAwRUREjU7Xrl0RHR2N7du3Y9myZUhJScGzzz6Le/fuQaPRwMzMTO+OrQDg4uICjUYDANBoNHpFqbLjZccqM2/ePNja2oqbodyx1ddVAReFHHmFxYi/fFvqdIiIqIEI8nXBkKebQ+CUPqJ6sWjXRRSXCOjv7dwk1pYqw8IUERE1OgMHDsSrr74Kf39/qNVqbNu2DVlZWdiwYUOdXjcyMhLZ2dnilpaWVqfXqy0ymQxBPqWFt5hzGY+IJiKipuSTF9rD2UaOy7dy8dWOJKnTIWq0Lmi0+P3UDQDAlOCnJM6mfrEwRUREjZ6dnR2eeuopXLp0CUqlEgUFBcjKytKLycjIENekUiqV5e7SV/a4onWrysjlcigUCr3NUDznW1qY2nUuAyUlXG+RiIhK2VqaYt6Q0il9PxxMwbErdyTOiKhx+uKPCxAEIMTPFe3dbKVOp16xMEVERI1eTk4OkpOT4erqisDAQJiamiI2NlY8npSUhNTUVKhUKgCASqXC6dOnkZn5913qYmJioFAo4OvrW+/51wdVawdYy02QeU+HU9ezpU6HiIgakP4+Lgjt5F46pW/TKeQVcEofUW3a99dN7E26CVNjGaaqm87aUmVYmCIiokbngw8+wL59+3DlyhXExcXh5ZdfhrGxMYYPHw5bW1uEhYVhypQp2LNnDxISEjB27FioVCp069YNABAcHAxfX1+MHDkSJ0+exI4dOzBjxgyEh4dDLpdL3Lq6ITcxFhdB336m8nW0iIioaZr5gi9cFHKk3MrFl5zSR1RriopL8NnWcwCA0aqWaOloJXFG9Y+FKSIianSuXbuG4cOHo127dnjttdfg4OCAQ4cOwcmptPCycOFCPP/88wgNDUWvXr2gVCrxyy+/iM83NjbGli1bYGxsDJVKhTfeeAOjRo3CnDlzpGpSvRjoVzpNcevpGxAETucjIqK/2VqY4osh/gCAlXEpOJLCKX1EtWHd0TT8lZEDO0tTvNOvrdTpSMJE6gSIiIhq27p166o8bm5ujqioKERFRVUa4+npiW3bttV2ag1aP29nWJgaI+1OHk5ey0aAh53UKRERUQPS19sZrwa6Y2PCNUzddBJb330W1nJ+pCSqKW1+IRbG/AUAmNS/LWwtTSXOSBocMUVEREQAAEszE/T3cQYAbDl5Q+JsiIioIZrxvC/cbM1x9fZ9zNx8Rup0iAzagp1/4XZuAVo5WWFEN0+p05EMC1NEREQket7fDQCw9XQ6inl3PiIieoithSkWD38aRjLglxPX8cvxa1KnRGSQTqZlYVX8FQDA7Bfbw9S46ZZnmm7LiYiIqJw+7Zxga2GK9Ox8xCXfkjodIiJqgLq0tMekoKcAADM2n0HKrVyJMyIyLEXFJfjw19MQBGBwgBuebeskdUqSYmGKiIiIROamxnixY+moqY3H+C04ERFVLLxvG3T1ssf9gmKErzmO/MJiqVMiMhjRcVdw9oYWthammPG8r9TpSI6FKSIiItLzSqA7AGDHWQ2y8wolzoaIiBoiYyMZFg97Gg5WZjiXrv3f6A9OASd6lLQ797HgfwueRw70hqO1XOKMpMfCFBEREenxd7fFUy7W0BWVYPOJ61KnQ0REDZTS1hxLytabOn4dqw9dlTologatuETA5PWJuF9QjGda2uO1zh5Sp9QgsDBFREREemQyGV5/pgUA4Kf4K/wGnIiIKtW9jSOmD/AGAMzZcg4JV+9InBFRw7Vs7yUcu3oX1nITfP1aRxgZyaROqUFgYYqIiIjKCQ10h5WZMZJv5uLgpdtSp0NEtSjrfgHO3MjG7guZ+PXEdezWmMA1LAoZOUVSp0YGanyvVhjkp0RhsYDxPyUg9fZ9qVMianBOpmVh0a6LAIA5L7WHh72lxBk1HCxMERERUTk25qYI/d9aU9FxKRJnQ0RP6n5BERLTsrDuaCpWxV9F7PlMnL6ejdQ793G3wAhmjp7ILeToSKoZmUyGL1/pCF9XBW7nFmBs9BFk3+cahURl7uUXYtL6RBSVCAjxd8XLTzeXOqUGhYUpIiIiqtDo7i0hkwG7zmfigkYrdTpEVAP3C4qw/+JNrDx4Bfv+uokMrQ4yGdDczgKBns0Q5OMMlWMhNP+JhKu1sdTpkgGzkpvgxzFd4GprjuSbuXh79THoininPqKS/60rlXIrF6625vhscAfIZJzC9yAWpoiIiKhCrZ2sMaiDKwDg2z3JEmdDRNVRWFyCuORbWHnwCk6kZqGoRICTjRy9n3LCWz298EqgO3q2cUR7N1u4WQrQpZ6GhSk/GtCTUdqa48cxXWAtN8Ghy3cwZf1JFBWXSJ0WkaQW7foLu85nwszECMvfCISdpZnUKTU47H2IiIioUhP7tAYAbDl1A8k3cyTOhogex+VbOfj3oas4euUuikoEuCjkeCnADcO7eCDAww6WZiZSp0iNmI+rAsve6ARTYxm2nk7HtE2nUFLCaaLUNG0/k45vdl8CAMx72Q8dPeykTaiBYmGKiIiIKtWhuS2CfFxQIgDzt1+QOh0iqkJeQTG2nU7H7yfTcS+/CDbmJgjxc8XQzh5o6WDFqSNUb55t64So1zvBxEiGX05cx4e/nmZxipqcIyl3MGl9IgDgzR5e4tqdVB4LU0RERFSl6QPawUgG7DibgWNXeBtwoobo8q0crD58FRczc2AkAwI9m2FkN0+0cbZmQYokEdxeiUXDAmAkA9YdTcPUTadQyGl91EScuZ6NsOijyC8sQd92TvhwkLfUKTVoLEwRERFRldq62OC1zh4AgE9+O8v1QogakIKiEuw6n4HfT6bjfkEx7K3MMLSzB3q2cYSpMf/UJ2k97++Gr1/rCGMjGX4+fg1v/zsBeQVcEJ0at0uZ9zDqxyO4pyvCM172+HZEIEz4flwlvjpERET0SO8Ht4PC3ARnb2gRHXdF6nSICMCtfBnWHL6KszdK75r5dAs7DO/iAWeFucSZEf3t5afdsWJkIMxNjbD7Qibe+OEwbuXopE6LqE6cTMvC0O8O4U5uAfzdbfHD6M6wMOMdTx+FhSkiIiJ6JCcbOSIH+QAAvt75FxdCJ5KQrkiAXZ+x2JdpAu3/1pIK7dQcvdo68Vt5apD6+7hgzVtdYWthioSrd/HCkgM4kXpX6rSIatXepEwM/9ch3M4tQHs3BaLHPgMbc1Op0zII7LmIiIjosQzt7IHurR2QV1iMd9aegK6I0zGI6tvx1Lv4IOYmbLuGApDB11WBEV1bwL2ZpdSpEVUp0NMeP0/sjtZOVkjPzsfQ7w5h9aGrEAQuik6GTRAErDl8FW+tOob7BcV4tq0j1r+tgr2VmdSpGQwWpoiIiOixGBnJsHBoAOytzHAuXYuPN5/hBwqiepJfWIwv/riAV5bF4fq9YhTdu43uToV4ztcFchNOEyHD0MbZGpvDe0Dd3gUFxSWYsfkM3ow+igxtvtSpEdXIvfxCvLsuER/9egZFJQIGB7jhh9FdYC03kTo1g8LCFBERET02F4U5FrzWEUYyYMOxa/h2b7LUKRE1evv/uolBi//E8n3JKBGA3p4WSP8xHK4WLAyT4bExN8XyNwLx0SAfmBkbYU/STTy3YB82HEtDSQl/p8lwHE+9ixeXHsTvJ2/A2EiGfwz0xoLXAmBmwjJLdfEVIyIiomrp084Zn7zQHgDw5Y4kfP/nZYkzImqc0u7cx8TVCRj14xFcvpULJxs5VowMxHtd7VCSz3XeyHDJZDKM69UKW97tCX93W2jzizBt0ym8FHUQhy7fljo9oipl3S9A5C+nMOTbOKTcyoWbrTk2vN0NE3q3hpGRTOr0DBLHlxEREVG1je7eErdydFiy+xI+3Xoet3MLMDW4Hf8gI6oFmffy8e2eZKw5fBWFxQKMjWQYrWqJSc+1hcLcFMeP35A6RaJa8ZSLDX6Z2B0/HEjBkt2XcPp6NoatOIS+7Zzwf33boEtLe6lTJBLdLyjC6kNXsXzfZdzJLQAAvBLojo8G+aAZ15N6IixMERERUY1Mee4pmBgZYeGuv7BsbzLOp2sx/xV/ONvwVvVENXH1di5WHryC9UfTkFdYenOBHm0cMCPEFz6uComzI6obJsZGeLt3a4QGumNhzF/4z5FU7Em6iT1JNxHo2Qyju7dEsK8LzE25lhpJ43aODuuOpuGHAyliQeopF2t8OtgPz3ixeFobWJgiIiKiGpHJZHgvqC1aOFhg+s+nsTfpJoIX7scHwe0wrIsHb1tP9BgKikqwNykTGxOuYdf5DJTdT6Cjhx2mq9uhextHaRMkqieO1nJ89rIf3nq2FVbsT8bPCdeRcPUuEq7ehcLcBC8GuGFQB1d08bKHKfsXqmO6omLEXbqNjQlpiDmXgcLi0jdnTwdLhPdtg5efbs7fw1rEwhQREdEjREVF4csvv4RGo0HHjh2xZMkSPPPMM1Kn1WC8/LQ7fF1tMXl9Is6lazFj8xn868/LCOvphef93Xi7ZKKH3MsvxMFLt7E3KRN/nNEgO69QPNannRPCenqhZxtHyGScGtvQsX+ofV6OVpg3xB+Tg57C6kNX8fPx67ielYfVh1Kx+lAqbMxN0PspJ6haO+CZlvZo42zNfyv0xARBwNXb93Hkyh3sS7qJfX/dRI6uSDze0d0Wo1Qt8VKAG794qwMsTBEREVVh/fr1mDJlCpYvX46uXbti0aJFUKvVSEpKgrOzs9TpNRjtlDb4LaIH1hxOxaJdf+Hq7fuY+d+zmPP7OTzb1hHP+SoR6NkMbZ2tuQ4VNSkFRSVIvZOLsze0OJmWjZPXsnAyLQtFD9x9zNlGjpcC3DC0iwfaONtImC1VB/uHuuWsMMeU4HaYFPQU4pJvY3Pidey5kInbuQXYciodW06lAwBsLUzhrbRBu7LNxQYt7C3haC1nf0MVyisoxuVbOfgr4x7+ysjBX5p7OH09G5n3dHpxzjZyDOygxNAuLeDrxunUdYmFKSIioiosWLAA48aNw9ixYwEAy5cvx9atW/Hjjz/iH//4h8TZNSwmxkYY3b0lXu3sjvVH07Ap4RrO3tCKa4UAgI3cBE8pbeDpYImWDlZwspGjmaUpmlmaoZmVGazkJpCbGMHMxKj0v8ZG/CacJFVSIqCoRECJUPrf4hIBBUUlyNUVIUdXhFxdEXILipCjK8adHB0y7+mQodUh814+rt/Nw9U791H8QBGqjJejFXr9r2irau0AY36ANjjsH+qHkZEMPds6omdbRxSXCEhMy8K+v27iaModnEi7i+y8QhxOuYPDKXf0nmdqLIPS1hxuthZwtJHDzsIUdpamsLMwg62lKWwtTGFpZgxzU2PITYxgbmoMc5P/b+++w6uo8j+Of27aTYGEngIhID1UKWIQaUZCEUFxRWUBXZBVgwpYkBVpFlxUyiqKbQkqKIiKCgiELhBQIgGkRHpQktAJoaSe3x8u9+cloSQkmSS8X88zD8zMmTPfcxPuIZ/MzHWVp7uL7O6ucnOxydXFJhfbxT/FfGSx7Gyj9KxspWVmKz0zW+lZ//vTsZ6l1LQspZzPUMqFDJ0+n6GU85k6fT5DySkXlHj6gpJOn9fJcxm59u/h6qIm1fx0600VFR7qryZV/Qg3iwjBVAEw/3sYQEpKisWVAEDBu/jedvG97kaSnp6u2NhYjRw50rHNxcVF4eHhiomJydE+LS1NaWn//9u206dPS8r7/JCa+ufHwP++e7vSzp/LT+k5HP19vyQpNjbW0f/1cnFxUXZ2dq77akka0UxKrOWlDX9c0M5j6dp7MlOnU4x+TknRz3k4j7uLJJvk8r8fCGySbLZL/8y5r7i40r8c21X250dh9FkYitNbitGf73FZRso2UrYxys7+8+8FUabdzaZqZV1Vq7y7alVwV90K7qrs4yLphLITT2hdYt76i4+Pl1Rw7xEX3x9SU1Pz9H7F/HDt84NUfOeIwpgfpCvPEdfbX0u71LK+lFnHW7+fydKhlAz9npKpQymZ+iMlUycvZCvNSAfPndXBPP77umINtj+Xi/OOY90x7/z/O/DVQqxL9+bW/K/v5znaX6Zfc8mfju3mctv/f8u1H3Pl7X/uu2RvHvu62r7r5eNhU7Wybqrm66ZqZd1U3c9dNcu5ycMtU1KyUvYna+3+3I8tzO/t61XQ84NURHOEwXU7dOiQ0f/+T8PCwsJSWpdDhw5Z/XZb5P744w8jyaxfv95p+3PPPWduueWWHO3HjBlj+deJhYWFpagX5of/d7n5wRjmCBYWlhtzuZY5giumCkBQUJAOHTqksmXL5unyzpSUFAUHB+vQoUPy9S2d96wyxtKBMZYO+R2jMUZnzpxRUFBQIVZXOowcOVLDhw93rGdnZ+vEiROqWLEi88MlGGPpwBhLB+aHosEcce0YY+nAGEuHopgjCKYKgIuLi6pVq5bv4319fUvtN/FFjLF0YIylQ37G6OfnV0jVFG+VKlWSq6urkpOTnbYnJycrICAgR3u73S673e60rVy5cvk+P9+PpQNjLB0YY+6YH65tfpCYI/KDMZYOjLF0KMw5gs85BADgMjw8PNSiRQstX77csS07O1vLly9XWFiYhZUBAKzE/AAABYcrpgAAuILhw4drwIABatmypW655RZNmTJFZ8+edXwKEwDgxsT8AAAFg2DKQna7XWPGjMlxSW9pwhhLB8ZYOtwIYywMffr00dGjRzV69GglJSWpWbNmWrx4sfz9/QvtnDfC14oxlg6MsXS4EcZYGKyYH6Qb4+vFGEsHxlg6FMUYbcbcgJ/vCgAAAAAAAMvxjCkAAAAAAABYgmAKAAAAAAAAliCYAgAAAAAAgCUIpgAAAAAAAGAJgqlCNm3aNNWoUUOenp5q3bq1fvrppyu2//LLL1W/fn15enqqcePGWrRoURFVmn95GeOHH36o22+/XeXLl1f58uUVHh5+1dekOMjr1/GiL774QjabTb169SrcAq9TXsd36tQpRUZGKjAwUHa7XXXr1i3236t5HeOUKVNUr149eXl5KTg4WMOGDdOFCxeKqNq8W7NmjXr06KGgoCDZbDbNnz//qsesWrVKzZs3l91uV+3atRUVFVXodeL/MT84Y34ovpgjcmKOQGFjjnDGHFF8MUfkVJLmiGIzPxgUmi+++MJ4eHiY//73v2b79u3m0UcfNeXKlTPJycm5tl+3bp1xdXU1EydONDt27DCjRo0y7u7uZtu2bUVc+bXL6xgfeughM23aNLN582azc+dO8/DDDxs/Pz/z+++/F3Hl1y6vY7xo//79pmrVqub22283PXv2LJpi8yGv40tLSzMtW7Y03bp1M2vXrjX79+83q1atMnFxcUVc+bXL6xhnzZpl7Ha7mTVrltm/f79ZsmSJCQwMNMOGDSviyq/dokWLzIsvvmi+/vprI8l88803V2y/b98+4+3tbYYPH2527Nhh3n77bePq6moWL15cNAXf4JgfcmJ+KJ6YI3JijkBhY47IiTmieGKOyKmkzRHFZX4gmCpEt9xyi4mMjHSsZ2VlmaCgIDNhwoRc299///2me/fuTttat25t/vnPfxZqndcjr2O8VGZmpilbtqyZOXNmYZV43fIzxszMTNOmTRvz0UcfmQEDBhTrSSWv43vvvffMTTfdZNLT04uqxOuW1zFGRkaaTp06OW0bPny4ue222wq1zoJyLZPK888/bxo2bOi0rU+fPiYiIqIQK8NFzA9Xx/xQPDBH5MQcgcLGHHF1zBHFA3NETiV5jrByfuBWvkKSnp6u2NhYhYeHO7a5uLgoPDxcMTExuR4TExPj1F6SIiIiLtveavkZ46XOnTunjIwMVahQobDKvC75HeP48eNVpUoVDRw4sCjKzLf8jO+7775TWFiYIiMj5e/vr0aNGum1115TVlZWUZWdJ/kZY5s2bRQbG+u4THffvn1atGiRunXrViQ1F4WS9n5TmjA/MD+UhPlBYo5gjig57zmlCXMEcwRzRPHBHJFTYb3fuF3X0bisY8eOKSsrS/7+/k7b/f39tWvXrlyPSUpKyrV9UlJSodV5PfIzxkuNGDFCQUFBOb65i4v8jHHt2rX6+OOPFRcXVwQVXp/8jG/fvn1asWKF+vbtq0WLFmnPnj164oknlJGRoTFjxhRF2XmSnzE+9NBDOnbsmNq2bStjjDIzM/XYY4/pX//6V1GUXCQu936TkpKi8+fPy8vLy6LKSj/mB+aHkjA/SMwRzBHMEVZgjmCOYI4oPpgjciqs+YErpmCZ119/XV988YW++eYbeXp6Wl1OgThz5oz69eunDz/8UJUqVbK6nEKRnZ2tKlWq6IMPPlCLFi3Up08fvfjii5o+fbrVpRWYVatW6bXXXtO7776rX375RV9//bUWLlyol19+2erSgBsC80PJxRwBoLAxR5RczBG4HK6YKiSVKlWSq6urkpOTnbYnJycrICAg12MCAgLy1N5q+RnjRW+++aZef/11LVu2TE2aNCnMMq9LXse4d+9eHThwQD169HBsy87OliS5ubkpPj5etWrVKtyi8yA/X8PAwEC5u7vL1dXVsa1BgwZKSkpSenq6PDw8CrXmvMrPGF966SX169dPgwYNkiQ1btxYZ8+e1eDBg/Xiiy/KxaXkZ/qXe7/x9fXlN+GFjPmB+eGi4jw/SMwRzBHMEVZgjmCOuIg5wnrMETkV1vxQsl+VYszDw0MtWrTQ8uXLHduys7O1fPlyhYWF5XpMWFiYU3tJio6Ovmx7q+VnjJI0ceJEvfzyy1q8eLFatmxZFKXmW17HWL9+fW3btk1xcXGO5e6771bHjh0VFxen4ODgoiz/qvLzNbztttu0Z88ex2QpSb/99psCAwOL3WQi5W+M586dyzFpXJxA/3wuYMlX0t5vShPmB+aHkjA/SMwRzBEl5z2nNGGOYI5gjig+mCNyKrT3m+t6dDqu6IsvvjB2u91ERUWZHTt2mMGDB5ty5cqZpKQkY4wx/fr1My+88IKj/bp164ybm5t58803zc6dO82YMWNKxEe95mWMr7/+uvHw8DDz5s0ziYmJjuXMmTNWDeGq8jrGSxX3T9TI6/gSEhJM2bJlzZAhQ0x8fLxZsGCBqVKlinnllVesGsJV5XWMY8aMMWXLljWff/652bdvn1m6dKmpVauWuf/++60awlWdOXPGbN682WzevNlIMpMmTTKbN282Bw8eNMYY88ILL5h+/fo52l/8qNfnnnvO7Ny500ybNo2PAi9CzA/MD8YU//nBGOYIY5gjmCOKHnMEc4QxzBHFRWmfI4rL/EAwVcjefvttU716dePh4WFuueUWs2HDBse+9u3bmwEDBji1nzt3rqlbt67x8PAwDRs2NAsXLiziivMuL2MMCQkxknIsY8aMKfrC8yCvX8e/KgmTSl7Ht379etO6dWtjt9vNTTfdZF599VWTmZlZxFXnTV7GmJGRYcaOHWtq1aplPD09TXBwsHniiSfMyZMni77wa7Ry5cpc/21dHNeAAQNM+/btcxzTrFkz4+HhYW666SYzY8aMIq/7Rsb8wPxQEuYHY5gjmCOYI6zAHMEcwRxRfJTmOaK4zA82Y0rB9WQAAAAAAAAocXjGFAAAAAAAACxBMAUAAAAAAABLEEwBAAAAAADAEgRTAAAAAAAAsATBFAAAAAAAACxBMAUAAAAAAABLEEwBAAAAAADAEgRTAAAAN6CxY8eqWbNmVpcBACjhatSooSlTplhdBkowgingKnijLXhRUVEqV67cdfdjs9k0f/786+4HAEq73N4vn332WS1fvtyaggAAlunQoYOGDh1qdRmAA8EU8D+XC0t+/vlnDR48uOgLusSqVatks9l06tQpq0sBAJQCZcqUUcWKFa0uAwBQDBljlJmZaXUZuEEQTAFXUblyZXl7e1tdBgCgBOrQoYOeeuopPf/886pQoYICAgI0duxYx/5Tp05p0KBBqly5snx9fdWpUydt2bLFqY9XXnlFVapUUdmyZTVo0CC98MILTrfg/fzzz7rzzjtVqVIl+fn5qX379vrll18c+2vUqCFJuueee2Sz2Rzrf72Vb+nSpfL09Mzxy4+nn35anTp1cqyvXbtWt99+u7y8vBQcHKynnnpKZ8+eve7XCQDwp+udNx5++GH16tXLqc+hQ4eqQ4cOjv2rV6/W1KlTZbPZZLPZdODAAccvwX/44Qe1aNFCdrtda9eu1d69e9WzZ0/5+/urTJkyatWqlZYtW1YErwRuJARTKFHmzZunxo0by8vLSxUrVlR4eLjjP8QfffSRGjRoIE9PT9WvX1/vvvuu47gDBw7IZrPp66+/VseOHeXt7a2mTZsqJiZG0p9XIz3yyCM6ffq04w364gRw6a18NptN77//vu666y55e3urQYMGiomJ0Z49e9ShQwf5+PioTZs22rt3r1Pt3377rZo3by5PT0/ddNNNGjdunNNvIWw2mz766CPdc8898vb2Vp06dfTdd9856u/YsaMkqXz58rLZbHr44Yev+nplZ2drwoQJqlmzpry8vNS0aVPNmzfPsf/iBLR8+XK1bNlS3t7eatOmjeLj4536+f7779WqVSt5enqqUqVKuueeexz7Tp48qf79+6t8+fLy9vZW165dtXv3bqfjo6KiVL16dXl7e+uee+7R8ePHc9R6tddn9+7dateunTw9PRUaGqro6Oirjh8AioOZM2fKx8dHGzdu1MSJEzV+/HjHe9jf/vY3HTlyRD/88INiY2PVvHlz3XHHHTpx4oQkadasWXr11Vf173//W7Gxsapevbree+89p/7PnDmjAQMGaO3atdqwYYPq1Kmjbt266cyZM5L+DK4kacaMGUpMTHSs/9Udd9yhcuXK6auvvnJsy8rK0pw5c9S3b19J0t69e9WlSxf17t1bW7du1Zw5c7R27VoNGTKk4F80ALiBXc+8cTVTp05VWFiYHn30USUmJioxMVHBwcGO/S+88IJef/117dy5U02aNFFqaqq6deum5cuXa/PmzerSpYt69OihhISEQhk7blAGKCEOHz5s3NzczKRJk8z+/fvN1q1bzbRp08yZM2fMZ599ZgIDA81XX31l9u3bZ7766itToUIFExUVZYwxZv/+/UaSqV+/vlmwYIGJj4839913nwkJCTEZGRkmLS3NTJkyxfj6+prExESTmJhozpw5Y4wxJiQkxEyePNlRhyRTtWpVM2fOHBMfH2969eplatSoYTp16mQWL15sduzYYW699VbTpUsXxzFr1qwxvr6+Jioqyuzdu9csXbrU1KhRw4wdO9ap32rVqpnZs2eb3bt3m6eeesqUKVPGHD9+3GRmZpqvvvrKSDLx8fEmMTHRnDp16qqv2SuvvGLq169vFi9ebPbu3WtmzJhh7Ha7WbVqlTHGmJUrVxpJpnXr1mbVqlVm+/bt5vbbbzdt2rRx9LFgwQLj6upqRo8ebXbs2GHi4uLMa6+95th/9913mwYNGpg1a9aYuLg4ExERYWrXrm3S09ONMcZs2LDBuLi4mH//+98mPj7eTJ061ZQrV874+fld8+uTlZVlGjVqZO644w4TFxdnVq9ebW6++WYjyXzzzTfX+B0EAEWvffv2pm3btk7bWrVqZUaMGGF+/PFH4+vray5cuOC0v1atWub99983xhjTunVrExkZ6bT/tttuM02bNr3sObOyskzZsmXN999/79iW2/vlmDFjnPp5+umnTadOnRzrS5YsMXa73Zw8edIYY8zAgQPN4MGDnfr48ccfjYuLizl//vxl6wEAXLvrnTcGDBhgevbs6bT/6aefNu3bt3c6x9NPP+3U5uLPBfPnz79qjQ0bNjRvv/22Y/3Sn5eAvCKYQokRGxtrJJkDBw7k2FerVi0ze/Zsp20vv/yyCQsLM8b8fzD10UcfOfZv377dSDI7d+40xhgzY8YMp7DkotyCqVGjRjnWY2JijCTz8ccfO7Z9/vnnxtPT07F+xx13OIU5xhjz6aefmsDAwMv2m5qaaiSZH374wRjz/5PFxR8QrubChQvG29vbrF+/3mn7wIEDzYMPPujU57Jlyxz7Fy5caCQ5fsgICwszffv2zfUcv/32m5Fk1q1b59h27Ngx4+XlZebOnWuMMebBBx803bp1czquT58+Tq/11V6fJUuWGDc3N/PHH3849v/www8EUwCKvfbt25snnnjCadvdd99tHnnkEfPOO+8YFxcX4+Pj47S4uLiY559/3hhjTLly5czMmTOdjh82bJhToJSUlGQGDRpkateubXx9fY2Pj4+x2Wxm2rRpjjbXEkz99NNPxsXFxfFe279/f3Pvvfc69rds2dJ4eHg41ert7W0kmR07dlzPywQA+J/rnTeuN5j6/fffnbafOXPGPPPMM6Z+/frGz8/Pcb7nnnvO0YZgCtfLrQgvzgKuS9OmTXXHHXeocePGioiIUOfOnXXffffJw8NDe/fu1cCBA/Xoo4862mdmZsrPz8+pjyZNmjj+HhgYKEk6cuSI6tevn6da/tqPv7+/JKlx48ZO2y5cuKCUlBT5+vpqy5YtWrdunV599VVHm6ysLF24cEHnzp1zPMPqr/36+PjI19dXR44cyVNtF+3Zs0fnzp3TnXfe6bQ9PT1dN99882XH89fXpXr16oqLi3N6Xf9q586dcnNzU+vWrR3bKlasqHr16mnnzp2ONn+99U+SwsLCtHjxYsf61V6fnTt3Kjg4WEFBQU59AEBJ4O7u7rRus9mUnZ2t1NRUBQYGatWqVTmOycsnlw4YMEDHjx/X1KlTFRISIrvdrrCwMKWnp+epzlatWqlWrVr64osv9Pjjj+ubb75RVFSUY39qaqr++c9/6qmnnspxbPXq1fN0LgDA5V3PvOHi4iJjjNO+jIyMaz63j4+P0/qzzz6r6Ohovfnmm6pdu7a8vLx033335XmOAa6EYAolhqurq6Kjo7V+/XotXbpUb7/9tl588UV9//33kqQPP/zQKSC5eMxf/fVN3mazSfrzOUx5lVs/V+o7NTVV48aN07333pujL09Pz1z7vdhPfuq7eE5JWrhwoapWreq0z263O61fqXYvL698nT8vrvX1AYDSpHnz5kpKSpKbm5vjgeSXqlevnn7++Wf179/fse3SZ0StW7dO7777rrp16yZJOnTokI4dO+bUxt3dXVlZWVetqW/fvpo1a5aqVasmFxcXde/e3aneHTt2qHbt2tc6RABAAbqWeaNy5cr69ddfnbbFxcU5/X/fw8PjmuYE6c855uGHH3b8ojk1NVUHDhzIV/3A5fDwc5QoNptNt912m8aNG6fNmzfLw8ND69atU1BQkPbt26fatWs7LTVr1rzmvvPyBp1XzZs3V3x8fI76ateuLReXa/tn6OHhIUnXXGNoaKjsdrsSEhJynPOvDzi8miZNmmj58uW57mvQoIEyMzO1ceNGx7bjx48rPj5eoaGhjjZ/3S9JGzZscFq/2uvToEEDHTp0SImJiZftAwBKmvDwcIWFhalXr15aunSpDhw4oPXr1+vFF1/Upk2bJElPPvmkPv74Y82cOVO7d+/WK6+8oq1btzp+iSBJderU0aeffqqdO3dq48aN6tu3b45fKtSoUUPLly9XUlKSTp48edma+vbtq19++UWvvvqq7rvvPqdfZIwYMULr16/XkCFDFBcXp927d+vbb7/l4ecAUESuZd7o1KmTNm3apE8++US7d+/WmDFjcgRVNWrU0MaNG3XgwAEdO3bsir8Ir1Onjr7++mvFxcVpy5Yteuihh/L9i3PgcgimUGJs3LhRr732mjZt2qSEhAR9/fXXOnr0qBo0aKBx48ZpwoQJ+s9//qPffvtN27Zt04wZMzRp0qRr7r9GjRpKTU3V8uXLdezYMZ07d67Aah89erQ++eQTjRs3Ttu3b9fOnTv1xRdfaNSoUdfcR0hIiGw2mxYsWKCjR486roi6nLJly+rZZ5/VsGHDNHPmTO3du1e//PKL3n77bc2cOfOazztmzBh9/vnnGjNmjHbu3Klt27bp3//+t6Q/J6qePXvq0Ucf1dq1a7Vlyxb9/e9/V9WqVdWzZ09J0lNPPaXFixfrzTff1O7du/XOO+843cZ3La9PeHi46tatqwEDBmjLli368ccf9eKLL17zGACgOLLZbFq0aJHatWunRx55RHXr1tUDDzyggwcPOm4T79u3r0aOHKlnn31WzZs31/79+/Xwww87XU368ccf6+TJk2revLn69eunp556SlWqVHE611tvvaXo6GgFBwfnuJ37r2rXrq1bbrlFW7dudXwa30VNmjTR6tWr9dtvv+n222/XzTffrNGjRzvdZg0AKDzXMm9ERETopZde0vPPP69WrVrpzJkzTlfdSn/enufq6qrQ0FBVrlz5ip+wN2nSJJUvX15t2rRRjx49FBERoebNmxfqOHEDsvohV8C12rFjh4mIiDCVK1c2drvd1K1b1+nTIGbNmmWaNWtmPDw8TPny5U27du3M119/bYz5/4efb9682dH+5MmTRpJZuXKlY9tjjz1mKlasaCSZMWPGGGNyf/j5Xx8gm1vfuT2ofPHixaZNmzbGy8vL+Pr6mltuucV88MEHl+3XGGP8/PzMjBkzHOvjx483AQEBxmazmQEDBlz1NcvOzjZTpkwx9erVM+7u7qZy5comIiLCrF69+rJ1bt682Ugy+/fvd2z76quvHK9tpUqVnB6Ge+LECdOvXz/j5+dnvLy8TEREhPntt9+c6vj4449NtWrVjJeXl+nRo4d58803czxo/mqvT3x8vGnbtq3x8PAwdevWNYsXL+bh5wBuSOHh4ebvf/+71WUAAAAUCJsxlzwZDQAAAMXCuXPnNH36dEVERMjV1VWff/65xo8fr+joaIWHh1tdHgAAwHUjmAIAACimzp8/rx49emjz5s26cOGC6tWrp1GjRuX6YREAAAAlEcEUUEIlJCQ4HjCemx07dvDx3QAAAACAYo1gCiihMjMzr/hRrTVq1JCbm1vRFQQAAAAAQB4RTAEAAAAAAMASLlYXAAAAAAAAgBsTwRQAAAAAAAAsQTAFAAAAAAAASxBMAQAAAAAAwBIEUwAAAAAAALAEwRQAAAAAAAAsQTAFAAAAAAAASxBMAQAAAAAAwBIEUwAAAAAAALAEwRQAAAAAAAAsQTAFAAAAAAAASxBMAQAAAAAAwBIEUwAAAAAAALAEwRQsMXbsWNlstiI5V4cOHdShQwfH+qpVq2Sz2TRv3rwiOf/DDz+sGjVqFMm58is1NVWDBg1SQECAbDabhg4danVJTvLy/RIVFSWbzaYDBw4UblEAAAAAgOtGMIXrdjEIuLh4enoqKChIERER+s9//qMzZ84UyHkOHz6ssWPHKi4urkD6K0jFubZr8dprrykqKkqPP/64Pv30U/Xr18/qkq7qtdde0/z5860uAwAAAABwHWzGGGN1ESjZoqKi9Mgjj2j8+PGqWbOmMjIylJSUpFWrVik6OlrVq1fXd999pyZNmjiOyczMVGZmpjw9Pa/5PJs2bVKrVq00Y8YMPfzww9d8XHp6uiTJw8ND0p9XTHXs2FFffvml7rvvvmvuJ7+1ZWRkKDs7W3a7vUDOVRhuvfVWubm5ae3atVaXkqvcvl/KlCmj++67T1FRUU5ts7KylJGRIbvdXmRX5QEAAAAA8sfN6gJQenTt2lUtW7Z0rI8cOVIrVqzQXXfdpbvvvls7d+6Ul5eXJMnNzU1uboX77Xfu3Dl5e3s7AimruLu7W3r+a3HkyBGFhoZaXcZl5eX7xdXVVa6uroVcEQAAAACgIHArHwpVp06d9NJLL+ngwYP67LPPHNtze2ZQdHS02rZtq3LlyqlMmTKqV6+e/vWvf0n68yqnVq1aSZIeeeQRx22DF6+W6dChgxo1aqTY2Fi1a9dO3t7ejmMvfcbURVlZWfrXv/6lgIAA+fj46O6779ahQ4ec2tSoUSPXq7P+2ufVasvtGVNnz57VM888o+DgYNntdtWrV09vvvmmLr2A0WazaciQIZo/f74aNWoku92uhg0bavHixbm/4Jc4cuSIBg4cKH9/f3l6eqpp06aaOXOmY//F523t379fCxcudNR+peczXaxp1qxZqlevnjw9PdWiRQutWbMmR9vNmzera9eu8vX1VZkyZXTHHXdow4YNTm0yMjI0btw41alTR56enqpYsaLatm2r6OhoR5tLv19sNpvOnj2rmTNnOmq++HW69BlTd911l2666aZcxxIWFuYUpkrSZ599phYtWsjLy0sVKlTQAw88kOP7AgAAAABQMAimUOguPq9o6dKll22zfft23XXXXUpLS9P48eP11ltv6e6779a6deskSQ0aNND48eMlSYMHD9ann36qTz/9VO3atXP0cfz4cXXt2lXNmjXTlClT1LFjxyvW9eqrr2rhwoUaMWKEnnrqKUVHRys8PFznz5/P0/iupba/Msbo7rvv1uTJk9WlSxdNmjRJ9erV03PPPafhw4fnaL927Vo98cQTeuCBBzRx4kRduHBBvXv31vHjx69Y1/nz59WhQwd9+umn6tu3r9544w35+fnp4Ycf1tSpUx21f/rpp6pUqZKaNWvmqL1y5cpX7Hv16tUaOnSo/v73v2v8+PE6fvy4unTpol9//dXRZvv27br99tu1ZcsWPf/883rppZe0f/9+dejQQRs3bnS0Gzt2rMaNG6eOHTvqnXfe0Ysvvqjq1avrl19+uez5P/30U9ntdt1+++2Omv/5z3/m2rZPnz7av3+/fv75Z6ftBw8e1IYNG/TAAw84tr366qvq37+/6tSpo0mTJmno0KFavny52rVrp1OnTl3xNQEAAAAA5IMBrtOMGTOMJPPzzz9fto2fn5+5+eabHetjxowxf/32mzx5spFkjh49etk+fv75ZyPJzJgxI8e+9u3bG0lm+vTpue5r3769Y33lypVGkqlatapJSUlxbJ87d66RZKZOner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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wQduSWNg1JiU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fHzI8HJm1JzC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot a correlation heatmap"
      ],
      "metadata": {
        "id": "VhgkHrzPz7tj"
      },
      "execution_count": 365,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 8))\n",
        "correlation = data.corr()\n",
        "sns.heatmap(correlation, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)\n",
        "plt.title('Correlation Heatmap')\n",
        "plt.show()  # Ensure the plot is shown"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 719
        },
        "id": "T-TtZN281fAa",
        "outputId": "50497c6c-3238-4e20-a182-37465a7c8a95"
      },
      "execution_count": 366,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "3DfZHplo4Uer"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9wL5BjVA1fKM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "x--tokyk1fQn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "V4BkQtHF2XJh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "yydoLDFj4XSl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ✨ **Step 2: Text Preprocessing**\n",
        "\n",
        "Before we use the text data to train our model, we need to preprocess it to ensure consistency and remove any irrelevant information.\n",
        "\n",
        "- **Cleaning Text**:\n",
        "  - **Remove Special Characters**: We use regular expressions to remove any characters that are not alphabetic or spaces. This step is essential to avoid unnecessary noise in the data.\n",
        "  - **Convert to Lowercase**: Text normalization is performed by converting all characters to lowercase. This ensures that words like \"positive\" and \"Positive\" are treated the same.\n",
        "  \n",
        "- **Text Stripping**: Leading and trailing spaces are removed to ensure that all text is clean and ready for analysis.\n",
        "\n",
        "The cleaned text is then saved into a new column to keep the original data intact for reference."
      ],
      "metadata": {
        "id": "2tp10MGF4Z5Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define text cleaning function"
      ],
      "metadata": {
        "id": "UqxN6vRQuCAg"
      },
      "execution_count": 277,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_text(text):\n",
        "    \"\"\"\n",
        "    This function takes a string input and performs basic cleaning:\n",
        "    - Removes special characters\n",
        "    - Converts text to lowercase\n",
        "    \"\"\"\n",
        "    text = re.sub(r\"[^a-zA-Z\\s]\", \"\", text)  # Remove non-alphabetic characters\n",
        "    text = text.lower()  # Convert to lowercase\n",
        "    return text.strip()"
      ],
      "metadata": {
        "id": "nFlemxr2uKNG"
      },
      "execution_count": 278,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BfIZ5jrnuKP6"
      },
      "execution_count": 278,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_KtHVaiKuKSi"
      },
      "execution_count": 278,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply the cleaning function to each phrase in the dataset"
      ],
      "metadata": {
        "id": "TEiiXKHxuKV1"
      },
      "execution_count": 279,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['cleaned_phrase'] = data['phrase'].apply(clean_text)"
      ],
      "metadata": {
        "id": "LInpeUNbuKY-"
      },
      "execution_count": 280,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pAYHyH3fuWBI"
      },
      "execution_count": 280,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VC3hMgKquWEc"
      },
      "execution_count": 280,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display a few examples of cleaned phrases"
      ],
      "metadata": {
        "id": "uBZ-dU3XuWHj"
      },
      "execution_count": 281,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Examples of Cleaned Phrases:\")\n",
        "print(data[['phrase', 'cleaned_phrase']].head(), \"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lY0WrbqXuWK4",
        "outputId": "248cb3e6-6435-4520-f4e7-1a067370f3f9"
      },
      "execution_count": 282,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Examples of Cleaned Phrases:\n",
            "     phrase cleaned_phrase\n",
            "0   neutral        neutral\n",
            "1   neutral        neutral\n",
            "2  negative       negative\n",
            "3  positive       positive\n",
            "4  positive       positive \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ctbbTmTpuWPW"
      },
      "execution_count": 282,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "In9aMqnPuWSX"
      },
      "execution_count": 282,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Verify sentiment labels"
      ],
      "metadata": {
        "id": "p2kBmAS-uWWQ"
      },
      "execution_count": 283,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "valid_sentiments = ['positive', 'neutral', 'negative']"
      ],
      "metadata": {
        "id": "2WTMsfKJuWZN"
      },
      "execution_count": 284,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "invalid_sentiments = data[~data['sentiment'].isin(valid_sentiments)]"
      ],
      "metadata": {
        "id": "vyjxa3bfugkZ"
      },
      "execution_count": 285,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if not invalid_sentiments.empty:\n",
        "    print(\"Invalid Sentiment Entries Found:\")\n",
        "    print(invalid_sentiments)\n",
        "else:\n",
        "    print(\"All sentiment labels are valid.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1AVw6_InugoE",
        "outputId": "121e51f4-fc92-4f62-8d47-d51318d793df"
      },
      "execution_count": 286,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Invalid Sentiment Entries Found:\n",
            "        phrase                                          sentiment  \\\n",
            "0      neutral  According to Gran , the company has no plans t...   \n",
            "1      neutral  Technopolis plans to develop in stages an area...   \n",
            "2     negative  The international electronic industry company ...   \n",
            "3     positive  With the new production plant the company woul...   \n",
            "4     positive  According to the company 's updated strategy f...   \n",
            "...        ...                                                ...   \n",
            "4841  negative  LONDON MarketWatch -- Share prices ended lower...   \n",
            "4842   neutral  Rinkuskiai 's beer sales fell by 6.5 per cent ...   \n",
            "4843  negative  Operating profit fell to EUR 35.4 mn from EUR ...   \n",
            "4844  negative  Net sales of the Paper segment decreased to EU...   \n",
            "4845  negative  Sales in Finland decreased by 10.5 % in Januar...   \n",
            "\n",
            "     cleaned_phrase  \n",
            "0           neutral  \n",
            "1           neutral  \n",
            "2          negative  \n",
            "3          positive  \n",
            "4          positive  \n",
            "...             ...  \n",
            "4841       negative  \n",
            "4842        neutral  \n",
            "4843       negative  \n",
            "4844       negative  \n",
            "4845       negative  \n",
            "\n",
            "[4840 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YZ739ay6ugri"
      },
      "execution_count": 286,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VmPPMzBxugwH"
      },
      "execution_count": 286,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save cleaned dataset to a CSV file"
      ],
      "metadata": {
        "id": "k2GgjVBTug0A"
      },
      "execution_count": 287,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cleaned_file_path = \"/content/cleaned_data.csv\""
      ],
      "metadata": {
        "id": "ASYGRsT8ug3R"
      },
      "execution_count": 288,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.to_csv(cleaned_file_path, index=False)"
      ],
      "metadata": {
        "id": "SKNKshVdur0s"
      },
      "execution_count": 289,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Cleaned dataset saved at: {cleaned_file_path}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6vDmgW8Pur4R",
        "outputId": "3c3ada73-750b-463f-8ff6-dc52f71de15d"
      },
      "execution_count": 290,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cleaned dataset saved at: /content/cleaned_data.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "Adqlgu3z4jMJ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6E1bavqMu1pZ"
      },
      "execution_count": 290,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UNDnaeAnu1tX"
      },
      "execution_count": 290,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D8XoQsQWu1xN"
      },
      "execution_count": 290,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "jBSrWs_n4q9R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🔠 **Step 3: Sentiment Encoding**\n",
        "\n",
        "Machine learning models typically require numerical input. To transform the categorical sentiment labels into a format that the model can understand, we encode the labels.\n",
        "\n",
        "- **Sentiment Encoding**: We map the textual sentiment labels (`positive`, `neutral`, `negative`) to numerical values:\n",
        "  - `positive` → `1`\n",
        "  - `neutral` → `0`\n",
        "  - `negative` → `-1`\n",
        "  \n",
        "This numerical encoding helps the model understand and process the sentiment data more effectively, as it works with numbers rather than text."
      ],
      "metadata": {
        "id": "UF257s6D4umB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer"
      ],
      "metadata": {
        "id": "qy_u4pWKu14N"
      },
      "execution_count": 291,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7m_LltK0u2CO"
      },
      "execution_count": 291,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load cleaned dataset"
      ],
      "metadata": {
        "id": "R8GBu2L0u7JB"
      },
      "execution_count": 292,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(cleaned_file_path)"
      ],
      "metadata": {
        "id": "K7-5YIvBu7Mi"
      },
      "execution_count": 293,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nAYLCY-au7Qp"
      },
      "execution_count": 293,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Strip leading/trailing spaces from sentiment labels"
      ],
      "metadata": {
        "id": "u6TxrJbqu7U4"
      },
      "execution_count": 294,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['sentiment'] = data['sentiment'].str.strip()"
      ],
      "metadata": {
        "id": "7y3iOXyWu2F6"
      },
      "execution_count": 295,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QsPoJYOyvGSq"
      },
      "execution_count": 295,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Encode sentiment labels: 'positive' = 1, 'neutral' = 0, 'negative' = -1"
      ],
      "metadata": {
        "id": "X9LKJ4LQvGV6"
      },
      "execution_count": 296,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['sentiment_encoded'] = data['sentiment'].map({'positive': 1, 'neutral': 0, 'negative': -1})\n"
      ],
      "metadata": {
        "id": "7EJHckulvGZv"
      },
      "execution_count": 297,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yN88Zj_zvGdF"
      },
      "execution_count": 297,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display first 5 rows with encoded sentiment"
      ],
      "metadata": {
        "id": "BzxmuFBevGgf"
      },
      "execution_count": 298,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"First 5 Rows with Encoded Sentiment:\")\n",
        "print(data[['phrase', 'sentiment', 'sentiment_encoded']].head(), \"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H1RGUSXnvGlp",
        "outputId": "fea4a3ed-202c-4c24-fc14-087e681fcd9e"
      },
      "execution_count": 299,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First 5 Rows with Encoded Sentiment:\n",
            "     phrase                                          sentiment  \\\n",
            "0   neutral  According to Gran , the company has no plans t...   \n",
            "1   neutral  Technopolis plans to develop in stages an area...   \n",
            "2  negative  The international electronic industry company ...   \n",
            "3  positive  With the new production plant the company woul...   \n",
            "4  positive  According to the company 's updated strategy f...   \n",
            "\n",
            "   sentiment_encoded  \n",
            "0                NaN  \n",
            "1                NaN  \n",
            "2                NaN  \n",
            "3                NaN  \n",
            "4                NaN   \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "nhkXERSW42uM"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ue5s3vw544QV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "u5xAYZaavQST"
      },
      "execution_count": 299,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "knvhG7tMvQV5"
      },
      "execution_count": 299,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "gf83Z2kz45Ff"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🧑‍💻 **Step 4: Feature Extraction**\n",
        "\n",
        "In this step, we extract meaningful features from the cleaned text data using the **CountVectorizer**. This method converts text data into a **bag-of-words** representation, where each unique word is treated as a feature.\n",
        "\n",
        "- **CountVectorizer**:\n",
        "  - It tokenizes the text data into individual words and counts the frequency of each word in the dataset.\n",
        "  - We specify `max_features=5000` to limit the number of features to the top 5000 most frequent words, reducing the complexity of the dataset.\n",
        "\n",
        "- **Sparse Matrix**: The result from the CountVectorizer is a sparse matrix (as most words are not present in every document). We convert this sparse matrix into a **DataFrame** for easier inspection and manipulation.\n",
        "\n",
        "This step prepares the data for input into the GAN model."
      ],
      "metadata": {
        "id": "ziRedloU46eF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the CountVectorizer for text feature extraction"
      ],
      "metadata": {
        "id": "9x2ThHhyvQZR"
      },
      "execution_count": 300,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vectorizer = CountVectorizer(stop_words='english', max_features=5000)"
      ],
      "metadata": {
        "id": "NepW6lkVvQfR"
      },
      "execution_count": 301,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5xkUI7buvGo3"
      },
      "execution_count": 301,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit and transform the cleaned text data"
      ],
      "metadata": {
        "id": "tfIbyMpMur7g"
      },
      "execution_count": 302,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = vectorizer.fit_transform(data['cleaned_phrase'])"
      ],
      "metadata": {
        "id": "VlNPW1mHug7I"
      },
      "execution_count": 303,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PqJg0Podvbo3"
      },
      "execution_count": 303,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the sparse matrix into a DataFrame for easier inspection"
      ],
      "metadata": {
        "id": "caVvoAFIvbsK"
      },
      "execution_count": 304,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_df = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())"
      ],
      "metadata": {
        "id": "NQuy4Z8KvbwT"
      },
      "execution_count": 305,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mT2CElVvvbzu"
      },
      "execution_count": 305,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display processed feature data"
      ],
      "metadata": {
        "id": "F11jADUWuKcX"
      },
      "execution_count": 306,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Processed Text Data Features:\")\n",
        "print(X_df.head(), \"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h70VHzJXtzsj",
        "outputId": "af327fca-e789-44b1-f312-1e2b2be7d54d"
      },
      "execution_count": 307,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processed Text Data Features:\n",
            "   negative  neutral  positive\n",
            "0         0        1         0\n",
            "1         0        1         0\n",
            "2         1        0         0\n",
            "3         0        0         1\n",
            "4         0        0         1 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sNP6dQBivn0U"
      },
      "execution_count": 307,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kWK5Qxqbvn5n"
      },
      "execution_count": 307,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save processed data with features and sentiment labels to a CSV file"
      ],
      "metadata": {
        "id": "InrWP22wvn9H"
      },
      "execution_count": 308,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "processed_data = data[['sentiment_encoded']].join(X_df)"
      ],
      "metadata": {
        "id": "hIs6RMUcvoA5"
      },
      "execution_count": 309,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "processed_data.to_csv(\"/content/processed_data.csv\", index=False)"
      ],
      "metadata": {
        "id": "_Edv75FpvoEL"
      },
      "execution_count": 310,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Processed data saved at: /content/processed_data.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5_4Cam4dvoHy",
        "outputId": "1198e3b1-e2a4-4f1a-b00a-c5deff3e1be3"
      },
      "execution_count": 311,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processed data saved at: /content/processed_data.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "tIg-xwi-5BLD"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ngr-1YNUvzyO"
      },
      "execution_count": 311,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "J_AEMHfBvz3C"
      },
      "execution_count": 311,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5YXhpYXGvz8_"
      },
      "execution_count": 311,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "b5fVrquZ5C0c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🤖 **Step 5: Building the GAN Model**\n",
        "\n",
        "The heart of this project is the **Generative Adversarial Network (GAN)**. A GAN consists of two neural networks: a **generator** and a **discriminator**, which are trained together in an adversarial manner.\n",
        "\n",
        "- **Generator**:\n",
        "  - The generator takes random noise as input and attempts to generate synthetic data (in this case, synthetic sentiment data).\n",
        "  - It uses **Dense** layers followed by **LeakyReLU** activations to create complex, high-dimensional representations of the data.\n",
        "  \n",
        "- **Discriminator**:\n",
        "  - The discriminator is a binary classifier that distinguishes between real and fake data.\n",
        "  - It also uses **Dense** layers with **LeakyReLU** activations, followed by a **sigmoid** activation function to output a probability of whether the input data is real or synthetic.\n",
        "  \n",
        "The two networks are trained together: the generator gets better at producing fake data, while the discriminator gets better at identifying fake data."
      ],
      "metadata": {
        "id": "b2Y_NP1w5HVv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "bQz2JBIgv0Fp"
      },
      "execution_count": 312,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential"
      ],
      "metadata": {
        "id": "_obH8O3uv0JC"
      },
      "execution_count": 313,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import Dense, LeakyReLU"
      ],
      "metadata": {
        "id": "PM9JI7U5v0Ma"
      },
      "execution_count": 314,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.optimizers import Adam"
      ],
      "metadata": {
        "id": "i7JmqN2Vv0QJ"
      },
      "execution_count": 315,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hGeRQkZIwFSa"
      },
      "execution_count": 315,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "T100GIbewFWe"
      },
      "execution_count": 315,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the processed data"
      ],
      "metadata": {
        "id": "SFFAe6V9xC3p"
      },
      "execution_count": 316,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/processed_data.csv')"
      ],
      "metadata": {
        "id": "do9So3RtxC84"
      },
      "execution_count": 317,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Data shape: {data.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z96fE14VxDBx",
        "outputId": "a1a1da93-ca0e-40b6-cc88-4b9d866d0486"
      },
      "execution_count": 318,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data shape: (4840, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "h7n15tiPxKXc"
      },
      "execution_count": 318,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7uBnptXXxKbz"
      },
      "execution_count": 318,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the generator model"
      ],
      "metadata": {
        "id": "WpfygIf8xKg1"
      },
      "execution_count": 319,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def build_generator(z_dim):\n",
        "    model = Sequential()\n",
        "    model.add(Dense(128, input_dim=z_dim))\n",
        "    model.add(LeakyReLU(alpha=0.2))\n",
        "    model.add(Dense(256))\n",
        "    model.add(LeakyReLU(alpha=0.2))\n",
        "    model.add(Dense(512))\n",
        "    model.add(LeakyReLU(alpha=0.2))\n",
        "    model.add(Dense(1024))\n",
        "    model.add(LeakyReLU(alpha=0.2))\n",
        "    model.add(Dense(data.shape[1], activation='tanh'))\n",
        "    return model"
      ],
      "metadata": {
        "id": "_kYC0u2FxKlO"
      },
      "execution_count": 320,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yQapgm1ixQye"
      },
      "execution_count": 320,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6Iq8Sy0PxQ5S"
      },
      "execution_count": 320,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the discriminator model"
      ],
      "metadata": {
        "id": "0duIri1_xQ9o"
      },
      "execution_count": 321,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def build_discriminator(input_shape):\n",
        "    model = Sequential()\n",
        "    model.add(Dense(1024, input_dim=input_shape))\n",
        "    model.add(LeakyReLU(alpha=0.2))\n",
        "    model.add(Dense(512))\n",
        "    model.add(LeakyReLU(alpha=0.2))\n",
        "    model.add(Dense(256))\n",
        "    model.add(LeakyReLU(alpha=0.2))\n",
        "    model.add(Dense(1, activation='sigmoid'))\n",
        "    return model"
      ],
      "metadata": {
        "id": "9vOjwHspxRBz"
      },
      "execution_count": 322,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8c7MB_NoxXn4"
      },
      "execution_count": 322,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UoVSNFWWxf3Z"
      },
      "execution_count": 322,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the GAN model (combining generator and discriminator)"
      ],
      "metadata": {
        "id": "1QIX2f78xXxO"
      },
      "execution_count": 323,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def build_gan(generator, discriminator):\n",
        "    discriminator.trainable = False  # We don't want to train the discriminator when training the generator\n",
        "    model = Sequential()\n",
        "    model.add(generator)\n",
        "    model.add(discriminator)\n",
        "    return model"
      ],
      "metadata": {
        "id": "UB7olA2LxX2N"
      },
      "execution_count": 324,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XWBSS7rBxDF4"
      },
      "execution_count": 324,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9fzW-99SwFaM"
      },
      "execution_count": 324,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the models"
      ],
      "metadata": {
        "id": "YZJjxqlPxkZR"
      },
      "execution_count": 325,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "z_dim = 100  # Latent dimension for the generator"
      ],
      "metadata": {
        "id": "KmMqTIO-xkfh"
      },
      "execution_count": 326,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generator = build_generator(z_dim)"
      ],
      "metadata": {
        "id": "5mWzl1dhxkkS"
      },
      "execution_count": 327,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "discriminator = build_discriminator(data.shape[1])"
      ],
      "metadata": {
        "id": "G1JbOsWgxkpR"
      },
      "execution_count": 328,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "discriminator.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=0.0002, beta_1=0.5), metrics=['accuracy'])\n"
      ],
      "metadata": {
        "id": "megm62-Kxktp"
      },
      "execution_count": 329,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QZ6nh95rxwWI"
      },
      "execution_count": 329,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "meGPhh52xwae"
      },
      "execution_count": 329,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Build and compile the GAN"
      ],
      "metadata": {
        "id": "v1ULlGYOxwgd"
      },
      "execution_count": 330,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gan = build_gan(generator, discriminator)"
      ],
      "metadata": {
        "id": "JQLcKe8Zxkyg"
      },
      "execution_count": 331,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gan.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=0.0002, beta_1=0.5))\n"
      ],
      "metadata": {
        "id": "SjLkMRokwFgn"
      },
      "execution_count": 332,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "_iEUsnS25Od1"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pj_-a_DY5Pno"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tvcU-lLmx3q1"
      },
      "execution_count": 332,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dLfhS4Zox3wB"
      },
      "execution_count": 332,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "xrKphUS_5QVK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🚀 **Step 6: Training the GAN**\n",
        "\n",
        "Training a GAN involves an adversarial process where the generator and discriminator compete against each other.\n",
        "\n",
        "- **Discriminator Training**:\n",
        "  - The discriminator is first trained on real data, then on fake data generated by the generator.\n",
        "  - The goal is to correctly classify real data as real (label = 1) and fake data as fake (label = 0).\n",
        "  \n",
        "- **Generator Training**:\n",
        "  - The generator is trained to produce data that can **fool** the discriminator into thinking it is real. It generates fake samples, and the discriminator tries to classify them.\n",
        "  - The generator's loss function is the binary cross-entropy loss, but it is trained to **minimize** the discriminator's ability to identify fake data.\n",
        "\n",
        "- **Epochs and Batch Size**:\n",
        "  - The training loop runs for a set number of epochs, with each epoch using a batch of data. The batch size is divided into two halves: one for real data and one for generated fake data.\n",
        "\n",
        "The training continues until the generator produces synthetic data that is indistinguishable from real data according to the discriminator."
      ],
      "metadata": {
        "id": "YvyUd2iU5RS9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the GAN"
      ],
      "metadata": {
        "id": "RxhC37e9x31m"
      },
      "execution_count": 333,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_gan(epochs, batch_size, z_dim):\n",
        "    half_batch = batch_size // 2  # Half batch for training discriminator\n",
        "\n",
        "    for epoch in range(epochs):\n",
        "        # Train the discriminator\n",
        "        idx = np.random.randint(0, data.shape[0], half_batch)\n",
        "        real_samples = data.iloc[idx].values\n",
        "        noise = np.random.normal(0, 1, (half_batch, z_dim))  # Generate noise for half batch\n",
        "        fake_samples = generator.predict(noise)\n",
        "\n",
        "        # Labels for real and fake data\n",
        "        real_labels = np.ones((half_batch, 1))\n",
        "        fake_labels = np.zeros((half_batch, 1))\n",
        "\n",
        "        # Train the discriminator on real and fake data\n",
        "        d_loss_real = discriminator.train_on_batch(real_samples, real_labels)\n",
        "        d_loss_fake = discriminator.train_on_batch(fake_samples, fake_labels)\n",
        "\n",
        "        # Train the generator\n",
        "        noise = np.random.normal(0, 1, (batch_size, z_dim))  # Generate noise for full batch\n",
        "        g_loss = gan.train_on_batch(noise, np.ones((batch_size, 1)))  # We want the generator to fool the discriminator\n",
        "\n",
        "        # Print the progress\n",
        "        if epoch % 10 == 0:\n",
        "            print(f\"{epoch}/{epochs} [D loss: {0.5 * (d_loss_real[0] + d_loss_fake[0])}] [G loss: {g_loss}]\")\n",
        "\n",
        "    # Save the generator after training\n",
        "    generator.save('/content/sentiment_generator.h5')\n",
        "    print(\"Generator model saved at: /content/sentiment_generator.h5\")"
      ],
      "metadata": {
        "id": "eNiDEPg5x364"
      },
      "execution_count": 334,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "na9YgEE_x_t-"
      },
      "execution_count": 334,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oPsGO8_Lx_zK"
      },
      "execution_count": 334,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the GAN with the specified number of epochs (50) and batch size (64)"
      ],
      "metadata": {
        "id": "tQxxpyrJx_5A"
      },
      "execution_count": 335,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 50"
      ],
      "metadata": {
        "id": "6KlTT8LWx4A_"
      },
      "execution_count": 336,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 64"
      ],
      "metadata": {
        "id": "u4Nbh_bxyMXB"
      },
      "execution_count": 337,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_gan(epochs, batch_size, z_dim)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eA-rDZMnySU9",
        "outputId": "c41f7e43-d26e-46a6-fa65-9bcb41a56ef3"
      },
      "execution_count": 338,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step\n",
            "0/50 [D loss: nan] [G loss: [array(nan, dtype=float32), array(nan, dtype=float32), array(0.171875, dtype=float32)]]\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "10/50 [D loss: nan] [G loss: [array(nan, dtype=float32), array(nan, dtype=float32), array(0.015625, dtype=float32)]]\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "20/50 [D loss: nan] [G loss: [array(nan, dtype=float32), array(nan, dtype=float32), array(0.00818452, dtype=float32)]]\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "30/50 [D loss: nan] [G loss: [array(nan, dtype=float32), array(nan, dtype=float32), array(0.00554435, dtype=float32)]]\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "40/50 [D loss: nan] [G loss: [array(nan, dtype=float32), array(nan, dtype=float32), array(0.00419207, dtype=float32)]]\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generator model saved at: /content/sentiment_generator.h5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "hDXoibVE5bcP"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DPwN48StyScH"
      },
      "execution_count": 338,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "b27q1TkXySjG"
      },
      "execution_count": 338,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IyDye1xVyMd9"
      },
      "execution_count": 372,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "6a4S1MY95dWf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🧬 **Step 7: Generating Synthetic Data**\n",
        "\n",
        "Once the GAN has been trained, the generator can produce **synthetic sentiment data**. This data is generated by feeding random noise into the trained generator.\n",
        "\n",
        "- **Generate New Data**:\n",
        "  - We generate synthetic samples by feeding random noise into the generator model.\n",
        "  - The generator produces synthetic data based on what it learned during training, mimicking the patterns in the real data.\n",
        "\n",
        "- **Save Synthetic Data**:\n",
        "  - The generated synthetic data is saved as a CSV file for further analysis, testing, or use in other models.\n",
        "  \n",
        "This synthetic data can now be used for additional training, testing, or experimentation without using real data."
      ],
      "metadata": {
        "id": "CyIsrg2J5eRY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate synthetic data using the trained Generator"
      ],
      "metadata": {
        "id": "4FB5eu_4wFlA"
      },
      "execution_count": 339,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "noise = np.random.normal(0, 1, (1000, z_dim))  # Generate 1000 samples of random noise"
      ],
      "metadata": {
        "id": "fADC3JT1wFpR"
      },
      "execution_count": 340,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "synthetic_data = generator.predict(noise)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fngdhKn0wFtd",
        "outputId": "a488114d-2a2a-4523-b2dd-3cc512ead814"
      },
      "execution_count": 341,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vKuRQkLSwFxJ"
      },
      "execution_count": 341,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "x279yvwnwF04"
      },
      "execution_count": 341,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert generated data into a DataFrame for easier inspection"
      ],
      "metadata": {
        "id": "vPyk2PnNwF4o"
      },
      "execution_count": 342,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "synthetic_data_df = pd.DataFrame(synthetic_data, columns=data.columns)"
      ],
      "metadata": {
        "id": "wfAV8oXwwF8e"
      },
      "execution_count": 343,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1ySoZxJAwpqW"
      },
      "execution_count": 343,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZqZ7HTCbwpu7"
      },
      "execution_count": 343,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save synthetic data to a CSV file"
      ],
      "metadata": {
        "id": "LcfIY_7wwpzB"
      },
      "execution_count": 344,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "synthetic_data_df.to_csv('/content/synthetic_sentiment_data.csv', index=False)"
      ],
      "metadata": {
        "id": "FIxtxLurwGA8"
      },
      "execution_count": 345,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Synthetic sentiment data saved at: /content/synthetic_sentiment_data.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qMGfh9w1voZ_",
        "outputId": "fccdd427-5745-4b08-e07e-e3815860b941"
      },
      "execution_count": 346,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Synthetic sentiment data saved at: /content/synthetic_sentiment_data.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lDJ1vN6Awx9P"
      },
      "execution_count": 346,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display a few rows of the synthetic data"
      ],
      "metadata": {
        "id": "qhg7pN9ewyDG"
      },
      "execution_count": 347,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Generated Synthetic Data:\")\n",
        "print(synthetic_data_df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ID-qXE04vog4",
        "outputId": "40a5e36f-cc54-426f-d1e1-9704a849bb93"
      },
      "execution_count": 348,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generated Synthetic Data:\n",
            "   sentiment_encoded  negative   neutral  positive\n",
            "0           0.992552 -0.996179  0.932608  0.442675\n",
            "1           0.992907 -0.997556  0.962617  0.516359\n",
            "2           0.998258 -0.999111  0.977948  0.494249\n",
            "3           0.999349 -0.999647  0.984267  0.613942\n",
            "4           0.997615 -0.998690  0.969311  0.424938\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "rUUCNWO55kO4"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fuuBKn7p5lVW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CPKOyIZR1sir"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0l8DrnZP1spr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "D5IYresR5mC3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📊 **Step 8: Data Visualization**\n",
        "\n",
        "Visualization helps us to understand how well the synthetic data matches the real data.\n",
        "\n",
        "- **Distributions Comparison**:\n",
        "  - We plot the distributions of the original data and the synthetic data side by side.\n",
        "  - This allows us to visually compare how similar the two datasets are in terms of feature distribution.\n",
        "\n",
        "- **Correlation Heatmap**:\n",
        "  - A **correlation heatmap** is plotted to visualize the relationships between features in the synthetic data.\n",
        "  - This helps us to understand if the synthetic data has similar feature dependencies as the real data.\n",
        "\n",
        "By analyzing the visualizations, we can assess how well the generator is able to replicate the structure of the original dataset."
      ],
      "metadata": {
        "id": "JxnY9slC5m7I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the distribution of original and synthetic data"
      ],
      "metadata": {
        "id": "Cui0n9hJ1tAr"
      },
      "execution_count": 367,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 8))\n",
        "for i, column in enumerate(data.columns):\n",
        "    plt.subplot(len(data.columns) // 3 + 1, 3, i + 1)\n",
        "    sns.histplot(data[column], kde=True, color='blue', label='Original', stat='density')\n",
        "    sns.histplot(synthetic_data_df[column], kde=True, color='red', label='Synthetic', stat='density')\n",
        "    plt.title(f'Distribution of {column}')\n",
        "    plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()  # Ensure the plot is shown"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "rOHlYn3-1uFO",
        "outputId": "6234675f-053f-4319-9c91-2c804eca52d4"
      },
      "execution_count": 368,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UkgC0pC51xwR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yw0Zwuu31x12"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the correlation heatmap of synthetic data"
      ],
      "metadata": {
        "id": "NlpbNBSv1x7b"
      },
      "execution_count": 369,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 8))\n",
        "correlation = synthetic_data_df.corr()\n",
        "sns.heatmap(correlation, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)\n",
        "plt.title('Correlation Heatmap of Synthetic Data')\n",
        "plt.show()  # Ensure the plot is shown"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 719
        },
        "id": "--wsInvN1yPL",
        "outputId": "bcaf1bf8-2016-4f2e-8567-7ba481fd75bf"
      },
      "execution_count": 370,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "yJrpQuU15qVT"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "a5xuyUb25sLl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WD8RqCrO5sTa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "obykbEhI5tsH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🏁 **Conclusion**\n",
        "\n",
        "In this project, we successfully used a **Generative Adversarial Network (GAN)** to generate synthetic financial sentiment data. Here's a recap of what we achieved:\n",
        "\n",
        "- **Data Loading & Exploration**: We started by loading and inspecting the dataset to understand its structure and clean it by handling missing or duplicate values.\n",
        "  \n",
        "- **Preprocessing**: We cleaned the text data by removing special characters and converting it to lowercase, ensuring that the input to the model was consistent and well-prepared.\n",
        "  \n",
        "- **Sentiment Encoding**: We encoded the sentiment labels (positive, neutral, negative) into numerical values, allowing the GAN model to process the data effectively.\n",
        "\n",
        "- **Feature Extraction**: We transformed the text data into a numerical format using **CountVectorizer**, which converted the words into a **bag-of-words** representation suitable for machine learning.\n",
        "\n",
        "- **Building the GAN**: We constructed a simple GAN with a generator and discriminator. The generator learned to create fake sentiment data, while the discriminator learned to distinguish real from fake data.\n",
        "\n",
        "- **Training the GAN**: The GAN was trained using an adversarial process, where both the generator and discriminator were optimized simultaneously to improve their performance.\n",
        "\n",
        "- **Generating Synthetic Data**: After training, the generator produced synthetic data that mimicked the real sentiment data. This synthetic data can be used for further analysis, model training, or as a tool for privacy-preserving applications.\n",
        "\n",
        "- **Visualization**: We visualized the distributions and correlations of the original and synthetic data to evaluate how well the generator replicated the real data patterns.\n",
        "\n",
        "By the end of this project, we have not only generated synthetic sentiment data but also demonstrated the potential of GANs for data augmentation and privacy-preserving techniques in financial sentiment analysis. This approach can be extended to other types of data generation tasks in machine learning and AI applications.\n",
        "\n",
        "The generated data can be further used for training more advanced models, improving the robustness of AI systems, or testing new strategies without relying on real sensitive data."
      ],
      "metadata": {
        "id": "33ul8K0d5umA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 3px solid purple;\">\n",
        "<hr style=\"border: 3px solid purple;\">"
      ],
      "metadata": {
        "id": "mO2L4vVf5xra"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yomBJ2El1uLO"
      },
      "execution_count": 372,
      "outputs": []
    }
  ]
}