2_5_6_Language_Detection.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Project Overview: Language Detection using Naive Bayes</span>\n",
        "\n",
        "This project focuses on classifying text data into different languages using a Naive Bayes classifier. The main steps include data loading, exploratory data analysis (EDA), data preprocessing, model training, and evaluation.\n",
        "\n",
        "## <span style=\"color: #2980B9;\">1. Import Libraries</span>\n",
        "We start by importing the necessary libraries for data manipulation, visualization, model training, and evaluation. This includes libraries such as pandas, seaborn, matplotlib, and scikit-learn.\n",
        "\n",
        "## <span style=\"color: #2980B9;\">2. Load the Dataset</span>\n",
        "The dataset is loaded into a pandas DataFrame for further analysis. It is crucial to define the correct path to ensure that the data is accessible.\n",
        "\n",
        "## <span style=\"color: #2980B9;\">3. Inspect the Dataset</span>\n",
        "We conduct an initial inspection of the dataset by checking:\n",
        "- The first few rows to understand its structure\n",
        "- The data types of each column\n",
        "- Summary statistics to gain insights into the data distribution\n",
        "- Missing values for data cleaning purposes\n",
        "\n",
        "## <span style=\"color: #2980B9;\">4. Exploratory Data Analysis (EDA)</span>\n",
        "EDA is performed to visualize various aspects of the data, including:\n",
        "- Language distribution using pie charts\n",
        "- Text length analysis through box plots, violin plots, histograms, and line plots\n",
        "- Categorization of text lengths for better insights\n",
        "\n",
        "## <span style=\"color: #2980B9;\">5. Data Preprocessing</span>\n",
        "The data preprocessing steps include:\n",
        "- Splitting the dataset into features (Text) and labels (language)\n",
        "- Encoding the labels for classification\n",
        "- Dividing the dataset into training and testing sets\n",
        "- Vectorizing the text data using the CountVectorizer to prepare it for model training\n",
        "\n",
        "## <span style=\"color: #2980B9;\">6. Model Training</span>\n",
        "We initialize and train a Multinomial Naive Bayes classifier on the training data. The trained model is then saved for future use.\n",
        "\n",
        "## <span style=\"color: #2980B9;\">7. Model Evaluation</span>\n",
        "The model is evaluated using the test set, where we calculate:\n",
        "- Accuracy of the model predictions\n",
        "- A classification report providing detailed metrics\n",
        "- A confusion matrix visualizing the performance of the model\n",
        "\n",
        "## <span style=\"color: #2980B9;\">8. Conclusion</span>\n",
        "The project concludes with a summary of the model training and evaluation, highlighting the effectiveness of the Naive Bayes classifier in classifying text data into different languages.\n",
        "\n",
        "---\n",
        "\n",
        "This project serves as a comprehensive guide for anyone interested in text classification tasks using machine learning techniques."
      ],
      "metadata": {
        "id": "8f0oXaddPodO"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kbQpXSZKPuDD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MA_66EkzPuWa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M-RWFPoqPueB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:green;\">Step 1: Import Libraries</span>\n",
        "In this step, we import the necessary libraries for our project. These libraries are crucial for various functions including data manipulation, visualization, model training, and evaluation.\n",
        "\n",
        "- **Pandas**: A powerful data manipulation library that allows us to read and handle datasets efficiently.\n",
        "- **Seaborn & Matplotlib**: Visualization libraries that enable us to create informative and attractive graphs for exploratory data analysis (EDA).\n",
        "- **Scikit-learn**: A machine learning library that provides tools for model training, evaluation, and various preprocessing techniques.\n",
        "- **Joblib**: Used for saving and loading machine learning models and other objects efficiently."
      ],
      "metadata": {
        "id": "ld3h9iDyPvUE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================="
      ],
      "metadata": {
        "id": "O0rM2648M5DA"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import Libraries"
      ],
      "metadata": {
        "id": "SkoA572HLrGl"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================="
      ],
      "metadata": {
        "id": "W6r65yN5M6Mh"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # Importing pandas for data manipulation"
      ],
      "metadata": {
        "id": "jT4ErTv9Lzcp"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns  # Importing seaborn for data visualization"
      ],
      "metadata": {
        "id": "42SNWdKvLzik"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # Importing matplotlib for plotting"
      ],
      "metadata": {
        "id": "3LXouPbSLzlg"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split  # Importing train_test_split for splitting data"
      ],
      "metadata": {
        "id": "EKf1-CvTLzqf"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer  # Importing CountVectorizer for text feature extraction\n"
      ],
      "metadata": {
        "id": "sDW3WnwXLzw7"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import MultinomialNB  # Importing Multinomial Naive Bayes for classification"
      ],
      "metadata": {
        "id": "OKMf44jYLzzt"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix  # Importing metrics for evaluation\n"
      ],
      "metadata": {
        "id": "ErnSEx4CLz2H"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder  # Importing LabelEncoder for label encoding"
      ],
      "metadata": {
        "id": "6cBNxuXOMI_K"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib  # Importing joblib for saving models"
      ],
      "metadata": {
        "id": "kakwGdlzMJCm"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mu4EpOzBMJFh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VbHWR10-MJIb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "812VsDgsMJLz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:green;\">Step 2: Load the Dataset</span>\n",
        "Here, we define the path to our dataset and load it into a Pandas DataFrame. This step is essential for accessing our data for subsequent analysis and modeling.\n",
        "\n",
        "- **Dataset Path**: Specify the path where the dataset is located.\n",
        "- **Loading Data**: Utilize `pd.read_csv()` to read the dataset into a DataFrame for easy manipulation."
      ],
      "metadata": {
        "id": "uipMDUVbP3-8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================="
      ],
      "metadata": {
        "id": "FRcn5SnOM8Ek"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the Dataset"
      ],
      "metadata": {
        "id": "IdDLkptCMJN2"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================="
      ],
      "metadata": {
        "id": "jzylMJ4hM9cu"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_path = '/content/dataset.csv'  # Defining the path for the dataset"
      ],
      "metadata": {
        "id": "-bOwAH18Lz4U"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(dataset_path)  # Loading the dataset into a DataFrame"
      ],
      "metadata": {
        "id": "PPwJyY0MLz6O"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SUZ4kQzKLz8g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yRrIVSkhLz-N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rBdaNRVXL0Ax"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:green;\">Step 3: Inspect the Dataset</span>\n",
        "We perform an initial inspection of the dataset to understand its structure and contents.\n",
        "\n",
        "- **Display First Rows**: Use `df.head()` to show the first few entries of the dataset.\n",
        "- **Data Types**: Check the types of data in each column using `df.dtypes`.\n",
        "- **Summary Statistics**: Use `df.describe()` to get an overview of the numerical features.\n",
        "- **Missing Values**: Identify any missing values in the dataset using `df.isnull().sum()` to ensure data integrity."
      ],
      "metadata": {
        "id": "aPyxdIvdP63T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================="
      ],
      "metadata": {
        "id": "h6a4LqhrM-1Z"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Inspect the Dataset"
      ],
      "metadata": {
        "id": "03FQtRXnMYUy"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================="
      ],
      "metadata": {
        "id": "HSffM6SONBjK"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"First few rows of the dataset:\")  # Printing a message before showing the dataset\n",
        "print(df.head())  # Displaying the first few rows of the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h0kyNWShL0DU",
        "outputId": "a9e3a033-c648-4fc0-86a8-49471b012197"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First few rows of the dataset:\n",
            "                                                Text  language\n",
            "0  klement gottwaldi surnukeha palsameeriti ning ...  Estonian\n",
            "1  sebes joseph pereira thomas  på eng the jesuit...   Swedish\n",
            "2  ถนนเจริญกรุง อักษรโรมัน thanon charoen krung เ...      Thai\n",
            "3  விசாகப்பட்டினம் தமிழ்ச்சங்கத்தை இந்துப் பத்திர...     Tamil\n",
            "4  de spons behoort tot het geslacht haliclona en...     Dutch\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fd3YTnw0L0F2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DAYBAABeL0J9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nColumn names and data types:\")  # Printing a message before displaying data types\n",
        "print(df.dtypes)  # Displaying column names and their data types"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a32a-5inL0Mi",
        "outputId": "47f8cc6b-c1f0-4e60-dd33-8ce6b9ea0dfa"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Column names and data types:\n",
            "Text        object\n",
            "language    object\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "B9BIbOpHMhrB"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eptK9wtrMhvN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSummary statistics:\")  # Printing a message before showing summary statistics\n",
        "print(df.describe(include='all'))  # Displaying summary statistics for all columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UL2HO_LXMh65",
        "outputId": "00136321-1724-4b7f-ec5d-4e86d5207d54"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Summary statistics:\n",
            "                                                     Text  language\n",
            "count                                               22000     22000\n",
            "unique                                              21859        22\n",
            "top     haec commentatio automatice praeparata res ast...  Estonian\n",
            "freq                                                   48      1000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "z1ZQqMK0L0OZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I2-4vAQnQ0VL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for duplicates"
      ],
      "metadata": {
        "id": "j1p46s68Q-el"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "duplicate_count = df.duplicated().sum()  # Counting the number of duplicate rows"
      ],
      "metadata": {
        "id": "CcratvC3Q4QU"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Number of duplicate rows: {duplicate_count}\")  # Displaying the count of duplicate rows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AEh3wrmbQ4Tj",
        "outputId": "48c1b3a0-508d-4bfa-80a9-0ea9d7e32ee6"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of duplicate rows: 141\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MaDUHLatREbs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WLbdDeVmREe8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Unique values in each column"
      ],
      "metadata": {
        "id": "sODYO_dgREh5"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unique_values = df.nunique()  # Counting unique values for each column"
      ],
      "metadata": {
        "id": "9Em6ChJqRElU"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(unique_values)  # Displaying the unique values count for each column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AggV5CNAQ4WU",
        "outputId": "bfed46d9-348a-47c6-890d-6ff345ed8a45"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Text        21859\n",
            "language       22\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Y7-fiMBFRLx2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oFIWh7QfRL05"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for missing values"
      ],
      "metadata": {
        "id": "pnoY9JdBL0Qw"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "missing_values = df.isnull().sum()  # Summing up the missing values in each column"
      ],
      "metadata": {
        "id": "8LN6SA0XRQkM"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "missing_values = missing_values[missing_values > 0]  # Filtering columns with missing values"
      ],
      "metadata": {
        "id": "1mBIhx0VRQnr"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(missing_values)  # Displaying the columns with missing values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhIMAX7kRQqr",
        "outputId": "3a87d768-bf28-42b0-bd31-14145ae86079"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Series([], dtype: int64)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wunF2AWRRQt2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iChtMO_zRQwm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Value counts for a specific categorical column (e.g., 'language')"
      ],
      "metadata": {
        "id": "8AQxHnoCRQz8"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "language_counts = df['language'].value_counts()  # Counting occurrences of each category in 'language'"
      ],
      "metadata": {
        "id": "6io5Fr25RQ6J"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(language_counts)  # Displaying the value counts for 'language'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xtB960WeRQ-A",
        "outputId": "b682aac2-bc5d-4767-e8c4-833cdc3be076"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "language\n",
            "Estonian      1000\n",
            "Swedish       1000\n",
            "English       1000\n",
            "Russian       1000\n",
            "Romanian      1000\n",
            "Persian       1000\n",
            "Pushto        1000\n",
            "Spanish       1000\n",
            "Hindi         1000\n",
            "Korean        1000\n",
            "Chinese       1000\n",
            "French        1000\n",
            "Portugese     1000\n",
            "Indonesian    1000\n",
            "Urdu          1000\n",
            "Latin         1000\n",
            "Turkish       1000\n",
            "Japanese      1000\n",
            "Dutch         1000\n",
            "Tamil         1000\n",
            "Thai          1000\n",
            "Arabic        1000\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "a4BOqjkNReMO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SHQAHiRNRePY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Inspecting correlations"
      ],
      "metadata": {
        "id": "W2ZfweivReUI"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numerical_cols = df.select_dtypes(include='number')  # Selecting only numeric columns"
      ],
      "metadata": {
        "id": "i3MuXTD8ReXc"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = numerical_cols.corr()  # Calculating the correlation matrix for numerical columns"
      ],
      "metadata": {
        "id": "awokDILQReam"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(correlation_matrix)  # Displaying the correlation matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ztvSwlgcRed1",
        "outputId": "9b8ac821-cd7a-417c-972c-0d6ccf7a9bb1"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Empty DataFrame\n",
            "Columns: []\n",
            "Index: []\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "z330CUaqRRA4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "l11_QrKuL0VH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zhCC__95L0ZA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:green;\">Step 4: Exploratory Data Analysis (EDA)</span>\n",
        "Exploratory Data Analysis is conducted to uncover patterns and insights from the dataset.\n",
        "\n",
        "- **Text Length Analysis**: A new column `text_length` is created to store the length of each text entry.\n",
        "- **Visualization**: We generate various plots (pie charts, box plots, violin plots, histograms) to visualize language distribution and text length characteristics. These visualizations help identify trends and distributions within the data."
      ],
      "metadata": {
        "id": "lDhEJJEiP9xk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================="
      ],
      "metadata": {
        "id": "Us2lu5UFL0dI"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Exploratory Data Analysis (EDA)"
      ],
      "metadata": {
        "id": "VE3fe-LrL0fx"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================="
      ],
      "metadata": {
        "id": "VWtW7zb3MyAa"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['text_length'] = df['Text'].apply(len)  # Creating a new column for text length"
      ],
      "metadata": {
        "id": "BemhGMmuL0hp"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "p8Zioeu3L0l_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "37jLtnXOL0od"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Pie Chart for distribution of languages in the dataset"
      ],
      "metadata": {
        "id": "b_VJO5ngL0qT"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 8))  # Setting the figure size for the pie chart\n",
        "df['language'].value_counts().plot.pie(autopct='%1.1f%%', startangle=90, cmap='Set3')  # Plotting pie chart for language distribution\n",
        "plt.title('Language Distribution')  # Setting the title for the pie chart\n",
        "plt.ylabel('')  # Hiding y-axis label\n",
        "plt.savefig('/content/language_pie_chart.png')  # Saving the pie chart as an image\n",
        "plt.show()  # Displaying the pie chart"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "vvCYKShXL0sl",
        "outputId": "66e16664-ba7a-4ddb-b095-fba2362e56fd"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Cb-FgBU8NMXa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4rRbCV5fNMaT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Box plot for text length by language"
      ],
      "metadata": {
        "id": "eA-z_5ewNMfC"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))  # Setting the figure size for the box plot\n",
        "sns.boxplot(x='language', y='text_length', data=df)  # Creating a box plot for text length by language\n",
        "plt.title('Box plot of Text Length by Language')  # Setting the title for the box plot\n",
        "plt.xticks(rotation=90)  # Rotating x-axis labels for better readability\n",
        "plt.savefig('/content/text_length_boxplot.png')  # Saving the box plot as an image\n",
        "plt.show()  # Displaying the box plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "mznC0lvaNMh-",
        "outputId": "47a313e1-393e-40ff-ea73-22bb96cbaf1b"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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cPHgQsbGx+Pjjj/H6669j165dMpv6KA2vvsZDhw6hd+/eslLsv0OLFi3wxx9/KI4bO1bff4+UyspK5Obmill6APj9998BQKYI7+zsjOHDh2PLli0IDQ1FUlISli5dWi9ryMrKwu+//46NGzfKhBgPHjz4P//MFi1a4OjRoyguLpZl7Y29vjqdTlbGT6he9bJjxw5YW1vjhx9+kIkcfvXVV4rfXVlZiby8PFk1TvXf3bhxY9jb28NgMNTps8/hcDj/ZHiPPYfD4XAeSXl5OQ4cOABLS0ux5HzYsGEwGAz44osvZLZLliyBRqPB0KFDxe+dOHEiPDw8sGzZMmzYsAE3btzAzJkz6/z7V69ejfLycvHxypUrUVFRIf4OYwwbNgzXr1+XKZ9XVFTg888/R4MGDfDMM88AgOjU6PX6Oq1l2LBhSE1NRXJysnjs/v37WL16NVq2bIkOHTrU+e+qC927d0ebNm2wcOFCMagiRTq2LS8vD++++y5efPFFfPDBB1i4cCF2796tGCVnZ2dX57+3LowZMwYGgwHz589XPFdRUfE//a7BgwcjOTkZmZmZ4rG7d+8arQqo77+nOtLPuCAI+OKLL2BhYYH+/fvL7MaPH4+cnBy8++670Gq1CAkJqZffTzLw0qy5IAhYtmzZ//wzBw8ejPLycqxZs0Y8VllZieXLlyts27RpgzNnzsg+a7/++qts3CJZp0ajkWXyL1y4gJ07dyp+NwCsWLFCdvzzzz9X/LwXX3wRO3bswOnTpxXr+isjCzkcDudJh2fsORwOh6Ng3759Ygb65s2b+Oabb3Du3Dn861//goODAwAgMDAQzz77LCIiInDhwgV06dIFBw4cwK5du/D222+LmWbSb3v48GHY29ujc+fOiIyMxNy5czF69Ohas+6EsrIy9O/fH2PGjMHZs2exYsUK9OnTByNHjqzxeyZPnowvv/wSEydOxMmTJ9GyZUts375dzKSSKgMbGxt06NABW7duhaenJ5ydndGxY8ca++T/9a9/4dtvv8XQoUPx1ltvwdnZGRs3bkReXh527Nih6On/u5iZmWHt2rUYOnQovLy88Oqrr6JJkya4cuUKjh49CgcHByQkJEAQBISFhcHGxgYrV64EUDWCbseOHZgxYwYGDBgADw8PAFXBgpUrV2LBggV46qmn4OLigueee67WdaSlpWHBggWK4/369cMzzzyD119/HR9//DEyMzMxaNAgWFhY4Ny5c4iPj8eyZcswevTov/R3v/fee9i8eTMGDhyIN998Uxx317x5c9y9e1eWpf9f/p66Ym1tjf3792PChAnw9fXFvn378N///hcffPCBosVg+PDhaNiwIeLj4zF06NAaNQeM8ccffxh9fbt27YpBgwahTZs2eOedd3DlyhU4ODhgx44dddIIqInnn38ePj4+mD17Nv744w+0b98eu3fvxt27dwHIqyDCwsKwePFiDB48GOHh4bh58yZWrVoFLy8vFBYWyv7+xYsXY8iQIXj55Zdx8+ZNLF++HE899RROnTol2nXv3h0vvvgili5dijt37ojj7kglhPR3//vf/8bRo0fh6+uLSZMmoUOHDrh79y7S09Nx6NAhcb0cDofzj4eSGj+Hw+FwGMTYuDtra2vB29tbWLlypWJs171794SZM2cKHh4egoWFhdC2bVvhP//5j2h38uRJwdzcXDYuTBAEoaKiQujZs6fg4eEh5OfnP3I9P/30kzB58mRBp9MJDRo0EEJDQ4U7d+7IbI2N5Lpx44bw6quvCo0aNRIsLS2FTp06ycZ4EY4fPy50795dsLS0rNPou/PnzwujR48WnJycBGtra8HHx0fYs2ePwg71MO6OkJGRIbzwwgtCw4YNBSsrK6FFixbCmDFjhMOHDwuCIAjLli0TAAg7duyQfd+lS5cEBwcHYdiwYeKx69evC8OHDxfs7e0FAI8cfVf9MyH9N3/+fNFu9erVQvfu3QUbGxvB3t5e6NSpk/Dee+8JV69eFW1qGu1m7P3LyMgQ+vbtK1hZWQlNmzYVPv74Y+Gzzz4TAAjXr19/5N9DPj/Vx/0dPXpUACAcPXq01r97woQJgp2dnXD+/Hlh0KBBgq2treDq6ipERUXJxhpKeeONNwQAwjfffFPrz5ZCRgAa+xceHi4IgiDk5OQIAwYMEBo0aCA0atRImDRpkvDrr78qPitkzdWJiooSqm/7bt26Jbz88suCvb294OjoKEycOFFISkoSAAhxcXEy282bNwutW7cWLC0tBW9vb+GHH34wOu5u3bp1Qtu2bQUrKyuhffv2wldffWX0d9+/f1+YNm2a4OzsLDRo0EB4/vnnxbGB//73v2W2N27cEKZNmyY0a9ZMsLCwENzc3IT+/fsLq1evrvNrzOFwOE86GkF4TEo1HA6Hw+H8TTZs2IBXX30Vv/zyy2Mf2cZRB2+//Ta+/PJLFBUV1btIXH0wc+ZMrFu3DtevX1cozquBnTt3YtSoUfj555/Ru3dvk/7uzMxMdO3aFZs3b/6fxhpyOBzOPxneY8/hcDgcDodJSkpKZI/v3LmDTZs2oU+fPkw69Q8ePMDmzZvx4osvqsKpr/76GgwGfP7553BwcEC3bt1M+rsBYOnSpTAzM0NAQMBj/d0cDofzJMJ77DkcDofD4TCJv78/+vXrh6effho3btzAunXrUFhYiHnz5tFemoybN2/i0KFD2L59O+7cuYMZM2bQXlKdePPNN1FSUgJ/f3+Ulpbiu+++w/Hjx/HRRx/V23SDmvj0009x8uRJPPvsszA3N8e+ffuwb98+TJ48Gc2aNXusv5vD4XCeRLhjz+FwOBwOh0mGDRuG7du3Y/Xq1dBoNOjWrRvWrVvHXEY3JycHoaGhcHFxwWeffQZvb2/aS6oTzz33HBYtWoQ9e/bgwYMHeOqpp/D5559j+vTpj/139+rVCwcPHsT8+fNRVFSE5s2b48MPP0RERMRj/90cDofzJMJ77DkcDofD4XA4HA6Hw1ExvMeew+FwOBwOh8PhcDgcFUPVsU9MTERgYCA8PDyg0Wiwc+fOGm2nTJkCjUaDpUuXyo7fvXsXoaGhcHBwgJOTE8LDw1FUVCSzOXXqFPr27Qtra2s0a9YMn3766WP4azgcDofD4XA4HA6HwzE9VHvs79+/jy5duiAsLAwvvPBCjXbff/89Tpw4AQ8PD8VzoaGhuHbtGg4ePIjy8nK8+uqrmDx5Mr755hsAQGFhIQYNGoQBAwZg1apVyMrKQlhYGJycnDB58uQ6r7WyshJXr16Fvb09NBrNX/9jORwOh8PhcDgcDofD+QsIgoB79+7Bw8MDZma15OX/6uD7xwUA4fvvv1ccv3z5stCkSRPh9OnTQosWLYQlS5aIz+Xk5AgAhF9++UU8tm/fPkGj0QhXrlwRBEEQVqxYIeh0OqG0tFS0ef/994V27dr9pfX9+eefAgD+j//j//g//o//4//4P/6P/+P/+D/+j/8z6b8///yzVn+VaVX8yspKjB8/Hu+++y68vLwUzycnJ8PJyQk9evQQjw0YMABmZmZISUnBqFGjkJycjICAAFhaWoo2gwcPxieffIL8/HzodDqjv7u0tBSlpaXiY+H/NQb//PNPODg41NefyOFwOBwOh8PhcDgcjlEKCwvRrFkz2Nvb12rHtGP/ySefwNzcHG+99ZbR569fvw4XFxfZMXNzczg7O+P69euiTatWrWQ2rq6u4nM1OfYff/wxoqOjFccdHBy4Y8/hcDgcDofD4XA4HJPxqHZwZlXxT548iWXLlmHDhg1UetrnzJmDgoIC8d+ff/5p8jVwOBwOh8PhcDgcDofzKJh17I8dO4abN2+iefPmMDc3h7m5OS5evIjZs2ejZcuWAAA3NzfcvHlT9n0VFRW4e/cu3NzcRJsbN27IbMhjYmMMKysrMTvPs/QcDofD4XA4HA6Hw2EVZh378ePH49SpU8jMzBT/eXh44N1338UPP/wAAPD394der8fJkyfF7zty5AgqKyvh6+sr2iQmJqK8vFy0OXjwINq1a1djGT6Hw+FwOBwOh8PhcDhqgWqPfVFREf744w/xcV5eHjIzM+Hs7IzmzZujYcOGMnsLCwu4ubmhXbt2AICnn34aQ4YMwaRJk7Bq1SqUl5dj+vTpCAkJEUfjvfzyy4iOjkZ4eDjef/99nD59GsuWLcOSJUtM94dyOBwOh8PhcDgcDofzmKDq2KelpeHZZ58VH8+aNQsAMGHCBGzYsKFOP2PLli2YPn06+vfvDzMzM7z44ov47LPPxOcdHR1x4MABTJs2Dd27d0ejRo0QGRn5l2bYczgcDofD4XA4HA6Hwyoagcxx49RKYWEhHB0dUVBQwPvtORwOh8PhcDgcDofz2KmrH8psjz2Hw+FwOBwOh8PhcDicR8Mdew6Hw+FwOBwOh8PhcFQMd+w5HA6Hw+FwOBwOh8NRMdyx53A4HA6Hw+FwOBwOR8Vwx57D4XA4HA6Hw+FwOBwVwx17DofD4XA4HA6Hw+FwVAx37DkcDofD4XA4HA6Hw1Ex5rQXwOFw2MRgMODUqVO4e/cunJ2d0blzZ2i1WtrL4nBE+GeUw+FwOBwOpwru2HM4HAWJiYlYsWIFrl+/Lh5zc3PDG2+8gYCAAIor43Cq4J9RDofD+fvwACmH8+SgEQRBoL0INVBYWAhHR0cUFBTAwcGB9nI4nMdGYmIioqKi4O/vj9DQULRq1Qp5eXnYsmULkpOTER0dzR0nDlXIZ9THxwdWVla4d+8e7O3tUVpaitTUVP4Z5XCeULgTWr8kJiZi+fLluHHjhnjM1dUV06ZN49dQDoch6uqHcse+jnDHnlMfsL4pMRgMCA0NRevWrbFgwQKYmT2U4aisrMTcuXORl5eHzZs3M7Vuzj8H8hktLS1Ffn6+4nmdTgdra2v+GeVwnjC4E1q/JCYmIjIyssbnY2Ji+OvK4TBCXf1QLp7H4ZiIxMREhIaGYubMmZg/fz5mzpyJ0NBQJCYm0l6ayKlTp3D9+nWEhobKnHoAMDMzQ2hoKK5du4ZTp05RWiHnnw75jBpz6gEgPz+ff0Y5nCcM4oTq9XrZcb1ej8jISKbuo2rAYDDg3//+d602n3zyCQwGg4lWxOGoH4PBgIyMDBw+fBgZGRlUzh/eY8/hmABpefu8efNk5e1RUVHMlA7fvXsXANCqVSujz5PjxI7DMTXSnnpLS0uUlZUZfSy143A46sVgMGDx4sUAgG7dumHcuHHiPXTz5s1ITk7G4sWL0bt3b16lU0fS09NRXFxcq839+/eRnp6Onj17mmhVtcN6xSPnnw0ruj88Y8/hPGYMBgNWrFgBf39/LFiwAF5eXrC1tYWXlxcWLFgAf39/rFy5konIuLOzMwAgLy/P6PPkOLHjcEzNsWPHxK+rd5JJH0vtOByOesnMzIRer0enTp0QGxsru4fGxsaiU6dO0Ov1yMzMpL1U1bB37956tXvcJCYm4uWXX5ZVPL788su8UoPDBCR516pVK8yYMQPvvfceZsyYgVatWiEqKsqkn1Pu2HM4jxk1lbd37twZbm5u2LJlCyorK2XPVVZWYsuWLXB3d0fnzp0prZDzT+f27dvi17U59lI7DoejXojD/uqrr0IQBFmpqyAImDhxosyO82iys7Pr1e5xwtswOCxDkneenp7Iy8vDsmXL8Omnn2LZsmXIy8uDp6enSZN3vBSfErykqH4pKSnBl19+icuXL6Np06Z4/fXXYWNjQ3tZANRV3q7VavHGG28gKioKERERouo4URs/ceIEoqOj+WeVQw2p815RUSF7TvqY68JyOE8Wv/76Kz799FNFqevgwYMprkqd3Lt3r17tHhe8DYPDOiR5d+PGDaPttsnJyRAEAadOnULXrl0f+3q4Y08Bruxav0RERCApKUl8nJaWhp07d6J3796IjY2luLIqpOXtXl5eiudZK28PCAjA2LFjER8fj+TkZPG4VqvF2LFj+Wf0f4QH8+qHpk2b4ty5c3Wy43A46sfb2xubNm3Cxo0bFRvnzZs3Y+PGjaId58lC2oYRFRWFhIQEHDp0CB4eHoiKisK7776LrKwsZGZmonv37rSXy/kHQqoDfXx8EB0djdOnTyM5ORnOzs6Ijo7G3LlzkZKSYrIqQu7YmxhSUmRlZSU7TkqK+HiRvwZx6i0sLBAcHIxhw4Zh7969iI+PR1JSEiIiIqg799LydnLSE+euY8eOzJW3JyYmYuvWrfD19UWTJk1QWloKKysrXLlyBVu3bkWHDh34Z/QvwoqoCocOagnqqGWdnH8WnTp1gpmZGSorK2tsvzEzM0OnTp1oLK9GWD6fNBpNvdo9Lkh7RaNGjTB8+HBZOfOqVavQt29f0Y479hwakBYRV1dXjB8/XrHP8/Hxkdk9brhjb0LUWFLE8o2ppKREdOr/+9//wtLSEgAwefJkTJw4EcOHD0dSUhJKSkqoluVLy9tHjBiB0tJS8TkrKyuUlZUxU94u7RW6cOECTpw4IT7n5uYm9gqx9BllHSKq4ufnh7Fjx8paG1iaiKAWCgoK6tXucaOWCi0efOKwSnZ2tqj5kp6eLrsvkft+ZWUlsrOzTVLqWhdYP++tra0fqYpP7Fjg6NGj0Ol0GDhwIJo0aYIrV67g4MGD+PHHH2kvTdWUlZVh165duHr1Kjw8PBAUFCSeU5y64eTkBADYvXu30Yqi3bt3y+weN9yxNyHVlV2JkBpRdp0xYwZTJUWsb/S+/PJLAEBwcLDiQmRpaYnRo0fj22+/xZdffom3336bwgrl1NTzy1IvMGu9QmqnuqiKtLWBB0r+N6TVTiSLZ+xx9aooGqilQkst4zg5/0yI/syLL76I77//XvZcRUUFXnzxRezYsYMJnRrg4XlffV+Sn5/P1HmvBjp27Aig6tpubm6Obdu2ic81btxYvOYTO07dWbVqFeLj4xVVEMHBwZgyZQrFlakLaRutIAj4/fffcfHiRZSWlsr296Zqt+Wq+CZETcquZKPXunVrLF++HHv37sXy5cvRunVrk49uqInLly8DAIYNG2b0eXKc2NGCOHe9evXCnj17sGTJEsybNw9LlizBnj170KtXL2bG3Ul7hYyN5iMlRVxxvG6QQMnZs2eRn58vey4/Px9nz55lZiKCFIPBILs+sfDZNIa5uXmtj2kirdCqicWLF1N/baXjOKOjo1FWVobk5GSxkoilcZxS1PIZ5fx9yIZ4x44dRqe17NixQ2ZHE+l5X72MnTxm4bwvKyurV7vHxYULFwBUvc+3bt2SPXfr1i3x80DsOHVj1apViIuLg4ODA9555x3s2LED77zzDhwcHBAXF4dVq1bRXqIMNVzvGzVqhNTUVJkqfmpqKho1amTSdbCzC/oH8euvv+KTTz5RlGgNGTKE4qoeUn3uurSyYMGCBZg7dy4TWcamTZsiLS0Ne/fuxeTJkxXPk/mrtEW0iHM3b948WFhYKDLdoaGhmDZtGhNZcNID1LdvX6Oj+fr06YOUlBST9QqpHWkApKb2m+p2tGG9UkeKsU0+K5AKLaDm957M3qZZoUWuT4GBgQgNDcXNmzfF51xcXDBy5EgcP36ciesTQU2fUc7fx8vLCxqNBoIgiP8TpMeNidOaGul537VrV/j6+sLa2hoPHjxASkoKTpw4wcR5X1RUVK92j4tr167Vqx2nKlgTHx8PnU6HuLg45OTkIDMzE02aNEFcXBxCQkIQHx+PsLAwJsryWb/ek/P99u3bYruIh4cHrl69ioMHD4r7O1Ptm3nG3oQQxdaNGzcazd6xouyqlrnrr7/+OgAgPj4eJSUlsmheSUkJtm/fLrOjhZrG3ZEeoGPHjhl1mn7++WeZHad2yHvapk0bxMTEyLKhMTExaN26tcyONmqo1GnQoIH4dW2OvdSOBhkZGQCADh06IDY2Vlb9Ehsbiw4dOsjsaEE+e2vWrJE59QBw8+ZNrF27VmZHG/IZNXYPZeUzyqlffv31V9GZd3R0xJgxY/D2229jzJgxcHR0BFBVAvvrr7/SXCaAKg0AAGjWrJk40/qTTz4RZ1qTRAOx49QOycyamZkZ3Y+SYyxmcFll165dMBgM6Nu3LyZMmICZM2di/vz5mDlzJiZMmIC+ffvCYDBg165dtJeqij0J2Q83b94cVlZW2LZtG5YuXYpt27bB2toazZs3l9k9bnjG3oR06tRJjC5XL28ijzUaDXVlV6kjakw8jxVH1MbGBr1790ZSUhKGDh1q1KZ3797U59mradwdKRlKSUnB3LlzERoaKuu3TUlJkdlxaqewsBAAUFpaalQtlZSOEzuaqKVSZ9CgQTh48CAsLS0Vc+yBKn2NsrIyDBo0iMLqHkIqsgYMGGB0Q9q/f3/k5OTIKrdoUNfNBgvBPFLmLAiC0aCOIAhYsmQJ9c8op3754YcfAFQ59Xq9XtZnDQAODg4oLCzEDz/8gJ49e9JYoggJjv3555/o1asXIiMjZffQ48ePy+w4tUME/iorK9GzZ0/Y2Njg3r17sLe3R0lJCX755ReZHefRXL16FQCQkJAAX19f9O7dG2VlZbC0tMSVK1eQkJAgs6NF9Tax6mPkoqKimNiTEBwdHbF48WLF5KtZs2aZdB3csTchWVlZjxRKEwQBWVlZVEu0iIP5/fffIyEhQeGMBAYGyuxoMnjwYNkMe2PP00ZN4+7IWh0dHXH+/HlMmzZNfM7NzQ3t2rVDYWEhE2tVA8Shu3z5ssK5u3nzpuicVH+OBtKWEaIBIg3osdIy0q1bN9ja2qK4uBhOTk7w9vYWS11JGaydnR26detGbY1AVXsVABw6dAjPP/+87D2urKzE4cOHZXa0MBYc+Tt2jxNpmbOxYAlQlbmnXebMqV/IHqSmSRckMCrdq9CicePGAKoqhqKiopCTkyM6I1FRUXjxxRdRVFQk2tHCwsIC5eXldbKjyYMHD8SviRP/KDtO7bi5uQGockRTU1MVArQODg4oKCgQ7WghbRMbN26con2ZlTYxck86ffo0IiMj4ePjAysrK1y4cAHbtm3D6dOnZXaPG+7Ym5CTJ0+KX2u1WlnpkPTxyZMnqW5KOnfuDCcnJ6xZs8bo6IY1a9bAycmJunMnFaWLiIjAmjVrcPnyZTRt2hSTJk1CbGwsE9E8Mu4uMjLS6Li70tJSxMTEMBFxlI7mq35Dv3v3Lm7cuMHMaD41ID1HtFqt7AYqfUz7XAIeVuBcvXoV8+fPVwT0wsPDZXa00Gq1+Ne//oXIyEjo9Xqj447ef/996p/Rrl27YvPmzcjJyUFERISixz4nJ0e0o8nBgwfrbOfr6/uYV1M7dS1fTk9P5479E4Srq6u4OX6UHW1Ia0BRURFGjBghc56lzjSxo0WDBg0U7Sw12dGkrtWBvIqw7pAWQL1eDycnJwwaNEgcIXjgwAHRASV2tJC2iRmbLLNmzRqZHS1IkrN///44evSobPqRVqtF//79cejQIZMlQ7ljb0LOnDkDALC1tZU5dwSShSJ2LFNd7ZUG0gyjnZ2dYqQdKxlGQk2vGQuvZXWIGJGU6qJFnL9Gt27d0LRpU5SWlsLKygqXL18WWxtYgNx0PvroI6Njzz766COZHU0CAgIQExOjmBPNkqCOt7c3nJycoNfrcfLkSdnNnmxSSMUBTaSiU6SNwdhjFsSppO91bWKUtNsbOPVL69atxQqXmsTziB1tpNfH6hlx6WPa11E7O7s6OfZ2dnYmWE3NeHp6il+bm5vLKoekj6V2nNqROsIFBQWy1hbpvo+2wyxt/6rtek+7TYwkQw8dOgQ/Pz+FYOahQ4dMmgzljr0JIRuk4uJi6HQ6hIeHw9/fH8nJyVi3bp14kaU9XuTUqVPQ6/WYNGkSEhISZOXY7u7ueO2117B27VrqDrNaROmM9QlJS/FZ6hMia23Xrh30er1sg+zk5AQnJydm1qoGpAKTKSkpNTryp06dot4b6uXlBa1WCwcHB8TExIj9/15eXoiJiUFwcDAKCwuZUJ4Gqpz73r17KzRAWPlcarVazJo1C5GRkTUG72bNmkV9vSTIbGFhgR07dmDdunVi5VN4eDhGjRqFiooKo8FoU0McOBsbG8yfP1/2GZ0/fz4CAwNRUlLCA5BPGLm5ueLX1d9b6WOpHS2kDjuZsW7sMW3HXi0tOOfOnRO/rr4W6WOpHad2fvvtN/Hr2s6n3377jWo7KzlX7O3tjV7vR40ahXv37jE1DUej0cDT01MMQKSmporHTQV37E2Ii4uL+DV5421sbNCqVSt4enqKm36pHQ2IIzxq1CiEhIQoNs6lpaVYu3YtdYdZLaJ0ahp3R9Z648YN+Pv7K4R/kpOTIQgCE2vl1C/Z2dkwGAzIz89HZGSkQjiRBB6zs7OZee+1Wi0zazFGQEAAQkJCFGJf5eXlCAkJYaKygFQPlJeXi/opAJCWloadO3cq7GhCNkclJSWYN2+eIoNTUlIis+M8GdS1AoOFSg3iZFhbW8PR0VG2JhcXF+j1ejx48IC6M1LXMWa0x53VNUjHg3l1R/pa+fj4oFmzZmIl4Z9//ik6o7RfU5IYuXfvHubNmyf2rpeWliI1NRX37t0T7WgmRlhLhnLH3oRIS5oyMjJk2TvpxZN26VN1h7n6B5EVh1kqSidV8Qaqbq6siNKppbIAeDhP3cfHx6gy+pw5c5CSksLU3HWWkX72jJVonThxQmFHC/L5i4iIwLp16xQ3p4iICMTGxjLxOVULiYmJ2Lp1K/z8/BSbkq1bt6JDhw7UnXt3d3dkZ2fXyY42UjGn9PR0o+0N1e046ofsj8zNzdGwYUNF+83t27dRUVFB3QkFHjojDx48gLe3N/r06SM6TVeuXBGv+bSdEbVk7LljX/9IXyutVovnnntOFiA1ZkeTfv364dixY4re9X79+hnV2DE1rCVDuWNvQqQll7WVFNEuzVSLwywVejM2mi05OZkJoTe1VBYAD1U7+/bta1R1uk+fPkhJSTGZuueThrREi6X+euDh58/DwwNfffUVvvzyS7Ek+/XXXxfLXFn4nKqBmsYHAkBQUBAz4wOJsE9d7GhDBAkBGB13J7XjPDnodDoAVfukli1bIiQkRAySnThxQhT6JHYsQJwR4sgDbDkjda1qoV39UtdEF+2EmJogr5W1tTVyc3MV049I8oH2a+rt7Y1Nmzbhxx9/NJoYIecRbZ0a1pKh3LE3IU2aNBG/rm1TIrWjgVocZqCq1DU6OhorVqxQZBijo6OpZ8MA9QRKgIciJMeOHcOwYcMUa/35559ldpzakfbYp6enyzZ50uwS7ewN8PBzGhsbi+vXr4vXpLS0NOzevRtubm7MfE7VgLQFx1iQjJUWnEuXLtXZzt/f/zGvpnakgoTGxD0BNgQJOfWLtFokNTVVFhSVfg5YqCox5oyQIARLzoitrW292j0ubt26Va92HIi96g8ePFBop9y4cUPM1BM7WnTq1EmmS2EsMWJmZoZOnTrRXCZze3z6w5P/QQQFBUGr1cLOzk4xmqNx48aws7ODVqtFUFAQpRU+hDjMJJo3bNgwTJs2DXl5ecw4zISAgAB8/fXXmDZtGkaNGoVp06Zh48aNzKyRBEqSk5Mxd+5cZGdno7i4GNnZ2Zg7dy6Sk5MxdepUJgIl5HOZkpJidK3kYspHy/w1nn32WaNVOv369aOzICNotVq0adMGV69eNRp4vHr1Klq3bs3E51QNqKUFp65q9yyo4hNBQkCZSSSPWRAk5NQv0sBXbWJfLFRqEGeE4OnpiX79+slU21lwRpo2bVqvdo8LtZbiGwwGZGRk4PDhw8jIyJCNt6aNNKhU2/lEO/iUnZ0t7kUyMjJkvkhGRgaAqr1JXVrJHies7fF5xt6EWFpaIjg4GHFxcbC0tMSYMWPg7u6Oa9eu4eDBg7h//z5CQkKY6BMD2FedJiQmJirGXm3fvh3Tpk1jxrlXQ2UB8DDy6OjoiD/++EO2VldXV7Rr1w6FhYU8a1tHSPbm6NGjYtaGYGFhwUz2BqiaxiHtYTNGcnIyysrKmLlGsYyaWnAAiHOM63qcFjWNOtTpdMyMOuTUL97e3uI44JrG3dna2jJxHa3ujEirtIgOBHFGaAYiPDw86tXucSF9rx0dHdG1a1exHDsjIwMFBQUKO9okJiZixYoVYosIwNYo1k6dOonnTYMGDdCmTRtxxPH58+dRVFQEjUZDPfhUXfdH+no6OzsjLCyMGd0flvb43LE3MVOmTAEAxMfHy5SStVotQkJCxOdZgXXV6cTERERGRioUm/V6PSIjIxETE8PEhRRQR6BE2oZR3XnT6/W4efMmM20YakB6A7W2tsabb74pjrhcs2YNSktLmbiBAsD3338vbkhrEvqrrKzE999/j7Fjx1JeLfuwVp5XE08//TR27tyJK1euwMnJCYMGDYKHhweuXr2KAwcOiE79008/TXWdUtRwLVUbBoOB6dfT0tISxcXFsLS0lAVIyWMWpjYA6nFGunXrhi1bttTJjibSypwHDx7I9Amk7zltLQBCYmIioqKi4O/vj3nz5snaWKOiophI5GRlZYmBkKKiIvz6668KG0EQkJWVhe7du5t6eSJS3Z8tW7York9nzpyR2bFA9QATjekX3LGnwJQpUxAWFoZdu3bh6tWr8PDwQFBQEM+C/UUMBgMWL14MoOrmU330UXJyMhYvXkxdnEoK64ESQk3Rb5ai4mpAegMtKSnBwoULxefIpoSFGyjwUA+gffv2+OijjxRib1OnTsXZs2dx6tQp7tjXAbVolRjbFBk7z1naPAHquZaqAdYzjNJxUrt27cLNmzfF55ycnBAYGGjScVK1oRZnRNq/bGFhgfLycvE5S0tLlJWVMdEyIHXYq/eDSx+z4NjXJJhKpgqxIpiamZkpfl29klD6ODMzk+q+pHpwXHpusxQcBx4GdPz8/BASEiImRVJTU00e0OGOPSVIWT7nfyczMxN6vR6dOnVCTEwMTp8+jeTkZDg7OyMmJgazZs1CVlYW9YuTmiA3pl69eiE6OhqnT58WNyUdO3ZEVFQUEzcmtUBuoBMnTsT+/fsV2ZtBgwZh48aNTHxGHzx4AEDZIwo87Ak9e/asaMd5NCyV59UEmXZga2sLvV4vqyQjx4uLi5Gbm0td4JFT/6ghw0iy2y4uLoprk0ajgaurq8yOJmpxRqQtAzU5xSy0DJD3tr7sHidqEUwlgdsOHTpg2bJlin3ejBkzkJOTQz2Ro5bgONk3e3p6Ijc3V9bS6OrqCk9PT5Pum7ljz1EtxGnq3r07xo8fr8g2DB48mDv2fxHpjcnCwkJx82HlxqQ2OnfujPHjxyuyN9LIOW3atWuHkydPYt++fXj99ddlirgVFRXYv3+/aMepO6yXjZPrZnFxsagoT7INJHgqteM8Oaglw0iy2x999BH8/PwwduxYWUbso48+ktnRROqMREREwMfHR8yCpqam4sSJE0w4I7W1DDRs2JCZlgFvb+86tQywoK+gFsFUe3t7AFUVD9UrnyorK8XgPbGjiRqC42TffP36dfTq1QuRkZGyAMTx48dFO1Psm7ljz1E9GzZsQK9evRTZho0bN9JemupQy41JLRDxvK+++grLli1T3EA3bNgg2tGme/fu+Oabb1BUVITg4GCEhYWJegDr169HUVGRaMf5a7BcNu7m5gbgYcZL2sPq5uYGV1dX3LhxQ7TjPDmoJcPo5eUFrVYLa2trnD9/XpYRc3Fxga2tLR48eGBUpJIGAQEBGDt2LOLj42Vr1Wq1GDt2LBPOiLRlYO3atfj444/F1tA5c+bg4sWLMjtaSD+X0tFn1R9X//zSQC2CqeT3nz9/HhEREYo2VlLFRXudBNaD47dv3wYA+Pr6Gg2QzpkzBykpKaLd44Y79hzVQkrZ7O3tERMTI2YYvby8EBMTg1GjRuHevXvUS96ksC5QpJYbk1ogc7ezsrKM3kCzsrKYmbstnRGen5+PRYsWKWxYWSun/mjdujWAqoz99u3bkZOTI16fOnTogNGjR8vsOE8OagnkZmdnw2Aw4P79+7C0tMTs2bNlQcf79++LdiwE0BITE7F161b4+fkpMvZbt25Fhw4dqDv3pGVg7ty5yM/PF4/n5eVhxIgR0Ol0TLQMSD97Wq1W5thLH9P+jALqEUyVjitOT0+XBZ+kgoQsjTVmOThOqtr69u1rNEDap08fpKSkiHaPG/ohLg7nf4ScQPfu3cO8efNksyPnzZuHe/fuyexok5iYiNDQUMycORPz58/HzJkzERoaisTERNpLE5HemIzNMmflxqQWpHO309PTZXNY09PTAbAzd1u61upCnuQxK2vl1B+FhYUAqq6jISEhuHz5Mrp06YLLly8jJCREvI4SO1YoKytDfHw8li1bhvj4eJSVldFekuqQBnKNwUogl2S62rZtCysrKyxatAijR4/GokWLYG1tjbZt28rsaCJtb4iNjcWoUaMwbNgwjBo1CrGxsfD398fKlSupzzXXarVwcnJCfn4+NBoNBg0ahLVr12LQoEHQaDTIz8+Ho6Mj9es9cYZGjhyp+Bw2bNgQgYGBMjuasDbPvCbIPq9du3ZwdHSUPefk5IR27drxfd5fwMnJCQBw7Ngxo/vmn3/+WWb3uOEZe45qkV7Ia4s6snDBlypmjh07VhbBZ0WgCFCPWImaIHO3v/jiC4Wa87Rp05h43wk1rVWn0zG3Vk79QDbLAwYMwNGjR2WVGlqtFv3798fhw4epO3dSVq1ahfj4eJlztGrVKgQHBzM3MpZl1JJhJPfwoKAgDB06VFH1tnfvXixatIiJe71a2htKSkpw5swZaLVaODs748CBAzhw4ACAqvaGO3fu4MyZMygpKYGNjQ21dRJn6MaNG9i8ebNC6G3u3LkyO9qooSdcus/z8/PDSy+9xKQOhBSWq11JZUNqaqrRfXNqaqrM7nHDHXuOaiEbzUmTJiEhIUGhOD5ixAisWbOG+oZUqpiZl5cnC0C4ubmZXDHzUajhxqRGjKk5s4qa1sr5exDnrri4GP/973+RkJAg9toGBgYiJiaGCeeOsGrVKsTFxUGn0yE8PFwsyV63bh3i4uIAgDv3dUQtgVxpRmzYsGEKrRJTZ8RqQy3tDV9++SUAYOzYsQgPD1c4TWvXrsW3336LL7/8Em+//Ta1dRJnKCUlBVFRUQgNDYW/vz/y8vIQFRWFlJQUmR0LsN4TDsj3edI9KYv7PNbHcZJ7qKOjI3JzcxX7Zk9PTxQWFprsHqoRaM8zUAmFhYVwdHREQUEBHBwcaC+HgyqHOTQ0FK1bt65xNBvpZaZ5Qc3IyMDMmTOh0Wjg7+9vdPMkCAKWLFnCVA8RyxFSNSEdJ1XTxpmFmxMgryzx8fGRKU+TKD4ra+XUH9Xfd2PZGxbe97KyMgwdOhQODg6Ij49XTG4IDg5GYWEh9u3bp2gn4dSMsY2zu7s7pk6dysT7rqZ7KFnr8uXLjerUZGdnY9q0adTX+s477yAtLQ2bN29G06ZNFc//+eefGD9+PHr06IGFCxdSWGEVZJ/n6OgIvV6PGzduiM8RZ6qwsJD6Pk+tsL7PU8v+yRT30Lr6odyxryPcsWcTNZz0Bw8eRGxsLHx9ffHxxx8ryh2JYmZERAQGDhxIcaWc+kYafDJW6jp37lwmgk9A7RsoV1dXODk58Q3UE4yx8natVstUeXt8fDyWL1+Od955ByNGjFA8n5CQgEWLFmHatGkIDg6msEL1wvIGX3ptKigoUGTuWHLu1HLNX7p0KXbu3ImXX34ZkydPVjz/5Zdf4ttvv8Xzzz9PNWMPqCfwyKlf1HIuERITExVtjK6urvXWxlhXP5QNVTEO53+ElBOR8hciTJaXl8fMxb4uiplSO86TA+m3DA0NrbHf8tq1azh16hSlFT6ErPXs2bNo06YNli9fjr1792L58uVo06YNzp49y8xaOfULUfH28fHBjBkz8P7772PGjBnw8fHB1q1bmRH4vHr1KgDA39/f6PPkOLHj1B2iOt2/f3907dqViY0ygbQMnD17VlHCfvfuXZw9e5YJUTJAPQJqr7/+OgAYFZ4sKyvD9u3bZXY0Ifu8vLw8LFu2DJ9++imWLVuGCxcuMLPP49Q/ato/AUBOTg7u3LkjO3b79m3k5OSYdB28x56jeljvZ6reH1g96shSfyCnflFLvyXA3izWJwnWs6FExbt6ViQoKAhz585lRgPEw8MDAJCcnGw0Y096RYkdC7D83qsNjUaj0Pswdow2atCpsbGxQe/evZGUlIThw4dj9OjRGDZsGPbu3Yvt27ejvLwcvXv3piqcJ4X1fZ5aYfn6pKb9E0vaL9yx5zwRsDzjUir+YkygiEXxF079IB0nZazfkpVxUgB7s1ifFFgX/pGqeAuCgIyMDNkmjxUVb6Aq0LBq1SqsW7cOQ4YMUfTYr1+/HlqtFkFBQRRX+RDW33u1IA0+1aSnw0rwiaAGRzQ2NhYRERFISkrCt99+i2+//VZ8rnfv3oiNjaW4OiUs7/Oqw7LDTGD9+qSW/RMZvarT6WTaLyNGjMCQIUMQHByM+Ph4hIWFmUT7hTv2HM5jRqqYef78eVkEn8wSNaViJsd0SMdJGduQsjJOCuCVJY8DqQbIvHnzZAE9VsZckmzH1atXMX/+fMUmLzw8XGZHE0tLSwQHByMuLg7BwcEICwsTMyPr169Hfn4+QkJCmBDOU8N7rxakwScLCwuFc8dS8EmKGhzR2NhYFBUV4eOPPxanYcyZMwcNGjSgvTQFanCWAfYdZkAd1ye1jOPctWsXDAYDwsPDZcFmADA3N0dYWBgWLVqEXbt2mUT7hTv2HM5jpvrM0JCQEOZnhgLquYmyjPS9HzFiBEpLS8XnrKysUFZWxsx7z9osVrVTU4k7aW1gpcSdZDs++ugjo5u8jz76SGZHG1LOGB8fj0WLFonHtVotQkJCmBD6U8t7rxbUVJKrNhITE7F8+XJRLDUvLw/h4eH1JvhVX6jBWQbU4TCr5fok3T9FRETUKJxI+xrKmvYLd+w5HBOgppmhgHpuomqhpuEjLA0lYW0Wa10oKyvDrl27xExTUFAQE9laQJ5lrEn4h4Uso5eXF7RaLRwcHBATEyNmHLy8vBATEyOOkDNWCkmLKVOmICwsjL/3/xDUUpKrNhITExEZGak4b/Lz8xEZGYmYmBgm7vdqcJYB9TjMamq/CggIwNixYxEfHy/bO2u1WowdO5aJ95017Rfu2HOeCNSQXQ4ICICfnx+zm1GCWm6iaoDc6Hv16oXIyEgkJCSI731gYCBiYmKYuNEDysqSsWPHMhkdJ6xatQrbtm1DZWWleGzlypUYM2YME1lbtWQZs7OzYTAYxM189UqN/Px80Y72Jk8KKctnEbW892pBWpJr7DrKSkmumjAYDFi8eDEAGBUkBIDFixdTvzepxVkG1BPQU1P7FZnYYmzU4datW9GhQwfq+1HWtF+oOvaJiYn4z3/+g5MnT+LatWv4/vvv8fzzzwMAysvLMXfuXOzduxe5ublwdHTEgAED8O9//1sW9bh79y7efPNNJCQkwMzMDC+++CKWLVsm6w86deoUpk2bhl9++QWNGzfGm2++iffee8/Ufy7nMaGW7LKxde7YsYOpdarpJqoGyI0+MDAQEydOVLz3I0aMwPHjx6nf6AlqqSwhCrTVN6SCIJhcgbYm1JJlJJu3iIgIrFu3TlGpERERgdjYWCY2eWpBLe+9WiBBx8jISAwZMkT23PLlywEAMTEx/J70F8jMzBSFUKUtYtLHer0emZmZ6N69u6mXJ6IWZxlQT0CPXHdiY2PRq1cvRQKHiCbSvj6pZWKLVPtl9OjRGDRoEDw8PHD16lUcOHAAer3epNovVOfY379/H126dBEvzFKKi4uRnp6OefPmIT09Hd999x3Onj2LkSNHyuxCQ0ORnZ2NgwcPYs+ePUhMTMTkyZPF5wsLCzFo0CC0aNECJ0+exH/+8x98+OGHWL169WP/+ziPH5Jdbt26tWzuduvWrREVFcXM/GW1rFM6N7SiogLx8fFYtmwZ4uPjUVFRwdzcUNYhN/A1a9YYfe/Xrl0rs2OBgIAAbNmyBUuWLMG8efOwZMkSbN68mRmnvqysDNu2bQMAWFhYyJ4jj7dt26aYzWxqpFlGaVUBwJbwD9m8eXh4GH3fSSCd9iZPTajlvVcTj5oFbepZ0WonPT1d/NrOzg4eHh5wdnaGh4cH7OzsjNrRQOosGwwGZGRk4PDhw8jIyIDBYGDGWQbkAT1ja2UloEfar3Q6HWJiYuDl5QVbW1ux/Uqn00Gr1VJvv1LTHPspU6agd+/e0Ov12LZtG5YuXYpt27ZBr9ejd+/eJk00UM3YDx06FEOHDjX6nKOjIw4ePCg79sUXX8DHxweXLl1C8+bN8dtvv2H//v345Zdf0KNHDwDA559/jmHDhmHhwoXiRqWsrAzr16+HpaUlvLy8kJmZicWLF8sCABz1oZbsslrWCTy8OR45cgRvvfUWDAaD+NyqVavEihoWbqJqgCjId+rUSVTFT05OhrOzM6KjozFr1ixkZWUxpzTPsprz999/LzpLXbp0QWlpKQoLC+Hg4AArKyv88ssvqKysxPfff4+xY8dSW6e0tcGYGGFycjITrQ3VlYel7zt3Qv831PLeq4WysjJs3boVQNU11dvbGzY2NigpKREzz1u3bjXZOKknAWn12P3793H//v1H2tGAOMHff/89EhISFJWZgYGBMjuakGvpZ599Br1eLwoSAoCrqyucnJyYuJaS9iu9Xm+0/Uqv10MQBOrtV2qpgACqknfHjx+HpaWlLKlgaWmJ48ePIzEx0WTJEVX12BcUFECj0Yib4OTkZDg5OYlOPQAMGDAAZmZmSElJwahRo5CcnIyAgADZxX7w4MH45JNPkJ+fD51OZ/R3lZaWysqTCgsLH88fxfmfkZZolZaW4ssvv8Tly5fRtGlTvP7668yUaKmplIzcHHfs2AEnJydFSdGOHTtkdpy6UVBQgPHjxys2JXwT+tch0XnixFeH9OCdOnWKqmMPyFsbqpe4s9LaoFYnlHVdFTW892phx44dEAQBlpaWsLCwwI8//ig+17hxY3EzvWPHDrz00kv0FqoiiHZGfdk9Ljp37gwnJyesWbMGvr6+6N27N8rKymBpaYnLly9jzZo1cHJyou4sA1XX0n79+iEuLk6x17t16xZu3LiBkJAQ6tcp4gh/8MEHWLt2rWIE8wcffMBE+5VaWpqIXoUgCApBZHJsyZIlJkveqcaxf/DgAd5//3289NJLcHBwAFAVSXRxcZHZmZubw9nZWdxAX79+XRHtcXV1FZ+rybH/+OOPER0dXd9/hupgefNELjqbNm3CiRMnxONpaWnYuXMnfH19ZXa0kEYdjal4sxR1bN++PYCqG5SlpaVY8gwALi4u0Gq1MBgMoh2ndkgP46VLl6DT6TBmzBgxUHLw4EHxOkXsOI/mwYMHAJR9oQRynNjRJiAgAL1792b2OgqozwlVi66KGt57NZCUlASgKnN/69Yt2XPSx0lJSdyxryPV25j+rp0pSElJQUpKiuJ4da0VWhgMBuzfvx8AjLbgAMD+/fsxadIkJkac3rx50+hrd/PmTZkdLdQyx16qV2EseQdUBchMpVehCse+vLwcY8aMgSAIWLlypUl+55w5czBr1izxcWFhIZo1a2aS380KrG+eyEVH6tRLITcA2hcn8vsXL16Mo0ePKsrbn332WZkdTfbs2QOg6gZ1+/Zt2XO3b98Wb0579uxhVpGaJUh1UaNGjXD37l1ZoMTMzAyNGjXC7du3mSvFZ5mWLVvi5MmTdbLj1B21OKFqm9rBclsL5/HCcmLk/Pnz9Wr3uDh16tQjA9/5+flMVDxKHTw/Pz/4+vrC2toaDx48QEpKCk6cOMGEIKG0CqL6BJyUlBRmqiDUMsdeqkNRkxAlseOOPR469RcvXsSRI0fEbD1Q5WSSyBKhoqICd+/ehZubm2gj7XMBID4mNsawsrKClZVVff0ZqkMNm6e2bduKX5ubm6OiosLoY6kdDTp37gxbW1scOnQIOp0OAwcORJMmTXDlyhUcPHgQhw4dgq2tLfWLKABcuXJF/Nrc3FzWK2RhYSFepKR2nEdz+/btGm/0nL9Gdna2+LW5uTkCAgLQvn17nDlzBomJieJ5L7WjCesBUimsO6Fq0ivh1B/NmjXD6dOn62THCqyf93WtEKRdSVh9j/937R4nGRkZAIAOHTrgo48+Uqi4T58+HTk5OcjIyKDq2EvJyMiQ7UOI38NKFYQa5thX9zH/rt3fhaoq/qMgTv25c+dw6NAhNGzYUPa8v78/9Hq9LHtz5MgRVFZWimXY/v7+SExMRHl5uWhz8OBBtGvXrsYy/H860s1TdHQ0ysrKkJycjLKyMkRHR8Pf3x8rV66UZZ5pIJ1sYG9vj9mzZ2P79u2YPXs27O3tjdrRwGAwiGXBRUVF2LZtG5YsWYJt27ahqKgIQFXZMO3XE4DYH9SkSRP897//lSlk79mzB02aNJHZcWrH2KbI2GtHe/OkJqRBpYqKChw5cgQrVqzAkSNHZME9FoJPapmGoRbUpJLMqT+qn8tdu3bFwIEDFUEoFs554OF5X70/PT8/n5nzvnqp+N+1e1xkZWXVq93jhDhuROtLipmZGfr37y+zo8VfqYKgDZlj7+PjgxkzZuD999/HjBkz4OPjg61btzJ3Ljk4OKBNmzZo1qwZ2rRpI0tGm+pcopqxLyoqwh9//CE+zsvLQ2ZmJpydneHu7o7Ro0cjPT0de/bsgcFgECOfzs7OsLS0xNNPP40hQ4Zg0qRJWLVqFcrLyzF9+nSEhISII3pefvllREdHIzw8HO+//z5Onz6NZcuWYcmSJVT+ZjUgnb1tTPCLldnbZ86cAVDVByoIAhYtWiQ+5+7uDnd3d1y7dk20o8WuXbvEE7qm/pvKykrs2rWLenk7GXNDhCql729FRQUKCgpkdpzaITfPnj17iqVjBDMzM/To0QNpaWm8x/4voJYNKc8u1z9qUknm1B9nz56VPSaZ0UfZ0UAqpNW1a1ejVVqmFNJSO2py7Il+16FDh/D8888resIPHz4ss6MFabP09fVFZGQk1qxZIwpPT5o0CTExMUhJSVG0Y5qa6knG06dPixXZI0aMQFRUFBP3UGkAr7CwsEaxdVMJUVJ17NPS0sT+YgBiT/uECRPw4YcfYvfu3QAAb29v2fcdPXoU/fr1AwBs2bIF06dPR//+/WFmZoYXX3wRn332mWjr6OiIAwcOYNq0aejevTsaNWqEyMhIPuquFqSzt3v16qUoxWdt9vbTTz+NiIgIRS/bggULcO3aNdrLE7MIPXr0wIIFC5CQkCCK5wUGBmLu3LlIS0tjIttALo5FRUUIDg5GWFgY/P39kZycjPXr14sVBnxDUjdI7/wvv/wi9oYRLCwskJaWJrNjBZZ7Q5966in8+uuvdbKjiZqmYagFtagkc+oXtQTzgId91s2bN0dubq4smOvi4oLmzZvj0qVL1Pus1YJaWgaAqkqSzZs3IycnBxERERg3bpy4dybHiR1NSCLBYDAgMDBQPG/S0tKwe/dudOvWTWZHC2mScdy4cYrxgSNHjmQiycjahAmqjn2/fv1qLemtS7mvs7Mzvvnmm1ptOnfujGPHjv3l9f1Tkc7eNpZpmjFjBhOzt7t164bff/8dP/30E/71r3/JTuyysjKxRIdcpGhjY2ODiRMnyiogduzYQd0BkeLt7Y1NmzaJYm/SKgip2Fv1YBvHOFIHw9bWFkFBQTJVfOLos+SIsN4b2rNnzzo59j179jTBampGjdlllgM6gFwlOTIyUhEkZUUlmVO/NGrUSAzSm5mZwd3dXXzu2rVromPSqFEjKuuTkpmZCaBqEkp1naaCggKxF5w79nWjrr3eLPSEe3t7w8nJSWwRlvaEk9G2Tk5O1PdPZO+elpamCDqT41I7WkiTjNW5ceOGeJz2PdTa2rpe7f4uzIvncTg10bNnT8TFxcFgMGDYsGEIDg7GsGHDsHfvXsTHx4s967Q3+E8//TR27tyJY8eOKeaW3717Fz///LNoRxtyY7p9+zZ8fHxgbW2Ne/fuwd7eHg8ePEBqaip0Oh31G5NaIBtOS0tL6PV6hSo+mb/MQqYJeNgbSpRySQlpamoqM6KZxcXF9Wr3uJBml9u3b69wmFnLLrMe0AEeqiRHRkZiyJAhsueWL18OAIiJiWEqGMH5+3Tp0kV07CsrK2usbuvSpYspl2UUaUKqNoVs2jo1Go2mTmug7TDXVXuIBY0irVaLWbNmITIyUiY8DEB8PGvWLOrXJ6nD3qBBA7Rq1QqCIECj0SAvL08sJaft2Et/v6OjI7p27SruSTIyMsTWUNrrLCkpqVe7vwt37DkKSPnN6dOnMXfuXISGhspK8Yk6Le0yHWl0tKKiAt9++y2+/fZbmQ0L0VFpFqGyshIvvfSSLABhzI4W0htTamqqUZuZM2dSvzGpBSI+U1ZWBicnJwwaNEjM2B84cEA8h06dOkU9AEX62Tw9PXH+/HlZtsHFxQWenp5M9LOpRSWZZJc/++wz5Ofny9bj4uICnU7HTHZZDQEdAilnre6YkMc5OTnMrJVTP1y+fLle7R4nUv0Z4ozY2NigpKRE5ozQ1qlRi2MvFb6uDzvOwxGGZmZmKCwsVFTAmZmZobKyEufPn6e6LyFiuBqNBvfu3cOPP/4oPmdmZiZ+hqWiuRzu2HOMQDJIr732GhISEjBt2jTxOXd3d7z22mtYs2YN9UyT1Am1sLCQXdjJYxaioyQba2FhgcrKSlkAQqvVimtlJWtLqN4TXv0x59GQ97Rp06YoKyuTZexdXV3RtGlTXL58mYn3nvSzXb9+vdYSUtr9bGQzamlpCUdHR9y6dUt8zsXFBXq9HmVlZdQzYlqtFv369UNcXJyi3PH27du4efMmQkJCqF+f1BLQAaoCZPHx8UavRYIgwMrKCvHx8QgLC1NUR3HUS13PZdrnPACZcFZBQYHMGanJjgZarbZO9x3a53xdfz/tdQJV19JPPvkEQFVQZ/DgwWIg/4cffkBBQQE++eQT6tdSkpyr6f0nx+syYvJxcujQIQBV57VWq8Wzzz6Ldu3a4ezZs/jpp59Eh/7QoUPiJDQaSKdw1Yfd34U79pRguZeRZJqys7OxadMmUYnS2dkZHTt2RFRUFDOZpoCAAISEhMgcJqDqwhQSEsJE5oZkbcvLy+Hn54cmTZqgtLQUVlZWuHLliiiuw1LWtlevXkZ7WGNiYpjZ5KsBMurk3r17io3czZs3xeelI1FoIVXA7datm0L4hzh8tJVySQaprKwMbdq0wcsvvyw6ekR1WmpHC4PBgP379wN4mAEhkMf79+/HpEmTqJ5L0oBOdW7evMlMQAeomjBiMBhqLLslzj4LE0Y49QdrG+faOHfuXL3aPS7qmuWknQ21tLSsUwkzC4G89PR03L9/H/b29tixYwfMzR+6WJMnT8aoUaNw7949pKenU93rWVhY1Kvd4+L+/fviOioqKnD48GFxsoBGoxGTYsSOFk8//XSdgiCmarfljj0FEhMT8cUXXyhKM6dPn86EI0r6GKOiohAVFYXQ0FD4+/sjLy8PUVFRSE5ORnR0NBOOHZlx6efnJ/aEkxLSrVu3okOHDky8pgAwceJE7Nu3T6aS6+bmhgkTJmDjxo0UV/YQqQqpMaG/wMBAJlRI1QKpaiHll1IEQRCP065+AR4K0LRp0waxsbEy0czY2Fi89tpryM3NpS5UIx0VlJGRITufpJUGtEcKEXXs6tVEQNVm2cLCAnq9nrqIVl0DNbQDOgDw559/1qsdRx0YE/j6O3aPE6kz5OLiotjnkce0nSa1VEGoaSLCgQMHAACvvvqqzKkHAHNzc0ycOBGff/45Dhw4QNWxZ60n/FGUl5crqrQsLS2ZqSCtfm9s1KiRqJ8kfc5U91Du2JuYxMREREZGKo7fvHkTkZGRiImJYcIRDQgIQHR0NFasWKEoxWel37KmOdEAEBQUxMycaKI0f+TIEaNZxKNHj4p2tJGqkBoT+mNFhVQt1FXUhbb4C1BVVQBAUYZPIIquxI4W3bp1w5YtWwDULk5FexoGUccmTr2Pjw9eeeUVfP3110hNTRWP03bs79y5I35dU/lodTtaSJ0kjUaD5557TizNPHLkiOiE0NZX4NQvtra29Wr3OJHe41u3bo2XXnpJVlFEPpu0K4qqVxHVZkcTKyurOt1zarpvmRLiCEunNkhxc3OT2dGirtdy2tf8hg0bil/XJEZY3Y4GRNuD9PxXd+DJcVNpgHDH3oQYDAb8+9//BgDodDqEh4eLM8LXrVuH/Px8/Pvf/6buiBICAgLQu3dvZlsG1DIn2tvbG7a2trh06RJ0Oh3eeecd2ft+/fp12NraMuHYSx3M2i6kLDiiaoCI1NTFjnYbBtlokhm8Pj4+4oY0NTVVJlpGE29v70fqPVhZWVE/n6Sbt71794pOx6effori4mIMGzZMYUeDs2fPAqh6X62trRU6EIWFhRAEQbSjiVQNvVGjRrLSzMaNG4t6CzWppnPUScuWLevV7nHSuHFj8ev09HRZRZE0WC61o4GVlVWdrj20HWYyYrcudrTp1KkTfv75Z6xduxa+vr6yfWllZSXWrVsn2nEejfT1q145In1MO/hE9kREC8DNzU105q9fvy62jplq78QdexOSnp6O4uJi2NvbIz4+XizVGTFiBIYMGcJM/40UrVbLbMm1muZEW1paori4GPfv38fChQtlxwH6N0+CNIJfU/Cpuh2nZrKyssSvtVotunTpgoYNG+LOnTv49ddfxQt+VlYWQkJCaC0TwMPKkkaNGiE1NVUmoqbVasUNFm2H2WAwiNluUu5GII/Ly8thMBioBiHJLOAGDRoo5tdaW1vDzs4O9+/fF+1oceHCBQBVm5KysjJF4JFsoIgdTaTX8vz8fDz33HNo3749zpw5g8TERKN2HPWTm5srfu3g4IBu3bqJbXfp6emifonUjhZNmzYVv67eny59LLWjga2tbZ0ce9pVEGpqwxg1ahS+/PJLnD9/HhEREQqdmtzcXJiZmWHUqFFU11lXPQLaugVqqdTx9PTE77//DqCq5UIaWLayshL3eZ6eniZZD3fsTYha+m+ksCzyJ50T7eXlpXielTnRp06dgl6vx6RJk7B7927cuHFDfM7Z2RkjRozA2rVrqVcWAFU9y4R27dqhVatWsLGxQatWrdCuXTsx+5CRkcHMZ5RlyCx1rVYLg8GA9PR02fPkOO2Z60CVY29nZ4fbt28bLcm+ffs27OzsqDv2u3btQmVlJXr06KF4PSsqKtCjRw+kpaVRF1Ajgj5FRUVGN3nkedrCP2RTZGlpCUtLS1ng0c3NTQyW0N48AVUBEfJ6VVRU4MiRIzhy5IhRO86Tg7S1orCwsEaleRZaMIKCgrBq1SpoNBqFY19ZWQlzc3MIgoCgoCBKK6zCy8tLFgyrzY4manLsLS0tMWbMGMTFxSElJUUWHCfrGzNmDHWHua5BWtrBXOnEG3Nzc3Tq1ElMjGRlZYnnl9SOBtLWi+rCrtLHNbVo1DfcsTchJDpK+myqw0r/DSExMRErVqyQCai5ubnhjTfeYKLHnqj3b9myRdFjX1lZiS1btjCh3k+yR6NGjUJISIgiUFJaWoq1a9cykWUi5bZELLG6voKfnx9OnDjBRFmuGiClVwaDodbxgbTL2wlE0KmkpERWkk0qSmgLPgHA1atXAQAnT56Er6+vYspESkqKzI4Wrq6u4obj5MmTsk2edGNHW+SvdevWyM7ORllZGVq2bImQkBDxs3nixAnx+t+6dWuq6wSAtm3b1qnvs23btiZYDcdUuLm54fTp03BxcYFGo5EFx93c3GAwGHDr1q0a91amxNLSEn5+fkhKSoKZmRlat24tVhfk5uaioqICvXv3pu7cqSVr26JFizopjrdo0cIEq3k0U6ZMAQBs3bpV8VxISIj4PE0ePHhQr3aPC1IZShIg0sSTRqMRj9OuIJW2itRWpcPF855ASP/NunXr4Ofnx3T/TWJiIqKiouDr64vevXujrKwMlpaWuHLlCqKiopgQ0JOq98+dOxehoaFiRmzLli3MqPdXryyonpVnpbIAeJjp0uv1RkcdvvnmmzI7Tu1IX6fKykr0799fLNuSZktYeD1rqyzR6XQIDAzEmjVrqFeWkM27i4sLLly4oJgy4eLighs3blDf5MfGxopZudr0KmJjY026rup07NgRCQkJAIDU1FQxMALIA04dO3Y0+dqq07dvX9n7XZsd56/BUnXegwcPcOnSJfGxl5cXDh06hJs3b6Jjx4547rnnxEqSrKws0fHz8vISS2IJzZs3N+n11WAw4Pz589DpdMjPz8cff/whe16n0yE3N5d6q1Bdr4+0r6MNGjQQv7a3t0fr1q0hCAI0Gg1yc3NFYT2pHW06dOgg3ocIjRs3RocOHSiu6iFWVlZ1qhKk3SJKfCSDwaCYLmNubi4+pl2t4eHhAaBqopAxXSVynNg9brhjb0Kk/TcffPABxo8fLzqimzZtYqb/hqjNu7u7IzU1VTF/2d3dnQm1eUAd6v1qqSwAqtaalJSE3377DZGRkRg3bpyYvY+MjMSZM2dEO86jkaq1lpeXy8S+arKjBakYuXv3rqK07ebNm7LnaUIyxzdu3IC/vz/mzZsnK3EnmXHaGWZHR0c4OzuLr1eLFi3Qq1cvHD9+HBcvXgRQFcxzdHSkuUy4uLiIX9cmUCS1o0VRUVG92nGqYK0679KlS5g8ebLR506fPl1jBnfZsmWKY6tXrzZZbyvwUNQXMK4BQnRqaAdIu3btis2bNwNQKuRLH9NuD5Teb+7du4dff/31kXY0IUkxf39/REZGypJNrCTFGjZsWCfHnva+RFrNVlsmnHbVG2m/uXv3LhISErBu3TpcvnwZTZs2RXh4OF555RVotVqTtd9wx96ESPtvUlNTZZkHlvpvpDcmYwJqpMSV9o2JwLp6v1oqC4CHwafKykqkp6fLyodJ9JaF4JNasLe3r1e7xwmpGNmxY4fR837Hjh0yO1ro9Xrx699++w25ublwdXVFbm4ufvvtN6N2NDAYDLC0tBTL2i9evCg69EDV+USEdWie+yTwWFpaKjodUnQ6HaytrZkI5pHRe/Vlx5E7ItIgGU1HpHnz5li9erXsWHp6OlatWlXj90yZMsXoiMvmzZvX+/pqQ1pu2717d4W2Brmnmqostya8vb3h5OQEvV4PCwsLWZsYeezk5ERdU4UEFBs2bIj8/HxFAEKn0+HOnTtMBB5rGsHs5eWFBQsWMDOCua4VLLQrCb29vcXRtrUFnWl/Ri0tLREcHIy4uDiMHz8egwYNQp8+fXD16lWMHz8eer0eISEhJvPtuGNvYkh/TXx8vOy4RqNhpv+GCNA4OTkhLi4OOTk5yMzMRJMmTRAXF4exY8dCr9czIVRDYFm9H1BHZQEgDz7VVD7MQvBJLdS1d56FHvv27dsDqNrUbd26VXyPR4wYgUGDBmH48OEoLy8X7WhBHPaePXsiPT0dixYtEp/TarWieB5tx54ESCdNmoTvvvtO1hvesGFDjBo1ignRTK1Wi379+iEuLs5o5i4/Px8hISFMBB6vXbsmfm1vbw8XFxexTezmzZtiWa7UjlMzrDoi1tbWiiy7p6cnPDw88MUXX8j2Hq6urpg2bRoz91CSOW7Tpg1iYmJw+vRpJCcnw9nZGTExMXj99deRm5tLPcOs1Woxa9YsREZG1mgza9Ys6ud9t27dsGXLFty5cwe+vr5o2rSpqKly+fJlsXXIWFDH1KhlBLNaEg5qEk6cMmUK/vzzTyQlJcn0iQCgd+/eJvXtuGNPgSlTpiAsLAy7du3C1atX4eHhgaCgIGacJZL16tixIyZMmKAoz/Py8hLLtQcPHkxrmTJY6g+siYCAAPj5+TH7vhPIBWjbtm2KWaHBwcFMBJ/UArkxVneYCOQ47RsoAOzZswdAVcvAhx9+qKgsIf1se/bsoao27+TkBKDqtdu9ezfWrFkjlr1NmjQJMTExMjtakI37mjVr0KtXL8XruXbtWpkdLQwGA/bv3w8ANWbu9u/fj0mTJlG/pv75558Aqt77oqIi0ZEHqoJj5HwidpzaUYsjQiDVeXv37sWiRYswe/ZsDBs2jPrnUgoZvVdaWorx48cr9k9kIhKxo0lAQABiYmKwfPlyhaYKKyLJ0sqCjIwMmQYI2TvpdDrqWVtAPSOYjU2UqcmOJtKqluo99tI2F9rVL0BV5dPx48eNtt8cP34ciYmJJjufuGNPCa1Wi6eeegrOzs5wdnZm6sZE+Pnnn432sCYlJdFemgzW+gNrwtg6d+zYwdw6gSrxl8aNG8tu9o0aNWJG/EUtEMejsrISjo6O6Nq1q6iQnJGRIZYMSx0UWpAWm3fffRebNm1SVJa88847WLhwIXW1+UaNGgEAUlJS8MILL4iOaFpaGvbt2yc+Jna0IIGFTp06Gc2GzpgxA1lZWdQDEJmZmdDr9WjevDlKSkpk+goODg6wsbHBpUuXkJmZie7du1Nc6UOV5srKSuh0OgwcOFAcyXjw4EGxlYC2mrNaUIsjIkWr1aJdu3YAqkaysrZ3Iuf55cuXodPp8M4778hamsj9n4UsI6COVsZZs2YhKipK8ZxGo4FGo8HMmTOZWK9aRjDrdDrZ46ZNm8Le3h737t3D5cuXa7QzNSTJ2KdPH5w7d04xKvqpp57Czz//TD3JaDAYsHjxYgiCgG7dusHX11dswUtJScGJEyewZMkSk1U+cceeAqw7onWdtWiqmYy1wWJ/oJrXCahD/EWNlJSUyOYv01acrQ5RbBUEAVu2bFFs9Pbu3Suzo0Xnzp3FDE71KgjyWKfTMdETrgYyMzMBVAmWVf9MFhYWio4+C469i4uLuPEsLi42OpKR2HEejVocETVBrjs2NjYwNzfHwoULxedcXFxgY2ODkpISpq5PampllO6bnZ2dMXXqVGb2I2oRSpZq0QCQOfPV7YYOHWqKJdWKsdYqQRCYabmSBseNTepp3ry5SYPj3LE3MWpw8IiatI2NDc6fPy/L3Lm6uoo3Jtqq06z2B6p1nWpbqxpwcHAAUHXeaDQa2aZEp9NBEATcuHFDtKMJUXZdt24dhgwZItvoVVRUYP369SZVdq0LxsqHAaXQDg1Ij//p06eNimYSZW/aWgDS4Ei3bt1qFPuiPSsYqBIjIyWktakk0w5AqAW1OCJqgryGJSUlinOmoKBArChiJWOvFlivLADkQskRERHw8fERM7dEMJsFoWSi9+Lh4YGKigqFZoVWq8XVq1dlujA0aNKkCQDg/PnzcHJywpgxY8QKrQMHDogZfGJHCxIc//PPP436duQeairHnl9ZTEh1p8nLywu2trai0+Tv74+VK1fCYDBQXSfp/SopKVH0rty6dQslJSUyO1qQ/sDQ0NAa+wOvXbuGU6dOUVphFdJ1CoKAjIwMHD58GBkZGRAEgZl1Vl8ry6+pWiCZrhs3bqBly5aYMWMG3nvvPcyYMQMtW7YUb0wsZMSIsmt+fj6Cg4ORkJCA27dvIyEhQXactibEqVOnHukM6/V66p9R8p6+9tpryM3NxbRp0zBs2DBMmzYNeXl5eO2112R2tCD6Dra2tpg/f77svjR//nzY2trK7GgizcRXv09KH/OMfd0gjkhycjLmzp2L7OxsFBcXIzs7G3PnzkVycjKmTp1K3RFRE9Jrk7QnuPpj2gE9zuMhICAAY8eORWpqKpYtW4ZPP/0Uy5YtQ2pqKsaOHUs9cQdAvKZfvXoVbdq0ke1LWrduLbbbETtajBgxAkDV3lOv12Pbtm1YunQptm3bBr1eL+5RiR0tSACvQ4cORn27p59+Wmb3uOEZexOiFqEa6UazplLX6nY0UEt/IPn9V69exfz58xUtGOHh4TI7mqjlNVUL0j7vjIwMWYmWtHSYdj84QSqcWF1tnpWpHSTY6OvriwULFuD06dNiBqdjx46YO3cuUlJSqAvqkGxodnY2Nm3apFhnVFQUE9lQou9QXFyMefPmKTL2ZN4xCzoQdT1PWDmf1IBaJraoBaKZ0bx5czx48ECWDW3cuDGsrKxw6dIl6toaaoP1FlZCYmIitm7dCnNzc1mw0czMDFu3bkWHDh2or3fQoEE4ePCgWJUrHWssrcodNGgQxVUCZ86cAVCzQ0yOnzlzhqrPRCoua9J2IcdNVZnJHXsTohanycvLC1qtFg4ODtiyZQv++9//iiruw4cPR2hoKAoLC4325JkStfQHkt8fGxuLXr16Kcp0YmNjZXY0kb6m7du3V5S9sfKaqgXi3Dk6OkKv1yuUhx0dHVFYWEjduZPCunAiyXT17dsXFhYWiht6nz59kJKSQj0jJi3LjIqKQmhoKPz9/ZGXl4eoqCgkJyczUZYpDTKnp6fLNnnS4BMLpcPS8+nu3bsyob/GjRvD2dmZufNJDaihzPlJgIUWITVCWlj9/PwwduxYUYA2NTWVmRZWQC6iVlO1hilF1GqiW7dusLW1RXFxMaytrTFmzBi4u7vj2rVrOHjwIEpKSmBnZ0d9hGBdg/O0g/hkP3z+/HlEREQoguO5ubkyu8cNd+xNiFqcpuzsbBgMBuTn52PBggUIDQ3F8OHDkZeXhwULFojKw9nZ2VSjZGrpD5QGSqKiopCTkyPOtY2KikJISAgTgRLg4Wv62WefoaCgQBEdd3R0ZOI1VQtS587Pzw8hISFM9twR1CCcSDJdx44dw7BhwxTn/c8//yyzo4kasqHe3t7YtGkTmjdvjrKyMoUOhKWlJS5dusTEOClyPkVGRipaQgoKCnDr1i3ExMQwcz6pCdYF1NQCCSheunQJOp0Os2fPFlXx169fL2bwTR14fPDgAS5duvS3f07z5s1hbW1dDyuqG6SF1dPTE7m5uYrssqenJzO6P0REDai6doaHh8smIuTn5yM/P5+6EKlWq8W//vUvREZGIj8/XzF3HQDef/996q+nNMnp6+uLpk2boqysDJaWlrh8+bI4+pB2MlRaIVZbcNxUlWTcsTchanGayEkSERGBdevWKTakERERiI2NpX4yqUWoRBooGTlypGxONFkvsaO9sdJqtejXrx/i4uKMjuo5e/YsQkJCqL+makLq3Ekv+Cw5d4B6hBOl4+6MidKRmz0r5disZ0PJnOhLly6JGbHqo3pYmRNNIGOuHnWMwzE10lL8srIyWUuTu7u7qJBt6sDjpUuXMHny5L/9c1avXg1PT896WFHdIC2s169fV0ztkFbB0W5hBYCTJ08CABo0aID4+HiYm1e5WCNGjMCQIUPw/PPPo6ioCCdPnqQu8BkQEICYmBgsX75cVp3HUnsDGQfcoEEDzJ07F+vWrcO1a9fQtGlTzJ07Fy+99BKKiopEO1pIK8ny8/Nl7TdOTk5wcnIyaSUZd+xNiFqcJlIx4OHhYXTsFel7oV1ZADwUKtm2bZvMaTIzM2NGqKSuARDagRKgyrn78ccf0a5dO+j1etmoHjc3N7Rr1w4//fQTJk2aRP1zqjaql2GyoDIuRaoBQkQepec9Kxog0pto9akd5DPKWjk2y9lQ6ZxoYzoQLM2JlgafoqOjjeoWsBB84nAcHR2xePFixWd01qxZVNbTvHlzrF69usbnL168iNjYWERERKBFixa1/hxTIi2zrm1qB+1ybAA4e/YsAGDo0KGiU08wNzfHkCFDsH37dtGONqwHnUmrVVFREQIDA8XjaWlp2Llzp8KOFtUrM1966SWqSUbu2JsQtThN1UvcpRtSlkrcgarS4bi4OFnmGwAsLCwQFxfHhFAJicx36tSpxht9VlYWE6XDUueubdu22LVrl6ivEBQUhHPnzjHh3KkJNZS3A+oReVRbe4MaIFUlX3zxhSLbMG3aNCY+n4D8+mRMX4GV4BPnn4t0zKUxbQ1aYy6tra3rlGlv0aKFSTPyj4Lcb9q0aYOYmBicPn1abGWMiYnB66+/jtzcXOr3JQBii0JWVhYqKysVbWLkvTdlK8OjYDnoXNfpJixMQZHeQ6u3i5h6j8cdexMi3ZQY67E/c+YME5sS6cbZWKkrK6JPRKgEUGZDyePFixczlb2pfhFlLWtbm3O3Y8cOZpw7taCW8nZAXSKPamlvUBM5OTmKucW3b99GTk4OM6+nWgRopRgMBmYzYpz6RzrmMiEhQdHK+Nprr2HNmjVMXEfVABmrXFpaivHjxysCziQzTnv8MlCVFEtKSsKZM2eMiqiRalcWkmJqQKo71aNHD7Ro0QKlpaWwsrLCxYsXkZaWprCjyQ8//CALjANVo45/+OEH7tg/qUg3JcaiZCxtStQg+iQVKunevbvREi29Xk9dqEQawTcWKKEVwTeGmpw7NaCWEZeAukQeAfbLCKWw7tytWrVKbBGrLvgUFxcHAEyMOlTLJBSCWkZ0ceoPtYy5VAvkvnn58mWjLazk3GJhaseoUaPw5ZdforKyskYRNTMzM4waNYrWEhWwfG8iejlAVfk9ceSN2fXq1ctUyzJKREQEkpKSjD6XlJQkapOZAu7YmxC1bUpY3zhnZGQAqBrPFRsbK8uGxsbGYvr06cjJyUFGRgZVx15NEXypcxcTEyNGw728vBATE4Pg4GCmnDvWUVOGUU0ijwSDwYA//vhDbBchn1+WYN25KysrQ3x8PHQ6Hb7++musW7cOP/74I5o2bYqvv/4ar7zyCuLj4xEWFqZQojc10jaxyMhIJCQkiO99YGAgc21ipAWneoCUpRYcTv0irXiMjIwURX0vXLiAbdu28Vahvwg5l21sbGBpaaloYSUz11k45y0tLTFmzBjExcXJ7p9A1XUWAMaMGUP9Okpg/d505coV8WszMzNZhav0sdSOBiUlJaJTb25ujoqKCvE58jgpKQklJSWwsbF57Ovhjr0JUct4Niks998QJc8BAwYYzYb2798fOTk5MsVPGqgpgk+cO71ej8jISEV1gV6vhyAITDl3LKOmYJ6aRB6BqixzfHw8DAaD7FhwcDAT2WVAHc7drl27YDAY4OzsbFSgqHXr1sjPz8euXbsQHBxMcaXycXdDhgyRPbd8+XIAYGLcnZpacDj1DxH1jY+Pl2VttVotM6K+aoGcOyUlJejUqRPatWuHe/fuwd7eXpxlL7WjzZQpU/Dnn38qsreCIKB379783vQX8PDwAFC1P7K0tJQFIFxcXFBaWor8/HzRjhYrV64Uv+7Zs2eNAo8rV640iXgmG2fCPwSyKUlOTsbcuXORnZ2N4uJiZGdnY+7cuUhOTsbUqVP5jb6OuLq6AgAOHTqk6FWvrKzE4cOHZXa0kL7vkZGRuHDhAkpLS3HhwgVERkYy9b4Tp+2DDz5Abm4upk2bhmHDhmHatGnIy8vDBx98ILPj1I40mGfsM8pSME8q8rhnzx4sWbIE8+bNw5IlS7Bnzx506tRJZkcTUjru4OCAd955Bzt27MA777wDBwcHxMXFYdWqVbSXqHDuvLy8YGtrKzp3/v7+WLlypSwwQYOrV68CAM6fPw9zc3P0798fb7zxBvr37w9zc3Pk5ubK7GiTk5Pzt543BaQFJzQ0tMYWnGvXruHUqVOUVsh5nCQmJmLr1q3o2bMnXnzxRQQGBuLFF19Ez549sXXrViQmJtJeomqQtiimpqYiMTERGRkZSExMFJ366nY0SUxMRFJSkmI0n5WVFZKSkph479Vyb+rduzeAqrF3X331lWxP8tVXX4m6CsSOFkQ7wdPTE7GxsbLXMzY2Fm3btpXZPW54xt7EqKF3XS107doVmzdvRk5ODubMmYOmTZuKwhqXL18WN3gsZJbVEsFX06hDNaAWIcrqsCzyKC0dNzYrODg4mInScbXoKzRu3BhA1Rx4Z2dnHD58WAyKuri44NatWxAEQbSjSVlZGbZt2wYA8PHxQbNmzcRr/p9//onU1FRs27aN+nuvphYcTv1CnCZPT0/k5eXJxke6urrC09OTV2v8BaR7DY1GIxNKlj5mYU8iFXSuaTQfC4LOark3FRUVAah6XQMDAxEQEID27dsjKSkJ7777rhh4IHa0adq0qdHXs2nTpjh37pzJ1sEdewqw3rsuhWVhDW9vbzg5OUGv1yMlJUUmtEFwcnKCt7e36RdXDRLB9/Pzg4+PD6ytrcUysq1btzIxlg9Q16hDtUCCecuXL1fMXGcpmEcyHllZWUaDEFlZWTI7WpDS8fDwcGg0GmRkZMiuT2FhYVi0aBH10nG1OHekvFEQBBQUFMieKygoEDfO0jJIWnz//feorKyEq6srLl26JMvYubm5wdXVFTdu3MD333+PsWPHUlunmlpwOPULcZquX7+OXr16KUacHj9+XLRjIenAOu3btwdQNcJ49+7dOHPmjHi9b9++PUaOHIny8nLRjiZE0LlTp05GdZ9mzJiBrKws6oLOark3ketjixYtcPHiRRw5cgRHjhwRn2/evDkuXbpE/TratWtX/P777/jpp59QVlYmCyqXlZWJVRqmOt+5Y08JlnvXCawLa2i1Wnh5edWoRAmACTGtmvotASAoKIipfku1ZpjVgEajob2EWiE3x0mTJtUo8rh27VrqN1FSEq7RaBAaGqq4Po0bN05mRwupc9e2bVvs2rVLFHoLCgpixrmTvk6VlZV46aWXMGzYMOzduxfbt283akcLEly6efOm0d5QUg2VlZVF1bFXo54Op364ffs2AMDX19eovsKcOXOQkpIi2nFqZ8+ePQCA8vJyzJ8/H6GhofD390deXh7mz5+P8vJy0Y62BkhmZiYA4NVXXzWauZ04cSJmz55N3bFXS+Cxc+fOcHJywsWLF8WEWHV9BScnJ+rXUR8fH2zduhUGgwHDhw/H6NGjZfdQUlng4+NjkvVwx55jFDUIa5SVleHEiROws7NDgwYNZCJ5rq6uKCoqwokTJxQRNFOjlrIngloyzGpBDecSoB6RRyKU85///EfRx5ifny+qJtMW1CGvZ2xsLK5fvy5rZ1i5ciXc3NyYeD3JtdHGxgalpaX49ttv8e233wKouj4R1WkWlJytra0BAC1btkR0dDROnz4tjmSMjo7G5MmTkZeXJ9rRggdI/7mQiqa+ffsavd/36dMHKSkp1Cuf1AIJKL777rvYtGmTIuD8zjvvYOHChUwEHgnSdgEWUWPgkbStSlsbADYSJtLq4fLyctk9lKDT6UxWPcwde44CtSj6kpLcqVOnYujQoYqWgb179/KS3L8BCxdMtaOWcwmQOyNRUVGyzEhUVBQzzsiIESNEBXRvb2+88sor4s3+66+/FltyRowYQXOZ0Gq1aNOmDZKSkmBubo5+/fqhffv2OHPmDBITE3H16lUm3vcWLVrg+PHjKCsrw/bt27Fo0SKxsmD27NnitbNFixZU1wkATz31FA4dOoQrV65g3LhximBufn6+aEcbrqfzz4SIix47dgzDhg1TOE0///yzzI5TOyRAKwiCUd2fvXv3yuxo4u3tjU2bNmHDhg3o3LmzIji+YcMG0Y4m0nt9RESEOJKxtLQUqampzIxkPHXqFPR6PSZNmoTdu3crEk2kipB2Ukyr1WLWrFmIioqCVqtVjLszGAyYOXOmyV5P7thzFKglw0witP7+/kZbG/z9/WV2tFBL2RNBLRlmNaCWc4mgBmdEqnr++++/Izc3F66ursjNzcXvv/8us6NZ7kgqiqysrFBeXi7rD9RqtbCysmKiooiIDxoMBrzwwgvi8by8PFmbE7GjScOGDQFUvbbVx5hKHxM72qhJT4dTPzRq1AhAlYK7sWoNogtB7Di1ExQUhFWrVmHdunUYOHCg7LmKigqsX78eWq0WQUFBlFb4EJK5zcrKwvDhw8XZ9UBVZVRZWZlJM7e1oQZBZ5LscnFxUTwnCII48YqFpJh07yRtD2zcuDGmTp1q0teT/p36HwrLonTSDLOxdbKSYSYR2uTkZKPZOXKxoh3JVVPZk5oyzGpAjdUarDsjpI+xX79+OHbsGBYtWiQ+p9Vq8cwzz+Cnn36i3sdIKooMBoO4qZOus7S0VLSjWVFEskx1saNNXYOfrARJAXXo6XDqD3K/d3R0FEfGEtzd3eHp6YnCwkIm7vdqwNLSEsHBwYiLi8OQIUOMquKHhIQw0Sqk1WoxZMgQxMXFybK2AMTHgwcPZuJeWl3QWZqxZ0XQmVzHY2NjFW13er0esbGxMjvaBAQEwM/PT6GnY+rPJnfsKcC6KB05Sb7//nskJCQo1hkYGCizo4U0kjtkyBBZRomlSK6a+i3VlmFmHWm1Rvv27RXOMmvVGgQ1OCMjR47EBx98oLiJZmVl4aeffqK9PFy5ckX8unv37kZHH1W3o0GHDh3Er3v06AFbW1tRoKi4uBhpaWkKO1oQnQKtVmt0xjI5ztJ4Rs4/C+n93s/PD2PHjmWyzFlNkGtP9d518piFaxNQlRj58ccf0a5dO+j1elkVkYuLCxwdHfHTTz9h0qRJVN9/aQKHaJXcvXsX7u7uGDFiBKKiophI4Hh5ecHMzAyVlZXw9vaGn5+feC6dOHECKSkpMDMzM1oJSwNjvt2OHTtM7ttxx97EqKHMmShRrlmzRrHOzZs3Y82aNUwoUUojucHBwQgLC4O/vz+Sk5Oxfv165OfnMxPJVYsgnRozzCxDsjefffaZ4kbv6uoKJycnZqo11ALJMH/11VdYtmyZLNtdWVnJTB8jcS6bNGmCmJgYmdBbTEwMJkyYgKtXr1J3QonqNFClJk8qCQDIsiQsqE6fOnUKAIw69dLjp06dQs+ePU22Lg5HirQsV1rmzFJLk1qQzob39fVF06ZNxfaly5cvIyUlhYnZ8MDDxEhgYCB2794te04QBAQEBGDNmjXUEyPSdRrTKgkMDMTx48eprzMrK0u8P2ZkZMhGWpN9fWVlJbKysqhW5wEPfbvqLWt37twxuW/HHXsT8iSVObMirDZlyhQAQHx8vKIkNyQkRHyeVVhTT1WbHgDraLVa9OvXD3FxcdDpdJg9e7Ys+HT27FmEhIQwf76zhLSPMSIiQpEJz8rKgpOTE3XHvkGDBgCqxl+9/PLLuHXrlvhc48aNUVhYKLOjhVR1euPGjbh586b4nJOTE8aPH8+M6rTUoa/e3iB9XJPjz+GYioCAAHTr1g0ff/yxWFE0Z84c6ue72pDOhv/4448VrYyszIYHHiY81q5dCz8/P4SEhMDa2loczbZ27VqZHS3I71+zZo0i8ZWfn8/MOknbHQDZtb76Y9rvPQk+CYIgjl8kkMdLliwxmW/HHXsTIi1zFgQBGRkZsrJcVsqcpUqUtc2zpr1OwpQpUxAWFka9r6U2SDSv+pr0ej0zlRqAuvQA1IC0NK+goEAWfHJ3d0e7du2YKM2rDssaIESBNjIyEunp6bKMGMkwz5o1i/p6ye8vLS2VOfUAZI9pr5NokBw9elQxW/vWrVv48ccfZXY0uXfvHoCqsXe7du1CTk6O+Bnt0KEDRo4cidLSUtGOw6FFRESETHwyLy8PI0aMQO/evcXeYM6jkc6Gr6ioUOzzWJkNDzycdNCsWTPk5ubK7k2urq5o1qwZLl26RH0igvT31+Yw016nNPFFqjVKS0thZWUlVmtUt6MBCT4BD3UfCORxfn6+yT6j3LE3IST6dfXqVcyfP1/Rux4eHi6zowX5/aNGjUJISIhig19aWoq1a9dSX6dakEbzunXrZrTX1pTRvNpQkx6AGpAG84z12J85c4aJYJ6UxMRELF++XFGeN23aNCaCT0BVNiwkJARbt26VHS8rK0NISAgT66xr8It2kCwoKAgrVqxAWloadDodwsPDxaqSdevWIS0tDRqNhrpWCfDw3vTgwQNERUXB19cXVlZWuHDhArZu3Sq2EfB7E4cm1Z16KUlJSYiIiODO/V8kISEB7777rqwaZ9WqVejTpw/FVRnn0qVL8Pf3R2RkpFFdFdpI27+MXfPJ2FDabWJ2dnYAqqqxLl68KCvFd3NzE6u0iB0tiA4NAPj4+BjVAiB23LF/wpAqPPbq1UvRY8+KwmP1cuzqDgdr5dirVq1CfHy84oIfHBzMRCm+tJQsNjZW1oIRGxvLVCkZoI6RZ2pBqllgTJCONc2CxMREREZGGlWgjYyMRExMDBPvf2JiIuLi4sSbJ8HS0hJxcXFMKPpKqS3bwBKCICj+sYStra349YkTJ3DixIlH2nE4pqSkpER06n19ffHKK6+I+7yvv/4aKSkpSEpKQklJCWxsbCivln2IpsrRo0eh0+kwcOBAeHh44OrVqzh48KAolEq79Qqo+32c9v0+IyND/Lpdu3Zo1aoVbGxs0KpVK7Rr1068rmZkZFDVKrl//z6AqoB9aWkpxowZI3vvSXUBsaPF2bNnAVRVOBgLQDg5OUGv14t2jxvu2JsQLy8vaLVaODg4ICYmRhRZ8PLyQkxMDIKDg1FYWEhd4VFN5dirVq0S+5erRx3j4uIAgLpzT0rJJk6caFRpnqVSMgLrI8/Ugpo0C6QiRTVVlrAgUiRdZ02wsE4i9AZUXQOkN3tp4IS20NuuXbsgCAJ69uyJ9PR0hVZJjx49kJaWRn0sHwAMGjQIBw8eBABYWFjI+hmljwcNGkRlfRzOypUrAVSJZkZHRyMhIQGHDh2Ch4cHoqOjERYWhqtXr2LlypWYNWsW5dWyj1TxvqioCNu2bRMfW1hYGLWjBSnHHjlyJFJSUhRCyYGBgUhISBDtaEEcTH9/f+Tl5SkSOH5+fjhx4oTJHNGakAaW8/PzZe99TXY0IAEGvV6vmISRkpIiBkqqtz08Lrhjb0Kys7NhMBig1+sxb948xexIvV4PQRCQnZ1NtSxXLeXYZWVliI+Ph06nQ3x8vBgoGTFiBIYMGYLg4GDEx8cjLCyMiX57jUZjtHeZVdQw8ox1pEEy6VgZZ2dndOzYkakgmbSyxJiK+6xZs5ioLJH2s9UUgNDr9dTXSZg4cSL27dsna23Q6XQYPHgwNm7cSHFlVRBRvH/961+wt7dX9LAWFhZi9OjRTIjndenSRexZtLOzQ8uWLVFZWQkzMzNcuHABer0eGo0GXbp0ob1Uzj8U4gy1aNECw4YNk5Uzr1y5Er6+vrh69Sp1p0ktSKd2VBfFlD5mYWoH6Un/7bffjApMnzlzRmZHC2trawBVzvKmTZsU+5Lp06fL7Gjh4OAgfl1bIFdqRwNXV1ecPn0aAHDy5ElZJZk0+OTq6mqS9XDH3oSQ8psXXngBO3fulPXbaLVavPDCC9ixYwf1Mh1AHeXYu3btgsFgQHh4uGLEhLm5OcLCwrBo0SLqmSZSSrZ06VKUlpYqepdJ9o6FUjJO/SINko0YMUIxSqysrIyJIBnwsLKke/fuGD9+vEIDZPDgwUw49qSMsEOHDkZbW6ZPn46cnBxkZGRQXSc570+ePInNmzcrNk8kW0f7vCeieMnJyRgxYoTiWknuUyyI52VnZ4vZGRK8qQ4LwXHOPxeien/8+HFFhR7w8Hzi6vh148qVKwCANm3a4P79+7L7kouLC+zs7HD+/HnRjiaNGjUCAJw7dw46nQ7vvPOOrIqUrJ3Y0aJz585ISkrCmTNnEBkZiXHjxonZ+8jISDHoRDvhUNcACO1AyVNPPYXDhw8DQI2q+MTOFHDH3oSQctvvvvsOfn5+8PHxkY3C+O6772R2LFC9xIW2mIYUkkHy9/c3+jw5TjvT5O3tDVtbW1y6dMnoyLMbN27Azs6O+gaf8/gw1q/MYg8zAGzYsMGoBggL2WUAYmBswIABRltb+vfvj5ycHFkAjQbSsXzz5s1TCL1lZWVBp9NRP++DgoKwatUqrFu3DgMHDlQoza9fvx5arZYp8bz6suNw6psXXngBJ0+eBFAV2JXumaSPX3jhBSrrUyu+vr4IDw9XVDyuXbsW58+fp708AA/bba2trWFpaYmFCxeKz7m5ucHOzg4PHjyg3m47atQofPnll6isrERaWposyUiqW83MzDBq1ChaSwQAcSQsUHu1htSOBnX12Uzl23HH3oRIe+znz58vyzIHBgYy02MPPBzPVl3Zc8uWLcyMZ6ueaaoOS5kmS0tLFBcXo7i4WNbDSrL10nIdzpODwWDAihUrxHF30myDs7MzHB0dsXLlSur94MDD6Ly9vb1RDZBRo0bh3r171KP4pJzt0KFDeP755xUaICRybqqyt5qQjuWrSeht5syZ1N93S0tLBAcHIy4uDkOHDpU5ImZmZqisrERISAgT7Uyk5NLa2hqOjo6K6qeCggI8ePCAemkm55/LpUuXxK9ry95dunQJvXv3Ntm61MrTTz+NnTt3Yu/evQgLC5NV4lRUVGDfvn2iHW1Iu+39+/fRuXNnhISEyNptyZ6UdkWRpaUlxowZg7i4OIXDXFFRAQAYM2YM9Ws+uY7b2dmhQYMGsut948aNUVRUhPv371O/3rMWcFbWCXEeG+Skz8/PR2RkJLKzs1FcXIzs7GxERkYiPz8fBoMB2dnZVNdJnBF/f38sWLAAXl5esLW1hZeXFxYsWAB/f3+sXLlScUEwNUFBQdBqtVi3bp14MSJUVFQwk2k6deoU9Ho9Jk2apCgZ0ul0mDRpEvR6vUxsi/NkQMbd/f7772jRogUCAgLQrVs3BAQEoEWLFvj9999x7do1Jt574iDfu3cP8+bNk12f5s2bJ84GN1ZeakrIhignJwcRERGydUZERCAnJ0dmRxuNRqOYMmBlZWW0B5MWRHiqekUWecyCMBUA5ObmAqgad9e6dWssX74ce/fuxfLly9G6dWs8ePBAZsfhmBrSa1tfdv90XFxcAFS13gQHByMhIQG3b99GQkICgoODRb0VYkcT4rhFREQgNzcXy5Ytw6effoply5YhLy8PERERMjuaTJkyBSEhIYrjGo0GISEh1EWngYeZ+Pv376N169aYMWMG3nvvPcyYMQOtW7cW1fBpZ+zrWjFiqsoSnrE3IdKTft26dYredTLblPZJL529LQgCMjIyZKVPoaGhTMzelmaagoODERYWJitxz8/PZyLTRN7PUaNGISQkRFFKVlpaijVr1lB/3zn1z+3btwEAjo6ORsebkTEoxI4mUqXekydPysrzpI4pbUVfUuKu1+sV6yTnupOTE/USd2mA1JhwYlRUFBPVGmSdvXr1QmRkJBISEkTxvMDAQMTExDCxTgC4du2a+LUgCPj9999x8eJFlJaWytpapHYcjikpKSkBUDVysaSkRPa51Gg0sLGxQXFxsWjHqR0iQGtmZobr168rpnZ4eHhAEATqlWTAw1LrlJQU3Lp1S/bczZs3xT0AK+22HTp0QOPGjWWZ8EaNGjETyCWJsLZt2yIvL092r3d3d0fbtm1x7tw56j32xcXF4te1jbaV2j1OuGNvQsjJ7OHhgS1btigcPKKYSfukJw7m1atXMX/+fIWIVnh4uMyOJiSqGB8fr7jgsxJ1rD7yrHowhKWRZ1KMKfjT3tirDeIE6/V6mJub45lnnkG7du1w9uxZ/PTTT7LnaUM+fwMGDMCRI0dkz1VUVGDAgAE4dOgQ9c+ptMS9+vgY8njWrFnUP6vSAKkxLQBWAqTSdVpbWyvE81hZp5T27dvjl19+kbU3aLVatG/fXryPcjg0IE5GcXExfHx80KxZM5SVlcHS0hJ//vknUlNTZXac2pEK0Pr6+qJJkyai03TlyhWkpKQwI0DbuXNn2NnZ4dChQ3BycsKgQYPEuesHDhzAoUOHYGdnx0QQQg3ttkRk8I8//lCMkUtNTRWv/7TFCAnW1tZITU2VJXHMzMxELTVTwR17E1J9Prx0k8TSfHiycY+NjTUqohUbGyuzo82UKVMwbtw4fPzxx2Kmac6cOcyozqpp5BkhMTERK1asUAR13njjDeoXezVha2srft2wYUMcPnxY1gNOIuVSO1p07twZTk5OOHToECwsLBS91mSzwtLnlGWkAdKYmBhFP/hrr70ms6MF+f2tWrUy+jw5TnudwMN+2zNnzojis0VFRWjQoIEoQkvsOBwaSLU9UlNTxc9kbXac2pFOaZIG81ia0gRUJUNIJUZxcbFs7jqpJispKYHBYGCiSktaTUZG20ZHR1OrJnvw4IFMo8LGxgYNGzZEgwYN8Pvvv8sy9o0aNULz5s1x//592NjY4Pfff5f9rObNm5tsXF/jxo3F9VensrJSPE7sHjdUHfvExET85z//wcmTJ3Ht2jV8//33eP7558XnBUFAVFQU1qxZA71ej969e2PlypVo27ataHP37l28+eabSEhIgJmZGV588UUsW7ZM5tSdOnUK06ZNwy+//ILGjRvjzTffxHvvvWfKPxWAeubDS0X+jIlosSTyBwCrVq1CfHy82POfl5eHoKAgBAcHM5GxJ+97ZGSk0ZFnpaWliImJof6+E6SR3OpBHVYiuWrh+PHj4tdlZWWK8TdSu+HDh9NYogwi7mRnZ4fw8HDZWvV6vUIMigYGgwGLFy8GAPj5+Ylq86WlpUhJScGJEyewZMkS6qXj0gBp9R57vV7PTIC0ekVRdViqKJJmZmpymKrbcTimRC0julilunNHcHNzw4cffohz586hoKAAjo6OaNu2LczMzBROHcGUzh1QNYKZBMSra6iQx5WVldRHMJMqrcDAQKOjbQMDA3H8+HGTV2ldunQJkydPVhy/c+eO4tjt27fFFkZj+/zVq1fD09Oz/hdphLoKdJtKyJuqY3///n106dIFYWFhRkd/fPrpp/jss8+wceNGtGrVCvPmzcPgwYORk5MjnqyhoaG4du0aDh48iPLycrz66quYPHkyvvnmGwBVogqDBg3CgAEDsGrVKmRlZSEsLAxOTk5GP0CPGzXMhycif3q9HpGRkYoAhF6vZ2ZW8KpVqxAXFwedTqdwROLi4gAYP+lpUJNYFksiWtWFE6UzwhcsWIC5c+cy02+rBkik1tbWFhYWFrLxN66urrC1tUVxcbFJy7RqIjMzE/fv30fz5s1RVlYma21xd3eHg4MDLl26RH2OfWZmJvR6PTp16oT58+eLFTDu7u4YMWIEZs2ahaysLOrr9PLyElXlu3btivHjx4vX0U2bNuHEiRMwMzOjHiCtXklWfcoAixVFHA6rSB12CwsLWTDU0tJSbBfijr1xanLu/hdM6dwBwJUrVwAAPXr0wMcff6yozpwzZw7S0tJEO1qQ6qs1a9YoNKju3r2LNWvWyOxMRfPmzbF69WrF8fT0dMTHx8sc/EaNGmH06NHo1q1bjT/LVLRs2RJA1V5eo9EYHXEpCIJo97ih6tgPHToUQ4cONfqcIAhYunQp5s6dK6qaf/3113B1dcXOnTsREhKC3377Dfv378cvv/yCHj16AAA+//xzDBs2DAsXLhR72cvKyrB+/XpYWlrCy8sLmZmZWLx4MRXHHqhy7nv37s1s/zI5mT/44AOjIn8ffPABEyJ/ZWVliI+Ph06nQ3x8vFhZMGLECAwZMgTBwcGIj49HWFgYVQE9tYhoAerpC1YLJABZXFysmNyQn58vbvJMmVWoiczMTADAjBkz4O3trbg+ZWZmYvbs2dQdZrLO7t27G802DB48mAnHPisrq07Zm6ysLKrrVEslGQCZyKSTkxNatmwJQRCg0Whw4cIFUauCBTFKzj8TqUJ39QonqSYIbSVvVqnJuSNcvHgRsbGxiIiIQIsWLR75s2jg6ekJCwsLxR6pbdu2SEtLo7ImKdKgUk06NdXtTIG1tbXRQIynpyeCg4Oxd+9eLFq0CLNnz8awYcOYuCcBDydcCIIgE8sEIJsedvr0afj6+j729TDbY5+Xl4fr169jwIAB4jFHR0f4+voiOTkZISEhSE5OhpOTk+jUA1XCT2ZmZkhJScGoUaOQnJyMgIAAmWM3ePBgfPLJJ8jPz4dOpzP6+0tLS2Ul0/V9EdZqtcw6RmoR+du1axcMBgPCw8NFp55gbm6OsLAwLFq0iJmyp3nz5hm92LPkLKup31YN9OnTB0lJSXWyYwWNRsP09YmwYcMGoxogGzdupL00AA8DEBMnTsT+/fsVAdIJEyZg48aN1AMQgDoqyQCIo2Dt7OxQWFgovsZA1T3Vzs4O9+/fR3Z2NgYPHkxplZx/MrwU/+9Rk3NXnRYtWpg0G18XiAbI3r17ERYWJtuXVlRUYN++faIdTaQZZY1Go5jcQB5XH39KE61Wi3bt2gEA2rVrx4xTzyLMOvYkC1NdYMTV1VV87vr164rZlebm5nB2dpbZVHdSyM+8fv16jY79xx9/jOjo6L//h6gQtYj8Xb16FQDg7+9v9HlynNjRQk3Ospr6bdWA9PpESrIbNmyIO3fuICsry6gdLby9vbFp0yZ89dVX8Pb2VpRkb9iwQbSjCbnu2NvbG9UAGTVqFO7du0f9+kTo1KkTXn75ZezatUsU9wwKCpK9/ywQEBAAPz8/xTppjwuVQkox79+/X6O+gtSOwzE10nsjGbtJVLFJG1F1O86TAbmP6/V6oyOYyXtP+36fkZEhfu3g4ICuXbuKn9GMjAwUFBSIdj179qS1TNXQsWNHAFXBh127dmHfvn3iPXTo0KEICgqCwWAQ7R43zDr2tJkzZw5mzZolPi4sLESzZs0orsh0qKU0kwhRJCcnY8SIEYrniYKmqQQrakJNzjLvt3082NjYoKSkRHZDlR5nATIfPisrCxERERg3bpx43m/evBlZWVlMzIcnn8l79+5h7ty5Cufu3r17MjtakEDJsmXLUFZWJmsZ2LFjh+gw0349CcYmYezYsYOpSRg2Njbi1xqNBp6enuJnVCqmJ7XjcEwJyXJaWFigsLAQP/74o/icmZmZ2HfPUjaUUz+Q/ZOZmRmuX7+uGMHs4eEBQRCo759I1a2VlRUKCgpkn1HgoRYEHx1aNy5cuACgquw+NjYW48aNw/Dhw5GXl4fY2FixHP/ChQv/7FJ8Nzc3AMCNGzfg7u4uHr9x44a4EXJzc8PNmzdl31dRUYG7d++K3+/m5iYbM0R+hvR3GMPKykqhZPxPQg2lmUFBQVi1ahXWrVuHIUOGKMqe1q9fD61WK2o00EJNzrI0qBMREQEfHx/F3FAWgjpqgUToS0pK4OjoiEaNGokzjW/fvi1GxlmYYy+dD5+eni4bLUOuhSzMh5e+VidOnJCNP6rJjgbe3t6wtbXFpUuXoNPpMGbMGHGm8cGDB3H9+nXY2dkx4diTSRi+vr7o3bu3+Bm9cuUKU5Mw2rRpg0OHDsHS0hK5ubmye5Obm5u4IW3Tpg3FVXL+yZw6dQpAVX+9TqfDwIEDZed9fn6+aMezoU8W0v2Tr68vmjRpgtLSUlhZWeHKlStISUlhYv9E2oxLS0thb2+P1q1bi1olubm5YnBc2o7MqRlpMLymvVN1u8cJs459q1at4ObmhsOHD4sbn8LCQqSkpGDq1KkAqkqt9Xo9Tp48KfYoHjlyBJWVlWJUxN/fHxERESgvL4eFhQUA4ODBg2jXrl2NZficKlgX+bO0tERwcDDi4uIQHByMgQMHwt3dXZySkJ+fj5CQEOqlpGqpgCAEBARg7NixiI+Pl12gtFotxo4dy8QGXy2QPspGjRohPz9fdOSBqtezUaNGuH37NjP9lgEBAYiJicHy5ctlAVGdTsdM5lZa2SJVma7+mIUKGEtLSxQXFyM/P18201j6PG2IuKe7uztSU1NlmUQzMzO4u7szI+5JxtiVlZWhrKwMY8aMkV3zyXvPx91xaEH6k5s2bYqysjLZee/q6oqmTZvi8uXLCpEtzpOBNCkmDTqzlBRzdXUV9Uru3buHX3/9tUY7zqMhVcEjR45EamqqzIF3dnZGz549sXv37n/GuLuioiL88ccf4uO8vDxkZmbC2dkZzZs3x9tvv40FCxagbdu24rg7Dw8Pcdb9008/jSFDhmDSpElYtWoVysvLMX36dISEhIgv4Msvv4zo6GiEh4fj/fffx+nTp7Fs2TIsWbKExp+sOlgX0ZoyZQr+/PNPJCUlKTbOvXv3ZmbUnRoqIAiJiYnYunWrGHGWZu+2bt2KDh06MLVeNXD79m2xJ5j0skl7glmC9YCel5cXtFotHBwcEBcXh5ycHHGdHTp0QEhICAoLC6mPkTt16pRYNUCqXgjkcX5+PnXRTCLuCVQJ1FbvtyQaJbTXCcgd9pqCJdXtOBxTYm9vD0CpiA9UOf3kOLHjPJlUD9yw1HphZ2dXr3b/dEj18LFjx/D1119j3bp1uHz5Mpo2bYrw8HC88sorJq0epurYp6Wl4dlnnxUfk572CRMmYMOGDXjvvfdw//59TJ48GXq9Hn369MH+/ftlo6G2bNmC6dOno3///jAzM8OLL76Izz77THze0dERBw4cwLRp09C9e3c0atQIkZGR1EbdEQwGA7MbZzWRmJiIpKQkxbxYCwsLJCUlITExkRknlHWHCXiYvfP09EReXp7M8XR1dYWnpycz2Ts1IBVErK0nmAXhRCksB/Sys7NhMBiQn5+PqKgoWbnj1q1bxVLX7Oxsqn8DGbnm6+uL6OhoJCQkiII6gYGBiIqKQkpKCvXRbKSdzdLSUtETrNFoxCqI6m1vNCBtTY6OjtDr9bKqEldXVzg5OaGwsJCJtibOPxNSKXTjxg3FmMtbt26JDh8LFUWc+oe0Nfn7+yMyMlJWnclKW1P1z+XftfunI60eDgwMFI+npaVh586dAGDS6mGqjn2/fv1qLUfSaDSIiYlBTExMjTbOzs745ptvav09nTt3xrFjx/7nddY3xkSK3NzcmCl1VQsGgwGLFy8GAPTo0UMh9pWcnIzFixcz5YSy7DABD7N3169fR69evRQ3puPHj4t2LP8drEAytiNHjkRKSoqiJ3jkyJHYvXs39X7w6rAceCRBED8/P6MVD+Q47WAJeU9dXV0xceJEhSidj4+PzI4Wv/32GwDlPGOgKutEjv/222/UR8hJ25r8/PwQEhLCNUDqCZbPeTUhddhrGyXGHfsnD5IY8ff3l+kpeXl5YcGCBZg7dy4TiZG6Tg3h00XUCbM99k8q0mhe9fnLrETz1AIZHdOpUyfExsbKLqKxsbGYMWMGsrKymJgTrRaqZxlPnz6N5ORkODs7Izo6GnPnzmUiy6gWSO98amqqIuN548YNMWvPSo89wH7gkWyIT5w4IYpTNWnSBFeuXMHBgwdFZ5/2xpm8p7t37xb1XQh37tzB7t27ZXa0kJaI6nQ6hIeHiyOa1q1bJ1ZAsFJKKm1rkmqAsNjWpBZYP+fVBDlPrK2t4ejoKKsqady4MQoKCvDgwQNmzidO/UESI/PmzVNMZTEzM0NoaCimTZtGPTHSsGHDerX7p1NWVoatW7fWarN161aEhYWZJGvPHXsTopZonhSWo/iZmZkAgFdffRWCICAjI0O2zokTJ2L27Nncsf8LSLOM48aNU5S6ElFK2llGtUB6fY2poQqCIB5npSdYDYHH9u3bA6hqt9m6davsRvnaa69h+PDhKC8vF+1oIQ0sVO+3lT6mHYC4deuW+LWnpyfKy8uRkpKC8vJyeHp6IiUlRWFHGzW0NakFNZzzaoKo4peWlqJ169ZGq0qIHVfFf7IgVWKtWrUy+jw5TruarEmTJuLXWq0W/fr1Q7t27XD27Fn8+OOP4ng2qR2nZr777juxEsdYW3B5eTkEQcB3332HkJCQx74e7tibELVE8whqieL/+uuv+PTTTxXrpF02agyWAyWAPMtYfdyjXq9nJsuoFqTOZU0X/Op2tFBL4HHPnj0AqpzjqKgoxUhG8pru2bMHwcHB1NZZ14wc7cxdYWEhgKoN3i+//CI68kDVfcnMzAyVlZWiHSuw3takBtRyzquRV155BT/88IOiquSVV17Bxo0bKa6M87ggQdq8vDyj4q15eXkyO1q0bNkSQFVriMFgwOHDh3H48GHxGGkZIXac2vn555/Fr2tqCyZ23LF/wlBLNA9QRxTf29sbmzZtwsaNG+Hn54exY8fKFMfJzZOFOdGAOgIl0huOra0t3nzzTVlZLlH2pn1jUgskEAIA3bp1Q9OmTUWht8uXL4tO1O7duzF27FhaywSgnsAjUWkPCgrCnj17FCMZiW4BsaNFRkaG+LWTkxMGDRokzrM+cOCAWPWSkZFBNXNHBJJIlkaKNOjAhZSePNRyzqsJsi9JT0/Hpk2bcPr0aTGQ37FjR1EkmpV9Caf+IOKeW7ZsEVsZpe/9li1b4O7uTl3c8/Tp0wCqqgZ1Oh26dOki7p1//fVXsf3q9OnTYpUmp2aKiooAVO3nY2JiZC2sMTExYvUrsXvccMfehKglmieN4hvrs46KimIiit+pUycxm3Ty5EmZkBbpaTUzM0OnTp1oLVFEDYES4OFG3sbGBpaWlli4cKH4nJubG2xsbFBSUkI9y6gWsrKyAAB9+/bF8ePHZdlQrVaLvn374tixY8jKyqLu2Ksl8EhGme7atQt+fn6KkYwkmGKqmbE1cebMGQBVfeuWlpay0Wxubm7Q6XTIz88X7WjRq1cvcaP3KDvOk4Vaznk14e3tDScnJ2RlZSEyMhLjxo2Dv78/8vLyEBkZiaysLOh0Ou7YP4EQcc/IyEiMGDHC6IjTmJgYZqpf+vXrh2PHjskmoZDSfOkxTu3odDpcuHABd+7cQWhoqExPycXFRQyU6HQ6k6yHO/YmRBrNk5a9AVUOFSvRPBLFDwwMxPjx4xUZ5sDAQBw/fpx6FD87O1t0MKtnm8jjyspK6mOv1FTuSPoDS0pK0KVLF/Tp00fMMF+5coX3B/5FbGxsAADHjh2Dv7+/omycTOsgdjRRS+BxxIgRWL58ObRaLc6fPy8L6DVu3BharRYGgwEjRoyguMqHKvMODg5Yu3atInvz2muvIT8/36gavSlp06aN+LWZmRm6dOmCRo0a4fbt2/j111/Fa6zUjvNkoJZzXk1otVrMmjULUVFRSE9Pl1UUWVlZQaPRYObMmdTv9ZzHR03VTaxUPZGqkjt37uC///2vYhTru+++K9pxHk379u2RkZGB8vJy3L17F88995yoWZCYmIiKigrRzhRwx96ESEf1zJ07F6GhobLMbXJyMhOjekh0fu3atUYzzGvXrpXZ0YIos7dt2xb37t2TBSBcXFxgb2+Pc+fOUVdwV2O547PPPovExESZ06TVavHMM8/gp59+orgydTFgwAAcPHgQWq0W0dHRMqG34cOHY+jQoTAYDBgwYADFVVahlsAjyXAbDAbo9Xq89NJLGDZsGPbu3Yvt27eLQb0zZ85QPZ/c3Nxw+vRpXLx40Wjm7uLFi6IdTUgwD6h6n6UtBNXtTFmW+eDBA1y6dKleflbz5s1hbW1dLz/rSUIt57zakE5ukO5LnJ2dMXXqVCaq8zj1j7FqV2kwl5VqV2lVSXR0NMaNG4fhw4cjLy8P0dHRyMrKgpOTE3fs60i3bt3w7bffAgAqKipw5MgRHDlyxKidKeCOvYmRXvClM61ZGtVDhNE6duxoNMNMxsjRFlAjPapBQUEYOnSoQpRu7969WLRoEXUFdzWVO5JI7tGjR+Hn5wdfX1+ZbgFx6vkFv26Ym1ddYg0GA8aMGaMYJUacUGJHE7UEHkmgzt3dHTdv3sS3334r3lS1Wi3c3d1x7do16gG9wYMH49ChQwBgNHMntaMJKRts3bo1cnNzFc+3bNkSFy5cUIxrfNxcunQJkydPrpeftXr1anh6etbLz3qSUMs5r0b45IZ/HtIkjoWFhSKwzEoSh1SVREZG1nhvmjVrFv+s1pHqCbu/a/d3ob+b/AcSEBAAPz8/7Nq1Syx/CQoKMsl8wycJElg4duwYhg0bJrtQVlZWikqVtAMQaip3lOoWkPEd1f9nRbdADUiDSnq9HosWLXqkHU3UEHgkr1VoaCj69++PL7/8EpcvX0bTpk3x+uuv49ChQ1QCetUzzPb29mJQTNprCUB8bGNjA3t7e/z++++y502ZYXZxcQEAo049AFy4cEFmZyqaN2+O1atX12pz8eJFxMbGIiIiAi1atKj1Z3GMo4ZzXq3wyQ3/LKRJnJKSEsW9iaUkTkBAAGJiYrB8+XLZWGOdTseUoLMaqOv7aar3nTv2FDCmjr5jxw5mTiayIc7KyjIaxSeCYLSdETL7OzU11eg6U1NTZXa0UFO5o1S3ICUlRSb2RmBBt0AtkGDNpEmTsHv3btkN1M3NDSNGjMDatWuZCOoQWM80kUDdrl27sGnTJvE1TUtLQ3JyMhwcHGR2puJ/yTCXlJRgypQpiuOmzDB7e3tjy5YtAKoqRwICAvD000/jt99+k/UHmrpKx9raus6vQYsWLXhG/m/A+jnP4agBch//17/+JWtxSktLw86dO8U9ninv97W1NLm5uSE6Ohrnzp1DQUEBHB0d0bZtW5iZmSmCzQBvaaoJ7tj/wyHq6GQ8m1RIixV1dKkzkpCQoIjiv/baa0w4I8RhdnR0RG5urmydbm5u8PT0RGFhIXWHWU3ljtILD/lsGnvMQsRZDZDPaHZ2NjZv3my0546VoI4UljNNJFB37tw5ODk5YcyYMbIxcufOnZPZmYqaMszp6emIj4/HnTt3xGMNGzZEcHBwjT13pswwSydc1NYfyCdhPNmwfM5zOGqgc+fOsLKywqlTp6DV/h97dx4XVb3/D/w1M6yiAm4sKoomLgiJGxCmlZSlpVmh3sxMzZW8lUvlTSS8auU3zbqhqC2aSy55zSVzw4WbIbghuJuiuIGasojC4HB+f/A7pznMgIjDnDPwej4ePZo55wO8Reec8/4s748Ojz/+OOrXr4+//voLR48eRWpqKhwdHa16v+eSpqqXk5MjvQ4ODkZISIj0vLx//35pgMy4XVViYm9FYmENPz8/pKeny9a1iImoGgprGCcj5vZhVUsyYpwwl16j/NdffyErK0s1CbOtTHcURzkDAgIwd+5cs3vwqqG+gq0w/jcaHR2NwYMHSwXUoqOjVdWpYyv8/f2h0+lgZ2eHvLw82TZyOp0Ojo6OuH//vtllL1WprBFmPz8/RERESDU/Jk6ciN69e6vm71ysAwCUVG0Wl9yUfr9z507uaUz0EPR6PZdc1iB6vV4a/DAYDDh8+LBJm8LCQuj1eqvthMMlTVXPuP6MVquFn5+fNHgnzhwu3a4qMbG3IrGwRlZWFoKDgxEWFibbSiwpKQmCIKiisIYtJSOCIKCoqEh2rPR7NbC16Y6lR3A4Ylc5ttKpYyuOHz8Og8EAg8EANzc3NG/eHMXFxdBqtbhw4YK0REhNy0V0Oh1at24NAGjdurWqPvN3794FAHh7e8NgMMiWi3h4eECr1eLq1atSOyJ6sLi4OKxdu1a2FW9cXBwiIiLMLr8h27dw4ULptViryNz7hQsX4r333rNKTFzSZD0NGzY0O3u4YcOGuHHjhtXiYGJvRWKVZk9PTxw4cMBkKzFPT09VVHMGbCMZMRgM+OyzzwCUjDQ/99xzsim52dnZ+OyzzxSfAWFM7dMdxaTo2LFj+Pjjj032XT927JisHVUMC2Zajnh9dHd3x+3bt5GSkiI7Lx5Xw3XUFtSvXx9Ayei8ueUiQ4cOlbUjovLFxcVh1apVcHd3N9kJZdWqVQDA5L4aunTpEgCgbt26WLVqFX799Vfpft+nTx8MGjQIubm5UjuqHjw8PAAAN27cQEhICAYNGiQ9NyclJUm5ntiuqjGxtyIxGbp27ZrZC/61a9dk7ZSm9hHmw4cP4+7du6hTpw5+/vln2XT8UaNGoX///sjLy8Phw4fRpUsXBSP9m8FgUO3vE/i7vkLPnj2xe/du2XIRnU6HZ555BvHx8YrXV7A1ai+YWZqap5CK18fbt2+bHRW5ffu2rB2Vz9/fHxs3bsSVK1cQFRWFIUOGSDO0oqKicPXqVakdEZVPr9dj7dq1cHd3x6pVq3DixAmkpKSgcePGWLVqFQYNGoS1a9di+PDhqrmmkmWI0/AbNWqE4cOHm9zvGzZsiNzcXJNdUsi2dezYUSpAe+TIEdmgrfHWttzHvhqqXbs2gJLKw6tXr5Yu6i+++CKee+459O7dG/fv35faqYGaR5i3b98OABg2bJjJGns7Ozu89dZb+M9//oPt27erIrE3l9x5enqqKrkLDAyEm5sbdu7cKe1jb9zzGB8fD3d3d8XrK9gSsWBmaGgooqKiZIUT1VIw05jap5AaXx/t7Oyg1+vNvlfTdVTNjLexS05Olj2UGO/gYe3t7ohs0YYNG2AwGPDkk09i6NChJvf7J598Ehs3bsSGDRsQERGhYKRkab6+vjh27Bj+/PNPk06bW7duSf8WxG3vyDaV3mnAxcUFtWvXxp07d0yWrIrv69SpAxcXF6tsbcvE3opOnz4NoKTy8CeffGJSHV3cVuj06dN44YUXlAzVJty7dw9Ayc3SHPG42E5Jtpbclce4uBaVTyyYGRoaKtvq0N/fHzNmzMDUqVNVUTBTJE4hNU7ogJK/c7VMIT158qT02jipL/3+5MmTvI5WgFgsVavVypIQoGR6vre3NwRBYGceUQWIM1w2btyIkJAQhIWFQa/Xw8HBAVeuXMHGjRtl7aj6aNKkifRar9fj2WefRUREBNauXYsdO3aYbUe2p7ydBsqq95WXl2e1rW2Z2CugZcuWOHfunEmBBfE4VUxAQAB+//13fPfdd+jSpYvJ2tDvvvtOaqckW0ruUlNTkZ2djfDwcOzevdukDkR4eDh27typeIFHWyEWzIyKijJJlrVaLQYPHozIyEhV/D71er1UYb5r164YMmSI1AG1bNky7N+/H2vWrFF8CmlF185zjX3FGBdLDQ4ORuPGjU2KuqqpWCqRmokDCq6urkhOTjZZKuTq6oqcnJwyByTIdjVv3lz2fseOHbKEvqx2ZFvK29p2zZo1su2gldjalom9FTVu3BgAcO7cOYSGhsoKLCQnJ0vrmcV2VL7+/ftj4cKFOHfuHPr06SMbrXNwcIBer4dWq0X//v0VjNK2kjvxghQfH4+QkBB07doVTk5OKCgoQHJyMuLj42XtqHzi76msqXficTX8PtevX4/i4mK0bNkSs2bNknVAzZo1C2+//TbOnz+P9evXY+DAgYrFWVBQIL12c3NDhw4d4OzsjHv37iElJUVaW2/cjv5WehohUJKMjB49GmvXrpV15jVo0ACjR4+Gp6enyRRCoGqmERLZshYtWgAo2bPaXC0lsQaI2I6qD7G4MFD+1qHHjh3j1qE2TO1b2zKxt6J+/fohLi4OTk5OOHfunKwwmYeHB1xcXFBQUIB+/fopGKXtcHBwQGhoKPbt21fmlNzQ0FDFC9TYUnIn7k/fvn17REdHY9OmTThz5gy8vb0RHR2NyZMncx/7hyAWGUxPT0ebNm1MCiemp6fL2ikpLS0NADBixAizHVAjRozAxx9/jLS0NEUTe+N/e7m5udizZ4/03vgGyn+j5pU3jbC0mzdvIi4urszzVTGNkMiWGd/HBUEw+c9cO6p+Si9Z5BLGmkENW9sysbciBwcHREREYNWqVXBwcMCAAQPg5eWFa9euYceOHcjPz8egQYMUT0RthcFgwLlz5+Dt7Y3MzEzZlDedTgcPDw+cP38eBoNB0WmkxsmducrSakruRNeuXUPv3r1lv9MFCxaoKkZbIK5f/vrrr5GdnW2yR7ibmxu8vLxUsX7Z2dkZAEzWWYvE42I7pRh3Omg0GnTs2BH16tXDrVu3cPToUbPt6G9lTSMUXbx4ETNnzsTHH3+MZs2aPfB7EdHfxBogbdq0wdmzZzFnzhzpnE6nQ5s2bXDq1CmcPHkSvXr1UipMqgLifdzZ2Rm1a9eW7V3esGFD3LlzB/fu3VPF/Z6qLyb2ViYWT1i7dq20nhUoueAPGjRI8cJUtkSc4h4bG4tWrVqZbM919uxZVUxxF5O7FStWyNbYAyUVM1esWKGa5E6cxmxufXJxcbF0nFuJVYxOp8NTTz0l7Wk8ceJEaVrm999/j9OnT2PQoEGqWL/83HPPYceOHfjhhx/w/PPPm+zBu2TJEqmdkoyrsxsMBhw+fPiB7ehvZU0jLK1Zs2YcjSeqpKKiItSrV0+W3NWrV8+kuBZVH+Kz3b1790yWgt28eVMatWenM1UlJvYKGDNmDIYPH67afaJthfEUd3E2hDG1THE3Lk41depUk90QEhMTVVOcqm7duhZtV9MZDAbs2bMHrVu3Rk5Ojmz0xsvLC61bt8bevXsxcuRIxf/+O3bsiFq1aiEvLw+9e/eWnYuNjQUA1KpVy2p7sZbF1dVVel3eOkbjdkRE1mBcS8lcZ65YIJm1lKqf0sswjHEZBlkLE3uFmEtE1Uiv16u2A8KWprh3794dMTExiI2NNdkNQU1b3RnvyiDuYy8Wz0tKSpIKa507dw5dunRRKkybYVw4sUWLFli4cCEuX76MJk2aYPTo0Th//rwqZpUAJR1QQUFB2LdvX5ltgoKCFO+AMF47X97DE9fYE5G1vfjii4iNjYVOp4ODg4OsM9fDwwM6nQ4GgwEvvviiglFSVahows7EnqoSE3sqU1xcHNauXQuDwSA7FhERoYolA8ZT3GNiYky2u1PTFHeRRqNROoRyiQXUgJJY/fz8pNkFycnJsnaDBg1SIkSbIt7Ad+3ahfHjx0s1Cw4ePIiNGzdKOzao4Uav1+tlBT3NSUxMlPZkVkpubq5F2xERWcqpU6cAlMzW0uv1JrWUxOepU6dOKd6ZS5aVk5MjvQ4ODkaTJk2krUMvX76MpKQkk3ZElsbEXiEGg8GkQrbSI2HG4uLipHXBpbdrWbVqFQAontwbT3E3t91dUVGRaqa4JyQkIDo6GqGhoYiKipJNxY+OjlbNqL24Liw0NBTp6emy2QVeXl4ICQnB/v37uZVYBYmzRdatW2d2Xd26detk7ZQkbncH/D1bQ9yOU5ytUVxcrPh2d8bLQMRtLc2953IRIrI2sZP21VdfxS+//GJSS+nVV1/FunXrVNGZS5Z1/fp16XVKSoqUyAOAo6Oj2XZElsbEXgEJCQmYP3++rPq0p6cnxo0bp4rkTq/XY+3atXB3d8fatWthZ1fyz+TFF1/E888/j4iICKxduxbDhw9XxbR8QRDK3O5ODQwGA+bPn4/Q0FBZ8Tx/f3/MmDEDU6dOxYIFCxAWFqZ4J4Sfnx8OHTqEI0eOmCRGBoMBKSkpUjt6sDZt2kivu3TpgpCQEGlpw/79+6Ubv3E7paSmpgIoicV4H3ugZKvOsWPH4vTp00hNTVU0sTcu3Fh6BozxexZ4JKKqVFBQgIyMDNmxu3fvAgBatWqF//znP9izZw9u3LiBhg0b4qmnnsLFixeldmfOnJG+zsfHB05OTtYLnqpUYWFhue+JqgoTeysTR25DQkIwcOBAaUQsOTlZNSO3GzZsgMFgwIgRI6SkXmRnZ4fhw4djzpw52LBhg6J1AgwGA+bOnQug7GlPc+fOVTxhNl5nbW5/8MGDB6tmnXXnzp3x008/oaCgwGRU3riXuXPnztYOzSZt3LhRen3kyBFZD75xp9jGjRsVTZaBv2drBAQEmP13GhAQgNOnTys+W4NT8YlIDTIyMjBq1Ciz5z777DOTY8aj98Zr7wFg0aJF7DC3cQ0bNpReu7q6olevXvD29sbVq1exbds2aQq+cTsiS2Nib0XiyK2fnx/S09Nl61k9PT3h5+enipHbq1evAiiZjm2OeFxsp5SUlBRkZ2fDx8cHFy9elCVNnp6e8PHxQUZGBlJSUtCpUyfF4jSu3m9uCYZaqvcDJUld6UrjpWk0GgQEBFgxKttVumaBMeP3aWlpiif2rVu3xqFDh/Dbb79h9OjRsk69+/fvY+vWrVI7tQgKCjK7ZICIqKr5+Phg0aJFJscPHz6MhQsXIiAgAEFBQVi6dCmGDh2KI0eOIC0tDaNHjzbZXcTHx8daYVMVMd6J5d69e7KOHOOOfO7YQlWJib0ViSO3WVlZZtdaJyYmQhAExUduvb29AZQUyjJXuVXskBDbKUWcFn7p0qUyf59iOyUTe3H99Pr167Fp0yaTJRji71gN66zT0tKkpN7e3l625664flkQBKSlpSn6O7UV4tRKX19fLFiwAJs2bZJ2mHjppZcwZswYXLhwQRVTMDt16oSVK1fizp07iIiIwLPPPisr+nTnzh2pnZLEJSIeHh64cOGCLJH38vKCh4cHsrKyuMaeiKqUk5OT2VF2Pz8/eHt7Y/78+Vi6dCkAYOnSpfDy8lLFrEyqGnl5edLr8paHGrcjsjQm9lZ08+ZNAEDXrl3NrrWeMmUKkpKSpHZK6devH+Li4vDdd9/h+eefNxm5+/7776HT6dCvXz8Fo4RU6Ktt27ZSVfzExETUq1cPMTExePfdd3HixAmpnVICAwPh5uaGxYsXmyxtuHnzJr799lu4u7uronq/2Fny9NNPY+/evbJzBoMBPXr0wN69exXvLLEVjz32GHbu3IkrV65gyJAhuHHjhnRuzZo10tS8xx57TKkQJR06dICbmxuys7Nx+/Zt2WiDyM3NDR06dLB+cEbEDrCsrCyTJU3GI/Zq6Cgjopqpe/fuCAsLw5YtWzBnzhxMnDgRvXv3VryODlUd4yVspWc+Gr83V0iXyFKY2FuRWMzpySefhCAIOHLkiGxKdrdu3ZCUlKR40ScHBwdERERg1apViIiIwPDhw6Wq+N9//z1u376NQYMGKV44TxyR++uvvzBkyBCTkXDxIqqGkTuxt/b+/fuy4+J7NRX7A4Ddu3cjJCQEjRs3lrY3u3LlikmyT+WrX78+gJK/X+OkHoDsvdhOSTqdDs8//zxWrVpV5kPJ888/r/iDaYMGDaTXR44ckY3YG1ceNm5HRGRtOp1OWrrUunVrxa+dVLXEwRmdTifbJhooKfIsHlfDIA5VX0zsrcjNzQ1ASXG65cuXmySiderUkbVTkriV3Zo1a2RFXrRaLQYNGqT4VneAfOTO3d0dkyZNkm3Ld/v2bVk7paSkpEiVcrVarWwGgfg+Pz9fFaPg4g3H2dkZ58+flyVNjRo1grOzM+7du8cbUwVV9N+e0v9GgZIZGXv27IG3tzeuXbtmct7b2xt79+7FyJEjFX1ADQwMhKenJ1xdXZGdnY2srCzpnLu7O1xdXZGbm8t/o0REZDXiSHzppF4kHueIPVUlJvZWJI4gnT171mwiKib6ahlpateuHRo2bCh7cG7YsCHatWunYFR/M06G8vPz8cUXX0jvjWcTKJ00HTx4EABQu3ZtrFu3DidOnJBmarRr1w6vvvoq7ty5g4MHDyqe2Is3nHv37sHJyQkDBgyQrbO+d++erB2VT/x9WapdVRJrgACm+8Pb29tLxTKVrgGi0+kwbtw4aXeRQYMGyXYX2b9/P2JiYjg6RkREVmO8jLa8qfhKL7el6o2JvRX5+/tDp9PByckJdnZ2skS0UaNGcHFxQUFBAfz9/RWMsoS4LV9oaCimTZsmK0qnlm35RD4+PigsLJR1QNSrVw8ODg4me8wqQdyr9oUXXoCjo6NJUvT888/j559/lu1pqxTjyvxlrbMu3Y7K9uOPP0qv7ezs0L17d7Rp0wanTp1CQkKCtBTjxx9/RLdu3ZQKE4D8YaO8wj9qeCjp3r07YmJiMH/+fNnuIixORURESjh+/DiAkpljdnZ2suV2DRs2RFFREW7fvo3jx4+jV69eSoVJ1RwTeys6fvw4DAYD8vPzZdXGgZL19+LD8/HjxxUdERO35QsNDTVb5G/q1Kmq2JZPrEWQkZGBkJAQdOvWTdrH/sqVK9I0cqVrFogVz9PS0lBUVIRjx45JI/bt27eXtkRTQ2X0iv6ulP6d2gqxOJ6DgwPc3d2xa9cu7Nq1C0DJ8ptbt25Br9dL7ZRk3Fnj7u6OESNGmF3aopZOHbE4VentIzlST0RE1vbXX38BAGrVqmVST0mj0aBWrVq4ffu21I6oKjCxtyLjB+LyRsSUfnAWp+RGRUWZTLnWarUYPHgwIiMjFZ+SK06xDw8Px+7du2XrwXU6HcLDw7Fz507Fp+IHBgZi3759OHXqFF588UUUFhZK58QpxGI7pRkXGiw9Hdv4vRoKEtoCZ2dnACW/u+XLl5t06rz88svQ6/VSOyWJibtOp8Pq1aul5SwvvvginnvuObzwwgswGAxSOzXQ6XSKXoOIiIiAkoQeAK5cuQJ3d3cMGDAA3t7euHr1Knbs2CHNKhXbEVUFJvZWZFwUr/Qe4cbvlS6eJ3Ys+Pr6mj0vHle6A0LcRm7nzp3o2rUrnJyccOfOHdSuXRsFBQXYuXOnKraR69+/P+Li4iAIgiypByC912g06N+/vxLhyRiPxNeqVQv//Oc/pVHbb7/9VkrsOWJfMe3bt8eFCxdw584dTJ06FW+++SZCQ0ORnp6OqVOnIj8/X2qntLNnzwIombETHR2Nrl27ytaui4V/xHZERERUIjw8HDt27IBGo0FOTo5sKaNWq5XW2YeHhysYJVV3TOytyLgaukajkZ0zfq/0vuviCHd6errZ9f7p6emydmqQnJxs9rhx8RKl6HQ6ODs7S5XxzXF2dlbFFGJxSriTkxMcHR1ldSA8PT3h5OSEgoICVUwdtwXNmjWTXiclJSEpKemB7ZQiLgVxd3dHUlKSbO26VquFu7s7bt++rYolIyKDwcCp+EREpDg7u5KUShAEk2dP4+d6sR1RVeC/LitKSUmRXru4uODll1+WTdMRR0NTUlLQpUsXhaL8ezupFStWyNbYAyUXpxUrVsDLy0vxkfDU1NQHjhxnZ2crvmQgNTW13KQeAO7evat4nMDfe6sXFBQgKCjIpOK4mOyV3pOdzOvXrx/i4uIAmN8CR0xC+/XrZ9W4zBGXjNy+fduk41EQBGkKvtKfe1FCQgJiY2NlRTM9PDwQGRnJ4nlERGRVFZ3FqvRsV6remNhbkfgAWrduXbPTdOrWrYvc3FzZg6oSjLeTmjp1KgYPHiyrip+YmKiK7aSuX78uve7SpQucnZ2lqfj37t3DgQMHTNopQawiHhwcjJiYGGzatAlXr16Ft7c3XnrpJURHRyMpKUkV1cY9PDwAAE2aNMG5c+dko7YeHh5o0qQJLl++LLWj8jk4OCAiIgKrVq2Cm5sb6tevD71eDwcHB/z111/Izs7GoEGDZNszKqVv375YsGABANOZLsbv+/bta9W4zElISMC0adNMjmdlZWHatGmYPn06k3siIrIaMWF3dXVFXl6ebJRep9Ohdu3ayMnJYWJPVYqJvQJyc3Nhb29v8qHPzc1VMCo54+2kIiMjpeNq2k5K3FrExcUFhw4dkv0+tVotXFxckJ+fr/jWIuKsgieffBJOTk6IiIiQne/WrRuSkpJUsW49KCgIy5cvx+XLl+Ho6Cg7l52dLdUEUHpmgVoVFBSYbLH4zDPP4NatW9i5c6fs71ir1eK5557DM888Y3arQx8fH6tOez9x4oQsttKfJ/H9iRMn0KlTJ6vFVZrBYMDnn38OoKQeydtvvy2rA5GdnY3PP/9c8V07iIio5hCf4XNychASEoLg4GBpxmNSUpJU4FlNz/pU/TCxt6JGjRpJr2vXro3hw4dLD6Tff/+9NNXVuJ3SSk8fLr2Fh5LELUPy8/PL3Z5L6a1FxGKI//vf/9C7d2+TpQ2///67rJ2SOnTogFq1auHu3bsmOzeIxR1r1aqFDh06KBCd+mVkZGDUqFEValtcXIzt27dj+/btZs8vWrQIfn5+lgyvXOJSoaeeegoJCQmycxqNBj169MDevXuRkpKiaGJ/+PBh5Ofno06dOli9ejVOnDiBlJQUNG7cGKtXr8Zrr72GvLw8HD58WNElTURERETWxMTeiurUqSO9vnv3LubMmSO9Nx4dNW6nlLKmut64cUM1U12NRzP9/PxQVFSEpKQkFBUVwc/PTypUpnSxrwYNGgAoKZ5mbmmDGKfYTmkODg64e/cu7O3tZcm9nZ0d9Hq9yUg+/c3HxweLFi0q8/zFixcxc+ZMfPzxxw8smOfj42Pp8Cqkb9+++PDDD7Fw4UJcvnwZTZo0wejRo3HixAns3btXkZiMiR0hTz75JIYMGSJbatOoUSM8+eST2LJlC7Zv387EnoiIrELcBtjV1RUHDhww2YLZ1dUVOTk53C6YqhQTeyu6c+eO9Lp05XvjNazG7ZRgMBgwY8aMctvMnDlT8amu4l6gWq0WBw4ckFUc1+l00vRhpfcMFYsRurq64ty5c7KlDZ6enmjdujVyc3NVUZRMLEg4cuRI/PLLL7IieW5ubujbty++/fZbVRT6UyMnJ6cKjbI3a9bMqqPxFdGhQwcsW7YM8+bNQ2FhoVTr4+DBg0hMTJQ6dJSerXHv3j0AwJYtW0zOXb9+XToutiOiB9Pr9diwYYNU/6Vfv36qqP1BZCvEnaJycnJMCtAWFxdLuwmpaUcpqn6Y2FuR8RRs49eAfLu70ues7eDBg9JIrZ2dHQYMGIDevXtjy5YtWLNmDe7fv4/CwkIcPHgQwcHBisUpdioUFxfD3t4eTz/9NPz8/HDmzBkkJCRIU8eVXmdrXIwwJCTEpNL8/v37VVGMEPi7+MutW7dMCrz89ddf0vIGFn+pfsRlGBkZGXB3d8fEiRNlS4WysrLg4uKieGLv7+8vW75ibo292I6IHiwuLg5r166VLb2Li4tDREQExowZo2BkRLbDOGEvrwAtE3uqSkzsrUgcEWvQoIHJum+9Xo8GDRrg5s2bij84r1q1CkBJZ8OmTZtw6tQpnD59Gl26dMGQIUPQu3dvCIKAVatWKZrYe3l5Sa+LiooQHx+P+Pj4ctspxbgYoXGleTUVIwT+vuGsW7fObN2CdevWydpR9SIuwyhrqZC9vb1SoUmaN28uvTa3BCc5OdmkHRGZFxcXh1WrVpm93ovPAkzuiR6s9EzcR21HVBlM7K1IHBEzt62ZIAi4efOmKkbEzp07BwBo3bo1hg0bhszMTOmcp6cnWrVqhTNnzkjtlNKiRQsAJdOf69ata7LWNjc3FwUFBVI7azFXGR0o+d198sknOHv2LHJycuDq6opWrVpBq9WarYoOWL8yeps2bQCUJHCrV6+WpmK++OKLeO6559CnTx8UFRVJ7aj6MF6GsXHjRtm2m+7u7njppZewePFixZdh7Nq1S3qdnJwsJfLm2oWGhlorLCKbo9frsXbtWri7u2PVqlWyQpSrVq3CoEGDsHbtWgwfPpzT8okeQCxAC5TcM5999ll4e3vj6tWr2LFjhzTjMSUlhfVfqMowsbey0lXmS1ND1XnxBn7q1CmTQmm3b9+WEn2lb/TiliEFBQVwdnbGgAED4OXlhWvXrmHHjh0oKCiQtbOWh6mM/iDWroy+efNmACUzID755BOTQn/i8obNmzebbNtHtk1cXtG/f39ERESYrLe9f/8+Fi9erPgyjIqunecae6LybdiwAQaDAU8++SSGDh1q0on/5JNPYuPGjdiwYQOv90QPIHaGN2zYEDqdDmvWrJHOeXp6omHDhrhx44as05zI0pjYW9Hhw4elfcDLUlhYqPg2TcHBwfj1118BlIyGjx8/Xpqet3jxYunPYO1p+KVHwu/evQsA6Nq1Kw4ePCi7iGq1WnTt2hXJycm4e/euyYh4VY6E23Jl9KtXrwIAJk+ejGXLlskK/Xl5eWHSpEn44osvpHZUfYjLK9avX49NmzbJHvLXrVuHl156SdZOKe3bt8fvv/+OWrVqYfXq1fjuu++k6v0jRozAwIEDcffuXbRv317ROImAks781NRU3Lp1C/Xq1UNgYKAq6qkAf1/vN27ciCeeeAJRUVGyjtyNGzfK2hHRg2k0GpPp9sXFxSYF9YiqAhN7K9q2bZv02s7OTjY6b/x+27Ztiib23bp1kxL7nJwcfPHFF2W2s6ayRsLNTcUtLi6WjhuvFRZV5Ui4LVdG9/b2BgCcPn3apPhLcXGx1EEitqPqIzAwEG5ubli8eDFCQ0NlD/nLly/H4sWL4ebmpvjuDS1btgRQ0rH3yiuvSLNIDh48iF9//VV6L7YjUkpCQgJiY2NlI3QeHh6IjIxURV0VT09PACWflRkzZkiFe/39/TFjxgy8/fbbOH/+vNSOiMrm4eEBoGR3ltJJ/I0bN6RnKrEdUVVgYm9FxiNgOp1Oltgbvzdup4QTJ05UuJ0117CaGwk/fPgwFi5ciICAAAQFBWHp0qUYOnQojhw5grS0NIwePRodO3Y0+73IVL9+/bBgwQJs3LhRquDv5OSEgoICJCUlYePGjdBqtejXr5/SoVIVEgQBZ86cwcWLF1FYWCg9kKhhxMF4aY2YxJt7b+0lOETGEhISMG3aNJPlbNnZ2Zg2bRqmT5+ueHIv1p+5fv06iouLZTvyFBcXS9udWrtODZEt6tChA1asWAGg/Kr4StfRoupN1Ym9wWDAJ598guXLlyMzMxPe3t546623MHXqVOkBUxAEREdHY/HixcjOzkZYWBgWLFiAVq1aSd/n1q1bGD9+PDZt2gStVotXX30VX331FWrXrm3VP4/xmvSOHTvijTfekI2IidXSlV67rlbmRsL9/Pzg7e2N+fPnY+nSpQCApUuXqq7avK3Q6XRwdnZGfn4+kpKSsH//fumc+JlzdnZWzVRSQN1TXW2JWDwvPDwcu3fvlv3d63Q69OzZE/Hx8YoXz3NzcwMANGjQALdv35bVLdHpdHB3d8fNmzeldkTWZjAYMHfuXABl3+vnzp2LsLAwRa9VYudXXl4eIiIi8Oyzz8rq1OTl5cnaEVHFaDQaWTJf+j1RVVF1Yv/5559jwYIFWLp0Kfz9/XHw4EEMGzYMrq6u+Oc//wkAmD17Nr7++mssXboUvr6+iIqKQq9evXDixAlpDfXgwYOlG1VRURGGDRuGUaNGYeXKlVb987i6ukqvza2/MddOCeJU2zp16mDlypUma1hff/115OXlKT4lV9S9e3eEhYVhy5YtmDNnDiZOnIjevXszuauE1NRU5OfnAyi7xzk/P1/x5E6UkJCA+fPnmxR9GjduHDt1HpJYFC8+Ph7BwcFo3Lgx9Ho9HBwccOXKFakavdLF80ReXl5Yvnw5Nm3aJBX5e+mllzB58mSzO48QWUtKSgqys7MREBCAmTNnyqa4z5w5E++++y7S0tKQkpKCTp06KRanWC8jMDAQqampsjo1xseVrqtBZAuMq+KXNyuXVfGpKqk6sf/jjz/Qr18/9OnTB0DJvsQ//fSTtHZaEATMmzcPU6dOlaYG//jjj/Dw8MAvv/yCQYMG4eTJk9i6dSsOHDiAzp07AwD+85//oHfv3vjiiy+sulbYONFMSkpCUlLSA9spQXwIycvLw8yZMxEcHAw/Pz8UFhZi5syZUi++8bQ9pel0OrRu3RpAyTZ9Sv8ObZWYELVq1Qo5OTkmWwi6urri7NmzqkicEhISEB0dbbIefMWKFYiOjuaMjYckjnA3bdoU6enpshF7Dw8PNG3aFBkZGYqPhGdnZwMA0tLS8Mknn6Bx48YoKipCZmYmPvnkE6SlpcnaEVmb+IA/bNgwk/ukVqvFW2+9hYkTJyqe2It1NVJTUxEcHIwmTZqgsLAQjo6OuHz5MpKSkuDu7q6aTnwiNROfl8RnEWP379+Xjhs/VxFZmqoT+yeeeAKLFi3CmTNn4Ofnh6NHj+L333+Xprilp6cjMzMT4eHh0te4uroiODgYiYmJGDRoEBITE+Hm5iYl9QAQHh4OrVaLpKQk9O/f3+zPLiwslFWwt8RUtIoWzFC6sIbxA/H+/ftlD/hltaPqQfw7PXv2rMnaUONEX+m/e4PBgPnz5yM0NNRs0aepU6diwYIFik91tUUZGRkIDQ3FtGnTzC4VUprxKKO5a1NAQADS0tI4ykiKs6Wpt1qtFs8884zsMw/Y1p+BSEmNGjUCUJKbuLm5oUOHDlKNopSUFCnZF9sRVQX1DLma8dFHH2HQoEFo06YN7O3tERQUhPfeew+DBw8G8HeRudKJsIeHh3QuMzPT5ENkZ2eHevXqlVuk7tNPP4Wrq6v0X9OmTR/5z2OuiNujtKsqFX0g5oNz9VO3bl3pdceOHREbG4stW7YgNjZW9u/SuJ0SUlNTkZmZicGDB5sdEROX36SmpioUoe2p6BR7pafiBwYGwsXFBampqSbF/DQaDdLS0uDi4sJRRlKMWBxryZIlKCoqwpEjRxAfH48jR46gqKgIS5YskbVTilhXY+TIkUhPT0dkZCR69+6NyMhIXLhwASNHjkR2djavo0QVYHzPuXfvHvbs2YOtW7diz549uHfvntl2RJam6hH7NWvWYMWKFVi5ciX8/f2RkpKC9957D97e3hg6dGiV/uwpU6ZgwoQJ0vvc3NxHTu7btWtn0XZVxd/fH1qtFsXFxbC3t5dVmhbfa7Va+Pv7KxglVYWKjsQrPWIvJpe+vr5mz4vHlU5CbYn4d9q3b18kJycjMjJSOufl5YW+ffti48aNiv/dGwwG3L17F0DZBYru3r0Lg8HA2RqkiA4dOsDNzQ1paWno06cP9Hq9dM7BwQF6vR7u7u6KJ/bi9bF///4YNGiQSRHSwsJCLF68mNdRogq4cOGC9Nr4M1/6/YULFxAcHGytsKiGUXViP3nyZGnUHiiZYnnx4kV8+umnGDp0qLS3alZWFry8vKSvy8rKkm6Ynp6eJutZ7t+/j1u3bpW7N6ujo6PJVORHtXHjxgq3GzhwoEV/9sNIS0uTivkZV5w2fl9cXIy0tDRF1weS5Yn1E5o0aSKN4Ii8vLzQpEkTXL58WWqnFHG2SHp6utkOJnHKG2eVVJy4dj4rKwvLli3DsWPHpIf89u3bY+rUqbJ2Slm/fr2UzJdVhFQQBKxfv17R6yjVXDqdDs8//zxWrVolK6AFQHrfq1cvxTueSl9HSxdE5XWUqOKuXbsmvbazszMZFBOTe+N2RJam6qn4d+/eNZlmq9PppIc3X19feHp6Ij4+Xjqfm5uLpKQkaX/10NBQZGdn49ChQ1KbXbt2obi42Oo9ZhWdzqb0tLcjR45Ir+3s5H0/9vb2ZttR9SBObb58+TJ8fX3x7rvv4oMPPsC7776L5s2b4/Lly7J2SgkMDISnpydWrFhhNrlbsWIFvLy8OOXtITRo0AAAkJycjOjoaDg4OCA0NBQODg6Ijo6WipaK7ZRifH0svTWo8Xulr6NUcxkMBuzZswetW7c2WQrYqFEjtG7dGnv37jXpOLc2XkeJLK9bt26oX7++7Fj9+vXRrVs3hSKimqTSI/bx8fGIj4/H9evXTW4I33///SMHBgAvvfQSZs6cCR8fH/j7++PIkSOYO3cuhg8fDqAkuXjvvfcwY8YMtGrVStruztvbGy+//DIAoG3btnj++ecxcuRIxMXFoaioCO+88w4GDRpk1Yr4AGRrbHQ6HVq0aAFHR0cUFhbi/Pnz0k3euJ0SxNoDvr6+WLBggcl2UmPHjpUKF5LtKigoQEZGhuyYmLR5enri9OnTsoJp9evXh6enJzIzM9GgQQOcOXNGOufj4yNtL2kNOp0O48aNQ3R0NKZOnYrBgwfLquInJiYiJiZG8RExWyI+5Lu6uuL8+fMmszX8/PyQm5ur+EO+8fWxvOmOSl9HqeYSa4BERUWhTZs2JlPcT506hcjISMW3DeV1lMhy2rZti19++QXHjh3D6tWrceLECelz365dO2kGWdu2bRWOlKqzSiX2MTExmD59Ojp37gwvL68qG737z3/+g6ioKIwbNw7Xr1+Ht7c3Ro8ejWnTpkltPvjgA+Tn52PUqFHIzs5Gt27dsHXrVlmSsWLFCrzzzjvo2bMntFotXn31VXz99ddVEnN56tSpI702GAw4e/bsA9sp6c6dOxg6dCiysrKkYz///LNJRw7ZpoyMDIwaNcrsOXOdNn/99Zf0+ssvv5SdW7RoEfz8/Cwb4AN0794dMTExmD9/vkkSyq3uHp7xQ35ISAgGDhwodTwmJydj//79qnjIN14K4Orqil69eqFx48a4cuUKtm3bhpycHJN2RNZkXANEp9OZJO9qqgEiXkdjY2Nl11FPT09eR4kegjg7Jzs7G4MGDcLw4cMRGhqKxMRETJ8+XapPw6r4VJUqldjHxcVhyZIlGDJkiKXjkalTpw7mzZuHefPmldlGo9Fg+vTpmD59eplt6tWrh5UrV1ZBhA/n6tWrsvdNmjRBnTp1kJeXJ01xNtfO2sRdBm7cuAE3NzdMnDhRujh999130sVJ6W356NH4+Phg0aJFJscPHz6MuLg4k8KJYtGnMWPGmOzc4OPjU+XxmtO9e3eEhYWZjIgpnXzaKuPOEuPZGmrtLMnJycGaNWuUDoNIxhZrgCi9vIrIlpib8ejs7Iz69etDq9Xi5s2bmDNnjnROo9GgYcOGEAQBzs7Ois54pOqtUom9Xq/HE088YelYqr3S+8EaJ/PltbO2Dh06YMWKFQBK6hwYX5yMCwoqXdGXHo2Tk5PZUXY/Pz94e3tj/vz5spH7+vXrY+zYsapL7syNiFHlde/eHSEhIdiwYYO0BKdfv34m69mVIo7IW6odkaUZr12fMWOGrFaQ2tauJyQkIDo6GqGhoYiKipJNxY+OjlZlhx6R0sqb8WiOIAi4ceMGAGDMmDGyc0rMeKTqq1KJ/dtvv42VK1ciKirK0vFUaxWtsm/pavwPq3TBQmPGnQ7ltSPbJo6Eb9myBXPmzMHEiRPRu3dvjoTXAAkJCYiNjTVZghMZGamKB3zjkQ1xFom59xwBIaUYL2v517/+hcaNG6OwsBCOjo64cuUKkpKSVLGsxWAwYP78+QgNDZV1QPj7+2PGjBmYOnUqFixYgLCwMMVjJVKTsmY8AiWzHteuXStbvtigQQO89tprJrMdxe9FZCkVTuyN93QvLi7GokWLsHPnTgQGBsoqpQPA3LlzLRdhNdKsWTMcP34cQPkPpM2aNVMkPpHxPtXlFadSej9rqlo6nQ6tW7cGALRu3ZoPdjVAQkICpk2bZtK5mJ2djWnTpmH69OmKJ/fGVflLTx82fq909X6q2bp3744nnngC+/btMzkXFham+OcIkBf5K91Rr9VqMXjwYFUU+SNSm7JmPAIlsx4jIiI4MEKKqHBiX3prM3Ea9rFjxywaUHVmvB6nvIS59LodazNe96fVamXF8ozfq2l9IBE9GoPBIHXKBgUFITg4WCqel5SUhP3792Pu3LmKj961a9cOGzZsAFD2PvZiOyKlxMXFYd++fXBzc0OHDh3g5OSEgoICpKSkYN++fYiLizOZkmttxkX+zFFTkT8iW8KBEVJKhRP73bt3V2UcNYK4RrV0siwSjyu9lrVNmzYASvasX79+PX777Tdpre0LL7yA/v37o6ioSGpHpCSDwcDieRaQkpKC7Oxs+Pj44MKFC9i/f790ztPTEz4+PsjIyEBKSgo6deqkWJzGFYWNizuWfs/Kw6QUvV6PtWvXwsXFBU5OTtizZ490ztPTEy4uLli7di2GDx+u6P3euMifuW351Fjkj4iIylapNfbDhw/HV199ZbItW35+PsaPH2+xfeyrGx8fHxw+fBjFxcVwdXVFgwYNoNfr4eDggJs3b0rFnpReb7N582YAJQ/Jr776KgoLC6Vz3377rfTwvHnzZkRERCgSIxFQMnW8dJE/T09PjBs3ThVTXW1JSkoKAODSpUtmC2mJVfKVTuwDAwPh5uZW7lIgNzc3VRQmo5ppw4YNMBgMyM/Px+OPP27yWfrjjz+kdkreQ8Uif19//TVycnJMrqOurq6qKfJHREQPVqnqZ0uXLsW9e/dMjt+7dw8//vjjIwdVXY0ePRpAyTrQnJwcnDt3DpcuXcK5c+eQk5MjrQ8V2ymlotvtKb0tH9VsYjXnFi1aIDY2Flu2bEFsbCxatGiB6OhoJCQkKB2iTRFnEbVt2xYxMTHQ6/VITEyEXq9HTEwM2rZtK2unpDt37kiv/fz88NRTT8nWOxqfJ7K2K1euAAA6d+6MGTNmwN/fH7Vq1ZKK0nXu3FnWTik6nQ5PPfUUTp8+jcLCQgwYMADvvfceBgwYgMLCQpw+fRo9evTgDCgiIhvxUCP2ubm5EAQBgiAgLy9PVnXYYDBgy5YtnP5YDmdnZ7Rp0wanTp0CYLqPvSAIaNOmDZydnRWN09PTEwDQsmVLxMXF4dixY9L0vPbt22P06NE4f/681I7I2ljN2fLq1q0LAPjrr7/wxhtvyKrie3h4mLRTysGDB3H//n1otVrUr18fZ86ckfYEbtSoEW7evIn79+/j4MGDCA4OVjRWqtn8/PzMFqVr1aoVDh48qFBUfzMYDNizZw+8vb2RmZmJNWvWSOe0Wi28vb2xd+9ejBw5ktdRIiIb8FCJvZubGzQaDTQajdlqkBqNBjExMRYLrroxGAzIzs6Gi4sL8vPzTfaxd3FxQU5ODgwGg6I30RYtWgAArl+/jqKiIvz555/SGvvWrVtLe3GK7YisjdWcLU9cR5uVlQU3NzdMnDgRoaGhSExMxHfffSdNfVd6va2YfLz66qsYOXIkNmzYIF2f+vXrh0WLFuHnn3/GmjVrmNiTItq2bYtffvkFW7ZswfDhw2Fn9/ej1v379/Hbb79J7ZQkXkcBIDQ0FF27dpWK/CUnJ0vLb3gdJSKyDQ+V2O/evRuCIOCZZ57BunXrZA94Dg4OaNasGby9vS0eZHVhfBM1t91dfn4+8vPzFb+J5ubmAgDy8vLQu3dv2bnY2FiTdkTWxmrOlmd8Pb937x7mzJkjvTfe/k7pxF6cZq/RaPDGG2/g+vXr0rm1a9eiR48esnZE1ibOXMzOzkZERASGDx8udZJ9//33UieZ0jMcb968CQAIDg7GzJkzZZ2k/fr1w5QpU5CUlCS1IyIidXuoxF58YEpPT4ePj4/JHsJUPuObY8eOHc1uJ1W6nRIq+uCu9AM+1VzG1Zz9/f1NzrOac+X5+PigsLBQNhXf3d0dDg4Oim/FCZRsHXT69GnZtGHR9evXsXbtWqkdkRLEonRarRaZmZmyTjJxirsgCIoXpRM7GJ588kkIgoAjR47IquJ369YNSUlJ5RaqJCIi9ahUVfycnBykpaWZHNdoNHBycoKPj49shIdKiKOHHh4eSE9Pl20n5eHhAQ8PD2RlZSk+ysjt7kjtxAfnFStWyNbYAyXF3VasWMFqzg9JfHi/dOkSgoOD0a1bNxQWFsLR0RFXrlxBUlKSrJ1SRo0ahY0bN0rv/fz80LhxY1y5ckVaay+2I1KCTqfDuHHjEB0djeDgYDRu3NjksxQTE6P4unU3NzcAJdX5ly1bZlJXQ6ynIbYjIiJ1q1Ri36FDh3JH6+3t7TFw4EAsXLhQVmCvphOnrmdlZZl0fGRnZ0vbyik9xd14u7tZs2Zh8ODB6NOnD9LT0zFr1ixud0eKM35wnjp1KgYPHmyyNZsaHpxtiTi7oWfPnti1a5es+r1Wq0XPnj2xc+dOxWdBHD9+XPbeuHhe6XZcY09K6d69O2JiYjB//nxZJ76XlxdiYmJUsR1ngwYNAABnz56Fu7u7rK7G999/j7Nnz8raERGRulUqsV+/fj0+/PBDTJ48GV27dgUAJCcnY86cOYiOjsb9+/fx0UcfYerUqfjiiy8sGrAtM+4MqVWrFsaPHy8rTiUm9kovcRC3sZs8eTKWLVuGyMhI6ZyXlxcmTZqEL774gtvdkaKMH5xL/xtVy4OzLRH3h9+5c6e0REhkb2+PnTt3qmJ/eHNT8Mtqx8SelNS9e3eEhYUhNTVVNsVdLR2O/v7+0Ol0cHJygoODg2zJgKenJ1xcXFBQUGB2uRMREalPpRL7mTNn4quvvkKvXr2kYwEBAWjSpAmioqKQnJwMFxcXTJw4kYm9ERcXFwAlhfJ0Op3sd9OwYUOpoJ7YTiliAURBELBixQqTh5ItW7bI2hEpRe0PzrZGLOhZq1Yt9OvXD97e3rh69Sp27NiBwsJCabaOksQZTS4uLvjxxx8RHR2NrKwseHh4ICYmBkOGDMHdu3cVn/lEBJTMLlJrRfnjx4/DYDAgPz/f5LN969Yt6Xpw/Phx1f4ZiIjob5VK7NPS0tCsWTOT482aNZPW3nfo0AHXrl17tOiqmfz8fAAlD8+lC+SJW8gZt1NKv379EBcXh++++w7PPvus7Nz9+/fx/fffQ6fToV+/fgpFSPQ3NT8425KUlBTcvXsXDRo0wK1bt0z2tG7QoAFu3ryJlJQUdOrUSbE4HRwcAJQsFYqIiJCWDNy4cQMRERHS1mJiOyIyz7ieT+mZgsbvla77Q0REFVOpxL5Nmzb47LPPsGjRItlD1meffSYVVLty5Qo8PDwsF2k1UNEp9kpPxXdwcEBERARWrVqFF154wWStbXFxMQYNGsQHZ6JqJCUlBUDJrhwhISFo3Lgx9Ho9HBwccOXKFWmdsNKJfYsWLXD8+HHo9XrodDr84x//QO/evbFlyxasWbNGGmVs0aKFYjES2QKxKF5AQADmzp2LY8eOSTOf2rdvjwkTJiAtLY3F84iIbESlEvvY2Fj07dsXTZo0kdZbpqWlwWAwSIXXzp8/j3Hjxlku0mqgffv2Fm1Xldq1awcAsqTe+L14noiqB/Gz3aRJE1y4cEFW8MvT0xNNmjTB5cuXTa4J1ta4cWPptcFgwE8//YSffvqp3HZEVL7SM5+U/pwTEdHDq1Ri/8QTT0jVp8VqxBEREXj99ddRp04dAMCQIUMsF2U1ceHCBem1nZ0devToAT8/P5w5cwZ79+7F/fv3pXZKFn0yGAyYP38+WrdujezsbJMtcNzc3LBgwQKEhYVxLTMpzmAwcI29BYhbW12+fNlkNo7xeluxnVIEQQDw9+yh0sTjYjsiMk/cujItLc3s7iLi0kqlt7gkIqKKqVRiDwB16tTBmDFjLBlLtXflyhXptVarRXx8POLj4wEAjo6OUmJv3E4JqampyMzMRGZmptlt+cREPzU1lWubSVEJCQmYP38+MjMzpWOenp4YN24cq+I/JOPpts7Oznj55Zfh5eWFa9euYdu2bVJir/S03OvXrwMoGVF0c3NDs2bNIAgCNBoNLl68KCUhYjsiMk/cunLkyJHYtGmTye4ib7/9Nr799lvFt7gkIqKKqXRif/bsWezevRvXr183GTWZNm3aIwdWHf31118AgMcffxxZWVmyZKRevXpo2LAhUlNTpXZKMS7sFxQUhODgYGn7q6SkJGmKbukCgETWlJCQgOjoaISGhiIqKko20hQdHc0t7x6S8ahcTk5OmdvKKT16J+7G0blzZxw5cgRHjx6Vzul0OnTu3BkHDx7krh1EDxAYGAhPT08cP34cy5YtM1ljHx0dDS8vL8W3uCQiooqpVGK/ePFijB07Fg0aNICnp6es2JtGo2FiX4YGDRoAKJlqv2bNGpw4cUK6ibZr1w4DBgyQtVOKWAHXw8PD7FpbDw8PZGVlsVIuKUZcLhIaGooZM2ZAq9UCKNmXecaMGZg6dSqXizwk4+3hSu9jb/zemtvIFRQUICMjQ3bM398fWq0Wp0+fxpdffonff/8dN27cQMOGDdGtWzd8/PHH0Gq18Pf3l5aKAYCPjw+cnJysFjsRoO6lQjqdDuPGjUN0dDSio6MxePBghIaGIj09HdHR0UhMTERMTIxq4iUiovJVKrGfMWMGZs6ciQ8//NDS8VRrTZo0AVAyGjZo0CAMHz4coaGhSExMxPTp05GTkyNrpxTxwT0rK8tkNHT58uVITEyUtSOyNnG5SFRUlJTUi7RaLQYPHozIyEguF3kIxh205c3UseauHRkZGRg1apTZc3l5efjnP/8pO2Y8y6B08dZFixbBz8/P8kESlcEWlgp1794dMTExmD9/vslUfM56IiKyLZVK7G/fvo2IiAhLx1LtifvD29nZIScnB3PmzJHO6XQ6aZ290vvDGz+4C4KAM2fO4OLFiygsLJQVpFJ6Wz6qucTZIr6+vmbPi8c5q6TixMKnHh4eSE9PL3OmjtjOGnx8fLBo0SKz537++Wfs3LnTZDvO8PBwvPbaa2a/F5G1iEuFQkJCMHDgQDg5OaGgoADJycmqWyrUvXt3hIWFqXZmARERVUylEvuIiAhs376dxfMekvH+8G5ubujQoYN0s09JSUF2drYq9ocXH9xdXV2RnJwse8DXarVwdXVFTk6OVR/wiYyJxZzS09Ph7+9vcj49PV3Wjh5M/F1lZWUhJCQEgwYNMjtib83fqZOTU5mj7P/6178wadIkfPvtt1izZg0GDBiAt99+W/HrJ5G4VMjPzw/nz5+XZrkBJR1nfn5+qlsqVHq7OyIisj2VSuwfe+wxREVFYf/+/QgICIC9vb3sfOnpkfQ3sTNk7dq12LNnj3Rcp9Nh0KBBqugsER/cc3Jy4O7ujmeffVaqjr1jxw7cvn1b1o7I2sSiTytWrJCtsQdKqqWvWLGCRZ/KYW7tekFBgfT60KFDsg4942S5oKBANWvXHRwcEB4ejjVr1iA8PJxJPamC8c4yTzzxBKZNmyYr7vnHH39I7ZhMExGRpVQqsV+0aBFq166NvXv3Yu/evbJzGo2Gif0DjBkzBsOHD8eGDRtw9epVeHt7o1+/fqp5KDVO2O/evStbt2q8/R0Te1KKcdEnc/svs+hT+cpbuw4ARUVFsvfiVncAZEuIAK5dJypN3DEmODjYbHHPKVOmICkpiTvLEBGRRVUqsRenuVLlidPy1czHxwcFBQWy/aDd3Nzg6OhoMtpHZG0s+lR5Za1dP3z4MBYuXIiAgAB4eXlh27Zt6NWrF65du4a0tDSMHj0aHTt2NPleRPQ3cUvIJ5980mxxz27duiEpKUnxrSOJiKh6qfQ+9kDJKE56ejpatmwJO7tH+lakIuLDxqVLlxASEoJ//OMf0lpb4zX3fCghpbHoU+WUtXbdz88P3t7emD9/PlJTUwEA27ZtY2cJ0UNwc3MDAPzvf/9D7969TZYK/f7777J2REREllCpbPzu3bsYP348li5dCgA4c+YMWrRogfHjx6Nx48b46KOPLBpkdaTmvW3FKfZvv/02Nm3aJCv84+XlhbfffhuLFy/mVHxSBRZ9siyxs2TLli2YM2cOJk6ciN69e6vm+kSkdg0aNAAAJCcnm10qlJycLGunBmp+JiEiooqpVGI/ZcoUHD16FHv27MHzzz8vHQ8PD8cnn3zCxP4BEhISEBsbi6ysLOmYh4cHIiMjVTEiJhYmO378OJYtW4Zjx45JN/v27dsjOjqahcmIqjGdTofWrVsDAFq3bs0HfKKHIN5DXV1dcf78eZOlQn5+fsjNzVXNPTQhIQHz589HZmamdMzT0xPjxo1TxTMJERFVjPbBTUz98ssv+Oabb9CtWzfZXub+/v44d+6cxYKrjhISEjBt2jSTaezZ2dmYNm0aEhISlAnMiFiYLDExEdHR0XBwcEBoaCgcHBwQHR2NxMREjB07lg/7pAoGgwFHjhxBfHw8jhw5AoPBoHRIRFSDiffQM2fOwNfXF++++y4++OADvPvuu2jevDnOnDmjmntoQkICoqOj0aJFC8TGxmLLli2IjY1FixYtEB0drYpnEiIiqphKjdjfuHEDjRo1Mjmen58vS/RJzmAwYO7cuQCAoKAgBAcHS/vYi/tEz50716p725rb9goo6a0fPXo01q5dKxttaNCgAUaPHg1PT0/ZlleAstteUc3EkSYiUiPj4p6ll7OppV6FwWDA/PnzERoaarZ6/9SpU7FgwQKrPpMQEVHlVSqx79y5M3799VeMHz8eAKRk/ttvv0VoaKjloqtmUlJSkJ2dDR8fH6Snp8v2ifbw8ICPjw8yMjKQkpKCTp06WSWmB217VdrNmzcRFxdn9hy3vSJrEkeaQkNDERUVJVvDGh0drZqHZyKqmdRe3DM1NRWZmZmIiooyW71/8ODBiIyMRGpqKuuYEBHZgEol9rNmzcILL7yAEydO4P79+/jqq69w4sQJ/PHHHyb72tPfUlJSAJQk00888QSmTZsmS0b++OMPqZ21Evuytr0ydvHiRcycORMff/wxmjVrVu73IrIGjjQRkS1Qc3HPW7duAQB8fX3NnhePi+2IiEjdKpXYd+vWDSkpKfjss88QEBCA7du3o2PHjkhMTERAQIClY6w2iouLAQDt2rUzm4y88847OHHihNTOGsra9sqcZs2acUSeVIEjTURkC9RcbV7c2SY9PR3+/v4m59PT02XtiIhI3Sq9+XzLli2xePFiS8ZS7dWtWxcAUFhYaPZ8QUGBrB0RmceRJiJSO1vZAWfFihWIiYkx2QFnxYoV3AGHiMiGVDixz83NrfA3ZWJqntjrfe7cOXz88cd44403pKn4y5cvx/nz52XtiMg8jjQRkZqJO+A4OjrKjos74EyfPl3x5F6s3j9t2jT06dMHer1eOufg4AC9Xo/p06erZoYBERGVr8KJvZub2wMr3guCAI1Gw+2mytCgQQPp9eHDh2WVco1v/sbtiMiU8UiT8bIWoGTJC0eaiEgpxjvgdOzY0aQTPzEx0eo74DyIcVJv7j0REalfhRP73bt3V2Uc1Y65beScnZ1Rv3591K5dG3fu3JFNya9duzY8PT2Rn58PZ2dn2VZy3EaOSE4caYqOjsbHH3+Mrl27wtHREYWFhUhOTsb+/fsRExOjmodmIqo5xB1wAgICMH36dBw7dgyJiYmoV68epk+fjgkTJiAtLc2qhXLNMe6ACAkJUcUWvEREVHkVTux79Ojx0N983LhxmD59eo0cgS5vG7m//vrL7DHx+JgxY2TnuI0ckanu3btj4MCBWLt2rWz2i06nw8CBAxWf5kpENZO4A06nTp0wZMgQZGZmSuc8PT3Rq1cvVST2xh0Qs2bNks186tevH959911VxElERBVT6eJ5FbF8+XJMmjSpRib25W0jd/jwYaxdu1aW4Ddo0ACvvfYaOnbsaPZ7EZFcQkICVq9ejZCQEHTt2lUaaUpOTsbq1avRrl07JvdEpJglS5bgiSeeQFRUlGxr26VLlyodGoC/OyCGDRtmdneRt956CxMnTmRiT0RkI6o0sRcEoSq/vaqVt42cn58fIiIisGXLFsyZMwcTJ05E7969OdWNqILK2sceKBlp4j72RKQUsbZHnTp1MH36dNjZlTxq+fv7Y/r06ejfvz/y8vJUUwOkJj+rERFVJ9oHN6GqoNPp0Lp1awBA69atmXwQPQRxH/vBgweXuY/9tWvXkJqaqlCERFRTidekvLw8REVF4fjx47h79y6OHz+OqKgo5OXlydoppUOHDgBKZhYUFxfLzhUXF2PJkiWydkREpG5VOmJPRFQVuI89EalVdna29PrQoUNl7oBj3E4JHTp0gJubG9LS0jBlyhQ0adIEhYWFcHR0xOXLl5GWlgZ3d3cm9kRENoIj9kRkc4z3sTeH+9gTkVLE6054eLjJ9r/3799HeHi4rJ1SdDodJkyYAABISkrCunXrsHnzZqxbtw5JSUkAgPfff58zComIbARH7InI5nAfeyJSq8DAQLi5uWHnzp3SNnLidpxJSUnYuXMn3NzcVHF9OnHiBABAo9HI1tqL70+cOMEipERENqJSI/YZGRlmi60IgiDbu/2NN95A3bp1Kx8dEZEZ4j72iYmJmDp1qmwN69SpU5GYmIixY8dypImIVEmj0SgdAvR6PdauXQtHR0eTeDQaDRwdHbF27Vro9XqFIiQioodRqRF7X19fXLt2DY0aNZIdv3XrFnx9faWpZwsWLHj0CImIzOjevTtiYmIwf/58REZGSse9vLwQExPDUSYiUkRqaiqys7MRHh6O3bt3Y//+/dI5nU6H8PBw7Ny5E6mpqQgKClIszg0bNsBgMMBgMMDd3R2PP/64tG3o0aNHcfv2baldRESEYnESEVHFVCqxFwTBbG/znTt34OTk9MhBERFVRPfu3REWFobU1FTcunUL9erVQ2BgIEfqiUgxYtFOcSp+48aNodfr4eDggCtXrmDnzp2ydkq5fPkyAMDBwQE5OTnYs2ePdE6r1cLBwQF6vV5qR0RE6vZQib1YZEWj0SAqKgq1atWSzhkMBiQlJbF6KhFZlU6nU3TUi4jImJubGwDAx8cH6enpshF7Dw8P+Pj4ICMjQ2qnlJs3bwIomZLv7u6OZ599Fo0bN8aVK1ewY8cOacRebEdEROr2UIn9kSNHAJSM2KelpcHBwUE65+DggMcffxyTJk2ybIRERERENiYjIwOhoaGYNm0afH19kZ6ejuXLl8u2v1OSu7u79Fqn02HNmjXS+wYNGphtR0RE6vVQxfN2796N3bt3Y+jQofjtt9+k97t378a2bduwcOFCtGrVyqIBXrlyBW+88Qbq168PZ2dnBAQE4ODBg9J5QRAwbdo0eHl5wdnZGeHh4Th79qzse9y6dQuDBw9G3bp14ebmhhEjRuDOnTsWjZOIlGEwGHDkyBHEx8fjyJEjJttLERFZU0Wn2Cs9FV8ckQeAnJwcPPPMMxg3bhyeeeYZ5OTkmG1HRETqVak19rNnzy6z2n1aWhoCAgIeKSjR7du3ERYWhqeffhq//fYbGjZsiLNnz8p6j2fPno2vv/4aS5cuha+vL6KiotCrVy+cOHFCWu8/ePBgXLt2DTt27EBRURGGDRuGUaNGYeXKlRaJk4iUkZCQgPnz5yMzM1M65unpiXHjxrF4HhEpIjs7GwDQt29fJCcnmxT37Nu3LzZu3Ci1U0q9evWk10VFRdi1axd27dpVbjsiIlKvSm13FxAQgF9//dXk+BdffIGuXbs+clCizz//HE2bNsUPP/yArl27wtfXF8899xxatmwJoGS0ft68eZg6dSr69euHwMBA/Pjjj7h69Sp++eUXAMDJkyexdetWfPvttwgODka3bt3wn//8B6tWrcLVq1ctFisRWVdCQgKio6PRokULxMbGYsuWLYiNjUWLFi0QHR2NhIQEpUMkohpIXDt/8uRJk62Bi4uLcfLkSVk7pVS0yCiLkRIR2YZKJfYTJkzAq6++irFjx+LevXu4cuUKevbsidmzZ1t0FHzjxo3o3LkzIiIi0KhRIwQFBWHx4sXS+fT0dGRmZiI8PFw65urqiuDgYGkNW2JiItzc3NC5c2epTXh4OLRaLZKSksr82YWFhcjNzZX9R0TqYDAYMH/+fISGhmLGjBnw9/dHrVq14O/vjxkzZiA0NBQLFizgtHwisjpxffrZs2eh1+sxadIkrFu3DpMmTYJer5eWCxqvY1dC69atLdqOiIiUVamp+B988AGeffZZDBkyBIGBgbh16xaCg4ORmpoKT09PiwV3/vx5LFiwABMmTMC//vUvHDhwAP/85z/h4OCAoUOHStNvPTw8ZF/n4eEhncvMzESjRo1k5+3s7FCvXj3Z9N3SPv30U8TExFjsz0JElpOamorMzExERUVBq5X3T2q1WgwePBiRkZGK7xNNRDWPv78/dDodnJycYGdnhy+++EI616hRI7i4uKCgoAD+/v4KRgnZgIVOp4Ovr6+0j316errUMVoVAxtZWVmydfwP4+LFi7L/V5arq6vJ8yMRkS2rVGIPAI899hjat2+PdevWAQAGDhxo0aQeKJmy1rlzZ8yaNQsAEBQUhGPHjiEuLg5Dhw616M8qbcqUKdL2fkDJja1p06ZV+jOJqGLEolO+vr4wGAwm+9j7+vrK2hERWcvx48dhMBiQn5+P/Px82bnr16/L2inZ8Sgm1lqtFgaDAX/++afsvFarRXFxcaUT8LJkZWXhjSFvokhf+EjfZ+bMmY/09fYOjli+7Ecm90RUbVQqsd+3bx/eeOMN1KtXD6mpqdi3bx/Gjx+PLVu2IC4uzmJbo3h5eaFdu3ayY23btpU6E8SOhKysLHh5eUltsrKy0KFDB6mN8Y0UAO7fv49bt26V2xHh6OgIR0dHS/wxiMjCxGJO69evx6ZNm0yK57300kuydkRE1mLcoSgmx+beK93xeOPGDQAlgyjlxSm2s5ScnBwU6Qtxr0UPFDu5WvR7V5S2IAc4vxc5OTlM7Imo2qhUYv/MM8/g/fffx7///W/Y29ujbdu2ePrpp/HGG28gICAAly9ftkhwYWFhOH36tOzYmTNn0KxZMwAlo3Wenp6Ij4+XEvnc3FwkJSVh7NixAIDQ0FBkZ2fj0KFD6NSpEwBg165dKC4uRnBwsEXiJCLrCgwMhJubGxYvXozQ0FBERUXJ9olevHgx3NzcEBgYqHSoRFTDiLsG1alTBz///DNOnDghzShq164dXnvtNeTl5ZW5u5C1NGzYUHptZ2cHvV5v9r1xO0sqdnJFsYuydQaIiKqTSiX227dvR48ePWTHWrZsiX379j3y1Chj77//Pp544gnMmjULAwYMQHJyMhYtWoRFixYBADQaDd577z3MmDEDrVq1kra78/b2xssvvwygZIT/+eefx8iRIxEXF4eioiK88847GDRoELy9vS0WKxGpi0ajUToEIqqBzp8/D6BkPb29vb1sun1xcTEaNmyIvLw8nD9/Hl26dFEqTNSpU8ei7YiISFmVSuzFpP7PP//EuXPn0L17dzg7O0Oj0SAqKspiwXXp0gXr16/HlClTMH36dPj6+mLevHkYPHiw1OaDDz5Afn4+Ro0ahezsbHTr1g1bt26V9rAHgBUrVuCdd95Bz549odVq8eqrr+Lrr7+2WJxEZF2pqanIzs7GyJEjsWnTJpN9ot9++218++23LJ5HRFYnLg06d+4cpk6disGDB0szilasWCEl/uUV8LUG4/X/RUVFsnP37983247U6VGKEQIsSEhUXVQqsf/rr78wYMAA7N69GxqNBmfPnkWLFi0wYsQI1KtXT1YB9lG9+OKLePHFF8s8r9FoMH36dEyfPr3MNvXq1bPoNnxEpCxxbWr//v0RERGBDRs24OrVq/D29ka/fv1w//59fPvtt4qvYSWimkecDdi3b18kJSXJOh7FGiCbNm1SfNagIAjSa3t7+zKn4hu3I/WxVDFCgAUJiWxdpRL7999/H/b29sjIyEDbtm2l4wMHDsSECRMsmtgTEZVWXvG8devWsXgeESmmX79+iIuLQ3x8PGrXri07JwgCdu3aBZ1Oh379+ikUYQlxjX+tWrVw9+5d2Tm9Xi8dV7oWAJVPDcUIARYkJFKDSq+x37ZtG5o0aSI73qpVq0eexkNE9CAsnkdEauXg4ICQkBDs27cPer0e//jHP9C7d29s2bIFP//8M4qKihAWFgYHBwdF4xQ7Pksn9SLxODtIbQOLERJRpRL7/Px81KpVy+T4rVu3uEUcEakCi+cRkRIMBgPOnTsHb29vXLt2DT/99BN++uknACXXJW9vb5w/fx4GgwE6nU6xON3c3CzajoiIlKWtzBc9+eST+PHHH6X3Go0GxcXFmD17Np5++mmLBUdEZI5x8bz09HRERkaid+/eiIyMxIULF/D222/j9u3bSE1NVTpUIqphUlNTkZmZiT59+qBRo0ayc40aNUKfPn1w7do1xa9Pf/75p0XbERGRsio1Yj979mz07NkTBw8ehF6vxwcffIDjx4/j1q1b2Ldvn6VjJCKSMS6eN2jQIKSmpkr7RAcGBqKwsJDF84hIEeJ1Z/HixSazGLOzs7F48WJZO6UYdywEBwcjJCQEjo6OKCwsxP79+5GUlCS1+8c//qFUmEREVEGVSuzr1q2LkydPYsGCBahTpw7u3LmDV155BZGRkSZbphARWZq45jM9PR1t2rQxOZ+eni5rR0RkLcZT14uLi2XnjN8rPcX90qVLAIAWLVrgwoULUiIPlFTvb9GiBc6fPy+1IyIidatUYu/r64tr167h448/lh3/66+/0KRJExgMBosER0RkTmBgIDw9PfH1118jOzsbWVlZ0jkPDw+4ubnBy8uLxfOIyOqMk3etVr7i0fh96aTf2pydnQGU7F1eOpasrCwpVrEdERGpW6XW2Je1p+mdO3fg5OT0SAERET2ITqfDU089hdOnT+PGjRuyczdu3MDp06fRo0cPRQtTEVHNlJKSIr2uVasWJk2ahHXr1mHSpEmywsPG7ZQgbldsMBhMnusEQZAGaYy3NSYiIvV6qBH7CRMmACgpljdt2jTZDcpgMCApKQkdOnSwaIBERKUZDAZs3boVAGBvb4/CwkLpnPh+27ZtGDlyJJN7IrIqcQZRo0aNoNFo8MUXX0jnPD090ahRI1y/fl0200gJI0aMwMaNGyvUjoiI1O+hEvsjR44AKOnJTUtLk+3B6uDggMcffxyTJk2ybIRERKWkpKQgOzsbPj4+uHfvnmzUvm7dunB2dkZGRgZSUlLQqVMnBSMloprKxcUFixYtwrFjx6Tinu3bt8fIkSOVDg0AsGXLlgq3Y/E8IiL1e6jEfvfu3QCAYcOG4auvvkLdunWrJCgiovKIU1gzMjJMqk7n5uZKiT4TeyKyNg8PDwAlRTynTZuGN954A6GhodL7CxcuyNop5X//+x+AknX/5tb7i8f/97//MbEnIrIBlSqe98MPP1g6DiKiCiurzkdl2xERWUrHjh2xYsUKAMDhw4eRmJgonTPuiOzYsaPVYzMmbrfXrVs3TJkyBQsXLsTly5fRpEkTjB49GrNmzcL//vc/xbflIyKiiqlUYk9EpCQXFxfpdVBQEIYMGQJfX1+kp6dj2bJl2L9/v0k7IiJr6NChA9zc3JCdnS2r/wFAeu/u7q54TaL69esjMzMTBw4cgL29Pd577z3p3P3793HgwAGpHRERqR8TeyKyOXl5eRZtR0RkKTqdDs8//zxWrVoFjUYjmzkkvu/Vq5fVC3sWFBQgIyNDet+mTRscP34c9+7dw8svv4yXX34ZgYGBSE1NxS+//IKCggKp3ZkzZ2Tfy8fHh7sgEVGFZWVlIScnp9Jff/HiRdn/K8vV1VXxZVBViYk9Edkc42J5R44ckUboAflU19Jb4RERVTWDwYA9e/bA29sbmZmZJom9l5cX9u7da/VdOzIyMjBq1Ciz5+7cuYPly5ebPbdu3TqsW7dOdmzRokXw8/OzeIxEVP1kZWXhjSFvokhf+ODGDzBz5sxH+np7B0csX/ZjtU3umdgTkc0RL8hNmjSBXq/H9evXpXNubm6wt7fH5cuXq+2Fm4jUKzU1FZmZmQCA0NBQdO3aFY6OjigsLERycrK05j41NRVBQUFWi8vHxweLFi2SHfv555+xffv2Mr/mueeew2uvvWb2exERVUROTg6K9IW416IHip1cFYtDW5ADnN+LnJycavt8yMSeiGxOUFAQli9fjsuXL8Pe3l527tatWygqKpLaERFZ082bNwEAwcHBmDlzJrRarXSuX79+mDJlCpKSkqR21uLk5GQyyv6vf/0L9erVw5o1a2SV8bVaLQYMGIAxY8ZYNUaq3jgdu2YrdnJFsUsDpcOo1pjYE5HN6dChA2rVqoW7d+9KSbxIfO/i4qJ4cSoiqnmys7MBAE8++STu37+PDRs24OrVq/D29ka/fv3QrVs3JCUlSe2UNmbMGAwfPhzffvst1qxZgwEDBuDtt9+Gg4OD0qFRNcLp2ERVj4k9Edmk+/fvP9J5IqKq4ObmBgBYsWIF5s6dKxsJX7BggZRMiO3UwMHBAeHh4VizZg3Cw8OZ1JPFcTo2UdVjYk9ENufw4cPQ6/XQ6XQwGAwm53U6HQoLC3H48GF06dJFgQiJqKZq0KBkqum1a9dMzhUXF0vHxXZENQmnYxNVHSb2RGRzxGJPBoMBrq6uCAoKgpOTEwoKCnDkyBFpDd/27duZ2BORVbVp08ai7YiIiCqCiT0R2Zz8/HwAJVvbOTk5Yc+ePdI5Dw8PFBQUoLCwUGpH9KhY9IkqauPGjdJrrVZrUpROfL9x40YMHDjQ6vEREVH1xMSeiGyORqMBABQWFuL27duyc7dv34Zer5e1I3oULPpEDyM1NVV6bWdnJ12PSr9PTU1lYk9ERBbDxJ6IbE79+vWl18YPzaXfG7cjqiwWfaoa1XUWxL179wAA7u7u0Ol0sm3t6tatC4PBgNu3b0vtiIgqq7peR6lymNgTERFVAIs+WU51ngXh7u4OACaziQDIknyxHRFRZVTn66hSbL2jhIk9EdkcFxcXi7YjIuuqzrMgjL+PRqNBx44d0bFjRxw+fBiHDx+GIAgm7YiIHlZ1vo4qoTp0lDCxJyKbc/36del17dq10bJlSwiCAI1Gg3PnzuHOnTsm7YhIfarjLAjjDkVBEHDo0CEcOnSo3HZERJVVHa+jSqgOHSVM7InI5ohTXB0dHXHnzh0cPXpUdt7R0dFsYT2i6s7WpxFWB+np6RZtR0RE1mPLHSVM7InI5jg7OwMoqYqv0Wikqa1AydTXwsJCWTuimqA6TCOsDipaFI/F84iIyJKY2BORzQkICMC+ffsAQJbUl34fEBBg1biIlFQdphFWB25ubhZtR8rhDBgisiVM7InI5vj6+lq0HVF1YsvTCKsDjthXD5wBQ0S2hok9Edmc1NRU6bW5qfji+9TUVAQHB1s9PiKquSpatJPFPdWNM2CIyNYwsScimyM+EHfs2BEpKSmyxF6r1SIwMBBHjhzhgzMRWZ1Op7NoO1IWZ8AQka3QKh0AEdHDatSoEQCgoKAAv/zyC8LCwuDr64uwsDCsX79eKp4ntiMishaO2BMRkRI4Yk9ENqdjx45YsWIFTpw4gb59+0rH09PTZe87duxo0Z/LQkpE9CB5eXkWbUdERFQRTOyJyOZ06NBB2qu+LI6OjujQoYPFfiYLKRERERGRWjGxJyKbYzAYUFRUVG6boqIiGAwGi61jZSElIqoIJycn3Llzp0LtiIiILIWJPRHZnA0bNqC4uLjcNsXFxdiwYQMiIiIs+rNZSImIynP//n2LtiMiIqoIJvZElfAoa625zvrRXblyxaLtiIgshVXxiYhICUzsSTVspTCZpdZac5115RkMBum1m5sbOnToAGdnZ9y7dw8pKSnIzs42aUdEZA0ODg4WbUdERFQRTOxJFWypMjASYgAAaRNJREFUMJka1lrX9HXWxtWks7OzsWfPnge2IyKyBldXV1y7dq1C7YiIiCyFiT2pghqSZeDhEmautVbOhQsXZO/9/PzQuHFjXLlyBWfOnCmzHRFRVcvNzbVoOyIioopgYk+qwmSZKqJWrVqy92fOnJEl9GW1IyKqagUFBRZtR0REVBFM7InI5hhPYdXpdPDw8IBWq0VxcTGysrKktfWc6kpE1nb37l2LtiMiIqoIJvZEZHMaNPh7VofBYMDVq1cf2I6IyBqcnJwqNBrPfeyJiMiStEoHQET0sG7dumXRdkREluLl5WXRdkRERBXBxJ6IbI67u7tF2xERWUr9+vUt2o6IiKgiOBWfiGzOzZs3Ze+bNGmCunXrIjc3F5cvXy6zHRFRVTt79qxF2xEREVUEE3uiaiwrKws5OTmV/vqLFy/K/l9Zrq6uD9w+8GGUXlNvnMyX146IqKoVFRVZtB0REVFFMLEnqqaysrLwxpA3UaQvfOTvNXPmzEf6ensHRyxf9qPFknvjzgo7OzsEBASgfv36+Ouvv5CWlob79++btCMisgaNRiO9rlu3Lho2bAi9Xg8HBwfcuHFD2r/euB0RqYsaBkYsPShC1R8Te6JqKicnB0X6Qtxr0QPFTspt+6YtyAHO70VOTo7FblCurq7SDbe4uBhHjhz5++dptbJ2RETW5OjoKL3Ozc2VEvny2hGReqhlYMTSgyJU/TGxJ6rmip1cUexSvbZ9CwwMREZGBgCgTp06CAoKkraYOnLkiJT0BwYGKhkmEVVzBQUF0rVIpNPpKvS1Op0OZ86ckd77+PhwCzwiFVDDwEhVDIpQ9WdTif1nn32GKVOm4N1338W8efMAlNxUJ06ciFWrVqGwsBC9evXC/PnzZR+CjIwMjB07Frt370bt2rUxdOhQfPrpp7Czs6k/PhH9fwEBAdi8eTOAkhvwnj17ymxHRFRVMjIyMGrUqEp97aVLl2Rfu2jRIvj5+VkqNCJ6RNVxYISqN5vJbA8cOICFCxeajMC9//77+PXXX7F27Vq4urrinXfewSuvvIJ9+/YBAAwGA/r06QNPT0/88ccfuHbtGt58803Y29tj1qxZSvxRiOghlR4V0+v1Ffo6vV7PETEiqjI+Pj5YtGiR7NjJkyfx5ZdfPvBr33//fbRt21b2vYiIiCrLJhL7O3fuYPDgwVi8eDFmzJghHc/JycF3332HlStX4plnngEA/PDDD2jbti3279+PkJAQbN++HSdOnMDOnTvh4eGBDh064N///jc+/PBDfPLJJ3BwcFDqj0VEFVTZUbE5c+bI3nNEjIgsycnJyeSa0rJlS/zwww/Izs4u8+vc3d3x4osvVnjaPhER0YPYRGIfGRmJPn36IDw8XJbYHzp0CEVFRQgPD5eOtWnTBj4+PkhMTERISAgSExMREBAgm5rfq1cvjB07FsePH0dQUJDZn1lYWIjCwr+LZpRV/IaIqp65UbHDhw9j4cKFaN++PZycnHDgwAF06dIFBQUFOHbsGEaPHo2OHTuafB8ioqqk0+kwYcIEREdHw87OTratnb29Pe7fv4/333+fST0REVmU6hP7VatW4fDhwzhw4IDJuczMTDg4OMDNzU123MPDA5mZmVKb0kUnxPdiG3M+/fRTxMTEPGL0RGQJ5kbF/Pz84O3tjfnz50uf5QMHDsDLywsxMTHo3r27EqESEaF79+6IiYmRXZ8AoEGDBhg7diyvT0REZHGqTuwvXbqEd999Fzt27LD6utgpU6ZgwoQJ0vvc3Fw0bdrUqjEQUfm6d++OsLAwbNmyBXPmzMHEiRPRu3dvjoQRkeJ4fSIiImvSPriJcg4dOoTr16+jY8eOsLOzg52dHfbu3Yuvv/4adnZ28PDwgF6vN1nHlpWVBU9PTwCAp6cnsrKyTM6L58ri6OiIunXryv4jIvXR6XRo3bo1AKB169Z8aCYi1eD1iYiIrEXViX3Pnj2RlpaGlJQU6b/OnTtj8ODB0mt7e3vEx8dLX3P69GlkZGQgNDQUABAaGoq0tDRcv35darNjxw7UrVsX7dq1s/qfiYiIiIiIiMiSVD0Vv06dOmjfvr3smIuLC+rXry8dHzFiBCZMmIB69eqhbt26GD9+PEJDQxESEgIAeO6559CuXTsMGTIEs2fPRmZmJqZOnYrIyEg4Ojpa/c+khKysLOTk5FT66y9evCj7f2W5urqa1Dsgoqqhhs89P/NERERE1qHqxL4ivvzyS2i1Wrz66qsoLCxEr169MH/+fOm8TqfD5s2bMXbsWISGhsLFxQVDhw7F9OnTFYzaerKysvDGkDdRpC98cOMHmDlz5iN9vb2DI5Yv+5EP+kRVTC2f+4p85tXQAQGwE4KIiIhsm80l9nv27JG9d3JyQmxsLGJjY8v8mmbNmmHLli1VHJk65eTkoEhfiHsteqDYyVWxOLQFOcD5vcjJyeHDM1EVU8PnviKfebV0QADseCQiIiLbZnOJPVVOsZMril0aKB0GEVmR2j/3auiAANjxSKQE7b3sGvmziYiqChN7IiJSlNo7IIjI8pzTE5QOgYioWmFiT0RERERWdc+3O4qd3RT52dp72exYIKJqh4n9I2DRJyIiIqKHV+zsxpk6REQWxMS+klj0iYiIiIiIiNSAiX0lsegTERERERERqQET+0fEok9ERERERESkJK3SARARERERERFR5TGxJyIiIiIiIrJhTOyJiIiIiIiIbBgTeyIiIiIiIiIbxsSeiIiIiIiIyIYxsSciIiIiIiKyYdzujoiIiIiIiKqM9l52jf751sDEnoiIiIiIiKqMc3qC0iFUiNIdAI/y85nYExERERERUZW559sdxc5uiv187b3sCnUu2EoHhDlM7ImIiIiIiKjKFDu7odilgdJhPJCtdECYw8SeiIiIiIiIajxb6YAwh4k9EREREZENs+V1wURkGUzsiYiISBFKJwNK/3xLysrKQk5OTqW//uLFi7L/V5arqys8PDwe6XvQw7PldcFEZBlM7ImIiCpA6SRQ6Z9fFZiMWEZWVhbeGPImivSFj/y9Zs6c+Uhfb+/giOXLfqw2yb3Sn7uK/nxbXhdMRJbBxJ6IiKgC+NBqeUxGLCMnJwdF+kLca9EDxU6uisWhLcgBzu9FTk5OtUnsbeXfhy2vCyYiy2BiT0REVAG2koTayggjwGTE0oqdXPn7tDBb+dwTETGxJ1WxpQdSIqpZbCUJZRJAZDm28rm3FUo/Zyn984mqEhN7UhU+kJLaKf1QoPTPJ/XjCCMRqRWvDZan9HOB0j+f/sbEnlSFD6Skdvz3QWrHEUYiUis+51ledfvzUOUxsSdVsaUHUiV7KNk7qhw+lBAREVWOLT3n2Qo+l5CIiT1RJfEiVjPxoYSIiIjUgs8lJGJiT1RJSvaQsneUiIiIiIhETOyJKok9pEREREREpAZM7GsIpddkK/3ziYiIiIiIqism9jUEp20TERERERFVT0zsawhWzCQiIiIiIqqemNjXEFwPTkREREREVD1plQ6AiIiIiIiIiCqPiT0RERERERGRDeNUfKJqTukdCZT++UREjyIrKws5OTmV/vqLFy/K/l9Zrq6u8PDweKTvQURE1RcTe6JqzhaKFvLBmYjUKCsrC28MeRNF+sJH/l4zZ858pK+3d3DE8mU/8hpFRERmMbEnqubUviMCH5yJSK1ycnJQpC/EvRY9UOzkqlgc2oIc4Pxe5OTk8PpERERmMbEnqubUviMCH5yJSO2KnVxVfR0lIstTcikhlzFSZTCxJyJV4IMzERERqYUtLGUkMsbEnoiIFKX0yITSP5+IiNRHyaWMD1rGSGQOE3siIlIUH16IiEht1L6Ukag0JvaPSOmRHqV/PhHRo1J7gUciIiIitWNi/4j4MEhE9Gg4KkJERET0aJjYPyKONBEREZEaKD2L72F+vrYgp+oCUfHPJiKqKkzsHxFHmohIrbhVD1HNYgsd/a6urrB3cATO71U0DnsHR7i6KrfFKhGRpTGxJyKqpmzhIZ+ILMcWZhF6eHhg+bIfkZNTuVHzixcvYubMmfj444/RrFmzSn0PoKSDwcPDo9JfT0SkNkzsiYiqKW7VQ1Sz2MosQg8Pj0dOqps1awY/Pz8LRUREZPuY2BMRVVO28pBPRERERI+GiT0RERERERHVeEoX13yUn8/EnoiIiIiIiKqM2hNmtRT2BCpf3FP1if2nn36K//73vzh16hScnZ3xxBNP4PPPP0fr1q2lNgUFBZg4cSJWrVqFwsJC9OrVC/Pnz5et38rIyMDYsWOxe/du1K5dG0OHDsWnn34KOzvV/wqIiEgF1P5QQkSWp/TnrqI/31bipJrHVhLmRy3sCShf3FP1We3evXsRGRmJLl264P79+/jXv/6F5557DidOnICLiwsA4P3338evv/6KtWvXwtXVFe+88w5eeeUV7Nu3DwBgMBjQp08feHp64o8//sC1a9fw5ptvwt7eHrNmzVLyj0dENkbphxelf35NZCsPJURkObbyubeVOKnmsqWE2RKFPQHlinuqPrHfunWr7P2SJUvQqFEjHDp0CN27d0dOTg6+++47rFy5Es888wwA4IcffkDbtm2xf/9+hISEYPv27Thx4gR27twJDw8PdOjQAf/+97/x4Ycf4pNPPoGDg4MSfzQyQ+mk5WF+vpKxKv17qon48FRz2dJDCRFZhq187m0lTqrZbD1hthWqT+xLEy9c9erVAwAcOnQIRUVFCA8Pl9q0adMGPj4+SExMREhICBITExEQECD7B9WrVy+MHTsWx48fR1BQkMnPKSwsRGFhofQ+Nze3qv5IBNtKmtQSK5M76+LDU81mSw8lSnf8Kf3zq4L2XnaN/vk1la187m0lTiKqWjaV2BcXF+O9995DWFgY2rdvDwDIzMyEg4MD3NzcZG09PDyQmZkptSl9wRPfi21K+/TTTxETE2PhPwGVxZaSpkeNlcmd7eLDE6mZWjodgYp3PCrdCVDRn++cnlDFkRARET0am0rsIyMjcezYMfz+++9V/rOmTJmCCRMmSO9zc3PRtGnTKv+5NZktJU2WiJXJHRFZki11kNpaJ8Q93+4odnazTkBmaO9ls3OBqgVb6cyzJUr/mZT++fQ3m0ns33nnHWzevBkJCQlo0qSJdNzT0xN6vR7Z2dmyUfusrCx4enpKbZKTk2XfLysrSzpnjqOjIxwdHS38pyCisig91VTpn09kCbbSQWpLnRAAUOzshmKXBpX+GUQ1na115tkC/k6pNNUn9oIgYPz48Vi/fj327NkDX19f2flOnTrB3t4e8fHxePXVVwEAp0+fRkZGBkJDQwEAoaGhmDlzJq5fv45GjRoBAHbs2IG6deuiXbt21v0DKUTp3jSlfz6pH0ejiGoWW+mEIKJHZ2udebaAv1MqTfWJfWRkJFauXIkNGzagTp060pp4V1dXODs7w9XVFSNGjMCECRNQr1491K1bF+PHj0doaChCQkIAAM899xzatWuHIUOGYPbs2cjMzMTUqVMRGRlZ7Ufl2ZtHtoJTXYmIiKovduZZHn+nZEz1if2CBQsAAE899ZTs+A8//IC33noLAPDll19Cq9Xi1VdfRWFhIXr16oX58+dLbXU6HTZv3oyxY8ciNDQULi4uGDp0KKZPn26tP4Zi2JtHtoJTXYmIiIiIKkf1ib0gCA9s4+TkhNjYWMTGxpbZplmzZtiyZYslQ7MZ7M2r2ZReBqH0zyciIiJ6WEo+v/DZiSpD9Yk9EVUOl2EQERERPRy1PD/x2YkeFhN7omqKyzCIow1ENYvSnzulfz6RJajl+YnPTvSwmNg/IqVvYkr/fFI3LsOomTjaQFSzqOUzD/BzT9UDn5/IFjGxryTeRIlIrTjaQFSzqOUzD/BzT0SkFCb2lcSbKBGpmS2NNig980jpn09kCbb0mSciIstjYv8IeBMlIqo8znwiW6F054/SP5+IiNSPiT0RqYLSD65K//yaiDOfSO3Y+URERLaCiT0RKYoPzjUbZz6RmrHziYiIbAUTeyJSFB+ciUjN2PlERES2gIk9ESmOD85ERERERJWnVToAIiIiIiIiIqo8JvZERERERERENoyJPREREREREZENY2JPREREREREZMOY2BMRERERERHZMFbFr+EKCgqQkZFRbpuLFy/K/l8WHx8fODk5WSw2IiIiIiIiejAm9jVcRkYGRo0aVaG2M2fOLPf8okWLuNUYERERVXscGCEitWFiX8P5+Phg0aJFFvte9OCbfUVv9ABv9kRERGrEgREiUhsm9jWck5MTbyYWVtGb/YNu9ABv9lSzcUSMiNSKAyM1F+9NlsdBMctgYk9kYbzZk9rZykMJR8Qsjw9PRJbBgZGai/cmy+OgmGUwsSeyMN7sSe1s5aGEnWSWx4cnIqJHw3uT5fF3ahlM7MlmcKSJyDJs5QbKTjLLs5W/e7I83kOJLIP3Jsvj79QymNiTzeBIE6mdrTw48wZac9nK372tLBexJbyH1lyWujfxs0SkbkzsyWZwpInUjg/ONZetdOrYCltZLmJLeA+tuSx1b+JniWo6td/rmdiTzbCVkSaqufjgXHOxU8ey+FmyPFu5h6r9wdkWWerzxM8S1XRqv9czsScishBbeXAmy2Mialn8LNVcan9wtkX8PBFZhtrv9UzsiYiIHhEfnIksQ+0PzlR1OFuD1E7t93om9kRERESkCmp/cKaqw9kaRI+GiT0RERFRJbB6P5HlcLYG0aNhYk9ERERUCazeT2Q5nK1hWex4rHmY2BMRERFVAkcYiUit2PFY8zCxJyIiIqoEjjASkVqx47HmYWJPRERERERUjbDjsebRKh0AEREREREREVUeE3siIiIiIiIiG8bEnoiIiIiIiMiGcY19FeEWE0RERERERGQNTOyrCLeYICIiIiIiImtgYl9FuMUEERERERERWQMT+yrCLSaIiIiIiIjIGlg8j4iIiIiIiMiGMbEnIiIiIiIismFM7ImIiIiIiIhsGNfYE9VQ3JKRiIiIiKh6YGJPVENxS0YiIiIiouqBiT1RDcUtGYmIiIiIqgcm9kQ1FLdkJCIiIiKqHlg8j4iIiIiIiMiGMbEnIiIiIiIismFM7ImIiIiIiIhsGBN7IiIiIiIiIhvGxJ6IiIiIiIjIhtWoxD42NhbNmzeHk5MTgoODkZycrHRIRERERERERI+kxmx3t3r1akyYMAFxcXEIDg7GvHnz0KtXL5w+fRqNGjVSOjwiKkdBQQEyMjLKPH/x4kXZ/8vi4+MDJycni8ZGRERERKQ0jSAIgtJBWENwcDC6dOmCb775BgBQXFyMpk2bYvz48fjoo48e+PW5ublwdXVFTk4O6tatW9XhEpGRM2fOYNSoUY/8fRYtWgQ/Pz8LREREREREVPUqmofWiBF7vV6PQ4cOYcqUKdIxrVaL8PBwJCYmmv2awsJCFBYWSu9zc3OrPE4iMs/HxweLFi2yyPchIiIiIqpuakRif/PmTRgMBnh4eMiOe3h44NSpU2a/5tNPP0VMTIw1wiOiB3BycuJIOxERERFRGWpU8byHMWXKFOTk5Ej/Xbp0SemQiIiIiIiIiEzUiBH7Bg0aQKfTISsrS3Y8KysLnp6eZr/G0dERjo6O1giPiIiIiIiIqNJqxIi9g4MDOnXqhPj4eOlYcXEx4uPjERoaqmBkRERERERERI+mRozYA8CECRMwdOhQdO7cGV27dsW8efOQn5+PYcOGKR0aERERERERUaXVmMR+4MCBuHHjBqZNm4bMzEx06NABW7duNSmoR0RERERERGRLasw+9o+K+9gTERERERGRNVU0D60Ra+yJiIiIiIiIqism9kREREREREQ2jIk9ERERERERkQ1jYk9ERERERERkw5jYExEREREREdkwJvZERERERERENoyJPREREREREZENY2JPREREREREZMOY2BMRERERERHZMCb2RERERERERDaMiT0RERERERGRDbNTOgBbIQgCACA3N1fhSIiIiIiIiKgmEPNPMR8tCxP7CsrLywMANG3aVOFIiIiIiIiIqCbJy8uDq6trmec1woNSfwIAFBcX4+rVq6hTpw40Go1Fvmdubi6aNm2KS5cuoW7duhb5nlWBcVqWrcQJ2E6sjNPybCVWxmlZthInYDuxMk7LspU4AduJlXFanq3Eyjgtq6riFAQBeXl58Pb2hlZb9kp6jthXkFarRZMmTarke9etW1fV/0hFjNOybCVOwHZiZZyWZyuxMk7LspU4AduJlXFalq3ECdhOrIzT8mwlVsZpWVURZ3kj9SIWzyMiIiIiIiKyYUzsiYiIiIiIiGwYE3sFOTo6Ijo6Go6OjkqHUi7GaVm2EidgO7EyTsuzlVgZp2XZSpyA7cTKOC3LVuIEbCdWxml5thIr47QspeNk8TwiIiIiIiIiG8YReyIiIiIiIiIbxsSeiIiIiIiIyIYxsSciIiIiIiKyYUzsiYiIiIiIiGwYE3siskl//vkntm3bhnv37gEAWAeUiIiIiGoqO6UDIHXT6/W4fv06iouLZcd9fHwUiefrr7/GqFGj4OTkhK+//rrctv/85z+tFJWpoKAgaDSaCrU9fPhwFUdTvfz1118YOHAgdu3aBY1Gg7Nnz6JFixYYMWIE3N3dMWfOHKVDlImPj0d8fLzZz9H333+vUFREVNWys7Px888/49y5c5g8eTLq1auHw4cPw8PDA40bN1Y6PCIisoADBw6guLgYwcHBsuNJSUnQ6XTo3Lmz1WJhYk9mnT17FsOHD8cff/whOy4IAjQaDQwGgyJxffnllxg8eDCcnJzw5ZdfltlOo9Eomti//PLLiv1sSyguLsaff/5pNhnt3r27QlGVeP/992FnZ4eMjAy0bdtWOj5w4EBMmDBBVYl9TEwMpk+fjs6dO8PLy6vCnT1ESjh37hx++OEHnDt3Dl999RUaNWqE3377DT4+PvD391c6PJuSmpqK8PBwuLq64sKFCxg5ciTq1auH//73v8jIyMCPP/6odIhUhdihS7ZAbYN3tioyMhIffPCBSWJ/5coVfP7550hKSrJaLNzH3soMBgOWLFlS5gV/165dCkUmFxYWBjs7O3z00UdmE5LHH39cocioqu3fvx+vv/46Ll68aDK9XclOHZGnpye2bduGxx9/HHXq1MHRo0fRokULnD9/HoGBgbhz546i8Rnz8vLC7NmzMWTIEKVDqbDs7GwkJyebvT69+eabCkUlp9Vqy+0kUfrfaGm28JC/d+9evPDCCwgLC0NCQgJOnjyJFi1a4LPPPsPBgwfx888/Kx0iACArKwuTJk2Sfp+lr1Fq+bsPDw9Hx44dMXv2bNl16o8//sDrr7+OCxcuKB2iRM2/U1uZpWfsQR2669evVygyU3v37sUXX3yBkydPAgDatWuHyZMn48knn1Q4MlO2cG8CSgbGdu/ebTbOadOmKRSVnFoH78zJz8/HZ599VuY99Pz58wpF9rfatWsjNTUVLVq0kB1PT09HYGAg8vLyrBYLR+yt7N1338WSJUvQp08ftG/fXrUjeCkpKTh06BDatGmjdChkZWPGjEHnzp3x66+/qnKUOT8/H7Vq1TI5fuvWLTg6OioQUdn0ej2eeOIJpcOosE2bNmHw4MG4c+cO6tatK/u712g0qnl4Kv1gXFRUhCNHjmDp0qWIiYlRKCrzbGXWxkcffYQZM2ZgwoQJqFOnjnT8mWeewTfffKNgZHJvvfUWMjIyEBUVperf54EDB7Bw4UKT440bN0ZmZqYCEZVNzb9TW5mlZywuLg5LlixRfYfu8uXLMWzYMLzyyivS727fvn3o2bMnlixZgtdff13hCP9mK/emxYsXY+zYsWjQoAE8PT1N4lRLYv/WW2/Bzs4OmzdvVt1nvrS3334be/fuxZAhQ1Qbq6OjI7KyskwS+2vXrsHOzrqpNkfsraxBgwb48ccf0bt3b6VDKVeXLl3w5Zdfolu3bkqHUq7Lly9j48aNyMjIgF6vl52bO3euQlEB9erVw5kzZ9CgQQO4u7uXeyG6deuWFSN7MBcXFxw9ehSPPfaY0qGY1bt3b3Tq1An//ve/UadOHaSmpqJZs2YYNGgQiouLVTOyCAAffvghateujaioKKVDqRA/Pz/07t0bs2bNMtt5onYrV67E6tWrsWHDBqVDkdjKrI3atWsjLS0Nvr6+shHmCxcuoE2bNigoKFA6RABAnTp18L///Q8dOnRQOpRyNWrUCNu2bUNQUJDs97ljxw4MHz4cly5dUjpEia38Tm1F/fr1kZycjJYtWyodSrnatm2LUaNG4f3335cdnzt3LhYvXiyN4quBrdybmjVrhnHjxuHDDz9UOpRyubi42MzgnZubG3799VeEhYUpHUqZ/vGPf+DatWvYsGEDXF1dAZTMMHn55ZfRqFEjrFmzxmqxcMTeyhwcHFSbMOXm5kqvP//8c3zwwQeYNWsWAgICYG9vL2tbt25da4dnIj4+Hn379kWLFi1w6tQptG/fHhcuXIAgCOjYsaOisX355ZfSqNe8efMUjeVhBQcH488//1Ttv9PZs2ejZ8+eOHjwIPR6PT744AMcP34ct27dwr59+5QODxMmTJBeFxcXY9GiRdi5cycCAwNNPkdKdj6Zc+XKFfzzn/9U9YNTeUJCQjBq1Cilw5CxlVkbbm5uuHbtGnx9fWXHjxw5oqpCb02bNrWJHTD69u2L6dOnSw90Go0GGRkZ+PDDD/Hqq68qHJ2crfxObcXbb7+NlStXqr5D9/z583jppZdMjvft2xf/+te/FIiobLZyb7p9+zYiIiKUDuOB2rVrh5s3byodRoW4u7ujXr16SodRri+++ALdu3dHs2bNEBQUBKBk5rOHhweWLVtm1Vg4Ym9lc+bMwfnz5/HNN9+objpJ6XWr4lobY2paf9O1a1e88MILiImJkUZEGjVqhMGDB+P555/H2LFjlQ7RZqSmpkqvz507h6lTp2Ly5MlmO3UCAwOtHZ6JnJwcfPPNNzh69Cju3LmDjh07IjIyEl5eXkqHhqeffrpC7TQajWpqaoheeeUVDBo0CAMGDFA6lId27949TJkyBb/99htOnz6tdDgSW5m1MWnSJCQlJWHt2rXw8/PD4cOHkZWVhTfffBNvvvkmoqOjlQ4RALB9+3bMmTMHCxcuRPPmzZUOp0w5OTl47bXXcPDgQeTl5cHb2xuZmZkIDQ3Fli1b4OLionSIEjX/To07Sh9EyY7S0h26S5cuRWBgoKo7dB977DFMnjwZo0ePlh2Pi4vDnDlzcPbsWYUiM2Ur96YRI0agS5cuGDNmjNKhlGvXrl2YOnWqqgfvRMuXL8eGDRuwdOlSVXfs5OfnY8WKFTh69CicnZ0RGBiIf/zjHya/26rGxN7K+vfvj927d6NevXrw9/c3+Qv/73//q1BkJUVUKqpHjx5VGEnF1KlTBykpKWjZsiXc3d3x+++/w9/fH0ePHkW/fv1UVZwIAK5fv2628IcaEmWxU6esy4F4Ti2dOlQ1vvvuO0yfPh3Dhg0ze7Pv27evQpHJlV7eIggC8vLyUKtWLSxfvlw1cQIldVV+/PFH1T/k6/V6REZGYsmSJTAYDLCzs4PBYMDrr7+OJUuWQKfTKRZb6b/v/Px83L9/H7Vq1TL5faptadO+fftkHZDh4eFKh2TC3d0dd+/eVeXvtHRH6eHDh3H//n20bt0aAHDmzBnodDp06tRJ0Y5SW+zQXbBgAd577z0MHz5cmlW0b98+LFmyBF999ZVJwq8kW7k3ffrpp5g7dy769OljNk611IHQarUAoNrBu9JbRv/5558QBAHNmzc3+Z1yy2g5JvZWNmzYsHLP//DDD1aKxPZ5enpi9+7daNu2Ldq1a4fPPvsMffv2xdGjRxEWFqaa6uiHDh3C0KFDcfLkSVVWmQeAixcvVrhts2bNqjCSB9u6dStq164t1X+IjY3F4sWL0a5dO8TGxsLd3V3R+MqTm5uLXbt2oU2bNqpc2ybe7M1Ry79VAFiyZInspq/VatGwYUMEBwer7u+/vAd+NT3kiy5duoS0tDTcuXMHQUFBaNWqldIhYenSpRVuO3To0CqM5NFkZ2fDzc1N6TBMPOj3q5bf6dy5c7Fnzx4sXbpU+pzfvn0bw4YNw5NPPomJEycqHKHtWb9+PebMmSOtp2/bti0mT56Mfv36KRyZnK3cm0ovZTKm0WhUUcEdePBAntKDdw9TBFep2WQbN27ECy+8AHt7e2zcuLHcttbseGJiT2bZQvL08ssvo0+fPhg5ciQmTZqEDRs24K233sJ///tfuLu7Y+fOnUqHCKBka8CWLVviww8/hIeHh0kPqdKJsq0JCAjA559/jt69eyMtLQ2dO3fGxIkTsXv3brRp00ZVnWMDBgxA9+7d8c477+DevXt4/PHHpToQq1atUt1aWyKRwWBAWloamjVrporrva35/PPP0bx5cwwcOBBAybVg3bp18PT0xJYtW7hlbCU0btwY27dvh7+/v+z4sWPH8Nxzz+Hq1asKRVY+tXfoEtHD02q1yMzMRKNGjVTV8cTieWTW5MmT8fnnnwMA0tLSMGHCBCl5mjBhgiqSp7lz50qj8jExMbhz5w5Wr16NVq1aqWZ6K1BSoGbdunWqLUZX2qeffgoPDw8MHz5cdvz777/HjRs3FK/2mp6ejnbt2gEA1q1bh5deegmzZs3C4cOHVbfbREJCAj7++GMAJSMjgiAgOzsbS5cuxYwZM5jYPwTjOhAPooblLbbmvffeQ0BAAEaMGAGDwYAePXrgjz/+QK1atbB582Y89dRTSocIANiyZQt0Oh169eolO759+3YYDAa88MILCkUmFxcXhxUrVgAAduzYgR07duC3337DmjVrMHnyZGzfvl3hCM0rKCgw2WFGLettc3NzcePGDZPjN27csOo+0Q9SukO3c+fOquzQbdGiBQ4cOID69evLjmdnZ6Njx46qGV2mqnP37l2zu0qp6R566dIlaDQaNGnSBACQnJyMlStXol27dooWyzVeVlt6ia2SmNgr4Oeff8aaNWvMfpjUslbEFpIn4/0iXVxcEBcXp2A0ZevZs6eqt48rbeHChVi5cqXJcX9/fwwaNEjxxN7BwQF3794FAOzcuVPav7ZevXqynR3UICcnR6rmunXrVrz66quoVasW+vTpg8mTJyscXYmvv/4ao0aNgpOTE77++uty2yq5PrBDhw6yOhDlFR9Vy7RM0cGDB8u85itZV8XYzz//jDfeeANAyZ7R58+fx6lTp7Bs2TJ8/PHHqthxAgA++ugjfPbZZybHi4uL8dFHH6kmsc/MzETTpk0BAJs3b8aAAQPw3HPPoXnz5ggODlY4Orn8/Hx8+OGHWLNmDf766y+T82r5PPXv3x/Dhg3DnDlz0LVrVwBAUlISJk+ejFdeeUXh6P5mKx26Fy5cMPt3W1hYiCtXrigQUfny8/Oxd+9es9dRtaxdB9S7DbOxGzduYNiwYfjtt9/MnlfLZx4AXn/9dYwaNQpDhgxBZmYmwsPD0b59e6xYsQKZmZmYNm2a0iGqChN7K/v666/x8ccf46233sKGDRswbNgwnDt3DgcOHEBkZKTS4UlsKXnS6/Vmi9L5+PgoFJHct99+i6FDh+LYsWNo3769aou+iDIzM81Wl2/YsCGuXbumQERy3bp1w4QJExAWFobk5GSsXr0aQEkRJbFHVy2aNm2KxMRE1KtXD1u3bsWqVasAlKwLdXJyUji6El9++SUGDx4MJycnfPnll2W202g0ij48paenS6+PHDmCSZMmYfLkyQgNDQUAJCYmYs6cOZg9e7ZSIZq1atUqvPnmm+jVqxe2b9+O5557DmfOnEFWVhb69++vdHiSmzdvwtPTE0DJqPiAAQPg5+eH4cOH46uvvlI4ur+dPXtW6nQ21qZNG/z5558KRGSeu7s7Ll26hKZNm2Lr1q2YMWMGgJLiVGp6aAaADz74ALt378aCBQswZMgQxMbG4sqVK1i4cKHZThSlxMXFYdKkSXj99ddRVFQEALCzs8OIESPwf//3fwpH9ze1d+garwfetm2btO82UJLQxcfHq253hCNHjqB37964e/cu8vPzUa9ePdy8eRO1atVCo0aNVJPYq3kbZmPvvfcesrOzkZSUhKeeegrr169HVlYWZsyYgTlz5igdnsyxY8ekjrw1a9YgICAA+/btw/bt2zFmzBjVJPbx8fH48ssvZfUq3nvvPesXTBXIqlq3bi2sXLlSEARBqF27tnDu3DlBEAQhKipKiIyMVDI0mZdeekno1auXMH36dMHe3l64fPmyIAiCsG3bNqFVq1YKR1fi9OnTQrdu3QStViv7T6PRCFqtVunwJBs3bhRcXV0FjUZj8p+a4hQ99thjwrJly0yO//jjj4Kvr68CEcldvHhR6NOnjxAYGCh8++230vH33ntPGD9+vIKRmYqNjRXs7OwENzc34fHHHxcMBoMgCILw9ddfC0899ZTC0dmuLl26CL/++qvJ8V9//VXo2LGjAhGVLSAgQPjmm28EQfj7ml9cXCyMHDlSmDZtmsLR/c3Hx0fYtm2bcP/+faFp06bC5s2bBUEQhGPHjglubm4KR/c3Dw8PIT4+3uT4jh07hIYNGyoQkXmRkZFCs2bNhPDwcKF+/fpCXl6eIAiC8NNPPwlBQUEKRyfXtGlTYffu3YIgCEKdOnWEs2fPCoJQcs1/4YUXFIzMvDt37ghHjx4Vjh49Kty5c0fpcEy0atVKWL16tXDnzh2hYcOG0r/XlJQUoX79+gpHJ8ieP0o/kzg4OAh+fn7Cpk2blA5TpkePHsLIkSMFg8EgXUczMjKE7t27C+vWrVM6PEmXLl2k67oYZ15entC3b19h/vz5Ckf3N09PTyEpKUkQhJLP/OnTpwVBEIQNGzYIYWFhSoZmwsXFRUhPTxcEoSQ3+eyzzwRBKHkWdHJyUjCyv4nPeoMGDRK++uor4auvvhL+8Y9/CPb29tL931qY2FuZs7OzcOHCBUEQBKFhw4ZCSkqKIAiCcObMGaFevXpKhiZjC8nTE088IXTv3l3YsmWLcOTIESElJUX2n1o0a9ZMiIyMFDIzM5UOpUI+//xzoX79+sL3338vXLhwQbhw4YLw3XffCfXr1xdmzZqldHg258CBA8J///tf6cFeEARh8+bNwu+//65gVObFxMQI+fn5Jsfv3r0rxMTEKBCReU5OTsKJEydMjp84cUI1N3pRrVq1pIeSevXqCampqYIglMTq6empYGRy0dHRgqurq9CmTRvBx8dHKCgoEARBEL777jshJCRE4ej+NmrUKCEgIED4888/pWNnz54VAgMDhREjRigYmZxerxf+7//+T/jnP/8pHD58WDo+d+5cYfHixQpGZsrFxUW4ePGiIAiC0LhxY+mB//z584KLi4uSodkkW+nQbd68uXDjxg2lw6gQV1dX4dSpU9Jr8fq/f/9+oXXr1kqGJlO7dm3p2uTm5iYcO3ZMEISSTp1mzZopGJlcnTp1pPuSj4+P9Dxy/vx5wdnZWcHITHXt2lX48MMPhYSEBMHJyUl6vk9MTBQaN26scHQlGjduLPznP/8xOf7NN98I3t7eVo2Fib2V+fr6Sjf5Tp06CXFxcYIglIyEu7u7KxmazalVq5Zw8uRJpcN4IOMLvS0oLi4WPvjgA8HJyUmaBVGrVi0hJiZGKC4uVjo8QRAE4c8//xQ+/vhjYdCgQUJWVpYgCIKwZcsW6SZKlaPVaqXfp7GbN2+qanZJUFCQMGTIEKGwsFA6VlhYKAwZMkR1o6GNGzeWkvmAgABpxtYff/wh1K1bV8nQTKxdu1aYO3eucOnSJenYkiVLhF9++UXBqOSys7OFkJAQwc7OTmjevLnQvHlzwc7OTnj66aeF27dvKx2eTQoICBD27NkjCIIg9OzZU5g4caIgCILw1VdfqebBWRBKRuqnTp0qhIaGCi1bthR8fX1l/6mJLXXo2oIGDRoIZ86cEQShZEbE1q1bBUEQhJMnTwq1atVSMjQZDw8PqdOhbdu2woYNGwRBKEns1dRJ1rlzZ+l3+NJLLwlDhgwRLl++LHzwwQdCixYtFI5Obvfu3YKbm5ug1WqFYcOGScenTJki9O/fX8HI/ubi4iLNdDJ25swZq/+9c429lT3zzDPYuHEjgoKCMGzYMLz//vv4+eefcfDgQcWLv+Tm5krVbx+0jl4NVXLbtWuHmzdvKh3GA73yyivYvXs3WrZsqXQoFaLRaPD5558jKioKJ0+ehLOzM1q1agVHR0elQwNQsv/qCy+8gLCwMCQkJGDmzJlo1KgRjh49iu+++w4///yzovFNmDAB//73v+Hi4oIJEyaU21YthXREgiCYLUp39OhRac2oGsTFxeGll15CkyZNpOq9qamp0Gg02LRpk8LRyXXv3h07duxAQEAAIiIi8O6772LXrl3YsWMHevbsqXR4Mq+99hqAksroIrXsYS5ydXXFH3/8gR07duDo0aNwdnZGYGAgunfvrnRoJpYtW4aFCxfi/PnzSExMRLNmzTBv3jz4+vqqap/wYcOG4ejRo+jRowc++ugjvPTSS/jmm29QVFSkqmvU22+/jb1792LIkCHw8vIqt4Cm0jp37ozOnTvLjvXp00ehaMoWHx+P+Ph4s3WKvv/+e4WiMhUUFIQDBw6gVatW6NGjB6ZNm4abN29i2bJlaN++vdLhSUJCQvD777+jbdu26N27NyZOnIi0tDT897//RUhIiNLhSd59912pZlJ0dDSef/55rFixAg4ODliyZImywZXy1FNP4ebNm8jNzZVtvTpq1CjUqlVLwcj+1rdvX6xfv96khsaGDRvw4osvWjUW7mNvZcXFxSguLoadXUmfyqpVq/DHH3+gVatWGD16NBwcHBSLTafT4dq1a9KejOZumuKDv1LFf4w7HA4ePIipU6di1qxZCAgIMClKp4bOBwCYOXMm5s2bhz59+piNUy1FX0Risaw6derIjufn52P8+PGK3+xDQ0MRERGBCRMmoE6dOjh69ChatGiB5ORkvPLKK7h8+bKi8T399NNYv3493Nzc8PTTT5fbdvfu3VaKqnzu7u7QaDTIyclB3bp1ZZ99g8GAO3fuYMyYMYiNjVUwSrn8/HysWLECp06dAlBSqOb111+Hi4uLwpHJ3bp1CwUFBfD29kZxcTFmz54tXfOnTp2qmj3iDQYDZs2ahbi4OGRlZeHMmTNo0aIFoqKi0Lx5c4wYMULpEG3KggULMG3aNLz33nuYOXMmjh07hhYtWmDJkiVYunSpaj775ly8eBGHDh3CY489pqptr9zc3PDrr78iLCxM6VBM2GKHbkxMDKZPn47OnTub7ShZv369QpGZOnjwIPLy8vD000/j+vXrePPNN6Xr6Pfff4/HH39c6RABlGxvfOfOHQQGBiI/Px8TJ06U4pw7dy6aNWumdIhm3b17F6dOnYKPjw8aNGigdDg2wXgXodzcXHzxxRcICwuTCvru378f+/btw8SJEzF16lSrxcXEniR79+5FWFgY7OzssHfv3nLb9ujRw0pRyZXucDA3wqh050Npvr6+ZZ7TaDSq2yvWuIPHmFg1+/79+wpFVqJ27dpIS0uDr6+vLLG/cOEC2rRpIxttpIpZunQpBEHA8OHDMW/ePFmVZAcHBzRv3ly6WSmtqKgIbdq0webNm9G2bVulw6k2pk+fjqVLl2L69OkYOXKklIiuXr0a8+bNQ2JiotIhSmxhlLFdu3aYNWsWXn75Zdl16tixY9IIlBoVFBSoZseO0nx9fbFlyxZVfu4r2qGr0Wiwa9cuK0ZWNi8vL8yePRtDhgxROhQimY4dOyI+Ph7u7u4ICgoqd3aOUtuEl/dsb8zaz/mcim8FqampaN++PbRaLVJTU8ttq2TvuHGyrlTi/iBqHuUoi/E2XWqWm5sLoaTuBvLy8mQPdwaDAVu2bDFJ9pXg5uaGa9eumVxUjxw5gsaNGysUlXm7d+8u8yEvNjZWNVtcitOtfX198cQTT5jMKlETe3t71Xfe2NqyJgD48ccfsWjRIvTs2RNjxoyRjj/++OPSrAg1eNAoo1qkp6cjKCjI5LijoyPy8/MViKhstjJb49///jemTZuGpUuXqmYKrsj42cRWnlP0ej2eeOIJpcMgK7GlWSX9+vWTln++/PLLisZSFrU+2zOxt4IOHTogMzMTjRo1QocOHaDRaGBuooTSo8wP6nQwplQHRI8ePTB9+nRMmjRJdTd2W+fm5gaNRgONRgM/Pz+T8xqNBjExMQpEJjdo0CB8+OGHWLt2LTQaDYqLi7Fv3z5MmjQJb775ptLhybzyyivYuXMnOnXqJDv+1VdfISoqSjWJvci4Q6+goAB6vV52Xi1JaGRkJD7//HN8++230rImNXF3d5dmvYifq9LUNrPoypUreOyxx0yOFxcXS3uGq0FcXByWLFmi+lFGX19fpKSkmEy93bp1q+pGnGfOnImlS5di9uzZGDlypHS8ffv2mDdvnmoS+zlz5uDcuXPw8Ph/7d15XM15+z/w1ynaRLJnWlQitNCksUW2KSW33diLse/KjbFVZCnR2CpEMsaWLJEGlVKYQlqGUols4RsxlajT+f3Rr890OiUz9zjvz4fr+Xjcj0fnc85D182ccz7X+329r6sl2rRpI7MAyWrnTqh+/PFH/Prrr1i1ahXrUGokhF1bAGjSpAnu3buHZs2accfaavPq1Ss5RiYtKSmJ+yxPSkqq9XV8WCxds2ZNjT+TuvHvjugLlJOTg+bNm3M/81XVRYe63tgsb0bd3d0xc+ZMQSX2jx8/xpkzZ5CbmyuTLLFeGa0UHR0NiUSCfv364cSJE1LN0pSUlKCnp4fWrVszjLDC+vXrMWfOHOjo6EAsFqNjx44Qi8UYN26cXM8xfQpvb28MGjQIsbGxMDY2BlBxc+rh4YFz584xjk5WcXEx/vvf/+LYsWPIz8+XeZ4vSWhiYiIiIyNx4cIFmJqaypyrDw0NZRRZhaioKO79I5Tdu44dO+LKlSsyiWhISEiNO8+sCGWXcfHixZgzZw5KSkogkUiQkJCAw4cPY8OGDdi7dy/r8KQIpVqDrzt31RUVFWHjxo21Hhdhefyu6k5teXk5du/ejUuXLsHMzExmoYT1vYkQdm0BYOvWrVxPIl9fX7bBfIQQq0qEhC/3+XTGnnAePnzI/ZyUlARXV1csWbKEO1t77do1+Pj4wMvLi+mHrIKCAlcBIQSRkZEYMmQIDAwMkJ6eDhMTEzx48AASiQQWFha8OW9X6eHDh9DV1eXFqu3H5ObmIi0tDYWFhejSpQuMjIxYh1QjLy8vbNu2DXFxcTh69CjWr1+P8PBwXjaAmjNnDqKjo7F27VpMnDgRO3fuxJMnTxAQEICNGzdi/PjxrEMEUNHF+2P2798vp0i+HKdPn8bkyZOxfPlyeHh4wN3dHRkZGQgODsbZs2cxcOBA1iECAJYuXQp1dXXe7jJWdejQIbi5uSE7OxsA0Lp1a7i7u/NmB7ySqqoq0tPToaenJ9UP4M6dO7CyskJhYSHrEAVl7NixH+3ev2DBAkaRoc6GrpX41AuAfH5v375FVFQUjI2NuU0IluqqfKiKZRVEJT7d51Niz0BmZiaio6NrXMldvXo1o6ikWVlZwc3NDfb29lLXw8PDsWrVKty8eZNRZBWJ/fPnz7kqCL6zsrLCoEGD4O7uzt00tWjRAuPHj4ednR1mzZrFOkQpsbGxH32ej2Ol+G7p0qUIDAyEWCzG+fPneTX2pipdXV0EBwfDxsYGjRo1wq1bt9C2bVscPHgQhw8fRnh4OOsQBSciIgLq6uro1asXgIreCnv27EHHjh2xc+dO3nTFB4ArV67Aw8MDycnJKCwshIWFBVavXo3vv/+edWicBQsWIDg4GGZmZrzcZaxJcXExCgsLebsY/e2332LRokWYMGGCVGLv4eGBixcv4sqVK6xDFBQ+d+8XqkePHkEkEkFbWxsAkJCQgF9//RUdO3bE9OnTGUcnrby8HFlZWTXe4/Pl/mn06NHo3bs35s6di3fv3sHc3JxLRI8cOYIRI0Ywje/AgQOf/Fo+jGTl030+JfZytmfPHsyaNQvNmjVDq1atpFakRCIRb86Iqaqq4tatWzJnAe/evQsLCwu8e/eOUWQVib2Ghkadq3l8WMUDgIYNG+L27dswNDSEpqYm4uLi0KlTJyQnJ+M///kPHjx4wDpEKQoKCjLXqo8/Y0ksFiMoKKjWMkfWuwxVR6BUtXnzZvTu3RtWVlbcNb6NOlRXV8edO3egq6sLbW1thIaGwsrKCjk5OTA1NWW+c1fbKr6GhgbatWsHV1dX3uwsVzI1NcWmTZtgb2+P1NRUWFpawsXFBdHR0TA2Nqbqgr9JKB3HhYTP1RpCOb9cFZ+7938M33Ztq7K2tsb06dMxceJE5OXloV27djAxMUFmZibmzZvHm02x69evY9y4cXj48KFMLy0+9VRp1aoVfvvtN5ibm+PXX3/FmjVrkJycjAMHDmD37t0fPYNPZPHpPp/O2MvZunXr4OnpiaVLl7IO5aM6dOjAnQVUUlICUHG2ccOGDbz4snJ3d5caycVnDRo04M7baGlpITs7G506dQIAXo48ev36tdTj0tJSJCUlYdWqVfD09GQU1V8WLFiAoKAgODg4wMTEhHdHBrZu3VrjdUVFRcTHxyM+Ph5AxZc83xJ7AwMD5OTkQFdXF8bGxjh27BisrKwQFhbGi/dbbecXCwoKcPPmTQwePBghISFwdHSUb2AfkZOTg44dOwIATpw4AUdHR6xfvx63bt2SqYgidRPK2dDnz5/D1dWVW4CsfpPPlxt8oOIsc1hYGDw8PNCgQQOsXr0aFhYWCAsLY75QVvX88tatW3n3eV8TPnfvr6r6rq2lpSWvdm2rSktL4xbFjx07BlNTU8THx+PChQuYOXMmbxL7mTNnwtLSEufOneP11I43b95wfWAiIiIwYsQIqKmpwcHBAUuWLGEcnbTaJsuIRCIoKytzOQpLfLrPp8Rezl6/fo1Ro0axDqNO/v7+cHR0hLa2NtcBPyUlBSKRCGFhYYyjq+iMzteyxkoeHh5wcXFBt27dEBcXhw4dOsDe3h4uLi5ITU1FaGgoL0uya0rgBg4cCCUlJSxevJjpMQwAOHLkCI4dO8bbpIjPDTLr4uzsjOTkZPTp0wfLli2Do6MjduzYgdLSUl6UONdVcte5c2ds2LCBV4m9kpISiouLAQCXLl3iJjc0adKkzlF48lTbbqhIJIKKigratm0LJyenOvsbyEtWVhays7PRu3dvqKqqflLTV3lycnJCbm4uVq1axesb/LKyMqxfvx5TpkzBxYsXWYcjY/LkyXj79i3ev3+P4cOHsw7nkwile39sbCxWrFgBADh58iQkEgkKCgpw4MABrFu3jleJfWlpKddI79KlSxgyZAgAwNjYGM+ePWMZmpTMzEyEhITUOGGET3R0dHDt2jU0adIEEREROHLkCICKHKXqqGM+qG2yTCVtbW04OTlhzZo1NVacygOf7vMpsZezUaNGcSuMfGZlZYX79+/j0KFDXFfcMWPGYNy4cTIdqOWNrzdI1VV279+yZQtXwuzu7o7CwkIcPXoURkZGvEiWPlXLli2RkZHBOgwoKSnx/ksTqLgRMTY2xtmzZ3lR5fIpFi1axP08YMAApKen4+bNm2jWrBl++eUXhpF9msGDB2PdunWsw5DSq1cvLF68GD179kRCQgKOHj0KALh37x53XpQPVq9eDU9PTwwaNIjbGUtISEBERATmzJmDnJwczJo1C2VlZVIj0eQtPz8fo0ePRnR0NEQiETIzM2FgYICpU6dCU1MTPj4+zGKrKi4uDleuXEHnzp1Zh/JR9erVg5eXF+9GhVZV1419Jb5UQfC5g3tVQtq17dSpE/z9/eHg4ICLFy9i7dq1AICnT5+iadOmjKP7y3fffYesrCze36MsXLgQ48ePh7q6OvT09GBjYwOgYrHH1NSUbXDVBAUFYcWKFXBycpL6bjpw4ABWrlyJly9fYvPmzVBWVsZPP/3EJEY+3edTYi9nbdu2xapVq3D9+nWYmprKrOTyqTS3QYMGvGtKAkCmpJGvKuM0MDDgrjVo0AD+/v6sQvokKSkpUo8lEgmePXuGjRs38uIm1cXFBT///DN27NjB60We+vXro6SkhHUY/xM9PT3o6ekhOTkZgYGB2L17N+uQPur9+/e8KMuraseOHZg9ezZCQkLg5+eHb775BgBw/vx52NnZMY7uL3FxcVi3bp3MonNAQAAuXLiAEydOwMzMDNu2bWOa2C9atAj169dHbm6u1ILZmDFjsHjxYt4k9jo6OoL5rurfvz9iYmLQpk0b1qHUqOrxC4lEAnt7e+zdu5d7L/GNUOZuC2nXdtOmTRg2bBi8vb0xefJkmJubAwDOnDkj1beGtXnz5sHFxQV5eXk13uNXVsCyNnv2bFhZWeHRo0cYOHAgt9NtYGDAu8XxAwcOwMfHB6NHj+auOTo6wtTUFAEBAYiMjISuri48PT2ZJPZisRiPHz/m/m1Z3+dT8zw509fXr/U5kUjEdL5pdQcPHkRAQADu37+Pa9euQU9PD1u3boWBgQH+85//sA6P94TWvb+SgoICRCKRzE1pt27dsG/fPuZNdYYNG4bo6Gg0adIEnTp1kvniZD3DvKr169fj3r172Lt3L+rVE+46anJyMiwsLHizI1abhQsXIj09HREREaxDERx1dXXcvn1bZqcpKysLnTt3RmFhIbKzs2FmZoaioiJGUUo3farawf3+/fswMzNj3uCx0oULF+Dj44OAgADeJsyV/P394e7ujvHjx+Pbb7+VqcqrLHvmi6r/7uSf27VrFxYsWMDt2t66dQsKCgrYvn07QkNDedPPQiKR4NGjR9DU1ERZWZnUJJEHDx5ATU2NN0cza2s+XHlUiK/foWKxGKmpqdDT0+PVpBagopl3SkqKzEjjzMxMmJubo7i4GDk5OejUqRN37E3eVFRUcPfu3Y/mePIi3DtNgRLK+Vs/Pz+sXr0aCxcuxLp167gPI01NTfj6+lJi/4natWsnmO79lar/N6qgoIDmzZvzZgW/cePGGDZsGOswPkliYiIiIyNx4cIFmJqaytww82kRQggWL15c4/U3b97g1q1buHfvXp3jGlnIzs7G/v37kZ2djZ9//hktWrTA+fPnoauryzXYYa1JkyYICwuTOo4BAGFhYVy5blFREdfIjJWioqIaG5K9evWKO4PLB2PGjEFxcTEMDQ2hpqYmswDJp8/92bNnA6h5VCCfkxG+EovF2Lp1K44dO4bc3FyuqVYlvvzbC2XXViKRoG3btvjjjz9kkju+LZoJ5R5/4cKFMDU1xdSpUyEWi9GnTx9cvXoVampqOHv2LFeazwc6OjoIDAzExo0bpa4HBgZCR0cHQMURLZYLEiYmJrh//z4l9l+7yh1RPpYTb9++HXv27MHQoUOl3kyWlpZwdXVlGJmwCKl7P1AxfzUyMhKhoaF48OABRCIR9PX1MXLkSEycOJEX/60KaTxY48aNedWASOhqG8HTqFEjDBw4EKGhobz4Yq0qJiYGgwYNQs+ePREbGwtPT0+0aNGCO94QEhLCOkQAwKpVqzBr1ixER0dzpa2JiYkIDw/nygovXryIPn36sAwT1tbWCA4O5s7YikQilJeXw8vL66Oj8OSttgkOfFR9ZCj537i7u2Pv3r1wcXHBypUrsWLFCjx48ACnTp3iTff2SpaWlrC0tJS65uDgwCiamikoKMDIyAj5+fkyiT3f6OnpsQ7hk4SEhGDChAkAKhZvc3JykJ6ejoMHD2LFihXc9B4+2Lx5M0aNGoXz58+ja9euAIAbN24gPT2d+/5MTEzEmDFjmMW4bt06uLq6Yu3atTVWPTVq1EhusVApPgPBwcHw9vZGZmYmgIpd3SVLlmDixImMI/uLqqoq0tPToaenJ1X2lpmZCTMzM6Zz7IVCQUEBeXl5vCkRq4tEIoGjoyPCw8Nhbm4OY2NjSCQS3L17F6mpqRgyZAhOnTrFOkzyGdTVbbqgoAAxMTG0c/cPdO/eHaNGjcLixYulPksTEhIwfPhwPH78mHWInPj4eOzYsYNrktm+fXvMmzcPPXr0YBzZX9LS0tC/f39YWFggKioKQ4YMwR9//IFXr14hPj4ehoaGrEMkn1nDhg2RkpLCu0W8SoaGhti2bRscHByk5ltv27YN169fx6+//so6RADAlClTPvr8vn375BRJ3cLCwuDl5QU/Pz+YmJiwDqdOd+7cqbFagy/HWlRUVJCVlQVtbW1Mnz4dampq8PX1RU5ODszNzXk1sQWoqIQICAjAvXv3AFR8N82YMYM3FRtVj2BU3QBjcQSDduzlbMuWLVi1ahXmzp2Lnj17AqhoWjRz5kz83//9n0wZJCv6+vq4ffu2zOpjRESEYDp8s8aH3e2/IygoCLGxsYiMjJTZ+YqKisLQoUMRHBzMi+7JISEhtZY58mWUkJDUVVWioaHBi393IUpNTa3xRr5FixZyn29bl549e3LfS3xlYmKCe/fuYceOHWjYsCEKCwsxfPhwzJkzB1paWkxje/v2LbczU9eNsTx3cD5FTEwMNm/ejLt37wIAOnbsiCVLlsDa2ppxZLILjyUlJZg5cyZvjzZVNk4DKnpXvHnzBkDF1I5Vq1axDE3K69evpR6XlpYiLS0NBQUF6NevH6OoajZp0iQUFxfD3NwcSkpKUFVVlXqeL8cb7t+/j2HDhiE1NVWqV1Hl/SBfFsdbtmyJO3fuQEtLCxEREfDz8wMAFBcXQ1FRkXF0svT19WVK8fnkY/0oUlNT5RgJJfZyt337dvj5+UndJA8ZMgSdOnWCm5sbbxL7xYsXY86cOSgpKYFEIkFCQgIOHz6MDRs2YO/evazDEwShFcMcPnwYP/30U43lrP369cOyZctw6NAh5gnetm3buNEnp0+fhrOzM7Kzs5GYmIg5c+Ywja06fX39jy7w8KVZppCONwhN48aN8ezZM5ndxaSkJF519c7Nzf3o87q6unKKpHalpaWws7ODv78/N3+bTzQ1NfHs2TO0aNGi1hFtfGyi9csvv8DZ2RnDhw/nJvPEx8ejf//+CAoKwrhx45jGV33hsbKEmK+0tbXx7Nkz6OrqwtDQEBcuXICFhQUSExN51Qfi5MmTMtfKy8sxa9Ys3lW+COVoy4IFC6Cvr4/IyEjo6+sjISEB+fn5cHFxwebNm1mHx3F2dsbo0aOhpaUFkUiEAQMGAAB+//135g2Sa1JQUICEhAS8ePFC5ugQ63tSADJH1P78808cPnwYe/fuxc2bNzF37ly5xUKl+HKmoqKCtLQ0mc7DmZmZMDU15dV4rEOHDsHNzQ3Z2dkAgNatW8Pd3R1Tp05lHBn5HFq1aoWIiIhaR9olJSVh0KBByMvLk29g1RgbG2PNmjUYO3asVGnz6tWr8erVK+zYsYNpfFX9/PPPUo9LS0uRlJSEiIgILFmyBMuWLWMUGZEXV1dX/P777zh+/DjatWuHW7du4fnz55g0aRImTZrEm9FYldMwasOXRLR58+a4evUqL8/axsTEoGfPnqhXrx5iYmI++lrWvQqq6tChA6ZPny6zsbBlyxbs2bOH28Unn2bZsmVo1KgRfvrpJxw9ehQTJkxAmzZtkJubi0WLFvF65xEAMjIyYGNjg2fPnrEORXCaNWuGqKgomJmZQUNDAwkJCWjfvj2ioqLg4uJSa58YFkJCQvDo0SOMGjUK2traACpGyzVu3JhXDbLDwsIwfvx4FBYWolGjRlLfUyKRiDfVGgAQGxuLwMBAnDhxAq1bt8bw4cMxYsQIrjeAPFBiL2cmJiYYN26czKzFdevW4ejRo3Iv2fgUxcXFKCwsFMxZcfLPKCkp4eHDh7WWsz59+hT6+vp4//69nCOTpqamhrt370JPTw8tWrTAxYsXYW5ujszMTHTr1g35+flM4/sUO3fuxI0bN2in/Cvw4cMHzJkzB0FBQRCLxahXrx7EYjHGjRuHoKAg3pQ9JicnSz2uXITasmULPD096+zDIC+LFi2CsrIy75MjIVFWVsYff/xR46hDExMTXm04CNG1a9dw7do1GBkZwdHRkXU4dQoPD8fkyZPx8uVL1qFIEcJ0EU1NTdy6dQv6+vowNDTE3r170bdvX2RnZ8PU1JTZODYha9euHezt7bF+/foaJ6KwlpeXh6CgIAQGBuLt27cYPXo0/P39kZycjI4dO8o9HirFlzN3d3eMGTMGsbGx3FnG+Ph4REZG4tixY4yj+8u+ffvQt29f6OvrQ01NjZdvJvLvqkw6aqOoqIiysjI5RlSzVq1a4dWrV9DT04Ouri6uX78Oc3Nz5OTkCOb4w6BBg7B8+XJK7L8CSkpK2LNnD1atWoW0tDQUFhaiS5cuvNtxNjc3l7lmaWmJ1q1bw9vbmzeJfVlZGfbt24dLly7V2H24ppFtrPC9fLSSjo4OIiMjZRL7S5cucTt55J/r3r07unfvzjoMGdXHh0okEjx79gznzp3D5MmTGUVVM6FMFzExMUFycjL09fXx3XffwcvLC0pKSti9ezcMDAxYh8fx8PD46PN8mt7w5MkTzJ8/n5d5iKOjI2JjY+Hg4ABfX1/Y2dlBUVGRmyTDAiX2cjZixAj8/vvv2Lp1K9dhvEOHDkhISECXLl3YBlfFhg0bMG3aNHzzzTfo06cP+vTpAxsbG5kvfvLlkEgkcHJyqvUMIOud+kr9+vXDmTNn0KVLFzg7O2PRokUICQnBjRs3eJN81CUkJISbDU6+Drq6urw4p/53tW/fHomJiazDwP3799GmTRukpaXBwsICALgOyZX41LC0rvJRPiX2Li4umD9/Pm7fvs1NQIiPj8f+/ft5dS5YSDIyMrB9+3buGEOHDh0wb948tG/fnnFkf0lKSpJq8KagoIDmzZvDx8enzo758rZs2TKsW7eOmy5SqV+/frw6frdy5UoUFRUBqEieBw8eDGtrazRt2hRHjx5lHN1fqvdXKC0tRU5ODurVqwdDQ0NeJfa2tra4ceMGrxZGKp0/fx7z58/HrFmzeLNYT6X4pFZPnjzB5cuXERsbi5iYGGRmZkJLSws2Njb45ZdfWIdH/mXOzs6f9DrWu8zl5eUoLy/nqguOHDnCnbmdMWMGlJSUmMYHVHyhu7i4oFevXjKjT/Ly8vDy5Uvs2rUL06dPZxglkQexWIygoCBERkbWuHMbFRXFKDJp1bu4V+7eubm5IT09Hbdv32YT2P+nqKjINaYDgDFjxmDbtm1o2bIl07hqw/fyUQDYunUrd67+5MmT8PHxkUpEZ8yYAX9/f17NtBaCEydO4IcffoClpSW3U3/9+nUkJibiyJEjGDFiBNP4ysvL4e3tjdOnT6O0tBT9+vWDm5ubTKd5PlFXV0dqair09fWleus8ePAAxsbGvD4u8urVK2hqavJq4bEmb9++hZOTE4YNG8ar8duBgYHw8PCAs7MzTE1NUb9+fannWY4QvH79OgIDA3H06FF06NABEydOxA8//AAtLS1mpfiU2MtZ9ZuTSvn5+WjRogVvGhRVVVxcjCtXruDw4cM4dOgQJBIJL0qyCeGryvf5rl27pL7MK3dEbGxseNl5lvz75s6di6CgIDg4OHAdiKvaunUro8ik1dQ8TyKRQEdHB0eOHGFeSqygoIC8vDzuu7NRo0a4ffs2L3dxAKBBgwZITU3lbXwAoKqqioCAgBqrBwoLC2Fra4v8/Hykp6cziE64DA0NMX78eJly5zVr1uCXX37hGhKzsnbtWri5uWHAgAFQVVXFb7/9hrFjx/Jqbn112traOHbsGHr06CGV2J88eRKurq7M/06/FKmpqXB0dMSDBw9Yh8KpOiO+Or5MGCkqKsLRo0exb98+JCQkQCwWY8uWLZgyZYpUhYk8UGIvZ9VvTio9ffoUhoaGePfuHaPIpF24cAGXL1/G5cuXkZSUhA4dOnDl+L1794ampibrEMlX7PXr1wgMDJSauezs7Myb8vba3ufk69OsWTMEBwfD3t6edSgfVb2Le+UiVNu2bT/ae0Neqr+nqt7c89Hw4cPxww8/YPTo0axDqVVISAgmTpyIo0ePSu16FRUVwc7ODi9evEB0dDRat27NMErhUVNTQ0pKSo3Tj8zNzZk3UDMyMoKrqytmzJgBoKKXgoODA969e/fRJIoloUwXKSkpwfbt2xEdHV1jhdatW7cYRfZp4uLi4OjoiNevX7MORbAyMjIQGBiIgwcPoqCgAAMHDsSZM2fk9vvZf1t/JbZt2wagYnVp7969UFdX554Ti8WIjY3l1Q6enZ0dmjdvDhcXF4SHh6Nx48asQyIEQMU4kSFDhqBRo0awtLQEUPH+8vDwQFhYGHr37s04wgp8L7sj8qGkpCSI3iQikQg9evSQSeLLysoQGxvL/H0lEolk3lN8e49VvXlzcHDAkiVLcOfOHd6Vj1YaOXIkCgoKMHbsWJw7dw42NjZcUp+Xl4fLly9TUv8P2NjY4MqVKzLv+7i4OFhbWzOK6i+5ublSC40DBgyASCTC06dPedsscf369Zg7dy50dXVRVlaGjh07ctNFVq5cyTo8ztSpU3HhwgWMHDkSVlZWvPuMqlSZk1SqPHp18OBB2NnZMYpKmr29PQ4fPgwNDQ0AwMaNGzFz5kwuH8nPz4e1tTXu3LnDMEpZ7du3h5eXFzZs2ICwsDC5V8LQjr2c6OvrAwAePnwIbW1tqRFHSkpKaNOmDTw8PPDdd9+xClGKr68vYmNjERsbC2VlZW633sbGBu3atWMdHvmKmZqaonv37vDz8+PeR2KxGLNnz8bVq1d5MTJSQUEBGhoadX6p82n+Kvk8fHx8cP/+fezYsYO3N3kA/4+JKSgoYNCgQVxzz7CwMPTr10+mK35oaCiL8AB8vGS0Kr6Uj1by8vKCp6cnTp8+jdWrV+PJkyeIiYnhbZLHd/7+/li9ejVGjx6Nbt26Aag4i3v8+HG4u7tLLZawWOBRVFREXl4emjdvzl1r2LAhUlJSuHtVvqjsB3DmzBl8+PABZmZmGDFiBG+ni2hoaCA8PJybesVX1f+dKyu0+vXrh+XLl8u9fLwm1b+Tqh+/ev78OVq3bs2rz1I+oMRezvr27YvQ0FBBlbKnpqYiJiYGUVFROHv2LFq0aIHHjx+zDot8pVRVVXH79m2Z7sIZGRno3LkzL46zKCgowNfXl1tprg3fRgqRf9+wYcMQHR2NJk2aoFOnTjI7tywT0aoUFBTw/PlzqZt9oKLzvKWlpUxzPXkTSnNPoVq2bBm8vb3Rpk0bXL58GTo6OqxDEiy+L/BUXyQDal4o48Nnk9D6AXTs2BFHjhyBmZkZ61D+tpKSEuzcuRPe3t7Iy8tjHU6dx68osa8ZleLLWXR0tNRjsViM1NRU6Onp8S7Zl0gkSEpKwuXLlxEdHY24uDiUl5fL3PgRIk8WFha4e/euTGJ/9+7dGmdxs/LDDz/QGXuCxo0bY9iwYazDqFXliEiRSCQz7lIsFiMlJYUbgcaSUBL2qKgozJ07F9evX0ejRo2knnvz5g169OgBf39/XpRkVx8PWr9+fTRr1gwLFiyQus6HBE9Iqp+r5puaFpQnTJjAIJK6BQcHY9euXTL9APbu3cvLfgA+Pj5YunQp/P39oaenxzocGe/fv4ebmxsuXrwIZWVlLFmyBEOHDsX+/fuxcuVKKCoqcpMyiDBRYi9nCxcuhKmpKaZOnQqxWIzevXvj2rVrUFNTw9mzZ2FjY8M6RACAo6Mj4uPj8fbtW5ibm8PGxgbTpk1D79696bw9YWr+/PlYsGABsrKypMocd+7ciY0bNyIlJYV7LatVcz6XXBP54ntCWllVIpFI0LBhQ6mRV0pKSujWrRumTZvGKjzB8fX1xbRp02SSeqDi73rGjBnYsmULLxL76hVFY8eOZRTJl+HatWvIz8/H4MGDuWvBwcFYs2YNioqKMHToUGzfvl1q8YwFvn8mVSW0fgCWlpYoKSmBgYEB1NTUZCq0WB+/W716NQICAjBgwABcvXoVo0aNgrOzM65fvw4fHx+MGjVK6qgwS0Loq8JHlNjL2fHjx7mV0bCwMDx48ADp6ek4ePAgVqxYwZt5scbGxpgxYwasra3rLCcmRJ4qbz7/+9//1vicSCSCRCJheo6VTjiR6l6+fImMjAwAFc11+FL5tH//fu6/1+3bt0s1diV/X3JyMjZt2lTr899//z02b94sx4hqJ6QETwg8PDxgY2PDJfapqamYOnUqnJyc0KFDB3h7e6N169Zwc3NjG6iAlJWVQUVFRepa/fr1UVpayiiijxs7diyePHmC9evXo2XLlrxLRI8fP47g4GAMGTIEaWlpMDMzQ1lZGZKTk3kXq0QikaoiKykpwcyZM7njIu/fv2cZHm/RGXs5U1FRQVZWFrS1tTF9+nSoqanB19cXOTk5MDc3Z36OUSgrzuTr9fDhw09+LR9L4cjXpaioCPPmzUNwcDBXoquoqIhJkyZh+/btUFNTYxxhRemwiooK/vjjD941oxIaFRUVpKWl1ToJISsrC6amprzoBUL+XVpaWggLC+OmtaxYsQIxMTGIi4sDUJFUrVmzhnddvPlMSP0AgIpRh9euXePVscCqlJSUkJOTg2+++QZARc+ihIQEmJqaMo5MFvVV+Wdox17OWrZsiTt37kBLSwsRERHw8/MDABQXF/Oi/IVWnAnfUbJOhGTx4sWIiYlBWFgY1yk5Li4O8+fPh4uLC/cdwJKCggKMjIyQn59Pif3/6JtvvvloYp+SkgItLS05R0Xk4fXr12jZsiX3OCYmBoMGDeIed+3aFY8ePWIRmmAJqR8AUFHtyudFO7FYDCUlJe5xvXr1eFulRQn7P0M79nLm5uYGX19faGlpobi4GPfu3YOysjL27duHPXv24Nq1a0zjoxVnIhR37txBbm4uPnz4IHWdD/OhCanUrFkzhISEyPRPiY6OxujRo/Hy5Us2gVUTFhYGLy8v+Pn5wcTEhHU4gjVv3jxcvnwZiYmJMiXE7969g5WVFfr27SszR5oIn56eHg4ePIjevXvjw4cPaNy4McLCwtC/f38AFRslffr0YX7Omnw+Fy5cgLu7Ozw9PWFqaipzxr6m3hvyJISxoeR/Qzv2cubm5gYTExM8evQIo0aN4t5cioqKWL58OePoaMWZ8N/9+/cxbNgwpKamcufpgb+aqtDoE8InxcXFUp+plVq0aIHi4mIGEdVs0qRJKC4uhrm5OZSUlKSa6AHsmz4JxcqVKxEaGop27dph7ty53PSO9PR07Ny5E2KxGCtWrGAcJfkc7O3tsWzZMmzatAmnTp2CmpqaVJPElJQUGBoaMoyQfG52dnYAwC3mVGLd96dS9QoIPlc/kH+GEns5sbe3x+HDh6GhoYGRI0di48aNUuUvgwcPhrW1NfPdxpYtWyInJwc6Ojr48OEDbt26BXd3d+75P//8U2YFkhB5WrBgAfT19REZGQl9fX0kJCQgPz8fLi4uvGlKRUil7t27Y82aNQgODuZ2cN+9ewd3d3d0796dcXR/8fX1ZR3CF6Fly5a4evUqZs2aheXLl0stPNra2mLnzp01LvQQ4Vu7di2GDx+OPn36QF1dHQcOHJAqe963bx++//57hhGSz636SGu+ofL2Lx+V4suJoqIinj17xs21btSoEW7fvg0DAwMAwPPnz9G6dWvmq3mzZs3iuvqeOnUKBw4cwNOnT7kvp0OHDsHX1xeJiYlM4yRfr2bNmiEqKgpmZmbQ0NBAQkIC2rdvj6ioKLi4uCApKYl1iIRw0tLSYGtri/fv33MNlZKTk6GiooLffvsNnTp1Yhwh+Vxev36NrKwsSCQSGBkZQVNTk3VIRA7evHkDdXV1mb5Jr169grq6ulSyTwgh/ybasZeT6usnfF1PoRVnwndisRgNGzYEUJHkP336FO3bt4eenh43TowQvjAxMUFmZiYOHTqE9PR0ABUjkcaPHy9T7s6aWCzGqVOncPfuXQBAp06dMGTIEF40dhUiTU1NdO3alXUYRM5qGxHcpEkTOUdCWCgoKEBgYKDU5+iUKVNodDSRC9qxlxMFBQXk5eVxO/YNGzZEcnIy73bsK9GKM+Era2truLi4YOjQoRg3bhxev36NlStXYvfu3bh58ybS0tJYh0iI4GRlZcHe3h5PnjzhzoVnZGRAR0cH586do7PBhBBShxs3bsDW1haqqqqwsrICACQmJuLdu3e4cOECLCwsGEdIvnSU2MuJoqIi8vLy0Lx5cwAViX1KSgr09fUB8C+xJ4SvfvvtNxQVFWH48OHIzMyEo6Mj7t27h6ZNm+LIkSMyTWsIkbczZ8588mtZ91WpZG9vD4lEgkOHDnE7i/n5+ZgwYQIUFBRw7tw5xhESQgi/WVtbo23bttizZw/q1asoii4rK8OPP/6I+/fvIzY2lnGE5EtHib2c1DVi4v3794iIiKDEnpB/4NWrV9DU1OQ64xPCkoKCgtTjqtMbql4D+DPFoUGDBrh+/TpMTU2lricnJ6Nnz54oLCxkFBkhhAiDqqoqkpKSYGxsLHX9zp07sLS05NUkFPJlojP2cvIpIyYmTZokr3AIEZzhw4fX+Zp69eqhVatWGDhwIBwdHeUQFSGyysvLuZ8vXbqEpUuXYv369VwX/GvXrmHlypVYv349qxBlKCsr488//5S5XlhYSEevCCHkEzRq1Ai5ubkyif2jR4+43kCEfE60Y08IEQRnZ+c6X1NeXo4XL14gJiYGrq6u8PDwkENkhNTOxMQE/v7+6NWrl9T1K1euYPr06VyDJdYmTZqEW7duITAwkDsb+vvvv2PatGn49ttvERQUxDZAQgjhufnz5+PkyZPYvHkzevToAQCIj4+Hq6srRowYgZ9//plxhORLR4k9IeSLc/bsWcyePRu5ubmsQyFfOVVVVSQmJsLExETqekpKCr777ju8e/eOUWTSCgoK4OTkhLCwMKmzoUOGDEFQUBB1dCaEkDp8+PABS5Ysgb+/P8rKyiCRSKCkpITZs2fD09OTd5NQyJeHEntCyBenoKAAU6ZMQWhoKOtQyFeud+/eUFFRwcGDB9GyZUsAFc1SJ02ahJKSEsTExDCNr7y8HN7e3jhz5gw+fPgAXV1dTJ48GSKRCB06dEDbtm2ZxkcIIUJTXFyM7OxsAIChoSH8/Pzg7e2NvLw8xpGRLx2dsSeEfHEaN25MST3hhX379mHYsGHQ1dWFjo4OgIrzlkZGRjh16hTb4AB4enrCzc0NAwYMgKqqKsLDw6GhoYF9+/axDo0QQgTh/fv3cHNzw8WLF6GsrIwlS5Zg6NCh2L9/P+zs7KCoqIhFixaxDpN8BWjHnhBCCPmMJBIJLl68iPT0dABAhw4dMGDAAF5McTAyMoKrqytmzJgBoKLZn4ODA969eyfT3Z8QQoispUuXIiAgAAMGDMDVq1fx8uVLODs74/r16/jpp58watQoKCoqsg6TfAUosSeEEEK+UsrKysjKyuKqCQBARUUFWVlZ0NbWZhgZIYQIg4GBAXx9fTFkyBCkpaXBzMwMTk5OCAwM5MUCLvl6UCk+IYQQ8hlFRkYiMjISL168kBqFB4B5yXtZWRlUVFSkrtWvXx+lpaWMIiKEEGF5/Pgxvv32WwAVk1CUlZWxaNEiSuqJ3FFiTwghhHwm7u7u8PDwgKWlJbS0tHh3oyeRSODk5ARlZWXuWklJCWbOnIkGDRpw16hnBSGE1EwsFkNJSYl7XK9ePairqzOMiHytqBSfEEII+Uy0tLTg5eWFiRMnsg6lRs7Ozp/0uv3793/mSAghRJgUFBQwaNAgboE0LCwM/fr1k1ocBWiBlHx+lNgTQgghn0nTpk2RkJAAQ0ND1qEQQgj5DGiBlPAFJfaEEELIZ7J06VKoq6tj1apVrEMhhBBCyBeMztgTQgghn0lJSQl2796NS5cuwczMDPXr15d6fsuWLYwiI4QQQsiXhHbsCSGEkM+kb9++H30+OjpaTpEQQggh5EtGiT0hhBBCCCGEECJgVIpPCCGE/MuGDx9e52tEIhFOnDghh2gIIYQQ8qWjxJ4QQgj5l2loaLAOgRBCCCFfESrFJ4QQQgghhBBCBEyBdQCEEEIIIYQQQgj55yixJ4QQQgghhBBCBIwSe0IIIYQQQgghRMAosSeEEEK+AjY2Nli4cCHrMAghhBDyGVBiTwghhBBCCCGECBgl9oQQQgghhBBCiIBRYk8IIYR8ZQ4ePAhLS0s0bNgQrVq1wrhx4/DixQvu+cuXL0MkEiEyMhKWlpZQU1NDjx49kJGRIfXnrFu3Di1atEDDhg3x448/YtmyZejcuTP3fE3l/0OHDoWTk9MnxwIAZ86cgZGREVRUVNC3b18cOHAAIpEIBQUF3Gvi4uJgbW0NVVVV6OjoYP78+SgqKvqf/64IIYQQIaDEnhBCCPnKlJaWYu3atUhOTsapU6fw4MEDqWS70ooVK+Dj44MbN26gXr16mDJlCvfcoUOH4OnpiU2bNuHmzZvQ1dWFn5/fvx5LTk4ORo4ciaFDhyI5ORkzZszAihUrpP6M7Oxs2NnZYcSIEUhJScHRo0cRFxeHuXPn/u14CCGEECESSSQSCesgCCGEEPJ52djYoHPnzvD19ZV57saNG+jatSv+/PNPqKur4/Lly+jbty8uXbqE/v37AwDCw8Ph4OCAd+/eQUVFBd26dYOlpSV27NjB/Tm9evVCYWEhbt++XevvHDp0KBo3boygoKAa46wey7Jly3Du3DmkpqZyr1m5ciU8PT3x+vVrNG7cGD/++CMUFRUREBDAvSYuLg59+vRBUVERVFRU/vlfHCGEECIAtGNPCCGEfGVu3rwJR0dH6OrqomHDhujTpw8AIDc3V+p1ZmZm3M9aWloAwJXJZ2RkwMrKSur11R//G7FkZGSga9euH/09ycnJCAoKgrq6Ovc/W1tblJeXIycn52/HRAghhAhNPdYBEEIIIUR+ioqKYGtrC1tbWxw6dAjNmzdHbm4ubG1t8eHDB6nX1q9fn/tZJBIBAMrLyz/5dykoKKB6YWBpaek/iuVjCgsLMWPGDMyfP1/mOV1d3U/+cwghhBChosSeEEII+Yqkp6cjPz8fGzduhI6ODoCK8ve/q3379khMTMSkSZO4a4mJiVKvad68OZ49e8Y9FovFSEtLQ9++fT85lvbt2yM8PFzqWvXfY2FhgTt37qBt27Z/+/8HIYQQ8iWgUnxCCCHkK6KrqwslJSVs374d9+/fx5kzZ7B27dq//efMmzcPgYGBOHDgADIzM7Fu3TqkpKRwO/sA0K9fP5w7dw7nzp1Deno6Zs2aJdXJ/lNimTFjBtLT07F06VLcu3cPx44d487nV/6upUuX4urVq5g7dy5u376NzMxMnD59mprnEUII+WpQYk8IIYR8RZo3b46goCAcP34cHTt2xMaNG7F58+a//eeMHz8ey5cvh6urKywsLJCTkwMnJyepRnVTpkzB5MmTMWnSJPTp0wcGBgbcbv2nxqKvr4+QkBCEhobCzMwMfn5+XFd8ZWVlABW9AGJiYnDv3j1YW1ujS5cuWL16NVq3bv1P/ooIIYQQwaGu+IQQQgj5VwwcOBCtWrXCwYMHP+vv8fT0hL+/Px49evRZfw8hhBAiFHTGnhBCCCF/W3FxMfz9/WFrawtFRUUcPnwYly5dwsWLF//137Vr1y507doVTZs2RXx8PLy9vanMnhBCCKmCEntCCCGE/G0ikQjh4eHw9PRESUkJ2rdvjxMnTmDAgAH/+u+qPMP/6tUr6OrqwsXFBcuXL//Xfw8hhBAiVFSKTwghhBBCCCGECBg1zyOEEEIIIYQQQgSMEntCCCGEEEIIIUTAKLEnhBBCCCGEEEIEjBJ7QgghhBBCCCFEwCixJ4QQQgghhBBCBIwSe0IIIYQQQgghRMAosSeEEEIIIYQQQgSMEntCCCGEEEIIIUTAKLEnhBBCCCGEEEIE7P8BVPsTESiax/oAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VanxZ34-NMk4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YLZH8P0ENMwx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Violin plot for text length by language"
      ],
      "metadata": {
        "id": "Bh22sAtINM8Z"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))  # Setting the figure size for the violin plot\n",
        "sns.violinplot(x='language', y='text_length', data=df)  # Creating a violin plot for text length by language\n",
        "plt.title('Violin plot of Text Length by Language')  # Setting the title for the violin plot\n",
        "plt.xticks(rotation=90)  # Rotating x-axis labels for better readability\n",
        "plt.savefig('/content/text_length_violinplot.png')  # Saving the violin plot as an image\n",
        "plt.show()  # Displaying the violin plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "Wu9x0eD5L0uc",
        "outputId": "4e7e2687-d659-4a28-d0e7-97b261eed497"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "11ldAnIWL0wp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M-XTkd0gL0yh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Histogram of text lengths"
      ],
      "metadata": {
        "id": "d7qffJlML00-"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Setting the figure size for the histogram\n",
        "df['text_length'].plot.hist(bins=30, edgecolor='black', color='skyblue')  # Creating a histogram of text lengths\n",
        "plt.title('Histogram of Text Lengths')  # Setting the title for the histogram\n",
        "plt.xlabel('Text Length')  # Setting the x-axis label\n",
        "plt.ylabel('Frequency')  # Setting the y-axis label\n",
        "plt.savefig('/content/text_length_histogram.png')  # Saving the histogram as an image\n",
        "plt.show()  # Displaying the histogram"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Hdwn5OrqIIpJ",
        "outputId": "e8db7c7c-b98a-4de2-c919-6a277b412dd2"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wHkrHptaIIsG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zU7S7KZsIIuO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Line plot for text length by index"
      ],
      "metadata": {
        "id": "HcdxDEdtIIw9"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Setting the figure size for the line plot\n",
        "plt.plot(df.index, df['text_length'], color='orange')  # Creating a line plot of text length by index\n",
        "plt.title('Line plot of Text Length by Index')  # Setting the title for the line plot\n",
        "plt.xlabel('Index')  # Setting the x-axis label\n",
        "plt.ylabel('Text Length')  # Setting the y-axis label\n",
        "plt.savefig('/content/text_length_lineplot.png')  # Saving the line plot as an image\n",
        "plt.show()  # Displaying the line plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Zeyg5k_aIIz2",
        "outputId": "681a9fb6-6043-4f65-a0a6-b29ba2b13b1d"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UHvIrfutII2j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7CQ8k9AWII5U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Smoothed Line plot (Rolling Average) of Text Length by Index"
      ],
      "metadata": {
        "id": "UDRN2G1tII8a"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Setting the figure size for the smoothed line plot\n",
        "df['text_length'].rolling(window=100).mean().plot(color='purple')  # Creating a smoothed line plot of text length by index\n",
        "plt.title('Smoothed Line Plot (Rolling Average) of Text Length by Index')  # Setting the title for the smoothed line plot\n",
        "plt.xlabel('Index')  # Setting the x-axis label\n",
        "plt.ylabel('Rolling Average of Text Length')  # Setting the y-axis label\n",
        "plt.savefig('/content/text_length_curve.png')  # Saving the smoothed line plot as an image\n",
        "plt.show()  # Displaying the smoothed line plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "N6d-gG2FII_E",
        "outputId": "58b2d4c0-6198-4699-eef1-339c43f28452"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ROdxemi-NnYr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "v79CfHpVNncH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Pie chart for text length distribution into categories"
      ],
      "metadata": {
        "id": "PCYz8OaQNney"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_length_categories = pd.cut(df['text_length'], bins=[0, 50, 100, 150, 200, df['text_length'].max()],\n",
        "                                labels=['0-50', '51-100', '101-150', '151-200', '201+'])  # Categorizing text lengths into bins\n",
        ""
      ],
      "metadata": {
        "id": "_Gi97Z9XNnhb"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AdZG2jfnNnj3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 8))  # Setting the figure size for the pie chart\n",
        "text_length_categories.value_counts().plot.pie(autopct='%1.1f%%', startangle=90, cmap='Blues')  # Plotting pie chart for text length categories\n",
        "plt.title('Text Length Categories Distribution')  # Setting the title for the pie chart\n",
        "plt.ylabel('')  # Hiding y-axis label\n",
        "plt.savefig('/content/text_length_pie_chart.png')  # Saving the pie chart as an image\n",
        "plt.show()  # Displaying the pie chart"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "dfqqJlUjNnns",
        "outputId": "7bd4bf7a-ecb1-4fc8-edae-d163f8403abb"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ybVR2p1KNnq-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dmpny5uqQaUM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the distribution of text lengths"
      ],
      "metadata": {
        "id": "rgw91nOaNnze"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Add a column for text length"
      ],
      "metadata": {
        "id": "_7Mhxg5yRzPY"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['text_length'] = df['Text'].apply(len)  # Creating a new column for text length"
      ],
      "metadata": {
        "id": "Gx9-ofAKRzUh"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0eHoyAlaRzZl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 5))  # Creating a figure with specified size\n",
        "sns.histplot(df['text_length'], bins=30, kde=True, color='blue')  # Plotting a histogram for text lengths with kernel density estimate\n",
        "plt.title('Distribution of Text Lengths')  # Setting the title of the plot\n",
        "plt.xlabel('Text Length')  # Labeling the x-axis\n",
        "plt.ylabel('Frequency')  # Labeling the y-axis\n",
        "plt.show()  # Displaying the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "TwA0e4MjRzd5",
        "outputId": "4f0b0683-942a-4951-f245-adb6ca1e0a71"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JhvZJj3lR7VW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "F2hMSoAOR7ch"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BH2-KvbVR7i1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:green;\">Step 5: Data Preprocessing</span>\n",
        "Data preprocessing is vital for preparing the dataset for model training.\n",
        "\n",
        "- **Feature and Label Definition**: The text data is separated into features (`X`) and labels (`y`).\n",
        "- **Label Encoding**: Convert categorical labels into numerical format using `LabelEncoder` to facilitate model training.\n",
        "- **Data Splitting**: The dataset is divided into training and testing sets (80% train, 20% test) to evaluate model performance.\n",
        "- **Text Vectorization**: We apply `CountVectorizer` to convert the text data into a format suitable for modeling (Bag-of-Words approach)."
      ],
      "metadata": {
        "id": "qhy3aTfiQBrj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================="
      ],
      "metadata": {
        "id": "SexAHNB6Nn20"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#  Data Preprocessing"
      ],
      "metadata": {
        "id": "gA-Rbkl_N2P8"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================="
      ],
      "metadata": {
        "id": "MS2frLm8IJBq"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Split the data into features (Text) and labels (language)"
      ],
      "metadata": {
        "id": "5A11J6ZSIJER"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df['Text']  # Defining features (Text)"
      ],
      "metadata": {
        "id": "aFrmF42IIJIs"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['language']  # Defining labels (language)"
      ],
      "metadata": {
        "id": "9j53RdugIJLD"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vR3qXFmCIJOQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1vdpO3fgIJQy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Encode the labels"
      ],
      "metadata": {
        "id": "-ou1uRS8IJS3"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label_encoder = LabelEncoder()  # Creating an instance of LabelEncoder"
      ],
      "metadata": {
        "id": "pPGLXRo5IJVO"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_encoded = label_encoder.fit_transform(y)  # Encoding the labels"
      ],
      "metadata": {
        "id": "AJgejx9FIJXg"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "l3YNgYlBIJe1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g1H_O8FYIJhN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the encoded labels for future reference"
      ],
      "metadata": {
        "id": "UfZV3RJ1IJji"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label_mapping = dict(zip(label_encoder.classes_, label_encoder.transform(label_encoder.classes_)))  # Creating a label mapping dictionary\n"
      ],
      "metadata": {
        "id": "SsH-pSs4IJmG"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label_df = pd.DataFrame(list(label_mapping.items()), columns=['Language', 'Encoded_Label'])  # Creating a DataFrame for label mapping\n"
      ],
      "metadata": {
        "id": "grzfDo2mIJoI"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label_df.to_csv('/content/label_mapping.csv', index=False)  # Saving the label mapping to a CSV file"
      ],
      "metadata": {
        "id": "hvQeYNsnIJqj"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Kvq1UbHnIJuz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bFkF8iuhIKZ3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Split the dataset into training and testing sets (80% train, 20% test)"
      ],
      "metadata": {
        "id": "l9f79YNSIKcw"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.2, random_state=42)  # Splitting data into training and testing sets\n"
      ],
      "metadata": {
        "id": "vjTUQZ92IKhA"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_khbPzknIKjf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1FtVpZWPIKls"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Vectorize the text data using CountVectorizer (Bag-of-Words approach)"
      ],
      "metadata": {
        "id": "K5o3o0XKIKnu"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vectorizer = CountVectorizer()  # Creating an instance of CountVectorizer"
      ],
      "metadata": {
        "id": "bY4sS5crIKpz"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_vect = vectorizer.fit_transform(X_train)  # Fitting and transforming the training data"
      ],
      "metadata": {
        "id": "psW6zv9jIKxY"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_vect = vectorizer.transform(X_test)  # Transforming the test data"
      ],
      "metadata": {
        "id": "nb4dTVPwIKzv"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VmzChb7VIK73"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KLRNZwe_IK-X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the vectorizer for future use"
      ],
      "metadata": {
        "id": "flcGaF5eILCr"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(vectorizer, '/content/vectorizer.pkl')  # Saving the vectorizer to a file"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SLOmhc94ILE7",
        "outputId": "0c1047cd-6468-4767-9caf-c575912b8f4e"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/vectorizer.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bt7YrQjJILHK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UbmYB21gILJr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "j35Mh95eILLo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:green;\">Step 6: Model Training</span>\n",
        "In this step, we initialize and train the chosen classification model.\n",
        "\n",
        "- **Naive Bayes Classifier**: We use the `MultinomialNB` model, which is well-suited for text classification tasks.\n",
        "- **Model Training**: The model is trained on the vectorized training data."
      ],
      "metadata": {
        "id": "1STJ_9pmQFFj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================="
      ],
      "metadata": {
        "id": "lP9XQSzTOgub"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#  Model Training"
      ],
      "metadata": {
        "id": "hSspfVCIOizC"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================="
      ],
      "metadata": {
        "id": "O5P1KRWjILN6"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Initialize and train the Naive Bayes Classifier"
      ],
      "metadata": {
        "id": "rv4SrpwAILSm"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nb_classifier = MultinomialNB()  # Creating an instance of Multinomial Naive Bayes"
      ],
      "metadata": {
        "id": "tikWQcFDILWj"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nb_classifier.fit(X_train_vect, y_train)  # Training the model on the training data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "EDIC-CvcILY8",
        "outputId": "f23a9460-1b5c-4773-d94a-1d0a15ffbd17"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MultinomialNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.naive_bayes.MultinomialNB.html\">?<span>Documentation for MultinomialNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MultinomialNB()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oIK4j_Y_ILbY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EzHyMWYpILdn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the trained model"
      ],
      "metadata": {
        "id": "o3uXhK8sILfq"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(nb_classifier, '/content/naive_bayes_model.pkl')  # Saving the trained model to a file"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LbRPq8aVHIJd",
        "outputId": "fcadb759-7039-4d19-a856-6cb274b0b6c2"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/naive_bayes_model.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eRxtyLRVOxU0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "f6sMSRR8OxYO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6Twe8j9DOxbO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:green;\">Step 7: Model Evaluation</span>\n",
        "After training the model, we evaluate its performance on the test set.\n",
        "\n",
        "- **Predictions**: Use the trained model to predict labels for the test set.\n",
        "- **Accuracy Calculation**: Compute the accuracy of the model using `accuracy_score`.\n",
        "- **Classification Report**: Generate a detailed classification report that includes precision, recall, and F1-score metrics.\n",
        "- **Confusion Matrix**: Visualize the performance of the classification model using a confusion matrix to see where the model makes correct and incorrect predictions."
      ],
      "metadata": {
        "id": "OX1AGpJzQHuX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================="
      ],
      "metadata": {
        "id": "PBi9u5FaOxes"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Evaluation"
      ],
      "metadata": {
        "id": "WDs04Y6nO0ij"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================="
      ],
      "metadata": {
        "id": "d3Sb1-R9OxhP"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Predict on the test set"
      ],
      "metadata": {
        "id": "b6Whqbm_Oxj0"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = nb_classifier.predict(X_test_vect)  # Making predictions on the test data"
      ],
      "metadata": {
        "id": "121xV9kcOxnG"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rwaC3j5pO8_r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Calculate accuracy"
      ],
      "metadata": {
        "id": "D2DJo486O9C5"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, y_pred)  # Calculating accuracy of the predictions"
      ],
      "metadata": {
        "id": "AFhBdhSEO9Hs"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Accuracy: {accuracy * 100:.2f}%\")  # Printing the accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wdDAhjIrO9Kh",
        "outputId": "dcb8598a-ebd7-4764-de63-84909c883952"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 94.23%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZLuqNK6tO9PH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gdz4p3nEO9Sk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Generate classification report"
      ],
      "metadata": {
        "id": "wDxiQINBO9dS"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_report = classification_report(y_test, y_pred, target_names=label_encoder.classes_)  # Generating a classification report\n"
      ],
      "metadata": {
        "id": "d7fLtC39O9hL"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nClassification Report:\\n\", class_report)  # Printing the classification report"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vD7WBmX1Gove",
        "outputId": "f3d3f0e0-1420-45e4-95a5-7cabafb426f0"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "      Arabic       1.00      1.00      1.00       202\n",
            "     Chinese       0.96      0.49      0.65       201\n",
            "       Dutch       0.98      0.98      0.98       230\n",
            "     English       0.68      1.00      0.81       194\n",
            "    Estonian       0.99      0.95      0.97       200\n",
            "      French       0.94      0.99      0.97       188\n",
            "       Hindi       1.00      0.99      0.99       208\n",
            "  Indonesian       1.00      0.98      0.99       213\n",
            "    Japanese       0.98      0.64      0.78       194\n",
            "      Korean       0.99      0.99      0.99       190\n",
            "       Latin       0.98      0.90      0.94       210\n",
            "     Persian       1.00      0.99      1.00       196\n",
            "   Portugese       0.99      0.96      0.98       194\n",
            "      Pushto       1.00      0.96      0.98       196\n",
            "    Romanian       0.98      0.98      0.98       197\n",
            "     Russian       0.99      0.99      0.99       213\n",
            "     Spanish       0.97      0.99      0.98       199\n",
            "     Swedish       0.60      1.00      0.75       179\n",
            "       Tamil       1.00      0.99      1.00       198\n",
            "        Thai       1.00      0.98      0.99       196\n",
            "     Turkish       0.99      0.98      0.98       199\n",
            "        Urdu       1.00      0.98      0.99       203\n",
            "\n",
            "    accuracy                           0.94      4400\n",
            "   macro avg       0.96      0.94      0.94      4400\n",
            "weighted avg       0.96      0.94      0.94      4400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GFf4VlGPPKpK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "19ldJCB8PKss"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Plot confusion matrix"
      ],
      "metadata": {
        "id": "nRI80UmUGoyX"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conf_matrix = confusion_matrix(y_test, y_pred)  # Calculating confusion matrix\n",
        "plt.figure(figsize=(10, 8))  # Setting the figure size for the confusion matrix\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=label_encoder.classes_, yticklabels=label_encoder.classes_)  # Plotting heatmap for confusion matrix\n",
        "plt.title('Confusion Matrix')  # Setting the title for the confusion matrix\n",
        "plt.xlabel('Predicted Label')  # Setting x-axis label\n",
        "plt.ylabel('True Label')  # Setting y-axis label\n",
        "plt.savefig('/content/confusion_matrix.png')  # Saving the confusion matrix as an image\n",
        "plt.show()  # Displaying the confusion matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 780
        },
        "id": "NdUeJMgwGo09",
        "outputId": "f3670963-ec3f-467c-a517-eae81ba85e07"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vcQ4PB7WPPDT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1w7nzxq0PPGF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rswWXXagPPIr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================="
      ],
      "metadata": {
        "id": "GQyP7ppxPPLQ"
      },
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#  Conclusion"
      ],
      "metadata": {
        "id": "iTtXZXHxPWX8"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================="
      ],
      "metadata": {
        "id": "asZHCc17PWa1"
      },
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Model training and evaluation completed.\")  # Printing a message to indicate completion"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8MRwm6guPbLl",
        "outputId": "c9badcf1-80a0-4254-bdef-53593dd4835c"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model training and evaluation completed.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wqS4jUntQOBL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vNvIA0yEQOEl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iU1R2ub2QOP9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:green;\">Step 8: Conclusion</span>\n",
        "In this section, we conclude the project by summarizing the key steps and findings from our language classification task.\n",
        "\n",
        "1. **Project Overview**:\n",
        "   - The primary goal of this project was to develop a model capable of accurately classifying text data into predefined language categories. Through systematic data processing and analysis, we aimed to achieve high classification accuracy.\n",
        "\n",
        "2. **Key Steps**:\n",
        "   - We began by importing essential libraries that facilitate data manipulation, visualization, and machine learning.\n",
        "   - The dataset was then loaded and inspected to understand its structure, distribution, and any missing values.\n",
        "   - Exploratory Data Analysis (EDA) was conducted to visualize language distribution and identify trends within the text lengths.\n",
        "   - Data preprocessing steps were performed, including feature extraction, label encoding, data splitting, and text vectorization.\n",
        "   - A Naive Bayes classifier was trained on the processed data, leveraging its effectiveness for text classification tasks.\n",
        "   - Model evaluation was conducted to assess performance through accuracy scores, classification reports, and confusion matrices.\n",
        "\n",
        "3. **Results**:\n",
        "   - The model demonstrated a satisfactory level of accuracy, indicating its potential for reliable language classification.\n",
        "   - Detailed evaluation metrics such as precision, recall, and F1-score provided insights into the model's strengths and weaknesses, helping us understand areas for improvement.\n",
        "\n",
        "4. **Future Work**:\n",
        "   - To enhance the model's performance further, future work may involve experimenting with different classification algorithms (e.g., Support Vector Machines, Random Forests) or fine-tuning hyperparameters.\n",
        "   - Incorporating more advanced techniques such as word embeddings (e.g., Word2Vec, GloVe) or deep learning models (e.g., LSTM, BERT) could improve classification accuracy.\n",
        "   - Additionally, expanding the dataset to include a more diverse set of languages or text samples could lead to a more robust model.\n",
        "\n",
        "5. **Final Thoughts**:\n",
        "   - This project has provided valuable insights into the processes of text classification, model training, and evaluation.\n",
        "   - The skills and techniques acquired throughout this project can be applied to various other natural language processing (NLP) tasks, opening the door for further exploration in the field."
      ],
      "metadata": {
        "id": "7TqQCYwVQOqz"
      }
    }
  ]
}