intent_classification_shallow_ml.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOPM7kG2Dubm1PwX03/OT0G",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PrithvirajKhelkar/intent-classification-shallow-ml/blob/main/intent_classification_shallow_ml.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import required libraries"
      ],
      "metadata": {
        "id": "PUVqDriBx0hI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import PorterStemmer\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM, Dense, Embedding\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.svm import SVC\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "import seaborn as sns\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import time"
      ],
      "metadata": {
        "id": "W1g9H08-x3s9"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## loads the train and test datasets from [here](https://www.kaggle.com/datasets/hassanamin/atis-airlinetravelinformationsystem)"
      ],
      "metadata": {
        "id": "bYzTKBNOyYyR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = pd.read_csv('atis_intents_train.csv', names=['intent', 'query'])\n",
        "test_df = pd.read_csv('atis_intents_test.csv', names=['intent', 'query'])\n"
      ],
      "metadata": {
        "id": "QJpuGkHSypo2"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Text preprocessing:\n",
        "First downloading the list of stop words from NLTK library, initializing the PorterStemmer object from NLTK, and defining a preprocess() function to clean and normalize the text data. The preprocess() function performs the following operations:\n",
        "* Converts the text to lowercase.\n",
        "* Removes non-alphabetic characters using regular expressions.\n",
        "* Tokenizes the text into words.\n",
        "* Stems each word using PorterStemmer object.\n",
        "* Removes stop words.\n",
        "* Joins the remaining words into a string.\n",
        "* Finally, the preprocessed text is stored in a new column named \"preprocessed_text\" in both train and test dataframes."
      ],
      "metadata": {
        "id": "pK8M3zQ_0Jev"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocessing the data\n",
        "nltk.download('stopwords')\n",
        "stop_words = set(stopwords.words('english'))\n",
        "ps = PorterStemmer()\n",
        "\n",
        "def preprocess(text):\n",
        "    text = text.lower()\n",
        "    text = re.sub('[^a-zA-Z]', ' ', text)\n",
        "    words = text.split()\n",
        "    words = [ps.stem(word) for word in words if word not in stop_words]\n",
        "    return ' '.join(words)\n",
        "\n",
        "train_df['preprocessed_text'] = train_df['query'].apply(preprocess)\n",
        "test_df['preprocessed_text'] = test_df['query'].apply(preprocess)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RfzuWQiO0Ubj",
        "outputId": "4ca88201-d38c-4cbb-e134-a249410f9d52"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Bar chart of intent label distribution\n",
        "train_df['intent'].value_counts().plot(kind='bar')\n",
        "plt.title('Intent Label Distribution in Training Set')\n",
        "plt.xlabel('Intent Label')\n",
        "plt.ylabel('Count')\n",
        "plt.show()\n",
        "\n",
        "# Histogram of sequence length distribution\n",
        "sequence_lengths = [len(seq) for seq in X_train_seq]\n",
        "plt.hist(sequence_lengths, bins=20)\n",
        "plt.title('Sequence Length Distribution in Training Set')\n",
        "plt.xlabel('Sequence Length')\n",
        "plt.ylabel('Count')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "GM4UJfieVrQk",
        "outputId": "32b5dc14-1d1a-403f-ee6d-62734cfd0ccd"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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KmlPKPgS8VtJ0wMB1wFsAbF8u6VSqjuuFwAG2HwKQ9DbgDGBF4Djbl/cx7oiIGKGfo6HOAdRj0+ljPOdQ4NAe5aeP9byIiOivXMEdERG1kiwiIqJWkkVERNRKsoiIiFpJFhERUSvJIiIiaiVZRERErSSLiIiolWQRERG1kiwiIqJWkkVERNRKsoiIiFpJFhERUSvJIiIiaiVZRERErXElC0k7jKcsIiKWTeOtWXxlnGUREbEMGnOlPEnbA88Bpkp6d9emNamWOI2IiOVA3bKqqwCrl/3W6Cq/C9izX0FFRES7jJksbJ8NnC3peNvXDyimiIhomfH2Wawq6RhJv5L0m85trCdI2lDSWZKukHS5pHeW8rUlzZJ0Tfk5pZRL0hGS5kq6RNKzuo61b9n/Gkn7LvVvGxERS6WuGarje8DXgG8AD43zOQuB99i+SNIawIWSZgFvBM60/VlJHwQ+CHwA2B3YpNy2BY4GtpW0NnAwMANwOc5ptm8fZxwREfEojTdZLLR99EQObPsm4KZy/25JVwLrA3sAO5XdTgB+S5Us9gC+ZdvAuZLWkrRe2XeW7dsASsLZDfjOROKJiIilN95mqJ9K+k9J65VmpLXLGf+4SJoGbAWcB6xbEgnAzcC65f76wA1dT5tXykYrH/kaMyXNljR7wYIF4w0tIiLGYbw1i04/wfu6ygw8te6JklYHfgAcaPsuSYsOYFuSxxnDmGwfAxwDMGPGjEk5ZkREVMaVLGxvtDQHl7QyVaI4yfYPS/EtktazfVNpZppfym8ENux6+gal7EYWNVt1yn+7NPFERMTSGVeykPSGXuW2vzXGcwQcC1xp+4tdm06jqql8tvz8SVf52ySdQtXBfWdJKGcAn+6MmgJ2AQ4aT9wRETE5xtsMtXXX/dWAFwAXAaMmC2AHYB/gUklzStmHqJLEqZL2B64H9irbTgdeDMwF7gP2A7B9m6RPAheU/T7R6eyOiIjBGG8z1Nu7H0taCzil5jnnABpl8wt67G/ggFGOdRxw3HhijYiIybe0U5TfCyxVP0ZERAyf8fZZ/JRq9BNUEwj+C3Bqv4KKiIh2GW+fxWFd9xcC19ue14d4IiKihcbVDFUmFLyKaubZKcA/+hlURES0y3hXytsLOB94NdXopfMkZYryiIjlxHiboT4MbG17PoCkqcCvge/3K7CIiGiP8Y6GWqGTKIpbJ/DciIgYcuOtWfyyXEndmen1NVQX0UVExHKgbg3up1HNEvs+Sa8Ediyb/gic1O/gIiKiHepqFl+izMNUJgL8IYCkZ5ZtL+1jbBER0RJ1/Q7r2r50ZGEpm9aXiCIionXqksVaY2x7zCTGERERLVaXLGZLevPIQklvAi7sT0gREdE2dX0WBwI/kvQ6FiWHGcAqwCv6GFdERLTImMnC9i3AcyTtDDyjFP/c9m/6HllERLTGeNezOAs4q8+xRERES+Uq7IiIqJVkERERtZIsIiKiVt+ShaTjJM2XdFlX2ccl3ShpTrm9uGvbQZLmSrpa0q5d5buVsrmSPtiveCMiYnT9rFkcD+zWo/xw29PL7XQASZsBewObl+d8VdKKklYEjgJ2BzYDXlv2jYiIARrvrLMTZvt3kqaNc/c9gFNsPwD8RdJcYJuyba7tawEknVL2vWKy442IiNE10WfxNkmXlGaqKaVsfeCGrn3mlbLRyiMiYoAGnSyOBjYGpgM3AV+YrANLmilptqTZCxYsmKzDRkQEA04Wtm+x/ZDth4Gvs6ip6UZgw65dNyhlo5X3OvYxtmfYnjF16tTJDz4iYjk20GQhab2uh68AOiOlTgP2lrSqpI2ATYDzgQuATSRtJGkVqk7w0wYZc0RE9LGDW9J3gJ2AdSTNAw4GdpI0HTBwHfAWANuXSzqVquN6IXCA7YfKcd4GnAGsCBxn+/J+xRwREb31czTUa3sUHzvG/ocCh/YoP52s9x0R0ahcwR0REbWSLCIiolaSRURE1EqyiIiIWkkWERFRK8kiIiJqJVlEREStJIuIiKiVZBEREbWSLCIiolaSRURE1EqyiIiIWkkWERFRK8kiIiJqJVlEREStJIuIiKiVZBEREbWSLCIiolaSRURE1OpbspB0nKT5ki7rKltb0ixJ15SfU0q5JB0haa6kSyQ9q+s5+5b9r5G0b7/ijYiI0fWzZnE8sNuIsg8CZ9reBDizPAbYHdik3GYCR0OVXICDgW2BbYCDOwkmIiIGp2/JwvbvgNtGFO8BnFDunwC8vKv8W66cC6wlaT1gV2CW7dts3w7MYskEFBERfTboPot1bd9U7t8MrFvurw/c0LXfvFI2WnlERAxQYx3ctg14so4naaak2ZJmL1iwYLIOGxERDD5Z3FKalyg/55fyG4ENu/bboJSNVr4E28fYnmF7xtSpUyc98IiI5dmgk8VpQGdE077AT7rK31BGRW0H3Fmaq84AdpE0pXRs71LKIiJigFbq14ElfQfYCVhH0jyqUU2fBU6VtD9wPbBX2f104MXAXOA+YD8A27dJ+iRwQdnvE7ZHdppHRESf9S1Z2H7tKJte0GNfAweMcpzjgOMmMbSIiJigXMEdERG1kiwiIqJWkkVERNRKsoiIiFpJFhERUSvJIiIiaiVZRERErSSLiIiolWQRERG1kiwiIqJWkkVERNRKsoiIiFpJFhERUSvJIiIiaiVZRERErSSLiIiolWQRERG1kiwiIqJWkkVERNRqJFlIuk7SpZLmSJpdytaWNEvSNeXnlFIuSUdImivpEknPaiLmiIjlWZM1i51tT7c9ozz+IHCm7U2AM8tjgN2BTcptJnD0wCONiFjOtakZag/ghHL/BODlXeXfcuVcYC1J6zUQX0TEcqupZGHgV5IulDSzlK1r+6Zy/2Zg3XJ/feCGrufOK2URETEgKzX0ujvavlHSE4BZkq7q3mjbkjyRA5akMxPgyU9+8uRFGhERzdQsbN9Yfs4HfgRsA9zSaV4qP+eX3W8ENux6+galbOQxj7E9w/aMqVOn9jP8iIjlzsCThaTHSVqjcx/YBbgMOA3Yt+y2L/CTcv804A1lVNR2wJ1dzVURETEATTRDrQv8SFLn9U+2/UtJFwCnStofuB7Yq+x/OvBiYC5wH7Df4EOOiFi+DTxZ2L4W2LJH+a3AC3qUGzhgAKFFRMQomurgjgma9sGfT/oxr/vsv036MSNi2dSm6ywiIqKlkiwiIqJWkkVERNRKsoiIiFpJFhERUSvJIiIiaiVZRERErSSLiIiolYvyYlLl4sGIZVNqFhERUSvJIiIiaiVZRERErSSLiIiolWQRERG1MhoqljvDMmJrWOKM5UNqFhERUSvJIiIiaiVZRERErfRZRMSjkr6V5cPQJAtJuwFfBlYEvmH7sw2HFBFDJEnt0RmKZCFpReAo4EXAPOACSafZvqLZyCIiJldbk9qw9FlsA8y1fa3tfwCnAHs0HFNExHJDtpuOoZakPYHdbL+pPN4H2Nb227r2mQnMLA//Gbh6ksNYB/jbJB+zHxLn5Eqck2sY4hyGGKE/cT7F9tReG4aiGWo8bB8DHNOv40uabXtGv44/WRLn5Eqck2sY4hyGGGHwcQ5LM9SNwIZdjzcoZRERMQDDkiwuADaRtJGkVYC9gdMajikiYrkxFM1QthdKehtwBtXQ2eNsXz7gMPrWxDXJEufkSpyTaxjiHIYYYcBxDkUHd0RENGtYmqEiIqJBSRYREVErySIiImolWUREdJH02KZjaKMkizFIOnM8ZW0gaUdJ+5X7UyVt1HRMI0laV9Kxkn5RHm8maf+m4xpJ0tMlnSnpsvJ4C0kfaTqukSTtIGmWpP+VdK2kv0i6tum4RhqW/yNJz5F0BXBVebylpK82HNZiJL1d0pQmXjvJogdJq0laG1hH0hRJa5fbNGD9hsNbgqSDgQ8AB5WilYFvNxfRqI6nGv78pPL4f4EDmwpmDF+nei8fBLB9CdW1PW1zLPBFYEdga2BG+dkKw/Z/BBwO7ArcCmD7T8DzGo1oSetSTaR6qqTdJGlQL5xk0dtbgAuBTcvPzu0nwJENxjWaVwAvA+4FsP1XYI1GI+ptHdunAg9Ddf0M8FCzIfX0WNvnjyhb2EgkY7vT9i9sz7d9a+fWdFBdhu3/CNs3jChq1efT9keATahOFN4IXCPp05I27vdrD8VFeYNm+8vAlyW93fZXmo5nHP5h25IMIOlxTQc0insl/RPQiXM74M5mQ+rpb+WfrxPnnsBNzYbU01mS/gv4IfBAp9D2Rc2FtDjbG0n6mO1PNB3LONwg6TmAJa0MvBO4suGYllD+128GbqY6iZkCfF/SLNvv79fr5qK8GuXDM42uxGr7W40F1IOk91KdbbwI+Azw/4CT25boJD0L+ArwDOAyYCqwZ2nmaQ1JT6W6OvY5wO3AX4DX276uybhGknRWj2Lbfv7Ag+lB0hzb0yVdZPtZTcdTR9I6VAusvRAQ8CvgnW2qrUl6J/AGqtlmvwH82PaDklYArrHdtxpGksUYJJ0IbAzMYVF11Lbf0VhQI5Q2yw2oqvq7UH3Iz7A9q9HARiFpJaop5AVcbfvBhkMaVamhrWD77qZjGUaSvkPVj/Ik4M/dm6j+j7ZoJLAhJukQqumOru+x7V9s960mlGQxBklXApu55W+SpEttP7PpOOpIOgA4yfYd5fEU4LW22zbiZFXgVSxZo2xVU4qkxwMHs6gT9mzgE7Zb07Qn6YlUgxpeNnJbry+8JpURhG9nyb/7ErE3RdKJtvepK+uH9FmM7TLgibSzvbrbRZK2tn1B04HUeLPtozoPbN8u6c1Aq5IFVQfsnVSdsQ/U7Nuk46g+o3uVx/sA3wRe2VhEI9i+Gdiy6TjG6cdUHcc/pQzCaKHNux+UJaefPYgXTrLoQdJPqTo31wCukHQ+i3cgtuZMo9gWeJ2k66lGRLW1mr+iJHVqauWDvkrDMfWyge3dmg5iHDa2/aqux4dImtNUMKORtAlVX9pmwGqdcttPbSyo3u63fUTTQfQi6SDgQ8BjJN3VKQb+wYBmn02y6O2wpgOYoF2bDmCcfgl8V9J/l8dvKWVt8wdJz7R9adOB1Pi7pB1tnwPVRXrA3xuOqZdvUjWXHQ7sDOxHO4ftf7lcs/QrWja6zPZngM9I+oztg2qf0Afps1iGSHoCi5+5/V+D4SyhjNh4C/CCUjQL+IbtVo1lL1fxPo1qFNQDtLSmJmk6cALweKoYbwPeWC4maw1JF9p+dnffWqes6di6SfoMVVPen1nUDNWK0WWSNrV9VRlRuIRBJLQkizFIupsy1r7LncBs4D22WzG1gqSXAV+gGnUyH3gKcKXtzcd8YvQk6Sm9ytvWIdshaU0A23fV7dsESX+gusr8+8BvqJZE/qztf240sBEkzaUa0PKPpmMZSdIxtmc2OVw6yWIMkj4JzANOpjpz25tqKO1FwH/Y3qm56BaR9Cfg+cCvbW8laWeq6wJaMe+SpFNt7yXpUpZMvrTljF3SmrbvKlNULMH2bYOOqRdJr7f9bUnv7rXd9hcHHdNYJG1NdXHbWsAngTWB/7J9bpNxjSTpx8BM2/ObjmU0klazfX9dWT+kz2JsL7PdPZLjmHKh0QckfaixqJb0oO1bJa0gaQXbZ0n6UtNBdXln+fmSRqOodzJVjBdSJbXueXcMtKVDtnOFfq8pXVp19lcGMbzG9nuBe6j6K9pqLeAqSRfQ3gEtfwBGNkX1Kpt0SRZju0/SXlTVZ4A9gU4Gb9M/5R2SVgd+B5wkaT5lnqg2sN0Zevwq4JQyd1Xr2H5J+dm6GXu72e4MEPi17f/p3lY6uVvD9kOSdmw6jnE6uOkARlOuV1mfajTUViw6kVkTGMiU6mmGGkOZ9uHLwPZUyeFc4F1Uba7P7oxCaYqkVW0/UK40vp/qA/Q6qg7Pk9o0TQE8MjvuXlQdsd8Fvmf7lmajWmS0zsOONoyK6dZrGo02Tq0h6WiqL7rv0XUSY/uHjQU1ZCTtSzVx4AyqPtOOu4HjB/FeJlkMsc4Xw6Cu4JwskrYAXkNV05hn+4UNhwSMOtdSRytGxQBI2p5q3qoDqYajdqwJvGJE02njJH2zR7Ft/7+BB9ODpHNs79hjQEtnFNyaDYW2BEmvsv2DJl47zVA9SHq/7c9L+gq9O2TbMjfUKpL+HXiOpCWu2m3xmdt8qhkzbwWe0HAsj7C9cxneu/3I5p2WWQVYner/t7vf4i6qptJWsd3mfgps71h+tnFa/5F+Vv7npzHgqWiSLHrrTMY1e8y9mvdWqmantYCXjthmqqmrW0PSf1I1Q02lapJ4s+0rmo1qcbYflnQksFXTsYzG9tnA2ZKOb+tw3m6STqCavfWO8ngK8IW21Cw6mpx3aQIam4omyaIH2z8tP09oOpax2D6njGGfZ/vQpuMZhw2AA23PaTqQGmdKehXww5ZPInmfqvUsNmfxizFb0VzWZYtOooBH5gRrYzIeOe/SSgxo3qUJaGwqmiSLHrrmhuqpTUPpypnwnkCrk0UZQvlK220acjyatwDvBhZK6gwcaFXbdXES1UCBl1DVMvcFFjQaUW8rSJpi+3aAch1La7572jDv0gQ0NhVNOrh7kPSvY20vzQCtIekw4I+0/ExY0k+At7dtGpJh1TWNxiWdCxslXWC7NetwA0h6A9WX8fdK0auBQ22f2FxUS6qbd0nS5rYvH2RMPWJobCqaJIseJJ1p+wWSPmf7A03HU6eM4ngc1RKLrT0TlvQ7qr6A81l8CGUramptmH9nIiSda3s7SWcARwB/Bb7vPq6WNlFlwMB2wB1UswwA/KZtfVXj0YZhyU1ORdOaqmDLrKdqOdWXSTqFxa/kbd2XxpCM4gD4aNMB1Hg3MJNqnq2RzKIvu7b4lKoFkN5DtVztmlTXAbVGaSY9yvZWwNAliBFUv0t/dZLCyElDByE1ix5KH8D+VJOfXcCIaR/a0oE4bGfC8MiZ0Sa2fy3pscCKzrKly7RhaSat05KaRWOThiZZjEHSR21/coztjbZhtmEmyolQtSreTGBt2xurWhTna7ZfUPPUgZP0DJZcrOdbzUW0yBBdBwQMTzNpnZYki8YmDU0z1BjGShTFiQxgAq/R2J5Zfu7cVAwTdACwDXAegO1rSnW6Vcq0JDtRJYvTgd2Bc4BWJAuG5zogYKiaSeu0YeryxiYNTbJ4dBpvw+xo85lwlwds/0Oq3rYyjr2NVds9qdaNvtj2fpLWBb7dcEyP6FwHBFzaxqbGjmFrJu0MbBmtzPZ2zUS2mMYmDU2yeHRa8UU3BGfCHWeXqd0fI+lFwH8CP615ThP+XjpmF6paWGg+sGHTQfXwhTIb6feB79q+rOmARhiKAQOSVqOauXWdcnV594yu6zcWWG97UDXlvYtFk4b2faoPSJ/Fo9KGNswSx6UsOhPesnMmbPtFDYe2mDKMcn9gF6p/yDOollVt1YdQ0leprgvYm2qk0T3AnDbOcVSSxV5UEzOuSZU0PtVsVMNF0jupJmV8EtWM0p1kcRfwddtHNhRaqyRZPAqdce4tiON829tIuhDYmWra4ittb9pwaKMqV/FuYPuSpmMZi6RpwJrdcTY9sKEXSc8E3k+10NAqTccz0jA0k0p6u+2vNB3HWEbMjLsKsDJw7yAGC6QZagyqFpKZY/teSa+n6sz+cmescxsSRTFb0lrA16kmGLuHaqhiq0j6LfAyqs/dhcB8SX+w3aprA7rZvq5HcaMDGzok/QtVjWJP4G9UU3+8p9GgehiWZlLbXynXV01j8RldWxNn92ABVZ1/e1Bd9Nh3qVmMQdIlVM07WwDHA98A9rI95nQgTWrzmbCki8twvzcBG9o+uHuqimHR+T1aEMcfgVOoFpFq5eqDMFTNpCcCGwNzgIdKsds2FHmkQX0eU7MY20LblrQHcKTtYyX1fTzzo9HmM2FgJUnrUbWxf7jpYB6FVpxh2d5e0mOAJzcdS41hGTAwA9isbX1o3bT4ujUrUMV8/yi7T6oki7HdXWakfD3wvNJBu3LDMS2Ntgzx/QRVp/Y5ti9QtWztNQ3HNLQkvRQ4jKrteiNJ04FPtGWurS5D0UwKXAY8EbipbscGda9bsxC4jqopqu/SDDWGMtLk34ELbP9e0pOBndrUhjkebRm1VUfSQbY/03QcdVo0sOFCquGnv+00Q0i61PYzm41sdG1sJtWiJQnWAKZTTXT5yMJCLUy+jUiyWA4MUbJoRZx1AxvaomvW2Yu7ksUw9gE1+nfXEC1JIOmIsbb3s38lzVA9aIgWcB+nNkxTMB5taS47GthS0pZUo4u+QTVyp20DGy5XtR7zimWerXcAf2g4pqXR6N+9TclgHFajGlX23fL41VSz+fa9WS81i2XAsJwJ12n6DHNkHJI+BtxYBja0IrZuZdbeD1Nd5AhVf9CnbA+kw3OytOW97XFyCNV617OB99i+dvBRLU7SucCOtheWxysDvx9Es+gK/X6BYVaG0tWWtcDRVOsxd86E/0zLxrCPU1tqFt0DG37exoENqpap/bntD9veutw+MmyJomW+BLyPaoqPDYD3AidTDU8+rrmwFjOF6kr9jtVLWd8lWYxtGBZwhzLEl2pUxJG2j6LqrBs236vfZSBeQ9XBub/tm6m+OP6r2ZAWZ/sh4GFVix8Nu7Y0k77M9n/bvtv2XbaPAXa1/V0G9IU8Dp8FLpZ0vKQTgIuATw/ihdMM1YO6FnAH7uva9CBwjMdYp7cJks4GfgnsBzyPahz7n9oyKkajrLvQ0faLntpK1ZrmWwGzWHyZ2la9n8PSTFoucjycamJGqK6Mf3cZRDDH9vTGgutSRmluWx6eV05oOtv6NrIsyWIMkj4DfB54OovmtLHt3zUX1ZLaPsRX0r7l7g706Jyz/dZGAhth2AY2dL2vi7F9wqBjGcuwzIRQrvv5MrA91d//XKrZXW8Enm37nAbDG5d+9v8kWYxB1cpu76BqhphDNQfLH92yFeiGRZOdc8sqSasAm1J9uV1tuy1NOo8YlgEDy4J+Tv2RobNjewewNXCu7Z0lbcqA2gfHY9jOhFnUOXdbeTywzrmJkHSi7X3qypom6cXAf1MNaBDVVdxvsf2LZiNbQqtnQtCQLVNbo29n/0kWY7vf9v2SkLSqq1W//rnpoDps71h+Dktndqdz7iyqL7fnAR9vNKLehmVgwxeBnW3PBZC0MfBzoG3J4jVUzaT72765NJO2acDAUC1T25Q0Q41B0o+oOo0PpJpW4XZgZdsvbjKukYblTBjG7pxr2hAObLjA9tZdjwWc310Wy5d+TkWTZDFOZUqAxwO/bFu78Mj233ImfIntzRoMqydJ6wNPYfH1Ato2YKDVAxu6Zh59EdV7eSpV88Orgf+z/Z9NxdZtWJpJu+aG6qlNc0M1ObIszVDj1MYpAbrPhCXd1bXpQeCYZqIanaTPUTVJXA48XIpNtfh8m1xLFdNiAxtoyZrRLD7z6C0smoZkAV0r0TVtiJpJD2s6gAlobCqa1CyWAW0/E+6QdDWwhe0HandukKrFejoDG6Z3BjbYfmXNU6OHtjeTSjrT9gskfc72B5qOZyxNjixLzWLZ0PYz4Y5rqUbBtDpZ0PKBDR1d1wVsR1VD+yPwrjbMYTRC2wcMrKdqOdWXSTqFEdPO2L6ombB6amxkWZLFsqHVQ3y73AfMkXQmi68X0LahifNULdbzY2CWpNuBVl1tXJwMHAW8ojzeG/gOiwYQNGqImkk/BnyU6mTrCyyeLEy7TroaG1mWZqhlQGdUjKQ5wLa2H5B0ue3N6547SMNyxXG3lg9sWGLtCkl/sr1lUzH1MkTNpB+1/ckxtrdiLfumpGaxbBiKM+E2J4XRtHRgw9rl7i8kfZBqVlRTnXWe3lhgoxuKZtKxEkXR2Fr2bRhZlprFMqblZ8J/ofcVsk9tIJyh1fU+9prS3W17P5eVAQP9nEpjGKRmsYxp45lwlxld91ejui5g7VH2jVHY3qjpGCZoKAYMjEPjZ9ZNjixLsoiBsX3riKIvSbqQqoMxloKkZ1DN5PvI9RVtmW24y1A0kw6JxkaWJVnEwEjqbu9dgaqmkc/gUpJ0MLATVbI4HdgdOIeWrZJouzNa6+NlXrDHU62/Mmwaa9Ztw8iy9FnEwJQvio6FwHXAYbavbiai4Vb6ArYELra9paR1gW/bflHDoQ2lJqfSGK8mR5blrC4GxvbOTcewjPm77YclLZS0JtUKiRs2HdQQa2wqjQlobGRZ1uCOgZH0eElflDS73L6gZWMN6abMLn0BXwcupFqP+Y+NRjTchmEt+84FuNeXk6+tgDsG8cKpWcQgHQdcBuxVHu8DfBMYqiGUbdE1u+zXJP0SWNP2JZ3ty/tFZEuh1Ys0FY2NLEuyiEHa2Paruh4fUq46j0fJ9nU9ihu7iGxItX2RJmhwZFk6uGNgJP0ReJ/LwvelQ/Ew29s3G9myaXm/iGxZN+gLcFOziEF6K/Ctrn6K24Ge80XFpMiZ4Di0YSqNpTHoC3CTLGIgJK0I7FOGeK4JYPuumqdF9N0QLdLUqIyGioGw/RDQ+ae8K4liIFo1N1jbSTpxPGXLq9QsYpAulnQa8D3g3k6h7R82F9LwqruIzPZ2jQY4fNq+SFOjUrOIQVoNuJXqAqKXlttLGo1ouB0N3Nd1EdmfadlUH8NA0kGlv2ILSXd1blTrm/+k4fBaI6OhIoZUk+sxL4uGZZGmpqQZKgZG0hE9iu8EZtvOGdzEDcNFZMNkKBZpakqaoWKQVgOmA9eU2xZU/5j7S/pSc2ENrddQrWW+v+2bqd7Ltl1ENkwam0pjGKQZKgZG0rnADmVkVKcD8fdUo6Qutb1Zk/HF8m1Y1rJvSpqhYpCmAKtTNT0BPA5Y2/ZDkh5oLqzhMqwXkQ2BLNI0hiSLGKTPA3Mk/Zbqi+15wKclPQ74dZOBDZNcRNYfy9AiTX2RZqgYKEnrAduUhxfY/mvXtsySOgFNrsccy58ki2iNDPucmJHvV+kDuiR9P9EPGQ0VbaKmAxgGuYgsmpCaRbRGahYTk4vIYpDSwR0xvHIRWQxMmqGiTTJL6sTkIrIYmCSLGBhJO5Rhskh6vaQvSnpKZ3tmSZ2w+23fDzyyHjMwkPWYY/mTZBGDlFlSJ9fIi8h+Qi4iiz5JB3cMTGZJ7Z9Br8ccy590cMcgZZbUPhn0esyx/EkzVAxSZkmNGFJphoqIiFpphoq+yyypEcMvNYuIiKiVPosYGEknjqcsItonySIGabEVx8osqc9uKJaImIAki+i7zJIaMfzSZxEDk1lSI4ZXRkPFIGWW1IghlWaoGKTMkhoxpJIsYpAyS2rEkEozVAzSyFlSbyezpEYMhXRwRyMyS2rEcEmyiIiIWumziIiIWkkWERFRK8kilmuS7hnHPgdKeuyjeI2dJD1nlG1vlHTkBI51naR1JrD/hI4fMZoki4h6BwJLnSyAnYCeySJiWCRZRPDI2f9vJX1f0lWSTlLlHcCTgLMknVX23UXSHyVdJOl7klYv5ddJOqSUXyppU0nTgLcC75I0R9JzxxnP0ZJmS7pc0iEjNr+/HP98SU8r+0+V9ANJF5TbDpP13kRAkkVEt62oahGbAU8FdrB9BPBXYGfbO5cmoI8AL7T9LGA28O6uY/ytlB8NvNf2dcDXgMNtT7f9+3HG8mHbM4AtgH+VtEXXtjttPxM4EvhSKftyeY2tgVcB35jYrx4xtlyUF7HI+bbnAUiaA0wDzhmxz3ZUyeR/JAGsQjW/VccPy88LgVc+ilj2kjST6n90vfKal5Rt3+n6eXi5/0JgsxITwJqdGk/EZEiyiFjkga77D9H7/0PALNuvrTnGaM+vJWkj4L3A1rZvl3Q8i2bphcWXpu3cXwHYrjOdStexliaEiCWkGSqi3t3AGuX+ucAOXX0Fj5P09Ak8fzzWBO4F7pS0LrD7iO2v6frZqdX8Cnh7ZwdJ0yfwehG1kiwi6h0D/FLSWbYXAG8EviPpEqov601rnv9T4BVjdHC/UdK8zg24FbgYuAo4GfifEftPKa/9TuBdpewdwAxJl0i6gqpTPWLSZLqPiIiolZpFRETUSrKIiIhaSRYREVErySIiImolWURERK0ki4iIqJVkERERtZIsIiKi1v8HaU/LoRfFNOsAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* encode the target variable \"intent\" into numerical labels using the LabelEncoder() function from scikit-learn library.\n",
        "* The fit_transform() method is used on the training data to fit the encoder and transform the labels, and the transform() method is used on the test data to transform the labels using the fitted encoder."
      ],
      "metadata": {
        "id": "cBiU-XB920jp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder()\n",
        "y_train_encoded = le.fit_transform(train_df['intent'])\n",
        "y_test_encoded = le.transform(test_df['intent'])"
      ],
      "metadata": {
        "id": "z2sRZnCC2578"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* tokenize the preprocessed text data into sequences using the Tokenizer() function from Keras.\n",
        "* The num_words parameter is set to 5000, which means only the top 5000 most frequent words in the dataset will be kept and the rest will be discarded.\n",
        "* The fit_on_texts() method is used on the training data to fit the tokenizer, and the texts_to_sequences() method is used on both training and test data to convert the preprocessed text data into sequences of numerical indices based on the learned vocabulary from the training data."
      ],
      "metadata": {
        "id": "samuGOCW2-Qv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vectorizer = TfidfVectorizer(max_features=5000)\n",
        "vectorizer.fit(train_df['preprocessed_text'])\n",
        "\n",
        "X_train_vectorized = vectorizer.fit_transform(train_df['preprocessed_text'])\n",
        "X_test_vectorized = vectorizer.transform(test_df['preprocessed_text'])"
      ],
      "metadata": {
        "id": "AENgUBim3F3q"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SVM Classifier"
      ],
      "metadata": {
        "id": "jexbFjdg3J_h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svm = SVC(kernel='linear')"
      ],
      "metadata": {
        "id": "MMPpCX1c3TA2"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the model\n",
        "start_time = time.time()\n",
        "svm.fit(X_train_vectorized, y_train_encoded)\n",
        "end_time = time.time()\n",
        "svm_train_time = end_time - start_time\n",
        "print('SVM training time:', svm_train_time, 'seconds')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7V1rU9nr3VzL",
        "outputId": "479bc2d0-a888-45e0-e367-b72909737925"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM training time: 0.5405542850494385 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Making predictions on test data\n",
        "start_time = time.time()\n",
        "y_pred_svm = svm.predict(X_test_vectorized)\n",
        "end_time = time.time()\n",
        "svm_pred_time = end_time - start_time\n",
        "print('SVM prediction time:', svm_pred_time, 'seconds')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IGH77cXQSrep",
        "outputId": "3c7f269f-eea2-40c9-e2f9-56b1a5401b0f"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM prediction time: 0.06821250915527344 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluating the model\n",
        "accuracy_svm = svm.score(X_test_vectorized, y_test_encoded)\n",
        "print('SVM Accuracy:', accuracy_svm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uI-8XwnD3cam",
        "outputId": "b99be67c-244c-493e-912e-2c84ff00f302"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM Accuracy: 0.96625\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating confusion matrix\n",
        "cm = confusion_matrix(y_test_encoded, y_pred_svm)\n",
        "plt.figure(figsize=(8, 8))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=le.classes_, yticklabels=le.classes_)\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.ylabel('True Label')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "id": "hAsSab8DTrxA",
        "outputId": "f6da2b28-b8de-476c-ee7f-557c0d08a037"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Logistic Regression Classifier"
      ],
      "metadata": {
        "id": "N-lG6T4DauxE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LogisticRegression(max_iter=5000)"
      ],
      "metadata": {
        "id": "z4L1AWbjaxsJ"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the Logistic Regression model\n",
        "start_time = time.time()\n",
        "lr.fit(X_train_vectorized, y_train_encoded)\n",
        "end_time = time.time()\n",
        "lr_train_time = end_time - start_time\n",
        "print('Logistic regression training time:', lr_train_time, 'seconds')"
      ],
      "metadata": {
        "id": "vgU7h0oJbA4G",
        "outputId": "db721436-6a9a-400c-f654-47c20d46a560",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic regression training time: 0.12191987037658691 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Making predictions on test data\n",
        "start_time = time.time()\n",
        "y_pred_lr = lr.predict(X_test_vectorized)\n",
        "end_time = time.time()\n",
        "lr_pred_time = end_time - start_time\n",
        "print('Logistic regression prediction time:', lr_pred_time, 'seconds')"
      ],
      "metadata": {
        "id": "RfXkm1kIbKdZ",
        "outputId": "f1ed7f6a-21aa-4d45-9841-9b122939abf1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic regression prediction time: 0.001065969467163086 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluating the Logistic Regression model\n",
        "accuracy_lr = lr.score(X_test_vectorized, y_test_encoded)\n",
        "print('Logistic Regression Accuracy:', accuracy_lr)"
      ],
      "metadata": {
        "id": "KcXbCOlXbOB_",
        "outputId": "5cb88550-f74a-4204-be83-502b8f8fb62c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression Accuracy: 0.9625\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating confusion matrix for Logistic Regression\n",
        "cm_lr = confusion_matrix(y_test_encoded, y_pred_lr)\n",
        "plt.figure(figsize=(8, 8))\n",
        "sns.heatmap(cm_lr, annot=True, fmt='d', cmap='Blues', xticklabels=le.classes_, yticklabels=le.classes_)\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.ylabel('True Label')\n",
        "plt.title('Confusion matrix for Logistic Regression')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Vqp1c-ElbTc1",
        "outputId": "a5fb3d7e-6a28-4edd-8241-ed6229ffcd98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 602
        }
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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sDNwKnBER7wBXAr9J3/MTLV5zA3BmRGwIjAF+XLKtRxrHyS3W19y0aVM567Qf8qPTz2bRRRelqamJTz/9hGtvvJUTTz6Nc844haRKaJYfs2bN5PFh/2TXr++WdShm1op6TGJOkvQS8AzJMKs1y+w7AjhG0gXABhHxWZl9D5b0AvAisB6wbsm2v5R83apk/e0RMSci/kPS0WjtdP0jEfFx+vzrwFmSRgHDgN7AV4HbgQObz00yrKyllYGHJI0BTk/jalM68+GSEfF4uup6kpkQm92Vfn2eJAFr6zhzJye67tqry52yIrNnzeKsU09mtz32YseddwVguYblGbjzrkhivQ02pFu3bkyeNGmBz1VtyzU0MOGDCXOXJzY20tDQUOYV2ctbzHmLt9STTzzB2uusy1eWWSbrUNqVt+uct3ghnzHPlW3H3prJPoISaYfWXYCtImIjkoSjd1v7R8Rwkl/i44HrJB3ZxnFXBU4Ddk4rGA+0OG5U8Lx0eWrp4YFBadWkf0R8NSJei4jxwEdpxecQkspMS78HLk+rQN8p914r9Hn6tYkynbYjYkhEDIiIAUcfe/wCnTAi+NlP/h/9Vl2Nw444eu76HXbciedHPAfAu/99h1mzZrHkUkst0LlqYb31N+Ddd99h3Lj3mDVzJkMffIAddtwp67DKylvMeYu31NAHH8hFUxLk7zrnLV7IZ8xzZTdPTE3V2+ikJYBJETFN0trAlun6WZJ6RsSs0p0lrUIyic7VkhYiaeZprQPs4iSJxyeSGkhuMDWsZPshJMO7DgGeLll/kKTrSaZNXg14A9i4xbEfAk6UdGI66+DGEfFiuu024AxgiYgY3cb7bZ7E56iS9Z+lMc8jIj6RNEnSdmkz0xHA4y3360wvjXqBv99/H2usuRbfOnh/AE448WT23u8Afvbj8/jmoH3o2bMnP/7pxagOPvAt9ejRg7PPPZ8TBh/HnDlN7Lf/INZYo1zxL3t5izlv8TabPm0azz79FOf9uGUXuvqUt+uct3ghnzEXneqpn0KaiNxD0hTyBrAkcAFJ0rEP8EJEHC5pSkQsKukokmaYWcAU4MiIeLuNY19HMuLnPeAT4L6IuC4dYn1beo7PgW9GxNh0/xkkfXMWB06JiPslHU0yJPsH6XH7kPSj2ZqksvV2RDR3Am4gSVJ+GhE/SdfNfX3aCfg3wCTgn8BmETFQ0lokzU9zSMbX7wxMiYhfSupP0mdmYZImrmMiYpKkYcBpETFS0jLAyIjo1941nzy9qX4+ABXo3bN71iFYHZpTRz/HKtWtDpN6y17vHtTkg9Fnl0tq8p9k+j/OyvSDXFdJjHU+JzFWBE5irCicxHRMvTUnmZmZWbUVNGkuXBIj6VlgoRarj4iI+pyoxMzMzOZL4ZKYiNgi6xjMzMzqSh0Mh66FwiUxZmZm1kJBm5OKmZqZmZlZ4bkSY2ZmVnQFbU4q5rsyMzOzwnMlxszMrOgK2ifGSYyZmVnRuTnJzMzMrH64EmNmZlZ0BW1OciXGzMzMcsmVGDMzs6IraJ8YJzFmZmZFV9AkppjvyszMzArPlRgzM7Oic8deMzMzs/rhSoyZmVnRuU+MmZmZWf1wJcbMzKzoCtonxkmMmZlZ0bk5yczMzKx+uBLTxfXu2T3rEDpkzpzIOoQO69atmGXcetKtoKVys6op6P8RV2LMzMwsl1yJMTMzKzgVtBLjJMbMzKzgiprEuDnJzMzMcsmVGDMzs6IrZiHGlRgzMzPLJ1dizMzMCq6ofWKcxJiZmRVcUZMYNyeZmZlZLrkSY2ZmVnCuxJiZmZnVEVdizMzMCq6olRgnMWZmZkVXzBzGzUlmZmaWT67EmJmZFVxRm5NciTEzM7NcciXGzMys4FyJMTMzM6sjrsSYmZkVXFErMU5izMzMCq6oSYybk8zMzCyXXIkxMzMrumIWYlyJaUnSOS2W/1XFY18jad1qHa/Fsf8iabSkH0k6WdLCtThPe556Yjj77PkN9tptV669ekgWIXTYTTdcx6D99uLA/ffmrDNO4fPPP886pHbl7TrnLV5wzJ0hb/Gef97ZDNxuKw7Yd6+sQ7GUk5gvmyeJiYitq3XgiDguIl5tuV5S9/ZeK6nNqpmk5YHNImLDiPgNcDLQ6UlMU1MTF190IX+88hruvu8Bhj54P2+OHdvZYXTIxMZG/nLLjdx8653cefffmNM0h4f+/kDWYZWVt+uct3jBMXeGvMULsO9+B3DFVddkHcZ8kVSTR9a6dBIj6R5Jz0t6RdJgSZcAfSSNknRzus+U9OsKkoan216WtF2Z414haWR63J+UrB8maUDzcSX9StJLwFaSjkwrKS9JujHd5zpJV0p6FviFpM0lPS3pRUn/kvS19NAPAyulsf0YWBF4TNJjNbhsbXp5zGj69l2Flfv2pWevXuy2x54Me+zRzgxhvjTNbuLzz2cwe/ZsZsyYzrLLLZd1SGXl7TrnLV5wzJ0hb/ECbDpgMxZfYomsw5gvRU1iunqfmG9HxMeS+gAjgB2AH0RE/1b2PQx4KCIuSisn5Sod56bH7Q48KmnDiBjdYp9FgGcj4lRJ6wHnAVtHxP8kLV2y38rp+iZJiwPbRcRsSbsAFwODgH2A+5vjlnQMsGNE/K9jl2PBTGxsZPkVlp+7vFxDA2NGt3zb9WW5hgaOPPrb7L7rTizUeyG22mobttp626zDKitv1zlv8YJj7gx5i9fqU5euxAAnpZWQZ4C+wJpl9h0BHCPpAmCDiPiszL4HS3oBeBFYD2itH0wT8Nf0+U7AHc1JR0R8XLLfHRHRlD5fArhD0svAb9Jjd1hadRopaWQe2qFr6dNPPmHYY49y/9B/8PCjw5k+fToP/O2+rMMyM6uqolZiumwSI2kgsAuwVURsRJJw9G5r/4gYDmwPjAeuk3RkG8ddFTgN2DkiNgQeaOO4M0qSk3Kmljz/KfBYRKwP7F0u3nIiYkhEDIiIAcceP3h+DtGq5RoamPDBhLnLExsbaWhoqNrxa+HZZ55mxZVWZumll6Znz57stMuuvPTSi1mHVVbernPe4gXH3BnyFq/Vpy6bxJBUNSZFxDRJawNbputnSerZcmdJqwCNEXE1cA2wSRvHXZwk8fhEUgOwewWx/BM4SNJX0nMt3cZ+S5AkUQBHlzneZ8BiFZy3qtZbfwPeffcdxo17j1kzZzL0wQfYYcedOjuMDll+hRUYM/olpk+fTkTw3LNPs+qqq2UdVll5u855ixccc2fIW7y5pxo9MtaV+8QMBb4r6TXgDZImJYAhwGhJL0TE4SX7DwROlzQLmAK0WomJiJckvQi8DrwHPNVeIBHxiqSLgMclNZFUhY5uZddfANdLOo+kwtOWIcBQSe9HxI7tnb9aevTowdnnns8Jg49jzpwm9tt/EGusUa6FLnsbbLgRu+z6dQ47+AC69+jB2muvw6CDDsk6rLLydp3zFi845s6Qt3gBzjztFEaOeI7Jkyex607bc8L3T+SAQQdlHVZFsmz6kfQOyR/XTcDsiBiQ/rF+G9APeAc4OCImKQn0MmAPYBpwdES80OaxI6K20VtdmzGbXH0A5szJVbgAdOtWB3+umFku9O5Rm/pGw3F31OSHZ+M1B7Ubb5rEDCgdbCLpF8DHEXGJpLOApSLiTEl7ACeSJDFbAJdFxBZtHbsrNyeZmZl1CXXYsXdf4Pr0+fXAfiXrb4jEM8CSklZo6yBduTlpgaXztyzUYvURETEmi3jMzMzqUAAPSwrgqogYAjRExAfp9glAc6/ulUi6YjQbl677gFY4iVkA5UpcZmZm9aJWfWIkDQZKh7kOSZOUUttGxHhJywGPSHq9dGNERJrgdJiTGDMzM5svacJSdsKxiBiffp0o6W5gc6BR0goR8UHaXDQx3X08ybxtzVbmi1G5X+I+MWZmZgWXVZ8YSYtIWqz5OfB14GXgPuCodLejgHvT5/cBRyqxJfBJSbPTl7gSY2ZmVnTZDZJsAO5OE54ewC0RMVTSCOB2SccC/wUOTvd/kGRk0liSIdbHlDu4kxgzMzOriYh4C9iolfUfATu3sj6A71d6fCcxZmZmBVcP9zmqBfeJMTMzs1xyJcbMzKzgilqJcRJjZmZWcEVNYtycZGZmZrnkSoyZmVnRFbMQ40qMmZmZ5ZMrMWZmZgVX1D4xTmLMzMwKrqhJjJuTzMzMLJdciTEzMys4V2LMzMzM6ogrMWZmZgXnSoyZmZlZHXElxszMrOiKWYhxEmP50q1b/v4nfjZ9dtYhdMhiffL3YyEi6wg6rqDVfatTbk4yMzMzqyP5+5PLzMzMOsSVGDMzM7M64kqMmZlZwRW0EOMkxszMrOjcnGRmZmZWR1yJMTMzK7iCFmJciTEzM7N8ciXGzMys4IraJ8ZJjJmZWcEVNIdxc5KZmZnlkysxZmZmBZfH+85VwpUYMzMzyyVXYszMzArOfWLMzMzM6ogrMWZmZgXnIdZmZmaWSwXNYdycZGZmZvnkSoyZmVnBFbU5yZUYMzMzyyVXYszMzAquqJUYJzFmZmYFV9Acxs1JVl1PPTGcffb8BnvttivXXj0k63AqkpeYm5qaOOawQZxx8vfmWf/bSy9m1+0GZBRVZfJyjVtqamrikAP348TvfSfrUCqSt+uct3ghnzEXmZOYMiSd02L5X1U89jWS1m1j23aSXpE0SlKfap2z1pqamrj4ogv545XXcPd9DzD0wft5c+zYrMMqK08x3/GXG1ll1dXmWff6qy/z2aefZhRRZfJ0jVu65aYbWHW11bMOoyJ5u855ixfyGXMzSTV5ZM1JTHnzJDERsXW1DhwRx0XEqy3XS+oOHA78X0T0j4jp7R1Licy/ly+PGU3fvquwct++9OzVi9322JNhjz2adVhl5SXmiY0TePqp4ey936C565qamvjDZb/khB+emmFk7cvLNW6pccIEnhg+jAMGHZh1KBXJ23XOW7yQz5iLLvNffPVC0j2Snk8rIIMlXQL0SashN6f7TEm/riBpeLrtZUnblTnuFZJGpsf9Scn6YZIGNB9X0q8kvQScDRwM/FTSzZIWlfSopBckjZG0b/qafpLekHQD8DLQV9LpkkZIGl16rs4ysbGR5VdYfu7ycg0NNDY2dnYYHZKXmH/3q0s44aRTKc1V/3r7LWy7/Y4ss8yyGUbWvrxc45Yu/fnFnHzK6dTB3wcVydt1zlu8kM+Ym0m1eWQtH/87O8e3I2JTYABwEnApMD2thhzeYt/DgIcioj+wETCqzHHPjYgBwIbADpI2bGWfRYBnI2KjiPgZcB9wenreGcD+EbEJsCPwK31Rw1sT+GNErAd8LV3eHOgPbCpp+9YCSpO0kZJGuk23/j31xDCWXHpp1l5nvbnr/vfhRB77x0MMOqTlR9OqYfiwx1hq6aVZd731sw7FrCqK2pzk0UlfOEnS/unzviQJQVtGAH+S1BO4JyJGldn3YEmDSa71CsC6wOgW+zQBf23j9QIuThOSOcBKQEO67b8R8Uz6/Ovp48V0edH0PQxvecCIGAIMAZgxmygTe4cs19DAhA8mzF2e2NhIQ0NDmVdkLw8xj3npRZ4aPoxnnnqCmTM/Z+qUqXzr4H3p1asnh+6/OwAzZszgkP1247Z7hmYc7Zfl4Rq3NOrFF3h82D958onhzPz8c6ZOncI5Z57GxT//ZdahtSlv1zlv8UI+Yy46V2IASQOBXYCtImIjkkSgd1v7R8RwYHtgPHCdpCPbOO6qwGnAzhGxIfBAG8edERFNbZzucGBZYNO08tNYcoyppafji340/SNijYi4tq33UAvrrb8B7777DuPGvcesmTMZ+uAD7LDjTp0ZQoflIebv/uBH3P3gP7nzb49wwUW/ZNPNtmDoY09z30PDufNvj3Dn3x6hd+/edZnAQD6ucUsn/ehUHn50OH9/+J9ccumv2WzzLes6gYH8Xee8xQv5jLlZUZuTXIlJLAFMiohpktYGtkzXz5LUMyJmle4saRVgXERcLWkhYBPghlaOuzhJovGJpAZgd2DYfMQ2MSJmSdoRWKWN/R4i7UcTEVMkrQTMioiJHTzffOvRowdnn3s+Jww+jjlzmthv/0GssUa5glb28hhz3vgad468Xee8xQv5jLnoFFG11oTcShORe4B+wBvAksAFJEnHPsALEXG4pCkRsaiko4DTgVnAFODIiHi7jWNfB2wNvAd8AtwXEddJGgacFhEjm4/b4jX3R8SdkpYB/kbSPDSSJMHaPd31/ohYv+R1PwSOSxenAN+KiDfLvfdqNidZ6z6bPjvrEDpksT75+9smjz/G6uGvWKs/vXtQk0/GZhcNq8n/khHnDsz0k+wkpotzElN7TmJqL48/xpzEWGucxHRM/n5amZmZWYcUNWl2ElMlkp4FFmqx+oiIGJNFPGZmZs3qYTh0LTiJqZKI2CLrGMzMzLoSJzFmZmYFV9BCjOeJMTMzs3xyJcbMzKzg3CfGzMzMcqmgOYybk8zMzCyfXIkxMzMruKI2J7kSY2ZmZrnkSoyZmVnBFbQQ4yTGzMys6NycZGZmZlZHnMSYmZkVnKSaPCo8d3dJL0q6P11eVdKzksZKuk1Sr3T9Quny2HR7v/aO7STGzMzMaumHwGslyz8HfhMRawCTgGPT9ccCk9L1v0n3K8tJjJmZWcFJtXm0f16tDOwJXJMuC9gJuDPd5Xpgv/T5vuky6fad1U65x0mMmZmZzRdJgyWNLHkMbrHLb4EzgDnp8leAyRExO10eB6yUPl8JeA8g3f5Jun+bPDrJzMys4Go1OikihgBD2jjnXsDEiHhe0sBanN9JjJmZWcFlNMJ6G2AfSXsAvYHFgcuAJSX1SKstKwPj0/3HA32BcZJ6AEsAH5U7gZuTzMzMrOoi4uyIWDki+gGHAv+MiMOBx4AD092OAu5Nn9+XLpNu/2dERLlzuBJjZmZWcHU22d2ZwK2Sfga8CFybrr8WuFHSWOBjksSnLCcxZmZmVlMRMQwYlj5/C9i8lX1mAAd15LhOYsxqbLE++fpvNn1mU9YhdNhCPfLXMl5nfxlbwRX145avn65mZmbWYd0KmsXk788XMzMzM1yJMTMzK7yCFmJciTEzM7N8ciXGzMys4IrakdxJjJmZWcF1K2YO4+YkMzMzyydXYszMzAquqM1JrsSYmZlZLrkSY2ZmVnAFLcS4EmNmZmb55EqMmZlZwYlilmKcxJiZmRWch1ibmZmZ1RFXYszMzArOQ6zNzMzM6ogrMWZmZgVX0EKMkxgzM7Oi61bQLMbNSWZmZpZLrsSYmZkVXEELMa7EmJmZWT45ibGqeuqJ4eyz5zfYa7ddufbqIVmHUxHHXH2NEz7ge8cfzaEH7MU3B+3NbbfcCMB/3nid4478JocftC+n/vB7TJ0yJeNI23bLTTdw4P57M2i/vbj5xuuzDqci9f65aClv8UI+Y4ZkiHUtHllzElMBSee0WP5XFY99jaR129g2TNKA9PmDkpas1nlroampiYsvupA/XnkNd9/3AEMfvJ83x47NOqyyHHNtdO/eg5NOOYNb77qfa264lTtvu4W33xzLxReez/dOOoWb77iXgTvuzE3X/ynrUFs19j//5q6/3sGNt9zObXfew/DHh/Huu//NOqyy8vC5KJW3eCGfMTeTavPImpOYysyTxETE1tU6cEQcFxGvtlwvqXuL/faIiMnVOm8tvDxmNH37rsLKffvSs1cvdttjT4Y99mjWYZXlmGtjmWWXZe11ktx8kUUWod+qqzHxw4m8++47bLzpAAA233JrHnv04SzDbNPbb73F+htsSJ8+fejRowebDtiMf/7jkazDKisPn4tSeYsX8hlz0TmJaUHSPZKel/SKpMGSLgH6SBol6eZ0nynp1xUkDU+3vSxpuzLHvULSyPS4PylZX1ptmSLpV5JeArZq8fp3JC0jqZ+k1yRdnR7rYUl90n1WlzQ0jf8JSWtX/wq1bWJjI8uvsPzc5eUaGmhsbOzMEDrMMdfe+++P599vvMb662/IaqutwfBhyQ/9Rx95iImNEzKOrnWrr7kmL74wksmTJzF9+nSefOJxJkz4IOuwysrb5yJv8UI+Y27WTarJI2tOYr7s2xGxKTAAOAm4FJgeEf0j4vAW+x4GPBQR/YGNgFFljntuRAwANgR2kLRhK/ssAjwbERtFxJNljrUm8IeIWA+YDAxK1w8BTkzjPw34Y5ljmNXctGlTOfu0H3LyaWezyKKLcu4FP+Ovt9/KUYcdyLRpU+nRs2fWIbZqtdVW5+hvH8/3Bh/L9797PF9bex26d+/e/gvNrFN5iPWXnSRp//R5X5KEoS0jgD9J6gncExGjyux7sKTBJNd8BWBdYHSLfZqAv1YQ49sl53oe6CdpUWBr4I6SzlYLtfbiNI7BAJf/8SqOPX5wBads33INDUz44Iu/rCc2NtLQ0FCVY9eKY66d2bNmcfZpJ/ON3fdix513BaDfqqvxuyuuAeDd/77Dv54YnmWIZe1/wIHsf8CBAPz+sl/T0LB8O6/IVl4+F83yFi/kM+Zm2ddMasOVmBKSBgK7AFtFxEbAi0DvtvaPiOHA9sB44DpJR7Zx3FVJKiM7R8SGwANtHHdGRDRVEOrnJc+bSBKjbsDktGLU/FinjbiHRMSAiBhQrQQGYL31N+Ddd99h3Lj3mDVzJkMffIAddtypasevBcdcGxHBRT/5f/RbdTUOO+Loues//vgjAObMmcOfr76S/Q88OKMI2/fxR0msH3zwPv/8xyPsvsdeGUdUXh4+F6XyFi/kM+aicyVmXksAkyJiWtqfZMt0/SxJPSNiVunOklYBxkXE1ZIWAjYBbmjluIsDU4FPJDUAuwPDqhl4RHwq6W1JB0XEHUrKMRtGxEvVPE85PXr04Oxzz+eEwccxZ04T++0/iDXWKFfIyp5jro2XRr3A3x+4j9XXXIsjDkkKmyf84GTee+9d7rztFgAG7rQre+17QJZhlnXaKScxefJkevTowVnnns9iiy+edUhl5eFzUSpv8UI+Y25WD8Oha0ER0foGaZNyL4yIF2oSUYbSROQeoB/wBrAkcAFJ0rEP8EJEHC5pSkQsKuko4HRgFjAFODIi3m7j2NeRNPe8B3wC3BcR10kaBpwWESObj1vymtJt75D001kUuD8i1k/3OQ1YNCIuSCs+V5A0V/UEbo2IC8u95xmzaf0DYF3W9JmVFAPry0I98ldU7tatmL9UbMH07lGblp/DbxxVk5/1Nx/RP9MPcrkk5rEyr4uIcA2tAJzEWEtOYjqHkxhrjZOYjmmzOSkiduzMQMzMzKw2itqc1G6fGEkLA6cAX42IwZLWBL4WEffXPLockvQsXx4VdEREjMkiHjMzs6KqpGPvn0mG8TbPUjseuANwEtOKiNgi6xjMzMxKFbQQU1ESs3pEHCLpmwDpyJ2CXg4zM7PiKeqv7Up6w81Mp7UPSKa2Z955SszMzMw6XSWVmB8DQ4G+6b2DtgGOrmVQZmZmVj1FHQzXbhITEY9IeoFk4jcBP4yI/9U8MjMzM7MyKp2xdwdgW5ImpZ7A3TWLyMzMzKqqqH1iKhli/UdgDeAv6arvSNolIr5f08jMzMysKoqZwlRWidkJWCfSqX0lXQ+8UtOozMzMzNpRSRIzFvgq8N90uW+6zszMzHKgW1drTpL0N5I+MIsBr0l6Ll3eAniuc8IzMzMza125SswvOy0KMzMzq5mCFmLK3gDy8c4MxMzMzGqjqKOT2p2xV9KWkkZImiJppqQmSZ92RnBmZmZmbamkY+/lwKEkN30cABwJrFXLoMzMzKx6ClqIqejeSUTEWKB7RDRFxJ+B3WoblpmZmVl5lVRipknqBYyS9AvgAypMfszMzCx7RR1iXUkyckS63w+AqSTzxBxQy6DMzMzM2lPJDSCbJ7mbAfwEQNJtwCE1jMvMzMyqpKCFmIpvANnSVlWNwszMzGqmyw6xNjMzM6tH5W47sElbm4CetQnHzLLWp1f3rEPosKU2+0HWIXTYpBGXZx2CdSFFrViUa076VZltr1c7EDMzM7OOKHfbgR07MxAzMzOrjaL2iZnfjr1mZmaWE92KmcMUtpnMzMzMCs6VGDMzs4LrspUYJb4l6fx0+auSNq99aGZmZpZXknpLek7SS5JekdQ8Ye6qkp6VNFbSbemtjZC0ULo8Nt3er71zVNKc9EeSye2+mS5/Bvxh/t6SmZmZdTZJNXm043Ngp4jYCOgP7CZpS+DnwG8iYg1gEnBsuv+xwKR0/W/S/cqqJInZIiK+T3LbASJiEtCrgteZmZlZHeim2jzKicSUdLFn+ghgJ+DOdP31wH7p833TZdLtO6udTKmSJGaWpO7piZG0LDCngteZmZlZgUkaLGlkyWNwi+3dJY0CJgKPAG8CkyNidrrLOGCl9PlKwHsA6fZPgK+UO38lHXt/B9wNLCfpIuBA4LxK3pyZmZllr1bTxETEEGBIme1NQH9JS5LkEmtX8/yV3MX6ZknPAzuT3HJgv4h4rZpBmJmZWXFFxGRJj5H0sV1SUo+02rIyMD7dbTzQFxgnqQewBPBRueNWMjrpq8A04G/AfcDUdJ2ZmZnlQDepJo9yJC2bVmCQ1AfYFXgNeIykVQfgKODe9Pl96TLp9n9GRJQ7RyXNSQ+Q9IcR0BtYFXgDWK+C15qZmVnXtAJwfdqvthtwe0TcL+lV4FZJPwNeBK5N978WuFHSWOBj4ND2TlBJc9IGpcvp3a2/16G3YWZmZpnJYnr+iBgNbNzK+reAL803FxEzgIM6co4Oz9gbES9I2qKjrzMzM7NsFPT+j+0nMZJOKVnsBmwCvF+ziMzMzMwqUEklZrGS57NJ+sj8tTbhmJmZWbW11wk3r8omMWlnnMUi4rROisfMzMysIm0mMc1juCVt05kBmZmZWXUVtBBTthLzHEn/l1GS7gPuAKY2b4yIu2ocm5mZmVVBe/c5yqtK+sT0Jpkxbye+mC8mACcxZmZmlplyScxy6cikl/kieWlWdgY9MzMzqx9F7dhbbv6b7sCi6WOxkufNjy5N0jktlv81H8c4SNJrkh6TNFDS/en6fSSd1c5r5+7fyraTJS3c0Xiq4aknhrPPnt9gr9125dqr27wnWN2Y8MEHHHv0Eey/9x7sv8+e3Hzj9e2/KGN5jLmePxdLLNqHWy49llF3nceLfz2PLTZclYtP3o9Rd53Hc7edzW2/Op4lFu0DwKG7D+CZW8+a+5j6/O/YcK2V2jlD56nn69yavMV7/nlnM3C7rThg372yDsVSauu2BJJeiIhNOjme3JA0JSIWKJmTNBT4WUQ8KWkgcFpEVPS/o9z+kt4BBkTE/9o7zozZ1auqNTU1sc+e3+Cqq/9MQ0MDhx1yIJdc+mtWX2ONap2i6j78cCL/+/BD1ll3PaZOncKhBw3it7/7g2Ouos74XCy12Q/m+7VXX3gET704luvufpqePbqzcO9eDFh/FYaN+DdNTXP42Un7AnDe7+6d53XrrbEit//6eNbb5yfzdd5JIy6f75hbk7f/f3mLF+D5kSNYeOGFOffsM7nr3lb/hlxgvXtQk5LJT/8xtiYtKP9vlzUyLfGUq8QUs/Y0HyTdI+l5Sa9IGizpEqCPpFGSbk73mZJ+XUHS8HTby5K2a+OY5wPbAtdKurTFtqMlXZ4+X13SM5LGSPpZ83lSi0q6U9Lrkm5W4iRgReCx9I6hneblMaPp23cVVu7bl569erHbHnsy7LFHOzOEDlt22eVYZ93kNmCLLLIoq622GhMnNmYcVXl5i7mePxeLL9qbbTdZnevufhqAWbOb+GTKdB595nWamuYA8NyYt1mpYckvvfbg3Tbljode6Mxwy6rn69yavMULsOmAzVh8iSWyDmO+dFNtHlkrl8Ts3GlR1L9vR8SmwADgJOBSYHpE9I+Iw1vsexjwUET0BzYCRrV2wIi4EBgJHB4Rp5c592XAZek9rMa12LYxcDKwLrAasE1E/I5kRuUdI2LHit9hFUxsbGT5FZafu7xcQwONjfX7y7Wl8ePH8fprr7HBhhtlHUrF8hBzPX8u+q34Ff43aQpDfvItnv7Lmfzx/MNYuHevefY5ct+teOipV7/02gO/vgm3Dx3ZWaG2q56vc2vyFq/VpzaTmIj4uDMDqXMnSXoJeAboC6xZZt8RwDGSLgA2iIjPFvDcW5EMbwe4pcW25yJiXETMIUmW+lVywLSaNFLSyDy0Q3eGaVOncurJJ3H6Weew6KL56PKVx5jrTY8e3em/dl+uvuMJtvrmz5k2/XNO+/auc7efcew3aGqaw60PjpjndZutvwrTZszi1Tc/6OyQzeaLavQvax2+AWRXk/Y92QXYKiKmSRpGMuy8VRExXNL2wJ7AdZJ+HRE31Ci8z0ueN1Hh9zMihgBDoLp9YpZraGDCBxPmLk9sbKShoaFah6+ZWbNmccrJJ7HHnnuzy65fzzqciuQp5nr+XIxvnMT4iZMZ8fJ/Abj7H6M49ZgkifnW3luwx/brs/t3fvel1x30jU3rqgoD9X2dW5O3eK0+ZXF37rxZApiUJjBrA1um62dJ6tlyZ0mrAI0RcTVwDcmEgQviGWBQ+vzQCl/zGfPe86pTrLf+Brz77juMG/ces2bOZOiDD7DDjjt1dhgdEhFccP65rLbaahx59DFZh1ORvMVcz5+Lxo8+Y9yESay5ynIADNz8a7z+1gR23XodTjl6Fw48+Sqmz5g1z2skMejrm3DHQ89nEXKb6vk6tyZv8eZdUfvEuBLTvqHAdyW9BrxBklRAUskYnY7iKu0XMxA4XdIsYApw5AKe/2TgJknnprF8UsFrhgBDJb3fmf1ievTowdnnns8Jg49jzpwm9tt/EGusUa7lLXsvvvA89993L2uutRYHH5CMQjnx5FPYbvsdMo6sbXmLud4/F6f8/A7+fPHR9OrRnXfG/4/BP76JJ286g4V69eD+K5JRT8+NeYeTLroVgG03WYNxEybxzviPsgz7S+r9OreUt3gBzjztFEaOeI7Jkyex607bc8L3T+SAQQdlHVaX1uYQa6sP6Xwv0yMiJB0KfDMi9q3W8avZnGSWlQUZYp2Vag+xtmKo1RDrXzz2Zk1+1p+x4+qZ1mNcial/mwKXSxIwGfh2tuGYmVneqKAz9jqJ6QSSngUWarH6iIgY095rI+IJkqHaZmZmVsJJTCeIiC2yjsHMzLqueuiEWwsenWRmZma55EqMmZlZwRW0S4yTGDMzs6LrVtAsxs1JZmZmlkuuxJiZmRWcO/aamZmZ1RFXYszMzAquoF1inMSYmZkVXbfa3M0gc25OMjMzs1xyJcbMzKzgitqc5EqMmZmZ5ZIrMWZmZgXnIdZmZmZmdcSVGDMzs4Ir6m0HnMSYmZkVXEFzGDcnmZmZWT65EmNmZlZwRW1OciXGzMzMcsmVGDMzs4IraCHGSYyZ5d+kEZdnHYJZXStqs0tR35eZmZkVnCsxZmZmBaeCtie5EmNmZma55EqMmZlZwRWzDuMkxszMrPA8T4yZmZlZHXElxszMrOCKWYdxJcbMzMxyypUYMzOzgitolxhXYszMzCyfXIkxMzMruKJOduckxszMrOCK2uxS1PdlZmZmBedKjJmZWcEVtTnJlRgzMzPLJVdizMzMCq6YdRgnMWZmZoXn5iQzMzOzOuJKjJmZWcEVtWJR1PdlZmZmBeckxszMrOAk1eRRwXn7SnpM0quSXpH0w3T90pIekfSf9OtS6XpJ+p2ksZJGS9qk3PGdxJiZmRWcavSowGzg1IhYF9gS+L6kdYGzgEcjYk3g0XQZYHdgzfQxGLii3MGdxFhVPfXEcPbZ8xvstduuXHv1kKzDqYhjrr28xXv+eWczcLutOGDfvbIOpUPydp3zFi/kM+YsRcQHEfFC+vwz4DVgJWBf4Pp0t+uB/dLn+wI3ROIZYElJK7R1/EIlMZLOabH8r/k4xkGSXkvLXwMl3Z+u30fSWe28du7+rWw7WdLCtY4/S01NTVx80YX88cpruPu+Bxj64P28OXZs1mGV5ZhrL2/xAuy73wFccdU1WYfRIXm7znmLF/IZczOpNo+OxaB+wMbAs0BDRHyQbpoANKTPVwLeK3nZuHRdqwqVxADzJAERsfV8HONY4PiI2LHFse6LiEsWILaTgbJJDNWJPzMvjxlN376rsHLfvvTs1Yvd9tiTYY89mnVYZTnm2stbvACbDtiMxZdYIuswOiRv1zlv8UI+Y641SYMljSx5DG5jv0WBvwInR8SnpdsiIoCYn/PnNomRdI+k59OOQoMlXQL0kTRK0s3pPlPSrytIGp5ue1nSdm0c83xgW+BaSZe22Ha0pMvT56tLekbSGEk/az5PalFJd0p6XdLNaSelk4AVgcckPdbGucvFP1DS45LulfSWpEskHS7puTSG1dP9lpX0V0kj0sc2832B58PExkaWX2H5ucvLNTTQ2NjYmSF0mGOuvbzFm1d5u855ixfyGXOzbqgmj4gYEhEDSh5famOT1JMkgbk5Iu5KVzc2NxOlXyem68cDfUtevnK6ro33lV/fjohNgQHAScClwPSI6B8Rh7fY9zDgoYjoD2wEjGrtgBFxITASODwiTi9z7suAyyJiA5JSV6mNSaou6wKrAdtExO+A94EdW1Z4Ss59Vpn4SeP+LrAOcASwVkRsDlwDnFgS128iYjNgULrNzMwsE0qGMF0LvBYRvy7ZdB9wVPr8KODekvVHpgWALYFPSpqdviTPScxJkl4CniHJ2tYss+8I4BhJFwAbpJ2LFsRWwB3p81tabHsuIsZFxBySZKnfAp6r2Yi0g9TnwJvAw+n6MSXn2AW4XNIokg/C4mkJbx6l5b9qdkxbrqGBCR9MmLs8sbGRhoaGMq/InmOuvbzFm1d5u855ixfyGXOzDPvEbEPyh/dOaUvDKEl7AJcAu0r6D8nvrubuGg8CbwFjgauB75U7eC6TGEkDSd70VhGxEfAi0Lut/SNiOLA9SUnqOklH1jC8z0ueN1G9WZFLjzunZHlOyTm6AVum1Zz+EbFSRJQ2dQHMU/479vhWmy/ny3rrb8C7777DuHHvMWvmTIY++AA77LhT1Y5fC4659vIWb17l7TrnLV7IZ8zNVKN/7YmIJyNCEbFhye+mByPio4jYOSLWjIhdIuLjdP+IiO9HxOoRsUFEjCx3/LzedmAJYFJETJO0NsnYc4BZknpGxKzSnSWtAoyLiKslLQRsAtywAOd/hqS55jbg0Apf8xmwGPC/Mvu0Gn8HPEzStHQpgKT+ETFqPo/VYT169ODsc8/nhMHHMWdOE/vtP4g11ihXIMueY669vMULcOZppzByxHNMnjyJXXfanhO+fyIHDDoo67DKytt1zlu8kM+Yi05Jp+B8SRORe0iaUd4AlgQuIJkkZx/ghYg4XNKUiFhU0lHA6cAsYApwZES83caxhwGnRcTItOJzWkTsJeloYEBE/EDSmsBNQB9gKEkfmpVK90+PdTkwMiKuk3Qi8APg/bb6xUj6eRvxtzxuWzEuA/yBpN9MD2B4RHy33LWcMXv+eoSbmVn19e5R6RxyHfPgKxNr8rN+j/WWy/T22LlMYrKWzvcyPSJC0qHANyNi36zjmh9OYszM6oeTmI7Ja3NS1jYl6UArYDLw7WzDMTMza1u32uRGmeuySYykZ4GFWqw+IiLGtPfaiHiCZMhzp5/bzMysozo6u25edNkkJiK26IrnNjMzK4oum8SYmZl1FUWtxORynhgzMzMzV2LMzMwKrpKJ6fLISYyZmVnBdStmDuPmJDMzM8snV2LMzMwKrqjNSa7EmJmZWS65EmNmZlZwHmJtZmZmVkdciTEzMyu4ovaJcRJjZmZWcB5ibWZmZlZHXIkxMzMruKI2J7kSY2ZmZrnkSoyZmVnBFXWItZMYMzOzgitoDuPmJDMzM8snV2LMzMwKrltB25NciTEzM7NcciXGzMzaNfXz2VmH0GGLLORfcc2KWYdxEmNmZlZ8Bc1i3JxkZmZmueRKjJmZWcF5xl4zMzOzOuJKjJmZWcEVdIS1kxgzM7OiK2gO4+YkMzMzyydXYszMzIquoKUYV2LMzMwsl1yJMTMzKzgPsTYzMzOrI67EmJmZFZyHWJuZmVkuFTSHcXOSmZmZ5ZMrMWZmZkVX0FKMKzFmZmaWS67EmJmZFVxRh1g7iTEzMyu4oo5OcnOSmZmZ5ZIrMWZmZgVX0EKMKzFmZmaWT67EmJmZFV1BSzGdXomRdE6L5X91dgwLStLRki7v5HOuKOnOzjzn/HjqieHss+c32Gu3Xbn26iFZh1MRx1x7eYv3/PPOZuB2W3HAvntlHUqH1Pt1/vzzzzn2iEM48pD9OfzAfbjmiuTH6M9+fA6D9vo6Rx16AEcdegD/fuO1jCNtW71f47aoRv+ylkVz0jxJTERsXYuTSMpdlalczBHxfkQc2JnxdFRTUxMXX3Qhf7zyGu6+7wGGPng/b44dm3VYZTnm2stbvAD77ncAV1x1TdZhdEgernOvXr34/VV/4obb7ub6v/yVZ55+kpdHvwTA908+letvvYvrb72Ltb62TsaRti4P17irqWkSI+keSc9LekXSYEmXAH0kjZJ0c7rPlPTrCpKGp9telrRdmeMeK+nfkp6TdHVzVUTSdZKulPQs8AtJ/SU9I2m0pLslLZXuN0zSgPT5MpLeSZ8fLekuSUMl/UfSL0rOeUzzOYFt2nnfB6Xv4SVJw9N13SVdKmlEGs930vUDJT0h6T7gVUmXSPp+ybEukHSapH6SXi451i/Tc4yWdGK6flNJj6fX/CFJK3ToG7aAXh4zmr59V2Hlvn3p2asXu+2xJ8Mee7QzQ+gwx1x7eYsXYNMBm7H4EktkHUaH5OE6S2LhhRcBYPbs2cyePRvlaOxvHq5xW6TaPLJW60rMtyNiU2AAcBJwKTA9IvpHxOEt9j0MeCgi+gMbAaNaO6CkFYH/B2xJkkys3WKXlYGtI+IU4AbgzIjYEBgD/LiCmPsDhwAbAIdI6psmAz9Jz7ctsG47xzgf+EZEbATsk647FvgkIjYDNgOOl7Rqum0T4IcRsRZwG3BwybEOTteVGgz0A/qn7+1mST2B3wMHptf8T8BFFbzfqpnY2MjyKyw/d3m5hgYaGxs7M4QOc8y1l7d48yov17mpqYmjDj2APXfZjs222Ir1NtgQgCF/+B1HHLw/l/3yEmbOnJlxlK3LyzXuSmqdxJwk6SXgGaAvsGaZfUcAx0i6ANggIj5rY7/Ngccj4uOImAXc0WL7HRHRJGkJYMmIeDxdfz2wfQUxPxoRn0TEDOBVYBVgC2BYRHwYETP5clLR0lPAdZKOB7qn674OHClpFPAs8BW+uB7PRcTbABHxIrBc2gdmI2BSRLzX4vi7AFdFxOz0NR8DXwPWBx5Jz3EeSUL3JWlVbKSkkXlq0zWz/OvevTvX33oX9wz9J6+9MoY3x/6H7/7gR/zlrvu59qbb+PTTT7jpunw15eWBavTIWs36jUgaSPLLdquImCZpGNC7rf0jYrik7YE9SRKAX0fEDfNx6qkV7DObLxK4ljF9XvK8ifm4RhHxXUlbkLyX5yVtSvL9PjEiHirdN71OLWO+AzgQWJ72E6a5hwJeiYitKohvCDAEYMZsosLjt2u5hgYmfDBh7vLExkYaGhqqdfiacMy1l7d48ypv13mxxRZnkwGb8+y/nuSwI48Bkj4ze+6zP7fccF22wbUhb9e4K6hlJWYJkirCNElrkzT/AMxKmz7mIWkVoDEirgauIWliac0IYAdJS6UdYQe1tlNEfAJMKulbcwTQXJV5B9g0fV5JZ9ln03N+JY39oHI7S1o9Ip6NiPOBD0mqUA8BJzS/d0lrSVqkjUPcBhyaxtay0gTwCPCd5o7AkpYG3gCWlbRVuq6npPUqeG9Vs976G/Duu+8wbtx7zJo5k6EPPsAOO+7UmSF0mGOuvbzFm1d5uM6TJn3MZ599CsDnM2Yw4pmnWaXfqvzvww8BiAiGP/Yoq62xRpZhtikP17hNBS3F1HIEz1Dgu5JeI/kF+0y6fggwWtILLfrFDAROlzQLmAIc2dpBI2K8pIuB54CPgdeBT9qI4SjgSkkLA28Bx6TrfwncLmkw8EB7byQiPkibuZ4GJtNGf50Sl0pak+Rb/CjwEjCapB/LC0p6sn0I7NfG+V6RtBgwPiI+aGWXa4C1SK7jLODqiLhc0oHA79KmtB7Ab4FX2nt/1dKjRw/OPvd8Thh8HHPmNLHf/oNYY41yLYjZc8y1l7d4Ac487RRGjniOyZMnsetO23PC90/kgEFl/3bJXB6u80cffshPf3wOc5rmMCfmsPOu32Cb7Qfyg8HHMHnyJCKCNddamzPOPT/rUFuVh2vclnoYDl0Liqhaa0KnkbRoRExJKxF3A3+KiLuzjiuPqtmcZGbFNfXz2VmH0GGLLJS7mTbo3aM22cYr46fW5Gf9eistkml2lL/vcOICSbuQ9Gd5GLgn23DMzMzqVz0Mh66Fuk5i0vleFmqx+oiIOC2LeFqSdC5f7h9zR0R06tBmMzOzriiXzUlWPW5OMrNKuDmpc9SqOem192vTnLTOim5OMjMzs1oqaHNSFvdOMjMzsy5A0p8kTWy+bU66bmlJj6S393mk5JZAkvQ7SWPTW+q0NdXKXE5izMzMCi7Du1hfB+zWYt1ZJLPjr0kyDclZ6frdSWayX5Pk9jpXtHdwJzFmZmZWExExnGROt1L7ktwKiPTrfiXrb4jEM8CS7d3I2EmMmZlZwdXqLtal9+JLH4MrCKehZCLXCUDzvRtWAkrvFTguXdcmd+w1MzMruFr16y29F998vj4kzffIKVdizMzMrDM1NjcTpV8npuvHk9xrsNnK6bo2OYkxMzMruvq6AeR9JPc2JP16b8n6I9NRSlsCn7Rx/8C53JxkZmZmNSHpLyQ3eF5G0jjgx8AlJDdhPhb4L3BwuvuDwB7AWGAaX9y0ue3je8bers0z9ppZJTxjb+eo1Yy9/2mcXpOf9Ws29Ml0Gj03J5mZmVku5S9NNTMzsw7xXazNzMwslwqaw7g5yczMzPLJlRgzM7OiK2gpxpUYMzMzyyVXYszMzAquwjtO546TGDMzs4Ir6ugkNyeZmZlZLrkSY2Zm7crj7Lf2hYIWYlyJMTMzs3xyam1mZlZ0BS3FOIkxMzMruKKOTnJzkpmZmeWSKzFmZmYF5yHWZmZmZnXElRgzM7OCK2ghxpUYMzMzyydXYszMzAquqH1inMSYmZkVXjGzGDcnmZmZWS65EmNmZlZwRW1OciXGzMzMcsmVGDMzs4IraCHGSYyZmVnRuTnJzMzMrI64EmNmZlZwvou1mZmZWR1xJcbMzKzoilmIcRJjZmZWdAXNYdycZGZmZvnkSoyZmVnBeYh1wUg6p8Xyv7KKpRxJR0tasWT5Gknrps/PafuV2XjqieHss+c32Gu3Xbn26iFZh9OuCR98wLFHH8H+e+/B/vvsyc03Xp91SBXJ23XOW7zgmDtD3uI9/7yzGbjdVhyw715Zh2KpLpvEAPMkABGxdVaBtONoYG4SExHHRcSr6WJdJTFNTU1cfNGF/PHKa7j7vgcY+uD9vDl2bNZhldW9R3dOO+Ms7v7bg9z0l9u49S+31H3MebvOeYsXHHNnyFu8APvudwBXXHVN1mHMF9XoX9a6RBIj6R5Jz0t6RdJgSZcAfSSNknRzus+U9OsKkoan216WtF2Z4x4j6d+SnpN0taTL0/XXSTqwZL/mYy8q6VFJL0gaI2nfdH0/Sa+lx3hF0sOS+qTHGADcnMbTR9IwSQNavgdJF0o6ueScF0n6YdUvZhkvjxlN376rsHLfvvTs1Yvd9tiTYY892pkhdNiyyy7HOuuuB8AiiyzKaqutxsSJjRlHVV7ernPe4gXH3BnyFi/ApgM2Y/Ellsg6DCvRJZIY4NsRsSlJQnAScCkwPSL6R8ThLfY9DHgoIvoDGwGjWjugpBWAnwDbANsC61YQxwxg/4jYBNgR+JU0t6VyTeAPEbEeMBkYFBF3AiOBw9NYpzcfKCLOavEe/gQcmcbWDTgUuKmCmKpmYmMjy6+w/Nzl5RoaaGys74Sg1Pjx43j9tdfYYMONsg6lrLxd57zFC465M+Qt3txTjR4Z6ypJzEmSXgKeAfqSJAxtGQEcI+kCYIOI+KyN/bYAhkXEhxExE7itgjgEXCxpNPAPYCWgId32dkSMSp8/D/Sr4HhzRcQ7wEeSNga+DrwYER+1GkRSjRopaWQe2qE7w7SpUzn15JM4/axzWHTRRbMOx8ysqgqawxR/dJKkgcAuwFYRMU3SMKB3W/tHxHBJ2wN7AtdJ+nVE3NDB084mTRDTqkivdP3hwLLAphExS9I7JbF8XvL6JqBPB88JcA1JH5rlSSozrYqIIcAQgBmzifk4T6uWa2hgwgcT5i5PbGykoaGhzCvqw6xZszjl5JPYY8+92WXXr2cdTrvydp3zFi845s6Qt3itPnWFSswSwKQ0gVkb2DJdP0tSz5Y7S1oFaIyIq0mSgk3aOO6zwA6SvpIe56CSbe8Am6bP9wGaz7MEMDFNYHYEVqkg/s+AxdrY1vI93A3sBmwGPFTBsatqvfU34N1332HcuPeYNXMmQx98gB123Kmzw+iQiOCC889ltdVW48ijj8k6nIrk7TrnLV5wzJ0hb/HmnVSbR9YKX4kBhgLflfQa8AZJkxIklYjRkl5o0S9mIHC6pFnAFNJ+Ji1FxAdpk9PTJH1YRpVsvhq4N23CGgpMTdffDPxN0hiSvi6vVxD/dcCVkqYDW7XYNs97iIiZkh4DJkdEUwXHrqoePXpw9rnnc8Lg45gzp4n99h/EGmuUa7nL3osvPM/9993LmmutxcEH7AvAiSefwnbb75BxZG3L23XOW7zgmDtD3uIFOPO0Uxg54jkmT57ErjttzwnfP5EDBh3U/gutZhRRtdaELk3S0cCAiPhBhjF0A14ADoqI/1Tymmo2J5mZ2YLp3aM2XU0+ntpUk5/1Sy/SPdN6TFdoTuoS0gnwxgKPVprAmJlZ11DU5iRXYiog6VlgoRarj4iIMVnEU02uxJiZ1Y9aVWImTatNJWaphbOtxHSFPjELLCK2yDoGMzMzm5ebk8zMzCyXXIkxMzMruHrov1ILTmLMzMwKrh5u1lgLbk4yMzOzXHIlxszMrOCK2pzkSoyZmZnlkisxZmZmBVfQQowrMWZmZpZPrsSYmZkVXUFLMU5izMzMCs5DrM3MzMzqiCsxZmZmBech1mZmZmZ1xJUYMzOzgitoIcZJjJmZWeEVNItxc5KZmZnlkpMYMzOzglON/rV7Xmk3SW9IGivprGq/LycxZmZmVnWSugN/AHYH1gW+KWndap7DfWLMzMwKLqMh1psDYyPirSQG3QrsC7xarRM4ienieveoTXcvSYMjYkgtjl0reYs5b/FC/mLOW7zgmDtD3uKF2v6sBwaXrBpScm1WAt4r2TYO2KKa53dzktXK4PZ3qTt5izlv8UL+Ys5bvOCYO0Pe4q2ZiBgSEQNKHp2a3DmJMTMzs1oYD/QtWV45XVc1TmLMzMysFkYAa0paVVIv4FDgvmqewH1irFZy1V6cylvMeYsX8hdz3uIFx9wZ8hZvJiJitqQfAA8B3YE/RcQr1TyHIqKaxzMzMzPrFG5OMjMzs1xyEmNmZma55CTGzMzMcslJjJmZmeWSkxjr0iQ9Wsm6eiJpW0nHpM+XlbRq1jEVTR4/FwCSFs46hkpJapB0raS/p8vrSjo267jaIulESUtlHYfNy0mMVY2kbSQ9Iunfkt6S9Lakt7KOqzWSektaGlhG0lKSlk4f/Uimyq5Lkn4MnAmcna7qCdyUXUTtk7SWpEclvZwubyjpvKzjak2OPxdbS3oVeD1d3kjSHzMOqz3XkQy9XTFd/jdwclbBVKABGCHp9vTOzNncjcjm4STGqula4NfAtsBmwID0az36DvA8sHb6tflxL3B5hnG1Z39gH2AqQES8DyyWaUTtu5ok6ZoFEBGjSSa9qkd5/Vz8BvgG8BFARLwEbJ9pRO1bJiJuB+ZAMqcI0JRtSG2LiPOANUl+zh0N/EfSxZJWzzSwLs6T3Vk1fRIRf886iEpFxKqSzo+IC7OOpQNmRkRICgBJi2QdUAUWjojnWvzhOjurYMqJiMuAyySdGBG/zzqejoiI91pc47pNCFJTJX0FaP4sbwl8km1I5aX/9yYAE0g+w0sBd0p6JCLOyDa6rslJjFXTY5IuBe4CPm9eGREvZBdSm44BLgP2A/KUxNwu6SpgSUnHA98mqXTUs/+lf602/7I6EPgg25DKi4jfS9oa6EfJz8mIuCGzoMp7L403JPUEfgi8lnFM7TmFZAr61SU9BSwLHJhtSG2T9EPgSOB/wDXA6RExS1I34D+Ak5gMeMZeqxpJj7WyOiJip04Pph2S/kLS3LUi8GbpJpKYN8wksDLSNviVSZo6vk4S60MR8UimgbVD0mok07RvDUwC3ga+FRHvZBlXOZJuBFYHRvFFRSMi4qTMgipD0jIkSfkuJJ+Lh4EfRsRHmQbWDkk9gK+RxPxGRMzKOKQ2SfoJybT5/21l2zoRUe9JYyE5ibEuS9LyJB0L92m5rbUfVPVA0piI2CDrOOZH2vTVLSI+yzqW9kh6DVg3/AOyZiR9H7g5Iiany0sB34yIuuyQLOnGiDiivXXWudycZFUjaQngx3zRofBx4MKIqMt27oiYAGyUdRwd9IKkzSJiRNaBVErSQsAg0qaZ5n4bdd4X6WVgeeq82atZOsz+RL7c/PWlBL2OHB8Rf2heiIhJaRNpXSYxwHqlC5K6A5tmFIulnMRYNf2J5If/wenyEcCfgQMyi6gdktYE/g9YF+jdvD4iVsssqPK2AA6X9F+SEUp12/xV4l6SDpvPU9JXqh5J+htJ353FgFclPce8/bvqNSm4h2TUzN9IR/vkQHdJaq52pUlBr4xj+hJJZwPnAH0kfdq8GpiJ72adOTcnWdVIGhUR/dtbV08kPUlSPfoNsDdJh99uEXF+poG1QdIqra2v1+YvAEkvR8T6WcdRCUk7lNseEY93ViwdIenZiNgi6zg6Ih0EsApwVbrqO8B7EXFqdlG1TdL/RcTZ7e9pnclJjFWNpKdJeuw/mS5vA/wyIrbKNrK2SXo+IjYt7WvSvC7r2MqRtBzzVo7ezTCcsiQNAX4fEWOyjqWoJB1GMofJw9T/yEAA0lE93wF2Tlc9AlwTEXU1NFzS2hHxuqRNWttez9e4K3ASY1UjqT9wPbAESbn1Y+DodOKtuiTpXyST890J/BMYD1wSEV/LNLA2SNoH+BXJqKqJJH/JvhYR65V9YYbSmWTXIBmV9Dk5aAKT9BnpkPASnwAjgVMjoq5mopb0fyTNt2/yRXNSXY4MzBtJQyJicJ5GX3YlTmKs6iQtDhARn7a3b9YkbUYyn8aSwE+BxYFLI+KZLONqi6SXgJ2Af0TExpJ2JBmuXM/3nMljE9hPgXHALSRJ16EkQ65fAE6IiIHZRfdlksaSjKaamXUs7ZF0e0QcLGkMX04UqdfkVlLviJjR3jrrXE5ibIFJ+lZE3CTplNa2R8SvOzumSqQdCX8eEadlHUulJI2MiAFpMrNxRMyR9FJE1N0oK0mLR8Sn6b2IviQiPu7smCrV2jVt7t9Vj9db0j3A4IiYmHUs7ZG0QkR8kLfkVtILEbFJe+usc3l0klVD89T3rd3Dp26z5IhokrRt1nF00GRJiwLDgZslTSS9j1IdugXYi2RUUpBUNJoFUK8jwACmSTqYpJkRkplkm//irsfP9JLA65JGUOejqSKiedj6IODW9P5fdSudT2olktFJG/PF53hxIDd3DS8qV2KsaiRtExFPtbeunki6guQH1B2UJAMRcVdmQbVC0kIR8Xk6YdwMkh+kh5P0P7q53mdmzZt0luHLgK1IkpZngB+R9JnatLnzer1oa1RVvY6mgrl3ZD+YpO/cbcAdEdGYbVRfJukokhs+DiDpE9XsM+C6evtZ0dU4ibGqyWO5VdKfW1kdEfHtTg+mjObrmKcZQtsazdHMozoMQNKGwCEklZlxEbFLxiG1StKgiPhr1nHYvNycZAtM0lYk98VZtkW/mMWB7tlEVZmIOCbrGCrUKx1Gu7WkL00eWKd/Df6qzLYg6aBcVySdERG/kPR7Wu90Wlf3TpL0ZERs28poquYRYItnFFpHTCS5K/RHwHIZx1LO/en/wX7MOytyPc88XXhOYqwaegGLknyeSvvFfEod35UWQNL1JDfKm5wuLwX8qt4qMcB3SZqPliSZlK9UkNw5vK5ExI7pXCBb1XOTYgvNN/EbWXavOhER26ZfW+uPVtckfY+kOWlZkubc4yPi1WyjKis3M093JW5OsqqRtEq9jixoi6QXI2Lj9tbVgzQhODsiLso6lo6o1+tZJHm8OaGki4HbI2JU1rFUIk8zT3clrsRYNU1LpxJfj3lnk627ZoMS3SQtFRGTANLhwHX5/yIdTn0gkKskBnhU0iDgrqjzv5pK7p3Uqnoc7ZNqeXPCHtTxzQnT6Q0OiIhzso6lA/4laQPPPF1f6vKHteXWzSSjDPYiaf44Cvgw04ja9yvgaUl3pMsHUd9JQm4SghLfAU4BZktqHllVr/01fpl1AB2R15sTptMbvCHpq/V8y4wWtgWOlpSbmae7AjcnWdWU3IdodPN/bEkjImKzrGNrTdo8syUwmS86mf6zntvl0w6ciwCz+WKodb0mBLkj6dGI2FnSzyPizKzjqVR7NyeUtF5EvNKZMbVH0nBgY+A55p3eoC6rXXmbnK+rcCXGqmlW+vUDSXsC7wOtztZaD9LmmT+k/TXqNnEplacOnDm9cd4KkrYG9pF0K/NO0FevMVMugUndCNTbVAf/L+sAOqI5WWl581XLlisxVjWS9gKeAPoCvycZYv2TiLgv08DKkPRL4GnqvHkmjwlBHm+cl/Y5Opak6WAELWYZrseYK1GvnavT6saaEfEPSQsD3SPis6zjak0eb77aFTiJsS4tL80zeUwI8kzS/4uIn5bZXnfNM+XU46STko4HBgNLR8TqktYEroyInTMOrVV5vPlqV+AkxhZY3iYIs84naX1gXeYdtXZDdhEtmHpMCsqpx3gljQI2B55trhJJGhMRG2QaWBvydPPVrsR9YqwacjVBGOSzeaZZ3hKC9B45A0lifhDYHXgSqNuYK6D2d6krM7MOoBWfR8RMKbmU6bDwev6rOk83X+0yXImxqpG0ST3/8i+V1+aZthKCiKjbmZEljQE2Al6MiI0kNQA3RcSuGYc23+qtstE8qqq9dfVE0i9IRgYeCZwIfA94NSLOzTKutvjmq/XJSYxVTZoQLA/cCdwWES9nHFLh5DEhkPRcRGwu6XlgR5K7/74WEWtnHNp8q5ckRlJvYGHgMZLktrlCtDgwtJ6vcTrFwbHA10nifgi4pp472Fv9cXOSVU16r5zlSe6HcpWkxUmSmZ9lHFpZOWuemZ62xc9Or+9EktFg9WykpCWBq0nuOzOFZERYntVL88x3gJNJRsw8zxdJzKfA5RnFVJGImEPymbg6nSl75XpOYFrcZLMX0BOYWm+DALoaV2KsJiRtAJwBHBIRvbKOpy15a56R9EeSGVoPBU4lSQhGRU7uxi2pH7B4RIwuWVd3I30kbUNyXadK+hbJHCuX1evEZpJOjIjfZx1HR0gaBuxD8sf08yQJ+b8i4kdZxlUJJR159gW2jIizso6nK3MSY1UjaR3gEJI7V/+P5BYEf42IiZkGVkYem2ea5SUhaE+9NM2UkjSa5HOxIXAdcA1wcETskGVc5aST9PWjpMJexxXFuXPXSDoO6BsRPy6d7TsP6nX+na7EzUlWTX8CbgW+HhHvZx1MhfLYPANARLzTyup6nJm1PfU40md2RISkfYHLI+JaSXU7H4ikG4HVgVFAU7o6qO8RYD0krUDS/FyXnXlLSTqgZLEbMICko69lyEmMVU1EbCWpD/DVrGPpgKL116jHhKA99VgO/iy9ueK3gO3TTqg9M46pnAHAuvXcp6QVF5J05n0yIkZIWg34T8YxlbN3yfPZwDskTUqWITcnWdVI2pvkLsC9ImJVSf2BC+v1hm4tFaF5ph6bZtpTjzGnHdQPA0ZExBOSvgoMrNfmGSV3YT8pIj7IOpZqkXR2RPxf1nFYfXMSY1WTDqHdCRiWhxk4K1GPv2DLyVu8AJKeiYgts44jjyT9jaSStRjQn+SO0J83b8/LHxCtqbfPsqTfldvumcmz4eYkq6ZZEfFJ8wycqbxnyXlrnqmXob9ztTfSp54SGElPRsS2LYbTQp3eU4uk8llU9fZ/rzfJKMbb0uWDgFfJd/Nz7jmJsWp6RdJhQPf0Zm4nAf/KOKYFVVdJWJ4SghJXABtJ2ohkWPg1JB1O626kT0Rsm35dLOtYKhERj2cdQw3V1f89kpFq20bEbABJVwJPRMR3sw2ra+uWdQBWKCcC65GUs28BPiGZiMuq5wpgWklC8Cb1PQIF0pE+JJ0gL4+IP5A0f9StdLRPu+vqhaTPJH3a4vGepLvTDrN5VG+VmKVIZkJutmi6zjLkSoxVhaTuwAMRsSM5GC7ZAfXWPJOrob+pvI30gSQZnyu9OeGmGcVSid8C40j+eBDJZIirAy+QTH0wMKvAFsAdWQfQwiXAi+ntVQRsD1yQaUTmjr1WPZIeBQ6IiE+yjqVSOZyZ9XFgKHAMyQ/RicBL9dx5Ok8jfdJk6xygDzCtZNMsYEhEnJ1JYO2Q9FJEbNRi3aiI6N/atixJ+j1lmorquYNs+lneIl18NiImlGzL1UjGonASY1Uj6V5gY+ARSm5RX+c/lHI1M2ueEoI8k/R/wC+AtfjinloREcOzi6ptkp4GfkNy81VIZs0+JSK2bE5mMguuBUlHpU+3oZWOsnntY1Jvo6m6CicxVjUlP5zmERHXd3YslWr+wSPpfGB82jzjH0ZVkMORPnNJOp6kY/rKJLPgbgk8HRE7ZRlXW9J+L5cBW5Fc62eAHwHjgU0j4skMw2uVpGeYt6NsT5KOsvXYOb1dvgVBNtwnxqomIq6X1AtYm+QH6RsRUW99SlrKRX+NPCYEeRvp08JJwGbAM5HcnX1t4OKMY2pTRLzFvDPKlqq7BCbV3FH243Q57x1lXRHIgJMYqxpJewBXkYyYEbCqpO9ExN+zjaysQ0iaZ46NiAlp88ylGcf0JXlOCCTdGBFHtLeuzsyIiBmSkLRQRLwu6WtZB9WSpDMi4hdt9TOp56Zc3FHWqsBJjFXTr4EdI2IsgKTVgQeAuk1i0o55vy5Zfpc6HrKc04QgbyN9AMal99S6B3hE0iSgHjt7v5Z+HZlpFPMhIv4s6e980VH2zNKOsjlU71XnQnKfGKsaSSMiYrOSZQHPla6rF3lsnoEvdx5ME4LREbFuhmG1Kq8jfVqStAOwBDA0B82juSJpJWAVSv6gruPO07kaydhVOImxBVZyi/pdSX4g3U6SGBwEvBsR38sqtqLIc0KQt5E+eVJy76RW1fO9kyT9nKQ59xVgTro66jXmvI1k7CrcnGTVUNqhsJEvppP/kC9+adWlvDTPpHfz/b+2EoLMAqvMW8BwWoz0IblZqC2YPN87aT/gaxHxeXs71ok8TjRZeE5ibIFFxDFZx7AA8tZfI48JQa5G+uTM+RGxs6SfR8SZWQfTQW+RjATMSxKTi5GMXY3vnWRVI2k1SX+T9KGkiZLurdf7tkg6O+0Ps2Hp/WZIKkn3ZhxeOc0JwX/TWzxsDEzONKL2zYiIGcDckT5A3Y30yakVJG0N7CNpY0mblD6yDq4d04BRkq6S9LvmR9ZBlXEIScJ1bNoBeWXqcCRjV+M+MVY16eRVfwD+kq46FDgxIrZo+1XZylt/jebO05JGAVtExOeSXomI9dp7bVYk3U1ym4STSSpGk4CeEbFHlnEVgaQDgWOBbYERzHvTxKjXyfkgn5NjWv1xEmNVI2l0RGzYYl1d3belpRzOzJrrhMAjfWpD0v+LiJ+W2e77+synvI5k7CqcxNgCk7R0+vRMkl+qt5L8Zz8EWKrOR86M4Yv+Gv2b+2tExAHtvDRzTgisUvV4Kw1Jb9P6BH112QRt9ckde60anif5YdRcyv5OybYA6jaJISczs7YmIh7POgbLDbW/S6cbUPK8N8mUDEu3sW/m8jKSsatxEmMLLCJWzTqGBZCXmVnNFkTdldwj4qMWq34r6Xng/CziqUDeRjJ2CU5irKokrQ+sS8n8MBFRt9P4R8T+6dML0nu4LAEMzTAksy6hxeipbiSVmbr7nVQ60WQ6grHZLGBINlFZM/eJsaqR9GNgIEkS8yCwO/BkRByYZVxmXZ2kZyJiy6zjKJX+0dBsNvAO8MuIeCObiMrL20jGrsJJjFVN2kl2I+DFiNhIUgNwU0TsmnFoZoXm+/rUXt5GMnYVnuzOqml6RMwBZktaHJgI9M04JrOu4ApgmqSNgFOBN6nju7EDSFpC0q8ljUwfv5K0RNZxlZHHiSYLz0mMVdPItJPs1SQjll4gmRLfzGprdiRl9eb7+vwBWCzjmNrzJ+Az4OD08Snw50wjKs8zT9chNydZTUjqByweEaNL1nnCLbMakPQ4SYf0Y4DtSaqgL0XEBpkGVoakURHRv7119SLvE00WlZMY6zT1OOGWWRFIWh44DBgREU9I+iowsJ5HBkp6Gjg9Ip5Ml7ch6di7VbaRtc8TTdYPJzHWaSS9GBEbZx2HmWUv7b9zA0kyAEll46jS6q1Ze+puTL4VmjNmsyrK6319JHUHjkhHMS4OEBGftvMysy9xEmNmllMRsW36td478c4jIpokNcfu5MXmm0cnWWdy27FZDUi6sZJ1deZFSfdJOkLSAc2PrIOyfHElxqqmvQm36m3GULMCyeN9fXoDH5GM9GkWwF3ZhGN55I69VjWSRpPM2LshcB1wDXBwROyQZVxmRVV6Xx9gWsmmWcCQiKjnO8ibLTAnMVY1zUOoJZ0PjI+Iaz2s2qz28nhfH0m/a2X1J8DIiLi3s+OxfHJzklXTZ+lfht8CtpfUDeiZcUxmXcFbwHBa3NeHeZtq6k1vYG3gjnR5EPA2sJGkHSPi5KwCs/xwJcaqJo8TbpkVQXrz1c2AZyKiv6S1gYsjom47ykp6BtgmIprS5R7AE8C2wJiIWDfL+CwfXImxqomICcCvS5bfpc5vQmdWEDMiYoakuff1kVTv9/VZCliUpAkJYBFg6XT49efZhWV54iTGFlheJ9wyK5Bx6c1X7wEekTQJ+G+mEbXvF8AoScNIflZsD1wsaRHgH1kGZvnh5iQzswLJ0319JK0AbJ4ujoiI90u2+Yax1i4nMVY1km6MiCPaW2dm1h6PbLRKeMZeq6Y8TrhlZvVJWQdg9c9JjC0wSWen/WE2lPRp8wNoBDzfg5nNDzcTWLvcnGRVk8cJt8ysPrk5ySrh0UlWTXmccMvM6lNdd0q2+uDmJKumk0gm3PpvROwIbAxMzjQiM6tLkrZJh1Mj6VuSfi1plebtvmGsVcJJjFXTjIiYAcydcAuo9wm3zCwbVwDTJG0EnAq8iSfHtA5yEmPV1HLCrXup/wm3zCwbsyPplLkvcHlE/AFYLOOYLGfcsddqIk8TbplZ55P0ODAUOIZktt6JwEsRsUGmgVmuOIkxM7NO5xvGWjU4iTEzM7Nc8hBrMzPrNL5hrFWTKzFmZmaWSx6dZGZmnU7SjZWsMyvHSYyZmWXBN4y1BeYkxszMOo1vGGvV5D4xZmbW6XzDWKsGj04yM7Ms+IaxtsDcnGRmZlnwDWNtgTmJMTOzLPiGsbbA3JxkZmZZaHnD2En4hrHWQe7Ya2ZmmfINY21+OYkxMzOzXHKfGDMzM8slJzFmZmaWS05izKxDJDVJGiXpZUl3SFp4AY51naQD0+fXSFq3zL4DJW09H+d4R9Iyla5v4xhHS7q8Guc1s+pxEmNmHTU9IvpHxPrATOC7pRvTe+B0WEQcFxGvltllINDhJMbMistJjJktiCeANdIqyROS7gNeldRd0qWSRkgaLek7AEpcLukNSf8Alms+kKRhkgakz3eT9IKklyQ9KqkfSbL0o7QKtJ2kZSX9NT3HCEnbpK/9iqSHJb0i6RpAlb4ZSZtLelrSi5L+Jal03pK+aYz/kfTjktd8S9JzaVxXSeo+/5fTzDrC88SY2XxJKy67A0PTVZsA60fE25IGA59ExGaSFgKekvQwyaysXwPWBRqAV4E/tTjussDVwPbpsZaOiI8lXQlMiYhfpvvdAvwmIp6U9FXgIWAd4MfAkxFxoaQ9gWM78LZeB7aLiNmSdgEuBgal2zYH1gemASMkPQBMBQ4BtomIWZL+CBwO3NCBc5rZfHISY2Yd1UfSqPT5E8C1JM08z0XE2+n6r5PcpfjAdHkJYE1ge+AvEdEEvC/pn60cf0tgePOxIuLjNuLYBVhXmltoWVzSouk5Dkhf+0A6iVqllgCul7QmEEDPkm2PRMRHAJLuArYFZgObkiQ1AH2AiR04n5ktACcxZtZR0yOif+mK9Bf41NJVwIkR8VCL/faoYhzdgC2bp65vEcv8+inwWETsnzZhDSvZ1nJSrSB5n9dHxNkLclIzmz/uE2NmtfAQcIKkngCS1pK0CMldiw9J+8ysAOzYymufAbaXtGr62qXT9Z8Bi5Xs9zBwYvOCpP7p0+HAYem63YGlOhD3EsD49PnRLbbtKmlpSX2A/YCngEeBAyUt1xyrpFU6cD4zWwBOYsysFq4h6e/ygqSXgatIKr93A/9Jt90APN3yhRHxITAYuEvSS8Bt6aa/Afs3d+wluQvygLTj8Kt8MUrqJyRJ0CskzUrvlolztKRx6ePXwC+A/5P0Il+uVD8H/BUYDfw1Ikamo6nOAx6WNBp4BFihwmtkZgvItx0wMzOzXHIlxszMzHLJSYyZmZnlkpMYMzMzyyUnMWZmZpZLTmLMzMwsl5zEmJmZWS45iTEzM7NcchJjZmZmufT/Ad7wlY0xi5dNAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}