Data_visualization.ipynb

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        "<a href=\"https://colab.research.google.com/github/Hamza-t/Language-Identification/blob/main/Data_visualization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "#Hello!\n",
        "In this notebook, we will visualise our data distribution.\n"
      ],
      "metadata": {
        "id": "8QcvcitZ4ZMM"
      }
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        "id": "n4GblE0o4Xiv",
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          "output_type": "stream",
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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "#Import data file \n",
        "#drive mount\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#verify the path\n",
        "folder_path = \"/content/drive/MyDrive/My_work/NLP-Project-INSAT\"\n",
        "!ls \"/content/drive/MyDrive/My_work/NLP-Project-INSAT\""
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "FLM1V2XM5Uk9",
        "outputId": "06586697-8507-4875-ab46-fc12b108d928"
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          "text": [
            " cleaned_data.csv\t   Data_cleaning.ipynb\t    Data_visualization.ipynb\n",
            "'Data augmentation'\t  'data collection '\t   'General Data'\n",
            " Data_Augmentation.ipynb   Data_Preparation.ipynb   prepared_data.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##assign data path \n",
        "data_path = folder_path + \"/\" + \"cleaned_data.csv\""
      ],
      "metadata": {
        "id": "hQz1T2Um5bKJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#read data\n",
        "import pandas as pd\n",
        "data = pd.read_csv(data_path)\n",
        "data = data[[\"text\",\"label\"]]"
      ],
      "metadata": {
        "id": "Rwb6Q0p65ifu"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
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              "                                                text  label\n",
              "0  ana fil ghorba w sout el athan ahla haja 3ijbi...      3\n",
              "1                           man eheb rasoule mohamed      3\n",
              "2                        rabi yerhmou sofiane cha3ri      3\n",
              "3     rabi yarahmak ya sbou3i wou yarham kol om mita      3\n",
              "4                              merci beaucoup salime      1"
            ],
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              "      <td>ana fil ghorba w sout el athan ahla haja 3ijbi...</td>\n",
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              "      <td>man eheb rasoule mohamed</td>\n",
              "      <td>3</td>\n",
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              "      <th>2</th>\n",
              "      <td>rabi yerhmou sofiane cha3ri</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>rabi yarahmak ya sbou3i wou yarham kol om mita</td>\n",
              "      <td>3</td>\n",
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              "      <th>4</th>\n",
              "      <td>merci beaucoup salime</td>\n",
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      "source": [],
      "metadata": {
        "id": "mCIgQ27G5qL2"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##let's create beautiful graphs\n",
        "- The total number of comments.\n",
        "- Plot the distribution of each class. Balanced data?\n",
        "- The unique word count in each class.\n",
        "- The max and mean sentence length in each class.\n",
        "- Word frequency analysis"
      ],
      "metadata": {
        "id": "gEhniYYMQaVr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Import library\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.graph_objects as go\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "A0I_jwrwQjgj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
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      "execution_count": null,
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    },
    {
      "cell_type": "markdown",
      "source": [
        "####The total number of comments && the distribution of each class."
      ],
      "metadata": {
        "id": "PnxHe_06XYnT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#total number\n",
        "print(f\"the total number of comments is {len(data['text'])}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TpgllyljXZtX",
        "outputId": "7abcae7b-4062-4a6b-bd88-4dae1a8570f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the total number of comments is 8336\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#number of each class\n",
        "langage = {0:'Arabic', 1:'French', 2:'English', 3:'Tunizi', 4:'Code-switching'}\n",
        "length = []\n",
        "for i in langage.keys():\n",
        "  print(f\"the total number of {langage[i]} is {len(data[data['label']==i])}\")\n",
        "  length.append(len(data[data['label']==i]))"
      ],
      "metadata": {
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      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "the total number of Arabic is 2225\n",
            "the total number of French is 373\n",
            "the total number of English is 86\n",
            "the total number of Tunizi is 4398\n",
            "the total number of Code-switching is 1254\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#distribution\n",
        "sns.set(rc={'figure.figsize':(12,6)})\n",
        "#per_lang = final_scraped_data['labels'].value_counts()\n",
        "sns.barplot(x=list(langage.values()), y=length).set(title='Number of comment in each class')"
      ],
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          "output_type": "execute_result",
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            "text/plain": [
              "[Text(0.5, 1.0, 'Number of comment in each class')]"
            ]
          },
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          "execution_count": 9
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QYgls3xUZgEZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####The unique word count in each class."
      ],
      "metadata": {
        "id": "awze6ViKaca4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#count the number of unique word in each class and visualise it!\n",
        "\n",
        "#count the number of unique word function\n",
        "def unique_word(lang):\n",
        "  df = data[data['label']==[k for k, v in langage.items() if v == lang][0]]\n",
        "  #print(len(df))\n",
        "  texts = \"\"\n",
        "  for text in list(df['text']):\n",
        "    texts += str(text)\n",
        "  return len(set(texts.split(' ')))"
      ],
      "metadata": {
        "id": "PHIAEiUyZmul"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#count the number of unique word in each class\n",
        "number_unique_word = []\n",
        "for lang in list(langage.values()):\n",
        "  number_unique_word.append(unique_word(lang))"
      ],
      "metadata": {
        "id": "ICVG7GPUb4IO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#visualise! \n",
        "sns.set(rc={'figure.figsize':(12,6)})\n",
        "#per_lang = final_scraped_data['labels'].value_counts()\n",
        "sns.barplot(x=list(langage.values()), y=number_unique_word).set(title='Number of unique word in each class')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "9QH70_S_b8Gy",
        "outputId": "76b12820-a0ea-40d4-8354-24eb6e30b305"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'Number of unique word in each class')]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HOXtl2O3eBvb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####The max and mean sentence length in each class."
      ],
      "metadata": {
        "id": "sDWT3BxredRo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#return the max and the mean sentence lenght function\n",
        "\n",
        "def max_mean(lang):\n",
        "  df = data[data['label']==[k for k, v in langage.items() if v == lang][0]]\n",
        "  lens = [] #all comments lengths \n",
        "  for text in list(df['text']):\n",
        "    lens.append(len(str(text)))\n",
        "  return max(lens), sum(lens)/len(lens)"
      ],
      "metadata": {
        "id": "jWMSOqoPeeHD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#return the max and the mean sentence lenght in each class\n",
        "max_class = []\n",
        "mean_class = []\n",
        "for lang in list(langage.values()):\n",
        "  max_class.append(max_mean(lang)[0])\n",
        "  mean_class.append(max_mean(lang)[1])"
      ],
      "metadata": {
        "id": "gy6LlgtGfgun"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#visualise! \n",
        "sns.set(rc={'figure.figsize':(12,6)})\n",
        "#per_lang = final_scraped_data['labels'].value_counts()\n",
        "sns.barplot(x=list(langage.values()), y=max_class).set(title='the max sentence lenght in each langage')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "8-mKyxknf054",
        "outputId": "000950d1-cb24-475e-be2b-26a6c7635cca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'the max sentence lenght in each langage')]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualise! \n",
        "sns.set(rc={'figure.figsize':(12,6)})\n",
        "#per_lang = final_scraped_data['labels'].value_counts()\n",
        "sns.barplot(x=list(langage.values()), y=mean_class).set(title='the mean sentence lenght in each class')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "rOkYMM1vf_yc",
        "outputId": "3b134b2c-8605-4189-948f-dd433a7dd9cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'the mean sentence lenght in each class')]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KDOLuNePgh8W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Word frequency analysis\n",
        "1.   Remove stop words from the text\n",
        "2.   Get a list of all words after removing stop words in each langage\n",
        "3.   Analysing word frequency and plot the graph in each langage"
      ],
      "metadata": {
        "id": "XnGtfocwiY7a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = data.copy()"
      ],
      "metadata": {
        "id": "EvYFjRIsjPSs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#####Remove stop words from the text "
      ],
      "metadata": {
        "id": "uvnmQNWhjLew"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "from nltk.corpus import stopwords\n",
        "import nltk"
      ],
      "metadata": {
        "id": "V9HMqjUBjOtO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QkrPlxpjjuod",
        "outputId": "62344574-8b3d-48fe-a2cd-c95fce7f2f2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "print(stopwords.words('french'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T6G_EubXjwMi",
        "outputId": "7d8159c2-a12f-4dfe-ffee-6f8f23007a0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['au', 'aux', 'avec', 'ce', 'ces', 'dans', 'de', 'des', 'du', 'elle', 'en', 'et', 'eux', 'il', 'ils', 'je', 'la', 'le', 'les', 'leur', 'lui', 'ma', 'mais', 'me', 'même', 'mes', 'moi', 'mon', 'ne', 'nos', 'notre', 'nous', 'on', 'ou', 'par', 'pas', 'pour', 'qu', 'que', 'qui', 'sa', 'se', 'ses', 'son', 'sur', 'ta', 'te', 'tes', 'toi', 'ton', 'tu', 'un', 'une', 'vos', 'votre', 'vous', 'c', 'd', 'j', 'l', 'à', 'm', 'n', 's', 't', 'y', 'été', 'étée', 'étées', 'étés', 'étant', 'étante', 'étants', 'étantes', 'suis', 'es', 'est', 'sommes', 'êtes', 'sont', 'serai', 'seras', 'sera', 'serons', 'serez', 'seront', 'serais', 'serait', 'serions', 'seriez', 'seraient', 'étais', 'était', 'étions', 'étiez', 'étaient', 'fus', 'fut', 'fûmes', 'fûtes', 'furent', 'sois', 'soit', 'soyons', 'soyez', 'soient', 'fusse', 'fusses', 'fût', 'fussions', 'fussiez', 'fussent', 'ayant', 'ayante', 'ayantes', 'ayants', 'eu', 'eue', 'eues', 'eus', 'ai', 'as', 'avons', 'avez', 'ont', 'aurai', 'auras', 'aura', 'aurons', 'aurez', 'auront', 'aurais', 'aurait', 'aurions', 'auriez', 'auraient', 'avais', 'avait', 'avions', 'aviez', 'avaient', 'eut', 'eûmes', 'eûtes', 'eurent', 'aie', 'aies', 'ait', 'ayons', 'ayez', 'aient', 'eusse', 'eusses', 'eût', 'eussions', 'eussiez', 'eussent']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create a list of stop words \n",
        "stop_words = stopwords.words('french')+stopwords.words('arabic')+stopwords.words('english')"
      ],
      "metadata": {
        "id": "h4vYLhcEjyaR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sjub_cbEkO3B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#####Get a list of all words after removing stop words in each langage"
      ],
      "metadata": {
        "id": "3Pk8_0OSk5Oe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def word_list(lang):\n",
        "  df = data[data['label']==[k for k, v in langage.items() if v == lang][0]]\n",
        "  words = []\n",
        "  for text in list(df['text']):\n",
        "    for word in str(text).split(\" \"):\n",
        "      if word not in stop_words:\n",
        "        words.append(word)\n",
        "  return words"
      ],
      "metadata": {
        "id": "QT8iGiNOk6Js"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#####Analysing word frequency and plot the graph in each langage"
      ],
      "metadata": {
        "id": "jil3TmxqmXfj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "word_list(\"Arabic\")[:4]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nL08z3cllfHO",
        "outputId": "a6171ad2-e259-413d-ac4e-0f62c8a65ea5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['أحترماتي', 'أحمق', 'أسفي', 'بلديهل']"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_style('darkgrid')\n",
        "nlp_words=nltk.FreqDist(word_list(\"Arabic\"))\n",
        "nlp_words.plot(20,title='Word frequency in Arabic text');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "RN-hVis2lhKX",
        "outputId": "35e7c61c-f8fe-4f81-993c-7758e8d3c52f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nlp_words=nltk.FreqDist(word_list(\"French\"))\n",
        "nlp_words.plot(20,title='Word frequency in French text');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "ei4DhHJPl259",
        "outputId": "213f0d0f-9fd0-42f7-99fd-8d4df8957884"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nlp_words=nltk.FreqDist(word_list(\"English\"))\n",
        "nlp_words.plot(20,title='Word frequency in English text');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "8xQ68SkTm1gw",
        "outputId": "912d3367-07c8-48c0-c8e3-95764f43fb87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nlp_words=nltk.FreqDist(word_list(\"Tunizi\"))\n",
        "nlp_words.plot(20,title='Word frequency in Tunizi text');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "id": "sExyMya1m1i5",
        "outputId": "09e8ec4f-07d8-46c0-ca61-d780d6c678a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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b7dvl97KztystbbQk6dMD25STX6Eao4dGpV1Z7+1R3wzUpz71u259f+iB+tSnPvV9yWJp/WKqT6eRvPvuu7ruuusUHR0tSYqNjVVqaqpWrlwpSVq5cqVSU1MVExPT6lggmdByo+RGppIAAAB0OT4P21+eQvL000/rjTfe0I033qg33nhDzzzzTJvGAsWYIfGyWQ3tO1KmsirfT2cBAACAeXw6jWT16tUXHOvfv7/efvvti57f2ligCAsO0sj+cdp28LQ2Zxdq+rheZrcEAAAAH2EHSR8YP7R5KsmGLKaSAAAAdCWEbR8Y0T9WYcE25Z+u1vGiarPbAQAAgI8Qtn0gyGbRmCHxkrhREgAAoCshbPvImVVJNmcX+nwpQgAAAJiDsO0jA5K7Ka5bsMqqnNp/jO3bAQAAugLCto80b9/OmtsAAABdCWHbh85MJdl64LScjS6TuwEAAEBHI2z7UGJMqPomRcrZ4NLOnGKz2wEAAEAHI2z72ES2bwcAAOgyCNs+NiY1XlaLoazDpaqsaTC7HQAAAHQgwraPRYbaNaxvjNwejzbvKzS7HQAAAHQgwrYJztwouYmpJAAAAJ0aYdsEowbEKdhuVV5BlQpKasxuBwAAAB2EsG0Ce5BVmYPZvh0AAKCzI2yb5OxUkkK5PWzfDgAA0BkRtk0yuFeUoiMcKq6o16H8CrPbAQAAQAcgbJvEYhgaPzRBElNJAAAAOivCtokmDG2eSrJlX5Eam9i+HQAAoLMhbJuoZ/dw9YoPV62zSbtzS8xuBwAAAO2MsG2y8S1XtzdkMZUEAACgsyFsm2xcWoIMQ9qdW6Lqukaz2wEAAEA7ImybLDrCobTe0XK5Pdqyv8jsdgAAANCOCNt+4Mya26xKAgAA0LkQtv3A6EHdZQ+y6FB+hYrK68xuBwAAAO2EsO0Hgu02jR7UXZK0iavbAAAAnQZh209MbFmVZGPWKXnYvh0AAKBTIGz7idQ+0YoMs6uwrE55BVVmtwMAAIB2QNj2E1aLRePTWrZvZ81tAACAToGw7UfObN++eV+hmlxuk7sBAADAV0XY9iO9EsLVIy5M1XWNysorNbsdAAAAfEWEbT9iGIYmDG2eSsKqJAAAAIGPsO1nxqc1TyXZkVOs2vomk7sBAADAV0HY9jOx3YI1OCVKjU1ubTvA9u0AAACBjLDth9i+HQAAoHMgbPuhzMHdZbNadOBYuUor681uBwAAAFeJsO2HQoODNGpgnDySNmUXmt0OAAAArpLPwrbT6dT8+fM1bdo03XzzzfrFL34hScrLy9OsWbN04403atasWTpy5Ij3Na2NdXZnViVh+3YAAIDA5bOw/fzzz8vhcGj16tVasWKF5syZI0maP3++Zs+erdWrV2v27NmaN2+e9zWtjXV2w/vFKjwkSCeKa3S8qNrsdgAAAHAVfBK2a2pqtGzZMs2ZM0eGYUiS4uLiVFJSouzsbM2YMUOSNGPGDGVnZ6u0tLTVsa7AZrVoTGq8JGkD27cDAAAEJJ+E7ePHjysqKkovv/yybr/9dt11113aunWrCgoKlJCQIKvVKkmyWq2Kj49XQUFBq2NdxcQz27dnF8rtZioJAABAoLH5oojL5dLx48eVlpamxx9/XLt27dL999+v3/72tx1eOycnS06ns8PrfFl29vav/B4ej0dRYRaV1zTog8++UJ/4IJ/W/yqoT33qd936/tAD9alPfer7isPhUGzs+EuO+yRsJyUlyWazeaeEjBw5UtHR0QoODlZhYaFcLpesVqtcLpeKioqUlJQkj8dzybErMXDgMJ9fFc7O3q60tNHt8l7XleVp+ed5OlEVoW9en+bz+leD+tSnftet7w89UJ/61Ke+L1ksRuvjvmgiJiZG48aN0/r16yU1rzJSUlKiPn36KDU1VStXrpQkrVy5UqmpqYqJiVFsbOwlx7qSM6uSbD94Ws4Gl8ndAAAA4Er45Mq2JD3zzDN68skntXDhQtlsNj333HOKjIzU008/rSeeeEKvvvqqIiMjtXDhQu9rWhvrKuKjQ9U/OVK5Jyq1Pee0JrTM4wYAAID/81nYTklJ0V/+8pcLjvfv319vv/32RV/T2lhXMnFoonJPVGpj1inCNgAAQABhB8kAMCY1QVaLob1HSlVR7fubPQEAAHB1CNsBIDwkSCP6x8rjaV4GEAAAAIGBsB0gzkwf2biXsA0AABAoCNsBYuSAWIU4bDpaWKUTxTVmtwMAAIA2IGwHiCCbVWOGdJckbWT7dgAAgIBA2A4gZ6aSbMo+JbeH7dsBAAD8HWE7gAxMiVJspEOllU4dPFZudjsAAAC4DMJ2ALEYhsZ7b5RkKgkAAIC/I2wHmDNTSbYeKFJDI9u3AwAA+DPCdoDpERem3okRqnO6tCu3xOx2AAAA0ArCdgDyrrnNqiQAAAB+jbAdgMalJchiGNpzuERVtQ1mtwMAAIBLIGwHoG5hdg3tGyOX26Mv9hWZ3Q4AAAAugbAdoCYMTZAkbWJVEgAAAL9F2A5Q6YO6y2G3KvdkpQpLa81uBwAAABdB2A5QjiCrMga1bN/O1W0AAAC/RNgOYBOGtWzfvrdQHrZvBwAA8DuE7QCW2itaUeF2FZXXKfdkpdntAAAA4EsI2wHMYjE0Po01twEAAPwVYTvAjW9ZleSLfYVqcrlN7gYAAADnImwHuF4JEerZPUw19U3aw/btAAAAfoWw3Ql4t29nVRIAAAC/QtjuBMalJciQtPNQiWrrG81uBwAAAC0I251ATGSwhvSOVpPLrS372b4dAADAXxC2O4mzU0kKTe4EAAAAZxC2O4mMwd0VZLPo4PFyVdS6zG4HAAAAImx3GiEOm9IHxkmS9uUzbxsAAMAfELY7kTNTSbLzG9i+HQAAwA8QtjuRoX1jFBEapNJqt46cqjK7HQAAgC6PsN2J2KwWjUtr3lFywx7W3AYAADAbYbuTuWZYkiRpM9u3AwAAmI6w3cn0SghXXIRF1XWN2nWI7dsBAADMRNjuZAzD0NAUuyRpQ1aByd0AAAB0bYTtTii1p12GIe3OLVFlbYPZ7QAAAHRZhO1OKDzYomF9Y+Vye7Q5mx0lAQAAzELY7qSuGd685jarkgAAAJjH5qtCkydPlt1ul8PhkCTNnTtXkyZN0s6dOzVv3jw5nU4lJyfr+eefV2xsrCS1OobWpQ+MU4jDpqOFVco/Xa2e3cPNbgkAAKDL8emV7RdffFHLly/X8uXLNWnSJLndbj322GOaN2+eVq9erczMTC1atEiSWh3D5QXZrBqbGi9J2pDF1W0AAAAzmDqNJCsrSw6HQ5mZmZKkO++8Ux988MFlx9A2Z9bc3rj3lFxu1twGAADwNZ+G7blz5+rmm2/W008/rcrKShUUFKhHjx7e8ZiYGLndbpWXl7c6hrbpnxyphOgQVVQ3KPtImdntAAAAdDmGx+Px+KJQQUGBkpKS1NDQoF/96leqqanR1KlT9c477+i1117znjdy5Eh9+umn2rx58yXHoqKi2lx306ZNcjqd7fpZAsnGg/Vav79eQ5KDNCMjzOx2AAAAOhWHw6Hx48dfctxnN0gmJTVPabDb7Zo9e7YeeOAB3X333Tp58qT3nNLSUlksFkVFRSkpKemSY1di4MBhcrt98n3CKzt7u9LSRvu05qXqxyfXaf3+jcotdKlPvxEKDe74v3J/+vzUpz71u14P1Kc+9anvSxaL0fq4L5qora1VVVWVJMnj8ej9999Xamqqhg0bpvr6em3dulWStGTJEk2fPl2SWh1D28V1C9GQXlFqbHJry37W3AYAAPAln1zZLikp0SOPPCKXyyW3263+/ftr/vz5slgseu655zR//vzzlveT1OoYrsw1w5O0/1i51med0nWjks1uBwAAoMvwSdhOSUnRsmXLLjo2evRorVix4orH0HYZg7vrjTUHdSi/QoVltUqIDjW7JQAAgC6BHSS7gGC7TRmDu0tiR0kAAABfImx3EdcMa9m+PeuU3L5ZgAYAAKDLI2x3EYN7Rysm0qGSynodPMZa5QAAAL5A2O4iLIahCUObr26vzyowuRsAAICugbDdhUxsmUqy9cBpORtcJncDAADQ+RG2u5Ck2DD17xEpZ4NL2w4Wmd0OAABAp0fY7mImDm/eyXM9q5IAAAB0OMJ2FzM2NV42q0X7j5aptLLe7HYAAAA6NcJ2FxMWHKRRA+PkkbRxL1e3AQAAOhJhuws6s+b2+j2n5GHNbQAAgA5D2O6ChvWLUWSYXadKa3W4oNLsdgAAADotwnYXZLVYND4tQRLbtwMAAHQkwnYXdU3LqiRf7CtUY5Pb5G4AAAA6J8J2F5USH65e8eGqqW/SrkPFZrcDAADQKRG2u7Cza26zfTsAAEBHIGx3YePTEmS1GNpzuFQVNQ1mtwMAANDpELa7sMgwu4b3i5Xb49Fm1twGAABod4TtLm7imTW3swjbAAAA7Y2w3cWNHBCnsGCbjhdV61hhldntAAAAdCqE7S4uyGbR2NSWNbe5ug0AANCuCNvQxOHNU0k27T2lJhdrbgMAALQXwjbULylSiTGhqqxtVFZeqdntAAAAdBqEbcgwDF3TcnV7A2tuAwAAtBvCNiRJE4YmypC081CxqusazW4HAACgU7jqsL1p0yZ98cUX7dkLTBQTGazUPtFqcnm0ZX+R2e0AAAB0Cm0O29/73ve0bds2SdJrr72mn/zkJ/rpT3+qxYsXd1hz8K1rhjVv385UEgAAgPbR5rCdk5OjUaNGSZLefvttvf7661q6dKmWLFnSYc3Bt0YP6i6H3arck5UqKKkxux0AAICA1+aw7Xa7ZRiGjh07Jo/HowEDBigpKUkVFRUd2R98yGG3aszgeEmsuQ0AANAe2hy2MzIytGDBAi1cuFBTp06VJB07dkzR0dEd1hx878yqJBv3npLb4zG5GwAAgMDW5rD97LPPKjIyUoMHD9YjjzwiSTp8+LDuvvvuDmsOvjcwJUpx3YJVWunU/qNlZrcDAAAQ0Noctjdt2qSf/OQnevTRRxUaGipJuv7665WYmNhhzcH3LIahicOa/07X72EqCQAAwFfR5rD91FNPXfT4vHnz2q0Z+IczYXvbwSLVOZtM7gYAACBw2S53wvHjxyVJHo/H+/jcMbvd3jGdwTTx0aEa2LObcvIrtO3AaV07IsnslgAAAALSZcP21KlTZRiGPB6P98bIM+Li4rzzt9G5XDM8STn5FdqQVUDYBgAAuEqXDdv79++X1LypzRtvvNHhDcE/ZA6O118/PKj9x8pVXF6nuKgQs1sCAAAIOG2es03Q7lpCg21KHxgnSdqwlxslAQAArkabw/bx48f105/+VN/85jd1/fXXn/dzJV5++WUNHjxYBw8elCTt3LlTt9xyi2688UZ9//vfV0lJiffc1sbQ8a4Z3rJ9e9YpeVhzGwAA4IpddhrJGXPnzlVKSooef/xxhYRc3ZSCvXv3aufOnUpOTpbUvCvlY489pmeffVaZmZl69dVXtWjRIj377LOtjsE3hvaJUbdwu4rK6nToRIUG9owyuyUAAICA0uYr2zk5OXruued03XXXaezYsef9tEVDQ4MWLFigp59+2nssKytLDodDmZmZkqQ777xTH3zwwWXH4BsWi6EJQ1lzGwAA4Gq1OWyPGTNG2dnZV13ot7/9rW655Rb17NnTe6ygoEA9evTwPo+JiZHb7VZ5eXmrY/Cda1rW3N6yv1ANjS6TuwEAAAgsbZ5GkpycrB/+8IeaOnWq4uLizhubM2dOq6/dsWOHsrKyNHfu3Kvr8ivIycmS0+n0ed3s7O0+r9lR9RO6WVVY4dLKf27RkOS2ravemT4/9alP/cDrgfrUpz71fcXhcCg2dvwlx9sctuvq6nTDDTeoqalJp05d2ZSCLVu2KDc3V1OmTJEknTp1Sj/4wQ9011136eTJk97zSktLZbFYFBUVpaSkpEuOXYmBA4fJ7fbtzX3Z2duVljbapzU7sv6U2uN686McHS0L0e1TR/q8/pWiPvWpb159f+iB+tSnPvV9yWIxWh1vc9j+Kjcm3nfffbrvvvu8zydPnqzFixdrwIABWrp0qbZu3arMzEwtWbJE06dPlyQNGzZM9fX1Fx2Db41LS9Bbaw8pK69E5dVORYU7zG4JABes8kkAACAASURBVAAgILQ5bH95q/ZzpaSkXFVxi8Wi5557TvPnz5fT6VRycrKef/75y47BtyJC7RrRP1Y7coq1aW+hpo/rZXZLAAAAAaHNYfvcbdvPMIzmy+b79u27oqJr1671Ph49erRWrFhx0fNaG4NvXTM8STtyirU+q0A3jk3x/t0DAADg0tocts9s237G6dOn9fLLL3uX5kPnNqJ/rMJDgnTidI2OFVard2KE2S0BAAD4vTYv/fdl3bt311NPPaUXXnihPfuBn7JZLRqXliBJWr+nwORuAAAAAsNVh21JOnz4sOrq6tqrF/i5a4Y3r7m9KbtQTS63yd0AAAD4vzZPI5k9e/Z583Tr6up06NAhPfTQQx3SGPxP74QIJceF6URxjfbklih9UHezWwIAAPBrbQ7bd9xxx3nPQ0JCNGTIEPXp06e9e4KfMgxDE4cn6u1PcrU+6xRhGwAA4DLaHLZvu+22juwDAWJ8WqL+/s9c7TpUrOq6RoWHBJndEgAAgN9q85ztxsZGvfjii5oyZYqGDx+uKVOm6MUXX1RDQ0NH9gc/Ex3h0NA+MXK5PdqcXWh2OwAAAH6tzVe2n3/+ee3evVvPPPOMevTooZMnT+rVV19VdXW1nnzyyY7sEX5m4vBEZeWVav2eAk3J6Gl2OwAAAH6rzWH7gw8+0PLlyxUdHS1J6tevn9LS0jRz5kzCdhczemB3hTisOnKqSieKa5QcF2Z2SwAAAH6pzdNIzt05si3H0XnZg6waMyRekrSBNbcBAAAuqc1he/r06XrggQe0bt065ebm6rPPPtNDDz2k6dOnd2R/8FMThyVJkjbuPSW3my9cAAAAF9PmaSSPPfaYfve732nBggUqKipSQkKCbrrpJj3wwAMd2R/81MCe3RQfFaKi8jplHynVsH6xZrcEAADgdy57ZXvbtm16/vnnZbfbNWfOHH344YfatWuX1qxZo4aGBmVnZ/uiT/gZwzA0cVjzjpLrs06Z3A0AAIB/umzY/v3vf68xY8ZcdGzcuHFavHhxuzeFwHAmbG8/eFp1ziaTuwEAAPA/lw3b+/bt06RJky46NnHiRGVlZbV7UwgMcVEhGpwSpcYmt7bsLzK7HQAAAL9z2bBdXV2txsbGi441NTWppqam3ZtC4Jg4vPnqNquSAAAAXOiyYbtfv376/PPPLzr2+eefq1+/fu3eFAJH5uB42YMsOphfoaLyOrPbAQAA8CuXDdv33HOP5s+frzVr1sjtdkuS3G631qxZo6efflr33ntvhzcJ/xXisCljUHdJXN0GAAD4sssu/XfzzTeruLhYjz/+uBobGxUVFaXy8nIFBQXp0Ucf1YwZM3zRJ/zYxOFJ2ri3UBuyTumWa/ua3Q4AAIDfaNM62/fee6/uuOMO7dixQ+Xl5YqKilJ6errCw8M7uj8EgNRe0YqOcKi4ol45x8vNbgcAAMBvtHlTm/Dw8EuuSoKuzWJpXnN71cajWp91SuP7mN0RAACAf2jzdu1Aa86sub11f5Eam9i+HQAAQCJso50kxYapb1Kk6htcyjl18aUiAQAAuhrCNtrNNS1rbu893mByJwAAAP6BsI12MzY1QTaroaOnm1hzGwAAQIRttKPwkCCNTU2QJL31cY7J3QAAAJiPsI129a3r+ivIKu3IKdauQ8VmtwMAAGAqwjbaVXSEQ9cMCZYkvfnRQTU0ukzuCAAAwDyEbbS79L4OJceF6XR5vf7f5mNmtwMAAGAawjbandVi6HvTBkmSVm08qqKyWpM7AgAAMAdhGx1icK9oTRiaoCaXW29+lCOPh41uAABA10PYRof5zg0DFOKwanduiXbmcLMkAADoegjb6DDdwh26dVI/SdKbH+XIyc2SAACgiyFso0NNHp2slPhwlVTWa9XGI2a3AwAA4FOEbXQoq8Wiu6YNliR9sPmYTpVysyQAAOg6CNvocAN6dtO1w5PU5PLorx8e5GZJAADQZfgsbD/44IO65ZZbdOutt2r27Nnat2+fJCkvL0+zZs3SjTfeqFmzZunIkSPe17Q2hsDy7ev7K9Rh0968Um07cNrsdgAAAHzCZ2F74cKFeu+997Rs2TJ9//vf15NPPilJmj9/vmbPnq3Vq1dr9uzZmjdvnvc1rY0hsESG2fWt65pvlvzbxzlyNnCzJAAA6Px8FrYjIiK8j6urq2UYhkpKSpSdna0ZM2ZIkmbMmKHs7GyVlpa2OobAdN2oZPVOjFBZlVPvbcgzux0AAIAOZ/Nlsaeeekrr16+Xx+PRH/7wBxUUFCghIUFWq1WSZLVaFR8fr4KCAnk8nkuOxcTE+LJttBOLxdBd0wbrV69v1ZovjuuaYUnqERdmdlsAAAAdxvCYcLfasmXLtGrVKs2ZM0ePP/64Vq1a5R375je/qeeff14ej+eSY0OHDm1zrU2bNsnpdLZr//hq1uyq1e6jDeoVZ9MdE8JkGIbZLQEAAFwVh8Oh8ePHX3Lcp1e2z7j11ls1b948JSYmqrCwUC6XS1arVS6XS0VFRUpKSpLH47nk2JUYOHCY3G7ffp/Izt6utLTRPq0ZSPV79W3U//f7jTpW3KQaS0+NTU3waf2ORn3qd+X6/tAD9alPfer7ksXS+kVDn8zZrqmpUUFBgff52rVr1a1bN8XGxio1NVUrV66UJK1cuVKpqamKiYlpdQyBLTwkSN++vr8kacnHOapzNpncEQAAQMfwyZXturo6zZkzR3V1dbJYLOrWrZsWL14swzD09NNP64knntCrr76qyMhILVy40Pu61sYQ2CaN7KHPdhUor6BS763P06zJA81uCQAAoN35JGzHxcVp6dKlFx3r37+/3n777SseQ2CzGIbuunGQfvmnrfpwS76uGZ6knt3DzW4LAACgXbGDJEzTJzFS149Oltvj0Rtr2FkSAAB0PoRtmOr2r/VTRGiQDh4v16a9hWa3AwAA0K4I2zBVWHCQ7rh+gCTprU8OqbaemyUBAEDnQdiG6SYOT9SA5G6qrGnQsnWHzW4HAACg3RC2YTqLYeh70wbJMKSPt+frWGGV2S0BAAC0C8I2/EKvhAhNGd1THo/0xpqDcnOzJAAA6AQI2/Abt07qp8gwuw6dqNCGPafMbgcAAOArI2zDb4QG2zTrhuabJd/+5yHV1Dea3BEAAMBXQ9iGXxk/NEGDUqJUVduof3zGzZIAACCwEbbhV4yWmyUthqF/bj+hI6cqzW4JAADgqhG24Xd6dg/X1zN7yiNulgQAAIGNsA2/NPPavooKt+vwyUp9vrvA7HYAAACuCmEbfinEYdOsyQMlSX//Z66q67hZEgAABB7CNvzW2NR4pfaOVnVdo975NNfsdgAAAK4YYRt+yzAM/cvUQbJaDH2286QOn+RmSQAAEFgI2/BrPeLCNG1sijyS/rLmgNxubpYEAACBg7ANv3fzxD6KjnDo6KkqfbrzhNntAAAAtBlhG34v2G7Td6c03yz5zqeHVVnbYHJHAAAAbUPYRkDIGNxdQ/vGqNbZpL9/ws2SAAAgMBC2ERAMw9D3pg6SzWro8z0FOpRfYXZLAAAAl0XYRsBIiAnV9HG9JDXfLOlyu03uCAAAoHWEbQSUmyb0UWxksI4XVeuT7dwsCQAA/BthGwHFEWTV7K833yz57rrDqqh2mtwRAADApRG2EXBGDYzTiP6xqnO6tJSbJQEAgB8jbCPgGIah2V8fKJvVoo17T+nAsTKzWwIAALgowjYCUnx0qG6a0FuS9MaHB9Xk4mZJAADgfwjbCFjfGNdL3aOCdeJ0jdZuyze7HQAAgAsQthGw7EFWzf76IEnSss/zVFbFzZIAAMC/ELYR0EYOiFP6wDjVN7i09JNDZrcDAABwHsI2At53pwyU3WbR5uxC7TtSanY7AAAAXoRtBLy4qBDdNLGPpOabJV1uj7kNAQAAtCBso1OYPraXEqJDVFBSq225zN0GAAD+gbCNTiHIZtG/TGu+WXLDwXp9uvMEywECAADTEbbRaQzrG6sJQxPV5JL+/MEB/fx/Nmvj3lNyM60EAACYxGZ2A0B7+sGMVMUGV2pLnlRYWqv/WZGt9zce1a2T+mn0oDgZhmF2iwAAoAshbKNTsRiGhiTbNXPKKG3Yc0rvrc/TieIavfLuHvVJjNDtX+unoX1jCN0AAMAnfBK2y8rK9LOf/UzHjh2T3W5X7969tWDBAsXExGjnzp2aN2+enE6nkpOT9fzzzys2NlaSWh0DWmO1WDRpZA+NH5qoz3ad1MoNR3TkVJVeWLpLg3p20+3X9deglCiz2wQAAJ2cT+ZsG4ahH/7wh1q9erVWrFihlJQULVq0SG63W4899pjmzZun1atXKzMzU4sWLZKkVseAtgqyWTQlo6d+c/8E3XF9f4UF23Qwv0K/+et2vfDWTuUVVJrdIgAA6MR8ErajoqI0btw47/NRo0bp5MmTysrKksPhUGZmpiTpzjvv1AcffCBJrY4BV8oRZNU3xvfWwvsn6pZr+ijYblVWXql++eetevkfe3TidLXZLQIAgE7I53O23W63/va3v2ny5MkqKChQjx49vGMxMTFyu90qLy9vdSwqin/+x9UJDbbp1kn99PXMFL2/6ajWbsvX9oOntePgaY0bmqCZ1/ZVQnSo2W0CAIBOwvB4PD5dF+2ZZ55RYWGhXn75ZX344Yd655139Nprr3nHR44cqU8//VSbN2++5NiVhO1NmzbJ6WSTE1xcdb1bmw7Wa/fRBrk9kmFIw3vZNX5QsCJDWBkTAAC0zuFwaPz48Zcc9+mV7YULF+ro0aNavHixLBaLkpKSdPLkSe94aWmpLBaLoqKiWh27EgMHDvP5OsvZ2duVljbapzWpf/X1x46Wisvr9N76I1qfVaDdRxuUnd+o69OTddOEPuoWZu/Q+u2N+tQ3s74/9EB96lOf+r5ksbS+wpnPLt298MILysrK0iuvvCK7vTm8DBs2TPX19dq6daskacmSJZo+ffplx4D2FhcVou/flKr/+OE4jU2NV5PLo4+25uvxxRv0zqe5qqlvNLtFAAAQgHxyZTsnJ0e///3v1adPH915552SpJ49e+qVV17Rc889p/nz55+3vJ8kWSyWS44BHSUpNkz3zxymb46v0rJ1edp5qFirNh7V2u0nNH1sir6emaIQB8vTAwCAtvFJahg4cKAOHDhw0bHRo0drxYoVVzwGdKReCRF69NsjlHuiQv/47LD2HS3Tu+vy9OHWfN00obduSE+WPchqdpsAAMDPcQcY0Ir+yd302HfT9dh309U/OVLVdY16a+0hPfH7jfpkxwk1udxmtwgAAPwYYRtog9Te0Xryexma8+0R6hUfrvLqBv1l9QE9+domrd9T4PObcAEAQGBg8inQRoZhaOSAOA3vH6ttB05r2brDKiip1f+u2qf3Nx3VbZP6afTg7ma3CQAA/AhhG7hCFsPQmCHxyhjUXRv3ntLyz/NUUFKrV5dlqVdCuNJ7udWrb6PCQ4LMbhUAAJiMsA1cJYvF0DXDkzQuLUHrdhdoxfo8HSus1rFCafmWdYqOcCglPvy8n4To0MuuxwkAADoPwjbwFdmsFt2QnqxrhiXqkx0n9On2PJVWe1RW5VRZlVO7c0u859ptFiV3D1PP7ueH8NBgroIDANAZEbaBdmIPsurGsb2UEl6sIUPSVVhWq+NF1co/Xa3jhdU6frpapZVO5RVUKa+g6rzXxkY6lBIfoZ7x4eoVH66e8eGKjwrhKjgAAAGOsA10AIvFUFJsmJJiwzQ2NcF7vKa+UflF1TpWVN0cxIuqdaK4RiWVTpVUOrXzULH3XHuQRT27h19wFZxNdQAACBz8XxvwobDgIA3uFa3BvaK9x1xutwpL63T8TAA/3fy7rMqpwycrdfhk5XnvEdct+Lzw3TM+XN2jQmQxuAoOAIC/IWwDJrNaLOoRF6YecWEal3b2KnhVbYPyi6p1/HSNjhdV6XhRtU4W16i4ol7FFfXakXP2KrjDblXP7mFKiY+QpdGpKhUqLMSmsOAghYUEKTzYpmCHjUAOAICPEbYBPxURaldqnxil9onxHmtyuVVYWuu9Cn68qHkueEV1g3JPVCr3RPNV8LVZey94P8NovrIeFmxTWEhQSxC3nXcs/NxjIc3HQ4NtslrY/woAgKtB2AYCiM1qUXL3cCV3D9f4oWePV9Y2NAfvwmodOHxMQSFRqqlrVE19o2rqmlRT36j6Bpeq6xpVXdcoldVdUd0Qh+2cQH6xsH72cXmNq50/NQAAgYuwDXQCkaF2De0To6F9YtQrolhpacMuOKfJ5VZtfZM3gFfXN7YE8qazwfwij2vrm1TnbP4prqhvUz+f7NuqyRk9lTk4XkE2rooDALouwjbQRdisFkWG2RUZZr+i17k9HtU5m84L5tXnXDE/+7t5/HhhhXJPVir3ZLbe+jhHXxvVQ9ePSlZMZHAHfTIAAPwXYRtAqyyG4Z0q0hY7d29ThTtRH287ofzT1Vq54aje33hM6YPiNGV0Tw3uFSWDGzUBAF0EYRtAu7LbDF2XlqyvjeyhnPwKrd2er20HTnt/kuPCNDmjpyYMTVCwnf8EAQA6N/5PB6BDGIahQSlRGpQSpbIqpz7deUKf7jypE8U1+svqA/r7Pw/pmmFJumF0spJiw8xuFwCADkHYBtDhoiMcunVSP82Y2EfbDpzW2u35ysmv0Efb8vXRtnwN7RujKaN7akT/WLaoBwB0KoRtAD5js1o0Li1B49ISdKywSmu352vT3kLtzSvV3rxSxXUL1g2jkzVpRA+Fh7RtjjgAAP6MsA3AFL0SInTPN1L17esH6PPdBfpkR75Ol9fr7U9ytWxdnsalJmhKRk/1Towwu1UAAK4aYRuAqcJDgjR9XC9NG5OiPYdLtHb7Ce05XKLP9xTo8z0F6p8cqSmjeypzSLxsVtbsBgAEFsI2AL9gsRgaOSBOIwfEqbC0Vp/sOKF1uwtatqHP1pK1h3TdyB66Pj1Z0REOs9sFAKBNCNsA/E5CTKjunDJQt03qp43Zp7R2W77yT9doxYYjWrXxqEYPitOUjJ4alMKa3QAA/0bYBuC3HHarrh+VrOtG9tDB4+Vau/2Eth04ra0tP8ndwzRldE+NZ81uAICf4v9OAPyeYRga3Ctag3tFe9fs/ufOkzpxukavrz6gt/+Zq2uHJ2ny6GSzWwUA4DyEbQAB5dw1u7ceKNLabSd06ESFPtx6XB9uPa7ecTZllB9R78QI9U6IUGSY3eyWAQBdGGEbQECyWS0an5ao8WmJOnqqZc3u7EIdLW7S0c8Oe8+LjnCod0JEc/huCeDcYAkA8BXCNoCA1zsxQvd+M1V33DBA/++zbWq0xujYqSodLapWWZVTZVVO7TxU7D2/W5hdvRMj1CshQn1aAnhMpIObLQEA7Y6wDaDTCA8J0tAUu9LSBkmS3B6PCktrdbSwSsdOVevIqUodLaxWRU2DdueWaHduyXmvPXPl+8xV8O7dggngAICvhLANoNOyGIaSYsOUFBum8WnNxzwej05X1Ovoqarmn8Lm39V1jd5t488IddjUKyFcfRIj1Sux+Xd8dIgsBHAAQBsRtgF0KYZhKD4qRPFRIRozJF5ScwAvrXTqaGGVjpyq0rGW35U1Ddp/rFz7j5V7X++wW9U7Ply9EyPVOzFcvRMilBQbJouFAA4AuBBhG0CXZxiGYrsFK7ZbsEYP6u49Xlbl9F75PnMVvKzKqYP5FTqYX+E9z26zKCWhOXgHuZxSaKliIoMVHeGQI8hqxkcCAPgJwjYAXEJ0hEPREQ6NGhDnPVZR06BjXwrgxRX1LdvKV0qSVu/a6T0/LNimmMhgxUQ4vAE8JtKhmIhgxUQ6FB0RrCCbxeefDQDgG4RtALgC3cLsGt4vVsP7xXqPVdc1ttyEWaW9h46pyQhTWVW9yqqcqqlvUk19tY4XVV/yPSNCg84J344Lwnl0hEM2K4EcAAIRYRsAvqLwkCAN7ROjoX1i1DuyRGlpoyU1r4ZSVduo0sp6lVY6VVZVr9IqZ/PzKqfKKp0qr3aqqrZRVbXNgf1iDEmRYXbvlfAzQdwbziOCFRVhl9VCIAcAf0PYBoAOYjEMdQuzq1uYXX2TLn6O2+1RRU2DSqvqVVbpPD+MVzWH9PJqpypqGlRR06C8gksEckOKCncoxNak0cW5GtE/Tv2SIrlxEwBM5pOwvXDhQq1evVonTpzQihUrNGhQ8xq4eXl5euKJJ1ReXq6oqCgtXLhQffr0uewYAHQWFovhnSqiHhc/x+V2q6K64WwQr3Q2h/Mqp/dxZXVD8wY+kk5uOKqVG44qPCRIw/vFauSAWA3rG6PQ4CCffjYAgI/C9pQpU3T33XfrX/7lX847Pn/+fM2ePVszZ87U8uXLNW/ePL3++uuXHQOArsRqsbRMGwmWkrtd9Jwml1vl1U5t2r5bFU3R2nWoWMUV9dq495Q27j0li2FoUEo3jegfp5EDYpUYE8qGPQDgAz4J25mZmRccKykpUXZ2tv7v//5PkjRjxgz98pe/VGlpqTwezyXHYmJifNEyAAQUm9WiuG4h6pcQpLS0QZr99YEqKKnVrtxi7T5Uopz8Cu+a4Us/OaTuUcEa2T9OIwbEanBKNCuiAEAHMW3OdkFBgRISEmS1Nq9Ba7VaFR8fr4KCAnk8nkuOEbYB4PIMw1CPuDD1iAvTN8b1Vk198w6Zuw4Va8/hUp0ur9dH2/L10bZ8OYKsSusTrZED4jSif6yiwh1mtw8AnUanv0EyJydLTqfT53Wzs7f7vCb1qU996rdWP1zSNf2lCf1CVVDm0uHCRh0ubNTpSpd25BRrR06xJCmhm1X9EmzqlxCkxCjrVU038dc/A+pTn/rUb28Oh0OxseMvOW5a2E5KSlJhYaFcLpesVqtcLpeKioqUlJQkj8dzybErNXDgMLndng74BJeWnb3du/SXGahPfepT/3KGnfO4tLJeu3JLtPtQsfYdLVNhhUuFFS5tPOhUZJhdI/rFakT/WA3tG6MQx+X/txEofwbUpz71qd8eLrfqk2lhOzY2VqmpqVq5cqVmzpyplStXKjU11TtNpLUxAED7iYkM1g3pybohPVkNjS7tP1amXYdKtDu3WCWVTn2+p0Cf7ymQ1WJoUEqURg6I08j+sUqICTW7dQDwez4J2//xH/+hNWvWqLi4WPfee6+ioqK0atUqPf3003riiSf06quvKjIyUgsXLvS+prUxAEDHsAdZNaJ/nEb0j5PHM0gnTtdoV26xduWWKPdEhfYdLdO+o2Va8nGOEmJCNbJ/rEb2j9XAlCh2uQSAi/BJ2P75z3+un//85xcc79+/v95+++2Lvqa1MQBAxzMMQz3jw9UzPlw3Teij6rpG7Tlcot25JdqTW6LC0lqtKa3Vmi3HFWy3aljfGA3rF6uy4gY12ItlMQxZLYYsFkMWo3kJQ8Oi5mNG8/FzH3t/n3e8+Xyj5b1YrhBAoOn0N0gCANpHeEiQJgxN1IShiXK53co9Ualdh4q1O7dEJ4prtPXAaW09cLr55C27O6QHQ/KGccNiyHpOQG8O9IbcrkaFbtwsm9WiIKtFQTaLbFaLbFbD+9j722qRzWZceOzc5y3jZ441v6bl/c59f5tFFr4MAPgSwjYA4IpZLRYNSonSoJQo3XHDABWX12lXboly8stVWlaq0LBucns8crvP/rg8HrndzVvUu9weeTzNv8+cd+5jt/exmo+3PPeo+bnrMje+l9XU+OYP4kusFkOGPLJ+8OlFvwhccAX/3HNaxqwt51sslpar+ee8tuU1xpf+VeDcx2WldTpcfuTsl4OWLwYXfPk488XBapx9bDv7ZcNm5V8SgPZA2AYAfGVxUSGaktFTUzJ6tqwGMLLda3g8Hnk8kvtMSG8J4C63R56WAO5ye3TgYJZ690lVk8utxia3Gl1uNTW5m5+3HGtyeS5yzK2mJk/z+eccO/t6z/nHzj2nyaMml9v7JaCpwdXun/+KHDzcLm9z7r8GnP2XgLP/SnA2mJ8N6s66Wh0uP6KI0CBFhtoVEWpXRFjz42D71S0lCQQywjYAICAYRvNVXosM2ayXPu90uFUp8eG+a6zFmSv1e/fu0KDBIy/4InDulwO3R+ddwXe5v3xF/0tX+j2Sy+1uOUfnn/Ol1xScOqHomMQLvhg0NZ37BcJz9ti5XzZcHu+XC5fb0/ylxOWSdGVfHrYfvnjYt1mN5vDtDeJBigi1KzLMroiQIEWEnR2LDLXLYW/lLxoIEIRtAADagWEYLVd8jTatR95RsrNLlZbW/yu/j9tz7tV/jzeYe3+fF9Y93iv9uUfyFBoZr6qaRlXWNqiqtlFVLb+djS6VVTlVVtW2zebsNsvZcB52Tjg/L6gHKSKk+bk9iHAO/0PYBgAAF7AYhuxB1isOsDG2AqWlDbjomLPR5Q3elTXnB/HK8343qLKmUQ1NbpVU1quksr5NtR12q+xWtyI3bFaww6oQh00hdlvzb4fV+zj4zOPgM+Nnz7UHWZjqgnZF2AYAAD7hCLLK0S1Ecd1CLnuux+NRfYNLVXWNqqppuOAquff5OWPOBpeckqrqrv4GWYthKMRhVfC5Id3R8thubQnrZx97j7cE96o6typrG7w3o7JkJQjbAADA7xj/f3v3HhdVnf4B/DMzMCCoaIquy5praOElEzUFwUzQXqICgrVpoWZJXjJMqfAuXlollNq8USqabpmaeN1SNiXUQhTbvJSsShr6U1BAZYDhMjPn9wfOxABqG5zvGeTzfr14yZxhfL4HmHMevt/nPEelsiSzrZr9vuRcX2rAqbOn0PZRDxSXGlBSZqj4t9QIfZkB+lID9KVGlJTe3V5mvPu8AfoyI/SlBpQbTCgqMaCoxPDHB//vo1YPf2sbqbp7gWnV7i+/dYSpfkGq6p5tKe3sVNW2Zd82oHleERzurko42GvYWUZhTLaJiIio3lOpVHBytIeLkxp/qcUFsgajyToJv5ug/5ashgi1TwAAHCNJREFUV0nSSyuSdPPzxfpSQKW5e3Gp+ULTihp3fR3u730dTrN6qFLdXVW4+6G1V1sl4+bHDvaailIcu7vPa6u/5rfHGjjYq6G11/DusQ/AZJuIiIjoLjuNGo0bqdG4kf0fen1F68selsemu8m29cWlUg2dYKwvNK3WSaZKa0rr1/8Wo0BXCJXGAWXlRpSVG1FaboTBWFGSUyJTS0qNWmVJvlWSAU2OHa9I1O0qknGHGpJ2bQ3Jv+Vx5dfaa6BW1+9ZeSbbRERERDJRq1XQqv/3C03/qKrJPlDRNrK0zITSciPKDEaUlhlRVl7xuLRSUl56d1uZ1XZTRS285bWmSl9f8WE0SXdn/Svi3SoqrNN9sjfPtP+ORL2FgwGd6zR67THZJiIiInqIadRqODmq4eRY92mfJFX0Yzcn5z+dO4NH23n8loyX3U3S7ybtvyXyJquE3ZzYV030y8qNFTenMpjwe+pw3B7RwN+nznezVphsExEREdEfolJV9Ja3t1MDjezRookG7f7UpM7+f0mSUGYwVU/IqyTxpXeT8kamnDqLXVeYbBMRERGRTVKpVJYSkd/j559zZR7R/46XjxIRERERyYTJNhERERGRTJhsExERERHJhMk2EREREZFMmGwTEREREcmEyTYRERERkUyYbBMRERERyYTJNhERERGRTJhsExERERHJhMk2EREREZFMmGwTEREREcnETukByE2tVgmP6eDgoEhcxmd8xmd8pePbwhgYn/EZn/FFelA8lSRJkqCxEBERERE1KCwjISIiIiKSCZNtIiIiIiKZMNkmIiIiIpIJk20iIiIiIpkw2SYiIiIikgmTbSIiIiIimTDZJiIiIiKSCZNtIiIiIiKZMNkmIiIiIpIJk20iIiIiIpkw2a4j27dvx5UrV5QeBhERNWB37txRegiKyMzM/F3biJSgkiRJUnoQD4MFCxYgNTUV5eXl8PLygre3N7y8vNCyZUthY9DpdLh06RJKS0st255++mlZY5aVlUGr1UKv19f4fKNGjWSNb2vKyspgNBotj+Xe/6+//hoBAQH47LPPanz+5ZdfljW+rdi9ezeCg4MfuO1h9ssvvyAjIwNlZWWWbcOHD1dwRGLl5eVh8+bNuHLlCgwGg2X7P/7xDyHxL168iA4dOlht++677+Dj4yNbzIyMDMyaNQtqtRoxMTGIiYlBWloamjVrhvj4eHTq1Em22LYmJCQEO3fufOA2ueTn52PRokX4/vvvoVKp4OPjg9mzZ+ORRx4REh9Q9hig1+uRnZ1tdf6r+n6QQ305B9opPYCHxfz58wEA2dnZ+PbbbxEXF4fr16/j3LlzQuJ/9dVXiImJQUFBAVq1aoWsrCx4eHjIfqDp3bs3fvzxR3h6ekKlUqHy324qlUr2/ffy8oJKpaq2XZIkqFQqpKamyhrf7N///jcWLVqEmzdvWsWXe/8vXLiAgIAAnD17VtY4D5KRkYH58+dXO9CL+v3fuHFjtcS6pm1y+eGHHxAbG4srV67AaDQK//3btGkTtm7dips3b+LJJ59Eeno6nn76aSEn2hEjRtT4HjT78ssvZR8DALz55ptwd3eHt7c3NBqNkJiVRUZGYt26dXB1dQUAnDhxAgsXLsSBAwdki7l48WK88cYb0Ol0GD9+PKZNm4ZPPvkEhw4dQkxMDDZu3Chb7KquX7+O2NhYZGRkWE34HDx4UNa4+fn5yM/PR2lpKTIzMy3nIJ1Oh+LiYlljVzZ//nx06NABM2bMAABs3boV8+bNw8qVK4XEV/IY8Nlnn2HZsmVo1qyZ5VigUqlk/9kDtnMOfBAm23Xk7NmzSE1NRWpqKm7evAlfX194e3sLix8fH4/ExES89tpr2LVrF7777jtZD/Jmjz32GICKpHvTpk2yx6tqx44dls91Oh0MBgOaN28OSZIgctHm/fffx4cffoju3btDrRZXnRUREQEAWLJkibCYNYmOjsZbb72FJUuWYN26dfjss8/g7Owse9wzZ87g9OnTuHXrltXMRmFhIcrLy2WPbzZ79mxMnjxZ+M/fbNu2bdi+fTtGjRqF9evX4/z581i1apWQ2FFRUULiPEhBQQEWLVqkWPyZM2di8uTJ+PTTT3Hx4kXMnj0b8fHxssYsKiqCv78/gIoZ/KCgIACAn58fPvroI1ljVzVr1iwMGTIEGRkZWLZsGbZs2YJHH31U9rh79+7Fp59+ihs3biA8PNyyvUmTJhg/frzs8c2ysrKwYsUKy+OIiAihK2tKHgMSEhKwb98+uLm5CYlXmfkcuGDBAmi1WqvnCgoKhI/nXphs15Hnn38enp6emD59uuylGzWxs7NDixYtLEs4Pj4+WLZsmexxS0pKcODAAVy/fh0pKSnVnu/fv7+s8c1v7pSUFMybNw8ajQaHDh3CmTNnsGrVKtlPdmYuLi7o0aOHkFg1MRgM2Lp1K9LS0gBUzPj/7W9/g52dmLd4WVkZvL29IUkSWrVqhWnTpmHEiBF4/fXXZY2bk5ODs2fPQq/XW81sODs7C/0DxNHREYGBgcLiVaXVauHk5ASTyQRJkvD444/j8uXLQmL37t3b6rF5NtHJyUlIfLOOHTsiJycHrVu3FhrXzMvLC2PGjEF4eDhyc3OxcuVKy2SEXCpPKFQtVzGZTLLGrurWrVt44YUXsGnTJnh6euKpp57Ciy++iClTpsgad+zYsRg7dizi4+MxceJEWWPdj8lkQl5eHlq0aAGgoqxJ5M9AyWOAq6urIol2ZTNnzsTy5cstjwsLCzF+/Hhs27ZNwVH9hsl2Hdm2bRuOHTuG1atXIy8vDz169EDfvn3x3HPPCYmv1WohSRLatWuHzZs3w83NTcgS2vTp07F161bk5uZi3bp1Vs+pVCrZk22zjz76CF9++aVlZuPJJ59EVlaW7HHNteqDBg3C559/jiFDhsDBwcHyvKia9QULFuDatWuWJcPdu3cjIyMDCxcuFBLfvGzv4uKCjIwMtG7dGrdu3ZI97sCBAzFw4EAcPXoUvr6+sse7l2eeeQYpKSnCft+ratSoEcrLy+Hh4YHY2Fi0adNGeLJ15coVREZG4ty5c1CpVOjcuTNiY2PRtm1bIfELCgoQFBQET09Pq/eg3DXbVWtF9Xo9evXqhRMnTuDEiROy1oy6ubmhsLAQjRs3xuLFiy3bs7OzhV8vY29vD6Dij6xr166hZcuWyM/PFxZ/4sSJSE1NRWZmJsLCwpCXl4eCggK0b99eSPzXXnsNw4cPx7PPPgugYgIoMjJSSGxAmWPAxYsXAQB9+/bF+++/j6FDh1q990TUbJu1bNkSsbGxeOedd1BSUoIJEyYoOgFSFS+QrGM5OTlITk7GJ598IrRmOyUlBT169EBeXh6io6Oh0+kQGRmJvn37Com/ZMkSzJw5U0ismowYMQI7duzA8OHDsWvXLgCw+lwuHh4eNdaqi6rZNhs8eDC++uorSwmDwWDA0KFDhZQSAcCGDRswfPhwnDlzBlOnToXJZEJERARee+01WeOePHkSPXv2rHFVBZB/ZcXMy8sLt2/fhrOzs+UPX5E12+fPn8df/vIX6PV6xMXFQafTYdKkSUIvkBs3bhyGDh2KESNGAAASExOxb98+bNiwQUj8e12fEhISImvcBx33lCjxKi4uhl6vt8yyihATE4MJEybg22+/xdKlS6HVajF48GDMmjVLSPxPPvkEKSkpuHnzJpKSkpCdnY1p06Zhy5YtQuIDFe/D48ePAwD69OmDjh07Co0t+hjg5+d3z+dE1WybSZKEqVOnwtPTE0eOHIG3t7dVWZHSmGzXkYULF+LYsWMoKSmBl5eX5aNVq1ZC4vv4+CAwMBAvvfSSkDo5WzNmzBjExcUhPDwcO3fuRFpaGlauXInNmzcrPTQhRo4ciY0bN8LR0RFARXnPuHHjhJ5ozMrLy1FaWorGjRvLHmvOnDlYvHgxRo8eXe05lUol7DqC//u//6txu9JLqyIFBwdj9+7dD9xGD79r166hsLAQjz/+uLCYQUFB2LFjB1544QXLJEtgYCD27t0rJP6uXbswePBgyzFYtNTU1GrXidW07WFTuRNaSUkJwsPD0bt3b7z55psAbKcjGstI6sgTTzyBV155RbFEd8+ePdi6dSvGjh0Ld3d3hIWFWZazGoK3334b4eHhuHr1KkaPHo3Lly9jzZo1wschuvWfeQm7Y8eOePHFFzFkyBAAwP79+/Hkk0/KGruqrKwsZGVlWe2/3DPL5qVzpf+oMi/n//rrr+jSpYuwuO+///59n3/33XcFjQRQq9X45ZdfLHXKly5dEtoV5PLly5g5cyZycnJw6NAh/PTTTzh06JDlpCtCQ2y/WFPb1+bNm6N58+bQ6/XCkh1HR0dLKYvZ/brk1DVzBxg/Pz+EhoaiZ8+ewmIDFceCqqs7NW2rS7bQ+rdyJzTzv2fPnkVCQoLQ1eUHYbJdR1588UVF47do0QKTJ0/GhAkTcPDgQURHR0OtViMsLAwvv/yyVR3Vw6hbt27YtGkTfvjhBwAVb8CmTZsKi5+UlITFixfjxo0bQstIKl8U2LlzZ8sFMR4eHkK7cSxfvhzbt2+Hu7u7pZRFZM3+iRMn0LlzZzg7O2P79u04c+YMwsPDhdULK3WBruiLEO9n2rRpePnlly3L1hkZGQ/8Y6AuRUdHY9KkSZaLpDp16oR3331XWLKtZOs1JdXU9tVMZLLzpz/9Cenp6VCpVDCZTIiPjxdaxvHRRx/h9u3b2Lt3L9577z0UFRUhNDQUEyZMkDXur7/+isuXL6OwsNCqnE6n090zCa4rSrf+BSqOM/WCRA+N4uJiacuWLVJgYKA0evRoac+ePdLs2bOl0aNHKz20h97AgQOlkydPSkajUemhKGLgwIGSTqdTLP6wYcMkk8kknT9/XgoICJDWrFkj9Pc+NDRUunHjhhQcHGzZFhAQICy+LSgrK5Py8vKkQ4cOSYcOHZLy8vKkO3fuCIsfGhoqSZJk9TOo/Lnchg4dKhUVFUlBQUGSJEnSf//7XykiIkJYfFty69Yt4TFv3LghjRs3TurSpYvUtWtX6ZVXXpFyc3OFj0OSKvZ/7ty5koeHh+yxEhMTpbCwMKl79+5SWFiY5WPixIlScnKyrLFDQkIkSZKYY/wOnNl+SCxcuBBJSUnw8/PDsmXLLLVygYGBGDx4sMKje/gp3foPUHYJ29XVVUiN9r3Y2dlBpVLh8OHDGDVqFEaPHo39+/cLHYP5ZiZmVXu+yu3o0aM4d+6c1Q1F5G67VtmMGTOwfPlyDBgwAEBF661XX31VWOstjUaD8vJyS+lATk6O0J7nSrZeU5L5LpYajQZLly5FTEwMjh8/DhcXF3z88cfw8PAQMg5XV1ckJCRAr9fDZDIJ6fNfmdFoxOHDh5GYmIiTJ0/C398f//znP2WPGxISgpCQECQmJiI0NFT2eJUp3fq3PmGy/ZBwc3PDv/71L7i4uFR7TombzTQUttL6T6klbPMBtnv37pg+fToGDx5stf+iDrYGgwGnTp2y3MkTgFXtuNycnZ2Rm5trSfTS0tLQpEkTYfGXLVuGM2fO4OLFi/D398fBgweFXxhVtfXWxIkThbbeeumllzBlyhTcunULK1aswK5duzBt2jRh8W2h/aIS7ncXy6VLlwq7i6XSpWT9+/fH448/juHDhyM2Nlb4hZJt27ZFUVGR0P23lda/9QG7kRDVgq20/hs2bBi2bduGUaNGYffu3Za7h8ndY7imLiBmIruBfPPNN1ixYgW8vLwwc+ZMXLp0CcuXLxd2q+RTp04hOjoaV69ehYeHh+UC3a5duwqJHxgYiJ07dyI0NBR79uxBTk4O5syZg7Vr1wqJD9hG66309HQkJydDkiT4+fmhV69ewmLbQvtFJYSEhFguwhswYACSk5Mtz4lov2oWGBiIPXv24OLFi5g6dSqCgoLw/fffCzsGXb9+HW3atBESqyZK7r/SrX/rA85sE9WCrVycodQSttJdQMzMN7cxa9++vbBEG6i4OLXqBboiabVaSylNeXk5WrdujezsbCGxK1+EtWDBAkvrrbCwMKHdKACgV69eQhNsM6PRiP379yMiIgJOTk547733hI9BKZKN3MVS6VKyNm3aKFrKp+T+M9F+MCbbRA8BpZewd+3ahQEDBljKmG7fvo3Dhw8jKChI2BiUrFk21yubl01F1ys7OztDr9fD09MTM2bMgKurq7BlbFtpvfXLL78gPj4eWVlZMBgMlu1ffvml7LE1Gg0OHz6MiIgI2WPZGlu5i6XSpWRKd6NRev/p/phsEz0E5s+fj/LycsyYMQNxcXG4evWq0LZrCQkJVieVZs2aISEhQViyrXTNstL1ynFxcdBoNIiKisKGDRug0+lkLyEys5XVnalTpyI4OBghISFC+3ubPfvss1i/fj2GDx9u1ZLRVm6qIZdVq1bVuL1p06ZYvXq1sHFMnToV8+bNg5eXFzp27IhLly6hXbt2wuJv27YN27dvx6hRo7B+/XpLKZ8oSu8/3R9rtonqOaPRiFWrVik6qxYUFIQ9e/ZYbRN59zala5ZtoV65oatcO6yEyl03lLhug5QVGhqKxMRES+20SqXiHVTJgjPbRPWcLSxhu7q6IikpCc899xwA4MCBA2jRooWw+ErVLNtKvbKSJRS2ol+/fkhJSVGsA4KtzPA3ZEqWkildygco3/6T7o0z20QPgZUrV6JRo0aKLWFnZmZi8uTJlpOLRqPB6tWrLbfultuYMWPw8ccfIyYmBjqdDq6urjh58iS2b98ua9zK3WiUuoMaUDGzHxwcjC5duliVUPTu3VtIfFuQmpqKyZMnQ61WQ6vVWn4mqampSg+NBLhXKdmyZcuExFe6G43S+0/3x2Sb6CGg5BK2yWTCkSNH4Ovri0uXLgGo6AYism42NzcXTZs2hdFotNQsjx49Gn/+85+FjUFJSpdQ2IJBgwYhMjISXbp0sbqZjZubm4KjIlGULCWzhVI+pUvp6P5YRkL0EFByCVutVuPDDz9E//790aFDB0XG0LJlS8vnkydPVmQMSlK6hMIWuLi48G65DZiS7S9toZRPyf2nB2OyTUS15uHhgdOnT6Nbt26KxG/oNcve3t4NvoRi4MCB2LJlCwICAhS5iyspS8n2l4Dy3WiU3n+6P5aREFGtBQUFITMzE+3atbM60YhKdht6zTJLKNgNpKFTupSs8u+fmcjfP6X3n+6PyTYR1drx48dr3C4q2W3oNcvPP/98g5nFJyKqb5hsE1G9FxcXh549ezbYmuX4+Hi4uLiwhIIaLFsoJcvPz8epU6cAAN27d0fz5s2FxbaF/ad7Y7JNRLWm0+mwdu3aaj1eN23aJCR+Q2/7xhIKauiULiVLSkrC3Llz0aVLFwDAuXPnsGjRIgwcOFBIfKX3n+6PF0gSUa3NmjUL7u7uuHz5MqZOnYodO3ZYTjoizJs3D0uWLKlWs9xQ8IYq1NDZ2dlh/PjxisX/4IMP8MUXX6B9+/YAgMuXL2PSpEnCkm2l95/ur+GdlYiozv36669466234OjoiGHDhuHjjz9Genq6sPjmtm9t27aFm5ub5YOIGgZz+0ulODg4WBJtAPjrX/8qtBuI0vtP98eZbSKqNa1WCwCwt7fH7du34eLigvz8fGHx2faNqGFTqv2lXq8HAPj7+2PNmjV4/vnnIUkSEhMT4e/vL2vsytj+07axZpuIau3tt9/GnDlzsHPnTnzxxRdo0qQJ2rZtiw8++EBIfNYsEzVsSrW/9PDwsBxzqhJ5DGL7T9vGZJuI6lR6ejp0Oh369esHOzsunhGR/Bp6+8uGvv+2ThMdHR2t9CCIqP7Lz8/HsWPHUFZWhqeeegrOzs5KD4mIGoj8/HxcuHABbdu2hSRJMBgMMBgMsLe3V3poQjT0/bd1nNkmolozt73q2rUrJEkS3vaKiBq2hl5K1tD339Yx2SaiWgsICMDq1aurtb36+uuvFR4ZERGRstj6j4hqTem2V0RERLaKM9tE9IeZ216tW7cOdnZ2Vm2vDAYDpkyZovAIiYiIlMVkm4j+MFtpe0VERGSrmGwTEREREcmENdtERERERDJhsk1EREREJBMm20RE9D9ZsWIF3n77baWHQURULzDZJiKqR9LT0zFy5Ej07NkTvXv3xsiRI3H69Gmlh0VERPdgp/QAiIjo9yksLMTEiRMRHR2NgIAAlJeXIz09HVqtVumhERHRPXBmm4ionrh06RIAYNiwYdBoNHB0dISvry88PDyQlZWFMWPGoE+fPujTpw8iIyNRUFBgea2fnx/WrVuHwMBAdO/eHbNmzUJubi7Gjx8PT09PvPLKK7hz5w4A4OrVq3jiiSewdetW+Pr6wtfXF+vXr7/nuH788UeMHDkSvXr1QlBQENLS0izPJSYmwt/fH56envDz88OePXtk+u4QEdkmJttERPVE+/btodFoEBUVhZSUFEtyDACSJGHChAk4cuQIvv76a2RnZ2PFihVWr09KSsKGDRtw4MABJCcnIzw8HNOnT8exY8dgMpmwefNmq69PS0tDUlIS1q9fj7Vr1+L777+vNqacnBxMmDABkyZNwvHjxxEVFYWIiAjk5+ejuLgYixcvxtq1a/Gf//wHX3zxBTp16iTPN4eIyEYx2SYiqicaN26Mzz//HCqVCnPnzoW3tzcmTpyI3NxctGvXDj4+PtBqtXjkkUcwbtw4nDhxwur1YWFhaNmyJVq3bo1evXqhW7du6Ny5MxwcHDBo0CD8/PPPVl//xhtvwMnJCU888QRCQ0Oxb9++amPavXs3nnnmGfTv3x9qtRo+Pj7o2rUrUlJSAABqtRoXLlxASUkJWrVqhY4dO8r3DSIiskGs2SYiqkfc3d2xdOlSAEBmZibeeecd/P3vf8esWbPw3nvvIT09HUVFRZAkCU2bNrV6bcuWLS2fOzg4WD12dHREcXGx1de3adPG8rmbmxvOnz9fbTzXrl3D/v37kZycbNlmMBjQp08fODk54YMPPkBCQgJmz56NHj16ICoqCu7u7rX7JhAR1SOc2SYiqqfc3d0RGhqKCxcuIC4uDiqVCnv37sUPP/yA2NhY1PYGwdevX7d8fu3aNbRq1ara17Rp0wbBwcFIT0+3fPz44494/fXXAQD9+vXDhg0bcPToUTz22GOYO3durcZERFTfMNkmIqonMjMzkZCQgOzsbAAVyfC+ffvw1FNPoaioCE5OTmjSpAlycnKwbt26WsdbvXo19Ho9Lly4gMTERAwZMqTa1wQFBSE5ORlHjhyB0WhEaWkp0tLSkJ2djdzcXHzzzTcoLi6GVquFk5MT1GqedoioYWEZCRFRPdG4cWOcOnUKGzZsgE6nQ5MmTTBgwAC8++67uH79OqKiotCrVy88+uijCA4OxsaNG2sVr3fv3hg0aBAkScKrr74KX1/fal/Tpk0brF69GrGxsYiMjIRarUa3bt0QHR0Nk8mEjRs3IioqCiqVCp06dUJ0dHStxkREVN+opNquMxIR0UPl6tWr8Pf3x08//QQ7O87JEBHVBtfziIiIiIhkwmSbiIiIiEgmLCMhIiIiIpIJZ7aJiIiIiGTCZJuIiIiISCZMtomIiIiIZMJkm4iIiIhIJky2iYiIiIhkwmSbiIiIiEgm/w9an3eI5ZPf4wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nlp_words=nltk.FreqDist(word_list(\"Code-switching\"))\n",
        "nlp_words.plot(20,title='Word frequency in Code-switching text');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "id": "eW2YMMoim1mX",
        "outputId": "7c489a86-18bc-4d8e-ae6d-7e454c17fdee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BRnDkbOcnD_j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Remark"
      ],
      "metadata": {
        "id": "om5YXraEnWiQ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Lh4C-3jLvPaG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   The data is not balanced: a major difference between the English/French languages and tunizi in terms of the number of comments and the number of unique words.\n",
        "*   There is a difference between code-switching/tunizi and other classes: the largest number of unique words and max/mean sentence is important.\n",
        "*   The data must be balanced in terms of number of word and unique word (vocab) in all the text\n",
        "*   To get closed to the number of comments that we shoold add to our data in each class, we will use a new feature alpha of each class\n",
        "*   alpha = (Num of unique words / mean_all_comments)\n",
        "*   Num of words : number of unique words in each class\n",
        "*   mean_all_comments : the mean length of all comments "
      ],
      "metadata": {
        "id": "RMeb_tfOnpEV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "zs4UeRJuoLI3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "alpha = np.array(number_unique_word)/(sum(mean_class)/len(mean_class))"
      ],
      "metadata": {
        "id": "qybX_3kNzjfR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "alpha"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KPZEvxO_ug_X",
        "outputId": "c7b2f827-5cd8-4263-89b2-7a7dcb1ca415"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([182.92139804,   9.69338339,   5.01154515, 268.93094269,\n",
              "       248.20336779])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualise! \n",
        "sns.set(rc={'figure.figsize':(12,6)})\n",
        "#per_lang = final_scraped_data['labels'].value_counts()\n",
        "sns.barplot(x=list(langage.values()), y=alpha).set(title='*')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "AUYELV_5z_5K",
        "outputId": "2fa14dce-b3b7-4e9a-8c15-62af32c65873"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, '*')]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**We need a data augmentation action in English/French/Arabic language (+ ~2000 comments for the arabic/ ~3500 comments for english and frensh),  and Code-switching class (+ ~2000 comments)**"
      ],
      "metadata": {
        "id": "6tcpVuhq0uf8"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w7bdzfHY0Yir"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}