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      "execution_count": null,
      "metadata": {
        "id": "aqyJiQOJ6Kbh"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
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        },
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        "outputId": "a78155ed-b94c-41f7-f71e-3a4d77326006"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.27.4-py3-none-any.whl (6.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.8/6.8 MB\u001b[0m \u001b[31m60.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (23.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.9/dist-packages (from transformers) (4.65.0)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m106.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers) (2.27.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers) (3.10.7)\n",
            "Collecting huggingface-hub<1.0,>=0.11.0\n",
            "  Downloading huggingface_hub-0.13.3-py3-none-any.whl (199 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.8/199.8 KB\u001b[0m \u001b[31m26.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.5.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (1.26.15)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2.0.12)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.13.3 tokenizers-0.13.3 transformers-4.27.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import os\n",
        "import string\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "# plotting\n",
        "import seaborn as sns\n",
        "from wordcloud import WordCloud\n",
        "import matplotlib.pyplot as plt\n",
        "# nltk\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "import nltk\n",
        "nltk.download(['punkt', 'stopwords'])\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.tokenize import RegexpTokenizer\n",
        "from nltk.probability import FreqDist\n",
        "# sklearn\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.naive_bayes import BernoulliNB\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "#Counter\n",
        "from collections import Counter\n",
        "#transformers\n",
        "from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3bJPwmbm6e0P",
        "outputId": "051f58b7-fc71-43ae-b9db-b4102d620ce3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.chdir(\"/content/drive/MyDrive/Teknofest-Hive Heroes/sentiment_analiz_example/\")"
      ],
      "metadata": {
        "id": "bAv-jxV36kpT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "STOPWORDS_FILEPATH_opt3 = \"data/stopwords_tr.txt\"\n",
        "DATA_PATH=\"train_with_preprocess_nolemma.csv\""
      ],
      "metadata": {
        "id": "ob6LgfG66otD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(DATA_PATH,encoding=\"utf-8\")\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "fXggnsCZ6pz-",
        "outputId": "41b8c05c-de96-48d2-b3eb-305b40ef2c57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     id  \\\n",
              "0  81c11060-a240-4d54-841b-9e2916039e85   \n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3cbdddbf-088c-4a7c-9ce9-92daa6f8f7cf button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3cbdddbf-088c-4a7c-9ce9-92daa6f8f7cf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "def vectorizer_methods(X_train, X_test, option):\n",
        "  if option==0: #tf-idf bu daha iyi çıktı aslında\n",
        "    vectorizer = TfidfVectorizer()\n",
        "\n",
        "  elif option==1: #boW\n",
        "    vectorizer= CountVectorizer()\n",
        "  X_train = vectorizer.fit_transform(X_train)\n",
        "  X_test = vectorizer.transform(X_test)\n",
        "  return X_train,X_test"
      ],
      "metadata": {
        "id": "LFTvn9AC7O0O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def model_Evaluate(model=None,model_name=str,X_test_vec=None,y_test=None,opt=None,y_pred=None): #for is_offensive\n",
        "  \n",
        "# Predict values for Test dataset\n",
        "  \n",
        "  if opt==0:\n",
        "    y_pred = model.predict(X_test_vec)\n",
        "  # Print the evaluation metrics for the dataset.\n",
        "  elif opt==1:\n",
        "    threshold = 0.5\n",
        "    y_pred=model.predict(X_test_vec)\n",
        "    y_pred= np.where(y_pred >= threshold, 1, 0) \n",
        "  elif opt==2:\n",
        "    pass\n",
        "  print(classification_report(y_test, y_pred))\n",
        "  # Compute and plot the Confusion matrix\n",
        "  cf_matrix = confusion_matrix(y_test, y_pred)\n",
        "  categories = ['Negative','Positive']\n",
        "  group_names = ['True Neg','False Pos', 'False Neg','True Pos']\n",
        "  group_percentages = ['{0:.2%}'.format(value) for value in cf_matrix.flatten() / np.sum(cf_matrix)]\n",
        "  labels = [f'{v1}n{v2}' for v1, v2 in zip(group_names,group_percentages)]\n",
        "  labels = np.asarray(labels).reshape(2,2)\n",
        "  sns.heatmap(cf_matrix, annot = labels, cmap = 'Blues',fmt = '',\n",
        "  xticklabels = categories, yticklabels = categories)\n",
        "  plt.xlabel(\"Predicted values\", fontdict = {'size':14}, labelpad = 10)\n",
        "  plt.ylabel(\"Actual values\" , fontdict = {'size':14}, labelpad = 10)\n",
        "  plt.title (model_name, fontdict = {'size':18}, pad = 20)\n",
        "  fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
        "  roc_auc = auc(fpr, tpr)\n",
        "  plt.figure()\n",
        "  plt.plot(fpr, tpr, color='darkorange', lw=1, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "  plt.xlim([0.0, 1.0])\n",
        "  plt.ylim([0.0, 1.05])\n",
        "  plt.xlabel('False Positive Rate')\n",
        "  plt.ylabel('True Positive Rate')\n",
        "  plt.title('ROC CURVE')\n",
        "  plt.legend(loc=\"lower right\")\n",
        "  plt.show()\n",
        "  return y_pred"
      ],
      "metadata": {
        "id": "Sh2FpUq07UtM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Sentiment Analysis"
      ],
      "metadata": {
        "id": "PH4JjyFt7b1F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#load model and tokenizer\n",
        "#https://huggingface.co/savasy/bert-base-turkish-sentiment-cased\n",
        "from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"savasy/bert-base-turkish-sentiment-cased\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"savasy/bert-base-turkish-sentiment-cased\")\n",
        "# create pipeline\n",
        "sa = pipeline(\"sentiment-analysis\", tokenizer=tokenizer, model=model)"
      ],
      "metadata": {
        "id": "f9JvR8O2j03q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
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            "10a9d31a3d1b46a2b4cf754757584099",
            "4df8603f41f84122a4862709df14e693",
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        "outputId": "36143927-f307-4fb6-ace5-e1500979664f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/596 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0da310784a5742fdbb3cf1dbcf7ae754"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/443M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fc545f40eb744bffa953ecafb8cf55fc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/39.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "09458e42da5b49e98f4f6bc4555ccbce"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c00d60b03dcd45fcb302e13c9f7343d6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "43bb80e6abe64a5982c081ffdc9bc30f"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_sentiment_analysis(tweet):\n",
        "    \"\"\"True if tweet has positive compound sentiment, False otherwise.\"\"\"\n",
        "    p = sa(str(tweet))\n",
        "    #print(\"label\", p[0][\"label\"])\n",
        "    #print(\"score\", round(p[0][\"score\"],4))\n",
        "    return pd.Series([p[0][\"label\"], round(p[0][\"score\"],4)])"
      ],
      "metadata": {
        "id": "0jeRpUIwgiTI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "jrrkOEFvAsu2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, auc\n",
        "def sentiment_analiz(model_opt,X,y):\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.05, random_state =26105111)\n",
        "  X_train_vec, X_test_vec = vectorizer_methods(X_train,X_test, 1)\n",
        "  if model_opt==1:\n",
        "    model = BernoulliNB()\n",
        "    model_name = \"Bernoulli NB\"\n",
        "  elif model_opt==2:\n",
        "    model = LinearSVC()\n",
        "    model_name = \"Linear SVC\"\n",
        "  elif model_opt==3:\n",
        "    model = DecisionTreeClassifier()\n",
        "    model_name = \"Decision Tree Classifier\"\n",
        "  elif model_opt==4:\n",
        "    model = XGBClassifier()\n",
        "    model_name = \"XGB Classifier\"\n",
        "  model.fit(X_train_vec, y_train)\n",
        "  #model=None,X_test_vec=None,y_test=None,opt=None,y_pred=None\n",
        "  y_pred= model_Evaluate(model=model,model_name=model_name,X_test_vec=X_test_vec,y_test=y_test,opt=0)\n",
        "\n",
        "  return y_pred"
      ],
      "metadata": {
        "id": "NQORBDo77bdw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import LSTM, Dense, Embedding, Dropout\n",
        "from keras.models import Sequential\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from keras.utils import to_categorical\n",
        "#from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.optimizers import RMSprop\n",
        "\n",
        "def lstm_model(X,y_number,num_class):\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y_number, test_size=0.02, random_state=42)\n",
        "  tokenizer = Tokenizer(num_words=5000, lower=True)\n",
        "  tokenizer.fit_on_texts(X_train)\n",
        "  X_train = tokenizer.texts_to_sequences(X_train)\n",
        "  X_test = tokenizer.texts_to_sequences(X_test)\n",
        "  y_train_cat = to_categorical(y_train, num_classes=num_class)\n",
        "  y_test_cat = to_categorical(y_test, num_classes=num_class)\n",
        "  max_len = len(max(X_train, key=len))\n",
        "  X_train = pad_sequences(X_train, padding='post', maxlen=max_len)\n",
        "  X_test = pad_sequences(X_test, padding='post', maxlen=max_len)\n",
        "  model = Sequential()\n",
        "  model.add(Embedding(input_dim=5000, output_dim=50, input_length=max_len))\n",
        "  model.add(LSTM(units=128, dropout=0.2, recurrent_dropout=0.2))\n",
        "  model.add(Dense(units=num_class, activation='softmax'))\n",
        "  model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "  model.fit(X_train, y_train_cat, validation_data=(X_test, y_test_cat), epochs=10, batch_size=64)\n",
        "  return [model,X_test,y_test]"
      ],
      "metadata": {
        "id": "_V1o5GDaA-8b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X=df.str_tkn_p_sw\n",
        "y=df.is_offensive"
      ],
      "metadata": {
        "id": "3awcaNsw9CsT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#model=None,X_test_vec=None,y_test=None,opt=None,y_pred=None\n",
        "pred=sentiment_analiz(1,X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_n82RBxfA4nb",
        "outputId": "2146e852-b645-4d8a-88d3-683fb3959f6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.27      0.42       190\n",
            "           1       0.76      1.00      0.86       434\n",
            "\n",
            "    accuracy                           0.77       624\n",
            "   macro avg       0.86      0.63      0.64       624\n",
            "weighted avg       0.82      0.77      0.73       624\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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sHOIhIiKSR2WwUVu5cuVQp04dqTIdHR0YGxtLyocMGYIJEybAyMgIenp6GDNmDBwcHNC8eXMAgJOTE+zs7ODu7o5FixYhNjYW06dPh5eXV7GGmfIxQSEiIpJHn7DB2ue0fPlyqKiowNXVFRkZGXB2dsbq1asl51VVVREQEIBRo0bBwcEBOjo68PT0xJw5c0p0H5EgCIKsg1dUz14VvSacSFndjH5T1iEQyZ0e9sWfS1FaWl1WyqyttMNjZdbWl8I5KERERCR3OMRDREQkj+R0iOdLYYJCREQkj/g0YyIiIiL5wh4UIiIieaTkPShMUIiIiOSRks9BUe70jIiIiOQSe1CIiIjkEYd4iIiISO5wiIeIiIhIvrAHhYiISB5xiIeIiIjkjpIP8TBBISIikkMiJU9QlLv/iIiIiOQSe1CIiIjkkLL3oDBBISIikkfKnZ9wiIeIiIjkD3tQiIiI5BCHeIiIiEjuKHuCwiEeIiIikjvsQSEiIpJDyt6DwgSFiIhIDil7gsIhHiIiIpI77EEhIiKSR8rdgcIEhYiISB4p+xAPExQiIiI5pOwJCuegEBERkdxhDwoREZEcUvYeFCYoREREckjZExQO8RAREZHcUegE5dy5cxgwYAAcHBwQFRUFAPj7779x/vz5Mo6MiIjoE4lkeCgghU1Q9uzZA2dnZ2hpaeHGjRvIyMgAACQmJmL+/PllHB0REdGnEYlEMjsUkcImKL/88gvWrFmDdevWQV1dXVLesmVLXL9+vQwjIyIiUkx+fn6oW7cu9PT0oKenBwcHBxw5ckRyvl27dgWSn5EjR0q1ERERga5du0JbWxsmJiaYPHkysrOzSxyLwk6SDQkJQZs2bQqU6+vrIyEh4csHREREJENl0fNhaWmJBQsWoHr16hAEAZs2bUKPHj1w48YN1K5dGwAwbNgwzJkzR3KNtra25OucnBx07doVZmZmuHjxImJiYuDh4QF1dfUSj24obIJiZmaGJ0+eoEqVKlLl58+fR9WqVcsmKCIiIhmRZYKSkZEhmQqRTywWQywWS5V1795d6vW8efPg5+eHS5cuSRIUbW1tmJmZFXqf48eP4/79+zh58iRMTU1Rv359zJ07F1OmTIGPjw80NDSKHbPCDvEMGzYMY8eOxeXLlyESiRAdHQ1/f39MmjQJo0aNKuvwiIiI5Iavry/09fWlDl9f3w9ek5OTg+3btyMlJQUODg6Scn9/f5QvXx516tSBt7c3UlNTJeeCg4Nhb28PU1NTSZmzszOSkpJw7969EsWssD0oU6dORW5uLjp27IjU1FS0adMGYrEYkyZNwpgxY8o6PCIiok8jwxEeb29vTJgwQars/d6TfHfu3IGDgwPS09Ohq6uLffv2wc7ODgDQv39/WFlZwcLCArdv38aUKVMQEhKCvXv3AgBiY2OlkhMAktexsbElillhExSRSISff/4ZkydPxpMnT5CcnAw7Ozvo6uqWdWhERESfTJZDPIUN5xTF1tYWN2/eRGJiInbv3g1PT08EBQXBzs4Ow4cPl9Szt7eHubk5OnbsiNDQUFSrVk1m8QIKPMSzZcsWpKamQkNDA3Z2dmjatCmTEyIi+mqU1TJjDQ0N2NjYoFGjRvD19UW9evWwcuXKQus2a9YMAPDkyRMAefND4+LipOrkvy5q3kpRFDZBGT9+PExMTNC/f38cPnwYOTk5ZR0SERHRVyc3N7fABNt8N2/eBACYm5sDABwcHHDnzh3Ex8dL6pw4cQJ6enqSYaLiUtgEJSYmBtu3b4dIJMJ3330Hc3NzeHl54eLFi2UdGhER0Scrix4Ub29vnD17FuHh4bhz5w68vb1x5swZuLm5ITQ0FHPnzsW1a9cQHh6OgwcPwsPDA23atEHdunUBAE5OTrCzs4O7uztu3bqFY8eOYfr06fDy8ir2EFM+hZ2Doqamhm7duqFbt25ITU3Fvn37sHXrVrRv3x6WlpYIDQ0t6xCJiIhKrSz2QYmPj4eHhwdiYmKgr6+PunXr4tixY+jUqROeP3+OkydPYsWKFUhJSUGlSpXg6uqK6dOnS65XVVVFQEAARo0aBQcHB+jo6MDT01Nq35TiUtgE5V3a2tpwdnbGmzdv8OzZMzx48KCsQyIiIlI469evL/JcpUqVEBQU9NE2rKyscPjw4U+ORWGHeAAgNTUV/v7+6NKlCypWrIgVK1agV69eJV5rTUREJHeU/GGBCtuD0q9fPwQEBEBbWxvfffcdZsyYIbWRDBERkSJT1If8yYrCJiiqqqrYuXMnnJ2doaqqWtbhEBERkQwpbILi7+9f1iEQERF9NuxBUSCrVq3C8OHDoampiVWrVn2w7o8//viFoiIiIpI9ZU9QRIIgCGUdRHFZW1vj33//hbGxMaytrYusJxKJ8PTp0xK3/+xV4RvRECmzm9FvyjoEIrnTw75ku6KWRiWvAzJr6/nvPWTW1peiUD0oYWFhhX5NRET01VHuDhTFXWY8Z84cqUc850tLSyvVhjBERETypKyexSMvFDZBmT17NpKTkwuUp6amYvbs2WUQEREpin8Dj2CmR9eyDoPog5Q9QVGoIZ53CYJQ6Id+69YtGBkZlUFEX45Ti7ofPD9g8Eh4DP3hi8QyyWswbt/4F96zF6J9JxdJ+d4df2PfDn/8vffoF4njXf9euoDN61fjWVgoNDTEsK/fEMPHTIKZecUir9m68Q9cuXgOoY9DoKaujn3HL0idP/7PASyZN6PQa3cEBMLQyBhPQh5g6fxZiIp8hnoNm2DyjHnQ09MHAORkZ2PMMDf8OHk6atrZy+7NKrEdv/ni2pmC318//eqP8uaWZRDRf/4NPIKdvy8AkPdLRs+wPKrXa4QuA0ZCV9/ws98/OvwJAvf5I/zhHaS8TYRRBTM0d+qBVl2/ldS5c+ksLh3fj+jwJ8jOyoJppSro9N0g2NZvWmS7oXdv4FzALjx/8gDpaakob26Jtt/0Q8M2naTqnQvYheDjB5DwMg465fRh37wdXNyGQV0j71ks18+ewBH/tchMT0Pj9i7oPnC05NrX8TH4c+4k/LjwD2hq68j4kyFFonAJiqGhoSQjrFGjhlSSkpOTg+TkZIwcObIMI/z8th86Lfk66ORRbPpzNf7aflBSpqWlLflaEATk5uRAVe3z/VNraIix6Y/f0Lq9I9TU1D/bfYojJjoSs6aOhWs/d0ydtQApKW+xZuVizPEej9UbdxZ5XXZ2Flp3cEKtOvVwNGBfgfNtHZ3RuHlLqbIlv0xHZmYmDI2MAQDLFvigfqOm+HnuIizz9cH2TeswfMwkAMDubZtR274BkxMZs63fFN95TZUq09EzKJtg3qOprYPJK/9GriAgJvwJdq5eiKTXrzB0xpLPfu+o0BDo6hui34/TYVDeBM9C7mLPmiUQqaigpUtvAEDYg1uoXrcxOvcfBk2dcvj39GFsXOCN0fP9ULFqjULbfRZyF+ZW1dCuZ3+UMzDEg2vB2PHbfGhq68CucQsAwI1zJ3DE/w/0+eEnWNnWwcvoSOz43RciEdB94GikJCVg95pF+M7LG8am5vhr/lRUq9NQcv2+dcvh4jaCyQm4ikfhEpQVK1ZAEAQMHjwYs2fPhr6+vuSchoYGqlSp8tXvKGtkXF7ytY5uOYhEIknZretXMXn0EPyy9Hds/OM3hIc+hu+KtTh++ACS377F7IUrJdf6rViI0MchWPL7XwDyHqm9Y8tfOHxgN968egXLylboP3A42nRw+mA87Tu5IPj8GRw+sAffuPYrst7Fs4HY8pcfnoU/hXH5Cujk8g36ew6TJE8R4WFYvsAHjx7eg7mFJX4YPxVTxw7HLN8VaNm2A2JjouDh6oKZ85fhwO5teHjvDipWqowfJ8+AnX09AMDjh/eRm5OLgcPHQEUlbwTz2/6e8JkyFtnZWUUmUB5DvQDk9ZQURizWhFisKXmd8OY1bl67ggne/w0nPg8Pw9RZC2BZuQrad3LB5QtnAQAxUZE4GrAPv/+1/YOfI5WcmroGyhkaFyg/e2gH/g08gldxMdDWLQe7xi3QZcBIiN9J3t8VHf4EBzf8isjQEIhEIpQ3t0Tv4RNRyaYmACDswW0c8f8DkU9DoFNOH3WatoaL23BoaGp9IDqRJDZ9o/Jo5dIbx3b8hayMDKiqq+PUns24ciIAyUkJMLG0Qhe34bBt0AwAkJ2VhYBNv+POpSCkpSRDV98QzZ2+QYfeAwAAP33bFq4jJ+Ph9WA8unkVekbl0c3TC7Wb5CXRTTpKD18Zm1rgWcg93L18VpKgfDNojFQdF7fhuH/1Au5fu1hkgtLB1V3qdauu3+LRrau4e/msJMF4FnIPVWzroEHrvF4VIxNz1G/VEc8f5z0j7VVcDDS1dVG/ZQcAQLU6DRAf9Qx2jVvgxvmTUFVVg33zNh/4XJUHExQF4+npCSBvyXGLFi2grl62f7HLq7/8VmLY6Ikwt7CErp5esa7ZvvlPnDr2D8ZOnoGKlaxw5+Y1LJwzDQaGRqjboHGR12nr6OB7z2Hw37AWnbp8I9WDk+/OzWtYNPdn/DB+CuzrNUJ01HOsWJj3y919yCjk5OTAZ+pYmJiaY9U6f6SlpmDtr4X/pblh7a8YPnoiKlaqjA1rf4XvrCnYuDMAqmpqqF7TDioqIhz7Zz+cuvRAeloqTh0NQIPGzWXau3PyyCGINbXQusN/XdtVbWrg+tVgVLSshBv/Xoa1Td5/5FcumouhP4yHtg7/IvxSRCIVfDP4RxiZmON1XAz2rVuOw1vWoNewCYXW37byF1S0tkHv4RMgUlFFTNhjSeL8KjYK6+f9BOd+Q9DHaypSEhOwf/0K7F+/At95eRc7JjUNMYTcXOTk5iD4nwM4d2gneg+fCAvr6nm9FwunYcLyTahgbokLh/fg/r8XMGCCDwwqmCLhZTwSXsVLtXdy10Z0cR+Jru6jcOHIXmxbORfT/HZCu1zhP+/pqSnQ1i36vwW5ubnISE/9YJ2i2jWpaCV5bWVbG9fPnkDE4weoXL0WXsVFI+T6JTRsk/eHTnlzS2RlpCPq6SMYVjBD5JOHaNKhC1KT3+L49r8wwmdFie5PXy+FnSTbtm1bSXKSnp6OpKQkqeNjMjIyClyTkfH17IPiMdQLjZo6wMKykmQexIdkZmZi2+Y/MXHaHDRu3hLmFS3h1LUHOjp3xT/7d330+u69+0JDQ4w92/8u9PyWv9agr/tgOHXpAfOKlmjU1AGew0bjnwO7AQDXrwYjJioSP82ch2rVbVGnXkMMGlH4Znvf9vdEs5ZtYFm5CjyG/oC42GhERT4HAJhbWMJ3xVpsWLMKXds1Ri+nlngZH4fpvyz+6HsoiaMB+9C+k4tUr8p4bx+cCzwBzz5doa6ujn4eQ/6fyGjC1q42vMeNxMA+XbFh7a8yjUWZPbgWjOkDOkuOv5fMBAC07tYHNnUawsjEHDb2DeH8/RDcunimyHYSXsbBpm5jmFS0QgVzS9Rt0R4WVWwAAKf3+aNBa0e07tYHFcwtUaVmHfQY/COuBR1HVmbx/pvxIiYSl04chGU1W2hqaePsoR1o1+N71G/VESYVK6OL+0hYVLHB+YBdknjKm1miSq26MKxgButaddGglaNUm43buaBBK0eUN7eES/9hyExPw/MnhT/JPfzhXdy6eBrNHLsXGePZg9uRkZ6Gei3aF+s9AcCti6fx/MlDNO7w3/yzBq07wanvIPjNGI2pfTtgodf3qFq7vqT3RVu3HPqO9saO3+bjV++RaNjWGbb1m+KfzavRonMvvI6PwYpJQ7B0/EDcDj5T7Fi+SnxYoGJKTU3FTz/9hJ07d+LVq1cFzufk5Hzwel9f3wKrfcZO/hnjpxQ+EVLR1KhlV6L60ZERyEhPx9Rxw6XKs7OyUK1GzY9er6GhAY9hP+D3ZQvQvdd3Bc4/ffII927fxLZN6yRluTm5yMzMQHp6GiKfhaOCqanU8JWtXZ1C71W12n/dz0bGFQAACW9eoXIVa7x+9RLLF8xGpy7foL2jC1JTU7D5z9WY+/NELFj5h0y6TO/fuYWI8Kf4aeZ8qfIqVW2wdPUGyeukxARsXr8aS1dvwO/LFsDOvh5m+i7DmCH9UbO2PRxatfvkWJRdtTr1pXpF8odcHt/+F4F7/REfHYH01BTk5uYgOzMTmRnp0HgnqczXutt32O23CNeDjqN63Uao69AOxmZ5k6pjwp8g5tlT3Dh3UlJfEAQIubl4HR8DU8sqhcaWnpqM6QM6Q8jNRXZWJqrUtMe3o35CemoKkl6/hFVN6flIVrb2iHn2BADQqL0L/pwzEYt/HADb+k1Rq1EL1KjfRKq+uVVVqfetqa2D5MSCm+rFRjzFpkXT0KnPwAJt5Ltx7gRO7NqEgVPmFXsS75O717Hz94X4duQkmFX6b+PM0Ls3cHqfP3oOHZ/XgxIbhYMbfsXJXZvg2CevB7xOszao0+y/YZzQezcR8ywUPYaMxcLR/dF/3EyUMzDCb94jUdWu3heZWCyPOMSjoCZPnozAwED4+fnB3d0dv//+O6KiorB27VosWLDgo9d7e3tjwgTp7t7YgquWFZbme2PjKiIV4L1Ng7OzsyVfp6Xl7Snzy5LfYVzBRKqeurpGse7Z0bkbdm/dhK0b/4CpuYXUubTUVHgMHYWW7RwLXKfx/5n9xaX2zoTf/J/f/A2RD+7ZDh0dXQzz+u/fdsqs+XDr6YSH926jVp16JbpXYY4c2otq1WuiRs0PJ4FrVi1Gr+8GoIKJGW5fv4qBw0dDS0sbTVu0xu3r/zJBkQENsVaBFTuv42OwwdcbzZ16wLn/UGjr6iH8wW3s8luEnOwsoJAExanvIDRo7YgH14IRcuMyju/YALfxM1GnWRtkpqeheafuaNnFtcB1BuVNi4xNrKWNsYvWQaSiAj0DY6iL877P01NTPvq+LKvWwNTV2/HwxmU8uf0vtizzQfW6jeA+6b89nlQKTHwX4f2NweOeh+OP2RPQzLE7On7rUei9bp4/hd1+izFg4mxUr1v0UO67Qu/dxMYF09B9oBcatessde7Y9vVo2MYJzRy7AQDMraohMyMde9YsQQdXd8ncsHzZWZnYv245+v74M17FRCE3JwfVatcHkDccFPH4PuwaS09QJ+WgsAnKoUOHsHnzZrRr1w6DBg1C69atYWNjAysrK/j7+8PNze2D14vFYojF0r8Y32R9PUM879M3MET40ydSZaGPQyS/7K2qVIO6hgbiY2M+ON/kQ1RUVDB45FjMmTYe3d7rRbGxrYXnEc9Q0bJyoddaWlXBi7g4vHn9SrIq5tGDuyWOISM9HSIV6b86VFTynnadK4OnOqSlpuLs6WMYPHLsB+vd+PcSnoeHYdLPcwEAObm5koQw553EkGQv6ukjCEIuunn+IPllePti4Eevq2BRCRUsKqFN9+/gv3w2rgYeQZ1mbWBhXQNxkc9KvHRZJFIp9BpNbR3oGZXHs4d3JL+IAeBZyB1UsqklVa9+yw6o37ID7B3aYf0vk5H6NqnIOSbvi30ehj98xqNRO2d07j+s0Do3zp/ErtUL4TZuFmo1Kt7igtC7N7BhgTe6uI1A807fFDiflZkBlff+8hflJyWF/Aye2v03ajRoCsuqNRD19BFyc//r/c7JyUFubm6x4voaKXsPisLOQXn9+jWqVs3r4tTT08Pr168BAK1atcLZs2fLMjS5VL9RUzx6eA8njhxE1PNn2Pzn71IJi7aODr793hNrVi3G8cMHEB35HI9D7mP/rq04frj4z4No1rINbO3s8c/+3VLlAwaNwMkjh/D3ej+EP32CiPCnCDxxRDIfo2ETB5hXtMTiuT//fzjoBjau/Q1AyX5Im7ZojUcP7mHLX2sQ9fwZHofcx9J5M2BqZgGb/w9VPbx/B4P7fYOXL+Ik18XHxiD00UPEx8UgNzcHoY8eIvTRQ6S9t1vxmVNHkZOdg47ORW/ylZmRgd+W+mLclJmSX5C169bHwT3bEfo4BOfOnETtug2K/Z6oZIzNKiInOxsXj+zFq7hoXAs6hkvHDxZZPysjA/v/XIHQuzfw5kUswh/eQWRoCEz/P/Gzfc/+eBZyF/v/XIHosMd4EROJe1fOY/+fK0odY9tv+uHMgW24eeE04qMicHjLWkSHP0HL/+9TcvbQDtw4fxLxUc/wIvo5bgefQTkDI2jq6Bar/diIp1g7axxq1GuMNt2+w9s3r/D2zSskJyZI6tw4dwI7fp2Pbh4/oHL1WpI6aSn/dSVfOLIXf/iMl7x+cvc6/vKdipYuvWHfvI3kmtS3/837q9WoBYKPH8DN86fwOi4Gj25dxfHtf6FW4xZQUVWVijPueThuXTwN576DAQAmFa0gEqngyql/8OBaMF5ERaBStY8PMX+tRCLZHYpIYXtQqlatirCwMFSuXBk1a9bEzp070bRpUxw6dAgGBgZlHZ7cady8JdwGDcefvy9HZmYmnLv2RCeX7ggLfSypM3D4aBgYGGL75vWIjZ4NHd1yqG5bC/08hpboXkN/GI9xI6SXIzZu3hJzF/+KLRvWYueWDVBVU0Mlqypw6Z635FFVVRU+C1Zi+QIfjBnyPcwsLDFs9ATMnDwGGhrFG2ICgAaNm2GqzwLs8t+Anf4bIBZrwq5OPcxb5ieZ0JqRno7IiHCpIa5Nf/6OE4f/+yU2amBeD9Di39ajXsP/xu2PHdqHlu06QvcDf8X+/dcaNGvRWmruzg/jp2LBrKmY+MMgdHDqglaFDHWRbFhUsUE3Ty8E7t+KI/5/wNquHjq7DcOOX+cXWl+kooKUt4nY8dt8vE14Ax09fdRp1hqd+g4CAJhXqYaRc1bi6NY/sXpG3tJcY1OLEk0mfV/LLq5IT03BP5tWIznpDUwsq2DglPmo8P8eF7GmNoL2b8PL2CioqKjAslpNDJ62sMDwSFFuBwchJSkB18+ewPWzJyTlhhXM4O23AwBw+UQAcnNysP/PFVLJVqN2ndF3dN7qpJSkRLyKi5acu3bmGLIy0hG4zx+B+/wl5VXt6mPknLwtDDp+6w6RSIRj29cj8fUL6OoZoFajFujcX/q/I4IgYM/aJejm6SWZO6QuFuM7r6nY/+cKZGdnoceQsdD//zwzUj4K9TTjdy1fvhyqqqr48ccfcfLkSXTv3h2CICArKwvLli3D2LEf7oIvDJ9mLF/u3b6B8SM9sXHnP7CwrFTW4SgtPs2YqKAv8TTj6pNltxP348WdP15JzihsD8r48f91Ozo6OuLhw4e4du0abGxsULfuh7eCJ/l0PugUtLS0UbFSZURHPoffioWoXbcBkxMiUkqKOjQjKwqboLzPysoKVlZWH69IcistNQXrV69AfFwM9PUN0KBJc4z4/1bxRETKRtknySpsgrJq1apCy0UiETQ1NWFjY4M2bdpA9b1JWSS/Orl8g04uBVcFEBGR8lHYBGX58uV48eIFUlNTYWiYt4nPmzdvoK2tDV1dXcTHx6Nq1aoIDAxEpUocIiAiIsWi5B0oirvMeP78+WjSpAkeP36MV69e4dWrV3j06BGaNWuGlStXIiIiAmZmZlJzVYiIiBSFiopIZociUtgelOnTp2PPnj2oVq2apMzGxgZLliyBq6srnj59ikWLFsHVteDuj0RERCTfFDZBiYmJkdrHIl92djZiY2MBABYWFnj79u2XDo2IiOiTcYhHQbVv3x4jRozAjRs3JGU3btzAqFGj0KFDBwDAnTt3YG1tXVQTREREckskEsnsUEQKm6CsX78eRkZGaNSokeS5Oo0bN4aRkRHWr18PANDV1cXSpUvLOFIiIiIqKYUd4jEzM8OJEyfw8OFDPHr0CABga2sLW1tbSZ327Uu/FTUREVFZUtCOD5lR2AQlX9WqVSESiVCtWjXJk3mJiIgUnaIOzciKwg7xpKamYsiQIdDW1kbt2rUREREBABgzZgwWLFhQxtERERHRp1DYBMXb2xu3bt3CmTNnoKmpKSl3dHTEjh07yjAyIiKiT1cWk2T9/PxQt25d6OnpQU9PDw4ODjhy5IjkfHp6Ory8vGBsbAxdXV24uroiLi5Oqo2IiAh07doV2traMDExweTJkwtddfsxCpug7N+/H7/99htatWol9eHXrl0boaGhZRgZERHRpxOJZHcUl6WlJRYsWIBr167h33//RYcOHdCjRw/cu3cPQN6Deg8dOoRdu3YhKCgI0dHR6N27t+T6nJwcdO3aFZmZmbh48SI2bdqEjRs3YubMmSV+/wo7aePFixcwMTEpUJ6SkqL043ZERKT4ZPm7LCMjAxkZGVJl+Stg39W9e3ep1/PmzYOfnx8uXboES0tLrF+/Hlu3bpVs57FhwwbUqlULly5dQvPmzXH8+HHcv38fJ0+ehKmpKerXr4+5c+diypQp8PHxgYaGRrFjVtgelMaNG+Off/6RvM7/h/zzzz/h4OBQVmERERHJHV9fX+jr60sdvr6+H7wmJycH27dvR0pKChwcHHDt2jVkZWXB0dFRUqdmzZqoXLkygoODAQDBwcGwt7eHqamppI6zszOSkpIkvTDFpbA9KPPnz4eLiwvu37+P7OxsrFy5Evfv38fFixcRFBRU1uERERF9ElkOBnhP9caECROkyt7vPcl3584dODg4ID09Hbq6uti3bx/s7Oxw8+ZNaGhowMDAQKq+qampZAf32NhYqeQk/3z+uZJQ2B6UVq1a4ebNm8jOzoa9vT2OHz8OExMTBAcHo1GjRmUdHhER0SeR5SRZsVgsmfiafxSVoNja2uLmzZu4fPkyRo0aBU9PT9y/f/8Lv3sF7kEBgGrVqmHdunVlHQYREdFXQ0NDAzY2NgCARo0a4erVq1i5ciX69u2LzMxMJCQkSPWixMXFwczMDEDeJqpXrlyRai9/lU9+neJSuB4UFRUVqKqqfvDghm1ERKToymIVT2Fyc3ORkZGBRo0aQV1dHadOnZKcCwkJQUREhGTup4ODA+7cuYP4+HhJnRMnTkBPTw92dnYluq/C/Sbft29fkeeCg4OxatUq5ObmfsGIiIiIZK8sVqR6e3vDxcUFlStXxtu3b7F161acOXMGx44dg76+PoYMGYIJEybAyMgIenp6GDNmDBwcHNC8eXMAgJOTE+zs7ODu7o5FixYhNjYW06dPh5eXV5FDSkVRuASlR48eBcpCQkIwdepUHDp0CG5ubpgzZ04ZREZERKTY4uPj4eHhgZiYGOjr66Nu3bo4duwYOnXqBABYvnw5VFRU4OrqioyMDDg7O2P16tWS61VVVREQEIBRo0bBwcEBOjo68PT0LNXvZZEgCILM3tkXFh0djVmzZmHTpk1wdnaGr68v6tSpU+r2nr3K+HglIiVzM/pNWYdAJHd62JdsPkVpNJ1/RmZtXZnWTmZtfSkKNwcFABITEzFlyhTY2Njg3r17OHXqFA4dOvRJyQkREZE8KYut7uVJqYd4cnNzoaIind8EBwcjICAAmpqaGDRoECwtLT85wPctWrQICxcuhJmZGbZt21bokA8REREptlIN8YwfPx5+fn6IjY2VLDXavXs3+vXrJ5mgWr58eVy/fl3mSYqKigq0tLTg6OgIVVXVIuvt3bu3xG1ziIeoIA7xEBX0JYZ4mi+Q3aajl6a2lVlbX0qpelACAwPRoUMHqXXQM2fOhL6+PlauXInY2Fh4e3tjyZIlWLFihYxCzePh4aGw3VVERETFpey/60qVoDx//hxt2/6XjYWFheHhw4eYNWsWBgwYAAA4d+4cjh49Kpso37Fx40aZt0lERCRvlDw/Kd0k2ZSUFOjo6EheBwUFQSQSwcXFRVJmZ2eHyMjIT4+QiIiIlE6pelAsLCwQEhIieX306FHo6upKPQMnKSmpxJuyEBERUR4O8ZRC27ZtsW3bNvz222/Q1NTE3r170bNnT6lJq6GhoZ9lFQ8REZEyUPL8pHRDPD///DO0tLQwduxYDB8+HGKxGD4+PpLzb9++xdmzZ9GyZUtZxUlERERKpFQ9KDY2Nrh//z727NkDAOjevTusrKwk5x8/fowRI0agf//+somSiIhIyXCIp5TMzc0xevToQs81bNgQDRs2LHVQREREyo4Jyie6f/8+Hj58iJSUFLi7u8siJiIiIlJypX4Wz9WrV1G/fn3Y29ujT58+GDhwoOTc2bNnoa2tjYMHD8oiRiIiIqUjEsnuUESlSlDu3buHDh06ICwsDOPHj5fa/wQAWrdujfLly2PXrl0yCZKIiEjZKPvDAkuVoMyaNQsAcO3aNSxZsgRNmjSROi8SieDg4ICrV69+eoRERESkdEqVoAQFBcHV1RU2NjZF1qlcuTJiYmJKHRgREZEyU/YhnlJNkn379i1MTEw+WCctLQ05OTmlCoqIiEjZKerQjKyUKkGpVKkS7ty588E6169fR7Vq1UoVFBERkbJT8vykdEM83bp1w/Hjx3Hy5MlCz+/cuROXLl1Cz549PyU2IiIiUlKl6kGZNm0adu/ejS5dusDT0xOxsbEAgNWrVyM4OBjbtm1DlSpVMGHCBJkGS0REpCxUlLwLpVQJSoUKFRAUFAR3d3esX79eUp6/s2yzZs2wbds26OvryyZKIiIiJaPk+Unpd5KtWrUqLly4gJs3b+LSpUt4/fo19PT00KxZswLLjomIiIhK4pO3uq9fvz7q168vg1CIiIgoH1fxEBERkdxRUe78pHQJyuDBg4tVTyQSSc1RISIiIiqOUiUoGzdu/OB5kUgEQRCYoBAREZUSh3hKISwsrNDyxMREXL9+HfPmzUODBg2waNGiTwqOiIhIWSl5flK6BMXKyqrIc3Xr1oWLiwvs7e3xzz//wMvLq9TBERERKSsRlDtDKdVOsh9jamqK7t2747fffvsczRMREdFX7rOt4ilXrhzCw8M/V/NERERfNa7i+QwSEhJw4MABmJqafo7miYiIvnqcJFsKc+bMKbQ8OzsbUVFROHjwIF6/fg0fH59PiY2IiIiUVKkSlI8lHuXKlYO3tzdmzJhRmuaJiIiUXll0oPj6+mLv3r14+PAhtLS00KJFCyxcuBC2traSOu3atUNQUJDUdSNGjMCaNWskryMiIjBq1CgEBgZCV1cXnp6e8PX1hZpa8dOOUiUogYGBhZarqKjA0NAQtra2UFdXL03TREREhLJ5mnFQUBC8vLzQpEkTZGdnY9q0aXBycsL9+/eho6MjqTds2DCp0RRtbW3J1zk5OejatSvMzMxw8eJFxMTEwMPDA+rq6pg/f36xYylVgtK2bdvSXEZERERlICMjAxkZGVJlYrEYYrFYquzo0aNSrzdu3AgTExNcu3YNbdq0kZRra2vDzMys0HsdP34c9+/fx8mTJ2Fqaor69etj7ty5mDJlCnx8fKChoVGsmD/LMmMiIiL6NCKR7A5fX1/o6+tLHb6+vh+NITExEQBgZGQkVe7v74/y5cujTp068Pb2RmpqquRccHAw7O3tpRbKODs7IykpCffu3Sv2+y9WD8rZs2eL3eD73s24iIiIqHhkuYrH29sbEyZMkCp7v/fkfbm5uRg3bhxatmyJOnXqSMr79+8PKysrWFhY4Pbt25gyZQpCQkKwd+9eAEBsbGyBVbz5r2NjY4sdc7ESlHbt2pX6g8rJySnVdURERCQbhQ3nfIyXlxfu3r2L8+fPS5UPHz5c8rW9vT3Mzc3RsWNHhIaGolq1ajKJFyhmgjJz5kylX49NRET0JZXlr93Ro0cjICAAZ8+ehaWl5QfrNmvWDADw5MkTVKtWDWZmZrhy5YpUnbi4OAAoct5KYYqVoHA/EyIioi+rLFbxCIKAMWPGYN++fThz5gysra0/es3NmzcBAObm5gAABwcHzJs3D/Hx8TAxMQEAnDhxAnp6erCzsyt2LJ9tq3siIiIqvbLoQPHy8sLWrVtx4MABlCtXTjJnRF9fH1paWggNDcXWrVvRpUsXGBsb4/bt2xg/fjzatGmDunXrAgCcnJxgZ2cHd3d3LFq0CLGxsZg+fTq8vLxKNMzEVTxEREQEAPDz80NiYiLatWsHc3NzybFjxw4AgIaGBk6ePAknJyfUrFkTEydOhKurKw4dOiRpQ1VVFQEBAVBVVYWDgwMGDBgADw+PInehL0qpe1Devn2L3377DSdPnkR0dHSB9dVA3gzk0NDQ0t6CiIhIaZXF3E9BED54vlKlSgV2kS2MlZUVDh8+/EmxlCpBefHiBVq0aIHQ0FDo6ekhKSkJ+vr6yMzMRFpaGgDAwsKCu8kSERGVkrI/zbhUQzw+Pj4IDQ3F5s2b8ebNGwDA+PHjkZKSgsuXL6Np06aoUqVKiTZkISIiIspXqgTl8OHD6NixIwYMGFCgC6pJkyY4cuQIwsPDMXv2bJkESUREpGxEIpHMDkVUqgQlJiYGDRo0kLxWVVWVDO0AgKGhIVxcXLBz585Pj5CIiEgJyXKre0VUqgRFX18fWVlZkteGhoaIjIyUqqOnpyfZmIWIiIioJEqVoFStWhXh4eGS1w0aNMCJEyfw6tUrAEBaWhoOHTqEypUryyRIIiIiZcMhnlJwcnLCqVOnJE8vHDFiBOLj41GvXj306dMHderUQWhoKAYOHCjLWImIiJSGikh2hyIqVYIycuRIrFu3TpKg9O7dG4sXL0ZKSgr27NmD2NhYTJgwAZMnT5ZpsERERKQcRMLHdmUpgZycHLx8+RImJiYK2aX07FXBzeaIlN3N6DdlHQKR3OlhX/yH3pXWoO13ZNbWhn72MmvrSylVD0pycnKh5aqqqjA1NVXI5ISIiEieiGR4KKJSJSimpqZwc3PD0aNHkZubK+uYiIiIlJ6KSCSzQxGVKkGpVq0atm3bhq5du8LCwgLjx4/HtWvXZB0bERERKalSJSi3b9/GzZs3MX78eKiqqmLlypVo2rQp7Ozs4Ovri4iICFnHSUREpFS4UVsp1a1bF0uWLEFkZCSOHTsGNzc3REZG4ueff0bVqlXRrl07rF+/XpaxEhERKQ1l3wdFpqt40tLSsG/fPvz99984efIkBEFAdna2rJr/7LiKh6ggruIhKuhLrOIZvkt2D9z9o09tmbX1pajJsrHs7GxkZGQgIyODk2eJiIg+gYJ2fMjMJycoOTk5OHz4MLZs2YKAgACkp6dDRUUFTk5OcHd3l0WMRERESkdRV9/ISqkTlEuXLmHLli3YuXMnXr16BUEQUL9+fbi7u6N///4wNTWVZZxERESkREqVoFSvXh1Pnz6FIAioWLEiJk+eDHd3d9SurXhjXERERPJIyTtQSpegxMbGwsPDA+7u7mjfvr3CzhAmIiKSV8r+u7VUCUp8fDy0tLRkHQsRERERgFImKF9rcmKqLy7rEIjkTj/HX8o6BCK5k3bjt89+j1JvVPaVkOkyYyIiIpINDvEQERGR3FFR7vxE6XuQiIiISA6xB4WIiEgOKXsPChMUIiIiOaTsc1A4xENERERyp1g9KBEREaW+QeXKlUt9LRERkbLiEE8xVKlSpVRdTSKRCNnZ2SW+joiISNkp+QhP8RIUDw8PpR8LIyIioi+nWAnKxo0bP3MYRERE9C4VJe8Y4CRZIiIiOaQiw6O4fH190aRJE5QrVw4mJibo2bMnQkJCpOqkp6fDy8sLxsbG0NXVhaurK+Li4qTqREREoGvXrtDW1oaJiQkmT55c4ikfTFCIiIgIABAUFAQvLy9cunQJJ06cQFZWFpycnJCSkiKpM378eBw6dAi7du1CUFAQoqOj0bt3b8n5nJwcdO3aFZmZmbh48SI2bdqEjRs3YubMmSWKRSQIglCaN5GTk4OdO3fi5MmTiI6ORkZGRsHGRSKcOnWqNM2XiXTO5yUqwLDJ6LIOgUjufImHBf585JHM2prZwarA72mxWAyx+MMPyX3x4gVMTEwQFBSENm3aIDExERUqVMDWrVvx7bffAgAePnyIWrVqITg4GM2bN8eRI0fQrVs3REdHw9TUFACwZs0aTJkyBS9evICGhkaxYi7VRm0pKSlwcnLCpUuXIAgCRCIR3s1z8l9zYi0REVHpyHIOiq+vL2bPni1VNmvWLPj4+HzwusTERACAkZERAODatWvIysqCo6OjpE7NmjVRuXJlSYISHBwMe3t7SXICAM7Ozhg1ahTu3buHBg0aFCvmUg3x/PLLLwgODsbs2bPx8uVLCIIAHx8fxMTEYMeOHahatSr69OlTaK8KERERfZxIJLvD29sbiYmJUoe3t/cH75+bm4tx48ahZcuWqFOnDgAgNjYWGhoaMDAwkKpramqK2NhYSZ13k5P88/nniqtUPSh79+5F8+bNMX369AIB9OnTBw4ODqhXrx4WL1780Q+AiIiIPq/iDOe8z8vLC3fv3sX58+c/U1QfVqoelIiICDRv3vy/RlRUpHpLLC0t0bVrV2zatOnTIyQiIlJCKiLZHSU1evRoBAQEIDAwEJaWlpJyMzMzZGZmIiEhQap+XFwczMzMJHXeX9WT/zq/TrHef8nDBnR0dKCi8t+l+vr6iImJkapjZmb2SVvkExERKTMVkUhmR3EJgoDRo0dj3759OH36NKytraXON2rUCOrq6lILYEJCQhAREQEHBwcAgIODA+7cuYP4+HhJnRMnTkBPTw92dnbFjqVUQzxWVlZSyUedOnVw+vRpZGRkQCwWQxAEnDp1Cubm5qVpnoiIiMqAl5cXtm7digMHDqBcuXKSOSP6+vrQ0tKCvr4+hgwZggkTJsDIyAh6enoYM2YMHBwcJCMrTk5OsLOzg7u7OxYtWoTY2FhMnz4dXl5eJRpmKlUPSseOHREYGCjZdMXT01OSPU2ePBmtWrXCzZs34erqWprmiYiIlJ4sJ8kWl5+fHxITE9GuXTuYm5tLjh07dkjqLF++HN26dYOrqyvatGkDMzMz7N27V3JeVVUVAQEBUFVVhYODAwYMGAAPDw/MmTOnZO+/NPugPH78GHv37oWHh4ekl2TMmDFYvXq1ZLmxq6srNm3aBG1t7ZI2X2a4DwpRQdwHhaigL7EPyrxTT2TW1s8dbWTW1pdS6o3aCvPixQs8ffoUVlZWJZoIIy+YoBAVxASFqCAmKJ9fqeagFKVChQqoUKGCLJskIiJSSiIo92anMk1QiIiISDZKszz4a1KqBKVq1arFqicSiRAaGlqaWxAREZESK1WCkpubW+hzdhITEyWbt5ibmxf7gUBEREQkjT0opRAeHv7BcxMmTEBcXBxOnDhR2riIiIiUmrI/cLdU+6B8SJUqVbBjxw68efMGP//8s6ybJyIiUgpludW9PJB5ggIA6urq6NSpE3bu3Pk5miciIqKv3GdbxZOamorXr19/ruaJiIi+ako+wvN5EpRz585h27ZtsLW1/RzNExERffVK8pC/r1GpEpQOHToUWp6dnY2oqCjJJNqZM2eWOjAiIiJSXqVKUM6cOVNouUgkgqGhIZycnDBhwgR06tTpU2IjIiJSWoo6uVVWSr0PChEREX0+Sj7C83lW8RARERF9ilIlKFWrVsWqVas+WOf3338v9pb4REREJE0FIpkdiqjUO8nmb2lflISEBDx79qw0zRMRESk9DvF8JomJiRCLxZ+reSIiIvqKFbsH5ezZs1Kvw8PDC5QBQE5ODp4/fw5/f3/UqFHj0yMkIiJSQlzFU0zt2rWTPLhIJBJh06ZN2LRpU6F1BUGASCTCggULZBMlERGRkuFGbcU0c+ZMiEQiCIKAOXPmoG3btmjXrl2BeqqqqjAyMkL79u1Rq1YtWcZKRESkNJQ8Pyl+guLj4yP5OigoCIMGDYKHh8fniImIiIiUXKlW8QQGBso6DiIiInqHsg/xlGoVz8WLFzFhwgTExsYWej4mJgYTJkzApUuXPik4IiIiZSUSye5QRKVKUJYuXYpDhw7BzMys0PPm5uYICAjA8uXLPyk4IiIiUk6lSlCuXr2KVq1afbBOmzZt2INCRERUSioyPBRRqeagxMfHo2LFih+sY2Zmhvj4+FIFRUREpOxEijo2IyOlSqwMDAwQERHxwTrPnj2Drq5uqYIiIiIi5VaqBKV58+bYt28fnj9/Xuj5iIgI7N+/Hy1atPik4IiIiJSVSIaHIipVgjJhwgSkpqaiZcuW2Lx5M2JiYgDkrd7ZtGkTWrZsibS0NEycOFGmwRIRESkLFZFIZociKtUclDZt2mDZsmWYOHEiBg0aBACSXWYBQEVFBStXrkSbNm1kFykREREpjVIlKAAwduxYtG/fHmvWrMHVq1eRmJgIAwMDNG3aFCNHjkSdOnVkGScREZFSUcx+D9kpdYICAHXr1sXq1auLPJ+RkQGxWPwptyAiIlJKCjoyIzOfZXn09evX4eXlBQsLi8/RPBER0VdPJBLJ7CiJs2fPonv37rCwsIBIJML+/fulzg8cOLBA+507d5aq8/r1a7i5uUFPTw8GBgYYMmQIkpOTSxTHJ/WgvCshIQFbtmzB+vXrcfv2bQiCAC0tLVk1T0RERF9ASkoK6tWrh8GDB6N3796F1uncuTM2bNggef3+aImbmxtiYmJw4sQJZGVlYdCgQRg+fDi2bt1a7Dg+OUE5efIk1q9fjwMHDiAjIwOCIMDBwQGDBg1C3759P7V5IiIipSTLIY6MjAxkZGRIlYnF4kKnYbi4uMDFxeWD7YnF4iIfd/PgwQMcPXoUV69eRePGjQEAv/76K7p06YIlS5YUe3SlVO//+fPnmDNnDqytreHs7IwdO3bA2NgYgiBg4MCBuHDhAoYOHYpy5cqVpnkiIiKlJ8shHl9fX+jr60sdvr6+pY7tzJkzMDExga2tLUaNGoVXr15JzgUHB8PAwECSnACAo6MjVFRUcPny5WLfo9g9KFlZWdi/fz/Wr1+PU6dOIScnBzo6OnBzc4OHhwc6dOgANTU1qKnJbNSIiIiIZMDb2xsTJkyQKivtIpbOnTujd+/esLa2RmhoKKZNmwYXFxcEBwdDVVUVsbGxMDExkbpGTU0NRkZGiI2NLfZ9ip1NWFhY4PXr1xCJRGjfvj08PDzQu3dv6OjoFP9dERERUbHIchFPUcM5pdGvXz/J1/b29qhbty6qVauGM2fOoGPHjjK5B1CCBOXVq1dQUVHB+PHj8dNPP6FChQoyC4KIiIikKcrDAqtWrYry5cvjyZMn6NixY6EPC87Ozsbr16+LnLdSmGLPQRk4cCC0tLSwbNkyWFpa4ptvvsGuXbuQmZlZ/HdBREREX5XIyEi8evUK5ubmAAAHBwckJCTg2rVrkjqnT59Gbm4umjVrVux2i52g/PXXX4iJicHatWvRsGFDBAQEoF+/fjA1NcWIESNw/vz5ErwdIiIi+hAVGR4lkZycjJs3b+LmzZsAgLCwMNy8eRMRERFITk7G5MmTcenSJYSHh+PUqVPo0aMHbGxs4OzsDACoVasWOnfujGHDhuHKlSu4cOECRo8ejX79+pVofzSRkP8AnRJ68OAB/vzzT2zZsgUvXryQdEW1atUKmzdvhpWVVWmaLVPp2WUdAZH8MWwyuqxDIJI7aTd+++z32He7+BNKP6ZX3eIPrZw5cwbt27cvUO7p6Qk/Pz/07NkTN27cQEJCAiwsLODk5IS5c+fC1NRUUvf169cYPXo0Dh06BBUVFbi6umLVqlXQ1dUtdhylTlDyZWdnS1b3nDhxArm5uVBRUUHbtm0xcOBAuLu7f0rzXxQTFKKCmKAQFfQ1Jyjy4pMTlHdFRkZiw4YN2LhxI8LCwiASiZCTkyOr5j87JihEBTFBISroSyQo+2WYoPRUwARFps/isbS0xIwZMxAaGooTJ05ILUUiIiKi4hOJZHcoos+2q1rHjh1luh6aiIhImajIdCcUxfNZnmZMRERE9Cm4Lz0REZEcUtShGVlR6B6Uc+fOYcCAAXBwcEBUVBQA4O+//+aeLEREpPBEMvyfIlLYBGXPnj1wdnaGlpYWbty4IXmMdGJiIubPn1/G0REREdGnUNgE5ZdffsGaNWuwbt06qKurS8pbtmyJ69evl2FkREREn46reBRUSEgI2rRpU6BcX18fCQkJXz4gIiIiGeIqHgVlZmaGJ0+eFCg/f/48qlatWgYRERERkawobIIybNgwjB07FpcvX4ZIJEJ0dDT8/f0xadIkjBo1qqzDIyIi+iQc4lFQU6dORW5uLjp27IjU1FS0adMGYrEYkyZNwpgxY8o6PCIiok+iqImFrMj0WTxlITMzE0+ePEFycjLs7OxK9KTE9/FZPEQF8Vk8RAV9iWfxHH/wQmZtOdWqILO2vhSF7UHZsmULevfuDW1tbdjZ2ZV1OERERDKlqPuXyIrCzkEZP348TExM0L9/fxw+fFihnppMRET0MSoi2R2KSGETlJiYGGzfvh0ikQjfffcdzM3N4eXlhYsXL5Z1aERERJ+MO8kqKDU1NXTr1g3+/v6Ij4/H8uXLER4ejvbt26NatWplHR4RERF9AoWdg/IubW1tODs7482bN3j27BkePHhQ1iERERF9EmVfxaOwPSgAkJqaCn9/f3Tp0gUVK1bEihUr0KtXL9y7d6+sQyMiIvokyj7Eo7A9KP369UNAQAC0tbXx3XffYcaMGXBwcCjrsIiIiEgGFDZBUVVVxc6dO+Hs7AxVVdWyDoeIiEimFHX1jawo7BBP/tAOk5PiObBvL1o1b1zWYRARUTFxiEeBrFq1CsOHD4empiZWrVr1wbo//vjjF4rqy5kxbSoOHthXoPzQ4eOobGVVBhH958C+vZg53RstWraC3x/rJeVJSUlo7dAEf27YjCZNm33RmDIyMrB00QIcPXIYmZmZaNGyFX6eMQvG5csXeY3f77/i6JF/EBsbC3V1ddjZ1cboseNRt249AEBUVCT+WLMaVy5fwquXL1HBxARdu32DYcNHQl1DQ1JnuvcU3L9/D3Z2tfGL70JUrGgpucfoH0agZ8/ecHRy/rwfgJL42I6ev6w5jHlrD3+RWI6tG4s2jasDANIzshAW9RJrtp/FH7vOfZH7A4C+rhZ8RndHjw71YKSvjYiYN5i8ZDeOnb8PANDVFmPWD93wTYd6qGCoi1shkZi0aDeu3Y8oss0eHephWJ/WqGtbEWJ1NTx4Gotf1hzGyeD/FiQM69MKw75tDSsLIwDAg6exmP/HERy/cF9SZ+HE3hjQvRlS0zIxY9UBbD/yr+Rcb8cG6N+tKb4dt1bWHwkpKIVKUJYvXw43Nzdoampi+fLlRdYTiURfZYICAC1btcacX3ylygyNjMooGmlqamq4fCkYVy5fQtNmzcs6HCxeOB/ngoKweNkKlCtXDr7z5mLC2NHY5L+9yGusrKrA++eZsLSshPSMdGzZvBGjhg3GoSMnYGRkhPCnT5GbK2DGrDmoXNkKTx4/wmyfGUhLS8PEyVMAAEsXLYSJiSl85s7Db6tWYNniRVi6Ii+hPnrkMFREIiYnMlTF0Vvy9bdOjTBjVFfU6zVHUpacmiFVX1VVBTk5uZ8tnvV7LmCuXwC0NDXg1q0ZVk7ri4S3qdh59Npnu2c+dTVV/LNmNOJfv4Xb5PWIik9AZQsjJL5Nk9Txm9kfdjYWGDx9E2JeJOL7Lk3xz5oxaOj6C6JfJBbabquGNjh96SFm/XoQCclp8PimOfasHIE27ktwKyQSABAVl4AZvx7Ak4gXEEGEAd2bYdfy4WjebwEePI1FlzZ18F3nxuj+w++wqVwBa2a54UTwA7xKSIGeriZ8RndH15G/fvbPSJEo+yoehUpQwsLCCv1amWhoaKB8hYLPVNi8cQMO7N+LyMjn0NfXR9u27TF+4mRo6+gU2k7Iw4dYtGAe7t+7C5FIhMpWVTBj1mzUrmMPALh+7V+sWrEM9+/dhYGhITp07IQfx02AtrZ2kbFpaWnBydkFK5cvhf/2XUXWi42JwdLFCxB88QJEIhU0bNQIP3n/LOllyM7OxpJFCxBwcD9UVFTRy/VbvHr5EsnJb7Hi19UAgCED3VG9hi3EGhrYu2c31NXV0advP4zyyntQ5Nu3b7Fvzx4sWLQEzZrnTZ6e88t89OzeBbdv3UTdevULja1Lt+5Sryf95I19e3bj8aMQNGvugJat26Bl6zaS85aVKiE8PAw7d2yTJChhT0Mx6aepsLKqgh49e2Hp4kUA8nqTfl+1Auv+2lTkZ0MlF/fqreTrxOQ0CBAkZa0bVcfxP8eix+jVmPVDN9SpboFuo36H+zfNYFBOC99NWCe5dvEkV9S1tYTzsJUA8v7QmTioE4b0bgFTYz08jojHgnVHse/kzQ/Gk5aeKbn/vLWH0delMbq2tcfOo9dQycwQS6f0QfumtsjNzcWJiw8wYeEuxL/Oq29foyIWT3JFQ7vKEAQBTyJeYMy87bh+PwIDujfD4smucJ+yAYsnucLSzBAXb4RihM8WxL5MAgB49nSAoZ422g1ciuzsvCQsIua1JDZNsTp6dqyPPuP/wIXroZIYu7Spg2F9WmP26oBC39PkJXukXs/67RC6tauLLm3rSBKUw2fvStXx+f0QhvVphaZ1rfHgaSxqWpvh3LXHuH4/AtfvR2DRJFdUsTDGq4QUzBvbE+t2ncPz2Dcf/GyVjZLnJ4o7B2XOnDlITU0tUJ6WloY5c+YUcsXXTUVFhCneP2PvgQDMnbcAV65cwvKli4us7z1lEkzNzLB1x25s27UXg4cOg5qaOgDgeUQEfhgxDI6dnLBr30EsWrIcN65fg++8uR+NY6TXaDx5/Agnjh0t9HxWVhZGDR8CbR0dbNjsj01btkFbWxs/jBiKrMxMAMCG9etwOOAQZv/ii01btiIlJRmBp08WaOvQgX3Q0tbGlu07MX7iZKz1+x3BFy8AAO7fu4vs7Cw0c2ghqW9dtRrMzS1w6+bNj74PAMjKzMSeXTtQrlw51LC1LbJe8tu30NfXl7yuYVsTly4FIzc3F8EXLqBGjbxrly9ZhL7f94eZuXmx7k+yM/fHbzBj1UHU7/0L7j6OKtY1kwc7wa1rU4yZtwMNv52HX7cE4q9fPNGqkU2J7p2WkQkNdTWIRCLsXD4cRnracBq6At1G/YYqluXx98LBkrob5nkiKi4BrQYsRgu3RVi64QSysv97jIe2pgbGeXTEkBmb0WnIclQyN4Tv+F6S813b2uPy7TCsmNoX4Sfn499d0zB5sBNU/j/bUk1VBWpqqkjPzJKKMT0jCy0aFH+DS5FIhHLaYrxJLPjfYCDvv0d9nBtBR0sDl2/n/TF5+1EUGtaqDINyWmhQqxK0xOoIff4CLepXRYNalfD7tjPFvj8pB4VNUGbPno3k5OQC5ampqZg9e/ZHr8/IyEBSUpLUkZGR8dHrytrZoDNo3riB5Jg0Pm8oa4DHQDRt1hwVK1qiWXMHjB4zDsePHSmyndiYaDRv3gLWVavByqoKnJxdYFuzJgBg/Z9r0aVbdwzwGAgrqyqo36Ahpnj/jICD+z/6GZmYmKL/AA/8umo5srMLPh762NHDyBVy4TNnHqrXsEXVatUw5xdfxMbE4OrVKwCAbf5bMGTYcHR07ATrqtXg/fNMlCunV6Ct6jVsMfKH0bCyqoLuPXrCrnYdXL4UDAB49fIl1NXVoacnfZ2RsTFevvzwE0KDzgSieeMGaNKwLv7evBFr1v0FQ8PCh9Einj3Dtq1b8G2ffpKyCZOnICzsKVw6dcCziGeYMHkKrv17FSEPH6D7Nz0xecJYdHHuiLmzZ0qSMvq85vr9g9OXHyIs8iXeJBX+S/VdGupq+GmIE0bO9sfJ4AcIj3qFLYcuY9vhqxjq2qpY91RREaFflyaoW8MSZ648QvumtqhjY4GB0zbixoPnuHr3GYbO2Iw2jaujkV1lAEAlM0OcvhKCR+FxCI14gb0nb+DOo/8SKg11NUmPys2HkViz/SzaNf0vebauaIxejg2gqipCrzF+WLDuKMa6d8TUoZ0B5A13Xbr1FN7DXGBeQV8SY7O61jArX/BnrCjjPTpCR1uMPcevS5XXtrHAiwtLkXh5BVb93Bd9J67Dw6exAICTwQ+w7fBVnN/yE/6Y7Y5hM/9GSlomVk7rhzHztmN4n9a4tW8GTm8Yj1pVzYody9dMRSSS2aGIFGqI512CIEBUyId+69YtGBVjToavr2+BRObnGbMwfaaPrEL8LJo0bYafZ/hIXmtpawEALgVfxPp1axEW9hQpycnIyclBRkYG0tLSoKWlVaAdd89BmD1rOgIOHUCz5i3g5NwZlSrn/Ufy0cOHePQoBIcDDknqCxCQm5uLqMhIVP3IowQGDRmG3Tt3YP/ePXDq7CJ17lHIQzyPiIBDk4ZS5RkZGYh8HoG3b9/i1auXqGNfV3JOVVUVtWrXhpArPW8gv2ciX4UKFfD69asPxlYcTZo2w849+5GQ8AZ7du/E5InjsGXbLhgbG0vVi4uLww8jhqKTc2e49vlOUm5qaorfVv830S8zMxOjhg/BL/MX4I+1ftDW0cGBgKP4YcRQ7Nq1A/3d3D85Zvqw6/eKngBamGqVykNHS4wAv9FS5Rrqqrj1MPKD1w7/rjUG9moBDXVV5OTkYtWW0/hj1zmM6tcGkXFvEBmXIKn78Gks3iSlwraqGa7dj8CqLYHwm9Ef/bs2QeDlEOw5cQNhkS8l9VPSMqRex75MhImRruS1iooKXrx+C6+525CbK+DGg+ewMDHAOI+OmP9H3h8sg6dvxlofNzw9Pg/Z2Tm4+fA5dh79Fw1qVS7WZ9O3c2NMG+GCPuP/wIs30n8kPgqPQ7N+vtDX1UIvxwZYN8cdTkNXSpKUeWulJyxPG+6CwMsPkZWdgylDO6PJd/Ph0roO/pzrgZZui4oVz9dMMdMK2VG4BMXQ0BAikQgikQg1atSQSlJycnKQnJyMkSNHfrQdb29vTJgwQapMUBXLPF5Z09LSKrBiJyoqEmN+GIHv+n6PMWPHQ09fHzeuX4PPjJ+RlZVVaIIyymsMXLp2w7mgIJw/fxZ+v6/CwiXL0dGxE1LTUvHtd/0K/cVpXozhCT09PQwZNhxr/H5Dm3btpM6lpqaill1t+C5cUuC6kk72VVOT/vYViUQQcgUAgHH58sjKykJSUpJUL8rrV69QvnzBOTzv0tbWRmUrK1S2skLdevXR3cUJ+/fuxpBhIyR14uPjMHSQB+o1aICZPh8e+vrzjzVwaNESdrXrYPasGRj94zioq6ujo6MTrly+xATlC0hJk+6pys0VCsxAVFP7b8sCXe28/xb0+tEP0fEJUvUyMwv2DL5r++F/sXD9UaSnZyHmZRIEQSh2nPPWHsaOI//CpXVtOLW0w/SRXeAxdQMOBt4GAKnhHgAQhLykJF/sy0RkZefkvb//exgWC/MK+lBXU0VWdg7CIl/CaehKaGtqQE9XE7Evk/D3gkEIi3qJj+nj3AirZ/aH20/rEXg5pMD5rOwcPH2e186NB8/RqHZleH3fDmPmFZyYXqOKKb7v2gTN+y2AZ08HXLj+BC/fJGPP8ev4Y/YA6GqLC0xwVjpKnqEoXIKyYsUKCIKAwYMHY/bs2VJj/xoaGqhSpUqxdpQVi8UQi6UTkvQP/3dHbj24dw+5uQIm/jRV8h+r40eLHt7JV6WKNapUsYa750BMmTQBB/btQUfHTqhVyw5PQ5980tLl793csdX/b/j/vVmqvFat2jh25AiMjI2hq6tb6LXGxuVx9+4dNGrcBEBe4vnw/n3JEFRx2NWuAzU1dVy5FCxZMRMe9hQxMdGoV79+id5LrpCLzHeGYuLi8pITO7vamPOLr9QviPc9DQ3FkX8CsGPP/ry2cnKQnZ03/p+dnYXc3Jwir6XP5+WbZNS2kU6269lWRNb/J5Y+eBqL9IwsVDIzxPlrT0rUdlJymuSX9LsePo2DpakhLE0NJL0oNauawVBPW9LDAABPIuLxq388fvUPxCbfgXDv4SBJUD4m+OZT9HVpnJes/z8xql7ZBDEvEgskN6npmUhNz4RBOS04tqiFn1cc+GDb33VuhDWz3ODhvQFHzxfvcSIqIhHEGoX/mvltej9MWboXKWmZUFVRgfr/E8T8/1f9wM8VKQeFS1A8PT0BANbW1mjRogXU1dXLOKKyV6myFbKzs7DN/2+0bdcBN25cw66dRS+lTU9Px7Ili9DJyRkVLS0RFxuLe3fvoGMnJwB5QzTu/fti/i9z0Nu1D7S0tfA09AmCL17EtOkzixWTWCzGKK8x8P1FesJyl27dsXHDeowdPQpeY8bCxNQUMdHROHXyBAYNHgpTMzN87zYAf61bi8qVK8Pauiq2+m9BUlJiidbclStXDr1cXbFk0QLo6etDV1cXC+b/gnr1G0it4OnRrTN+HDcxr+coNRV//rEG7dp3QPkKFZDw5g22b/NHfFwcOjnnjeHHxcVh6EB3mFtYYMLkKXjz+r8VEu+vrhIEAXN8ZmDSFG/J6qf6DRpiz+5dsLKyxqGDB+DSpWux3xPJzpmrjzDesyP6d2uKy7fD8H2XJrCrZiFZkZKcmoEVm09h0URXqKio4OKNUOjrasKhfjUkpaTD/9DlEt/z9OWHuPskGhvmD8TkxbuhpqqKFdP64uy/eStbNMXq8B3XE3tP3sSz6JeoaGKIRnZW2H/qZrHvsW7XOYzs2wZLf/oWq7cFwaZyBUwe4oTV24IkdRwdakEkAh6Fx6NapQqYP74nHoXFYfPBYEmdOWO+gYWJPobO+BtA3rDOujnumLR4N67eCYepcTkAQFpGFpKS0yXXHLtwD89j3qCcjib6ujRGm8bV0f2H1QXiHNSrBV6+SZas/Am++RQ/j+iCpvZV4NTSDvdDY5CYnFbgOmWjqBusyYpCJSjvdtc3aNAAaWlpSEsr/Jv4/cmRXzPbmjUx6SdvbFi/DqtWLEPDRo3x47gJmO49pdD6qioqSExIwHTvKXj16iUMDA3R0dEJP4zOm3Bbw7Ym1m/8G7+uWoFBHv0hCEClSpXg7NKlRHF906MXNm/cgKeh//0FqqWlhQ2btmDFsiWYMHY0UlJSYGJqimbNHKDz/x6VQUOG4eXLl5juPQUqKqpw7fMdWrRsBRWVku0aPHnKNKiIVDBx3I/IzPr/Rm3TZ0nVCQ8LQ/LbvCWeqqqqCAt7ioMH9iHhzRsYGBigdh17bNjsDxubvM23Ll28gIiIZ4iIeAanDm2k2rp1T7rLe/euHTA2Lo+27dpLykZ6jYH3TxMx4Ps+aNmqNfp+71ai90SycTL4AXzXHcW8sT2hKVbD5gOXsPWfK6htYyGpM3t1AF6+ScbkQZ1gPeN7JLxNw80Hz7Hor2Olvu934//A0il9cGL9eKllxgCQk5MLIwMdrJ/rDhPjcniVkIIDp29h7pp/it1+ZFwCvvFajUUTe+PqTm9Exyfg961nsHTjCUkdfV1NzBnzDSqaGuB1YioOnLqJWb8fkixLBgCz8nqoZPbfkOtg15ZQV1fFyml9sXJaX0n53wcvYfisLQCACka6WD/XA2bl9ZCYnI67j6PQ/YfVOH35oVSMJkblMGWoM9oPXCYp+/feM6zccgp7V43Ci9dvMWzm38V+z18zBZ3bKjMioSQDpGVMVVUVMTExMDExgYqKSqGTZPMnz+bklLzrXFGHeL52ubm56NndBU7OLhj947iyDkfpGDYZ/fFKRErmYzsYy8KVp4VvnFcaTavqf7zS/509exaLFy/GtWvXEBMTg3379qFnz56S84IgYNasWVi3bh0SEhLQsmVL+Pn5oXr16pI6r1+/xpgxY3Do0CGoqKjA1dUVK1euLHJovzAK1YNy+vRpyQqdwMDAMo6GPpfo6CgEX7iARk2aICszE9u2+iMqMgpdunb/+MVERF+JsupASUlJQb169TB48GD07t27wPlFixZh1apV2LRpE6ytrTFjxgw4Ozvj/v370NTUBAC4ubkhJiYGJ06cQFZWFgYNGoThw4dj69atxY5DoXpQPjf2oMiH2JgYTJk0Hk+ePIYgCLCpXgNjx0+UTJqlL4s9KEQFfYkelKthsutBqWuhWWAfq8IWi7xPJBJJ9aAIggALCwtMnDgRkyZNAgAkJibC1NQUGzduRL9+/fDgwQPY2dnh6tWraNw47yG1R48eRZcuXRAZGQkLC4uibidFYadJHz16FOfPn5e8/v3331G/fn30798fb95wu2RFZmZujk3+23Hh8jVcvHIdm/23MzkhIvoEvr6+0NfXlzp8fX0/fuF7wsLCEBsbC0dHR0mZvr4+mjVrhuDgvInWwcHBMDAwkCQnAODo6AgVFRVcvlz8CeYKm6BMnjwZSUl5z5+4c+cOJkyYgC5duiAsLKzA/iZERESKRiTD/3l7eyMxMVHq8Pb2/ngQ74mNzVsSb2pqKlVuamoqORcbGwsTExOp82pqajAyMpLUKQ6FmoPyrrCwMNjZ2QEA9uzZg+7du2P+/Pm4fv06unQp2WoTIiIieSPLVTzFGc6RNwrbg6KhoSF5WODJkyfh5JS3h4eRkZGkZ4WIiIhkx8ws7zlJcXFxUuVxcXGSc2ZmZoiPj5c6n52djdevX0vqFIfCJiitWrXChAkTMHfuXFy5cgVdu+ZtePXo0SNYWlqWcXRERESfRiTDQ1asra1hZmaGU6dOScqSkpJw+fJlyS7uDg4OSEhIwLVr1yR1Tp8+jdzcXDRr1qzY91LYBOW3336Dmpoadu/eDT8/P1SsWBEAcOTIEXTu3LmMoyMiIvpEZZShJCcn4+bNm7h58yaAvCkVN2/eREREBEQiEcaNG4dffvkFBw8exJ07d+Dh4QELCwvJSp9atWqhc+fOGDZsGK5cuYILFy5g9OjR6NevX7FX8ABcZiyFy4yJCuIyY6KCvsQy4xvP3sqsrQZW5Ypd98yZM2jfvn2Bck9PT2zcuFGyUdsff/yBhIQEtGrVCqtXr0aNGjUkdV+/fo3Ro0dLbdS2atWqEm3UptAJSk5ODvbv348HDx4AAGrXro1vvvkGqqol2xI9HxMUooKYoBAV9DUnKPJCYVfxPHnyBF26dEFUVBRsbW0B5K3zrlSpEv755x9Uq1atjCMkIiIqPWV/Fo/CzkH58ccfUa1aNTx//hzXr1/H9evXERERAWtra/z4449lHR4REdEnkcdJsl+SwvagBAUF4dKlS5Jn8wCAsbExFixYgJYtW5ZhZERERPSpFDZBEYvFePu24PhccnIyNDQ0yiAiIiIiGVLUrg8ZUdghnm7dumH48OG4fPkyBEGAIAi4dOkSRo4ciW+++aaswyMiIvokstzqXhEpbIKyatUq2NjYoEWLFtDU1ISmpiZatmwJGxsbrFy5sqzDIyIiok+gcEM8ubm5WLx4MQ4ePIjMzEz07NkTnp6eEIlEqFWrFmxsbMo6RCIiok+m7Kt4FC5BmTdvHnx8fODo6AgtLS0cPnwY+vr6+Ouvv8o6NCIiIplR8vxE8YZ4Nm/ejNWrV+PYsWPYv38/Dh06BH9/f+Tm5pZ1aERERCQjCpegREREoEuXLpLXjo6OEIlEiI6OLsOoiIiIZEzJN0JRuCGe7OxsaGpqSpWpq6sjKyurjCIiIiKSPUVdfSMrCpegCIKAgQMHQiwWS8rS09MxcuRI6OjoSMr27t1bFuERERHJBCfJKhhPT88CZQMGDCiDSIiIiOhzUbgEZcOGDWUdAhER0Wen5B0oipegEBERKQUlz1AUbhUPERERff3Yg0JERCSHuIqHiIiI5I6yr+LhEA8RERHJHfagEBERySEl70BhgkJERCSXlDxD4RAPERERyR32oBAREckhruIhIiIiuaPsq3iYoBAREckhJc9POAeFiIiI5A97UIiIiOSRknehMEEhIiKSQ8o+SZZDPERERCR32INCREQkh7iKh4iIiOSOkucnHOIhIiIi+cMEhYiISB6JZHgUk4+PD0QikdRRs2ZNyfn09HR4eXnB2NgYurq6cHV1RVxc3Ce/1cIwQSEiIpJDIhn+ryRq166NmJgYyXH+/HnJufHjx+PQoUPYtWsXgoKCEB0djd69e8v6rQPgHBQiIqKvXkZGBjIyMqTKxGIxxGJxgbpqamowMzMrUJ6YmIj169dj69at6NChAwBgw4YNqFWrFi5duoTmzZvLNGb2oBAREckhkUh2h6+vL/T19aUOX1/fQu/7+PFjWFhYoGrVqnBzc0NERAQA4Nq1a8jKyoKjo6Okbs2aNVG5cmUEBwfL/P2zB4WIiEgOyXIVj7e3NyZMmCBVVljvSbNmzbBx40bY2toiJiYGs2fPRuvWrXH37l3ExsZCQ0MDBgYGUteYmpoiNjZWhtHmYYJCREQkh2S5D0pRwznvc3FxkXxdt25dNGvWDFZWVti5cye0tLRkF1AxcIiHiIiICmVgYIAaNWrgyZMnMDMzQ2ZmJhISEqTqxMXFFTpn5VMxQSEiIpJLZbDO+D3JyckIDQ2Fubk5GjVqBHV1dZw6dUpyPiQkBBEREXBwcCj1PYrCIR4iIiI5VBZb3U+aNAndu3eHlZUVoqOjMWvWLKiqquL777+Hvr4+hgwZggkTJsDIyAh6enoYM2YMHBwcZL6CB2CCQkRERP8XGRmJ77//Hq9evUKFChXQqlUrXLp0CRUqVAAALF++HCoqKnB1dUVGRgacnZ2xevXqzxKLSBAE4bO0rIDSs8s6AiL5Y9hkdFmHQCR30m789tnvEZ2QKbO2LAw0ZNbWl8IeFCIiIjmk7E8z5iRZIiIikjvsQSEiIpJDJX2GzteGCQoREZE8Uu78hEM8REREJH/Yg0JERCSHlLwDhQkKERGRPFL2VTxMUIiIiOSQsk+S5RwUIiIikjvsQSEiIpJHyt2BwgSFiIhIHil5fsIhHiIiIpI/7EEhIiKSQ1zFQ0RERHKHq3iIiIiI5Ax7UIiIiOSQsg/xsAeFiIiI5A4TFCIiIpI7HOIhIiKSQ8o+xMMEhYiISA4p+yoeJihERERySNl7UDgHhYiIiOQOe1CIiIjkkJJ3oDBBISIikktKnqFwiIeIiIjkDntQiIiI5BBX8RAREZHc4SoeIiIiIjnDHhQiIiI5pOQdKExQiIiI5JKSZygc4iEiIiK5wx4UIiIiOcRVPERERCR3lH0Vj0gQBKGsgyB6V0ZGBnx9feHt7Q2xWFzW4RDJBf5ckLJhgkJyJykpCfr6+khMTISenl5Zh0MkF/hzQcqGk2SJiIhI7jBBISIiIrnDBIWIiIjkDhMUkjtisRizZs3iRECid/DngpQNJ8kSERGR3GEPChEREckdJihEREQkd5igEBERkdxhgkJERERyhwkKKbwqVapgxYoVZR0G0Wdx5swZiEQiJCQkfLAefw7oa8MEhT5o4MCBEIlEWLBggVT5/v37IfrCT7LauHEjDAwMCpRfvXoVw4cP/6KxEL0v/2dFJBJBQ0MDNjY2mDNnDrKzsz+p3RYtWiAmJgb6+voA+HNAyoMJCn2UpqYmFi5ciDdv3pR1KIWqUKECtLW1yzoMInTu3BkxMTF4/PgxJk6cCB8fHyxevPiT2tTQ0ICZmdlH/yDgzwF9bZig0Ec5OjrCzMwMvr6+RdY5f/48WrduDS0tLVSqVAk//vgjUlJSJOdjYmLQtWtXaGlpwdraGlu3bi3QJb1s2TLY29tDR0cHlSpVwg8//IDk5GQAed3cgwYNQmJiouSvVB8fHwDSXdv9+/dH3759pWLLyspC+fLlsXnzZgBAbm4ufH19YW1tDS0tLdSrVw+7d++WwSdFyk4sFsPMzAxWVlYYNWoUHB0dcfDgQbx58wYeHh4wNDSEtrY2XFxc8PjxY8l1z549Q/fu3WFoaAgdHR3Url0bhw8fBiA9xMOfA1ImTFDoo1RVVTF//nz8+uuviIyMLHA+NDQUnTt3hqurK27fvo0dO3bg/PnzGD16tKSOh4cHoqOjcebMGezZswd//PEH4uPjpdpRUVHBqlWrcO/ePWzatAmnT5/GTz/9BCCvm3vFihXQ09NDTEwMYmJiMGnSpAKxuLm54dChQ5LEBgCOHTuG1NRU9OrVCwDg6+uLzZs3Y82aNbh37x7Gjx+PAQMGICgoSCafF1E+LS0tZGZmYuDAgfj3339x8OBBBAcHQxAEdOnSBVlZWQAALy8vZGRk4OzZs7hz5w4WLlwIXV3dAu3x54CUikD0AZ6enkKPHj0EQRCE5s2bC4MHDxYEQRD27dsn5H/7DBkyRBg+fLjUdefOnRNUVFSEtLQ04cGDBwIA4erVq5Lzjx8/FgAIy5cvL/Leu3btEoyNjSWvN2zYIOjr6xeoZ2VlJWknKytLKF++vLB582bJ+e+//17o27evIAiCkJ6eLmhrawsXL16UamPIkCHC999//+EPg+gD3v1Zyc3NFU6cOCGIxWKhZ8+eAgDhwoULkrovX74UtLS0hJ07dwqCIAj29vaCj49Poe0GBgYKAIQ3b94IgsCfA1IeamWaHZFCWbhwITp06FDgL7Zbt27h9u3b8Pf3l5QJgoDc3FyEhYXh0aNHUFNTQ8OGDSXnbWxsYGhoKNXOyZMn4evri4cPHyIpKQnZ2dlIT09HampqscfW1dTU8N1338Hf3x/u7u5ISUnBgQMHsH37dgDAkydPkJqaik6dOkldl5mZiQYNGpTo8yB6X0BAAHR1dZGVlYXc3Fz0798fvXv3RkBAAJo1ayapZ2xsDFtbWzx48AAA8OOPP2LUqFE4fvw4HB0d4erqirp165Y6Dv4c0NeACQoVW5s2beDs7Axvb28MHDhQUp6cnIwRI0bgxx9/LHBN5cqV8ejRo4+2HR4ejm7dumHUqFGYN28ejIyMcP78eQwZMgSZmZklmvzn5uaGtm3bIj4+HidOnICWlhY6d+4siRUA/vnnH1SsWFHqOj6EjT5V+/bt4efnBw0NDVhYWEBNTQ0HDx786HVDhw6Fs7Mz/vnnHxw/fhy+vr5YunQpxowZU+pY+HNAio4JCpXIggULUL9+fdja2krKGjZsiPv378PGxqbQa2xtbZGdnY0bN26gUaNGAPL+gnt3VdC1a9eQm5uLpUuXQkUlb2rUzp07pdrR0NBATk7OR2Ns0aIFKlWqhB07duDIkSPo06cP1NXVAQB2dnYQi8WIiIhA27ZtS/bmiT5CR0enwM9BrVq1kJ2djcuXL6NFixYAgFevXiEkJAR2dnaSepUqVcLIkSMxcuRIeHt7Y926dYUmKPw5IGXBBIVKxN7eHm5ubli1apWkbMqUKWjevDlGjx6NoUOHQkdHB/fv38eJEyfw22+/oWbNmnB0dMTw4cPh5+cHdXV1TJw4EVpaWpKlkzY2NsjKysKvv/6K7t2748KFC1izZo3UvatUqYLk5GScOnUK9erVg7a2dpE9K/3798eaNWvw6NEjBAYGSsrLlSuHSZMmYfz48cjNzUWrVq2QmJiICxcuQE9PD56enp/hUyNlVr16dfTo0QPDhg3D2rVrUa5cOUydOhUVK1ZEjx49AADjxo2Di4sLatSogTdv3iAwMBC1atUqtD3+HJDSKOtJMCTf3p34ly8sLEzQ0NAQ3v32uXLlitCpUydBV1dX0NHREerWrSvMmzdPcj46OlpwcXERxGKxYGVlJWzdulUwMTER1qxZI6mzbNkywdzcXNDS0hKcnZ2FzZs3S00OFARBGDlypGBsbCwAEGbNmiUIgvTkwHz3798XAAhWVlZCbm6u1Lnc3FxhxYoVgq2traCuri5UqFBBcHZ2FoKCgj7twyKlVtjPSr7Xr18L7u7ugr6+vuT7+9GjR5Lzo0ePFqpVqyaIxWKhQoUKgru7u/Dy5UtBEApOkhUE/hyQchAJgiCUYX5ESioyMhKVKlXCyZMn0bFjx7IOh4iI5AwTFPoiTp8+jeTkZNjb2yMmJgY//fQToqKi8OjRI8m4OBERUT7OQaEvIisrC9OmTcPTp09Rrlw5tGjRAv7+/kxOiIioUOxBISIiIrnDre6JiIhI7jBBISIiIrnDBIWIiIjkDhMUIiIikjtMUIiIiEjuMEEhIiIiucMEhYiIiOQOExQiIiKSO0xQiIiISO4wQSEiIiK5wwSFiIiI5A4TFCIiIpI7TFCIiIhI7jBBISoj4eHhEIlEGDhwoFR5u3btIBKJyiaoEqpSpQqqVKlS1mEAkK9YiOjTMUGhr15+IvDuoaGhgUqVKqF///64fft2WYcoUwMHDoRIJEJ4eHhZh0JEVGpqZR0A0ZdSrVo1DBgwAACQnJyMS5cuYdu2bdi7dy9OnTqFli1blnGEeTZv3ozU1NSyDoOIqEwxQSGlYWNjAx8fH6my6dOnY968efj5559x5syZMonrfZUrVy7rEIiIyhyHeEipjRkzBgBw9epVSZlIJEK7du0QFRUFDw8PmJmZQUVFRSqBOXv2LLp3747y5ctDLBajevXqmD59eqE9Hzk5OVi4cCFsbGygqakJGxsb+Pr6Ijc3t9CYPjQH5cCBA3BycoKxsTE0NTVRpUoVuLu74+7duwDy5mFs2rQJAGBtbS0Z0mrXrp1UO2FhYRg6dCgqV64MsVgMc3NzDBw4EM+ePSvyvk2aNIGWlhZMTU0xbNgwvHnzpvAPtRB///03RCIR5syZU+j569evQyQSwc3NTVIWGBiIwYMHw9bWFrq6utDV1UXjxo3xxx9/FPu+Hxru8vHxgUgkKjQxLcm/7549e9C2bVuYmJhAU1MTFhYWcHR0xJ49e4odJxEVxB4UIqBAQvDq1Ss4ODjAyMgI/fr1Q3p6OvT09AAAfn5+8PLygoGBAbp37w4TExP8+++/mDdvHgIDAxEYGAgNDQ1JW8OHD8dff/0Fa2treHl5IT09HcuWLcPFixdLFOPEiROxbNkyGBkZoWfPnjAxMcHz589x8uRJNGrUCHXq1MG4ceOwceNG3Lp1C2PHjoWBgQEASE0evXz5MpydnZGSkoJu3bqhevXqCA8Ph7+/P44cOYLg4GBUrVpVUn/z5s3w9PSEnp4e3N3dYWBggICAADg6OiIzM1PqvRald+/eGDVqFPz9/TFz5swC5//++28AgLu7u6Rs4cKFePLkCZo3b45evXohISEBR48exYgRIxASEoKlS5eW6PMrrpL8+/r5+eGHH36Aubk5evXqBWNjY8TGxuLKlSvYt28fXF1dP0uMREpBIPrKhYWFCQAEZ2fnAudmzpwpABDat28vKQMgABAGDRokZGdnS9W/d++eoKamJtSrV094+fKl1DlfX18BgLBkyRJJWWBgoABAqFevnpCcnCwpj4yMFMqXLy8AEDw9PaXaadu2rfD+j+ahQ4cEAIK9vX2B+2ZlZQmxsbGS156engIAISwsrMD7zczMFKpUqSKUK1dOuH79utS5c+fOCaqqqkK3bt0kZYmJiYKenp6go6MjhISESLXTpk0bAYBgZWVV4D6FGTBggABAuHz5slR5dna2YGpqKpiZmUl93k+fPi3QRlZWltCpUydBVVVVePbsmdQ5KyurArF86LOYNWuWAEAIDAyUlJX037dhw4aChoaGEBcXV6D9968nopLhEA8pjSdPnsDHxwc+Pj6YPHky2rRpgzlz5kBTUxPz5s2TqquhoYFFixZBVVVVqnzt2rXIzs7Gr7/+CmNjY6lzP/30EypUqIBt27ZJyjZv3gwAmDlzJnR0dCTlFStWxNixY4sd++rVqwEAK1euLHBfNTU1mJqaFqudgIAAhIeHY/LkyWjQoIHUuVatWqFHjx44fPgwkpKSAAD79+9HUlISBg8ejBo1akjqqqurF/jMPia/d2TLli1S5cePH0dcXBz69esn9XlbW1sXaENNTQ0jR45ETk4OAgMDS3T/4ijpvy+Q91moq6sXaOv964moZDjEQ0ojNDQUs2fPBpD3S8XU1BT9+/fH1KlTYW9vL1XX2toa5cuXL9DGpUuXAADHjh3DqVOnCpxXV1fHw4cPJa9v3boFAGjdunWBuoWVFeXKlSsQi8Vo27Ztsa8pTH78ISEhBSYMA0BsbCxyc3Px6NEjNG7c+IPxOzg4QE2t+P8J6dixI8zNzbF9+3YsW7ZMcm1+wvLu8A4AvH37FkuWLMH+/fsRGhqKlJQUqfPR0dHFvndxlfTft1+/fvjpp59Qp04d9O/fH+3bt0erVq0kw4FEVHpMUEhpODs74+jRo8WqW1SPxOvXrwGg2L0HiYmJUFFRKTTZKW6vR347FStWhIrKp3V65sfv7+//wXr5yUBiYiIAwMTEpEAdVVXVEvUSqKqqon///li6dCmOHTuGrl27Ijk5Gfv374ednR0aNmwoqZuZmYl27drh+vXraNCgAdzd3WFsbAw1NTWEh4dj06ZNyMjIKPa9i6uk/76TJk2CsbEx/Pz8sHTpUixZsgRqamro2rUrli9fXmgvEBEVD4d4iApR1Cqa/L+Mk5KSIAhCkUc+fX195Obm4uXLlwXaiouLK3Y8BgYGkt6NT5Ef/6FDhz4Yf35Pjb6+PgAgPj6+QFs5OTl49epVie7//jDPnj17kJqaWqD35MCBA7h+/TqGDBmC69evw8/PD7/88gt8fHzQuXPnYt8vP6HLzs4ucC4/+XpXSf99RSIRBg8ejKtXr+LFixfYt28fevfujQMHDqBbt27IyckpdqxEJI0JClEJNGvWDMB/QwEfU69ePQDAuXPnCpwrrKwoTZs2RUZGBoKCgj5aN38eR2G/HPPjDw4OLtZ9PxR/cHBwob/4P9aevb09Dhw4gLdv32LLli0FlhcDecNxANCjR48CbZTkczM0NAQAREVFFTh348aNAmUl/fd9l7GxMXr27IkdO3agQ4cOuH//Pp48eVLidogoDxMUohL44YcfoKamhjFjxiAiIqLA+YSEBKlffPk9A3PmzJGaQxEVFYWVK1cW+75eXl4AgLFjx0qGIfJlZ2dL9cYYGRkBAJ4/f16gnR49eqBy5cpYtmwZzp49W+B8VlYWzp8/L1VfT08Pf/31Fx49eiRVb/r06cWO/13u7u5IS0vDqlWrcPr0abRt2xaVKlWSqmNlZQUAUrEAQFBQENatW1fsezVp0gQAsHHjRqny3bt3F5rslfTf98yZM1I9KkDeZ5P/b6SpqVnsWIlIGuegEJVAnTp1sHr1aowaNQq2trbo0qULqlWrhrdv3+Lp06cICgrCwIEDsWbNGgBA+/btMWjQIGzYsAH29vbo1asXMjIysGPHDjRv3hwBAQHFum+XLl0wadIkLFmyBNWrV0evXr1gYmKCqKgonDp1CpMmTcK4ceMAAB06dMCSJUswfPhwuLq6QkdHB1ZWVnB3d4dYLMbu3bvh4uKCtm3bokOHDrC3t4dIJMKzZ89w7tw5GBsbSyaC6uvrY9WqVRg4cCCaNGmCfv36QV9fHwEBAdDS0oK5uXmJP8P8icmzZ89Gbm5ugeEdAOjevTuqVKmCRYsW4e7du6hTpw5CQkIQEBCAXr16Yffu3cW6V48ePVCtWjVs3LgRz58/R4MGDfDgwQOcPn0aXbp0weHDh6Xql/Tft2fPntDT00Pz5s1hZWWFrKwsnDhxAvfv38e3334rSbSIqBS+5JpmorLwoX1QCgNAaNu27QfrXLlyRejXr59gYWEhqKurC+XLlxcaNmwoTJ06VXjw4IFU3ezsbMHX11eoWrWqoKGhIVStWlWYP3++8OTJk2Lvg5Jvz549Qvv27QV9fX1BLBYLVapUEdzd3YW7d+9K1Vu0aJFQvXp1QV1dvdD3ExkZKYwdO1aoXr26IBaLBT09PaFWrVrC0KFDhVOnThW47759+4RGjRoJYrFYMDExEYYOHSq8fv260L1HisPR0VEAIGhqagqJiYmF1nn69Kng6uoqVKhQQdDW1haaNGkibN++XbK3zKxZs6TqFxVLWFiY0LNnT6FcuXKCjo6O0LFjR+Hq1auF7oOSr7j/vqtXrxa++eYbwcrKStDU1BSMjY2Fpk2bCn5+fkJmZmaJPxci+o9IEN7rnyQiIiIqY5yDQkRERHKHCQoRERHJHSYoREREJHeYoBAREZHcYYJCREREcocJChEREckdJihEREQkd5igEBERkdxhgkJERERyhwkKERERyR0mKERERCR3mKAQERGR3PkfpKAjaQ9NjRAAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred=sentiment_analiz(2,X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xuTk1qYu-B4D",
        "outputId": "fc7de0cb-06c3-437d-b6f7-37f412175938"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.66      0.75       190\n",
            "           1       0.87      0.95      0.91       434\n",
            "\n",
            "    accuracy                           0.87       624\n",
            "   macro avg       0.86      0.81      0.83       624\n",
            "weighted avg       0.87      0.87      0.86       624\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred=sentiment_analiz(3,X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "21Yh8jk7A6r-",
        "outputId": "8fd6c131-61a2-4643-85d0-10092411ec99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.72      0.59      0.65       190\n",
            "           1       0.84      0.90      0.87       434\n",
            "\n",
            "    accuracy                           0.81       624\n",
            "   macro avg       0.78      0.75      0.76       624\n",
            "weighted avg       0.80      0.81      0.80       624\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred=sentiment_analiz(4,X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2rcKqrdqA7Jb",
        "outputId": "7f6f3922-66d1-4c8a-a9cd-a623d07b7690"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.34      0.49       190\n",
            "           1       0.77      0.97      0.86       434\n",
            "\n",
            "    accuracy                           0.78       624\n",
            "   macro avg       0.81      0.66      0.67       624\n",
            "weighted avg       0.79      0.78      0.75       624\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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joGCQ1YlE8hydLiAi4oyunoXwVik3NO29HvxLW51IJE9SERIRcTbxMTC/DVw5BWE/p9ypXUTSpSIkIuJMEi/Dgg5w4SD0Wge3VLU6kUiepiIkIuIskuJgURc4/Qf0XANBta1OJJLnqQiJiDiD5ET4vhcc3wDdVkLJxlYnEskXVIRERPI7WzKs6AdHVkKXJRDS3OpEIvmGipCISH5mbPDDY7A/HDrNgwptrU4kkq+oCImI5FfGwI9DYNdMaDcbKne1OpFIvqMiJCKSHxkD60dC5AfQ6mOo/oDViUTyJV1ZWkQkP/p1Ivz2BtzzNtR63Oo0IvmWipCISH6zdSpsHANNJ0D9oVanEcnXVIRERPKTHZ/AuqHQcDjcMcbqNCL5noqQiEh+secbWP0E1BkId00CNzerE4nkeypCIiL5wV+LYEV/uH0A3PueSpBINlEREhHJ646sgmVhULk7tP4U3PRPt0h20W+TiEhe9s/PsLgrlGsN7b8Edw+rE4k4FRUhEZG86sQWWNgRSjVJuWq0h5fViUScjoqQiEhedHoHLGgLxWpC58VQwMfqRCJOSUVIRCSvObcPwltBQAXougy8ClmdSMRpqQiJiOQlMYdhXkvwLQbdV4FPYasTiTg1FSERkbzi4jGYF5pyGKzHGihYzOpEIk5PN10VEckLrpyG8FCwJULv9VCopNWJRFyCipCIiNXizqeMCYo7n1KCAspZnUjEZagIiYhYKeEiLGgHF49C2E9QpLLViURcioqQiIhVEq/Awk5wdg/0+hGK1bA6kYjLURESEbFCUjws6Q7Rv0GPHyC4vtWJRFySipCISG6zJcHyPnB0bcp1gko3szqRiMtSERIRyU3GBisfgoNL4L4FUK6l1YlEXJqKkIhIbjEG1jwNe7+B9t9ApU5WJxJxeSpCIiK5wRj46XnY8TG0+RyqhlmdSETQlaVFRHLH5vGw9W24932o8ZDVaUTk/6kIiYjktN8mpxShu16HuoOsTiMi/6IiJCKSkyI/hJ9fhDvGQKPhVqcRkf9QERIRySm7ZkHEQKj3LDSdYHUaEUmHipCISE7YHw6rHoaaj8I9b4Obm9WJRCQdKkIiItnt0DJYdj/c1htCp6sEieRhKkIiItkp6seUW2dU7AhtZ4K7h9WJROQ6VIRERLLLsU2w6D4o0xw6zAEPT6sTicgNqAiJiGSHk9tgYXsIqgedF0IBb6sTiUgmqAiJiNysM7sgvDUUuQ26LgXPglYnEpFMUhESEbkZ5w9AeCvwLw3dVoB3gNWJRMQBKkIiIlkVGwXzWoJXAHT/AXyLWp1IRBykIiQikhWXoyE8FNzcoeca8Au2OpGIZIHuPi8i4qirZ1MOhyVehrD14F/G6kQikkUqQiIijoiPgflt4PJJCPsZCle0OpGI3AQVIRGRzEq8DAs7woWD0Gst3FLV6kQicpMsHyM0bdo0ypcvj4+PD40bN2bLli3XnX/q1Kncdttt+Pr6EhISwtChQ4mLi8ultCLispLiYHFXOBWZcnZYUB2rE4lINrC0CM2dO5dhw4Yxbtw4tm3bRu3atWnTpg2nTp1Kd/5vvvmGESNGMG7cOPbs2cNnn33G3LlzGTVqVC4nFxGXkpwIS8Pg2Hro+j2UusPqRCKSTSwtQm+//TaPPfYYDz30ENWrV2f69OkULFiQzz//PN35N23aRLNmzejTpw/ly5endevW3H///TfciyQikmW2ZFjRHw6vgPsWQMg9VicSkWxkWRFKSEhg69athIaG/i+MuzuhoaFs3rw53WWaNm3K1q1b7cXn0KFDLF++nPbt22f4PvHx8cTGxqZ6iIhkirHB6sdh/zzoOAcqtLM6kYhkM8sGS585c4bk5GSCg1NfeyM4OJi9e/emu0yfPn04c+YMd955J8YYkpKSePLJJ697aGzSpEmMHz8+W7OLiAswBtYOhZ1fQLvZULmb1YlEJAdYPljaEevWrWPixIl8+OGHbNu2jQULFrBs2TJeeeWVDJcZOXIkMTEx9sfRo0dzMbGI5FsbRsP29yD0I6j+gNVpRCSHWLZHqFixYnh4eHDy5MlU00+ePEmJEiXSXeall16iX79+PProowDUrFmTy5cv8/jjjzN69Gjc3dP2Om9vb7y9dRdoEXHArxNhyyRoPgVqP2F1GhHJQZbtEfLy8qJ+/fpERETYp9lsNiIiImjSpEm6y1y5ciVN2fHw8ADAGJNzYUXEdWx7N2VvUNPx0GCY1WlEJIdZekHFYcOGMWDAABo0aECjRo2YOnUqly9f5qGHHgKgf//+lC5dmkmTJgHQqVMn3n77berWrUvjxo05cOAAL730Ep06dbIXIhGRLNvxKax9Fhq8AHe8ZHUaEckFlhahsLAwTp8+zdixY4mOjqZOnTqsXLnSPoA6Kioq1R6gMWPG4ObmxpgxYzh27BjFixenU6dOvPbaa1Z9BBFxFnu+TTlDrPbTcPcb4OZmdSIRyQVuxsWOKcXGxhIYGEhMTAwBAQFWxxGRvODAYljSPWVQdJvPU+4oLyJ5Sk59f+u3XURc25EfYGmvlNPjW3+qEiTiYvQbLyKu65/1sLgLlGsF7b8Cd92HWsTVqAiJiGuK/g0WdoBSTaDjPPDwsjqRiFhARUhEXM/pHTC/DdxSAzovBk9fqxOJiEVUhETEtZzbD+GtIKA8dFsOXoWsTiQiFlIREhHXEXME5rUE32LQ/QfwKWx1IhGxmIqQiLiGS8dTSlABb+ixGgoWszqRiOQBOkVCRJzfldMwLxRsidB7PRQqZXUiEckjVIRExLnFXYDw1hB3DsJ+hoByVicSkTxERUhEnFfCRVjQDi5GQa91ULSK1YlEJI9RERIR55R4FRbdB2d3Qc8foXhNqxOJSB6kIiQizic5Ab7vDid+TTk7rEQDqxOJSB6lIiQizsWWBMv6QFQEdFkKZe60OpGI5GEqQiLiPIwNVj0MBxfDfQugfCurE4lIHqciJCLOwRiIGAi7v4IO30KlTlYnEpF8QEVIRPI/Y+CnF+CP6dD6M6gaZnUiEckndGVpEcn/Nk+ArVOgxXtQ82Gr04hIPqIiJCL5229vweaX4c5JUG+w1WlEJJ9RERKR/CvyI/j5BWg8GhqPsDqNiORDKkIikj/tmg0RT0O9IdDsFavTiEg+pSIkIvnP/vmw6iGo+Sjc8w64uVmdSETyKRUhEclfDi2HZffDbWEQOl0lSERuioqQiOQfUWtTbp1RoT20nQXuHlYnEpF8TkVIRPKH45thUScofTd0nAsenlYnEhEnoCIkInnfye2woB0E1YPOC6GAt9WJRMRJqAiJSN52djfMbw2FK0PXpeBZ0OpEIuJEVIREJO+6cBDmhYJfSei+CrwDrE4kIk5GRUhE8qbYozCvJXj5Q4/V4FvU6kQi4oRUhEQk77kcDeEtATfoGQF+wVYnEhEndVNFKC4uLrtyiIikuHoWwltB4mXouQb8y1idSEScmMNFyGaz8corr1C6dGkKFSrEoUOHAHjppZf47LPPsj2giLiQ+FiY3zZlj1CPNVC4ktWJRMTJOVyEXn31VWbOnMmbb76Jl5eXfXqNGjX49NNPszWciLiQxMuwsANcOJAyJuiWalYnEhEX4HARmj17NjNmzKBv3754ePzvqq61a9dm79692RpORFxEUjws7gqntkO3FRBUx+pEIuIiCji6wLFjx7j11lvTTLfZbCQmJmZLKBFxIcmJsDQMjq1PKUGl7rA6kYi4EIf3CFWvXp3169enmR4eHk7dunWzJZSIuAhbMqwcAIeXQ6f5EHKP1YlExMU4vEdo7NixDBgwgGPHjmGz2ViwYAH79u1j9uzZLF26NCcyiogzMjZY/QTsmwsdv4OK7a1OJCIuyOE9Qp07d+b7779nzZo1+Pn5MXbsWPbs2cP3339Pq1atciKjiDgbY2DtUNj5GbSdCVW6W51IRFyUmzHGWB0iN8XGxhIYGEhMTAwBAbpcv4glNoyBX1+D0I+g9pNWpxGRfCCnvr8d3iNUsWJFzp49m2b6hQsXqFixYraEEhEn9uuklBLU/C2VIBGxnMNF6MiRIyQnJ6eZHh8fz7Fjx7IllIg4qW3vwYZR0ORlaPCc1WlERDI/WHrJkiX2v69atYrAwED78+TkZCIiIihfvny2hhMRJ/LnZ7B2CDR4HpqMtTqNiAjgQBHq0qULAG5ubgwYMCDVa56enpQvX54pU6ZkazgRcRJ7voUfHoPaT8Hdb4Kbm9WJREQAB4qQzWYDoEKFCvz2228UK1Ysx0KJiBM5sBhW9IPq/aDlBypBIpKnOHwdocOHD+dEDhFxRkdWw9JeULkrtPkM3BweligikqMcLkIAly9f5qeffiIqKoqEhIRUrz3zzDPZEkxE8rl/1sPizlA2FNp/De5Z+udGRCRHOfwv0/bt22nfvj1Xrlzh8uXLFC1alDNnzlCwYEGCgoJUhEQEon9PuZN8yTugUzh4eFmdSEQkXQ7vpx46dCidOnXi/Pnz+Pr68ssvv/D3339Tv3593nrrrZzIKCL5yek/YX4buKUGdFkCnr5WJxIRyZDDRSgyMpLnnnsOd3d3PDw8iI+PJyQkhDfffJNRo0blREYRyS/O7YfwVuBfFrotB69CVicSEbkuh4uQp6cn7u4piwUFBREVFQVAYGAgR48ezd50IpJ/xByBeS3Bpyj0+AF8CludSETkhhweI1S3bl1+++03KleuTPPmzRk7dixnzpzhyy+/pEaNGjmRUUTyukvHITw0ZSxQzzVQsLjViUREMsXhPUITJ06kZMmSALz22msUKVKEp556itOnT/Pxxx9ne0ARyeOunE45HJYUDz0joFApqxOJiGSa7j4vIlkXdwHm3ZuyRyjsZyhaxepEIuKk8szd5zOybds2OnbsmF2rE5G8LuESLGgPsUegx2qVIBHJlxwqQqtWreL5559n1KhRHDp0CIC9e/fSpUsXGjZsaL8Nh4g4ucSrsOg+OLsTuq+C4jWtTiQikiWZHiz92Wef8dhjj1G0aFHOnz/Pp59+yttvv83gwYMJCwtj586dVKtWLSezikhekJwA3/eAE7+klKASDa1OJCKSZZneI/Tuu+/yxhtvcObMGb777jvOnDnDhx9+yJ9//sn06dNVgkRcgS0JlveFqDXQeTGUucvqRCIiNyXTg6X9/PzYtWsX5cuXxxiDt7c3a9eupVmzZjmdMVtpsLRIFhkbrHwI9nwN9y2AW++zOpGIuJCc+v7O9KGxq1evUrBgQQDc3Nzw9va2n0YvIk7OGIgYBLu/hA7fqASJiNNw6IKKn376KYUKpVwyPykpiZkzZ1KsWLFU8+imqyJOxhj4+UX44yNo/RlU7W11IhGRbJPpQ2Ply5fHzc3t+itzc7OfTZZZ06ZNY/LkyURHR1O7dm3ef/99GjVqlOH8Fy5cYPTo0SxYsIBz585Rrlw5pk6dSvv27TP1fjo0JuKgzRNg0zho8S7U0//oiIg1LD80duTIkWx702vmzp3LsGHDmD59Oo0bN2bq1Km0adOGffv2ERQUlGb+hIQEWrVqRVBQEOHh4ZQuXZq///6bwoULZ3s2EQF+n5JSgu6cqBIkIk7J0itLN27cmIYNG/LBBx8AYLPZCAkJYfDgwYwYMSLN/NOnT2fy5Mns3bsXT0/PLL2n9giJZNIf02HNU9B4FNz5mtVpRMTF5fkrSzsqISGBrVu3Ehoa+r8w7u6EhoayefPmdJdZsmQJTZo0YeDAgQQHB1OjRg0mTpxIcnJybsUWcQ27v4Q1T0PdZ6DZq1anERHJMQ7ffT67nDlzhuTkZIKDg1NNDw4OZu/evekuc+jQIX788Uf69u3L8uXLOXDgAE8//TSJiYmMGzcu3WXi4+OJj4+3P4+Njc2+DyHijPbPh5UPQo2HocU7cIOxgSIi+Zlle4SywmazERQUxIwZM6hfvz5hYWGMHj2a6dOnZ7jMpEmTCAwMtD9CQkJyMbFIPnN4BSy7H6r0glYfg1u++idCRMRhlv0rV6xYMTw8PDh58mSq6SdPnqREiRLpLlOyZEmqVKmCh4eHfVq1atWIjo4mISEh3WVGjhxJTEyM/XH06NHs+xAizuToOljSDSq0g3azwd3jRkuIiOR7WSpCBw8eZMyYMdx///2cOnUKgBUrVrBr165Mr8PLy4v69esTERFhn2az2YiIiKBJkybpLtOsWTMOHDiQ6uau+/fvp2TJknh5eaW7jLe3NwEBAakeIvIfx3+BhR2h9F3QcS54ZO1kBBGR/MbhIvTTTz9Rs2ZNfv31VxYsWMClS5cA+OOPPzIcp5ORYcOG8cknnzBr1iz27NnDU089xeXLl3nooYcA6N+/PyNHjrTP/9RTT3Hu3DmGDBnC/v37WbZsGRMnTmTgwIGOfgwRueZUJCxoB0F1ofNCKOBjdSIRkVzj8GDpESNG8OqrrzJs2DD8/f3t0++99177afCZFRYWxunTpxk7dizR0dHUqVOHlStX2gdQR0VF4e7+v64WEhLCqlWrGDp0KLVq1aJ06dIMGTKE4cOHO/oxRATg7B4IbwWFb4WuS8HTz+pEIiK5yuHrCBUqVIg///yTChUq4O/vzx9//EHFihU5cuQIVatWJS4uLqeyZgtdR0jk/104CHPuAt9boNe6lD9FRPKoPHMdocKFC3PixIk007dv307p0qWzJZSI5LDYozCvJXj5Q4/VKkEi4rIcLkK9e/dm+PDhREdH4+bmhs1mY+PGjTz//PP0798/JzKKSHa6fBLC//9Cpj3WgF/6Z2mKiLgCh4vQxIkTqVq1KiEhIVy6dInq1atz991307RpU8aMGZMTGUUku1w9lzImKOEi9IyAAF1XS0RcW5bvNRYVFcXOnTu5dOkSdevWpXLlytmdLUdojJC4rPjYlD1BMYch7Ce4pbrViUREMs3yu89fs2HDBu68807Kli1L2bJlsy2IiOSgxCsp1wk6vx96rlUJEhH5fw4fGrv33nupUKECo0aNYvfu3TmRSUSyU1I8LO4Kp7ZBtxUQXNfqRCIieYbDRej48eM899xz/PTTT9SoUYM6deowefJk/vnnn5zIJyI3IzkRlobBsZ+hy/dQKv2rtouIuCqHi1CxYsUYNGgQGzdu5ODBg/Ts2ZNZs2ZRvnx57r333pzIKCJZYUtOuYv84eXQaT6UbWF1IhGRPCfLg6WvSU5OZsWKFbz00kvs2LGD5OTk7MqWIzRYWlyCMbD6cdj5ecq9w6r0sDqRiMhNyTMXVLxm48aNPP3005QsWZI+ffpQo0YNli1blm3BRCSLjIF1Q+HPT6HNFypBIiLX4fBZYyNHjmTOnDkcP36cVq1a8e6779K5c2cKFiyYE/lExFGbxsK2d6Hlh3C7LnIqInI9Dhehn3/+mRdeeIFevXpRrFixnMgkIln16+vwy6tw92So85TVaURE8jyHi9DGjRtzIoeI3Kxt78OGkdBkHDR83uo0IiL5QqaK0JIlS2jXrh2enp4sWbLkuvPed9992RJMRBzw5+ew9hmo/1xKERIRkUzJ1Flj7u7uREdHExQUhLt7xuOr3dzcdNaYSG7bOweW9YHaT6SMC3JzszqRiEi2s/QWGzabLd2/i4jFDiyBFf2g+gPQcppKkIiIgxw+fX727NnEx8enmZ6QkMDs2bOzJZSIZMLfa2BpT6jUGdp8Dm5ZvhqGiIjLcviCih4eHpw4cYKgoKBU08+ePUtQUJAOjYnkhn82wPw2ENIcOi8CDy+rE4mI5Kg8c0FFYwxu6ex+/+effwgMDMyWUCJyHdG/w8IOULJRyq0zVIJERLIs06fP161bFzc3N9zc3GjZsiUFCvxv0eTkZA4fPkzbtm1zJKSI/L8zO1P2BN1SHbosAU9fqxOJiORrmS5CXbp0ASAyMpI2bdpQqFAh+2teXl6UL1+e7t27Z3tAEfl/5/+CeaHgXxa6LQcvf6sTiYjke5kuQuPGpVybpHz58oSFheHj45NjoUTkP2L/hnktwaco9PgBfIpYnUhExCk4fGXpAQMG5EQOEcnIpRMpJcjdE3qshoLFrU4kIuI0MlWEihYtyv79+ylWrBhFihRJd7D0NefOncu2cCIu78oZCA+FpHjovR78S1udSETEqWSqCL3zzjv4+/vb/369IiQi2STuAsxvDVfPQNjPEFje6kQiIk7H4esI5Xe6jpDkCwmXUs4OO7cHeq2D4rWsTiQiYqk8cx2hbdu28eeff9qfL168mC5dujBq1CgSEhKyLZiIy0qKg8Wd4cyf0H2VSpCISA5yuAg98cQT7N+/H4BDhw4RFhZGwYIFmTdvHi+++GK2BxRxKckJ8H0POL4Zui6FEg2tTiQi4tQcLkL79++nTp06AMybN4/mzZvzzTffMHPmTObPn5/d+URchy0Jlj8Af6+GzguhzN1WJxIRcXpZusXGtTvQr1mzhvbt2wMQEhLCmTNnsjediKswNvjhUfhrAXT8Dsq3sTqRiIhLcLgINWjQgFdffZUvv/ySn376iQ4dOgBw+PBhgoODsz2giNMzBiIGw67Z0O5LuLWz1YlERFyGw0Vo6tSpbNu2jUGDBjF69GhuvfVWAMLDw2natGm2BxRxasbAz8Phjw+h9SdQ7X6rE4mIuJRsO30+Li4ODw8PPD09s2N1OUanz0uesvkV2DQWWkyFekOsTiMikmfl1Pe3w7fYuGbr1q3s2bMHgOrVq1OvXr1sCyXiEn5/O6UE3fmaSpCIiEUcLkKnTp0iLCyMn376icKFCwNw4cIFWrRowZw5cyheXPdBErmhHTPgp+eg0UhoPMrqNCIiLsvhMUKDBw/m0qVL7Nq1i3PnznHu3Dl27txJbGwszzzzTE5kFHEuu7+C1U9C3cEpe4NERMQyDo8RCgwMZM2aNTRsmPpCb1u2bKF169ZcuHAhO/NlO40REkv9tQC+7wW3D0gZHO3m8P+LiIi4pDxziw2bzZbugGhPT0/79YVEJB2HV8LS3lClB7SaoRIkIpIHOPwv8b333suQIUM4fvy4fdqxY8cYOnQoLVu2zNZwIk7j6E+wpCuUb5tyrSB3D6sTiYgIWShCH3zwAbGxsZQvX55KlSpRqVIlKlSoQGxsLO+//35OZBTJ3078Cgs7Qqk7odN34JG3LzEhIuJKHD5rLCQkhG3bthEREWE/fb5atWqEhoZmeziRfO/UHzC/LRSvDV0WQQEfqxOJiMi/OFSE5s6dy5IlS0hISKBly5YMHjw4p3KJ5H9n90B4KyhcCbotA08/qxOJiMh/ZLoIffTRRwwcOJDKlSvj6+vLggULOHjwIJMnT87JfCL504VDEB4KfsHQfRV4B1qdSERE0pHpMUIffPAB48aNY9++fURGRjJr1iw+/PDDnMwmkj9d/AfmtYQCBaHHavC9xepEIiKSgUwXoUOHDjFgwAD78z59+pCUlMSJEydyJJhIvnT5ZEoJMjboGQF+JaxOJCIi15HpQ2Px8fH4+f1vjIO7uzteXl5cvXo1R4KJ5DtXz8H81pBwEcJ+hoCyVicSEZEbcGiw9EsvvUTBggXtzxMSEnjttdcIDPzf+Ie33347+9KJ5BfxsbCgHVw8Br1/hiK3Wp1IREQyIdNF6O6772bfvn2ppjVt2pRDhw7Zn7u5uWVfMpH8IvEKLOoE5/dBzx/hlupWJxIRkUzKdBFat25dDsYQyaeS4mFJNzi5Fbr/AMH1rE4kIiIOcPiCiiLy/5ITYVlvOLoOui2H0k2tTiQiIg5SERLJClsyrHwQDi2Fzoug7L1WJxIRkSxQERJxlDGw5inYNwc6zIGKHaxOJCIiWaQiJOIIY2DdMPjzE2g7E27raXUiERG5CQ7ffV7EpW0aB9umQstpcPuAG84uIiJ5W5aK0Pr163nggQdo0qQJx44dA+DLL79kw4YN2RpOJE/Z8gb88grc/SbUedrqNCIikg0cLkLz58+nTZs2+Pr6sn37duLj4wGIiYlh4sSJ2R5QJE/YPg3Wj4A7xkLDF6xOIyIi2cThIvTqq68yffp0PvnkEzw9Pe3TmzVrxrZt27I1nEiesPML+HEQ1B8GTV+2Oo2IiGQjh4vQvn37uPvuu9NMDwwM5MKFC9mRSSTv2DsXfngUaj0Bzd8CXT1dRMSpOFyESpQowYEDB9JM37BhAxUrVsyWUCJ5wsHvYcUDULUPhH6oEiQi4oQcLkKPPfYYQ4YM4ddff8XNzY3jx4/z9ddf8/zzz/PUU0/lREaR3Pf3Gvi+J1S6D9p+AW46wVJExBk5/K/7iBEj6NOnDy1btuTSpUvcfffdPProozzxxBMMHjw4SyGmTZtG+fLl8fHxoXHjxmzZsiVTy82ZMwc3Nze6dOmSpfcVSdexjbCoc8rVojt8C+663JaIiLNyM8aYrCyYkJDAgQMHuHTpEtWrV6dQoUJZCjB37lz69+/P9OnTady4MVOnTmXevHns27ePoKCgDJc7cuQId955JxUrVqRo0aIsWrQoU+8XGxtLYGAgMTExBAQEZCmzOLGTW+G7e1Nuntp1OXj6Wp1IRETIue/vLBeh7NK4cWMaNmzIBx98AIDNZiMkJITBgwczYsSIdJdJTk7m7rvv5uGHH2b9+vVcuHBBRUhu3pmdMLc5FKkMPVaDl7/ViURE5P/l1Pe3w/v8W7Rogdt1Bo3++OOPmV5XQkICW7duZeTIkfZp7u7uhIaGsnnz5gyXmzBhAkFBQTzyyCOsX7/+uu8RHx9vv9YRpPwgRdI4/xeEtwL/EOi2QiVIRMRFOFyE6tSpk+p5YmIikZGR7Ny5kwEDHLvlwJkzZ0hOTiY4ODjV9ODgYPbu3ZvuMhs2bOCzzz4jMjIyU+8xadIkxo8f71AucTGxf8O8luBdGHr8AD5FrE4kIiK5xOEi9M4776Q7/eWXX+bSpUs3Heh6Ll68SL9+/fjkk08oVqxYppYZOXIkw4YNsz+PjY0lJCQkpyJKfnPpBMwLTRkQ3WMNFMx4XJqIiDifbDsd5oEHHqBRo0a89dZbmV6mWLFieHh4cPLkyVTTT548SYkSJdLMf/DgQY4cOUKnTp3s02w2GwAFChRg3759VKpUKdUy3t7eeHt7O/JRxFVcOZNyOCzpKvReD/6lrU4kIiK5LNsujrJ582Z8fHwcWsbLy4v69esTERFhn2az2YiIiKBJkyZp5q9atSp//vknkZGR9sd9991HixYtiIyM1J4eybz4GJjfBq6ehp4REFjB6kQiImIBh/cIdevWLdVzYwwnTpzg999/56WXXnI4wLBhwxgwYAANGjSgUaNGTJ06lcuXL/PQQw8B0L9/f0qXLs2kSZPw8fGhRo0aqZYvXLgwQJrpIhlKvAwL2kPsYei1DoreZnUiERGxiMNFKDAwMNVzd3d3brvtNiZMmEDr1q0dDhAWFsbp06cZO3Ys0dHR1KlTh5UrV9oHUEdFReHurqv6SjZJiku5WOLpHSl7gorXsjqRiIhYyKHrCCUnJ7Nx40Zq1qxJkSL588waXUfIhSUnwJLuEBUB3VdCmbQ3DxYRkbwpp76/HdrV4uHhQevWrXWXecl/bMmw/AE4sgo6L1QJEhERIAuDpWvUqMGhQ4dyIotIzjA2+OFR+GsBdJwL5dtYnUhERPIIh4vQq6++yvPPP8/SpUs5ceIEsbGxqR4ieYox8OMzsGsWtJsNlbtanUhERPKQTI8RmjBhAs899xz+/v+79cC/b7VhjMHNzY3k5OTsT5mNNEbIhRgD60fAb29Cq0+g1qNWJxIRkSyy/KarHh4enDhxgj179lx3vubNm2dLsJyiIuRCfnkVNr4E97wD9Z+1Oo2IiNwEy2+6eq0v5fWiIwLA1ndSSlCzV1WCREQkQw6NEbreXedF8owdM2DdMGg0AhqPsjqNiIjkYQ5dULFKlSo3LEPnzp27qUAiN2XP17D6SagzCO6cCCrvIiJyHQ4VofHjx6e5srRInvHXQlgxAG5/EO59VyVIRERuyKEi1Lt3b4KCgnIqi0jWHV4JS8OgSg9o/Qm46bYsIiJyY5n+ttD4IMmzjv4ES7pC+bbQ7ktw97A6kYiI5BOZLkIO3JJMJPec2AILO0KpZtDpO/DwtDqRiIjkI5k+NGaz2XIyh4jjTv0B89tA8drQZTEU8LE6kYiI5DMaSCH509m9EN4KAitCt2Xg6Wd1IhERyYdUhCT/iTkM4aFQMAi6rwJvnckoIiJZoyIk+cvFf2BeSyjgCz1WQ8FiVicSEZF8TEVI8o8rp2BeKNiSoWcEFCppdSIREcnnHLqOkIhlrp5LGROUEANh6yGgrNWJRETECagISd6XcBEWtIOLxyDsJyhyq9WJRETESagISd6WeCXlOkHn9kKvtVDsdqsTiYiIE1ERkrwrKR6WdIfo31MGRgfXszqRiIg4GRUhyZtsSbDsfji6Froug9JNrU4kIiJOSEVI8h5jg5UPwqHv4b6FUK6l1YlERMRJqQhJ3mIMrHkK9n4LHb6FSh2tTiQiIk5MRUjyDmPgp+dgxwxo8wXc1svqRCIi4uR0QUXJOza9DFvfgXs/gBoPWp1GRERcgIqQ5A1b3oRfJsBdb0DdgVanERERF6EiJNaL/BDWD4c7XoJGL1qdRkREXIiKkFhr1yyIGAj1h0LT8VanERERF6MiJNbZ9x2sehhqPQ7Np4Cbm9WJRETExagIiTUOLoXlfaHq/dDyQ5UgERGxhIqQ5L6/I+D7HlCxE7SdCe4eVicSEREXpSIkuevYRlh0H4S0SLlgorsuZSUiItZREZLcc3IbLGgPJRrCffOhgLfViURExMWpCEnuOLMLwlvDLdWg6/fgWdDqRCIiIipCkgvOH4DwUPAvA91WgJe/1YlEREQAFSHJabFRMK8leAVCjx/Ap4jViUREROxUhCTnXDqRUoLcPaBnBBQMsjqRiIhIKjplR3LG1bMQ3gqSrkLv9eBf2upEIiIiaagISfaLj4H5beDKKQj7GQIrWJ1IREQkXSpCkr0SL8OCDnDhIPRaB7dUtTqRiIhIhlSEJPskxcGiLnD6D+i5BoJqW51IRETkulSEJHskJ8L3veD4Bui2Eko2tjqRiIjIDakIyc2zJcOKfnBkJXRZAiHNrU4kIiKSKSpCcnOMDX54FPaHQ6d5UKGt1YlEREQyTUVIss4Y+HEI7JoF7WZD5a5WJxIREXGIipBkjTGwfiREfgCtPobqD1idSERExGG6srRkza8T4bc34J53oNbjVqcRERHJEhUhcdzWqbBxDDR7Beo/a3UaERGRLFMREsfs+ATWDYWGw6HxaKvTiIiI3BQVIcm8Pd/A6iegzkC4axK4uVmdSERE5KaoCEnm/LUIVvSH2wfAve+pBImIiFNQEZIbO7IKloVB5e7Q+lNw0382IiLiHPSNJtf3z8+wuCuUaw3tvwR3D6sTiYiIZBsVIcnYiS2wsCOUapJy1WgPL6sTiYiIZCsVIUnf6R2woC0UqwmdF0MBH6sTiYiIZDsVIUnr3D4IbwUBFaDrMvAqZHUiERGRHKEiJKnFHIZ5LcG3GHRfBT6FrU4kIiKSY1SE5H8uHkspQQV8oMcaKFjM6kQiIiI5Kk8UoWnTplG+fHl8fHxo3LgxW7ZsyXDeTz75hLvuuosiRYpQpEgRQkNDrzu/ZNKVUxAeCrYk6BkBhUpanUhERCTHWV6E5s6dy7Bhwxg3bhzbtm2jdu3atGnThlOnTqU7/7p167j//vtZu3YtmzdvJiQkhNatW3Ps2LFcTu5E4s5DeGuIv5BSggLKWZ1IREQkV7gZY4yVARo3bkzDhg354IMPALDZbISEhDB48GBGjBhxw+WTk5MpUqQIH3zwAf3797/h/LGxsQQGBhITE0NAQMBN58/3Ei6mDIw+fwDC1kGxGlYnEhERSSOnvr8t3SOUkJDA1q1bCQ0NtU9zd3cnNDSUzZs3Z2odV65cITExkaJFi6b7enx8PLGxsake8v8Sr8DCTnB2D/RYpRIkIiIux9IidObMGZKTkwkODk41PTg4mOjo6EytY/jw4ZQqVSpVmfq3SZMmERgYaH+EhITcdG6nkBQPS7pD9G/QbTkE17c6kYiISK6zfIzQzXj99deZM2cOCxcuxMcn/Qv+jRw5kpiYGPvj6NGjuZwyD7IlwfI+cHQtdFkCpZtZnUhERMQSBax882LFiuHh4cHJkydTTT958iQlSpS47rJvvfUWr7/+OmvWrKFWrVoZzuft7Y23t3e25HUKxgYrH4KDS+C+BVCupdWJRERELGPpHiEvLy/q169PRESEfZrNZiMiIoImTZpkuNybb77JK6+8wsqVK2nQoEFuRHUOxsCap2HvN9DuK6jUyepEIiIilrJ0jxDAsGHDGDBgAA0aNKBRo0ZMnTqVy5cv89BDDwHQv39/SpcuzaRJkwB44403GDt2LN988w3ly5e3jyUqVKgQhQrpVhAZMgZ+eh52fAxtPoeqYVYnEhERsZzlRSgsLIzTp08zduxYoqOjqVOnDitXrrQPoI6KisLd/X87rj766CMSEhLo0aNHqvWMGzeOl19+OTej5y+bx8PWt+He96HGQ1anERERyRMsv45QbnPJ6wj9Nhl+fhHueh0aDbc6jYiIiMOc8jpCkgsiP0wpQXeMUQkSERH5DxUhZ7ZrFkQMhHrPQtMJVqcRERHJc1SEnNX+cFj1MNR8FO55G9zcrE4kIiKS56gIOaNDy2DZ/XBbbwidrhIkIiKSARUhZxP1Y8qtMyp2hLYzwd3D6kQiIiJ5loqQMzm2CRbdByH3QIc54OFpdSIREZE8TUXIWZzcBgvbp9w89b4FUEC3FREREbkRFSFncGYXhLeGIrdB16XgWdDqRCIiIvmCilB+d/4AhLcC/9LQbQV4+VudSEREJN9QEcrPYqNgXkvwCoDuP4BvUasTiYiI5CsqQvnV5WgIDwU3d+i5BvyCrU4kIiKS71h+01XJgqtnUw6HJV6GsPXgX8bqRCIiIvmSilB+Ex8D89vA5ZMQ9jMUrmh1IhERkXxLRSg/SbwMCzvChYPQay3cUtXqRCIiIvmailB+kRQHi7vCqUjosRqC6lidSEREJN9TEcoPkhNhaRgcW59yinypO6xOJCIi4hRUhPI6WzKs6A+HV0CXxSm3zxAREZFsoSKUlxkbrH4c9s+DTt9BhXZWJxIREXEqKkJ5lTGw9lnY+QW0mw2Vu1mdSERExOmoCOVVG0bD9vchdDpUf8DqNCIiIk5JV5bOi36dCFsmQfMpUPsJq9OIiIg4LRWhvGbbuyl7g5pOgAbDrE4jIiLi1FSE8pIdn6aMC2r4Itwxxuo0IiIiTk9FKK/Y823KGWK1n4a7Xgc3N6sTiYiIOD0VobzgwGJY0Q9u7w8t31cJEhERySUqQlY78gMs7ZVyenzrT8FNm0RERCS36FvXSv+sh8VdoFwraP8VuOtqBiIiIrlJRcgq0b/Bwg5Qqgl0nAceXlYnEhERcTkqQlY4vQPmt4FbakDnxeDpa3UiERERl6QilNvO7YfwVhBQHrotB69CVicSERFxWSpCuSnmCMxrCb7FoPsP4FPY6kQiIiIuTaNzc8ul4yklqIA39FgNBYtZnUhEMskYQ1JSEsnJyVZHEXFqnp6eeHh45Op7qgjlhiunYV4o2BKh93ooVMrqRCKSSQkJCZw4cYIrV65YHUXE6bm5uVGmTBkKFcq9YSMqQjkt7jyEt4a4cxD2MwSUszqRiGSSzWbj8OHDeHh4UKpUKby8vHDTBU9FcoQxhtOnT/PPP/9QuXLlXNszpCKUkxIuwoL2cDEKwn6ColWsTiQiDkhISMBmsxESEkLBggWtjiPi9IoXL86RI0dITExUEcr3Eq/Covvg7G7o9SMUq2F1IhHJInd3nVcikhus2OOqIpQTkhPg++5wYgt0XwXB9a1OJCIiIulQEcputiRY1geiIqDrMihzp9WJREREJAPa35udjA1WPQwHF0OncCgXanUiERFx0L59+yhRogQXL160OorTueOOO5g/f77VMVJREcouxkDEQNj9FbT7Cip1sjqRiLiwBx98EDc3N9zc3PD09KRChQq8+OKLxMXFpZl36dKlNG/eHH9/fwoWLEjDhg2ZOXNmuuudP38+99xzD4GBgRQqVIhatWoxYcIEzp07l8OfKPeMHDmSwYMH4+/vb3WUHDNt2jTKly+Pj48PjRs3ZsuWLTdc5sKFCwwcOJCSJUvi7e1NlSpVWL58eap5jh07xgMPPMAtt9yCr68vNWvW5Pfff7e/PmbMGEaMGIHNZsv2z5RVKkLZwRj46QX4Yzq0/hSqhlmdSESEtm3bcuLECQ4dOsQ777zDxx9/zLhx41LN8/7779O5c2eaNWvGr7/+yo4dO+jduzdPPvkkzz//fKp5R48eTVhYGA0bNmTFihXs3LmTKVOm8Mcff/Dll1/m2udKSEjIsXVHRUWxdOlSHnzwwZtaT05mvFlz585l2LBhjBs3jm3btlG7dm3atGnDqVOnMlwmISGBVq1aceTIEcLDw9m3bx+ffPIJpUuXts9z/vx5mjVrhqenJytWrGD37t1MmTKFIkWK2Odp164dFy9eZMWKFTn6GR1iXExMTIwBTExMTPatdOPLxryFMVvfy751iojlrl69anbv3m2uXr1qdRSHDRgwwHTu3DnVtG7dupm6devan0dFRRlPT08zbNiwNMu/9957BjC//PKLMcaYX3/91QBm6tSp6b7f+fPnM8xy9OhR07t3b1OkSBFTsGBBU79+fft608s5ZMgQ07x5c/vz5s2bm4EDB5ohQ4aYW265xdxzzz3m/vvvN7169Uq1XEJCgrnlllvMrFmzjDHGJCcnm4kTJ5ry5csbHx8fU6tWLTNv3rwMcxpjzOTJk02DBg1STTtz5ozp3bu3KVWqlPH19TU1atQw33zzTap50stojDF//vmnadu2rfHz8zNBQUHmgQceMKdPn7Yvt2LFCtOsWTMTGBhoihYtajp06GAOHDhw3Yw3q1GjRmbgwIH258nJyaZUqVJm0qRJGS7z0UcfmYoVK5qEhIQM5xk+fLi58847b/j+Dz30kHnggQfSfe16v3M58v1tjNEeoZv121uw+WW4cxLUG2x1GhHJDYlX4OS23H0k3tyVrXfu3MmmTZvw8vKyTwsPDycxMTHNnh+AJ554gkKFCvHtt98C8PXXX1OoUCGefvrpdNdfuHDhdKdfunSJ5s2bc+zYMZYsWcIff/zBiy++6PChkVmzZuHl5cXGjRuZPn06ffv25fvvv+fSpUv2eVatWsWVK1fo2rUrAJMmTWL27NlMnz6dXbt2MXToUB544AF++umnDN9n/fr1NGjQINW0uLg46tevz7Jly9i5cyePP/44/fr1S3M46b8ZL1y4wL333kvdunX5/fffWblyJSdPnqRXr172ZS5fvsywYcP4/fffiYiIwN3dna5du1735zNx4kQKFSp03UdUVFS6yyYkJLB161ZCQ/83htXd3Z3Q0FA2b96c4XsuWbKEJk2aMHDgQIKDg6lRowYTJ05MdduZJUuW0KBBA3r27ElQUBB169blk08+SbOuRo0asX79+gzfK7fprLGbEfkR/PwCNB4NjUdYnUZEcsu5vfBVLl8W44GtEFzPoUWWLl1KoUKFSEpKIj4+Hnd3dz744AP76/v37ycwMJCSJUumWdbLy4uKFSuyf/9+AP766y8qVqyIp6enQxm++eYbTp8+zW+//UbRokUBuPXWWx1aB0DlypV588037c8rVaqEn58fCxcupF+/fvb3uu+++/D39yc+Pp6JEyeyZs0amjRpAkDFihXZsGEDH3/8Mc2bN0/3ff7+++80Rah06dKpyuLgwYNZtWoV3333HY0aNcow46uvvkrdunWZOHGifdrnn39OSEgI+/fvp0qVKnTv3j3Ve33++ecUL16c3bt3U6NG+tefe/LJJ1OVqfSUKpX+rZzOnDlDcnIywcHBqaYHBwezd+/eDNd36NAhfvzxR/r27cvy5cs5cOAATz/9NImJifbDrYcOHeKjjz5i2LBhjBo1it9++41nnnkGLy8vBgwYkCrb0aNHsdlseeIaXSpCWbVrNkQ8DfWGQLNXrE4jIrmpaNWUYpLb7+mgFi1a8NFHH3H58mXeeecdChQokOaLN7OMMVlaLjIykrp169pLUFbVr5+6eBYoUIBevXrx9ddf069fPy5fvszixYuZM2cOAAcOHODKlSu0atUq1XIJCQnUrVs3w/e5evUqPj4+qaYlJyczceJEvvvuO44dO0ZCQgLx8fFprjb+34x//PEHa9euTfe+WQcPHqRKlSr89ddfjB07ll9//ZUzZ87Y9wRFRUVlWISKFi160z9PR9lsNoKCgpgxYwYeHh7Ur1+fY8eOMXnyZHsRstlsNGjQwF786taty86dO5k+fXqqIuTr64vNZiM+Ph5fX99c/RzpURHKiv3zYdVDUPNRuOcd0L2HRFyLZ0GH985Ywc/Pz7735fPPP6d27dp89tlnPPLIIwBUqVKFmJgYjh8/nmYPQkJCAgcPHqRFixb2eTds2EBiYqJDe4Vu9EXn7u6epmQlJiam+1n+q2/fvjRv3pxTp06xevVqfH19adu2LYD9kNmyZctSDegF8Pb2zjBPsWLFOH/+fKppkydP5t1332Xq1KnUrFkTPz8/nn322TQDov+b8dKlS3Tq1Ik33ngjzftc2wvXqVMnypUrxyeffEKpUqWw2WzUqFHjuoOtJ06cmGovU3p2795N2bJl0/18Hh4enDx5MtX0kydPUqJEiQzXV7JkyTR3hq9WrRrR0dEkJCTg5eVFyZIlqV69eqrlqlWrluZ0+XPnzuHn55cnShDorDHHHVoOy+6H28IgdLpKkIjkC+7u7owaNYoxY8Zw9epVALp3746npydTpkxJM//06dO5fPky999/PwB9+vTh0qVLfPjhh+mu/8KFC+lOr1WrFpGRkRmeXl+8eHFOnDiRalpkZGSmPlPTpk0JCQlh7ty5fP311/Ts2dNe0qpXr463tzdRUVHceuutqR4hISEZrrNu3brs3r071bSNGzfSuXNnHnjgAWrXrp3qkOH11KtXj127dlG+fPk0Gfz8/Dh79iz79u1jzJgxtGzZkmrVqqUpYel58skniYyMvO4jo0NjXl5e1K9fn4iICPs0m81GRESE/RBiepo1a8aBAwdSjV3av38/JUuWtI87a9asGfv27Uu13P79+ylXLvXNxnfu3HndvXK5LluHXucDNzXq/O8fjZnqY8zCzsYkZTxyXkScg7OdNZaYmGhKly5tJk+ebJ/2zjvvGHd3dzNq1CizZ88ec+DAATNlyhTj7e1tnnvuuVTLv/jii8bDw8O88MILZtOmTebIkSNmzZo1pkePHhmeTRYfH2+qVKli7rrrLrNhwwZz8OBBEx4ebjZt2mSMMWblypXGzc3NzJo1y+zfv9+MHTvWBAQEpDlrbMiQIemuf/To0aZ69eqmQIECZv369Wleu+WWW8zMmTPNgQMHzNatW817771nZs6cmeHPbcmSJSYoKMgkJSXZpw0dOtSEhISYjRs3mt27d5tHH33UBAQEpPr5ppfx2LFjpnjx4qZHjx5my5Yt5sCBA2blypXmwQcfNElJSSY5Odnccsst5oEHHjB//fWXiYiIMA0bNjSAWbhwYYYZb9acOXOMt7e3mTlzptm9e7d5/PHHTeHChU10dLR9nn79+pkRI0bYn0dFRRl/f38zaNAgs2/fPrN06VITFBRkXn31Vfs8W7ZsMQUKFDCvvfaa+euvv8zXX39tChYsaL766qtU79+8eXMzYcKEdLNZcdaYilBmHdtkzLt+xsxrbUxiXM6EE5E8xdmKkDHGTJo0yRQvXtxcunTJPm3x4sXmrrvuMn5+fsbHx8fUr1/ffP755+mud+7cuebuu+82/v7+xs/Pz9SqVctMmDDhuqfPHzlyxHTv3t0EBASYggULmgYNGphff/3V/vrYsWNNcHCwCQwMNEOHDjWDBg3KdBHavXu3AUy5cuWMzWZL9ZrNZjNTp041t912m/H09DTFixc3bdq0MT/99FOGWRMTE02pUqXMypUr7dPOnj1rOnfubAoVKmSCgoLMmDFjTP/+/W9YhIwxZv/+/aZr166mcOHCxtfX11StWtU8++yz9qyrV6821apVM97e3qZWrVpm3bp1OV6EjDHm/fffN2XLljVeXl6mUaNG9ssZ/PvzDBgwINW0TZs2mcaNGxtvb29TsWJF89prr6UqjMYY8/3335saNWoYb29vU7VqVTNjxoxUr//zzz/G09PTHD16NN1cVhQhN2OyOAIun4qNjSUwMJCYmBgCAgIyt9DJ7TCvBRSrBd1XpowPEBGnFxcXx+HDh6lQoUKaAbTivKZNm8aSJUtYtWqV1VGczvDhwzl//jwzZsxI9/Xr/c5l6fs7EzRY+kbO7ob5raFIFei6VCVIRMTJPfHEE1y4cIGLFy869W02rBAUFMSwYcOsjpGKitD1XDgI80LBryR0Wwne2ddARUQkbypQoACjR4+2OoZTeu6556yOkIbOGstI7FGY1xK8/KHHavDN3Ws2iIiISM5TEUrP5WgIbwm4Qc8I8Au+4SIiIiKS/+jQ2H9dPQvhrSDxMoT9DP5lrE4kIhZzsXNKRCxjxe+a9gj9W3wszG+bskeoxxooXMnqRCJioWsX57ty5eZueCoimXPtitr/voJ1TtMeoWsSL8PCDnDhAPRaC7dUszqRiFjMw8ODwoULc+rUKQAKFiyIm64mL5IjbDYbp0+fpmDBghQokHv1REUIICkeFneFU9tT9gQF1bE6kYjkEdfuv3StDIlIznF3d6ds2bK5+j8cKkLJibA0DI6th24roNQdVicSkTzEzc2NkiVLEhQUlO7NQEUk+3h5eeHunrujdvJEEZo2bRqTJ08mOjqa2rVr8/7779OoUaMM5583bx4vvfQSR44coXLlyrzxxhu0b9/e8Te2JcPKAXB4OXReBCH3ZPkziIhz8/DwyNVxCyKSOywfLD137lyGDRvGuHHj2LZtG7Vr16ZNmzYZ7obetGkT999/P4888gjbt2+nS5cudOnShZ07dzr2xsYGq5+AfXOhw7dQMQtFSkRERPI1y+811rhxYxo2bMgHH3wApAyWCgkJYfDgwYwYMSLN/GFhYVy+fJmlS5fap91xxx3UqVOH6dOn3/D97PcqWfIEAfs/hnazoXq/7PtAIiIiku1y6l5jlu4RSkhIYOvWrYSGhtqnubu7ExoayubNm9NdZvPmzanmB2jTpk2G82dox8cQ+pFKkIiIiAuzdIzQmTNnSE5OJjg49ZWbg4OD2bt3b7rLREdHpzt/dHR0uvPHx8cTHx9vfx4TEwNAbJ0xUKEPxMbezEcQERGRXBD7/9/X2X0gK08Mls5JkyZNYvz48Wmmh3R8FXg19wOJiIhIlp09e5bAwMBsW5+lRahYsWJ4eHhw8uTJVNNPnjxpv3bHf5UoUcKh+UeOHMmwYcPszy9cuEC5cuWIiorK1h+kOC42NpaQkBCOHj2arcd7JWu0PfIObYu8Q9si74iJiaFs2bIULZq9N0G3tAh5eXlRv359IiIi6NKlC5AyWDoiIoJBgwalu0yTJk2IiIjg2WeftU9bvXo1TZo0SXd+b29vvL2900wPDAzUf9R5REBAgLZFHqLtkXdoW+Qd2hZ5R3ZfZ8jyQ2PDhg1jwIABNGjQgEaNGjF16lQuX77MQw89BED//v0pXbo0kyZNAmDIkCE0b96cKVOm0KFDB+bMmcPvv//OjBkzrPwYIiIikg9ZXoTCwsI4ffo0Y8eOJTo6mjp16rBy5Ur7gOioqKhU7a9p06Z88803jBkzhlGjRlG5cmUWLVpEjRo1rPoIIiIikk9ZXoQABg0alOGhsHXr1qWZ1rNnT3r27Jml9/L29mbcuHHpHi6T3KVtkbdoe+Qd2hZ5h7ZF3pFT28LyCyqKiIiIWMXyW2yIiIiIWEVFSERERFyWipCIiIi4LBUhERERcVlOWYSmTZtG+fLl8fHxoXHjxmzZsuW688+bN4+qVavi4+NDzZo1Wb58eS4ldX6ObItPPvmEu+66iyJFilCkSBFCQ0NvuO3EMY7+blwzZ84c3Nzc7Bc+lZvn6La4cOECAwcOpGTJknh7e1OlShX9W5VNHN0WU6dO5bbbbsPX15eQkBCGDh1KXFxcLqV1Xj///DOdOnWiVKlSuLm5sWjRohsus27dOurVq4e3tze33norM2fOdPyNjZOZM2eO8fLyMp9//rnZtWuXeeyxx0zhwoXNyZMn051/48aNxsPDw7z55ptm9+7dZsyYMcbT09P8+eefuZzc+Ti6Lfr06WOmTZtmtm/fbvbs2WMefPBBExgYaP75559cTu6cHN0e1xw+fNiULl3a3HXXXaZz5865E9bJObot4uPjTYMGDUz79u3Nhg0bzOHDh826detMZGRkLid3Po5ui6+//tp4e3ubr7/+2hw+fNisWrXKlCxZ0gwdOjSXkzuf5cuXm9GjR5sFCxYYwCxcuPC68x86dMgULFjQDBs2zOzevdu8//77xsPDw6xcudKh93W6ItSoUSMzcOBA+/Pk5GRTqlQpM2nSpHTn79Wrl+nQoUOqaY0bNzZPPPFEjuZ0BY5ui/9KSkoy/v7+ZtasWTkV0aVkZXskJSWZpk2bmk8//dQMGDBARSibOLotPvroI1OxYkWTkJCQWxFdhqPbYuDAgebee+9NNW3YsGGmWbNmOZrT1WSmCL344ovm9ttvTzUtLCzMtGnTxqH3cqpDYwkJCWzdupXQ0FD7NHd3d0JDQ9m8eXO6y2zevDnV/ABt2rTJcH7JnKxsi/+6cuUKiYmJ2X6DPVeU1e0xYcIEgoKCeOSRR3IjpkvIyrZYsmQJTZo0YeDAgQQHB1OjRg0mTpxIcnJybsV2SlnZFk2bNmXr1q32w2eHDh1i+fLltG/fPlcyy/9k1/d3nriydHY5c+YMycnJ9ttzXBMcHMzevXvTXSY6Ojrd+aOjo3MspyvIyrb4r+HDh1OqVKk0/6GL47KyPTZs2MBnn31GZGRkLiR0HVnZFocOHeLHH3+kb9++LF++nAMHDvD000+TmJjIuHHjciO2U8rKtujTpw9nzpzhzjvvxBhDUlISTz75JKNGjcqNyPIvGX1/x8bGcvXqVXx9fTO1HqfaIyTO4/XXX2fOnDksXLgQHx8fq+O4nIsXL9KvXz8++eQTihUrZnUcl2ez2QgKCmLGjBnUr1+fsLAwRo8ezfTp062O5nLWrVvHxIkT+fDDD9m2bRsLFixg2bJlvPLKK1ZHkyxyqj1CxYoVw8PDg5MnT6aafvLkSUqUKJHuMiVKlHBofsmcrGyLa9566y1ef/111qxZQ61atXIypstwdHscPHiQI0eO0KlTJ/s0m80GQIECBdi3bx+VKlXK2dBOKiu/GyVLlsTT0xMPDw/7tGrVqhEdHU1CQgJeXl45mtlZZWVbvPTSS/Tr149HH30UgJo1a3L58mUef/xxRo8eneom4ZKzMvr+DggIyPTeIHCyPUJeXl7Ur1+fiIgI+zSbzUZERARNmjRJd5kmTZqkmh9g9erVGc4vmZOVbQHw5ptv8sorr7By5UoaNGiQG1FdgqPbo2rVqvz5559ERkbaH/fddx8tWrQgMjKSkJCQ3IzvVLLyu9GsWTMOHDhgL6MA+/fvp2TJkipBNyEr2+LKlStpys61gmp0685clW3f346N48775syZY7y9vc3MmTPN7t27zeOPP24KFy5soqOjjTHG9OvXz4wYMcI+/8aNG02BAgXMW2+9Zfbs2WPGjRun0+eziaPb4vXXXzdeXl4mPDzcnDhxwv64ePGiVR/BqTi6Pf5LZ41lH0e3RVRUlPH39zeDBg0y+/btM0uXLjVBQUHm1VdfteojOA1Ht8W4ceOMv7+/+fbbb82hQ4fMDz/8YCpVqmR69epl1UdwGhcvXjTbt28327dvN4B5++23zfbt283ff/9tjDFmxIgRpl+/fvb5r50+/8ILL5g9e/aYadOm6fT5a95//31TtmxZ4+XlZRo1amR++eUX+2vNmzc3AwYMSDX/d999Z6pUqWK8vLzM7bffbpYtW5bLiZ2XI9uiXLlyBkjzGDduXO4Hd1KO/m78m4pQ9nJ0W2zatMk0btzYeHt7m4oVK5rXXnvNJCUl5XJq5+TItkhMTDQvv/yyqVSpkvHx8TEhISHm6aefNufPn8/94E5m7dq16X4HXPv5DxgwwDRv3jzNMnXq1DFeXl6mYsWK5osvvnD4fd2M0b48ERERcU1ONUZIRERExBEqQiIiIuKyVIRERETEZakIiYiIiMtSERIRERGXpSIkIiIiLktFSERERFyWipCIpDJz5kwKFy5sdYwsc3NzY9GiRded58EHH6RLly65kkdE8jYVIREn9OCDD+Lm5pbmceDAAaujMXPmTHsed3d3ypQpw0MPPcSpU6eyZf0nTpygXbt2ABw5cgQ3NzciIyNTzfPuu+8yc+bMbHm/jLz88sv2z+nh4UFISAiPP/44586dc2g9Km0iOcup7j4vIv/Ttm1bvvjii1TTihcvblGa1AICAti3bx82m40//viDhx56iOPHj7Nq1aqbXndGdw3/t8DAwJt+n8y4/fbbWbNmDcnJyezZs4eHH36YmJgY5s6dmyvvLyI3pj1CIk7K29ubEiVKpHp4eHjw9ttvU7NmTfz8/AgJCeHpp5/m0qVLGa7njz/+oEWLFvj7+xMQEED9+vX5/fff7a9v2LCBu+66C19fX0JCQnjmmWe4fPnydbO5ublRokQJSpUqRbt27XjmmWdYs2YNV69exWazMWHCBMqUKYO3tzd16tRh5cqV9mUTEhIYNGgQJUuWxMfHh3LlyjFp0qRU6752aKxChQoA1K1bFzc3N+655x4g9V6WGTNmUKpUqVR3dgfo3LkzDz/8sP354sWLqVevHj4+PlSsWJHx48eTlJR03c9ZoEABSpQoQenSpQkNDaVnz56sXr3a/npycjKPPPIIFSpUwNfXl9tuu413333X/vrLL7/MrFmzWLx4sX3v0rp16wA4evQovXr1onDhwhQtWpTOnTtz5MiR6+YRkbRUhERcjLu7O++99x67du1i1qxZ/Pjjj7z44osZzt+3b1/KlCnDb7/9xtatWxkxYgSenp4AHDx4kLZt29K9e3d27NjB3Llz2bBhA4MGDXIok6+vLzabjaSkJN59912mTJnCW2+9xY4dO2jTpg333Xcff/31FwDvvfceS5Ys4bvvvmPfvn18/fXXlC9fPt31btmyBYA1a9Zw4sQJFixYkGaenj17cvbsWdauXWufdu7cOVauXEnfvn0BWL9+Pf3792fIkCHs3r2bjz/+mJkzZ/Laa69l+jMeOXKEVatW4eXlZZ9ms9koU6YM8+bNY/fu3YwdO5ZRo0bx3XffAfD888/Tq1cv2rZty4kTJzhx4gRNmzYlMTGRNm3a4O/vz/r169m4cSOFChWibdu2JCQkZDqTiIBT3n1exNUNGDDAeHh4GD8/P/ujR48e6c47b948c8stt9iff/HFFyYwMND+3N/f38ycOTPdZR955BHz+OOPp5q2fv164+7ubq5evZruMv9d//79+02VKlVMgwYNjDHGlCpVyrz22muplmnYsKF5+umnjTHGDB482Nx7773GZrOlu37ALFy40BhjzOHDhw1gtm/fnmqeAQMGmM6dO9ufd+7c2Tz88MP25x9//LEpVaqUSU5ONsYY07JlSzNx4sRU6/jyyy9NyZIl081gjDHjxo0z7u7uxs/Pz/j4+NjvpP32229nuIwxxgwcONB07949w6zX3vu2225L9TOIj483vr6+ZtWqVdddv4ikpjFCIk6qRYsWfPTRR/bnfn5+QMrekUmTJrF3715iY2NJSkoiLi6OK1euULBgwTTrGTZsGI8++ihffvml/fBOpUqVgJTDZjt27ODrr7+2z2+MwWazcfjwYapVq5ZutpiYGAoVKoTNZiMuLo4777yTTz/9lNjYWI4fP06zZs1Szd+sWTP++OMPIOWwVqtWrbjtttto27YtHTt2pHXr1jf1s+rbty+PPfYYH374Id7e3nz99df07t0bd3d3++fcuHFjqj1AycnJ1/25Adx2220sWbKEuLg4vvrqKyIjIxk8eHCqeaZNm8bnn39OVFQUV69eJSEhgTp16lw37x9//MGBAwfw9/dPNT0uLo6DBw9m4Scg4rpUhESclJ+fH7feemuqaUeOHKFjx4489dRTvPbaaxQtWpQNGzbwyCOPkJCQkO4X+ssvv0yfPn1YtmwZK1asYNy4ccyZM4euXbty6dIlnnjiCZ555pk0y5UtWzbDbP7+/mzbtg13d3dKliyJr68vALGxsTf8XPXq1ePw4cOsWLGCNWvW0KtXL0JDQwkPD7/hshnp1KkTxhiWLVtGw4YNWb9+Pe+884799UuXLjF+/Hi6deuWZlkfH58M1+vl5WXfBq+//jodOnRg/PjxvPLKKwDMmTOH559/nilTptCkSRP8/f2ZPHkyv/7663XzXrp0ifr166cqoNfklQHxIvmFipCIC9m6dSs2m40pU6bY93ZcG49yPVWqVKFKlSoMHTqU+++/ny+++IKuXbtSr149du/enaZw3Yi7u3u6ywQEBFCqVCk2btxI8+bN7dM3btxIo0aNUs0XFhZGWFgYPXr0oG3btpw7d46iRYumWt+18TjJycnXzePj40O3bt34+uuvOXDgALfddhv16tWzv16vXj327dvn8Of8rzFjxnDvvffy1FNP2T9n06ZNefrpp+3z/HePjpeXV5r89erVY+7cuQQFBREQEHBTmURcnQZLi7iQW2+9lcTERN5//30OHTrEl19+yfTp0zOc/+rVqwwaNIh169bx999/s3HjRn777Tf7Ia/hw4ezadMmBg0aRGRkJH/99ReLFy92eLD0v73wwgu88cYbzJ07l3379jFixAgiIyMZMmQIAG+//Tbffvste/fuZf/+/cybN48SJUqkexHIoKAgfH19WblyJSdPniQmJibD9+3bty/Lli3j888/tw+Svmbs2LHMnj2b8ePHs2vXLvbs2cOcOXMYM2aMQ5+tSZMm1KpVi4kTJwJQuXJlfv/9d1atWsX+/ft56aWX+O2331ItU758eXbs2MG+ffs4c+YMiYmJ9O3bl2LFitG5c2fWr1/P4cOHWbduHc888wz//POPQ5lEXJ7Vg5REJPulN8D2mrffftuULFnS+Pr6mjZt2pjZs2cbwJw/f94Yk3owc3x8vOndu7cJCQkxXl5eplSpUmbQoEGpBkJv2bLFtGrVyhQqVMj4+fmZWrVqpRns/G//HSz9X8nJyebll182pUuXNp6enqZ27dpmxYoV9tdnzJhh6tSpY/z8/ExAQIBp2bKl2bZtm/11/jVY2hhjPvnkExMSEmLc3d1N8+bNM/z5JCcnm5IlSxrAHDx4ME2ulStXmqZNmxpfX18TEBBgGjVqZGbMmJHh5xg3bpypXbt2munffvut8fb2NlFRUSYuLs48+OCDJjAw0BQuXNg89dRTZsSIEamWO3XqlP3nC5i1a9caY4w5ceKE6d+/vylWrJjx9vY2FStWNI899piJiYnJMJOIpOVmjDHWVjERERERa+jQmIiIiLgsFSERERFxWSpCIiIi4rJUhERERMRlqQiJiIiIy1IREhEREZelIiQiIiIuS0VIREREXJaKkIiIiLgsFSERERFxWSpCIiIi4rJUhERERMRl/R8tTUci89+7rAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "LSTM\n"
      ],
      "metadata": {
        "id": "VmhhXwGlBRoQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lstm_model,X_test,y_test=lstm_model(X,y,2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JHLUhb_XDY01",
        "outputId": "fc46a753-f116-451c-da03-f7be68bfad9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "191/191 [==============================] - 40s 168ms/step - loss: 0.4104 - accuracy: 0.8138 - val_loss: 0.2904 - val_accuracy: 0.8640\n",
            "Epoch 2/10\n",
            "191/191 [==============================] - 20s 107ms/step - loss: 0.2024 - accuracy: 0.9217 - val_loss: 0.2755 - val_accuracy: 0.8880\n",
            "Epoch 3/10\n",
            "191/191 [==============================] - 20s 103ms/step - loss: 0.1523 - accuracy: 0.9426 - val_loss: 0.2717 - val_accuracy: 0.8720\n",
            "Epoch 4/10\n",
            "191/191 [==============================] - 20s 102ms/step - loss: 0.1345 - accuracy: 0.9475 - val_loss: 0.2842 - val_accuracy: 0.9080\n",
            "Epoch 5/10\n",
            "191/191 [==============================] - 17s 87ms/step - loss: 0.1163 - accuracy: 0.9585 - val_loss: 0.2872 - val_accuracy: 0.8800\n",
            "Epoch 6/10\n",
            "191/191 [==============================] - 17s 92ms/step - loss: 0.1049 - accuracy: 0.9595 - val_loss: 0.2704 - val_accuracy: 0.9080\n",
            "Epoch 7/10\n",
            "191/191 [==============================] - 17s 88ms/step - loss: 0.0953 - accuracy: 0.9620 - val_loss: 0.2666 - val_accuracy: 0.9160\n",
            "Epoch 8/10\n",
            "191/191 [==============================] - 17s 88ms/step - loss: 0.0938 - accuracy: 0.9630 - val_loss: 0.2902 - val_accuracy: 0.8960\n",
            "Epoch 9/10\n",
            "191/191 [==============================] - 18s 95ms/step - loss: 0.0861 - accuracy: 0.9661 - val_loss: 0.2808 - val_accuracy: 0.8960\n",
            "Epoch 10/10\n",
            "191/191 [==============================] - 18s 96ms/step - loss: 0.0856 - accuracy: 0.9665 - val_loss: 0.3208 - val_accuracy: 0.8880\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred= model_Evaluate(lstm_model,model_name=\"LSTM\",X_test_vec= X_test,y_test=y_test,opt=1)\n",
        "#model=None,model_name=str,X_test_vec=None,y_test=None,opt=None,y_pred=None,"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "ariGLtPgaX9w",
        "outputId": "d86eac5e-1a2d-4d4d-c4fc-c5805173be11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 0s 6ms/step\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-33-57cdd13d3ae0>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my_pred\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mmodel_Evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlstm_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmodel_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"LSTM\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX_test_vec\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m#model=None,model_name=str,X_test_vec=None,y_test=None,opt=None,y_pred=None,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-30-c913263562ef>\u001b[0m in \u001b[0;36mmodel_Evaluate\u001b[0;34m(model, model_name, X_test_vec, y_test, opt, y_pred)\u001b[0m\n\u001b[1;32m     12\u001b[0m   \u001b[0;32melif\u001b[0m \u001b[0mopt\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m   \u001b[0;31m# Compute and plot the Confusion matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m   \u001b[0mcf_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mclassification_report\u001b[0;34m(y_true, y_pred, labels, target_names, sample_weight, digits, output_dict, zero_division)\u001b[0m\n\u001b[1;32m   2308\u001b[0m     \"\"\"\n\u001b[1;32m   2309\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2310\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2311\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2312\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m     96\u001b[0m             \"Classification metrics can't handle a mix of {0} and {1} targets\".format(\n\u001b[1;32m     97\u001b[0m                 \u001b[0mtype_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype_pred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Classification metrics can't handle a mix of binary and multilabel-indicator targets"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bert Model"
      ],
      "metadata": {
        "id": "kKfiqniA26PX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data=df"
      ],
      "metadata": {
        "id": "2UCoK-4Ip3Fe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"sentiment_label\"] = \"-\"\n",
        "data[\"sentiment_score\"] = -1\n",
        "data[[\"sentiment_label\", \"sentiment_score\"]] = data[\"text\"].apply(get_sentiment_analysis)\n",
        "data['sentiment_score'].apply(lambda x: float(x))\n",
        "y_pred = np.where(data['sentiment_label'] ==\"positive\", 0, 1) # DataFrame sütunundaki değerlere göre yuvarlama işlemi\n",
        "y_test=data[\"is_offensive\"]\n",
        "model_Evaluate(y_test=y_test,opt=2,y_pred=y_pred)\n"
      ],
      "metadata": {
        "id": "eksh-TmRpjoU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Target Classification"
      ],
      "metadata": {
        "id": "kbXgrYzpHxAz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X=df.str_tkn_p_sw\n",
        "y=df.target\n",
        "y_number=df.y_target_number\n"
      ],
      "metadata": {
        "id": "Ls_SHvLnHwi8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def conf_matrix(y_test,y_pred):\n",
        "  cm = confusion_matrix(y_test,y_pred, normalize=\"true\")\n",
        "  sns.heatmap(cm, annot=True, cmap=\"Blues\",xticklabels=[\"INSULT\",\"OTHER\",\"PROFANITY\",\"RACIST\",\"SECIST\"],yticklabels=[\"INSULT\",\"OTHER\",\"PROFANITY\",\"RACIST\",\"SECIST\"] )\n",
        "  plt.xlabel('Tahmin Edilen Sınıf')\n",
        "  plt.ylabel('Gerçek Sınıf')\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "q9kW_ipI3olc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def multiple_choic(X,y,opt):\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)\n",
        "  X_train_boW, X_test_boW=vectorizer_methods(X_train,X_test, 0)\n",
        "  if opt==0: #y_train and test are str\n",
        "    svc_model = LinearSVC()\n",
        "    svc_model.fit(X_train_boW, y_train)\n",
        "    y_pred=svc_model.predict(X_test_boW)\n",
        "    print(classification_report(y_test, y_pred, digits = 4))\n",
        "    conf_matrix(y_test,y_pred)\n",
        "  elif opt==1: #y_train and test are int\n",
        "    xgboost_model = XGBClassifier()\n",
        "    xgboost_model.fit(X_train_boW, y_train)\n",
        "    y_pred=xgboost_model.predict(X_test_boW)\n",
        "    print(classification_report(y_test, y_pred, digits = 4))\n",
        "    conf_matrix(y_test,y_pred)\n",
        "  elif opt==2:\n",
        "    model,X_test,y_test=lstm_model(X,y,num_class=5)\n",
        "    y_pred=model.predict(X_test)\n",
        "    y_pred = np.argmax(y_pred, axis=1)\n",
        "    print(classification_report(y_test, y_pred, digits = 4))\n",
        "    conf_matrix(y_test,y_pred)\n",
        "  elif opt==3:\n",
        "    model,X_test,y_test=DecisionTreeClassifier(X,y,num_class=5)\n",
        "    y_pred=model.predict(X_test)\n",
        "    y_pred = np.argmax(y_pred, axis=1)\n",
        "    print(classification_report(y_test, y_pred, digits = 4))\n",
        "    conf_matrix(y_test,y_pred)\n"
      ],
      "metadata": {
        "id": "0g7d4Xhb40uH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "multiple_choic(X,y,0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "wTh0Dov_682U",
        "outputId": "3de61953-ed32-451f-cb95-1bdb09b68069"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      INSULT     0.7219    0.7201    0.7210      1161\n",
            "       OTHER     0.7646    0.8550    0.8073      1835\n",
            "   PROFANITY     0.9082    0.8360    0.8706      1171\n",
            "      RACIST     0.8735    0.7677    0.8172      1016\n",
            "      SEXIST     0.8813    0.8830    0.8821      1051\n",
            "\n",
            "    accuracy                         0.8168      6234\n",
            "   macro avg     0.8299    0.8124    0.8196      6234\n",
            "weighted avg     0.8210    0.8168    0.8173      6234\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "multiple_choic(X,y_number,1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "qpPpKfC66-Fz",
        "outputId": "24f75337-82d6-4c18-8afb-c4a89fcfd6ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.6833    0.5202    0.5907      1161\n",
            "           1     0.5748    0.8937    0.6997      1835\n",
            "           2     0.9646    0.7216    0.8256      1171\n",
            "           3     0.9282    0.5856    0.7182      1016\n",
            "           4     0.8765    0.8173    0.8459      1051\n",
            "\n",
            "    accuracy                         0.7287      6234\n",
            "   macro avg     0.8055    0.7077    0.7360      6234\n",
            "weighted avg     0.7767    0.7287    0.7307      6234\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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PGAwAsLC0gry8PGLevoV9g0YAitrzY1wcatQUngzLYrGg8/Xx3uALZ6GnVwMWlvUl1gaS9GN7tv2xPcPDMbCEe83667029Md7LUz4XhPHqeNH0bptW6hraJTp+ApDQsGFt7c3liwRnhjr6emJxYsXF8vL5XJRWFgIXV3hx8l1dXXx/PlzkeUPGTIEXC4XLVq0AJ/PR0FBAcaPHy/2sAijwUVJQx61atXCP//8g6FDh/52WIRpZ54kYEorY7ziZuJlYhZ6WOlAoQobV14URe5TWxkjKSsP++99BAD0t6uBF4lfEJ+ei6ryHPSy1oO2ijwuRxf9cuKwgNntTWGiqYzl/74CmwVBz8iX3EIU8Bh7uEfirr9OxmD7Gniflo3YlBy0NlGHPIeNO++LAoHB9jWQnlOAc88SUcDjI/6nCZrfenO+pWfl85D103ybQn7RPI7EzL9rcuewESPhuXAeLCytYGVtgwP7diM7Oxs9excF7B7z50JbVwdTps0EAAweOhyuo0Zg7+6daNGqDS5dOIenUVFY4OEFoOgP4dyZ0/D82VP4+PqjkFcomAegqqoKuXIK2CVh+AgXLFowF5b1rWBlZYP9X9um19e2Weg+Bzo6upg6o6hthgwbgTEuw7EncCdatmqN4Avn8TTqCTwWF7UNi8XC0OEjsG3rFhgaGUFf3wCbfTdAW0dH8AdXRUUF/QYMwha/TdDVq4GaNWti964dAICOHb+vyRC4czuat2gJFpuNK5cvYef2bVi11qdCr+szdMRIeC6YB8v6VqhvbYMDe4XvtUXz50JHRwdTpn9rz+Fwdfl6r7Vsg4vBRffaQk8vQZlpaamI//QJiV/H/WNiit6QramlJegpAoDY2Hd4cP8eNvptLa/LrfBE9daL6rUoq2vXrmH58uXw8/ODg4MDXr16hWnTpmHp0qVYtGhRqcthNLjYtavizo0orVtvU1BdsQoGN6wJNSU5vE3KxtKLL5H2tWtfS0Ve6BExFQUOJrYwgpqSHL7kFuJNUhbmn32OD18XjtKoKo8mRmoAgHV9LIXOtehcNKK+/sr/G0R8zICKPAedzbRRXYGDuPRcbA1/L+ipUVeSA3MPSldsHTt3RUpKMvz9Nn1dKMoCm7ZsE3RVx8d/BOuHRztt7Rpg2Yo12LLJB5s3roehoTHWbvBFnbr1AACJnxNw/doVAMDg/r2FzhWwYzcaNf718GRF0qlLUdts8d0ILjcRZuYW8PPfLuiW//TpE1js778i7ewbYPnKNdi8yQebNqyDoZEx1m/cLGgbABg5yhXZ2dlYutgDGRnpsG/QEH7+24W+1GfMnIMqnCpY6D4Hubk5sLK2xdadu1H9hx9It/67ge3b/JGfl4d6Zubw2bRZMO+iourUuStSkpOxZXPRvWZmbgFf/22C9oz/9BFsVvF7zc/XB74b1sPQyBjrfrjXAOD61StYvOj7L2H32UV/LMdOmITxE6cI0k+dOAZdXT04Nmsu7cuUPgmtUVHSEIgoWlpa4HA4SEgQXhYhISEBenp6Io9ZtGgRhg8fjjFjxgAArK2tkZmZibFjx2LBggVgs0vXA8PoOhcluX79OjIzM+Ho6Ah1dfG7ZstznYu/UXmtc/E3Kq91Lv5GklrnorKS1joXlUG5rHPRK+D3mUoh+9Q4sfI7ODigSZMmgmUdeDweDA0NMXnyZMybN69Y/oYNG8LJyQkrV64UpB08eBCjR49GRkZGqXvZGO25WLlyJb58+YKlS5cCKFpit0uXLrh06RIAQEdHByEhIahfv2KORxJCCCGlwtDqmm5ubnB2dkajRo3QpEkT+Pj4IDMzU7A45YgRI6Cvrw9vb28AQI8ePbBu3TrY29sLhkUWLVqEHj16iDV8x2hwERQUhLlz5wo+Hz16FDdu3MDNmzdhYWGBESNGYMmSJTh8+DCDtSSEEEJk08CBA5GYmAgPDw/Ex8fDzs4OwcHBgkmesbGxQkMdCxcuBIvFwsKFCxEXFwdtbW306NEDy5aJtzIqo8Mi6urqCA0NhYVF0QxsFxcXFBYWYs+ePQCA8PBw9O/fH++/LlBTWjQs8mdoWKTsaFik7GhY5M/QsEjZlcuwSJ/tEikn+8QYiZQjbYyu3FJQUCA0MSUsLAzNmn1fNrZmzZolrqpHCCGEyIxK9uIyRoMLU1NT3LhxA0BR18yLFy/QqlUrwf4PHz4UW0aWEEIIIRUbo3MuJk2ahMmTJ+PmzZsIDw+Ho6MjLC2/P3555coV2NvbM1hDQggh5M+xZKjXQRIYDS5cXV3B4XBw5swZtGrVCp6enkL7P378iFGjRjFUO0IIIUQyKLgoZ6NGjSoxgPDz8yvn2hBCCCHkTzEeXBBCCCF/vcrVccFscMFms3/bVcRisVBQUPwtmYQQQoisoGGRcnTixIkS94WFhWHjxo3g8f6uV40TQgghfztGg4tevXoVS4uOjsa8efNw5swZDB06FF5eXiKOJIQQQmRHZeu5YHSdix99/PgRrq6usLa2RkFBASIiIrB7924YGRkxXTVCCCHkj7BYLIlssoLx4CItLQ1z585FnTp1EBUVhZCQEJw5cwZWVlZMV40QQgiRiMoWXDA6LLJq1SqsXLkSenp6OHjwoMhhEkIIIYTIFkaDi3nz5kFJSQl16tTB7t27sXv3bpH5jh8/Xs41I4QQQiRIdjodJILR4GLEiBEy1c1DCCGElEVl+1vHaHARGBjI5OkJIYQQIgWMBhd9+/b9bR4Wi4Vjx46VQ20IIYQQ6aCei3KkqqrK5OkJIYSQckHBRTnatWsXk6cnhBBCiBTQi8sIIYQQKaOeC0IIIYRIVuWKLZhfoZMQQgghfxfquSCEEEKkjIZFCCGEECJRFFwQQgghRKIqW3BBcy4IIYQQIlHUc0EIIYRIW+XquKDgghBCCJE2GhYhhBBCCPkDf2XPxbZBtkxXQaYZtJjOdBVk1vKuvkxXQWYV8vhMV0Gm5RfymK6CDONI/QyVrefirwwuCCGEkIqksgUXNCxCCCGEEImi4IIQQgiRMhaLJZGtLDZv3gxjY2MoKirCwcEBd+7cKTFvmzZtRJ63W7duYp2TggtCCCFE2lgS2sQUFBQENzc3eHp64sGDB7C1tUWnTp3w+fNnkfmPHz+OT58+CbYnT56Aw+Ggf//+Yp2XggtCCCFERuTm5iI9PV1oy83NLTH/unXr4OrqChcXF1haWsLf3x/KysrYuXOnyPwaGhrQ09MTbP/++y+UlZUpuCCEEEIqGkkNi3h7e0NVVVVo8/b2FnnOvLw83L9/H05OToI0NpsNJycnhIWFlareO3bswKBBg1C1alWxrpeeFiGEEEKkTFJPi7i7u8PNzU0oTUFBQWReLpeLwsJC6OrqCqXr6uri+fPnvz3XnTt38OTJE+zYsUPselJwQQghhEiZpIILBQWFEoMJSduxYwesra3RpEkTsY+lYRFCCCHkL6SlpQUOh4OEhASh9ISEBOjp6f3y2MzMTBw6dAijR48u07kpuCCEEEKkjYGnReTl5dGwYUOEhIQI0ng8HkJCQuDo6PjLY48cOYLc3FwMGzZMvJN+RcMihBBCiJQxtUKnm5sbnJ2d0ahRIzRp0gQ+Pj7IzMyEi4sLAGDEiBHQ19cvNil0x44d6N27NzQ1Nct0XgouCCGEkL/UwIEDkZiYCA8PD8THx8POzg7BwcGCSZ6xsbFgs4UHMaKjo/Hff//h0qVLZT4vi8/n/3VvC0rKLGC6CjKNXlxWdil36cVlZUUvLvsz9OKyslNTkv6Ly4ymnpFIOe829pBIOdJGPReEEEKIlNGLywghhBBC/kCFDS5ycnKwZs0apqtBCCGE/DEmX1zGBEaDi8TERJw9exaXLl1CYWEhACA/Px8bNmyAsbExVqxYwWT1CCGEEMlg6MVlTGFszsV///2H7t27Iz09HSwWC40aNcKuXbvQu3dvVKlSBYsXL4azszNT1SOEEEJIGTHWc7Fw4UJ07doVkZGRcHNzw927d9GnTx8sX74cT58+xfjx46GkpMRU9QghhBCJoWGRcvL48WMsXLgQVlZW8PLyAovFwqpVq9CvXz+mqkQIIYRIRWULLhgbFklJSYGWlhYAQElJCcrKyrCysmKqOoQQQojUyFBcIBGMrnPx9OlTxMfHAwD4fD6io6ORmZkplMfGxoaJqhFCCCGkjBgNLtq3b48fFwjt3r07gKLuIz6fDxaLJXiKhBBCCJFVsjSkIQmMBRdv375l6tSEEEJIuapksQVzwYWRkRFTpyaEEEKIFDH2tMiqVauQnZ0t+Hzr1i3k5uYKPmdkZGDixIlMVI0QQgiRqMr2tAhjwYW7uzsyMjIEn7t06YK4uDjB56ysLAQEBDBRNUIIIUSiWCzJbLKCseDi5ze9/4VvfieEEEIqJXrlOiGEECJlbLYMdTtIAAUXhBBCiJTJ0pCGJDAaXGzfvh0qKioAgIKCAgQGBgpW7fxxPgYhhBBCZAdjwYWhoSG2bdsm+Kynp4e9e/cWy1PRHAs6gP17diE5iYs69czgNmc+LK1KXkX0yr8XsXXLJsR/jIOBoREmTnVDsxatBPv/5zkf58+cEjrGwbE51m/eKvgcuD0Aof/dwMsXzyFXRQ6XboRL/sIYMm5AK8xwbg9dzep4/CIObiuP4F7UuxLzTx7SBq79W6KWnjqSUjNx4vJDLNp0Grl5BQAAFWUFeE7sjp7tbKGtroJH0R8wa9VR3H8aW16XJBGHDuzH7l07wOUmop6ZOebNXwTrX6xWe+niBWzetAEf4+JgaGSM6W6z0LJVa8F+Pp8PP9+NOH70CDIy0mFn3wALPBbDyMhYkGdbwBbcvHEd0c+fQU5ODv+F3yt2nhXL/4eIhw/w6uULmJiY4vDxU8XyVERBB/djT+AOJHG5qGdmjjnuC2FlXXJ7/nsxGFt8N+DjxzgYGhph6oxZaPFDe4ZcvoRjhw/h2dMopKWl4eCREzAztxDs/xj3Ad07O4kse+UaH3To1FlyF1cOjhw6gP27dyIpiYu69cwwc+4C1P9F+4VcCkaA3yZ8+hiHWoZGmDTNDc1bfm+/bVt88e/FC0iIj4ecnBzMLS0xfvI0WFnbAgDu372Dia4jRZa9a18QLK2sJXp95UGWnvSQBMYmdMbExODt27e/3SqSyxcvYOO6VRg1diJ2HTiCOnXNMGPSOCQnJ4nM//jRQ3jOn40evfoi8MBRtGrTDvPcpuD1q5dC+Zo2a4Ezl64JtiXeq4X2F+Tno51TR/TpN1Bq18aEfh0bYOXMPlgWcAGOQ1Yi8kUcTvtNgra6isj8Azs3wtKpvbA84ALs+v4P45fsR79ODeE1pacgzxaPIWjX1ByjFu5GowHLcTnsOc75T0FNbdXyuqw/FnzhPNas8sa4iZNw6MgJmJmZY8K40UhKEn2fRTx8gHmzZ6JP334IOnoSbdu1x/Qpk/Dy5QtBnl07tuHg/r1Y6LkY+w4ehpKSEiaMHS30+Hd+fj46dOyM/gMH/7J+vfv8g05dukrmYsvBxeDzWLd6BcaOn4QDh4+jbj0zTBo3BskltOejiAeYP3cmevXthwNHTqBNOye4TZuMVz+0Z3Z2NuzsG2LqjFkiy9DVq4FLV28KbeMnToGysjKat2wpleuUln8vXsCGtSsxetxE7D54FHXqmWPaxLElfu9FRjzEIvfZ6NG7L/YcOoZWbdtjzgzh7z1DI2PMmrcAB46exNZde1Gjpj6mTnBFSnIyAMDGzg7nL18X2nr16Yea+gawqC+b76Cip0VIiQ7t342effqhe68+qG1SB3MWeEJBURFnTx0Xmf/wgX1wcGyBoc6jYGxiirETp8LM3BLHgg4I5ZOTl4emlrZgq15d+A/hmAmTMWiYM0zr1JXatTFh6rB22HU8FHtPh+P5m3hMWXYI2Tl5cO7tKDJ/U9vaCIt4g6Dge4j9lIyQ8Oc4HHwPjeoXLcimqCCH3u3tsMDnJG49eI0377lYFnAer98nwrW/7Hyh7929C337DUDvPv/AtE4dLPRcAkVFRZw8fkxk/v379qBZi5YYOWoMTExNMXnqdFhYWuLQgX0Ainot9u/dA9dxE9C2nRPqmZnjf96rkPj5M66EXBaUM3HyVAx3Hom6deuVWLd58xdi0JChMDCoJdmLlqL9ewLR55/+6NXnH5iY1sECjyVQVFLEqROi2/PAvr1wbN4Czi6jYWJiiolTpsHc0hJBB/cL8nTv0QtjJ0yCQ1PR9yqHw4GWlrbQdvXKZXTo1AXKylWlcp3ScnBvIHr17Y8evfvCxLQO5i30hKKiIs6cFP29F3RgL5o2a4HhI0ejtokpxk+aCjMLSxw59L39OnXtjiZNm0HfoBZM6tTFtJlzkfnlC169jAYAyMkJfyeqqqrhxrUr6N6rj8z2AFS2dS4YGxbZuHFjqfJNnTpVyjUpnfz8PEQ/e4rhLq6CNDabjcYOTfEk8pHIY548jsCgoc5CaQ6OzXHjWohQ2sN7d9G1fUtUr14dDRs7YOzEqVBVU5P4NVQkclU4sLeohdU7LwnS+Hw+rtyORhOb2iKPCX/0FoO6NUaj+ka4F/UOxvqa6NS8Pg6cuwMAqMJho0oVDnLy8oWOy8nNRzN7U+ldjATl5+Xh2dMojHYdJ0hjs9lo2rQZIh89FHlMZEQEhjuPFEpr1rwFrn4NHOI+fACXmwiHps0E+6tVqwZrG1tEPnqILl27Sf5CKoj8/KL2dBk9VpDGZrPh0NQRkY8iRB7z+FEEho4YKZTm2Kw5rl0JEZm/NJ5GPUH082eYt2BRmctgQn5+Hp4/ewrnUT9/7znicWSEyGMeR0Zg8LCRQmlNHZvj+rUrJZ7j5LHDUFGphrr1zEXmuXH9KtLSUtG9V58yXQcpf4wFF+vXrxf6/P79e9SoUQNVqnyvEovF+m1wkZubK9S1CwC5BRwoKChIrrIAUlNTUVhYCA0NTaF0DQ1NvIsRPXyTxOVCXVM4v7qmplD3tkOzFmjdzgk1axrgw4f3CPD1gduUcdgaeAAcDkei11CRaKmroEoVDj4nC0/c/ZyUDjNjXZHHBAXfg6Z6VYTsmgEWWJCT42DrkZuCAOVLVi7CH72Bu2sXRL9NQEJSOgZ0bgQHm9p4/T5R6tckCSmpKSgsLITmT/eNpqYm3r59I/IYLpcLTU2tYvm5Sdyv+4uuXVOreJlcLldSVa+QUlOK2lPjp/bU0NRCTAnDrkXt+XNbaSHpD9rq1IljqG1iClu7BmUugwmpKV+/9366vzQ0NfEuRvT9mMTlimzvn9vvvxvXsHDuTOTk5EBLSxub/LdDTV1dZJmnTxyDg2Nz6Orq/cHVMEuWeh0kgbFhkZ/nVigpKeH69etCaW/eiL55f+Tt7Q1VVVWhzWfNynK4Asno0KkrWrZuB9O69dC6bXus3uCHZ1FP8PDeXaarVuG0bFgXs0d1wjTvIDgOWYmBblvRpUV9zHP9Pjlu1MI9YLGAN5eWIe22DyYNbo3DwffA49EibYQZOTk5uHD+LHr3/YfpqlQoDRs3wd6g49i2+wCaNm+B+XPcRM7jSEiIx+2wW+jZR7bbr7LNuZD5dS7c3d3h5uYmlPalQPK/+NXU1MDhcIrd/MnJScWi+m80tbSQ8tOksZSkpGK/in6kb1ALamrq+PA+Fo0cmv55xSsobsoXFBQUQkejmlC6jmZ1xCelizzGc2I3HDx3B4EnwgAAUa8+QllJAZsXDsbK7RfB5/Px9gMXHcdsgLKiPKqrKCKem469K1zwNk42fqGrq6mDw+EUm7yZlJQkeEz7Z1paWkhK4hbP//W+1NLSLkrjJkFbW0coj5m56G7ov4WaelF7/jx5MzmpeG/PN0Xt+XP7c6FZQvv/zuV/LyInOwfde/Qu0/FMUlP/+r330/2VnJQEjRLaQ1NLS3R7/5RfSUkZtQyNUMvQCNY2tvinR2ecPnEMI38YwgKAs6dOQFVVDa1at5XAFZHyIvMTOhUUFFC9enWhTdJDIkDRBCMzC0vcv/P9MVAej4d7d27DysZW5DFW1na4d0f4sdE7t8NgZWNX4nk+J8QjLS0Vmtpl+yKTFfkFhXj47D3aOpgJ0lgsFto2qYc7kaK7q5UU5Yv1QPB4vK/HCufNyslDPDcdatWU4NTMAmevPZbsBUiJnLw8LCzr43Z4mCCNx+Ph9u0w2NjaizzGxs4Ot8OF77PwsFDY2NkBAPQNDKClpY3bt7+X+eXLFzyOfFRimX8LObmi9rxzW7g974SHw8bWTuQx1rZ2QvkB4HZYaIn5f+fU8aNo3bYt1DU0ynQ8k+Tk5GFuYYm7P33v3b0TDusSvsesbUR874WHwbqE78lv+Hw+8vPyiqWdPXUCXXr0RBU5ubJdRAVBEzpJiQYNdcb/POfD3LI+LOtbI+jAXuRkZ6N7z6JJRl6L3KGto4MJU2YAAAYMGYaJriNxYG8gmrVohcsXL+D50yeYu3AxACArKxM7A7agTfsO0NTSQtz799i8YS0MahnCwbGF4Lzxnz4iPT0NCfGfwOMV4kX0MwCAQS1DmZt5/qON+65gm9dw3H8ai3tPYjB5SFsoKylgz6miL6btS4fj4+c0eGw6DQA4f+MJpg5ri0fRH3DncQxMa2nDY0J3nL/xWBB0ODlagMUCXsR8hmktbSyf0Rsv3iZgz+mwEutR0Qx3dsGi+XNRv74VrKxtsG/vbmRnZ6N3n74AgAXuc6Cjo4tpM2YCAIYOG4HRI4djd+BOtGrVGsEXziPqyRMsWuwFoOhLbejwEdgWsAVGhkbQNzDA5k0boK2jg3btv6/F8OnjR6SlpeHTp48oLCzE82dF95mhoSGUqxbdZ7Hv3iErKwtcbiJycnMEeUxNTSEnL19ubSSOoSNGwnPBPFjWt0J9axsc+NqePXsXteei+XOho6ODKdOL2nPIsOFwdRmBvbt3okXLNrgYfA5Po6Kw0NNLUGZaWiriP31C4ufPAICYr/OuNLW0BD1FABAb+w4P7t/DRr/v69bImsHDR8JrkTssLK1gaWWNQ/v3FH3vfZ1cuXjhPGjr6GDS1KIe5IFDhmP8GGfs37MLzVu2xr/B5/Hs6RO4eywBAGRnZ2HXtgC0bNMOWlpaSE1NxdGgA0j8nID2HToJnfvenXB8jPuAXn36le9FS4EMxQUSwVhwkZ4u3PXNYrHw5cuXYunVq1cvz2r9klOnLkhNSca2Lb5ITuKirpk51vkGCIZFEuI/Ca0fb21rjyXLVmGr30YE+PrAwNAIK9ZtEjxSymFz8OplNM6fPYUvGenQ0tZBk6bNMHbiFMj/8EW93d9XaKGtkYOL/qH5bt2FBo2alMelS8XRSw+gpa4CjwndoKtZDZHRceg1abNgkmctPQ2hnooV24PB5/PhObE7auqogpvyBeduPMFi3zOCPKoqivCa0hP6umpITsvCqZAIeG4+g4ICXrlfX1l17tIVKcnJ8PPdCC43EWbmFvAL2C7oVo7/9Als1vdORzv7BvBetQa+G32wyWcdDI2M4bNps9AjpS6jXZGdnQ2vxR7IyEiHfYOG8AvYLtTL5+e7EadPnRB8HtivNwBg+649aNzEAQCwxHMh7t29UyzP+Ush0Nc3kHhbSEKnzkXtuWXzJiR9bU9f/20/tOdHsH/45re1a4BlK9bAz9cHvhvWw9DIGOs2+KLOD+15/eoVLF40X/DZfXbRH9axEyZh/MQpgvRTJ45BV1cPjs2aS/sypabD1++9rVs2CRYh8/ELEAwrJfx0P9rY2WPp8lXw37wRWzb5oJahEVat//69x2Zz8C7mLc7PnIbU1BSoqqnBor4VAnbuhclPj9ufPnEcNrb2MK5tUn4XTCSCxWfodaRsNluoi4fP54v8XFhYKHbZSZkFEqljZWXQYjrTVZBZKXd9ma6CzCqkSbd/JL9QdgLoikZNSfpP5jVcelUi5dxfJBtzTxjrubh6VTINTQghhFR0NCxSTt69e4eBAwdKZfIlIYQQQops3rwZq1evRnx8PGxtbbFp0yY0aVLykHpqaioWLFiA48ePIzk5GUZGRvDx8UHXrqVf9p+xp0VcXFyQlpbG1OkJIYSQcsPU0yJBQUFwc3ODp6cnHjx4AFtbW3Tq1Amfv05G/lleXh46dOiAmJgYHD16FNHR0di2bRv09fXFOi9jPRcMTfUghBBCyh1TwyLr1q2Dq6srXFxcAAD+/v44d+4cdu7ciXnz5hXLv3PnTiQnJyM0NBRyXx//NTY2Fvu8jK5zIUvP7BJCCCFlJamei9zcXKSnpwttP78C45u8vDzcv38fTk7fHzlns9lwcnJCWJjox/NPnz4NR0dHTJo0Cbq6urCyssLy5cvFfriC0XUu2rdvL/QuEVEePHhQTrUhhBBCKjZvb28sWbJEKM3T0xOLFy8ulpfL5aKwsBC6usLva9LV1cXz589Flv/mzRtcuXIFQ4cOxfnz5/Hq1StMnDgR+fn58PT0LHU9GQ0uOnXqBBUVFSarQAghhEidpDrqRb3yQpIPRvB4POjo6GDr1q3gcDho2LAh4uLisHr1atkJLmbPng0dHZ3fZySEEEJkmKSmASgoKJQ6mNDS0gKHw0FCQoJQekJCAvT0RL9htkaNGpCTkxN6K7eFhQXi4+ORl5cntMDjrzA254LmWxBCCCHSIy8vj4YNGyIkJESQxuPxEBISAkdHR5HHNG/eHK9evRK8twkAXrx4gRo1apQ6sAAYDC5+flqEy+WCy5WNN1cSQggh4mDqletubm7Ytm0bdu/ejWfPnmHChAnIzMwUPD0yYsQIuLu7C/JPmDABycnJmDZtGl68eIFz585h+fLlmDRpkljnZWxY5O3bt5CTk8OkSZMQFBSElJQUAIC6ujoGDRqE//3vf1BTU2OqeoQQQojEMNVbP3DgQCQmJsLDwwPx8fGws7NDcHCwYJJnbGws2Ozv/Qy1atXCxYsXMWPGDNjY2EBfXx/Tpk3D3LlzxTovY+8WSU5OhqOjI+Li4jB06FBYWFgAAJ4+fYoDBw6gVq1aCA0Nhbq6uthl07tF/gy9W6Ts6N0iZUfvFvkz9G6RsiuPd4s0X31TIuXcmt1SIuVIG2M9F15eXpCXl8fr16+LPSbj5eWFjh07wsvLC+vXr2eohoQQQohkVLZphozNuTh58iTWrFlTLLAAAD09PaxatQonTpwQcSQhhBAiW5ha/pspjAUXnz59Qv369Uvcb2Vlhfj4+HKsESGEEEIkgbHgQktLCzExMSXuf/v2LTQ0NMqvQoQQQoiUUM9FOenUqRMWLFiAvLy8Yvtyc3OxaNEidO7cmYGaEUIIIZLF1KOoTGF0QmejRo1Qt25dTJo0Cebm5uDz+Xj27Bn8/PyQm5uLvXv3MlU9QgghRGJkqddBEhgLLgwMDBAWFoaJEyfC3d1dsKgWi8VChw4d4Ovri1q1ajFVPUIIIYSUEaPvFqlduzYuXLiAlJQUvHz5EgBQp04dmmtBCCHkr1LJOi6YDS6+UVdXR5MmTZiuBiGEECIVlW1YhLEJnYQQQgj5O1WIngtCCCHkb1bJOi4ouCCEEEKkjV3JogsaFiGEEEKIRFHPBSGEECJllazjgoILQgghRNoq29MiFFwQQgghUsauXLFF6eZcNGjQACkpKQCKlu3OysqSaqUIIYQQIrtKFVw8e/YMmZmZAIAlS5bgy5cvUq0UIYQQ8jepbG9FLdWwiJ2dHVxcXNCiRQvw+XysWbMGKioqIvN6eHhItIKEEEKIrJOhuEAiWPxvbwz7hejoaHh6euL169d48OABLC0tUaVK8biExWLhwYMHUqmoOBIzCpiugkyLTaJhr7LqsjSY6SrIrNiAAUxXQabxeL/9KiclUJaX/l/+bgF3JFLOuXGy8aqMUvVcmJmZ4dChQwAANpuNkJAQ6OjoSLVihBBCyN+ChcrVdSH20yI8Hk9iJ//48SNq1qwpsfIIIYSQiqiyPS0idnBx+vTpX+7v2bNnqcuqX78+Nm/ejCFDhohbDUIIIYRUUGIHF7179y5xH4vFQmFhYanLWrZsGcaNG4cTJ04gICAAGhoa4laHEEIIqfBk6UkPSRD73SI8Hq/ETZzAAgAmTpyIyMhIJCUlwdLSEmfOnBG3OoQQQkiFx2JJZpMVjK/QWbt2bVy5cgW+vr7o27cvLCwsij2JUhGeQCGEEEJI6ZQpuAgJCUFISAg+f/5cbILnzp07xS7v3bt3OH78ONTV1dGrVy+Rj7kSQgghsqqyvXJd7L/iS5YsgZeXFxo1aoQaNWr88TjStm3bMHPmTDg5OSEqKgra2tp/VB4hhBBS0VSy2EL84MLf3x+BgYEYPnz4H5+8c+fOuHPnDnx9fTFixIg/Lo8QQgipiCrbhE6xg4u8vDw0a9ZMIicvLCxEZGQkDAwMJFIeIYQQQpgn9tMiY8aMwYEDByRy8itXrkBeXl4iZRFCCCEVFT0t8hs5OTnYunUrLl++DBsbG8jJyQntX7duXanLKsVrTQghhBCZx+SEzs2bN2P16tWIj4+Hra0tNm3ahCZNRL+jJDAwEC4uLkJpCgoKyMnJEeucYgcXkZGRsLOzAwA8efJEaF9lG1MihBBCKrKgoCC4ubnB398fDg4O8PHxQadOnRAdHV3iO8KqV6+O6Ohoweey/G0XO7i4evWq2Cf5le3bt5f4+vZvpk6dKtFzEkIIIeVJUj+9c3NzkZubK5SmoKAABQUFkfnXrVsHV1dXQW+Ev78/zp07h507d2LevHmi68piQU9P74/qyfiCEv7+/uBwOCXuZ7FYFFwQQgiRaZLq2ff29saSJUuE0jw9PbF48eJiefPy8nD//n24u7sL0thsNpycnBAWFlbiOb58+QIjIyPweDw0aNAAy5cvR/369cWqp9jBRd++fX+5//jx42KVd+/ePXp9OyGEEFIK7u7ucHNzE0orqdeCy+WisLAQurq6Qum6urp4/vy5yGPMzMywc+dO2NjYIC0tDWvWrEGzZs0QFRUl1pOdYgcXqqqq4h5SIpqjQQghpDKQ1CvXfzUEIgmOjo5wdHQUfG7WrBksLCwQEBCApUuXlrocsYOLXbt2iXtIiehpEUIIIZUBEz+mtbS0wOFwkJCQIJSekJBQ6jkVcnJysLe3x6tXr8Q6t9jrXPzs+vXrOH/+PFJSUsQ+1tPT87eTOQkhhBAiPnl5eTRs2BAhISGCNB6Ph5CQEKHeiV8pLCzE48ePUaNGDbHOXeqei5UrV+LLly+CbhE+n48uXbrg0qVLAAAdHR2EhISINemjT58+pYqGbGxsSl0mIYQQUtEwNQvAzc0Nzs7OaNSoEZo0aQIfHx9kZmYKnh4ZMWIE9PX14e3tDQDw8vJC06ZNUadOHaSmpmL16tV49+4dxowZI9Z5Sx1cBAUFYe7cuYLPR48exY0bN3Dz5k1YWFhgxIgRWLJkCQ4fPlzqk9vZ2YHFYokcHvmWzmKxUFhYWOoyCSGEkIqGqTmGAwcORGJiIjw8PBAfHw87OzsEBwcLJnnGxsaCzf4+iJGSkgJXV1fEx8dDXV0dDRs2RGhoKCwtLcU6L4tfyokP6urqCA0NhYWFBQDAxcUFhYWF2LNnDwAgPDwc/fv3x/v370t98nfv3pUqn5GRUanLBIDEjAKx8hNhsUlZTFdBZnVZGsx0FWRWbMAApqsg03g8msNWVsry0v/DP/JgpETKCRwsGz35pe65KCgoEJqhGhYWhunTpws+16xZE1wuV6yTixs0EEIIIaTiK3VwYWpqihs3bsDExASxsbF48eIFWrVqJdj/4cMHaGpqinXy2NjYUuUzNDQUq1xCCCGkIqlsSy+UOriYNGkSJk+ejJs3byI8PByOjo5CYzBXrlyBvb29WCc3NjYW2eDf5loARf9BCgpomIMQQojsqlyhhRjBhaurKzgcDs6cOYNWrVrB09NTaP/Hjx8xatQosU7+8OFDkel8Ph+HDh3Cxo0b6VFVQgghRMaItYjWqFGjSgwg/Pz8xD65ra1tsbTLly9j3rx5ePHiBebMmYOZM2eKXS4hhBBSkTD5ynUmMP7ism8ePHiAuXPn4ubNmxgzZgzOnz9P7xwhhBDyV6hkscWfr9D5p16/fo2BAweiSZMm0NbWxtOnT+Hr60uBBSGEECKjGA0uJk6cCEtLS6SlpeHevXs4cOAATExMmKwSIYQQInEsFksim6xgdFjE398fioqK+Pz58y8ngz548KAca0UIIYRIlgzFBRIhdnBx9epVtG3bVuS+zZs3Y9KkSaUu6+cnTmTBscMHcHDvLiQncWFa1wwzZs+HpVXJK6ZduXwR27dsQvynOBjUMsKEKW5wbNFKKE/M29fYsnEdIh7cQ2FhIYxNTPC/VT7Q06sJAFi1bDHu3QkHl/sZykrKsLKxw4SpbjAy/vt6eS6dPoyzR/chLTkJhiZ14TxxNuqYi35fzZXzJ3Dz8nm8f/caAFC7jjkGukwqMf/fZlTbOpjY2Qw6qoqIep+K+Qce4uHbZJF5T8xug+bmxYca/438iKEb/kMVDgvufazR3loPRtoqyMjOx42nCVh6LBIJqTnSvhSJO3RgP3bv2gEuNxH1zMwxb/4iWP/iHUWXLl7A5k0b8DEuDoZGxpjuNgstW7UW7Ofz+fDz3YjjR48gIyMddvYNsMBjMYyMjAV5tgVswc0b1xH9/Bnk5OTwX/g9oXOcOnEcHgvdRZ7/yo1QsdcJKk9BB/djd+AOJHG5qGdmjrnuC2FlXXJ7/nsxGH6+G/DxYxwMDY0wdYZwe4ZcvoSjhw/h2dMopKWl4dCREzAztxBZFp/Px+QJYxF66ybW+fiibXsniV8fkTyxg4u+ffvi8uXLaNiwoVD6hg0bsGjRor86uAi5dAG+61dhlrsnLK2scfjgXrhNGYeDx85CXaP4F8PjRw+xZMFsjJs0Hc1atsa/wefgPmsKdu47CpM6dQEAcR9iMXHMcHTv2Rejx01GVZWqePv6FRTkv6+GamZhiY5dukNXrwbS09OwM2AzZkxyxZHTl8DhcMrt+qUt7Nol7Nvqg1FT5qGOuRUunDiIFQumYO2Oo1BV0yiW/2nkfTRr2xF1LW0gJ6eAM4d3Y8X8yVi1NQgaWn/3nJ1ejWthyUBbzN57Hw/eJGNsh7oImtEKzRZcADcjt1h+F79QyHO+j4Kqq8jj6uKOOH3vAwBASb4KbAzVsO7MU0S9T4NaVTn8b7A99k5pgY5LL5fbdUlC8IXzWLPKGws9l8Da2hb79+7GhHGjcepssMg/4BEPH2De7JmYOt0NrVq3xflzZzB9yiQcOnocdevWAwDs2rENB/fvxdLlK6Cvb4DNmzZgwtjROHH6vGDl4vz8fHTo2Bk2tnY4efxosfN06tIVzVu0FEpbtGAe8vLyKnRgcTH4PNauXoEFixbDysYWB/buxsRxY3DyzAVoiGrPiAdwnzsTU6a5oWXrNrhw7izcpk3GwcPHUOdre2ZnZ8POviE6dOqCpYsX/fL8+/fulqnhgJJUtqdFxJ5zsXr1anTp0gXPnz8XpK1duxYeHh44d+6cxCqWnp6OLVu2oFGjRhIr808d2r8bPXr3Q7eefVDbpA5mu3tCUVERZ08fF5n/yKF9cHBsgSEjRsG4tilcJ0xFPXNLHDt8QJBn6+aNcGzWChOnzUI9cwvoGxiiRet2QsFKr74DYNegEWrU1IeZuSVcJ07F54R4xH+Kk/o1l6fzxw+gbefeaNOpJwyMTDB6qjsUFBRx/eJpkfknz/sfOvToD2NTM+gbGmPsjIXg8/l48vBuOde8/I3vWA/7brzBoVsxePEpHbP33kd2XgEGt6gtMn9qZh4+p+cIttaWusjOK8SZu0XvAsrIzkf/dTdw+t4HvE7IwP03yXDf/wB2xhrQ11Auz0v7Y3t370LffgPQu88/MK1TBws9l0BRUREnjx8TmX//vj1o1qIlRo4aAxNTU0yeOh0WlpY4dGAfgKJfzvv37oHruAlo284J9czM8T/vVUj8/BlXQr4HXhMnT8Vw55GCgORnioqK0NLWFmxsDgd3bt9G777/SL4RJGjfnkD0/ac/evX5B6amdbDAYwkUlRRx8oTo9jy4by+aNW8BZ5fRMDExxaQp04ra8+B+QZ7uPXph3IRJaNr016/9jn7+DHt378Lipcskek1MYLEks8kKsYOLMWPGYNasWXByckJMTAxWrlwJLy8vnD9/Hi1btvx9Ab9x9epVDB8+HDVq1MDSpUvh4ODwx2VKQn5+Hl48f4pGDt//MbDZbDRq0hRRkY9EHvMkMgKNmjQVSnNwbI4njyMAADweD6G3rqOWkRHcJruie4eWcHUehBvXQkqsR3Z2Fs6fPoEa+gbQ0dX78wurIAry8/H25XNYNWgiSGOz2bCyb4KXTx+Xqozc3BwUFBRApVp1aVWzQpDjsGFrpI4bzxIEaXw+cOPpZzQyLd0v4CEta+PEnVhk5ZX8xuHqSnLg8fhIy8r74zqXl/y8PDx7GoWmjs0EaWw2G02bNkPkI9GL9kVGRBT7I9eseQtERkQAAOI+fACXmwiHpt/LrFatGqxtbEssszTOnD4JJSVFdOjYucxlSFt+flF7/njtbDYbDk0dEfkoQuQxkY8ihPIDgGOz5iXmL0l2djbc587CvAUe0NLSFrfqFQ5N6CyFOXPmICkpCY0aNUJhYSEuXryIpk2b/v7AEsTFxSEwMBC7du1CamoqUlJScODAAQwYMOC3jZmbm4vcXOFu4Nw8jtBL1iQhLTUVhYWF0Php+ENDQxPvYt6KPCY5iVtsuERdQxPJSUkAgJTkJGRnZWFf4A64TpiCCVPcEB72HxbMnoaN/rtg37Cx4LjjRw5iy8a1yM7OhqFRbfhs3gY5OXmJXiOTMtJTweMVFhv+UFXXwMf3MaUq4+COTVDX1BIKUP5GGtXkUYXDRmK68H2fmJ6DOjWq/fZ4+9oasDRQw4zAeyXmUajCxqJ+NjhxJxZfcmRn+f2U1BQUFhYWG2bQ1NTE27dvRB7D5XKhqalVLD83ift1f2JRmlbxMsV9WeOPTh47ii5du0NRUbHMZUhbSkpRe/48/KGpqYWYt6K/97hcrsj8SWK21dpV3rC1s0fbdu3FqzSpEEoVXGzcuLFYmr6+PpSVldGqVSvcuXMHd+7cAQBMnTq11Cc/duwYduzYgRs3bqBLly5Yu3YtunTpgqpVq8La2rpUUZq3tzeWLFkilDZr3iLMme9R6now5dvb7lu0bouBQ50BAHXNLPDkUQROHgsSCi46dumOxg7NkMRNxMG9u7Bo3kxs2bFP4kGUrDodFIiwa/9i0Wp/yMtTm/zK0Ba18fR9aomTP6twWNg2wREsFguz994v59pVDo8iHuLNm9dYtmIV01WpkK5dvYI7d27j0BHRQ86yiPFFpcpZqYKL9evXi0zncDi4desWbt26BaCo20ec4GLgwIGYO3cugoKCUK3a739xieLu7g43NzehtPQ8yU9yVFVTA4fDQXJyklB6cnJSsV8932hoaiHlp/wpyUmCqL6ozCowrm0qlMeotgkeRwg/fquiUg0qKtVQy9AI9a1t0KVtM9y4ehkdOnf700urEKpVVwObzUFaqvAfvLSUZKip/7qr/+yRvTgdtBvzV2yGoUldaVazQkjOyENBIQ/a1YWDKO3qivic9usnO5TlOejdpBZWnooSub8Kh4Xt4x1RS7Mq+q6+JlO9FgCgrqYODoeDpCThf3dJSUnQ0hL971RLSwtJSdzi+b/+u/7WJZ/ETYK2to5QHjNz8zLV8/ixIzAzt4BlfasyHV9e1NWL2jO5WHsW7+35RktLS3T+EtpflLt3wvHhfSxaNRPuhZzlNhX2DRpi+669pS6ropClIQ1JKFUw9fbt21Jtb96I7nYsyejRo7F582Z07twZ/v7+SElJEfsCFBQUUL16daFNGr/m5eTkUc/cEvfvhAvSeDwe7t+9jfo2xd+RAgBWNna4dzdcKO3u7TBYWdsJyrSob4X372KE8ryPfQfdGjVLrAufX9TrkZ8vO2Phv1NFTg6165oj6ofJmDweD1ERd1HX0rrE484c3oMTB3Zg7rKNMKlnWWK+v0l+IQ+P3qWgpYWuII3FAlpa6ODe66RfHAn0aFwL8nIcHA17V2zft8Citm419FtzHSmZsnd/ycnLw8KyPm6HhwnSeDwebt8Og42t6Lc229jZ4Xa48L/T8LBQ2NjZAQD0DQygpaWN27e/l/nlyxc8jnxUYpm/kpWZiUvBF9Cnbz+xjy1vcnJf2/O2cHveCQ+Hja2dyGNsbO1w54f8wNf2LCG/KC6jXXH42CkcOnJCsAHAzDnzsGSpt9jXQcpfmXtq8vLyEB0d/UevQw8ICMCnT58wduxYHDx4EDVq1ECvXr3A5/PB4/HKXK60DBrqjDMnj+LC2ZOIefsaa7y9kJ2djW49+gAAlnq4w9/3ey9P/0HDcDv0Fg7uC8S7mDfYEbAZz58+wT8DhgjyDB7ugpB/L+D0iSP48P4djgXtR+jNa+jTfxAAIO7De+zdtQ3Pn0UhPv4jHj96iEVzZ0BBUQGOzYXXy5B1XfsOwdULJ3Hj37OIi32LnZtWICcnG6079gAA+K3yxKGdvoL8p4N248gef4xz84C2bg2kJnORmsxFTnYWU5dQbvwvvcCwViYY2MwIdWtUw+phDaGsUAWHbhWNg/uOboIFfYsHZUNb1MaFh3HFAocqHBZ2TGgGW2MNTNwaDg6bBZ3qitCprgg5jmx16A53dsHxo4dx+uQJvHn9Gv/zWozs7Gz07tMXALDAfQ42rF8ryD902AiE3rqJ3YE78fbNa2zZvAlRT55g0JBhAIp+cQ4dPgLbArbg2pUQvHwRjYXuc6Cto4N2P6y58OnjRzx/9gyfPn1EYWEhnj97hufPniErM1OofsHB51FYWIhuPXpKvzEkYNiIkThx7AhOnzqBN29eY/nSovbs1buoPRfOn4uNPt/bc/Cw4Qi99R/27N6Jt2/ewN9vE55GRWHQ4KGCPGlpqYh+/gyvXxetURMT8xbRz58J5rdoaWmjTt16QhsA1NCrCX0Dg/K6dIlisySzyQqxJ3RmZWVhypQp2L17NwDgxYsXMDExwZQpU6Cvr4958+aJVZ6SkhKcnZ3h7OyMly9fYteuXbh37x6aN2+Obt26oV+/fujbt6+41ZSK9h27IDUlGdv9fZGcxEWdeuZYuykAGl+7BxPiP4H9w399a1t7eC5bhW1+G7F1sw8MahnBe80mwRoXANC6rRNmuXtiX+A2+KzxhqGRMf630ge2dkXriCgoKODRw/s4fHAvMtLToKGpBVv7hvDfsV/k2hqyzLFNR6SnpeLongCkpiTByKQe5i3bCNWvwyJJifFC7Xv53DEU5OfD539zhcrpO8wV/YaPLde6l7dTd99Ds5oC5vS2gk51RTx5n4pB628IJnnqayiD93VOzzemutXQtJ42+q+9Xqy8GmpK6GKvDwC4uqST0L7eq64iNDpRSlcieZ27dEVKcjL8fDeCy02EmbkF/AK2C7rl4z99Apv1PWCys28A71Vr4LvRB5t81sHQyBg+mzYLPVLqMtoV2dnZ8FrsgYyMdNg3aAi/gO1CvaR+vhtx+tQJweeB/XoDALbv2oPGTb4/9Xby+DG0d+qA6tVl46mmTp2L2nPL5k1I+tqem/23/dCeH4XWcLCza4DlK9Zgs68PfDesh6GRMdZt8BUECABw/eoVeC6aL/g8b3bR0Pa4CZMwfuKUcrqy8iVLgYEksPj8n76BfmPatGm4desWfHx80LlzZ0RGRsLExASnTp3C4sWL8fBh6R/N4vF4YLOL/yri8Xg4d+4cduzYgQsXLhR7GuR3EjNka5y4oolN+vt/+UtLl6XBTFdBZsUGDGC6CjKNxxPrq5z8QFle+n/53U4//32mUljXs2zzfMqb2P2dJ0+ehK+vL1q0aCE0QaV+/fqCLq7SkpOTw+fPnwWfZ8+ejeTkZLDZbPTo0QMnT57E+/fvxa0iIYQQUqFUtnUuxA4uEhMTRb4OPTMzU+wL/7nTJCAgAKmpqUJp9Op1Qgghsq6yzbkQO7ho1KiR0DLf3wKK7du3w9Hx10u5/o6YIzSEEEIIqYDEntC5fPlydOnSBU+fPkVBQQE2bNiAp0+fIjQ0FNevF58oRgghhFR2MjSiIRFiBxctWrRAREQEVqxYAWtra1y6dAkNGjRAWFgYrK1LXo+gJB4eHlBWLnoxUl5eHpYtWwZVVVWhPOvWrRO7XEIIIaSiqGxvRS3Tu0VMTU2xbdu2Pz55q1atEB0dLfjcrFmzYgtxydIEFkIIIUQU2Vot5s+JHVycP38eHA4HnToJPwt/8eJF8Hg8dOnSpdRlXbt2TdzTE0IIIaSCEzuYmjdvHgoLi7+mmc/nY968eXjx4gU2bNiAx49L95rsH3G53D96yyAhhBBSEbFYktlkhdjBxcuXL2FpWfwdDubm5nj8+DFmzZqFGzduoEePHqUqLzU1FZMmTYKWlhZ0dXWhq6sLLS0tTJ48udhjqYQQQogsYrNYEtlkhdjDIqqqqnjz5g2MjY2F0l+9egUNDQ2cPn0ab9++hZXV79/2l5ycDEdHR8TFxWHo0KGwsLAAADx9+hSBgYEICQlBaGgo1NXVxa0mIYQQQhgids9Fr169MH36dKHVOF+9eoWZM2eid+/eAAAVFRWcOnXqt2V5eXlBXl4er1+/RkBAAKZPn47p06dj69atePXqFeTk5ODl5SVuFQkhhJAKhclhkc2bN8PY2BiKiopwcHDAnTt3SnXcoUOHwGKxBH/bxSF2cLFq1SpUrVoV5ubmqF27NmrXrg0LCwtoampizZo1AABtbW04OTn9pqSipcTXrFkDXV3dYvv09PSwatUqnDhxQsSRhBBCiOxgaoXOoKAguLm5wdPTEw8ePICtrS06deok9OoNUWJiYjBr1iy0bNmyTNdbpmGR0NBQ/Pvvv3j06BGUlJRgY2ODVq3Ef/33p0+fUL9+/RL3W1lZIT4+XuxyCSGEEFK0TpSrqytcXFwAAP7+/jh37hx27txZ4lvMCwsLMXToUCxZsgQ3b94s0/xHsYKL/Px8KCkpISIiAh07dkTHjh3FPuGPtLS0EBMTAwMDA5H73759Cw0NjT86ByGEEMI0SU3GzM3NLfamcAUFBSgoKBTLm5eXh/v378Pd3f17PdhsODk5ISwsrMRzeHl5QUdHB6NHj8bNmzfLVE+xhkXk5ORgaGgo8lHUsujUqRMWLFiAvLy8Yvtyc3OxaNEidO7cWSLnIoQQQpgiqTkX3t7eUFVVFdq8vb1FnpPL5aKwsLDY1ANdXd0SRwX+++8/7Nix448XyhR7WGTBggWYP38+9u7d+8e9Cl5eXmjUqBHq1q2LSZMmwdzcHHw+H8+ePYOfnx9yc3Oxd+/ePzoHIYQQ8rdwd3eHm5ubUJqoXouyyMjIwPDhw7Ft2zZoaWn9UVliBxe+vr549eoVatasCSMjI1StWlVo/4MHD0pdloGBAcLCwjBx4kS4u7sL3orKYrHQoUMH+Pr6olatWuJWkRBCCKlQJPW69JKGQETR0tICh8NBQkKCUHpCQgL09PSK5X/9+jViYmKE1qni8XgAgCpVqiA6OhqmpqalOrfYwUVZHkn5ldq1a+PChQtISUnBy5cvAQB16tShuRaEEEL+GiyU/wJY8vLyaNiwIUJCQgR/u3k8HkJCQjB58uRi+b8thvmjhQsXIiMjAxs2bBDrx77YwYWnp6e4h5SKuro6mjRpUiz96NGj6Nevn1TOSQghhJQHSfVciMvNzQ3Ozs5o1KgRmjRpAh8fH2RmZgqeHhkxYgT09fXh7e0NRUXFYgtgqqmpAUCpFsb8UZle1Jaamort27fD3d0dycnJAIqGQ+Li4sQuq6CgAE+ePMGLFy+E0k+dOgVbW1sMHTq0LFUkhBBCKr2BAwdizZo18PDwgJ2dHSIiIhAcHCyY5BkbG4tPnz5J/Lxi91xERkbCyckJqqqqiImJgaurKzQ0NHD8+HHExsZiz549pS7ryZMn6N69O96/fw+gaPXPLVu2YMCAAXjy5AlcXV1x7tw5catICCGEVChM9VwAwOTJk0UOgwC/fzt5YGBgmc4pds+Fm5sbRo4ciZcvX0JRUVGQ3rVrV9y4cUOssubOnYs6derg1KlTGDRoEE6ePIk2bdqgR48e+PDhA1asWFHiGhiEEEKIrGCxWBLZZIXYPRd3795FQEBAsXR9fX2xV9O8e/cuLl26BDs7O7Rs2RIHDx7E/PnzMXz4cHGrRQghhJAKQuzgQkFBAenp6cXSX7x4AW1tbbHK4nK5qFmzJoCiZcWrVq2Kpk2bilslQgghpEJjcliECWIPi/Ts2RNeXl7Iz88HUNTVExsbi7lz5+Kff/4RqywWi4WMjAykp6cjLS0NLBYL2dnZSE9PF9oIIYQQWcbkW1GZIHbPxdq1a9GvXz/o6OggOzsbrVu3Rnx8PBwdHbFs2TKxyuLz+ahXr57QZ3t7e6HPLBZLYsuNE0IIIUT6yvRW1H///Rf//fcfIiMj8eXLFzRo0KBUr1j/2dWrV8U+hhBCCJE1knpxmawQO7j4pkWLFmjRosUfnbxFixZYs2YNTp8+jby8PLRv3x6enp5QUlL6o3IJIYSQiqSyzbkodXCRnZ2NkJAQdO/eHUDRy1N+fO0rh8PB0qVLhR5P/Z3ly5dj8eLFcHJygpKSEjZs2IDPnz9j586dYlwCIYQQQiqSUgcXu3fvxrlz5wTBha+vL+rXry/oZXj+/Dlq1qyJGTNmlPrke/bsgZ+fH8aNGwcAuHz5Mrp164bt27eDzS7T4qGEEEJIhVPJRkVK/7TI/v37MXbsWKG0AwcO4OrVq7h69SpWr16Nw4cPi3Xy2NhYdO3aVfDZyckJLBYLHz9+FKscQgghpCJjgyWRTVaUuufi1atXsLa2FnxWVFQU6l1o0qQJJk2aJNbJCwoKig2jyMnJCR5zLSs++H90fGUXxU1jugoyKzZgANNVkFnt199kugoy7dK0P5sDR6SrsvVclDq4SE1NFZpjkZiYKLSfx+MJ7S8NPp+PkSNHCr2bPicnB+PHj0fVqlUFacePHxerXEIIIYQwp9TBhYGBAZ48eQIzMzOR+yMjI8V+D4izs3OxtGHDholVBiGEEFLR0dMiJejatSs8PDzQrVu3YkMZ2dnZWLJkCbp16ybWyXft2iVWfkIIIUQW0ToXJZg/fz4OHz4MMzMzTJ48WbCyZnR0NHx9fVFQUID58+dLraKEEEIIkQ2lDi50dXURGhqKCRMmYN68eeDziyZNslgsdOjQAX5+ftDV1ZVaRQkhhBBZVck6LsRbobN27doIDg5GcnIyXr16BQCoU6cONDQ0pFI5Qggh5G9AwyKloKGhgSZNmki6LoQQQgj5C5T53SKEEEIIKZ1K1nFBwQUhhBAibZXthRaV7XoJIYQQImXUc0EIIYRIGauSjYtQcEEIIYRIWeUKLSi4IIQQQqSusj2KSnMuCCGEECJRjAYXe/bsEftNqoQQQoisYUlokxWMBhcuLi5IS0tjsgqEEEKI1LFYktlkBaPBxbf3kxBCCCHk78H4hM7K9ngOIYSQyqey/a1jPLho3749qlT5dTUePHhQTrUhhBBCJK+yPT3BeHDRqVMnqKioMF0NQggh5K+0efNmrF69GvHx8bC1tcWmTZtKfPno8ePHsXz5crx69Qr5+fmoW7cuZs6cieHDh4t1TsaDi9mzZ0NHR4fpahBCCCFSw9SwSFBQENzc3ODv7w8HBwf4+PigU6dOiI6OFvm3V0NDAwsWLIC5uTnk5eVx9uxZuLi4QEdHB506dSr1eRntqalsY1CEEEIqJ6YeRV23bh1cXV3h4uICS0tL+Pv7Q1lZGTt37hSZv02bNujTpw8sLCxgamqKadOmwcbGBv/9959Y56WnRQghhBAZkZubi/T0dKGtpPWi8vLycP/+fTg5OQnS2Gw2nJycEBYW9ttz8fl8hISEIDo6Gq1atRKrnowGF2/fvoW2tnax9IKCAnz58oWBGhFCCCGSx2KxJLJ5e3tDVVVVaPP29hZ5Ti6Xi8LCQujq6gql6+rqIj4+vsS6pqWlQUVFBfLy8ujWrRs2bdqEDh06iHW9jAYXkZGR2L17t1DasmXLoKKiAjU1NXTs2BEpKSkM1Y4QQgiRDLaENnd3d6SlpQlt7u7uEq1rtWrVEBERgbt372LZsmVwc3PDtWvXxCqD0eBi7dq1yMzMFHwODQ2Fh4cHFi1ahMOHD+P9+/dYunQpgzUkhBBC/pykei4UFBRQvXp1oU1BQUHkObW0tMDhcJCQkCCUnpCQAD09vRLrymazUadOHdjZ2WHmzJno169fib0jJZYhVm4Je/r0KZo1ayb4fPToUXTo0AELFixA3759sXbtWpw5c4bBGhJCCCGySV5eHg0bNkRISIggjcfjISQkBI6OjqUuh8fjif0eMEYfRc3IyICmpqbg83///Yf+/fsLPtevXx8fP35komqEEEKIxDD1bKSbmxucnZ3RqFEjNGnSBD4+PsjMzISLiwsAYMSIEdDX1xf0THh7e6NRo0YwNTVFbm4uzp8/j71792LLli1inZfR4EJfXx/Pnj2DoaEhvnz5gkePHmH9+vWC/UlJSVBWVmawhoQQQsifY2rlhYEDByIxMREeHh6Ij4+HnZ0dgoODBZM8Y2NjwWZ/H8TIzMzExIkT8eHDBygpKcHc3Bz79u3DwIEDxTovo8FF//79MX36dMyfPx/nz5+Hnp4emjZtKth/7949mJmZMVhDQgghRLZNnjwZkydPFrnv54ma//vf//C///3vj8/JaHDh4eGBuLg4TJ06FXp6eti3bx84HI5g/8GDB9GjRw8Ga0gIIYT8OTZjAyPMYDS4UFJSwp49e0rcf/Xq1XKsTekcP3wQB/fuQnISF6Z1zTB99nxYWlmXmP/q5YvYvsUX8Z/iYFDLCOOnzIBjC+HFSGLevob/xvWIeHAPhYWFMDYxwf9W+UBXrwY+fYzDgJ6il1z1WrEWbZ1KvxxrRXTv0imEnzuML2nJ0DU0RUfnydA3NReZ9/ndm7h16iBSEuLAKyyEuq4+mnbtB+uW35+/vnFsN56GXUN6ciI4nCrQq10XbQaMgn4di/K6JKk5dGA/du/aAS43EfXMzDFv/iJY29iUmP/SxQvYvGkDPsbFwdDIGNPdZqFlq9aC/Xw+H36+G3H86BFkZKTDzr4BFngshpGRMQAgLu4Dtvr74c7tcCRxudDW0UG37j3hOnY85OTlpX25UtfXvgaGNDaARlV5vPr8BetDXuNZvOj1dbrW18GCrsK9qLkFPLRbf0vwWV1ZDhNb10YTYzWoKFRBxIc0rL/8Gh9Sc6R6HeUh6OB+7AncgSQuF/XMzDHHfSGsrEu+9/69GIwtvhvw8WMcDA2NMHXGLLT44d4LuXwJxw4fwrOnUUhLS8PBIydgZv793+jHuA/o3tlJVNFYucYHHTp1ltzFlZPKtiB1ZXtR2x8JuXQBvutXYaTrBGzfdwR16plh5pRxSElOEpn/8aOHWLJgDrr16oMd+4+gZZt2mD9rKt68einIE/chFpPGjIChcW1sDNiFwEPH4Dx6POS/fnnr6OrhZPA1oW3UuElQUlaGQ7OW5XLd0vI07Cou7/dHy77DMfp//tAxNMGhFfOQmSZ6bROlqtXQvNcQjFy8Ea7eW2HbuhPObF2N15F3BXk09AzQaeRkuK7YihGePlDV1sPBFXORmZ5aTlclHcEXzmPNKm+MmzgJh46cgJmZOSaMG42kJNH3XsTDB5g3eyb69O2HoKMn0bZde0yfMgkvX74Q5Nm1YxsO7t+LhZ6Lse/gYSgpKWHC2NGCWeExb96Ax+NjkacXjp86h9lz3HHk8CFs3LBe5DllSXszLUxpY4KdobEYtechXiVmYl1/K6gpy5V4zJfcAvTwCxds/wTcEdq/oo8laqoqYu6Jp3DZ/RDx6bnYMMAainKy/TV7Mfg81q1egbHjJ+HA4eOoW88Mk8aNQXIJ996jiAeYP3cmevXthwNHTqBNOye4TZuMVz/ce9nZ2bCzb4ipM2aJLENXrwYuXb0ptI2fOAXKyspo3lK2v/cqCxafwTW47e3tS/V+EXFfuf45I7+sVfqlsc6DYWFphRlzFwAoejznn25O+GfgEAwbOaZYfk/3mcjOzsYqHz9B2riRQ1C3nhlmzff8mmcWqlSpgkVLV5S6HqOG9EM9cwvM85DOGiDB0SWv3CZJuzwmo4aJGTqPnAIA4PN42Dh1MBp37I1mPQeXqoztC8ajjp0D2vR3Ebk/NysTa1x7YYj7KtS2aiCxupdkgF0tqZQ7dFB/1LeyxvyFHgCK7r2O7Vtj8JDhGO06tlj+2TOnIzs7G75+AYK0YYMHwMzcHIs8vcDn8+HUpiVGjHSBs8toAEVPb7Vr1Qxey1agS9duIusRuHM7DgcdxPmLISL3/4n2629KvMySbB1qi+fxX7Au5DWAopn8J8Y3wdEHH7Hvzodi+bvW18HUdqbovEn0ksm11JVwaEwjDNt5H2+TsgRlnpnogICbMTjzOEHkcZJ0aVoLqZQ7YsgAWNa3wrwF3++9Lh3aYNDgYXAZU/zemztrBrKzs7Bx8/d7b8TQgTAzM8cCjyVCeb/1UPzccyHK4P59YG5hCU+vZRK4KmFV5aXfrXDuyWeJlNPNSjZe9MnosEjv3r2ZPL1Y8vPz8eL5Uwxz+R5EsNlsNGrSFFGRj0Qe8yTyEQYOdRZKa+LYDDevXQFQ9I807NYNDBkxCm6Tx+Jl9HPUqKmPYS5j0KpNe5FlRj+LwssXzwUBjqwqLMjHp7cvhIIIFpuN2lYN8OHl098ez+fzERP1EMmfPsBwkGuJ53h49RwUlKtC18hUYnUvb/l5eXj2NAqjXccJ0thsNpo2bYbIRw9FHhMZEYHhziOF0po1b4GrIZcBAHEfPoDLTYRD0+/rzFSrVg3WNraIfPSwxODiS0YGVFVV//CKmFWFzYKZXjXsvf09iOADuPcuFVY1q5d4nJI8B8fGNgaLxcKLhC8IuBkjCCTkOEV/nPIKeUJl5hXyYWOgWi7BhTTk5xfdey6jvwcRbDYbDk0dEfkoQuQxjx9FYOiIkUJpjs2a49qVsgekT6OeIPr5M8xbsKjMZTCtsg2LMBpceHp6/nEZubm5xRb3yM1jl7hiWVmlpaagsLAQGhqaQunqGpp4F/NW5DHJSdxi+TU0tJCcxAUApCQnIzsrC/sDd2DMhCmYMMUNt8P+w8LZ07HBfyfsGzYuVubZU8dhVNsE1rb2EroyZmRlpIHP46GqqrpQetXq6kj6+L7E43KyvmDj5EEoLMgHi81G55FTYWLdUCjPywfhOOH7P+Tn5UJFTQND5q2EcjXZ/YOY8vXe+3FNGADQ1NTE27dvRB7D5XKhqalVLD/3673H5SYWpWkVL5PL5YosM/bdOxw8sA9us+aW6ToqCjUlOVRhs5CclSeUnpyVB0MNJZHHvEvJhnfwC7xOzERV+SoY3Fgf/kNtMWznfSR+ycO75GzEp+VgXEtjrL70Ctn5hRjYSB+61RWgWVV256ekpnz93vvp3tPQ1ELMW9Hfe0X33s/3lRaSSrivSuPUiWOobWIKWzvp9z4SyWB0MDAnJwenT59GRkZGsX3p6ek4ffr0b1cFE/USl41rV0qryhLF5xf9ymnRui0GDh2BumbmGDZyDJq1aI1Txw4Xy5+bk4PLwefRvVff8q5qhaGgqIwxywPg4rUZbfqPwuX9/nj3NEIoj5GlLcYsD8BIzw0wtWmM45v+V+I8DlI6CQkJmDhuDDp06ox/+g9gujrlLupjBoKjPuPl50xEfEjD/FPPkJqVj962NQAAhTw+5p96BkMNJQRPdUTIjOZoYKiKsDfJ4NHbn/9ITk4OLpw/i959/2G6Kn+EDZZENlnBaM9FQEAATp8+jZ49exbbV716dWzcuBGxsbElPp8LFL3Exc3NTSgtLU/yMZOqmjo4HA6Sf5q8mZKcVOwX4jcamlrF8icnc6HxNX9RmVVgXFu4y96otgkiI4rPM7kacgk5Odno1K14e8ka5WqqYLHZxf7oZ6anFOvN+BGLzYaGnj4AQM+4DrgfYxF6+iCMLO0EeeQVlYry6OlDv64l/NycEXHtApr3GiKVa5E29a/33s+TN5OSkqClJfre09LSQlISt3j+r/eellbR24iTuEnQ1tYRymNmLvy0zufPCRjjMgK29vbwWCz77/pJzc5HAY8PDWXhHgUNZXkkZ5ZuvlYhj48Xn79AX11RkBad8AUjdz9EVXkO5DhspGbnF83tSJDdNzyrqX/93vvp3ktOKt4z9k3RvffzvcqFZgn36u9c/vcicrJz0L1H7zIdX1FUtmERRnsu9u/fj+nTp5e4f/r06b98VBWAWC9x+RNycnKoZ26J+3duC9J4PB7u372N+ja2Io+xsrHF/bvhQmn3bofBytpWUKZF/fqIfSfcvfg+NgZ6NWoWK+/cqeNo3qot1NU1/vRyGMepIocateshJup7EMXn8RDz5CEM6lqWuhw+n4eCgl//QeDzeSj8TZ6KTE5eHhaW9XE7/PtkQh6Ph9u3w2BTwvCYjZ0dbocL33vhYaGwsbMDAOgbGEBLSxu3b38v88uXL3gc+UiozISEBIweOQKWlvXh9T9voZX8ZFUBj4/o+Aw0MlITpLEANDRSw5OP6aUqg80CTLWqIikzr9i+zLxCpGbnw0BNEeZ61fDfK9FPVcgCObmie+/ObeF77054OGxs7UQeY21rJ5QfAG6HhZaY/3dOHT+K1m3bQl1Dtr/3WCzJbLKC0W+Kly9fwtZW9B9mALCxscHLly9L3F/eBg4dgbMnj+LC2VOIefsaa72XIjs7G12/RtT/83CHv+/3x/T6DRqG26G3cGhfIN7FvMHOgM14/jQKfQd8/wU9eLgLrvwbjNMnjuLD+1gcCzqA0JvX0af/IKFzf3gfi0cP76NHb9nuGvyRQ5d/8PDqeUTeuARu3Dtc2LUB+bk5sGld9Az76S0rcPXQdkH+W6cO4M3j+0j5/BHcuHcIP3cET/67DKvmRc/D5+Vk42rQDsS9fIq0xAR8evsCZ7auRkYKFxYOrUXWQVYMd3bB8aOHcfrkCbx5/Rr/81qM7Oxs9O5TNES2wH0ONqxfK8g/dNgIhN66id2BO/H2zWts2bwJUU+eYNCQYQCK3tA4dPgIbAvYgmtXQvDyRTQWus+Bto4O2rUvas+EhASMGTkcNWrUgNvsuUhJTgY3MRHcxMRyv35JC7oXhx42euhSXwdGGkqY1bEOFOXYOPekaOLlwq71ML6lsSC/i6MhmhiroaaqIurpVIVHNzPoVVfAmcjvEzXb1tOCfS1V1FRVRIs6GvAZYI2br5JwJya1nK9OsoaOGIkTx47gzKkTePPmNZYvLbr3evYuuvcWzZ+LTT7f770hw4Yj7NZ/2Lt7J96+eQN/v014GhWFgYOHCvKkpaUi+vkzvHld9LROTMxbRD9/JpgL9E1s7Ds8uH8Pvfv2B5EtjA6LFBQUIDExEYaGhiL3JyYmoqCgoJxrVbL2HbsgNSUFO/x9kZzERZ165lizyV8wzJEQ/wmsH37ZWdvaw3PZSmzz24StmzfAoJYRlq/ZCJM6dQV5WrV1wix3D+wL3I4Na7xhaGSMpSvXw+aniUvnTh+Hto4uGv8wu1/WWTq2RWZGGq4fDURmWgp0jUwxaK43VL4Oi6QlfQaL9b0983NzELxrIzKSE1FFXgGaNWuh14R5sHRsCwBgszlI+vgeR29eQnZGOpRUqqOGST2MWLQe2gbGTFyixHTu0hUpycnw890ILjcRZuYW8AvYLuhqjv/0Cewf2srOvgG8V62B70YfbPJZB0MjY/hs2oy6desJ8riMdkV2dja8FnsgIyMd9g0awi9gu6DnLzz0FmJj3yE29h06thNe+O1RVHQ5XLX0hERzoaYshzHNjaBRVR4vP3/BzKNRSMkq6uHSraaAH6dKVFOsgrkd60KjqjwycgsQHf8F4w48QszXp0UAQFNFHlPamkCjqhySvuQhOOozdoXFlvelSVynzkX33pbNm5D09d7z9d/2w733EewfflLb2jXAshVr4OfrA98N62FoZIx1G3xR54d77/rVK1i8aL7gs/vsoqHtsRMmYfzEKYL0UyeOQVdXD47Nmkv7MqWOJUPzJSSB0XUumjZtij59+mDuXNGzz729vXHq1CmE/9S9+zvSWueisiivdS7+RtJa56IyKM91Lv5G0lrnojIoj3UuQp6X/WmZH7U3L9vclfLG6LDIqFGjsHTpUpw9e7bYvjNnzmDZsmUYNWoUAzUjhBBCSFkxOiwyduxY3LhxAz179oS5ubngDajPnz/HixcvMGDAAIwdW3wFOEIIIUSWVLZhEcanfu/btw+HDh1CvXr18OLFC0RHR8PMzAwHDx7EwYMHma4eIYQQ8scq29MijPZcfDNgwAAMGFB8YR4ej4fz58+je/fuDNSKEEIIIWVRIYKLn7169Qo7d+5EYGAgEhMTkZ9PEzQJIYTILhoWYUh2djb27NmDVq1awczMDKGhofDw8MCHD8XfUEgIIYTIEjZLMpusYLzn4u7du9i+fTsOHToEU1NTDB06FKGhofDz84OlZelXaiSEEEJIxcBocGFjY4P09HQMGTIEoaGhqF+/PgBg3rx5TFaLEEIIkSgaFilH0dHRaNWqFdq2bUu9FIQQQv5ale1pEUaDizdv3sDMzAwTJkyAgYEBZs2ahYcPH4IlSy1ICCGE/AZLQpusYDS40NfXx4IFC/Dq1Svs3bsX8fHxaN68OQoKChAYGIgXL14wWT1CCCGElEGFeVqkXbt22LdvHz59+gRfX19cuXIF5ubmqFOnDtNVI4QQQv4Im8WSyCYrKkxw8Y2qqiomTpyI//77D6tXr8b79++ZrhIhhBDyR2hYpBzl5ubC3d0djRo1QrNmzXDy5EkAwK5du2BqaooNGzbAy8uLySoSQgghREyMPorq4eGBgIAAODk5ITQ0FP3794eLiwvCw8Oxdu1a9O/fHxwOh8kqEkIIIX9OlrodJIDR4OLIkSPYs2cPevbsiSdPnsDGxgYFBQV49OgRPTFCCCHkr0HrXJSjDx8+oGHDhgAAKysrKCgoYMaMGRRYEEIIITKM0Z6LwsJCyMvLCz5XqVIFKioqDNaIEEIIkbzK9puZ0eCCz+dj5MiRUFBQAADk5ORg/PjxqFq1qlC+48ePM1E9QgghRCIqWWzB7LCIs7MzdHR0oKqqClVVVQwbNgw1a9YUfP62EUIIIaRsNm/eDGNjYygqKsLBwQF37twpMe+2bdvQsmVLqKurQ11dHU5OTr/MXxJGey527drF5OkJIYSQ8sFQ10VQUBDc3Nzg7+8PBwcH+Pj4oFOnToiOjoaOjk6x/NeuXcPgwYPRrFkzKCoqYuXKlejYsSOioqKgr69f6vOy+Hw+X5IXUhF8zshnugoyLTg6nukqyKwBdrWYroLMar/+JtNVkGmXprVgugoyq6q89P/y33ubLpFyrGsqIDc3VyhNQUFBML3gZw4ODmjcuDF8fX0BADweD7Vq1cKUKVNK9QbywsJCqKurw9fXFyNGjCh1PSvcCp2EEELI30ZSb0X19vYuNnXA29tb5Dnz8vJw//59ODk5CdLYbDacnJwQFhZWqnpnZWUhPz8fGhoaYl0vo8MihBBCCCk9d3d3uLm5CaWV1GvB5XJRWFgIXV1doXRdXV08f/68VOebO3cuatasKRSglAYFF4QQQoiUSWrg5VdDIJK2YsUKHDp0CNeuXYOioqJYx1JwQQghhEgbAxM6tbS0wOFwkJCQIJSekJAAPT29Xx67Zs0arFixApcvX4aNjY3Y56Y5F4QQQshfSF5eHg0bNkRISIggjcfjISQkBI6OjiUet2rVKixduhTBwcFo1KhRmc5NPReEEEKIlDH1bhE3Nzc4OzujUaNGaNKkCXx8fJCZmQkXFxcAwIgRI6Cvry+YFLpy5Up4eHjgwIEDMDY2Rnx80dODKioqYq2gTcEFIYQQImVMLf89cOBAJCYmwsPDA/Hx8bCzs0NwcLBgkmdsbCzY7O+DGFu2bEFeXh769esnVI6npycWL15c6vNScEEIIYT8xSZPnozJkyeL3Hft2jWhzzExMRI5JwUXhBBCiJRVtneL/JUrdKZkFTJdBZnGzchjugoyq5amEtNVIJWUerc1TFdBZmVfnCX1czx6nyGRcmxrVZNIOdJGT4sQQgghRKJoWIQQQgiRMqaeFmEKBReEEEKIlDH1tAhTKLgghBBCpKySxRY054IQQgghkkU9F4QQQoi0VbKuCwouCCGEECmrbBM6aViEEEIIIRJFPReEEEKIlNHTIoQQQgiRqEoWW9CwCCGEEEIki3ouCCGEEGmrZF0XFFwQQgghUkZPixBCCCGE/AHGggsvLy9kZWUxdXpCCCGk3LBYktlkBWPBxZIlS/DlyxemTk8IIYSUG5aENlnB2JwLPp/P1KkJIYSQ8iVLkYEEMDrngiVLfTyEEEIIKRVGnxapV6/ebwOM5OTkcqoNIYQQIh2V7WkRRoOLJUuWQFVVlckqEEIIIVJX2TrqGQ0uBg0aBB0dHSarQAghhBAJYyy4oPkWhBBCKovK9hePnhYhhBBCpK2SRReMBRc8Hk9k+rt375CZmQlzc3Ow2bSAKCGEECJrGPvrvXPnTqxbt04obezYsTAxMYG1tTWsrKzw/v17hmpHCCGESA5LQv+TFYwFF1u3boW6urrgc3BwMHbt2oU9e/bg7t27UFNTw5IlS5iqHiGEECIxlW35b8aGRV6+fIlGjRoJPp86dQq9evXC0KFDAQDLly+Hi4sLU9UjhBBCSBkx1nORnZ2N6tWrCz6HhoaiVatWgs8mJiaIj49nomqEEEKIRFW2d4swFlwYGRnh/v37AAAul4uoqCg0b95csD8+Pp4W2CKEEPJ3YDC62Lx5M4yNjaGoqAgHBwfcuXOnxLxRUVH4559/YGxsDBaLBR8fnzKdk7HgwtnZGZMmTcLSpUvRv39/mJubo2HDhoL9oaGhsLKyYqp6hBBCiMQwNaEzKCgIbm5u8PT0xIMHD2Bra4tOnTrh8+fPIvNnZWXBxMQEK1asgJ6eXpmvl7HgYs6cOXB1dcXx48ehqKiII0eOCO2/desWBg8ezFDtCCGEENm3bt06uLq6wsXFBZaWlvD394eysjJ27twpMn/jxo2xevVqDBo0CAoKCmU+L2PBBZvNhpeXFx4+fIgLFy7AwsJCaP+RI0cwevRohmpXsqNBB9C7qxNaOdhh1PCBiHoS+cv8If8GY2CfbmjlYIeh/Xsh9Ob1EvOu/N9iNLW3xKH9e4TSZ02bhF5d2qGVgx26dWiFxQvnIrGEqFPWnD1+CKMGdEEfpyZwGzcM0U8fl5j33dtXWL5wJkYN6ILurexw6vC+YnnOnzyMySP7o3/n5ujfuTlmThiBe+H/SfMSpOLQgf3o0qEdGttbY+ig/ngc+ev77NLFC+jVvTMa21vjn949cPOG8H3G5/OxedMGtG/dAk0a2GDs6JF49y5GKM/USePRqX0bNLa3RvvWLTB/3mx8/pwglOfWfzcxbPAAODa2R5sWTeE2bQri4j5I5JolidpPusb1sMPz3a5IOTMdNzYMRSOzX//CndynAR5tH4Xk09Pwct9YrBrXBgpyHMH+WQOb4L+Nw/D5xFS8C5qIw569UNdA/Rclyh5JPS2Sm5uL9PR0oS03N1fkOfPy8nD//n04OTkJ0thsNpycnBAWFibV66VVqsTw78UL2LB2JcaMm4jdB46ibj1zTJ84FsnJSSLzR0Y8hIf7bPTo3Re7Dx5DqzbtMcdtCl6/elks77Url/Hk8SNoaxd/10rDxk2wbOV6BJ04B+/VGxD3/j3mz54u6csrdzdCLmL75rUYPHIcNmw/iNp16sFj1kSkpoh+E25uTg70aurDedw0qGtoicyjqa0L53FT4bPtAHy2HYBtg8b43/zpePf2lTQvRaKCL5zHmlXeGDdxEg4dOQEzM3NMGDcaSUmi77OIhw8wb/ZM9OnbD0FHT6Jtu/aYPmUSXr58Iciza8c2HNy/Fws9F2PfwcNQUlLChLGjhb6UGjdpitXrfHDqXDDW+mzEh/fvMWvGNMH+Dx/eY/qUiWji0BSHj53Clq07kJqaArdpU6TXGGVA7Sdd/VqbYeXYNli2PwyOk/Yi8s1nnF7WD9qqyiLzD2xrjqWjWmH5/lDYue7C+HUX0a+1ObxcWgrytLSpBf8zD9F6+n50dz+CKhwOzi7vD2UFufK6LKmT1JQLb29vqKqqCm3e3t4iz8nlclFYWAhdXV2hdF1dXak/MMFYcKGurg4NDY3fbhXJwX2B6NW3P7r36ovapnUwd4EnFBUVcfbkcZH5gw7uRdNmLTDMeTRqm5hi3KSpMLOwxNFD+4Xyff6cgLUrl2HJ8lXgVCn+dPDgYc6wsrFFjZr6sLGzx3CXMXjy+BEK8vOlcp3l5eThvejUvS86dO0NQ2NTTJq5EAqKivj33EmR+etZWGHURDe0bt8ZcvKiv3QcmrdGY8eW0K9lBP1aRhjhOgWKSsqIjiq5R6Si2bt7F/r2G4Deff6BaZ06WOi5BIqKijh5/JjI/Pv37UGzFi0xctQYmJiaYvLU6bCwtMShA0U9O3w+H/v37oHruAlo284J9czM8T/vVUj8/BlXQi4LyhnuPBI2tnaoWVMfdvYNMGq0KyIfRSD/6332LCoKPB4Pk6dORy1DQ1hY1seIkaMQ/fyZIE9FQO0nXVP7NsKu4MfYe+kJnscmYcrGf5Gdmw/nTqLnyDW11EdYVByCrj5HbEI6Qh68w+Frz4V6O3otOIZ9/0bh2bskPH6TiLFrL8BQtzrs6+qKLLMyc3d3R1pamtDm7u7OdLWKYWydi7LOQGVKfn4eop89hfMoV0Eam81GYwdHPI6MEHnMk8gIDB42UiitqWNzXL96RfCZx+NhycJ5GOY8CiamdX9bj7S0VFy8cBbWtvaoIie7UX1+fj5evXiG/sNGCdLYbDbsGjrgedSvu7BLq7CwEP9d+xc5Odkwt7KRSJnSlp+Xh2dPozDadZwgjc1mo2nTZoh89FDkMZERERjuPFIorVnzFrj69Q9f3IcP4HIT4dC0mWB/tWrVYG1ji8hHD9Gla7diZaalpuLcuTOwtbOH3Nf7zKJ+fbBYLJw8cQy9evdFVlYWzp05BQfHZoI8TKP2ky65KmzY19XF6kO3BWl8PnDlYSyaWNYUeUz40zgMameBRmZ6uBcdD2M9VXRqXBsHQp6WeJ7qVYvG+lMyciR7AQyS1AJYCgoKpZ4LoaWlBQ6Hg4QE4eG5hISEP5qsWRqMBRfOzs4SKSc3N7fYeFNuYZU/mogiSmpKKgoLC6HxU3e8uqYmYmLeiDwmicuFhobmT/m1kJTEFXzeu2s7OBwOBgwe9svz+25Yi6OHDiAnJxtW1rZYu3FLGa+kYkhPSwGvsBBq6sLto6ahiQ+xMX9Udszrl5g1cQTy8vKgpKSEBf9bB0Nj0z8qs7ykpKagsLAQmprC7aKpqYm3b0XfZ1wuF5qaWsXyc7/eZ1xuYlGaVvEyuVyuUNr6tatx6OB+5GRnw8bWDpv8/AX7DAxqwX/bTsyeOR3/W+KJwsJC2NrZw3fL1rJdrBRQ+0mXVnUlVOGw8Tk1Uyj9c0omzGqJ7mkOuvocmtWVELJ2MFgsQK4KB1vPRggFKD9isYDV49si9MkHPH3HFZlHNpX/KhXy8vJo2LAhQkJC0Lt3bwBFP2hDQkIwefJkqZ6bsWGRlJQUbNq0Cenp6cX2paWllbjvZ6LGn9avWSGNKkvc86dRCDq4F4uWLP/tK+iHjRiFPYeOYcOW7WBzOFiyaB69WbYE+obG2LgjCOv896JLrwFYv9wDsTGvma6WTBg5ajSCjp6A/7adYLPZWOg+V3CfcRMTscRzEXr27I39QUexc/c+yMnJYdaMqXQvfkXtV1xLm1qYPagppvlehuOkvRi45CS6NDHBvCFNReb3meyE+kZaGOF9tpxr+ndyc3PDtm3bsHv3bjx79gwTJkxAZmamYAXsESNGCA2r5OXlISIiAhEREcjLy0NcXBwiIiLw6pV489YY67nw9fVFZGQkpkwpPplJVVUVN2/eRHp6OhYsWPDLctzd3eHm5iaUllUo+ctSU1cDh8NBcrJwJJ2SlFTsV883mlpaxSZ7piR9/5UU8fA+UpKT0btre8H+wsJCbFy3Cof278HJ89/Hc9XU1aGmrg5DI2PUrm2Cnp3b4UnkI1jb2knoCstXdVV1sDkcpKYIt09qclKJkzVLS05ODjUNDAEAdcws8fJ5FE4fOYDJsxf9UbnlQV1NHRwOp9jkw6SkJGhpiW4XLS3h3jBB/q/3mZaWdlEaN0lownBSUhLMzM2Fz6+uAXV1DRgb14aJiSk6tm+NyEcRsLWzx6GD+1FNRQUzZs0R5F++YjU6tm+Nx5GPYFMB7kVqP+nipmejoJAHHbWqQuk66lURn5Ip8hhP5+Y4GPIUgcFF856iYrhQVpTD5mkdsfJgOH6Mq9ZPao+uDiZwmhmEOO4XqV0HE5h6L8jAgQORmJgIDw8PxMfHw87ODsHBwYJJnrGxsUJvIP/48SPs7e0Fn9esWYM1a9agdevWuHbtWqnPy1jPxbFjxzB+/PgS948bNw5Hjx79bTkKCgqoXr260CbpIREAkJOTh5mFJe7eDhek8Xg83L0TDmsbO5HHWNnY4e6dcKG0O+FhsLaxBQB06dYT+w6fxJ5DxwWbtrYOho4YhQ1+20qsy7fX1efl5/3hVTFHTk4OdepZ4NH97yvF8Xg8PHpwB+b1JTs/gs/jIV9G2kpOXh4WlvVxO/z7Y2I8Hg+3b4fBxtZe5DE2dna4HS58n4WHhcLGzg4AoG9gAC0tbdy+/b3ML1++fP2DJrrMb+cFin7JAEBOTg5YbOGvDDaHLZSXadR+0pVfwMPDlwloa28oSGOxgLZ2hrjz9KPIY5QUqoD3U88Mj8f/euz3v7jrJ7VHz2Z10HnOYbxLSJNC7ZnF5PLfkydPxrt375Cbm4vbt2/DwcFBsO/atWsIDAwUfDY2Ngafzy+2iRNYAAz2XLx+/Rp165Y8gbFu3bp4/bpidWUPHjYSSz3cYWFpBUsrawQd2IOc7Gx069UHALBk4Txo6+hg4tSinpSBg4djgqsz9u/ZheYtW+Pfi+fx7OkTzFu0BACgqqYGVTU1oXNwqlSBppYWjIxrAwCePH6EZ1FPYGvfANWqVUfch/cI8NsEg1q1SgxqZEXvAcOx3nsR6ppZop6FFU4dKRqrduraCwCwdtlCaGrpYOS4qQCKJoG+/zq8UZBfgCTuZ7x5+RyKSsqCnorAgI1o5NAc2rp6yM7KwrXLF/A44h681vgxc5FlMNzZBYvmz0X9+lawsrbBvr27kZ2djd59+gIAFrjPgY6OLqbNmAkAGDpsBEaPHI7dgTvRqlVrBF84j6gnT7BosReAoi/wocNHYFvAFhgZGkHfwACbN22Ato4O2rUvev49MvIRoh4/hn2DhqiuWh3vY2Pht2kDatUyhK1d0R/Qlq1aY9+eQPj7+aJLt+7IyszERp91qFlTH+YWlgy0lGjUftK18fg9bJvVBfdfJOBe9CdM7tMQyopy2HPpCQBg++wu+Mj9Ao9dNwEA58PfYGrfhnj0KgF3nsfDVF8NHs7Ncf72a0GQ4TPZCQPbmqP/4pP4kp0HXfWix1rTMvOQk1fAzIWSP8JYcMHhcPDx40cYGhqK3P/x40ehrpqKoEOnLkhNSca2LZuQlMRFXTNzrN8cIBjmiI//JPTLxMbOHl7LVyFg80b4+/qglqERVq3bBNM6v38q5BtFRSVcu3IZ2/x9kZOdDU0tbTRt1gIurusgLy8v8WssT63ad0Jaagr27dyClGQuTOqYwWuNH9S/ToJNTPgE9g+/bJK5nzF19CDB5+OH9uD4oT2wsmuIFRt3AADSUpKxbvlCJCdxUbWqCoxN68FrjR/sGzuW78X9gc5duiIlORl+vhvB5SbCzNwCfgHbofm1Wz/+0yewWd/vMzv7BvBetQa+G32wyWcdDI2M4bNpM+rWrSfI4zLaFdnZ2fBa7IGMjHTYN2gIv4Dtgl4+JUVFhFy+hC2bNyE7Owta2tpo3qIlVo2bKLjPHJo6wnvVWgTu3I7AnTugqKQIW1s7+AVsg6KiYjm20K9R+0nX0evR0FJVhseI5tBVV0bkm0T0WnAUn1OzAAC1tKsLggYAWHEgDHw+H54jW6Cmpgq4adk4F/4aiwO/L243rocdAODfNYOEzuW65gL2/Rsl/YsqB7L0unRJYPEZmknUtm1bODg4YMUK0ZMv586dizt37uDq1atil52SVfin1avUuBmyMYRQEdXSVGK6CqSSUu+2hukqyKzsi7Okfo74NMmsZaKnKhuPLTPWczF58mQMGjQIBgYGmDBhAjicoqVgCwsL4efnh/Xr1+PAgQNMVY8QQgiRnErWc8FYcPHPP/9gzpw5mDp1KhYsWAATExMAwJs3b/DlyxfMnj0b/fr1Y6p6hBBCCCkjxoILAFi2bBl69eqF/fv349WrV+Dz+WjdujWGDBmCJk2aMFk1QgghRGIqWccFs8EFADRp0oQCCUIIIX+1yjahk7HHMVatWoXs7GzB51u3bgkt452RkYGJEycyUTVCCCGE/AHGggt3d3dkZGQIPnfp0gVxcXGCz1lZWQgICGCiaoQQQohEsST0P1nB2LDIz0/A/s1r6xNCCKnkZCcukIiKtUoVIYQQQmQe4xM6CSGEkL9dJeu4YDa42L59O1RUVAAABQUFCAwMFLy58Mf5GIQQQogsq2xPizC2/LexsbHQG/FK8vbtW7HLpuW//wwt/112tPw3YQot/1125bH8d1KmZF7ApllVNgYcGKtlTEwMU6cmhBBCypUsPekhCYxN6AwLC8PZs2eF0vbs2YPatWtDR0cHY8eOFVr3ghBCCJFVLJZkNlnBWHCxZMkSREV9f5Xu48ePMXr0aDg5OWHevHk4c+YMvL29maoeIYQQQsqIseDi0aNHaN++veDzoUOH4ODggG3btsHNzQ0bN27E4cOHmaoeIYQQQsqIsTkXKSkp0NXVFXy+fv06unTpIvjcuHFjvH//nomqEUIIIRIlS0MaksBYz4Wurq7gSZC8vDw8ePAATZs2FezPyMiAnJwcU9UjhBBCJKayLf/NWHDRtWtXzJs3Dzdv3oS7uzuUlZXRsmVLwf7IyEiYmpoyVT1CCCGElBFjwyJLly5F37590bp1a6ioqGD37t2Ql5cX7N+5cyc6duzIVPUIIYQQialswyKMBRdaWlq4ceMG0tLSoKKiAg6HI7T/yJEjgtU7CSGEEFlWyWIL5t8toqqqKjJdQ0OjnGtCCCGEEElgPLgghBBC/nqVrOuCggtCCCFEymTpSQ9JYOxpEUIIIYT8najnghBCCJEyelqEEEIIIRJVyWILCi4IIYQQqatk0QXNuSCEEEKIRFHPBSGEECJlle1pEQouCCGEECmrbBM6aViEEEIIIRLF4vP5fKYrUZnk5ubC29sb7u7uUFBQYLo6MoXa7s9Q+5UdtV3ZUdtVThRclLP09HSoqqoiLS0N1atXZ7o6MoXa7s9Q+5UdtV3ZUdtVTjQsQgghhBCJouCCEEIIIRJFwQUhhBBCJIqCi3KmoKAAT09PmthUBtR2f4bar+yo7cqO2q5yogmdhBBCCJEo6rkghBBCiERRcEEIIYQQiaLgghBCCCESRcEFIYQQQiSKgotfGDlyJHr37i34/ywWCytWrBDKc/LkSbB+eiPNtm3bYGtrCxUVFaipqcHe3h7e3t4iy/3RtWvXwGKxkJqaCgAIDAyEmpqaUB5jY2OwWKwSt5EjR/7pZUvM+/fvMWrUKNSsWRPy8vIwMjLCtGnTkJSUhJiYmF9eB4vFQmBgYLE2+ZGxsTF8fHyEPosq59t/s5/PqaGhgdatW+PmzZtSuf5v9wyLxYK8vDzq1KkDLy8vFBQUCK7r26atrY2uXbvi8ePHYrXjj9q0aSPy+gsKCgR5xo0bBw6HgyNHjhQ7z+LFi8FisTB+/Hih9IiICLBYLMTExAD43o4RERGCY361OTk5oVOnTsXO5+fnBzU1NXz48KEszSvSj20uJyeH2rVrY86cOcjJyRHK9+HDB8jLy8PKykpkOXw+H1u3boWDg4Pg33GjRo3g4+ODrKwsAEXtZWdnJzgmKysL7u7uMDU1haKiIrS1tdG6dWucOnWq1Pc70xITEzFhwgQYGhpCQUEBenp66NSpE27dugXg9//Gvjl27BjatGkDVVVVqKiowMbGBl5eXkhOTgZQ/LutsLAQK1asgLm5OZSUlKChoQEHBwds374dAH7bdosXLy6X9iGlR29FFYOioiJWrlyJcePGQV1dXWSenTt3Yvr06di4cSNat26N3NxcREZG4smTJxKpw927d1FYWAgACA0NxT///IPo6GjBsrpKSkoSOc+fevPmDRwdHVGvXj0cPHgQtWvXRlRUFGbPno0LFy4gLCwMnz59EuRfs2YNgoODcfnyZUGaqqoqbt++LdZ5vby84OrqKpRWrVo1oc+XL19G/fr1weVysWzZMnTv3h0vXryArq5uGa701zp37oxdu3YhNzcX58+fx6RJkyAnJwdHR0cAEPy3+/jxI2bPno1u3brh1atXkJeXB/D7dgwPD4eGhobgfK6urvDy8hKqQ5UqRf/Ms7KycOjQIcyZMwc7d+5E//79i9VXUVERO3bswMyZM1G3bt3fXt+sWbOEgpHGjRtj7NixQv8N8vPzYW1tjYCAAIwbNw4A8PbtW8yZMwdbtmyBgYFBaZuzVL61eX5+Pu7fvw9nZ2ewWCysXLlSkCcwMBADBgzAjRs3cPv2bTg4OAiVMXz4cBw/fhwLFy6Er68vtLW18ejRI/j4+MDY2Fjkj4Px48fj9u3b2LRpEywtLZGUlITQ0FAkJSWhVq1apbrfmfbPP/8gLy8Pu3fvhomJCRISEhASEiIUyP7u39iCBQuwcuVKzJgxA8uXL0fNmjXx8uVL+Pv7Y+/evZg2bVqx8y5ZsgQBAQHw9fVFo0aNkJ6ejnv37iElJQUAhNouKCgIHh4eiI6OFqSpqKhIrA2IhPBJiZydnfm9evUS/P/u3bvzzc3N+bNnzxbkOXHiBP/HZuzVqxd/5MiRpS73R1evXuUD4KekpPD5fD5/165dfFVV1RLL+Tl/RdK5c2e+gYEBPysrSyj906dPfGVlZf748eOF0j09Pfm2trbFyvnVNRoZGfHXr19f4uefvX37lg+A//DhQ0FaZGQkHwD/1KlTpbkssYj679yhQwd+06ZNRV7X6dOn+QD4jx49EqSJ046tW7fmT5s2rcT6BAYG8ps2bcpPTU3lKysr82NjY4X2f/tv0KFDB37//v0F6Q8fPuQD4L99+5bP54tux29K+m8QGBjIV1FR4b9584bP4/H4bdu25ffp06fEupaVqDbv27cv397eXvCZx+PxTUxM+MHBwfy5c+fyXV1dhfIHBQXxAfBPnjxZrHwej8dPTU3l8/nF71lVVVV+YGBgqepZ0v3OpJSUFD4A/rVr10rM87t/Y7dv3+YD4Pv4+JR4Dj6/+Hebra0tf/HixaWq5+++F0nFQMMiYuBwOFi+fDk2bdpUYleunp4ewsPD8e7du3KuXcWRnJyMixcvYuLEicV6UvT09DB06FAEBQWBz/ASK9nZ2dizZw8ACHoKpE1JSQl5eXnF0tPS0nDo0CGhuki6HXfs2IFhw4ZBVVUVXbp0KbEbfsWKFTh27Bju3bsnxpX9mrOzM9q3b49Ro0bB19cXT548QUBAgMTKL8mTJ08QGhoq9N/36tWryMrKgpOTE4YNG4ZDhw4hMzNTsH///v0wMzNDr169ipXHYrFK7GHQ09PD+fPnkZGRIfkLKQcqKipQUVHByZMnkZubW6Yy9u/fDxUVFUycOFHk/p+Heb/R09PDlStXkJiYWKbzkoqHggsx9enTB3Z2dvD09BS539PTE2pqajA2NoaZmRlGjhyJw4cPg8fjlXNNmfPy5Uvw+XxYWFiI3G9hYYGUlBSxvkgMDAwEX37fttjY2GL55s6dWyzfz3MqmjVrBhUVFVStWhVr1qxBw4YN0b59e/EuUkx8Ph+XL1/GxYsX0a5du2LXpaamhgMHDqBnz54wNzcHULZ29PPzE7r2mTNnCsoKDw/HwIEDAQDDhg3Drl27RAYmDRo0wIABAzB37lyJXT8AbN26FU+ePMH06dOxdetWaGtrS7T8b86ePQsVFRUoKirC2toanz9/xuzZswX7d+zYgUGDBoHD4cDKygomJiZCc1BevnwJMzMzsc+7detWhIaGQlNTE40bN8aMGTMEcxVkQZUqVRAYGIjdu3dDTU0NzZs3x/z58xEZGSmU71f/xl6+fPn/9u48JorzDeD4dxGMCIrWg6OtgsKiiFgVYmK6IEZYoKGGihBBRS2Kd4m1UKiCom3VtHhWa6MoiUe09ahWtFoKigpUEbQYCvGAkhTEqlhptXVxfn8YN64cFVik/ng+ySTMzDvvMezuPPu+7+zQr18/zMzMmlR2cnIyN2/exMbGBjc3N2bOnMnRo0eN1jbx4klw0QwrV64kNTWVoqKiOvtsbW3Jzs7m559/5r333kOn0xEREYGfn1+7CjAAo/ZMZGVlUVBQYLDY2dnVSffBBx/USefu7m6QZs+ePeTn57Nv3z4cHR3Zvn17kz8Mn9fTFzp/f39CQ0MNJp9lZWWRl5fH9u3bUavVfPnll3XyaMp5DA8PN2h7XFwc8HgukFarpWfPngAEBARw9+5dfvzxx3rzWb58OVlZWRw/frwJrW1c7969iYqKYuDAgfXOWTAWb29vCgoKyM3NJSIigqlTpzJu3DgAqqur2b9/PxMnTtSnnzhxIlu3btWvN/d16+npybVr10hPTyc4OJjLly+j0WhYtmxZyxr0Ao0bN47ffvuNQ4cO4efnR2ZmJsOGDTPo5WrsPdbcc+fi4kJhYSE5OTlMmzaNqqoqAgMDiYyMNEazRBuQCZ3N4OnpiVarJS4ursG7M1xdXXF1dWX27NnMnDkTjUbDyZMn8fb2pmvXrvUOm1RXV9OhQwcsLCxauQWty9HREZVKRVFREUFBQXX2FxUV0b179yZ9c3VwcKjTpfpkouLTevbsiaOjY6N5vf766zg5OeHk5IROpyMoKIjCwsJWefaBt7c3mzZtomPHjtjZ2dWp85N2OTs7U1VVRWhoKKdOnQKadx6trKzqtL+2tpbU1FQqKysNyq+trSUlJaXeXpv+/fszffp0PvzwQ4MLb0uZmprW+38zJgsLC/05SElJYciQIWzdupV3332XXbt28eDBA4MJnIqi8OjRI0pKSlCr1ajVan755ZdmlW1mZoZGo0Gj0RAbG8vy5ctJSkoiNjb2hQ29tVSnTp3w8fHBx8eHxYsXExkZSWJiov6zrrH3mFqt5vTp0zx8+LDJAbuJiQkeHh54eHgQHR3Njh07mDRpEh999BEODg4tbZZ4waTnoplWrFjB4cOHyc7O/te0Li4uAPpxXWdnZy5fvlxnXPPChQs4ODi02rfoF6VHjx74+PiwceNG7t+/b7CvsrKSnTt3EhoaWucW3rYQHByMqakpGzdubJX8n1zo+vTp868X1Tlz5lBYWMiBAwcA453HJ/MA8vPzDb5t7t69m/3799d7my9AQkICJSUl+rkgLyMTExPi4+NZtGgR9+/f198J8/R5uHjxIhqNhpSUFADCwsIoKSnh22+/rZOfoijcvXv3uct3cXFBp9PVuRX2ZeLi4mIwJ6UxYWFh1NTUNPh+aui11lC5wHOXLf5bJLhopsGDBxMeHs66desMts+aNYtly5Zx5swZysrKyMnJYfLkyfTq1Ut/+2F4eDgqlYrJkyeTl5fHlStXSElJYc2aNfox8idqa2vrdEHWNxzzX7Nhwwb+/vtvtFotp06dory8nGPHjuHj48Orr77Kxx9/3Crl3rt3j8rKSoPljz/+aDC9SqVi/vz5rFixQv/7BW2lc+fOTJ8+ncTERH33sjHO49atW3nrrbcYMmSIvkfN1dWVkJAQunXrxs6dO+s9ztramgULFtR5jb9sxo8fT4cOHfjiiy+4cOECkZGRBufB1dWVCRMmkJqaik6nIyQkhNDQUCZMmMAnn3zC+fPnKSsr47vvvmPMmDFkZGTUW86oUaPYvHkzeXl5lJaWkpaWRnx8vL638r/u1q1bjB49mh07dnDp0iWuX7/O119/zapVqwwmtzb2HhsxYgQxMTG8//77xMTEkJ2dTVlZGenp6YwfP57U1NR6yw4ODmb16tXk5uZSVlZGZmYmc+bMQa1W6+cgiZeLBBctkJSUVGcexZgxY8jJyWH8+PGo1WrGjRtHp06dSE9Pp0ePHsDjGdNZWVk8fPiQt99+mzfeeIN169aRnJys/x2AJ2pqahg6dKjBEhgY+MLa2FxOTk6cP3+efv36ERISQv/+/ZkxYwbe3t5kZ2cb/DaDMSUkJGBra2uwxMTENHpMREQEDx8+ZMOGDa1Sp6aYO3cuRUVF+gmGLT2PN27c4MiRI/o5B08zMTEhKCio0WGPhQsXvvS/IWBqasrcuXOJi4vD3t6+3otVUFAQVVVVpKWloVKp2LVrF8nJyRw8eBAvLy/c3NxYsmQJY8eOrfcHwQC0Wi2pqan4+voycOBA5s2bh1arZe/eva3dRKOwtLRkxIgRrF69Gk9PT1xdXVm8eDHTp083eG/823ts5cqV7Nq1i9zcXLRaLYMGDWLBggW4ubkRERFRb9larZbDhw8TGBiIWq0mIiKCAQMGcPz48VYfRhOtQx65LoQQQgijkp4LIYQQQhiVBBdCCCGEMCoJLoQQQghhVBJcCCGEEMKoJLgQQgghhFFJcCGEEEIIo5LgQgghhBBGJcGFEEIIIYxKggshnoO9vT1r1qwxer5Tpkxp1SeENubZNqlUKg4ePAhAaWkpKpWKgoKCNqmbsR08eBBHR0c6dOhAdHR0W1dHiP97ElyIdkGlUjW6PP0Y9Bdp7dq1Bo+zbo4lS5bU26amPpOhoqICf3//FtWlqf766y/i4uLo378/nTp1olevXnh5edX70LCWiIqKIjg4mPLy8pfqEehCvKzkR9tFu1BRUaH/e8+ePSQkJFBcXKzf1lbPz7CysjJKPoMGDeKHH34w2NbUZzLY2NgYpS5NMXPmTHJzc1m/fj0uLi7cunWLs2fPcuvWLaOVUVNTQ1VVFVqtFjs7O6PlK4RomPRciHbBxsZGv1hZWaFSqfTrf/75J+Hh4VhbW2NpaYmHh0edCzU8/pY9bdo0unTpQp8+ffjqq6/0+54MI+zduxeNRoO5uTkeHh6UlJRw7tw53N3dsbS0xN/fn5s3b+qPe3ZYZNSoUcyfP5+YmBheeeUVbGxsnqtXxdTU1KCNNjY29OzZU7+/qqqKwMBAzM3NcXBwqPdJqE8Pi9SnsLAQf39/LC0tsba2ZtKkSfz+++8tqvuhQ4eIj48nICAAe3t7hg8fzrx585g2bZo+TX3DN1u2bCEoKIjOnTvj5OTEoUOH9PszMzNRqVRUV1eTmZlJly5dABg9ejQqlYrMzMxG6ySEaDkJLkS7V1NTQ0BAAOnp6eTn5+Pn50dgYCC//vqrQbrPP/8cd3d38vPzmT17NrNmzTLo/QBITExk0aJFXLhwAVNTU8LCwoiJiWHt2rVkZWVx5coVEhISGq1PamoqFhYW5ObmsmrVKpKSkjhx4kSL2jhlyhTKy8vJyMjgm2++YePGjVRVVT338dXV1YwePZqhQ4dy/vx5jh07xo0bNwgJCWlR3W1sbEhLS+PevXtNas/SpUsJCQnh0qVLBAQEEB4ezu3bt+ukGzlypP5/tG/fPioqKhg5cmSTyhJCNIMiRDuzbds2xcrKqtE0gwYNUtavX69f79u3rzJx4kT9+qNHj5TevXsrmzZtUhRFUa5fv64AypYtW/Rpdu/erQBKenq6ftunn36qODs769cjIiKUsWPH6te9vLyUN99806AuHh4eSmxsbIN1TUxMVExMTBQLCwuDJSoqSlEURSkuLlYA5aefftIfU1RUpADK6tWr9dsA5cCBAwbtyc/PVxRFUZYtW6b4+voalFteXq4ASnFxcbPrfvLkSeW1115TzMzMFHd3dyU6Olo5ffq0QZq+ffvWqeeiRYv06zU1NQqgHD16VFEURcnIyFAA5c6dO4qiKMqdO3cUQMnIyGiwHkII45I5F6Ldq6mpYcmSJRw5coSKigp0Oh3379+v03Ph5uam//vJsMqz3/6fTmNtbQ3A4MGDDbb9W4/B03kA2Nra/usxzs7OBkMDAF27dgWgqKgIU1NThg8frt83YMAAunXr1mieT7t48SIZGRn1zk25evUqarW6WXX39PTk2rVr5OTkcPbsWdLT01m7di1Lly5l8eLFDR73dDkWFhZ07dq1ST0xQojWJcGFaPcWLlzIiRMn+Oyzz3B0dMTc3Jzg4GD++ecfg3RmZmYG6yqVikePHjWYRqVS1bvt2WOe9TzlPKtjx444Ojo2mqYlampqCAwMZOXKlXX22dra6v9uTt3NzMzQaDRoNBpiY2NZvnw5SUlJxMbG0rFjxwaPaWo5QogXR4IL0e6dOXOGKVOmEBQUBDy+kJaWlrZtpYxowIAB6HQ68vLy8PDwAKC4uJjq6urnzmPYsGHs27cPe3v7Jt+F0lQuLi7odDoePHjQYHAhhPhvkwmdot1zcnJi//79FBQUcPHiRcLCwl66b8E6nY7KykqD5caNG8DjIRM/Pz+ioqLIzc0lLy+PyMhIzM3Nnzv/OXPmcPv2bSZMmMC5c+e4evUq33//PVOnTqW2trbZ9R41ahSbN28mLy+P0tJS0tLSiI+Px9vbWz+sI4R4+UhwIdq95ORkunfvzsiRIwkMDESr1TJs2LC2rlaTXL58GVtbW4Olb9+++v3btm3Dzs4OLy8v3nnnHWbMmEHv3r2fO387OzvOnDlDbW0tvr6+DB48mOjoaLp164aJSfM/RrRaLampqfj6+jJw4EDmzZuHVqtl7969zc5TCNH2VIqiKG1dCSGEEEL8/5CeCyGEEEIYlQQXQgghhDAqCS6EEEIIYVQSXAghhBDCqCS4EEIIIYRRSXAhhBBCCKOS4EIIIYQQRiXBhRBCCCGMSoILIYQQQhiVBBdCCCGEMCoJLoQQQghhVP8DWYgTeSKJvR0AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "multiple_choic(X,y_number,2)"
      ],
      "metadata": {
        "id": "FEm4EdYgF6Id",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2772c532-dbd6-4efa-a6c2-c2941fd7ed66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "191/191 [==============================] - 44s 188ms/step - loss: 1.2435 - accuracy: 0.4649 - val_loss: 0.8720 - val_accuracy: 0.6760\n",
            "Epoch 2/10\n",
            "191/191 [==============================] - 36s 187ms/step - loss: 0.5927 - accuracy: 0.7867 - val_loss: 0.6888 - val_accuracy: 0.7560\n",
            "Epoch 3/10\n",
            "191/191 [==============================] - 33s 171ms/step - loss: 0.4209 - accuracy: 0.8575 - val_loss: 0.6866 - val_accuracy: 0.7600\n",
            "Epoch 4/10\n",
            "191/191 [==============================] - 19s 101ms/step - loss: 0.3575 - accuracy: 0.8797 - val_loss: 0.6689 - val_accuracy: 0.7480\n",
            "Epoch 5/10\n",
            "191/191 [==============================] - 18s 93ms/step - loss: 0.3177 - accuracy: 0.8949 - val_loss: 0.6746 - val_accuracy: 0.7640\n",
            "Epoch 6/10\n",
            "191/191 [==============================] - 17s 88ms/step - loss: 0.2752 - accuracy: 0.9087 - val_loss: 0.7450 - val_accuracy: 0.7640\n",
            "Epoch 7/10\n",
            "191/191 [==============================] - 17s 90ms/step - loss: 0.2557 - accuracy: 0.9166 - val_loss: 0.7969 - val_accuracy: 0.7560\n",
            "Epoch 8/10\n",
            "191/191 [==============================] - 17s 88ms/step - loss: 0.2297 - accuracy: 0.9251 - val_loss: 0.8019 - val_accuracy: 0.7680\n",
            "Epoch 9/10\n",
            "191/191 [==============================] - 20s 106ms/step - loss: 0.2146 - accuracy: 0.9288 - val_loss: 0.7007 - val_accuracy: 0.7840\n",
            "Epoch 10/10\n",
            "191/191 [==============================] - 17s 90ms/step - loss: 0.1970 - accuracy: 0.9352 - val_loss: 0.7407 - val_accuracy: 0.7800\n",
            "8/8 [==============================] - 0s 7ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.7317    0.6977    0.7143        43\n",
            "           1     0.7209    0.8052    0.7607        77\n",
            "           2     0.8333    0.7843    0.8081        51\n",
            "           3     0.7812    0.8333    0.8065        30\n",
            "           4     0.8837    0.7755    0.8261        49\n",
            "\n",
            "    accuracy                         0.7800       250\n",
            "   macro avg     0.7902    0.7792    0.7831       250\n",
            "weighted avg     0.7849    0.7800    0.7807       250\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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cXFC/QQNMmDobADB42EjMmOiEgMO+aNOuI65duYhXUZGYu2gZ33mzvn3D9eArmDJzbiVfUdU1ytEJSxctgJGRMYxNTHHooB+ys7PRf0DNaKYf5uCIlW6LoG9oBEMjEwQcOYic7Gz0/n8g4L606FmbPL3oWRs6YiSmjB+DIwd90bZ9R1y9fBEvXzzHgiXLAADyCgqQV1DgK6NWrVpQVlaBukZzvvTw+2H48vkT+vQfVOHXSUSL0eBC3F4zLY1T9z5Apa40Fg02RQN5WTz7kIZBa68hKbNokGcT5TrgcLl8x2g3rIe2+g3Q3+MqE1VmjHWn7viakY4TB3chIy0F6pq6WLByK69bJCUxnq9PNi0lCYun/vy2eOHkIVw4eQgGJi2xZP0uAMD3798QsH87UpMTISdXD63b/4OhY6YUe9VN3P3TzQ7paWnw2e31/4mN9LF+y05eU3ViQhzYv4wzMTZtgaUr1mLfzm3Y470FTZqqY9X6rdDU0uE7b/B/F8HlctHVtmelXk9V1sOuJ9JSU+HttRXJyUnQ0zeA9669UK4B3SIAYGNrh/S0VOzZUfSs6ejpY5PXLt6zlhAfBzb758+piVkLLF+1Dru9t2KXlyeaNFPHmk3boKWtU1IRJTp35iRMzMyh0VxTZNfDGDEajCkKLC73t790VcCNGzeQlZUFa2trKCoqCn28gsOhCqhVzXF1ZfX6ll+Zmij9/a0gIphCHTFv9mZYVi4t9FhWynUq/suHbL9dIjlP9pmJf89UBTD6dW7t2rX49u0bVqxYAaCof83Ozg5XrlwBADRo0ADBwcEwMqp+UzcTQgipQWpYywWjI0wCAgJgbPxzKtcTJ07g5s2buHXrFpKTk2FhYYHly5czWENCCCGECIvRlovo6GiY/rL4T1BQEAYPHox27doBAJYsWYIhQ8r/5gAhhBDCqBr2tgijV1tQUMD3GmloaCjatm3L+9yoUaMaMQseIYSQao5m6Kw8WlpauHnzJgAgNjYWr1+/RseOP5fl/fTpU7FpdwkhhBBStTHaLTJ16lRMmzYNt27dwr1792BtbQ1DQ0Pe/mvXrqFFixYM1pAQQggpv5q2miujwcX48eMhISGBc+fOoWPHjnBzc+Pb/+XLF4wdO5ah2hFCCCGiQcFFJRs7dmyJAYS3t3cl14YQQggh5cV4cEEIIYRUezWr4YLZ4ILNZv+1qYjFYqGggGaeI4QQIr6oW6QSnTp1qsR9oaGh2Lp1KzgcTiXWiBBCCCHlxWhw0a9fv2Jpr169wsKFC3Hu3Dk4ODjA3d2dgZoRQggholPTWi6qzJRhX758wfjx42FiYoKCggI8efIEfn5+UFdXZ7pqhBBCSLmwWCyRbOKC8eAiIyMDCxYsgLa2NiIjIxEcHIxz587xrTlCCCGEiDMmg4vt27dDQ0MDMjIysLKywv3790vM27lzZ4Hl9urVS6gyGQ0u1q1bB01NTZw/fx5Hjx7F3bt30aFDByarRAghhFQbAQEBcHZ2hpubGx49egQzMzPY2toiMTFRYP7AwEDExcXxtufPn0NCQkLodb5YXC6XK4oLKAs2mw1ZWVnY2NhAQkKixHyBgYFCnVfB4VB5q1ajXV3Zl+kqiK0mSrJMV0FsKdSRZLoKYi0rl96qKyvlOhU//FB+xEGRnCdx/1Dk5ubypUlLS/Ot0/UrKysrtG7dGl5eXgAADoeDpk2bYvr06Vi4cOFfy/P09ISrqyvi4uJQp06dUteT0ZaL0aNHY+jQoVBSUoK8vHyJGyGEECLORNUt4uHhUexvpIeHh8Ay8/LyEB4eDhsbG14am82GjY0NQkNDS1Xvffv2YdiwYUIFFgDDb4v4+voyWTwhhBAiVlxcXODs7MyXVlKrRXJyMgoLC6GqqsqXrqqqipcvX/61rPv37+P58+fYt2+f0PVkNLgYOHDgX/OwWCycPHmyEmpDCCGEVAxRvenxpy4QUdu3bx9MTExgaWkp9LGMBhfU5UEIIaQmYOI1UhUVFUhISCAhIYEvPSEhAWpqan88NisrC/7+/mWea4rR4GL//v1MFk8IIYRUW1JSUmjVqhWCg4PRv39/AEUDOoODgzFt2rQ/Hnv8+HHk5uZi5MiRZSqbFi4jhBBCKhhTE2A5OzvD0dERFhYWsLS0hKenJ7KysuDk5ASg6MWKxo0bFxsUum/fPvTv3x/KysplKpeCC0IIIaSiMTS5pr29PZKSkuDq6or4+HiYm5vj0qVLvEGesbGxYLP5Xxx99eoVbt++jStXrpS5XAouCCGEkGps2rRpJXaDXL9+vVianp4eyjsFFgUXhBBCSAUTp3VBRIGCC0IIIaSCUXBBCCGEEJGqacEF46uiEkIIIaR6oZYLQgghpKLVrIYLCi4IIYSQikbdIoQQQggh5VAtWy5e7bBnugpiTcN2KdNVEFuJIauZroLYyviez3QVxJpULfquWJXVtJaLahlcEEIIIVVJTQsuKNQlhBBCiEhRywUhhBBSwWpaywUFF4QQQkhFq1mxBXWLEEIIIUS0qOWCEEIIqWDULUIIIYQQkaLgghBCCCEiVdOCCxpzQQghhBCRopYLQgghpKLVrIYLCi4IIYSQikbdIoQQQggh5UAtF4QQQkgFq2ktFxRcEEIIIRWspgUX1C1CCCGEEJGqssFFTk4ONmzYwHQ1CCGEkHJjsVgi2cQFo8FFUlISzp8/jytXrqCwsBAAkJ+fjy1btkBDQwNr1qxhsnqEEEKIaLBEtIkJxsZc3L59G71790ZmZiZYLBYsLCywf/9+9O/fH7Vq1cKyZcvg6OjIVPUIIYQQUkaMtVwsWbIEPXv2REREBJydnfHgwQMMGDAAq1evxosXLzBp0iTIysoyVT1CCCFEZKhbpJI8e/YMS5YsgbGxMdzd3cFisbBu3ToMHjyYqSoRQgghFaKmBReMdYukpaVBRUUFACArK4vatWvD2NiYqeoQQgghFUaM4gKRYHSeixcvXiA+Ph4AwOVy8erVK2RlZfHlMTU1ZaJqhBBCCCkjRoOLrl27gsvl8j737t0bQFHzEZfLBYvF4r1FQgghhIgrcerSEAXGgovo6GimiiaEEEIqVQ2LLZgLLtTV1ZkqmhBCCCEViLG3RdatW4fs7Gze5zt37iA3N5f3+evXr5gyZQoTVSOEEEJEism3RbZv3w4NDQ3IyMjAysoK9+/f/2P+9PR0TJ06FQ0bNoS0tDR0dXURFBQkVJmMBRcuLi74+vUr77OdnR0+f/7M+/z9+3fs2rWLiaoRQgghIsViiWYTVkBAAJydneHm5oZHjx7BzMwMtra2SExMFJg/Ly8P3bp1Q0xMDE6cOIFXr15hz549aNy4sVDlMtYt8utATkGfCSGEEFI+mzZtwvjx4+Hk5AQA2LlzJy5cuAAfHx8sXLiwWH4fHx+kpqbi7t27kJSUBABoaGgIXW6VXbiMEEIIqS7YbJZIttzcXGRmZvJtvw4p+FVeXh7Cw8NhY2PzSz3YsLGxQWhoqMBjzp49C2tra0ydOhWqqqowNjbG6tWrhX5zk4ILQgghpIKJqlvEw8MD8vLyfJuHh4fAMpOTk1FYWAhVVVW+dFVVVd4cU797//49Tpw4gcLCQgQFBWHp0qXYuHEjVq5cKdT1MjrPxd69eyEnJwcAKCgogK+vL2/Wzl/HYxBCCCGkaLyis7MzX5q0tLTIzs/hcNCgQQPs3r0bEhISaNWqFT5//oz169fDzc2t1OdhLLho1qwZ9uzZw/uspqaGgwcPFstT1QQeOwr/Q/uRmpIMLR09zJy3CIZGJiXmD7l6Gft2eiE+7jMaN1XHpOmzYd2uI1+emOh32LltM54+eojCwkJoNNfEinWeUFVrCACYMXEMnjx6yHdM34FDMNel9P/QVdXEQW0w26ETVJXk8OxtHJw3ncXDF59KzD/Nvh3GD2iDpmoKSEnPwqmQ51i64xJy8woAAO3Mm2O2Q0e01GuMhvXrYeiCAzh380VlXU6FOuZ/GAf9fJCSnAwdXX3MW7gYxiYlz2B79col7Ni+FXFfPqNpM3VMnzUH7Tt0AgAU5OfD22sL7ty+ic+fPkGurhwsrawxfeYc1G/QgO88t29ex55dO/D2zStISUmjpUVrbPT0qtBrFTX6uS2fEwFHcMjPB6kpydDW1cOcBYthZFzysxf83yXs9t7Ge/amznBG2/8/ewCwZ6cXrl6+iIT4eEhKSkLPwBCTps2EsYkZL8/LqBfYvmUjoiKfgy3BRpeu3TFzznzUrl2nQq+1oohqEi1paelSBxMqKiqQkJBAQkICX3pCQgLU1NQEHtOwYUNISkpCQkKCl2ZgYID4+Hjk5eVBSkqqVGUz1i0SExOD6Ojov25VSfCVi9juuQ5jxk3G3oPHoa2jh7nTJyItNUVg/mdPH8N9yXz06jcAew8dR4dO/2Dx3Bl4//YNL8/nT7GYNn401DWaY8uu/dh/9CRG/zup2D9gn/6Dceridd42efqcCr3WyjC4qynWzuiNVfuuwnrMNkS8icPZzf+ivqLgXx723c2wYnIPrPa5CvNhmzBp9UkM7moK90m2vDx1ZCTx7E0cZm08U1mXUSmuXArC5g1rMX7iVBzyPwldPT1MnzweqSmCn72nTx5j8cK56DdgEA4HBKJzl66YO2s63r55DQDIycnBy5cvMG7CZBwKOIn1m7biQ0wMnGfyv/4dfPUKXBcvRJ9+A3Dk2Cns8zuMHna9Kvx6RYl+bsvnv8sXsWXjWoybOAV+R05AR1cfs6ZMQGoJ9y/iyWO4usxDn/4D4Xf0JDp27or5ztPx7pf710xdA3MWLMbh46exa/9BNGzUGDOnjEdaaioAICkxETMmjUWTps2w76A/PLfvxvt3b7HCdXGlXHNFYOJtESkpKbRq1QrBwcG8NA6Hg+DgYFhbWws8pl27dnj79i04HA4v7fXr12jYsGGpAwuAxlwI5diRA+jdfzB69h0ADU0tzHFxhYyMDC6cPSUw/wn/Q7C0bofho8ZCo7kWxk2eDl19QwQeP8LLs8d7K9q07YDJM+ZAV88AjZs0Q/tOXaCopMx3LmkZGSirqPC2Ov/vThJnM4a3x/6z93HwQjhexiRi+rrTyM7Ng2NvC4H525ioI/TZBwRceYrY+DQE33+DY/89hYVhU16eK/deY/nuKzh7I7KyLqNSHD7oh/4Dh6Bv/4HQ1NKGy5JlkJGRwdnTgQLz+x8+AOu27TF6zL9orqmFydNmQt/AAMf8i549ubp14b3LB91s7aCh0RwmpuaY77IEUS8iER/3BUBRV+XGtasxY/ZcDB46DOoazaGppY1utnaVdt2iQD+35XP0kC/6DRyC3v0GormWNhYsdoOMjAzOl/DsBRw9iDZt22OkY9GzN3HqDOgZGOKE/2FeHlu73rBs0xaNmzSFppYOZs1ZgKxv3/D2zSsAwJ1b1yFRSxLzXJZCXaM5DI1MsGCxG0KCr+Bj7IfKuGyRY2qeC2dnZ+zZswd+fn6IiorC5MmTkZWVxXt7ZPTo0XBxceHlnzx5MlJTUzFz5ky8fv0aFy5cwOrVqzF16lShymWsW2Tr1q2lyjdjxowKrknp5Ofn4/XLFxg5Zhwvjc1mo5VlG0Q+eyrwmMhnTzF0hCNfmmWbtrh14xqAoggy9M5NjBg1FnOmT8CbVy/RsFFjjBwzDh06d+U77r9LF/DfxfNQUlZB2w6d4DhuEmRkZEV8lZVHspYEWug1xvoD13lpXC4X1x68haWx4Nlb7z37gGG2LWBh2AQPX3yCRiMl2LbVw5FLjyqp1szIz8/Dy6hIOP07npfGZrNh2cYaERFPBB4TEfEUDqP4nz3rtu1xPSRYYH4A+PbtK1gsFuTq1gNQ1CydmJgANpuNEUMHIiUlCXp6Bpgxey60dXTLf2GVgH5uyyc/Pw+vol7AcSz/s9fayhrPSnj2nkc8wfCRY/jS2li3w42QayWWcTrwGOTk6kJHVx9A0VsOkpKSYLN/fv/90RXw9MkjNG1GMzyXlr29PZKSkuDq6or4+HiYm5vj0qVLvEGesbGxfPe5adOmuHz5MmbPng1TU1M0btwYM2fOxIIFC4Qql7HgYvPmzXyfP378iIYNG6JWrZ9VYrFYfw0ucnNzi72Gk5vLFukAFwDISE9DYWFhsW8mSkrKiI0R3H2TmpIMJWX+/IpKKkhNSQYApKWmIvv7dxz224dxk6dj0jRnhIXexpL5s7Blhw/MW7UGANjY9oJaw0ZQrl8f7968xi6vzYj9EINV67eI9Bork4pCbdSqJYHE1G986Ymp36CnXl/gMQFXnkJZvg6Cd04Ci8WCZC0J7A68h/V+1yuhxsxJT0tHYWFhsWdJSVkZMSV0HaYkJ0NJWaVY/pTkZIH5c3Nzsc1zI2ztevEGWX/+9BEAsHunF2bPXYhGjRrj0IH9mDjOEYFnL0JeXqGcV1bx6Oe2fHjPnhL/s6SorIyYmPcCj0lJTobSb/dbUVkFKSn8z97tm9exdOEc5OTkQEWlPrbu3AsFRUUAgIWlFbZsWodDfvtgP2IUsrOz4b216G9GSlKSqC6vUjG5cNm0adMwbdo0gfuuX79eLM3a2hr37t0rV5lVZuGyunXr4saNG9DU1BTqPB4eHli+fDlf2pyFSzDPxbXcdaxoXG5Rn1b7Tl0wdMRoAICOnj6eRzzBmcBjvF9SfQcO4R2jpa0LZZX6mD3lX3z+FIvGTareoNeK0qGFJuY5dsHM9Wfw4EUstJqoYMOsPohz+gdr9gv+VkT+riA/HwvnzQaXy8XCxT8HG/6Y2G7suEnoatMdAODmvho9u3fG1SuXMWiIPSP1ZRr93IpGq9aWOOAfiIz0dJwJPI7F852x76A/lJSUoamlA1f31diycS12bPMEm83G0OEjoaSsDBZbPHvzaeEyMSPotZz0XNE/fPIKipCQkCg2CCw1NaXYN8QflJRVig24S0v9+Y2y6Jy1oN5ciy+PenNNPHtSclO/oXHRKPfPHz+K7S+p5PTvKCgoRAMl/j7oBkpyiE/5JvAYtwndcPTSI/ieewAAiHyXgNoykti+cCDW+oZU21leFRQVICEhUexZSk1JgbKK4GdPWeXnN+0/5f8RWMTHfcGOPft5rRYAoKJS1IKkqfnz+ZSSkkLjxk0RHx9XrmuqLPRzWz68Zy+V/1lKS0mBcgn3T1lFpdhgz7SU5GL5ZWVro2kzdTRtpg5jUzMM7tsD506dhOO/EwAUjcuwteuNlJRkyMrKgsVi4eghPzRu0kSEV0gqiniGgL+QlpZGvXr1+DZRd4kAgKSkJHT1DRH+IIyXxuFw8OhBGIx+eX3qV0YmZnj0gL9p6UFYKC+/pKQk9A2N8PEDfyvOp9gYqDVsVGJd3r5+CQAl/mERB/kFhXj86jO6WGjz0lgsFrpYaOP+c8EDtmRlJMHh8AcQPz5X528FkpJS0Dcwwv2wn88Sh8PBg7B7MDU1F3iMqakZHoTxP3th9+7C5Jf8PwKL2NgP8N7lAwUFRb78+oZGkJKSQswv3QcF+fmI+/IZDf/wfFYl9HNbPpKSUtAzMOR7ljgcDh7cv8f3LP3K2NQcD+7z37/790JhYir4fv/A5XKRl59XLF1ZWQW1a9fB1csXISUlDcs2bYW/kCqAyYXLmCD2wUVlGjpiNM6fPoGL588gJvodNq5ZgezsbPTs0x8AsMrNBbu8fo4lGTxsJMJC78D/kC8+xLyHz+7teBUViYFDRvDyDB/lhGv/XcK5Uyfw6WMsTh47gru3bqD/4GEAil5589u7E6+iIhH35TNu3wjBKrdFMGthAS0dvUq9flHbevQ2nPq2hkPPltBTr4+t8/ujtowUDpwPBwDsdR0K98k/XzMNuv0S4we2wRAbU6g3VMQ/rbXhOqEbgm5H8YKMOrJSMNVpCFOdorkGNBopwVSnIZqqylf+BYqQwyhHnA48jvNnTyP6/Tt4rFyO7Oxs9Ok/AADgungBvLZs4uUf5jAad+/exiG//YiJfo9dO7zwIjISQ4cVPXsF+fmYP3cWol5EYqXHehRyCpGcnITk5CTk//8XvJycHAYNscfuHV64d/cOYmKi4bGqqAvSprstxAX93JbP8JFjcPbUCVz4/7O3bvVy5GRno1e/omdv+ZKF8N7689mzHz4K9+7exuEDRc/enp1eiHrxHIOHOQAAsrO/Y8e2zXge8RRxXz7j5YtIrFy2GEmJCeja7edzddz/MF5GvUDshxicCDiCDWtXYfL0Waj7/wHH4oaphcuYwli3SGZmJt9nFouFb9++FUuvV6/qPEhdu9shPT0NPru8/j+ZjD42bN3Jay5NiI8Di/UzXjMxawHXlWuxd8c27PHegiZN1bFqw1Zoauvw8nTsYoM5Lq445LsXWzZ6oFkzDbiv3QxT85YAgFq1JPHw/j0c9z+InOxs1FdVQ6d/umH02ImVe/EV4ERwBFQU68B1XDeoKtdFxJsv6DfbB4lpRd0iTVUV+Foq1vheA5fLhdvE7mhUXx7JaVm4cCcKy3Ze5uVpqd8EV7wn8D6vm9kbAHDwQjgmrDxeSVcmet179ERaWhp2em9FSnIydPUMsM17N6+pOT4+jm/Et5l5C6zyWA9vry3Yvm0zmjZTxwbPbby3PBITE3HzetE4lRFDB/CVtXOvHyxaWwIAZs6eBwmJWnBdvAC5uTkwMjHFjj37Ua+e+ARr9HNbPt1s7ZCeloo9O7YhJSUZOnr62Lx9F9+z9+s4CFPzFnBfvQ67tm/FTi9PNG2mjnWbtkHr//ePzZZATEw0gs7NRHp6GuTlFWBgZIydPgehqfXzHr94/gx7dnoh+/t3qGtoYuHiZbDr3bdyL56UGYvLUEc1m83ma+LhcrkCPwu7WAoAJGTmi6SONZWG7VKmqyC2EkNWM10FsfU9T/ifdfKTVC1qiC4rxdoSf89UTq1WhIjkPOFLu4jkPBWNsZaLkBDR3GhCCCGkqhOnLg1RYCy4+PDhA+zt7Stk8CUhhBBCmMNYO5qTkxMyMjKYKp4QQgipNDXtbRHGWi6q65wEhBBCyO/EKC4QCUYn0RKnKIwQQggpq5r2947R4KJr1658a4kI8uhR9V6UihBCCKluGA0ubG1t+aYbJoQQQqqjGtZwwWxwMW/ePDRo0IDJKhBCCCEVrqZ1izD2tkhNu9GEEEJITVFl3hZJTi5adU9FjBb1IYQQQkqjpn2fZqzlIjo6GpKSkpg6dSpUVFSgqqoKVVVVqKioYNq0aUhPT2eqaoQQQohI0TwXlaRu3bpo06YNPn/+DAcHBxgYGAAAXrx4AV9fXwQHB+Pu3btQVFT8y5kIIYQQUpUwFly4u7tDSkoK7969g6qqarF93bt3h7u7OzZv3lzCGQghhBDxIEaNDiLBWLfI6dOnsWHDhmKBBQCoqalh3bp1OHXqFAM1I4QQQkSrpnWLMBZcxMXFwcjIqMT9xsbGiI+Pr8QaEUIIIUQUGAsuVFRUEBMTU+L+6OhoKCkpVV6FCCGEkApCLReVxNbWFosXL0ZeXl6xfbm5uVi6dCl69OjBQM0IIYQQ0WKxRLOJC0YHdFpYWEBHRwdTp06Fvr4+uFwuoqKi4O3tjdzcXBw8eJCp6hFCCCEiI06tDqLAWHDRpEkThIaGYsqUKXBxceFNqsVisdCtWzd4eXmhadOmTFWPEEIIIWXE6NoizZs3x8WLF5GWloY3b94AALS1tWmsBSGEkGqlhjVcMBtc/KCoqAhLS0umq0EIIYRUiJrWLcLYgE5CCCGEVE9VouWCEEIIqc5qWMMFBReEEEJIRWPXsOiCukUIIYQQIlLUckEIIYRUsBrWcEHBBSGEEFLR6G0RQgghhIgUmyWarSy2b98ODQ0NyMjIwMrKCvfv3y8xr6+vb7H1TGRkZIS/3tJkatmyJdLS0gAUTdv9/ft3oQsihBBCSOUKCAiAs7Mz3Nzc8OjRI5iZmcHW1haJiYklHlOvXj3ExcXxtg8fPghdbqmCi6ioKGRlZQEAli9fjm/fvgldECGEEFJTMbUq6qZNmzB+/Hg4OTnB0NAQO3fuRO3ateHj4/PHuqqpqfE2VVVVocst1ZgLc3NzODk5oX379uByudiwYQPk5OQE5nV1dRW6EoQQQkh1JqohF7m5ucjNzeVLk5aWhrS0dLG8eXl5CA8Ph4uLCy+NzWbDxsYGoaGhJZbx7ds3qKurg8PhoGXLlli9ejWMjIyEqmepggtfX1+4ubnh/PnzYLFYuHjxImrVKn4oi8WqEsFFAYfLdBXE2rMzbkxXQWw1sFvFdBXEVuKlxUxXQaxJStAQuprAw8MDy5cv50tzc3PDsmXLiuVNTk5GYWFhsZYHVVVVvHz5UuD59fT04OPjA1NTU2RkZGDDhg1o27YtIiMj0aRJk1LXs1TBhZ6eHvz9/QEURT3BwcFo0KBBqQshhBBCajIWRNN04eLiAmdnZ740Qa0WZWVtbQ1ra2ve57Zt28LAwAC7du3CihUrSn0eoV9F5XA4wh5Soi9fvqBRo0YiOx8hhBBSFZX1TY/fldQFIoiKigokJCSQkJDAl56QkAA1NbVSnUNSUhItWrTA27dvhaqn0MHF2bNn/7i/b9++pT6XkZERtm/fjhEjRghbDUIIIYT8gZSUFFq1aoXg4GD0798fQFEDQXBwMKZNm1aqcxQWFuLZs2fo2bOnUGULHVz8qKAgLBYLhYWFpT7XqlWrMHHiRJw6dQq7du2CkpKSsNUhhBBCqjymJtFydnaGo6MjLCwsYGlpCU9PT2RlZcHJyQkAMHr0aDRu3BgeHh4AiqabaNOmDbS1tZGeno7169fjw4cPGDdunFDlCj0CiMPhlLgJE1gAwJQpUxAREYGUlBQYGhri3LlzwlaHEEIIqfJYLNFswrK3t8eGDRvg6uoKc3NzPHnyBJcuXeIN8oyNjUVcXBwvf1paGsaPHw8DAwP07NkTmZmZuHv3LgwNDYW7Xi6XWyVerfDy8sLs2bNhYGBQ7E2UR48eCXWuz+l5oqxajZOdJ1yQSH4ysd/AdBXEFr0tUj70tkjZyVTCQhj99z4UyXlOj7MQyXkqWpluaXBwMIKDg5GYmFhsgOefJuYoyYcPHxAYGAhFRUX069dP4GuuhBBCiLiqaUuuC/1XfPny5XB3d4eFhQUaNmxY7n6kPXv2YM6cObCxsUFkZCTq169frvMRQgghVU0Niy2EDy527twJX19fjBo1qtyF9+jRA/fv34eXlxdGjx5d7vMRQgghVVFNWxVV6OAiLy8Pbdu2FUnhhYWFiIiIEGrWL0IIIYRUbUKPABo3bhyOHDkiksKvXbsGKSkpkZyLEEIIqaqYeluEKUK3XOTk5GD37t24evUqTE1NISkpybd/06ZNpT5XFXlRhRBCCKlQNKDzLyIiImBubg4AeP78Od++mtanRAghhJDihA4uQkJCRFqBvXv3lrh8+w8zZswQaZmEEEJIZappX70Zn1Bi586dkJCQKHE/i8Wi4IIQQohYq2kt+0IHFwMHDvzj/sDAQKHO9/DhQ1q+nRBCCKlGhA4u5OXlRVZ4TYvkCCGE1EyiWnJdXAgdXOzfv19khdPbIoQQQmqCmvZlutxjLm7cuIGsrCxYW1tDUVFRqGPd3Nz+OpiTEEIIIeKl1MHF2rVr8e3bN6xYsQJAUauDnZ0drly5AgBo0KABgoODYWRkVOrCBwwYgLdv3/41n6mpaanPSQghhFQ1NazhovTBRUBAABYsWMD7fOLECdy8eRO3bt2CgYEBRo8ejeXLl+PYsWOlLtzc3BwsFktg98iPdBaLhcJCWgKcEEKI+KJukRJER0fztSAEBQVh8ODBaNeuHQBgyZIlGDJkiFCFR0dHC5WfEEIIEUc0oLMEBQUFkJaW5n0ODQ3FrFmzeJ8bNWqE5ORkoQpXV1cXKj8hhBBCqr5SBxdaWlq4efMmNDU1ERsbi9evX6Njx468/Z8+fYKysrJQhcfGxpYqX7NmzYQ6LyGEEFKVULdICaZOnYpp06bh1q1buHfvHqytrWFoaMjbf+3aNbRo0UKowjU0NATe8B9jLYCif5CCggKhzksIIYRUJTUrtBAiuBg/fjwkJCRw7tw5dOzYEW5ubnz7v3z5grFjxwpV+OPHjwWmc7lc+Pv7Y+vWrfSqKiGEECJmWNwqNpPV1atXsXDhQrx+/RrOzs6YM2cO6tatK9Q5PqfnVVDtaobsPHo7p6xM7DcwXQWxlXhpMdNVEGuSEmymqyC2ZCphla1xAc//nqkU9tobi+Q8FY3xhct+ePToERYsWIBbt25h3LhxCAoKojVHCCGEVAs1bMgFGA913717B3t7e1haWqJ+/fp48eIFvLy8KLAghBBCxBSjwcWUKVNgaGiIjIwMPHz4EEeOHIGmpiaTVSKEEEJEjsViiWQTF4x2i+zcuRMyMjJITEz842DQR48eVWKtCCGEENESo7hAJIQOLkJCQtClSxeB+7Zv346pU6eW+ly/v3EiDk4fP4qAw75ITUmGlo4eps9xgYGRSYn5rwdfxv5dXoiP+4ImTZth/NTZaNPu5/wg/1gJPnbCNGcMG+UEAPgYG4NdWzfiecQTFOTnQ1NbF04Tp6GFhaVoL44B5wP9cfKoH9JSU9BcSxeTZi2AnqHge/Ih+i0O7duBt69eIDE+DuOnz0X/oSP58hw7uA93bwbj04cYSElLw8DYDE6TZ6FJM41KuJrKNbG/BWbbW0NVSQ7P3iXAeeslPHz5RWDey5tHoaO5RrH0i/feYKCLPwCgjowkVk7oij7t9aBUTxYxcenwDryPveeqZ3B/zP8wDvr6ICU5GTq6+pjnshjGJiWvY3T1yiXs8NqKuC+f0bSZOqbPnoP2HToBAAry8+HttQV3bt3E50+fIFdXDpZW1pg+aw7q1/AuXv8jh+G3fx+Sk5Ogq6ePhYuWwoTWi6r2hH5bRFFREVevXkWrVq340rds2YKlS5ciMzNTpBUsi4p6WyTkv0tYs3wRZi1YCgMjU5z0P4gb167A79g5KCoVn0DsecQTzJo0BuMmz4R1+04IvnwB/gd9sOvAMTTX0gEApKbwz2oadvcWNqxyw8GTF9CocVMAwOjBvdG4aTOMmzIT0tIyOOl/EJcvnMWhwCAoKauI/Dor622Rm8GXsXHVEkybsxh6hiY4ffwwbof8h91HzkBBUalY/tdRz3Hr2hVo6xliz7YNGOwwplhwsXTOFHTsagtdAyMUFhbCb9c2fIh+h50HAyEjK1vh11RZb4sM7mKIvQv7YfrmIDyI+oxpg60wsJMBzEZ7Iyn9e7H8inVlIFVLgvdZSb427u+dgMnrz+HQ5QgAgNecXujcQgOT15/Hh/h02LTWxJZZPTHM9Tgu3H1d4ddUmW+LXLkUBLfFC+GydBmMTUxx9NABXL1yGSfPBkFJwGSAT588xgSnUZg6YzY6dOqMS0Hn4eezD4cCTkBbRxffvn7F/DkzMWDQEOjo6uNrZgY2rPUAh1OIg/4nKuWaquLbIpcuBmGJy3wscVsOExMzHD7ohytXLuHM+UtCT7pYkSrjbZHJJ1+I5Dw7Bhn+PVMVIPTTuH79etjZ2eHly5e8tI0bN8LV1RUXLlwQWcUyMzOxY8cOWFhYiOyc5XX86AH07DcIdn0GQENTC7MXukJaRhYXz50SmD8w4BAs27TDsFFOUG+uibGTpkNHzxCnjx/l5VFSVuHb7t4MgXkrS15gkZGehk8fP2D46H+hpaOHJs3UMX7qbOTkZCP63ZtKue6KcirgIHr0GYhuvfqjWXMtTJu7BDIyMrhy4bTA/LoGxvh3qjM62fSApJSkwDwrNnqjW89+UG+uDU1tPTgvckdSQhzevhLND3ZVMWNIG+y/8BgHLz3Fyw/JmL7pArJz8uFoZy4wf9rXHCSkZfG2rq2a43tOPgJvRPHytDFqgkOXI3Dr6QfEJmTA5/xjRLxLgIV+o0q6qspz+IAf+g8agr79B0JTSxsuS5dBRlYGZ08HCszvf/gArNu1x2inf9FcUwuTp82EvoEBjvkfAQDI1a0L790+6GZrB43mzWFiZo75i5Yg6kUk4uMEtybVBAf99mPg4KHoP2AQtLS1scRtOWRkZHA68CTTVat0LJZoNnEhdHAxbtw4zJ07FzY2NoiJicHatWvh7u6OoKAgdOjQodwVCgkJwahRo9CwYUOsWLECVlZW5T6nKOTn5+P1yxdoZdmGl8Zms9GqdRu8ePZU4DEvnj1Fy9Zt+NJat2mLyBLyp6Yk496dW+jZdwAvrZ68Apqqa+DKxXPIzv6OwoICnDt1HIqKStDVF48IVpD8/Hy8fR0F81Y//33ZbDbMLazwMjJCZOVkZX0DAMjVkxfZOZkmWYuNFroNcS3858J/XC5w7VE0LI2alOocjj1b4HhIJL7n5PPS7kV+Qu+2umikUjSvTEdzdeg0UcLVh+9FewEMy8/Pw8uoSFi1sealsdlsWFpZI+LpE4HHRDx9Cksra74067bt8ayE/ADw7dtXsFgsyNWtJ4pqi538vDxEvYhEG+u2vDQ2m402bdoi4qngCRSrMxrQWQrz589HSkoKLCwsUFhYiMuXL6NNmzZ/P7AEnz9/hq+vL/bv34/09HSkpaXhyJEjGDp06F9vZm5uLnJzc39LY/EtsiYKGelp4BQWFuv+UFRSRuwHwau7pqYkC8yfliJ4gbcrQWdRu05tdOhsw0tjsVjYsG0Pls6fid5d2oDFZkNRUQlrtuxEXTH+g5mZUXQ/FX67PwqKyvj4IUYkZXA4HOzeuh6GJubQ0NQWyTmrAhX52qglwUZi2je+9MS0LOg1+3s3mYV+IxhrNsDk9ef40p23XsL2Ob3w7vgs5BcUgsPhYsrGC7gTUbo1gMRFelo6CgsLi3V/KCkrI6aElZpTkpOLdUEqKSsjpYTFGnNzc7Ft80bY2vWqsbMMp6WnobCwsFj3h7KyMqKjq1fASoorVXCxdevWYmmNGzdG7dq10bFjR9y/fx/3798HAMyYMaPUhZ88eRL79u3DzZs3YWdnh40bN8LOzg516tSBiYlJqaI0Dw8PLF++nC9t9oIlmLNwaanrUVVcPHcKXW17QeqXwIjL5WLL+lVQUFTCll1+kJKWRtDZQCyeMw07fP2hrFKfwRpXbTs2eeBD9Fus3+7LdFWqFMee5nj2LqHY4M8pA1rD0qAJBi3yR2xCBtqbNoPnzB6IS/6KkEeC/+iS4gry87Fw7mxwuVwsXCJ+g9ZJxah6I2IqVqmCi82bNwtMl5CQwJ07d3Dnzh0ARd+yhQku7O3tsWDBAgQEBAg9xfcPLi4ucHZ25ktLzhZ905G8giLYEhJIS03hS09LTYGSgMGcQNF4CkH5FQUMwox4HI6PH2LgupJ/QODjh2G4d+cmzvx3B3X+/w1IV98Q4WGhuHzhDEY4jivPZTGmnnzR/Uz/7f6kpwm+P8LasdkD90NvYu02H6g0UC33+aqS5IzvKCjkoIEi/zfiBop1EJ/6rYSjitSWkcSQLkZY4XuDL11GqhaWj/sH9q7HcOneWwDA8/eJMNVWwyz7NtUquFBQVICEhARSU/ifvdSUFCirCH72lFVUig2+FpS/ID8fC+fNRnzcF+zYu7/GtloAgKKCIiQkJJDy231OSUmBSgn3uToTpy4NUShVMBUdHV2q7f174Zq6/v33X2zfvh09evTAzp07kZaWJvQFSEtLo169enybqLtEAEBSUhK6+oZ49CCMl8bhcPDowT0YmpgJPMbQxAyPHobxpT28HwojAfkvnguErr4htHT1+NJzcnIAFPVV/orFZqOKLQsjFElJSWjrGuBJ+H1eGofDwZPw+9A3KvtralwuFzs2eyD05jWs9twNtUaNRVHdKiW/gIPHr+PQpaUGL43FArq0bI77kZ/+eOzATgaQlqqFo/8940uXrMWGlKQEOBz+Z6qQwwG7mv1SlJSUgr6BEe6H3eOlcTgcPAi7B1Mzc4HHmJqZ4cEv+QEg7N5dmPyS/0dgEfvhA7x3+0BBQbEiqi82JKWkYGBohLB7obw0DoeDsLBQmJoJt4I2ET9lbqnJy8vDq1evyrUc+q5duxAXF4cJEybg6NGjaNiwIfr16wculwsOh1Pm81aUIcNH48KZk7h84Qw+RL+H59oVyMnJRo/e/QEAHssWYc92T17+gfYj8SD0Do4d9kNszHv47vHG66hI9B8ynO+8Wd++4Ubwf+jZb1CxMo1MzCBXtx7WLF+Md69f4WNsDHZu3Yj4L5/Qpm3HYvnFyQD7Ubh8PhBXL55FbMx7bN+4CjnZ2ejWsx8AYOPKJfDd+bNLLj8/H+/evMS7Ny9RkF+AlKREvHvzEl8+/RwT4L1pNUKuXMA8Vw/I1q6D1JRkpKYkIzc3p9KvryJtPX4PTr1bwsHWFHrNVLB1dk/UlpHEgUtFg4X3uvSD+7h/ih03pmcLnLv9CqmZ2XzpX7/n4eaTGKyeZIMOZupQV1PASFtTOHQ3xdnbryrlmiqTw2hHnD55HOfPnEb0+3fwWLkc2dnZ6NO/aDC166IF8NqyiZd/mMNo3L17G4f89iMm+j12eXvhRWQkhg4bAaAosJg/ZxaiIiOxcs16FHIKkZychOTkJOTn19yFFEc5OiHwxDGcPX0K79+9w0r3ZcjOzkb/AQOZrlqlY7NEs4kLoQd0fv/+HdOnT4efnx8A4PXr19DU1MT06dPRuHFjLFy4UKjzycrKwtHREY6Ojnjz5g3279+Phw8fol27dujVqxcGDx6MgQOrxoPYpVsPpKenYv/u7UhLSYaWrj7Weu7kDfRKTIgD+5d/fWNTcyxesQY+O72wb8cWNG6qDvd1W3hzXPwQ8t9FcLlc/NPdrliZ8gqKWLtlJ/bt2Io5U/9FQUEBNDS1sGL91mKtHOKmY1dbZKSn4dC+HUhLTYamth7cN3jzBsEmJcTxNSWmJidixthhvM+B/gcQ6H8AJuatsGbbPgBA0OnjAICFM/i7i2a5LOcFLdXBiZAXUJGvDdcxnaCqJIeIdwnot+AIEtOyAABNG9Qr1gqh01QZ7UybodfcQwLPOdo9EO7j/4Hv4v5QrCeL2IQMLNsXgj1nwyv8eipb9x49kZaWhp3eW5GSnAxdPQNs27Ebyv//WY6Pj+NrLTQzb4FVa9bDe9sWbN+6GU2bqWPDlm3Q1tEFACQmJuLm9WsAgBFDBvCVtXOfHyxai/+Ed2XRw64n0lJT4e21FcnJSdDTN4D3rr0ldj9VZ0wGBtu3b8f69esRHx8PMzMzbNu2DZaWf38m/f39MXz4cPTr1w+nT58WqkyhJ9GaOXMm7ty5A09PT/To0QMRERHQ1NTEmTNnsGzZMjx+XPpXjDgcTrHm/h/pFy5cwL59+3Dx4sVib4P8DS25Xj605HrZ0ZLrZUdLrpdPVZxES1xUxiRazmdf/j1TKWzqqy9U/oCAAIwePRo7d+6ElZUVPD09cfz4cbx69eqPC4TGxMSgffv20NTUhJKSktDBhdBP4+nTp+Hl5YX27dvzfas0MjLCu3fvhDqXpKQkEhMTeZ/nzZuH1NRUsNls9OnTB6dPn8bHjx+FrSIhhBBSpYhqnovc3FxkZmbybX/6Ar5p0yaMHz8eTk5OMDQ0xM6dO1G7dm34+PiUeExhYSEcHBywfPnyMi8mKnRwkZSUJDDaycrKEno07O+NJrt27UJ6ejpfGi29TgghRNyJasyFh4cH5OXl+TYPDw+BZebl5SE8PBw2Nj/nTmKz2bCxsUFoaKjAYwDA3d0dDRo0wL///lv26xX2AAsLC75pvn8EFHv37oW1tXVJh5WKOL/9QAghhFQ0FxcXZGRk8G0uLi4C8yYnJ6OwsBCqqvyv46uqqiI+Pl7gMbdv38a+ffuwZ8+ectVT6J6m1atXw87ODi9evEBBQQG2bNmCFy9e4O7du7hx48bfT0AIIYTUMKJ6o1taWrpCplsAgK9fv2LUqFHYs2dPueciETq4aN++PZ48eYI1a9bAxMQEV65cQcuWLREaGgoTk5KXHi+Jq6srateuDaCoCWfVqlWQl+ef1nrTpk2CDiWEEELEAhPzxaioqEBCQgIJCQl86QkJCVBTUyuW/927d4iJiUGfPn14aT+mhahVqxZevXoFLS2tUpVdpjGyWlpa5W4yAYCOHTvi1auf79C3bdu22ERcNW1WM0IIIdUPE+/ySElJoVWrVggODkb//v0BFAULwcHBmDZtWrH8+vr6ePaMf4K9JUuW4OvXr9iyZQuaNm1a6rKFDi6CgoIgISEBW1tbvvTLly+Dw+HAzq74XA0luX79urDFE0IIIaSUnJ2d4ejoCAsLC1haWsLT0xNZWVlwcnICAIwePRqNGzeGh4cHZGRkYGxszHe8goICABRL/xuhg4uFCxdizZo1xdK5XC4WLlwILS0tXLx4Ef/884/Q3STJ/19hsCbOO08IIaT6YqoR3t7eHklJSXB1dUV8fDzMzc1x6dIl3iDP2NhYgfNNlZfQk2jJysoiKioKGhoafOkxMTHQ1NRE7969ISkpifDwcMTExPz1fOnp6Vi8eDECAgJ4a4soKipi2LBhWLlyJS9qEgZNolU+NIlW2dEkWmVHk2iVD02iVXaVMYnW0ktvRHKeFT10/p6pChD6lsrLy+P9+/fFgou3b99CSUkJZ8+eRXR0dKmaUFJTU2FtbY3Pnz/DwcEBBgYGAIAXL17A19cXwcHBuHv3LhQVa/YCQIQQQog4ETq46NevH2bNmoVTp07xRo2+ffsWc+bM4Q0YkZOTw5kzZ/56Lnd3d0hJSeHdu3fF3sN1d3dH9+7d4e7uXuKS74QQQog4qGnvJgjdjrZu3TrUqVMH+vr6aN68OZo3bw4DAwMoKytjw4aiJuH69evzzQhWktOnT2PDhg3FAgsAUFNTw7p163Dq1Clhq0gIIYRUKbQq6l/Iy8vj7t27+O+///D06VPIysrC1NQUHTsKv/x3XFwcjIyMStxvbGxc4ixihBBCCKmahAou8vPzISsriydPnqB79+7o3r17uQpXUVFBTEwMmjRpInB/dHQ0lJSUylUGIYQQwjQmJtFiklDdIpKSkmjWrBkKC0XzNoGtrS0WL16MvLzib3fk5uZi6dKl6NGjh0jKIoQQQpjCYolmExdCd4ssXrwYixYtwsGDB8vdquDu7g4LCwvo6Ohg6tSp0NfXB5fLRVRUFLy9vZGbm4uDBw+WqwxCCCGEVC6hgwsvLy+8ffsWjRo1grq6OurUqcO3/9GjR6U+V5MmTRAaGoopU6bAxcWFtyoqi8VCt27d4OXlJdR0o4QQQkhVJE6DMUVB6ODix+umotK8eXNcvHgRaWlpePOmaJIRbW1tGmtBCCGk2mChZkUXQgcXbm5uFVEPKCoqwtLSslj6iRMnMHjw4AopkxBCCKkMNa3lokzzxaanp2Pv3r1wcXFBamoqgKLukM+fPwt9roKCAjx//hyvX7/mSz9z5gzMzMzg4OBQlioSQgghhCFCBxcRERHQ1dXF2rVrsWHDBqSnpwMAAgMD4eLiItS5nj9/Dm1tbZiZmcHAwAADBw5EQkICOnXqhLFjx8LOzg7v3r0TtoqEEEJIlVLTJtESOrhwdnbGmDFj8ObNG8jIyPDSe/bsiZs3bwp1rgULFkBbWxtnzpzBsGHDcPr0aXTu3Bl9+vTBp0+fsGbNmhLnwCCEEELEBYvFEskmLoQec/HgwQPs2rWrWHrjxo2Fnk3zwYMHuHLlCszNzdGhQwccPXoUixYtwqhRo4StFiGEEEKqCKGDC2lpaWRmZhZLf/36NerXry/UuZKTk9GoUSMARdOK16lTB23atBG2SoQQQkiVJk5dGqIgdLdI37594e7ujvz8fABFTT2xsbFYsGABBg0aJNS5WCwWvn79iszMTGRkZIDFYiE7OxuZmZl8GyGEECLOaIbOv9i4cSMGDx6MBg0aIDs7G506dUJ8fDysra2xatUqoc7F5XKhq6vL97lFixZ8n1kslsimGyeEEEJIxSvTqqj//fcfbt++jYiICHz79g0tW7Ys1RLrvwsJCRH6GEIIIUTc1LSFy4QOLn5o37492rdvX67C27dvjw0bNuDs2bPIy8tD165d4ebmBllZ2XKdlxBCCKlKatqYi1IHF9nZ2QgODkbv3r0BAC4uLsjNzeXtl5CQwIoVK/heT/2b1atXY9myZbCxsYGsrCy2bNmCxMRE+Pj4CHEJhBBCCKlKSh1c+Pn54cKFC7zgwsvLC0ZGRrxWhpcvX6JRo0aYPXt2qQs/cOAAvL29MXHiRADA1atX0atXL+zduxdsdpkmDyWEEEKqnBrWK1L6t0UOHz6MCRMm8KUdOXIEISEhCAkJwfr163Hs2DGhCo+NjUXPnj15n21sbMBisfDlyxehzkMIIYRUZWywRLKJi1K3XLx9+xYmJia8zzIyMnytC5aWlpg6dapQhRcUFBTrRpGUlOS95lpWEjWtc0vEaknQ/SurtP+WMl0FsaXYehrTVRBraQ+8mK4C+YOa1nJR6uAiPT2db4xFUlIS334Oh8O3vzS4XC7GjBkDaWlpXlpOTg4mTZqEOnXq8NICAwOFOi8hhBBCmFPq4KJJkyZ4/vw59PT0BO6PiIgQeh0QR0fHYmkjR44U6hyEEEJIVVfTGtRLHVz07NkTrq6u6NWrV7GujOzsbCxfvhy9evUSqvD9+/cLlZ8QQggRRzTPRQkWLVqEY8eOQU9PD9OmTePNrPnq1St4eXmhoKAAixYtqrCKEkIIIUQ8lDq4UFVVxd27dzF58mQsXLgQXC4XQNH6IN26dYO3tzdUVVUrrKKEEEKIuKphDRfCzdDZvHlzXLp0CampqXj79i0AQFtbG0pKShVSOUIIIaQ6oG6RUlBSUoKlpaWo60IIIYSQaqDMa4sQQgghpHRqWMMFBReEEEJIRatpC1rUtOslhBBCSAWj4IIQQgipYCwWSyRbWWzfvh0aGhqQkZGBlZUV7t+/X2LewMBAWFhYQEFBAXXq1IG5uTkOHjwodJkUXBBCCCEVjCWiTVgBAQFwdnaGm5sbHj16BDMzM9ja2iIxMVFgfiUlJSxevBihoaGIiIiAk5MTnJyccPnyZeGul/tjwopqJD6zfAuf1XQ5+YVMV0FsqcnL/D0TEYgWLisfWris7GQqYfThofBPIjnPyFbCLbNhZWWF1q1bw8ur6PngcDho2rQppk+fjoULF5bqHC1btkSvXr2wYsWKUpdLLReEEEKImMjNzUVmZibfVtKioXl5eQgPD4eNjQ0vjc1mw8bGBqGhoX8ti8vlIjg4GK9evULHjh2FqiejwcWBAweEXkmVEEIIETei6hbx8PCAvLw83+bh4SGwzOTkZBQWFhabPVtVVRXx8fEl1jUjIwNycnKQkpJCr169sG3bNnTr1k2o62X0VVQnJyf06NEDDRo0YLIahBBCSIUS1TwXLi4ucHZ25kuTlpYWzcn/r27dunjy5Am+ffuG4OBgODs7Q1NTE507dy71ORgNLqrhcA9CCCGkwkhLS5c6mFBRUYGEhAQSEhL40hMSEqCmplbicWw2G9ra2gAAc3NzREVFwcPDQ6jggvExF2V9tYYQQggRF0y8iiolJYVWrVohODiYl8bhcBAcHAxra+tSn4fD4Qg9hIHxGTq7du2KWrX+XI1Hjx5VUm0IIYQQ0WPqm7yzszMcHR1hYWEBS0tLeHp6IisrC05OTgCA0aNHo3HjxrxxGx4eHrCwsICWlhZyc3MRFBSEgwcPYseOHUKVy3hwYWtrCzk5OaarQQghhFQ79vb2SEpKgqurK+Lj42Fubo5Lly7xBnnGxsaCzf4Z+mRlZWHKlCn49OkTZGVloa+vj0OHDsHe3l6ochmd54LNZiM+Pl7kAzppnovyoXkuyo7muSg7mueifGiei7KrjHkujj35IpLzDDVvJJLzVDRGWy5ovAUhhJCaoKb9tWN0QCe9LUIIIYRUP4y2XERHR6N+/frF0gsKCpCTk0NjMQghhFQLNa2lntGWi4iICPj5+fGlrVq1CnJyclBQUED37t2RlpbGUO0IIYQQ0WCLaBMXjNZ148aNyMrK4n2+e/cuXF1dsXTpUhw7dgwfP34UaqEUQgghpCpicsl1JjDaLfLixQu0bduW9/nEiRPo1q0bFi9eDACQkZHBzJkzsWnTJqaqSAghhBAhMdpy8fXrVygrK/M+3759G127duV9NjIywpcvonl9hxBCCGGKqBYuExeMBheNGzdGVFQUAODbt294+vQpX0tGSkoKateuzVT1CCGEEJFgsUSziQtGg4shQ4Zg1qxZOHjwIMaPHw81NTW0adOGt//hw4fQ09NjsIaEEEIIERajYy5cXV3x+fNnzJgxA2pqajh06BAkJCR4+48ePYo+ffowWENCCCGk/Nhi1alRfowGF7Kysjhw4ECJ+0NCQiqxNqVz6thR+B/aj9SUZGjp6GHmvEUwMDIpMX/I1cvw2emF+LjPaNxUHZOmz0abdh358sREv8OubZvx9NFDFBYWQr25Jlas84SqWkPEffmMYf1sBZ57mcdGdLERvE9cnT3pjxOH/ZCamgxNbV1McV4IfUPB9zfm/Vsc2OuNty+jkBD/BRNnzsNA+5GVXGPx4H/kMPz270NychJ09fSxcNFSmJiaMl0tRk0c2hGzHbtCVbkenr3+DOe1x/Ew8kOJ+aeN6IzxQzqgqZoiUtKzcOrqYyzddha5eQUAgPFD2mP84A5Qb6QEAIh6H4/Vuy/iyp0XlXI9VRU9e0XEqUtDFMTptVnGXbtyEds918Fx3GTsOXgcWjp6mDt9ItJSUwTmf/70MVYsmY+e/QZgz6Hj6NDpHyyeOwPv377h5fn8KRbTx49GM43m8Ny1Hz5HT8Lx30mQkpICADRQVUPgxet8m9OEqZCtXRtWbTtUynVXlutXL2H31g1wGDsR2/f7Q1NbD4tnT0Z6Cfc3NycHDRs1wdjJM6CkrFLJtRUfly4GYcM6D0ycMhX+x09BT08fkyf+i5QUwfe1JhjcvSXWzhmAVbsuwnrEWkS8/oyz3lNRX1HwxH32PSywYkY/rN51EeYDV2LS8sMYbNsK7tP78vJ8TkjH0m1n0NZhHdo5rMf1+69xfPMEGGiqVdZlVTn07NVcjC5c1qJFi1K9tyvskusVtXDZpDHDoW9ojFnzi16V5XA4GNLbBgOHjoDDmHHF8i9zmYOcnGys2ezNS5vsNALaunqY4+IGAFi+aC4katXCEvc1pa7Hvw6DoatvgAVLK2YOEKYWLpsxzgG6BkaYNmcRgKL7O7J/d/QbPBz2o//947GjB9qhv70D4y0XVXHhModhQ2BkbIJFS1wBFN3X7l07YfiIUfh3/ASGa/dTZS5cdvPAXIRHfsDstccBFM1B8PbSCuzwv4EN+/8rln/zgiHQa66GnpO28dLWOA9Aa2MNdB27ucRyPl9fi0Wep+F3OlT0F/Gbqrhwmbg8e5WxcNmF54kiOU8vY9Eu9FlRGO0W6d+/P5PFCyU/Px+vX77gCyLYbDZaWbZB5LOnAo+JfPYUQ0c48qW1btMWt29cA1D0gxZ65yaGjxqLudMn4M2rl2jYqDEcxoxDh85dBZ0Sr6Ii8fb1S8z+f4BTXeTn5+PNqygMG/UziGCz2WjRug1ePI9gsGbiLT8vD1EvIvHv+Im8NDabjTZt2iLi6WMGa8YcyVoSaGHQFOt9rvDSuFwuroW9gqVpc4HH3HsajWG9WsPCSB0PIz9Ao7EybNsZ4ciF+wLzs9ksDOrWEnVkpRAWEV0h11HV0bPHr6Z1izAaXLi5uZX7HLm5ucjNzf0tjQ1paelyn/tXGelpKCwshKKSMl+6opIyYmME//JITUmGovLv+VWQmpIMAEhLTUX29+844rcP/06ejonTnHE/9DaWzp8Fzx0+MG/Vutg5L5wJhHpzTRibtRDRlVUNmelp4BQWQkHA/f34oWb+chaFtP8/t8q/PYfKysqIjn7PUK2YpaIoh1q1JJCY+pUvPTElE3oaqgKPCbj0EMqKdRC8fzZYYEFSUgK7j9/iC1AAwEi7Ea77zYGMVC18y86F/Zw9ePk+vsKupSqjZ69mY3TMRU5ODs6ePYuvX78W25eZmYmzZ88WCxx+5+HhAXl5eb5t26a1FVVlkeJyOQCAdp26YOiI0dDR04fDmHGwbt8JZwKPFcufm5OD4MtB6NV3YGVXlZAarUMrHcwba4uZHgGwHrEW9s67YdfeCAvH9+DL9zomAVbDPNBx9AbsOX4be9xHQb8Gj7kgP7HBEskmLhhtudi1axfOnj2Lvn37FttXr149bN26FbGxsZg2reS+WBcXFzg7O/OlpeWKPmaSV1CEhIREscGbaakpJQ4mVFJWQVrK7/mTefmLzlkLGs21+PKoN9fEsyfFx5lcv3YFOTnZsO1V/H6Ju3oKimBLSBQbvJmWmgJFJRqsWVaK/39ufx9Al5KSAhWVmnlfk9O+oaCgEA2U6vKlN1Cuh/iUTIHHuE3phaMX7sP3VNHYici3X1BbVhrblwzH2r2X8WPoWn5BId5/LGqZfBz1Ea2MmmHq8M6Yvsq/Aq+oaqJnj19N6xZhtOXi8OHDmDVrVon7Z82a9cdXVQFAWloa9erV49tE3SUCAJKSktDVN0T4gzBeGofDwaMHYTAyMRN4jJGJGcIf3ONLexgWyssvKSkJfUMjxP7W7P8xNgaqDRsVO1/QmUC069gFCopK5b2cKkdSUhI6egZ4HM5/f588DIOhcc17bU1UJKWkYGBohLB7PwcUcjgchIWFwrSada2VVn5BIR5HfUQXq58T9LFYLHSx1MX9EsZHyMpIgcPhH/vO4XD+f2zJZbFZLEhLMfodjjH07PGjGTor0Zs3b2BmJvgPMwCYmprizZs3Je6vbENHjMaF0ydw6fwZxES/w6Y1K5CdnQ27Pv0BAKvcXLDb6+fI8cHDRuJ+6B0EHPLFh5j32L97O15FRWLAkBG8PMNGOSHkv0s4d+oEPn2MReCxIwi9dQP9Bw/jK/vTx1g8fRyOXv0GVcq1MmHgsFG4eDYQ/wWdRWzMe2xbvxI5Odno3rs/AGCd+2L47NjCy5+fn493r1/i3euXyC/IR0pSIt69fonPn2IZuoKqaZSjEwJPHMPZ06fw/t07rHRfhuzsbPQfUHO717YeuganAW3h0McKes1VsXWRPWrLSuPAmaIvA3tXjOJ7zTTo5nOMH9IeQ2xbQb2RMv6x0ofr5N4IuvmMF3S4T++Ldi210KyhEoy0G8F9el90tNCBf9BDRq6xKqBnr+ZiNKQuKChAUlISmjVrJnB/UlISCgoKKrlWJfunux3S09Pgs8sLqSnJ0NbVx/qtO3ndHInxcWCzfsZrxmYtsHTlWuzbsQ17vLegSVN1rNqwFZraOrw8HbvYwNnFFYd992LrRg80a6YB97WbYWrekq/soLOBqN9AFa3btEV11dmmBzLS03BgjzfSUpOhqaOHVZu8eYNokxLiwWb/vL8pyYmYMsae9/nEET+cOOIH0xYWWL99X6XXv6rqYdcTaamp8PbaiuTkJOjpG8B7114o18Cm6R9OXHkEFUU5uE7uBVXluoh49Rn9pm7nDfJsqqbE11KxZu8lcLlcuE3pjUYN5JGc9g0Xbj7HMq9zvDz1leSwb8VoqKnUQ8a3HDx/8xl9pnjjWtjLSr++qoKevZ9YYjReQhQYneeiTZs2GDBgABYsWCBwv4eHB86cOYN79+4J3F+SiprnoqZgap6L6qAqznMhLipznovqqCrOcyEuKmOei+CXySI5T1d98QjMGO0WGTt2LFasWIHz588X23fu3DmsWrUKY8eOZaBmhBBCCCkrRrtFJkyYgJs3b6Jv377Q19fnrYD68uVLvH79GkOHDsWECVVnFjdCCCGkLGpatwjja4scOnQI/v7+0NXVxevXr/Hq1Svo6enh6NGjOHr0KNPVI4QQQsqtpr0tUiXekRo6dCiGDh1aLJ3D4SAoKAi9e/dmoFaEEEIIKYsqEVz87u3bt/Dx8YGvry+SkpKQn08DNAkhhIgv6hZhSHZ2Ng4cOICOHTtCT08Pd+/ehaurKz59+sR01QghhJByYbNEs4kLxlsuHjx4gL1798Lf3x9aWlpwcHDA3bt34e3tDUNDQ6arRwghhBAhMRpcmJqaIjMzEyNGjMDdu3dhZGQEAFi4cCGT1SKEEEJEirpFKtGrV6/QsWNHdOnShVopCCGEVFs17W0RRoOL9+/fQ09PD5MnT0aTJk0wd+5cPH78GCxxuoOEEELIX7BEtIkLRoOLxo0bY/HixXj79i0OHjyI+Ph4tGvXDgUFBfD19cXr16+ZrB4hhBBCyqDKvC3yzz//4NChQ4iLi4OXlxeuXbsGfX19aGtrM101QgghpFzYLJZINnFRZYKLH+Tl5TFlyhTcvn0b69evx8ePH5muEiGEEFIuTHaLbN++HRoaGpCRkYGVlRXu379fYt49e/agQ4cOUFRUhKKiImxsbP6YvySMBhe5ublwcXGBhYUF2rZti9OnTwMA9u/fDy0tLWzZsgXu7u5MVpEQQggRWwEBAXB2doabmxsePXoEMzMz2NraIjExUWD+69evY/jw4QgJCUFoaCiaNm2K7t274/Pnz0KVy+iS6wsWLMCuXbtgY2ODu3fvIikpCU5OTrh37x4WLVqEIUOGQEJCQujz0pLr5UNLrpcdLbledrTkevnQkutlVxlLrt97ly6S87TRUhAqv5WVFVq3bg0vr6Lng8PhoGnTppg+fXqppn0oLCyEoqIivLy8MHr06FKXy+g8F8ePH8eBAwfQt29fPH/+HKampigoKMDTp0/pjRFCCCHVhqjmucjNzUVubi5fmrS0NKSlpYvlzcvLQ3h4OFxcXHhpbDYbNjY2CA0NLVV5379/R35+PpSUlISqJ6PdIp8+fUKrVq0AAMbGxpCWlsbs2bMpsCCEEEIE8PDwgLy8PN/m4eEhMG9ycjIKCwuhqqrKl66qqor4+PhSlbdgwQI0atQINjY2QtWT0ZaLwsJCSElJ8T7XqlULcnJyDNaIEEIIET1RfWd2cXGBs7MzX5qgVgtRWLNmDfz9/XH9+nXIyAjX5ctocMHlcjFmzBjejcnJycGkSZNQp04dvnyBgYFMVI8QQggRCVG1x5fUBSKIiooKJCQkkJCQwJeekJAANTW1Px67YcMGrFmzBlevXoWpqanQ9WQ0uHB0dOT7PHLkSIZqQgghhFQvUlJSaNWqFYKDg9G/f38ARQM6g4ODMW1ayQOo161bh1WrVuHy5cuwsLAoU9mMBhf79+9nsnhCCCGkcjA0lNDZ2RmOjo6wsLCApaUlPD09kZWVBScnJwDA6NGj0bhxY964jbVr18LV1RVHjhyBhoYGb2yGnJycUMMWGF9ynRBCCKnumFoV1d7eHklJSXB1dUV8fDzMzc1x6dIl3iDP2NhYsNk/3+3YsWMH8vLyMHjwYL7zuLm5YdmyZaUul9F5LioKzXNRPjTPRdnRPBdlR/NclA/Nc1F2lTHPRXhMpkjO00qjnkjOU9Gq3PTfhBBCCBFv1C1CCCGEVLCaNnsTBReEEEJIRath0QV1ixBCCCFEpKjlghBCCKlgTL0twhQKLgghhJAKVtOWzKJuEUIIIYSIFLVcEEIIIRWshjVcVM/gQqG2JNNVEGvRiXlMV0FsZWbTBG5llXhvK9NVEGuKtquZroLYyg5eVPGF1LDogrpFCCGEECJS1bLlghBCCKlK6G0RQgghhIhUTXtbhIILQgghpILVsNiCxlwQQgghRLSo5YIQQgipaDWs6YKCC0IIIaSC1bQBndQtQgghhBCRopYLQgghpILR2yKEEEIIEakaFltQtwghhBBCRItaLgghhJCKVsOaLii4IIQQQioYvS1CCCGEEFIOjAUX7u7u+P79O1PFE0IIIZWGxRLNJi4YCy6WL1+Ob9++MVU8IYQQUmlYItrEBWNjLrhcLlNFE0IIIZVLnCIDEWB0zAVLnNp4CCGEEFIqjL4toqur+9cAIzU1tZJqQwghhFSMmva2CKPBxfLlyyEvL89kFQghhJAKV9Ma6hkNLoYNG4YGDRowWQVCCCGEiBhjwQWNtyCEEFJT1LS/ePS2CCGEEFLRalh0wVhwweFwBKZ/+PABWVlZ0NfXB5tNE4gSQggh4oaxv94+Pj7YtGkTX9qECROgqakJExMTGBsb4+PHjwzVjhBCCBEdloj+ExeMBRe7d++GoqIi7/OlS5ewf/9+HDhwAA8ePICCggKWL1/OVPUIIYQQkWFy+u/t27dDQ0MDMjIysLKywv3790vMGxkZiUGDBkFDQwMsFguenp5lKpOx4OLNmzewsLDgfT5z5gz69esHBwcHtGzZEqtXr0ZwcDBT1SOEEELEXkBAAJydneHm5oZHjx7BzMwMtra2SExMFJj/+/fv0NTUxJo1a6CmplbmchkLLrKzs1GvXj3e57t376Jjx468z5qamoiPj2eiaoQQQohIiWptkdzcXGRmZvJtubm5JZa7adMmjB8/Hk5OTjA0NMTOnTtRu3Zt+Pj4CMzfunVrrF+/HsOGDYO0tHSZr5ex4EJdXR3h4eEAgOTkZERGRqJdu3a8/fHx8TTBFiGEkOpBRNGFh4cH5OXl+TYPDw+BRebl5SE8PBw2Nja8NDabDRsbG4SGhlbQhRZh7G0RR0dHTJ06FZGRkbh27Rr09fXRqlUr3v67d+/C2NiYqeoRQgghIiOqwZguLi5wdnbmSyuphSE5ORmFhYVQVVXlS1dVVcXLly9FUp+SMBZczJ8/H9+/f0dgYCDU1NRw/Phxvv137tzB8OHDGaodIYQQUvVIS0uXq7uisjAWXLDZbLi7u8Pd3V3g/t+DjerA/8hh+O3fh+TkJOjq6WPhoqUwMTVlulqMCjoVgFMBB5CemgINLV2MnzEfugaCW6xio9/hyP4dePc6CkkJcRg7dQ76DnYo8dwnj+zHwT3b0HvQcIybNq+iLqHSBB47iqMH9yM1JRlaOnqYNW8RDI1NSswfcvUy9u7wQnzcZzRpqo5J02fDun1Hvjwx0e+wc+tmPHn0EIWFhdDQ1MTKdZ5QVWsIADgbeBz/XbqA16+i8D0rC0Ehd1G3bj1BxYmdY/6HcdDXBynJydDR1cc8l8UwNin55/HqlUvY4bUVcV8+o2kzdUyfPQftO3QCABTk58Pbawvu3LqJz58+Qa6uHCytrDF91hzUr4ZLHEzs1wqzh1pBVUkOz94lwHnbFTx8FScw7+WNDuhorl4s/eK9txi4+BgAoI6MJFaO74I+7XShVE8WMfEZ8A58gL3nH1fodVQmJialVlFRgYSEBBISEvjSExISyjVYszRolqpKculiEDas88DEKVPhf/wU9PT0MXniv0hJSWG6aoy5fe0yfHZswjDHCdi0+wg0tHSwfP5UpKcJXgk3NzcHao0aY/SEGVBUUvnjud+8jMTlcyehoalTEVWvdMFXLsJr8zqMGT8Zew8dh7auHuZMn4i0VMHPz7Onj7F88Xz06jcA+w4fR4fO/2DR3Bl4//YNL8/nT7GYOm40mmk0x9Zd++HrfxKO/06ClJQUL09OTg6s2rbHKKfxFX6NlenKpSBsXr8W4ydNxaGAk9DV08P0SeORWsLP49Mnj7F4wVz0GzAIh48FovM/XTF35nS8ffMaQNF9ehn1AuMmTsahgJNYv2krPsTEwHnGlMq8rEoxuLMB1k7qilUHbsN6kg8i3iXi7NphqK9QW2D+YctOQmPwFt7WcuxuFBRyEHgzipdn7WQbdGutCSePszB32g2vk/exeYYtellXj59fQHQDOoUhJSWFVq1a8b15yeFwEBwcDGtr63Jdz98wFlwoKipCSUnpr1t1cdBvPwYOHor+AwZBS1sbS9yWQ0ZGBqcDTzJdNcacOX4Y3XsNQFe7fmiqoYnJzoshLSOD4ItnBObX0TfCmEmz0eEfW9SSlCzxvNnZ37F51WJMnbsUdarJt+yAwwfQp/9g9Oo7AM01tTDXxRUyMjK4cPaUwPwn/A/B0rodRoweC43mWhg3eTp09Q0ReOwIL8/u7VvRpm0HTJk5B7r6BmjcpBnad+oCRSVlXp6hI0Zh5JhxMDKuXi1shw/4of+gIejbfyA0tbThsnQZZGRlcPZ0oMD8/ocPwLpde4x2+hfNNbUwedpM6BsY4Jh/0f2Uq1sX3rt90M3WDhrNm8PEzBzzFy1B1ItIxMd9qcxLq3AzBltif9ATHLwcgZcfkjHd8yKycwvg2MNMYP60rzlISMvibV1bNcf3nHwE3vjZ59/GqAkOXXmGW09jEZuQAZ8LTxDxLgEW+o0q67KqLWdnZ+zZswd+fn6IiorC5MmTkZWVBScnJwDA6NGj4eLiwsufl5eHJ0+e4MmTJ8jLy8Pnz5/x5MkTvH37VqhyGesWKevEHOIoPy8PUS8i8e/4ibw0NpuNNm3aIuJp9Wn2E0Z+fj7evY7CIAcnXhqbzYZZSyu8iowo17l3e65BqzbtYdbKCscO7i1vVRmXn5+P1y9fYKTTOF4am82GhWUbREY8FXjM84insHdw5EuztG6LW9evASj69hJ65yZGjB4L52kT8ObVSzRs1BgjncahY+euFXcxVUB+fh5eRkXCadzP1hg2mw1LK2tEPH0i8JiIp0/hMIr/flq3bY/rISXPxfPt21ewWCzIVZMAFwAka7HRQrch1h/9+aYBlwtcexQNS8PGpTqHo50Zjoe8wPecfF7avchP6G2tgwOXnuJL8jd0NFeHThMlzPe+KvJrYApTa3Xa29sjKSkJrq6uiI+Ph7m5OS5dusQb5BkbG8u31MaXL1/QokUL3ucNGzZgw4YN6NSpE65fv17qchl9W0QUcnNzi73jy5WoWgNe0tLTUFhYCGVlZb50ZWVlREe/Z6hWzPqakQ4OpxAKivytU/KKSvgUG1Pm8966dhnv3rzEhp0Hy1nDqiPj/8+PkhL/86OopIwPMdECj0lNSS6WX0lJBakpyQCAtNRUZH//jsO++zBu8nRMnu6MsNDbWDJvFrbs9EGLVq0r5mKqgPS09KL7+dvPo5KyMmKiBd/PlORkKCmrFMufkpwsMH9ubi62bd4IW7tekJOTE03FqwAV+dqoJcFGYloWX3piWhb0miqXcNRPFnoNYazZAJM3XOBLd/a6gu3OdngXMAP5BYXgcLiYsikId55VpyUgmJu6e9q0aZg2bZrAfb8HDBoaGiJZWJSxbpG0tDRs27YNmZmZxfZlZGSUuO93gt75Xb9W8Du/pHpLSozHXq/1cF68ElJSVSe4rIq43KKFA9t36gJ7h9HQ0dPHyDHj0LZ9J5w5eYzh2om3gvx8LJw7G1wuFwuXuDFdnSrFsac5nr1PLDb4c0p/C1gaNMagJcfQdrIPFu4MhucMW3RpqcFMRUm5MdZy4eXlhYiICEyfPr3YPnl5edy6dQuZmZlYvHjxH88j6J1frkTV+sOiqKAICQmJYoM3U1JSoKLy54GJ1VVdeQWw2RLFBm9mpKXy9fkL493rKGSkpcJ5ws83SDicQryIeISgU8dw/Mo9SEhIlKveTJD///OT+tvgzbTUFCgrC35+lJRViuVPTf357bvonLWg0VyLL496c01EPHkkwtpXPQqKCkX387efx9SUFCiX8POorPKz1edP+Qvy87Fw3mzEx33Bjr37q1WrBQAkZ3xHQSEHDRTr8KU3UKyD+NSsEo4qUltGEkM6G2CF3y2+dBmpWlj+b2fYu53ApbB3AIDn75Ngqq2KWUOsEPIoRqTXwBSmukWYwljLxcmTJzFp0qQS90+cOBEnTpz463mkpaVRr149vq0qdYkAgKSUFAwMjRB272c/JYfDQVhYKEzNWvzhyOpLUlISWroGiHj0cwEdDoeDiEf3oWdUtsGDZi0tscXnGDbvPcrbtPUM0dHGDpv3HhXLwAIoule6+oYIvx/GS+NwOAh/EAYjU8GD6IxNzRD+4B5f2sOwUBibmPHOaWBkhNgP/N0AH2NjoNaweg+ik5SUgr6BEe6H/bw/HA4HD8LuwdTMXOAxpmZmeBDGfz/D7t2FyS/5fwQWsR8+wHu3DxQUFFHd5Bdw8Ph1HLq00OClsVhAlxYauP/i8x+PHdhJH9JStXD06nO+dMlabEhJSoDzW1N8IYcLNrv6/EVm4m0RJjHWcvHu3Tvo6JT8mpGOjg7evXtXiTWqWKMcnbB00QIYGRnD2MQUhw76ITs7G/0HDGS6aozpN8QBW9a4QVvXEDoGRjh34ghycrLRtUdfAIDn6qVQrt8Ao8YXtW7l5+fj44eiMSoFBflITU7E+7evICsri4aNm0G2dh2oN9fmK0NaRhZ168kXSxc39g6jsXrZYugbGsHAyBjHjxxCdnY2evbpDwBY6eoClQYNMGnabADA4GEjMX2CE/wP+cK6fUcEX76Ily8iMW/RMt45h49ygpvLXJi1tEBLC0uE3b2Nu7duYOuu/bw8KcnJSE1JxqdPsQCA92/foHbtOlBVa4h6Yjw9v8NoRyxb4gJDQ2MYmZjgyKEDyM7ORp/+AwAArosWoIGqKqbNLGoVHeYwGhPGjsYhv/1o37ETLl8MwovISCxyLVq5uSA/H/PnzMKrqBfY7LUDhZxCJCcnAShqiZWUlBJcETG09cR97FnQB+Gv4/Dw5RdMG2SJ2jKSOHC5aCD23gV98CX5K1z3Xec7boydOc7deY3UzGy+9K/f83DzyQesntAV2bkFiE3IQAezZnDoZowFO2jxSnHFWHAhISGBL1++oFmzZgL3f/nyhW8Eq7jrYdcTaamp8PbaiuTkJOjpG8B7194Sm2Frgvb/2CIjIw1HfXcgLTUFzbX04LbWCwr/7xZJSowH65dnIDUlCc7jf87aejrgIE4HHISRWSus8txT6fWvTF272yE9LQ37dnohNSUZ2rr62LBtJ6+bIyE+ju9emZi1gNuqtdjjvQ27t29Bk6bqWL1hKzS1fwb0HbvYYK6LKw757sWWDR5opq6BFWs3w9S8JS/PmZMB2L9nB+/ztPFFA7Fd3FbyAhtx1L1HT6SlpWGn91akJCdDV88A23bs5nUzxcfH8f3+MTNvgVVr1sN72xZs37oZTZupY8OWbdDW0QUAJCYm4ub/38QZMWQAX1k79/nBorVlJV1ZxTtxPQoq8rXhOqYjVBXrIOJdAvotDOAN8mzaoF6xVgidJkpoZ9IUveYfEXRKjF55Gu7jOsN3UT8o1pVBbEImlvncwJ5z1aeLrqZ1i7C4ohgWWgZdunSBlZUV1qxZI3D/ggULcP/+fYSEhAh97pyC8tauZotO/HPfKSmZct3q8w21sslKiWe3VVXRoKfg36Xk77KDF1V4GfEZ+X/PVApq8iXP8VOVMNZyMW3aNAwbNgxNmjTB5MmTef3hhYWF8Pb2xubNm3HkiOAolxBCCBErNazlgrHgYtCgQZg/fz5mzJiBxYsXQ1NTEwDw/v17fPv2DfPmzcPgwYOZqh4hhBBCyoix4AIAVq1ahX79+uHw4cN4+/YtuFwuOnXqhBEjRsDSsvr0URJCCKnZaljDBbPBBQBYWlpSIEEIIaRaq2kDOhl7HWPdunXIzv75StKdO3f4pvH++vUrpkypfisKEkIIIdUdY8GFi4sLvn79yvtsZ2eHz59/TsLy/ft37Nq1i4mqEUIIISLFEtF/4oKxbpHf34Bl6I1YQgghpOKJT1wgEtVnlipCCCGEVAmMD+gkhBBCqrsa1nDBbHCxd+9e3qqBBQUF8PX15a0S+ut4DEIIIUSc1bS3RRib/ltDQwOsUtzt6Ojov+b5HU3/XT40/XfZ0fTfZUfTf5cPTf9ddpUx/XdKlmj+MCnXEY8OB8ZqGRMTw1TRhBBCSKUSpzc9RIGxAZ2hoaE4f/48X9qBAwfQvHlzNGjQABMmTOCb94IQQggRVyyWaDZxwVhwsXz5ckRGRvI+P3v2DP/++y9sbGywcOFCnDt3Dh4eHkxVjxBCCCFlxFhw8fTpU3Tt2pX32d/fH1ZWVtizZw+cnZ2xdetWHDt2jKnqEUIIIaSMGBtzkZaWBlVVVd7nGzduwM7Ojve5devW+PjxIxNVI4QQQkRKnLo0RIGxlgtVVVXemyB5eXl49OgR2rRpw9v/9etXSEpKMlU9QgghRGRq2vTfjAUXPXv2xMKFC3Hr1i24uLigdu3a6NChA29/REQEtLS0mKoeIYQQQsqIsW6RFStWYODAgejUqRPk5OTg5+cHKamfcwT4+Pige/fuTFWPEEIIEZma1i3CWHChoqKCmzdvIiMjA3JycpCQ4J9A5/jx47zZOwkhhBBxVsNiC+bXFpGXlxeYrqSkVMk1IYQQQogoMB5cEEIIIdVeDWu6oOCCEEIIqWDi9KaHKDD2tgghhBBCqidquSCEEEIqGL0tQgghhBCRqmGxBQUXhBBCSIWrYdEFjbkghBBCqrHt27dDQ0MDMjIysLKywv379/+Y//jx49DX14eMjAxMTEwQFBQkdJkUXBBCCCEVjKm1RQICAuDs7Aw3Nzc8evQIZmZmsLW1RWJiosD8d+/exfDhw/Hvv//i8ePH6N+/P/r374/nz58Ld71cLpcrdG2ruJwCpmsg3qITs5iugthSriv190xEIFkpib9nIiVq0HMN01UQW9nBiyq8DFH9XZIRcjCDlZUVWrduDS8vLwAAh8NB06ZNMX36dCxcuLBYfnt7e2RlZeH8+fO8tDZt2sDc3Bw7d+4sdbnUckEIIYSIidzcXGRmZvJtubm5AvPm5eUhPDwcNjY2vDQ2mw0bGxuEhoYKPCY0NJQvPwDY2tqWmL8k1XJAp7CRXWXKzc2Fh4cHXFxcIC0tzXR1BDJoVIfpKggkDveuKqP7V3bicO8q49t3WYjDvasMovq7tGylB5YvX86X5ubmhmXLlhXLm5ycjMLCQqiqqvKlq6qq4uXLlwLPHx8fLzB/fHy8UPWklotKlpubi+XLl5cYaZKS0b0rH7p/ZUf3ruzo3omWi4sLMjIy+DYXFxemq1VMFf6OTwghhJBfSUtLl7oFSEVFBRISEkhISOBLT0hIgJqamsBj1NTUhMpfEmq5IIQQQqohKSkptGrVCsHBwbw0DoeD4OBgWFtbCzzG2tqaLz8A/PfffyXmLwm1XBBCCCHVlLOzMxwdHWFhYQFLS0t4enoiKysLTk5OAIDRo0ejcePG8PDwAADMnDkTnTp1wsaNG9GrVy/4+/vj4cOH2L17t1DlUnBRyaSlpeHm5lajBzaVFd278qH7V3Z078qO7h2z7O3tkZSUBFdXV8THx8Pc3ByXLl3iDdqMjY0Fm/2zE6Nt27Y4cuQIlixZgkWLFkFHRwenT5+GsbGxUOVWy3kuCCGEEMIcGnNBCCGEEJGi4IIQQgghIkXBBSGEEEJEioILQgghhIgUBRd/MGbMGPTv35/3/ywWC2vW8C8OdPr0abBY/CvV7dmzB2ZmZpCTk4OCggJatGjBe83n9/P+6vr162CxWEhPTwcA+Pr6QkFBgS+PhoYGWCxWiduYMWPKe9ki8/HjR4wdOxaNGjWClJQU1NXVMXPmTKSkpCAmJuaP18FiseDr61vsnvxKQ0MDnp6efJ8FnefHv9nvZSopKaFTp064detWhVz/j2eGxWJBSkoK2tracHd3R0FBAe+6fmz169dHz5498ezZM6Hu4686d+4s8PoLCn6umDRx4kRISEjg+PHjxcpZtmwZWCwWJk2axJf+5MkTsFgsxMTEAPh5H588ecI75k+bjY0NbG1ti5Xn7e0NBQUFfPr0qSy3V6Bf77mkpCSaN2+O+fPnIycnhy/fp0+fICUlVeIIeC6Xi927d8PKyor3c2xhYQFPT098//4dQNH9Mjc35x3z/ft3uLi4QEtLCzIyMqhfvz46deqEM2fOlPp5Z1pSUhImT56MZs2aQVpaGmpqarC1tcWdO3cA/P1n7IeTJ0+ic+fOkJeXh5ycHExNTeHu7o7U1FQAxX+3FRYWYs2aNdDX14esrCyUlJRgZWWFvXv3AsBf752gqa8Js+hVVCHIyMhg7dq1mDhxIhQVFQXm8fHxwaxZs7B161Z06tQJubm5iIiIEHq52pI8ePAAhYWFAIqWxh00aBBevXqFevXqAQBkZWVFUk55vX//HtbW1tDV1cXRo0fRvHlzREZGYt68ebh48SJCQ0MRFxfHy79hwwZcunQJV69e5aXJy8sjLCxMqHLd3d0xfvx4vrS6devyfb569SqMjIyQnJyMVatWoXfv3nj9+nWx+fRFoUePHti/fz9yc3MRFBSEqVOnQlJSkjchzY9/uy9fvmDevHno1asX3r59CympotVV/3Yf7927ByUlJV5548ePh7u7O18datUq+jH//v07/P39MX/+fPj4+GDIkCHF6isjI4N9+/Zhzpw50NHR+ev1zZ07ly8Yad26NSZMmMD3b5Cfnw8TExPs2rULEydOBABER0dj/vz52LFjB5o0aVLa21kqP+55fn4+wsPD4ejoCBaLhbVr1/Ly+Pr6YujQobh58ybCwsJgZWXFd45Ro0YhMDAQS5YsgZeXF+rXr4+nT5/C09MTGhoaAr8cTJo0CWFhYdi2bRsMDQ2RkpKCu3fvIiUlBU2bNi3V8860QYMGIS8vD35+ftDU1ERCQgKCg4P5Atm//YwtXrwYa9euxezZs7F69Wo0atQIb968wc6dO3Hw4EHMnDmzWLnLly/Hrl274OXlBQsLC2RmZuLhw4dIS0sDAL57FxAQAFdXV7x69YqXJicnJ7J7QESES0rk6OjI7devH+//e/fuzdXX1+fOmzePl+fUqVPcX29jv379uGPGjCn1eX8VEhLCBcBNS0vjcrlc7v79+7ny8vIlnuf3/FVJjx49uE2aNOF+//6dLz0uLo5bu3Zt7qRJk/jS3dzcuGZmZsXO86drVFdX527evLnEz7+Ljo7mAuA+fvyYlxYREcEFwD1z5kxpLksogv6du3Xrxm3Tpo3A6zp79iwXAPfp06e8NGHuY6dOnbgzZ84ssT6+vr7cNm3acNPT07m1a9fmxsbG8u3/8W/QrVs37pAhQ3jpjx8/5gLgRkdHc7lcwffxh5L+DXx9fblycnLc9+/fczkcDrdLly7cAQMGlFjXshJ0zwcOHMht0aIF7zOHw+FqampyL126xF2wYAF3/PjxfPkDAgK4ALinT58udn4Oh8NNT0/ncrnFn1l5eXmur69vqepZ0vPOpLS0NC4A7vXr10vM87efsbCwMC4ArqenZ4llcLnFf7eZmZlxly1bVqp6/u33IqkaqFtECBISEli9ejW2bdtWYlOumpoa7t27hw8fPlRy7aqO1NRUXL58GVOmTCnWkqKmpgYHBwcEBASAy/AUK9nZ2Thw4AAA8FoKKpqsrCzy8vKKpWdkZMDf35+vLqK+j/v27cPIkSMhLy8POzu7Epvh16xZg5MnT+Lhw4dCXNmfOTo6omvXrhg7diy8vLzw/Plz7Nq1S2TnL8nz589x9+5dvn/fkJAQfP/+HTY2Nhg5ciT8/f2RlZXF23/48GHo6emhX79+xc7HYrFKbGFQU1NDUFAQvn79KvoLqQRycnKQk5PD6dOny7zI2OHDhyEnJ4cpU6YI3P97N+8PampquHbtGpKSkspULql6KLgQ0oABA2Bubg43NzeB+93c3KCgoAANDQ3o6elhzJgxOHbsGDgcTiXXlDlv3rwBl8uFgYGBwP0GBgZIS0sT6hdJkyZNeL/8fmyxsbHF8i1YsKBYvt/HVLRt2xZycnKoU6cONmzYgFatWqFr167CXaSQuFwurl69isuXL+Off/4pdl0KCgo4cuQI+vbtC319fQBlu4/e3t581z5nzhzeue7duwd7e3sAwMiRI7F//36BgUnLli0xdOhQLFiwQGTXDwC7d+/G8+fPMWvWLOzevRv169cX6fl/OH/+POTk5CAjIwMTExMkJiZi3rx5vP379u3DsGHDICEhAWNjY2hqavKNQXnz5g309PSELnf37t24e/culJWV0bp1a8yePZs3VkEc1KpVC76+vvDz84OCggLatWuHRYsWISIigi/fn37G3rx5A01NTUhKSgpV9qZNm5CUlAQ1NTWYmppi0qRJuHjxosiujVQ+Ci7KYO3atfDz80NUVFSxfQ0bNkRoaCiePXuGmTNnoqCgAI6OjujRo0eNCjAAiLRl4tatW3jy5Anf1qhRo2L55s2bVyyfhYUFX56AgAA8fvwYJ0+ehLa2Nnx9fYX+ZVhav/6hs7Ozg729Pd/gs1u3biE8PBy+vr7Q1dXFzp07i51DmPvo4ODAd+0/lmL28fGBra0tVFRUAAA9e/ZERkYGrl27JvA8K1euxK1bt3DlyhUhrvbPGjRogIkTJ8LAwEDgmAVR6dKlC548eYKwsDA4OjrCyckJgwYNAgCkp6cjMDAQI0eO5OUfOXIk9u3bx/tc1ue2Y8eOeP/+PYKDgzF48GBERkaiQ4cOWLFiRfkuqBINGjQIX758wdmzZ9GjRw9cv34dLVu25Gvl+tPPWFnvnaGhIZ4/f4579+5h7NixSExMRJ8+fTBu3DhRXBZhAA3oLIOOHTvC1tYWLi4uJb6dYWxsDGNjY0yZMgWTJk1Chw4dcOPGDXTp0gX16tUT2G2Snp4OCQkJ1KlTp4KvoGJpa2uDxWIhKioKAwYMKLY/KioKioqKQn1zbd68ebEm1R8DFX+loqICbW3tP56radOm0NHRgY6ODgoKCjBgwAA8f/68QtY+6NKlC3bs2AEpKSk0atSoWJ1/XJeenh4SExNhb2+PmzdvAijbfZSXly92/YWFhfDz80N8fDxf+YWFhfDx8RHYaqOlpYXx48dj4cKFfH94y6tWrVoC/91EqU6dOrx74OPjAzMzM+zbtw///vsvjhw5gpycHL4BnFwuFxwOB69fv4auri50dXXx8uXLMpUtKSmJDh06oEOHDliwYAFWrlwJd3d3LFiwoNK63spLRkYG3bp1Q7du3bB06VKMGzcObm5uvN91f/oZ09XVxe3bt5Gfny90wM5ms9G6dWu0bt0as2bNwqFDhzBq1CgsXrwYzZs3L+9lkUpGLRdltGbNGpw7dw6hoaF/zWtoaAgAvH5dPT09REZGFuvXfPToEZo3b15h36Iri7KyMrp16wZvb29kZ2fz7YuPj8fhw4dhb29f7BVeJgwePBi1atWCt7d3hZz/xx+6Zs2a/fWP6tSpU/H8+XOcOnUKgOju449xAI8fP+b7tnn06FEEBgYKfM0XAFxdXfH69WveWBBxxGazsWjRIixZsgTZ2dm8N2F+vQ9Pnz5Fhw4d4OPjAwAYMWIEXr9+jTNnzhQ7H5fLRUZGRqnLNzQ0REFBQbFXYcWJoaEh35iUPxkxYgS+fftW4s9TSc9aSeUCKHXZpGqh4KKMTExM4ODggK1bt/KlT548GStWrMCdO3fw4cMH3Lt3D6NHj0b9+vV5rx86ODiAxWJh9OjRCA8Px9u3b+Hj4wNPT09eH/kPhYWFxZogBXXHVDVeXl7Izc2Fra0tbt68iY8fP+LSpUvo1q0bGjdujFWrVlVIuV+/fkV8fDzflpmZWWJ+FouFGTNmYM2aNbz5C5hSu3ZtjB8/Hm5ubrzmZVHcx3379qFXr14wMzPjtagZGxtj6NChUFBQwOHDhwUep6qqCmdn52LPuLgZMmQIJCQksH37djx69Ajjxo3juw/GxsYYPnw4/Pz8UFBQgKFDh8Le3h7Dhw/H6tWr8fDhQ3z48AHnz5+HjY0NQkJCBJbTuXNn7Nq1C+Hh4YiJiUFQUBAWLVrEa62s6lJSUvDPP//g0KFDiIiIQHR0NI4fP45169bxDW7908+YlZUV5s+fjzlz5mD+/PkIDQ3Fhw8fEBwcjCFDhsDPz09g2YMHD8bmzZsRFhaGDx8+4Pr165g6dSp0dXV5Y5CIeKHgohzc3d2LjaOwsbHBvXv3MGTIEOjq6mLQoEGQkZFBcHAwlJWVARSNmL516xby8/PRt29fmJubY+vWrdi0aRNvHoAfvn37hhYtWvBtffr0qbRrLCsdHR08fPgQmpqaGDp0KLS0tDBhwgR06dIFoaGhfHMziJKrqysaNmzIt82fP/+Pxzg6OiI/Px9eXl4VUidhTJs2DVFRUbwBhuW9jwkJCbhw4QJvzMGv2Gw2BgwY8Mduj7lz54r9HAK1atXCtGnT4OLiAg0NDYF/rAYMGIDExEQEBQWBxWLhyJEj2LRpE06fPo1OnTrB1NQUy5YtQ79+/QROCAYAtra28PPzQ/fu3WFgYIDp06fD1tYWx44dq+hLFAk5OTlYWVlh8+bN6NixI4yNjbF06VKMHz+e72fjbz9ja9euxZEjRxAWFgZbW1sYGRnB2dkZpqamcHR0FFi2ra0tzp07hz59+kBXVxeOjo7Q19fHlStXKrwbjVQMWnKdEEIIISJFLReEEEIIESkKLgghhBAiUhRcEEIIIUSkKLgghBBCiEhRcEEIIYQQkaLgghBCCCEiRcEFIYQQQkSKggtCCCGEiBQFF4SUgoaGBjw9PUV+3jFjxlToCqF/8vs1sVgsnD59GgAQExMDFouFJ0+eMFI3UTt9+jS0tbUhISGBWbNmMV0dQqo9Ci5IjcBisf64/boMemXasmUL33LWZbFs2TKB1yTsmgxxcXGws7MrV12E9f37d7i4uEBLSwsyMjKoX78+OnXqJHDRsPKYOHEiBg8ejI8fP4rVEuiEiCuatJ3UCHFxcbz/DwgIgKurK169esVLY2r9DHl5eZGcx8jICFevXuVLE3ZNBjU1NZHURRiTJk1CWFgYtm3bBkNDQ6SkpODu3btISUkRWRnfvn1DYmIibG1t0ahRI5GdlxBSMmq5IDWCmpoab5OXlweLxeJ9zsrKgoODA1RVVSEnJ4fWrVsX+0MNFH3LHjt2LOrWrYtmzZph9+7dvH0/uhGOHTuGDh06QFZWFq1bt8br16/x4MEDWFhYQE5ODnZ2dkhKSuId93u3SOfOnTFjxgzMnz8fSkpKUFNTK1WrSq1atfiuUU1NDSoqKrz9iYmJ6NOnD2RlZdG8eXOBK6H+2i0iyPPnz2FnZwc5OTmoqqpi1KhRSE5OLlfdz549i0WLFqFnz57Q0NBAq1atMH36dIwdO5aXR1D3zd69ezFgwADUrl0bOjo6OHv2LG//9evXwWKxkJ6ejuvXr6Nu3boAgH/++QcsFgvXr1//Y50IIeVHwQWp8b59+4aePXsiODgYjx8/Ro8ePdCnTx/Exsby5du4cSMsLCzw+PFjTJkyBZMnT+Zr/QAANzc3LFmyBI8ePUKtWrUwYsQIzJ8/H1u2bMGtW7fw9u1buLq6/rE+fn5+qFOnDsLCwrBu3Tq4u7vjv//+K9c1jhkzBh8/fkRISAhOnDgBb29vJCYmlvr49PR0/PPPP2jRogUePnyIS5cuISEhAUOHDi1X3dXU1BAUFISvX78KdT3Lly/H0KFDERERgZ49e8LBwQGpqanF8rVt25b3b3Ty5EnExcWhbdu2QpVFCCkDLiE1zP79+7ny8vJ/zGNkZMTdtm0b77O6ujp35MiRvM8cDofboEED7o4dO7hcLpcbHR3NBcDdu3cvL8/Ro0e5ALjBwcG8NA8PD66enh7vs6OjI7dfv368z506deK2b9+ery6tW7fmLliwoMS6urm5cdlsNrdOnTp828SJE7lcLpf76tUrLgDu/fv3ecdERUVxAXA3b97MSwPAPXXqFN/1PH78mMvlcrkrVqzgdu/ena/cjx8/cgFwX716Vea637hxg9ukSROupKQk18LCgjtr1izu7du3+fKoq6sXq+eSJUt4n799+8YFwL148SKXy+VyQ0JCuAC4aWlpXC6Xy01LS+MC4IaEhJRYD0KIaNGYC1Ljffv2DcuWLcOFCxcQFxeHgoICZGdnF2u5MDU15f3/j26V37/9/5pHVVUVAGBiYsKX9rcWg1/PAQANGzb86zF6enp8XQMAUK9ePQBAVFQUatWqhVatWvH26evrQ0FB4Y/n/NXTp08REhIicGzKu3fvoKurW6a6d+zYEe/fv8e9e/dw9+5dBAcHY8uWLVi+fDmWLl1a4nG/llOnTh3Uq1dPqJYYQkjFouCC1Hhz587Ff//9hw0bNkBbWxuysrIYPHgw8vLy+PJJSkryfWaxWOBwOCXmYbFYAtN+P+Z3pSnnd1JSUtDW1v5jnvL49u0b+vTpg7Vr1xbb17BhQ97/l6XukpKS6NChAzp06IAFCxZg5cqVcHd3x4IFCyAlJVXiMcKWQwipPBRckBrvzp07GDNmDAYMGACg6A9pTEwMs5USIX19fRQUFCA8PBytW7cGALx69Qrp6emlPkfLli1x8uRJaGhoCP0WirAMDQ1RUFCAnJycEoMLQkjVRgM6SY2no6ODwMBAPHnyBE+fPsWIESPE7ltwQUEB4uPj+baEhAQARV0mPXr0wMSJExEWFobw8HCMGzcOsrKypT7/1KlTkZqaiuHDh+PBgwd49+4dLl++DCcnJxQWFpa53p07d8auXbsQHh6OmJgYBAUFYdGiRejSpQuvW4cQIn4ouCA13qZNm6CoqIi2bduiT58+sLX9Xzt2bCIhEAVg+N1WYCRoYryBiQ2YOblFmAl2JKb2YQmWI152cNndMrCwfB9MNPB40fAzKbque/da/3KeZ1RV9es0TfNzv65r1HUdfd/HOI4xTVOUZfnn+XVdx3EccV1XDMMQbdvGsixRFEU8Hq8/Iyml2LYthmGI5/MZ8zxHSin2fX95JvB+X/d93+9eAgD4HH4uAICsxAUAkJW4AACyEhcAQFbiAgDISlwAAFmJCwAgK3EBAGQlLgCArMQFAJCVuAAAsvoGObVeEgJjdxQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "multiple_choic(X,y,3)"
      ],
      "metadata": {
        "id": "NPYYReRBiV_N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_class=5\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y_number, test_size=0.02, random_state=42)\n",
        "\n",
        "# Gönderilerin uzunluğunu eşitleyin (padding)\n",
        "# Kelime seviyesinde Tokenization ve embedding yapmak için tokenizer oluşturun\n",
        "tokenizer = Tokenizer(num_words=5000, lower=True)\n",
        "tokenizer.fit_on_texts(X_train)\n",
        "\n",
        "# Train ve test verilerinde Tokenization işlemi\n",
        "X_train = tokenizer.texts_to_sequences(X_train)\n",
        "X_test = tokenizer.texts_to_sequences(X_test)\n",
        "\n",
        "# Hedef değişkeninizi 5 sınıfı olan bir çoklu-etiketleme formatına dönüştürün\n",
        "y_train_cat = to_categorical(y_train, num_classes=num_class)\n",
        "y_test_cat = to_categorical(y_test, num_classes=num_class)\n",
        "\n",
        "# Her bir gönderinin maksimum uzunluğunu belirleyin\n",
        "max_len = len(max(X_train, key=len))\n",
        "\n",
        "# Gönderilerin uzunluğunu eşitleyin (padding)\n",
        "X_train = pad_sequences(X_train, padding='post', maxlen=max_len)\n",
        "X_test = pad_sequences(X_test, padding='post', maxlen=max_len)\n",
        "\n",
        "# Model oluşturma\n",
        "model = Sequential()\n",
        "\n",
        "# Embedding katmanı\n",
        "model.add(Embedding(input_dim=5000, output_dim=50, input_length=max_len))\n",
        "\n",
        "# LSTM katmanı\n",
        "model.add(LSTM(units=128, dropout=0.2, recurrent_dropout=0.2))\n",
        "\n",
        "# Tam bağlantı (fully connected) katmanı\n",
        "model.add(Dense(units=num_class, activation='softmax'))\n",
        "\n",
        "# Model derleme\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "# Model eğitimi\n",
        "model.fit(X_train, y_train_cat, validation_data=(X_test, y_test_cat), epochs=10, batch_size=64)\n"
      ],
      "metadata": {
        "id": "lOsAUX6b4peZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d49bbb5-bce4-461c-d8bd-474bd51f0da7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "191/191 [==============================] - 28s 120ms/step - loss: 1.3087 - accuracy: 0.4280 - val_loss: 0.9998 - val_accuracy: 0.6480\n",
            "Epoch 2/10\n",
            "191/191 [==============================] - 12s 65ms/step - loss: 0.7077 - accuracy: 0.7375 - val_loss: 0.7147 - val_accuracy: 0.7280\n",
            "Epoch 3/10\n",
            "191/191 [==============================] - 14s 71ms/step - loss: 0.4794 - accuracy: 0.8369 - val_loss: 0.6985 - val_accuracy: 0.7440\n",
            "Epoch 4/10\n",
            "191/191 [==============================] - 16s 84ms/step - loss: 0.3847 - accuracy: 0.8715 - val_loss: 0.7304 - val_accuracy: 0.7720\n",
            "Epoch 5/10\n",
            "191/191 [==============================] - 13s 68ms/step - loss: 0.3410 - accuracy: 0.8877 - val_loss: 0.6568 - val_accuracy: 0.7720\n",
            "Epoch 6/10\n",
            "191/191 [==============================] - 11s 57ms/step - loss: 0.2992 - accuracy: 0.8993 - val_loss: 0.6579 - val_accuracy: 0.7800\n",
            "Epoch 7/10\n",
            "191/191 [==============================] - 13s 69ms/step - loss: 0.2536 - accuracy: 0.9153 - val_loss: 0.6906 - val_accuracy: 0.7760\n",
            "Epoch 8/10\n",
            "191/191 [==============================] - 14s 75ms/step - loss: 0.2343 - accuracy: 0.9196 - val_loss: 0.6410 - val_accuracy: 0.7760\n",
            "Epoch 9/10\n",
            "191/191 [==============================] - 13s 70ms/step - loss: 0.2244 - accuracy: 0.9253 - val_loss: 0.6482 - val_accuracy: 0.7760\n",
            "Epoch 10/10\n",
            "191/191 [==============================] - 13s 70ms/step - loss: 0.1985 - accuracy: 0.9340 - val_loss: 0.6741 - val_accuracy: 0.7920\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f23fa44c700>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "id": "sDae61NTA6Ns",
        "outputId": "e82b6a76-704c-4133-ce1b-c7aee5659862",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  49,   19, 3109, ...,    0,    0,    0],\n",
              "       [  26,  736, 1858, ...,    0,    0,    0],\n",
              "       [ 669,    2,  158, ...,    0,    0,    0],\n",
              "       ...,\n",
              "       [2693,    0,    0, ...,    0,    0,    0],\n",
              "       [   2,   48,  853, ...,    0,    0,    0],\n",
              "       [   2, 2310,  229, ...,    0,    0,    0]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=model.predict(X_test)\n",
        "y_pred = np.argmax(y_pred, axis=1)\n",
        "print(classification_report(y_test, y_pred, digits = 4))\n",
        "conf_matrix(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "8s-qRzJ88z8X",
        "outputId": "4872acf5-c9f9-4f65-d972-8ebb8d260ae8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 1s 21ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.6596    0.7209    0.6889        43\n",
            "           1     0.7750    0.8052    0.7898        77\n",
            "           2     0.9091    0.7843    0.8421        51\n",
            "           3     0.8125    0.8667    0.8387        30\n",
            "           4     0.8298    0.7959    0.8125        49\n",
            "\n",
            "    accuracy                         0.7920       250\n",
            "   macro avg     0.7972    0.7946    0.7944       250\n",
            "weighted avg     0.7977    0.7920    0.7934       250\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "W575R91UZO8u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X=df.str_tkn_p_sw\n",
        "y=df.y_target_number\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.02, random_state=42)\n",
        "X_train, X_test= vectorizer_methods(X_train, X_test, 1)"
      ],
      "metadata": {
        "id": "aqRCxyQc4PAL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import VotingClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "# Örnek veri oluşturma\n",
        "\n",
        "# Sınıflandırıcıların oluşturulması\n",
        "svc_clf = SVC(probability=True)\n",
        "xgb_clf = XGBClassifier()\n",
        "dt_clf = DecisionTreeClassifier()\n",
        "knn_clf = KNeighborsClassifier()\n",
        "\n",
        "# Oy verme sınıflandırıcısının oluşturulması\n",
        "voting_clf = VotingClassifier(estimators=[('svc', svc_clf),  ('xgb', xgb_clf), ('lr', lr_clf), ('dt', dt_clf), ('knn', knn_clf)], voting='hard')\n",
        "\n",
        "# Oy verme sınıflandırıcısının eğitilmesi ve değerlendirilmesi\n",
        "voting_clf.fit(X_train, y_train)\n",
        "print(\"Accuracy:\", voting_clf.score(X_train, y_train))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PZ3V4aarUVsw",
        "outputId": "75d0f8d5-e0f5-499d-b057-bdb3c2858556"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9829745436686584\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "f1_score(y_test, y_pred, average='macro')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c5oz8z-5V5kr",
        "outputId": "f747603c-12cd-4b8c-e7d5-90871e370ca0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7899083918671297"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Code:\n",
        "# import libraries\n",
        "from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer, AutoModelForSequenceClassification\n",
        "tokenizer= AutoTokenizer.from_pretrained(\"savasy/bert-turkish-text-classification\")\n",
        "\n",
        "# build and load model, it take time depending on your internet connection\n",
        "model= AutoModelForSequenceClassification.from_pretrained(\"savasy/bert-turkish-text-classification\")\n",
        "\n",
        "# make pipeline\n",
        "nlp=pipeline(model=model, tokenizer=tokenizer)\n",
        "\n",
        "# apply model\n",
        "nlp(\"bla bla\")\n",
        "# [{'label': 'LABEL_2', 'score': 0.4753005802631378}]\n",
        "#INSULT\",\"OTHER\",\"PROFANITY\",\"RACIST\",\"SECIST\"\n",
        "code_to_label={\n",
        " 'LABEL_0': 'INSULT ',\n",
        " 'LABEL_1': \"OTHER\",\n",
        " 'LABEL_2': \"PROFANITY\",\n",
        " 'LABEL_3': \"RACIST\",\n",
        " 'LABEL_4': \"SEXIST\",}\n",
        " \n",
        "code_to_label[nlp(\"bla bla\")[0]['label']]\n",
        "# > 'kultur '"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377,
          "referenced_widgets": [
            "43d452db1ccc46ec9c24e95d5faf6b05",
            "0171b9c9d51047afb14695d537f63ebe",
            "912d3a889f084a9dba6e11e1ee217ab4",
            "d7111b0073864399b5d45c076acd115c",
            "4d088460317e4d9fb62c1a9424d52ec6",
            "0a113da5c963459f9ed82c74e6727db8",
            "6b150e6a697f4980abb5967ceab0752c",
            "4ce2aad1d47e4f54ae4c8c4165ecae0e",
            "712369449e3b428689757536b39032ea",
            "cfde2dee069d45c38d11838ff603712d",
            "d872fdca83f5412394f6c8148683460b",
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        "id": "IbtiXQOMXNWA",
        "outputId": "d624db68-cdfc-4ca2-fb76-e4aab5746a67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/40.0 [00:00<?, ?B/s]"
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          "output_type": "display_data",
          "data": {
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          "output_type": "display_data",
          "data": {
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              "Downloading pytorch_model.bin:   0%|          | 0.00/443M [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-62-55655322ec55>\u001b[0m in \u001b[0;36m<cell line: 23>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     21\u001b[0m  'LABEL_4': \"SEXIST\",}\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mcode_to_label\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnlp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"bla bla\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'label'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0;31m# > 'kultur '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'culture'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=df.str_tkn_p_sw\n",
        "y=df.y_target_number\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.02, random_state=42)\n",
        "\n",
        "#y_train=pd.Categorical(y_train)\n",
        "# Birleştirilmiş verilerinizi bir veri çerçevesine dönüştürün\n",
        "Xy_train = pd.concat([X_train, y_train], axis=1)\n",
        "X_train_df = pd.DataFrame(Xy_train)\n"
      ],
      "metadata": {
        "id": "LvdTfd2fcm1t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "ZtMW94p1dySd",
        "outputId": "b7fcd2df-c1c9-487c-8c30-90779535b971"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-931765772341>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'X_train' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df =  pd.concat([y_train, X_train], axis=1)"
      ],
      "metadata": {
        "id": "xaTLi4DwdNV8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install simpletransformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4XN0LxTbgMjN",
        "outputId": "a9567ad1-d27b-47a5-f54b-f60f053a18f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: simpletransformers in /usr/local/lib/python3.9/dist-packages (0.63.9)\n",
            "Requirement already satisfied: datasets in /usr/local/lib/python3.9/dist-packages (from simpletransformers) (2.11.0)\n",
            "Requirement already satisfied: seqeval in /usr/local/lib/python3.9/dist-packages (from simpletransformers) (1.2.2)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (from simpletransformers) (1.4.4)\n",
            "Requirement already satisfied: transformers>=4.6.0 in /usr/local/lib/python3.9/dist-packages (from simpletransformers) (4.27.4)\n",
            "Requirement already satisfied: tokenizers in /usr/local/lib/python3.9/dist-packages (from simpletransformers) (0.13.2)\n",
            "Requirement already satisfied: streamlit in /usr/local/lib/python3.9/dist-packages (from simpletransformers) (1.20.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from simpletransformers) (2.27.1)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.9/dist-packages (from simpletransformers) (2022.10.31)\n",
            "Requirement already satisfied: tqdm>=4.47.0 in /usr/local/lib/python3.9/dist-packages (from simpletransformers) (4.65.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from simpletransformers) (1.22.4)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.9/dist-packages (from simpletransformers) (1.10.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.9/dist-packages (from simpletransformers) (1.2.2)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.9/dist-packages (from simpletransformers) (0.1.97)\n",
            "Requirement already satisfied: wandb>=0.10.32 in /usr/local/lib/python3.9/dist-packages (from simpletransformers) (0.14.0)\n",
            "Requirement already satisfied: tensorboard in /usr/local/lib/python3.9/dist-packages (from simpletransformers) (2.12.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from transformers>=4.6.0->simpletransformers) (6.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers>=4.6.0->simpletransformers) (3.10.7)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers>=4.6.0->simpletransformers) (23.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.9/dist-packages (from transformers>=4.6.0->simpletransformers) (0.13.3)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from wandb>=0.10.32->simpletransformers) (4.5.0)\n",
            "Requirement already satisfied: Click!=8.0.0,>=7.0 in /usr/local/lib/python3.9/dist-packages (from wandb>=0.10.32->simpletransformers) (8.1.3)\n",
            "Requirement already satisfied: sentry-sdk>=1.0.0 in /usr/local/lib/python3.9/dist-packages (from wandb>=0.10.32->simpletransformers) (1.18.0)\n",
            "Requirement already satisfied: setproctitle in /usr/local/lib/python3.9/dist-packages (from wandb>=0.10.32->simpletransformers) (1.3.2)\n",
            "Requirement already satisfied: protobuf!=4.21.0,<5,>=3.15.0 in /usr/local/lib/python3.9/dist-packages (from wandb>=0.10.32->simpletransformers) (3.20.3)\n",
            "Requirement already satisfied: GitPython!=3.1.29,>=1.0.0 in /usr/local/lib/python3.9/dist-packages (from wandb>=0.10.32->simpletransformers) (3.1.31)\n",
            "Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.9/dist-packages (from wandb>=0.10.32->simpletransformers) (5.9.4)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.9/dist-packages (from wandb>=0.10.32->simpletransformers) (67.6.1)\n",
            "Requirement already satisfied: appdirs>=1.4.3 in /usr/local/lib/python3.9/dist-packages (from wandb>=0.10.32->simpletransformers) (1.4.4)\n",
            "Requirement already satisfied: docker-pycreds>=0.4.0 in /usr/local/lib/python3.9/dist-packages (from wandb>=0.10.32->simpletransformers) (0.4.0)\n",
            "Requirement already satisfied: pathtools in /usr/local/lib/python3.9/dist-packages (from wandb>=0.10.32->simpletransformers) (0.1.2)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->simpletransformers) (2022.12.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->simpletransformers) (1.26.15)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->simpletransformers) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->simpletransformers) (3.4)\n",
            "\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "5J9FCLvAg16f",
        "outputId": "9e054ecb-0619-4267-c305-37b15e902df1"
      },
      "execution_count": null,
      "outputs": [
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              "       y_target_number                                       str_tkn_p_sw\n",
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              "9450                 2                                   yalanını sikeyim\n",
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              "11964                0           kadar korkak olmayıp beni korusan olurdu\n",
              "5191                 4                      kadinlar surekli dir dir eder\n",
              "5390                 2                                         gay menisi\n",
              "860                  2             günes senin götüne koyarım bi doğarsan\n",
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              "\n",
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              "          const element = document.querySelector('#df-484dce80-ae43-4ed4-b706-9b1dabd88618');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.columns=[\"labels\",\"text\"]"
      ],
      "metadata": {
        "id": "5cVyTNX1gonF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F9YvulzKsSAG",
        "outputId": "08f64475-6cca-4b99-dd7d-81f6d0d3fc4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'text', 'is_offensive', 'target', 'y_target_number',\n",
              "       'preprocessed_text', 'first_p_sec_sw', 'tkn_p_sw', 'str_tkn_p_sw'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df[[ \"str_tkn_p_sw\",\"target\"]]\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "09eREEcxuXFJ",
        "outputId": "d953b89c-fd14-4507-ad89-fb8434dcaed7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            str_tkn_p_sw     target\n",
              "0                                            çürük dişli     INSULT\n",
              "1      adamın islama müslümanlara verdiği zararı gavu...     RACIST\n",
              "2                                   erkekler zora gelmez     SEXIST\n",
              "3      utanmazın götüne kazık sokmuşlar tıkırtı nered...  PROFANITY\n",
              "4           otomasyon sistemlerine doğrudan bağlanabilir      OTHER\n",
              "...                                                  ...        ...\n",
              "12462  uyuma taklidi yapan tehlikeli hayvanın gözüne ...      OTHER\n",
              "12463          yolda at kavga eden üç oğlan çocuğu görür      OTHER\n",
              "12464  sizin köpeklerinizin burnu bile iyi koku alıyo...      OTHER\n",
              "12465  hayalleri gerçek etmek birisinin delilik yapma...      OTHER\n",
              "12466  deliklerden birini bulsan diğerini bulamıyorsu...      OTHER\n",
              "\n",
              "[12467 rows x 2 columns]"
            ],
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>str_tkn_p_sw</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>adamın islama müslümanlara verdiği zararı gavu...</td>\n",
              "      <td>RACIST</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>erkekler zora gelmez</td>\n",
              "      <td>SEXIST</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>utanmazın götüne kazık sokmuşlar tıkırtı nered...</td>\n",
              "      <td>PROFANITY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>otomasyon sistemlerine doğrudan bağlanabilir</td>\n",
              "      <td>OTHER</td>\n",
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              "      <td>uyuma taklidi yapan tehlikeli hayvanın gözüne ...</td>\n",
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              "      <td>OTHER</td>\n",
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              "    <tr>\n",
              "      <th>12464</th>\n",
              "      <td>sizin köpeklerinizin burnu bile iyi koku alıyo...</td>\n",
              "      <td>OTHER</td>\n",
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              "    <tr>\n",
              "      <th>12465</th>\n",
              "      <td>hayalleri gerçek etmek birisinin delilik yapma...</td>\n",
              "      <td>OTHER</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12466</th>\n",
              "      <td>deliklerden birini bulsan diğerini bulamıyorsu...</td>\n",
              "      <td>OTHER</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertTokenizer\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained(\"dbmdz/bert-base-turkish-128k-uncased\")\n",
        "\n"
      ],
      "metadata": {
        "id": "wK2DAlwlcAEm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels = {'INSULT':0,\n",
        "          'OTHER':1,\n",
        "          'PROFANITY':2,\n",
        "          'RACIST':3,\n",
        "          'SEXIST':4\n",
        "          }"
      ],
      "metadata": {
        "id": "3fuQfynos_Sf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import torch\n",
        "import numpy as np\n",
        "from transformers import BertTokenizer\n",
        "class Dataset(torch.utils.data.Dataset):\n",
        "\n",
        "    def __init__(self, df):\n",
        "\n",
        "        self.labels = [labels[label] for label in df['target']]\n",
        "        self.texts = [tokenizer(text, \n",
        "                               padding='max_length', max_length = 512, truncation=True,\n",
        "                                return_tensors=\"pt\") for text in df['str_tkn_p_sw']]\n",
        "\n",
        "    def classes(self):\n",
        "        return self.labels\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "    def get_batch_labels(self, idx):\n",
        "        # Fetch a batch of labels\n",
        "        return np.array(self.labels[idx])\n",
        "\n",
        "    def get_batch_texts(self, idx):\n",
        "        # Fetch a batch of inputs\n",
        "        return self.texts[idx]\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "\n",
        "        batch_texts = self.get_batch_texts(idx)\n",
        "        batch_y = self.get_batch_labels(idx)\n",
        "\n",
        "        return batch_texts, batch_y"
      ],
      "metadata": {
        "id": "J44gNWjzt76S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(112)\n",
        "df_train, df_val, df_test = np.split(df.sample(frac=1, random_state=42), \n",
        "                                     [int(.8*len(df)), int(.9*len(df))])\n",
        "\n",
        "print(len(df_train),len(df_val), len(df_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DULO1btuuCZk",
        "outputId": "495b5237-5717-4d3b-e643-ac99a6913279"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9973 1247 1247\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import nn\n",
        "from transformers import BertModel\n",
        "\n",
        "class BertClassifier(nn.Module):\n",
        "\n",
        "    def __init__(self, dropout=0.5):\n",
        "\n",
        "        super(BertClassifier, self).__init__()\n",
        "\n",
        "        self.bert = BertModel.from_pretrained(\"dbmdz/bert-base-turkish-128k-uncased\")\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "        self.linear = nn.Linear(768, 5)\n",
        "        self.relu = nn.ReLU()\n",
        "\n",
        "    def forward(self, input_id, mask):\n",
        "\n",
        "        _, pooled_output = self.bert(input_ids= input_id, attention_mask=mask,return_dict=False)\n",
        "        dropout_output = self.dropout(pooled_output)\n",
        "        linear_output = self.linear(dropout_output)\n",
        "        final_layer = self.relu(linear_output)\n",
        "\n",
        "        return final_layer"
      ],
      "metadata": {
        "id": "z0YanVcMvALV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.optim import Adam\n",
        "from tqdm import tqdm\n",
        "\n",
        "def train(model, train_data, val_data, learning_rate, epochs):\n",
        "\n",
        "    train, val = Dataset(train_data), Dataset(val_data)\n",
        "\n",
        "    train_dataloader = torch.utils.data.DataLoader(train, batch_size=2, shuffle=True)\n",
        "    val_dataloader = torch.utils.data.DataLoader(val, batch_size=2)\n",
        "\n",
        "    use_cuda = torch.cuda.is_available()\n",
        "    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
        "\n",
        "    criterion = nn.CrossEntropyLoss()\n",
        "    optimizer = Adam(model.parameters(), lr= learning_rate)\n",
        "\n",
        "    if use_cuda:\n",
        "\n",
        "            model = model.cuda()\n",
        "            criterion = criterion.cuda()\n",
        "\n",
        "    for epoch_num in range(epochs):\n",
        "\n",
        "            total_acc_train = 0\n",
        "            total_loss_train = 0\n",
        "\n",
        "            for train_input, train_label in tqdm(train_dataloader):\n",
        "\n",
        "                train_label = train_label.to(device)\n",
        "                mask = train_input['attention_mask'].to(device)\n",
        "                input_id = train_input['input_ids'].squeeze(1).to(device)\n",
        "\n",
        "                output = model(input_id, mask)\n",
        "                \n",
        "                batch_loss = criterion(output, train_label.long())\n",
        "                total_loss_train += batch_loss.item()\n",
        "                \n",
        "                acc = (output.argmax(dim=1) == train_label).sum().item()\n",
        "                total_acc_train += acc\n",
        "\n",
        "                model.zero_grad()\n",
        "                batch_loss.backward()\n",
        "                optimizer.step()\n",
        "            \n",
        "            total_acc_val = 0\n",
        "            total_loss_val = 0\n",
        "\n",
        "            with torch.no_grad():\n",
        "\n",
        "                for val_input, val_label in val_dataloader:\n",
        "\n",
        "                    val_label = val_label.to(device)\n",
        "                    mask = val_input['attention_mask'].to(device)\n",
        "                    input_id = val_input['input_ids'].squeeze(1).to(device)\n",
        "\n",
        "                    output = model(input_id, mask)\n",
        "\n",
        "                    batch_loss = criterion(output, val_label.long())\n",
        "                    total_loss_val += batch_loss.item()\n",
        "                    \n",
        "                    acc = (output.argmax(dim=1) == val_label).sum().item()\n",
        "                    total_acc_val += acc\n",
        "            \n",
        "            print(\n",
        "                f'Epochs: {epoch_num + 1} | Train Loss: {total_loss_train / len(train_data): .3f} \\\n",
        "                | Train Accuracy: {total_acc_train / len(train_data): .3f} \\\n",
        "                | Val Loss: {total_loss_val / len(val_data): .3f} \\\n",
        "                | Val Accuracy: {total_acc_val / len(val_data): .3f}')\n",
        "                  \n",
        "EPOCHS = 5\n",
        "model = BertClassifier()\n",
        "LR = 1e-6\n",
        "              \n",
        "train(model, df_train, df_val, LR, EPOCHS)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295,
          "referenced_widgets": [
            "8aaee3d170d94fb48275ec543afb7968",
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            "697646746e0a49f9a4b508a67acddaf2",
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            "6b2bbd6cefa6447e960ce134762d0212"
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        },
        "id": "vGu1HKclvN6c",
        "outputId": "569f0449-37be-4f00-bf7d-1d4f265db550"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/740M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at dbmdz/bert-base-turkish-128k-uncased were not used when initializing BertModel: ['cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "100%|██████████| 4987/4987 [20:12<00:00,  4.11it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epochs: 1 | Train Loss:  0.515                 | Train Accuracy:  0.622                 | Val Loss:  0.229                 | Val Accuracy:  0.880\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4987/4987 [20:13<00:00,  4.11it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epochs: 2 | Train Loss:  0.150                 | Train Accuracy:  0.915                 | Val Loss:  0.152                 | Val Accuracy:  0.904\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4987/4987 [20:14<00:00,  4.11it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epochs: 3 | Train Loss:  0.083                 | Train Accuracy:  0.954                 | Val Loss:  0.158                 | Val Accuracy:  0.897\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4987/4987 [20:13<00:00,  4.11it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epochs: 4 | Train Loss:  0.048                 | Train Accuracy:  0.977                 | Val Loss:  0.139                 | Val Accuracy:  0.919\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4987/4987 [20:13<00:00,  4.11it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epochs: 5 | Train Loss:  0.027                 | Train Accuracy:  0.989                 | Val Loss:  0.154                 | Val Accuracy:  0.917\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate(model, test_data):\n",
        "\n",
        "    test = Dataset(test_data)\n",
        "\n",
        "    test_dataloader = torch.utils.data.DataLoader(test, batch_size=2)\n",
        "\n",
        "    use_cuda = torch.cuda.is_available()\n",
        "    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
        "\n",
        "    if use_cuda:\n",
        "\n",
        "        model = model.cuda()\n",
        "\n",
        "    total_acc_test = 0\n",
        "    output_indices = []\n",
        "    test_labels=[]\n",
        "    with torch.no_grad():\n",
        "\n",
        "        for test_input, test_label in test_dataloader:\n",
        "\n",
        "              test_label = test_label.to(device)\n",
        "              mask = test_input['attention_mask'].to(device)\n",
        "              input_id = test_input['input_ids'].squeeze(1).to(device)\n",
        "\n",
        "              output = model(input_id, mask)\n",
        "             \n",
        "              acc = (output.argmax(dim=1) == test_label).sum().item()\n",
        "              total_acc_test += acc\n",
        "\n",
        "              batch_indices = output.argmax(dim=1).tolist()\n",
        "              output_indices.extend(batch_indices)\n",
        "              test_labels.extend(test_label)\n",
        "\n",
        "    \n",
        "    print(f'Test Accuracy: {total_acc_test / len(test_data): .3f}')\n",
        "    return output_indices, test_labels\n",
        "y_pred,y_test=evaluate(model, df_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UuXLpmtAvrYt",
        "outputId": "12bf0a05-5f3a-4006-a607-6d22aa195fd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy:  0.928\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_tensor = torch.tensor(y_pred)\n",
        "y_test_tensor = torch.tensor(y_test)\n"
      ],
      "metadata": {
        "id": "fdRo3ptI8F32"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "report = classification_report(np.array(y_pred_tensor.cpu()), np.array(y_test_tensor.cpu()), output_dict=True)\n"
      ],
      "metadata": {
        "id": "XGNmRONhwk9Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "report"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YBksgKk23vvK",
        "outputId": "8bbca7a4-cc4e-4e05-bdc3-5c5754f1226d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'0': {'precision': 0.907258064516129,\n",
              "  'recall': 0.8333333333333334,\n",
              "  'f1-score': 0.8687258687258688,\n",
              "  'support': 270},\n",
              " '1': {'precision': 0.9058171745152355,\n",
              "  'recall': 0.956140350877193,\n",
              "  'f1-score': 0.930298719772404,\n",
              "  'support': 342},\n",
              " '2': {'precision': 0.919831223628692,\n",
              "  'recall': 0.9646017699115044,\n",
              "  'f1-score': 0.941684665226782,\n",
              "  'support': 226},\n",
              " '3': {'precision': 0.9731182795698925,\n",
              "  'recall': 0.9576719576719577,\n",
              "  'f1-score': 0.9653333333333334,\n",
              "  'support': 189},\n",
              " '4': {'precision': 0.958139534883721,\n",
              "  'recall': 0.9363636363636364,\n",
              "  'f1-score': 0.9471264367816091,\n",
              "  'support': 220},\n",
              " 'accuracy': 0.9278267842822775,\n",
              " 'macro avg': {'precision': 0.9328328554227341,\n",
              "  'recall': 0.929622209631525,\n",
              "  'f1-score': 0.9306338047679994,\n",
              "  'support': 1247},\n",
              " 'weighted avg': {'precision': 0.928100288818587,\n",
              "  'recall': 0.9278267842822775,\n",
              "  'f1-score': 0.9273093000411817,\n",
              "  'support': 1247}}"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(np.array(y_pred_tensor.cpu()), np.array(y_test_tensor.cpu()), output_dict=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9E81YUYe28WT",
        "outputId": "c3faffb2-4791-474e-f400-8141eb3df235"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'0': {'precision': 0.907258064516129, 'recall': 0.8333333333333334, 'f1-score': 0.8687258687258688, 'support': 270}, '1': {'precision': 0.9058171745152355, 'recall': 0.956140350877193, 'f1-score': 0.930298719772404, 'support': 342}, '2': {'precision': 0.919831223628692, 'recall': 0.9646017699115044, 'f1-score': 0.941684665226782, 'support': 226}, '3': {'precision': 0.9731182795698925, 'recall': 0.9576719576719577, 'f1-score': 0.9653333333333334, 'support': 189}, '4': {'precision': 0.958139534883721, 'recall': 0.9363636363636364, 'f1-score': 0.9471264367816091, 'support': 220}, 'accuracy': 0.9278267842822775, 'macro avg': {'precision': 0.9328328554227341, 'recall': 0.929622209631525, 'f1-score': 0.9306338047679994, 'support': 1247}, 'weighted avg': {'precision': 0.928100288818587, 'recall': 0.9278267842822775, 'f1-score': 0.9273093000411817, 'support': 1247}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def conf_matrix(y_test,y_pred):\n",
        "  cm = confusion_matrix(y_test,y_pred, normalize=\"true\")\n",
        "  sns.heatmap(cm, annot=True, cmap=\"Blues\",xticklabels=[\"INSULT\",\"OTHER\",\"PROFANITY\",\"RACIST\",\"SECIST\"],yticklabels=[\"INSULT\",\"OTHER\",\"PROFANITY\",\"RACIST\",\"SECIST\"] )\n",
        "  plt.xlabel('Tahmin Edilen Sınıf')\n",
        "  plt.ylabel('Gerçek Sınıf')\n",
        "  plt.show()\n",
        "conf_matrix(np.array(y_pred_tensor.cpu()), np.array(y_test_tensor.cpu()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "p-p4oK_A8Wkt",
        "outputId": "08dd5114-421f-4190-a47d-9750d8c8a117"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "f1_score(np.array(y_test_tensor.cpu()),np.array(y_pred_tensor.cpu()), average='macro')"
      ],
      "metadata": {
        "id": "C8i7SJIJ8jBe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04e9a25a-9178-4cc3-bd19-a0fc7183c2de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9306338047679994"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Nk2zmAmj7Ko8"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}