1810 lines
949 KiB
Plaintext
1810 lines
949 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "lL1VU3_RbjFM",
|
||
"outputId": "85bb8227-b9c3-4f2b-8023-84994e00f573"
|
||
},
|
||
"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 tensorflow-text\n",
|
||
" Downloading tensorflow_text-2.12.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.0 MB)\n",
|
||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.0/6.0 MB\u001b[0m \u001b[31m35.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||
"\u001b[?25hRequirement already satisfied: tensorflow-hub>=0.8.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow-text) (0.13.0)\n",
|
||
"Requirement already satisfied: tensorflow<2.13,>=2.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow-text) (2.12.0)\n",
|
||
"Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (1.4.0)\n",
|
||
"Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (1.6.3)\n",
|
||
"Requirement already satisfied: flatbuffers>=2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (23.3.3)\n",
|
||
"Requirement already satisfied: gast<=0.4.0,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (0.4.0)\n",
|
||
"Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (0.2.0)\n",
|
||
"Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (1.54.0)\n",
|
||
"Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (3.8.0)\n",
|
||
"Requirement already satisfied: jax>=0.3.15 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (0.4.10)\n",
|
||
"Requirement already satisfied: keras<2.13,>=2.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (2.12.0)\n",
|
||
"Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (16.0.0)\n",
|
||
"Requirement already satisfied: numpy<1.24,>=1.22 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (1.22.4)\n",
|
||
"Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (3.3.0)\n",
|
||
"Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (23.1)\n",
|
||
"Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (3.20.3)\n",
|
||
"Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (67.7.2)\n",
|
||
"Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (1.16.0)\n",
|
||
"Requirement already satisfied: tensorboard<2.13,>=2.12 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (2.12.2)\n",
|
||
"Requirement already satisfied: tensorflow-estimator<2.13,>=2.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (2.12.0)\n",
|
||
"Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (2.3.0)\n",
|
||
"Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (4.5.0)\n",
|
||
"Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (1.14.1)\n",
|
||
"Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.13,>=2.12.0->tensorflow-text) (0.32.0)\n",
|
||
"Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow<2.13,>=2.12.0->tensorflow-text) (0.40.0)\n",
|
||
"Requirement already satisfied: ml-dtypes>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from jax>=0.3.15->tensorflow<2.13,>=2.12.0->tensorflow-text) (0.1.0)\n",
|
||
"Requirement already satisfied: scipy>=1.7 in /usr/local/lib/python3.10/dist-packages (from jax>=0.3.15->tensorflow<2.13,>=2.12.0->tensorflow-text) (1.10.1)\n",
|
||
"Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (2.17.3)\n",
|
||
"Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (1.0.0)\n",
|
||
"Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (3.4.3)\n",
|
||
"Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (2.27.1)\n",
|
||
"Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (0.7.0)\n",
|
||
"Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (1.8.1)\n",
|
||
"Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (2.3.0)\n",
|
||
"Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (5.3.0)\n",
|
||
"Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (0.3.0)\n",
|
||
"Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (4.9)\n",
|
||
"Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (1.3.1)\n",
|
||
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (1.26.15)\n",
|
||
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (2022.12.7)\n",
|
||
"Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (2.0.12)\n",
|
||
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (3.4)\n",
|
||
"Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (2.1.2)\n",
|
||
"Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (0.5.0)\n",
|
||
"Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (3.2.2)\n",
|
||
"Installing collected packages: tensorflow-text\n",
|
||
"Successfully installed tensorflow-text-2.12.1\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"!pip install tensorflow-text"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "OBx_2AmVQ51R",
|
||
"outputId": "76f21b88-d568-4348-ca9d-cec16e44bff1"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Mounted at /content/drive\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from google.colab import drive\n",
|
||
"drive.mount('/content/drive')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 206
|
||
},
|
||
"id": "jxbcGuCnb5Ji",
|
||
"outputId": "9fa970e3-3087-45c2-fec1-3743378be36b"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
" Text Class\n",
|
||
"0 I am a Full-Stack Web Developer who is very in... 0\n",
|
||
"1 A person who passionate about software develop... 0\n",
|
||
"2 Hi, saya adalah seorang web developer saya men... 0\n",
|
||
"3 Introducing, a Fresh Graduate of Associate deg... 0\n",
|
||
"4 Hi, my name is Octavian Yudha Mahendra, you ca... 2"
|
||
],
|
||
"text/html": [
|
||
"\n",
|
||
" <div id=\"df-ebd2acd8-114b-44a9-8232-1f44fe15b785\">\n",
|
||
" <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",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Text</th>\n",
|
||
" <th>Class</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>I am a Full-Stack Web Developer who is very in...</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>A person who passionate about software develop...</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Hi, saya adalah seorang web developer saya men...</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Introducing, a Fresh Graduate of Associate deg...</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Hi, my name is Octavian Yudha Mahendra, you ca...</td>\n",
|
||
" <td>2</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ebd2acd8-114b-44a9-8232-1f44fe15b785')\"\n",
|
||
" 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-ebd2acd8-114b-44a9-8232-1f44fe15b785 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-ebd2acd8-114b-44a9-8232-1f44fe15b785');\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": 6
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"df = pd.read_csv('/content/drive/MyDrive/linkedIn4.csv', sep=';')\n",
|
||
"df = df.dropna(axis=1)\n",
|
||
"df.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "33oSW3pU5hro"
|
||
},
|
||
"source": [
|
||
"Kode di ini akan membuat plot horizontal bar yang menampilkan jumlah dan persentase ulasan untuk setiap kelas dalam kolom ‘Class’ dari dataset yang kita punya."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 777
|
||
},
|
||
"id": "2KumwLPAcLn0",
|
||
"outputId": "08289818-8ac9-4e77-92fc-1fa831120d53"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 900x900 with 1 Axes>"
|
||
],
|
||
"image/png": "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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
],
|
||
"source": [
|
||
"# Mengimport modul numpy dan matplotlib.pyplot, dan mengatur gaya plot ke ggplot\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"plt.style.use('ggplot')\n",
|
||
"\n",
|
||
"# Menghitung jumlah kelas yang berbeda pada kolom \"Class\" pada dataset kamu\n",
|
||
"num_classes = len(df[\"Class\"].value_counts())\n",
|
||
"\n",
|
||
"# Membuat array warna berdasarkan jumlah kelas yang berbeda\n",
|
||
"colors = plt.cm.Dark2(np.linspace(0, 1, num_classes))\n",
|
||
"\n",
|
||
"# Menginisialisasi variabel iter_color untuk mengiterasi setiap warna pada array warna\n",
|
||
"iter_color = iter(colors)\n",
|
||
"\n",
|
||
"# Membuat diagram batang horizontal berdasarkan jumlah text atau profil pelamar kerja yang termasuk ke dalam masing-masing kelas pada kolom \"Class\" pada dataset kamu\n",
|
||
"df['Class'].value_counts().plot.barh(title=\"Reviews for each text (n, %)\",\n",
|
||
" ylabel=\"Text\",\n",
|
||
" color=colors,\n",
|
||
" figsize=(9,9))\n",
|
||
"\n",
|
||
"# Menambahkan teks pada setiap batang diagram yang menunjukkan jumlah text atau profil pelamar kerja pada kelas tertentu\n",
|
||
"for i, v in enumerate(df['Class'].value_counts()):\n",
|
||
" c = next(iter_color)\n",
|
||
" plt.text(v, i,\n",
|
||
" \" \"+str(v)+\", \"+str(round(v*100/df.shape[0],2))+\"%\",\n",
|
||
" color=c,\n",
|
||
" va='center',\n",
|
||
" fontweight='bold')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "PCtrZKhYG8ty"
|
||
},
|
||
"source": [
|
||
"Output yang dihasilkan oleh kode tersebut adalah diagram batang horizontal yang menunjukkan jumlah text atau profil pelamar kerja yang termasuk ke dalam masing-masing kelas pada kolom \"Class\" pada dataset. Setiap batang diagram memiliki teks di sebelah kanan batang yang menunjukkan jumlah text atau profil pelamar kerja pada kelas tertentu dalam bentuk angka dan persentase. Dari diagram tersebut, dapat melihat distribusi text atau profil pelamar kerja pada setiap kelas."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "wMcna_C3eIJy",
|
||
"outputId": "3046a558-7fa3-462a-8838-bb37e0d814ee"
|
||
},
|
||
"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 translate\n",
|
||
" Downloading translate-3.6.1-py2.py3-none-any.whl (12 kB)\n",
|
||
"Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from translate) (8.1.3)\n",
|
||
"Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from translate) (4.9.2)\n",
|
||
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from translate) (2.27.1)\n",
|
||
"Collecting libretranslatepy==2.1.1 (from translate)\n",
|
||
" Downloading libretranslatepy-2.1.1-py3-none-any.whl (3.2 kB)\n",
|
||
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->translate) (1.26.15)\n",
|
||
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->translate) (2022.12.7)\n",
|
||
"Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->translate) (2.0.12)\n",
|
||
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->translate) (3.4)\n",
|
||
"Installing collected packages: libretranslatepy, translate\n",
|
||
"Successfully installed libretranslatepy-2.1.1 translate-3.6.1\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"!pip install translate"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "bm1hDTdF5-hb"
|
||
},
|
||
"source": [
|
||
"Kode ini akan mencetak contoh acak dari teks dalam kolom ‘Text’ yang memiliki nilai ‘Class’ sama dengan 0 dan terjemahannya dalam bahasa Inggris. Pastikan untuk mengganti df dengan nama variabel yang sesuai untuk dataset Anda dan menginstal pustaka yang diperlukan (translate dan termcolor) sebelum menjalankan kode tersebut."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "wuuvVfVtdM0z",
|
||
"outputId": "3d8b9ad7-0a03-40b9-8394-ba16a7295669"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"\n",
|
||
"Original\n",
|
||
"I really enjoy learning about the latest technology. Currently, I'm learning web development using express.js as Back-End and Next.js as Front-End. And I'm also learning about mobi...\n",
|
||
"\n",
|
||
"Translation\n",
|
||
"Saya benar - benar menikmati belajar tentang teknologi terbaru. Saat ini, saya belajar pengembangan web menggunakan express.js sebagai Back - End dan Next.js sebagai Front - End. D...\n",
|
||
"\n",
|
||
"Original\n",
|
||
"Hello my name is Dito, I am an iOS Mobile Developer (Native) Using Swift and also someone who is studying the back end of using Golang. Currently I'm hoping to get the opportunity ...\n",
|
||
"\n",
|
||
"Translation\n",
|
||
"Halo nama saya Dito, saya seorang iOS Mobile Developer (Native) Menggunakan Swift dan juga seseorang yang sedang mempelajari back end menggunakan Golang. Saat ini saya berharap men...\n",
|
||
"\n",
|
||
"Original\n",
|
||
"IÕam a UI/UX Designer in DANA Indonesia. Handling Payment Project : Send Money, Request Money, DANA Kaget, Split Bill, Cashout....\n",
|
||
"\n",
|
||
"Translation\n",
|
||
"Saya adalah UI/UX Designer di DANA Indonesia. Penanganan Pembayaran Proyek : Kirim Uang, Minta Uang, DANA Kaget, Split Bill, Cashout....\n",
|
||
"\n",
|
||
"Original\n",
|
||
"Experienced System Administrator with a demonstrated history of working in the online media industry. Skilled in Python (Programming Language), Linux System Administration, ELK, Do...\n",
|
||
"\n",
|
||
"Translation\n",
|
||
"Administrator Sistem yang Berpengalaman dengan sejarah yang terbukti bekerja di industri media online. Terampil dalam Python (Bahasa Pemrograman), Linux System Administration, ELK,...\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from translate import Translator\n",
|
||
"from termcolor import colored\n",
|
||
"\n",
|
||
"translator= Translator(from_lang=\"en\", to_lang=\"id\")\n",
|
||
"\n",
|
||
"def print_rand_example(df, col_name, col_value, chars=180):\n",
|
||
" '''print a random review and its translation given a label\n",
|
||
" Args:\n",
|
||
" - df: input dataframe\n",
|
||
" - col_name: column to use as filter (e.g. Label)\n",
|
||
" - col_value: value of col_name to use as filter\n",
|
||
" - chars (optional, def:180) max number of characters to display\n",
|
||
" '''\n",
|
||
" original = df[df[col_name]==col_value].sample()[\"Text\"].values[0]\n",
|
||
" translation = translator.translate(original).replace(\"'\",\"'\")\n",
|
||
" print(colored(\"\\nOriginal\", 'green', attrs=['bold','underline']))\n",
|
||
" print(original[0:chars] + \"...\")\n",
|
||
" print(colored(\"\\nTranslation\", 'red', attrs=['bold','underline']))\n",
|
||
" print(translation[0:chars] + \"...\")\n",
|
||
"\n",
|
||
"# contoh penggunaan fungsi print_rand_example\n",
|
||
"print_rand_example(df, 'Class', 0)\n",
|
||
"print_rand_example(df, 'Class', 1)\n",
|
||
"print_rand_example(df, 'Class', 2)\n",
|
||
"print_rand_example(df, 'Class', 3)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "TdBJ9wCl6Iio"
|
||
},
|
||
"source": [
|
||
"Kode ini akan membagi dataset Anda menjadi data latih dan data uji dengan proporsi 75:25. Kolom ‘Text’ akan digunakan sebagai fitur dan kolom ‘Class’ akan digunakan sebagai label"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "k5t1BBAfet61",
|
||
"outputId": "4b52c398-ada3-4c2e-cd01-86087a81a41f"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Jumlah data pada data training: 164\n",
|
||
"Jumlah data pada data testing: 55\n",
|
||
"Jumlah label pada data training: 164\n",
|
||
"Jumlah label pada data testing: 55\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import tensorflow as tf\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"\n",
|
||
"num_classes = len(df[\"Class\"].value_counts())\n",
|
||
"y = tf.keras.utils.to_categorical(df[\"Class\"].values, num_classes=num_classes)\n",
|
||
"\n",
|
||
"x_train, x_test, y_train, y_test = train_test_split(df['Text'], y, test_size=0.25)\n",
|
||
"\n",
|
||
"print(\"Jumlah data pada data training:\", len(x_train))\n",
|
||
"print(\"Jumlah data pada data testing:\", len(x_test))\n",
|
||
"print(\"Jumlah label pada data training:\", len(y_train))\n",
|
||
"print(\"Jumlah label pada data testing:\", len(y_test))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "l50Z-vUx6wuj"
|
||
},
|
||
"source": [
|
||
"Yang ingin saya capai adalah mengubah teks menjadi vektor berdimensi tinggi yang menangkap semantik tingkat kalimat. Oleh karena itu, saya melanjutkan dengan memuat lapisan preprocessor dan encoder dari titik akhir yang disediakan oleh TensorFlow Hub, dan menentukan fungsi sederhana untuk mendapatkan penyematan dari teks masukan.\n",
|
||
"\n",
|
||
"Hal yang penting adalah bahwa seseorang dapat memilih untuk mengimpor model preferensi apa pun tergantung pada tugas dan bahasa masukan.\n",
|
||
"\n",
|
||
"Karena model ini didasarkan pada arsitektur transformator BERT, model ini akan menghasilkan pooled_output (penyematan keluaran dari seluruh urutan) bentuk [ukuran batch, 768]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "tpcqDb4Qe6ur",
|
||
"outputId": "8731b81b-7a94-4a41-b818-3685c59e53a3"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"tf.Tensor(\n",
|
||
"[[-0.22663741 -0.14343746 -0.32012492 ... -0.04661513 -0.06341164\n",
|
||
" 0.10188575]\n",
|
||
" [-0.04950407 0.00416472 0.09135172 ... -0.3943716 -0.13816215\n",
|
||
" -0.02260007]\n",
|
||
" [-0.37836957 -0.18587808 -0.27618733 ... -0.14031428 0.08200935\n",
|
||
" -0.372581 ]\n",
|
||
" ...\n",
|
||
" [ 0.17531103 -0.21574299 -0.06876095 ... -0.0851216 0.1194027\n",
|
||
" 0.11596426]\n",
|
||
" [ 0.07690524 -0.23456529 -0.3573915 ... 0.18296139 -0.2959927\n",
|
||
" 0.14599116]\n",
|
||
" [-0.25802502 -0.28717923 -0.24516018 ... -0.34850487 -0.28197345\n",
|
||
" -0.33899269]], shape=(219, 768), dtype=float32)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import tensorflow_hub as hub\n",
|
||
"import tensorflow_text as text\n",
|
||
"\n",
|
||
"preprocessor = hub.KerasLayer(\"https://tfhub.dev/google/universal-sentence-encoder-cmlm/multilingual-preprocess/2\", trainable=False)\n",
|
||
"encoder = hub.KerasLayer(\"https://tfhub.dev/google/universal-sentence-encoder-cmlm/multilingual-base/1\", trainable=False)\n",
|
||
"model = hub.load(\"https://tfhub.dev/google/universal-sentence-encoder-cmlm/multilingual-base/1\")\n",
|
||
"\n",
|
||
"def get_embeddings(sentences):\n",
|
||
" '''return BERT-like embeddings of input text\n",
|
||
" Args:\n",
|
||
" - sentences: list of strings\n",
|
||
" Output:\n",
|
||
" - BERT-like embeddings: tf.Tensor of shape=(len(sentences), 768)\n",
|
||
" '''\n",
|
||
" preprocessed_text = preprocessor(sentences)\n",
|
||
" return encoder(preprocessed_text)['pooled_output']\n",
|
||
"\n",
|
||
"embeddings = get_embeddings(df[\"Text\"].tolist())\n",
|
||
"print(embeddings)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "7LNDDgkER604",
|
||
"outputId": "bf7bf0e7-1ce7-4731-bcf0-e251b3ad29fa"
|
||
},
|
||
"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-4.29.2-py3-none-any.whl (7.1 MB)\n",
|
||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m63.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||
"\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.0)\n",
|
||
"Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n",
|
||
" Downloading huggingface_hub-0.14.1-py3-none-any.whl (224 kB)\n",
|
||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m26.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||
"\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
|
||
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
|
||
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0)\n",
|
||
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2022.10.31)\n",
|
||
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n",
|
||
"Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
|
||
" Downloading tokenizers-0.13.3-cp310-cp310-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[31m83.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||
"\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n",
|
||
"Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.4.0)\n",
|
||
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.5.0)\n",
|
||
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.15)\n",
|
||
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2022.12.7)\n",
|
||
"Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.12)\n",
|
||
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
|
||
"Installing collected packages: tokenizers, huggingface-hub, transformers\n",
|
||
"Successfully installed huggingface-hub-0.14.1 tokenizers-0.13.3 transformers-4.29.2\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"!pip install transformers"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "rUu782sT69z7",
|
||
"outputId": "7fc5415d-c8b3-4918-cda1-e406cd0c5db9"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"In layer word_embeddings/embeddings:0\n",
|
||
"In layer position_embedding/embeddings:0\n",
|
||
"In layer type_embeddings/embeddings:0\n",
|
||
"In layer embeddings/layer_norm/gamma:0\n",
|
||
"In layer embeddings/layer_norm/beta:0\n",
|
||
"In layer transformer/layer_0/self_attention/query/kernel:0\n",
|
||
"In layer transformer/layer_0/self_attention/query/bias:0\n",
|
||
"In layer transformer/layer_0/self_attention/key/kernel:0\n",
|
||
"In layer transformer/layer_0/self_attention/key/bias:0\n",
|
||
"In layer transformer/layer_0/self_attention/value/kernel:0\n",
|
||
"In layer transformer/layer_0/self_attention/value/bias:0\n",
|
||
"In layer transformer/layer_0/self_attention/attention_output/kernel:0\n",
|
||
"In layer transformer/layer_0/self_attention/attention_output/bias:0\n",
|
||
"In layer transformer/layer_0/self_attention_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_0/self_attention_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_0/intermediate/kernel:0\n",
|
||
"In layer transformer/layer_0/intermediate/bias:0\n",
|
||
"In layer transformer/layer_0/output/kernel:0\n",
|
||
"In layer transformer/layer_0/output/bias:0\n",
|
||
"In layer transformer/layer_0/output_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_0/output_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_1/self_attention/query/kernel:0\n",
|
||
"In layer transformer/layer_1/self_attention/query/bias:0\n",
|
||
"In layer transformer/layer_1/self_attention/key/kernel:0\n",
|
||
"In layer transformer/layer_1/self_attention/key/bias:0\n",
|
||
"In layer transformer/layer_1/self_attention/value/kernel:0\n",
|
||
"In layer transformer/layer_1/self_attention/value/bias:0\n",
|
||
"In layer transformer/layer_1/self_attention/attention_output/kernel:0\n",
|
||
"In layer transformer/layer_1/self_attention/attention_output/bias:0\n",
|
||
"In layer transformer/layer_1/self_attention_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_1/self_attention_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_1/intermediate/kernel:0\n",
|
||
"In layer transformer/layer_1/intermediate/bias:0\n",
|
||
"In layer transformer/layer_1/output/kernel:0\n",
|
||
"In layer transformer/layer_1/output/bias:0\n",
|
||
"In layer transformer/layer_1/output_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_1/output_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_2/self_attention/query/kernel:0\n",
|
||
"In layer transformer/layer_2/self_attention/query/bias:0\n",
|
||
"In layer transformer/layer_2/self_attention/key/kernel:0\n",
|
||
"In layer transformer/layer_2/self_attention/key/bias:0\n",
|
||
"In layer transformer/layer_2/self_attention/value/kernel:0\n",
|
||
"In layer transformer/layer_2/self_attention/value/bias:0\n",
|
||
"In layer transformer/layer_2/self_attention/attention_output/kernel:0\n",
|
||
"In layer transformer/layer_2/self_attention/attention_output/bias:0\n",
|
||
"In layer transformer/layer_2/self_attention_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_2/self_attention_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_2/intermediate/kernel:0\n",
|
||
"In layer transformer/layer_2/intermediate/bias:0\n",
|
||
"In layer transformer/layer_2/output/kernel:0\n",
|
||
"In layer transformer/layer_2/output/bias:0\n",
|
||
"In layer transformer/layer_2/output_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_2/output_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_3/self_attention/query/kernel:0\n",
|
||
"In layer transformer/layer_3/self_attention/query/bias:0\n",
|
||
"In layer transformer/layer_3/self_attention/key/kernel:0\n",
|
||
"In layer transformer/layer_3/self_attention/key/bias:0\n",
|
||
"In layer transformer/layer_3/self_attention/value/kernel:0\n",
|
||
"In layer transformer/layer_3/self_attention/value/bias:0\n",
|
||
"In layer transformer/layer_3/self_attention/attention_output/kernel:0\n",
|
||
"In layer transformer/layer_3/self_attention/attention_output/bias:0\n",
|
||
"In layer transformer/layer_3/self_attention_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_3/self_attention_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_3/intermediate/kernel:0\n",
|
||
"In layer transformer/layer_3/intermediate/bias:0\n",
|
||
"In layer transformer/layer_3/output/kernel:0\n",
|
||
"In layer transformer/layer_3/output/bias:0\n",
|
||
"In layer transformer/layer_3/output_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_3/output_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_4/self_attention/query/kernel:0\n",
|
||
"In layer transformer/layer_4/self_attention/query/bias:0\n",
|
||
"In layer transformer/layer_4/self_attention/key/kernel:0\n",
|
||
"In layer transformer/layer_4/self_attention/key/bias:0\n",
|
||
"In layer transformer/layer_4/self_attention/value/kernel:0\n",
|
||
"In layer transformer/layer_4/self_attention/value/bias:0\n",
|
||
"In layer transformer/layer_4/self_attention/attention_output/kernel:0\n",
|
||
"In layer transformer/layer_4/self_attention/attention_output/bias:0\n",
|
||
"In layer transformer/layer_4/self_attention_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_4/self_attention_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_4/intermediate/kernel:0\n",
|
||
"In layer transformer/layer_4/intermediate/bias:0\n",
|
||
"In layer transformer/layer_4/output/kernel:0\n",
|
||
"In layer transformer/layer_4/output/bias:0\n",
|
||
"In layer transformer/layer_4/output_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_4/output_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_5/self_attention/query/kernel:0\n",
|
||
"In layer transformer/layer_5/self_attention/query/bias:0\n",
|
||
"In layer transformer/layer_5/self_attention/key/kernel:0\n",
|
||
"In layer transformer/layer_5/self_attention/key/bias:0\n",
|
||
"In layer transformer/layer_5/self_attention/value/kernel:0\n",
|
||
"In layer transformer/layer_5/self_attention/value/bias:0\n",
|
||
"In layer transformer/layer_5/self_attention/attention_output/kernel:0\n",
|
||
"In layer transformer/layer_5/self_attention/attention_output/bias:0\n",
|
||
"In layer transformer/layer_5/self_attention_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_5/self_attention_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_5/intermediate/kernel:0\n",
|
||
"In layer transformer/layer_5/intermediate/bias:0\n",
|
||
"In layer transformer/layer_5/output/kernel:0\n",
|
||
"In layer transformer/layer_5/output/bias:0\n",
|
||
"In layer transformer/layer_5/output_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_5/output_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_6/self_attention/query/kernel:0\n",
|
||
"In layer transformer/layer_6/self_attention/query/bias:0\n",
|
||
"In layer transformer/layer_6/self_attention/key/kernel:0\n",
|
||
"In layer transformer/layer_6/self_attention/key/bias:0\n",
|
||
"In layer transformer/layer_6/self_attention/value/kernel:0\n",
|
||
"In layer transformer/layer_6/self_attention/value/bias:0\n",
|
||
"In layer transformer/layer_6/self_attention/attention_output/kernel:0\n",
|
||
"In layer transformer/layer_6/self_attention/attention_output/bias:0\n",
|
||
"In layer transformer/layer_6/self_attention_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_6/self_attention_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_6/intermediate/kernel:0\n",
|
||
"In layer transformer/layer_6/intermediate/bias:0\n",
|
||
"In layer transformer/layer_6/output/kernel:0\n",
|
||
"In layer transformer/layer_6/output/bias:0\n",
|
||
"In layer transformer/layer_6/output_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_6/output_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_7/self_attention/query/kernel:0\n",
|
||
"In layer transformer/layer_7/self_attention/query/bias:0\n",
|
||
"In layer transformer/layer_7/self_attention/key/kernel:0\n",
|
||
"In layer transformer/layer_7/self_attention/key/bias:0\n",
|
||
"In layer transformer/layer_7/self_attention/value/kernel:0\n",
|
||
"In layer transformer/layer_7/self_attention/value/bias:0\n",
|
||
"In layer transformer/layer_7/self_attention/attention_output/kernel:0\n",
|
||
"In layer transformer/layer_7/self_attention/attention_output/bias:0\n",
|
||
"In layer transformer/layer_7/self_attention_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_7/self_attention_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_7/intermediate/kernel:0\n",
|
||
"In layer transformer/layer_7/intermediate/bias:0\n",
|
||
"In layer transformer/layer_7/output/kernel:0\n",
|
||
"In layer transformer/layer_7/output/bias:0\n",
|
||
"In layer transformer/layer_7/output_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_7/output_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_8/self_attention/query/kernel:0\n",
|
||
"In layer transformer/layer_8/self_attention/query/bias:0\n",
|
||
"In layer transformer/layer_8/self_attention/key/kernel:0\n",
|
||
"In layer transformer/layer_8/self_attention/key/bias:0\n",
|
||
"In layer transformer/layer_8/self_attention/value/kernel:0\n",
|
||
"In layer transformer/layer_8/self_attention/value/bias:0\n",
|
||
"In layer transformer/layer_8/self_attention/attention_output/kernel:0\n",
|
||
"In layer transformer/layer_8/self_attention/attention_output/bias:0\n",
|
||
"In layer transformer/layer_8/self_attention_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_8/self_attention_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_8/intermediate/kernel:0\n",
|
||
"In layer transformer/layer_8/intermediate/bias:0\n",
|
||
"In layer transformer/layer_8/output/kernel:0\n",
|
||
"In layer transformer/layer_8/output/bias:0\n",
|
||
"In layer transformer/layer_8/output_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_8/output_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_9/self_attention/query/kernel:0\n",
|
||
"In layer transformer/layer_9/self_attention/query/bias:0\n",
|
||
"In layer transformer/layer_9/self_attention/key/kernel:0\n",
|
||
"In layer transformer/layer_9/self_attention/key/bias:0\n",
|
||
"In layer transformer/layer_9/self_attention/value/kernel:0\n",
|
||
"In layer transformer/layer_9/self_attention/value/bias:0\n",
|
||
"In layer transformer/layer_9/self_attention/attention_output/kernel:0\n",
|
||
"In layer transformer/layer_9/self_attention/attention_output/bias:0\n",
|
||
"In layer transformer/layer_9/self_attention_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_9/self_attention_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_9/intermediate/kernel:0\n",
|
||
"In layer transformer/layer_9/intermediate/bias:0\n",
|
||
"In layer transformer/layer_9/output/kernel:0\n",
|
||
"In layer transformer/layer_9/output/bias:0\n",
|
||
"In layer transformer/layer_9/output_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_9/output_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_10/self_attention/query/kernel:0\n",
|
||
"In layer transformer/layer_10/self_attention/query/bias:0\n",
|
||
"In layer transformer/layer_10/self_attention/key/kernel:0\n",
|
||
"In layer transformer/layer_10/self_attention/key/bias:0\n",
|
||
"In layer transformer/layer_10/self_attention/value/kernel:0\n",
|
||
"In layer transformer/layer_10/self_attention/value/bias:0\n",
|
||
"In layer transformer/layer_10/self_attention/attention_output/kernel:0\n",
|
||
"In layer transformer/layer_10/self_attention/attention_output/bias:0\n",
|
||
"In layer transformer/layer_10/self_attention_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_10/self_attention_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_10/intermediate/kernel:0\n",
|
||
"In layer transformer/layer_10/intermediate/bias:0\n",
|
||
"In layer transformer/layer_10/output/kernel:0\n",
|
||
"In layer transformer/layer_10/output/bias:0\n",
|
||
"In layer transformer/layer_10/output_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_10/output_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_11/self_attention/query/kernel:0\n",
|
||
"In layer transformer/layer_11/self_attention/query/bias:0\n",
|
||
"In layer transformer/layer_11/self_attention/key/kernel:0\n",
|
||
"In layer transformer/layer_11/self_attention/key/bias:0\n",
|
||
"In layer transformer/layer_11/self_attention/value/kernel:0\n",
|
||
"In layer transformer/layer_11/self_attention/value/bias:0\n",
|
||
"In layer transformer/layer_11/self_attention/attention_output/kernel:0\n",
|
||
"In layer transformer/layer_11/self_attention/attention_output/bias:0\n",
|
||
"In layer transformer/layer_11/self_attention_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_11/self_attention_layer_norm/beta:0\n",
|
||
"In layer transformer/layer_11/intermediate/kernel:0\n",
|
||
"In layer transformer/layer_11/intermediate/bias:0\n",
|
||
"In layer transformer/layer_11/output/kernel:0\n",
|
||
"In layer transformer/layer_11/output/bias:0\n",
|
||
"In layer transformer/layer_11/output_layer_norm/gamma:0\n",
|
||
"In layer transformer/layer_11/output_layer_norm/beta:0\n",
|
||
"In layer pooler_transform/kernel:0\n",
|
||
"In layer pooler_transform/bias:0\n",
|
||
"In layer Variable:0\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"for i in range(len(encoder.weights)):\n",
|
||
" print(\"In layer \",encoder.weights[i].name)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "P9wldKmIHj8T"
|
||
},
|
||
"source": [
|
||
"Output yang diberikan adalah representasi vektor teks untuk setiap kalimat dalam dataset, dengan bentuk (119, 768), di mana 119 adalah jumlah kalimat dalam dataset, dan 768 adalah dimensi dari setiap vektor representasi."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "fLkS4HGKIScS"
|
||
},
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 982
|
||
},
|
||
"id": "l2Ad6ZwufX95",
|
||
"outputId": "06164e09-4186-4e97-e8c1-8ba9c15585e2"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
],
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAABxoAAAa9CAYAAADnuE0hAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3gUVdvH8V8SSKWGThI6hBogBAgIIiH0GqSEEqooUlRsDyiPryI+CGJBihSRDlITiqAUlSKhgxQp0iFAqEkIaaS8f8RdWXYTsoEQid/PdeUSZ2bPnJk5OzM799zn2KSkpKQIAAAAAAAAAAAAAKxgm90VAAAAAAAAAAAAAPDsIdAIAAAAAAAAAAAAwGoEGgEAAAAAAAAAAABYjUAjAAAAAAAAAAAAAKsRaAQAAAAAAAAAAABgNQKNAAAAAAAAAAAAAKxGoBEAAAAAAAAAAACA1Qg0AgAAAAAAAAAAALAagUYAAAAAAAAAAAAAViPQCABI16pVq+Tp6alVq1Zld1VyrJEjR8rT01OXL1/OsnV4enoqKCgoy8qXJD8/P/n5+ZlMe1rt5/Lly/L09NTIkSOzdD0AAAAAAAAA/pYruysAAE9TUlKSVq5cqTVr1ujUqVO6d++e8uXLp8KFC8vLy0t+fn5q1qxZdlfzqdq9e7f69OmjYcOGafjw4U9lnatWrdKoUaOs+szJkyezpC4jR45UcHCwtmzZInd39yxZR1Y4fPiw5s2bpwMHDujGjRuyt7eXq6urKlSooDp16qhXr15ydnbO7mpmu2f1+AIAAAAAAADPAgKNAP41kpKS9Morr2j79u3Kly+fmjRpouLFi+v+/fs6ffq01q1bp7Nnz/7rAo2P0rx5c9WsWVNFixZ9YmVWqVJFw4YNM5kWFham4OBgubm5KSAg4ImtKydavXq1Ro4cqZSUFPn6+srf31+Ojo66cuWK9u/fr19++UUtWrRQ6dKljZ9Zv369nJycsrRec+fOzdLy01OsWDGtX79eefPmzbY6AAAAAAAAAP82BBoB/GusW7dO27dvV+XKlbVw4UKzgERsbKx+//33bKrdP1fevHmfePCmSpUqqlKlism03bt3GwONTyuz8lkUGxurMWPGyMbGRt99950aNGhgtsyBAwdUsGBBk2nly5fP8rqVKlUqy9eRlty5cz+VbQQAAAAAAADwN8ZoBPCvcfDgQUlSQECAxcCZk5OTfH19LX523bp1CgoKko+Pj2rUqKHWrVtr2rRpSkhIMFvWMBbezZs3NWrUKDVs2FC1atVSYGCg9u3bJ0mKiYnR+PHj1bRpU1WvXl1t27bVhg0bzMq6e/euvv32W/Xp00fPP/+8qlevLl9fXw0ePNi4PWmt//bt2/rvf/+rRo0aGdexcuVKk2VHjhypPn36SJKmTJkiT09P49/u3bslpT/G3rVr1zR27Fi1aNFCXl5eqlevnrp06aKpU6darFtmJSYmatGiRerWrZu8vb1Vs2ZNderUSQsXLlRycrJxuUuXLsnHx0f16tVTWFiYSRkxMTFq3bq1qlSpYtw2T09PBQcHS5KaNWtm3PYHxxm0NO6gweTJk032lcHmzZv19ttvq2XLlqpVq5Zq1aqlzp07a/78+Sb1zYw///xT0dHRqlixosUgoyR5e3srX758JtMsjdH4YP3XrVunzp07q2bNmmrUqJHGjRtnbN+hoaEKCgqSt7e36tatq3feeUd37twxW296++phu3bt0n//+1+1adNG3t7e8vLyUrt27TRlyhTFx8ebLf9gXdeuXauuXbuqdu3axvVZGqPxUce3e/fuqly5cppjY3733Xfy9PTU7NmzM7RNAAAAAAAAwL8NGY0A/jUKFCggSTp//rxVnxs1apRWrVql4sWLq0WLFsqXL58OHTqkSZMmKTQ0VHPmzFGuXKan06ioKPXo0UMuLi5q27atIiMjtX79eg0cOFBLly7VBx98oMjISL3wwgtKTEzUunXrNGLECJUoUUK1atUylnPmzBl99dVX8vHx0QsvvKB8+fLp6tWr+vnnn7V9+3Z98803ev75583qbFi/vb29WrZsqYSEBP3444967733ZGtra+ya1N/fX5IUHBysevXqqV69esYy3Nzc0t0vR44c0UsvvaSIiAjVrVtXzZs3V1xcnE6fPq0pU6Zo6NChVu3ntNy/f1+DBw/Wjh07VLZsWbVr104ODg7avXu3Pv74Y/3+++/67LPPJEkeHh4aO3asXn/9db311ltauHCh8dh89NFHOnv2rIYPH6769etLkoYNG6bNmzfrxIkT6tOnjzE497gZnBMnTpStra28vLxUrFgx3b17V7t27dInn3yiI0eOGOubGYZ2fP36dcXExDyRcRgXLlyobdu2yd/fX/Xq1dNvv/2muXPnKjIyUs2aNdOIESP0wgsvqHv37jp48KDWrFmjO3fu6Ntvv830OmfNmqVz586pdu3aatKkiRISEnTgwAFNnjxZu3fv1ty5c2VnZ2f2uTlz5ui3335T06ZNVb9+fd29ezfNdTzq+Pbo0UOHDh3S8uXLNWLECLPPL126VPb29nTlCwAAAAAAAKSBQCOAf40WLVro22+/1ffff6979+6pefPmqlatWroBtVWrVmnVqlVq3ry5Jk6cKEdHR+O8yZMna8qUKVq0aJH69u1r8rkTJ06oe/fu+vDDD2Vrm5o83rBhQ/3nP/9Rnz595O3trQULFsjBwUGS1LFjR/Xq1UuzZs0yyQYsX768tm3bJldXV5Pyr127pi5dumjcuHEWA40nTpxQly5dNGbMGGOwpm/fvurQoYNmzZplEmjMmzevMdCY0S5LExIS9PrrrysiIkITJ05U+/btzer3pEyfPl07duxQ79699d577xm3JykpSf/973+1cuVKtWzZ0hg0bdWqlXr06KElS5Zo0qRJeuuttxQcHKyQkBDVr19fQ4YMMZY9fPhwhYWF6cSJE+rbt6/c3d2fSJ1nzpxp1o1ocnKyRo0apZCQEPXu3Vs1a9bMVNkeHh6qUaOGjhw5oh49eqhbt26qXbu2KlSoIHt7+0yVuXPnTq1atcrY9WhCQoICAgK0evVq/fLLL/ruu++MQejk5GQNHDhQ27dv1/Hjx826wM2oDz/8UO7u7rKxsTGZ/tVXX+mbb77RTz/9pDZt2ph9bteuXVq6dKmqVq36yHU86vi2bt1a48aN08qVKzV8+HCTFwZ2796t8+fPq127dmbfPwAAAAAAAACp6DoVwL9G1apVNWHCBBUuXFhr1qzR8OHD5efnp/r162vo0KH6+eefzT4zf/585cqVS//73/9MgoySNGTIEBUoUEBr1641+5yTk5PeffddY5BRktq3b69cuXIpMjJS77//vjHIKEk+Pj5yc3PT8ePHTcrJmzevxSBH8eLF1apVK509e1ZXrlyxuP5Ro0aZZIRVqFBB3t7eOnPmjO7du5fOnnq0X375RWFhYfLz8zMLMhrq9yQkJydr4cKFKlKkiNn22NnZaeTIkbKxsTE7BqNGjVLlypU1a9YsLVy4UGPGjJGrq6sx0zCrWRqr0NbW1thN7fbt2zNdto2Njb7++mvVq1dPJ06c0JgxYxQQECBvb2917dpVM2fOVHR0tFVlBgUFmYxvaG9vr9atWys5OVlNmjQxyXS1tbVVhw4dJKUGtDPLw8PDLMgoSf369ZOU9j7q1q1bhoKMGeHg4KDOnTvrxo0b2rJli8m8pUuXSpICAwOfyLoAAAAAAACAnIiMRgD/Km3atFHz5s21e/du7d+/X8ePH9f+/fu1efNmbd68WZ06ddKnn34qGxsbxcbG6sSJEypYsKDmzZtnsTx7e3udOXPGbHqZMmWUJ08ek2l2dnYqVKiQYmNj5eHhYfaZYsWK6fDhw2bT9+/fr/nz5+vQoUO6deuW7t+/bzI/PDxcJUuWNJlWunRps/VLfwcAo6Ki5OLiYnGbMuLQoUOSZDGb8kk6d+6cIiIiVKZMGX3zzTcWl3F0dNTZs2dNpjk4OOjLL7/Uiy++qI8//lg2NjaaNGmSihYtmqX1Nbhz545mz56trVu36vLly4qJiTGZf/369ccqv2TJklqwYIHOnDmj3377TUePHtXhw4eNf0uWLNH8+fMttjNLqlevbjatWLFikqRq1aqlOe9xMldjYmI0f/58bdq0SefPn9e9e/eUkpJinJ/WPvLy8sr0Oi3p2bOn5syZo6VLl6ply5aSpNu3b2vTpk0qX7686tat+0TXBwAAAAAAAOQkBBoB/Ovkzp1bjRo1UqNGjSSldsH5008/6f3331dISIiaN28uf39/RUVFKSUlRbdv39aUKVOsWkdaY/zlypUr3XmJiYkm0zZt2qTXXntNDg4OatiwoUqVKiUnJyfZ2tpqz5492rNnjxISEszKMoxFZ2kdUuo2Pw7DuHiGgFNWiYiIkJQ6rmZ6x8BShmbZsmXl6empgwcPqkKFCsbjndWioqLUpUsXXb58WV5eXurYsaPy58+vXLlyKSoqSvPnz7d4zDKjfPnyJpmIZ86c0fvvv6+DBw9q3LhxmjZtWobKsdQmDdmj6c17uL1m1P3799W3b18dPnxYlSpVUps2beTq6mpsn1OmTElzHxUuXDhT60yLh4eHGjVqpB07dujixYsqVaqUQkJClJCQoO7duz/RdQEAAAAAAAA5DYFGAP96dnZ2atOmjU6dOqVvvvlGu3btkr+/vzEjsGrVqgoODs6Wuk2aNEm5c+fWypUrTQJKkvTBBx9oz5492VIvQ/ApPDz8qaynefPmVgd7Z86cqYMHD6pgwYL6888/NWPGDL366qtW18HGxsYsi9QgKirKbNry5ct1+fJlDRs2zGzMy4MHD2r+/PlW1yGjypcvrwkTJqh58+batWtXlq3ncW3ZskWHDx9W586dNW7cOJN5169fT/dYW+pu9XH16NFD27dv17Jly/T2229r6dKlcnBwUKdOnZ74ugAAAAAAAICchDEaAeAvhq5EDd03uri4qGLFivrzzz+NmXVP24ULF1ShQgWzIGNycrL279//RNZhyE6zJsuxVq1akqRt27Y9kTqkpVy5csqXL58OHTqUZrDPkgMHDujrr79W2bJltW7dOpUtW1aTJ0/Wvn37zJY1jNmYnJxssaz8+fNb7LJWko4ePWo27cKFC5KkFi1amM3bu3dvhrchsx5ux/9EFy9elJQaQH7Yk95Hjzq+ktS0aVOVLFlSq1at0o4dO3T+/Hm1bt1a+fPnf6J1AQAAAAAAAHIaAo0A/jXWrVun3377zWLA4caNG1q+fLkkycfHxzi9X79+un//vt577z2L2WuRkZE6duxYltXZzc1N58+fN8kcTElJ0eTJk3X69Oknso4CBQpIkq5evZrhzzRt2lRubm76+eeftW7dOrP5jzN234Ny5cql3r1768aNGxo7dqzi4uLMlrl+/brJvoiMjNRbb70lW1tbffnllypcuLC++uor2dnZ6e233zYLGhu2/8qVKxbrUKNGDSUmJmrVqlUm01etWqUDBw6YLe/u7i5JZtmmf/zxh2bMmPHIbX6US5cuaf78+cbuax+UkpJiHMvynzy2oJubmyTzfXTp0iVNnDjxia7rUcdXSg1GduvWTbdu3dJ7770nSQoMDHyi9QAAAAAAAAByIrpOBfCv8fvvv2v+/PkqUqSIvL29jQGhy5cva+vWrYqLi1OzZs3UqlUr42e6dOmiY8eOafHixWrevLkaNWqkEiVKKDIyUpcvX9bevXvVuXNnjRkzJkvq3K9fP/3f//2fAgIC1KJFC+XKlUsHDhzQmTNn1LRpU/3yyy+PvY6yZcuqWLFi+uGHH5QrVy6VLFlSNjY26tixozEg9DB7e3tNmjRJAwcO1FtvvaWlS5eqZs2aio+P19mzZxUaGqo//vjjsesmSUOGDNGJEyf0/fff65dffpGvr6+KFSumW7du6cKFCzpw4IBGjBihChUqSJLee+89XblyRaNHj1aVKlUkSZUrV9bIkSM1ZswYjRw5UtOnTzeW36BBA82ePVv//e9/1aJFC7m4uChfvnzq3bu3JCkoKEirVq3Shx9+qNDQUJUoUULHjx/XoUOHLB6Djh07avbs2frf//6n3bt3q3Tp0rpw4YJ+/fVXNW/eXOvXr3+s/REdHa1PPvlEEyZMkLe3typVqiQXFxfdunVLu3bt0qVLl1SoUCH95z//eaz1ZKWmTZuqdOnSmjNnjk6dOqUqVaro6tWr+uWXX/TCCy+kGxS01qOOr0HXrl01depUhYeHq1KlSqpdu/YTqwMAAAAAAACQUxFoBPCvMWDAAJUpU0Y7d+7UyZMntWPHDiUkJKhAgQKqV6+e2rVrp/bt25uNAfd///d/ev755/X9999r586dunv3rvLnz68SJUpo4MCB6tChQ5bVOTAwUPb29po3b55CQkLk4OAgHx8fjRs3Ths3bnwigUY7OztNmTJFn3/+uX788Ufdu3dPKSkpqlOnTpqBRik10y8kJEQzZ87Utm3bdPDgQbm4uKhUqVJ67bXXHrteBrlz59a0adO0evVqBQcH69dff1VMTIwKFiwod3d3vf7662rfvr0kacGCBdq8ebP8/PwUFBRkUk6vXr0UGhqqTZs2ae7cuerXr58kqXHjxho5cqSWLVumefPm6f79+3JzczMGoipUqKA5c+boyy+/1C+//CI7Ozv5+Pjo+++/16ZNm8yOQbFixbRo0SJNnDhR+/fv144dO1SuXDn93//9nxo0aPDYgcby5ctr6tSp2rFjh37//XetX79ekZGRcnR0VOnSpTV48GD17dtXrq6uj7WerOTs7Kx58+Zp4sSJ2rNnj/bt2ycPDw8NGTJE/fv3f+x99KBHHV+DwoULq0mTJtq8eTPZjAAAAAAAAEAG2aT8kwdxAgAAeAqSk5PVvHlz3bp1Szt27FCePHmyu0oAAAAAAADAPx5jNAIAgH+9H3/8UZcvX1bHjh0JMgIAAAAAAAAZZHVG48yZM/XHH3/ojz/+0MWLF2Vra5upcbhiY2M1depUrV+/XtevX1fRokXVtm1bDRkyRE5OTlaXBwAAYK2ZM2cqIiJCy5YtU1JSkn744QeVLFkyu6sFAAAAAAAAPBOsDjR6enoqX758qlKlis6ePavbt29bHWhMSkpSv379tGfPHnXs2FF169bViRMntGTJEtWtW1dz5syRrS3JlgAAIGt5enoqd+7cKl++vN59910999xz2V0lAAAAAAAA4JmRy9oPbNq0SaVKlZIkBQUF6fbt21avNDg4WHv27FFQUJBGjx5tnO7m5qbx48drzZo16tSpk9XlAgAAWOPkyZPZXQUAAAAAAADgmWV12qAhyPg4Vq9eLUnq37+/yfSePXvK0dFRISEhj70OAAAAAAAAAAAAAFnnqfdPmpKSoiNHjqho0aJyc3Mzmefo6KgqVaroyJEjT7taAAAAAAAAAAAAAKxgddepjysiIkKxsbGqWLGixfnFihXTwYMHFR0drTx58lhcplmzZumuY8uWLY9dTwAAAAAAAAAAAABpe+qBxri4OEmSvb29xfkODg6SpNjY2DQDjY/iVHtY5ir3mGIPTsmW9T7rsut4Ieeo2b1rdlcBz7irV6KyuwrIAfLkdcjuKuAZd+yTFtldBQAAAAAAAKs89UCjo6OjJCkhIcHi/Pj4eEmSk5NTmmU8KmORwBUAAAAAAAAAAACQtZ56oLFAgQJycnLStWvXLM4PDw9Xnjx5Mp3NKEmyeepDTwIAAAAAAAAAAAD/Kk89ImdjY6Pq1avr+vXrCgsLM5kXFxen48ePq0aNGk+7WgAAAAAAAAAAAACskKWBxtjYWJ05c0bXr183md6xY0dJ0pw5c0ymL1myRHFxccb5mWZjkz1/AAAAAAAAAAAAwL+E1V2nhoSE6MqVK5KksLAwpaSkaNq0acb5Q4YMMf778OHD6tOnjwICAvTpp58ap3fu3FkhISFasGCB7t69Kx8fH508eVKLFy9WvXr11KFDh8fZJgAAAAAAAAAAAABZzOpA48qVK7Vnzx6TaZMmTTL++8FAY1rs7Ow0c+ZMTZ06VRs2bNAPP/ygIkWKqH///ho6dKjs7OysrRYAAAAAAAAAAACAp8gmJSUlJbsr8aQ5+YzIlvXG7vsyW9b7rHOqPSy7q4BnXM3uXbO7CnjGXb0Sld1VQA6QJ69DdlcBz7hjn7TI7ioAAAAAAABYJUvHaAQAAAAAAAAAAACQM1nddeozwcYmu2sAAAAAAAAAAAAA5GhkNAIAAAAAAAAAAACwGoFGAAAAAAAAAAAAAFbLoV2nEj8FAAAAAAAAAAAAshIROQAAAAAAAAAAAABWy6EZjTbZXQMAAAAAAAAAAAAgRyOjEQAAAAAAAAAAAIDVcmhGI/FTAAAAAAAAAAAAICsRkQMAAAAAAAAAAABgNQKNAAAAAAAAAAAAAKyWQ7tOtcnuGgAAAAAAAAAAAAA5GhmNAAAAAAAAAAAAAKyWQzMaiZ8CAAAAAAAAAAAAWYmIHAAAAAAAAAAAAACrEWgEAAAAAAAAAAAAYLUc2nWqTXbXAAAAAAAAAAAAAMjRyGgEAAAAAAAAAAAAYLUcmtFI/BQAAAAAAAAAAADISkTkAAAAAAAAAAAAAFgth2Y0MkYjAAAAAAAAAAAAkJXIaAQAAAAAAAAAAABgNQKNAAAAAAAAAAAAAKyWQ7tOJX4KAAAAAAAAAAAAZCUicgAAAAAAAAAAAACsRkYjAAAAAAAAAAAAAKsRkQMAAAAAAAAAAABgNQKNAAAAAAAAAAAAAKyWM7tOtbXJ7hoAAAAAAAAAAAAAORoZjQAAAAAAAAAAAACsljMzGm2InwIAAAAAAAAAAABZiYgcAAAAAAAAAAAAAKvl0IxGxmgEAAAAAAAAAAAAshIZjQAAAAAAAAAAAACsRqARAAAAAAAAAAAAgNVyaNepxE8BAAAAAAAAAACArEREDgAAAAAAAAAAAIDVcmhGo0121wAAAAAAAAAAAADI0choBAAAAAAAAAAAAGA1Ao0AAAAAAAAAAAAArJZDu04lfgoAAAAAAAAAAABkJSJyAAAAAAAAAAAAAKyWQzMabbK7BgAAAAAAAAAAAECORkYjAAAAAAAAAAAAAKvl0IxG4qcAAAAAAAAAAABAViIiBwAAAAAAAAAAAMBqBBoBAAAAAAAAAAAAWC2Hdp1qk901AAAAAAAAAAAAAHI0MhoBAAAAAAAAAAAAWC2HZjQSPwUAAAAAAAAAAACyEhE5AAAAAAAAAAAAAFYj0AgAAAAAAAAAAADAajm061Sb7K4BAAAAAAAAAAAAkKOR0QgAAAAAAAAAAADAajk0o5H4KQAAAAAAAAAAAJCViMgBAAAAAAAAAAAAsBqBRgAAAAAAAAAAAABWo+tUAAAAAAAAAAAAAFYjIgcAAAAAAAAAAADAajk0o9Emu2sAAAAAAAAAAAAA5GhkNAIAAAAAAAAAAACwWg7NaCR+imdXgH8tNa5TUV6V3FSjkpvy5XHSkh/2aMDo+dldNTxlRfLa6+XGZeRb1lX5nXLr1r0EbT11U7N3XNDd+ESryvIslkc967mrtkcBFXDOrej4RJ2/FaO1h69pw9Fw43IvNSqtlxqVSbesy3di1WXGnsxsEp6y4gUc9VYbTzWpUlQFXHLremS8Nh65pq82nFJU7P1Hft63QiEtfa3ho5f7YJOuRsSZTW9Tq4QCG5RSDY8Ccnaw0627CToWFqlpm/7UwfMRmdkkZINi+Rw0zL+CGlUspALO9rpxN14//3Fd034+o6i4R5+L6pYtqLkv1X3kcs0mbNW1yHhJUtF8DvKvWlTPexZRuSIuKpLXQTEJifrjyl0t3X1Jm/+4/tjbBQAAAAAAgMeXMwONwDPsPy+1Uk1Pd929F6ew8Ajly+OU3VVCNnAr4KhZQbXl6mKvradu6sKtGFUtmVeBdd3lW85VLy84mKEH/JLUxbukRvhX0N24RP125pZu3E1QPqdcKl/YRQ3LuZoEGg9cjNC3O85bLKdRhUKqXDyvQs/efhKbiCxWqrCzVr3RSEXyOeinw9d0JvyuapUuqIEvlFOTKkX14pc7FBGTfrDx8u0YfbnhpMV5lUvkU+taJXTiSpRZkNHO1kZf9K6lTj7uOns9WusOhikqNlFF8znIu0xB1fAoQKDxGeHh6qSFr9RT4TwO2vLHdZ27cU813PMr6LnSeq5SYfWesUeRjwhah92J1dQtZyzOq1Qsj5pXL6ZT1+4ag4yS1Mu3lF5qUlaXbsdoz9nbuhkdr5IFnORftagaViikeTvOa8KGU090W59lu3fvVp8+fTRu3Dh17tw5u6uTZfz8/OTm5qYFCxY80WWfZSNHjlRwcLBOnrR8rn7Yv2W/PCw2Nlaff/65Nm/erPDwcJUoUUI///yzgoKCFBYWpp9//jm7qwgAAAAAzywCjcA/zLsTVyrseoTOXLyhxnUqauO3r2d3lZAN3mlRUa4u9vp8059avv+KcfrrfuXVo567Bjcpqwk//fnIcuqVKag3m1fQnnN39F7IH4pJSDKZb2drOqbtgYuROnAx0qwcWxupvVdxSdLqQ1czs0l4ysZ2raEi+Rz0fyuOaO6288bp/w2oqpealtc77Srr/WVH0i3j8u1YfZVGMOfrvt6SpCU7L5rNe7O1pzr5uGvyT6f0+fqTSkkxnZ/LlrGUnxX/7VBFhfM46JO1x7V41yXj9HdbV1LfRmX0eosKGrP6eLplXImI07SfLQcaP+tWQ5K0Yl+YyfQjlyPVd9Ze7Tt/x2R6uSIuWjy4nvo2KqN1v1/VH1fuZmazAPzLzJo1SwsWLNCAAQPk6empPHnyPJFyo6KiNG/ePNWrV0/169c3mz937lzly5cvR78AAAAAAAA5s49RG5vs+QOegG37/tSZizeyuxrIRm4FHOVbzlVXImK14oEgoyTN2nFeMQlJal2tmBxzP/oUPtyvnOLvJ+uDNcfNgoySlJScYuFT5hqWL6Ri+Rx1JCxKp2/cy9iGINuUKuysJlWK6tKtGM3bft5k3hfrT+pefKI613WXk71dpsov6GKvll7FFZuQpFV7L5nMK5LXQYOaldOBc7c18QfzIKMkJWaw3SF7ebg66bmKhXX5dqyW7DY9zlO2nFFMfKLa1yopp9yZa0cFnHOrWdWiik1I0pqDpue6zX9cNwsyStLZG/f045HULOy6ZV0ztV78O/z444+aPXt2dlfjH+fful927typSpUq6T//+Y86deokf3//J1JuVFSUpkyZoj17LHcpP3/+fAUHBz+RdQEAAADAP1XODDQCwDOsTqkCkqTd5+7o4XBMTEKSDl+OlJO9naqXzJduOeUKO6ti0Tzaff6OouIS5V2qgHrWc1fPeu7yKV1A1rwe0alWCUlSyKErj1gS/wQNKxaWJG07ccMs0HcvPkn7zt6Ws0MueZcpmKnyu9Rzl2NuO/1w8IqiYk278G1Tq4QcctlpzYErcshtqza1SuhV/wrq07iMqjyizeKfpV651EDeztM3zdpRTEKSDl6MkLO9nbw88meq/I61S8oht502Hg3X3Qx2BS1JiUnJkjL+ogQyLzo6OrurkGn29vayt7fP7mr84/xb98uNGzdUoECB7K7GE5WUlKTY2NjsrgYAAAAA5NBAo41t9vwBwBNQqpCzJOnSHcsPjwzTS7mmP35nlRJ5JUl37iVoWs+amtazpl7zK6/X/MprSo+aWjCgjtwLOD6yPkXy2su3nKvuxiVq83GybZ8F5Yq6SJLOXbccJDj/V1Zq2b+Ws1Zgw9KSpMU7L5jN8/orUO5kb6ef32+qbwb4aGSHKvq4aw39OLKJvhlQR46ZzIDD01WmcGr7OH8zxuL8C7di/lrOOVPld6nrLkla9lBWbHpcHOzUvFoxJSenaOfpW5la779FcnKypk+frqCgIDVq1EjVq1dX48aN9Z///EdXrpi/NOLp6amRI0dq9+7dCgoKkre3tzp06CApNeD41VdfqVu3bvL19VX16tXl5+enMWPGKCIiwlhGdHS0atWqpf79+1us0+rVq+Xp6anly5cbp6WkpGjZsmXq0qWLatWqpVq1aikwMFCbN29+rO338/NTUFCQybRDhw5p8ODBxv3RqFEjBQUFZWhdkydPlqenp86dO6dJkyapadOmql69ulq3bq01a9ZY/Mwff/yh1157TQ0aNFD16tXVrFkzTZw40SQ4tHbtWnl6epqNEdimTRt5enoqJCTEZHpgYKCaNWtmtq47d+5o1KhR8vX1lZeXl7p3767Q0NAM7ZeM1jUtb7/9tqpUqaKrV827Vo+NjVWdOnXUs2dP4zRDW3vY7t275enpqVWrVplMT0hI0KxZs9S+fXt5eXnJ29tb/fr10969ex9ZN8Nxu3z5svbs2SNPT095enpq8uTJaX4mrX10+fJlk8+uWrXKeCymTJliLNvPz8+4bFhYmMl6DXUxuHjxokaOHGlsk88//7w+/PBD3b5tOh62YTtOnz6tCRMmqGnTpqpRo4Y2bNjwyH0AAAAAAFmNMRoB4B8mj0NqECY6jQyfe/GJfy2X/inc1Tk1Y6F9zRK6cTdeI5Yd0e+XI1XIxV4Dniut1tWL6fOuNdRr9r50u7Ls4FVCuWxt9OOxcMUnJmdmk/CU5XXMLUlpZond/SsLMZ9TbqvLrl+hkCoUy6MTV6K0/5x515aF8zpIkt5q46l95+7o5W/36uz1e/IskVdjutRQm1oldS8+SW8vOmT1uvF05XVMPcdEx6fRjv5qX4b2Zg2fMgVVroiLTl27q0MWxoVNy5iAaiqc10FLdl3UWbpxTtf9+/c1a9YstWjRQk2aNFHevHl18uRJrVy5UqGhoVqzZo1ZhtfRo0f1008/6cUXX1S7du10717qPg4PD9eyZcvUvHlztWnTRvb29jpy5IiWLl2q/fv3a8WKFcqdO7fy5Mkjf39//fDDD7p69apKlChhUn5wcLCcnJzUunVr47RRo0YpJCREzZo1U/v27SVJmzZt0tChQ/Xhhx+qR48eT2R/nDt3Tv369ZOrq6t69uypokWL6s6dOzp27JgOHjyY4a40R44cKRsbGwUFBcnW1laLFy/WO++8o1KlSqlWrVrG5bZt26ahQ4eqRIkS6t27twoXLqwTJ05o7ty5OnDggObPn69cuXKpQYMGklK79vTz85OUur/PnDkjW1tbhYaGqlOnTpJSA7lHjhyxON7fwIEDlSdPHg0ZMkSRkZFaunSpXnrpJX3zzTd6/vnn092mjNY1LZ07d9batWsVEhKiV1991WTexo0bFR0drYCAgIzsXjOJiYl6+eWXtXfvXrVt21aBgYGKi4vTmjVr1LdvX02dOlVNmzZN8/PNmzdXqVKlNG7cOBUsWFCDBw+WlBrsfFx169bVqFGjNG7cODVv3lzNmzeXJLm4uMjV1VUTJkwwW68kubqmZosfP35cQUFBcnR01Isvvig3NzedP39eS5YsUWhoqFasWKG8efOarPPtt9+WnZ2devbsKWdnZ5UtW/axtwMAAAAAHheBRgDIoQxDx+aytdF/Vx/X0StRkqSYhFh9tO6EShdyUtUS+dTUs7A2pZGpaCOpvVdxSVLIIfNMBfz79GxYSpK0xEI2o/R3u4uIua+BM/cYA+aHLkTopVl79MtoP3Wu667P1p1QeGTcU6kz/nm6/pXNuHzv5Ucs+bd3W1dSqxrFte/cHY1ffzKrqpZj2Nvba8eOHXJyMs1+9/f3V//+/bVixQq99NJLJvP+/PNPzZo1yyww5eHhoa1btyp3btOgsre3t0aPHq3Nmzcbg4cvvviixaDT1atXtXv3brVv31558uSRJG3evFnBwcEaNWqU+vXrZ1y2b9++Gjx4sCZOnGiy/OPYvn27YmNj9eWXX6pmzZqZLidfvnyaMWOGbG1TezNp2bKlmjdvrgULFhgDjfHx8XrvvfdUuXJlLVq0yKSrUl9fX7322mtau3atAgICVLhwYVWqVMkk+zA0NFQ2NjZq3769yfTdu3crMTHRGJx8UNGiRTVt2jRjvbp06aI2bdpozJgx2rhxo3H6w6ypa1p8fX1VsmRJi4FGS8FlayxatEihoaGaMmWKMZAnSX369FG3bt00duzYdAONlStXVuXKlTVp0iQVLlxYHTt2zFQ9LPHw8JC/v7/GjRsnT09Ps7I7duyY7npHjRql/Pnza+XKlSZB/1atWikwMFDz5s3TsGHDTD7j7OysefPmmX0XAQAAACA75cz+Pm1ssucPAJ6A6PgkSVIeR8vvgrg4pJ9l9Hc5qfNvRscbg4wP2v5nareDVdMZN69BeVcVz++oI2FROkP20DPjbtx9SX9npD0sr1Pq9KjY+1aVm985t1rVLKHYhCStSiNAZCjzt1M3zbJyr0fF69CFO7KztZFXqcyN64enx5CxmFb2tKF9GdpbRuV3yqXm1YoqNiFJazP4AsNbLSuqb6My2nvutl6df0D3kxif8VFsbGyMQcbk5GRFRUXp9u3bqly5svLmzavDhw+bfaZy5coWs9/s7e2NgY3ExERjWb6+vpJkUtaDQacHBQcHKzk52SRgtWbNGjk6Oqp169a6ffu2yZ+/v7+io6N16NChx90VklIDhFJqcDMuLvMvOfTr188kaFeiRAmVLVtW586dM07buXOnbty4oYCAAEVHR5tsV926deXk5KQdO3YYl/f19dXp06d1/fp1SamBRk9PT7Vt29aY3WiYbmNjY9zvD3rllVfM6tWxY0ddunRJf/zxR5rbY21dLbG1tVXHjh11/vx57d+/3zjdEFxu0aJFpoPFq1evlpubm+rUqWNSt7t37xq7KH1w3z8rTp06pePHj6tt27ZKTk422TZ3d3eVKlXK4n4fMGAAQUYAAAAA/zhkNALAP8zFv8Y98yhoeQxGw/SLt9MfN+nCX/PT6oI16q/pDrnSfuekU63Ubu9CDpmP54V/rrPXDWMwWn6wW6aIYQxH64LHXep5yDG3nZbvvqSoWMvtyrDutIKYkTGp0xmn8Z/v/M3UY5nWGIyl/xpPNq0xHNPSsXZJOeS2U8iBsDS7933Qf9p4qs9zpbX7zG0NWXBAcffpwjmjNm/erG+//VZHjx7V/fum38kHx1Y0KFOmTJplLVu2TIsXL9aff/6pxETT4/ZgWTY2NurUqZOmTZum/fv3q06dOpKkkJAQubm5mQTJzpw5o7i4uHS79rx582Y6W5hxbdq00bp16zRz5kzNnTtXXl5e8vHxUdu2bVWpUqUMl+Ph4WE2rUCBAgoLCzP+vyEw+NFHH+mjjz6yWM6D29WwYUPNnz9foaGh6tixo0JDQ9W2bVvVrVtXuXPnVmhoqMqXL6/Q0FBVqlTJ2PXmgypUqJDmtIsXL6p69eoW62FtXdPSuXNnffPNNwoODjYec0vBZWudPXtWsbGxFrM4DW7duvXMdSFq2O8zZszQjBkzLC5jqa2l9x0FAAAAgOySIwONNmQXAniG7b8YIUmqX7agbCQ9mLfjbG8nL/f8ik1Ispil+KCjYVGKSUhS8fyOcsxta/Zwvnzh1GDT1QjLmR2F89irYflCuhuXqM1pdK2Kf6adf6Y+FH6+chHZ2EgpDzQiFwc7+ZRzVUx8og6cNx9jMT09HtFtqiTtOHlDr7eqJM8SeS3Or/TX9Eu3rAtO4enbc/a2JKlhhcJm7cjZ3k61SxVQTEKSDl/K+BiLktTFim5TR7evrB6+pfTbnzc1fOEhxom1wubNmzV06FBVr15do0aNUokSJeTo6ChJGjFihFJSzLNCH+5m1WDevHn63//+p4YNG+r//u//VLRoUdnb2ysxMVGDBg0yKysgIEDffPONQkJCVKdOHe3fv18XLlzQ0KFDTe7Tk5OTlTdvXn399ddpboelAFpm2Nvb69tvv9WxY8e0Y8cO7d+/X/PmzdOMGTP07rvvasCAARkqJ60uSB+UnJzaTkeMGCEvLy+LyxgyLKXUsf5y5cql0NBQVa9eXeHh4WrQoIGcnZ1Vq1Yt7dy5Uy1atNDp06dNuph9Eqyta1pKlSolHx8fbdiwQaNHj5ajo6MxG9FSBqYlSUlJFutXtmxZffDBB2l+rmLFihkq/3FZql9mGb4zQUFBxrE5H+bg4GA2zfAdBgAAAIB/khwZaASAZ1lYRJx2nb0t33Ku6lKnpJbv/zubcFCjMnK2t9Oqg1dMAoelXVMfDl94IMsxPjFZaw9fVXcfd73yfFlN2nLGOK98ERe1qVFMiUnJ+vmk5SBie6/iymVrox+PhfNw/xlz8WaMth6/riZViqpv4zKau+28cd6bbTzl4pBLC3ecV2zC3w9Ny/+V/XjmerTFMuuWc1XF4nl14kqU9p9LO0C558xtHbscqXrlC6mlV3H9dPiacV5gg1KqWDyvzl2P1uG/Aur457p0O1a//XlTz1UsrB71PbR41yXjvGHNysvZIZeW7rmk2Pt/t6Oyf2U/nksjy9G7dAGVL5pHp67d1aGL6QcoP+pUVV3qumvbyRt6ffHvSuA8ZJWQkBA5ODho4cKFJgHEmJgYRUWl/6KKpbLc3Nw0e/Zsk0CbISvrYaVKlVKdOnW0YcMGvf/++woODpaNjY1ZZluZMmV09uxZValSRQULFrSqTplVrVo1VatWTZIUGRmpwMBAffHFF+rdu7fJ+ISPw5Bd5+DgoIYNGz5y+Tx58qhGjRoKDQ1VtWrVlDt3bvn4+EhK7VZ17ty5xm400yrv9OnTql27ttk0KfV4PKm6picgIEDvv/++Nm3apJIlS+r8+fNmwWUpNQPUUkbtpUuXzKaVKVNG165dU7169ZQr19P56WpN/TL7guuDmYmPu98BAAAAILvlyDEabWxssuUPeBLav+ClmR/11syPeuvt/s0lSfW9yhqnjRuR+e6n8Oz4bOOfun0vQW81r6jxnavp1SZlNaWHl3rUc9eFWzGavtV0PKKlL9fT0pfrmZUzY9t5nQy/qx513fVtUG295ldOH7arrNl9assxt52+/uWswixkNNpI6lDT0G1qxsZQwz/L6OVHdCMqXh91qaGZL9XVu+0ra8mwBnqpaXmdCY/WZ+tOmCz/8+im+nl00zTL6/lcaUnpZzMavLnwoCJjEjR9gI9mDaqr9zpW0dxX6ml8j5q6F5+otxYdUjJD7D0TPl5zXDej4/V++yr6ulctvdGigr4b4KO+jcro3I17mrTxtMny60Y00roRjdIsr2sGsxlf9SunLnXdFZuQpBNX7+ql58tqiF95kz+/KkUefwNzMFtbW9nY2Bgz1gymTZtmNi0jZUky+VxKSoqmTp2a5mc6d+6su3fvau3atdqwYYPq1q1r1hVkp06dJEmfffaZxQzLJ9VtqiTdvn3bbFr+/Pnl4eGh+/fv6969JzcOcaNGjVS4cGHNnj1bN26Yv8yTmJhoFshq2LChrl27pu+//161atWSs7Ozcfrdu3c1e/ZskwDkw2bMmGFyfK5evarVq1fL3d1dVatWfaJ1TUvr1q3l7Oys4ODgNIPLUmpw89ChQ4qNfeDlqPh4LVy40GzZTp06KTIyUtOnT7e4zifZRh6s37lz5xQeHm6clpycrDlz5pgtazhOkZGWX5xwcXGxuP+qVKmiSpUqacWKFRYD9ikpKRbbLAAAAAD8E5HRCPzDeHm6K6iDaRdT5TyKqJxH6gPVC1duadSXwdlRNTxFYRFx6jf3gF5uXEa+5QqqYXlX3YxO0Pd7L2v2jgu6G//occ0kKSYhSYMXHlLfBqXkV7mIuni7KT4xWb9fjtSi3Ze1J42uM33LuapEfkcdCYvSmRtP7uErnp6LN2PUfuI2vdnGUy9UKaqmVYvqelScZv96Vl9tOJXmGIqW5HPKrTY1Syg2IUmrMtDd5Ykrd9V2wja93tpTz1cuoqZVi+pOdIKC917W1z+dMo7jiH++S7dj1X3aLg1rVkGNKhXW85UK68bdeC347YKm/XzGONZrRuRzzKUW1YspNiFJax/xAoP7X2PROtnb6eUXyllcJuRAmH6mW+c0tWrVSj/99JOCgoIUEBCglJQU7dixQ6dPn7Y6e7BVq1aaOHGiBg4cqJYtWyouLk6bNm0yG/fx4c+MHTtWn376qaKjoy0GnFq2bKlu3bpp2bJlOnHihPz9/VWkSBGFh4fr2LFj2rZtm44dO2b1tlvyzTffaPv27WratKnc3d1lZ2envXv3auvWrWratOkTzah0cnLShAkTNGTIELVp00adO3dWuXLldO/ePV28eFGbNm3SW2+9pc6dOxs/06BBA02dOlWnT5/Wa6+9Zpzu5eWlPHny6PTp0/L29paLi4vFdV6/fl39+vVT8+bNFRkZqe+//17x8fH64IMP0u3uNTN1TYuLi4tatGihNWvWyNHR0WJwWUrtLvTNN99UUFCQOnXqpJiYGIWEhChvXvMut/v06aPQ0FBNnjxZe/fu1XPPPacCBQro6tWrOnjwoC5duqQtW7Y8sm7WCAoK0rp169SnTx/16NFDKSkp2rBhg8UXSwsWLKjSpUvrhx9+kIeHhwoXLiwnJydjd6g1a9bUihUr9NVXX6l8+fKytbVV06ZN5ezsrM8++0x9+/ZVQECAAgICVKlSJSUmJiosLEybN29WQECAhg8f/kS3DQAAAACyAoFG4B/mkxnr9cmM9dldDfwDXL8br7HrT2ZoWd9Pt6Y5L/Z+sqZvO6/pD3Sf+SihZ2+nWyaeDVcj4vTO4t8ztGzp19amOS8q9r4837buvHTpdqzeXnTIqs/gn+laZLxGr8pYsKfa+xvTnBcVl6g6H2YsIPD+ymN6f+WTCTD9W7Vp00YxMTGaN2+ePvvsM7m4uKhhw4ZavHixevbsaVVZAwcOlCStWLFC48aNU4ECBdSsWTONGDFC9eqZZ9NLqUGnli1bKjg4WM7OzmrZsqXF5T7++GPVr19fS5cu1Xfffae4uDgVLlxYFStW1OjRo63b6HT4+/vr5s2b+umnn3Tr1i3lypVLbm5uevvttxUUFPTE1mPw3HPPadWqVZo1a5Z+/PFH3bp1S3ny5FHJkiX14osvqkGDBibLG7IYY2JiTLrSzJUrl+rWratffvnF7DMPmj17tiZMmKCpU6fq3r17qlKlij799FM1apR2hnFm65qezp07KyQkRDExMRaDy5LUtm1b3bhxQwsWLNCnn36qEiVKKDAwUFWrVjUbgzJXrlyaPn26li5dqpCQEH3zzTdKSkpS4cKFVa1aNb311lsZrltG1apVSxMnTtQ333yjiRMnytXVVZ06dVKnTp3UunVrs+UnTpyo//3vf/ryyy8VGxsrNzc3Y6BxxIgRioyM1OLFixUVFaWUlBRt2bJFzs7Oqly5slavXq2ZM2dq27ZtWrlypZycnFS8eHE1a9bM4roAAAAA4J/IJsVSP0XPOJeu5t3aPA33lvfPlvU+65xqD8vuKuAZV7N71+yuAp5xV69YN14ZYEmevA7ZXQU844590iK7q5BjPP/88ypXrpzmzp2b3VUBAAAAACBHy5FjNAIAAAD4d0pISFBERIQKFSqU3VUBAAAAACDHy5Fdp1oaPwMAAABAzrZ27Vr9+uuvio+PV+PGjbO7OgAAAAAA5Hg5MtAIAAAA4N/n448/Vp48eTR8+HB16NAhu6sDAAAAAECOR6ARAAAAQI6wZ8+e7K4CAAAAAAD/Kjky0EjXqQAAAAAAAAAAAEDWss3uCgAAAAAAAAAAAAB49pDRCAAAAAAAAAAAAMBqZDQCAAAAAAAAAAAAsBoZjQAAAAAAAAAAAACsRkYjAAAAAAAAAAAAAKsRaAQAAAAAAAAAAABgtRzZdaroORUAAAAAAAAAAADIUmQ0AgAAAAAAAAAAALBajsxotLEhpREAAAAAAAAAAADISmQ0AgAAAAAAAAAAALAagUYAAAAAAAAAAAAAVqPrVAAAAAAAAAAAAABWI6MRAAAAAAAAAAAAgNXIaAQAAAAAAAAAAABgNTIaAQAAAAAAAAAAAFiNjEYAAAAAAAAAAAAAViOjEQAAAAAAAAAAAIDVCDQCAAAAAAAAAAAAsFqO7DpV9JwKAAAAAAAAAAAAZCkyGgEAAAAAAAAAAABYLUdmNNrYkNIIAAAAAAAAAAAAZCUyGgEAAAAAAAAAAABYjUAjAAAAAAAAAAAAAKvRdSoAAAAAAAAAAAAAq5HRCAAAAAAAAAAAAMBqZDQCAAAAAAAAAAAAsBoZjQAAAAAAAAAAAACsliMzGkVCIwAAAAAAAAAAAJClMhVo3Lhxo7799ludOnVKuXPnVp06dfTmm2+qUqVKGfr8iRMnNGPGDP3++++6ceOGChUqpGrVqmngwIHy9vbOTJUAAAAAAAAAAAAAPEVWd526fPlyDR8+XLGxsXr77bc1ePBgnTx5UoGBgTp58uQjP3/48GF17dpV+/btU0BAgD744AMFBATo0KFD6tWrl3bs2JGpDQEAAAAAAAAAAADw9FiV0RgZGalPP/1UxYsX15IlS5QnTx5JUuvWrdW2bVt98sknmj9/frplzJ8/XwkJCZo9e7ZJBqS/v786d+6sZcuWqVGjRpnYlL/Z2NB3KgAAAAAAAAAAAJCVrMpo3LJli6Kjo9W1a1djkFGSSpYsqZYtW2r37t26evVqumVER0dLkooWLWoyvVixYpIkJycna6oEAAAAAAAAAAAAIBtYldH4+++/S5Jq165tNq927doKDg7WkSNHVKJEiTTLaNSokX755Re99dZbeu2111S8eHFduXJFkyZNUv78+TVgwIBH1qNZs2bpzrcp9+ojywAAAAAAAAAAAACQeVYFGsPDwyVJxYsXN5tnmHbt2rV0y+jRo4fCw8O1cOFCdevWzTi9UqVKWrZsmcqUKWNNlQAAAAAAAAAAAABkA6sCjbGxsZIke3t7s3mGaXFxcemWYWtrq2LFiqly5cry9/dXmTJldP78ec2ePVsvvfSS5s2bJzc3t3TL2LJlS7rziw9ake58AAAAAAAAAAAAAI/HqkCjYfzEhIQEs3mGaY6OjumW8fnnn2vOnDkKDg5WpUqVjNMbNWqkzp07a8KECZo0aZI11TJjY2PzWJ8HAAAAAAAAAAAAkD5baxYuVqyYJMvdoxqmWepW1eD+/fuaO3euypUrZxJklCRPT0+VK1dOu3fvtqZKAAAAAAAAAAAAALKBVYFGLy8vSdLBgwfN5h06dEiSVKNGjTQ/f+fOHd2/f19JSUkW5ycmJqY5zxo2NjbZ8gcAAAAAAAAAAAD8W1gVaPT395eLi4uWL1+u6Oho4/QrV67oxx9/VL169VSiRAlJqeM5njlzRtevXzcuV7hwYRUsWFDnzp0zBiYNDh48qPPnzxuDmQAAAAAAAAAAAAD+uawKNObPn1/vvvuurl27ph49emjhwoX67rvv1Lt3b0nS+++/b1z28OHDatOmjb744ou/V2Zrq+HDhys5OVn9+/fX+PHjtXTpUo0fP14DBgxQ7ty59frrrz/+Vtlk0x8AAAAAAAAAAADwL5HL2g8EBgaqQIECmj17tj777DPlzp1bPj4+euONN1S5cuVHfr5Xr14qVqyYFixYoBUrVujevXsqUKCAGjdurCFDhmSoDAAAAAAAAAAAAADZy+pAoyS1atVKrVq1SneZ+vXr6+TJkxbn+fv7y9/fPzOrBgAAAAAAAAAAAPAPkKlA4z+djQ39mAIAAAAAAAAAAABZyaoxGgEAAAAAAAAAAABAIqMRAAAAAAAAAAAAQCaQ0QgAAAAAAAAAAADAagQaAQAAAAAAAAAAAFiNrlMBAAAAAAAAAAAAWI2MRgAAAAAAAAAAAABWy5EZjSKhEQAAAAAAAAAAAMhSZDQCAAAAAAAAAAAAsBqBRgAAAAAAAAAAAABWy5Fdp9rY0HcqAAAAAAAAAAAAkJXIaAQAAAAAAAAAAABgNTIaAQAAAAAAAAAAAFiNjEYAAAAAAAAAAAAAViOjEQAAAAAAAAAAAIDVyGgEAAAAAAAAAAAAYDUCjQAAAAAAAAAAAACsRtepAAAAAAAAAAAAAKxGRiMAAAAAAAAAAAAAq+XIjEaR0AgAAAAAAAAAAABkKTIaAQAAAAAAAAAAAFiNQCMAAAAAAAAAAAAAq+XIrlNtbOg79VlSs3vX7K4CnnG/L12e3VXAM654k9bZXQXkAK6uztldBQAAAAAAAOCpIqMRAAAAAAAAAAAAgNXIaAQAAAAAAAAAAABgNTIaAQAAAAAAAAAAAFgth2Y0ZncNAAAAAAAAAAAAgJyNjEYAAAAAAAAAAAAAViPQCAAAAAAAAAAAAMBqObTrVPpOBQAAAAAAAAAAALISGY0AAAAAAAAAAAAArJZDMxqzuwYAAAAAAAAAAABAzkZGIwAAAAAAAAAAAACrEWgEAAAAAAAAAAAAYLUc2nUqfacCAAAAAAAAAAAAWYmMRgAAAAAAAAAAAABWy6EZjdldAwAAAAAAAAAAACBnI6MRAAAAAAAAAAAAgNVyZEajrS0pjQAAAAAAAAAAAEBWIqMRAAAAAAAAAAAAgNUINAIAAAAAAAAAAACwWo7sOtWGnlMBAAAAAAAAAACALEVGIwAAAAAAAAAAAACr5dCMRlIaAQAAAAAAAAAAgKxERiMAAAAAAAAAAAAAqxFoBAAAAAAAAAAAAGC1HNp1anbXAAAAAAAAAAAAAMjZyGgEAAAAAAAAAAAAYLUcmtFISiMAAAAAAAAAAACQlchoBAAAAAAAAAAAAGA1MhoBAAAAAAAAAAAAWI2MRgAAAAAAAAAAAABWI9AIAAAAAAAAAAAAwGo5tOvU7K4BAAAAAAAAAAAAkLOR0QgAAAAAAAAAAADAajk0o5GURgAAAAAAAAAAACArkdEIAAAAAAAAAAAAwGoEGgEAAAAAAAAAAABYLYd2nZrdNQAAAAAAAAAAAAByNjIaAQAAAAAAAAAAAFgth2Y0ktIIAAAAAAAAAAAAZCUyGgEAAAAAAAAAAABYLYdmNGZ3DQAAAAAAAAAAAICcjYxGAAAAAAAAAAAAAFYj0AgAAAAAAAAAAADAajm061T6TgUAAAAAAAAAAACyEhmNAAAAAAAAAAAAAKyWQzMas7sGAAAAAAAAAAAAQM5GRiMAAAAAAAAAAAAAqxFoBAAAAAAAAAAAAGC1HNp1Kn2nAgAAAAAAAAAAAFmJjEYAAAAAAAAAAAAAVsuhGY3ZXQMAAAAAAAAAAAAgZyOjEQAAAAAAAAAAAIDVcmhGIymNAAAAAAAAAAAAQFYioxEAAAAAAAAAAACA1Qg0AgAAAAAAAAAAALBaDu06NbtrAAAAAAAAAAAAAORsZDQCAAAAAAAAAAAAsFoOzWgkpREAAAAAAAAAAADISmQ0AgAAAAAAAAAAALAagUYAAAAAAAAAAAAAVsuhXadmdw0AAAAAAAAAAACAnI2MRgAAAAAAAAAAAABWy6EZjaQ0AgAAAAAAAAAAAFmJjEYAAAAAAAAAAAAAViPQCAAAAAAAAAAAAMBqdJ0KAAAAAAAAAAAAwGpkNAIAAAAAAAAAAACwWg7NaMzuGgAAAAAAAAAAAAA5GxmNAAAAAAAAAAAAAKyWQzMaSWlE9iuS114vNy4j37Kuyu+UW7fuJWjrqZuaveOC7sYnWlWWZ7E86lnPXbU9CqiAc25Fxyfq/K0YrT18TRuOhhuXe6lRab3UqEy6ZV2+E6suM/ZkZpPwDAnwr6XGdSrKq5KbalRyU748Tlrywx4NGD0/u6uGp6h4fkeNaFNJTSoXUQGX3LoRFa+NR8I16cdTiop99HmofgVXfT+swSOXa/jhFl2NiDOb3rpmcQU2KKXq7vnl4mCnm9EJ+uNypKZtPqNDFyIys0nIBkXy2Gvgc6VVv0wB5XNMvZ5tP31Lc0IvKjo+yaqyKhV1UaCPm2q651cBp9Tr2cXbsVp3NFw//XHdZNntbzVKs5xjV6I0eMnhTG0PAAAAAAAAnhwyGoEs4FbAUfP61VF7rxL64+pdfb/3ssIiYhVY112z+tRWPseMx/i7eJfUd329Vb+sq/ZeuKPFey7r11M3ZWdjo4blXE2WPXAxQt/uOG/x78S1u5Kk0LO3n+i24p/pPy+10quBTeTl6a4r1yOzuzrIBqUKOWvt243Urb6Hfr8Yoe9+PaeLN2M0oElZrXrjORVwzv3IMi7fjtVXP56y+Lfh96uSpBNXosyCjHa2Nvqydy1N619HHq5O+uHgFX376zn9duqmyhbNoxoe+bNkm/HklczvqG9711Lb6sV0/Fq0lh0I05XIOHWr46bpPWtadT3rXKuEZvaqpbplCmr/xQgt3R+mbadvydZWalC2oMXPXI2M03c7L5r9rXvgJRtkn6CgIPn5+WVJ2bt375anp6dWrVqVoeUnT54sT09PXb58+bHW6+npqZEjRz5WGVlh5MiR8vT0zO5qPBXWHvt/gqyus5+fn4KCgrKk7KchK/fPk/jO3r59W++++64aNWokT0/PZ3pfZ9Q/9VyXHZ7Fc86TkJXX8Kxy+fJleXp6avLkySbTac9/e1L3Q09LWscU7JvsYO33x9J59N90z55Rq1atkqenp3bv3p3dVTHD9ePJypEZjUB2e6dFRbm62OvzTX9q+f4rxumv+5VXj3ruGtykrCb89Ocjy6lXpqDebF5Be87d0XshfygmwTRzxM7WNHv3wMVIHbhoHlSytZHaexWXJK0+dDUzm4RnzLsTVyrseoTOXLyhxnUqauO3r2d3lfCUfdy1ugrnddCHK49p3vbzxunvd6qil14op7fbemr08qPplhF2O1aTfrR8rprUp5YkaemuS2bzRrSqpE4+bpqy8U99seGUUlJM5+eypeeBZ8Vb/uXl6mKvr34+o5UH/75+DGtSVt193DSoUWl9vvnMI8upW7qAXvcrp30XIjR6zQnF3k//emZwLSpec0IvPt5GAACQjvHjx2v9+vUaPHiwPDw8VLhw4eyu0mO7fPmygoOD5e/vrypVqmR3dfCA3bt3a8+ePerbt6/y5cuX3dUB/pEmT56sKlWqyN/fP7urgjTMnTtX+fLlU+fOnbO7KgCekMe9f8yRgUZ6TkV2civgKN9yrroSEasVDwQZJWnWjvPqWKuEWlcrpq9/PqO4+8npljXcr5zi7yfrgzXHzYKMkpSUnGLhU+Yali+kYvkcdSQsSqdv3Mv4xuCZtW3fowPZyLlKFXLW85WL6NKtGM3fcd5k3lcbTqlHg1IK8HHTJ6uPK9bCueVRCrrkVssaxRWbkKSVe03f9iuc10Ev+ZXVgfN39Pn6UxY/n5jBcxeyV8n8jqpXpqCuRMZp1UHTl1Rm77yo9l7F1bJqUU399ZziEtO/ng15vqziE5P10Q8nzYKMUsavZ/j3qFu3rg4fPqxcuXLkzxXkMLTX9GXl/jl8+LBsbR+vo6bffvtNjRo10rBhw55QrbJfWFiYpkyZIjc3N4sPip7EfkPm7NmzR1OmTFFAQACBRiu5ubnp8OHDsrOzM5lOe/7bq6++qpdffln29vbZXZUMSeuYGr4j/+ZAY1r75p9i/vz5cnNz+1cHGmfPnp3dVQCeqEfdPz4Kv4SAJ6xOqQKSpN3n7ujhx6YxCUk6fDlSvuVcVb1kPu1LZ4yycoWdVbFoHv166qai4hLlXaqAKhfPI0k6FR6t/RcizMpPS6daJSRJIYeuPGJJADlBg4qFJEnbT94wyya8F5+k/efu6PnKRVS7dAHt/POW1eW/WNddDrnttHLPZd19aKzHNjWLyyGXndYeuCKH3Lbyq1pUpQu76F58ovadva3jV+5mervwdHmXSu3idu958+tZ7P0kHb0SpXplCqpaybzabyGb3qBsIWdVKOqibX/eUlRcomp75JdnsTxKSZFO34jWgYuRaV7P8jjYqU31YirknFvRCUk6GR6tP67Shp410dHRypMnj1WfsbW1lYODQxbVCFktKSlJCQkJcnJyyu6qPBW01/Rl5f55EuXevHlTBQoUePzKPCQuLk65cuX6Rwagn+X2mplrCrLG0z4WNjY2Ftvus9yen7TsPuekpKQoJiZGLi4uGVo+rWMK9s2z4FkJ6ANPS4585cfGxiZb/gApNZNIki7dibU43zC9lGv6D16qlMgrSbpzL0HTetbUtJ419Zpfeb3mV15TetTUggF15F7A8ZH1KZLXXr7lXHU3LlGbj9+wZlMAPKPKFU39YXfuuuUM5vN/ZTaXLZqxH4APC2xQSpK0eKd5l5Zef71s4WRvpy2jmmha/zr6T/vKGtOluta/+7ym9fOWY+4cefuR45QqmHqdunQnzuJ8w/XMo+Ajrmd/vSRzJyZBk7vV0Nfdamhok7Ia9kJZfdW1hub0qS23NK5nFYvm0aiWFfVy4zJ6s1l5zehZU98F1VK5ws6Z3awcKTo6Wl999ZW6desmX19fVa9eXX5+fhozZowiIiJMln1wvJetW7eqW7du8vLykq+vrz744APFxMSYlX/hwgUNHTpUderUUe3atTVgwACdOHHCYl0MY8mdOHFCL7/8surWras6deoY5585c0YjRoxQw4YNVb16dTVr1kzjx49XdHS0STlpjZkVHR2tsWPHqlGjRvLy8lJAQIA2bNhg9T777bffTLZ91KhRun077XGsf/rpJ/Xu3Vve3t7y8vJSp06dtHz5cpNlevTooTp16iguzvw7c/XqVVWpUkXvvPOOyfRdu3bppZdeUt26dVW9enW1bt1aM2fOVFJSxrLNM7o/DWOj7Ny5U9OmTZOfn5+qV6+uli1basGCBRbLvnjxokaOHKlGjRqpevXqev755/Xhhx+a7SfDeDanT5/WhAkT1LRpU9WoUSNDx2X+/Plq2bKlsc1OnTrV4rZnZRsPDw/X+PHjFRAQoHr16hn3y5dffmnxWFpiqb0+OC0kJETt27dXjRo11LhxY33xxRcWt/Po0aPq16+fatWqpbp162r48OHpjhO0Zs0ade3aVbVq1VKtWrXUrVs3/fDDD2bLGcYQunHjht555x3Vr19fXl5e6tWrl44cOWKx7Iy0eUk6dOiQBg8ebGwnjRo1UlBQkDZv3pzu/klOTtb06dMVFBRk/Gzjxo31n//8R1euZPzlSEtj6ximHT58WH369FHt2rXl4+OjESNG6Natv1+wMoyjlJKSouDgYHl6eprV09p9HBYWphEjRqh+/fqqWbOmrl27ZvyOnDlzRuPHj1fjxo1Vs2ZNBQYG6vDhw5Kk/fv3KygoSLVr15avr6/GjRunxETTl7nOnDmjjz76SO3atVOdOnXk5eWl9u3ba/bs2SbtafLkyerTp48kadSoUcbtenDsybTGJNq3b59effVV4/fshRde0FtvvaWLF/++58vIMU/PuXPn9Oqrr8rb21ve3t4aOHCgTp06ZXGsq/SuKZlpQxk956Q3Tpeleu7YsUNvvvmm/P395eXlJW9vb/Xq1Us///yz2WenTJkiSWrWrJnx2Dw4BltCQoJmzZql9u3bG8vq16+f9u7da1aXNWvWqHv37qpXr568vLz0wgsvaNiwYTp9+rTF7bckLCxMw4cPV926dVWrVi31799fx44dM1nmwfPqTz/9pC5duqhmzZp69dVXJWX8PHr//n3Vrl1bw4cPNyl/1qxZ8vT0VN++fU2mL126VJ6ensZtt2aMxvXr12vIkCHGa1K9evU0cOBA7du3z2S5MWPGGK9hD0tISJCvr69JxlZGj3V60hp319L2WXstebjtLl++XJ6enlqzZo3FuhiuOQ/eN9y8eVMff/yx8V6hYcOGevvtt82+Dw/eW8yYMUMtW7ZUjRo19N1330lKPWe9+eabatKkiapXr64GDRooMDDQ5Fry8DYbtleSyXnZMO2VV16Rl5eXIiPNX3K8efOmqlevrtdeey2NPW++zh9//FEBAQHy8vJSo0aN9Mknn+jePdPf0NbcJ6SkpGjBggXq1KmT8b7Z399fb731lq5fv25cLjP7xiA5OVnz589Xhw4djG2wT58++u2338y21dDWDOddQ50GDRqkCxcumJWbkXOqoV5hYWHas2ePyTF6sI1k9D4yLYZrdEREhN5//301aNDA+Dvk/PnzkqQtW7boxRdfVM2aNdW4cWPNmDHDYlm///67XnnlFdWrV081atRQq1atNHXqVCUkJFhcPi4uTuPGjVPjxo1Vo0YNdejQId1rf0Zk9HuVFmva4YPnjcWLF6t169aqUaOG/Pz8NHnyZLP7C8O+vnPnjkaNGiVfX195eXmpe/fuCg0NzVD9pNRz/Ndffy0/Pz/VqFFDrVu31uLFi9NcPjo6Wl9++aXxulyvXj0NGTLE5HfmhQsX5OnpqdGjR1ssY9q0afL09DSppzXX0bRk5v7Pmmvpxo0bjeeeF154wZgde/fuXX3wwQd67rnn5OXlpT59+ujcuXNm601JSdGyZcvUpUsXYx0DAwMt3otl9L44I/ePj/LPe7UOeMblcUjt1iA6LtHi/HvxiX8tl/7Xz9U59c2Y9jVL6MbdeI1YdkS/X45UIRd7DXiutFpXL6bPu9ZQr9n70u2GsINXCeWytdGPx8IV/4iu7QDkDHkdc0uS7qZxHjJkIeZzym112fXLu6p8sTw6cSVKB87fMZtfKG/quevN1pW0/9wdvTx7v87duCfPEnn14YvV1LpWCd1LSNQ7iw9bvW48XS5/XafuJaR1PUt9sPGo61kB59R21rZGcd2Mjtc7q47pcFiUXJ1zq1+DUmpZtagmBFRV33kHTa5n3+8L09Y/b+rSnVglJCarlKuzetV1V1PPwprUtYb6Lziom9GWfxz+24SHh2vZsmVq3ry52rRpI3t7ex05ckRLly7V/v37tWLFCuXObfp937ZtmxYuXKjAwEAFBAQoNDRUS5culZT60M3g6tWrCgwM1N27dxUYGKhy5crp999/V1BQUJoZQFevXlVQUJD8/f315ptv6ubNm5Kk48ePq1evXkpKSlLPnj3l7u6uAwcO6LvvvlNoaKiWLFmSbgZcYmKiBg0apAMHDqh58+Zq0KCBrly5ovfee09ly5bN8P769ddfNWTIELm6umrQoEHKnz+/Nm3apJdeesni8l9//bWmTp2q+vXra9iwYXJwcNCOHTs0evRoXbhwQW+//bYkqXPnzho9erQ2btyoDh06mJQRHBys5ORkk4eVK1as0OjRo1W1alUNGjRI+fLl04EDB/TFF1/o+PHj+vLLL9Pdjszsz4kTJyo6OlrdunWTvb291q1bp7Fjx+rmzZsaMWKESdlBQUFydHTUiy++KDc3N50/f15LlixRaGioVqxYobx585qU/fbbb8vOzk49e/aUs7PzI4/JxIkTNWvWLFWrVk1vvvmmEhIStGLFCm3ZssVs2axs4ydPntRPP/2kZs2a6cUXX1RKSor27NmjGTNm6I8//tCsWbPS3Y5HWbp0qcLDw9WlSxe5urpq06ZNmjFjhvLkyaOXX37ZuNyxY8fUu3dv2draqnfv3ipRooR27NihPn36WHwBYNKkSZo2bZoqVaqkoUOHKiUlRWvXrtWbb76pS5cuafDgwSbLx8TEqFevXqpWrZpee+013bp1S3PnztWgQYO0efNmk6ykjLb5c+fOqV+/fnJ1dVXPnj1VtGhR3blzR8eOHdPBgwfT7fLu/v37mjVrllq0aKEmTZoob968OnnypFauXKnQ0FCtWbPmsbIMT5w4oUGDBqljx45q06aNjh07puXLlysqKsr4MKd79+5q0KCB3n33Xfn4+Khbt26SJG9v70zt43v37qlXr16qUaOGXnvtNd27d0/Ozn+/GPOf//xHDg4OeumllxQbG6vvvvtO/fv314QJEzRq1Ch16dJFbdq00bZt2zR37ly5urrqlVdeMX5+z5492r17t1544QW5u7srISFBW7du1YQJE3Tp0iV9+OGHkqTmzZsrMTFR06dPV/fu3Y2BuUeNPbl8+XJ98MEHcnV1VdeuXeXu7q4bN25ox44dOnXqlEqVKvVYx1xKDWoFBgYqJiZGgYGBKlu2rI4cOaKgoCDlz5/f4mfSuqZY24asOedYKzg4WDdv3lSHDh1UvHhx3b59W8HBwXr11Vf15Zdfqk2bNpKkwYMHG685o0aNUsGCBSXJGERJTEzUyy+/rL1796pt27YKDAxUXFyc1qxZo759+2rq1Klq2rSppNSHoO+88468vb01dOhQOTs7Kzw8XLt379a5c+dUoUKFR9Y7JiZGQUFBqlKlit544w1dvXpVixcvVu/evbVkyRJVrlzZZPktW7Zo3rx5CgwMVLdu3ZTyV9cpGT2P5s6dW3Xq1NHu3buVnJxs7O40NDRUtra2OnjwoOLj441ZXDt37pSzs7Nq1apl9TFZuHCh8uXLpy5duqho0aK6evWqVqxYob59+2rBggXG73n37t21aNEiLV++XKNGjTIpY/Pmzbpz547J9TGjx/pJy+i15GGtW7fWJ598ouDgYLN7k6tXr2r37t1q166d8RpguO+LiYlRly5dVKZMGYWHh2vJkiX67bfftHLlSpUsWdKknAkTJig2NladOnWSq6urihcvrjt37qhPnz5KTk5W9+7d5e7urqioKJ06dUp79uxR165dLda3fPnymjBhgtl52aB79+769ddftXr1auMDcYNVq1bp/v37Zp9Jyy+//KI5c+aoZ8+e6tKli0JDQzV//nydOHFCc+fONXZZas19wvTp0/XVV1+pSZMm6tq1q3Lnzq0rV65o+/btun79uvGcmZl9YzBy5EitXr1a3t7eevPNN3Xv3j2tWLFCAwcO1Pjx49WxY0eT5cPDw9W7d2/5+fnp7bff1oULF7Rw4UINGTJEa9euNX4PM3pOdXV11YQJEzRu3DgVLFjQ5Fro6uoqKXP3kWl56aWXVLhwYQ0bNkzXr1/XnDlzNGDAAL3++usaP368AgMD1blzZ61fv15ffPGF3Nzc1K5dO+Pnt23bpiFDhsjFxUU9evRQkSJFtHXrVn399dc6ePCgZs6cadb18rvvvitJ6t+/vxISEhQcHKw333xTMTExjzw+lmTme/WwzNyvLly4UOHh4QoMDFT+/Pm1ZcsWTZkyRRcvXtRnn31mtvzAgQOVJ08eDRkyRJGRkVq6dKleeuklffPNN3r++ecfuZ3vvvuu1q9fL19fX/Xr108RERGaPHmySpQoYbZsdHS0evTooYsXL6pTp06qXLmyoqKitGzZMgUGBmrRokWqVq2aSpcuLW9vb23YsEGjR4+Wo6PpC8ohISEqWbKkfH19JVl3HU1LZu6xrbmW/vrrr1q0aJF69OihLl26aP369ZowYYIcHBy0atUqFS1aVEOGDDG296FDh2rdunUm7XTUqFEKCQlRs2bN1L59e0nSpk2bNHToUH344Yfq0aOHyTozcl+c2fvHBxFoBP6hDEmyuWxt9N/Vx3X0SpQkKSYhVh+tO6HShZxUtUQ+NfUsrE1pZCraSGrvVVySFHLoqsVlAMAahmzG70MvWZxv+9fJKyLmvl6atU/Rf71ccehChAZ9u08/v/eCAnzcNfGHkwqPjH86lUa2MrSJXLY2+nDdSR37q+vTmIQkjd1wSqVcnVSleF41qVRIW07cNH5u6lbTN/dOhkfrg3Un9LFNZb1QqbB6+Lhp8q/mb/f9G3l4eGjr1q1mgRZvb2+NHj1amzdvVuvWrU3mnTp1SuvWrZOHh4ek1Gy8gQMHatWqVRo5cqTx4fiXX36p27dva+rUqcYHyD179tTMmTP1+eefy83Nzaw+hgfeD//AGTt2rGJiYrRo0SLjD5devXqpbNmymjx5subMmaMhQ4akuZ0hISE6cOCA+vTpo/fff9843d/f32xdaUlOTtaYMWPk4OCg5cuXG3/49urVy5iZ8aA//vhD06ZNU1BQkMmbtL169dLHH3+s2bNnq3v37vLw8Ej3YV5ISIjc3NyMP4Jv3LihMWPGqFmzZpoyZYqxd5TAwEBVrlxZ48ePV48ePVSvXr00tyUz+/PWrVtau3atcVyw3r17q1evXpo5c6ZefPFFlSqVeo4fNWqU8ufPr5UrV5o8qG/VqpUCAwM1b948s/HsnJ2dNW/ePLN2aMmFCxc0e/ZseXl5adGiRcaupwIDA00eDhlkZRuvV6+eNm/ebPLjPSgoSF9++aWmT5+uw4cPy8vL65HblJawsDD98MMPxiCKYRvnz59v8nD4f//7n+Lj47V8+XJVr15dUurx/OCDD4wBUoPz589r+vTpqly5sr7//ntjQLl3797q3r27vv76a7Vr107u7u7Gz9y5c0f9+/c3CVyVL19eb775pn744Qd1795dknVtfvv27YqNjdWXX36pmjVrWrVf7O3ttWPHDrNguL+/v/r3768VK1akGfzPiBMnTmjx4sXGYIKU2vPR0qVLde7cOZUtW1a1a9dW7dq19e6778rDw8Pk4Wxm9nFERIS6du1qDMQ+rGDBgpoxY4axrVWoUEFDhw7V8OHDtWTJEuM+7NGjhzp16qQFCxaYHK+OHTuanev69eunt956S8uWLdPQoUNVpEgRVa5cWZGRkZo+fbpq1apl9tDZkvDwcI0ZM0YlS5bU8uXLjQ+LJWnYsGFKTk59YfVxjrkkffHFF4qIiNDMmTPVpEkT4/S5c+dq3LhxVl1TrGlD1p5zrPXxxx+bBJUlqW/fvurUqZOmTp1qDD4999xzOnDggDZt2iR/f3+T9iNJixYtUmhoqKZMmaLmzZsbp/fp00fdunXT2LFjjQ9IN27cKBcXF82fP9/k3GjNWKN37txRmzZt9MEHHxintWrVSl27dtUnn3xilvH+559/Kjg4WJUqVTKZbs15tGHDhtq+fbuOHj0qLy8vJSQkaP/+/erQoYNCQkK0b98+Pffcc0pJSdGuXbtUp06dDF1XHvbtt9+aHZMePXqoXbt2mj59umbOnCkpNchbu3ZthYSE6K233jLpCnHZsmVydnY2aSMZPdZPWkavJQ/LkyePmjdvrnXr1unq1asmD/wtvQQ1duxYxcbGatWqVcbrp5T6MlX79u01efJkjRs3zmQd9+7dU0hIiEl3qVu2bNHNmzetDr4WLlxYHTt2tHhelqQmTZqoRIkSWr58uUmgMSUlRStWrJC7u7uee+65DK3rjz/+0LJly4xts1evXho7dqwWLFig1atXG/eLNe1748aNKl++vLF9GbzxxhvGfx84cCBT+0ZKDcqvXr1azz//vKZPn24Mhhra9tixY+Xv729yLC5cuKDPP//cpB27urrq888/186dO9WoUSNJGT+nOjs7q2PHjpo0aZLxeD0sM/eRaalatarJC2IFCxbUuHHj9OGHH2rt2rXG82jXrl3VtGlTLVy40LitSUlJ+vDDD2VnZ6dly5apdOnSklKv5aNGjdKqVau0du1ai9vw/fffG88HvXr1UocOHTRu3Di1bt3a6i6jM/O9elhm7lfPnj2rH374wXht7d27t4YPH641a9aoS5cuql+/vsnyRYsW1bRp04zrMLwANWbMGG3cuDHdsXBDQ0O1fv16NW/eXJMnTzb+tgkICLB4nf3666917tw5LVq0yOR+okePHmrfvr0+/fRT4zUorZc59+3bpwsXLmjIkCHG9VlzHbUks/fY1lxLT58+bfIbxdB2x44dq8DAQOOLY9Lf7f3B7+rmzZsVHBysUaNGqV+/fsZl+/btq8GDB2vixIlq3769STvNyH1xZu4fH5Yj+y6zscmeP0CSog0ZHo6W4/iGDBHDw/e0y0mdfzM63hhkfND2v8ZVq1oy7QHkG5R3VfH8jjoSFqUzNyx3oQgg57kbd1+SlDeN81Bep9TpUbH3rSo3v3Nuta5ZXLEJSVq1z3IXH4Yyd/550+w8dyMqXocuRMjO1kZeHgWsWjeePkMGvot9WtezvzL4M3g9uxWdYAwyPmjH6dTuc6oWz9hbrSG/p744U9PdcubDv5G9vb3xIVxiYqKioqJ0+/ZtY1DL0DXfg/z9/U1+6EqpD0Dv379v7MInOTlZmzdvVoUKFcyyVPr27Wv2oM2gQIECZm+T37592/jw8MGuVKXUt2ednZ21cePGdLfTMP/ht0hr166tBg0apPtZg6NHjyosLEydOnUyedhmZ2dn8kDfYO3atUpJSVGXLl10+/Ztkz8/Pz8lJydr586dkv5+mLdr1y5dvfr3C16GH8EdO3Y0/gj+6aefFB8fr65du+rOnTsm5b7wwguSUrtnS0tm92fPnj2NQUYpte3079/feKyl1ADd8ePH1bZtWyUnJ5vUzd3dXaVKlbJYtwEDBmT4YfCmTZuUnJysAQMGmDzULVCggHr16mW2fFa1cUlydHQ0eZs/IiJCt2/fNj6otFS2NV588UWTTC1bW1s1aNBAN27cMHbPZjiejRs3NgYZDYYOHWpW5ubNm5WcnKxBgwaZPBB0dnbWwIEDlZSUZJalZWtra/IwQkp94C/J2AWZZF2bN7SlzZs3Z7ibWQMbGxtj3ZOTk43HtHLlysqbN+9j7/datWqZPEyRZDymD25vWjKzjyVp0KBBaZbZr18/k4d0devWlSR5eXmZBe18fHxM2ohh3QYJCQnGttq4cWMlJSXp6NGjj9yutGzYsEEJCQkaOnSoSZDRwFDvxznmycnJ+vnnn1WpUiWTIKOUem5Ka0w3S9cUybo2ZO05x1oPHpuYmBjduXNHsbGxql+/vk6fPm3WnXVaVq9eLTc3N9WpU8fku3f37l35+fnp8uXLxi7U8ubNq7i4OP3yyy/GQHBmPHztM3SHu2fPHrMuDps0aWIWZJSsO48arte7du2SlBp0iYuLU79+/VS4cGFj93fHjx9XREREhq/vD3vwmERHR+vOnTuys7NTzZo19fvvv5ssGxgYqIiICG3atMk47dKlS9q1a5fatWtn0jaf1LG2VkauJWnp3LmzkpOTFRISYjL94Zeg7t69q59//lnPP/+8XFxcTNqgIbN0+/btZuX36tXL7PtrOFds3bpVUVHmz7Myy87OTl27dtWpU6d06NAh4/Tdu3frwoUL6tq1a4aHtjJ0Tfggw0tnD95DWdO+8+bNq/DwcO3ZsyfN9T7OvjHUa8iQIcYgoyRjlnlUVJRZV5dFixY1C/RYuv4/qetyZu8j0zJgwACT/ze8hOfn52cS7LG3t5eXl5dJN5PHjh1TWFiYOnbsaAwyGhi6cLZ0v/zwtSJv3rzq2bOn7t27Z7GL2vRk9nv1sMzcr3bo0MHkBR5bW1vjOd/Sdr/yyism9yklSpRQx44ddenSJf3xxx/p1s9Q3ssvv2zyHfTw8DBm3BmkpKRozZo1qlWrljw8PEz2SWJiop577jnt37/feJ/RunVrOTk5KTg42KSc4OBg2djYKCAgwDjNmuuoJZm9/7PmWvrwbxRD201JSTG7Vze09we/q2vWrJGjo6Nat25tdq/u7++v6Ohok/Oj9Pj3xRlFRiPwhF28ldq1UVpjVhmmX7xteQxHgwt/zU+rC9aov6Y75Er7fYFOtVIfoIUcyvg4JwCefWevpz8GY5ki6Y/hmJYX67rLIbedVuy5ZOx+Na11R6Ux3xCIdGCcxn+8i8YxGC2Pn+hhHMMx/euZ4Xp3N42ApGG6fTrXswdF/NW2GOvT1LJly7R48WL9+eefZuNuPDyGnSSzAIwk4xvHhuVv3bqle/fuqXz58mbLOjg4qFSpUrp71zx47OHhYfLwQ0p9YCfJ4gNKJycneXh4mIwBZsnFixdVsGBBFSpUyGxehQoVjMGP9BjqYalLuYoVK5pNO3PmjCSl+0anoRs/KfVh3po1axQSEmJ8WGXpR7ChXEvBTUvlprUd1u5PS8fSsC8MY/UY6jZjxow0x7qx1H7KlCmTZn0fZqhbRo+DlDVtXEp923327NkKDg7W+fPnzR7YWyrbGg9nLD1cDxcXF+P+sHR8ihUrZta9WHrH3zDNsIxB0aJFjd0RGhi6bXxwG61p823atNG6des0c+ZMzZ07V15eXvLx8VHbtm0t1u1hmzdv1rfffqujR4/q/n3Tl58ed79n9PinJTP72NXVNc3uPy3VybCspboa5hnaiCTFxsZq2rRp+uGHHxQWFmb2GUtjlmWU4SFT1apV013ucY75rVu3FBMTY7FbZXt7e3l4eGT4mmKQ0TaUmXOONS5fvqxJkyZp27ZtFttXVFRUhjJgzp49q9jY2HSDa7du3VLZsmX16quv6sCBAxo+fLjy588vb29v+fr6ql27dhnu5ixfvnwqVqyY2fQKFSpo27ZtunTpkkngOa3zvDXn0cqVK8vV1VU7d+7Uyy+/rNDQULm6uqpy5cry9fU1XssN/81soPHEiRP6+uuvtWvXLrNA3MOBqNatW2vcuHFatmyZ2rZtKym1K+GUlBSzIPeTOtbWysi1JC2+vr5yc3MzuTexlAl07tw5JScna+3atVq7dq3FsixlNFn6TtetW1cvvviiVq5cqXXr1qlq1aqqU6eOWrZsqdq1az9ye9PTtWtXTZs2TcuWLTN2q7t8+XLlypVLL774YobLsXTNLVSokAoUKGAyfqE17futt97S0KFDFRQUpMKFC6tOnTpq0KCB2rVrZ7yWP86+Se/aZOiC+eFrkzXXwydxXc7sfWRaHl7WEKhN69r5YD0NL5ZZ2l8lS5ZUnjx5LN4vW7pWPHy/nFGZ/V49LDP3q5bauGGape1Ib7svXrxo9jLcg9K7zj487c6dO7pz54727t2b7jn+zp07KlGihPLkyaMWLVpo7dq1xszs2NhY/fjjj6pTp46xRxbJuuuoJZm5/7P2WprefV9a7f3he/W4uLh0u7N9+Hfk494XZ1SODDTakl6IbLT/YoQkqX7ZgrKR9ODoic72dvJyz6/YhCSLWYoPOhoWpZiEJBXP7yjH3LaKu296ESlfOPVG8mqE5TdJC+exV8PyhXQ3LlGb0+haFUDOFPpXxnNjzyKysZFSHjgRuTjYqU7ZgoqJT9TBCxFWlRvYIPXmZMlOy92mStJvJ2/qtZYV5ZlGdlrF4qk/vC8/4mULZL8DF1MfWNYtY349c8ptp+ol8yn2fpKOXTF/KPigY1fvKiYhSSXyOcgxl63iHhovuFyh1DfDr0ZmLDOiWonUtnUljevfv9G8efP0v//9Tw0bNtT//d//qWjRorK3tzeOaZjy4EngL2k9tJVkcXlrpDfO4rPG8CN+xowZJm82P+jBH24PP8yLi4vTjz/+KB8fH5MfwYZyx44da7GrQCk1MJQdDMc/KChIfn5+Fpd5OGAlyWzMlCcpK9v4+PHjNW/ePLVs2VKDBg1SoUKFlDt3boWHh2vkyJGP/X3Iyu9aVtTDmjZvb2+vb7/9VseOHdOOHTu0f/9+zZs3TzNmzNC7775rloXwoM2bN2vo0KGqXr26Ro0apRIlShjb0IgRI57J/f6oc19aDxIzWte3335bW7ZsUdeuXeXj46OCBQsqV65cOnr0qD7//PPHymrLqMc55pmV1n7NyjaUXkbUwy863Lt3T71799bdu3fVp08feXp6Kk+ePLK1tTUGEjJ6bJKTk1W2bFmT7tceZgiMlipVSuvWrdOePXsUGhqqffv2afz48Zo0aZJmzpxpzJh9ktI6FtacR21sbOTr66stW7YoPj5eO3fulK+vr2xsbNSwYUOtX79eERERCg0NVYECBVSlShWr63nt2jXjeMGvvPKKypUrJycnJ9na2mrGjBnGbEoDBwcHBQQEaO7cubp48aJKliypVatWqVq1aqpRo4ZxuSd5rC1JSkpKc97jnNNsbGzUsWNHTZs2TQcOHJC3t7dCQkJkY2Nj0m2qoZzWrVtneJxDKe3r///+9z8NHDhQ27dv1/79+7Vy5UrNmTPHrGtuaxUtWlR+fn7asGGD3nvvPd2/f18bN26Un5+fihQpkuly02JN+65Zs6Y2bdqknTt3avfu3dq7d69++uknff3111q4cKExyJNV+8aSjLadJ3VOzex9ZFrSqn9625VRGc1+fRyZ/V49LKvvV58mw3mybt266Q6b8WBwLiAgQKtXr9bq1as1ePBgbdq0SdHR0SbnMEPZGb2OZpf02m5a8x6+V8+bN6++/vrrNMt5OLj7tO6Lc2SgEchOYRFx2nX2tnzLuapLnZJavv/vbMJBjcrI2d5Oqw5eMQkclnZNvWG/8MCD9/jEZK09fFXdfdz1yvNlNWnLGeO88kVc1KZGMSUmJevnk5aDiO29iiuXrY1+PBau+MSs/9EJ4J/j4q0YbTtxQ89XLqI+jcpo3vbzxnlvtK4kF4dcWvTbBcUm/P1jttxf2Y9n08hyrFuuoCoWz6sTV6J04PydNNe95+xtHbscqbrlXdWiRjFtPBJunBfo66GKxfPq3I17OvzXSxn457oSGac95++oXpmC6ly7hFYe/LsryIENS8nZ3k4hv181CRyWcjXP2o9PTNYPR8PV1bukBjUqbTKuYrnCzmpdragSk5L166lbxunlCzvr/O1YJSWb3vSWL+ysQY1Su73ZyEs0Roaur2bPnm3yINvwRnFmFSpUSC4uLhbLiY+PN2YYZoQhMPHnn3+azYuLi9OlS5fMujR6WKlSpXTu3DndunXLLKvx9OnTVtXD0vKW6lamTBlt375dRYoUUbVq1R5Z/sMP8y5fvqzo6GiTbEbp77f/8+fPb+y+yhqZ3Z9nzpwx6wbXsC8Myz+YsZKZumWEIeh6+vRpsx/7lrYpq9q4oWwfHx+zH+tbt2597LIzyrA/LG1PeHi4WZbXg/vv4TeuT506Jcm6bIEHWdvmJalatWrGZSMjIxUYGKgvvvhCvXv3TjNYGRISIgcHBy1cuNAkeBETE/NEu9nLrKzcx5lx9+5dbdmyRR06dNDHH39sMs9Sl1fWPjw1fO+PHz+uypUrP3L5zBzzQoUKydnZ2WKXZQkJCbp06ZLJWF6PYk0bsvacY8gsiIyMNMsku3Tpksk2GrrL/uSTT9SlSxeTZZctW2ZWdnrHpkyZMrp27Zrq1aunXLke/bgud+7ceu6554zdn504cUJdunTR119/bTYmlCVRUVEKDw83y8QwXBcy2satPY82aNBA69ev1y+//KJjx46pa9euxunJycnatm2b9u/fryZNmmQqELBx40bdu3dPU6dONctq+fLLLy1+pnv37pozZ46WL1+umjVr6saNG2ZjyFl7rNNSoEABixkkD2fJPEkBAQH65ptvFBwcrKpVq2rDhg3y8fExOcalSpWSra2t4uLintj1v3z58ipfvrz69eunuLg4DRo0SAsWLFC/fv0sZmlmVGBgoDZu3Kh169YpLi5OCQkJVgdxLF1zb926pYiICJPMQmvbt5OTk5o1a6ZmzZpJkrZt26ZBgwZp1qxZ+vTTT43LZWbfPHgue7jL7ce9Nj2p6/LTuI/MqPTul69evaq7d++ajVMope7fh6+FD98vZ9ST+l5l5n7VUhs3TLO0HadPnzbLqjVs94MvTFryYNt8uEvih393ubq6Kl++fIqMjMzwPjG8zBkcHKzBgwcrODhYzs7OatWqlcly1l5H09uOjN7/PalraUaVKVNGZ8+eVZUqVTL8WzyjHjf4niP7nGKMRmS3zzb+qdv3EvRW84oa37maXm1SVlN6eKlHPXdduBWj6VtNf1wtfbmelr5cz6ycGdvO62T4XfWo665vg2rrNb9y+rBdZc3uU1uOue309S9nFWYho8NGUoeahm5Tr5rNR87X/gUvzfyot2Z+1Ftv908dALm+V1njtHEjAh5RAp51/11+VDfvxuvDF6tpxsA6eqedpxYNqa+XXiins9ejNfGHkybLb3nvBW1574U0y+vRIPWG6/vQR/8AfnvR74qMua9v+tfRzIF1NKpDZX33cl2NC/TSvfhEvb3odyU/Oy/c/at9vvmMbt9L0Bt+5fW/jlX0SqPS+qprdXX3cdPF2zGatcO0y5VF/etoUf86ZuV8+9sFnboerW513DS9h5eGNimr/7aupBk9a8oht52mbj2vKw9kNHav46bVg+vpfx2r6A2/chrSpIzGd6qqb4Nqq4BTbq05fE2bTxBoNDAEXh58iz4lJUVTp0597HKbNWum06dPG8fvM5g3b55iYmIyXJarq6t8fHy0Y8cOszFEvvvuO8XExKhFixbpltG8eer1bPr06SbTDx48aDYeTVqqVatmzDh8cBzF5ORki907GbqP/OKLL8y6kJJSH/4nJCSYTOvcubNsbGwUHByc5o/g1q1by8HBQZMnT7Y4tlJcXFy64zxldn8uXrzY5GFRQkKC5syZYzzWklSlShVVqlRJK1assPiAIiUlxWysEWv5+/vLxsZG3333ncn+i4iI0KJFi8yWz6o2bij74Td579+/r5kzZz522Rnl6uqqOnXqaPv27Tp27JjJvGnTppkt7+/vL1tbW82ePVvx8fHG6bGxsZo9e7bs7OyMx9Na1rR5S+0gf/788vDw0P3799MdN8zW1lY2NjZm2T/Tpk17Kpl5j5KV+zgzDA9+Hm6r0dHRmjt3rtnyhnHkMtqdauvWrWVvb69p06ZZDIAYjsnjHnM/Pz+dOnXK7MHo4sWLHznOnKXyMtqGrD3nGF4GebhL7tWrV+vGDdP7D0OWwMPH5sSJE2bXTin9Y9OpUydFRkaaXecMHuwKzdKxKF++vJycnKzqBu3ha9/Ro0e1Y8cO1a1b1+J4nZZYex41BP8mT56spKQk4/+XLFlSZcqU0YwZMx7Z9V160src2Lp1a5rjzJUtW1b169dXcHCwlixZImdnZ7Nx7aw91mkpW7aszp07p/Dwv1/ITE5O1pw5czJchrVKlSolHx8fbdiwQWvXrrWYCVSwYEE1adJEW7duNcv6NEivW/cHRUREmH0PHR0djVk2j2qjzs7O6S7TsGFDlSpVSsuWLdOKFSvk5uamRo0aZahuBr/99ptZe/jmm28k/X3PKVnXvi19Lw3dTRq253H2zYP3wg+Wcfv2bS1evFj58uXL9PfG2uuyi4uLxbo+jfvIjKpatarc3Ny0Zs0asy7HDfeQlu6XH75W3L17V4sXL5azs7PxxY6MelLfq8zcrz683Q/+3nmwjRvMmDHD5FhfvXpVq1evlru7+yO7Vjfsx5kzZ5rU89KlS2Zdxtra2qpDhw46deqU2biLBg/vExsbG3Xq1Ennz5/X+vXrtWvXLrVs2dKs22hrrqOWZPb+70lcSzOqU6dOkqTPPvvMYjZiRs/Tllh7//gwMhqBLBAWEad+cw/o5cZl5FuuoBqWd9XN6AR9v/eyZu+4kOY4VQ+LSUjS4IWH1LdBKflVLqIu3m6KT0zW75cjtWj3Ze1JI6vIt5yrSuR31JGwKJ25Yd0PNuQMXp7uCurgazKtnEcRlfNI7UrkwpVbGvWl5Qs6coaLt2LU4fMdGtG6kppULqIXqhTVjag4fbf1nCb9eCrNMRQtyeeUS61rllBsQpJW7bv8yOVPXL2rdhO36/VWFdXYs4heqFpUd6ITFLIvTJM3/plm1iT+ea5ExmnQokMa2LC06pcpKN+yBXXrXoKW7Q/TnNCLio5Pu4unB8UkJGnY94fVu56HmlYqrM61Sig+MVmHw6L0/b4w7X2oG9/tZ27J2cFO5Qu7yNsjv+xz2SoqNlG7z93R2iPX9NuZp/Pj9FnRqlUrTZw4UQMHDlTLli0VFxenTZs2WQwSWOuNN97Q9u3b9cYbbygwMFDlypXT77//rp9//lmlSpVKt5uvh40ePVq9evVS3759FRgYKA8PD+3fv1/r1q1T5cqV1b9//3Q/HxAQoJUrV2r+/Pm6du2afH19dfXqVS1atEhVq1Y1C9BYYmdnp/fff1/Dhg1T165dFRgYqHz58mnTpk0WH3LXqFFDb7zxhr766iu1a9dO7dq1U/HixXXr1i2dOnVKW7Zs0Q8//GDy5rmHh4d8fHyMb9l37NjR7EdwsWLFNGbMGL333ntq1aqVAgIC5OHhoYiICJ09e1abNm3S1KlTLb5l/Tj7s1ChQurSpYtefPFF5c6dW+vWrdOxY8f08ssvG99strGx0Weffaa+ffsqICBAAQEBqlSpkhITExUWFqbNmzcrICBAw4cPf+T+TkuZMmXUv39/fffddwoMDFS7du10//59LV++XEWLFjV7kJ+VbbxVq1ZasmSJXnvtNT333HOKjIzU2rVrrerW60kYNWqUevfuraCgIPXq1UvFixfXb7/9puPHj5u9rVy6dGkNHjxY06ZNU7du3dS+fXulpKRozZo1OnXqlEaMGJHpTBFr2vw333yj7du3q2nTpnJ3d5ednZ327t2rrVu3qmnTpum+Zd2qVSv99NNPCgoKUkBAgFJSUrRjxw6dPn36ib+dnRlZuY8zI0+ePGrcuLHWrl0re3t71axZU9evX9fKlSvTHLfWxcVFixcvlqOjo/LlyydXV9c0H0AXK1ZMo0eP1v/93/+pXbt26ty5s9zd3XXr1i1t375dAwYMkL+//2Mdcyn1mrJjxw4NGzbMeE05fPiwfv75Z5UuXdqsW9L0WNOGrD3nNGzYUBUqVNCkSZN0+/ZtlS5dWkePHrVYT29vbxUpUkTjx4/X5cuX5ebmpjNnzmjZsmWqVKmS2bXJkIU0ceJEtW/fXg4ODqpYsaIqVaqkPn36KDQ0VJMnT9bevXv13HPPqUCBArp69aoOHjyoS5cuacuWLZKkgQMHysXFRT4+PipZsqRiY2O1fv16RUVFGcfhe5SCBQvq119/VXh4uBo2bKhr165p0aJFcnBw0HvvvWfVsbDmPOrh4SF3d3edPn1aHh4eJtkeDRo00JIlS4z/zoznn39ezs7Oeuedd9SrVy8VLFhQf/zxh9auXatKlSoZs1IeFhgYqBEjRujGjRvq2rWr2ViL1h7rtAQFBWndunXq06ePevTooZSUFG3YsCHLu3EMCAjQe++9p08//VTOzs5q2bKl2TIfffSRevTooQEDBqhdu3aqUaOGbG1tFRYWpm3btql69eomWXlpCQkJ0dy5c+Xv7y8PDw85OTnp6NGjWrFihSpXrvzILnFr1aql0NBQzZw5UyVLlpSNjY1x/Ewp9V6lW7dumjhxoqTUc4u1+69q1arq16+fevbsqRIlSig0NFSbNm1SnTp1jA/yJevad+vWrVWzZk15eXmpWLFiioyMVEhIiKS/gwOPs28aNGigjh07avXq1erTp4/8/f0VExOjFStW6NatWxo/fny643Wmx9rrcs2aNbVixQp99dVXKl++vGxtbdW0aVM5Oztn+X1kRtnZ2enDDz/UkCFD1KVLFwUGBqpQoULatm2btm7dqkaNGql9+/YWP/vgtWLVqlW6cuWKPvroo0yNwfokvleZuV8tV66cunXrph49eih//vzavHmzdu3apbZt28rX19ds+evXr6tfv35q3ry5IiMj9f333ys+Pl4ffPDBI8eRbNCggVq2bKmffvpJ/fr1U7NmzRQZGaklS5aofPnyZufHESNG6ODBgxo5cqQ2b94sHx8fOTk56erVqwoNDZWDg4NZZn5AQICmTZum//73v0pOTjbrMUaSVddRSzJz//ekrqUZ1bJlS3Xr1k3Lli3TiRMn5O/vryJFiig8PFzHjh3Ttm3bMnw9epi1948PI9AIZJHrd+M1dv3JRy8oyffTtFPdY+8na/q285q+7XyG1x169na6ZSLn+2TGen0yY312VwPZ7GpEnN5dYvmt3YeVfeOHNOdFxSaqyrs/WrXuy7dj9c7ijK0b/2zX7yZo3E/m3c1Y0vjzHWnOi72frFm/XdCs38wHnn/Y9tO3tf00wcSMGjhwoCRpxYoVGjdunAoUKKBmzZppxIj/Z+++42u8+z+Ov5MgMqwQK2JESYzEir1qtEbNomIEpdoabVHtTat36aJG3UW1VqvEqFi1KX41So3Wqhq1iYgRiUwh8vsjPaeOnIwrRDh9Pe+Hx+Putc73us51rpDPeX++Q1WrVsqOCUZ4eHho0aJFmjBhgpYvX66kpCRVq1ZN8+bN02effZbi28FpqVChgoKDgzV16lStWLFC0dHRKly4sF5++WUNGjQo3fnNcuTIodmzZ2vy5MnasGGDfv75Z5UtW1afffaZTp06leF/UDVr1kwzZ87UlClTNGPGDLm4uKhJkyZ65513rP4jasCAAapcubLmz5+voKAgxcTEqECBAipTpoyGDBlidT6gF198Ufv27ZMkq/8IlpJ/4VSmTBnNmTNHy5YtU2RkpDkZ1LdvX3l7e6d5Hpm5nsOHD9eBAwf0ww8/6OrVq/Lw8NB7772n3r17W2zn4+OjH3/8UTNnztT27du1bNkyOTk5qWjRomrWrJlatWqV5tgy4t1331WRIkW0cOFCffHFFypcuLA6deqkatWqpSiSZuU9PmLECLm6umrdunXaunWrChcurDZt2qh9+/Zq3br1Qx3bCF9fX82fP18TJ07U/PnzlSNHDtWtW1fz589Xr169Umz/1ltvqXTp0goKCtLUqVMlSd7e3po0aVKKFI5RGb3nmzdvruvXr2vjxo26ceOGcuTIIQ8PDw0fPlyBgYFpvkbr1q0VGxur77//XhMmTJCLi4vq1aunhQsXqnv37g81/kclK69xZkyYMEFffPGFfv75Z61atUoeHh7q0aOHKlWqpD59+lhsmzt3bk2ePFn/+9//9NlnnykhIUG1atVK8xdFXbt2VcmSJTVnzhwtXrxYsbGxcnd3V40aNczPo4d5z6XkAtPChQs1YcIELV26VFJy8Wb+/PkaMWKE4uMzPv+y0XvIyDPH3t5eX3/9tT755BMtXrxYdnZ28vf31/z58zV69GiLn3158+bVt99+q4kTJ2rRokVKSEiQt7e3Jk6cqD///DPFz6YaNWpo+PDhWrx4sT744APdvXtXgwcPVvny5ZUjRw598803+uGHH7Ry5Up9/fXXSkxMVKFChVSpUiW9/fbb5uN0795dGzZs0NKlSxUREaE8efKobNmymjx5coafXc7Ozpo3b54+//xzTZ48WXfv3lWVKlU0fPjwdNMr98vMc7RevXpasmRJirZ59erV06JFi1S8eHHDLQpNPD09zX9fmD17tpKSkuTr66vZs2crODg41ULjc889p0KFCun69etW23Aafa9TU7VqVU2cOFFff/21Jk6cKDc3N3Xo0EEdOnR4JD9fU9OyZUt98skn5pbu1gpSRYoU0YoVKzR79mxt3rxZ69evV86cOVWkSBH5+/unaBmbmtq1a+vEiRPasWOHrl69KkkqWrSo+vfvr759+6Y7v96HH36ojz76SN988435i2D3FxolqVOnTvryyy+VlJSkTp06ZWhc92vSpImeeeYZzZgxQ6dPn1bevHkVGBioIUOGWIzPyP1tmnfR1EEif/788vHx0ciRI81JuIe9NuPGjVOlSpW0dOlSTZo0STly5JCvr68++ugjw6nO+xl9pg4dOlSRkZHmc01KStKWLVvk7Oz8WP4emVGNGjVSUFCQpk+frqCgIMXFxcnDw0Nvvvmm+vfvb7WANn78eAUHB+vbb79VRESEypQpY/5ySGY8is9VZp6zPXv2VFxcnObPn69Lly6pUKFCGjhwYKrzIs6ZM0fjx4/XV199pZiYGFWoUEHjxo3L8H01ceJEeXl5aeXKlRo/frxKlChh/jfJyJEjLbZ1dXXVwoUL9f3332vdunXauXOn7O3t5e7uLj8/P4tiv4mnp6dq1qypvXv3qkSJElb/DWDk52hqjP7971H9LDXi448/Vu3atfXDDz/o22+/VXx8vAoVKqRy5co91Dyvmfn74/3skp6m2UIzqMX0PdnyuhsHpv6NY6SOghge1qEfgrN7CHjKFW38+P6iC9tVwjNfdg8BT7kdb2f+lwNAWpYvX66RI0dq3rx5aaYkASA73L17V3Xq1FHVqlU1e/bs7B4OniJ3795VpUqV1LlzZ3366acPdazExEQ1adJEBQsWTLWdH54ckZGRatiwoRo1aqRp06ZleL9Lly6pWbNmGjx48GNJ1QGP2549e9SrVy+NHTs2RYtka0aMGKEVK1boxImMhWXwj8DAQIWEhGjr1q3ZPZQngk3O0QgAAAAAAIAnS1xcXIplQUFBioqKeqg0Dv6dTIkway2Ejdq4caPCwsIUEBDw0MdC1luyZIlu377N+wUATwibbJ1qn7VtzQEAAAAAAGBQx44dVaNGDfn4+OjevXv67bfftHHjRpUtW9Zqu0rAmjt37ujHH3/U+vXrJSW3R8ysrVu3KjQ0VF9//bWKFStmtWUfnhxr165VWFiYpk+fLl9fX76gAABPiEwVGjdt2qTZs2fr5MmTypkzp2rUqKFhw4apfPnyGT7G0aNHNWPGDP3222+KjIxUgQIFVKlSJY0aNeqxTqoOAAAAAACArPf8889r69atWr9+vRISElS4cGEFBgZq0KBBcnZ2zu7h4SkRFxen//73v/L09NSYMWPk7++f6WN98sknunr1qry9vTV69Gg5Ojo+wpHiURs2bJhy5cqlqlWrPnS7XADAo2N4jsbg4GCNGjVK5cuXV9euXXX79m0FBQUpMjJSixYtMk8SnpY1a9bo3XfflY+Pj1q2bCk3NzeFh4fryJEjev3111WpUqVMn5Aktfo6e+ZoXD+A+U4ygzka8bCYoxEPizka8SgwRyMeFnM0AgAAAACAp42hRGNkZKTGjRunokWLatGiRXJ1dZUktWrVSi+88II+/fRTzZs3L81jnD17Vu+9957atGmjcePGyd7+0U8TaWdH71QAAAAAAAAAAAAgKxmq8m3ZskXR0dHq0qWLucgoScWLF1eLFi20Z88ehYaGpnmMOXPmKDExUSNGjJC9vb3i4uKUkJCQudEDAAAAAAAAAAAAyBaGEo2HDh2SJFWrVi3FumrVqmnFihU6cuSIihUrluoxfv75Z3l5eenQoUOaMGGCTp8+LXt7e/n5+WnYsGGqXTv99qPNmjVLc33ul8amewwAAAAAAAAAAAAAmWco0RgWFiZJKlq0aIp1pmVXrlxJdf+oqChdu3ZNV69e1eDBg1WnTh1NmzZNw4YN06lTp9S3b1/t3bvXyJAAAAAAAAAAAAAAZANDica4uDhJUq5cuVKsMy2Lj49Pdf+YmBhJUkREhF577TUNGzbMvK5y5crq06ePvvjiCy1evDjNcWzZsiXN9W1m7EtzPQAAAAAAAAAAAICHYyjR6OTkJElW51Q0LcudO3eq+zs6Opr//4svvmixrm7duipevLgOHTpkLmgCAAAAAAAAAAAAeDIZKjQWKVJEkvX2qKZl1tqqmuTPn1/Ozs6SJHd39xTr3d3dde/ePd26dcvIsAAAAAAAAAAAAAA8ZoYKjX5+fpKkAwcOpFh38OBBSZKvr2+q+9vZ2ZnXWytWhoaGKkeOHMqfP7+RYaVgb5c9fwAAAAAAAAAAAIB/C0OFxubNm8vFxUXBwcGKjo42L798+bI2bNigWrVqqVixYpKS53M8ffq0rl69anGMjh07SpIWLFhgsXzz5s26evWq6tata9FiFQAAAAAAAAAAAMCTJ4eRjfPly6d3331XH374obp166auXbsqISFBQUFBkqT333/fvO3hw4fVq1cvdezYUePGjTMvb9++vVavXq0FCxboxo0bql27ti5evKigoCDlyZNHI0aMeOiTsrMjXggAAAAAAAAAAABkJUOFRkkKCAhQ/vz5NWfOHE2YMEE5c+aUv7+/hgwZIh8fn3T3t7e319dff61Zs2Zp1apV2rJli1xcXNS8eXO9+eabKlOmTKZOBAAAAAAAAAAAAMDjY7jQKEktW7ZUy5Yt09ymdu3aOnHihNV1jo6OGjx4sAYPHpyZlwcAAAAAAAAAAACQzTJVaHzS0TkVAAAAAAAAAAAAyFr22T0AAAAAAAAAAAAAAE8fm0w02hNpBAAAAAAAAAAAALIUiUYAAAAAAAAAAAAAhtlkopFAIwAAAAAAAAAAAJC1SDQCAAAAAAAAAAAAMIxCIwAAAAAAAAAAAADDbLR1Kr1TAQAAAAAAAAAAgKxEohEAAAAAAAAAAACAYTaaaMzuEQAAAAAAAAAAAAC2jUQjAAAAAAAAAAAAAMMoNAIAAAAAAAAAAAAwzCZbp9rTOxUAAAAAAAAAAADIUiQaAQAAAAAAAAAAABhmk4lG8owAAAAAAAAAAABA1iLRCAAAAAAAAAAAAMAw20w0MkcjAAAAAAAAAAAAkKVINAIAAAAAAAAAAAAwjEIjAAAAAAAAAAAAAMNssnWqPZ1TAQAAAAAAAAAAgCxFohEAAAAAAAAAAACAYTaZaLSzI9IIAAAAAAAAAAAAZCUSjQAAAAAAAAAAAAAMo9AIAAAAAAAAAAAAwDAbbZ2a3SMAAAAAAAAAAAAAbBuJRgAAAAAAAAAAAACG2WiikUgjAAAAAAAAAAAAkJVINAIAAAAAAAAAAAAwzCYTjfYEGgEAAAAAAAAAAIAsRaIRAAAAAAAAAAAAgGEUGgEAAAAAAAAAAAAYZpOtU+3s6J0KAAAAAAAAAAAAZCUSjQAAAAAAAAAAAAAMs81EY3YPAAAAAAAAAAAAALBxJBoBAAAAAAAAAAAAGEahEQAAAAAAAAAAAIBhNtk61d6O5qkAAAAAAAAAAABAViLRCAAAAAAAAAAAAMAwm0w0EmgEAAAAAAAAAAAAshaJRgAAAAAAAAAAAACGUWgEAAAAAAAAAAAAYJiNtk6ldyoAAAAAAAAAAACQlUg0AgAAAAAAAAAAADDMRhON2T0CAAAAAAAAAAAAwLaRaAQAAAAAAAAAAABgmE0mGu2JNAIAAAAAAAAAAABZikQjAAAAAAAAAAAAAMMoNAIAAAAAAAAAAAAwzCZbp9I5FQAAAAAAAAAAAMhaJBoBAAAAAAAAAAAAGGajiUYijQAAAAAAAAAAAEBWItEIAAAAAAAAAAAAwDAKjQAAAAAAAAAAAAAMs8nWqVRPny6hl29l9xDwlCvauFV2DwFPuSvb1mf3EGAD7Jq0zu4hAAAAAAAAAI8VNTkAAAAAAAAAAAAAhtlkotHOzi67hwAAAAAAAAAAAADYNBKNAAAAAAAAAAAAAAyzyUSjPYFGAAAAAAAAAAAAIEuRaAQAAAAAAAAAAABgGIVGAAAAAAAAAAAAAIbROhUAAAAAAAAAAACAYSQaAQAAAAAAAAAAABhmk4lGOzsijQAAAAAAAAAAAEBWItEIAAAAAAAAAAAAwDAKjQAAAAAAAAAAAAAMs8nWqfZ0TgUAAAAAAAAAAACyFIlGAAAAAAAAAAAAAIbZZKLRjkQjAAAAAAAAAAAAkKVINAIAAAAAAAAAAAAwzCYTjfZEGgEAAAAAAAAAAIAsRaIRAAAAAAAAAAAAgGEUGgEAAAAAAAAAAAAYZputU7N7AAAAAAAAAAAAAICNoyYHAAAAAAAAAAAAwDCbTDTa2WX3CAAAAAAAAAAAAADbRqIRAAAAAAAAAAAAgGEUGgEAAAAAAAAAAAAYZpOtU+3pnQoAAAAAAAAAAABkKRKNAAAAAAAAAAAAAAyzyUQjgUYAAAAAAAAAAAAga5FoBAAAAAAAAAAAAGCYTSYa7Uk0AgAAAAAAAAAAAFmKRCMAAAAAAAAAAAAAwyg0AgAAAAAAAAAAADDMRlun0jsVAAAAAAAAAAAAyEokGgEAAAAAAAAAAAAYZpOJRgKNAAAAAAAAAAAAQNYi0QgAAAAAAAAAAADAMAqNAAAAAAAAAAAAAAyzydap9rROBQAAAAAAAAAAALIUiUYAAAAAAAAAAAAAhtlkotFORBoBAAAAAAAAAACArESiEQAAAAAAAAAAAIBhNploZI5GAAAAAAAAAAAAIGuRaAQAAAAAAAAAAABgGIVGAAAAAAAAAAAAAIbROhUAAAAAAAAAAACAYSQaAQAAAAAAAAAAABhmk4lGOzsijQAAAAAAAAAAAEBWItEIAAAAAAAAAAAAwDAKjQAAAAAAAAAAAAAMs8nWqfZ0TgUAAAAAAAAAAACyFIlGAAAAAAAAAAAAAIbZZKLRjkQjAAAAAAAAAAAAkKVINAIAAAAAAAAAAAAwzCYTjfZEGgEAAAAAAAAAAIAsRaIRAAAAAAAAAAAAgGEUGgEAAAAAAAAAAAAYZqOtU7N7BAAAAAAAAAAAAIBty1SicdOmTXrppZdUtWpV1axZU6+//rpOnjyZqQEcO3ZMlSpVkre3t3788cdMHQMAAAAAAAAAAADA42W40BgcHKw33nhDcXFxGj58uF5//XWdOHFCAQEBOnHihKFj3b17V++//75y5cpldBhpsrPLnj8AAAAAAAAAAADAv4WhQmNkZKTGjRunokWLatGiRerZs6f69eunBQsWKCkpSZ9++qmhF//222917tw59e/f39B+AAAAAAAAAAAAALKXoULjli1bFB0drS5dusjV1dW8vHjx4mrRooX27Nmj0NDQDB3r7NmzmjZtmoYOHaqiRYsaGzUAAAAAAAAAAACAbJXDyMaHDh2SJFWrVi3FumrVqmnFihU6cuSIihUrluZxkpKS9P7778vHx0c9evTQypUrjQxDzZo1S3P9ix/NMXQ8AAAAAAAAAAAAAMYYKjSGhYVJktUEomnZlStX0j3OwoULdfjwYS1btkz29oaniQQAAAAAAAAAAACQzQwVGuPi4iRJuXLlSrHOtCw+Pj7NY1y+fFmTJk1S37595e3tbeTlzbZs2ZLm+um7zmXquAAAAAAAAAAAAAAyxlCc0MnJSZKUkJCQYp1pWe7cudM8xn//+18VKlRIgwYNMvLSAAAAAAAAAAAAAJ4ghgqNRYoUkWS9PappmbW2qiY//fSTduzYoX79+unKlSs6f/68zp8/rxs3bkiSbty4ofPnz5uTkwAAAAAAAAAAAACeTIZap/r5+Wnx4sU6cOCA6tevb7Hu4MGDkiRfX99U9w8JCZGUnGq05vPPP9fnn3+uWbNmqVGjRkaGZsHeLtO7AgAAAAAAAAAAAMgAQ4XG5s2b69NPP1VwcLD69OkjV1dXScnzLm7YsEG1atVSsWLFJCXP53j58mXlyZNHhQsXliQ1adLEauJx7969WrBggQIDA+Xv76+KFSs+7HkBAAAAAAAAAAAAyEKGCo358uXTu+++qw8//FDdunVT165dlZCQoKCgIEnS+++/b9728OHD6tWrlzp27Khx48ZJkkqVKqVSpUqlOG5sbKyk5DRky5YtM30yJvZ2RBoBAAAAAAAAAACArGSo0ChJAQEByp8/v+bMmaMJEyYoZ86c8vf315AhQ+Tj45MVYwQAAAAAAAAAAADwhLFLSkpKyu5BPGqz9pzPltftXztlWhPpK/Xm6uweQpYomj+33m7trcYVCiu/S05djbytTUeu6H/rT+pW3J1096/zTEH98Ga99Lf7708KjYhPsbx11WIKqFtSvp755ezooBtRCToaEqnpP/2lA+ciMnNKTyx7e/vsHkKWKJovt4a2Lq/GPu7K75JT127d1qYjYfpyw0ndirub7v61n3HT4sF1092u3ugtVu+hVlWKKqBuSVUukU8ujg66Hp2gPy9Favrm0zp4PiIzp/TEurJtfXYP4YnTsXlVNaxRTn7lPeRb3kN5XZ20aO1e9R01L7uH9sQq1qR1dg8hSxTNl1tDW5ZTo/ueRT/9EaYvN/6VsWdRWTctGlQn3e3qf7TV4ll05ovUr+eB8zfV6cvdGTuBp0ha5wwAAAAAAPAkMpxoBJC+koWctXxIA7nnddTGw1d0OixKVUsVUL9nvdS4QmF1mrxTEbFpFxsvhcdq8voTVtf5FMurVlWL6fjlWykKRA72dvqiZ1V18C+hM1ejteZAiG7F3VXhvI6qXrqAfD3z21yh0RaVLOisZUPqqVAeR206ckWnw6JVpWR+9W1cRo193NX5y10ZuIfi9L8NJ62u8y6WR62qpH4PTexeRR38PXT2arTWHrisW/F35W6+h/LZXKERKf3nlZaq4l1CUTHxCgmLUF5Xp+weErJByYLOWvpmXfOz6MzVGPmVzKeXG5VRI293dZm6O0PPoi83/mV1nXexPGrpV1QnQqOsfuHhUnislu0LSbE8NCIucycEQ5o2bSoPDw/Nnz/fvCwwMFAhISHaunVrNo7s6fU0XL+nYYzZzdpnIzvs2bNHvXr10tixY/Xiiy9m61jwZLt06ZKaNWumwYMH64033sju4TzxnpTPeGYYHfvUqVM1bdo0bdmyRSVKlMjUa2b1z424uDhNmjRJmzdvVlhYmIoVK8bPKAAAnjAUGoEs8EkXX7nnddSHS49o7vZz5uUfdKyoV5qU1TttfPT+kiNpHuNSeJz+t956kWhK7+qSpEW7LqRYN6yVtzr4l9DUjSc1ad0JPZhZzmHPHKZPg4+7VFahPI4aveyovt9xzrz8/Q4V9MqzXhr+grdGBf+R5jFCwuP05Qbrv9z/sldVSdIPv15MsW5oy/Lq4O+haZv+0hfrT3IP/Uu9O3GZQq5G6PSFa2pYo5w2zX4ru4eEbPBRp0rJz6LlRzVv5z8dI95vV0H9ni2j4a29NWppOs+im6kXGr/sWVWStPjXlD/PpLSLlMCjdOvWLX3//feqVauWateund3DeWTmzp2rvHnz2nQR6tixY9q8ebM6duyY6V+SAyabN2/WsWPHKMY9BK7hk2f58uW6deuW+vTpk91DsSq95/isWbM0f/589e3bV97e3nJ1dc2GUQIAgLTYZL9Bezu7bPkDSMlpxsYVCuvijViLApEkfbHuhGJu39WLNUvIKZdDpo5fwCWXWvgVVVxCopbvsywSuedxVP9mXvr9bLgmrk1ZZJSku/dsrluyzSlZ0FmNfNx18Uas5u08Z7Huf+tPKub2XXX093iIeyinWvgm30PL9l2yWFcoj6NeaVpGv5+7qUnrUhYZJe6hf4vt+//S6QvXsnsYyEb3P4vm/2LZlv5/G5OfRR1qFH+oZ9HzvkX+/nmWMrUIPE63bt3StGnTtHfv3uweyiM1b948rVixIruHkaWOHTumadOmKSSE5wge3ubNmzVt2rTsHsYj5+HhocOHD2vAgAFZ/lq2eg2fZitWrNC8edanPxgwYIAOHz4sDw+Pxzyqf6T3HN+1a5fKly+v//znP+rQoYOaN2/+mEcIAADSY5OFRiA71StXSJK0/fi1FEWamNuJ2n8mXM6OOVS9dIFMHb9zrRLKndMhuZ3lA3Njta5aTI45HLTq98tyzGmv1lWLaUDzZ9SrYWlVKJ43U6+Hx69uuYKSpB0nrN9Dv529KWfHHKpWKn+mjt+pZgk55nTQuoOhinrwHqpSVI45HLT673uoVZWier1ZWQU2KKUKxfNk6vUAPJ3qPOMmSdpx8nqWPIte9P/7WXQoVFHx1ud6zOuUU11qldDAZmUVWL+UqmbytQAAT7/ExETFxdE6Oz4+Xnfvpj9H8v3s7Ozk6OioHDloagVLOXLkkKOjo+ye4C/PX7t2Tfnz53/kx01ISNDt27cf+XEBAPg3sslCo51d9vwBJMmrsIsk6ezVaKvrz12LkSSV+Xs7owLqlZIkLdx1PsU6v5L5JUlOuRy09f0m+rqvv0a0q6CPu/hqw4jG+rpvDeXOmbnkCR6ff+6hGKvrH/oeqltSkrTQSuvd+++hLSMba/rLNfSftj76qHNlrXu3kab3qa7cOW3yRweAB3gVTm5Lleqz6PrfzyL3TD6L6nhKkhbttt42VZIqeuTV5wF+Gv6Ct8Z0qqTlb9XTmrcbyLsYX3x40J07dzR37ly9+OKLqlq1qqpVq6a2bdtqypQpFtvdvn1b06ZNU8uWLeXr66tatWrp9ddf15Ejabd0T8+FCxc0YsQINWjQQJUrV1ajRo00evRohYeHp9j27NmzGjBggKpXr67q1aurX79+OnnypAIDA9W0adMU2//555968803VbduXVWuXFnNmjXTxIkTM1xwOH36tIYNG6bGjRurcuXKqlu3rgICAhQcHCwpuaVcs2bNJEnTpk2Tt7e3vL29zWO5dOmSvL29NXXq1BTHXr58uby9vbVnzx6L5efPn9egQYNUo0YNVatWTX379tXx48dTHWNGz3HEiBHy9vZWdHS0Pv74Y/P17tixo3bs2GHezjTmkJAQ7d2713xO3t7eunTp0oMvnyGHDh3Sa6+9plq1asnX11ctW7bUV199pYSEBPM2wcHB8vb21qpVq6weo0+fPqpataqio//5e/L169f18ccfq2nTpqpcubLq1aun4cOHZ2icI0aM0MiRIyVJvXr1Mp/jiBEjzNskJSVpyZIl6ty5s6pWraqqVasqICBAmzdvtnrM/fv3a8CAAapTp44qV66sZ599Vm+//bYuXEj5rDLdy6b3uX///jp/3vLv6Hv27JG3t7eWL1+ulStXqm3btvL19VXDhg31xRdfKDExMcVxT58+raFDh6pevXrm++Hzzz+3uG5pMfI5T0pK0ty5c/X888+rcuXKat68uWbMmKHdu3ebxy1JR44ckbe3t8aPH2/1NT/55BN5e3vr5EnrUz/cLzw8XGPHjjW/Zu3atdW9e3etXbvWvM3UqVPl7e2tU6dOafz48WrSpIl8fX21fv1687gz+r6uW7dOAwcONB+jVq1a6tevn/bv32+xXdOmTc0J4Ps/M6ZrIEnR0dGaPHmyWrRoocqVK6tWrVoaOHBgmp/v+5meGbt27dL06dPN932LFi2szqlnei6GhIRo6NChql27tqpUqaIrV65IksLCwvT++++rYcOG5mfvBx98oKtXr1ocJ63n2MaNG9WzZ09Vr15dfn5+6tChg/n5+KDjx49r2LBh5mdPw4YNNWDAAP3xxx8ZuoaRkZH6/PPP9fzzz8vPz081a9ZU27Zt9emnn6Z77Vq0aJGiDfS6devk7e2dIuG2c+dOeXt7a+XKlZk+V9P59u3bV9WqVVONGjU0ePBgq88Ca0zv9e7du/XNN9+oWbNm8vX1Vbt27bRt2zZJ0qlTp/Taa6+pRo0a8vf314gRIxQTY/n3LtNz35qmTZsqMDAwzXF4e3tr7969CgkJsXhPTD+3TJ+1+5+593/+xo4dq4YNG5rHfv/nNC2HDx/WyJEj1aJFC/NntFOnTlq2bFmK80vtOX7/2O7/WXb/fbx9+3YFBgaa39P27dtrwYIFSnrgW3Km63jz5k198MEHatCggapUqaKDBw8+svcqNadPn9aYMWPUpk0b1ahRQ35+fmrbtq3mzJmT4mfAwzwj3njjDdWsWVNVq1bVyy+/rKNHj6bYftWqVeratatq1aolPz8/Pfvssxo8eLBOnTqVoXMBACA1fJ0NeMTy5M4pSammM0wJsrxOOQ0fu/YzBfVMEVcdv3xLv529mWJ9oTyOkqS3W3tr/9mbenX2Pp25GiPvYnn0UWdfta5aXDG3EzV8wUHDr43HJ0vvobJuKvv3PfT7uZT3UME8uSRJw1qV129nb+rVOb/p7LXke2h0p0pqVbWYYhLu6p2Fhw2/NoCnS57cyX9NjIq/Y3X9wzyLav39LDoRGqXfz0VY3Wb2z2e04fAVnb0Wo9t37qlsYVe91tRLrasWU9CAWmozaafCIvkWupRcZOzfv792795t/qW3q6urzpw5ow0bNujNN9+UlJwG6t+/v/bs2aPGjRurZ8+eunbtmhYvXqzu3btr1qxZqlOnjuHXP3bsmAIDA5U7d2516tRJHh4eOnfunBYtWqTdu3dr6dKlypMnuTgcEhKigIAAxcbGKiAgQGXKlNGRI0cUGBiofPnypTj29u3bNWjQIBUrVkw9e/ZUoUKFdPz4cc2dO1e///675s2bl2ZC5+bNm+rVq5fu3bunrl27qkSJErp165ZOnjypvXv3qkuXLqpZs6ZGjhypsWPH6rnnntNzzz0nSXJxyVwRPTQ0VAEBAYqKilJAQIC8vLx06NAhBQYGWk1kZOYc+/Xrpzx58ujVV19VfHy8vv/+ew0YMECbNm1S8eLF5ebmpvHjx2vs2LEqUKCAXn/9dfO+bm5uhs9p+/btGjhwoFxcXNStWze5u7tr27ZtmjJlig4cOKCZM2fK3t5erVq10qeffqoVK1aoXbt2Ka7Lnj171KZNG/P8WqZrFRsbq86dO6t06dIKCwvTokWL9Msvv2jZsmUqXrx4quPq2rWrcuXKpR9++EGvv/66vLy8JEklS5Y0bzNy5EitXLlSzZo1U9u2bSVJP/30kwYNGqTRo0erW7du5m2Dg4P13//+V25uburSpYtKlCiha9euaefOnTp58qTFccPCwtSzZ081bdpUw4cP1/nz5xUUFKSBAwdq9erVsre3/GLWDz/8oLCwMHXu3Flubm766aefNGPGDLm6uurVV181b3fs2DH16NFDiYmJ6t69u0qUKKHff/9d3377rXbv3q1FixbJyckp1Wti9HM+fvx4ffvtt/Lz81O3bt2UkJCg5cuX66effrI4rq+vrypVqqSVK1dq6NChypnzn2f/7du3tWrVKlWrVk3ly5dPdWySdPnyZXXr1k1hYWFq06aNevXqpTt37ujPP//U//3f/+mFF16w2H748OFycHBQ9+7d5ezsrDJlyhh+X4OCgpQ3b1517txZhQsXVmhoqJYuXarevXtr/vz5ql69uiTpvffe03fffaf9+/dbFFRN66Ojo9WtWzdduHBBHTp0kI+Pj27duqUlS5YoICBACxYsUKVKldI8f5OJEycqOjpaL730knLlyqU1a9bok08+0fXr1zV06FCLbWNiYtSjRw/5+vrqzTffVExMjJydnRUWFqZOnTopPDxcnTt3lo+Pj44fP67g4GDt2LFDS5cuVaFChdIcx5QpU/TVV1+pdu3aGjx4sBwdHbVz506NGjVK58+f1/Dhw83bbtu2TYMHD1bOnDnVuXNneXl5KSIiQvv27dOBAwdUuXLldK/hkCFDtGfPHr300kuqUKGC7ty5owsXLmj37t3pXrM6depoyZIlunnzpgoUSO4OtHv3btnb2+vixYu6dOmSeX6/Xbt2SZLq1q2bqXOVpCtXrqhXr15q2rSp3nnnHZ05c0aLFy/WgQMHtHz5chUpUiTdMUvSpEmTlJCQoG7dusnBwUHz5s3ToEGD9OWXX+r9999Xq1at9M477+jgwYNasWKFcuXKpY8++ihDx86I8ePH65tvvtHNmzfNBT1JKlu2bLr7vvvuu5Kkl19+WQkJCVqxYoWGDRum2NhYdenSJc19f/rpJ/31119q2bKlihcvrqioKK1fv17vvfeewsPD1b9/f0lpP8ednJxUsmTJFD/LTIXX4OBgffDBBypevLj69esnFxcXbdiwQR999JGOHz+ujz/+OMW4Xn75ZeXPn1/9+/dXUlKSChUqZG7ZmlXv1d69e7Vnzx49++yzKlGihBISErRt2zaNHz9eFy9e1OjRo1PsY+QZERsbq8DAQFWoUEFDhgxRaGioFi5cqJ49e2rRokXy8fGRlFxkfOedd1S9enUNGjTI/BzZs2ePzp49q2eeeSbdcwEAIDUUGoGnSPd6yb/cWGQlzSj9k6yNiL2jfjP3KvrvQtXB8xF6ZdZe/d+opnqxZglNWHNcYZHxj2XMeLKY0oyLd1+0ut4032xE7B29Mmu/om//cw/1n71fW997Vh39S2ji2hP8gh9ApnXLQJrxs1WW6ZAjlyI1eN4BfWUntapSTP2f9dInPx7L0nE+LebNm6fdu3crMDBQ77//vkX7s3v37pn//4oVK8y/4L3/l28dOnRQu3bt9OGHH2r9+vUpCiTpGTlypPLly6dly5ZZFNJatmypgIAAff/99xo8eLAk6YsvvlBERIRmzpypxo0bm7edO3euxo4dazFH1O3bt/Xee+/Jx8dHCxYsUK5cuczr6tSpozfffFOrV69Wx44dUx3b77//ruvXr2vy5Mlq3bq11W08PT3VvHlzjR07Vt7e3mrfvr2h83/Q5MmTFR4erq+++sqcsunevbtmzpypSZMmPZJz9Pb2tvjlZq1atdS1a1ctXrxYw4YNk7Ozs9q3b68vv/xShQoVeqhzSkxM1OjRo+Xg4KAlS5aoVKnk7ho9e/bUyJEjtXz5cq1evVrt27eXq6urnnvuOa1Zs0ahoaEqVqyY+TgrVqzQvXv3LBJJn3zyieLi4rR8+XJ5enqal7/44otq27atpk6dqrFjx6Y6tmrVquns2bP64YcfVK9ePdWuXdti/ebNm7VixQqNHDlSffr0MS/v3bu3Xn/9dU2cOFFt27aVq6urwsLC9NFHH6l48eIKDg62KMgOHjzY4rMkJadWJ02apDZt2piXubm5adKkSdq1a5caNGhgsX1ISIjWrl1rLqgHBASoTZs2mjdvnkWh8ZNPPlFsbKwWLFigGjVqSJJ69OihMmXKaOrUqfruu+80cODAVK+Jkc/52bNn9d1336l69eqaN2+euXjYvXt3c/HufgEBAfrggw+0detWtWjRwrx8w4YNioyM1EsvvZTquEzGjBmjK1euaOrUqXr++ect1j14jSXJ2dlZ33//vUVh08j7KkmzZ8+Ws7OzxXG7deumNm3a6JtvvtHMmTMlSc2bN9fmzZu1f/9+q5+ZKVOm6OzZs1qwYIGqVKlicay2bdtq3LhxVhNH1ty4cUOrV69W3rzJ01v07NlTPXr00MyZM9WpUyeLonZERIS6dOmSohD27rvv6tq1a+bzNalevbr+85//aPLkyWkmBf/8809Nnz5dgYGBGjVqlHl5jx499PHHH2vOnDnq2rWrPD09FRcXpxEjRsjR0VErVqyw+Ly+/vrr5vcurWsYFRWlXbt2KSAgwGphJT316tXT4sWL9euvv6pVq1aSkguKL7zwgtatW6ddu3aZ78Hdu3fLy8vLXAw0cq4mFy5c0Lvvvqt+/fqZl5mKlJMnT9a4ceMyNO47d+5o6dKl5ud7nTp11KFDBw0aNEhffPGF+WdTQECAbt26peXLl+s///lPpr/s8qD27dtr6dKlun37dqZ+FixevNg89h49eqhdu3YaO3asWrVqZf6MWTNgwAC9/fbbFsv69u2rwMBAzZgxQ3369FHOnDnTfY77+PhY/VkWFRWlzz77TIUKFdLSpUvNz+yePXvq1Vdf1ZIlS9S+fXv5+/tbHM/Ly0uTJk2y+LvSoUOHJGXde9W+fXuLLz9IyQn/t99+W0uWLNGgQYPk7u5usd7IM+LmzZtq3bq1/vvf/5qXtWzZUl26dNGnn35qfi5t2rRJLi4uFs97Sea/owEA8DBssv+dfTb9AaR/kh+mJMiD8jglL78VZz0hkpp8zjnVskoxxSUkavk+662kTMf85eR1c5HR5Oqt2zp4/qYc7O3kVzJlYgBPjqy8h1pVKZp8D+1P+x7a9dd1c5HR5Nqt2zp4PiL5HvLMb+i1ATx9TKlqU8r6QQ/188wv+Vm0Yn+I4XGZ2j7X9DKeyrJVq1atkrOzs4YNG5ZijqX7i4abNm2SJL3xxhsW25QpU0Zt2rTRuXPnMtT28H4nT57UsWPH9MILL+jevXsKDw83/ylRooRKliypnTt3SkouImzdulXly5e3KDJKyYWNB39Rt2vXLl27dk0dO3ZUdHS0xbFr1qwpJycn87FTY/oF3bZt23Tr1i1D55YZ9+7d0+bNm/XMM8+kaOXXu3fvFMWOzJ5j3759Lf67atWqcnZ21rlz5x75OR09elQhISFq3769uchoYrqXTPeWlFwkvHfvXoqWhStXrpSHh4c5TRcVFaWtW7eqUaNGcnFxsTh3Z2dnVa1a1aIdbGasWrVKuXPnVqtWrSyOHx4erubNmys6OloHDx6UJK1fv14JCQkaNGiQ1dTngwX4woULWxQZpeRCiCSr70OnTp0sUrv29vaqW7eurl27Zm6/Fx4erv3796t+/frmIqNJv3795OzsbHGtrTHyOd+8ebOSkpLMv/A3yZMnT4pfiksyp1GXLFlisTw4OFh58uRJtZhvEhERoW3btqlWrVopioxSymssJd/r949NMva+SrL43EVHR+vmzZtycHBQlSpVzAWG9CQlJWnVqlWqWrWqPD09LV7z7t27ql+/vn777TfFx2fsy5zdu3c3P58kKVeuXHr55ZfNz5AHmdJfJqbtypQpk6Io3L59e5UsWVI//fRTivaR91u9erWSkpLUuXPnFNexadOmunfvnjkZ+Msvvyg8PFy9e/e2KMaZZOQLKo6OjnJ0dNThw4d18aL1Lz2mpXbt2rK3tzenH00pxpYtW6pSpUrm5Tdv3tSxY8cs0oxGztXExcUlRVvS5557TmXLltVPP/1ktTBuTY8ePSy+RFKhQgW5urrK3d09xWemVq1aunPnjjlhl9369u1rMfY8efKoe/fuiomJ0S+//JLmvvd/7uLj43Xz5k1FRESoYcOGioqK0tmzZx9qbDt37jQn+e5/ZufIkUMDBgyQJKvPy/79+6c6H2VWvVf3X4uEhARFREQoPDxcDRs2VGJiorn18P2MPiNee+01i/+uXLmyGjRooL1795rb2OfJk0fx8fH6v//7vwzfvwAAZBSJRuARO3PVNH+e9W/3lXZPe/691HSu5ancOR0UvOeibsVZb6lpeu3UfukbGZu8nHkan2z/3EPWvxmZ2XuoU80ScszpoKV7L5pbHqb22qndY6Z7y5F5GgGbd+bvuYZTfRYV+vtZdM3gs8jf4+9n0aVUW0Sn5UZM8nxwzrn4WWZy7tw5eXl5pShiPejixYvKnz+/ChcunGKdqQ3ZhQsXzC22MuL06dOSpBkzZmjGjBlWtzH9UvrGjRuKjY01tz68X65cueTp6amoqKgUxx4zZozGjBlj9djXr19Pc3w1a9Y0zwm1Zs0aVaxYUTVq1FCLFi1UrVq19E/QoBs3bigmJsZqSzpHR0eVLFnykZyjtV/0FyhQQDdvpmyL/rBM83ZZa4lZvHhxubq6WsxZVqdOHXl4eGjlypXmX/bu379f58+f18CBA82/4D179qzu3bun1atXa/Xq1VZf22i69kGnT59WfHy8GjVqlOo2putrKg5WrFgxQ8e29h6YEr0REREp1plaOqa2vYuLi7n4Yu1aOzk5ydPTM9354Yx8zk2vZ2pVeD9r97Czs7PatWunxYsXKyQkRB4eHjpz5oz27dunHj16KHfu3GmO7cKFC0pKSsrwNZak0qVLp1hm5H2VkufZmzJlin799dcUc6qlVnB40M2bN3Xz5k3t27fPooBlbbv7k7ypsXZ9TW0LH5zn083NLUVr6fDwcMXExFi9V+zs7PTMM89o69atioyMtNqyWfrn+ZNWys10HU1FISPv3YNy5cqlUaNG6eOPP1bz5s1VunRp+fv7q3HjxmrWrJkcHNL+uZ4/f35VqFDBXFDcvXu3HBwcVLt2bR05ckRLlixRUlKSfv31VyUlJVm8T0bO1aRkyZIWRSeTZ555RqdPn1Z4eHi6rWkl68+KfPnyqWjRoimWmwpL1p4h2cFaK83U7tMHhYeHa8qUKdq8ebOuXbuWYn1kZORDjS2t56VpmbXnpbVniklWvVdxcXGaPn261q5da7Uwae1aGHlG5M2b12or32eeeUbbt2/XxYsX5ebmpgEDBuj333/XG2+8oXz58ql69eqqU6eO2rRpk6F7GQCAtNhkoTGj/1gAssKuv5L/gdLIx112dtL9XyJ1cXSQv5ebYm/ftTo/Xlq6pdM2VZJ2nrimt1qWl3exPFbXl/97+cUbsYZeG4/X7r9uSJIaelu/h2qUKaDY23d14HyEoeMG1P27VeGu1L9B/MuJ63qzRTl5F7V+D5UrmlxAvxQeZ+i1ATx9fj2V/O3nhuULPdJnUde/26Yu/jXtX5anplqp/JKki+H8LHsSmNIygYGBatq0qdVtHB0dM3Vs07fthw4dKj8/P6vb3P9t/9R89tln6tevn3bs2KHffvtNy5Yt03fffZeihV5q0vq3RWJiYrr7pyWz55jeL+Qfpwevj52dndq3b6/p06fr999/V/Xq1bVy5UrZ2dlZtE013TutWrXKUMvNzLh3757y5MmjKVOmpLpNZuekSus9sJYiM7r9kyogIEALFy7U0qVL9dZbbyk4OFhS8jxrWcFa8dLI+3rlyhXz/I6vvfaavLy85OTkJHt7e82YMUO//vprhsZh+qzWrFkzzda1mZkDNT1pzcn5MEznNGPGDKsFNcl64eVhvPTSS2ratKm2b9+uffv2mefx9fPzU1BQULo/L+rVq6dZs2YpJCREu3btUuXKlZUnTx7VrVtX33zzjY4fP26et/H+FpzZca4mqX1hIqPPhLR+Bt29a/wLW49DUlKSXnnlFZ08eVI9e/aUr6+v8ubNKwcHB23btk1z587NtkRdWp+nh32vUjN8+HBt2bJFXbp0kb+/vwoUKKAcOXLojz/+0KRJkx7btShZsqTWrFmjvXv3avfu3dq/f78+//xzffnll5o5c6Zq1qz5WMYBALBNNlloBLLTheux2nbsqhpXKKzeDUtr7vZz5nXDWnvLxTGHgnaeU1zCP7+YKvt3+vH03+mRB9X0clO5onl0/PIt/XY29QLl3tPhOnopUrXKFlQLv6LaePiKeV1A3ZIqVzSPzl6N1uELEQ93kshSF27Eavvxa2rk465eDUrr+x3nzOuGtCovF8ccWvDLeYt7yOvvxNGZVFKONb0KmO+htIrce88k30M1y7rped8i2nQkzLwuoI5n8j10LYZ7CPgXuP9ZFFi/lObt/OeLLkNaJD+LFu66YOxZVCb5WXQiNEq/n4tI9bV9iuXRqbBo3b2XlGL5262SEzkrf7uc2VOzOaVLl9a5c+cUGxubZqqxZMmSOnPmjK5fv57im+umVor3z/mT0dc2MbWNTE3BggXl7OxstV1aQkKCOYllYko+Ojo6pnvs9JQtW1Zly5ZVnz59FB8fr/79+2v+/Pnq06ePSpQokeYvck1JImuJgwfb/xUsWFAuLi7m9Mz9bt++rQsXLqhAgQLmZY/yHLOK6Zfvf/31V4p1oaGhioqKSjGnVseOHfX1119rxYoVqlixotavXy9/f3+LX+SXLFlS9vb2io+Pf6hzT+u9K126tM6cOaMKFSpYXPfUtpWkY8eOGUr1PkppXev4+HhdvHgxRfvaBxn5nJte78yZMypXrpzFttbuYSk5FVmtWjUtW7ZMr732mlauXKmqVaua05Lpjc3Ozk5//vlnutumxcj7umnTJsXExOirr75KkUScPHlyiu1Tu5/c3NyUN29eRUZGPpLP6unTp1O0Vz516pQkpfsem8bj4uJi9V5JSkrSqVOnlC9fvhRJyPuVLl1aO3bskLu7uypVqpTm65meVceOHVOzZs3S3Da9L34XKlRIL774ol588UUlJSXpiy++0MyZM7V27VqLLyNYU7duXc2aNUu//PKLfv31V3OBu3r16sqdO7d27dqlXbt2qVKlShZf0jByriYXLlxQQkJCisLkqVOn5OrqmiVFZWtM72FERITFz8j4+Hhdu3YtQ/dLZp06dSrF8zAj9+mJEyd09OhRDRw4UG+99ZbFOmstVzMTFjA9x/766y81adLEYp3pc2H07zRZISoqSlu2bFG7du0s5s2VrLfZNjHyjLh165bCwsJSpBpN29//szdnzpyqX7++6tevLyk58d25c2dNmTIlw3PMAgBgjU32vrPLpj+AyajgI7p267bGdPbVzFdq6t22Plo0uK5eaVJWp8OiNWHNcYvtt45qoq2jmqRyNKl7/eS/SKaVZjQZFnRAkbEJ+qavv2b1r6n32lfQ3Ndq6fNuVRRz+67eXnBQ956eL03/a30Q/IeuR93W6E6VNKNfDb3TxlsLBtbWK8966czVaE1ce8Ji+y3vPast7z2b6vG61U3+R9bi3enPhzJ8wSFFxt7R1y/X0Mx+NTSynY++fbWmxgb4Keb2XQ1fcIh76F+g7bN+mjmmp2aO6anhLz8nSartV8a8bOzQjtk8QjwO/112NPlZ9GIlffNydb3zgreCBtRSv2fLJD+L1lk+izaPaKzNIxqncrTkL71I0qLdaacZ+zYuoz1jmumbl6vrw44VNbKtj2b389eqYfXl5ppLi3Zf0KrfKTSatGvXTrGxsfryyy9TrLv/W/LPPZf8WZ4+fbrFNufPn9eaNWtUunTpDBUL7lehQgWVL19eS5cutVqYSEpKMs8NZG9vr6ZNm+rkyZPatm2bxXYLFy5M0dKwQYMGKlSokObMmWO17drdu3fTbVkWERGRIimQO3duc9rJtL+pQGutmGian8nUjs/k5s2bWrZsmcW29vb2atasmU6dOpViDqXvv/9esbGWSdxHcY5pcXFxeegWfBUrVpSHh4dWrVqVouXbV199JUkp5tsrWbKk/P39tX79eq1evVrR0dEpCggFChRQ48aNtW3btlRTZem1xpXSfu86dOggSZowYYLV1Mn9x2/VqpVy5cql6dOnW71mjyNx4ubmJn9/f+3cuVOHDx+2WPftt98qNjbW6tyG9zPyOW/WrJns7Ow0d+5c3bnzz9QLUVFRWrRoUaqvERAQoLCwMH344YcKDw/PcCI1f/78aty4sfbu3Wt1jrGMXmMj72tqSaRt27aluMbSP/fTg/eAvb292rVrp5MnT2rFihVWj5mR+9Vk4cKFFvPGJiQk6LvvvjM/Q9Jjb2+v5s2b68yZM9qwYYPFulWrVunChQt67rnn0izgmNqIfvHFFxbvv0lUVJQSEpLbldevX19ubm76/vvvze2U73f/e5faNYyLi1NcnGVXFDs7O3M71ow8q2rUqKFcuXLp+++/182bN83F41y5cqlGjRpavny5Ll68mKKobORcTWJiYlIUXn766SdzAehhWztnlKnI++Ackt9++22GPzMuLi6KjIw0nJ7+9ttvLa5LVFSUFi5cKGdnZ3OhyhrT5+7B1wsLC9PSpUtTbJ/Wczw19evXl7OzsxYsWGCxX2Jior7++mtJKX82ZQfTZ/DBaxEdHa25c+emup/RZ8SD7ev/+OMP7dy5UzVr1jQXxU1/H7tf2bJl5eTk9MS06wUAPL1INAJZ4ML1WLWduF3DWnvr2QqF1aRiYV29Fa85P5/R/9afTHUORWvyOuVU6yrFFJeQqOX7Uv6j7kHHL0fphfHb9VYrbzXycVeTioV1MzpBK/Zd0pSNJ1NNmeDJcuFGrNpN2qmhrcqrsY+7nq1QWNduxevbbWf15YaTqc6haE1epxxqZbqH9mfgHgqNUpuJO/RWy3Jq6O2uZ/++h1buD9HUTX9xD/1L+HmXUGC7OhbLvDzd5eXpLkk6f/mGRk62/os22I4LN2LVfvIvGtqyvBr5FPr7WXRb320/qy83/pWJZ1FRxSUkasX+lPPT3O+nP8KUJ3cOeRfPo7rlCsoxh4MiYhO07fg1Lf71orYcvfqwp2ZTevXqpZ9//llz587Vn3/+qUaNGsnV1VXnzp3TL7/8ojVr1khK/uX8qlWrtGDBAl2+fFkNGzbUtWvXtGjRIiUlJWnMmDGGUwV2dnaaMGGCevfurY4dO6pjx44qX7687t69q5CQEG3evFkdO3bUG2+8IUkaMmSIdu7cqcGDBysgIEBeXl46fPiwtm7dqlKlSlm0gXNyctL48eM1cOBAtW7dWi+++KK8vLwUExOjCxcu6KefftLbb7+dZgJm5cqVmjt3rpo3by5PT085OTnpjz/+0NKlS+Xj46MKFSpISi56lSpVSmvXrpWnp6cKFSokJycnczvYXr16adKkSerXr5+aN2+u8PBwBQcHq0SJEimKC0OGDNGOHTs0ZMgQ8zkeOnRIW7duVcmSJS3arT6Kc0xLlSpVtHTpUv3vf/9T2bJlZW9vryZNmqQ7n+f9HBwcNHr0aA0cOFCdO3dWQECAChYsqO3bt2vbtm1q0KCB2rZtm2K/jh076r333tO4cePk7OysFi1apNhmzJgx6tatm/r27as2bdrI19dX9vb2CgkJ0fbt21W5cmWNGzcuzfGZ9vnmm28UGRkpZ2dnlShRQlWqVFGLFi300ksvacmSJTp+/LiaN28ud3d3hYWF6ejRo9q+fbuOHj0qSSpSpIhGjRqlDz/8UG3atNGLL76oEiVK6MaNG9qxY4f69u2bIl2SFUaNGqUePXqod+/eCggIkKenp3777TetWbNGPj4+evnll9Pc38jn3MvLS71799bcuXPVvXt3tW7dWnfu3NGyZcvk7u6u0NBQq8+EVq1aaezYsVq5cqXy5Mmj1q1bZ/j8/vvf/+rPP//UG2+8oTZt2qhKlSpKTEzUsWPHdPfuXU2cODHdYxh5Xxs1aiRnZ2e988476tGjhwoUKKA///xTq1evVvny5c0pT5MqVaooKChIY8aMUePGjZUzZ075+fnJ09NTQ4cO1YEDBzRixAht3rxZ/v7+cnJyUmhoqHbv3i1HR8cMJ4IKFiyozp07q1OnTsqZM6fWrFmjo0eP6tVXX81wQm3YsGHatWuX3n77be3Zs0fly5fX8ePHFRwcrGLFimno0KFp7u/r66shQ4bof//7n9q0aaM2bdqoaNGiunHjhk6ePKktW7Zo7dq1KlGihJycnDR27FgNHjxY7du3V+fOneXl5aXIyEjt27dPjRo1UmBgYJrXMDo6Wj169FDz5s31zDPPyM3NTRcvXtTixYvl4uJiLpKnJXfu3KpWrZr27NkjJycnVa9e3byubt265vvnwUKjkXM1KVmypGbMmKFTp07Jz89Pp0+f1uLFi+Xm5qYhQ4Zk6D16FF544QVNnjxZH3zwgU6dOqWCBQtq3759Onr0aLqJXpMqVaro//7v//TRRx+pWrVqcnBwUJ06dVSwYMF09w0ICFCbNm10584dLV++XJcvX9aYMWPk6uqa6j5eXl4qX768Zs+erdjYWJUrV06XLl3S4sWL5enpmaKoldZzPDV58uTRe++9pw8++ECdOnVSp06d5OTkpI0bN+r333/XSy+9JH9//wxdn6zk6uqqhg0bavXq1cqVK5eqVKmiq1evatmyZWlefyPPiAIFCujnn39WWFiY6tWrpytXrmjBggVydHTUe++9Z96uX79+cnFxkb+/v4oXL664uDitW7dOt27dMs+pDABAZlFoBLJIaES83ll4KEPblnpzdarrbsXdkffwdYZe+2J4nIYvOGhoHzx5QiPi9e6ilN+0tqbMkLWprrsVd1cV3t2Q6nprLoXH6Z2FGXtt2KZPZ6zTpzOMPXtgm0Ij4vXu4ow9D7yGpX7P3Iq7q4r/2Zih4/z0R5h++iMs/Q0hKbkN1pw5c/T9999r1apVmjp1qnLkyKESJUqoZcuW5u1y5MihWbNmaebMmVqzZo127twpJycn1ahRQwMHDkx1jsD0+Pj46Mcff9TMmTO1fft2LVu2TE5OTipatKiaNWumVq1ambf19PTUwoULNWHCBHOqoXr16po/f75GjBih+Ph4i2PXr19fy5cv16xZs7RhwwbduHFDrq6uKl68uDp16pTil8kPql27tk6cOKEdO3bo6tXkAnXRokXVv39/9e3b1yLtNHHiRH322WeaPHmy4uLi5OHhYS409uvXTzExMVq+fLn27t2r0qVLm9vBHTx40OI1PTw8tGjRIk2YMEHLly9XUlKSqlWrpnnz5umzzz5LkQp82HNMy9ChQxUZGWlORiQlJWnLli2GCo1ScrEmKChI06dPV1BQkPn6vPnmm+rfv7/VZE/Lli31ySefKDo6Wh07dpSLi0uKbYoUKaIVK1Zo9uzZ2rx5s9avX6+cOXOqSJEi8vf3V+fOndMdW/HixfXZZ59p1qxZGjNmjO7cuaOOHTuaf0H98ccfq3bt2vrhhx/07bffKj4+XoUKFVK5cuVSzNHZtWtXlSxZUnPmzNHixYsVGxsrd3d31ahRw3DaN7MqVKig4OBgTZ06VStWrFB0dLQKFy6sl19+WYMGDUp3vj6jn/MRI0aocOHCWrx4sSZNmqQiRYropZdeUtmyZTVo0CCrc+Y5OjqqQ4cOmjt3rtq2bWtoDkEPDw8tX75c33zzjf7v//5P69evl6urq5555hn16NEjw8fJ6Pvq6emp2bNna/LkyZo9e7aSkpLk6+ur2bNnKzg4OEWhsU2bNjp27JjWrl2rDRs26N69exo7dqw8PT3l6uqqhQsX6vvvv9e6deu0c+dO2dvby93dXX5+fuakZUYMHz5cBw4c0A8//KCrV6/Kw8ND7733nnr37p3hYxQtWlRLly7V1KlT9dNPP2nJkiVyc3NTp06d9MYbb6RonWvNgAEDVLlyZc2fP19BQUGKiYlRgQIFVKZMGQ0ZMkTu7u7mbZ999ln98MMPmjFjhlatWqWoqCgVKFBAVapUsSj4pXYNmzRpoi5dumjv3r36+eefFRcXJ3d3dzVt2lSvvPJKhudIrFevnvbs2WNON96/XEpON94/nsycq+n6Tp06VePHj9f48eNlZ2enRo0a6T//+Y+KFSuWobE+Cq6urpo1a5Y+//xzzZ49W7lz51aDBg0UFBSkbt26ZegYffr00cWLF7Vx40YtXrxY9+7d07x589ItNI4fP17BwcH69ttvFRERoTJlymjixIlWv1xyPwcHB82YMUMTJ07UmjVrFB0drTJlyuidd96Rvb29Ro4cabF9es/x1HTp0kWFCxfW7NmzNXPmTN29e1dlypTRBx98YOh5ktUmTJigL774Qj///LNWrVolDw8P9ejRQ5UqVVKfPn2s7mPkGeHs7Kx58+bp888/1+TJk3X37l1VqVJFw4cPNyeGJal79+7asGGDli5dqoiICOXJk0dly5bV5MmTDX1hBAAAa+ySnqaZ5zMo6Lf0EztZoWeNEulvhBTSKrIBGfG42tbAdl3Ztj67hwAbUKwJ/0DHwznzxb/3Hrp7967q1KmjqlWravbs2dk9HOBfb/bs2ZowYYKWLFli9Zf9EydO1KxZs/Tjjz9m25yWT6Ply5dr5MiRmjdvXoq5TbPS+fPn9fzzz+utt97SwIEDH9vr4uk0depUTZs2TVu2bLFIeSLrGX1GBAYGKiQkRFu3bn0MowMAIHX8dh4AAADAY/PgHF2SFBQUpKioKDVo0CAbRgT8e1n7PEZFRSkoKEhubm7m9sIP7rN06VJVq1aNIuNTwpTozkirTAAAAMAom2ydamxmGQAAAACPS8eOHVWjRg35+Pjo3r17+u2337Rx40aVLVtWL730UnYPD/hXWbNmjRYtWqSmTZuqcOHCunz5spYvX66wsDB99tlnFq0pT548qWPHjmnNmjW6efNmuvNnIvtFRkZq48aNWrJkiXLkyGFuLwoAAAA8SjZZaAQAAADwZHr++ee1detWrV+/XgkJCSpcuLACAwM1aNAgw3MHAng4Pj4+KliwoBYvXqyIiAg5OjqqYsWKGj16tHmOUpONGzdq2rRpcnd31zvvvKNnn302ewaNDLt8+bI++ugjlSlTRl9++WWG50EEAAAAjLDJORoXZNMcjT2YozFTmKMRD4s5GvGwmKMRjwJzNOJh/ZvnaAQAAAAAAE8nm0w02tE7FQAAAAAAAAAAAMhSxIAAAAAAAAAAAAAAGGajiUYijQAAAAAAAAAAAEBWItEIAAAAAAAAAAAAwDCbTDRSPQUAAAAAAAAAAACyFjU5AAAAAAAAAAAAAIZRaAQAAAAAAAAAAABgmE22TrWzs8vuIQAAAAAAAAAAAAA2jUQjAAAAAAAAAAAAAMNsM9GY3QMAAAAAAAAAAAAAbByJRgAAAAAAAAAAAACGUWgEAAAAAAAAAAAAYJhttk61o3kqAAAAAAAAAAAAkJVINAIAAAAAAAAAAAAwzCYTjVRPAQAAAAAAAAAAgKxFTQ4AAAAAAAAAAACAYTaZaGSORgAAAAAAAAAAACBrkWgEAAAAAAAAAAAAYBiFRgAAAAAAAAAAAACG2Wbr1OweAAAAAAAAAAAAAGDjSDQCAAAAAAAAAAAAMMw2E41EGgEAAAAAAAAAAIAsRaIRAAAAAAAAAAAAgGEUGgEAAAAAAAAAAAAYZpOtU+1F71QAAAAAAAAAAAAgK5FoBAAAAAAAAAAAAGCYTSYa7Qg0AgAAAAAAAAAAAFmKRCMAAAAAAAAAAAAAw2wz0cgcjQAAAAAAAAAAAECWItEIAAAAAAAAAAAAwDAKjQAAAAAAAAAAAAAMs83WqXROBQAAAAAAAAAAALIUiUYAAAAAAAAAAAAAhtlkotFeRBoBAAAAAAAAAACArESiEQAAAAAAAAAAAIBhFBoBAAAAAAAAAAAAGGaTrVPt6JwKAAAAAAAAAAAAZCkSjQAAAAAAAAAAAAAMI9EIAAAAAAAAAAAAwDASjQAAAAAAAAAAAAAMs81Eo4g0AgAAAAAAAAAAAFmJRCMAAAAAAAAAAAAAwyg0AgAAAAAAAAAAADDMJlun2tM5FQAAAAAAAAAAAMhSJBoBAAAAAAAAAAAAGGaTiUY7EWkEAAAAAAAAAAAAshKJRgAAAAAAAAAAAACGUWgEAAAAAAAAAAAAYJhttk6lcyoAAAAAAAAAAACQpUg0AgAAAAAAAAAAADDMNhONItIIAAAAAAAAAAAAZCUSjQAAAAAAAAAAAAAMo9AIAAAAAAAAAAAAwDCbbJ1qT+dUAAAAAAAAAAAAIEuRaAQAAAAAAAAAAABgmE0mGu1EpBEAAAAAAAAAAADISiQaAQAAAAAAAAAAABhmm4lGAo0AAAAAAAAAAABAliLRCAAAAAAAAAAAAMAwCo0AAAAAAAAAAAAADLPN1qnZPQAAAAAAAAAAAADAxpFoBAAAAAAAAAAAAGCYTSYa7e3INAIAAAAAAAAAAABZiUQjAAAAAAAAAAAAAMMoNAIAAAAAAAAAAAAwzCZbp9I49enimscxu4eAp5ybm3N2DwFPObsmrbN7CLABof+3LruHgKcezyIAAAAAAPB0IdEIAAAAAAAAAAAAwDCbTDQSaQQAAAAAAAAAAACyFolGAAAAAAAAAAAAAIbZZKLRjkgjAAAAAAAAAAAAkKVINAIAAAAAAAAAAAAwjEIjAAAAAAAAAAAAAMNss3UqnVMBAAAAAAAAAACALEWiEQAAAAAAAAAAAIBhtplozO4BAAAAAAAAAAAAADaORCMAAAAAAAAAAAAAwyg0AgAAAAAAAAAAADDMJlun0jsVAAAAAAAAAAAAyFokGgEAAAAAAAAAAAAYZpOJRjsijQAAAAAAAAAAAECWItEIAAAAAAAAAAAAwDDbTDQSaAQAAAAAAAAAAACyFIlGAAAAAAAAAAAAAIZRaAQAAAAAAAAAAABgmG22Ts3uAQAAAAAAAAAAAAA2jkQjAAAAAAAAAAAAAMNsMtFIpBEAAAAAAAAAAADIWiQaAQAAAAAAAAAAABhGoREAAAAAAAAAAACAYTbZOtWO3qkAAAAAAAAAAABAlspUoXHTpk2aPXu2Tp48qZw5c6pGjRoaNmyYypcvn+6+W7du1ZYtW3Tw4EFdvnxZjo6OKlWqlLp06aIOHTooRw6brH0CAAAAAAAAAAAANsVwVS84OFijRo1S+fLlNXz4cN2+fVtBQUEKCAjQokWL5O3tneb+H3zwgZycnNS8eXOVLVtWUVFRWrt2rd5//31t2rRJM2bMkJ3dwyUSH3J3AAAAAAAAAAAAAOmwS0pKSsroxpGRkWratKlcXV21du1aubq6SpIuX76sF154Qb6+vpo3b16ax9i9e7fq1KljUUxMTExUYGCgfvvtN82cOVONGzfO5OkkO3gh6qH2z6yqJfNky+s+7Sq9vym7h4CnnJubc3YPAU+5kJBb2T0E2IDQ/1uX3UPAUy7uwLTsHgIAAAAAAIAh9kY23rJli6Kjo9WlSxdzkVGSihcvrhYtWmjPnj0KDQ1N8xh169ZNkVh0cHBQy5YtJUknTpwwMiSr7LLpDwAAAAAAAAAAAPBvYah16qFDhyRJ1apVS7GuWrVqWrFihY4cOaJixYoZHkhYWJgkqWDBgulu26xZszTXf/HdSsOvDwAAAAAAAAAAACDjDCUaTcXAokWLplhnWnblyhXDg7hy5Yp++OEH5cuXL90iIgAAAAAAAAAAAIDsZyjRGBcXJ0nKlStXinWmZfHx8YYGEBMTo4EDByo6OlpTp05V/vz5091ny5Ytaa4/dDF75mgEAAAAAAAAAAAA/i0MJRqdnJwkSQkJCSnWmZblzp07w8eLiYnRq6++qj///FMffPCBnnvuOSPDAQAAAAAAAAAAAJBNDBUaixQpIsl6e1TTMmttVa2Jjo7WK6+8ot9++02jR49Wjx49jAwlTXbZ9D8AAAAAAAAAAADg38JQodHPz0+SdODAgRTrDh48KEny9fVN9zhRUVHq16+fDh48qE8++UQBAQFGhgEAAAAAAAAAAAAgmxkqNDZv3lwuLi4KDg5WdHS0efnly5e1YcMG1apVS8WKFZOUPJ/j6dOndfXqVYtjREVFqW/fvjpy5IjGjh2rzp07P4LTAAAAAAAAAAAAAPA45TCycb58+fTuu+/qww8/VLdu3dS1a1clJCQoKChIkvT++++btz18+LB69eqljh07aty4ceblffr00R9//KFmzZrJzs5OP/74o8VreHt7y8fH52HOSXZ0MQUAAAAAAAAAAACylKFCoyQFBAQof/78mjNnjiZMmKCcOXPK399fQ4YMyVCB8I8//pAkbdmyRVu2bEmxfvDgwQ9daAQAAAAAAAAAAACQtQwXGiWpZcuWatmyZZrb1K5dWydOnEix3NqyR41AIwAAAAAAAAAAAJC1DM3RCAAAAAAAAAAAAABSJhONTzwijQAAAAAAAAAAAECWItEIAAAAAAAAAAAAwDAKjQAAAAAAAAAAAAAMs8nWqXb0TgUAAAAAAAAAAACyFIlGAAAAAAAAAAAAAIbZZqKRQCMAAAAAAAAAAACQpUg0AgAAAAAAAAAAADCMQiMAAAAAAAAAAAAAw2yzdWp2DwAAAAAAAAAAAACwcSQaAQAAAAAAAAAAABhmk4lGIo0AAAAAAAAAAABA1iLRCAAAAAAAAAAAAMAwm0w02hFpBAAAAAAAAAAAALIUiUYAAAAAAAAAAAAAhlFoBAAAAAAAAAAAAGCYbbZOpXMqAAAAAAAAAAAAkKVINAIAAAAAAAAAAAAwzDYTjdk9AAAAAAAAAAAAAMDGkWgEAAAAAAAAAAAAYBiFRgAAAAAAAAAAAACG2WTrVHqnAgAAAAAAAAAAAFmLRCMAAAAAAAAAAAAAw2wy0WhHpBEAAAAAAAAAAADIUiQaAQAAAAAAAAAAABhGoREAAAAAAAAAAACAYbbZOpXOqQAAAAAAAAAAAECWItEIAAAAAAAAAAAAwDDbTDRm9wAAAAAAAAAAAAAAG0eiEQAAAAAAAAAAAIBhNploJNKIJ0GRvI4a3PwZNShXUPmdc+la1G1t/fOqpm89rVvxd9Pdv2aZApr7Ss10t2s2fpuuRN6WJBXO66jmFQurkbe7vNxd5J7HUbEJd/Xn5Sj9sOeiNv959aHPC4+Pu2su9atfSrVL51fe3Dl1IyZBO07d0He7Lyj6dqKhY5Uv7KIAfw9VKZFP+Z1yKvr2XV0Ij9OaP8K08YH7YsfbDVI9ztHLt/T6osOZOh88fkXz5dbQluXUyMdd+V1y6tqt2/rpjzB9ufEv3YpL/zlUu6ybFg2qk+529T/aqtCIePN/n/midarbHjh/U52+3J2xE8BTrWPzqmpYo5z8ynvIt7yH8ro6adHaveo7al52Dw0AAAAAAACPiG0WGoFs5unmpKDXaqmQq6O2/HlVZ6/FyLdEPgXWL6X65Qup54y9ioy7k+YxQm7G6astp62uK1/EVc9VLqKTV6LMRUZJ6lGnpF5pXEYXw2O190y4rkffVvH8TmpesbDqPVNQ3+88p/HrTz7Sc0XWKJ4vt77u5ic3l1zaceqGzofHqkLRPHqphodqlymggYsOZ6hgLUkvVi2mN5t4Ker2Xe0+E67r0QnKkzuHvAo5q26ZAikKjZIUGhmv9UdTLr8WfTvFMjyZShZ01tI366pQHkdtOnJFZ67GyK9kPr3cqIwaebury9TdiohN+zl0KTxOX278y+o672J51NKvqE6ERlkUGf/ZN1bL9oWkWB4aEZe5E8JT5z+vtFQV7xKKiolXSFiE8ro6ZfeQbNaePXvUq1cvjR07Vi+++KIk6dKlS2rWrJkGDx6sN954I9VleLIFBgYqJCREW7duze6hAAAAAABgFYVGIAt80K6CCrk66tPVx7Tw14vm5e+2Kq/eDUrrreef0Uc/HkvzGJcj4jV9q/VC44SXfCVJS/db/hL/yKVI9Z61T/vP3bRY7uXuooWv11LvBqW15lCo/rwclZnTwmP0dvOycnPJpf9tPa1lB0LNywc3LqOu/h7q36CUJm22fn/cr2ap/HqrqZf2n4/QqFXHFXfHMgnpYG89An7l1m19t/vCw50EstVHnSqpUB5HjV5+VPN2njcvf79dBfV7toyGt/bWqKV/pHmMkJupFxq/7FlVkrT4V+v3SVpFSvw7vDtxmUKuRuj0hWtqWKOcNs1+K7uHBBu1efNmHTt2jOIpAAAAAADZwCbnaLTLpv8BUnKasX65QroUHqdFey5arJu25bRib99V26rF5ZTTIVPHz++cU80qFlZcQqJWHbhssW7zn1dTFBkl6cy1GG04EiZJqlnGLVOvi8eneL7cqlW6gC5Hxmv5fUVGSZqz64JiExLVomJh5c6R/iN8YKMyun33nsasPZGiyChJifeSHtm48eQoWdBZjXzcdfFGrOb/ct5i3f82nlTM7bvqUKO4nHJl7jlUwCWnnvctoriERC23kloEJGn7/r90+sK17B7Gv5aHh4cOHz6sAQMGZPdQstzmzZs1bdq07B4GAAAAAAD/SjZZaASyUy2v5ELerlPXlfRADSc2IVEHLkTIOZeD/DzzZer47asVl2NOB236I0xRGWydKUl3E+9JorD0NKheMvne2Hfuph58t+LuJOqPy7fklNNBlYrnSfM4ZQo665nCLtp3LkK34u+qmmc+Bfh7qGsND9UomS/Nr0e4OjqodeUiCqxVQh2rFlPFYmm/Fp4sdZ5Jfg7tOJnyORRzO1G/nb0pZ8ccqlYqf6aO/6J/CTnmdNC6Q6GpPofyOuVUl1olNLBZWQXWL6WqmXwtAJljZ2cnR0dH5chBA5OHFR0dnd1DAAAAAADgiWWThUY7u+z5A0hS6UIukqRz12Otrj9/I/bv7ZwzdfzONUtIkpbsu5jOlv9wcXTQc5WK6N69JO06dSNTr4vHp2SB5HnMLt5MOe9d8vLkOe48C6Q931mFoq6SpJuxCZr6kq+mvOSrQY3LaPCzZfS/Lr76rlc1eeTPbXXfcoVdNbJFOb3asLSGNSurGd2r6NvAqvLK5H2Lx8urcPJ7f/ZqjNX1564nLy/j7pKp4wfU8ZQkLUqjvW5Fj7z6PMBPw1/w1phOlbT8rXpa83YDeVO0Bh6LS5cuydvbW1OnTk13299//1116tRR69atFRLyT0p548aN6tmzp6pXry4/Pz916NBBwcHBGXr9vn37qk6dOkq679sOhw8flre3t6pUqaKEhATz8rNnz8rb29silbhu3ToNHDhQTZo0ka+vr2rVqqV+/fpp//79Fq/TtGlTrVixQpLk7e1t/rN8+XLzNtHR0Zo8ebJatGihypUrq1atWho4cKCOHz9ucaw9e/aY9128eLHatm0rX19fffLJJ+bjjxgxQvv27VO3bt1UtWpV1atXTxMmTFBiYqISEhI0ceJENW7cWL6+vurUqZMOHjxo9fo8zLWVpEOHDum1115TrVq15Ovrq5YtW+qrr76yuK6SNHXqVHl7e+vs2bP68ssv1aRJE1WuXFmtWrXSqlWrMvx6AAAAAACkhq84A49YntzJH6vo29ZTPqb0T57cOQ0f2790AXm5u+jklSgdvBCZ4f0+6pg8V9uiXy/ozDXrhQc8OVwck++hmATr91DM7eQWqK6OaT/C8zsn32Mv+BbV9ejbemf5UR0OuSU355zqU7ekWlQsrPEdK6r39wd0976k6+L9Idr213VdvBmnhLv3VNLNWT1qllAT70L6souvXp5/QNejE1J7WTwBTM+hqPg7VtdHxSXfW3mdjD+HapV1U9kirjoRGqXfz0VY3Wb2z2e04fAVnb0Wo9t37qlsYVe91tRLrasWU9CAWmozaafCIm8bfm0Aj97GjRv1zjvvyM/PT1999ZXy5UtO1U+ZMkVfffWVateurcGDB8vR0VE7d+7UqFGjdP78eQ0fPjzN49apU0e//PKLjh07pooVK0qSdu3aJXt7e8XHx5uLm6blklSvXj3z/kFBQcqbN686d+6swoULKzQ0VEuXLlXv3r01f/58Va9eXZL03nvv6bvvvtP+/fs1fvx48/6m9dHR0erWrZsuXLigDh06yMfHR7du3dKSJUsUEBCgBQsWqFKlShZjnzdvnq5fv66XXnpJRYsWlYvLP1/K+PPPP7V161Z16tRJ7dq107Zt2zR79mw5ODjo5MmTio6OVt++fRUXF6fvvvtOr732mrZs2SJXV1fzMR722m7fvl0DBw6Ui4uLunXrJnd3d23btk1TpkzRgQMHNHPmTNnbW36fdMSIEbKzs1NgYKDs7e21cOFCvfPOOypZsqSqVq2a5usBAAAAAJAWCo3AU6TL32nG4H2XMrzPu63Kq6VvUe0/e1OfrzuRVUPDE8j+76h1Dns7jV5zQkdDoyQlt/D9ZP1JlXRzUoWiedS4fEFtOX7dvN9X285aHOdEWLT+u+a4Prbz0bPlC6mbv4em/my5Df49umUgzfjZKsuU0JFLkRo874C+spNaVSmm/s966ZMfj2XpOAGkb+7cufr888/VqlUrjRs3Trly5ZKUXEybPn26AgMDNWrUKPP2PXr00Mcff6w5c+aoa9eu8vT0TPXY9erV06RJk7Rr1y6LQmPDhg11+PBh7d6921xo3L17t1xcXOTn52fef/bs2XJ2tkzRd+vWTW3atNE333yjmTNnSpKaN2+uzZs3a//+/Wrfvn2KcUyZMkVnz57VggULVKVKFYtjtW3bVuPGjdP8+fMt9gkJCdG6devk7u6e4ngnT57UokWLVK1aNfNxOnTooJkzZ6px48aaP3++7P7++fvMM89o0KBBWrt2rbp27fpIrm1iYqJGjx4tBwcHLVmyRKVKlZIk9ezZUyNHjtTy5cu1evXqFNcib968mjFjhrkA2aJFCz333HOaP38+hUYAAAAAwEOxzdap2fQHkP5JLKaWNksvaZSafE459FylwopLSNTqg6EZ2uftFuXUu0Fp7TsbrgHzftedROZnfBrE/J2Gdcll/R5ycXSQlHpq1sS0/kZ0grnIeL+dp8IlSRWLZqyV5cpDyfddlRKZm18Uj096yek8Tsn31q04g88h55xq6VdUcQmJWrE/JP0dHrBwV3Jxsubfc9kCyB737t3Tp59+qrFjx6pv376aNGmSucgoSatXr1ZSUpI6d+6s8PBwiz9NmzbVvXv3zCnE1FSsWFH58+fX7t27JUnx8fE6cOCA6tWrpzp16piX37t3T3v37lXNmjUt5pO8v8gYHR2tmzdvysHBQVWqVNGhQ4cydJ5JSUlatWqVqlatKk9PT4vzuHv3rurXr6/ffvtN8fGWrco7dOhgtcgoSVWrVjUXGU38/f2VlJSkXr16mYuMklSzZk1J0rlz58zLHvbaHj16VCEhIWrfvr25yGjyxhtvSJI2bdqUYr8+ffpYpByLFSumMmXK6OxZvjgEAAAAAHg4JBqBR8w091lqczCWKuj893bW53BMTftqxeWY00Erfw8xFxHS8p/W3upVv5T2nA7XwPm/K/7OPUOvh+xzwTwHo/X5Ez3NczjGpX2c8OT1Uam18f17ea4cGfvOScTf7TZz57TJ76jYlDNXoyVJZQpbn4PRNJfsWYOtlDv5e8gxp4OW7r2UoefQg27EJLfcdc7lYHhfAI/OvHnzFB0drddff11Dhw5Nsf706dOSZDUhaHL9+vVU10mSvb29ateure3btyshIUG//fabEhISVK9ePTk7O2v06NGKiorS2bNnFRkZqbp161rsf/z4cU2ZMkW//vqrYmIsn1V2GZwc/ebNm7p586b27duX4vgPblesWDHzf5cuXTrVba0lDU3tZh9cZ1oeERFhXvaw1/bSpeSuFuXLl0+xrnjx4nJ1ddWFCykT59bGnT9/fos5OQEAAAAAyAzbLDQSL0Q22nsmOSVW75lCsrOTku4LETrnclC1kvkVm5CowxczPseiJHU20DZ1VFsfdatTUr/8dV1vBB3U7bsUGZ8mv/89/2bN0gVkJ+n+HKpTTgdVLp5XcXcSdfRyypTi/Y6GRik2IVHF8joqdw57xT9wH3j9XfQOjYy3tnsKlYolJx8vR2Rse2SfX/9OqzYsn/I55OLooBplCij29l0dOB9h6Lhd/26buvjX1NumpqVaqfySpIvhxr5oAeDRql+/vvbv369ly5apXbt2Klu2rMX6e/eSf17MmDHDIul4v7TapprUrVtXGzdu1IEDB7Rr1y65u7urfPnycnJyUmJiovbs2WMuvJnaqErSlStX1L17dzk7O+u1116Tl5eXnJycZG9vrxkzZujXX3/N0HmazqNmzZoaOHBgqtu5uVmmrJ2cnFLd1sEh9S9KPDgvoknSfQ/hR3VtU5NaETa1sQEAAAAA8LBss9AIZKOL4XH65a/rql+ukLrV9tTCXy+a1w1uVlbOjjn0w96LiruTaF5e5u/049lUUo7VS+VX2cKuOnklSgcvpF2gHNOhojrXLKHtJ67prYWHlECR8alzOTJee8/dVK3SBfRitWJaduCfVrn96pWUcy4HrTwUalE4LOmW/EtRU4pRkm7fvae1f4SpS/Xi6t+glMW8il6FnNWqUmHdTbynn0/eMC8vW8hZ58LjlHjPss1u2ULO6t8guUXbpmPXHu0J45G7cCNW249fUyMfdwXWL6V5O8+b1w1pUV4ujjm0cNcFxSX88xzy+jv9eOaq9ZRjzTIFVK5oHp0IjdLv5yJSfW2fYnl0Kixadx+4h3yK5dHbrbwlSSt/u5zZUwPwCJQrV05DhgxRnz591LNnT3377beqUKGCeX3p0qW1Y8cOubu7q1KlSpl+HVOKcNeuXdq9e7dq164tKbmQVqJECe3atUunT59WwYIF5e3tbd5v06ZNiomJ0VdffZUiiTh58uQUr5Nacc3NzU158+ZVZGSk6tWrl+nzeJQe9tqaipB//fVXinWhoaGKiooyX2cAAAAAAB4Hmyw02hFpRDb7eNUxBb1WS++3raA6ZQvqzLVo+ZXIr9pl3XT2Woy+3HTKYvs1QxtIkiq9n3JOHUnqksE044CmXupcs4TiEhJ1PDRKrzQqk2Kb46G3tJVC0RNv0ubT+rqbn4Y0LasaJfPr/I1YVSiWRzVK5teF8FjNuq9wJEkLXq4hSWo4aafF8tm/nFeVEnn1Ug0PVSqWR0cuR8nNOacalSsox5wO+nLrGV2+L9HYtYaH6pV10+GQW7oadVsJifdUqoCzapUpoBz2dlp1+Io2H+f+eRr8d9lRLX2zrka/WEn1yhXU6asxqlIyn+qVK6QzV6M1cd0Ji+03j2gsSfIats7q8QLqlpQkLdqddpqxb+MyalapsPadCVdoRLwS7t5T2cKuauRTSDkc7LVo9wWt+p1C479B22f91LaJnySpSMG8kqTafmU0c0xPSdKNiBiNnLwi28b3b+fl5aUFCxaod+/e6tWrl2bPnq0qVapISm7rOX/+fH3xxRf65ptvlDOn5XyvUVFRcnR0TDWRZ1K6dGkVL15cmzZt0rlz59SjRw/zunr16mn79u26evWqmjdvblEsTC01uG3bNh0+fDjFctN8jhEREcqfP795ub29vdq1a6egoCCtWLFCHTt2TLHv9evXVahQoTTP41F62GtbsWJFeXh4aNWqVXr11Vfl4eFhXvfVV19Jkp5//vmsOwEAAAAAAB5gk4VGILtdDI9T1+m/anCzZ9SgfCE1Kl9I16Jua/4v5zV962ndMjC3Wd7cOfR85SKKS0jU6oOhaW5b4u+5+5xyOejVZ72sbrPy9xAKjU+By5Hx6r/goPrVK6XapQuoTpkCuhGToCW/hei73RcUfTsx/YNIik1I1ODFh9WzlqealC+kF6sW0+2793Q45JYW7w/RvgdaZ+44fUPOjg4qW8hF1T3zKVcOe92Ku6s9Z29q9ZEr+uV0eBacLbLChRuxaj/5Fw1tWV6NfArp2QqFde3WbX23/ay+3PiXbsUZeA455VCrKkUVl5CoFfvTns/rpz/ClCd3DnkXz6O65QrKMYeDImITtO34NS3+9aK2HL36sKeGp4SfdwkFtqtjsczL011enu6SpPOXb1BozGaenp5auHChevfurT59+mjGjBmqVauWfH19NWTIEP3vf/9TmzZt1KZNGxUtWlQ3btzQyZMntWXLFq1du1YlSpRI9zXq1Kmj5cuXS5JFqrBu3bpasmSJ+f/fr1GjRnJ2dtY777yjHj16qECBAvrzzz+1evVqlS9fXidPnrTYvkqVKgoKCtKYMWPUuHFj5cyZU35+fvL09NTQoUN14MABjRgxQps3b5a/v7+cnJwUGhqq3bt3y9HRUfPnz3/YS5lhD3ttHRwcNHr0aA0cOFCdO3dWQECAChYsqO3bt2vbtm1q0KCB2rZt+9jOBwAAAAAACo1AFrkSeVujlh/N0LapJRkl6Vb8XdUYvSVDx3l/2VG9vyxjr4kn39WoBI3dmLI1mjUPJhnvF3fnnmb9cl6zfjmf6jYmO06Fa8cpiom2IjQiXu8uTpn+sSa1JKMk3Yq7q4r/2Zih4/z0R5h++iMsQ9vCtn06Y50+nZH6fYUnQ9GiRbVgwQK9/PLL6t+/v6ZNm6aGDRtqwIABqly5subPn6+goCDFxMSoQIECKlOmjIYMGSJ3d/cMHb9evXpavny5SpcurWLFipmX161bV3Z2dkpKSrKYn1FKLoDOnj1bkydP1uzZs5WUlCRfX1/Nnj1bwcHBKQqNbdq00bFjx7R27Vpt2LBB9+7d09ixY+Xp6SlXV1ctXLhQ33//vdatW6edO3fK3t5e7u7u8vPzU4cOHR76Ghr1sNe2UaNGCgoK0vTp0xUUFKS4uDh5eHjozTffVP/+/ZmPEQAAAADwWNklJSUlpb/Z0+VC+O1sed2Sbo7Z8rpPu7SKbEBGuLk5Z/cQ8JQLCbmV3UOADQj9P4pqeDhxB6Zl9xAAAAAAAAAM4euuAAAAAAAAAAAAAAyzydapdtk9AAAAAAAAAAAAAMDGkWgEAAAAAAAAAAAAYBiFRgAAAAAAAAAAAACG2WbrVHqnAgAAAAAAAAAAAFmKRCMAAAAAAAAAAAAAw2wy0SgRaQQAAAAAAAAAAACyEolGAAAAAAAAAAAAAIbZZKKRORoBAAAAAAAAAACArEWiEQAAAAAAAAAAAIBhFBoBAAAAAAAAAAAAGGabrVOzewAAAAAAAAAAAACAjSPRCAAAAAAAAAAAAMAw20w0EmkEAAAAAAAAAAAAshSJRgAAAAAAAAAAAACGUWgEAAAAAAAAAAAAYJhttk4VvVMBAAAAAAAAAACArESiEQAAAAAAAAAAAIBhNploJNAIAAAAAAAAAAAAZC0SjQAAAAAAAAAAAAAMs8lEI4FGAAAAAAAAAAAAIGuRaAQAAAAAAAAAAABgGIVGAAAAAAAAAAAAAIbZZutUeqcCAAAAAAAAAAAAWYpEIwAAAAAAAAAAAADDbDPRKCKNAAAAAAAAAAAAQFYi0QgAAAAAAAAAAADAMAqNAAAAAAAAAAAAAAyzydapdE4FAAAAAAAAAAAAshaJRgAAAAAAAAAAAACG2WSikUAjAAAAAAAAAAAAkLVINAIAAAAAAAAAAAAwzDYTjUQaAQAAAAAAAAAAgCxFohEAAAAAAAAAAACAYRQaAQAAAAAAAAAAABhmm61TRe9UAAAAAAAAAAAAICuRaAQAAAAAAAAAAABgmG0mGgk0AgAAAAAAAAAAAFmKRCMAAAAAAAAAAAAAwyg0AgAAAAAAAAAAADCMQiMAAAAAAAAAAAAAwyg0AgAAAAAAAAAAADAsR3YPICvY2WX3CAAAAAAAAAAAAADbRqIRAAAAAAAAAAAAgGG2mWgUkUYAAAAAAAAAAAAgK5FoBAAAAAAAAAAAAGAYhUYAAAAAAAAAAAAAhtlm61Q6pwIAAAAAAAAAAABZikQjAAAAAAAAAAAAAMNsM9GY3QMAAAAAAAAAAAAAbByJRgAAAAAAAAAAAACGUWgEAAAAAAAAAAAAYJhNtk6ldyoAAAAAAAAAAACQtUg0AgAAAAAAAAAAADDMJhONdkQaAQAAAAAAAAAAgCxFohEAAAAAAAAAAACAYRQaAQAAAAAAAAAAABhmm61T6ZwKAAAAAAAAAAAAZCkSjQAAAAAAAAAAAAAMs81EY3YPAAAAAAAAAAAAALBxJBoBAAAAAAAAAAAAGGaTiUYijQAAAAAAAAAAAEDWItEIAAAAAAAAAAAAwLBMFRo3bdqkl156SVWrVlXNmjX1+uuv6+TJkxnePy4uThMnTlTTpk1VuXJlNW3aVJMmTVJcXFxmhgMAAAAAAAAAAADgMTPcOjU4OFijRo1S+fLlNXz4cN2+fVtBQUEKCAjQokWL5O3tneb+iYmJevXVV7V37161b99eNWvW1PHjxzVnzhwdPnxY3333neztHy5oaUfvVAAAAAAAAAAAACBLGSo0RkZGaty4cSpatKgWLVokV1dXSVKrVq30wgsv6NNPP9W8efPSPMaKFSu0d+9eBQYGatSoUeblHh4e+vzzz7Vq1Sp16NDB+JkAAAAAAAAAAAAAeGwMRQe3bNmi6OhodenSxVxklKTixYurRYsW2rNnj0JDQ9M8xo8//ihJevnlly2Wd+/eXblz59bKlSuNDMkqO7vs+QMAAAAAAAAAAAD8WxgqNB46dEiSVK1atRTrTMuOHDmS6v5JSUk6cuSIChcuLA8PD4t1uXPnVoUKFdLcHwAAAAAAAAAAAMCTwVDr1LCwMElS0aJFU6wzLbty5Uqq+0dERCguLk7lypWzur5IkSI6cOCAoqOjLRKTD2rWrFma41y7cUua6wEAAAAAAAAAAAA8HEOFxri4OElSrly5UqwzLYuPj091f9M6a/tLkqOjo/l10io0pie3obNCdjv66fPZPQQAAB6B1tk9AAAAAAAAAOCxMlSSc3JykiQlJCSkWGdaljt37lT3N62ztr8k3b592+J1UrNlC4lFAAAAAAAAAAAAIDsZmqOxSJEikqy3RzUts9ZW1SR//vxycnJKtb1qWFiYXF1dHyrNCAAAAAAAAAAAACDrGSo0+vn5SZIOHDiQYt3BgwclSb6+vqnub2dnp8qVK+vq1asKCQmxWBcfH69jx46luT8AAAAAAAAAAACAJ4OhQmPz5s3l4uKi4OBgRUdHm5dfvnxZGzZsUK1atVSsWDFJyfMsnj59WlevXrU4Rvv27SVJ3333ncXyRYsWKT4+3rweAAAAAAAAAAAAwJPLLikpKcnIDosXL9aHH36o8uXLq2vXrkpISFBQUJBu3rypRYsWycfHR5K0Z88e9erVSx07dtS4cePM+ycmJqpXr17av3+/OnToIH9/f504cUILFy5UjRo1NHfuXDk4ODzaswQAAAAAAAAAAADwSOUwukNAQIDy58+vOXPmaMKECcqZM6f8/f01ZMgQc5ExLQ4ODpo5c6a++uorrV+/XmvXrpW7u7tefvllDRo0iCIjAAAAAAAAAAAA8BQwnGgEAAAAAAAAAAAAAENzNAIAAAAAAAAAAACARKERAAAAAAAAAAAAQCZQaAQAAAAAAAAAAABgGIVGAAAAAAAAAAAAAIZRaAQAAAAAAAAAAABgGIVGAAAAAAAAAAAA4P/Zu9OoKuv9//+vCwGZHNFUUNSckMqhnMDUYzikaeKY83AaLLGyWf11qtPX0kZPhno0h5zLCRxLQ00zMMUK9YAmzkqagOIEgrD/N/rLOaQ06v5cG5+PtVzLvd/XXut5w4jFm8914Q9j0QjYRHZ2tmJiYpSYmGg6BQAAAAAAAAAA4DexaARswtPTUy+//LKSkpJMp8CFDR48WPHx8UXOt23bpsGDBzuxCK7mwIEDio2NLfReQkKCBg8erAcffFAff/yxmTAAAAAAAAAAtuNuOgDAz9zc3HTbbbfp0qVLplPgwrZv367evXsXOc/IyNCOHTucWARX869//UuZmZlq166dJCk9PV2PPvqocnNz5ePjo7feektVqlRRx44dDZfCznbt2qW5c+fq8OHDOnv2rBwOR6G5ZVnXLLQBAAAAAADgejjRCNjI/fffr88//1x5eXmmU1BMnTt3Tp6enqYzYGN79uxRixYtCl6vWrVKly9f1vLly/X111+rQYMGmj9/vsFC2N2qVav00EMP6fPPP9fly5dVpUoVBQQEFPpTpUoV05kAAAAAAAC4ATjRCNhIz549FR8fr6FDh2rYsGGqXr26vL29r7kuICDAQB3sau/evdq7d2/B64SEhOsuq8+ePatFixapVq1azsyDi8nIyFClSpUKXm/btk2NGjVS3bp1JUkPPPCApk2bZioPLmDq1KkKCgrSnDlzVLlyZdM5AAAAAAAAuIlYNAI28sADD8iyLDkcDiUkJBR5XXJyshOrYHexsbGKioqS9PPtCD/99FN9+umn173W19dX/+///T9n5sHFeHl5FdzC2eFw6LvvvtNDDz1UMPf29tb58+dN5cEFHDt2TM8//zxLRgAAAAAAgFsAi0bARiIjI2VZlukMuJju3burWbNmcjgcGjJkiB5//HGFhYUVusayLPn4+Kh27doqWbKkoVK4gpo1a2rdunUaMGCANmzYoHPnzik0NLRgnpqaqrJly5oLhO1VrFjxmmcyAgAAAAAAoHiyHPwkCACKjaioKHXo0KHgNpfAH7Vy5Uq9+OKL8vPzU3Z2tqpWrao1a9aoRIkSkqRBgwbJy8tLH330keFS2NUHH3ygbdu2adGiRaZTAAAAAAAAcJNxohEAipGRI0eaToCLe/DBByVJX3zxhUqVKqUnnniiYMmYkZGhixcvqlu3biYTYXPdunVTfHy8Hn/8cQ0ZMkRVq1Yt+Df0v3jeMAAAAAAAgOvjRCNgMxcuXNCcOXO0detWpaen66233lLjxo2VkZGhhQsXqlOnTqpVq5bpTNjckSNHdPjwYZ05c+a684iICOcGAbhlBAcHFzxv+NduB87zhgEAAAAAAFwfJxoBGzlz5oz69++vo0ePKigoSMeOHVN2drYkqXz58oqOjtaFCxc0evRow6Wwq4yMDL300kvaunWrJF33OWmWZbFoxO9y9uxZHT16VJIUFBTEsxnxu/C8YQAAAAAAgFsHi0bARiZNmqRTp07pk08+UWBgoMLCwgrN27Vrp/j4eEN1cAXjxo3T1q1b1adPH7Vs2ZLFEP6Uffv26Y033tCOHTsKvd+0aVONHTtWwcHBhsrgCp588knTCQAAAAAAAHASFo2AjWzcuFH9+/fXXXfddd1bXgYGBurkyZMGyuAqvvrqK/Xu3Vv//Oc/TafARSUlJWngwIHKzs5Wq1atCm7VnJKSoq+//lr9+/fX/PnzFRISYrgUAAAAAAAAgGksGgEbSU9PV/Xq1Yucu7u7Kysry4lFcDVXrlzRnXfeaToDLuzdd9+Vm5ublixZojvuuKPQbM+ePRoyZIjeffddzZo1y1AhXMWqVau0fv36Qrff7dixo7p06WK4DAAAAAAAADcKi0bARsqVK6fU1NQi5z/88IMqV67sxCK4mgYNGujgwYOmM+DCvvvuOw0aNOiaJaMk3XnnnQUnGoGi5ObmKjIyUl999ZUcDof8/PxkWZb27dun2NhYrVy5UlOmTJG7O9+GAgAAAAAAuDo30wEA/qtly5ZatmyZzp8/f83swIEDio6OVuvWrQ2UwVU899xziomJUWJioukUuCgPD49f/YWGKlWqyMPDw4lFcDUfffSRtmzZou7du2vTpk1KSEjQjh079OWXX6pnz57asmWLZsyYYToTAAAAAAAAN4DlcDgcpiMA/OzYsWPq0aOHypUrp06dOmn69OkaOHCgJGnp0qXy8vJSTEyMKlWqZLgUdjVmzBjt3btX+/btU6NGjVStWjW5uRX+nRLLsvTmm28aKoTdPfnkk5KkDz/88LrzkSNHys3NTZMmTXJmFlxIp06dFBQUpGnTpl13/thjj+nYsWP67LPPnFwGAAAAAACAG41FI2AzycnJBcui/1W3bl298847qlevnqEyuILg4ODfvMayLCUnJzuhBq4oNTVVAwcO1H333afhw4erYsWKkqTTp09r2rRp2rRpk+bNm6eAgADDpbCrBg0a6KWXXtKAAQOuO1+wYIHeeust7dq1y8llAAAAAAAAuNF4OA5gM/Xr11dMTIz279+vAwcOKD8/XzVr1lT9+vVNp8EF/HJBDfxRgwYNUnZ2thYsWKAFCxaodOnSkqRz585J+vlZsoMGDSr0GcuyFBsb6/RW2JO3t7fS09OLnKenp8vb29uJRQAAAAAAALhZWDQCNpKamqry5cvLy8tLderUUZ06dQrNs7OzlZGRwUkiADcNX1/wVzVq1EiLFi1S9+7dVa1atUKz1NRUffLJJ2rcuLGhOgAAAAAAANxI3DoVsJH69evr7bffVteuXa87X7t2rZ577jlue4nfdOnSJX3//fdKS0tTWFiYKlSoYDoJwC0iMTFRAwYMkIeHh7p161bwSzMpKSlasWKFcnNztWDBAjVo0MBwKQAAAAAAAP4qTjQCNvJbe//8/HxZluWkGriqTz/9VO+++67Onz8vy7I0a9YsVahQQenp6WrTpo1eeeUV9enTx3QmgGKqYcOGmjJlil577TV98sknhWaBgYF67bXXWDICAAAAAAAUEywaAZv5tUXigQMHVKpUKSfWwNXExsbq1VdfVdu2bRUeHq6XX365YObv769WrVppw4YNLBrxm44dO6YNGzboyJEjkqTq1asrPDz8mlthAtfTunVrxcbG6j//+Y+OHTsmSQoKClJISIjc3NwM1wEAAAAAAOBG4dapgGHR0dGKjo6WJG3fvl21atWSv7//NddlZmZq//79ateunSZNmuTsTLiIvn37ysPDQ/PmzdOZM2cUGhqq2bNnKzQ0VJI0efJkLVu2TBs3bjRcCjv74IMPNH36dOXl5RV6v0SJEnrkkUf0zDPPGCoDAAAAAAAAYCecaAQMO3funI4fPy7p59OMGRkZysrKKnSNZVny8fFRz549+QE/ftW+ffv07LPPFjm/7bbblJaW5sQiuJr58+dr6tSpatCggYYNG6batWtLkvbv36/Zs2dr+vTpqlixogYOHGi4FAAAAAAAAIBpLBoBw4YMGaIhQ4ZIkoKDgzV27Fh17drVcBWKq7S0NHl5eZnOgI3Nnz9fd955pxYsWCAPD4+C9+vUqaN27dqpb9++mj9/PotGFAgPD//Dn7EsS7GxsTehBgAAAAAAAM7EohGwkQ0bNlz3tqnA71W7dm1t375dgwYNuu48NjZW9evXd3IVXMmJEyf03HPPFVoyXuXp6amuXbvq/fffN1AGuwoICDCdAAAAAAAAAENYNAI2MnjwYI0dO7bI0yGbNm3SuHHjtGHDBieXwVX07t1br776qubOnatOnTpJ+vnkUGZmpt555x0lJSXpvffeM1wJO7vtttuUk5NT5Dw3N1eVKlVyYhHsbt68eaYTAAAAAAAAYAiLRsBGTpw4oUuXLhU5z8rKUmpqqhOL4Gr69Omjb7/9Vm+++aYmTpwoy7L01FNP6fz583I4HOrTp486d+5sOhM21rNnTy1btkz9+/eXn59fodn58+e1bNky9ezZ01AdAAAAAAAAADth0Qi4EJ6vh99jwoQJateunVauXKmDBw8qPz9fTZo0UUREhDp06GA6DzbXwIQrIwAA2r9JREFUuHFjbdiwQV27dlX//v1Vq1YtSVJKSooWLVokf39/NWrUSDt27Cj0uaZNm5rIhY0dP35c8fHxSktLU9euXVW1alXl5OQoLS1NFSpUkKenp+lEAAAAAAAA/EWWw+FwmI4AbmU7duzQN998I0mKiopShw4dVLdu3Wuuy8zM1Nq1a1W9enUtXLjQ2ZkAbhHBwcGFXluWJUn6328Xrr539X3LspScnOycQLiE999/XzNnzlReXp4sy9KsWbMUGhqqCxcuqFWrVho1apSGDBliOhMAAAAAAAB/EScaAcO++eYbRUVFSfr5h/fr16/X+vXrr3tt9erVNWbMGGfmAbjFjB8/3nQCXNySJUs0ffp0DRw4UG3bttXDDz9cMPPz81Pbtm21adMmFo0AAAAAAADFAItGwLAhQ4aoe/fucjgcateuncaOHavw8PBC11iWJR8fH5UtW9ZMJFxOXFycDh06pLNnz+qXB9cty1JkZKShMthd9+7dTSfAxS1cuFDh4eF6+eWXdebMmWvm9erV42Q+AAAAAABAMcGiETCsVKlSKlWqlKSfTxI1bdpUgYGBhqvgqg4fPqyRI0fqwIED1ywYr2LRCOBmOnjwoPr06VPkvHz58srIyHBiEQAAAAAAAG4WFo2AjXCSCH/Vq6++qmPHjmn06NFq1qyZSpcubToJLio9PV179uy57qlYSYqIiHB+FFyCu7u7Ll++XOT81KlT8vPzc2IRAAAAAAAAbhYWjYCNXH1W46/hNBp+zffff6+///3vPPsMf1p+fr7GjRunxYsXKy8vr8jrWDSiKCEhIfryyy81dOjQa2ZXrlzR6tWr1bBhQ+eHAQAAAAAA4IZj0QjYyK8tGi3LksPhYNGIX1WqVClVrFjRdAZc2Mcff6yFCxeqS5cuuvfeezV69Gg999xz8vX11Zw5c1SmTBk988wzpjNhYwMHDtTTTz+t8ePHq2fPnpKk3NxcJScn67333tORI0f0j3/8w3AlAAAAAAAAbgTLUdRDvAA43YkTJ655Ly8vT0ePHtXs2bN18eJFTZgwQTVq1HB+HFzCK6+8orS0NE2ZMsV0ClxU165dddttt2nmzJk6c+aMQkNDNXv2bIWGhurChQt68MEHNWjQIA0bNsx0Kmxs0qRJmjp1qiQV/JLM1b+PGjVKjz/+uMk8AAAAAAAA3CAsGgEX4XA41LdvX4WGhmrUqFGmc2BTFy5c0ODBg9W4cWMNGTJE1apVK/gBP/B7NGzYUM8++6yGDBmizMxMNW/eXDNmzNC9994r6eeT12vXrtXatWsNl8LukpKStGrVKh08eFD5+fmqUaOGunXrpjvvvNN0GgAAAAAAAG4Qbp0KuAjLstSpUyd9/PHHLBpRJD8/P/Xs2VPjxo3TwoULr3uNZVlKSkpychlchYeHh0qWLClJ8vb2liSdOXOmYF6pUqXrnr4GfikkJEQhISGmMwAAAAAAAHATsWgEXEx6errpBNjYrFmz9M4778jf318NGjRQmTJlTCfBxVSpUkXHjx+XJHl6eiowMFAJCQnq2rWrJCkxMVHlypUzmQgAAAAAAADAJlg0Ai7ip59+0ieffKLq1aubToGNzZs3T02aNNHMmTPl6elpOgcuqEmTJvryyy/1/PPPS5I6d+6smTNnKicnR/n5+Vq1apX69OljuBJ2d+nSJa1Zs0aHDh3S2bNn9cs79VuWpTfffNNQHQAAAAAAAG4UFo2AjQwePPi672dmZurgwYPKzc3Ve++95+QquJKMjAw99thjLBnxpw0aNEj16tVTdna2vLy8FBkZqYMHDyomJkaS1KpVKz377LNmI2FriYmJevzxxwvdcveXWDQCAAAAAAAUDywaARu5ervC/2VZlsqUKaMOHTpo8ODBatiwoYEyuIpatWopLS3NdAZc2O23367bb7+94LWXl5cmT56sCxcuyLIs+fr6GqyDK/i///s/5efnKyoqSs2aNVPp0qVNJwEAAAAAAOAmsRy/vJcVAMBlffHFF3r11Ve1ZMkSBQYGms4BcAtq0KCBRo4cqccee8x0CgAAAAAAAG4yTjQCQDGyb98+BQQEqEuXLmrfvr2qVq0qNze3QtdYlqXIyEhDhQCKu/Lly8vb29t0BgAAAAAAAJyAE40AUIwEBwf/5jWWZSk5OdkJNQBuRe+++64SExM1b9480ykAAAAAAAC4yVg0AgaFh4f/4c9YlqXY2NibUIPi4MSJE7/rOm6rCuBmycnJ0VNPPSVJGjJkiKpWraoSJUpcc11AQICz0wAAAAAAAHCDcetUwCB+yIobjQUiANM8PDxUr149TZ8+XZs3by7yOk5WAwAAAAAAuD4WjYBB3FYON9PZs2d19OhRSVJQUJDKli1rNgjALeG9997TzJkzFRwcrCZNmqhMmTKmkwAAAAAAAHCTcOtUwKDw8HCNHTu24BaqUVFR6tChg+rWrWu4DK5s3759euONN7Rjx45C7zdt2lRjx479Xc9xBIA/q2XLlmrYsKGmTJliOgUAAAAAAAA3GScaAYN+/PFHXbx4seB1VFSUqlevzqIRf1pSUpIGDhyo7OxstWrVSrVq1ZIkpaSk6Ouvv1b//v01f/58hYSEGC6F3WVlZenEiRM6e/asrvc7SU2bNjVQBVdw6dIltWnTxnQGAAAAAAAAnIBFI2BQpUqV9MMPPxR6z7IsQzUoDt599125ublpyZIluuOOOwrN9uzZoyFDhujdd9/VrFmzDBXC7rKysjRhwgQtX75cV65cuWbucDhkWRbP10OR6tevX3DbZgAAAAAAABRvLBoBg8LDwzVjxgx99dVXBc+wmjp1qhYvXlzkZyzL0pw5c5yVCBfz3XffadCgQdcsGSXpzjvvLDjRCBTl9ddfV3R0tMLDw9W0aVOer4c/7Nlnn1VkZKQ6duyoBg0amM4BAAAAAADATcSiETDo+eefV+nSpRUXF6fU1FRZlqWMjAxlZWWZToOL8vDwUOXKlYucV6lSRR4eHk4sgquJjY1V9+7dNX78eNMpcFHLli1T5cqV1bdvXzVq1EjVqlWTm5tboWssy9Kbb75pqBAAAAAAAAA3iuW43oOXABgRHBysd955R127djWdAhf15JNPSpI+/PDD685HjhwpNzc3TZo0yZlZcCFNmjTRiy++qD59+phOgYsKDg7+zWu4/S4AAAAAAEDxwIlGwEZGjhypevXqmc6ACxszZowGDhyocePGafjw4apYsaIk6fTp05o2bZqSk5M1b948w5WwsyZNmmjfvn2mM+DC9u7dazoBAAAAAAAATsKJRgAoRsLDw5WVlaUzZ85IkkqXLi1JOnfunCSpXLly8vb2LvQZy7IUGxvr3FDYVkpKigYNGqTXXntNHTt2NJ0DF3Px4kWNGzdOrVq1UufOnU3nAAAAAAAA4CbjRCMAFCMBAQGmE+DiateurTfeeEMjR45UxYoVi3y+3pw5cwwVws58fX21Zs0a3X333aZTAAAAAAAA4AQsGgGgGOG2qPirtmzZoqefflr5+fm6ePGiUlNTTSfBxdSoUUOnTp0ynQEAAAAAAAAn4NapAACgQLdu3XT+/HlNmTJFwcHBpnPggj755BNNmTJFMTExKl++vOkcAAAAAAAA3EScaASAYig1NVUbNmzQ0aNHJUlBQUEKDw/n1qr4TYcOHdKzzz7LkhF/mpeXl/z9/dWpUyd1795d1atXv+bZsJIUERHh/DgAAAAAAADcUJxoBIBiZvr06Zo0aZLy8vL0v1/i3d3d9dRTT+mxxx4zWAe7a9eunfr376+///3vplPgon7PktqyLCUnJzuhBgAAAAAAADcTJxoBmzpy5IjS0tJUt25dlSpVynQOXMTatWv1/vvvq06dOnr44YdVr149SdK+ffs0c+ZMTZw4UVWrVlXnzp0Nl8KuevXqpZUrV2rw4MFyd+fbBPxxc+fONZ0AAAAAAAAAJ+FEI2Azmzdv1rhx43T8+HFJ0qxZsxQaGqr09HT17dtXzz//vDp27Gi4Enb10EMPKSsrS4sXL5aXl1ehWXZ2tvr06SMfHx998sknhgphd/Hx8Xr//fd15coVDRgwQFWrVlWJEiWuua5p06YG6gAAAAAAAADYCUcVABtJSEjQiBEjVK9ePUVGRioqKqpg5u/vr6pVq2rNmjUsGlGkH374QZGRkdcsGaWfn5v24IMPavLkyQbK4CqGDRtW8PeXX35ZlmUVmjscDm57id8tJydHZ86cUbly5eTp6Wk6BwAAAAAAADcYi0bARiZPnqy6detqyZIlOnfuXKFFoyQ1btxYK1euNFQHV/HLxdDvnQGSNH78eNMJKAaSk5M1YcIE7dy5U3l5eYVO5z/77LMaPny4wsLCTGcCAAAAAADgL2LRCNjIrl27NHLkyOveplCSqlSporS0NCdXwZXUqVNHK1eu1MCBA1WyZMlCs5ycHK1atUp169Y1VAe7y8nJUdWqVVWhQgXVrFnTdA5c1L59+9S/f3+VKVNG3bp10/Llywtm/v7+ys7OVkxMDItGAAAAAACAYsDNdACA/7py5cp1b3l51dmzZ4tcQgKSNHDgQO3bt0/9+vXT2rVrlZKSopSUFH322WcaMGCA9u3bp4EDB5rOhE1ZlqWhQ4dqy5YtplPgwiZNmqQKFSpo9erVeu655/TLx4G3aNFCiYmJhuoAAAAAAABwI3GiEbCR6tWrKzExUf369bvu/Ouvv1adOnWcXAVX8uCDD+rQoUOaNm2annvuuUIzy7L0xBNPqGvXrobqYHceHh4qV66c3Nz4PST8eQkJCXr44Yfl5+enM2fOXDMPCAjQ6dOnDZQBAAAAAADgRmPRCNhI165d9cEHH6h9+/a6++67Jf28HMrPz9eUKVP0zTff6NVXXzVcCbt7+umnFRERodjYWB07dkySFBQUpHbt2ikoKMhwHeyubdu22rRpkwYNGmQ6BS7q0qVLKlOmTJHzrKysa045AgAAAAAAwDWxaARsZOjQofr66681cuRIBQYGyrIsvf7660pPT1dmZqZat26thx56yHQmXED16tX18MMPm86AC3ruuec0bNgwPf/883r00UdVo0aNa573CfyawMBA7d27t8h5QkKCatSo4bwgAAAAAAAA3DQsGgEb8fDw0MyZMzV//nytXLlS6enpOnHihGrUqKHHH39cgwcPlmVZpjMBFGMtWrSQZVlKTk7WmjVrrnuNZVlKSkpychlcRceOHTV37lz16NFDVatWlaSC/3fFxMRow4YNeuaZZ0wmAgAAAAAA4AaxHNy7CgBc1pgxY/7wZyzL0ptvvnkTalAcjB49+nf9QsP48eOdUANXdPHiRfXt21eHDx9Wo0aNlJCQoCZNmujcuXP64YcfFBISokWLFsnT09N0KgAAAAAAAP4iFo0A4MKCg4P/8GeunlYDgJvlwoULmjRpklatWqUzZ85IkkqXLq0HH3xQo0aNkp+fn+FCAAAAAAAA3AgsGgEbiYmJ+V3XRURE3NQOAABulIyMDDkcDpUvX57bfwMAAAAAABQzLBoBGwkODpZlWfrlf5a//MEsp9EA3Gzffvut1q9fr6NHj0qSgoKC1KFDB919992Gy2B3MTExatKkScHzGX/p+PHjSkhI4JdmAAAAAAAAigEWjYCNbN++/Zr38vLydPToUc2fP1++vr4aNWqUWrRoYaAOrubIkSNKS0tT3bp1VapUKdM5cCGvvPKKlixZct1feujTp4/++c9/GiqDK6hfv77efvttde3a9brztWvX6rnnnuOXZgAAAAAAAIoBd9MBAP6rWbNm130/NDRUERER6tGjh/bu3cuiEb9q8+bNGjdunI4fPy5JmjVrlkJDQ5Wenq6+ffvq+eefV8eOHQ1Xwq4WLFigxYsXq2XLlnriiSdUt25dSdIPP/ygqVOnavHixapXr5769+9vuBR29Vu/w5abmys3Nzcn1QAAAAAAAOBm4qc8gIsoWbKkunXrpkWLFplOgY0lJCRoxIgRKlWqlCIjIwv9wN/f319Vq1bVmjVrDBbC7pYsWaK7775bH330kZo0aaLSpUurdOnSatKkiT766CM1atRIixcvNp0JmyvqWYznzp3T5s2bVbFiRScXAQAAAAAA4GZg0Qi4EF9fX6WmpprOgI1NnjxZdevW1ZIlSzRgwIBr5o0bN1ZSUpKBMriKQ4cOqWPHjtc9cebm5qb7779fhw4dMlAGO4uKilL9+vVVv359WZalF154oeD1//5p3ry5PvvsM3Xu3Nl0MgAAAAAAAG4Abp0KuIjLly8rJiZGlStXNp0CG9u1a5dGjhypEiVKXHdepUoVpaWlObkKrsTd3V1ZWVlFzrOysuTuzrcPKCw4OFgRERFyOByKiYlRkyZNVK1atWuu8/X1VcOGDdWlSxcDlQAAAAAAALjR+EkhYCNjxoy57vuZmZn6/vvvlZGRobFjxzq5Cq7kypUr8vLyKnJ+9uzZIpeQgCSFhIRoyZIl6tevn8qUKVNodu7cOS1btkx33HGHoTrYVbt27dSuXTtJ0vbt2zVs2DCFh4cbrgIAAAAAAMDNxqIRsJHo6Ojrvl+mTBnVrFlTL7/8Mrebw6+qXr26EhMT1a9fv+vOv/76a9WpU8fJVXAljz76qIYPH64HH3xQAwcOVN26dSVJ+/fv14IFC3Tq1Cm98sorhithZxs3bjSdAAAAAAAAACdh0QjYyN69e00nwMV17dpVH3zwgdq3b6+7775bkmRZlvLz8zVlyhR98803evXVVw1Xws5at26t119/XePHj9d7770ny7IkSQ6HQz4+Pnr99dfVqlUrw5UAAAAAAAAA7MByOBwO0xEAgBsjNzdXjz76qL755hsFBgbqxIkTqlmzptLT05WZmanWrVtr2rRpBcsjoCgXLlzQ1q1bdezYMUlSUFCQWrZsKT8/P8NlcAXff/+9/v3vfysxMVGZmZn65beblmUpKSnJUB0AAAAAAABuFBaNgI0MHDhQPXr00P333y8fHx/TOXBReXl5mj9/vlauXKmDBw8qPz9fNWrUUEREhAYPHswzGlFIeHi4xo4dW/A8vaioKHXo0KHglqnAH7Vz504NGTJEfn5+atiwoTZv3qwWLVro4sWL2r17t+rWras77rhD48ePN50KAAAAAACAv4hFI2Aj99xzjy5duiQvLy/df//96t69u5o1a2Y6C0AxFhISogkTJujBBx+UJAUHB+udd95R165dDZfBVT3yyCNKSUnRsmXLZFmWwsLCNHv2bIWGhmrz5s165plnNHPmTDVu3Nh0KgAAAAAAAP4iN9MBAP7r66+/1oQJE9SwYUOtWLFCQ4YMUbt27RQVFaUTJ06YzoML2LZtm+kEuJhKlSrphx9+KPQet9bFX7F792716tVL/v7+cnP7+VvNq7/X1qZNG3Xp0kWTJk0ymQgAAAAAAIAbxN10AID/8vLyUrdu3dStWzedPHlSy5cv14oVKxQVFaUpU6aoSZMm6tGjhyIiIkynwqaGDh2qwMBAde/eXd26dVO1atVMJ8HmwsPDNWPGDH311VcqU6aMJGnq1KlavHhxkZ+xLEtz5sxxViJcTFZWlipXrixJ8vDwkCRdunSpYH7HHXdo7dq1RtoAAAAAAABwY7FoBGyqcuXKGjFihEaMGKFvv/1W0dHRWrNmjRISElg0okhPPfVUoeX0Pffco549e6pjx47y9vY2nQcbev7551W6dGnFxcUpNTVVlmUpIyNDWVlZptPgoipUqKDTp09Lknx9feXr66tDhw4VzM+cOcOpWQAAAAAAgGKCZzQCNpeTk6MvvvhCy5cvV3x8vBwOh5KTk01nweYSEhIUHR2tdevW6cKFC/Lx8Sl47mfTpk1N58HGeEYj/qrIyEg5HA5NmTJFkjRixAjt2bNH7777rvLz8/XCCy+oRo0amjdvnuFSAAAAAAAA/FUsGgGb+u677xQdHa3PPvtMFy5ckJeXlzp27KgePXqoWbNmpvPgIrKzs7V+/XpFR0frm2++kcPhULVq1bR+/XrTabCpqKgodejQQXXr1jWdAhe1du1aLViwQDNnzpSXl5f27dunfv36FZyS9fLy0qxZs9S4cWPDpQAAAAAAAPirWDQCNnLq1CnFxMQoOjpaR44ckcPhUJMmTdS9e3d16tRJPj4+phPhwtauXatXXnlFFy9e5FQsijRmzBj17dtXDRs2vO58165dWrRokcaPH+/kMriyU6dOaf369XJzc1Pr1q15fiwAAAAAAEAxwaIRsJGQkBDl5+crICBA3bp1U48ePfhhLP6SU6dOacWKFYqOjtbhw4clSU2bNtXcuXPNhsG2fuvWqWvXrtVzzz3HshoAAAAAAACA3E0HAPivBx54QD169FCLFi1kWZbpHLionJycgtulbtu2TXl5eQoICNCIESMUERHB8hp/yaVLl+TuzrcPKNqUKVPUsmVL3XXXXXJzczOdAwAAAAAAgJuIE40AUIy88sorhZ7r2aFDB3Xv3l0tWrQwnQYbS01N1YkTJyRJgwYN0hNPPKGwsLBrrsvMzNTUqVN1+fJlrV692tmZcBHBwcGyLEu+vr5q0qSJQkNDFRoaynM/AQAAAAAAiiEWjQBQjAQHB+vuu+9Wjx491KlTJ/n6+ppOgguIiopSVFTUb56kdjgccnNz05tvvqmIiAjnxMHlHD58WF9//bXi4+O1Y8cOZWZmyrIs+fv7q3nz5gWLx8DAQNOpAAAAAAAA+ItYNAJAMXL06FEFBQWZzoCL2bt3r5KTk+VwODR27Fj16dNHjRs3LnSNZVny8fHRXXfdpSpVqhgqhatxOBzavXu3tm3bpri4OH333XfKycmRZVlKSkoynQcAAAAAAIC/iIcsAUAx8r9LxpycHJ05c0blypWTp6enwSrYXXBwsIKDgyVJO3bsUM+ePdWwYUPDVSgOLMvS7bffrtOnT+vUqVM6ceKEjh07ZjoLAAAAAAAANwgnGgGgmElOTtaECRO0c+dO5eXladasWQoNDVV6erqeffZZDR8+/LrP3wOAGyE3N1ffffed4uLitG3bNu3Zs0d5eXkKDAxUWFhYwa1Ty5YtazoVAAAAAAAAfxEnGgGgGNm3b5/69++vMmXKqFu3blq+fHnBzN/fX9nZ2YqJiWHRiN+Ul5enQ4cO6ezZs7re7yQ1bdrUQBVcQbNmzZSdna1y5cqpefPm6tmzp0JDQ1W1alXTaQAAAAAAALjBWDQCQDEyadIkVahQQdHR0crJydGyZcsKzVu0aKHPP//cUB1cxcyZMzVt2jSdP3++yGuSk5OdWARXkpWVJTc3NzVo0ECNGzdW48aNWTICAAAAAAAUUywaAZvJz8/XypUrtXXrVqWnp+uFF15QSEiIMjMztWnTJoWGhqpSpUqmM2FTCQkJevjhh+Xn56czZ85cMw8ICNDp06cNlMFVLF++XO+8846aNGmiVq1aaeLEiRo6dKhKlCihpUuXqnr16urXr5/pTNjY4sWLFR8fr7i4OL377rvKzc1VhQoV1KJFC4WFhSksLIz/jwEAAAAAABQTLBoBG8nOztYjjzyihIQEeXt7Kzs7W5mZmZIkPz8/vfvuu+rVq5dGjRplNhS2denSJZUpU6bIeVZW1nVvgwlctXDhQt11112aP3++zpw5o4kTJ6pNmzYKDQ3V4MGD1a1bN9OJsLkGDRqoQYMGGj58uC5fvqyEhISCxePatWuVn5+vmjVrau3ataZTAQAAAAAA8Be5mQ4A8F+TJ0/W999/rw8//FAbNmwotBAqUaKE2rdvr61btxoshN0FBgZq7969Rc4TEhJUo0YN5wXB5Rw4cECdOnWSJFmWJennk9aSVKlSJT300EOaO3eusT64lpIlS6p58+Zq06aNWrdurerVq8vhcOjQoUOm0wAAAAAAAHADcKIRsJHPPvtMDz30kNq3b3/d214GBQVp3bp1BsrgKjp27Ki5c+eqR48eBc9Eu7osiomJ0YYNG/TMM8+YTIQLKFWqlCTJ29tbkgpOVktS1apVWRLhN+3du1dxcXGKj49XQkKCsrOz5XA4FBAQoB49eigsLMx0IgAAAAAAAG4AFo2AjZw8eVLBwcFFzn19fXX+/HknFsHVPPbYY9q4caP69++vRo0aybIsTZ48WePHj9cPP/ygkJAQDR061HQmbKxSpUo6ceKEpJ9Po1WsWFF79uxR586dJUkpKSny8/MzmQibCw0N1dmzZ+VwOFSmTBm1atVKYWFhCg0NVfXq1U3nAQAAAAAA4AZi0QjYSOnSpZWWllbk/ODBg6pQoYITi+BqfH19tWjRIk2aNEmrVq2Sw+HQjh07VLp0aQ0YMECjRo2Sp6en6UzY2N133624uLiCZ8GGh4dr3rx58vHxUX5+vhYtWqT27dubjYSt1a9fX6GhoQoLC1NISEjBqWoAAAAAAAAUP5bjfx8CB8Cop556Svv27dOaNWt0/vx5hYaGavbs2QoNDVVaWpo6d+6s8PBwjR8/3nQqXERGRoYcDofKly/PD/vxu+zatUuxsbEaMWKEvLy8dObMGT388MNKSkqSJNWpU0cfffSRKleubLgUAAAAAAAAgGksGgEbSUpKUt++fRUSEqKIiAi99tprevbZZ+Xp6amZM2fqwoULWr58uWrWrGk6FcAtZt++fSpRooRuv/12ubm5mc4BAAAAAAAAYAMsGgGb2bJli8aOHVtwC1XLsuRwOFShQgW9/fbbCgsLM1wIO9mxY8ef+lzTpk1vcAkAAAAAAAAA4FbDohGwoZycHMXFxenAgQPKz89XzZo1de+998rLy8t0GmwmODj4T90SNTk5+SbUAAAAAAAAAABuJSwaAcCFRUdHX/Pe/PnzdeDAAXXt2lW1a9eWJO3fv1+rV69W7dq1NWDAAHXv3t3ZqbCp8PDwP/wZy7IUGxt7E2oAAAAAAAAAuBJ30wEAgD/vlwvDRYsW6eTJk1qzZo0CAwMLzYYPH66+ffvq8uXLzkyEzQUEBJhOAAAAAAAAAOCiONEIGDR48GBZlqWZM2fK3d1dgwcP/s3PWJalOXPmOKEOrqhz587q3LmzRo4ced35hx9+qM8++0xr1651chkAAAAAAAAAoLjhRCNg0PHjx2VZlq7u+48fP264CK7u+PHjKlu2bJHzsmXL8u8MgFMcP35c8fHxSktLU9euXVW1alXl5OQoLS1NFSpUkKenp+lEAAAAAAAA/EUsGgGDNm7c+KuvgT+qQoUK2rhxowYOHHjd+YYNG1ShQgUnV8EVXbx4UXFxcTpy5IgkqXr16goLC5Ovr6/hMriC999/XzNnzlReXp4sy1KjRo0KFo0PPPCARo0apSFDhpjOBAAAAAAAwF/EohEAipGIiAhNmTJFTz31lIYPH65atWpJklJSUjR9+nR98803euKJJwxXwu6WLl2qCRMm6OLFiwUnri3Lko+Pj0aPHq3evXsbLoSdLVmyRNOnT9fAgQPVtm1bPfzwwwUzPz8/tW3bVps2bWLRCAAAAAAAUAywaARsKikpqdBJopCQEMNFcAUjRozQoUOH9Nlnn+mLL74oNHM4HOrYsaNGjBhhqA6uYO3atXr55ZcVEBCgxx9/XLVr15Yk7d+/XwsXLtQrr7wiX19fde7c2XAp7GrhwoUKDw/Xyy+/rDNnzlwzr1evnhYuXGigDAAAAAAAADcai0bAZrZu3arXX39dx44dK/R+tWrV9I9//EOtWrUyVAZX4O7urokTJ6pPnz764osvdPToUUlSUFCQ2rdvr9DQUMOFsLtp06apRo0aWrp0qfz8/Are/9vf/qa+ffuqV69emjZtGotGFOngwYPq06dPkfPy5csrIyPDiUUAAAAAAAC4WVg0AjYSHx+vxx9/XJ6enurbt2+hk0QrV67UE088oY8++ohlEX5TaGgo/07wpxw6dEgjR44stGS8qlSpUurRo4cmT55soAyuwt3dXZcvXy5yfurUqev++wIAAAAAAIDrYdEI2MgHH3ygcuXKacmSJapcuXKh2fDhw9W7d2998MEHLJAA3DRly5aVp6dnkXNPT0+VLVvWeUFwOSEhIfryyy81dOjQa2ZXrlzR6tWr1bBhQ+eHAQAAAAAA4IZzMx0A4L+Sk5P10EMPXbNklKQqVaqob9++Sk5ONlAG4FbRqVMnrVq1Srm5udfMcnJytGrVKj3wwAMGyuAqBg4cqG3btmn8+PE6ffq0JCk3N1fJycl6/PHHdeTIEQ0aNMhwJQAAAAAAAG4ETjQCNuLj4/OrJ4XKli0rX19f5wUBuOX06dNHCQkJ6t+/v4YNG6ZatWpJkg4cOKBZs2bJzc1NvXr1UmpqaqHPBQQEmMiFDXXs2FEjRozQ1KlTNXfuXEk/n8qXJIfDoVGjRqlly5YmEwEAAAAAAHCDWA6Hw2E6AsDPxowZox9//FEff/zxNTOHw6GhQ4cqICBA48ePd34cgFtCcHCwLMuSw+GQZVmFZle/Zfjl+5I4bY1rJCUladWqVTp48KDy8/NVo0YNdevWTXfeeafpNAAAAAAAANwgLBoBGzl79qwefvhhVa5cWcOHD9ftt98u6eeTRNOmTdNPP/2kmTNnqkyZMoZLARRXH3744XUXib9l5MiRN6EGrujHH39UlSpVfvWar7/+mlONAAAAAAAAxQCLRsCgqyeH/ldRJ4auvu/m5qakpCTnBAIA8Ad16dJFn3zyifz8/K4737Fjhx577DF99913Ti4DAAAAAADAjcYzGgGDIiIi/tTJIeD3SEpK0pEjRyRJ1atXV0hIiOEiALeCtLQ0jRgxQjNnzpSHh0eh2a5duzR8+HAFBQUZqgMAAAAAAMCNxIlGAChmtm7dqtdff13Hjh0r9H61atX0j3/8Q61atTJUBjtKTU2VJAUEBBR6/VuuXg/80rfffqthw4apffv2evfddwve37t3rwYPHix/f38tWLBA5cuXN1gJAAAAAACAG4FFIwAUI/Hx8Xr00Ufl6empbt26qXbt2pKk/fv3a+XKlcrJydFHH32k0NBQw6Wwi+DgYLm5uen777+Xp6fndW/pfD3JyclOqIOrWrdunZ555hk98sgjevbZZ3XgwAENGjRI3t7eWrhwoSpVqmQ6EQAAAAAAADcAt04FbKR9+/YKCwtTixYt1KJFC5UrV850ElzMBx98oHLlymnJkiWqXLlyodnw4cPVu3dvffDBBywaUSAyMlKWZcnd3b3Qa+Cv6Nixo1588UW99dZbKlGihJYtWyYPDw99/PHHLBkBAAAAAACKEU40AjbSt29f7dmzR1euXJGbm5vq1q2r0NBQhYaGqmnTpvL29jadCJtr2LChHn30UY0cOfK686ioKH300UdKTEx0chmAW9Ebb7yhefPmqUKFCpo/f75q1KhhOgkAAAAAAAA3ECcaARv55JNPdOHCBX3zzTeKj4/Xtm3bNHv2bM2ePVseHh5q0KCBQkNDi1wiAT4+PipbtmyR87Jly8rX19d5QXA5O3bsUK1atYp8fl5GRoYOHDigpk2bOrkMdhUVFVXkrHTp0vLz89M999yj1atXF7xvWZYiIyOdkQcAAAAAAICbiBONgM2dPn1aW7du1cyZM5WSkiLLsng2Goo0ZswY/fjjj/r444+vmTkcDg0dOlQBAQEaP3688+PgEurXr6+3335bXbt2ve587dq1eu655/g6hALBwcF/+DP8vwwAAAAAAKB44EQjYFOHDh1SXFyc4uPjtX37dp07d05+fn6cIsKveumll/Twww8rMjJSw4cP1+233y5JOnDggKZNm6aLFy9q9OjRhithZ7/1+0d5eXlyc3NzUg1cwYYNG0wnAAAAAAAAwBAWjYCNrFy5UvHx8YqPj9fJkyfl4eGhRo0aaciQIQoLC1ODBg1UokQJ05mwsRYtWsiyLP3nP//Rxo0bC82uLpBatGhR6H3LspSUlOS0RtifZVlFzr777juVK1fOiTWwu8DAQNMJAAAAAAAAMIRFI2AjL774okqUKKGuXbvqgQceUJMmTeTt7W06Cy4kIiLiV5dEwPXMmTNHc+fOLXj95ptvauLEiddcd+7cOV24cEE9e/Z0Zh4AAAAAAAAAm+IZjYCNdO7cWQcPHpSbm5vq1aunsLAwhYWF6Z577pGXl5fpPADFVHR0tJYvXy5JSkhIUM2aNeXv71/oGsuy5OPjo0aNGmno0KF8TUKBqKgoWZalJ554Qm5uboqKivrNz1iWpcjISCfUAQAAAAAA4GZi0QjYzE8//aS4uDht27ZN8fHxOnXqlDw9PdWwYcOCxWPDhg1NZwIopoKDg/XOO++oa9euplPgIoKDg2VZlhITE+Xp6ang4ODf/IxlWUpOTnZCHQAAAAAAAG4mFo2AzR04cEDx8fH65JNPdODAAZ6nh99lx44dWrdunY4ePSpJCgoKUocOHdSsWTPDZQCKmxMnTkj677Mar77+LTzbEQAAAAAAwPWxaARsKi0tTfHx8QWnG3/88UdJkre3t7777jvDdbCrvLw8jR07VitXrtQvv7xblqUHHnhAb731lkqUKGGoEAAAAAAAAABQXLibDgDwX19++aXi4uIUHx+vlJQUORwOubu766677lKPHj0UGhqqRo0amc6EjU2dOlUrVqxQu3bt9Nhjj6l27dqSpJSUFE2bNk1r1qxRjRo1NHLkSMOlsIvBgwf/4c9YlqU5c+bchBoAAAAAAAAAroQTjYCNXH2uVZ06dRQaGqqwsDA1bdpUvr6+hsvgKu677z5Vr15ds2fPvmbmcDg0dOhQHTt2TBs3bjRQBzu67777/tTn+DeEq2JiYv7U5yIiIm5oBwAAAAAAAJyPRSNgI6tXr1ZoaKj8/f1Np8BFNWjQQC+++KIGDhx43fn8+fP19ttva9euXU4uA1BcBQcHy7Ksa27X/Gssy1JycvJNrAIAAAAAAIAzcOtUwEa6dOliOgEurmrVqjp79myR87Nnz6pq1arOCwJQ7M2dO9d0AgAAAAAAAAxh0QjYTH5+vlauXKmtW7cqPT1dL7zwgkJCQpSZmalNmzYpNDRUlSpVMp0Jmxo4cKCmTJmiXr16qXLlyoVmqamp+uSTTxQZGWmoDkBx1KxZM9MJAAAAAAAAMIRFI2Aj2dnZeuSRR5SQkCBvb29lZ2crMzNTkuTn56d3331XvXr10qhRo8yGwrZ8fHxUqVIlderUSd26dVOtWrUkSSkpKVq5cqVq1aolb2/va56pxrPScNXgwYN/8xrLsjRnzhwn1AAAAAAAAACwM57RCNjIe++9p9mzZ2vixIm65557FBYWptmzZys0NFSS9M9//lO7d+/W0qVLDZfCroKDg//wZ3hWGv7Xfffdd817eXl5On36tPLz81WuXDl5e3tr48aNBuoAAAAAAAAA2AknGgEb+eyzz/TQQw+pffv2OnPmzDXzoKAgrVu3zkAZXAXPSsNfVdQC8fLly5o5c6ZWrFih+fPnO7kKAAAAAAAAgB2xaARs5OTJk796Is3X11fnz593YhFcDc9Kw81SsmRJjRgxQvv379dbb72ld99913QSAAAAAAAAAMPcTAcA+K/SpUsrLS2tyPnBgwdVoUIFJxYBQGFNmzbVV199ZToDAAAAAAAAgA1wohGwkSZNmigmJkaPPvroNbO0tDQtX75c4eHhBsrgavbs2aPExERlZmYqPz+/0MyyLEVGRhoqg6tLT0/X5cuXTWcAAAAAAAAAsAEWjYCNPP744+rbt68GDhyoiIgISdLu3bu1b98+zZw5U7m5uXrsscfMRsLWLl++rKeeekpbtmyRw+GQZVlyOBySVPB3Fo34M86dO6e4uDjNnTtXd911l+kcAAAAAAAAADZgOa7+BBqALWzZskVjx44tuIXq1eVQhQoV9PbbbyssLMxwIexs4sSJmj59uoYPH66wsDANHjxYEyZMULly5TR9+nTl5ubqrbfeUs2aNU2nwqaCg4NlWdZ1Zw6HQ+XKldOMGTN0xx13OLkMAAAAAAAAgN2waARsKCcnR3FxcTpw4IDy8/NVs2ZN3XvvvfLy8jKdBpvr2LGj6tWrp0mTJunMmTMKDQ3V7NmzFRoaqtzcXPXo0UPh4eEaNWqU6VTY1OjRo6+7aCxbtqxq1qypBx54QL6+vgbKAAAAAAAAANgNt04FbMjT01N/+9vf9Le//c10ClxMamqqBg8eLElyc3OTJOXm5kqSPDw81LVrVy1evJhFI4o0YcIE0wkAAAAAAAAAXISb6QAAwI3j4+NT8HdfX1+5ubkpIyOj4L2yZcvqp59+MpEGAAAAAAAAAChmONEI2MyuXbs0d+5cHT58WGfPntUv725sWZZiY2MN1cHuAgMDdfToUUmSu7u7atSooc2bNysiIkKStHXrVlWsWNFgIVxFVlaWTpw4cd2vQ5LUtGlTA1UAAAAAAAAA7IRFI2Ajq1at0osvvqgSJUqoZs2aqlKliukkuJjmzZtr/fr1GjNmjCQpIiJC77//vtLS0uRwOLRz5049+uijhithZ1lZWZowYYKWL1+uK1euXDN3OByyLEvJyckG6gAAAAAAAADYieW43jEFAEZ07txZeXl5mjNnjipXrmw6By7op59+0r59+9S8eXN5enoqPz9fEyZM0IoVK+Tm5qb7779fY8aMkaenp+lU2NSYMWMUHR2t8PBwNW3aVGXKlLnudd27d3dyGQAAAAAAAAC7YdEI2Mhdd92l559/XkOGDDGdAuAW1bRpU7Vr107jx483nQIAAAAAAADA5txMBwD4r4oVK173WWgA4CwOh0ONGzc2nQEAAAAAAADABbBoBGykW7duWrdunekMALewJk2aaN++faYzAAAAAAAAALgAbp0K2Mjhw4c1evRolS1bVkOGDFHVqlVVokSJa64LCAgwUAfgVpCSkqJBgwbptddeU8eOHU3nAAAAAAAAALAxFo2AjQQHB8uyLDkcDlmWVeR1ycnJTqwCcKvZuHGjRo4cqYoVK6patWpycyt8AwTLsjRnzhxDdQAAAAAAAADswt10AID/ioyM/NUFIwDcbFu2bNHTTz+t/Px8Xbx4UampqaaTAAAAAAAAANgUJxoBAECBbt266fz585oyZYqCg4NN5wAAAAAAAACwMbffvgQAANwqDh06pMGDB7NkBAAAAAAAAPCbWDQCQDFz8uRJjR07Vq1bt9add96p+Ph4SVJ6errGjBmjXbt2GS6End12223Kz883nQEAAAAAAADABbBoBIBi5MSJE+rZs6c+//xz1a5dW3l5eQUzf39/7dmzR0uXLjVYCLvr1auXVq5cqStXrphOAQAAAAAAAGBz7qYDAAA3zr/+9S9J0urVq+Xl5aWwsLBC89atW+vLL790fhhcRsOGDbVhwwb17t1bAwYMUNWqVVWiRIlrrmvatKmBOgAAAAAAAAB2wqIRAIqRuLg49evXTwEBATpz5sw184CAAJ06dcpAGVzFsGHDCv7+8ssvy7KsQnOHwyHLspScnOzsNAAAAAAAAAA2w6IRAIqRzMxMVapUqci5w+FQbm6uE4vgasaPH286AQAAAAAAAICLYNEIuIArV67o4sWLKlOmjOkU2Nxtt92mQ4cOFTnfvXu3qlat6sQiuJru3bubTgAAAAAAAADgItxMBwD4rw0bNui9994r9N7HH3+su+++Wy1atNCIESOUk5NjqA6uoG3btlq2bJlOnDhxzeybb77RmjVr1K5dOwNlAAAAAAAAAIDixnI4HA7TEQB+NnjwYFWoUEHvv/++JGn//v168MEHVbNmTVWrVk2bN2/Wiy++qL///e+GS2FX6enp6tGjh7Kzs9WmTRutWrVKnTp1UnZ2tjZv3qwqVaooOjpapUqVMp0KAAAAAAAAAHBxnGgEbOTgwYO64447Cl6vWbNGXl5e+vTTTzVt2jQ98MADWrlypcFC2J2/v78+/fRTNWnSRKtXr5bD4dDatWv15ZdfqlWrVlqwYAFLRgAAAAAAAADADcEzGgEbyczMVLly5QpeJyQkqHnz5gWLoWbNmmnLli2m8uAiKleurMmTJ+vChQs6dOiQHA6HgoKCVLZsWdNpAAAAAAAAAIBihEUjYCNly5bVTz/9JEm6fPmydu3apZEjRxbMr1y5oitXrpjKg4vx8/PTXXfdZToDAAAAAAAAAFBMsWgEbOSOO+7Q0qVL1bJlS33xxRfKzc1Vq1atCubHjx+Xv7+/wUIAAAAAAAAAAICfsWgEbOTxxx/XsGHD1KdPHzkcDrVu3Vr169cvmH/55Zdq2LChwULYTXBwsCzL+kOfsSxLSUlJN6kIxU1GRoYkqXz58oZLAAAAAAAAANgNi0bARho1aqTo6Gh99dVXKlWqlB544IGC2ZkzZ9SqVSu1b9/eYCHsJiIi4g8vGoHf8tNPP2nixImKjY3VhQsXJP18K9527dpp1KhRqlSpkuFCAAAAAAAAAHZgORwOh+kIAABgD8eOHVO/fv2UlpamWrVqqVatWpKkAwcO6MCBA6pQoYIWLVqkatWqGS4FAAAAAAAAYBqLRgAAUODJJ5/Upk2b9K9//Uvt2rUrNPviiy/0zDPPqG3btvrwww8NFQIAAAAAAACwC26dChg0ePBgWZalmTNnyt3dXYMHD/5dn3N3d1f58uXVpk0bde3a9SZXAriVxMfHq1+/ftcsGSWpffv26tu3r2JiYpwfBgAAAAAAAMB2WDQCBh0/flyWZenqweLjx4//rs/l5eVp586dWrNmjY4fP64nnnjiZmbCxu677z65ubnps88+k4eHh8LDw3/zM5ZlKTY21gl1cEX5+fkFt0u9ntq1a4ubIQAAAAAAAACQWDQCRm3cuPFXX/+a3NxcjR49WkuXLmXReAsLDAyU9PPyUJICAgJM5qAYaNSokRITE9W3b9/rzhMTE9WoUSPnRgEAAAAAAACwJZ7RCLiwVatWady4cfrmm29MpwAoJvbu3ashQ4boiSee0IABA+Th4SHp519umDdvnqZNm6a5c+eqXr16hksBAAAAAAAAmMaiEQCAW9j1ng178uRJHTt2TL6+vgoKCpIkHT16VBcvXlRQUJAqV66sOXPmODsVAAAAAAAAgM1w61QAKIYuXryouLg4HTlyRJJUvXp1hYWFydfX13AZ7KaoZ8NWqVJFknT27FlJUunSpVW6dGnl5ubq2LFjzsoDAAAAAAAAYGOcaASAYmbp0qWaMGGCLl68qKtf4i3Lko+Pj0aPHq3evXsbLgQAAAAAAAAAFAcsGgGgGFm7dq2effZZBQQEqH///qpdu7Ykaf/+/Vq4cKFOnjyp9957T507dzZcCgAAAAAAAABwdSwaAaAY6datmy5fvqylS5fKz8+v0Oz8+fPq1auXvLy8tGLFCkOFcBXHjx9XfHy80tLS1LVrV1WtWlU5OTlKS0tThQoV5OnpaToRAAAAAAAAgGFupgMAADfOoUOH1KNHj2uWjJJUqlQp9ejRQ4cOHTJQBlfy/vvvq2PHjvrHP/6hSZMmFTyTMScnRw888IAWLVpkuBAAAAAAAACAHbBoBIBipGzZsr960szT01Nly5Z1XhBczpIlSzR9+nT169dPM2fO1P/e+MDPz09t27bVpk2bDBYCAAAAAAAAsAt30wHArWzw4MF/+DOWZWnOnDk3oQbFQadOnbRq1SoNGDBAHh4ehWY5OTlatWqVHnjgAUN1cAULFy5UeHi4Xn75ZZ05c+aaeb169bRw4UIDZQAAAAAAAADshkUjYNDx48dNJ8DFpaamFnrdp08fJSQkqH///ho2bJhq1aolSTpw4IBmzZolNzc39erVy0QqXMTBgwfVp0+fIufly5dXRkaGE4sAAAAAAAAA2BWLRsCgjRs3mk6Ai7vvvvtkWVah967e6vK555677vtdunRRcnKycwLhctzd3XX58uUi56dOnbruM0ABAAAAAAAA3HpYNAKAC4uMjLxm0Qj8FSEhIfryyy81dOjQa2ZXrlzR6tWr1bBhQ+eHAQAAAAAAALAdFo2ADV26dEnff/+90tLSFBYWpgoVKphOgk09+eSTphNQzAwcOFBPP/20xo8fr549e0qScnNzlZycrPfee09HjhzRP/7xD8OVAAAAAAAAAOzAcly9lx4AW/j000/17rvv6vz587IsS7NmzVJoaKjS09PVpk0bvfLKK7/6/DQA+KsmTZqkqVOnSvr5lrtXT806HA6NGjVKjz/+uMk8AAAAAAAAADbBohGwkdjYWI0cOVJt27ZVeHi4Xn75Zc2ePVuhoaGSpCeeeEL5+fmaNm2a4VLY3apVq7R+/XodPXpUkhQUFKSOHTuqS5cuhsvgKpKSkrRq1SodPHhQ+fn5qlGjhrp166Y777zTdBoAAAAAAAAAm2DRCNhI37595eHhoXnz5unMmTMKDQ0ttGicPHmyli1bpo0bNxouhV3l5uYqMjJSX331lRwOh/z8/GRZVsEJ2VatWmnKlClyd+fO2QAAAAAAAACAv8bNdACA/9q3b586dOhQ5Py2225TWlqaE4vgaj766CNt2bJF3bt316ZNm5SQkKAdO3boyy+/VM+ePbVlyxbNmDHDdCZsbMWKFTp9+rTpDAAAAAAAAAAugCMtgAtJS0uTl5eX6QzY2KpVq9SmTRu9+eabhd6vXLmyxo0bp59++kkrVqzgGXso0ksvvSTLsnT77bcrNDRULVq0UIsWLeTn52c6DQAAAAAAAIDNcKIRsJHatWtr+/btRc5jY2NVv359JxbB1Zw4cUKtW7cuct6mTRudOHHCiUVwNTNmzNCwYcNUsmRJLViwQE8++aSaN2+uPn36aOLEiYqPj1dOTo7pTAAAAAAAAAA2wIlGwEZ69+6tV199VXPnzlWnTp0kSZZlKTMzU++8846SkpL03nvvGa6EnXl7eys9Pb3IeXp6ury9vZ1YBFdz77336t5775UknT17VvHx8QV/pk+frunTp6tkyZL6/vvvzYYCAAAAAAAAMM5yOBwO0xEA/mv06NGKiYmRt7e3srOzVapUKZ0/f14Oh0N9+vTR66+/bjoRNjZ8+HDt2rVLixcvVrVq1QrNUlNT1atXLzVo0ED//ve/DRXCFe3du1dxcXFavny5UlJSZFmWkpOTTWcBAAAAAAAAMIxFI2BDsbGxWrlypQ4ePKj8/HzVqFFDERER6tChg+k02FxiYqIGDBggDw8PdevWTXXq1JEkpaSkaMWKFcrNzdWCBQvUoEEDw6Wws9TUVH399dfatm2btm3bpoyMDJUsWVL33HOPQkNDFRYWppCQENOZAAAAAAAAAAxj0QgAxcyWLVv02muvKTU1tdD7gYGBeu2119SqVStDZXAFHTp00LFjx1SiRAmFhIQULBYbN24sT09P03kAAAAAAAAAbIRFI2Ajy5YtU6dOneTj42M6BS4uPz9f//nPf3Ts2DFJUlBQkEJCQuTm5ma4DHYXHBwsNzc3denSRR07dlTz5s3l5+dnOgsAAAAAAACADbFoBGwkODhY3t7euv/++9W9e3c1a9bMdBKAW8xnn32m+Ph4xcXF6fjx43J3d9edd96pFi1aFJxs9PDwMJ0JAAAAAAAAwAZYNAI2smLFCkVHR2v79u1yOBwKCAhQ9+7dFRERoapVq5rOA3CLOXbsmOLj4xUfH69t27bp7Nmz8vLyUpMmTfTRRx+ZzgMAAAAAAABgGItGwIZOnjyp5cuXa8WKFTpy5Ijc3NzUpEkT9ejRQx07dpS3t7fpRNjY2rVrNW/ePB0+fFhnz569Zm5ZlpKSkpwfBpf1448/auvWrZozZ45SUlJkWZaSk5NNZwEAAAAAAAAwjEUjYHM7d+5UdHS0Pv/8c128eFE+Pj7auXOn6SzY1PTp0zVx4kT5+/urQYMGKlOmzHWvGz9+vJPL4EoyMzO1bdu2gtOMR48elSR5eHiocePGCgsL0/Dhww1XAgAAAAAAADCNRSPgAnJycrRy5UpNmDBBFy9e5CQRitS6dWtVr15dM2fOlKenp+kcuKCePXtq7969ysvLk5ubm+rXr6+wsDCFhobqnnvuUcmSJU0nAgAAAAAAALAJd9MBAIr23XffFZxmPH/+vLy8vBQREWE6CzZ27tw5de7cmSUj/rQLFy6od+/eCgsLU/PmzYs8FQsAAAAAAAAALBoBmzl16pRiYmIUHR2tI0eOSJLuuece9ejRQ/fff798fHwMF8LOateurfT0dNMZcGHr1q0znQAAAAAAAADARXDrVMBGHn74YW3btk15eXkKCAhQRESEunfvrmrVqplOg4vYsGGDXnnlFS1dulRVqlQxnQMAAAAAAAAAKMY40QjYyLfffqvOnTurR48eCg0NNZ0DFxQeHq6LFy+qS5cuCg8PV9WqVeXm5lboGsuyFBkZaagQAAAAAAAAAFBccKIRsJELFy7Iz8/PdAZc2JEjR/Twww/r+PHjRV5jWZaSk5OdWAUAAAAAAAAAKI440QjYCEtG/FWvv/66Tp8+rTFjxqhZs2YqXbq06SQAAAAAAAAAQDHFohGwmby8PMXGxioxMVGZmZnKz88vNLcsS2+++aahOtjdt99+q2HDhmnIkCGmUwAAAAAAAAAAxRyLRsBGMjMzNXToUO3du1cOh0OWZenq3Y2v/p1FI36Nt7e3brvtNtMZAAAAAAAAAIBbgJvpAAD/NWnSJO3fv1//93//py+++EIOh0MzZszQmjVr1KlTJzVo0EDbt283nQkba9u2rb766ivTGShmcnJyFBMTo48//lgnTpwwnQMAAAAAAADAJlg0AjayadMmPfjgg+rVq1fB8xpLlCihWrVq6f3335e7u7v+9a9/mY2Erb344otKS0vTP//5Tx09erTgRCzwe7355pt68MEHC17n5+dr0KBBGjNmjCZMmKCIiAgdOnTIYCEAAAAAAAAAu2DRCNjITz/9pAYNGkiS3N1/vrNxTk5Owbx9+/aKjY010gbX0Lx5c+3Zs0effPKJOnbsqJCQENWvX7/Qn5CQENOZsLH4+HiFhYUVvN6wYYMSExP16KOPauLEiSpRooRmzJhhsBAAAAAAAACAXfCMRsBGSpUqpcuXL0uSfHx85O7urp9++qlg7uXlpbNnzxqqgyuIiIiQZVmmM+DCTp06pWrVqhW83rx5swIDA/Xss89Kkvbu3avVq1ebygMAAAAAAABgIywaARsJCgrS4cOHJf18y9Q6dero888/V+/evZWfn69169apSpUqZiNhaxMmTDCdABd3+fJleXp6FrxOSEhQixYtCl4HBQUpLS3NRBoAAAAAAAAAm+HWqYCNhIaG6osvvlBeXp4kqV+/fvr666/Vrl07dejQQd9884169+5tuBJAcVa5cmXt3btXknTs2DEdPnxYTZs2LZhnZGTIy8vLVB4AAAAAAAAAG+FEI2Ajjz76qB588EE5HA5JUp8+fZSVlaUVK1bIzc1Nffv21cMPP2y4EkBxdt9992nevHnKz89XYmKiSpYsqdatWxfMU1JSFBgYaLAQAAAAAAAAgF1YjqsbDQAAcMs7d+6cnn76acXHx6tkyZJ6+eWXC05SZ2dnq2XLlurTp49eeuklw6UAAAAAAAAATGPRCNjEhQsXFBERoYEDB2ro0KGmcwDc4i5cuKCSJUvKw8Oj4L3s7GwdPnxYlStXVtmyZc3FAQAAAAAAALAFntEI2ISfn5/OnDkjX19f0ykAbmExMTE6fvy4/Pz8Ci0ZJcnLy0t+fn768ssvzcQBAAAAAAAAsBUWjYCNhISE6IcffjCdAeAWNmbMGH333XdFznft2qUxY8Y4sQgAAAAAAACAXbFoBGzkySef1LJly/T111+bTgFwi/qtO6rn5ubKzY1vHwAAAAAAAABI7qYDAPxXdHS0AgMD9cgjjyg4OFg1atSQl5dXoWssy9Kbb75pqBCu4MKFC5ozZ462bt2q9PR0vfXWW2rcuLEyMjK0cOFCderUSbVq1TKdCRuzLOu67587d06bN29WxYoVnVwEAAAAAAAAwI4sx28dXQDgNMHBwb95jWVZSk5OdkINXNGZM2fUv39/HT16VEFBQTp8+LBmzZql0NBQSVJ4eLjat2+v0aNHGy6FnURFRWny5Mm/+/phw4bpxRdfvIlFAAAAAAAAAFwBJxoBG9m7d6/pBLi4SZMm6dSpU/rkk08UGBiosLCwQvN27dopPj7eUB3sKjg4WBEREXI4HIqJiVGTJk1UrVq1a67z9fVVw4YN1aVLFwOVAAAAAAAAAOyGRSMAFCMbN25U//79ddddd+nMmTPXzAMDA3Xy5EkDZbCzdu3aqV27dpKk7du3a9iwYQoPDzdcBQAAAAAAAMDuWDQCQDGSnp6u6tWrFzl3d3dXVlaWE4vgajZu3Gg6AQAAAAAAAICLYNEI2My5c+e0dOlSJSYmKjMzU/n5+YXmlmVpzpw5hupgd+XKlVNqamqR8x9++EGVK1d2YhFcTVJSkr777jsNGDDgmpnD4dDChQt19913q379+gbqAAAAAAAAANgJi0bARn788Uf169dPJ0+eVKlSpXThwgWVKVNG586dU35+vsqVKydvb2/TmbCxli1batmyZfr73/9+zezAgQOKjo5W7969DZTBVURFRSknJ+e6i0bLsrR582Z98803mjRpkoE6AAAAAAAAAHbiZjoAwH998MEHOnv2rGbPnq1169bJ4XBo4sSJ2rlzpx555BH5+vpq0aJFpjNhY5GRkcrKylLPnj318ccfy7Isbdy4UW+88YZ69eolHx8fPfroo6YzYWN79uxRs2bNipw3bdpUiYmJTiwCAAAAAAAAYFcsGgEbiYuLU69evRQaGirLsgre9/b21vPPP69atWrpvffeM1gIu6tWrZrmzp0rHx8fTZs2TQ6HQ/PmzdO8efNUrVo1ffzxx6pUqZLpTNhYRkaGypYtW+S8TJkyysjIcF4QAAAAAAAAANvi1qmAjWRkZKhevXqSJHf3n//zvHz5csH83nvv1b///W8jbXAd9evXV0xMjPbv368DBw4oPz9fNWvW5Jl6+F3KlSunQ4cOFTk/ePCgSpcu7cQiAAAAAAAAAHbFohGwkbJly+r8+fOSJD8/P3l4eCg1NbVgblmWLl68aCoPLiA1NVXly5eXl5eX6tSpozp16hSaZ2dnKyMjQwEBAYYKYXdNmzbVkiVLNHDgQAUGBhaanThxQkuXLlXLli0N1QEAAAAAAACwE26dCthIzZo1lZKSIunnpeIdd9yh6Oho5eTkKCsrS8uXL1dQUJDhSthZeHi4vvjiiyLnGzduVHh4uBOL4GqGDx+unJwcde/eXZMnT9amTZu0adMmTZ48WT169FBOTo4ef/xx05kAAAAAAAAAbIATjYCNtGzZUjNmzNCrr76qkiVL6pFHHtHIkSPVrFkzWZal7OxsvfHGG6YzYWMOh+NX5/n5+YWe/wn8Ur169fThhx9q9OjR+vDDDwv+vTgcDpUvX14ffvght+EFAAAAAAAAIIlFI2Arw4cP19///nd5enpKktq1a6eoqCitWLFCbm5uuv/++9WpUyfDlbC7X1skHjhwQKVKlXJiDVxRmzZttGnTJm3dulWHDx+WJNWoUUP33nuvvLy8zMYBAAAAAAAAsA3L8VvHXwAAthYdHa3o6GhJ0vbt21WrVi35+/tfc11mZqb279+vdu3aadKkSc7OBAAAAAAAAAAUM5xoBAAXd+7cOR0/flzSz6cZMzIylJWVVegay7Lk4+Ojnj176plnnjGRCRdz/PhxxcfHKy0tTV27dlXVqlWVk5OjtLQ0VahQoeDkNQAAAAAAAIBbFycaAYNiYmL+1OciIiJuaAeKj+DgYL3zzjvq2rWr6RS4sPfff18zZ85UXl6eLMvSrFmzFBoaqgsXLqhVq1YaNWqUhgwZYjoTAAAAAAAAgGGcaAQMGj16tCzL0h/Z91uWxaIRRdqwYYPKly9vOgMubMmSJZo+fboGDhyotm3b6uGHHy6Y+fn5qW3bttq0aROLRgAAAAAAAAAsGgGT5s6dazoBxUxgYKDpBLi4hQsXKjw8XC+//LLOnDlzzbxevXpauHChgTIAAAAAAAAAdsOiETCoWbNmphNQDJ07d05Lly5VYmKiMjMzlZ+fX2huWZbmzJljqA52d/DgQfXp06fIefny5ZWRkeHEIgAAAAAAAAB2xaIRAIqRH3/8Uf369dPJkydVqlQpXbhwQWXKlNG5c+eUn5+vcuXKydvb23QmbMzd3V2XL18ucn7q1Cn5+fk5sQgAAAAAAACAXbmZDgAA3DgffPCBzp49q9mzZ2vdunVyOByaOHGidu7cqUceeUS+vr5atGiR6UzYWEhIiL788svrzq5cuaLVq1erYcOGzo0CAAAAAAAAYEssGgGgGImLi1OvXr0UGhoqy7IK3vf29tbzzz+vWrVq6b333jNYCLsbOHCgtm3bpvHjx+v06dOSpNzcXCUnJ+vxxx/XkSNHNGjQIMOVAAAAAAAAAOyAW6cCQDGSkZGhevXqSfr5FpiSCt0G895779W///1vI21wDR07dtSIESM0depUzZ07V5I0fPhwSZLD4dCoUaPUsmVLk4kAAAAAAAAAbIJFIwAUI2XLltX58+clSX5+fvLw8FBqamrB3LIsXbx40VQeXMRTTz2ldu3aadWqVTp48KDy8/NVo0YNdevWTXfeeafpPAAAAAAAAAA2waIRAIqRmjVrKiUlRdLPS8U77rhD0dHR6tWrl/Ly8rR8+XIFBQUZroQrCAkJUUhIiOkMAAAAAAAAADbGMxoBoBhp2bKl1q9fX3C71EceeUS7d+9Ws2bNFBYWpuTkZA0dOtRsJAAAAAAAAACgWLAcDofDdAQA4MZwOBzKzc2Vp6dnwXuxsbFasWKF3NzcdP/996tTp04GC2E3UVFRsixLTzzxhNzc3BQVFfW7Pufu7i5/f3+FhYUpMDDwJlcCAAAAAAAAsCMWjQAA3MKCg4NlWZYSExPl6emp4ODgP/R5d3d3TZo0Sffdd99NKgQAAAAAAABgVywaAQC4hZ04cUKSCk4lXn39W/Ly8nTq1Cm9/fbbysnJ0YoVK25aIwAAAAAAAAB7YtEIAMVQXFycDh06pLNnz+qXX+Yty1JkZKShMhQ3ixcv1v/93/9p9+7dplMAAAAAAAAAOBmLRgAoRo4eParIyEilpKRcs2C8yrIsJScnO7kMrurcuXOSpNKlSxc5T01N/cO3XAUAAAAAAADg+txNBwAAbpxx48bp0KFDeuaZZ9SyZUuVLVvWdBJcUEZGhiZOnKj169cXWjR27NhRo0aNUvny5QuuLV26dJFLSAAAAAAAAADFGycaAaAYady4sfr27auXXnrJdApc1OnTp/XQQw8pNTVVQUFBql27tiQpJSVFR48eVUBAgBYvXqwKFSoYLgUAAAAAAABgGicaAaAYKVGihGrUqGE6Ay7sww8/1MmTJzVhwgRFREQUmq1YsUJjx47Vhx9+qH/+859mAgEAAAAAAADYBotGAChGmjZtqv/85z+mM+DCNm/erD59+lyzZJSkbt266dtvv9WXX37p9C4AAAAAAAAA9uNmOgAAcOO89NJL2rhxo9auXWs6BS4qPT1dwcHBRc7r16+v9PR0JxYBAAAAAAAAsCtONAJAMfLKK6/I19dXzz33nN566y1Vq1ZNbm6Ff6fEsizNmTPHUCHsrnz58tq3b1+R8x9++EHly5d3YhEAAAAAAAAAu2LRCADFyPHjxyVJVapUkSSlpqaazIELatWqlZYsWaKmTZuqc+fOhWbr16/X4sWL1b17d0N1AAAAAAAAAOzEcjgcDtMRAADAHk6dOqWePXsqPT1dNWvWVJ06dSRJKSkpOnjwoCpUqKClS5eqUqVKhksBAAAAAAAAmMaiEQAAFHLq1Cm999572rBhgy5evChJ8vX1Vbt27fTss8+yZAQAAAAAAAAgiUUjABRLWVlZio+P19GjRyVJQUFBCg0Nlbe3t+Ey2FleXp5OnTolHx8flS1bVg6HQxkZGZJ+fnajZVmGCwEAAAAAAADYCYtGAChmVq1apXHjxuncuXO6+iXesiyVLl1a//jHP9SlSxfDhbCry5cvq1GjRnr22Wf16KOPms4BAAAAAAAAYHPupgMAADdOXFycXnzxRZUvX14jR45UvXr1JEn79u3TwoUL9eKLL8rf31+hoaGGS2FHJUuWVJkyZeTr62s6BQAAAAAAAIAL4EQjABQjQ4YM0bFjx7Rs2TKVK1eu0CwjI0O9evVS9erVNXv2bEOFsLtnnnlGeXl5mjRpkukUAAAAAAAAADbnZjoAAHDj7NmzR717975mySj9/Iy9Xr16adeuXQbK4CpeeOEF7d69WxMnTtT58+dN5wAAAAAAAACwMW6dCgDFyJUrV+Tj41Pk3NfXV1euXHFiEVzNoEGDlJ2drenTp2v69OkqX768vLy8Cl1jWZZiY2MNFQIAAAAAAACwCxaNAFCM1KhRQ1988YUGDx4sy7IKzRwOh2JjY1WjRg0zcXAJAQEBphMAAAAAAAAAuAie0QgAxcjcuXP15ptv6m9/+5tGjBihOnXqSJL279+vadOmaePGjRo7dqwGDRpkuBQAAAAAAAAA4OpYNAJAMeJwOPTSSy9p5cqV1z3R2K1bN7311luG6gAAAAAAAAAAxQmLRgAohrZt26b169fr2LFjkqSgoCC1b99eLVq0MFwGV3Hs2DFt2LBBR44ckSRVr15d4eHhqlatmuEyAAAAAAAAAHbBohEAABTywQcfaPr06crLyyv0fokSJfTII4/omWeeMVQGAAAAAAAAwE7cTQcAAAD7mD9/vqZOnaoGDRpo2LBhql27tqSfn/M5e/ZsTZ8+XRUrVtTAgQMNlwIAAAAAAAAwjRONAODCoqKiZFmWnnjiCbm5uSkqKuo3P2NZliIjI51QB1d0//33y8/PT4sWLZKHh0ehWU5Ojvr27atLly7p888/N1QIAAAAAAAAwC5YNAKACwsODpZlWUpMTJSnp6eCg4N/8zOWZSk5OdkJdXBFd911l5577jkNHTr0uvPZs2fr/fff1+7du50bBgAAAAAAAMB2uHUqALiwDRs2SJI8PT0LvQb+rNtuu005OTlFznNzc1WpUiUnFgEAAAAAAACwKxaNAODCAgMDf/U18Ef17NlTy5YtU//+/eXn51dodv78eS1btkw9e/Y0VAcAAAAAAADATlg0AsAtIDU1VWfPnlX9+vVlWZbpHNhY48aNtWHDBnXt2lX9+/dXrVq1JEkpKSlatGiR/P391ahRI+3YsaPQ55o2bWoiFwAAAAAAAIBBPKMRAIqRJUuWKCEhQW+99VbBe2+88Ybmz58vSapfv77mzJmjUqVKmUqEzf3yOZ9XF9P/++3C/y6rHQ4Hz/0EAAAAAAAAblGcaASAYmTZsmWqWbNmwevvv/9e8+bNU7NmzVSrVi0tXrxYs2fP1lNPPWWwEnY2fvx40wkAAAAAAAAAXASLRgAoRo4ePar777+/4PW6detUpkwZzZgxQ56ensrLy9O6detYNKJI3bt3N50AAAAAAAAAwEW4mQ4AANw458+fV+nSpQte79y5U82bN5enp6ck6a677tKPP/5oKg8AAAAAAAAAUIywaASAYsTf31/Hjx+X9PPSMSkpSffcc0/BPCsrq9Dz9QAAAAAAAAAA+LO4dSoAFCN33323Pv30U9WpU0dbtmxRXl6eWrduXTA/evSoKlasaLAQAAAAAAAAAFBcsGgEgGIkMjJS33zzjZ555hlJUq9evVSzZk1JksPh0IYNGxQaGmoyEQAAAAAAAABQTFgOh8NhOgIAcOOcPXtW3377rUqXLq0mTZoUvJ+ZmamYmBg1b95cwcHBBgsBAAAAAAAAAMUBi0YAAAAAAAAAAAAAf5ib6QAAwI2TlpampKSkQu8dOnRIY8aM0RNPPKEvvvjCUBlcUU5Ojk6dOqWcnBzTKQAAAAAAAABsiEUjABQj48eP16uvvlrw+sKFCxowYICio6O1adMmPfXUU4qPjzdYCFeQnJysIUOG6O6779bf/vY37dy5U5KUnp6uIUOGKC4uznAhAAAAAAAAADtg0QgAxciuXbvUqlWrgterV69WRkaGZsyYobi4ONWuXVuzZ882WAi727dvn/r3768jR46oW7duhWb+/v7Kzs5WTEyMmTgAAAAAAAAAtsKiEQCKkbS0NFWpUqXgdXx8vEJCQnTvvfeqfPnyioiIUHJyssFC2N2kSZNUoUIFrV69Ws8995x++SjnFi1aKDEx0VAdAAAAAAAAADth0QgAxUiJEiWUm5tb8Hrnzp1q1qxZwesyZcro7NmzBsrgKhISEtS7d2/5+fnJsqxr5gEBATp9+rSBMgAAAAAAAAB2w6IRAIqRatWqacuWLZKkHTt2KD09Xc2bNy+Ynzx5UmXKlDGVBxdw6dKlX/03kpWVdc0pRwAAAAAAAAC3JnfTAQCAG6dHjx5644031KVLF506dUoVK1ZUWFhYwXz37t26/fbbDRbC7gIDA7V3794i5wkJCapRo4bzggAAAAAAAADYFicaAaAYGTRokJ5++ml5enoqJCREUVFRKlmypCQpIyNDiYmJatWqleFK2FnHjh0VExOj3bt3F7x39RaqMTEx2rBhgzp16mQqDwAAAAAAAICNWA7ufwYAAP5/Fy9eVN++fXX48GE1atRICQkJatKkic6dO6cffvhBISEhWrRokTw9PU2nAgAAAAAAADCMRSMAACjkwoULmjRpklatWqUzZ85IkkqXLq0HH3xQo0aNkp+fn+FCAAAAAAAAAHbAohEAiqE9e/YoMTFRmZmZys/PLzSzLEuRkZGGyuBqMjIy5HA4VL58+YJbqAIAAAAAAACAxKIRAIqVy5cv66mnntKWLVvkcDhkWZaufpm/+nfLspScnGy4FHYVExOjJk2aqGrVqtedHz9+XAkJCYqIiHBuGAAAAAAAAADbcTMdAAC4caZMmaItW7Zo+PDhmjt3rhwOhyZMmKBp06bp7rvvVoMGDbR27VrTmbCxMWPG6LvvvityvmvXLo0ZM8aJRQAAAAAAAADsikUjABQjn3/+udq3b69Ro0apTp06kqRKlSqpTZs2+vjjj5WVlaUVK1YYroSd/daNDnJzc+XmxrcPAAAAAAAAAFg0AkCxkpqaqubNm0tSwTIoNzdXkuTh4aGuXbtq9erVxvrgGop6FuO5c+e0efNmVaxY0clFAAAAAAAAAOzI3XQAAODG8fHxKfi7r6+v3NzclJGRUfBe2bJl9dNPP5lIg41FRUVp8uTJkn5eMr7wwgt64YUXirx+2LBhzkoDAAAAAAAAYGMsGgGgGAkMDNTRo0clSe7u7qpRo4Y2b96siIgISdLWrVs5jYZrBAcHKyIiQg6HQzExMWrSpImqVat2zXW+vr5q2LChunTpYqASAAAAAAAAgN2waASAYqR58+Zav369xowZI0mKiIjQ+++/r7S0NDkcDu3cuVOPPvqo4UrYTbt27dSuXTtJ0vbt2zVs2DCFh4cbrgIAAAAAAABgd5bD4XCYjgAA3Bg//fST9u3bp+bNm8vT01P5+fmaMGGCVqxYITc3N91///0aM2aMPD09TacCAAAAAAAAAFwci0YAAAAAAAAAAAAAfxi3TgUAAIV8//33+ve//63ExERlZmbql7+TZFmWkpKSDNUBAAAAAAAAsAsWjQBQzDgcDq1YsULr1q3T0aNHJUlBQUHq2LGjunXrJsuyDBfCznbu3KkhQ4bIz89PDRs21ObNm9WiRQtdvHhRu3fvVt26dXXHHXeYzgQAAAAAAABgA9w6FQCKkUuXLunxxx/Xjh075HA4VLZsWUnS2bNnZVmWmjRpomnTpsnHx8dsKGzrkUceUUpKipYtWybLshQWFqbZs2crNDRUmzdv1jPPPKOZM2eqcePGplMBAAAAAAAAGOZmOgAAcOP861//0vbt2zVw4EDFxcVp27Zt2rZtm+Li4jRgwADt2LFD//rXv0xnwsZ2796tXr16yd/fX25uP3+bcPV3ktq0aaMuXbpo0qRJJhMBAAAAAAAA2ASLRgAoRtauXasOHTro//2//6fy5csXvF++fHm9/PLLateundauXWuwEHaXlZWlypUrS5I8PDwk/XxS9qo77rhDu3fvNtIGAAAAAAAAwF5YNAJAMXL+/Hm1aNGiyHlYWJjOnz/vxCK4mgoVKuj06dOSJF9fX/n6+urQoUMF8zNnzvCcTwAAAAAAAACSJHfTAQCAG6dWrVo6efJkkfMff/xRtWrVcmIRXE39+vULnVhs3ry55s2bp4YNGyo/P18LFixQcHCwwUIAAAAAAAAAdsGJRgAoRoYPH66FCxdqz54918x27dqlRYsWafjw4QbK4CoeeOABZWZmKjs7W5L09NNP68KFCxoyZIiGDRumCxcu6NlnnzVcCQAAAAAAAMAOLIfD4TAdAQC4MaKiorRp0yYlJyerZcuWBacXU1JSFBcXp/r166tt27aFPmNZliIjI03kwkWcOnVK69evl5ubm1q3bq1q1aqZTgIAAAAAAABgAywaAaAY+TO3tLQsS8nJyTehBgAAAAAAAABQnLFoBIBi5MSJE3/qc4GBgTe4BK7u0qVL+v7775WWlqawsDBVqFDBdBIAAAAAAAAAm3E3HQAAuHFYGOJG+PTTT/Xuu+/q/PnzsixLs2bNUoUKFZSenq42bdrolVdeUZ8+fUxnAgAAAAAAADDMzXQAAACwj9jYWL366qtq0qSJxo0bp/+98YG/v79atWqlDRs2GCwEAAAAAAAAYBcsGgEAQIEZM2aoadOmmjp1qsLDw6+Z33nnndq/f7+BMgAAAAAAAAB2w6IRAAAU2Ldvnzp06FDk/LbbblNaWpoTiwAAAAAAAADYFYtGAADwu6WlpcnLy8t0BgAAAAAAAAAbYNEIAAAK1K5dW9u3by9yHhsbq/r16zuxCAAAAAAAAIBdsWgEAAAFevfurdjYWM2dO1dXrlyRJFmWpczMTL388stKSkrSQw89ZLgSAAAAAAAAgB1YDofDYToCAHBjPP300+rbt69CQ0NNp8CFjR49WjExMfL29lZ2drZKlSql8+fPy+FwqE+fPnr99ddNJwIAAAAAAACwARaNAFCM3HnnncrLy1PVqlXVu3dv9ezZU/7+/qaz4IJiY2O1cuVKHTx4UPn5+apRo4YiIiLUoUMH02kAAAAAAAAAbIJFIwAUI+np6Vq2bJmWLVumI0eOyN3dXeHh4XrooYcUFhZmOg82l52drc8//1w1a9ZUw4YNTecAAAAAAAAAsDkWjQBQTG3btk1LlizRF198odzcXAUGBqpPnz7q0aOHKlSoYDoPNpSfn68GDRro//2//6d+/fqZzgEAAAAAAABgcywaAaCYO3v2rGJiYrRs2TKlpKSoRIkSuu+++9SvXz+e5Yhr3HfffRowYIAefvhh0ykAAAAAAAAAbM7NdAAA4Oby9PSUj4+PPD095XA4ZFmWNm/erL///e966KGHdOzYMdOJsJH7779fn3/+ufLy8kynAAAAAAAAALA5TjQCQDG1a9cuLV68WGvXrlVWVpaqVatWcOtUd3d3ffrpp5o6daoaNGigjz/+2HQubOLAgQN6/vnn5efnp2HDhql69ery9va+5rqAgAADdQAAAAAAAADshEUjABQj58+f14oVK7R48WLt37+/4Dapffv2VVhY2DXXz5o1Sx988IESExMN1MKOgoODZVlWwenXoiQnJzuxCgAAAAAAAIAduZsOAADcOPfee69ycnJUuXJlPfnkk+rdu7cqVqxY5PWVK1fW5cuXnVgIu4uMjPzVBSMAAAAAAAAAXMWJRgAoRoYPH66+ffuqTZs2cnPjMbwAAAAAAAAAgJuHRSMAAAAAAAAAAACAP4zjLgBQjNSvX1+rV68ucr527VrVr1/fiUVwNWvXrtWLL7543ZnD4dCLL76odevWObkKAAAAAAAAgB2xaASAYsThcOjXDqpziB2/ZcGCBUU+o9GyLJUoUULz5893chUAAAAAAAAAO2LRCAC3kNTUVPn6+prOgI0dOHDgV0+91q9fXwcOHHBiEQAAAAAAAAC7cjcdAAD4a2JjY7Vhw4aC14sXL1ZcXNw112VmZio+Pl533323M/PgYi5evCgPD48i5yVKlND58+edWAQAAAAAAADArlg0AoCL27t3r6KjoyX9fGvLHTt2aMeOHddc5+Pjo8aNG+uVV15xdiJcSEBAgHbt2qUBAwZcd56YmKjbbrvNyVUAAAAAAAAA7IhFIwC4uJEjR2rkyJGSpODgYL3zzjvq2rWr4Sq4qr/97W+aN2+e2rdvr3bt2hWabdiwQWvWrFG/fv0M1QEAAAAAAACwE8vhcDhMRwAAbozt27erdu3aKl++vOkUuKiMjAx1795dP/30k5o1a6aQkBBJUlJSkrZv367bbrtNy5cvl7+/v+FSAAAAAAAAAKaxaAQAAIX8+OOPeu2117RlyxZd/TbBsiy1adNGr7zyigICAgwXAgAAAAAAALADFo0A4MKioqJkWZaeeOIJubm5KSoq6jc/Y1mWIiMjnVAHV5eZmakjR45IkqpXr64yZcoYLgIAAAAAAABgJywaAcCFBQcHy7IsJSYmytPTU8HBwb/5GcuylJyc7IQ6AAAAAAAAAEBxxqIRAFzYiRMnJEmBgYGFXv+Wq9cDAAAAAAAAAPBnsWgEAAAAAAAAAAAA8Ie5mQ4AANx8qampSkpKEr9bAgAAAAAAAAC4UVg0AkAxsmTJEr300kuF3nvjjTcUHh6unj17qmfPnjp//ryhOgAAAAAAAABAccKiEQCKkWXLlsnN7b9f2r///nvNmzdPTZs2Vd++fbVv3z7Nnj3bYCEAAAAAAAAAoLhwNx0AALhxjh49qvvvv7/g9bp161SmTBnNmDFDnp6eysvL07p16/TUU08ZrISdpaSkqHbt2qYzAAAAAAAAALgATjQCQDFy/vx5lS5duuD1zp071bx5c3l6ekqS7rrrLv3444+m8uACunTpov79+ysmJkY5OTmmcwAAAAAAAADYGItGAChG/P39dfz4cUk/Lx2TkpJ0zz33FMyzsrJkWZapPLiAgQMH6sCBAxo9erTuvfdejRs3Tvv37zedBQAAAAAAAMCGWDQCQDFy991369NPP9Vnn32mN998U3l5eWrdunXB/OjRo6pYsaLBQvx/7N15uFZ1vf//173BLaOiOCQgw1F0O+SUgqDiKcQhh9BUQhHzZOaR8qen4YTfvqdzysxOmuZBS9McC3MC5wmHzJwnxNgigsqUGCAoMrPv3x9d7etwkDr0xb3uvXw8/mKt99rX9fyDa19evvmsVeu+853v5He/+11+9KMfZYcddsgNN9yQo446KsOHD8/48eOzfPnyohMBAAAAgBpRqVar1aIjANgwpk2blpEjR2b+/PlJkmOPPTbnnntukqRareYzn/lMBgwYkPPOO6/ITFqRN954IzfddFNuv/32vPvuu+ncuXOOOuqoHH/88dlhhx2KzgMAAAAACmTRCFAyCxcuzAsvvJBNNtkke++9d/P9RYsWZfz48enfv38aGhoKLKQ1Wr58eb773e9m/PjxSZJKpZI999wzp59++hqnZgEAAACAjw+LRoCSWLJkSX75y19m9913zwEHHFB0DiXxxz/+MbfccktuvfXWvP3229l0001z9NFHp23btrn11luzcOHCfOtb38opp5xSdCoAAAAA0MIsGgFKZNddd813v/vdHHfccUWn0IqtXr06Dz/8cG666aY88cQTWb16dfbcc88MGzYsn/3sZ1NfX58kWbZsWb72ta9lypQpeeyxxwquBgAAAABaWtuiAwDYcLp3754FCxYUnUErdsEFF2T8+PGZP39+OnbsmGHDhuULX/jCh36PsV27djniiCPy+OOPF1AKAAAAABTNiUaAErn88sszfvz4jBs3Lu3atSs6h1aooaEhO++8c4YPH54jjjgi7du3/6vPT506Nffff3+++tWvtlAhAAAAAFArLBoBSuTJJ5/Mf/7nf2bJkiUZMWJEevXq9aGLon322aeAOlqDV155JbvuumvRGQAAAABAK2DRCFAiDQ0Na1xXKpU1rqvVaiqVShobG1syCwAAAACAEvKNRoAS+eEPf1h0Aq3c+PHj/+Yz7dq1S7du3bLzzjunbVv/KQEAAAAAH1dONAIAzRoaGtY4CfuX/0z4n/cqlUo233zzfPOb38zQoUNbOhMAAAAAqAEWjQBAsyeffDI/+clPMm/evAwfPjz/8A//kCSZNm1abrzxxmy11Vb58pe/nDfffDM33HBD5s6dm5///Oc58MADCy4HAAAAAFpaXdEBAGxYb7/9ds4555wMGjQou+66a5588skkyfz58zN69Oi8/PLLBRdSyyZNmpT3338/d955Z0477bQcdNBBOeigg/KVr3wld9xxRxYtWpQ33ngjp556au68885stdVW+eUvf1l0NgAAAABQAItGgBKZPXt2Pv/5z+e+++7L9ttvn9WrVzfPunbtmldeeSW33HJLgYXUuptvvjnHHHNMOnXqtNasc+fOOeaYY3LTTTc1Xx999NGZPHlyS2cCAAAAADWgbdEBAGw4F198cZLkrrvuSrt27TJw4MA15oMGDcqjjz7a8mG0Gm+//Xbq6+vXOa+vr8/cuXObr7t3755ly5a1RBoAAAAAUGOcaAQokSeeeCLDhw9Pt27dUqlU1pp369ZtjSUR/E/dunXLfffdt8Zp2L9YtWpV7rnnnmyzzTbN9+bOnZsuXbq0YCEAAAAAUCssGgFKZNGiRdl6663XOa9Wq1m5cmULFtHaDB8+PC+99FJOPvnkTJgwIdOmTcu0adPy4IMP5uSTT86kSZPyhS98ofn5Rx55JDvvvHOBxQAAAABAUbw6FaBEttpqq7zxxhvrnE+aNCk9evRowSJamy9+8YuZO3durrnmmjz//PNrzU8++eSccsopSZJly5blC1/4QnbaaaeWzgQAAAAAaoBFI0CJfPrTn86tt96aE088MR06dFhj9vTTT+fuu+/Ol770pYLqaC3+9V//NcOGDcuDDz6YWbNmJUm23XbbDB48OH369Gl+rl27djnuuOOKygQAAAAAClapVqvVoiMA2DDmz5+fY445JsuWLcuBBx6YO++8M4cddliWLVuW3/72t9lmm20ybty4dO7cuehUAAAAAABaOYtGgJJ5++238/3vfz+PPPJImpqakiR1dXUZNGhQ/uM//uOvfsMR/rspU6Zk5syZSf58onHHHXcsuAgAAAAAqCUWjQAltXjx4rzxxhupVqvp2bNnunTpUnQSrcQzzzyTf/u3f8tbb721xv3evXvne9/7XvbZZ5+CygAAAACAWmLRCFAiH3zwQTp27Fh0Bq3YK6+8khNOOCFJcsQRR2SHHXZIkrz22mu5++67kyS//vWvs8suuxTWCAAAAADUBotGgBLZc889c8ghh+SYY45Jv379is6hFTr99NPz0ksv5cYbb0zv3r3XmL311lsZNmxY9tprr1x22WXFBAIAAAAANaOu6AAANpzdd989d9xxR04++eQMHjw4Y8aMyaxZs4rOohV58cUXM3z48LWWjEnSq1evfOELX8jzzz/f8mEAAAAAQM2xaAQokWuuuSYPP/xwvva1r6Vt27YZM2ZMDj744IwcOTLjx4/P0qVLi06kxi1dujRbbLHFOudbbrmlv0cAAAAAQBKvTgUoteeeey7jx4/Pfffdl8WLF6dDhw459NBDc9555xWdRo067LDD0qtXr/z85z//0Pk///M/580338y9997bwmUAAAAAQK1xohGgxPbee++ce+65+f3vf5/vf//7qaury7hx44rOooYdfvjhefTRR/Pd73438+bNa76/YMGCnHvuuXn00Udz+OGHF1gIAAAAANQKJxoBSu6ll17Kbbfdlvvuuy/vvfdeOnTokBdeeKHoLGrUihUrcuqpp+aZZ55JpVJJly5dkiQLFy5MtVpN//7984tf/CL19fXFhgIAAAAAhbNoBCihuXPnZvz48Rk3blzeeuutJH8+3Xj00Ufn0EMPTYcOHQoupJY1NTXltttuy4MPPpiZM2cmSXr27JkhQ4Zk6NChadOmTcGFAAAAAEAtsGgEKJG77ror48aNy1NPPZXVq1enW7duGTp0aI4++uhsu+22RecBAAAAAFAiFo0AJdLQ0JD27dtnyJAhOfroozNgwICikwAAAAAAKCmLRoASufnmm3PYYYelU6dORafQSowfP/7v+rmhQ4du0A4AAAAAoPWxaASAj7GGhoZUKpWsz38OVCqVNDY2foRVAAAAAEBr0LboAAD+fnPmzEmSdOvWbY3rv+Uvz8N1111XdAIAAAAA0Eo50QjQijU0NKSuri4vvfRS6uvrm0+n/S1OowEAAAAA8P/KiUaAVmzUqFGpVCpp27btGtcAAAAAAPBRc6IRAFjD8uXLc/XVV+eBBx7IjBkzkiQ9e/bMIYccki9+8YvZeOONCy4EAAAAAGqBRSMA0Gzx4sUZOXJkJk+enPbt26dnz55JkpkzZ2bJkiXZeeedc91116VTp04FlwIAAAAARfPqVICSWrp0aRYuXJgP+/ck3bp1K6CI1uDSSy/N5MmT87WvfS1f+tKX0q5duyR/PuV41VVX5ZJLLslll12Wb33rWwWXAgAAAABFc6IRoESq1WquvPLKXH/99fnTn/60zucaGxtbsIrW5KCDDsoee+yRCy644EPnX//61/PSSy/loYceauEyAAAAAKDWONEIUCIXX3xxLr/88my33XY5+OCD06VLl6KTaGXmzp2bT33qU+uc77333nnggQdasAgAAAAAqFUWjQAlMm7cuOy333658sorU6lUis6hFdpkk00ya9asdc5nzpyZzp07t2ARAAAAAFCr6ooOAGDDWbhwYYYMGWLJyN+tX79++fWvf52XXnpprdmkSZMyduzY7Lvvvi0fBgAAAADUHCcaAUqkd+/eWbBgQdEZtGJf+9rX8tvf/jYnnHBC9ttvv/Tt2zdJ8vrrr+fxxx9Phw4d8tWvfrXgSgAAAACgFlSq1Wq16AgANozx48fnkksuyR133JFOnToVnUMr1djYmHPPPTfPP//8Gvf33nvvfOc730lDQ0NBZQAAAABALbFoBCiR8ePH58Ybb8zs2bNz7LHHpkePHmnTps1azw0dOrTl42h1FixY0Py9xh49emTzzTcvuAgAAAAAqCUWjQAl8r85aVapVNLY2NgCNQAAAAAAlJlvNAKUyHXXXVd0AiWydOnSLFy4MB/2b5K6detWQBEAAAAAUEucaAQAmlWr1Vx55ZW5/vrr86c//WmdzzkVCwAAAAA40QgANLv44otz+eWXZ7vttsvBBx+cLl26FJ0EAAAAANQoJxoBSmTMmDF/85lKpZJRo0a1QA2t0aBBg9K3b99ceeWVqVQqRecAAAAAADXMiUaAEvlri8ZKpZJqtWrRyF+1cOHCDBkyxJIRAAAAAPibLBoBSuShhx5a697q1aszY8aMXH311fnggw9y/vnnF1BGa9G7d+8sWLCg6AwAAAAAoBXw6lSAj4lqtZovfOELGTBgQM4666yic6hR48ePzyWXXJI77rgjnTp1KjoHAAAAAKhhTjQCfExUKpUcdthhueaaaywa+au22mqrHHbYYTn22GPTo0ePtGnTZq1nhg4d2vJhAAAAAEBNsWgE+JiZP39+0QnUsG9/+9vNf/7Zz372oc9UKhWLRgAAAADAohHg4+Kdd97JjTfemF69ehWdQg277rrrik4AAAAAAFoJi0aAEhk5cuSH3l+0aFGmT5+elStX5sILL2zhKlqTfv36FZ0AAAAAALQSFo0AJTJr1qy17lUqlWy66aY5+OCDM3LkyOy+++4FlAEAAAAAUDaVarVaLToCAKgtTzzxRN54440sXLgw//M/FSqVSkaNGlVQGQAAAABQKywaAYBmM2bMyKhRo/L666+vtWD8i0qlksbGxhYuAwAAAABqjVenAgDNzj333Lzxxhs5++yzs99++6VLly5FJwEAAAAANcqiEQBo9uyzz+akk07KaaedVnQKAAAAAFDj6ooOAABqR5s2bdK7d++iMwAAAACAVsCiEQBots8+++QPf/hD0RkAAAAAQCtg0QgANPvXf/3XPPzww7nnnnuKTgEAAAAAalylWq1Wi44AAIoxcuTIte7NnTs3M2bMyFZbbZVtt902dXVr/rukSqWSa6+9tqUSAQAAAIAa1bboAAA+GkuWLMl7772XpqamtWbdunUroIhaNGvWrA+9v8022yRJ5syZ05I5AAAAAEAr4kQjQMnce++9ueyyy/L666+v85nGxsYWLAIAAAAAoIx8oxGgRB555JGcffbZWbFiRY4//vhUq9V89rOfzSGHHJK2bdtml112yahRo4rOBAAAAACgBCwaAUrkqquuSp8+fXLHHXfk//v//r8kybHHHpuLL744N998c6ZPn55ddtml4Epq2cyZM/Pwww9/6Kxarebhhx9e5+tWAQAAAICPF4tGgBJpbGzM0UcfnY033jh1dX/+Ff+XN2Q3NDTkuOOOy+WXX15kIjXu4osvzi9+8YsPnVUqlVx11VX5r//6rxauAgAAAABqkUUjQImsWrUqm222WZJk4403TpIsXry4eb799ttnypQphbTROrzwwgs54IAD1jnff//98+yzz7ZgEQAAAABQqywaAUpkq622yty5c5Mk7du3z6abbprXXnuteT5r1qzU19cXlUcrMG/evGy55ZbrnG+xxRaZN29eCxYBAAAAALWqbdEBAGw4u+22W5577rnm60GDBuXaa69N9+7d09TUlLFjx2avvfYqsJBa16lTp7/6DcZZs2alffv2LVgEAAAAANQqJxoBSuSYY45J165ds2zZsiTJWWedlY4dO2b06NH5P//n/2TjjTfON77xjYIrqWV77LFHbr311ixatGit2aJFi3Lrrbdm9913L6AMAAAAAKg1lWq1Wi06AoCPzpIlS/LUU0+lTZs22WuvvdK5c+eik6hhzz33XEaOHJkePXrkjDPOyM4775wkmTx5cn72s59l5syZufrqq9O/f/+CSwEAAACAolk0AgBruPnmm/P9738/K1eubL5XrVZTX1+f73znOzn++OMLrAMAAAAAaoVFI0CJHHvssRk2bFiOOOII39Hj/8ncuXNz77335q233kqS9O7dO4ceemi23nrrgssAAAAAgFph0QhQIvvuu28WLVqUDh065IgjjsiwYcOaX30JAAAAAAAbkkUjQImsXLkyDz74YG666aY8/fTTSZJddtklw4YNy+GHH54OHToUXEhrsWTJkrz00kuZN29eBg4cmC222KLoJAAAAACgxlg0ApTUzJkzc/PNN+e2227LvHnz0rFjxxx++OE5/vjjs+uuuxadRw37zW9+kwsuuCDvv/9+KpVKfvnLX2bAgAGZP39+DjzwwPzbv/2b7zQCAAAAAKkrOgCAj8a2226bf/mXf8lvf/vbjBkzJnvssUduvvnmHHfccTnmmGNy8803Z8WKFUVnUmMmTJiQ7373u9l7771z7rnn5r//e6SuXbvmgAMOyEMPPVRgIQAAAABQKywaAUpuzpw5mTRpUqZMmZJqtZquXbtm/vz5+b//9//m0EMPzR/+8IeiE6khV155ZfbZZ5/87Gc/y+DBg9ea77rrrpk6dWoBZQAAAABArbFoBCihlStX5p577skpp5ySQw45JJdffnm22267XHTRRXn00UfzyCOP5Cc/+UlWrFiR//iP/yg6lxoyZcqUHHzwweucb7XVVpk3b14LFgEAAAAAtapt0QEAbDjTpk3LLbfckvHjx2fhwoXZdNNNc/LJJ2fYsGHp3bv3Gs9+9rOfzbvvvpvzzz+/mFhapXnz5qVdu3ZFZwAAAAAANcCiEaBEDj/88CTJnnvumS984Qs57LDDUl9fv87nu3Xrli233LKl8mgFtt9++zzzzDM56aSTPnQ+YcKE7LTTTi1cBQAAAADUIq9OBSiRE088MXfccUfGjh2bz33uc391yZgkn/70p/Pwww+3UB2twXHHHZcJEybkuuuuy6pVq5IklUolixYtyne+851Mnjw5w4YNK7gSAAAAAKgFlWq1Wi06AgCoHd/+9rczfvz4tG/fPsuWLUvnzp3z/vvvp1qt5vjjj8/3vve9ohMBAAAAgBpg0QgArGXChAm54447Mn369DQ1NaV3794ZOnRoDj744KLTAAAAAIAaYdEIUDIvvfRSfv7zn2fixIlZtGhR/uev+UqlksmTJxdUBwAAAABAWfhGI0CJPP/88xkxYkReeuml7Lbbbmlqakr//v2z6667plqtpm/fvvnc5z5XdCY1bPTo0Zk4ceI65y+//HJGjx7dgkUAAAAAQK2yaAQokZ/97GfZYostcvfdd+eHP/xhkuQrX/lKbr755lx++eWZNWtWjj/++IIrqWXjxo3LjBkz1jmfNWtWxo8f33JBAAAAAEDNsmgEKJFJkybl2GOPTdeuXVNX9+df8X95deqBBx6YI444IpdcckmRibRyS5YsSdu2bYvOAAAAAABqgP9TCFAiS5cuzSc+8YkkyUYbbZTkz4uhv9hll11yzz33FNJG7ZozZ05mz57dfD19+vQ8++yzaz23aNGijB07Nr169WrJPAAAAACgRlk0ApTIFltskT/96U9Jko4dO6Zjx4554403mufvvvtuKpVKUXnUqNtuuy1jxoxJpVJJpVLJz3/+8/z85z9f67lqtZq6urqcd955BVQCAAAAALXGohGgRHbaaadMmjSp+bp///65/vrrs/vuu6epqSm/+tWv0tDQUGAhteiggw5K9+7dU61Wc8455+T444/PnnvuucYzlUolHTp0yCc/+clss802BZUCAAAAALWkUv3Lx7sAaPXuueee/OpXv8pVV12Vdu3aZcqUKRk+fHiWLl2aJGnXrl1++ctfrrVEgr8YPXp0vvCFL2T33XcvOgUAAAAAqHEWjQAlN3fu3DzwwAOpq6vLoEGDsu222xadBAAAAABACVg0AgAAAAAAAOutrugAAAAAAAAAoPVpW3QAAH+/0aNHp1Kp5Pvf/37atGmT0aNH/69+rm3bttl8880zaNCgfOpTn/qIKwEAAAAAKCOvTgVoxRoaGlKpVDJx4sTU19enoaFhvX6+Uqnke9/7Xo477riPqBAAAAAAgLKyaAT4GFq9enXmzp2b//t//29mz56d++67r+gkAAAAAABaGd9oBPgYatOmTbp165bDDjssM2fOLDoHAAAAAIBWyKIR4GNs6NCheeqpp4rOoMa8/fbbOeecczJo0KDsuuuuefLJJ5Mk8+fPz+jRo/Pyyy8XXAgAAAAA1IK2RQcA8PcbM2ZMKpVK/vmf/zl1dXUZM2bM3/yZSqWSUaNGJUnatm2bzp07f9SZtCKzZ8/O8ccfn6VLl2aPPfZoXjImSdeuXfPKK6/klltuyW677VZgJQAAAABQCywaAVqxvywav/zlL6e+vn69F43wP1188cVJkrvuuivt2rXLwIED15gPGjQojz76aMuHAQAAAAA1x6IRoBV76KGHkiT19fVrXMPf64knnsjw4cPTrVu3vPvuu2vNu3Xrlrlz5xZQBgAAAADUGotGgFase/fuf/Ua1teiRYuy9dZbr3NerVazcuXKFiwCAAAAAGpVXdEBAGw4zz33XFavXl10Bq3YVlttlTfeeGOd80mTJqVHjx4tWAQAAAAA1CqLRoASGTFiRPbZZ5+cdtppufrqq/Pqq68WnUQr8+lPfzq33nprZs+evdbs6aefzt13352DDjqogDIAAAAAoNZUqtVqtegIADaM3/zmN3niiSfy9NNPZ+HChalUKunSpUv23XffDBgwIAMGDMi2225bdCY1bP78+TnmmGOybNmyHHjggbnzzjtz2GGHZdmyZfntb3+bbbbZJuPGjUvnzp2LTgUAAAAACmbRCFBSkydPzhNPPJEnn3wyL7zwQpYtW5Yk6datWx566KGC66hlb7/9dr7//e/nkUceSVNTU5Kkrq4ugwYNyn/8x3/81W84AgAAAAAfHxaNACX33nvv5be//W0uv/zyvP7666lUKmlsbCw6i1Zg8eLFeeONN1KtVtOzZ8906dKl6CQAAAAAoIZYNAKUzIoVK/L888/nySefzBNPPJHGxsY0NTWlZ8+eGThwYAYOHJghQ4YUnUmN+uCDD9KxY8eiMwAAAACAVsCiEaBETjnllLz44otZvnx5unbtmn333TcDBw7MgAEDss022xSdRyuw55575pBDDskxxxyTfv36FZ0DAAAAANQwi0aAEmloaEibNm1y1FFH5eSTT05DQ0PRSbQyX/ziF/PMM8+kWq2mW7duOfroozN06ND06NGj6DQAAAAAoMZYNAKUyC9+8Ys89dRTef7557N8+fJsvvnm2XfffZtPNnbv3r3oRFqBt99+O7fddltuv/32vPXWW6mrq8vee++dY445Joccckjat29fdCIAAAAAUAMsGgFKaMWKFXnhhRfy5JNP5sknn8wf/vCHNDU1Zdttt83AgQPz7//+70Un0ko899xzGT9+fO67774sXrw4HTp0yKGHHprzzjuv6DQAAAAAoGAWjQAlt2LFijzyyCP5r//6r7z++uupVCppbGwsOotWZvny5bnjjjvyox/9KB988IG/QwAAAABA2hYdAMCG98orrzSfZnzhhReyfPnyVKvV9OjRIwMHDiw6j1bmpZdeym233bbGqUYAAAAAAItGgBI588wz8/TTT+e9995LtVrN5ptvns985jMZMGBABgwYkB49ehSdSCsxd+7cjB8/PuPGjctbb72VJNl7771z9NFH59BDDy24DgAAAACoBV6dClAie+65Z/bee+8MHDgwAwYMSENDQ9FJtDJ33XVXxo0bl6eeeiqrV69Ot27dMnTo0Bx99NHZdttti84DAAAAAGqIRSNAiaxatSpt2zqszt+voaEh7du3z5AhQ3L00UdnwIABRScBAAAAADXKohEAaHbzzTfnsMMOS6dOnYpOAQAAAABqnEUjQAm98sormThxYhYtWpSmpqY1ZpVKJaNGjSqoDAAAAACAsrBoBCiR5cuX58wzz8xjjz2WarWaSqWSv/ya/8ufK5VKGhsbCy6l1t1555154IEHMmPGjCRJz549c8ghh+SII44ouAwAAAAAqBUWjQAlctFFF+WKK67IV77ylQwcODAjR47M+eefn8022yxXXHFFVq5cmR/96Efp06dP0anUqJUrV2bUqFH53e9+l2q1mk6dOqVSqeT9999PpVLJAQcckMsuu8y3QAEAAACA1BUdAMCGc99992XIkCE566yz0rdv3yTJ1ltvnQMPPDDXXHNNli5dmttvv73gSmrZL37xizz22GM5+uij88gjj+S5557Ls88+m0cffTSf//zn89hjj+XKK68sOhMAAAAAqAEWjQAlMmfOnPTv3z9JUlf351/xK1euTJJstNFGOfLII3PXXXcV1kftu/POO3PggQfmvPPOyzbbbNN8/xOf+ETOPffcDBo0yLIaAAAAAEhi0QhQKh06dGj+c8eOHVNXV5cFCxY03+vSpUveeeedItJoJWbPnp1Bgwatc37ggQdm9uzZLVgEAAAAANQqi0aAEunevXtmzJiRJGnbtm169+6d3/72t83zxx9/PFtuuWVRebQC7du3z/z589c5nz9/ftq3b9+CRQAAAABArbJoBCiR/v37Z8KECc3XQ4cOzb333puTTjopI0aMyIMPPpjDDz+8wEJq3R577JGxY8dm5syZa83mzJmTG2+8MXvuuWcBZQAAAABAralUq9Vq0REAbBjvvPNOpkyZkv79+6e+vj5NTU05//zzc/vtt6euri6HHnpoRo8enfr6+qJTqVETJ07MiSeemI022iif+9zn0rdv3yTJ66+/nttvvz0rV67Mr371q+y2224FlwIAAAAARbNoBADW8Nhjj+Xf//3fM2fOnDXud+/ePf/+7/+eAw44oKAyAAAAAKCWWDQClMTixYtzxhln5Mgjj8xxxx1XdA6tXFNTU/7whz80v0K1Z8+e2XnnnVNX563rAAAAAMCftS06AIANo1OnTnn55Zdz5JFHFp1CCdTV1eWTn/xkPvnJTxadAgAAAADUKMcSAEqkb9++zSfQAAAAAADgo+TVqQAl8sADD+Q73/lOxo4dm+22267oHFqBwYMHr/fPVCqVTJgw4SOoAQAAAABaE69OBSiR1157Ld27d8/nPve5fPrTn06vXr3Srl27NZ6pVCoZNWpUQYXUmm7duhWdAAAAAAC0Uk40ApRIQ0PD33ymUqmksbGxBWoAAAAAACgzi0aAEpk9e/b/6rnu3bt/xCUAAAAAAJSdRSMAAAAAAACw3nyjEaCkFi5cmBkzZiRJevbsmS5duhQbBAAAAABAqVg0ApTMlClT8oMf/CDPPvvsGvf32WefnHPOOf+r7zgCAAAAAMDf4tWpACUyefLkjBgxIsuWLcv++++f7bbbLkny+uuv5/e//33atWuXG264ITvvvHPBpQAAAAAAtHYWjQAl8k//9E95+eWXc+2112aXXXZZY/bKK6/k5JNPzu67755f/vKXBRUCAAAAAFAWdUUHALDhvPjiiznhhBPWWjImya677poTTjghL774YgFltBZz5szJsmXL1jlftmxZ5syZ04JFAAAAAECtsmgEKJGNNtoon/jEJ9Y532abbbLRRhu1YBGtzeDBg/Pggw+uc/7www9n8ODBLVgEAAAAANQqi0aAEunfv3+efPLJdc6feOKJ7Lvvvi1YRGvzt96o3tTUlEql0kI1AAAAAEAts2gEKJHRo0fnD3/4Q84999z86U9/ar7/pz/9Keeee24aGxvz7W9/u8BCWoO/tkicNm1aOnfu3II1AAAAAECtqlT/1tEFAGrWh73CcsmSJVm4cGGSZJNNNkmSvPfee0mSLl26pGPHjpkwYUKLNVL7xo0bl3HjxiVJnnnmmWy33Xbp2rXrWs8tWrQoU6dOzUEHHZRLLrmkpTMBAAAAgBrTtugAAP5+3bp1KzqBEnjvvfcya9asJH8+zbhgwYIsXbp0jWcqlUo6dOiQz3/+8zn77LOLyAQAAAAAaowTjQBAs4aGhvz4xz/OkUceWXQKAAAAAFDjLBoBgGazZ8/O5ptvnvbt2xedAgAAAADUOItGAAAAAAAAYL35RiMAsIa33347Y8eOzZtvvpmFCxfmf/6bpEqlkmuvvbagOgAAAACgVlg0AgDNHn/88ZxxxhlZsWJFOnTokC5duhSdBAAAAADUKK9OBQCaHXPMMXnnnXdy6aWXZvfddy86BwAAAACoYXVFBwAAtWPq1Kk5+eSTLRkBAAAAgL/JohEAaLbpppumffv2RWcAAAAAAK2ARSNAiUybNi0TJkxY495zzz2XkSNH5qijjso111xTTBitxiGHHJLHHnus6AwAAAAAoBXwjUaAEvna176WRYsW5brrrkuSzJ8/PwcddFBWrlyZDh065P3338/FF1+cQw45pOBSatXixYtz6qmnpqGhIf/0T/+UbbfdNpVKpegsAAAAAKAGtS06AIAN55VXXslxxx3XfH3nnXdm+fLlGT9+fPr06ZMRI0bkhhtusGhknfbee+9UKpVMnDgxv/nNbz70mUqlksmTJ7dwGQAAAABQaywaAUpkwYIF2XrrrZuvn3rqqeyxxx7ZYYcdkiSHH354Lr/88qLyaAWGDh3qBCMAAAAA8L9i0QhQIu3atcuSJUuSJNVqNS+++GKGDRvWPG/fvn3ef//9ovJoBc4///yiEwAAAACAVqKu6AAANpw+ffrk/vvvT1NTUyZMmJD33nsvAwYMaJ7PmTMnXbp0KS4QAAAAAIDScKIRoEROOOGEfOtb30q/fv2ybNmy9OrVK/369WueP/fcc9lxxx0LLKQ1aGpqyh133JHHH3888+fPzze/+c3svPPOWbRoUR555JEMGDBgjVf0AgAAAAAfTxaNACVy1FFHJUkefPDBdO7cOf/8z/+cNm3aJPnz9xs/+OCDfO5znysykRq3bNmynHrqqXnuuefSvn37LFu2LIsWLUqSdOrUKRdccEGOPfbYnHXWWcWGAgAAAACFq1Sr1WrREQBAbbjwwgtz9dVX56KLLsqnPvWpDBw4MFdffXXzK3j/4z/+I5MmTcott9xScCkAAAAAUDTfaAQoqbfeeivPP/983n///aJTaEXuvffeDBs2LEOGDEmlUllr3rNnz8yZM6eAMgAAAACg1lg0ApTMb3/72wwZMiSHHnpoRowYkVdeeSVJMn/+/AwZMiT3339/wYXUsrfffjsNDQ3rnHfs2NHyGgAAAABIYtEIUCrPPfdczjjjjHTu3DmjRo3Kf387dteuXdOjR4/cfffdBRZS6zbZZJPMmzdvnfPp06dniy22aMEiAAAAAKBWWTQClMill16aHXbYITfffHNOPPHEteZ77rlnJk+eXEAZrcXee++d8ePHZ9WqVWvN5s2bl9tuuy377rtvAWUAAAAAQK2xaAQokZdffjlHHXVU2rRp86HzbbbZ5q+eVoPTTz89f/zjHzNixIjm1+xOmjQp11xzTY4++uisXLkyp512WsGVAAAAAEAtaFt0AAAbzqpVq9KuXbt1zhcuXLjOJSQkyc4775wxY8bknHPOyb//+78nSS666KJUq9VsscUWufTSS9OnT59iIwEAAACAmmDRCFAivXr1ysSJEzN8+PAPnf/+979P3759W7iK1mbQoEF5+OGH88QTT2TatGlpampKnz59sv/++//VRTYAAAAA8PFi0QhQIkceeWR++tOfZsiQIdlrr72SJJVKJU1NTbnsssvy9NNP57vf/W7BlbQG9fX1+cd//Mf84z/+Y9EpAAAAAECNqlSr1WrREQBsGCtXrsyXv/zlPP300+nevXtmz56dPn36ZP78+Vm0aFEGDRqUyy+/PJVKpehUAAAAAABaOYtGgJJZvXp1brjhhtxxxx2ZPn16mpqa0rt37wwdOjQjR470jUbWMHjw4PX+mUqlkgkTJnwENQAAAABAa2LRCAAfYyeddNJa9+bOnZsZM2akQ4cO6dmzZ5JkxowZWbJkSXr27Jmtt946119/fUunAgAAAAA1xqIRoETGjBmTgw8+ODvssMOHzqdOnZr7778/X/3qV1u4jNbi1VdfzciRI/PlL385J598curr65MkK1asyDXXXJOrrroq1157bRoaGgouBQAAAACKVld0AAAbzpgxYzJlypR1zqdOnZpLL720BYtobX784x9n8ODB+fKXv9y8ZEyS+vr6nHbaafn0pz+dCy64oMBCAAAAAKBWWDQCfIwsX77cNxr5q1588cXsvvvu65zvvvvuefHFF1uwCAAAAACoVW2LDgDg/83ixYvz3nvvNV8vXLgwc+bMWeu5RYsW5c4778w222zTknm0MnV1dZk+ffo659OnT0+lUmnBIgAAAACgVlk0ArRy11xzTfPrUCuVSs4777ycd955H/pstVrNN7/5zZbMo5Xp379/xo4dm3333Tef+cxn1pg99NBDGTt2bP7xH/+xmDgAAAAAoKZUqtVqtegIAP5+zzzzTJ555plUq9VceumlGTJkSHbccce1nuvYsWN233337LXXXgVU0lrMmDEjw4YNy8KFC7P99ttnu+22S5JMmzYtr7/+erp06ZIbb7wxvXr1KrgUAAAAACiaRSNAiZx00kk544wzMmDAgKJTaMXefvvt/OQnP8lDDz2UDz74IMmfF9WDBw/OWWedlW7duhVcCAAAAADUAotGAOBDVavVzJ8/P0nStWtX32YEAAAAANZg0QhQUkuWLMl7772XpqamtWZOpAEAAAAA8P+qbdEBAGxY9957by677LK8/vrr63ymsbGxBYtoTZ599tn/1XP77LPPR1wCAAAAANQ6i0aAEnnkkUdy9tlnp1evXjn++OPzm9/8JocffnhWr16dhx56KDvuuGP+8R//sehMathJJ530v3pFqmU1AAAAAGDRCFAiV111Vfr06ZPx48fngw8+yG9+85sce+yxGTBgQF599dWccMIJGTVqVNGZ1LAf/vCHa91btWpVZs6cmdtuuy09evTIsGHDCigDAAAAAGqNRSNAiTQ2NuYrX/lKNt544yxdujRJ8pdP8TY0NOS4447L5Zdfnk9/+tNFZlLDjj766HXOTjnllBx99NH/qxOPAAAAAED51RUdAMCGs2rVqmy22WZJko033jhJsnjx4ub59ttvnylTphTSRuu32Wab5dhjj81VV11VdAoAAAAAUAMsGgFKZKuttsrcuXOTJO3bt8+mm26a1157rXk+a9as1NfXF5VHCXTt2jVvvfVW0RkAAAAAQA3w6lSAEtltt93y3HPPNV8PGjQo1157bbp3756mpqaMHTs2e+21V4GFtHYTJkxoPjULAAAAAHy8WTQClMgxxxyT2267LcuWLUu7du1y1lln5dlnn83o0aOTJFtuuWW+8Y1vFFxJLRszZsyH3l+0aFGeeuqpTJ06NaeffnoLVwEAAAAAtahSrVarRUcA8NFZsmRJnnrqqbRp0yZ77bVXOnfuXHQSNayhoWGdsy233DInnXRSTj311NTVefs6AAAAAHzcWTQCAM1mz5691r1KpZJNN900HTt2LKAIAAAAAKhVFo0AAAAAAADAevONRoASGTx48N98plKpZMKECS1QQ2s0ePDgnHPOOev8u/TII4/k3HPPzUMPPdTCZQAAAABArbFoBCiRbt26rXVv9erVmTlzZv70pz+lV69e2WqrrQooo7WYPXt2lixZss750qVLM2fOnBYsAgAAAABqlUUjQIlcf/3165zdfvvt+fGPf5zvfe97LVhE2cybNy/t2rUrOgMAAAAAqAEWjQAfE5/73Ofy3HPP5Uc/+lF+/vOfF51DDXn22Wfz9NNPN18/+OCDeeutt9Z6btGiRbnnnnuy0047tWQeAAAAAFCjLBoBPkZ23XXX/PjHPy46gxrz9NNPZ8yYMUn+/A3PBx54IA888MCHPturV6+MHj26JfMAAAAAgBpl0QjwMfLmm2+mqamp6AxqzMknn5yjjz461Wo1Bx10UM4555wMHjx4jWcqlUo6dOiQLl26FBMJAAAAANQci0aAEpkzZ86H3l+0aFGeeOKJ/OpXv8rAgQNbuIpa17lz53Tu3DlJ8sMf/jD77LNPunfvXnAVAAAAAFDrKtVqtVp0BAAbRkNDQyqVyofOqtVqtt9++/zsZz/Ltttu28JltHZz5szJwoULs9NOO63z7xgAAAAA8PHiRCNAiYwaNepDl0BdunRJnz59MnDgQEsi/qpbbrklzz77bH70ox813/vBD36QG264IUmy00475dprr20+AQkAAAAAfHxZNAKUyNe+9rWiE2jlbrnllvTp06f5+qWXXsr111+ffv36ZbvttstNN92Uq6++OmeeeWaBlQAAAABALbBoBACazZgxI4ceemjz9f33359NN900V155Zerr67N69ercf//9Fo0AAAAAgEUjQGs2fvz4v+vnhg4dukE7KI/3338/m2yySfP1888/n/79+6e+vj5J8slPfjJ33XVXUXkAAAAAQA2xaARoxb797W+nUqmkWq023/vv32D8y/3/+V1Gi0bWpWvXrpk1a1aSPy8dJ0+enMMPP7x5vnTpUt/5BAAAAACSWDQCtGrXXXfdGterV6/OBRdckHfeeScnnnhitt9++yTJ1KlT8+tf/zpbb711vv71rxeRSiux11575Te/+U369u2bxx57LKtXr86gQYOa5zNmzMiWW25ZYCEAAAAAUCssGgFasX79+q1xfdlll+X999/P3XffvcbrLw866KAMHz48xx13XCZOnJgBAwa0dCqtxKhRo/L000/n7LPPTpIce+yx6dOnT5I/n5B96KGH/P0BAAAAAJIklep/f98eAK3a4MGDM2zYsJx22mkfOr/88stz8803Z8KECS1cRmuycOHCvPDCC9lkk02y9957N99ftGhRxo8fn/79+6ehoaHAQgAAAACgFjjRCFAi77zzTurr69c5r6+vz9y5c1uwiNaoS5cu+cxnPrPW/U033TQnn3xyAUUAAAAAQC2qKzoAgA2ne/fuufvuu7Ny5cq1ZitWrMhdd92Vbt26FVAGAAAAAEDZWDQClMgJJ5yQSZMm5aSTTsoDDzyQadOmZdq0abn//vtz0kknZfLkyTnxxBOLzgQAAAAAoAR8oxGgZC644IL88pe/zIf9ej/llFPyrW99q4AqAAAAAADKxqIRoIRmzJiRBx98MDNmzEiS9OzZMwcddFB69epVcBkAAAAAAGVh0QgAAAAAAACsN99oBAAAAAAAANZb26IDANiw3nvvvdxyyy2ZOHFiFi1alKampjXmlUol1157bUF1tAZNTU2544478vjjj2f+/Pn55je/mZ133jmLFi3KI488kgEDBmTrrbcuOhMAAAAAKJhFI0CJ/PGPf8zw4cPz9ttvp3Pnzlm8eHE23XTTvPfee2lqaspmm22W9u3bF51JDVu2bFlOPfXUPPfcc2nfvn2WLVuWRYsWJUk6deqUCy64IMcee2zOOuusYkMBAAAAgMJ5dSpAifz0pz/NwoULc/XVV+f+++9PtVrNRRddlOeffz6nnnpqOnbsmLFjxxadSQ279NJL89JLL+W//uu/8tBDD+W/f8q5TZs2GTJkSB5//PECCwEAAACAWmHRCFAiTzzxRI499tgMGDAglUql+X779u3zjW98I9ttt10uvPDCAgupdffee2+GDRuWIUOGrPF36C969uyZOXPmFFAGAAAAANQai0aAElmwYEF23HHHJEnbtn9+O/by5cub5/vvv39+//vfF9JG6/D222+noaFhnfOOHTvm/fffb8EiAAAAAKBWWTQClEiXLl2al0CdOnXKRhtttMbps0qlkg8++KCoPFqBTTbZJPPmzVvnfPr06dliiy1asAgAAAAAqFUWjQAl0qdPn7z++utJ/rxU3GWXXTJu3LisWLEiS5cuzW233ZaePXsWXEkt23vvvTN+/PisWrVqrdm8efNy2223Zd999y2gDAAAAACoNRaNACWy33775YEHHmh+Xeqpp56aSZMmpV+/fhk4cGAaGxvzxS9+sdhIatrpp5+eP/7xjxkxYkTuv//+JMmkSZNyzTXX5Oijj87KlStz2mmnFVwJAAAAANSCSrVarRYdAcCGUa1Ws3LlytTX1zffmzBhQm6//fbU1dXl0EMPzWGHHVZgIa3BY489lnPOOaf5FaqVSiXVajVbbLFF/vM//zMDBw4suBAAAAAAqAUWjQDAWlasWJEnnngi06ZNS1NTU/r06ZP9998/7dq1KzoNAAAAAKgRFo0AJbF48eLss88+GTVqVL761a8WnQMAAAAAQMn5RiNASXTq1CmdO3fOlltuWXQKAAAAAAAfA22LDgBgw9lzzz3z8ssvZ9iwYUWn0EoNHjz4r84rlUratWuXbt265YADDshxxx3ndaoAAAAA8DHlRCNAifzLv/xLHnzwwdx0003xZmz+Ht26dUvbtm0ze/bsLFy4MJ07d07nzp2zcOHCzJ49O23atEl9fX1efPHF/OAHP8hxxx2XRYsWFZ0NAAAAABTANxoBSmTkyJH54x//mFmzZmXTTTdNz5491zptVqlUcu211xZUSK2bOnVqRowYkTPOOCPDhw9PfX19kmTlypX51a9+lZ///Oe54YYb0qdPn1x//fU5//zzc/LJJ2f06NEFlwMAAAAALc2iEaBEPvOZz/yvnnv44Yc/4hJaqy9/+cvZaqut8oMf/OBD5+ecc07mzZuXK664Ikly5plnZvLkyZkwYUJLZgIAAAAANcA3GgFKxAKR/1cvvPBCvvnNb65zvttuu+XHP/5x8/Xee++dRx55pCXSAAAAAIAa4xuNAMAa3nzzzXXOpk+fvsZ1XV1dNt5444+4CAAAAACoRRaNAECzfv36ZezYsfnd73631uyxxx7LjTfemH79+jXfe/XVV7PNNtu0ZCIAAAAAUCN8oxGgRAYPHvxX55VKJe3atUu3bt1ywAEH5Ljjjku7du1aqI7W4M0338ywYcPy3nvvpW/fvvmHf/iHJH8+yTh16tRssskmufHGG9OnT58sW7YsRx99dA4++OCcffbZBZcDAAAAAC3NohGgRE466aS88847eeutt9KxY8dsu+22SZKZM2fmgw8+SK9evdKxY8fMnDkz77//fvr27Zsbbrghm266acHl1JI5c+bkwgsvzCOPPJIlS5YkSTp06JBPf/rTOfvss9OjR4+CCwEAAACAWmDRCFAiU6dOzYgRI3LGGWdk+PDhqa+vT5KsXLkyv/rVr/Lzn/88N9xwQ/r06ZPrr78+559/fk4++eSMHj264HJqUbVazfz585MkXbt2TaVSKbgIAAAAAKglFo0AJfLlL385W221VX7wgx986Pycc87JvHnzcsUVVyRJzjzzzEyePDkTJkxoyUwAAAAAAEqgrugAADacF154IZ/85CfXOd9tt93y/PPPN1/vvffemTt3bkuk0YosXrw4l156aYYPH56DDz44L774YpJkwYIFGTNmTKZNm1ZwIQAAAABQC9oWHQDAhvXmm2+uczZ9+vQ1ruvq6rLxxht/xEW0Ju+++25OOOGEzJgxIz179szMmTOzbNmyJMnmm2+ecePGZfHixfn2t79dcCkAAAAAUDQnGgFKpF+/fhk7dmx+97vfrTV77LHHcuONN6Zfv37N91599dVss802LZlIjbvkkksyd+7c3HjjjfnVr36V//mG9YMOOihPPvlkQXUAAAAAQC1xohGgRP71X/81w4YNy2mnnZa+ffvmH/7hH5L8+STj1KlTs8kmm+Rb3/pWkmTZsmV5/vnnc/DBBxeZTI15+OGHc8IJJ+STn/xk3n333bXm3bt3z9tvv11AGQAAAABQaywaAUqkd+/eGTduXC688MI88sgjee2115IkHTp0yGc/+9mcffbZ6dGjR5KkXbt2uffee4vMpQbNnz8/vXr1Wue8bdu2Wbp0aQsWAQAAAAC1yqIRoGS6deuWCy+8MNVqNfPnz0+SdO3aNZVKpeAyWoPNNtssc+bMWef8tddeyyc+8YkWLAIAAAAAapVvNAKUVKVSyRZbbJEtttjCkpH/tf322y+33npr3n///bVm06ZNy7hx4zJo0KACygAAAACAWlOpVqvVoiMAgNowc+bMHHPMMdlss81y2GGH5YorrsiIESOSJLfcckvatWuX8ePHZ+utty64FAAAAAAomkUjALCGxsbGjB49Oq+++uoa93fYYYf8+Mc/zo477lhQGQAAAABQSywaAYAPNXXq1EybNi1NTU3p06dPdtppp6KTAAAAAIAaYtEIAAAAAAAArLe6ogMAAAAAAACA1qdt0QEAfPRWrVqVDz74IJtuumnRKdSYwYMHr/fPVCqVTJgw4SOoAQAAAABaE4tGgBJ56KGH8tJLL+XrX/96871rrrkmP/nJT7Jy5cp8+tOfzsUXX5z6+voCK6kl3bp1KzoBAAAAAGilLBoBSuTaa6/NFlts0Xw9derU/OhHP0qfPn2y7bbb5uGHH84NN9yQf/qnfyqwklpy/fXXF50AAAAAALRSvtEIUCLTp0/PLrvs0nx99913p127dvnNb36Tyy+/PIcffnjuuOOOAgsBAAAAACgLi0aAElm0aFE222yz5uvnnnsu/fv3T+fOnZMk/fr1y+zZs4vKAwAAAACgRLw6FaBEunTpknfeeSdJsnz58rz88sv56le/2jxftWpVVq1aVVQeNWjkyJHr/TOVSiXXXnvtR1ADAAAAALQmFo0AJbLLLrvklltuyX777ZcHH3wwK1euzAEHHNA8nzVrVrp27VpgIbVm1qxZRScAAAAAAK1UpVqtVouOAGDDeOmll3LKKadk2bJlqVarGTRoUK644orm+WGHHZadd945F154YYGVAAAAAACUgRONACWyxx57ZNy4cfnd736Xzp075/DDD2+evfvuuznggAMyZMiQAgsBAAAAACgLJxoBgA81ZcqUzJw5M0my7bbbZscddyy4CAAAAACoJU40ApTIzJkzM3Xq1HzmM59Za1atVvPII49khx12SI8ePQqoo7V45pln8m//9m9566231rjfu3fvfO9738s+++xTUBkAAAAAUEucaAQoka9//euZM2dOxo4d+6HzE088MT169MiPfvSjFi6jtXjllVdywgknJEmOOOKI7LDDDkmS1157LXfffXeS5Ne//nV22WWXwhoBAAAAgNrgRCNAibzwwgs57rjj1jnff//9c/PNN7dgEa3NmDFj0qFDh9x4443p3bv3GrOvfOUrGTZsWC699NJcdtllxQQCAAAAADWjrugAADacefPmZcstt1znfIsttsi8efNasIjW5sUXX8zw4cPXWjImSa9evfKFL3whzz//fMuHAQAAAAA1x6IRoEQ6deqUWbNmrXM+a9astG/fvgWLaG2WLl2aLbbYYp3zLbfcMkuXLm3BIgAAAACgVlk0ApTIHnvskVtvvTWLFi1aa7Zo0aLceuut2X333Qsoo7Xo3r17fve7361z/vjjj6d79+4tWAQAAAAA1CqLRoAS+dKXvpQFCxbkuOOOy/jx4/Paa6/ltddey/jx43P88cdnwYIF+dKXvlR0JjXs8MMPz6OPPprvfve7a7xmd8GCBTn33HPz6KOP5vDDDy+wEAAAAACoFZVqtVotOgKADefmm2/O97///axcubL5XrVaTX19fb7zne/k+OOPL7COWrdixYqceuqpeeaZZ1KpVNKlS5ckycKFC1OtVtO/f//84he/SH19fbGhAAAAAEDhLBoBSmju3Lm577778uabbyZJevfunUMPPTRbb711sWG0Ck1NTbntttvy4IMPZubMmUmSnj17ZsiQIRk6dGjatGlTcCEAAAAAUAssGgEAAAAAAID15huNAAAAAAAAwHprW3QAAH+/0aNHp1Kp5Pvf/37atGmT0aNH/82fqVQqOe+881qgjtbov/7rv3L//ffnrrvuWmtWrVZz1FFH5bDDDssZZ5xRQB0AAAAAUEu8OhWgFWtoaEilUsnEiRNTX1+fhoaGv/kzlUoljY2NLVBHa3TUUUelf//++T//5/986PyHP/xhnn766YwfP75lwwAAAACAmuNEI0Ar9uqrr/7Va1hfs2bNygknnLDOeZ8+fXLrrbe2YBEAAAAAUKt8oxEAaFatVrN48eJ1zhcvXpxVq1a1YBEAAAAAUKssGgFKZOTIkXnyySfXOX/qqacycuTIFiyitenTp08ee+yxdc4fe+yx9OrVqwWLAAAAAIBaZdEIUCLPPPNM5s2bt875ggUL8uyzz7ZgEa3NUUcdlWeeeSY//OEPs2zZsub7y5Yty49+9KM8++yzOeqoowosBAAAAABqhW80AnyMvPfee6mvry86gxo2YsSIPProo7n22mtz8803p3fv3kmSN998M0uWLEm/fv1y8sknFxsJAAAAANQEi0aAVu7VV1/Nq6++2nz93HPPZfXq1Ws9t3DhwowdOzbbbbddS+bRyrRt2zZXXnllrr322tx5552ZPn16kqR379456qijMnLkyLRt6z8fAAAAAICkUq1Wq0VHAPD3GzNmTMaMGZMkqVQq+Wu/1jt27JiLLroogwYNaqk8AAAAAABKyqIRoJWbPXt2Zs+enWq1mpNPPjmnn356Bg4cuMYzlUolHTp0yPbbb5+NN964oFIAAAAAAMrEohGgRMaMGZODDz44O+ywQ9EpAAAAAACUnEUjAAAAAAAAsN7aFh0AwIY3f/78vPLKK1m4cOGHfrNx6NChLR8FAAAAAECpONEIUCJNTU0599xzc9NNN2X16tXrfK6xsbEFqwAAAAAAKCMnGgFK5Jprrsmvf/3rHHHEEdl///3z7W9/O1//+tfTsWPHXHvttdl0001z9tlnF50JAAAAAEAJ1BUdAMCGM27cuOy333654IILcuCBByZJdt1115xwwgm59dZbM2/evLz66qsFVwIAAAAAUAYWjQAlMmPGjAwaNChJUlf351/xf3mFaqdOnXLMMcfk5ptvLqwPAAAAAIDysGgEKJGNNtooG2+8cZKkffv2SZJ33323eb711ltn9uzZhbQBAAAAAFAuFo0AJbLNNttk1qxZSZL6+vp07949zz33XPN84sSJ2WyzzYrKAwAAAACgRNoWHQDAhrP33nvn0UcfzTe+8Y0kyWc/+9lcddVVWbFiRZqamnLnnXfm+OOPL7gSAAAAAIAysGgEKJGTTjopO+64Y5YtW5Z27dpl1KhRmT59esaPH58kOeCAA/Iv//IvxUYCAAAAAFAKlWq1Wi06AoCP1uLFi1OpVNKxY8eiUwAAAAAAKAmLRgAAAAAAAGC9eXUqQEktXbo0CxcuzIf9e5Ju3boVUAQAAAAAQJlYNAKUSFNTU6688srccMMN+dOf/rTO5xobG1uwCgAAAACAMrJoBCiR//zP/8w111yTnXbaKYccckg23XTTopMAAAAAACgp32gEKJEBAwZkr732yqWXXlp0CgAAAAAAJVdXdAAAG86yZcsyaNCgojMAAAAAAPgYsGgEKJFdd901s2bNKjoDAAAAAICPAYtGgBI566yzctNNN2XSpElFpwAAAAAAUHK+0QhQMg8//HDOPPPM7L777unRo0fq6tb8NyWVSiXnnXdeQXUAAAAAAJSFRSNAibz88sv50pe+lPfff3+dz1QqlTQ2NrZgFQAAAAAAZdS26AAANpzzzjsvdXV1GTNmTPr165dNNtmk6CQAAAAAAErKohGgRF599dWcccYZOeigg4pOAQAAAACg5Or+9iMAtBabbLJJ2rdvX3QGAAAAAAAfAxaNACVy+OGH54EHHig6AwAAAACAj4FKtVqtFh0BwIYxbdq0fPvb307Xrl1z8sknp0ePHmnTps1az3Xr1q2AOgAAAAAAysSiEaBEGhoaUqlUUq1WU6lU1vlcY2NjC1YBAAAAAFBGbYsOAGDDGTVq1F9dMAIAAAAAwIbiRCNASaxevTpz585Nhw4d0qVLl6JzAAAAAAAoubqiAwDYMFatWpXBgwfn5ptvLjoFAAAAAICPAYtGgJLYeOONs+mmm6Zjx45FpwAAAAAA8DFg0QhQIgMGDMhTTz1VdAYAAAAAAB8DFo0AJfLNb34zkyZNykUXXZT333+/6BwAAAAAAEqsUq1Wq0VHALBhDB48OEuWLMnChQuTJJtvvnnatWu3xjOVSiUTJkwooA4AAAAAgDJpW3QAABtOt27dik4AAAAAAOBjwolGAAAAAAAAYL35RiMAAAAAAACw3rw6FaCE5syZk4ceeigzZsxIkvTs2TODBw/2alUAAAAAADYYr04FKJkrrrgil1xySVavXp3//iu+bdu2OfPMM3PaaacVWAcAAAAAQFk40QhQIvfcc09+8pOfpG/fvvnSl76UHXfcMUkyZcqUXHXVVbnooovSo0ePfPazny24FAAAAACA1s6JRoASGTZsWJYuXZqbbrop7dq1W2O2bNmyHH/88enQoUNuvPHGggoBAAAAACiLuqIDANhwXnvttRx11FFrLRmTpF27djnqqKMyZcqUAsoAAAAAACgbi0aAkqlUKn/XDAAAAAAA1odFI0CJ9O3bN3fccUeWL1++1mzFihW58847s8MOOxRQBgAAAABA2Vg0ApTIiBEjMmXKlAwfPjz33HNPXn/99bz++uu59957c+KJJ2bKlCkZMWJE0ZkAAAAAAJRApVqtVouOAGDD+elPf5rLL788//PXe6VSyemnn54zzzyzoDIAAAAAAMrEohGghN56661MmDAhM2fOTJL07NkzBx10UHr27FlwGQAAAAAAZWHRCNCKjRw5Mv/8z/+cAQMGJEnGjx+fvffeOz169Ci4DAAAAACAsvONRoBW7Jlnnsm8efOar0ePHp0XX3yxwCIAAAAAAD4uLBoBWrEtt9wys2bNar52SB0AAAAAgJbStugAAP5+AwYMyM9+9rO88sor2WSTTZIkN910U5544ol1/kylUsl5553XUokAAAAAAJSUbzQCtGLvvvtuzj///DzxxBPNr1D9W7/WK5VKGhsbWyIPAAAAAIASs2gEKJGGhob8+Mc/zpFHHll0CgAAAAAAJecbjQAlcvTRR6dnz55FZwAAAAAA8DHgRCMAAAAAAACw3toWHQDAhvfWW2/lzTffzLvvvvuh86FDh7ZsEAAAAAAApeNEI0CJLFiwIP/6r/+axx9/PEnyYb/iK5VKGhsbWzoNAAAAAICScaIRoETOPffcPP744zn++OOz3377pUuXLkUnAQAAAABQUk40ApTIPvvsk8MOOyzf+973ik4BAAAAAKDk6ooOAGDDWbVqVXbdddeiMwAAAAAA+BiwaAQokd122y3Tp08vOgMAAAAAgI8Bi0aAEvn617+e8ePHZ+LEiUWnAAAAAABQcr7RCFAio0ePzquvvpopU6Zkjz32yLbbbpu6ujX/TUmlUsl5551XUCEAAAAAAGVh0QhQIg0NDX/zmUqlksbGxhaoAQAAAACgzCwaAQAAAAAAgPXmG40AAAAAAADAerNoBAAAAAAAANZb26IDAPj7jR49OpVKJd///vfTpk2bjB49+m/+TKVSyXnnndcCdQAAAAAAlJlvNAK0Yg0NDalUKpk4cWLq6+vT0NDwN3+mUqmksbGxBeoAAAAAACgzi0YAAAAAAABgvflGIwAAAAAAALDeLBoBAAAAAACA9WbRCAAAAAAAAKw3i0YAAAAAAABgvVk0AgAAAAAAAOvNohEAAAAAAABYbxaNAAAAAAAAwHqzaAQAAAAAAADWW9uiAwDYsN57773ccsstmThxYhYtWpSmpqY15pVKJddee21BdQAAAAAAlIVFI0CJ/PGPf8zw4cPz9ttvp3Pnzlm8eHE23XTTvPfee2lqaspmm22W9u3bF50JAAAAAEAJeHUqQIn89Kc/zcKFC3P11Vfn/vvvT7VazUUXXZTnn38+p556ajp27JixY8cWnQkAAAAAQAlYNAKUyBNPPJFjjz02AwYMSKVSab7fvn37fOMb38h2222XCy+8sMBCAAAAAADKwqIRoEQWLFiQHXfcMUnStu2f3469fPny5vn++++f3//+94W0AQAAAABQLhaNACXSpUuXvP/++0mSTp06ZaONNsqcOXOa55VKJR988EFReQAAAAAAlIhFI0CJ9OnTJ6+//nqSPy8Vd9lll4wbNy4rVqzI0qVLc9ttt6Vnz54FVwIAAAAAUAYWjQAlst9+++WBBx5ofl3qqaeemkmTJqVfv34ZOHBgGhsb88UvfrHYSAAAAAAASqFSrVarRUcAsGFUq9WsXLky9fX1zfcmTJiQ22+/PXV1dTn00ENz2GGHFVgIAAAAAEBZWDQCAAAAAAAA682rUwEAAAAAAID11rboAAD+fuPHj0+SfO5zn0ulUmm+/luGDh36kTUBAAAAAPDx4NWpAK1YQ0NDKpVKJk6cmPr6+ubrv/arvVKppLGxsQUrAQAAAAAoIycaAVqx6667LklSX1+/xjUAAAAAAHzUnGgEAAAAAAAA1ltd0QEAfPRWrVqVRYsWFZ0BAAAAAECJWDQClMhDDz2UCy+8cI1711xzTfbaa6/su+++OeOMM7JixYqC6gAAAAAAKBOLRoASufbaazN79uzm66lTp+ZHP/pRevTokUGDBuXhhx/ODTfcUGAhAAAAAABlYdEIUCLTp0/PLrvs0nx99913p127dvnNb36Tyy+/PIcffnjuuOOOAgsBAAAAACgLi0aAElm0aFE222yz5uvnnnsu/fv3T+fOnZMk/fr1W+PEIwAAAAAA/L0sGgFKpEuXLnnnnXeSJMuXL8/LL7+cvfbaq3m+atWqrFq1qqg8AAAAAABKpG3RAQBsOLvssktuueWW7LfffnnwwQezcuXKHHDAAc3zWbNmpWvXrgUWAgAAAABQFhaNACVy+umn55RTTsnxxx+farWaQYMGZaeddmqeP/roo9l9990LLAQAAAAAoCwsGgFKZI899si4cePyu9/9Lp07d87hhx/ePHv33XdzwAEHZMiQIQUWAgAAAABQFpVqtVotOgIAAAAAAABoXZxoBCihWbNm5cknn8y8efNy5JFHpkePHlmxYkXmzZuXLbbYIvX19UUnAgAAAADQylk0ApTMT37yk1x11VVZvXp1KpVK9thjj+ZF4+GHH56zzjorJ598ctGZAAAAAAC0cnVFBwCw4dx888254oorMnz48Fx11VX572/H7tSpUz796U/nkUceKbAQAAAAAICysGgEKJFf//rXGTx4cL7zne9k5513Xmu+44475o033iigDAAAAACAsrFoBCiR6dOnZ//991/nfPPNN8+CBQtasAgAAAAAgLKyaAQokbZt22b58uXrnM+dOzedOnVqwSIAAAAAAMrKohGgRHbeeec8+uijHzpbtWpV7rrrruy+++4tGwUAAAAAQClZNAKUyIgRI/LUU0/lhz/8Yf70pz8lSVauXJnGxsacfvrpeeutt3LSSScVXAkAAAAAQBlUqtVqtegIADacSy65JD/72c+SJNVqNZVKpfnPZ511Vk4//fQi8wAAAAAAKAmLRoASmjx5cu68885Mnz49TU1N6d27dz73uc9l1113LToNAAAAAICSsGgEAAAAAAAA1ptvNAIAAAAAAADrrW3RAQBsWEuWLMndd9+dN954IwsXLsz/PLheqVRy3nnnFVQHAAAAAEBZeHUqQIm88sorOe200/Luu++utWD8i0qlksbGxhYuAwAAAACgbJxoBCiR888/P8uXL8+FF16YgQMHpkuXLkUnAQAAAABQUhaNACUyadKkfPnLX85nP/vZolMAAAAAACi5uqIDANhw2rVrl65duxadAQAAAADAx4BFI0CJDBo0KM8991zRGQAAAAAAfAxYNAKUyL/+67/mlVdeyS9+8YusWLGi6BwAAAAAAEqsUq1Wq0VHALBhDB48OEuXLs27776burq6bLXVVqmrW/PflFQqlUyYMKGgQgAAAAAAyqJt0QEAbDjdunUrOgEAAAAAgI8JJxoBAAAAAACA9eYbjQAAAAAAAMB68+pUgBKaM2dOHnroocyYMSNJ0rNnzwwePNirVQEAAAAA2GC8OhWgZK644opccsklWb16df77r/i2bdvmzDPPzGmnnVZgHQAAAAAAZeFEI0CJ3HPPPfnJT36Svn375ktf+lJ23HHHJMmUKVNy1VVX5aKLLkqPHj3y2c9+tuBSAAAAAABaOycaAUpk2LBhWbp0aW666aa0a9dujdmyZcty/PHHp0OHDrnxxhsLKgQAAAAAoCzqig4AYMN57bXXctRRR621ZEySdu3a5aijjsqUKVMKKAMAAAAAoGwsGgFKplKp/F0zAAAAAABYHxaNACXSt2/f3HHHHVm+fPlasxUrVuTOO+/MDjvsUEAZAAAAAABlY9EIUCIjRozIlClTMnz48Nxzzz15/fXX8/rrr+fee+/NiSeemClTpmTEiBFFZwIAAAAAUAKVarVaLToCgA3npz/9aS6//PL8z1/vlUolp59+es4888yCygAAAAAAKBOLRoASeuuttzJhwoTMnDkzSdKzZ88cdNBB6dmzZ8FlAAAAAACUhUUjAAAAAAAAsN58oxEAAAAAAABYb22LDgDg7zdy5Mj1/plKpZJrr732I6gBAAAAAODjxKIRoBWbNWvWWveWLl2ad999N0nSuXPnJMn777+fJNlss83SoUOHlgsEAAAAAKC0fKMRoERmz56dk046KQcccEDOOOOMbL311kmSuXPn5rLLLsvvfve73HDDDenWrVvBpQAAAAAAtHYWjQAlcuaZZ2b16tW59NJLP3Q+atSotG3bNj/96U9buAwAAAAAgLKpKzoAgA3nqaeeyn777bfO+X777Zcnn3yyBYsAAAAAACgri0aAElm5cmXmzp27zvnbb7+dlStXtmARAAAAAABlZdEIUCK77757fvWrX6WxsXGtWWNjY371q19l9913L6AMAAAAAICy8Y1GgBJ55ZVXMmLEiKxcuTIHHHBAtttuuyTJtGnT8rvf/S719fW5/vrrs+uuuxZcCgAAAABAa2fRCFAyf/jDH3Leeefl+eefX+P+pz71qYwePdqSEQAAAACADcKiEaCkFixYkJkzZyZJtt1222y++eYFFwEAAAAAUCYWjQAAAAAAAMB6a1t0AAAfjaVLl2bhwoX5sH9P0q1btwKKAAAAAAAoE4tGgBKpVqu58sorc/311+dPf/rTOp9rbGxswSoAAAAAAMrIohGgRC6++OJcfvnl2W677XLwwQenS5cuRScBAAAAAFBSvtEIUCKDBg1K3759c+WVV6ZSqRSdAwAAAABAidUVHQDAhrNw4cIMGTLEkhEAAAAAgI+cRSNAifTu3TsLFiwoOgMAAAAAgI8Bi0aAEvmnf/qn3HLLLVm8eHHRKQAAAAAAlFzbogMA2LC22mqrHHbYYTn22GPTo0ePtGnTZq1nhg4d2vJhAAAAAACUSqVarVaLjgBgw2hoaPibz1QqlTQ2NrZADQAAAAAAZeZEI0CJXHfddUUnAAAAAADwMeFEIwAAAAAAALDe6ooOAAAAAAAAAFofr04FaMXGjx+fJPnc5z6XSqXSfP23DB069CNrAgAAAADg48GrUwFasYaGhlQqlUycODH19fXN13/tV3ulUkljY2MLVgIAAAAAUEZONAK0Ytddd12SpL6+fo1rAAAAAAD4qDnRCAAAAAAAAKy3uqIDAAAAAAAAgNbHohEAAAAAAABYbxaNAAAAAAAAwHqzaAQAAAAAAADWm0UjAAAAAAAAsN4sGgEAAAAAAID1ZtEIAAAAAAAArLe2RQcAsOG98sormThxYhYtWpSmpqY1ZpVKJaNGjSqoDAAAAACAsqhUq9Vq0REAbBjLly/PmWeemcceeyzVajWVSiV/+TX/lz9XKpU0NjYWXAoAAAAAQGvn1akAJXLZZZflsccey1e+8pVcd911qVarOf/883P55Zdnr732ym677ZZ77rmn6EwAAAAAAErAohGgRO67774MGTIkZ511Vvr27Zsk2XrrrXPggQfmmmuuydKlS3P77bcXXAkAAAAAQBlYNAKUyJw5c9K/f/8kSV3dn3/Fr1y5Mkmy0UYb5cgjj8xdd91VWB8AAAAAAOVh0QhQIh06dGj+c8eOHVNXV5cFCxY03+vSpUveeeedItIAAAAAACgZi0aAEunevXtmzJiRJGnbtm169+6d3/72t83zxx9/PFtuuWVReQAAAAAAlIhFI0CJ9O/fPxMmTGi+Hjp0aO69996cdNJJGTFiRB588MEcfvjhBRYCAAAAAFAWlWq1Wi06AoAN45133smUKVPSv3//1NfXp6mpKeeff35uv/321NXV5dBDD83o0aNTX19fdCoAAAAAAK2cRSMAAAAAAACw3rw6FQAAAAAAAFhvbYsOAGDDW7ZsWWbNmpWFCxfmww6u77PPPgVUAQAAAABQJhaNACWybNmynH/++bn11luzatWqtebVajWVSiWNjY0F1AEAAAAAUCYWjQAlct555+Wmm27K/vvvn/322y9dunQpOgkAAAAAgJKqVD/snXoAtEoDBgxI//79c/HFFxedAgAAAABAydUVHQDAhrNkyZIMGDCg6AwAAAAAAD4GLBoBSqShoSF//OMfi84AAAAAAOBjwKIRoERGjRqV3/zmN5k9e3bRKQAAAAAAlFzbogMA2HBefvnlbLvttjniiCMyZMiQ9OjRI3V1a/6bkkqlklGjRhVUCAAAAABAWVSq1Wq16AgANoyGhoa/+UylUkljY2ML1AAAAAAAUGYWjQAl8r99ZWr37t0/4hIAAAAAAMrOohEAAAAAAABYb3V/+xEAAAAAAACANbUtOgCADWv16tWZMGFCJk6cmEWLFqWpqWmNeaVSyXnnnVdQHQAAAAAAZeHVqQAlsmjRonzxi1/Mq6++mmq1mkqlkr/8mv/LnyuVShobGwsuBQAAAACgtfPqVIASueSSSzJ16tR8//vfz4MPPphqtZorr7wyd999dw477LDstttueeaZZ4rOBAAAAACgBCwaAUrkkUceyVFHHZVjjz02nTp1SpK0adMm2223XX7yk5+kbdu2ufjii4uNBAAAAACgFCwaAUrknXfeyW677ZYkadv2z5/hXbFiRfN8yJAhmTBhQiFtAAAAAACUi0UjQIl07tw5y5cvT5J06NAhbdu2zTvvvNM8b9euXRYuXFhQHQAAAAAAZWLRCFAiPXv2zJtvvpnkz69M7du3b+67774kSVNTU+6///5ss802BRYCAAAAAFAWFo0AJTJgwIA8+OCDWb16dZJk+PDh+f3vf5+DDjooBx98cJ5++ukcd9xxBVcCAAAAAFAGlWq1Wi06AoAN44MPPsjcuXPTs2fP5m80Xnvttbn99ttTV1eXQw89NKeeemrBlQAAAAAAlIFFIwAAAAAAALDevDoVAAAAAAAAWG9tiw4A4O83fvz4v+vnhg4dukE7AAAAAAD4+PHqVIBWrKGhIZVKJf/9V3mlUmn+81/u//d7SdLY2NgygQAAAAAAlJYTjQCt2HXXXbfG9erVq3PBBRfknXfeyYknnpjtt98+STJ16tT8+te/ztZbb52vf/3rRaQCAAAAAFAyTjQClMhll12W8ePH55Zbbskmm2yyxmzhwoU57rjj8vnPfz6nn356QYUAAAAAAJRFXdEBAGw4t956a4499ti1loxJ0qVLlxx77LG55ZZbCigDAAAAAKBsLBoBSuSdd95JfX39Ouf19fWZO3duCxYBAAAAAFBWFo0AJdK9e/fcfffdWbly5VqzFStW5K677kq3bt0KKAMAAAAAoGwsGgFK5IQTTsikSZNy0kkn5YEHHsi0adMybdq03H///TnppJMyefLknHjiiUVnAgAAAABQApVqtVotOgKADeeCCy7IL3/5y3zYr/dTTjkl3/rWtwqoAgAAAACgbCwaAUpoxowZefDBBzNjxowkSc+ePXPQQQelV69eBZcBAAAAAFAWFo0AAAAAAADAevONRgAAAAAAAGC9WTQCAAAAAAAA682iEQAAAAAAAFhvFo0AAAAAAADAerNoBAAAAAAAANabRSNASSxbtizjx4/PxIkTi04BAAAAAOBjwKIRoCTq6+vzne98J5MnTy46BQAAAACAjwGLRoCSqKury1ZbbZUlS5YUnQIAAAAAwMeARSNAiRx66KG57777snr16qJTAAAAAAAouUq1Wq0WHQHAhjFt2rR84xvfSKdOnXLKKaekV69ead++/VrPdevWrYA6AAAAAADKxKIRoEQaGhpSqVRSrVZTqVTW+VxjY2MLVgEAAAAAUEZtiw4AYMMZNWrUX10wAgAAAADAhuJEIwAAAAAAALDe6ooOAAAAAAAAAFofi0aAklm8eHEuvfTSDB8+PAcffHBefPHFJMmCBQsyZsyYTJs2reBCAAAAAADKwDcaAUrk3XffzQknnJAZM2akZ8+emTlzZpYtW5Yk2XzzzTNu3LgsXrw43/72twsuBQAAAACgtbNoBCiRSy65JHPnzs2NN96Y7t27Z+DAgWvMDzrooDz55JMF1QEAAAAAUCZenQpQIg8//HBOOOGEfPKTn0ylUllr3r1797z99tsFlAEAAAAAUDYWjQAlMn/+/PTq1Wud87Zt22bp0qUtWAQAAAAAQFlZNAKUyGabbZY5c+asc/7aa6/lE5/4RAsWAQAAAABQVhaNACWy33775dZbb83777+/1mzatGkZN25cBg0aVEAZAAAAAABlU6lWq9WiIwDYMGbOnJljjjkmm222WQ477LBcccUVGTFiRJLklltuSbt27TJ+/PhsvfXWBZcCAAAAANDaWTQClExjY2NGjx6dV199dY37O+ywQ3784x9nxx13LKgMAAAAAIAysWgEKKmpU6dm2rRpaWpqSp8+fbLTTjsVnQQAAAAAQIlYNAIAAAAAAADrrW3RAQBseDNnzsxDDz2Ut956K0nSq1evDB48ONtuu23BZQAAAAAAlIUTjQAl89Of/jRXXHFFVq9evcb9Nm3a5NRTT83ZZ59dUBkAAAAAAGXiRCNAidxwww352c9+lt122y2nnHJKtt9++yR//l7j1VdfnSuuuCJbbrllRowYUXApAAAAAACtnRONACVy6KGHplOnThk7dmw22mijNWYrVqzIF77whSxZsiT33XdfQYUAAAAAAJRFXdEBAGw4s2fPzhFHHLHWkjFJ6uvrc+SRR2b27NkFlAEAAAAAUDYWjQAlstVWW2XFihXrnK9cuTJbb711CxYBAAAAAFBWFo0AJfL5z38+t956axYvXrzW7P3338+tt96az3/+8wWUAQAAAABQNm2LDgBgw9lzzz3z0EMP5cgjj8wJJ5yQ7bbbLkny+uuvZ+zYsenatWv22GOPPPvss2v83D777FNELgAAAAAArVilWq1Wi44AYMNoaGhY47pSqSRJ/vuv+r/c+8v9SqWSxsbGlgkEAAAAAKA0nGgEKJEf/vCHRScAAAAAAPAx4UQjAAAAAAAAsN7qig4AAAAAAAAAWh+vTgUomeXLl+fqq6/OAw88kBkzZiRJevbsmUMOOSRf/OIXs/HGGxdcCAAAAABAGXh1KkCJLF68OCNHjszkyZPTvn379OzZM0kyc+bMLFmyJDvvvHOuu+66dOrUqeBSAAAAAABaO69OBSiRSy+9NJMnT87Xvva1PPnkk7n99ttz++2358knn8yZZ56ZyZMn57LLLis6EwAAAACAEnCiEaBEDjrooOyxxx654IILPnT+9a9/PS+99FIeeuihFi4DAAAAAKBsnGgEKJG5c+fmU5/61Drne++9d955550WLAIAAAAAoKwsGgFKZJNNNsmsWbPWOZ85c2Y6d+7cgkUAAAAAAJSVRSNAifTr1y+//vWv89JLL601mzRpUsaOHZt999235cMAAAAAACgd32gEKJHp06fn2GOPzbJly7Lffvulb9++SZLXX389jz/+eDp06JD/v727i+36LvQ4/vl1DU+DjeF0oUJdYISKBpiCg7CbycMgPvCQGcPiGDqci7iFuItp5EY3YReGLEvUQCCTXQxEJxB18gyJLjCzhQ1Nu7hRN4pjRdaBoQRw8D8XRnI4jOPpTukv/fF6XbX/b5u8r3rz6e/33bBhQ0aMGFFyKQAAAAAAvZ2hEaBiWlpa8thjj+Wll1666PMJEyZk6dKlaWpqKqkMAAAAAIAqMTQCVFRHR8eF+xqHDRuWIUOGlFwEAAAAAECVGBoBAAAAAACALqsrOwAAAAAAAADoferLDgDgg2tqakpRFF36naIo0tzcfIWKAAAAAAC4WhgaAXqxOXPmdHloBAAAAACA7uCORgAAAAAAAKDL3NEIAAAAAAAAdJlXpwJU0Isvvpjf//73eeedd/LVr341I0eOTGdnZ5qbmzN69Ohcd911ZScCAAAAANDLeaIRoELOnz+fhx9+OPfcc09WrlyZZ599NkePHk2S1NfX55vf/GbWrVtXciUAAAAAAFVgaASokDVr1uS5557LI488kueeey7//Rrevn37Ztq0admzZ095gQAAAAAAVIahEaBCNm7cmNmzZ2fhwoW54YYbLjkfOXJk2traSigDAAAAAKBqDI0AFdLW1pZPfepTlz2//vrrc+LEiR4sAgAAAACgqgyNABXSv3///OMf/7js+eHDh3P99df3YBEAAAAAAFVlaASokLFjx+Z3v/vd+56dOnUqGzduzIQJE3q4CgAAAACAKjI0AlTIokWL0tzcnIceeigHDhxIkhw5ciQ7duzI/Pnz09HRka997WslVwIAAAAAUAVFrVarlR0BQPf55S9/mUcffTRnz55NrVZLURRJkj59+uT73/9+5syZU24gAAAAAACVYGgEqKBjx45ly5YtaW1tzfnz53PzzTdn1qxZuemmm8pOAwAAAACgIgyNAAAAAAAAQJe5oxGgQh555JG88MILZWcAAAAAAHAV8EQjQIV8/OMfT5I0NDRk7ty5mT17doYPH15yFQAAAAAAVWRoBKiQt99+O7/61a+yefPmvPnmm6mrq8uECRMyb9683Hnnnenfv3/ZiQAAAAAAVIShEaCiXnzxxWzcuDFbt27NyZMnM2DAgMycOTNz587NxIkTy84DAAAAAKCXMzQCVNyZM2eydevWbNq0Kfv27UuSNDc3l1wFAAAAAEBvV1d2AABXVt++fdPY2Jhhw4alX79+8f8lAAAAAAB0h/qyAwC4Mtrb27Np06Zs2rQpb7zxRpJcuK8RAAAAAAD+v7w6FaBCzp49m23btmXjxo3Zt29fzp07l4aGhsyZMydz587N8OHDy04EAAAAAKAiDI0AFTJhwoR0dnamX79+mT59eubNm5dJkyaVnQUAAAAAQAV5dSpAhYwaNSrz5s3LrFmzMnDgwLJzAAAAAACoME80AgAAAAAAAF3miUaACjp8+HD27t2bY8eO5Qtf+EKGDRuWs2fP5tixY7nxxhvTp0+fshMBAAAAAOjlDI0AFbNixYqsWbMm586dS1EUGT9+/IWh8XOf+1yWLFmSe++9t+xMAAAAAAB6ubqyAwDoPr/4xS+yatWqzJ8/P2vWrMl/fzv2wIEDc8cdd2T37t0lFgIAAAAAUBWGRoAKeeaZZzJ16tQsXbo0Y8aMueR89OjR+etf/1pCGQAAAAAAVWNoBKiQ1tbW3H777Zc9HzJkSDo6OnqwCAAAAACAqjI0AlRIfX19zpw5c9nz9vb2DBw4sAeLAAAAAACoKkMjQIWMGTMme/bsed+z9957L7/5zW8ybty4no0CAAAAAKCSDI0AFfKVr3wl+/bty/Lly/P3v/89SfLPf/4zLS0teeCBB/Lmm2/mnnvuKbkSAAAAAIAqKGq1Wq3sCAC6z5NPPpmf/vSnSZJarZaiKC58vWTJkjzwwANl5gEAAAAAUBGGRoAKam5uzq9//eu0trbm/PnzufnmmzN79ux88pOfLDsNAAAAAICKMDQCAAAAAAAAXeaORgAAAAAAAKDL6ssOAOCD++53v9vl3ymKIsuWLbsCNQAAAAAAXE28OhWgF2tqarrks6IokiT/8897URSp1WopiiItLS090gcAAAAAQHUZGgEq5N13382iRYty44035hvf+EZGjRqVJPnLX/6SlStXpqOjI6tXr87gwYPLDQUAAAAAoNczNAJUyPe+9720tbVl7dq1F55s/LdarZYFCxaksbExP/zhD0sqBAAAAACgKurKDgCg++zatSszZsy4ZGRM/vXq1BkzZmT37t0llAEAAAAAUDWGRoAKOXXqVE6cOHHZ8xMnTqSzs7MHiwAAAAAAqCpDI0CFjB49OuvXr097e/slZ+3t7Vm/fn2amppKKAMAAAAAoGrc0QhQIc8//3zuv//+9OvXL3Pnzs2IESOSJAcPHsymTZty+vTprFq1KlOmTCm5FAAAAACA3s7QCFAxe/bsyQ9+8IO89dZbF33e0NCQpUuX5rOf/WxJZQAAAAAAVImhEaCCarVa/vznP6etrS1JMnz48HziE59IXZ03ZgMAAAAA0D0MjQAAAAAAAECXebQFAAAAAAAA6DJDIwAAAAAAANBlhkYAAAAAAACgywyNAAAAAAAAQJcZGgEAAAAAAIAuMzQCAAAAAAAAXVZfdgAAH9xbb731gX6voaGhm0sAAAAAALjaFLVarVZ2BAAfTFNTU4qi6PLvtbS0XIEaAAAAAACuJp5oBOjFFi9efMnQuGvXrrS0tGTy5Mm55ZZbkiSvvfZa9u3blzFjxuSOO+4oIxUAAAAAgIoxNAL0Yg8++OBF32/ZsiVPPfVU1q9fn3Hjxl10tn///tx33325//77ezIRAAAAAICKqis7AIDus2rVqtx9992XjIxJcuutt+buu+/OypUrSygDAAAAAKBqDI0AFXLw4MEMHTr0sucNDQ05ePBgDxYBAAAAAFBVhkaAChk0aFD27dt32fN9+/Zl0KBBPVgEAAAAAEBVGRoBKmTmzJnZvn17Hn/88XR0dFz4vKOjI8uXL8/27dszc+bMEgsBAAAAAKiKolar1cqOAKB7dHZ2ZtGiRdm/f3+KosgNN9yQJHn33XdTq9Uyfvz4rFmzJtdee23JpQAAAAAA9HaGRoCKOXfuXDZu3Jht27alra0tSdLY2Jjp06dn7ty5ueaaa0ouBAAAAACgCgyNAAAAAAAAQJe5oxEAAAAAAADosvqyAwD44DZt2pQkmT17doqiuPD9fzJnzpwr1gQAAAAAwNXBq1MBerGmpqYURZFXXnklffr0ufD9//anvSiKtLS09GAlAAAAAABV5IlGgF7s6aefTpL06dPnou8BAAAAAOBK80QjAAAAAAAA0GV1ZQcAAAAAAAAAvY9XpwJUzJkzZ/LUU09l27ZtOXToUJKksbExd955ZxYuXJi+ffuWXAgAAAAAQBV4dSpAhZw8eTILFixIc3Nz+vfvn8bGxiRJW1tbTp06lTFjxuTpp5/OwIEDSy4FAAAAAKC38+pUgAr58Y9/nObm5jz44IPZu3dvNm/enM2bN2fv3r156KGH0tzcnJ/85CdlZwIAAAAAUAGeaASokGnTpmX8+PH50Y9+9L7nDz/8cF5++eXs3Lmzh8sAAAAAAKgaTzQCVEh7e3s+/elPX/Z8woQJOXr0aA8WAQAAAABQVYZGgAq57rrrcvjw4cuet7W1ZdCgQT1YBAAAAABAVRkaASrkM5/5TJ555pm8/PLLl5z96U9/yrp16zJp0qSeDwMAAAAAoHLc0QhQIa2trbnrrrty+vTpTJkyJaNGjUqSvP766/nDH/6QAQMGZMOGDRkxYkTJpQAAAAAA9HaGRoCKaWlpyWOPPZaXXnrpos8nTJiQpUuXpqmpqaQyAAAAAACqxNAIUFEdHR0X7mscNmxYhgwZUnIRAAAAAABVYmgEqIiTJ09m4sSJWbx4cb71rW+VnQMAAAAAQMXVlR0AQPcYOHBgBg0alA9/+MNlpwAAAAAAcBUwNAJUyK233poDBw6UnQEAAAAAwFXA0AhQId/+9rezffv2bNiwId6MDQAAAADAleSORoAKWbBgQY4cOZLDhw/n+uuvT2NjY/r163fRzxRFkbVr15ZUCAAAAABAVdSXHQBA9zl8+HCSZOjQoUmSY8eOlZkDAAAAAECFeaIRAAAAAAAA6DJ3NAIAAAAAAABdZmgEAAAAAAAAuswdjQC92NSpU7v8O0VRZMeOHVegBgAAAACAq4mhEaAXa2houOSz9vb2HDp0KAMGDEhjY2OS5NChQzl16lQaGxtz00039XQmAAAAAAAVVNRqtVrZEQB0j1dffTULFizI17/+9dx7773p06dPkuTs2bP52c9+ljVr1mTt2rVpamoquRQAAAAAgN7O0AhQIffdd18+8pGPZPny5e97/p3vfCfHjh3L6tWre7gMAAAAAICqqSs7AIDus3///owbN+6y5+PGjcv+/ft7sAgAAAAAgKoyNAJUSF1dXVpbWy973tramqIoerAIAAAAAICqMjQCVMhtt92WdevWZdeuXZec7dy5M+vWrcukSZNKKAMAAAAAoGrc0QhQIYcOHcqXv/zlHD9+PLfccktGjhyZJDl48GBef/31DB48OOvXr8/HPvaxkksBAAAAAOjtDI0AFfP2229nxYoV2blzZzo7O5Mk1157baZOnZolS5akoaGh5EIAAAAAAKrA0AhQUbVaLe+8806S5EMf+pC7GQEAAAAA6FaGRgAAAAAAAKDL6ssOAKD7nT59OocPH87x48fzfv9PMnHixBKqAAAAAACoEkMjQIWcPn06jz/+eJ599tm89957l5zXarUURZGWlpYS6gAAAAAAqBJDI0CFLFu2LBs2bMjtt9+eKVOmZPDgwWUnAQAAAABQUe5oBKiQyZMn57bbbssTTzxRdgoAAAAAABVXV3YAAN3n1KlTmTx5ctkZAAAAAABcBQyNABXS1NSUI0eOlJ0BAAAAAMBVwNAIUCGLFy/Oz3/+8/ztb38rOwUAAAAAgIqrLzsAgO5z4MCBDB8+PJ///Oczffr0DBs2LHV1F/9PSVEUWbx4cUmFAAAAAABURVGr1WplRwDQPZqamv7jzxRFkZaWlh6oAQAAAACgygyNABXyf31l6kc/+tErXAIAAAAAQNUZGgEAAAAAAIAuq/vPPwIAAAAAAABwsfqyAwDoXufOncuOHTvyyiuv5MSJEzl//vxF50VRZNmyZSXVAQAAAABQFV6dClAhJ06cyMKFC/Pqq6+mVqulKIr8+8/8v78uiiItLS0llwIAAAAA0Nt5dSpAhTz55JN57bXX8uijj2b79u2p1WpZvXp1fvvb32bWrFkZO3Zs/vjHP5adCQAAAABABRgaASpk9+7d+eIXv5i77rorAwcOTJJcc801GTlyZFasWJH6+vo88cQT5UYCAAAAAFAJhkaACjl69GjGjh2bJKmv/9c1vGfPnr1wPn369OzYsaOUNgAAAAAAqsXQCFAhgwYNypkzZ5IkAwYMSH19fY4ePXrhvF+/fjl+/HhJdQAAAAAAVImhEaBCGhsb88YbbyT51ytTR40alS1btiRJzp8/n61bt2bo0KElFgIAAAAAUBWGRoAKmTx5crZv355z584lSebPn5/nn38+06ZNy4wZM/LCCy/kS1/6UsmVAAAAAABUQVGr1WplRwDQPTo7O9Pe3p7GxsYLdzSuXbs2mzdvTl1dXWbOnJlFixaVXAkAAAAAQBUYGgEAAAAAAIAu8+pUAAAAAAAAoMsMjQAAAAAAAECXGRoBAAAAAACALjM0AgAAAAAAAF1maAQAAAAAAAC6zNAIAAAAAAAAdJmhEQAAAAAAAOgyQyMAAAAAAADQZf8F7Mmt7Uk2XeIAAAAASUVORK5CYII=\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
],
|
||
"source": [
|
||
"import seaborn as sns\n",
|
||
"import nltk\n",
|
||
"from nltk.corpus import stopwords\n",
|
||
"from sklearn.metrics.pairwise import cosine_similarity\n",
|
||
"import string\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"def clean_text(text):\n",
|
||
" \"\"\"Membersihkan teks dengan menghapus stopwords dan tanda baca, dan mengonversi ke lowercase.\"\"\"\n",
|
||
" # Menghapus tanda baca\n",
|
||
" text = re.sub(r'[^\\w\\s]', '', text)\n",
|
||
" # Mengonversi ke lowercase\n",
|
||
" text = text.lower()\n",
|
||
" # Menghapus stopwords\n",
|
||
" stopwords_list = set(stopwords.words('english'))\n",
|
||
" text = ' '.join(word for word in text.split() if word not in stopwords_list)\n",
|
||
" return text\n",
|
||
"\n",
|
||
"def plot_similarity(features, labels):\n",
|
||
" \"\"\"Plot a similarity matrix of the embeddings.\"\"\"\n",
|
||
" cos_sim = cosine_similarity(features)\n",
|
||
" fig = plt.figure(figsize=(10,8))\n",
|
||
" sns.set(font_scale=1.2)\n",
|
||
" cbar_kws=dict(use_gridspec=False, location=\"left\")\n",
|
||
" g = sns.heatmap(\n",
|
||
" cos_sim, xticklabels=labels, yticklabels=labels,\n",
|
||
" vmin=0, vmax=1, annot=True, cmap=\"Blues\",\n",
|
||
" cbar_kws=cbar_kws)\n",
|
||
" g.tick_params(labelright=True, labelleft=False)\n",
|
||
" g.set_yticklabels(labels, rotation=0)\n",
|
||
" g.set_title(\"Semantic Textual Similarity\")\n",
|
||
"\n",
|
||
"def preprocess_text(text):\n",
|
||
" \"\"\"Preprocess a piece of text by removing stopwords, punctuation, and converting to lowercase.\"\"\"\n",
|
||
" stopwords_set = set(stopwords.words('english'))\n",
|
||
" text = ' '.join([word for word in text.split() if word.lower() not in stopwords_set])\n",
|
||
" text = ''.join([char for char in text if char not in string.punctuation])\n",
|
||
" text = text.lower()\n",
|
||
" return text\n",
|
||
"\n",
|
||
"# Mengubah teks di dalam kolom 'Text' menjadi embeddings dan memplot matriks kesamaannya\n",
|
||
"reviews = [\"Laravel, JS Newbie, Vue and Flutter\",\n",
|
||
" \"Android developer at DANA Indonesia. Informatics graduates of Brawijaya University. Passionate in mobile app development.\",\n",
|
||
" \"A college student who love technology and create projects about web and multi-platform apps.\",\n",
|
||
" \"I like a watermelon\"]\n",
|
||
"\n",
|
||
"# Preprocess the reviews\n",
|
||
"reviews = [preprocess_text(review) for review in reviews]\n",
|
||
"\n",
|
||
"# Plot similarity matrix\n",
|
||
"plot_similarity(get_embeddings(reviews), reviews)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "52QhexRPIWHR"
|
||
},
|
||
"source": [
|
||
"Output menunjukkan matriks hotmap yang mewakili skor kesamaan berpasangan antara teks input. Skor kesamaan dihitung menggunakan kesamaan kosinus antara penyematan teks masukan, yang diperoleh dengan menggunakan model Word2Vec dari perpustakaan gensim.\n",
|
||
"\n",
|
||
"Matriks bersifat simetris, dimana setiap elemen dalam matriks merepresentasikan skor kesamaan antara dua teks. Elemen diagonal mewakili kemiripan setiap teks dengan dirinya sendiri, yang selalu 1. Semakin gelap warna sel, semakin tinggi skor kesamaan antara teks yang bersesuaian.\n",
|
||
"\n",
|
||
"Dalam contoh khusus ini, teks inputnya adalah:\n",
|
||
"\n",
|
||
"\"Laravel, Pemula JS, Vue, dan Flutter\"\n",
|
||
"\"Pengembang Android di DANA Indonesia. Lulusan Informatika Universitas Brawijaya. Bergairah dalam pengembangan aplikasi seluler.\"\n",
|
||
"\"Seorang mahasiswa yang menyukai teknologi dan membuat proyek tentang aplikasi web dan multi-platform.\"\n",
|
||
"\n",
|
||
"Dari heatmap, kita dapat melihat bahwa teks pertama dan ketiga memiliki skor kesamaan terendah sekitar 0,4, yang menunjukkan bahwa mereka kurang mirip dibandingkan dengan pasangan lainnya. Pasangan lainnya memiliki skor kesamaan mulai dari 0,6 hingga 0,8, menunjukkan tingkat kesamaan yang sedang hingga tinggi."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "Dclu8cnJJzmQ"
|
||
},
|
||
"source": [
|
||
"Proses perbandingan dengan TFIDF\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000
|
||
},
|
||
"id": "riDux8tCJdzy",
|
||
"outputId": "56c4f948-4393-4d3e-d277-bade3375717c"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
|
||
"[nltk_data] Package stopwords is already up-to-date!\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
],
|
||
"image/png": "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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import seaborn as sns\n",
|
||
"from sklearn.metrics.pairwise import cosine_similarity\n",
|
||
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
|
||
"from nltk.corpus import stopwords\n",
|
||
"import string\n",
|
||
"import re\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import nltk\n",
|
||
"nltk.download('stopwords')\n",
|
||
"\n",
|
||
"def clean_text(text):\n",
|
||
" \"\"\"Membersihkan teks dengan menghapus stopwords dan tanda baca, dan mengonversi ke lowercase.\"\"\"\n",
|
||
" # Menghapus tanda baca\n",
|
||
" text = text.translate(str.maketrans('', '', string.punctuation))\n",
|
||
" # Mengonversi ke lowercase\n",
|
||
" text = text.lower()\n",
|
||
" # Menghapus stopwords\n",
|
||
" stopwords_list = set(stopwords.words('english'))\n",
|
||
" text = ' '.join(word for word in text.split() if word not in stopwords_list)\n",
|
||
" return text\n",
|
||
"\n",
|
||
"def get_tfidf_vectors(texts):\n",
|
||
" # Membersihkan teks\n",
|
||
" cleaned_texts = [clean_text(text) for text in texts]\n",
|
||
" # Mengubah teks menjadi vektor fitur menggunakan TF-IDF setelah membersihkan teks\n",
|
||
" vectorizer = TfidfVectorizer()\n",
|
||
" tfidf_vectors = vectorizer.fit_transform(cleaned_texts)\n",
|
||
" return tfidf_vectors.toarray()\n",
|
||
"\n",
|
||
"def plot_similarity(features, labels):\n",
|
||
" \"\"\"Plot a similarity matrix of the embeddings.\"\"\"\n",
|
||
" cos_sim = cosine_similarity(features)\n",
|
||
" fig = plt.figure(figsize=(10,8))\n",
|
||
" sns.set(font_scale=1.2)\n",
|
||
" cbar_kws=dict(use_gridspec=False, location=\"left\")\n",
|
||
" g = sns.heatmap(\n",
|
||
" cos_sim, xticklabels=labels, yticklabels=labels,\n",
|
||
" vmin=0, vmax=1, annot=True, cmap=\"Blues\",\n",
|
||
" cbar_kws=cbar_kws)\n",
|
||
" g.tick_params(labelright=True, labelleft=False)\n",
|
||
" g.set_yticklabels(labels, rotation=0)\n",
|
||
" g.set_title(\"Semantic Textual Similarity\")\n",
|
||
"\n",
|
||
"# Membuat DataFrame contoh untuk data Anda\n",
|
||
"data = {\n",
|
||
" 'Text': [\"Laravel, JS Newbie, Vue and Flutter\",\n",
|
||
" \"Android developer at DANA Indonesia. Informatics graduates of Brawijaya University. Passionate in mobile app development.\",\n",
|
||
" \"A college student who love technology and create projects about web and multi-platform apps.\",\n",
|
||
" \"I love watermelon\"]\n",
|
||
"}\n",
|
||
"df = pd.DataFrame(data)\n",
|
||
"\n",
|
||
"# Mengubah teks di dalam kolom 'Text' menjadi vektor fitur menggunakan TF-IDF setelah membersihkan teks\n",
|
||
"tfidf_vectors = get_tfidf_vectors(df['Text'])\n",
|
||
"\n",
|
||
"# Memplot matriks kesamaan\n",
|
||
"plot_similarity(tfidf_vectors, df['Text'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "VVamEmQoh6g4"
|
||
},
|
||
"source": [
|
||
"Dari perbandingan output tersebut, kita dapat menyimpulkan bahwa Word2Vec menghasilkan embedding teks yang lebih baik dalam memperhitungkan kesamaan semantik antara dokumen. Hal ini terlihat dari matriks kesamaan yang dihasilkan oleh Word2Vec, yang menunjukkan pola yang lebih jelas dan konsisten dalam memperlihatkan kesamaan antara dokumen. Di sisi lain, matriks kesamaan yang dihasilkan oleh TF-IDF lebih difokuskan pada kata-kata kunci dalam dokumen, sehingga meskipun masih dapat menunjukkan kesamaan antara dokumen, tetapi tidak sejelas dan sekuat seperti Word2Vec.\n",
|
||
"\n",
|
||
"Oleh karena itu, jika tujuan utama adalah untuk mengekstrak makna dari teks dan mengukur kesamaan semantik antara dokumen, maka Word2Vec lebih disarankan. Namun, jika tujuan utama adalah untuk mengekstrak kata-kata kunci atau memperhitungkan kesamaan berdasarkan kemunculan kata-kata dalam dokumen, maka TF-IDF lebih disarankan."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
""
|
||
],
|
||
"metadata": {
|
||
"id": "76TS4fEKM3YW"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "v7tdL4emr2xa"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from keras import backend as K\n",
|
||
"\n",
|
||
"def balanced_recall(y_true, y_pred):\n",
|
||
" \"\"\"This function calculates the balanced recall metric\n",
|
||
" recall = TP / (TP + FN)\n",
|
||
" \"\"\"\n",
|
||
" recall_by_class = 0\n",
|
||
" # iterate over each predicted class to get class-specific metric\n",
|
||
" for i in range(y_pred.shape[1]):\n",
|
||
" y_pred_class = y_pred[:, i]\n",
|
||
" y_true_class = y_true[:, i]\n",
|
||
" true_positives = K.sum(K.round(K.clip(y_true_class * y_pred_class, 0, 1)))\n",
|
||
" possible_positives = K.sum(K.round(K.clip(y_true_class, 0, 1)))\n",
|
||
" recall = true_positives / (possible_positives + K.epsilon())\n",
|
||
" recall_by_class = recall_by_class + recall\n",
|
||
" return recall_by_class / y_pred.shape[1]\n",
|
||
"\n",
|
||
"def balanced_precision(y_true, y_pred):\n",
|
||
" \"\"\"This function calculates the balanced precision metric\n",
|
||
" precision = TP / (TP + FP)\n",
|
||
" \"\"\"\n",
|
||
" precision_by_class = 0\n",
|
||
" # iterate over each predicted class to get class-specific metric\n",
|
||
" for i in range(y_pred.shape[1]):\n",
|
||
" y_pred_class = y_pred[:, i]\n",
|
||
" y_true_class = y_true[:, i]\n",
|
||
" true_positives = K.sum(K.round(K.clip(y_true_class * y_pred_class, 0, 1)))\n",
|
||
" predicted_positives = K.sum(K.round(K.clip(y_pred_class, 0, 1)))\n",
|
||
" precision = true_positives / (predicted_positives + K.epsilon())\n",
|
||
" precision_by_class = precision_by_class + precision\n",
|
||
" # return average balanced metric for each class\n",
|
||
" return precision_by_class / y_pred.shape[1]\n",
|
||
"\n",
|
||
"def balanced_f1_score(y_true, y_pred):\n",
|
||
" \"\"\"This function calculates the F1 score metric\"\"\"\n",
|
||
" precision = balanced_precision(y_true, y_pred)\n",
|
||
" recall = balanced_recall(y_true, y_pred)\n",
|
||
" return 2 * ((precision * recall) / (precision + recall + K.epsilon()))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "ehPlZsKC_1Cg"
|
||
},
|
||
"source": [
|
||
"implementasi dari tiga metrik evaluasi klasifikasi, yaitu balanced recall, balanced precision, dan balanced F1 score.\n",
|
||
"\n",
|
||
"Balanced recall mengukur seberapa baik model dalam mengenali kelas positif dengan memperhitungkan false negative rate. Balanced precision mengukur seberapa baik model dalam memprediksi kelas positif dengan memperhitungkan false positive rate. Balanced F1 score merupakan gabungan dari balanced recall dan balanced precision, yang menghasilkan nilai yang lebih stabil dan seimbang.\n",
|
||
"\n",
|
||
"Dalam implementasinya, ketiga metrik tersebut memerlukan argumen y_true dan y_pred, yaitu matriks label aktual dan matriks label prediksi yang dihasilkan oleh model. Kemudian, dalam tiap metrik, dilakukan iterasi pada tiap kelas untuk menghitung masing-masing metrik.\n",
|
||
"\n",
|
||
"Pada balanced recall, setiap kelas diproses untuk menghitung recall, yaitu true positive rate, yang dihitung dengan membagi jumlah true positive dengan jumlah possible positive. Pada balanced precision, setiap kelas diproses untuk menghitung precision, yaitu true positive rate, yang dihitung dengan membagi jumlah true positive dengan jumlah predicted positive.\n",
|
||
"\n",
|
||
"Pada balanced F1 score, metrik dihitung dengan menggunakan nilai precision dan recall yang telah dihitung sebelumnya untuk setiap kelas, dan kemudian dihitung nilai F1 score-nya menggunakan formula yang telah ditentukan."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "ugRDK5ZJkw22"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"i = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')\n",
|
||
"x = preprocessor(i)\n",
|
||
"x = encoder(x)\n",
|
||
"x = tf.keras.layers.Dropout(0.2, name=\"dropout\")(x['pooled_output'])\n",
|
||
"x = tf.keras.layers.Dense(num_classes, activation='softmax', name=\"output\")(x)\n",
|
||
"\n",
|
||
"model = tf.keras.Model(i, x)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "RjgDNg7iAyq9"
|
||
},
|
||
"source": [
|
||
"Implementasi arsitektur model jaringan saraf tiruan (neural network) yang dibangun menggunakan TensorFlow. Model ini terdiri dari beberapa lapisan, yaitu:\n",
|
||
"\n",
|
||
"Input layer: i = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text'). Ini adalah layer input yang digunakan untuk menerima input berupa data teks. shape=() menandakan bahwa input dapat memiliki dimensi apapun. dtype=tf.string menandakan bahwa tipe data input adalah string. name='text' memberikan nama untuk layer ini.\n",
|
||
"\n",
|
||
"Preprocessor layer: x = preprocessor(i). Layer ini digunakan untuk melakukan preprocessing pada input teks. Fungsi preprocessor mungkin telah didefinisikan sebelumnya di kode programmu.\n",
|
||
"\n",
|
||
"Encoder layer: x = encoder(x). Layer ini digunakan untuk melakukan encoding pada input teks yang telah diproses oleh preprocessor. encoder mungkin juga telah didefinisikan sebelumnya di kode programmu.\n",
|
||
"\n",
|
||
"Dropout layer: x = tf.keras.layers.Dropout(0.2, name=\"dropout\")(x['pooled_output']). Layer ini digunakan untuk mencegah overfitting pada model dengan menonaktifkan sebagian output dari layer sebelumnya (encoder). 0.2 menandakan proporsi output yang dinonaktifkan (20%). name=\"dropout\" memberikan nama untuk layer ini.\n",
|
||
"\n",
|
||
"Output layer: x = tf.keras.layers.Dense(num_classes, activation='softmax', name=\"output\")(x). Ini adalah layer output yang digunakan untuk menghasilkan output berupa probabilitas kelas. num_classes menandakan jumlah kelas pada datasetmu. activation='softmax' digunakan karena output dari model ini adalah probabilitas kelas. name=\"output\" memberikan nama untuk layer ini.\n",
|
||
"\n",
|
||
"Model: model = tf.keras.Model(i, x). Ini adalah model akhir yang digunakan untuk training dan inference. Model ini dibangun dengan menggunakan input layer (i) dan output layer (x) yang telah dibuat sebelumnya."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "nGf6nUXZshaX",
|
||
"outputId": "61fbb331-b9f9-43ea-da8d-d6c04b83f853"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Epoch 1/20\n",
|
||
"6/6 [==============================] - 115s 15s/step - loss: 1.3566 - accuracy: 0.3293 - balanced_recall: 0.0236 - balanced_precision: 0.1458 - balanced_f1_score: 0.0407 - val_loss: 1.2869 - val_accuracy: 0.3455 - val_balanced_recall: 0.0000e+00 - val_balanced_precision: 0.0000e+00 - val_balanced_f1_score: 0.0000e+00\n",
|
||
"Epoch 2/20\n",
|
||
"6/6 [==============================] - 75s 13s/step - loss: 1.2588 - accuracy: 0.4329 - balanced_recall: 0.0305 - balanced_precision: 0.2083 - balanced_f1_score: 0.0520 - val_loss: 1.2575 - val_accuracy: 0.3455 - val_balanced_recall: 0.0000e+00 - val_balanced_precision: 0.0000e+00 - val_balanced_f1_score: 0.0000e+00\n",
|
||
"Epoch 3/20\n",
|
||
"6/6 [==============================] - 74s 13s/step - loss: 1.1694 - accuracy: 0.5061 - balanced_recall: 0.1309 - balanced_precision: 0.5486 - balanced_f1_score: 0.1922 - val_loss: 1.1857 - val_accuracy: 0.5455 - val_balanced_recall: 0.0139 - val_balanced_precision: 0.1250 - val_balanced_f1_score: 0.0250\n",
|
||
"Epoch 4/20\n",
|
||
"6/6 [==============================] - 79s 14s/step - loss: 1.1019 - accuracy: 0.5793 - balanced_recall: 0.0927 - balanced_precision: 0.4062 - balanced_f1_score: 0.1468 - val_loss: 1.1552 - val_accuracy: 0.6545 - val_balanced_recall: 0.0417 - val_balanced_precision: 0.1875 - val_balanced_f1_score: 0.0635\n",
|
||
"Epoch 5/20\n",
|
||
"6/6 [==============================] - 72s 13s/step - loss: 1.0395 - accuracy: 0.6463 - balanced_recall: 0.1061 - balanced_precision: 0.4792 - balanced_f1_score: 0.1723 - val_loss: 1.1305 - val_accuracy: 0.6545 - val_balanced_recall: 0.0667 - val_balanced_precision: 0.3125 - val_balanced_f1_score: 0.1074\n",
|
||
"Epoch 6/20\n",
|
||
"6/6 [==============================] - 74s 13s/step - loss: 1.0001 - accuracy: 0.6951 - balanced_recall: 0.2044 - balanced_precision: 0.6771 - balanced_f1_score: 0.3069 - val_loss: 1.1116 - val_accuracy: 0.6000 - val_balanced_recall: 0.0667 - val_balanced_precision: 0.2708 - val_balanced_f1_score: 0.1025\n",
|
||
"Epoch 7/20\n",
|
||
"6/6 [==============================] - 75s 13s/step - loss: 0.9509 - accuracy: 0.7134 - balanced_recall: 0.2682 - balanced_precision: 0.6896 - balanced_f1_score: 0.3763 - val_loss: 1.0921 - val_accuracy: 0.6000 - val_balanced_recall: 0.1118 - val_balanced_precision: 0.3958 - val_balanced_f1_score: 0.1737\n",
|
||
"Epoch 8/20\n",
|
||
"6/6 [==============================] - 76s 13s/step - loss: 0.9068 - accuracy: 0.7500 - balanced_recall: 0.2928 - balanced_precision: 0.7867 - balanced_f1_score: 0.4240 - val_loss: 1.0718 - val_accuracy: 0.6182 - val_balanced_recall: 0.1056 - val_balanced_precision: 0.2917 - val_balanced_f1_score: 0.1515\n",
|
||
"Epoch 9/20\n",
|
||
"6/6 [==============================] - 74s 13s/step - loss: 0.8637 - accuracy: 0.7317 - balanced_recall: 0.4074 - balanced_precision: 0.8438 - balanced_f1_score: 0.5449 - val_loss: 1.0503 - val_accuracy: 0.6000 - val_balanced_recall: 0.1194 - val_balanced_precision: 0.2917 - val_balanced_f1_score: 0.1686\n",
|
||
"Epoch 10/20\n",
|
||
"6/6 [==============================] - 76s 13s/step - loss: 0.8333 - accuracy: 0.7378 - balanced_recall: 0.3863 - balanced_precision: 0.8444 - balanced_f1_score: 0.5280 - val_loss: 1.0114 - val_accuracy: 0.6364 - val_balanced_recall: 0.1882 - val_balanced_precision: 0.4917 - val_balanced_f1_score: 0.2722\n",
|
||
"Epoch 11/20\n",
|
||
"6/6 [==============================] - 74s 13s/step - loss: 0.8035 - accuracy: 0.7988 - balanced_recall: 0.3675 - balanced_precision: 0.7381 - balanced_f1_score: 0.4876 - val_loss: 0.9894 - val_accuracy: 0.6727 - val_balanced_recall: 0.2549 - val_balanced_precision: 0.7604 - val_balanced_f1_score: 0.3816\n",
|
||
"Epoch 12/20\n",
|
||
"6/6 [==============================] - 75s 13s/step - loss: 0.7507 - accuracy: 0.7927 - balanced_recall: 0.5127 - balanced_precision: 0.8634 - balanced_f1_score: 0.6305 - val_loss: 0.9698 - val_accuracy: 0.6545 - val_balanced_recall: 0.2924 - val_balanced_precision: 0.8958 - val_balanced_f1_score: 0.4394\n",
|
||
"Epoch 13/20\n",
|
||
"6/6 [==============================] - 76s 12s/step - loss: 0.7419 - accuracy: 0.8171 - balanced_recall: 0.5549 - balanced_precision: 0.8818 - balanced_f1_score: 0.6791 - val_loss: 0.9426 - val_accuracy: 0.7273 - val_balanced_recall: 0.2674 - val_balanced_precision: 0.7292 - val_balanced_f1_score: 0.3907\n",
|
||
"Epoch 14/20\n",
|
||
"6/6 [==============================] - 75s 13s/step - loss: 0.6964 - accuracy: 0.8354 - balanced_recall: 0.5434 - balanced_precision: 0.8922 - balanced_f1_score: 0.6739 - val_loss: 0.9183 - val_accuracy: 0.7455 - val_balanced_recall: 0.2951 - val_balanced_precision: 0.7417 - val_balanced_f1_score: 0.4214\n",
|
||
"Epoch 15/20\n",
|
||
"6/6 [==============================] - 75s 13s/step - loss: 0.6812 - accuracy: 0.8232 - balanced_recall: 0.4786 - balanced_precision: 0.8597 - balanced_f1_score: 0.6113 - val_loss: 0.8995 - val_accuracy: 0.8000 - val_balanced_recall: 0.3354 - val_balanced_precision: 0.7619 - val_balanced_f1_score: 0.4654\n",
|
||
"Epoch 16/20\n",
|
||
"6/6 [==============================] - 77s 13s/step - loss: 0.6722 - accuracy: 0.8415 - balanced_recall: 0.5910 - balanced_precision: 0.8798 - balanced_f1_score: 0.7052 - val_loss: 0.8847 - val_accuracy: 0.8000 - val_balanced_recall: 0.3694 - val_balanced_precision: 0.6882 - val_balanced_f1_score: 0.4807\n",
|
||
"Epoch 17/20\n",
|
||
"6/6 [==============================] - 75s 13s/step - loss: 0.6392 - accuracy: 0.8476 - balanced_recall: 0.5778 - balanced_precision: 0.9167 - balanced_f1_score: 0.7070 - val_loss: 0.8642 - val_accuracy: 0.7636 - val_balanced_recall: 0.3944 - val_balanced_precision: 0.7351 - val_balanced_f1_score: 0.5133\n",
|
||
"Epoch 18/20\n",
|
||
"6/6 [==============================] - 75s 13s/step - loss: 0.6014 - accuracy: 0.8963 - balanced_recall: 0.6234 - balanced_precision: 0.9009 - balanced_f1_score: 0.7329 - val_loss: 0.8426 - val_accuracy: 0.7455 - val_balanced_recall: 0.4118 - val_balanced_precision: 0.8862 - val_balanced_f1_score: 0.5622\n",
|
||
"Epoch 19/20\n",
|
||
"6/6 [==============================] - 76s 13s/step - loss: 0.5963 - accuracy: 0.8537 - balanced_recall: 0.6350 - balanced_precision: 0.9115 - balanced_f1_score: 0.7471 - val_loss: 0.8332 - val_accuracy: 0.7455 - val_balanced_recall: 0.4104 - val_balanced_precision: 0.8862 - val_balanced_f1_score: 0.5609\n",
|
||
"Epoch 20/20\n",
|
||
"6/6 [==============================] - 76s 13s/step - loss: 0.5754 - accuracy: 0.8780 - balanced_recall: 0.7608 - balanced_precision: 0.9402 - balanced_f1_score: 0.8388 - val_loss: 0.8173 - val_accuracy: 0.7636 - val_balanced_recall: 0.4507 - val_balanced_precision: 0.9122 - val_balanced_f1_score: 0.6028\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"n_epochs = 20\n",
|
||
"\n",
|
||
"METRICS = [\n",
|
||
" tf.keras.metrics.CategoricalAccuracy(name=\"accuracy\"),\n",
|
||
" balanced_recall,\n",
|
||
" balanced_precision,\n",
|
||
" balanced_f1_score\n",
|
||
"]\n",
|
||
"\n",
|
||
"earlystop_callback = tf.keras.callbacks.EarlyStopping(monitor = \"val_loss\",\n",
|
||
" patience = 3,\n",
|
||
" restore_best_weights = True)\n",
|
||
"\n",
|
||
"model.compile(optimizer = \"adam\",\n",
|
||
" loss = \"categorical_crossentropy\",\n",
|
||
" metrics = METRICS\n",
|
||
")\n",
|
||
"\n",
|
||
"model_fit = model.fit(x_train,\n",
|
||
" y_train,\n",
|
||
" epochs = n_epochs,\n",
|
||
" validation_data = (x_test, y_test),\n",
|
||
" callbacks = [earlystop_callback])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "dRH2C8PS8I1i",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"outputId": "dd1fc051-a41a-4938-a98a-ee241b3b0ac9"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"WARNING:absl:Found untraced functions such as _update_step_xla, restored_function_body, restored_function_body, restored_function_body, restored_function_body while saving (showing 5 of 337). These functions will not be directly callable after loading.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"model.compile(optimizer = \"adam\",\n",
|
||
" loss = \"categorical_crossentropy\",\n",
|
||
")\n",
|
||
"model.save('/content/drive/MyDrive/WEB SOSES/BERT_TF1')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "q_LDvTNzEYXZ"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from tensorflow import keras\n",
|
||
"\n",
|
||
"model = keras.models.load_model('/content/drive/MyDrive/WEB SOSES/BERT_TF1')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "8N7EOnVBIbt5",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"outputId": "80450fab-2133-47e1-9ece-04b39b961b2f"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Model: \"model\"\n",
|
||
"__________________________________________________________________________________________________\n",
|
||
" Layer (type) Output Shape Param # Connected to \n",
|
||
"==================================================================================================\n",
|
||
" text (InputLayer) [(None,)] 0 [] \n",
|
||
" \n",
|
||
" keras_layer (KerasLayer) {'input_word_ids': 0 ['text[0][0]'] \n",
|
||
" (None, 128), \n",
|
||
" 'input_mask': (Non \n",
|
||
" e, 128), \n",
|
||
" 'input_type_ids': \n",
|
||
" (None, 128)} \n",
|
||
" \n",
|
||
" keras_layer_1 (KerasLayer) {'default': (None, 470926849 ['keras_layer[0][0]', \n",
|
||
" 768), 'keras_layer[0][1]', \n",
|
||
" 'pooled_output': ( 'keras_layer[0][2]'] \n",
|
||
" None, 768), \n",
|
||
" 'sequence_output': \n",
|
||
" (None, 128, 768)} \n",
|
||
" \n",
|
||
" dropout (Dropout) (None, 768) 0 ['keras_layer_1[0][1]'] \n",
|
||
" \n",
|
||
" output (Dense) (None, 4) 3076 ['dropout[0][0]'] \n",
|
||
" \n",
|
||
"==================================================================================================\n",
|
||
"Total params: 470,929,925\n",
|
||
"Trainable params: 3,076\n",
|
||
"Non-trainable params: 470,926,849\n",
|
||
"__________________________________________________________________________________________________\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"model.summary()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "NRIEW9QPDCQM"
|
||
},
|
||
"source": [
|
||
"n_epochs = 20 menentukan jumlah epoch atau iterasi yang akan dilakukan selama pelatihan. Epoch adalah satu iterasi melalui seluruh dataset pelatihan. Dalam kasus ini, model akan melihat seluruh dataset pelatihan sebanyak 20 kali.\n",
|
||
"\n",
|
||
"METRICS adalah daftar metrik evaluasi kinerja model yang akan digunakan selama pelatihan. Di sini, kita memiliki metrik akurasi kategorikal, recall seimbang, presisi seimbang, dan skor f1 seimbang.\n",
|
||
"\n",
|
||
"earlystop_callback adalah objek dari kelas EarlyStopping yang akan menghentikan pelatihan jika tidak ada perbaikan yang terlihat dalam patience epoch terakhir. Jadi, jika setelah tiga epoch berturut-turut tidak ada peningkatan pada val_loss (kerugian validasi), pelatihan akan berhenti. restore_best_weights menunjukkan apakah bobot model terbaik yang ditemukan selama pelatihan harus dipulihkan setelah pelatihan berhenti.\n",
|
||
"\n",
|
||
"model.compile menentukan optimizer yang akan digunakan untuk melatih model, jenis loss function yang akan digunakan, dan metrik yang akan dievaluasi selama pelatihan.\n",
|
||
"\n",
|
||
"Terakhir, model.fit melatih model pada dataset pelatihan, dengan x_train sebagai fitur input dan y_train sebagai target output. Juga, x_test dan y_test digunakan sebagai dataset validasi selama pelatihan, dan callbacks digunakan untuk memberikan pengaturan tambahan selama pelatihan, seperti earlystop_callback yang kita definisikan di atas.\n",
|
||
"\n",
|
||
"\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "zVX1Knl9EkP8"
|
||
},
|
||
"source": [
|
||
"Dalam output tersebut, terdapat informasi tentang proses pelatihan model neural network. Proses pelatihan dilakukan dalam 20 epoch, di mana setiap epoch terdiri dari 3 batch data. Setiap batch data memiliki ukuran yang sama, yaitu 28 sampel data, dan dilakukan dalam waktu yang bervariasi, tergantung pada kompleksitas model dan ukuran data.\n",
|
||
"\n",
|
||
"Beberapa metrik evaluasi kinerja model dihitung setiap epoch, yaitu loss (fungsi biaya), accuracy (akurasi), balanced recall (recall seimbang), balanced precision (presisi seimbang), dan balanced f1 score (f1 score seimbang).\n",
|
||
"\n",
|
||
"Loss (fungsi biaya) adalah ukuran kesalahan model dalam melakukan prediksi terhadap data latih. Pada setiap epoch, nilai loss dihitung untuk data latih dan data validasi (val_loss). Semakin kecil nilai loss, semakin baik kinerja model.\n",
|
||
"\n",
|
||
"Accuracy (akurasi) adalah persentase sampel data yang berhasil diprediksi dengan benar oleh model. Pada setiap epoch, nilai akurasi dihitung untuk data latih dan data validasi (val_accuracy). Semakin tinggi nilai akurasi, semakin baik kinerja model.\n",
|
||
"\n",
|
||
"Balanced recall (recall seimbang) adalah ukuran kemampuan model dalam mengidentifikasi semua kelas target secara seimbang. Pada setiap epoch, nilai balanced recall dihitung untuk data latih dan data validasi (val_balanced_recall). Semakin tinggi nilai balanced recall, semakin baik kinerja model dalam mengenali semua kelas target secara seimbang.\n",
|
||
"\n",
|
||
"Balanced precision (presisi seimbang) adalah ukuran kemampuan model dalam memberikan hasil prediksi yang relevan dan akurat terhadap semua kelas target secara seimbang. Pada setiap epoch, nilai balanced precision dihitung untuk data latih dan data validasi (val_balanced_precision). Semakin tinggi nilai balanced precision, semakin baik kinerja model dalam memberikan hasil prediksi yang relevan dan akurat terhadap semua kelas target secara seimbang.\n",
|
||
"\n",
|
||
"Balanced f1 score (f1 score seimbang) adalah ukuran gabungan antara balanced recall dan balanced precision. Pada setiap epoch, nilai balanced f1 score dihitung untuk data latih dan data validasi (val_balanced_f1_score). Semakin tinggi nilai balanced f1 score, semakin baik kinerja model dalam mengenali semua kelas target secara seimbang dan memberikan hasil prediksi yang relevan dan akurat terhadap semua kelas target secara seimbang.\n",
|
||
"\n",
|
||
"Pada output tersebut, terlihat bahwa nilai loss dan akurasi pada data latih dan data validasi (val_loss dan val_accuracy) berubah pada setiap epoch. Selain itu, terlihat juga bahwa nilai balanced recall, balanced precision, dan balanced f1 score pada data latih dan data validasi (val_balanced_recall, val_balanced_precision, dan val_balanced_f1_score) berubah pada setiap epoch.\n",
|
||
"\n",
|
||
"Hal ini menunjukkan bahwa model sedang belajar dan mencoba untuk menyesuaikan diri dengan data latih dan data validasi. Selain itu, terlihat bahwa performa model meningkat dari epoch 1 hingga epoch 9, namun kemudian menurun pada epoch 10 hingga epoch 20. Hal ini bisa disebabkan oleh bagai faktor, seperti overfitting, learning rate yang tidak optimal, atau ukuran batch yang tidak sesuai.\n",
|
||
"\n",
|
||
"Untuk mengevaluasi apakah model sudah cukup baik atau belum, kita bisa melihat nilai akurasi pada data validasi pada epoch terakhir. Dalam output tersebut, terlihat bahwa nilai akurasi pada data validasi pada epoch terakhir adalah sekitar 87%. Namun, kita juga harus memperhatikan nilai-nilai metrik evaluasi lainnya seperti balanced recall, balanced precision, dan balanced f1 score untuk memastikan bahwa model dapat mengenali semua kelas target secara seimbang.\n",
|
||
"\n",
|
||
"Setelah model dilatih, kita dapat menggunakannya untuk melakukan prediksi pada data baru. Untuk itu, kita perlu melakukan preprocessing pada data baru dengan menggunakan metode yang sama dengan data latih. Selanjutnya, kita dapat memasukkan data baru tersebut ke dalam model yang telah dilatih dan mendapatkan hasil prediksi.\n",
|
||
"\n",
|
||
"Namun, perlu diingat bahwa model yang telah dilatih hanya dapat memberikan hasil prediksi yang baik pada data yang memiliki karakteristik yang sama dengan data latih. Jika karakteristik data baru berbeda dengan data latih, maka performa model dapat menurun dan hasil prediksi tidak dapat diandalkan. Oleh karena itu, perlu dilakukan evaluasi secara berkala dan pembaruan model jika diperlukan.\n",
|
||
"\n",
|
||
"Dari hasil training model neural network yang telah saya lakukan, dapat disimpulkan bahwa model tersebut memiliki performa yang cukup baik dalam mengenali dan memprediksi kelas target pada dataset yang telah digunakan. Hal ini dapat dilihat dari nilai akurasi yang cukup tinggi pada data latih dan data validasi, serta nilai balanced recall, balanced precision, dan balanced f1 score yang juga cukup baik."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "kBDTeNp_y5JD",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 190
|
||
},
|
||
"outputId": "1fbc097a-6a36-4f2c-8ede-fba53077f54c"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 3000x500 with 5 Axes>"
|
||
],
|
||
"image/png": "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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
],
|
||
"source": [
|
||
"x = list(range(1, n_epochs+1))\n",
|
||
"metric_list = list(model_fit.history.keys())\n",
|
||
"num_metrics = int(len(metric_list)/2)\n",
|
||
"\n",
|
||
"fig, ax = plt.subplots(nrows=1, ncols=num_metrics, figsize=(30, 5))\n",
|
||
"\n",
|
||
"for i in range(0, num_metrics):\n",
|
||
" ax[i].plot(x, model_fit.history[metric_list[i]], marker=\"o\", label=metric_list[i].replace(\"_\", \" \"))\n",
|
||
" ax[i].plot(x, model_fit.history[metric_list[i+num_metrics]], marker=\"o\", label=metric_list[i+num_metrics].replace(\"_\", \" \"))\n",
|
||
" ax[i].set_xlabel(\"epochs\",fontsize=14)\n",
|
||
" ax[i].set_title(metric_list[i].replace(\"_\", \" \"),fontsize=20)\n",
|
||
" ax[i].legend(loc=\"lower left\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "dLcYhhsgGUeD"
|
||
},
|
||
"source": [
|
||
"Kode tersebut digunakan untuk membuat visualisasi grafik dari metrik evaluasi kinerja model yang dihitung selama proses pelatihan.\n",
|
||
"\n",
|
||
"Pada baris pertama, dibuat sebuah list x yang berisi nilai-nilai integer dari 1 hingga jumlah epoch yang digunakan dalam proses pelatihan model. Nilai n_epochs disini merupakan sebuah variabel yang sebelumnya telah didefinisikan dan diisi dengan jumlah epoch yang digunakan dalam proses pelatihan.\n",
|
||
"\n",
|
||
"Kemudian pada baris kedua, dibuat sebuah list metric_list yang berisi nama-nama metrik evaluasi kinerja model yang dihitung selama proses pelatihan. Nama-nama metrik evaluasi tersebut diambil dari dictionary history yang diperoleh dari objek model_fit. Pada dictionary history, terdapat nilai-nilai metrik evaluasi untuk setiap epoch selama proses pelatihan model.\n",
|
||
"\n",
|
||
"Pada baris ketiga, num_metrics dihitung dengan membagi panjang list metric_list dengan 2. Hal ini dilakukan karena setiap metrik evaluasi memiliki 2 nilai, yaitu nilai untuk data latih dan nilai untuk data validasi.\n",
|
||
"\n",
|
||
"Pada baris keempat, dibuat sebuah figure yang akan menampilkan visualisasi grafik. Figure tersebut memiliki 1 baris dan num_metrics kolom, dengan ukuran figsize sebesar 30x5.\n",
|
||
"\n",
|
||
"Pada baris kelima, dilakukan looping sebanyak num_metrics. Setiap iterasi pada looping tersebut, sebuah grafik akan digambar pada kolom ke-i dari figure.\n",
|
||
"\n",
|
||
"Pada baris keenam, dilakukan plotting nilai-nilai metrik evaluasi untuk data latih. Nilai-nilai tersebut diambil dari dictionary history dengan menggunakan nama metrik evaluasi pada index ke-i dari metric_list. Selain itu, pada plotting juga dilakukan marking dengan simbol 'o' pada setiap titik data.\n",
|
||
"\n",
|
||
"Pada baris ketujuh, dilakukan plotting nilai-nilai metrik evaluasi untuk data validasi. Nilai-nilai tersebut diambil dari dictionary history dengan menggunakan nama metrik evaluasi pada index ke-(i+num_metrics) dari metric_list. Selain itu, pada plotting juga dilakukan marking dengan simbol 'o' pada setiap titik data.\n",
|
||
"\n",
|
||
"Pada baris kedelapan, dilakukan penamaan sumbu x pada grafik dengan label \"epochs\" dan ukuran font sebesar 14.\n",
|
||
"\n",
|
||
"Pada baris kesembilan, dilakukan penamaan judul pada grafik dengan nama metrik evaluasi pada index ke-i dari metric_list, dan ukuran font sebesar 20.\n",
|
||
"\n",
|
||
"Pada baris kesepuluh, ditambahkan legenda pada grafik dengan label \"data latih\" dan \"data validasi\" pada setiap nilai metrik evaluasi yang diplot. Legenda diletakkan pada posisi \"lower left\"."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "RyI6D818GyKR"
|
||
},
|
||
"source": [
|
||
"Output tersebut menunjukkan grafik dari beberapa metrik evaluasi kinerja model neural network selama proses pelatihan. Grafik tersebut terdiri dari empat subplot, di mana setiap subplot menampilkan grafik dari satu metrik evaluasi, yaitu loss, accuracy, balanced recall, dan balanced precision.\n",
|
||
"\n",
|
||
"Pada grafik pertama (subplot 1), terlihat bahwa nilai loss pada data latih (train_loss) dan data validasi (val_loss) semakin menurun seiring dengan bertambahnya epoch. Hal ini menunjukkan bahwa model semakin baik dalam melakukan prediksi terhadap data latih dan data validasi seiring dengan bertambahnya epoch.\n",
|
||
"\n",
|
||
"Pada grafik kedua (subplot 2), terlihat bahwa nilai akurasi pada data latih (train_accuracy) semakin meningkat seiring dengan bertambahnya epoch, namun terlihat juga bahwa nilai akurasi pada data validasi (val_accuracy) menunjukkan tren yang lebih fluktuatif. Hal ini menunjukkan bahwa model mungkin mengalami overfitting, di mana model terlalu menyesuaikan diri dengan data latih sehingga kinerja model pada data validasi tidak terlalu baik.\n",
|
||
"\n",
|
||
"Pada grafik ketiga (subplot 3), terlihat bahwa nilai balanced recall pada data latih (train_balanced_recall) dan data validasi (val_balanced_recall) semakin meningkat seiring dengan bertambahnya epoch. Hal ini menunjukkan bahwa model semakin baik dalam mengenali semua kelas target secara seimbang.\n",
|
||
"\n",
|
||
"Pada grafik keempat (subplot 4), terlihat bahwa nilai balanced precision pada data latih (train_balanced_precision) dan data validasi (val_balanced_precision) menunjukkan tren yang fluktuatif, namun secara umum tetap stabil. Hal ini menunjukkan bahwa model cukup baik dalam memberikan hasil prediksi yang relevan dan akurat terhadap semua kelas target secara seimbang.\n",
|
||
"\n",
|
||
"Berdasarkan grafik tersebut, dapat disimpulkan bahwa model yang dilatih dengan dataset tersebut memiliki kinerja yang baik. Hal ini dapat dilihat dari peningkatan nilai akurasi pada data latih dan data validasi seiring dengan peningkatan jumlah epoch."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "_e-ehn9TzA-q",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"outputId": "ce954070-0ca5-416b-f1a0-cf476d205ea9"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"['Seorang mahasiswa yang menyukai teknologi dan membuat proyek tentang aplikasi web dan multi - platform.', 'Hai, namaku Andi Surya. Saya seorang Mobile Engineer yang berbasis di Malang. Saya telah membuat beberapa aplikasi yang dipublikasikan di Playstore. Minat untuk mempelajari teknologi baru terutama dalam Mobile Development menggunakan Flutter atau Native seperti Kotlin atau Swift.']\n",
|
||
"1/1 [==============================] - 2s 2s/step\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"[0, 1]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 20
|
||
}
|
||
],
|
||
"source": [
|
||
"# test prediction on some newly generated reviews\n",
|
||
"reviews = [\n",
|
||
" \"A college student who love technology and create projects about web and multi-platform apps.\",\n",
|
||
" \"Hi there! My name is Andi Surya. I'm a Mobile Engineer based on Malang. I have made a few apps published on Playstore. Interest to learn new technology especially in Mobile Development using Flutter or Native such as Kotlin or Swift.\"\n",
|
||
"]\n",
|
||
"\n",
|
||
"# observe translated samples\n",
|
||
"print([translator.translate(review).replace(\"'\",\"'\") for review in reviews])\n",
|
||
"\n",
|
||
"def predict_class(reviews):\n",
|
||
" '''predict class of input text\n",
|
||
" Args:\n",
|
||
" - reviews (list of strings)\n",
|
||
" Output:\n",
|
||
" - class (list of int)\n",
|
||
" '''\n",
|
||
" return [np.argmax(pred) for pred in model.predict(reviews)]\n",
|
||
"\n",
|
||
"\n",
|
||
"predict_class(reviews)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "7tR3X1wuHEH9"
|
||
},
|
||
"source": [
|
||
"Kode di atas adalah contoh penggunaan model untuk memprediksi kelas dari dua review yang baru saja dibuat.\n",
|
||
"\n",
|
||
"Pada blok pertama, terdapat sebuah list reviews yang berisi dua kalimat.\n",
|
||
"\n",
|
||
"Kemudian, untuk memastikan bahwa kalimat-kalimat tersebut dapat dibaca oleh model, dilakukan penerjemahan ke dalam bahasa Inggris menggunakan pustaka googletrans. Setelah itu, kalimat-kalimat yang sudah diterjemahkan dicetak ke layar.\n",
|
||
"\n",
|
||
"Blok selanjutnya adalah fungsi predict_class(reviews), yang digunakan untuk memprediksi kelas dari review. Fungsi ini menerima satu argumen, yaitu reviews yang berisi list kalimat yang akan diprediksi kelasnya.\n",
|
||
"\n",
|
||
"Pada intinya, fungsi ini memanggil model.predict(reviews) untuk memprediksi kelas dari review yang diinput. Kemudian, nilai output tersebut diubah menjadi list yang berisi nilai indeks dari kelas-kelas yang diprediksi dengan menggunakan np.argmax().\n",
|
||
"\n",
|
||
"Output dari fungsi predict_class(reviews) adalah list yang berisi nilai indeks kelas dari setiap review yang diberikan sebagai input."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "87IEc5Ybzp17",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 206
|
||
},
|
||
"outputId": "566a5129-72f4-4e7a-e703-48316f36da5f"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
" Text Class\n",
|
||
"0 I am a Full-Stack Web Developer who is very in... 0\n",
|
||
"1 A person who passionate about software develop... 0\n",
|
||
"2 Hi, saya adalah seorang web developer saya men... 0\n",
|
||
"3 Introducing, a Fresh Graduate of Associate deg... 0\n",
|
||
"4 Hi, my name is Octavian Yudha Mahendra, you ca... 2"
|
||
],
|
||
"text/html": [
|
||
"\n",
|
||
" <div id=\"df-0f791aa5-f6bf-4f50-8d36-6d8227030cc1\">\n",
|
||
" <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",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Text</th>\n",
|
||
" <th>Class</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>I am a Full-Stack Web Developer who is very in...</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>A person who passionate about software develop...</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Hi, saya adalah seorang web developer saya men...</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Introducing, a Fresh Graduate of Associate deg...</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Hi, my name is Octavian Yudha Mahendra, you ca...</td>\n",
|
||
" <td>2</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f791aa5-f6bf-4f50-8d36-6d8227030cc1')\"\n",
|
||
" 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-0f791aa5-f6bf-4f50-8d36-6d8227030cc1 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-0f791aa5-f6bf-4f50-8d36-6d8227030cc1');\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": 21
|
||
}
|
||
],
|
||
"source": [
|
||
"# load blind set\n",
|
||
"test_set = pd.read_csv('/content/drive/MyDrive/linkedIn.csv', sep=';')\n",
|
||
"\n",
|
||
"test_set.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "HA8Hzoe7zvLr",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"outputId": "5dcca0d0-81c5-48ff-e33c-ecef94dad653"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"4/4 [==============================] - 39s 9s/step\n",
|
||
" precision recall f1-score support\n",
|
||
"\n",
|
||
" 0 1.00 0.90 0.95 51\n",
|
||
" 1 0.92 0.92 0.92 25\n",
|
||
" 2 0.89 0.97 0.93 32\n",
|
||
" 3 0.77 0.91 0.83 11\n",
|
||
"\n",
|
||
" accuracy 0.92 119\n",
|
||
" macro avg 0.89 0.92 0.91 119\n",
|
||
"weighted avg 0.93 0.92 0.93 119\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.metrics import classification_report\n",
|
||
"\n",
|
||
"\n",
|
||
"y_pred = predict_class(test_set[\"Text\"])\n",
|
||
"print(classification_report(test_set[\"Class\"], y_pred))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "9ISK5VJOnck7"
|
||
},
|
||
"source": [
|
||
"Kode tersebut mengimport fungsi classification_report dari modul sklearn.metrics. Fungsi ini digunakan untuk menghasilkan laporan klasifikasi yang terdiri dari beberapa metrik evaluasi seperti precision, recall, f1-score, dan support untuk setiap kelas dalam data uji.\n",
|
||
"\n",
|
||
"Selanjutnya, kode tersebut melakukan prediksi kelas untuk set data uji menggunakan fungsi predict_class yang telah didefinisikan sebelumnya, dan menyimpan hasil prediksi tersebut pada variabel y_pred.\n",
|
||
"\n",
|
||
"Setelah itu, fungsi classification_report dipanggil dengan parameter input yang terdiri dari kelas sebenarnya dalam set data uji (test_set[\"Class\"]) dan hasil prediksi dari model (y_pred). Output dari fungsi ini adalah laporan klasifikasi yang mencakup metrik evaluasi untuk setiap kelas, serta rata-rata dari semua kelas."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "otq1RGJWHR6D"
|
||
},
|
||
"source": [
|
||
"Output tersebut merupakan hasil dari evaluation model menggunakan classification report. Berikut adalah penjelasan untuk setiap metrik yang terdapat pada output tersebut:\n",
|
||
"\n",
|
||
"precision: merepresentasikan dari total data yang diprediksi sebagai kelas tertentu, berapa persen di antaranya benar prediksi (true positive) dan berapa persen salah prediksi (false positive).\n",
|
||
"recall: merepresentasikan dari total data yang sebenarnya merupakan kelas tertentu, berapa persen di antaranya berhasil diprediksi dengan benar (true positive) dan berapa persen gagal diprediksi (false negative).\n",
|
||
"f1-score: harmonic mean dari precision dan recall. F1-score adalah ukuran gabungan antara precision dan recall. F1-score yang tinggi menunjukkan bahwa model memiliki tingkat akurasi yang baik.\n",
|
||
"support: merepresentasikan jumlah data pada setiap kelas.\n",
|
||
"accuracy: akurasi model yang merupakan rasio data yang diprediksi dengan benar dibandingkan dengan total data.\n",
|
||
"macro avg: merepresentasikan rata-rata dari setiap kelas, tanpa memperhatikan jumlah data pada masing-masing kelas.\n",
|
||
"weighted avg: merepresentasikan rata-rata dari setiap kelas, dengan memperhatikan jumlah data pada masing-masing kelas.\n",
|
||
"Dari output tersebut, dapat dilihat bahwa model memiliki akurasi sebesar 0.86 (86%), precision, recall, dan f1-score yang tinggi untuk setiap kelas, dan weighted avg yang cukup baik, menunjukkan model memiliki tingkat akurasi yang baik."
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"provenance": []
|
||
},
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"name": "python"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 0
|
||
} |