{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The watermark extension is already loaded. To reload it, use:\n",
" %reload_ext watermark\n",
"Romell D.Z. \n",
"last updated: 2019-01-20 \n",
"\n",
"numpy 1.14.6\n",
"pandas 0.23.4\n",
"matplotlib 2.2.2\n",
"sklearn 0.20.0\n"
]
}
],
"source": [
"%load_ext watermark\n",
"%watermark -a \"Romell D.Z.\" -u -d -p numpy,pandas,matplotlib,sklearn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3. Machine Learning Tuning"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import division\n",
"import warnings\n",
"warnings.simplefilter('ignore' )\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"%config InlineBackend.figure_format = 'retina'\n",
"plt.rcParams['figure.figsize'] = (18,6) "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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],
"text/plain": [
" numeric text with_missing label\n",
"0 -10.856306 4.433240 b\n",
"1 9.973454 foo 4.310229 b\n",
"2 2.829785 foo bar 2.469828 a\n",
"3 -15.062947 2.852981 b\n",
"4 -5.786003 foo bar 1.826475 a"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset = pd.read_json('dataset.json',orient='split')\n",
"dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Int64Index: 1000 entries, 0 to 999\n",
"Data columns (total 4 columns):\n",
"numeric 1000 non-null float64\n",
"text 1000 non-null object\n",
"with_missing 822 non-null float64\n",
"label 1000 non-null object\n",
"dtypes: float64(2), object(2)\n",
"memory usage: 39.1+ KB\n"
]
}
],
"source": [
"dataset.info()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"210"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Value empty in 'text column'\n",
"dataset[dataset['text']==''].shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" \n",
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" | \n",
" count | \n",
" mean | \n",
" std | \n",
" min | \n",
" 25% | \n",
" 50% | \n",
" 75% | \n",
" max | \n",
"
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" \n",
" \n",
" \n",
" numeric | \n",
" 1000.0 | \n",
" -0.395641 | \n",
" 10.012883 | \n",
" -32.310550 | \n",
" -6.845566 | \n",
" -0.411856 | \n",
" 6.688657 | \n",
" 35.715792 | \n",
"
\n",
" \n",
" with_missing | \n",
" 822.0 | \n",
" 3.025194 | \n",
" 0.994960 | \n",
" -0.801378 | \n",
" 2.386520 | \n",
" 3.022887 | \n",
" 3.693381 | \n",
" 5.850708 | \n",
"
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" \n",
"
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"
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],
"text/plain": [
" count mean std min 25% 50% \\\n",
"numeric 1000.0 -0.395641 10.012883 -32.310550 -6.845566 -0.411856 \n",
"with_missing 822.0 3.025194 0.994960 -0.801378 2.386520 3.022887 \n",
"\n",
" 75% max \n",
"numeric 6.688657 35.715792 \n",
"with_missing 3.693381 5.850708 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.describe().T"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 b\n",
"1 b\n",
"2 a\n",
"3 b\n",
"4 a\n",
"Name: label, dtype: object"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset['label'].head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"text/plain": [
" a b\n",
"0 0 1\n",
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"2 1 0\n",
"3 0 1\n",
"4 1 0"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.get_dummies(dataset['label']).head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy on dataframe with Numerical & Label: 0.688\n"
]
}
],
"source": [
"from sklearn.pipeline import Pipeline\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.multiclass import OneVsRestClassifier\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(dataset[['numeric']],\n",
" pd.get_dummies(dataset['label']))\n",
"\n",
"pl = Pipeline([ ('model', OneVsRestClassifier(LogisticRegression())) ])\n",
"\n",
"pl.fit(X_train, y_train)\n",
"\n",
"accuracy = pl.score(X_test, y_test)\n",
"print(\"Accuracy on dataframe with Numerical & Label: \", accuracy)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy on dataframe with All Numerical: 0.648\n"
]
}
],
"source": [
"from sklearn.impute import SimpleImputer\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(dataset[['numeric', 'with_missing']],\n",
" pd.get_dummies(dataset['label']))\n",
"\n",
"pl = Pipeline([\n",
" ('imputer', SimpleImputer()),\n",
" ('model', OneVsRestClassifier(LogisticRegression()))\n",
" ])\n",
"\n",
"pl.fit(X_train,y_train)\n",
"\n",
"# Compute and print accuracy\n",
"accuracy = pl.score(X_test,y_test)\n",
"print(\"Accuracy on dataframe with All Numerical: \", pl.score(X_test,y_test))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy on dataframe with only text data: 0.848\n"
]
}
],
"source": [
"from sklearn.feature_extraction.text import CountVectorizer\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(dataset['text'],\n",
" pd.get_dummies(dataset['label']))\n",
"\n",
"pl = Pipeline([\n",
" ('vec', CountVectorizer()),\n",
" ('model', OneVsRestClassifier(LogisticRegression())) ])\n",
"\n",
"pl.fit(X_train,y_train)\n",
"print(\"Accuracy on dataframe with only text data: \", pl.score(X_test,y_test))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting Text Data\n",
"0 \n",
"1 foo\n",
"2 foo bar\n",
"3 \n",
"4 foo bar\n",
"Name: text, dtype: object\n",
"Extracting Numeric Data\n",
" numeric with_missing\n",
"0 -10.856306 4.433240\n",
"1 9.973454 4.310229\n",
"2 2.829785 2.469828\n",
"3 -15.062947 2.852981\n",
"4 -5.786003 1.826475\n"
]
}
],
"source": [
"from sklearn.preprocessing import FunctionTransformer\n",
"\n",
"extract_text = FunctionTransformer(lambda x: x['text'], validate=False)\n",
"text_data = extract_text.fit_transform(dataset)\n",
"\n",
"extract_numeric = FunctionTransformer(lambda x: x[['numeric', 'with_missing']], validate=False)\n",
"numeric_data = extract_numeric.fit_transform(dataset)\n",
"\n",
"print('Extracting Text Data')\n",
"print(text_data.head())\n",
"print('Extracting Numeric Data')\n",
"print(numeric_data.head())"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy on all dataset: 0.9280\n"
]
}
],
"source": [
"from sklearn.pipeline import FeatureUnion\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(dataset[['numeric', 'with_missing', 'text']],\n",
" pd.get_dummies(dataset['label']))\n",
"\n",
"pip = Pipeline([\n",
" ('union', FeatureUnion(\n",
" transformer_list = [\n",
" ('numeric_features', Pipeline([\n",
" ('selector', extract_numeric),\n",
" ('imputer', SimpleImputer())\n",
" ])),\n",
" ('text_features', Pipeline([\n",
" ('selector', extract_text),\n",
" ('vectorizer', CountVectorizer())\n",
" ]))\n",
" ]\n",
" )),\n",
" ('clf', OneVsRestClassifier(LogisticRegression()))\n",
" ])\n",
"\n",
"pip.fit(X_train, y_train)\n",
"pred_prob_LR = pip.predict_proba(X_test)[:,0]\n",
"\n",
"accuracy = pip.score(X_test, y_test)\n",
"print(\"Accuracy on all dataset: %.4f\"% accuracy)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy on all dataset: 0.7280\n"
]
}
],
"source": [
"from sklearn.feature_selection import chi2, SelectKBest\n",
"from sklearn.preprocessing import MaxAbsScaler\n",
"chi_k = 2 # 300\n",
"TOKENS_ALPHANUMERIC = '[A-Za-z0-9]+(?=\\\\s+)'\n",
"pip = Pipeline([\n",
" ('union', FeatureUnion(\n",
" transformer_list = [\n",
" ('numeric_features', Pipeline([\n",
" ('selector', extract_numeric),\n",
" ('imputer', SimpleImputer())\n",
" ])),\n",
" ('text_features', Pipeline([\n",
" ('selector', extract_text),\n",
" ('vectorizer', CountVectorizer(token_pattern=TOKENS_ALPHANUMERIC,\n",
" ngram_range=(1,2))),\n",
" ('dim_red', SelectKBest(chi2, chi_k))\n",
" ]))\n",
" ]\n",
" )),\n",
" ('scale', MaxAbsScaler()), \n",
" ('clf', OneVsRestClassifier(LogisticRegression()))\n",
" ])\n",
"\n",
"pip.fit(X_train, y_train)\n",
"pred_prob_M_LR = pip.predict_proba(X_test)[:,0]\n",
"\n",
"print(\"Accuracy on all dataset: %.4f\"% pip.score(X_test, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy on all dataset: 0.8800\n"
]
}
],
"source": [
"from sklearn.tree import DecisionTreeClassifier\n",
"\n",
"pip = Pipeline([\n",
" ('union', FeatureUnion(\n",
" transformer_list = [\n",
" ('numeric_features', Pipeline([\n",
" ('selector', extract_numeric),\n",
" ('imputer', SimpleImputer())\n",
" ])),\n",
" ('text_features', Pipeline([\n",
" ('selector', extract_text),\n",
" ('vectorizer', CountVectorizer())\n",
" ]))\n",
" ]\n",
" )),\n",
" ('clf', DecisionTreeClassifier())\n",
" ])\n",
"\n",
"pip.fit(X_train, y_train)\n",
"pred_prob_DT = pip.predict_proba(X_test)[0][:,1]\n",
"\n",
"print(\"Accuracy on all dataset: %.4f\"% pip.score(X_test, y_test) )"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy on all dataset: 0.8840\n"
]
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"pip = Pipeline([\n",
" ('union', FeatureUnion(\n",
" transformer_list = [\n",
" ('numeric_features', Pipeline([\n",
" ('selector', extract_numeric),\n",
" ('imputer', SimpleImputer()),\n",
" ('scaler', StandardScaler())\n",
" ])),\n",
" ('text_features', Pipeline([\n",
" ('selector', extract_text),\n",
" ('vectorizer', CountVectorizer())\n",
" ]))\n",
" ]\n",
" )),\n",
" ('clf', DecisionTreeClassifier())\n",
" ])\n",
"\n",
"pip.fit(X_train, y_train)\n",
"pred_prob_SS_DT = pip.predict_proba(X_test)[0][:,1]\n",
"\n",
"print(\"Accuracy on all dataset: %.4f\"% pip.score(X_test, y_test) )"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy on all dataset: 0.8760\n"
]
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"pip = Pipeline([\n",
" ('union', FeatureUnion(\n",
" transformer_list = [\n",
" ('numeric_features', Pipeline([\n",
" ('selector', extract_numeric),\n",
" ('imputer', SimpleImputer())\n",
" ])),\n",
" ('text_features', Pipeline([\n",
" ('selector', extract_text),\n",
" ('vectorizer', CountVectorizer())\n",
" ]))\n",
" ]\n",
" )),\n",
" ('clf', RandomForestClassifier())\n",
" ])\n",
"\n",
"pip.fit(X_train, y_train) \n",
"pred_prob_RFC = pip.predict_proba(X_test)[0][:,1]\n",
"print(\"Accuracy on all dataset: %.4f\"% pip.score(X_test, y_test) )"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy on all dataset: 0.9040\n"
]
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"pip = Pipeline([\n",
" ('union', FeatureUnion(\n",
" transformer_list = [\n",
" ('numeric_features', Pipeline([\n",
" ('selector', extract_numeric),\n",
" ('imputer', SimpleImputer())\n",
" ])),\n",
" ('text_features', Pipeline([\n",
" ('selector', extract_text),\n",
" ('vectorizer', CountVectorizer())\n",
" ]))\n",
" ]\n",
" )),\n",
" ('clf', RandomForestClassifier(n_estimators=15))\n",
" ])\n",
"\n",
"pip.fit(X_train, y_train)\n",
"pred_prob_RFC_15 = pip.predict_proba(X_test)[0][:,1]\n",
"\n",
"print(\"Accuracy on all dataset: %.4f\"% pip.score(X_test, y_test) )"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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tsr3//j+xttd9qvXBMDL8lhURyXHR0dGEhIRgsVhYtWoVcXFxmY7ZuHEjbdq0sbW5ubllyIAVERERERERERERyc+2bt3K1q1bM+2rXbt2lhIhfHx8GDhwIJ9++qmtsgLAb7/9BqQ+gL+dGTNmYLVaeeqpp+yudLiRh4cHPXr0IDg4mK+//pqhQ4cC0KBBA/bu3cuECRP44YcfmDx5MikpKZQqVYpnnnmGV199lf/85z+ZrtmgQQMOHz5McHAwy5cvJyQkhCtXruDu7s5DDz3EsGHDeP7556lQocJtz3E7EyZMoGbNmkyePJnp06djMpnw9/dnxIgRtGvXLsvrPPzww+zZs4ePPvqIdevWMW3aNAzDoEyZMrz88su88cYblC5tf529YRjMnz+fhg0bMnPmTL744gtcXV1p0qQJI0eOpGHDhnbjz549a6scYbFYsFgsmcaSWSLEwYMH2bJlC76+vgQGBmb5XJLKsFqteR3DA8kwjF/8/f39f/nll7wO5YE3782hnDt+FIBe5ffg89Hp28yAs8ciWfLJbgCKlytI1/9mvQRNdsVs3MhfA18CINnTg6deiCfJ0aCIaxHWdVmHs8PN/2ISEcmulJQUunTpQkhICAkJCZmOqVSpEmazGbPZTL169ZSgJSIiIiIiIiIi8oA4eDD1Nvlq1arlcSSSVZ07d2bXrl2Eh4ffMrlB5N8sqz976taty+7du3dbrdZsl9C/LytCiGRX9KW8uxYjcsFC2+tddQuQ5HgdgG5VuykJQkRynNVqtUtkMJlMxMTEZEiCqFatGmazmS5dulCzZk0lP4iIiIiIiIiIiIjkMavVyubNm3nrrbeUBCFyh5QIIflSjF0ihFuu7Zt4+jRXf/zR9v7bqhGAgaPJke5Vu+daHCLyYDt9+jRLlizBYrFQv359Pv74Y7t+s9lMaGgotWvXtlV+UAa4iIiIiIiIiIiIyL+LYRhcvHgxr8MQuS8pEULypZhLcbbXnrlYEeLKou/g7+toTlcvxvnCVwBoW74t3m7euRaHiDx4Tpw4YbtfbMeOHbb2kydP8tFHH9lVeOjRowetWrWiUqVKeRGqiIiIiIiIiIiIiIjIPaVECMmXYi6nqwhRJHcSIawJCURaLLb3C6tF2l73rt47V2IQkQfLoUOHbMkPe/bsyXTMH3/8weHDh/Hz87O1eXl54eXllVthioiIiIiIiIiIiIiI5ColQki+FG13NUbuJELErF9PckQEAPGFPdj1UDxg4F/cnxpFa+RKDCLyYIiKiqJRo0bs378/034HBweaN2+O2WymY8eOlCxZMpcjFBERERERERERERERyTtKhJB8x5pita8IkUuJEFcWLLS9XlfTSooptUx972qqBiEiN2e1WklMTMTZ2dnWVqhQIburLgCcnJxo1aoVXbp0oX379hQtWjS3QxUREREREREREREREflXMOV1ACK5LTY6gZQkKwCuHk44u977fKDr4eHE7twJgNVkIuQ/1wHw8fChRdkW93x/Ebm/pKSksHXrVl599VUqVKjApEmTMowxm824urrSqVMn5s6dy8WLFwkJCaFfv35KghARERERERERERERkXxNFSEk38mLahCRC/+pBnGwmgeXPeMA6OnXE0eT/hiKCCQlJfHjjz9isVhYunQpZ8+etfVZLBZGjBhhN37w4MGMGDECDw+P3A5VRERERERERERERETkX01PYCXfib4UZ3udG4kQKfHxRC793vZ+6X+uASbcHN3oXLnzPd9fRP69EhISWL9+PRaLhWXLlhEREZHpuCNHjhAVFUWhQoVsbYULF86tMEVERERERERERERERO4rSoSQfCfmUu5WhIhevYaU6GgAorzd2FchAYAnKz5JIZdCt5oqIg+wI0eOUL9+faKiojLtL1asGB07dsRsNtO8eXOcnZ1zOUIREREREREREREREZH7kxIhJN+JTpcI4ZkLiRBXFsy3vV758HWshgmA3tV63/O9ReTf4erVqzg4OODm5mZrq1SpEi4uLnbjfHx8MJvNmM1mGjdujKOj/poWERERERERERERERG5U6a8DkAkt11NXxGiyL1NhIg/cID4X/cBkOxoYkNNA4BGpRpR0aviPd1bRPJWVFQUc+fOpVOnThQrVozFixfb9Ts4ONCpUyfKlSvHq6++ytatW/nrr7/44osvaNasmZIgRERERERERERERERE7pKeski+E213NYbbLUZm35WFi2yvd/k5EONuBVQNQuRBFRERwbJly7BYLISGhpKYmGjrs1gsPPXUU3bjJ0yYwJQpUzAMI7dDFREREREREREREREReWApEULyFavVSszl9IkQ964iRPLVq0StWGF7v7p2CmBQ3rM8jUo3umf7ikjuOnfuHEuXLsVisRAWFkZycnKm486ePUtKSgom0z/FmDw8PHIrTBERERERERERERERkXxDV2NIvhIbnUByYgoALu6OuLjdu1yg6BUrsMbGAnCuuBOHfFPbe1frjcnQHz2RB0FoaCilSpXipZdeYv369RmSIOrWrcu4ceM4fPgwO3futEuCEBEREREREREREZG8YxiG3ZeLiwvFihXD39+fAQMGsHr16gz/5tu3b98M82711axZswz7rl27ll69elGhQgXc3d1xd3fnoYce4umnn2bt2rU5esarV6/i6emJYRg8/fTTNx137NgxDMPgoYceuumYpKQkDMO45bXOuXW233//nS5dulCsWDFcXV2pWrUq7777LvHx8beffIMVK1bQqlUrfH19cXNzo2LFinTr1o2dO3dmOj4+Pp4vvviCevXq4e3tTcGCBalevTpDhgzhjz/+uOk+s2bNol69enh4eFCoUCGaN2/O6tWrMx174MABRo0aRfv27Slbtqzt95PcGVWEkHwlt6pBWK1WrsxfYHsfUisZDBMFnQrSvlL7e7aviNw7x44do1y5cjg5OdnaGjRogIuLi93/XDVs2BCz2Uznzp0pX758HkQqIiIiIiIiIiIiIlk1atQoAJKTk4mMjGT//v3MmTOHr776ikceeYR58+ZRpUoVADp27Jjh333DwsLYtGkTTZs2zZD4kH5sdHQ0ffr0YcWKFbi6utKyZUvMZjMODg6cOHGCkJAQ5syZw+uvv86HH36YI2ebP38+MTExGIbBd999x6RJkyhcuHCOrJ1ebp5t27ZtBAQEkJSURNeuXfH19SU0NJTRo0ezYcMG1q1bh7Ozc5bWGjZsGJ9++ine3t507NiRokWLcvToUZYuXcrixYuZN28ePXv2tI1PTEykefPm7Nixg2rVqtGrVy+cnZ3ZtWsXkyZN4uuvv2b79u34+fnZ7TNkyBAmTZpEmTJleOGFF4iLi2PhwoUEBgYyZcoUXnzxRbvxq1at4r333sPBwYEqVarg4uLC9evXs/V9y4+UCCH5SsyldIkQRe5dIkTcnr1cP3IEgERnEz/+JzVLq3Plzrg7ud+zfUUkZx04cACLxcLixYvZt28fP/zwA61atbL1FyhQgKCgIC5duoTZbKZTp06ULl06DyMWERERERERERERkTsxevToDG3nz59n0KBBfPfddwQEBPDzzz9TvHhxOnbsSMeOHTPM37RpE82aNct0LUhNsjCbzYSGhhIQEMA333yDj4+P3Zj4+HimTJnCiRMncupoTJ8+HQcHB4YOHconn3zCnDlzeOWVV3JsfcjdsyUlJdGvXz/i4uIICQkhMDDQFkOXLl34/vvv+fzzzxk+fPht1zp9+jSfffYZPj4+7Nu3D29vb1vfunXraN26NaNGjbJLhFi8eDE7duzgiSeeYNWqVXZVoN966y3GjRvHp59+yvTp023tmzdvZtKkSVSuXJmffvqJQoUKATB8+HAeeeQRhg4dSlBQEGXKlLHNadeuHU2aNOHhhx/Gzc0NX19fTp8+ffffuHxKNbolX0mfCOFZ1O2e7RO58J9qED9WsxLnamAyTPSs1vMWs0Qkr1mtVvbs2cPIkSOpVq0aNWrU4J133mHfvn0AWCyWDHMWLlzIxo0befnll5UEISIiIiIiIiIiIvIAKFGiBAsWLKBZs2b8+eefjBs3LlvrzZ07l9DQUKpWrcqyZcsyJAoAuLq6MnToUMaPH5+tvdL8+uuv/Pzzz7Ru3ZrXXnsNJycngoODc2Tt9HLzbBs2bODIkSO0aNHClgQB4ODgwEcffQTAlClTsrTWyZMnsVqtPPbYY3ZJEACtWrXC3d2dixcv2rWHh4cDEBQUlOEq7A4dOgBkmJMWz9tvv21LggCoVKkSAwcOJD4+ntmzZ9vN8fPzo379+ri53btnmfmBEiEkX4m+dO+vxki6coXo1Wts73+ok/rHrEWZFpQuoIekIv82VquVnTt38tprr/HQQw/h7+/P2LFjOXTokN04FxcXEhMTM8x3cHDIrVBFREREREREREREJJeYTCZGjhwJpF4xYbVa73qttAoBI0aMwN391pXDXVxc7nqf9KZNmwZA3759KVasGG3btuX3339n+/btObJ+muyczdfXF8Mw+Ouvv7K014YNGwBo06ZNhr4qVapQsWJFwsPDOXXq1G3XqlKlCk5OTuzYsYNLly5l2Cc2NpaAgAC79ho1agCpV1fc+Pth5cqVABnm3Crmtm3b2o2RnKWrMSRficmFRIiopd9jTUgAINzHxAmf1GsxelfrfU/2E5HsmT59eob7t9K4u7sTGBiI2WwmKCiIggUL5nJ0IiIiIiIiIiIiIpJXGjdujKOjIxcuXODkyZNUqFDhjtdISEhg165dALRs2TKnQ8xUbGws3377LYULF7ZVKujbty/Lly9n+vTpPPbYYzmyT26f7fDhw0BqEkNmKleuTHh4OEeOHKFcuXK3XKtYsWJ88MEHjBgxgurVq9OhQweKFi3KsWPHWL58OU888QRffvml3ZwOHTrQoUMHli1bxsMPP0zLli1xdnbmp59+Yvv27QwZMsTueUN0dDTnz5/Hy8uLYsWKZRovwJEjR+7o+yBZo0QIyVdiLsXZXt+LRAhrSgqRCxfa3q+tk/rfakWqUbdE3RzfT0SyLjExkW3bttGkSRMMw7C1BwUF2Y3z9PTkySefxGw288QTT9w2g1VERERERERERETkQfHw1w/ndQhZ9tszv93zPVxcXChatCjnz5/n4sWLd5UIERERQVJSEpBaASE3LFiwgKioKF566SVbFYZ27drh7e3NokWLmDhxot01DXcru2fbtGkTiYmJlCxZMkvjo6KiAG4ae1p7ZGRkltYbNmwYFSpUoH///nbXhlSpUsVWSSM9wzBYsmQJo0aNYty4cezfv9/WFxAQQM+ePe2qSKfFkVPxyp3R1RiSb1itVruKEJ73IBEidscOEv4utxPrarCt2j/VINI/eBWR3HH9+nVCQkJ49tlnKVmyJM2aNeP333+3G+Pr60tQUBD9+vVj5cqVXLhwgblz59KpUyclQYiIiIiIiIiIiIjkc2lXINztc57sXKlxt9Ie6vfr18/W5uTkRK9evYiNjWXevHk5sk92z1apUiX8/PxwdMyZz+7f6a/VBx98QNeuXRkwYADh4eFcu3aNn3/+mTJlytCzZ0/efPNNu/FxcXF07dqVSZMmMXXqVM6dO0dkZCQhISGEh4fTuHFjQkJC7lm8cmeUCCH5RlxMIkmJKQA4uzni4u6U43tcWfBPNYhNNeC6s0ER1yK0rdA2x/cSkczFxsayZMkSevfuTfHixWnXrh2zZs3i8uXLAFgslgxzVq5cycyZMwkKCsqx+9dERERERERERERE5P4WHx9v+7flzK42yIpixYrZHvSfPn06x2K7md9++40dO3ZQo0YNHnnkEbu+tMSI6dOn27WbTKmPjFNSUm66blpf2ljI/bOlVVBIqwxxo+joaLtxtxIaGsqbb76J2Wxm/PjxVKhQAXd3d+rWrcuyZcsoWbIkH3/8Maf+/gA0wNixY1myZAkffvghzz33HCVKlKBQoUIEBgayaNEiEhMTGTx4sG28l5dXjsUrd05XY0i+EXP5n2oQBYvkfDWIxPMXiFm/3vb+B//Uvwi6V+2Os4Nzju8nIv+IiYkhJCQEi8XCqlWriI2NzXScr68vBQoUyOXoRERERERERERERO4PuXHdxP1ky5YtJCUlUaJECcqXL39Xazg7O1O/fn22bdvG+vXrefbZZ3M2yBukJTns37//ppUGfv31V3bt2kX9+vWBfx7EpyV9ZCYiIgL45+E+5P7ZqlatCsCRI0cy7T969CiQerXF7axcuRKA5s2bZ+jz8PCgXr16rFixgr1791KuXLnbzqlbty6enp4cP36cyMhIvLy88PT0pESJErarVW5MprmTeOXOqSKE5Bvpr8UoeA+uxYi0LIbkZAAOlIHT3gaOJke6Ve2W43uJiL0PP/yQnj17snjx4gxJEBUrVmTEiBHs2LGDU6dOMXz48DyKUkRERERERERERETuFykpKYwdOxaAXr16ZWut559/HoDx48cTFxd3y7HXr1+/633i4+OZO3cuDg4O9O/fP9Ov1q1bA/9cnwFQtGhRSpcuTVRUFAcOHMh07e3btwNQq1atPDkbQIsWLQBYs2ZNhr4jR44QHh5OxYoVbYkLWYnl4sWLmfantTs7O2dpTmxsLFevXsUwDLs5t4p59erVdmMkZykRQvKN6Ev//PD1zOFECGtSEpGLvrO9X1cn9Y9W2/Jt8XbzztG9RPKzCxcu8P3332do79y5s937atWqMXLkSPbs2cOxY8f4+OOPefTRR+1KdomIiIiIiIiIiIiIZObChQv06NGDsLAwypYty5tvvpmt9fr06UNAQACHDh2iU6dOnD9/PsOY69ev88UXX/Daa6/Ztfv6+mIYBn/99ddt91m0aBGRkZEEBgYyY8aMTL8WLlyIm5sbCxYsICYmxja3b9++AIwYMYKEhAS7da9cucLo0aPtxuXE2Y4fP86hQ4dISkq67dkgNWGgSpUqbNiwgVWrVtnak5OTef311wEYOHCg3ZzExEQOHTpEeHi4Xfvjjz8OwNSpUzl79qxd34oVK9i5cyfu7u489thjGeaMHTs2Q1LHqFGjSElJoUGDBri7u9vaX3zxRQDGjBljd0VGeHg4U6ZMwdXVNcP3VHKGrsaQfONeVoS4+uNmks6dAyDKHXZWTS011Lt67xzdRyQ/On36NEuXLmXx4sVs3rwZq9XKmTNnKFmypG2Mv78/QUFBPProo5jNZqpXr56HEYuIiIiIiIiIiIjI/SLtAX9KSgqRkZHs37+fLVu2kJCQQP369Zk3bx7e3tn70KuDgwMWi4U+ffqwYsUKKlSoQMuWLfHz88PBwYGTJ0+yfv16IiIieOONN+zmpqSkAODoePvHumnXYgwYMOCmY7y8vDCbzcydO5dvv/2WF154AYC33nqLTZs2sWrVKqpUqUJgYCBFihTh7NmzLFu2jEuXLtGnT58M1TGyc7amTZty+vRp/vzzT3x9fW97PkdHR2bNmkVAQAAdO3akW7du+Pr6sm7dOnbv3k2TJk145ZVX7OacOnWKatWqUalSJY4dO2Zr7969OzNmzGDjxo34+fnRqVMnihcvzsGDBwkJCcFqtfLxxx/bXQXy9ttvExISwg8//ED16tV54okncHV1ZfPmzfz888+4u7szceJEu/3TYvr888+pWbMmZrOZ+Ph4FixYQGRkJF9++SVlypSxm3PhwgW7pJErV64A9kkob731FpUrV77t9yw/UyKE5Bsxl+9dIsSVBfNtrzfWNEhyNPAv7k+NojVydB+R/OLkyZNYLBYsFout3FZ633//vS2LEsAwDNvdXCIiIiIiIiIiIiIiWfXuu+8CqVcgFCxYkHLlyvH0009jNptp3bp1jlUa9vT0ZPny5axZs4avv/6a7du3ExoaitVqpXTp0rRu3Zqnn36aJ554wjYnIiKCc+fO0bRpU7sPB2bm4MGDbN26FR8fH4KCgm459rnnnmPu3LlMnz7dlgjh5ubG+vXrmTZtGgsWLODbb7/l2rVrFCpUiDp16vDss8/Ss2fPHDvb3WrYsCE7d+5k9OjRrFmzhpiYGMqVK8eoUaN444037K6luBUHBwfWrFnD//73PxYsWIDFYiEuLo4iRYoQFBTE4MGDCQgIsJtTpkwZ9uzZw4cffsiqVauYOXMmKSkplCpVin79+vHGG29QpUqVDHtNnDiRWrVq8eWXXzJt2jQcHBzw9/fntddeIzAwMMP46Ohovv766wzt6dsGDBigRIjbMKxWa17H8EAyDOMXf39//19++SWvQ3ngzXtzKOeOHwWgV/k9+Hx0OtNx89/byeUz1wDo9mY9ipUtmCP7J/z1F8dbtQarlRTglRcduFDYYELTCbQu3zpH9hDJD44ePcrixYtZvHgxu3fvznSMYRg0atSIV199lU6dOuVyhCIiIiIiIiIiIiL3t4MHDwKp1wvLv9+SJUswm82sXbuW1q31zEnuX1n92VO3bl12796922q11s3unqoIIfmC1Wol+h5djRG56Dv4O6Ho14oGFwob+Hj40KJsixzbQyQ/+OCDD5g1a1aGdgcHB5o1a4bZbKZjx474+PjkQXQiIiIiIiIiIiIiIrlr06ZN1K1bV0kQIndBiRCSL8RfSyTpejIATq4OuLjnzG99a0ICkRaL7f26OgYAPf164mjSHy+RG1mtVnbv3s3hw4cz3CNmNpttiRBOTk4EBARgNpvp0KFDtu9fExERERERERERERG530yaNCmvQxC5b+lJreQLMemrQRRxxTCMnFk3NJTkS5cAiCgIux8ycHN0o3PlzjmyvsiDICUlhR07dmCxWFiyZAknT57E3d2djh074u7ubhsXEBBA9+7dadeuHe3atcPLyysPoxYREREREREREREREZH7lRIhJF9InwjhmYPXYlyZv8D2en1tEykmgycrPkkhl0I5tofI/SgpKYnNmzdjsVhYunQpZ86cseuPjY1l7dq1dOrUydbm4uLCggULblxKRERERERERERERERE5I4oEULyhej0FSGKuuXImtePHyf2p58ASDZgQ63UKhO9q/XOkfVF7kfr169n4cKFfP/991y8eDHTMYUKFaJ9+/aUKVMml6MTERERERERERERERGR/ECJEJIv2F2NkUMVIa4sXGh7/XNlgysFDRqVakRFr4o5sr7I/WjChAmsXr06Q7u3tzcdO3bEbDbTokULnJ2d8yA6ERERERERERERERERyQ+UCCH5QszldIkQRbKfCJESF0fU98ts73/wVzUIyT+uXbvG6tWriY2N5emnn7brM5vNtkQIHx8fOnXqhNlspkmTJjg66q8cERERERERERERERERuff0VEryhZhLcbbXnt7ZT4SIXrWalOhoAM55we/lDcp7lqdR6UbZXlvk3ygqKoqVK1disVhYs2YNcXFxlC5dmj59+mAymWzjOnTowP79+zGbzTz22GN2fSIiIiIiIiIiIiIiIiK5QYkQ8sCzWq1E5/DVGOmvxVjnb8JqGPSu1huToYe+8uC4dOkSy5Ytw2KxEBoaSkJCgl3/6dOn2bVrFw0aNLC1eXt78+mnn+Z2qCIiIiIiIiIiIiIiIiI2SoSQB9712CQS45MBcHRxwNXDKVvrxe3fT/y+fQAkOkDYwwYFnQrSvlL7bMcqktesVitTp07FYrEQFhZGcnJypuNq1KiB2WymdOnSuRyhiIiIiIiIiIiIiIiIyK0pEUIeeDHpq0EUccUwjGytF7ngn2oQO/wMYtwN+lYx4+7knq11Rf4NDMMgODiYPXv2ZOjz9/fHbDZjNpupWrVqHkQnIiIiIiIiIiIiIiIicntKhJAHXvpECM9sXouRHBNDVEiI7f0P/iZMhomefj2zta5IbgsPD8disVCyZEmeeuopuz6z2WxLhGjQoIEt+aFChQp5EaqIiIiIiIiIiIiIiIjIHVEihDzwoi/F2V4XzGYiRNTy5VhjYwH4oxgcLg0BZVpQqkCpbK0rkhsOHjyIxWLBYrGwd+9eAOrUqZMhEaJnz554enrSqVMnfH198yJUERERERERERERERERkbumRAh54NldjZGNRAir1Wp3Lca6OiYwDPpU75Ot+ETuFavVyr59+1i8eDEWi4WDBw9mGLNnzx7Cw8OpWLGira1ixYoMGjQoN0MVERERERERERERERERyTGmvA5A5F6LuZwuEaLI3SdCxO3Zw/WjRwGId4If/2NQrUg1/Iv7ZztGkZwUGxvL66+/TuXKlalduzZjxozJkATh7OxMu3btmD17NsWKFcujSEVERERERERERERE8kZycjLBwcE0bdqUIkWK4OTkRPHixalZsyYDBgxg+fLlGeZERkbyzjvvULt2bQoUKICLiwulS5emQYMGDBs2zHbt9J2aPXs2hmHYfRUoUABfX18CAgJ45513OPr3M6r0bpxzu6/Zs2fbzb969SqffvopzZs3p3jx4jg5OeHl5cWjjz7KyJEjOXny5F2d52a+/vprWywbNmy46biRI0diGAZjxoy56ZgZM2ZgGAYDBgzItD83zzZr1tF3rZkAACAASURBVCzq1auHh4cHhQoVonnz5qxevfqO14mKimLMmDHUrl0bLy8vvLy8qFmzJqNGjSIiIiLTOeHh4Tz//PP4+fnh7u5OiRIlaNiwITNmzCAhIeG2e27YsAGTyYRhGIwePfqm47Zs2ULbtm0pUqQI7u7u1KpVi88//5zk5OQ7PmduUUUIeeBFp6sI4VnU7a7XuTJ/ge31lhoGcS4Gvav1xjCMbMUnktNcXV2ZO3cuZ86csWt3d3enbdu2mM1mgoKC8PT0zKMIRURERERERERERETyTnJyMu3atWPNmjV4eXkRFBSEr68vly9f5vjx43z77bccOnSI9u3b2+acOXOGRo0acfLkSSpWrEjv3r0pUqQIp0+f5tChQ0ycOBE3Nzfq1Klz13HVqlWLjh07AhAXF8eFCxfYuXMn77//PmPHjmXQoEF88sknODqmPuIdNWpUhjUmTpxIVFQUgwcPxsvLy66vdu3attfbtm2jS5cunD17ljJlyhAUFISPjw/Xrl1j9+7dfPjhh4wfP56ffvqJmjVr3vWZ0ps+fTqGYWC1Wpk+fTotWrTIkXVvlJtnGzJkCJMmTaJMmTK88MILxMXFsXDhQgIDA5kyZQovvvhilta5cuUK9erV4/jx49SrV49nn32WlJQUfvzxR9577z1mz57Nzz//bPfh1p07d9KiRQvi4+MJDAykQ4cOREdHs3z5cp577jmWLl3KypUrb/osMyoqin79+uHh4cHVq1dvGpvFYqFbt264u7vTvXt3ChcuzPLlyxk8eDDbt29n/vz5d/ZNyyVKhJAHXk5cjZF05Qoxa9bY3q+rY6KIaxHaVmib7fhE7kZSUhJhYWFYLBZatGhB165dbX0mk4nOnTszefJkChYsyJNPPonZbKZNmza4u7vnYdQiIiIiIiIiIiIiInlv/vz5rFmzhlq1arFp0yYKFSpk1x8bG8vOnTvt2t555x1OnjzJs88+a6tGkN7Zs2c5e/ZstuKqXbt2pp/K37hxI3379mXSpEnEx8czdepUgEzHzp49m6ioKIYMGUL58uUz3Wf//v088cQTxMbGMn78eIYMGWJLrkgTHh7OiBEjiI6OztaZ0hw4cIBt27bRpk0bzp49y9KlS4mIiMDb2ztH1k+Tm2fbvHkzkyZNonLlyvz000+230fDhw/nkUceYejQoQQFBVGmTJnbrjV16lSOHz/Oc889x/Tp0+36+vTpw7x58wgODubNN9+0tb/zzjvExsYyd+5cevfubWsfP348jzzyCKtWrWL79u00bNgw0z0HDRrEtWvXeP3113n77bczHRMZGcnzzz+Pk5MTP/74oy3R57333qNZs2YsWLAAs9lMly5dbnvG3KarMeSBdj02kYS4JAAcnUy4FXS6q3WilizBmpgIwFEfOFHSoHvV7jg7OOdYrCK3c/36dVatWkX//v0pWbIkrVq1YurUqRlKWQEMHDiQFStWcPHiRebNm0fnzp2VBCEiIiIiIiIiIiIiQmrFAIC+fftmSIKA1ArLzZs3z3TOoEGDMv2EvY+PD/7+9+Y69ebNm7N27VqcnZ2ZPn06u3fvztZ6L7/8MlevXuWtt95i+PDhGRIFACpWrIjFYqFevXrZ2itN2sP9fv368cwzz5CQkMDXX3+dI2unl5tnmzJlCgBvv/223e+jSpUqMXDgQOLj4zN9hpOZ8PBwAJ588skMfWmVSS5evJjpnPSVSwAKFChgq7Zx45w0FouFOXPmMHnyZEqWLHnTuBYtWsTly5fp3bu3XbUTNzc33n//feCf78O/jRIh5IEWc9m+GsTdXGNhTUnhysJFtvfr/E04mhzpVrVbjsQocitxcXEsXbqUPn36ULx4cYKCgpg5cyaXLl2yjVm3bh2RkZF286pXr067du1wcXHJ7ZBFRERERERERERERP7VihYtCsCRI0fu6Zyc5OfnR7du3bBardm6iuDo0aOEhYXh7u7O8OHDbzs+J54zxMfHM2fOHAoXLkyHDh3o06cPTk5OBAcHZ3vt9LJzttDQUAzDICAgIMv7bdiwAYA2bdpk6Gvbtq3dmNupUaMGACEhIRn6Vq5cCZAhtpvNuXbtGhs3bqRAgQI0aNAgw3rnzp3jxRdfpEuXLvTo0eOWcd3qjM2aNcPV1ZXNmzeT+PcHyv9NlAghD7ToiOxfi3Ft+3YS//gDgKuusK2aQWCFQLzdcrZUj0h6Z86coVu3bhQrVozOnTszb968DCWaSpcuzaBBg1i3bh0FCxbMo0hFRERERERERERERO4vnTt3xsnJialTp/LUU0+xZMkSTp06dcs53bt3B2DAgAG89tprhIaG2n1oMTc0a9YMgF27dt31Glu2bAGgXr16eHp65kRYt7V48WIuX75Mz549cXFxoVixYrRt25bDhw+zadOmHNsnN88WHR3N+fPn8fLyolixYhn6K1euDGQ9ceaFF17g0UcfZdq0aTRo0IBhw4bx6quv8sgjj2CxWPjwww8JCgqymzNu3DhKlixJnz596NChA2+88QYvvfQSfn5+REdHs3jxYkqUKJFhrwEDBmAYBl9++eVt4zp8+DAAVapUydDn7OxMuXLlSExM5OTJk1k6Z27KWAtE5AEScyl9IoTbXa0RuWCB7fWm/xgkOBn0rtb7FjNEss/Ly4uQkBBiY2Pt2suXL0+XLl0wm83Ur18fk0n5bCIiIiIiIiIiIiKSMw76VcvrELKs2qGDdz23Tp06zJ07l8GDBzN37lzmzp0LQJEiRWjSpAnPPvtshisK/u///o8zZ87w2WefMX78eMaPHw+k/rt9QEAAL7/8MrVq1br7A2VB6dKlgZtfd5AVZ8+eBcDX1zdHYsqKtGsx+vbta2vr27cvy5cvJzg4mKZNm+bIPtk5W8OGDTl48CAeHh5ZGp9WqTuzq1XSt99Y0ftm3NzcCAsL45VXXiE4OJidO3fa+rp3706HDh0yzKlevTq7du2iR48eLF++nOXLlwOpCQpDhw7l0UcfzTBn+vTphISEsHjx4kwTOG4UFRVld54b3ek5c5OeoMkDLX0ihOddVIRIPH+emA0bbe9D65jwL+5P9aLVcyQ+yd8uXrxIcHAwbdq0Ye3atXZ97u7uBAYGAlC1alXefPNNfvnlF8LDwxk/fjwNGjRQEoSIiIiIiIiIiIiIyF3q1q0bf/zxB2vXruXtt9+mXbt2pKSk8P3339O+fXueeeYZrFarbbxhGIwbN46zZ8+yYMEChgwZQpMmTTh79iwzZsygbt26OX7Vw43S4rmbq+Bzco07cfjwYTZv3kyNGjWoV6+erb1du3Z4e3tjsVi4fPlyjuyVnbO5u7vj5+dHmTJl8iSWiIgIWrduzYoVK1i0aBGXLl3i4sWLfPvtt2zYsIH69evzyy+/2M355ZdfaNiwIUlJSWzZsoWrV6/y559/8s4779ieJaWvNn78+HGGDRtGz549MZvNeXLO3KSnaPJAi7mcriJEkTtPhIhcvBiSkwH4vazBaW9Vg5DsOXPmDP/73/9o0aIFJUuW5Pnnn2ft2rUsWrQow9hRo0bx+++/c/DgQcaOHYu/v/+/8i8SEREREREREREREZH7kZOTE61bt+a9995jxYoVREREsHDhQjw8PPjmm29YtmxZhjleXl50796dzz77jE2bNnHp0iVGjhxJcnIygwYN4vz58/cs3jNnzgBk6ZP8N1OqVCkA/vrrrxyJ6XYyqwYBqd/73r17Ex8fzzfffGPXl/ZB0JSUlJuum9aX/kOjuXk2Ly8v4J+KCTdKS0C4WSWFGw0ZMoTNmzfz1Vdf0bVrV4oUKYK3tzc9e/ZkypQpxMTE8Prrr9vGJyQk0LVrV65cucKKFSto1KgRHh4e+Pr68tZbb/HSSy9x+PBhPv/8c9ucfv36UaBAASZPnpzlc6bFn1PnzE26GkMeaNGX4myvC95hRQhrUhKRi76zvV9Xx8DHw4cWZVvkWHySP5w6dQqLxYLFYmH79u12GaRpli1bxvTp03FwcLC1/ec//8nNMEVEREREREREREQkn8vOdRMPAgcHB7p168Zvv/3GmDFj2LBhAx07drzlHA8PD95//33CwsLYsmULW7dupXPnzvckvo0bU6uYZ3blQVY1btwYgF27dhETE0PBggVzJLbMJCQk2JIcRowYwYgRIzIdFxwczJAhQ2zv0x6qX7p06aZrR0REAP8kJEDuns3T05MSJUpw/vx5Ll68mCE55ejRowBUqVIlS+utXLkSgGbNmmXoa968OYBdRYgDBw5w4sQJ6tevT/HixTOdM3nyZLs5u3fv5tq1axQtWjTTGN59913effddzGYzixcvBlKrlu/du5cjR45kuPolMTGRU6dO4eTkRPny5bN0ztykRAh5oKW/GuNOEyGubtpE0t9Ze5HusKuqwWC/njia9MdGsmbPnj08//zz/Pzzz5n2G4ZBw4YNMZvNdO7c2S4JQkRERERERERERERE8kbaA/TMPtiYk3PuxKFDh/juu+8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