test3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import StratifiedKFold,cross_val_score\n",
    "from lightgbm import LGBMClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "color = sns.color_palette()\n",
    "sns.set_style('darkgrid')\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from bayes_opt import BayesianOptimization\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "import gc\n",
    "gc.enable()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read data and test\n",
      "('data test Shapes : ', (307511, 122), (48744, 121))\n",
      "('all_data Shape : ', (356255, 122))\n",
      "['NAME_CONTRACT_TYPE', 'NAME_TYPE_SUITE', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'OCCUPATION_TYPE', 'WEEKDAY_APPR_PROCESS_START', 'ORGANIZATION_TYPE', 'FONDKAPREMONT_MODE', 'HOUSETYPE_MODE', 'WALLSMATERIAL_MODE', 'EMERGENCYSTATE_MODE']\n",
      "('all_data Shape : ', (356255, 255))\n",
      "('all_data Shape : ', (356255, 261))\n",
      "('bureau  buro_bal Shape : ', (1716428, 17), (27299925, 3))\n",
      "['STATUS']\n",
      "['CREDIT_ACTIVE', 'CREDIT_CURRENCY', 'CREDIT_TYPE']\n",
      "('bb_agg Shape : ', (817395, 13))\n",
      "('bureau Shape : ', (1716428, 52))\n",
      "('bureau_agg Shape : ', (305811, 71))\n",
      "('bureau_agg Shape : ', (305811, 107))\n",
      "('bureau_agg Shape : ', (305811, 145))\n",
      "('all_data Shape : ', (356255, 405))\n",
      "('prev Shape : ', (1670214, 37))\n",
      "['NAME_CONTRACT_TYPE', 'WEEKDAY_APPR_PROCESS_START', 'FLAG_LAST_APPL_PER_CONTRACT', 'NAME_CASH_LOAN_PURPOSE', 'NAME_CONTRACT_STATUS', 'NAME_PAYMENT_TYPE', 'CODE_REJECT_REASON', 'NAME_TYPE_SUITE', 'NAME_CLIENT_TYPE', 'NAME_GOODS_CATEGORY', 'NAME_PORTFOLIO', 'NAME_PRODUCT_TYPE', 'CHANNEL_TYPE', 'NAME_SELLER_INDUSTRY', 'NAME_YIELD_GROUP', 'PRODUCT_COMBINATION']\n",
      "('prev_agg Shape : ', (338857, 208))\n",
      "('prev_agg Shape : ', (338857, 258))\n",
      "('prev_agg Shape : ', (338857, 307))\n",
      "('all_data Shape : ', (356255, 711))\n",
      "('pos Shape : ', (10001358, 8))\n",
      "['NAME_CONTRACT_STATUS']\n",
      "('pos_agg Shape : ', (337252, 24))\n",
      "('all_data Shape : ', (356255, 735))\n",
      "('ins Shape : ', (13605401, 8))\n",
      "[]\n",
      "('ins_agg Shape : ', (339587, 29))\n",
      "('all_data Shape : ', (356255, 764))\n",
      "('cc Shape : ', (3840312, 23))\n",
      "['NAME_CONTRACT_STATUS']\n",
      "('cc_agg Shape : ', (103558, 81))\n",
      "('all_data Shape : ', (356255, 845))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def one_hot_encoder(df, nan_as_category = True):\n",
    "    original_columns = list(df.columns)\n",
    "    categorical_columns = [col for col in df.columns if df[col].dtype == 'object']\n",
    "    print (categorical_columns)\n",
    "    df = pd.get_dummies(df, columns= categorical_columns, dummy_na= nan_as_category)\n",
    "    new_columns = [c for c in df.columns if c not in original_columns]\n",
    "    return df, new_columns\n",
    "\n",
    "print('Read data and test')\n",
    "data = pd.read_csv('./input/application_train.csv')\n",
    "test = pd.read_csv('./input/application_test.csv')\n",
    "print('data test Shapes : ', data.shape, test.shape)\n",
    "\n",
    "y = data['TARGET']\n",
    "del data['TARGET']\n",
    "gc.collect()\n",
    "all_data = pd.concat((data, test)).reset_index(drop=True)\n",
    "\n",
    "inc_by_org = all_data[['AMT_INCOME_TOTAL', 'ORGANIZATION_TYPE']].groupby('ORGANIZATION_TYPE').median()['AMT_INCOME_TOTAL']\n",
    "all_data['NEW_INC_BY_ORG'] = all_data['ORGANIZATION_TYPE'].map(inc_by_org)\n",
    "print('all_data Shape : ', all_data.shape)\n",
    "\n",
    "for bin_feature in ['CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY']:\n",
    "    all_data[bin_feature], uniques = pd.factorize(all_data[bin_feature])\n",
    "all_data, cat_cols = one_hot_encoder(all_data)\n",
    "print('all_data Shape : ', all_data.shape)\n",
    "# Some simple new features (percentages)\n",
    "#all_data['DAYS_EMPLOYED'].replace(365243, np.nan, inplace= True)\n",
    "n_data = data.shape[0]\n",
    "\n",
    "all_data['INCOME_DAYS_PERC'] = all_data['AMT_INCOME_TOTAL'] / all_data['DAYS_BIRTH']\n",
    "all_data['DAYS_EMPLOYED_PERC'] = all_data['DAYS_EMPLOYED'] / all_data['DAYS_BIRTH']\n",
    "all_data['INCOME_CREDIT_PERC'] = all_data['AMT_INCOME_TOTAL'] / all_data['AMT_CREDIT']\n",
    "all_data['INCOME_PER_PERSON'] = all_data['AMT_INCOME_TOTAL'] / all_data['CNT_FAM_MEMBERS']\n",
    "all_data['ANNUITY_INCOME_PERC'] = all_data['AMT_ANNUITY'] / all_data['AMT_INCOME_TOTAL']\n",
    "all_data['PAYMENT_RATE'] = all_data['AMT_CREDIT'] / all_data['AMT_ANNUITY']\n",
    "print('all_data Shape : ', all_data.shape)\n",
    "del data,test\n",
    "gc.collect()\n",
    "\n",
    "bureau = pd.read_csv('./input/bureau.csv')\n",
    "buro_bal = pd.read_csv('./input/bureau_balance.csv')\n",
    "print('bureau  buro_bal Shape : ', bureau.shape,buro_bal.shape)\n",
    "buro_bal, bb_cat = one_hot_encoder(buro_bal)\n",
    "bureau, bureau_cat = one_hot_encoder(bureau)\n",
    "bb_aggregations = {'MONTHS_BALANCE': ['min', 'max','mean']}\n",
    "for col in bb_cat:\n",
    "    bb_aggregations[col] = ['mean']\n",
    "    bb_agg = buro_bal.groupby('SK_ID_BUREAU').agg(bb_aggregations)\n",
    "bb_agg.columns = pd.Index([e[0] + \"_\" + e[1].upper() for e in bb_agg.columns.tolist()])\n",
    "bb_agg['BB_COUNT'] = buro_bal.groupby('SK_ID_BUREAU').size()\n",
    "print('bb_agg Shape : ', bb_agg.shape)\n",
    "bureau = bureau.join(bb_agg, how='left', on='SK_ID_BUREAU')\n",
    "bureau.drop(columns= 'SK_ID_BUREAU', inplace= True)\n",
    "print('bureau Shape : ', bureau.shape)\n",
    "del buro_bal,bb_agg\n",
    "gc.collect()\n",
    "bureau['PEC_ANNUITY_DEBT'] = bureau['AMT_ANNUITY']/bureau['AMT_CREDIT_SUM_DEBT']\n",
    "num_aggregations = {\n",
    "    'DAYS_CREDIT': ['min', 'max', 'mean', 'var'],\n",
    "    'CREDIT_DAY_OVERDUE': ['max', 'mean'],\n",
    "    'DAYS_CREDIT_ENDDATE': ['min', 'max', 'mean'],\n",
    "    'AMT_CREDIT_MAX_OVERDUE': ['mean'],\n",
    "    'CNT_CREDIT_PROLONG': ['sum','mean'],\n",
    "    'PEC_ANNUITY_DEBT':['min','max','mean'],\n",
    "    'AMT_CREDIT_SUM': ['max','mean', 'sum'],\n",
    "    'AMT_CREDIT_SUM_DEBT': ['max','mean', 'sum'],\n",
    "    'AMT_CREDIT_SUM_OVERDUE': ['mean'],\n",
    "    'AMT_CREDIT_SUM_LIMIT': ['mean', 'sum'],\n",
    "    'DAYS_CREDIT_UPDATE': ['min','max','mean'],\n",
    "    'AMT_ANNUITY': ['max', 'mean'],\n",
    "    'MONTHS_BALANCE_MIN': ['min'],\n",
    "    'MONTHS_BALANCE_MAX': ['max'],\n",
    "    'MONTHS_BALANCE_MEAN': ['mean'],\n",
    "    'BB_COUNT': ['mean', 'sum']\n",
    "}\n",
    "cat_aggregations = {}\n",
    "for cat in bureau_cat: cat_aggregations[cat] = ['mean']\n",
    "for cat in bb_cat: cat_aggregations[cat + \"_MEAN\"] = ['mean']\n",
    "bureau_agg = bureau.groupby('SK_ID_CURR').agg(dict(num_aggregations, **cat_aggregations))\n",
    "bureau_agg.columns = pd.Index(['BURO_' + e[0] + \"_\" + e[1].upper() for e in bureau_agg.columns.tolist()])\n",
    "bureau_agg['BURO_COUNT'] = bureau.groupby('SK_ID_CURR').size()\n",
    "bureau_agg['BURO_MAX_OVERDUE_DEBT'] = bureau_agg['BURO_AMT_CREDIT_MAX_OVERDUE_MEAN']/bureau_agg['BURO_AMT_CREDIT_SUM_DEBT_MEAN']\n",
    "print('bureau_agg Shape : ', bureau_agg.shape)\n",
    "\n",
    "active = bureau[bureau['CREDIT_ACTIVE_Active'] == 1]\n",
    "active_agg = active.groupby('SK_ID_CURR').agg(num_aggregations)\n",
    "active_agg.columns = pd.Index(['ACT_' + e[0] + \"_\" + e[1].upper() for e in active_agg.columns.tolist()])\n",
    "active_agg['ACT_COUNT'] = active.groupby('SK_ID_CURR').size()\n",
    "bureau_agg = bureau_agg.reset_index().join(active_agg, how='left', on='SK_ID_CURR')\n",
    "print('bureau_agg Shape : ', bureau_agg.shape)\n",
    "del active, active_agg\n",
    "gc.collect()\n",
    "\n",
    "closed = bureau[bureau['CREDIT_ACTIVE_Closed'] == 1]\n",
    "closed_agg = closed.groupby('SK_ID_CURR').agg(dict({'DAYS_ENDDATE_FACT': ['min','max','mean']},**num_aggregations))\n",
    "closed_agg.columns = pd.Index(['CLS_' + e[0] + \"_\" + e[1].upper() for e in closed_agg.columns.tolist()])\n",
    "closed_agg['CLS_COUNT'] = closed.groupby('SK_ID_CURR').size()\n",
    "bureau_agg = bureau_agg.join(closed_agg, how='left', on='SK_ID_CURR')\n",
    "print('bureau_agg Shape : ', bureau_agg.shape)\n",
    "del closed, closed_agg, bureau\n",
    "gc.collect()\n",
    "\n",
    "all_data = all_data.merge(bureau_agg, how='left', on='SK_ID_CURR')\n",
    "print('all_data Shape : ', all_data.shape)\n",
    "del bureau_agg\n",
    "gc.collect()\n",
    "\n",
    "prev = pd.read_csv('./input/previous_application.csv')\n",
    "print('prev Shape : ', prev.shape)\n",
    "prev, cat_cols = one_hot_encoder(prev, nan_as_category= True)\n",
    "# Days 365.243 values -> nan\n",
    "prev['DAYS_FIRST_DRAWING'].replace(365243, np.nan, inplace= True)\n",
    "prev['DAYS_FIRST_DUE'].replace(365243, np.nan, inplace= True)\n",
    "prev['DAYS_LAST_DUE_1ST_VERSION'].replace(365243, np.nan, inplace= True)\n",
    "prev['DAYS_LAST_DUE'].replace(365243, np.nan, inplace= True)\n",
    "prev['DAYS_TERMINATION'].replace(365243, np.nan, inplace= True)\n",
    "# Add feature: value ask / value received percentage\n",
    "prev['APP_CREDIT_PERC'] = prev['AMT_APPLICATION'] / prev['AMT_CREDIT']\n",
    "# Previous applications numeric features\n",
    "num_aggregations = {\n",
    "    'AMT_ANNUITY': ['min','max', 'mean'],\n",
    "    'AMT_APPLICATION': ['min','max', 'mean'],\n",
    "    'AMT_CREDIT': ['min','max', 'mean'],\n",
    "    'APP_CREDIT_PERC': ['min', 'max', 'mean', 'var'],\n",
    "    'AMT_DOWN_PAYMENT': ['min', 'max', 'mean'],\n",
    "    'AMT_GOODS_PRICE': ['min', 'max', 'mean'],\n",
    "    'HOUR_APPR_PROCESS_START': ['min', 'max', 'mean'],\n",
    "    'RATE_DOWN_PAYMENT': ['min', 'max', 'mean'],\n",
    "    'DAYS_DECISION': ['min', 'max', 'mean'],\n",
    "    'SELLERPLACE_AREA': ['min', 'max', 'mean'],\n",
    "    'DAYS_FIRST_DRAWING': ['min', 'max', 'mean'],\n",
    "    'DAYS_FIRST_DUE': ['min', 'max', 'mean'],\n",
    "    'DAYS_LAST_DUE_1ST_VERSION': ['min', 'max', 'mean'],\n",
    "    'DAYS_LAST_DUE': ['min', 'max', 'mean'],\n",
    "    'DAYS_TERMINATION': ['min', 'max', 'mean'],\n",
    "    'CNT_PAYMENT': ['mean', 'sum']\n",
    "}\n",
    "# Previous applications categorical features\n",
    "cat_aggregations = {}\n",
    "for cat in cat_cols:\n",
    "    cat_aggregations[cat] = ['mean']\n",
    "\n",
    "prev_agg = prev.groupby('SK_ID_CURR').agg(dict(num_aggregations, **cat_aggregations))\n",
    "prev_agg.columns = pd.Index(['PREV_' + e[0] + \"_\" + e[1].upper() for e in prev_agg.columns.tolist()])\n",
    "prev_agg['PREV_COUNT'] = prev.groupby('SK_ID_CURR').size()\n",
    "print('prev_agg Shape : ', prev_agg.shape)\n",
    "# Previous Applications: Approved Applications - only numerical features\n",
    "\n",
    "ap_num_aggregations = {\n",
    "        'AMT_ANNUITY': ['min','max', 'mean'],\n",
    "        'AMT_APPLICATION': ['min','max', 'mean'],\n",
    "        'AMT_CREDIT': ['min','max', 'mean'],\n",
    "        'APP_CREDIT_PERC': ['min', 'max', 'mean', 'var'],\n",
    "        'AMT_DOWN_PAYMENT': ['min', 'max', 'mean'],\n",
    "        'AMT_GOODS_PRICE': ['min', 'max', 'mean'],\n",
    "        'HOUR_APPR_PROCESS_START': ['min', 'max', 'mean'],\n",
    "        'RATE_DOWN_PAYMENT': ['min', 'max', 'mean'],\n",
    "        'DAYS_DECISION': ['min', 'max', 'mean'],\n",
    "        'SELLERPLACE_AREA': ['min', 'max', 'mean'],\n",
    "        'DAYS_FIRST_DRAWING': ['min', 'max', 'mean'],\n",
    "        'DAYS_FIRST_DUE': ['min', 'max', 'mean'],\n",
    "        'DAYS_LAST_DUE_1ST_VERSION': ['min', 'max', 'mean'],\n",
    "        'DAYS_LAST_DUE': ['min', 'max', 'mean'],\n",
    "        'DAYS_TERMINATION': ['min', 'max', 'mean'],\n",
    "        'CNT_PAYMENT': ['mean', 'sum'],\n",
    "        'NEW_PA_AMT_UNREPAID':['min','max','mean', 'sum']\n",
    "    }\n",
    "approved = prev[prev['NAME_CONTRACT_STATUS_Approved'] == 1]\n",
    "approved['NEW_PA_AMT_UNREPAID'] = approved[approved['DAYS_LAST_DUE_1ST_VERSION'] > 0]\\\n",
    "    ['DAYS_LAST_DUE_1ST_VERSION']/30*(approved[approved['DAYS_LAST_DUE_1ST_VERSION'] > 0]['AMT_ANNUITY'])\n",
    "approved_agg = approved.groupby('SK_ID_CURR').agg(ap_num_aggregations)\n",
    "approved_agg.columns = pd.Index(['APR_' + e[0] + \"_\" + e[1].upper() for e in approved_agg.columns.tolist()])\n",
    "approved_agg['APR_COUNT'] = approved.groupby('SK_ID_CURR').size()\n",
    "prev_agg = prev_agg.reset_index().join(approved_agg, how='left', on='SK_ID_CURR')\n",
    "print('prev_agg Shape : ', prev_agg.shape)\n",
    "# Previous Applications: Refused Applications - only numerical features\n",
    "refused = prev[prev['NAME_CONTRACT_STATUS_Refused'] == 1]\n",
    "refused_agg = refused.groupby('SK_ID_CURR').agg(num_aggregations)\n",
    "refused_agg.columns = pd.Index(['REF_' + e[0] + \"_\" + e[1].upper() for e in refused_agg.columns.tolist()])\n",
    "refused_agg['REF_COUNT'] = refused.groupby('SK_ID_CURR').size()\n",
    "prev_agg = prev_agg.join(refused_agg, how='left', on='SK_ID_CURR')\n",
    "print('prev_agg Shape : ', prev_agg.shape)\n",
    "del refused, refused_agg, approved, approved_agg, prev\n",
    "gc.collect()\n",
    "\n",
    "all_data = all_data.merge(prev_agg, how='left', on='SK_ID_CURR')\n",
    "print('all_data Shape : ', all_data.shape)\n",
    "del prev_agg\n",
    "gc.collect()\n",
    "\n",
    "pos = pd.read_csv('./input/POS_CASH_balance.csv')\n",
    "print('pos Shape : ', pos.shape)\n",
    "pos, cat_cols = one_hot_encoder(pos, nan_as_category= True)\n",
    "# Features\n",
    "aggregations = {\n",
    "    'MONTHS_BALANCE': ['min','max', 'mean'],\n",
    "    'CNT_INSTALMENT': ['sum', 'mean'],\n",
    "    'SK_DPD': ['sum','max','min','mean','size'],\n",
    "    'SK_DPD_DEF': ['sum','max', 'min','mean','size']\n",
    "}\n",
    "for cat in cat_cols:\n",
    "    aggregations[cat] = ['mean']\n",
    "\n",
    "pos_agg = pos.groupby('SK_ID_CURR').agg(aggregations)\n",
    "pos_agg.columns = pd.Index(['POS_' + e[0] + \"_\" + e[1].upper() for e in pos_agg.columns.tolist()])\n",
    "# Count pos cash accounts\n",
    "pos_agg['POS_COUNT'] = pos.groupby('SK_ID_CURR').size()\n",
    "print('pos_agg Shape : ', pos_agg.shape)\n",
    "del pos\n",
    "gc.collect()\n",
    "\n",
    "all_data = all_data.join(pos_agg, how='left', on='SK_ID_CURR')\n",
    "print('all_data Shape : ', all_data.shape)\n",
    "del pos_agg\n",
    "gc.collect()\n",
    "\n",
    "ins = pd.read_csv('./input/installments_payments.csv')\n",
    "print('ins Shape : ', ins.shape)\n",
    "ins, cat_cols = one_hot_encoder(ins, nan_as_category= True)\n",
    "# Percentage and difference paid in each installment (amount paid and installment value)\n",
    "ins['PAYMENT_PERC'] = ins['AMT_PAYMENT'] / ins['AMT_INSTALMENT']\n",
    "ins['PAYMENT_DIFF'] = ins['AMT_INSTALMENT'] - ins['AMT_PAYMENT']\n",
    "# Days past due and days before due (no negative values)\n",
    "ins['DPD'] = ins['DAYS_ENTRY_PAYMENT'] - ins['DAYS_INSTALMENT']\n",
    "ins['NEW_IP_DPD_RATIO'] = ins['DAYS_ENTRY_PAYMENT'] / ins['DAYS_INSTALMENT']\n",
    "ins['DBD'] = ins['DAYS_INSTALMENT'] - ins['DAYS_ENTRY_PAYMENT']\n",
    "ins['NEW_IP_DBD_RATIO'] = ins['DAYS_INSTALMENT'] / ins['DAYS_ENTRY_PAYMENT']\n",
    "ins['DPD'] = ins['DPD'].apply(lambda x: x if x > 0 else 0)\n",
    "ins['DBD'] = ins['DBD'].apply(lambda x: x if x > 0 else 0)\n",
    "# Features: Perform aggregations\n",
    "aggregations = {\n",
    "    'NUM_INSTALMENT_VERSION': ['nunique'],\n",
    "    'DPD': ['max','mean', 'sum'],\n",
    "    'NEW_IP_DPD_RATIO':['max', 'mean','min'],\n",
    "    'NEW_IP_DBD_RATIO':['max', 'mean','min'],\n",
    "    'DBD': ['max', 'mean', 'sum'],\n",
    "    'PAYMENT_PERC': ['max', 'mean', 'sum', 'var'],\n",
    "    'PAYMENT_DIFF': ['max', 'mean', 'sum', 'var'],\n",
    "    'AMT_INSTALMENT': ['max', 'mean', 'sum'],\n",
    "    'AMT_PAYMENT': ['min', 'max', 'mean', 'sum'],\n",
    "    'DAYS_ENTRY_PAYMENT': ['max', 'mean', 'sum'],\n",
    "    'DAYS_INSTALMENT': ['max', 'mean', 'sum']\n",
    "}\n",
    "for cat in cat_cols:\n",
    "    aggregations[cat] = ['mean']\n",
    "ins_agg = ins.groupby('SK_ID_CURR').agg(aggregations)\n",
    "ins_agg.columns = pd.Index(['INS_' + e[0] + \"_\" + e[1].upper() for e in ins_agg.columns.tolist()])\n",
    "# Count installments accounts\n",
    "ins_agg['INS_COUNT'] = ins.groupby('SK_ID_CURR').size()\n",
    "print('ins_agg Shape : ', ins_agg.shape)\n",
    "del ins\n",
    "gc.collect()\n",
    "\n",
    "all_data = all_data.join(ins_agg, how='left', on='SK_ID_CURR')\n",
    "print('all_data Shape : ', all_data.shape)\n",
    "del ins_agg\n",
    "gc.collect()\n",
    "\n",
    "cc = pd.read_csv('./input/credit_card_balance.csv')\n",
    "print('cc Shape : ', cc.shape)\n",
    "cc, cat_cols = one_hot_encoder(cc, nan_as_category= True)\n",
    "# Features: Perform aggregations\n",
    "num_aggregations = {\n",
    "    'MONTHS_BALANCE': ['min','max', 'mean'],\n",
    "    'AMT_BALANCE': ['min','max', 'mean','sum'],\n",
    "    'AMT_CREDIT_LIMIT_ACTUAL': ['min','max', 'mean','sum','var'],\n",
    "    'AMT_DRAWINGS_ATM_CURRENT': ['min','max', 'mean','sum'],\n",
    "    'AMT_DRAWINGS_CURRENT': ['min','max', 'mean','sum'],\n",
    "    'AMT_DRAWINGS_OTHER_CURRENT': ['min','max', 'mean','sum'],\n",
    "    'AMT_DRAWINGS_POS_CURRENT': ['min','max', 'mean','sum'],\n",
    "    'AMT_INST_MIN_REGULARITY': ['min','max', 'mean','sum','var'],\n",
    "    'AMT_PAYMENT_CURRENT':['min','max', 'mean','sum'],\n",
    "    'AMT_PAYMENT_TOTAL_CURRENT':['min','max', 'mean','sum'],\n",
    "    'AMT_RECEIVABLE_PRINCIPAL':['min','max', 'mean','sum','var'],\n",
    "    'AMT_RECIVABLE':['min','max', 'mean','sum','var'],\n",
    "    'AMT_TOTAL_RECEIVABLE':['min','max', 'mean','sum','var'],\n",
    "    'CNT_DRAWINGS_ATM_CURRENT':['mean','sum'],\n",
    "    'CNT_DRAWINGS_CURRENT':['mean','sum'],\n",
    "    'CNT_DRAWINGS_OTHER_CURRENT':['mean','sum'],\n",
    "    'CNT_DRAWINGS_POS_CURRENT':['mean','sum'],\n",
    "    'CNT_INSTALMENT_MATURE_CUM':['mean','sum'],\n",
    "    'SK_DPD':['max', 'mean', 'sum'],\n",
    "    'SK_DPD_DEF':['max', 'mean', 'sum']\n",
    "}\n",
    "cat_aggregations = {}\n",
    "for cat in cat_cols:\n",
    "    cat_aggregations[cat] = ['mean']\n",
    "# General aggregations\n",
    "cc_agg = cc.groupby('SK_ID_CURR').agg(dict(num_aggregations,**cat_aggregations))\n",
    "cc_agg.columns = pd.Index(['CC_' + e[0] + \"_\" + e[1].upper() for e in cc_agg.columns.tolist()])\n",
    "# Count credit card lines\n",
    "cc_agg['CC_COUNT'] = cc.groupby('SK_ID_CURR').size()\n",
    "\n",
    "#cc_agg['INSTALLMENTS_PER_LOAN'] = cc_agg['CNT_INSTALMENT_MATURE_CUM_SUM'] / cc_agg['CC_COUNT']\n",
    "del cc\n",
    "print('cc_agg Shape : ', cc_agg.shape)\n",
    "gc.collect()\n",
    "\n",
    "all_data = all_data.join(cc_agg, how='left', on='SK_ID_CURR')\n",
    "print('all_data Shape : ', all_data.shape)\n",
    "del cc_agg\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(307511, 830)\n",
      "(48744, 830)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['SK_ID_CURR']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data_na = (all_data.isnull().sum() / len(all_data)) * 100\n",
    "all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index).sort_values(ascending=False)\n",
    "missing_data = pd.DataFrame({'Missing Ratio' :all_data_na})\n",
    "\n",
    "missing_data\n",
    "\n",
    "drop_col = missing_data[missing_data['Missing Ratio']==100]\n",
    "missing_data = missing_data[missing_data['Missing Ratio']<100]\n",
    "\n",
    "all_data.drop(drop_col.index.values,1,inplace=True)\n",
    "\n",
    "data = all_data[:n_data]\n",
    "test = all_data[n_data:]\n",
    "#del avg_buro, avg_prev,avg_pos,avg_cc_bal,avg_inst\n",
    "del all_data\n",
    "gc.collect()\n",
    "print data.shape\n",
    "print test.shape\n",
    "\n",
    "exclude_fea = []\n",
    "exclude_fea.append('SK_ID_CURR') \n",
    "exclude_fea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "folds = KFold(n_splits=5, shuffle=True, random_state=1700)\n",
    "oof_preds = np.zeros(data.shape[0])\n",
    "sub_preds = np.zeros(test.shape[0])\n",
    "feature_importance_df = pd.DataFrame()\n",
    "feats = [f for f in data.columns if f not in exclude_fea]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(feats)\n",
    "seeds = [1500,1600,1700,1800,1900]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 300 rounds.\n",
      "[100]\ttraining's auc: 0.753077\tvalid_1's auc: 0.739431\n",
      "[200]\ttraining's auc: 0.782726\tvalid_1's auc: 0.762276\n",
      "[300]\ttraining's auc: 0.801927\tvalid_1's auc: 0.775639\n",
      "[400]\ttraining's auc: 0.813935\tvalid_1's auc: 0.782417\n",
      "[500]\ttraining's auc: 0.822775\tvalid_1's auc: 0.785507\n",
      "[600]\ttraining's auc: 0.83018\tvalid_1's auc: 0.787615\n",
      "[700]\ttraining's auc: 0.836294\tvalid_1's auc: 0.788811\n",
      "[800]\ttraining's auc: 0.841926\tvalid_1's auc: 0.789645\n",
      "[900]\ttraining's auc: 0.847108\tvalid_1's auc: 0.790455\n",
      "[1000]\ttraining's auc: 0.85178\tvalid_1's auc: 0.790868\n",
      "[1100]\ttraining's auc: 0.856158\tvalid_1's auc: 0.791541\n",
      "[1200]\ttraining's auc: 0.860392\tvalid_1's auc: 0.79208\n",
      "[1300]\ttraining's auc: 0.864606\tvalid_1's auc: 0.79237\n",
      "[1400]\ttraining's auc: 0.868635\tvalid_1's auc: 0.792427\n",
      "[1500]\ttraining's auc: 0.872363\tvalid_1's auc: 0.792672\n",
      "[1600]\ttraining's auc: 0.876097\tvalid_1's auc: 0.792844\n",
      "[1700]\ttraining's auc: 0.87964\tvalid_1's auc: 0.792886\n",
      "[1800]\ttraining's auc: 0.882969\tvalid_1's auc: 0.792771\n",
      "[1900]\ttraining's auc: 0.886312\tvalid_1's auc: 0.792759\n",
      "Early stopping, best iteration is:\n",
      "[1696]\ttraining's auc: 0.879494\tvalid_1's auc: 0.792907\n",
      "Fold  1 AUC : 0.792907\n",
      "Training until validation scores don't improve for 300 rounds.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-80745fd20332>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m     clf.fit(trn_x, trn_y, \n\u001b[1;32m     19\u001b[0m             \u001b[0meval_set\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrn_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrn_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mval_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m             \u001b[0meval_metric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'auc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mearly_stopping_rounds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m  \u001b[0;31m#30\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m            )\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/lightgbm/sklearn.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, init_score, eval_set, eval_names, eval_sample_weight, eval_class_weight, eval_init_score, eval_metric, early_stopping_rounds, verbose, feature_name, categorical_feature, callbacks)\u001b[0m\n\u001b[1;32m    673\u001b[0m                                         \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeature_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    674\u001b[0m                                         \u001b[0mcategorical_feature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcategorical_feature\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 675\u001b[0;31m                                         callbacks=callbacks)\n\u001b[0m\u001b[1;32m    676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/lightgbm/sklearn.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, init_score, group, eval_set, eval_names, eval_sample_weight, eval_class_weight, eval_init_score, eval_group, eval_metric, early_stopping_rounds, verbose, feature_name, categorical_feature, callbacks)\u001b[0m\n\u001b[1;32m    467\u001b[0m                               \u001b[0mverbose_eval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeature_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    468\u001b[0m                               \u001b[0mcategorical_feature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcategorical_feature\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 469\u001b[0;31m                               callbacks=callbacks)\n\u001b[0m\u001b[1;32m    470\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    471\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mevals_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/lightgbm/engine.pyc\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(params, train_set, num_boost_round, valid_sets, valid_names, fobj, feval, init_model, feature_name, categorical_feature, early_stopping_rounds, evals_result, verbose_eval, learning_rates, keep_training_booster, callbacks)\u001b[0m\n\u001b[1;32m    199\u001b[0m                                     evaluation_result_list=None))\n\u001b[1;32m    200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m         \u001b[0mbooster\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m         \u001b[0mevaluation_result_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/lightgbm/basic.pyc\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, train_set, fobj)\u001b[0m\n\u001b[1;32m   1522\u001b[0m             _safe_call(_LIB.LGBM_BoosterUpdateOneIter(\n\u001b[1;32m   1523\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1524\u001b[0;31m                 ctypes.byref(is_finished)))\n\u001b[0m\u001b[1;32m   1525\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__is_predicted_cur_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mFalse\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__num_dataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mis_finished\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for n_fold, (trn_idx, val_idx) in enumerate(folds.split(data)):\n",
    "    trn_x, trn_y = data[feats].iloc[trn_idx], y.iloc[trn_idx]\n",
    "    val_x, val_y = data[feats].iloc[val_idx], y.iloc[val_idx]\n",
    "    \n",
    "    clf = LGBMClassifier(\n",
    "        n_estimators=10000,\n",
    "        learning_rate=0.02,\n",
    "        num_leaves=31,\n",
    "        colsample_bytree=0.9338690,\n",
    "        subsample=0.97291513,\n",
    "        max_depth=8,\n",
    "        reg_alpha=0.06663684,\n",
    "        reg_lambda=0.0988343,\n",
    "        min_split_gain=0.07916152,\n",
    "        min_child_weight=82.8850799,\n",
    "        random_state=1700,\n",
    "    )\n",
    "    clf.fit(trn_x, trn_y, \n",
    "            eval_set= [(trn_x, trn_y), (val_x, val_y)], \n",
    "            eval_metric='auc', verbose=100, early_stopping_rounds=300  #30\n",
    "           )\n",
    "    \n",
    "    oof_preds[val_idx] = clf.predict_proba(val_x, num_iteration=clf.best_iteration_)[:, 1]\n",
    "    sub_preds += clf.predict_proba(test[feats], num_iteration=clf.best_iteration_)[:, 1] / folds.n_splits\n",
    "    fold_importance_df = pd.DataFrame()\n",
    "    fold_importance_df[\"feature\"] = feats\n",
    "    fold_importance_df[\"importance\"] = clf.feature_importances_\n",
    "    fold_importance_df[\"fold\"] = n_fold + 1\n",
    "    feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)\n",
    "    print('Fold %2d AUC : %.6f' % (n_fold + 1, roc_auc_score(val_y, oof_preds[val_idx])))\n",
    "    del clf, trn_x, trn_y, val_x, val_y\n",
    "    gc.collect()\n",
    "    \n",
    "print('Full AUC score %.6f' % roc_auc_score(y, oof_preds)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['TARGET'] = sub_preds\n",
    "\n",
    "test[['SK_ID_CURR', 'TARGET']].to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAALICAYAAABijlFfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3Xl4Tdf++PH3SUhyIlQEETW1RMwkYhbVGDLnxBB15UaQEnOrXJQ2auw13A70ShQxl6IRkRNDQ0yNxpTrkhY1/UqLtCI4mRPn90e+9nUkSCLk0M/refI8OWuvvdZn75VWPtlrra3S6/V6hBBCCCGEEEI8M5PyDkAIIYQQQgghXhWSYAkhhBBCCCFEGZEESwghhBBCCCHKiCRYQgghhBBCCFFGJMESQgghhBBCiDIiCZYQQgghhBBClBFJsIQQQvwlHT9+HDc3t2LVTUxMpFu3bs85IlFShw4dYvTo0eUdxgv1zTff0LlzZxwdHbl9+/YT6wYGBrJly5Yij127dg0HBwfy8vKe2Ma+fft4//33Sx2vEH9FkmAJIYR4pbm6upKQkFCo3NnZmd27d5dJH1OnTuXzzz8vVK7VavH396dNmzZ06tQJf39/NmzYwINXUE6dOpUWLVrg6OiIo6Mjffv25ejRo8r5kZGRODg4MG/ePIN24+LicHBwYOrUqUXGk5iYSJMmTZR2HR0dGTly5HO7zvLy+eefM2LEiPIO44XJzc3ln//8JxERESQlJWFtbf3c+3R1deXChQucPXv2ufclxKtCEiwhhBDiOYiIiGDu3LkEBwdz+PBhEhISmDlzJidPniQ3N1epFxwcTFJSEidPnuRvf/sb48aNIz8/Xzler149du7cafCkISoqigYNGjyx/5o1a5KUlKR8hYeHl/k1ltTTnpaUxH//+190Oh1t2rQpszaNSVH36tatW2RnZ9OoUaMXGouXlxebN29+oX0K8TKTBEsIIcRf0qPT/pKTk/Hz88PR0ZHx48fz/vvvF3paExERQadOnejatSvfffcdAN9++y07duxg5cqVypOie/fusXjxYmbMmIG7uztWVlaoVCqaNWvGv/71L8zMzArFo1Kp8Pb2Ji0tjT///FMpr169Oo0bN+bw4cMApKWlkZSUhKura6mu+/79+3z99df07NmTDh068N5775GWlqYcHz9+PF26dKFt27YEBATwyy+/PPY6ARwcHPh//+//Kec//JTrwT3++uuv6dKlCx9++CEA8fHxaDQanJ2dGThwoMHTka+//hoXFxccHR1xc3PjyJEjRV7HwYMHadeunUHZnDlzeOutt3BycqJv374cP34cgJs3b9KqVSuD6/zpp5/o0KEDubm55Ofn889//pMOHTrg6urK+vXrnzh97uLFiwQGBuLs7IyXlxd79+4F4NSpU3Tp0sUgQf7+++/x8fF56r1/MGVvy5YtdO/enaCgIIM+L1++jLu7OwDt2rVj8ODBAJw8eZJ+/frRtm1b+vXrx8mTJ4uMOT8/n/nz59OhQwd69OjBgQMHDI5HRkbSo0cPHB0dcXV1JTo6WjnWvn179u/fX2S7QojCJMESQgjxl5eTk8PYsWPp06cPR48exdvbm7i4OIM6f/75J/fu3ePgwYPMnTuXWbNmcefOHd555x18fHyUJ1Hh4eEkJSWRk5NDjx49ih1Dfn4+UVFR1KlTh+rVqxsc8/PzIyoqCiiYdtijR48ik7TiWLduHXFxcaxfv55Dhw7x2muvMWvWLOV4t27d2L17N0eOHKFZs2ZMmjQJoMjrLI4///yTO3fuEB8fz+zZs/npp5+YNm0as2bNIjExkXfeeYfRo0eTk5PDpUuX2LBhA1u3biUpKYmVK1fy+uuvF9nu+fPneeONNwzKWrZsSVRUlDKG7733HtnZ2dja2tKmTRv27Nmj1N2xYwdubm5UrFiRzZs3c/DgQbZv3862bdsKjf3DcnNzGTlyJF26dCEhIYGPPvqISZMmcenSJVq3bo1arebHH3806OdBgvW0ew9w7NgxYmNjWblypUH5G2+8QUxMjFJn7dq1pKWlERISQmBgIImJiQwdOpSQkJAi12Zt3ryZ+Ph4oqKi+O6779i1a5dyLCMjgzlz5rB8+XKSkpLYtGkTTZs2VY43bNiQ3377DZ1O99j7IoT4H0mwhBBC/OWdOnWKvLw8Bg8eTMWKFenduzctW7Y0qFOhQgXGjBlDxYoVeeutt7C0tOTy5ctFtnf79m2sra2pUKGCUjZw4ECcnZ1p1aoVx44dU8ojIiJwdnbG0dGRefPm8d5772FqamrQXq9evTh69Cj37t1j+/btaDSap15TSkoKzs7OyldsbCwAmzZtYsKECdSqVQszMzPGjh3L7t27lac1/fv3x8rKCjMzM8aNG8fZs2e5d+9e8W5kEUxMTBg/fjxmZmZYWFjw7bff8s4779C6dWtMTU3p06cPFStW5D//+Q+mpqbk5ORw8eJFcnNzqVOnDvXq1Suy3Xv37lGpUiWDMo1Go9z3YcOGkZOTo4yRj4+PkqDo9XpiY2OVxGfnzp0MHjyYWrVq8dprrz1xXdepU6fIyMhgxIgRmJmZ0alTJ95++220Wi1QMJ3uQT86nY6DBw/i5eUFPP3eA4wbNw5LS0ssLCyeem/3799P/fr18fPzo0KFCnh7e/Pmm28SHx9fqO7OnTsJCgrCzs6OqlWrEhISYnDcxMSEX375haysLGrWrIm9vb1y7MF9vnv37lNjEkJAhadXEUIIIV5tKSkp2NraolKplDI7OzuDOlWrVjVImNRqNRkZGUW2V7VqVW7fvk1eXp5yzqZNm4CCJ0T3799X6g4bNowJEyag1+v55ZdfGDZsGK+99hpvvfWWUsfCwoK33nqLpUuXkpaWRtu2bTl48OATr6lmzZpF1vn9998ZM2YMJib/+xuriYkJt27donr16nz++efs2rWL1NRUpc7t27epXLnyE/t7HGtra8zNzQ36j4qKYv369UpZbm4uKSkptG/fnmnTprFkyRIuXLhA165dmTp1Kra2toXarVKlCunp6QZlK1euZOvWraSkpKBSqdDpdMrTnN69ezN79mxSUlK4cuUKJiYmODs7AwXj//B416pV67HXk5KSQq1atQzuX+3atbl58yZQkMgNHDiQmTNn8v3339OsWTPlKdyT7n1x+i4qltq1axuUPRzLo3UfvsaHz7O0tOTzzz8nIiKC6dOn4+TkxJQpU2jYsCGAcp+rVKlS7NiE+CuTBEsIIcRfXo0aNbh58yZ6vV5Jsq5fv07dunWLdf7DiRmAo6MjZmZm7N27t9hbwatUKho3boyTkxMHDhwwSLCgYJpgUFAQY8eOLVZ7j1OrVi3mzZtH27ZtCx2Liopi7969rFq1ijp16nDv3j3atWun7Hr46HVCQaKZmZmpfP7jjz8MEqJHz7Gzs2PkyJGMGjWqyPh8fHzw8fFBp9MRGhrKokWLWLhwYaF6Dg4OXLlyRfl8/PhxVqxYwerVq7G3t8fExMQg9tdee40uXboQGxvLpUuX8PT0VGKrUaMGN27cUNp6+PtH1axZkxs3bnD//n0lUbp+/bqy6UijRo2oXbs2Bw8eJCYmBm9vb+XcJ937a9euFXm/nqRmzZr8/vvvBmXXr1/HxcWlUN0aNWpw/fp1g3oPc3FxwcXFhaysLL744gs+/vhjvvnmG6Bgzdnrr7+OlZVVsWMT4q9MpggKIYR45eXm5pKdna18Pbp5QZs2bTA1NWX9+vXk5eURFxfH6dOni92+jY2N8gsyFPylf8yYMcycOZNdu3ah0+m4f/8+P//8s0Ey8qiLFy9y8uTJIneJa9++PatWreLvf/97seMqyt/+9je++OILfvvtNwBSU1OVNUfp6emYmZlhbW1NZmYmn3322ROvE6BJkybExMSQn5/PwYMHDaY/FsXf359NmzZx6tQp9Ho9GRkZ7N+/H51Ox6VLlzhy5Ag5OTmYmZlhbm5u8LTnYW+99ZZBX+np6ZiamlKtWjXy8vL46quvCq0Z8vHxYfv27ezevVuZHgjg4eHB2rVruXnzJnfv3mX58uWPjb9Vq1ZYWFiwYsUKcnNzSUxMZN++fXh6eip1vL29WbNmDceOHVM2poAn3/vSeOutt7hy5Qo7duwgLy+P2NhYLly4QPfu3QvV9fDwYN26ddy4cYM7d+7w9ddfK8f+/PNP4uLiyMjIwMzMDEtLS4P7fuzYMXkPnBAlIAmWEEKIV96IESNo1aqV8rVkyRKD42ZmZixZsoStW7fSrl07oqOj6d69e7E3kujfvz8XLlzA2dlZefHt8OHDmTp1KitWrKBLly507tyZ0NBQJk2ahKOjo3Lug1352rRpQ3BwMH379mXgwIGF+lCpVHTq1ImqVas+w52AwYMH4+rqyrBhw3B0dGTAgAH897//BQqektWuXRsXFxe8vLwKbYFe1HVOnz6d+Ph4nJ2d2bFjBz179nxi/y1btmT27NnMmjWLdu3a0bt3byIjI4GCzUb+9a9/0aFDB7p27UpqaioffPBBke00b94cKysrTp06BUDXrl1xcXHBzc0NV1dXzM3NC03zdHV15cqVK1SvXp0mTZoo5QMGDKBLly74+vri5+fHW2+9RYUKFQqthYOCn5Xw8HAOHjxIx44dmTlzJgsWLFCm00FBgnXs2DE6duxItWrVinXvS8Pa2prw8HBWrVpFhw4dWLFiBeHh4QZ9PnyNXbt2RaPR0KdPH3r37q0cu3//PqtXr8bFxYX27dtz7NgxPvnkE+W4Vqst8mdSCFE0lf7Bs3MhhBBCKPz9/Rk4cCD9+vUr71DEYxw+fJhvvvmGpUuXlmm7Bw4c4JNPPilys4i/mn379rF9+3a+/PLL8g5FiJeGJFhCCCEEcPToUd544w2sra3ZsWMHM2bMIC4ujpo1a5Z3aOI5y8rKIjExkS5dunDr1i3GjRtH69atmT59enmHJoR4CckmF0IIIQQFL3J9//33yczMpE6dOixevFiSq78IvV7P4sWLef/997GwsKB79+6899575R2WEOIlJU+whBBCCCGEEKKMyCYXQgghhBBCCFFGZIqgEK+gzMwcdLrs8g5DFMHKylzGxgjJuBgvGRvjJWNjvGRsXowaNYp+Abs8wRLiFVShQuGthYVxkLExTjIuxkvGxnjJ2BgvGZvyJWuwhHgF/RG2vrxDEEIIIUR56q8p7wheefIESwghhBBCCCGeM0mwhBBCCCGEEKKMSIIljE7Tpk3RaDR4e3szfvx4MjMzlWNxcXE4ODhw8eJFALKzs3F3d+fcuXNKnRUrVhAaGsq1a9dwcHDg888/V46lpqbSvHlzZs2aBcCSJUtwcXFBo9EoX3fv3iUxMREHBwf27dunnBsSEkJiYiJjxoxBo9HQq1cv2rZtq5x38uTJIq8nMDAQNzc3fH196devHz///LPB8Z9//hkHBwcOHjwIwO3bt5U2u3TpYhBfTk6Ocn8efH399dfPeMeFEEIIIURZkV0EhdGxsLBg+/btAEycOJFNmzYxdOhQAGJiYmjbti1arZbx48djbm7OtGnTmDlzJhs2bCAlJYVNmzbx3Xffce/ePerUqcOBAweYMGECALt27aJRo0YG/Q0ZMoTg4OBCcdSqVYvw8HBcXV0Nyv/9738DkJiYSEREBMuWLXvqNS1atIiWLVvy3XffsWDBAlatWqUce/iaunXrhrW1tXL9S5YswdLS0iC+h++PEEIIIZ7u5PWrbElOIjMvt7xDeXEO7y7vCF4otdoCf/9BODk5l3cokmAJ4+bs7Kw8nUpPT+fEiROsXbuWkSNHMn78eAC6devGd999R1RUFPv372fs2LG89tpr3Lt3D7VaTcOGDTl9+jQtW7Zk586deHh4kJKS8tS+mzRpQl5eHj/88ANdunQpk+tp06YNK1euVD7r9Xp27drFqlWrGDRoENnZ2Zibm5dJX0IIIYQosOPcGS6n3SrvMF4s3d3yjuCFi4mJkgRLiCfJy8vj4MGDuLi4ALB3715cXFx44403sLa25syZM7Ro0QKAadOm4e/vT/369fHz8zNox9PTk9jYWKpXr46JiQk1a9Y0SLBWr15NdHQ0AFWqVGHdunXKsZEjR/Lll1+WWYJ16NAhevbsqXw+efIkderUoV69enTo0IH9+/fj5ub2xDaysrLQaP63M1BISAienp5lEp8QQgjxKvJxaEFWXu5f6wmWlVV5R/BCqdUWeHv7Pb3iCyAJljA6DycQzs7O9O/fHwCtVsvgwYOBgqRJq9UqCZatrS0dO3ake/fuhdpzcXHhyy+/xMbGpshE5HFTBAHatWsHwPHjx5/pmiZNmkRubi4ZGRkG0/u0Wi1eXl7KNW3fvv2pCZZMERRCCCFKxsmuLk52dcs7jBdLtmkvN5JgCaNTVAKRlpbGjz/+yPnz51GpVOTn56NSqZg8eTIqlQoAExMTTEwK79tiZmZG8+bNWbVqFVqt1mDjiuIYOXIkYWFhVKhQ+v9cFi1aRIsWLViwYAGzZ8/mq6++Ij8/nz179rB3717Cw8PR6/WkpaWh0+mw+ov91UkIIYQQ4lUhuwiKl8Lu3bvRaDTEx8ezb98+Dhw4QJ06dYr9ZGnYsGFMmjSJqlWrlrjvrl27cvfuXYOdCktDpVLx3nvv8Z///IeLFy9y5MgRHBwcOHDgAPv27SM+Pp7evXsTFxf3TP0IIYQQQojyI0+wxEshJiaG4cOHG5T17t2bmJgYZRrfk9jb22Nvb1/ksYfXYMH/dgl82MiRIxk9enQJoy7MwsKCYcOGsXLlSvR6vcF6LCi4po0bNxZaR/awR9dgubi4MGnSJIM6Vd/9G2lpGc8cryh7VataytgYIRkX4yVjY7xkbIyXjE35Uun1en15ByGEKFu5ufnyP1YjJf/oGScZF+MlY2O8ZGyMl4zNi1GjRuUiy2WKoBBCCCGEEEKUEXmCJUQZGTNmDNeuXTMomzRpkrLN/Iv0R/jXL7xPIYQQQhipfn8r7wheSY97giVrsIQoI0Wt3RJCCCGEEH8tMkVQCCGEEEIIIcqIPMESRqlp06Y0btxY+ezl5UVwcDD+/v58+OGHys6Bw4YNw9/fn4iICHJycrhz5w5ZWVnY2toCBU+V6tSpU6j9rVu3smbNGgD0ej3vv/8+PXv2RK/XExYWRlRUFCqVipo1axIaGqrsQOjo6EhSUpLSTmRkJGfOnCE0NJQlS5awefNmqlWrRm5uLqNHj8bb21upu3LlSrZs2YK5uTkVKlQgMDAQPz8/AgMDSUlJwcLCAoD69euzePHiIu/LqlWr2LJlC6amplSrVo158+bx+uuvP8utFkIIIYQQZUgSLGGUinrZMMCMGTP46KOPiIyMZPfu3ahUKjw8PPDw8AAME57HuXHjBuHh4Wzbto3KlSuTnp5OamoqABs2bCApKYnt27ejVqs5fPgwo0aNQqvVYm5u/tS4hwwZQnBwMFeuXKFv3764ublRsWJFNm7cSEJCAlu3bsXKygqdTsf333+vnLdo0SJatmz51PabNm3Kd999h1qt5ptvvmHhwoV88cUXTz1PCCGEEC+fk9d/Z8uZM2Tm5T5bQ4cOlU1A/0ettsDffxBOTs5l2u6rQhIs8VJp3bo1jo6OfPXVV+zYsYNVq1aVuI1bt25RqVIlLC0tAahUqRKVKlUCYPny5axfvx61Wg0UvGTY0dGR6Oho/P39i91HgwYNUKvV3L17FxsbG5YtW8a6deuwsrICwMrKij59+pQ49o4dOyrft2nTxuD9XUIIIYR4tew4d5bLabefvSGd7tnbeERMTJQkWI8hCZYwSo++TDckJARPT08APvjgA7p3705QUBD169cvcdtNmjShevXq9OjRg06dOtGrVy9cXV3R6XRkZmZSt25dg/otWrTgwoULJeojOTmZ+vXrY2Njg06nIz09vVC7D5s0aZIyRbBz585MmTLlqX1s3bqVbt26lSguIYQQQrw8fByakJWb9+xPsKyqlE1A/0ettsDb269M23yVSIIljNLjpggCHD9+HCsrK86fP1+qtk1NTVmxYgWnT5/myJEjfPrppyQnJzN06NBStadSqZTvV69eTWRkJFeuXCEsLKzYbRR3iuAD27dv58yZM6xfv75EsQohhBDi5eFkVxsnu9rP3pBs0/5CyS6C4qWSkZHBwoULWbNmDampqRw4cKBU7ahUKlq1akVISAifffYZe/bswcrKCrVazdWrVw3qJicn06hRIwDMzc3JyclRjt25cwdra2vl85AhQ9BqtSxevJjp06eTnZ2NlZUVlpaWhdotrYSEBMLDwwkLC8PMzKxM2hRCCCGEEGVDEizxUvn3v/+Nu7s7DRs2ZMaMGcybN4/s7OwStXHz5k2Sk5OVz2fPnqV27YK/DgUHBzNnzhyysrKAgmTmxIkT+Pj4ANC+fXtl3VNWVhY7d+6kQ4cOhfro0aMHLVq0YNu2bQCMGDGCmTNnovu/OdDp6elERUWV8Orhp59+IjQ0lLCwMGxsbEp8vhBCCCGEeL5kiqAwSo+uwXJxcUGj0RAXF6dMHWzWrBkuLi4sX76csWPHFrvtvLw85s+fT0pKCubm5lSrVo2ZM2cCEBgYyN27d/Hx8cHExIQaNWqwdOlSZX3U9OnTCQ0NZd26dej1evz8/JQt4x81ZswYJk6cyIABAxg0aBAZGRn069ePihUrUqFCBYMpiQ+vwbK2tmb16tVFtrlgwQIyMjJ47733ALCzsyM8PLxQvarBwaSlZRT7nogXp2pVSxkbIyTjYrxkbIyXjI3xkrEpXyq9Xq8v7yCEEGUrNzdf/sdqpOQfPeMk42K8ZGyMl4yN8ZKxeTFq1KhcZLlMERRCCCGEEEKIMiJTBMUrzd/f32BTCiiYZufg4FBOERVPWFgYu3btMihzd3dn1KhRxTo/dYW8fNhYpZZ3AKJIMi7GS8bGeMnYvBgm/UeUdwiihGSKoBCvoJth/yrvEIQQQghRBkqTYMkUwRdDpggKIYQQQgghxHMmUwSF0WratCmNGzdWPnt5eREcHIy/vz8ffvihsnvfsGHD8Pf3JyIigpycHO7cuUNWVha2trZAwdbuderUKdT+1q1bWbNmDQB6vZ7333+fnj17otfrCQsLIyoqCpVKRc2aNQkNDcXe3h4AR0dHkpKSlHYiIyM5c+YMoaGhLFmyhM2bN1OtWjVyc3MZPXo03t7eSt2VK1eyZcsWzM3NqVChAoGBgfj5+REYGEhKSoqyk2D9+vVZvHhxkfdl48aNfPPNN5iYmGBpacns2bOV93QJIYQQQojyJQmWMFoWFhbKluwPmzFjBh999BGRkZHs3r0blUqFh4cHHh4egGHC8zg3btwgPDycbdu2UblyZdLT00lNLZhNvmHDBpKSkti+fTtqtZrDhw8zatQotFot5ubmT417yJAhBAcHc+XKFfr27YubmxsVK1Zk48aNJCQksHXrVqysrNDpdHz//ffKeYsWLaJly5ZPbd/Hx4e//a3gjex79+7l008/ZeXKlU89TwghhBBlL+l6CluSz5OVl/d8Ojj8nxKfYmqqIj//yauA1GoL/P0H4eTkXNrIxGNIgiVeOq1bt8bR0ZGvvvqKHTt2sGrVqhK3cevWLSpVqoSlpSUAlSpVolKlSgAsX76c9evXo1arAejatSuOjo5ER0fj7+9f7D4aNGiAWq3m7t272NjYsGzZMtatW4eVlRUAVlZW9OnTp8SxPzgfIDMzE5VKVeI2hBBCCFE2Ys5d4kra3efXge75raWKiYmSBOs5kARLGK1HXzYcEhKCp6cnAB988AHdu3cnKCiI+vXrl7jtJk2aUL16dXr06EGnTp3o1asXrq6u6HQ6MjMzqVu3rkH9Fi1acOHChRL1kZycTP369bGxsUGn05Genl6o3Yc9/LLhzp07M2XKlMfW3bBhA6tWrSI3N1eZ5iiEEEKIF8/b4U0y8/Ke3xMsq6olPqW4T7C8vf1KG5V4AkmwhNF63BRBgOPHj2NlZcX58+dL1bapqSkrVqzg9OnTHDlyhE8//ZTk5GSGDh1aqvYefoq0evVqIiMjuXLlCmFhYcVuo7hTBAECAgIICAhgx44dhIWFMX/+/BLHLIQQQohn52hXE0e7ms+tfdlF8OUjuwiKl05GRgYLFy5kzZo1pKamcuDAgVK1o1KpaNWqFSEhIXz22Wfs2bMHKysr1Go1V69eNaibnJysbCRhbm5u8G6tO3fuYG1trXweMmQIWq2WxYsXM336dLKzs7GyssLS0rJQu8/Ky8uLuLi4Mm1TCCGEEEKUniRY4qXz73//G3d3dxo2bMiMGTOYN28e2dnZJWrj5s2bJCcnK5/Pnj1L7dq1AQgODmbOnDlkZWUBkJCQwIkTJ/Dx8QGgffv2REdHAwXTGHfu3EmHDh0K9dGjRw9atGjBtm3bABgxYgQzZ85Ep9MBkJ6eTlRUVAmvHq5cuaJ8v3///lJNkRRCCCGEEM+HTBEURuvRNVguLi5oNBri4uKUqYPNmjXDxcWF5cuXM3bs2GK3nZeXx/z580lJScHc3Jxq1aoxc+ZMAAIDA7l79y4+Pj6YmJhQo0YNli5dqqyPmj59OqGhoaxbtw69Xo+fn5+yZfyjxowZw8SJExkwYACDBg0iIyODfv36UbFiRSpUqGAwJfHhNVjW1tasXr26yDbXr1/PkSNHqFChAlWqVClyemC1d9+XqQFGSqZtGCcZF+MlY2O8ZGyEKJpKr9c/eQWcEOKlk5ubL//oGSn5hcQ4ybgYLxkb4yVjY7xkbF6MGjUqF1kuUwSFEEIIIYQQoozIFEHxyvP39zfYlAJgwYIFODg4lFNExRMWFsauXbsMytzd3Rk1atRTz/1z+cfPKyzxjP4s7wBEkWRcjJeMjfGSsTFUwf/D8g5BGAmZIijEK+j60mnlHYIQQgjxl2JMCZZMEXwxZIqgEEIIIYQQQjxnMkVQGKWmTZvSuHFj5bOXlxfBwcH4+/vz4YcfKrv2DRs2DH9/fyIiIsjJyeHOnTtkZWVha2sLFGzpXqdOnULtb926lTVr1gCg1+t5//336dmzJ3q9nrCwMKKiolCpVNSsWZPQ0FDs7e0BcHR0JCkpSWknMjKSM2fOEBoaypIlS9i8eTOlN5jdAAAgAElEQVTVqlUjNzeX0aNH4+3trdRduXIlW7ZswdzcnAoVKhAYGIifnx+BgYGkpKQoOwjWr1+fxYsXF3lfjh07xrx58zh37hyfffYZ7u7uz3KbhRBCCCFEGZMESxglCwsLZSv2h82YMYOPPvqIyMhIdu/ejUqlwsPDAw8PD8Aw4XmcGzduEB4ezrZt26hcuTLp6emkpqYCsGHDBpKSkti+fTtqtZrDhw8zatQotFot5ubmT417yJAhBAcHc+XKFfr27YubmxsVK1Zk48aNJCQksHXrVqysrNDpdHz//ffKeYsWLaJly5ZPbd/Ozo5PP/2UiIiIp9YVQgghBCRdv813P10jK+/+8+3oh9HPtXm12gJ//0E4OTk/137Es5MES7xUWrdujaOjI1999RU7duxg1apVJW7j1q1bVKpUCUtLSwAqVapEpUqVAFi+fDnr169HrVYD0LVrVxwdHYmOjsbf37/YfTRo0AC1Ws3du3exsbFh2bJlrFu3DisrKwCsrKzo06dPiWN/8DTOxERm9wohhBDFoT1/nSsvYj2S7vfn3kVMTJQkWC8BSbCEUXr0JcMhISF4enoC8MEHH9C9e3eCgoKoX79+idtu0qQJ1atXp0ePHnTq1IlevXrh6uqKTqcjMzOTunXrGtRv0aIFFy5cKFEfycnJ1K9fHxsbG3Q6Henp6YXafdjDLxnu3LkzU6ZMKfF1CSGEEKIwr8Z2ZOXlP/8nWFbVnmvzarUF3t5+z7UPUTYkwRJG6XFTBAGOHz+OlZUV58+fL1XbpqamrFixgtOnT3PkyBE+/fRTkpOTGTp0aKnaU6lUyverV68mMjKSK1euEBYWVuw2ijtFUAghhBAl42hnjaOd9XPvx5h2ERTlS+YZiZdKRkYGCxcuZM2aNaSmpnLgwIFStaNSqWjVqhUhISF89tln7NmzBysrK9RqNVevXjWom5ycTKNGjQAwNzc3eKfWnTt3sLb+3/+0hwwZglarZfHixUyfPp3s7GysrKywtLQs1K4QQgghhHj1SIIlXir//ve/cXd3p2HDhsyYMYN58+aRnZ1dojZu3rxJcnKy8vns2bPUrl0bgODgYObMmUNWVhYACQkJnDhxAh8fHwDat29PdHQ0UDCNcefOnXTo0KFQHz169KBFixZs27YNgBEjRjBz5kx0Oh0A6enpREVFlfDqhRBCCCGEsZMpgsIoPboGy8XFBY1GQ1xcnDJ1sFmzZri4uLB8+XLGjh1b7Lbz8vKYP38+KSkpmJubU61aNWbOnAlAYGAgd+/excfHBxMTE2rUqMHSpUuV9VHTp08nNDSUdevWodfr8fPzU7aMf9SYMWOYOHEiAwYMYNCgQWRkZNCvXz8qVqxIhQoVDKYkPrwGy9ramtWrVxfZ5n//+1/Gjh3L3bt3iY+PZ8mSJWi12kL1qg+fLS8YNFLy8kfjJONivGRsjJeMjRBFU+n1en15ByGEKFu5ufnyj56Rkl9IjJOMi/GSsTFeMjbGS8bmxahRo3KR5TJFUAghhBBCCCHKiEwRFK80f39/g00pABYsWICDg0M5RVQ8YWFh7Nq1y6DM3d2dUaNGFev8G+GDn0dYogzcKO8ARJFkXIyXsY2NxcDw8g5BCGHkZIqgEK+gq0sCyjsEIYR4JUmC9T8yDc14ydi8GDJFUAghhBBCCCGeM5kiKF45TZs2pXHjxuTl5WFqaoqfnx9DhgzBxOR/f0+YO3cuu3bt4sCBA5iYmPDLL78wduxYtm/fruzmN2LECHx9fenYsSPTp0/n+vXr5OXl8frrr7N8+fIi+7527Rqenp688cYb6PV6LC0tmTdvHm+++SaJiYlERESwbNkyIiMjWbBgAba2tmRnZzNw4ECGDBliMDXw/PnzNG7cGIB+/fpx584dLC0tCQ4OVvpzdXVl69atVKv2fN8eL4QQQgghikcSLPHKsbCwULZyv3XrFhMnTkSn0zF+/HgA7t+/T1xcHHZ2dhw9epSOHTtib29Pr169CAsLY8KECcTFxZGXl4e3tzehoaF07tyZoKAgoOC9WU9Sr149pf9NmzaxbNky5s+fX6iep6cnoaGh3L59G3d3d9zc3Bg1apSyzsrR0VFpB2DJkiXPfnOEEOIv5D83stj2s46svLJbDaH6cXSZtfWAWm2Bv/8gnJycy7xtIcSLJwmWeKXZ2Ngwe/Zs+vfvz7hx41CpVCQmJtKoUSM8PT3RarV07NgRKHhvlZ+fH+7u7ixatIjw8IJ59ikpKXTp0kVps0mTJsXuX6fTUaVKlSfWsba2pn79+vzxxx/Y2dmV4iqFEEIUZecv6fy/O3ll22j672Xb3v+JiYmSBEuIV4QkWOKVV7duXfLz87l16xbVq1dHq9Xi5eVFz549+eyzz8jNzaVixYqo1WqmTJlCQEAAQ4cOpUGDBgAEBAQwYcIE1q9fT+fOnenbty+2traP7e/XX39Fo9GQnp5OVlYWmzdvfmJ8v//+O9nZ2cXa2XD16tVER0crn1NSUop3E4QQ4i/Iw74SWXll/ASr8uP//19aarUF3t5+Zd6uEKJ8SIIl/lJycnI4cOAAU6dOxcrKitatW3P48GHefvttoGBNU5UqVRg0aJByjouLC3FxcRw6dIiDBw/Sp08fYmJiHrvu6eEpgrGxsXz88cesXLmyUL3Y2FiOHTvG5cuX+fjjjzE3N39q/EOGDCm0BksIIUTR2tSyoE0tizJt02Lg0jJtTwjx6pFdBMUr7+rVq5iammJjY8Phw4e5d+8evr6+uLq6cuLECWJiYgzqm5iYoFKpDMqqVq2Kj48PCxcupGXLlhw7dqxYfbu6unL8+PEij3l6erJjxw42btzIv/71L/7444/SXaAQQgghhDAakmCJV1pqaiozZswgICAAlUqFVqtlzpw57Nu3j3379rF3714SEhLIzMx8bBtHjhxRjut0On799ddir5U6ceIE9erVe2Kdli1b4uvry9q1a4t/YUIIIYQQwijJFEHxysnKykKj0SjbtGs0GoYOHUpmZiaHDh1i5syZSl1LS0vatm1LfHw8np6eRbaXnJzM7NmzMTU1Ra/X4+/vT6tWrR7b/4M1WHq9nooVKzJnzpynxjx8+HD69u1LSEgIVlZWJb9oIYQQQghhFFR6vb7sVn4KIYxCbm6+vMHdSFWtailjY4RkXIyXjI3xkrExXjI2L0aNGpWLLJcpgkIIIYQQQghRRmSKoBClcO7cOSZPnmxQZmZmxpYtW8opIkMXv+5b3iGIx7hV3gGIIhnDuNgMWF/eIQghhCgDkmAJUQoODg7KVuxCCCGEEEI8IFMEhRBCCCGEEKKMSIIlXkpxcXE4ODhw8eJFAK5du4aDgwOff/65Uic1NZXmzZsza9YswsLC0Gg0aDQamjZtqnz/tK3RNRoNEyZMMCibOnUqLi4u5OTkKP08eOHvgzjWrVun1J81axaRkZEABAYGcvr0aeXYtWvX8Pb2BiAxMZGQkBC+++47Jb4WLVrg4+ODRqNh/PjxuLm5kZWVpZw/YsSIQu/xEkIIIYQQ5UemCIqXUkxMDG3btkWr1TJ+/HgA6tSpw4EDB5SEaNeuXTRq1AiAUaNGMWrUKAAcHR2LNb3v4sWL3L9/n+PHj5ORkYGlpaVyzNTUlK1btzJo0KBC59nY2LB27VreeecdzMzMSnxt/fr1o1+/fkDBi4rXrFlDtWrVAFi0aBFhYWFMmDCBuLg48vLylARNCPH8nbmeR8zPOWTnlf0GvCYJo5+5DbXaAn//QTg5OZdBREIIIUpDEizx0klPT+fEiROsXbuWkSNHKgmWWq2mYcOGnD59mpYtW7Jz5048PDxISUkpVT8xMTH4+vpy6dIl9u7di4+Pj3IsKCiINWvWMGDAgELnVatWDScnJ6Kiooo8/izGjBmDn58f7u7uLFq0iPDw8DJtXwjxZHG/5HA17f7zaVz3e5k0ExMTJQmWEEKUI0mwxEtn7969uLi48MYbb2Btbc2ZM2eoWrUqAJ6ensTGxlK9enVMTEyoWbNmqROs2NhYVq1axaVLl1i/fr1BgmVnZ4eTkxPbt2/n7bffLnTu8OHDeffdd5UnUWVFrVYzZcoUAgICGDp0KA0aNCjT9oUQT9bT3oysvOf0BMvK7pnbUKst8Pb2K4NohBBClJYkWOKlo9VqGTx4MFCQUGm1WgICAgBwcXHhyy+/xMbGBk9Pz1L3cfr0aaytralduza2trZMmzaNtLQ0JZEDCAkJYfTo0XTv3r3Q+XXr1qV169bs2LHjqX2pVKoSxebq6kqVKlWKnJ4ohHi+WthVoIXd8/mn02bA0ufSrhBCiBdLEizxUklLS+PHH3/k/PnzqFQq8vPzUalUSrJhZmZG8+bNWbVqFVqtln379pWqH61Wy+XLl5XNK3Q6HXv27DGY8tegQQOaNm3Kzp07i2wjJCSE9957j3bt2ill1tbW3L17V/l8584dg6StuExMTEqcmAkhhBBCiOdPEizxUtm9ezcajYZZs2YpZX//+9+5ceOG8nnYsGG0b9++VIkLwP3799m5cyfR0dHY2toC8OOPP7J06dJCa6pGjhxJSEhIke00bNiQhg0bEh8fT8uWLQFo37490dHRdO7cGZVKxbZt2+jQoUOp4hRCCCGEEMZHtmkXL5WYmBh69uxpUNa7d2+WLVumfLa3t6dPnz6l7uP48ePY2toqyRVAu3btuHjxYqH1XPb29jRr1uyxbY0aNcog+RswYACVKlXC19cXX19fMjIyCA4OLnWsQgghhBDCuKj0en3Zr9QVQpSr3Nx80tIyyjsMUYSqVS1lbIyQjIvxkrExXjI2xkvG5sWoUaNykeXyBEsIIYQQQgghyoiswRJ/aWFhYezatcugzN3dXXkp8cvq9ErZptlYXS3vAESRjG1c6vb7prxDEEIIUUqSYIm/tFGjRr30yZQQQgghhDAeMkVQCCGEEEIIIcpIuSRYTZs2RaPR4O3tzfjx48nMzCxUPnLkSOV9QdeuXaNVq1ZoNBrlKyoqig8//JBNmzYZtB0XF8e777771L69vLzw9fUlIiKC+/fvG9SZO3cuLi4uSvkvv/yCm5sbWVlZSp0RI0YQExPDn3/+SUhICL6+vnh6ejJ8+PDH9n3t2jW8vb2LPJaXl0fHjh1ZtGiRQXl8fDx+fn5K+5s2bSIsLEy5Dw+uR6PRsHbt2iLbXrJkCS4uLmg0Gnr37s3YsWO5cOGCctzV1ZXU1FTlc2JiorL1eGRkJB07djS49w+f+6jg4GCcnZ0LbV3+rNdx9OhR3nnnnUL3rHPnzty8eZOpU6fi6uqqtDFw4MBC8bu7u7N69Wrl/EuXLhEYGIhGo8HDw4OPP/640PVDwc+Uj48PHh4e+Pj4EBcXpxybOnUqLi4u5OTkAJCamqq8O6so165dw8HBgc8//1wpS01NpXnz5gZbzwNoNBomTJhgUDZy5EiioqKUzx999BErVqx4bH9CCCGEEOLFKpcpghYWFmzfvh2AiRMnsmnTJoYOHWpQPmXKFDZs2KBM36pXr55y7IHq1auzbNky5ZdpKHhB7OOSmEf7vnXrFhMnTkSn0zF+/Hig4B1IcXFx2NnZcfToUTp27Ii9vT29evUiLCyMCRMmEBcXR15eHt7e3oSGhtK5c2eCgoIAOHv2bKnuyQ8//ECDBg3YtWsXEydORKVSkZuby8cff8zWrVupVasWOTk5XLt2jTfffFO5L46OjoXuS1GGDBmibAceGxtLUFAQO3bsoFq1ak8919PTk9DQ0GJdx7vvvktmZibffvutUlYW1+Hs7MyNGzf47bffeP311wFISEigUaNGynbqkydPxt3d/bHx3759G3d3d9zc3LCzs2Pu3LkEBQUp276fO3eu0Llnz55l/vz5REREULduXa5evcqwYcOoU6cOTZo0AcDU1JStW7cqLzt+mjp16nDgwAEledq1axeNGjUyqHPx4kXu37/P8ePHycjIwNLSEihIqAYPHoyrqysXLlzg1KlTfPLJJ8XqVwghhBBCPH/lvgbL2dm5yF9s27RpU2T5wzp16sTUqVNJSUmhZs2aZGRkkJCQwOzZs4vVt42NDbNnz6Z///6MGzcOlUpFYmIijRo1wtPTE61WS8eOHQEYM2YMfn5+uLu7s2jRIsLDwwFISUmhS5cuSpsPfukuKa1Wy+DBg9m4cSNJSUk4OTmRnp5Ofn6+8sJcMzMz3nzzzVK1/zBPT0/279/Pjh07lMSwrHTq1InExESDsrK4DhMTEzw8PNBqtYwYMQIoSBSflEw/ytramvr16/PHH39gZ2dHSkoKtWrVUo47ODgUOmflypWEhIRQt25dAOrWrcuIESNYuXIlCxcuBCAoKIg1a9YUegnx46jVaho2bMjp06dp2bIlO3fuxMPDw+AdWzExMfj6+nLp0iX27t2Lj48PUJCcvfPOOyxcuJBTp04RGhpKhQrl/p+xEOIpfr6ez+4zOWTnFa9+hUOji1VPrbbA338QTk7OzxCdEEKIslSua7Dy8vI4ePAgjRs3NijPz8/nyJEjBlOtfv31V4NpasePH8fU1JTevXuzc+dOoGAaWocOHbCysip2DHXr1iU/P59bt24BBYmOl5cXvXr1Yv/+/eTm5gIFvxRPmTKFgIAAvLy8aNCgAQABAQFMnz6dwMBAwsLCuHnzZonvQ3Z2NgkJCbi6uuLt7Y1WqwWgatWquLq68vbbb/PBBx8QHR1daDpjaTVr1oxLly4Vq25sbKzBvX94qmRxlNV1eHl5ERsbC0BOTg4HDhygd+/eyvEFCxYoMU6cOLHQ+b///jvZ2dlKIjVkyBCCgoJ49913Wb16tTIl9WEXLlygRYsWBmUtW7Y0mCZpZ2eHk5NTsZ4kPuDp6UlsbCzXr1/HxMSEmjVrGhyPjY3Fy8sLLy8v5efhgWHDhnHo0CHs7e1p165dsfsUQpSf/edy+S1Nz5+64n3duPF7sb4uX75ETEzU0wMQQgjxwpTLn76zsrLQaDRAwROs/v37G5TfvHmThg0bGjwZKmqKIBT80r1gwQKCgoLQarVKu6Xx4Jf2qVOnYmVlRevWrTl8+DBvv/02ULBWqUqVKgZTwVxcXIiLi+PQoUMcPHiQPn36EBMTU6ypdw88SAwtLCzo3bs3S5cuZdq0aZiamjJ37lzOnTvHkSNHiIiIICEhgX/+85+lvsbiUqlUyvclmSL4OGVxHS1btiQjI4NLly5x6dIlWrdurTwVg8dPEYyNjeXYsWNcvnyZjz/+GHNzcwD69etH165dOXToEHv37mXTpk1ER0eX6vpCQkIYPXo03bt3L1Z9FxcXvvzyS2xsbPD09DQ4dvr0aaytralduza2trZMmzaNtLQ05VrPnTvH/fv3uXz5Mvfv38fERPaqEcLYdXeoSHZuCZ5gWdkVq55abYG3t7yWQQghjEm5r8EqqjwzM5Pg4GA2bNjA4MGDn9iWk5MTf/zxB2fPniUpKclg84DiuHr1KqamptjY2BAfH8+9e/fw9fUFIDMzE3NzcyXBgoKpag8nH1DwhMbHxwcfHx9CQkI4duwYbm5uxY5Bq9Vy4sQJ5YldWloaP/74o5JgOjg44ODggK+vLz169CiTBOunn35SnsxUrVqVu3fvKknhnTt3sLa2fuY+HlUW1/HgKdbFixfx8vIq1jkPEsTTp08THByMq6srNWrUAMDW1pb+/fvTv39/vL29OX/+vMG5DRs25MyZMwZTP8+cOVNozVSDBg1o2rSp8jT1aczMzGjevDmrVq1Cq9Wyb98+5ZhWq+Xy5cvKz4NOp2PPnj0MGDCA+/fvM3PmTBYuXMimTZvYuHEjAQEBxepTCFF+mtqZ0tROXez6dfstfY7RCCGEeJ6M8k/farWajz76iFWrVpGX9+Q/96lUKjw8PJgyZQrdunVTnk4UR2pqKjNmzCAgIACVSoVWq2XOnDns27ePffv2sXfvXhISEpRdDoty5MgR5bhOp+PXX3/Fzq54f3l8cM7x48fZv3+/0m9oaCgxMTGkp6cbrGc6e/asssHDs9i9ezc//PCDsn6pQ4cOSsKbn59PdHQ0HTp0eOZ+HijL6/D29iY6Opoff/yRHj16lOjcli1b4uvrq+xSePDgQWUK6B9//EFaWpqyYcYDwcHBfP3111y7dg0o2AVw2bJlDBs2rFD7I0eOJCIiotjxDBs2jEmTJhk8hbt//z47d+4kOjpa+XlYunQpMTExAGzatIn69evToUMHpk6dyvLlyw12gBRCCCGEEOXLaFfHN2vWDAcHB2JiYnB2dlbWYD3Qr18/5emWt7c3K1asKHLdzaMeTEPMy8vD1NQUjUbD0KFDyczM5NChQ8ycOVOpa2lpSdu2bYmPjy80jeuB5ORkZs+ejampKXq9Hn9/f1q1avXY/i9fvky3bt2UzxMmTKBjx46YmZkpZT169GDhwoVMmzaNFStWEBoaioWFBWq1mk8//fSp11iU1atXEx0dTWZmJvb29qxZs0Z5YjV69Gg++eQTfH190ev1uLi4KE/xoGCK3YkTJ5TPM2bMwMnJqch+Bg0axKVLl8jIyKBbt27MnTsXR0fHMruOhg0bolarad68ubKz3gMLFiwgLCxM+bxly5ZC5w8fPpy+ffsSEhLCDz/8wNy5c5Wk/B//+Ac1atQwWJvWtGlTJk2axKhRo8jNzaVixYr84x//oGnTpoXatre3p1mzZvz000/FuhZ7e3vs7e0Nyo4fP46tra1BoteuXTsuXrzIb7/9xooVK5QdGm1tbQkKCmLhwoWlvp9CCCGEEKJsqfR6vb68gxBClK3c3HzS0jLKOwxRhKpVLWVsjJCMi/GSsTFeMjbGS8bmxahRo3KR5UY5RVAIIYQQQgghXkZGO0XwWdy+fZshQ4YUKl+9evVz2bzhUefOnWPy5MkGZWZmZkVOWStLYWFh7Nq1y6DM3d1deZlvWXne1/eiruN5Kq+fgQeOrir9bppCvEwa99lY3iEIIYQQBmSKoBCvoB++Lv4LmIV4mZVVgiXTaYyXjI3xkrExXjI2L4ZMERRCCCGEEEKI50wSLPHcNW3aFI1Gg5eXF76+vkRERHD//n2DOnPnzsXFxUUp/+WXX3BzcyMrK0upM2LECGJiYvjzzz8JCQnB19cXT09Phg8f/ti+r127pmxHn5iYSNu2bfHz88PNzY2AgADi4+OfGPuSJUtwcXFBo9Hg7e3N3r17AZg6dWqhaYyOjo5Kn61atUKj0eDr68vAgQOVnQkTExMJCQkp1E9gYCCnT58GYOvWrcp71by9vYmLi3tqn0IIIYQQwji8kmuwhHF5+MXSt27dYuLEieh0OsaPHw8UvPspLi4OOzs7jh49SseOHbG3t6dXr16EhYUxYcIE4uLiyMvLw9vbm9DQUDp37kxQUBBQ8F6t4nJ2dmbZsmUA/Pzzz4wZMwYLCws6der02HOGDBlCcHAwFy9eZNCgQRw5cuSp/dSrV0+55k2bNrFs2TLmz5//1PNu3LhBeHg427Zto3LlyqSnp8t7roQQQgghXiKSYIkXysbGhtmzZ9O/f3/GjRuHSqUiMTGRRo0a4enpiVarpWPHjgCMGTMGPz8/3N3dWbRoEeHh4QCkpKTQpUsXpc0mTZqUKpamTZsyevRo1q9f/8QE64GGDRtSoUIFbt++XaJ+dDodVapUKVbdW7duUalSJeUdX5UqVaJSpUol6k+Il8253/PZdyaXnNySn1vxwOhS9alWW+DvPwgnJ+dSnS+EEEI8jiRY4oWrW7cu+fn53Lp1i+rVq6PVavHy8qJnz5589tlnygt91Wo1U6ZMISAggKFDh9KgQQMAAgICmDBhAuvXr6dz58707dvX4MW8JdG8eXNWrlxZrLqnTp1CpVIpL2h+kgcvxk5PTycrK4vNmzcXq48mTZpQvXp1evToQadOnejVqxeurq7K8UdfpizEq+CHs3lcv13K/ZZ0v5e635iYKEmwhBBClDlJsES5ysnJ4cCBA0ydOhUrKytat27N4cOHefvttwFwdXWlSpUqDBo0SDnHxcWFuLg4Dh06xMGDB+nTpw8xMTHFSnweVZxNNFevXk10dDSVKlXiiy++QKVSoVKpnnjOw1MEY2Nj+fjjj4uVyJmamrJixQpOnz7NkSNH+PTTT0lOTmbcuHEATJ48GXd3d6W+rMESr4IuTSqQnVfKJ1hWdqXqU622wNvbr1TnCiGEEE8iCZZ44a5evYqpqSk2NjbEx8dz7949fH19AcjMzMTc3FxJsABMTEwKJTRVq1ZVNoIICQnh2LFjuLm5lTiWn376iYYNGz6xzoM1WI/2f/fuXeVzWlraY9+x5urqyocffljsmFQqFa1ataJVq1Z07tyZadOmKQmWEK8ih9qmONQ2LdW5jfssLeNohBBCiGcjuwiKFyo1NZUZM2YQEBCASqVCq9UyZ84c9u3bx759+9i7dy8JCQlkZmY+to0jR44ox3U6Hb/++it2diX/K/bZs2dZunQpAQEBJT63ffv2xMbGkpOTA8C2bdvo0KFDkXVPnDhBvXr1itXuzZs3SU5ONoixdu3aJY5PCCGEEEKUD3mCJZ67rKwsNBoNeXl5mJqaotFoGDp0KJmZmRw6dIiZM2cqdS0tLWnbti3x8fF4enoW2V5ycjKzZ8/G1NQUvV6Pv78/rVq1KlYsx48fx8/Pj8zMTGxsbPjoo4+KtcHFo95++22Sk5Pp168fJiYm1KtXz+A6HqzB0uv1VKxYkTlz5ijHjhw5Qrdu3ZTPX375pfJ9Xl4e8+fPJyUlBXNzc6pVq2bQrhBCCCGEMG4qfXEWoQghXiq5ufnyBncjVbWqpYyNEZJxMV4yNsZLxsZ4ydi8GDVqVC6yXKYICiGEEEIIIUQZkSmC4pVw7tw5Jk+ebFBmZmbGli1binV+WFgYu3btMihzd3dn1KhRZRbji3RgjU95hyDEc9Na8215hyCEEEI8lkwRFOIVFLei6PVrQtofp7IAACAASURBVLwKnkeCJdNpjJeMjfGSsTFeMjYvhkwRFEIIIYQQQojnTBIsYZQevED32rVrODg4sG7dOuXYrFmziIyMBOA///kP/v7+aDQaPDw8WLJkyWPbjIyMpGPHjvj5+dG7d2+Cg4M5efKkcnzq1Km4urqi0Wjo06cPSUlJBuW+vr64ubkxefJkbty48cT4XV1dDV6ODKDRaPD29gYgMTGRtm3botFolK+EhASlblxcHA4ODly8eFEpe9q9EEIIIYQQ5U8SLGH0bGxsWLt2rfLOqYdNmTKF2bNns337dmJiYvDw8HhiW56enkRFRbFnzx6GDx/OuHHjDJKYyZMns337diZOnEhoaKhBeXR0NLt27aJZs2YEBQUVGc/D0tPTuX79OoBBHw84Ozuzfft25atz587KsZiYGNq2bYtWqy32vRBCCCGEEOVPNrkQRq9atWo4OTkRFRXFgAEDDI6lpqZSo0YNAExNTWnUqFGx2+3YsSMDBgzg22+/Zdq0aQbH2rVrx6+//lroHJVKxZAh/5+9e4+Lqtof//+amUQg5MNFRQq8AaKJWUjfwCOphEiADGnYCY5X8gZmeiT0mEePGlpImZrXxFtWpkmiM0gmIGqmpqc84iVJDeWUeUErEAQGfn/wYx8n7oiK+n4+HvPHrL32Wu+9l/WYxXrvtYfz1VdfsWfPHnx9fatt/4UXXiA5OZmIiAh0Oh2BgYFs27at1rjy8/M5cuQI69evZ+zYsUyYMEE5VtO9EOJ+9uN/Dew9ZuBmSe11m6dH1qlNMzNTQkPDcHf3uM3ohBBCiLqTFSxxXxg1ahQJCQkYDAaj8mHDhuHv709UVBQbN27k5s2b9Wq3a9eunD17tlJ5WloanTp1qva8J554osrzbuXn58dXX30FQHp6Oj4+PkbHDx8+bJQiWDGhS01Nxdvbmw4dOmBtbU1mZqbRedXdCyHuZwdPGbh4rYxrf9T+uXjx5zp9zp07i0639V5fmhBCiIeMrGCJ+4KjoyPdu3dn+/btRuXjx48nODiYffv2odPp0Ov1Rs8o1ebPm2jGxcWxbNkybGxsiI2NrfN5VbGyssLS0hK9Xo+TkxOmpqZGxz08PFixYkWl8/R6PUOHDgXKUxr1ej1ubm7K8eruhRD3s2c7aygqruMK1qP2dWrTzMyUoKCQ24xMCCGEqB+ZYIn7xpgxY3j99dd55plnjMrbtm1LWFgYgwcPxsvLi2vXrmFtbV2nNk+cOIGTk5PyPSYmBn9//1rPO3nyJF5eXrXWCwgIYPbs2cybN69O8Vy/fp0DBw5w+vRpVCoVBoMBlUpV6R1f1d0LIe5Xzo9rcH5cU6e63bVL73A0QgghRMNJiqC4bzg5OeHk5ER6erpStnv3bmU1KTs7G7VajaWlZZ3aO3ToEJs2barXs0xlZWWsX7+ey5cv4+3tXWt9X19fIiIi6NWrV53a//LLL9FqtaSnp5OWlkZGRgYODg4cPnzYqF5V90IIIYQQQtx7soIl7ivjxo0jJOR/KT9JSUnMmzcPU1NTNBoN8fHxaDTV/xU8OTmZI0eOUFhYiIODA4sWLTJawapOXFwcS5cupbCwkO7du7N+/XpMTExqPc/CwoLRo0dXeaziGaxbr02n0zFq1Cijen5+flWW//leCCGEEEKIe09VVpeHSYQQ95XiYoO8wb2JsrIyl7FpgmRcmi4Zm6ZLxqbpkrG5O1q1alFluaQICiGEEEIIIUQjkRRB8cDZsmUL69evNypzd3dn5syZjd5XaGhopZf+xsXF4erq2uh9CSGEEEKIpk9SBIV4AOlWv3CvQxCi3p4dsOme9S3pNE2XjE3TJWPTdMnY3B2SIiiEEEIIIYQQd5hMsIQQQgghhBCikcgzWA+ZXbt2ERUVRXJysrI9+blz55g7dy7Z2dk8+uijtG3blv79+7NixQoAzp8/T+vWrTE1NcXV1ZW4uLhK7R48eJDIyEgcHR0pKCigZcuWvPrqq/Tt29eonlarpWPHjixYsACATZs2sX//ft5//30A8vLyCAkJYc2aNVy9epXY2FiKioooKioiICCA1157rdpry8jIYOHChRQWFmJiYoKnpydTp05l8eLFbNq0CRsbG4qLi4mMjCQoKAiAqVOncujQIVq0KF/iNTMzY+PGjSQmJhIXF0ebNm24ceMGjo6OREVF4e7urpzXp08ftm/fTk5ODjdu3CA3NxcHBwcAZs6cqdS91ZAhQ7hw4QLp6emoVCoAIiMj+eabb/juu++UemvXruXdd99l//79Smw7d+7k448/Zt26dUD5Nu9z5sxhy5YtPPKI/KcshBBCCNEUyK+yh4xOp6NHjx7o9XomTJjAzZs3GTNmDFOnTsXHxwconyxZW1uTlJQElE8KYmJi6NatW41te3h4KJOykydPEhUVhampKV5eXgCcOXOG0tJSDh8+zI0bNzA3Nyc0NJTExET2799Pz549WbhwIYMGDcLR0ZFXX32VhQsX0rlzZwwGA+fOnau279OnTzNnzhxWrFiBk5MTBoOBzz77TDk+fPhwIiIi+Omnnxg4cCD9+/enWbNmAMTExODv71+pzYCAAGbMmAHAgQMHeO2111i/fr3Re7OWLFmi3LPVq1cr11+TFi1acOTIETw8PPj999+5fPlypTp6vZ5u3bqxc+dOBg0aBJS/D2vz5s1s374df39/Zs2axaxZs2RyJZqEs/8t5Zv/GCgqbvhjvZ+mRjboPDMzU0JDw3B392hw30IIIURjkRTBh0h+fj5HjhwhNjYWvV4PwPbt23nqqaeUyRXAs88+S6dOnW6rry5duhAZGcmGDRuUMp1OR3BwML169SI1NRUAlUrFv/71L+bOncuxY8c4cOAAERERAOTm5tKqVSsANBoNzs7O1fa3atUqxo4dq0x+NBoNYWFhleq1b98eMzMzfv/993pdj6enJ4MHDzaatDVUYGAgycnJQPmqVL9+/YyOnz9/nhs3bjBx4kRlnCrMmDGD999/nw8++IBu3bpVuUomxL1w5ISBS7llXP+DBn8uXvy5QZ9z586i022917dACCGEAGQF66GSmpqKt7c3HTp0wNramszMTLKysujatesd6a9r164kJCQo35OTk1mzZg1nz55lw4YNDBgwAIDOnTvTq1cvhg8fztKlSzExMQFg2LBh+Pv78//+3//D29ubF198kebNm1fZV1ZWFiNHjqw1puPHj9OuXTtsbW2Vsri4OJYtWwaAs7Mz7777brXXs3HjxrpdfA28vLyYPn06BoOB5ORkZs+erfQP5atXAQEBeHh4cO7cOa5cuULLli0BcHR0JCAggA0bNvDVV1/ddixCNJYeT2goKrm9FSzTRx9r0HlmZqYEBYU0uF8hhBCiMckE6yGi1+sZOnQoUJ7+9ufVkcZ26xsAjh07hrW1NY899hh2dnZMmzaN69evY2VlBUB4eDh79uzh2WefVc4ZP348wcHB7Nu3D51Oh16v56OPPmpQLGvXriUxMZGffvrJaDID1acI1nQ9t0OtVitpmoWFhcpzWxX0ej0ffPABarUaPz8/UlJS+Nvf/gaAwWDg66+/xtzcnJ9//hkbG5tGiUmI29XxcTUdH7+9pIhnByxtpGiEEEKIe0cmWA+J69evc+DAAU6fPo1KpcJgMKBSqYiKiuLbb7+9I32eOHFCSdnT6/WcO3dOSUXMy8tj586dDB48GChPFVSrK/84a9u2LWFhYQwePBgvLy+uXbuGtbV1pXrOzs5kZmbSuXPnKmOpeAYrNTWVN998k127dlW7GlaX67ldgYGBjB8/nvHjxxuV//DDD/z000/KalxRUREODg7KBOuTTz7B1dWViRMnMnv2bD777DNlswwhhBBCCHHvyTNYD4kvv/wSrVZLeno6aWlpZGRk4ODgQLt27fjuu+/YvXu3Uvfbb7/l9OnTt9XfqVOnWLp0KeHh4ZSWlrJjxw62bdtGWloaaWlpLF26FJ1OV2Mbu3fvVlaNsrOzUavVWFpaVlk3IiKCFStWKBthlJaW8umnn1aq9/zzz+Pm5sYXX3xRr+s5dOgQmzZtUiaEt8vDw4PRo0cTGBhoVK7X63nttdeU+7Rv3z4uXbrEf//7Xy5fvsyaNWt44403eO6557Czs2Pz5s2NEo8QQgghhGgcsoL1kNDpdIwaNcqozM/PD71ez/Lly5k7dy5z587lkUcewdXVlTfffLPefRw+fJiQkBAKCgqwtbVl+vTpeHl5cejQIezs7LCzs1PqPvPMM5w5c4ZLly7RunXrKttLSkpi3rx5mJqaotFoiI+PR6PRVFm3c+fOTJs2jcmTJ1NQUIBKpaJPnz5V1o2KimLy5MnKZOnWZ7AAZdKSnJzMkSNHlDS+RYsWNdoKlkqlUjbzuJVer2flypVGZf369UOv1/PDDz/w6quvKmmB06ZNIzw8HD8/PyXVskL/ITp5g3sTZWVlLmMjhBBCPMBUZY31YIkQoskoLjbIj/gmSiZYTZOMS9MlY9N0ydg0XTI2d0erVi2qLJcUQSGEEEIIIYRoJJIiKOpl7969xMfHG5U5ODgoL9y907Zs2cL69euNytzd3Zk5c+Zd6b+uoqKiyMnJMSqLjo7G29v7rvSftCGw9kpCNILeQfIcoBBCCHErSREU4gH0+Zrat50XojE8KBMsSadpumRsmi4Zm6ZLxubukBRBIYQQQgghhLjDJEVQNJpdu3YRFRVFcnIyTk5O5OTk8PzzzzN27FgmTZoEQG5uLt7e3rz88su0atWKlJQUAE6fPk2nTp0AGDRokPJC5D9LSkpi1apVlJaWotFo6NatG1OmTMHS0pKioiLmz5/P7t27UalUODk5MXPmTNq0aQPAxYsXmTVrFmfOnKG0tJQ+ffoQExODiYkJBw8eJDIyEkdHRwoKCmjZsiWvvvoqffv2BeDs2bPMnDmT33//naKiIjw8PJgzZ06VMVa05eDgQFFRkfLOq1vLb968Sd++fZkyZQoAiYmJZGZmMmPGDAC2bt3KqlWrUKlUaDQaBgwYQEREBFOnTuXQoUO0aFH+FxMzMzM2btzYGMMnhBBCCCEagUywRKPR6XT06NEDvV7PhAkTgPLnszIyMpQJVkpKCs7OzgCMGzeOcePGAfD000+TlJRUY/t79uxh3bp1rFq1Cjs7OwwGA1988QVXrlzB0tKSBQsWkJ+fT0pKChqNhi1btjB+/Hhl2/Xx48fzyiuvsGzZMgwGA//85z9ZsGCBMsnx8PBgxYoVAJw8eZKoqChMTU3x8vIiNjaWYcOG4evrC5S/ELgmFW3duHGDkJAQZaJWUV5YWEhISAi+vr706NHD6NyMjAzWrVtHQkICdnZ2FBUVsXXrVuV4TEwM/v6SAigaT3ZOKYf+Y6C4uP7nfrEr8rb7NzMzJTQ0DHd3j9tuSwghhLjXZIIlGkV+fj5Hjhxh/fr1jB07VplgmZmZ4eTkxLFjx+jWrRs7duzghRde4NKlS/XuY/ny5cTExCjv09JoNLz00ksAFBQUkJiYSGpqqvKurEGDBrFlyxYOHDgAQPPmzRk0aJBy7rRp03j++eeVWG/VpUsXIiMj2bBhA15eXly6dElZCQNwdXWtU8zm5uZ07dqV7OxsbG1tlXJTU1O6dOnCr7/+WumclStXGl2niYlJo73gWIiqfH+ilCu5DTv3tz9+bpQYdLqtMsESQgjxQJAJlmgUqampeHt706FDB6ytrcnMzFRefhsQEEBycjItW7ZErVbTunXrBk2wfvzxR7p27VrlsezsbOzt7bGwsDAqd3NzIysrC6DSuRYWFtjb25OdnV1lm127diUhIQGA4cOHM2zYMJ5++ml69erFwIEDsbS0rDXma9eucfToUSIjI8nN/d8v2N9++43s7GyeeeaZSudkZWXh5uZWbZu3vhjZ2dmZd999t9Y4hKjJU0+oKSpp2AqW2aOP3Xb/ZmamBAWF3HY7QgghRFMgEyzRKPR6vfLcVEBAAHq9nvDwcAC8vb1ZuHAhtra2BAQENEp/P/zwAzExMeTn5/P3v/+djh07Nkq7t7p1g81BgwbRq1cv9u7dS2pqKhs3bmTbtm2YmJhUee7hw4cJCQlBrVYzatQoXFxcOHjwIIcPHyY4OJjs7GyGDRtGq1at6h2XpAiKxtbOQU07h4btedQ7aGkjRyOEEELc32QXQXHbrl+/zoEDB5g+fTo+Pj4kJCSwY8cOZYJiYmJC165dWbNmDf37929wP87Ozhw/fhwoT9FLSkriueeeo7CwkLZt2/LLL7+Ql5dndM7x48dxcXExOrdCXl4ev/zyC+3atauyvxMnTuDk5KR8t7Oz46WXXmLZsmU88sgjnD59utpYPTw82Lp1K4mJibzyyitG5du2bUOn0/H5559z8uTJKq8zMzOz9hsihBBCCCGaHJlgidv25ZdfotVqSU9PJy0tjYyMDBwcHLh48aJSZ+TIkURHRytpgw0xZswY4uLijNotLCwEyp91CgkJ4e2338ZgMADlO/EVFBTg6emJl5cXBQUFymYRBoOBt99+mxdffBEzM7NKfZ06dYqlS5cqq3B79uyh+P/Pn7p8+TLXr19XnpFqCEdHR0aPHs2HH35Y5XXOnz+fy5cvA1BUVKRs1CGEEEIIIZo2SREUt02n0zFq1CijMj8/P2VHPgAXFxdcXFxuq5/evXuTm5vLqFGjMBgMWFpa4uLiQq9evQCYPHky77zzDv3790etVtOxY0eWLFmCSqUCYMmSJcyaNYulS5dSWlpK7969+fvf/660X5HWV1BQgK2tLdOnT8fLywuAr7/+mtjYWJo3bw7AG2+80aD0vlv99a9/JSEhgZycnErXeeXKFUaMGEFZWRkqlUrZnAOMn8EC2Lx5c6VURe3f9PKCwSZKXv4ohBBCPNhUZbc+aCKEeCAUFxvkR3wTJROspknGpemSsWm6ZGyaLhmbu6NVqxZVlkuKoBBCCCGEEEI0EkkRFE3OsmXLSElJMSrz9/dXXkrcVOzdu5f4+HijMgcHB5YsWXKPIvqfzz5unN0ahWgM/QM/v9chCCGEEHeNpAgK8QDasLbhuzUK0djuhwmWpNM0XTI2TZeMTdMlY3N3SIqgEEIIIYQQQtxhMsF6yOzatQtXV1fOnDkDQE5ODq6urnz00UdKndmzZ5OYmAjA1KlT8fb2pqioCIDc3Fx8fHwAOHjwIGPGjDFqf+rUqUp635AhQzh27BihoaFotVr69OmDp6cnWq0WrVbL5MmT+eSTT5Rzjx49yoABA5Tt0P/Mx8eH3NxcoPw9WG+//bZyLCEhgcWLFyvft27dSlBQEAMGDCAkJISEhASg/OXBS5cuxc/Pj/79+zNkyBCysrKM+ggLCzPqV6vVEhQUpFxzjx49lGvQarXs37+/2vvdpUsX5fwJEyZQUFBgVF7xWblypXLP+vfvT3BwMIMGDTJ6T1Z+fj4zZszA19eXgQMHMmTIEI4ePVpt30IIIYQQ4u6TZ7AeMjqdjh49eqDX65kwYQIAtra2rF+/npdffrnSdt8AGo2Gzz//vNLEo64q3uGUmJhIZmYmM2bMAODKlSu8/PLL+Pv7Y2VlxezZs5k5cybNmjWrtU0TExN27tzJ6NGjsbGxMTqWkZHBunXrSEhIwM7OjqKiIuX9Vx9//DHfffcdSUlJmJmZsW/fPsaNG4der1e2YM/Pz+eXX37B3t5emYjeysPDw2gL+pqYmpqSlJQElG8jv3HjRkaMGGFU/mfx8fF069aNLVu2EBcXx5o1awCYPn06Dg4O7Ny5E7VazYULF6qMT4i76UJOKd8dLaWav4sAsOOryFrbMTMzJTQ0DHd3j0aMTgghhLj7ZAXrIZKfn8+RI0eIjY1Fr9cr5TY2Nnh5eSmTkD8bNmwY69ato6SkpFHjadmyJSNHjmT+/Pl8+umnuLq64uFRtx9XjzzyCC+//DLr1q2rdGzlypXExMQoLwI2MTFh8ODBAHz44YfMmDFDeblwr169ePrpp9m2bZty/gsvvEBycjJQPiENDAy8reus4OHhQXZ2dp3rP/XUU/z6668AnD9/nqNHjzJx4kTU6vL/bB0dHenTp0+jxCZEQ2WeKOVqLvz+R/Wfixd/rvVz7txZdLqq/x8khBBC3E9kgvUQSU1Nxdvbmw4dOmBtbU1mZqZybNSoUSQkJGAwGCqdZ29vj7u7e7UrLrfjlVde4ccffyQhIYE33nijXueGh4ezfft2/vjjD6PyrKws3NzcKtXPy8ujoKAAR0dHo3I3Nzd+/PFH5bufnx9fffUVAOnp6UpKZIXDhw8bpfedP3++1lhLSkrYs2cPnTp1AqCwsNCojYoJ3a327t2Lr6+vck1dunRBo9HU2pcQd5PbE2psbcCyRfWfNm0eq/XToUNHgoJC7vXlCCGEELdNUgQfInq9nqFDhwIQEBCAXq8nPDwcKF8N6d69O9u3b6/y3DFjxhAZGWm0YqJSqaqsW115VdRqNS+//DKZmZlYW1vX+TwACwsLtFot69evx9TUtF7n1sTKygpLS0v0ej1OTk6V2q5PimDFRKrivJdeegmgxhTB6OhoiouLuXHjxh2Z1ArRmBwd1Dg61Py3uv6BS+9SNEIIIcS9JxOsh8T169c5cOAAp0+fRqVSYTAYUKlURs9VjRkzhtdff51nnnmm0vnt27enS5cu7NixQymzsrLit99+q9RPfSdKarVaSXurr2HDhjFw4EAGDhyolDk7O5OZmYmXl5dRXQsLC8zMzLhw4YLRKtbx48crXXNAQACzZ89m3rx5DYqrQk0TqerEx8fj5uZGXFwcc+bM4YMPPsDFxYVTp05hMBhkFUsIIYQQogmTFMGHxJdffolWqyU9PZ20tDQyMjJwcHDg4sWLSh0nJyecnJxIT0+vso2xY8eyevVq5Xv79u25dOmSstHCf//7X3744Qe6dOlyZy/mFlZWVvj7+/P55/97z86YMWOYP38+ly9fBqCoqEjZaCMiIoK33nqLwsJCAPbv38+RI0cYMGCAUbu+vr5ERETQq1evu3QlxlQqFa+//jrff/89Z86coW3btri5ubFo0SIqXl2Xk5PD7t2770l8QgghhBCiarKC9ZDQ6XSMGjXKqMzPz69Sqtu4ceMICan6OQgXFxeeeOIJTpw4AZRvHjF//nz+8Y9/cPPmTR555BHeeustWrSo+qVrd8rIkSP5+OOPle+9e/fmypUrjBgxgrKyMlQqFYMGDQLKt0H//fffGTBgAGq1mlatWrF06dJKaYAWFhaMHj26yv4qnsGqMG7cOPz9/esV862pgwDe3t5ER0cb1TE1NWXkyJEkJCQwd+5cYmNjefvtt+nXrx+mpqZYW1tX+9zay+HJ8oLBJkpe/iiEEEI82FRlFX8OF0I8MIqLDfIjvomSCVbTJOPSdMnYNF0yNk2XjM3d0apV1YsKkiIohBBCCCGEEI1EUgRFkxMaGkpRUZFRWVxcHK6urvcooppdu3aN4cOHVypfu3ZtvTf8aCzrPw24J/2Kh0fwC5/XXkkIIYR4CMkESzQ5FRtS3C+sra1lO3UhhBBCCAFIiqAQQgghhBBCNBpZwRJ10qVLFzp16kRJSQkajYaQkBCGDx9u9P6q2NhYUlJSyMjIQK1Wk5WVxfjx40lKSlJ26Rs9ejTBwcF4enry5ptv8ssvv1BSUsLjjz/Ohx9+WGXfOTk5BAQE0KFDB4qLi3FzcyM2NpZmzZpx8OBBIiMjcXBwUOpPmTKFnj17cuXKFebNm8f333/P//3f/9GsWTNeffVV+vXrx8GDB1m9ejUrVqzgypUrlWKJjo4mJiYGgF9++QULCwtatGiBtbU1b731VrXxVHcvtmzZwvr16wE4c+YMHTp0QK1W4+3tTceOHcnMzGTGjBkAfPbZZ6xZswYo381w6tSpeHh4AOW7IObn55OYmAjAsWPHiIuL46OPPmqUcRZCCCGEELdHJliiTm59Ye7Vq1eZPHkyeXl5TJgwAYDS0lJ27dqFvb09hw4dwtPTExcXF/r168eyZcuYNGkSu3btoqSkhKCgIGbMmEHPnj0ZNmwYAKdOnaqx/7Zt25KUlITBYGDEiBHs2LGD4OBgADw8PCptN19WVkZUVBQhISG8++67QPl7utLS0iq1vWjRokqxuLq6Ktc7depU+vTpo2zFnpOTU2M8Vd2LQYMGKVvF+/j4sG7dOmxsbACUyRJAeno6n332GZ988gk2NjYcP36cqKgoNm/eTKtWrQDIzc0lIyOD3r171z5wQlTjvzml/Oe7UkpKGnZ+2peRt9W/mZkpoaFhuLt73FY7QgghRFMjEyxRb7a2tsyZM4eXXnqJ1157DZVKxcGDB3F2diYgIAC9Xo+npyeAMsnx9/cnPj6e5cuXA3Dp0iX+8pe/KG127ty5Tn1rNBqefPJJfv311xrrHThwgGbNmvHKK68oZY8//jhDhgypVLehsVQXT3X3oi4+/PBD3njjDWXy1bVrV0JCQvj444+ZOHEiUP6y5OXLl8sES9yWk5mlXMtt+Pl//P7zbceg022VCZYQQogHjkywRIM4OjpiMBi4evUqLVu2RK/XExgYiK+vL++99x7FxcU0a9YMMzMzpkyZQnh4OCNGjKB9+/YAhIeHM2nSJDZs2EDPnj0ZOHAgdnZ2tfZ78+ZNjh49yptvvqmU/fnFv4sXLyYrK4snnniiTtfS0Fiqi6e6e1EXP/74I25ubkZlbm5ubN26Vfn+1FNP8dVXX3HgwAEeffTROrUrxJ91cVNTXNzwFaxHzR+7rf7NzEwJCqr6peZCCCHE/UwmWOK2FRUVkZGRwdSpU7GwsKB79+7s27ePvn37AuUpcZaWloSFhSnneHt7s2vXLvbu3cuePXt48cUX0el0ysrNn50/fx6tVktOTg59+vQxWmWqKkXwz2bNmsWRI0do1qwZW7ZsMTpW31hqiqe2e9FYxo0bx7Jly4iOjm7UdsXD43EHNY87NHyfo+AXLQd4rQAAIABJREFUljZiNEIIIcSDQ3YRFA1y4cIFNBoNtra27Nu3jz/++IPg4GB8fHw4cuQIOp3OqL5arUalUhmVWVlZMWDAAObPn0+3bt349ttvq+2v4pmnr776iszMTFJTU2uMz8XFhRMnTijfZ86cydq1a7l27VqV9esTS03x1OVe1MTJyYnMzEyjsuPHj+Ps7GxU5uXlpayeCSGEEEKIpkMmWKLecnNzmTlzJuHh4ahUKvR6PW+99RZpaWmkpaWRmprK/v37KSgoqLaNb775Rjmel5fH+fPnsbe3r7VvGxsboqOjWblyZY31PD09uXnzJp988olSVlhY2KixVBVPQ+7FrV599VXi4+OVieDJkyf54osvjFb/KowbN45Vq1bVqV0hhBBCCHF3SIqgqJPCwkK0Wq2yTbtWq2XEiBEUFBSwd+9eZs2apdQ1NzenR48epKenExAQUGV7x48fZ86cOWg0GsrKyggNDeXJJ5+sUyy+vr4sXryYw4cPA5WfwRo3bhz+/v4sWbKEefPmsWrVKmxsbDAzM6sype52Yrk1nkOHDjXoXtzq+eef59dff+Wvf/0rKpWKRx99lPnz59O6detKdXv37l1tGuPQV5K5fv1Gna9B3D1WVuYyNkIIIcQDTFVWVlZ2r4MQQjSu4mKD/IhvomSC1TTJuDRdMjZNl4xN0yVjc3e0atWiynJJERRCCCGEEEKIRiIpgqLJ+OGHH4iJiTEqMzExYfPmzfcoovvXys9qT0cUoq4G9//8XocghBBC3DdkgiWaDFdXV5KSku51GEIIIYQQQjSYpAgKIYQQQgghRCORFayHVJcuXejUqZOyK2BISAjDhw9Hrf7fnDs2NpaUlBQyMjJQq9VkZWUxfvx4kpKSMDU1BWD06NEEBwfj6enJm2++yS+//EJJSQmPP/44H374YZV95+TkEBAQQIcOHZSyESNGEBISgo+PD23atDHaXl2r1WIwGNDpdBw8eJDIyEgcHBwoKioiMDCQ8ePHc/DgQVavXl3phcNFRUXMnz+f3bt3o1KpcHJyYubMmdjZ2REWFsbYsWPp3bs3ADt27ODzzz8nISFBuT8VAgMDGT16NEOGDOHSpUuYmJhQXFxMz549mThxIpaWlrXea4PBQMeOHXnnnXcwMzOrtY/mzZvTrFkz3nrrLbp06QJAfn4+77zzDvv378fS0pJHH32U6OhounfvXuuYCyGEEEKIO08mWA8pU1NTJR3v6tWrTJ48mby8PCZMmABAaWkpu3btwt7enkOHDuHp6YmLiwv9+vVj2bJlTJo0iV27dlFSUkJQUBAzZsygZ8+eDBs2DIBTp07V2H/Fi3qrkp+fzy+//IK9vT1nzpypdNzDw4MVK1Zw48YNQkJC6Nu3b7X9LFiwgPz8fFJSUtBoNGzZsoXx48ezefNmZs2axeuvv46npyclJSUsWLBAea/Urffnz+Lj4+nWrRtFRUW89957REZGsmHDhmpjuLWtyZMns3HjRkaMGFGnPrZs2UJcXBxr1qwBYPr06Tg4OLBz507UajUXLlyo8h4JUZVfLpRy8rtSSorrd97+HZEN6s/MzJTQ0DDc3T0adL4QQghxP5IUQYGtrS1z5szh448/pmLX/oMHD+Ls7Mwrr7yCXq9X6kZFRZGSksLJkyeJj49nxowZAFy6dIk2bdoo9Tp37tzgeF544QWSk5MB0Ol0BAYGVlnP3Nycrl27kp2dXeXxgoICEhMTmTZtGhqNBoBBgwZhYmLCgQMH6NSpE3379uXDDz9kyZIlaLVa2rZtW+c4TUxMeOONN/j5559rnVBW8PDwqDbeqjz11FP8+uuvAJw/f56jR48yceJEZaXR0dGRPn361Lk98XDLyizl+lXI+71+n4sXf27Q59y5s+h0W+/1ZQshhBB3laxgCaD8h7rBYODq1au0bNkSvV5PYGAgvr6+vPfeexQXF9OsWTPMzMyYMmUK4eHhjBgxgvbt2wMQHh7OpEmT2LBhAz179mTgwIHY2dlV29/58+eNXg78z3/+Ew+P8r9y+/n5MW3aNCIiIkhPTyc+Pp5t27ZVauPatWscPXqUyMhIcnNzKx3Pzs7G3t4eCwsLo3I3NzeysrLw8vJi/PjxvPjii5iYmLBlyxalTsWLlSuMGTOmyhcFazQaOnfuzNmzZ2udVJaUlLBnzx68vb3r3MfevXvx9fUFICsriy5duiiTRSHqy8VNTUlx/VewLMwfa1B/ZmamBAWFNOhcIYQQ4n4lEyxRSVFRERkZGUydOhULCwu6d+/Ovn37lFQ8Hx8fLC0tCQsLU87x9vZm165d7N27lz179vDiiy+i0+mwsbGpso+aUgStrKywtLREr9fj5OSkPO9V4fDhw4SEhKBWqxk1ahQuLi4cPHiwQddqbm5OQEAA5ubmmJiYKOU1pe/9WW3v6r51IuXh4cFLL71Uax/R0dEUFxdz48YN2VlRNBp7RzX2jvVPXBjcf+kdiEYIIYR4MMkESwBw4cIFNBoNtra2pKen88cffxAcHAyUp9o1b97c6FkntVqNSqUyasPKyooBAwYwYMAAxowZw7fffkv//v0bFE9AQACzZ89m3rx5lY5VPINVm7Zt2/LLL7+Ql5dntIp1/PjxStdy6+Ye9WEwGDh9+jQdO3astk59JmsV4uPjcXNzIy4ujjlz5vDBBx/g4uLCqVOnMBgMsoolhBBCCNFEyTNYgtzcXGbOnEl4eDgqlQq9Xs9bb71FWloaaWlppKamsn//fgoKCqpt45tvvlGO5+Xlcf78eezt7Rsck6+vLxEREfTq1avBbZibmxMSEsLbb7+NwWAAYOvWrRQUFODp6dngdisUFxfz7rvvYm9vf1vPnFVHpVLx+uuv8/3333PmzBnatm2Lm5sbixYtUlbNcnJy2L17d6P3LYQQQgghGkZWsB5SFWlrFdu0a7VaRowYQUFBAXv37mXWrFlKXXNzc3r06EF6enqVzyFB+arQnDlz0Gg0lJWVERoaypNPPllt/39+BmvQoEEMHTpU+W5hYcHo0aPrdU3ffPMNzz33nPJ94cKFTJ48mXfeeYf+/fujVqvp2LEjS5YsqbT69md/fj7K29ub6OhooDx9z8TEhKKiInr27MnSpQ1Ln6qpjwqmpqaMHDmShIQE5s6dS2xsLG+//Tb9+vXD1NQUa2tr3njjjUptj345mevXbzQoLnFnWVmZy9gIIYQQDzBVWW0PkAgh7jvFxQb5Ed9EyQSraZJxabpkbJouGZumS8bm7mjVqkWV5ZIiKIQQQgghhBCNRFIExR3zww8/EBMTY1RmYmLC5s2b71FEd861a9cYPnx4pfK1a9dibW191+NZ+HnVqZxCNJZhvp/f6xCEEEKIJkkmWOKOcXV1fWi2GLe2tn5orlUIIYQQQlRPUgSFEEIIIYQQopHICpZ44OzatYuoqCiSk5NxcnIiJyeH559/nrFjxzJp0iSgfGt6b29vXn75ZVq1akVKSgoAp0+fplOnTkDlnQ1vtXXrVlatWoVKpUKj0TBgwAAiIiKYOnUqhw4dokWLFpSVlfGPf/wDLy8vAIYMGcKlS5eUFye3a9eORYsWsXjxYjZt2oSNjQ0FBQV06tSJiRMn4uzsrJwXExPD7NmzKSoq4rfffqOwsBA7OzsAlixZgoODw527oUIIIYQQos5kgiUeODqdjh49eqDX65kwYQIADg4OZGRkKBOslJQUZQIzbtw4xo0bB8DTTz9da6pfRkYG69atIyEhATs7O4qKiti6datyPCYmBn9/fw4cOMCMGTPYuXOnciw+Pp5u3bpVanP48OFEREQAkJyczLBhw9i+fTs2NjZKnYpn1xITE8nMzGTGjBn1vjdC1ObS+VKyjpRSUlxzve90kXVqz8zMlNDQMNzdPRohOiGEEKLpkxRB8UDJz8/nyJEjxMbGotfrlXIzMzOcnJw4duwYADt27OCFF15oUB8rV64kJiZGWUEyMTFh8ODBleo9/fTT/Prrr/VuPyAggL/85S9s3769QfEJcTvO/aeU36/Cjd9r/ly8+HOdPufOnUWn21p7x0IIIcQDQlawxAMlNTUVb29vOnTogLW1NZmZmVhZWQHlE5fk5GRatmyJWq2mdevWXLp0qd59ZGVl4ebmVmu9vXv34uvra1QWHR2tpAj27NmTKVOmVHnuE088wdmzZ+sdmxC3q8OTakqKa1/BsjR/rE7tmZmZEhQU0giRCSGEEPcHmWCJB4per1eemwoICECv1xMeHg6At7c3CxcuxNbWloCAO7eNeVxcHAsWLODixYt89tlnRseqSxEUoqlo3VZN67a1JzcM8116F6IRQggh7j8ywRIPjOvXr3PgwAFOnz6NSqXCYDCgUqkICwsDylP5unbtypo1a9Dr9aSlpTWoH2dnZzIzM5XNK/6s4hmsjz76iGnTppGYmFjvPk6cOFGnVTIhhBBCCNG0yDNY4oHx5ZdfotVqSU9PJy0tjYyMDBwcHLh48aJSZ+TIkURHRytpgw0xZswY5s+fz+XLlwEoKiqq8uXJf/vb3ygtLWXv3r31vo6vv/6aoKCgBscohBBCCCHuDVnBEg8MnU7HqFGjjMr8/PxYsWKF8t3FxQUXF5fb6qd3795cuXKFESNGUFZWhkqlYtCgQZXqqVQqxo0bx6pVq/D29gaMn8GytrZm7dq1AKxdu5Zt27ZRUFCAi4sL69atM9pBUAghhBBC3B9UZWVlZfc6CCFE4youNnD9+o17HYaogpWVuYxNEyTj0nTJ2DRdMjZNl4zN3dGqVYsqyyVFUAghhBBCCCEaiaQIClGNZcuWkZKSYlTm7++vvJS4KYv94s7tkihEVaL6fn6vQxBCCCGaBJlgCVGNcePG3ReTKSGEEEII0XRIiqAQQgghhBBCNBJZwaqjXbt2ERUVRXJyMk5OTgCcO3eOuXPnkp2dzaOPPkrbtm3p37+/smvd+fPnad26Naampri6uhIXF1ep3YMHDxIZGYmjoyMFBQW0bNmSV199lb59+xrV02q1dOzYkQULFgCwadMm9u/fz/vvvw9AXl4eISEhrFmzhqtXrxIbG0tRURFFRUUEBATw2muvVXttGRkZLFy4kMLCQkxMTPD09GTq1KksXryYTZs2YWNjQ3FxMZGRkcrW4VOnTuXQoUO0aFH+cJ+ZmRkbN24kMTGRuLg42rRpw40bN3B0dCQqKgp3d3flvD59+rB9+3ZycnK4ceMGubm5ODg4ADBz5kyl7q2GDBnCpUuXlB342rVrx6JFi1i8eDGrVq0iLS0NW1tbAJ5++mm+++47ALp06UKnTp0oKSlBo9EQEhLC8OHDUavVDb73s2bN4t///jfFxcXk5OTQoUMHoHzFa/fu3VXel6okJibyj3/8gzVr1tCzZ0/gf//OFi5ciL+/P0OGDCEmJoZu3brh4+ND165dWbx4MQApKSns3r2bt99+u9qxFUIIIYQQd5dMsOpIp9PRo0cP9Ho9EyZM4ObNm4wZM4apU6fi4+MDlE+WrK2tSUpKAjD6cVwTDw8PZVJ28uRJoqKiMDU1VV5ke+bMGUpLSzl8+DA3btzA3Nyc0NBQEhMT2b9/Pz179mThwoUMGjQIR0dHXn31VRYuXEjnzp0xGAycO3eu2r5Pnz7NnDlzWLFiBU5OThgMBj777DPl+PDhw4mIiOCnn35i4MCB9O/fn2bNmgH/e6HunwUEBDBjxgwADhw4wGuvvcb69euViSnAkiVLlHu2evVqo63UqxMfH1/lvbS2tmb16tW88cYblY6Zmpoq43H16lUmT55MXl4eEyZMABp272fOnAlATk4OY8eOVdoH2L17d7X3pSqdOnVCr9crEyydTkfnzp2rrX/8+HF+/PFHnJ2d69S+EI0pN7uU7MOlGIorH5uYFFnlOWZmpoSGhuHu7nGHoxNCCCGaBkkRrIP8/HyOHDlCbGwser0egO3bt/PUU08pkyuAZ599lk6dOt1WX126dCEyMpINGzYoZTqdjuDgYHr16kVqaipQ/o6lf/3rX8ydO5djx45x4MABIiIiAMjNzaVVq1YAaDSaGn+Mr1q1irFjxyqTH41GQ1hYWKV67du3x8zMjN9//71e1+Pp6cngwYONJm2NbdCgQezYsYPr16/XWM/W1pY5c+bw8ccfU9XbCep67xuTh4cH//nPfyguLiY/P5/z58/TpUuXauuPGDGCZcuWNXocQtRFztFS8q5AwW+VPxcv/lzl59y5s+h0W+916EIIIcRdIxOsOkhNTcXb25sOHTpgbW1NZmYmWVlZdO3a9Y7017VrV86ePat8T05OJjAwkMDAQGWCB9C5c2d69erF8OHDmT59OiYmJgAMGzYMf39/oqKi2LhxIzdv3qy2r6ysLNzc3GqN6fjx47Rr105JwwOIi4tDq9Wi1WqZPHlyna+noaKjo5X+3nnnHaXc3NycgQMHsn79+lrbcHR0xGAwcPXq1TrFWt29r0ld7wuUT5R79uzJvn37SE1NNZqwV+WFF17gxIkTZGdn1ykWIRqTQ3c1Fi3B7P8qf9q0eazKT4cOHQkKCrnXoQshhBB3jaQI1oFer2fo0KFAefpbXX9oN9StqyvHjh3D2tqaxx57DDs7O6ZNm8b169exsrICIDw8nD179vDss88q54wfP57g4GD27duHTqdDr9fz0UcfNSiWtWvXkpiYyE8//VRp5aSuqXCN9S7r6lIEAYYOHUpISAgjR468rT7qc++rU58UQYDAwEDWr19PXl4eU6ZMqTFdUq1WExERwYoVK3juuefq3IcQjcGmnRqbdlX/XS6q79K7HI0QQgjRNMkKVi2uX7/OgQMHmD59Oj4+PiQkJLBjxw6cnZ05fvz4HenzxIkTSsqeXq/n3Llz+Pj40K9fP/Ly8ti5c6dSV6VSoVZXHsa2bdsSFhbG2rVrOXXqFNeuXauyL2dnZzIzM6uNZfjw4ej1ehYtWsSbb75Z42pYXa7nTrG0tCQoKIhPPvmkxnoXLlxAo9EYrcTdqj73vrE8+eSTnD59mmvXrikbZtREq9Vy+PBhLl682OixCCGEEEKI2yMTrFp8+eWXaLVa0tPTSUtLIyMjAwcHB9q1a8d3333H7t27lbrffvstp0+fvq3+Tp06xdKlSwkPD6e0tJQdO3awbds20tLSSEtLY+nSpeh0uhrb2L17t7ISk52djVqtxtLSssq6FashFRthlJaW8umnn1aq9/zzz+Pm5sYXX3xRr+s5dOgQmzZtYvDgwfU6ryGGDx/Oxo0bKSkpqfJ4bm4uM2fOJDw8HJVKVel4Y9z7hpo8eTKTJk2qU91mzZoxbNgw1q5de0diEUIIIYQQDScpgrXQ6XSMGjXKqMzPzw+9Xs/y5cuZO3cuc+fO5ZFHHsHV1ZU333yz3n0cPnyYkJAQCgoKsLW1Zfr06Xh5eXHo0CHs7Oyws7NT6j7zzDOcOXOGS5cu0bp16yrbS0pKYt68eZiamqLRaIiPj0ej0VRZt3PnzkybNo3JkydTUFCASqWiT58+VdaNiopi8uTJymQpLi7OKG1w8+bNQPlzS0eOHKGwsBAHBwcWLVrUKCtY0dHRyjbt1tbWlSYYNjY29OvXz6i8sLAQrVarbNOu1WoZMWKEcryx7z1UfV8qno+rTu/evetyCxShoaGy2YUQQgghRBOkKmusB2SEEE1GcbGB69dv3OswRBWsrMxlbJogGZemS8am6ZKxabpkbO6OVq1aVFkuKYJCCCGEEEII0UgkRfAu2bt3L/Hx8UZlDg4Oygt377QtW7ZU2sbc3d1deWluUxEVFUVOTo5RWXR0NN7e3vcoott3L+79G9sC71jb4uHx5nOb73UIQgghxH1HUgSFeABN3FL3beKFqM7DNMGSdJqmS8am6ZKxabpkbO4OSREUQgghhBBCiDtMJlgPgaeffhqAnJwcXF1djV46PHv2bBITEwH4/vvvCQ0NRavV8sILL7B48eJa246MjKy0BfvixYtxdXUlOztbKVu7di2urq4cO3ZM6aNPnz54enqi1WrRarWVUgMr+Pj4MGDAAAYMGMDIkSO5fPmycuzkyZO4urqyZ88eoPxFwa+88goZGRlKnR07dhAREQGAq6sr0dHRyrGSkhI8PT0ZM2YMAImJiUYxabVafvzxxxrv3axZs9BqtQQEBPDkk08q56WkpFR5PdXd58WLF5OQkFDp2nNzc+sUuxBCCCGEuPfkGayHjK2tLevXr+fll1+utHX4lClTWLhwIZ07d8ZgMCjvxqrO77//zvHjxzE3N+fChQs4Ojoqxzp16oRerycyMhKAlJQUXFxcgP9t556YmEhmZiYzZsyoNe5169ZhY2PDe++9x4oVK5g+fTpQvo1+jx490Ov1PPfcc6hUKmbNmsXrr7+Op6cnJSUlLFiwgFWrVgFgbm5OVlYWhYWFmJqa8vXXXxttxQ4QEBBQKaacnJxq713Fs1Q5OTmMHTuWpKSkGq+lvve5Ql1iF0IIIYQQ95ZMsB4yNjY2uLu7s3Xr1korT7m5ubRq1QoAjUaDs7NzjW3t3LmTvn370rJlS/R6PWPHjlWO+fr6kpqaSmRkJOfPn6dFixY0a9bstuP38PBQVpHKyspISUlhzZo1hIWFcfPmTZo3b06nTp3o27cvH374ITdu3ECr1dK2bVuljd69e7N79278/f3R6/UEBgZy5MiRWvuu6d7VR33v860aGrsQf/b7T6X8+q2B0qLq60xMjKy1HTMzU0JDw3B392jE6IQQQoj7l6QIPoRGjRpFQkICBoPBqHzYsGH4+/sTFRXFxo0buXnzZo3t6PV6goKCCAwMRK/XGx2zsLDA3t6e06dPo9frCQgIaJTYd+/eTadOnQD497//jYODA23btuXZZ59l9+7dSr3x48ezfft29u7dW+lF0QEBASQnJ3Pz5k1++OEHunfvbnQ8OTnZKEWwsLBQOVbdvauP+t7n+sQuRF1d+b6UwstQ9Fv1n4sXf671c+7cWXS6rff6coQQQogmQ1awHkKOjo50796d7du3G5WPHz+e4OBg9u3bh06nQ6/XGz1zdKsrV66QnZ1Njx49UKlUPPLII5w+fVqZ/ED5ZECv17Nv3z7WrVunPOvVEMOGDUOtVuPq6srEiRMBlBWcir6SkpLo378/UJ5OFxAQgLm5eaVUyM6dO5OTk4NOp6N3796V+qoqRbBCdfeuPqq7zyqVqsr6t5bXFrsQddXyKTWG4ppXsGzMHqu1HTMzU4KCQhoxMiGEEOL+JhOsh9SYMWN4/fXXeeaZZ4zK27ZtS1hYGIMHD8bLy4tr165hbW1d6fwdO3bw22+/8fzzzwOQl5eHXq83mmD17duX+fPn4+bmhoWFxW3FW/EMVgWDwcDOnTtJTU1l+fLllJWVcf36dfLy8pS+1Go1anXVi7Q+Pj7ExcWxfv16rl+/Xq9Yqrt39VHVfbaysjLawAMgPz8fS0vLRotdiAqW7dVYtq85ieHN55bepWiEEEKIB4ekCD6knJyccHJyIj09XSnbvXs3Fa9Fy87ORq1WV/pxX0Gv17Nq1SrS0tJIS0tjy5YtldIEzczMiI6ONno2q7F88803uLq6kpGRQVpaGunp6fj5+bFr1646nf/SSy8RFRWFq6trvfuu6t7VR3X32cPDg7S0NPLy8oDyZ9xcXV3RaDSNFrsQQgghhLizZAXrITZu3DhCQv6X2pOUlMS8efMwNTVFo9EQHx9f6cc9lO+W99///pennnpKKXN0dKRFixYcPXrUqG5FCl9j0+v1+Pr6GpX5+fnx6aefGl1Tddq0acPQoUOrPJacnGy0ccTMmTNp3bq1UZ0/37v6qO4+d+7cmfDwcMLCwlCpVNja2hIbG1uv2IUQQgghxL2lKqv4U7oQ4oFRXGyQN7g3UVZW5jI2TZCMS9MlY9N0ydg0XTI2d0erVi2qLJcUQSGEEEIIIYRoJJIiKGq0ZcsW1q9fb1Tm7u6uvFy3MYWGhlJUZLylWVxc3H37rNGsWbP497//bVQ2dOhQBg0adMf7Dk++M6mZomlb0nPzvQ5BCCGEeOhJiqAQD6DBSf73OgRxD8gEq+EknabpkrFpumRsmi4Zm7tDUgSFEEIIIYQQ4g6TCVYT0qVLF7RaLYGBgQQHB7N69WpKS0uN6sTGxuLt7a2UZ2Vl0b9/fwoLC5U6o0ePRqfTceXKFcaMGUNwcDABAQGMGjWq2r5zcnIICgqq8lhJSQmenp7Ex8cblaenpxMSEqK0v3HjRpYtW4ZWq0Wr1SrXo9VqK6UZVli8eDHe3t5otVqCgoJITU1VyhMSEozq+vj4kJubC8DFixcZN24cfn5++Pr68tZbbynphQcPHsTV1ZW0tDTl3DFjxnDw4EEAhgwZQv/+/ZXYJkyYUO19Adi6dStBQUEMGDCAkJAQJa6pU6eSkpJiVPfpp582+r527Vq6devGH3/8oZTVFl9JSQnvvfcefn5+SozLli1T6t56X7VaLStXrqwxfiGEEEIIcffIM1hNiKmpKUlJSQBcvXqVyZMnk5eXp0wASktL2bVrF/b29hw6dAhPT09cXFzo168fy5YtY9KkSezatYuSkhKCgoKYMWMGPXv2ZNiwYQCcOnWqQXF9/fXXtG/fnpSUFCZPnoxKpaK4uJh//vOffP7557Rp04aioiJycnLo2LEj48aNA8onGxXXU5Phw4cTERHBmTNnCAsL45tvvqmxfllZGePHj+eVV15h2bJlGAwG/vnPf7JgwQKmTJkClG9lvnz5cnx8fKpsIz4+nm7dutUaW0ZGBuvWrSMhIQE7OzuKiorYunVrredV0Ov1dOvWjZ07dxo9e1VTfO+//z5Xrlxh+/btNG/enLy8PNasWaMcv/XfiRBCCCGEaFpkgtVE2draMmfOHF566SVee+01VCoVBw8exNnZmYCAAPR6PZ6engBERUUREhKCv78/8fHxLF++HIBLly7xl7/8RWmzc+fODYpFr9czdOhKpj+GAAAgAElEQVRQPv30U7777jvc3d3Jz8/HYDBgZWUFgImJCR07dryta3ZycuKRRx7h2rVrNdY7cOAAzZs3VyYsGo2GadOm8fzzzyuT0c6dO1NSUsLXX39tdA/qa+XKlcTExGBnZweUX+fgwYPrdO758+e5ceMGM2fOZPny5UYTrOriKygoYPPmzaSmptK8eXMALCwseO211xp8DaLpKzpXyo0DBsqKb6+diZsibzsWMzNTQkPDcHf3uO22hBBCiIeRTLCaMEdHRwwGA1evXqVly5bo9XoCAwPx9fXlvffeo7i4mGbNmmFmZsaUKVMIDw9nxIgRtG/fHoDw8HAmTZrEhg0b6NmzJwMHDlQmCnV18+ZN9u/fz+zZs/njjz/Q6/W4u7tjZWWFj48Pffv2xcvLiz59+hAUFIRa3fCs06NHj6JSqbCxsQHK0+u2bdumHL906RJQnhbZtWtXo3MtLCywt7cnOztbKRs7diwLFy6scoIVHR2NqakpAD179lRWvv4sKysLNze3amOOi4szSt+7lV6vJyAgAA8PD86dO8eVK1do2bJljfFlZ2djb2+PhYVFtX0WFhai1WqV72PGjCEgIKDa+qLpK/h3KYbLt9/ORX6+/UYAnW6rTLCEEEKIBpIJ1n2iqKiIjIwMpk6dioWFBd27d2ffvn307dsXKH8+ydLSkrCwMOUcb29vdu3axd69e9mzZw8vvvgiOp1OmcDURXp6Os8++yympqb4+fmxdOlSpk2bhkajITY2lh9++IFvvvmG1atXs3//ft5+++16X1vFROrRRx/l/fffR6VSAf9LHaxQXbpfdZ555hkADh8+XOlYXVMEaxMTE4O///927Lv1GSy9Xs8HH3yAWq3Gz8+PlJQU/va3v9UpvgoV2+Rfv36djRs3Ym9vLymCDyAzdzU3im5/Bau16WO3H4uZKUFBIbfdjhBCCPGwkglWE3bhwgU0Gg22trakp6fzxx9/EBwczP/H3r1Hx3jtjx9/T0YjScOJW0ldKxjqHmklKlQQMblMIqItX4KUuKtS0bSiCaqHlLZaqdPQ0DpVRV1mSB2EIkSpailBpTQOFRKXRK6T/P7IL88xcptEkOrntVbW6uxnP3vv59nTtWbbn703FIaS1axZUxlgAVhYWCiDkyJ2dnZ4e3vj7e1NcHAwP/zwAwMGDDC7DQaDgaNHjyqDmxs3bnDo0CFl1kWj0aDRaPDx8aFv376VGmDdO5AqT6tWrfjuu+9M0tLT07l8+TLNmzfn559/VtLHjRtHVFQUNWpU7qveqlUrTpw4gYuLS4XuS0xM5Pfff2f06NFA4QC5SZMmJgOsktrXvHlzLl++THp6Ora2tvj7++Pv74+XlxdGo7FSzyCqP8tnLLB85v73HPqgx7IqaI0QQggh7ofsIlhNpaamMmfOHIYNG4ZKpcJgMDBv3jx2797N7t272bVrF/Hx8WRmZpZaxsGDB5Xr6enpXLx4EXt7e7PbkJ6ezpEjR9izZ49Sb1hYGHq9noyMDGXXOyjcQKNx48aVf+AKcHFxITMzU9lswmg08t577+Hn54e1tbVJ3p49e3Lr1i0SExMrVVdwcDCLFi0iJaUwfisnJ4dvvin/rCGDwcDkyZOV97Z//36uXr3KpUuXymyftbU1/v7+zJ07l+zsbOX5cnPvc2pDCCGEEEI8FDKDVY0Ura3Jy8tDrVaj0+kYNWoUmZmZ7Nu3j/DwcCWvjY0N3bp1Iy4urtT1NydPnmTu3Lmo1WoKCgoICAigU6dOpdaflJREr169lM/Tpk3D2dkZS0tLJa1v374sWrSI0NBQoqOjCQsLw8rKCmtraxYsWFAFb6F8KpWKTz75hPDwcJYtW0Z+fj69e/fm9ddfLzH/uHHjmDDBdPH/3Wuw6tSpQ0xMTIn39u7dm2vXrjFq1CgKCgpQqVQmm1WUxmAwFNs+vX///hgMBjp37lxm+6ZNm8aHH36Il5cXTz75JFZWVvj6+vLUU08Bxddgubq6MmPGjHLbJIQQQgghHjxVQUFBwaNuhBCiauXmGuUE92rKzs5G+qYakn6pvqRvqi/pm+pL+ubhaNCgVonpEiIohBBCCCGEEFVEQgT/ZhITE5k5c6ZJmqWlpVnriu5HVFQUsbGxJmkeHh7KocSPWnVvX0X5bBv7qJsgHpHVPT541E0QQggh/tYkRFCIx9DAzebvyigeLzLAqhwJp6m+pG+qL+mb6kv65uGQEEEhhBBCCCGEeMBkgFWF2rVrh06nw8vLi3HjxnHr1i0AkpOT6dSpEzqdTvkr2mI8IyODOXPm0K9fP/z8/Bg0aBDr1q1T7vPy8lLKP3LkCIMHD8bDwwMPDw++/vpr5drSpUvp3Lkz169fV9LuPvS2vPZOmTJF2dL9ypUrjB8/Hnd3d/r168e8efPIyckBICEhgW7duqHT6Rg4cCAff/wxABs3biQiIsKk/OHDh/PLL78AhYcEp6amltiOCRMmMGTIEOVzVFSU8p6K2qjT6Vi9ejVLly5lxYoVABQUFLBs2TLc3d0ZMGAAw4cP5+zZs0o5bm5uTJ48WfkcGxvLrFmzSn0fGzduxNnZGZ1Oh4eHh8nOgkuXLsXV1dWkD2/dukVCQgLBwcEllpeamkr79u356quvlLSAgAB0Oh0vvviiUpdOpyM5OVl5R8OHD2ffvn0mZcXExDBnzpwyv0tCCCGEEOLRkzVYVcjKyorNmzcDEBISwpo1a5Q1PM2aNVOu3e3tt9+madOm7NixAwsLC1JTU1m/fn2xfCkpKcyYMYNPPvmE9u3bk5qayquvvkrDhg158cUXgcLtxleuXMkbb7xR4fZOnz6dtWvXMnLkSCZNmsQrr7xCVFQURqOR2bNns2TJEkJCQgBwcnJi+fLl3LlzB19fX5PDjivq1q1bnDx5EhsbG/744w+aNm3K+PHjlffWtWtXk/e2dOlS5b/XrFnDsWPH2Lx5M9bW1uzfv5/x48djMBioWbMmULhV/blz52jVqpVZ7dFqtYSFhZGWloaHhwcDBgxQzg6r6IHIsbGxdO7cGYPBwCuvvAKgrHXbuHEjJ06cICwsrNh9Xl5ebNu2DVdXVyVt27ZtSr+W9l0SQgghhBCPngywHpAuXbqUe7jtxYsX+fnnn3n//fexsCicTKxbty5jxxbfoGDNmjX4+fnRvn17Jd8bb7zB0qVLlQGWv78/3377LWPGjMHOzq5C7XVyciIxMZFDhw5Rs2ZN5awntVpNaGgoffv2ZcqUKSb32NjY0L59ey5cuFChuu62Y8cO+vTpQ/369TEYDIwbN87sez/77DO+/PJL5XDhnj170rVrV7Zs2UJAQAAAo0aNIioqivfff79C7apTpw7NmzcnJSWlQocz381gMDBr1iymT5/OlStXaNSokVn3DRgwgA8++ICcnBwsLS1JTk7m6tWrODk5FTuoWDxe8pPuYDx0g4Lc/EqX8dq6CeVnKoO1tRUBAUNxdHS6r3KEEEKIvysZYD0ARqORgwcPMnjwYCXt4sWLJofDzp49m5s3b9K2bVtlcFWWc+fO4evra5LWoUMHzp07p3y2sbFh0KBBrF69uthgqCx5eXl8//33uLq6cvbsWWUQV8TW1hZ7e/tiA6m0tDSOHz/OhAkTlFDAijIYDEycOJF69eoxZcoUswdY6enpZGZm0rRpU5P0e9/JwIED+fe//13hQeB///tfsrOz0Wg0SlpMTAxbtmwBoHbt2nzxxRel3n/58mVSUlLo1KkTAwcOZNu2bYwePdqsuu3s7OjUqRPff/89/fr1Y9u2bQwcOBCVSgWU/F1ycpIfw48D4483KUjJua8yrvDf+26HXr9JBlhCCCFEJckAqwplZWWh0+n4888/cXBw4IUXXlCulRTWtWvXLpPPRVuFX79+nf3791eqDSNGjMDX19esH/NF7YXCGazBgwezdu3acu87cuQIvr6+WFhYMGbMGFq3bs2JEydKzFs0KCjJtWvXuHDhAt26dUOlUlGjRg3OnDlDmzZtym2DuSwsLAgKCmL58uX06tWr3Pzbtm3jhx9+ICkpidmzZyuhhlCxEMGiQREUhh2GhoaaPcAC8PT0ZNu2bfTr1w+DwcD8+fOVaxIi+PhSO/4DY879zWA1snrqvtpgbW2Fl5dv+RmFEEIIUSIZYFWhojVNmZmZBAUFsWbNGkaMGFFq/latWnH69Gny8/OxsLBQ1h6VtDmFg4MDJ0+epF+/fkraiRMniq0tql27Nl5eXvz73/82u733tum7774zSUtPT+fy5cs0b96cn3/+WVmDdTc7Oztu3rxpknbjxg3q1KlTav3bt2/n5s2b9O3bV6nHYDCYNcCytbXF2tpaWbdV5OTJkzz33HMmeXU6Hf/617/MKrdoDdYvv/xCUFAQbm5uNGjQoNz77mUwGEhJSWHr1q0AXL16ld9//50WLVqYdX/fvn1ZsGABJ0+eJCsriw4dOlS4DeKvx+IZGyyesbmvMj6QbdqFEEKIR0p2EXwArK2tefvtt/n888/Jy8srNV/z5s3p0KEDH3zwAUajEYDs7GxKOpps2LBhfPvtt5w6dQooDM+LjIzk1VdfLZZ35MiRrF27tsy6S+Pi4kJmZqayM53RaOS9997Dz89PWetUko4dO3Ls2DFSUlIA+OWXX8jJySlz/ZLBYCA6Oprdu3eze/duNmzYgMFgMLutQUFBzJs3j6ysLADi4+M5evQo3t7eJvmeeOIJAgMDTXYFLE/Hjh3x8fFh9erVZt9TJCkpiYyMDPbt26c829ixY9Hr9WaX8eSTT9K9e3dCQ0Px9PSscBuEEEIIIcSjITNYD8izzz6LRqNBr9fj5ORUbN2Mv78/I0aMYP78+SxcuJD+/ftjZ2eHlZVVibsAPvXUUyxatIi3336bjIwMCgoKCAwMxM3NrVjeunXr0r9//woNKIqoVCo++eQTwsPDWbZsGfn5+fTu3ZvXX3+9zPvq169PaGgoY8eOJT8/HxsbGxYvXmyyvszHx0f53KlTJy5dukSXLl2U602bNqVWrVocP36czp07l9vW4cOHc+vWLby9vbGwsKBBgwYsW7YMKyurYnkDAgKIiooy9zUAMGbMGAYNGqRsw373GiyATz75BICDBw+ahB8OGTKE/v37m5Tl7u7OtGnTmDRpktn1e3l5MXHiRBYvXmySXtp3SQghhBBCPHqqgpKmS4QQf2m5uUY5wb2asrOzkb6phqRfqi/pm+pL+qb6kr55OBo0qFViuoQICiGEEEIIIUQVkRDBx1xaWhojR44slh4TE1PmBhSPsw0bNhRbW+Xo6MicOXMeUYuEEEIIIcTjQkIEhXgMaTeFPOomiAds1QtvP+omPFYknKb6kr6pvqRvqi/pm4dDQgSFEEIIIYQQ4gGTAZYQQgghhBBCVBFZgyUeunbt2tGmTRvy8vJQq9X4+voycuRIky3d58+fT2xsLHv37sXCwoKzZ88yadIkNm/erGzDPnbsWHx8fHB2duatt97i8uXL5OXl0bhxYz777LMS605OTkar1fLMM88oaaNGjcLX1xc3NzcaNWpkckizTqfDaDSi1+tJSEhgwoQJNGnShJycHDw9PZk0aRIJCQmsXLmy2OHLOTk5LFq0iD179qBSqXBwcGDOnDk0bNiQoUOHMm7cOHr37g0UHrq8fv16VqxYobyfIp6enowdO5bhw4dz9epVLC0tyc3NpUePHrz22mvUrl37/jtFCCGEEEJUCRlgiYfOysqKzZs3A3D9+nWmT59Oeno6U6ZMASA/P5+dO3dib2/P4cOHcXZ2pnXr1vTv35+oqCimTZvGzp07ycvLw8vLi7CwMHr06EFgYCAAp0+fLrP+Zs2aKfXfKyMjg8uXL2Nvb89vv/1W7LqTkxPLly/nzp07+Pr60qdPn1LrWbJkCRkZGcTGxqJWq9mwYQOTJk3im2++ITw8nKlTp+Ls7ExeXh5LliwhOjq62Pu5V2RkJB07diQnJ4fFixczYcIEvvzyyzKfV1RPxqQbGBP+C7nGSt3/2jcTKl23tbUVAQFDcXR0qnQZQgghhCiZDLDEI1WvXj3mzp3L4MGDmTx5MiqVioSEBFq1aoVWq8VgMODs7AzAxIkT8fX1xcPDg8jISD799FMArl69ygsvvKCU2bZt20q3Z+DAgWzbto2goCD0ej2enp4mhwsXsbGxoX379ly4cIF69eoVu56ZmcnGjRvZtWsXarUaKDwQeMOGDRw6dAgXFxf69OnDZ599xp07d9DpdDRr1szsdlpaWvLGG2/Qv39/Tp8+fV/PLB4N47ErFKRUfgHyFf57X/Xr9ZtkgCWEEEI8ADLAEo9c06ZNMRqNXL9+nfr162MwGPD09KRfv34sXryY3NxcnnjiCaytrQkJCWHYsGGMGjWKFi1aADBs2DCmTZvGl19+SY8ePRg0aBANGzYstb6LFy+i0+mUz7Nnz8bJqfCHpru7O6GhoQQFBREXF0dkZGSJA6y0tDSOHz/OhAkTSE1NLXb9woUL2NvbY2tra5LeoUMHzp49i4uLC5MmTcLPzw9LS0s2bNig5MnKyjJpX3BwMFqttlgdarWatm3bcv78eRlg/QWpuzbCmFP5GayGVsUH9uaytrbCy8u30vcLIYQQonQywBLVSk5ODnv37mXWrFnY2trSuXNn9u/fr4Tiubm5Ubt2bYYOHarc4+rqys6dO9m3bx/ff/89fn5+6PV66tatW2IdZYUI2tnZUbt2bQwGAw4ODsp6ryJHjhzB19cXCwsLxowZQ+vWrUlISKjUs9rY2KDVarGxscHS0lJJLytE8F5yysJfl/oZO9TP2FX6/g9km3YhhBCiWpIBlnjk/vjjD9RqNfXq1SMuLo7bt2/j4+MDFIba1axZ02Stk4WFBSqVyqQMOzs7vL298fb2Jjg4mB9++IEBAwZUqj1arZaIiAgWLFhQ7FrRGqzyNGvWjMuXL5Oenm4yi3Xy5Mliz3L35h4VYTQaOXPmDC1btqzU/UIIIYQQourJNu3ikUpNTWXOnDkMGzYMlUqFwWBg3rx57N69m927d7Nr1y7i4+PJzMwstYyDBw8q19PT07l48SL29vaVblO/fv0ICgqiZ8+elS7DxsYGX19f3nvvPYzGwhCwTZs2kZmZqawpux+5ubm8//772NvbS3igEEIIIUQ1IjNY4qErWmNUtE27Tqdj1KhRZGZmsm/fPsLDw5W8NjY2dOvWjbi4uBLXIUHhrNDcuXNRq9UUFBQQEBBAp06dSq3/3jVY/v7+jBgxQvlsa2vL2LFjK/RMBw8epFevXsrnDz/8kOnTp/PPf/6TAQMGYGFhQcuWLfnkk0+Kzb7d6941WK6ursyYMQOAGTNmYGlpSU5ODj169GDZsmUllrHZ8105wb2asrOzkb4RQgghHmOqAlnEIcRjJzfXKD/iqykZYFVP0i/Vl/RN9SV9U31J3zwcDRrUKjFdQgSFEEIIIYQQoopIiKB4LCUmJjJz5kyTNEtLS7755ptH1KKHS6cvvkGHePyt6jn1UTdBCCGE+NuTAZZ4LGk0GrO3OhdCCCGEEKKqSIigEEIIIYQQQlQRGWCJv5SuXbsCkJycjEaj4YsvvlCuRUREsHHjRgB++uknAgIC0Ol0DBw4kKVLl5Za5saNG3F2dsbX1xd3d3eCgoL48ccfleuzZs3Czc0NnU6Hh4cHH3/8sXJt+PDhDBgwAG9vbzw8PIiIiODWrVtlPkNUVBSenp54e3uj0+k4fvw4UHiIcmpqqpIvISGB4OBgpY0ajYb4+Hjl+s6dO9FoNMTGxpb73oQQQgghxMMhIYLiL6tevXqsXr2al156CUtLS5NrISEhfPjhh7Rt2xaj0UhSUlKZZWm1WsLCwgA4dOgQkydPZvXq1Tg4OAAwc+ZMPDw8yM7ORqvVotPpaNq0KQCRkZF07NiRnJwcFi9ezIQJE/jyyy9LrOfYsWPs2bOHb7/9FktLS1JTU8nNzTXredu0aYPBYKBHjx4A6PV6OQPrMWf8/RrGhCTIMZqV/7X1iWbls7a2IiBgKI6OTvfTPCGEEEKUQAZY4i+rbt26ODo6smnTJoYMGWJyLTU1lQYNGgCgVqtp1aqV2eU6OzszZMgQvv76a0JDQ02uZWdnA4Xnc93L0tKSN954g/79+3P69OkSBz8pKSnUqVNHGRDWrVvX7HY5OTlx5MgRcnNzycnJ4eLFi7Rr187s+8Vfj/HHPyhISTc7/5WbpR/IfS+9fpMMsIQQQogHQAZY4i9tzJgxvPrqq/j7+5ukBwYG4uHhwfPPP4+rqyt+fn7UrFnT7HLbt2/P2rVrlc8LFy4kKiqKixcvMnz4cOrVq1fifWq1mrZt23L+/PkSB1gvvPACn3zyCQMGDMDFxQWtVsvzzz9vVptUKhU9evRg//793L59Gzc3N5KTk81+JvHXo3ZsijE3z+wZrIbWdczKZ21thZeX7/00TQghhBClkAGW+Etr2rQpnTt3ZuvWrSbpkyZNwsfHh/3796PX6zEYDCbrtcpz7/nbRSGCGRkZjBw5kh9//BFHR0ez7r3bk08+ycaNGzly5AgJCQlMmzaN6dOnM2jQoBLzq1Qqk8+enp6sXr2a9PR0QkJCWL58udnPJP561C3qo25R3+z8H8g27UIIIcQjJ5tciL+84OBgoqOjiw1smjVrxtChQ4mJieH06dOkpaWZXeavv/6qrL+625NPPsnzzz/P0aNHS7zPaDRy5swZWrZsWWrZarWa7t27M2XKFGbPns2OHTsAsLOzM9kg4+bNm9SpYzoj0alTJ86cOUNaWhrPPPOM2c8jhBBCCCEeDhlgib88BwcHHBwciIuLU9L27NmjDLguXLiAhYUFtWvXNqu8w4cPs27dumLrugDy8vL4+eefadasWbFrubm5vP/++9jb25e6+cT58+f5/ffflc+nTp3i6aefBqB79+7K2V1Go5EtW7bQvXv3YmVMnz6dadOmmfUsQgghhBDi4ZIQQfFYGD9+PL6+/1tTsnnzZhYsWICVlRVqtZrIyEjUanWp92/bto2jR4+SlZVFkyZN+Oijj0xmsIrWYOXm5uLi4oK7u7tybcaMGVhaWpKTk0OPHj1YtmxZqfXcuXOHefPmcevWLdRqNc2bNyciIgKACRMm8M477+Dj40NBQQGurq74+PgUK6N3797lvo/NXm9y48adcvOJh8/Ozkb6RgghhHiMqQrKWjAihPhLys01yo/4akoGWNWT9Ev1JX1TfUnfVF/SNw9Hgwa1SkyXEEEhhBBCCCGEqCISIij+NjZs2MDq1atN0hwdHZkzZ06V15WWlsbIkSOLpcfExBTbuOJB8N36wQOvQ1Q/Ma5jH3UThBBCiL89GWCJvw1/f/9i52U9KHXq1FE2rBBCCCGEEH8fEiIohBBCCCGEEFXkkc5gtWvXjjZt2lBQUIBarWb27Nk4OjqSkJDAypUrTQ5RnTVrFi+++CIeHh4MHz6cq1evUrNmTZ544gnmzZtHu3btALh9+zZz587l2LFjFBQU4OjoyOzZs6lVq+RFaEViYmJ4//33iY+PV/ImJCQwYsQI5s2bR0BAAFC4rbavry8zZ84kOTmZH3/8kdzcXJKTk5VzicaPH4+Hh0eJ9axYsYJvvvmGmjVrUqNGDYYPH46vr2+Zz+Tm5saTTz6JhUXhePi5557j7bffZtasWRw+fBhbW1uys7Pp3Lkzr7/+Oo0aNVLuW7duHUFBQQBcu3YNCwsL6tatC8A333yDpaVlsTZqNBq8vb2JjIwECrcm79mzJ507dzbpkwkTJnDt2jXWrVunpM2bNw87OzsmTZoEQFRUFFevXi01DK+goICoqCg2bdqESqXiqaeeIiwsjNatW/Pmm2/SuXNnXn75ZSX/zp07Wbt2LdHR0cr3p4inpydjx441611C4Vbo/fv3Z8KECdSsWdPs752VlRUAzZs356OPPirxuZYuXcrHH3/Mjh07aN68OVD4HVuwYAHr16+nY8eOpfbr3e988ODBzJgxQyl3+PDhZGRksHHjRgB++eUXFi5cWKFDlIUQQgghxIPzSAdYVlZWShjVvn37WLx4MV9++aVZ90ZGRtKxY0c2bNjAwoUL+fzzzwF46623aN26NQsXLgTgo48+4q233ir1h3ARg8FAx44d2bFjh0kYWZs2bdi+fbsywNLr9coZR0WDhuTkZMaNG1duSNhXX31FfHw869evx9bWlvT0dP7zn/+U+0wAq1atUgZGd5s5cyYeHh4UFBSwatUqAgMD2bp1qzJwsrCwUNq1dOlSbGxslAFXaWxsbDh79ixZWVlYWVlx4MABGjZsaJLn1q1bnDx5EhsbG/744w+aNm0KwGuvvYZOp0On0wGFg7hNmzaVWteaNWs4duwYmzdvxtramv379zN+/HgMBgOenp4sX77cZIBlMBjw8vICTL8/9zLnXWZkZBAWFkZYWBj//Oc/y3wn95ZrjjZt2mAwGJgwYQIAsbGxtG7d2iRPaf164MABWrRoQWxsLNOnT0elUinXUlNT2bt3r1nbtYvHg/H3q+QdPgM5eWXme23DT6Ves7a2IiBgKI6OTlXdPCGEEELcpdqECKanp5t9EOzdunTpwp9//gkUHih74sQJ5QctwMSJEzlx4gQXL14stYyLFy9y584dXnvtNQwGg8m1p59+muzsbK5du0ZBQQH79u2jV69eFW4nwPLly3nnnXewtbUFwNbWFj8/vzKfyVwqlYqRI0dSv359vv/++0q17269e/dmz549AMpg5247duygT58+eHp6mrwzW1tbpk2bRkREBBEREUyZMqXMfv3ss88ICwvD2toagJ49e9K1a1e2bNmCi4sLSUlJXL16FSg8Qyo+Pp5+/fqZ/Rxlvcsnn3yS8PBwdu7cyY0bN8wu01z9+vVj165dQOF3rFatWmZvcGEwGBgxYgT29vYcO3bM5FpQUBCffvpplbdXVF95x85TkHKLgpt3yvy7cuW/pSBWWekAACAASURBVP4lJZ1Hry/9HzuEEEIIUTUe6QxWVlYWOp2O7OxsUlJSWLVqVYXL2Ldvn/KD+9y5c7Rr187kQFm1Wk27du04e/YszZo1K7EMg8GAVqvFycmJpKQkrl27Rv369ZXrAwYMIDY2lnbt2tG+ffsSw+rKk56eTkZGhjLTY+4zFQkMDFRCyfz8/ErcoQ7g2Wef5fz58xVu3720Wi3Lli2jT58+JCYm4u/vz9GjR5XrBoOBiRMnUq9ePaZMmcK4ceOUa15eXnzxxRdYWFiYHP57r/T0dDIzM4u9kw4dOnDu3DnUajXu7u5s376dwMBA4uLi6N69uzJALfr+FAkODkar1ZqUVdK7vJutrS1NmjThwoULZr2XGTNmKCGCPXr0ICQkpMyy7e3tOXPmDLt27UKr1SqhfUVK6tfs7Gzi4+OJiIjg9u3bGAwGHB0dlXu6dOnCf/7zHw4dOqSEO4rHW42uLcnLzSt3BquhtV2p16ytrfDyKv3/RyGEEEJUjWoTInjs2DFCQkLQ6/Um4VB3uzt9xowZ5ObmcufOnfverc1gMPDxxx9jYWGBu7s7sbGx/N///Z9yfeDAgUybNo3z58/j6elZbEahqpT1TKWFkt2rqs6Nbtu2LcnJyej1+mKhaNeuXePChQt069YNlUpFjRo1OHPmjLIe6sqVK6SkpKBSqcjIyLivQYCnpycLFy4kMDAQg8FgMqAqK0SwIt+PondmzveuIiGCUDhQNRgM7N+/n1WrVhUbYJXUr0UDSSsrK9zd3Vm2bBmhoaEm/3Awfvx4oqKiTNZniceXusVTqFs8VW6+D2SbdiGEEOKRqzYhgl27diUtLY3U1FTs7Oy4efOmyfUbN26YhFdFRkaya9cu/Pz8mDt3LgCtWrXi1KlT5OfnK/ny8/M5deoUrVq1KrHexMREfv/9d0aPHo2bmxsGgwG9Xm+Sp0GDBtSoUYMDBw7g4uJSqeeztbVV1iuVpqRnqqhTp07h4OBQqXvv5ebmxsKFC4uFB27fvp2bN2/St29f3NzcuHTpkkmY4Pz585k8eTIDBw7k448/LrV8W1tbrK2ti72TkydPKv3l6OhISkoKp0+f5tixY7z44otmtd3cd5mens6lS5do0aKFWd+7iurTpw9btmzh6aefVmbeymMwGDh48CBubm74+/tz48YNDh06ZJLHxcWF7Oxsjh8/Xum2CSGEEEKIqldtBli//fYbRqMROzs7WrRowdWrV/ntt98AuHTpEomJicpOcEVUKhVTp07lp59+4rfffqN58+Y8++yzLFu2TMmzbNky2rdvr+zkdi+DwcDkyZPZvXs3u3fvZv/+/Vy9epVLly6Z5JsyZQpvvPGGySxCRY0dO5bw8HDS09MByMjIKLYBxL3PZK6CggJWr15NSkoKrq6ulW7j3QYPHszEiRPRaDQm6QaDgejoaOWdbdiwQRlg7d27l+vXr+Pr68uECRP4z3/+w7lz50qtIygoiHnz5pGVlQVAfHw8R48exdvbGyh8HwMHDiQkJIRevXpRs2ZNs9tf3rvMyMggPDycfv368Y9//MPs711FWFtbM2PGDJMQyrKkp6dz5MgR9uzZo7zfsLCwYoN+KJzFio6OrnTbhBBCCCFE1asWa7CgcIDwz3/+E7VajVqtZtGiRbz55ptkZ2dTo0YN5s2bV+JW61ZWVowePZoVK1bw7rvvMn/+fObOnausu+nSpQvz588vtQ0Gg4F//etfJmn9+/fHYDDQuXNnJe3uNTCVNXToUO7cuYO/vz9PPPEENWrUYNSoUeU+E5iu1dFoNMouiQsXLmTZsmVkZWXRuXNnVq9eXak1YiVp1KgRI0aMMElLTk7m0qVLdOnSRUlr2rQptWrV4vDhw7z77rt89NFHqFQqbGxsmDlzJhEREaxevbrEOoYPH86tW7fw9vbGwsKCBg0asGzZMmWdExSu6YqOjmb69Okm9967BsvV1bVYyFxp77KgoID8/Hxlm3YAS0vLcr93d6/BqlOnDjExMeW+x3tnAO92b7+6uLjg7Oxs0od9+/Zl0aJF5OTkmNzbu3fvUsNGN3m/xo0bd8ptm3j47OxspG+EEEKIx5iqoKoW7Qghqo3cXKP8iK+mZIBVPUm/VF/SN9WX9E31JX3zcDRoUPI5u9UmRFAIIYQQQggh/uoeaYjgw5SYmMjMmTNN0iwtLfnmm2+qvK7w8HB+/PFHk7QRI0aYHGD8qKWlpZW41XtMTMx9bepQmr/CO6mMqKgoYmNjTdI8PDwYP378I2pRId+tUY+0flF5Ma6Bj7oJQgghhLgPEiIoxGPIc2PpuzeK6k0GWI+GhNNUX9I31Zf0TfUlffNwSIigEEIIIYQQQjxgMsCqpK5duwKFu+ppNBq++OIL5VpERIRyoOxPP/1EQEAAOp2OgQMHsnTp0lLL3LhxI87Ozvj6+uLu7k5QUFCxsLq8vDycnZ2JjIxU0pYsWcKiRYuUz5cuXaJv377cunWLuLg4fH198fHxQavVsnbt2lLrX7p0Ka6uruh0OuXv1q1bJCQkoNFo2L17t5I3ODiYhIQEJk6ciE6no3///nTr1k2578cff2T48OEMGDAAHx8f/P39OXXqFADTp0/n3//+t1LW8ePH8fb2Jjc3t8R2ubm54e3tjbe3N6NHjyYlJUW5durUKTQaDd9//z1QuBvlK6+8wt69e5U827dvJygoCCjcqe/unQaL3mdwcLBJH9z9Ds6dO1dmP4eHh6PT6dBqtXTq1Em5797QwSKzZs2ic+fOynb9UHh2mEajITU1VUnbuXMnGo3GZIv5X375BU9PT2VHwYsXL9K3b1+TsoQQQgghxKPzt1mD9SDVq1eP1atX89JLLxXbIj0kJIQPP/yQtm3bYjQaSUpKKrMsrVZLWFgYAIcOHWLy5MmsXr1aOTz4wIEDtGjRgtjYWKZPn45KpWL8+PHodDoGDRqEg4MD8+fPZ+rUqVhbWzN79mzWr19Po0aNyMnJITk5ucz6R44cqQxG7taoUSM+/fRT3NzcTNI/+eQTABISEli5ciXLly83uR4ZGUnHjh3ZsGEDCxcu5PPPP+fNN9/kpZdewsPDAzs7OyIiIpgzZw5PPPFEqe1atWoVdevWZfHixSxfvpy3334bAL1eT7du3TAYDPTq1QuVSkV4eDhTp07F2dmZvLw8lixZopwXZWNjw9mzZ8nKysLKyooDBw7QsGHDUvugSHJycqn9PGfOHCXPuHHj2Lx5c5nvGKBZs2bs2rULnU5Hfn4+hw4dKtaOu59typQpAHTs2JHnnnuOlStXMm7cOMLDw5k2bZrZhxiLh8f4+xXyDp+CnJL/4aA0r21IqFB+a2srAgKG4ujoVKH7hBBCCPFgyAxWFahbty4uLi7FDg0GSE1NpUGDBgCo1WpatWpldrnOzs4MGTKEr7/+WkkzGAyMGDECe3t7jh07BhSe9RQaGkp4eDh79+4lIyMDHx8fMjIylMOboXBTj5YtW1bqGdu2bUutWrU4cOBApe7v0qULf/75JwD169dn9OjRLFq0iK+++gqNRoOTk3k/Dp2cnLhw4QJQOFsVGxvLe++9x4EDB8jOzgagTZs29OnTh88++4xPPvkEnU5Hs2bNlDJ69+7Nnj17gML3WdY5VXcrq58rytPTk+3btwOFg1NHR0dq1Pjfv3dkZGRw9OhR5s+frxziXOT1119n3bp1fPbZZxiNRry8vO67PaLq5R07S0HKDQpuZlTo78qV/1boLynpPHr9/X8nhRBCCFE1ZIBVRcaMGcOKFSswGo0m6YGBgXh4eDBx4kTWrl2rDALM1b59e86fPw9AdnY28fHxuLm54eXlZfLDu3fv3vzjH/8gJCREmVGxs7PDzc2NPn368Prrr7Nlyxby8/PLrC8mJkYJcRs+fLjJtXHjxhEVVbnd6fbt26cc/gzwyiuvcO7cOVasWMEbb7xhdjl79uyhTZs2APz44480adKEZs2a0b17d2XQBDBp0iS2bt3Kvn37GDNmjEkZWq2Wbdu2kZ2dTWJiosmB0gDbtm0zCRHMyspSrpXWzxXVokULUlNTuXnzZomDvF27duHq6sozzzxDnTp1OHHihHKtdu3ajB07lsWLFxebaRPVR42urVE1sEP1jycr9Neo0dMV+nvmmZZ4efk+6scVQgghxP8nIYJVpGnTpnTu3JmtW7eapE+aNAkfHx/279+PXq/HYDCYrOMpz92bPMbFxdG9e3esrKxwd3dn2bJlhIaGolarARg6dChZWVkms1Tz588nMTGRgwcPsnLlSuLj43nvvfdKra+0EEGA5557DoAjR46Y3f4ZM2aQm5vLnTt3TELnLCwseOmllzhx4oRZ28IHBgZiYWGBRqPhtddeA0xnn7RaLZs3b2bAgAFAYSigVqvFxsamWNhm27ZtSU5ORq/X07t372J1lRQiWKS0fq6M/v37YzAYOH78OBERESbXimYqi9pjMBjo0KGDcv3777+nXr16/Pbbb5WelRQPlrpFI9QtGlX4vg9kF0EhhBDiL00GWFUoODiYqVOnKgORIs2aNWPo0KEMGTIEFxcX0tLSzD5r6tdff1XWXxkMBo4ePaqsg7px4waHDh3ihRdeAAoHLRYWxSclNRoNGo0GHx8f+vbtW+YAqzxFs1h3h7OVJTIykg4dOrBw4ULmzp3Lxx//b/vw0tpbkqI1WEWMRiM7duxg165dfPrppxQUFHDjxg3S09OV9Uhlle/m5sbChQtZvXo1N27cMKsNRUrr54rSarUMGjQIPz8/k3YW9euZM2dQqVQYjUZUKhUzZ85EpVIRFxfH7du3WbFiBZMmTaJnz55YW1vfV1uEEEIIIUTVkBDBKuTg4ICDgwNxcXFK2p49e5RZqAsXLmBhYUHt2rXNKu/w4cOsW7eOIUOGkJ6ezpEjR9izZw+7d+9m9+7dhIWFodfrS70/IyODhIT/LZg/ffo0jRs3ruTTFerZsye3bt0iMTHR7HtUKhVTp07lp59+MtkR734cPHgQjUbD3r172b17N3Fxcbi7u7Nz506z7h88eDATJ05Eo9FUuO6S+rkyGjduzLRp0xg6dKhJ+nfffYdOpyMuLo7du3ezd+9emjRpwpEjR8jKyuK9995jzpw5aDQa+vbty6effnpf7RBCCCGEEFVHZrCq2Pjx4/H1/d96iM2bN7NgwQKsrKxQq9VERkYqIX0l2bZtG0ePHiUrK4smTZrw0Ucf4eDgwLfffouzs7NJuFvfvn1ZtGgROTk5xcLgoDC8MDo6mrCwMKysrLC2tmbBggVltj8mJoYtW7Yon4t2CbzbuHHjmDBhQpnl3MvKyorRo0ezYsUK3n333QrdWxKDwWCypgvA3d2dr776yuT9l6ZRo0ZKCN69ivqgyJw5c3jqqadM8tzbz5X18ssvF0vT6/XF1o25u7uj1+uVtWxFm6VMmjQJnU6Hn58fLVq0UPJv8h4vBwxWU3L4oxBCCPF4UxXcvchHCPFYyM01yo/4akoGWNWT9Ev1JX1TfUnfVF/SNw9Hgwa1SkyXEEEhhBBCCCGEqCISIvgIbNiwgdWrV5ukOTo6KturP2hRUVHExsaapHl4eDB+/PiHUn9pAgICyMnJMUlbuHBhpdZJVQfh4eH8+OOPJmkjRozA39//gdftu2XFA69DPFwxvV551E0QQgghhBkkRFCIx5Dnhn896iaIKiYDrAdLwmmqL+mb6kv6pvqSvnk4JERQCCGEEEIIIR4wCRH8m0lJSeHdd9/ll19+oXbt2tSrV4/Q0FAmT55cbMv3n376ifnz55OTk0NOTg5arZbJkyeXWO7GjRtZuHAhjRo14s6dOzRt2pSJEyfi6Oio5MnLy6Nnz54MHjyYGTNmALBkyRLy8vJ44403ALh06RIjRozg22+/5ejRo3z44Yfk5+eTl5fHiBEjStx1r8imTZuIjo5GpVKhVqvx9vYmKCiIWbNmcfjwYWrVqkVBQQFvvvkmLi4uAAwfPpyrV69iZWUFQPPmzfnoo49YunQp69ato27dumRmZtKmTRtee+01Zfe+4cOHM3PmTCIiIsjJyeHmzZtkZWXRsGFDoHD3xSZNmhRro5ubG40aNeLf//63kqbT6TAajSbvf/78+cTGxrJ3717ljKzPP/+cM2fOKDtBbtmyBb1ez7/+JbNVQgghhBDVhQyw/kYKCgqYNGkSvr6+LFmyBCg8G+v69esl5g8JCeHDDz+kbdu2GI1GkpKSyixfq9USFhYGwKFDh5g8eTKrV69WDko+cOAALVq0IDY2lunTp6NSqRg/fjw6nY5Bgwbh4ODA/PnzmTp1KtbW1syePZv169fTqFEjcnJySE5OLrXuvXv3smrVKlasWEHDhg3Jyclh06ZNyvWZM2fi4eHBoUOHCAsLY8eOHcq1yMhIOnbsWKzMkSNHEhQUBBRu3R4YGMjWrVtNDjz+5ptvgMIB5okTJ5TnL0tGRgaXL1/G3t6+xHPB8vPz2blzJ/b29hw+fBhnZ2egcFDn7+/P0aNHad26NR988AGrVq0qtz7x6Bh//y95P5yAnNz7Luu1jfsqdZ+1tRUBAUNxdHS67zYIIYQQonwSIvg3cujQIWrUqMErr/xvLUfbtm1p1KhRiflTU1Np0KABAGq1Wpm9MYezszNDhgzh66+/VtIMBgMjRozA3t6eY8eOAYXnY4WGhhIeHs7evXvJyMjAx8eHjIwMjEYjdnZ2AFhaWtKyZctS6/vXv/7FzJkzlRkkS0tLhgwZUixf165d+fPPP81+jiJarZYXXniBrVu3Vvjeew0cOJBt27YBhWdeeXp6mlxPSEigVatWvPLKKxgMBiW9Ro0azJkzh4iICBYtWoS/vz9Nmza97/aIByfvp9MUpKRRcDP9vv+uXPlvpf6Sks6j128qv7FCCCGEqBIyg/U3cvbsWdq3b292/sDAQDw8PHj++edxdXXFz8+PmjVrmn1/+/btWbt2LQDZ2dnEx8cTERHB7du3MRgMSvhg7969Wb9+PSEhIUronJ2dHW5ubvTp0wcXFxdefPFFvLy8lHC5kp6tQ4cO5bap6KDeu82YMUMJEezRowchISEl3vvss89y/vx58x6+DO7u7oSGhhIUFERcXByRkZEmhzsbDAY8PT3p168fixcvJjc3lyeeeAIo3G3SwcGB+Ph4tm/fft9tEQ9WjS5tycvNq5IZrIbWtSt1n7W1FV5e938othBCCCHMIwMsUapJkybh4+PD/v370ev1GAwGvvjiC7Pvv3uDyri4OLp3746VlRXu7u4sW7aM0NBQ1Go1AEOHDiUrK8tklmr+/PkkJiZy8OBBVq5cSXx8PO+9916lnmXhwoUsWbKEK1eumMyqQekhgg+KnZ0dtWvXxmAw4ODgoAzuAHJycti7dy+zZs3C1taWzp07s3//fvr06QMUhheeOHGCvLw8UlNTS519FNWDusXTqFs8XSVlfSC7CAohhBB/CRIi+DfSunVrTp48WaF7mjVrxtChQ4mJieH06dOkpaWZfe+vv/6qrL8yGAwcPHgQNzc3/P39uXHjBocOHVLyWlhYlDg7pdFoGDlyJCtXruS7774rta5WrVpx4sSJUq/PnDmT7777jhkzZhAaGmr2M5T2PPdLq9USERFRLDxw//793L59Gx8fH9zc3Dh69KjJ5hdLly7Fx8eHcePG8e6771ZJW4QQQgghRNWRAdbfiLOzMzk5OSYzOKdPn+bKlSsl5t+zZ48yC3XhwgUsLCyoXdu8MKXDhw+zbt06hgwZQnp6OkeOHGHPnj3s3r2b3bt3ExYWVmzXwrtlZGSQkJBg0s7GjRuXmj84OJhFixaRkpICFM4EFW1Acbf/+7//Iz8/n337KrZhwHfffceBAwfw8vKq0H2l6devH0FBQfTs2dMk3WAwMG/ePOU97dq1i/j4eDIzM0lMTGTPnj2MGTOGl156iUuXLnHgwIEqaY8QQgghhKga5YYIXrt2jcWLF3P16lWio6M5d+4cx44dIyAg4GG0T1QhlUrFxx9/zLvvvstnn31GzZo1ady4MaGhoSQlJdGrVy8l75tvvsmOHTtYsGABVlZWqNVqIiMjlZC+kmzbto2jR4+SlZVFkyZN+Oijj3BwcODbb7/F2dkZS0tLJW/fvn1ZtGgROTk5JulFCgoKiI6OJiwsDCsrK6ytrZXtyUvSu3dvrl27xqhRoygoKEClUuHv71/iOxg/fjzR0dG4uroCpmuw6tSpQ0xMDAAxMTFs2bKFzMxMWrduzapVq0x2ELwftra2jB071iQtMzOTffv2ER4erqTZ2NjQrVs34uLi+OKLL3jzzTeVdXDvvPMOISEhbNq0qdg73OQTJAcMVlNy+KMQQgjxeFMV3L1QpgSvvvoqgwYN4tNPP2XLli3k5eXh5+dXJbupCSEejNxco/yIr6ZkgFU9Sb9UX9I31Zf0TfUlffNwNGhQq8T0ckME09LS0Gq1yvqYGjVqlLqTmxBCCCGEEEL8nZUbImhjY0NaWhoqlQqAn376iVq1Sh6ticffhg0bWL16tUmao6Mjc+bMeSj1R0VFERsba5Lm4eHB+PHjH0r95goICCAnJ8ckbeHChWg0modSv98W83d7FH89n/cqHv4qhBBCiOqh3BDBkydPMnfuXM6ePUvr1q1JS0vjww8/pG3btg+rjUKICvLaEPOomyAeIBlgVT0Jp6m+pG+qL+mb6kv65uEoLUSwzBms/Px8srOz+fLLL0lKSqKgoIBnnnlGOfRUCCGEEEIIIcT/lLmYysLCgoiICGrUqEHr1q1p06aNDK4eknbt2qHT6fDy8mLKlClkZmYWSx83bhy3bt0CIDk5mU6dOqHT6ZS/TZs28eabb7J27VqTsnfu3Mmrr75abt2enp74+PiwcuVK8vPzTfLMnz8fV1dXJf3s2bMMGDCArKwsJc/YsWPR6/Vcu3aN4OBgfHx80Gq1jBkzptS6k5OTS90KPS8vD2dnZyIjI03S4+Li8PX1Vcpfu3YtUVFRynsoeh6dTlcsvLHI0qVLcXV1RafT4e7uzqRJkzh37pxy3c3NjdTUVOVzQkICwcHBAGzcuBFnZ2eTd3/3vfc+n0ajYcmSJUpaamoq7du3JyIiwiSvTqdj2rRpJmnjxo1j06ZNyue3336b6OjoEusSQgghhBAPX7lrsFxcXPjuu+9wd3dX1mGJB8/KyorNmzcDMH36dNauXcuoUaNM0kNCQlizZo2y/qhZs2bKtSL169dn+fLlvPzyy0qawWAo8zynu+u4fv0606dPJz09nSlTpgCFM5s7d+7E3t6ew4cP4+zsTOvWrenfvz9RUVFMmzaNnTt3kpeXh5eXF2FhYfTo0YPAwECg8Eyryjhw4AAtWrQgNjaW6dOno1KpyM3NZfbs2axfv55GjRqRk5NDcnIyLVu2VN5L165di72XkowcOZKgoCCgcMv5wMBAtm7datbW7FqtlrCwMLOeo0mTJuzdu1cZPMXGxtKqVSuTPL/99hv5+fkcOXKEO3fuYGNjAxQOqEaMGIGbmxvnzp3j+PHjvPPOO2bVK6ov4+/J5P5wHHJyzcr/2sZdZV63trYiIGAojo5OVdE8IYQQQlRAudsBrl27lqlTp9KxY0ccHR3p2rUrjo6OD6Nt4v9zcnLiwoULxdK7dOnCn3/+Wea9Li4uJCUlcfXqVQDu3LlDfHw8/fr1M6vuevXqMXfuXNasWaMcOpyQkECrVq145ZVXMBgMSt6JEycSGxvLqVOniIyMVAYcV69epVGjRkq+yq7fMxgMjBgxAnt7e44dOwYUHkhsNBqxs7MDwNLSkpYtW1aq/LtptVpeeOGFB3IcgbW1NQ4ODvzyyy8AbN++nYEDB5rk0ev1+Pj40LNnT3bt+t+P6SZNmvDSSy+xaNEi3nnnHcLCwqhRo9x/JxHVXN5Pv1KQkkrBzdtm/V258t8y/5KSzqPXbyq/YiGEEEJUuXJ/mRX9kBWPRl5eHt9//71yKG4Ro9HIwYMHGTx4sJJ28eJFdDqd8nn27Nk4OTnh7u7O9u3bCQwMJC4uju7du2Nra2t2G5o2bYrRaOT69evUr18fg8GAp6cn/fr1Y/HixeTm5vLEE09gbW1NSEgIw4YNY9SoUbRo0QKAYcOGMW3aNL788kt69OjBoEGDaNiwYYXeQ3Z2NvHx8URERHD79m0MBgOOjo7Y2dnh5uZGnz59cHFx4cUXX8TLy6tKjhJ49tlnOX/+vFl5iw5ZLvL1118rhxeXRKvVsm3bNurXr4+FhQVPPfWUMgguKu/zzz/n/PnzfPnll3h7eyvXRo8eTf/+/enWrRvPPfdcJZ5MVDc1ujxLbm6u2TNYDa3L3snV2toKLy/fqmiaEEIIISqo3AHWDz/8UGK6/LB7sLKyspTBkpOTkzKQKkr/888/cXBw4IUXXlDuKSlEEMDT05OFCxcSGBiIwWAwGYRVVE5ODnv37mXWrFnY2trSuXNn9u/fT58+fYDCtUq1a9dm6NChyj2urq7s3LmTffv28f333+Pn54derzcr9K5I0cDQysoKd3d3li1bRmhoKGq1mvnz55OYmMjBgwdZuXIl8fHxvPfee5V+RnPdHTJbkRBBKHwnH374IfXq1UOr1Zpc++WXX6hTpw5PP/00DRs2JDQ0lBs3biizdImJieTn55OUlER+fr6cS/cYULdogrpFE7PzfyC7CAohhBDVVrkDrBUrVij/nZ2dzc8//0z79u1L3SxAVI2710GVlJ6ZmUlQUBBr1qxhxIgRZZbl6OhISkoKp0+f5tixYyYbLJjjjz/+QK1WU69ePeLi4rh9+zY+Pj4AZGZmUrNmTWWABYWbo9y7Xs/Ozg5vb2+8vb0JDg7mhx9+YMCAAWa3wWAwcPToUdzc3AC4ceMGhw4dUgaYGo0GjUaDj48Pffv2rZIB1q+//kqHDh2U9t+6dUsZFN68eZM6depUumxLS0vat2/P559/jsFgYPfu3co1urAmhgAAIABJREFUg8FAUlKS8qzp6ens2LGDIUOGkJ+fT3h4OIsWLWLt2rV89dVXDBs27D6eUgghhBBCVKVyB1iffvqpyefLly/z7rvvPrAGCfNYW1vz9ttvM3HiRJPZopKoVCoGDhxISEgIvXr1ombNmmbXk5qaypw5cxg2bBgqlQqDwcC8efOUTTLu3LlD3759yczMxNrausQyDh48SJcuXbC2tiY9PZ2LFy9ib29vdhvS09M5cuQIe/fuxdLSEig88Fiv19OlSxdOnDhB9+7dgcINNBo3bmx22aX57rvvOHDgALNmzQKge/fubN68malTp2I0GtmyZYvZ69hKM3r0aJ5//nllZgoKNxDZvn07W7ZsUcIoDx06xLJlyxgyZAhr166lefPmdO/enRYtWvDSSy8xcODACs0GCiGEEEKIB6fCq+MbNWrEb7/99iDaIiro2WefRaPRoNfrcXJyKrYGy9/fX5nd8vLyIjo6munTp5dbblEYYl5eHmq1Gp1Ox6hRo8jMzGTfvn2Eh4creW1sbOjWrRtxcXHFQt2KFB1WrVarKSgoICAggE6dOpVaf1JSEr169VI+T5s2DWdnZ2VwBdC3b18WLVpEaGgo0dHRhIWFYWVlhbW1NQsWLCj3GUsSExPDli1byMzMpHXr1qxatUoZuEyYMIF33nkHHx8fCgoKcHV1VWbxoPgarDlz5pS7GUzr1q1p3bq1SdqRI0do2LChyRq15557jt9++41Lly4RHR3N119/DUDDhg0JDAxk0aJFlX5mIYQQQghRtVQFRVvDlWLu3LlKuFd+fj6nTp2icePGxc4iEkJUH7m5RjnBvZqys7ORvqmGpF+qL+mb6kv6pvqSvnk4GjQoedOpcmewitagAKjVajw9PenWrVvVtUwIIYQQQgghHhPlDrBu3bqlHBBbZNWqVcXSxF9LWloaI0eOLJYeExNzX5s3mCsxMZGZM2eapFlaWvLNN9880HqjoqKIjY01SfPw8FAOJa4qj+r5ivhtXvtQ6hGP1ue9fcrPJIQQQoiHqtwQQT8/P7799luTNF9fXzZtkkMshaiuvNavedRNEA+BDLCqjoTTVF/SN9WX9E31JX3zcFQ4RFCv16PX60lOTmbcuHFKekZGBv/4xz+qvoVCCCGEEEII8RdX6gmlXbt2ZdSoUbRs2ZLRo0crf7NmzTI5G6uyUlJSmDZtGv369WPQoEGMGTOGpKQkkpKSGDNmDO7u7vj5+TF16lSuXbtWajk///wzw4YNY8CAAfj6+vLWW2+RmZnJxo0badu2LadPn1byenl5kZycTEBAADqdjhdffBFnZ2d0Oh06nY7k5OQS63Bzc1POcNJqtSxZsoTs7GwAkpOT6dSpEzqdDq1Wy8yZM8nNzTW5f/78+bi6upKfnw9AQUEB3bt35+bNmwBcvXoVjUbDkSNHlHucnZ1JS0tj6dKlyvueNWsWrq6u5OTkAIVbqBedlQTw+++/ExwcrLzT4cOHKwdFX7t2jeDgYHx8fNBqtYwZM6bcPoqJiaFjx47cvn0bgH379invqmvXrgwYMACdTsfMmTNJSEhAo9GYhMCdOnUKjUZT7vdlxYoVeHh4oNPp8Pf3V2ZH3dzcSE1NVfIlJCQQHBwMwMaNG5W+8/DwICYmRsm3dOlSXF1dlT7R6/XKtVmzZuHm5qY8x8svv6yUV5Xfl3u3ztfpdMrW9gkJCXTr1k0pR6fTER8fr+TduXMnGo3GZLfO5ORkNBoNX3zxhZIWERHBxo0by3y3QgghhBDi4Sp1Bqtx48Y0btxY2RK6KhUUFDBp0iR8fX2VQ29Pnz7N9evXCQ0NVX4EQ+GP0dTUVOrXr1+snGvXrjF16lQWL15M165dAYiNjSUjIwMo3FL+008/5YMP/h979x7VVZU//v8JKAKi4zWlAUFRARVFxI9okYoICMgbRco0QEVDwUzTEFNxKGlGMtOs0PLe9M2ZjPHCQXK4qHi/kal5x0Qs866hyOUNvz/4cca33BWV9PVY671W7LPP2Xuf/W6t93a/9t4Lde4rHQTEx8dz9OhRoqKiqqxz6Zbdd+7cISoqiqioKObNmwdAmzZt2LBhA1qtltGjR7N582Z1C++ioiKSk5MxMzNj3759ODs7o6enh4ODAz/++CN9+/YlIyODTp06kZGRgZOTE5mZmTRp0qTctVAGBgasW7euzA/4vLw8QkNDiYiIYMCAAQCcOnWKo0eP0rNnTz799FP69Omjrp27fyBREUVRsLe3Z8uWLfj7++Pi4oKLiwsAgYGBREREYG9vD5T0U8eOHdm8eTMBAQFAySyora1tpWV8++237Nq1i3Xr1mFqakpOTg7//e9/q6wbgJeXF1FRUdy4cQNPT088PDzU87VGjRpFSEgIv/zyC0OHDsXDw4P69esDEBERgaenZ5nn1eb35c6dO/z222+YmZmVe6yBk5MTS5cuLffehIQEevTogaIoTJo0SU1v3rw5a9as4bXXXtPZsl48H7TnL1Cw7xDc9w84k/+TVCafsbERAQEjcHR0epLVE0IIIcT/r8IZrFI//vgj/v7+dO/enS5dumBnZ1fl+T5V2bNnD/Xq1eP1119X02xtbfnll19wcHDQmZXp1asXHTt2LPc533zzDX5+furgCko2LCgdjPXr148zZ86QmZn5SPW9X8OGDYmOjiY5OZmbN2/qXDMwMKBr1678/vvvatrevXtp3749r7/+OoqiqOndu3fn0KFDAGRkZBAcHMyPP/6o/l3ROw4ODmb16tUUFhbqpG/cuBEHBwd1cAXQsWNHhg4dCpTMkrVu3Vq9VtXAJysri7t37zJ58mSdelfmxRdfJC8vj6tXr1JcXEx6errOeVblWbp0KX/7298wNTUFwNTUlCFDhlSrvFJNmzbF0tKSK1eulLlmZWWFsbExt2/frvI5tfl9GTRoEImJiUDJgMnb27ta9925c4eDBw8SExNT5r03a9aM3r17y/rH51Thj0covnqN4lu31c+lS7+W+Zw7l0lCgnxHhBBCiKelygHW+++/z4IFC7C0tOTw4cPMnTuXkSNHPlKhp0+fpnPnztVOr+lzSunr6zN27NgKZwoelqmpKebm5pw/f14nPS8vj8OHD6uzPFAyC+Tt7c3AgQPZunWrGj7o6OhIRkYGUBLmOHDgQH777TegZIB1/6DxfmZmZjg6OrJhwwad9DNnztCpU6cK6zxy5EhmzpxJYGAgcXFxOoPA8iiKgpeXF05OTpw7d67SMM37eXh4kJSUxKFDh+jcuXOlMy05OTncuXMHCwuLaj27Ir/++it5eXnY2NiUuXbs2DEsLS1p3ry5mhYbG6uG5t1/8HJtfl/c3d3Vmbi0tDSdfzSAkgOF7w8RzMrKAiAlJQUXFxfatm1L06ZNOXr0qM5948aNY/ny5Wi12keuo/hzqedgj16L5uj9pbH6ad36xTKftm3b4ePj97SrK4QQQjy3qtymHcDS0hKtVouBgQH+/v74+fnp/DCty3x8fIiLi+PChQu1+tz7N1/MyspS1+T069dPnR3Kz89n27ZtREZGYmpqSrdu3dixYwf9+/fH3t6e48ePc/fuXQoLC2nYsCEWFhacP3+ejIwMRo8eXWHZoaGhhIWF0a9fvwrzhIeHc/78eaysrPjss89wcXEhOTmZ9PR0tm/fzpAhQ0hISKBZs2bl3q8oCp999hn6+vq4u7uTlJTEG2+8UeV7GTRoEFOmTCEzMxNvb291EFlbSg+9BkhMTGT//v2cO3eO2bNn06BBA/XaqlWriI+P55dffiEuLk7nGRWFCELtfV+aNGlC48aNURQFa2trjIyMdK5XFCKoKApBQUFASQikoig6Z9FZWFjQrVs3Nm3a9Ej1E38+BpYWGFjq/mPEQtlFUAghhKhzqpzBMjY2Jj8/Hzs7O2JjY1m1apW6WcPD6tChA8eOHSuT3r59+3LTK1Kd/PXq1WPMmDF89dVXNa5nRXJycrh48SJWVlbA/9Zg/fe//+Xo0aOkpKQAsGPHDv744w98fX1xdXXl4MGD6oYLxsbGWFpa8v3336szTw4ODmzbto1r167Rrl27Csu3srLCzs6OzZs3q2nt27fn559/Vv/+/PPP+fvf/65upAElP/oHDx7MRx99hL29vboBxoNOnjzJL7/8wpgxY3B1dUVRFJ2NIirTsmVL6tWrx86dO+ndu3eleU1NTTExMalwMNOkSROd0L5bt27prEvz8vJi06ZNfPvtt3z88cc6IYKjRo1CURQ+/fRTZs6cqW5KUpXa/L54eXnx/vvvVzs88ObNm+zZs4dZs2bh6urK8uXL2bx5Mw+epBAaGsqyZcvKpAshhBBCiKevygFWbGwsxcXFREVFYWJiwm+//cbixYsfqVBnZ2fy8/N1NtA4ceIEbdu2JSMjg61bt6rp+/fv59SpU+U+54033mD9+vUcPnxYTduyZUuZcLYhQ4awe/dunR3pHtadO3eIjo7Gzc2tzHb1zZo1Y9q0aXz55ZdAyWzE3LlzSU1NJTU1lZSUFHbt2kVubi5Qsg5r9erVODg4ACUDrDVr1uDg4KAzU1Oe8ePHs2LFCvXvwYMHc+jQIXVwB3Dv3j31v3fv3q2Wm5OTQ1ZWlrohxIMUReGtt95S671jxw4uX77MxYsXq/WOJk2axLvvvouBgUGVed98802io6PJyckBSt5v6RqjXr16qaGQWq2WjRs30qtXrzLPsLe3x9fXlzVr1pS5NmDAALp06VLmLLfK1Nb3xc3NjZCQEF5++eVq5f/hhx/QaDSkpaWRmprKtm3bMDc319ldEsDa2hpra2vS0tIeqX5CCCGEEKL2VTnA+utf/0pxcTGXL19m4sSJzJgxA0tLy0cqVE9Pj88++4xdu3bh5uaGt7c3CxYsoEWLFixZsoSvv/4ad3d3vLy8+H//7/9VGMbWokULFixYwLx58/Dw8GDQoEHs2LGDhg0b6uQzNDQkMDCQa9euPXSdg4OD8fHxISAggBdffJH333+/3Hxubm7k5uayb98+0tPTdcL4TExM6NGjh/rD2NHRkQsXLqjrrTp37sylS5cqXH91vw4dOuisuTIyMmLJkiWsXbuWAQMG8NprrxEXF8eECROAkrVI/v7+DB48mOHDhxMQEEDXrl3LfbaiKLi5uemkDRw4sNqbXTg6Opa5vyIjRoygV69e+Pv74+Pjw8iRI9XBZVhYGFlZWfj6+uLn50ebNm3U3RkfNG7cOOLj49WB2v3Cw8NZuXKlOvN6/xosjUajbntfqja+L1AyQ/fmm2+Wuw7twTVYSUlJJCQklHlv7u7u5c4eTpgwgUuXLj1S/YQQQgghRO3TK64izig1NZV58+ZRUFBAamoqx48fZ9GiRSxZsuRJ1VEIUUMFBVo5wb2OatLERPqmDpJ+qbukb+ou6Zu6S/rmyWjZslG56VXOYH322WesW7eOxo0bA2BnZ1ftUDEhhBBCCCGEeJ5UuYtgvXr1aNSo/NHZk5Kens78+fN10szNzfn8889rtZyAgIAy4WKxsbHlbv/9LDh58iQRERE6aYaGhurBurUpOjpaPferVFBQEP7+/rVe1pNSl78vQzZ8/7SrIGrRyr6DnnYVhBBCCFFNVQ6w2rdvz6ZNm9Bqtfzyyy98/fXX1VojVJtcXFx0zpZ6XB7HwKIus7GxKXOe1uMyZ86cJ1LOk/S8fV+EEEIIIUTVKgwRfPfdd4GSLcjPnDmDoaEh77zzDqampsycOfOJVVAIIYQQQggh/iwqHGAdO3aM33//ncTEREaPHs3y5ctZsWIFo0ePVrf7FuJxunTpEhMmTMDd3R03Nzfmzp1Lfn4+fn5+HD9+HIDCwkK6d++uMxM3dOhQjh07Rnx8PLa2tpw4cUK95uPjQ3Z2doVl3rlzh6ioKNzc3Bg6dCiBgYE6xwAkJydjY2PD2bNn1bTs7Gy6du2KRqPBy8uLiIgICgoKqmxfTEwMLi4uZc6V2759O8OGDcPT0xONRsPkyZP59ddfAYiMjMTV1VXdfXD48OFVliOEEEIIIZ6cCkMEhw8fzqhRo7hw4YLOOpni4mL09PR0zlsSorYVFxczceJEXn/9deLi4tBqtcyePZtPPvkER0dHDh06hJ2dHSdOnMDKyoqMjAw0Gg13794lKysLW1tbTp48SevWrVmyZAkLFy6sVrmzZs3C3NycLVu2oK+vz4ULF3QGUwkJCfTo0QNFUZg0aZKaXnrYtFarZfTo0WzevLnCLeUBioqKSE5OxszMjH379uHs7AzAqVOnmDt3LnFxcVhbWwOQkpLCxYsXefHFFwGIiIjA09Ozxu9U1D3a8+cp2HcAqhiQT/7PpiqfZWxsREDACBwdnWqrekIIIYR4CBUOsIKCgggKCmLOnDlER0c/yToJwZ49e2jQoIE6uDcwMOC9995jwIABzJo1i23btjFy5EgyMjIYPny4epDwTz/9ROfOndVDjvv168eBAwfIzMykXbt2lZaZlZXF4cOHmT9/Pvr6JZO7FhYWWFhYACWzWwcPHmTNmjWMHz9eZ4BVysDAgK5du/L7779XWtbevXtp3749Xl5eKIqiDrC++uorQkND1cEVlByWLJ5NhT8epviBg9HLc+nWrWo9LyFhvQywhBBCiKesym3aZXAlnobTp0/TuXNnnTRTU1PMzMxo1aoVGRkZAGRkZODk5ET9+vXJyckhIyNDZxMWfX19xo4dy9KlS6tVpp2dnTo4e1BKSgouLi60bduWpk2bcvTo0TJ58vLyOHz4cJWbsiiKgre3NwMHDmTr1q1qSOGZM2fKtPtB9x+UPHXq1CrbJequeg7d0GvRAr2//KXST+vWL1b5adu2HT4+fk+7SUIIIcRzr8pdBIWoaxo3bkxBQQFXrlxRZ6bs7e356aefyMjI4I033tDJ7+PjQ1xcHBcuXHikchVFISgoCECdeerSpQtQMvul0WjIzs6mX79+2NraVvic/Px8tm3bRmRkJKampnTr1o0dO3bQv39/nXw3btxg1KhR3Lt3j1dffZWQkBBAQgSfJQaWlhhYWlaZb6Fs0y6EEEL8acgAS9RJ7du354cfftBJy8nJ4bfffsPS0pLu3buTlJREy5Yt0dPTw8HBgUOHDvHTTz+VOUagXr16jBkzhq+++qrSMjt06MCJEyfQarVlZrFu3rzJnj17OHXqFHp6emi1WvT09NRzxErXYF2/fp3XX3+dlJSUCkP7duzYwR9//KGu0crNzaVBgwb079+f9u3bc+zYMWxtbWnatCkbNmxg+fLl3L0rp7ELIYQQQvwZVBkiKMTT0Lt3b3Jzc1m/fj0AWq2Wf/zjHwwZMgRjY2O6d+/O6tWrcXBwAMDBwYH169fTsmXLcg/GHjJkCLt37+b69esVltmmTRu6dOnCp59+SnFxMVCyQ+DWrVv54Ycf0Gg0pKWlkZqayrZt2zA3N+fAgQM6z2jWrBnTpk3jyy+/rLAcRVGYO3cuqamppKamkpKSwq5du8jNzWXs2LEsWbJEZ2MN2bVTCCGEEOLPQwZYok7S09Pj888/JykpCXd3dzw8PGjQoAHvvPMOAI6Ojly4cEGdrXrhhRcoKiqq8BBsQ0NDAgMDuXbtWqXlxsTEcO3aNQYOHIiPjw8zZsygWbNmJCQk4ObmppPX3d2dhISEMs9wc3MjNze3zOALSgZL6enp9OvXT00zMTGhR48epKWlYWNjw8yZM4mIiMDDw4Phw4eTmZmJj4+Pmv/+NVgajYb8/PxK2ySEEEIIIZ4cveLSf6oXQjwzCgq03LwpYYV1UZMmJtI3dZD0S90lfVN3Sd/UXdI3T0bLlmWjpkBmsIQQQgghhBCi1sgmF+K5FBAQUCa0LjY2Fhsbm1orIz09nfnz5+ukmZub8/nnn9daGRUZuqHqg2lF3beir5yBJoQQQvzZyABLPJe+++67x16Gi4tLledhCSGEEEKIZ4uECAohhBBCCCFELZEZrD8BOzs7OnbsiFarpV27dsybNw9jY2OddHNzc2JjY2ncuDHZ2dl4eXnRtm1b9RmjR49m7969dOvWjeHDh6vpycnJrF27lmXLlpVb9pUrV/jwww85cuQIjRs3pnnz5rz33nvUr1+fAQMGMGvWLAIDAwF4//336dKlC0eOHOHQoUMUFBSQnZ2t1mPChAnlHpAbGRnJvn37aNSoEfr6+kRFRam7ARYWFvLyyy8zbNgwpk2bBsAnn3xCYWEh7777LgAXL14kKCiI//znP4SHh3PhwgXS0tLQ09MDICwsjN27d5ORkVHhu/Hz88PV1ZXOnTuzePFiAJKSkti6dSs9e/ZkzZo1AJw9e5a2bduir6+Pi4uLWqf7xcfHM2PGDFauXEmfPn3U9xweHs6iRYvw9PQkMDCQy5cvY2RkBIClpSWffvqp+gyNRkO7du345JNPdN7Tzp07SUlJwdDQkOvXrzNs2DBSU1PL7TshhBBCCPHkyQDrT8DIyIgNGzYAMHXqVNauXcvo0aN10qdPn84333zDhAkTgP8dfHu/Fi1asHTpUp0BlqIoOluA36+4uJiJEyfi5+en/tA/ceIE165do3Xr1jRv3pw1a9bw2muvYWhoqN43Z84coOQMqfHjx5epR3kiIiLw9PRkx44dREVFsWlTyRqinTt3YmVlRVJSElOnTkVPT48JEyag0WgYOnQo1tbWxMTE8Pbbb9O4cWMAGjVqxMGDB3FycuL27dtcuXJFp6zy3k2pY8eOcebMGdq3b6+m+fv74+/vD4CrqyurV6+mWbNmlbanY8eOKIqiDrASEhKwtbXVyTN//nzs7e3L3Hv27FmKioo4cOAAd+/excTERL1mYGDAunXrGDFiRKXlCyGEEEKIp0MGWH8yTk5OnDx5sky6g4NDuen36927N5GRkVy+fJkXXniBu3fvsmvXLj744INy8+/Zs4d69erx+uuvq2mlg4Ts7GyaNWuGo6Mj69ev59VXX32EVv1Pz549ycrKUv9WFIWgoCC+/fZbMjIycHR0xMjIiPfee4/o6GhCQkK4c+cOvr6+6j3e3t4kJibi5OTEli1bGDhwIGfOnKlW+aNHjyYuLo6PP/74kdrh5OTEgQMHKCgoID8/n6ysLOzs7Kp1b0JCAr6+vmRmZpKSksLgwYPVa8HBwaxevbrW3rd4urTnz5G/by8UFJR7ffJ/vq/wXmNjIwICRuDo6PS4qieEEEKIhyBrsP5ECgsL2b59Ox07dtRJ12q17N69G1dXVzUtKytL5zDaAwcOYGBggLu7O5s3bwYgLS2NXr16YWpqWm55p0+fpnPnzpXWady4cSxfvhytVvuIrSuRmpqqti8vL49du3bh6uqKj48PiqKo+fr27ctf/vIXpk+frs6Ylerduzf79+9Hq9WSmJiIl5eXzvXy3k2pQYMG8fPPP3P+/PlHaoeenh59+vRhx44dpKSk6PRNqWnTpql1mDdvnpqemJiIt7c33t7eOm0GMDMzw9HRsVqzgqLuK/gxg+KrVyi+dbPcz6VLv1b4OXcuk4SE9U+7CUIIIYR4gMxg/Qncu3cPjUYDlMyMDBs2TCf9999/x9rampdeekm9p6IwOG9vb2JjYwkODkZRFPW5D8vCwoJu3bqpIX0PKzY2lri4OJo1a0ZMTAzwvwGgkZER7u7ufPHFF7z33nsYGBgAMGLECO7du0e7du10nqWvr0+PHj1QFIV79+5hbm6uc72yEEF9fX1CQkJYunQpr7zyyiO1ydvbmzVr1pCTk8P06dNZunSpzvXyQgSPHDlC06ZNefHFF2nVqhXvvfceN2/epEmTJmqe0NBQwsLC6Nev3yPVTzx99R26lxwXUMEMVitjk3LToWQGy8fH73FVTQghhBAPSQZYfwL3r7UqLz03N5eQkBC++eYbgoKCKn2Wo6MjV65c4cSJE2RkZOhsovCgDh068MMPP1RZv9DQUN5++2169uxZdWMqULoG636KonDw4EF19ufmzZvs2bNHHUjq6+ujr1/+JKy3tzcTJ05k4sSJNa6LRqPhyy+/LDNTWFNdu3bl1KlTGBsb62yqURlFUTh37pza5pycHLZs2aITEmhlZYWdnZ06Eyn+vAws22JsWfF3Y6GcgyWEEEL86UiI4DPA2NiYWbNmsXLlSgoLCyvNq6enx6BBg5g+fTqvvPIKDRo0qDCvs7Mz+fn5/Otf/1LTTpw4oRNSB2BtbY21tTVpaWmP1pD75OTkcODAAbZu3UpqaiqpqalERUWRkJBQrfudnJx488038fb2rnHZ9evXJzg4mFWrVtX43gdNnTqVKVOmVCtvUVERmzdvZuPGjWqbv/jii3LbPH78eFasWPHI9RNCCCGEELVLBljPiE6dOmFjY6P+GH9wnVHpNuMAPj4+nDhxosrBh56eHp999hm7du3Czc0Nb29vFixYQIsWLcrknTBhApcuXaq19vz3v//F2dlZZ3fCAQMGkJaWVhJSVQU9PT1CQkLK3e2vsndTKiAgoMrBanX07dsXZ2fncq/dvwZr1KhRHDhwgFatWtGqVSs1T8+ePTl79iyXL1/WubdDhw506tTpkesnhBBCCCFql15xcXHx066EEKJ2FRRouXnz7tOuhihHkyYm0jd1kPRL3SV9U3dJ39Rd0jdPRsuWjcpNlxksIYQQQgghhKglssmF4MaNG4waNapM+qpVq2jatGmtlRMdHc2hQ4d00oKCgtRDfP9svv/++zLhhY6OjmW2jX8ahm2oenMS8XSs03g87SoIIYQQ4jGSEEEhnkGadYlPuwqiAus0HhK2UQdJOE3dJX1Td0nf1F3SN0+GhAgKIYQQQgghxGMmA6znTPfu3QHIzs7GxsaGr7/+Wr32/vvvEx8fD8CPP/5IQEAAGo2GQYMGsXjx4iqfHRYWpnNeE8DixYuxsbHh/PnzatqqVauwsbHhyJEjahn9+vXD2dlZ3VUvOzu73DJcXV0ZPHgwgwcPZsyYMVy5ckW9dvz4cWxsbNhhoDwfAAAgAElEQVS+fTsAxcXFvP7662zbtk3Ns3nzZkJCQgCwsbFh2rRp6rXCwkKcnZ0JDQ0FID4+XqdOGo2GM2fOVPruoqOj0Wg0eHl50bVrV/W+pKSkctsTGRlJt27dyMnJUdNiYmKwsbHh+vXrQPX7TAghhBBCPH2yBus51rx5c9asWcNrr72msx06wPTp01m0aBG2trZotVrOnTtX6bNu377NsWPHMDEx4cKFC1hYWKjXOnbsiKIohIWFAZCUlESHDh0A+O6774CSwczRo0eJioqqst6rV6+mWbNmLFiwgKVLlzJr1iwAEhIS6NGjB4qi8Morr6Cnp0d0dDRvv/02zs7OFBYW8sknn7Bs2TIATExMOH36NPfu3cPIyIidO3fqbJEO4OXlVaZO2dnZFb670vVX2dnZjB8/vtwDoh/Upk0bUlJS0Gg0FBUVsWfPnjL1KFVZnwkhhBBCiKdPBljPsWbNmuHo6Mj69evLzDxdv36dli1bAmBgYED79u0rfdaWLVvo378/LVq0QFEUxo8fr15zc3MjJSWFsLAwsrKyaNSoEfXr13/k+js5OamzOcXFxSQlJbFy5UpGjBhBXl4eDRo0oGPHjvTv35+vvvqKu3fvotFoaNOmjfqMvn37snXrVjw9PVEUBW9vbw4ePFhl2ZW9u5ry9vZm8+bNaDQa9u7di6OjI+np6Y+9XPHwCs9nkr9vJ8UFBTW+N2TjWrTami19NTY2IiBgBI6OTjUuTwghhBBPloQIPufGjRvH8uXL0Wq1OunBwcF4enoSHh7O2rVrycvLq/Q5iqLg4+ODt7c3iqLoXDM1NcXMzIxTp06hKApeXl61UvetW7fSsWNHAA4dOoS5uTlt2rShV69ebN26Vc03ceJENm3aRHp6OuPGjdN5hpeXF4mJieTl5XHy5Em6deumcz0xMVEnRPDevXvqtYreXU1ZWVlx/fp1bt26pQ7yKlNb5YqHl//jfoquXqb41o0afy5evMilS7/W6HPuXCYJCeufdrOFEEIIUQ0yg/Wcs7CwoFu3bmzatEknfeLEifj6+rJjxw4SEhJQFEVn7c/9rl69yvnz5+nRowd6enrUq1ePU6dOqYMfKBnIKIrCjh07WL169SOtGwoODkZfXx8bGxsmT54MoDMw8fLyYsOGDXh4lGyHbWJigpeXFyYmJmXC6mxtbcnOziYhIYG+ffuWKau8EMFSFb27hzFw4EAUReHw4cO8//77leatzXLFwzF06El+fv5DzWC9aGryUDNYPj5+NS5LCCGEEE+eDLAEoaGhvP322/Ts2VMnvU2bNowYMYJXX32V3r17c+PGjXLPxdq8eTO3bt1iwIABAOTk5KAois4Aq3///nz00Ud06dIFU1PTR6pv6RqsUlqtli1btpCSksKSJUsoLi7m5s2b5OTkqGXp6+ujr1/+hK2rqyuxsbGsWbOGmzdv1qguFb27mvLy8mLo0KEMGTKkwno+jnLFw6ln2Y56lu0e6t7lsk27EEII8UyTEEGBtbU11tbWpKWlqWlbt26l9Ii08+fPo6+vT+PGjcu9X1EUli1bRmpqKqmpqXz//fdlwgSNjY2ZNm2aztqs2rJ7925sbGzYtm0bqamppKWl4e7uTnJycrXuHzZsGOHh4djY2NS47PLe3cP461//ypQpUxgxYsQTLVcIIYQQQtQumcESAEyYMAE/v/+FIG3YsIG///3vGBkZYWBgwPz58zEwMChzX3Z2NhcvXsTBwUFNs7CwoFGjRhw+fFgnb1Vrix6Woii4ubnppLm7u/Ptt9/qtKkirVu3JigoqNxriYmJOptezJkzhxdeeEEnz4Pv7mENHz68Rvlrq1whhBBCCFF79IpLpymEEM+MggKthKHVUU2amEjf1EHSL3WX9E3dJX1Td0nfPBktWzYqN11CBIUQQgghhBCilkiIoKi277//njVr1uikOTo6qofr1qaAgADy8/N10mJjYx9qnVRdEB0dzaFDh3TSgoKC8Pf3f0o1EkIIIYQQj4OECArxDPJbV70NPkTd8FXfXk+7Cs89Caepu6Rv6i7pm7pL+ubJkBBBIYQQQgghhHjMHtsAy87ODo1Gg4+PD5MmTSI3N7dM+vjx47l9+zZQshtd165d0Wg06mf9+vXMmDGDtWvX6jw7OTmZsWPHVlm2t7c3vr6+rFixgqKiIp08MTExuLi4qOmnT5/Gw8ODe/fuqXnefPNNEhISuHr1KqGhofj6+uLl5cW4ceMqLDs7OxsfH59yrxUWFuLs7Mz8+fN10tPS0vDz81Ofv3btWuLi4tT3UNoejUZTJkSv1OLFi3FxcUGj0eDu7s7EiRM5c+aMet3V1ZXr16+rf+/du5fQ0FAA4uPjcXZ21nn399/7oJCQEJycnNT7a6sd+/bt47XXXivzzvr06cPvv/9OZGQkrq6u6jNKd927v/6enp6sWrVKvT8zM5PAwEA0Gg2DBg1i9uzZZdoPJd+pwYMHM2jQIAYPHqyzxXtkZCQuLi5qyOL169dxdXWt8P1kZ2djY2PDJ598oqZdv36dzp07q4cI399fpZ/S/xeg7PeztJ22tracOHFCTfPx8SE7O7vCugghhBBCiCfrsa3BMjIyYsOGDQBMnTqVtWvXMnr0aJ306dOn88033zBhwgSg5GDb0mulWrRowdKlS3W2sFYUpcJBzINlX7t2jalTp5KTk8OkSZMAKCoqIjk5GTMzM/bt24ezszMdOnRg4MCBxMXFMWXKFJKTkyksLMTHx4eoqCj69OlDcHAwgM4P3JrYuXMnVlZWJCUlMXXqVPT09CgoKGD27NmsW7eO1q1bk5+fT3Z2Nu3atVPfS/fu3cu8l/KMGjWKkJAQoGR78eDgYDZt2qRzKG9FvLy8iIqKqlY7xo4dS25uLv/617/UtNpoh5OTE5cuXeLixYv89a9/BWDXrl20b9+eVq1aARAREYGnp2eF9b9x4waenp54eHhgZmZGTEwMwcHB6jbuJ0+eLHPviRMnmDdvHitWrMDCwoILFy4wZswYzM3NsbW1BcDAwIB169ZV+5wqc3Nztm3bxpQpUwBISkqiffv2Onnu76/7lff9LNW6dWuWLFnCwoULq1UPIYQQQgjxZD2RTS6cnJzK/WHr4OBQbvr9evfuTWRkJJcvX+aFF17g7t277Nq1iw8++KBaZTdv3pwPPviAYcOG8dZbb6Gnp8fevXtp3749Xl5eKIqi/oANDw/Hz88PT09P5s+fz5IlSwC4fPkyL730kvrM0h/dNaUoCkFBQXz77bdkZGTg6OjInTt30Gq1NGnSBABDQ0PatWv3UM+/n5eXF1u3bmXTpk3qwLC29O7dm7179+qk1UY79PX1GTRoEIqi8OabbwIlA8XKBtMPatq0KZaWlly5cgUzMzMuX75M69at1evlbZKxfPlyQkNDsbCwAErO8XrzzTdZvnw5H330EQDBwcGsXr2aV199tVr1MDY2xtramiNHjmBvb8/mzZsZNGgQly9frvLeir6fAP369ePAgQNkZmbWyvdEPD6F58+Qty+d4oK8KvNO/s/qKvMYGxsREDACR0en2qieEEIIIR6Tx74Gq7CwkO3bt9OxY0eddK1Wy+7du3VCrbKysnRCpg4cOICBgQHu7u5s3rwZKAlD69WrF6amptWug4WFBVqtlmvXrgElAx1vb28GDhzI1q1bKSgoAEp+FE+fPp2RI0fi7e2NlZUVACNHjmTmzJkEBgYSFxfH77//XuP3kJeXx65du3B1dcXHxwdFUQBo0qQJrq6u9O/fn3feeYeNGzeWCWd8WJ06dSIzM7NaeRMTE3Xe/f2hktVRW+3w9vYmMTERgPz8fLZt24a7u7t6PTY2Vq3j1KlTy9z/66+/kpeXpw6kRo0aRXBwMGPHjmXVqlU6YXilzpw5Q5cuXXTS7O3tdcIkzczMcHR0rNZMYikvLy8SExP57bff0NfXL3NA8apVq9S2BAYGqukVfT+hZBA6duxYli5dWu16iKcj/8e9FF29RPGtG1V+Ll36tcrPuXOZJCSsf9rNEkIIIUQVHtsM1r1799BoNEDJDNawYcN00n///Xesra11ZobKCxGEkh/dsbGxBAcHoyiK+tyHUfqjPTIyElNTU7p168aOHTvo378/ULJWqXHjxjqhYC4uLiQnJ5Oens727dsZMmQICQkJ1Qq9K1U6MDQyMsLd3Z0vvviC9957DwMDA2JiYjh58iS7d+9mxYoV7Nq1i3/84x8P3cbq0tPTU/+7JiGCFamNdtjb23P37l0yMzPJzMykW7du6qwYVBwimJiYyP79+zl37hyzZ8+mQYMGAPj7+/Pyyy+Tnp5OSkoKa9euZePGjQ/VvtDQUMLCwujXr1+18ru4uLBo0SKaN2+Ol5dXmevlhQhW9f2EknVXcXFxXLhw4aHaIZ4MQ4de5OXnV2sGq5WxcZV5jI2N8PHxq42qCSGEEOIxeiJrsMpLz83NJSQkhG+++YagoKBKn+Xo6MiVK1c4ceIEGRkZOpsHVMeFCxcwMDCgefPmpKWl8ccff+Dr6wtAbm4uDRo00PkBq6+vrzP4gJIZmsGDBzN48GBCQ0PZv38/Hh4e1a6DoigcPHhQnbG7efMme/bsUQeYNjY22NjY4Ovry4ABA2plgPXzzz+rMzNNmjTh9u3b6qDw1q1bNG3a9JHLeFBttKN0Fuvs2bN4e3tX657SAeKRI0cICQnB1dWVli1bAtCqVSuGDRvGsGHD8PHx4dSpUzr3Wltbc/ToUZ3Qz6NHj5ZZM2VlZYWdnZ06m1oVQ0NDOnfuzMqVK1EUhdTU1Crv2bFjR5Xfz3r16jFmzBi++uqratVDPB31LNtTz7J91RmBhbJNuxBCCPHMeGrbtBsbGzNr1ixWrlxJYWFhpXn19PQYNGgQ06dP55VXXlFnJ6rj+vXrzJkzh5EjR6Knp4eiKMydO5fU1FRSU1NJSUlh165d6i6H5dm9e7d6PScnh6ysLMzMzKpdh5ycHA4cOMDWrVvVcqOiokhISODOnTs665lOnDihbvDwKH744Qd27typrl/q1auXOuDVarVs3LiRXr1q70ddbbbDx8eHjRs3smfPHgYMGFCje+3t7fH19VV3Kdy+fbsaYnflyhVu3rypbphRKiQkhC+//FLdjS87O5ulS5cyZsyYMs8fP348K1asqHZ9xowZw7Rp03Rm4SpT3e/nkCFD2L17t87OkEIIIYQQ4ul7IptcVKRTp07Y2NiQkJCAk5OTugarlL+/vzq75ePjw7Jly8pdd/Og0jDEwsJCDAwM0Gg0jB49mtzcXNLT04mOjlbzmpiY0KNHD9LS0soN4wI4duwYH3zwAQYGBhQXFxMQEEDXrl0rLP/cuXO88sor6t9TpkzB2dkZQ0NDNW3AgAF89NFHvPfeeyxbtoyoqCiMjIwwNjbm73//e5VtLM+qVavYuHEjubm5dOjQgdWrV6szVmFhYfztb3/D19eX4uJiXFxc1FkSKAmxO3jwoPr3nDlzcHR0LLecESNGkJmZyd27d3nllVeIiYmhe/futdYOa2trjI2N6dy5MyYmJjrXYmNjiYuLU//+7rvvytw/btw4hg4dSmhoKDt37iQmJkYdlL/77ru0bNlSZ22anZ0d06ZNY8KECRQUFFC/fn3effdd7Ozsyjy7Q4cOdOrUiZ9//rlabenQoQMdOnQo91ppf5X6+OOPK/1+3s/Q0JDAwEBiYmKqVQ8hhBBCCPFk6BUXFxc/7UoIIWpXQYFWTnCvo5o0MZG+qYOkX+ou6Zu6S/qm7pK+eTJatmxUbvpTCxEUQgghhBBCiGfNUw0RfBQ3btxg1KhRZdJXrVr1WDZveNDJkyeJiIjQSTM0NCw3ZK02xcXFkZSUpJPm6empHuZbWx53+55UOx6np/UdEEIIIYQQdZeECArxDBry/Y6nXQUBfPlKtzJpErZRN0m/1F3SN3WX9E3dJX3zZEiIoBBCCCGEEEI8ZjLAEkIIIYQQQoha8qddg1UbkpOTCQ8PJzExEWtra6Bki/UPP/yQ8+fP07BhQ9q0aYOHhwdLly4FICsrixdeeAEjIyNsbGyIjY2t8PkxMTEkJSWxbds29PVLxrLx8fHMmDGDlStX0qdPH516LFq0iE2bNpGdnc3du3e5fv065ubmQMXbphcUFLBo0SK2bNlCw4YNMTQ0JCwsjL59++Lq6krDhg0B+Mtf/sK8efPUs6ns7Ozo2LGj+hxvb2/efPNNAgMDuXz5MoaGhhQUFNCnTx8mT55M48aNAejevTtr165V1x799ttvmJqa0qhRI5o2bcqqVavK1LGoqIgPP/yQPXv2oKenh6GhIQsXLsTCwoLu3buTkZGh5o2Pj+fo0aNERUWxePFiPvvsM7Zs2YKlpSVQssbu73//O+vWrcPe3r7c935/u7VaLQMHDiQsLIwGDRqQnZ2Nl5cXbdu2VfOPHj0aPz8/9T59fX2Kiop4++236dGjh7rW7+rVq+jr66tb33/33Xc6W++XsrGxYfDgwcyfPx+AwsJCXn75Zbp168bSpUuJj48nNjZW5zyujz/+WD3YeNWqVXz88cfs2rWLRo1Kpp737t1LUFAQcXFx6mHVoaGhjBkzplbPMxNCCCGEEI/muR5gJSQk0KNHDxRFYdKkSeTl5REaGkpkZKT6I3bv3r00bdpUPaQ3MDCQiIiICn/clyoqKiI5ORkzMzP27duHs7Ozeq1jx44oiqIOsBISErC1tQXg888/V8tdsWKFOrCryKJFi7hy5QoJCQkYGhpy9epV9u3bp14vPQvr008/JS4ujrlz5wJgZGSktulB8+fPx97envz8fBYsWEBYWBj//Oc/1es2NjbqvZGRkfTr1w9PT88K65iYmMjly5fZuHEj+vr6XLp0CWNj40rbVar0XYWFhQGQlJRU4blS9ytt9507d4iKiiIqKop58+YB0KZNmwrbXnpfZmYmISEhpKWlqXkXL16MiYkJISEhlZZtYmLC6dOnuXfvHkZGRuzcubPM4cZeXl5ERUWVe7+iKNjb27Nlyxb8/f3V9NatW7NkyRL1uymenoJfTpG3PxXy8yvNNzm+7KHoBgZ6aLXlL301NjYiIGAEjo5OtVJPIYQQQjx5z22I4J07dzh48CAxMTEoigLApk2bcHBw0PkB26tXL52Znurau3cv7du35/XXX1efX8rJyYmffvqJgoIC7ty5Q1ZWVrmH2lYlNzeX7777jtmzZ6szKS1atCj3wGQHBwd+//33Gj3f0NCQd999l19//ZUTJ07UuH6lrly5QsuWLdVZvNatW/OXv/ylWve6ubmRkpIClMwels6UVVfDhg2Jjo4mOTmZmzdvVvu+nJwcddbuYfTt25etW7cCJQMmb2/vat2XlZXF3bt3mTx5cpnvja2tLY0aNWLnzp0PXS9RO/J/3EnRld8ounWt0s+lS7+W+Vy8eLHc9EuXfuXcuUwSEtY/7eYJIYQQ4hE8tzNYKSkpuLi40LZtW5o2bcrRo0c5ffo0nTt3rpXnl/6odnNzY8GCBRQUFFC/fn0A9PT06NOnDzt27OCPP/7A1dWV7OzsGpdx/vx5zMzMMDU1rTJveno6bm5u6t/37t1Do9Gof4eGhpY7MDMwMMDW1pbMzEx1lq2mBg0axIgRIzhw4AC9e/fG19eXTp06VeteU1NTzMzMOHXqFCkpKXh5eREfH1+j8k1NTTE3N+f8+fM0b96crKwsnbbPnj0bJ6eSGYPg4GCKi4u5cOECCxcurFE59/Py8uKLL76gf//+nDx5En9/fw4ePKheT0xM1Pn7X//6F0ZGRiiKgpeXF05OTpw7d46rV6/SokULNd/48eNZtGgRL7300kPXTTw6Q4eXyCvIq3IG6wXjms9g+fj41UodhRBCCPF0PLcDLEVRCAoKAkp+DD84W/Ao8vPz2bZtG5GRkZiamtKtWzd27NhB//791Tze3t6sWbOGnJwcpk+fXmUo4MMKDg7m5s2bmJiY8Pbbb6vplYUIPuhRd/Jv3bo1SUlJ7N69mz179jBq1CgWLVpE7969y82vp6en83dp/+zYsYPVq1fXeIAFum2oTohgVlYWo0aN4v/+7//U9Vw1YWtrS3Z2NgkJCfTt27fM9YpCBBVF4bPPPkNfXx93d3eSkpJ444031Os9e/YE4MCBAzWuk6g99a06Ut+q6pnthbJNuxBCCPHceS4HWDdv3mTPnj2cOnUKPT09tFotenp6hIeHs3///kd+funMlK+vL1ASytegQQOdAVbXrl05deoUxsbGOhsu1ISlpSW//fYbOTk5Fc5irV69msaNGzNt2jQWL17MjBkzalSGVqvl1KlTtGvX7qHqWMrQ0JC+ffvSt29fWrRoQXJyMr1796ZBgwbk5+erIY63bt0qEwLYv39/PvroI7p06VKt2boH5eTkcPHiRaysrPjjjz+qdU+bNm1o3rw5Z8+epWvXrjUuE0o224iNjWXNmjXVCk88efIkv/zyC2PGjAFKBurm5uY6AywomcWKi4ujXr3n8n9fIYQQQog67blcg/XDDz+g0WhIS0sjNTWVbdu2YW5ujqWlJRkZGeraGYD9+/dz6tSpGj1fURTmzp1LamoqqamppKSksGvXLnJzc3XyTZ06lSlTpjx0O4yNjfH39ycmJob8/z9U6fr162zevFknX7169XjvvfdYv359jdYhFRQU8PHHH2NmZvbQ4YEAx44dU9d/FRUVcfLkSV588UUA/u///o+NGzcCJWGLmzdvLrMrnrGxMdOmTWP8+PE1LvvOnTtER0fj5uZW7XVfANeuXSM7O1ut58MYNmwY4eHh2NjYVCu/oii89dZb6vdmx44dXL58mYsXL+rke/nll7l9+zYnT5586LoJIYQQQojH47n8J/CEhATGjRunk+bu7o6iKCxZsoQPP/yQDz/8kHr16mFjY8PMmTOr/ezc3FzS09OJjo5W00xMTOjRowdpaWk6ecsLHaupyZMns3DhQry9vWnQoAHGxsZMmjSpTL4XXngBHx8fvvnmG8LDw8uswXJxcWHatGkATJs2DUNDQ/Lz8+nTpw9ffPHFI9Xx2rVrzJ49Wx0E2tvbq7MyM2fOJCoqiq+//pri4mL8/PzUMLj7VXeTiFKla6mKiorUbdpLPbgGy9/fXw0XDQ4ORl9fn8LCQqZOnaqz/qmmWrdurT73QQ+uwZozZw6KovDll1/q5Bs4cCCKotCtm26o2fjx43Xa9KB/+/aWMDQhhBBCiKdAr/hRF9gIIeqcggKtDLDqKFmDVTdJv9Rd0jd1l/RN3SV982S0bNmo3PTnMkRQCCGEEEIIIR6H5zJEsLakp6czf/58nTRzc3P1sODaFB4eXmYr92nTpuHi4lLrZT2skydPEhERoZNmaGjId99991jKCwgIUMMOS8XGxlZ7zdOjuHHjBqNGjSqTvmrVqhqd0/W4DN+Y8bSrIKqw5JXH/z0VQgghxJMnIYJCPIP8v5dt3Os6GWDVLRJOU3dJ39Rd0jd1l/TNkyEhgkIIIYQQQgjxmMkA6xlhZ2eHRqPB19eXIUOGcOjQIQD27t1LaGioTt7IyEiSkpIACAwMxMPDA19fX/z9/Tl+/Lia748//iAiIoKBAwfi5uZGREREpedIZWdn07VrV/z8/Bg0aBDDhg0r91DgsLAwXn31VfXvnTt38tprr6mHAWu1Wvz8/Dh06BCZmZkEBgai0WgYNGgQs2fPrvQ9/PTTT4wcORIPDw/8/PyYOXMmubm5xMfH4+zsjEajwdPTk1WrVqn3LF68GBcXFzQajfq5ffs2e/fupUePHvj5+eHh4cHIkSN1doJcvHgxy5cvJzo6Go1Gg5eXF127dlWfUfqOHxQZGUm3bt3IyclR02JiYrCxseH69etqWnJyMjY2Npw9e1ZNO3LkCN7e3mpoZFZWFgMGDNB5lhBCCCGEeHpkDdYzwsjIiA0bNgAla8MWLFjAP//5z2rdO3/+fOzt7fn++++JjY1l5cqVQMkW6h06dCA2NhaATz/9lJkzZ/Lpp59W+Kw2bdqwfv16AC5cuMDEiRMpLi7G398fgNu3b3Ps2DFMTEy4cOECFhYWvPTSS6xbt45169YREBDA119/TZcuXXB0dCQkJITg4GDc3NwAKj376erVq7z99tssWLCA7t27A5CUlMSdO3cA8PLyIioqihs3buDp6YmHhwdmZmYAjBo1ipCQkDLPdHJyYunSpQAcP36c8PBwjIyM6N27t5pnzpw5QMkAc/z48Wo/VKZNmzakpKSg0WgoKipiz549tGrVSidPQkICPXr0QFEUdet9e3t7evbsyYoVKxg/fjzR0dFMmTLloQ5gFrWr4Jfj3Nv/X4rz86qVf3K8YbXyGRsbERAwAkdHp0epnhBCCCGeEJnBegbl5OTQuHHjGt/n4OCgHgh8/vx5jh49qnPWUnh4OEePHiUrK6taz7OwsCAyMpKvv/5aTduyZQv9+/fH29sbRVHU9BkzZrB06VJOnz7NN998o57JdfnyZVq3bq3mq2wDi2+++QY/Pz91cAXg6elZ5iyrpk2bYmlpyZUrV6rVjlJ2dnaEhYVVe+BaGW9vb/VA6L179+Lo6Ei9ev/79447d+5w8OBBYmJidN4TwDvvvMO///1vvvrqK7RaLT4+Po9cH/Ho7v24He2VixTdulqtz6VLv1brc+5cJgkJ659284QQQghRTTKD9YwoPTg4Ly+PK1eusHr16ho/Iz09XZ0pOnPmDHZ2dhgYGKjXDQwMsLOz4/Tp07Rp06Zaz+zcuTOZmZnq34qiEB4eTvPmzZk0aRLjx48HSg5CDg4O5rXXXmPWrFk0adIEKJlZCg4Opnv37rz88ssMHTq0wsHj6dOn8fPzq7JOv/76K3l5eTqDtVWrVrFx40YAGjdurDMofLA9y5cvr1bbK2NlZUVqaiq3bt1CURR8fX1JT09Xr6ekpODi4kLbtm1p2rQpR48epUuXLmr93myHx4MAACAASURBVHzzTaKjo8sMvsTTY+TwCvcK8qo9g/WCcfVnsHx8qv5eCyGEEKJukAHWM+L+EMGMjAymT59OQkICenp65ea/P33atGkUFBRw9+7daoW31cT9m1RevXqV8+fP06NHD/T09KhXrx6nTp2iY8eOAIwcOZKPP/6YoUOHqvf4+/vz8ssvk56eTkpKCmvXrmXjxo0YGlbvx+n9EhMT2b9/P+fOnWP27Nk0aNBAvVZRiGBl7XlUAwcORFEUDh8+zPvvv69zTVEUgoKCgJLQRkVR1AEWwPbt22nevDlnz56lXbt2tVYn8fDqW9lR38qu2vkXyi6CQgghxDNJQgSfQd27d+fGjRtcv36dJk2acOvWLZ3rN2/e1Dmraf78+aSkpDBkyBA++OADANq3b8/x48cpKipS8xUVFXH8+HHat29f7br8/PPPWFtbA7B582Zu3brFgAEDcHV15eLFizozMPr6+uUOCFu1asWwYcOIi4tTB2Xlad++PceOHauwLl5eXmzatIlvv/2Wjz/+uMYhgg+251F5eXmxaNEiXnrpJfT1//e/4s2bN9mzZw+zZs3C1dWV5cuXs3nzZnVwl5aWxh9//MHy5cuJjY0lNze3VuojhBBCCCEenQywnkFnz55Fq9XSpEkTrKysuHz5sroT3cWLFzl58iR2drr/0q6np8fbb7/Njz/+yNmzZ7G0tKRTp0588cUXap4vvviCzp07Y2lpWa16ZGdnExsbyxtvvAGUzMosW7aM1NRUUlNT+f7776sMcdu+fTsFBQUAXLlyhZs3b5bZDKLUG2+8wfr16zl8+LCatmXLFq5evaqTz97eHl9fX9asWVOtdpQ6ceIEX3zxBSNHjqzRfRX561//ypQpUxgxYoRO+g8//IBGoyEtLY3U1FS2bduGubk5Bw4c4N69e/zjH/9gzpw52NjYMGDAAJYsWVIr9RFCCCGEEI9OQgSfEaVrsKAkjG3evHkYGBhgYGDARx99xIwZM8jLy6NevXrMnTuXRo3KHoxmZGTEmDFjWL58OR9++CExMTF88MEH6rosBwcHYmJiKq1HVlYWfn5+5OXl0bBhQwIDAxk6dCjZ2dlcvHgRBwcHNa+FhQWNGjXi8OHDdOvWrdzn7dy5k5iYGDWc791336Vly5bl5m3RogULFixg3rx5XLt2DX19fXr27ImLi0uZvOPGjWPo0KHqFvb3r8EC+PzzzwE4cOAAfn5+5Obm0rx5c2bNmqWzg+CjGj58eJm0hIQExo0bp5Pm7u5OQkKCuk6udBZx4sSJaDQahgwZgpWVlZp/rW93OWCwjpLDH4UQQohnm15xbS4qEULUCQUFWvkRX0fJAKtukn6pu6Rv6i7pm7pL+ubJaNmy7IQFSIigEEIIIYQQQtQaCREUNXby5EkiIiJ00gwNDfnuu++eSPnp6enMnz9fJ83c3FwN66sroqOjOXTokE5aUFCQeujy4zRyY8UHMosn7/NXLJ52FYQQQgjxhEiIoBDPoFe///lpV0Hc5/4BloRt1E3SL3WX9E3dJX1Td0nfPBkSIiiEEEIIIYQQj5mECFYgOTmZ8PBwEhMT1XOPzp07x4cffsj58+dp2LAhbdq0wcPDg6VLlwIlO+i98MILGBkZYWNjQ2xsbJnn7t27l7CwMCwsLMjNzaVFixaMHTuW/v376+TTaDS0a9eOTz75BIB///vf7Nq1i4ULFwKQk5ODn58fK1eu5Nq1a8TExJCfn09+fj5eXl689dZbFbZt27ZtLFq0iHv37mFoaIizszORkZEsXryYf//73zRr1oyCggLCwsLw8fEBIDIykn379qm7DxobG7N27Vri4+OJjY2ldevW3L17FwsLC8LDw3F0dFTv69evH5s2bSI7O5u7d+9y/fp1zM3NAZgzZ46a936BgYFcvnwZIyMjACwtLfn0009ZvHixutV78+bNgZJzvzIyMgCws7OjY8eOFBYWYmBggJ+fH6NGjUJfX/+h331pqF9BQQHZ2dm0bdsWgAkTJrB169Zy30t54uPjmTFjBitXrqRPnz7A/75nixYtwtPTE4Dr16/j4uLCrFmzeP3113X6e9myZVhZWVFQUMDQoUOZO3duhTswCiGEEEKIJ08GWBVISEigR48eKIrCpEmTyMvLIzQ0lMjISFxdXYGSwVLTpk3ZsGEDUDIoiIiIwN7evtJnOzk5qYOy48ePEx4ejpGRkbr999mzZykqKuLAgQPcvXsXExMTAgICiI+PZ9euXfTp04dFixbh7++PhYUFY8eOZdGiRdja2qLVajl37lyFZZ86dYoPPviApUuXYm1tjVar5V//+pd6fdSoUYSEhPDLL78wdOhQPDw8qF+/PgARERHqIOB+Xl5eREVFAbBnzx7eeust1qxZo3Mgb+n6qL1797JixQq1/ZWZP39+ue+yadOmrFixgnfffbfMNSMjI7U/rl27xtSpU8nJyWHSpEnAw737OXPmACXneo0fP159PsDWrVsrfC/l6dixI4qiqAOshIQEbG1tdfIkJSXRrVs3FEVRB1impqa88847fPDBByxfvpwVK1bQvXt3GVw9Bfm/HOXu/kSK8+9V+57J8fXV/zYw0EOrrTwy29jYiICAETg6Oj10PYUQQgjxdEiIYDnu3LnDwYMHiYmJUQ/C3bRpEw4ODurgCqBXr1507Njxkcqys7MjLCyMf/7zn2paQkICvr6+vPzyy6SkpAAlBwH/7W9/48MPP+TIkSPs2bOHkJAQoGTGo/RsKAMDA/WMpPIsW7aM8ePHq4MfAwODMgfdAlhZWWFsbMzt27dr1B5nZ2f+P/buPS7qKn/8+GsYQkB0vaGowKqgYGgoUmFpKF6CAR0MscIQU0sFrfxpSmaY1zZWt7wkutqGuO56SRaTUXMVMe/32rQQU0KgvF8I5C6/P3jw+TrOAIOhjPZ+Ph7zeDTncz7nMqc/OJ73OWfYsGF6k7a6FhISwrZt27h582a1+Zo3b86cOXNYu3YtxrYamvrb1yVvb2/+97//UVJSQn5+PhcuXDC49Fmn0xEdHc2lS5e4ePGikq7RaABYuXIl69at4//9v/9X5+0TNSv4dhdlV7K4c+uKyZ+LF39RPjk5OXrfjX0yMs6TnJxU310VQgghxH2QFSwjdu3aRe/evWnfvj1Nmzbl1KlTnD17Fg8PjwdSn4eHB59//rnyfevWrXzxxRecP3+ef/7znwwaNAgAd3d3evXqxciRI1m2bBlWVlYARERE4O/vzzPPPEPv3r0ZMmSIcjHvvc6ePcuoUaNqbNPp06f585//rIThAcTGxhIXFweAq6srCxcurLI/VYXJ1caUKVOUEMHnnnuOadOmAWBra8tLL71EQkKCsjJVFScnJ8rKyrh27VqVbTXlt6+Oqb8LVEyUn3vuOfbt28dvv/2Gn58f2dnZyvNff/2VK1eu8NRTTxEQEMDWrVv1xmv69OloNBrmzJlDkyZNamybqHs23fpxu6SoVitYLW1qv4IVFBR8320UQgghRP2RCZYROp2OESNGABWrBpWrWA/K3asr33//PU2bNqVNmza0atWK6dOnc/PmTeWP6eHDh/PNN9/w7LPPKu9MmDCBwYMHs2/fPpKTk9HpdKxZs+a+2hIfH09iYiI///yzMmmoZGooXF0dTFlViCBUHHceHBxs0mSxOrX57atSmxBBgMDAQBISEsjLy2PatGl64ZJbt24lICAAqPh/b/r06Xp93Lt3L/b29qSnp5tcn6hbVu26YNWuS63e+VROERRCCCH+MGSCdY+bN29y6NAh0tPTUalUlJWVoVKpiIqK4ujRow+kzh9++EEJ2dPpdGRkZCihiHl5eezYsYNhw4YBFSsgFhaGkZ3Ozs6EhYUxbNgwevbsyY0bN2jatKlBPldXV06dOmWw76dS5R6sXbt28f7777Nz584qV8NM6c+D0rhxY4KCgvjXv/5Vbb6srCzUajXNmzfn3LlzBs9r89vXlaeeeor09HRsbGyUAzMq6XQ6rly5wpYtWwC4fPkyP//8M+3atePSpUusWbOGL7/8khEjRpCWllblOAohhBBCiPohe7Du8fXXX6PVatm9ezcpKSns2bMHR0dH/vznP3Py5ElSU1OVvEePHv3dKwlpaWksW7aM4cOHc+fOHbZt28ZXX31FSkoKKSkpLFu2jOTk5GrLSE1NVVZiMjMzsbCwoHHjxkbzjh49mhUrVigHYdy5c4d///vfBvn69etHly5d+M9//lOr/hw5coQNGzbU+aTEmJEjR7Ju3TpKS0uNPr9+/TozZ85k+PDhqFQqg+d18dvfr8mTJzNp0iS9tIyMDPLz89m7d6/ShjfffFNpw0cffcS4ceNwcHAgOjqa2bNn19lqoRBCCCGEqBuygnWP5ORk3njjDb20gQMHotPpWL58OfPnz2f+/PlYWlri5ubG+++/X+s6jh07RnBwMAUFBTRv3pwZM2bQs2dPjhw5QqtWrWjVqpWS9+mnn+bcuXNcvnyZli1bGi1v8+bNfPTRR1hbW6NWq1mwYAFqtdpoXnd3d6ZPn87kyZMpKChApVLRp08fo3mjoqKYPHmyMlm6e68RwMaNG4GKsLbjx49TWFiIo6MjixcvrpMVrLv3YDVt2pT4+Hi9582aNWPAgAF66YWFhWi1WuWYdq1Wy+uvv648r+vfHoz/LpX746ri6+trkKbT6RgwYIBe2sCBA5k0aRLdu3fn119/ZejQoQD4+fmxYcMGkpKSGDJkiEFZawe7SRiaEEIIIUQ9UJXLP4EL8dgpKSmTCZaZkj1Y5knGxXzJ2JgvGRvzJWPzcNjbNzKaLiGCQgghhBBCCFFHJETwAdm7dy8LFizQS3N0dFQu3H3QNm3aREJCgl6al5eXcmmuuYiKitI7phwqQgN79+5dTy36/czht39zS85Dq0tUiO1teKiMEEIIIf54JERQiMfQ64kX6rsJfzimTrAkbMM8ybiYLxkb8yVjY75kbB4OCREUQgghhBBCiAdMJljikdK9e3cAsrOzcXNz07tQefbs2SQmJgLw7bffEhoailarJSAggCVLllRZZmJiIj4+PgQHBzNw4EBGjx7NiRMnlOfR0dH4+fmh1Wrx9/dn6dKlyrPw8HBefPFFBg0ahL+/P7NnzyY3N7faPri5uTFlyhTle2lpKT4+PowdO1YvX2RkpMFx93PnztWrPy4ujlmzZlVbnxBCCCGEeHhkD5Z4ZDVv3pyEhARefvllg2PRp02bxqJFi3B3d6esrEy596sqGo2GmJgYAA4dOsTEiRNJSEhQjpufOnUq/v7+FBUVodFo0Gq1ODk5AbBgwQK6du1KcXExf/vb34iMjOSf//xnlXXZ2tpy9uxZCgsLsba2Zv/+/XrHwwPk5uZy+vRpbG1tycrKUup655130Gq1aLVaoOJI+KSkpFr8aqImBT9/x80j/6G8uLBW772zyfjVCPdSq1WUlZVjY2NNaGgYXl7e99NMIYQQQpgpmWCJR1azZs3w8vIiKSnJYKXn+vXr2NvbA6BWq3F1dTW5XB8fH4YNG8b69euZPn263rOioiKgYpJ0LysrK959910GDBhAWloa7u7uVdbh6+tLamoq/v7+6HQ6AgMDOX78uPJ8x44d9O3blxYtWqDT6Rg3bhwAdnZ2TJo0idmzZwPw1ltvVXmptLg/uSe3UXIls9bvXbxV+7qSk5NkgiWEEEI8ZiREUDzS3njjDT7//HPKysr00iMiIvD39ycqKop169YpEyNTeXh4cP78eeV7bGwsWq0WX19fAgMDad68udH31Go17u7ueu8ao9Fo2Lp1K0VFRZw5cwZPT0+95zqdjqCgIAIDA9HpdHrPgoKCyM3NJS8vj+Dg4Fr1S9SscfcAnrD/M5Z/alWrj4NDG5M+bdu2xcGhDe3bdyAoSMZPCCGEeNzICpZ4pDk5OeHp6cmWLVv00idMmMDgwYPZt28fycnJ6HQ6vf1aNbn3cM3KEMH8/HxGjhzJiRMn8PLyMuldY9zd3cnOziY5ORlfX1+9Z1evXiUzM5MePXqgUqmwtLQkPT2dTp06AXDx4kWuXLmCSqUiPz+fhg0bmtwvUTObdp7YtPOsOeM95BRBIYQQQoCsYInHwNixY1m1apXBxMbZ2ZmwsDDi4+NJS0vjxo0bJpf5ww8/KPuv7tawYUOeeeYZvXC+u5WVlZGenk6HDh1qrMPPz4/Y2FgCAwP10rdt28atW7fo168ffn5+5OTk6K1izZs3j4kTJxIQEKB34IUQQgghhKh/MsESjzwXFxdcXFzYvXu3kpaamqpMuDIzM7GwsDB5r9KRI0fYsGGDwb4uqDjx73//+x/Ozs4Gz0pKSli4cCGtW7eudv9VpaFDhxIVFYWbm5teuk6nY9WqVaSkpJCSksKmTZuUCdaePXu4du0awcHBREZG8t///peffvrJpH4JIYQQQogHT0IExWNh/PjxevuRNm/ezEcffYS1tTVqtZoFCxagVld9ytvWrVs5fvw4hYWFODo6snjxYr0VrNjYWOLi4igpKaFnz54MHDhQeTZlyhSsrKwoLi7mueeeY9myZSa12cHBgREjRuilZWdnk5OTQ7du3ZQ0JycnGjVqxJEjR5g/fz6LFy9GpVJha2vL1KlTmT17NgkJCXrl/H1QWwlDE0IIIYSoB6pyUzaMCCEeKSUlZTLBMlOyB8s8ybiYLxkb8yVjY75kbB4Oe/tGRtMlRFAIIYQQQggh6oiECIo/jE2bNhmE0nl5eTFz5sw6r+vGjRuMHDnSID0+Pp6mTU07be73mJd86YHXIWoW1cv4v2wJIYQQ4vElEyzxhxESEkJISMhDqatp06Zs3rz5odQlhBBCCCHMh4QICiGEEEIIIUQdkRWsetS5c2c6depEWVkZHTp04OOPP8bGxkYv3dHRkdjYWBo3bkx2djYajYb27dsrZbz++uscPnwYT09PXnnlFSV9586drFu3jlWrVlVZ/86dO4mKimLr1q3KiXl311FSUoK3tzcffvghv/zyi9F0Cwvjc/SMjAzmz59PZmYmDRs2xNnZmQ8++IBz584RGRmJo6MjRUVF9O3bl2nTpgGQmJhIbGwsrVq1UspZuHAh1tbWaDQaOnToQFFREQ0bNiQsLIyXXnpJee/UqVPY29uzfft2AL2LeUNCQgxO6wNYsmQJGzZsoFmzZpSVlTFp0iT69eunl15pzZo1/Pjjj1W2HSqOUF+0aBGFhYVYWVnh4+NDdHS0Qb1Hjhxh4cKFrF+/XkkrLS3lhRde4D//+Q+tWrWitLSUXr16MXToUKZMmaLkCw8P5/LlyzRo0IAnnniCuXPn0rlz5yrHWAghhBBCPFwywapH1tbWShjZ5MmTWbduHa+//rpe+rRp01i7di3jx48HKi7PvTf0rEWLFqxYsUJvgqXT6QgKCqq2/uTkZHr06IFOp+Ott95S0ivrKC0tJSIigp07d/Lkk08aTb/7uPJKRUVFjB07lujoaPz8/AA4fPgw169fB8Db25sVK1ZQWFhIcHAw/fv3p0ePHgBoNBpiYmL0ysvOzsbZ2ZmkpCQAsrKymDBhAuXl5Xohf+PHj1d+p+7du5sUojdy5EhGjx7NuXPnCAsL4+DBg3rp96qq7enp6cyZM4cVK1bg4uJCWVmZ3gTq3jIuXrxITk4Obdu2BeDAgQO4uroqk8v9+/fTrl07tm/fzuTJk1GpVMr7CxYsoGvXrmzatInY2Fi++OKLGvsp6s71n78l8/AmyooLa8z7zpeG/wChVqsoK9M/vNXGxprQ0DC8vLzrrJ1CCCGEqB8SImgmvL29yczMNEjv1q0bly5Vf2BBz549ycjI4PLlywDcvn2bAwcO0L9//yrfyc/P5/jx48ybN0+5xPZelpaWdO/e3aBdVaVX2rJlC926dVMmVwDPPvussqJUydrams6dO9fYv3s5OTkRHR3NmjVravVedVxcXLC0tOTGjRsm5b+37atWrWLcuHHKSqBarSYsLMzouxYWFgQEBOj97lu3btWbEOt0OkaMGEHr1q05efKk0XJM+X9D1L3sEzryrvxMwa2LNX4uXvzF4JOTk2OQlpFxnuTkpPrumhBCCCHqgEywzEBpaSnffPONwQSkrKyMgwcP6k1ULly4gFarVT7Hjh1DrVYzcOBAtm3bBsDu3bt59tlnsbOzq7LOXbt20bt3b9q3b0/Tpk05deqUQZ6CggIOHjxo0K6q0iudPXsWDw+PGvt969YtMjMzefrpp5W0rVu36vWvsND4KoGHhwfnz5+vsQ5Tfffdd6hUKiUsMD4+XmlDeHh4jW0/e/YsXbp0Mbm+wMBAtm7dCkBxcTF79uxRVgOLioo4cOAAfn5+BAUFVTkB3rt3b7WTaPFgOHoFYmffDps/OdT4cXBoY/Bp27atQVr79h0ICgquuXIhhBBCmD0JEaxHhYWFaLVaoGIFa+jQoXrply5dwsXFheeff155x1iIIFT8wR4bG0tERAQ6nU4ptyqVKyRQEZan0+mUCULlJE6lUtGvXz98fX3Jzs42mn4/jh07xuDBg8nMzCQiIgJ7e3vlmbEQQWPq6n7s+Ph4vvrqKxo2bMinn36qhOJVFSJYXdtro2vXrty+fZvz589z/vx5PD09adKkCfB/E2Rra2sGDhzIsmXLmD59Omq1GoApU6ZQUlLC7du35aTCetCsXTeatetmUl5jx7TL5Y9CCCHE400mWPXo7r1WxtILCgoYPXo0a9euNXpIw928vLy4cuUKaWlpnDx5kk8++aTKvDdv3uTQoUOkp6ejUqkoKytDpVIxdepUoOpJXFXp93J1deXo0aNVPq/cx5SVlcXLL79MQEBArQ9q+OGHH5RwvN+jqolUVapqu6urK6dOncLd3d3ksipXsc6dO0dgYKCSrtPpOH78uLJyWTlelRPtBQsW0KVLF2JjY5kzZw5Lly41uU4hhBBCCPFgSYigGbOxsWHGjBl88cUXlJaWVptXpVIREBDAtGnTeOGFF2jQoEGVeb/++mu0Wi27d+8mJSWFPXv24OjoyLFjx+qk3YMGDeLkyZOkpqYqaUePHiU9PV0vn5OTE2+++SYrV66sVfnZ2dnExsby2muv1UVz78u9bR89ejQrVqwgIyMDgDt37vDvf/+72jKCgoL46quvOHToEP369QMgLy+PY8eOkZqaSkpKCikpKcTExJCcnKz3rkql4u233+bbb7/l3LlzD6CHQgghhBDifsgEy8w9+eSTuLm5KX9g37sHKyEhQckbFBREWlqa3mqIMcnJyQZ7dwYOHGjwR/z9sra2Zvny5axZs4aBAwei0Wj417/+pXfseaVXXnmFo0ePkp2dDRjuwTpx4gRQ0e/g4GACAgJ45513CA8Pf6CXBt+9B0ur1Srtq6rt7u7uTJ8+ncmTJxMQEEBQUBBZWVnV1uHi4oKNjQ0+Pj7Y2toC8N///hcfHx+srKyUfP369WP37t0UFxfrvW9tbc2oUaP4/PPP66DHQgghhBCiLqjK62ozixDCbJSUlMk+HzMle7DMk4yL+ZKxMV8yNuZLxubhsLc33GsNsoIlhBBCCCGEEHVGDrl4jN24cYORI0capMfHx9O0adPfXf6ZM2eUgzEqWVlZsXHjxt9ddl2Ki4tj+/btemn+/v7KpcSPY91/T776QMsX+ob1sq3vJgghhBDCTEiIoBCPoc/+IxcQP0y1mWBJ2IZ5knExXzI25kvGxnzJ2DwcEiIohBBCCCGEEA+YTLDq2ZUrV5g0aRL9+/fnpZde4o033iAjI4OgoCCDvN9++y2hoaFotVoCAgJYsmRJjeVHRkYybNgwvbQlS5bg5uZGZmamkhYfH4+bmxvff/+9UkefPn3w8fGp9iQ9gPz8fGJiYpQ+hIeH89133wHQuXNntFotQUFBjBs3jtzcXKDiqPWnnnpK76S+pKQkAPz8/Bg0aBCDBg1Co9HwySefUFRUpLwXFBTE3r17lfe6d+/Oiy++iFarNQhZrFRQUMDkyZMZNGgQQUFBvPrqq+Tn5yvl3fv7VJ7MFx0djaenJ3l5ecrzefPm4ebmxvXr16v83d3c3JgyZYryvbS0FB8fH8aOHQtAYmKi3m+r1Wr56aef9Maja9eu/Pbbb0ra4cOHcXNzIyUlRUkbO3Yshw8frrIdQgghhBDi4ZI9WPWovLycCRMmEBwcrFwMnJaWxrVr14zmnzZtGosWLcLd3Z2ysjLlzqWq5Obmcvr0aWxtbcnKysLJyUl51qlTJ3Q6HZGRkQBs376djh07Aih7qBITEzl16hQxMTHV1jNjxgwcHR3ZsWMHFhYWZGVlKXcz3X2Z8rRp01i7dq2y/6i6i4tXr15Ns2bNlMlbTEwMH3/8sfK8d+/e9O7dG4Dw8HCmTp1K165dq2xjQkICLVq0YOHChQCcP3+eJ554otp+VXJ2dmbXrl1otVru3LnDoUOHaNWqVbXv2NracvbsWQoLC7G2tmb//v0G72g0mip/W51OR9euXdmxY4fecfQODg4sX75cuYRY3L9ffz7Jj4c3Ulpc+LvLOvCl6f9WpVarKCszjMy2sbEmNDQMLy/v390eIYQQQtQfmWDVo0OHDmFpacmrr76qpLm7u1e5UnT9+nXs7e0BUKvVuLq6Vlv+jh076Nu3Ly1atECn0zFu3DjlWf/+/dm1axeRkZFcuHCBRo0amTzhuNuFCxf47rvvWLBgARYWFX9kOjk56U3mKnXr1o0zZ87UqvyGDRsya9YsfH19uXnzZq3bV+nKlSu0adNG+d6hQweT3w0MDGTbtm1otVoOHz6Ml5cXe/furfE9X19fUlNT8ff3R6fTERgYyPHjx2t878KFC9y+fZuZM2eyfPlyvQmWu7s7paWl7N+/n+eff97kPghDZ08kc/PKz3VSVt6tOimG5OQkmWAJIYQQjziZYNWjs2fP4uHhYXL+iIgI/P39eeaZZ+jduzdDhgyhQYMGVebX6XRERUXRvHlzpUk1BwAAIABJREFU3nrrLb0Jlp2dHa1btyY9PZ1du3ah0WhITEy8rz507twZtVpdbb6ysjIOHjzI0KFDlbTKS5MrffDBB3h7G/5xaWdnh6OjI5mZmTRv3rzWbQQICQlh1KhRfP311/j4+DBkyBDatWtn0rvt2rUjJSWFW7duodPpGDx4sEkTLI1Gw7Jly+jbty9nzpwhJCREb4K1detWve/r16/H2toanU6HRqPB29ubjIwMrl69SosWLZR848aNY9GiRTLB+p06egVRWlJQJytYdjZ1s4IVFBT8u9sihBBCiPolE6xHyIQJExg8eDD79u0jOTkZnU7HmjVrjOa9evUqmZmZ9OjRA5VKhaWlJenp6XTq1EnJo9Fo0Ol07Nu3j9WrV9/XBKsmhYWFaLVaLl26hIuLi96koLoQwXv93sMuO3fuzM6dO9m/fz8HDhxg6NChyoTGGJVKpfd9wIAB6HQ6vvvuO2bPnm1SnZWrkcnJyfj6+ho8rypEUKfTsXTpUiwsLBg4cCDbt2/ntddeU54//fTTABw7dsykdgjjWrfrTut23eukLDlFUAghhBCV5JCLetSxY0dOnz5dq3ecnZ0JCwsjPj6etLQ0bty4YTTftm3buHXrFv369cPPz4+cnBx0Op1enr59+/LVV1/Rpk0b7Ozs7rsPaWlplJWVGX1euQdr9+7dlJeXs3bt2lrXkZeXR05OjskrTlVp2LAhAwcO5MMPP2Tw4MHs2bOHJk2acOuWfnzXrVu3DO4J02g0yqpRZSikKfz8/IiNjSUwMNCk/GfOnOHnn39m1KhR+Pn5odPpSE5ONsg3btw44uLiTG6HEEIIIYR4OGSCVY98fHwoLi5m/fr1SlpaWhoXL140mj81NVVZycnMzMTCwoLGjRsbzavT6Vi1ahUpKSmkpKSwadMmgwmWjY0NU6ZM0QsdrC1nZ2e6dOnC4sWLlbZlZ2eTmppqUNeMGTP44osvKC0tNbn8/Px8Zs2aRf/+/fnTn/503+08fvy4MpEqLi7mp59+ok2bNjRs2BB7e3sOHjwIwM2bN9m7dy89evTQe79t27ZMmjSJsLCwWtU7dOhQoqKicHNzMym/Tqdj4sSJyrjt27ePy5cvk5OTo5evV69e5Obm1npPmxBCCCGEeLAkRLAeqVQqli5dyvz581m5ciUNGjSgbdu2TJ8+nYyMDF544QUl73vvvceOHTv46KOPsLa2Rq1Ws2DBAqN7n7Kzs8nJyaFbt25KmpOTE40aNVKOT69k6spKdebNm8df/vIXBgwYgLW1NU2bNuXdd981yPfkk0/i5uZGcnIy3t7eBnuwQkJCGDFiBFCx36y8vJw7d+4wYMAA5bTD+5WVlcWHH34IwJ07d/D19eXFF18EIDY2llmzZvGXv/wFgKioKJydnQ3KeOWVV2pdr4ODg9Kne927B2vmzJnodDr+/ve/6+WrDE/09PTUSx83btzv/l2EEEIIIUTdUpX/3s0tQgizU1JSJvt8zJTswTJPMi7mS8bGfMnYmC8Zm4fD3r6R0XQJERRCCCGEEEKIOiIhgo+4TZs2kZCQoJfm5eXFzJkz67yu0NBQiouL9dJiY2NN3l/0MOzdu5cFCxbopTk6OvLZZ5/VeV03btxg5MiRBunx8fEGh2Q8bBu+ul6v9f/RDXzB+OmUQgghhHj8SYigEI+htZuu1HcT/tCqm2BJ2IZ5knExXzI25kvGxnzJ2DwcEiIohBBCCCGEEA+YTLCE2eneveLy1+zsbNzc3PQuU549e7ZyIfK3335LaGgoWq2WgIAAlixZUmWZiYmJ+Pj4EBwczMCBAxk9ejQnTpxQnkdHR+Pn54dWq8Xf35+lS5cqz8LDw3nxxRcZNGgQ/v7+zJ49m9zc3Gr74ObmxpQpU5TvpaWl+Pj4MHbsWL32aLVa5fPTTz8p+ePj4+natSu//fabknb48GHc3NxISUlR0saOHcvhw4erbYsQQgghhHh4ZA+WMGvNmzcnISGBl19+GSsrK71n06ZNY9GiRbi7u1NWVkZGRka1ZWk0GmJiYgA4dOgQEydOJCEhARcXFwCmTp2Kv78/RUVFaDQatFotTk5OACxYsICuXbtSXFzM3/72NyIjI/nnP/9ZZV22tracPXuWwsJCrK2t2b9/P61ataqyPffS6XR07dqVHTt2EBISoqQ7ODiwfPly/Pz8qu2rqDtZP5/gxNGNlBQXmPzO1sSq/+1KrVZRVqYfmW1jY01oaBheXt733U4hhBBCmAdZwRJmrVmzZvTs2ZOkpCSDZ9evX8fe3h4AtVqNq6uryeX6+PgwbNgwvUueKxUVFQEVk6R7WVlZ8e677/LLL7+QlpZWbR2+vr7Khcs6nc7kO8cuXLjA7du3eeeddwwuh3Z3d6dRo0bs37/fpLLE7/f9t1u4diWD3FsXTf5cvPhLlZ+cnByDtIyM8yQnG/4/LoQQQohHj6xgCbP3xhtvMGbMGL2VHKi4jNjf359nnnmG3r17M2TIEBo0aGByuR4eHqxbt075HhsbS1xcHBcuXCA8PJzmzZsbfU+tVuPu7s758+dxd3evsnyNRsOyZcvo27cvZ86cISQkRO9i4XsvGl6/fj3W1tbodDo0Gg3e3t5kZGRw9epVWrRooeQbN24cixYt4vnnnze5r+L+de02iJKSwlqtYNna1H4FKygo+L7bKIQQQgjzIRMsYfacnJzw9PRky5YteukTJkxg8ODB7Nu3j+TkZHQ6nd5+rZrce4BmZYhgfn4+I0eO5MSJE3h5eZn0rjHu7u5kZ2eTnJyMr6+vwfOqQgR1Oh1Lly7FwsKCgQMHsn37dl577TXl+dNPPw3AsWPHamyD+P2c2nnh1M74/wdVkVMEhRBCiD8uCREUj4SxY8eyatUqg4mNs7MzYWFhxMfHk5aWxo0bN0wu84cfflD2X92tYcOGPPPMM3qrS3crKysjPT2dDh061FiHn58fsbGxJocHnjlzhp9//plRo0bh5+eHTqcjOTnZIN+4ceOIi4szqUwhhBBCCPHwyARLPBJcXFxwcXFh9+7dSlpqaqoy4crMzMTCwoLGjRubVN6RI0fYsGEDw4YNM3hWWlrK//73P5ydnQ2elZSUsHDhQlq3bl1teGCloUOHEhUVZfJlzDqdjokTJ5KSkkJKSgr79u3j8uXL5OTk6OXr1asXubm5nDlzxqRyhRBCCCHEwyEhguKRMX78eIKD/2+fyubNm/noo4+wtrZGrVazYMEC1Gp1le9X7nkqLCzE0dGRxYsX661gVe7BKikpoWfPngwcOFB5NmXKFKysrCguLua5555j2bJlJrXZwcGBESNGVNueSjNnzkSn0/H3v/9dL9+AAQPQ6XR4enrqpY8bN47IyEiT2iGEEEIIIR4OVbkpm0mEEI+UkpIy2edjpmQPlnmScTFfMjbmS8bGfMnYPBz29o2MpkuIoBBCCCGEEELUEQkRFI+VTZs2kZCQoJfm5eXFzJkz67yuGzduMHLkSIP0+Ph4mjZtWuf11ca2JNMP+xAPW5Het559TL9aQAghhBDmT0IEhXgMfbXxan03QZhIJljmQcJpzJeMjfmSsTFfMjYPh4QICiGEEEIIIcQDJiGCwqxduXKF+fPn8/3339O4cWOaN2/O9OnTKS0tZc6cOVy6dIny8nK0Wi2RkZGoVCoSExOJjY3FwcGB27dv4+TkRFRUlHJpcHR0NEeOHKFRo4p/dbCxsWHdunVVtuGbb75h8eLF5OXl0aBBA9q3b8/UqVNp06ZNlWUlJiYyffp0kpKSlOPcg4KCWL58OY6Ojvj5+dGwYUOg4l6tAQMGEBkZSYMGDcjOzkaj0dC+fXulDa+//jrBwcF67/3pT3/i448/pm3btnX/wwshhBBCiPsiEyxhtsrLy5kwYQLBwcF88sknAKSlpXHt2jWio6P58MMP6dWrFwUFBUycOJF//etfDB8+HACNRkNMTAwAhw4dYuLEiSQkJCjHsk+dOhV/f/8a25Cens7cuXOJi4tT3t21axc5OTm0adOm2rIcHBxYvnw5n376qdGyV69eTbNmzcjPzycmJoaYmBg+/vhjoOIC5c2bN1f73uLFi4mLi2Pu3Lk19kMIIYQQQjwcMsESZuvQoUNYWlry6quvKmnu7u5s3LgRLy8vevXqBVSsGsXExBAeHq5MsO7m4+PDsGHDWL9+PdOnT69VG1auXMnYsWP17svq16+fSe/26dOHY8eOcf78eTp06FBlvoYNGzJr1ix8fX25efOmyW3r1q0ba9asMTm/MA8ZmSc4dHQDxSWFAKxPUuk9t7GxJjQ0DC8v7/ponhBCCCF+J9mDJczW2bNn8fDwMEj/6aefDNKdnZ25ffs2eXl5Rsvy8PDg/PnzyvfY2Fi0Wi1arZbJkydX2QZjdd2rqrIsLCwYM2YMK1asqPZ9ADs7OxwdHcnMzATgwoULSplarZZjx44ZvLN371769+9fY9nCvBz/dguXr2Zw89av3Lz1Kxcv/qL3ycg4T3JyUn03UwghhBD3SVawxB/CvYdlmhoieLfKY9kLCwsZNmwYo0ePrrGsoKAg4uLiyMrKqlUbqwsRjIiI4ObNm9ja2vL222/Xqg+i/vXoNoiSkgJlBcvaxnAFKygouD6aJoQQQog6IBMsYbY6duzI119/bZDu6urK0aNH9dKysrKwtbXFzs7OaFk//PCDXpifqVxdXTl9+jTu7u40bdqUzZs38/nnn3P7tmlHn1paWjJq1ChWrlxZbb68vDxycnJo164dv/32W7V5V69eTePGjZkyZQpLlizhvffeM7k/ov61/7MX7f/spXyXY9qFEEKIx4uECAqz5ePjQ3FxMevXr1fS0tLSaN++PcePH+fAgQMAFBYWMnfuXMaMGWO0nCNHjrBhwwaGDRtW6zaMGTOG5cuXc+7cOSWtoKCgVmUMGTKEgwcPcv36daPP8/PzmTVrFv379+dPf/qTSWVaWloqpxTWZt+WEEIIIYR4sGQFS5gtlUrF0qVLmT9/PitXrqRBgwa0bduW6dOns2zZMubOncusWbO4c+cOWq2W1157TXl369atHD9+nMLCQhwdHVm8eLHeClZsbCxxcXHK940bN2JlZWXQBjc3N95//32mTp1KXl4eTZs2pU2bNkycOLHasu5mZWVFeHg48+bN00uPiIigvLycO3fuKMe0V6rcg1UpJCSEESNG6L3fsmVLgoKCWLt2LVFRUTX+nkIIIYQQ4sFTld+7OUUI8cgrKSmTG9zNVJMmtjI2ZkjGxXzJ2JgvGRvzJWPzcNjbNzKaLiGCQgghhBBCCFFHJERQCGDTpk0kJCTopXl5eTFz5sx6atHvs2ej8f1ewhwU4jnAur4bIYQQQogHRCZYQlCxxykkJKS+myGEEEIIIR5xEiIohBBCCCGEEHVEVrCEonv37pw8eZLs7Gz69evHjBkzCA8PB2D27Nl06dKFl156CYDPP/+cjRs30qBBAywtLQkPDyc4OJji4mL++te/kpqaikqlwsXFhZkzZ+Lg4ABUnMo3aNAgFixYAEBpaSm9evXC09OTFStWkJiYSGxsLK1atVLatXDhQlxdXY22OSMjg/nz55OZmUnDhg1xdnbmgw8+4Ny5c0RGRuLo6EhRURF9+/Zl2rRpAFXWYW1tjUajoUOHDhQVFdGwYUPCwsKUPicmJnLq1Cns7e3Zvn07AOnp6XTq1AkwftIfwJIlS9iwYQPNmjWjrKyMSZMm0a9fP730SmvWrOHHH3+ssu0Ae/bsYdGiRRQWFmJlZYWPjw/R0dG1GWohhBBCCPGAyARLGNW8eXMSEhJ4+eWXDY4v//e//82BAwf48ssvsbOzIy8vj//+978AfPLJJ+Tn57N9+3bUajWbNm1iwoQJbNy4EZVKha2tLWfPnqWwsBBra2v279+vN9EB0Gg0xMTE1NjGoqIixo4dS3R0NH5+fgAcPnxYuW/K29ubFStWUFhYSHBwMP3796dHjx5V1pGdnY2zszNJSUlAxeXFEyZMoLy8XC98cPz48YwfPx6omJRu3ry5xraOHDmS0aNHc+7cOcLCwjh48KBe+r2qant6ejpz5sxhxYoVuLi4UFZWpndPmBBCCCGEqF8ywRJGNWvWDC8vL5KSkgwu6F2xYgVr1qzBzs4OADs7O4YMGUJBQQGJiYns2rULtVoNVKzqbNq0iUOHDtGzZ08AfH19SU1Nxd/fH51OR2BgIMePH691G7ds2UK3bt2UyRXAs88+C1RMtCpZW1vTuXNnLl26VKvynZyciI6O5uOPP66z/VkuLi5YWlpy48YNk/Lf2/ZVq1Yxbtw45U4vtVpNWFhYnbRN1K2fLpxg74mNFJUYXkzdQGcYnW1jY01oaBheXt4Po3lCCCGEeEBkgiWq9MYbbzBmzBi9yUVeXh75+fk4OTkZ5M/MzKR169bKxKtSly5dOHv2rDLB0mg0LFu2jL59+3LmzBlCQkL0JliVlwRXWr9+PdbWhqeunT17Fg8Pjxr7cevWLTIzM3n66aerrcMYDw8Pzp8/X2Mdpvruu+9QqVRKWGB8fDxfffUVAI0bN2bNmjXVtv3s2bOMGjWqztojHpzD32/h4rUM4w9zjScnJyfJBEsIIYR4xMkES1TJyckJT09PtmzZUqfluru7k52dTXJyMr6+vgbPTQ0RrMmxY8cYPHgwmZmZREREYG9vX+s66uoe7sqJVMOGDfn0009RqVRA1SGC1bVdPBqe7TqI4pJC4ytYtsZXsIKCgh9G04QQQgjxAMkES1Rr7NixvP3228oKip2dHba2tmRlZRmsYjk7O/Prr7+Sl5ent4p1+vRp+vbtq5fXz8+P2NhYEhISuHnz5n21zdXVlaNHj1b5vHIfU1ZWFi+//DIBAQF07ty5VnX88MMPSjje71HVRKoqVbXd1dWVU6dO4e7u/rvbJB4sV2cvXJ29jD6Te7CEEEKIx5cc0y6q5eLigouLC7t371bS3nzzTWbNmkVeXh4A+fn5JCUlYWtrS3BwMH/5y18oKysDICkpiYKCAnx8fPTKHTp0KFFRUbi5ud132wYNGsTJkydJTU1V0o4ePUp6erpePicnJ958801WrlxZq/Kzs7OJjY3ltddeu+82/l73tn306NGsWLGCjIyK0LM7d+7w73//u97aJ4QQQggh9MkKlqjR+PHjCQ7+v9ClsLAwbt++TUhICE888QSWlpa8/vrrAEyePJmPP/6YF198EQsLCzp06MBnn32mhMRVcnBwMHqkORjuj5o5cyZeXoYrAdbW1ixfvpz58+czf/58LC0tcXNz4/333zc4ROKVV17h888/Jzs7u8o6WrZsyYULFwgODlaOaQ8PD1eOaX8Q7t6DBfDZZ58Z5Lm77e7u7kyfPp3JkydTUFCASqWiT58+D6x9QgghhBCidlTldbXJRAhhNkpKyrh583Z9N0MY0aSJrYyNGZJxMV8yNuZLxsZ8ydg8HPb2jYymS4igEEIIIYQQQtQRCREUZu/MmTNMnTpVL83KyoqNGzfWU4uMi4uLY/v27Xpp/v7+yqXEQgghhBDi8SchgkI8hg4kXKnvJoi7dAz4v1MDJWzDPMm4mC8ZG/MlY2O+ZGweDgkRFEIIIYQQQogHTCZYj6Du3bsDFceIu7m5sWbNGuXZ7NmzSUxMBODbb78lNDQUrVZLQEAAS5YsqbLMxMREfHx80Gq1aDQaNmzYoPc8MjKSYcOGKd/379/Pyy+/rFzEW1ZWRnBwMCdOnGDJkiW4ubmRmZmp5I+Pj8fNzY3vv/8eqLgHa9CgQWi1WrRaLXPnzgUgOjqa3r17U1xcDMD169fx8/PjzJkzSt5nnnkGPz8/tFotI0eONNqf7OxsnnrqKaU/MTEx3LlzRy+98pOUlKTXpkGDBvHaa6+Rk5OjlHflyhUmTZpE//79eemll3jjjTeUo9KN1e3m5sYnn3yipF2/fh0PDw9mz54NwJIlS+jdu7deO3Jzc5X88+bNo3fv3ty5c0dvjNzd3UlLS1PSgoKClJMRhRBCCCFE/ZM9WI+45s2bk5CQwMsvv4yVlZXes2nTprFo0SLc3d0pKyurckJQqXIicu3aNQIDA/Hz86NFixbk5uZy+vRpvQuGn3/+eb788ku+/PJLQkNDWbNmDV26dMHLy4v9+/fTqVMndDodkZGRAGzfvp2OHTvq1bd69WqaNWtm0A61Ws2XX35JWFiYkubm5sbmzZuBiklYnz598Pf3r7Y/zs7ObN68mdLSUiIiIti5cydPPvmkkm5MZZsWL15MXFwcc+fOpby8nAkTJhAcHKxMmtLS0rh27Rrt27c3Wo6joyN79uxh0qRJSv9dXV318lR1+fCdO3fYuXMnrVu35siRI3p3iDk4OLB8+XI+/fTTavsuhBBCCCHqh0ywHnHNmjXDy8uLpKQkvRUmqFg1sbe3ByomLff+gV+V5s2b4+zszC+//EKLFi3YsWMHffv2pUWLFuh0OsaNGwfAe++9R1hYGN26dWPt2rV6h07079+fXbt2ERkZyYULF2jUqBFPPPGESfVHRESwevVqg/7cL0tLS7p3705mZiZPPvmkSe9069ZNWRk8dOgQlpaWvPrqq8pzd3f3at+3sbHBxcWF77//nq5du7Jt2zYCAgK4fPlyjXUfPnwYV1dXNBoNOp1Ob4LVp08fjh07xvnz5+nQoYNJfREPx5nsE6Sc3EhRaYHBsye+/r9gAbVaRVlZxcqvjY01oaFheHl5P7R2CiGEEOLBkhDBx8Abb7zB559/TllZmV56REQE/v7+REVFsW7dOoqKikwqLysri6ysLJydnQHQ6XQEBQURGBiITqdT8rVs2ZKIiAhefvllxo8fT5MmTZRndnZ2tG7dmvT0dHQ6HRqNxqCeiIgIJTwuPj5eSW/dujVeXl5VrjLVVkFBAQcPHqRTp04AXLhwQS8079ixYwbv7N27l/79+wNw9uxZPDw8al2vRqNh69at/Prrr1hYWNCyZUu95/Hx8UobwsPDlXSdTkdgYCADBgwgNTWVkpIS5ZmFhQVjxoxhxYoVtW6PeLD2ndrCL9czuJZ70eBz8eIvyicnJ0f574yM8yQnJ9V304UQQghRh2QF6zHg5OSEp6cnW7Zs0UufMGECgwcPZt++fSQnJ6PT6fT2a91r69atHD9+HCsrK2bPnk2TJk24evUqmZmZ9OjRA5VKhaWlJenp6cpkZfjw4SxcuJCXXnrJoLzKFZh9+/axevVqZW9YpapCBAHGjh1LZGQkffr0qeWv8X8qJ1IqlYp+/frh6+tLdnZ2tSGCERER3Lx5E1tbW95+++37rhugd+/eLFq0iObNmxudYBoLESwuLmbPnj1ER0djZ2eHp6cn+/bto2/fvkqeoKAg4uLiyMrK+l3tE3WrV5dBFJcUGl/Bsq16BSsoKPihtVEIIYQQD55MsB4TY8eO5e233+bpp5/WS3d2diYsLIxhw4bRs2dPbty4QdOmTY2WUbkH627btm3j1q1b9OvXD4C8vDx0Op0ywbKwsEClUhktr2/fvvz1r3+lS5cu2NnZ1ao/7dq1o3Pnzmzbtq1W792tuolUVVavXk3jxo2ZMmUKS5Ys4b333qNjx458/fXXta7fysoKDw8PvvjiC3Q6HSkpKTW+s2/fPn777TcGDx4MVKy+NWjQQG+CZWlpyahRo1i5cmWt2yQeHDdHL9wcvYw+k2PahRBCiD8OCRF8TLi4uODi4sLu3buVtNTUVOWUv8zMTCwsLGjcuHGtytXpdKxatYqUlBRSUlLYtGmTXphgdWxsbJgyZYqyZ6u2xo0bxz/+8Y/7evf3sLS0ZPr06SQlJXHz5k18fHwoLi5m/fr1Sp60tDSjoYX3GjVqFFOmTNELn6yOTqdj7ty5yu+9a9cuDhw4QEGB/qrIkCFDOHjwINevX69d54QQQgghxAMlK1iPkfHjxxMc/H/hRps3b+ajjz7C2toatVrNggULUKvVJpeXnZ1NTk4O3bp1U9KcnJxo1KgR3333HZ6enjWWERgYWOWziIgILCwq5vhubm7ExsbqPe/YsSNPPvkkP/zwg8ltNkVl6GClkJAQRowYoZenZcuWBAUFsXbtWqKioli6dCnz589n5cqVNGjQgLZt2zJ9+vQa6+rYsaPB6YmV4uPj+eqrr5TvCxcuZO/evcyaNUtJs7W1pUePHnoTZ6hYHQsPD2fevHkm9VkIIYQQQjwcqvLKJQ4hxGOjpKRMwtDMlIQImicZF/MlY2O+ZGzMl4zNw2Fv38houoQICiGEEEIIIUQdkRDBP5hNmzaRkJCgl+bl5cXMmTPrqUW/z5kzZ5g6dapempWVld6dXI9j3UIIIYQQwjxJiKAQj6GTq2q+0FjUDUetTa3yS9iGeZJxMV8yNuZLxsZ8ydg8HBIiKIQQQgghhBAPmEywhBBCCCGEEKKOyASrjnXu3BmtVktQUBBvvfWWcn/R3enjxo0jNzcXqDgK/amnnkKr1SqfpKQk3nvvPdatW6dX9s6dOxkzZkyNdQcGBjJ48GD+8Y9/cOfOHb088+bNo3fv3kr62bNnefHFFyksLFTyvPnmmyQnJ3P16lXGjh3L4MGD0Wg0vPHGG0brPXPmjNL2Z555Bj8/P7RaLSNHjqyyfwB+fn4MGjSIQYMG8dprr5GTk6OU6ebmxpQpU5TvpaWl+Pj4MHbsWAASExOZPXs2AEuWLMHT05Nr164p+bt3727w27m5uXHu3DmT2hwUFKS8e+zYMYYOHYq/vz/+/v5692GZUve9aupbpcjISIYNG6aXNnfuXJYuXap8j4uL0zvWXQghhBBC1C855KKOWVtbs3nzZgAmT57MunXreP311/XSp02bxtq1axk/fjwAzs7OyrNKLVq0YMWKFbzyyitKmk6n0/vDv7q6r127xuTJk8nLy+Ott94C4M6dO+zcuZPWrVtz5MgRfHx86NixIwMGDCAuLo5Jkyaxc+dOSktLCQoKIiYmhucn6tDSAAAgAElEQVSee46IiAig4nJdY9zc3JR6o6Oj6dOnD/7+/kDFBNJY/yqtXr2aZs2asXjxYuLi4pg7dy5Qcf/T2bNnKSwsxNramv3799OqVasq+960aVP+8Y9/8O677xp9npycTI8ePdDpdLz11ls1trnSlStXmDJlCp999hkeHh5cv36dMWPG0KpVK/r06WNS3fcypW+5ubmcPn0aW1tbsrKycHJyAuCdd95RJoYAGzduVCas4v78kHOCr7//kqLSgpozG2G5u3b/TqVWq7CyakBoaBheXt73VacQQgghzJesYD1A3t7eZGZmGqR369aNS5cuVftuz549ycjI4PLlisMKbt++zYEDB+jfv79JdTdv3pw5c+awdu1aKs8xOXz4MK6urrz66qvodDolb1RUFNu3b+fHH39kwYIFxMTEAHD58mUcHByUfO7u7ibVfT+M/Sa+vr6kpqYCFZPL6i4tDgkJYdu2bdy8edPgWX5+PsePH2fevHl6/TbF2rVrGTJkCB4eHgA0a9aMd999l7///e8m1V2Vmvq2Y8cO+vbtS2BgoF6b7ezsmDRpErNnz2b27Nm89dZbNG7cuFZ9EvpS05LJuZHB1d8u3tfn4sVfavXJyckhI+M8yckyMRZCCCEeRzLBekBKS0v55ptv6NSpk156WVkZBw8exM/PT0m7cOGCXgjdsWPHUKvVDBw4kG3btgGwe/dunn32Wezs7Exug5OTE2VlZUr4WuUf8gMGDCA1NZWSkhIAbGxsmDZtGsOHDycwMJB27doBMHz4cN5//33Cw8OJi4urcVJYFWP9u9fevXsNJo8ajYatW7dSVFTEmTNn8PT0rLIOW1tbXnrpJYMj6AF27dpF7969ad++PU2bNuXUqVMmt/2nn35SJleVunTpwk8//WRS3VWpqW+Vq5X3TrAAgoKCyM3NJS8vj+DgYJPrFMb1cQ+ibdP2tGjkcF8fB4c2tfq0bduW9u07EBQkYyeEEEI8jiREsI4VFhYq4Vve3t4MHTpUL/3SpUu4uLjw/PPPK+9UFUIXGBhIbGwsERER6HQ6pdz7UVxczJ49e4iOjsbOzg5PT0/27dtH3759gYr9UI0bNyYsLEx5p3fv3uzcuZO9e/fyzTffMGTIEJKTk2nWrFmt6q4uRDAiIoKbN29ia2vL22+/rffM3d2d7OxskpOT8fX1rbGeESNGEBwczKhRo/TSdTodI0aMAComNjqdji5dutSqD/dbd1Wq69vVq1fJzMykR48eqFQqLC0tSU9PVybrFy9e5MqVK6hUKvLz82nYsGGd9uWP5sm2XjzZ1uu+35dj2oUQQghxN1nBqmOV+6A2b97MBx98gJWVlV767t27KS8vZ+3atTWW5eXlxZUrV0hLS+PkyZPKnh9TZWVloVarad68Ofv27eO3335j8ODB+Pn5cfz4cZKTk/XyW1hYoFKp9NKaNGnCoEGD+Otf/0rXrl05evRordpQk9WrV7N79246d+7MkiVLDJ77+fkRGxtbbXhgpcaNGxMUFMS//vUvJe3mzZscOnSIGTNm4Ofnx+eff862bdsw9fo3FxcXTp8+rZd26tQpXF1da6y7JlX1bdu2bdy6dYt+/frh5+dHTk6O3irWvHnzmDhxIgEBAXoHXgghhBBCiPonE6yHzMbGhhkzZvDFF19QWlpabV6VSkVAQADTpk3jhRdeoEGDBibXc/36dWbOnMnw4cNRqVTodDrmzp1LSkoKKSkp7Nq1iwMHDiinHBpz8OBB5XleXh4XLlygdevWJrfBVJaWlkyfPp2kpCSDfUxDhw4lKioKNzc3k8oaOXIk69atU37br7/+Gq1Wy+7du0lJSWHPnj04OjoaDVM0Zvjw4fznP//hxx9/BODGjRssWLDA6GmO99Zdk6r6ptPpWLVqlTJWmzZtUiZYe/bs4dq1awQHBxMZGcl///tfvXBFIYQQQghRvyREsB48+eSTuLm5kZycjLe3t7JHqVJISIgS0hYUFMSqVauYPHlyjeVWhiGWlpaiVqvRarW8/vrrFBQUsHfvXr3jvG1tbenRowe7d+9Go9EYLe/06dPMmTMHtVpNeXk5oaGhPPXUU7Xub3X9q9SyZUuCgoJYu3YtUVFRSrqDg4NB3uo0a9aMAQMGEB8fD1ScHnjv8fIDBw4kOTmZp59+usbyWrZsyV//+ldmzJhBfn4+5eXlRERE6O2hq6rumhjrW3Z2Njk5OXTr1k1Jc3JyolGjRhw5coT58+ezePFiVCoVtra2TJ06ldmzZxvs/+oS0VzC0IQQQggh6oGq3NRYKSHEI6OkpEwmWGZK9mCZJxkX8yVjY75kbMyXjM3DYW/fyGi6hAgKIYQQQgghRB2REMFHzI0bNxg5cqRBenx8PE2bNn3g9Z85c4apU6fqpVlZWbFx48YHXvejpL7H6fzfrz7wOkTNmg2zre8mCCGEEOIhkxBBIR5DZz67vzvLRN0yNsGSsA3zJONivmRszJeMjfmSsXk4JERQCCGEEEIIIR6wP1yIYOfOnenUqRPl5eWo1Wo++OADvLy8OHz4MP/4xz9YsWKFkjc6Opo+ffrg7+9PeHg4ly9fpkGDBjzxxBPMnTuXzp07A/Dbb78xZ84cTp48SXl5OV5eXnzwwQc0amR8VpudnY1Go6FDhw4UFRXRsGFDwsLCeOmll/TyRUZGcvXqVTZs2ADA/v37Wbx4MevWrUOlUlFWVkZISAgxMTE0adKEmTNnkpubS3FxMd7e3syZM6fK3+F///sfH3/8MVevXsXGxgYPDw9mzJjBtm3biI2NpVWrVhQVFfHKK68ooW5Llixhw4YNehcNr1mzhh9//JHIyEicnJwoKCigRYsWjBkzRrnEeMmSJdja2pKdnc2JEycoKSkhOzub9u3bAzB+/Hj8/f0N2hgdHc2RI0eU39HGxoZ169aRmJioHOvu7u4OVJy2uHz5chwdHfHz81Mu3y0rK2PAgAFERkbSoEGD+/7t4+Li2L59O4Depb8hISHcunXL6O/SuHFjgz4dPnyYESNGMHfuXEJDQwH48ccfCQ4OZurUqYwePRqA0tJSevXqxdChQ5kyZYrSl9DQUN577z3lBMRRo0YRGhpKQEBAlWMthBBCCCEenj/cBKvywl+AvXv38re//Y1//vOfJr27YMECunbtyqZNm4iNjeWLL74A4P3336djx47ExsYCsHjxYt5//30WL15cZVnOzs4kJSUBFRcCT5gwgfLyckJCQgDIzc3l9OnT2NrakpWVhZOTE88//zxffvklX375JaGhoaxZs4YuXbrg5eXF6NGjiYiIoH///kDFXqmqXL16lbfffpu//e1vdO/eHYDt27eTn58PgEajISYmhhs3buDv78+LL76o3H81cuRIZRJwN29vb2Vy+uOPPxIVFYW1tTU9e/ZU8sycOROomGCOGzdOGYfqTJ061ejky8HBgeXLl/Ppp58afW/16tU0a9aM/Px8YmJiiImJ4eOPPwbu77cfP34848ePB6B79+56bV+yZEmVv4sxnTp1Ytu2bcoEKzk5WZkoVtq/fz/t2rVj+/btTJ48GZVKhVqtZubMmcyYMYPExES+/vpr5a40UT++//UkyT9upKi00OhziwOGQQJqtQorqwaEhobh5eX9oJsohBBCiIfsDx0imJeXZ3SVoSbdunXj0qWKPS6ZmZmcOnWKyMhI5XlUVBSnTp3iwoULJpXn5OREdHQ0a9asUdJ27NhB3759CQwMVC6ZBXjvvf/P3r3H9Xz3jx9/fCop4lcOc1jOhySnwsTWEJJqfcLYroaaZiizy6WRHDaHbBK7sDEbFnZwmTXj86n4KoccYg5zYShKlo0ihxUdVL8/uvW+fNbnk08JbZ73263b7er1fr3fr5Prduu11/P1es1g9erVJCcn8/XXXyurGxkZGTRu3FjJV97FvF9//TU+Pj7K5ArA3d2dBg0a6OSzsbGhRYsWZGZmGtWOUvb29gQGBho9ca2Mfv36ceHCBVJSUsrNV7t2bebOncuuXbvKXGIMFev7qtK0aVPy8vK4fv06xcXFJCQk8PLLL+vk0Wq1jBkzhiZNmnDixAklvWvXrjg6OvLJJ5+wdOlS5syZU+X1E8bblazh11uXyMi+qvfn6tXfyvxcuXKF1NQUNJqtT7v6QgghhHgMnrkVrNLLePPy8sjMzGT9+vUV/kZCQoKyUnThwgXs7e0xNTVVnpuammJvb09ycjLNmzc36psODg46kwWtVktQUBD169dn8uTJTJgwASi5+NbPz4/XXnuNWbNmYW1tDZSsLPn5+eHo6MhLL73EsGHDDE4ek5OT8fHxeWidfvvtN/Ly8nQma5GRkWzbtg2AunXr6kxM/tyetWvXGtX28oSHh7Nq1SoA2rZty5IlSwAwMTHhrbfeYvXq1crKlCFWVlbY2tqSlpZG/fr19dbVmL4vj7H9Umrw4MHExsZib2+Pg4MD5ubmyrO8vDwOHjzIvHnz+OOPP9BqtTg5OSnP//Wvf9GvXz/8/Pxo0aLFQ+smHp+B7bzIvX/P8AqWleEVLC+vh/9/UAghhBB/Pc/cBOvBEMETJ04wffp0NBoNKpVKb/4H04ODgykoKODu3btGhbdVxIOHOV6/fp20tDS6d++OSqXCzMxMZ9/PG2+8wZIlS3T2DQ0fPpyXXnqJhIQE4uLi2LRpE9u2bdP5w91Y0dHR/PTTT6SmpjJ79mxq1qypPDM2FK6qDqc0FCIIJfuuVq1axa+//vpI9alI3xtSkRBBgCFDhjBlyhRSUlLw9PTUWaXavXs3vXr1wsLCAjc3N1auXEloaKgyiT969ChWVlYkJSUZXZ54PDo3caRzE0eDz+UUQSGEEOLZ80yHCDo6OnLz5k2ysrKwtrbm9u3bOs9v3bqlc2dRREQEcXFxDB06VDlAom3btpw9e5aioiIlX1FREWfPnqVt27ZG1+WXX36hTZs2AMTExHD79m0GDBiAq6srV65c0QlVMzEx0TshbNSoEa+++iqrVq1SJgb6tG3bljNnzhisi4eHB9u3b+fbb79lyZIlFQ4R/HN7HhczMzPGjh3LF198UW6+7Oxsrly5QsuWLfU+r0jfV5WGDRtiZmbGgQMHdPapQckK2qFDh3B1dWX48OHcunWLxMREAO7evcvixYtZv349WVlZ7N27t8rrJoQQQgghKu+ZnmBdvHiRwsJCrK2tadmyJRkZGVy8eBGAK1eucP78eeWkwFIqlYp3332Xn3/+mYsXL9KiRQs6duzIypUrlTwrV67EwcHB6PCt9PR0wsPDGTVqFFDyB/aaNWuIj48nPj6e77///qF/5O/bt4+CggIAMjMzuXXrFo0aNdKbd9SoUWzdupWTJ08qaTt37uT6dd3LaTt37oy3tzcbNmwwqh2lzp07x8qVK3njjTcq9F5lDB06lEOHDpGVlaX3eU5ODnPnzmXgwIH8v//3/8o8r4q+r6zJkyfz3nvv6YSXZmdnc/ToUfbs2aPUYc6cOWg0GgA+/fRT3N3dadOmDe+//z4LFy4kLy/vsdRPCCGEEEJU3DMXIli6BwtKQsMWLVqEqakppqamLF68mBkzZpCXl4eZmRkLFizQe9S6hYUFY8eOZe3atSxcuJCwsDDmz5+v7Mvq1q0bYWFh5dbj8uXL+Pj4KEeFjx49mmHDhpGens6VK1fo1q2bkrdZs2bUqVOHkydP0rVrV73fO3DgAGFhYUo433vvvUfDhg315m3QoAFLly5l0aJF3LhxAxMTE3r27ImLi0uZvOPGjWPYsGGMHz8e0N1rBCV/8ENJ2JqPjw/37t2jfv36zJo1q8zKTGU8uAcL4LvvvtN5bm5uzujRo8v0t5+fH8XFxRQVFSnHtJeq6r4H/f1ia2tbbtse3FdV6v/+7/9wdnbWCe0cMGAAixcv5ty5c+zatUsJT+3YsSMuLi588cUXTJo0Sec7rd9uIGFoQgghhBBPgaq4qjbLCCGqjYKCQplgVVOyB6t6knGpvmRsqi8Zm+pLxubJaNhQ/523z3SIoBBCCCGEEEJUpWcuRPBJOn/+PNOmTdNJMzc3LxPm9rgkJCQQERGhk2Zra6uE9VUXc+fO5fjx4zppY8aMUS7+/St62n1/dXnFDyYRFWcxpvbTroIQQgghqhkJERTib+jXJVefdhWeCZWZYEnYRvUk41J9ydhUXzI21ZeMzZMhIYJCCCGEEEII8ZjJBOsRZGZmMmXKFAYOHMiwYcMYN24cqampeHl5lcn7888/M2LECNRqNUOGDGHFihUGvxsVFYWzszM+Pj64ubkREBBQJoTu/v37ODs764ShffzxxyxevFj5/cqVKwwYMIA7d+6we/dufHx88Pb2xsPDg02bNpXbtq1bt+Ll5cUrr7yCj48Pa9euBSAkJARXV1fUajXe3t4cOnRIeWf06NEMHjwYtVqNWq1m8uTJAKxYsQIXFxfUajVubm5MmjSJCxcu6Lx36tQppX/69euHs7Oz8p309HS9dXR01L3gNSoqinnz5pUp08vLi7i4OKPqApCVlYWDgwPffvutklZe3VxdXXnllVeUtAULFhjs15CQELp27Up2draSFhYWhp2dnc5R87t27cLOzk65NgDg1KlTeHp6kp+fD5SchjhgwACdbwkhhBBCiKdL9mBVUnFxMZMmTcLHx4ePP/4YKLn/6caNG3rzT58+nWXLltGhQwcKCwtJTU0t9/seHh7MmTMHgMTERN555x02bNigXIh74MABWrZsSWxsLFOnTkWlUjFx4kTUajXDhg2jTZs2hIWF8e6772Jpacns2bPZsmULjRs3Jj8/3+CkBWDv3r2sX7+etWvX0qhRI/Lz89m6davyfNq0abi7u5OYmMicOXPYuXOn8iwiIoLOnTuX+aa/vz8BAQEAREdH4+fnx/bt26lXr56Sp3RvWlRUFKdPn1baX1mlZV68eBFfX19lMviwusTGxtK1a1e0Wi3/+Mc/jKrb+vXrddpSnubNmxMXF4daraaoqIjExMQyd5ZpNBq6d++OVqtVJqqdO3emZ8+erFu3jgkTJjB37lymTJmClZVVJXtIPMzPGSf4IWkLufdz9T5XHX/4f6OytLRgxAhfnJx6VHX1hBBCCFENyQpWJSUmJmJmZqb8AQ7QoUMHGjdurDd/VlaWci+Vqakpbdu2NbosZ2dnRo4cyX/+8x8lTavVMmbMGJo0acKJEyeAkvu5QkNDmTt3Lnv37iUnJwdvb29ycnKUC5Wh5KCN1q1bGyzv888/Z9q0acof/ebm5owcObJMPkdHR65du2Z0O0p5eHjw4osvsn379gq/Wxlt2rTBzMyMmzdvGlUXrVZLSEgI165d4+rVqt/L5OnpSUxMDACHDx/GyckJM7P//beOnJwcjh07RlhYWJlLjv/1r3+xefNmvvjiCwoLC/WuloqqE5OiJe3OJa7dvar35+rV3x76k5qagkaz9eGFCSGEEOJvQVawKik5ORkHBwej8/v5+eHu7s4LL7yAi4sLQ4cOVS4FNoaDg4MS1peXl8fBgweZN28ef/zxB1qtVrm0tm/fvmzZsoXp06fzzTffAGBtbY2rqyv9+/end+/e9OvXDy8vL0xM9M+vk5OT6dSp00PrlJCQoFyuXCo4OBgLCwsA+vTpw/Tp0/W+27FjR1JSUoxr/CM6efIkKpXK4ArTg3X5/fffyczMpEuXLgwZMoTo6GjGjh370DL8/PyU/hw6dCj+/v4G87Zs2ZL4+Hhu376NVqvF29ubhIQE5XlcXBwuLi60atUKGxsbTp8+rYxH3bp1efvtt5k7d26ZyZeoekNae5J7/57hFay6xq1geXn5VHXVhBBCCFFNyQTrCZk0aRLe3t7s378fjUaDVqtl48aNRr//4GGPu3fvplevXlhYWODm5sbKlSsJDQ3F1NQUAF9fX3Jzc3VWqcLCwjh//jyHDh1i3bp1HDx4kI8++qhSbQkPD+fjjz/m6tWrOqtqYDhE8ElRqVTK/46MjGTbtm3Url2bf//73zrPDImOjmbIkCFAyepWaGioUROsioQIAgwaNAitVsvJkyeVfWOlSlcnS+ug1Wp1Jrz79u2jfv36XLx4sdyVSPHouj3nSLfnHA0+l2PahRBCCPFnMsGqpHbt2rFjx44KvdO8eXN8fX0ZOXIkvXv35ubNm9jY2Bj17i+//KLsv9JqtRw7dgxXV1cAbt26RWJiIi+++CIAJiYmelen7OzssLOzw9vbmwEDBhicYLVt25bTp0/Tu3dvvc9L92Bt3LiR0NBQoqKijGrDn9tjzCpZeWrWrEl+fj7m5uYA3L59W6c/H9xrZWxdtFotmZmZSshgRkYGly5domXLlo9U1z/z8PBg2LBhDB06VGesSscyKSkJlUpFYWEhKpWKadOmoVKp2L17N3/88Qdr165l0qRJvPTSS1haWlZp3YQQQgghROXJHqxKcnZ2Jj8/X2cF59y5cwb37OzZs0dZhUpLS8PExIS6desaVdaRI0fYvHkzI0eOJDs7m6NHj7Jnzx7i4+OJj49nzpw5aDQag+/n5ORw+PBhnXo+//zzBvOPHz+exYsXk5lZclltfn6+3suRR40aRVFRkU54mzF27NjBgQMHHnn/0AsvvMC2bdsAyM3NJSYmhl69elW6LqmpqeTk5JCQkKD07dtvv11u31bW888/z5QpU/D19S1TH7Vaze7du4mPj2fv3r3Y2tpy9OhRcnNz+eijj3j//fexs7NjwIABfPbZZ1VeNyGEEEIIUXmyglVJKpWKTz75hIULF/LFF19Qs2ZNnn/+eUJDQ0lNTeXll19W8s6YMYOdO3fy4YcfYmFhgampKREREUpInz7R0dEcO3aM3NxcbG1tWb58OW3atOGHH37A2dlZWbUBGDBgAIsXL9ZZzXlQcXExa9asYc6cOVhYWGBpacmHH35osOy+ffty/fp13nzzTYqLi1GpVAwfPlxvH0ycOJE1a9bg4uIC6O7BsrGxITIyEvhfuN69e/do165dhUPq9Jk5cyZz5sxh48aNFBcX4+PjQ8+ePR/6nqG6fPPNNwwaNEgnr5ubG1OmTGHSpEnlfvPBPVh2dnaEh4c/tB6vv/56mTSNRsO4cePK1EGj0Sh73koPSJk0aRJqtZqhQ4eWWWFrPLmhXDAohBBCCPEUqIof3NwjhPhbKCgolAlWNWVtXUvGphqScam+ZGyqLxmb6kvG5slo2LCO3nQJERRCCCGEEEKIKiIhgk/R999/z4YNG3TSnJyceP/9959I+atWrSI2NlYnzd3dnYkTJz6R8o01YsQI8vPzddLCw8Oxs7N7SjV6uLlz53L8+HGdtDFjxugNtXwcbvz71ydSjihh6l//aVdBCCGEENWEhAgK8Td0dfGlp12FZ0pFJlgStlE9ybhUXzI21ZeMTfUlY/NkSIigEEIIIYQQQjxmMsGqpnbt2oWdnR0XL15U0lJTUxk3bhxubm4MHTqUd999l+joaNRqNWq1GkdHRwYPHoxarWbatGnlfj8sLAwXFxeKioqUtKioKOzs7Dh48GCZesTGxhIUFIRarWbQoEF0795dKffPoXClCgoKiIiIUOr72muvsXfvXgBcXV155ZVXeOWVVxg1ahRXrlxR3rO3t1e+rVar+fzzzwEYPXo0gwcP5pVXXsHd3Z158+Zx584d5T1HR0fOnz+vvPfCCy/g6uqKWq3G399fbx2LiopYsGABXl5evPLKKwwfPpxff/1V+d6DoqKilEuBV6xYgZ2dHWlpacrzyMhI7OzsOHXqlMF+37Jli9JuLy8vdu3apbTtwffS09OVY+wPHz6MnZ2dzlH5Z8+exc7OjrVr1xosSwghhBBCPHmyB6ua0mg0dO/eHa1Wy+TJk8nLy2P8+PGEhIQoFwwfPnwYGxsbfvzxR6Dkj/Rp06bRuXPncr9dVFTErl27aNKkCUeOHMHZ2Vl51r59e7RaLX369FHq0aFDBwA+/fRTpdx169axevXqcstZtmwZmZmZaDQazM3NuX79OkeOHFGelx6Pvnz5clatWsWCBQsAsLCwUNr0ZxEREXTu3Jn8/HyWLl1KYGAgX331lfLczs5OeTckJIR+/frh7u5usI7R0dFkZGSwbds2TExMuHr1qtEX95b2VWBgIACxsbG0a9fOYP6rV6/y2Wef8cMPP1CnTh1ycnLIysoyuqyYmBhGjBgB6I6LqFonMk6y5cKP5N7PNf6lnw1fuQBgaWnBiBG+ODn1eMTaCSGEEKK6kwlWNZSTk8OxY8fYsGEDEyZMYPLkyWzfvp1u3bopkyugwpfqljp8+DBt27bFw8MDrVarM8Hq0aMHR48epaCggPz8fC5fvoy9vX2Fy7h37x7fffcdcXFxyt1cDRo0wMPDo0zebt26sXHjxgp939zcnPfee49BgwZx7ty5Sk82MjMzadiwoXKHVePGjY1+d+DAgcTFxREYGMjly5epU6cONWrUMJj/xo0b1K5dm1q1agFQu3ZtateubVRZTZs2JTs7m+vXr1O/fn0SEhLo27ev0XUVxtOm7uDSnbSHZ3yQEWHuGs1WmWAJIYQQzwCZYFVDcXFxuLi40KpVK2xsbDh9+jTJyck4ODhUyfe1Wi2enp4MHDiQpUuXUlBQoEwMVCoVffr0Yf/+/fzxxx+4urqSnp5e4TLS0tJo0qQJVlZWD81beoFuqdzcXNRqtfL7+PHj9U7MTE1N6dChAykpKZWeYA0ZMgRfX1+OHj1K79698fb2pmPHjka9a2VlRZMmTUhKSiIuLg4PDw+ioqIM5u/QoQMNGjRgwIAB9O7dm0GDBulMmB9m8ODBxMbGYm9vj4ODg95LpcWj82w1mHuFuRVbwar78BUsLy+fR6yZEEIIIf4KZIJVDWm1WsaMGQOgrDJVlfz8fPbu3UtISAhWVlZ07dqV/fv3079/fyWPp6cnGzZsIDs7m+nTpz80FLCy/Pz8uHXrFrVq1eLdd99V0ssLEfyzRxu6xqcAACAASURBVD0Es3HjxsTGxnLo0CESExPx9/dn2bJl9O7dW29+lUql83vp+Ozfv5/169eXO8EyNTVlzZo1nDp1ikOHDvHhhx9y5swZ3nnnHaPKGjJkCFOmTCElJQVPT09OnDhRwdYKYzg+1xXH57pW6B05pl0IIYQQpWSCVc3cunWLxMREkpKSUKlUFBYWolKpCAoK4qeffnrk75euTHl7ewMloXw1a9bUmWB16dKFpKQkLC0tadWqVaXKadGiBb///jvZ2dkGV7HWr19P3bp1CQ4OZsWKFcyYMaNCZRQWFpKUlETr1q0rVcdS5ubm9O3bl759+9KgQQN27dpF7969qVmzJvn5+cpK0e3bt7GxsdF5t3///ixevJhOnToZtVqnUqno0qULXbp0oU+fPoSGhvLOO+9gY2Ojc2DH7du3sba21nm3YcOGmJmZceDAAWbOnCkTLCGEEEKIakhOEaxmduzYgVqtZvfu3cTHx7N3715sbW1p0aIFJ06cYM+ePUren376iaSkpAp9X6vVsmDBAuLj44mPjycuLo6DBw9y7949nXxTp05lypQplW6HpaUlw4cPJywsTLkkOCsri5iYGJ18ZmZmhIaGsnXrVm7dumX09wsKCliyZAlNmjR5pMMezpw5w7Vr14CSwz/Onz9P06ZNAXjhhRfYtm0bUBK2GBMTU2bfm6WlJcHBwUyYMOGhZV27do0zZ84ov587d65MWaUrcj/88IPePXaTJ0/mvffew9S0/JA0IYQQQgjxdMgKVjWj0WgYN26cTpqbmxtarZbPPvuMhQsXsnDhQszMzLCzs2PmzJlGf/vevXskJCQwd+5cJa1WrVp0796d3bt36+StigMU/vnPf/Lvf/8bT09PatasiaWlJZMnTy6T77nnnsPLy4uvv/6aoKCgMnuwXFxcCA4OBiA4OBhzc3Py8/Pp06cPK1eufKQ63rhxg9mzZyuTwM6dOzNq1CgAZs6cyZw5c9i4cSPFxcX4+PjQs2fPMt/w9PQ0qqz79++zaNEiMjIyqFmzJvXq1VPGYuTIkaSkpODt7Y1KpaJTp05MnTq1zDecnJyMKqv+P5vJBYNCCCGEEE+BqvhRN7EIIaqdgoJCmWBVU9bWtWRsqiEZl+pLxqb6krGpvmRsnoyGDevoTZcQQSGEEEIIIYSoIhIi+DeVkJBARESETpqtra1yWXBVCgoKKnOUe3BwMC4uLlVeVmWdP3+eadOm6aSZm5vz3XffPZbyRowYoYQdlgoPD8fOzu6xlPdnN1acfyLliLJMRzd72lUQQgghxFMkIYJC/A1dXfrL067CM+thEywJ26ieZFyqLxmb6kvGpvqSsXkyJERQCCGEEEIIIR4zmWCVIzMzkylTpjBw4ECGDRvGuHHjSE1NxcvLq0zen3/+mREjRqBWqxkyZAgrVqww+N2oqCicnZ3x8fHBzc2NgIAAjh8/rpPn/v37ODs764T5ffzxxyxevFj5/cqVKwwYMIA7d+6we/dufHx88Pb2xsPDg02bNpXbtq1bt+Ll5cUrr7yCj48Pa9euBSAkJARXV1fUajXe3t4cOnRIeWf06NEMHjwYtVqNWq1WTgRcsWIFLi4uqNVq3NzcmDRpEhcuXNB579SpU0r/9OvXD2dnZ+U7fw4vLOXq6sorr7yi5FuwYIFSRxcXF53j311dXQFIT0+nS5cu+Pj4MGTIEF599VWdy38r2/dBQUGo1WoGDRpE9+7dlTodP37cYL/os2LFCuzs7EhLS1PSIiMjsbOz49SpU0ra2bNnsbOzY9++fUra77//jqurq3Kc/e3bt3F1dTXYf0IIIYQQ4smTPVgGFBcXM2nSJHx8fPj444+BknuLbty4oTf/9OnTWbZsGR06dKCwsJDU1NRyv+/h4cGcOXMASExM5J133mHDhg20adMGgAMHDtCyZUtiY2OZOnUqKpWKiRMnolarGTZsGG3atCEsLIx3330XS0tLZs+ezZYtW2jcuDH5+fnl/tG9d+9e1q9fz9q1a2nUqBH5+fls3bpVeT5t2jTc3d1JTExkzpw57Ny5U3kWERFB586dy3zT39+fgIAAAKKjo/Hz82P79u3Uq1dPyVO63ykqKorTp08r7S/P+vXrdb5RytTUlC1btuDr61vmWfPmzZX2/Prrr0yaNIni4mKGDx8OVK7vS/euHT58mHXr1rF69WqdMg31iz7t27dHq9USGBgIQGxsLO3atdPJo9Fo6N69O1qtlpdffhmAJk2a8I9//IMlS5Ywf/58lixZwmuvvYatra1R5YpHd+LaabYkRZNbmGs407Ea5X7DyqoWw4a9jpNTjyqunRBCCCGqA1nBMiAxMREzMzP+8Y9/KGkdOnSgcePGevNnZWXRsGFDoOSP/7Zt2xpdlrOzMyNHjuQ///mPkqbVahkzZgxNmjThxIkTAFhYWBAaGsrcuXPZu3cvOTk5eHt7k5OTQ2FhIdbW1kDJ4Q2tW7c2WN7nn3/OtGnTaNSokZJ/5MiRZfI5Ojoql/BWhIeHBy+++CLbt2+v8LvG8vPzY/369dy/f7/cfM2aNSMkJISNGzfqfW5s31elgQMHEhcXB8Dly5epU6cONjY2yvPi4mJiY2P56KOPOHDgAHl5ecozf39/fv75ZyIjIzl27Bhjx46t8voJw7QpcVy68ytXczIN/1z9rdyfCxcuoNFsfXhhQgghhPhLkhUsA5KTk3FwcDA6v5+fH+7u7rzwwgu4uLgwdOhQatasafT7Dg4OSlhfXl4eBw8eZN68efzxxx9otVrlgtm+ffuyZcsWpk+fzjfffAOAtbU1rq6u9O/fn969e9OvXz+8vLwwMdE/f05OTqZTp04PrVNCQgIDBw7USQsODsbCwgKAPn36MH36dL3vduzYkZSUFOMaXw4/Pz+lHUOHDsXf3x8oWc1xcnLixx9/pH///uV+w8HBody6GNv35TG2XwCsrKxo0qQJSUlJxMXF4eHhoRPGePz4cWxtbWnevDm9evViz549DB48GIAaNWowbdo03nrrLdatW0eNGuWvloiq5dl6APfu55W/glXn4StYXl4+VVwzIYQQQlQXMsGqIpMmTcLb25v9+/ej0WjQarUGV030efAwx927d9OrVy8sLCxwc3Nj5cqVhIaGYmpqCoCvry+5ubk6q1RhYWGcP3+eQ4cOsW7dOg4ePMhHH31UqbaEh4fz8ccfc/XqVZ2VHahYKFxVMBQiCDB+/HgCAwPp169fud942EGZFel7QyraLx4eHmi1Wvbv38/69et1JlharRZPT08l348//qhMsAD27dtHw4YNSU5O5sUXXzS6TPHoHBt1wrFR+f9xQk4RFEIIIZ5tEiJoQLt27Thz5kyF3mnevDm+vr5ERkZy7tw5bt68afS7v/zyi7IHSKvVcujQIVxdXRk+fDi3bt0iMTFRyWtiYqJ3dcrOzg5/f3/WrVvHjh07DJbVtm1bTp8+bfD5tGnT2LFjB8HBwYSGhhrdBkPteVxatmyJvb09MTExj1SXivR9Venfvz/btm2jadOmWFlZKemFhYXs3LmTTz/9FFdXVxYsWEBCQgLZ2dlAyeEXBw8eZPPmzURGRpKRkVHldRNCCCGEEJUnEywDnJ2dyc/P11nBOXfuHFevXtWbf8+ePcpKSFpaGiYmJtStW9eoso4cOcLmzZsZOXIk2dnZHD16lD179hAfH098fDxz5sxBo9EYfD8nJ4fDhw/r1PP55583mH/8+PEsXryYzMxMAPLz8/VeuDtq1CiKiopISEgwqh2lduzYwYEDB/SetljVJkyYwLp16ww+T09PJzw8nFGjRul9/qh9X1mWlpYEBwczYcIEnfRDhw5hZ2fH3r17iY+PZ/fu3bi5ubFr1y6Ki4v54IMPCA0NpWnTpgQEBLBo0aIqr5sQQgghhKg8CRE0QKVS8cknn7Bw4UK++OILatasyfPPP09oaCipqanKyW4AM2bMYOfOnXz44YdYWFhgampKREREuWFl0dHRHDt2jNzcXGxtbVm+fDlt2rThhx9+wNnZGXNzcyXvgAEDWLx4Mfn5+TrppYqLi1mzZg1z5szBwsICS0tLPvzwQ4Nl9+3bl+vXr/Pmm29SXFyMSqVSTtj7cx9MnDiRNWvW4OLiAujuNbKxsSEyMhIoOWp827Zt3Lt3j3bt2pUb2lcRD+7BsrOzIzw8XOd5u3bt6NixI7/88r+LdS9fvoyPjw95eXnUrl2b0aNHM2zYMOV5VfZ9KUP9Up7SMMAHabXaMvve3Nzc+Pbbb8nLy6NJkyZKWKCvry9RUVEcOXKEF1544aHlCSGEEEKIx09V/LANKkKIv5yCgkLZ51NNyR6s6knGpfqSsam+ZGyqLxmbJ6Nhwzp60yVEUAghhBBCCCGqiIQIPkbff/89GzZs0ElzcnLi/ffffyLlr1q1itjYWJ00d3d3Jk6c+ETKN9aIESPIz8/XSQsPD8fOzu4p1ejRPe2+z/r05ydSjgCTN9o/7SoIIYQQohqREEEh/oau/fvY067CM6OiEywJ26ieZFyqLxmb6kvGpvqSsXkyJERQCCGEEEIIIR6zZz5EcNeuXQQFBREdHa3chZSamsrChQtJS0ujdu3aNG/enMGDB7N69Wqg5JS65557DgsLC70n2wEcPnyYwMBAmjVrxr1792jQoAFvvfUW/fv318mnVqtp3bo1H3/8MQCbN2/m4MGD/Pvf/wYgOzsbHx8fvvzyS27cuEFYWBj5+fnk5+fj4eHBO++8Y7Bte/fuZdmyZeTm5mJubo6zszMhISGsWLGCzZs3U69ePQoKCggMDFSOVA8JCeHIkSPUqVMyI7e0tGTTpk1ERUURHh5O48aNuXv3Ls2aNSMoKAgnJyflvX79+rF9+3bS09O5e/cuWVlZ2NraAvD+++8reR80evRoMjIylBP4WrRowfLly1mxYgVr1qwhPj6e+vXrA+Do6MiJEycAsLe3p3379ty/fx9TU1N8fHzw9/fHxMSk0n0/d+5cjh8/TkFBAenp6bRq1QqAiRMnsmfPHr39ok9UVBQzZszgyy+/pE+fPsD//p0tW7YMd3d3g+02VLfSPj5w4ABxcXGYm5uTlZXFq6++Snx8vMF/A0IIIYQQ4sl65idYGo2G7t27o9VqmTx5Mnl5eYwfP56QkBBcXV2BksmSjY0NP/74I1AyKZg2bRqdO3cu99s9evRQJmVnz54lKCgICwsLevfuDcDFixcpKiri6NGj3L17l1q1ajFixAiioqI4ePAgffr0YdmyZQwfPpxmzZrx1ltvsWzZMjp06EBhYSGpqakGy05KSmL+/PmsXr2aNm3aUFhYqHOnl7+/PwEBAVy6dIlhw4YxePBgatSoAZRcNOzu7l7mmx4eHsyZMweAxMRE3nnnHTZs2KBzie+nn36q9Nm6deuU9pcnIiJCb1/a2Niwbt063nvvvTLPLCwslPG4ceMGU6dOJTs7m8mTJwOV6/vSvXHp6elMmDBB+T6U3HNmqF/0ad++PVqtVplgaTQaOnToYFS79dWtlKmpKVu2bMHX19eoeohHd+LaWb47v5Pc+3n6M/xk+Ph+S0sLRozwxcmpx2OqnRBCCCGqm2c6RDAnJ4djx44RFhaGVqsFYPv27XTr1k2ZXAH06tWL9u0fbSO7vb09gYGBfPXVV0qaRqPB29ubl156ibi4OKDk7qkPPviAhQsXcurUKRITEwkICAAgKyuLhg0bAiV/aLdt29ZgeWvWrGHChAnK5MfU1FTvH+UtW7bE0tKSO3fuVKg9zs7OjBw5UmfSVtWGDx9OTEwMt27dKjdf/fr1mT9/Pl9//TX6thQa2/dVqUePHvz3v/+loKCAnJwcLl++jL29vVHvllc3Pz8/1q9fz/3796u8zkI/zcW9XLp9has51/X/XP3N4E9qagoazdan3QQhhBBCPEHP9AQrLi4OFxcXWrVqhY2NDadPnyY5ORkHB4fHUp6DgwMpKSnK79HR0Xh6euLp6alM8AA6dOjASy+9hL+/P7NmzVIuuPXz88Pd3Z2goCA2bdpEXp6B/6IOJCcn06lTp4fW6cyZM7Ro0UIJw4OSE/zUajVqtZqpU6ca3Z7KCg4OVspbtGiRkl6rVi2GDRtW5iRGfZo1a0ZhYSE3btwwqq6G+r48xvYLlEyU+/Tpw/79+4mLi9OZsJcy1O7y6takSROcnJx0VtfE4+XVpi8t/9/zNK7dQP9P46YGf1q1ao2Xl8/TboIQQgghnqBnOkRQq9UyZswYoCT8zdg/tCvrwdWVU6dOYWNjQ9OmTWnUqBGhoaHcunULa2trAN544w327dtHr169lHcmTZqEt7c3+/fvR6PRoNVq2bhxY6XqEhkZSVRUFJcuXWLVqlU6z4wNhauqAygNhcoBjBkzBh8fH8aOHftIZVSk7w2pSIgggKenJxs2bCA7O5vp06eXCZfU125j6jZ+/HgCAwPp16+f0XURlefYyB7HRoZXH+WYdiGEEEI86JmdYN26dYvExESSkpJQqVQUFhaiUqkICgrip59+eixl/vLLL0rInlarJTU1VVnZyM7OZufOnYwcORIoWQExMSm7wNi8eXN8fX0ZOXIkvXv35ubNm9jY2JTJ17ZtW06fPl1m30+p0j1YcXFxzJw5k127dlGzZs1Kt+dxqVu3Ll5eXnzzzTfl5vv1118xNTWlfv36XLx4sczzivR9VenSpQtJSUlYWloqB2Y8jDF1a9myJfb29sTExFRpfYUQQgghxKN7ZkMEd+zYgVqtZvfu3cTHx7N3715sbW1p0aIFJ06cYM+ePUren376iaSkpEcq79y5c6xcuZI33niDoqIiYmJi2LZtG/Hx8cTHx7Ny5Uo0Gk2539izZ4+yEpOWloaJiQl169bVmzcgIIDVq1crB2EUFRXx7bfflsk3YMAAOnXqxA8//FCh9hw5coTNmzdX+aREH39/fzZt2mRw31FWVhbvv/8+b7zxBiqVqszzquj7ypo6dSpTpkwxKm9F6jZhwgTWrVtX1dUVQgghhBCP6JldwdJoNIwbN04nzc3NDa1Wy2effcbChQtZuHAhZmZm2NnZMXPmzAqXcfToUXx8fLh37x7169dn1qxZ9O7dmyNHjtCoUSMaNWqk5O3ZsycXL14kIyOD5557Tu/3fvzxRz788EMsLCwwNTUlIiICU1NTvXk7dOhAaGgoU6dO5d69e6hUKoMhZUFBQUydOlWZLIWHh+uEDX733XdAyd6gY8eOkZubi62tLcuXL6+SFazg4GDluHIbGxsiIyN1nterV49BgwbppOfm5qJWq5Vj2tVqNW+++abyvKr7HvT3S+n+OEP69u1rdLsDAwPLrduD2rVrR8eOHfnll1/KLV8IIYQQQjxZquKq2kgjhKg2CgoK5Qb3asraupaMTTUk41J9ydhUXzI21ZeMzZPRsGEdvenPbIigEEIIIYQQQlS1ZzZEsKokJCQQERGhk2Zra6tcuPu4ff/992WOMXdyclIuza0ugoKCSE9P10kLDg7GxcXlKdXo0VXnvs9amfi0q/DMMvHt8rSrIIQQQoinSEIEhfgburbswNOuwjPrYRMsCduonmRcqi8Zm+pLxqb6krF5MiREUAghhBBCCCEes79liKC9vT3t27enuLgYU1NTZs+ejZOTE4cPH2bdunU6F76GhITQr18/3N3dGT16NBkZGdSsWZMaNWqwYMEC7O1LLhj9448/mD9/PidOnKC4uBgnJydmz55NnTr6Z66lIiMjWbJkCQcPHlTyHj58mDFjxrBgwQJGjBgBwNmzZ/Hx8WHatGmkp6dz/PhxCgoKSE9PV+5QmjhxosGLbteuXct3331HzZo1MTMzY/To0fj4+JTbJldXV2rXrq3ct9WzZ09mzZpFSEgIR44cwcrKiry8PLp27cq//vUvGjdurLy3efNmAgICALh+/TomJibUq1cPMHy63qpVq9BoNJiYmGBiYsK8efPo2rUrrq6ubNmyRXn/wXGKiopixowZfPnll/Tp0weAXbt2ERQUxLJlywz2R2m7zc3NKSgooE+fPvzzn/9UjrUv/TdSytPTk7ffflt5z8LCgvz8fPz9/XnttdcYMWIE+fn53L59m9zcXOWkv08//RRbW9sy5bu6utK4cWOd+7vUajWFhYVoNBoOHz5MYGCgzrvTp08v08bo6GjlpMb09HQGDBjArFmzGD16NADz5s2jU6dODBs2TG8/CCGEEEKIJ+tvOcGysLDgxx9/BEr2SC1dupSvvvrKqHcjIiLo3Lkz33//PeHh4Xz55ZcAzJw5k3bt2hEeHg7A8uXLmTlzJsuXLy/3e1qtls6dO7Nz506GDx+upLdv356YmBhlgqXRaJRLgUv38KSnpzNhwgSlLYZ8++23HDx4kC1btmBlZUV2djb/93//99A2Aaxfv16Z2Dxo2rRpuLu7U1xczPr16/Hz82P79u3KxMnExESp14oVK6hVq5Yy4dKn9G6xH374AXNzc7KysigoKCi3XaXat2+PVqtVJh8P9lV5Studn5/P0qVLCQwMVP4dPPhvxNB7t27dYtCgQQwdOlQ5qj4qKorTp08zZ86ch5afk5PD77//TpMmTfReftyjRw+dyf6DNBoN3bt3R6vVMnnyZCW9fv36bNiwgddee+2hR8SLx+PEtfN8dy6e3Pv5+jMc0b2w29LSghEjfHFy6vEEaieEEEKIp+1vHyKYnZ1t8DLe8nTr1o1r164BJZf6nj59msDAQOV5UFAQp0+f5vLlywa/cfnyZe7evcs///lPtFqtzrOmTZuSl5fH9evXKS4uJiEhgZdffrnC9QRYvXo1H3zwAVZWVgBYWVkxdOjQcttkLJVKhb+/Pw0aNGDfvn2Vqh9AZmYmNjY2yqSgXr16Ovc9ladHjx7897//paCggJycHC5fvqyswhnD3Nyc9957j99++41z584Z/d7du3extLQ0eNfYwwwZMoTo6GigZMLk6elp1Hs5OTkcO3aMsLCwMv9u6tWrR+/evdm6dWul6iQenebCAS7d/p2rOTf0/1z9TecnNTUFjUbGSwghhHhW/C1XsEovoc3LyyMzM5P169dX+BsJCQkMHDgQgAsXLmBvb6/zh7apqSn29vYkJyfTvHlzvd/QarV4eHjQo0cPUlNTuX79Og0aNFCeDx48mNjYWOzt7XFwcKjUikR2djY5OTk0a9asQm0q5efnp4QIDh06FH9/f73vduzYkZSUlArXr9SLL77Ip59+yuDBg+nduzceHh688MILRr2rUqno06cP+/fv548//sDV1bXMiYQPY2pqSocOHUhJSaFDhw7Kv5FS48ePx8PDAyg53dDc3Jy0tDRCQ0MrPcFyc3MjNDSUgIAAdu/eTUREBNu2bVOeHz16VKcOK1asoHnz5sTFxeHi4kKrVq2wsbHh9OnTdOrUSck3btw43nrrLZ0VUfHkeLV9kXv38wyvYNUpu4Ll5eXzBGomhBBCiOrgbznBejD868SJE0yfPh2NRoNKpdKb/8H04OBgCgoKuHv37kND8x5Gq9XyySefYGJigpubG7GxsYwaNUp5PmTIEKZMmUJKSgqenp6cOHHikcozpLw2GQoR/LNHPWyydu3aREVFcfToUQ4fPsyUKVOYOnWqwb1Dfx4rT09PNmzYQHZ2NtOnTzcYWleeB9tgTIhgVlYWr7/+Oi4uLjz//PMVLs/a2pq6deui1Wpp06YNFhYWOs8NhQhqtVrGjBkDgIeHB1qtVmeC1axZM7p27cr27dsrXCfx6Bwb2eHYyM7gczmmXQghhHi2/e1DBB0dHbl58yZZWVlYW1tz+/Ztnee3bt3CxsZG+T0iIoK4uDiGDh3K/PnzAWjbti1nz56lqKhIyVdUVMTZs2dp27at3nLPnz/PpUuXGDt2LK6urmi1WjQajU6ehg0bYmZmxoEDB+jdu3el2mdlZUWtWrX49ddfDebR16aKOnv2rHLYQmWZmprSq1cvJk+ezOzZs9m5cydQMhG5c+eOku/27ds6YwLQpUsXkpKSuHnzpnLoR0UUFhaSlJRE69atjX6nXr16dOzYkZMnT1a4vFIeHh7MmzfP6PDAW7dukZiYyKxZs3B1dWXt2rXExMSUmeCOHz+eNWvWPPLEVwghhBBCVK2//QTr4sWLFBYWYm1tTcuWLcnIyFAOHLhy5Qrnz58vs59HpVLx7rvv8vPPP3Px4kVatGhBx44dWblypZJn5cqVODg40KJFC73larVa3nnnHeLj44mPj2f//v1kZGRw5coVnXyTJ0/mvffeq3QYGsDbb7/N3Llzyc7OBkr28Px5j86f22Ss4uJiNmzYQGZm5iNdCpySksKlS5eU38+ePUvTpk0B6NWrl7KaVFhYyLZt2+jVq1eZb0ydOpUpU6ZUuOyCggKWLFlCkyZNjDoco9S9e/c4e/aswRBQYwwcOJCAgABeeuklo/Lv2LEDtVrN7t27iY+PZ+/evdja2nL06FGdfG3atKFNmzbs3r270nUTQgghhBBV728ZIvjg/pri4mIWLVqEqakppqamLF68mBkzZpCXl4eZmRkLFizQe9S6hYUFY8eOZe3atSxcuJCwsDDmz5+v7GHq1q0bYWFhBuug1Wr5/PPPddIGDRqEVqula9euSpqTk9Mjt9fX15e7d+8yfPhwatSogZmZGW+++eZD2wS6e7Ds7OyUUxLDw8NZuXIlubm5dO3alQ0bNjzSqXV3795lwYIF3LlzB1NTU1q0aMG8efMACAwM5IMPPsDb25vi4mJcXFzw9vYu842+fftWqMzSvVT5+fn06dNHZ4L85z1YLi4uBAcHK++VHtM+dOhQnfC8irKysuLtt9/W++zPe7AmTpyIRqNh3LhxOvnc3Nz0pk+cOBEfH9nbI4QQQghRnaiKJcZIiL+dgoJCucG9mrK2riVjUw3JuFRfMjbVl4xN9SVj82Q0bKj/Pty/fYigEEIIIYQQQjwpf8sQwSfp/Pnz95wZ9QAAIABJREFUTJs2TSfN3NxcuZi2Ks2dO5fjx4/rpI0ZM6ZaHdd98+ZNvUe9R0ZGljm4oioEBQWVObI9ODj4kfaLVcSIESPIz9c9rjs8PBw7O8OnzD0JNz/b+1TLf5aoXu/5tKsghBBCiGpEQgSF+BvKWBH/tKvwzKjoBEvCNqonGZfqS8am+pKxqb5kbJ4MCREUQgghhBBCiMdMQgT/Quzt7Wnfvj3FxcWYmpoye/ZsnJycOHz4MOvWrdO5tDYkJIR+/frh7u7O6NGjycjIoGbNmtSoUYMFCxYoR9P/8ccfzJ8/nxMnTlBcXIyTkxOzZ8/We7IiQHp6Oh4eHrRu3Zq8vDxq166Nr69vmQuDAwMDuX79Ops3bwbgwIEDLF++nE2bNqFSqSgsLGT48OHMmTMHa2tr3n//fe7cuUN+fj49evQo976u//73vyxatIjr169jaWmJg4MDs2bNIiYmhvDwcBo1akReXh6vv/66Eq64YsUKNm/erHOp8saNGzl79iyBgYE0a9aMe/fu0aBBA9566y369++vvFerVi3S09M5fvw4BQUFpKenK3dxTZw4EXd39zJ1DAkJISYmhgMHDmBlZQVAWFgYGzZs4NChQ9SrV08Zz1Kenp7KiYNZWVm4uLgwa9Ys/vGPfyh5XF1dcXBwYMWKFQDExsayZ88ePvroI4P9JYQQQgghnhyZYP2FWFhYKPdFJSQksHTpUr766iuj3o2IiKBz5858//33hIeH8+WXXwIwc+ZM2rVrpxzPvnz5cmbOnMny5csNfqt58+bKPVu//vorkyZNori4WNkLdufOHc6cOaNcgNysWTNefPFFtmzZwpYtWxgxYgQbN26kU6dOODk5ERAQgJ+fn3IE/vnz5w2Wff36dd59912WLl2Ko6MjUDLJyMnJAUou9p0zZw43b97E3d2dwYMH06RJEwD8/f0JCAgo880ePXook9OzZ88SFBSEhYWFzuXP77//PlAywZwwYYIyDuVp3rw5cXFxqNVqioqKSExMpFGjRsrzB8fzz2JjY+natStarVZnggVw5swZLly4YPCSayGEEEII8fTIBOsvKjs7m7p161b4vW7durF27VoA0tLSOH36NB9//LHyPCgoiEGDBnH58mWjLtht1qwZISEhLFq0SJlg7dy5k/79+9OgQQO0Wi0TJkwAYMaMGfj6+tKtWze+/vpr5SCQjIwMGjdurHyzvAMivv76a3x8fJTJFaB3BcnGxoYWLVqQmZmpTLCMYW9vT2BgIF999ZXOBKsyPD09iYmJQa1Wc/jwYZycnEhISDDqXa1WS0hICFOnTuXq1as6/fPmm2+yatUqlixZ8kj1E8Y7fjWZLWf3ce9+ftmHiV/q/GppacGIEb44OfV4QrUTQgghRHUie7D+Qkovx3V3d2fWrFkEBgZW+BsJCQnKStGFCxewt7fH1NRUeW5qaoq9vT3JyclGf9PBwYGUlBTld61Wi5eXF56enmi1WiX9ueeew8/Pj9dee42JEydibW0NlKws+fn58dZbbxEZGcmdO3cMlpWcnIyDg8ND6/Tbb7+Rl5enM1mLjIxErVajVqsZPXq00e2prJYtW5KVlcXt27fRarV4enrqPC8dz9Kf6OhoAH7//XcyMzPp0qULQ4YMUdJLDRkyhF9++YW0tLRHrqMwjiY5kdTbV7mak1X25+pvOj+pqSloNFufdpWFEEII8ZTICtZfyIMhZSdOnGD69OloNBpUKpXe/A+mBwcHU1BQwN27d40Kb6uIBw+ivH79OmlpaXTv3h2VSoWZmRlJSUnKXqM33niDJUuW6OzZGj58OC+99BIJCQnExcWxadMmtm3bhrm5eYXrEh0dzU8//URqaiqzZ8+mZs2ayjNDIYLltedRDRo0CK1Wy8mTJ5k3b57OM0MhgtHR0QwZMgQoCXkMDQ1l7NixynMTExMCAgJYvXo1L7/8cpXVVRjm1c6Z3Pv5+lew6ljo/GppaYGXl88TqpkQQgghqhuZYP1FOTo6cvPmTbKysrC2tub27ds6z2/duqVz71RERASdOnUiPDyc+fPn88knn9C2bVvOnj1LUVERJiYli5lFRUWcPXu2Qvt7fvnlF9q0aQNATEwMt2/fZsCAAUBJKKNWq1UmWCYmJnonhI0aNeLVV1/l1VdfxcvLi6SkJDp16lQmX9u2bTlz5oyyCvdnpXuwTp06RUBAAK6urjRs2NDotvy5PY/Kw8ODYcOGMXToUKWPH0ar1ZKZmcn27duBkhDKS5cu0bJlSyWPWq3m888/1zkkQzw+To3b4dS4nd5ncg+WEEIIIR4kIYJ/URcvXqSwsBBra2tatmxJRkYGFy9eBODKlSucP39eOSmwlEql4t133+Xnn3/m4sWLtGjRgo4dO7Jy5Uolz8qVK3FwcKBFixZG1SM9PZ3w8HBGjRoFlEwO1qxZQ3x8PPHx8Xz//fc6YYL67Nu3j4KCAgAyMzO5deuWzmEQDxo1ahRbt27l5MmTStrOnTu5fv26Tr7OnTvj7e3Nhg0bjGpHqXPnzrFy5UreeOONCr1nyPPPP8+UKVPw9fU1Kn9qaio5OTkkJCQoffj222+j0Wh08tWoUQM/Pz8iIyOrpJ5CCCGEEKJqyArWX0jpnh0oCWNbtGgRpqammJqasnjxYmbMmEFeXh5mZmYsWLBA71HrFhYWjB07lrVr17Jw4ULCwsKYP3++siLUrVs3wsLCyq3H5cuX8fHxUY5pHz16NMOGDSM9PZ0rV67QrVs3JW+zZs2oU6cOJ0+epGvXrnq/d+DAAcLCwpRwvvfee8/gqlODBg1YunQpixYt4saNG5iYmNCzZ09cXFzK5B03bhzDhg1j/PjxQMkerG3btinPP/30UwCOHj2Kj48P9+7do379+syaNeuRD7h40Ouvv643/cHxBHBxccHCwoJBgwbp5HNzc2PKlClMmjRJJ33EiBGsWrWqyuophBBCCCEenaq4KjecCCGqhYKCQrnBvZqytq4lY1MNybhUXzI21ZeMTfUlY/NkNGyo/95YCREUQgghhBBCiCoiIYJCr/PnzzNt2jSdNHNzc+XuqsctISGBiIgInTRbW1slrK+6mDt3LsePH9dJGzNmjHIn2NNyc/WOp1r+s0T1WtnwVCGEEEI8uyREUIi/oYxPoh+eSVSJik6wJGyjepJxqb5kbKovGZvqS8bmyZAQQSGEEEIIIYR4zCRE8DGyt7enffv2FBYW0rp1axYtWoSlpaVOuq2tLeHh4dStW5f09HQ8PDxo1aqV8o0333yTw4cP07VrV53T6Hbt2sWmTZtYs2aNwfJ37dpFUFAQ0dHRyr1OD5ZRUFBAjx49+OCDD/jtt9/0phu6uyk1NZWFCxeSlpZG7dq1ad68ObNnz+bixYsEBgZia2tLXl4e/fv3Z/r06QBERUURHh6ucwT7kiVLsLCwwMPDg9atWysnE/r6+iqXEUdFRXH69GkaNmxIbGwsgM7lxcOHD2fMmDFl6rhixQo2b95MvXr1KCwsZMqUKQwYMEAnvdTGjRs5e/aswboD7N27l2XLlpGbm4u5uTnOzs6EhITo7Z8VK1bwySefsHPnTuXI+8jISD788EO2bNlC586dcXV1pXbt2kof9+zZk1mzZgFw//59XnrpJV599VWCg4OV744ePZqcnByioqIAOHXqFOHh4WzcuFH/PwIhhBBCCPFEyQTrMbKwsODHH38EYOrUqWzatIk333xTJ3369Ol8/fXXTJw4EYDmzZsrz0o1aNCA1atX60ywtFotXl5e5Zav0Wjo3r07Wq2WyZMnK+mlZdy/fx8/Pz927dpFx44d9aa7ubmV+W5eXh7jx48nJCQEV1dXAA4fPkxWVhYAPXr0YPXq1eTm5uLj48PAgQPp3r078L+LgB+Unp5O8+bN2bp1KwC//vorkyZNori4WGcv08SJE5V+cnR0LNNP+vj7+xMQEMDFixfx9fXl0KFDOul/ZqjuSUlJzJ8/n9WrV9OmTRsKCwv5z3/+U27Z7du3R6vVEhgYCEBsbCzt2uleVrt+/XqdiV6pAwcO0LJlS2JjY5k6darO5cxZWVns3buXvn37PrT9QgghhBDiyZIQwSekR48epKWllUnv1q0b165dK/fd3r17k5qaSkZGBgB3797l4MGDyt1V+uTk5HDs2DHCwsIMXvRrZmaGo6NjmXoZSi+1fft2unXrpkyuAHr16qWsKJWysLDA3t7+oe37s2bNmhESElKlqzJt2rTBzMyMmzdvGpX/z3Vfs2YNEyZMUFYCTU1NH3p58MCBA4mLiwNK7g6rU6cONjY2RpWv1WoZM2YMTZo04cSJEzrPAgIC+Oyzz4z6jqhax6+mELp7I1N2rlN+/vnPQJ2fGTP+xfHjR592VYUQQgjxlMgE6wm4f/8++/btKzMBKSws5NChQzoTlcuXL6NWq5Wfo0ePYmpqipubGzExMQDs3r2bXr16YWVlZbDMuLg4XFxcaNWqFTY2Npw+fbpMnnv37nHo0KEy9TKUXio5ORkHB4eHtvv27dukpaXRs2dPJS06Olqnfbm5uXrfdXBwICUl5aFlGOvkyZOoVCpltSgyMlKpw+jRox9a9+TkZDp16lShMq2srGjSpAlJSUlotVo8PDzK5PHz81PqERkZCZSsEB48eBBXV1e8vLzKTJC7detGjRo1SExMrFB9xKPTJP9E6q0Mrubc/N/P1d90flJTU9Botj7tqgohhBDiKZEQwccoNzcXtVoNlKxgvfrqqzrp165do02bNrz44ovKO/pCBAE8PT0JDw/Hz88PrVarfNeQ0hUQKAnL02q1ygShdBKnUqkYMGAAffv2JT09XW96ZRw9ehRvb2/S0tLw8/Pj/7d370FRnecfwL+7S1dQaCAKGAt4iQqpShOKEwliFMJyWXApijEYYyjGGlCniY4lYI1NU2yJM2qsF6oJSMqgBlFklyoFhIQYAQktIUqgCgL+vA0BlYtclvP7w+GUlUu8LOwWv58ZZtz3vOec5+wzZ2cez3ve19raWtzW3xDB/uhrcsvExEScOHECY8aMwY4dO8ShdgMNERws9kfR890XFBTg4MGD4rtTPfobIthTQJuamkKhUGDPnj2Ijo6GTCYT+7z99tvYu3evzvtZNPQCps3G3a4OtHV2/rfRwkynj5mZKQICgoY5MiIiIjIWLLCGUO93rfprb2trQ3h4OJKTk/udpKE3FxcX3Lx5ExUVFSgtLcX27dsH7NvU1ISzZ8+isrISEokEWq0WEolEXNdqoCJuoPb7TZ06FcXFxQNu73mPqa6uDq+++ir8/Pzw3HPP/ehxezt//rw4HO9xDFRIDWSg2KdOnYry8nI4OTk91PkXLFiAjz76CDNnzhz0iWNvGo0GJSUl4pPNnnz2LsTd3Nywc+dO/Pvf/36oeOjxuIyfApfxU3TauA4WERER9cYhggZkZmaGTZs2ISEhAV1dXYP2lUgk8PPzw+9+9zvMmzcPo0aNGrDvqVOnoFKpcPr0aeTm5iI/Px92dnY4d04/74UEBgaitLQUeXl5YltxcTEqKyt1+tnb22PVqlXYv3//Qx2/vr4ecXFxeP311/UR7iO5P/bw8HDEx8ejuroaANDd3Y2UlJQfPY6ZmRk2bNiA1atXP9B5m5ubce7cOeTl5SE3Nxe5ubnYvHkz1Gp1n75vv/32oLNIEhEREdHw4xMsA/v5z38OR0dHqNVquLq6isP0evSegjwgIAAHDhzA+vXrBz2mWq3GW2+9pdOmUCj6bX8Upqam2LdvH2JjYxEbGwsTExM4OjoiJiamzyQSS5cuxSeffIL6+noA997BKikpEbe///77sLGxQW1tLYKCgsRp2pcvXy5O0z4UeoYO9ti9e3efPr1jd3JyQnR0NNavX4+2tjZIJBLMnz//gc6lVCoH3LZixQpxmnZHR0e4ublhzpw5kMvlYh8vLy989NFH6Ojo0Nn35Zdf7ncGQiIiIiIyHImgr5ddiMhodHZquYK7kbK0HM3cGCHmxXgxN8aLuTFezM3wsLa26LedQwSJiIiIiIj0hEME/4c1NjbizTff7NOemJj4wOstDeb7778XJ8boIZfL8fnnnz/2sfVp7969OHnypE6br6+vuCjxSD03ERERERkfDhEkGoFu7E778U702CRLvB96Hw7bME7Mi/FibowXc2O8mJvhwSGCREREREREQ4xDBEeg7OxsREZGIjMzU1xLqrq6GrGxsbh8+TLGjBkDBwcH+Pj4ID4+HsC9xYdtbGxgamoKR0dHxMXF9TluYWEhIiIiYG9vj7a2NowbNw4rV67EggULdPqpVCpMmTJFXKvryJEjOHPmDHbs2AHg3lTkQUFBSEhIQENDA/70pz+ho6MDHR0d8Pf3x9q1awe8tvz8fOzcuRN3796FXC7HnDlzEBUVhV27duHIkSN4+umn0dnZiYiICAQEBAAAoqKiUFRUBAuLe//LYGZmhkOHDiEtLQ1xcXEYP348WltbYW9vj8jISLi4uIj7zZ8/HxkZGaivr0drayt++OEH2NnZAbg3A2JP396WL1+Ouro6nD59WlzYOCIiAl9//TVKS0tRX18Pf39/TJ48WdwnLCwMQUH3Fqe9cOECgoKCsH//fsybN0/s4+joiLCwMERFRQEAPvnkE7S2tg76fRERERHR8GKBNQKp1Wr88pe/hEajwbp169De3o7f/OY3iIqKEhevLSwshJWVlbiw8PLly7Fx40bMmjVr0GP3LMQL3CsEIiMjYWpqCjc3NwDAxYsX0d3djXPnzqG1tRWjR49GSEgI0tLScObMGbz00kvYuXMnFi1aBHt7e6xcuRI7d+6Ek5MTtFqtuM5UfyorK/HHP/4R8fHxePbZZ6HVanH48GFxe8+iwjU1NQgODoaPjw9+8pOfAAA2btwIX1/fPsf09/fH5s2bAQBnz57F2rVrkZSUpLPIcc8U7oWFhfj000/F6x+MhYUFSkpK4Orqitu3b+PmzZs62wdb1Ll3/noXWHK5HFlZWVi1ahWnZyciIiIyUiywRpiWlhaUlJQgKSkJq1evxrp165CRkYHnn39eLK4A4MUXX3zscz333HOIiIjA3//+d7HAUqvVWLhwIS5duoScnBwEBgZCIpFgy5Yt2LBhA7Zu3YqzZ8/i6NGjAIAffvgB1tbWAACZTIapU6cOeL4DBw5g9erVYvEjk8kQGhrap9+kSZNgZmaG27dvY+zYsQ98PXPmzMGSJUtw+PBhREdHP/B+/VEqlcjMzISrqyuysrLg7e2N//znPz+6nyAIOHnyJBISEhAaGor29nZxUWkTExO8+uqrOHjwIN55553Hio8e3jdXa5B6oQhtXb3WIztzTKePmZkpQkJC4eLiOrzBERERkdHgO1gjTE5ODjw8PDB58mRYWVmhvLwcVVVVmDFjxpCcb8aMGbh06ZL4OTMzE0qlEkqlEhqNRmx3cnLC3Llz8eabb2LTpk3iQrorVqyAr68vIiMjcejQIbS3tw94rqqqKsycOfNHY/ruu+8wceJEneIqLi4OKpUKKpVq0IWa77+eR+Xm5obi4mJotVpkZmbC399fZ3vPgtI9f+fOnQMAfPPNN7Czs4ODgwNefPFF5OXl6ey3bNkyZGRk4M6dO48dIz0cdVUpqptu4lrzrf/+Xfs/nb/q6ktQq48bOlQiIiIyID7BGmE0Gg3eeOMNAPeGv/UucoZC70kov/32W1hZWWHChAmwtbVFdHQ0mpqaYGlpCeBecfDFF1/oPD1bs2YNFi5ciIKCAqjVamg0Gnz22WePFEtiYiLS0tJQU1ODvXv36mwbaIjgYNfzOKRSqTjM7+7du+J7Wz0GGiKo0WigVCoB3Mtfeno6fHx8xO3m5uZQqVRISkqCqampXmKlBxMw7QXc7erUfYJlPkanj5mZKQICgoY5MiIiIjImLLBGkKamJpw9exaVlZWQSCTQarWQSCSIjIxEcXHxkJzz/Pnz4pA9jUaD6upqcShic3MzsrKysGTJEgCARCKBVNr3oamDgwNCQ0OxZMkSuLm5obGxsd91vKZOnYry8nI4OTn1G0vPO1g5OTmIiYlBdna2OLzuUa7ncSmVSqxZswZr1qx5oP5arRZZWVnIycnBvn37IAgCmpqa0NzcDHNzc7HfihUrEBwcjODgYL3ESQ/G5ZlJcHlmkk7bo0zTTkRERCMbhwiOIKdOnYJKpcLp06eRm5uL/Px82NnZYeLEiSgtLdUZblZcXIzKysrHOl9FRQX27NmDZcuWobu7G//4xz9w4sQJ5ObmIjc3F3v27IFarR70GHl5eeJTo8uXL0MqleKnP/1pv33Dw8MRHx8vToTR3d2NlJSUPv28vLwwc+ZMHDt2rM+2wRQVFeHIkSNiQfi4XF1dsWrVKvGJ1I/5+uuv4ejoiPz8fOTm5uL06dNQKBTIzs7W6WdpaQlfX1+kpqbqJU4iIiIi0h8+wRpB1Go13nrrLZ02hUIBjUaDffv2ITY2FrGxsTAxMYGjoyNiYmIe+hznzp1DUFAQ2traMHbsWGzatAlubm4oKiqCra0tbG1txb6zZ8/GxYsXcePGDdjY2PR7vPT0dGzduhWmpqaQyWTYtm0bZDJZv32dnJwQHR2N9evXo62tDRKJBPPnz++3b2RkJNavXy8WS3FxcTrDBj///HMA994ZKykpEYfxffzxx3p7giWRSBAeHt7vtp53sHosWrQIFy5cwCuvvKLTT6FQICUlRZzCvcevf/1rJCcn6yVOIiIiItIfiaCvl06IyGh0dmq5gruRsrQczdwYIebFeDE3xou5MV7MzfCwtrbot50FFhERERERkZ5wiCD18eWXX2Lbtm06bXZ2duKCu0Pt6NGjSEpK0mlzcXHB+++/Pyznf1CRkZGor6/XaduwYQM8PDwMFBERERERGRqfYBEREREREekJZxEkIiIiIiLSExZYREREREREesICi2gE+eKLL+Dj4wNvb2/87W9/M3Q4TyRPT08EBgZCpVKJi0E3NTUhLCwMCoUCYWFhuHXrFgBAEAR8+OGH8Pb2RmBgIL777jtDhj7ivPfee3Bzc0NAQIDY9ii5OHbsGBQKBRQKxUOvr0f96y83u3btgoeHB1QqFVQqFfLz88Vt8fHx8Pb2ho+PD7788kuxnb95+nX16lUsX74c/v7+UCqVOHjwIADeN8ZgoNzwvjFSAhGNCF1dXYKXl5dQW1srtLe3C4GBgUJVVZWhw3riLFiwQGhoaNBp+8tf/iLEx8cLgiAI8fHxQlxcnCAIgpCXlyeEh4cL3d3dQmlpqbB48eJhj3ckKyoqEsrLywWlUim2PWwuGhsbBU9PT6GxsVFoamoSPD09haampuG/mBGmv9x8/PHHwoEDB/r0raqqEgIDA4X29nahtrZW8PLyErq6uvibNwSuX78ulJeXC4IgCHfu3BEUCoVQVVXF+8YIDJQb3jfGiU+wiEaIsrIyTJw4Efb29pDL5VAqlcjJyTF0WAQgJydHXCw6KCgI2dnZOu0SiQTPP/88bt++jRs3bhgy1BFl9uzZeOqpp3TaHjYXBQUFcHd3h6WlJZ566im4u7vr/E8wPZr+cjOQnJwcKJVKyOVy2NvbY+LEiSgrK+Nv3hCwsbHBjBkzAADm5uaYMmUKrl+/zvvGCAyUm4HwvjEsFlhEI8T169cxfvx48bOtre2gP740dMLDwxEcHIzDhw8DABoaGmBjYwMAsLa2RkNDA4C+ORs/fjxzNsQeNhe8r4ZXcnIyAgMD8d5774nD0AbKAXMztOrr63HhwgX84he/4H1jZHrnBuB9Y4xYYBER6VFKSgqOHTuG/fv3Izk5GcXFxTrbJRIJJBKJgaKj3pgL4/Laa6/hn//8J9LT02FjY4M///nPhg7pidXS0oJ169YhOjoa5ubmOtt43xjW/bnhfWOcWGARjRC2tra4du2a+Pn69euwtbU1YERPpp7vfOzYsfD29kZZWRnGjh0rDv27ceMGnn76abFv75xdu3aNORtiD5sL3lfDZ9y4cZDJZJBKpQgJCcG3334LYODfNuZmaHR2dmLdunUIDAyEQqEAwPvGWPSXG943xokFFtEIMWvWLNTU1KCurg4dHR3QaDTw9PQ0dFhPlNbWVjQ3N4v//uqrrzBt2jR4enri+PHjAIDjx4/Dy8sLAMR2QRDwr3/9CxYWFuIwHBoaD5uLuXPnoqCgALdu3cKtW7dQUFCAuXPnGvISRqze7x9mZ2dj2rRpAO7lRqPRoKOjA3V1daipqYGzszN/84aAIAiIiYnBlClTEBYWJrbzvjG8gXLD+8Y4mRg6ACLSDxMTE2zevBkrV66EVqvFokWLxB9aGh4NDQ2IjIwEAGi1WgQEBGDevHmYNWsWfvvb3yI1NRUTJkzAjh07AAAvv/wy8vPz4e3tDTMzM8TGxhoy/BHn3XffRVFRERobGzFv3jysXbsWq1ateqhcWFpaIiIiAosXLwYAREZGwtLS0mDXNFL0l5uioiJUVFQAAH72s5/hgw8+AABMmzYNfn5+8Pf3h0wmw+bNmyGTyQCAv3l6VlJSgvT0dEyfPh0qlQrAvVzxvjG8gXKjVqt53xghiSAIgqGDICIiIiIiGgk4RJCIiIiIiEhPWGARERERERHpCQssIiIiIiIiPWGBRUREREREpCcssIiIiIiIiPSEBRYRERH1sXTp0mE9X319PTIyMob1nEREQ4EFFhEREfVx6NChYTtXV1cXrly5ArVaPWznJCIaKlwHi4iIiPp44YUXUFpaisLCQuzatQsWFhaorKyEn58fpk+fjqSkJLS3t2P37t1wcHBAVFQU5HI5ysvL0dLSgqioKCxYsADt7e3YsmULysvLIZPJEBUVhTlz5iAtLQ1ZWVlobW1Fd3c3Ojo6cPHiRdjZ2eFXv/oVXnnlFWzcuBFtbW0AgN///vdwcXFBYWEh/vrXv8LKygqVlZWYMWMGtm3bBolEgrKyMsTGxqKZNv4kAAADHElEQVS1tRVyuRyJiYkwMzPDtm3bUFRUhI6ODixbtmzYn84R0ZPFxNABEBERkXGrqKhAZmYmLC0t4eXlhZCQEKSmpuLgwYP47LPPEBMTAwC4cuUKUlNTUVtbizfeeAMvvfQSkpOTAQAZGRm4ePEiwsPDcerUKQDA+fPnceLECVhaWqKwsBCffvop4uPjAQBtbW1ISEjAqFGjUFNTg3fffRdpaWnifhqNBjY2NnjttddQUlICZ2dnvPPOO9i+fTucnZ3R3NwMU1NTpKamwsLCAkePHkVHRweWLl0Kd3d32NvbG+CbJKInAQssIiIiGtSsWbNgY2MDAHBwcIC7uzsAYPr06SgsLBT7+fn5QSqVYtKkSbC3t8elS5dQUlKC119/HQDw7LPPYsKECaiurgYAuLu7w9LSst9zdnV14YMPPkBFRQWkUilqamrEbc7Ozhg/fjwAwMnJCVeuXIGFhQWsra3h7OwMADA3NwcAfPXVV/j+++/Fou7OnTu4fPkyCywiGjIssIiIiGhQcrlc/LdUKhU/S6VSaLVacZtEItHZ7/7P9zMzMxtwW2JiIsaNG4f09HR0d3eLhdP98chkMp0Y7icIAjZt2gQPD49BYyEi0hdOckFERER6cfLkSXR3d6O2thZ1dXWYPHkyXF1dxdkBq6urcfXqVUyZMqXPvmPGjEFLS4v4+c6dO7C2toZUKkV6evqgRRQATJ48GTdv3kRZWRkAoLm5GV1dXZg7dy5SUlLQ2dkpxtDa2qqvSyYi6oNPsIiIiEgvnnnmGSxevBgtLS34wx/+gFGjRiE0NBRbtmxBYGAgZDIZtm7dqvMEqoejoyOkUikWLlyI4OBghIaGYu3atTh+/Dg8PDwwevToQc8tl8uxfft2fPjhh7h79y5MTU2RkJCAkJAQXLlyBcHBwRAEAVZWVtizZ89QfQVERJxFkIiIiB5fVFQU5s+fD19fX0OHQkRkUBwiSEREREREpCd8gkVERERERKQnfIJFRERERESkJyywiIiIiIiI9IQFFhERERERkZ6wwCIiIiIiItITFlhERERERER68v/1hdM7shHcNQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = feature_importance_df[[\"feature\", \"importance\"]].groupby(\"feature\").mean().sort_values(by=\"importance\", ascending=False)[0:50].index\n",
    "best_features = feature_importance_df.loc[feature_importance_df.feature.isin(cols)]\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.barplot(x=\"importance\", y=\"feature\", data=best_features.sort_values(by=\"importance\", ascending=False))\n",
    "plt.title('LightGBM Features (avg over folds)')\n",
    "plt.tight_layout()\n",
    "plt.savefig('lgbm_importances-01.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-5e05747c624d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfolds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1700\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0moof_preds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0msub_preds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mfeature_importance_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mfeats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mf\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mexclude_fea\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "folds = KFold(n_splits=5, shuffle=True, random_state=1700)\n",
    "oof_preds = np.zeros(data.shape[0])\n",
    "sub_preds = np.zeros(test.shape[0])\n",
    "feature_importance_df = pd.DataFrame()\n",
    "feats = [f for f in data.columns if f not in exclude_fea]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 300 rounds.\n",
      "[100]\ttraining's auc: 0.742456\tvalid_1's auc: 0.730498\n",
      "[200]\ttraining's auc: 0.752584\tvalid_1's auc: 0.738657\n",
      "[300]\ttraining's auc: 0.767497\tvalid_1's auc: 0.750713\n",
      "[400]\ttraining's auc: 0.782488\tvalid_1's auc: 0.761905\n",
      "[500]\ttraining's auc: 0.793627\tvalid_1's auc: 0.769675\n",
      "[600]\ttraining's auc: 0.801849\tvalid_1's auc: 0.775303\n",
      "[700]\ttraining's auc: 0.808459\tvalid_1's auc: 0.779347\n",
      "[800]\ttraining's auc: 0.81397\tvalid_1's auc: 0.782101\n",
      "[900]\ttraining's auc: 0.818643\tvalid_1's auc: 0.784121\n",
      "[1000]\ttraining's auc: 0.822874\tvalid_1's auc: 0.78557\n",
      "[1100]\ttraining's auc: 0.826677\tvalid_1's auc: 0.786632\n",
      "[1200]\ttraining's auc: 0.830162\tvalid_1's auc: 0.787509\n",
      "[1300]\ttraining's auc: 0.833395\tvalid_1's auc: 0.788357\n",
      "[1400]\ttraining's auc: 0.836396\tvalid_1's auc: 0.788977\n",
      "[1500]\ttraining's auc: 0.83915\tvalid_1's auc: 0.789587\n",
      "[1600]\ttraining's auc: 0.841901\tvalid_1's auc: 0.790134\n",
      "[1700]\ttraining's auc: 0.844538\tvalid_1's auc: 0.790578\n",
      "[1800]\ttraining's auc: 0.846959\tvalid_1's auc: 0.790957\n",
      "[1900]\ttraining's auc: 0.84941\tvalid_1's auc: 0.791283\n",
      "[2000]\ttraining's auc: 0.851705\tvalid_1's auc: 0.791572\n",
      "[2100]\ttraining's auc: 0.854024\tvalid_1's auc: 0.791815\n",
      "[2200]\ttraining's auc: 0.856298\tvalid_1's auc: 0.792111\n",
      "[2300]\ttraining's auc: 0.858494\tvalid_1's auc: 0.792276\n",
      "[2400]\ttraining's auc: 0.860577\tvalid_1's auc: 0.79248\n",
      "[2500]\ttraining's auc: 0.862689\tvalid_1's auc: 0.792621\n",
      "[2600]\ttraining's auc: 0.864822\tvalid_1's auc: 0.792737\n",
      "[2700]\ttraining's auc: 0.866923\tvalid_1's auc: 0.792844\n",
      "[2800]\ttraining's auc: 0.868882\tvalid_1's auc: 0.792902\n",
      "[2900]\ttraining's auc: 0.870811\tvalid_1's auc: 0.79305\n",
      "[3000]\ttraining's auc: 0.872738\tvalid_1's auc: 0.793113\n",
      "[3100]\ttraining's auc: 0.874493\tvalid_1's auc: 0.79325\n",
      "[3200]\ttraining's auc: 0.876176\tvalid_1's auc: 0.793341\n",
      "[3300]\ttraining's auc: 0.87783\tvalid_1's auc: 0.793388\n",
      "[3400]\ttraining's auc: 0.879687\tvalid_1's auc: 0.793407\n",
      "[3500]\ttraining's auc: 0.881389\tvalid_1's auc: 0.793391\n",
      "[3600]\ttraining's auc: 0.883182\tvalid_1's auc: 0.793402\n",
      "[3700]\ttraining's auc: 0.88487\tvalid_1's auc: 0.793454\n",
      "[3800]\ttraining's auc: 0.886512\tvalid_1's auc: 0.793493\n",
      "[3900]\ttraining's auc: 0.888177\tvalid_1's auc: 0.793582\n",
      "[4000]\ttraining's auc: 0.889786\tvalid_1's auc: 0.793658\n",
      "[4100]\ttraining's auc: 0.89143\tvalid_1's auc: 0.793722\n",
      "[4200]\ttraining's auc: 0.892956\tvalid_1's auc: 0.7937\n",
      "[4300]\ttraining's auc: 0.894501\tvalid_1's auc: 0.793724\n",
      "[4400]\ttraining's auc: 0.896047\tvalid_1's auc: 0.793793\n",
      "[4500]\ttraining's auc: 0.897466\tvalid_1's auc: 0.793749\n",
      "[4600]\ttraining's auc: 0.898943\tvalid_1's auc: 0.793684\n",
      "Early stopping, best iteration is:\n",
      "[4396]\ttraining's auc: 0.895966\tvalid_1's auc: 0.793801\n",
      "Fold  1 AUC : 0.793801\n",
      "Training until validation scores don't improve for 300 rounds.\n",
      "[100]\ttraining's auc: 0.741983\tvalid_1's auc: 0.735689\n",
      "[200]\ttraining's auc: 0.750858\tvalid_1's auc: 0.742497\n",
      "[300]\ttraining's auc: 0.765652\tvalid_1's auc: 0.754548\n",
      "[400]\ttraining's auc: 0.780812\tvalid_1's auc: 0.767848\n",
      "[500]\ttraining's auc: 0.792094\tvalid_1's auc: 0.776191\n",
      "[600]\ttraining's auc: 0.800448\tvalid_1's auc: 0.7817\n",
      "[700]\ttraining's auc: 0.8071\tvalid_1's auc: 0.785485\n",
      "[800]\ttraining's auc: 0.812738\tvalid_1's auc: 0.788015\n",
      "[900]\ttraining's auc: 0.817541\tvalid_1's auc: 0.789884\n",
      "[1000]\ttraining's auc: 0.821664\tvalid_1's auc: 0.791202\n",
      "[1100]\ttraining's auc: 0.825354\tvalid_1's auc: 0.792295\n",
      "[1200]\ttraining's auc: 0.828787\tvalid_1's auc: 0.793132\n",
      "[1300]\ttraining's auc: 0.831913\tvalid_1's auc: 0.793823\n",
      "[1400]\ttraining's auc: 0.834967\tvalid_1's auc: 0.794424\n",
      "[1500]\ttraining's auc: 0.837905\tvalid_1's auc: 0.794961\n",
      "[1600]\ttraining's auc: 0.840618\tvalid_1's auc: 0.795347\n",
      "[1700]\ttraining's auc: 0.843265\tvalid_1's auc: 0.795625\n",
      "[1800]\ttraining's auc: 0.845779\tvalid_1's auc: 0.795919\n",
      "[1900]\ttraining's auc: 0.848317\tvalid_1's auc: 0.796226\n",
      "[2000]\ttraining's auc: 0.850791\tvalid_1's auc: 0.79658\n",
      "[2100]\ttraining's auc: 0.85327\tvalid_1's auc: 0.796864\n",
      "[2200]\ttraining's auc: 0.8556\tvalid_1's auc: 0.797058\n",
      "[2300]\ttraining's auc: 0.857786\tvalid_1's auc: 0.797251\n",
      "[2400]\ttraining's auc: 0.859849\tvalid_1's auc: 0.797509\n",
      "[2500]\ttraining's auc: 0.861971\tvalid_1's auc: 0.79775\n",
      "[2600]\ttraining's auc: 0.863942\tvalid_1's auc: 0.797889\n",
      "[2700]\ttraining's auc: 0.865881\tvalid_1's auc: 0.797994\n",
      "[2800]\ttraining's auc: 0.867974\tvalid_1's auc: 0.798083\n",
      "[2900]\ttraining's auc: 0.869948\tvalid_1's auc: 0.798138\n",
      "[3000]\ttraining's auc: 0.87182\tvalid_1's auc: 0.798163\n",
      "[3100]\ttraining's auc: 0.873654\tvalid_1's auc: 0.798147\n",
      "[3200]\ttraining's auc: 0.8755\tvalid_1's auc: 0.798142\n",
      "[3300]\ttraining's auc: 0.877294\tvalid_1's auc: 0.798271\n",
      "[3400]\ttraining's auc: 0.87913\tvalid_1's auc: 0.798343\n",
      "[3500]\ttraining's auc: 0.880899\tvalid_1's auc: 0.798375\n",
      "[3600]\ttraining's auc: 0.882587\tvalid_1's auc: 0.798419\n",
      "[3700]\ttraining's auc: 0.884244\tvalid_1's auc: 0.798456\n",
      "[3800]\ttraining's auc: 0.885961\tvalid_1's auc: 0.798441\n",
      "[3900]\ttraining's auc: 0.887665\tvalid_1's auc: 0.798482\n",
      "[4000]\ttraining's auc: 0.889225\tvalid_1's auc: 0.798543\n",
      "[4100]\ttraining's auc: 0.89079\tvalid_1's auc: 0.798509\n",
      "[4200]\ttraining's auc: 0.892404\tvalid_1's auc: 0.798496\n",
      "[4300]\ttraining's auc: 0.893867\tvalid_1's auc: 0.7985\n",
      "Early stopping, best iteration is:\n",
      "[4024]\ttraining's auc: 0.88958\tvalid_1's auc: 0.798558\n",
      "Fold  2 AUC : 0.798557\n",
      "Training until validation scores don't improve for 300 rounds.\n",
      "[100]\ttraining's auc: 0.740133\tvalid_1's auc: 0.73532\n",
      "[200]\ttraining's auc: 0.751594\tvalid_1's auc: 0.744216\n",
      "[300]\ttraining's auc: 0.766634\tvalid_1's auc: 0.755395\n",
      "[400]\ttraining's auc: 0.781839\tvalid_1's auc: 0.766817\n",
      "[500]\ttraining's auc: 0.793228\tvalid_1's auc: 0.774288\n",
      "[600]\ttraining's auc: 0.801708\tvalid_1's auc: 0.779146\n",
      "[700]\ttraining's auc: 0.808337\tvalid_1's auc: 0.782213\n",
      "[800]\ttraining's auc: 0.813948\tvalid_1's auc: 0.784521\n",
      "[900]\ttraining's auc: 0.818776\tvalid_1's auc: 0.786217\n",
      "[1000]\ttraining's auc: 0.822945\tvalid_1's auc: 0.787384\n",
      "[1100]\ttraining's auc: 0.826578\tvalid_1's auc: 0.788324\n",
      "[1200]\ttraining's auc: 0.830033\tvalid_1's auc: 0.789028\n",
      "[1300]\ttraining's auc: 0.833266\tvalid_1's auc: 0.789648\n",
      "[1400]\ttraining's auc: 0.836283\tvalid_1's auc: 0.790116\n",
      "[1500]\ttraining's auc: 0.839027\tvalid_1's auc: 0.79057\n",
      "[1600]\ttraining's auc: 0.841807\tvalid_1's auc: 0.790833\n",
      "[1700]\ttraining's auc: 0.844442\tvalid_1's auc: 0.791211\n",
      "[1800]\ttraining's auc: 0.847059\tvalid_1's auc: 0.791503\n",
      "[1900]\ttraining's auc: 0.849539\tvalid_1's auc: 0.791779\n",
      "[2000]\ttraining's auc: 0.852036\tvalid_1's auc: 0.792107\n",
      "[2100]\ttraining's auc: 0.854245\tvalid_1's auc: 0.792417\n",
      "[2200]\ttraining's auc: 0.85652\tvalid_1's auc: 0.792579\n",
      "[2300]\ttraining's auc: 0.858799\tvalid_1's auc: 0.792783\n",
      "[2400]\ttraining's auc: 0.860966\tvalid_1's auc: 0.792899\n",
      "[2500]\ttraining's auc: 0.863048\tvalid_1's auc: 0.793005\n",
      "[2600]\ttraining's auc: 0.865249\tvalid_1's auc: 0.793147\n",
      "[2700]\ttraining's auc: 0.867171\tvalid_1's auc: 0.793182\n",
      "[2800]\ttraining's auc: 0.869186\tvalid_1's auc: 0.79329\n",
      "[2900]\ttraining's auc: 0.871134\tvalid_1's auc: 0.793375\n",
      "[3000]\ttraining's auc: 0.872939\tvalid_1's auc: 0.793431\n",
      "[3100]\ttraining's auc: 0.874898\tvalid_1's auc: 0.793491\n",
      "[3200]\ttraining's auc: 0.876764\tvalid_1's auc: 0.793424\n",
      "[3300]\ttraining's auc: 0.878508\tvalid_1's auc: 0.793476\n",
      "[3400]\ttraining's auc: 0.88037\tvalid_1's auc: 0.793553\n",
      "[3500]\ttraining's auc: 0.882218\tvalid_1's auc: 0.79359\n",
      "[3600]\ttraining's auc: 0.883992\tvalid_1's auc: 0.793604\n",
      "[3700]\ttraining's auc: 0.885674\tvalid_1's auc: 0.793624\n",
      "[3800]\ttraining's auc: 0.887255\tvalid_1's auc: 0.793665\n",
      "[3900]\ttraining's auc: 0.888988\tvalid_1's auc: 0.79368\n",
      "[4000]\ttraining's auc: 0.890492\tvalid_1's auc: 0.793708\n",
      "[4100]\ttraining's auc: 0.892044\tvalid_1's auc: 0.793698\n",
      "[4200]\ttraining's auc: 0.893552\tvalid_1's auc: 0.793626\n",
      "[4300]\ttraining's auc: 0.895094\tvalid_1's auc: 0.7937\n",
      "[4400]\ttraining's auc: 0.896551\tvalid_1's auc: 0.79366\n",
      "[4500]\ttraining's auc: 0.89789\tvalid_1's auc: 0.793636\n",
      "Early stopping, best iteration is:\n",
      "[4289]\ttraining's auc: 0.894899\tvalid_1's auc: 0.793722\n",
      "Fold  3 AUC : 0.793727\n",
      "Training until validation scores don't improve for 300 rounds.\n",
      "[100]\ttraining's auc: 0.739804\tvalid_1's auc: 0.73254\n",
      "[200]\ttraining's auc: 0.751109\tvalid_1's auc: 0.74172\n",
      "[300]\ttraining's auc: 0.766152\tvalid_1's auc: 0.753803\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[400]\ttraining's auc: 0.781889\tvalid_1's auc: 0.766586\n",
      "[500]\ttraining's auc: 0.793294\tvalid_1's auc: 0.774712\n",
      "[600]\ttraining's auc: 0.801658\tvalid_1's auc: 0.779703\n",
      "[700]\ttraining's auc: 0.808325\tvalid_1's auc: 0.78306\n",
      "[800]\ttraining's auc: 0.813775\tvalid_1's auc: 0.785441\n",
      "[900]\ttraining's auc: 0.818448\tvalid_1's auc: 0.787259\n",
      "[1000]\ttraining's auc: 0.8225\tvalid_1's auc: 0.788523\n",
      "[1100]\ttraining's auc: 0.826136\tvalid_1's auc: 0.789334\n",
      "[1200]\ttraining's auc: 0.82957\tvalid_1's auc: 0.790172\n",
      "[1300]\ttraining's auc: 0.832552\tvalid_1's auc: 0.790875\n",
      "[1400]\ttraining's auc: 0.835523\tvalid_1's auc: 0.791283\n",
      "[1500]\ttraining's auc: 0.838473\tvalid_1's auc: 0.791689\n",
      "[1600]\ttraining's auc: 0.841202\tvalid_1's auc: 0.792049\n",
      "[1700]\ttraining's auc: 0.843738\tvalid_1's auc: 0.792379\n",
      "[1800]\ttraining's auc: 0.846382\tvalid_1's auc: 0.792674\n",
      "[1900]\ttraining's auc: 0.848936\tvalid_1's auc: 0.792949\n",
      "[2000]\ttraining's auc: 0.851345\tvalid_1's auc: 0.793263\n",
      "[2100]\ttraining's auc: 0.853715\tvalid_1's auc: 0.793613\n",
      "[2200]\ttraining's auc: 0.855973\tvalid_1's auc: 0.793856\n",
      "[2300]\ttraining's auc: 0.858277\tvalid_1's auc: 0.793963\n",
      "[2400]\ttraining's auc: 0.860353\tvalid_1's auc: 0.794109\n",
      "[2500]\ttraining's auc: 0.862462\tvalid_1's auc: 0.794256\n",
      "[2600]\ttraining's auc: 0.864456\tvalid_1's auc: 0.794338\n",
      "[2700]\ttraining's auc: 0.866527\tvalid_1's auc: 0.794429\n",
      "[2800]\ttraining's auc: 0.86847\tvalid_1's auc: 0.794461\n",
      "[2900]\ttraining's auc: 0.870538\tvalid_1's auc: 0.794501\n",
      "[3000]\ttraining's auc: 0.872421\tvalid_1's auc: 0.794526\n",
      "[3100]\ttraining's auc: 0.874229\tvalid_1's auc: 0.794601\n",
      "[3200]\ttraining's auc: 0.876018\tvalid_1's auc: 0.794669\n",
      "[3300]\ttraining's auc: 0.877728\tvalid_1's auc: 0.794658\n",
      "[3400]\ttraining's auc: 0.879613\tvalid_1's auc: 0.794706\n",
      "[3500]\ttraining's auc: 0.881571\tvalid_1's auc: 0.794715\n",
      "[3600]\ttraining's auc: 0.883355\tvalid_1's auc: 0.794778\n",
      "[3700]\ttraining's auc: 0.885083\tvalid_1's auc: 0.794761\n",
      "[3800]\ttraining's auc: 0.886902\tvalid_1's auc: 0.794766\n",
      "[3900]\ttraining's auc: 0.888592\tvalid_1's auc: 0.794781\n",
      "Early stopping, best iteration is:\n",
      "[3623]\ttraining's auc: 0.883768\tvalid_1's auc: 0.794814\n",
      "Fold  4 AUC : 0.794814\n",
      "Training until validation scores don't improve for 300 rounds.\n",
      "[100]\ttraining's auc: 0.742854\tvalid_1's auc: 0.730186\n",
      "[200]\ttraining's auc: 0.753538\tvalid_1's auc: 0.738331\n",
      "[300]\ttraining's auc: 0.76834\tvalid_1's auc: 0.749726\n",
      "[400]\ttraining's auc: 0.783167\tvalid_1's auc: 0.760374\n",
      "[500]\ttraining's auc: 0.794299\tvalid_1's auc: 0.767832\n",
      "[600]\ttraining's auc: 0.802467\tvalid_1's auc: 0.773109\n",
      "[700]\ttraining's auc: 0.808984\tvalid_1's auc: 0.776862\n",
      "[800]\ttraining's auc: 0.814455\tvalid_1's auc: 0.77957\n",
      "[900]\ttraining's auc: 0.819073\tvalid_1's auc: 0.781651\n",
      "[1000]\ttraining's auc: 0.823054\tvalid_1's auc: 0.783122\n",
      "[1100]\ttraining's auc: 0.826711\tvalid_1's auc: 0.784343\n",
      "[1200]\ttraining's auc: 0.830165\tvalid_1's auc: 0.785294\n",
      "[1300]\ttraining's auc: 0.833373\tvalid_1's auc: 0.785957\n",
      "[1400]\ttraining's auc: 0.836522\tvalid_1's auc: 0.786565\n",
      "[1500]\ttraining's auc: 0.839348\tvalid_1's auc: 0.787132\n",
      "[1600]\ttraining's auc: 0.842073\tvalid_1's auc: 0.787576\n",
      "[1700]\ttraining's auc: 0.844692\tvalid_1's auc: 0.788007\n",
      "[1800]\ttraining's auc: 0.847112\tvalid_1's auc: 0.788261\n",
      "[1900]\ttraining's auc: 0.849662\tvalid_1's auc: 0.788523\n",
      "[2000]\ttraining's auc: 0.852113\tvalid_1's auc: 0.788727\n",
      "[2100]\ttraining's auc: 0.854461\tvalid_1's auc: 0.789026\n",
      "[2200]\ttraining's auc: 0.856769\tvalid_1's auc: 0.789204\n",
      "[2300]\ttraining's auc: 0.859007\tvalid_1's auc: 0.789409\n",
      "[2400]\ttraining's auc: 0.860998\tvalid_1's auc: 0.789569\n",
      "[2500]\ttraining's auc: 0.863004\tvalid_1's auc: 0.789735\n",
      "[2600]\ttraining's auc: 0.865189\tvalid_1's auc: 0.789841\n",
      "[2700]\ttraining's auc: 0.867182\tvalid_1's auc: 0.789965\n",
      "[2800]\ttraining's auc: 0.869193\tvalid_1's auc: 0.790148\n",
      "[2900]\ttraining's auc: 0.871167\tvalid_1's auc: 0.790272\n",
      "[3000]\ttraining's auc: 0.873139\tvalid_1's auc: 0.790347\n",
      "[3100]\ttraining's auc: 0.875029\tvalid_1's auc: 0.790389\n",
      "[3200]\ttraining's auc: 0.87688\tvalid_1's auc: 0.790412\n",
      "[3300]\ttraining's auc: 0.878646\tvalid_1's auc: 0.790449\n",
      "[3400]\ttraining's auc: 0.880389\tvalid_1's auc: 0.790475\n",
      "[3500]\ttraining's auc: 0.882142\tvalid_1's auc: 0.790521\n",
      "[3600]\ttraining's auc: 0.883964\tvalid_1's auc: 0.790555\n",
      "[3700]\ttraining's auc: 0.885681\tvalid_1's auc: 0.790599\n",
      "[3800]\ttraining's auc: 0.887254\tvalid_1's auc: 0.790648\n",
      "[3900]\ttraining's auc: 0.888959\tvalid_1's auc: 0.79067\n",
      "[4000]\ttraining's auc: 0.890651\tvalid_1's auc: 0.790679\n",
      "[4100]\ttraining's auc: 0.892271\tvalid_1's auc: 0.790697\n",
      "[4200]\ttraining's auc: 0.893847\tvalid_1's auc: 0.790687\n",
      "[4300]\ttraining's auc: 0.895302\tvalid_1's auc: 0.790736\n",
      "[4400]\ttraining's auc: 0.896762\tvalid_1's auc: 0.790699\n",
      "[4500]\ttraining's auc: 0.898228\tvalid_1's auc: 0.790673\n",
      "Early stopping, best iteration is:\n",
      "[4286]\ttraining's auc: 0.895131\tvalid_1's auc: 0.79075\n",
      "Fold  5 AUC : 0.790749\n",
      "Full AUC score 0.794305\n"
     ]
    }
   ],
   "source": [
    "for n_fold, (trn_idx, val_idx) in enumerate(folds.split(data)):\n",
    "    trn_x, trn_y = data[feats].iloc[trn_idx], y.iloc[trn_idx]\n",
    "    val_x, val_y = data[feats].iloc[val_idx], y.iloc[val_idx]\n",
    "    \n",
    "    clf = LGBMClassifier(\n",
    "        n_estimators=10000,\n",
    "        learning_rate=0.01,\n",
    "        num_leaves=31,\n",
    "        colsample_bytree=0.9338690,\n",
    "        subsample=0.97291513,\n",
    "        max_depth=8,\n",
    "        reg_alpha=0.06663684,\n",
    "        reg_lambda=0.0988343,\n",
    "        min_split_gain=0.07916152,\n",
    "        min_child_weight=82.8850799,\n",
    "        random_state=1700,\n",
    "    )\n",
    "    clf.fit(trn_x, trn_y, \n",
    "            eval_set= [(trn_x, trn_y), (val_x, val_y)], \n",
    "            eval_metric='auc', verbose=100, early_stopping_rounds=300  #30\n",
    "           )\n",
    "    \n",
    "    oof_preds[val_idx] = clf.predict_proba(val_x, num_iteration=clf.best_iteration_)[:, 1]\n",
    "    sub_preds += clf.predict_proba(test[feats], num_iteration=clf.best_iteration_)[:, 1] / folds.n_splits\n",
    "    fold_importance_df = pd.DataFrame()\n",
    "    fold_importance_df[\"feature\"] = feats\n",
    "    fold_importance_df[\"importance\"] = clf.feature_importances_\n",
    "    fold_importance_df[\"fold\"] = n_fold + 1\n",
    "    feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)\n",
    "    print('Fold %2d AUC : %.6f' % (n_fold + 1, roc_auc_score(val_y, oof_preds[val_idx])))\n",
    "    del clf, trn_x, trn_y, val_x, val_y\n",
    "    gc.collect()\n",
    "    \n",
    "print('Full AUC score %.6f' % roc_auc_score(y, oof_preds)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "folds = KFold(n_splits=5, shuffle=True, random_state=1700)\n",
    "oof_preds = np.zeros(data.shape[0])\n",
    "sub_preds = np.zeros(test.shape[0])\n",
    "feature_importance_df = pd.DataFrame()\n",
    "feats = [f for f in data.columns if f not in exclude_fea]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "938"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 300 rounds.\n",
      "[100]\ttraining's auc: 0.742456\tvalid_1's auc: 0.730498\n",
      "[200]\ttraining's auc: 0.752584\tvalid_1's auc: 0.738657\n",
      "[300]\ttraining's auc: 0.767497\tvalid_1's auc: 0.750713\n",
      "[400]\ttraining's auc: 0.782488\tvalid_1's auc: 0.761905\n",
      "[500]\ttraining's auc: 0.793627\tvalid_1's auc: 0.769675\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-1d8c737f0e94>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m     clf.fit(trn_x, trn_y, \n\u001b[1;32m     19\u001b[0m             \u001b[0meval_set\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrn_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrn_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mval_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m             \u001b[0meval_metric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'auc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mearly_stopping_rounds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m  \u001b[0;31m#30\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m            )\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/lightgbm/sklearn.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, init_score, eval_set, eval_names, eval_sample_weight, eval_class_weight, eval_init_score, eval_metric, early_stopping_rounds, verbose, feature_name, categorical_feature, callbacks)\u001b[0m\n\u001b[1;32m    673\u001b[0m                                         \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeature_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    674\u001b[0m                                         \u001b[0mcategorical_feature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcategorical_feature\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 675\u001b[0;31m                                         callbacks=callbacks)\n\u001b[0m\u001b[1;32m    676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/lightgbm/sklearn.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, init_score, group, eval_set, eval_names, eval_sample_weight, eval_class_weight, eval_init_score, eval_group, eval_metric, early_stopping_rounds, verbose, feature_name, categorical_feature, callbacks)\u001b[0m\n\u001b[1;32m    467\u001b[0m                               \u001b[0mverbose_eval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeature_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    468\u001b[0m                               \u001b[0mcategorical_feature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcategorical_feature\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 469\u001b[0;31m                               callbacks=callbacks)\n\u001b[0m\u001b[1;32m    470\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    471\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mevals_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/lightgbm/engine.pyc\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(params, train_set, num_boost_round, valid_sets, valid_names, fobj, feval, init_model, feature_name, categorical_feature, early_stopping_rounds, evals_result, verbose_eval, learning_rates, keep_training_booster, callbacks)\u001b[0m\n\u001b[1;32m    199\u001b[0m                                     evaluation_result_list=None))\n\u001b[1;32m    200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m         \u001b[0mbooster\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m         \u001b[0mevaluation_result_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/lightgbm/basic.pyc\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, train_set, fobj)\u001b[0m\n\u001b[1;32m   1522\u001b[0m             _safe_call(_LIB.LGBM_BoosterUpdateOneIter(\n\u001b[1;32m   1523\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1524\u001b[0;31m                 ctypes.byref(is_finished)))\n\u001b[0m\u001b[1;32m   1525\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__is_predicted_cur_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mFalse\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__num_dataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mis_finished\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for n_fold, (trn_idx, val_idx) in enumerate(folds.split(data)):\n",
    "    trn_x, trn_y = data[feats].iloc[trn_idx], y.iloc[trn_idx]\n",
    "    val_x, val_y = data[feats].iloc[val_idx], y.iloc[val_idx]\n",
    "    \n",
    "    clf = LGBMClassifier(\n",
    "        n_estimators=10000,\n",
    "        learning_rate=0.01,\n",
    "        num_leaves=31,\n",
    "        colsample_bytree=0.9338690,\n",
    "        subsample=0.97291513,\n",
    "        max_depth=8,\n",
    "        reg_alpha=0.06663684,\n",
    "        reg_lambda=0.0988343,\n",
    "        min_split_gain=0.07916152,\n",
    "        min_child_weight=82.8850799,\n",
    "        random_state=1700,\n",
    "    )\n",
    "    clf.fit(trn_x, trn_y, \n",
    "            eval_set= [(trn_x, trn_y), (val_x, val_y)], \n",
    "            eval_metric='auc', verbose=100, early_stopping_rounds=300  #30\n",
    "           )\n",
    "    \n",
    "    oof_preds[val_idx] = clf.predict_proba(val_x, num_iteration=clf.best_iteration_)[:, 1]\n",
    "    sub_preds += clf.predict_proba(test[feats], num_iteration=clf.best_iteration_)[:, 1] / folds.n_splits\n",
    "    fold_importance_df = pd.DataFrame()\n",
    "    fold_importance_df[\"feature\"] = feats\n",
    "    fold_importance_df[\"importance\"] = clf.feature_importances_\n",
    "    fold_importance_df[\"fold\"] = n_fold + 1\n",
    "    feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)\n",
    "    print('Fold %2d AUC : %.6f' % (n_fold + 1, roc_auc_score(val_y, oof_preds[val_idx])))\n",
    "    del clf, trn_x, trn_y, val_x, val_y\n",
    "    gc.collect()\n",
    "    \n",
    "print('Full AUC score %.6f' % roc_auc_score(y, oof_preds)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = feature_importance_df[[\"feature\", \"importance\"]].groupby(\"feature\").mean().sort_values(by=\"importance\", ascending=False)[0:50].index\n",
    "best_features = feature_importance_df.loc[feature_importance_df.feature.isin(cols)]\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.barplot(x=\"importance\", y=\"feature\", data=best_features.sort_values(by=\"importance\", ascending=False))\n",
    "plt.title('LightGBM Features (avg over folds)')\n",
    "plt.tight_layout()\n",
    "#plt.savefig('lgbm_importances-01.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}