analyze_face_detection_ensemble.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "import torch\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import patches\n",
    "import matplotlib.lines as mlines\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "\n",
    "from source.constants import RESULTS_PATH, PLOTS_PATH\n",
    "from source.data.face_detection import get_fair_face, get_utk\n",
    "from source.utils.metrics import accuracy, aod, eod, spd, get_prs, get_tprs, get_fprs\n",
    "\n",
    "os.makedirs(PLOTS_PATH, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "method_seeds = [42, 142, 242, 342, 442]\n",
    "dseed = 42\n",
    "\n",
    "model = [\"resnet18\", \"resnet34\", \"resnet50\", \"regnet\", \"efficientnet\", \"efficientnet_mcdropout\"][2]\n",
    "\n",
    "targets = [\"age\", \"gender\", \"race (old)\", \"race\"]\n",
    "# predicting race does not give high unfairness (with either pa) for eod and aod\n",
    "# predicting gender also not too nice (only unfairness with age)\n",
    "target = 0 # 0, 1, 2, 3\n",
    "pa = 1 # 0, 1, 2, 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/system/user/publicwork/kschweig/disparate-benefits/source/data/face_detection.py:117: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['gender'].replace(gender_dict, inplace=True)\n",
      "/system/user/publicwork/kschweig/disparate-benefits/source/data/face_detection.py:117: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df['gender'].replace(gender_dict, inplace=True)\n",
      "/system/user/publicwork/kschweig/disparate-benefits/source/data/face_detection.py:118: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['age'].replace(age_dict, inplace=True)\n",
      "/system/user/publicwork/kschweig/disparate-benefits/source/data/face_detection.py:118: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df['age'].replace(age_dict, inplace=True)\n",
      "/system/user/publicwork/kschweig/disparate-benefits/source/data/face_detection.py:119: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['race'].replace(race_dict_ff, inplace=True)\n",
      "/system/user/publicwork/kschweig/disparate-benefits/source/data/face_detection.py:119: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df['race'].replace(race_dict_ff, inplace=True)\n",
      "/system/user/publicwork/kschweig/disparate-benefits/source/data/face_detection.py:117: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['gender'].replace(gender_dict, inplace=True)\n",
      "/system/user/publicwork/kschweig/disparate-benefits/source/data/face_detection.py:117: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df['gender'].replace(gender_dict, inplace=True)\n",
      "/system/user/publicwork/kschweig/disparate-benefits/source/data/face_detection.py:118: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['age'].replace(age_dict, inplace=True)\n",
      "/system/user/publicwork/kschweig/disparate-benefits/source/data/face_detection.py:118: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df['age'].replace(age_dict, inplace=True)\n",
      "/system/user/publicwork/kschweig/disparate-benefits/source/data/face_detection.py:119: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['race'].replace(race_dict_ff, inplace=True)\n",
      "/system/user/publicwork/kschweig/disparate-benefits/source/data/face_detection.py:119: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df['race'].replace(race_dict_ff, inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10843 10843 10954 23705\n",
      "52.875661849975586 52.27167010307312\n",
      "44.896841049194336 55.19510507583618\n"
     ]
    }
   ],
   "source": [
    "# no need to define targets and protected attributes, are queried directly afterwards\n",
    "ff_train_ds, ff_test_ds = get_fair_face(binarize=True, augment=False)\n",
    "utk_test_ds = get_utk(binarize=True)\n",
    "\n",
    "run_path = os.path.join(RESULTS_PATH, f\"fairface_target{target}_{model}_mseed{method_seeds[0]}_dseed{dseed}\")\n",
    "fair_inds = torch.load(os.path.join(run_path, \"fair_inds.pt\"))\n",
    "val_inds = torch.load(os.path.join(run_path, \"val_inds.pt\"))\n",
    "\n",
    "print(len(fair_inds), len(val_inds), len(ff_test_ds), len(utk_test_ds))\n",
    "\n",
    "# get targets and protected attributes\n",
    "y_fair_t = ff_train_ds.targets[target, fair_inds]\n",
    "a_fair_t = ff_train_ds.targets[pa, fair_inds]\n",
    "y_val_t = ff_train_ds.targets[target, val_inds]\n",
    "a_val_t = ff_train_ds.targets[pa, val_inds]\n",
    "y_ff_test_t = ff_test_ds.targets[target]\n",
    "a_ff_test_t = ff_test_ds.targets[pa]\n",
    "y_utk_test_t = utk_test_ds.targets[target]\n",
    "a_utk_test_t = utk_test_ds.targets[pa]\n",
    "\n",
    "# get fraction of protected attribute for testsets\n",
    "p_a_ff_test = a_ff_test_t.float().mean().item() * 100\n",
    "p_a_utk_test = a_utk_test_t.float().mean().item() * 100\n",
    "print(p_a_ff_test, p_a_utk_test)\n",
    "\n",
    "# get fraction of targets for testsets\n",
    "p_y_ff_test = y_ff_test_t.float().mean().item() * 100\n",
    "p_y_utk_test = y_utk_test_t.float().mean().item() * 100\n",
    "print(p_y_ff_test, p_y_utk_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load probits\n",
    "fair_probits, val_probits, ff_test_probits, utk_test_probits = list(), list(), list(), list()\n",
    "for mseed in method_seeds:\n",
    "    path = os.path.join(RESULTS_PATH, f\"fairface_target{target}_{model}_mseed{mseed}_dseed{dseed}\")\n",
    "\n",
    "    fair_probits.append(torch.load(os.path.join(path, f\"fair_probits_t{target}.pt\")))\n",
    "    val_probits.append(torch.load(os.path.join(path, f\"val_probits_t{target}.pt\")))\n",
    "    ff_test_probits.append(torch.load(os.path.join(path, f\"ff_test_probits_t{target}.pt\")))\n",
    "    utk_test_probits.append(torch.load(os.path.join(path, f\"utk_test_probits_t{target}.pt\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate accuracies and fairness measures\n",
    "fair_accs, val_accs, ff_test_accs, utk_test_accs = list(), list(), list(), list()\n",
    "fair_spds, val_spds, ff_test_spds, utk_test_spds = list(), list(), list(), list()\n",
    "fair_eods, val_eods, ff_test_eods, utk_test_eods = list(), list(), list(), list()\n",
    "fair_aods, val_aods, ff_test_aods, utk_test_aods = list(), list(), list(), list()\n",
    "\n",
    "for m in range(len(method_seeds)):\n",
    "    fair_accs.append([accuracy(p.argmax(dim=1), y_fair_t) for p in fair_probits[m]])\n",
    "    val_accs.append([accuracy(p.argmax(dim=1), y_val_t) for p in val_probits[m]])\n",
    "    ff_test_accs.append([accuracy(p.argmax(dim=1), y_ff_test_t) for p in ff_test_probits[m]])\n",
    "    utk_test_accs.append([accuracy(p.argmax(dim=1), y_utk_test_t) for p in utk_test_probits[m]])\n",
    "\n",
    "    fair_spds.append([spd(p.argmax(dim=1), a_fair_t) for p in fair_probits[m]])\n",
    "    val_spds.append([spd(p.argmax(dim=1), a_val_t) for p in val_probits[m]])\n",
    "    ff_test_spds.append([spd(p.argmax(dim=1), a_ff_test_t) for p in ff_test_probits[m]])\n",
    "    utk_test_spds.append([spd(p.argmax(dim=1), a_utk_test_t) for p in utk_test_probits[m]])\n",
    "    \n",
    "    fair_eods.append([eod(p.argmax(dim=1), y_fair_t, a_fair_t) for p in fair_probits[m]])\n",
    "    val_eods.append([eod(p.argmax(dim=1), y_val_t, a_val_t) for p in val_probits[m]])\n",
    "    ff_test_eods.append([eod(p.argmax(dim=1), y_ff_test_t, a_ff_test_t) for p in ff_test_probits[m]])\n",
    "    utk_test_eods.append([eod(p.argmax(dim=1), y_utk_test_t, a_utk_test_t) for p in utk_test_probits[m]])\n",
    "\n",
    "    fair_aods.append([aod(p.argmax(dim=1), y_fair_t, a_fair_t) for p in fair_probits[m]])\n",
    "    val_aods.append([aod(p.argmax(dim=1), y_val_t, a_val_t) for p in val_probits[m]])\n",
    "    ff_test_aods.append([aod(p.argmax(dim=1), y_ff_test_t, a_ff_test_t) for p in ff_test_probits[m]])\n",
    "    utk_test_aods.append([aod(p.argmax(dim=1), y_utk_test_t, a_utk_test_t) for p in utk_test_probits[m]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "ff_test_m_accs, utk_test_m_accs = list(), list()\n",
    "ff_test_m_spds, utk_test_m_spds = list(), list()\n",
    "ff_test_m_eods, utk_test_m_eods = list(), list()\n",
    "ff_test_m_aods, utk_test_m_aods = list(), list()\n",
    "\n",
    "for m in range(len(method_seeds)):\n",
    "    ff_test_m_accs.append(accuracy(torch.mean(ff_test_probits[m], dim=0).argmax(dim=1), y_ff_test_t))\n",
    "    utk_test_m_accs.append(accuracy(torch.mean(utk_test_probits[m], dim=0).argmax(dim=1), y_utk_test_t))\n",
    "\n",
    "    ff_test_m_spds.append(spd(torch.mean(ff_test_probits[m], dim=0).argmax(dim=1), a_ff_test_t))\n",
    "    utk_test_m_spds.append(spd(torch.mean(utk_test_probits[m], dim=0).argmax(dim=1), a_utk_test_t))\n",
    "    \n",
    "    ff_test_m_eods.append(eod(torch.mean(ff_test_probits[m], dim=0).argmax(dim=1), y_ff_test_t, a_ff_test_t))\n",
    "    utk_test_m_eods.append(eod(torch.mean(utk_test_probits[m], dim=0).argmax(dim=1), y_utk_test_t, a_utk_test_t))\n",
    "\n",
    "    ff_test_m_aods.append(aod(torch.mean(ff_test_probits[m], dim=0).argmax(dim=1), y_ff_test_t, a_ff_test_t))\n",
    "    utk_test_m_aods.append(aod(torch.mean(utk_test_probits[m], dim=0).argmax(dim=1), y_utk_test_t, a_utk_test_t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fPY6lnmCAXSAYYPfKblLkexd1NwCgd9tuD2g6mkcDTUf36l222yu2ewKglHJ/F4RjjnTGIp40KS/1ie0ep+QEvUcSyO1tuxVuplLXGtt1LnWtuT3d6+5u250U2z0BUN0/a2HcIg6oCUIsaWLajkRjxgG5Nte26QrDWlnIpsAxe5XM9TZ8mrspyXMq9UxukqI7/lHKyavXd5IdYKbRghE0n+tkUek4dsvHYHjx1M/JjbV2vI1uGFA/5Ug/9hEhGo2yePFi6uvrWbVqFW1tbdx99910dHRw//33D3ru8uXLefHFF7n11luZNWsW7777LqtWraKzs5Pvfe97uXFbtmzh2muv5YILLuAHP/gBHo+HTZs2kU6nc2Msy+KGG27ANE3uvfdeLMvivvvuY8mSJTzxxBNjkhlo2Q7ReJbyEh+2LYuhscbdsLjBAGWbKDMNZqZAKUU3Ws7x32O7fTl7LrZ7/NPbdiulUPGDYFto4Zpc5oITbcHp2ItWEsGonp47N/vB7/BPmQE1VWPy7MLYYTuKjkSGSJkPZQ89XhhdVLczqdt2Z1NgZbsEvXvZbjeGS79ArqaD0RPIFf2l8Y3rPFI9ttu2UIl2QKGHa3Pj7IM7UYl29Ip69LIad2w6Tubj1ymZcyZIL4FxizigJgiZrmhMJOST1rBHENVdSmGbbieLbBrMtDvp2TbubKejGR53Y6LL5DZclFKo6AGUGUOVnpg77iQ7cdp2oflDGDXH5Y7brZ+irCxGRQOav9S9RiaBE20GTwCjor7nGtEDKCuLHqpC87k6asrKuhOZx4deWpF/3XQcvWoaejDsnh9vw9q5Ec1finfGWT1jm7ehEu14pp7a44ByLJzO5tx9utF0j7vpPUp58skniUajrF27lsrKSgAMw2Dp0qV861vfYsaMGQXPsyyL559/nhtuuIHrr78egLPPPpu9e/fy3HPP5Tmgli9fzgUXXMD3v//93LFzzz0373rr1q1jy5YtPPvss8yaNQuA2tparrnmGl599VXmz59/WN+7WOIpCR6MBd0bFmWbrrC36ep2qO4W2HTr5Hm69DrcTYqUMBePsi3XxqoMhE/OHXc69uPEW9HLatDL69yxysHe/xFoGkbtjB7nT6INlexEC4bRQz2OHrttNwB6+SR3fsW18yodR/OXoAXKuq6rsPd9CI6JMXlObqzdugP7wDb0yGQ89e68omka5q53wbHxnnAu+Erca5gpnOgB9L4/e93jZi8LxyTJlEUma+M1xHofKXIZqTnbne1lu3saMmiaAYbXFfT2dK+7xXYXi8qmceIt4DEgPCt33G75GJVJoldORS8p7xqbxD64C83jz1uLOx37UWYKvay6xx5bWZzoATTdgx6Z1DM22QFWFi0Q7lkzZ1PYrZ+AZuCZ3JQba+1+D6ezGaN+DkZFg3vQTGPt3AgeH75eDiiVirr3K4lAWdfB7vnCtg7PN0sYFcQBNUGwHUVnVzRGGB1Ub0eTZUI22fW1nb9hMTxdXSxEb2m4uM6fNkDLbUw0TcPavxXsDPbkqeRCFtkUTsc+tFBln0lvn+soClX3ckAlsZu3o5WU5zmg7JZPUalOtGmn9zig0jGsXe+6G55eTiUn1upGUspqoMsBhW506bnkb0K6J1t61f5rvhKMyU1onvy/UaPhRDQrOdJv2bhnw4YNnHPOOTnnE8All1zCsmXLWL9+/YAOKKUUtm1TVlaWdzwcDuelTm/fvp133nmHf/zHfxz0OdavX09TU1PO+QQwd+5cGhoaWL9+/Zg5oDIZi0TKJFwipRyjweAbFqun1XWu62cg56QQikdlkjjJdjRfSY/j3rGx92xCGRqqcXZurJPqdG231w9ddh6lcA7uBMCoOb7nuvE27JZP0Kum5jug9m0BpVx73PXzcqIHsJu3oVc25DmVnI694NgYtTNyY9E0NzOizyZEL63oV1anl1bA5Nm5+aQb36yz8XptZBtzbJLJWsSSJtXlAQn8jgKDVhPk5Ck013Yb0rF5pDipKCrZiV5WhdbtdE/HsPdugZIyOK5nzeTEDqKSHXlZRsrM4BzcieYvzQ8Gd+x1M0q9foxuB5SZxt67Gc0byHdAte7AiR7AaDgRw9flVHJsnLY94PFBLwcUaO7PvrftNrxowXJX67YXemQyWkkk5ywDwOPHf+ICdJ+UT49nZBU2gUimLDKmg1cXp8ehoJRyHQqO1TPpmSmwzMIbFq9PJr0RoDJJVCqKVtKrTC3ZgbXrPbRAWc4BBV1Rbix3Y9g152iBEMakE9xNTC/0SL0bSfH2yq31BtArGvplH+mlFSivH3pfQ/eglVb0dxRVNKDKavI2IVoghHfWuWDkT3qevMmya6zHh1E1rf9xw4vmHL2/P9u3b+fKK6/MO+bz+Zg2bRrbt28f8Dyv18uXv/xlfvrTnzJ37lxmzpzJe++9x1NPPcV1112XG7dx40YAYrEYf/VXf8VHH33EpEmT+Nu//dtc5lT3cxRyds2cOXPQ5xhtbEcRTWSJhHy5RmfCyMjfsJgoM9NVQtcdKFBuJFw2LCNGKYVKx1DpmLu478rmdTr2uo6iioacA0rz+tHDtRiBoPv970Ivq0HzBtC6HfnuaHfzonDLYbqPBsPuNYORvGdwyymUq6fVjdfvziee/DnBqD0e0Nxstu5nKJ/c5bzqY7unf6bfO2uBstwGKu+4x4emZwp/o4SjHqWgPZ6hqlzqeA6VftUEZhqyhaoJusS9vYZUEgwT5dioVAysNHp5j/PHbv4IFW8DbQ5GpeuA0oJh9LJq9FB5XmDMqJqKCvdZB3u7Mp+M/DWzHqpCeQNovl5jdcN1XvWxu5q/1LXdvedjr78raJA/1qibhVE3M++45vXjnfHZfu/cO2iRG6tpru3WsoW+TcI4QRxQE4h01iKeMqkq80s0pkiUclBmBpXN9tqwpHPOJ3cxrHWV0HVFx0XXY1go5UAmiTLT6GXVuePW3g9Qifa8iIcWCLtRjBJ30uvOIDMmzcLwaBglJRBNdo0NYQRC/e7XW6OjGz1Yht5wYv+xk2b1H1tSjt54Zv/jkcn9jmm6Af6SgV5dwNWACofD/Y6Hw2E6OzsHPXf58uXcfvvtXHXVVblj119/Pd/+9rdzX7e2tgLw3e9+l//yX/4Lp512Gi+//DIrVqygvLycL37xi7nn6JtN1f0cgzmgvvCFLwz42W9/+1sM49AdGImUScZ08EjwoGiUY7u2O5PJ02tSygbLAg13g6J7ZMMyQpRjozIJN9OoVwTZ+vQtsC23FLnLMaOVlLuO+z422TPtNAyPhu71Q8q13XqoEkKVeeM0XceoK2CPw7V50XZwNxCeaaf1G2tE6jEi9f2PVx/X75gbKff2Oy4IwyGdcbtQl/qlC3WxdGs0Odlsr/LnjFQTHEaUbaHSMdfR0u0syqawPvkj6AbecG1uPtRLq1Canue417x+PNM/g+HJL13s7bjKjfWVFLTdhdbimr+0sO2um0nflZRmeLuCB32O9wk6C0cn4oCaQCgFHbEMlWXyx1kMysqSPdCC1daBne29YfG6mh/eEpn0hkm3swnd6Mk2yiQxt70Ghgfv7Aty31O9tAJHOW42QheaL1AwiiEcffR2MA7E/fffzyuvvMKdd95JY2MjmzZtYtWqVYTDYW655RYAHMdNo77yyiu58cYbAVcraufOnTz00EM5BxRQ8H7FPMdok87aJNImFSEJHhSDk0mQbW7Gao9iW25KZP6GRWz3cFGO3aOf1BVZdjr3Y+/5AK20IueU1zQNvbTSzVJQKtcoSi+ryYm8CsKxgmk6RBMmZUHpQl0MTrKDTCqF1RHDsbqcTVJNcEgo23SdTSUVuXnPbv4Ip203RvVxPYFWf4lr3/2lblaZx3VAuWVzx43JswvCQIgDaoKRzlikMzZ+r0R6B0M5Nna8BcdruaUAhojLdtOdbtv9/VC2CZbpOpV6RR6c+EFwHLRQZW7RYO/70J30ahrdFFlwM4Q8Pre23Dbdem7AqJ3hptcKRzXhcJhoNNrveCwWG1D/CWDr1q088sgjrF69OpeFNG/ePDRN49577+Xaa6+lqqqK8nI3M+Pss8/OO//ss89mw4YNmKaJ1+sd9DkKZWh189JLLw36fodDt8lxFJ3xLBUSPBgSZWVwYi04QQPNH0SXuS5HX2eqsrI5m9vtVFK2hUp2AORnpH7yJ1QqimfaaWhdGUdaIOxqa/QpRy4UwRaEY5VYIotZEZRGEkOgMgnsWAuUh9D8peheWXN30892m2k3G8wbyK2vlZlGpeNgeHsEwB0bc8t6UApv0/nQJT2hBcOuDEWvig1N013JCEGYAIgDaoKRydrEUiYlgYC09R4ApRRO7CB6JoYerkOz02hHQe60m3IbRwv4gC4hwS4RWJSDMeWUHkfRwV047bvRy+tygq9KKczNL4Pj4G2an9NFctp2u8KuFfV4Gk7K3c/a+S44ljuhdZWhaYEytzNQLxFXTdPxNs0XB98xyowZM/qVuGWzWXbu3NlPG6o327ZtA2DOnDl5x+fMmYNlWezZs4eqqqpBnVi63uNYnjFjBps3by54n89//vNFv89okUxLR6WhUI6FE2tFN9PolZPQrJSr6zTBUVYWlYqil5WTs93pOHbLx2D48NT3CHhbezejEh0YdTNypWlOKor18R/RPD53E9KFvXezK+xaPwejcop70Exj7XgHDC++ORfkxmqBkLvB6SXsqgVCeGcvENstCIOQli7UQ6KsDE60BV0D3V+ClklOeNutlHLLvtNR9IqeMmEn1oLTvhetJJJXhmZ+/EewTTzTT8+Jfdttu7H3bUEP1+KZempurPXxmygzg2fGWTmtPCfRhr17k6vN1KVVp+kGmj/kyoaYmZz2qR6p7+kQJ4wLPvnkE1asWMFbb71FMBjksssuY+nSpQQCA2vI7d69e0AZCK/Xy/vvv5/7urm5mbvvvptXX30Vx3E466yz+Kd/+iemTp2aG/PGG2/wN3/zN/2udemll/LDH/7wEN7u8CMOqAlIZzxDtYgiDohKdaIS7WiloQmr56QcB5WJowXK+qXcanXHQU13KYSG09kMgOE4PV3Z7CwqHUeVRHLXzNtk9HbI6V2tyPvop2jBMrflbe/68MhkV+y7z4ZFNjDHLvPnz+fBBx+kvb2digpXnPiFF14gm82yYMGCAc9raHAXT5s2baK+vkfXpXvCnTLF3VB/5jOfoby8nNdeey2vk91rr73GjBkz8HjcaWzBggU8/fTTeWLkGzduZM+ePYM+x5Eik7VIpC0qRcOvIEo5OLE2VCqOFgpPWJuibAvMdJ5WkrXjHdcB1XgaVEbccY6F09ncr3ECZgaVibuZqV1omgbK6de9Dd3o6frWjeFxNzR9jhuTZ2PU52cCT9TvsSAcSWxH0SFdqAdEORZOtAVlZdDLImP9OCPGzSi10Lp1P5WD+dHvQSk8ofOBrq7L2RRO9AB6nzWzyibByuZ0rgB3/ayU25GzN7oXjPxjmuFzbXefOcFz/Lx+pYtiu8cX0WiUxYsXU19fz6pVq2hra+Puu++mo6OD+++/f8Dzamtr+dnPfpZ3TCnFN77xDc46q6dLt23b3HDDDaRSKe644w78fj8//vGPWbx4Mc888wylpfkdXO+++26OP75HX6t7bT6eEAfUBCSdsUhmRBSxECqTwIm1dpXdTYxfb7ezk51rL5pLuXXsAim3/nxHkaZhTJ7tTnK9nG1ua9KKfmJ+3pnnuuN6fW+MqmkFu7d5Cwh1S/2+0Jerr76axx9/nCVLlrBkyRIOHjzIypUrufzyy/Oyl5YtW8batWv54IMPADj55JM59dRTuf3222ltbaWxsZH33nuP1atXc+mll1JZ6YoY+3w+lixZwv33309ZWRmnnXYav/vd73jllVf43//7f+euf/HFF9PU1MQtt9zCd77zHWzb5t577+WMM87g/PPPZ6xRCjriGarCAdzuB0JvnGQnKtnudsuZIIGD7lbh3XONm6n0ZlemUo+zVAuGwbFch373MW8QY/LsXOlcN3rt8ejV0/I6C+ErdeeCPhsez5ST+z2T5g3gnXFW/+NiuwVhxCRTFumsjU8yWPPIBQ7Scbekd4KgbAs0PTfX2G27sfduRg/X4Jl2OtCVfRQMg+O4Yupd6KUVUD87l+XUjWfqKe7/eHscSHq4zu3U1sf+emed0++Z9LLqvLLpbsR2j3+efPJJotEoa9euza1dDcNg6dKlfOtb3xowk9/n83H66afnHXvjjTeIxWIsWrQod+z5559n69at/OpXv6Kpye3Cfcopp3DhhRfy85//nK9//et515g1axannHLK4XvBUWBi7NCFPExLRBELoUw3BVhDG7ddFPrqLzkd+7H2bkYPVeZ0NzTdQPOVuOUS2XS/lFvDkx/BNqqm0hfNV9JvcnSPS+accHgJh8M89thjrFixgptvvplAIMCiRYtYunRp3jjHcbDtnsigYRg89NBDPPDAA6xZs4bW1lYmT57Mddddx0033ZR37te//nU0TePf/u3fWL16NVOnTuWee+7hwgsvzI3xeDysWbOGu+66i+9+97tomsbChQtZtmzZuIkWptMWqayNzxgfzzNeUOk4Kn7Q7UI6TgMHSjl5Xfbs/R9hH9zpat3VHAfQy+ZqKNvMOZeMybNdW93bdnv9BW23Huy/idN0HXSx3YIwVmSyFrGkSU15QDJYe+EkOroCB+O34qCv7TY/fRsVP4jnuLlooSoAt8wN8kqUATyNri6l3tt2B8owAv077uqllf2OaYanf5aqcNSxYcMGzjnnnJzzCeCSSy5h2bJlrF+/flApib48++yzhEIhFi5cmDv2wQcfUFNTk3M+AdTV1TFr1ixefvnlfg6oiYD8VUxQRBQxH1c7pCsFOFg+9AljgLX7fZxYqysCW9qVDun1ubXdmUTeWM9xc11xWCmXECYAjY2NPPzww4OOWblyJStXrsw7VlVVxR133FHUPRYvXszixYsHHVNbW8sDDzxQ1PXGgoxpE09mqZZNTA5luqLj4zVwoBwb69O3Uek43qbzexxkHq9bFpeJ58ZqhsfVw+sj6i22WxAmNt0ZrCJ/0cN4DxyodAxr9yYAvDN7mphohgeFWzFBtwMqGMY754J+Galiu4Vi2L59ez/NU5/Px7Rp0/pppA6GaZqsW7eOiy66CL+/Zz2UyWTw+fqXAPt8Pj7++ON+x7/5zW/S0dFBTU0Nl112GX/3d383qBbVWDD+LIZQFOmsRSJlEi7xHZYuTRMZNwX4oJsCXCB6fMSfJ5PAbv7IrRvvEhIE3Lpw20SlotDlgNKC5XhnnpMT+e6m7wZGmJi4JToOWl/tFuGYRClXw69KNjGAG212AwfZcRE4cOIHsVs/RQuU4Zl0AtBV/mCm3UBBOpYLHuiRevRwXa5Euhux3RMfpZTrXLQU6Mf2+kroIZ2xSKRF/gLcjm1OrAVN08dF4MBu24XTsQ+9YgpGRZempOFDpWOgaSjbyjnJjLqZblZqL1vtZm9JSH+io5QCx0ahYJiVi3v37uX6668f8PPBOiZHo9GC3ZbD4TCdnZ1FP8OGDRvo6OjIK78DN8i7f/9+mpubqaurAyCRSLBt2zbS6XRuXFlZGTfccAPz5s3D7/fz+uuv88gjj/Dxxx/zL//yL0U/x5FAHFATFNtWdCayREI+elW1HJO4KcAdbgqwdmQnECfehhM7gB7qVbutGzjRFnfSc+xc/bZe04he05hL9YWuzU0vsVph4qCU4wpL5v5roxwH6HY2aaBpaLrullF6vGANdkXhWCCdsUlnbPzeY3uxq5SDEx+7wIHTsR8n3opRO6NHCNyxUfE26KX3AWBMOQnN44deZc3iaJq4qJzdtnP/dQXe85tzKI8H3V8KSpbKApimyF9Ad+CgFWWb6EdY90k5Nk77XlSqE6PhpJ4MJTONSnaiAiHockBpXj+eaad3dW/u8UYUkqcQJgYqZ7PdNTeOk2+7NQ00HYUH3V/WTzfxiD+vUsPKonvmmWeorq7mnHPyNcIWLVrEqlWruO2221i+fDl+v5977rmHZDKZa8QDcOKJJ3LiiSfmvj7nnHOora3ljjvu4N133+XUU09lvCCz6gQmkTLJWA6eYzhFNJcC7A2OegqwcmxUKopWEunRcIq34hzcBY7T44Dy+DHqZ6P5y/I7yI2D7KzxjnIclG11afVqXT6cIz+BdEfA6bNRUY4N3T9STQPdQENzuwgaPreLicfjHtd097iuY/i8eMvKoD0x6H2Fo5+MaRNLmZQEAtj2sRtGP5KBA2WbqEwSvaQny8pu3+12Sy2JYFS6HRe1kghG/Ry0PtlYhbQ9hHyUY3f9U+Sc72OwNumx3Xa+7VZOj39J19zgj6aDYYAvgGZ40QwPWpfNRjfweL34qsrQO1I41rHrcBB6ONblL5Tj4MRbUZmE69gZ7fuZGbDNXl1FNezmbeBY6FXTcsELLTwJj78MraSP7Q7XIAyOsnvZbk0Dxsp2Fx/URTPcoK7hddfduuHa9K5/Hq8HX1WYZEcSirTd9fX1g2Y5DUY4HCYajfY7HovFitZ/SiQSvPLKK3zlK1/BMPLTt8rLy/nBD37AbbfdxkUXXQTAvHnzuOKKK3j99dcHve5f/uVfcscdd/D++++LA0o4PGQyNvGkecy29VZmGid6AE0zRj0arZSD+eGrYJtuyVzXZKiX1YBy3P92oWkaRmV/cVmhP8pxwDbBNnE0B8djoswMyla5jlHuhoIexw/0+lpzv1D0mji7jucmUC33Gf0+I9+51CcCrmmaO9F5fWB40T2+Xs6lnskOTR90wh4LJ5owfulMZKiJHLtleE46hoq3HpnAQTqOuf110D14Z8/P/S3qkclQEslzSmkeX84ZJQyOcmywTFdsXVc4nlJUNo2yeqLReba7ux15X1ve49Hvb7u77Xa/Y3Rdq0AEHHpstKaD1w8eH7rhdY/3stnonkGFkzVDF9st5JHOWMSTJpGQ75hcdzuJdlSy84gEDpyOfVi730cLVeI97gzALZUzqqa6f7u91v16sAyCo+8QOxpQtgV2FuVYaDoobwiVSaHsAWx3Do08O5tnu/vY7DxbXsB2H2JQF80ownYfOSfajBkz+mk9ZbNZdu7c2U8baiBeeOEFUqkUl19+ecHPzz33XH73u9/x6aef4vP5mDp1Kt/85jf7ddGbKIgDagLjKHXMtvVWtummADvWYU8BVukYdssnoOm5NteapqMFyyCdQJnpXDRGL61wW7IKRaEcG2zL1cJyLNB1V/QxGMIIluKvjpDyp7siFsqdqHL/BZRya7tVr8+6jzvd53RFUJRyoyi5Y72v1/V114SHpycCntukaN0OJtmECIeXTMYikbEo9RnHnJaIyqZQ0RY0zXPYAwdOrAWnbTdaSUWuMx3+kq7NihfMDHSV2xkVDYf13kc77qbFQtkmYHc5bzxoJeUYJSVdtjvl2u6c5l1vW83gtrvbodRtn52uazi95wLcjQt0ZS95wOfrlb3UY7O77beICAuHE9tRdCQyRMqOvRJcJxVFJboqDvRhCuwMgX1wF07nfoyaxlw1gRYMu2s0x8krZTLqZh7Wex/t9HY4oRQYHjTdixYMYwSD+KoieHzJwW139/E+a3K3JK73Grv7Ggqc3mO7v6YrMODryl7qst3DDOqON+bPn8+DDz5Ie3s7FRXunvCFF14gm82yYMGCoq7x7LPPMm3aNE477bQBxxiGkcuo2r59O3/4wx9Ys2bNoNf99a9/DcApp5xS1HMcKcQBNcFJpS2SGQu/59jZILspwAcPSwqwsrJuGYa/tFeKLzidza4GhOPkvOyeKae6hnsCGcWxpsfhlHU3GbqGZvggGEL3Bt0NqMdNn9U9Orq/BM2r0LSBU2YP5buv8jY/bnjncC+kBGEosqZDPGlSFvAeU1oibuCgBWVbh1ySrLJJnHgberjOdS7h2nMn1opmWzkHlKbpeGedmxsjFEePwykLKNeZY3jRAhE0r9/VxPJ40TTdtd2+IJrXGUXb3V1C12XDNX3ctn0Xjm6SKYt01sZnHDu/fyqbQsVaXafzIQQOlFKudEaqMy/bVGXiqGQHTvxgj5yFrwRv0wKx3cPAFeHuCvLaXVqGhse13cEwujcAHl+uy7ZruwNoXntUbLfKOawgZ7uPwqDu1VdfzeOPP86SJUtYsmQJBw8eZOXKlVx++eV5JXjLli1j7dq1fPDBB3nnt7W18dprr/GNb3xjwHvcd999nH766YRCIT788EMefPBBrrjiijy9qKVLlzJ9+nROPPHEnAj5o48+yhe+8AVxQAmHl2zWJp4yCR4jbb2VUm4KcKIDLVA2bCPWWxQcwG7+CKd9L0bNcRiBWe5BfwijbhZaaSTP6sokODSqq9Ofm+Hk9Mpwcie+3g6nscAtqRMHojD2ROMZaiPBsX6MI0ZOOySbQhtB1mpf223t+DMqE3cX1uVuVxg9VAWTm/ppNontHhpl99609HY4VaD5Am7goMvhNBa4JXVjcmtByCOTtYglTWqOlXW3le0KHNhuqdtwz+9tux0L6+M3QCn0UDWazy1F1yP1roOktCp3nqZprs6PMCA5h5Nlopy+Dqdy9K4S5G6H05FGyyvJO3oJh8M89thjrFixgptvvplAIMCiRYtYunRp3jjHcbALdA77zW9+g2VZA5bfAezfv5/ly5fT2dlJQ0MDN954I4sXL84bM2vWLJ555hkeeeQRTNOkoaGBm266iW9+85uH50UPI5pSx1oBwNihlKK1NV7UWF3XOBjNsGNff1GzvpSFfMxsKO8S/xw+Ho9ORUUp7e0JrHEutOmkojid+9E8gSGjMIZHIxwuIRpNYmVtrJ3voJIdeGedl2sb63Tsw27dgV5Rj1E17Ui8wiGhbBNdmZSVeIjFM13ZE1qP3oWmd5UfaLmvR3PSGdDh5Aui+YLu/3v8RUWqJ9Lv4XAZ7rtVV4ck026cMBy7DWAYOjuaY7S2pwYd5/XoNNaHKQ2MvKX3RPmbUUq5WauxVjdwMIQDOs92J+JYu98H28Qz69zc34W1fysq2YlRc1yeBt94RCkFtomuspSF/MSiKWxHAV32We9lv7vt9ihn9+Q5nJST27TgKxm2w2mi/B4Ol5G8l9ju8cNwbbemw0e7OoknzSHHHsq6eyL9vSjHwYk2uw14AuEhf7d7226z/QD2vq1owTI8U3vEj81P33a1Uutm5VUejEeUcsAy0clSFgoQi6W71t1dNrpbJ7T3mnsUbXchh5NmeMDwgq902A6nifS7OBxkzT3+kQyoo4BjRU/ETQFucWuXh3A+KeWgTAtw261quu6WgjmO23mpK2KulU/CG5k82o9+SCjLBCuDUra7SQgE8VbVYnjTKNNyNxKO1fV+di9hVlforzvztVsU8FCcVd0OJ2WZgOOe053h5OsqqTN8UhohCENgWg7RpElZydHf0lulY6h4G5qvdEjnk7JMVG+nh8ePSsddJ4mZ7tFwqps1rheM3U4nZWYAx9W5CJbhq6rBMJIo0+xx4HeXKjuWq5OUE9buzUDOquLKiAtnOPnQgmVdJXXdmxax3YIwFOmMRSJtUuofeQBhvONWHLS5a+YinE/KTKOMXs01DC8qm0Q5Vp6Gk2f6Z8a57XbAyrrlx8rNoNVLKvBVVWIYiV7rbjO3r3DX2l1r7tyiu5uRO6vceaRbN7WXw8nrR/NF3MY4Y5jhJAgjRRxQRwFZ0yGWyFIWKD1qNzLKyuJEW1C2gx4sHXyscrD3fICd7sApPY/u3E9jcpMb1fX1lL2MV4OtrKw7ASrLdfD4g+j+EJovgOEP4AmF0M0Eujf/553fgrrn/1WXMKCy7V6TZvHOKrdDnO12njC8aCVhN8NJHE6CMGJiiSxWRfCozk5X2aQbODC8Q5bCKdvE2vE2jsdAhT8HuIttz/TT3a5LXZmrMD5tt1LKtdtWFtfp1GUr/SVo3gAevx+jtBQ960G3Ctluu6cNdZcQt+q24b2cVAM7q7q7FOm5cuNcd8+CDiffuPw+CsJ4xzQdogmTsuDRG0DoCRyEhg4cZBJYn7yFqqhFhecCroi4Z9rpaKUVeXZmPNoc5XQ7nTKAhubxoZVWoPtKwOvH8PkGsd1Oj93u6u7mNlfo6vSWs9tm8c4q6BINx+0CJw4n4Shj3DmgPvnkE1asWMFbb71FMBjksssuY+nSpQQCg7esTiaTrF69mueff56Wlhbq6uq4/PLLufHGG/H53GyZeDzOsmXLeP/992ltbaWkpISTTz6ZW265hVNPPTXvei0tLdx1111s2LABXddZuHAhy5YtIxKJjNarHxLRRJbayNG5kVGOjRM7iMom0YLlQ59gW6hUJ5qVwU7HQXdr1vWSyOg+6CHQHS3Hyrj18h4v+EvRA6Vo3vxyw8EmHa07wtJnsVDojJyzqs/kWchZpRk+tyxDHE6CcNjIZG0SaYuyoJejsRq+O3CA46AFBg8cQFeWazaJsnWcbAZw7Yweqhr8xDGku0RDWRlA5ZxOur/U3TQYxWmYuLbb0/3KPccL3nMQZ1X3hqdr06N5A13Cs15xOAnCYSSWyGJWBPv+yR4V9AQOfEVp6KlUDGVnUclO19lCV/fo8Pgtj853OoHm8aOFqtB9QfAGitYq1TQdCgjSF7bd/dfcBZ1Vjo3mq3BL6gyvOJyEMUMpB2WZ7u/iYWRcOaCi0SiLFy+mvr6eVatW0dbWxt13301HRwf333//oOcuX76cF198kVtvvZVZs2bx7rvvsmrVKjo7O/ne974HgGma+P1+br75ZiZPnkwsFuOxxx5j8eLF/Md//AeNjY0AWJbFDTfcgGma3HvvvViWxX333ceSJUt44oknxqURyGSOzo1MTnQ83dklOj70917z+PAcdyaaGcMbqSMVTR6BJx0+hUo0CIbQff2dTqNBzlmFAb3m2fH32y0IRyeW7RBNZCkv9WHbR4/dhl6BAzNdtOi4HgzjOe4MDI+OEQyBOV5td/8SDa0kgu4vcTcuxugurYbjrBIEYXRIZyziSZNIyHdUiZH3BA4U2hDB/270yCQ8uo4nXNHldB9aR2ssUI7dY7s1zXU6BavR/cEuvdLRbZCTk8AQ2y2MUxwrS2LT70lue5v07i3YiU7AzUb3Vk8leNzJhE6ej7/uuEO6z7hyQD355JNEo1HWrl1LZaXbxcYwDJYuXcq3vvWtvFaGvbEsi+eff54bbriB66+/HoCzzz6bvXv38txzz+UcUBUVFdx33315537uc5/jrLPO4re//S033XQTAOvWrWPLli08++yzzJrldkarra3lmmuu4dVXX2X+/Pmj8v6HgmU7dCayREI+LOsomghT0aK0Q5RSkE2i+d0ou+b1YwSLmziPJEOVaBQbLRcE4eggnjTJWg7GUbQCzWmHpDvR/IMHDrp1kDRvVzekYBjdM/6+GQVLNEoqupxOfjR9XC2nBEEYZWxH0RHPECkb3WDhkcQNHLQUFThQZtrNzOlam+vhWrRxabttt7rAyrqNcjwBtJIadF8APAHJ6BcEwDEzdL72NJ1//DVOJom3qp7gcadglJajGV7sdByro5nYxhfpfOMZAlOaqFx4PYEpTSO637haMW3YsIFzzjkn53wCuOSSS1i2bBnr168f0AGllMK2bcrK8tuDhsPhIbOBSkpK8Pv9WJaVO7Z+/XqamppyzieAuXPn0tDQwPr168elAwogkcySztp49PE3AYwElUngxFvdhf4gjhmlFPb+rTjte/BMP71fC+6x5nCVaAiCcPSRybpituUlvqMme1WlOlHx9qEDB46NtXMjZFN4jjsDrZc+33jgcJVoCIJwdJJMW6SzNr4CJVgTDbdbaRsqFRtSdFxl01if/gm8AVfnaZSzPoeLsi3XdjumK/bt8aOFI1222y/NFgShD7se/Da6N0DkvCsJnTQfTyhScJxSivSO94n9+WX2PX47VZfcQPgzFw77fuPKYmzfvp0rr7wy75jP52PatGls3759wPO8Xi9f/vKX+elPf8rcuXOZOXMm7733Hk899RTXXXddv/GO4+A4Dm1tbTz88MPous5f/dVf5T1HIWfXzJkzB32Ow41lO9hOYXFDDQ29l6PJdhwSaZPORIaKkL9fOrCn1+Ro9RVM1NyOTJbt/ht0bB9Ga6yhLJxYK5qjsH0+t156oLG4ziocGzuTxAlG3A9s3f0e2gqr63xPr0iHrZxBu5cYmpabgIc71unauGB1dRzyeNEC5Wj+ILoviN5VU+84alABS0Pvua7jKByl+v28utF1Db3v2AE4ImOVGjQtXdd6focdpbBsp+B7FRo7nOsWO1Yp1dUa/dDHahoY3UKSXe8mCIWwHUU0kaEi5MO2x/ppDh03cHBwyMAB4OoUZdNu5zsrOy4cUGNdoiEIwsQhk7WIJU1qygMTvgzPDRy0uQ0fhurOZmVQtomG5uoYjQMHlOt0yrji3bqB5vGhByvRPH5xOgnCEFTMv5qyUy8Yco2jaRrB404heNwpmPP/GivaOqL7jb3F6EU0GiUc7p/yGQ6H6ezsHPTc5cuXc/vtt3PVVVfljl1//fV8+9vf7jf2gQce4KGHHgKgqqqKn/zkJ0ydOjXvOfpmU3U/x2AOqC984QsDfvbb3/4Wwyhu4ZpMm9z35EZQUFdZeEEeCfmZPa0i9/XbW1uwHcW72w8SKfOjek2E1eVBzjt1cu7rF/64i4zZs9PRdY1g0EcqlSVc6uOC0xtyn7389h6S6cK13GUlPr5wxpTc1+s37iWWzBYcWxLwcvG8nu/x79/bR0csU3Cs3wMXz/bnUoA3bm2hfYCxhqGx8DNT8Ew7DZVo48/N0Prxntx7BQJe0mkztzC46MyeZ9j0cRvN7amC1wX4/NwGPF3OlC07Otjbmhhw7ILT6vEaGlhZtu88wL7WBLbuwzQC2EYAWzdQWgJIcPG8qZR0/eV9sKONbbsH/t1eOHcK4VI3vXvrrg627GzP+3n1XvAsOL2BijJXJG773k42fdI24HXPO2Uy1RH3d+vT/THe3T6wATn7pElMqiwBYHdLnLe3tgw4dt7sWhpqQgDsa03wxy0HBhw794QaptW5f2cH2lO8ubm54HsBnDqjmuPrXdvQ1pnm9+/tG/C6JzVWMmtKBIDOeJb1G/cMOHb2tApmT3f/jmJJk5ff3j3g2JlTyjm50RVDTmUs1v1x14BjGyeHOW1mNeB2qfztH3fyX684dcDxwrFNImWRMZ0Jn72qrAxOtAWtSO0QzevH03gmykyhlxTRYGKU6F+i4ZcSDUEQhkQp6IhnqC4ff5IPw0FlEm7Qt0gNO72kHM/0ua6tPMzixMMh53SyTTA8btOFQJVb0u3xj0vNXkEYj4RPH9iHMRDeikl4KyaN6H7jygE1EEqpIY3I/fffzyuvvMKdd95JY2MjmzZtYtWqVYTDYW655Za8sV/72te48MILaWlp4amnnuKb3/wmjz76KCeddFJuTKH7FfMch4OM6bBjfwyAyjI/Xm/xi9/uDBJjohpdpfBno6hUyZCi414rgWO4zg5NN9DKaqB5YOfIqKAUhjJR6SjK425cLH+ERCCErftQEnERxjGOo8haNgHfhJgKjlrcbnhmwezViYJy3KxVZWUG1Q5RjgPZBFrAdT5r3rHZwOScTraUaAgTD7Hd44d0xi2jLvV7Bs2UH6/kAgdog9piZWVBOT16fWMUNOhxOllgGK7TqawKzRsEj3T6FISJwLiaucLhMNFotN/xWCw2oP4TwNatW3nkkUdYvXp1Lgtp3rx5aJrGvffey7XXXktVVU8b57q6Ourq6gC44IIL+NKXvsSqVav4l3/5lyGfo1CGVjcvvfTSoO9XrL5HRZmfmQ3lbNvTSdDv4TMnVPcbo/XpmTD3BLfVqabBlNoyqgdJB76oVyYSgMejE4mU0tGRwLLyS4UWzm2gWBacXl/02PNOmVzwuJNoR8WyaP7SXNT59Fn9399p+QTnwE70ybPzjp86oyq3APAYOmXhILFoqmAJ1EnHV3LiEGV13cyeHqFpWgTo6YCE1ZXt5fFhBMswujogzanSmD3YdXtlOZw4vTIvk22wsSdMjTBzSvmAP6/eJZkz6stpnDzw72rvscdNKmNaXaiosVNqQtRXD9xOvffYydWlLPrccQOP7fX9ra0I8sXzGgf8Pew9trI8UPR1y0O+oseWlXiLHhv0ewYd23v94/PqfPG8xgHHHkmyps2Gd/bwp83NbP70IB3xLCiFx2MwrS7EqTNruOCMKTTWj102yrGI4yg649lcBuNEQykHJza0dohyHOw97+PEDnbp9Q1s+0blOfuV1wXc7nXidBLGOWK7Rx9XsqGw9EUh2Yvc/2cc2qIZgtWenKTCRJG9UI6F09GCYWXQg+7vjlXg/ZWVxdnxNsqx8TeemSuX7idP0Uf64nDJXijHAjMLdhZ0D4a/BL2sGs0XwNY8qO49kaOA/JsUlLIY6BkGGltA+mKiy144ziCSHiJ7IYwyh8UBdbgiMTNmzOhX4pbNZtm5c2c/bajebNu2DYA5c+bkHZ8zZw6WZbFnz548B1RvdF1nzpw5bNy4Me85Nm/eXPA+n//854t9nUPic6dMYtueTj7c1cEZTTVDevSNXkY+kTKZVFmCoxX+Y/X0EUv0GDpej+4eV4OPHYxDHavScUi1o/lK8lKAPX3KH5RSoGzQNAzyjYyh6bl+poah4TF0DEMD1f9+vccOhY4GdpcYrQLN44NwFbqvpJ8Y7XAUQnRdc689jLGD/bwO5bqHfaymoRfZ2kvXinuvkVy32LGapuEZtbFju7HNmDb/8fJH/OrVj0mmTRpqyzh1Vg2RkB+vRyeeNNl/MMFv39jB2vXbmH1cJX+76CRmHze+BP2PZpJpi0zWxjsBxWydZCcq2V6Edohyo9bKcXVDjgB5QuLdmk4lNeJ0EiYEo227P/nkE1asWMFbb71FMBjksssuY+nSpQSGKKFNJpOsXr2a559/npaWFurq6rj88su58cYb8fl6usI1Nzdz99138+qrr+I4DmeddRb/9E//lCd7AdDS0sJdd93Fhg0b0HWdhQsXsmzZMiKRyLC/ZyOhtSPFv/5mM5GQv+BGdyDZi258HoOKsB+NCSR7oRSBbDshEpxzRk/TpY0ftfaTvTCcDDWxNgxNMee4nnX3u9sP0tqRznu33tIXhyJ7sa8liuGYGMrE0QxX1sJTiqXrXHz2JAJ+d5+waVsrn+zrnzTQzcXzplIScPUIRyJ70f1efSUiJrrsxeub9g8o6SGyF8JoMyKP0WhFYubPn8+DDz5Ie3s7FRXuL+gLL7xANptlwYIFA57X0OAa702bNlFf35OF8/777wMwZcqUgucBmKbJu+++mzcZLliwgKeffjpPjHzjxo3s2bNn0Oc4nJw1p47/+8JWDkbTtHSmqY0UL86aytgkMhYlPmPCpAMrM+PWnw+RAgzuht6om4UeqkYPjd4GWSnlbly6u9dJ221hgnLj3S8S8Bn89UUnsGDuFCrKCm8ulFK8u62VF/+4k2UP/ic3fulULjl7+hF+2mOTTNYinraoKptYZXgqHUfFD7rZRENoh2i64er1pWPoJZHRe6aC3eu6hcRF00mYOIym7Y5GoyxevJj6+npWrVpFW1sbd999Nx0dHdx///2Dnrt8+XJefPFFbr31VmbNmsW7777LqlWr6Ozs5Hvf+x4Atm1zww03kEqluOOOO/D7/fz4xz9m8eLFPPPMM5SWutnUlmVxww03YJom9957L5Zlcd9997FkyRKeeOKJI1JSte5Pu9i8o4MpNaWDZnkPhGU7ZE2HgG/iNCnwmTH8VgwrUDqkI97W/bSUzcGjO2j+4X9/iqW7NNrIxvA6GSzdR8YIYxt+bM2bSy+XMjtBODLYiU70krLDHqzT1DD6PheKxMyYUt4vErN1VweptDnsSEw0GmXRokU0NDSwZMkSDh48yMqVKznvvPPyJsNly5axdu1aPvjgA8Cd5K6++mr27NnDzTffTGNjI++99x6rV6/mggsu4Ic//CEAP/vZz3j33Xf53Oc+R01NDS0tLTz55JO88847PProo5x55pmAOxleeeWVWJbFd77zHWzb5t5776W2tvaQJkOlFK2t8aLG6rrGD5/6M3/e1srpM6uZf1rx5W0A9bUhJleWDNphrRuPR6eiopT29v6lT0cCZVs4nftRmUQuBbgQTqIdrSRS9Pff8GiEwyVEo0lsq7hfc6UU2CbKzACO28XJX4ruLwVfYFw4ncb65zVaHK3vBcN/t+rq0GFdYL3wxg4WzpuWV9I5FPsPJmhpT3HKzP4lsMcSw7HbAIahs6M5Rusgkd6BiIT9HF8fxrGLs1dj/TejzAxOx15wBt6UKKVQyY5hlduNzHY7vQIGmpulGgi5AYNx4nQa65/XaCHv1cNEst0/+clPWL16NS+//DKVle46/ZlnnmHp0qU899xzA0pfWJbF3LlzueGGG/I0VpcvX866dev4wx/+AMCvf/1rvvOd7/CrX/2KpqYmwM2IuvDCC/mHf/gHvv71rwPw3HPPceutt/Lss88ya5abifP2229zzTXXsGbNGubPn1/0u/dmOLb7j1sO8ODa9ykLerj+kqZ+P8PBSvC6qQgHOG5yGY6tBi1/6yujMBYleCodx+nc5wp1e3x5lQbdJXjKtiCbRAvmyzkMVlbXV/qimBK87tJo3TbRDB3NE8DxlYB34CzV3qVytjNEad9hKMErJH1xNJTgDSjpMcFL8DRdo6a6bMzW3EcLiQ/f5MAvf0BJ02epveLvD6sTali76dGOoofDYR577DFWrFjBzTffTCAQYNGiRSxdujRvnOM42L36VRuGwUMPPcQDDzzAmjVraG1tZfLkyVx33XXcdNNNuXEzZ85k3bp13HXXXUSjUWpqajjllFP4xS9+wezZPVpCHo+HNWvWcNddd/Hd734XTdNy6cBH8hf0zKZa/rytlQ93tXPuKZOHtQCJxjPDypoaK5RycOIHUel4v0muN3bbbuy9m9Erp2JM7r84OLRncJ1OWBmUcp1OWknYdToV2RFEEMYzF501/CymSVWlTKoavUjn0YhpOWz4814qw/6CGxQYfCMTT2SJJU38np5JfrxqibiBg2bIZtGCZdD1Hr03HKZt4+zbgurYiz55NnpFvqbggJuTPjoiA411nU5mlx6fcgVogxV4AqVoPrc0Orc5GeAdj+hGZiC9jQm+kbFsp+B7FRo70TYyY81o2u4NGzZwzjnn5JxPAJdccgnLli1j/fr1AzqglFLYtt2vW3Q4HM77nn3wwQfU1NTknE/garDOmjWLl19+OeeAWr9+PU1NTTnnE8DcuXNpaGhg/fr1I3ZADYfTZ1ZREvAQS1nsO5hiau3AupiQL3vRTdZ0sGyFr4DMRd+vx1L2QplpnEQruuFF8/Xfx3l0HWVbWLv+jMrEMaZ9Br00UvC6faUsBpO+6D3WzVLN5OnxEarIlUYbw9joFvpZDMRIJSeGkoiYiLIXulGkpIfIXhyTJLb+keZffh9sm8Tm1zgAh9UJNayd9bWXzC4qEqNpGqfNquG0WTVce8lsWoYRCW5sbOThhx8edMzKlStZuXJl3rGqqiruuOOOQc8744wzhrx2N7W1tTzwwANFjR0tmqZFCPo9pDIWO/bHcvW4xZDO2sTTJmWB8d2Vw0l0oJIdrnbIYL/Umu6uDA9jFFtZXU4nx0LzeCEYQveHutrQeg/bfQRBODZ4a+sBHv3NFqrKA8wYwF4PpiWiAe990kaJ34NSavxqiUTTBLPt+KwYWaMUNFd/w+fVWXBazzP8+aNW1IF2QpkobdGDJHukYTAMjYWf6XmG3loifXVEgJyWiFKKDz7az8G2KBoKW/diGkFsI4CtOygtzqLPVeccVn/edpCdzbGC7wbwl2dNx99VNvP+x22jqiUykN7GRNcSeXNzc8H3gomvJXI0s3379n76qj6fj2nTpvXTY+2N1+vly1/+Mj/96U+ZO3cuM2fO5L333uOpp57iuuuuy43LZDJ5elC97/Hxxx/nPUchZ9fMmTMHfY7Diddj8Nk5tbzyzl4272gf0gFViEzWIpY0qRmkCdBYo+yubqWOhT5It1Kga719+Nbdg+rxefzjIktVEISezKfQnM8R3/R7SpvOIrn1jxxY+78OmxNqWA4oiaIfWQxDp2lqhI3bWtmys31YDijLcojGs5SX+IoqwxsLctoh3uCQWUZGRb3bGS84cIelou7Z7XRSVk95XaDUdTp5+i+UBEEQimVqrbspb+tMM70uNOwonMLdxJT4x3fWpd+M4rNimEYwv+VjXzSNjuA0Er5qTM8hrAOUQlnZXGk0OGS9ISwjiK37UNrE0V0RhPFCNBot2Nk5HA7T2TmwcxXccrvbb7+dq666Knfs+uuv59vf/nbu68bGRvbv309zc3Ou83QikWDbtm2k0z3C1dFotF82VfdzDOaA6u56XYjf/va3GMbw7MLnTpnMK+/sZfueTrKfqcfnGd75SkFHPEN1+eAC7mOFchyceCsqk0AL9P9+90YzPHimnQ7ZFFpg+M643D0LlEZroapxVRotCEI+VucBSuecQ80Xbyb+/qsEZ3yGslM/T8cbT6PMbMHMyeEyvle5AnOmV7BxWysf74uSzlrD6jQYT5lkLYcisxaPKMpM40QPoGnGgI4fJ37Q1Xzq6jCnl4ysvbCyTVQ6LU4nQRBGlYbqUqbVhth5IE64xMdJjf31D7U+6fRzT6jJ+9rvMTiuIUzA23/zc9G8/M5RA+k3ACycm1/uNhgLTi9eY/DcWSGcmjh4KwraT6UUKtaKVlbN6bOK1w87dUZVLlvXY+iEygLE2qNYmZRb3udYudLok6p9nDSIHl/vLO3TZlZx6ozCXXD7jj35+MqCP7NCY0+cXpmXyTbY2BOmRpg5pXxgvY1eY2fUl9M4eeBgU++xx00qY1rdwJvD3mOn1IQGFVfuPXZydSmLPnfcwGN7OR1rK4J88bzGAX8Pe4+tLA8Ufd3ykK/osWUl3qLHBv2eQcf29qf6vMfm5lgpNWSg7/777+eVV17hzjvvpLGxkU2bNrFq1SrC4XBOF2rRokWsWrWK2267jeXLl+P3+7nnnntIJpN4PPl/v4XuV8xzHE5mNoSpKPPTHsuwfU+UOdOL163rJp22SKRNSv3jq/pAKYWTaEclOtAChQWFlWO7en0h115qugEjcD4p5bj6gJl0rmu0FqrqKq/L7xotCML4o/yzi/odK5l1BiWzzjhs9xAH1DinJhKkujxAa2earbs6B11I9yWTsUikTCIh37hKB1a2iRNrGTQF2Olsxtr9HlppBZ5pp49owlK2iR1vR5kW+IPogTJxOglCLzrjGcpKfHmbT+HQOPvkSex8eRsf7enk1BlDO2D66ldYjiKddSgv9WH3ESMfay0RlU2hJw6iewtrhwDYzduwWz5Br5qGZ3JTwTGF6K0PojtZ9EwWHQdPabmb/dqrNHo4C5fh6IOMtpZIUXobE1BLpJj3Gsl1x4OWyHjlcNjucDhMNNq/5DQWiw2o/wSwdetWHnnkEVavXp3LQpo3bx6apnHvvfdy7bXXUlVVRXl5OT/4wQ+47bbbuOiii3LjrrjiCl5//fWinqNQhlY3L7300qDvN1wNL03TOOX4Kjb8eS9bdrSPyAFlWg6dCZOy4PiqPlDpGCpxEM1XUnA9rRwHe9e7OPGDGPUnYlQMr/FRN04mgWO4ndF7ukaL00kQhHyOzfDOBKM7yrplZ/uwzrMdRUciQ5FrziOCsjJdKcBJNP8gkRWvH3QDzRtw9Z+Gex8zjcqk8JRX46maih6pRw+GxfkkCF28/v4+/vbOddz/xFvjykE90Tn7xEkA7GlJDKirNBSd8Qzj6UeilEJlU27gwLbRfCUDD/a5ZXmDjhnsXuk4yjbxVtTjqZ6KUT7JDR6ILp8gAIfPds+YMaNfiVs2m2Xnzp2DOqC2bdsGwJw5c/KOz5kzB8uy2LOnR7vr3HPP5Xe/+x3PPfccL774Io8//jitra2cfvrpgz5H930Ge47R4OTj3QzIXS3xEdvveCKLOU6cT0o5rk2NtaLpnoHXwJrmdp3T9BGV1yjl4KQ60XQPvuopGFVTMcI1bvBAnE+CIPThsDigOuMZ2cCMIk3TImga7G9L0h5LD31CL5Ipi0zWHnrgKKKUg8oksDubsdt2o5LRIUXH9ZII3uM/i1E/Z1hRSKWUO9kqGyMyCU95LZrXP64jmYJwpHnj/X3c829/wrId/vPPe8QJdRipKg/kBJo/GkSkejDSWbeMY6zNlnJsnHQMp2Ova7uL0AMxKhrwzjgbo2rqoOP638vdwGB4MCL1eMoqxOkkCH04nLZ7/vz5vP7667S39wQ3X3jhBbLZLAsWLBjwvIYGt7x306ZNecfff/99AKZMmZJ33DAMZsyYwdSpU9m+fTt/+MMf+OpXv5r7fMGCBWzdujXPCbVx40b27Nkz6HOMBpGQn4auUtUtOztGdI10xiKeNMc0s1jZJk6yE6dtN3b7XnCcQYMCmqZhTG7CO+Ms9NKBy5AL38tCpaJo/lKMyGSMkrB0jxYEYVAO2QElUfTRpzTgZXrXhma4E2ImaxFPWWMyEeZNgG17UMlONN2HHiwvGBFx4gdRZo+DTfOXDktpXzkOKh0Fj9eNmpeUi+NJEPrw+vv7uOenf+K80+rRgHNOqeeN9/eJDT+MdJdKb93VMaLzTdMhljTRx0igVVkZ7HgbzsFdOO17IZNC8wZd213ApjqdzSinJ9AxXNFaZVuodBQtEEKvmIwekMYlgtCXw227r776asrKyliyZAmvvvoqa9eu5c477+Tyyy/PyzxatmwZJ554Yu7rk08+mVNPPZXbb7+df//3f+f1119nzZo1/OhHP+LSSy+lsrLHgXHffffxwgsv8Nprr/Hoo4/yta99jSuuuIJzzjknN+biiy+mqamJW265hZdeeol169axdOlSzjjjDM4///yRfbMOge7Suy072oddxgdd1QfxzOANGkaB7kxVO9bq2u7OfWCZ7lq6gE1WysHp2Jd7R03T0PzDs73KyqKyCbTSCvTySWhe/2F5F0EQjm4OyUXdHYmxHTcSA7D02jNET2QUmD2tgk/3x9iys52zT6wr2rHiduVIUxk+MpOCUgrMNE4mAakoys6i6d4h03CdeBvWzo1oHj+exnnDnsSUbbmdPYIh9LIaKbUThAFobkty7qn13HrNXNa/s5szZtfyhXlT+eUr28maNoFx3oFtInDS8ZU899qnHOhI0R5LU1E2/JKGWCKLXTGyMraRoJQD2RROOo7KxN2Ni8c/oGBtN/bBndj7PkQLVeKZ9plhdzVSZgZlpdFKK9HLKtEGERcXhGOZw227w+Ewjz32GCtWrODmm28mEAiwaNEili5dmjfOcRxsu8fBbBgGDz30EA888ABr1qyhtbWVyZMnc91113HTTTflnbt//36WL19OZ2cnDQ0N3HjjjSxevDhvjMfjYc2aNdx1111897vfRdM0Fi5cyLJly8YkiDijoZxXNu6hPZ6huT3FpMrh2+Fk2iKdtfANsxPqSFCOjcomUakoKpME5aB5AmiBgYOwSinsPR/gdOxDT8fwTDph+PfNJFHKRiurQS+NHJbW7IIgHBuMeKX3+vv7uLcrErPhnd15kRhxQh1+jq8P4/PqxJImu1sSTK0tPsKcTtukMlbBrkqHi5FMgL3RfCW5zQ7DLLtQZgZlpt0ITFmV1JsLwiD81fz+mhrzTpzEvC7tIuHQKQ14mVZXxqf7Y3y4qyOnCzUcMlmbRNokFBjdbkrKNt2NRKoTlXUzUDVvEK2kuEi4FigD3YNeEhm+8ymTQKHQwrVdGauygRGEgRgN293Y2MjDDz886JiVK1eycuXKvGNVVVXccccdQ17/+9//flHPUVtbywMPPFDU2NHG7zWYUV/Oh7s62LyjfUQOqEzWIpY0qSkPjFpmsbIyOOmuYK+VcbtKe4NFlb9pmoZWEoHOZvSSyPDuqxxUJo5meDHKJg8741UQBGHEq73ekRgFnDG7lv/f4nl0xDJkzbHVHDoa8Rg6sxoiwPDFyDOmTSxlYhTZ+WU4DFqqMQztJc0XwNM4D2PKycPaxKhMAuWY6JE69HCNOJ8EYQz45JNP+K//9b9y+umnc84557BixQrS6aH16pLJJPfffz8XXnghp512GhdffDE/+tGPyGbzxV+bmpr6/Tv33HP7Xa+lpYW///u/Z+7cuZx55pn89//+3+no6DhcrzksTpgaAdwyvJGUcZiWQ3SUyvAGK9VwmzUUHwTQSyvwzjwHveb4Ydy/S+9J1zHKJ2GUVojzSRCEccPsrjK8j3Z3YI1AUNytPsgM1hRyRPTTVI21gHIDAVogNCztJaNyCt5Z56KHa4u/v2Oj0jE0Xwl6RJxPgiCMjBFnQEkU/cgzZ3oFmz5tY9vuThacXo/PU7yzpTOWpaY8eFieo3epBpk4qshSjb44yQ5AQy8pBxhW2Z1yHFQmhubx5zptCIJw5IlGoyxevJj6+npWrVpFW1sbd999Nx0dHdx///2Dnrt8+XJefPFFbr31VmbNmsW7777LqlWr6Ozs5Hvf+17e2Ouvv55FixblvvZ6850klmVxww03YJom9957L5Zlcd9997FkyRKeeOKJI17KcfzkMB5DoyOepaUjRe0IyuniiSxmRfCwtas91EzVbpzO/WglEbdLKQyra5JbLh139Z7KakQzRBCEccfU2hClAQ+JtMWn+2PMbCgf9jXSabeZRKn/0LNYB8xU9RW/9lVK4bTvRo/U54K1w7LdVhZlptBKIuihKhEaF4RjgOm3/iu6f/gyEkMh1mMCMbmqhPJSH52JLNv3RHNCicWQyVok0yalh1DOkZsA01FUJgUaaJ4gepGlGnnXSsewdrwDgOe4M9CD4WE8R68NTLgGzSMbGEEYK5588kmi0Shr167Nic8ahsHSpUv51re+NWAbbcuyeP7557nhhhu4/vrrATj77LPZu3cvzz33XD8H1OTJk/Nad/dl3bp1bNmyhWeffZZZs2YBblnHNddcw6uvvsr8+fMPw9sWj89r0Dg5zEe7O/lwV8eIHFCZrEUibREOekeURdXNoZRq9MXp3I+1+300bxDP8Z8dVrZUbgNTWiEbGEEQxi26pjF7WgVvbW1hy472ETmgTMuhM2FSFvRhjyiLamSaqgNhN3+E07oDFWvFmHb68DpMZ5MoxxK9J0E4xjBKykblumJBJhCapuXSgrfsGF4Znmk5dI6gnKNgqYaZdUs1AsMr1cjDG0QLht2sqWFkLykz09VxoxI9MkmcT4IwxmzYsIFzzjknr/PRJZdcgs/nY/369QOep5TCtm3KyvInt3A4PCJny/r162lqaso5nwDmzp1LQ0PDoM8xmjT1KsNzRvBOlq2IJrIj0lTsV6oRPQBKjahUozdasBzNG0Arq4JhXENlkq7zq6zGDRyI80kQhHFM93r70/1RkhlrRNeIJbKYw3Q+KcfGScdwOva6tjt+EDTDzVQdofMJQC+rBt2DFq4dRiMjhZOKuudHJmOEKsX5JAjCISNWZIIxZ5o7Ie5qiRNLZocYnU98GBPhaE2A3WiGB8+00/FMO73oa3XrPWnhWvRwtXRLEoRxwPbt2/tlOfl8PqZNm8b27dsHPM/r9fLlL3+Zn/70p/z5z38mkUjw+uuv89RTT3Httdf2G/+Tn/yEk046iTPPPJO///u/Z+/evUM+B8DMmTMHfY7RZFpdGX6vQSJtsaclMaJrJJImWav4DYyyTZxkJ07bbuy2PahUJ5ruc0XCfcFD3jxoPjfzyZjUVNQmRikHJx0FDQzZwAiCMEGoCgeojQRxFHy0q2NE18hkLOJJs6ggwuHSVB0IvbQS7wnnYlQ0FDXe1XuKovkC6JHJ6IHRyYQQBOHYQ3bwE4xwqY+G6lL2tCbYsrODebOLFw9MZyziKZPyEt+AGQaHs1Qj77qOg9O2C2wTo24mQPGOp7yOGzUieigI44hoNEo43L+ENhwO09nZOei5y5cv5/bbb+eqq67KHbv++uv59re/nTfuiiuu4IILLqC6upqtW7fy4IMP8rWvfY2nn36a8vLy3HP0zabqfo7BHFBf+MIXBvzst7/9LYYxcme7x9CZ2VDOpk/b2LqrY1jdS7vJZC0SKZNIyDdgN6XuTNXDVaqRd23LxG75GL2kHL3c1XjUPL7iznVs13b7S9HLqnOaUYIgCBOBOdMrONCRYvOOdk6bWT3s821H0RHPECnzQwFJ8t6aqioTd5tBjEBTtRAqk8Ru/gijdkZu3Vy07bbMrmqDcvRQNdowu1MLgiAMhjigJiCzp1e4Dqgd7ZzZVFN0VMR2FNFEhoqQD7tPo0I7ncDqaMZJRkcsKj4YKtWBvX8raJrbOaPIsjsRrBWEiYlSakjbdP/99/PKK69w55130tjYyKZNm1i1ahXhcJhbbrklN+6ee+7J/f+8efM444wz+PKXv8xTTz3FN77xjdxnhe5XzHOMJidMjbjNI/a4zSM8xvBsqu0oOhMZKsr6bxyUcrCTUez2/djJ+IhFxQfD6diDc3AnKupHKyu+02iPYG33BkaWG4IgTCxmTY3w6rt7OdCR4mA0TVV4+E70ZNoinbXw9bL9yrbcTNVYe76o+Ag0VQfCPrANJ3oAlINn+meKPk9lU730niqG1ZlaEAShGGRFOAGZ2VDO+o17aI9naG5PMamyeHHbRNIiYzp4eqUDq0wSM9mOSiVA941IVLwQynFyE5deWole2YAWLAdfcc8rgrWCMLo8/j//kqD/0P6uwuEw0Wi03/FYLDagADnA1q1beeSRR1i9enUuC2nevHlomsa9997LtddeS1VVVcFzZ8+enXNYFfMchTK0unnppZcG/Aw4JPFvgIaa0lw3pZ3NMY6vH76YbTLV324DqFSMbLwDlc0elkzV3HV72+7KqahEB3rV1OKdT70Fa0sisoERhMPM4bDdwtCU+D0cNynMx/uibNnRzrmnTB72NTJZi1jSpKY8kMtiNaOt2O37AOOwZKpC11ylVM7eGnUzwXFyVQdFnZ+Jg66jl9d1BaHHLngjCMKRY9dP/p7K+ddQOvusosZbsXY6/vAfeMoqiHzuy8O+n6wKJyB+r8GMrk3M5mGKkWeyNvFUfj26k46DY6MHy4pOzx0M5djYB7ZjbX8NZfcIN3rqT8SoaChON0QEawVh1AmX+vB6Dm0amDFjRr8St2w2y86dOwd1QG3btg2AOXPm5B2fM2cOlmWxZ8+eQe/b1zFU6Dm67zPYc4w2uqYxa0oEgA9HqiNi9rfbSjk4yU40w4t+CKLivVFmBmv3+9i7/pz7/mq6gWf66eihws7AvPOVwknFgF6CteJ8EoTDzuGw3UJx5Jr/7GwfUTMJpaAjnskV4Ckri5OKofmDh8/5lI5jffoWTusnuWOarwTP9NOLkq1Qjo1KdYLHjx6pRw+GxfkkCMcQoTmf48Azq9jxoxtp+93jJLe/g53ozK0FHTND9sBOou+8yP6f/TM7f3wTmf3bKZk1b0T3Oyy7eonEHHlmT6/gw10dbN3VwfmnTi66rMNRbj16ZdgtZVNWFpWOQaQMLHuIs4vH6djnapJEm4sWPIReek+6B6N8sug9CcI4Z/78+Tz44IO0t7dTUeEu1F944QWy2SwLFiwY8LyGBtcubNq0ifr6+tzx999/H4ApU6YMeO7mzZv59NNPufLKK3PHFixYwNNPP50nRr5x40b27Nkz6HMcCZqmRdi4rZVP9kXJWjY+z/A2HI6j6IxnqQwHyOmIZFNuhqivBrKZw/OgjoXT2QzKQU/H0YLFi87m9J58Qbdc2hc8PM8kCIIwhhw3qYyAz20msetAnOl1wxfjTqctEmmTUr/HDbDaWbeLs3VoGbbdqEwClWjHTsfRq6YPKyCh7C69p2A5elnVYQlEC8KxxieffMKKFSt46623CAaDXHbZZSxdupRAYOCy3d27dw+oQ+r1enPrYYDm5mbuvvtuXn31VRzH4ayzzuKf/umfmDp1at55LS0t3HXXXWzYsAFd11m4cCHLli0jEokM+vwV519F2ekX0fnHZ4lufImOP6yFLie0Znh6EkqUIjBtDnVfupXS2WcP/Y0ZgMPiNQqXirE60kytDeXKOj7dH2NmQ/FlHam0RSpj4/foqGwSzTbRvX5IJUf8PCqbym04NN3AmDwbHBstXLxIugjWCsLE4+qrr+bxxx9nyZIlLFmyhIMHD7Jy5Uouv/zyvMyjZcuWsXbtWj744AMATj75ZE499VRuv/12WltbaWxs5L333mP16tVceumlVFZWAvDwww+za9cuPvvZz1JZWclHH33EQw89xKRJk/jqV7+au/7FF19MU1MTt9xyC9/5znewbZt7772XM844g/PPP//IflP6UBsJEgn56Ihn+XhvlNld3UyHQypjkclaeLuCDU46jo526CLjvW23vxRjchNaoAx9OM6nbsHaknLXdotgrSAIRwkeQ2fWlAjvfXyQLTvaR+SAMi2HzoRJyO/BSUbRSnxwCDFfpRwws2g+d52shWsxame4GqvDcT5l064zLFSNXioZq4IwEqLRKIsXL6a+vp5Vq1bR1tbG3XffTUdHB/fff/+A59XW1vKzn/0s75hSim984xucdVZPKZxt29xwww2kUinuuOMO/H4/P/7xj1m8eDHPPPMMpaWudI5lWdxwww2Ypsm9996LZVncd999LFmyhCeeeGLIrEZPWQVVC6+ncsE1ZPZ+RHr3h1jxdpSZxSgpw1vVQHD6yXjCQ2fED4WkLU1QdE1j9rQK3trawuYd7cNyQGWzNrGkSbDch52Moh/iZsHavxXn4E48005HL3O7hHT/t1iUlXWdYaWRLr0n2cAIwkQgHA7z2GOPsWLFCm6++WYCgQCLFi1i6dKleeMcx8Hu1f3AMAweeughHnjgAdasWUNrayuTJ0/muuuu46abbsqNa2xsZN26dTz33HMkEgkqKipYsGABf//3f5+n7eTxeFizZg133XUX3/3ud9E0LRf5GetSAk3TOGFqhDc3H2Drro4ROaAyWYt4yqIq7MfOZiATR/ONvCmDsi3sPZtw4gfxzvxcbiNjVA6ceVbwOjnBWtnACIJwdDJnegXvfXyQ7Xs7yZg2fu/wHf+xRBazxEHPptAitdAlPj5cVCaBtft9cGw8M85G03U0TcOoPb74aygFmQTomqv3JCV3gjBinnzySaLRKGvXrs0FTw3DYOnSpXzrW98aUAbC5/Nx+umn5x174403iMViLFq0KHfs+eefZ+vWrfzqV7+iqakJgFNOOYULL7yQn//853z9618HYN26dWzZsoVnn32WWbNmAa6T65prruHVV19l/vz5Rb2PZngITJ1DYOqcoQePEHFATWBmT3cdUDv2R0lmLEqKLINUQGciQ3XQRplpKB1+NKf/RRVOom3YjifoI1grHTcEYcLR2NjIww8/POiYlStXsnLlyrxjVVVV3HHHHYOet3DhQhYuXFjUc9TW1vLAAw8UNfZI09TlgNrRHBuWve6mW0ekqtzv2kzLRCs5hBJl3UDZJigHlWxH8w1PXFcEawVBOFaoqwhSEfLTHs+wfU8nJx5XOexrpDMWifYYYY92aOtcj89duyvlVg0EB26yUQjlOKhMDM0bcDVWi2wMJAhCYTZs2MA555yTcz4BXHLJJSxbtoz169cPS4f02WefJRQK5a17P/jgA2pqanLOJ4C6ujpmzZrFyy+/nHNArV+/nqamppzzCWDu3Lk0NDSwfv36oh1QRwJxQE1gqsIBaiuCHGhPsXVXB6fPLN75k85YxDtjBDSGPRE6yU40bwDN60bfjZpG9FBVUSK1vVFKodJxNMNAL58kGxhBGCX+230vc91fzOacU+qHHgy0RdP8/KWtVIYDfPULJ4zy0x0bVJQFqIkEaelIsW13J6fOGH4KczpjkUyZeJPRYet0KKVQsVa0UFUuYu6pn+N2TRqm1p5ybFQ6huYr6drAiN6TIIwGYrvHB5qmMXt6Ba9t2s/mHe0jckApM0Oso53I5OGdqxwHlTiIXlbjPovhxTP1NDR/yfDnAdtEZRJowbBbLi16T8NCKYVpZsf6MQ4bjqORThtksxls+/DokY0HBnovw/Cgj0KSw/bt2/M0ScHNbpo2bVrB5jgDYZom69at46KLLsLv78lwz2Qy+Hz9/1Z9Ph8ff/xx3nMUcnbNnDlzWM9xJBAH1ARnzrQKDrSn2LyjfVgOKCudJml1Ulo1vOwnu+VT7OaP0COT8Uw5GXAnQ224zicRrBWEI8Z5pzXww39/m5+sfZ8L5k7hlBnVzJhSTrjUh6ZpZEyb/a0Jtuxo541N+3jnwwPMmlrBX371uLF+9KOKpqkRWjpSbN3dMSIHVDZrk4hFqbDSKN/wnEb27vdwOpsxJjdhVE0DXM2n4SKCtYJw5BDbPX6YPS3Ca5v2s6c1QTSRHbb+rW6myFhp0o5OsStv5dhY299AZRJ4jp+HXhJxr1UaGda9AZSZRllZtFAVeqjysHTfO5bIZrPs3bvT1d86imht1XGco+udYOD3CgZDhMOV/RIe9u7dy/XXXz/g9V566aUBP4tGo3mSEN2Ew2E6OzuLfuYNGzbQ0dGRV34HbpXB/v37aW5upq6uDoBEIsG2bdtIp3tKeaPRKGVl/a1LOBye2A4oicSMP06YGuHVd/fS0pHiYGeaqvLihLt1K00yk8KprmQ4vmAtVAkHNNA0lHLQtOF7kkWwVhCOLNdc3MQlZ0/nVxu2s+6NHfy/332EBqBpeHQNy3YnaQWc2FjFd687k8+dWpydF4pn1pRyfv/ePva2Jogls5SVDDN6DcTa2oiE9WFnrmqllRBrcWv5RkiPYG1Vl96TbGAEYTQR2z1+KCvxMbUmxK6WOFt2tvPZOXXFn+zY6GacDG7zoJoiT9N0A60k4pZL2+aInrtb70npGnp5LVqwXKoNholSir1796LrOuXlNSPa+4xXDEM7qrKfuun7XkopstkM8Xg7AOXlhy6kPRRKqWH9rT3zzDNUV1dzzjnn5B1ftGgRq1at4rbbbmP58uX4/X7uuecekskkHk++K6fQ/Yb7HEeCYTmgJBIz/gj6PRw3KczH+6Js3tnOeacUoeOhHPRsjIxmkMxalHsHdgA58YNgW+jl7kSrB8N4Z5074owllU2hbFMEawXhCFMZDvD1RSdx3V/O4cMd7Xy4o42D0TRZ0yFc6mNKbYhTZlRTHZFsxNGirMRHQ3Upe1oTbN3VwRlNxXcJBdDsLFY6RiZcwWChBqUcnPa9bje7ErdBhV5Rjx6qzomND4duvSfVrfckgrWCcMQQ2z1+mD29gl0tcTbvaGfe7Nqi7aBuZ9CtDI4vRDSZxbILt8BTtoVzcCd65ZRcdqlRNwtj0qwRBWtzek8eP0a4ZkRZrwI4jk0ikaSsrBLfCObQ8YzHo2NZR18GVKH38nU1bonH2ykrq8grx6uvrx80y2kwwuEw0Wi03/FYLFa0/lMikeCVV17hK1/5CoaRH9wrLy/nBz/4AbfddhsXXXQRAPPmzeOKK67g9ddfL+o5CmVojSXDckBJJGZ8Mmd6BR/vi/LhznY+d9IkdH3wCVG30uh2BstbSjyRJTJAGrETa8HasREML97SitxkOBLnk1KOW3Kne9AjovckCGOFx9A56fgqTjp+9KM/Qn+apkbY05rgwxE4oHQziWlmSZoaJSUD20+n5RPsAx+7ke7j56FpmhuxHYnzqbtc2hvECFeLYK0gjBFiu8eeGQ1hXnlHpzORZX9bkslVxTl09KzbsAFNw8xaxFMmRgETbu9+DyfWirKyeOpnA6B5RlYloCwTZSbQAuGucumRd0091rG79reGIco1E51uJ5RtW+j64ZEQmDFjRr8St2w2y86dO/tpQw3ECy+8QCqV4vLLLy/4+bnnnsvvfvc7Pv30U3w+H1OnTuWb3/xmXhe9GTNmsHnz5n7nbtu2jc9//vNFv09m33b8k4sXTh8Jw/5LkkjM+OO4yWUEfAaJtMWuA3GmTxq8ulzPJgANNJ1kxiJjORRSE9FCVW6kuyQCh+AsUrblbmACIbfkznt0RQ8EQRCKZUZDOa9s3EtrZ5qD0TRV4SLtoWNjZOMo3UcsYVJZFsAYoAJOr5iC074XPTIJNyQ0MvutzAzKSrvl0qEqKZcWBOGYxucxmNFQzpad7Wze0V6UA0qzM+hWEsdwN76mpYgnslSU+oD8TCi9ajoqk0APDV/kvDcqk0QpCy1U3dVdWsqlDw23lEsC5xOf0fgZzp8/nwcffJD29nYqKioA16GUzWZZsGBBUdd49tlnmTZtGqeddtqAYwzDyGVUbd++nT/84Q+sWbMm9/mCBQt4+umn88TIN27cyJ49e4p+DoA9j/wj/oZZhM/8S0JzPoc2Co7XEdc/dUdivvz5WXzjr07hv33lNK7/yzl8/oyp4nw6whi6zglTIgBs3tk+6NjcROhxNz0Z0yGVsVBKYXc2Y+16zy23ADRNx9M4D8/kphFvPFQ2jcom0Uor3cwncT4JgnAME/R7mD7Jdflv3dVR9Hm6nUazsyjDT9a0SGVdPRDlOFgtn2I3b8uN1bx+PLPOxaiaNjKdvq4Opcox0cpq0MO14nwSBEHArToA2Lq7I1f5MRi6mUJTNug9m7h42iKZTGPt24LTsb9nbKgSz8zPoYeHlx3bjXIcnFQn6Bp6ZLIbOBDnk9DF/v37ueyyL4z1Yxx1XH311ZSVlbFkyRJeffVV1q5dy5133snll1+eV4K3bNkyTjzxxH7nt7W18dprr3HZZZcNeI/77ruPF154gddee41HH32Ur33ta1xxxRV5elEXX3wxTU1N3HLLLbz00kusW7eOpUuXcsYZZ3D++ecX/T41l38blKLl6VXs/NGNtL3y71jRg0WfXwySS3iUMGd6Be9+fJDtezrJmDZ+b+EJx50ILZTullEoRxFNZJmUzWDt3oRjWm5b7fJJACPWaFLKQaUTaIZohgiCIPTmhKkRPtkX48NdHZx9Yt3QtlGpvBIOy1YkUhbhEh92vB1r30coBXpkck7jY8S227FR6RiaL4hRVi2aIYIgCL2YUlNKKOglnjL5eF80FwAuSC5zNb/8LWvaRNt3EIrvRkUPoIVrco6iEdtu20RlEmjBMlfvzysld8cCX/nK5bS3t+X0jMrLI/ziF88UHDtp0iR+/euR6RwJAxMOh3nsscdYsWIFN998M4FAgEWLFrF06dK8cY7jYBfQf/vNb36DZVkDlt+B6zxcvnw5nZ2dNDQ0cOONN7J48eK8MR6PhzVr1nDXXXfx3e9+F03TWLhwIcuWLRvWHrzs1AsoO/UC0nu3Ef3Tc3S+8Ss6XltLyawzKJ93KcHpJxd9rYEQB9RRQm1FkIoyP+2xDNt2d3JSY4H03QEmwnTGIm3rGLUz0Ew34n0o5Eru/CXoZTUjFiwXBEE4Gjl+cjkeYzfRRJbm9hSTKgfXVdLsLLqVypVwACRSJpZSeMJVGFVTUL4yOER9JmVl3YzV7pI7z+HRRxAEQTha0DSN2dMq+NOHB9iyo31QB5RuuZmrjq+/0EWnt5ZgMI6/dvohZyn1NPiRDqXHIg888BAnn3zKIV3Dsqx+HdWE4mlsbOThhx8edMzKlStZuXJlv+PXXnst11577aDnfv/73y/qOWpra3nggQeKGjsUgfqZBL54C/aFXyf6zgvE3l7Hvif+J97qBsrPvJTQqRegj3CdKL9pRwmapjFnegV/eH8/m3e2F3RAdZdw9J0Is6ZDeyxNpPY4LPPQOiHkNENKK7o0Q+RXTBAEoTdej86M+nI+3NXB1l0dQzqgdMst4VC9Sjgypk0qbREJg7dhDrZ1aG2UVSaBUg5auBa9JCIdSgVBEAZgzvQIf/rwADuaYyTTJiWBAiXKSqGbPZmrfcla0BlpYnI4hLJHZr+lwY/Ql87ODv7n//wffPjhB2iazoIFn+fv/m4pPp+Pffv2cu21X+Hll/8AwHnnncnSpf/I44//G/X1DZx22mfYu3cPmUyGN954jcbG47njjruZNMntsL59+zZ+8IN72L59G/X1Dfz3/76M2bNPxHEcfvjD+3j55XVYlsX06Y2sXv1/0HW94HFxdE0sNMOD7vWD4QGlUGaG1t/8hPbf/4K6K5cSaDhh2NeUFeZRRNPUCAB7WxN0xjP5H/Yp4egm0PExnmQLiXiaZMYeqVZtl2ZIDKUs9PI6t4xPnE+CIAgFOaHLXm/d1YGjBtl8FMpcdSx8rVtJHGzOafaNFOXYXZohBkZkMkaoUpxPgiAIg1BRFqCusgSl4MMBtPw0O4PRJ3NVz8bxd3yClokDEEuaxFPmiBr9KNtCpaJovhL0inp0kbo4YiilyGTtUft3KPO64yiuvPIqfvnL3/Doo//O5s2bWLv2/w04/o9/fJN/+7cnue++/wXAhg2/46tfvZrf/OZlpk6dxr/+qytynUwmWbr0Fq666mv8+tcv8rd/ewPLln2XTCbDm2++zubNm/j5z3/Fc8+9zC23/AOapg14XJgYZJo/peW5h9ix6hscfPmnBBpOoP5vVzLtvz1Iww334ymrpPW5h0Z0bfEQjGM0zf1XrB0qK/ExtTbErgNxtuzs4KwT63quZWf7T4RmEn98LyT2ka2spT2apq6ypPgbdiFtugVhYtAZz/D8a5/y/scHaYumAbez6akzq7nk7OMIl0rJ1ZFiWl2IgM8gmbHYfSDOtLrC3Ut1K41uZ7B9PZ970234Es3YexIkjps+4ialysqizKS06RaEcY7Y7vHHnGkRmtuSbN7Rzmdm9Zeu0M0kOA54e7ZavmQz/sQ+NK8DpTOwLEV7LENpwNvdaK0oVDaNsrNug5+ySjRdtnNHCqUUdz/+Ntv2dI7aPWZOKee2a+cW7ay59db/hmG4gaMrrvgKN930bQCqq6v54he/zJ/+9AZXXXVNwXP/5m/+lpKSHq3HefPO5rTTPgPAhRdezP/5P/8CwB/+8CozZsxkwYLPA3D++Rfw6KMPs2nTe3g8HpLJBDt37qSpaXauHHCg48L4Jv7BfxL9029I7/4QoyRM+WcvJ3zGJXhCFbkx/rrjqPz8tez79ztHdA+xWOMUx1GUBr0EfB5SGavo8+ZMr2DXgTibd7bz2Tm1OePlToR23kSoNIN02RQMbHTDSyKVJZH2Egp4UU5xM6G06RaEicGft7Zw92NvksxY6JpGuNSHAvYciPPnj1r45SvbWPb1z3LyjOqxftRjAkPXmdlQzvuftLF1V0dhB1RXCYfSjbwIue0pIVM6iYyvhAMHk0RKve7Hw9nASJtuQZgQiO0en5wwNcKGP++jtTNNS0eKmt4dwB0Lw0ygjHzHoBWoxHRMjFBdzl4nkybxZJayEt+Qa29p8DNOGGff8h/+8H/nnDvJZII77/z/8/bbfyKRSOA4NnPmnDTguXV1dXlfV1T0cjL4A6RSSQCam/fz1lt/5C/+4oLc55Zl0draysUX/wWXX/4l7rrrdqLRKF/84pf4r//1Rs4887MFjwvjmwO//CG+SY3ULFpC6KTzBtzbe8prCJ08f0T3GJEDSiIxR4aAV6e0xDssB9SM+nK8nj1EE1n2HUxSX13qlnCYcZSRH91WHj+Z8uPQdY0SwLQUbdEMpf6hnUhKKcgkUBpum+7SyIjafQuCMPp0xjPc89M/URL0cvNVn+GMObUEfK75T2ct/vhBM488s4mV//ZHVv/3L4gNP0I0TY3w/idtbNvbyQV2Ax4j34b2lHAE8o47vhBp30x0XSOeNkmns9RUlODRtSGdUMpxUJkYmseHXjYZzR+SDYwgjFPEdo9fAj4PjZPL2L43ypad7XkOKN1KoztZbG9+YMEKVOCUVFJS4oNEFgDbUbTFMpQGvYP6NaTBz/hA0zRuu3Yu2UPUzB0Mn1cf8bz85JNPEItF+dd//b9EIhHWrv1/vPjibwccX+x9ampqOfvsc7n77vsLfn7NNddxzTXXsWvXTv7u777Fqaeexrx5Zw94XBi/1P/NnQSmzhlynLdiErWXf3tE9xi2x+DPW1u48e4XeeK3W3hvWyuxRJZoIst721r56W82c9PKF3l/e+uIHkbIx3GgPOTvtykZDK/HjaoDbN7RDrgCtrpj9ovEFCKZMokms2j6wAZJOTYq1Qkeb49miDifBGHcsu6NHThKce+3z+fc0+pzGxhwF9Hnn97APd8+D8tWvPDGjjF80mOL+mq3nXfWdNixP9bv81wJxxDZSdGEyf62JJajBo3MKttEpaNogRB6pB5dBGsFYVwjtnt8M2e6my3y4c4OnO7sJaVc3T7NU7S2UzJt0ZnIohuFxyszg8om3AY/kXpxPo0xmqbh9xmj9u9Q5uVkMonP5ycUCrF//z5++ctfHJZ3/tznzufDDzezYcMrWJZFJpPm9df/QDweZ8uWD9iyZTO2bRMKhTAMA103BjwujG/89bNwsumCnznZNMouPjFmIIaVAXUkIjGffPIJK1as4K233iIYDHLZZZexdOlSAoHAoOclk0lWr17N888/T0tLC3V1dVx++eXceOON+Hy+3LUff/xxXnvtNfbu3UtFRQXnnHMOt956KzU1+fXbLS0t3HXXXWzYsAFd11m4cCHLli0jEokM+51GilKKUNBDwG8QTxbvaZ8zvYLNO9r5aHcHC06bTMBMoLT8Eg5P6iCO4cfxltJ7x+I4ivZohlDQW9A7KW26BWHisXFrCxd9dhrVkYEXrbUVJVw4bxpvf3iAKxfOOoJPd+yiaRonTCnn7Y9a+XBXBzO6ggdATwlHHxvrTTRj+ctRnvw5MRZ3o+mTKgtnQqlsEmVb0qZbECYQYrvHN9MnleW0/HY2xzhuchjNzqDb6fzMVcfGm2rBClaB3n/drBxFRzRDWdBHbx+UW20QR+m9S+4k4CsMzFe/ejW3376Mv/zLzzNt2nHMn38B77zz1iFfNxQKce+9/4sf/egH3H33HXg8Hk455TROPvlU4vE4DzxwP/v37yMYLOGyy77IGWfM409/erPgcWF80/LcgyjLpO5L3+n3WetzD6F5fNQsWnJI9xiWA6p3JKbvZNgdiWmaXsEt33+FF97YMeyJMBqNsnjxYurr61m1ahVtbW3cfffddHR0cP/9hVP+ulm+fDkvvvgit956K7NmzeLdd99l1apVdHZ28r3vfQ+A//zP/+TNN9/kqquuYs6cOezfv58f//jH/PVf/zXPPPMMpaWuCJtlWdxwww2Ypsm9996LZVncd999LFmyhCeeeOKIRow9uk445CeeNIs+p6G6lLISL7Gkyc49rcyJ9CnhUIpgxzZ02yRefTKqpCLv/FTGojOeparcj9PVGlYpBdmktOkWhAnIruYYi85rHHLcScdXsuGd3UfgiYRuTphWwdsftfLJvigZ08bvdR1DhUo4NCtNSftHKDRikz/bbyNTyAklbboFYeIitnt8Y+g6TVMj/Hn7QTbvbOe4yWF0M4nm2ODtcfJ7022UtG/Dju0hOfmMgtdKZy06EhlqygM4tpIGP0JR/OIXz+R9XVc3iYceeqTg2MmT63n55T/kvv797/+Ex6NjWW6SQ199prlzz+T//t+eDnozZ87igQce7HfdM8/8LD/96VNFHxfGN+lP36dy4XUFPyuZdSZtv3vikO8xLAfUaEdinnzySaLRKGvXrqWyshIAwzBYunQp3/rWt5gxY0bB8yzL4vnnn+eGG27g+uuvB+Dss89m7969PPfcczkH1KWXXsq1116bt/huamrir/7qr1i3bh1f+tKXAFi3bh1btmzh2WefZdYs9x1qa2u55pprePXVV5k/f2SCWyPBth3CJV4Oeg0ypl3UOZqmMXtaBX/ccoBPduxnTrgsbyLUHAvbF4ZsDNsf7pfppJSiI56hrNSLR9N6JkGPH6OsDi0QOoxvKAjCaJNImZSHhu5wVh7yk0gV7+wWDp2a8gAVZX7aYxk+3ht1SzqUwsjG+pVwaI7lZj+hoYzCeiG9nVCGslHpuFtyV1aN5h08k1gQhPGF2O7xz5zpFfx5+0E+3hslm84QMhM4fTJUlaZhe0sxAxUDluUp5VaahEu8eGxTGvwIgjAm2IlOjFBFwc+MUAQ70XHI9xhWCsuu5hgnNlYOOe6k4yvZ1dxfz2IoNmzYwDnnnJNzPgFccskl+Hw+1q9fP+B5Sils26asLF/sLxwOu5k7XVRWVvaL/DY1NWEYBgcOHMgdW79+PU1NTTnnE8DcuXNpaGgY9DlGi6DfQyAwPL34OdMqMLCJtR0kaeX/mJXhJVk1h9ikM2GAVN5MxqI9lkFTpitYGwijV0wW55MgTEBM2ylKS87QNSx79IQ1hf5omkbT1AgAH+5ydfs0O4NupXH6NI5wfCESNaeQrD5x0GvG4ln2H2jDSifQQ5Vu5pM4nwRhwiG2+8ihazoez/Az+2siQSrDfmxH8enuZnQ7i9LzHUZWsJp43WfIhKcNeq1M2qL9YBsaltvgJ1wrzidBEI4oeqAUs21/wc/Mtv1ovkNfTw7LqzHakZjt27dz5ZVX5h3z+XxMmzaN7du3D3ie1+vly1/+Mj/96U+ZO3cuM2fO5L333uOpp57iuusKp5B1884772Dbdl521fbt2wtmW82cOXPQ5xgtNKCizE8snsVRxfXZjpT5mV7pgajF9uY0Jx1fWuDCA0+0Coh3dBLWA/gj1Wgl0qZbECYyuw/E0QdpLtA9RjjynDA1wusfNLPrQJxk2iSsEmiogcXHB9MAUQrdTBC3DFQgQn1pNT5Dp8ipQxCEcYbY7iODUopIyE80VvxaG9wgwpxpFfzh/X3s2bWPU2omDSw+PqjtdtCzcWJGKeXBGkrKwmK3BUE44gSmn0THH/6D0tlnYQR7knvsVIyO135J8LhTDvkew3JAjXYkJhqNEg6H+x0Ph8N0dnYOeu7y5cu5/fbbueqqq3LHrr/+er797YHbA5qmyT//8z/T2NjIBRdckPccfbOpup9jMAfUF77whQE/++1vf4thjMyB4ziKUNBLwG+QTBepPK8cTp7kYWOnwfZ9UU5sdLO/NCsNutEvOtP3XMNMkNG9tGsRppbXYktkTRAmNA88+faQYxSDNlETRolIyE9dRZDm9hTbdx3kjLpsv+wn3UzgeIKDb2Ac2x3nLcEKVNCe9aNaE0ypCeE1NNnMCMIERGz3kaF7re33GaQyw+vy1DStgrc37STeGaUjW0+41xJbz8bdhj+DaO9pdhbdTmP5ysh6KziYNigt11C2GG1BEI4sFfP/mj2P/CO7Hvw2oTnnYpRVYsUOktj8GsqxqZj/14d8j+HVdTE2kRil1JCiqffffz+vvPIKd955J42NjWzatIlVq1YRDoe55ZZbCp5z55138tFHH/H444/j8eR/Kwrdr5jnGC38Xp3SEl/RDijdztBY4+VPW72k4lnaYhmqwgGCnR/jSbeTiszCLK3tf2JX5yXLV4YdiNBheilPZgkFvHnljIIgTBz+7q8/M9aPIAzBCVMjNLen2Ln7APOqS7F9+Y0jSls3oSmHePXJOL7+pdCanUGzstj+MFagAnR3TuuIZgDECSUIExCx3UcWd63tGbYDKhT0MqPaINkGH++LcfpM135rVoayAxtxDD+xurkFs1p1MwkozEAVtj8Mmk5nPEO0zE+ZrL0FQTjC+KoaqP+bFRx88VGiG18ExwFdJzjtRCov/Dq+qoZDvsewHVCjGYkJh8NEo9F+x2Ox2IAC5ABbt27lkUceYfXq1bkspHnz5qFpGvfeey/XXnstVVVVeef8+Mc/5he/+AU/+tGPOOWU/FSywZ6jUIZWNy+99NKg73cok4jjQHnIR3s0netWMBi6mcTj0amvCbOjOc7He6NUlfnR7CyaUti+/iV5mpVGt7JYgQosfwR0g6zp0NqRJjTZ26+ttyAIE4MvzBtcd0IYe06YEuH37+4l0dFBLFNKib+X+Lidyf2/4+3TCUkp9GwCW4FVUo3tC/XLkhInlCBMTMR2H1kcB8pL/bRHM9jDyD7SbJMTJ/n4/UEvH++NctqMajRNw7ASKN2D4/H3dz4pBz0TRxk+rGBlnm03TYfWjhRlsvYWBGEM8NcdR/21y3HMDE46gR4MoXt8Q59YJMNyQI12JGbGjBn9Styy2Sw7d+7spw3Vm23btgEwZ86cvONz5szBsiz27NmT54B64okn+NGPfsQdd9xRsGxuxowZbN68ueB9Pv/5zw/rnQ4XSilCAS8Bn4e4lR10rGabbhmG4WdGQzk7muN8sq+TuSfUkKg9Hd1M5m9ilELLxNFQZEtq+6UKxxJZOuNZIiEfjiMzoSBMdJRSRBNZNE2jrMQ7ZpmdQg+lQS/HV/tw2rN83JLh5F6xDuUJEJs0D91K5zuXlIOeiaE8XixfhVuiNwDihBKEiY/Y7tFFKUVp0F1rD0fLVrdSTKnyozxe4imLA+1J6ipLsQKVRCd/Fs3us263LQwzTtZXih2oQBn9N3bxhClrb0EQxhTd60f3Dq3/PVyG5YAa7UjM/PnzefDBB2lvb6eiwm3/98ILL5DNZlmwYMGA5zU0uKlgmzZtor6+Pnf8/fffB2DKlCm5Y7/+9a9ZsWIFt9xyC3/914VrGBcsWMDTTz+dJ0a+ceNG9uzZM+hzjDaGDuVlPuLJwR1Qup1Gt01sf5D6Ki8Bn0E6a7O3NcHU2lC/CLpmZ8DvxyytxtH6T4KW7dDSmXIXO4f1jQRBOJJs+bSNX7z8Ee9uayGTtQHw+wxOnVnDV78wi6bpQ3c5FYamGK3EQsyZ7GNTO3y8P87JM2ryP9Q0HG++g0k3UzilEUxfCEcNrTEoTihBmJiI7T5yeA2dslJf8Q4o5aCbcXSvj+Pqyti2J8r2vVHqKrsqDTQd5cnvGqWbSaxABMtTPmCzidzau1TW3oIgHFnMtr1E336BbOtuVN/EF02j/trlh3T9YZfgZUyb19/bx4H2JOUhP2edNKmoznjFcPXVV/P444+zZMkSlixZwsGDB1m5ciWXX355XgnesmXLWLt2LR988AEAJ598Mqeeeiq33347ra2tNDY28t5777F69WouvfRSKivdifnNN9/kH//xHznzzDM599xz2bhxY+6alZWVTJvmOtguvvhimpqauOWWW/jOd76Dbdvce++9nHHGGZx//vmH5V1Hgm0rwiU+Wn1GbgHSD+WgZ2Oorratuq7RODnMlh0H2b6ng6m1BbRDHBPlr0Q5fhgg5TiRNGmPZaiJBIaVliwIwvjg1//5CWvWvgfAzCkR6ipLUMCBtiR/2tzMnzY3840rTuGycxvH9kEnOI7jECrx4vHoRZVLd6PZJsdX6mzUvMTjWdpjaSrKAuDYA3fDUzbKFwLTM6Dt7os4oQRhYiG2+8jiOA7hUi8HO3TMYiQvrAy6lcbxljKjPsy2PVE+bY4xr6kar7dAwx/HRnl0HE8paIMHDnJr73JZewujy+rVq3jmmbVMmTKFNWv+bawfRxhDsgd2sufR2/CUVWK278dXOx07GcWOteEJV+GpmHTI9xiWA+pgZ4rb/vd/0tyWyJUkP/KMl+XfOJvZhyH6Eg6Heeyxx1ixYgU333wzgUCARYsWsXTp0rxxjuNg2z0OGMMweOihh3jggQdYs2YNra2tTJ48meuuu46bbropN+6NN97ANE3efPPNftlPX/rSl1i5ciUAHo+HNWvWcNddd/Hd734XTdNYuHAhy5YtG/N056DfIBjwDOiA6j0RdjOjPkxs9zYmRz+GTg3Ke4mHKYUGKE8QBkmschzFwc405SE/hoRiBGFCsWVHGz9Z+x5nzq7jW1eeSnUkP5OmtSPF6v/3Z9asfY9ZUyOcMK1ijJ504qMUlAY8BHwG8WE4oHQrhddwqK0uZ9eBBJ/si1JRFiDY/hGGlSJV3ogdiOTGa44Jugfl8YM5QEBiAMQJJQgTA7HdRx7XhnsJ+D2YQ0heAOhmHNBA06mtKCEU9BBPmahP3qC0opxkxay8DCjNMVFG0C27G2KKcBzFwY405aWy9hYK85WvXE57exu6rlNSUsoXvnAR/+2//f2wOq/v37+Pp5/+f/ziF88W7AIvHFu0vfIEweNPo+7L/8And/81NZctwT/5eJIfvUXLr/83lRdcc8j3GJYD6vHfbOFgZ4qrLmyiaXoFe1vj/PzFj3jwF+/ywD9ccMgPA9DY2MjDDz886JiVK1fmnEXdVFVVcccddwx63s0338zNN9/8/7H35nFy1WW+//vstXf1ku50Z98ICasBhLAksuqwuTEuF5BxBBFGcfTGO2Ouv4Gr4cIFxhnQi45cHREdlVEHERHZJEFUUJQlgQAJ2dfeaq866/f3x6mq7kpXd3pNupPzfr0CVWepOtXd9f1+z+d5ns8zrOtobW3l7rvvHtaxhxQBjXGDTM6qWxMu2zk/VbefT0hTIsSsaAnNstnTmae9oe94ybPwFM2/ibGGTjculGy60kU6mqO47vBvrAICAg4vDz2zmcWzG/mfH39n3S6mLckwX/r46fzj//0tP/vNJv7xmtMOw1UeOaiyX8KRK4ykhMPPXJ3bnvAFqL1Z3rGwGa3UiyRchHxAp1bXxtPDoGjAyAQoCESogICpQDB2Hx5kCRpiBrm8NaQHuOTayE4BofiVIJIkMb8jwea3d5NNpWmJKgi5NgtK9mzQW8CTGY7DeKFk05MtMb0xEqy9A+py993f5PjjT2D79q18+tPXM3v2HN73viuGda7j2Ozbt5fm5pZRiU+O4wzoJB8wtTH3vk3Lez7Z5wct/HEnsugUGk6/nJ7f/ICOq78ypvcY0V/MS2/u568vOIaPXLi4vKWN9uYoa77zfF+5QMCE4nmCWFgnpKsUSrU3N/5EWMRTBv4e7LYTeXPzVrRuhfaFted4eqUGfeibJSH8m5bGuIGhysENS0DAFOG1Ld387eXH172BqSDLEhefOZfv/GLDIbyyIxPP84hHdbpTRSx7OCUcJWTHxNOizGwxUFWJfNGhM2Mit5+KWkrVZLUCSJ6De2BHvBFSK0LJh73dtyRJSDKYtse+ngK26aAoEpoiocgynicQYmwdZQMCphLB2H14cF1BPKKhD2V5gZ+5KrkOntE3Fs/vaOCVzT08lZrN+49pRetfQi08QEJoITAHvl49hIDedIlkVEcbpb9gwNHB7NlzOfHEk3n77c385jdP8u1vf4vu7i5OPPEk/vEf/z8aG5v485//xF133cbKlefx85//jCVLlvCXv/wF27a48MJzuOKKj3D99X/Hgw/+kP/8zx9SLBZZseJdfPaz/x3DCPHoo7/g8cd/xfTp7Tz99JP8/d+v4jvf+RYf/OCHefjhn9HT0811193A0qXH87//9/+iu7ubv/mbT/DhD18JwG9/u5Zvfete9uzZQ3NzC5/61N/xrnf5DcFuvfUWEokEmza9xWuvbeCEE07kllv+d7UD/Ysv/pF7772HHTu209TUxBe/+E+cdNI7yGTSfPWrd/CnP71AOBzhb//2Oi677PLD9nuY6nilPEo4hiTJoCi4pXx1n9G+gN7f/ueY32NEI1lv1uS4+c01205Y0IIAUtlhjqQBY0ZXZeLRgXXlslNEdu2q/1N/5s5ooEfE2ZtySOfLvyt/JT/AHHEoiqZDZ6qENMRiKCAgYHKRLdhMSw7eIa3CtMYI2eFm7QQMihAQNVQMY3gxHtnOUynh0FSZ2WWvvi17sghZw45Mq+lMiufiyQpiHFripjImOztz2K53WErMJQkURcYD0gWLbXuzvL07zY69WbbsTrNpR5q3dmTYsjvD/lSJdMGiZLt4gKxIKIqMLEsEzcACjkQmeuzesmULn/jEJzj55JNZvnw5a9asoVQqHfS8QqHAXXfdxQUXXMBJJ53ERRddxNe+9jUsq7ZkbefOnXz+85/n7LPP5h3veAfvf//7efjhhwe8XmdnJ3//93/PsmXLOPXUU/kf/+N/kEqlRvx5xpOK5cWgVD1Xa8fhRERnWjJESei80Vsnc7VSdTACCiWHroyJEtThTRqEbfr/+gVEhOv421x7kGP7AlLCKx97QJmnsEd/P7116xZeeeUlpk9v52tf+xe+/OX/zS9+8Thz5szlrrtuqx63a9dOwuEwDz/8a/7P//kqd911N7NmzeaJJ57l+uv/juef/z3/8R/f45//+R5+9KP/YteuXXz3u33VSX/5y4ucfPIyHnvsN5x//oUAPP/877jvvu9x993f4N577+GHP/w+9977be6555v827/dW/0+RyJR1qz5P/z618/w2c9+nltv/V90d3dVX/upp57gs5/97zzyyBO4rsvPfvYgALt372L16lX87d9+kl/96mn+9V+/QXNzCwBf+co/0draxs9+9kv++Z/v5pvf/DpvvfXmqH+ORztqvAm3kAFAa5xOaftr1X3W/m3I2tgTjkaUAeUJgaHV1pRq5edu0CL0kOEbJOr0pEt9BonlEg6vTitXgIih0dESZVdnns27Miw7ZhqS5yAUFW+EE2Ema5KL60QNLYhEBwRMARJRjc7eAtA85HGdqSKJOuJ2wMiRJWiIGuRyByvhsPwSjn7j8NzpDby9O8u2vRlOW9w6IPtB9iw8xUDIYxeg4PBkQlU+U850yOaKZPIWJcvBdQWKIqFqKrbjVY13cwDpErIsoSoyall8ChkKYV1D12V0VUFV/P0SfqaU/2/CP05AwIQwkWN3JpPhmmuuoaOjg3vuuYeenh5uu+02UqkUd91115Dn3nLLLTz55JN87nOfY9GiRbzyyivcc889pNNpvvSlLwFgmiaf+MQnAL95UDKZ5JFHHuELX/gCoVCIiy66CPBLeK699lps2+aOO+7AcRzuvPNObrzxRn7wgx8cPu9VAcmYQSZr4dUZRGSnhOyaA7JTwfde7UyV2Lw7zXFzm6qfQfYsvFBjjU3GcElnSjTGdMK6Eoxpk4Dcv18PQPTqe5DCfoaO9fKjWH/6GdqxKwit+Nu+Yx/4DDgW0Y/eiRT3O9zaG57C/P0PUReeQfi8Pr/i/A9XEfvY10Z0LZ/73N+hKDLxeAMXX3wZO3bs4IMf/BDz5/slL3/zN9fxV391Lo7jAGAYIa688hoURUFVB/4tPvXU47zvfR9k9uy5AHz849dy663/i+uv/zsAZs2azXvec0n1tQCuuOIjxGIxjj12KY2NTZx77gUkEgkSiQRtbW1s27aFZPIdLFt2avV9li8/m/nzF7Bx4+ucdZbf5Ov88y+sXvfKlefx5z//EYAnnniMs89eWT1u+nTfCLu7u4uXX36J22//KoqiMHv2XC688D0888xTzJvXr+QnYNgYM5dQ2vkG0cWnEzt+Bb3rfoyb60VSVLKv/IbY8SvG/B4jLtrcuT9Xsxiu+BDt3J8bcOzCmcnRX1nAoNQzSOxfwtEfxcwQymzDirYxvyPBrs48b+/J8I5FLciehatGQR7Zn4Fpu3SlSsSma8EkGBAwBVgyr5lHf7eVFe+YOWgph+cJfvnbt1k6b+gbnXps2bKFNWvW8OKLLxIOh7nkkktYtWoVodDQUZJCocC9997LY489RmdnJ21tbVx22WVcf/316Hp9cWXNmjU88MADXHnllfzTP/1Tzb7Ozk5uvfVW1q1bhyzL1eYRyWRyxJ9prLiuIB4eRgmHXS7hUPtKONqbI8w20kS9HD17Y7R0tNee5Nl4RpLxTPs5FCJUtcTOcsnlHFLZEkXTxRqBibrnCSzPrVoWZvMARV+0UmT/nyoTNhRCuoquymiaglYWrYQQeF5QxhcwNZjIsftHP/oRmUyGhx56qNotWlEUVq1axQ033FDTfbo/juPw2GOPce2113L11VcDcMYZZ7B7924effTRqgD16quvsnXrVu6//37OOOMMAM4880xefvllHn300aoA9fjjj7Nx40YeeeQRFi1aBPg+rB/96Ed59tlnWbFi7Dc7o8G3vNAwdIWi6QzYL1t9mav9CaU2szTksl4xSeegJ1OiuSHsVxwIRlR10J+S5dKVLjG7LYYIOuIF9ONf/uX/cvzxJ1Sfr1p1E0888SseeODfq9sURaWnpxvwfZOHMinv6uriHe84pfp8+vT2miyl1ta2Aeckk30NEAzDqFl3GUaIYrEIwMsvv8Q3v3kPW7duRQiPYrFIJpPu9zp9Tc1CoRCFgn9eZ+d+Ojr6NdEqs2/fXkqlIpdccn51m+t6vOc9fzXo5wsYmsazP4iT7QEgufx9uLkUuQ3rAIno0jNpvuCaMb/HiAWou3/057rb/+U/Xqw+FoAE/Pyu9472ugIOgiL7kZmKQeJgE6FW7EQ103iKwaxpC9FUiULJYW9PgZkxF08LM5pbmEzeIp23aIjqdc3QAwICJg/vW7mAf/j6b/nf332BG684iaZE7QK4O13kGz99hbd2pLjufScM8ir1megoen/eeOMNfvrTnxKLxQbsm4xR9JChEDKGEKA8F9nODSjhUGSJpUmTQjpD5/7OWgFKeICMp+gjq6EfBhMhQkkSyLKM43pkCxbpnEm+6FCynHENYLiuwHVdzLIhezrbV95XyZjSVIVISMXQFDStL2NKlqSyv1QwlwVMLiZy7F63bh3Lly+vik8A7373u1m9ejVr164dVIASwv+uHWhYnEgkar5DlWyLA4+Lx+M1x61du5bFixdXxSeAZcuWMWPGDNauXXvYBCgAQ5OJRtQBApTkmshOAe9AMclz0fP7MYTL7JZWXtsn2Lw7Q3NDuNz9Thu0UmE4ZHIm2bhBLBRUIBxuYh//N/9Bv1J4/aSL0U94N8i1s3Ps6nJGk9qXpagddz7ase8aEEiKfnToddNwmDatlU9+8u/40IcGdivbuXPHQddDLS0t7Nu3t/q8YlLex+jXU2vW/BPXXPO3vOc9l6KqKtdf//Fh/S23traxffu2AdunTWslFovzy18+WfO5VFXGGUEn4oA+tMbpaI1+hpkkK7S8+xO0vPsT4/oeIxKgPvvhd4zrmweMHtcVxKN+ZMYsFgeUcFQwYzMQioGjJ1AVmXnTE7y5M83W3b10HNuEUIxRDSOO49GVKhKPjE8JSEBAwMRx7Jwmrr38eP7fw+v5xJrHWTgzSVuTny25ryfPpp0pPAHXvff4Ebfxnugoen++8pWv8PGPf5yHHnpowL7JGEWXgGQsRCZn1RVbZNf0M1f1gYKaOuNYdnZvZE+3yiLXQy2bz0quhafoA0Sr8WK8RKhKtkbedMjki2RyfSV2hwoh/LmqbxFq05sBWZJQVAlV9rOlQoZCPKwTCanomgJCBIGVgEnBRI7dmzdv5oMf/GDNNl3XmT17Nps3bx70PE3T+MAHPsADDzzAsmXLWLhwIa+++ioPPvggV111VfW4U045hYULF/LVr36VW265hWQyyaOPPsr69ev57Gc/W3Md9eaJhQsXDnkdhwLP80upezNmzdgl20Uk4SDkAxpBSDL55iVopV6m6y28tm83W/ZkOGVxK5pr4eqxEVcd9MeyPb8CoV0bTgO9gAlE0gbec0mKCsrA32/dY2W17t9CvWNHysUXX8ZXvvJPvOMdp7Bo0TFkMmlefvkvnHPOu4Z1/rnnXsCdd/5vLrjg3TQ2NvLd7/4/zjvvgjFfF0ChkCceb0BRFJ555ineeOP1YZ13wQXv5uMf/2/8/ve/5fTTz6Szcz+O4zBjxkyOPXYp9933DT72sb9FVVU2b95EJBJi1qy543LNRxOebbLj3k/T8lefJHrMxHVVHdEoeP5psyfqOgJGQVhXiYQ17Ew3knDwDpwI8VN9zfjM6vP5M3wBau/+XqxjW8c0EWYLNr05k+a4ESzWAwImOZedM58FMxv4z6feYv3mLt7Y3guAoSm8Y3Erf33eMSyZ13SQVxnIREfRKzz88MPs3LmT++67r64ANRmj6H4Jh4qhq5TqlnDk/EhpnWhkY0sL+7QOikWHnZ055k73PSZk18IONY3KQ2S4jFaEkiQJWYaS7ZHLWaMqsTsUeELg2QIbD0y/jK9bLmFoCoah0BA1iBgqIUMpZ0d5Qbl5wGFjosbuTCZT7S7Vn0QiQTqdrnNGH7fccgs333wzH/rQh6rbrr76aj796U9Xn2uaxve+9z1uuOEGLrjgguq222+/neXLl9dcR73274lEYkgB6vzzzx90369//eshS4yGixCCaFgjpKvki+W6X89FsXIIuY5QIEm4oSRuKEmHJwjrCkXLZXdnlrmNEp4aGUPuiE82b5HOWSRjQQVCQH1OOOEk/u7vPsuaNTezZ89uEokE5513wbAFqOXLz+LDH/5vfPazN2CaJuecs5KPf/y6cbm2v//7L/Av/3IHt932vzj33As5+eRlwzqvo2MGa9bcwb333s0//dNqmptb+OIX/4kZM2Zy881f4Wtf+xc+9KH3Yts28+cv4HOf++/jcr1HG7JmIBwLWR+70fhQjF596Ee2YPHTp99i294szQ0hLjtnPnOmD5zUAsYXIQTJiErByeMN04x2WkOYeESDUoG3uzzmN4x+KvQ8QVeqSEN0/EtBAgICxp+l85q5+dpmPE+Qyfv+cYmoPmSL74Mx0VF0gFwuxx133MHq1asJh+t3hJqsUXRDVwkbAwWoagmHUj/aKUkSc6fH2bCll617Mr4AJTyEJI3aQ2QkjESEUpRyiV3R9kvsCva4l9hNNJ4nKJoORdMhnTXRVQVdV4hHdWIh/3eoKnJQqhdwWBjO2J3OmTTExp49IYQ4aInOXXfdxTPPPMNXvvIV5s2bx4YNG7jnnntIJBLcdNNNAJRKJW666SZc1+XrX/86sViMp59+mi9+8YskEomaoEC99xvOdRwKNEUmHtWrApTslvxM1DqZq/2RZYl5HQle29rL1t29zG5uRSj6mAUox/XoTBdJRIMKhAD4yU9+UXf7ypXnsXLleQO2L1t2Kv/xHz896LYPf/hKPvzhKwecf/HFl3HxxZcNeQ0HvtZ3v/sf1ccXXvgeLrzwPXWv+X/+z1uGfK/TTjudf//3/+BAGhqSfOlL/6tmW1CCN3rCc0+guOUVwnNHVtY9EkYkQH374fX89uXd/Pv/d1F1W8l0+Ny/rKWzt1DNBn32pV3882dXMLN1YEQjYPzwPEFUdTBkl4I0sA49lNmKHW7B1RPVCLskSSxsj/LG21le21lgfv3khGFTKNp0Z0pMb4zgusEXPSBgMrJ+cxcLZiYJG/6QL8sSyfjAG5V0zuSFDXu58PQ5w37tiY6iA3z9619nzpw5XHzxxUNex6SMogtBMq6Tzpk10Wq/hMNFHJCFKls5tGIXdqSV+e0JNmzpZWdXHst2MSQHIetj8hAZCUOJULIsIYCi6ZBJl0jnTEzTwTkCzHGF8JttmLZLNm+hqTK6phAJq36pXtg3N0cQZCAEHFIOHLuFELy4cT+PP7+NP762j/+647Ihzq4lkUiQyWQGbM9ms4NmrgK8+eabfOc73+Hee++tjp+nnXYakiRxxx13cOWVV9Lc3MxPfvITXn75ZdatW1fNkF2+fDm7d+/mzjvvrApQQ11HvbmlwlNPPTXk5xsvodjvPK3RnZKxbXfQzFW12IXsmNiRFkQ5sDC/3RegOrvTlLwOZGV8uszmCzY92RItiVAwBgUEBIwrybM+wL6f3omkakQXn4ESaxxg+6WEx6bxjEiA2ri1hxUn1zrQP/LcFvb3FnjvigV89KLFbN+X5bbv/pGfPP0Wf/+R4aXVBYwOIQSKlSMc0SkUav8ytFIPRm4PWqmXbNspNfsWtIZ5dbPKnq4S2YJV90Z0+NcAvekSDVEdXQnyoAICJiP/8xvPcedNK6oeIZ4n+OA//oK7blrBgn7dSvd25/n6f740IgFqMMYrir5p0yZ+8IMf8OCDDx70PSdjFN3zBNFQuZNSqZwFNUQJh17Yj5HbjeyUSDYtpiGmkc7ZbN+fZfE0BcdoAHnspSXDpb8IpasykgSm7ZHNl0vsSpOvxG68sR0P2/HIF+1qqV7IUElEfd+osK4gSZSzow731QYcDezpyvPEC9t4+k876M2UUBWZM09sP/iJ/ViwYMEAcd6yLLZv3z4gq7U/mzZtAmDJkiU125csWYLjOOzatYvm5mY2bdpEW1tbTXl25bjnnnuu5jpef32gD8ymTZs499xzR/SZJgIhIGL4naedUhHZKdbNXDWyu1GtDCCwytYXTYkQjXEdJ1/kzf0OxybH55o8T9CdLpGMGUEFQkBAwLiy69v/A4DedQ/S++x/1j1m/ur624fLiASovT0FLl9RGxV5YcNeGqIGf3PpcSiyxLFzmnjfygU88tyWMV1YwDBwTNxSjlg8StoycZy+la+rhrEibbhaeECUJmZAItnInh6JjdtTnHHcwHaaI6FQcuhOl5gxLYp3BES/AwKONA78VgrA9cS4eJhOdBT9tttu4z3veQ8zZsyovo/nedi2TSaTIRaLIcvypI6iG5pMNKxVBSjZGbyEwzGSyE4JO9qKJEnMb2/gL291sWV3hmNaGvHU+iWIE0lFhGqKh8gWLHIFG9Ny8Y5CtaV/qV4qW0LXyqV6kaBUL2BisWyX3768myde2MZrb3dXO06/d+VCrjhv0YhLslasWME3vvENent7aWz0gxNPPPEElmWxcuXKQc+bMcMPRG/YsIGOjo7q9vXr1wMwc6YvvnR0dLBv3z66u7tpbm6uOa7yGgArV67k5z//eU0Z9UsvvcSuXbuGvI5DiSJDQ8yg0L2/buYqQmBHpgFgh6fV7Fo4Pcb6TVk27Mpz7DHjd01BBUJAQMBEkDznr5HGXCw8NCMSoPJFu6YFrOt6vLUjxRnHT0fpV4c+f0YDvZnS+F1lQF08s4BwXSLROEbWxXHsvn16jGLTooEnCQ8kmXlzWnmjp5ON23o5fWnrmK8llTVJxg0iuhJEgAMCjiImOoq+ZcsWfvvb3/Lwww/XHPfggw/y4IMP8uijj7JgwYJJHUX3Oynp9GZKuI6HbA9uPu6Em3DCfRkDc6cn+MtbXXT3ZCk6TciHqPzuQFIZk2zOwg3KPaoIAablYlou2VxfqV40rBGLaEQMFV0LSvUCxsab23t54oXtPPuXnRRNB0NXOf+02Zx5Ygdf/vYfeOfStlH5AX3kIx/h+9//PjfeeCM33ngj3d3d3H777Vx22WU1wYPVq1fz0EMP8dprrwFw/PHHc+KJJ3LzzTfT1dXFvHnzePXVV7n33nu5+OKLqxlPl19+Od/61re47rrruO6660gkEjzxxBP85je/4ZZbbqm+/kUXXcTixYu56aab+PznP4/rutxxxx2ccsopnHPOOWP74Y0TriuIhSTCFLAGMR+3Yu1YsYFZaPPaDP68SWNvj0VvtkRLcnyCCJUKhGRURwsqEAICAsaJphUfnvD3GJEAlYwb9PQTljbvSuN6HotmJWuOkyUJTQ0Gw4lEeA4UM0iKgQQkIjqFon1Q8UdybYSsMXdmC+orXfTmTPb2FIjHxzYhmpZLd6pIdHocEWRBBQQcNUx0FP2rX/0qpmnWnPv5z3+ek08+mY997GPVcydzFL1/J6WCmUUZpISjHvGIxrRkiEI6xeZOm0XTxqV3yKgIxKehqSnVS0nolVK9mE7EUNE0v3TS7xYolefroGwvYHA+c9dv2L7Xz+xcPKeJC985m3NOnkHI6NeVbZQkEgnuv/9+1qxZw2c+8xlCoRCXXnopq1atqjnO8zxct6/MVlEUvvnNb3L33Xdz33330dXVRXt7O1dddRWf+tSnqsdNnz6dBx54gH/9139lzZo1FAoF5syZw5o1a7jiiiuqx6mqyn333cett97KF77wBSRJ4rzzzmP16tWTwoS8QgiLkOxgKiNrAhHRJJpamti7z+P1bSnOGScBCvwKhK6MycyWCG6w9p5A/L/DIKt16iNEkC04GRjRSnbhzCSP/2EbZ5/UgSRJPPPnnUjAiYtq00137s/SGJ/4Lj1HM8IqIhwTKRRHeIJoWEXXFEzLRSt04hiJqglif2TPwjaS6LrGgo4G3tiR4vWtvSya01znXUZGOmeRKdgkwloQ7Q0IOEqY6Cj6ySefPOA9DcOgra2N008/vbptskfRNUUmFtEo9RT8lCjtwBIOD63QiR1uhgPKO+ZNT/B6pofX95ksWnoILzpg1Lh1SvXCIZVpRQfHKnfTkiQk2RejFElCLgtTkgSy5BtOy5LUT6DqE6sq4lXf44AjkW17M0jAqUumc80lS5g9zh2m582bx7e//e0hj7n99tu5/fbba7Y1Nzfz5S9/+aCvv2TJEv7t3/7toMe1trZy9913H/S4w4UQAooZYtEImUytECE7RWS7gBNqBOmA4Lvn+lUHs1t5bd9eNm7v5ewTp4/rtaUzJRpjOuGgAmHCUFUVWZbI5dLEYg2TShgdK54nHZHi5YGfSwiB6zpksykkSUZVx6chQIUtW7awZs0aXnzxRcLhMJdccgmrVq0iFBpcD9m5c+egjXA0TasGZCvHfvWrX+WFF14gn88zd+5cPv7xj3P55ZdXj3n++ef52Mc+NuC1Lr74Yv7lX/5lDJ9u/BmRAPXBcxfyP77+Wz51+1MkojpvbO9l6bxmFvYzsQV44bV9LJqdrPsaAWNHCIEoZpAkBak82emaQthQsAp5Ij1vIJDItr8T0b/jhvA9X0TZQ2TJnEbe2JFi444UF49D/bjteHSlisRC4/ulDggIGDs79+eqLbsrAvHO/bkBx4yUiY6iD5fJHkX3PI9YSCbjFXHrlNGpxR4ivW/hZneSa1tWU543t9Xg1TcUdvc4pHImyXFotR5w6KiU6jmuh5AK5HIlPM83x5clPyOq5rFc+1iTJRRFQpFlVFUuC1UgyzJKRaiSZf8cWUKmT9wSnhfclE5hrn3v8Tz1wg7++Ppe/vT6XhbNbuTCd87hnJM7Dn5ywPhhl3CLecLxKHqxiGn1zWVafh+h7E6syDSKTYtrTpM8G0/RmNPRhK7tJ1e02bE/x9IxVh30p2S5dKVLzGmLB15QE4Qsy8yaNYutW7fR01M83JczrsiyjOcdeX83g30uXQ+RSDSN67owk8lwzTXX0NHRwT333ENPTw+33XYbqVSKu+66a9DzWltb+fGPf1yzTQjBddddVxNgNU2TT3ziE4AfzE0mkzzyyCN84QtfIBQKcdFFF9W8xm233cb8+fOrzyvVCcPl7VuvqGsR0Z9DakK+eE4TX/r4O/nZM5vI5i0uOn0O11xSG47tzZToShW54LTZY7qwgCGwSwizgKT1TWDCg3hUJ9fr4ugJkKRa8QmQPAsha9UW3jNbY0TDGvmizVs7UsxqiYz50rI5i1TOpCluBFlQAQGTiLt/9OcB2/7lP16seV4xtR0pEx1FP5Cnn3667vbJHEUXAqKyjSE75Kl/8+GqYd//6YCJP6JCY2OS7V0Kb+5I8c4lY2scEXD4EcJfaPrL45HPlX6WlFQWqfoe9xe0dE2mvTmCoSnBfDxFufycBVx+zgLe2tHLE89vZ91Lu/i/P3mJ//fzVzl1SRsS9bt/BowvnpkH4WHoOpGQVSNAISl4ioYdGlhJILk2rt6EqqocMzPJ+i09vL6tl6ULpg04dixkciaZuEEspAVlYhNELBajvX02pmkd7ksZNxRFoqEhQjpdOKKyoAb7XLIsI8vKuI+ZP/rRj8hkMjz00EPV7H1FUVi1ahU33HDDoA15dF0fkOX//PPPk81mufTSS6vbXn31VbZu3cr999/PGWecAcCZZ57Jyy+/zKOPPjpAgFq0aBEnnHDCqD9PPRNyt5CmuOVlhOcRP+Fdo37tCiM2kzht6XROWzp4+mhjIsTXVh3+tqlHMpWJUFL6/fqEIGJoaNEG8uqJviJ1AJJr4xqJagtvWZI4dlaSF9/s5OW3OpnVMvbW664n6EqXaIjqE+yfHxAQMFw+++F3HO5LOOoRQiCZWSKREPl8HfPxSAu5cDP1xAjJc5gxcxovd6V4Y0eK045tDW46j3KEAFcIQIA7+HGW49HREiEe1oPsiCnMolmNLJrVyLXvPb7aCe93r+xGAPf8+C+8Z/lczj9t9qjMyAOGRrhlz1U1hOdBPKKTztlVUddMzMKMz6xzoucHg1U/Y/XY2Y2s39LDWzvTWPYQX9pRYNkeXakSsXZtNHp2wDCRZRlNO3K+Y6oqEwqFKBZdHOfImR8O9edat24dy5cvr4pPAO9+97tZvXo1a9euHbIj9IE88sgjxGIxzjvvvOo2x/E7KMfj8Zpj4/H4hAjOg5mQC9dmzw+/ghIdeyn44XMzDRgVwnWglEVSB9aUqopELKJRNJ2Bdeh+uBXvgPOWzGnkxTc7eWtHildaYxw3t4mxUijY9GRMWhvDwYI3IGAScH6QkXr4sUt4pTyRRAzNLGI7dRYNksSAHDTPwZMVZs+YhvJKmt6sSVe6xLRxNLINOHLJ5S12OB5tzRGaEgbeERTlPhrRNYXzTp3FeafOYk9Xnide2MbTf9rBvz+yge8/tpGf3n7pwV8kYEQIq4BwLKRwAwhB2NAwNMVfa1eoExCoNP2pNJxob47QENVJ5y02buth/vT4gHPGQjZvkc5ZJGP6Ycl4lCQJRZFxXA9FkfE8Uc30DAg4ktm8efOArs+6rjN79uwBXaKHwrZtHn/8cS688EIMo89q4ZRTTmHhwoV89atf5ZZbbiGZTPLoo4+yfv16PvvZzw54nU9+8pOkUimmTZvGJZdcwmc/+9khvaiGi6RoNJx6Md1PP0DilPeM6bUCAWqKIawCwjb9ifAA3GKOSFhHy8rYByi+kmcjFHVA56WmRIjlx7Xx+w37ePrPO4kaKnPbx6ZsekLQnS7RENNR5SBKHxAQEOCZeYTnEYmG0HUb2+nrYCXbeTw1UvcmRvZsPMVAN0LMbU+weVeaN3ekAgEqYNgUTYddnTksx6OtMYwIyvGOCNpbonzs4qVc9Z4l/GnjPp58YfvhvqQjDiE833NV0apZp6oiEYloFEsWsmPiafXH4krTn0pAWJIkjp3TyPOv7eOVt7rGXYByXI/OdPGQZsFJkp8V5LgeuaJNtjuPrCq4jouhKeiqjK4qaJqMpvg/ByFE+d8hu8yAgIOye/durr766kH3P/XUU4Puy2QyJBID750TiQTpdHrY17Bu3TpSqVRN+R34huTf+973uOGGG7jggguq226//XaWL19ePS4ej3Pttddy2mmnYRgGf/jDH/jOd77D22+/PaxmEMNBUnXcXGrMrxMIUFOIqvl4v4mw/z5ny5/R8AhFj8GmVumUPBtXjQ7orgRw+tI2CpbHy2918qvnt/PBlfNpbRybH1SxZNOVLjEjaA0bEBBwlCNcu1rCIUsQD+sUCrbvueWYxPb9BaGGyLa+o1oiXcWz8YwkSBKLZyXZvCvNGztSnHn8dEbn2BVwNGLbHvu68tiOR0dzBFkKuucdKciyxDuXTuedQ9hjBIwSu4SwCkh6tLrJ8wTxkErOThHa/xp2qIlCywHtSatNf2rX4sfO9gWoLXsyZAsWEWN8m/bkCza9WZPmxMT6sCqKhCcgX7LJ5m0yed8XSyCIxULkciVcV6DIflaUqkioikzIUAgbGprmC1P+dgkJKRCmAo44hBAjskv4xS9+QUtLS42oBFAqlbjppptwXZevf/3rxGIxnn76ab74xS+SSCRYsWIFAEuXLmXp0r6xaPny5bS2tvLlL3+ZV155hRNPPHFMn8fNp0n94edozWNvghEIUFOJOhNh/32U/SDiyUZyPcWaQVwS7qBRGkmSuOSsufRmimzfl+Ph323lQ+9aOKYoigBSWZPGmEFIl4MJJSAg4KhFWEWEayGFGvBcQTSkomkylu2hOHnfxFbWB4pPwgPkauOIudPj6KpMrmizu7vA7LbYof8wAVMW1xN09RZwHJeOlmhgTh4QcBC8Uh6EQOo/NgsIGxohLATSAGsLKFcd9Cu/q9AQ1ZkxLcquzjy/W7+X85fNHFc/P88TdKWLNMR05IMfPiJkWUIAJdMlm7FJ50xKplNTcaEoBxgXewLXc7HKCb+ZPEARpSxIqYqMqkqEdRXDUDFUGU2V0RQFVZWqglQwTgVMNB0dHUNmOQ1FIpEgk8kM2J7NZoft/5TP53nmmWe44oorUJTateBPfvITXn75ZdatW1f1mVq+fDm7d+/mzjvvrApQ9firv/orvvzlL7N+/fphC1Dbv37DgPimcBzcQhokiel//Y/Dep2hCASoKYRXyg2cCMtIehh18dlgFogYBnq2X5cOz8GTVIQyeOtuRZa59My5PPj0JrrSJR5+bgt//a6FGPrA9xouJdOhK11kdtAaNiAg4CilWsIh92Wu6rqCrqtYtoUTaiLT/k5kb2BnHcm18BQdURagVEVmwYwGXt/Wy5s7UoEAFTBihIDejOlnQrVEiUcCc/KAgHoIxxrUc1WWIdy+gH1KE3UbR7gWrh4fGFQA3nlsKw91beG1rb2EdJWzjp8+riJUoWjTnSkxvTEy5u+2LEsggWm55HIOqbLoVNMFcBS4rsB1XcxyB4U0FpLk34uoioRSFqIihoahy2jlcj5NkVDkwF8qYHKxYMGCAV5PlmWxffv2Ad5Qg/HEE09QLBa57LLLBuzbtGkTbW1tNSbnAEuWLOG5554b/YUPQmjOUg5UoCRVQ2toJbr0LLRk65jfIxCgpgjCtQedCCtIkgyhGIYiEQkp1QlCdi08xUAoQ6f6GprCZWfO5cFnNtOTNfnlH7by3rPnocijj6Okg9awAQEBRzN1MlclIBHRyRcsPztUVvDkgRmqkmvjhptqmkosnpXk9W29vLUzxbnLZhyCDxBwJJIr2Gzfl2N6c4SmRAgvEKECAmron7l6INVM1lAIyz7guyMEEmLQqoO57QkuOWsej/x2C39+sxNDkznt2Lbxu24BvekSyahe9V0aCZIkIcsSluORyVmk8iaFooNpORNazSCE72PluED5/iWFiSRRzZZSFBldkwmHVAxVQdfKWVSyjKz4jeP7yviCcr6AQ8OKFSv4xje+QW9vL42NjYAvKFmWxcqVK4f1Go888gizZ8/mpJNOGrCvo6ODffv20d3dTXNzc3X7+vXrmTFj6HXgL3/5SwBOOOGE4X4cWi/7zLCPHS3jJkCt39zFwplJQkagaU0EwiwMOhEKz0PqJxJ5HsSjBpmcjesJPwMqPLzudvGIzuVnzuUnazezszPPky/u5KJTZ406OnOoW8NKkj95ShJ4whfAbNdD6VdjHqTyBgQEHCq84sDMVeEJIiEFXZUw7UHGI+GBRLWFd4WZ02JEDJWC6bB9X5YTEoEZecDoKJkOu/YH5uQBAQfiZ66mazJXa/Z7HoahYBh+Jmt/JM/Bkwc2/enPO45pJZM1Wffybn6/YR+6qnDSwpZxu/5CyaErYzJzmD6sVTNxzyNXssnkLHIFm5LlHPY1sxBgO15tc6W0n52llDOiFFlCUSUMTcHQVfRyaZ+m+D5TlXuAiigVeE0FjCcf+chH+P73v8+NN97IjTfeSHd3N7fffjuXXXZZTQne6tWreeihh3jttddqzu/p6eH3v/891113Xd3Xv/zyy/nWt77Fddddx3XXXUcikeCJJ57gN7/5Dbfcckv1uFWrVjFnzhyWLl1aNSH/7ne/y/nnnz8iAUq4DsJ1kPWBSS+eVUJSVCRlbHrPuKhFrif4n994jn/++5UsnJkcj5cM6IcQHqKUGXQidHdtQFgFlOmLkKNNIAQRXcXQFQoF04+uK8P3c5qWDHPxGXN4+LktvLE9RSKis/y40ZtbZvMm6ZwxIa1hK5EagcByPEqmS8l0yRdtHNfDCGmUihayLBExVEKG/3MxNKUaGfJTeYOZKODoJAgeTBzCscDMIdWJhGuqTKLzJfK2QrFx4QCzWr+Ft+57Q/VDliUWzWzg5c3dbNzWywmLxp4KHXD0Yjtlc3Lbo70lghKYkwcEgFVE2KW6nqvCsbDfeg4lMY144yJyebtmDSl5Fq4artv0pz+nLJ5GyXJ44fX9rH15N7qmsGRO47h9hHSmRNNBfFgrvk550yGbL/pm4qaLMwUyIj3PDyjbDLxWWfJFJ7lsgq7IErom++KUKqOqflc+tWyQrshSP2EqEKcCRkYikeD+++9nzZo1fOYznyEUCnHppZeyatWqmuM8z8N1B5av/upXv8JxnLrldwDTp0/ngQce4F//9V9Zs2YNhUKBOXPmsGbNGq644orqcYsWLeIXv/gF3/nOd7BtmxkzZvCpT32KT37ykyP6PJ2//AbCtWl7/+cH7Ot69JtIqs60S28c0WseyLjdcQTf1QnELiGsYv2J0HPxcl3gOiD1RdgVRSIW1Snlsr6HiDwyQ/E5bXHOWzaTp17cyR837icR0Tlu3vCyqA7EcQWd6SLxqDamnk192U0SriswHRfTcimaDrmSjWP7EZLKxKkoErKqUDQdXFeQyfk15pWJR9P8+vKQoRDS1HIqr3+FnhfUlgcc+QTBg4lFWAWEYyNHBnYVFaUcIWFi2i5CHlgeLXsWjtFQ10PkmFlJXt7czebdGWxnbF4cAQGuJ+hKFbBdlxmBOfmUIQgeTBxeKQdQ13PVy3aC6+CVCkTCOrpm1ngiSZ6L0IbXSfr0JW1YtsdLm7p48k870FXf5288KFluXR/Wiq9T0XTJFWxS+bKZ+IGlhFMYTwg8pzKG1c6RvtdUOXNKkZBl32/K0GUMTS3fI0ioql/ap8jln1lAwBDMmzePb3/720Mec/vtt3P77bcP2H7llVdy5ZVXDnnukiVL+Ld/+7chj7n++uu5/vrrD36xB6G0bT1N511Vd19k0an0/OYHY36PcZu1gq/mxOEVB58IJVlBW3QWXrYTKZzoO8cVxEIqacmjqEb8EXeEHDe3iWze4oWN+3n6LzuJhTXmTI+P6jPkCza9GZNpydCw0oGhkt0EAgnb8TAtB9PyyJUsSqaLU07JHclCWQi/HbVte1CCdNZClsoTTdn0MBrWCJWzpHRNRq1GRgJRKuDII/iLnhiEVzYfH8R7T9JjGEvOxtm1r073u3LnH7V+ed30pgiJiE6mYPHm9hRzWut0Rg0IGAFCQCpj4jge7S1REoE5+aQmCB5MHNXM1UE8V+VkB5IRA+GiqjIRQ6lt+nOQ8rv+SJLEOSe2Y9kur23r5VcvbOfyM+cyu210a+0DSecssnGLeMSfh0q2Ry5vkc6ZFEsOluUedWsA32tK4Lgu2AP3K7JUFaaUcvle1NDIWx6yEGiqDIGdR8ARjJtPo8TqZ2MqsSRuPjXm9wgyoCY5/kR4EPNxVUdpHGhCpqsSuqGRlwc/92CcvrSNTMFi4/YUjz6/jStWLmBacuSeI54n6E6XaIgZKINoYbLsZzd5QmDZLiXbo2Q65Io2tu362U2ON+5/a5X3s+xyN46siSxLfnquKqNrCtGwVi3d01U/U6pSuhdoUgFTmSB4MEHYRYRVQjIGF4eMSAyjUaKQMWu2S56NULRBS6clSWLx7CR/3Lifp/64nYveOYv2pkCEChg7uYLNjn052pojtCRCgQg1iQmWHhODn7nqIEfqj6mSJCFF/Cwlz4NYVCedt/E8gezaftOfOlmtgyFJEuctm4npeGzeleaR32/lfWfPp6Nl7GO6Zbt0pko4riBdNRN38YKF66C4nvD9c/uV9uULNkXHwzZtDF2lIaYTDWkYmhxUTAQcccihKHbPXsJzjh+wz+7Zi1THG2qkjIsApcgS/2/1hTQ1jP2CAmo52EQ4FJJjkUhESZWMUS9UJEni/FNmkiva7OzM8/BzW/jQuQuJR0ZW0gdQKPmtYdubInieVzULd1yBabuULJdiySFfcnBcD9t2y5PAocfzBKbnYtq+n1RvpoQi92VKGf1FKdXvyiFLclA/HjDlCP5UJwavlAOpfuZq9RgP4hGNdM6qiaZKno2rRYf0EDlhfjNv7EiRzlv85282c/LCFs48fjrqKLoeBQT0p2Q67N6fww7MySc1QfBg/BGehyhkkNRhCkhCEDE0DM23e0A4eFpyxFUHsizx7tNm8Yjjsn1fjod/t4UPrhhdwPdAUtkS6ZwZZOyMESH8ssZ80SGVNQnp/n1AImYQC6moioznecH6P2DKE5pzHKnf/YzosaejhPuyMd1iltTv/4vw3OEbmg/GuGVAtTYNr945YPhUJ8JBSjjc3l2IfA9y4yzkaHLAfs82iTQ0Y6BSLDmjvg5FlrnkjLn859pN9GRMHn5uK1e8awGGNviNVT0qrWGjIf/Pzh/IbUzLLXe4cCf1wO16Atfyfafy2PSkS6iK5JsYqgohQyFcMTlXFbSaTKkgQhIw+QiCBxPDwUo4nL1vghAoTbMIG5G+m5cyknDx1KHn1FhY46qLjuH3G/bxlzc7eWlTF9v2ZrnwtFlMD+bjgDFiOx57u/LYtkt7SxRFkoI5bJIR/DYmALtsPm7EBuwSQuDueBkpFEduml0VqVRFIhLRKBZNkORhl98diKr4a+2f//ZtdncXeOi3b3PFygU0xsc2Pwfrz/HH8wSFkkOh5NCbKWHoKomoTjyqETE0FJlh240EBEw2Gld8mF3f+Qd2fOPTxJachRJvwsl2k3/99wjPpXHFh8f8HoFz4WRmiIkQwOvdjSikkMINcIAAJYQHSBiRCDGLMQlQAIau8N6z5vHgbzbRnSnx6B+2cflZ81BGaMxXNB22783iumJKdNk4GNU6csslV/C3VUQppZwpFSmn6eqaL0ppgdF5wCQiCB6MP33m44M0jujdBa6D3NCGqkSJhNWqACV5Np6sIobRudTQFC49ez5z2mI88ccd9OZM/vOZTZy6uJV3LmlFkYNsqIDR43mCrlQR2/XoaIkSCszJJw1B8GBi8EpZAKQ6Y6coZfEynZDrRm6e03eOJ4iHVDKSg61oiEGCxsNBU2UuO2seP1u3mc5Uif96dgtXrFxAIjryqoOAQ4PjCpyiTb5o05WSCRkqDTGDeNhvciRBMG4GTCn05hl0fGwN3U9+l8xLT/o3rLJMePZSmi74G/TmgbY/IyUQoCYxfV046t9EKNOPwUvvRU60Ddzp2EiqhlBCNMQ9ejOlMQs+8YjOZWfN46fPbGbH/hxP/3knF5wyE2mEqcb9u4UcifQXpSqZUtXyvbKvVDTkG53rmoymKejlshkRGBsGBExp/MzVNJI6yA2DJKHOPAEv140UbsDzBNGwRipr4roCybVx1dCIbmLmtSe48sJjWPvSbt7YkeKPG/ezZU+Gi06bRUvD2Es4Ao5eKubkdtmcvCEwJ580BMGD8UU4JsLMI2n1x0xJj6DOPN4PDCv9bp8EhA0NQ/YwpQRIYxP+DU3hvWfP56drN9ObNfmvZ9/mr9+1gEho9MJWwKHBr+awyOYtDN2vjEjGQsTCfnUEIhCjAqYGRttcOq68Bc828Up55HAMebB17SgIwqOTlGoJxyATIYAcaUBtX4ykDUz3FY4JehQkmVhIG7c2va3JMH91+mwkCV7f1ssLG/ePy+se6bie8Ev3ijbprMnuzhxbd2d4e1eazTtSvLUzxa7OHD1Zk4Lt4ngCSZZQFLlszn64P0FAQMCwsIsI24RByu8kSUaOt/hjtySBgGjZQwRA8pxht/DuT0hXefc7Z/NXp88mpCt0pUv86KlN/HHj/mDBexTieWJcjYbzBZud+3J0pUsogc9YwBGIZxaqwdt6SIqKnGxHmTZv4D48YlEDoY6u/O5AIobK+86e53sE5i0e+u0WStbYKhkCDi2m5ZLOWmzfm2Hzrgxb92TpzZm4AhRFHnHwPiDgUCFcB88qASBrBmq8qSo+eVYJ4Y59LAoyoCYrnotwHWR95ObjflmXQDb8mxhFhoa4Tq5gjculzW1PcO7JM3j6L7t4/rV9JCIaS+Y0jctrH0343fcElu1HkzM5C0nyfQAqmVJhQyFsaOjlEj5dlYLFf0DAJMafmMWgmav1kGW/k1KhUMKTlUG73w2HRTOTzGiJ8tSfd7FlT4bfb9jL23syXHTqzDF7iQRMXkzLZU9Pnj3dBfZ059nbU0CSJE5a0MKyY1oI6WNf7pVMh92dOSzHZXpTJDAnDziycO0hm0YMhWeaRKJhNCWCaY/P9yIe0Xn/OfP5ydrNdKVLPPzcVt53zjx0dXTXGHB4EMIfO0tmP/PyiE5DVCcamJcHTEI6H/0GwrFpe//nB+zrevSbSKrOtEtvHNN7TIgAZdkuqZxJa2OQHjwRiFIOL7MPuWF6/Rbfru0bl5czo1xXkAjrdOnKuJW/HT+/mXTB4sU3OnnqxZ3EwhqzWuMHPzFgSISopPB6YEI2D1CsClKqIhMJqTSXHGRPoGtyUF8eEDBF8DKdCLuE3NBWU6LneYJISEWXHCzFQMhjS3OOhDQuXT6HjdtTrH15F/t6CvzHk29x1vHtnLSw+YiLvAohsF0P03KxbA/Tdmv+VbdZlee1+3RNpqUhzLRkmJaGENOSYaIhddL+nIQQpPNWVWza012gO1OqdyR/emM/r2zu4uRFLbxj0bQRNw85ENvx2NddwHZ8Xyhlkv6MAgLGE7d7B5KiIcVbasvvygjXxEg0E0LHtM1xe99kzOB9Z8/jp2vfZm9PgUd+t43Lz5obdDudotSYl6dLGIbim5dHNKIhDVkKzMsDDj+lretpOu+quvsii06l5zc/GPN7TIgA9cfX93HH9/7Iz+9674jP3bJlC2vWrOHFF18kHA5zySWXsGrVKkKhoSO3hUKBe++9l8cee4zOzk7a2tq47LLLuP7669H1vsX8//2//5c//elPvPLKK+RyOX7yk59wwgkD2wl2dnZy6623sm7dOmRZ5rzzzmP16tUkk8kRf6bxxkvtwe3aijDzqLNOHHiAY0I4VtM9LxxSCIfUcfVfOvO46WQLNm/uSPHL32/jinctCPxGJgjH9aoeXkXTxnQFlmmja3JQXx4wbgTBg4nF7d6GyPeCcFFa5vbtEBDWVQzFoyRFR9zCux6SJLFkTiMzp0V58sWd7NifY90ru9m8O80Fp86iYZKa2goh6EyV2NldIJ0tUSr1F4v6C0tezbaxRI8LJqRyFpt2pavbQrrCtIYwLUlfkJrWECYZN0bceGM8cFyPzlSRPd0FdpcFp/5dEys0RHXam6N0NEdob46Sypn84bV9dGdKvPD6fl7e1M2yY1o4aUEL+hiEKM8TdKeK2I7HjGlRYlrgTRNw5CI8F3f/ZnBt1LmnIMVqM/4rTX8UPUIyrpLJmeOazdLSEOa9Z8/jv559m52dOR57fjt/dcacwzIWBYwfjuvhFDzyBZsurWxeHjWIRzTChoIod9AOGBmSBHLw3RgTbj6NEmusu0+JJXHzqTG/x6QqwctkMlxzzTV0dHRwzz330NPTw2233UYqleKuu+4a8txbbrmFJ598ks997nMsWrSIV155hXvuuYd0Os2XvvSl6nE//vGPmT17NmeddRa//vWv676W4zhce+212LbNHXfcgeM43Hnnndx444384Ac/OOxRUSnSgBxvQW6YXne/EN7A0j3hR1IyOWvcBApJkrjglJnkija7u/I8/NxWPnTuQmLhYDF6KLBsl2LJIZOzMHSVaNifvGIRDS1I6Q0YBWMJHgQMjRACOdGK53nIiYFjtyRcotEwKXN8haF4ROd9Z89j/ZYefvvKHnZ15fmPJ9/knBPbOW5u02Gfzyr0ZEq8uSPFGztSpPOjKxeXJdA1BUNTMHT///5zvyNp5V/1mHJpc9F06EqX6EwV6UwX6c2alCyXHZ05dnTm+l5flmhOhJiWDPkZUw0hWpLhMWcVHUjBdKqZTXu68+zvLeIeMG/LskRrMlwVm9qbIwNMipsbQszvSPDWrjTPv7aP3qzJ7zfs4y9vdXHKMdM4cUELmjq6TAohIJ01cRyPGW0xGhoC0fpwEgQPJhAhUJpn4eV7kQ7oOA1UfaM8RScWVgjpal2BeCxMb4pw2Zlz+flvt/D2ngxPvriDi06dNWnG74CxYdsetm2Ry1noukI0otHSECIW1vCCjKgh8X1yJQQCy/GwTBcrb1F0BCEl+H6MBjkUxe7ZS3jO8QP22T17kfSx2zmMSID64eNvDOu4Hfuyo7qYH/3oR2QyGR566CGamvwIg6IorFq1ihtuuIEFCxbUPc9xHB577DGuvfZarr76agDOOOMMdu/ezaOPPlojQD3zzDPIsszzzz8/qAD1+OOPs3HjRh555BEWLVoEQGtrKx/96Ed59tlnWbFixag+33ghJ1qRE6119wnXRlJUJK32j8PzBPGITkhXKJTGb2JUFZlLl8/hP5/xu3U8/JzfMnYs0dWAkVFTX54xMQyVeEQjEdWJhjQUWQq6FgUEHGYkSUJpno3SPLvufs80iUSiaHJ43DxE+r/3CfObmdUa48k/7WB3d4Gn/7yLzbsynH/KzMMWNMgWrKro1JXuKyFTFYm2pgiaIqOrco2opKsKhi7XEZMUVEUa9Q3Z7La+EnLH9ejJlOhMlehMF+lKFelMl7AdPxOpM1UEeqvHJyIaLeUsqZZkiGkNYeIRbVjXIoSgJ2vWCE6p3EABLqQrNdlNrY3hYZXhSJLEMTOTLJzRwJs7Urzw+j5SOYvn1u/1hajF0zhhfvOoS3ryRZud+7IoqoIsBIosofXr6ur/G9VLB4yAIHgwcUiKitK6gMFWtcKxkCIJJFnFUCQi4fEXoABmTotx8Rlz+OXvt/LG9hS6qvCukzsCEeoIQuD7+ZmWSz5v0xA3aEmGCOtqsI7Hz26SJL8xk+OKakZ0seSQNx0c28N2PYQQLJ5vQCBAjYrQnONI/e5nRI89HSXctzZyi1lSv/8vwnMHVo6NlBEKUBuR8L8gB2M0v/J169axfPnyqvgE8O53v5vVq1ezdu3aQQUoIQSu6xKP13oQJRKJsiF3H/IwjGHXrl3L4sWLq+ITwLJly5gxYwZr16497ALUkDgW6KG6LcB1VSYW0cdVgAK/+9LlZ83jwd9soitd4lfPb+fSM+cG6cGHAdcTFIo2haJNT7qEoas0xHXiYY2woQZ+UUcpEx08CBg7wrUwGloJoY6rh0h/kjGDD6xcwEtvdfH7DXvZti/LD554k5Und7B4VvKQ3MgUSg5v7Urx5o4Ue7oL1e2y5ItAi2clWTizgabGKLlc6bD4YaiKTGtjpCabRAhBJm/RmS5VBamudJFswSZT/vf27kz1eF2TfUGq7CnVkgwzLRnCdlx27s+xszNfFZ1Me2BpfFPcqGY2tTdHScb0Mf1+ZEni2NmNHDMzycbtvbzw+n4yBYtnX9nDn9/s5NRjWzlubtOohCjTctnTlccs2Uj4P7+QoRDS+xpoaIpUfe1AmAo4UvDvMTzksh+r8KAhZpDKmAOyFseDee0JLjxtFr9+YQevvt2NrsmcdXz7uL9PwOHHtF329xTI5i0aG0I0J0LoqnRUeURVspv8pk1+6X2p3FHcsl1sx8NxvAEdX5VAeBoTjSs+zK7v/AM7vvFpYkvOQok34WS7yb/+e4Tn0rjiw2N+jxEJUImozhnHt/Oxi5cOedwLG/bwtQdfGvHFbN68mQ9+8IM123RdZ/bs2WzevHnQ8zRN4wMf+AAPPPAAy5YtY+HChbz66qs8+OCDXHVVfROtg11HPbFr4cKFQ17HRCM8D5HdjxSrb4IIfgaUbLTU3ed5Hg0xnd6MH8kdTxqiOpefOZefrtvMtn1ZnvnLLs5bNiOIzBxGfDNzi1zBwtAUQiEl8Is6Spno4EHA0AiriLAKSNFGJGngDb7w/PFY0UIkY8q4e4j0R5Yklh0zjTnT4zzxpx3s7y3y+B93sHl3hnPfMYOIMf6V+abt8vbuNG/sSLFjf67ms82YFmXxzCQLZjQQLr/3ZFw8SpJEQ8ygIWawcEZDdXvJcuhM+WJUpYyvJ2Ni2R67uvLs6spXj/VjMtKAxbKqSLQ1RvoJTpFx6VpXD1mWWDq3icWzG3l9Ww8vvL6fXNFm7Uu7efGNTt55bCtL5jaNOIAkhF8GVrk5ypQbaChl4UlRZFRFIqyrhAwVTZPRVT9rTVUkJKRDKkxJ5d9FZYlSiap7QuC54AqB63ikc+Zh9RMJggeHF6+QBklCCsXrr2fLVQeVpj9CCGJhjZChki/aE3JNi2c1YtseT/9lFy++0YmhKZy6uH5VRMDUp2g6lDpzpLMmzckQTfEQsnTkrd8lSUKWQSDhuB4l28WyXAolm0LJxXE9bMc9qgS4w4XePIOOj62h+8nvknnpSfA8kGXCs5fSdMHfoDfPGPN7jGiFM7+jgd2deRIHMS8Nh0aXzp/JZEgkEgO2JxIJ0ul0nTP6uOWWW7j55pv50Ic+VN129dVX8+lPf3pU13FgNlXlOoYSoM4///xB9/36179GUcZWliZyXTg7XkUyIqgLzxwwGQrXgTrld9X9AqIhf2K0ndF5bAxFW1OE97xzNr/8/TY2bO0hEdU57dhgUpwMVNJUK35RkbBKMvCLOmqY6OBBwNB4vbtwO7cgJ9tRZw6sqce1kFSj7CEiYegqpQko4ehPcyLEX79rIS++sZ8XXt/H5l1pdnflOO8dM1nQT2AZLY7rsXVvljd2pNi6J1OTDdDaGOaYmUmOmZWc8p6BIV1lVmuMWa2x6jbX8+jJmHSli1VxqjNVKmc6CaJhlfYmX2zqaI7Skgwf8oxhRZY4fl4zx85u5LWtPfxxoy9EPf2XXfzpjU7euaSVY2c3jll8cV0/Qx38LK8M/tpDVaSyKOV3eA3rCoah+iWXqoKmyPhLppEJU5USDf+x/3+B743pugJXgOcKHK/83BM4rofl+NF01xV4nv9PkqG1JUZbgzGmn8FYCIIHhxd3/2ZErhulfXH98mnHAiNa0/RHU2RiEW3CBCjwO1Gbtstz6/fyu/V70VWFExc0T9j7BRxehPDLnYumQypr0pIMk4zqCMGASp+pQF8pnYTr+d5NpmVTLGc32ZXsJje4NzlcGG1z6bjyFjzbxCvlkcMx5HJ1lZtPo0THtk4ckQA1r6OBx5/fdtDjQrpCyzgaIQohDppJc9ddd/HMM8/wla98hXnz5rFhwwbuueceEokEN91004jfs977Dec6JhQhkPQwUnxa/etwLCTNgDrldxUU2U8PzuWtYS1oRsr8jgZWnNzB2pd28/sNe4lHNI6dXd9JP+DQ098vKl3xiwprJGKBX9SRzEQHDwIOgiyDqiPH62enCtdCijQiyQqGIhMJTbwABb4I8c4lbcxtT/DEH3fQnSnxyz9s49jZSVac1DHiLBzPE+zYn+PNnSk27UrXZNo2xg2OmZVk8cwkyfjhu6E/FCiy7HfOS4ZZMsffJoSgYDpEoway8PAmyTCrKjInLmhh6dwm1r/dw5/e8EvznnxxJ3/cuJ/Tl7ZxzKwk8jivfRxX4LguZlmYSuMLKJVMKaUsTEUMP2NX12S0sjClljPkXM8XkDxP4An6RCXXw/VE9SbGdr2qqOSVM3/9x2LILAJFkQ77zU8QPDh8CCGQVA0hK8ixQcZu4SCHapv+eJ5HQ1SnJz3+1Qb9OWVxK6bt8ac39vPMS7vQNXnKrreFKBtI2y5W/w6njv+8Un7Vf5vteLQ0hFg6t/Go6cDteYJMzqJQckhFdVqSIWJh3a+QmeRCjSxLIFHz+8yVLEyzPE473hGX1XUkIGsGsmb4a5hNL5J56SkKm15k/j/+eEyvO6LV5UcuWswlZ8076HGnHNvGt//nhSO+mEQiQSaTGbA9m80O6v8E8Oabb/Kd73yHe++9t5qFdNpppyFJEnfccQdXXnklzc3DjwwMdR31MrQqPPXUU0O+7lhVarmhDSnR6heZ13t9z0bSh/bxcF1BPKJh6Aola6DvxHhw0oIWsnmLP7/VxZN/2kksrDFzWuzgJwYcUvr7RXVnSoR0lYaYXm4BG/hFjReToSXs4QoeBPgo0+YjN8+tu08IAZ5A1sOVDTTEdFJZ85B9/1qTYT583kKef30ff36jk43bU+zozHPBspnMmT4wG7g/Qgj2dBd4c0eKt3alKJp980osrPmi06wkLQ2ho7okW5IkElGdWMwglysxvJyWQ4eqyJy8qIXj5jXxyuYuXnyzk3Te4vE/7qgKUYtmNEzo71BQbk3uQiVjKp01kSSq2VKKIqGpMqK/kNRPTKo8nuw3Y8MlCB4cPiRJQp15AsJzkeSBFQzCtUHR/MBv/+0CIiENQ5+YaoP+LD+uDctxeWVzN0/8aQe6KjO/Y+wZrKPB9QS9uRJdWYt0pkjR9EUky+knHpUFJn9bn7hkjVKo29mZ46VNXb4QNaeJY2YnJ6SMfLLhOB496RK5gm9UPm2SGpVXRKei6Wc2pfMWluViu753U8Dkx+7dS/alp8i++gxuLoWkqESPPWPMrzuib2nYUKseDRPBggULBpS4WZbF9u3bB3hD9WfTpk0ALFmypGb7kiVLcByHXbt2jUiAWrBgAa+//nrd9zn33HOH/ToTgSRJINWZCD0XJKnvJmYIIoZKOKRNmAAFcNYJ7WQKNpt2pfnl77dxxbsW0JwYe9vGgInBcTxyZb8oXVMIGwrJeJ9flPCmZprvoaR/SrEAXNfDdn3jRCdvEY0evr//iQ4eBBwcabAGGI6FpGpQLp32POGXSo9zx9KDoSq+me389gRP/GkHqZzFz5/bwvHzmjj7hPaazqZCCLrSJd7ckeLNnSmyhb5Sk5CusGimLzq1N0eOatFpKqKpMqcsbuWE+c28vLmbP7/ZSW/W5LHnt/PHRIgzlrYxvyNxSH+vQvhz1NF4wxIEDw4/9cQnoK/qQBkoDioyNMR1coWJFaAkSWLlSR1YtsfG7b386vntXH7WXGa1Dh04GCtCCLIFm729Bfb1FNjbU6AzVcQZoz+PLEsYZX84v8tpXxfU/h1RdU1GliS27M2yZU+GrnSJda/s5rev7mbu9ARL5jQytz2OMozGU1MZy3bp7CmQy1skEwYtDeHDblQ+UHQyKZXFyOA2YmrgORb5139H9qWnKe143Z+EJYmG0y8lufwDKJGxjy+TSiZesWIF3/jGN+jt7aWx0U8jfeKJJ7Asi5UrVw563owZvhnWhg0b6OjoqG5fv349ADNnzhzRdaxcuZKf//znNWbkL730Ert27RryOiYSYeZBH2Ix7/geIqgHL20QQpCM66RzExdhlySJi06bRb5ks6e7wMPPbeFD5y4kGkTpJj1WOfU5k+/zi2qMGcQj+hFpfDhS+reBBXBdsMseIpbjUTIdipaD4/ilII7rEYvozJ1x+H5uEx08CKiP8DxwSkj64DeG1Rbe/RpLGJpMNKwdUgGqQntzlI+efwy/W7+Hlzd3s35LD9v35bjw1JlEwxpv7kjxxo4Uvdm+Tn2aKrOgo4FjZiWZ1RoLOqAeAeiawmnHtnLigmZeequLv7zVWS3RnJYMccbS6cydPogxc8C4EQQPDg+inLlUr6N09RjPQQ411/0OuK4gEdbp0hXMCQz2gr8eueCUmViOy9u7Mzzyu22875x5tDdHD37yMDFtl309Bfb1FtjbU2RfT4FCnTJxXZOJR3Q0RR4gGOmagqH6jwdu85+PtAvn0rlNFE2HN3em2Litl329Rd7ek+HtPRlCusLiWUmWzGlkWjJ8RI9VRdOh1OWQyVk0N4RoTBgoknTI1uuy7AdeKx3qAtFpalLavYnsS0+Rf+23eFYJSTOIn/guoseewd4f30Zk0anjIj7BJBOgPvKRj/D973+fG2+8kRtvvJHu7m5uv/12LrvsspoSvNWrV/PQQw/x2muvAXD88cdz4okncvPNN9PV1cW8efN49dVXuffee7n44otpamqqnvvCCy/Q09NTzZr6wx/+wK5du5gxYwYnnHACABdddBGLFy/mpptu4vOf/zyu63LHHXdwyimncM455xzCn4iPsE3sTb9H0iOo899ZtwOecC2kcMvgUfZ+eJ4gHtEJGSqFCTRJVBWZS5fP5T+f2UQqZ/GL57bygZXz0dXRm7ELIShZLkXToWA6Nf8vlvzH1eemg+0KXzCgTzSoeFnI5eeV7f2PkSRp4PN+22V/J77Q7++LhFQWz2pkVlts3P0yDgf9/aIyWYuQodKYMGiI6hiaclQYl1e6ckiSVC4NEb7QZHuYlkvRcrBtryw0+YJTvR9JkD12dCLyPTjb/oKcaEWdfdLA/eUW3pJRK1B5nu/90pMpHZZIpqbKrDx5BvM7GnjyxR1kChY/Xfd2zTGKLDF3epxjZiWZ154Y8Y1DwNTA0BROX9rGSQub+ctbXby0qYvOVIlf/G4rbY1hzlg6nXkdE5ttcTQTBA8OD17PTtzOt1GmzUNpHWgB4lcdyIM2/QEIGQohQ51wAQp8AeA975zNL363lR37czz83FY+uHL+qLyRXE/QnS76QlOvn93UP+BQfU8JmhvCTG+KML0pTFtjhJZkiHg8TC536OausKFy0oIWTlrQQnemxMZtvWzc3ku+5PDy5m5e3txNcyLEkjmNLJ6dPGID4f2NyntzJtMawiRjE2dUHohORxY77/scVudOAIwZxxA/6TxiS89C1kN4pfxBzh45k2pWSyQS3H///axZs4bPfOYzhEIhLr30UlatWlVznOd55a4qPoqi8M1vfpO7776b++67j66uLtrb27nqqqv41Kc+VXPu1772NV544YXq87vuuguA97///dx+++0AqKrKfffdx6233soXvvAFJEnivPPOY/Xq1YdFQRelrJ92oaj1xSfhARKyPvwSH02RiUf1CRWgwJ8YLj9rHg/+ZhP7U0Uee347ly6fW+OJYzteXUGpUHLqbh/9wDbxI+LG7SniEY0lcxpZOqfpoL4NUwXH9cgVLPJFiy5DJRHVaYgbRI8Qr6j+AqMnRNW41i6bYRZNxy+lc/uymoIJNuBgiFLOH7u1QTJTXQdJUf3s1f7nCUE0rBHSJ66V93CY1Rrjv11wDM++spvXtvYilbcdMyvJghkNGNrYOrsGTB1Cusry46Zz8sIW/vxmJy9v7mJfb5GfP7eFjpYIK94xk2REG1OAKSBgsiDMnH9HP5ithWOWqw4GX+NJQDJmkMmZh2S9UAn6/tezb7O3p8BDz27hipULhmz6UC2lK5fR7estsL+3WNO1tEIiotPWVBGcIkxLhgcEHg53llFzIsRZJ7Sz/LjpbN+fY+O2HjbvztCdKfHbV/fw3Po9zGmLs2RO4xEbOPE8QTZnUSw69EZ1piVDxCP6uASNA9HpyMXavwMkicjCZTSdexX6tFkT+n6SCELzhwwhBF1dueEdaxVxu7cjh30zQeE6/oRnDEypFbYJCOTmWYPXqh+AJEHedNiyK4MnBLFYaEIjFnu68/xs3du4nqCt0U+FLZSzluxRmOYZZZ+iSDk66PtaqTXPYxGNZEOYXM7EcfvaOAshym2Z+z0X1GzzRnCsEH5nnX29Bd7Yniq32vaZ1RrjuLlNzO8Yv4lOUaQJ/30NB11T/PK8uEEioqMq8pgMEFVVprExSm9vfsK9Pvwadck3QqwYYjoeRdPGNMsZTZ6H6/iGtmMlEdNZtnQ6uWxpWJ+tpSV22BdyI2HLli2sWbOGF198kXA4zCWXXMKqVasIhYYWxQuFAvfeey+PPfYYnZ2dtLW1cdlll3H99dej6/7CPpfLsXr1atavX09XVxeRSITjjz+em266iRNPPLHm9To7O7n11ltZt24dsixXAwfJZHLUn20k4zaAV0jjpff2jd3l8blepFyUcmBEUBo7BuxTFJmdnTn2dRcGfa9DORZkCxaK7Hfom2gmyxg33hxJn6tQsvnTG528+nZ3zc1q2FBpThg0xUM0JUI0JQyaE6EpmcWjKBIz2xuYnjQwzeFlsUy1sftIZqRjt5vZD4U0UsjP6BOlHGihuoFfr5hGik9DiTUN2Ncf2/V4e1eGYr9ytYkeB0zL5afrNtOVLhGPaFyxcgHxiF7dV8lqqpTTFeuU0hmaQltjmLay2NTWGBnW2D8ZxzjTcnlrZ4rXtvWyt6dvPjU0hWPKJXqV+5LBmIyfa7jomkJDzKAlGSJi1BqVD2fdPRVFJ0WRWDy/hagmY9sHH7uDcRvSf/wl2Zd/g7VvK0gSRsdC4iedT2zpWSA8tv7zNbRf/b8Izz5uXN5v6q0IjlIkRYU6kyCAcEykaOOwxSfwAzvRkEYoNLFleBXam6O8552z+eUftrGvtzhgvyJLfUKSoRLpJyYNFJiUYRkLKopELGogC3FIJowlcxo5+4R2Nu9O89qWXnZ05tix3/9XqUU/bl7TEdMutuIVlc1ZhEIqybhfnhfWFTxvcnUh8kvpJFxPULL87LpMwe/G4bcDD9q/jpZMJsM111xDR0cH99xzDz09Pdx2222kUqlqhulg3HLLLTz55JN87nOfY9GiRbzyyivcc889pNNpvvSlLwFg2zaGYfCZz3yG9vZ2stks999/P9dccw0/+9nPmDfP90dxHIdrr70W27a54447cByHO++8kxtvvJEf/OAHh21xcWCHpP4Iz0UO1e8Q6nke8ahOd3p4ouVEU7mBCQgAv8vXipM6WHbMNP78Zidb9mZI5yyKpsPOToednbUp+2FDGSBKNcVDh0TQDAgYDdIgY7PwvGE3/TF0P1BXT+SZKAxd4X1nz+MnazeTyln817Nv094cHbKUriXpl9BVyumSMeOIuSE3dIXj5zdz/PxmerMlXt+WYuP2XnJFm1ff7ubVt7tpjBssmdPIsbMbiYWPrBI9y3bp7C2QLVg0lsdeQ5OHvC+aiqJTwNhoOO0SGk67BHP3JjIvP0X+tefoevSbdD/x70QWLvMzVxi/MSGY+Sc5QnhI0uBii5/AJoY1ER5IJT24WDo0JR4LZjTwgRXz6c2aRMoiU0Vg0lX5iJjsVEVm8axGFs9qJJ23eH1rD69t8ye6Si16a2OY4+Y2ccys5BFRwuJ6gnzBplCw6TZU4hGNZNwgFtYOa3leJcvJclyKRZtc0SZftLFsD9t2J1kT9KnLj370IzKZDA899FDVb09RFFatWsUNN9xQ49/XH8dxeOyxx7j22mu5+uqrATjjjDPYvXs3jz76aFWAamxs5M4776w598wzz+T000/n17/+dbXM+vHHH2fjxo088sgjLFq0CIDW1lY++tGP8uyzz7JixYoJ+fyDcdCx23VAUQYVqISAWLkbXm4SCFABAfWIhTXOXTaDy2IL6OnN05Uq0ZMt0Z0x6cmU6MmYZAoWRdNll5lnV1etMBXSFZoSobIgZZQfG4QN9YhYEwRMLYQQcJCxG8dCUvRhNf1BQEPUIJUx65a1TRSRkMb7z5nPfz7ji1CpXF83vkRUZ3q/7KZ6pXRHKo3xEGceP50zjmtj5/4cr2/vZfOuNL1Zk9+t38vv1u9ldmuMJXMamd/RgKYeOT+Xkumwt2xU3tTgj7f97VAC0SkAwOhYyLSOhTRf+PFqJ7z8xj+AEHT98l7i77iI+InnjtmMPBCgJjFCCNzNzyNpYZT2xUj1RCbXRlK0wT1GhsA3I9fo0Q7dn8HMaTFmTqsfVTrSaIjqnHHcdN65tI3t+3K8trWHt3dn2N9bZH/vLp59ZTcLZzSwdG4TM1qiU36xLegzLU9lTSIhlcZEiETU74gylvK84TBYlpNZnkAP5eLvaGLdunUsX768ptnDu9/9blavXs3atWsHFaCEELiuSzxeO4klEomDGmZGIhEMw8Bx+qLKa9euZfHixVXxCWDZsmXMmDGDtWvXHlIBSjg2zhvPIsdbUNqPrZ+d6ph+WV6dFt4VFFkiETPIFQ6fD1RAwHDRNYW2pghtTbWm+rbj0dtflCo/zuQtSpbL7q48uwcRppri5WyphC9ORQJhKmACEYUU7vaXkBtnorTOr3+MayLFmofd9CcW0QgZh97PLx7R+cCK+fxx437iYa363YxMwXLY8UaWJGa3xZndFsc82WXTrjSvb+tld1ee7ftzbN+fQ1d3sWimX6I3s3X8OgrWo2L3UakeUBVpQsa5/kblqaxJW3OEcNigZLtkclYgOgVUkVWd+AnvIn7Cu7B795J96Smyrz5Dz1Pfo3ftD5n3Dz8c0+sHo9BkxiogSjmEVURRBkkJdUwIx3wRahSEyunBh6BJx1GLLPndouZOj1MwHd7Y3suGLT30ZE02bk+xcXuKhqjO0rlNLJlzZKT/2o5HOmeRLdiEDZVETCcZ86PawhPj1pEjyHI6/GzevJkPfvCDNdt0XWf27Nls3rx50PM0TeMDH/gADzzwAMuWLWPhwoW8+uqrPPjgg1x11VUDjvc8D8/z6Onp4dvf/jayLPPe97635jrqiV0LFy4c8jomApHvBsdClHKDlkYP1cK7gud5xCIauiZj2UEWVMDURFNlWhsjtDbWF6Z6sibd5WypnkyJ9BDClKEpvhgVD9EYN2iKGzTGDeJR/YjoPhtweBG5LoRtIpyBpWrQv+nP8KsONEUmFtEOS0OJZMzgwlMn1kx4qmNoCsfNbeK4uU2kcyavb+9l47ZeMgWbDVt72LC1h2RMZ3pLFNvyg5l9XrG+B6zn9T32/y8QXu0x1e3l46uPxUDLikhIZeGMBhbNaKC9JTruY5vnCbJ5C9N2yJVcMtkixdJYmjwFHMlojdNpOvdKGt/1UQqb/kz25afH/JqBADWJkYwo2sLlCDNX1wQRyh4i+hiUeeFPUF1Z6+DHBoyZiKHyjkXTOHlhC3t7Cry2tZc3d6ZI5y1+v2Evf3htL3Pa4hw3t4m57QkUeWovqD1PkC+LQj3pkm8MHw8RD2vI0sjL86pZTq6gaPtZTtkgy+mwkslkSCQSA7YnEgnS6fSQ595yyy3cfPPNfOhDH6puu/rqq/n0pz894Ni7776bb37zmwA0NzfzrW99i1mz+hbWmUxmQDZV5TqGEqDOP//8Qff9+te/RlFGXiYrJdpQ420g6iv71RbeBynhEAKihophqFh2MEYHHFkMJkw5rkdvf1EqW/KFqZyFabvs6S6w5wBzfkWWaCyLUb4wFao+PlrKiwLGjjxtAXLjTCQ9Uv8Ax0ZSR1Z14HkeiahOT7qEHZRTT2oaYgZnLJ3O6Uva2NWV5/VtvWzamR5QxjjRFEoOr2zu5pXN3URDKgtmNLBoZpKO5si4Zka5rqBoOphWkPEUcHAkSSa66FSii04d82sFAtQkRwrFBjdCdP2JsF53peHieYJYWCdTOnQGiQG+kNLeHKW9Oco5J7WzaWeaDVt72NNdYOveLFv3ZokYKsfOaeS4uY00xkf/O54smJaLabmksxbhft3zDE3B8wZflMmyn4psVrKcCjb5ko1lu9i2F2Q5TVKEEAddKN11110888wzfOUrX2HevHls2LCBe+65h0QiwU033VRz7H/7b/+NCy64gM7OTh588EE++clP8t3vfpfjjuvryFHv/YZzHeONJEnIkYbBD3AsX3waxk2MLPkeIrmcFfytBwwbTZUJh1Q8zxd0HNfDdabGeKkqMtOSYaYla7NMKsJUT7ZEb9b0H2dMUjnfX6crXaIrXRrweomITlNioDg1FTvzBUwskiwjJVoH3S8cCynSgCQP/2+n0vTH0FVsJwgkTAUkSapahqw8uYPt+7IIScay/HslP/leQpYkJIny/yVkud9jqS9oWvf4etvLQec9XXne2pVm8+40+QPEqIUzGlg4AWJUQMChJJh9pzK2CUYYSR1bdyJNlYhHdHp7C1OuveiRgK4qLJ3bxNK5TfRkSry2rZfXt/VSMB3+/GYnf36zk/bmCMfNbWLRzOSoMjImE47rkc1Z5PIWYUMlHjNIxnQSUf9zSZLfgt71BEUryHKa7CQSCTKZzIDt2Wx2UP8ngDfffJPvfOc73HvvvdUspNNOOw1Jkrjjjju48soraW5urh7f1tZGW1sbAO9617t4//vfzz333MO//du/HfQ66mVoVXjqqaeG/HzjVS5a85quhRSZNrTRbRnXFcTDGrquYAa10gEHQVVkYlGNlmSYRFhD4Je6WbaL6XiUSg5F08FxPWzXw3X8UpCpwGDClCcEmbzVJ0qVBaqejIlpu2QKFpmCxda92ZrzQrpSLuErl/Ml/MeJiBbc2AUMwJ8LPGRjkOyoIVBkaIjr5AqBADXV0FWFxbMbicVC5HKlQ3KfNLc9wdz2BOd5M9i+L8emfmJUpaFRIEYFTGUCAWqS4uzeiJfei9S+eNBUYOG5yMbYDb09TzCjJQaeR2dPgXzRX5wGHHqaEiHOPqGd5cdNZ+ueDBu29rBtb7ZacrD25d0snp3k5GNaUSXQVRlDU6bkxCOEn2ZcKDn0pkvEYzozXCgWLDI5K8hymiIsWLBgQImbZVls3759gDdUfzZt2gTAkiVLarYvWbIEx3HYtWtXjQDVH1mWWbJkCS+99FLNdbz++ut13+fcc88d7scZM862vyBcB6k9UtebT5Sz/WR9+FmNIUMhZKiBABUwKIoiEYtoNDeEaYjqIPpKnFVZQgupxCQJKRFCILAdge24mLZLyXQpmA6O42dKOY43pYR+WZJIxgySMYN57X3bhRAUTbfqM9VfnMoWbN9nqrvA7kHK+ZoTIRbNaeSDK+Yd4k8UcKgRjoWz5U9IRhSlbVH94IBr+3YYo2j6UwkkGLoSrK8Dho0iy8xrTzCvPcG57gx27B9ajFo0M0l7IEYFTAECAWoSIjwXr2srXq4b0TKnrgDV18J7fEqzdF2hMWYQC2lkChY9mRL5gh3Uqx8mFFliwYwGFsxoIFe0eX1bL69t7SGdt1j/dg/r3+6pHitJENJVwrp/kxrWFf+5ceD/VUK6QlhX0TV5Uk1Qlu2SypTwkMjnSoHh8hRixYoVfOMb36C3t5fGxkYAnnjiCSzLYuXKlYOeN2PGDAA2bNhAR0dHdfv69esBmDlz5qDn2rbNK6+8UuMBtXLlSn7+85/XmJG/9NJL7Nq1a8jrGE+8QhovtRdh5WH64voHueXyu+G08C4j4Xv1ZXJm4NMQUIMsS0QjGi0NIRqixqDeekJUMjj8fYoEiubPB3LZOs12RV+2lO1RKAcBHFdUxamphCRJREIqkVCMGQd037Udj96cSW+mnC1VFqh6Dyjne2NHirltMZYtmnaYPsXEsmXLFtasWcOLL75IOBzmkksuYdWqVYRCQ68tC4UC9957L4899hidnZ20tbVx2WWXcf3116Prflb+z372M774xS/WPf/ss8/m29/+dvV5Z2cnt956K+vWrUOWZc477zxWr15NMpkct886FF5qN16uB6mQRmk7pv5BjgWh6Kib/oQNlZChBllQAaNCVQaKUW/tTPP2nkCMCph6BALUpERCnX0yzq4NSJHG+oc4FpJmwBjL7/oj/BUqyahOMqqTKdp0p0rkihZ2IAgcNmJhjdOObeXUxdOqpoh7ugv+zYHjIQQUTb+sgmz9zi0HIpdFq1A/0aoiUNWIVYZKNKQSCx+akgTPE1Mq8h4AH/nIR/j+97/PjTfeyI033kh3dze33347l112WU0J3urVq3nooYd47bXXADj++OM58cQTufnmm+nq6mLevHm8+uqr3HvvvVx88cU0NTUB8OMf/5hXXnmFM888k2nTptHZ2cmPfvQjtm/fzpe//OXq61900UUsXryYm266ic9//vO4rssdd9zBKaecwjnnnHNIfhaSEUGZdQJe7y7fqLYOwrWQoo2Ddserh+/Vp2LoKiUz8OsL8IWnSEijJRkiGesTnkY6fAohcMuJdTJgqDIhTSn7kYRxXD9TynI8LNujYNqUTLcvW2qKiVIVNFWmNRmm9cByPk+Ug3Am6bxJOKxx8qKWw3SVE0smk+Gaa66ho6ODe+65h56eHm677TZSqRR33XXXkOfecsstPPnkk3zuc59j0aJFvPLKK9xzzz2k02m+9KUvAX6p9I9//OOa87Zu3co//MM/sGLFiuo2x3G49tprsW2bO+64A8dxuPPOO7nxxhv5wQ9+cEjWHnKiDWX6MUiOOej7CeEgG6Nv+lMJJOSLgQAVMDb6i1FOfzHqwMyosMbCjsQhFaM8T1AwHQolm3zJIV/+f6FoY7sesiyhVLyxyv9qnksSSr/HslzvOUPsk1BV2U+g0IIGFJOVQICahEiyjJycjuJag0+Eno0capqQwaQSPY2HNOLtfuvYnkyJbN7GtIMSkMNFxRRxzvR4tRa9YuxdtByKpkvJcihZLkWz7//F8raS6VC0XGzHwxP4E8QwRauwoTK9KUJ7c4TpTRHaGiNoajCwB/jeS/fffz9r1qzhM5/5DKFQiEsvvZRVq1bVHOd5Hq7bN34oisI3v/lN7r77bu677z66urpob2/nqquu4lOf+lT1uIULF/L4449z6623kslkmDZtGieccAI/+clPOPbYY6vHqarKfffdx6233soXvvAFJEmqRtEPVQRQUjSUppmDdy31ey4ja8Nv4V3B0FUioUCAOtqRJIiENZoTvm+RIkujEp6GQvRrFQ5+G3ldlZHCEi1SCE8IHFdgOi5WeU4RsoznlH36XIHrTs3SaVnuK+dTFImZ7Q2EDRXTPPLWPj/60Y/IZDI89NBDVcFfURRWrVrFDTfcMKiHn+M4PPbYY1x77bVcffXVAJxxxhns3r2bRx99tCpANTU1VV+3wrPPPouiKFx88cXVbY8//jgbN27kkUceYdGiRQC0trby0Y9+lGeffbZGrJooJD2MMm0uFOp3bhWuDYrmB35HSf9AQkDAeFFfjErx9u4M+aJdI0YtmtHAwhkNoxKjHNfzhaR+wlKhIjAV+54XJskaJf7UW9xx41kYh+heZTTZpDt37hy0E7OmadWKgMqxX/3qV3nhhRfI5/PMnTuXj3/841x++eU15x3ubNLhEoyCUxC/hbd00BbeY34fIaDcvSMe0cmWbHrTJTJ5K/AimSSoiowalomGh58S7rieL0iVRaui5VAyK88PELAsh3zR375lT4Yte3yTZ0mCloYw7U0RpjdHaG+KkIjqQarvUcq8efNqyinqcfvtt3P77bfXbGtubq7JYqrHKaecctDXrtDa2srdd989rGMPC45VbuE9itJpIWiI6aSyZt0Sq4AjG0mCcEijKRGiKWGgKRKuKw7Z30K9Er6IphAzVFRVJh4P05PKY1peNWPKLM8xFU8px/V8YSr4850UrFu3juXLl9eIRO9+97tZvXo1a9euHVSA8rPmXOLxeM32RCJx0KYNjzzyCGeccQbTpvWVNK5du5bFixdXxSeAZcuWMWPGDNauXXtIBKiDUqk6UMZWdWDoCpGQhhU0/AmYAGrFKK9sYN4nRr20qYuXNnVVxahjZieZo6t+kkG5w3S+2F9k6hOWRpKAIEG5/FkjGlKJlv+vawqu5ze+8DxRrXrw+m1zD3xcfl73sQduv9fyPFF9bugqinxo7klGm03a2to6IEtUCMF1113H6aefXt1mmiaf+MQnAKpi0iOPPMIXvvAFQqEQF110ETA5skmHSyBATUWckXuIjAV/sSGI6gqxtphvGp01SeesIBo/BVEVmVhYJjZM0cpxPTpTRfZ0F9jbU2BPT4F80aYzVaQzVeSVt7sBP0uqIkgFWVIBAQPxW3gnBs2QGgrPE0RDGiFdoVAKxt2jBQkIh1SSiRBNcQNDU3Bdb9J0rK0s+itlFLoiYagqkiQhSSHAL/GzXRfb9cv4LNulaLrYju8vVfk8U6Ub35HC5s2bBzSK0HWd2bNnD2gs0R9N0/jABz7AAw88wLJly1i4cCGvvvoqDz74IFddddWg57366qts3bqV66+/fsB11BO7Fi5cOOR1HEqEayPHm8d+AyegIabTnQ3K8AImFlWRmd+RYH5Hnxj11q4UWw4Qo0aCIktEDxSWwgOFppChIh9GsUOWYfH8FsK6gn0IKndGm02q6zonn3xyzbbnn3+ebDbLpZdeWt1WGTvvv/9+zjjjDADOPPNMXn75ZR599NGqADUZskmHSyBATUGEayGFW5DkQ3tzLwQIVxDWFaLTojQ3hHyfhKxJyXSmZLp9wMFRFZn25ijtzX3eB9mCxZ6eAnu7fUGqs7dI0XR4e0+GtydRlpTrCYr9atH99GD/cbGfeCpJEhKABBISksSA55XjKp9NOshzKq8jQTSsMXdmEn3yBB8CDjGVFt7SKFp4VzA0P9sxEKCODsKGSjJh0BQ3COlqWaiZ/J5LB2ZLQV8ZXyxUGRcl30PKE1iOh2N7mI5LqeRg2v7ndD0P5xBmeR1tZDIZEonEgO2JRIJ0un4pWoVbbrmFm2++mQ996EPVbVdffTWf/vSnBz3nkUcewTCM6s1S/+s4MJuqch1DCVCDla4A/PrXv0ZRhu+zNxR+0x91XJr++GV4GtliMIYHHDoGilFZ3tqVZsvuDJbjoWsyUUMjElaJGv0EpbBWIzhNla7bfgDk0F3naLNJ6/HII48Qi8U477zzqtscxx8vDhwn4/F4TdbplMgmLRMIUFMMITxAGlEL7/G/Br+lrK7IzGiJ0pww6M1ZpDImRdMO0uuPAuIRnXhE55iZSaA2S8oXpvLkS86QWVLtTRFaR5ElJYTAtN2aWvRC5bFZeeynDZcmUaloImbwwRXzD/dlBBwuXAdJUcdUOu15kIjq9GRKkyYDJmD8CekKDXGD5oYQ4SkkPB2MPmEKKuKUKkmomoKkq2Xjc7+00PH6zM9tx6NoOpiWi+t6VWP0YK0xMQghDnrzdtddd/HMM8/wla98hXnz5rFhwwbuueceEokEN91004DjPc/j0Ucf5V3vehexWGzA/nrvN5zrOCS4FpKqj1vVQSWQMLLck4D+qIpc7eZcLDlB9uQI8MWoBuZ3NIAE4bCOZdrBmmIMjDab9EBs2+bxxx/nwgsvxDD6xptTTjmFhQsX8tWvfpVbbrmFZDLJo48+yvr16/nsZz9bcx2TPZu0QiBATTUc258IxyESMx64roemyLQ3RWiKG6RyFr2Z0lE5IchlM9ijkQOzpIQQ5Ip2X9led57OVGlAlpRczpKa3hxhRkuU2R0NpNJFsgWbQtXg0M9aqj4e4d+WJEGkX0QnElKJGBphQ0GWJP+mCFG9mRFCVOP2/jZRPqbv5qnm2APO6/+8cmw4pPLuM+aM7ocbcGTgmGBE/fF7lAghiIY1QrpKvmiP48UFTAYMXSERM2hJhIiElKqZ99HAgcbnigSKphAuC1OSJCHwzc9t2yNXsunNmpRKzpTtxHe4SSQSZDKZAduz2eyQEfs333yT73znO9x7773VLKTTTjsNSZK44447uPLKK2lubq455/nnn2f//v1cdtllI7qOehlaFZ566qlB9wEczI9quAjXRookx00M8zxBMm6wS5VrGnMEDI4kga4p6JpCLKIRDWvVsaE7U6I7VQosQUaBIkvomoJlBuuJ3bt3V5sq1GOo8WYs2aT9WbduHalUqqb8Dvyy5+9973vccMMNXHDBBdVtt99+O8uXL6+5jtFkkx4OAgFqiiEcc8QtvA8FruuhyhJtjWEa4wapnElvpuSLBUeYKCMBiir7BuCKhKErhA0NXZVJ5U0yWeuoXxBLktSXJTUrCQyeJbU/VWR/qsgrm7tH9B6GphAJqdW6c19YOkBoCmmE9cOfMpyI6UxvjpLLlg7rdQQcPoRwkUOjb+FdQVNkYhEtEKCOIHRNJhHTaWoIEwupCE8E0egyfSK///OQ8TNIwkaI5kSIXNGmN1siX5hcGa9TgQULFgy4KbEsi+3btw+I5vdn06ZNACxZsqRm+5IlS3Ach127dg0QoH7xi18Qj8dZuXJl3et4/fXX677PueeeO+zPMxEIzwNJQtZH3rl00NcUEI/qJKI6ecnvLh183weiKr44EjJU4hGdSEglpMvIkozn9TUzmN4YIRHW6UwVSAXr74BJxEizOH/xi1/Q0tJSIyoBlEolbrrpJlzX5etf/zqxWIynn36aL37xiyQSiZrSukmdTdqPQICaQlQ8FcZzIhxvXNdDkaA16RumpvIW3ekSxaKNOwWFKFmSUFWpLDbJhA3fXM/QZDRNQVclFFmuimzJmE4qatGZKpIv2EddFthQHCxLam9Pgd6s6XeJMdRq3XkkVKlJ7xOXwoaKqgQG5wFTA+E6ICtjauFdwfM8ElGd7nRp3CL8RysSoGkKmir72TdCIIRvhi28vucT9WPWVJl4VKelIUQ8ouN5Hl5wIzosKnNuPKyRiGqULI903iRVzoqaiuuNQ82KFSv4xje+QW9vL42NjQA88cQTWJZVVyiqMGPGDAA2bNhAR0dHdXulZfjMmTNrjrcsiyeeeIKLLroIXR+YAbpy5Up+/vOf15SPvPTSS+zatWvI6zgkOBaSMn7ldxV0TWFue4KS6VIwHXJFm3zBxrJdbNs9Kj1VJQk01c9yileynAwVXZWq2ZHCA5dagcl1PUK6zOy2OPGoSWdviXzRCkp0A0ZER0fHQbMqB2O02aT9yefzPPPMM1xxxRUD/Ot+8pOf8PLLL7Nu3bqqz9Ty5cvZvXs3d955Z1WAGm026eEgEKCmEq6NpIyyhfchxnUFEtAcN0jGdDJlIapQtHEm6QJbUfqEJk2TiYQ0QpqCrsnomoKqSEhI5RuSclmWB45XOxkmYzrxiEZ3xqQnXQwMgwfhwCwpRZGIxULkcoG/TcARhmP6BrZjbOENfvS80g2vGJQcjBhZrkTVFRLlqLqhK77ghN/W2fO8mnbQrkfZe8jDcTxsTyC8ilAl8MreRp4naoSswW6AVEUmHlVpSYaJhzUQHDWlduONEALhgq5ITG+M0JIIkS345XmFoj2i1uFHGx/5yEf4/ve/z4033siNN95Id3c3t99+O5dddlnNTdPq1at56KGHeO211wA4/vjjOfHEE7n55pvp6upi3rx5vPrqq9x7771cfPHFfichGQAALLJJREFUNUa84BvjZjKZuuV3ABdddBGLFy/mpptu4vOf/zyu63LHHXdwyimncM4550zcD2AYCNdEijVPSNMf4QkUCRJhjWRUx/UEJcu3GsjkLSzLxbTdI66KoD9KJctJV0lEdSKGSqhsj+CPp8PLBq34yzXGDOIRnc5Ukd50KciKDDgkjDabtD9PPPEExWKx7ji5adMm2traBoytS5Ys4bnnnqu5jsmaTXoggQA1lXBMCMdH1cL7cOF5vhDVGDNIRg3SeYueTAnH9aoTRmXxLqr/r9023kgSVaFJUWRChkLY8G9CdFVBUyVUWSpff5+PgB+ZPvgFVT5zW2OYRFSjM1UinTWxgoVwQMBRifAc5NA4tPAuo8gSiZgRCFDDRFXlasluPKITDqmENKUqGiH8eUFGAgVQlJpOlhX6d7l0vT7Pov7CleuJPvHK80uPK+KViyAa0WhtMIiVhSdxBN9cHmpc10PCDwIl4zqFkkM6Z5HOW5TMI88OYKwkEgnuv/9+1qxZw2c+8xlCoRCXXnopq1atqjnO87waryJFUfjmN7/J3XffzX333UdXVxft7e1cddVVfOpTnxrwPr/4xS+YNm0ap59+et3rUFWV++67j1tvvZUvfOELSJLEeeedx+rVqw9r2Uhf05+JrTroL7KEND8DvDUZpmS7FEsO2aJNoVjOjnKmtlBdyTrVNZloRCcW9m0SdE2GapaTYLQ5YJ4nkIGOZr/r8v7eIpmcGQQ1AyaU0WaT9ueRRx5h9uzZnHTSSQP2dXR0sG/fPrq7u2vKm9evX1/NSIVJnk16AJIIcvgPGUIIurpywzvWKuJ2b0cON1S3eYUUcmMHcnh80+hUVaaxMUpvbx5ngic3WfZbY3rCw/X6jJ09j+qkI8oLeK8sRHllPwzP81s2u67AFR7CqxWuDhSyJBmi0RClooWEX0oXKZfQVbKaNEUuG1FPXKmFosikCxZdqSLZnDXm0oAjNVPoSP1c4HtALVs6nVy2NKzvWEtLbNLVax+tjGTcBvAKabz03urYLTwXYRVQmmYijdONjCRB3nLZvieDEdKPuO/MWMcCSQJdVdB1hXhUr5btaorcJzpNEAcKV5Xn4M+1sViITKaIfQQFJA7lGmIk+F31ZCzHI1uw6M36vpS2PbxrVBSJme0NTE8amObwfl/B2D15GOnY7Wb2QyGNFPJNfIVtAh5y8ywkefwCvyP5vkiShCxLOK5H0XIolFwyeRPLcrFsb9LZPNQbuytG10a/rNOQrqLIfVlOE4Ek+f/pzZp09hYpFO1RlzYeyvWpoStoqh8gqQQ0KsGN8f5RHanrbkWRWDy/hagmD2uuHeu4nclkuPTSS5kxY0ZNNunZZ5/NXXfdVT3uwGzSCj09PZxzzjlcd911/P3f//2A19+7dy+XXnops2fP5rrrriORSPDEE0/wwx/+kFtuuYWPfvSjADiOwwc/+EEcx6nJJm1tbeUHP/jBpJqbpk4qzVGOcGwkVfPLOKYw/sK/z0wU8GcJBb/tDfSLONdfxFe2Vcoc+gtZwisLVvhZSOGwjm05KJKEpvrvWFtCN/pIy3BxXY94SCM+XaMnZ9LVW6RQsoP69ICAowHHRFINGAf/pwpCQNRQMYxgCq8gyxKGpmAYKg0DSjn8jNtDUebWv3Nmv61ARRCRAu+uQ0Tld65I0BQ3aIob5Eo2qaxFtuBnRQW/ioDBEI6FFGkYV/FpxNdQzo6S8Mf8eFhjWjKEabsUTYdswaJQcrFsd1KJv5oqEzZkoiGNWEQjHFLRVQWqWacT32hBlNsSN8X9jNPOdIlUujQpy3IVRSJkqDTEDBIR3/uqmk3rCmzPz6S1HYFtu5iOi+OUA/WewC37B3qeOCr9ww43o80mrfCrX/0Kx3EGLVOePn06DzzwAP/6r//KmjVrKBQKzJkzhzVr1nDFFVdUj5us2aT1CDKgDiFjyYASpRwYYZTGGQc5c+RM1ujlWJmMn0uWJWxX0JUu0psxR9U29kiOWByJnwuCDKipzFgzoLxiGik+DSXWdJAzR4aiSHRmSqTzzhH3nRnuWKCpfiZrJKwRj/gNC2puciYZk3FOGg+m0ueqiICm7ZHJW/TmTIolp+51BxlQU5uxZEAJIRClDErjDKRQbFyva7y+L7LsZ0dZjkfBdCgUfUHKsn1Barzu7iSpnIklSdXvj//c3y5JErICquyXOieTYSRPoKvyhGc5jQRZkcgVbPaPohphItankgSGrhINqyRjvkimKnLdQEnld+A/lqpl4J4nypUhHo4rcFwP2/XKfwNetXqktjS87/qP1HX3oc6AChg5Qfh0iiA8F9kY30kw4NDjlU0nO5ojNER9o8R0zpr0i/aAgICR09fCe/wzV11XEA/rFMyjZ+yQJL97lK77pRzRsEpY9ztiVm5yvMDMO2AI/KwogSpLtDSEaEqEyllRJrmCjWk6QQZBQL+mP+Pb/W488cpiggzEQyoNEZ22pjAly6VQcqpZfqbt+llUkt/ZWZL7xCO5IiDJfeV+ar+GPIoiI0t+GV1F8FJkf5v/T0aS/ccgoSgSDQ2Rqrg2mUQNzxVEQxrzpmt0Z0t0pfzGSIcaTZUJhXzRKRHRqk0w/Gy3+vNXxVqk/Ky6XQI0WUKTFSS9VqSC/k00yllUjm9lYtseluPieB4hQ8WxVWzbz7IKOogGHAoCAWoKIFzHN0Wd4uV3AX24riCsK8xui5OOWXSmiuQL9qSM2gcEBIwSd2JaeFfwvTQUZFmaVAv98USRJXS93CUpVi6t0xVkiWrXuaCDXMBoqMy38ZBGIqJRslwyeZtUthQY/B/tOBaEo74INQXoPw4aqkw4YdDSYGA5goLpZ/jJsu+Jpsi+EKXIcjWjSa7+X6pmTvXPWqpfWlze5oFbfixJ498tcDypfKZpDSG/W15vkVS2hDVMX7jRIksSIUMhFtFpiOlEQ1p1DvPGae4eTKSS8bO/NEVB0tVyNhVUBMNwxCCVLmBaLo4rsB0/g6pk+ab3ftmYX+Y3ET5UAUcngQA1FXAsX3xSx97CO2DyUKlPT0Z14mGN7oxJT7pIoTT5Fr5+LCUgIGAkCNdCijYhycoEvYFgbnuCkCbTkylRKDqY1tT3tfH9Q1QieoSI4RuID+ySFBAwPgghEC7oikxbY4jmhEG2aJPOW8hBWcZRiRAOsh493JcxairiqiJBIqwhSX0iEjBwjhB+hpB3lKz0XFegyRKzWmMkYjqdPQWyExAE1jWFcEilsexDZWhK2ZPQb5x0qKlttuQLhpWGTJJGNYuqWubn+iV+ldI+x/GwbA/TdqolfhVhyi13Nw8IGA6BADUFEJ6NHGoK6lOPUDzPN5hsawzTENXoTJVIZU2sw2SUqCgSmiL7rcsNhaihI8vlZYuomL/XdhysPPboM3evdCX0APpNetVIWvn1KttkGQxNwdGVctSFajfEgICphhACPDGhLbyFgHBIo7Gcym9aLrmiQypXomS6mNbUkWn8zqQqiahOIqbT2hyjVDSx7fJi/QjN8AqYXFQMn5NRnZaGEJKqIJyp8z0KGDvCtUGZ+k1/KkxUh+cjAdf1SIQ1oh0JutPjEwRWZN9QPBHTaYjqhA0ViUpH78mdrduXRdXPJ0oCRZUJaXJVmAIJgcBxKgKVny1lO4KS7WBZblmcEtWyvqDCI6A/gQA1yRGeC5Lkd1EKOKJxXQ9NkZnZGiMZ032jxLw1oaU1sux3B1QVPwoSDWuEdAVDk9FUBeUgHZv6dywcfF9lQvMnrErmlwdIQvIjbsJPEY5GDbLZEpbtlicufwKzHRfL8bDLngKVzh8VgcoLJreAyYZjIanahJXf9aci0GiKTHPCoClhUDJdMkWLdNbCtBzsSeYzJ1H2czIUGqIGkbBKpNyaW5b9VtSF/OQwrw04+qjMKY1Rg97e/OG+nIBDiWMhaQZMkfK7gLHRPwiciGrlsjxzRHOmhD9nRcKabyge0dAU2c92OkI609Ur8ZMl0BUJQ1XBkGrM0h3PN0avZE7ZjodpORRKLrbjYtteEGA+igkEqMmOY417C++AyY3ner5RYrtGb9akK1UkX7THHMGSJMpik4Km+e1xQ4aCoSnomlyeLPuymUbWJnc4x9UeI5W3yeUnqiwRCWmYRQuZvhr1/vXqlc4fbll4qqT/OmWhyp/kfLHKdeuLVF4QDQw4BPgtvBNIyqGdZitCrKHJTA+FmdYQplCySRdscnmLkuUcNr+oiol4SFdIxAyiIb+8rs/PqdxyfJL7iAQEBBy5CNdGjjcHVQdHGa7rYagys9piJKJ+EDift4cUSVRVJqSrJOMGiahOSO/rwjrZs53Gk1qPMP/nVWuQrlbFKU94lCyPkuWQK9oUS345n+2MX+fGgMlPIEBNcoRrIYVbggX5UYYQflZQU9wgHtHpShfpzZiUhmmMKuFPjKoqo6my76MSUstik4KqSOWU4L73mmyd+AaaXh5gqihJqKpU/m5I1YyrSopwRaDyhC9aORUjxUq7WselZPtRGMf1/x2pRs4BhwMPyYgc1iuo/D1HQyrxiI7dGCZftEnnTfIFB9NyJzwCWWk1HapkOpWN0yslCYGfU0BAwGRBeB4o6hFTfhcwMoQA4QoaojqxsEZnukRPuoTdrwxXkiBkqMTCOg1xnVhIQ5H9+TbowlqfvjLQcoBMlQnrBk3xEK4rMG2XguWQK1qUSq7foc8+WhzJjk4CAWoKMBEtvAOmBp4nUCToaI6QjBp0pgpk67SNVZU+sSlsKIRDGoYmY2gqmioh4ZfSVbIjjiQ/laGEKoly/boioSv129Q6nsB2BJbtYtouRdOhZLplocr/F0RlAkaE6yDpoUlTOl3pkCQDiYhOMqZj2h65ok0qZ/oRSMsdt8WeLEsYZfPVRFQnGlIJ6Wp1DDpSShICAgKOMFwLyWg4JKXTAZOXSllee1OEhohOV6YEskQiZpCI6MQj/hq7EsR1gyjKiPHvR/oytsOGwbSGELbjUbRdTNMlW7AomeWSvUkWJA8YG4EANcnxy+8CAepox3UFIV1mVlucXMkmb7oIV/Ozm0Iahq4Q0hQ0VS630e1nDO7WGgoezdSrYZfoq2FPRLRyirBft27Zrt/xw3LJmzaOI3AdvxtI4DkVMCRaBOn/b+/Ow6Kq9z+Avwdmhn1ABDIoFdHhok4gBYryQ8XrcktFycoiUx4iEROXx5SEbrlL17Jwq+uSaXorUzPN7XYr1Mf09uQSiWSCNxGvhRsj62zn9wfNXEcWARnnnPH9eh6emu+c5XMO+p7xczYRPrnU/GVZ7iSDr1fd/aKqa42WZlRtraFVj6R2dpLBRXlr00lh9eS6++lyBCKSKJkMcPHk5XcEoO7AjavSCZ07eAFyZ8BQd3DSZGrJLSqoOcy3yZABcFc4w9NFDj9vF+iNAmp0BlT/0ZDS6eq+lxv4nULS2IASOxcP2z3CmyTFfF8mHw8XBAa4oKKi5n+XsQj/e/pc253HcH+5/ekfcpkMChc5PF3rHkkroO4yRXNjqlZvQlWNHjq98Y9L+kx1j6G161aQKDg5Q+Yq/kd4W+4XJXeCm7cr/LxdUVlrwM1KHbSVOtTWGpv8kid3lkGplNdd4mduOslldY1vE59cR0QSI1fa9MmlJD3m74btPOseSMCDj/eGeT87AfBwkcPTVQF/H7e6M6R0BlTXGlBRqYNOb4LOYGRDUGLYgBIxmbOCH4RUjyAIUCrq7qEitvs2OZrbm1JOsrp/rLsqnCGTAU5ObpanfOj/aErV1NZ9MBosl/DxQ/F+I5Mr656iJCHmL3seSmd4uXogwMcNlTUGlFfqUFmlQ43OCJNJgFzuBBeFMzzdFfByV8LdVW71AAN+CSQiqZI5K3n5HZHI3Ppd3FkGeLnK4e2ugNDODTq9CdU6I6pq9Kio1kOvN8Fo4r+NxI4NKDFTKHn5HZEIWS5v/OMf7QonGZSucni6ySCTuUKAAINRsFzCp//jtGK6TyhcAGfxXX7XHOb7RckAeLkp4O2hhM5gQkW1DrV6Ezxc5XB3kUNu1XTilz0ikjoZ4OoJmRMf+kMkZnXfU/64WsFJBpWbAj4eSpiEP+7najBBKeffYzFjA0qs5Eo4ube754/wJqLWqXe2FABXhTPclHIoFE6Qy3kp7f1ApnSDTOHiEPcQMZ/R5CwD2nm61D1d0iRYmlRERI7Cyc0L4BOniSTn1rOvFc5OcHeVw8fHHdevV9q5MmqM6JL2/PnzSElJQUREBGJiYrBgwQLU1NTccb6qqiosXboUf/7znxEeHo4hQ4Zg+fLl0Ol0VtPp9Xq89dZbiI2NRXh4OMaNG4fCwsJ6yysrK8O0adMQGRmJxx57DLNmzcKNGzfaajPvSObkDJmL+O8hQkRNu/Xpg+T46i6/c7wzV803XeUTIYnIEckUrqJ8cAQRtQy/p4ifqE6v0Wq1GD9+PAIDA5Gbm4tr165h8eLFuHHjBpYuXdrkvG+88Qa++uorTJ8+Hd26dcOPP/6I3NxclJeXIzs72zLd4sWL8fnnnyMzMxNBQUFYu3YtJkyYgF27dsHf3x8AYDAY8OKLL0Kv1+PNN9+EwWDA3/72N6Snp2Pz5s0OcWSbiIiIiIiIiOheEVUD6uOPP4ZWq8Xnn38OX19fAICzszNmzpyJSZMmISQkpMH5DAYD9u3bhxdffBHjxo0DAPTp0weXLl3Cnj17LA2o3377DR9//DGysrLw9NNPAwDCw8MxaNAgfPjhh5g5cyYA4MCBAygsLMTu3bvRrVs3AEBAQACeffZZHDp0CHFxcTbdD0REREREREREjkRUl+AdPHgQMTExluYTAAwdOhRKpRJ5eXmNzld37acRXl5eVuMqleqPe7LUOXz4MIxGI5544gnLmKenJ+Lj462Wn5eXh9DQUEvzCQAiIyMRFBTUZB1ERERERERERFSfqBpQRUVF9c5yUiqV6NixI4qKihqdT6FQIDExEZs2bcKpU6dQWVmJo0eP4tNPP0VSUpLV8v38/ODj42M1f0hICM6fPw/TH49tbKgOAOjatWuTdRARERERERERUX2iugRPq9VCpVLVG1epVCgvL29y3jfeeAOvv/665dI6ABg3bhxefvllq+XffpYUAHh7e0Ov16Oqqgqenp6NTqdSqZpsQA0aNKjR9/bv3w9nZz4Fi4iIiIiIiIjuP6JqQDVGEIQ73vh76dKl+PbbbzF//nwEBwfj9OnTyM3NhUqlQkZGhmW6hpYjNHC7/Mama+0NyM3NJz8/z1bNfy/4+LjbuwSb4HZJi6NuF+DY2+bIxJzbgOP+ueJ2SQu3i8RGzNntyH+uHHXbuF3S4qjb5QhE1YBSqVTQarX1xm/evNnoDcgB4OzZs1i/fj1WrVplOQspKioKMpkMb775JpKSktC+fftGl6/VaqFQKODu7n7HOho6Q8vsX//61x23Ucwc9el+3C5pcdTtAhx72xyVFH5nUqixNbhd0sLtIjER++9N7PXdDUfdNm6XtDjqdjkCUd0DKiQkpN4lbjqdDhcuXGiyAXXu3DkAQFhYmNV4WFgYDAYDSktLLcu/evUqbty4YTVdUVERgoOD4eTk1Ggd5vU0VQcREREREREREdUnqgZUXFwcjh49iuvXr1vG/vnPf0Kn06F///6NzhcUFAQAOH36tNX4Tz/9BAB46KGHAACxsbFwcnLC3r17LdNUVlbi66+/tlp+//79cfbsWasm1MmTJ1FaWtpkHUREREREREREVJ9MaOgGSHai1WoxfPhwBAUFIT09HVevXsWSJUsQGxuLpUuXWqabM2cOPv/8cxQUFAAAjEYjxo4di9LSUkyZMgXBwcHIz8/HqlWrMGDAACxbtswy77x587Bz505kZmYiMDAQ69evx08//YTdu3fD398fAGAwGPDkk0/CYDBgxowZMBqNePPNNxEQEIDNmzfzlD4iIiIiIiIiohYQVQMKAM6fP48FCxbghx9+gKurK4YPH46ZM2fC1dXVMk1mZiZ27NiBn3/+2TJ29epVvPvuuzh8+DCuXLmCBx98EEOGDEFaWho8PDws0+l0OuTm5mLHjh24efMmwsPDkZWVhT/96U9Wdfz+++9YuHAhDh06BJlMhvj4eMyZMwft2rWz/U4gIiIiIiIiInIgomtAERERERERERGRYxHVPaCIiIiIiIiIiMjxsAFFREREREREREQ2xQYUERERERERERHZFBtQRERERERERERkU2xAERERERERERGRTbEBRURERERERERENsUGFBERERERERER2RQbUPepvXv3Ij09Hf3790dERARGjBiBLVu2wGQy2bu0NlVZWYm4uDiEhoYiPz/f3uW0ia1bt2LkyJHQaDSIiYlBWlqavUu6a1999RWeeuopREZGom/fvnj55ZdRXFxs77Ja5Ndff8Vf//pXJCQkoHv37hg+fHiD0+Xl5WHUqFHQaDQYPHgwNm/efI8rJSljdksTc1ucmNt0rzC7pYnZLU7MbmmT27sAso8PPvgAgYGBmDVrFtq3b49jx45h4cKFKCkpwezZs+1dXptZtWoVjEajvctoM8uXL8eGDRuQlpaG8PBwlJeX49ChQ/Yu664cOXIEL7/8MkaOHIlp06ZBq9VixYoVSE5OxpdffglPT097l9gsv/zyC/Ly8hAeHg6TyQRBEOpNc+LECaSnpyMhIQGZmZk4fvw4FixYAKVSiaeeesoOVZPUMLulh7ktXsxtuleY3dLD7BYvZrfECXRfunr1ar2xRYsWCRqNRqitrbVDRW3v3LlzQkREhPCPf/xDUKvVwo8//mjvku7KuXPnhLCwMOHQoUP2LqVNzZkzRxg4cKBgMpksY6dOnRLUarXw7bff2rGyljEajZb/nz17tvDEE0/UmyYlJUUYM2aM1Vh2drbQr18/q/mJGsPslhbmtrgxt+leYXZLC7Nb3Jjd0sZL8O5Tvr6+9cbCwsJQW1uLGzdu3PuCbGDhwoUYO3YsgoOD7V1Km9i+fTsefvhhxMbG2ruUNmUwGODh4QGZTGYZ8/LysmNFrePk1HSc6nQ6HD16FE888YTV+IgRI1BWVoaCggJblkcOgtktLcxtcWNu073C7JYWZre4MbuljQ0osvjhhx/g4+OD9u3b27uUu7Zv3z4UFhZi8uTJ9i6lzZw6dQpqtRorV65ETEwMevbsieeffx5nzpyxd2l3ZcyYMSguLsamTZug1Wpx8eJF5OTkICQkBDExMfYur81cuHABer0eXbp0sRrv2rUrAKCoqMgeZZEDYHaLF3Nb2pjbZEvMbvFidksbs1vc2IAiAEB+fj62b9+O8ePHw9nZ2d7l3JXq6mosWbIEM2bMkMy1zM1RVlaGw4cPY9euXZg7dy6WL1+O6upqJCcnQ6vV2ru8VouKisKKFSuwbNkyREVFYdCgQSgpKcH69euhVCrtXV6bKS8vBwCoVCqrcfNr8/tELcHsFjfmtrQxt8lWmN3ixuyWNma3uLEBRSgrK0NGRgY0Gg1SU1PtXc5dW716Ndq3b4/ExER7l9KmBEFAVVUVli9fjiFDhmDgwIFYvXo1Kisr8cknn9i7vFY7fvw4XnnlFTz55JPYsGEDVqxYAVdXV6SmpqKiosLe5bW5W097bs44UWOY3eLH3HYMzG1qS8xu8WN2OwZmtzjxKXj3uZs3byI1NRWurq5YvXo1FAqFvUu6K6WlpVi/fj1WrlxpCdKqqirLfysrK+Hh4WHPElvN29sbfn5+6Natm2UsICAAXbp0wblz5+xY2d1ZsGAB+vTpg6ysLMvYo48+iri4OGzduhXJycl2rK7teHt7A6h/1MV8JO32ozRETWF2SwNzW9qY29TWmN3SwOyWNma3uLEBdR+rra3FpEmTcOXKFXzyySdo166dvUu6axcvXoRer8dLL71U770XXngB4eHh+PTTT+1Q2d0LCQnBpUuX6o0LgnDHm/GJWVFREeLj463GfH19ERAQgAsXLtipqrbXsWNHKBQKFBcXIy4uzjJu/iITEhJir9JIYpjd0sHcljbmNrUlZrd0MLuljdktbmxA3acMBgOmTp2KwsJCfPTRRwgKCrJ3SW0iLCwMGzdutBo7c+YMFi9ejLlz50Kj0dipsrs3YMAA7NixA2fPnoVarQYA/PbbbyguLpb0ac+BgYE4ffq01VhZWRl+//13h/lzCQBKpRJ9+vTB3r17MWHCBMv47t274e/vj+7du9uvOJIMZre0MLeljblNbYXZLS3MbmljdosbG1D3qXnz5uGbb77BK6+8gpqaGpw8edLyXteuXSV7E0GVSoXevXs3+F6PHj3Qo0ePe1xR2xk8eDB69OiBKVOmYOrUqVAqlVi5ciV8fX3x9NNP27u8VktKSsL8+fMxb948DBo0CFqtFu+//z7c3d0xcuRIe5fXbNXV1cjLywNQd0p6RUUF9u3bBwCIjo6Gr68vJk+ejOeffx7Z2dkYMWIEjh8/jq1bt2LevHmSPqJG9w6zW1qY2+LG3KZ7hdktLcxucWN2S5tMEATB3kXQvRcfH4/S0tIG39u4cWOjHyZSdOzYMbzwwgv47LPPJHskxuzq1atYtGgR8vLyYDAYEBUVhVdffbXeY0alRBAEfPrpp9iyZQsuXLgAd3d3aDQaTJ8+HaGhofYur9kuXryIQYMGNfjerX+n8vLy8Pbbb6OoqAgdOnRAcnIykpKS7mWpJGHMbulhbosXc5vuFWa39DC7xYvZLW1sQBERERERERERkU3x/DMiIiIiIiIiIrIpNqCIiIiIiIiIiMim2IAiIiIiIiIiIiKbYgOKiIiIiIiIiIhsig0oIiIiIiIiIiKyKTagiIiIiIiIiIjIptiAIiIiIiIiIiIim2IDioiIiIiIiIiIbIoNKGqWwsJChIaGori4GACwYcMGxMfHN2veY8eOITQ0tNGf7du327J0m7t48SJCQ0Oxbt26O067fft2hIaG4uLFi2267tDQUCxfvrzBaV599VXLNPZg3ub8/Hy7rJ/ofsbsbhyzu2nMbiL7YG43jrndNOY2SYHc3gWQNOTn50OlUiE4OBgAcPLkSWg0mhYtY8aMGejdu3e98Y4dO7ZJjfczDw8P7NixA5MnT4aT0//6ypWVldi3bx88PT1RUVFhxwqJyB6Y3eLG7Cai2zG3xY25TXR3eAYUNUt+fj4eeeQRyGQyAMCpU6fwyCOPtGgZnTp1QkRERL0fX19fW5R8X3n88cdRWlqK7777zmp8z549MJlMzT5yJmXV1dX2LoFIdJjd4sbsZnYT3Y65LW7MbeY23R02oKhZ8vPzLUdfysrKcOnSpRZ/GDZHfHw8Jk6ciIMHD2L06NF45JFHMGzYMHz22WdW01VXVyMnJwfx8fHQaDSIjo5GYmIidu/eXa/utLQ0REdHQ6PRYNSoUdizZ4/VNObTVb/77jtkZ2ejd+/eiIyMxKxZs1BVVYWysjJMnToVjz32GGJjY5GTkwO9Xl+vdpPJhNWrV2PAgAHQaDRITEys9+HUmCNHjmD8+PGIjIxEeHg4xo4d2+x5ASA4OBi9evXCtm3brMa3bduGwYMHw8vLq8H59uzZg2eeeQYRERHo1asXUlJSUFBQYDVNZmYmevXqhaKiIqSkpCAiIgKxsbH4+9//DqDuyNyzzz6LiIgIDB06FDt27GhwXVqtFq+++iqio6MRERGBtLQ0lJSUtGpfLF++HKGhoTh9+jQyMjIQFRWFwYMHAwBKSkowffp0xMbGomfPnujbty/Gjx+PM2fONG9nEjkQZjezm9lNJC3MbeY2c5scGS/Bo0bFx8ejtLTU8rqgoACrV6+2vH7++ecBAKNHj8aSJUvuuDyTyQSDwVBvXC63/mNYWFiInJwcpKamws/PD1u3bkVWVhY6deqEqKgoAMDixYvxxRdfYNq0aQgLC0N1dTXOnj2LGzduWJZz9OhRvPjiiwgPD8cbb7wBLy8v7NmzB9OnT0dNTQ0SExOt1pudnY0hQ4bg7bffRkFBAZYtWwaj0Yjz589j8ODBeOaZZ3DkyBGsWbMGAQEBSE5Otpp/8+bNCAwMxJw5c2AymbB27VqkpqZi06ZN6NWrV6P7ZefOnZg9ezYGDRqEnJwcyOVyfPLJJ0hJScG6desQExNzx30LAGPGjMG8efNQXl4Ob29vFBcX48SJE5g2bRoOHDhQb/r33nsP77zzDhITEzFp0iTo9XqsW7cOSUlJ2Lp1K7p27WqZVq/XY8qUKRg7dixSUlKwa9cuvPXWW6ioqMD+/fuRmpqKDh064KOPPkJmZia6deuGnj17Wq0vKysLffv2xdKlS3H58mW88847GDduHL744guoVKpW7YspU6bg8ccfx9ixY1FVVQUASE1NhclkwiuvvILAwEBcv34dJ06cgFarbdZ+JJI6Zjez24zZTSQNzG3mthlzmxyeQNSIX375RSgoKBDWrFkj9OjRQ/jxxx+FgoICIT09XRg7dqxQUFAgFBQUCKWlpU0u5+jRo4JarW7057///a9l2oEDBwoajcZqmTU1NUJ0dLTw2muvWcaGDx8upKenN7neYcOGCaNGjRL0er3V+MSJE4V+/foJRqNREARB2LZtm6BWq4X58+dbTZeeni6o1Wrhgw8+sBpPSEgQRo8ebXldUlIiqNVqITY2VqipqbGM37x5U4iOjhYmTJhgGTOvq6SkRBAEQaiqqhKio6OFiRMnWq3DaDQKI0eOFMaMGdPkNprXvXbtWqGiokKIiIgQPvroI0EQBCEnJ0eIj48XTCaTMHfuXEGtVlvmu3TpktC9e/d621xRUSH069dPmDp1qmVs9uzZglqtFvbv328Z0+v1Qp8+fQS1Wi2cPn3aMn79+nUhLCxMWLx4cb1tnjx5stW6fvjhB0GtVgurVq1q8b7Izc0V1Gq18O6771pNe+3aNUGtVgsbNmxocr8ROTJmN7NbEJjdRFLC3GZuCwJzm+4PvASPGtW1a1eEhYXh999/h0ajgUajQVhYGP7zn/+gX79+CAsLQ1hYGAIDA5u1vJkzZ+Kzzz6r99O+fXur6W5fpouLCzp37oxLly5ZxjQaDQ4ePIilS5fi2LFjqKmpsVrGr7/+iuLiYowYMQIAYDAYLD9xcXEoKyvD+fPnreYZMGCA1euQkBAAQP/+/euN33qUymzIkCFwcXGxvPb09MTAgQPx/fffw2g0NrhPTpw4gRs3bmD06NFWNZpMJvzf//0f8vPzLUcZ7sTDwwPDhg3Dtm3bYDAYsHPnTiQmJlruIXCrw4cPw2AwICEhwWq9Li4uiIqKwr///W+r6WUymdV+kMvl6NSpE/z9/dG9e3fLuI+PD3x9fa1+V2bm34VZZGQkgoKCcOzYsVbviyFDhli99vHxQceOHbFu3Tp88MEHKCgogMlkatb+I3IUzG5mtxmzm0gamNvMbTPmNjk6XoJHDTIajRAEAQDw/fffo1+/fjAYDCgvL0dRUREiIyNhMBggk8ng7OzcrGU+/PDDzXqKh4+PT70xpVKJ2tpay+vs7Gx06NABe/bswZo1a+Di4oLY2FjMmjULnTt3xpUrVwAAOTk5yMnJaXA9169ft3rt7e1t9VqhUDQ6rtPp6i3Pz8+vwTG9Xo+qqqoGrwk315mRkdFgjQBQXl4Od3f3Rt+/1ZgxY/Dcc8/hvffew7Vr1+qd8nz7eseMGdPg+7c+1QMA3NzcrD7ogbr90JzflVlj+8d8Cndr9kVAQIDV+zKZDBs2bMDKlSuxdu1aLFmyBD4+PhgxYgSmTZsGT0/PRpdN5AiY3czuWzG7icSPuc3cvhVzmxwdG1DUoAkTJlh15AsKCrBmzRrLa/O12NHR0di0adM9r8/d3R0ZGRnIyMjAlStXcPDgQbz11ltIS0vDvn370K5dOwDAxIkTLTfKu5358bZtxRzmt48pFIpGP8zMdb722msIDw9vcJrbj1Y15dFHH0VwcDBWrlyJvn374sEHH2xyvbm5uc0+mna3Gts/5kcCt9W+CAoKwqJFiwAA58+fx969e7FixQrodDrMmzevteUTSQKzu+WY3U1jdhPZFnO75ZjbTWNuk5ixAUUNmjt3LiorK3HkyBGsWrUKGzduhJOTE1auXImKigrMnj0bQN0pqPbm5+eHxMREFBYW4sMPP0R1dTW6dOmCzp07o7CwEDNmzLgndRw4cACzZs2yHLWoqKjAN998g8cee6zRI1aRkZFQqVQ4d+6c5QaTd2vSpEnYv38/kpKSGp0mNjYWcrkcFy5cwNChQ9tkvXeya9cuq3UdP34cpaWlliNCttgXwcHBSE9Px4EDB+o9aYTIETG7W47Z3TRmN5FtMbdbjrndNOY2iRkbUNSgLl26AAC2bNmCmJgYS3f8zJkzeOmll5p1Wu/tfv31V5w8ebLeeIcOHdChQ4cWLeupp57CgAEDEBoaCm9vbxQVFWHnzp3o1asX3NzcANR9oKempiIlJQWjR4/GAw88YDmd+fTp08jNzW3xNjTF2dkZycnJSE5Ohslkwpo1a1BRUYEpU6Y0Oo+Hhweys7ORmZmJ8vJyDB06FO3bt8e1a9dQWFiIa9euYe7cuS2qIyEhAQkJCU1O89BDDyEjIwPvvPMOSkpKEBcXB5VKhStXriA/Px9ubm5NnpbbGj/99BOysrIwbNgwXL58GcuWLcMDDzyA5557DkDb7IvCwkLMnz8fw4YNQ6dOnaBQKHD06FH8/PPPeOmll9p0e4jEiNndcszupjG7iWyLud1yzO2mMbdJzNiAokaZTCZ8++23mDZtGoC6U4IvX76MgQMHtmp5b7/9doPjaWlpmD59eouW1adPH3z99deWoy8PPPAARo0ahbS0NKtptm7divfeew+LFi2CVquFj48PQkJC8Je//KVV29CUpKQk1NbWYsGCBbh69Sq6deuG999/H48++miT8yUkJCAwMBBr167F66+/jsrKSvj6+iIsLAyjR49u8zrNJk6ciJCQEGzcuBFffvkldDod/P390bNnTzz77LNtvr6FCxdi586dmDFjBnQ6HXr37o2srCyra9rvdl/4+/ujY8eO2LJlCy5fvgyg7j4Is2fPxrhx49p8m4jEiNndMszupjG7iWyPud0yzO2mMbdJzGSC+a53RERERERERERENuB050mIiIiIiIiIiIhajw0oIiIiIiIiIiKyKTagiIiIiIiIiIjIptiAIiIiIiIiIiIim2IDioiIiIiIiIiIbIoNKCIiIiIiIiIisik2oIiIiIiIiIiIyKbYgCIiIiIiIiIiIptiA4qIiIiIiIiIiGyKDSgiIiIiIiIiIrIpNqCIiIiIiIiIiMim2IAiIiIiIiIiIiKb+n8CS7pIwDZlsAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ensemble_members = list(range(1, len(ff_test_probits[0]) + 1))\n",
    "\n",
    "ff_test_fm_accs, utk_test_fm_accs = list(), list()\n",
    "ff_test_fm_spds, utk_test_fm_spds = list(), list()\n",
    "ff_test_fm_eods, utk_test_fm_eods = list(), list()\n",
    "ff_test_fm_aods, utk_test_fm_aods = list(), list()\n",
    "\n",
    "for m in range(len(method_seeds)):\n",
    "\n",
    "    ff_test_fm_accs_, utk_test_fm_accs_ = list(), list()\n",
    "    ff_test_fm_spds_, utk_test_fm_spds_ = list(), list()\n",
    "    ff_test_fm_eods_, utk_test_fm_eods_ = list(), list()\n",
    "    ff_test_fm_aods_, utk_test_fm_aods_ = list(), list()\n",
    "\n",
    "    for em in ensemble_members:\n",
    "        preds = torch.mean(ff_test_probits[m][:em], dim=0).argmax(dim=1)\n",
    "        ff_test_fm_accs_.append(accuracy(preds, y_ff_test_t))\n",
    "        ff_test_fm_spds_.append(1 - spd(preds, a_ff_test_t))\n",
    "        ff_test_fm_eods_.append(1 - eod(preds, y_ff_test_t, a_ff_test_t))\n",
    "        ff_test_fm_aods_.append(1 - aod(preds, y_ff_test_t, a_ff_test_t))\n",
    "\n",
    "        preds = torch.mean(utk_test_probits[m][:em], dim=0).argmax(dim=1)\n",
    "        utk_test_fm_accs_.append(accuracy(preds, y_utk_test_t))\n",
    "        utk_test_fm_spds_.append(1 - spd(preds, a_utk_test_t))\n",
    "        utk_test_fm_eods_.append(1 - eod(preds, y_utk_test_t, a_utk_test_t))\n",
    "        utk_test_fm_aods_.append(1 - aod(preds, y_utk_test_t, a_utk_test_t))\n",
    "\n",
    "    ff_test_fm_accs.append(ff_test_fm_accs_)\n",
    "    utk_test_fm_accs.append(utk_test_fm_accs_)\n",
    "    ff_test_fm_spds.append(ff_test_fm_spds_)\n",
    "    utk_test_fm_spds.append(utk_test_fm_spds_)\n",
    "    ff_test_fm_eods.append(ff_test_fm_eods_)\n",
    "    utk_test_fm_eods.append(utk_test_fm_eods_)\n",
    "    ff_test_fm_aods.append(ff_test_fm_aods_)\n",
    "    utk_test_fm_aods.append(utk_test_fm_aods_)\n",
    "\n",
    "ff_test_fm_accs = np.asarray(ff_test_fm_accs)\n",
    "utk_test_fm_accs = np.asarray(utk_test_fm_accs)\n",
    "ff_test_fm_spds = np.asarray(ff_test_fm_spds)\n",
    "utk_test_fm_spds = np.asarray(utk_test_fm_spds)\n",
    "ff_test_fm_eods = np.asarray(ff_test_fm_eods)\n",
    "utk_test_fm_eods = np.asarray(utk_test_fm_eods)\n",
    "ff_test_fm_aods = np.asarray(ff_test_fm_aods)\n",
    "utk_test_fm_aods = np.asarray(utk_test_fm_aods)\n",
    "\n",
    "\n",
    "# FairFace Plotting\n",
    "acc_axs = list()\n",
    "f, axs = plt.subplots(1, 3, figsize=(12, 3))\n",
    "axs[0].plot(ensemble_members, np.mean(ff_test_fm_spds, axis=0), \"-\")\n",
    "axs[0].fill_between(ensemble_members, \n",
    "                   np.mean(ff_test_fm_spds, axis=0) - np.std(ff_test_fm_spds, axis=0), \n",
    "                   np.mean(ff_test_fm_spds, axis=0) + np.std(ff_test_fm_spds, axis=0), alpha=0.3)\n",
    "axs[0].hlines(np.mean(ff_test_fm_spds, axis=0)[0], ensemble_members[0], ensemble_members[-1], color=\"C0\", linestyle=\"--\", alpha=0.5)\n",
    "acc_axs.append(axs[0].twinx())\n",
    "axs[0].set_ylabel(\"1 - SPD ($\\\\rightarrow$)\", color=\"C0\")\n",
    "axs[1].plot(ensemble_members, np.mean(ff_test_fm_eods, axis=0), \"-\")\n",
    "axs[1].fill_between(ensemble_members,\n",
    "                   np.mean(ff_test_fm_eods, axis=0) - np.std(ff_test_fm_eods, axis=0), \n",
    "                   np.mean(ff_test_fm_eods, axis=0) + np.std(ff_test_fm_eods, axis=0), alpha=0.3)\n",
    "axs[1].hlines(np.mean(ff_test_fm_eods, axis=0)[0], ensemble_members[0], ensemble_members[-1], color=\"C0\", linestyle=\"--\", alpha=0.5)\n",
    "acc_axs.append(axs[1].twinx())\n",
    "axs[1].set_ylabel(\"1 - EOD ($\\\\rightarrow$)\", color=\"C0\")\n",
    "axs[2].plot(ensemble_members, np.mean(ff_test_fm_aods, axis=0), \"-\")\n",
    "axs[2].fill_between(ensemble_members,\n",
    "                   np.mean(ff_test_fm_aods, axis=0) - np.std(ff_test_fm_aods, axis=0), \n",
    "                   np.mean(ff_test_fm_aods, axis=0) + np.std(ff_test_fm_aods, axis=0), alpha=0.3)\n",
    "axs[2].hlines(np.mean(ff_test_fm_aods, axis=0)[0], ensemble_members[0], ensemble_members[-1], color=\"C0\", linestyle=\"--\", alpha=0.5)\n",
    "acc_axs.append(axs[2].twinx())\n",
    "axs[2].set_ylabel(\"1 - AOD ($\\\\rightarrow$)\", color=\"C0\")\n",
    "\n",
    "axs[1].set_title(\"FairFace\")\n",
    "\n",
    "for ax in axs:\n",
    "    ax.yaxis.grid(False)\n",
    "    ax.set_xlabel(\"# Ensemble Members\")\n",
    "    ax.yaxis.set_major_formatter(FuncFormatter(lambda x, _: f\"{x:.3f}\"))\n",
    "for a, ax in enumerate(acc_axs):\n",
    "    ax.yaxis.grid(False)\n",
    "    ax.plot(ensemble_members, np.mean(utk_test_fm_accs, axis=0), \":\", c=\"C1\", alpha=0.5)\n",
    "    ax.fill_between(ensemble_members, \n",
    "                    np.mean(utk_test_fm_accs, axis=0) - np.std(utk_test_fm_accs, axis=0), \n",
    "                    np.mean(utk_test_fm_accs, axis=0) + np.std(utk_test_fm_accs, axis=0), alpha=0.15, color=\"C1\")\n",
    "    if a == 2:\n",
    "        ax.yaxis.set_major_formatter(FuncFormatter(lambda x, _: f\"{x:.3f}\"))\n",
    "        ax.set_ylabel(\"Accuracy ($\\\\rightarrow$)\", color=\"C1\")\n",
    "    else:\n",
    "        ax.set_yticks([])\n",
    "\n",
    "handle_1 = mlines.Line2D([], [], color='C0', linestyle=\"solid\", label='Fairness')\n",
    "handle_2 = mlines.Line2D([], [], color='C1', linestyle=\"dotted\", label='Performance')\n",
    "axs[2].legend(handles=[handle_1, handle_2], loc=\"center right\", fontsize=\"x-small\")\n",
    "f.tight_layout()\n",
    "plt.savefig(os.path.join(PLOTS_PATH, f\"n_members_target{target}_pa{pa}_{model}_ff.pdf\"))\n",
    "plt.show()\n",
    "\n",
    "# UTKFace Plotting\n",
    "acc_axs = list()\n",
    "f, axs = plt.subplots(1, 3, figsize=(12, 3))\n",
    "axs[0].plot(ensemble_members, np.mean(utk_test_fm_spds, axis=0), \"-\")\n",
    "axs[0].fill_between(ensemble_members, \n",
    "                   np.mean(utk_test_fm_spds, axis=0) - np.std(utk_test_fm_spds, axis=0), \n",
    "                   np.mean(utk_test_fm_spds, axis=0) + np.std(utk_test_fm_spds, axis=0), alpha=0.3)\n",
    "axs[0].hlines(np.mean(utk_test_fm_spds, axis=0)[0], ensemble_members[0], ensemble_members[-1], color=\"C0\", linestyle=\"--\", alpha=0.5)\n",
    "acc_axs.append(axs[0].twinx())\n",
    "axs[0].set_ylabel(\"1 - SPD ($\\\\rightarrow$)\", color=\"C0\")\n",
    "axs[1].plot(ensemble_members, np.mean(utk_test_fm_eods, axis=0), \"-\")\n",
    "axs[1].fill_between(ensemble_members,\n",
    "                   np.mean(utk_test_fm_eods, axis=0) - np.std(utk_test_fm_eods, axis=0), \n",
    "                   np.mean(utk_test_fm_eods, axis=0) + np.std(utk_test_fm_eods, axis=0), alpha=0.3)\n",
    "axs[1].hlines(np.mean(utk_test_fm_eods, axis=0)[0], ensemble_members[0], ensemble_members[-1], color=\"C0\", linestyle=\"--\", alpha=0.5)\n",
    "acc_axs.append(axs[1].twinx())\n",
    "axs[1].set_ylabel(\"1 - EOD ($\\\\rightarrow$)\", color=\"C0\")\n",
    "axs[2].plot(ensemble_members, np.mean(utk_test_fm_aods, axis=0), \"-\")\n",
    "axs[2].fill_between(ensemble_members,\n",
    "                   np.mean(utk_test_fm_aods, axis=0) - np.std(utk_test_fm_aods, axis=0), \n",
    "                   np.mean(utk_test_fm_aods, axis=0) + np.std(utk_test_fm_aods, axis=0), alpha=0.3)\n",
    "axs[2].hlines(np.mean(utk_test_fm_aods, axis=0)[0], ensemble_members[0], ensemble_members[-1], color=\"C0\", linestyle=\"--\", alpha=0.5)\n",
    "acc_axs.append(axs[2].twinx())\n",
    "axs[2].set_ylabel(\"1 - AOD ($\\\\rightarrow$)\", color=\"C0\")\n",
    "\n",
    "axs[1].set_title(\"UTKFace\")\n",
    "\n",
    "for ax in axs:\n",
    "    ax.yaxis.grid(False)\n",
    "    ax.set_xlabel(\"# Ensemble Members\")\n",
    "    ax.yaxis.set_major_formatter(FuncFormatter(lambda x, _: f\"{x:.3f}\"))\n",
    "for a, ax in enumerate(acc_axs):\n",
    "    ax.yaxis.grid(False)\n",
    "    ax.plot(ensemble_members, np.mean(utk_test_fm_accs, axis=0), \":\", c=\"C1\", alpha=0.5)\n",
    "    ax.fill_between(ensemble_members, \n",
    "                    np.mean(utk_test_fm_accs, axis=0) - np.std(utk_test_fm_accs, axis=0), \n",
    "                    np.mean(utk_test_fm_accs, axis=0) + np.std(utk_test_fm_accs, axis=0), alpha=0.15, color=\"C1\")\n",
    "    if a == 2:\n",
    "        ax.yaxis.set_major_formatter(FuncFormatter(lambda x, _: f\"{x:.3f}\"))\n",
    "        ax.set_ylabel(\"Accuracy ($\\\\rightarrow$)\", color=\"C1\")\n",
    "    else:\n",
    "        ax.set_yticks([])\n",
    "\n",
    "handle_1 = mlines.Line2D([], [], color='C0', linestyle=\"solid\", label='Fairness')\n",
    "handle_2 = mlines.Line2D([], [], color='C1', linestyle=\"dotted\", label='Performance')\n",
    "axs[2].legend(handles=[handle_1, handle_2], loc=\"center right\", fontsize=\"x-small\")\n",
    "f.tight_layout()\n",
    "plt.savefig(os.path.join(PLOTS_PATH, f\"n_members_target{target}_pa{pa}_{model}_utk.pdf\"))\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$\\boldsymbol{0.022_{\\pm 0.001}}$ & $\\boldsymbol{0.633_{\\pm 0.005}}$ & $\\boldsymbol{0.676_{\\pm 0.005}}$ & $\\boldsymbol{0.758_{\\pm 0.005}}$ \\\\\n",
      "$\\boldsymbol{0.015_{\\pm 0.001}}$ & $\\boldsymbol{0.391_{\\pm 0.006}}$ & $\\boldsymbol{0.505_{\\pm 0.007}}$ & $\\boldsymbol{0.597_{\\pm 0.006}}$ \\\\\n"
     ]
    }
   ],
   "source": [
    "# test for significance of disparate benefits\n",
    "ff_test_fm_delta_accs = ff_test_fm_accs[:, -1] - np.mean(np.asarray(ff_test_accs), axis=1)\n",
    "ff_test_fm_delta_spds = ff_test_fm_spds[:, -1] - np.mean(np.asarray(ff_test_spds), axis=1)\n",
    "ff_test_fm_delta_eods = ff_test_fm_eods[:, -1] - np.mean(np.asarray(ff_test_eods), axis=1)\n",
    "ff_test_fm_delta_aods = ff_test_fm_aods[:, -1] - np.mean(np.asarray(ff_test_aods), axis=1)\n",
    "utk_test_fm_delta_accs = utk_test_fm_accs[:, -1] - np.mean(np.asarray(utk_test_accs), axis=1)\n",
    "utk_test_fm_delta_spds = utk_test_fm_spds[:, -1] - np.mean(np.asarray(utk_test_spds), axis=1)\n",
    "utk_test_fm_delta_eods = utk_test_fm_eods[:, -1] - np.mean(np.asarray(utk_test_eods), axis=1)\n",
    "utk_test_fm_delta_aods = utk_test_fm_aods[:, -1] - np.mean(np.asarray(utk_test_aods), axis=1)\n",
    "for s, samples in enumerate([ff_test_fm_delta_accs, ff_test_fm_delta_spds, ff_test_fm_delta_eods, ff_test_fm_delta_aods,\n",
    "                            utk_test_fm_delta_accs, utk_test_fm_delta_spds, utk_test_fm_delta_eods, utk_test_fm_delta_aods]):\n",
    "    # perform t-test for the null hypothesis that the mean of the samples is equal to zero\n",
    "    t_statistic, p_value = stats.ttest_1samp(samples, 0)\n",
    "    if p_value < 0.05:\n",
    "        print(f\"$\\\\boldsymbol{'{'}{np.mean(samples):.3f}_{'{'}\\pm {np.std(samples):.3f}{'}'}{'}'}$\", end=\"\")\n",
    "    else:\n",
    "        print(f\"${np.mean(samples):.3f}_{'{'}\\pm {np.std(samples):.3f}{'}'}$\", end=\"\")\n",
    "    if s % 4 < 3:\n",
    "        print(\" & \", end=\"\")\n",
    "    else:\n",
    "        print(\" \\\\\\\\\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$0.812_{\\pm 0.007}$ & $0.810_{\\pm 0.009}$ & $0.835_{\\pm 0.010}$ & $0.874_{\\pm 0.008}$ \\\\\n",
      "$0.793_{\\pm 0.005}$ & $0.691_{\\pm 0.009}$ & $0.748_{\\pm 0.009}$ & $0.796_{\\pm 0.008}$ \\\\\n"
     ]
    }
   ],
   "source": [
    "for s, samples in enumerate([ff_test_fm_accs, ff_test_fm_spds, ff_test_fm_eods, ff_test_fm_aods]):\n",
    "    print(f\"${np.mean(samples):.3f}_{'{'}\\pm {np.std(samples):.3f}{'}'}$\", end=\"\")\n",
    "    if s < 3:\n",
    "        print(\" & \", end=\"\")\n",
    "print(\" \\\\\\\\\")\n",
    "for s, samples in enumerate([utk_test_fm_accs, utk_test_fm_spds, utk_test_fm_eods, utk_test_fm_aods]):\n",
    "    print(f\"${np.mean(samples):.3f}_{'{'}\\pm {np.std(samples):.3f}{'}'}$\", end=\"\")\n",
    "    if s < 3:\n",
    "        print(\" & \", end=\"\")\n",
    "print(\" \\\\\\\\\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$0.794_{\\pm 0.001}$ & $0.173_{\\pm 0.001}$ & $0.153_{\\pm 0.002}$ & $0.113_{\\pm 0.001}$ \\\\\n",
      "$0.782_{\\pm 0.001}$ & $0.296_{\\pm 0.003}$ & $0.240_{\\pm 0.003}$ & $0.195_{\\pm 0.003}$ \\\\\n"
     ]
    }
   ],
   "source": [
    "for s, samples in enumerate([np.mean(np.asarray(ff_test_accs), axis=1), \n",
    "                             np.mean(np.asarray(ff_test_spds), axis=1), \n",
    "                             np.mean(np.asarray(ff_test_eods), axis=1), \n",
    "                             np.mean(np.asarray(ff_test_aods), axis=1)]):\n",
    "    print(f\"${np.mean(samples):.3f}_{'{'}\\pm {np.std(samples):.3f}{'}'}$\", end=\"\")\n",
    "    if s < 3:\n",
    "        print(\" & \", end=\"\")\n",
    "print(\" \\\\\\\\\")\n",
    "for s, samples in enumerate([np.mean(np.asarray(utk_test_accs), axis=1), \n",
    "                             np.mean(np.asarray(utk_test_spds), axis=1), \n",
    "                             np.mean(np.asarray(utk_test_eods), axis=1), \n",
    "                             np.mean(np.asarray(utk_test_aods), axis=1)]):\n",
    "    print(f\"${np.mean(samples):.3f}_{'{'}\\pm {np.std(samples):.3f}{'}'}$\", end=\"\")\n",
    "    if s < 3:\n",
    "        print(\" & \", end=\"\")\n",
    "print(\" \\\\\\\\\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ensemble_members = list(range(1, len(ff_test_probits[0]) + 1))\n",
    "\n",
    "for delta in [False, True]:\n",
    "\n",
    "    ff_test_fm_prs_1, ff_test_fm_prs_0 = list(), list()\n",
    "    ff_test_fm_tprs_1, ff_test_fm_tprs_0 = list(), list()\n",
    "    ff_test_fm_fprs_1, ff_test_fm_fprs_0 = list(), list()\n",
    "    utk_test_fm_prs_1, utk_test_fm_prs_0 = list(), list()\n",
    "    utk_test_fm_tprs_1, utk_test_fm_tprs_0 = list(), list()\n",
    "    utk_test_fm_fprs_1, utk_test_fm_fprs_0 = list(), list()\n",
    "\n",
    "    for m in range(len(method_seeds)):\n",
    "\n",
    "        ff_test_fm_prs_1_, ff_test_fm_prs_0_ = list(), list()\n",
    "        ff_test_fm_tprs_1_, ff_test_fm_tprs_0_ = list(), list()\n",
    "        ff_test_fm_fprs_1_, ff_test_fm_fprs_0_ = list(), list()\n",
    "        utk_test_fm_prs_1_, utk_test_fm_prs_0_ = list(), list()\n",
    "        utk_test_fm_tprs_1_, utk_test_fm_tprs_0_ = list(), list()\n",
    "        utk_test_fm_fprs_1_, utk_test_fm_fprs_0_ = list(), list()\n",
    "\n",
    "        for em in ensemble_members:\n",
    "            preds = torch.mean(ff_test_probits[m][:em], dim=0).argmax(dim=1)\n",
    "            pr1, pr0 = get_prs(preds, a_ff_test_t)\n",
    "            tpr1, tpr0 = get_tprs(preds, y_ff_test_t, a_ff_test_t)\n",
    "            fpr1, fpr0 = get_fprs(preds, y_ff_test_t, a_ff_test_t)\n",
    "            ff_test_fm_prs_1_.append(pr1)\n",
    "            ff_test_fm_prs_0_.append(pr0)\n",
    "            ff_test_fm_tprs_1_.append(tpr1)\n",
    "            ff_test_fm_tprs_0_.append(tpr0)\n",
    "            ff_test_fm_fprs_1_.append(fpr1)\n",
    "            ff_test_fm_fprs_0_.append(fpr0)\n",
    "\n",
    "            preds = torch.mean(utk_test_probits[m][:em], dim=0).argmax(dim=1)\n",
    "            pr1, pr0 = get_prs(preds, a_utk_test_t)\n",
    "            tpr1, tpr0 = get_tprs(preds, y_utk_test_t, a_utk_test_t)\n",
    "            fpr1, fpr0 = get_fprs(preds, y_utk_test_t, a_utk_test_t)\n",
    "            utk_test_fm_prs_1_.append(pr1)\n",
    "            utk_test_fm_prs_0_.append(pr0)\n",
    "            utk_test_fm_tprs_1_.append(tpr1)\n",
    "            utk_test_fm_tprs_0_.append(tpr0)\n",
    "            utk_test_fm_fprs_1_.append(fpr1)\n",
    "            utk_test_fm_fprs_0_.append(fpr0)\n",
    "            \n",
    "        if delta:\n",
    "            ff_test_fm_prs_1.append([t - ff_test_fm_prs_1_[0] for t in ff_test_fm_prs_1_])\n",
    "            ff_test_fm_prs_0.append([t - ff_test_fm_prs_0_[0] for t in ff_test_fm_prs_0_])\n",
    "            ff_test_fm_tprs_1.append([t - ff_test_fm_tprs_1_[0] for t in ff_test_fm_tprs_1_])\n",
    "            ff_test_fm_tprs_0.append([t - ff_test_fm_tprs_0_[0] for t in ff_test_fm_tprs_0_])\n",
    "            ff_test_fm_fprs_1.append([t - ff_test_fm_fprs_1_[0] for t in ff_test_fm_fprs_1_])\n",
    "            ff_test_fm_fprs_0.append([t - ff_test_fm_fprs_0_[0] for t in ff_test_fm_fprs_0_])\n",
    "\n",
    "            utk_test_fm_prs_1.append([t - utk_test_fm_prs_1_[0] for t in utk_test_fm_prs_1_])\n",
    "            utk_test_fm_prs_0.append([t - utk_test_fm_prs_0_[0] for t in utk_test_fm_prs_0_])\n",
    "            utk_test_fm_tprs_1.append([t - utk_test_fm_tprs_1_[0] for t in utk_test_fm_tprs_1_])\n",
    "            utk_test_fm_tprs_0.append([t - utk_test_fm_tprs_0_[0] for t in utk_test_fm_tprs_0_])\n",
    "            utk_test_fm_fprs_1.append([t - utk_test_fm_fprs_1_[0] for t in utk_test_fm_fprs_1_])\n",
    "            utk_test_fm_fprs_0.append([t - utk_test_fm_fprs_0_[0] for t in utk_test_fm_fprs_0_])\n",
    "        else:\n",
    "            ff_test_fm_prs_1.append(ff_test_fm_prs_1_)\n",
    "            ff_test_fm_prs_0.append(ff_test_fm_prs_0_)\n",
    "            ff_test_fm_tprs_1.append(ff_test_fm_tprs_1_)\n",
    "            ff_test_fm_tprs_0.append(ff_test_fm_tprs_0_)\n",
    "            ff_test_fm_fprs_1.append(ff_test_fm_fprs_1_)\n",
    "            ff_test_fm_fprs_0.append(ff_test_fm_fprs_0_)\n",
    "\n",
    "            utk_test_fm_prs_1.append(utk_test_fm_prs_1_)\n",
    "            utk_test_fm_prs_0.append(utk_test_fm_prs_0_)\n",
    "            utk_test_fm_tprs_1.append(utk_test_fm_tprs_1_)\n",
    "            utk_test_fm_tprs_0.append(utk_test_fm_tprs_0_)\n",
    "            utk_test_fm_fprs_1.append(utk_test_fm_fprs_1_)\n",
    "            utk_test_fm_fprs_0.append(utk_test_fm_fprs_0_)\n",
    "\n",
    "    ff_test_fm_prs_1 = np.asarray(ff_test_fm_prs_1)\n",
    "    ff_test_fm_prs_0 = np.asarray(ff_test_fm_prs_0)\n",
    "    ff_test_fm_tprs_1 = np.asarray(ff_test_fm_tprs_1)\n",
    "    ff_test_fm_tprs_0 = np.asarray(ff_test_fm_tprs_0)\n",
    "    ff_test_fm_fprs_1 = np.asarray(ff_test_fm_fprs_1)\n",
    "    ff_test_fm_fprs_0 = np.asarray(ff_test_fm_fprs_0)\n",
    "\n",
    "    utk_test_fm_prs_1 = np.asarray(utk_test_fm_prs_1)\n",
    "    utk_test_fm_prs_0 = np.asarray(utk_test_fm_prs_0)\n",
    "    utk_test_fm_tprs_1 = np.asarray(utk_test_fm_tprs_1)\n",
    "    utk_test_fm_tprs_0 = np.asarray(utk_test_fm_tprs_0)\n",
    "    utk_test_fm_fprs_1 = np.asarray(utk_test_fm_fprs_1)\n",
    "    utk_test_fm_fprs_0 = np.asarray(utk_test_fm_fprs_0)\n",
    "\n",
    "    f, axs = plt.subplots(1, 3, figsize=(12, 3))\n",
    "    for t, test_pr in enumerate([ff_test_fm_prs_0, ff_test_fm_prs_1]):\n",
    "        axs[0].plot(ensemble_members, np.mean(test_pr, axis=0), \"-\", color=[\"C4\", \"C2\"][t], \n",
    "                    label=f\"A={t} ({(p_a_ff_test if t == 1 else 100 - p_a_ff_test):.0f}%)\")\n",
    "        axs[0].fill_between(ensemble_members, \n",
    "                            np.mean(test_pr, axis=0) - np.std(test_pr, axis=0), \n",
    "                            np.mean(test_pr, axis=0) + np.std(test_pr, axis=0), color=[\"C4\", \"C2\"][t], alpha=0.3)\n",
    "    axs[0].set_ylabel(\"$\\Delta$ PR\" if delta else \"PR\")\n",
    "    for t, test_tpr in enumerate([ff_test_fm_tprs_0, ff_test_fm_tprs_1]):\n",
    "        axs[1].plot(ensemble_members, np.mean(test_tpr, axis=0), \"-\", color=[\"C4\", \"C2\"][t], \n",
    "                    label=f\"A={t} ({(p_a_ff_test if t == 1 else 100 - p_a_ff_test):.0f}%)\")\n",
    "        axs[1].fill_between(ensemble_members,\n",
    "                            np.mean(test_tpr, axis=0) - np.std(test_tpr, axis=0), \n",
    "                            np.mean(test_tpr, axis=0) + np.std(test_tpr, axis=0), color=[\"C4\", \"C2\"][t], alpha=0.3)\n",
    "    axs[1].set_ylabel(\"$\\Delta$ TPR\" if delta else \"TPR\")\n",
    "    for t, test_fpr in enumerate([ff_test_fm_fprs_0, ff_test_fm_fprs_1]):\n",
    "        axs[2].plot(ensemble_members, np.mean(test_fpr, axis=0), \"-\", color=[\"C4\", \"C2\"][t], \n",
    "                    label=f\"A={t} ({(p_a_ff_test if t == 1 else 100 - p_a_ff_test):.0f}%)\")\n",
    "        axs[2].fill_between(ensemble_members,\n",
    "                            np.mean(test_fpr, axis=0) - np.std(test_fpr, axis=0), \n",
    "                            np.mean(test_fpr, axis=0) + np.std(test_fpr, axis=0), color=[\"C4\", \"C2\"][t], alpha=0.3)\n",
    "    axs[2].set_ylabel(\"$\\Delta$ FPR\" if delta else \"FPR\")\n",
    "    axs[2].legend(loc=\"upper right\")\n",
    "\n",
    "    axs[1].set_title(\"FairFace\")\n",
    "\n",
    "\n",
    "    for i, ax in enumerate(axs.flatten()):\n",
    "        # ax.yaxis.grid(False)\n",
    "        ax.set_xlabel(\"# Ensemble Members\")\n",
    "        ax.yaxis.set_major_formatter(FuncFormatter(lambda x, _: f\"{x:.3f}\"))\n",
    "        if delta:\n",
    "            ax.hlines(0, 1, len(ensemble_members), color=\"black\", linestyle=\"--\", alpha=0.3)\n",
    "\n",
    "    f.tight_layout()\n",
    "    if delta:\n",
    "        plt.savefig(os.path.join(PLOTS_PATH, f\"n_members_target{target}_pa{pa}_{model}_delta_rates_ff.pdf\"))\n",
    "    else:\n",
    "        plt.savefig(os.path.join(PLOTS_PATH, f\"n_members_target{target}_pa{pa}_{model}_rates_ff.pdf\"))\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "\n",
    "    f, axs = plt.subplots(1, 3, figsize=(12, 3))\n",
    "    for t, test_pr in enumerate([utk_test_fm_prs_0, utk_test_fm_prs_1]):\n",
    "        axs[0].plot(ensemble_members, np.mean(test_pr, axis=0), \"-\", color=[\"C4\", \"C2\"][t], \n",
    "                       label=f\"A={t} ({(p_a_utk_test if t == 1 else 100 - p_a_utk_test):.0f}%)\")\n",
    "        axs[0].fill_between(ensemble_members, \n",
    "                            np.mean(test_pr, axis=0) - np.std(test_pr, axis=0), \n",
    "                            np.mean(test_pr, axis=0) + np.std(test_pr, axis=0), color=[\"C4\", \"C2\"][t], alpha=0.3)\n",
    "    axs[0].set_ylabel(\"$\\Delta$ PR\" if delta else \"PR\")\n",
    "    for t, test_tpr in enumerate([utk_test_fm_tprs_0, utk_test_fm_tprs_1]):\n",
    "        axs[1].plot(ensemble_members, np.mean(test_tpr, axis=0), \"-\", color=[\"C4\", \"C2\"][t], \n",
    "                       label=f\"A={t} ({(p_a_utk_test if t == 1 else 100 - p_a_utk_test):.0f}%)\")\n",
    "        axs[1].fill_between(ensemble_members,\n",
    "                            np.mean(test_tpr, axis=0) - np.std(test_tpr, axis=0), \n",
    "                            np.mean(test_tpr, axis=0) + np.std(test_tpr, axis=0), color=[\"C4\", \"C2\"][t], alpha=0.3)\n",
    "    axs[1].set_ylabel(\"$\\Delta$ TPR\" if delta else \"TPR\")\n",
    "    for t, test_fpr in enumerate([utk_test_fm_fprs_0, utk_test_fm_fprs_1]):\n",
    "        axs[2].plot(ensemble_members, np.mean(test_fpr, axis=0), \"-\", color=[\"C4\", \"C2\"][t], \n",
    "                       label=f\"A={t} ({(p_a_utk_test if t == 1 else 100 - p_a_utk_test):.0f}%)\")\n",
    "        axs[2].fill_between(ensemble_members,\n",
    "                            np.mean(test_fpr, axis=0) - np.std(test_fpr, axis=0), \n",
    "                            np.mean(test_fpr, axis=0) + np.std(test_fpr, axis=0), color=[\"C4\", \"C2\"][t], alpha=0.3)\n",
    "    axs[2].set_ylabel(\"$\\Delta$ FPR\" if delta else \"FPR\")\n",
    "    axs[2].legend(loc=\"upper right\")\n",
    "\n",
    "    axs[1].set_title(\"UTKFace\")\n",
    "\n",
    "\n",
    "    for i, ax in enumerate(axs.flatten()):\n",
    "        # ax.yaxis.grid(False)\n",
    "        ax.set_xlabel(\"# Ensemble Members\")\n",
    "        ax.yaxis.set_major_formatter(FuncFormatter(lambda x, _: f\"{x:.3f}\"))\n",
    "        if delta:\n",
    "            ax.hlines(0, 1, len(ensemble_members), color=\"black\", linestyle=\"--\", alpha=0.3)\n",
    "\n",
    "    f.tight_layout()\n",
    "    if delta:\n",
    "        plt.savefig(os.path.join(PLOTS_PATH, f\"n_members_target{target}_pa{pa}_{model}_delta_rates_utk.pdf\"))\n",
    "    else:\n",
    "        plt.savefig(os.path.join(PLOTS_PATH, f\"n_members_target{target}_pa{pa}_{model}_rates_utk.pdf\"))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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heUdHJ5w9myKXnpOTjblzZyEwMAizZs2RO+7q6gaRSIT8/DzY2nb93/M9BgBYW3eRy3/t2lU8fvwQS5f+E0DtWmA1NbXLzdf9XUcgECA/Pw+9ejk1eO2ENIXWg0RWVhZSU1Ph5uYGsVgMiUT5FUjfffddLF68WCata9euMo9XrVqFpKQkREdHw9bWFjt37sT06dNx4sQJ6ReHrmAyGfAd5oAzxzIV5vEZZt8sN60bo2p3U0hIKGJilsHZ2QW9e/fBwYP/RlVVFYKCRit9jpMnj2PQIB84OvaSSe/Rww5xcbE4d+40JkyYrFJ3k5mZGYKDxyIubgPYbA5MTEyxceNa9O3bT+FNawDw8vLGtm1bwOeXSm845+RkIzLyM3h6DkRISCiKil4CAJhMlvRezoABnujVyxmrVn2DyMgoiMUSxMaugYeHl0zrAgAEgleIjV2D5ctXSifIubq64ejRQxg/fhJSUy9g9uz50vwZGbegr28gM+KKEE3TepAICAjAsGHDAADR0dHIyFB+whWbzYa7u7vC4wUFBdi/fz+WLl2KyZNrf8G6ublh6NCh2L17NxYuXNikujcHO6eOGPl3F1w+90BmGKyZuSF8htnDzkm3ApsiQ4eOAI9Xgp07f0RxcREcHHph/fo4cLmvZ5fHx29DSkoyDh8+IVe+uLgIv/12GcuWrZA7xmQyMXjw33Dy5HFMmKB8y6TOnDlRYDCYWLr0c1RXC+Hp6Y0FCxY3WMbe3gHOzi64cOEcxo2bCKB2aC6PV4KzZ1NkWhmdO3eRXhOTycR3323Ahg3f4R//+ATGxsYYOHAQZs+eJ/ccu3btxKBBg+Ho+LplMG/eIixfvhSzZ4dj+PBA+PsHSI+dP38WI0YEwsjISOXXgBBlMSSq/HRvZnVBQtnuJhMTE2zbtk1hniNHjmDJkiX4z3/+AwsLC2n6F198gYyMDJw4If/lpCyRSIzi4gq59OpqIYqKnqFDhy7Q11f/3oHgVQ0SNqYDAEZN6ot3enJbpAXRkkuFr1jxTwCQdq3ouitX0rFlywYkJh7U+lIYPB4PU6ZMwM6de2BjY6swn6bej82lWijCztjLAICPo3x1coE/PT0mLC1NUVJS0aaW0edyTWW6cRXRekuiKa5evQp3d3eIRCK4ublh7ty58PDwkB7Pzs6GlZWVTIAAAHt7e5w4cQJisVjrH3ZFq8DWVL9+MxoY6KGosP5d7lrzKrA3b/4hM6pH13l7++DJk8d48aIQ1tadtVqX58+fYsGCxQ0GCEI0odUGCQ8PD4wdOxY9evRAYWEh4uPjMWPGDCQmJqJ///4Aakd+vD0eHgA4HA6qq6tRWVkJMzMzteugpycfYMRi1X7tK7MKbN1Cf/XR5CqwdSNAGYzaIaHN7dChpi+x0dImT/5A21UAADg7u8DZufHJfXVYLEa971dtk4hfv9H09Jg6Wce6X9vK/Opui1ptkIiMjJR57O/vj+DgYPzwww/YsWOHNL2+se+a6GFjMhmwtJS/WfrqFQsvXzKV/lD2G2ALe2f17zOYmhlo/IPVXj8MbZFYzACTyQSHY6KT9y6EgtcjtiwsTGBgqLtfSWy2sbaroBW6+z+iIhMTE/j5+eHMmTPSNDabLbOiZh0+nw99fX2YmJio/XxisQR8fqVculAogFgshkgkUar/0tBYH4bG+mrXA4DG+kkZjNoAIRKJW6QlQZqfSCSBWCxGaWklqqpEjRdoYW9uOsTjVerkPQkWiwk22xh8fhVEorZzT4LNNm779yTe9nYLwd7eHkVFReDxeDL3JbKzs9GzZ88m34+o78tZJGq93651Lx8FiLZH2R8tLe3NOtXUiMHQwvBuZYlEYp18DZtbm+lXqKysRGpqKlxdX4919/X1BZPJRErK6+GJFRUVuHDhAvz8/LRRTULIG8Rv3JN4+oQn85joBq23JKqqqpCamgoAyM/PR3l5OU6fPg0A8PT0BJfLxZIlS5CUlITMzNpJZteuXUN8fDyGDx8OGxsbFBYWYteuXXjx4gU2bdokPbe1tTVCQkKwbt066OnpwcbGBgkJCQCAadOmtfCVEkLelHPvBS6fe73q8alDGTA1N4DvMIdWMx+oPdB6kCgqKsLcuXNl0uoe79mzB15eXv/r43/dd9mxY0cIhULExsaCx+PB2NgY/fv3x/Lly9Gvn+zs0+joaJiYmGDjxo0oKyuDm5sbdu/erTOzrUsFfJQK618TSBkcAzY4hmwN1oiQ5le3b8rb6vZNGfl3FwoUOkKnJtO1JpqaTHcy5yxOPVR/S85RPYYhyG6E2uXf1pKT6Ujz08XJdGKxBHu3/t7oGmWhs7y0sgTN22gyHdEqX9uBcO0oP969WlSD2D9+AABEvRsBfVb9/1UcA2pFtJTSUh5CQydhx47d0n2uteXKlXRs2/Y9EhL2an1CqKp0Yd8UorzW9e5qgziGbHQz7yr3x8bs9YzeV6JX6GpmU28+TXQ1SSQSzJ0bgaio2XLHjh49hMBAfxQWFqh83iNHDmLixNEICBiE8PBpyMxUfl2ut61ZswJDhnji4kX1W111jh8/itmzP8GIEX7w9R2AsrIypcrt2ZMAH5/B0gCRlXUfy5YtwfjxQQgI8EFo6EQcPPiTXLlffjmH6dOnYOhQH0yYEIx//3uPzPH79+9ixowpGD58MBYvni8zbLumpgYffRSKO3duy5Tx9vYBk8nEmTOnVL18rdOFfVOI8ihI6KCbhX/h29/XSR//8GcCvvptFW4WKt6DuSkYDAaWLPkamZkZOHbssDT96dN8bN0ah3nzFqFTJ2uVzvnLL2exZcsGzJgRjvj4vXBw6IWoqDkoKSluvPBbXr16hYsXzyE0dJpae2S/TSB4BS+vQTLbqzamqqoKycnHMXr0OGnavXt3YGlpia+++gaJiQcwdepH2LZtC44cOSDNc+VKOr755kuMGzcee/YcwIIF0Thw4N8yeVavjsGAAV6Ij9+L8vJyJCbukh776ae96NfPHb1795Gr06hRwThy5KCKV699urJvClEOBQkdc7PwL+zISJS7mc0TlGJHRmKzBQpr686YO3ch4uI24unTfEgkEqxe/S08PLwQGBik8vn279+H0aPHIShoDHr2tMOiRV/AyMhIrS/5ixfPw87OAVOmTMXNm9dRUPBc5XO8afLkKQgLm44+fRreg+JNV66kQ19fX2ZZ7uDgsZg3bxH6938PtrZdMXLkKIwaNQapqRelec6eTcHgwf4YN24ibG27YtAgX4SFTce+fXuk83oePcpFcPBYdOvWHcOGjcTDhzkAgPz8PJw8+bPclqV1fH39cPduJvLynqjzMmhN3b4pDdHWvilEHt2T0CFiiRiHshr+Ej2c9TP6dewDJkPz8f3994Nx6dKvWLlyOfz9A5CTk43ExNpfqqrsTFddXY379+/K/FJnMpkYMMATt2/fUrleycnHMWJEIMzNzTFw4CCkpCRj+vSPpcdV2ZlOXbdu3YSTU+NrJVVUlEv3mwBqZ+C/vRyGoaERCgsL8Pz5M3TpYgMHh164evV32Np2xfXrV2Fn5/C/Oq9CRESkwr0yOnfuAi63A/7880aTt5ZtSbq8bwqRR0FChzzg5YInKG0wT4mgFA94uehlad9gPnVFR3+JKVMmYfPmWMTEfCfdPEeVnelKS3kQiURy24FyuVw8evRQpfo8efIYmZkZWLlyLQBg5MggxMVtwLRpM6XrcqmyM526nj9/CisrqwbzZGTcwoUL57B27UZpmqenN+LiYvH++1fx7rsDkJf3BPv37wUAFBW9RJcuNli8+EvExq7B/v174erqhrCwGUhJSYaRkRF693ZBVNRs5OfnYejQEXKtCisrKzx//qxJ16YNbWXflPaAgoQO4QuUmy+hbD51cLlcjBnzd1y69Kt061FA9Z3p6iOR1L/gYkOSk4/D03MgOBwLALU3bFev/hZ//HFNuse1KjvTqUsoFMLAQPGS7Lm5OYiOXoCwsBnw8BgoTR8z5u/Iz8/D55/Ph0hUA2NjY0ya9AF27dohHZVkZ2cvs2R6aSkPCQk78P3327Fhw1q4urphxYq1CA+fCheXvjJ7XBsaGkIgEDTDFTc/O6eOsO1uqZV9U4jyKEjoELaSI5WUzacuFosF1ltDblXpbuJwLMBisVBcLHuTuqSkWK510RCRSITTp0+iuLgIfn5eMunJycelQaIlups4HAuUldUfnHNzcxAZ+RmCgsZg5sxPZY4xGAxERETi00//geLiIlhYWOLatdq92RUNo928ORaTJ3+ATp2scePGdYSHz4KxsTG8vX1x48Z1mSDB5/NhYWGp9nVp25sBweYdCwoQOoiChA5xsOgJC0NOg11OloYcOFj0bMFa1VKlu0lfXx+9ejnj+vWr0taIWCzG9ev/xfjxym83euVKOiorK5GQsE9m0k9OTjZWrvwGfD4fbDa7RbqbHB2dZLYofbMuc+fOQmBgEGbNmqOwPIvFQseOnQAAFy6cQ9++/WBpKR8wr127isePH0p36xOLRaipqV1Ou+7vOgKBAPn5eejVy+nt0xCiMRQkdAiTwcQkxzHYkZGoMM9ExzHNctO6Map2N4WEhCImZhmcnV3Qu3cfHDz4b1RVVSEoaLTS5zh58jgGDfKBo2MvmfQePewQFxeLc+dOY8KEySp3NxUVvURxcRHy8/MAADk5D2BiYgJr684yN53f5OXljW3btoDPL5XmycnJRmTkZ/D0HIiQkFAUFb0EADCZLOm9HB6Ph19/PY/+/QdAKBQgJSUZFy6cQ1yc/La7AsErxMauwfLlK6VdUa6ubjh69BDGj5+E1NQLmD17vjR/RsYt6OsbyIy4IkTTKEjoGPdOrgjvG4aD94/LDIO1NORgouMYuHdybaC07hg6dAR4vBLs3PkjiouL4ODQC+vXx4HL7SDNEx+/DSkpyTh8WH6v8eLiIvz222UsW7ZC7hiTycTgwX/DyZPHMWGC8i2TOklJR7Br1+uNqf7xj3AAwJIlyzBqVP1BzN7eAc7OLrhw4RzGjZsIoHZoLo9XgrNnU2RaGZ07d5G5ppSUk/j++02QSCTo06cfNm/eVm/LZ9eunRg0aDAcHV+3DObNW4Tly5di9uxwDB8eCH//AOmx8+fPYsSIQJ3cTIi0HbR2k5o0tXaTIlU1r7Aw7WsAQITbR+jN7dUiLYiWXLtpxYp/AoC0a0XXXbmSji1bNiAx8aDWl8Lg8XiYMmUCdu7c0+A+17q4dtObqoUi7Iy9DAD4OMpXJzcdorWbiFYpWgW2WvS6/9mIZYS88qf1lm/Nq8DevPmHzKgeXeft7YMnTx7jxYtCWFt3brxAM3r+/CkWLFjcYIAgRBMoSGjZ5fzfG10Ftm6hv/poehXYlnToUNOX2Ghpkyd/oO0qAACcnV3g7Nz45D5CmoqChJYpWgVWWbQKLCGkOVGQ0DKOYevtLiKEtH20wB8hhBCFKEgQQghRiIIEIYQQheiehJZVlAtQWa7+DlwmZgYwNVO88BwhhDQFBQkty7zxDNfSH6ldfoBPd3gM7qG5ChFCyBsoSGiZS/8u6OHYQS69plqMpH03AQDjQt2hp19/z6CJme7Nom2rSkt5CA2dhB07ditcwbWlXLmSjm3bvkdCwl6tz/4mbRu9u7TM1MwQHTuby/3hdny9YJ1QWIMOnczqzaeJriaJRIK5cyMQFTVb7tjRo4cQGOiPwsIClc558+Yf+Pzz+Rg7NhC+vgOQlvZrk+q4Zs0KDBniiYsXG554qAyBQID169dg1KihGD58MJYuXYTi4qJGy+3ZkwAfn8HSAJGVdR/Lli3B+PFBCAjwQWjoRBw8+JPcc61Y8U9Mnfp/8PPzwhdfLJA77/37dzFjxhQMHz4YixfPB5//egZ+TU0NPvooFHfu3JYp4+3tAyaTiTNnTqnzEhCiNAoSOijn3gsc2Plf6eNThzKwd+vvyLn3olmej8FgYMmSr5GZmYFjxw5L058+zcfWrXGYN28ROnWyVumcVVVVcHBwRFTU502u36tXr3Dx4jmEhk5Ta4/st8XFxSI9PQ3ffrsacXHb8fLlSyxduqjBMlVVVUhOPo7Ro8dJ0+7duwNLS0t89dU3SEw8gKlTP8K2bVtw5MgBaR6xWAxDQ0NMnBiC997zrPfcq1fHYMAAL8TH70V5eTkSE3dJj/3001706+eO3r37yJUbNSoYR44cVPHqCVENBQkdk3PvBc4cy5TZ0hEAKsqEOHMss9kChbV1Z8yduxBxcRvx9Gk+JBIJVq/+Fh4eXggMDFL5fN7ePvjkkwj4+QU0nrkRFy+eh52dA6ZMmYqbN6+joOC52ucqLy9HcvJxzJkzH++95wFn595YsmQZ/vrrFjIy/lJY7sqVdOjr68ssyx0cPBbz5i1C//7vwda2K0aOHIVRo8YgNfWiNI+xsTEWLvwCY8b8HR06yHcrAsCjR7kIDh6Lbt26Y9iwkXj4MAcAkJ+fh5Mnf5bbsrSOr68f7t7NRF7eE3VeCkKUQvckdIhYLMHl8w8azJN+Phs9HK2aZQev998PxqVLv2LlyuXw9w9ATk42EhNrf6mqsjOdpiUnH8eIEYEwNzfHwIGDkJKSjOnTP5YeV2Vnunv37qCmpgYDBrze6a579x6wtu6M27dvoW/f+pdiv3XrJpycGl8+paKiXOGeFIo4OPTC1au/w9a2K65fvwo7O4f/1XkVIiIiFe6V0blzF3C5HfDnnzeavLVsc1M0iq+m+vWqqi8Lyhu890aj+LRD60Hi0aNHiI+Px59//omsrCzY2dkhOTm5wTLl5eXYtWsX0tLSkJubCz09PfTp0wdRUVHo00e2We7kJL9rl5WVFdLT0zV6HZrw7EkpKsoaHg5bXibAsyelsO1u0Sx1iI7+ElOmTMLmzbGIiflOunmOKjvTadKTJ4+RmZmBlSvXAgBGjgxCXNwGTJs2U7pftio70xUVFUFfXx/m5uYyx7lcLoqKFN+XeP78KaysrBp8joyMW7hw4RzWrt3Y2GXJWLz4S8TGrsH+/Xvh6uqGsLAZSElJhpGREXr3dkFU1Gzk5+dh6NARcq0KKysrPH/+TKXn0wZlRvHVDdSoD43i0x6tB4msrCykpqbCzc0NYrEYymxv8fTpUxw4cAATJkxAZGQkampqsGfPHoSEhGD//v1ygSIsLAzBwcHSx/r6+hq/Dk2orFBuvoSy+dTB5XIxZszfcenSr9KtRwHVd6bTlOTk4/D0HAgOxwJAbTfW6tXf4o8/rkn3uFZ1Z7r6SCSQBp36CIVCGBgo/iWbm5uD6OgFCAubAQ+PgSo9t52dvcyS6aWlPCQk7MD332/Hhg1r4erqhhUr1iI8fCpcXPrK7HFtaGgIgUCg0vNpg6JRfMqiUXzao/UgERAQgGHDhgEAoqOjkZGR0WiZrl274ty5czA2NpamDRo0CEOHDsXevXuxatUqmfxdunSBu7u7RuvdHExMlfsgKJtPXSwWCyyW7FtDG91NIpEIp0+fRHFxEfz8vGTSk5OPS4OEKt1NHTp0QHV1NcrKymRaEyUlxeBy5fecrsPhWKCsTH7fD6A2QERGfoagoDGYOfNTVS6xXps3x2Ly5A/QqZM1bty4jvDwWTA2Noa3ty9u3LguEyT4fD4sLCyb/JzNzdTMkLqLWimtBwl1xnibmJjIpRkaGsLe3h6FhYWaqJZWdHmHA1Nzgwa7nMzMDdHlHdX6vDVBG91NV66ko7KyEgkJ+2R20MrJycbKld+Az+eDzWar1N3k5NQbenp6uH79Kvz9hwIAHj9+hIKC5+jTR/Fe0Y6OTjJblL5Zl7lzZyEwMAizZs1R5zJlXLt2FY8fP5Tu1icWi1BTU7sBVd3fdQQCAfLz89Crl3yXKiGaovUgoSmVlZW4c+cOxo4dK3ds+/btiI2NhbGxMXx9ffH555/Dxka7k6Hqw2Qy4DvMAWeOZSrM4zPMvlluWjdG1e6myspK5Oe/HnXz7Fk+srLuwdycg86dldvV7eTJ4xg0yAeOjr1k0nv0sENcXCzOnTuNCRMmq9TdZGZmhuDgsYiL2wA2mwMTE1Ns3LgWffv2U3jTGgC8vLyxbdsW8Pml0hvTOTnZiIz8DJ6eAxESEoqiopcAACaTJb2XA9S2NGpqqsHnl6KyshJZWfcAQGYvawAQCF4hNnYNli9fKf3x5OrqhqNHD2H8+ElITb2A2bPnS/NnZNyCvr6BzIgrQjStzQSJjRs3oqqqCh9++KFM+rhx4+Dv7w8rKyvcv38fW7duxZQpU3D8+HFwOE37Ra6nJ98KEoub9gVu59QRI//ugsvnHsgMgzUzN4TPMHvYOXVs0vkbUtcl30DXvNLu3s1EZORn0sdxcRsA1I6gqvuVHB+/DSkpyTh8+IRc+eLiIvz222UsW7ZC7hiTycTgwX/DyZPHMWHCZJXrNmdOFBgMJpYu/RzV1UJ4enpjwYLFDZaxt3eAs7MLLlw4h3HjJgKoHZrL45Xg7NkUmVZG585dZK5p0aK5MjeXZ8wIBQBcvnxN5jl27dqJQYMGywSPefMWYfnypZg9OxzDhwfC3//1kOLz589ixIhAGBkZNXrNLBaj3vcraVxdK1aZ/aDbIoZEmTvFLaTunkRjo5veduLECSxcuBBff/01QkNDG8x79+5djB8/HvPnz0d4eLjadZVIJPXe6Hz16hWys3NgZdW5wRudjRG8qsH2dZcAAKND+qGbHVcrLYjm9M03ywAAX3+9XMs1Uc5vv6Vj06b1+Omnw1pfCoPHK8HkyePxr3/tbXCfa6FQgJcvn8Pe3k6pYELI21p9SyI9PR1ffPEFZs6c2WiAAABnZ2f07NkTt2/fbjRvQ8RiCfj8Srl0oVAAsVgMkUiCmhpxPSVlKTN+nMViouBp/TdNNTl+nMGofS6RSIyW+Olw48Z1bNmyXanXSRd4enpj7NgJePbsOaytlesyay55eflYsGAxOnXq0uDrJxJJIBaLUVpaiaoqUQvWsO1gsZhgs43B51dBJGod71VlsNnGSrWOWnWQuHXrFmbPno3AwEAsWtTwsgpv0lTjqb4Pp0ik2rl1afx43cvSUm3LQ4eavsRGS5s8+QNtVwEA4OzsAmdn5fdGV/ZHC1FMJBK3y9ew1QaJ7OxshIeH491338WqVasaHOP+pjt37uDhw4eYMGFCM9dQOTR+nBCiy7QeJKqqqpCamgoAyM/PR3l5OU6fPg0A8PT0BJfLxZIlS5CUlITMzNpRP0VFRZg5cyb09fXx8ccfy3QdGRgYwMWl9hdWfHw8njx5Ij1PVlYWfvzxR3Tu3BmTJjU85r+l0PhxQogu03qQKCoqwty5c2XS6h7v2bMHXl5e/+vjf92f+uDBAzx7VjtaZPr06TJlbW1tceHCBQBAz549cfbsWZw6dQoVFRWwtLSEn58f5s2b1yxLSBBCSFujU6ObWhORSIzi4gq59OpqIYqKnqFDhy7Q1299XUF6esx22e/aVrX296Mu0NNjwtLSFCUlFW3qs8Hlmip147p9DvwlhBCiFK13N7V3pQI+SoX1D29VBseADY4hdZ0RQpoHBQktu5z/O049VH9LzlE9hiHIboQGa0QIIa9RkNAyX9uBcO0oP969WlSD2D9+AABEvRsBfVb9/1UcA2pFtJTSUh5CQydhx47d0n2uteXKlXRs2/Y9EhL2an32N2nb6N2lZRxDNrqZd5X7Y2P2ekbvK9ErdDWzqTefJrqaJBIJ5s6NQFTUbLljR48eQmCgPwoLC1Q6582bf+Dzz+dj7NhA+PoOQFrar02q45o1KzBkiCcuXlS/1VXn+PGjmD37E4wY4Qdf3wEoKytTqtyePQnw8RksDRBZWfexbNkSjB8fhIAAH4SGTsTBgz8pLJ+X9wTDhw9BYKC/TPr9+3cxY8YUDB8+GIsXzwef/7r7saamBh99FIo7d2RXCPD29gGTycSZM6eUvGpC1ENBQgfdLPwL3/6+Tvr4hz8T8NVvq3CzUPEezE3BYDCwZMnXyMzMwLFjh6XpT5/mY+vWOMybtwidOlmrdM6qqio4ODgiKurzJtfv1atXuHjxHEJDpyE5uemztAWCV/DyGoSwsBlKl6mqqkJy8nGMHj1Omnbv3h1YWlriq6++QWLiAUyd+hG2bduCI0cOyJWvqanBP/+5FG5u7nLHVq+OwYABXoiP34vy8nIkJu6SHvvpp73o188dvXv3kSs3alQwjhw5qPQ1EKIO6m7SMTcL/8KOjES5dJ6gFDsyEhHeNwzunRQvaa0ua+vOmDt3ITZs+A7vveeFLl1ssHr1t/Dw8EJgYJDK5/P29oG3t49G6nbx4nnY2TlgypSpGDcuEAUFTVs7afLkKQCAP/641kjO165cSYe+vr7MstzBwbLL0tvadkVGxl9ITb2ICRP+T+bY9u0/oHv37njvPU9kZNySOfboUS6WLYtBt27dMWzYSPz2W+3Cjvn5eTh58mckJMi/HwDA19cPGzeuQ17eE53f45q0XhQkdIhYIsahrIZ/KR/O+hn9OvYBk6H5RuD77wfj0qVfsXLlcvj7ByAnJxuJibW/VLWxM12d5OTjGDEiEObm5hg4cBBSUpIxffrH0uOq7Eynrlu3bsLJqfG1kioqyqX7TdS5fv2/uHjxF/zrX/uQmnpRroyDQy9cvfo7bG274vr1q7Czc/hfnVchIiJS4V4ZnTt3AZfbAX/+eYOCBGk2FCR0yANeLniC0gbzlAhK8YCXi16W9s1Sh+joLzFlyiRs3hyLmJjvpJvnaGNnOgB48uQxMjMzsHLlWgDAyJFBiIvbgGnTZkrX61JlZzp1PX/+FFZWVg3myci4hQsXzmHt2o3StNJSHlas+Ce+/vpbmJqa1Vtu8eIvERu7Bvv374WrqxvCwmYgJSUZRkZG6N3bBVFRs5Gfn4ehQ0fgk08iZMpaWVnJ7FVBiKZRkNAhfIFy8yWUzacOLpeLMWP+jkuXfsWQIf7SdFV3ptOU5OTj8PQcCA7HAkBtN9bq1d/ijz+uSfe4VmVnOnUJhcIG9wfJzc1BdPQChIXNgIfHQGn6mjUrMHx4INzd31VY1s7OHlu2bJc+Li3lISFhB77/fjs2bFgLV1c3rFixFuHhU+Hi0ldmj2tDQ0MIBIImXh0hilGQ0CFsJUcqKZtPXSwWC6y3htxqo7tJJBLh9OmTKC4ugp+fl0x6cvJxaZBoie4mDscCZWX1B+fc3BxERn6GoKAxmDnzU5ljf/zxX6Snp2H//r0AakeSicVi+Pl5YdGiJXL3NQBg8+ZYTJ78ATp1ssaNG9cRHj4LxsbG8Pb2xY0b12WCBJ/Ph4WFpdw5CNEUChI6xMGiJywMOQ12OVkacuBg0bMFa1VLG91NV66ko7KyEgkJ+2TWmMnJycbKld+Az+eDzWa3SHeTo6OTzBalb9Zl7txZCAwMwqxZc+SO//jjLojFrxenvHQpFfv27cGPP8bDyqqTXP5r167i8eOH0i1exWIRampqAED6dx2BQID8/Dz06uX09mkI0ZhmCRJpaWlYvnw5fvnll+Y4fZvFZDAxyXFMvaOb6kx0HNMsN60bo2p3U2VlJfLzn0gfP3uWj6ysezA356BzZ+VGJp08eRyDBvnA0bGXTHqPHnaIi4vFuXOnMWHCZJW7m4qKXqK4uAj5+XkAgJycBzAxMYG1dWe5m851vLy8sW3bFvD5pdI8OTnZiIz8DJ6eAxESEoqiopcAACaTJb2X06OHbEC/e/cOmEyG9Ob0mwSCV4iNXYPly1dKJ8i5urrh6NFDGD9+ElJTL2D27PnS/BkZt6CvbyAz4ooQTWuWb5uqqio8ffq0OU7d5rl3ckV43zC5mdSWhpxmG/7aHO7ezcSMGaGYMaN2S9m4uA2YMSMU8fE/SvPEx2/DxImj6y1fXFyE3367DD+/oXLHmEwmBg/+G06ePK5W3ZKSjmDGjFCsWRMDAPjHP8IxY0YoLl9OU1jG3t4Bzs4uuHDhnDTt4sXz4PFKcPZsCsaODZT+CQ+fqla9du3aiUGDBsPR8XXLYN68RXjw4B5mzw7HoEG+8PcPkB47f/4sRowIpL2rSbNqlqXCz5w5g3nz5uHOnTuaPrXOaO6lwqtqXmFh2tcAgAi3j9Cb26tFWhAtuVT4ihX/BABp14quu3IlHVu2bEBi4kGtL4XB4/EwZcoE7Ny5BzY2tgrz0VLhTdfelwqnexJapmgV2GrR6/5nI5YR8srrb5m15lVgb978Q2ZUj67z9vbBkyeP8eJFYZMm82nC8+dPsWDB4gYDBCGaQEFCy5RZBbZuob/6tOZVYA8davoSGy1t8uQPtF0FAICzswucnRuf3EdIU1GQ0DJFq8Aqi1aBJYQ0J5WCRP/+/aWzXBsiFredfrvmxjFsvd1FhJC2T6UgMXLkSKWCBKmdNEWIttH7kDSVSkFi9erVzVWPNoPFYgEAhEJBg8s4ENIShMLaJTvenkFPiLKa5Z2TkZGBffv2YdWqVc1xep3GZLJgbGyG8vISAICBgWGran2JxQyIRPTrs7WTSCQQCgUoLy+BsbGZ1ofsktarWYJEfn4+kpKS2mWQAAA2mwsA0kDRmjCZTLqn1IYYG5tJ34+EqIPaoM2AwWCAw+kAc3NLiEQ1jRfQESwWAxyOCUpLK6k10QawWHrUgiBNRkGiGTGZTDCZrWeWq54eE0ZGRqiqErWpmaWEEPXRzwxCCCEKaT1IPHr0CF9//TXGjh0LFxcXBAcHK1322LFjCAwMhKurK4KDg5GSIr+Uc3V1NdavXw9fX1+4ubkhLCwMd+/e1eQlEEJIm6VSd9Nnn32mVL6XL18qfc6srCykpqbCzc0NYrFY6XHdp0+fRnR0ND755BP4+Pjg/PnzmD9/PszNzeHr6yvNt2rVKiQlJSE6Ohq2trbYuXMnpk+fjhMnTjR5jwGiWYrWsVJWa17HihBdpdIqsAEBAY1nesOFCxcazSMWi6U316Kjo5GRkYHk5ORGy73//vvo1asXNm3aJE2bOXMmysrKcPDgQQBAQUEB/va3v2Hp0qUIDa1dsrq8vBxDhw7FpEmTsHDhQpWu502KVoFtzbS92uXJnLONrmPVkNa8jhXRXdr+XDSXZlkFVpkvfVWpM/riyZMnyMnJQVRUlEx6cHAwvvjiCxQXF4PL5eLy5csQiUQICgqS5jEzM0NAQABSU1ObFCSI5ilax6paVCNd5DDq3QjoK5gYRutYEaJ5rXJ0U05ODgDAzs5OJt3e3h4SiQQ5OTngcrnIzs6GlZUVLCws5PKdOHFCphVDtE/ROlYCkVD6767mNjBktZ4RY4S0dioFidGj699FrD4MBgM//9w8S0GXltbuAf32nsocDkfmOJ/Ph7m5uVx5DoeD6upqVFZWwszMTO166Om1rQBT1/RUpgnakkR4PWNdj8Voc6870W26+rloKSoFibd/kdensrISt2/fbpGlKN5+jrrbK2+m11cPTSx6xmQyYGmp/L7KrQmbbaztKsh4VfP6bWphaQojPVoTi7Q8XftctBSVgkRiYqLCYzU1NThw4AB++OEHMBgMlYayqurNFoOVlZU0nc+vHRlT18Jgs9nStDfx+Xzo6+vDxMRE7TqIxRLw+ZVql9dFLBYTbLYx+PwqiES6c4NOUCOQ/ptXUgFDvdYzi520frr6uWgqNtu45bYvTUlJwcaNG/H48WMMGjQICxcuRO/evTVx6nrV3YvIycmBvb29ND07OxsMBkN63N7eHkVFReDxeDKtoOzsbPTs2bPJ9yPa0kiHN4lEYp26tpo3lgipEUnAgu7UjbQfuva5aClN+pb8z3/+g0mTJmH+/PkwNTVFQkIC4uPjmzVAAMA777wDOzs7nDp1SiY9OTkZ/fr1A5dbu6CZr68vmEymzCS7iooKXLhwAX5+fs1aR0IIaQvUakncu3cP69atw+XLl9G1a1esX79eZpipKqqqqpCamgqgdvXY8vJynD59GgDg6ekJLpeLJUuWICkpCZmZmdJykZGRmD9/Prp164ZBgwbhl19+QXp6Onbu3CnNY21tjZCQEKxbtw56enqwsbFBQkICAGDatGlq1ZcQQtoTlYLEs2fPsHHjRiQnJ4PD4WDJkiUICQmBvr6+2hUoKirC3LlzZdLqHu/ZswdeXl4Qi8UQiUQyed5//328evUKP/74I+Lj49G9e3ds2LBBZrY1UDtBz8TEBBs3bkRZWRnc3Nywe/dumm1NCCFKUGnGdb9+/VBdXY3Bgwfj448/hqlpw6N7+vTp0+QK6iqacd1yBCIholK/BADE+sXQPAnSonT1c9FUzTLjWiisndSUlpaGS5cuKcwnkUjAYDBw584dVU5PCCFEx6gUJNrrTnNEu8SS17/eHvBy0JvbC0xG+5zYREhLU6m7ibxG3U0t42bhXzh4/7jM6rAWhhxMchwD906uWqwZaS908XOhCcp2N9HPMaKzbhb+hR0ZiXLLh/MEpdiRkYibhX9pqWaEtB8UJIhOEkvEOJTV8Npfh7N+lumKIoRoHgUJopMe8HLBE5Q2mKdEUIoHvNwWqhEh7RMFCaKT+ALldqhTNh8hRD0UJIhOYiu5Damy+Qgh6qEgQXSSg0VPWBhyGsxjaciBg0XPFqoRIe0TBQmik5gMJiY5jmkwz0THMTRfgpBmRp8worPcO7kivG+Y3N7VloYchPcNo3kShLSAVrnHNWk/3Du5wonriIVpXwMAItw+ohnXhLQg+qQRnfdmQHCwsKMAQUgLok8bIYQQhShIEEIIUYiCBCGEEIUoSBBCCFGIggQhhBCFKEgQQghRiIIEIYQQhShIEEIIUYiCBCGEEIUoSBBCCFGI1m4iOqNUwJfbzxoAqkU10n/nlT2FPqv+ty3HgA0O7S9BiEZRkCA643L+7zj18HyDeWL/+EHhsVE9hiHIboSmq0VIu0ZBgugMX9uBcO3oonb5t5cUJ4Q0nU4EidzcXMTExOD69eswNjZGUFAQFi5cCCMjI4Vl8vLyMHTo0HqP6evrIyMjQ/rYyclJLo+VlRXS09ObXnmiMRxD6i4iRNdoPUjw+XxMmzYNNjY22Lx5M4qLi7Fq1SrweDysW7dOYblOnTrhwIEDMmkSiQTh4eHw8vKSyx8WFobg4GDpY319fc1dBCGEtFFaDxL79+8Hn89HUlISuFwuAIDFYmHhwoWYNWsW7O3t6y1nYGAAd3d3mbT//Oc/KCsrkwkGdbp06SKXnxBCSMO0PgQ2LS0N3t7e0gABACNHjoSBgQFSU1NVOldycjLMzMwQEBCg6WoSQki7pPUgkZ2dLddaMDAwQLdu3ZCdna30eaqrq3H27FkMHz4choaGcse3b9+OPn36YMCAAZg3bx6ePn3a5LoTQkhbp/XuJj6fDzZb/mYlm81GaWmp0udJS0sDj8ert6tp3Lhx8Pf3h5WVFe7fv4+tW7diypQpOH78ODgcjtp119PTeozVKBaLKfM3IYQ+F1oPEopIJBIwGAyl8584cQJWVlbw9vaWO7ZmzRrpvz08PPDee+9h/PjxOHjwIMLDw9WqH5PJgKWlqVpldR2bbaztKhCic9rr50LrQYLNZoPPl59lW1ZWpvCm9dsqKirw66+/YuLEiWCxWI3md3Z2Rs+ePXH79m2V61tHLJaAz69Uu7wuYrGYYLONwedXQSQSa7s6hOgEXfhclAr4KBXIf08qq77h5Wy2sVKtI60HCXt7e7l7D0KhEI8fP8aECROUOse5c+dQVVWF0aNHK/28EolEpXrWp6ambX6RikTiNntthKhLm5+LXx/91uhqBA1pymoEWg8SQ4YMwdatW1FSUgJLS0sAtV/6QqEQfn5+Sp0jOTkZ3bp1g5ubm1L579y5g4cPHyodhAghRJsUrUZQLaqRLlUT9W5Eg+uaqUvrQSIkJAR79+5FREQEIiIiUFRUhNWrV2P06NEy3U1LlixBUlISMjMzZcoXFxfjypUrCu8txMfH48mTJ/D09ASXy0VWVhZ+/PFHdO7cGZMmTWrWayOEEE1QtBqBQCSU/ruruQ0MWQYaf26tBwk2m43du3cjJiYGc+bMgZGREYKDg7Fw4UKZfGKxGCKRSK58SkoKampqFHY19ezZE2fPnsWpU6dQUVEBS0tL+Pn5Yd68efWOqiKEEPIaQ6KJzvl2SCQSo7i4QtvV0Cg9PSYsLU1RUlJB9yQI+R9d/lwIREJEpX4JAIj1i1GpJcHlmip147p9DvwlhBCiFAoShBDSSoklr1s2D3g5Mo81Rev3JAghhKjuZuFfOHj/uPTxD38mwMKQg0mOY+DeyVVjz0MtCUIIaWVuFv6FHRmJctv98gSl2JGRiJuFf2nsuShIEEJIKyKWiHEo6+cG8xzO+lljXU8UJAghpBV5wMsFT9Dw4qclglI84OVq5PkoSBBCSCvCV3INJ2XzNYaCBCGEtCJsJfeBVzZfYyhIEEJIK+Jg0RMWhg3vg2NpyIGDRU+NPB8FCUIIaUWYDCYmOY5pMM9ExzFgMjTz9U5BghBCWhn3Tq4I7xsmt7qrpSEH4X3DNDpPgibTEUJIK+TeyRVOXEcsTPsaABDh9hF6c3tprAVRh1oShBDSSr0ZEBws7DQeIAAKEoQQQhpA3U0aVlEuQGW5sPGMCpiYGcDUzFCDNSKEEPVRkNCwzBvPcC39kdrlB/h0h8fgHpqrECGENAEFCQ1z6d8FPRw7yKXXVIuRtO8mAGBcqDv09Ovv6TMx0/z2g4QQoi4KEhpmamZYb3dRtfD11qtW1mbQN2C1ZLUIIa1YqYAvt+IrAFSLaqT/zit7Cn1W/V/pHIP698hWBgUJQgjRcZfzf8eph+cbzBP7xw8Kj43qMQxBdiPUem4KEoQQouN8bQfCtaOL2uXfnnSnCgoSLUQslkj//fQJD+/05ILJZGixRoSQ1oJjqH53UVNRkGgBOfde4PK5B9LHpw5lwNTcAL7DHGDn1FGLNSOEkIbRZLpmlnPvBc4cy0TFW3MnKsqEOHMsEzn3XmipZoQQ0jgKEs1ILJbg8vkHDeZJP58t0xVFCCG6hIJEM3r2pBQVZQ3Pvi4vE+DZk4a3IiSEEG2hINGMKiuUW55D2XyEENLSKEg0IxNT5WZPK5uPEEJamk4EidzcXMycORPu7u7w9vZGTEwMXr161Wi5sLAwODk5yf3Jzs6WyVddXY3169fD19cXbm5uCAsLw927d5vrcqS6vMOBqXnDAcDM3BBd3ml4K0JCCNEWrQ+B5fP5mDZtGmxsbLB582YUFxdj1apV4PF4WLduXaPl3333XSxevFgmrWvXrjKPV61ahaSkJERHR8PW1hY7d+7E9OnTceLECXTs2HxDUJlMBnyHOeDMsUyFeXyG2dN8CUKIztJ6kNi/fz/4fD6SkpLA5XIBACwWCwsXLsSsWbNgb2/fYHk2mw13d3eFxwsKCrB//34sXboUkydPBgC4ublh6NCh2L17NxYuXKixa6mPnVNHjPy7Cy6feyAzDNbM3BA+w+xpngQhRKdpvbspLS0N3t7e0gABACNHjoSBgQFSU1ObfP7Lly9DJBIhKChImmZmZoaAgACNnF8Zdk4d8X8fe0gfj5rUF6GzvChAEEJ0ntaDRHZ2tlxrwcDAAN26dZO7t1Cfq1evwt3dHa6urvjwww/x3//+V+78VlZWsLCwkEm3t7dHbm4uxGJxk69BGW92Kdm8Y0FdTISQVkHr3U18Ph9stvyaJGw2G6WlDc8f8PDwwNixY9GjRw8UFhYiPj4eM2bMQGJiIvr37y89v7m5uVxZDoeD6upqVFZWwszMTK266+kpH2Mlb0yY09NjqlS2pbBYTJm/CSH0udB6kFBEIpGAwWj413ZkZKTMY39/fwQHB+OHH37Ajh07pOn1nUciadosZyaTAUtLU6XzCwWv1323sDCBgaHOvvRgs421XQVCdE57/Vxo/ZuKzWaDz5ffTKOsrKzRm9ZvMzExgZ+fH86cOdPo+fl8PvT19WFiYqJ6pVG75AafXymXXlEmkFunCQBqql9vOpR1twB6+vVvOmRqZgBTc+3scc1iMcFmG4PPr4JI1DLdcITourb6uWCzjZVqHWk9SNjb28vdexAKhXj8+DEmTJig8vnebiHY29ujqKgIPB5P5r5EdnY2evbsCSZT/SZkTY38G+bWtfxG97g+sueGwmO6sMe1SCSu99oIac/a6+dC60FiyJAh2Lp1K0pKSmBpaQkAOHfuHIRCIfz8/FQ6V2VlJVJTU+Hq6ipN8/X1BZPJREpKCj744AMAQEVFBS5cuIBJkyZp7kL+R9Ee18qiPa4JIbpE60EiJCQEe/fuRUREBCIiIlBUVITVq1dj9OjRMt1NS5YsQVJSEjIzayemXbt2DfHx8Rg+fDhsbGxQWFiIXbt24cWLF9i0aZO0nLW1NUJCQrBu3Tro6enBxsYGCQkJAIBp06Zp/HoU7XFNCCGtkdaDBJvNxu7duxETE4M5c+bAyMgIwcHBcpPcxGIxRKLX/fodO3aEUChEbGwseDwejI2N0b9/fyxfvhz9+vWTKRsdHQ0TExNs3LgRZWVlcHNzw+7du5t1tjUhhLQFDElTh/m0UyKRGMXFFdquhkbp6TFhaWmKkpKKdtn3Skh92urngss1VerGdfsc+EsIIUQpFCQIIYQoREGCEEKIQnRPQk0SiaRN7k3NYjHb1IQhQjShLX4umExGo6taABQkCCGENIC6mwghhChEQYIQQohCFCQIIYQoREGCEEKIQhQkCCGEKERBghBCiEIUJAghhChEQYIQQohCFCQIIYQoREGCEEKIQhQkCCGEKERBghBCiEIUJAhyc3Mxc+ZMuLu7w9vbGzExMXj16pW2q0WIVj169Ahff/01xo4dCxcXFwQHB2u7Slqh9T2uiXbx+XxMmzYNNjY22Lx5M4qLi7Fq1SrweDysW7dO29UjRGuysrKQmpoKNzc3iMVitNcFsylItHP79+8Hn89HUlISuFwuAIDFYmHhwoWYNWsW7O3ttVxDQrQjICAAw4YNAwBER0cjIyNDyzXSDupuaufS0tLg7e0tDRAAMHLkSBgYGCA1NVWLNSNEu5hM+noEKEi0e9nZ2XKtBQMDA3Tr1g3Z2dlaqhUhRFdQkGjn+Hw+2Gy2XDqbzUZpaakWakQI0SUUJEi9JBKJUvvfEkLaNgoS7RybzQafz5dLLysrq7eFQQhpXyhItHP29vZy9x6EQiEeP35MI5sIIRQk2rshQ4bg999/R0lJiTTt3LlzEAqF8PPz02LNCCG6gOZJtHMhISHYu3cvIiIiEBERgaKiIqxevRqjR4+mlgRp16qqqqTDwPPz81FeXo7Tp08DADw9PWWGjbdlDEl7nUZIpHJzcxETE4Pr16/DyMgIwcHBWLhwIYyMjLRdNUK0Ji8vD0OHDq332J49e+Dl5dXCNdIOChKEEEIUonsShBBCFKIgQQghRCEKEoQQQhSiIEEIIUQhChKEEEIUoiBBCCFEIQoShBBCFKIgQQghRCEKEoToiE8//RQDBgzAs2fP5I7xeDz4+voiJCQEYrFYC7Uj7RUFCUJ0RExMDFgsFr788ku5Y99++y0qKiqwZs0a2laTtCh6txGiIzp27Ihly5bh8uXL2L9/vzT93LlzSE5OxqJFi9C9e3ct1pC0R7R2EyE6JioqChcvXsSJEydgamqKoKAgODs7IyEhQdtVI+0QBQlCdAyPx0NwcDDs7OzA5XKRnp6OEydOoHPnztquGmmHKEgQooNSU1PxySefAAC+++47jB07Vss1Iu0VBQlCdNT//d//gcfj4cyZM9quCmnH6MY1ITrKwMAA+vr62q4GaecoSBBCCFGIggQhhBCFKEgQQghRiIIEIYQQhWh0EyGEEIWoJUEIIUQhChKEEEIUoiBBCCFEIQoShBBCFKIgQQghRCEKEoQQQhSiIEEIIUQhChKEEEIUoiBBCCFEIQoShBBCFKIgQQghRCEKEoQQQhT6fxhScQue/OL4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZzPC6msUMQcM70heAgVHyNxAul4M+A72UfkBq+A/0NMhP5LpO+4SHRyAubiHat++IDh064eefd+PFixcIDh6i8WMcPXoQvXv7w9u7rdzxVq3aIDExASkpJxAaOqpO0z729vYICRmGxMRVEAgcYWtrh9Wrv4WPTxeVJ3sBoGdPPyQlrYNQWCw7oZuVlYmZM6eiR49eCA+PQH7+cwAAl8uTnSsBqn8ZiMVVEAqLUV5ejnv37gCo/kJ5VWVlBRISlmPRoqWyC7c6d+6KX37ZhxEjRiI19TRmzPhU1v7GjWuwtOTLrUAyVoZezEA0Y7TJPzs7G3Fxcbh8+TJsbGwQHByM2NhYWFtbq+1XXl6O9evX48SJE3j27BlcXV0xZMgQTJkyBXy+cV0h26adC4KGd0R6Sobcck97Byv4D/Q0mZHRgAHvoaioEJs3/4CCgnx4ebXFd98lwtn55XLHLVuScPz4Eezff1ihf0FBPs6fT8fChUsU7uNyuejb9x0cPXoQoaGa/5KoERMzGxwOF/Pnf46qKhF69PDDZ5/NVdvH09ML7dt3xOnTKfjggzAA1UtQi4oK8dtvx+V+FTRr5ib3mubMmSV3UjYqKgIAkJ5+Se45tm3bjN69+8p9KXzyyRwsWjQfM2ZMwrvvDkK/fv1l95069Rvee29Qre9/Y2AMixlI7TgMwxjdpu5CoRAhISFwd3dHdHQ0CgoKEB8fj759+2LlypVq+37++ec4deoUPv30U3h7e+PatWtYu3YtwsPDsWDBAq3ikUikKCgoUzheVSVCfv4jNG7sBktL7b9YKivE2Lr6HABg8EgfvZwU0/eWzkuWfAMAsikOY3fhwjmsW7cKO3f+bPAtFYqKivDRR6HYvHkH3N09VLbT1fuxvu7deopTh/6utd3AoR3g3bGpHiJSzcKCCycnOxQWlpnNFufOznZyU6eqGOXIf8+ePRAKhUhOTpadmOPxeIiNjcW0adPg6emptJ9YLMaJEycwceJEREZGAgB69eqFhw8f4tixY1onf11RtaunuOrlm47Pt0D+U+VVwUx5V8+rV/+UW+Vi7Pz8/PHgQQ6ePXsKV9dmBo3l8eOH+OyzuWoTvzExlsUMmjhy5BD27v0RO3bsMXQoemeUyT8tLQ1+fn5yKzKCgoIwb948pKamqkz+DMNAIpEorOsWCAQwhh84muzqWbPBmzKmvKvnvn3136pB30aNGm3oEAAA7dt3RPv2tV90ZiyMeTHDqxiGwRdfzMHDh3k4ezYVvXsH1N7JjBhl8s/MzERoaKjcMT6fjxYtWiAzM1NlP0tLS4wYMQI7d+5Et27d4OXlhevXr+Pnn3/GmDFjGjrsWqna1VNTtKsnMQXGvJjhVWfO/AcPH+YBAJYuXYzDh38zmTKsumCUyb9mWd/rBAIBiouL1fb95ptvsHDhQowa9fLkYGRkJGbMmFGvmCwsFOfQpNK6vVFUFbzQt5r3N4dTfcETMS88Hkfp+1Wf2nZyBZfHRdrJu3K/AOwFVuj7nje82ht2MQPDMFi+PA4cDgcMw+CPP/6LCxfOyl3saO6MMvmrwjBMrd/MK1euxO+//47FixejdevWuHnzJtauXQuBQICZM2dq9bxcLgdOTorLCysqeHj+nGsUHzZtaHJSiJgOqZQDLpcLR0dbo1gV1N3PDp3f9MCKBScBAKMndodnu6YGH/EDwMmTJ3Hlyp+y2zweD8uXL8HQoYNZM/o3yuQvEAjk9jmvUVJSonK+HwDu3r2LrVu3Yv369RgwoPqinu7du4PD4WDFihWIiIhQuduiOlIpA6GwXOG4SFQJqVQKiYQxqZUCHE514pdIpDTyNyMSCQOpVIri4nK8eCGpvYMevFrG0dHZBsXFip8jfWMYBl9+OQ88Hk9Wh1sikeDChQs4dOiYyY/+BQIb013t4+npqTC3LxKJkJOTo3Au4FUZGdUXlnTo0EHueIcOHSAWi5GXl6dV8gegNLlLJKaZOWsSPiV+82RMg5FX4xCLpeAYwaj/9OlTcqP+GjweD0uW/At+fn1ZMfo3yuQfEBCADRs2oLCwUHb1ZEpKCkQiEQIDA1X28/CoXgp38+ZNuLu/LL1340Z1VanmzZs3YNS1K64UolikfM8YTTjyBXC0UjwXQgjRTHXtiji5UX8NiUSC//3vD6Snp6FvX9V5xlwYZfIPDw/Hrl27EB0djejoaOTn52PZsmUYMmSI3LTPvHnzkJycjFu3qlcV+Pj4oEuXLli4cCGeP3+O1q1b4/r161i/fj0GDx5cp43GGkJ63kUc+0f70oSDWw1EcJv3dBgRIexy5sx/cPWq4qi/BofDwbJli9GnT4DZj/6NMvkLBAJs374dcXFxiImJgbW1NUJCQhAbGyvXrnq+/eW3N4/Hww8//IA1a9Zg06ZNeP78Odzc3DBmzBhMnTpV3y9DQR+PXujsorheu0oiRsKf6wEAs7tFw5Kn/J/FkU+jfn2hAu7m6datm2rvZxgGd+7chlQqBY/H01NUhmGU2zsYm4be3uGFuAKxaV8DAKK7jkcH57bgchrmQ84wDD75ZDosLLj47rt1cvf98ss+bNz4PXbs2FvnOr4HDvyMn37aiYKCfHh6euPTT+fUuvumKsuXL8HRowexaNFSvPPOQK0eo8bBg78gJeUE7t69g/LyMhw/fkbhIkBlEhMTUFpaii+/rP53uXfvLnbt+jeuX/8LRUVFcHNzw7BhoXIXguXk/INvv43HP/9ko6ysFI0bu+Ddd4MwfvxkWFhUf6HfvXsb8fH/Qm7uA3Tr5ov58xfJljWLxWJMnjwOc+bMU6jjO378GIwcGY733w9RGbOxbO/wqiqRBJsT0gEAE2f3gSXfsAmVYRiUlb38LG/e/APWrVuNGzfuys5P8Pl8o9sHrC403d6BhhEGdvXpdSy++HK/ovV/bcVX5+Nx9anqGrP1wbYC7pWVFejZs7dcmcnaaFvAncezwKBBwUhIWIfduw9g1qzZOHw4GVu2JMnaUAF3w+JwOLC3t5f9sbKyUjhmyom/Lij5G9DVp9ex6cZOhZPARZXF2HRjZ4N9AbClgDsAjBr1ESIjP0anTpr/CtG2gLuHR3MEBw+Ft3dbNGvmhj59AvHee4Pw119XZG2ogDsxFkY5588GUkaKfffUJ8f99w6hi0unBpkCYkMBd23pqoB7bu4D/Pe/F9C3bz/ZMSrgTowFJX8DySjKRlGl+q0qCiuLkVGUjbZOqi9sqw9zL+CurfoWcJ86dTzu3r0NkUiEoUOHY9KkabL7zLWAuyY71j5/UgoLS+UDGVPesdZUUfI3EGGlZuv9NW2nDbYWcK9NfQu4L1q0FOXl5cjIuIv169fip592IiJiHADzLeDO5h1rTRUlfwMRaHixlqbttGXOBdy1Vd8C7jX7/7du3QZSqRQrVizB6NGRSpdpmksBd9qx1vRQ8jcQr0at0cjKUe3Uj5OVI7watdZjVNXMpYC7tnRZwJ1hGIjFYkilUoXkb04F3I1lx1qiOUr+BsLlcDHSeyg23dipsk2Y99AGW++vjrkUcAeA/PznKCjIR15eLgAgKysDtra2cHVtpvJkrbYF3H/77Th4PAt4enrB0tISd+7cRlLS9xgw4D3ZOv8a5lzAnZgGSv4G9GbTzpjkE4mf7x6UW+7pZOWIMO+heLNpZwNGpzljLuCenHwA27Ztkt2ePn0SAGDevIUYPFj5l5O2Bdx5PB5+/HE7HjzIAcDA1dUNI0aMxKhRHyk8hzkXcCemga7w1YA5XeFbgwq4q0cF3Nlhw4ZEJCSsQHZ2ntHshFpfJl3A3Vyp2tWzSvJyfteaZ43c0odK+5vyrp5UwF17plbAnZgGSv56pMmunjUbvCljyrt6UgF37ZlaAXdiGij565GqXT01Rbt6EkJ0hZK/Hjlame60DSHEvNDGboQQwkKU/AkhhIUo+RNCCAvRnL8eqdr5UFO08yEhRFco+euRJjsfqkM7HxJCdIWSvx6p2vlQXCWVbXf7QcSbavc8J/pBBdyJuaN3kh7Z2VvBpZmDwh9nl5cblYlEYjRuaq+0nS6mfBiGwaxZ0Zg1S7Fc4C+/7MOgQf3w9OmTOj3m1at/4vPPP8WwYYPQp48v0tJ+r1eMy5cvQUBAD5w5o/6COE1UVlbiu++WY/DgAXj33b6YP38OCgrya+23Y8dW+Pv3lSX+e/fuYuHCeRgxIhj9+/sjIiIMP//8k0I/hmGwe/dOhIePwDvv+OGDD97H9u1bZPffvXsbUVEf4d13+2Lu3E8hFL684lssFmP8+Aj8/fdNucf08/MHl8vFyZPHtP1rIEQBJX8Dy7rzDHs3/092+9i+G9i14SKy7jxrkOdriALuL168gJeXN2bP/rze8em6gHtiYgLOnUvD4sXLkJi4Ec+fP8f8+XPU9tG2gDsArFmzEkeOJGPGjFn48cf9WLYsAR07vizITgXcibGgaR8DyrrzDCd/vaVwvKxEhJO/3kLQ8I5o065+e9MrU1PAfeXK5ejRoxfc3NzrVcDdz88ffn7+Oont1QLuH3wwCE+ePNZ6b53S0lIcOXIQCxfGyYrBzJu3EBERYbhx4zp8fJTvmqqqgPurPDya48aN60hNPYPQ0A8BAP/8k41ff92PnTv3okWLVgCgsB/P/fvZWLgwTlbA/fz5swBeFnDfulX5Ft99+gRi9eqVyM19QDV8iU5Q8jcQqZRB+qkMtW3OncpEK+8m4HLrVg5RE7oq4K5ruizgfufO3xCLxfD1fVkZrGXLVnB1bYabN6+pTP7aFnA/dy4N7u4euHjxPD77bBYYRgpf3x6Ijp4pa0cF3ImxoORvII8eFKOsRP2yz9KSSjx6UAyPlo0aJAZdFHDXJV0XcM/Pz4elpSUcHBzk7nd2dkZ+vup5f20LuD98mIcnTx7j1KnfsGDBN5BIJEhMTMBXX32BNWs2ADDfAu7E9FDyN5DyMs3W+2vaThsNWcBdG/oq4F5bcXltC7hLpQxEIhEWLFiEFi1aAgDmzv0KEydGIifnH7Ro0cpsC7gT00PJ30Bs7TRbtqlpO23Vt4C7rjREAffGjRujqqoKJSUlcqP/2orLa1vAvUmTJuDxeLLEDwCtW1fXYH7y5LHsPMCrzKWAOzE9lPwNxO0NR9g58NVO/dg7WMHtDeV1ZhuSIaZ9GqKAe7t2HWBhYYHLl/9Av34DAAA5Offx5MljdOqkuhautgXcO3fuColEgry8XHh4NP//58sBALi6uim0N6cC7sT0UPI3EC6Xgz4DvZSu9qnhP9CzQU721qau0z7l5eXIy3sgu/3oUR7u3bsDBwdHNGum2Uqdhijgbm9vj5CQYUhMXAWBwBG2tnZYvfpb+Ph0UXmyF9C+gLuvbw+0bdse8fH/wsyZsyGVMkhIWI7u3XvK/RoAqIA7MTxa529Abdq5IGh4R9i9duWuvYNVgy3zbAi3b99CVFQEoqIiAACJiasQFRWBLVt+kLXZsiUJYWHKC6bXFHAPDBygcN+rBdy1ERMzG71798X8+Z9jxoxJcHZujCVLVqjt82oB9xqvFnAfNmyQ7M+kSWPlYl2xYhUcHR0xffpkfP75J2jVqjUWLVKcplJVwD0j4w5mzJiE3r37UAF30qCogLsGGrqAe2WFGFtXnwMADB7pgzdaOzf4iJ8KuKtHBdzZgQq4E71QtaunuOrlm47Pt0D+01Kl/U15V08q4K49KuBOGgIlfz3SZFfPmg3elDHlXT2pgLv2qIA7aQhGm/yzs7MRFxeHy5cvw8bGBsHBwYiNjdVozrOoqAirV6/GqVOnUFxcDHd3d0RFRSE8PFwPkaumaldPTdGunoQQXTHK5C8UCjFu3Di4u7tj7dq1KCgoQHx8PIqKirBy5Uq1fcvKyhAZGQkrKyvMmzcPjRs3xv3791FVVaWn6FWzs7cy2WkbQoh5Mcrkv2fPHgiFQiQnJ8suxuHxeIiNjcW0adPg6empsm9SUhIqKiqwb98+2a+Enj17qmxPCCFsZJRLPdPS0uDn5yd3FWZQUBD4fD5SU1PV9j1w4ADCwsJoSRwhhKhhlMk/MzNTYXTP5/PRokULZGZmquz34MEDPH/+HAKBAFOmTIGPjw969uyJRYsWoaKioqHDJoQQk2GU0z41l/K/TiAQoLi4WGW/58+rr7pcsWIFBg0ahE2bNiEjIwMJCQmoqqpCXFyc1jFZWCh+T0qldVuLX1wpRLFI+Z4xmnDkC+BoVf9tFWr2NONwqjc5I+aFx+Mofb8SRTXX02iyLt7cGGXyV4VhGLW7MUql1evlPT09ER8fDwDw8/ODWCzGihUrMGvWLNm+L3XB5XLg5KS4pUBFBQ/Pn3M1/rCd/+e/OJKVUms7VULavIshXkFa938dG9/w5kwq5YDL5cLR0ZamPTVkbW0JABAIbAwcif4ZZfIXCARytU1rlJSUqD3Z26hRIwBAr1695I736tULUqkUmZmZWiV/qZSBUFiucFwkqoRUKoVEwmh0dWBvt57o1LiDwvEqiRgJf64HAMzuFg1LnvJ/Fke+QCdXIXI41YlfIpHSyF8FUyzgLpEwkEqlKC4ux4sXEj1GaLoqKqpXAQqFLyCRmMcVvgKBjele4evp6akwty8SiZCTk4PQ0FCV/d544w1YWloqHK/ZwaI+l+krS7oSSd0yp6OV8mmbF+KX5yMqJBVo7dgWXE7DjMqrC7hPh4UFF999t07uvl9+2YeNG7/Hjh1761TH9+rVP7F7907cufM38vOfY+nSlXL1Aepq+fIlOHr0IBYtWop33hmo9eMAwMGDvyAl5QTu3r2D8vIyHD9+RqG4izLKCrjv2vVvXL/+F4qKiuDm5oZhw0IVLgT7z39SsHPnNjx4cB+NGjkhNHQUPvro5f4/d+/eRnz8v5Cb+wDduvli/vxFsilOsViMyZPHYc6ceXJ1fP38/LFp0wacPHkM778fUmvsmg5GSPXADqjewoVtf2dG+bs/ICAAFy9eRGFhoexYSkoKRCIRAgMDVfbj8/nw9/fHhQsX5I5fuHABFhYW8PLyarCYtXX16XUsvvjy2oX1f23FV+fjcfXp9QZ5PrYVcK+srEDPnr0RGRmlcR9tC7hfuHAO//rXAnzwwQjs2LEXn332Bfbu3S3Xhgq4E2NhlMk/PDwcDg4OiI6OxtmzZ5GcnIzFixdjyJAhctM+8+bNQ8eO8pe9T58+HXfu3MHnn3+O9PR0/Pvf/0ZiYiIiIiLUFvAwhKtPr2PTjZ0KJ4GLKoux6cbOBvsCqCng/v33a/DwYR4Yhql3AffJk6MRGNi/9sa1eLWA+9Wrl/HkyeN6Pd6oUR8hMvJjdOqkvgbAq1QVcP/kkzl466234eHRHEFBgzF48FCkpp6Rtfntt+Po27cfPvggDB4ezdG7dx9ERn6MH3/cIfv1ef9+NkJChskKuP/zTxaAlwXcXy/dWKNPn0Dcvn0LubkPlN5PSF0Z5bSPQCDA9u3bERcXh5iYGFhbWyMkJASxsbFy7arn2+XnNrt06YKkpCR89913mDp1Kho1aoQxY8Zg1qxZ+nwJtZIyUuy7p35ku//eIXRx6dQgU0BsKOCuLW0LuItElQonWq2srPH06RM8fvwIbm7uVMCdGA2jTP5Adfm7LVu2qG2zbNkyLFu2TOG4v78//P39Gyo0ncgoykZRpeplqwBQWFmMjKJstHVSfZK7Psy9gLu2tC3g3qOHHxITE/D++3+gWzdf5OY+wJ49uwAA+fnP4ebmTgXcidGoU/Lv0KED/v7774aKhVWElZqt99e0nTbYWsC9NtoWcB86dDjy8nLx+eefQiIRw8bGBiNHjsa2bZtkiw2ogDsxFnVK/lT3RXcEGl6spWk7bZlzAXdtaVvAncPhIDp6JqZMmY6Cgnw0auSES5f+AACVy0WpgDsxlDol/1cvsCooKDC6E6imxKtRazSyclQ79eNk5QivRq31GFU1cyngri1tC7jX4PF4cHFpCgA4fToFPj5d4OSk+FmhAu7EkLSe8w8ICED//v0RGhqKgIAAtVfeEkVcDhcjvYdi042dKtuEeQ9tsPX+6phLAXegeq69oCAfeXm5AICsrAzY2trC1bWZ3MnaV2lbwL2oqAi//34Kb73lC5GoEsePH8Hp0ylITExSeA4q4E4MTevMEhQUhLS0NEydOhWBgYFYtWoVcnJydBmb2XuzaWdM8omEI19+FO1k5YhJPpF4s2lnA0VWN8ZcwD05+QCioiKwfHn1vk7Tp09CVFQE0tPTVPbRtoA7ABw/fhQTJ0Zi2rQJyMzMxNq1SUp/qVABd2JodSrg/voJ39LSUhw+fBi//PILrl+/Dg6HA19fX4SFhWHQoEGwsjKPwiUNXcD9hbgCsWlfAwCiu45HB+eGu8K3BhVwV48KuLMDFXDXkr29PUaPHo3Ro0cjMzMT+/fvx+HDhzF37lwsXrwYwcHBCA0NRZcu9FMVUL2rZ5Xk5fyuNc8auaUPlfbX1a6ehkAF3LVHBdxJQ6jXyF8ZiUSC33//HQcOHEBaWhokEgm8vLxw+PDhegdrKLoa+R/N+g3H/jmldRyDWw1EcJv3tO7/Kn2P/EnDo5F/3dHIX4d4PB4GDBiArl27YvPmzdi+fTsyMjJ0/TQmqY9HL3R2qf3KUVVePzdACCHa0mnyl0gkOH36NA4cOID09HSIxWK0a9cOYWFhunwak6VqV09CCNE3nST/e/fu4cCBAzh06BAKCwthb2+PsLAwhIWFwcdH8w21TBVd/EaMAb0PSV1onfxrVvocOHAAN2/eBACzXOmjDo/HA1C9oZe67QAI0QeRqHrrh9ev2CZEGa3fJf7+/hCJRHBxccHkyZMRGhqKFi1a6DI2o8fl8mBjY4/S0uq6A3y+lclc7CaVcupcjIYYJ4ZhIBJVorS0EDY29gZfmkpMg9bJv2/fvggLC0NAQACr32wCQfVl+zVfAKaCy+XKah4T82BjYy97PxJSG62T/7p162pvxAIcDgeOjo3h4OAEiURcewcjwONx4Ohoi+Lichr9mwkez4LVgzBSd1on/5p5fk116qRYms6ccLlccLmmsbbawoILa2trvHghMZu1zYSQutF6S+fQ0FCN5rcZhgGHw6E6AIQQYkS03tI5Pj5e58EQQgjRD62nfYYPH67LOAghhOhRvRcEFxQUYM+ePbh06RKePn0KAGjatCl69uyJUaNGyfY6J4QQYjzqlfwvXLiAmJgYlJaWgserLmrBMAyys7Nx/vx5bN26FevWrUP37t11FS8hhBAd0HptWEFBAT755BM4ODhg9erVuHTpEtLT03Hu3DlcunQJCQkJsLGxwcyZM1FYaFpr4AkhxNxpnfz3798PqVSKn376CYMGDYKNjY3sPhsbGwwePBi7d++GWCzG/v37dRIsIYQQ3dA6+aenpyM0NFRtjVZ3d3eMGDECZ8+e1fZpCCGENACtk39WVhbefvvtWtv5+voiKytL26chhBDSALQ+4SsUCuHsXPs+Is7OzhAKFUsXEvI6KSNFRlE2hJVCCKwE8GrUusFrGRPCVlpf4SsSiWBpaVn7E1hYoKqqqu6REbOlrJbxnYIM/CcnDSVVpbJjDpb2GNAiAO2cveTamnItY0KMRb2WemZlZcn2tFfXhpBXpedd1KiWcUlVKZIzjwGZ8sd1WcuYELaqV/L/8ssva21Ts7cPITVerWUsZaT44a9/y434X+fAt8fULh/LpoColjEh9Ud7+xC9e7WW8d3CTLWJHwBKRKUQSarQ1slTH+ERwgpaz/nT3j5EF4SVmi0G0LQdIUQzdVpKcfv27YaKg+gJwzDo2fMtzJo1y9ChAAAEGp641bQdIUQztI6OZc6cOYV79+4hKSkJZWVlhg4HXo1ao5GVo9o2TlaO8GrUWk8REcIOlPxZhGEYxMfHAQAqKyuxZcsmg8RRXClETkkuckpykVv6EP2a+6ttH9jcH7mlD2V9imkKiJB6q/eWzg0lOzsbcXFxuHz5MmxsbBAcHIzY2FhYW1tr/BgpKSmYMWMGvL29ceTIkQaM1jScOXMKf/11RXZ71aqVGDduAuzs7PQah6ZLPWu8vtyTlnoSUn9GmfyFQiHGjRsHd3d3rF27FgUFBYiPj0dRURFWrlyp0WNUVFQgPj4eTZo0aeBoTUPNqJ/L5UEqlQAAhMJibNu2GTNm6Hf+/9Wlnq+SMlLkljxEaVUZ7C3t0NzBXekVvrTUk5D6M8rkv2fPHgiFQiQnJ8u2kODxeIiNjcW0adPg6Vn7kr+kpCS4u7ujefPmuHHjRkOHbPReH/UD1V8Ia9asRFTURL2O/l9d6vm6VoIWeouDEDYzyjn/tLQ0+Pn5ye0dFBQUBD6fj9TU1Fr75+TkYNu2bViwYEFDhmkyXh31v04oFGLbts0GiIoQYkhGmfwzMzMVRvd8Ph8tWrRAZmamil4vLVmyBMOGDUP79u0bKkSTUjPqr5nueVXN6N8YVv4QQvTHKKd9hEIhBALFaQGBQIDi4mK1fU+fPo0rV67gxIkTOo3JwsIovyc1smLFUrX3FxcXY8eOrYiJMY61/4ToC5dbvWsBj2e6n29tGWXyV6W2fYIqKyuxdOlSxMTEaLTdtKa4XA6cnPS7IkaXPDzcUVT0spRmTk4O7Ozs0LhxY9mxxo0dTfo1EqKJ27dv48CBA7Lb586lo6KiAomJCbJjzZo1w/jx481+TzKjTP4CgUBpDYCSkhK1J3u3b98OLpeL4OBgWf+qqipIpVIIhUJYW1uDz+fXOR6plIFQWF7nfsZi+/af5G537/4mQkNHYP78byCRSGXHCwtp6oeYt507d2Pp0sXg8XjgcDiQSqWQSqX45ptvAAASiQRWVlYYNmxkrTsWGyuBwEajXzJGmfw9PT0V5vZFIhFycnIQGhqqsl9WVhbu378PPz8/hfu6d++Ob775BqNHj9YqJrFYWnsjEyORSM3ydRGiyujRkfj22+WoqhLJHReLxQAALpeHCRMmg2E4Zv/ZMMrkHxAQgA0bNqCwsBBOTk4Aqi/YEolECAwMVNlv0qRJChvObdy4EdnZ2YiPj0erVq0aMmxCiJFzdW2GqKiJ2LIlCRKJ4gIIS0sLREez49yXUZ7lCA8Ph4ODA6Kjo3H27FkkJydj8eLFGDJkiNy0z7x589Cx48uLhTw9PdGzZ0+5Py4uLrC1tUXPnj3h6upqiJdDCDEiMTGfKF32zOXyMHHiFLi4uBggKv0zyuQvEAiwfft22NraIiYmBsuWLUNISAji4uLk2kmlUqXf3oQQokrN6P/1OX02jfoBgMO8ukk/UUoikaKgwHxOhvr5dUNo6Ah88cXXZj+vSYgyT548RrduPrK5fy6Xh2nTpmPhwrhaeuqWsnrWQN22Onn9anlnZzvTPeFLCCENqWb0v2nTBjAMY7BRf103OXxdfTY5pORPCGGlmJhPZCd+J0+eapC5/tc3ObxTkFG9i60KH3gORjtnL9nt+mxyaJRz/oQQ0tBcXZuhb99AcDgcve9sW8PRSoAWDs3RwqE5mtu74/fcc2rbp+aeQ3N7d1kfVRskaoKSPyGEtXbv/hl//fUXXFyaGjoUZBRlo6hS/fY1hZXFyCjK1snzUfInhLCWtbU1OnfubOgwAABCDSvUadquNpT8CSHECAg0nMLRtF1tKPkTQogR8GrUGo2sHNW2cbJyhFej1jp5Pkr+hBBiBLgcLkZ6D1XbJsx7qNL1/tqgpZ6EEGIgr1/k5WzjhA88B+NUTipKq15eWOrAt8eANwLgbOOEnJJc2XFlF3lpipI/IYQYiKYXeZWISqvX/79WyJAu8iKEEBP0+kVedVWfi7wo+RNCiIE4Wmk/bVNfdMKXEEJYiJI/IYSwECV/QghhIUr+hBDCQpT8CSGEhSj5E0IIC1HyJ4QQFqLkTwghLETJnxBCWIiSPyGEsBAlf0IIYSFK/oQQwkKU/AkhhIUo+RNCCAtR8ieEEBai5E8IISxEyZ8QQliIkj8hhLAQJX9CCGEhSv6EEMJClPwJIYSFLAwdgCrZ2dmIi4vD5cuXYWNjg+DgYMTGxsLa2lpln9LSUmzbtg1paWnIzs6GhYUFOnXqhNmzZ6NTp056jJ4QQoybUY78hUIhxo0bh7KyMqxduxZz587F4cOHsWDBArX9Hj58iL1796J3795YtWoV4uPjIZVKER4ejps3b+opekIIMX5GOfLfs2cPhEIhkpOT4ezsDADg8XiIjY3FtGnT4OnpqbRf8+bNkZKSAhsbG9mx3r17Y8CAAdi1axfi4+P1Ej8hhBg7oxz5p6Wlwc/PT5b4ASAoKAh8Ph+pqakq+9na2solfgCwsrKCp6cnnj592mDxEkKIqTHK5J+Zmakwuufz+WjRogUyMzPr9Fjl5eX4+++/0aZNG12GSAghJs0op32EQiEEAoHCcYFAgOLi4jo91urVq/HixQuMGTOmXjFZWBjl92S98Hjm95oIqYuazwAbPwtGmfxVYRgGHA5H4/aHDx/G9u3b8fXXX6Nly5ZaPy+Xy4GTk53W/Y0Nl1v9dygQ2NTSkhB2YONnwSiTv0AggFAoVDheUlKi8mTv686dO4cvv/wSEyZMQERERL3ikUoZCIXl9XoMYyKVMgAAofAFJBKpgaMhxHB4PC4EAhuz+iwIBDYa/ZIxyuTv6empMLcvEomQk5OD0NDQWvtfu3YNM2bMwKBBgzBnzhydxCQWm8cb41USidQsXxchdcXGz4JRTnQFBATg4sWLKCwslB1LSUmBSCRCYGCg2r6ZmZmYNGkSunXrhvj4+DpNExFCCFsYZfIPDw+Hg4MDoqOjcfbsWSQnJ2Px4sUYMmSI3LTPvHnz0LFjR9nt/Px8TJgwAZaWlpg4cSJu3ryJq1ev4urVq7h165YhXgohhBglo5z2EQgE2L59O+Li4hATEwNra2uEhIQgNjZWrp1UKoVEIpHdzsjIwKNHjwAAH3/8sVxbDw8PnD59usFjJ4QQU8BhGIYxdBDGTiKRoqCgzNBhaC0n577sSxEApkyJQr9+gRgzJgoSSfU/f6dOnWBv72CoEAkxCAsLLpyc7FBYWGY2c/7OznYanfCl5K8BU0/+7dq1QmFhgdo2kZEf47vv1uopIkKMA5uTv1HO+RPdGjZseK0nvocOHa6naAghxoCSPwvMmvUZuFzl/9Q8Hg9vv90dAQH99BsUIcSgKPmzgIdHc0RGfgwej6dwn0QiwRdfLKAlsYSwDCV/lpg16zOFYzTqJ4S9KPk3AKmUQd79Ity79RR594tk2ykYkrLRP436CWEvWu2jgbqs9sm68wzppzJQViKSHbNz4KPPQC+0aefSUCFqJC8vF76+nSGRSMDhcPD22744evQUJX/CWrTah+hE1p1nOPnrLbnEDwBlJSKc/PUWsu48M1Bk1WpG/0D1Dqnz5n1NiZ8QlqKRvwZUjfzLSitRXlqd6Bkpg2P7b+BFeZXKx7GxtcTgMB9w/n9LZVt7PuzsrRomaBXy8nLx1lsd4e7ujuvX78gu8iKEjdg88jfK7R1Mxa0rj3Dp3H2N278or8KBHVdkt339W6J731YNEJlqHh7NsXjxUrzzTsD/j/op+RPCRjTy14Cqkf+zJyUofF69z//jPCFu/vmw1sfq1M0dzTyqq5Q5NbGFi6v+t1Qwx9EOIdowx88Cjfz14J+7+XUa+QPAzT8fyr4kfP1bGiT5E0IIJf966PiWG1p5Nwag/Zw/IYQYAiX/erCzt5I7YRsQ5I2Tv6quGxAQ5I2m7oqF6QkhRN9oqacOtWnngqDhHWHnID+it3ewQtDwjgZf508IITVo5K9jbdq5oJV3Ezx6UIzyMhFs7fhwe8MRXC6tpyeEGA9K/g2Ay+XAo2UjQ4dBCCEq0bQPIYSwECV/QghhIbrISwMMwxjFzpy6xONxIZGYx0UthNSHuX0WuFyORnt2UfInhBAWomkfQghhIUr+hBDCQpT8CSGEhSj5E0IIC1HyJ4QQFqLkTwghLETJnxBCWIiSPyGEsBAlf0IIYSFK/oQQwkKU/AkhhIUo+RNCCAtR8meR7OxsTJgwAW+++Sb8/PwQFxeHiooKQ4dFiN7dv38fX3/9NYYNG4aOHTsiJCTE0CHpHVXyYgmhUIhx48bB3d0da9euRUFBAeLj41FUVISVK1caOjxC9OrevXtITU1F165dIZVKwcbNjSn5s8SePXsgFAqRnJwMZ2dnAACPx0NsbCymTZsGT09PA0dIiP70798fAwcOBAB88cUXuHHjhoEj0j+a9mGJtLQ0+Pn5yRI/AAQFBYHP5yM1NdWAkRGif1wupT76G2CJzMxMhdE9n89HixYtkJmZaaCoCCGGQsmfJYRCIQQCgcJxgUCA4uJiA0RECDEkSv4sxzCMRvU+CSHmhZI/SwgEAgiFQoXjJSUlSn8REELMGyV/lvD09FSY2xeJRMjJyaGVPoSwECV/lggICMDFixdRWFgoO5aSkgKRSITAwEADRkYIMQRa588S4eHh2LVrF6KjoxEdHY38/HwsW7YMQ4YMoZE/YZ0XL17Iljjn5eWhtLQUJ06cAAD06NFDbkm0ueIwbLy0jaWys7MRFxeHy5cvw9raGiEhIYiNjYW1tbWhQyNEr3JzczFgwACl9+3YsQM9e/bUc0T6R8mfEEJYiOb8CSGEhSj5E0IIC1HyJ4QQFqLkTwghLETJnxBCWIiSPyGEsBAlf0IIYSFK/oQQwkKU/AnRkylTpsDX1xePHj1SuK+oqAh9+vRBeHg4pFKpAaIjbEPJnxA9iYuLA4/Hw4IFCxTuW7x4McrKyrB8+XIqMUj0gt5lhOiJi4sLFi5ciPT0dOzZs0d2PCUlBUeOHMGcOXPQsmVLA0ZI2IT29iFEz2bPno0zZ87g8OHDsLOzQ3BwMNq3b4+tW7caOjTCIpT8CdGzoqIihISEoE2bNnB2dsa5c+dw+PBhNGvWzNChERah5E+IAaSmpmLy5MkAgBUrVmDYsGEGjoiwDSV/Qgzkww8/RFFREU6ePGnoUAgL0QlfQgyEz+fD0tLS0GEQlqLkTwghLETJnxBCWIiSPyGEsBAlf0IIYSFa7UMIISxEI39CCGEhSv6EEMJClPwJIYSFKPkTQggLUfInhBAWouRPCCEsRMmfEEJYiJI/IYSwECV/QghhIUr+hBDCQpT8CSGEhSj5E0IIC/0fYat5meLgLewAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ff_test_member_loss_y0_a1, ff_test_member_loss_y1_a1, ff_test_member_loss_y0_a0, ff_test_member_loss_y1_a0 = list(), list(), list(), list()\n",
    "ff_test_member_loss_a1, ff_test_member_loss_a0 = list(), list()\n",
    "ff_test_ensemble_loss_y0_a1, ff_test_ensemble_loss_y1_a1, ff_test_ensemble_loss_y0_a0, ff_test_ensemble_loss_y1_a0 = list(), list(), list(), list()\n",
    "ff_test_ensemble_loss_a1, ff_test_ensemble_loss_a0 = list(), list()\n",
    "utk_test_member_loss_y0_a1, utk_test_member_loss_y1_a1, utk_test_member_loss_y0_a0, utk_test_member_loss_y1_a0 = list(), list(), list(), list()\n",
    "utk_test_member_loss_a1, utk_test_member_loss_a0 = list(), list()\n",
    "utk_test_ensemble_loss_y0_a1, utk_test_ensemble_loss_y1_a1, utk_test_ensemble_loss_y0_a0, utk_test_ensemble_loss_y1_a0 = list(), list(), list(), list()\n",
    "utk_test_ensemble_loss_a1, utk_test_ensemble_loss_a0 = list(), list()\n",
    "\n",
    "preds = torch.stack(ff_test_probits)\n",
    "member_loss = - torch.log(torch.gather(preds, dim=3, index=y_ff_test_t.reshape(1, 1, -1, 1).expand_as(preds[..., :1])))[..., 0]\n",
    "\n",
    "ff_test_member_loss_y1_a1 = member_loss[..., (y_ff_test_t == 1) & (a_ff_test_t == 1)].mean(dim=2).reshape(1, -1)\n",
    "ff_test_member_loss_y0_a1 = member_loss[..., (y_ff_test_t == 0) & (a_ff_test_t == 1)].mean(dim=2).reshape(1, -1)\n",
    "ff_test_member_loss_y1_a0 = member_loss[..., (y_ff_test_t == 1) & (a_ff_test_t == 0)].mean(dim=2).reshape(1, -1)\n",
    "ff_test_member_loss_y0_a0 = member_loss[..., (y_ff_test_t == 0) & (a_ff_test_t == 0)].mean(dim=2).reshape(1, -1)\n",
    "ff_test_member_loss_a1 = member_loss[..., a_ff_test_t == 1].mean(dim=2).reshape(1, -1)\n",
    "ff_test_member_loss_a0 = member_loss[..., a_ff_test_t == 0].mean(dim=2).reshape(1, -1)\n",
    "\n",
    "S, M, N, P = preds.shape\n",
    "# m_preds = torch.mean(preds.reshape(S * M, N, P), keepdim=True, dim=0)\n",
    "# ensemble_loss = - torch.log(torch.gather(m_preds, dim=2, \n",
    "#                                          index=y_ff_test_t.reshape(1, -1, 1).expand_as(m_preds[..., :1])))[0][..., 0]\n",
    "ensemble_loss = - torch.log(torch.gather(torch.mean(preds, dim=1, keepdim=True), dim=3,\n",
    "                                            index=y_ff_test_t.reshape(1, 1, -1, 1).expand_as(torch.mean(preds, dim=1, keepdim=True)[..., :1])))[:, 0][..., 0] \n",
    "ff_test_ensemble_loss_y1_a1 = ensemble_loss[..., (y_ff_test_t == 1) & (a_ff_test_t == 1)].mean(dim=1).reshape(1, -1)\n",
    "ff_test_ensemble_loss_y0_a1 = ensemble_loss[..., (y_ff_test_t == 0) & (a_ff_test_t == 1)].mean(dim=1).reshape(1, -1)\n",
    "ff_test_ensemble_loss_y1_a0 = ensemble_loss[..., (y_ff_test_t == 1) & (a_ff_test_t == 0)].mean(dim=1).reshape(1, -1)\n",
    "ff_test_ensemble_loss_y0_a0 = ensemble_loss[..., (y_ff_test_t == 0) & (a_ff_test_t == 0)].mean(dim=1).reshape(1, -1)\n",
    "ff_test_ensemble_loss_a1 = ensemble_loss[..., a_ff_test_t == 1].mean(dim=1).reshape(1, -1)\n",
    "ff_test_ensemble_loss_a0 = ensemble_loss[..., a_ff_test_t == 0].mean(dim=1).reshape(1, -1)\n",
    "\n",
    "preds = torch.stack(utk_test_probits)\n",
    "member_loss = - torch.log(torch.gather(preds, dim=3, index=y_utk_test_t.reshape(1, 1, -1, 1).expand_as(preds[..., :1])))[..., 0]\n",
    "\n",
    "utk_test_member_loss_y1_a1 = member_loss[..., (y_utk_test_t == 1) & (a_utk_test_t == 1)].mean(dim=2).reshape(1, -1)\n",
    "utk_test_member_loss_y0_a1 = member_loss[..., (y_utk_test_t == 0) & (a_utk_test_t == 1)].mean(dim=2).reshape(1, -1)\n",
    "utk_test_member_loss_y1_a0 = member_loss[..., (y_utk_test_t == 1) & (a_utk_test_t == 0)].mean(dim=2).reshape(1, -1)\n",
    "utk_test_member_loss_y0_a0 = member_loss[..., (y_utk_test_t == 0) & (a_utk_test_t == 0)].mean(dim=2).reshape(1, -1)\n",
    "utk_test_member_loss_a1 = member_loss[..., a_utk_test_t == 1].mean(dim=2).reshape(1, -1)\n",
    "utk_test_member_loss_a0 = member_loss[..., a_utk_test_t == 0].mean(dim=2).reshape(1, -1)\n",
    "\n",
    "S, M, N, P = preds.shape\n",
    "# m_preds = torch.mean(preds.reshape(S * M, N, P), keepdim=True, dim=0)\n",
    "# ensemble_loss = - torch.log(torch.gather(m_preds, dim=2,\n",
    "#                                         index=y_utk_test_t.reshape(1, -1, 1).expand_as(m_preds[..., :1])))[0][..., 0]\n",
    "ensemble_loss = - torch.log(torch.gather(torch.mean(preds, dim=1, keepdim=True), dim=3,\n",
    "                                            index=y_utk_test_t.reshape(1, 1, -1, 1).expand_as(torch.mean(preds, dim=1, keepdim=True)[..., :1])))[:, 0][..., 0]       \n",
    "utk_test_ensemble_loss_y1_a1 = ensemble_loss[..., (y_utk_test_t == 1) & (a_utk_test_t == 1)].mean(dim=1).reshape(1, -1)\n",
    "utk_test_ensemble_loss_y0_a1 = ensemble_loss[..., (y_utk_test_t == 0) & (a_utk_test_t == 1)].mean(dim=1).reshape(1, -1)\n",
    "utk_test_ensemble_loss_y1_a0 = ensemble_loss[..., (y_utk_test_t == 1) & (a_utk_test_t == 0)].mean(dim=1).reshape(1, -1)\n",
    "utk_test_ensemble_loss_y0_a0 = ensemble_loss[..., (y_utk_test_t == 0) & (a_utk_test_t == 0)].mean(dim=1).reshape(1, -1)\n",
    "utk_test_ensemble_loss_a1 = ensemble_loss[..., a_utk_test_t == 1].mean(dim=1).reshape(1, -1)\n",
    "utk_test_ensemble_loss_a0 = ensemble_loss[..., a_utk_test_t == 0].mean(dim=1).reshape(1, -1)\n",
    "\n",
    "#### YA difference\n",
    "\n",
    "plt.figure(figsize=(4, 3))\n",
    "\n",
    "for t, test_ent in enumerate([ff_test_member_loss_y0_a0, ff_test_member_loss_y0_a1]):\n",
    "    plt.errorbar([t / 5 * 2 - 0.2], torch.mean(test_ent[0], dim=0), yerr=torch.std(test_ent[0], dim=0), fmt=\"o\", \n",
    "                 color=[\"C4\", \"C2\"][t], capsize=6,\n",
    "                 label=f\"Y=0, A={t} ({((100 - p_y_ff_test) * (p_a_ff_test if t == 1 else 100 - p_a_ff_test) / 100):.0f}%)\")\n",
    "for t, test_ent in enumerate([ff_test_member_loss_y1_a0, ff_test_member_loss_y1_a1]):\n",
    "    plt.errorbar([t / 5 * 2 + 0.8], torch.mean(test_ent[0], dim=0), yerr=torch.std(test_ent[0], dim=0), fmt=\"o\", \n",
    "                 color=[\"C4\", \"C2\"][t], capsize=6,\n",
    "                 label=f\"Y=1, A={t} ({(p_y_ff_test * (p_a_ff_test if t == 1 else 100 - p_a_ff_test) / 100):.0f}%)\")\n",
    "# for t, test_ent in enumerate([ff_test_ensemble_loss_y0_a0, ff_test_ensemble_loss_y0_a1]):\n",
    "#     plt.scatter([t / 5 * 2 - 0.2], torch.mean(test_ent[0], dim=0), color=[\"C4\", \"C2\"][t], marker=\"x\")\n",
    "# for t, test_ent in enumerate([ff_test_ensemble_loss_y1_a0, ff_test_ensemble_loss_y1_a1]):\n",
    "#     plt.scatter([t / 5 * 2 + 0.8], torch.mean(test_ent[0], dim=0), color=[\"C4\", \"C2\"][t], marker=\"x\")\n",
    "plt.title(\"FairFace\")\n",
    "plt.ylabel(\"NLL\")\n",
    "plt.xlabel(\"Y\")\n",
    "plt.xticks([0, 1], [\"0\", \"1\"])\n",
    "# plt.legend(handles=[patches.Patch(edgecolor=[\"C4\", \"C2\"][t], facecolor=\"none\", label=f\"A={t} ({(p_a_ff_test if t == 1 else 100 - p_a_ff_test):.0f}%)\") for t in range(2)])\n",
    "plt.legend(fontsize='small')\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(PLOTS_PATH, f\"n_members_target{target}_pa{pa}_{model}_likelihood_ya_ff.pdf\"))\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(4, 3))\n",
    "\n",
    "for t, test_ent in enumerate([utk_test_member_loss_y0_a0, utk_test_member_loss_y0_a1]):\n",
    "    plt.errorbar([t / 5 * 2 - 0.2], torch.mean(test_ent[0], dim=0), yerr=torch.std(test_ent[0], dim=0), fmt=\"o\", \n",
    "                 color=[\"C4\", \"C2\"][t], capsize=6,\n",
    "                 label=f\"Y=0, A={t} ({((100 - p_y_utk_test) * (p_a_utk_test if t == 1 else 100 - p_a_utk_test) / 100):.0f}%)\")\n",
    "for t, test_ent in enumerate([utk_test_member_loss_y1_a0, utk_test_member_loss_y1_a1]):\n",
    "    plt.errorbar([t / 5 * 2 + 0.8], torch.mean(test_ent[0], dim=0), yerr=torch.std(test_ent[0], dim=0), fmt=\"o\", \n",
    "                 color=[\"C4\", \"C2\"][t], capsize=6,\n",
    "                 label=f\"Y=1, A={t} ({(p_y_utk_test * (p_a_utk_test if t == 1 else 100 - p_a_utk_test) / 100):.0f}%)\")\n",
    "# for t, test_ent in enumerate([utk_test_ensemble_loss_y0_a0, utk_test_ensemble_loss_y0_a1]):\n",
    "#     plt.scatter([t / 5 * 2 - 0.2], torch.mean(test_ent[0], dim=0), color=[\"C4\", \"C2\"][t], marker=\"x\")\n",
    "# for t, test_ent in enumerate([utk_test_ensemble_loss_y1_a0, utk_test_ensemble_loss_y1_a1]):\n",
    "#     plt.scatter([t / 5 * 2 + 0.8], torch.mean(test_ent[0], dim=0), color=[\"C4\", \"C2\"][t], marker=\"x\")\n",
    "plt.title(\"UTKFace\")\n",
    "plt.ylabel(\"NLL\")\n",
    "plt.xlabel(\"Y\")\n",
    "plt.xticks([0, 1], [\"0\", \"1\"])\n",
    "plt.legend(fontsize='small')\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(PLOTS_PATH, f\"n_members_target{target}_pa{pa}_{model}_likelihood_ya_utk.pdf\"))\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# YA difference\n",
    "\n",
    "ff_test_member_loss_y0_a0 = torch.mean(ff_test_member_loss_y0_a0.reshape(len(method_seeds), -1), dim=1).reshape(1, -1)\n",
    "ff_test_member_loss_y0_a1 = torch.mean(ff_test_member_loss_y0_a1.reshape(len(method_seeds), -1), dim=1).reshape(1, -1)\n",
    "ff_test_member_loss_y1_a0 = torch.mean(ff_test_member_loss_y1_a0.reshape(len(method_seeds), -1), dim=1).reshape(1, -1)\n",
    "ff_test_member_loss_y1_a1 = torch.mean(ff_test_member_loss_y1_a1.reshape(len(method_seeds), -1), dim=1).reshape(1, -1)\n",
    "utk_test_member_loss_y0_a0 = torch.mean(utk_test_member_loss_y0_a0.reshape(len(method_seeds), -1), dim=1).reshape(1, -1)\n",
    "utk_test_member_loss_y0_a1 = torch.mean(utk_test_member_loss_y0_a1.reshape(len(method_seeds), -1), dim=1).reshape(1, -1)\n",
    "utk_test_member_loss_y1_a0 = torch.mean(utk_test_member_loss_y1_a0.reshape(len(method_seeds), -1), dim=1).reshape(1, -1)\n",
    "utk_test_member_loss_y1_a1 = torch.mean(utk_test_member_loss_y1_a1.reshape(len(method_seeds), -1), dim=1).reshape(1, -1)\n",
    "\n",
    "plt.figure(figsize=(4, 3))\n",
    "\n",
    "test_ents_y0 = [ff_test_member_loss_y0_a0 - ff_test_ensemble_loss_y0_a0, ff_test_member_loss_y0_a1 - ff_test_ensemble_loss_y0_a1]\n",
    "for t, test_ent in enumerate(test_ents_y0):\n",
    "    plt.errorbar([t / 5 * 2 - 0.2], torch.mean(test_ent[0], dim=0), yerr=torch.std(test_ent[0], dim=0), fmt=\"o\", \n",
    "                 color=[\"C4\", \"C2\"][t], capsize=6,\n",
    "                 label=f\"Y=0, A={t} ({((100 - p_y_ff_test) * (p_a_ff_test if t == 1 else 100 - p_a_ff_test) / 100):.0f}%)\")\n",
    "test_ents_y1 = [ff_test_member_loss_y1_a0 - ff_test_ensemble_loss_y1_a0, ff_test_member_loss_y1_a1 - ff_test_ensemble_loss_y1_a1]\n",
    "for t, test_ent in enumerate(test_ents_y1):\n",
    "    plt.errorbar([t / 5 * 2 + 0.8], torch.mean(test_ent[0], dim=0), yerr=torch.std(test_ent[0], dim=0), fmt=\"o\", \n",
    "                 color=[\"C4\", \"C2\"][t], capsize=6,\n",
    "                 label=f\"Y=1, A={t} ({(p_y_ff_test * (p_a_ff_test if t == 1 else 100 - p_a_ff_test) / 100):.0f}%)\")\n",
    "ylims = plt.gca().get_ylim()\n",
    "arrow = patches.FancyArrowPatch((0, torch.mean(test_ents_y0[1][0], dim=0)), \n",
    "                                (0, torch.mean(test_ents_y0[0][0], dim=0)), \n",
    "                                arrowstyle='<|-|>', \n",
    "                                color='black', \n",
    "                                mutation_scale=15, shrinkA=0, shrinkB=0)\n",
    "plt.gca().add_patch(arrow)\n",
    "arrow = patches.FancyArrowPatch((1, torch.mean(test_ents_y1[1][0], dim=0)), \n",
    "                                (1, torch.mean(test_ents_y1[0][0], dim=0)), \n",
    "                                arrowstyle='<|-|>', \n",
    "                                color='black', \n",
    "                                mutation_scale=15, shrinkA=0, shrinkB=0)\n",
    "plt.gca().add_patch(arrow)\n",
    "plt.ylim(ylims)\n",
    "plt.title(\"FairFace\")\n",
    "plt.ylabel(\"$\\\\overline{\\mathrm{DIV}}$\")\n",
    "plt.xlabel(\"Y\")\n",
    "plt.xticks([0, 1], [\"0\", \"1\"])\n",
    "# plt.legend(handles=[patches.Patch(edgecolor=[\"C4\", \"C2\"][t], facecolor=\"none\", label=f\"A={t} ({(p_a_ff_test if t == 1 else 100 - p_a_ff_test):.0f}%)\") for t in range(2)])\n",
    "plt.legend(fontsize='small')\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(PLOTS_PATH, f\"n_members_target{target}_pa{pa}_{model}_likelihood_ratio_ya_ff.pdf\"))\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(4, 3))\n",
    "\n",
    "test_ents_y0 = [utk_test_member_loss_y0_a0 - utk_test_ensemble_loss_y0_a0, utk_test_member_loss_y0_a1 - utk_test_ensemble_loss_y0_a1]\n",
    "for t, test_ent in enumerate(test_ents_y0):\n",
    "    plt.errorbar([t / 5 * 2 - 0.2], torch.mean(test_ent[0], dim=0), yerr=torch.std(test_ent[0], dim=0), fmt=\"o\", \n",
    "                 color=[\"C4\", \"C2\"][t], capsize=6,\n",
    "                 label=f\"Y=0, A={t} ({((100 - p_y_utk_test) * (p_a_utk_test if t == 1 else 100 - p_a_utk_test) / 100):.0f}%)\")\n",
    "test_ents_y1 = [utk_test_member_loss_y1_a0 - utk_test_ensemble_loss_y1_a0, utk_test_member_loss_y1_a1 - utk_test_ensemble_loss_y1_a1]\n",
    "for t, test_ent in enumerate(test_ents_y1):\n",
    "    plt.errorbar([t / 5 * 2 + 0.8], torch.mean(test_ent[0], dim=0), yerr=torch.std(test_ent[0], dim=0), fmt=\"o\", \n",
    "                 color=[\"C4\", \"C2\"][t], capsize=6,\n",
    "                 label=f\"Y=1, A={t} ({(p_y_utk_test * (p_a_utk_test if t == 1 else 100 - p_a_utk_test) / 100):.0f}%)\")\n",
    "ylims = plt.gca().get_ylim()\n",
    "arrow = patches.FancyArrowPatch((0, torch.mean(test_ents_y0[1][0], dim=0)), \n",
    "                                (0, torch.mean(test_ents_y0[0][0], dim=0)), \n",
    "                                arrowstyle='<|-|>', \n",
    "                                color='black', \n",
    "                                mutation_scale=15, shrinkA=0, shrinkB=0)\n",
    "plt.gca().add_patch(arrow)\n",
    "arrow = patches.FancyArrowPatch((1, torch.mean(test_ents_y1[1][0], dim=0)), \n",
    "                                (1, torch.mean(test_ents_y1[0][0], dim=0)), \n",
    "                                arrowstyle='<|-|>', \n",
    "                                color='black', \n",
    "                                mutation_scale=15, shrinkA=0, shrinkB=0)\n",
    "plt.gca().add_patch(arrow)\n",
    "plt.ylim(ylims)\n",
    "plt.title(\"UTKFace\")\n",
    "plt.ylabel(\"$\\\\overline{\\mathrm{DIV}}$\")\n",
    "plt.xlabel(\"Y\")\n",
    "plt.xticks([0, 1], [\"0\", \"1\"])\n",
    "plt.legend(fontsize='small')\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(PLOTS_PATH, f\"n_members_target{target}_pa{pa}_{model}_likelihood_ratio_ya_utk.pdf\"))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Weighting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select the best temperature on validation set\n",
    "temperatures = np.logspace(2, -6, 300)\n",
    "\n",
    "optim_temps_spd, optim_temps_eod, optim_temps_aod = list(), list(), list()\n",
    "\n",
    "for m in range(len(method_seeds)):\n",
    "\n",
    "    best_temp_spd, best_temp_eod, best_temp_aod = temperatures[0], temperatures[0], temperatures[0]\n",
    "    best_spd, best_eod, best_aod = float(\"inf\"), float(\"inf\"), float(\"inf\")\n",
    "\n",
    "    for temperature in temperatures:\n",
    "    \n",
    "        preds = torch.sum(val_probits[m] * torch.softmax(-torch.abs(torch.tensor(fair_spds[m])) / temperature, dim=0).reshape(-1, 1, 1), \n",
    "                        dim=0).argmax(dim=1)\n",
    "        if np.abs(spd(preds, a_val_t)) < np.abs(best_spd):\n",
    "            best_spd = spd(preds, a_val_t)\n",
    "            best_temp_spd = temperature\n",
    "        preds = torch.sum(val_probits[m] * torch.softmax(-torch.abs(torch.tensor(fair_eods[m])) / temperature, dim=0).reshape(-1, 1, 1),\n",
    "                        dim=0).argmax(dim=1)\n",
    "        if np.abs(eod(preds, y_val_t, a_val_t)) < np.abs(best_eod):\n",
    "            best_eod = eod(preds, y_val_t, a_val_t)\n",
    "            best_temp_eod = temperature\n",
    "        preds = torch.sum(val_probits[m] * torch.softmax(-torch.abs(torch.tensor(fair_aods[m])) / temperature, dim=0).reshape(-1, 1, 1),\n",
    "                        dim=0).argmax(dim=1)\n",
    "        if np.abs(aod(preds, y_val_t, a_val_t)) < np.abs(best_aod):\n",
    "            best_aod = aod(preds, y_val_t, a_val_t)\n",
    "            best_temp_aod = temperature\n",
    "    \n",
    "    optim_temps_spd.append(best_temp_spd)\n",
    "    optim_temps_eod.append(best_temp_eod)\n",
    "    optim_temps_aod.append(best_temp_aod)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "ff_test_fm_spd_accs, utk_test_fm_spd_accs = list(), list()\n",
    "ff_test_fm_spds, utk_test_fm_spds = list(), list()\n",
    "ff_test_fm_eod_accs, utk_test_fm_eod_accs = list(), list()\n",
    "ff_test_fm_eods, utk_test_fm_eods = list(), list()\n",
    "ff_test_fm_aod_accs, utk_test_fm_aod_accs = list(), list()\n",
    "ff_test_fm_aods, utk_test_fm_aods = list(), list()\n",
    "\n",
    "for m in range(len(method_seeds)):\n",
    "    preds = torch.sum(ff_test_probits[m] * torch.softmax(-torch.abs(torch.tensor(fair_spds[m])) / optim_temps_spd[m], dim=0).reshape(-1, 1, 1), \n",
    "                      dim=0).argmax(dim=1)\n",
    "    ff_test_fm_spd_accs.append(accuracy(preds, y_ff_test_t))\n",
    "    ff_test_fm_spds.append(spd(preds, a_ff_test_t))\n",
    "    preds = torch.sum(ff_test_probits[m] * torch.softmax(-torch.abs(torch.tensor(fair_eods[m])) / optim_temps_eod[m], dim=0).reshape(-1, 1, 1),\n",
    "                        dim=0).argmax(dim=1)\n",
    "    ff_test_fm_eod_accs.append(accuracy(preds, y_ff_test_t))\n",
    "    ff_test_fm_eods.append(eod(preds, y_ff_test_t, a_ff_test_t))\n",
    "    preds = torch.sum(ff_test_probits[m] * torch.softmax(-torch.abs(torch.tensor(fair_aods[m])) / optim_temps_aod[m], dim=0).reshape(-1, 1, 1),\n",
    "                        dim=0).argmax(dim=1)\n",
    "    ff_test_fm_aod_accs.append(accuracy(preds, y_ff_test_t))\n",
    "    ff_test_fm_aods.append(aod(preds, y_ff_test_t, a_ff_test_t))\n",
    "\n",
    "    preds = torch.sum(utk_test_probits[m] * torch.softmax(-torch.abs(torch.tensor(fair_spds[m])) / optim_temps_spd[m], dim=0).reshape(-1, 1, 1),\n",
    "                        dim=0).argmax(dim=1)\n",
    "    utk_test_fm_spd_accs.append(accuracy(preds, y_utk_test_t))\n",
    "    utk_test_fm_spds.append(spd(preds, a_utk_test_t))\n",
    "    preds = torch.sum(utk_test_probits[m] * torch.softmax(-torch.abs(torch.tensor(fair_eods[m])) / optim_temps_eod[m], dim=0).reshape(-1, 1, 1),\n",
    "                        dim=0).argmax(dim=1)\n",
    "    utk_test_fm_eod_accs.append(accuracy(preds, y_utk_test_t))\n",
    "    utk_test_fm_eods.append(eod(preds, y_utk_test_t, a_utk_test_t))\n",
    "    preds = torch.sum(utk_test_probits[m] * torch.softmax(-torch.abs(torch.tensor(fair_aods[m])) / optim_temps_aod[m], dim=0).reshape(-1, 1, 1),\n",
    "                        dim=0).argmax(dim=1)\n",
    "    utk_test_fm_aod_accs.append(accuracy(preds, y_utk_test_t))\n",
    "    utk_test_fm_aods.append(aod(preds, y_utk_test_t, a_utk_test_t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "FairFace Test Set | Target: age | Protected Attribute: gender\n",
      "################################################################################\n",
      "Individual Models:\n",
      "Accuracy: Mean = 79.47, Std = 0.45\n",
      "SPD: Mean = 0.1701, Std = 0.0079\n",
      "EOD: Mean = 0.1467, Std = 0.0103\n",
      "AOD: Mean = 0.1093, Std = 0.0076\n",
      "--------------------------------------------------------------------------------\n",
      "Individual Models (all):\n",
      "Accuracy: Mean = 79.43, Std = 0.30\n",
      "SPD: Mean = 0.1728, Std = 0.0070\n",
      "EOD: Mean = 0.1534, Std = 0.0121\n",
      "AOD: Mean = 0.1126, Std = 0.0076\n",
      "--------------------------------------------------------------------------------\n",
      "Fairest Model (on Fairness Dataset):\n",
      "Accuracy (SPD): Mean = 79.43, Std = 0.32\n",
      "SPD: Mean = 0.1711, Std = 0.0069\n",
      "Accuracy (EOD): Mean = 79.42, Std = 0.27\n",
      "EOD: Mean = 0.1493, Std = 0.0097\n",
      "Accuracy (AOD): Mean = 79.39, Std = 0.31\n",
      "AOD: Mean = 0.1102, Std = 0.0081\n",
      "--------------------------------------------------------------------------------\n",
      "Fairest Model (on Validation Dataset):\n",
      "Accuracy (SPD): Mean = 79.48, Std = 0.32\n",
      "SPD: Mean = 0.1705, Std = 0.0066\n",
      "Accuracy (EOD): Mean = 79.41, Std = 0.32\n",
      "EOD: Mean = 0.1523, Std = 0.0112\n",
      "Accuracy (AOD): Mean = 79.48, Std = 0.32\n",
      "AOD: Mean = 0.1099, Std = 0.0068\n",
      "--------------------------------------------------------------------------------\n",
      "Uniform Average:\n",
      "Accuracy: Mean = 81.64, Std = 0.15\n",
      "SPD: Mean = 0.1943, Std = 0.0041\n",
      "EOD: Mean = 0.1708, Std = 0.0040\n",
      "AOD: Mean = 0.1294, Std = 0.0042\n",
      "--------------------------------------------------------------------------------\n",
      "Fairness Weighted Average:\n",
      "Accuracy (SPD): Mean = 79.47, Std = 0.44\n",
      "SPD: Mean = 0.1701, Std = 0.0089\n",
      "Accuracy (EOD): Mean = 79.60, Std = 0.17\n",
      "EOD: Mean = 0.1495, Std = 0.0124\n",
      "Accuracy (AOD): Mean = 79.59, Std = 0.38\n",
      "AOD: Mean = 0.1075, Std = 0.0114\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print(\"#\" * 80)\n",
    "print(f\"FairFace Test Set | Target: {targets[target]} | Protected Attribute: {targets[pa]}\")\n",
    "print(\"#\" * 80)\n",
    "\n",
    "# Individual models\n",
    "# only first model per seed\n",
    "print(\"Individual Models:\")\n",
    "print(f\"Accuracy: Mean = {np.mean(np.asarray(ff_test_accs)[:, 0] * 100):.2f}, Std = {np.std(np.asarray(ff_test_accs)[:, 0] * 100):.2f}\")\n",
    "print(f\"SPD: Mean = {np.mean(np.asarray(ff_test_spds)[:, 0]):.4f}, Std = {np.std(np.asarray(ff_test_spds)[:, 0]):.4f}\")\n",
    "print(f\"EOD: Mean = {np.mean(np.asarray(ff_test_eods)[:, 0]):.4f}, Std = {np.std(np.asarray(ff_test_eods)[:, 0]):.4f}\")\n",
    "print(f\"AOD: Mean = {np.mean(np.asarray(ff_test_aods)[:, 0]):.4f}, Std = {np.std(np.asarray(ff_test_aods)[:, 0]):.4f}\")\n",
    "print(\"-\" * 80)\n",
    "\n",
    "# all models over all seeds\n",
    "print(\"Individual Models (all):\")\n",
    "print(f\"Accuracy: Mean = {np.mean(np.asarray(ff_test_accs) * 100):.2f}, Std = {np.std(np.asarray(ff_test_accs) * 100):.2f}\")\n",
    "print(f\"SPD: Mean = {np.mean(ff_test_spds):.4f}, Std = {np.std(ff_test_spds):.4f}\")\n",
    "print(f\"EOD: Mean = {np.mean(ff_test_eods):.4f}, Std = {np.std(ff_test_eods):.4f}\")\n",
    "print(f\"AOD: Mean = {np.mean(ff_test_aods):.4f}, Std = {np.std(ff_test_aods):.4f}\")\n",
    "print(\"-\" * 80)\n",
    "\n",
    "# Fairest model on Fairness dataset\n",
    "print(\"Fairest Model (on Fairness Dataset):\")\n",
    "fairest_acc_spd = np.asarray(ff_test_accs)[:, np.argmin(np.asarray(fair_spds), axis=1)]\n",
    "print(f\"Accuracy (SPD): Mean = {np.mean(fairest_acc_spd * 100):.2f}, Std = {np.std(fairest_acc_spd * 100):.2f}\")\n",
    "fairest_spds = np.asarray(ff_test_spds)[:, np.argmin(np.asarray(fair_spds), axis=1)]\n",
    "print(f\"SPD: Mean = {np.mean(fairest_spds):.4f}, Std = {np.std(fairest_spds):.4f}\")\n",
    "fairest_acc_eod = np.asarray(ff_test_accs)[:, np.argmin(np.asarray(fair_eods), axis=1)]\n",
    "print(f\"Accuracy (EOD): Mean = {np.mean(fairest_acc_eod * 100):.2f}, Std = {np.std(fairest_acc_eod * 100):.2f}\")\n",
    "fairest_eods = np.asarray(ff_test_eods)[:, np.argmin(np.asarray(fair_eods), axis=1)]\n",
    "print(f\"EOD: Mean = {np.mean(fairest_eods):.4f}, Std = {np.std(fairest_eods):.4f}\")\n",
    "fairest_acc_aod = np.asarray(ff_test_accs)[:, np.argmin(np.asarray(fair_aods), axis=1)]\n",
    "print(f\"Accuracy (AOD): Mean = {np.mean(fairest_acc_aod * 100):.2f}, Std = {np.std(fairest_acc_aod * 100):.2f}\")\n",
    "fairest_aods = np.asarray(ff_test_aods)[:, np.argmin(np.asarray(fair_aods), axis=1)]\n",
    "print(f\"AOD: Mean = {np.mean(fairest_aods):.4f}, Std = {np.std(fairest_aods):.4f}\")\n",
    "print(\"-\" * 80)\n",
    "\n",
    "# Fairest model on Validation dataset\n",
    "print(\"Fairest Model (on Validation Dataset):\")\n",
    "fairest_acc_spd = np.asarray(ff_test_accs)[:, np.argmin(np.asarray(val_spds), axis=1)]\n",
    "print(f\"Accuracy (SPD): Mean = {np.mean(fairest_acc_spd * 100):.2f}, Std = {np.std(fairest_acc_spd * 100):.2f}\")\n",
    "fairest_spds = np.asarray(ff_test_spds)[:, np.argmin(np.asarray(val_spds), axis=1)]\n",
    "print(f\"SPD: Mean = {np.mean(fairest_spds):.4f}, Std = {np.std(fairest_spds):.4f}\")\n",
    "fairest_acc_eod = np.asarray(ff_test_accs)[:, np.argmin(np.asarray(val_eods), axis=1)]\n",
    "print(f\"Accuracy (EOD): Mean = {np.mean(fairest_acc_eod * 100):.2f}, Std = {np.std(fairest_acc_eod * 100):.2f}\")\n",
    "fairest_eods = np.asarray(ff_test_eods)[:, np.argmin(np.asarray(val_eods), axis=1)]\n",
    "print(f\"EOD: Mean = {np.mean(fairest_eods):.4f}, Std = {np.std(fairest_eods):.4f}\")\n",
    "fairest_acc_aod = np.asarray(ff_test_accs)[:, np.argmin(np.asarray(val_aods), axis=1)]\n",
    "print(f\"Accuracy (AOD): Mean = {np.mean(fairest_acc_aod * 100):.2f}, Std = {np.std(fairest_acc_aod * 100):.2f}\")\n",
    "fairest_aods = np.asarray(ff_test_aods)[:, np.argmin(np.asarray(val_aods), axis=1)]\n",
    "print(f\"AOD: Mean = {np.mean(fairest_aods):.4f}, Std = {np.std(fairest_aods):.4f}\")\n",
    "print(\"-\" * 80)\n",
    "\n",
    "# BMA\n",
    "print(\"Uniform Average:\")\n",
    "print(f\"Accuracy: Mean = {np.mean(np.asarray(ff_test_m_accs) * 100):.2f}, Std = {np.std(np.asarray(ff_test_m_accs) * 100):.2f}\")\n",
    "print(f\"SPD: Mean = {np.mean(ff_test_m_spds):.4f}, Std = {np.std(ff_test_m_spds):.4f}\")\n",
    "print(f\"EOD: Mean = {np.mean(ff_test_m_eods):.4f}, Std = {np.std(ff_test_m_eods):.4f}\")\n",
    "print(f\"AOD: Mean = {np.mean(ff_test_m_aods):.4f}, Std = {np.std(ff_test_m_aods):.4f}\")\n",
    "print(\"-\" * 80)\n",
    "\n",
    "# Tempered average\n",
    "print(\"Fairness Weighted Average:\")\n",
    "print(f\"Accuracy (SPD): Mean = {np.mean(np.asarray(ff_test_fm_spd_accs) * 100):.2f}, Std = {np.std(np.asarray(ff_test_fm_spd_accs) * 100):.2f}\")\n",
    "print(f\"SPD: Mean = {np.mean(ff_test_fm_spds):.4f}, Std = {np.std(ff_test_fm_spds):.4f}\")\n",
    "print(f\"Accuracy (EOD): Mean = {np.mean(np.asarray(ff_test_fm_eod_accs) * 100):.2f}, Std = {np.std(np.asarray(ff_test_fm_eod_accs) * 100):.2f}\")\n",
    "print(f\"EOD: Mean = {np.mean(ff_test_fm_eods):.4f}, Std = {np.std(ff_test_fm_eods):.4f}\")\n",
    "print(f\"Accuracy (AOD): Mean = {np.mean(np.asarray(ff_test_fm_aod_accs) * 100):.2f}, Std = {np.std(np.asarray(ff_test_fm_aod_accs) * 100):.2f}\")\n",
    "print(f\"AOD: Mean = {np.mean(ff_test_fm_aods):.4f}, Std = {np.std(ff_test_fm_aods):.4f}\")\n",
    "print(\"-\" * 80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################################################################\n",
      "UTK Test Set | Target: age | Protected Attribute: gender\n",
      "################################################################################\n",
      "Individual Models:\n",
      "Accuracy: Mean = 78.07, Std = 0.28\n",
      "SPD: Mean = 0.2908, Std = 0.0076\n",
      "EOD: Mean = 0.2345, Std = 0.0067\n",
      "AOD: Mean = 0.1903, Std = 0.0073\n",
      "--------------------------------------------------------------------------------\n",
      "Individual Models (all):\n",
      "Accuracy: Mean = 78.15, Std = 0.38\n",
      "SPD: Mean = 0.2961, Std = 0.0080\n",
      "EOD: Mean = 0.2401, Std = 0.0117\n",
      "AOD: Mean = 0.1954, Std = 0.0085\n",
      "--------------------------------------------------------------------------------\n",
      "Fairest Model (on Fairness Dataset):\n",
      "Accuracy (SPD): Mean = 78.16, Std = 0.36\n",
      "SPD: Mean = 0.2967, Std = 0.0077\n",
      "Accuracy (EOD): Mean = 78.14, Std = 0.36\n",
      "EOD: Mean = 0.2416, Std = 0.0105\n",
      "Accuracy (AOD): Mean = 78.12, Std = 0.38\n",
      "AOD: Mean = 0.1975, Std = 0.0069\n",
      "--------------------------------------------------------------------------------\n",
      "Fairest Model (on Validation Dataset):\n",
      "Accuracy (SPD): Mean = 78.21, Std = 0.35\n",
      "SPD: Mean = 0.2957, Std = 0.0086\n",
      "Accuracy (EOD): Mean = 78.23, Std = 0.31\n",
      "EOD: Mean = 0.2379, Std = 0.0108\n",
      "Accuracy (AOD): Mean = 78.21, Std = 0.35\n",
      "AOD: Mean = 0.1950, Std = 0.0088\n",
      "--------------------------------------------------------------------------------\n",
      "Uniform Average:\n",
      "Accuracy: Mean = 79.64, Std = 0.13\n",
      "SPD: Mean = 0.3132, Std = 0.0029\n",
      "EOD: Mean = 0.2552, Std = 0.0044\n",
      "AOD: Mean = 0.2072, Std = 0.0032\n",
      "--------------------------------------------------------------------------------\n",
      "Fairness Weighted Average:\n",
      "Accuracy (SPD): Mean = 78.16, Std = 0.49\n",
      "SPD: Mean = 0.2910, Std = 0.0127\n",
      "Accuracy (EOD): Mean = 78.33, Std = 0.18\n",
      "EOD: Mean = 0.2353, Std = 0.0110\n",
      "Accuracy (AOD): Mean = 78.26, Std = 0.38\n",
      "AOD: Mean = 0.1934, Std = 0.0109\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print(\"#\" * 80)\n",
    "print(f\"UTK Test Set | Target: {targets[target]} | Protected Attribute: {targets[pa]}\")\n",
    "print(\"#\" * 80)\n",
    "\n",
    "# Individual models\n",
    "# only first model per seed\n",
    "print(\"Individual Models:\")\n",
    "print(f\"Accuracy: Mean = {np.mean(np.asarray(utk_test_accs)[:, 0] * 100):.2f}, Std = {np.std(np.asarray(utk_test_accs)[:, 0] * 100):.2f}\")\n",
    "print(f\"SPD: Mean = {np.mean(np.asarray(utk_test_spds)[:, 0]):.4f}, Std = {np.std(np.asarray(utk_test_spds)[:, 0]):.4f}\")\n",
    "print(f\"EOD: Mean = {np.mean(np.asarray(utk_test_eods)[:, 0]):.4f}, Std = {np.std(np.asarray(utk_test_eods)[:, 0]):.4f}\")\n",
    "print(f\"AOD: Mean = {np.mean(np.asarray(utk_test_aods)[:, 0]):.4f}, Std = {np.std(np.asarray(utk_test_aods)[:, 0]):.4f}\")\n",
    "print(\"-\" * 80)\n",
    "\n",
    "# all models over all seeds\n",
    "print(\"Individual Models (all):\")\n",
    "print(f\"Accuracy: Mean = {np.mean(np.asarray(utk_test_accs) * 100):.2f}, Std = {np.std(np.asarray(utk_test_accs) * 100):.2f}\")\n",
    "print(f\"SPD: Mean = {np.mean(utk_test_spds):.4f}, Std = {np.std(utk_test_spds):.4f}\")\n",
    "print(f\"EOD: Mean = {np.mean(utk_test_eods):.4f}, Std = {np.std(utk_test_eods):.4f}\")\n",
    "print(f\"AOD: Mean = {np.mean(utk_test_aods):.4f}, Std = {np.std(utk_test_aods):.4f}\")\n",
    "print(\"-\" * 80)\n",
    "\n",
    "# Fairest model on Fairness dataset\n",
    "print(\"Fairest Model (on Fairness Dataset):\")\n",
    "fairest_acc_spd = np.asarray(utk_test_accs)[:, np.argmin(np.asarray(fair_spds), axis=1)]\n",
    "print(f\"Accuracy (SPD): Mean = {np.mean(fairest_acc_spd * 100):.2f}, Std = {np.std(fairest_acc_spd * 100):.2f}\")\n",
    "fairest_spds = np.asarray(utk_test_spds)[:, np.argmin(np.asarray(fair_spds), axis=1)]\n",
    "print(f\"SPD: Mean = {np.mean(fairest_spds):.4f}, Std = {np.std(fairest_spds):.4f}\")\n",
    "fairest_acc_eod = np.asarray(utk_test_accs)[:, np.argmin(np.asarray(fair_eods), axis=1)]\n",
    "print(f\"Accuracy (EOD): Mean = {np.mean(fairest_acc_eod * 100):.2f}, Std = {np.std(fairest_acc_eod * 100):.2f}\")\n",
    "fairest_eods = np.asarray(utk_test_eods)[:, np.argmin(np.asarray(fair_eods), axis=1)]\n",
    "print(f\"EOD: Mean = {np.mean(fairest_eods):.4f}, Std = {np.std(fairest_eods):.4f}\")\n",
    "fairest_acc_aod = np.asarray(utk_test_accs)[:, np.argmin(np.asarray(fair_aods), axis=1)]\n",
    "print(f\"Accuracy (AOD): Mean = {np.mean(fairest_acc_aod * 100):.2f}, Std = {np.std(fairest_acc_aod * 100):.2f}\")\n",
    "fairest_aods = np.asarray(utk_test_aods)[:, np.argmin(np.asarray(fair_aods), axis=1)]\n",
    "print(f\"AOD: Mean = {np.mean(fairest_aods):.4f}, Std = {np.std(fairest_aods):.4f}\")\n",
    "print(\"-\" * 80)\n",
    "\n",
    "# Fairest model on Validation dataset\n",
    "print(\"Fairest Model (on Validation Dataset):\")\n",
    "fairest_acc_spd = np.asarray(utk_test_accs)[:, np.argmin(np.asarray(val_spds), axis=1)]\n",
    "print(f\"Accuracy (SPD): Mean = {np.mean(fairest_acc_spd * 100):.2f}, Std = {np.std(fairest_acc_spd * 100):.2f}\")\n",
    "fairest_spds = np.asarray(utk_test_spds)[:, np.argmin(np.asarray(val_spds), axis=1)]\n",
    "print(f\"SPD: Mean = {np.mean(fairest_spds):.4f}, Std = {np.std(fairest_spds):.4f}\")\n",
    "fairest_acc_eod = np.asarray(utk_test_accs)[:, np.argmin(np.asarray(val_eods), axis=1)]\n",
    "print(f\"Accuracy (EOD): Mean = {np.mean(fairest_acc_eod * 100):.2f}, Std = {np.std(fairest_acc_eod * 100):.2f}\")\n",
    "fairest_eods = np.asarray(utk_test_eods)[:, np.argmin(np.asarray(val_eods), axis=1)]\n",
    "print(f\"EOD: Mean = {np.mean(fairest_eods):.4f}, Std = {np.std(fairest_eods):.4f}\")\n",
    "fairest_acc_aod = np.asarray(utk_test_accs)[:, np.argmin(np.asarray(val_aods), axis=1)]\n",
    "print(f\"Accuracy (AOD): Mean = {np.mean(fairest_acc_aod * 100):.2f}, Std = {np.std(fairest_acc_aod * 100):.2f}\")\n",
    "fairest_aods = np.asarray(utk_test_aods)[:, np.argmin(np.asarray(val_aods), axis=1)]\n",
    "print(f\"AOD: Mean = {np.mean(fairest_aods):.4f}, Std = {np.std(fairest_aods):.4f}\")\n",
    "print(\"-\" * 80)\n",
    "\n",
    "# BMA\n",
    "print(\"Uniform Average:\")\n",
    "print(f\"Accuracy: Mean = {np.mean(np.asarray(utk_test_m_accs) * 100):.2f}, Std = {np.std(np.asarray(utk_test_m_accs) * 100):.2f}\")\n",
    "print(f\"SPD: Mean = {np.mean(utk_test_m_spds):.4f}, Std = {np.std(utk_test_m_spds):.4f}\")\n",
    "print(f\"EOD: Mean = {np.mean(utk_test_m_eods):.4f}, Std = {np.std(utk_test_m_eods):.4f}\")\n",
    "print(f\"AOD: Mean = {np.mean(utk_test_m_aods):.4f}, Std = {np.std(utk_test_m_aods):.4f}\")\n",
    "print(\"-\" * 80)\n",
    "\n",
    "# Tempered average\n",
    "print(\"Fairness Weighted Average:\")\n",
    "print(f\"Accuracy (SPD): Mean = {np.mean(np.asarray(utk_test_fm_spd_accs) * 100):.2f}, Std = {np.std(np.asarray(utk_test_fm_spd_accs) * 100):.2f}\")\n",
    "print(f\"SPD: Mean = {np.mean(utk_test_fm_spds):.4f}, Std = {np.std(utk_test_fm_spds):.4f}\")\n",
    "print(f\"Accuracy (EOD): Mean = {np.mean(np.asarray(utk_test_fm_eod_accs) * 100):.2f}, Std = {np.std(np.asarray(utk_test_fm_eod_accs) * 100):.2f}\")\n",
    "print(f\"EOD: Mean = {np.mean(utk_test_fm_eods):.4f}, Std = {np.std(utk_test_fm_eods):.4f}\")\n",
    "print(f\"Accuracy (AOD): Mean = {np.mean(np.asarray(utk_test_fm_aod_accs) * 100):.2f}, Std = {np.std(np.asarray(utk_test_fm_aod_accs) * 100):.2f}\")\n",
    "print(f\"AOD: Mean = {np.mean(utk_test_fm_aods):.4f}, Std = {np.std(utk_test_fm_aods):.4f}\")\n",
    "print(\"-\" * 80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "disparate_benefits",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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