meta-new-users.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Library imports**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Library imports\n",
    "import pyforest\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from matplotlib import pyplot as plt\n",
    "from tqdm import tqdm\n",
    "from pprint import pprint\n",
    "from time import sleep\n",
    "import time\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "from turtle import forward\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import math\n",
    "import torch\n",
    "from torch.nn.parameter import Parameter\n",
    "from torch.nn.modules.module import Module\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "\n",
    "import models \n",
    "from models import get_accuracy\n",
    "import inspect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Load dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(953, 1682, 176439, 40381)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = 'movielens'\n",
    "n_users = 943\n",
    "n_items = 1682\n",
    "n_new_users = 10\n",
    "\n",
    "# load original training edges\n",
    "train_edges = np.load('data/' + dataset + '/train_edges.npy')\n",
    "test_edges = np.load('data/' + dataset + '/test_edges.npy')\n",
    "\n",
    "# create list of new edges\n",
    "new_users_edges = []\n",
    "for u in range(n_users, n_users + n_new_users):\n",
    "    for i in range(n_items):\n",
    "        new_users_edges.append([u, i, 0])\n",
    "\n",
    "# shuffle the new user edges so that it's not in any particular order\n",
    "new_users_edges = np.array(new_users_edges)\n",
    "np.random.seed(0)\n",
    "np.random.shuffle(new_users_edges)\n",
    "\n",
    "# concatenate with original training edges\n",
    "train_edges = np.concatenate((train_edges, new_users_edges))\n",
    "np.random.seed(0)\n",
    "np.random.shuffle(train_edges)\n",
    "\n",
    "# proceed with creating users, items and ratings as usual\n",
    "user_list_train = train_edges[:, 0]\n",
    "user_list_test = test_edges[:, 0]\n",
    "item_list_train = train_edges[:, 1]\n",
    "item_list_test = test_edges[:, 1]\n",
    "rating_list_train = train_edges[:, 2].astype('float32')\n",
    "rating_list_test = test_edges[:, 2].astype('float32')\n",
    "\n",
    "# compute new n_users, n_items and n_samples\n",
    "n_users = max(user_list_train.max(), user_list_test.max()) + 1 \n",
    "n_items = max(item_list_train.max(), item_list_test.max()) + 1\n",
    "n_samples_train = len(rating_list_train)\n",
    "n_samples_test = len(rating_list_test)\n",
    "\n",
    "n_users, n_items, n_samples_train, n_samples_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Code for surrogate attack using new users**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> Algorithm:  meta-new(10)\n",
      "\n",
      "-> T (surrogate):  20\n",
      "-> T (target):  20\n",
      "-> Delta: 10000 (5.67%)\n",
      "-> Embedding size (surrogate):  8\n",
      "-> Embedding size (target):  8\n",
      "-> Device:  cuda:2\n",
      "\n",
      "-> Surrogate:  CF\n",
      "-> Target:  CF\n",
      "-> Surrogate optimizer:  adam\n",
      "-> Target optimizer:  adam\n",
      "-> Surrogate learning rate:  0.3\n",
      "-> Target learning rate:  0.3\n",
      "-> Surrogate seed:  0\n",
      "-> Target seed:  7\n",
      "\n",
      "-> Retain graph:  True\n",
      "-> Create graph:  False\n",
      "-> Save results:  True\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "-> Perturbations: 100%|██████████| 10000/10000 [1:22:10<00:00,  2.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> Execution time: 01h 22m 11s\n",
      "-> File name: CF(8,adam)-CF(8,adam)-meta-new(10)-D=10000-T(20,20)-lr(0.3,0.3)-seed(0,7)-drop(0,0)\n"
     ]
    }
   ],
   "source": [
    "# model settings\n",
    "algorithm = 'meta-new({})'.format(n_new_users)\n",
    "surrogate = 'CF'\n",
    "target = 'CF'\n",
    "opt_surrogate = 'adam'\n",
    "opt_target = 'adam'\n",
    "lr_surrogate = 0.3\n",
    "lr_target = 0.3\n",
    "seed_surrogate = 0\n",
    "seed_target = 7\n",
    "dropout_surrogate = 0\n",
    "dropout_target = 0\n",
    "\n",
    "# start execution\n",
    "start_time = time.time()\n",
    "\n",
    "# GPU settings (set use_gpu = -1 if you want to use CPU)\n",
    "use_gpu = 2\n",
    "if use_gpu == -1:\n",
    "    device = 'cpu'\n",
    "else:\n",
    "    device = torch.device('cuda:{}'.format(str(use_gpu)) if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "# some hyperparameters\n",
    "T_surrogate = 20\n",
    "T_target = 20\n",
    "Delta = 10000 # 5% ~ 10K perturbations for movielens\n",
    "n_factors_surrogate = 8\n",
    "n_factors_target = 8\n",
    "save_results = True\n",
    "retain_graph = True \n",
    "create_graph = False\n",
    "\n",
    "if Delta < 100:\n",
    "    save_results = False\n",
    "\n",
    "# initialize list of perturbations\n",
    "perturbations = dict()\n",
    "perturbations['edges'] = []\n",
    "perturbations['metagrad'] = []\n",
    "\n",
    "perturbations['accuracy-before-surrogate'] = []\n",
    "perturbations['accuracy-after-surrogate'] = []\n",
    "perturbations['accuracy-unseen-surrogate'] = []\n",
    "\n",
    "perturbations['loss-before-surrogate'] = []\n",
    "perturbations['loss-after-surrogate'] = []\n",
    "perturbations['loss-unseen-surrogate'] = []\n",
    "\n",
    "perturbations['accuracy-before-target'] = []\n",
    "perturbations['accuracy-after-target'] = []\n",
    "perturbations['accuracy-unseen-target'] = []\n",
    "\n",
    "perturbations['loss-before-target'] = []\n",
    "perturbations['loss-after-target'] = []\n",
    "perturbations['loss-unseen-target'] = []\n",
    "\n",
    "perturbations['auc-before-surrogate'] = []\n",
    "perturbations['auc-after-surrogate'] = []\n",
    "perturbations['auc-unseen-surrogate'] = []\n",
    "\n",
    "perturbations['auc-before-target'] = []\n",
    "perturbations['auc-after-target'] = []\n",
    "perturbations['auc-unseen-target'] = []\n",
    "\n",
    "# print hyperparam configuration\n",
    "print('-> Algorithm: ', algorithm)\n",
    "print()\n",
    "print('-> T (surrogate): ', T_surrogate)\n",
    "print('-> T (target): ', T_target)\n",
    "print('-> Delta: {} ({}%)'.format(Delta, round(Delta * 100 / n_samples_train, 2)))\n",
    "print('-> Embedding size (surrogate): ', n_factors_surrogate)\n",
    "print('-> Embedding size (target): ', n_factors_target)\n",
    "print('-> Device: ', device)\n",
    "print()\n",
    "print('-> Surrogate: ', surrogate)\n",
    "print('-> Target: ', target)\n",
    "print('-> Surrogate optimizer: ', opt_surrogate)\n",
    "print('-> Target optimizer: ', opt_target)\n",
    "print('-> Surrogate learning rate: ', lr_surrogate)\n",
    "print('-> Target learning rate: ', lr_target)\n",
    "print('-> Surrogate seed: ', seed_surrogate)\n",
    "print('-> Target seed: ', seed_target)\n",
    "print()\n",
    "print('-> Retain graph: ', retain_graph)\n",
    "print('-> Create graph: ', create_graph)\n",
    "print('-> Save results: ', save_results)\n",
    "\n",
    "# load users, items and ratings as tensors\n",
    "users = torch.tensor(user_list_train, device = device)\n",
    "items = torch.tensor(item_list_train, device = device)\n",
    "ratings = torch.tensor(rating_list_train, device = device, requires_grad = True)\n",
    "\n",
    "# only those edges are allowed to be perturbed whose mask entry is False\n",
    "mask = torch.ones_like(ratings).bool()\n",
    "mask[users >= 943] = False\n",
    "\n",
    "# perturbs keeps track of perturbed edges by marking them as False\n",
    "perturbs = torch.ones_like(ratings).bool()\n",
    "\n",
    "# generate tensors out of validation data\n",
    "users_test = torch.tensor(user_list_test, device = device)\n",
    "items_test = torch.tensor(item_list_test, device = device)\n",
    "ratings_test = torch.tensor(rating_list_test, device = device)\n",
    "\n",
    "# sample random negative edges to perturb for baseline\n",
    "if 'base' in algorithm:\n",
    "    edges = mask.int().detach().to('cpu').numpy()\n",
    "    neg_edges = np.where(edges == 0)[0]\n",
    "    np.random.seed(0)\n",
    "    edges_to_perturb = np.random.choice(neg_edges, size=Delta, replace = False) # sample Delta edges randomly and perturb one by one inside loop \n",
    "\n",
    "# for each perturbation do the following\n",
    "for delta in tqdm(range(Delta), desc='-> Perturbations'):\n",
    "\n",
    "    # define surrogate model and it's parameters\n",
    "    torch.manual_seed(seed_surrogate)\n",
    "    model = getattr(models, surrogate)(n_users, n_items, n_factors_surrogate, dropout_surrogate).to(device)\n",
    "\n",
    "    # define optimizer and loss function\n",
    "    if 'adam' in opt_surrogate:\n",
    "        optimizer = torch.optim.Adam(model.parameters(), lr = lr_surrogate)\n",
    "    else:\n",
    "        optimizer = torch.optim.SGD(model.parameters(), lr = lr_surrogate)\n",
    "    loss_fn = nn.BCELoss(reduction = 'mean')\n",
    "\n",
    "    # inner loop training process\n",
    "    model.train()\n",
    "    for i in range(T_surrogate):\n",
    "        y_hat = model(users, items).reshape(ratings.shape)\n",
    "        loss = loss_fn(y_hat, ratings)\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward(retain_graph=retain_graph, create_graph=create_graph)\n",
    "        optimizer.step()\n",
    "    model.eval()\n",
    "\n",
    "    # compute and store accuracy of model after T training steps\n",
    "    with torch.no_grad():\n",
    "        # training accuracy and loss including perturbed edges\n",
    "        y_hat = model(users, items).reshape(ratings.shape)\n",
    "        perturbations['accuracy-before-surrogate'].append(get_accuracy(y_hat, ratings))\n",
    "        perturbations['loss-before-surrogate'].append(loss_fn(y_hat, ratings).item())\n",
    "\n",
    "        # compute training AUROC including perturbed edges\n",
    "        y_pred = y_hat.detach().clone().to('cpu').numpy()\n",
    "        y_actual = ratings.detach().clone().to('cpu').numpy() \n",
    "        perturbations['auc-before-surrogate'].append(roc_auc_score(y_actual, y_pred))\n",
    "\n",
    "        # training accuracy and loss excluding perturbed edges\n",
    "        y_hat_masked = torch.masked_select(y_hat, perturbs)\n",
    "        ratings_masked = torch.masked_select(ratings, perturbs)\n",
    "        perturbations['accuracy-after-surrogate'].append(get_accuracy(y_hat_masked, ratings_masked))\n",
    "        perturbations['loss-after-surrogate'].append(loss_fn(y_hat_masked, ratings_masked).item())\n",
    "\n",
    "        # compute training AUROC excluding perturbed edges\n",
    "        y_pred = y_hat_masked.detach().clone().to('cpu').numpy()\n",
    "        y_actual = ratings_masked.detach().clone().to('cpu').numpy()\n",
    "        perturbations['auc-after-surrogate'].append(roc_auc_score(y_actual, y_pred))\n",
    "\n",
    "    # compute and store accuracy of surrogate model on unseen data\n",
    "    with torch.no_grad():\n",
    "        y_hat = model(users_test, items_test).reshape(ratings_test.shape)\n",
    "        perturbations['accuracy-unseen-surrogate'].append(get_accuracy(y_hat, ratings_test))\n",
    "        perturbations['loss-unseen-surrogate'].append(loss_fn(y_hat, ratings_test).item())\n",
    "\n",
    "        # compute unseen AUROC\n",
    "        y_pred = y_hat.detach().clone().to('cpu').numpy()\n",
    "        y_actual = ratings_test.detach().clone().to('cpu').numpy()\n",
    "        perturbations['auc-unseen-surrogate'].append(roc_auc_score(y_actual, y_pred))\n",
    "\n",
    "    # compute meta gradient\n",
    "    if 'meta' in algorithm:\n",
    "        meta_grad = torch.autograd.grad(loss, ratings)[0]\n",
    "\n",
    "    # define evaluation model\n",
    "    torch.manual_seed(seed_target)\n",
    "    eval_model = getattr(models, target)(n_users, n_items, n_factors_target, dropout_target).to(device)\n",
    "\n",
    "    # define optimizer and loss function for evaluation\n",
    "    if 'adam' in opt_target:\n",
    "        optimizer_eval = torch.optim.Adam(eval_model.parameters(), lr = lr_target)\n",
    "    else:\n",
    "        optimizer_eval = torch.optim.SGD(eval_model.parameters(), lr = lr_target)\n",
    "    loss_fn_eval = nn.BCELoss(reduction = 'mean')\n",
    "\n",
    "    # detach ratings and perturbs for eval model\n",
    "    ratings_eval = ratings.detach().clone()\n",
    "    perturbs_eval = perturbs.detach().clone()\n",
    "\n",
    "    # inner train  evaluation model\n",
    "    eval_model.train()\n",
    "    for i in range(T_target):\n",
    "        y_hat = eval_model(users, items).reshape(ratings_eval.shape)\n",
    "        loss_eval = loss_fn_eval(y_hat, ratings_eval)\n",
    "        optimizer_eval.zero_grad()\n",
    "        loss_eval.backward(retain_graph=retain_graph, create_graph=create_graph)\n",
    "        optimizer_eval.step()\n",
    "    eval_model.eval()\n",
    "\n",
    "    # compute and store accuracy of eval model after T training steps\n",
    "    with torch.no_grad():\n",
    "        # training accuracy and loss including perturbed edges\n",
    "        y_hat = eval_model(users, items).reshape(ratings_eval.shape)\n",
    "        perturbations['accuracy-before-target'].append(get_accuracy(y_hat, ratings_eval))\n",
    "        perturbations['loss-before-target'].append(loss_fn_eval(y_hat, ratings_eval).item())\n",
    "\n",
    "        # compute training AUROC including perturbed edges\n",
    "        y_pred = y_hat.detach().clone().to('cpu').numpy()\n",
    "        y_actual = ratings_eval.detach().clone().to('cpu').numpy() \n",
    "        perturbations['auc-before-target'].append(roc_auc_score(y_actual, y_pred))\n",
    "\n",
    "        # training accuracy and loss excluding perturbed edges\n",
    "        y_hat_masked = torch.masked_select(y_hat, perturbs_eval)\n",
    "        ratings_masked = torch.masked_select(ratings_eval, perturbs_eval)\n",
    "        perturbations['accuracy-after-target'].append(get_accuracy(y_hat_masked, ratings_masked))\n",
    "        perturbations['loss-after-target'].append(loss_fn_eval(y_hat_masked, ratings_masked).item())\n",
    "\n",
    "        # compute training AUROC excluding perturbed edges\n",
    "        y_pred = y_hat_masked.detach().clone().to('cpu').numpy()\n",
    "        y_actual = ratings_masked.detach().clone().to('cpu').numpy()\n",
    "        perturbations['auc-after-target'].append(roc_auc_score(y_actual, y_pred))\n",
    "\n",
    "    # compute and store accuracy of target model on unseen data\n",
    "    with torch.no_grad():\n",
    "        y_hat = eval_model(users_test, items_test).reshape(ratings_test.shape)\n",
    "        perturbations['accuracy-unseen-target'].append(get_accuracy(y_hat, ratings_test))\n",
    "        perturbations['loss-unseen-target'].append(loss_fn(y_hat, ratings_test).item())\n",
    "\n",
    "        # compute unseen AUROC\n",
    "        y_pred = y_hat.detach().clone().to('cpu').numpy()\n",
    "        y_actual = ratings_test.detach().clone().to('cpu').numpy()\n",
    "        perturbations['auc-unseen-target'].append(roc_auc_score(y_actual, y_pred))\n",
    "\n",
    "    # select best edge and perform perturbation\n",
    "    with torch.no_grad():\n",
    "        if 'meta' in algorithm:\n",
    "            meta_grad[mask == True] = 0\n",
    "            best_edge = meta_grad.argmax().item()\n",
    "            \n",
    "            if mask[best_edge] == False:\n",
    "                ratings[best_edge] = 1\n",
    "                perturbs[best_edge] = False\n",
    "                mask[best_edge] = True\n",
    "            else:\n",
    "                print('Perturbation #{}: No more edges left to perturb'.format(delta))\n",
    "\n",
    "            perturbations['edges'].append(best_edge)\n",
    "            perturbations['metagrad'].append(meta_grad[best_edge].item())\n",
    "\n",
    "        else:\n",
    "            best_edge = edges_to_perturb[delta]\n",
    "            ratings[best_edge] = 1 \n",
    "            perturbs[best_edge] = False\n",
    "            mask[best_edge] = True\n",
    "\n",
    "            perturbations['edges'].append(best_edge)\n",
    "            perturbations['metagrad'].append(-1)\n",
    "\n",
    "sleep(1)\n",
    "# compute execution time\n",
    "exec_time = int(time.time() - start_time)\n",
    "exec_time = time.strftime(\"%Hh %Mm %Ss\", time.gmtime(exec_time))\n",
    "print('-> Execution time: {}'.format(exec_time))\n",
    "\n",
    "# process results\n",
    "perturbations = pd.DataFrame(perturbations)\n",
    "filename = '{}({},{})-{}({},{})-{}-D={}-T({},{})-lr({},{})-seed({},{})-drop({},{})'.format(surrogate, n_factors_surrogate, opt_surrogate, target, n_factors_target, opt_target, algorithm, Delta, T_surrogate, T_target, lr_surrogate, lr_target, seed_surrogate, seed_target, dropout_surrogate, dropout_target)\n",
    "print('-> File name: {}'.format(filename))\n",
    "if save_results:\n",
    "    perturbations.to_csv('results/' + dataset + '/' + filename + '.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File name:  CF(8,adam)-CF(8,adam)-meta-new(10)-D=10000-T(20,20)-lr(0.3,0.3)-seed(0,7)-drop(0,0)\n",
      "Max meta-gradient:  0.0001862233184510842\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>edges</th>\n",
       "      <th>metagrad</th>\n",
       "      <th>accuracy-before-surrogate</th>\n",
       "      <th>accuracy-after-surrogate</th>\n",
       "      <th>accuracy-unseen-surrogate</th>\n",
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       "      <th>auc-unseen-surrogate</th>\n",
       "      <th>auc-before-target</th>\n",
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       "      <th>auc-unseen-target</th>\n",
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       "      <td>0.310088</td>\n",
       "      <td>0.310028</td>\n",
       "      <td>0.523068</td>\n",
       "      <td>0.867456</td>\n",
       "      <td>0.867460</td>\n",
       "      <td>0.790694</td>\n",
       "      <td>0.302897</td>\n",
       "      <td>0.302875</td>\n",
       "      <td>0.527346</td>\n",
       "      <td>0.939803</td>\n",
       "      <td>0.939813</td>\n",
       "      <td>0.867409</td>\n",
       "      <td>0.942858</td>\n",
       "      <td>0.942864</td>\n",
       "      <td>0.870440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>167880</td>\n",
       "      <td>1.534826e-04</td>\n",
       "      <td>0.864083</td>\n",
       "      <td>0.864093</td>\n",
       "      <td>0.787895</td>\n",
       "      <td>0.310127</td>\n",
       "      <td>0.310022</td>\n",
       "      <td>0.522757</td>\n",
       "      <td>0.867484</td>\n",
       "      <td>0.867494</td>\n",
       "      <td>0.790619</td>\n",
       "      <td>0.302935</td>\n",
       "      <td>0.302884</td>\n",
       "      <td>0.527216</td>\n",
       "      <td>0.939799</td>\n",
       "      <td>0.939817</td>\n",
       "      <td>0.867444</td>\n",
       "      <td>0.942848</td>\n",
       "      <td>0.942860</td>\n",
       "      <td>0.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>116647</td>\n",
       "      <td>1.521438e-04</td>\n",
       "      <td>0.864123</td>\n",
       "      <td>0.864138</td>\n",
       "      <td>0.788044</td>\n",
       "      <td>0.310167</td>\n",
       "      <td>0.310047</td>\n",
       "      <td>0.522771</td>\n",
       "      <td>0.867422</td>\n",
       "      <td>0.867436</td>\n",
       "      <td>0.790545</td>\n",
       "      <td>0.302975</td>\n",
       "      <td>0.302897</td>\n",
       "      <td>0.527242</td>\n",
       "      <td>0.939787</td>\n",
       "      <td>0.939811</td>\n",
       "      <td>0.867451</td>\n",
       "      <td>0.942837</td>\n",
       "      <td>0.942855</td>\n",
       "      <td>0.870442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>81614</td>\n",
       "      <td>1.470985e-04</td>\n",
       "      <td>0.864095</td>\n",
       "      <td>0.864114</td>\n",
       "      <td>0.787871</td>\n",
       "      <td>0.310199</td>\n",
       "      <td>0.310069</td>\n",
       "      <td>0.522762</td>\n",
       "      <td>0.867467</td>\n",
       "      <td>0.867487</td>\n",
       "      <td>0.790644</td>\n",
       "      <td>0.303012</td>\n",
       "      <td>0.302911</td>\n",
       "      <td>0.527261</td>\n",
       "      <td>0.939778</td>\n",
       "      <td>0.939805</td>\n",
       "      <td>0.867454</td>\n",
       "      <td>0.942826</td>\n",
       "      <td>0.942850</td>\n",
       "      <td>0.870425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>119379</td>\n",
       "      <td>3.545470e-07</td>\n",
       "      <td>0.812133</td>\n",
       "      <td>0.824379</td>\n",
       "      <td>0.794582</td>\n",
       "      <td>0.383431</td>\n",
       "      <td>0.367335</td>\n",
       "      <td>0.510937</td>\n",
       "      <td>0.834186</td>\n",
       "      <td>0.837795</td>\n",
       "      <td>0.792155</td>\n",
       "      <td>0.364845</td>\n",
       "      <td>0.357857</td>\n",
       "      <td>0.515720</td>\n",
       "      <td>0.905548</td>\n",
       "      <td>0.917108</td>\n",
       "      <td>0.870123</td>\n",
       "      <td>0.916522</td>\n",
       "      <td>0.921439</td>\n",
       "      <td>0.870123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>8377</td>\n",
       "      <td>3.506421e-07</td>\n",
       "      <td>0.812088</td>\n",
       "      <td>0.824408</td>\n",
       "      <td>0.794606</td>\n",
       "      <td>0.383462</td>\n",
       "      <td>0.367334</td>\n",
       "      <td>0.510912</td>\n",
       "      <td>0.834226</td>\n",
       "      <td>0.837818</td>\n",
       "      <td>0.791882</td>\n",
       "      <td>0.364835</td>\n",
       "      <td>0.357855</td>\n",
       "      <td>0.515764</td>\n",
       "      <td>0.905524</td>\n",
       "      <td>0.917109</td>\n",
       "      <td>0.870132</td>\n",
       "      <td>0.916532</td>\n",
       "      <td>0.921443</td>\n",
       "      <td>0.870120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>144286</td>\n",
       "      <td>3.371793e-07</td>\n",
       "      <td>0.812088</td>\n",
       "      <td>0.824437</td>\n",
       "      <td>0.794458</td>\n",
       "      <td>0.383462</td>\n",
       "      <td>0.367352</td>\n",
       "      <td>0.510978</td>\n",
       "      <td>0.834096</td>\n",
       "      <td>0.837721</td>\n",
       "      <td>0.791932</td>\n",
       "      <td>0.364868</td>\n",
       "      <td>0.357871</td>\n",
       "      <td>0.515779</td>\n",
       "      <td>0.905528</td>\n",
       "      <td>0.917097</td>\n",
       "      <td>0.870107</td>\n",
       "      <td>0.916515</td>\n",
       "      <td>0.921436</td>\n",
       "      <td>0.870113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>111452</td>\n",
       "      <td>3.094013e-07</td>\n",
       "      <td>0.812037</td>\n",
       "      <td>0.824605</td>\n",
       "      <td>0.794879</td>\n",
       "      <td>0.383452</td>\n",
       "      <td>0.367355</td>\n",
       "      <td>0.510954</td>\n",
       "      <td>0.834062</td>\n",
       "      <td>0.837696</td>\n",
       "      <td>0.792006</td>\n",
       "      <td>0.364891</td>\n",
       "      <td>0.357877</td>\n",
       "      <td>0.515755</td>\n",
       "      <td>0.905533</td>\n",
       "      <td>0.917095</td>\n",
       "      <td>0.870104</td>\n",
       "      <td>0.916504</td>\n",
       "      <td>0.921433</td>\n",
       "      <td>0.870119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>120430</td>\n",
       "      <td>3.284003e-07</td>\n",
       "      <td>0.812054</td>\n",
       "      <td>0.824609</td>\n",
       "      <td>0.794928</td>\n",
       "      <td>0.383461</td>\n",
       "      <td>0.367405</td>\n",
       "      <td>0.510975</td>\n",
       "      <td>0.834084</td>\n",
       "      <td>0.837707</td>\n",
       "      <td>0.792130</td>\n",
       "      <td>0.364893</td>\n",
       "      <td>0.357879</td>\n",
       "      <td>0.515745</td>\n",
       "      <td>0.905528</td>\n",
       "      <td>0.917070</td>\n",
       "      <td>0.870089</td>\n",
       "      <td>0.916504</td>\n",
       "      <td>0.921433</td>\n",
       "      <td>0.870122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       edges      metagrad  accuracy-before-surrogate  \\\n",
       "0      42421  1.862233e-04                   0.864191   \n",
       "1      64940  1.584444e-04                   0.864174   \n",
       "2     167880  1.534826e-04                   0.864083   \n",
       "3     116647  1.521438e-04                   0.864123   \n",
       "4      81614  1.470985e-04                   0.864095   \n",
       "...      ...           ...                        ...   \n",
       "9995  119379  3.545470e-07                   0.812133   \n",
       "9996    8377  3.506421e-07                   0.812088   \n",
       "9997  144286  3.371793e-07                   0.812088   \n",
       "9998  111452  3.094013e-07                   0.812037   \n",
       "9999  120430  3.284003e-07                   0.812054   \n",
       "\n",
       "      accuracy-after-surrogate  accuracy-unseen-surrogate  \\\n",
       "0                     0.864191                   0.787648   \n",
       "1                     0.864179                   0.787722   \n",
       "2                     0.864093                   0.787895   \n",
       "3                     0.864138                   0.788044   \n",
       "4                     0.864114                   0.787871   \n",
       "...                        ...                        ...   \n",
       "9995                  0.824379                   0.794582   \n",
       "9996                  0.824408                   0.794606   \n",
       "9997                  0.824437                   0.794458   \n",
       "9998                  0.824605                   0.794879   \n",
       "9999                  0.824609                   0.794928   \n",
       "\n",
       "      loss-before-surrogate  loss-after-surrogate  loss-unseen-surrogate  \\\n",
       "0                  0.310002              0.310002               0.523046   \n",
       "1                  0.310088              0.310028               0.523068   \n",
       "2                  0.310127              0.310022               0.522757   \n",
       "3                  0.310167              0.310047               0.522771   \n",
       "4                  0.310199              0.310069               0.522762   \n",
       "...                     ...                   ...                    ...   \n",
       "9995               0.383431              0.367335               0.510937   \n",
       "9996               0.383462              0.367334               0.510912   \n",
       "9997               0.383462              0.367352               0.510978   \n",
       "9998               0.383452              0.367355               0.510954   \n",
       "9999               0.383461              0.367405               0.510975   \n",
       "\n",
       "      accuracy-before-target  accuracy-after-target  accuracy-unseen-target  \\\n",
       "0                   0.867439               0.867439                0.790669   \n",
       "1                   0.867456               0.867460                0.790694   \n",
       "2                   0.867484               0.867494                0.790619   \n",
       "3                   0.867422               0.867436                0.790545   \n",
       "4                   0.867467               0.867487                0.790644   \n",
       "...                      ...                    ...                     ...   \n",
       "9995                0.834186               0.837795                0.792155   \n",
       "9996                0.834226               0.837818                0.791882   \n",
       "9997                0.834096               0.837721                0.791932   \n",
       "9998                0.834062               0.837696                0.792006   \n",
       "9999                0.834084               0.837707                0.792130   \n",
       "\n",
       "      loss-before-target  loss-after-target  loss-unseen-target  \\\n",
       "0               0.302870           0.302870            0.527337   \n",
       "1               0.302897           0.302875            0.527346   \n",
       "2               0.302935           0.302884            0.527216   \n",
       "3               0.302975           0.302897            0.527242   \n",
       "4               0.303012           0.302911            0.527261   \n",
       "...                  ...                ...                 ...   \n",
       "9995            0.364845           0.357857            0.515720   \n",
       "9996            0.364835           0.357855            0.515764   \n",
       "9997            0.364868           0.357871            0.515779   \n",
       "9998            0.364891           0.357877            0.515755   \n",
       "9999            0.364893           0.357879            0.515745   \n",
       "\n",
       "      auc-before-surrogate  auc-after-surrogate  auc-unseen-surrogate  \\\n",
       "0                 0.939820             0.939820              0.867422   \n",
       "1                 0.939803             0.939813              0.867409   \n",
       "2                 0.939799             0.939817              0.867444   \n",
       "3                 0.939787             0.939811              0.867451   \n",
       "4                 0.939778             0.939805              0.867454   \n",
       "...                    ...                  ...                   ...   \n",
       "9995              0.905548             0.917108              0.870123   \n",
       "9996              0.905524             0.917109              0.870132   \n",
       "9997              0.905528             0.917097              0.870107   \n",
       "9998              0.905533             0.917095              0.870104   \n",
       "9999              0.905528             0.917070              0.870089   \n",
       "\n",
       "      auc-before-target  auc-after-target  auc-unseen-target  \n",
       "0              0.942863          0.942863           0.870444  \n",
       "1              0.942858          0.942864           0.870440  \n",
       "2              0.942848          0.942860           0.870455  \n",
       "3              0.942837          0.942855           0.870442  \n",
       "4              0.942826          0.942850           0.870425  \n",
       "...                 ...               ...                ...  \n",
       "9995           0.916522          0.921439           0.870123  \n",
       "9996           0.916532          0.921443           0.870120  \n",
       "9997           0.916515          0.921436           0.870113  \n",
       "9998           0.916504          0.921433           0.870119  \n",
       "9999           0.916504          0.921433           0.870122  \n",
       "\n",
       "[10000 rows x 20 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('File name: ', filename)\n",
    "if 'meta' in algorithm:\n",
    "    print('Max meta-gradient: ', perturbations['metagrad'].max())\n",
    "perturbations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File name:  CF(8,adam)-CF(8,adam)-meta-new(10)-D=10000-T(20,20)-lr(0.3,0.3)-seed(0,7)-drop(0,0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Lk2mhuXDhQtSrVw9//fUX9PX1sWfPHpmS7c927dqF8PBwHDlyBGZmZnj+/Dm6d++u8FEBwLTKff/+vUrV7NlZWlpi+fLlbOktO0XP8/766y9wOBycPn0axsbGuHjxIlu127VrV4X5rYyVlRUqV66MoKAghdvt7e0RHh6OCxcuwMnJCbVq1UJkZCSuXr0q80wzKw/yqnbt2jI1IJ8+fYJQKJT53r59+5a9+QSYxocfP36UWadOqKq2BBkYGMDX1xeLFy/GxYsXkZaWBqFQiGvXrrHVf126dMGWLVuQkJCAhIQEbNq0Cd7e3vl+z//++w9BQUHIzMzE3r17wefz0bhxY6SlpYHD4bB3lceOHZN5BpWb0NBQPHnyBEKhEDo6OuDz+eDxeODxePDw8MCaNWuQnJyMiIgI7N69W6UvfJZGjRpBR0cHf//9N4RCIe7du4fLly/D09Mz12MzMjJw7tw5LF68GCdPnmT/zZs3D/7+/sjMzETjxo3B5/Oxfft2ZGRkIDU1FX/99RcaNGiASpUqKTxvy5YtYW5uzrYAHDZsGJKTkzFnzhzExcUhIyMDZ86cwdatWzF9+nRwOBzY2NhgwIABmDJlCu7duweBQICMjAycPXsW27dvl3sPKysr2NvbY/Xq1cjIyMCLFy9w9OjRfF0HJiYmsLCwwKlTpyASiXD06FGZZ+A5+fLlCy5duoTU1FTw+Xzo6uqyJa1+/fph+/bt7DWTlJSEc+fOAQDatm2L9+/f4+TJkxAKhRAKhQgNDZXrxpMX7u7uuHr1KoKDgyEQCLB+/Xq2oRnAlFL09PSgp6eHt2/f4uDBgzLHV6xYUebzpqSkQEtLC4aGhvj27Rs2btyY4/u3adMm16rSnPTv3x9r165lg2RCQgIuXrwIgPnbcLlcufTp6urC0NAQMTExbKkyPxo1agR9fX1s374d6enpEIlEePXqFfs4RkdHBw0aNMCBAwfY55n29vY4fPiwTOBUlAdCoRAZGRmQSCTIzMxERkYGRCIRAKaF7JUrVxAcHIzU1FSsW7cOHTt2lKlBefDgAVq3bs0uh4aGolKlSkq/dyWNAmcJGz58OGbOnInNmzejefPmaNu2LQ4cOIAOHToAAHx8fNCgQQP27rJ+/foF6njcvn17BAQEwMnJCadOncKGDRugqamJWrVqYcSIEejXrx9atGiBV69eoWnTpnk+b0pKCubOnQtnZ2e0a9cORkZG7OAI8+bNg46ODjp06IABAwbAy8sLvXr1UjntfD4fW7ZswfXr19GsWTO2kUjNmjVzPfbixYvQ1tZG9+7dYWZmxv775ZdfIBKJcOPGDTZo3r9/H61bt0aHDh0QGxuLtWvX5ljaHjVqFP7++28IBAIYGxvjn3/+QUZGBrp06QIXFxfs3r0bK1eulAnwc+fOZau2nZyc0KFDB1y4cAHt2rVT+B6rV69GREQEWrVqhYkTJ2LSpElo2bKlynkIMI21du7cCRcXF7x58wb29vZ5Ok4sFmP37t1o1aoVnJ2d8eDBA7aqsGPHjhg1ahR+//13NG3aFF5eXmyndn19fezcuRMBAQFo1aoVXF1dsWrVqnx1cK9duzbmz5+PqVOnolWrVjA0NJSpup0xYwbOnDmDpk2bYt68eXI3VRMnTsTMmTPh6OiIgIAADB06FBkZGWjWrBn69u2LVq1a5fj+3bp1w7Vr15Cenq5y2gGmVsbNzQ0jRoyAvb09+vTpIxO4xo0bh/79+8PR0RGPHz/GxIkT8ezZMzg6OmLMmDHo1KlTvt4XAHg8HrZs2YIXL16gffv2aNasGebOnStTjerk5ITMzEy2RO3s7IyUlBSZwFm/fn3o6+uzbSMA5jveqFEj9iaxUaNG7DP42rVrY9GiRZg6dSpatGiBlJQU9roBmCCpo6MjU4r39/cvkr6yhYVDE1mXHxs2bMCHDx+watWqkk4KIaXW6tWrYWJigmHDhpV0UkrMzZs38c8//8i08M+vSZMm4ZdffmH7gMbHx2PQoEE4efIktLS0Cnz+okCBsxyhwEkIIQVHVbWEEEKICqjESQghhKiASpyEEEKICihwEkIIISqgwEkIIYSogEYO+uHr1xSIxQV73Gtqqo/4+OTcdyyHKG+Uo7xRjvJGOcob5Qojb7hcDoyNFY/JS4HzB7FYUuDAmXUeohjljXKUN8pR3ihHeaNcUeYNVdUSQgghKqDASQghhKiAAichhBCiAnrGmQORKBNfv8YhMzNvg1HHxnJlZmogUpQ3yqlD3mho8GFsbAYej34SCMkNfUty8PVrHLS1daGnZ5nrXJQAoKHBRWYmBQdFKG+UK+m8kUgkSElJxNevcahY0arE0kFIaUFVtTnIzBRAT88wT0GTkNKKw+FAT88wzzUrhJR3FDhzQUGTlAd0nROSdxQ4S5nExES4ubXAunV/yazfuXMbNm5cK7Pu2LHDWLZsIQDg0aNgtG/fEsOGDcDgwX0wceIYfPjwXmb/06dPYNCgPhg48Bf069cTe/fulHn2lpqagjVrVqJv3+4YPLgPBg/ug337dhXFxyx2YrEY48ePRGxsTEknRSVHjvyDr18T8rTv5s3rcOFCYBGniJCyjwJnKXPhwjnUr98QFy+eh1AoVOlYG5sa2LPnH+zffwT16jXA+vWr2W2BgWdx5MhBrFq1HgcOHMX27Xvw4ME9NjBKJBJMm/YrJBIJ/Pz+xf79R/D33/ugq6tbqJ+vpFy5chHVq9eAublFoZwvMzMzx+XCcuTIwTwHzv79h2DXru0l3hCJkNKu2BoHhYeHY+bMmfj27RuMjIywYsUK2NjYyOwTHx+PWbNmISoqCkKhEM2aNcPcuXOhoaGB6dOn4+XLl+y+L1++xKZNm9C+fXts2rQJAQEB4PF40NDQwG+//YZWrVoV10crVmfPnoaPz2Ts378HN29eQ7t2HfJ1Hnt7B9y+fZNd3rlzO6ZMmQFLS0sAgKGhIaZOnYWRIwdhwIAhePIkBFFRkVi3bgs0NJjLRktLG7/80k/u3FFRkRg1ajDOnr2UbXkIzp69yG7r2rUn7t69hfT0dMycOR+NGzfB168JWLhwLr5+jQcAODo6w9d3CgDgwIG9uHr1EkQiESpWNMeMGXNgaloRQqEQ27dvxuPHDyEUZqJmzZqYMmUWdHV1sWzZQvD5fHz69BGxsTGoX78h5s5dpLBa8vTpExg2bBS7vGvXdly8eB58vhY4HGD9+m1ITk5S8LmY5azXPXv2QXDwfbi7d8bFi0Fo2LAxnj17Cj6fjz/+WI0tWzbg3r3bAAAXlxYYP34SNDS4iIuLxdKlCxAfH49KlSpBIgFcXJqhV6++CAoKxL//HkRmJnOjNGHCr3B0dMbevTvx5Usc5s6dAT5fCwsWLEXlylWU5oexsTGsrSvj4cP7cHJqlq/rhhBSjIFzwYIFGDBgALp164ZTp05h/vz52Ldvn8w+W7duRc2aNbF9+3YIhUIMGDAAQUFB8PT0xMqVK9n9Xrx4gaFDh7LBsVGjRhgxYgR0dHTw4sULDBo0CDdv3oS2tnahpf/wYQ0cPKiZ4z4cDgf5md60f38h+vbNvUTy+vUrJCYmwsHBCQkJ8Th79nS+AqdYLMbNm9fQoUMnAEwVbFRUBOrXbyizn41NdWhqauLz54949eoFbG3rskGzIL5//44GDRph7NgJCAo6h61b12PLll0ICjoHS0tLrFu3GQBTLQ0A588H4PPnz9i2bQ+4XC5OnDiKjRvXYsGCpThwYC/09PSwYwdzLW3evB779+/G2LETAADv3r3F2rWbweVyMXz4QAQH35MLGpmZmQgLC0W9evXZ9z140A9nzgRBS0sbqakp4PO1kJyclOvnsrGpjpEjxwIALl4Mwrt3b/DXXxugoaGBEyeO4vXrV9i16wAAYOpUX5w+fQK9e/fB2rV/wt7eAcOGjUJ0dBSGDOkHFxcmnS4uzdCxozs4HA4+fnyPyZN9cOJEAIYOHQl//5NYunQFatSoBQDYs+fvHPOjQYOGCA5+QIGTkAIolsAZHx+PZ8+eYffu3QAALy8vLFmyBAkJCTAxMWH343A4SElJgVgshkAggFAohIWFfNXZ0aNH4e3tDT6fDwAypUtbW1tIJBJ8+/aNLT2VFWfPnoKHRxdwOBy0adMOa9b8ibi4WJiZmStt3JF9/fv37zBs2AB8+RILQ8MK2Lo19+eTWTcChTnfuY6OLlq2ZP5m9es3ZJ/N1q/fEIcP/4NNm9ahSZOmcHFpDgC4efM6Xrx4jhEjBgFg+tfq6+sDAG7duo6UlBRcvXoZACAUClCrVm32vVq1agstLS0AzLUREfEZTk6y6fn27Rs0NTWgpcXcaOnp6aFq1WpYvHgeXFxaoEWLVtDVVTzYc3Z8vhbc3DrKrOvY0YO92QgOvgdPTy9oajI3YJ6e3rh+/Qp69+6DR48e4tdfpwEALC2t4OAgTWRExGcsXDgHcXFx0NDQQEJCPOLjv8DUtKJcGnLLDxMTUzx5EpLrZyGlR1wcBzNnauGvv9JhZFTSqSkfiiVwRkVFwcLCAjweDwDA4/Fgbm6OqKgomcDp4+ODSZMmwdXVFWlpaRg4cCAcHBxkziUQCODv7489e/YofK+TJ0+iatWqhR40+/bNzLVUWJT98YRCIS5cCASfr4XAwLMAmJLSuXNnMGTICBgZGSM6OkrmGKZa3JhdtrGpgZ0790MoFGLBgtlYteoPLF78P+jq6sHKqhL++y8MzZq1YPd//z4cmZmZqFSpCmxt7XD8+L/IzMzMtdTJ4/FkBlgWCGS7OfD50pI7l8uFSMTka4MGjbB79wE8eHAP588HwM9vD7Zs2QmJRIKhQ0fAy6ub3HtJJMCUKTNlAk12Wlr8bO/Fg0gkUrCPlkwaeTwetm3bjbCwJ3j0KBgjRw7CX39tgKGhYY6fS0dHW+4GRkdH+gxYIpFvvZp9WdnNz8KFczBx4m9o3botxGIxOnRwlXvv7O+RU34IBAL2RoKUDfXrMzeRLVqIMHKkEP/+qwEvr0zo6JRwwsowtRoAITAwELa2tti7dy9SUlIwevRoBAYGwsPDg93n4sWLsLa2hp2dndzx9+/fx7p167Brl+otPU1N9eXWxcZyoaGhWvspVffPq6tXr6FaterYvl362cLCnmDRovkYMWIUnJycsG/fTiQkxMHc3ALfv3/H5csXMHXqDGhocMHjccHhMOnT0NDCjBmz0bt3d7x79xp16thi1Kgx2Lx5HWrXrgULC0skJibir7/+wJAhw6Gnp4NmzZrBwsISmzevw6RJv0JTUxMZGRk4fPgghgwZJpNWc3MziESZiIr6jCpVquLSpfNs3vB4XAAcNp+yL0dGRsDc3BweHp3RtKkDevfuBi4XaNOmDQ4fPgg3t/YwNDSEQCDAhw/vUbt2HbRu3QZHjhxA48aNoa2tjZSUFMTGxqB69RrgcDjgcqXv9fNyFmPjCjAxMUVsbDSsra2RkpKCtLRUODk5wcnJCf/9F4YPH96hffuOCj4XR+Hnyno/Hk+6zsWlGc6dO4NOnZgq8sDAs2jXrj0AoGlTBwQGnsGwYSMRExONR4+C4ezsDA0NLpKTk1GlSmVoaHBx8uRJCAQC8HjMtamnp4e0tFT2PXLKDwD4+PE96tSpo/A65XK5MDMzUP3iLGLqmKbCkJwM6Mv/7Kjk2jVp3ixbpo1Zs5hakwkTmJuo8qwor5tiCZxWVlaIiYmBSCQCj8fc9cfGxsLKSnaUEj8/PyxfvhxcLhcGBgZwc3PDvXv3ZALnsWPH0KtXL7n3CAkJwbRp07B582bUqFFD5TTGxyfLTUMjFotVKkEWZYnT3/80Onb0kDm/nV1DiMViBAcHo0mTppg06XdMnfobxGIxJBIJevXqC3t7J2RmiiESiSGRgD2+QgVj9O8/CDt2bMP//rcK7u5dkJaWhsmTJ0AikUAkEsHDowsGDx7BHrNq1Tps3boJffv2ZJ8fd+zYWcFn5sLXdwp8fX1gYWGJpk0dAYBNByBhj8m+/ODBAxw65AceTwMSiRhTp86CWAx07OiJhISvGD+eabwjFovRo0dvVK9eCwMGDMXOndswfPggcLlM8BoxYjSqVLGBRMJMFZf1Xj8vZ9eqVVvcvn0L3bv3wvfviZgzZzoEggyIxWLUqVMXrq5tlXwuicLPlfV+IpF0nZdXd3z8+BGDB/cHADg7N0eXLkwp2td3CpYuXYALF4JQrZoNGjZsBB0dPWRmiuHr+zumT/8dFSuaoUmTpqhQoQJEIuba/OWXfliyZCG0tbWxYMHSXPPjwYP7GDRomMI8EIvFiIvL+TlucTMzM1C7NBWGdev4WLZMC3fuJKNmTfkI9/07cPy4JgYPFkJZBY+ZmQF695YuJ/80/WRZzLe8KozrhsvlKCxQAQBHUpgPr3IwePBg/PLLL2zjoKNHj2L//v0y+4wbNw4NGjTAxIkTIRAIMHbsWHTs2BEDBgwAAERHR8PDwwNXr16FUbbK/NDQUPj6+mLdunVo3LhxvtKnKHBGR3+ApWW1PJ+jpIdOU2fqnjeRkRFYuHAOtm3bXeyDAWhocJGSkgoeTwMaGhr48uULRo8egnXrNqNqVZtCe5979+4gKCgA8+YtUbhd1eu9OJTFwBkSwoW7O/PMfN++VHh4yD4+kEgACwumtLR3bxo6d1b8iEhLywAVKih/nyNHUtG2rfyjifKgqANnsfXjXLhwIfz8/ODu7g4/Pz8sWrQIADB69GiEhYUBAGbPno2HDx/C29sb3bt3h42NDfr06cOe48SJE2jXrp1M0ASARYsWIT09HfPnz0e3bt3QrVs3ma4rhOTG2roS+vUbhPj4LyXy/p8+fcKoUUMwdGh//PrreAwfPrpQgyYApKSkYPx430I9J1FdVtAEAD5ffvvKldKVW7YobskfH89hg+bff6fh0qUUuX369NHFxYu8giWWKFRsJU51RyXOokV5o5y65A2VOIvOjh2amDNHcfe42FjZz1evnh6+fOEq3Q4AdevqISGB2efu3WTUqCHB69dc7NqlCVdXEYYPZ1oGdesmxI4d6YX1MUqNMlPiJISQ8kpZ0MxOIACGDNGWCZoAYG5uALEYePCAixYtdHHzJo8NmgBQvTpzw1+7thj/+18GXFyk1bNRURxERHBw5QqVPAsTBU5CCClmr14lwdU1E0ZG0lquX3/VRmCg4qrZkSO10aWLHt684aFnT2kXp/nz0/HzI/mKFSW4coWpur1/XwP29vro21cX8fHMjs+fc2FubgBzcwMo6dVEckGBkxBCitGlSykwMgIaNBDLBK6jR2WD5rJl0irW2Fj5n2oXF2DiRMXjVdevL1/1/+QJF/7+GmjTRvqMtW5dfRTRMMplGgVOQggpYkZGEgwbJsCdO8lo2FDMrktN5UAgAM6f56FxY2kV69SpGRgxQohTp1IBAA8eyFe13rmT83tqacm22ejXTxcjR8qOipCczMGKFQpaKJEcUeAkhJAilJgIfPvGwfnzGjJ9Nu/eZYJh5coGGDxYF0+eSIPjtGkC8HhA8+bKu5Pk1msqNDQZ166lwN8/Ncf9Dh/OeQzupCTg61dg2zZN2NnpgSbXocBZ6tB8nEXj5/k4AwL88fHjh2JNw+vXL3Hp0gV2WSAQYOTIwUj+uWc7KVU2bmRKdLGxspGuaVPFQXHHjjSlQbFuXRHMzcXYvz/nYAgAxsaAnZ1YprFQltOnpcfXqaM4EgqFTHCvWdMAtrYGmDdPG/HxXFhaGuDLl/I98TkFzlKG5uMsGj/PxxkQ4I9Pnz6qfJ6CzLv5+vUrXLkiDZx8Ph/u7p1x+PCBfJ+TlLy1a5mxgUeNkv2+Tp+uuGVOt26y11BkZBLMzcVwcBDh0KE0PH2aAnd31QY2aNOGOef790mIjU1Cs2YiREUloXnzTFy/riFXikxKAipVMkDXroq/33376qBZs9wnPiir1GqsWpI7mo+z6OfjPHv2NF6+fI61a1dhx44tmDBhMkxMTPHXX38gPT0NAoEAXbv2QJ8+zIhWy5YthK6uLj59+oRv375i1y4/bNu2CZcvX4ChYQXY2zvg4cMH2LmTGSnr3LkzOH78X4hEIujr62PGjNnQ1zfE339vRWpqCoYNG4AmTezx66/T0KGDO0aOHMxOVUZKrxkzMmSWuVzAxSUT9+5Jf4b795e/GdbQAJ4+lR/gQBWHDqVBJJIdcIGX7bFpXBwHFhbSauSaNXMe5zUsjDn48mUe3NzK3+hEFDjz6PCLf3DwhV+O+3A4+RtYuX/dQehbd0Cu+9F8nMUzH2eXLl1x7twZ9O8/mJ3+LDU1BWvXbgafz0dqairGjBkKZ+fmsLGpDgB4+jQMGzduh46ODm7evI7bt29iz56D0NLSwty5M9j3evIkBJcvX8CmTTvA5/Nx584tLF26CFu27MSoUeNw+/YNLF0qnXvWxMQUGhqa+PDhPapVsylw3pOS4eaWqXBA99On0xAVxcGDBzzs26eJtWuLZrACHk82UGYZNUqIO3c0MGuWFs6c0UTDhiI2KCqyalU6pk6V9klNTS2fVbYUOEsRmo+zeObjVCQ9PR0bN/6BN29egcPh4suXOLx584oNnG3btofOj3mcQkKC4ebWgV3u3LkL9uzZyab3zZvXGDNmGAAmX3ObINvU1BSxsTEUOEuh9B9x0NhY8feHwwGsrSXo1i1Troq2OGSl68wZpoFQTkHz0KFUtGsnkgmcI0boYMGCdEyYkPfHRocOacDYWKJydbM6ocCZR33rDsi1VEjzcTJK+3ycimzbtgkmJqbYtesANDQ08NtvE2SO0dWVNvOXSCRKb2QkEqZEO2rUOHZdbteNQJBBc2iWUtHRzHWQ9YxR3dSsmfPv1bt3SYiK4uLbN8DJSfG+ixZpo3VrEdvNJje+vsx3ZdgwAQYPFub5OHVCjYNKievXr6JqVRucOBGAo0f9cfSoP9as2YiAAH8AQNOmjrh37w7bKjQxkZmP09m5mdy5NDU1MXXqTNy9exuvXzOD4Y8YMRqbNq1FTEz0j+MTsWrV/zBo0DBoaWnB0dEZFhYW2LhxLdsoKSMjA35+e+TOb2JiiszMTHz+/AkAcOFCYJ4+Y2RkBPT09NGhgzsmTfoNL1++gFgshqtra5w4cZStuhUIBHj9+hUAwNW1NQ4fPoCMDObWPjU1Be/fh+fp/bIYGBjA2NgEUVGR7Do9PT2kpEhbsyYnJ8Hc3AIaGhp49+4Nnjx5rPR8TZs64sqVi0hPT4dYLMb58wHstpYtWyEw8Cz7dxKJRHjx4hn7nj+3oBWJRIiMjECNGrVU+kyk5GVmAqdPMzeJVlbqOSR4Tul69iwZ+vrMUH7Zg6aiAeWzl0ITE4GhQ7XZ0Ym+f5fu988/0pvuPXv48PIqnY0LqcRZSgQE+KNTp84y6xo0aASxWIzHjx+hSZOm8PX9HTNnTpGZj9PR0Vnh+UxMTNG//yDs2rUD//vfKnTu7IWMjHRMmTJJZj7OIUNGAGCqfFetWo+tWzdh4MBfZObj/JmGhgYmT56C336bIDMfZ25CQh7KzMc5bdoscLlceHh0wffv3zBp0hgA0vk4a9eug0GDhmHnzm0YNWqIzPyTWVWoedW6dTvcu3cH3bszc7127doTmzatxcGD++HjMxlDh47EkiXzERR0DpUqVUKTJvZKz+Xq2gZhYaEYNqw/KlY0Q/36DZGUxFTHNmnSFGPG+GDmzN9/zKkpRPv2HVGrVl04ODjj4EE/DB3aH/b2TfHrr9MQFvYE9eo1YKumSelhbW2Q7bV6Bs7snj9PxtCh2pg1S4A6dcSoWFFxmhs2FCM2Nglbt2pi/nzmdyAkRFrFe+KEJs6dk9YqDRigi1270mBiIsGvv8oOwJCWxkH37jrw9RWUqkZGNDvKDzQ7StFS97wp7Pk4U1NToKurB7FYjD/+WIKKFc0wZoyPwn1zypuFC+egS5eucHJyKXCackOzoxSMRMI8s9y0SROLFsk+L3/3Lklh46CCKKy8uXmTh6AgDSxenJH7zj9JTgZq1GBuELJmcfnzTz7+/FP1RwuKZoHJr6KeHYVKnIRAdj7OihXNCny+JUsWIDo6EhkZGbC1tcPAgUNUPodAIECTJvbFEjRJwTRurIeoKC5mzMjAihXyQUOdKwxcXUVwdc1fae/nzxURwck1aM6fn47Fi+Ub4t27x1M4WIM6ohLnD1TiLFqUN8qpS95QiVN1b99yMGOGNq5fV14G+fQpCUXRtktd8qZ2bX18/86Bo6MIwcHSKttBgwQYOVKIdu1kB0qIjU2CQMAMNfizN2+SYGhY8DTRfJyEEKKmmjfXzzFoLlmSXiRBU53Y2DA3fdmDZuvWmVi9OgP164tlqmDHjWNaovP5YAew79hR2uJ42rTc5y1VBxQ4c0EFclIe0HVeeCZNysDEiczzQg8P9eyGUpj270+TW3f0qOy6li2ZfJg3T/octXlzEWJjk3DggHTfEydyHnBeXVDgzIGGBh8pKYn0o0LKNIlEgpSURGho0PRShWH4cCHmzxcgNjYJ1aqV/d8OS0uJzKDxipw4kYbY2CRoKomLt25Ju7ikF83gSYWKGgflwNjYDF+/xiE5+Vue9udyuTKziRApyhvl1CFvNDT4MDYueKOo8iTrftrISIJXr5Kxfj0fS5dqwdS07AfLnzVrJmKH49u1S74EmpvsAzF8/MhVOmOLuqDGQT8oahykKnV5WK+OKG+Uo7xRTp3zRihkZhABCrcrRV6pc97kx8yZWti1K2sKtoJ9LmocRAghaqhVq/I7rVZRGDlSOt5tau7TjZYoCpyEEKKizEzg3Tv6+SxMNWpIq2dtbHKe1qyk0V+eEEJUtGIFNaQqbIqmPVNXFDgJIUQFa9bwsW5dGe+cWULu3UtWuP7mTR7i4tRn7k9qVUsIISr43/8oaBaV6tUl6N9fiIMHNXHvHg96ehKkpHDQsyczi0pJNMJShAInIYTkg4eHEIGBpaPDfmmir8/0bvD2lp9y7OtXwNhYbnWxo6paQgjJo7RsXRQHDRIq35Hkm7a28m6Bly+rR1mPAichhORRZCTznM3QUIIWLURo0ECkcGJnkn+zZgmUbvvvP/UIWeqRCkIIUXMSCTOoOwBMnZoBfX3g8uVUNGyo3qPclDYaCgqV9+8zjYY2blSP58sUOAkhJA8WLJD+aHt5lf3B20vSz42AbGyk1bdTppR88KTASQghebB1q7TvZuXKNFJpUfv0KQnv3ychOpoJomPHMlW4+/fzcekSD2/elFz3FAqchBCSi3fvONleq0eXiLJOSwvQ1QW4P6LUokXSKcn699dFixb6OHOmZBoLUeAkhJBcbNrElDarVhVDX/G432XKmbenYb7ZEG++vsaT2BBsebyxpJMELheYMEG24dDRoyUTONWjbS8hhKixyEimjBEUVD5a0I44PwgA8OeD5bj++Sri0+Mhlogxwd63RNO1YEEGexMDAFZWJVNlTiVOQgjJxaVLTBlDHTrfF6cTb44hPj0eALDoztwSTg2jenWxwtfFiQInIYQoIBYDjx5xsXu3dHQgjvoMl1poUoQpeBIbAvPNhjDfbIj/3VusdN+41LhiTJlid+6ksH1n587VLpEpyKiqlhBCFNixQxPz5mmXdDKKXPUdVjLLax6uAgDoaOggLTNNZluyMAlmMCu2tCnC5UKm72z9+voID1c8OHyRpaFY340QQkqJkBDZea7u3i3eH+eCuvH5GqKSI3E36o7SfZ7HP1O6LWzoKzwfHg5f+98xptF4AEBI7EOZfYQiIeLT4iGRSPDHvSV4+iWscBKvgpSU4q8GoMBJCCEKnDghrZAbO1aAGjVKT9/NB9H30Ou0Nxrvq4uuJ9wRmRyhcL+EH88vf9bIrAkMtSrAVMcUc5svRD3TBgCAcRdGyuzX/ZQn7HZXR3jiO6x++CfcjrQs3A+Sg//9L73Y3utnFDgJIUQBiURakhk3Tvn4qepGLBGjy/GOMutOvTmBOTem42t6ArvuXtRd9DjVRWa/mPHfETXuK4J+uSqz3sWqGfu62nYLfEz8AIAJ0ADQ+qALuz1JkFgonyM3I0cK4eQkAgCcPVu8Tx0pcBJCSDbv33MwfbrssG6VKpWe0ubfoVvl1i24PRs7wraij38Pdt2T2Efs6w9jYhDrkwgOhwMelwcuRzY0VK9Qk32dlpkGR7+GMgFSIJbeWNz4fL1QPkdeJP+oPR8+XKfY3hOgwFloXia8QGRSZEkngxBSQKNH62DPHmlfwQULSq5KMD/m356tdNuTuBDEpMYAAB7GPGDX62jkHHi4HC5ifWRLkjX/rqxw32GBA/KaVDmpwlR8SfuS5/0vXpQ2qZUU470NBc5C0uqQMyqtrlTSySCEFNCTJ9JGQdu2pWHChNI176ZYwrQ49Wnii5cj3sttb7inNlbcX4YTb44BALxqdMvzuQfZDc3Tfq8SXuLgcz9IVIhmMakxsNlhiXq7a0AkFuXpGM1s84jHxhZfIyEKnIQQokSPHkU7C8qHxPcqBZfcfE76hEr6laGroYuFLZbCWNsEK1uvkdvvr+AV7OupTjPzfP7V7TYgatxX6GrosevWtdsMSz0rxPokgs9lSuquh5ww+YoP+vh3R6Y49zx88/U1ep/uyi7b7a6e5zRlKc65OilwFrJv6V9LOgmEEBWIxcw/QLa6z8enaBsEHXpxAE5+jdBknx2Co+8Xyjmb7q+PiOTPaG4tbd06tP4IHOxyFI3Mmig8xs6knkrvwePyAEgzqr/dIIQOfQkAGNPYR2bfa5+vwHqrSa7Vr11PeuBFwnN2+VvGtzynp2dPpkagXz/dPB9TUBQ4C0nvOv0AAMExhfMFIIQUj9q19eHuzvzoentLn/UtXJih7JBC4XuZ6RsZlRIJz+Md8CrhZYHON/vGNPb1pY8X2NccDgftq3XCuZ6X0MyqhcwxXWp0BScfwyFlbyyUnb6m4hHwn34JVXousUSML2n5H5Fo8+bifwZNgbOQjGo4FgAw4Gxv/PflKY69OqJ035iU6EKtniGE5F9SEgdPnvCQmQncv1883RqyGuhkF5VSsMaFf4dtY1+f7XlBbrsmTxOnewTiwxjpe2vx8jcp9LFupzGg7mBEjZOtYZto/yv7ekKTyezrPv7dkZ6pOMD9HFTtzZuCA06efyO5JRDFKHAWkiqG1djX7Y60wPiLo3D+/Tm5/d58fY2Ge+vAYkuF4kweISQXxdm45F7kbbl1vf27YWzQ8HzdVL/++op9HTXuK5wsXZTuq6OhA2MtZrT6V1/zV8o10TbFWrdNP6ptpfg8PqLGfcXTYW+woMUSvB75kd1Wdbu5wnOlZhvW72Lv69DT1IcEEpx5dzrP6WncmGlMVFzj1lLgLCQVdSqibsW6MusGB/TFxpB1AIDz78/BYX8Dmabav1+ZVKxpJIRIHT+uAUdHaSOXJk2Kb6LN5UoGUj/x5hhm35ymcFtOWh50ZF//HMwUeTb8HXya+MLP87DK75UbHpcHc10mSFbQMoKDhRO7TSCSf24ckxIFALjU+wYamTWBb9PfAQBHXv6T5/fMagltY2MAUd4a5BYIBc5C5FHTQ27d4jvzYL7ZEIMD+uJT0keZOzy/53uLbZQNQoisceN08PFjyfwENqzYGADQrkp7uW07w7bn+TxbHm+E/9tT7PKt/sF5Oo7H5WFhi6Ww1i/6LnTHuvqzr2NSo9nXv12ZiBX3l2F00DAAgAHfEADQpnI7AExhI6trTW7GjpUG5JCQov+bUuAsRHweP/edfvIo5iE97ySkmAUG5l4qKwrJgiSceXsap94eBwBY6FnK7aMomCryNPYpFtyejZHnBwMArPSsUdu4TuEltpDoauriny7/AgCmX/uNnb7swPN9Mt1iqv543JW9sdKi2/Py9B7z5mUAZs+AhRx43tfHu6/vCvETyKPAWYiWui1lX0eMVTx4MgAYaRlhbrOFAJjnGttDNxd10ggh2QwZUnxdF7Jbfm8xRpwfxC7PcVmAGc5z8HbUZxz2OgEAkCBvN9Lf0r/JLBe0cVFRstBlbhCyt/b92c/D/AHAlicb8nR+Ph/AhPrs8pqbW1RLoIoocBYiTZ4mPo2NQ8TYeGjyNBEz/rvM9mlOszC56RTcHRiCHrV/YdfPuzWruJNKSLnVt6/88HIVK+atSrAgPid9kmn5+nx4OCz0LDHFcQYM+IZoV5UpaV79dDlPtVDJgtIzzVl1I8XdV7IE9Lwos3x/4BP29bFXR3D548WfD2FliDLw/ad+nxsfrVI9kSootsAZHh6Ovn37wt3dHX379sX79+/l9omPj8eYMWPg7e0NDw8PLFy4EJmZzKgT06dPR7du3dh/devWxaVLlwAAIpEIixYtQocOHdCxY0f8+++/xfWx5GjxtKDJY8aB4nA4bKfjK31uY5rTLMxptgAm2qZK+zsRQorOt2/AlSvyXU4ePEhBgwbSViUaGoX/+KTp/voyy6Y6pkr3/ffVoVzP9z39e677qAt9TX2sabuRXe5flyl1X/jlGmJ9EuFo6Syzv02F6vCs7g0AGH9xFPqd6YmQGNm5QLNU2WaG2jurMgtfbJn/r80t5E8gq9gC54IFCzBgwACcP38eAwYMwPz58+X22bp1K2rWrAl/f3/4+/vjv//+Q1BQEABg5cqVOHXqFE6dOoUVK1agQoUKaNWqFQDA398fHz9+RFBQEA4fPowNGzbg8+fPxfXRcvRPl6P4q+161K/YQGa9sbaJzLKi1maEkMKVnCx9fjZtmnSAAz09oGZNaanT27toh9o73u2MwvVhw14DyNsIZP2OMYOuPB7yHHcHPJLpn6mOBtYbgtM9zmNwvWFY57YZsT6JaGxur3T/hS2Wyiy7H2snt49cybziS2ChBDsGLCmUNCtTLIEzPj4ez549g5eXFwDAy8sLz549Q0JCgsx+HA4HKSkpEIvFEAgEEAqFsLCwkDvf0aNH4e3tDT6faYwTEBCA3r17g8vlwsTEBB06dEBgYGDRf7A8MNc1x+B6wxRui/VJxLjGEwEASYKkYkwVIeVTerY++O7umVi5Mh3Hj8t3/tMo5HEQsg8n16pyW7hWaq1wP3MdcxjwDfE+MVzhdolEgmRhsswg6GY65qhhVCvXGU7UQTOr5vir7fo87VvN0EbptiRBIuruspFppQsAoxuOQ2xsEkaNKkgqc1csgTMqKgoWFhbg8ZiWbDweD+bm5oiKipLZz8fHB+Hh4XB1dWX/OTg4yOwjEAjg7++PXr16yZzf2tqaXbayskJ0tGyGqqt6pkz1TaKg9FS7EFJavXjB/AbNnp2BRo3EGDZMCFdX+Y5/HTsWbonzQ+J7AExJ81hX5R37ORwOqleogXff3yrcfuD5PtTYYQ2rrcbsuqxHQ2UNh8OBu01ndrmxmbR0WvPvykhIT4DbkZYyxyxrtbJY0la802bnIjAwELa2tti7dy9SUlIwevRoBAYGwsND2j/y4sWLsLa2hp2dXaG+t6lp4TxzNDMzUGn/KvFMazOOjkDlY0ubsv75CoLyRrnCzJsRI5j/dXS0YGYmO9yc1o/FwYOBUaN0kI8hXJXiRDEDkdevUhtmpjl/ntC4xwAADf1MGOtIA+S199fw+1XZQVP+aP9Hmb52vOw82RHYYtOiYWZmgNa7paX17IPHb/PaJpMXRZkvxRI4raysEBMTA5FIBB6PB5FIhNjYWFhZWcns5+fnh+XLl4PL5cLAwABubm64d++eTOA8duyYTGkz6/yRkZFo1KgRAPkSaF7ExydDLC5YgwAzMwPExalW5SpKY+6A7717BBt+3Vz2Lr3ykzflBeWNcoWfN8yPae3aqYiLky1pikTaADTRpk0avnwp3BLnvkd+AABBEgdx4pw/j5GWEb5lfMO2OzvRpUZXWOoxv5Nt97aV23eErU+ZvnZeRL1hX0clR8FxqxMexsgP8tDXdgB6VO3P5kVhXDdcLkdpgapYqmpNTU1hZ2eHM2eYB+JnzpyBnZ0dTExkG8hUrlwZ169fB8BUyd65cwe1a9dmt0dHR+Phw4fss9IsHh4e+PfffyEWi5GQkICLFy/C3d29iD9V4bA1ZoJlamYxDbJISDkVESEtQrq5yVfPLl2agTFjBOjcufAbBiX+GCHMXFe+zcbPNrRnuqzMujENjfbawnyzIXqekv3N29R+OyQLyv7AKb1t+8ksKwqaADDbRb6xaVEqtqrahQsXYubMmdi8eTMMDQ2xYgUzYsTo0aPh6+uLhg0bYvbs2ViwYAG8vb0hEong4uKCPn36sOc4ceIE2rVrByMjI5lzd+vWDU+ePEGnTp0AABMmTECVKlWK66MViKlORfA4PEQlq2/nZULKgrFjtXPcbmoqwdKlhT+V2MHnfrgbxQzqnpcpvBQ1HLoZwRQoWlVui4NdjuZrlLLSqJ5pfcT6JOJx7CN0OtqWXT+56RRUMaiK2NQYlSbiLiwcCY33BqDkqmoBMENQ6Vrgwi/XYKWvWhVzaUHVkcpR3ihXmHkzfboW9uzhIzg4GVWrFt3PXlpmGkTiTOjzDRD0/hwGBfRlt8X65G1savPNhgrXZz++PF03YokYlluM2OX3o6Ohq6l89KeirqpVq8ZB5Vlsagwa76uL/7X6Ex2quaOKQVVwwMnXJLOEEHl79jCltKIMmgBQbbvi6tisgd3zwqtGN5x5dyr3HcsJLoeLvZ0PopZRbbUYj5eG3FMzs25Mg5NfI1huMcLI80NKOjmElAkZhV8DK6PNoWaYcHGMTCvPn23usCPP59vlsZ99/fNwdOVV5+pd1CJoAhQ41cKfbdYqXH/m3SlsClkP882GePolrHgTRUgZEh3N1Nz8/nvhR9CPiR/wPOEZ/n11CPV211C4T+jQl7A1Ua3V/N7OB3Gu1yU4Wjrjcp9beDzkee4HkWJBgVMN9KrTB6MbjkMto9py2xbdYcZc/Ct4BU0/Rkg+3b7NdPuyti7871BuN7VHvE+yXUpU0bl6F3YS6AYVGxbL3JkkbyhwqgF9TX0sa7USQ+oPV7rP2XenUX2HdY6zBBBC5EkkwOTJzHB0Dg7y3VCKgo6GDu4ODEHUuK9oW8WtWN6TFB8KnGqkR+3e6FCV6VJTvYJ8lU9qZgr6nelZ3MkipFSbNk06QlDFitISp6NfIwwPHKToEJUMCxwAALjZ7wFWt2Xmj3w3KhI1KtQEj1syE2aTokWtatWIha4F/vE6irTMNGhyNWG91ST3gwghOdq3T9rnsYJRJsw3S4ex+/hjDNn8Ovjcj31d27gO6pjYYlC9oQU6J1F/VOJUQzoaOtDgaqCPbf+STgohpZog22x9lWwj4HRQfozrtMy0PJ3refwzuXYGk6/4sK+p61j5QYFTja1ttwl/tP4Lc5stlFkfk6re8+4Roi5On5ZWqjnPmSo3DRUATLnqm+t5HsUEo83hZrDYUoGd6SS7z2OVd0MhZQ8FTjWmwdXAiAaj4dv0d5kRQxrukW99SwiRt3MnU03bsmUmPqfKznFppceM0nX01eEczyGRSOBxTNrAx8mvEdIzmYk9LfWs4GDhWG6GwCMMCpylyJq2G0s6CYSUCp+TPkEkFuHhQ6ZxTvOZC/Eg+h4AJthFjfuKJ0NfsPvf+HxN6bl2hG6RW1d1uznMNxsiOiUK9UwbFHLqibqjwFmKDKwnHUnIfLMhMsWFP4sDIaXF/md7YL7ZEGKJGOHf3+Fe1F0ATGvZpvvrY8X9ZUCVW0DPQVj1cBl7XOjQl2xr16xW7L1Oe8uce/uTzQj6MQ/k3FvSQcQdLBzl0uH3bG/hfjCi9qhVbSl2LvwsvGt2K+lkEKKQRCLBoIA+GNPIB22qtCvw+ZbeWYiLH4Nwpsd56PMN2GeT2Qf/XtVmHdtSdu2jVcDIVTLnmNtskczyOrctqL+nJgDgWfx/yMhMh9eJThCKhXLv/2jwf7DQtUSlbaYy6+8ODCnoRyOlDJU4S5l/vaUDP488P5idLZ6QkvYi4Tk6H2uP2NRYPIv/Dy8SnuPCh/Po7Z/3m7utTzair38PdD3hgdjUWLYVq1gixvqQ1XgW/xTHXx/F1/QEhcdPvTZZ6bn/bLMWvk1/k1lnwDdgX7c93Bzux9opDJrzmi9GZYMq0ORp4lrfuxjZcAxifRIR65OosM81KdtoWrEfSnJaMVUJRUKZu968TlVUksrTFEiqKit5M/Xqr9j3bJfCbZ/Hfsm1AY1EIoHFlgpy67vW7AG3qh3w65UJ7DpdDT2kZqYoPVcVg6r4lPRRZt3ZnhfgZOkit29UciQa78t5HNmY8d/VrrtJWbluikJRTytGJc5SSJOnKbM88dLYEkoJIVIvEp4p3VZ5W8Vcj08UfFe4/vTbEzJBEwAbNCfa/yq3/4LmS/Fw8FMgZBhwYg+wNhy3+z9UGDQBKJ0Dd26zRTDTMUfUuK9qFzRJyaLAWUr97jidfX3k5UFkiJhZH86Fn8XHxA8llSxSjpnq5B4cc1J7Z1X2davKbRXuk9WFJMv85ovZ17vc/RAz/jsm2P/ol3lqN/BkKC4dM0Mt45y7cEWN+4qzPS/g3ehIjGk0Hrs9DsC36W/4b/gbGjaPyKHGQaXUiAZjsDp4JbvsfrQdLva+jqHnmNGGSkP1LSlbYlKi0LpyO1QzrIaQ2Ed4+iU0X+fZ0WkPutVixmQ2MzPA1ls7Mf7iKNwdGAIjLSPU3VUdANCgYiMAQMTYeIgkImhraMucp3FjEczMJGjYUJzre/K4PLZEutR1Rb7STcoPKnGWUua65oj1SWSH5XsW/xQxKfKjohBSHD4kvsej2Iew1LPEX23X43Kfm3gy5AVixn/Hbw5TASDH7lPvvr8FABhrGbNBM0uvOn3waWwcalSoCRNt6bP9S71vAGAeXfwcNAFmuD0+n5pwkMJHgbOUy/pRAoCDL6QDTj+MeVASySHllJMfU/o78vIgu85K3xocDgdRKVEAkOOkBYHhAQAAXU09hdu1eNIZTuY1X4w+tv1zfO4okQDPn/Pw+DFVs5LCR4GzlKuZbfLrlQ+Ws687H2sP882GOPH6aEkki5Qj2Rvm7+i0R277VEfpAALmmw0xJmgYXiZIR+2RSCRYeHsOANnuVspMsv8VG9tvy3Gf5GTm/8hI+okjhY+uqjIgbNhrpdvGXhhRjCkh5dGah3+yr3+uZgWAqobVZJZPvjmOVoecYb7ZED1OdkFcWhy7raZRrUJJU3w8tYIlRYcCZxlgoWuB/7Vifrwq6pjJbX8W/x8AICE9HquDV8pNjURIfoXGPcbljxcBAFyO8p+TT2PjFK6/FXkDlz4EscuF1e1jwwamz2jz5jQsJSl8FDjLiJENxyLWJxHPhr/FjX73Zba1Pdwch14cQN1d1fHH/aXwf3uyZBJJSqWdYdvge3m8zLoTr49i73+70OHf1rgfzYwR+08X5Y8FtHhaCqtxAemcloe8jhdKehMTgf37mcDZuTMFTlL4aOSgH0rTyEF58e77W9yOuInfr06S22aha4mwYa+KNT3qlDfqRt3zxnyzIQDg3agI6PMN2OWf5dYFKi41jh0XFgDcbTrj/I+B1AEgevw3uVJrfvLG01MXwcFMo6ArV1JQv37u3VFKI3W/bkoSjRxE8qVGhZqw1LNUuK2wniORsm9n2Hb29bpHq5UGzbww0zXDwS5H8XbUZ8T6JMLdxpPd5mzZLMeqXlVkBU0AqF27bAZNUrJoAIQyzIAvP+4nAITEPizmlJDSatYNaXendY/+UrjP57FfwOPkrdtH+2qd2Nfmuubs66zq3sLGp/mlSRGgEmcZ5mLVDJd630DM+O8YaCedyzMtM60EU0VKi/i0eKXbYn0S8UfrvxA8KAx8Hj9fw9IZaUn7dS5t+Ue+0qiIqyvzXDM4OLnQzklIdhQ4y7iGZo3B4XCwpt1GmWdQNAl22bc6eCVmXp+S7+NvfL4KAFjTdqPM+oM/GgGNaDBarquJKuoY12Fft6jUKt/n+RmXCzg4iFC1KjXfIEWDAmc549OEGQA7Pl15aYKUfoPO9sEf95di19Md+T5HkpBpXNG2ihtaVWrDrm9o1qSgyQMAVNAyYl83qNiwUM6Zmgpcv66BChUoaJKiQ4GzFLp8mYeQEC6Cg7lYvFj6ECcqigNzcwO8fq38z+pg4QQAiEuNVbg9Pi0eVz9dLtwEk2KVJEhE0IdAdvl7xrd8nefapysAAEMtQxzr5s+uN802XmxBcDgcRIyNR/T4b/k+R2oq8y/LggXM0Hy3btFQe6ToUOOgUqhfP12ZZX9/TXz4wIWdnQgAMHOmFo4dU/wc0+xHg4z338NR37SBXIdzu93MzBObO+zAL3X6FnbSSTHY9Hi9zHLtnVXZavpXCS8hhhh1TexyPEf3k564HXkTAKCnyTTJ3+95GGFxTwp1mq2f55ZVVa1a+uDzgffvmeeZe/cyN5IZGTRyECk6VOIsAz58YP6Mz58zP2guLiKl+5r/GFloxPlB2PvfLqX7+VwcXYgpJMUp+3RzWVbeXw6JRALXQ05ofUjxhM5Zzr8/xwZNQDoikLtNZ0x1mqnssBKRmclBaioTJD98oGBJigcFTjUXE8PBtm2akEiA4GAupkzRyvUYAwPlz3fMsnUBmH79N8SkxhRKOknhyxRn5jg8olgixpSrk/Hfl6fsuvq7pX10d7rvZ1+vCv4DHxLfs8sJOTzjHhwgrWkIGfxM1WQXm+xZc+0aD0uWSL8bd+5Qi1pSdChwqrmGDfUxb542GjXSg6enHjuUWE4OH1Ze/aWvaSCz/P57OPs6KjmSfZ31LJQUvdMvT6PpvvqYd5Mpzf378hDMNxvCeqsJLLZUQGLGd4XHTbnqi/3PdqPdkRaYe3MGBCIB4tKkz669a3ZDQM+L7HL2bWff+eNnkckRuBlxnV2+N/AxKhlULvDnKyoJCdISZkCABh49klYh16xJjYNI0aHAqcbCw6U/DDExOf+pHj2S3mF//Kh835+faSZmazjSeF9d9vXDmAcFGiWG5F23Q93wOfkTtoVuhkQiwYRLY2S23/pRbZqWmYZDLw7AfLMhJBIJDjzfx+6zPXQLlt9bzC4vbLEMAOBo6cyu63K8I/t6ylVfmfe4E3kLTfbZoecpL3Zd9Qo1CuHTFZ2ICOm1vHs3H9raTLB88YKGoSNFiwKnGouIyPufp3JlCQIDUwAAyckcHDqkvN3X+9HRmOY0CwAwMKAPjrw8SEGyhIjEss+jFZUERwQOwoX3geh2woMdbH3sheFy+2VvDe3TRDpGsVeNbgrfe+DZ3hCIBABkJ6AGlM9mok46dJCd9PrNG6bEaaJ8vmxCCgUFTjUzYoQ2qlZlWjFGRck3dujbV4jq1WXH3zxyhGmP37SpdP2dO8oDp66mrszkwhMvjVW677tvb/KWcJIvsT89Yx5xfpDcPiKJCAMD+uBxXAi77uQbZiaRqgbSAQiexTPPOpuaO8gc/7f7XsxynscuD6s/EgBw4cN5/PclDADQqjLTT7OOsS3uD3wCLV7uz9IJKa8ocKoRiQQ4c0YT6ekcbNyoiago+T/Phg3p2LlT2tXkxYsktG0rLbX07CkEABw8qAlzcwNcvqy464CyeQ9bV26HiLHShiPL7y3J12cheZMhylC67WLv60q3ZZnmNEtuOq56pg1klrkcLn5znIbdHgdwwPMIWli7stvcj7WD+WZDjLvABNPAXpdhU6G6Kh+BkHKHAqeaOHpUAxYW0oY7ixdrY+lS5q4/IiIJ0dFJiI1lnt00aCBGRASz/HO11KpV6TLLP/f5zG5T++1y6z4khkOTp4mrfe8AABoV0igxRLGsqtLWldvJrN/l7oeGFRvL7d+zdm/42v/OLuvzDeBWtQNGNZTWGsxtvlDhe3Wp4Y2ONh5oU6Wdwu1Z5yttXr6kZ5qkeFHgVBM+PjpKt2lqMuNv/rxOEX3F08cppGiAg6wuC7WNmHFERTSmbZEQiUW4G3UH96KZG5Rh9Ucievw3eNXohrnNFqJLDW9wOBxEj/+G1yM/ssetd9uCuc0XImrcV5zqfg6e1ZnGPMtb/YlYn0R8HvsFJrmM7GOsbYLnw8Nz3EddCQSAubkBTp1iHkXo6kpgbAzUqcPUumhqUmtaUvRo5KAySF9fguTk3DuDZ6+u3dFpD0YHDWPHss0aHSZTQoGzsJ16cxyjg4bJrNPT1AOXw8Uuj/0y67kcLipoGeFEt7O49PEC+DymOxKPy0Nz65Zy587anhtTHVPEjP8Oiy3M1HPne12BloZ2Pj5N8XrwgLkuR49mbjSzBj+4cSMV69bx4eZG1yspehxJTj2sy5H4+GSIxQXLioLMOm5trY/MTA5iY5NQo4Y+G/iOHEmVeYaZF8nJwNSp2jh+nCmWXrqUgoYNFU/o++brawBALePactuyWtoGDwor0CwYQNmfrT46JQqfkz6x3T+iU6JgrmshMzlzdEoUXA86I1Eg3y8z+8w1ROrn6yY0lCvTmtbaWozHj1NKImklrqx/pwqiMPKGy+XA1FRxFR6VONVEZqa09PfqVTIkEqaxUH4m4tXXB7ZuTWcDZ/v2etizJw2envJ344oC5s8c/RrSD3suGu21BQD8r9UqSCRizL45HYb8Cngz6hMAID0znd3nZ2Hjw4otnaWdUCi7vHlzuuIdCSlC9IxTDaT8uGHmcJgSr4YG8wyzoLPX//GH9Edl06aCnWz/sz0FS0wZFpMSzb6edWMqZt+cDgBIFHzHHz9aJY+9MELuuP51B+Fgl6NoYN5AbhtRLDpa9ierYkWqMCPFj0qcauDuXea5Tb9+hft8hpetJ0rjxqpV9/5sylVfDK43rGAJKqMa7q2jdNvqh3+iZaXWOBd+hl3H4/DwbPhbGGtTT31VZGQwM/9kV6eO4kcQhBQlCpxq4Nw55s+QobxLX76kZHv0o69f8DtzgUiQ58Yn5ZUB3xBJAtlq7V6nvdnXMeO/QygWUj7mw6RJ2rkOPUlIcaCrsIR9+MDBvn3Mj+jChYUbOcePlz4Qio9XfcqlewMfw7tmd3Y5JjVa+c4EsT6JeDvqMz6MiUHM+O8yfSsBpt8sh8OhoJlPQUHS+/wDB1Jx9Wr5bBRESh4FzhIWECD9MbCwKNznNRwO2EET8jKrys+qV6iBne77sKD5UgDA8Vf/Fmr6yoKskX+61+rJrtPR0AGHw8HkplPYdTaG1dHbtl+xp68syMhg+m5mdT0BgI4dRahXj6ppScmgwFnCFiyQ9p1TMgpeietWqwcAQJNKSnLiUpmpuoQKBoqw0LPE9o67AQAHutBNR359/66mXwxSblHgLGFeXkx1anR00fXHcnXNhJVV/u/OLfWsAABbHm8orCSVGaFxTwAALbON/5pd99q9EDXuK2obK29ARHI2caLswAx+fqkllBJCGBQ4S1ilShLo6UnkhtQrTCYmkgI1DtLgMtXJ9uZNCytJZcawwAEAAG0N5UMmZo3CRPLn6lXZNoydOhWshTghBUWBUw0UdRUtjweIRAV7EydLFyQJyu4oJW+/vYaqg2glZkhHABpoN6Swk0QIUVMUOEtIZibw9CkXEon8AO6FjcsFRAW8SbfSs1a7VrXXPl2B+WZD9t/cmzPydZ6YlGg0/8eBHbc1r5bfW8y+VjZNGylchw5RNS0pecUWOMPDw9G3b1+4u7ujb9++eP/+vdw+8fHxGDNmDLy9veHh4YGFCxciM1Pa6CIgIADe3t7w8vKCt7c3vnz5kqfj1NHMmVpwc9PD9u18JCYW7Y8ujweIC9gAUVdTF2++vcaF94GFk6hC0Nu/m8zy9tAtSBYm53hMsiAJU6/+im/pX9l1KZmqd2t4HPsIu57uAAD4eR5W+XiiutjYJLi5UTUtKXnFFjgXLFiAAQMG4Pz58xgwYADmz58vt8/WrVtRs2ZN+Pv7w9/fH//99x+CgoIAAGFhYdi4cSN27dqFM2fO4J9//oGBgUGux6mrrL6bxUFDQ1LgEmdg+FkAwMCAPrgdcROHXhxAcjFW3b5IeI74NGaC7VRhKiKSPivcr59/T7l17769YUulNf6uhH3PdqHOrmpYdncRAECswl1F0PtzCAwPQKejbdl1rpXaqPBJiKqMjCTo2FG9b4RJ+VIsgTM+Ph7Pnj2Dlxczd6CXlxeePXuGhIQEmf04HA5SUlIgFoshEAggFAphYWEBANizZw9GjBgBMzMzAICBgQG0tLRyPU5dDRggKLb34nKByMiC/alv9X/Ivu5+yhO+l8dj/q3Z2PZkEz4mfgAAXP54Ac5+jdH5mJvS81z6EIR6u2siRahaKa/1IRd0OtoGiRnfYbPDEvb767HbKulXZl/fj76LIy8PYmPIOthst4REIkGzfxQ3alr36C+IJeI8T50279YsDAroiyHnpP0x/9fqT+hqKp8snBRcejpQtSr12STqo1gCZ1RUFCwsLMD7MXgqj8eDubk5oqKiZPbz8fFBeHg4XF1d2X8ODg4AgLdv3+LTp08YOHAgevTogc2bN7ONOXI6Tl0ZGxffe/n5MaXbZ8+4CA7mYskSPlSdTM5M1wwe1bvIrAv6EIh5t2bB0a8hep3uin5neuF9YjgexgTj5OtjChvb9D/7C76kxeFB9L08ve+7b28wNmg4AOBT0kfU2llFZru7TWeEDHmGmPHShjoTL43F4jvzkJqZmutzS8stRmh9yIVdFksU/0Afe3UE255skls/osGYPH0Okj8CAZCezkFgII0OStSHWl2NgYGBsLW1xd69e5GSkoLRo0cjMDAQHh4eEIlEePnyJXbv3g2BQIBRo0bB2toa3bt3z/G4vFI275qqzMwM8rTfGemY31i8OO/HFUTbttJ5DBcv1oKRkWrHnxtyBpxF0uexsakx7Osbn6/K7DvmwnAkiGMww1XaYCdFM5593ce/O/s6Y26GwmHoopOj0Wxzzl1gAocGsK99nX2x/v56hfv92fFPtKzSEk6VnNDtUDcEvA5QuJ+WoQR3P98CBxy413IHADyKeoTxF0fJ7WtvaQ9zc8Mc05dXxfH3L43evmX+79ePS3mkAOWJckWZN8USOK2srBATEwORSAQejweRSITY2FhYWVnJ7Ofn54fly5eDy+XCwMAAbm5uuHfvHjw8PGBtbQ0PDw/w+Xzw+Xy0b98eoaGh6N69e47H5VVxTmS9c6cmPnyQdupu3z4FcXFFWRUlfwF9/JgMoVD1z3vE+6RM0PvZVMeZWBX8BwBg5qWZGGHrA/+3JzHyvPLuGrvv+cFS1wpVDauh6f76ONHtLF4kPMesG1NzTMuK1qtl8nuu41KlgXNobWbc2K/xaeBLtBXuAwDGK6RVAVlzkD758Jxd92BQKJz8GgEAzvW4UigTCdOExMr168dcu6am6YiLE+ayd/lC141yRT2RdY5Vtenp6Xj16pXCba9evUJGHqfzMDU1hZ2dHc78KGadOXMGdnZ2MDGRnVapcuXKuH79OgBAIBDgzp07qF2bmWjZy8sLN2/ehEQigVAoxN27d1G3bt1cj1NH167JdoivXbv4n9+k5rNVf9sqbgontdbT1MelPjcx3Xk2mppLq8kT0uPlgmb1CjVklsddGInupzzRdH99AECPU11kgub5XlfwasQHxPokItYnEZ/HfsE/Xf7F8AbypcCd7vvl1lU1qCazPNN5LtxtOmP/j9aws13mY7rTbKWfOejDOQBAI7MmqGZog8lNp2C/52FwOdSbq6gFBzP/C4qvSQAhucrxm//333/j6NGjCrcdP34cf//9d57faOHChfDz84O7uzv8/PywaBHTonH06NEICwsDAMyePRsPHz6Et7c3unfvDhsbG/Tp0wcA0KVLF5iamsLT0xPdu3dHrVq18Msvv+R6nDqqWlVa0jM2LvqJeGvXlm9SO2yYDmJj898N5nKfW+zrWJ9EhI+ORMOKTEksoNcldlvdXdXljr0z4JHMoOg5eT48HPYWDjDSlpYE+Tw+OlRzV7i/d81ucLBwAgAc7HIUdib1sMT1D5l9ahjVwn7Pw3C36YxYn0T86jAVUxyV9wF9/z0cABPAAWBOswVwt+mcp/ST/IuJkV6fAgH1kyXqgyPJYbgUT09P7N69W2EL1ZiYGAwfPhwBAYqfFZU2xVlVa24urToND0+Cnl4OOxeC79+B2rUV1/evW5eG/v3z19Q/6P05NLNqAUMt+QY4Vz9dVlily+fy8Xkc0//2RcJzmYY5PzvY5SjaV+ukcrpiUqLx7vtbNLduqdJxc25Mx46wrezypvbb0cLalW3Bq6ikXRioyk2xgQN1cOEC8zRp5840eHtTl5Ts6LpRrkSramNiYpR267CwsEBMTIzCbUS5iIhsjWtiiz5oAkCFCsx7hYcnYd8+2TrayZN18FOvoDzrZNNZYdAEmCrd7C4MvgBAdpYQU+2Kcsf9r9WqbOdon690WehZqhw0AWBZq5WIHv+NXZ5waYxMtxdSvLKCJgAKmkSt5Bg4dXR05LqMZImMjISOjvKBrYmUj482zM0NcO8eD6tXM61HPTyKv6GDnh7g4SFfbZvVXaWw3R/4hH3doUYHxPokok2Vduy6ijrygXNEg9Hs65IYHJ3L4aKyfhW59TaG8lXOpHjcvZvzaFCEFLccA2ebNm2wevVqhdvWrVuHNm1oxJS8OHpUEwAwebI2GjRgGgLNn5+3hlVF4f172SqMpUu1iuR9bCpUx5uRn/ByxHuF2zkcDtvgJ+sfh8OBV41ucKvaoUjSlBdnesqPOnV3YEgJpKT8Cg+X1szUqFH07QAIUUWOzzjj4uLQt29f6Ovro1OnTjAzM0NcXBwuXLiA5ORkHDp0iB3Jp7QrymecWc80K1YU48sX5l4lIiIJmpoFersCy/6sNSoqCbwiLOCVtucxNyOuo+cpZqSr3nX6YVOH7UX2XqUtb4pD9er6SElhgmdsLOWNInTdKFfUzzhz7MdpZmaGEydOYNeuXbhx4wa+ffsGIyMjtGvXDsOHD0eFCqrNJlEe3b4tjUZZQRNAiQfNn2VkALo0chzLtVJrDG8wCob8CpjTbEFJJ6fcyQqahKijXAdAqFChAn777bfiSEuZ1L176YhGb95w0agRjQea3YrWih9TEELKt1wDZ1JSEvbs2YNbt27h69evMDY2RosWLTB06FAqceYiJKT0dJDv0EEPnz8ngV98k7YQQkiplGPgjImJQf/+/aGnpyfzjDMoKAgnT57EwYMH1X4WkpLk7q64r8mnT+r5XOLxYy6cnanUSUpW9gHJevcuuXQQokyORaJVq1ahWbNmOH36NCZNmoR+/fph0qRJOH36NFxcXLBy5criSmepc/q09J5EX1+20ZFW0TRiVVn//kJwONK0eXnp4dWr0lNKJmXT27fSa3DPnpJLByHK5PgreePGDUyZMgUcjuyDeg6Hg99//x03b94s0sSVZn/8Ia3zXLIkAwcP5nNw2CK0bl06Zs+WHQTU1bUYRmQgRIk7d3jsLD6dOwupwRpRSzkGzoyMDBgYKB6qzdDQEAIaeZkVFsbFrl3M60OHNPDmjbQ1bZ8+Qjg6MgMPLF6cXhLJU2rUKPm/If1ZSUnp1k0aKTt2lB+sgxB1kGPgrFWrFi5cuKBw28WLF1GzZs0iSVRp1L69HkaOBMaM0cauXdLS5u7dadDUlA57N26cek2NpKfHjMzi6SlN16FDatZXhpRLXl7q9V0hJEuOjYN8fHwwdepUxMTEwN3dnW0cFBgYiC1bttAzTgVOnpQNOl26qP8YmzVqSGBvL0bWeP0VKzLPPePjOdDVlYBGViQlgappibrKMXC2a9cOy5Ytw8qVK/Hnn3+y6y0tLbF48WK4ubnlcDR58qT0jLGpqyttJLRqFR+enpmws2NGzaCRW0hhmjFDC7t38zF3bgbi4jjYu1cTHz/Kf1fUbZAQQrLk2o/Tw8MDHh4eePfuHduPs0aNGrkdVu7cuZOM5s1lh2eysio9Y2wOHSpERAQXmzfz8fRp8Q+uTsqP3buZRxnZx0j+/p15nJEdhwYPImoqz30PatSoAQcHBzZoxsfHY8WKFUWWsNKmZk0JDh4s6VTkH58PLFzIdKAbM4ZaB5Hi9fw5D8pHzSZEveQYOCUSCf79918sWbIE+/btg1AoRGJiIpYvX4727dvj7du3xZXOUsHbO/vr0tuwYft2PhKzzdl84QKVQEnhsbWVby3btasuYmOlRczevUvv94eUfTlW1a5YsQIBAQFo2rQpzp8/jydPniA0NBSNGzfGkSNHUKdOneJKZ6mgp1d2ngeOGCFtETRwoG6Z+VykZEkkwMuXim/EGjaUPurYtEm9um0Rkl2OgfPcuXM4cOAAqlSpgrdv36JLly5Yu3YtPDw8iit9pIQ8f04jCJHC9/lz7g8ud+5MK4aUEJJ/Of46JiUloUqVKgCAmjVrQkdHh4JmOREXJ3tpfPtWMukgZcunT9Lr6tatFIX71K5N4yUT9ZZjiVMikeDTp0/sMo/Hk1kGwAZWUrZFRXFhZEQ/aCR/hEJm8HZutvux2rXFiI1Nwtu3HJkW6RQ4ibrLMXCmpaWhU6dOkGRr7taxY0f2NYfDwfPnz4sudaTYvXiRhAMH+FiyRHYk+m/fqG8AyZ9jxzQwfbo2kpI4OHGCGbP5+HHp2M01a0p/X3bsSAOP2qIRNZdj4Hzx4kVxpYOoCRMTYNIkgVzgzN7ikRBVjB8vbWh2/jzzk/NzcLx/PxkVK0qgL9sVmhC1RC1AiEL378uO5LJrFw3jQlQXHy97w7V1KzP4wdevsuttbChoktIjxxJnmzZt5KYU09DQgLW1Nby8vNCnT58iTRwpOVnj1TZuLMKTJzzcuZPrIFOEyHF3VzzgLI0KREqzHH8Ns49PmyUzMxOfPn3Cnj17kJiYiFGjRhVZ4kjJ0ddn+qRKJICFBTO13NWrPLRtS1M9kbz7+FFxpVajRnQdkdIrx8Dp7Oyc47Zx48ZR4CzjspcMHj+mwEkKztc3A5Uq0fh6pPTK9zPO6tWrIz4+vjDTQtQUn8/8yCXR4EFERdnnec3y5QvV05LSLd+BMzQ0FJaWloWZFqKmDAyYwLlhg1YuexIiKyBAvlHZtGk0iQAp3XKsqj169KjcuszMTEREROD48eOYMmVKkSWMqA99fYAqF4iqgoOl9+WxsUkQCJjWtBYWVE1LSrccA+epU6fkD9DQgJWVFVasWIEWLVoUWcKI+jA0lP7QhYZy0agRjexCcufpqQdAev3w+aCgScqEHAPn/v37Fa5/8eIFTp06hZkzZ+LmzZtFkjCiPjSz1bZ16KBHM6UQlRw5kpr7ToSUInnunJeQkAB/f3+cPHkSL168gKOjI+bMmVOUaSNqInvLWh6PSgwkZ9m7MAFA06ZUQ0HKlhwDp1AoxOXLl3HixAncvHkTVatWRZcuXRAREYG1a9fC1NS0uNJJ1IRIxEF8PAemphRAiWJv3kifbdrbU/clUvbkGDhbtmwJDoeDnj17YtKkSahfvz4A4ODBg8WSOKIefh7lxc5On6priVLZayVojGNSFuXYHcXW1hZJSUl48uQJwsLC8P379+JKF1EjNDwaUYVQKL1gDh2iSalJ2ZNj4Ny/fz8uXLiAli1bYteuXWjZsiXGjRuH1NRUZGZmFlcaiZr4+2/pj+DduzT3E1FM8KOb5pQpGbC1peebpOzJdQCESpUqYcKECQgKCsKePXtgZmYGLpeLrl27YuXKlcWRRlLC9PSYqjdzcwmGD2d+Fbt2VTx4NyFZzzibNqXnm6RsUmnKC0dHRzg6OmLu3Lm4cOECTp48WUTJIupk/vwMGBtL0KSJCBs28Es6OUTNjR3LzL9JE1KTsipfQ+5paWnBy8sLf//9d2Gnh6ihBg3E2LYtHdrawLp16ex6euRNciKmWlpSRtFE1kQlWfN0AvSck8iTZOulZGCgfD9CSjMKnERlEyYwzzkHD6bnnOWJUMj8y0lKCvO/vb0ILi70jJOUTRQ4icpsbfP+g7h9uyYePKDLrCxo1kwPnTrlfLOUksJ0RenXL5cIS0gpplLjIEIAoFevTPj6AjY2yh9iSSRAUBAPc+dqs8ukdPv0KfcboKNHmZ+Ur1+p8y8pu6goQFSWNej7+/dchIYqvoQOH9agqtxyaO9eptX1ixf000LKLrq6SYGsWaO4e4qfn/wExqT0ymsLWVdXZmCUNm3o+SYpuyhwkgI5e1Y+QF6+zMP9+7JPAUT0O1qqpeZxZrDPn5mflJYtaWQxUnZR4CT5YmwsfWj58aPs86wDB+SD6bNnRZ4kUoSSkvL2zPLqVeaGiW6USFlGgZPky61bKezrxYu1ZLbp6Ulfb9zIjG87aFCxJIsUkeTkvAXOvn2Z1rQ1a1JrMFJ2UeAk+ZJ9IITTp2VLmAkJ0h/ZXr2YKrvQ0OJJFykaycnS1ykpwLlzGhgzRhvm5gaIjJT+vblcwNqahgwiZRsFTpJv0dHMnJw2NmIcOqSBoCBmJKH4eOaHtHnzTJnxSmNiqItCaZW9xLl8uRaGDtXByZPMDVOTJvpISQGePuUiJUU6KQAhZRX14yT5xuUyQfP9ey58fXXY9XZ2IrRrl4nDh2XnYmzYUB9v3ybRUGylUPbAuWOHfEvqgQN1cPs283NSvTqVOEnZRiVOUiDv38tfQs+f81CpkuIfz1ev6JIrbfbv18TQoTo57pMVNAEgPJz+xqRsoyucFImsbgkA8Pix9AHZ8OE5/wAT9TNlinZJJ4EQtVJsVbXh4eGYOXMmvn37BiMjI6xYsQI2NjYy+8THx2PWrFmIioqCUChEs2bNMHfuXGhoMMkMCAjAli1bIJFIwOFwsHv3blSsWDHXbaT4ZX+2aW0tfeYVHc3FunV8dO8uRLVq9CyMEFL6FFuJc8GCBRgwYADOnz+PAQMGYP78+XL7bN26FTVr1oS/vz/8/f3x33//ISgoCAAQFhaGjRs3YteuXThz5gz++ecfGPx4WJbTNlK0/v1Xcc94CwvZqtqHD6Wvly3TgpOTflEmixQSgUDx+mbNmNbSnp7yg7mvXp0ut46QsqRYAmd8fDyePXsGLy8vAICXlxeePXuGhIQEmf04HA5SUlIgFoshEAggFAphYWEBANizZw9GjBgBMzMzAICBgQG0tLRy3UaKVps2Ivz3XzLCwpIRG5uEjx+TsGhROpYsyZDZr2lTYPp02XUJCUDLlrpISirOFBNVTJ8u+z3K6mpy8mQa/v47DX36yI8QNGgQzYxCyrZiCZxRUVGwsLAA70f9HY/Hg7m5OaKiomT28/HxQXh4OFxdXdl/Dg4OAIC3b9/i06dPGDhwIHr06IHNmzdD8mPKjZy2kaJnZiaBhQWT39rawPjxQoUtZ3+eMaNuXQO8fs2jZ2hq7NIl2ac5jx6lICYmCVwu0LVrJpo0kQ4RZGYmRs+eFDRJ2adW3VECAwNha2uLvXv3IiUlBaNHj0ZgYCA8PDwgEonw8uVL7N69GwKBAKNGjYK1tTW6d++e47a8MjUtnKpDMzOqIlbm5EnFA8KfPKmJ48c1wSnH3TzV9bqJiZFdtrCQTeePSh4AwL59XHh4cAEU7gD/6po36oDyRrmizJtiCZxWVlaIiYmBSCQCj8eDSCRCbGwsrKysZPbz8/PD8uXLweVyYWBgADc3N9y7dw8eHh6wtraGh4cH+Hw++Hw+2rdvj9DQUHTv3j3HbXkVH58MsbhgpVQzMwPExVG9oyJmZga4di0Z9eopvkHp0CETa9emw9Ky/NUUqPd1I/3xOXAgFXFxigahZfaRSFIQF1e4fTjVO29KFuWNcoWRN1wuR2mBqliqak1NTWFnZ4czZ84AAM6cOQM7OzuYmJjI7Fe5cmVcv34dACAQCHDnzh3Url0bAPNc9ObNm5BIJBAKhbh79y7q1q2b6zaiPkxMlAfFy5c10KgRNRhSJ9kHap8wQYCOHXMeud3QsIgTRIiaKLZWtQsXLoSfnx/c3d3h5+eHRYsWAQBGjx6NsLAwAMDs2bPx8OFDeHt7o3v37rCxsUGfPn0AAF26dIGpqSk8PT3RvXt31KpVC7/88kuu24j64Ga72g4ezOM8VaTELFsmrVqfNy8jhz0Zpqblr7aAlE8cCbWiAUBVtUUtK2/MzZlqvdjYJPz3HxfGxhJMm6aNCxeYpwb37yfDxqZ8XZLqeN2MHauNEyekzypjY5WnL+tvGh2dJHNzVBjUMW/UBeWNcmWiqpYQRerXF8PaWoIDB6Rj2p47p1bt1cqlDx84MkEzN5s3p2HOnIxCD5qEqCu61EmxevIkGSEhyXLrW7dm+gMuWEBdU0paRIRqPwu//JKJyZOVjJRASBlEgZMUKysrCSpVkq+KPXJEWurctatwuzMQ1YwfL3vzcveu/I0OIeUZBU6iFrhcoGFDptXmzJlU6iwJHz5wEB/PQVSU9GehWbNM1KhRvp45E5IbeqBE1Eb2OR9J8VM0fnDbtjl3QSGkPKISJ1Eb0dEUOEvKkiXyozr17y/E6NH07JKQn1HgJGpjyxbprBqp1M2zWG3YID8pwrp16QrHHCakvKPASdSGp2cmpkxhOtr/PCA8IYSoCwqcRK3Urs2MdZqWlsuOpEjVqFG4Y84SUpZQ4CRqRUeH+T8tjUqcxSVdwbzTt2+nFH9CCCklKHAStaKjw3R9SEmhwFlcfm6UdelSCo0CREgO6OtB1IqhIRM4e/bUKeGUlB/fvzOBc8QIAWJjk9CwIVXTEpITCpxErWRNPZaZyWEHDydFa9YsZsCJVq2ozyYheUGBk6iVatVkR6kJC6NLtKgFB/NKOgmElCr0q0TUCocDNG1KJZ+SUKECDa1HSF5Q4CRqJzAwFZ06MbOl0Li1Ra9qVeaZpqMj3bAQkhcUOIlamj2bGQjhwQOqRixKO3dq4uNH5mdAm+5RCMkTCpxELdnZUcvO4pDVMIgQkncUOIla4mTrWmhuboA7d6jkSQhRDxQ4idqqVUv6zK1bN90STEnZVacOPdckRFUUOInasrKStvKsWZOqbgsqIQF49kz6lReJgFevqCRPiKoocBK1tXWrdBDVt2/pUi2ounUN0LatHrv88aO0PjwyMqkkkkRIqUS/RkRtVawowcyZGexyVBSNX1sYEhKY/3k/Cpv9+wuhoVFy6SGktKHASdQWhwP8/ruAXR43jlqAFobTpzUBSMeobdEisySTQ0ipQ4GTlBp37lCxqDDUri3Gp08ctG/PVNsGBlK+EqIKCpxE7S1fLn3WKRDksCNRSpSt8axQCDg46LPLjRtTwytCVEGBk6i9UaOE7Os//+SXYEpKr4QE6fPhzJ9qZn186G6EEFVQ4CSlys2bGhAKc9+PyPr6VRo4BwyQ7RPLp3sRQlRCgZOUKg8f8jBjhlZJJ6PUCQ2lrzohhYW+TaRUyD7CjZ8fFZFU5eOjU9JJIKTMoMBJSoVjx9JKOgmlUnIyEB7OwdCh9ByTkMJCgZOUChYWEtjbS0udYmoImicODvpwcdFHXJziwSNo8mpCVEeBk5Qa7u7S5qCvXtGlmxuBQNooKCBAk52wGgBCQpJx+3YyQkKSSyp5hJRa9OtDSo1ff5VWNybR0Kq5io+XLWV++SJdrlRJglq1JNDX//koQkhuKHCSUoPLBU6cSAUA/PEHtazNzfnzsiMCpaYygdPYmKpnCSkIGmuLlCo6OsyP/o0bdOnm5r//5O+Lz51LQaVKFDgJKQj69SGlir0985zOxYUGJs+Nnp78OgcHalVFSEFRVS0pVTgcoFWrTIjFNMVYbjZvpv6uhBQFCpyk1KlQQYLv30s6FeqvbVumVH7uXAoAwN8/tSSTQ0iZQVW1pNQxNpbg1SseEhIAE5OSTo36unqV+Xo7OIgRG0vNkAkpLFTiJKUO98dVW7euAWbO1EJEBFXbEkKKDwVOUupws121u3bxYW9PnRF/RvOWElJ0KHCSUif7FFnSdSWQEDWW/mPuby8vmoONkMJGgZOUOnPmZMits7U1KIGUqC+BgLm5aNlSlMuehBBVUeAkpU61ahLExiYhNjYJs2dLg+jmzZolmCr1kvmjm6sGNf8jpNBR4CSlmpaWdBSchQu1SzAl6iMzEzh8mLmJ4PNplCBCChvdj5JSzcsrEwsWSJeTk1FuBy5PSAAOHdJERAQXO3Ywgx8IhdTimJDCRoGTlGpVqkjw6lUS6tRhnnHGxHCgr18+S1mjR+vIjeFLs8gQUvioqpaUekZGwN69aQCA5s31YW+vYJDWckDRwPfaVHtNSKGjwEnKBF1daSkzIoIu6yzdutFg+IQUNvqFIaQUe/iQi5gYxc8xtbQkqFixfFZbE1KUKHCSMkFcTmbLCgvjwtzcAO/ecSAWA50766FhQ308eSL/Va5Tp5xkCiHFjAInKRNatZLv6H//PhcfP5aNVqUpKYC5uQEWLtQCAAQGauD9e+lnCwnhyR0za5b8QBGEkIKjwEnKBA0NYMeONHZ54EAdeHnpwdGxbPRN8fVlWvlkNQDi8wEdHen26dNlWwHNnJmBDh1o1CBCikKxBc7w8HD07dsX7u7u6Nu3L96/fy+3T3x8PMaMGQNvb294eHhg4cKFyMyUNm4ICAiAt7c3vLy84O3tjS9fvsgc/+7dOzRu3BgrVqwo6o9D1FC3bpnsSEIXLpStnlbVqslWu27YwEfjxvI3BYaGzDNNY2N6tklIUSm2wLlgwQIMGDAA58+fx4ABAzB//ny5fbZu3YqaNWvC398f/v7++O+//xAUFAQACAsLw8aNG7Fr1y6cOXMG//zzDwwMpOOTikQiLFiwAB06dCiuj0TUUNbkzdmV5mnHHj7kIikJsLaWDYRRUYq/us+fJ2PbtjQMGECDuxNSVIolcMbHx+PZs2fw8vICAHh5eeHZs2dISEiQ2Y/D4SAlJQVisRgCgQBCoRAWFhYAgD179mDEiBEwMzMDABgYGEBLS4s9dvv27Wjbti1sbGyK4yMRNdWkiRimprKls+/fS2fgTEtjGv/UrGmA6OjcP8OePWnQ1AR69MhEtq8GIaSQFUvgjIqKgoWFBXg8pgEDj8eDubk5oqKiZPbz8fFBeHg4XF1d2X8ODg4AgLdv3+LTp08YOHAgevTogc2bN0MiYe7CX7x4gZs3b2LYsGHF8XGImouPl72ss6bYKm2yB/z163OPhJ6e1GeTkOKgVg+CAgMDYWtri7179yIlJQWjR49GYGAgPDw8IBKJ8PLlS+zevRsCgQCjRo2CtbU1unTpgnnz5uF///sfG5jzw9S0cBqRmJnR9FbKFFfe9O8PHDwoXX70SA/u7sXy1vn2c95IJIC5ee7HTZsG7NoFDBxYdq+9svq5CgPljXJFmTfFEjitrKwQExMDkUgEHo8HkUiE2NhYWFlZyezn5+eH5cuXg8vlwsDAAG5ubrh37x48PDxgbW0NDw8P8Pl88Pl8tG/fHqGhoXB2dsbHjx8xZswYAEBiYiIkEgmSk5OxZMmSPKcxPj4ZYnHBGlSYmRkgLo4GB1WkOPNm8WLg4EHpl+bECRFGjUotlvfOD0V5w4wxm/sXf9q0JEybxryOiyv8tJU0+k4pR3mjXGHkDZfLUVqgKpaqWlNTU9jZ2eHMmTMAgDNnzsDOzg4mJiYy+1WuXBnXr18HAAgEAty5cwe1a9cGwDwXvXnzJiQSCYRCIe7evYu6devC2toa9+7dw+XLl3H58mUMHToUffr0USlokrKlQgXA11fah/HBg/zXRJSUwED5e1pjYwmiopIwfLgAAKCjQy1nCSkJxdaqduHChfDz84O7uzv8/PywaNEiAMDo0aMRFhYGAJg9ezYePnwIb29vdO/eHTY2NujTpw8AoEuXLjA1NYWnpye6d++OWrVq4Zdffimu5JNSZu5cAWJiSu/duKmpfFBctCgdPB6gp8dsGzaMWs4SUhI4kqwWNuUcVdUWrZLKG3Nzprrz+fNkhcFIHSjKm6x0r16djt9/ZwY36NxZiL1709GrFzN9WOvWmTh6NE3ufGUJfaeUo7xRrkxU1RJS0uzs9HH0qFq1hVMq+7i7HTtKW8pmlTBnzGCqob29qRUtISWBAicp0xwcpMPO+fjo5LCn+oiLk3ZDMTKSlpJbtmQ+i7OzGI8fJ2PIEKqqJaQklI5bcELyKSAgFRYW0tapEREcVKqknlW2WRo2ZKqH6tUTQUsLGDtWgC9fOODzpfv8PJIQIaT4UOAkZRrnpwF37O31ERqaDEtL9Qw8CxZIBzoYN45pPbtkCc1yQog6oapaUu6MH6+d+04lZMsWabEyPJy+noSoI/pmkjLv55lCbt1Sz4qWnyfjznqmSQhRL+r5C0JIIbp9OwXx8Ry8ecPFsGHq20Do2DHZryMFTkLUEwVOUuaZmkpgaipBnTpiVKggUdvZUrK3pgWAAgy9TAgpQlRVS8qVWrWY+tAXL9Tv0s9qsKSvL8Hdu8klnBpCiDLq9+tBSBGqVo0JnMOHq1+VbUoKU+K8eTMFNWqoZ6tfQggFTlLOxMYywentW/W79FNSmP+zxqIlhKgn9fv1IKQIDR+unqPtZGYCkZHM11FXt4QTQwjJETUOIuVK9vFd09IAHTWosb10CfD11cWzZzxwuRJoapZ0igghOaESJym3Bg1SPWrWqqWPadO0kJYGfPlS8Na5MTEcdOgAPHvGNKEVi9WzxS8hRIoCJyl35s1jhrC7cUODfa6YFxIJkJjIwd69fFSrZoB69RRPOaSKP//kyyxzufR8kxB1R4GTlDtdukifc1avbpDDnrIUBVlhAR+ZZjVWyhIRQd1QCFF3FDhJuZPfrh6JifLVqO3bF6wlj5OT7Dh7NOgBIeqPAicpl9asSVf5GEWB88WLgkW66Gh6pklIaUOBk5RLDRqoPg5sYqL0ddWq0pJiRj5m/RIKgX//1cCOHcwzzv37U/HmTZLqJyKEFDsKnKRcyj6ZtUCQt2OSkqSlw9GjpQddv656qdPTUxcTJkhb9bq7i2BoqPJpCCElgAInKZcqVpTA0pIpNZ46lbfuzNmramvXFkNbmwm+f/0lnXz67FkNmJsb4N075VWwycnAkyfSYFunjkpJJ4SUMAqcpNwaN44pNeZ1pJ6swLlnTxrc3ETo1IkZTIHPl5Zep01jguixY8pHMfj0SfZr9+pVnpNMCFEDFDhJueXqyjzn5OSxfU5W4GzdmgmYK1YwDzeNjKSB88sX5itlYqK85e7PLWf/+y9v708IUQ8UOEm5pa/PBLe8DoKwZAlTmtTTY5ZNTZnjAwOlpcvGjZlgvH277MAG2WUP1NbWYtSrl9cUE0LUAQVOUm5lBcDk5JyLnJ8+cSDO1t1SWQlVLJY+uwwPV/7VyvwxXO7QoQI8fqzC0EWEELVAgZOUW1nTd+VU4nz/ngMHB31YWioeYSirW0pmJlC7dt6G4Pv+nYm87dqp3iWGEFLyKHCSciurUdDu3cqrVc+ckW1xa2UlO9LPqFFMA6PPnzky3VUAJugq0q8f0w1FQ4PGpSWkNKLAScot7o+r/9MnLkJDuXj6VP7r8HM1blSU7D5xccx2Z2f50uahQ4pb1qamMsf8HGgJIaUDBU5CAHTooAc3Nz3cvy/7lVi9WktmeeJE2WGCLC2VlxptbMRKtwFAfDwFTkJKIwqcpFzbuTNNZtnLi2kxdPEiD+bmss81t25Nw5w5ssMM2doqD44SBTE1eyOjXr0y5XcghKg9CpykXGvbVj54paQAAwbIjooQG5uEnj0z5fpgauQw6NDkycyzzKpV9fHHH3wEBfHYRkZVq4rZ7iyEkNKFAicp1wwMgNOnU2XW/TxH5x9/qDaTyu+/S6tzY2I4SE/nYPVqLQwaJA3Gy5erPjsLIUQ9UOAk5V6zZiJs3JimdHubNsqrVFNT5dcNHCid3bphQ8VdVKytqbRJSGlFgZMQAH36ZMLTUyi3PjY2CTVr5n34PCDn4fay1K+fc8MhQoj6osBJyA8jRsgGzpxKmlmyAmerVtJ99fTkW99m17p1Zp7HxyWEqB8KnIT84OgoHcln2rQMHDmivPo2S1bgFP00CNCsWfKTfJ44kYqYmCQcPZr7eQkh6itvExESUg7o6gKnTqXCwECCBg3yVpWaFTgzMwFjYwkMDZlqWk1NYMuWNISG8tC4sQj/+58WmjUTUUmTkDKAAich2TRvrtr4sVmBUCzm4MqVFOjqSp9v9uqVyfbV7NmT+mwSUlZQ4CSkACpXZkqm7dplUktZQsoJCpyEFEClShKEhSXDzIyCJiHlBQVOQgrIwoKCJiHlCbWqJYQQQlRAgZMQQghRAQVOQgghRAUUOAkhhBAVUOAkhBBCVECBkxBCCFEBBU5CCCFEBRQ4CSGEEBVQ4CSEEEJUQIGTEEIIUQEFTkIIIUQFNFbtD1xu4UyUWFjnKYsob5SjvFGO8kY5yhvlCpo3OR3PkUgkNEI1IYQQkkdUVUsIIYSogAInIYQQogIKnIQQQogKKHASQgghKqDASQghhKiAAichhBCiAgqchBBCiAoocBJCCCEqoMBJCCGEqIACZyEIDw9H37594e7ujr59++L9+/clnaQi9fXrV4wePRru7u7w9vbGxIkTkZCQACDnvMjvttJo48aNsLW1xatXrwBQvmTJyMjAggUL0KlTJ3h7e2PevHkAKH8A4MqVK+jevTu6desGb29vBAUFASifebNixQq4ubnJfIeAosmLfOWThBTY4MGDJSdPnpRIJBLJyZMnJYMHDy7hFBWtr1+/Su7evcsu//HHH5JZs2ZJJJKc8yK/20qbp0+fSkaOHClp27at5OXLlxKJhPIly5IlSyTLli2TiMViiUQikcTFxUkkEsofsVgscXR0ZK+X58+fS5o0aSIRiUTlMm8ePHggiYyMlLRr147NE4mkaK6T/OQTBc4C+vLli8TBwUGSmZkpkUgkkszMTImDg4MkPj6+hFNWfAIDAyVDhw7NMS/yu620ycjIkPTp00fy8eNH9ktP+cJITk6WODg4SJKTk2XWU/4wgdPZ2VkSHBwskUgkkvv370s6depU7vMme+AsirzIbz7R7CgFFBUVBQsLC/B4PAAAj8eDubk5oqKiYGJiUsKpK3pisRgHDx6Em5tbjnkhkUjyta205eG6devQtWtXVKlShV1H+cL49OkTjIyMsHHjRty7dw96enqYPHkytLW1y33+cDgcrF27Fj4+PtDV1UVKSgq2bdtG1042RZEX+c0nesZJCmTJkiXQ1dXFoEGDSjopJS4kJARhYWEYMGBASSdFLWVmZuLTp0+oV68ejh8/jqlTp2LSpElITU0t6aSVuMzMTGzbtg2bN2/GlStXsGXLFvz222+UN2qKSpwFZGVlhZiYGIhEIvB4PIhEIsTGxsLKyqqkk1bkVqxYgQ8fPmDr1q3gcrk55oVEIsnXttLkwYMHePfuHdq3bw8AiI6OxsiRIzFr1qxynS9ZrK2toaGhAS8vLwBA48aNYWxsDG1t7XKfP8+fP0dsbCwcHBwAAA4ODtDR0YGWlla5z5ssRfH7kt98ohJnAZmamsLOzg5nzpwBAJw5cwZ2dnaltjokr9asWYOnT59i06ZN4PP5AHLOi/xuK03GjBmDmzdv4vLly7h8+TIsLS2xc+dOeHp6lut8yWJiYgIXFxfcunULANOaMT4+HjY2NuU+fywtLREdHY13794BAN6+fYsvX76gWrVq5T5vshTF70t+84kmsi4Eb9++xcyZM5GYmAhDQ0OsWLECNWrUKOlkFZnXr1/Dy8sLNjY20NbWBgBUrlwZmzZtyjEv8ruttHJzc8PWrVtRp04dypcfPn36hNmzZ+Pbt2/Q0NDAr7/+ijZt2lD+ADh9+jR27NgBDocDAPD19UWHDh3KZd4sXboUQUFB+PLlC4yNjWFkZISzZ88WSV7kJ58ocBJCCCEqoKpaQgghRAUUOAkhhBAVUOAkhBBCVECBkxBCCFEBBU5CCCFEBRQ4CSkiM2fOxJo1a0rkvSUSCWbNmgUnJyf88ssvJZKG/LC1tcWHDx8K5VyRkZGwt7eHSCQqlPMRkoUCJyk33Nzc0KJFC5lhzP79918MHjy4BFNVNB4+fIhbt27h2rVrOHr0aJG/3/Hjx9G/f/8if5+cuLm54fbt2+yytbU1QkJC2HFICSksFDhJuSISibBv376STobKVC01RUREoFKlStDV1S2iFEllZmaqxTkIKS4UOEm5MnLkSOzatQuJiYly2z5//gxbW1uZH/HBgwfj33//BcCUqvr164fly5fD0dER7du3x6NHj3D8+HG0adMGzZs3x4kTJ2TO+fXrVwwfPhz29vYYNGgQIiIi2G1v377F8OHD4ezsDHd3dwQEBLDbZs6ciQULFmD06NFo0qQJ7t27J5femJgYjBs3Ds7OzujYsSOOHDkCgClFz507F48fP4a9vT3Wr18vd2zWZ1myZAkcHBzg4eGBO3fusNuTkpIwe/ZsuLq6olWrVlizZg0bvLPng7OzM3777TcsWLCAfT9HR0e5vMs6Lnup1NbWFgcOHECnTp3QqVMndv21a9fQvn17uLi4YMWKFRCLxQCAjx8/YsiQIXBxcYGLiwumTJnC/h2nTZuGyMhIjBs3Dvb29tixY4fc31NZfgHAhg0bMHnyZEyfPh329vbo0qULwsLC2O3bt29Hq1atYG9vD3d3d5m8IuUPBU5SrjRo0ADOzs7YuXNnvo4PDQ2Fra0t7t27By8vL/z+++8ICwvDhQsX8Oeff2Lx4sVISUlh9/f394ePjw/u3buHunXrYurUqQCA1NRUjBgxAl5eXrh9+zZWr16NRYsW4fXr1+yxZ86cwbhx4/Do0SN28O/spkyZAktLS9y4cQPr16/H6tWrcefOHfTu3RuLFi1CkyZNEBISAl9fX6WfpUqVKrh79y58fX0xceJEfPv2DQAwY8YMaGhoICgoCCdPnsStW7dkgmDWsbdv38aff/4p837BwcF5zs+LFy/iyJEjMjcNFy5cwLFjx3DixAlcvnwZx44dA8A8tx07dixu3LiBc+fOITo6Ghs2bAAA/Pnnn7C2tsbWrVsREhKC0aNH5zm/sly+fBldunRBcHAw3NzcsGTJEgDAu3fvcODAARw9ehQhISHYuXMnKlWqlOfPSMoeCpyk3PH19YWfnx8SEhJUPrZy5cro1asXeDwePD09ERUVhQkTJoDP58PV1RV8Ph8fP35k92/bti2cnJzA5/Px22+/4fHjx4iKisLVq1dRqVIl9OrVCxoaGqhfvz7c3d1x/vx59tj27dvDwcEBXC4XWlpaMumIiorCw4cPMXXqVGhpacHOzg69e/fGqVOn8vxZTExMMHToUGhqasLT0xPVq1fH1atX8eXLF1y/fh2zZ8+Grq4uTE1NMWzYMJw9e5Y91tzcHIMHD4aGhgY7XnF+jBkzBkZGRjLnGD16NIyMjGBtbY0hQ4awA3BXq1YNLVu2BJ/Ph4mJCYYPH44HDx7k6X3ykl8ODg5o06YNeDweunXrhhcvXgBg5mgUCAR4+/YthEIhKleujKpVq+b7M5PSj6YVI+VOnTp10LZtW2zfvh01a9ZU6VhTU1P2ddaPfcWKFdl1WlpaMiVOS0tL9rWenh4qVKiA2NhYREREIDQ0lK3WBJjnmF27dmWXc5raKDY2FhUqVIC+vj67ztraGk+fPs3zZ7GwsGAHFM86PjY2FpGRkcjMzISrqyu7TSwWy6Qn++cqCEWfMfu6SpUqITY2FgAQHx+PpUuXIjg4GCkpKZBIJDA0NMzT++Qlv7L/HbW1tZGRkYHMzExUq1YNs2fPxoYNG/DmzRu4urpi5syZsLCwUPnzkrKBAicpl3x9fdGjRw+MGDGCXZfVkCY9PZ39gY2LiyvQ+0RHR7OvU1JS8P37d5ibm8PKygpOTk7YvXt3vs5rbm6O79+/Izk5mU1rVFSUSj/mMTExkEgkbPCMioqCm5sbLC0twefzcffuXWhoKP6JyB5wFS0DgI6ODtLS0tjlL1++5HqerHTUrl0bANOlxNzcHADw119/gcPh4PTp0zA2NsbFixexePHiPH3WguaXt7c3vL29kZycjPnz52PVqlX4888/83QsKXuoqpaUS9WqVYOnpyf279/PrjMxMYGFhQVOnToFkUiEo0eP4tOnTwV6n2vXriE4OBgCgQDr1q1D48aNYWVlhbZt2+L9+/c4efIkhEIhhEIhQkND8fbt2zyd18rKCvb29li9ejUyMjLw4sULHD16FN7e3nlOW0JCAvbt2wehUIhz587h7du3aNOmDczNzdGyZUv88ccfSE5OhlgsxsePH3H//n2l5zI1NUVMTAwEAgG7zs7ODhcuXEBaWho+fPiQ524xO3fuxPfv3xEVFYV9+/bB09MTAHPjoaurC0NDQ8TExODvv/+WOa5ixYpK/14Fya93797hzp07EAgE4PP50NLSoi4u5RwFTlJuTZgwQaZPJwAsWbIEO3fuhIuLC968eQN7e/sCvYeXlxc2bdoEFxcX/Pfff2wpRV9fHzt37kRAQABatWoFV1dXrFq1Sibw5Gb16tWIiIhAq1atMHHiREyaNAktW7bM8/GNGjXChw8f0KxZM6xduxbr16+HsbExAGDlypUQCoXw9PSEk5MTfH19cyx9N2vWDLVq1YKrqytcXFwAgH1+2qJFC8yYMSPPQb19+/bo2bMnunfvjrZt27IDOEycOBHPnj2Do6MjxowZI9MSF2Cel27ZsgWOjo4KG3/lN78EAgH++usvuLi4wNXVFQkJCfjtt9/y9FlI2UTzcRJSDh0/fhz//vsvDh48WNJJIaTUoRInIYQQogIKnIQQQogKqKqWEEIIUQGVOAkhhBAVUOAkhBBCVECBkxBCCFEBBU5CCCFEBRQ4CSGEEBVQ4CSEEEJU8H+DOBgQqaCt8AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Process perturbations data\n",
    "perturbations_reset = perturbations.reset_index()\n",
    "perturbations_reset.columns = ['# Perturbations', 'Perturbed edge', 'Meta gradient', 'Accuracy including perturbed edges (surrogate)', 'Accuracy excluding perturbed edges (surrogate)', 'Accuracy unseen (surrogate)', 'Loss including perturbed edges (surrogate)', 'Loss excluding perturbed edges (surrogate)', 'Loss unseen (surrogate)', 'Accuracy including perturbed edges (target)', 'Accuracy excluding perturbed edges (target)', 'Accuracy unseen (target)', 'Loss including perturbed edges (target)', 'Loss excluding perturbed edges (target)', 'Loss unseen (target)', 'AUROC including perturbed edges (surrogate)', 'AUROC excluding perturbed edges (surrogate)', 'AUROC unseen (surrogate)', 'AUROC including perturbed edges (target)', 'AUROC excluding perturbed edges (target)', 'AUROC unseen (target)']\n",
    "perturbations_reset\n",
    "\n",
    "# Figure specifications\n",
    "sns.set_theme()\n",
    "figsize = (7, 6)\n",
    "print('File name: ', filename)\n",
    "\n",
    "# Loss on training data plot\n",
    "plt.figure(figsize = figsize)\n",
    "sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'Loss excluding perturbed edges (surrogate)', color = 'blue').set_title('Comparison of loss on training data ({})'.format(algorithm))\n",
    "fig = sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'Loss excluding perturbed edges (target)', color = 'green').get_figure()\n",
    "plt.xlabel('Number of perturbations')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(labels = ['Loss (surrogate)', 'Loss (target)'])\n",
    "if save_results: \n",
    "    fig.savefig('plots/' + dataset + '/losses-' + filename + '.png')\n",
    "plt.show()\n",
    "\n",
    "# Loss on unseen data plot\n",
    "plt.figure(figsize = figsize)\n",
    "sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'Loss unseen (surrogate)', color = 'blue').set_title('Comparison of loss on unseen data ({})'.format(algorithm))\n",
    "fig = sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'Loss unseen (target)', color = 'green').get_figure()\n",
    "plt.xlabel('Number of perturbations')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(labels = ['Loss unseen (surrogate)', 'Loss unseen (target)'])\n",
    "if save_results: \n",
    "    fig.savefig('plots/' + dataset + '/losses-unseen-' + filename + '.png')\n",
    "plt.show()\n",
    "\n",
    "# Accuracy on training data plot\n",
    "plt.figure(figsize = figsize)\n",
    "sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'Accuracy excluding perturbed edges (surrogate)', color = 'blue').set_title('Comparison of accuracy on training data ({})'.format(algorithm))\n",
    "fig = sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'Accuracy excluding perturbed edges (target)', color = 'green').get_figure()\n",
    "plt.xlabel('Number of perturbations')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(labels = ['Accuracy (surrogate)', 'Accuracy (target)'])\n",
    "if save_results:\n",
    "    fig.savefig('plots/' + dataset + '/accuracy-' + filename + '.png')\n",
    "plt.show()\n",
    "\n",
    "# Accuracy on unseen data plot\n",
    "plt.figure(figsize = figsize)\n",
    "sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'Accuracy unseen (surrogate)', color = 'blue').set_title('Comparison of accuracy on unseen data ({})'.format(algorithm))\n",
    "fig = sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'Accuracy unseen (target)', color = 'green').get_figure()\n",
    "plt.xlabel('Number of perturbations')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(labels = ['Accuracy unseen (surrogate)', 'Accuracy unseen (target)'])\n",
    "if save_results: \n",
    "    fig.savefig('plots/' + dataset + '/accuracy-unseen-' + filename + '.png')\n",
    "plt.show()\n",
    "\n",
    "# AUROC on training data plot\n",
    "plt.figure(figsize = figsize)\n",
    "sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'AUROC excluding perturbed edges (surrogate)', color = 'blue').set_title('Comparison of AUROC on training data ({})'.format(algorithm))\n",
    "fig = sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'AUROC excluding perturbed edges (target)', color = 'green').get_figure()\n",
    "plt.xlabel('Number of perturbations')\n",
    "plt.ylabel('AUROC')\n",
    "plt.legend(labels = ['AUROC (surrogate)', 'AUROC (target)'])\n",
    "if save_results:\n",
    "    fig.savefig('plots/' + dataset + '/AUROC-' + filename + '.png')\n",
    "plt.show()\n",
    "\n",
    "# AUROC on unseen data plot\n",
    "plt.figure(figsize = figsize)\n",
    "sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'AUROC unseen (surrogate)', color = 'blue').set_title('Comparison of AUROC on unseen data ({})'.format(algorithm))\n",
    "fig = sns.lineplot(data = perturbations_reset, x = '# Perturbations', y = 'AUROC unseen (target)', color = 'green').get_figure()\n",
    "plt.xlabel('Number of perturbations')\n",
    "plt.ylabel('AUROC')\n",
    "plt.legend(labels = ['AUROC unseen (surrogate)', 'AUROC unseen (target)'])\n",
    "if save_results:\n",
    "    fig.savefig('plots/' + dataset + '/AUROC-unseen-' + filename + '.png')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "6f38835821387ecea7238337192aa99e87ed1a9c9c1fa6562e207de7e0c31193"
  },
  "kernelspec": {
   "display_name": "Python 3.7.11 ('PyG': conda)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}