examples/quickstart_ja/rec/rec_synthetic_discrete_zoo_ja.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quickstart: 様々なオフ方策推定量によるオフ方策評価 (離散行動空間)\n",
    "\n",
    "このノートブックでは推薦システムの人工データ上で様々なOPE推定量 (モデルベース，重点サンプリング，周辺化重点サンプリング，double reinforcement learningなど) を紹介します．\n",
    "\n",
    "特に、以下の2つの順番で解説していきます: \n",
    "1. セットアップ, 人工データの生成とオフライン強化学習\n",
    "2. 様々なオフ方策推定量によるオフ方策評価\n",
    "\n",
    "このライブラリは，オンラインとオフラインの方策学習およびモデルベースのオフ方策評価の一部で [d3rlpy](https://github.com/takuseno/d3rlpy)のアルゴリズムを利用しています．\n",
    "また、実装のワークフローは[Open Bandit Pipeline](https://github.com/st-tech/zr-obp)を参考にしています．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SCOPE-RL モジュールのインポート\n",
    "import scope_rl\n",
    "from recgym import RECEnv\n",
    "from scope_rl.dataset import SyntheticDataset\n",
    "from scope_rl.policy import OnlineHead\n",
    "from scope_rl.policy import EpsilonGreedyHead\n",
    "from scope_rl.policy import SoftmaxHead\n",
    "from scope_rl.ope.online import (\n",
    "    calc_on_policy_policy_value,\n",
    "    visualize_on_policy_policy_value,\n",
    ")\n",
    "\n",
    "# d3rlpy アルゴリズムのインポート\n",
    "from d3rlpy.algos import DiscreteRandomPolicyConfig as RandomPolicyConfig\n",
    "from d3rlpy.preprocessing import MinMaxObservationScaler\n",
    "\n",
    "# その他のライブラリのインポート\n",
    "import gym\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from glob import glob\n",
    "from tqdm import tqdm\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1.2\n"
     ]
    }
   ],
   "source": [
    "# バージョン\n",
    "print(scope_rl.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random stateの設定\n",
    "random_state = 12345"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# logディレクトリ\n",
    "from pathlib import Path\n",
    "Path(\"logs/\").mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# warnings\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. セットアップ, 人工データの生成とオフライン強化学習\n",
    "まず初めに推薦システム環境を紹介します．\n",
    "\n",
    "#### 強化学習での推薦システム環境のセットアップ\n",
    "推薦システムにおいて, 強化学習エージェントの目的は累積報酬を最大化することです．\n",
    "この問題を(部分観測)マルコフ決定過程((PO)MDP)として定式化します．\n",
    "- `状態`: \n",
    "   - ユーザーの持つ特徴ベクトルで、エージェントが提示する行動に応じて時間と共に変化する.\n",
    "   - 真の状態が観測できない場合，エージェントは状態の代わりにノイズがのった観測を用いる．\n",
    "- `行動`: どのアイテムをユーザに提示するかを表す．\n",
    "- `報酬`: ユーザーの興味の大きさを表す．0/1の二値または連続量．\n",
    "\n",
    "\n",
    "より詳細に環境の引数を確認したい場合は，次のノートブックを参照してください．[examples/quickstart_ja/rec/rec_synthetic_customize_env_ja.ipynb](https://github.com/hakuhodo-technologies/scope-rl/blob/main/examples/quickstart_ja/rec/rec_synthetic_customize_env_ja.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 標準的な環境\n",
    "env = gym.make(\"RECEnv-v0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 人工データ生成\n",
    "オフ方策評価の目標は，過去に集められたデータを用いて新たな方策を評価することです．\n",
    "そこで次のステップとして、データ収集方策$\\pi_0$によりデータセットを収集します（今回はシミュレーション環境をオンライン環境とみなした人工データです）．\n",
    "\n",
    "$$\\mathcal{D}_0 := \\{ \\{ s_t, a_t, s_{t+1}, r_t \\}_{t=1}^T \\}_{i=1}^n \\sim \\prod_{i=1}^n d_{s_0}(s_0) \\prod_{t=1}^T \\pi_0(a_t | s_t) p(s_{t+1} | s_t, a_t) p(r_t | s_t, a_t),$$\n",
    "\n",
    "ここで $s_t \\in \\mathcal{S}$ は時刻$t$で観測された状態，$a_t$ はデータ収集方策 $\\pi_0$によって選択された行動，$r_t$は状態と行動に応じて観測された報酬を表します．\n",
    "\n",
    "`SyntheticDataset` は以下の引数を持っています:\n",
    "- `env`: 前のセクションで紹介した推薦システム環境を定義.\n",
    "- `max_episode_steps`: 1エピソードにおける最大の時刻.\n",
    "\n",
    "より詳細にデータ収集と可視化について知りたい場合は，以下を参照してください．[examples/quickstart_ja/rec/rec_synthetic_data_collection_ja.ipynb](https://github.com/hakuhodo-technologies/scope-rl/blob/main/examples/quickstart_ja/rec/rec_synthetic_data_collection_ja.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# データ収集方策\n",
    "from d3rlpy.algos import DoubleDQNConfig\n",
    "from d3rlpy.models.encoders import VectorEncoderFactory\n",
    "from d3rlpy.models.q_functions import MeanQFunctionFactory\n",
    "from d3rlpy.dataset import create_fifo_replay_buffer\n",
    "from d3rlpy.algos import LinearDecayEpsilonGreedy\n",
    "\n",
    "# モデル\n",
    "ddqn = DoubleDQNConfig(\n",
    "    encoder_factory=VectorEncoderFactory(hidden_units=[30, 30]),\n",
    "    q_func_factory=MeanQFunctionFactory(),\n",
    "    target_update_interval=100,\n",
    ").create(device=device)\n",
    "\n",
    "# 再生バッファの設定\n",
    "buffer = create_fifo_replay_buffer(\n",
    "    limit=10000,\n",
    "    env=env,\n",
    ")\n",
    "\n",
    "# 探索\n",
    "explorer = LinearDecayEpsilonGreedy(\n",
    "    start_epsilon=1.0,\n",
    "    end_epsilon=0.1,\n",
    "    duration=1000,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 学習の開始\n",
    "# 事前学習したモデルを利用する場合はスキップ\n",
    "ddqn.fit_online(\n",
    "    env,\n",
    "    buffer,\n",
    "    explorer=explorer,\n",
    "    eval_env=env,\n",
    "    n_steps=100000,\n",
    "    n_steps_per_epoch=1000,\n",
    "    update_start_step=1000,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# モデルの保存\n",
    "ddqn.save_model(\"d3rlpy_logs/ddqn.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-07-22 18:11:27 [warning  ] Parameters will be reinitialized.\n"
     ]
    }
   ],
   "source": [
    "# モデルのリロード\n",
    "ddqn.build_with_env(env)\n",
    "ddqn.load_model(\"d3rlpy_logs/ddqn.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "behavior_policy = EpsilonGreedyHead(\n",
    "    ddqn,\n",
    "    n_actions=env.action_space.n,\n",
    "    epsilon=0.3,\n",
    "    name=\"ddqn_epsilon_0.3\",\n",
    "    random_state=random_state,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# データセットクラスの初期化\n",
    "dataset = SyntheticDataset(\n",
    "    env=env,\n",
    "    max_episode_steps=env.step_per_episode,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# データ収集方策によってログデータを集める\n",
    "# ログデータがある存在する場合はスキップ\n",
    "train_logged_dataset = dataset.obtain_episodes(\n",
    "    behavior_policies=behavior_policy,\n",
    "    n_trajectories=10000,\n",
    "    obtain_info=False,\n",
    "    random_state=random_state,\n",
    ")\n",
    "test_logged_dataset = dataset.obtain_episodes(\n",
    "    behavior_policies=behavior_policy,\n",
    "    n_trajectories=10000,\n",
    "    obtain_info=False,\n",
    "    random_state=random_state,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"logs/train_dataset_discrete_ddqn.pkl\", \"wb\") as f:\n",
    "    pickle.dump(train_logged_dataset, f)\n",
    "with open(\"logs/test_dataset_discrete_ddqn.pkl\", \"wb\") as f:\n",
    "    pickle.dump(test_logged_dataset, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"logs/train_dataset_discrete_ddqn.pkl\", \"rb\") as f:\n",
    "    train_logged_dataset = pickle.load(f)\n",
    "with open(\"logs/test_dataset_discrete_ddqn.pkl\", \"rb\") as f:\n",
    "    test_logged_dataset = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### オフライン強化学習\n",
    "さて，データ収集が済んだところで，[d3rlpy](https://github.com/takuseno/d3rlpy)のアルゴリズムを用いていくつかの\"候補\"方策を学習します．これは，オフ方策評価では評価対象となる新たな方策に対応します．\n",
    "\n",
    "オフライン強化学習の手順について詳細に知りたい方は，以下を参照してください[examples/quickstart_ja/rec/rec_synthetic_discrete_basic_ja.ipynb](https://github.com/hakuhodo-technologies/scope-rl/blob/main/examples/quickstart_ja/rec/rec_synthetic_discrete_basic_ja.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# d3rlpyのモジュールのインポート\n",
    "from d3rlpy.dataset import MDPDataset\n",
    "from d3rlpy.algos import DiscreteCQLConfig as CQLConfig\n",
    "\n",
    "# d3rlpy用にオフラインデータセットを変形\n",
    "offlinerl_dataset = MDPDataset(\n",
    "    observations=train_logged_dataset[\"state\"],\n",
    "    actions=train_logged_dataset[\"action\"],\n",
    "    rewards=train_logged_dataset[\"reward\"],\n",
    "    terminals=train_logged_dataset[\"done\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conservative Q-Learning 方策を利用\n",
    "cql = CQLConfig(\n",
    "    encoder_factory=VectorEncoderFactory(hidden_units=[30, 30]),\n",
    "    q_func_factory=MeanQFunctionFactory(),\n",
    ").create(device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cql.fit(\n",
    "    offlinerl_dataset,\n",
    "    n_steps=10000,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# モデルの保存\n",
    "cql.save_model(\"d3rlpy_logs/cql_discrete.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-07-22 18:13:08 [warning  ] Parameters will be reinitialized.\n"
     ]
    }
   ],
   "source": [
    "# モデルのリロード\n",
    "cql.build_with_env(env)\n",
    "cql.load_model(\"d3rlpy_logs/cql_discrete.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 様々なオフ方策評価によるオフ方策評価\n",
    "評価対象の方策が学習できたところで，方策の性能指標となる様々な統計量を，オフ方策評価を用いて（オフラインで）推定していきます．\n",
    "\n",
    "#### 推定目標\n",
    "ここでは，方策の期待性能 (*方策価値*) を推定します．\n",
    "\n",
    "$$ V(\\pi) := \\mathbb{E}\\left[\\sum_{t=1}^T \\gamma^{t-1} r_t \\mid \\pi \\right] $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SCOPE-RLからのopeモジュールのインポート\n",
    "from scope_rl.ope import CreateOPEInput\n",
    "from scope_rl.ope import OffPolicyEvaluation as OPE\n",
    "\n",
    "# 標準的な推定量\n",
    "from scope_rl.ope.discrete import DirectMethod as DM\n",
    "from scope_rl.ope.discrete import TrajectoryWiseImportanceSampling as TIS\n",
    "from scope_rl.ope.discrete import PerDecisionImportanceSampling as PDIS\n",
    "from scope_rl.ope.discrete import DoublyRobust as DR\n",
    "\n",
    "# self normalized推定量\n",
    "from scope_rl.ope.discrete import SelfNormalizedTIS as SNTIS\n",
    "from scope_rl.ope.discrete import SelfNormalizedPDIS as SNPDIS\n",
    "from scope_rl.ope.discrete import SelfNormalizedDR as SNDR\n",
    "\n",
    "# marginal推定量\n",
    "from scope_rl.ope.discrete import StateActionMarginalIS as SAMIS\n",
    "from scope_rl.ope.discrete import StateActionMarginalDR as SAMDR\n",
    "from scope_rl.ope.discrete import StateMarginalIS as SMIS\n",
    "from scope_rl.ope.discrete import StateMarginalDR as SMDR\n",
    "from scope_rl.ope.discrete import StateActionMarginalSNIS as SAMSNIS\n",
    "from scope_rl.ope.discrete import StateActionMarginalSNDR as SAMSNDR\n",
    "from scope_rl.ope.discrete import StateMarginalSNIS as SMSNIS\n",
    "from scope_rl.ope.discrete import StateMarginalSNDR as SMSNDR\n",
    "\n",
    "# double reinforcement learning推定量\n",
    "from scope_rl.ope.discrete import DoubleReinforcementLearning as DRL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from d3rlpy.algos import DiscreteRandomPolicyConfig\n",
    "\n",
    "random = DiscreteRandomPolicyConfig().create(device=device)\n",
    "random.build_with_env(env)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 評価方策を定義\n",
    "cql_ = EpsilonGreedyHead(\n",
    "    base_policy=cql,\n",
    "    n_actions=env.action_space.n,\n",
    "    name=\"cql\",\n",
    "    epsilon=0.0,\n",
    "    random_state=random_state,\n",
    ")\n",
    "ddqn_ = EpsilonGreedyHead(\n",
    "    base_policy=ddqn,\n",
    "    n_actions=env.action_space.n,\n",
    "    name=\"ddqn\",\n",
    "    epsilon=0.0,\n",
    "    random_state=random_state,\n",
    ")\n",
    "random_ = EpsilonGreedyHead(\n",
    "    base_policy=random,\n",
    "    n_actions=env.action_space.n,\n",
    "    name=\"random\",\n",
    "    epsilon=1.0,\n",
    "    random_state=random_state,\n",
    ")\n",
    "evaluation_policies = [cql_, ddqn_, random_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# まずOPEのインプットを準備\n",
    "prep = CreateOPEInput(\n",
    "    env=env,\n",
    "    model_args={\n",
    "        \"fqe\": {\n",
    "            \"encoder_factory\": VectorEncoderFactory(hidden_units=[30, 30]),\n",
    "            \"q_func_factory\": MeanQFunctionFactory(),\n",
    "            \"learning_rate\": 1e-4,\n",
    "        },\n",
    "    },\n",
    "    state_scaler=MinMaxObservationScaler(\n",
    "        minimum=test_logged_dataset[\"state\"].min(axis=0),\n",
    "        maximum=test_logged_dataset[\"state\"].max(axis=0),\n",
    "    ),\n",
    "    bandwidth=1.0,\n",
    "    gamma=0.95,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算に時間がかかります\n",
    "input_dict = prep.obtain_whole_inputs(\n",
    "    logged_dataset=test_logged_dataset,\n",
    "    evaluation_policies=evaluation_policies,\n",
    "    require_value_prediction=True,\n",
    "    require_weight_prediction=True,\n",
    "    n_trajectories_on_policy_evaluation=100,\n",
    "    random_state=random_state,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"logs/ope_input_dict_discrete_zoo.pkl\", \"wb\") as f:\n",
    "    pickle.dump(input_dict, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"logs/ope_input_dict_discrete_zoo.pkl\", \"rb\") as f:\n",
    "    input_dict = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "basic_estimators = [DM(), TIS(), PDIS(), DR(), SNTIS(), SNPDIS(), SNDR()]\n",
    "state_marginal_estimators = [SMIS(), SMDR(), SMSNIS(), SMSNDR()]\n",
    "state_action_marginal_estimators = [SAMIS(), SAMDR(), SAMSNIS(), SAMSNDR()]\n",
    "drl_estimators = [DRL()]\n",
    "all_estimators = (\n",
    "    basic_estimators\n",
    "    + state_marginal_estimators\n",
    "    + state_action_marginal_estimators\n",
    "    + drl_estimators\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "basic_estimators_name = [\"dm\", \"tis\", \"pdis\", \"dr\", \"sntis\", \"snpdis\", \"sndr\"]\n",
    "state_marginal_estimators_name = [\"sm_is\", \"sm_dr\", \"sm_snis\", \"sm_sndr\"]\n",
    "state_action_marginal_estimators_name = [\"sam_is\", \"sam_dr\", \"sam_snis\", \"sam_sndr\"]\n",
    "drl_estimators_name = [\"drl\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "ope = OPE(\n",
    "    logged_dataset=test_logged_dataset,\n",
    "    ope_estimators=all_estimators,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cql':            policy_value  relative_policy_value\n",
       " on_policy      1.052753               1.000000\n",
       " dm           109.204625             103.732442\n",
       " tis            0.943698               0.896410\n",
       " pdis           0.994452               0.944621\n",
       " dr            17.782391              16.891326\n",
       " sntis          1.184691               1.125326\n",
       " snpdis         0.983714               0.934421\n",
       " sndr           1.058056               1.005038\n",
       " sm_is          0.718542               0.682536\n",
       " sm_dr         -9.388991              -8.918513\n",
       " sm_snis        0.717821               0.681851\n",
       " sm_sndr      -15.307240             -14.540203\n",
       " sam_is         1.323398               1.257084\n",
       " sam_dr         4.175565               3.966330\n",
       " sam_snis       1.391744               1.322005\n",
       " sam_sndr       0.212890               0.202222\n",
       " drl           12.804063              12.162459,\n",
       " 'ddqn':            policy_value  relative_policy_value\n",
       " on_policy      0.921953               1.000000\n",
       " dm           180.812627             196.119099\n",
       " tis            0.971401               1.053634\n",
       " pdis           0.949966               1.030384\n",
       " dr             1.256556               1.362928\n",
       " sntis          0.960774               1.042107\n",
       " snpdis         0.935800               1.015019\n",
       " sndr           3.485956               3.781056\n",
       " sm_is          0.747866               0.811176\n",
       " sm_dr          5.168330               5.605849\n",
       " sm_snis        0.747824               0.811131\n",
       " sm_sndr       26.269648              28.493473\n",
       " sam_is         1.012810               1.098548\n",
       " sam_dr         6.576351               7.133064\n",
       " sam_snis       1.063689               1.153735\n",
       " sam_sndr       2.094914               2.272256\n",
       " drl           18.679539              20.260832,\n",
       " 'random':            policy_value  relative_policy_value\n",
       " on_policy     -0.358749                    NaN\n",
       " dm            -1.121451                    NaN\n",
       " tis            1.212819                    NaN\n",
       " pdis          -0.057954                    NaN\n",
       " dr            -1.045771                    NaN\n",
       " sntis          0.569506                    NaN\n",
       " snpdis        -0.053441                    NaN\n",
       " sndr          -1.034604                    NaN\n",
       " sm_is          0.616444                    NaN\n",
       " sm_dr         -0.680751                    NaN\n",
       " sm_snis        0.612679                    NaN\n",
       " sm_sndr       -1.034101                    NaN\n",
       " sam_is         0.113369                    NaN\n",
       " sam_dr        -0.619436                    NaN\n",
       " sam_snis       0.122395                    NaN\n",
       " sam_sndr      -0.588125                    NaN\n",
       " drl           -0.885275                    NaN}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# opeによる性能評価\n",
    "(\n",
    "    policy_value_df_dict,\n",
    "    policy_value_interval_df_dict,\n",
    ") = ope.summarize_off_policy_estimates(\n",
    "    input_dict,\n",
    "    random_state=random_state,\n",
    ")\n",
    "# 性能推定結果（辞書）\n",
    "policy_value_df_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# opeの結果の比較・可視化\n",
    "ope.visualize_off_policy_estimates(\n",
    "    input_dict,\n",
    "    compared_estimators=basic_estimators_name,\n",
    "    random_state=random_state,\n",
    "    sharey=False,\n",
    ")\n",
    "# データ収集方策に対する相対方策価値\n",
    "# ope.visualize_off_policy_estimates(input_dict, random_state=random_state, is_relative=True, sharey=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NCQnRU089ZXt1AAAAgJOzeAz1uHHj9MYbbyg1NVW+vr46efKkhg4dquDg4NqsDwAAAHBqFgfqnj17at68eTpw4IBKSkr04IMPqmPHjrVZGwAAAOD0avRgl7CwMA0cOLC2agEAAABcTo0CdXWys7OVn58vf3//KsdUAwAAAPWVxYF627Ztat++vUJCQszr9u3bp0WLFuns2bPmdVFRUZo4caJuvPFG+1YKAAAAOCGLZ/kovyGxXEpKiubOnauCggLdcccdeuSRRzR8+HBlZmZqzpw5OnXqVG3UCwAAADgVq4d8fPbZZwoODtbcuXPl5+dnXp+UlKRnnnlGS5cu1R/+8Ae7FAkAAAA4K4uvUP9aWVmZTp48qaSkpAphWpKCgoKUmJioo0eP2qVAAAAAwJlZFaivXLkiSWrWrFmV28PCwlRQUGB9VQAAAICLqNGQj7179yo3N1eS1LhxY2VnZ1fZLicnR76+vjYXBwAAADi7GgXqrVu3auvWreblnTt3KjExsVK7gwcPqkWLFrZXBwAAADg5iwP1W2+9VWmdwWCotC4vL0/NmjVTly5dbCoMAAAAcAUWB+rQ0FCL2vn7+2vKlClWFwQAAAC4Ers8KdFVHDx4UJs3b9YPP/ygCxcuKCAgQJ06ddK9996rwMDA6+7/r3/9S0uWLKm03tPTU5988kltlAwAAAAnV+NAvXfvXm3dulUnT55Udna2SkpK1LhxY0VGRurmm2/WwIEDFRwcXBu12uyTTz5Rfn6+evfurYiICJ0/f17//ve/tWfPHv3v//6vxY9NnzRpkho3bmxednOzarIUAAAA1AMWB+ri4mL97W9/04EDByqsd3d3V2RkpLKysrRs2TKtXr1a//Vf/6X4+Hi7F2urBx54QB07dqwQgLt06aJZs2ZpzZo1Gjt2rEXH6d27t/z9/WurTAAAALgQiwN1cnKyDh48qIceekhxcXFq1KiRjhw5onfffVcxMTG67777dObMGS1evFhvvfWWgoKCFBMTU5u111hV9cTExMjPz09nzpyp0bEKCwvl7e1d5Y2ZAAAAaDgsHquwbds2DR48WEOGDFGTJk3k5eWlLl26aNKkSfr666918eJFtWjRQtOnT1fbtm21dOnS2qzbboqKilRUVFSjK87Tpk3TxIkT9cADD+jNN980z80NAACAhsfiK9SXLl2qcm7pFi1ayGg0Kj09XU2bNpXBYFB8fLySk5PtWmhtWbVqlcrKytSnT5/rtvXz89OQIUPUvn17eXh4KDU1Vf/+97914sQJvfLKK/Lx8alyv9LSUpWWlpqXDQaDvL29zX/DvsrfU95boOb43NQuvp8A2zjrZ8fiQB0WFqZDhw5VepDL4cOHJanCDX3u7u4ymUz2qbAaRqNRZWVlFrX19PSs8j/AkSNHtGTJEt16663q1KnTdY8zbNiwCsu9e/dWu3bt9Oabb2rt2rW68847q9xv2bJlFWYHiY6O1ty5cy2eihDWCQ8Pd3QJ9dxRRxeAWhAREeHoEhoEvp8A6zjrd5TFgXrw4MFauHChvLy81KdPH3l5eSk1NVXLly9Xx44dK3w5nDp1qta/LI4eParZs2db1HbevHlq3rx5hXVpaWn661//qpYtW+p3v/ud1XXEx8dr0aJFOnjwYLWBetSoUUpKSjIvl4f7zMxMi38UwHIGg0Hh4eE6d+5crf+wA+qb9PR0R5dQr/H9BNimLr+jPDw8LL74aXGgHjJkiLKysrRq1Spt3LjRvL5du3Z68sknK7T18vLS0KFDLT20VZo3b27xA2R+O8d0VlaW5syZIx8fHz377LPm4RfWCg4OVn5+frXbPT095enpWeU2vlBrj8lk4v0FaojPTN3g+wmwjrN+bmo0D/WECROUlJSkY8eOqbS0VJGRkYqOjq7UbuLEifaqr1oBAQHq379/jfe7dOmSXn75ZZWVlemFF16w6IEu12IymZSZmVnl+wAAAID6r8YPdgkICFDPnj1ro5ZaV1RUpFdeeUXZ2dmaOXPmNcfhZGVlqbi4uMJQkby8vEqzgaxdu1Z5eXnq3LlzrdUNAAAA59WgHj3+5ptv6vjx47rtttt05syZCnNPN27cuMIPhbfeektHjhzRv/71L/O6KVOmqE+fPmrVqpU8PT2Vmpqqbdu2KSoqSgMHDqzT1wIAAADn0KAC9c8//yxJ+u677/Tdd99V2BYaGnrdK+/x8fE6duyYduzYoZKSEoWGhmrkyJG666671KhRo1qrGwAAAM6rQQXq+fPnW9x21qxZldbZMhsIAAAA6ieLn5QIAAAAoDICNQAAAGADAjUAAABgA6sDdVFRkRYsWKC0tLRK244dO6YFCxbYVBgAAADgCqwO1KWlpdq4caNycnIqbTt//nyFpykCAAAA9RVDPgAAAAAbEKgBAAAAG9R4HuqsrCxJUn5+viTp4sWL5nWSFBISYqfSAAAAAOdX40A9derUCstvvvlmheXk5GTbKgIAAABcSI0D9cyZMyVJBQUF+utf/6r7779fbdq0sXthAAAAgCuocaCOiYmRJF26dEmSFBUVZV4HAAAANDTclAgAAADYgEANAAAA2MDqQO3u7q6YmBj5+vpW2ta0aVOGgQAAAKBBqPEY6nI+Pj7mGxR/KzY2VrGxsVYXBQAAALgKhnwAAAAANrAoUBcXF1t9Alv2BQAAAJydRYF68uTJWrJkiXJyciw+cHZ2tpKTkzVlyhSriwMAAACcnUVjqCdNmqQvvvhCS5YsUYcOHXTzzTerTZs2CgsLk6+vr0wmkwoKCpSRkaETJ07o4MGD+vHHHxUREaFHHnmktl8DAAAA4DAWBeo+ffqod+/e2r17tzZs2KBly5aprKys6gN6eCg2NlZ/+MMf1KNHD7m5MUwbAAAA9ZfFs3y4ubmpZ8+e6tmzp0pLS3Xy5EmlpaUpPz9fkuTn56fmzZurTZs28vT0rLWCAQAAAGdi1bR5np6e6tChgzp06GDvegAAAACXwngMAAAAwAYEagAAAMAGBGoAAADABlY/etwVbdiwQQsWLKhy2zvvvKOAgIDrHuPMmTP66KOPlJqaKg8PD3Xr1k0PPvig/P397VwtAAAAXEGDCtTlxowZo7CwsArrfHx8rrvfhQsXNHPmTPn4+GjcuHEqKirSypUrdfr0ab3yyivy8GiQbycAAECD1iATYNeuXdW2bdsa77ds2TIVFxdr7ty5CgkJkSS1a9dOc+bM0YYNG5SYmGjvUgEAAODkrB5DnZWVpXfeeUe///3v9dBDD+nIkSOSpLy8PH3wwQf66aef7FZkbbh8+bKMRmON9tmxY4e6detmDtOSFBsbq4iICH3//ff2LhEAAAAuwKor1GfOnNELL7wgk8mkdu3a6dy5c+Zw6u/vrx9++EHFxcWaPHmyXYu1l9mzZ6uoqEgeHh7q3LmzHnjgAUVERFxzn+zsbF28eLHKK9vt2rXTvn37aqtcAAAAODGrAvXixYvl6+url19+WZL06KOPVtjetWtXp7xi6+Xlpf79++umm26St7e3Tp48qVWrVum5556rMIyjKjk5OZKkwMDAStsCAwOVn5+v0tLSKp8SWVpaqtLSUvOywWCQt7e3+W/YV/l7ynsL1Byfm9rF9xNgG2f97FgVqI8ePaq7775b/v7+unTpUqXtISEhys7Otrm4azEajSorK7OoraenpwwGg/r06aM+ffqY1/fs2VNdunTRzJkztXTpUv3Xf/1XtccoKSmRpCpvPCwP0SUlJVUG6mXLlmnJkiXm5ejoaM2dO1ehoaEW1Q/rhIeHO7qEeu6oowtALbjev9bBPvh+AqzjrN9RVgVqo9GoRo0aVbs9Ly+v1me8OHr0qGbPnm1R23nz5ql58+ZVbuvYsaPatWungwcPXvMYXl5eklRliC+/+lze5rdGjRqlpKQk83L5r6vMzEyLfxTAcgaDQeHh4Tp37pxMJpOjywFcSnp6uqNLqNf4fgJsU5ffUR4eHhZf/LQq9bZp00Z79+7V4MGDK227cuWKtm3bpvbt21tzaIs1b95cU6ZMsahtVcM0fi04OFhnz5616BjlQz9+LScnR35+flVenZauXsGubhtfqLXHZDLx/gI1xGembvD9BFjHWT83VgXqO++8U6+++qreffddxcXFSZJyc3OVkpKiZcuWKS0tTQ8//LBdC/2tgIAA9e/f3y7HysjIuO6DWYKCguTv768TJ05U2nb8+HFFRUXZpRYAAAC4FqsCddeuXTV16lQtXLhQ69evlyT9/e9/lyR5e3tr6tSpiomJsV+VdpKXl1cpOO/du1cnT57U0KFDK6w/d+6cpIrj3Hr16qWNGzcqKyvLfAPjwYMHlZ6eruHDh9dy9QAAAHBGVg907tu3r3r27KmUlBTztHnh4eHq3LmzeQYLZ/Pcc88pKipKbdu2lY+Pj3766Sd99913Cg4O1qhRoyq0femllyRJ8+fPN68bNWqUtm/frtmzZ2vYsGEqKirSihUr1KpVK9122211+loAAADgHGy6c7Bx48bq2bOnvWqpdX369NHevXuVkpKi4uJiBQYGasCAARo9erQCAgKuu39ISIhmzZqlRYsW6dNPP5WHh4e6du2qBx54oNox0gAAAKjfrArUWVlZFrW71rzOjjB27FiNHTvWora/vjL9ay1bttSMGTPsWRYAAABcmFWBeurUqRa1S05OtubwAAAAgMuwKlBX9Uhxo9GozMxMbdq0Sf7+/lVOqQcAAADUN1YF6mtNV3fHHXdoxowZKiwstLYmAAAAwGW42fuAjRs3Vv/+/bVq1Sp7HxoAAABwOnYP1NLVp9jk5ubWxqEBAAAAp2LTtHm/VVhYqKNHj2rFihWKjo6256EBAAAAp2RVoL733nuvuT0kJESTJk2yqiAAAADAlVgVqO+++24ZDIYK6wwGg3x9fdWsWTN17txZ7u7udikQAAAAcGZWBeoxY8bYuw4AAADAJdXKTYkAAABAQ2HRFeoFCxbU+MAGg6HKB8AAAAAA9YlFgfrw4cM1PvBvx1gDAAAA9ZFFgXr+/Pm1XQcAAADgkhhDDQAAANiAQA0AAADYwOonJe7bt09ff/21fvrpJxUWFspkMlVqk5ycbFNxAAAAgLOz6gr19u3b9eqrr+rixYvq06ePTCaT4uLiFBcXJy8vL7Vu3VqjR4+2d60AAACA07HqCvVXX32ldu3a6aWXXlJ+fr7WrVun22+/XZ06dVJGRoZmzJihsLAwe9cKAAAAOB2rrlCfOXNGcXFxcnNzMz9ivKysTJIUFhamwYMHa/ny5farEgAAAHBSVgXqRo0aycPj6sVtX19feXh4KDc317y9adOmysjIsEuBAAAAgDOzKlBHRkbqzJkz5uWoqCht2rRJV65cUUlJibZs2aKQkBC7FQkAAAA4K6sC9S233KJdu3aptLRUknTXXXfp8OHDmjhxoiZNmqTU1FTdeeed9qwTAAAAcEoW35S4Z88ede3aVW5ubho5cqRGjhxp3ta9e3fNmjVLO3bskJubm7p166ZOnTrVSsEAAACAM7E4UP/lL39RkyZNdOuttyohIUHt27evsP3GG2/UjTfeaPcCAQAAAGdmcaB+9NFHtXXrVq1bt05r165VaGio4uPjFR8frxYtWtRmjQAAAIDTsjhQJyYmKjExUdnZ2dq6dau2bt2qZcuWadmyZYqKilJCQoL69OmjoKCg2qwXAAAAcCo1frBLUFCQRowYoREjRujs2bPasmWLtm7dqo8//liLFy9WTEyMEhIS1KtXL/n4+NRGzVabNWuWjhw5UuU2d3d3ffbZZ9fcf/78+dq4cWOl9ZGRkXr99dftUSIAAABcjFVPSiwXGRmpMWPGaMyYMTp+/Li2bt2qbdu26e2339YHH3ygjz/+2F512sVdd92l22+/vcK64uJivfvuu4qNjbXoGJ6ennrssccqrHO2Hw4AAACoOzYF6l8LCwtTs2bNFBwcrNzcXJWUlNjr0HZTVWjetGmTJCkhIcGiY7i5ualv3752rQsAAACuy6ZAXVRUpB07dmjLli06dOiQjEajQkNDNWrUKMXHx9urxlq1detWNWrUSD169LB4H6PRqKKiIq5MAwAAoOaBuqysTHv37tWWLVu0b98+lZSUyM/PTwMGDFB8fLw6duxYG3XWiry8PKWkpOjWW29V48aNLdqnpKREDz74oIqLi+Xr66u4uDhNmDDB4v0BAABQv1gcqA8dOqQtW7Zox44dKiwslKenp7p3766EhAR17dpV7u7utVlnrdi2bZuuXLli8XCPwMBAjRw5UtHR0TKZTNq/f7/Wrl2rn3/+WbNmzar2PSgtLTU/VVKSDAaDvL29zX/DvsrfU95boOb43NQuvp8A2zjrZ8fiQP3SSy/JYDDopptuMs/iUR4KHcFoNKqsrMyitp6enlX+B9iyZYv8/f0tviFx/PjxFZbj4uIUERGhzz//XNu3b1dcXFyV+y1btkxLliwxL0dHR2vu3LkKDQ216LywTnh4uKNLqOeOOroA1IKIiAhHl9Ag8P0EWMdZv6MsDtT333+/4uLiFBgYWJv1WOzo0aOaPXu2RW3nzZun5s2bV1h3/vx5HTt2TEOGDLHp6npSUpKSk5N18ODBagP1qFGjlJSUZF4uD/eZmZkW/yiA5QwGg8LDw3Xu3DmZTCZHlwO4lPT0dEeXUK/x/QTYpi6/ozw8PCy++GlxoP51IHQGzZs315QpUyxqW9WPgC1btkiSzTdPenl5qUmTJsrPz6+2jaenpzw9Pavcxhdq7TGZTLy/QA3xmakbfD8B1nHWz43dps2rawEBAerfv7/V+2/ZskXNmjVT+/btbarj8uXLunTpkvz9/W06DgAAAFyTm6MLcISffvpJaWlp17w6fe7cOZ07d868XFJSosuXL1dq9+WXX8pkMqlLly61USoAAACcnMteobbF5s2bJV37YS4vvfSSpKuPG5ek3NxcTZ8+XXFxcYqMjJQkHThwQPv27VOXLl1qNI81AAAA6o8GF6iNRqO2bdum6OhoczC2hK+vr7p166aUlBRt3LhRRqNR4eHhGjdunEaMGCE3twZ5sR8AAKDBsypQb9u2TT169JCXl5e966l1bm5uevvtt6/brvzKdDlfX189/vjjtVUWAAAAXJRVgfqNN96Qt7e3evXqpYSEBHXq1MnedQEAAAAuwapA/eKLL2rz5s3avn27NmzYoKCgIMXHxyshIUGtWrWyd40AAACA07IqUHfo0EEdOnTQQw89pP3792vz5s1as2aNVqxYoVatWqlv376Kj493mofAAAAAALXFppsS3d3d1b17d3Xv3l1FRUXauXOnNmzYoMWLF+vTTz9VTEyM+vXrpz59+sjDo8Hd/wgAAIAGwG5TU5w+fVrHjx/X6dOnJUmRkZHKz8/X/Pnz9fjjjys1NdVepwIAAACchk2Xjc+ePastW7Zoy5YtOn/+vPz9/RUfH6++ffuqTZs2kqQTJ07o7bff1rvvvqu//e1vdikaAAAAcBZWBerVq1dr8+bNOnnypDw9PdW9e3dNnDhRXbp0qTQfc9u2bTV8+HCLpqoDAAAAXI1Vgfqjjz5Shw4d9Oijj6pPnz7y8fG5Zvu2bdvq7rvvtqpAAAAAwJlZFaj//ve/KywszOL2LVu2VMuWLa05FQAAAODUrLopMTg4WIWFhdVuLyws1JUrV6wuCgAAAHAVVgXqhQsX6vnnn692+/PPP69FixZZXRQAAADgKqwK1Pv371evXr2q3d67d2/t27fP6qIAAAAAV2FVoM7JyVFQUFC12wMDA5WdnW11UQAAAICrsCpQ+/n56ezZs9VuT0tLk7e3t9VFAQAAAK7CqkDdpUsXrV+/Xj/99FOlbSdPntT69evVtWtXm4sDAAAAnJ1V0+bde++92r9/v/70pz+pe/fu5inxfvnlF+3Zs0f+/v6699577VooANS1KyWXte+5JElS1zlfy92Lf3kDAFRmVaAOCgrSq6++qk8++US7d+/Wrl27JEne3t6Kj4/XuHHjrjnGGgAAAKgvrArU0tUbD6dNmyaTyaS8vDxJkr+/vwwGg92KAwAAAJyd1YG6nMFgUNOmTe1RCwAAAOByLArUS5YskSTdddddcnNzMy9fz+jRo62vDAAAAHABFgXqL774QpJ05513ys3Nzbx8PQRqAAAA1HcWBerk5ORrLgNoeJbf19HRJdS6wsJC3fDc1b+/GNuR+fUBAFWyah5qAAAAAFcRqAEAAAAbWDTkY+rUqTWeDs9gMOjvf/+7VUUBAAAArsKiQB0TE+O080vn5ORo9erVOn78uE6cOKGioiLNnDlTN910U5Xtf/jhBy1evFg//fSTvL29deutt2r8+PFq3LixRef7z3/+o5UrVyojI0PBwcEaOnSohg4das+XBAAAABdi8RVqZ3X27FktX75cERERatWqlY4dO1Zt21OnTunFF19UixYt9MADDyg7O1srV67UuXPn9Kc//em651q3bp3effdd9erVS8OHD1dqaqoWLlyo4uJi3XnnnXZ8VQAAAHAVNj/YxdHatGmjDz74QH5+ftq+fbtee+21att+9tln8vPz08yZM+Xj4yNJCg0N1T//+U8dOHBAnTt3rnbfkpISff755+rWrZv++7//W5KUmJgoo9GoL7/8UomJifLz87PviwMAAIDTszpQG41Gbdq0SXv37lVWVpYkKSQkRN27d1dCQoLc3OrmfkdLp7EqLCxUSkqKhg8fbg7TktSvXz999NFH+v77768ZqA8dOqRLly5p0KBBFdYPGTJEW7Zs0d69e9W3b1/rXgQAAABcllWpt7CwUM8//7z+8Y9/KCUlRVeuXNGVK1d08OBBLViwQM8//7wKCwvtXatNTp8+rStXrqhNmzYV1nt4eCgqKko//fTTNfc/deqUJKlt27YV1rdp00YGg8G8HUD94ePjo7Nnz8pkMlX4IQ4AwK9ZdYX6s88+08mTJ/Xwww9rwIAB8vC4epiysjL95z//0cKFC/X555/r4YcftmuxtsjNzZUkBQYGVtoWEBCg1NTUa+6fk5MjNzc3NW3atMJ6Dw8PNWnSRNnZ2dXuW1paqtLSUvOywWAwX1l31ps9XVn5e8p7C3ugP8Ge6E+AbZz1s2NVoN65c6cGDRqkwYMHVzyYh4cGDRqkM2fOaMeOHTUO1EajUWVlZRa19fT0rNGbWlJSYt7vt7y8vMzbr7V/+Q+Hqmq51v7Lli3TkiVLzMvR0dGaO3euQkNDLSkdVgoPD3d0CahH6E+wJ/oTYJ2IiAhHl1AlqwJ1fn6+IiMjq93evHlz5efn1/i4R48e1ezZsy1qO2/ePDVv3tziY3t5eUlShSvF5UpKSszbr7V/dWG/tLT0mvuPGjVKSUlJ5uXyHwKZmZkW/4CA5QwGg8LDw3Xu3DmZTCZHlwMXR3+CPdGfANukp6fX2bk8PDwsvvhpVaAODw/X7t27K12hLrd79241a9asxsdt3ry5pkyZYlHbqoZuXEtAQICkq0M3fis3N/e6xwsMDJTRaNTFixcrDPsoKyvTpUuXFBQUVO2+np6eVV4Zl8QXai0ymUy8v7Ab+hPsif4EWMdZPzdWBepBgwbpgw8+0CuvvKJhw4aZL7+fPXtW33zzjVJSUvTII4/U+LgBAQHq37+/NSVdV6tWreTu7q6TJ0+qT58+5vVlZWU6deqUbr311mvuHxUVJUk6ceKEunXrZl5/4sQJmUwm83YAAAA0LFYF6sGDB+vixYtavny59u/fX/GAHh4aPXp0penlHM3Hx0c333yzNm/erLvvvtt8U+CmTZtUVFRUIVAXFxcrKytLTZo0kb+/vySpU6dO8vPz09q1aysE6rVr16pRo0YV1gEAAKDhsHoe6jFjxmjIkCFKSUkxz0MdGhqqm2++2RxC68qXX34pSfrll18kXQ3J5bN23H333eZ2Y8eO1fPPP69Zs2ZpwIAB5icldu7cWV26dDG3O378uGbPnq3Ro0drzJgxkq6Oob733nv1/vvv67XXXlPnzp119OhRbd68WWPHjuWhLgAAAA1UjQJ1bm6uNmzYoIyMDPn7+6tXr16Kj4+vrdoslpycXGH5u+++M//960Ddpk0bPf/88/rkk0/00UcfydvbW7fffrvGjx9v0XkGDx4sd3d3ff3119q9e7eCg4P14IMPatiwYfZ5IQAAAHA5BpOFo7szMjL07LPPVpi9w2AwaNq0aU4Rql1RZmZmlbOOwDYGg0ERERFKT0932psX4DroT7An+lPdePPNNx1dAmrJE088UWfn8vT0tP8sH//6179UVFSkhx56SJ06ddK5c+e0cOFCffTRR+rTp0+dPWocAAAAcCYWB+offvhBiYmJGjJkiCSpRYsWcnNz09y5c5WWlqaWLVvWWpEAAACAs7L4snJWVpaio6MrrGvTpo0k6dKlS/atCgAAAHARFgdqo9FY6dHb7u7u5m0AAABAQ1SjWT5OnDhR4Yl/ly9fliSlpqaqoKCgUvtevXrZWB4AAADg3GoUqFevXq3Vq1dXWv/FF19U2f6309kBAAAA9Y3FgXrmzJm1WQcAAADgkiwO1DExMbVZBwAAAOCSmDwaAAAAsAGBGgAAALABgRoAAACwAYEaAAAAsAGBGgAAALABgRoAAACwgUXT5m3cuNGqg/fr18+q/QAAAABXYVGgXrBggVUHJ1ADAACgvrMoUL/11lsVlgsKCjR//nz5+Pho6NChioyMlCSlpaVpzZo1unz5sqZOnWr/agEAAAAnY1GgDg0NrbD8xRdfyN/fX88995wMBoN5fatWrdSrVy+9/PLLWrVqlaZMmWLfagEAAAAnY9VNibt27VLPnj0rhGnzAd3c1LNnT+3atcvm4gAAAABnZ1WgNplMSktLq3b7mTNnrC4IAAAAcCVWBepbbrlF69at09dff63i4mLz+uLiYq1cuVLr169Xjx497FYkAAAA4KwsGkP9Ww899JAyMjL08ccf69NPP1VgYKAkKScnR1euXFGHDh00ceJEe9YJAAAAOCWrArWPj49mz56tXbt2ad++fcrKypIkde7cWd26dVP37t2rHF8NAAAA1DdWBepyt9xyi2655RZ71QIAAAC4HJsCdXZ2to4cOaK8vDz16tVLwcHBMhqNKiwslI+Pj9zceLI5AAAA6jerArXJZNKiRYu0Zs0aGY1GSVfnoA4ODlZRUZGmTp2qMWPGaPjw4XYtFgAAAHA2VgXqFStWaPXq1brjjjt08803a86cOeZtPj4+6tmzp3bs2FEngTonJ0erV6/W8ePHdeLECRUVFWnmzJm66aabKrQrLi7Wd999p927d+v06dMqKipSeHi4EhMTlZiYaNHV9KlTpyozM7PS+sTERP3Xf/2X3V4TAAAAXIdVgfrbb79Vv379NH78eF26dKnS9tatW2v//v221maRs2fPavny5YqIiFCrVq107NixKtudP39eCxcuVKdOnZSUlCRvb28dOHBA7733no4dO6Zp06ZZdL6oqCglJSVVWFf+6HUAAAA0PFYF6gsXLqh9+/bVbm/UqJEKCwutLqom2rRpow8++EB+fn7avn27XnvttSrbBQQE6K9//atatmxpXjdw4EAtWLBAGzZs0OjRoxUeHn7d8wUFBalv3752qx8AAACuzaq7Bv39/XXhwoVqt588eVIhISFWF1UT3t7e8vPzu247f3//CmG6XM+ePSXV7OmOZWVlKioqsrxIAAAA1FtWXaHu1auX1q1bp/79+8vHx6fCtgMHDmjDhg2644477FJgbcvNzZV0NXBb4tChQ5owYYKMRqNCQ0M1fPhwDRs2rBYrBAAAgDOzKlCPGTNGhw8f1h//+Ed17NhRkrR8+XIlJyfr2LFjio6O1qhRo+xaaG0oKyvT6tWrFRYWprZt2163fatWrdSxY0dFRkbq0qVL2rhxoz788ENlZ2drwoQJ1e5XWlqq0tJS87LBYJC3t7f5b9hX+XvKewt7oD/BnuhPgG2c9bNj9ZMSX375Za1cuVLbt2+Xl5eXjhw5ovDwcN1zzz0aOXKkvLy8anxco9GosrIyi9p6enra/Ka+//77OnPmjP7nf/5H7u7u120/ffr0Csu33Xab/vznP2vVqlUaOnSogoODq9xv2bJlWrJkiXk5Ojpac+fOVWhoqE3149osGRMPWIr+BHuiPwHWiYiIcHQJVbL6wS5eXl66++67dffdd9utmKNHj2r27NkWtZ03b56aN29u9blWrFihb7/9Vvfee6+6detm1TEMBoOGDx+uAwcO6PDhw9XerDhq1KgKM4OU/xDIzMy0+AcELGcwGBQeHq5z587JZDI5uhy4OPoT7In+BNgmPT29zs7l4eFh8cVPqwL17Nmzddddd+nmm2+ucvuhQ4f05ZdfaubMmTU6bvPmzTVlyhSL2gYGBtbo2L+2YcMGffLJJxo4cKDNPwjKb77Mz8+vto2np6c8PT2r3MYXau0xmUy8v7Ab+hPsif4EWMdZPzdWBeojR45owIAB1W7Py8vTkSNHanzcgIAA9e/f35qSLLZr1y69/fbb6tmzpx555BGbj3f+/HlJlt/UCAAAgPrFqmnzrufcuXPmm+6cyZEjR/T666/rxhtv1BNPPFHt0xHLysqUlpamnJwc87r8/HzzY9Z/3e6rr76Sh4eHOnXqVKu1AwAAwDlZfIV6w4YN2rhxo3l56dKl+vbbbyu1Kyws1M8//6yuXbvap0ILfPnll5KkX375RZK0adMmpaamSpJ5SEdmZqb+8pe/yGAwqHfv3vr+++8rHKN169Zq3bq1JCk7O1tPPfWU+vXrp6lTp0qSdu/erS+//FK9e/dWWFiY8vPztWXLFv3yyy8aN26cAgIC6uKlAgAAwMlYHKhLSkqUl5dnXr58+XKlWTYMBoMaNWqkgQMHavTo0far8jqSk5MrLH/33Xfmv8sDdUZGhvnpje+//36lY4wePdocqKvSqlUrtWjRQps3b1ZeXp48PDwUFRWlp556Srfeeqs9XgYAAABckMFkxejuqVOn6qGHHlKPHj1qo6YGIzMzs8L81LAPg8GgiIgIpaenO+3NC3Ad9CfYE/2pbrz55puOLgG15Iknnqizc3l6etbuLB/z58+3ZjcAAACg3rF6Hupyly9fVmFhYZW/tMunlAMAAADqK6sD9dq1a/X111+bp42rym/HNgMAAAD1jVXT5q1du1bvv/++wsPDNXbsWEnS8OHDdeeddyogIEBRUVGaPHmyXQsFAAAAnJFVgXrNmjXq3Lmz/vSnPykxMVGS1K1bN40bN07z5s3T5cuXdenSJbsWCgAAADgjqwL1+fPn1b17d0mSu7u7pKsPOZEkHx8f3X777Vq7dq2dSgQAAACcl1WB2sfHR1euXDH/7eXlpaysLPN2b29v5ebm2qVAAAAAwJlZFahbtmypn3/+2bzcvn17rVu3TtnZ2crKytL69esVERFhtyIBAAAAZ2VVoE5ISNAvv/xifijJPffcozNnzmjy5MmaOnWqzp49a75ZEQAAAKjPrJo277bbbtNtt91mXu7YsaNee+017dmzR25uboqNjVVkZKTdigQAAACclc0PdinXrFkzDRs2zF6HAwAAAFyCzYHaaDSqsLCwym1+fn62Hh4AAABwalYF6rKyMi1fvlzfffedLly4IKPRWGU7npQIAACA+s6qQP3OO+9o48aNat++vW655Rb5+PjYuy4AAADAJVgVqLdv366+fftq6tSp9q4HAAAAcClWBepGjRrphhtusHctAAAAsFBJSYnmzZsnSXrqqafk5eXl4IoaLqvmoY6Li9PevXvtXQsAAADgcqy6Qj1hwgQtWLBAr776qm677TYFBwfLza1yNm/Tpo3NBQIAAADOzKpAXVpaKpPJpH379mnfvn3VtmOWDwAAANR3VgXqf/zjH9q5c6fi4uLUrl07ZvkAAABAg2VVoD5w4ICGDBmiiRMn2rkcAAAAwLVYdVOit7e3wsPD7V0LAAAA4HKsCtQDBgzQ1q1bq31CIgAAANBQWDXko0WLFtq9e7emT5+ufv36VTvLR69evWwuEAAAAHBmVgXq119/3fz3xx9/XG07ZvkAAABAfWdVoJ45c6a967BaTk6OVq9erePHj+vEiRMqKirSzJkzddNNN1VqO2vWLB05cqTS+s6dO2vGjBkWne8///mPVq5cqYyMDAUHB2vo0KEaOnSoza8DAAAArsmqQB0TE2PvOqx29uxZLV++XBEREWrVqpWOHTt2zfbBwcEaN25chXVBQUEWnWvdunV699131atXLw0fPlypqalauHChiouLdeedd1r7EgAAAODCrArUzqRNmzb64IMP5Ofnp+3bt+u11167ZnsfHx/17du3xucpKSnR559/rm7duum///u/JUmJiYkyGo368ssvlZiYKD8/P6teAwAAAFyXRYF69uzZMhgMmjFjhtzd3TV79uzr7mMwGPTCCy/YXOD1eHt713ifK1euqLS0VI0bN7Z4n0OHDunSpUsaNGhQhfVDhgzRli1btHfvXquCOgAAAFybRYHaZDJVWjYYDDXax1mcPXtW999/v8rKytS0aVMNGDBAo0ePlofHtd+KU6dOSZLatm1bYX2bNm1kMBh06tQpAjUAAEADZFGgnjVr1jWXXUWzZs100003qVWrViouLtb27du1dOlSpaen66mnnrrmvjk5OXJzc1PTpk0rrPfw8FCTJk2UnZ1d7b6lpaUqLS01LxsMBvOV9ev9MEHNlb+nvLewB/oT7In+BNjGWT87Vo2hPnLkiFq0aCF/f/8qt+fl5enMmTM1vnnRaDSqrKzMoraenp41flMnT55cYblv37765z//qW+//VbDhw9X+/btq923pKSk2qvYnp6eKikpqXbfZcuWacmSJebl6OhozZ07V6GhoTWqHzXD0zxhT/Qn2BP9CbBORESEo0uoklWBevbs2Xr88ccVHx9f5fZDhw7pjTfeqPE81EePHrVofLYkzZs3T82bN6/R8asyYsQIffvttzp48OA1A7WXl1e1Yb+0tFReXl7V7jtq1CglJSWZl8t/CGRmZlr8AwKWMxgMCg8P17lz55x26BFcB/0J9kR/AmyTnp5eZ+fy8PCw+OJnrczyUVpaWuWTE6+nefPmmjJlikVtAwMDa3z8qgQHB0uS8vPzr3s+o9GoixcvVhj2UVZWpkuXLl1z6j1PT095enpWuY0v1NpjMpl4f2E39CfYE/0JsI6zfm4sDtRZWVnKyMgwL6elpVX5kJTCwkKtX7/equEMAQEB6t+/f433s8X58+clqdrhK+WioqIkSSdOnFC3bt3M60+cOCGTyWTeDgAAgIbF4kD93XffVRgHvHTpUi1durTKtm5ubnr00Udtr86OCgsLK10pNplM5tfQuXNn8/ri4mJlZWWpSZMm5qDdqVMn+fn5ae3atRUC9dq1a9WoUaMK6wAAANBwWByob731VrVs2VLS1fHLQ4cOVceOHSu0MRgMatSokaKiohQQEGDXQq/lyy+/lCT98ssvkqRNmzYpNTVVknT33XdLkn766Se98cYbiouLU3h4uEpKSrRz50798MMPSkxMVJs2bczHO378uGbPnq3Ro0drzJgxkq6Oob733nv1/vvv67XXXlPnzp119OhRbd68WWPHjuWhLgAAOIknnnjC0SXUicLCQs2bN0+SNHXqVKuezQH7sDhQt2jRQi1atJB0dbaMmJgYhYWF1VphNfHbmx+/++4789/lgTo0NFQdO3bUzp07lZubKzc3NzVv3lyPPvqoEhMTLTrP4MGD5e7urq+//lq7d+9WcHCwHnzwQQ0bNsx+LwYAAAAuxWCy4+ju8+fPq7S01By8cW2ZmZkV5qeGfRgMBkVERCg9Pd1pb16A66A/wZ7oT7CnwsJC3XDDDZKu/us6V6jty9PTs3Zn+fjmm2/0ww8/6MknnzSvW7BggTZu3Cjp6jzLzz77bKWHoAAAAAD1Tc3ntpP07bffVgjL+/fv18aNG5WYmKiHH35Y58+f1xdffGG3IgEAAABnZdUV6szMzAoPVfn+++8VFhZmntkjNzdXmzZtsk+FAAAAgBOz6gr1b6WkpKhLly7m5dDQUOXm5trj0AAAAIBTsypQR0REaNeuXZKuDvfIzs5W165dzduzs7Pl6+trnwoBAAAAJ2bVkI8RI0bozTff1EMPPaSioiK1aNGiwoNRDh06xJMDAQAA0CBYFajj4uLUpEkT7d27V76+vub5mSUpPz9ffn5+6tu3r10LBQAAAJyRVYFakmJjYxUbG1tpvZ+fn55++mmbigIAAABchcVjqI8fP678/HyL2mZkZJjnpAYAAADqM4sD9YwZM7R//37zcn5+viZMmKAjR45UavvDDz9owYIFdikQAAAAcGZWT5tnMplUWloqo9Foz3oAAAAAl2KXeagBAACAhopADQAAANiAQA0AAADYoEbT5mVkZOjkyZOSpMLCQklSenq6fHx8KrUDAAAAGoIaBerk5GQlJydXWPfee+/ZtSAAAADAlVgcqCdPnlybdQAAAAAuyeJA3b9//1osAwAAAHBN3JQIAADggnx8fHT27FmZTKZK97OhbhGoAQAAABsQqAEAAAAbEKgBAAAAGxCoAQAAABsQqAEAAAAbEKgBAAAAGxCoAQAAABvU6NHjzignJ0erV6/W8ePHdeLECRUVFWnmzJm66aabKrTLyMjQtGnTqj3O7bffrt/97nfXPNeYMWOqXD9+/HjdeeedNa4dAAAArs/lA/XZs2e1fPlyRUREqFWrVjp27FiV7fz9/asM1Pv379eWLVvUuXNni84XGxurvn37VlgXHR1d88IBAABQL7h8oG7Tpo0++OAD+fn5afv27XrttdeqbNe4ceNKQViSNm7cKG9vb3Xv3t2i80VERFR5HAAAADRMLh+ovb29rd43JydHhw4dUr9+/eTl5WXxfiUlJZJUo30AAABQP7l8oLbF1q1bZTKZFB8fb/E+GzZs0Nq1a2UymdS8eXPdfffdNdofAAAA9UuDDtRbtmxRYGCgOnXqZFH7Dh066NZbb1VYWJiys7P173//W2+++aYKCws1aNCgavcrLS1VaWmpedlgMMjb21seHg367a81BoNBkuTp6SmTyeTgauDq6E+wJ/oT7I0+VXtqktOcKtEZjUaVlZVZ1NbT09Pciaxx9uxZnTx5UsOHD5ebm2WzB7700ksVlm+//XZNnz5dn332mfr371/tEJBly5ZpyZIl5uW4uDj9/ve/V2BgoNX14/pCQkIcXQLqEfoT7In+BHujTzmWUwXqo0ePavbs2Ra1nTdvnpo3b271ubZs2SJJSkhIsPoYHh4eGjJkiN59912dPHlSHTt2rLLdqFGjlJSUVGFdaWmpPD09rT43qnf58mXNmjVLs2bNsmmMPSDRn2Bf9CfYG33KOThVoG7evLmmTJliUVtbr+5u2bJFkZGRatOmjU3HCQ4OliTl5+dX28bT05PwXIdMJpN++ukn/ukLdkF/gj3Rn2Bv9Cnn4FSBOiAgQP3796/18/z44486d+5ctQ9qqYmMjAxJV+e5BgAAQMPTIB89Xj7co7rZOYqLi5WWlqa8vDzzul//Xe7y5ctatWqVmjRpYvOVbgAAALgmp7pCba0vv/xSkvTLL79IkjZt2qTU1FRJ0t13312hrdFo1LZt23TDDTcoPDy8yuMdP35cs2fP1ujRo81XsdesWaNdu3ape/fuCgkJUW5urr777jtlZWVp2rRpzNjhRDw9PTV69GiG2cAu6E+wJ/oT7I0+5RzqRQpMTk6usPzdd9+Z//5toE5JSdHFixd111131egcHTt21LFjx/Sf//xHly5dUuPGjdWuXTtNnjzZ4mn3UDc8PT3tMpwHkOhPsC/6E+yNPuUcDCZGsQMAAABWa5BjqAEAAAB7IVADAAAANiBQo8EbM2aM/vWvfzm6DNRTGRkZGjNmjDZs2ODoUlCPlT/YA6gtU6dO1fz58x1dhtMiUAMAAAA2qBezfAC2WLx4sdzd3R1dBgBY7bnnnnN0CUCDxhVqNHheXl4EajhcUVGRo0uAC/Pw8OB5CHC4hvw9xqcPDnX58mUlJydr165dysnJkY+Pj1q3bq377rtPbdq00axZs3Tp0iU98cQT+uCDD3TixAkFBgbqvvvuU+/evXXkyBEtXrxYP//8s0JCQvTII48oNja2RjWMGTOmwkN8rlcTnJMz9KWCggJ9+OGH2rlzpwwGg3r06KGkpKRK7ebPn6/t27frf//3f7Vw4UIdPXpUnTp10h//+Ed7vR24DmfoL1u3btWKFSuUnp4ug8GgkJAQDRgwQMOGDZMkbdiwQQsWLNCLL76oHTt2aNOmTSopKVFsbKwee+wx+fv7m49VPn761+Oov/nmG61bt04ZGRny9PRUs2bNlJSUVO1TgmGd+taXTCaTli5dqnXr1ik/P1833HCDHn744UrnLD/mrFmztG3bNm3fvl1XrlzRwoULbXtDXRRXqOFQ7777rtauXatevXpp0qRJGjFihLy8vJSWlmZuk5+fr1dffVU33HCDJkyYIE9PT73++uvatm2bXn/9dXXt2lX33XefiouL9dprr+ny5cu1XhOcj6P7kslk0l/+8hdt2rRJCQkJGjt2rLKzs6u9icdoNOrll1+Wv7+/7r//fvXu3dvm9wCWc3R/SUlJ0RtvvCE/Pz/dd999Gj9+vG666SbzU35/beHChfr55591zz33aODAgdqzZ4/ef//9ax5//fr1WrhwoVq0aKGJEyfqnnvuUVRUlH788UfL3yRYpL71peTkZCUnJ6t169aaMGGCwsLCNGfOnGqvPr/33ns6c+aMRo8erTvuuMPiuusbrlDDofbu3asBAwbogQceMK/77QcyJydHTzzxhPmqSmxsrJ588km98cYbmjNnjm644QZJUvPmzfXyyy9rx44d6t+/f63WBOfj6L60e/duHT16VBMmTNDIkSMlSYMGDdLs2bOrbF9aWqpbb71V48ePr+lLhR04ur/s3btX3t7emjFjhtzcrn1ty8/PT88995wMBoOkqz/evvnmGxUWFsrHx6fa47ds2VJ/+MMfLKoH1qtPfSkvL08rVqxQt27dNH36dHO7zz77TMuWLav2mC+88MJ1z13fNexXD4fz9fXV8ePHlZ2dXW2bxo0bKy4uzrwcGRkpX19ftWjRwvwlJMn89/nz52u9JjgfR/elffv2yd3dXYMGDTKvc3Nz05AhQ6rd59dtUbcc3V98fHxUXFyslJSU67ZNTEw0BxtJuvHGG2U0GpWZmVntPr6+vrpw4YKOHz9ucU2wTn3qSykpKSorK9OQIUMqtBs+fHi1xxwwYECDD9MSV6jhYPfdd5/mz5+vyZMnq02bNuratav69eunZs2amdsEBwdX+GBLV79AgoODK62Tro5jre2a4Hwc3ZcyMzMVEBCgxo0bV1gfGRlZZXt3d3cFBQVZfHzYl6P7y+DBg/X999/rz3/+s4KCghQbG6s+ffqoS5culdqGhIRUWPb19b3u+e644w4dPHhQf/rTnxQeHq7Y2FjFx8erY8eOFtcIy9SnvpSVlSVJioiIqNDO39/f3Pa3wsLCLK61PiNQw6H69OmjG2+8UTt37tSBAwe0cuVKLV++XE8//bS6du0qSdX+8q1uvclkqvWa4HycsS9di4eHB1d1HMjR/aVp06b63//9X+3fv1/79+/Xvn37tGHDBvXt21fTpk2z+XwtWrTQ66+/rr1792r//v3asWOH1q5dW+EGbNhHfe9L1+Pl5WX1vvUJ3+ZwuMDAQA0ePFh//OMf9dZbb6lJkyZaunQpNaHGHPnfLTQ0VLm5uZVu3Dl79mydnB815+jPuYeHh3r06KFJkybp73//uxITE7Vp0yadO3fOLsdv3Lix+vTpoylTpugf//iHunXrpqVLl6qkpMQux8f/qS99qfwKdnp6eoX1eXl5Nv/rb31HoIbDGI1GFRYWVljXtGlTBQYGqqysjJpgMWf479a1a1dduXJFa9eurVDXmjVr6uT8sJwz9JdLly5VWHZzc1Pr1q0lXb1h1d7H9/DwUIsWLWQymXTlyhWbj4+r6ltfio2Nlbu7u9asWVPhqvWqVatsL7SeY8gHHOby5cv63e9+p969e6t169Zq3LixDh48qBMnTlS4W7qh14Trc4b/bt27d1eHDh30ySefKCMjQy1atNDOnTsr/Z8tHM8Z+svbb7+t/Px8derUScHBwcrMzNSaNWsUFRWl5s2b23z8OXPmKCAgQB06dFBAQIDOnDmjf//73+rWrZu8vb3t8Aog1b++5O/vrxEjRuirr77Sq6++qq5du+rUqVPat2+fmjRpUkuvoH4gUMNhGjVqpMGDB+vAgQPauXOnjEajwsPDNWnSJIfNfuCMNeH6nOG/m5ubm6ZPn64PP/xQmzdvNj/Y5YEHHuCBLU7GGfpLQkKCvv32W61du1YFBQUKCAjQrbfeqjFjxthlbP3AgQO1efNmrVq1SkVFRQoKCtLQoUN111132aF6lKuPfWns2LHy8vLSunXrdPjwYd1www167rnn9Oqrr9ZC9fWHwVSbd90AAAAA9RxjqAEAAAAbMOQD9ZLRaFReXt412zRu3LjSnMHAb9GXUBP0F9gLfcm1EKhRL2VlZVWaf/O3mI8VlqAvoSboL7AX+pJrYQw16qWSkhKlpqZes02zZs14+iGui76EmqC/wF7oS66FQA0AAADYgJsSAQAAABsQqAEAAAAbEKgBAAAAGxCoAQAAABsQqAEAAAAbEKgBAAAAGxCoAQAAABsQqAEAAAAbEKgBAAAAGxCoAaCBi4qKksFg0KlTpyzep3///jIYDNqwYUOt1QUAroJADQAAANiAQA0AAADYgEANAAAA2IBADQANwJEjR3TPPfcoJCRE3t7e6tSpk/7617/qypUr1e7zyy+/6OGHH1ZERIQaN26sG264QTNmzNDly5evea7s7Gw9+eSTat26tRo1aqRWrVpp2rRpys7O1sSJE2UwGPThhx9W2OfX63/66Sfdf//9Cg8PV6NGjdS2bVs999xzKi4utsdbAQB25+HoAgAAtWvLli0aMmSICgoK1KZNGw0cOFBZWVn605/+pO3bt1e5T2pqqvr166eMjAxFRERo5MiRKigo0Lx58/Tdd99Ve67z588rISFBP/74owIDA5WUlCSj0ahPPvlEa9as0U033XTNWvfv36/f//73CgwMVL9+/ZSdna2tW7fq5Zdf1uHDh7Vs2TKb3gsAqA0EagCox4qKijR+/HgVFBToySef1F//+le5u7tLklJSUjRgwABlZWVV2u+BBx5QRkaGxowZo48++kiNGzeWJJ0+fVq33367Tpw4UeX5pk2bph9//FEJCQlauXKlmjZtKunqVethw4ZpxYoV16z3jTfe0IwZMzR79mxznYcOHVLv3r311Vdf6fvvv9ett95q9fsBALWBIR8AUI99+eWX+uWXX9SyZUv95S9/MYdUSYqNjdWMGTMq7bN161bt2rVLvr6+WrBggTlMS1KrVq3017/+tcpz/fLLL1q6dKkMBoPefvttc5iWpKCgIL399tvXrbd79+566aWXKtTZqVMn3X///ZKk9evXX/9FA0AdI1ADQD1WPk/0mDFj5OnpWWn7gw8+WO0+Q4YMUXBwcKXtd9xxR4WwXG7Tpk0yGo3q1q2bYmJiKm3v0qWLYmNjr1lvUlKSDAZDpfU33nijJCktLe2a+wOAIxCoAaAeO3PmjCQpOjq6yu2BgYGVwvH19jEYDIqKiqrxua63Tbp6Bbwq/v7+kq4OYQEAZ0OgBgA4DTc3/m8JgOvhmwsA6rHmzZtLUrWPFc/NzdXFixdrtI8k/fzzzzU+1/W2AYCrIlADQD3Wr18/SdK//vUvlZaWVtq+aNGiavdZs2aNsrOzK21fsWKFcnNzK63v27evDAaD9u7dq9TU1ErbDxw4oJSUlJq+BABwegRqAKjHRo8erebNm+v06dN69tlnZTQazdsOHTqkOXPmVNonISFB3bp1U35+vqZOnVrhgSq//PKLnn766SrP1apVK40aNUpGo1GTJ09WXl6eeVtOTo6mTJkik8lkx1cHAM6BQA0A9Zi3t7c++eQT+fj46G9/+5vat2+vcePGadCgQerWrZsSEhLUunXrSvt9/PHHCg0N1eeff642bdro3nvv1YgRI9SxY0cFBwdXOxf0/Pnz1bZtW23YsEHR0dG6++67ddddd6lNmzY6f/68Ro4cWdsvGQDqHIEaAOq5fv36aceOHbrrrruUk5OjZcuW6cyZM3rxxReVnJxc5T4xMTHavXu3Jk6cqCtXruirr77SkSNH9Pjjj+vbb7+Vl5dXlfuFh4drx44devzxx+Xj46Ovv/5au3bt0tixY7V9+3YFBgbW5ksFAIcwmPj3NwBAHZk4caI++ugjLVy4UBMnTnR0OQBgF1yhBgAAAGxAoAYAAABsQKAGAAAAbMAYagAAAMAGXKEGAAAAbECgBgAAAGxAoAYAAABsQKAGAAAAbODh6AIaspycHJWVlTm6jHopNDRUmZmZji4D9QT9CfZEf4K90adqh4eHh8VPdyVQO1BZWZlKS0sdXUa9YzAYJF19f5nEBraiP8Ge6E+wN/qUc2DIBwAAAGADAjUAAABgAwI1AAAAYAMCNQAAAGADAjUAAABgAwI1AAAAYAMCNQAAAGADAjUAAABgAwI1AAAAYAMCNQAAAGADAjUAAABgAwI1AAAAYAMCNQAAgAsqLCxUZGSkDAaDCgsLHV1Og0agBgAAAGxAoAYAAABs4OHoAiyVkZGh3bt3KzU1VWlpacrLy5PBYFCTJk3UvHlzdezYUT169FBYWJijSwUAAEAD4vSBes+ePVq5cqVSU1NlMpkUHh6usLAwtWzZUpJUUFCgn3/+WTt27NBHH32kjh07auTIkerevbuDKwcAAEBD4NSBesaMGTp16pRuueUWPfXUU7r55pvl4+NTZdvCwkKlpKRo+/btmjdvnlq3bq2XX365jisGAABAQ+PUgfqmm27SM888o4CAgOu29fHxUe/evdW7d2/l5uZq9erVtV8gAAAAGjynDtTjx4+3ar+AgACr9wUAAABqglk+AAAAABs49RVqSTp58mSN92nTpk0tVAIAAABU5vSB+tlnn63xPsnJybVQCQAAAFCZ0wfqyZMnO7oEAAAAoFpOH6j79+/v6BIAAACAarnETYnZ2dnKzs6+bpucnJw6qggAAAC4yukD9cmTJzVlyhRt27btmu22bdumKVOm6PTp03VUGQAAAOACgXrNmjWKiIjQ8OHDr9lu+PDhioyM5IEuAAAAqFNOH6gPHz6sW2+9VQaD4ZrtDAaDevfurUOHDtVRZQAAAIALBOrc3FyFhYVZ1DYkJIRx1AAAAKhTTh+oGzVqpPz8fIvaFhQUyMvLq5YrAgAAAP6P0wfq1q1ba8+ePRa13bNnj1q3bl3LFQEAAAD/x+nnoe7bt6/efvttffPNNxo6dGi17dasWaMjR47od7/7XR1WBwAAnM2bb77p6BLqRElJifnv+fPnN4h/pX/iiSccXUKVnD5Q9+vXT99//70+/PBD7du3TwkJCWrVqpW8vb11+fJlnT59Wps3b9aBAwcUGxvLg2AAAABQp5w+ULu5uenpp5/Wxx9/rPXr1+vAgQNVthk4cKAeeOCB684GAgAAANiT0wdqSfLy8tIjjzyiUaNGad++fUpLS1NhYaF8fHwUGRmprl27Kjg42NFlAgAAoAFyiUBdLigoSAMGDHB0GQAAAICZ08/yAQAAADgzAjUAAABgA5ca8lFbjh8/ro0bN+rw4cPKzMyUn5+fbrjhBo0dO1aRkZHmdvPnz9fGjRsr7R8ZGanXX3+9DisGAACAsyBQS1q+fLl++OEH9e7dW61bt1Zubq7WrFmj6dOn6+WXX1arVq3MbT09PfXYY49V2N/Hx6euSwYAAICTIFBLSkpK0u9//3t5ePzf29GnTx89/fTT+uqrrypMIu7m5qa+ffs6okwAAAA4IZcO1EajUT/++KOys7MVEBCg9u3by93dvcbH6dChQ6V1ERERatGihdLS0qo8b1FREVemAQAA4LqBOi0tTXPnztWFCxfk5+envLw8BQUF6ZlnnlFUVJTNxzeZTLp48aJatmxZYX1JSYkefPBBFRcXy9fXV3FxcZowYYIaN25s8zkBAADgelw2UL/33nvq0qWLJkyYIC8vL+Xl5en111/XP//5T73yyis2H3/z5s3Kzs7WmDFjzOsCAwM1cuRIRUdHy2Qyaf/+/Vq7dq1+/vlnzZo1q9qr46WlpSotLTUvGwwGeXt7m/+GfZW/p7y3sAf6E+yJ/gTYxlk/O04fqN955x2NHz9efn5+Fdanp6fr/vvvl5eXlyTJ399fPXv21Oeff27zOdPS0vT++++rffv26t+/v3n9+PHjK7SLi4tTRESEPv/8c23fvl1xcXFVHm/ZsmVasmSJeTk6Olpz585VaGiozbWieuHh4Y4uAfUI/Qn2RH8CrBMREeHoEqrk9IE6JydHjz/+uO655x4NGTJEbm5Xp86OiYnRxx9/rDFjxigwMFBpaWlatWqVYmJibDpfbm6uXn31Vfn4+OgPf/iD+XzVSUpKUnJysg4ePFhtoB41apSSkpLMy+W/rjIzM1VWVmZTvajMYDAoPDxc586dk8lkcnQ5cHH0J9gT/QmwTXp6ep2dy8PDw+KLn04fqKdPn679+/dr0aJFWrdunR588EF16dJFkyZN0nvvvaeXXnpJV65ckZubm2655RZNmjTJ6nMVFhbqz3/+swoKCvTiiy8qKCjouvt4eXmpSZMmys/Pr7aNp6enPD09q9zGF2rtMZlMvL+wG/oT7In+BFjHWT83Th+oJalLly6KjY3VN998ozfeeEPt27fXxIkT9cQTT2jatGm6dOmSmjRpct2ryddSUlKiuXPnKj09Xc8//7xatGhh0X6XL1/WpUuX5O/vb/W5AQAA4LpcIlBLV+d/Hj58uBISEvTZZ5/pmWee0aBBg3TPPfeoadOmNh3baDTq9ddf17Fjx/TMM8+offv2ldqUlJToypUr5psJy3355ZcymUzq0qWLTTUAAADUhJeXl6ZPn+7oMiAXCtRlZWUqKSmRv7+/HnvsMQ0ePFgLFy7UE088oXvvvVcDBgyw+s7PRYsWaffu3erevbvy8/O1adOmCtv79u2r3NxcTZ8+XXFxcebHkR84cED79u1Tly5d1KNHD5tfIwAAAFyP0wfqnJwc/eMf/1BKSopMJpPCw8P12GOPKSYmRrNnz9a2bdu0ePFirV27VhMnTrTqpsRTp05Jkvbs2aM9e/ZU2t63b1/5+vqqW7duSklJ0caNG2U0GhUeHq5x48ZpxIgRNg03AQAAgOsymJx1dPf/N3fuXJ07d06PPvqofH19tXTpUh06dEgLFixQo0aNJF0djvHVV19p5cqV6tq1q/7whz84uGrLZGZmVpifGvZhMBgUERGh9PR0p715Aa6D/gR7oj/VjTfffNPRJaCWPPHEE3V2Lk9PT4tn+XD6y6pHjx7VsGHDFBMTo9atW+u+++5Tfn6+zpw5Y27j5eWlMWPGaN68eU474TcAAADqJ6cP1IGBgfrxxx/Ny+V/BwQEVGobEhKip556qq5KAwAAAJx/DPW4ceP0xhtvKDU1Vb6+vjp58qSGDh2q4OBgR5cGAAAAOH+g7tmzp+bNm6cDBw6opKREDz74oDp27OjosgAAAABJLhCoJSksLEwDBw50dBkAAABAJS4RqKuTnZ2t/Px8+fv7VzmmGgAAAKhtTh+ot23bpvbt2yskJMS8bt++fVq0aJHOnj1rXhcVFaWJEyfqxhtvdESZAAAAaKCcfpaP8hsSy6WkpGju3LkqKCjQHXfcoUceeUTDhw9XZmam5syZY35ICwAAAFAXnP4K9W999tlnCg4O1ty5c+Xn52den5SUpGeeeUZLly51mQe7AAAAwPU5/RXqXysrK9PJkyeVlJRUIUxLUlBQkBITE3X06FEHVQcAAICGyKUC9ZUrVyRJzZo1q3J7WFiYCgoK6rIkAAAANHAuMeRj7969ys3NlSQ1btxY2dnZVbbLycmRr69vHVYGAACAhs4lAvXWrVu1detW8/LOnTuVmJhYqd3BgwfVokWLuiwNAAAADZzTB+q33nqr0jqDwVBpXV5enpo1a6YuXbrUQVUAAADAVU4fqENDQy1q5+/vrylTptRyNQAAAEBFLnVTIgAAAOBsnP4Kdbm9e/dq69atOnnypLKzs1VSUqLGjRsrMjJSN998swYOHKjg4GBHlwkAAIAGxukDdXFxsf72t7/pwIEDFda7u7srMjJSWVlZWrZsmVavXq3/+q//Unx8vIMqBQAAQEPk9IE6OTlZBw8e1EMPPaS4uDg1atRIR44c0bvvvquYmBjdd999OnPmjBYvXqy33npLQUFBiomJcXTZAAAAaCCcfgz1tm3bNHjwYA0ZMkRNmjSRl5eXunTpokmTJunrr7/WxYsX1aJFC02fPl1t27bV0qVLHV0yAAAAGhCnD9SXLl2qcm7pFi1ayGg0Kj09XdLVqfTi4+N1/Pjxui4RAAAADZjTB+qwsDAdOnSo0vrDhw9LkgICAszr3N3dZTKZ6qo0AAAAwPnHUA8ePFgLFy6Ul5eX+vTpIy8vL6Wmpmr58uXq2LGjwsPDzW1PnTpVYRkAAACobU4fqIcMGaKsrCytWrVKGzduNK9v166dnnzyyQptvby8NHTo0DquEAAAAA2Z0wdqSZowYYKSkpJ07NgxlZaWKjIyUtHR0ZXaTZw4se6LAwAAQIPmEoFaujpWumfPno4uAwAAAKjA6W9KBAAAAJwZgRoAAACwAYEaAAAAsAGBGgAAALABgRoAAACwAYEaAAAAsIHLBeqioiItWLBAaWlplbYdO3ZMCxYscEBVAAAAaKhcLlCXlpZq48aNysnJqbTt/PnzFZ6mCAAAANQ2lwvUAAAAgDMhUAMAAAA2cJlHj2dlZUmS8vPzJUkXL140r5OkkJAQh9QFAACAhs1lAvXUqVMrLL/55psVlpOTk+uyHAAAAECSCwXqmTNnSpIKCgr017/+Vffff7/atGnj4KoAAADQ0LlMoI6JiZEkXbp0SZIUFRVlXmer48ePa+PGjTp8+LAyMzPl5+enG264QWPHjlVkZGSFtmfOnNFHH32k1NRUeXh4qFu3bnrwwQfl7+9vl1oAAADgWrgpUdLy5cu1Y8cOderUSRMnTlRiYqKOHj2q6dOn6/Tp0+Z2Fy5c0MyZM3Xu3DmNGzdOI0aM0N69e/XSSy+prKzMga8AAAAAjuIyV6hrU1JSkn7/+9/Lw+P/3o4+ffro6aef1ldffaUnnnhCkrRs2TIVFxdr7ty55psg27Vrpzlz5mjDhg1KTEx0SP0AAABwHJe7Qu3u7q6YmBj5+vpW2ta0aVOrhoF06NChQpiWpIiICLVo0aLCExl37Nihbt26VZhRJDY2VhEREfr+++9rfF4AAAC4Ppe7Qu3j42O+QfG3YmNjFRsba5fzmEwmXbx4US1btpQkZWdn6+LFi2rbtm2ltu3atdO+ffvscl4AAAC4FpcL1HVl8+bNys7O1pgxYyTJ/KjzwMDASm0DAwOVn5+v0tJSeXp6VtpeWlqq0tJS87LBYJC3t7f5b9hX+XvKewt7oD/BnuhPgG2c9bPj1IG6uLhYjRo1qvN909LS9P7776t9+/bq37+/JKmkpESSKg0NkWQO0SUlJVUG6mXLlmnJkiXm5ejoaM2dO1ehoaFW1QfLhIeHO7oE1CP0J9gT/QmwTkREhKNLqJJTB+rJkydr2LBhGjBgQJVXhquSnZ2tdevWae3atXr//fdrfM7c3Fy9+uqr8vHx0R/+8Ae5uV0dZu7l5SVJVc7mUX71ubzNb40aNUpJSUnm5fJfV5mZmcwOUgsMBoPCw8N17tw5mUwmR5cDF0d/gj3RnwDbpKen19m5PDw8LL746dSBetKkSfriiy+0ZMkSdejQQTfffLPatGmjsLAw+fr6ymQyqaCgQBkZGTpx4oQOHjyoH3/8UREREXrkkUdqfL7CwkL9+c9/VkFBgV588UUFBQWZt5UH+vKhH7+Wk5MjPz+/Kq9OS1evYFe3jS/U2mMymXh/YTf0J9gT/QmwjrN+bpw6UPfp00e9e/fW7t27tWHDBi1btqzaK7oeHh6KjY3VH/7wB/Xo0cN8ZdlSJSUlmjt3rtLT0/X888+rRYsWFbYHBQXJ399fJ06cqLTv8ePHFRUVVaPzAQAAoH5w6kAtSW5uburZs6d69uyp0tJSnTx5UmlpacrPz5ck+fn5qXnz5mrTpk21V4Gvx2g06vXXX9exY8f0zDPPqH379lW269WrlzZu3KisrCzz1HkHDx5Uenq6hg8fbt0LBAAAgEtz+kD9a56enurQoYM6dOhg1+MuWrRIu3fvVvfu3ZWfn69NmzZV2N63b19JV8dCb9++XbNnz9awYcNUVFSkFStWqFWrVrrtttvsWhMAAABcg0sF6tpy6tQpSdKePXu0Z8+eStvLA3VISIhmzZqlRYsW6dNPP5WHh4e6du2qBx54wOqr4wAAAHBtBGpJs2bNsrhty5YtNWPGjNorBgAAAC7F5R49DgAAADgTAjUAAABgAwI1AAAAYAMCNQAAAGADl7spMSsrS0uXLtXhw4eVl5enZ555RjExMcrLy9OSJUt02223KTo62tFlAgAAoIFwqSvUZ86c0R//+Ed9//33CgsLU2FhoYxGoyTJ399fP/zwg9asWePgKgEAANCQuFSgXrx4sXx9ffXGG2/o8ccfr7S9a9euSk1NdUBlAAAAaKhcKlAfPXpUAwcOlL+/vwwGQ6XtISEhys7OdkBlAAAAaKhcKlAbjUY1atSo2u15eXny8HC5YeEAAABwYS4VqNu0aaO9e/dWue3KlSvatm2b2rdvX8dVAQAAoCFzqUB95513av/+/Xr33Xf1yy+/SJJyc3OVkpKiOXPmKC0tTXfccYeDqwQAAEBD4lLjI7p27aqpU6dq4cKFWr9+vSTp73//uyTJ29tbU6dOVUxMjCNLBAAAQAPjUoFakvr27auePXsqJSVF586dk9FoVHh4uDp37ixvb29HlwcAAIAGxuUCtSQ1btxYPXv2dHQZAAAAgGsF6qysLIvahYSE1HIlAAAAwFUuFainTp1qUbvk5ORargQAAAC4yqUC9eTJkyutMxqNyszM1KZNm+Tv76/Bgwc7oDIAAAA0VC4VqPv371/ttjvuuEMzZsxQYWFh3RUEAACABs+l5qG+lsaNG6t///5atWqVo0sBAABAA1JvArUkmUwm5ebmOroMAAAANCAuNeSjOoWFhTp69KhWrFih6OhoR5cDAACABsSlAvW99957ze0hISGaNGlSHVUDAAAAuFigvvvuu2UwGCqsMxgM8vX1VbNmzdS5c2e5u7s7qDoAAAA0RC4VqMeMGePoEgAAAIAK6tVNiQAAAEBdc+or1AsWLKjxPgaDocoHwAAAAAC1wakD9eHDh2u8z2/HWAMAAAC1yakD9fz58x1dAgAAAHBNjKEGAAAAbECgBgAAAGzg1EM+qrJv3z59/fXX+umnn1RYWCiTyVSpTXJysgMqAwAAQEPkUleot2/frldffVUXL15Unz59ZDKZFBcXp7i4OHl5eal169YaPXq0o8sEAABAA+JSV6i/+uortWvXTi+99JLy8/O1bt063X777erUqZMyMjI0Y8YMhYWFObpMAAAANCAudYX6zJkziouLk5ubm/kR42VlZZKksLAwDR48WMuXL3dkiQAAAGhgXCpQN2rUSB4eVy+q+/r6ysPDQ7m5uebtTZs2VUZGhoOqAwAAQEPkUoE6MjJSZ86cMS9HRUVp06ZNunLlikpKSrRlyxaFhIQ4sEIAAAA0NC4VqG+55Rbt2rVLpaWlkqS77rpLhw8f1sSJEzVp0iSlpqbqzjvvdGyRAAAAaFCc/qbEPXv2qGvXrnJzc9PIkSM1cuRI87bu3btr1qxZ2rFjh9zc3NStWzd16tTJgdUCAACgoXH6QP2Xv/xFTZo00a233qqEhAS1b9++wvYbb7xRN954o4OqAwAAQEPn9IH60Ucf1datW7Vu3TqtXbtWoaGhio+PV3x8vFq0aGGXcxQVFWnFihX68ccfdfz4cRUUFGjKlCnq379/hXbz58/Xxo0bK+0fGRmp119/3S61AAAAwLU4faBOTExUYmKisrOztXXrVm3dulXLli3TsmXLFBUVpYSEBPXp00dBQUFWnyMvL09LlixRSEiIoqKidPjw4Wrbenp66rHHHquwzsfHx+pzAwAAwLU5faAuFxQUpBEjRmjEiBE6e/astmzZoq1bt+rjjz/W4sWLFRMTo4SEBPXq1avGATcwMFDvvPOOAgICdOLECT377LPVtnVzc1Pfvn1tfTkAAACoJ1xqlo9ykZGRGjNmjN544w29/PLLGjp0qNLS0vT2229XunpsCU9PTwUEBFjc3mg0qrCwsMbnAQAAQP3jMleoqxMWFqZmzZopODhYubm5KikpqdXzlZSU6MEHH1RxcbF8fX0VFxenCRMmqHHjxrV6XgAAADgnlwzURUVF2rFjh7Zs2aJDhw7JaDQqNDRUo0aNUnx8fK2dNzAwUCNHjlR0dLRMJpP279+vtWvX6ueff9asWbPMj0P/rdLSUvPc2ZJkMBjk7e1t/hv2Vf6e8t7CHuhPsCf6E2AbZ/3suEygLisr0969e7Vlyxbt27dPJSUl8vPz04ABAxQfH6+OHTvWeg3jx4+vsBwXF6eIiAh9/vnn2r59u+Li4qrcb9myZVqyZIl5OTo6WnPnzlVoaGit1tvQhYeHO7oE1CP0J9gT/QmwTkREhKNLqJLTB+pDhw5py5Yt2rFjhwoLC+Xp6anu3bsrISFBXbt2rfaqcF1JSkpScnKyDh48WG2gHjVqlJKSkszL5b+uMjMzVVZWVid1NiQGg0Hh4eE6d+6cTCaTo8uBi6M/wZ7oT4Bt0tPT6+xcHh4eFl/8dPpA/dJLL8lgMOimm24yz+JRPlzCGXh5ealJkybKz8+vto2np6c8PT2r3MYXau0xmUy8v7Ab+hPsif4EWMdZPzdOH6jvv/9+xcXFKTAw0NGlVOny5cu6dOmS/P39HV0KAAAAHMDpA/Wvh0o4UklJia5cuVLp6viXX34pk8mkLl26OKYwAAAAOJTTB+q6smbNGhUUFCgnJ0eStHv3bl24cEGSNHToUOXn52v69OmKi4tTZGSkJOnAgQPat2+funTpoh49ejisdgAAADgOgfr/W7lypTIzM83LO3fu1M6dOyVJCQkJ8vX1Vbdu3ZSSkqKNGzfKaDQqPDxc48aN04gRI+Tm5pLPyAEAAICNCNT/3/z586/b5vHHH6+DSgAAAOBKuKwKAAAA2MClAvW2bdtq/dHiAAAAQE241JCPN954Q97e3urVq5cSEhLUqVMnR5cEAACABs6lAvWLL76ozZs3a/v27dqwYYOCgoIUHx+vhIQEtWrVytHlAQAAoAFyqUDdoUMHdejQQQ899JD279+vzZs3a82aNVqxYoVatWqlvn37Kj4+3mkfAgMAAID6x6UCdTl3d3d1795d3bt3V1FRkXbu3KkNGzZo8eLF+vTTTxUTE6N+/fqpT58+8vBwyZcIAAAAF+FSNyVW5fTp0zp+/LhOnz4tSYqMjFR+fr7mz5+vxx9/XKmpqQ6uEAAAAPWZS16+PXv2rLZs2aItW7bo/Pnz8vf3V3x8vPr27as2bdpIkk6cOKG3335b7777rv72t785uGIAAADUVy4VqFevXq3Nmzfr5MmT8vT0VPfu3TVx4kR16dKl0pMK27Ztq+HDh+vtt992ULUAAABoCFwqUH/00Ufq0KGDHn30UfXp00c+Pj7XbN+2bVvdfffddVQdAAAAGiKXCtR///vfFRYWZnH7li1bqmXLlrVYEQAAABo6l7opMTg4WIWFhdVuLyws1JUrV+qwIgAAADR0LhWoFy5cqOeff77a7c8//7wWLVpUhxUBAACgoXOpQL1//3716tWr2u29e/fWvn376rAiAAAANHQuFahzcnIUFBRU7fbAwEBlZ2fXYUUAAABo6FwqUPv5+ens2bPVbk9LS5O3t3cdVgQAAICGzqUCdZcuXbR+/Xr99NNPlbadPHlS69evV9euXR1QGQAAABoql5o2795779X+/fv1pz/9Sd27dzdPiffLL79oz5498vf317333uvgKgEAANCQuFSgDgoK0quvvqpPPvlEu3fv1q5duyRJ3t7eio+P17hx4645xhoAAACwN5cK1NLVGw+nTZsmk8mkvLw8SZK/v78MBoODKwMAAEBD5HKBupzBYFDTpk0dXQYAAAAaOKcO1EuWLJEk3XXXXXJzczMvX8/o0aNrsywAAADAzKkD9RdffCFJuvPOO+Xm5mZevh4CNQAAAOqKUwfq5OTkay4DAAAAjuZS81ADAAAAzoZADQAAANjAqYd8TJ06tcbT4RkMBv3973+vpYoAAACAipw6UMfExDC/NAAAAJyaUwfqqVOnOroEAAAA4JoYQw0AAADYwKmvUFfFaDRq06ZN2rt3r7KysiRJISEh6t69uxISEuTmxm8EAAAA1B2XCtSFhYV6+eWXdfz4cXl7e6tZs2aSpIMHD2rHjh1au3atZsyYIR8fHwdXCgAAgIbCpQL1Z599ppMnT+rhhx/WgAED5OFxtfyysjL95z//0cKFC/X555/r4YcfdnClAAAAaChcanzEzp07NWjQIA0ePNgcpiXJw8NDgwYN0sCBA7Vjxw4HVggAAICGxqUCdX5+viIjI6vd3rx5c+Xn59dhRQAAAGjoXCpQh4eHa/fu3dVu3717t3lcNQAAAFAXXCpQDxo0SCkpKXrllVd04MABZWRkKCMjQ/v379crr7yilJQUDRkyxNFlAgAAoAFxqZsSBw8erIsXL2r58uXav39/hW0eHh4aPXq0Bg0a5JjiAAAA0CC5VKCWpDFjxmjIkCFKSUkxz0MdGhqqm2++Wf7+/g6uDgAAAA2NSwTq3NxcbdiwQRkZGfL391evXr0UHx/v6LIAAAAA5w/UGRkZevbZZyvM3vHVV19p2rRpdgvVRUVFWrFihX788UcdP35cBQUFmjJlivr371+p7ZkzZ/TRRx8pNTVVHh4e6tatmx588EGujgMAADRQTh+o//Wvf6moqEgPPfSQOnXqpHPnzmnhwoX66KOP1KdPH7s8ajwvL09LlixRSEiIoqKidPjw4SrbXbhwQTNnzpSPj4/GjRunoqIirVy5UqdPn9Yrr7xSYW5sAAAANAxOnwB/+OEHJSYmmmfvaNGihdzc3DR37lylpaWpZcuWNp8jMDBQ77zzjgICAnTixAk9++yzVbZbtmyZiouLNXfuXIWEhEiS2rVrpzlz5mjDhg1KTEy0uRYAAAC4FqefNi8rK0vR0dEV1rVp00aSdOnSJbucw9PTUwEBAddtt2PHDnXr1s0cpiUpNjZWERER+v777+1SCwAAAFyL01+hNhqNlYZSuLu7m7fVlezsbF28eFFt27attK1du3bat29ftfuWlpaqtLTUvGwwGOTt7W3+G/ZV/p7y3sIe6E+wJ/oTYBtn/ew4faCWpBMnTsjT09O8fPnyZUlSamqqCgoKKrXv1auX3WvIycmRdHV4yG8FBgYqPz9fpaWlFeost2zZMi1ZssS8HB0drblz5yo0NNTudeL/hIeHO7oE1CP0J9gT/QmwTkREhKNLqJJLBOrVq1dr9erVldZ/8cUXVbZPTk62ew0lJSWSVOWNh+UhuqSkpMpAPWrUKCUlJZmXy39dZWZmqqyszO61NnQGg0Hh4eE6d+6cTCaTo8uBi6M/wZ7oT4Bt0tPT6+xcHh4eFl/8dPpAPXPmTEeXIEny8vKSpCoDcPlwjvI2v+Xp6Vll0JbEF2otMplMvL+wG/oT7In+BFjHWT83Th+oY2JiHF2CpP8b6lE+9OPXcnJy5OfnV21oBgAAQP3l9LN8OIugoCD5+/vrxIkTlbYdP35cUVFRdV8UAAAAHI5AXQO9evXS3r17lZWVZV538OBBpaenq3fv3g6sDAAAAI7i9EM+6sqaNWtUUFBgHtKxe/duXbhwQZI0dOhQ+fj4aNSoUdq+fbtmz56tYcOGmR9Z3qpVK912222OLB8AAAAOQqD+/1auXKnMzEzz8s6dO7Vz505JUkJCgnx8fBQSEqJZs2Zp0aJF+vTTT+Xh4aGuXbvqgQceYPw0AABAA0Wg/v/mz59vUbuWLVtqxowZtVwNAAAAXAVjqAEAAAAbOPUV6o0bN1q1X79+/excCQAAAFA1pw7UCxYssGo/AjUAAADqilMH6rfeeqvCckFBgebPny8fHx8NHTpUkZGRkqS0tDStWbNGly9f1tSpUx1RKgAAABoopw7Uv31++hdffCF/f38999xzMhgM5vWtWrVSr1699PLLL2vVqlWaMmVKXZcKAACABsqlbkrctWuXevbsWSFMl3Nzc1PPnj21a9cuB1QGAACAhsqlArXJZFJaWlq128+cOVOH1QAAAAAuFqhvueUWrVu3Tl9//bWKi4vN64uLi7Vy5UqtX79ePXr0cGCFAAAAaGicegz1bz300EPKyMjQxx9/rE8//VSBgYGSpJycHF25ckUdOnTQxIkTHVskAAAAGhSXCtQ+Pj6aPXu2du3apX379ikrK0uS1LlzZ3Xr1k3du3evcnw1AAAAUFtcKlCXu+WWW3TLLbc4ugwAAADANQN1dna2jhw5ory8PPXq1UvBwcEyGo0qLCyUj4+P3Nxcamg4AAAAXJhLBWqTyaRFixZpzZo1MhqNkq7OQR0cHKyioiJNnTpVY8aM0fDhwx1cKQAAABoKl7qUu2LFCq1evVojRozQc889V2Gbj4+PevbsqR07djioOgAAADRELhWov/32W/Xr10/jx49XVFRUpe2tW7dWenp63RcGAACABsulAvWFCxfUvn37arc3atRIhYWFdVgRAAAAGjqXCtT+/v66cOFCtdtPnjypkJCQOqwIAAAADZ1LBepevXpp3bp1On/+fKVtBw4c0IYNG3Trrbc6oDIAAAA0VC41y8eYMWN0+PBh/fGPf1THjh0lScuXL1dycrKOHTum6OhojRo1ysFVAgAAoCFxqSvUPj4+evnllzVy5EhlZ2fLy8tLR44cUWFhoe655x69+OKLatSokaPLBAAAQAPiUleoJcnLy0t333237r77bkeXAgAAALjWFerZs2fr4MGD1W4/dOiQZs+eXYcVAQAAoKFzqUB95MgRXbx4sdrteXl5OnLkSB1WBAAAgIbOpQL19Zw7d07e3t6OLgMAAAANiNOPod6wYYM2btxoXl66dKm+/fbbSu0KCwv1888/q2vXrnVZHgAAABo4pw/UJSUlysvLMy9fvnxZBoOhQhuDwaBGjRpp4MCBGj16dF2XCAAAgAbM6QP1oEGDNGjQIEnS1KlT9dBDD6lHjx4OrgoAAAC4yukD9a/Nnz/f0SUAAAAAFbhUoP61y5cvq7CwUCaTqdK2kJAQB1QEAACAhsjlAvXatWv19ddf6/z589W2SU5OrsOKAAAA0JC51LR5a9eu1fvvv6/w8HCNHTtWkjR8+HDdeeedCggIUFRUlCZPnuzgKgEAANCQuFSgXrNmjTp37qw//elPSkxMlCR169ZN48aN07x583T58mVdunTJwVUCAACgIXGpQH3+/Hl1795dkuTu7i5JKisrkyT5+Pjo9ttv19q1ax1WHwAAABoelwrUPj4+unLlivlvLy8vZWVlmbd7e3srNzfXQdUBAACgIXKpQN2yZUv9/PPP5uX27dtr3bp1ys7OVlZWltavX6+IiAgHVggAAICGxqUCdUJCgn755ReVlpZKku655x6dOXNGkydP1tSpU3X27FnzzYoAAABAXXCpafNuu+023Xbbbebljh076rXXXtOePXvk5uam2NhYRUZGOrBCAPVJYWGhbrjhBknS8ePH5e3t7eCKAADOyKUCdVWaNWumYcOGOboMAAAANFAuG6iNRqMKCwur3Obn51fH1QAAAKChcqlAXVZWpuXLl+u7777ThQsXZDQaq2xXW09KPHz4sGbPnl3ltjlz5qh9+/a1cl4AAAA4L5cK1O+88442btyo9u3b65ZbbpGPj49D6hg6dKjatm1bYV14eLhDagEAAIBjuVSg3r59u/r27aupU6c6tI4bb7xRvXv3dmgNAAAAcA4uNW1eo0aNzHfcO9rly5fND5kBAABAw+VSV6jj4uK0d+9eDRo0yKF1LFiwQEVFRXJzc9ONN96oCRMmVBoCAtR3d3yS6ugSat2Vksvmv+/5PFXuXvV/2rzl93V0dAkA4HJcKlBPmDBBCxYs0KuvvqrbbrtNwcHBcnOrfJG9TZs2tXJ+Dw8P9erVS127dpW/v7/OnDmjlStX6oUXXtCcOXMUHR1d5X6lpaXmh9FIksFgMM9nazAYaqXWhqz8PeW9BWqOz03t4vsJsI2zfnZcKlCXlpbKZDJp37592rdvX7XtamuWjw4dOqhDhw7m5R49eqh37956+umn9emnn2rGjBlV7rds2TItWbLEvBwdHa25c+cqNDS0VurEVdwoWtuOOroA1IKIiAhHl9Ag8P0EWMdZv6NcKlD/4x//0M6dOxUXF6d27do5bJaPXwsPD1ePHj20c+dOGY3GKq+Yjxo1SklJSebl8l9XmZmZKisrq7NaGwqDwaDw8HCdO3dOJpPJ0eUALiU9Pd3RJdRrfD8BtqnL7ygPDw+LL366VKA+cOCAhgwZookTJzq6lApCQkJUVlamoqKiKkO+p6enPD09q9yXL9TaYzKZeH+BGuIzUzf4fgKs46yfG5ea5cPb29sp/5ns/Pnz8vT0VOPGjR1dCgAAAOqYSwXqAQMGaOvWrdU+IbG25eXlVVp36tQp7d69W507d65yuAcAAADqN5ca8tGiRQvt3r1b06dPV79+/aqd5aNXr161cv558+bJy8tLHTp0MM/y8e2336pRo0YaP358rZwTAAAAzs2lAvXrr79u/vvjjz+utl1tzfJxyy23aMuWLfr66691+fJl+fv7q2fPnrrnnnuccigKANu4e3mrx1++dXQZAAAn51KBeubMmQ49/7BhwzRs2DCH1gAAAADn4lKBOiYmxtElAAAAABVwFx0AAABgA6e+Qj179mwZDAbNmDFD7u7umj179nX3MRgMeuGFF+qgOgAAAMDJr1D/duJ7SybzdtYJvwEAAFA/OfUV6lmzZl1zGQAAAHA0p75C/VtHjhyp8uEq5fLy8nTkyJE6rAgAAAANnUsF6tmzZyslJaXa7YcOHbJonDUAAABgLy4VqK+ntLSUx38DAACgTjn1GGpJysrKUkZGhnk5LS2tymEdhYWFWr9+vUJDQ+uyPAAAADRwTh+ov/vuOy1ZssS8vHTpUi1durTKtm5ubnr00UfrqjQAAADA+QP1rbfeqpYtW0qS5s2bp6FDh6pjx44V2hgMBjVq1EhRUVEKCAhwQJUAAABoqJw+ULdo0UItWrSQJE2ePFkxMTEKCwtzcFUAAADAVU4fqH+tf//+Va4/f/68SktLzcEbAAAAqCsuFai/+eYb/fDDD3ryySfN6xYsWKCNGzdKkqKjo/Xss8+qadOmDqoQAAAADY1LzTH37bffVgjL+/fv18aNG5WYmKiHH35Y58+f1xdffOHACgEAANDQuNQV6szMTDVv3ty8/P333yssLMw8s0dubq42bdrkqPIAAADQALnUFerfSklJUZcuXczLoaGhys3NdVg9AAAAaHhcKlBHRERo165dkq4O98jOzlbXrl3N27Ozs+Xr6+uo8gAAANAAudSQjxEjRujNN9/UQw89pKKiIrVo0UKdO3c2bz906JCioqIcVyAAAAAaHJcK1HFxcWrSpIn27t0rX19fDR48WO7u7pKk/Px8+fn5qW/fvg6uEgAAAA2JSwVqSYqNjVVsbGyl9X5+fnr66acdUBEAAAAaMqcfQ338+HHl5+db1DYjI8M8JzUAAABQF5w+UM+YMUP79+83L+fn52vChAk6cuRIpbY//PCDFixYUIfVAQAAoKFz+kD9WyaTSaWlpTIajY4uBQAAAHC9QA0AAAA4EwI1AAAAYAMCNQAAAGADl5g2LyMjQydPnpQkFRYWSpLS09Pl4+NTqR0AAABQl1wiUCcnJys5ObnCuvfee89B1QAAAAD/x+kD9eTJkx1dAgAAAFAtpw/U/fv3d3QJAAAAQLWcPlDXZ4fOF+hiYbGjy6h3DAaDAgs8lZNzSSaTydHlAC5l55lLji6hXuP7qW5keoY6ugTUkrr8jmrq00i3hVrWl5jlAwAAALABV6gdyK20RG4lRY4uo94xGAwyFBfJraSIK0C1qNGVEkeXgFrAd1Lt4vupbnheKXN0Cagldfkd5eZpsLitwcQn2mHOPXGfSk/84OgyAAAA8BuebTso/M1PLGrLkA8AAADABgz5cKAfHpqpiwWXHV1GvWMwGBQQEKTc3Gz+SbUW/WVLuqNLQC34Y3yEo0uo1/h+qhurV69xdAmoJcOGDamzczX19Va4hW0J1A5k9PSS0atuvlCNb82pk/M4i0xHF1CH3KY955DzFrt7OeS8qF1Gr8YOOe/OzQUOOa9jNKyn+vZM8K3zc5a6E2/qq7r8jjJ6Wv7/cwz5AAAAAGzAT7gaKi0tVXJysjZv3qz8/Hy1bt1aY8eOVWxsbI2P1amZr0pL6+Yq35ULR+vkPKh77i2aOLoE1CM9HdSfzrtdcch5Ufsc0ae2lzakf6dsWOqyP3l6elrclivUNTR//nytWrVK8fHxeuihh+Tm5qZXXnlFqampji4NAAAADkCgroHjx49r27ZtGjdunO6//34lJibqhRdeUEhIiBYvXuzo8gAAAOAABOoa2L59u9zc3JSYmGhe5+Xlpdtvv13Hjh1TVlaWA6sDAACAIxCoa+Cnn35SRESEfHx8Kqxv166dJOnUqVMOqAoAAACOxE2JNZCbm6vAwMBK68vX5eTkVLlfaWmpSktLzcsGg0He3t7y8Ki7t9+tbYc6OxfqlnsNbpqwpw5hfg45L2pXTW7CsaeQMMdM14fa54g+FRkZWefnRN2oy/5Uk5xGoK6BkpKSKv9Dlq8rKSmpcr9ly5ZpyZIl5uW4uDj9/ve/rzKc1xoLH50JWGrxg6GOLgH1yN330Z9gP0888YSjS0ADw5CPGvDy8qpwpblc+Tovr6qnwBs1apQ+/PBD8/8effTRKo8D+7h8+bKmT5+uy5d5CiVsR3+CPdGfYG/0KefAFeoaCAgIUHZ2dqX15UM9qrvi7Onp6bB/Rm2I/l97dx4QVdX/D/x9h5lh3xHZZFNcyFDABRFRE8UNy1IytdKyb6U+VtqT37KnsLS0vk+2qPW02UIZZpq7IhWKC6CJ4oKpKJqKAgKyygzM/f3hj3maWBxmYYbx/fon7rnnnvu545nbh8O554qiiAsXLvC1vmQQ7E9kSOxPZGjsU+aBI9RtEBgYiMLCQtTU1GiUnz17Vr2fiIiIiO4uTKjbICoqCiqVCmlpaeoypVKJ9PR0hISEwMPDw4TREREREZEpcMpHG4SEhCAqKgpr165FRUUFvLy8sGfPHhQXF+OZZ54xdXj0/8lkMkyaNInTbMgg2J/IkNifyNDYp8yDIHLSTZsoFAqkpKQgIyMD1dXV8Pf3x8MPP4y+ffuaOjQiIiIiMgEm1EREREREeuAcaiIiIiIiPTChJiIiIiLSAxNquuslJiZi3bp1pg6DLFRRURESExORnp5u6lDIgiUlJSEpKcnUYZAFmzNnDlatWmXqMMwWE2oiIiIiIj1w2Ty66yUnJ8PKysrUYRAR6ezVV181dQhEdzWOUNNdTy6XM6Emk7t165apQ6AOTCqVQirlGBmZ1t18H+O3j0yqtrYWKSkpOHToEMrKymBnZ4eAgABMmzYNwcHBSEpKQmVlJebNm4cvv/wS+fn5cHV1xbRp0xAVFYVTp04hOTkZFy9ehIeHB5588kmEhYW1KYbExERMmjQJiYmJWsVE5skc+lJ1dTW++uorZGdnQxAE9OvXD+PHj29Sb9WqVcjMzMS7776LNWvWIC8vD71798ZLL71kqI+D7sAc+sv+/fuxefNmFBYWQhAEeHh4YMSIERg7diwAID09HatXr8Ybb7yBrKws7N27FwqFAmFhYXj66afh5OSkbqtx/vRf51Hv2LEDu3fvRlFREWQyGTp37ozx48cjJiZG78+P/svS+pIoitiwYQN2796NqqoqhISE4IknnmhyzsY2k5KScODAAWRmZqKhoQFr1qzR7wPtoDhCTSb12WefITU1FQMHDsSsWbOQkJAAuVyOK1euqOtUVVVh2bJlCAkJwfTp0yGTyfD+++/jwIEDeP/99xEeHo5p06ahrq4O7733Hmpra40eE5kfU/clURTxzjvvYO/evRgyZAimTJmC0tLSFh/iUalUWLp0KZycnPDoo48iKipK78+AtGfq/pKbm4sPPvgADg4OmDZtGqZOnYp77rkHp0+fblJ3zZo1uHjxIiZPnoyRI0fi999/xxdffNFq+2lpaVizZg38/PwwY8YMTJ48GYGBgTh79qz2HxJpxdL6UkpKClJSUhAQEIDp06fD09MTS5YsaXH0+fPPP8fly5cxadIk3H///VrHbWk4Qk0mdeTIEYwYMQKPPfaYuuzvX8iysjLMmzdPPaoSFhaG559/Hh988AGWLFmCkJAQAICvry+WLl2KrKwsDBs2zKgxkfkxdV86fPgw8vLyMH36dEyYMAEAMGrUKCxevLjZ+kqlEoMGDcLUqVPbeqlkAKbuL0eOHIGtrS0WLVoEiaT1sS0HBwe8+uqrEAQBwO1f3nbs2IGamhrY2dm12H6XLl0wf/58reIh3VlSX6qoqMDmzZsRERGBhQsXquutXbsWGzdubLHN11577Y7ntnR399WTydnb2+PcuXMoLS1tsY6NjQ0GDx6s3vbx8YG9vT38/PzUNyEA6p+vX79u9JjI/Ji6L+Xk5MDKygqjRo1Sl0kkEowePbrFY/5al9qXqfuLnZ0d6urqkJube8e6cXFx6sQGAHr16gWVSoXi4uIWj7G3t8eNGzdw7tw5rWMi3VhSX8rNzUV9fT1Gjx6tUW/cuHEttjlixIi7PpkGOEJNJjZt2jSsWrUKzz77LIKDgxEeHo6hQ4eic+fO6jru7u4aX2zg9g3E3d29SRlwex6rsWMi82PqvlRcXAwXFxfY2NholPv4+DRb38rKCm5ublq3T4Zl6v4SHx+PgwcP4q233oKbmxvCwsIQHR2Nvn37Nqnr4eGhsW1vb3/H891///04fvw4XnnlFXh5eSEsLAwxMTHo2bOn1jGSdiypL5WUlAAAvL29Neo5OTmp6/6dp6en1rFaMibUZFLR0dHo1asXsrOzcezYMWzZsgWbNm3Ciy++iPDwcABo8TfflspFUTR6TGR+zLEvtUYqlXJUx4RM3V+cnZ3x7rvv4ujRozh69ChycnKQnp6O2NhYzJ07V+/z+fn54f3338eRI0dw9OhRZGVlITU1VeMBbDIMS+9LdyKXy3U+1pLwbk4m5+rqivj4eLz00ktYuXIlHB0dsWHDBsZEbWbKf7dOnTqhvLy8yYM7V69ebZfzU9uZ+nsulUrRr18/zJo1Cx999BHi4uKwd+9eXLt2zSDt29jYIDo6GrNnz8bHH3+MiIgIbNiwAQqFwiDt039ZSl9qHMEuLCzUKK+oqND7r7+Wjgk1mYxKpUJNTY1GmbOzM1xdXVFfX8+YSGvm8O8WHh6OhoYGpKamasS1c+fOdjk/ac8c+ktlZaXGtkQiQUBAAIDbD6waun2pVAo/Pz+IooiGhga926fbLK0vhYWFwcrKCjt37tQYtd62bZv+gVo4Tvkgk6mtrcUzzzyDqKgoBAQEwMbGBsePH0d+fr7G09J3e0x0Z+bw7xYZGYkePXrgu+++Q1FREfz8/JCdnd3kf7ZkeubQXz755BNUVVWhd+/ecHd3R3FxMXbu3InAwED4+vrq3f6SJUvg4uKCHj16wMXFBZcvX8auXbsQEREBW1tbA1wBAZbXl5ycnJCQkICff/4Zy5YtQ3h4OAoKCpCTkwNHR0cjXYFlYEJNJmNtbY34+HgcO3YM2dnZUKlU8PLywqxZs0y2+oE5xkR3Zg7/bhKJBAsXLsRXX32FjIwM9YtdHnvsMb6wxcyYQ38ZMmQIfvnlF6SmpqK6uhouLi4YNGgQEhMTDTK3fuTIkcjIyMC2bdtw69YtuLm5YcyYMXjwwQcNED01ssS+NGXKFMjlcuzevRsnT55ESEgIXn31VSxbtswI0VsOQTTmUzdERERERBaOc6iJiIiIiPTAKR9kkVQqFSoqKlqtY2Nj02TNYKK/Y1+itmB/IUNhX+pYmFCTRSopKWmy/ubfcT1W0gb7ErUF+wsZCvtSx8I51GSRFAoFTp8+3Wqdzp078+2HdEfsS9QW7C9kKOxLHQsTaiIiIiIiPfChRCIiIiIiPTChJiIiIiLSAxNqIiIiIiI9MKEmIiIiItIDE2oiIiIiIj0woSYiIiIi0gMTaiIiIiIiPTChJiIiIiLSAxNqIiIiIiI9MKEmIiKjCAwMhCAIKCgoMHUoRERGxYSaiIiIiEgPTKiJiIiIiPTAhJqIiIiISA9MqImIOgBBECAIAgBgzZo1GDRoEJydnTXmKF+8eBHLly/HfffdB39/f1hbW8PFxQUxMTH4z3/+A5VK1aTdgoICCIKAwMBAiKKITz/9FJGRkbC3t4ezszNGjRqFgwcPthjXqVOnMHnyZHh4eMDW1ha9e/fG//3f/6GhoaHV6yktLcUrr7yCe+65B3Z2dnB0dERkZCTeeecd1NbWNqmfnp4OQRAwbNgw1NXVYfHixejevTtsbGzg7++PhQsX4tatWwCAmzdv4sUXX0RwcDBsbGwQGBiIpKQk1NfXa/txExG1iSCKomjqIIiIqHWNyfTcuXOxevVqREdHo0uXLjh//jxSUlIQEBCAJUuW4F//+heCgoIQHByMTp06obCwEAcPHoRCocCDDz6I9evXq9sCbifUQUFBCAgIwLBhw/D9999jyJAh8PDwwNGjR3HmzBlYW1tjz549GDhwoEZM+/btw+jRo1FdXY3g4GAMGDAAJSUl2LNnDyZMmIDDhw/j4sWLuHDhAgIDA9XHnT9/Hvfddx8uXryITp06ITY2FkqlEr/99hsqKysRERGBtLQ0uLq6qo9JT0/H8OHDMWjQIMjlcuTk5GDYsGFQqVTIyMjAzZs3MX78eHz99deIjo5GaWkpYmNjUVlZib179+LWrVt45pln8PHHHxv3H4qI7k4iERGZPQAiANHJyUk8ePBgs3Wys7PF48ePNym/cuWK2KdPHxGAuG7dOo19Fy5cULcdEBAg/vHHH+p99fX14hNPPCECEEeNGqVxXG1trdilSxcRgPj888+L9fX16n3Hjh0TPTw81O1euHBB49iBAweKAMQJEyaIVVVV6vKioiIxIiJCBCBOnTpV45jffvtN3d6AAQPEkpIS9b6CggLR1dVVBCDee++9YkJCglhdXa3ef+jQIVEqlYoSiUS8ePFis58dEZE+mFATEXUAjcnkG2+8odPxu3btEgGIkydP1ij/a0K9efPmJscVFhaKAERra2tRoVCoy5OTk0UAYpcuXTTKG61YsaLZhDojI0MEINrZ2YnXrl1rctzhw4dFAKJEIhH//PNPdXljQi0IQrO/NMybN08EIDo4OIjXr19vsj8hIUEEIH799dfNf0BERHqQts84OBERGcKkSZNa3V9XV4fU1FQcOnQIRUVFqKurgyiKqKysBAD88ccfzR4nlUoxevToJuVeXl5wdXVFWVkZbty4AS8vLwC3p2AAQGJiImQyWZPjHn/8cbzwwgtNyhuPGz16NDp37txkf2RkJPr06YNjx45hz549mDZtmsZ+f39/9O7du8lxISEh6uM9PT1b3H/16tUm+4iI9MWEmoioA/nrXOS/y8zMxMMPP4xLly61WKeioqLZcm9v72YTYwBwcnJCWVmZ+qE/ALh8+TIAICgoqNljXF1d4ezsjJs3b2qUX7lypdXjAKBr1644duyYuu5f+fv7N3uMg4NDq/sdHR0BQOMaiIgMhat8EBF1ILa2ts2W19TU4IEHHsClS5cwc+ZMZGdno7S0FPX19RBFUT0yLbbwHLpE0jH+d3CnODvKdRCRZeEINRGRBdi7dy+uX7+OiIgIfPnll032nz171qDn8/X1BYAWXyteXl7eZHT6r8edP3++xbYb9zXWJSIyd/xVnojIApSWlgJoecpDcnKyQc83dOhQAMC6deugVCqb7P/mm2+aPW7YsGEAgJ07d+L69etN9ufk5ODo0aOQSCSIjY01XMBEREbEhJqIyAL06tULAPDLL7/g1KlTGvs+/fRTpKSkGPR8kyZNgq+vLy5duoSXX35Z46UxJ06cwJIlS5o9LiYmBgMHDkRtbS2efvpp1NTUqPeVlJTg6aefBgBMmTIFXbp0MWjMRETGwoSaiMgChIeH4/7770dlZSXCw8MRHx+PRx55BL169cIzzzyDV155xaDns7W1xXfffQc7Ozv8+9//Rvfu3fHII49g1KhRiIiIwJAhQxAQENDssd9//z0CAgKwadMmBAUFYfLkyXjggQfQtWtXHDp0CBEREVi5cqVB4yUiMiYm1EREFuLHH3/Eu+++ix49emDfvn1ITU2Fv78/du3ahVmzZhn8fEOHDkVWVhYefPBBlJWVYePGjbh8+TLeeOONVkfEg4ODceTIEbz88stwd3fH1q1bsXv3bnTt2hXLli3Dvn37NN6SSERk7vjqcSIiIiIiPXCEmoiIiIhID0yoiYiIiIj0wISaiIiIiEgPTKiJiIiIiPTAhJqIiIiISA9MqImIiIiI9MCEmoiIiIhID0yoiYiIiIj0IDV1AHezsrIy1NfXmzoMi9SpUycUFxebOgyyEOxPZEjsT2Ro7FPGIZVKtX5rKxNqE6qvr4dSqTR1GBZHEAQAtz9fvgiU9MX+RIbE/kSGxj5lHjjlg4iIiIhID0yoiYiIiIj0wISaiIiIiEgPTKiJiIiIiPTAhJqIiIiISA9MqImIiIiI9MCEmoiIiIhID0yoiYiIiIj0wISaiIiIiEgPTKiJiIiIiPTAhJqIiIiISA9MqMmi1NTUwMfHB4IgoKamxtThEBER0V2ACTUREVE74C/8RJaLCTURERERkR6YUBMRERER6UHa1gOKiopw+PBhnD59GleuXEFFRQUEQYCjoyN8fX3Rs2dP9OvXD56ensaIl4iIiIjIrGidUP/+++/YsmULTp8+DVEU4eXlBU9PT3Tp0gUAUF1djYsXLyIrKwtff/01evbsiQkTJiAyMtJowZP2Gp6aYOoQ2kVDfYP65/rZk9AgtTJhNO3D6rPNpg6BSG9bUspNHYLR1dX9d970th/LYG1dZ8Jo2k/Cwy6mDoHI6LRKqBctWoSCggL0798fL7zwAu69917Y2dk1W7empga5ubnIzMzEihUrEBAQgKVLlxo0aCIiIiIic6FVQn3PPffgn//8J1xcXO5Y187ODlFRUYiKikJ5eTm2b9+ub4xERERERGZLq4R66tSpOjXu4uKi87FERERERB0BV/kgIiIiItKD1g8lnj9/vs2NBwcHt/kYIn3YSa1waSwfhCUi82NtbYdP3jtp6jCIyAi0TqhffvnlNjeekpLS5mOIiIiIiDoSrRPqZ5991phxEBERERF1SFon1MOGDTNiGEREREREHVObHkosLS1FaWnpHeuUlZXpFRQRERERUUehdUJ9/vx5zJ49GwcOHGi13oEDBzB79mxcunRJ7+CIiIiIiMyd1gn1zp074e3tjXHjxrVab9y4cfDx8eELXYiIiIjorqB1Qn3y5EkMGjQIgiC0Wk8QBERFReHEiRN6B0dEREREZO60fiixvLwcnp6eWtX18PBo93nUSqUSKSkpyMjIQFVVFQICAjBlyhSEhYW1ety6deuwfv36JuUymQzfffddk/Jff/0VW7ZsQVFREdzd3TFmzBiMGTPGYNdBRERERB2L1gm1tbU1qqqqtKpbXV0NuVyuc1C6WLVqFbKysjB27Fh4e3sjPT0db7/9Nl5//XX07NnzjsfPmjULNjY26m2JpOng/e7du/HZZ59h4MCBGDduHE6fPo01a9agrq4ODzzwgCEvh4iIiIg6CK0T6oCAAPz+++8YP378Hev+/vvvCAgI0Cuwtjh37hwOHDiA6dOnY8KECQCA2NhYLFiwAMnJyViyZMkd24iKioKTk1OL+xUKBX744QdERERgwYIFAIC4uDioVCr89NNPiIuLg4ODg2EuiIiIiIg6DK3nUMfGxuLUqVPYsWNHq/V27tyJU6dOYejQoXoHp63MzExIJBLExcWpy+RyOe677z6cOXMGJSUlWrVTU1MDURSb3XfixAlUVlZi1KhRGuWjR49GXV0djhw5ovsFEBEREVGHpfUI9dChQ3Hw4EF89dVXyMnJwZAhQ+Dv7w9bW1vU1tbi0qVLyMjIwLFjxxAWFtauL4K5cOECvL29YWdnp1HerVs3AEBBQQE8PDxabWPu3Lm4desWrK2t0b9/fzz22GNwcXFR7y8oKAAAdO3aVeO44OBgCIKAgoICxMbG6n8xRERERNShaJ1QSyQSvPjii/j222+RlpaGY8eONVtn5MiReOyxx+64GoghlZeXw9XVtUl5Y1lrD0g6ODhg9OjR6N69O6RSKU6fPo1du3YhPz8fb7/9tjpJLysrg0QigbOzs8bxUqkUjo6Orb7wRqlUQqlUqrcFQYCtra36ZyJ9sA8ZV+Pny8+ZSDf87hgX71HmQeuEGrg9jeLJJ5/ExIkTkZOTgytXrqCmpgZ2dnbw8fFBeHg43N3djRVrixQKBWQyWZPyxjKFQtHisWPHjtXYjoqKQrdu3fDhhx8iNTVV/bChQqGAVNr8xyWTyVo9x8aNGzVWEgkKCsLy5cvRqVOnFo8xtD/b7UzU3ry9vU0dwl3By8vL1CFYOL5h11LxHtU+eI8yrTYl1I3c3NwwYsQIQ8eiM7lcrjEC3KixrK0rjsTExOCbb77B8ePH1Qm1XC5HfX19s/WVSmWr55g4caLGw5yNv0UWFxe32CaRtgoLC00dgkUTBAFeXl64du1ai89YEFHLeI8yLt6jjEcqlWo9+KlTQm1uXFxcmp1y0TjVo7npIHfi7u6usUygq6srVCoVbt68qTHto76+HpWVlXBzc2uxLZlM1uwIOgB2ftIb+1D7EEWRnzWRDvi9aR+8R5mW1qt8mLPAwEAUFhaipqZGo/zs2bPq/W0hiiKKi4s1ltFrbCM/P1+jbn5+PkRRbPM5iIiIiMgyWERCHRUVBZVKhbS0NHWZUqlEeno6QkJC1Ct8lJSU4MqVKxrHVlRUNGkvNTUVFRUV6NOnj7qsd+/ecHBwQGpqapO61tbWiIiIMOQlEREREVEHYRFTPkJCQhAVFYW1a9eioqICXl5e2LNnD4qLi/HMM8+o661cuRKnTp3CunXr1GWzZ89GdHQ0/P39IZPJcPr0aRw4cACBgYEYOXKkup5cLsfDDz+ML774Au+99x769OmDvLw8ZGRkYMqUKXypCxEREdFdyiISauD2OtIpKSnYu3cvqqur4e/vj4ULFyI0NLTV42JiYnDmzBlkZWVBoVCgU6dOmDBhAh588EFYW1tr1I2Pj4eVlRW2bt2Kw4cPw93dHY8//niTlUKIiIiI6O4hiAaYwa5SqXD27FmUlpbCxcUF3bt3h5WVlSHis2jFxcXNrk5iDA1PTWiX81D7s/pss6lDsGiCIMDb2xuFhYV84MeItqSUmzoEMpKEh11MHYJF4z3KeGQyWfut8nHlyhUsX74cN27cgIODAyoqKuDm5oZ//vOffFCPiIiIiCye3g8lfv755+jbty/WrFmD//znP/jPf/6Dzp074z//+Y8h4iMiIiIiMmtaJ9SffvqpxrrMjQoLCzFs2DD1i02cnJwwYMAALuRORERERHcFrRPqsrIy/OMf/8D27duhUqnU5aGhofj222+Rl5eHa9eu4ffff8e2bdvu+DAgEREREZEl0HoO9cKFC3H06FF888032L17Nx5//HH07dsXs2bNwueff44333wTDQ0NkEgk6N+/P2bNmmXMuImIiIiIzEKbHkrs27cvwsLCsGPHDnzwwQfo3r07ZsyYgXnz5mHu3LmorKyEo6MjJBKLeF8MEREREdEdtTnzlUgkGDduHD744AP1ah7ffPMN6urq4OzszGSaiIiIiO4qbc5+6+vrUVNTAycnJzz99NNYsmQJ8vPzMW/ePKSlpXENRCIiIiK6q2g95aOsrAwff/wxcnNzIYoivLy88PTTTyM0NBSLFy/GgQMHkJycjNTUVMyYMYMPJRIRERHRXaFNy+YVFxfjtddewzvvvIPAwED8+9//Rl1dHQAgOjoa77//Pvr164e3334b7733ntGCJiIiIiIyF1on1Hl5eRg7dixCQ0MREBCAadOmoaqqCpcvX1bXkcvlSExMxIoVKyAIglECJiIiIiIyJ1on1K6urjh79qx6u/FnFxeXJnU9PDzwwgsv6B8dEREREZGZ03oO9SOPPIIPPvgAp0+fhr29Pc6fP48xY8bA3d3dmPEREREREZk1rRPqAQMGYMWKFTh27BgUCgUef/xx9OzZ05ixERERERGZvTa92MXT0xMjR440VixERERERB1OmxLqlpSWlqKqqgpOTk7NzqkmIiIiIrJUWifUBw4cQPfu3eHh4aEuy8nJwTfffIOrV6+qywIDAzFjxgz06tXLsJESEREREZkhrVf5aHwgsVFubi6WL1+O6upq3H///XjyyScxbtw4FBcXY8mSJSgoKDBGvEREREREZkXnKR9r166Fu7s7li9fDgcHB3X5+PHj8c9//hMbNmzA/PnzDRIkEREREZG50nqE+q/q6+tx/vx5jB8/XiOZBgA3NzfExcUhLy/PIAESEREREZkznRLqhoYGAEDnzp2b3e/p6Ynq6mrdoyIiIiIi6iDaNOXjyJEjKC8vBwDY2NigtLS02XplZWWwt7fXOzgiIiIiInPXpoR6//792L9/v3o7OzsbcXFxTeodP34cfn5++kdHRERERGTmtE6oV65c2aRMEIQmZRUVFejcuTP69u2rV2BERERERB2B1gl1p06dtKrn5OSE2bNn6xwQEREREVFHotNDiUREREREdFub16E+cuQI9u/fj/Pnz6O0tBQKhQI2Njbw8fHBvffei5EjR8Ld3d0YsRIRERERmR2tE+q6ujr8+9//xrFjxzTKrays4OPjg5KSEmzcuBHbt2/H//zP/yAmJsbgwRIRERERmRutE+qUlBQcP34cM2fOxODBg2FtbY1Tp07hs88+Q2hoKKZNm4bLly8jOTkZK1euhJubG0JDQ40ZuwalUomUlBRkZGSgqqoKAQEBmDJlCsLCwlo9LisrCwcOHEB+fj7Ky8vh7u6OyMhIPPTQQ02W/pszZw6Ki4ubtBEXF4f/+Z//Mej1EBEREVHHoHVCfeDAAcTHx2P06NHqsr59+2LWrFl45513MH78ePj5+WHhwoV49dVXsWHDhnZNqFetWoWsrCyMHTsW3t7eSE9Px9tvv43XX38dPXv2bPG4Tz/9FK6urhgyZAg8PDxw6dIl7Ny5Ezk5OVi+fDnkcrlG/cDAQIwfP16jzMfHxyjXRERERETmT+uEurKystm1pf38/KBSqVBYWAhnZ2cIgoCYmBikpKQYNNDWnDt3DgcOHMD06dMxYcIEAEBsbCwWLFiA5ORkLFmypMVj58+fj3vuuUejLDg4GKtWrUJGRgZGjBihsc/NzQ2xsbGGvwgiIiIi6pC0XuXD09MTJ06caFJ+8uRJAICLi4u6zMrKCqIo6h+dljIzMyGRSDReMiOXy3HffffhzJkzKCkpafHYvyfTADBgwAAAwJUrV5o9pr6+Hrdu3dIzaiIiIiKyBFqPUMfHx2PNmjWQy+WIjo6GXC7H6dOnsWnTJvTs2RNeXl7qugUFBRrbxnbhwgV4e3vDzs5Oo7xbt27qeDw8PLRur/H16o6Ojk32nThxAtOnT4dKpUKnTp0wbtw4jB07VvfgiYiIiKhD0zqhHj16NEpKSrBt2zbs2bNHXd6tWzc8//zzGnXlcjnGjBljsCDvpLy8HK6urk3KG8vKysra1N6mTZsgkUgQFRWlUe7v74+ePXvCx8cHlZWV2LNnD7766iuUlpZi+vTpLbanVCqhVCrV24IgwNbWVv0zkT7Yh4yr8fPl50ykG353jIv3KPPQpnWop0+fjvHjx+PMmTNQKpXw8fFBUFBQk3ozZswwVHxaUSgUkMlkTcobyxQKhdZt7du3D7/++ismTJgAb29vjX0LFy7U2B4+fDjeeustbNu2DWPGjGlx/e2NGzdi/fr16u2goCAsX75c67dPGsKf7XYmam9/76dkHO35V7e7U9sGPqjj4D2qffAeZVptfrGLi4uLeo6xuZDL5RojwI0ay/6+UkdL8vLy8PHHH6NPnz545JFH7lhfEASMGzcOx44dw8mTJ1t8WHHixIkaK4M0/hZZXFyM+vp6rWIjaklhYaGpQ7BogiDAy8sL165da9dnQ4gsBe9RxsV7lPFIpVKtBz/bnFCbIxcXF5SWljYpb5zq0dx0kL8rKCjAO++8A39/fyxYsABWVlZanbtxbnZVVVWLdWQyWbMj6ADY+Ulv7EPtQxRFftZEOuD3pn3wHmVaWq/yYc4CAwNRWFiImpoajfKzZ8+q97fm2rVreOutt+Dk5ISXX34ZNjY2Wp/7+vXrAAAnJ6e2BU1EREREFsEiEuqoqCioVCqkpaWpy5RKJdLT0xESEqIeRS4pKWmyFF55eTmWLl0KQRCwaNGiFhPjqqoqqFQqjbL6+nr8/PPPkEql6N27t4GvioiIiIg6AouY8hESEoKoqCisXbsWFRUV8PLywp49e1BcXIxnnnlGXW/lypU4deoU1q1bpy5bunQprl+/jgkTJuD06dM4ffq0ep+Li4v61eWHDx/GTz/9hKioKHh6eqKqqgr79u3Dn3/+iUceeURjHW4iIiIiuntYREINAHPnzkVKSgr27t2L6upq+Pv7Y+HChXd8/fnFixcBAJs3b26yLzQ0VJ1Q+/v7w8/PDxkZGaioqIBUKkVgYCBeeOEFDBo0yPAXREREREQdgiByBrvJFBcXN7s6iTE0PDWhXc5D7c/qs6a/DJLhCIIAb29vFBYW8oEfI9qSUm7qEMhIEh52MXUIFo33KOORyWRar/Kh8xzqW7duYfXq1c2+nvvMmTNYvXq1rk0TEREREXUYOifUSqUSe/bsafYthNevX9d4myIRERERkaWyiFU+iIiIiIhMhQk1EREREZEe2rzKR0lJCYD/vhnw5s2b6jLgv28OJCIiIiK6G7Q5oZ4zZ47G9ocffqixnZKSol9EREREREQdSJsT6tdffx0AUF1djf/7v//Do48+iuDgYIMHRkRERETUEbQ5oW58UUplZSUAIDAw8I4vTyEiIiIislR8KJGIiIiISA9MqImIiIiI9KBzQm1lZYXQ0FDY29s32efs7MxpIERERER0V2jzHOpGdnZ26gcU/y4sLAxhYWE6B0VERERE1FFwygcRERERkR60Sqjr6up0PoE+xxIRERERmTutEupnn30W69evR1lZmdYNl5aWIiUlBbNnz9Y5OCIiIiIic6fVHOpZs2bhxx9/xPr169GjRw/ce++9CA4OhqenJ+zt7SGKIqqrq1FUVIT8/HwcP34cZ8+ehbe3N5588kljXwMRERERkclolVBHR0cjKioKhw8fRnp6OjZu3Ij6+vrmG5RKERYWhvnz56Nfv36QSDhNm4iIiIgsl9arfEgkEgwYMAADBgyAUqnE+fPnceXKFVRVVQEAHBwc4Ovri+DgYMhkMqMFTERERERkTnRaNk8mk6FHjx7o0aOHoeMhIiIiIupQOB+DiIiIiEgPTKiJiIiIiPTAhJqIiIiISA9MqImIiIiI9MCEmoiIiIhID0yoiYiIiIj0oNOyeQBQUlKCDRs24OTJk6ioqMA///lPhIaGoqKiAuvXr8fw4cMRFBRkyFiJiIiIiMyOTiPUly9fxksvvYSDBw/C09MTNTU1UKlUAAAnJyf88ccf2Llzp0EDJSIiIiIyRzqNUCcnJ8Pe3h5Lly4FADz11FMa+8PDw3Hw4EH9o2sDpVKJlJQUZGRkoKqqCgEBAZgyZQrCwsLueGxpaSm++uor5ObmQhRF3HPPPXj88cfRuXPnJnV//fVXbNmyBUVFRXB3d8eYMWMwZswYY1wSEREREXUAOo1Q5+XlYeTIkXBycoIgCE32e3h4oLS0VO/g2mLVqlXYtm0bYmJiMHPmTEgkErz99ts4ffp0q8fdunULixcvRl5eHiZOnIjJkyfjwoULSEpKQmVlpUbd3bt345NPPoGfnx9mzpyJ7t27Y82aNfj555+NeGVEREREZM50GqFWqVSwtrZucX9FRQWkUp2nZ7fZuXPncODAAUyfPh0TJkwAAMTGxmLBggVITk7GkiVLWjx2165dKCwsxFtvvYVu3boBuD3CvmDBAmzZsgVTp04FACgUCvzwww+IiIjAggULAABxcXFQqVT46aefEBcXBwcHByNfKRERERGZG51GqIODg3HkyJFm9zU0NODAgQPo3r27XoG1RWZmJiQSCeLi4tRlcrkc9913H86cOYOSkpJWj+3atas6mQYAX19f3HvvvRrTVk6cOIHKykqMGjVK4/jRo0ejrq6uxc+DiIiIiCybTgn1Aw88gKNHj+Kzzz7Dn3/+CQAoLy9Hbm4ulixZgitXruD+++83aKCtuXDhAry9vWFnZ6dR3pgkFxQUNHucSqXCpUuX0LVr1yb7unbtiuvXr6O2tlajjb/XDQ4OhiAILZ6DiIiIiCybTvMywsPDMWfOHKxZswZpaWkAgI8++ggAYGtrizlz5iA0NNRwUd5BeXk5XF1dm5Q3lpWVlTV7XFVVFZRKJVxcXFo91tbWFmVlZZBIJHB2dtaoJ5VK4ejo2OqccaVSCaVSqd4WBAG2trbqn4n0wT5kXI2fLz9nIt3wu2NcvEeZB50nOsfGxmLAgAHIzc3FtWvXoFKp4OXlhT59+qiTxfaiUCggk8malDeWKRSKFo/7a73WjlUoFC3OC5fJZC2eAwA2btyI9evXq7eDgoKwfPlydOrUqcVjDG7b4fY7F90V+r/7q6lDaEenTB1Auzn0z/tMct6nX/A2yXnJMv3v//6vqUMgI1m2bJmpQ2iWXk8O2tjYYMCAAYaKRWdyuVxjBLhRY5lcLm/xuL/Wa+1YuVyO+vr6ZttRKpUtngMAJk6ciPHjx6u3G3+LLC4ubrFN0p0gCPDy8sK1a9cgiqKpwyHqUAoLC00dgkXj/YlIP+15j5JKpVoPfuqUULf2kN9feXh46NJ8m7m4uDQ75aJxqkdz00EAwMHBATKZDOXl5Xc81tXVFSqVCjdv3tSY9lFfX4/Kykq4ubm1GJ9MJmt2FBwAb6hGJIoiP1+iNuJ3pn3w/kSkG3P93uiUUM+ZM0ereikpKbo032aBgYE4efIkampqNB5MPHv2rHp/cyQSCfz9/ZGfn99k37lz59C5c2f19JXGNvLz8xEREaGul5+fD1EUWzwHEREREVk2nRLqZ599tkmZSqVCcXEx9u7dCycnJ8THx+sdnLaioqKwZcsWpKWlqdehViqVSE9PR0hIiHqkvKSkBHV1dfD19VUfO3DgQHz//ffIz89Xr+Bx9epVnDhxAgkJCep6vXv3hoODA1JTUzUS6tTUVFhbW2uUEREREdHdQ6eEetiwYS3uu//++7Fo0SLU1NToGlObhYSEICoqCmvXrkVFRQW8vLywZ88eFBcX45lnnlHXW7lyJU6dOoV169apy+Lj4/HLL79g2bJlSEhIgJWVFbZu3QpnZ2eNhFoul+Phhx/GF198gffeew99+vRBXl4eMjIyMGXKFL7UhYiIiOguZfDXGdrY2GDYsGHYtm0bxo4da+jmWzR37lykpKRg7969qK6uhr+/PxYuXHjH5ftsbW2RlJSEr7/+Gj/99BNEUURoaChmzJgBJycnjbrx8fHqhPvw4cNwd3fH448/3q7XSURERETmxSjvBxdFsdkH/YxJLpfj0UcfxaOPPtpinaSkpGbL3d3dMX/+fK3OExcXp/FGRiIiIiK6uxk0oa6pqUFeXh42b96MoKAgQzZNRERERGSWdEqoH3744Vb3e3h4YNasWToFRERERETUkeiUUD/00ENNXnEpCALs7e3RuXNn9OnTB1ZWVgYJkIiIiIjInOmUUCcmJho6DiIiIiKiDkli6gCIiIiIiDoyrUaoV69e3eaGBUFo9gUwRERERESWRKuE+uTJk21u+O9zrImIiIiILJFWCfWqVauMHQcRERERUYfEOdRERERERHpgQk1EREREpAed35SYk5ODrVu34sKFC6ipqYEoik3qpKSk6BUcEREREZG502mEOjMzE8uWLcPNmzcRHR0NURQxePBgDB48GHK5HAEBAZg0aZKhYyUiIiIiMjs6jVD//PPP6NatG958801UVVVh9+7duO+++9C7d28UFRVh0aJF8PT0NHSsRERERERmR6cR6suXL2Pw4MGQSCTqV4zX19cDADw9PREfH49NmzYZLkoiIiIiIjOlU0JtbW0NqfT24La9vT2kUinKy8vV+52dnVFUVGSQAImIiIiIzJlOUz58fHxw+fJl9XZgYCD27t2LIUOGoKGhAfv27YOHh4fBgrRUJ65X42ZNnanDsDiCIMC1WoaysspmH5YlopZlX640dQgWjfen9lEs62TqEMhI2vMe5WxnjeGdtOtLOo1Q9+/fH4cOHYJSqQQAPPjggzh58iRmzJiBWbNm4fTp03jggQd0aZqIiIiIqEPReoT6999/R3h4OCQSCSZMmIAJEyao90VGRiIpKQlZWVmQSCSIiIhA7969jRIwEREREZE50Tqhfuedd+Do6IhBgwZhyJAh6N69u8b+Xr16oVevXgYPkIiIiIjInGmdUD/11FPYv38/du/ejdTUVHTq1AkxMTGIiYmBn5+fMWMkIiIiIjJbWifUcXFxiIuLQ2lpKfbv34/9+/dj48aN2LhxIwIDAzFkyBBER0fDzc3NmPESEREREZmVNq/y4ebmhoSEBCQkJODq1avYt28f9u/fj2+//RbJyckIDQ3FkCFDMHDgQNjZ2RkjZiIiIiIis6HTsnmNfHx8kJiYiMTERJw7dw779+/HgQMH8Mknn+DLL7/Et99+a6g4iYiIiIjMkk7L5jXH09MTnTt3hru7OwBAoVAYqmkiIiIiIrOl1wj1rVu3kJWVhX379uHEiRNQqVTo1KkTJk6ciJiYGEPFSERERERkttqcUNfX1+PIkSPYt28fcnJyoFAo4ODggBEjRiAmJgY9e/Y0RpxERERERGZJ64T6xIkT2LdvH7KyslBTUwOZTIbIyEgMGTIE4eHhsLKyMmacRERERERmSeuE+s0334QgCLjnnnvUq3jY2toaMzYiIiIiIrOndUL96KOPYvDgwXB1dTVmPDqprq5GcnIysrOzoVAo0K1bNzz66KMIDg5u9TiVSoW9e/ciKysLBQUFqKqqgqenJ6Kjo5GQkAC5XK5RPzExsdl2pk6digceeMBQl0NEREREHYjWCfX48eONGYfOVCoVli1bhoKCAkyYMAGOjo5ITU3F4sWLsWzZMnh7e7d4rEKhwOrVqxESEoKRI0fCyckJZ86cwbp163DixAm89tprEARB45iwsDDExsZqlAUFBRnl2oiIiIjI/Om1yoc5yMzMxB9//IH58+cjKioKABAdHY3nnnsO69atw3PPPdfisVKpFG+++SZ69OihLouLi4OnpyfWrVuH48ePIywsTOMYb2/vJgk1EREREd29DLYOtalkZmbC2dkZAwYMUJc5OTlh0KBBOHz4MJRKZYvHSqVSjWS6UWNbV65cafY4hULBdbaJiIiICIAFjFAXFBQgODgYEonm7wbdunVDWloaCgsL4e/v36Y2y8vLAQCOjo5N9qWnpyM1NRWiKMLX1xcPPfQQ19wmIiIiuot1+IS6rKwMvXr1alLu4uICACgtLW1zQr1p0ybY2toiPDxco7xHjx4YNGgQPD09UVpail27duHDDz9ETU0NRo0a1WJ7SqVSY6RcEATY2tpCEIQmc7RJfwKE//6XHy9Rm/CeZFy8PxHppz3vUW05l04J9YEDB9CvX78mq2DoS6VSob6+Xqu6MpkMgiBAoVBAJpM12d8YW1unZmzYsAHHjx/HrFmzYG9vr7HvzTff1Ni+7777sHDhQqxduxbDhg1r8fPYuHEj1q9fr94OCgrC8uXL4eTkBIl1Q5viI+25mOGKNJblsqkDICNwdXUzdQh3Bd6fiHTTnvcoB2vt37GiU0L9wQcfwNbWFgMHDsSQIUPQu3dvXZppIi8vD4sXL9aq7ooVK+Dr6wu5XN7sPOnGRLotSf+BAweQkpKC++67r9UR50ZSqRSjR4/GZ599hvPnz7f4lsiJEydqrJLS+BtPRUUFbtbUaR0faUeAABdXV5SXlUGEaOpwiDqUsrJSU4dg0Xh/ItJPe96jVHbWALT75VenhPqNN95ARkYGMjMzkZ6eDjc3N8TExGDIkCFtnl7xV76+vpg9e7ZWdRvXw3Z1dVXPef6rxjI3N+1+k8nNzcXKlSsRHh6Op556SqtjAMDd3R0AUFVV1WIdmUzW7Ci6KIoQRd5QDe7//4VGBD9forbid8bIeH8i0kt7fm/aci6dEuoePXqgR48emDlzJo4ePYqMjAzs3LkTmzdvhr+/P2JjYxETE9Pml8C4uLhg2LBhbTomMDAQeXl5UKlUGg8mnj17FtbW1q2uQ/3Xuu+++y66du2K+fPnt+k16kVFRQBuryxCRERERHcfvR5KtLKyQmRkJCIjI3Hr1i1kZ2cjPT0dycnJ+P777xEaGoqhQ4ciOjoaUqlxnn+MiopCZmYmsrOz1etQV1RUIDMzE5GRkRojw9euXQMAeHl5qcsuX76MZcuWwdPTEwsXLmxxikhFRUWTpLm2thbbtm2Do6PjHd/KSERERESWyWBZ7qVLl3Du3DlcunQJAODj44OqqiqsWrUKa9euxXPPPdfiHGN9REVFISQkBKtXr8bly5fVb0pUqVRNXhXe+FDhqlWrANxOiJcuXYqqqiokJCTgyJEjGvW9vLzQvXt3AMDOnTtx6NAhREZGwsPDA+Xl5fjtt99QUlKCuXPnGu0XBiIiIiIyb3plgVevXsW+ffuwb98+XL9+HU5OToiJiUFsbKx6xDY/Px+ffPIJPvvsM/z73/82SNB/JZFI8PLLLyM5ORk7duyAQqFA165dMXv2bPj4+LR6bGVlJW7cuAEA+P7775vsHzp0qDqh7tmzJ86cOYNff/0VlZWVsLGxQbdu3fDss88a7KFMIiIiIup4BFGH2d3bt29HRkYGzp8/D5lMhsjISAwdOhR9+/Zt8oIV4PbLUD755BP88MMPBgnaUvx2ooCrfBiBIAhwdXVDWVkpH/oxoqV7mn+TKHVsi4b6mjoEi8b7U/vYsmWrqUMgI0lIGH/nSgbibGeN4b0Dtaqr0wj1119/jR49euCpp55CdHQ07OzsWq3ftWtXPPTQQ7qcioiIiIjIrOmUUH/00Ufw9PTUun6XLl3QpUsXXU5FRERERGTWms7P0IK7uztqampa3F9TU4OGBr4BkIiIiIgsn04J9Zo1a/Cvf/2rxf3/+te/8M033+gcFBERERFRR6FTQn306FEMHDiwxf1RUVHIycnROSgiIiIioo5Cp4S6rKys1Vd6u7q6orS0/d61TkRERERkKjol1A4ODrh69WqL+69cuQJbW1udgyIiIiIi6ih0Sqj79u2LtLQ0XLhwocm+8+fPIy0tDeHh4XoHR0RERERk7nRaNu/hhx/G0aNH8corryAyMlK9JN6ff/6J33//HU5OTnj44YcNGqglkigVkChumToMiyMIAoS6W5AobvHFCUZk3aAwdQhkBLwnGRfvT+1D1lBv6hDISNrzHiWRCVrX1elNicDtedTfffcdDh8+jNraWgCAra0t+vfvj0ceeaTVOdZ027V506DM/8PUYRARERHR38i69oDXh99pVVenEWrg9oOHc+fOhSiKqKioAAA4OTlBELTP5omIiIiIOjqdR6hJf3tyzuBmda2pw7A4giDAxcUN5eWl/JOqEb2zr9DUIZARvBTjbeoQLBrvT+1j+/adpg6BjGTs2NHtdi5ne1sMDe+uVV2tRqjXr18PAHjwwQchkUjU23cyadIkrerdrVQyOVRy3lANTRAEiNY2UMlt+D8sI6qzkps6BDICldzG1CFYNN6f2ofSSuc/wJOZa897lEqm/f/ntOpxP/74IwDggQcegEQiUW/fCRNqIiIiIrJ0WiXUKSkprW4TEREREd2tdFqHmoiIiIiIbmNCTURERESkB62mfMyZM6fNy+EJgoCPPvpIp6CIiIiIiDoKrRLq0NBQri9NRERERNQMrUeoiYiIiIioKc6hJiIiIiLSg84rn6tUKuzduxdHjhxBSUkJAMDDwwORkZEYMmQIJBLm6kRERERk+XRKqGtqarB06VKcO3cOtra26Ny5MwDg+PHjyMrKQmpqKhYtWgQ7OzuDBktEREREZG50SqjXrl2L8+fP44knnsCIESMgld5upr6+Hr/++ivWrFmDH374AU888YRBgyUiIiIiMjc6zcvIzs7GqFGjEB8fr06mAUAqlWLUqFEYOXIksrKyDBYkEREREZG50imhrqqqgo+PT4v7fX19UVVVpXNQREREREQdhU4JtZeXFw4fPtzi/sOHD6vnVRMRERERWTKdEupRo0YhNzcXb7/9No4dO4aioiIUFRXh6NGjePvtt5Gbm4vRo0cbOlYiIiIiIrOj00OJ8fHxuHnzJjZt2oSjR49qNiiVYtKkSRg1apQh4tNadXU1kpOTkZ2dDYVCgW7duuHRRx9FcHDwHY9dtWoV9uzZ06Tcx8cH77//vkaZSqXCli1bkJqaivLycnh7e+OBBx5ATEyMoS6FiIiIiDoQndehTkxMxOjRo5Gbm6teh7pTp06499574eTkZLAAtaFSqbBs2TIUFBRgwoQJcHR0RGpqKhYvXoxly5bB29v7jm3IZDI8/fTTGmXNLfv3ww8/4Oeff8aIESPQtWtXHD58GB9++CEEQcDgwYMNdk1ERERE1DG0KaEuLy9Heno6ioqK4OTkhIEDB5rFyGxmZib++OMPzJ8/H1FRUQCA6OhoPPfcc1i3bh2ee+65O7YhkUgQGxvbap3S0lJs2bIF8fHxePLJJwEAI0aMQFJSEpKTkzFo0CC+0IaIiIjoLqN19ldUVIQFCxZg7dq1+OWXX7Bx40a8/PLL2LdvnzHj00pmZiacnZ0xYMAAdZmTkxMGDRqEw4cPQ6lUatWOSqVCTU1Ni/sPHTqEhoYGxMfHq8sEQcDIkSNx48YNnDlzRveLICIiIqIOSesR6nXr1uHWrVuYOXMmevfujWvXrmHNmjX4+uuvER0dbdKR2YKCAgQHBzeJoVu3bkhLS0NhYSH8/f1bbUOhUODxxx9HXV0d7O3tMXjwYEyfPh02NjbqOhcuXIC1tTV8fX2bnKdxf8+ePZu0rVQqNZJ6QRBga2sLQRAgCEKbr5daJ0D473/58RK1Ce9JxsX7E5F+2vMe1ZZzaZ1Q//HHH4iLi1Ov3uHn5weJRILly5fjypUr6NKlS9sjNZCysjL06tWrSbmLiwuA21M1WkuoXV1dMWHCBAQFBUEURRw9ehSpqam4ePEikpKSYGVlBeD2lBcXF5cmH7Crq6s6juZs3LgR69evV28HBQVh+fLlcHJygsS6oU3XStpz+f//LmQsl00dABmBq6ubqUO4K/D+RKSb9rxHOVhbaV1X64S6pKQEQUFBGmWNK2hUVlZqfcI7UalUqK+v16quTCaDIAhQKBSQyWRN9svlcgC3R59bM3XqVI3twYMHw9vbGz/88AMyMzPVDxsqFAqNN0P+NY7WzjNx4kSMHz9evd2YkFdUVOBmTV2rsVHbCRDg4uqK8rIyiBBNHQ5Rh1JWVmrqECwa709E+mnPe5TKzhqAdr/8ap1Qq1SqJslk48itSqXSPro7yMvLw+LFi7Wqu2LFCvj6+kIulzc7T7oxwW1MrNti/PjxSElJwfHjx9UJtVwubzbZbzx3S+eRyWTNJvyiKEIUeUM1uP//BwQR/HyJ2orfGSPj/YlIL+35vWnLudq0ykd+fr5GYlhbWwsAOH36NKqrq5vUHzhwYFuaB3D7teWzZ8/Wqm7jVAtXV1eUl5c32d9Y5ubW9j8PyOVyODo6arxC3cXFBSdPnoQoihrTPhqnerjyT3hEREREd502JdTbt2/H9u3bm5T/+OOPzdZPSUlpc0AuLi4YNmxYm44JDAxEXl4eVCqVxoOJZ8+ehbW1tVbrUP9dbW0tKisrNdbUDgwMxK+//oorV67Az89PXX7u3Dn1fiIiIiK6u2idUL/++uvGjEMvUVFRyMzMRHZ2tnod6oqKCmRmZiIyMlJjVP3atWsAAC8vLwC3p4U0NDTA1tZWo82ffvoJoiiib9++6rL+/fvj66+/xq5du9TrUIuiiN27d8PNzQ09evQw5mUSERERkRnSOqEODQ01Zhx6iYqKQkhICFavXo3Lly+r35SoUqmQmJioUffNN98EcPt148DtaSELFy7E4MGD4ePjAwA4duwYcnJy0LdvX/Tr1099rLu7O8aNG4fNmzejoaEBXbt2xaFDh5CXl4d58+bxpS5EREREdyGdXz1uTiQSCV5++WUkJydjx44dUCgU6Nq1K2bPnq1Okltib2+PiIgI5ObmYs+ePVCpVPDy8sIjjzyChISEJkny1KlTYW9vj7S0NKSnp8Pb2xv/+Mc/zOKNkURERETU/gSRjxmbzG8nCrhsnhEIggBXVzeUlZXyKXojWrrniqlDICNYNNT3zpVIZ7w/tY8tW7aaOgQykoSE8XeuZCDOdtYY3jtQq7qco0BEREREpAcm1EREREREemBCTURERESkBybURERERER60GqVjz179ujU+NChQ3U6joiIiIioo9AqoV69erVOjTOhJiIiIiJLp1VCvXLlSo3t6upqrFq1CnZ2dhgzZox6recrV65g586dqK2txZw5cwwfrYXp3dkeSqXc1GFYHEEQ4O3tgcJCJZelImqjAX6Opg7BovH+1D4ylcWmDoGMpD3vUX990/adaJVQd+rUSWP7xx9/hJOTE1599VUIgqAu9/f3x8CBA7F06VJs27YNs2fP1joQIiIiIqKOSKeHEg8dOoQBAwZoJNPqBiUSDBgwAIcOHdI7OCIiIiIic6dTQi2KIq5cafktaZcvX9Y5ICIiIiKijkSnhLp///7YvXs3tm7dirq6/746u66uDlu2bEFaWhr69etnsCCJiIiIiMyVVnOo/27mzJkoKirCt99+i++//x6urq4AgLKyMjQ0NKBHjx6YMWOGIeMkIiIiIjJLOiXUdnZ2WLx4MQ4dOoScnByUlJQAAPr06YOIiAhERkY2O7+aiIiIiMjS6JRQN+rfvz/69+9vqFiIiIiIiDocvRLq0tJSnDp1ChUVFRg4cCDc3d2hUqlQU1MDOzs7SCR8szkRERERWTadEmpRFPHNN99g586dUKlUAG6vQe3u7o5bt25hzpw5SExMxLhx4wwaLBERERGRudFpCHnz5s3Yvn07EhIS8Oqrr2rss7Ozw4ABA5CVlWWQAImIiIiIzJlOCfUvv/yCoUOHYurUqQgMDGyyPyAgAIWFhfrGRkRERERk9nRKqG/cuIHu3bu3uN/a2ho1NTU6B0VERERE1FHolFA7OTnhxo0bLe4/f/48PDw8dA6KiMgcNChqcfilETj80gg0KGpNHQ4REZkpnRLqgQMHYvfu3bh+/XqTfceOHUN6ejoGDRqkd3BEREREROZOp1U+EhMTcfLkSbz00kvo2bMnAGDTpk1ISUnBmTNnEBQUhIkTJxo0UCIiIiIic6TzmxKXLl2KLVu2IDMzE3K5HKdOnYKXlxcmT56MCRMmQC6XGzpWIjIjm6b1NHUIRldTU4OQ/7+Q0Y9TesLW1ta0ARERkVnS+cUucrkcDz30EB566CFDxkNERERE1KHoNId68eLFOH78eIv7T5w4gcWLF+scFBGRObCzs8PVq1chiiLs7OxMHQ4REZkpnRLqU6dO4ebNmy3ur6iowKlTp3QOioiIiIioo9Apob6Ta9euca4hEREREd0VtJ5DnZ6ejj179qi3N2zYgF9++aVJvZqaGly8eBHh4eGGiVBL1dXVSE5ORnZ2NhQKBbp164ZHH30UwcHBdzw2MTGxxX333nsv/vWvfwEAioqKMHfu3GbrPffccxg8eLBuwRMRERFRh6V1Qq1QKFBRUaHerq2thSAIGnUEQYC1tTVGjhyJSZMmGS7KO1CpVFi2bBkKCgowYcIEODo6IjU1FYsXL8ayZcvg7e3d6vHNJcnnz5/H9u3b0adPnyb7Bg8e3OQXhtbeHElERERElkvrhHrUqFEYNWoUAGDOnDmYOXMm+vXrZ7TA2iIzMxN//PEH5s+fj6ioKABAdHQ0nnvuOaxbtw7PPfdcq8fHxsY2KTt16hQEQWh21DkoKKjZY4iIiIjo7qPTsnmrVq0ydBx6yczMhLOzMwYMGKAuc3JywqBBg5CRkQGlUgmZTKZ1e0qlEllZWQgNDYW7u3uzdW7dugWpVAqpVOeVB4mIiIjIAuidDdbW1qKmpgaiKDbZ5+HhoW/zWikoKEBwcDAkEs1nLLt164a0tDQUFhbC399f6/ZycnJQXV2NmJiYZvevX78eycnJEAQBwcHBmDJlSrNTQ4iIiIjI8umcUKempmLr1q24fv16i3VSUlJ0bb5NysrK0KtXryblLi4uAIDS0tI2JdQZGRmQyWTq6SONJBIJ+vTpg/79+8PNzQ1FRUXYunUr3nrrLSxcuBARERHNtqdUKqFUKtXbgiCoV0H5+zx00l/jZ8rPlgyB/YkMif2JSD/m+t3RKaFOTU3FF198gT59+mD48OH44YcfMG7cOMhkMqSnp8PFxQVjxozRKSCVSoX6+nqt6spkMgiCAIVC0eyUjsbXnysUCq3PX1NTgyNHjiA8PBz29vYa+zw8PLBo0SKNstjYWLzwwgv45ptvWkyoN27ciPXr16u3g4KCsHz5cnTq1EnruKjtvLy8TB0CWRD2JzIk9ici3dxpoQlT0Smh3rlzJ/r06YNXXnkFlZWV+OGHHxAREYHevXvj/vvvx//+7/+isrJSp4Dy8vK0fsviihUr4OvrC7lcrjEC3KgxkW5MrLWRlZUFpVLZ4nSPv3NwcMDw4cPx888/48aNG83OuZ44cSLGjx+v3m787aq4uFjrXx5Ie4IgwMvLC9euXWt2KhJRW7A/kSGxPxHpp7CwsN3OJZVKtR781Cmhvn79OuLj4wEAVlZWAKBODO3s7HDfffchNTUVCQkJbW7b19cXs2fP1qquq6ur+r/l5eVN9jeWubm5aX3+ffv2wc7ODpGRkVof05hEV1VVNZtQy2SyFh+K5A3VeERR5OdLBsP+RIbE/kSkG3P93uiUUNvZ2aGhoUH9s1wuR0lJiXq/ra1tswmuNlxcXDBs2LA2HRMYGIi8vDyoVCqNBxPPnj0La2trrf88UFZWhhMnTmDYsGFtWhWkcR65k5NTm+ImIiIioo5Pp1ePd+nSBRcvXlRvd+/eHbt370ZpaSlKSkqQlpbWrnNcoqKicPPmTWRnZ6vLKioqkJmZicjISI3k+Nq1a7h27Vqz7ezfvx+iKGLIkCHN7v/ri20alZaW4rfffkNAQIB6xJyIiIiI7h46jVAPGTIEu3fvVq/vPHnyZLz55pt49tlnbzcqlWLBggUGDbQ1UVFRCAkJwerVq3H58mX1mxJVKlWT14q/+eabAJpfS3vfvn1wdXVFaGhos+dJTk7G9evX0bt3b7i6uqK4uBhpaWmoq6vDjBkzDH5dRERERGT+dEqohw8fjuHDh6u3e/bsiffeew+///47JBIJwsLC4OPjY7Ag70QikeDll19GcnIyduzYAYVCga5du2L27Nlax3H16lWcP38e48ePb7KedaOwsDDs3r0bu3btQnV1Nezs7NCrVy88+OCDCA4ONuQlEREREVEHIYjmOrv7LlBcXNzs6iSkH0EQ4O3tjcLCQrN9eIE6DvYnMiT2p/bx4YcfmjoEMpJ58+a127lkMplxV/n4K5VKhZqammb3OTg46Ns8EREREZFZ0ymhrq+vx6ZNm/Dbb7/hxo0bUKlUzdZrrzclEhERERGZik4J9aeffoo9e/age/fu6N+/P+zs7AwdFxERERFRh6BTQp2ZmYnY2FjMmTPH0PEQEREREXUoOq1DbW1tjZCQEEPHQkRERETU4eiUUA8ePBhHjhwxdCxERERERB2OTlM+pk+fjtWrV2PZsmUYPnw43N3dm127mWszExEREZGl0ymhViqVEEUROTk5yMnJabEeV/kgIiIiIkunU0L98ccfIzs7G4MHD0a3bt24ygcRERER3bV0SqiPHTuG0aNHY8aMGQYOh4iIiIioY9HpoURbW1t4eXkZOhYiIiIiog5Hp4R6xIgR2L9/f4tvSCQiIiIiulvoNOXDz88Phw8fxsKFCzF06NAWV/kYOHCg3gESEREREZkznRLq999/X/3zt99+22I9rvJBRERERJZOp4T69ddfN3QcREREREQdkk4JdWhoqKHjICIiIiLqkHR6KJGIiIiIiG7TaoR68eLFEAQBixYtgpWVFRYvXnzHYwRBwGuvvaZ3gERERERE5kyrEWpRFCGKosa2NscQEREREVk6rUaok5KSWt0mIiIiIrpb6TSH+tSpU6ioqGhxf0VFBU6dOqVzUEREREREHYVOCfXixYuRm5vb4v4TJ05oNc+aiIiIiKijM8oqH0qlstk3JxIRERERWRqt16EuKSlBUVGRevvKlSvNTuuoqalBWloaOnXqZJgIiYiIiIjMmNYJ9W+//Yb169ertzds2IANGzY0W1cikeCpp57SPzoiIiIiIjOndUI9aNAgdOnSBQCwYsUKjBkzBj179tSoIwgCrK2tERgYCBcXF4MGSkRERERkjrROqP38/ODn5wcAePbZZxEaGgpPT0+jBUZERERE1BFonVD/1bBhw5otv379OpRKpTrxJiIiIiKydDol1Dt27MAff/yB559/Xl22evVq7NmzBwAQFBSEl19+Gc7OzgYJsjVlZWXYvn07zp07h/z8fNy6dQuvv/467rnnHq3bKC0txVdffYXc3FyIooh77rkHjz/+ODp37tyk7q+//ootW7agqKgI7u7uGDNmDMaMGWPISyIiIiKiDkSnte1++eUXjWT56NGj2LNnD+Li4vDEE0/g+vXr+PHHHw0WZGuuXr2KTZs2obS0FP7+/m0+/tatW1i8eDHy8vIwceJETJ48GRcuXEBSUhIqKys16u7evRuffPIJ/Pz8MHPmTHTv3h1r1qzBzz//bKCrISIiIqKORqcR6uLiYvj6+qq3Dx48CE9PT/XKHuXl5di7d69hIryD4OBgfPnll3BwcEBmZibee++9Nh2/a9cuFBYW4q233kK3bt0AAOHh4ViwYAG2bNmCqVOnAgAUCgV++OEHREREYMGCBQCAuLg4qFQq/PTTT4iLi4ODg4NhL46IiIiIzJ5B3r6Sm5uLvn37qrc7deqE8vJyQzR9R7a2tnolspmZmejatas6mQYAX19f3HvvvTh48KC67MSJE6isrMSoUaM0jh89ejTq6upw5MgRnWMgIiIioo5LpxFqb29vHDp0CKNGjcLRo0dRWlqK8PBw9f7S0lLY29sbLEhjUalUuHTpEoYPH95kX9euXXHs2DHU1tbC1tYWBQUF6vK/Cg4OhiAIKCgoQGxsbLPnUSqVUCqV6m1BEGBra6v+mQyr8TPlZ0uGwP5EhsT+RKQfc/3u6JRQJyQk4MMPP8TMmTNx69Yt+Pn5oU+fPur9J06cQGBgoKFiNJqqqioolcpm18x2dXUFcPuhR1tbW5SVlUEikTR50FIqlcLR0RGlpaUtnmfjxo0aL8UJCgrC8uXL+TZJI/Py8jJ1CGRB2J/IkNifiHTj7e1t6hCapVNCPXjwYDg6OuLIkSOwt7dHfHw8rKysANxOUh0cHFocrW2NSqVCfX29VnVlMpnev6UoFAp1W821/9c6CoUCUmnzH5dMJlPXa87EiRMxfvx49XZj3MXFxVpfL2lPEAR4eXnh2rVrEEXR1OFQB8f+RIbE/kSGpFAosGLFCgDACy+8ALlcbuKIjK+wsLDdziWVSrUe/NQpoQaAsLAwhIWFNSl3cHDAiy++qFObeXl5WLx4sVZ1V6xYofFgpC4aO95fp2M0aixrrCOXy1tMfpVKZaudWCaTNZu0A+AN1YhEUeTnSwbD/kSGxP5EpBtz/d5onVCfO3cOXl5eWj0AWFRUhLy8PAwdOrRNwfj6+mL27Nla1W2ckqEPBwcHyGSyZh+gLCsr0ziPq6srVCoVbt68qTHto76+HpWVlXBzc9M7HiIiIiLqeLROqBctWoR//OMfiImJAXB7asczzzyDV155BaGhoRp1//jjD6xevbrNCbWLi0uLb2E0BolEAn9/f+Tn5zfZd+7cOXTu3Fn98GDjnPD8/HxERESo6+Xn50MUxQ4xZ5yIiIiIDE/nZfNEUYRSqYRKpTJkPEZVUlKCK1euaJQNHDgQ+fn5Gkn11atXceLECURFRanLevfuDQcHB6Smpmocn5qaCmtra40km4iIiIjuHjrPoTYnP/30EwDgzz//BADs3bsXp0+fBgA89NBD6norV67EqVOnsG7dOnVZfHw8fvnlFyxbtgwJCQmwsrLC1q1b4ezsjISEBHU9uVyOhx9+GF988QXee+899OnTB3l5ecjIyMCUKVP4UhciIiKiu5RFJNQpKSka27/99pv6578m1M2xtbVFUlISvv76a/z0008QRRGhoaGYMWMGnJycNOo2rmaydetWHD58GO7u7nj88ccxduxYw10MERER6WXevHmmDqFd1NTUqFf5mDNnjnqaKrU/i0io/zri3JqkpKRmy93d3TF//nyt2oiLi0NcXJy2oRERERGRhWtTQl1UVITz588DuP1bEXB7PUA7O7sm9YiIiIiI7gZtSqhTUlKaTK/4/PPPDRoQEREREVFHonVC/eyzzxozDiIiIiKiDknrhLo914cmIiIiIuoodF6HmoiIiIhMx87ODlevXoUoik2eZ6P2xYSaiIiIiEgPTKiJiIiIiPTAhJqIiIiISA9MqImIiIiI9MCEmoiIiIhID0yoiYiIiIj0wISaiIiIiEgPTKiJiIiIiPTAhJqIiIiISA9MqImIiIiI9MCEmoiIiIhID0yoiYiIiIj0IDV1AHczqZQfvzHx8yVDYn8iQ2J/IkNjnzK8tnymgiiKohFjISIiIiKyaJzyQRantrYWCxcuRG1tralDIQvA/kSGxP5EhsY+ZR6YUJPFEUURFy5cAP/4QobA/kSGxP5EhsY+ZR6YUBMRERER6YEJNRERERGRHphQk8WRyWSYNGkSZDKZqUMhC8D+RIbE/kSGxj5lHrjKBxERERGRHjhCTURERESkBybURERERER6YEJNRERERKQHJtR010tMTMS6detMHQZZqKKiIiQmJiI9Pd3UoZAFS0pKQlJSkqnDIAs2Z84crFq1ytRhmC0m1EREREREepCaOgAiU0tOToaVlZWpwyAi0tmrr75q6hCI7mocoaa7nlwuZ0JNJnfr1i1Th0AdmFQqhVTKMTIyrbv5PsZvH5lUbW0tUlJScOjQIZSVlcHOzg4BAQGYNm0agoODkZSUhMrKSsybNw9ffvkl8vPz4erqimnTpiEqKgqnTp1CcnIyLl68CA8PDzz55JMICwtrUwyJiYmYNGkSEhMTtYqJzJM59KXq6mp89dVXyM7OhiAI6NevH8aPH9+k3qpVq5CZmYl3330Xa9asQV5eHnr37o2XXnrJUB8H3YE59Jf9+/dj8+bNKCwshCAI8PDwwIgRIzB27FgAQHp6OlavXo033ngDWVlZ2Lt3LxQKBcLCwvD000/DyclJ3Vbj/Om/zqPesWMHdu/ejaKiIshkMnTu3Bnjx49HTEyM3p8f/Zel9SVRFLFhwwbs3r0bVVVVCAkJwRNPPNHknI1tJiUl4cCBA8jMzERDQwPWrFmj3wfaQXGEmkzqs88+Q2pqKgYOHIhZs2YhISEBcrkcV65cUdepqqrCsmXLEBISgunTp0Mmk+H999/HgQMH8P777yM8PBzTpk1DXV0d3nvvPdTW1ho9JjI/pu5LoijinXfewd69ezFkyBBMmTIFpaWlLT7Eo1KpsHTpUjg5OeHRRx9FVFSU3p8Bac/U/SU3NxcffPABHBwcMG3aNEydOhX33HMPTp8+3aTumjVrcPHiRUyePBkjR47E77//ji+++KLV9tPS0rBmzRr4+flhxowZmDx5MgIDA3H27FntPyTSiqX1pZSUFKSkpCAgIADTp0+Hp6cnlixZ0uLo8+eff47Lly9j0qRJuP/++7WO29JwhJpM6siRIxgxYgQee+wxddnfv5BlZWWYN2+eelQlLCwMzz//PD744AMsWbIEISEhAABfX18sXboUWVlZGDZsmFFjIvNj6r50+PBh5OXlYfr06ZgwYQIAYNSoUVi8eHGz9ZVKJQYNGoSpU6e29VLJAEzdX44cOQJbW1ssWrQIEknrY1sODg549dVXIQgCgNu/vO3YsQM1NTWws7Nrsf0uXbpg/vz5WsVDurOkvlRRUYHNmzcjIiICCxcuVNdbu3YtNm7c2GKbr7322h3Pbenu7qsnk7O3t8e5c+dQWlraYh0bGxsMHjxYve3j4wN7e3v4+fmpb0IA1D9fv37d6DGR+TF1X8rJyYGVlRVGjRqlLpNIJBg9enSLx/y1LrUvU/cXOzs71NXVITc394514+Li1IkNAPTq1QsqlQrFxcUtHmNvb48bN27g3LlzWsdEurGkvpSbm4v6+nqMHj1ao964ceNabHPEiBF3fTINcISaTGzatGlYtWoVnn32WQQHByM8PBxDhw5F586d1XXc3d01vtjA7RuIu7t7kzLg9jxWY8dE5sfUfam4uBguLi6wsbHRKPfx8Wm2vpWVFdzc3LRunwzL1P0lPj4eBw8exFtvvQU3NzeEhYUhOjoaffv2bVLXw8NDY9ve3v6O57v//vtx/PhxvPLKK/Dy8kJYWBhiYmLQs2dPrWMk7VhSXyopKQEAeHt7a9RzcnJS1/07T09PrWO1ZEyoyaSio6PRq1cvZGdn49ixY9iyZQs2bdqEF198EeHh4QDQ4m++LZWLomj0mMj8mGNfao1UKuWojgmZur84Ozvj3XffxdGjR3H06FHk5OQgPT0dsbGxmDt3rt7n8/Pzw/vvv48jR47g6NGjyMrKQmpqqsYD2GQYlt6X7kQul+t8rCXh3ZxMztXVFfHx8XjppZewcuVKODo6YsOGDYyJ2syU/26dOnVCeXl5kwd3rl692i7np7Yz9fdcKpWiX79+mDVrFj766CPExcVh7969uHbtmkHat7GxQXR0NGbPno2PP/4YERER2LBhAxQKhUHap/+ylL7UOIJdWFioUV5RUaH3X38tHRNqMhmVSoWamhqNMmdnZ7i6uqK+vp4xkdbM4d8tPDwcDQ0NSE1N1Yhr586d7XJ+0p459JfKykqNbYlEgoCAAAC3H1g1dPtSqRR+fn4QRRENDQ16t0+3WVpfCgsLg5WVFXbu3Kkxar1t2zb9A7VwnPJBJlNbW4tnnnkGUVFRCAgIgI2NDY4fP478/HyNp6Xv9pjozszh3y0yMhI9evTAd999h6KiIvj5+SE7O7vJ/2zJ9Myhv3zyySeoqqpC79694e7ujuLiYuzcuROBgYHw9fXVu/0lS5bAxcUFPXr0gIuLCy5fvoxdu3YhIiICtra2BrgCAiyvLzk5OSEhIQE///wzli1bhvDwcBQUFCAnJweOjo5GugLLwISaTMba2hrx8fE4duwYsrOzoVKp4OXlhVmzZpls9QNzjInuzBz+3SQSCRYuXIivvvoKGRkZ6he7PPbYY3xhi5kxh/4yZMgQ/PLLL0hNTUV1dTVcXFwwaNAgJCYmGmRu/ciRI5GRkYFt27bh1q1bcHNzw5gxY/Dggw8aIHpqZIl9acqUKZDL5di9ezdOnjyJkJAQvPrqq1i2bJkRorccgmjMp26IiIiIiCwc51ATEREREemBUz7IIqlUKlRUVLRax8bGpsmawUR/x75EbcH+QobCvtSxMKEmi1RSUtJk/c2/43qspA32JWoL9hcyFPaljoVzqMkiKRQKnD59utU6nTt35tsP6Y7Yl6gt2F/IUNiXOhYm1EREREREeuBDiUREREREemBCTURERESkBybURERERER6YEJNRERERKQHJtRERERERHpgQk1EREREpAcm1EREREREemBCTURERESkh/8H6Kf+g5WWlbQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# opeの結果の比較・可視化\n",
    "ope.visualize_off_policy_estimates(\n",
    "    input_dict,\n",
    "    compared_estimators=state_marginal_estimators_name,\n",
    "    random_state=random_state,\n",
    "    sharey=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# opeの結果の比較・可視化\n",
    "ope.visualize_off_policy_estimates(\n",
    "    input_dict,\n",
    "    compared_estimators=state_action_marginal_estimators_name,\n",
    "    random_state=random_state,\n",
    "    sharey=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# opeの結果の比較・可視化\n",
    "ope.visualize_off_policy_estimates(\n",
    "    input_dict,\n",
    "    compared_estimators=[\n",
    "        \"dm\",\n",
    "        \"pdis\",\n",
    "        \"dr\",\n",
    "        \"sm_is\",\n",
    "        \"sm_dr\",\n",
    "        \"sam_is\",\n",
    "        \"sam_dr\",\n",
    "        \"drl\",\n",
    "    ],\n",
    "    random_state=random_state,\n",
    "    sharey=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ope.visualize_off_policy_estimates(\n",
    "    input_dict,\n",
    "    compared_estimators=[\n",
    "        \"dm\",\n",
    "        \"snpdis\",\n",
    "        \"sndr\",\n",
    "        \"sm_snis\",\n",
    "        \"sm_sndr\",\n",
    "        \"sam_snis\",\n",
    "        \"sam_sndr\",\n",
    "    ],\n",
    "    random_state=random_state,\n",
    "    sharey=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OPEのより発展的なトピック(累積分布関数の推定など) やオフ方策選択 (OPS) は，以下を参照してください．[examples/quickstart_ja/rec/rec_synthetic_discrete_advanced_ja.ipynb](https://github.com/hakuhodo-technologies/scope-rl/blob/main/examples/quickstart_ja/rec/rec_synthetic_discrete_advanced_ja.ipynb). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 参考文献\n",
    "\n",
    "- Yuta Saito, Shunsuke Aihara, Megumi Matsutani, and Yusuke Narita. \\\n",
    "\"Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation.\", 2021.\n",
    "\n",
    "- Takuma Seno and Michita Imai. \\\n",
    "\"d3rlpy: An Offline Deep Reinforcement Library.\", 2021.\n",
    "\n",
    "- Christina J. Yuan, Yash Chandak, Stephen Giguere, Philip S. Thomas, and Scott Niekum. \\\n",
    "\"SOPE: Spectrum of Off-Policy Estimators.\", 2021.\n",
    "\n",
    "- Nathan Kallus and Masatoshi Uehara. \\\n",
    "\"Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes.\", 2020.\n",
    "\n",
    "- Masatoshi Uehara, Jiawei Huang, and Nan Jiang. \\\n",
    "\"Minimax Weight and Q-Function Learning for Off-Policy Evaluation.\", 2020.\n",
    "\n",
    "- Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, and Dale Schuurmans. \\\n",
    "\"Off-Policy Evaluation via the Regularized Lagrangian.\", 2020.\n",
    "\n",
    "- Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu. \\\n",
    "\"Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.\" 2020.\n",
    "\n",
    "- Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine. \\\n",
    "\"Conservative Q-Learning for Offline Reinforcement Learning.\", 2020.\n",
    "\n",
    "- Nathan Kallus and Masatoshi Uehara. \\\n",
    "\"Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning.\", 2019.\n",
    "\n",
    "- Hoang Le, Cameron Voloshin, and Yisong Yue. \\\n",
    "\"Batch Policy Learning under Constraints.\", 2019.\n",
    "\n",
    "- Qiang Liu, Lihong Li, Ziyang Tang, and Dengyong Zhou. \\\n",
    "\"Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation.\", 2018\n",
    "\n",
    "- Di Wu, Xiujun Chen, Xun Yang, Hao Wang, Qing Tan, Xiaoxun Zhang, Jian Xu, and Kun Gai. \\\n",
    "\"Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising.\", 2018.\n",
    "\n",
    "- Jun Zhao, Guang Qiu, Ziyu Guan, Wei Zhao, and Xiaofei He. \\\n",
    "\"Deep Reinforcement Learning for Sponsored Search Real-time Bidding.\", 2018.\n",
    "\n",
    "- Josiah P. Hanna, Peter Stone, and Scott Niekum. \\\n",
    "\"Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation.\", 2017.\n",
    "\n",
    "- Nan Jiang and Lihong Li. \\\n",
    "\"Doubly Robust Off-policy Value Evaluation for Reinforcement Learning.\", 2016.\n",
    "\n",
    "- Philip S. Thomas and Emma Brunskill. \\\n",
    "\"Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning.\", 2016.\n",
    "\n",
    "- Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. \\\n",
    "\"OpenAI Gym.\", 2016.\n",
    "\n",
    "- Philip S. Thomas, Georgios Theocharous, and Mohammad Ghavamzadeh. \\\n",
    "\"High Confidence Policy Improvement.\", 2015.\n",
    "\n",
    "- Philip S. Thomas, Georgios Theocharous, and Mohammad Ghavamzadeh. \\\n",
    "\"High Confidence Off-Policy Evaluation.\", 2015.\n",
    "\n",
    "- Adith Swaminathan and Thorsten Joachims. \\\n",
    "\"The Self-Normalized Estimator for Counterfactual Learning.\", 2015.\n",
    "\n",
    "- Hado van Hasselt, Arthur Guez, and David Silver. \\\n",
    "\"Deep Reinforcement Learning with Double Q-learning.\", 2015.\n",
    "\n",
    "- Miroslav Dudík, Dumitru Erhan, John Langford, and Lihong Li. \\\n",
    "\"Doubly Robust Policy Evaluation and Optimization.\", 2014.\n",
    "\n",
    "- Alex Strehl, John Langford, Sham Kakade, and Lihong Li. \\\n",
    "\"Learning from Logged Implicit Exploration Data.\", 2010.\n",
    "\n",
    "- Alina Beygelzimer and John Langford. \\\n",
    "\"The Offset Tree for Learning with Partial Labels.\", 2009.\n",
    "\n",
    "- Doina Precup, Richard S. Sutton, and Satinder P. Singh. \\\n",
    "\"Eligibility Traces for Off-Policy Policy Evaluation.\", 2000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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