From 5453d18d93d71e52c28ffcb96a2da93da6ce60c9 Mon Sep 17 00:00:00 2001
From: Tommy in Tongji <36354458+TommyZihao@users.noreply.github.com>
Date: Wed, 7 Jun 2023 12:23:01 +0800
Subject: [PATCH] Add files via upload

---
 ...347\273\203\346\227\245\345\277\227.ipynb" | 1109 +++++++++++++++++
 1 file changed, 1109 insertions(+)
 create mode 100644 "2023/0524/\343\200\220D3\343\200\221\344\270\211\350\247\222\346\235\277\347\233\256\346\240\207\346\243\200\346\265\213-\345\217\257\350\247\206\345\214\226\350\256\255\347\273\203\346\227\245\345\277\227.ipynb"

diff --git "a/2023/0524/\343\200\220D3\343\200\221\344\270\211\350\247\222\346\235\277\347\233\256\346\240\207\346\243\200\346\265\213-\345\217\257\350\247\206\345\214\226\350\256\255\347\273\203\346\227\245\345\277\227.ipynb" "b/2023/0524/\343\200\220D3\343\200\221\344\270\211\350\247\222\346\235\277\347\233\256\346\240\207\346\243\200\346\265\213-\345\217\257\350\247\206\345\214\226\350\256\255\347\273\203\346\227\245\345\277\227.ipynb"
new file mode 100644
index 0000000..ec2b46f
--- /dev/null
+++ "b/2023/0524/\343\200\220D3\343\200\221\344\270\211\350\247\222\346\235\277\347\233\256\346\240\207\346\243\200\346\265\213-\345\217\257\350\247\206\345\214\226\350\256\255\347\273\203\346\227\245\345\277\227.ipynb"
@@ -0,0 +1,1109 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0c136516-29cd-4fe7-bff2-0d43ed8f4937",
+   "metadata": {},
+   "source": [
+    "# 三角板目标检测-可视化训练日志\n",
+    "\n",
+    "训练模型时在`work_dirs`目录生成记录训练日志，解析其中损失函数、评估指标等信息，并可视化。\n",
+    "\n",
+    "同济子豪兄：https://space.bilibili.com/1900783\n",
+    "\n",
+    "20230607"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "095a94a6-89fb-4e7b-85b0-32244b3862c8",
+   "metadata": {},
+   "source": [
+    "## 进入mmdetection主目录"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7f50ec01-d4f2-4009-88d9-a8fb96d605b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.chdir('mmdetection')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ed5b805-2e18-45b5-a062-8b7338eb7b89",
+   "metadata": {},
+   "source": [
+    "## 导入工具包"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5245a2db-e693-4c0f-be85-33a93122bebd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.rcParams['axes.unicode_minus']=False  # 用来正常显示负号"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "398a5e05-9fca-445a-9c0d-3a1cad47187d",
+   "metadata": {},
+   "source": [
+    "## 载入训练日志"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ebe51f25-dd78-4931-8708-e5a0c7898ae9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 日志文件路径\n",
+    "log_path = 'work_dirs/faster_r_cnn_triangle/20230511_234855/vis_data/scalars.json'\n",
+    "\n",
+    "# log_path = 'work_dirs/rtmdet_tiny_triangle/20230511_234855/vis_data/scalars.json'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "639c2f24-7fb7-438e-851a-6b2d43ef6d9e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(log_path, \"r\") as f:\n",
+    "    json_list = f.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "465cd7c3-8a34-4e63-87db-d5d595cb379c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4310"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(json_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "204cd3ff-c1c8-4ef9-9797-5d6f63bd2b4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'lr': 0.00018016032064128258,\n",
+       " 'data_time': 0.10732541084289551,\n",
+       " 'loss': 1.554654097557068,\n",
+       " 'loss_rpn_cls': 0.7000267386436463,\n",
+       " 'loss_rpn_bbox': 0.01174525087699294,\n",
+       " 'loss_cls': 0.8412070512771607,\n",
+       " 'acc': 91.30859375,\n",
+       " 'loss_bbox': 0.0016750632668845356,\n",
+       " 'time': 0.3841512203216553,\n",
+       " 'epoch': 1,\n",
+       " 'memory': 3177,\n",
+       " 'step': 5}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eval(json_list[4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "10703123-7456-44a5-b41b-ad3256f9fdd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 4310/4310 [00:04<00:00, 985.66it/s] \n"
+     ]
+    }
+   ],
+   "source": [
+    "df_train = pd.DataFrame()\n",
+    "df_test = pd.DataFrame()\n",
+    "for each in tqdm(json_list):\n",
+    "    if 'coco/bbox_mAP' in each:\n",
+    "        df_test = df_test.append(eval(each), ignore_index=True)\n",
+    "    else:\n",
+    "        df_train = df_train.append(eval(each), ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9ef57471-a041-4f74-8b2e-9cb6d112aeac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lr</th>\n",
+       "      <th>data_time</th>\n",
+       "      <th>loss</th>\n",
+       "      <th>loss_rpn_cls</th>\n",
+       "      <th>loss_rpn_bbox</th>\n",
+       "      <th>loss_cls</th>\n",
+       "      <th>acc</th>\n",
+       "      <th>loss_bbox</th>\n",
+       "      <th>time</th>\n",
+       "      <th>epoch</th>\n",
+       "      <th>memory</th>\n",
+       "      <th>step</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.00002</td>\n",
+       "      <td>0.496813</td>\n",
+       "      <td>1.745113</td>\n",
+       "      <td>0.697028</td>\n",
+       "      <td>0.010730</td>\n",
+       "      <td>1.036911</td>\n",
+       "      <td>4.296875</td>\n",
+       "      <td>0.000443</td>\n",
+       "      <td>0.868021</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3018.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.00006</td>\n",
+       "      <td>0.255589</td>\n",
+       "      <td>1.723127</td>\n",
+       "      <td>0.701383</td>\n",
+       "      <td>0.013017</td>\n",
+       "      <td>1.008303</td>\n",
+       "      <td>4.296875</td>\n",
+       "      <td>0.000424</td>\n",
+       "      <td>0.570157</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3283.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.00010</td>\n",
+       "      <td>0.173261</td>\n",
+       "      <td>1.672904</td>\n",
+       "      <td>0.699922</td>\n",
+       "      <td>0.012187</td>\n",
+       "      <td>0.960405</td>\n",
+       "      <td>5.957031</td>\n",
+       "      <td>0.000391</td>\n",
+       "      <td>0.470132</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3283.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.00014</td>\n",
+       "      <td>0.132102</td>\n",
+       "      <td>1.623487</td>\n",
+       "      <td>0.700816</td>\n",
+       "      <td>0.013017</td>\n",
+       "      <td>0.909276</td>\n",
+       "      <td>33.398438</td>\n",
+       "      <td>0.000379</td>\n",
+       "      <td>0.419139</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3283.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.00018</td>\n",
+       "      <td>0.107325</td>\n",
+       "      <td>1.554654</td>\n",
+       "      <td>0.700027</td>\n",
+       "      <td>0.011745</td>\n",
+       "      <td>0.841207</td>\n",
+       "      <td>91.308594</td>\n",
+       "      <td>0.001675</td>\n",
+       "      <td>0.384151</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3177.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4295</th>\n",
+       "      <td>0.00020</td>\n",
+       "      <td>0.006442</td>\n",
+       "      <td>0.066295</td>\n",
+       "      <td>0.003812</td>\n",
+       "      <td>0.006429</td>\n",
+       "      <td>0.021561</td>\n",
+       "      <td>99.023438</td>\n",
+       "      <td>0.034493</td>\n",
+       "      <td>0.277901</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>3177.0</td>\n",
+       "      <td>4296.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4296</th>\n",
+       "      <td>0.00020</td>\n",
+       "      <td>0.006451</td>\n",
+       "      <td>0.067425</td>\n",
+       "      <td>0.003638</td>\n",
+       "      <td>0.006775</td>\n",
+       "      <td>0.022045</td>\n",
+       "      <td>98.730469</td>\n",
+       "      <td>0.034965</td>\n",
+       "      <td>0.278211</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>3283.0</td>\n",
+       "      <td>4297.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4297</th>\n",
+       "      <td>0.00020</td>\n",
+       "      <td>0.006454</td>\n",
+       "      <td>0.066920</td>\n",
+       "      <td>0.003666</td>\n",
+       "      <td>0.006712</td>\n",
+       "      <td>0.021902</td>\n",
+       "      <td>99.316406</td>\n",
+       "      <td>0.034640</td>\n",
+       "      <td>0.277896</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>3177.0</td>\n",
+       "      <td>4298.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4298</th>\n",
+       "      <td>0.00020</td>\n",
+       "      <td>0.006462</td>\n",
+       "      <td>0.067548</td>\n",
+       "      <td>0.003785</td>\n",
+       "      <td>0.006692</td>\n",
+       "      <td>0.022116</td>\n",
+       "      <td>98.535156</td>\n",
+       "      <td>0.034956</td>\n",
+       "      <td>0.278304</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>3283.0</td>\n",
+       "      <td>4299.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4299</th>\n",
+       "      <td>0.00020</td>\n",
+       "      <td>0.006466</td>\n",
+       "      <td>0.067208</td>\n",
+       "      <td>0.003769</td>\n",
+       "      <td>0.006664</td>\n",
+       "      <td>0.022056</td>\n",
+       "      <td>98.828125</td>\n",
+       "      <td>0.034719</td>\n",
+       "      <td>0.278978</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>4573.0</td>\n",
+       "      <td>4300.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4300 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           lr  data_time      loss  loss_rpn_cls  loss_rpn_bbox  loss_cls  \\\n",
+       "0     0.00002   0.496813  1.745113      0.697028       0.010730  1.036911   \n",
+       "1     0.00006   0.255589  1.723127      0.701383       0.013017  1.008303   \n",
+       "2     0.00010   0.173261  1.672904      0.699922       0.012187  0.960405   \n",
+       "3     0.00014   0.132102  1.623487      0.700816       0.013017  0.909276   \n",
+       "4     0.00018   0.107325  1.554654      0.700027       0.011745  0.841207   \n",
+       "...       ...        ...       ...           ...            ...       ...   \n",
+       "4295  0.00020   0.006442  0.066295      0.003812       0.006429  0.021561   \n",
+       "4296  0.00020   0.006451  0.067425      0.003638       0.006775  0.022045   \n",
+       "4297  0.00020   0.006454  0.066920      0.003666       0.006712  0.021902   \n",
+       "4298  0.00020   0.006462  0.067548      0.003785       0.006692  0.022116   \n",
+       "4299  0.00020   0.006466  0.067208      0.003769       0.006664  0.022056   \n",
+       "\n",
+       "            acc  loss_bbox      time  epoch  memory    step  \n",
+       "0      4.296875   0.000443  0.868021    1.0  3018.0     1.0  \n",
+       "1      4.296875   0.000424  0.570157    1.0  3283.0     2.0  \n",
+       "2      5.957031   0.000391  0.470132    1.0  3283.0     3.0  \n",
+       "3     33.398438   0.000379  0.419139    1.0  3283.0     4.0  \n",
+       "4     91.308594   0.001675  0.384151    1.0  3177.0     5.0  \n",
+       "...         ...        ...       ...    ...     ...     ...  \n",
+       "4295  99.023438   0.034493  0.277901   50.0  3177.0  4296.0  \n",
+       "4296  98.730469   0.034965  0.278211   50.0  3283.0  4297.0  \n",
+       "4297  99.316406   0.034640  0.277896   50.0  3177.0  4298.0  \n",
+       "4298  98.535156   0.034956  0.278304   50.0  3283.0  4299.0  \n",
+       "4299  98.828125   0.034719  0.278978   50.0  4573.0  4300.0  \n",
+       "\n",
+       "[4300 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2faf2b47-4a9d-4e89-9e32-eae88c423a1a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>coco/bbox_mAP</th>\n",
+       "      <th>coco/bbox_mAP_50</th>\n",
+       "      <th>coco/bbox_mAP_75</th>\n",
+       "      <th>coco/bbox_mAP_s</th>\n",
+       "      <th>coco/bbox_mAP_m</th>\n",
+       "      <th>coco/bbox_mAP_l</th>\n",
+       "      <th>pascal_voc/mAP</th>\n",
+       "      <th>pascal_voc/AP50</th>\n",
+       "      <th>data_time</th>\n",
+       "      <th>time</th>\n",
+       "      <th>step</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.411</td>\n",
+       "      <td>0.948</td>\n",
+       "      <td>0.217</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.411</td>\n",
+       "      <td>0.948099</td>\n",
+       "      <td>0.948</td>\n",
+       "      <td>0.074389</td>\n",
+       "      <td>0.178963</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.818</td>\n",
+       "      <td>0.977</td>\n",
+       "      <td>0.957</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.818</td>\n",
+       "      <td>0.991736</td>\n",
+       "      <td>0.992</td>\n",
+       "      <td>0.058926</td>\n",
+       "      <td>0.160506</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.839</td>\n",
+       "      <td>0.979</td>\n",
+       "      <td>0.960</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.839</td>\n",
+       "      <td>0.995671</td>\n",
+       "      <td>0.996</td>\n",
+       "      <td>0.066158</td>\n",
+       "      <td>0.166594</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.842</td>\n",
+       "      <td>0.979</td>\n",
+       "      <td>0.960</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.842</td>\n",
+       "      <td>0.995671</td>\n",
+       "      <td>0.996</td>\n",
+       "      <td>0.065453</td>\n",
+       "      <td>0.166578</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.841</td>\n",
+       "      <td>0.979</td>\n",
+       "      <td>0.960</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.841</td>\n",
+       "      <td>0.995671</td>\n",
+       "      <td>0.996</td>\n",
+       "      <td>0.060269</td>\n",
+       "      <td>0.165117</td>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.847</td>\n",
+       "      <td>0.978</td>\n",
+       "      <td>0.960</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.847</td>\n",
+       "      <td>0.995671</td>\n",
+       "      <td>0.996</td>\n",
+       "      <td>0.073501</td>\n",
+       "      <td>0.173967</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.848</td>\n",
+       "      <td>0.979</td>\n",
+       "      <td>0.960</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.848</td>\n",
+       "      <td>0.995671</td>\n",
+       "      <td>0.996</td>\n",
+       "      <td>0.067540</td>\n",
+       "      <td>0.170206</td>\n",
+       "      <td>35.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.846</td>\n",
+       "      <td>0.979</td>\n",
+       "      <td>0.960</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.846</td>\n",
+       "      <td>0.995671</td>\n",
+       "      <td>0.996</td>\n",
+       "      <td>0.057506</td>\n",
+       "      <td>0.158793</td>\n",
+       "      <td>40.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.852</td>\n",
+       "      <td>0.979</td>\n",
+       "      <td>0.960</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.852</td>\n",
+       "      <td>0.997783</td>\n",
+       "      <td>0.998</td>\n",
+       "      <td>0.069616</td>\n",
+       "      <td>0.170459</td>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.848</td>\n",
+       "      <td>0.979</td>\n",
+       "      <td>0.960</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.848</td>\n",
+       "      <td>0.998878</td>\n",
+       "      <td>0.999</td>\n",
+       "      <td>0.063433</td>\n",
+       "      <td>0.165182</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   coco/bbox_mAP  coco/bbox_mAP_50  coco/bbox_mAP_75  coco/bbox_mAP_s  \\\n",
+       "0          0.411             0.948             0.217             -1.0   \n",
+       "1          0.818             0.977             0.957             -1.0   \n",
+       "2          0.839             0.979             0.960             -1.0   \n",
+       "3          0.842             0.979             0.960             -1.0   \n",
+       "4          0.841             0.979             0.960             -1.0   \n",
+       "5          0.847             0.978             0.960             -1.0   \n",
+       "6          0.848             0.979             0.960             -1.0   \n",
+       "7          0.846             0.979             0.960             -1.0   \n",
+       "8          0.852             0.979             0.960             -1.0   \n",
+       "9          0.848             0.979             0.960             -1.0   \n",
+       "\n",
+       "   coco/bbox_mAP_m  coco/bbox_mAP_l  pascal_voc/mAP  pascal_voc/AP50  \\\n",
+       "0             -1.0            0.411        0.948099            0.948   \n",
+       "1             -1.0            0.818        0.991736            0.992   \n",
+       "2             -1.0            0.839        0.995671            0.996   \n",
+       "3             -1.0            0.842        0.995671            0.996   \n",
+       "4             -1.0            0.841        0.995671            0.996   \n",
+       "5             -1.0            0.847        0.995671            0.996   \n",
+       "6             -1.0            0.848        0.995671            0.996   \n",
+       "7             -1.0            0.846        0.995671            0.996   \n",
+       "8             -1.0            0.852        0.997783            0.998   \n",
+       "9             -1.0            0.848        0.998878            0.999   \n",
+       "\n",
+       "   data_time      time  step  \n",
+       "0   0.074389  0.178963   5.0  \n",
+       "1   0.058926  0.160506  10.0  \n",
+       "2   0.066158  0.166594  15.0  \n",
+       "3   0.065453  0.166578  20.0  \n",
+       "4   0.060269  0.165117  25.0  \n",
+       "5   0.073501  0.173967  30.0  \n",
+       "6   0.067540  0.170206  35.0  \n",
+       "7   0.057506  0.158793  40.0  \n",
+       "8   0.069616  0.170459  45.0  \n",
+       "9   0.063433  0.165182  50.0  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7d3adaf-5349-4f63-ba43-c934d9e26fd4",
+   "metadata": {},
+   "source": [
+    "## 导出训练日志表格"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "41f7af2f-5787-4604-9813-db2f333ea083",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train.to_csv('训练日志-训练集.csv', index=False)\n",
+    "df_test.to_csv('训练日志-测试集.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d621e61-ada4-459f-93a7-47e11534c7f9",
+   "metadata": {},
+   "source": [
+    "## 设置Matplotlib中文字体"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "70c523df-edd2-4625-aee7-5788d6d3745b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # windows操作系统\n",
+    "# plt.rcParams['font.sans-serif']=['SimHei']  # 用来正常显示中文标签 \n",
+    "# plt.rcParams['axes.unicode_minus']=False  # 用来正常显示负号"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "aa2a07f6-e57d-4cf5-8fbc-a78063c4ace9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Mac操作系统，参考 https://www.ngui.cc/51cto/show-727683.html\n",
+    "# 下载 simhei.ttf 字体文件\n",
+    "# !wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220716-mmclassification/dataset/SimHei.ttf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a2d91da7-7c70-464c-bad2-597810e02b48",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2023-05-12 08:45:29--  https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220716-mmclassification/dataset/SimHei.ttf\n",
+      "正在连接 172.16.0.13:5848... 已连接。\n",
+      "已发出 Proxy 请求，正在等待回应... 200 OK\n",
+      "长度： 10050868 (9.6M) [application/x-font-ttf]\n",
+      "正在保存至: “/environment/miniconda3/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/ttf/SimHei.ttf”\n",
+      "\n",
+      "/environment/minico 100%[===================>]   9.58M  22.5MB/s    用时 0.4s    \n",
+      "\n",
+      "2023-05-12 08:45:30 (22.5 MB/s) - 已保存 “/environment/miniconda3/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/ttf/SimHei.ttf” [10050868/10050868])\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Linux操作系统，例如 云GPU平台：https://featurize.cn/?s=d7ce99f842414bfcaea5662a97581bd1\n",
+    "# 如果遇到 SSL 相关报错，重新运行本代码块即可\n",
+    "!wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220716-mmclassification/dataset/SimHei.ttf -O /environment/miniconda3/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/ttf/SimHei.ttf\n",
+    "!rm -rf /home/featurize/.cache/matplotlib\n",
+    "\n",
+    "import matplotlib \n",
+    "import matplotlib.pyplot as plt\n",
+    "matplotlib.rc(\"font\",family='SimHei') # 中文字体\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "da0ce49d-5ff5-471d-b19e-4037a2e1dd20",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot([1,2,3], [100,500,300])\n",
+    "plt.title('matplotlib中文字体测试', fontsize=25)\n",
+    "plt.xlabel('X轴', fontsize=15)\n",
+    "plt.ylabel('Y轴', fontsize=15)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70148398-3f1d-47bf-a7db-5f28c19d4ec1",
+   "metadata": {},
+   "source": [
+    "## 可视化辅助函数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3f468a09-d222-4a50-966a-7e592f5ed039",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import colors as mcolors\n",
+    "import random\n",
+    "random.seed(124)\n",
+    "colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'black', 'indianred', 'brown', 'firebrick', 'maroon', 'darkred', 'red', 'sienna', 'chocolate', 'yellow', 'olivedrab', 'yellowgreen', 'darkolivegreen', 'forestgreen', 'limegreen', 'darkgreen', 'green', 'lime', 'seagreen', 'mediumseagreen', 'darkslategray', 'darkslategrey', 'teal', 'darkcyan', 'dodgerblue', 'navy', 'darkblue', 'mediumblue', 'blue', 'slateblue', 'darkslateblue', 'mediumslateblue', 'mediumpurple', 'rebeccapurple', 'blueviolet', 'indigo', 'darkorchid', 'darkviolet', 'mediumorchid', 'purple', 'darkmagenta', 'fuchsia', 'magenta', 'orchid', 'mediumvioletred', 'deeppink', 'hotpink']\n",
+    "markers = [\".\",\",\",\"o\",\"v\",\"^\",\"<\",\">\",\"1\",\"2\",\"3\",\"4\",\"8\",\"s\",\"p\",\"P\",\"*\",\"h\",\"H\",\"+\",\"x\",\"X\",\"D\",\"d\",\"|\",\"_\",0,1,2,3,4,5,6,7,8,9,10,11]\n",
+    "linestyle = ['--', '-.', '-']\n",
+    "\n",
+    "def get_line_arg():\n",
+    "    '''\n",
+    "    随机产生一种绘图线型\n",
+    "    '''\n",
+    "    line_arg = {}\n",
+    "    line_arg['color'] = random.choice(colors)\n",
+    "    # line_arg['marker'] = random.choice(markers)\n",
+    "    line_arg['linestyle'] = random.choice(linestyle)\n",
+    "    line_arg['linewidth'] = random.randint(1, 4)\n",
+    "    # line_arg['markersize'] = random.randint(3, 5)\n",
+    "    return line_arg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83b62060-817f-458a-b63b-b71551d958e9",
+   "metadata": {},
+   "source": [
+    "## 训练集损失函数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "829f3a23-b3e0-4341-9ed3-44a91ad0581b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['lr', 'data_time', 'loss', 'loss_rpn_cls', 'loss_rpn_bbox', 'loss_cls',\n",
+       "       'acc', 'loss_bbox', 'time', 'epoch', 'memory', 'step'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "6c8df914-49bf-4aac-aec4-a5882fa20354",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics = ['loss', 'loss_bbox', 'loss_cls', 'loss_rpn_cls', 'loss_rpn_bbox']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "d9c217a6-7104-430e-bfc3-42b0e7673b53",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Jcx/vxPU+87nA2bgS5OXF2nlLnINKHQHPStSnAO1wbUV1rPtz0bWPuvf9LZpqPMRau9BLbA/hWtH6dFz7TpdeqOthXKOxObimCRcl1h2Bu3G90/wBcBXwFnA0rgW7PgZeLtZPiPv3UKm9nAGstfcA9wAYY+JwbbGVA7xpjDkL1yjui9baC8vvpST3atMvAZfiWlzrUmutZ8TXWvuhe2bADGCme3r7bdbaQ70HXiJpL0qa3fbw96rhy4FPS7Ut/vfgkKy1k40xS4FT3T9DgJvc1Q2NMXcB71trfyx2WTcg2v250BgTdogV1EVEpJ5Q4iwiIoGkg/uYi2u16D+MMXPcZYuLtSuaZlx8Ea3gUsciCfw9JTcf13vO/8E1Cvs9rkRtqbvd3dbat730ibU23xhzJa53jR82xiyz1q4oVj+r+E3do+LX4ErUtuNalKoXrsR5jbvuTVyj67nAY4fae7mUQybVxpgmwDtAU1wJfxYwHdeiaKOMMbdZaz8+7E2M6QW8CHTGtaL5+dbataXbWWtfNMbsBWbh2gf5bGPMIlwLmX3snjZdXOk/o+Km4foy4dsqrNZdhrX2J+AnY8xCXFPN/4FrEbhOuLYhG2SM6e3+8x3kfoZI4FegJ/C6MWawkmcRkfpNibOIiAQE97vDA92nY6213xhjLgJOxPUO7PxizYtGdg874myt/cEY8wyuVamXWGsPFLtnU1wrKR8LvGmtfdQY0wjXu8FRQHt304Puvn41xjwG3I9rxe0VFONOlvvgGim/HNeU7wXADdbancaY3u6mTmtthnsUeDQwEddewhtxjUovA5Zba/8q1X8crkW0TnIX5ZWqN+57T8Y1ov0e8JC1Ns8Y08cd97XAEmPMe8A4b+9NG2OOxbX412W4fsf/Ay6w1m4o3baItfZtY8xaXO8I93PHcS7wvTGmj3tf5CLh7uNFxpgTy+uzAlPe4fBfIsTg2m5sLK6EfaS19nljzPG4vlR4CIhy/7leh+vLlauBhcBKXCt0f2GMueowo+giIlKXWWv1ox/96Ec/+gmIH2AC8Kr7c2dco8EWVyJ9uGubuduursT9Tsb1bvIKINxdFo5r+yPr/llT6pow4NRSZU5gHq73ki2uvYM/As4p1W6ku35yqfIY4F/AxmL33QHEl2o3rVi9BW5xl5+JaxS9+PUzgRAvz9wT14irdcd7Qqn6EcX6yMc1Uh1ZyT/HC4Cv3H1081J/TannqM5PmT9vXFOtL3H/mWS72y0HjinVLhbX1PbdxX7npxSr7+wus7i+qHnQ3/9G9KMf/ehHP1X7MdaWXptERESk/jLGOK1rO6LjcO0RfDTQxVqbd5jrmuPaXmqttfbYStzPABG22H7IxpgHcC0U9gXwsLV2fQX66YhrVPxNYJa19ncvbcYDk4BXrLVXlhPLicAVwFJr7Vul6k/BlSCvxfXu78vWNcW4L/AZrhHVj3CNMpf7LrD7veXJuPa2vtCW+p8JY8xDwAnArbbk+7+VYoxpaa3dVNXrK9C/BdZbazuWKh8JTHWfLgcetda+7+X6Prj+zOJxTeO+2Vq7u1Sb1rimvYcDp1nt6SwiUi8pcRYRkYBmjGlord3v7zjqOmPMQFz7NR9uy6ri1wRZa/O9lJvSyXR9494z+2N7mOnV7uQ5yJbdmqx4mzCgWWV+tyIiUrcocRYRERERERE5BMfhm4iIiIiIiIgcuZQ4i4iIiIiIiByCtqOqoNjYWNuyZUt/hyEiIiIiIiI1YPXq1buttXHe6pQ4V1DLli1ZtWqVv8MQERERERGRGmCM+bO8Ok3VFhERERERETkEJc4iIiIiIiIih6DEWUREREREROQQlDiLiIiIiIiIHIISZxEREREREZFDUOIsIiIiIiIicghKnEVEREREREQOQYmziIiIiIiIyCEE+TsAERERERGpupycHPbs2cP+/fspKCjwdzgidYLT6aRhw4Y0adKE0NDQavenxFlEREREpJ7Kyclh8+bNREdH07JlS4KDgzHG+DssEb+y1pKXl0dGRgabN2+mefPm1U6eNVVbRERERKSe2rNnD9HR0cTGxhISEqKkWQQwxhASEkJsbCzR0dHs2bOn2n36PXE2xrQ1xlR5TokxZpMxxpbzM6FYu9mHaDfMF88iIiIiIlKb9u/fT1RUlL/DEKmzoqKi2L9/f7X78ctUbWOMA2gDdAMmUr0E/lYgslTZccBYIMtL+1uA9FJl/63G/UVERERE/KKgoIDg4GB/hyFSZwUHB/vk3X9/veMcD/zii46stW+VLjPG/Mf9cYmXS6ZZaw/64t4iIiIiIv6m6dki5fPVvw9/TdXeA5zt/vmxBvr/J7DJWvtDqfL9SppFRERERESkMvySOFtrc621H1prP8SVRPuMMaYnkAy866U6zRjzgDFmpzEmxxjztTHmBF/eX0RERERERAKL3xcHqwGD3EdviXNL4BLgMeBJ4HhgiTEmpnZCExERERERkfomEBPnfwKpwFelyn8E3gZ6W2v/ba29B5gANAJGeOvIGHO9MWaVMWbVrl27ajDk6pv48Qt8/5dPXhsXERERERGRYgIqcTbGHAO0AxZYawuL11lrn7LWXmSt3VeseKn72Nlbf9baGdbaHtbaHnFxcTUTtI/sOpBOelaGv8MQEREREfGblJQUWrZs6e8wJAAFVOIMXOg+epum7U3Rhl5NaiCWWhUWHEpOfq6/wxAREREREQk4gZY4DwIygE+KFxpjuhpjfjLG3FaqfTP3cWttBFeTwoJCOJif4+8wREREREREAk7AJM7GmNZAV2CRtbb00OtmoD1wmSm5kddl7uPiWgixRoUFh5KdpxFnERERERERX6uzibMx5jhjzDBjTNMKXlLuNG1r7V7gWaAb8IEx5iZjzEvA9bhGpys6tbvOCgsK0VRtEREREZFybNy4kcGDBxMdHU1ERAT9+/dn+fLlZdqtX7+eQYMGkZiYSEREBF27dmX27Nll2v31118MHz6c5ORkwsPDad++PU8++STW2lp4GqltQf4O4BAuAO4HTgF2VKD9hUAO8EE59eOB7cANwNPuPp8E7i+9kFh9FORwkl9Y4O8wRERERKQOOXbSZYdv5Gdrbp1X4/fYsGEDffr0ITc3l9GjRxMVFcW0adNISUlh/vz5DBgwAIDMzExOPfVUrLWMHj2amJgYli5dyvDhw2ncuDEXXHCBp88BAwawceNGxo0bR1JSEqtXr+b2228nODiYcePG1fgzSe3ye+JsrU0pp3wCru2iKtpPn8PUFwJPuH8CjtPhpECJs4iIiIgUUxtJaX0wduxY0tPTWblyJccffzwA1113HZ07d+a6665j06ZNBAcHs379erZv387kyZMZO3YsADfccANjxowhJ+fv9YTS0tJYu3Yt48aN48EHH/SUJyYmEhISUrsPJ7XC74mz+IbT4SC/QImziIiIiEhx2dnZLFmyhH79+nmSZoDo6GiGDRvGo48+ysqVK+nbty/t27cnPj6eadOmkZCQQM+ePWnVqhXPPPNMiT5jYmLo1KkTb7zxBl26dKFPnz60a9eOCRMm1PLTSW2ps+84S+UEOZwUWCXOIiIiIiLFpaWlkZ+fT1JSUpm65ORkAHbscL0ZGhkZyQcffEDbtm0ZMWIEbdq0IS4ujlGjRpGRkVHi2oULF5KSksKdd95Jp06diI6OZsiQIWzfvr3mH0pqnUacA4TTOCkorPevaouIiIiI+FRMTAxBQUFs3Vp2B9otW7YAkJCQ4Cnr1q0bCxcu9NS/8847jB8/npycHF544QVPu9atWzN37lwAdu7cyYcffsioUaPYsWMHS5curclHEj/QiHOAcDgcWhxMRERERKSUsLAwTj/9dL744gvWrFnjKd+7dy9z5swhMTGRnj17AjBjxgySkpJYtWoV4BqRHjt2LJ07d2bFihWeaxcvXkxSUhLz588HXIn3VVddRf/+/Uu0k8ChEecA4XQ4NOIsIiIiIuLF5MmT6dOnD/369fOsqj19+nRSU1N55513PAt6paSkMGbMGC699FKuueYaYmNj+fbbb1m7dq1nsTCA3r17k52dzY033sj3339PUlISP//8M4sWLWLgwIH+ekypQUqcA0SQcVJY/3fVEhERERHxuXbt2rFixQruvvtupkyZQm5uLr169WL27NmceOKJJdp9+eWXPPLII0ydOpW0tDSOOuoo7rnnHu69915PuyZNmvDVV18xceJEZs2aRWpqKvHx8YwYMYKJEyf64xGlhilxDhBOTdUWERERkSPcsmXLyq1r3bo1b7zxxmH76NmzJ+++++5h27Vv355XXnmlMuFJPaZ3nAOEw+HQPs4iIiIiIiI1QIlzgAjSqtoiIiIiIiI1QolzgHBqH2cREREREZEaocQ5QGhVbRERERERkZqhxDlABDmcesdZRERERESkBihxDhAO46BA21GJiIiIiIj4nBLnAKHtqERERERERGqGEucA4ZqqrRFnERERERERX1PiHCCcDieFmqotIiIiIiLic0qcA4TDaKq2iIiIiIhITVDiHCCCHA6tqi0iIiIiIlIDlDgHCKfecRYREREREakRSpwDhNNoH2cREREREZGaoMQ5QDgd2sdZRERERI5sKSkptGzZ0t9hVNqwYcMwxvisnfieEucA4XQ4NFVbRERERESkBihxDhCaqi0iIiIiIlIzgvwdgPiG0+Eg3ypxFhEREZG/TaritN6Ebt0Yunq117pXundn53ffVanfW62t0nUi/qYR5wARpFW1RURERETKtXHjRgYPHkx0dDQRERH079+f5cuXl2m3fv16Bg0aRGJiIhEREXTt2pXZs2eXaffXX38xfPhwkpOTCQ8Pp3379jz55JPYanw58PXXX9OtWzfCwsJo3749s2bNqnK7zMxMbr/9dpKTkwkNDaVr167MmTOnRJuPPvoIYwxjx471lO3du5eEhAQ6depETk5OlZ8l0GjEOUA4jINCJc4iIiIiImVs2LCBPn36kJuby+jRo4mKimLatGmkpKQwf/58BgwYALiSzVNPPRVrLaNHjyYmJoalS5cyfPhwGjduzAUXXODpc8CAAWzcuJFx48aRlJTE6tWruf322wkODmbcuHFVinPAgAFccsklDB06lNdee42rr76anJwcbrzxxkq1y87Opn///qxcuZLhw4fTpUsX3n33XYYNG8avv/7Kww8/DMCZZ57Jtddey9SpU7nmmmvo2rUr99xzD2lpabz//vuEhoZW6TkCkRLnAOF0ODVVW0RERETEi7Fjx5Kens7KlSs5/vjjAbjuuuvo3Lkz1113HZs2bSI4OJj169ezfft2Jk+e7BmFveGGGxgzZkyJ0de0tDTWrl3LuHHjePDBBz3liYmJhISEVDnOG264gccee8zzuXXr1tx///1cf/31OByOCrebMmUK3377Lc888wyjR48GYMyYMZxzzjk8+uijDB48mGOOOQaAp59+miVLljBq1Cj+85//MG3aNG6//XZ69uxZ5ecIREqcA0SQVtUWERERkVJq4p3i8t59rquys7NZsmQJ/fr18yTNANHR0QwbNoxHH32UlStX0rdvX9q3b098fDzTpk0jISGBnj170qpVK5555pkSfcbExNCpUyfeeOMNunTpQp8+fWjXrh0TJkyoVqzXXXed53NERAQXXHAB06ZN45dffqFjx44VbrdgwQJCQkIYOXKkp50xhnHjxvHBBx+wcOFCT+LcsGFDXnzxRc444wzOOussOnbsWO3nCER6xzlAOB1aVVtEREREpLS0tDTy8/NJSkoqU5ecnAzAjh07AIiMjOSDDz6gbdu2jBgxgjZt2hAXF8eoUaPIyMgoce3ChQtJSUnhzjvvpFOnTkRHRzNkyBC2b99e5VhLx9i0aVMA9uzZU6l2O3fupGnTpjidzkM+b5HTTjuNLl26sGvXLkaNGlWtUfNApcQ5QDiMQ4mziIiIiEgpMTExBAUFsXXr1jJ1W7ZsASAhIcFT1q1bNxYuXEh6ejqbN2/m3nvvZfr06dxyyy0lrm3dujVz584lNTWVHTt2MGXKFObPn8/QoUOrHGvphHbXrl0ANGrUqFLtEhIS2LFjBwUFJfMDb88LMHPmTNauXUurVq144IEH2L17d5WfIVApcQ4QQQ4nBVZTtUVEREREigsLC+P000/niy++YM2aNZ7yvXv3MmfOHBITEz3v886YMYOkpCRWrVoFuEZox44dS+fOnVmxYoXn2sWLF5OUlMT8+fMBVyJ61VVX0b9//xLtKqv46tg5OTksXLiQxo0b06FDh0q1O++888jNzWX69OmedtZannnmGYwxnHfeeZ7yDRs2cPPNN3PRRRfxySefcODAAa6//voqP0Og0jvOAcKpd5xFRERERLyaPHkyffr0oV+/fp5VtadPn05qairvvPOOZ2pySkoKY8aM4dJLL+Waa64hNjaWb7/9lrVr15bYsql3795kZ2dz44038v3335OUlMTPP//MokWLGDhwYJXjnDRpEqmpqbRr147XX3+dLVu2MGnSJIKCgirVbvTo0bz55puMHj2a77//3rOq9rJly7jjjjvo0qULAAUFBQwdOpSwsDCmTp1KfHw8jz76KKNHj+all17i6quvrvKzBBxrrX4q8NO9e3dbl+UV5NvjJw32dxgiIiIiUot+/vlnf4dQp/Tr18+2aNHCa92GDRvsJZdcYhs1amTDw8NtSkqK/fLLL8u0W7Fihb3gggtss2bNbGhoqG3VqpW95557bE5OTol269evt0OGDLHJyck2NDTUJicn25tuusnu3bu30nFfddVVFrBLliyxnTp1ssHBwbZ169b2ueeeq1I7a63dv3+/HT9+vG3WrJkNDg62xxxzjH3xxRdLtHnggQcsYF999VVPWWFhoT3ppJNsZGSk/f333yv9LHVRRf+dAKtsOfmgsTWw0l4g6tGjhy2aslEXWWs57qnB/DD+dYwx/g5HRERERGrBunXrSqy2LCJlVfTfiTFmtbW2h7c6TdUOEMYYHMZQYAsJMs7DXyAiIiIiIjVm3759HDx48LDtoqOjCQ0NrYWIpDqUOAeQoi2pghxKnEVERERE/Gns2LHMmTPnsO3ee++9ar0XLbVDiXMAcRotECYiIiIiUheMHz+eyy677LDtunfvXgvRSHUpcQ4gDuOgUFtSiYiIiIj4XZcuXTyrV0v9p32cA4jTocRZRERERETE15Q4BxDXO85KnEVERERERHxJiXMAcRgHBRpxFhERERER8SklzgFEi4OJiIiIiIj4nt8TZ2NMW2NMQTWun22MseX8DCvVtq8x5nNjzAFjzG5jzBxjTEy1H6KOcDiM3nEWERERERHxMb+sqm2McQBtgG7ARHyTwN8CpJcq+2+xex4LfArsAO4F4oHxwDHGmN7W2jwfxOBXGnEWERERERHxPX9tRxUP/OLjPqdZaw8eov5JwAmcbq39DcAYsxd4DBgMvOzjeGqdtqMSERERERHxPX9N1d4DnO3++dEH/e0/VNJsjEkETgM+KUqa3Wa5j1f4IAa/c2g7KhEREREREZ/zS+Jsrc211n5orf0QVxJdXWnGmAeMMTuNMTnGmK+NMScUq+8LGGBFqThSgc1AHx/E4Heaqi0iIiIiIuJ7fl8czEdaApfgmnb9JHA8sKTYwl8t3cdtXq7dATQ0xjSp4RhrnNPhpMBWeZ01EREREZF6LSUlhZYtW/o7jFoxe/ZsjDEsW7bM36EcEfz1jrMv/Qi8DVxjrd0HYIw5ADwKjMC1+FiEu623BcBy3ccISo1+G2OuB64HaN68uc8D9zWNOIuIiIiIiPhevR9xttY+Za29qChpdlvqPnZ2HzPdx0ZeumhUqk3xvmdYa3tYa3vExcX5JuAapHecRUREREREfK/eJ87l2O8+Fk2//sN9jPfSNg7YZ60tvZVVveMwDgqUOIuIiIiIiPhUvU6cjTFdjTE/GWNuK1XVzH3c6j4uByxwcqnrWwFN3fX1ntM4KNRUbRERERGRMjZu3MjgwYOJjo4mIiKC/v37s3x52TRg/fr1DBo0iMTERCIiIujatSuzZ88u0+6vv/5i+PDhJCcnEx4eTvv27XnyySex1lYpvoyMDG6++WaSkpKIiIigc+fOTJ48mfz8/Cr1l5uby4QJE2jbti3h4eEkJyczYsQI9u7dW6X+jnT1/R3nzUB74DJjzCT799/Sy9zHxeBaPdsY8xFwujGmg7V2fal2c2st4hqkqdoiIiIiUtzzX73JtK/fLlMe1yCapSOe5/mv3gRgxD8u5rTnR7Ars+wkzBv7DPLUzx36MD/v3MjYd5/0er+Pb3yOn3du5O01nzDlwtsZ/c4TfPHHd2XandyqG1MuvJ2fd/xBp6atqvmUh7dhwwb69OlDbm4uo0ePJioqimnTppGSksL8+fMZMGAAAJmZmZx66qlYaxk9ejQxMTEsXbqU4cOH07hxYy644AJPnwMGDGDjxo2MGzeOpKQkVq9eze23305wcDDjxo2rVHyZmZmcfPLJ/PTTT1xzzTUce+yxLFu2jJtvvpn//e9/zJw5s9LPfNttt/HMM89w3XXX0b17d7Zt28bTTz/N9u3bmT9/fqX7O+JZa/36AyxzhVGm/DhgGND0MNf/G9do8ofATcBL7vOlgKNYu65ANvAncDOuxcNygVVA0OHi7N69u63rhr8+wa748yd/hyEiIiIiteTnn3/2dwh1Sr9+/WyLFi3KlA8YMMA6nU773Xffecr27NljExMTbbNmzWxubq611tpVq1ZZwE6ePLnE9aNHj7bz5s3znO/evdsCdty4cSXa3X///Xbq1KmVjnvixIkWsNOnTy9RftJJJ1mn02n3799f5ppZs2ZZwH722Wde+zzmmGPscccdV6Lstddes2PHjq10fPVdRf+dAKtsOflgXZ6qfQEwC+hwmHbjgTuAtsDTwGm4tqQ619q/h1+ttWuBU4BNuFbavh6YB5xpra3a/Ic6xqkRZxERERGRErKzs1myZAn9+vXj+OOP95RHR0czbNgwtm3bxsqVKwFo37498fHxTJs2jXnz5vH7779jreWZZ57h0ksv9VwbExNDp06deOONN3jppZdYt24dBQUFTJgwgZEjR1Y6xsWLFxMaGsq1115bovyLL74gPz+fyMjISvd50kkn8dNPP/HII4+wYsUKDh48yOWXX87kyZMr3ZfUgXecrbUp1lrjpXyCtdZYa5cd5vpCa+0T1trW1toQa21za+3t1tqDXtp+ba3tZ61tYK2NsdZeaa1N8+Hj+JXDOMjXO84iIiIiIh5paWnk5+eTlJRUpi45ORmAHTt2ABAZGckHH3xA27ZtGTFiBG3atCEuLo5Ro0aRkZFR4tqFCxeSkpLCnXfeSadOnYiOjmbIkCFs37690jGmpqbStGlTHA7fpWdPPvkkY8eO5aWXXqJ3795ERUXRv39/vv32W5/d40ji98RZfMdpNOIsIiIiIlJcTEwMQUFBbN26tUzdli1bAEhISPCUdevWjYULF5Kens7mzZu59957mT59OrfcckuJa1u3bs3cuXNJTU1lx44dTJkyhfnz5zN06NBKxxgfH8+OHTvKLPT78ccfM2TIEFavXl3pPhs0aMCkSZPYsGEDGRkZvP/++2zbto0BAwaU+RJADk+JcwBxOLQdlYiIiIhIcWFhYZx++ul88cUXrFmzxlO+d+9e5syZQ2JiIj179gRgxowZJCUlsWrVKsA1Ij127Fg6d+7MihUrPNcuXryYpKQkzyJbCQkJXHXVVfTv379Eu4oaMGAAOTk5vPTSSyXKZ82axWuvvVbpqdrp6ekkJydzxx13AK6R9DPPPJPrr7+ePXv2sGHDhkrHeKSr76tqSzHajkpEREREpKzJkyfTp08f+vXr51lVe/r06aSmpvLOO+8QEhICQEpKCmPGjOHSSy/lmmuuITY2lm+//Za1a9cyduxYT3+9e/cmOzubG2+8ke+//56kpCR+/vlnFi1axMCBAysd39ixY3nzzTe58cYbWbVqFV27duXLL79k3rx5DBs2jPbt21eqv+joaNq2bcvTTz9NVlYWXbp0YdeuXfznP/+hadOmdOrUqdIxHumUOAcQbUclIiIiIlJWu3btWLFiBXfffTdTpkwhNzeXXr16MXv2bE488cQS7b788kseeeQRpk6dSlpaGkcddRT33HMP9957r6ddkyZN+Oqrr5g4cSKzZs0iNTWV+Ph4RowYwcSJEysdX2RkJF988QX33Xcfb775Ji+//DJt27Zl6tSp3HDDDVV65nfeeYfHH3+ct99+mxdffJEGDRpw0kkn8fDDDxMWFlalPo9kxlZxg+4jTY8ePWzRlI266taF/+b0dr05s0Mff4ciIiIiIrVg3bp1dOzY0d9hiNRpFf13YoxZba3t4a1OI84BxOlwasRZRERERKQO2LdvHwcPltnop4zo6GhCQ0NrISKpDiXOAcS1HVWBv8MQERERETnijR07ljlz5hy23XvvvVel96KldilxDiDajkpEREREpG4YP348l1122WHbde/evRaikepS4hxAtB2ViIiIiEjd0KVLF7p06eLvMMRHtI9zANF2VCIiIiIiIr6nxDmAaDsqERERERER31PiHECcxkGBRpxFRERERER8SolzAHFqxFlERERERMTnlDgHENd2VEqcRUREREREfEmJcwDRdlQiIiIiIiK+p8Q5gGhxMBEREREREd9T4hxAtDiYiIiIiIiI7ylxDiAOTdUWERERERHxOSXOAcTp0IiziIiIiBy5UlJSaNmypb/DEGDChAkYY9i0aZO/Q/EJJc4BRCPOIiIiIiIivqfEOYA4HU7yCwv8HYaIiIiIiEhACfJ3AOI72o5KRERERIozkyZV6bpuCQmsHjrUa133V17hu507q9SvvfXWKl0n4m8acQ4g2o5KRERERMS7jRs3MnjwYKKjo4mIiKB///4sX768TLv169czaNAgEhMTiYiIoGvXrsyePbtMu7/++ovhw4eTnJxMeHg47du358knn8RaW6X4ir8TvGbNGi6++GISEhJYuHChp82wYcMwxrB06VK6dOlCaGgobdu2ZcaMGV772rhxI5MmTaJly5ZERkZy8skns379+irFt337dq6++mri4+OJjIykW7duzJkzp0p9Aezbt49x48Zx9NFHEx4eztFHH83dd99NTk5OlfusSRpxDiBOYygorNo/VBERERGRQLVhwwb69OlDbm4uo0ePJioqimnTppGSksL8+fMZMGAAAJmZmZx66qlYaxk9ejQxMTEsXbqU4cOH07hxYy644AJPnwMGDGDjxo2MGzeOpKQkVq9eze23305wcDDjxo2rcqyffvopo0aNolOnTgwcOJDmzZuXaXP++eczdOhQrr76al5//XVuuOEGsrKyytz3rrvuYuXKlYwdO5Y//viDqVOnctlll/HDDz9UKqbt27fTq1cvdu3axciRI2ndujXvvvsuw4YNY+vWrfzrX/+q9HNeeeWVLF68mLFjx9KuXTt+++03nnzySbKyspg8eXKl+6tpSpwDiBYHExEREREpa+zYsaSnp7Ny5UqOP/54AK677jo6d+7Mddddx6ZNmwgODmb9+vVs376dyZMnM3bsWABuuOEGxowZU2IkNC0tjbVr1zJu3DgefPBBT3liYiIhISHVinXMmDE89dRTjBw5stw2t956Kw888AAAI0aMoG3btjz44IPcdNNNBAX9neJ9/vnnrF27lri4OAC2bt3K/PnzSUtLIyYmpsIx3XfffWzZsoUPP/yQM888E3D9Xtq0acOUKVOqlDh/+umnDBw4kEnFXido3rw5e/furXRftUGJcwBxOhwUKHEWEREREbeaeKe4vHef66rs7GyWLFlCv379PEkzQHR0NMOGDePRRx9l5cqV9O3bl/bt2xMfH8+0adNISEigZ8+etGrVimeeeaZEnzExMXTq1Ik33niDLl260KdPH9q1a8eECROqHe+gQYMOmTQDXH311Z7PYWFhnH/++UydOpVffvmFzp07e+ruuusuT9IM0KVLF+bPn8/+/fsrlTgvXryYNm3aeJJmAKfTycaNGyvcR2knnXQSS5cuZcqUKZx88sl06tSJ0aNHV7m/mqZ3nAOIwzgo1D7OIiIiIiIeaWlp5Ofnk5SUVKYuOTkZgB07dgAQGRnJBx98QNu2bRkxYgRt2rQhLi6OUaNGkZGRUeLahQsXkpKSwp133kmnTp2Ijo5myJAhbN++vVrx3nbbbYdt07Rp0xLnRclx6dHavn37ljh3OKqW/qWmpnLUUUdV6dryzJ49m8GDB/PEE09w3HHHERUVxfnnn88vv/zi0/v4ihLnAOJ0ODXiLCIiIiJSTExMDEFBQWzdurVM3ZYtWwBISEjwlHXr1o2FCxeSnp7O5s2buffee5k+fTq33HJLiWtbt27N3LlzSU1NZceOHUyZMoX58+cztJoj8pGRkYdtU/pZihL/Jk2alCiPjY2tVixF4uPj+euvv8qUz507lyFDhrB58+Yq9Tljxgy2bNnCnj17eO2111i5ciUDBw70Rcg+p8Q5gDiNgwLt4ywiIiIi4hEWFsbpp5/OF198wZo1azzle/fuZc6cOSQmJtKzZ08AZsyYQVJSEqtWrQJcI9Jjx46lc+fOrFixwnPt4sWLSUpKYv78+YAr8b7qqqvo379/iXY1ZebMmZ7PWVlZLFiwgLi4ONq1a1cj9xswYAAbNmxg6dKlnjJrLVOnTuXNN9+kcePGlervxx9/JCkpiSlTpgCuafMXXnghF198MRs2bGDPnj2+DN8n9I5zANF2VCIiIiIiZU2ePJk+ffrQr18/z6ra06dPJzU1lXfeecezoFdKSgpjxozh0ksv5ZprriE2NpZvv/2WtWvXehYLA+jduzfZ2dnceOONfP/99yQlJfHzzz+zaNGiWhkxnT59Ounp6XTo0IHXXnuN7du38+yzz+J0Omvkfg8++CAffvgh5513HiNHjqRly5YsWrSI5cuXM2HCBKKioirVX8eOHYmIiODuu+9m69attGnThj///JMXX3yRY489tszIeV2gxDmAuEaclTiLiIiIiBTXrl07VqxYwd13382UKVPIzc2lV69ezJ49mxNPPLFEuy+//JJHHnmEqVOnkpaWxlFHHcU999zDvffe62nXpEkTvvrqKyZOnMisWbNITU0lPj6eESNGMHHixBp/nkWLFnHTTTcxe/ZsWrRowcyZM7n22mtr7H6JiYme39+cOXPIzs6mY8eOzJs3j0svvbTS/QUFBbFs2TImTpzIm2++yfbt22ncuDGDBg3ikUceqYEnqD5T1Q26jzQ9evSwRVM26qqP1n/Nx79+w6TzbvZ3KCIiIiJSC9atW0fHjh39HYbUkmHDhjFnzhyUw1VORf+dGGNWW2t7eKvTiHMA0XZUIiIiIiJ1w759+zh48OBh20VHRxMaGloLEZW0Z88ecnNzD9suNja2xN7QRyr9BgKItqMSEREREakbxo4dy5w5cw7b7r333vPLStIXXnghn3/++WHb/fjjjxxzzDG1EFHdpsQ5gGjEWURERESkbhg/fjyXXXbZYdt17969wn3Onj2b2bNnVyOqvz3xxBMVWr26ZcuWPrlffafEOYBocTARERERkbqhS5cudOnSxd9hlOuEE07wdwj1ivZxDiDajkpERERERMT3lDgHEI04i4iIiIiI+J4S5wDiMBpxFhERERER8TUlzgFEi4OJiIiIiIj4nhLnAKLtqERERERERHxPiXMAcWjEWURERERExOeUOAeQIOOkoLDA32GIiIiIiIgEFL8nzsaYtsaYamV7xpjexpjPjTFZxph9xpi3jTHNS7WZbYyx5fwMq9ZD1BHajkpERERERMT3gvxxU2OMA2gDdAMmUo0E3hjTDvgEOADcDyQCY4G2xpjjrbWlk/JbgPRSZf+t6v3rEm1HJSIiIiIi4nt+SZyBeOAXH/U1DogATrPWfg1gjIkCrgHOAhaVaj/NWnvQR/euU7QdlYiIiIiIiO/5a6r2HuBs98+P1eyrF5BRlDS7rXYfO5Rquz9Qk2bQdlQiIiIicmRLSUmhZcuW/g4j4FT09xrIv3+/JM7W2lxr7YfW2g9xJdHV8SJwT6myZu5j6b7TjDEPGGN2GmNyjDFfG2NOqOb96wxtRyUiIiIiIuJ7/pqq7TPW2ueKn7unaQ8DsoCPSjVvCVwCPAbEALcCS4wxra21aTUebA3TVG0RERERERHfq/eJc3HGmDDgdSAJGGWt3Vas+kfgbeAaa+0+d/sDwKPACFyLlJXu73rgeoDmzZuXrq5zghwO8jXiLCIiIiIi4lN+347KV4wxjYAPgAHA3aVHoq21T1lrLypKmt2Wuo+dvfVprZ1hre1hre0RFxdXI3H7krajEhERERHxbuPGjQwePJjo6GgiIiLo378/y5cvL9Nu/fr1DBo0iMTERCIiIujatSuzZ88u0+6vv/5i+PDhJCcnEx4eTvv27XnyySex1lYpvgkTJmCMYdOmTaxZs4aLL76YhIQEFi5c6GkzbNgwjDEsXbqULl26EBoaStu2bZkxY4bXvjZu3MikSZNo2bIlkZGRnHzyyaxfv75K8QEsXLiQDh06EBoaynHHHcf7779f5Xa7du3ihhtuICEhgbCwMHr16sWiRSXXdZ4xYwbGGP797397yv744w/Cw8M57bTTqvy7roqAGHE2xsTj2pKqI3CDtXbGYS4pst99bFIjgdUybUclIiIiIsVNWLaMBz7/vEx5YmQk28aPZ8KyZa52KSk0e+opth84UKbt/f36eepXXX89q7dt47x587ze769bbmH1tm3M+O473hs8mHNff533f/21TLuB7drx3uDBrN62je7Nmnnpybc2bNhAnz59yM3NZfTo0URFRTFt2jRSUlKYP38+AwYMACAzM5NTTz0Vay2jR48mJiaGpUuXMnz4cBo3bswFF1zg6XPAgAFs3LiRcePGkZSUxOrVq7n99tsJDg5m3LhxVY71008/ZdSoUXTq1ImBAwd6nfl6/vnnM3ToUK6++mpef/11brjhBrKyssrc96677mLlypWMHTuWP/74g6lTp3LZZZfxww8/VDqu1NRUBg8ezMiRI4mLi2PmzJmcd955vP/++57fX0Xb7d69mz59+rB161ZGjBhB8+bNefnllzn33HOZNm0a119/PQDXX389b731FhMmTOCyyy4jMTGRsWPHEhwczIsvvogxptLPUWXWWr/+AMtcYVT5+gjgeyAHOK+cNl2Bn4DbSpWfAljgxcPdp3v37rau25O5z5405Rp/hyEiIiIiteTnn3/2dwh1Sr9+/WyLFi3KlA8YMMA6nU773Xffecr27NljExMTbbNmzWxubq611tpVq1ZZwE6ePLnE9aNHj7bz5s3znO/evdsCdty4cSXa3X///Xbq1KlViv3++++3gG3QoEG5fVx11VUWsPfdd5+n7ODBgzYpKclGR0fbvLy8En01bdrUpqametpecMEFFrC7d++uVGz9+vWzgH3++ec9Zdu2bbOhoaG2eJ5U0XYjR460gF24cKGnLCcnx3bt2tVGRESUiHnz5s02KirKXn755XbBggUWsNOnT69U/BX9dwKssuXkg4EwVfsR4DjgWmvtwnLabAbaA5eZkl9LXOY+Lq658GqPU1O1RURERERKyM7OZsmSJfTr14/jjz/eUx4dHc2wYcPYtm0bK1euBKB9+/bEx8czbdo05s2bx++//461lmeeeYZLL73Uc21MTAydOnXijTfe4KWXXmLdunUUFBQwYcIERo4cWa14Bw0adNg+rr76as/nsLAwzj//fNLT0/nll19KtLvrrrso/spply5dANi/fz+V5XQ6S9w3MTGRU045he+++46srKxKtVuwYAGtWrXi3HPP9bQLCQlh1KhRZGVl8fHHH3vKk5OTefrpp5k7dy7XXHMNZ5xxhmdEujbV2cTZGHOcMWaYMabpIdo0w7Ww1ybA6W5f/OcsAGvtXuBZoBvwgTHmJmPMS7gW/voEeLeGH6dWODRVW0RERESkhLS0NPLz80lKSipTl5ycDMCOHTsAiIyM5IMPPqBt27aMGDGCNm3aEBcXx6hRo8jIyChx7cKFC0lJSeHOO++kU6dOREdHM2TIELZv316teG+77bbDtmnatGSKVJQc7927t0R53759S5w7HFVP/+Li4ggJCSkTh7WW9PT0SrXbuXNnhf48ilx55ZXExsaye/dubr755io/Q3XU5XecLwDuxzWdekc5bdoBIbi2mZrlpf5z4EP35/HAduAG4Gl3n08C91sbGMO02o5KRERERKSkmJgYgoKC2Lp1a5m6LVu2AJCQkOAp69atm2dBri1btvDOO+8wfvx4cnJyeOGFFzztWrduzdy5cwFXIvjhhx8yatQoduzYwdKlS6mqyMjIw7bZunUrrVu39pwXJZpNmpRcuik2NrbKcZS2e/du8vPzCQr6O4XctWsXAFFRUZVql5CQUOE/D3Atdpaenk6LFi249dZbSUlJISwszEdPVjF+H3G21qZYa8u81W2tnWCtNdbaZYe4dpm7TXk/KcXaFlprn7DWtrbWhlhrm1trb7fWHqyZJ6t9QQ4n+YUF/g5DRERERKTOCAsL4/TTT+eLL75gzZo1nvK9e/cyZ84cEhMT6dmzJ+BaxTkpKYlVq1YBrhHQsWPH0rlzZ1asWOG5dvHixSQlJTF//nzAlehdddVV9O/fv0S7mjJz5kzP56ysLBYsWEBcXBzt2rWrsXvm5+fz6quves53797NsmXLOOaYY2jYsGGl2p133nn88ccfLF789xuzeXl5PPfcc4SHh3PGGWd4yr/66isef/xxbr75Zt544w3WrVvHXXfdVWPPWZ66POIsleR0ODVVW0RERESklMmTJ9OnTx/69evnWVV7+vTppKam8s4773imFqekpDBmzBguvfRSrrnmGmJjY/n2229Zu3YtY8eO9fTXu3dvsrOzufHGG/n+++9JSkri559/ZtGiRQwcOLDGn2f69Omkp6fToUMHXnvtNbZv386zzz6L0+mssXuGhoYycuRI1q9fT2xsLC+++CKZmZncf//9lW73wAMP8OGHH3LhhRcycuRIkpOTeeWVV1izZg3PPfecZ+r5/v37GTp0KK1ateLBBx8kPDycm2++maeffpqBAwfSv3//Gnve0pQ4BxCnw4ExUFBYiLMa7y+IiIiIiASSdu3asWLFCu6++26mTJlCbm4uvXr1Yvbs2Zx44okl2n355Zc88sgjTJ06lbS0NI466ijuuece7r33Xk+7Jk2a8NVXXzFx4kRmzZpFamoq8fHxjBgxgokTJ9b48yxatIibbrqJ2bNn06JFC2bOnMm1115bo/ds2rQpDz/8MPfddx9btmyhffv2vPXWWwwaNKjS7eLi4vjmm2/417/+xauvvkpGRgZdu3ZlwYIFnHfeeZ52Y8eOZdOmTXz++eeEh4cD8NBDD7Fw4UKuuuoqfvzxR6Kjo2v0uYsYW4ubRtdnPXr0sEVTNuqyEyYP5YtRLxIWHHL4xiIiIiJSr61bt46OHTv6OwypJcOGDWPOnDkoh6uciv47Mcasttb28FanEecAE+QIIq8wnzCUOIuIiIiI+Mu+ffs4ePDwyylFR0cTGhpaCxGVtGfPHnJzcw/bLjY2tsRCX0cq/QYCTLAziPyCfH+HISIiIiJyRBs7dixz5sw5bLv33nuvVt6LLu3CCy/k888/P2y7H3/8kWOOOaYWIqrblDgHmCCHk7xCJc4iIiIiIv40fvx4LrvsssO26969e4X7nD17NrNnz65GVH974okn2LNnz2HbtWzZ0if3q++UOAeYYEcQeRpxFhERERHxqy5dutClSxd/h1GuE044wd8h1CtaejnABDuDtJeziIiIiIiIDylxDjBBDqdGnEVERESOIFphWaR8vvr3ocQ5wGjEWUREROTI4XQ6ycvL83cYInVWXl4eTqez2v0ocQ4wQU694ywiIiJypGjYsCEZGRn+DkOkzsrIyKBhw4bV7keJc4AJ1lRtERERkSNGkyZNSE9PZ/fu3eTm5mratgiu6dm5ubns3r2b9PR0mjRpUu0+tap2gAnSVG0RERGRI0ZoaCjNmzdnz549bNq0iYIC/X+gCLheY2jYsCHNmzcnNDS02v0pcQ4wwZqqLSIiInJECQ0NJTExkcTERH+HIhKwNFU7wAQ5nOQVKnEWERERERHxFSXOASbYGUS+puiIiIiIiIj4jBLnAKMRZxEREREREd9S4hxgXCPOSpxFRERERER8RYlzgAl2BGnEWURERERExIeUOAeYII04i4iIiIiI+JQS5wAT7NB2VCIiIiIiIr6kxDnABDud5BdqVW0RERERERFfUeIcYII04iwiIiIiIuJTSpwDTLBTibOIiIiIiIgvKXEOMMHOIE3VFhERERER8SElzgEm2BFEbkGev8MQEREREREJGEqcA0xoUDA5+UqcRUREREREfEWJc4AJCw7lYF6Ov8MQEREREREJGEqcA0x4cCjZSpxFRERERER8RolzgAnXiLOIiIiIiIhPKXEOMGHBoRzMV+IsIiIiIiLiK0qcA4xGnEVERERERHxLiXOACQ/SO84iIiIiIiK+pMQ5wGjEWURERERExLeUOAcYJc4iIiIiIiK+pcQ5wIQFh5KtxcFERERERER8RolzgAkLCiU7Lxdrrb9DERERERERCQhKnAOM0+Eg2BlETn6ev0MREREREREJCEqcA5DrPedsf4chIiIiIiISEJQ4ByAtECYiIiIiIuI7SpwDxEMfv8DKzf8DihYIy/VzRCIiIiIiIoFBiXOASMvcx77sTEBTtUVERERERHxJiXOACA0KJq/AtSBYWFCIpmqLiIiIiIj4iN8TZ2NMW2NMQTX76GuM+dwYc8AYs9sYM8cYE1PVdvVRSLGVtMODw8hW4iwiIiIiIuITQf64qTHGAbQBugETqUYCb4w5FvgU2AHcC8QD44FjjDG9rbV5lWlXX4UEhZBbUJQ4a3EwERERERERX/FL4owraf3FR309CTiB0621vwEYY/YCjwGDgZcr2a5eCnEGeRLnsOAQLQ4mIiIiIiLiI/6aqr0HONv982NVOzHGJAKnAZ8UJcNus9zHKyrTrj47ofkxtItrAbimamtxMBEREREREd/wy4iztTYX+BDAGHNnNbrqCxhgRan+U40xm4E+lWxXb53SpofnsxYHExERERER8R2/Lw5WTS3dx21e6nYADY0xTSrRrt56/bsPef27DwGICA4jK1cjziIiIiIiIr7gr3ecfSXCffS2sFdusTYVbbeneIUx5nrgeoDmzZtXK9CadmaHvhj358jQCFIPpPk1HhERERERkUBR30ecM93HRl7qGhVrU9F2JVhrZ1hre1hre8TFxVUr0JqWm59LZu5BAKLCGpCRneXniERERERERAJDfU+c/3Af473UxQH7rLXplWhXb32wfjlvrlkKQMPQBuzPKfM9gIiIiIiIiFRBfU+clwMWOLl4oTGmFdDUXV+ZdvWW0+Ekv7AAcI04789W4iwiIiIiIuIL9TpxttamAh8BJxhjOhSrusx9nFuZdvWZ0+GgoLAQgIahEezP0VRtERERERERX6izi4MZY44DjgM+tNbuOETTO4BTgI+MMZNxTcceD6wG5lWhXb0UZJwUFBtxztCIs4iIiIiIiE/U5RHnC4BZQIdDNbLWrsWVEG8CJuJaBXsecKa1Nr+y7eorp8NBgXUlznrHWURERERExHf8PuJsrU0pp3wCMKGCfXwN9PNVu/rI9Y6za6p2ZGg4WXnZFBQW4nTU5e9GRERERERE6j6/J87iG/3bnsCJRx8HgMM4aBASwYGcLBqFR/o3MBERERERkXpOw5EBIq8gv8R7zVFhDcjIOeDHiERERERERAKDEucAsWH3Zpb88rXnvGFoBPuztbK2iIiIiIhIdWmqdoDo1aILvVp08Zw3Dm9I+sH9foxIREREREQkMGjEOUCs27mROSvf85zHNGhEWuZe/wUkIiIiIiISIJQ4B4hdB9JZuflnz3lcg2h2KXEWERERERGpNiXOAcLpcJDv3scZILZBY3ZnpvsxIhERERERkcCgxDlAOB1OCt37OAPERzZh5/49foxIREREREQkMFQrcTbGjDHG3Oj+3NQYs8QYs9cY87YxpqFvQpSKcBoHBYV/jzi3bNKMb//8kUJbeIirRERERERE5HCqnDgbY24F/g3Eu4ueBPoAy4DzgIeqG5xUnNPhJL9Yktw+vgX5hYXakkpERERERKSaqjPifC0ww1r7oDHGgStZHmmtvQB4Frig+uFJRUWFNaBtbHKJsviG0aQfzPBTRCIiIiIiIoGhOolzc2C1+3M7IBL40H3+P/4eiZZa0CY2mXtOv7ZEWZPwKPZk7fNTRCIiIiIiIoGhOonzbuAo9+c+wC5r7S73eRKQWp3ApHK27dvFf754vURZdEQj9mTt91NEIiIiIiIigSGoGtd+AtxsjIkCLgWWAhhjzgeuBz6ufnhSUZGh4Rx/VPsSZTENGpGmvZxFRERERESqpTojzv8C/gRuBvYC97rLnwWCgYnVikwqJTI0gp7NO5coi2sQzS7t5SwiIiIiIlItVU6crbXbrLXHArHW2mOstRvdVdcD7ay1v/kkQqmQLXt3csmcO0qUxUdGk3pAibOIiIiIiEh1VGeqNgDW2j2lzj+obp9SeU7jIL/YPs5QlDjvKecKERERERERqYjqTNWWOsTpcFJgSybOcZFNSN2vxFlERERERKQ6qpU4G2POM8Zc7P7c0BgzwxjzgzHmKWNMtUezpeKcDgeFhbZEWXyk3nEWERERERGprionzsaYq4B3gX+4ix4Fhrs/jwPuqVZkUilO4ywzVbtxeEOy83LJzsv1U1QiIiIiIiL1X3VGnG8B3rLWjnOfXwrcYq09DpgNXFGtyKRSgrxM1TbGcFSjOH7d9aefohIREREREan/qpM4t+HvvZtbAU2Ad9x13wBHVS80qYyIkDDuOe3aMuWntOnJVxt/qP2AREREREREAkR1Eud9uJJlgJ7AXmvtX+7zWCCtOoFJ5QQ5nJzY6rgy5e3imvNH2l9lLxAREREREZEKqU7i/DUwxhgzGrgP+BLAGNMduBpYUf3wpKLyCws4a8ZNZcqPahzPX/tS/RCRiIiIiIhIYKjOytd3A58B/wFS+fud5vlADHBZtSKTSgl2BvHlTS+WKW8WFce2jN1+iEhERERERCQwVHnE2Vr7C673nHsAra21P7irHgSOtdaurn54UhmvrFpEoS0sUdYkIoqsvIMczMvxU1QiIiIiIiL1W7X2cbbWZllrv7PWZhYrm2mt/a36oUllTf5iLgWFJRNnh3EQ1yCaXQe0n7OIiIiIiEhVVCtxBjDGOI0xvY0xFxhj+hhjqjP9W6rBYRxlEmeAuEglziIiIiIiIlVVrSTXGHMWMBNoVqx4mzHmBmvt4mpFJpXmMI4yU7UB4hpEk5qpxFlERERERKQqqjzibIzpAyxw9/EYMAp43H3+rrteapHT4aDAW+IcGc1ujTiLiIiIiIhUSXVGnB8CtgA9rLV7iwqNMU8AK931p1UrOqkUh3FQqKnaIiIiIiIiPlWdd5z7ANOKJ80A7vMZQO9q9C1VEBkS7nXEObZBY3ZpqraIiIiIiEiVVGfEOQdoWE5dQyC3Gn1LFXx4w7Ney2MaNCItc18tRyMiIiIiIhIYqjPi/BEwxhjTtXihMaYLMNpdL7XorTVLycw9WKY8tkFjdmfurf2AREREREREAkB1RpzvwvUO8ypjzOfAViAJOBnIAO6ufnhSGWlZ+ygoLChTHhPRiD1ZGnEWERERERGpiionztbaTcaY3sAzwOnuvvKBj4Fx1tqNvglRKuqGPoO8ljcOj2J/Thb5hQUEOZy1HJWIiIiIiEj9Vp2p2lhrf7fWnoPrneYkoKG19hxr7W8+iU4q5YY3H+avfallyp0OB1FhkaRnZfghKhERERERkfqtWolzEWttjrV2m7U2xxf9SdVsz9hNbn6e17qYiEakabq2iIiIiIhIpfkkcZa6wWkcXrejAq2sLSIiIiIiUlVKnAOI0+GksJzEuYkWCBMREREREamSCi8OZoz5o5J9W2tt60peI9XgcDgoKCxnxFmJs4iIiIiISJVUZlXtzYCtqUCk+pym/MS5SUQUuw7srd2AREREREREAkCFE2drbYovb2yM6QhMAk7ElZAvw7WN1aZK9rMM6FdO9Rxr7TB3uwnA/eW0e8BaO6Ey962Lzul0Ik0iorzWdWraime+nFfLEYmIiIiIiNR/Vd7HuTqMMUcBX7pPHwOcwG3AF8aYrtbavZXo7jFgdqmyJOAhIMtL+0eA0ttl/VCJ+9VZQ7oPKLeuXVwLtuzdUYvRiIiIiIiIBAa/JM64Rn5jgFOstcsAjDG/AfOAMcCDFe3IWvth6TJjzM3uj0u8XPKqtXZdZQOuD+5aNIUrug3gmMSyr5ZHhzckryCfjOxMosIa+CE6ERERERGR+qnWV9U2xgQDlwK/FCXNbm8D+4ArfHCbC3GNNn/kpW6nD/qvk8acNJjWsUle64wxJEbFsXN/Wi1HJSIiIiIiUr/5YzuqrkAUsKJ4obU2H1gDtDPGNKlq58aYBKAv8JG19mCp6jzgCmPMZmNMrjHmR2PM2VW9V12TkZ1Jbn5eufVNwhuSfnB/LUYkIiIiIiJS//kjcW7pPm7zUrejVJuq+Ceu53rXS10wcAfwHK7p4knAQmNM+2rcr854+vNX+WnH7+XWR0dEkZ6VUYsRiYiIiIiI1H/+eMc5wn30NjSaW6pNVVzo7vu9UuUbgEXASGvtZgBjzEbgdVwLk11buiNjzPXA9QDNmzevRki1I8jhpNB6344KoHF4Q9IPKnEWERERERGpDH+MOGe6j4281DUq1aZSjDHRQAqwrPTK3NbaV621A4uSZrel7mNnb/1Za2dYa3tYa3vExcVVJaRa5TAOCgoLyq2PDteIs4iIiIiISGX5I3H+w32M91JXlJ1uqmLf5+Kaju1tmrY3RS/8Vvmd6rrE6XBQcIgR58jQCLLysmsxIhERERERkfrPH4nzT7hWzz6peKExJhw4HlhnrU2vYt+DAAvML9V3jDHmJ2PM5FLtm7mPW6t4vzrFYRwUFpafOIcGBZNziMXDREREREREpKxaT5zdq2e/DjQzxpxerOqfQCgwtyr9GmMaAGcA31hrt5eq3oNrhPsSY0xYsfLL3MfFVblnXeM0hx5xDgkKJic/t9x6ERERERERKcsfi4MBPARcDMwzxkwCnMDtwBZgCoAxpg1wIvBfa+2GCvQ5AAjDyzRta601xjwMTAaWGWPmAkcDo4H/Ac9X94HqgttOvZIGIeHl1oc6Q8gt0IiziIiIiIhIZfhjqjbW2m24kuJvgLtwrWr9KXCytXafu9mJwCz3sSIudB+9vt9srf0PrpWzGwKTgMuBl4B+1tqsKjxGnZOZc5CDeTnl1muqtoiIiIiISOX5a8QZa+164JxD1M8GZleiv8HA4MO0eRF4saJ91jef/LaCjglHE3v0cV7rQ5zB5CpxFhERERERqRS/Jc7ie9f2/uch60ODQ8gp0DvOIiIiIiIilaHEOQCs3bWLY+fMAeDW4zvy+KkDcBhTpl2oRpxFREREREQqzS/vOIvvWGu5cMECz/mk79dx2v/9H/tyyr7rHBoUQo4WBxMREREREakUJc71XGpWFnuys0uUfbZlC6e/+Sb7c0tOyw5xanEwERERERGRylLiXM8lNGjAhL59y5Sv3LGDe/773xJlodrHWUREREREpNKUOAeA0ccf7zV5fv6HH0jNzPSchwTpHWcREREREZHKUuIcAIwx3N+3Lw8fH0+T0L/Xe8srLOTdDRs856FOraotIiIiIiJSWUqcA0jb2Gac0yKxRNmbv/zi+awRZxERERERkcpT4hxALj72NP514uklyj7bsoW97sXDXO8452Gt9Ud4IiIiIiIi9ZIS5wDy8S/fsHnXr3Rs0sRTVmgtq3buBMBhHAQ5neQXFvgrRBERERERkXpHiXMAadmkGS2bNKN3s2Ylylfv2OH5HOrUytoiIiIiIiKVocQ5gLRs0ozm0U3pkZBQorxoxBkgJChEibOIiIiIiEglBB2+idQXL69aREb2Afq2O7VE+fepqZ7P4cGhHMzLqe3QRERERERE6i2NOAcQp3FQaAvpHBNTovzPjAwKCgsBaBASRmZutj/CExERERERqZeUOAcQh8OVOEeGhBAbHo7DGFpERXHiUUeRkeuanh0RHEZm7kE/RyoiIiIiIlJ/aKp2AHEaQ0Gha6upH668kviICIKdzhJtIkLCOZinEWcREREREZGKUuIcQBzuqdoARzVs6LWNpmqLiIiIiIhUjqZqBxCnw0GBO3EuT7imaouIiIiIiFSKRpwDSO8WXemY0OqQbRqEhJOlqdoiIiIiIiIVpsQ5gCRGxRIf2eSQbRqEhJGlqdoiIiIiIiIVpqnaAWTJL9/wxGdzDtkmIkRTtUVERERERCpDI84B5JxOJ3JOpxMByMjJYfyyZaRmZbEzKwuAb664gojgMHbu3+PPMEVEREREROoVJc4BZGPaX/y5dwcprbsT4nTywo8/euqcxlBoresdZ03VFhERERERqTBN1Q4gv+7azPv/+wKAsKAgokJCPHUF1pKenU1kaAT7czL9FaKIiIiIiEi9o8Q5gJTejio+IqJE/c7MTJpERLEnK6O2QxMREREREam3lDgHEIdxUFj4d+Kc0KBBifqdWVnENGhEWta+2g5NRERERESk3lLiHEAcpUacE0qNOKdmZdEkohF7lDiLiIiIiIhUmBLnABJUasS5zFTtrCwigsOw1mqBMBERERERkQpS4hxAgp1BBDn/Xii99IjzzsxMjDHu95w16iwiIiIiIlIR2o4qgPRq0YVeLbp4zr294wy4p2tnkNQ4oVbjExERERERqY804hxAdu5P44P1yz3n3t5xBohp0FgLhImIiIiIiFSQEucAkleQz76D+z3n3rajAjRVW0REREREpBKUOAeQpMYJXHb8mZ7z0lO1PSPOEY1Iy1TiLCIiIiIiUhFKnAPIr7v+5JYFT3vOS484p3recY5iT1ZGrcYmIiIiIiJSXylxDiB5Bflsy9jlOY8KCSHE6fScZ+Xnk5mbS5MGjUnL2uuHCEVEREREROofJc4BxFFqH2djjGfU2WkMTRs0YG9OjmvEWVO1RUREREREKkTbUQWQIIeTAltYouzzSy+lUWgo0WFhOIwB4GBOI03VFhERERERqSAlzgHEYRwUFBaUKGvVuHGZdk0iGmk7KhERERERkQrSVO0A4nQ4yow4e9M4PJLM3IPkFeTXQlQiIiIiIiL1mxLnANIoLJILjkk5bDuHcdA4vCHpBzVdW0RERERE5HCUOAeQ6Igorul1QYXaxkc2YUdGWs0GJCIiIiIiEgCUOAeQXQfSGT5vQoXatolNYkPalpoNSEREREREJABocbAA0ji8IQ+dNaJE2eaMDF768UdSs7JIzcoiOSqKf59yCnGR0aRpSyoREREREZHD8tuIszGmozFmkTFmnzFmrzFmvjGmZRX6mWCMseX8TKiJe9ZlqQfSS5zvPniQB77+mufXrOHt337jkz//BCCmQWN2Z+71Q4QiIiIiIiL1i19GnI0xRwFfuk8fA5zAbcAXxpiu1tq9Vej2EeC3UmU/1PA965T9OVncvOApPh8101MWHxFRok1qVhYAsRGN+X7r+lqNT0REREREpD7y11Tt+4EY4BRr7TIAY8xvwDxgDPBgFfp81Vq7rpbvWac4jYOCwpLbUcWFh5c433XwIIXWEhcZzc79e2ozPBERERERkXqp1qdqG2OCgUuBX4oSWLe3gX3AFVXseqcf7lmnOBwOCkvt4xwaFESj0FDPeaG17Dl4kKTG8fy1L7W2QxQREREREal3/PGOc1cgClhRvNBamw+sAdoZY5pUss884ApjzGZjTK4x5kdjzNk1fM86x9uIM3ifrh3boDEHcrPIys2urfBERERERETqJX8kzi3dx21e6naUalNRwcAdwHO4pmQnAQuNMe1r8J51jsOUHXEG74mzwzg4Kiqev/btqq3wRERERERE6iV/JM5FWVyel7rcUm0qYgOwCOhrrX3MWvsoMALX+9u3VeeexpjrjTGrjDGrdu2q+wmm0+EgvzC/THl5C4QlRzflz3Rv3yWIiIiIiIhIEX8kzpnuYyMvdY1KtTksa+2r1tqB1trNxYqXuo+dq3NPa+0Ma20Pa22PuLi4iobkN0EOJ+8Of6pMeenEeac7cW4f14JfUv+sldhERERERETqK38kzn+4j/Fe6oqy003VvMd+97HoveXauKffGWPYnbm3zHvO8aVX1nYnzvENm5CWtbe2whMREREREamX/JE4/4RrJeuTihcaY8KB44F11tr0inRkjIkxxvxkjJlcqqqZ+7jV1/es62atWEhuQckZ6XGlp2ofPAhATEQjdmfuq7XYRERERERE6qNaT5zdK1m/DjQzxpxerOqfQCgwtxLd7cE1inyJMSasWPll7uPiGrhnnTZ10J2EB4eWKCs9VbtoxDm2QWPSMvfWVmgiIiIiIiL1kj9GnAEeAtKAecaYu4wx9wDTgC3AFABjTBtjzDBjTJvyOrHWWuBhIBFYZowZY4z5t7v//wHPV+aegWDixy+QkX2gRFl5i4MlNIxh675U8gsLai0+ERERERGR+sYvibO1dhtwIvANcBeu1a8/BU621hbNHT4RmOU+Hqqv/wDXAg2BScDlwEtAP2ttViXvWe99+cf3ZJbamzmu9DvO7qnaCQ2bEBYUws79abUWn4iIiIiISH0T5K8bW2vXA+ccon42MLuCfb0IvFjdewaCYGcQeQUlt6Qqb6o2QHxkE1IPpHNUI2/rpomIiIiIiIjfEmepGcGOsolzTHg4Sy66iPiICOIjIogtNgLdKDySfaWmdouIiIiIiMjflDgHmCCnk7zCkomz0+Hg9JYtvbZvGNqA/dkV3jZbRERERETkiOOvxcGkhgQ7gsgvqPhiXw1DIzTiLCIiIiIicghKnAPMMYltCA0KqXj7pq1ZvXVdDUYkIiIiIiJSvylxDjB3n3Y1beOSK9z+hOadWbvttxqMSEREREREpH5T4hxgXvx2PutTN1W4fULDGJwOB7/v3lpzQYmIiIiIiNRjWhwswBzbrB1NIqLKlH+3cyffbNtGek4O6dnZnN6iBWcefTTGGNrHtWTz3h20jk3yQ8QiIiIiIiJ1mxLnANO5aWuCHM4y5e/9/jsTli/3nIcFBXHm0UcDkBgVy/aM3bUWo4iIiIiISH2iqdoB5v4Pp7H0txVlyqPDwkqcp2dnez4nRsWyQ4mziIiIiIiIV0qcA0yQ00l+QX6Z8ujQ0BLnxRPnplGxbN+vxFlERERERMQbJc4BJtgRRF6hl8T5UCPODWM0VVtERERERKQcSpwDTLAzyOuIc+PSI845OZ7PesdZRERERESkfEqcA0ywM4g8b1O1S4047y2WOMc2iGZf9gGv14mIiIiIiBzplDgHmCCHk7yCgjLlh5qq7XQ4iIloxK4D6TUen4iIiIiISH2j7agCzDmdTiLEGVym3NviYNZajDEAJDRsws4De2jWKK5W4hQREREREakvNOIcYFo1OYpmUWWT3/DgYEKdf+/vnFdYSFZenue8acNYtu3bVSsxioiIiIiI1CdKnAPM699/xOyV73mtO9QCYa1ijuKPtK01GpuIiIiIiEh9pKnaAeaqngPLrYsOC2NnVpbnfG92NkkNGwLQKiaJxeu+qvH4RERERERE6huNOAeYX1L/5KuNP3itK7NAWLER5zaxyXzz51oO5GSRoRW2RUREREREPDTiHGDW7dzI6q0/84+jjytT522BsCKtYo7ipFbH848pVwMwou/F/LR9A92SOnB1r/NrNGYREREREZG6TCPOASY0OITs/FyvdYfakgrgsXPGeD6/vOp9vtz4Pe/+9JnvgxQREREREalHNOIcYMKDQsjO8544X9+1K2cffTTRYWFEh4XRLjq6RL3T4SAiOIysvGwycw8CsDl9B+lZGURHRNV47CIiIiIiInWRRpwDTGhQCDnljDifnJzMFZ06MaBVK/o0a0ZMeHiZNjMvuZdb+l0BwHW9/wnA+z9/qXeeRURERETkiKUR5wATFhxKdn7O4RuW45jE1hyT2Jqrep4LQIvoRO754DliI6M5u0NfX4UpIiIiIiJSb2jEOcCEB4cS5PDd9yFnd/wH0eFR7Mna57M+RURERERE6hMlzgGmQ3xLXrrsfp/1F+Rwck2v89mY9pfP+hQREREREalPlDgHmMzcg8z7/iOf9tm7RRe++fNHn/YpIiIiIiJSX+gd5wC0L/uA1/Ls/HxW7dhBek4O6dnZOIxhSKdOh+2vTWwyGdmZ7MjYTdOoWF+HKyIiIiIiUqcpcQ4wDULCuaHPIK91qVlZnDRvnue8WWRkhRJnYwx5BfmcOeMmHh84hrO0SJiIiIiIiBxBNFU7wBTaQs6ZOQZrbZm6xqGhJc7Ts7Mr3O9Fx/YH4I73n2Hvwf3VC1JERERERKQeUeIcYBzGwc4De8gtyCtT1zAkBKcxnvOD+fnk5Fdsf+bxKUN566onAOg39TqyciuedIuIiIiIiNRnSpwDUFhQCDn5uWXKjTE0DgsrUbY3p+J7PreNa85JRx8PwOd/fFe9IEVEREREROoJJc4BKCwolIN5ZRNngOhqTNcGmHLh7UQEh3Hn+89UOT4REREREZH6RIlzAIoICeNgnveEOLrUiHN6JUacwTVq/eZVj9MkohH5hQVVjlFERERERKS+UOIcgCJDwjmQc9BrXXUWCCuS1DiB8OBQuj99BRnlbH0lIiIiIiISKJQ4B6B/dj2VxuENvdaVHnGuzDvOxT0+cAwAq7asq9L1IiIiIiIi9YX2cQ5AFx97Wrl1ZaZqV2HEGaBLYhuGdj+Hzek7qnS9iIiIiIhIfaER5wD09LJX+Wj9117rqrs4WHHt41vw/V/rq3y9iIiIiIhIfaDEOQANO+E8Tmp1vNc6X404A3RL6sB/N/7A77u3VrkPERERERGRuk6JcwDKyj1I6oE9XuvKLA5WxXecARKjYskvLODC2bdyxav/qnI/IiIiIiIidZkS5wC05JdvePfHz7zW+WpxMACHcfDK5Q9xfe8L+WnH70z/+m2+/OP7KvcnIiIiIiJSF2lxsAAUGRrOXxm7vNb5cqo2QNdmbenarC05+bk899WbAKy5dV61+hQREREREalLlDgHoAYhEWTmZHmta9u4MXeccAKNQ0OJDgvj6EaNfHLPm068lDM79OHqeQ/4pD8REREREZG6wm9TtY0xHY0xi4wx+4wxe40x840xLavYVwd3X/uNMZnGmCXGmC6l2kwwxthyfib44pnqiqiwBuzNPuC1rkWjRjx28snc2asXNxx7LGe0bOmTe4YEBdMpoRXZ+bkczKv69G8REREREZG6xi8jzsaYo4Av3aePAU7gNuALY0xXa+3eSvQVAywDGgBPuvu6HfjYGNPOWptR6pJHgN9Klf1QyUeo05pERJGeVfqxa54xhpZNmvH576s5q0PfWr+/iIiIiIhITfDXVO37gRjgFGvtMgBjzG/APGAM8GAl+roGSAAut9a+7u4rD3gIGApMLdX+VWvtumpFX8dFh0eRfrD2E2eA449qzx3vP8PJrboRERJ2+AtERERERETquFqfqm2MCQYuBX4pSprd3gb2AVdUsste7uNHxcpWu48dvLTfWcn+652EhjG8dsXDfrn3hV1OBeDlVe/75f4iIiIiIiK+5o93nLsCUcCK4oXW2nxgDdDOGNOkEv29A9wLpBcra+Y+lt7MOA+4whiz2RiTa4z50RhzdqWirwecDge/7tpMTn5urd+7a7O2PHXezTy//C2/3F9ERERERMTX/JE4t3Qft3mp21GqzWFZa1+z1k601loAY0wQMAKwwIJSzYOBO4DncE0XTwIWGmPae+vbGHO9MWaVMWbVrl3et3eqqz765WsO5Bz0WncgN5c/9+3jh9RUPtu8udpbUpV2WrtedEpoxaotP3MwL4dFP/+Xrzet9ek9REREREREaos/3nGOcB/zvNTllmpTKcYYB/A80B14wlr7XbHqDcAiYKS1drO7/UbgdVwLk11buj9r7QxgBkCPHj1sVWLylwfPurHcuvPefZfPtmzxnC+56CJO99Hq2kVaNmnGyLcfK1G2+pbXCHI4fXofERERERGRmuaPEedM99HbBsKNSrWpMGNMKDAXVwI8HbizeL219lVr7cCipNltqfvYubL3q+tmfvMu3231vgZadFjJRbt8PeIMcGPfQZ7Pl3c7i+OOas+yDavKtPtq4xq27k31+f1FRERERER8xR8jzn+4j/Fe6uLcx02V6dAYE4FrNDkFeMhae18FL93vPlbmnep6YevenTSJiKJbUscydWUS5xzf77vcIjqRH8a/zsG8HCJCwvhw/XLmfvchp7Xr5Wnz6urFPPnZy4QGBfPZyBk0CAn3eRwiIiIiIiLV5Y8R559wrZ59UvFCY0w4cDywzlqb7u1Cb9zTs98G+uGahl0maTbGxBhjfjLGTC5VVbSI2NaKh18/JEbF8dc+7yO5jUNDS5zXxIgzuPZ1LtqSqn/bE9i8dwcbdm/BWsusFQt58rOX6d2iCzn5eVw974EaiUFERERERKS6aj1xdq+e/TrQzBhzerGqfwJF060rYwxwFnC/tfb5ctrswTXCfYkxpvhw62Xu4+JK3rPOaxObzPqdm7zWlR5x3lsDI86lBTuD6JnciUGzb2PNtl+Z/MVcrul1PtMv/hfTL/4X61M3sTl9x+E7Ar7843se/vhFDuRk1XDUIiIiIiIi/hlxBngISAPmGWPuMsbcA0wDtgBTAIwxbYwxw4wxbcrrxD1K/S9gL7DN3b74z0UA7hW3HwYSgWXGmDHGmH+74/gfrgXFAsrxR7Xn280/cTCvbFIcXUsjzqVd3/tCAK56/X6u6/1Pxpw0GIDeLboAcOXcewH4ffdWnvnydf77x/fsztxboo+d+/dw0zuP839rPuYfU67WllciIiIiIlLj/PGOM9babcaYE4GngLtwbR31KTDOWrvP3exEYBYwHNeK2N4kALHuzy94qf8TeMt9z/8YYw4AtwCTcO37/BJwl7U24IYuYxo0okV0Ilv27qBdXIsSdbWxOJg3R8ccxXHN2vHDtl85s33fEnVzhzzM5a/+i2MnXeYpe9G9m9iaW+cx/8dlZOZm8cRnL2MwfHTDs9z23mTeWfspg7ud5dM49x08QF5hPrENGvu0XxERERERqZ/8kjgDWGvXA+ccon42MPswfWwCTCXu+SLwYkXb13dXdD+bsKDQMuW1sThYeeZc/iA5+bmEBoWUKO/ctDXP/PM2xs2fxPnHpHBm+z58t3U9M755h2/+/JH7P5oGQHLjBOZf/TRBDic39xvCsNfvp3+7E4iP9N36ble9fh8b92zjlcsf4qhG8ezcn8Ydi54hxBnM28Oe9Nl9RERERESkfvBb4iw1b1DX/uQV5HPZK3dx+ylXelbYrq3FwcpTOmku0q91d74f/7rnvE/Lrnzxx3fc8ObDAEy76G46JbTy7AV9XLN2JDRswth3JzE+ZQhZudmc3LpbtWJbvWUdO/fvIcjhZKh76jiA0zgosIXs3J9GQsOYat1DRERERETqF+N6/VcOp0ePHnbVqrL7ENdle7IyuPyVu+nY9GhWb1nHouueoWFoBL/s2UOHl17ytGvduDEbrr3Wj5GWL68gn7/2pdIsKo6QoOAy9Vm52fR5Zpjn/JXLH6Jrs7ZVvt+9HzxPYlQsp7U7gan//T/Wbt9AsNPJR9dP5fnlb7F80xpeufwhjKnwRAcREREREakHjDGrrbU9vNYpca6Y+pg4A2TmHqRBSDhXz3uA1VvXsWLcy+zLySPh+b/XQ2sSFkbaTTf5Mcrq2bRnGxnZmXz0y3JeXf0Bd5w6jMur8N5zQWEh/Z+/gdeGPMxRjcpuM26t5awZNzH94n/RskkzLz2IiIiIiEh9dajE2V+rakstaRASDsDDA0bRMDSCT39b6XU7qsJSX6AU5uez6eOP2fb119jCwlqLtypaNmlG12ZtGX7C+XRJbMPjn84mryC/0v2s3f4bcZHRXpNmcO9LHRzG+S/dUt2QRURERESkHlHifIRIjIplfMpQdmXuJdjppEHw39OeC61lf65rW6fczEy+fughng4O5q0zzmBu37682quXv8KulNgGjXn1iokcd1R7bnzzYX7a/jvXvPEgd7z/DBt2b+FQsyustUz67GVOadPzkPd4/NwxgGtbLBEREREROTIocT6C/LPLKVzZw7WQubcFwg5s28YzkZF8dd99Jep2rlpF7v79tRZndY36xyWs2rqOK177F6u2/MzO/Wnc+OYj3PvB8+zP8b7z2PrUTfy2ezP/7HLKIftuF9eCi449jUU/f1mivC6+8rA5fQdZubW78JuIiIiISCDSqtpHkHU7N/Lzzj8Y1LU/57Zuzb6cHKLDwogOCyN/xw6mdejg9boOgwfjDC27rVVddULzznwzdg4PLpnJbadcSZOIKDJzDzLy7cd44/uPuLb3P8tc8+6Pn3Fhl/4kRsV66bFU/8mdeeSTl7jo2NO47v8eYn3qJsA1HX5gp5N8/TiVYq3l3R8/45FPXiKvIJ8O8S2ZN/RRLWYmIiIiIlINWhysgurr4mDFbdm7g83pO/jH0ceVKLfW8tYZZ/Dn0qXlXnvdpk1EJSeTk5FBWOPGNRtoDflq4w88tOQFxqcM4aRW3Xjo45nERDRi9dZ1/Jm+g3lDHyGpccJh+0nL3MclL9/B7sy9ZepWjHu53O22alqhLeT+D6ezasv/iAyN4Ma+FzHj63c4vV0vr18WiIiIiIjI3w61OJhGnI8gyY2bktSoZGJoreXtAQMOmTRfvnw5eZmZTE9O5sD27Rx91llcuGhRvRvF7J7Uie37d3Pre5MZ1vNc3i823Xpo93MqlDQDxDRoxOLrnuGM6aO49/Rr6d/2BApsIdfMe4BPflvBgI4n1tQjlOu17z7gqc9eocAWsuSGqZ69pltEJzLq7ceUOIuIiIiIVIMS5yPI+tRNTPhoOvOGPuopWzNtGps+/NBr+2s3bKBx69YA/LV8OfHHH8+BbdvY+MEHfDJ6NKc9+2ytxO0rYcEhfHfLXOZ+9wGTlr3CfWdcR4/kThQUFhLboHGl+goNCuHzUTM950HGybATzuOO9//Dlr07ee6rN1lw9dM0j26Kw9TsUgLP/vcNZn7zLud17se4kwcTU+xZWsckkZmbzbZ9u2jWKK5G4xARERERCVRKnI8g4cGhHCi2OFZBXh7fPvZYmXYDXnmFTkOGeM6ttbz+j3+UaPPD1KnEdu7McSNG1FzANcDpcHDZ8WfStGEMp7Xr5dNR85TW3Xni3HH83/dLADj/pVsY9Y9LuL7PhV7bb0z7i/zCQpIbJxAWXLXp3fO+/4i31nzCrMsm0C2p7DvqxhgGdPwHi9f9V6POIiIiIiJVpMT5CNIgJJzMYqssO4ODue7333nj/VfY+8yLZH72FSc//niJpBlcydfxN93E96VGmJeOHElhQQHHjxpVr6ZtBzuDOL19b5/3a4whpXV3Ulp3J68gn7fXfsKjn8zixW8X8Pi5Y9iRsZtLjjudHRlp3PTO4/yettVzbVxkNEtumOoZnbbWYrElRqv/2pdaYo9pay2zV7zHtIvvpkN8y3LjOrN9Hx77dJYSZxERERGRKtLiYBUUCIuDZeVmc8pz1/PtuJc9Zb/u2sIFc+7i9La9efrs63GGlD/yOamc5PjMl16iy/DhPo83EHy7+ScmfDiNbRm7S5S3jW3O3acNJzI0gsc+mc3qresA6JTQiukX/4uL5tzGzv17OKVNDzrEtyT1QDpvr/0EgGZRcQzqeiovrVhIYlQsb131xCG/uCi0hQyafTtDuw/gwq6n1tzDioiIiIjUY4daHEyJcwUFQuJsraXb05ez8uZX+WjjJq78YDHp2TlYIMaRweab7iYiJKzc6/dv3cr05GSvdddv3kzDpKR6NfJcmwoKC3n/5y+Y8NF0hnY/h1tSSo7qW2uZ+tX/8dK3CyiwhXRu2po2scmkZe7lvxt/AGBE34uJCmvA15vW8sUf3wEw85J7OaF558Pe/7ddm7lozu3cdsqVXHzsaX5b+VtEREREpK5S4uwDgZA4A/zjmeF8cP2zrNy5m9PefNNTnhhayIun/YOzO/7jEFdDxpYtzGje3GtdYq9eXPTRR4Q2auTTmI8kOfm5vPDNfK7ofjaNwxsCsOtAOo3DGxLs/PvNiryCfKy1hAQFV7jvdTs3ctkrdxEeHMp/R79EkMPp8/hFREREROqrQyXONbvcr9Q5d5w6jGBnEDFhJUeWg4Ib8MYPSzjcFylRyclc9sUXXuuOPvtsQqKifBbrkSg0KIRRJ17iSZrB9f5z8aQZXO9pVyZpBuiYcDTPXngHrWOS+HD9cp/EKyIiIiJyJFDifIQ5t/PJhDiDWbv8oxLlmfmWC445hbyCfPIK8snOyyU3P4/8woIyfSSddBJnzZ5doqzN+efTbexYjDFYa9n29ddkpqZ6Pm/86KPDJuVS805qdTyjTryE6V+/7fXPVkREREREylLifIQZN38S/92wmjcXvgTFEtk92dkM7Hwyc1a9z4CZY+j1nyvpOXkoU76c57WfY666ilYDB3rONyxYwL6NGz3naevX83xCAk85HMzt25e3zzqLpxwO/vjgg5p7OB8K5CS/T4uuxDeIZsqX8wL6OUVEREREfEXvOFdQoLzjXFBYSOrq1Vy44CHWhPTgYESEp277iBE0bdCA/MICHvtkFl9vWsvWfaksGzmD6AjvU7Dzc3LYuWoVDZo1o1HLlhTm57N25kw+GTWq3BjOfvll/vrvf0k6+WQ6Dh7MwbQ0Nn/2GUknnkhks2YVeo6DaWns/t//SOjenZAGDSr3SziEDQsWsHTUKHL376f9xRdz+vTpOJyB9y7w1r2pnP/SzQzq2p+bTryUz39fzZ6sfTQKa0jfo7sS1yBaC72JiIiIyBFFi4P5QKAkzr/u+pOZr0zm2+3r+YVjSE1I8NStueoqusbFec5z8/O478Pn6dvyWGIjG9O35bGH7f+rCRP4+oEHKhRLo6OPZsjKlbxxyins/vFHAE5+4gk6X3kl3/3nP/z+3nu0v+QSjr3xRiKKxQWw4MIL+e3dd2nSsSOXff55mfqq+POTT3jztNMAaHrCCZz40EMU5ufTasAAT5v8nByCQkOrfa+6ICP7ACc9e63nPKFhE3bu30PD0AYkN07AYrmyxzkc3eQowPWOtIiIiIhIoFLi7AOBkjh/+ttKbl7wFKdN/4z5p13IH61be+qWXnwx/Vu0KHPN6i3r2JO1j1vfm4zTOCiwheVug2QLC3mqgiO01/7xB42PPppf3nqL9y6++JBtr//zT6KKreaduXMnzzdt6jk/7+23+d+cOfT+179IPOGECt2/RNzWMqtTJ/asXw9AUFgYY/bvxxHkWpQrPzubDfPns/LJJ4np1IkBr7xS6XvURTv3p3Eg5yAtmiQS5HBSUFjIwbxsTn3+Bno178KG3VvYlrELgIlnj+S0dr0IDw6MLw5ERERERIrTqtrikdK6O9dM/ZrEDalEHjhQoi41K8vrNd2TO3JUo3gArup5Lg1Cwnl++Zte2xqHgxE7d5YoCworuzd03LHHsuqpp1j/f//HqkmT+MdDD+E8xEjujBYt2LRkCQC5mZkYp5Met97qqV84aBC/L1zIa7168fY552ALC8vty5vFQ4Z4kmZwJcp/ffUVAL+//z7TmjXj/cGD2fndd/z86qv88qb3569vEhrG0Do2ybM1ldPhIDI0ghXjXmHKhbez+LpnWD5mFlf2OId/f/4aA18Yy46M3X6OWkRERESkdgUdvokEkgNbt5K7aTMADTIzS9SVlzgDdGraijW3uhYKG3vyYHLz8/hswyq+/fMn+rbsSuemrYlp4Nq/uUF8PLeWmsmwedky/u+UUzznu9asoUmHDrS78ELev/RStn/77WFjXzd3LhhDckoK/3fKKRzYtg2MKbHIGcDGxYv5v9NO4/x33mHzJ58Q360bjY8uf5rxhgULXH2XUjQbI7ZLF/JK/a7eu+QSknftIiI29rBx12fGGBqEhDM+ZSi39BvC05+/ypkzbuLqE85nSPezaRLRSO9Ci4iIiEjAU+J8hPnhuec8n0uPOO86eLDC/YQEBdO7RRe6JrZhyS/fMPrdJ7jpxEtp2jCGczufXKZ985QUUp56imXjx3vKTp82DUdQEKP37uXZmBhswaG3R/rfnDlEtWxJy9NP55y5c3mxbdty22757DOejY72nF/9yy80adfOc75pyRLeOvPMcq8f/vPPxHTsCECjFi3o99RTfDp6dIk2r//jHzQ/9VTWTJsGQAt3XP5Kpgvz8z1Ty8GV+O/55RcK8/KI69Kl2v0bYxj1j0tJjIrj8U9n89KKBRzVKJ4O8S35M307AD2TO3N1r/OIj2xS7fuJiIiIiNQVese5ggLlHee5//gH25YvB2B5nz68e+GFnrrrunZlxhlnVKnfuxY9y+J1/wXgh/GvV2kU8q+vvuL7Z58lKzWVdhddhDMkhI+u/XvxqgaJiQz78UfCY2IA2PTxx7xVgXjPfvllOg8d6jn/+qGH+Oq++8ptf9yoUZz27LNlymd36cLun3467P1GbN9Og2LvX9ckW1jIyqee4ovbbyexVy/OffNNopKTASgsKCBz+3YWDBpE7DHHcOYLL/hsdDi/sIDc/DxmfvMuy35fzfmd+7Ercw9LfnHNHDgmsTXbM3Zzx6nD6JRwNKFBIV77KbSFbNu3i92Ze/n0t5XENGjEUY3iaRGdSNu45l6vERERERGpCVoczAcCIXE+mJbGc/Hxnvd/13bpwitXXumpP691axb8859V7t9ay3FPDea1Kx7mmMTWh7+gIn0WFnJg+3bSf/2VxN69CQ4PL1GfsWULGxYsICI+np9eeolNH31Uov6sWbNofe65hDVpgjGGwoIClo4cydoZM7zeLzwmhhE7d3rdgip7794So9jlGfjGG3S45JJy63MyMlg5aRLfPPQQAF2uvZZOQ4aQdPLJlUpsd6xezYILLmD/1q0AJHTvztBif0cL8vJ4f/Bgfnv7bQAGvPoqna64osL9V9VXG9ewfNMa/tqXylcb15BbkIfTOIhp0JizOvSh0BaybucmDLBq6zocxtAwtAFntu/Drsx0tmfs5q99u7ix7yAKreXM9r1JaBhT43GLiIiIyJFNibMPBELi/PNrr7F4yBDP+Z6TTuLR887znHdLSGB1sZHZqti5fw95BXn8umszp7btWa2+qmLbN98wt0+fEmXR7dpx9bp1GIdrLby9GzfyQqtWZa5teeaZDFq82NPOm9/fe493i/3OSrt02TJPArz755/55qGHSFu3joxNm4jp3Jmg8HA2f/LJIa9P7tevTHnW7t389s47/DRrFi369yfu2GN5r1Ry3mrgQC587z3A9SXGgn/+kw0LFpRoc82vvxJ9iCnuvmatZfPeHazbuZHG4Q1Z+uu3bM/YzX83/sDtp15F54RWNAiJoG1cconrFv70OTO+eYec/Dx2HUjnhOaduarnQHokd2LXgXTiI5sQEhRcoRjyCwv49s8fiW0QTfv4sqvGe1NQWEjqgT0kNHRNOXcYraMoIiIiEuiUOPtAICTO719+Oetff91zfvTddzMmOZnmDRuS3LAhnWNjub9v32rf56uNP1BoLZm5B9mcvgOnw0nvFsfQualvRqEPpzA/n5WTJpH+22/0uOUWYjt3LlP/dHDJpKvvAw/Q45ZbCImMPGz/Pzz/PEtHjixTnnTSSfS+5x5auqeP5x44wKyOHT0jwocSER/PGTNnsn/rVo4ZNozgiAj2b93KNxMnsmb69MNeX+ScuXPpOHgwAN8+/jhf3nmn13bD160jpkOHCvfrT5/8toK31nxC+sEM1u3cCEBcZDSnt+vFX/t2cWqbnpzT6UQ27dnOF398x4yv3yalTQ/+TN9O29hkvvzjB9IPZgBwWtsT6N/uBE5u1Y3I0AjPPdIy9xEWHMKsFQt5ZdUisvNzMRgslrjIaG4/5So6JRxNUuMErzECbNm7g4U/ffH/7Z1nlBzFubCfnpxnc46Kq4hyRBIZIRDZ5GSSbbBxNg7fdcDX19fm2sY2BttgAyaYnDMoIFBAOaddbc5xZifH+n5MYMPsagUSSHI95/TZneqa6uru6pp+33oDPb5eVIqKqs4GLpi4CJvBjEGjIxgJ0+7uptPj4IKJi9hQt4uDHXVcOuVMtjTupaozNk4MGh3Ti8aTYbKjU2uoyCnnYEcdk/JGo1V/4sPe4e6h0dnO6MwibAbzsbj0SSLRKOo+CiVfKIBaUQ2rvEh8p7qrCaNWz7sH1lPvaOOWORdRYD/yvOtNzna8QT8mnYECWzaVnfWMySqWSg2JRCKRSCRHDSk4HwVOdMFZRKP8JSsLf09PsuzqtWspPAqC8lA8seVNHlz7HJPzx7CntZrHr7mH8szCY3a8keJububVL30JU04OFz73XL+AWiPFWVvLrocfxlpczNTbbosVKsogU+uDL77Iq5ddNqI2Z9x1F6ffd1+yjRV33cW2P/95xH2a++Mfs+hXv+pXNlBZ0pfL332XsrPPHnH7xwONjnayLWlUdjTw4q4V5FozWVG5iQPttRg0OrIt6dw0ezm+UID3Dm7gvIqF5FgzWDxqBt6gnzf3fcTbB9azvekAX1vwJTLNdlp7O3n445cByLFk8LNzbmNqwTgseiP+UJAPq7fx5NY3OdhRz6Ly6Vwx7Wx2tx6ivqcVlUpFk7OdUDjE9uaDnDF2NqcUjONAex2v7vmAhWWn0Orq4lBXI2UZBdj0ZnyhAJWd9Vj1ZlyBWLT20vR8Th8zC51ai0at5oG1qdOdnTF2Nm2ubjQqFTuaKwFQUBidVYRapeb8CQvxBP1MzR9DcXoedd0tLBo1nVAkzNraHWxvOsCe1mpmFU+gydnOutqddHocWHRGxmQVc9GU0zh3/Hxe3r2afa01tLu7cfrd7G+vZWbRBEZlFrK2ZgfNvR1Y9SaunHYO43PKOLV8GiadgS6Pk39ufJlur4s3932EWWfEE4wFHTx9zCxCkTB722q4aPISqjobaHJ28H/Lv8WozMJ+z05tdzMrKjeRbrSyrekAHxzaglqlxqI30u7qIRQJERFRpuaPZfmkRSytWIDNcHil10gIRcL84+NXKLBlkW6yMTF3FGlGK8297fhCAcozCvGF/P2OFwjHxkkoGqHL4+CssXPIs322IIGNjjZsBsunUooIIWKuKSJKbXcLo47CvBsMh2hzd5FpSsOk659isNfvodfvJtea2U+583mTiJmgKEoyhaJEIpFIJEeCFJyPAie64Oyorubh0Z+s+Ortdu7s6krpy3sseGX3an6z8jF+fu5XOGf8PFwBL+tqdnCgo5ZlE05lTFbx4Rs5wRDRKE1r1/L04sFRxofiyg8+oLhP/UOvvcaKu+6it7Z2yO+otFpuPnAgZcotV2Mj/5o2DV9XV8rv2kpLufCFFwh5PBQuWPCplAhfNFERxR8KDnqZH4539q9nTfVW3AEvroCXCyctYVbxhGFf/NfV7uDeVY9T3dWIUavnupnLaHN1U55RgEln4LyKhdiNqYW3qIgmV0aFEPhCAUw6A6FIGIfPRbalv+98m6ubLHMaapWKLo8Dq97MyqpNNDjamF08kV0tVUwrHM+U/DE4fW7W1e3kJ2/cz7kVC1AUqOyoxxv00+hsx6o3JwW9xaOmI+J9aHd3s2zCQtKNNvJt2Wxt3Mf/rPgnAAW2bM4YOxu7wYJVb+LUUdPY2rifTo+T4rRc8qyZOP1uHtrwIq2uLrQqDXaDhaquBgwaPXm2LC6fegbhaJQLJy0mGAmTbrQSioR5bPPrHGyvw6gz8Mru1QBMzhuNRW+ipruJYnsumxv3sXjUDFp6O8mzZbJ84mLOHj8XlaKiw91Dj6+XInsur+9dwxt7P2J780H+cNF3OW3MTPa11WDWGWlzd5NlSiPDZOPl3avZ3LAXm8HCzuaDNDrbmVU8kUOdjZSk5zIxdzTZlnS0ag0v71pFl9fJvNIp1Pe0sretGrvBgtPfPwtButHKsgmnUtvdzP72WvQaHTmWdPzhIPvba7HqzZw/MTa3nT1uLkatfsggeQDd3l7cAS8d7h5WVW3m8S1vYNDo0Gm05FgysOhNtPR28P3TbmBsdgk9PhcH2+uYUTSB5t6YEuTNfR+RZrTiCwawGcx4gj5aXV1cNvUMyjMKiUSjXDHt7OSz0uPtRa1SJZUA3d5erHoTW5v28/imN5hRVEG+LYutjft5dsd7AJSk5+Hye7AazBTYstnedIBwNEI4GmFW0QS+segqrHozpRn5yfzwh6PR0Y476CUcidAbcFPT1YSiqLhmxtIRfV8IwYGOOv667nlWVW1Gp9ayaNR0lk9azKJR00fUDyEE25sPolapsBsslKbnD7K0OBKC4VBSeaGg0NzbQSQaZXRW0aC64WiEj2q20+11sqB0KrnWTKq7mihOy0Wn0RKKhL9QhUQCIQQCQTgSwRvyE4lGyDDF0lAeD2kJg+HQiF14JBKJZCik4HwUONEF56pXX+Xliy5Kfi5avJirPvjgc+3D9qYDZJnT+MMHT/LzpV/lp28/yIbaXXhDfgpsWfzmgm8yMW8Uu1uqmJw/ZsiXnTZXN+6AN+ULyPHGUJG/F//mN8z5wQ+AmOn4+3feSdPatZxy++3MuOsuAPw9PVS9+iod27ez5b77hjzG8ueeY/zllw+531FdzcNjxgzKdz2Q0cuXc8mrr/YrO/Dcc+x/5hmyJk1i9ve+h85qHbYNyfFFVETp9Xvo8fZSkp5/WCHAG/Sj02hHLPAk2FC3i9f2rOH6WedTkVN2RN8NRcKsrdnOpoY9TCsYT5Ozg4Xl0wb5vQ/H7pZD3PLMPfjDAQCyzelkmGwc6KjDoNFxxtg5CBFlSsFYDBodPd5eXAEvZ46dw7am/TT3dtLp7qHT6+TscXO5fOpZGLQxIXdd7Q4i0SiziycRFVFCkTCugJdHNr3KvrYazp+wkHmlU/utmociYfa0HuK5He+ztmY7oUiYcDTK+RMXcu2MZRi1ejY37EVRFDbW76bH62Jr037SjVbUKjUTcstZPGoG4WiY+WVTaentpNvbS3VXE3/68N9kmux0eZ39rsGFkxZz7cxlNDraKLTn4A54UavUGDQ6drZUUtnZwMH2OtxBH2UZ+Wxp2IfT70atqMizZWE3WNjbVg1AnjWTiyefRmVnAw2O1piFx5f/j5L0PDbW76Y4LZc2Vze7Ww4xIbeMqQXj8IcCXPPET2h3d6NVa/CFApw/8VTGZZXS7XUyNruE08bMYl3NDh7Z9CoWnYmS9Fyqu5rY1nQgqZwoSctjQm457x7YQKbZjk6tZXxOKQvKTkEIgVatoa6nhUZHGztaKtEoarq8ToKRENfNXMaX51yITq3l6W3v8GH1Npx+N6Ozijglfxzt7m6aezu5fOoZzC2dwnsHN/BRzXZ2tVRR39Pa73omLELOHjeXm2ZfiFqlojyjEIHAqNUPGoONjjYane1sbzrIrpZKdrVU9VO2JFw/Lpi4iJnFE8gw2QlHwnxwaCvr63ZSYMsi35bN6qot+MMBjFo9QgjGZpewq6WKOxZ+idvnXZocY4l3N0VReGv/Ohp6Wun29mI3mJleVIFRq+exTa9TaM+mx+siEA5iNZgpyyhgReVGqrsaGZ9dxqjMQjJMdnKs6eRaMhiTVYxGpUGv0WLRm+j1u3ltzxre2r+OQ52NREWUSPw56MtV08/lzoVXoFNr0Wu0IxKkX9y1kr989CzhaIRcSwanjprGTbOXH9Z6JByN8PjmN9jSsI/NjXspScuj29dLh7uHr8y/jGmF45icF3uHSKVQDUXCdHkcWPQmdrceIhKNMrd0MtFoFF8oMKQCtC9CCDo9Dsw64xEpbUdCp8fBR9Xb+aB6C0X2HE4dNR29WsuTW99izaGtmHRGLph4KrfNu2TIayWEYGP9bmYUTThqShchBK6Al92tVexsrmJpxXzSjFaqu5oYnVk0ousmkZwISMH5KHCiC849VVVUvvginbt20bl7N8VnnMHpv/vd594PIQS7WquYnDc6uQJX293Mr97/B10eJw9e/mP+8fHLfHnOhWxu2EtvwMOV085hddVmPqrZzjnj5xGJRqnuauKSKaexpXE/i0fNQFFiPpXHg1Z+KIIeD9379mEpLMSSn3/Y+iIaZct997H9gQcIulzM/fGPmXHXXYhIhIDTmUzLNVKGMxtPGz2ay997b9Cq9SOTJtG1d2/y88xvfYuFv/zliHzBJZLPk1AkTKurkzSjDWvcf72mqwmL3jRoRf/zJGE23eBo5U9rnmZb8wE63D3MK52CVqVhXtkULDoTi0fPIMNkG3G7/lAQlaKgUqlGrOgQQvDqng/wh4JMzh9NjiUDo1bPpoa9dHocXDb1DHyhmNDW13f806x4OnwuXt/7IfvbaglFw2xr2k+bq5sMk53pheO5cNJi9rXVUJSWy9KKBYPa94eC1Dta0Kg0bGncx4fV29jedIB0k43zKhaQbrQRFhEm5Y4i35ZNjiV9kLAmhOC5He/zxJY3STdaWVg+jebeDl7atYoMk51ur5NLp5zB+RNP5ZSCcck+9Hh7EYBKUfjDB0/y8u7VZJvT6fD0oKBw27xL6PI6KE7LS/6mfVS9naK0XPKtmUzOH8PZ4+ZSllEAQHNvB8VpudT3tLKmehsfHNrC5oa9jMooJNOcxt1n3JhMv9fm6sIT9CeVEy/vXsWYrGIe2fgqGpUajUpNq6uLVlcXFp2RipwyNjfuY2LuKDJMNsoyCtjedABv0E+BPZuyjAJ8IT9qRU2Tsx27MbaaPr1wPO3uHtYc2kq7u5vegJdOTw+9fg9qRUVERMmzZtLqilkr3TDrfM4dv4CitByCkRDugI9evztmtRP08dd1z1Pb3Zy89jaDmTnFk7h06pm4A14OdtSxvflg0rIizWjhYEc9Pzv3dhaWnUJNdzPPbH+XlZWbmJQ3mlEZhdiNFqYVjGNB+Sm4Az4aHK28sHMlmxv20u7u5opTzuaG2edT292CSWugpruJ9bU72dlSlVTgCCGYkj+G0VnFrIu7majjYzsiYhlGEvc2QZ41M26poGFr036mF45n+cTFrK/byc7mSjY37MWo1ROMhBBCkGPJoNHZztzSyZRnFJBhstPgiCliphdWkGa0svrQFgwaLRadiT1t1dT1tCBEbPX+wslLGJddytzSyXxUs53fr36CorQcitPyCEfDrK/dRbY5jbmlk1k0agbNzg7+tv55fKEgC8tPQaNSU5SWS5fHSU13E7taqghHwwTCIewGC2adkSn5YxifU0aDo5Vev4fFo2awZPQM0uNzzp7WQ1TklOMJ+qjtaSYcCdPm7iHLZKfR2c4ru1ezt62aQDiUvE5WvQlXwAuATq3l9DGzUKtUjMsuZWbRBBRFwawzkm1Jx6wzoKCwvm4nNd3NXDRpCWqVmo31uxmbVUK+LQtfKIDT7ybDZCMSjWLU6jnU1UieNTMZkyQQDtLt7cUb9JNtSafX78GsM2A3WthYv4cp+WMw64zJeTcQDqJSVLgDXv780dMU2nNodLTT5XUyOrOIqIhi0hnwBv28sHMlFTllfG3B5biCXmq6mqjraUGjUuMN+fGFApw+ehb5tiyK03J5Y99assx2vEE/hfYc9BotapWaXS1VrKvdwcGOekxaA9fMWMpFk0/DpDOwt7WasdklVHU20O7u5vQxs2h2dvBx/W6K0/LIsaSTaU5LXq9wNDJobuz1u3l7/3oqcsooyyjAqjfR5Gxnb1sNz2x7B28owJT8Mdw0e/mniicyUrq9vTQ626jILkOjVqNSVEnlO3DECvjjCSk4HwVOdMH5RCMSjfLQhpd4cuub9PpjfqBLKxZwzYylnFIwDoitMv3q/X/Q5upCp9biDvoosGfzjyt/mnxx/jTsaT2EEDAqs5BevxtFUfHrFf8k25zO1xZ+6Yhebo83Ak4nb15/PYfi0bcBsiZPZvlzz2FIT8ec+0nwq5DXy/3p6USCwZRtnfa731G2dCkZ48fjamzEUVmJWq8nY8IETFmfzb9TIjlZCUXCyWBv/0mEoxF8ocBnmpuPFq29nTT3djAlf+yIFAIOn4s0oxWnz029o5Vntr0be2HMKcfh62VH80H+94K7kr9NIyHxYj9SguEQG+p2EUVQmpZHrjWTvW3V9Hh7mV82tV+ww89CYiXZ6Xezt7WacdklI/LXD0cj9Hh7STNa8QR91PW0cKC9ll+veIQxWcWMyixifulUJuSWxwTuoI+JuaOSmQsS7G45xK6WSvQaHV1eJ6uqNrOn9RAAJq2B+WVTuXr6uZxSMG5Ys2xv0I/T78bpd7OvrYZ9bTVU5JZxoL2OK6edw6jMmOtCq6szLky1oVJU5NkyeXv/Op7Y8hZnj5tLq6uLup4WNtbt5rwJCxmdWUSONYN8axYziydQ3dWUdFewGcx8VL0dlUpFKBLGZjDT7OygN+Ahx5LOgfY6ZpdMojQ9n2xLOt6gj6rOBg521NPpcVDb3czkvNFcOe0cLpw8OLvGQHa1VLGxfjdtrm4URcGqN1HX08JZY+cyt3QyVr2ZFZUb2dywl2xLOjXdTZSlF9DgaOXNfWsJRyMU2LLItqQnY2YAFNpzYoKbEjsPnVrDldPPiSn6SqeQabYnFWu9fjdWvZluby//2vw6GSYbv//gSdKMVhSUuLuRE4veiFVvQq/REQiHaO7tAEhammjVGsKRCAKBRqUmHI0AMYHcoNVxavl0tsetgwC0ag2hSBijVo+CgtVgwulzY9DqMWkNtLo6mVk0kZ0tB8k2pxOKhim051CeUYhGpWZa4Tie2PIWk3JHERFROj0O7lj4JZ7c8hbranegVqkQAi6K3wedOmZF8dKuVUSiEVpdXVTklCUtP0w6A75QAFfAS5rRyvKJi1g0agYrKj/m3QMbqOysx6Q14gsFiIgI6UYbTr+LSDSKRW9iXukUDrbX0e3rxR8KEhXRZCrPyfmj0aq1/PSc21hbs4O/rX+BQnsO9T2tuAIe9BotgXCIU8unMat4IkIItjbt51BnA+UZhaSbbKyp3kp5RgFXzziPsvj4cwe8PLX1bTJMtqSAPqtoIq6gF6NGR72jjWAkRLY5nYMddWxr3I/VYMaiMxERET44tBUAT9BHpsmOXqNN3h+AAlsWozOLOX3sLC6cFItr8ta+tXhDfn581s3HdWBPKTgfBf4TBOfeQIC63l6mZB87DdWRIoRgb1s1ozOLk6aTA/fvaq2i2+Mkw2Tnrf1ruWHWBby8axVXz1hKk7OdD6u3MSqziHmlk4c1ARNCcO+qf/Hy7tXcMOt8phWO5429H/Lzc7/Ka3vWsPrQFlZVbeI7S67lxtnLj+VpH3Mc1dU8s2RJMuK31mJh2b/+xdg+ebxr332X588994jb1qel8eU9e7AUFCTLDjz/PL11dWRPmYI+LQ1FrSbodOKsqSHoduPv7sbb1oazpoZoJIIhPR1rSQnjLr2UwoULP/sJSyQSieRz50gVBKlw+Fzo1NqjbhJ9JLgC3mOu9Dka12qkxNxHImys343D5+Lc8Qvo9DjwhQJH5CYzUrY3HSAUCTOjaAIqRYllZlCp0Kg0CAQtvR1kmdMxavV0e3sJRcJoVCpMOiP722t578AG5pROZkZhBVa9CUVRcPhc2A0W2t09dHkcTMgtZ09rNVGiaBQ1nR4H47JLqOtpobm3kwsmLjoqVolCCIKR0LAxKwYSiUZZV7uDWcUTMWh0KIqCK+ClydmOXq3tFzi3uquJTJMdb8iPTq3hYEc925sO8Nf1LwBw16KruHnORShKbEV6f1st43NK+52bEIJ1tTvxBGOWGlPyx1LZUce7Bz+m3dVNc28HNoOZmUUTGZNVhFatoc3Vxcd1u8k02wlFIuRaMwlHwnT7ejFpDdgMZqbkj8GkNaBSqSiy5zC/bCpCCPa31xKKhClNz0/Gb9nVUsme1mr+tv7FpBvVwrJTWDRqOlePMH7FF4UUnI8CJ6vg7AoGufK119jd2UmDy4VVp8PxjW+gOg4CfXwW3tm/nhlFFVR3NfJh9XZqu5vZ2rSfK6adzTcXXZ3SpO83Kx9je/MBHrzsR0nzpYE8ve0dfr3iEX617E5OLZ/Goc5YtOS/rnue8TmlXDb1zOMiSEq3t5etjfspTc9LmgCmrHfwIL7OTjInTMCQ3t+ctXXzZjb+9rccfC51hOdUKCoV5//731QMyDG96rvfZcvvf39kJwFUXH01C372MzLGjx9RfSEEIhLpF+QsEgzi6+zEmJ2NWisDx0gkEolEIjlxEELQ5GzHqDWQabZ/5vY+z4CDiaCCCoMzzxyvSMH5KHCyCs5CCDLuvx9HIJAs23XjjUw+jladjxYOn4vbn/sVB9preePWP+EKeChOyyUcjXDXS7/FGwzwl8t+OMh0bCB7W6t558B6vnTKWTyz/V1umXsxP3nzL+xoPkieNYvZJRMxaPR8/dQrP3VE1lQEwsGUGs66nhZqupo4bcwsntr6NtMLx6MoCn9c82+2NO7ltnmXcuvciz/1hCWEoH7lSl48/3wifcbJQBSVimVPPJHMI92Xzr17eXRAPu2RMPPb3+b0FAJ3yOfj1csvR2MwoLfb0aen03PwINWvv56so9bpBpmZn/HnPzPj61/vV+Zpb6dr7178XV1429vpOXgQT1sbHTt2oLVYKJg/n+xTTiFt1Chypk9Hb7MRcDpx1taiNZtJHzNmUP8cNTW8cc01ZE6YQNqYMXTt2YOiUuGsqUGflkbO9OnYy8rwdXXRsmEDNW+9RdbUqZiys7n4lVcGRbv3Oxw4a2ow5eTgbWvD391N98GDqNRq8ufNw1ZSQsDpxNfZSdDtJnPCBEw5sXQ8qe57JBik7v33adm4kYaVKzHl5TH+S18ia8oUMisqiASDREKxqMBak2nEKyG+7m4a16zBWVODs7oalU5HxrhxNG/YgKJSEfJ4KDv7bMZeeim+ri7M+fnozIPTLQkhiIbDRPx+6t5/n6Z163DW1ODr7ESt02HIyCDi91O2dCnFS5aQOWHCYfs2UoQQuBoa0NlsGNLSRvSdkNeLq7ERtU6HzmbDmDH8HCKRSCQSieT4RArOR4GTVXAGOP+FF3izpib5+a4ZM/jjGWd8gT06diTSfswrmcJvVz3KdTPPxx8K8M6B9dw0e/mQK80jIRQJs7lhL1sa9/HQhpd4+vpfs6JyIyVpeZw+dja/XfkYk/NGM61wPAfaa6nraWFGUQXTCsezumoLyyYspNXVRTAS4u/rX6TR2U6Ts50cSwZPXvvfXPDwN3npy7+juruJmu5mOt09rKjcyJ7WapZWLODHZ93M1sb9jMosJD/uk9bm6uLrL/6GuSVT+Obiqz+zhrFj1y7e+8pXaF6/PuX+M++/n4nXXYfePlgj+tTChTSvW3dEx5tx112c8cc/Dio/+MILvDpMJPGhOO9f/2LS9dcnPztra3ly7ly87e0jbkOl0SCiUUQ0StnSpVz+1luD6tS9/z7PfYoc2UVLlnDV6tWDyg+98QYvXXDBEbenqFRcs24d+XPn9iv3dXfzysUX0/jhhyNuS2MwsOCee5jz/e8P2tfb0MCjkycT7O0dcXsZEyZgLy+naNEi5tx9d1Iw9/f08NfCQsI+34jbAhh/5ZVMve02Sk4/HWWAwqp5wwaa16/H39PD1vvuI2PCBHwdHVgKCtBarfQcPIhKo0FjMOB3OHDV1wMw5ZZbOOehh/opDYQQvH/HHez/978xZGSgMRr7BdBLkD11KjkzZqAxGEgbPZrsU04hf86clM8GxJRLnbt3421tpXnDBupXrsTb1kbR4sVYCmNmfN72doxZWRTMn09PZSVhr5cpt95K4YIFg9pz1tXxUFkZxqwsIoEA07/+dbImTyZ/7lxsZWU4a2pwNzcTDQYJulx0HzyIxmAgZ9o0ipf0968UQuBpbeXd22+n+vXXyT7lFHQWC7319RjicRD8PT1429rQGAzMvvtuMsaNo+SsszDnDJ9P2dfdjauhAa3JRDQcxtXQgKuhAb/Dga20lLzZszFmZSUVLN6ODkIeD/aysn7tBD0eVtx5J/ueegqtyUTRkiWMvfRScmfOJH3MGNR6PSISQVGpBo2PVARdLlq3bMFeXo69tPSw9T8tQgjcTU20b99Ob10dxsxMopEIIhLBkJlJ2qhRBHt7MeXlHbV+RBPXYYTK1KDbTdDlis19QqCoVPTW1lL77rvYy8rImjqVzAkTjji9ZUJJFfb50KenH3asDMTb0UHr5s1ozWa0JhOG9HTSRp+8sQP8Dgf+7m5CXi/pY8agMQxtui6EwN/Tg95mOyHTTEokXyTDCc7yaTrJ2fA//0P3/v0ULlxIwYIFZE2aNOil4aIxY/oJzg9u3863Z86kbIgXvBMZjUrNaaNnAvDTc25Plk/MG/WZ29aqNcwvm8r8sql8/dQrgViQDZWixCNRCp7Y+iZPbHmTorRc9Bod88umUtlRT11PC4qicMsz99DkbOf6meeztGIBBbZsgpFQLP3Gtf+NVq2h0dHG39Y9T013M3ctupqHrvivpEC8sPyUfn3KtWby8BU/5cdv3s+F//gWj11zD0atAZ1ac0T+OQmyp0zhmj7Cr7+nh56DB8mcPDnlymECT3s7o5cvT74UD2T8FVeQPnYsGRUVRAIBDjz/PLVvv03r5s0pVzsPPPvsEfcd6Od3DWAvK+P0++7j/a99jYDTOcS3+hMNf5KGJW/mzJR1jkQQ70ve7Nmp22ttTVl+ONQ6HdnTpg0q15rNtGzceERthf1+mteuhRSCszEzk5DHc0TtuRsbufT110kb1f/ZM6SnU3rWWf0C2I2EA888w4FnnuGbXi9ao7HfvpaPP2b1d76T/NwaP3dnn3kvFd729kFjT1EUDOnpBJzOYcdMx86ddOzc2a8sb/ZsLnnttX5B+BJs/8tf2P7AA4PKG9esGVR24Jlnkv/nTJ+eUnDu2LEDAF9nLFjLx7/+9ZB97UvujBlcv2VLvzJFUVBUqqRFR6JtYNDzHPb7Wf+LXyQ/28rKuH2I6/z47Nm0jUAhrdJoSBs7NmkVMu+//otT77mnXx2tyYSiUhENhQg4nRx69VUODUivB7G893qbDV9XF4aMDPzd3dxSWTnIcqRh9WpeuvBCIBa3wVJYSMjjIW3UKPRpafTW1zP9zjsxZmVR/frrqA0GnDU19Bw8yMTrr2fej3886NhBj4d3b72Vlo0bseTn46ytxd3UdNjzByiYP5+r167tNx5bNm7k41//GsehQ4Q8HpzV1cl9GRUVBOKCVsLyJqOigpDbjc5m49oNG1KmF9z/7LO8fuWVSYsdU24uvo4ORDR62D7e1ds7qE1PWxtde/bgrKnB1dSEt72dQE8P+556atD3E/ejaPFiipYsYeptt2Er7u9vW7diBevvuSflc5Egd8YMzAUFBBwOMioqSBs1CltpKRVXXZVSafLhT35C5UsvEXA48La3Y87NJW3sWNRaLfWrVlFx5ZVoTCbSRo0i5PUSiisSys87j3EpMlXUrViBs7oafVoahvR0dHY7nbt24Th0CHNuLiIaxZCRgdZsxlJQgK20FH16OopKhUY/ONUZxNyqnpg9u59yMmPChKSiIOhy0RhPMarW61FpNIQ8HhS1motffpnRI1C8BpxOXE1NhL3e5HlqTCa8ra1UvfIK7du3Yy8v57Tf/S6llU/Q5WLP44+DEGRNngzEnjeN0Yi9vBy1Xk/nrl10HzhAwbx5qA0GHJWVZFRUDPptBggHArRv24ZKoyFn+nTCfj+Oykrs5eWxa2UyxfpoNKLSagm53bHjDaNQ8HZ2EvZ6QVGwFhaCooAQuBob6dq7l2g4TMH8+bibm0EIIqEQIbcbtV5Pwbx5/doSQtC6aRO9dXWoNBpUWi3WoiIUjYbeujoCPT2Y8/OxFBRgHzUKtU53RJZ/QgiioRBqXf93NcehQ7R8/DGO6moKFyzAWlKCtagIEY2iNX0+wRdFNErTunV07NyJolJhLytDYzQSdLmS86W1sHBQ308GpOB8EhMJBtly3334OjrY+/jjAFz00kuMvfjifvVunDSJ//n4Y+riE3IoGqX8oYe4buJExqWnc2ZJCQsKCwc2LxkBNkNMmLTo4Zfn3TFkvWmFMR/eN2/7E1ERTRltMBHN+6xxczlr3NxB+4fCbrTwl8t+yPamA2SY7Dy97R18oQC3zL2Ixze/QTgawRv00+FxAKBXa/nStLMwaPTsbKnkgomL2NywF7POSKE9J3lOEBNyBq5mpsKck8PcH/6QuT/8IZFQCE9rK+a8vCF9jqfccgsQXxkZ8EMT9Hg41Mck+0gw5+UNKptw9dUULFjAK5dcQvu2bUfUXu6swQpJEY3SW1f3qfo3lODsbmn5VO0VLFiQ8kWsddOmYc3uh6L7wIGU5e3btiEikSNqK+hy8ca113LVmjWDxsGMb37ziAVngNKzzhokNENM6Pg0dO7Zk7J89ve/z/YHHyTgcBxRe96OjpRCM8QC9n0awn5/yvKBQvtI0aenTt3VW1v7qdpLCO6p8DQ3D7mvL9FwmO59+5KfU7lHKIqCbQQrstFQCF9XLMWSv7sbAEdV1aA2Ry9fTsVVV7H/6acJOBzJe933Orz95S+nPEai/YFEAgEOvfbaICH3cKi0Ws7++98HzYWRQICql19O+Z3u/fuHLQu6XCkF59ZNm2Jtx4Vtb1vbiPo45dZbU7a34utf5+Dzz4+ojcT9aFyzhsY1azjlK18ZVCfY2zus0AzQtnUrbI1F/G366CMAsqZMYcI116Ss37xuXb/x5W5ujglOcVIJ+RB7VlIJzi0ff8xHP/nJsH1MhaJScZfbnXIO662tHWTR071vX79+J4gEAsn5Xa3Vkj9A4AOofOklVn372xizslBpNPTW1+MZwe9M9/79LBpCCdeycSMr7rzzsG30ZfSFF3LRiy+m3BdwOnnvK1854rksa/JkbogL3AP5W2HhkJlChqNo0SKuSjHu3v7yl1NaHA2FMTsbRVG45LXXyJ8zJ2WdFXfdRcOqVfTW1SUFUXNeHmG/f9h5eMm99zL7e98bVN62bRvrfvYzuvbuJX38ePR2e1KB6G5qwu9wJC28jFlZ6KzWWCovp5OKq65i4nXXDWrzyfnzkwrooVBUqpiFlaJQetZZqA0GsqdOZfZ3vzvs9453pOB8EtO0di2+jo7kZ73dTtk55wyqp9do+MWCBdz09tv9yp+ITwY/XbuWWbm5XDF+PJeMHcuYIV6sJEeHYxWiPyGcXzdzWcz0Muij1dWFVqXBrDcyOW80URGN5+/MosvjQKUoCCF4dONr8dyhncwtmUyBPZvyjELOGjcHq96MLxQYccRTtVY7aBVhKFKZ/mn0ei5+6SU6d+8m6HajKApqgyFmZtvTQ97s2fi7uqh88UVyZ80i+5RTKD3zTNzNzUmT176E/X72PPYYvs5OsqdOZdT555M2Zgw6qxVfVxfO6mqa16+ne//+fkKA1mIhN8WKs6JSxSKCX345kUCAoMtFwwcfkDV5Mj0HDxIJBLAWFWEvL8dWWkrY78ecl4fGaCR97NhB7QkhYlp0k4mwz4fOak2+QOXNmUNvbS1hn4+gy4Var8eUm5s0NS5avDjlde05cABTbi7etjZKzjwTc24ude+/f9iVcsehQ0TD4UEvJImX7QS20tJPTArHjsWUnY3ObqfmzTf7rVy1bNjAjr/9bZDfecI819fVhaIolC9bxqhly7CXl+NubsbV2Ejde+/hamjop6QoOeuslP3OmjIFRa0+YuHeWV1NyOsdpMXXp6Ux5wc/4MM+q4oJ32tfZ2c/q4S+9NbW4m5pSZnH3Xno0BH1LUEqk/ZIMEjT2rWfqj39EH7dLR9//KnaG2oFDWK+4Z+GocxxTUdo6pugddMmypcOjvJ6xp/+RN377w8r/KdkiNXZ7X/5yxFbZgDM/dGPyI6v4PUlf948tBYLIbf7iNusfPllpt8xWJnbsmHDEbelqFTMufvulPvSx408RVdfTLm5KVchR11wQSzWwxFa9XTu2kXHrl1kT5nSr1wIccQK0wRDuaaUnn32pxKcRTRK1969KS2Ztv75z0fcHsDEG25ImR5SYzLRW1f3qZS8Q7mb7H3iiSNu69Crr1L18sspFRBEo3Ts2nXEbboaG4c0TzdmZfVTioyUkjPPHFSmKAoTrr32iO514p18429+w4XPPZfSAsJRVUXn7t3Jz30Vd8MxlAVV9tSpdO7ejbOmBscR/s6M+9KXUpZnVFQcVnAW0Sj+nlh+9ISV4NhLLwUpOEuOVxriZjsJxlx88ZBmHNdNnMi9mzaxZwhN+ea2Nja3tfGDNWtov+MOMgyGoxr4SvL5oigKFr2Ju8+4acg6NoM5mSLh/stiL0VdHgfvH9xIg6ONdbU7OHPsHN45sJ71tTv5xdKvcukj30NRFDJMNtwBH0X2HNKMVpZNPJWxWcU8t+N9bpq9nL1t1VR3NeHwufCG/NT1tNDm6mZpxQK+dMpZvLr7A04bM5MmZwc13U0UpeVSbM8lzWhFpdFQds45KZVAfRmoJR1oEpxAYzCw4Kc/ZcFPfwrEgr/ZjRbyrFlsbzrA9OzrWRJfZY+GwzgOHUJrscTMvAbQ6/cQjIQovvwSxl51JRqVut8qUTAcwh8OYDNYCEXC+EIBbAZz0hy91+8eFAROURTm3n03c+++GxGNjsg/MxIMEgkG0Q5hPj/llluSq/oJhBAxYdfjQWezodbrCXk8RPx+VBoNAaeTnqoqogOilkNsRfesBx6gaMkSsiZOHLZvQZcLtcGASq2OWR6kECL1Nht39lH6JfCFAgghMOkMyfsV6O1l+wMPUPP22ymFHwCt0UjpmWfiamoi0NODu7mZqbffHvNfLS/H393NnsceY9QFF5BRUUHZOecQDYfxd3ejTiH4Oaur8TsczP7BDyg54wwyJ0zAWlyMoihJi4rmtWvZ/8wzREMhIoFA8qWlef16xl166aA28+fOJWvKFFAUjBkZhDweLIWFWAoKCHk89FRVEfJ40Oj16Ox2RDhM/cqVKZUBap2OJffey+Qbb6R+1Sq0ZjONH3xAT2UlQZcrWUdjNGIuKCBt9GgCDgeRYHDI9G+Fp55K+bJl5M+dS8b48YS8Xrr27ME+ahR5s2bhqKqidfNmwj4fIa+Xzp076dy9G/UwZpMJk0lbSUnyBT4hFFkKC9GnpeGorBy0QiSESOnCUbZ0KRe+8AKm7Gxq3n6b1k2bqHvvvSGPnyCV20I0HKZz927s5eVHLDiHU1hziGh0WMEic9Ik7OXlBF0uHFVV6CyW2FhqbqZo0aKUz75aq6XkjDNSmqQfjm333z9IcI4Eg8kV2lRoDAYioVByzKm0WqKhEBkTJqR8ToCUysCBmPPz8Xd19bvPOdOnpzRrVWu1FMyfT9Urr3xyjHHjCDidh10d3/fUU2QPWC111taO2E1nIEN9L3fGDAzp6Umh4Ujo2LlzkODsqK6m+o03gJiVV9jnG9LSZCDTv/GNlOXWESqvU5HKbD/k9SYtG4+Ut2++mZxp0wYpxCpfegk+RRymxFyWykxYn57+6QTnIeL+VFx99adSklS++CIf/vjHLP7f/x20bzhT8+EYSnCueeutw7olDcVQ702mTxlA+NMo+Y43pOB8EnOozw8LDL36BKBWqXh2+XJueecdNhzGXMeu16cUmmudTowaDbnD+LpKTmwyzWlcOb2/wHrO+HksrYj5WD5y1c9pcrbj8LsQQuDwuWl1dVJgy0KlqFDHBckNdbup7KjDojchgDnFk8ky28k0pyGEoKa7mbNUMRPwVVWbaXS00+BoQ4goBq0ek9bAa7fex9Pb3kEIwdUzlvLblY/R4GijqrOeLHM6URHFHw7yjUVXMad4Epc88l1ev/WPfFi9jS2N+yiy5+DwuXAHvVR1NmLVm/i/C7/NutodzC+bilFr4L41T1HV2UCGyUahPQe70UKRPZcbSy6guquJFZUbuW3eJdzz7t9ZWbk5KfS6g15CkTA3zV7Ot5dcy2WPfp8HL/8Re1urufv1P6PXaPGG/IQiYTJMdjJMNl646V7+tfkN8m1ZnD9hEcse+gYGrZ40gwV30EeuNYPx2WV8adpZ2PRm3ty/lmtnnMe9q/5Fa28nVoMZlaLg9LlJM9lYPGo6S0bP5KVdq5Lm9m/vX4deo8OsMxKMhHD4XBi1en5y1i08tfUtcq2ZTC+s4LUD63B4XTj9brxBP6FomBxLOredeQmVjma2NR3gmhlLWV21GVfAS05+BvbLzmN9Rz2ebe9wSsE4RmUW8sz297hh1vm8tmcNj256DZ1ay8S8UeRaMmhwtDGjqIJLCgp4bc8allYsoLKjnuruJva1VeMPh9CrtXS4e9BptPxq2Z384+OXybGkc+nUMzn9gduxGywU2nMonJHNvEv+lyaLAb3PjVGrp7q7iYqcMnq8vfQGPEx87EF6fL24Az4Egnx7DlPyx/DW/nXMKDyL8puu59U9H9Du7mb1cz/FbrAwOrOIkq58bpq9nKrOBpx+N3NKJrE50gW3XEahPZsGdw9r2nbQU/sRF01aQqY5jYcOrea7V12POD02hityygDoOXgQUwpTbW/Qj/Ljr2JWa0k3WnH63Tg8PaTZc5hZPo02VzdFkTBFaTl4g350Gi3BcIjZkRBmnZFEkE9FUajsqGd0VhGaUSWoc+2kL5hCvi2b6fpfEIqEMXlj0dKNWVkocYsSfziIgkJLbwdt7m7e3r+OBkcb54yfR741i+3NB5kzYwaRX32LqnAQjUqNSZuLes5o2oJ+tHmZTJ4zh9Y54zitfBpdHgft7h5KrNnQOVh46PI4iYgIt9XWstfVgkcVJSqieIN+9Bod/lCAsswCpuSNibmSNDbhaW5GZ7djys5OvrT1Pe9gOISxuJC8/BxWV20m7ebLGH/nTZyXWYjicOHt6SZ99Gj8HZ3UvvtucuVlzEUXESiIKckA9BodOZYMMs12/JNHsfS5Z/AeqsHb0UE0HKZt61Zq3noLrdGI1mLB39ND565dWIuKmHLbbWRUVPRzuUgK+IrCzfv24W1vp2vfPtzNzai0WhCC/CWL8Zt1eIJ+ssxp2AxmGh3tWPVGrDoT7Z4eIoiUL2zzfvITRi9fDkKQPn481rgvtq+7G3t5ORqDgd7aWvwOB93eXvRqDRqdjn15BjbV72F0VjHpxphpplqn49uBAI7qajp378ZRWYm3s5OKK64gb86c5HjpK9A6fW60ajUGTWrBOXPixFiAtnHjsBYXY8jIINDTgyEjA1NJMZGZE9HZbFg1esLV9fRs3EL79u3D+uSe+Ze/MO3OO8mbNatfGsVQwI+7oZH2bdtwNTTEovvX1qKO+9mmEhbTysv5Wmsr3vZ21Ho9GqORlg0b6KmsRK3TEXS56Nq3DxGJYM7PT5qy6qzWIdMkKorC7O9/n9ZNm/D39ODv6SHY24uzpgZrURGFixYRCQbpra2lt7Y2ZlkTf6dKrDQKIXAFPFj1ZqzFxdxeW0vEbkFvNGHQ6Ag4HHTu2hUT3oXA3dyMxmikdfNm8ufMoeTMM2PBC4cQwlIpfgEUtTrmk1xWRiQQQKXTYcnPx5idTfrYsTGFXknxoHHgbm5m/Je+RM1bb2HIzCTY24shPR2NyYS/qwtXY2O/4+isVqKRCBG/H53FQtO6dYME59p3303Zx5HgbW/HWlSU8rx7DhwYbBWkKDE/ZbU6qZQw5eRgyMiImRynpaVU1pmysph4/fV07NxJNBxO+r/by8uxFhfjamige//+lJYm2x98kKm33TbovIdTNvZFZ7OhT0sj7PPhiwdNTMW2++8fUXupsA8hOBuzs9FZrRgyMsioqIj1IW4hptbp8LS2plRQ+Lq6Ptd85ceCL0xwVhRlAvB/wKmAAFYD3xJC1B6rto7mMY93PK2ttG/f/kmBosR+XIdhYlYW66+9lj2dnbxRXY0jEGBNYyPrmproq/MLR6PoUpjQ/uSjj3hq3z7Gpadz+bhxnFNWxsLCQjRHuDI98KFqdrkIRSKUxk0IO71eWt1uJn9KszzJ0aWvabndaMFutAxZ94ZZ5wNwy9yLhm3zm4tjKa0m5JZz7/JvJcu7PA7CfbTdyyacij8cW92ZWjCWGUUVFNpz8IUChCIh0ow2CuzZGLV6XrjpXrRqDcVpeexsqaS6q4l0k40cSwZzSiZTnhF7kbh13iXJ9h+/9peEoxEaelppcXXS7uqh0dlGIBwi3WhNCkR3LLyCry34ElnmtOTLJcQirUNMoWDU6jltzCzWf/MReryupK94g6ONrHhexkRQuaiI8swN/4sn6MPhc2HWGWlwtFHT3YRZZ8QfDmLSxn5cL5q8hKrOBuq6W8iypGPVm3D4XFj1MeuSPa2HuGDiIqx6M1MLxhIMh/CG/ATCGsZmlZBvy0RRFGYVTyLfloU36KOuu4V0o5VCew5mnQG9RkdzbwcalQa7wUJ5RkGy73vbamjp7eBAex3zyqaQZUqjJD2PQDiHUCQEwKS80fz3eXfgCfpYc2grroCHKfljMOtivnzbmg6wtGIBoWiYDw5tYWxWCaVGC56gnyn5Y5JjKnF9AF675T7a3d10uHuo62nl1d0f0Btw873TbiDTbOeh9S/yu4u+w2ObX+f9gzEz41xLBhkmO2ERoSKnjCn5Y+jx9hKKRAhG3DQ5O8g02/n24mtJM1o41NVEu6sbtUpFg6OVdGMszsC62h1sazqAPxSgJD2PMVnFZJhsaNVahBBMzY+tsu1rr+Hv616k3d2NWqXGrDOQb8vmgkmLuHbGedz89C/4zpLrMGh1vHNgPUIInH43NoMFk1bPi7tWMb9sKjubD9Lc28mNsy/gxn//lKrOBnRqLcH49dWqNdy16Gqum7mM//fWAzxx7X+zoW4XD657Hq1agyfgwxPyEQyHmFk0gQcu/xE/f+dvTCsYzznj57HkL7chEORZM8mxZJBmtFJkz0FBocvr5M19HzGnZBJ2g4UDDXtQK6qkQsWo1aPTaJmQW86rez7g1PJp7Guv5aH1L3Koq4mIiK1OGjR60k1WXrvlPj6o3oIv6Ofamct4fvOzccGdpIWGVq0lz5ZFj8/Flx77ASu+9lfe7NrHAysfxqDR0+v3EI6GCUcjTCscz2NX/4Jfvf8P5pdNZVrBODbU78LhddHjc1HT3YRBo8cV8PDMDf+LM+Lm8bRO/nDPPdz9+p9Y+eF9WPVmcq0ZqFDwhQJcOf0crpp+Lr9b/QT3Lv8Wm3wtPFCznpK0PMT8LHLP+RaeoA+7wcJPzr6F9w9+jBCCBePnceaDX8X32lsEIyFCkTAqRUGlqLj7jBu5YOJivrbyL/zrml/y+t4PeWrr27S5unA89SrpJhtmnZGoiPLaLfexqmoTaUYr5088lS8//XO8oQATc0dh1Opx+lx0+1zcfcaNjJo4hnc8+/j+6Tfy2KbX2b9/J1a9iWAkhHfnDjxBH9mWdH5+7lf41zt/5+zxc5maP5Z/vvsQTbtXUtfTgj8cJN1oI91k44dn3ERpcR51ioNzLr+cV3d/wAMNW6j/9xvUO9ro9jpRKyrOrZjPr8//Bne/8Se+u+R6nH4Xv3jn7xi0OvQaHSatAYNWT64lg7s3fsze1moOdTVy8eTTuG/NU3R4elhV+Q75H21BrVLh8Ln5zmnXcdrtt3LvM/fw1EUXsb52Jxvr9zAhtxyDRodKUajqbCQiotxy9kX86r1/sGT0DKYWjOX8h79JMBxGr9GiUhRGFxajKc7jx/fE3Cme3/E+5592PX9d9zzr63YRCAcJRyJ4Q36i0SiXn3Imt065hJ++/Ve+cs6lhE6dwZNb36TbGyBr7hIyTDZae7tw+t38/Yrv8tKuVewIuLkB+M4rv6e+pxWDVkeuJQOn34MYJbj+ip9w4agZ/PHDf/Otxdfw2p41rKvdQVUkAgoYNGdi0hmw6c3csfBLbKjdCYpCOBrhnL/egSfoR6vWYNTqCUVC+EJB/veCbzA1fyw3vPBz3rjtT6yv3cmGul0ErUb8oSDh8yeyq2U7xR+38udLf8D/rXqcJaNnMDa7hG++dC8alZp0k43RmUWMevMpAh0djPFpMUyeQHumgTMr5vHoxtfY21aNLxTAHw6gUlT0+j0sLJ/I10+9kov/+R3+fOkPONhRz0u7VqFSVNj0Zmy3LcVx/UJK0vK4c8HlvL73Q8Znl1Joz+GuF39LKBrGGwoQjkZYUDYVk9bA1aeciVqlZnXVZiYBa2u2s7+9FlBw3HUpnq+ehzoSZZopj9PGz2Wts5bitFzKMvJ55KMXMFlsqAIhRCBAmxIkXWdm6ahZhNOsrKzcxBljZ7Oxfg/vH/yYPa2H6L1iAtbrZ6FRqZmfMZobpp3L3/a+yyWTT8cvwvzvykc5JX8s7qCPSmc73V4nFr2JmyZP5oPqrXR5HFw29Uye2PIm+9tqKbBnE7ltGeHouWhUapaMnsn4nFJ+t/oJfnzWzWxu2MtLu1Zh05kI9DrpDXjRGY0Y1Xq+vfQ2VlZvIbc5yuT80fxu9eP4wyHcF51CeNkEFJ0WlcHA8uIZ5BxqZ3X1VhbmjOPtvDAr6rdj0BrItWZg0OhxBzxklU8H4GvP/5pfn/91mp0dbG86QN05p+C5eBaW9Ey0vR40Te1Mdapx5qdhzctnTMEo1noaMIQEti4PIX1MNAyrFbxmHRYhWFG5kbPGzWV/ey0rKzcRWVBK45O/pK6nhTxrJmOyivnGoqt4a99axmQVU5ZRwOs7VuHetA1zfTvRzm6UjDQ8owtw+t2kGQfHQzhR+ELSUSmKUggkwnL+DlAD3wecwFQhhONot/VZj3kipKN679Ah3q6q4vcbNvDL//kfdk+YQHFTE5k9PdRccAEz/+u/2N/ZSTASodhmY1peHueOGcPv169ndHo6cwoLuebFFwlFIujUatbU1ZFjNpNmMLB04kReq6ykqqeHvbfcwpM7dzI5J4dFJSV4QiG6vF6ufP11GvqaYSgKJo2GsXY7ZVYry8eNw+nxoFYUbp0xg+f27mV9YyP/c+aZLHnkEYKRCBVZWbx28CAFViu+UIhHLr6YJpeLzc3N3DVvHre8/DK729vRqFSMz8pibGYmaQYDZ4waxYy8PMbGtc+uQACH38/m5mYCkcSLm4bTy8owaDRsaGxkSVkZrkCAbp8vKZSHo1HqHA40KhUldjv7OzupyMqi1e2myeXig9paMoxGimw2bHo92WYz5Wlp1DudlKalEYlGUSkKwfg1HKhVC4TDaNVqVIpCJBo96ubuHV4v7R4P4zMzUSsnTrJ5ieRkJhQJExVRGhxteIN+wtEwM4om4A36MWh1RxTXoO8qa+KzLxRAq9YcNt1cVERpc3WTb8siEo2iKDHFV1REUTg284U/FERRYn/94QC51swj+n5CkerwufAG/fjDQXKtGWhVsXNVq9TDzqO+UABv0IfNYEGr1hCKhAmEg1j0Jup7Wskyp2HU6oc9916/h3Z3Nw2ONoLhEK6AB3VcADlt9Ex2txzCajBRmp5Pt7cXrVqDTq1Bo9IQFTFFn0alJioE1V2NjM0uoc3VTZuri1xrBlnm9OQ5hKMRNKrBiuk2VzcH2mvxhvykG62km2zkWTPRqDRsatjDktEzqeyoZ19bDZ6gD51Gi0lnxKTVU5yWx6jMoQN8BsJBur29OHwuKnLKqOpsYGvjfq6cfg7v7F+PN+SnJC2P4vRcssxpROOm8n3Hmy8UoKarCZVKRSAcxB3wEYyEaHa2c8W0c2h0tNHpcTC7ZBIfVm+j2+tkXHYpE3LL+/UlEo1S19PCqMxC9rZW886B9dR0NxONRgmLCCVpeUwvquC8igV0eRwYtTHFntPvTv6uOv1uOtzdhKNRTikYS1REOdBex+ySSexrq8EV8KLXaNGo1Fj1ZlSKCl8owNjsYrY27mdi7ig8QR/r63aiVqnxBf20u7spTc8nx5LBjKIKur29CARZ5jT2tB7CG/SjUWto6e0gw2RHURSK7Dnk27J4bNPr3DR7OR2eHj6u241WrUEAvqAfXziACoWrZyxlW9MBItEIs4on4vC5sBss9Phc+EIxS4zMeLsA7oAXi97Ex3W72N1ancyUEYlGmJw/hiJ7DukmGw2OVuwGC0atgV0tVYSiYbq9vWyq3027u4dgOMSPz7oZjUrNisqN3Dh7OWtrttPr92DU6tFrdAgEFr2JdKOV4rQ8QpEwWrWGlt5O9rXVJN2OnH4XdoOVYCTEFdPO5sPqbeRYMhidVcTmhr3o1Bp0ah2BcJDNjXsRQnDJlNNRKSpWVG7kqunnsqpqMzubK4mKKBa9CbvBgjfoJyqi3Dz3It7Y+xHTC8djN1p4ZOOrBMMhApEQoUiIInsuPb5eLplyBjaDmSe3vMU3F1/NG3s/oqa7ifmlU8kw2XAFvISjYfQaHZPyRrO1cT/FabnYDGY+rN5OTXcTOrWG8oxCssxp+MNBphWOo9nZgTfkZ1x2Kc9ufw9/OEiXx4FJZ0Cj0hCOhplTMpnpheN5YecKLpt6JtuaDlDd1Ygn6EelKFh0JqJEsektnDVuDutqd5BpSmNUZiFPb3sHnVqLVqNBHbfQUykK0wrGY9TquW/NU/xi6VfZ1nSAbHManqCf5t4OAuEg6UYrRWm5FNpzONhRR0laPlub9vHBoa1oVGrMOiMK4PR7UKsUvn/6jby2Zw16jY5zxs/j5+/8jZruZtp6u5LxagxaPbfOvZjTx8zie6/+gXsv/Bab6vewpXE/KkWFSWdgct5oHD4XKkXhtDGz4ilY88m3ZfI/K/5Jp9vB3rYatGoNapWK0ZlF/Oyc2ymwfzpT78+L4y6Ps6IofwduA04XQqyOl10JPA38TAhxzzBf/1RtfdZjHu+Cc53DQVk83+1pBgOn3XMPzbm56INBbC4XG7/zHUqnT6fAasWk1bKrvZ0Or5cXr7iCOqeTTKMRm17P+sZGhBBU9/RwUUUFwUiETq+XU3Jz+bC+ntqeHq6dOpX/XrOG7W1tvF1VhU2vp9BqZVtbGxiNEA7H/FK0WkgErlGUWMAUlSoWpCcRRVcI/nDmmXx50iSaXC72dXRgNxjIs1jINZsp+fvf8ff14YtGY20BRCKxz4ljRaNohaA8J4funh66PB7mFRVRYrcjAHcwyA8XLuSVQ4d4v6qKW2bN4oENG6jq6sKi0xEVAncwiE6tZlRODtkWC5vr6hiVl0e3y4XP72dhURG9caFcrVLx3fnzOa2sjJkPPcQZ48ezvaGBqrhywqDREBWCSDTK/NJSrps2jf/94APunDMHfzjMf61YQb7FQpfPR7HNhlatxqLTsfHWW3l0+3Zqe3spzcjgN2vWEAiHsev1aFQqwtEowUiEpePH4wwG2dHSwsT8fD6sqaGuuzt2HeL1skwmfn7aadwxezaPbNvG3/fswRsOk2+xML+ggKnZ2WhVKjQqVfKvRqXCrNVi1sY09yrAotORPYR/fHf8HiuKglalQq9Woz3CfJ5HQiQaJRCJEI5G+/X7cC/9oUiEQCRCVAgsutjqxUgYKKgcTyTGgmmI6OSflkg0Smf8vlp1upTtR4Wg0+vFpNVi1GiGFF76WpBEolHC0Sh6mVf0iAhHoygg40pIJBLJCUJUiBG/Z0iOL44rwVlRFC3QCbQIISr6lGvi5W1CiNSOI5+yraNxzONdcP7lypX89MMPWZmTw+Y77qDvo6pPS+P2ujr0NttRP267x4Ndr6fa6eT6N99kW3s70aHGlBCfCL19+PHcufxq0aJB5e5gEOuf/jTyziSOqyhcOX48/1q2DO0AgUoIgep3vxv8vT7fTdVHgLNLS3l3iAiD5X//O7W9vYMDWfQ9577txutdNHo09y5Zgj8cRq1SYdRoKE9P52BXFxe99BL7e3o+idDat48AiZfoSAQ0mthfIVg2diwvXXwxKkWh3ePBoNFg1+v5wXvv8cfdu2MBWILB2Hf6nq+ixMrC4VjbipJUgoxNT+ec0lJ0ajX5FguXTJhAnsXCO1VVfGXVKrpcrk+UGopCml6PQaPBqtdTmp6Oy+8nYWDt8PnwBAKYNBouHzsWrVqNTq3muqlTsev1rKqt5Y26Ora3tdHu8eCPRAhGIgSjUUKRCBEhYv3rczyEQB23cNh2ww0E40JyodVKutHIu4cO8eCuXbx+6FDyOurUamx6PRkGA+1eL75IBItGg1alQqfREAyFCITD9Ph8vHDxxZTa7ZTY7RjjSoUml4u3amt5bNcuFGIWDYFQKLaSRyytW67dTqfbTSgaxazX0+3xEI5GCUWj/HDOHKbl5lJgtWLR6TDrdFT39NDk8fC91avRqVQ4AwGi0Si+UIjeYJBgNIpQqQiGw4QS5x9X0lh0On65aBG3TZ2KgKTyptXtptnj4e4PPsATDBIIh4kIEXtOFYVwNIparSYYDscEcSFocbuTAWB+sWABZ5SUIISgLC2NYrudjU1NlKelkdMn77BOo8Gk0WDWatGrVAhFodvrxRkIoNdqCfQJ/FNut/O3s88GoMhmY0J2Nh83NjIhO5vLXnmFDo8nJigqCgatFotOR7rRiFpR8IfD+OMKkNhjEVNO5ZrN/OrUU/GFQnhCIWbk57OhsZGxGRl8+e232dbSgipuhaGLK3dMOh1atRpN/NlL1+sJxZURgUiEQCjEv5cvjx1DCErsdlrdbnRqNc8eOMCLlZUYNBqMGg1GtRqdSkVUiJjiSFFodbnwxJ9LlaLE6mq1/HLhwpTZCV6pquLPW7fiD4fp9Hrp9vnwhEJ4QyFQqbDElRhalQqdSoVOrU4e/7szZ7KwsJCIEBTZbHR5vTElXzDIza+/ji8cJhy/5xqVCotWizUe5NGi0xGI+/yZtdqkgu6ysWO5fPx4vKEQdoOBXW1tlNjt9Pj9XPDiiyjxtrQqFTpFocBmI8dkwqDR4AwE6A0G8YfD+MJhFIitShQX84M5cwjFXX6EEIj4fbzohRcIRaMxhaMQhKJRfJEIYSEwxBWLdr0eQVyBFg4TikTINZv5ydy5sRXGvDycfn/s/mq1fHfVKvZ0dSWPASRdj6KJuVVRiMbbA7Dq9Ri1Wp5ctgxzigBDv924kecPHkw+R1EhYi5MKhVWnY5evx+LwYA3GMQbDxSWyIX993POYWJWFhnxlEMJxdJ/f/QRj+3dS7bJhFGrjY3R+D2OAr5QiA6fLza+VKq4CXjs+X314otxBgKMy8ykx+fDrNOhU6t5rbKSa958E5NGg06lwqbXk2+x9HO9cgeDKPG5U6dWowD1LhcXjxnDzxcs6KesNet07Gpv54IXX0QNZFss5JlMmDUaVCoVqvhvUkSI2JzdZ+4WQnB2WRk/SpG+0OHzcd2bbxISglAkQig+R4bic75apUIdV8wGwuHY/Y8/9yVWK08vX45Jq42ZfPv9ZBiNhKNRfvbRR3zY1EQwrrALxtvSqVTo49dEHx8nGQYD1vh1yzAY+N4QqQH/sWsX76ZICaSKPwsaRUGtUuEKBqnv7U0KTwJ4bvlyige8h3lDIZ7cu5eHd+5EAM5gMDZ3q9UYtVqCkQi5JhPZJlPsOgAGtRq9RoNeo+Gi0aNZWFQ0SEBr83h49sABHHFFvyMQwB0M4guH6YjPK1Eh0CoKZq2WcDRKvtWKXq3mpsmTWVre3xIAoNHl4psrV9Lp89Hu8aCPK8i74j7BRo2GdIMh9nuk1ZJlNGLSajFoNJxRUsLyFNHwhRBc+NJLuEMhIvGxrVGpkr8liqKgQGyL/69SlNjvu8XC/yxahDo+RkVi7gW+9t57NLhisVYSc4o7FMIVDOINhfDFn3WdSoVBo8EUf+b0ajUrr7gi2U7y/gnBrz/+mNUNDcmx5wgE8IbDuIJBjBoNkfh8kigLRCJYdToKLBZevfhixmVkDDr3f+/fzzP79+MLBIgSey+xx69hND43mLVaTBoNaQYDJo0m+WzcOGkSRQNSwEWiUZ4/eJBHdu1CHZ8n1PExmZgzEmMl0meuTcxlXz3lFC5IcZ/cwSD3rF8fe0bi85KiKGgUhSjgDARocrsJRSL4IxHcwSDecJhgJMK5ZWX8OEVKtOOZ4QTnL0LtPxWwAf0iVwkhwoqi7AAWK4qSIYToPlptAeVH8ZjHHUGXi9NXruTeX/2KLcBAsW/pP/95TIRmgJx4ILAJmZlsvv56mlwuVjc08E5tLW/X1NDRN1XKEAJpZ4p0KhAzOz4i+rSfbjCk9MNOKdIPIyz3RT1MHXcoNKgPKT8PKG/z+RibOdh0cVxmJia9PlbvcKu3idW7eL0dHR3Jcy/oM6n+7txzefjAAXoTgmdiJT/x8pgQxPsK/3HhTKtWMz4zE08oRJvHQ1QIfKEQ6xoaYi+jfa0AAEf8R6lVUajs7Y0J6ipVrI9+PygK2RYLVr2eYCRCj88XUx4oCttaWtjU2squlpaY4N63T4l7ZTB8IuCr1eDzERECt6JwzhNPoIsL4788/XTOHzuWv27ejM5kin0nfq+CQtDpdtOZOHejkUAiUqlWG+tnnL9s2kSn18vBri6euOQSzh83jiWPPsrFU6awtr4+2Wayf4mtszN2zMR97GOB8ZdNmwhHIjS7XPzzoos4s7ycy559lh8sXszmxsbYNRuodEmce+J4Wi0Eg/iDQfxuN3e++ip3vvoqUSF49vLLOW/sWE579FH+snw5K6urY98bqMBRlJilSCgU+6zTQZ9n76FNm3g7nr/52/PmUWy384P33uPhCy+M3e+4YBCMb47EF02mpDInoFLFrkN8rDndbn67bh0A548dy4TsbP65bRvfX7iQdY2NePsGOemr2DIaY/+HQrHrEAgkx4hapWL1wYMYNRouGDeOGfn5rKmrwxcK0ex209jT80k7fdvU62P3Pxj85Nr2EfJn/f3vAFRkZbHyxht5bPt2csxm9nZ3895Q+Tt1uti98Xpjfe5znQBWxvPpWnQ6Kr/xDf6ycSPeUAi1Xs+KRF7WgfOSRoMbcDscYDYP6uf1tbWkGQxkGI3sueMOnt2zB7VKRWlGBttbWwefN8T6mBiXJlOsvcTYAl7asYPrgRn5+Wy87TZe3LeP+cXFWA0G9jQ09D/nuAKLRGCbYDB27n3uEcD7+/bx43feYXFpKatuvJGbX32Vs8rLuaiigjf374/N0QMVkDrdJ/1MjNU+z5xWpWJ7fT0TsrN54YoruHfdOkrtdm6bOZM/fvRR8oU22UeI3Xe1OnaPEtcz8QzEse3cyfjMTPbeeSc/XrGCXLOZb86bx69XrcIxMHJ24tnRaGJzR8L6KmGBFa+z/KmnsOp01H/72/x27Vqcfj+/OvNMXtm/n6rWVqoGnv9hxhJA0R/+wNiMDPbeeScv7d/P/s5Ofnv22Xz3nXdwOxz0jWO7N3HfdbrY9dTrY8cbEKW5sqmJ36xaRev3vseHdXW8tH8//7rkEn65Zg317e2gKNQkolgn5je9PtamThcrS7QZP58PDhzgJ2+/zfpbbkGv0fDlV15h21e+wk9Xr+aNRPqdvmM+MTcFArH2E/erD/tVKkoOHeLh5cs5taSEsx5/nD133MEvP/iAX3/4YVJZ0k/hrNfHxmeizcS83Oe63792LQ8tX86cwkKWPfUUa2++mb9t3sx333sPz8A8wPHfj+TzmLi2fe+jEEx98EF+feaZfHXWLMb9+c+su+UWXti7l++99x7ugW0qSvKZ3JNoM0Xwp/s/+ojfnn02d82dS+Zvf0vDt7/NGwcP8pXXX6cnMU769iMxL0ejsTb9/kHK+dd37eJHp57KTxYv5tJnnuEP555Ls8vFZc89R4vb3X9BQIjYuUejsbFuMPRvM34uT5pM3DBlCv93zjl8+ZVXuHnaNMrT05n38MM0DYy0rCifjKFA4JNnPkX+5Xf27WP7V7/Kd995hwnZ2Xxl5kzG338/db29BOO/P/3aTIyhxPtAijbH338/1d/8Jne/9x75VivfmjcPy69/jS8UGvwOmXg+fb7YuQ94Pl0eDwccDs587DEqv/ENHt66lQank9+cfTaTH3yQfZ2dn7xDJeYnjWbw8xkI9P9NEIIXd+7k/euvZ0NjI4/v3MlTl13GuU88wZr6ekIDsy2o1Z/cG622//PZh621tbx5zTVYdDq+9sYbvHf99fz6ww95eNs2qhMpsQY+S4n3ir7PUh+21NQwwW5nUWkp5z/1FB/feuugOicSX8SK82XA88BvhBA/HLDvGeAKYKYQYuvRaouY4PyZjnm8rzi3bNrEkymSqefNmcN1nzL/5mdFCEGDy8WWtjY+bmnhw8ZG9nV309PnYR2fkcHyUaO497TTBn1/f1cXV7/xBj1+P8b4SqBWrcYSX/GLCoECBKNRap1OauPaXRh6FTscjaL9/e8/1fksKy/njVS5BgHDH/6Q9KU+EjIMBroG5LBNMPPxx9l6mPQaQ1F5yy0pV7TyHniAtk+RO/WysWN5/qLUAb3S//znwS+RI2BqdjY7brwx5b5JjzzC3iFSow2HXa/HMUT6jatff52n4wILMHgFfxi6v/510lNEuvzGihXc/ylzgH509dUsTBHZ9IFt27hzxYpP1ebjy5ZxXYp0UKvq6zkjnkfxSPnWzJn84fTTB5VX9vQw7h//iH0Y+DtymGt6+9Sp/G2IdGLm++7DmyoP8oCXUFSqfvdwfEYG+2++OWWbS55+mjUDoroO2WZfdxBFwf+tb6U0Lf/Ku+/y9x07PikYqUmeELx56aVMz80lHI1SZLPR4fFg1ul4aOdOvrVq1bDfTR5rwP8vXHQRl6bImftubS3nPv/88G0OvJ7xdn86fz6/SJGe6pDDwZiHHx66f33aGPiSfdm4cTy9fHkyaGQoEkm6Wlj/+MdPlJBD9XXgucOw937hU0+xrqkp9fM+1D2L13XedVds9Su+8ieEQK/RcPObb/JInzyrybZGMAY2Xnsts+Mp2MLxmBgqRUn93A+8333PvQ/3LlnC91L8/q+ur+f0vs/9gPvbb6wP4J5TT+W/5s8fVF7ndFL20EPDn2RfBVWf410/cSKPnHdecsVLQPJ/ze9+F1NwDOzjYe7R9Nxctt5wQ8oqUx59lN2dnYMVRgPbHDA21CoVO66/nnyrFbtez/7OTibl5NDj83HL22/zUlXV4O8PmI+IRAYd56Orr2ZSdjZpBgOdXi+ZRiOKovCbjz/mh2vWDN2/vscZsO+liy5i2ahR6NRqvKEQRo2GQCTC8/v3c/2bbw5u8zDXE+Cn8+Zx54wZ5JjN7GprY0x8pXRdUxNnPfvs0Pel7xgdMLdePn48v168mDEZGTT19pJmMKDXaKju6WF84nekbzt9FfmJeXlA/xcUFrL2mmsGfFVQ1d3Nuc89R01vb38Bv69w2retRH/jq7vub30LiMWkScz9Qgiufv11nhn4DpFoMxL5pM8prsum665jVkFBctHBrNMhhODuDz7g3k2bhr6eff8fcF3vXbKEr8+enbRws8TbfHzPHm58++3B7Qx85lPMJd+fPZufLVyIoig4/X7yrVYOdHayt6uLSxOZevp+T63+ZKwPMT9dVVHBP847D0P8fo8ZsPJ+PHK8rTgnHCVT/ToGB9Q5Wm19qmMqinI7cDtASUnJCLv0xRBJIbgULVnCsn/96wvoTQxFUSix2Six2bgknssxlvokZiqaMMMZiorMTLYN8YOYCn84TIfXS28wSMYQ4fwV4L/mzSMYjeIMBJImfyatlhKrlWKrlQyjMWl6Z9Fqsep0RITAlsJkL3FOj513HqG4X6g6bhrT7vXS7fcTjEbRx3/Uuvx+1HEzo06fD6tOhz8cxpDixfy88nImxCcYTyiEIxBArXxiGmnX6ym2WrHGzRczjUayjUYWFBRgHMLn9dVLLuFgTw/rm5tp83gIi5hp3MC/nlAIbziMPxxGURRyhvBvhtjqvqIoROP3dqQKhOFW8Ic0949j0GjQKEqyv5F4fe0wPqAJ0y8hREwwG6mgM0x/QinyWY6UQRrhOEeihFAgaaYGMTPbVBxpZHuI3Vd3MEj7EIqWUCRCttGINxzGm0oTP0R/VYrC6HgwvlSEh7r3qQSeEd5D/VBWG6naHHCtgtEoqRLtBFK8GI8IRWFUejp5lk+iz2fHLXeGG79D9jf+f6o5BAZbIKVsc4jrOZRifcg2U12PFP3sOx77xkM47Dg9wvuebHOEQu3A4+jjJrFAPwsmm8Ew9EvyYeh7Rfueb8pxP8Ixv3OkeaYHfneYcxjqmRnRnDdEXw1xc+pktT77jBpNTGky0vuUELiHOYfuhJL+cPd/wL6IEOTH3wUAJsUzeKQbjZgTlmCpvt+3PMX1yzCZSIu/m2T1+U0NDqPAGK6fEJvzE2MzEYvCoNFg0GqPbIz2aVun1SatCaf0SZ9XYrcP32bf6zCgr2lGY1JgKuxjAVlgtY6szRR1UvkPK4rC2MxMzAYDDAhWm/L/Af3tqyTt+3/CxWfIdoazDFQUxsUtC1WKknT/SKSpG/LeH0bJd0p+fnLe1yerKVgGvqv2/W7f65iizVyLJdm/xHgan5VFZyAw9H3qe+4p2rQZDMm2TgSh+XB8ESvOlwIvAH8WQtw1YN+rwHJghhDisMs4I22L2IrzZzrm8b7iXL9yJc+eeSYA5cuWMfnLX2bcZZcNK5hKJMcCfzhMt98fi7bu89Ht9+MLh9Gp1WQbjUm/PZNWO6QAtbGlBU9ce15osWCM+3Pp435/A8d1QtERivvhHY5wNIo7GKTb78cVDGLSaskzm3EFg0Si0aTPjxAidvwhBNJ9XV3U9fYmffH6+uUl/I41cWVHv02JrZDa9INFslaPh8qeHryhEJnGWI5ejUpFltGIWatFG78GWpUKtUpFJBrFHffrTdPrUwby6vH7+bilpV8AuEQ/BvZNq1Il/VRHihCCQCSCNxTCGQjgj0eVt2i1pBsMBCMRjBpN8kUn4d+aivXNzYSj0X73wRP3TVMgORYGKl7MWi1LUuRpBahxOHCHQklftXD83obivo+haJTeYBBXMJj030r4u51aWJgy2N2ezk7qe3sJRCK4gkHavN7k2EzcM71aTZbRiE6t7ufveeHo0SnvfWVPD/u7u7HG/QLzzeakYiwiBN5QCH8kkgx0l1BU+SMRJmRkJF/0+9Lm8bC2qQmjRkNG/PkLxxWHoWgUtaLgDoXQx/1bPaEQYRHz65uUmcmU7MHRTwPhMPu6u2PXMhrFH4ngCATo8ftjysJIBLtej02ni73E9/EXzLdYmJEilzXAcwcOEBECjRJzD9HFAxUa4n59zkAAbyiWhzrhuwcxk/czhlBuf9zSkrRyUiA5dyRGz8DPEJvDgtEoF40Zk/LlvM7ppM3rTa4WqxUFf3z8J5RDfTdFiQVZVCkKFRkZKeeoLp+PWqcTRyCQHJOJvxBT/Jm0Wkxx5Z+IX08hBGV2+yBfR4jNc664n3kgEqHb76fV40n6lieuHYAv7o+YyAgxKze3n3CXIBiJUON00u33U9fbm/RhVysKenUscnji+UnM9YnnIt9sZlJWVsr79FZ1NYkAkwnrssQ4CMevxcCAkOq4IiaVdRXAiro6VHGBRxdvMypEMoZB4q87FMIR/y0Ixp+Jr02bhj3FM7qqvp42r7ffeEnci8S8FYory0tsNkxabTLbxtTs7JTzc5PLRWVPD2qVimyjEY1KhT8cxhu/rvUuV7/+Bvo899dNmECp3T6ozfXNzTy5dy9pBgNpej1pen0s/kTch9us1aLXaHDF571uvz+ZanRqVhYTU9wndzDIWzU12PV6rDodvnAYjaJQZLWiVhRcwSDuuP9wbzBIl8+HOxQiFIkwOi2Ni+MLKH0JRSK8WVODpU9shWAk0k8h2zcOgohfb61KRZnNxuIh5vxV9fW4489jIiaAIR7HIhHQUkDy2fAl4oYAc+MWIQPZ2dFBi9ud9LlPWD9adbqkH7VaUTDFy/RqNR0+H90+HxWZmSnnkt0dHRzs6Un6GEeEwOH3J4X2xJzsDYXoCQTwh8PJ8f/lyZMH+U1D7B1iU2trv3ki2qf9RHlikUelKMlzmpiZyagU72X1vb08tW8f7lAoOU8kxrxKUbDp9eSYTFji83XCL1unVlNosaTs5/HM8RYcbBqwDXhGCHHVgH3rgXlAhhCi52i1BZR+1mMe74KziAdGUB3DaMYSiUQikUgkEolEcrJyvJlq7yaWO7mfA6qiKEZgOrBvJELzkbSlKIrrKB7zuERRqQ5vkieRSCQSiUQikUgkkiPmc08KKYQIA/8GChRFObvPrkuImek/dbTbOprHlEgkEolEIpFIJBLJfxafu6k2gKIoBcBOYub7/weogR8Qy2QyRQjhVBRlDHAq8JEQouqztHUk9YbieDfVlkgkEolEIpFIJBLJp2c4U+3PfcUZQAjRTEwo3gD8CPg+sBJY3EeAPRV4JP73s7Y14noSiUQikUgkEolEIpH05QtZcT4RkSvOEolEIpFIJBKJRHLyctytOEskEolEIpFIJBKJRHKiIAVniUQikUgkEolEIpFIhkEKzhKJRCKRSCQSiUQikQyDFJwlEolEIpFIJBKJRCIZBik4SyQSiUQikUgkEolEMgxScJZIJBKJRCKRSCQSiWQYpOAskUgkEolEIpFIJBLJMEjBWSKRSCQSiUQikUgkkmGQgrNEIpFIJBKJRCKRSCTDIAVniUQikUgkEolEIpFIhkEKzhKJRCKRSCQSiUQikQyDFJwlEolEIpFIJBKJRCIZBkUI8UX34YRAUZQOoO6L7sdhyAI6v+hOSCSfE3K8S/7TkGNe8p+GHPOS/zTkmP/iKRVCZKfaIQXnkwhFUTYLIWZ90f2QSD4P5HiX/Kchx7zkPw055iX/acgxf3wjTbUlEolEIpFIJBKJRCIZBik4SyQSiUQikUgkEolEMgxScD65+PsX3QGJ5HNEjnfJfxpyzEv+05BjXvKfhhzzxzHSx1kikUgkEolEIpFIJJJhkCvOEolEIpFIJBKJRCKRDIMUnE9wFEWZoCjKG4qiOBVFcSiK8rKiKGVfdL8kkpGgKMpYRVEiQ+xboCjKB4qiuBVF6VQU5TFFUTKPdT2J5FigKEpFfK52KYriURTlXUVRpgyoM6L5/GjXk0iOBYqijFMU5c34eG9RFOUZRVGKBtSRY15y0qEoyvcVRRGKojw6oFyO9xMcaap9AqMoSiGwI/7xd4Aa+D7gBKYKIRxfUNckkiFRFEUFjAFmAP8NjBZCKAPqnAJ8DLQCfwRygO8Cu4B5QojQsagnkRwL4gqaPYAZuJfYXP0DYnP1OCFE70jn86NdTyI5FiiKYgH2AgL4M7Gx/x2gndj488kxLzkZURRlPLAdMACPCSFuipfL8X4yIISQ2wm6EQsgIIDT+pRdGS/76RfdP7nJLdUG5MXHaHJLUeddIASM7VN2d7z+Dceqntzkdiw2YkKyAK7uU/b/4mV3xj+PaD4/2vXkJrdjsfWZXyf3KftmvOzK+Gc55uV2Um3ELHnXERNeBfBon31yvJ8Em1xxPkFRFEULdAItQoiKPuWaeHmbEGL8F9U/iWQoFEXRAWfEP/4WmCL6rDgripIPNAHvCiGW9inPAdri5ece7XrH6HQlEhRFeQG4FMgUQnTHy84D3gTuJ7YSd9j5fKTzvvx9kHzRKIryPDBTCFHepywx5n8I/B455iUnGYqifBf4P+B2YoLtY0KIm472OJbj/YtD+jifuEwFbMDGvoVCiDAx041xiqJkfBEdk0iGQwgRFEK8LYR4G+hOUWUBoDB4bLcD9cD8Y1RPIjlWvAj8F9DTp6wg/rebkc/nR7ueRHJMEEJc3ldojjMp/rcBOeYlJxmKoowDfgk8DLw3YLcc7ycJUnA+cSmL/21Osa91QB2J5ESiLP53qLFtjf8gHO16EskxQQjxpBDiv0XcxCu+KvA1YiZ1rzDy+fxo15NIjjmKouQrinI18CPgEPAqcsxLTiLisVv+SWy197spqpTF/8rxfoIjBecTF1P8b6qgRsEBdSSSE4mRju2jXU8iOebEX7AeBGYC9wohtiLHvOTk5iHgKcAPXCyEcCPHvOTk4pvAQuA2IURviv1yvJ8kSMH5xMUT/2tPsc8+oI5EciIx0rF9tOtJJMcURVH0xASIW4G/EfP1BDnmJSc3vyK2ChcB1sSzHMgxLzkpUBRlDLEx/iKwQ1GUPCA7vtsY/xyIf5bj/QRH80V3QPKpqY7/zUmxL/HA1n4+XZFIjiqHG9tOIUSPoihHtd5n6rFEchgURTEBbwCnAb8UQvy0z+6RzufiKNeTSI45Qoj1wHpFUT4k5pN5D/Cz+G455iUnOqcCRmIBIC8dsO+K+Pbt+Gc53k9wZFTtE5Q+kfM8QojCPuVGYgFoqoUQE7+o/kkkI0FRlNXAkgFRtXOI+ehsEEIs6FM+iph/3FtCiGVHu96xPE/JfzZx8+w3gHOJpZ96cMD+Ec3nR7veMTpdyX84iqJkAR8Ry1hwV59yE7FVsAPAZOSYl5wExPMpTxlQnAs8CrxPLMfyVuAgcryf8EhT7ROUeOS8fwMFiqKc3WfXJUDCHFAiOeGIR7t+B5ijKEpFn11Xxf8+dSzqSSTHkLuApcDPBgrNMPL5/GjXk0iOEd1AFnBx3D0hwZz430NyzEtOFoQQTYlMIX0yhnwQ353Y144c7ycFcsX5BEZRlAJgJ7FUO/8HqIEfAA5iuXGdX1zvJJLDk2rFOV4+lZhJXxtwHzFzpO8SG+/z4j8aR72eRHK0ia8A1BNzjfoeMT/PvriFEM+PdD4/2vUkkmNBn3y264GnASvwdSADOF0IsU6OecnJiqIoZUAN8TzO8TI53k8GhBByO4E3oIKYCWAv4AReBsq+6H7JTW4j2YDVsWko5b75xLS2HqAL+BeQeazryU1uR3MjlhJEDLPV9qk7ovn8aNeTm9yOxQZcRkxwdhJzl3kFOGVAHTnm5XbSbX3m/UcHlMvxfoJvcsVZIpFIJBKJRCKRSCSSYZA+zhKJRCKRSCQSiUQikQyDFJwlEolEIpFIJBKJRCIZBik4SyQSiUQikUgkEolEMgxScJZIJBKJRCKRSCQSiWQYpOAskUgkEolEIpFIJBLJMEjBWSKRSCQSiUQikUgkkmGQgrNEIpFIJBKJRCKRSCTDIAVniUQikUj+Q1EU5WJFUX7+RfdDIpFIJJLjHUUI8UX3QSKRSCQSyReAoiiPAjcKIZQvui8SiUQikRzPyBVniUQikUgkEolEIpFIhkEKzhKJRCKRnGQoijJXUZR3FEXpUBTFpSjKx4qiXBDfd5qiKEJRFAHcGC8TfbayAW0VK4rymKIobYqiuBVF2agoysUD6tQqirJaUZRvK4rSoCiKX1GU9YqiLPqcTlkikUgkkmOKNNWWSCQSieQkQlGUEmAP0Ar8FfAClwFLgPlAI7A0Xv1WYCHw5T5NPC+EcMfbKgY2AR7gIaA33tYZxEy8/xWvVwvkAkHgd0AY+AaQCZwqhNh4bM5WIpFIJJLPByk4SyQSiURyEqEoymXA88DFQohX4mU24LfEhOL3+9R9lGF8nBVFeQS4EpgLdMSL1cSE6U4hxNR4vVqgFDhDCLEqXjYZ2Am8LoS48CifpkQikUgknytScJZIJBKJ5CQivuJ8ANgO/AbYIoRoGKLuowwvODcD+UMcSgBaIUQkLjgLIUT5gO/vBnKFENmf4lQkEolEIjlu0HzRHZBIJBKJRHL0EELUx/2ZfwQ8DljiAvCjwM+FEKEjaC6bmAD+oxHUbU1R1gGMP4LjSSQSiURyXCIFZ4lEIpFITjKEECuAFYqiKMBo4CbgJ4AP+O8jaKoT0Akh3u5bqChKKTGhWgVE4sVFKb6fB3QfUeclEolEIjkOkVG1JRKJRCI5iVAU5ZeKojQqipIrYlQB/wU4gTkDqnfGv2Mform3gYmKoszu074aeB14i0+EZoAiRVGW9qk3HagAPvqs5ySRSCQSyReN9HGWSCQSieQkQlGU04CVwA7gScBFLIr2xcC3hRD3Dai7ipgg/CqQAYwVQtwa35+Iqm0GHgDq4u2cDdwhhHgwXq8WSI83+3s+iaqdDswXQmw9BqcqkUgkEsnnhhScJRKJRCI5yVAU5Wzg+8ApgA2oAR4WQvw+Rd3rgR8CY4iZcj8rhLi9z/4SYubd5wEmYC9wrxDi2T51aoFa4Aliq9u5wFbgB0IIueIskUgkkhMeKThLJBKJRCL5TCQEZyHEaV9wVyQSiUQiOSZIH2eJRCKRSCQSiUQikUiGQQrOEolEIpFIJBKJRCKRDIM01ZZIJBKJRCKRSCQSiWQY5IqzRCKRSCQSiUQikUgkwyAFZ4lEIpFIJBKJRCKRSIZBCs4SiUQikUgkEolEIpEMgxScJRKJRCKRSCQSiUQiGQYpOEskEolEIpFIJBKJRDIMUnCWSCQSiUQikUgkEolkGP4/8RSRW5f8lCMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1152x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(16, 8))\n",
+    "\n",
+    "x = df_train['step']\n",
+    "for y in metrics:\n",
+    "    try:\n",
+    "        plt.plot(x, df_train[y], label=y, **get_line_arg())\n",
+    "    except:\n",
+    "        pass\n",
+    "\n",
+    "plt.tick_params(labelsize=20)\n",
+    "plt.xlabel('step', fontsize=20)\n",
+    "plt.ylabel('loss', fontsize=20)\n",
+    "plt.title('训练集损失函数', fontsize=25)\n",
+    "plt.savefig('训练集损失函数.pdf', dpi=120, bbox_inches='tight')\n",
+    "\n",
+    "plt.legend(fontsize=20)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30a80c4d-6331-4ffd-9067-2593d57c935c",
+   "metadata": {},
+   "source": [
+    "## 训练集准确率（如果图是空的，说明没有`acc`这个指标，跳过本图即可）"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "9ec337a1-384a-4f60-b6e0-8bfbb203170b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics = ['acc']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "4fba2265-ca28-497f-b0c9-0be37e344e99",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(16, 8))\n",
+    "\n",
+    "x = df_train['step']\n",
+    "for y in metrics:\n",
+    "    try:\n",
+    "        plt.plot(x, df_train[y], label=y, **get_line_arg())\n",
+    "    except:\n",
+    "        pass\n",
+    "\n",
+    "plt.tick_params(labelsize=20)\n",
+    "plt.xlabel('step', fontsize=20)\n",
+    "plt.ylabel('loss', fontsize=20)\n",
+    "plt.title('训练集准确率', fontsize=25)\n",
+    "plt.savefig('训练集准确率.pdf', dpi=120, bbox_inches='tight')\n",
+    "\n",
+    "plt.legend(fontsize=20)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f96dedcd-37b4-4036-9db8-ab46a9cc7f9e",
+   "metadata": {},
+   "source": [
+    "## 测试集评估指标-MS COCO Metric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "903eb6b6-4ec7-44fe-b163-48d33643ac1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['coco/bbox_mAP', 'coco/bbox_mAP_50', 'coco/bbox_mAP_75',\n",
+       "       'coco/bbox_mAP_s', 'coco/bbox_mAP_m', 'coco/bbox_mAP_l',\n",
+       "       'pascal_voc/mAP', 'pascal_voc/AP50', 'data_time', 'time', 'step'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_test.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "4209309a-5175-46c1-b465-2bf97f7b868c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics = ['coco/bbox_mAP', 'coco/bbox_mAP_50', 'coco/bbox_mAP_75', 'coco/bbox_mAP_s', 'coco/bbox_mAP_m', 'coco/bbox_mAP_l']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "8bc4ccd8-746b-4cdf-bf95-434dab3340a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(16, 8))\n",
+    "\n",
+    "x = df_test['step']\n",
+    "for y in metrics:\n",
+    "    try:\n",
+    "        plt.plot(x, df_test[y], label=y, **get_line_arg())\n",
+    "    except:\n",
+    "        pass\n",
+    "\n",
+    "plt.tick_params(labelsize=20)\n",
+    "# plt.ylim([0, 100])\n",
+    "plt.xlabel('Epoch', fontsize=20)\n",
+    "plt.ylabel(y, fontsize=20)\n",
+    "plt.title('测试集评估指标', fontsize=25)\n",
+    "plt.savefig('测试集分类评估指标.pdf', dpi=120, bbox_inches='tight')\n",
+    "\n",
+    "plt.legend(fontsize=20)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48520a3e-6e19-4f72-8541-3b20b51a0b92",
+   "metadata": {},
+   "source": [
+    "## 测试集评估指标-PASCAL VOC Metric（如果生成的图是空的，说明没有pascal voc指标，跳过本图即可）"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "495bf2dd-0c6b-4642-8811-a100dae97099",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics = ['pascal_voc/mAP', 'pascal_voc/AP50']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "6351fd44-99a0-4608-a8cd-948fe2cf6113",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(16, 8))\n",
+    "\n",
+    "x = df_test['step']\n",
+    "for y in metrics:\n",
+    "    try:\n",
+    "        plt.plot(x, df_test[y], label=y, **get_line_arg())\n",
+    "    except:\n",
+    "        pass\n",
+    "\n",
+    "plt.tick_params(labelsize=20)\n",
+    "# plt.ylim([0, 100])\n",
+    "plt.xlabel('Epoch', fontsize=20)\n",
+    "plt.ylabel(y, fontsize=20)\n",
+    "plt.title('测试集评估指标', fontsize=25)\n",
+    "plt.savefig('测试集分类评估指标.pdf', dpi=120, bbox_inches='tight')\n",
+    "\n",
+    "plt.legend(fontsize=20)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0e405d8-565e-4f79-afda-30aea4428044",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}