{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select Preference Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:11:04.757824Z",
     "start_time": "2024-05-26T00:11:04.555293Z"
    },
    "execution": {
     "execution_failed": "2024-09-02T04:29:36.833Z"
    }
   },
   "outputs": [],
   "source": [
    "# setup tailscale if you haven't\n",
    "# https://tailscale.com/kb/1031/install-linux\n",
    "!sudo tailscale up --accept-routes=true\n",
    "\n",
    "# setup autoload\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:11:08.392310Z",
     "start_time": "2024-05-26T00:11:04.759383Z"
    },
    "execution": {
     "execution_failed": "2024-09-02T04:29:36.833Z"
    }
   },
   "outputs": [],
   "source": [
    "# pip install psycopg2-binary\n",
    "# make sure sqlalchemy is >=2\n",
    "# pip install \"sqlalchemy>=2\"\n",
    "import os\n",
    "import datetime\n",
    "from collections import defaultdict, Counter\n",
    "import json\n",
    "from urllib.parse import quote\n",
    "\n",
    "import boto3\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sqlalchemy\n",
    "import tqdm\n",
    "from botocore.exceptions import ClientError\n",
    "from suno_analytics.preference_helper import get_preference_counts\n",
    "from suno_analytics.preference_data_selection import (\n",
    "    gather_data,\n",
    "    plot_clip_distribution,\n",
    "    parse_metadata_for_basics,\n",
    "    get_concat_clip_ids,\n",
    "    validate_preference_data,\n",
    "    run_bot_detection,\n",
    "    print_out_value_counts_nicely,\n",
    "    merge_concat_clips_with_reactions,\n",
    "    plot_clip_basic_distributions,\n",
    ")\n",
    "\n",
    "\n",
    "# setup some pandas display stuff\n",
    "pd.set_option(\"display.max_rows\", 500)\n",
    "pd.set_option(\"display.max_columns\", 500)\n",
    "pd.set_option(\"display.width\", 1000)\n",
    "\n",
    "\n",
    "def get_secret():\n",
    "    secret_name = \"rds!cluster-a3b66c33-40a7-47dd-bd6e-32b1c17c9124\"\n",
    "    region_name = \"us-east-2\"\n",
    "    # Create a Secrets Manager client\n",
    "    session = boto3.session.Session()\n",
    "    client = session.client(service_name=\"secretsmanager\", region_name=region_name)\n",
    "    try:\n",
    "        get_secret_value_response = client.get_secret_value(SecretId=secret_name)\n",
    "    except ClientError as e:\n",
    "        raise e\n",
    "    secret = get_secret_value_response[\"SecretString\"]\n",
    "    return json.loads(secret)\n",
    "\n",
    "\n",
    "my_secrets = get_secret()\n",
    "\n",
    "# alternative...\n",
    "engine = sqlalchemy.create_engine(\n",
    "    \"postgresql://postgres:%s@suno-main-pgdb-prod-analytics.cnfvffydbwvc.us-east-2.rds.amazonaws.com/suno_main\"\n",
    "    % quote(my_secrets[\"password\"])\n",
    ")\n",
    "\n",
    "\n",
    "home_dir = os.path.expanduser(\"~\")\n",
    "snow_password_path = os.path.join(home_dir, \".aws\", \"snow_pw.txt\")\n",
    "if os.path.exists(snow_password_path):\n",
    "    # !pip install snowflake\n",
    "    from snowflake.core import Root\n",
    "    from snowflake.snowpark import Session\n",
    "\n",
    "    with open(snow_password_path, \"r\") as fp:\n",
    "        fp_lines = fp.readlines()\n",
    "        snow_password = fp_lines[0].strip()\n",
    "        snow_username = fp_lines[1].strip()\n",
    "\n",
    "    CONNECTION_PARAMETERS = {\n",
    "        \"account\": \"fu90569.us-east-2.aws\",\n",
    "        \"user\": snow_username,\n",
    "        \"password\": snow_password,\n",
    "        \"role\": \"ACCOUNTADMIN\",\n",
    "        \"database\": \"SUNO_PROD\",\n",
    "        \"warehouse\": \"SUNO_PROD_LARGE\",\n",
    "        \"schema\": \"PROD\",\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validate some info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:11:08.550447Z",
     "start_time": "2024-05-26T00:11:08.397196Z"
    },
    "execution": {
     "execution_failed": "2024-09-02T04:29:36.833Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-06-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "# there are 4 hr time difference between eastern time and utc\n",
    "cutoff_date = \"2024-06-01 00:00:00\"  # super long -- careful if you run this\n",
    "# cutoff_date = \"2024-08-26 21:00:00\"  # v4-t3 out\n",
    "# cutoff_date = \"2024-09-2 04:55:00\"  # v4-t3-5 out\n",
    "# cutoff_date = \"2024-09-4 05:20:00\"  # v4-t2-16 out\n",
    "# cutoff_date = \"2024-09-4 15:45:00\"  # v4-t2-17 out\n",
    "# cutoff_date = \"2024-09-6 21:00:00\"  # v4-t3-r out\n",
    "# cutoff_date = \"2024-09-07 04:00:00\"  # v4-t3-r out\n",
    "# cutoff_date = \"2024-09-07 17:20:00\"  # v4-t3-r out\n",
    "# cutoff_date = \"2024-09-07 22:30:00\"  # v4-t3-repro out\n",
    "# cutoff_date = \"2024-09-07 14:00:00\"  # v4-t3-repro-4 out\n",
    "# cutoff_date = (datetime.datetime.now() - datetime.timedelta(hours=2)).astimezone(datetime.timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "print(cutoff_date)\n",
    "\n",
    "target_model_name = \"chirp-v3p5-engine-t-3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:11:09.349857Z",
     "start_time": "2024-05-26T00:11:08.551408Z"
    },
    "execution": {
     "execution_failed": "2024-09-02T04:29:36.833Z"
    }
   },
   "outputs": [],
   "source": [
    "df_all_tables = pd.read_sql_query(\n",
    "    \"SELECT table_name FROM information_schema.tables WHERE table_schema = 'public'\",\n",
    "    engine,\n",
    ")\n",
    "# should have all the basic table names here\n",
    "assert df_all_tables[\"table_name\"].nunique() >= 61"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get the full song query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Clips: 588,799 rows\n"
     ]
    }
   ],
   "source": [
    "\n",
    "generated_clip_query = f\"\"\"\n",
    "SELECT * FROM bots_generatedclip\n",
    "WHERE status='complete' AND created_at>='{cutoff_date}' AND (request_id IS NULL) AND (model_name IS NOT NULL AND model_name != '') AND (upvote_count >= 1)\n",
    "\"\"\"\n",
    "total_clip_df = pd.read_sql_query(generated_clip_query, engine)\n",
    "print(f\"Total Clips: {total_clip_df.shape[0]:,} rows\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_clip_df[[\"continued_parent\", \"duration\", \"source\", \"clip_type\", \"task\"]] = pd.DataFrame(\n",
    "    total_clip_df[\"metadata\"].map(parse_metadata_for_basics).tolist(), index=total_clip_df.index\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "execution_failed": "2024-09-02T04:29:36.833Z"
    }
   },
   "outputs": [],
   "source": [
    "# parse out the necessary metadata early\n",
    "total_clip_df[[\"continued_parent\", \"duration\", \"source\", \"clip_type\", \"task\"]] = pd.DataFrame(\n",
    "    total_clip_df[\"metadata\"].map(parse_metadata_for_basics).tolist(), index=total_clip_df.index\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "execution_failed": "2024-09-02T04:29:36.833Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total clips: 588799\n",
      "total without model: 0\n",
      "uploads: 0\n",
      "stems: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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Xv464rVLG9ta3vjU2bNgQN99884jfz507N1paWkZsfzTe+ta3xurVq2PhwoVDvxsYGIi2trbYYYcd4o1vfGPJt/ma17wmPvjBD8b9998fbW1tL/n7xo0bY86cObFq1aqSb7uY52Nz/u///b+xww47xHnnnRcdHR0v+fvTTz8dra2tERExbdq0iIihn/NuuummEX+PeOG5/+1vfzvicrfddlvBKuhibL/99pt9jQxvdxLxQjuW1772tSNed1vyox/9KPr7+4d+vuWWW2JgYGDosXvLW94SO+64Y1x//fUjLpeXy+VKuRujkvRzAABQz7RzAQCoc/fdd188+eSTsWHDhujo6IgHH3wwHnjggdhjjz3i2muv3WKrhe985zvx29/+NqZNmxZ77rlnZLPZ+OEPfxi77757vOENb4iIiNe+9rWx8847x6233hqveMUrYocddohDDjlkqMq2VOPGjYuPfOQjcdxxx0U2m43W1tbYZ5994kMf+tDQZT74wQ/Gf/7nf8b//b//N/75n/85nn766Zg3b95LWtOUMra3v/3tceSRR8Y3vvGNeOaZZ2LixInxwAMPxD333BMnn3zyFtvelOKEE06IH/3oR3HOOefE73//+9hzzz3jP//zP+ORRx6Jc8899yV9sIt1zjnnxMqVK+PCCy+Mn/3sZ/G2t70tdt555/jrX/8aP/3pT+PJJ5+MY445puTbLeb52JzXvva1cfnll8fnP//5eM973hPve9/7Yv/994++vr5YsmRJ/PSnPx1acPaAAw6ID3zgA/GjH/0ouru7441vfGP87ne/i7vuuive+c53xpvf/Oah2/3gBz8Y559/fnzmM5+JKVOmxJ/+9Ke4//7741WvelXJ9y3vH//xH+OWW26Ja665JvbZZ5/YZZddYvLkyXHMMcfEm970pvjHf/zHeOUrXxm/+93v4j//8z/jpJNOKup2+/v745RTTol//ud/jj//+c/xwx/+MN7whjfEO97xjoh4oe3NrFmz4qyzzorjjjsu3vOe98Quu+wSzz77bNx7771x+OGHx8yZM8u+X6VI+jkAAKhnQnQAgDr3rW99KyIixo4dG6985Stj//33j3PPPTeOO+64rQa2b3/72+OZZ56JO+64I/72t7/Fq171qnjTm94Un/nMZ2KnnXYaut1LLrkkrrzyypg1a1YMDAzExRdfXHaI/slPfjKWL18eN9xwQ6xduzYmT54c559//lBVecQLFbznnHNO3HTTTXHRRRfFQQcdFNddd118/etfH3FbpYxtm222iWuvvTa+9a1vxcKFC+POO++MPffcM84666w49dRTy7ovm/Pyl7882tra4vLLL4+77rorenp64nWve11cfPHFQ6FyObbffvv47ne/G3feeWf8+Mc/jmuuuSbWr18fr371q+PII4+Myy+/PHbbbbeSb7eY56OQd7zjHXH33XfHjTfeGPfcc0/ccsstsd1228XEiRPjnHPOGRHEX3jhhbHXXnvFXXfdFb/4xS9i/Pjx8YlPfCLOOOOMEbf5oQ99KNrb2+P222+PRYsWxRve8Ia46aab4pRTTin5vuV9+tOfjmeffTa+973vxdq1a+NNb3pTTJ48OaZPnx7/9V//FQ888ED09fXFHnvsEZ/73OfitNNOK+p2Z86cGfPmzYtvfetb0d/fH8ccc0ycd955I9rH/Mu//Eu8+tWvjhtuuCFuvPHG6Ovri9122y2OOOKIUb0eypHkcwAAUM9aBpNYGQkAAEjUgw8+GB/96Efjm9/8Zhx99NFJD6eu3HnnnfHlL385br/99jj44IOTHg4AAFWmJzoAAAAAABQgRAcAAAAAgAKE6AAAAAAAUICe6AAAAAAAUIBKdAAAAAAAKECIDgAAAAAABQjRAQAAAACgACE6AAAAAAAUMCbpAeTdcMMNccUVV8RHP/rR+Pd///eIiOjt7Y1LLrkkFi5cGH19fTF16tQ4//zzY/z48UPXe/bZZ2PWrFnx4IMPxg477BDvf//74wtf+EKMGVP6Xctm14RlVgEAAAAAGl9LS8Suu+601culIkR/9NFH49Zbb42JEyeO+P1FF10U9957b1x11VWx0047xQUXXBBnnHFG3HrrrRERsWHDhvjEJz4R48ePj1tvvTWee+65OPvss2Ps2LHxb//2byWPY3AwhOgAAAAAAAxJvJ3L2rVr40tf+lJceOGFMW7cuKHfr1mzJu64444455xzYvLkyXHQQQfFRRddFEuWLImlS5dGRMT9998fK1asiMsuuywOPPDAmDZtWnz2s5+Nm2++Ofr6+hK6RwAAAAAANIrEQ/SvfvWrMW3atJgyZcqI3y9btiz6+/tH/H7fffeNPfbYYyhEX7p0aey///4j2rtMnTo1enp6YsWKFTUZPwAAAAAAjSvRdi4LFiyIP/zhD3H77be/5G8dHR0xduzY2HnnnUf8ftddd43Vq1cPXWZ4gB4RQz/nLwMAAAAAAOVKLET/61//Gl/72tdizpw58bKXvSypYQAAAAAAQEGJhei///3vI5vNxnHHHTf0uw0bNsTDDz8cN998c9x4443R398f3d3dI6rRs9lsTJgwISJeqDp/9NFHR9xuR0dHRMTQZQAAAAAAoFyJhehvfvObY968eSN+9+Uvfzn+1//6X/Gxj30sXvOa18TYsWNj8eLF8e53vzsiIp588sl49tln47DDDouIiMMOOyyuu+66yGazseuuu0ZExK9//evYcccdY7/99qvp/QEAAAAAoPEkFqLvuOOOsf/++4/43Q477BCvfOUrh35//PHHxyWXXBLjxo2LHXfcMS688MKYNGnSUIg+derU2G+//eKss86KL33pS7F69eq46qqr4sQTT4ztttuu1ncJAAAAAIAGk+jColtz7rnnxjbbbBNnnnlm9PX1xdSpU+P8888f+vu2224b1113XcyaNStOOOGE2H777eMDH/hAnHnmmQmOGgAAAACARtEyODg4mPQg0qKjY014NAAAAAAAGl9LS8T48Ttt9XLb1GAsAAAAAABQl4ToAAAAAABQgBAdAAAAAAAKEKIDAAAAAEABQnQAAAAAAChAiA4AAAAAAAUI0QEAAAAAoAAhOgAAAAAAFCBEBwAAAACAAoToAAAAAABQgBAdAAAAAAAKEKIDAAAAAEABQnQAAAAAAChAiA4AAAAAAAUI0QEAAAAAoAAhOgAAAAAAFCBEBwAAAACAAoToVFwmsyouvfSiyGRWJT0UAAAAAIBREaJTcZnMqrj88kuE6AAAAABA3ROiAwAAAABAAUJ0AKAqtPcCAACgEQjRAYCq0N4LAACARiBEBwAAAACAAoToAAAAAABQgBAdSAW9kwEAAABIIyE6kAp6JwMAAACQRkJ0AAAAAAAoQIgOAAAAAAAFCNEBAAAAAKAAIToAAAAAABQgRAcaRnv7ymhvX5n0MAAAAABoIEJ0qLJMZlVceulFkcmsSnooDa29fWVMnnJETJ5yhCAdAAAAgIoRokOVZTKr4vLLLxGiV1kul43e9euid/26yOWySQ8HAAAAgAYhRAcAAAAAgAKE6AAAAAAAUIAQHQAAAAAAChCiUxSLYwIAAAAAzUiITlEsjslolHMSxokbAAAAANJAiA5UXTknYZy4AQAAACANhOgAAAAAAFCAEB0AAAAAAAoQogMAAAAAQAFCdKomm+2I8847O5YufSTpoQAAAAAAlEWITtWsWPFY3PDd6+LYfzk62ttXJj0cAAAAAICSCdGpmp6enojBwejrXR+5XDbp4QAAAAAAlEyIDgAAAAAABQjRAQAAAACgACE6AAAAAAAUIEQHAAAAAIAChOgAAAAAAFCAEB1IlUwmE+3tK5MeBgAAAABEhBAdSJkZp06PyVOOiEwmU9L1stlslUYEAAAAQDMbk/QAAIbr610fERHd3Z0lXS+X66jCaAAAAABodirRSZ1MZlVceulFkcmsSnooAAAAAECTE6KTOpnMqrj88kuE6AAAAABA4oToAA3ADA4AAACA6hCiAzQAMzgAAAAAqkOIDgAAAAAABQjRSZ1MJpP0EAAAAAAAIkKITo20ts4pqs1Ee/vKOGXGSREhTAcAAAAAkidEpyzt7SujvX1l0Zdva5tbVIiey2Wjv683IiK6uzvLHR4AAAAAQEUI0dmq9vaVI6rC29tXxuQpR8TkKUdsMUjv6uquxfBKUmr4D8Df2YcCAADQjMYkufEf/vCHccstt8QzzzwTERGvf/3r4/TTT49p06ZFRMT06dPjoYceGnGdE044Ib761a8O/fzss8/GrFmz4sEHH4wddtgh3v/+98cXvvCFGDMm0bvWMPKB+caNG4d+l8tlo3f9uqH/32uvvTd73etvuKbo7WQyq6K1dU688Y1Hjm7AW5C/LxERi3/924LjJh3mzbs76SEAw9iHAgAA0KwSTZp33333+OIXvxj77LNPDA4Oxo9//OP49Kc/HXfddVe8/vWvj4iID33oQ3HmmWcOXWf77bcf+v8NGzbEJz7xiRg/fnzceuut8dxzz8XZZ58dY8eOjX/7t3+r+f1pRMMD81JtGBgo+rKZzKq4/PJL4tprv1vWtopRbPhPOixcOC/pIQDD2IcCAADQrBJt5/L2t789pk2bFv/wD/8Qr3vd6+Lzn/987LDDDrF06dKhy7z85S+PCRMmDP3bcccdh/52//33x4oVK+Kyyy6LAw88MKZNmxaf/exn4+abb46+vr4E7hERFgQFAAAAABpHanqib9iwIRYsWBDPP/98TJo0aej38+bNiyOPPDKOPfbYuOKKK2Ldur9XRS9dujT233//GD9+/NDvpk6dGj09PbFixYqajp+/syDoC5X1l156UVGLqTaDbLZjxH8BAAAAoF4k3jh8+fLl8a//+q/R29sbO+ywQ3znO9+J/fbbLyIijj322Nhjjz3i1a9+dSxfvjwuv/zy+POf/xzf/va3IyKio6NjRIAeEUM/r169urZ3BIbJt6c5+uj3JD2UVMjlsiP+CwAAAAD1IvEQ/XWve138+Mc/jjVr1sR//ud/xtlnnx0/+MEPYr/99osTTjhh6HITJ06MCRMmxCmnnBJPP/10vPa1r01w1GxJNptLeggAAAAAABWReDuX7bbbLvbZZ5846KCD4gtf+EIccMAB8f3vf3+zlz300EMjIuKpp56KiBeqzjs6RraHyP88YcKEKo66eW2tHUd7+8qYNeu8Go0GAAAAAKC6Eg/RN7Vx48aCi4L+8Y9/jIi/B+SHHXZYPPbYY5HN/r1FxK9//evYcccdh1rCUFlba8eRy2VjYKC/RqMBAAAAAKiuRNu5XHHFFfHWt741XvOa18TatWtj/vz58dBDD8WNN94YTz/9dMybNy+mTZsWr3zlK2P58uVx8cUXxxvf+MY44IADIuKFRUT322+/OOuss+JLX/pSrF69Oq666qo48cQTY7vttkvyrlEB+mdTjExmVbS2zolddx2/9QsDAAAAQIkSDdGz2WycffbZ8dxzz8VOO+0UEydOjBtvvDGOOuqo+Otf/xqLFy+O73//+/H888/Ha17zmnjXu94Vp59++tD1t91227juuuti1qxZccIJJ8T2228fH/jAB+LMM89M8F5RES0tMWv2zPjnf/6X2GuvvZMeDSmWX8T1y1/WRqjZ5U+onHzyqbHbbrsnPRwAAACgQSQaol900UUF//aa17wmfvCDH2z1Nvbcc8/47ne/W8lhkQaDg9Hf1xe5XFaI3iQymUzSQ6DO5U+oHH30e4ToAAAAQMWkric60Hza21fGKTNOSnoYAAAAAPASQnQgcblcNvr7epMeRl3LL7A8fKFlAAAAAEZPiA7QAHK5jhH/rUft7SujvX1l0sMAAAAAGEGIDlCETGZVXHrpRZHJrGqobaVFe/vKmDzliJg85QhBOgAAAJAqQnTqWjOGjSQjv2hlrUL0Wm0rLXK5bPSuXxe969dFLqclDQAAAJAeQnTqWjOGjQAAAABA7QjRAQAAoI6ZoQsA1SVEBxpONlu/i2sCAKURHoIZugBQbUJ0oOHoqQ0AzUN4CABAtQnRAQAAAACgACE6dau9fWVkMpmkhwFQV9rbV0Z7+8qkhwEAAAB1Q4hOXWpvXxmTpxwRp8w4aeh3+mGyNV4jNLv8vnPylCME6TQ1nweQTt6bAEBaCdGpS7lcNnrXr4v+vt6h3+mHydZ4jdDs8vvO3vXrrB1AU/N5AOnkvQkApJUQHWgIXV3dSQ+BYUZbSaYSDQAAAEgLITpQ1zKZTERLS1x/wzVJD4VhRltJphINAAAASAshOlDXurs7IwYHY8PAQNJDAQAAAKABCdFJlc7OzqSHQIVlMqti3ry7kx4GAAAAAJRFiE6qdHd3lXW9bLajwiOhUjKZVbFw4bykhwEAAAAAZRGi0xByuWzSQwAAAAAAGpAQnabU2jrHgoUAAAAAwFYJ0WlKbW1zheg1kMmsitbWOUkPA+pKJrMqLr30oqbdRzX7/QcAACB9hOhA1WQyq6KtbW7Sw4C6ksmsissvv6RpQ+Rmv/8AAACkjxAdoAra21dGe/vKpIfR8DzOjUvbLQAAANJCiM4IptHTLKr5Wm9vXxmTpxwRk6ccIeCtIo9zY9N2CwAAiifPgeoSojOCafQ0i2q+1nO5bPSuXxe969fFH//4BwFvlQx/nHO5bNLDYTMymUzSQwAAgKYgz4HqEqJTM9lsR1VvX1hDGs04dXrZldJpbWehhUptZLMddV1JsnTpI/HRj344IuyfKY7qKQAAIK2E6NRMNStFc7lsnDLjpIgQ1pAufb3ry66UTmM7i1q0UKn2CbdaKzcYzOWydV1J8sQTj8eGDQMREdHd3ZnsYKgLw6unahWop/VkJQAAkC5CdBpCT09P9Pf1RoSwBqqpFi1UGq01i2mVULpavW/SeLISAABIHyE6DafRArh61d6+0qyABpTNen8BANXlGBIASBshOo2lpSVmzZ6pX3PC8i0/8i12KM3wL45p+xKZyzVWqxWqI5NZFfPm3Z30MACoM5lMJqKlJWacOt3xPDQpa6QAaSVEp6C6rCQeHIz+vj7V6AnLt/zIt9ihNMNbEmlPRD3KZFbFwoXzhn62TwagGN3dnRGDg9HXu95nBzQprRCBtBKis1mZTEYlMdBwVLYkwAwhAAAA6pwQnc3q7u5USQw0HJUtCTBDCKiR1tY59u8AAFSFEB0AAKh7bW1zhegAAFSFEJ2KymZVGmYyq6K1dU7Sw6BKVLkBAAAANBchOps1b97dZV0vl+sY1Xa7urpHdf00yGRWRVvb3KSHQZWockunbHZ0+54t0UcdgHLlP0OWLXvUZwkAQB0TorNZCxfOS2S7199wTVnX6+lZU+GRQGHVDGzTLJNZVfYJtmortt92/rkr5TnUR70+ONkBpFH+M2T58j/6LAEAqGNCdFJlw8BAWdfr6emp8EigsGZdIDGTWZXYCbatyeWykclkirrc8P/SOJzsAAAAoFrGJD0AaBbFBHyju/0XerGffPKpsdtuu1d1W/Uum+2I+fPTWVFNeWbNnhkRLUkPo65Uc0aF9TEAAABoJCrRocoymUxES0vMOHV6tLevrOJ2VGEWK5fL6lvfYPr7+qK/rzfpYdSValbjj3Z9DAAAAEgTITpUWXd3Z8TgYPT1rtdCgrpW7dkUjE57+8qqnqgDAACAZiVEp2mVGgjWYtG6Wi+MJ3RLv62deMm/Zjo7u6o6jvb2lXHKjJOqug3K196+MiZPOSImTznCexoAAEicvIFGI0SnaXV3d5Z0+Vq0S6llSxahWx1oaYlZs2du8fnJv2ZKfT2XKpfLapeSYrlcNnrXr4ve9evMeAEAABIlb6ARCdGhSQnd6sDgYPT39Xl+mlQ2m0t6CAAAACWTN9CIhOg0hCVLHkl6CFBVW2v1c9999xZ9W6bVlSapXvCzZn8l0e0DUB98rgMAVJ8QnZJ0dXVGa+ucpIfxEkuXLqnq7Wcyq1J5vxm91tY5NetBPxpba/XzwAOLirqdRppWl81Wv6JheC/4a665uqavlYH+vogovfUUAM2jkT7XAQDSTIhOSbq6uqKtbW7Sw6i5TGZVU97vZtDWNjd1IXo123g00rS6XK6jBtv4ey/4O+/8j9S9VgBobo30uQ4AkGZCdFItm61+SJZG2WzHFlt30JgymVVx3nlnx6xZ/570UACoMS05XvgcnDnz3Dj//HMdAwEAkCpCdFKhUFuGZq2oyeWyW2zdQWPKZFbFDTdcGwMDA0VfZ9P3zvz5d3vdpIhWUEAxtOR4QSazKq677ttx7bXf9llG6mxtfRoAoLEJ0UmFWrRloLm1t69syAUaN33vLFgwz5e7FNEKCkqXyWQacn+9JVpyQPptbX0agEZn1hzNbkzSAwCotnyF38aNGxMbw5/+9Kf46U8Xxsknnxq77bZ7YuOoB2ldwBiojRmnTo/BwcGkhwEAwIvy36kjIhb/+rex1157JzwiqD2V6DQtbS+aR77CL79AZM21tMTnPn+G6qUijXYB42ZdSwHSZDRtD/p61ye3v64QbR8AgEZi1hwI0Wli2l5QM4ODMdDfl/QomoaDOkhes7c9aPb7DwAAjUaIzhAL4AGQJvXaF1u/yL9rbZ0jSAagaD5DAUgrITpDLIBHEpy8gfpTk5Y5LS0x49TpdfdFeunSR+JNbzosJk95Q92NvRra2ubGH/6wTGsTALYq33N58pQjfIYCkDpCdCBRTt5A/alJy5zBwejrXV937XmeeOLxGBjoj9719Tf2asnlslqbALBVei7T3r6ybmciAo1PiA4JqpeFx+plnM2mvX1lZLO5pIdR10wZBqAUPjeqJ5NZFfPm3Z30MICE5GcinDLjpKSHArBZQnRqZv78u4Wwm6iHhcfyAXpS46xJ24g6lD/InDXrvKSHUrdMGQagFGn/3Kj3NQgymVWxcOG8pIexVfX+OMNwaSqWys9E6O/rTXooAJslRKdmFiyYl4oPZ0qTdLsVUzk3L3+QOTDQn/RQ6pYpwwCUIu2fG21tcx1r14DHmUZSD0VdAGkhRAdoUtlsR2oqT7LZ9IURSdEHsnQWKAZKkabKS9iU1ycApJMQHaBJpWmxv1zu7217Fi26N8GRJK+7uzPpIdSdpGfMAPWlHiovBanNqx5en1Aq+7Tm1to6J5Yte9RrgLonRAcgVe6/f1HSQ2hI7e0rG7LKvVHvF9DcBKlAI7FPa25tbXNj+fI/eg1Q94TojEqa2kGU47rrvlO3Y6exaEdBNeUXwztlxklJD6WiGvV+AY3BZzsAQONINET/4Q9/GP/yL/8Shx9+eBx++OFxwgknxL33/n0af29vb8yePTuOPPLImDRpUnzmM5+Jjo6OEbfx7LPPxsc//vE49NBDY/LkyfH1r389BgYGan1Xmlaa2kGU4/bbf7TVsfsCRC1oR1Ef6vXEYX4xvP6+3qSHUlGNer+A4qX5OM1nO8DotLevjPb2lUkPAyAiEg7Rd9999/jiF78Yd955Z9xxxx3x5je/OT796U/H448/HhERF110Ufzyl7+Mq666Ktra2uK5556LM844Y+j6GzZsiE984hPR398ft956a1xyySVx1113xbe+9a2k7hINaDRfgLLZjpg37+7KDggqrLV1Tt2Fwkl58skn6vrEYdqlOQwjGfZPbI2gGkgzvcDLl8lkYvKUI2LylCO2GKR7jIFaSTREf/vb3x7Tpk2Lf/iHf4jXve518fnPfz522GGHWLp0aaxZsybuuOOOOOecc2Ly5Mlx0EEHxUUXXRRLliyJpUuXRkTE/fffHytWrIjLLrssDjzwwJg2bVp89rOfjZtvvjn6+vqSvGsQES9USS5cOC/pYTSdXC6b9BDqSlvbXAedReru7kp6CA1NGMam7J8AqGd6gZevu7szetevi97167b4/c5jDNRKanqib9iwIRYsWBDPP/98TJo0KZYtWxb9/f0xZcqUocvsu+++scceewyF6EuXLo39998/xo8fP3SZqVOnRk9PT6xYsaLWdwFGRQVmhbS0xKzZM7dYrWB2ADSubNZJNACqy4LWANB8Eg/Rly9fHpMmTYqDDz44zj///PjOd74T++23X3R0dMTYsWNj5513HnH5XXfdNVavXh0RER0dHSMC9IgY+jl/GUhSKWFtvVRgpn663OBg9Pf1bbFaweyAdEj6tVTNsDV/3wS6tZfLdWz9QlCipPdXQIq0tMSMU6fr0wxFcExMXjabS3oIMGqJh+ive93r4sc//nHcdttt8eEPfzjOPvtsVeQ0jEYMa02Xo1KSfi1VM2zN3zeBLjSGpPdXQIoMDkZf73rtA6EIjonJW7t2TdJDgFFLPETfbrvtYp999omDDjoovvCFL8QBBxwQ3//+92P8+PHR398f3d3dIy6fzWZjwoQJEfFC1XlHx8idcf7n/GWobw5OAWgmKp7Tr719pQpUAABoMomH6JvauHFj9PX1xUEHHRRjx46NxYsXD/3tySefjGeffTYOO+ywiIg47LDD4rHHHhsxNejXv/517LjjjrHffvvVeuhNLZvtiEsvvSg6Oyu46F4Rva2h3uTfK2kIyDo7O5MeQlPJZlXgsHUqnmuvlPdmJpOJyVOOiMlTjnB8AkDDSNN3FIC0SjREv+KKK+Lhhx+O9vb2WL58eVxxxRXx0EMPxb/8y7/ETjvtFMcff3xccskl8Zvf/CaWLVsW5557bkyaNGkoRJ86dWrst99+cdZZZ8Wf/vSnWLRoUVx11VVx4oknxnbbbZfkXWs6uVw2Lr/8kuju7qzcjRbR27qRWKCoOeTfK2k4QO3uruBJL7aq3vdl9lE0qlLem93dndG7fl30rl9X9+9pAMhL03eUemBWGjSnREP0bDYbZ599dhx99NFxyimnxO9+97u48cYb46ijjoqIiHPPPTf+9//+33HmmWfGSSedFOPHj4+rr7566PrbbrttXHfddbHNNtvECSecEF/60pfi/e9/f5x55plJ3aWG19Ojj1U1ZDKZOGXGSUkPI5Xmz7/bwVwKWAyoubW3r6z6PiqTWRWtrXOquo0kadOSXj5naGaCIIDStLevLHpWmn1s6Rwzk2Zjktz4RRddtMW/v+xlL4vzzz8/zj///IKX2XPPPeO73/1upYdGAT09PVv8+3333VujkdROLaovu7s7o7+vt+rbqUcLFsyLz3/+S7HbbrsnPZSmZjGgdKv2SY5cLlv1fVQmsyra2uZWdRtJyrdpOfro99T1/ix/suPkk0+t6/sxnM8ZmlU+CIqIWPzr38Zee+2d8IgA0i+Xy0bv+nVD/19o32kfW55GOWamMaWuJzr17YEHFiU9hIqraIsaIsIZ+bTo6tLOpVFU8yRHe/vKRFu5aCOTLnq2Uwqf9+mWD4K0JwKovCT2sY6b659jp3QTogM1Vcr0ty3JT/Oq6GK2FVIXrU9aWuKGG65NehRVoTVD5eTfr4m1m2ppiRmnTncgCRVSyynSlfq8b0bVbm+Vfx3UxfEKAEUZ3n5RmF6fHDulnxAdSlSLlcvnzbu7aredtEqdkc9XQyYxU2BrIW1dtD4ZHIyBgYGkR1EVCxbMa4oQvRZhWP79Wot2U5sNcwYHo693vQpJmk613t+1nEmgyrl8W2tvNdrXR/51UBfHK0DdGu0Jwc7OzsoNpgkMb79oNn19cuyUfkJ0KFEtVi5fuHBe1W47SblctiHOitdzSNsIjz8vaLS2GsIc+LtGe39TWV4fsHmN0gahUe7HaNe76e5O34xjoLkJ0WEzrAhdHbNmz0yuLUTKZbOVCxALBeXDp/jROK699ttx++23Df3c2jqnpvuuSuwvM5lVDT0DB2qhEY5dat1epBazC/OK2cdpswLla5Q2CI1yP2g8irFAiA6bpcKnOvr7+mrSFqIeVWy61hZ6SA+f4lfvhK5/d8cdt8U99/x86Oe2trlF77sqFYCPdn+Zyaxq2Bk4FKfaPaCbQSMcu9R6RkotZhfmFbOPa9Q2K41wgof0S3sbhGKLHNJ+P6gPXV2dRe13i90/K8aCFwjRaRrOnNIUmqSHtNC1MmoZutkHsyWjnfINpFcjnOCB0SqlyAFGq6urq6j9brH750YqxoLREKJTkiVLHkl6CGVx5hSgsEwmU90pw1uYIQE0vqrvY6BMquQhvXp61iQ9hLqgFRjUjhCdkixduiTpIZTFmdN0uvbab8d11327arfvgIJC5s+/Ozo7LVaUN+PU6dXtvdkkMySAvxv+2VvOPqaSa4XQ2EYThKuSh/Tq6elJegh1oVFbgdVy3RIolhAdSMwdd9w2YkHESmvUAwpGb8GCedHd3Zn0MFKjr3e93ptARQ3/7C1nH2N/RLEE4QCNp5brlkCxhOg0vEovVpbN5ip2W1CqYhclSsvtUh0WYQSoLftdSqFNDJvjeBugvgnRaXiVXqxs7Vq92aitbLZj6It7tRYlsthRfanEfq2rq1Mg1AQEOVRas76mGmnx23yrnM7OzmQH0sBUx7M5jrdJEyeHoXRCdGhC7e0rI5PJJD0MipTLZRvmizvp0dXV5XXVBAQ5VJrXVP3Lt8rp7rY2CECzaqSTw1ArY5IeANSbrq70fOHIL9C11157l3SdyVOOiI0bN1ZrWFAx1VxYrquru2q3naf9U2PJ73PHjh0bra1z4uSTT43ddts94VFRS97TjcXzCVA9w4/j7W+BRqASHUrR0hI33HBt0qOIiL+H4ZOnHDEU7BQjl8tG7/p10d/XW8XRQWVUc2G562+4pmq3nTdr9ldKunwmsyrmzbu7SqOpnPnz7266KtTh+9xHH/2fpqzETWrabxpmTmUymYiWlpLf06ST5xNoJGltszX8OD6/v03DZ/pwtXzsitlWWp/LRpN/nJcte9TjTUmE6FCKwcEYGBhIehQR8fcwvHf9uqoGjdCoNtTgvTzQ31fS5TOZVbFw4bwqjaZyFiyY13QHm8P3ud3dnUkPJxFJTftNw+Pd3d35wjFAie/pNGtvX1nSSfhG0ojPJ+nWzO83qq8e2mzl97fV/EzPZDIlv89q+dgVs62knstyHrt6ln+cly//Y+rfO6SLEB1SrNJn6i0e0jw21wal3NdTvVRHA5SrtXVOU32BymQyZc1mq6Wurk7VYWVIW5Un5c8epTZU/jaOGadO9z4rk8cOiiNEh7RqaYkZp06v6AfZaKsIq9mfOk02PZiux5MPm85OaG9fGafMOKms26qX6mhKs2jRvUkPoSkJuNKprW1uUwUo3d2dQzMrvvGNy1J537u6ulSHlSENMzcKabaTVXlmj5Ynm+2oSbidtirubNZrpFx9veu9z4q06ffbUh+7NO7PnRCjFoTokFaDg9HXuz5VBwFpGks1bXow3Qgrl+dyWX3wK6Begudigtr7719Ug5GQP6BfvvxPVTk5Ohq+bBDRfCcQSI7XGqXI5bIlhduNEj7ncuksWnLM0FhG+/02jfvzck6I5Wdbp/GkAOkkRIcUsFo51Id6CZ7TXInYbPIH9O3tT6fu5Gjaqu+g2Zmpkpxm6wfciNIaPjcKxww0ovxs6zSeFCCdhOiQAvnVygFoHNlsx9BU2fvuq49ZDFSe6j2KMbztWjWLK2q5zkn+tV8PFcLV6gc8/HMA0qYeW1YCJEmITuptrh9eNQ7G8wf6y5Y9WtTBRCX7g+dXK6+2evgSU6z8479mzZqERwKV4YtM48nlskNTZR94oD5mMdSrNAfVqvcaQ7WrxIe3XVu7tvxjm/nz797ia62W65zkX/v1UCFcrV7Kwz8HIG0aoWUljWX496FanfCFUgjRSb3N9cOrxsF4/kB/+fI/FnUwkZbp+KWohy8xxco//mvX9iQ8EppRNcIUX2SotfzruBHaGAiqqbZ6aZO1YME87wMA6tLw70O1OuFbivb2lXV/zMzoCNGhSdRy+m415asNOzu7KnibepA2u5JmlhSxOKTXVLo1ygHwaKqvh7eOOGXGSVVpY0B6NcLxQCGVnClI+cqZXdVIMybLUU/tb2qhUdaMSusxYXv7ytSOLe3KeY9q7VQ71VoktL19ZUyecoRj5iYnRCf1Ojs7I8KB9WjVcvpuNeWrDStWEVZEIFoNjboCeP4L4IoVj0dE/bxvS5pZUsTikPVSsTga9foabqQD4Pz+8A9/WFZymD68dUR/X2/JbQzS8t4WOpWnEY4HCqnHmYKNqNDsqi29VxtpxmQ56qn9TdW1tFR9zahafX6k8ZgwfyyUP5lOacp5jzZia6e0HntVa5HQXC4bvevXVaX1V6Wluc1hvROiU3OlVgB2d79QceyAkqooIhCthkZdATz/BfCpp/4cEd63lZamCup6fQ3X0wFwsTbX9qz620zHe1voBOXr6qrcrL5iea9SlMHBqq8Z1cyfH/ljofzJdChHM7536oU2h9UjRKemMplMTSoAN7cYKVRSWs+8k7xqtU4aXkFt+i3D5ae8p6m3+fDXaCO3DqE0Wq0UZ9Gie4f+v9KzfoY/BzfccG3FbrfepHkWi31m6drbVzZM+5dSWZi+MZWTZzTrewBqSYhOTXV3d9akAjCJqrwk+VJae4XOvDdK73nKV63WScMrqNM4NZjay2QyI6a8zzh1eira1Azvtx7R2K1DKE2jzP6otvvvXzT0/5We9TP8ORgYGKjY7Sal3AAxzVXI9pmlyRcZzJp13tDvmum7kYXpayeTWRUzZ54bt99+W9W3VWyeMXwfWO0WSEkppnjIySRqZUzSAwBGL5fLxq67jq/pNlXCbl6j9J4H0q+7u3PElPe+3vUR8cJnwl577Z3YuIb3WweoJgEi+SKDTX8HlZbJrIrrrvt20sMYYfg+sNotkJJSTPGQzwJqRSU6FdHV1Zn0EKoim+2oi6riWh4o5isfa7kYZ62rSfLba6YqFpLnxBQAABSvvX2lY2igZoToVEQSCxPVQi6XTX9VcUtLzJo9s2YHD/nKx1ouxlnrapL89lSxUEtatFALae4DDPUm/35qlvaBzUyrACIq855vliKdWrS4zLfyGd5CDqCahOhQ7wYHo7+vTwAHUIRmr1ZKcx9gqDf595MQvfFpFUBEZd7zzVKkU4sWl/lWPlrIAbUiRIcGUYu2M/Pn3x2dnY056yDihWqGWi7IV86q6xER1133HVWkUIZNF7xMG5WOjae1dY79NaRQqSdU6+kEbGvrHCd2KFlnZ2fSQ4CGZNZaYxGi0/Qa5cttLdrOLFgwr6iK92o8pqW2ICh1quTy5X+MyVOOiMlTjohsNlfOEEtW7Krrm7r99h+pIk2BZpmOW0lJP2ZpX/By00rH0R50C+WT19Y21/4aUmb4CdW//OUvRV0nzTM+Nz1Gbmubm6qwRoBUPZX8nO/ubtxCKUiSWWuNRYhO0/PltvKq8ZiW2oKg1KmS7e0ro3f9uuhdvy7Wrl1TzhBpMitWPJb0EOpOs0xhrpTRHnRrP5Au9VTJCo1s+AnV556r/1BjS8fItZ5luTkCpOrxOQ9QW0J0oCRdXd1JDyHVkq60bRaVnu1Qcrj74oK+pXwxnT//7qb4AlmLhaSgnuRy2YiWlphx6vSqhFkWi2VT2nkQ8fdFFydPOaIi+560VZTb91XWokX3Jj0ENqPc9p+kTyaTKXlfnLb9LkJ0GpSebtVz7XXfSXoIqabSdqRqBccVne1QRiCeX9C3lOd7wYJ5TXEAVIuFpEgngcbm9fT0RAwORl/v+qp8RjTqYrG1DHQaLaRIWzsPkpFfdLF3/bqK7HvSVlHeqPu+pNx//6Kkh5B6SRzflNv+k9JV+wT0jFOnl3xSM237XYToNCg93apncOOGpIdAHamL4LiMQBx4KYEGlVTLQEdIATSKNLTwaVSObxpbtU9A9/Wur9hJTZIjRGdUarUAIwDAcLVaOLWcFkXDq9Xuu88U+dGoRS95x7OkUS2rXrUM2Lp6WKx7+fI/xpvedFhMnvKGhgjSlyx5JOkhVEzpxxHV+1yq1Gu5WVpVbk45rVloDEJ0RmXW7K9ERMTPf/6zoi6vzQoA9aYevjjXWhq+ONVqQbVyWhQNr1Z74IG/V1RbN6N03d2dVd+GBcUrb/jJj1qdpGi0XvC1rHrVMmDr6mERz/b2lTEw0B+966vTPqzWli5dkvQQKqbY44hMJvNiq8mvVG0slXot18WM4yoppzULjUGIzqgM9PdFRMRvf/tQUZfXZgWAepOWL85pmqLdzF+cRqMRQg0oxvCTH7U6SaEXPBSnFjN8KE93d2fE4OBQzkI6ac3SvIToDLEQWPlMgayurq5OVaB1rNmmu/li0ljSUoXe3r4yJk85oi6qXsw6oxnY16dTLavRG63ynfo3vH1YoZlP7e0r45QZJ9VqSNDU0vI9gsoRojPEQhnlMwWyurq6ulJRBUp5mm26Wy1aD1A71axC39IJ2Gy2Y8RBdy6Xjd716+qi6sWss9HLFzYocEiplpaYcer0igTp+f2A57oyalmNnobK9+EBTak9l5tZowZbw9uHFTpWyOWy0d/XW/WxaF8Gm/8e4b1R34ToAFSV6W6weVs6AZvLZZ08bGL5wgYFDiltgTM4GH296yty0jS/H/BcU47hAU2pazc0s7S0aWtkqdx3Qwp4b9Q3ITpQUCazKq699ttJD6Nh1WqhrdEyZT159VAxlc12qKasA/nql9tvvy3hkUARWlpi1uyZFZ/JVC+fv1Aun8X1KZNZZUYBDaEevrtAOYToUEGZTKZmB6212E4msyruuEPQsiVl98Ov8qrrlVSL9iSNFGhs6b6U+76th4qp1tabql5Naf2J0ctXv9xzz88THgmVlu9FX6njg1S83wYHo7+vr6JVW+3tK2PWrPNG/G7RonsLXJq8VLweKJqZDfUpk1mV+hkF1SyuqdX3aPuz6quH7y6NqL19ZdO0UE2KEB0qIJPJDPXHfPzxx7Zy2cp8aKfh4DhfeZrEAcjwhXOSVHY/fKuuD8lkMi8JNOrV5sKZ4dLwvq2WWnzpq7f1J0azwGbaKtHSVFGkh/Tm5XvRV2o/U2/vt2LlctkYGOgf8bv7719U4NLk5V8Pf/jDslSET/YD5KXt87JRDf++W62QrlbHyY36+Ub9G81nWnv7ypg85YiYPOUIM8mrSIgOFdDd3TnUH3Pt2jVbvGwjfWjnctnE7svwhXOob93dnS8JNOrV5sIZmtdoFthMWyVamiqK9JAeyRclainJY7/htrQfyGQyDVOJl+aqwrSMLW2fl5WUpv378O+7ejpDdYzm2DaXy0bv+nXRu35dTWaSNyshOg2ttXWOChXq3miqWRtRWvtFpqlSF2pl+Oyq/OftmjU9CY+qebS3r4xTZpwUERFdXd1V31YaArOt6erqTEWldKNK4+fvpmacOj0mTzkila/XUkLRXC47VFVY7fuSXy8j/9+tGV7xWO2xtbbOac73cxFV39UI2dMU3NN8NrcPmj//7pL3AcXuy0armtup1X2gNEJ0Glpb21yVatS90VSzpkmlvgSltV9kmip1m4WTpJtXy4Pu4bOr8p+3W5uRReXkctno7+uNiIh169ZWbTv1NEW4q6ur4pXSaT15m4Rafv6We9zQ17s+etevS1+1bImtMHp6eoaqCqt9X/K3X+x2hlc8VntsbW1zqxqil/PerklLy61UfQ8/iVoxVW7XkmZNe7ImZTb3el+wYF5kMqsKtsXdXCFTrfb/uVy2aj32U/cZRkQI0aGm0v7Fk/qRxoU429tXbvE1Xu0vQTQfJ0k3z0E3ldbsU4TTevK20TXccUMKW2HUywyTairnvZ2GtkbDT6JWTA1eo7lcNpWvuYbb3zSYTCYTjz76P5t93yVdyNRI7XrZOiE61MqLZ/YF6fVla8FwIlpaYtbsryQ9ihHyVYoVr4hpAvnqhc7OdM040J5m9BYtKm0B5NTta6ABmcHCcEuWPJL0EAoq9TOkVMNnmKSxOKNReGxHmjV7ZmrbLZFeM06dXtHvmY65KZcQHWrlxTP7o6ngKmaqkKlolZPvRZm6YHhwMAb6+5IexQj5KsVyKmKaved7vnph/vyfJD2UEZKu6ihXqX0TqzUFMyLi/vtLWwA5zRW+lQge6y28zL826m3cbJkZLJVRk3YWNbB06ZKkh1BQqZ8hpRo+w2RrbbhqdWK90fa37e0rY9as8yp2e9U8ZinFaNrG9ff1pbPdEqnW17u+pO+ZW/s+kOZj7krQgq56hOhQR4qZKmQqWuXke1FWfKpkHajl9N5G6fk+Wg88UN0vy41oc9Vd+b6JxTIFsziVCB7rLbzMvzbSPG6VVCQlDe0sqJ1anVhP8/62HLlcNgYG+it2e2k5ZhGAk3alfh9oNFrQVY8QndS7774XpjKWsypztaW1DUOjqfZ0VkYaPr13S0F6pWY95N/j9agWlXhdXZ1xzTXfGvG7Zq/ej6h8dRfUm6osKgdAVaXxOy1sSSO2eNRmiXIJ0Um9fHVmGs8m5qsBGn06UNKqPZ2VkYZP791SpUmlZj3UcwV2LSrxurq64s47bx/xuwULTNGrdHUX6ZV04JDWiruqLCpHzVSqXVCjtb+gOZTaliRtr/PRtEoo9TutVp0jpaWlTTPIP9Z/+MPv67LFYyGNWoiTtv1koxKiAzSgXC6dK99TGU4s0UySPone09OT2LbZunpraZMfb6XaBTVa+wsax5ZaA5baliRtr/NatkrQqnOktLS0aQb10NauHI1aiNNoz1NaCdGBhler1heZzKrULOKR9Mr3hVq01FvY4Yx+c2vE6atQjrTuu+uupU1LS8w4dbqT3DS84a0B07r/qIU//elPSQ+BLbjmmm/F8uWeo1ozu4J6JkQnMfmdZ5qCRxpTrRauzGRWVaUypadnTcnXSXrl+822aCkzPEhy2qYz+s2tVgupVcvw4KLRQ4xijyMq1UIj4oU1Eaot6VY2ES+8dtIaVNddS5vBwejrXZ/aFkFpJGypnmoWCgxvDdisbS/b21fG5z736aSHwRbcedcd8dnPnZH0MJpGfp2zWs2uqMXaVdXU1dWpmCeFhOgkJr/zrFbwCI2iVq0EMplMdavjygwPTNscvUYPUNm84cFFNUKMTCaTmtdWsccRlZyaXE4QWup1km5lE/HCa6daQfWW2j0UIy2vP6pHK4vqUShQXY3aMqKhDA7GQH9f0qNoGrVuR1mLtauqqaurq66LeRqVEJ2ml4Yqr6T5Erp55VSA17MZp05v+mm3jaju2h3UgWrMnqq7WVktLXHKjJPi5FNOjAifI0VpaYlZs2dq5fGi4e0eynlMmmnfllRrsS3NBPvLX/5S+wFBBZTbqs3nHABCdIYOkDs7a9PyIm3SUOWVKP05C2q2xeT6etc39bTb4Rppv5imdgfz598dy5Y9WtdTKyOqs6BY3c3KGhyM/r7eoQou+40iDA5Gf19f1Vt5XHnlZbFs2aNV3UYlDG/3UG5Vf1r2baORX7dlS583SVUMb2km2BVXfP3FyzR3sKjdTP0pp1VbM52025y6O9FP6tl3bl2zFfTVCyE6QwfIvgA3Kf054SXsF6tjwYJ5sXz5H+t6aiWk3eOPPxbLl/8xIkbfLoXSlPN459dtqbfPm4GBgYiov3FXmnYzzeGPf/xDQ5y0K1fdnegn9Rpl31nNDKXUgr5sNlelkTBcoiH69ddfH8cff3xMmjQpJk+eHKeffno8+eSTIy4zffr0mDhx4oh/M2fOHHGZZ599Nj7+8Y/HoYceGpMnT46vf/3rQwd2NJakprPS+J599tmabOf222+ryXYqpVoL55U7lTai/P1AM1XRJHlSrJ4O4LLZDgv2VEhXV+eo3l8+3ytvtO1SKI3Hm2bR2jqnqfbZzV6FXopan7htptdhqfKLeKr4rqIX2/SlYUZWe/vKmDXrvKSH0RQSDdEfeuihOPHEE+O2226Lm266KQYGBuK0006L559/fsTlPvShD8X9998/9O+ss84a+tuGDRviE5/4RPT398ett94al1xySdx1113xrW99q9Z3hxqohwVw7rvv3qSHQKlaWuIHP2gd1U0U+4Xinnt+Pqrt1Fq1wthyptLmlbsfqNcqmvxBcNES7ru8dm39TD3M5bIW7KmQrq6urb6/trQGST18vteb0bZLoTTN/nhvqX96sddvlhPd9a6tbW5T7bOr0TpqtOFvGk9kJHEisZleh6XKL+JZi4rv0e7/69aLbfrSMCPLQsa1k2iIfuONN8Zxxx0Xr3/96+OAAw6ISy65JJ599tn4/e9/P+JyL3/5y2PChAlD/3bcccehv91///2xYsWKuOyyy+LAAw+MadOmxWc/+9m4+eabo6/PSsvU3gMP1HbVaSpgcDA2bNgwqptoti8U1E7JK9nXqO9yxAu9cGtRfZGGCg9Gr+nXIIEGtqX+6cVevx5PdEM5RvudIY3fO5r9RGIzG+3+v1rqaXYs9SNVPdHXrHmhem3cuHEjfj9v3rw48sgj49hjj40rrrgi1q1bN/S3pUuXxv777x/jx48f+t3UqVOjp6cnVqxYUZuBU/eq1bKi0vIVB2mrPCC9stmOuPTSi8p+zYy2RUPS9AOurhmnTq/+FOcmWPy4Vicj0mDTfVGt36P18Dhnsx11ud9tpEq04fdlNO3HqI56ek4qFSam7btKOeFU/pi0EfYRQMq1tMSs2V9JehQ0oNSE6Bs3boyLLrooDj/88Nh///2Hfn/sscfGZZddFt///vfj4x//ePzkJz+JL33pS0N/7+joGBGgR8TQz6tXr67N4Kl7tTxb3tXVGRHlHQznKw7SVnlAeuVy2bj88kvKfs0U06IhrZYufSTe9KbDYvKUNzR0AJukvt711V9oK8WLH1cqkK3JyYiUGL4vGj71uxbVQsP72qa5OimXy9blfjetlWjlGH5fimk/Vqi1QtqCzy2pp7GOpiVcrZW6MFwhqfoMLDOcyh+TNsI+guZTqOVUPe07qyG1xX2DgzHQ39idKSpVvNBIRRC1kJoQffbs2fH444/HN77xjRG/P+GEE+Itb3lLTJw4Md773vfG17/+9fj5z38eTz/9dEIjpZLSUhGWy2VrNpaurq6hbQLV88QTj8fAQH/0rk9nAEv9q1QPxJqcjEih4VO/a9FLf3hf23rq3d9IGnVfXKi1Qj3d3xUrHkt6CNSLJginYFOFWk7V036+GhT3JadSxQuNVARRC6kI0b/61a/Gr371q2htbY3dd999i5c99NBDIyLiqaeeiogXqs47Oka+cfM/T5gwoQqjpWJenKKfhiB91uyZJVcBzp9/d3rPvJZhxYrH4n3v++dYtuzRpIdCBXV2diY9BBLS2jqnaQ+Giq0K8v4gjeqxlUtRNln0ePgxVLPtr7LZjqq3IynpGDXhBambXb4KcGvPWTWrBe+7r8RFzMuQxL6t3iosC70GKvl9uZG+vwIvSEOm1iwSDdEHBwfjq1/9avz85z+P1tbW2Hvvvbd6nT/+8Y8R8feA/LDDDovHHntsxIfBr3/969hxxx1jv/32q87AKduiRcMO0F6cop+G1Yz7+/pKrgJcsGBeYmdeq9Gn+qmn/hKLFz8Qy5f/saK3S7K6u7tGdf2eHtWa9aqtbW4iXxpH7OcTUmxV0GjfH0kqNK24XtUiwKkXSbRyqcmXr00WPR5+DNXWNjf+8Idldfeazrfo29TWTgrkctmqtyMp6Ri1hgtS81L5KsCtPWfVrBZ84IESFzEvQzL7tupWWFa6lUeh10Alvy83Q+VwI7VYqXX7uVquXdBIz1OSMplMwYJQa1FUXqIh+uzZs+Puu++OK664Il7xilfE6tWrY/Xq1bF+/fqIiHj66afjO9/5Tixbtiza29vjnnvuibPPPjve+MY3xgEHHBARLywiut9++8VZZ50Vf/rTn2LRokVx1VVXxYknnhjbbbddknePzbj//uofoCWhq6u7xtur3z7V1JdK9fKsBGfY60Oj7ufTptC04npViwBnuHxrNV6QhoKGeuwFX+h1lNRJTKB85RxnOvGUTpV+XtrbV9bse8imoXmt28/Vcu0C75/K6O7uLFgQai2KyhuT5MZvueWWiIiYPn36iN9ffPHFcdxxx8XYsWNj8eLF8f3vfz+ef/75eM1rXhPvete74vTTTx+67LbbbhvXXXddzJo1K0444YTYfvvt4wMf+ECceeaZNb0vNKdcLhvR0hLX33BN0kNhE7ffflu86lWvqtrtz59/d0ydOq1qt9/IstmOmD+/+GrDTCYz1P7pN4v/u4ojgxfC6dbWOXHyyacmPZSK8kVlpBtuuHbUt5H/HNhtty23ImSkcirPli//YxxyyGGVH8xWlPp5lVb5Ni177bX1Wb9p5CR65ffhxcxmSuJ1k9iiz8OOM+v1fdLoktoP5BdB37hxY022NWvWeVXfDslKwzF5o37fqYVEK9GXL1++2X/HHXdcRES85jWviR/84Afx4IMPxu9+97v42c9+FmeddVbsuOOOI25nzz33jO9+97vxP//zP/Gb3/wmzj777BgzJtHzA1TJ/Pl3RyazKrkDrE309PREDA7GhoGBmm5XBd3W3XPPz+P222+LiOrMFFiwYJ4zumUqdRp7d3fnUPun0Rx0mDLIpjbXeqYhF9dpon7HxU5bHajA53alPgfyX2SKuVwjTMkteT/e0hKf+/xnEnn9bu3zqtYzEcvR3r4y3jz5DfHGNx4aS5Y8UrVtVDPg2tJMiXprAVSWF/fhlXyMtzabKR8cTp5yxKjfe8U+R4kGiBU4zmxWxfb031RJbSZePMkx/LWYzXbU5P2fXwS9FgvA53LZGBjor/jtNtu6I6lWwjF5tT6zIxr0+06NpGJhUSjWggXz4tFH/6ekA6xGPLhet+75pIdQV9I+U6BWB4HFStNY5s+/Ozo7K3PSqNpfjNIY0tdi8bp6UOgLXtO0nnmx3/Hjjy9P1fu7Gupx2moms6qoE4ulfOGp5uLn+T7gNdvnpbVfd4IzEfOPfTHPQS6Xjb7e9bFhw0A8+eTjRW+j2JM7uVw2Jk85omA/1mpLawug/PtvzZoKtMV78T1Qy7ZL+eCwd/26Ub/3in2OqhUgVlO+wKuZ5T+bSj3eLOnzejMnOarRAqxRZ71oMZYiJRzTLF26ZOj/LQicHkJ06k53d2dJB1hpPbiOaNwP6rSp9UyBUqWtD2yaxrJgwbxU9OotRuoCnqjN4nX1oNhF20arvX1lamZKbU7a9jVUTzUXP8/PhkvjPq+mEpiJmPf3xVmr9xwMP7mTyWQKHrP29PRstUqzGWdQ5t9/te5nTG2Zmfp3jXB8US/fOWg+zbAgcL3Q8wQSkstl48zPnlHUZfPVQLvuOr7Iy2eivX1lzXv6NVqrgDQtqpmUak4jK0faqvZHI/++fuMbj0x6KEOS2nfUq+EBVn7qe9pP2kExiq1CTkotF3lrVsMf31FVmbe0VGQNgnqVtuOovDS+f9I4JgAYTiU6JKSnp6fo3mr5Kspiz47POHV6RXoYlmJ478Q0V2IWY82aF6qG7rzz9ppsL82h8PBpZGnQSJW0taqOLkUS+440KSk43KSnYX7qe71NRa+GtIZGxahE3/FiFuxLu2JbzNRSvn/u0qWPJNo+pFkMP+bs7+stvx/w4GBF1iAoR6Gey7WcFp+246i8tFXctrevLOk9nfYTfUBjsc8hT4gODaivd31FehiWYnjvxHqfurp27QsV6Bs2bKjJ9holFKb+JbHvSJOSgsO09mlOgbSGRsWoxEJLW1uwj/Lk++c+8cTjNVvkjfpWqOdymk5e84JcLlvSezqNJ/qgGmrdDqsei+FqMYvFPoc8ITqJ6+zsTHoIQIp1dXUnPQQqqB4PztPIAkPp1ezPTb76t9RFoeu9ep8XlPv8A2xNzRa0TosE2mHNmv2ViKif9kqlzmKphnyeVY/Hf62tc+py3EkSopO47u70fsnyhY5GUbev5ZaWuP6Ga2q2uXwrBwcT1VPvM1XSQiVlejX7c5Ov/i21XYTq/cLmz7+7bhYvLPf5r2eF2sZQHWlvq9AIx5KtrXNGvJ7Tcl9qMfuvEm3d8kb9uFWhHdbW3j8D/X0RUZuWT8M/2/KPe6knYEudxVIN+TwrqeO/0XzPb2ub2/THraUSolN3ivmSNZppT8OrJNPyha5ezgSTXml5LZdscLCmCzVu2qdcFTwASVuwYJ6ANsUKtY2hOtLeVqEaa97UunVcW9vcEa/nZgrZKtHWLS+Nj1ua3j/DP9tKXQOOv6vb7/l1SohO4ioeUo1y2lN+ClO1LVp0b1GXy2QyQ1OUfv7zn1VzSFAT9dTCqZZV8GmyYsVjqa7yAspTqCqu0Xv7V7KyEOqRavk6tslC5kD9SMssDipHiE6yqtGqYZTTnvJTmKrt/vsXFXW57u7OoSlKv/3tQ9UcEtREqS2chqt1AF+LKviurs6q3Xa5j9dTT/0lNVUqtTR81k8pM4DSPrUc8jZbFVelgOa++4orFqiFSlYWQj168sknyn4PZLMdDfUZV6u+2ls6BiupRZOFzEu2pcc3DcdszdDbvRnuYzEqNRuhntq6NTohOsmqcasGoL6NJoBPq9G0n9qaRny8qmn4FNJSppOWOjU2k8nUTUVZe/vKePjhBy0I28hKDGiKnUH4wAPFFQtArTXj/mw0xwO5XLahTqzXKoze0mOuRVN1benxTUM7k2Y4IdIM97GW7DPSY0zSAwBImyVLHinqcplMJjo6Vld5NFTCihWP1e/iqrxEvb/3Zpw6PVpaWmLOjd9PeigjbFo11N6+Mt48+fDo6+uPbbetbN1FGirBGk2xn12FFFU1VuPFnqHS2ttXxqxZ5yU9jIqpx3WTstmOmD/fMVm9y3+Ov/GNRyY9FGAUSl3MtdmpRIdRWLNmTdJDSKV6f1yWLl1S1OVOmXFSfPaznx76uRlD2hUrHov3ve+fY8WKx5MeyhY99dRfLLrSQDZ979Wbvt710bt+XdmLJ2WzHVXZ32xaNZTLZaOvtzdicGPFZ42loRKsXuV7G2/aZ7PYz65CiqoaM4OQhFSqr30ul42Bgf4KjSpZ7e0rh9ZNqieNVtnerKqxgCrpVOoxZyazqm6/F7e2zmn4iu9NC1ks5loaITqMwtq1PUkPIZWa5XF5oVf94NDPzRjSPvXUX2Lx4gfiqaf+nPRQaCKbvveaTS6Xbcr9zaaasSVDxAvPv+Bi6+qpbRJb10h97StVPZ7LZYfWTSI59jU0ulKPOTOZVXV7nNrWNrchPme2RCHL6AjRoYEldVDX09McITpQukWL0rPYIJXT01P9GUj5StTly//04mKYX6n6NiPStUBmLWxaoVRocbxyF7nq6uqMSy+9qOrTh2ecOj0mTzkiHn74wURaXlRrxkja5V8va9Y4FtyslpaYcer0WLLkkZIr65vx9VQP8vuaZgrSG7XfdS3vV3v7yqZ6zRTSDJXfNBYhOtShYvueJnVQd+edt9d0e1Bptfyi2mwB3f33W2ywEdXi5Gm+ErW9/emIwcEY6O+r+jYjGmeBzGKnV29aoVRocbxyF7nq6uqKyy+/pOrTh/Ntkz5w3L8k0vKiWWeM5F8va9ems7Xfpm2Qam5wMPp618eTTz5ecmV9Ka+n+fPvTv6+VlC5J+1qIb+vqXWwnNhJlZaWmDV7Zl3249+iGt6vXC4bk6cc0XQnXzanGSq/aSxCdKhDxfY9TeqgbsOGDTXdHlRaLYOPRgnogBcqy9LaZqaep1dHlN++p693vZYXL2rU6tFSPP74Y0kPoSYWLJiXSMunalXXlnvSrh6U+5gltj8fHIz+vr7G66Ncw/vV09MTvevXJfI9vZq0NqIZCNEBKFtaF5FN6zT6XC7r4LLBZLPp60ud5oq9RrZ06SPxpjcdFrNm/XvSQ6kLXV3dxV+4hu172ttXDlUipvWESFlerLJsqPtUoqVLH4mZM89NehgFlXrckrZK8/b2lUPVtQ1XpVwlDz/8YLx58htUJFdBfhHuRjkeSuPxZsQLLdryLeDys+C9/2lkQnQYhWLbqjS6bLZjRP9U0qPa/afTuohsWqfRz5o90xelGsr30K5m0JDGCqJGrthLsyeeeDwGBvpjYGAg6aHUhetvuKb4C9eofU8mk4nJU46Ik085MSIivjLzy1XfZs28WGWZ1jYrtfDEE4/Hxo3pnS1Z6nFL2hYXzuWyQ9W1DVelXAWZTCbe/4FjE5s5PFq3335b0kPYovwi3I1yPPTnPz+RypMCXV1dQy3g8q/l0bz/m63NJfVHiA6jUGxblUaXy2Wt8JxS+k+nS39fX11+UapX+R7aaQsayqGqZ/TS+himdVzVtiGFJxu6uzujd/26ocB+cOPGhEcENKru7s66bjV1zz0/T3oITSW/nkjaQvRK+P3vlw0dC2lzSdqNSXoAANS/rq7NLzwHjF4mk0lkYcRqGd4uo5bbTNtjmMmsiquv/kbcNPfGpIfCizo7y/8sS2MLMSqj+EV5X+gHvNdee9dgVKXZ9Ditmdv6kD6ZzKpobZ0Tu+46PumhNLxaHH91dnaWdPnPfu6MGDNGNEl9UInOEFNnaiOtPaRHwxdHbrjh2qSHQINrbZ2Tuv6vtVLv1WrD5XLZePPkN8T06f9a8+2m5THM9zXNZFbFDTdcG/19W25T0to6JzKZVUPXK/bLaalfYolRTUFPYwuxiNL6Ztei5+7m1mzIB2jFqvVJuGIX5c33A05dy7aWlhHHae3tK2PWrPOquslq7H+a9RigGeRnDm5tHzx//t1Vfx00+mdnLVotdXeXeEJ6cGNNWrZBJQjRGWLqTG2ktYf05hT7JSWtXxypHT2Aqba2trkN0Zal2fX09ERf7/pU9iWuVTBXajuntra5kcmsGrpesV9OS/4SS0MqZb9Zi1Zjm1uzIZNZVVJbwLT2205tb+vBwRHHablcNgYG+qu6yWrsf/Kv5WZtgcUL+49qHwv67GQ02ttXpu9EKhUlRIeUS+pseBqnvgONpRYLf5arq6uz6RZM7urqTOWiVeXKv76Kuj8tLTHj1Ol1Fc6YBQZbpmVJ43n44QeHFv5tNPX0+UPllTojh/TJ5bIxecoR6ZyRFAL+ShGiQ8otWFD7L8mZzKr4xjcuS83Ud6A8aW/TVauFP/MnI0vpd9zV1VV0ZWQaTwKUo9EWrcq/voq6P4OD0de7PrUVrptjFljpatGuJO3S/rlQMS0tMWv2V5IeRVOo1fsql8vG+z9wbNXbPpR0ArZSXjyRW48nftLa/iSt4yqk1Bk5aWFdrL/r6emJ3vXrUjkjKZPJDAX8TtiNjhAdUu7++2vfZifNH+L1eHAJm8pkMjU5gNGm6wX5qbnVCki1mYH6kLYvtZVQ6nFR03wuDA7qsVsjtXpf9fT01KTAp6QTsJXy4onctWvrb+2stLY/Seu4Gs26dc8nPQSK0N3dORTw11PBSBoJ0aFJFFspWYsFW0ZDVVH9W7So+lVwm1af1GKbpZhx6vQ4+ZSPJD2MgtL2eCUhzY9BV1fnVi+j4pZKKXVB9Hqr/kuLnp7Sw7NcLrvFBSIzmVUN0fanlu21VOiVRyuK6stmOzzGZUpkdkGTWLLkkaSHULI0Zx2VVswxgNkEpRGiQxNobZ0TTz75RFGXrcWCLaOhqqj+1WJ2xabVJ0nM6NiSvt71MdBf3UW9RiNtj1cSin0Muro6ax5SFXOw24gVtySj1AXRVf+Vp6en9IXnV6366xYXiMxkVjVE259S2muNyostNfSMLV2aZ7HWky21W8rlsh7jMiUxu6BRTmJuzdKlS5IeQsnSnHVUWjHHADfccG1EOIlcLCE6NIG2trmm7dSxcr5YA7XR1dXVECEVUH8cH1TYiy01nIQkKZVst+R1nKxGOYlJ4xsYGIiI6rW9bDRCdIA0a2mJO++8PelR1B3T0gAoVZrbOEGzc2xXgpaWmDV7ZmKzKjKZzFbXa/iP/7gtIpqrtUa90RqwdvIthzo7C+/nMpmMmVIpIEQHXqIZpp7VjcHB2LBhQ9KjqDu//vX9NdlOvvdva+ucLR708FK1WlyV5rK5L+2trXOK/pJe6iKNra1zYunSR+Lhhx+08PWLttSOIO20siqdEw/USr7lAEUYHIz+vr7EqtFnnDp9i+s1RET813/9PCIi/vznFSX1eq+X76m1Osat5kkIsxlqJ99yaEvV4DNOnR6Tpxwx6iC9mLWVKGxM0gMA0sfUM+pdOf35stmOmD+/tAPzfO/ftra58eUvb/nLAiPNOHV6DA4OlnQdCxayRS0tm118uq1tbkyZclTZ19+Stra5ceuPbon+/v7Ydtva1KaUswBlLVWyHUE5hLq15cQDtZJvOUD69fWuL/qypa55UC/fU2vVGqOZ+ns3u/z7KpfLxl577V32TAGzekZHiA4AYcGmWivlC1aeBQvZosHB0S0+Xeb1+/t6IyJiw8DG8rddAn2wt6xeQ13PK0DjMDuNaqv0TAEzD4qjnQsAZVuy5JGkhwBUWGvrnMhkViU9DGhYm5tVY/2Twhp9doHWZtWxbNmj8fnPf6ak62SzHSW1FtmSrq7uitxOXr20MWk2mwse29tXbrWdTV5a+47Pn393w7SqbLRe4lX5zEh4HYV6IkQHoGzltE0BSlPrL85tbXOF6FWWyayqWFBTKfPn3+15r5Hhs2rWrHmhPY/1Twqr19kFxapV24dm0tXVGZdf/vX43e/+p6TrVXJW4vU3XFOR28mrlzYmTeXF4HHJkkfi0ksvimXLHo1LL70oHn/8sRgY6C/qJtJa/btgwbyG2TedMuOkUfcST80CuC0tMePU6ZUPuxNeR6GeCNEBgESZ8rplvjiPVOnqviRkMqtS1z5qwYJ5QvQErF2rjQuNJ+n9dFdXV+KfnRv0cG98LwaPTz75eFx++SWxfPkf4/LLL9GnPGX6+3qjd/26UQXEqXlOBwejr3e9sDtBQnQgIv5eCQVQU2Us5liuRm8J0OgymUxES0tR1X2VmOqa1inWaZPJrIpLL70oPVVaDapWbZbyz2ejTONPq0ZfKLvSVdhURiazqqnbwqSldVI5x6P5RcUdmxDxwjGBz+lkCNGBiFAJBSRktItBlqBRWgLcd19zngzo7u6MGBwsqrqvElOQG6nKp5pfujOZVSrvaqDUNktdXZ1ltQzKP5/z5/+k5OtSvEZfKFsVdjplMqsSr9BPQv4k/IxTp6ciSC/neDS/+HQjHZs0s9Eel7W1zS37WLdZv0dUihAdAKCOPPBAY5wMoHZ86W4+XV1do2oZZD8DNIr8Sfi+3vUN0+eb+pbkcZnP99EZk/QAAKARaIkENKJ89VtaNHMrglJoX0U9SENVMGxJtV+j5Yap+QXKd911fIVH1Liy2Y649NKLPGaMikp0AKgALZGAYpXaPzzJoOnOO29PbNubM5pWBPPn312zHqJJh/1Jta8yTZyivdheo719ZdIjSUSt++LnQ9dKa9T3fGvrnFi69JE4ZcZJ1dtIS0vMmj2zrPdAvvVWNSvrk14guNJyuWzVH7O8fGCvb3njEaIDAEANldo/PMnp5xs2bEhs25W2YMG8WLny6Zpsqxn7DkeYJk4JXmyv0aztpmrdFz+TWTWqFk+FNOp7vq1tbjzxxOPR39dbvY0MDkZ/X19q3wPr1q1Negh1a2uBfVqfc7ZOiA4A1IVGrXZqBEuWPJL0EBpaV1dn4pXNjeL6G65Jeghla22dU9LiomlVzYVut7zdXFGXa5THeXOy2Y6qVCPTmDKZVappa8D7Mt0ymVWVPQYbxQwEkidEBwDqQqNWOzWCpUuXJD2EhtbV1TWqyuaurk5f0F+0YWAg6SGUra1tbkOEu0lU4LW3r4xZs84r6rKN8jhvTi6XrUo1ctpYE+ClSm0jFlGbliGNIN+ap9yTDc3yvqxXmcyqys4uS/kMBLZMiA4AAA2sq6vLF3SaWi6XjYGB/qSHQY0ksSZA2hdJLbWNGMXLt+ZxsgEanxAdgLrRaAvc0NyquahYo7X/aG2dU9Pp5LXeHjST+fPvbthK72L19KyJiORa2zSbTCZT3dYJTb5IarPTbhCaR1kh+jve8Y7429/+9pLfd3d3xzve8Y5RDwoAXqKlpaq9bG+//baq3TZsTjUXFRtt+4+0aWubW9MKr1pvr5los8CCBfPKDtHLaUmRRj09PRHRnIvLJXGfZ5w6PSZPOaJ6IfeLi6Q+/vhyrbNK1AifCbVoN2jtmfTKn0RphNcyWzemnCs988wzsXHjxpf8vq+vL/XTmACoU4ODVe1le889P6/abQPwgiTaLNA4VqxYHoODLUkPg1F45plna77Nvt71EVH9AF9v69L5TCiOtWfSK38SxWu5OZQUot9zzz1D/79o0aLYaaedhn7euHFjLF68OPbcc8/KjQ5ItWq2IgAAKkN1VLoMfz6atbow315jr732Lul6s2bPjMGNgyVdJ5vtiPnzS29vlcmsitbWOfHGNx5Z4vUUlW1JNWcVUt8apW1jGtvptbbOibPOOjfpYbAV7e3tSQ+BrSgpRP/0pz8dEREtLS1xzjnnjLyhMWNizz33fMnvgcZVzVYEAJXSjL0q8/12IUJ1VNoMfz6atbpwxqnTo6WlJRb/+rclXa+/r6/kbZVTHZwPwi+//JK49trvbvGyt912699/aGmJU2acFOfP/Gqpw2wa1ZxVSGXle/avWVObY4pGOcGSxnZ6bW1z4+STT016GE2j1BMp+c8cM1nSr6QQ/U9/+lNERLz97W+P22+/PXbZZZeqDAoA6kFXlxNJaZTLZUf0Pa1Fr8q0yffbHQ3VlEC11Kq9Rrm6uztjt912K+qyv/zl32drx+Bg9Pf1xtq1TmRS//Lvz7VrR39MUQwnWGgUxZxIGX6cbR2e+lHWwqL/9V//JUAHVDrS3Fpa4oYbrk16FGzGrNkzY/KUI4TAmyilBVcmk4lTZpxUvcEAUHNaMTanTGZVKluMJGFrj4P2Z6xY8XhceulFZS+AXZSWlphx6vRRLXbcjDNt06CshUUjIhYvXhyLFy+ObDb7kkVGL7744lEPDEi/SlQ6Qt0aHIwBFTOplJ/ur6pjpFJacHV3d0Z/X28VR5N+jdKbFRidtFbLl0MrxuaUyaxKZYuRJGztcdD+jKee+nNcfvklcfTR74nddtu9OhsZHIy+3vWRy2VLXhskrxln2qZBWSH6t7/97fjOd74TBx10UEyYMCFaWqyQDgBA41i3bm3SQwBSQNFI48hkMpHN5pIeBgB1qqwQ/dZbb42LL7443v/+91d4OEC1VGO6z5Ilj1T8NgFgc9asEWSlmVYBpJXXJnkzTp0eGzdsSHoYNaOtXeNr9pYe2WzjzBSiPpTVE72/vz8OP/zwSo8FqKJqTPdZunRJxW8TgGSkfZ2L0SzU19mZnhYGXV2dowr1ij2BvWZNbZ9PrQJIq2Z9bep//lJ9vetjYKA/6WHUxih7Lgvga2c079VqtvRIa8Hc8OA8l+tIcCQ0o7JC9P/zf/5PzJvXnAcjAACNqJFbFqSpP35XV9eoQr1iT2CvXdu4zycQW21Lov95kxvWc7kcf/nLE1rf1Eha36tpLZgTnJOkstq59Pb2xm233RaLFy+OiRMnxpgxI2/my1/+ckUGBwAAEemtiGok2WxHzJ+v9QWj5/1aXe3tK2PWrPOSHgYNbNbsmTG4cTDpYRT0H/9xW9JDqEtOjMDolBWiL1++PA444ICIiHjsscdG/M0io/XHVC0AIO1qXRHVjCFgLpeNtra5SQ+DBpDWCsZGkctlq9KWpKurM2bOPDc6Op6r+G1TX/r7+pIewhb913/9POkh1J1MJlOTk2+1bicHtVRWiN7W1lbpcZCgNE1xBqhXzRi40bgsxCcEJP187lBpXV1dcd113x7xu2bqrd5M95X6lcmsitbWOSVfr7u7syZrAqS1nVwa39/ZbEdceulFseuu45MeCkUqqyc6ADCSwI1G0qwL8UE98blDLaS1X3M1bO6+mrVN2mQyq8waK0Ma92W5XDYuv/wSha11pKxK9OnTp2+xbcv3v//9sgcEJKNeq5nqddwAUKyurvR98SMZmUxGT9saKHcxxnK287e/ddZkW9VSq8cqCe3tK+OUGSdV5bbvu+/eqtwuUH/sD+pHWZXoBx54YBxwwAFD//bbb7/o7++PP/zhD7H//vsXfTvXX399HH/88TFp0qSYPHlynH766fHkk0+OuExvb2/Mnj07jjzyyJg0aVJ85jOfiY6OkavxPvvss/Hxj388Dj300Jg8eXJ8/etfj4GBgXLuGjSteq1mqtdxA+nS2jonOjtLDyp7evR9pMpaWuKGG65NehSkxEdP/kjMmvXvSQ+jsbW0xKzZM0dVgfyLXxTXr/n8WV+Jyy+/pOztVFrJn2kVeKzSLJfLRn9fb1Vu+4EHFlXldjfV2jonstnGPdEBlbZoUe0D7VrtDxi9sirRzz333M3+/uqrr47nn3++6Nt56KGH4sQTT4yDDz44NmzYEFdeeWWcdtppsWDBgthhhx0iIuKiiy6Ke++9N6666qrYaaed4oILLogzzjgjbr311oiI2LBhQ3ziE5+I8ePHx6233hrPPfdcnH322TF27Nj4t3/7t3LuHgDQZNra5saXv1z6Yks9Pens+0gDGRxUHMKQDTXoZ9v0Bgejv69vVNPrH374waIuN9Cfruez5M+0CjxWVFdb29yYMuWopIdRl8z6aU733994gbbXcuVUtCf6e9/73rjjjjuKvvyNN94Yxx13XLz+9a+PAw44IC655JJ49tln4/e//31EvLCq7x133BHnnHNOTJ48OQ466KC46KKLYsmSJbF06dKIiLj//vtjxYoVcdlll8WBBx4Y06ZNi89+9rNx8803R1/KV5QGAKqnFgeM9dRSKonKGhqbCsfGMn/+3TV/PrPZjq1fqAGN5rMjm+0oa1FBmlcaF1SsB7NmlV5cQW0KTDKZVXHppReVNYu06bS0xKzZX0l6FA2joiH6kiVLYrvttiv7+mvWvDB9bNy4cRERsWzZsujv748pU6YMXWbfffeNPfbYYyhEX7p0aey///4xfvzfV7OdOnVq9PT0xIoVK8oeCwBQx2p0wFhPLaUasbKGZLW1zY1crjlD0Ea0YMG8mj+fae6nPW/e3VW77dF8duRyWYsKVkmjnmxO44KK9WCgRjN/8jlYQ2hpiTvvvL2qm5g//+74wx9+b0HOYg0OxkC/AuNKKaudyxlnnDHi58HBwVi9enUsW7YsTj/99LIGsnHjxrjooovi8MMPH+qr3tHREWPHjo2dd955xGV33XXXWL169dBlhgfoETH0c/4yAECTccAIwCgsXDgv6SFQY042N5566Ne/dm0DtQYcHIwNGzZUdRMLFsyLY499b1W3AYWUFaLvtNNOI35uaWmJ173udXHmmWfG1KlTyxrI7Nmz4/HHH48f/vCHZV0fAADSrlErHWkM+qayqa4uVczUn66uroiWlphx6vSY+ZXZSQ+HCqvmTKG8WpyA0Wqp/pQVol988cUVHcRXv/rV+NWvfhU/+MEPYvfddx/6/fjx46O/vz+6u7tHVKNns9mYMGHC0GUeffTREbfX0fHCNMT8ZQAAmklae7UnFSD39KRnqrRKR9Js7dr0vFfS4r77mvjEV0tL3HDDtVXdRGvrnDjqqLdUdRuMTlqPKbZk3brnIwYHo693vf1aA6rFTKFatIrRaqn+jKon+rJly+InP/lJ/OQnP4k//OEPJV9/cHAwvvrVr8bPf/7zaG1tjb333nvE3w866KAYO3ZsLF68eOh3Tz75ZDz77LNx2GGHRUTEYYcdFo899tiIRXB+/etfx4477hj77bdfeXcMAKCOpbVXe1IBci0WuQIa0wMPNPGJr8HBGBgYqOom2trmxlNP/bmq22B00npM0YxyuWxdtKiBRlVWJXo2m43Pf/7z8dBDDw1ViHd3d8eRRx4Z3/jGN2KXXXYp6nZmz54d8+fPj2uuuSZe8YpXDPUw32mnneLlL3957LTTTnH88cfHJZdcEuPGjYsdd9wxLrzwwpg0adJQiD516tTYb7/94qyzzoovfelLsXr16rjqqqvixBNPHNUipwAA1Keurs5obZ2T9DAAKMKaNU50QjFmzZ4ZES1JDyM1urq6q3bb8+ffHVOnTqva7VOfyqpEv+CCC2Lt2rWxYMGCeOihh+Khhx6K+fPnR09PT1x44YVF384tt9wSa9asienTp8fUqVOH/i1cuHDoMueee2787//9v+PMM8+Mk046KcaPHx9XX3310N+33XbbuO6662KbbbaJE044Ib70pS/F+9///jjzzDPLuWsAANS5rq6uaGubm/QwgK2YP//uETOK2bqVK59OeggVp90GFKe/ry/6+3qTHkY6tLTE9TdcU/TFS20puGDBvMhkVpU6KhpcWZXoixYtiptuuin23Xffod/tt99+cf7558epp55a9O0sX758q5d52cteFueff36cf/75BS+z5557xne/+92itwsAAECyFiyYF8ce+96kh1FX/vrXZ5MeAkDyBgdjQwntpqxJQyWUVYm+cePGGDt27Et+P2bMmNi4ceOoBwUAAABUTlMvkgoAo1RWiP7mN785vva1r41Y0CCTycTFF18ckydPrtjgAAAAgNFr6kVSm5iTJ1Aei7iyqbLaucycOTM+9alPxTve8Y7YfffdIyJi1apV8frXvz4uu+yyig4QAAAAgNI5eUJERDabS3oIdae7uzN22223pIdBipQVor/mNa+Ju+66K37961/Hk08+GRER++67b0yZMqWigwMAAGpj3ry7kx4CQN267bYfJT0E2LyWlpg1+ytJjwLqXkntXBYvXhzvec97oqenJ1paWuKoo46K6dOnx/Tp0+Pggw+OY445Jn77299Wa6wAAECVLFw4L+khABWihUft/fKXv0h6CGzGmjVrkh5C8gYHY6C/L+lRQN0rKURvbW2ND33oQ7Hjjju+5G877bRTnHDCCXHTTTdVbHAAAABAabTwgBesXduT9BCABlFSiL58+fJ4y1veUvDvRx11VPz+978f9aAAAID0+8tf/pL0EACgoCVLHkl6CECDKClE7+joiDFjCrdRHzNmTORyFisAAIBmcMUVX096CNBUBIJQmqVLlyQ9hC3ynob6UVKIvttuu8Xjjz9e8O/Lly+PCRMmjHpQAABA+g0MDCQ9BGgqaQ8EgdJ4T0P9KClEnzZtWnzzm9+M3t7el/xt/fr1cfXVV8fb3va2ig0OGpEzzQAAQKPo7OxMegiQuEWLLOYLja5wb5bN+NSnPhU/+9nP4t3vfneceOKJ8brXvS4iIp588sn44Q9/GBs2bIhPfvKTVRkoNApnmgGgtpzAhvSaN+/upIfAKHV3dyU9BEjc/fdbzBcaXUkh+vjx4+PWW2+NWbNmxZVXXhmDg4MREdHS0hJTp06NmTNnxvjx46syUAAAqqenpyfpIVRMV9fIQMcJbEivhQvnJT0EAHiJXC6b9BBImZJC9IiIPffcM7773e9GV1dXPPXUUxERsc8++8S4ceMqPjgAAGqgpSXuvPP2pEdRMTfccG3SQwCoO2btAI2uq6u7uAu2tMSs2TNj7k03V20saWsBlM12JD2E1Cs5RM8bN25cHHLIIZUcCwAASRgcjA0bNiQ9ioqx2CVA6czaARrd9TdcU9wFBwejv68vurs7qzaWtLUAUnm/dSUtLAoA0KjWrFmT9BAAgBRobZ0T2axACRrNBoUWjIIQHQAgItaubZye4ABA+dra5kYup7UBAH8nRAcAAAAYZt68u5MeAmXS359KyWZzSQ+BFBGiAwAAAAyzcOG8pIdAmfT3pyJaWuL8WeclPQpSRIgOAAAAAJA3OBgbBvqTHgUpIkQHAIjaT/297757a7o9AKgGn2cANAMhOgBA1H7q7wMPLKrp9gCgGnyeAdAMhOgAAAAAAFCAEB0AAAAAAAoQogMAABCLFultTe3Vek0SACiHEB0AAIC4/369ram9Wq9JAgDlEKIDAAAAAEABQnQAAAAAAChAiA4AAAAAAAUI0QEAAAAAoAAhOgAAAAAAFCBEBwAAAACAAoToAAAAAABQgBAdAAAazJIljyQ9BACAptXV1Z30EKgwIToAADSYpUuXJD0EAICmtW7d2qSHQIUJ0QEAAAAAoAAhOgAAAAAAFCBEBwAAAACAAoToAAAAAABQgBAdAAAAAAAKEKIDAAAAAEABQnQAAAAAAChAiA4AAAAAAAUI0QEAAAAAoAAhOgAAAAAAFCBEBwAAAACAAoToAAAAAABQgBAdAAAAAAAKEKIDAAAAAEABQnQAAAAAAChAiA4AAAAAAAUI0QEAAAAAoAAhOgAAAAAAFCBEBwAAAACAAoToAAAAAABQgBAdAAAAAAAKEKIDAAAAAEABQnQAAAAAAChAiA4AAAAAAAUI0QEAAAAAoAAhOgAAAAAAFCBEBwAAAACAAoToAAAAAABQgBAdAAAAAAAKSDREf/jhh+OTn/xkTJ06NSZOnBi/+MUvRvz9nHPOiYkTJ474d9ppp424TGdnZ3zhC1+Iww8/PI444og499xzY+3atbW8GwAAAAAANKgxSW78+eefj4kTJ8bxxx8fZ5xxxmYv85a3vCUuvvjioZ+32267EX//4he/GKtXr46bbrop+vv749xzz42ZM2fGFVdcUdWxAwAAAADQ+BIN0adNmxbTpk3b4mW22267mDBhwmb/9sQTT8SiRYvi9ttvj4MPPjgiIs4777z4+Mc/HmeddVbstttuFR8zAAAAAADNI/U90R966KGYPHlyvPvd747zzz8//va3vw39bcmSJbHzzjsPBegREVOmTIltttkmHn300SSGCwAAAABAA0m0En1r3vKWt8Q//dM/xV577RUrV66MK6+8Mj72sY/Fj370o9h2222jo6MjdtlllxHXGTNmTIwbNy5Wr16d0KgBAAAAgGbV09OT9BCosFSH6Mccc8zQ/+cXFn3nO985VJ0OAAAAAJAaLS1x5523Jz0KKiz17VyG23vvveNVr3pVPPXUUxERMX78+MjlciMuMzAwEF1dXQX7qAMAAAAAVMXgYGzYsCHpUVBhdRWir1q1Kjo7O4cC8kmTJkV3d3csW7Zs6DK/+c1vYuPGjXHIIYckNUwAAAAAABpEou1c1q5dG08//fTQz+3t7fHHP/4xxo0bF+PGjYtvf/vb8e53vzvGjx8fK1eujMsuuyz22WefeMtb3hIREfvuu2+85S1via985Ssxe/bs6O/vjwsuuCCOOeaY2G233ZK6WwAAAAAANIhEQ/Rly5bFRz/60aGfL7744oiI+MAHPhCzZs2Kxx57LH784x/HmjVr4tWvfnUcddRR8dnPfja22267oetcfvnlccEFF8TJJ58c22yzTbzrXe+K8847r+b3BQAAAACAxpNoiH7kkUfG8uXLC/79xhtv3OptvPKVr4wrrriiksMCAAAAAICIqLOe6AAAAAAAUEtCdAAAAAAAKECIDgAAAAAABQjRAQAAAACgACE6AAAAAAAUIEQHAAAAAIAChOgAAAAAAFCAEB0AAAAAAAoQogMAAAAAQAFCdAAAAAAAKECIDgAAAAAABQjRAQAAAACgACE6AAAAAAAUIEQHAAAAAIAChOgAAAAAAFCAEB0AAAAAAAoQogMAAAAAQAFCdAAAAAAAKECIDgAAAAAABQjRAQAAAACgACE6AAAAAAAUIEQHAAAAAIAChOgAAAAAAFCAEB0AAAAAAAoQogMAAAAAQAFCdAAAAAAAKECIDgAAAAAABQjRAQAAAACgACE6AAAAAAAUIEQHAAAAAIAChOgAAAAAAFCAEB0AAAAAAAoQogMAAAAAQAFCdAAAAAAAKECIDgAAAAAABQjRAQAAAACgACE6AAAAAAAUIEQHAAAAAIAChOgAAAAAAFCAEB0AAAAAAAoQogMAAAAAQAFCdAAAAAAAKECIDgAAAAAABQjRAQAAAACgACE6AAAAAAAUIEQHAAAAAIAChOgAAAAAAFCAEB0AAAAAAAoQogMAAAAAQAFCdAAAAAAAKECIDgAAAAAABQjRAQAAAACgACE6AAAAAAAUIEQHAAAAAIAChOgAAAAAAFCAEB0AAAAAAAoQogMAAAAAQAFCdAAAAAAAKECIDgAAAAAABQjRAQAAAACgACE6AAAAAAAUIEQHAAAAAIAChOgAAAAAAFBAoiH6ww8/HJ/85Cdj6tSpMXHixPjFL34x4u+Dg4PxzW9+M6ZOnRqHHHJInHLKKfGXv/xlxGU6OzvjC1/4Qhx++OFxxBFHxLnnnhtr166t4b0AAAAAAKBRJRqiP//88zFx4sQ4//zzN/v37373u9HW1hazZs2K2267Lbbffvs47bTTore3d+gyX/ziF2PFihVx0003xXXXXRe//e1vY+bMmbW6CwAAAAAANLBEQ/Rp06bF5z//+finf/qnl/xtcHAwvv/978enPvWpeOc73xkHHHBAXHrppfHcc88NVaw/8cQTsWjRorjwwgvj0EMPjSOOOCLOO++8WLBgQWQymVrfHQAAAAAAGkxqe6K3t7fH6tWrY8qUKUO/22mnneLQQw+NJUuWRETEkiVLYuedd46DDz546DJTpkyJbbbZJh599NGajxkAAAAAgMaS2hB99erVERGx6667jvj9rrvuGh0dHRER0dHREbvsssuIv48ZMybGjRs3dH0AAAAAAChXakN0AAAAAABIWmpD9AkTJkRERDabHfH7bDYb48ePj4iI8ePHRy6XG/H3gYGB6OrqGro+AAAAAACUK7Uh+l577RUTJkyIxYsXD/2up6cn/ud//icmTZoUERGTJk2K7u7uWLZs2dBlfvOb38TGjRvjkEMOqfmYAQAAAABoLGOS3PjatWvj6aefHvq5vb09/vjHP8a4ceNijz32iI9+9KNx7bXXxj777BN77bVXfPOb34xXv/rV8c53vjMiIvbdd994y1veEl/5yldi9uzZ0d/fHxdccEEcc8wxsdtuuyV1twAAAAAAaBCJhujLli2Lj370o0M/X3zxxRER8YEPfCAuueSS+NjHPhbr1q2LmTNnRnd3d7zhDW+I733ve/Gyl71s6DqXX355XHDBBXHyySfHNttsE+9617vivPPOq/l9AQAAAACg8bQMDg4OJj2ItOjoWBPN+GjccceP4lOf+ljSwwAAAAAAauzaa78bxx9/QtLDSERLS8T48Ttt9XKp7YkOAAAAAABJE6IDAAAAAEABQnQAAAAAAChAiA4AAAAAAAUI0QEAAAAAoAAhOgAAAAAAFCBEBwAAAACAAoToAAAAAABQgBAdAAAAAAAKEKIDAAAAAEABQnQAAAAAAChAiA4AAAAAAAUI0QEAAAAAoAAhOgAAAAAAFCBEBwAAAACAAoToAAAAAABQgBAdAAAAAAAKEKIDAAAAAEABQnQAAAAAAChAiA4AAAAAAAUI0QEAAAAAoAAhOgAAAAAAFCBEBwAAAACAAoToAAAAAABQgBAdAAAAAAAKEKIDAAAAAEABQnQAAAAAAChAiA4AAAAAAAUI0QEAAAAAoAAhOgAAAAAAFCBEBwAAAACAAoToAAAAAABQgBAdAAAAAAAKEKIDAAAAAEABQnQAAAAAAChAiA4AAAAAAAUI0QEAAAAAoAAhOgAAAAAAFCBEBwAAAACAAoToAAAAAABQgBAdAAAAAAAKEKIDAAAAAEABQnQAAAAAAChAiA4AAAAAAAUI0QEAAAAAoAAhOgAAAAAAFCBEBwAAAACAAoToAAAAAABQgBAdAAAAAAAKEKIDAAAAAEABQnQAAAAAAChAiA4AAAAAAAUI0QEAAAAAoAAhOgAAAAAAFCBEBwAAAACAAoToAAAAAABQgBAdAAAAAAAKEKIDAAAAAEABQnQAAAAAAChAiA4AAAAAAAUI0QEAAAAAoAAhOgAAAAAAFJDqEP3qq6+OiRMnjvh39NFHD/29t7c3Zs+eHUceeWRMmjQpPvOZz0RHR0eCIwYAAAAAoJGMSXoAW/P6178+brrppqGft91226H/v+iii+Lee++Nq666Knbaaae44IIL4owzzohbb701iaECAAAAANBgUh+ib7vttjFhwoSX/H7NmjVxxx13xOWXXx6TJ0+OiBdC9fe85z2xdOnSOOyww2o8UgAAAAAAGk2q27lERDz11FMxderUeMc73hFf+MIX4tlnn42IiGXLlkV/f39MmTJl6LL77rtv7LHHHrF06dKERgsAAAAAQCNJdSX6IYccEhdffHG87nWvi9WrV8d3vvOdOPHEE2PevHnR0dERY8eOjZ133nnEdXbddddYvXp1QiMGAAAAAKCRpDpEnzZt2tD/H3DAAXHooYfG2972tvh//+//xctf/vIERwYAAAAAQDNIfTuX4Xbeeef4h3/4h3j66adj/Pjx0d/fH93d3SMuk81mN9tDHQAAAAAASlVXIfratWtj5cqVMWHChDjooINi7NixsXjx4qG/P/nkk/Hss89aVBQAAAAAgIpIdTuXr3/96/G2t70t9thjj3juuefi6quvjm222SaOPfbY2GmnneL444+PSy65JMaNGxc77rhjXHjhhTFp0iQhOgAAAAAAFZHqEH3VqlXxb//2b9HZ2Rm77LJLvOENb4jbbrstdtlll4iIOPfcc2ObbbaJM888M/r6+mLq1Klx/vnnJzxqAAAAAAAaRapD9G984xtb/PvLXvayOP/88wXnAAAAAABURV31RAcAAAAAgFoSogMAAAAAQAFCdAAAAAAAKECIDgAAAAAABQjRAQAAAACgACE6AAAAAAAUIEQHAAAAAIAChOgAAAAAAFCAEB0AAAAAAAoQogMAAAAAQAFCdAAAAAAAKECIDgAAAAAABQjRAQAAAACgACE6AAAAAAAUIEQHAAAAAIAChOgAAAAAAFCAEB0AAAAAAAoQogMAAAAAQAFCdAAAAAAAKECIDgAAAAAABQjRAQAAAACgACE6AAAAAAAUIEQHAAAAAIAChOgAAAAAAFCAEB0AAAAAAAoQogMAAAAAQAFCdAAAAAAAKECIDgAAAAAABQjRAQAAAACgACE6AAAAAAAUIEQHAAAAAIAChOgAAAAAAE0ql8smPYTUE6IDAAAAADSjlpaYNXtmtLevTHokqSZEBwAAAABoRoOD0d/Xpxp9K4ToAAAAAABQgBAdAAAAAAAKEKIDAAAAAEABQnQAAAAAAChAiA4AAAAAAAUI0QEAAAAAoAAhOgAAAAAAFCBEBwAAAACAAoToAAAAAABQgBAdAAAAAAAKEKIDAAAAAEABQnQAAAAAAChAiA4AAAAAAAUI0QEAAAAAoAAhOgAAAAAAFCBEBwAAAACAAoToAAAAAABQgBAdAAAAAAAKEKIDAAAAAEABQnQAAAAAAChAiA4AAAAAAAUI0QEAAAAAoAAhOgAAAAAAFCBEBwAAAACAAoToAAAAAABQgBAdAAAAAAAKEKIDAAAAAEABQnQAAAAAAChAiA4AAAAAAAU0TIh+8803x9vf/vY4+OCD44Mf/GA8+uijSQ8JAAAAAIA61xAh+sKFC+Piiy+OT3/603HXXXfFAQccEKeddlpks9mkhwYAAAAAQB1riBD9pptuig996ENx/PHHx3777RezZ8+Ol7/85XHHHXckPTQAAAAAAOpY3YfofX198fvf/z6mTJky9LttttkmpkyZEkuWLElwZAAAAAAA1LsxSQ9gtP72t7/Fhg0bYtdddx3x+1133TWefPLJkm6rpaWSI6sfY8eOjZ122inpYQAAAAAACdh2222bMhst9j7XfYheSbvu2pxB8mmnnRynnXZy0sMAAAAAAEidum/n8qpXvSq23Xbblywims1mY/z48QmNCgAAAACARlD3Ifp2220X//iP/xiLFy8e+t3GjRtj8eLFMWnSpARHBgAAAABAvWuIdi4zZsyIs88+Ow466KA45JBDorW1NdatWxfHHXdc0kMDAAAAAKCONUSI/p73vCdyuVx861vfitWrV8eBBx4Y3/ve97RzAQAAAABgVFoGBwcHkx4EAAAAAACkUd33RAcAAAAAgGoRogMAAAAAQAFCdAAAAAAAKECIDgAAAAAABYxJegAAm+rr64uI/7+9+4/vud7/P37f2KK2U9haMSnDFsZ7fm9N0vLbCrUT+bWd+bFQncoxv48cPyKUH4kRw6QOQ0RR3xitJcnQGGloLG3DiTH26/X9w6X3pXe8z/mU7f7q/ep+vVzO5bT30+Fxbud9nq957v1+vQFPT0+TJ7E2deZQZxH5PbR3iMhvpX2j4l26dAl5eXkAAF9fX3h7e5s8kYi4Au3PHBXdWYfoLuz48eNISkpCeno68vPzAQA+Pj6w2Wzo168f6tWrZ/KE1qDOHKmpqUhMTER6ejoKCgoAAF5eXrDZbIiJiUFYWJjJE1qDOnOoM8f58+eRnJx8w/4cEhKCXr16oXr16iZPaB26FnJo7+DQ85lDnTm0b3CsXbsWy5cvx4kTJxwef+CBBxATE4OoqCiTJrMW7Rsc6syh/ZmD2dnNMAyj3H43oUlJScHw4cPRqFEjhIeHo0aNGgCAc+fOITU1FRkZGVi4cCHatm1r8qSuTZ05NmzYgPHjx6NTp0437bxt2zZMmTIFPXr0MHdQF6fOHOrMcfDgQQwaNAhVqlRBWFiYQ+e0tDRcvXoVS5cuRXBwsMmTuj5dCzm0d3Do+cyhzhzaNziWLl2KBQsWoH///ggPD4ePjw8AID8/H6mpqVi1ahVGjBiB2NhYkyd1bdo3ONSZQ/szB72zIS4pMjLSeOONN5yuz5s3z+jevTtxImtSZ46OHTsaSUlJTteTkpKMDh06ECeyJnXmUGeOqKgoY8KECUZZWdkNa2VlZcaECROMv/71ryZMZj26FnJo7+DQ85lDnTm0b3A88sgjxpYtW5yub9myxWjXrh1vIIvSvsGhzhzanznYnfXBoi7q5MmTiIyMdLrerVs3nDp1ijiRNakzR05ODkJDQ52uh4aG4uzZs8SJrEmdOdSZIzMzEwMHDoSbm9sNa25ubhg4cCCOHDliwmTWo2shh/YODj2fOdSZQ/sGx7lz5xAYGOh0vUGDBrhw4QJxImvSvsGhzhzanznYnXWI7qJq1aqFlJQUp+spKSmoWbMmcSJrUmeO+vXrY926dU7Xk5OTdV+2cqDOHOrM4ePjg0OHDjldP3TokP3t1nJrdC3k0N7BoeczhzpzaN/gCA4ORkJCAkpKSm5YKy0txZIlS3T7uHKgfYNDnTm0P3OwO+ue6C7qww8/xMiRI9G2bVuEhYU53JctLS0Nu3fvxuzZs9GpUyeTJ3Vt6syxZ88exMXFwd/f/6b3Ns7OzkZCQgJatmxp8qSuTZ051Jlj9erVePXVV/H0008jNDT0hv157dq1GDVqFPr27WvypK5P10IO7R0cej5zqDOH9g2OzMxMDBo0CMXFxWjZsqVD571798LDwwPLli1DgwYNTJ7UtWnf4FBnDu3PHOzOOkR3YV9//TVWrVqF9PR05OXlAQB8fX1hs9kwYMAAhISEmDyhNagzx+nTp7FmzRocOHDghs69e/eGv7+/yRNagzpzqDPH1q1bkZiYiIyMDJSWlgIAKlWqhEaNGiE6Ohpdu3Y1eULr0LWQQ3sHh57PHOrMoX2Do6CgAJs2bcKBAweQn58P4Pq74mw2GyIjI+Hl5WXyhNagfYNDnTm0P3MwO+sQXURERFxacXGx/V6k1apVg4eHh8kTiYiIiIiIiJXoEN0CLl265PDTFm9vb5MnsiZ1rnglJSU4fvy4Q+eAgAAdiJUzdeZQZ66ioiIAgKenp8mTWJuuhRVPewePns8c6lzxtG9w5OXlObwS3dfXF02aNIGvr6/Jk1mP9g0Oda542p85WJ0rl+vvJlRr167F8uXLceLECQCAYRhwc3PDAw88gJiYGERFRZk8oTWoc8UrKyvD3Llz8c477+DSpUsOa97e3ujbty+ef/55uLvrs5BvhTpzqDNPamoqEhMTkZ6ejoKCAgCAl5cXbDYbYmJiEBYWZvKE1qFrYcXT3sGj5zOHOlc87RscV65cwcSJE7F161a4ubnhzjvvBAD89NNPMAwD3bp1w+TJk1G1alWTJ3V92jc41LniaX/mYHfWIbqLWrp0KRYsWID+/fsjPDzc4cMgUlNTMW3aNFy8eBGxsbEmT+ra1Jlj1qxZ2LBhA15++eWbdp47dy6Ki4vxj3/8w+RJXZs6c6gzx4YNGzB+/Hh06tQJY8aMcfgQmdTUVAwZMgRTpkxBjx49zB3UAnQt5NDewaHnM4c6c2jf4Jg6dSoOHTqExYsXIywsDJUqVQIAlJaWIi0tDf/6178wdepUTJkyxeRJXZv2DQ515tD+zEHvbIhLeuSRR4wtW7Y4Xd+yZYvRrl073kAWpc4cYWFhxq5du5yu79q1ywgNDSVOZE3qzKHOHB07djSSkpKcriclJRkdOnQgTmRduhZyaO/g0POZQ505tG9wtGjRwti3b5/T9a+++spo0aIFcSJr0r7Boc4c2p852J31vgEXde7cOQQGBjpdb9Cggf1D1uT3U2eOy5cv4+6773a67uvri8LCQuJE1qTOHOrMkZOTg9DQUKfroaGhOHv2LHEi69K1kEN7B4eezxzqzKF9g6OsrOy/3lfXw8MDZWVlxImsSfsGhzpzaH/mYHfWIbqLCg4ORkJCAkpKSm5YKy0txZIlSxAcHGzCZNaizhytWrXCzJkzcf78+RvWzp8/j1mzZqFVq1YmTGYt6syhzhz169fHunXrnK4nJyejXr16xImsS9dCDu0dHHo+c6gzh/YNjkceeQQTJ07E4cOHb1g7fPgwJk2ahPbt25swmbVo3+BQZw7tzxzszm6GYRjl9rsJTWZmJgYNGoTi4mK0bNnS4V6we/fuhYeHB5YtW4YGDRqYPKlrU2eOH374AUOGDEFWVhYaNGjg0PnYsWMICAjA4sWLce+995o8qWtTZw515tizZw/i4uLg7++PsLAwh85paWnIzs5GQkICWrZsafKkrk/XQg7tHRx6PnOoM4f2DY6ffvoJL7/8Mj777DPceeedqF69OoDrBzQXL15EeHg4Zs+ejb/85S8mT+ratG9wqDOH9mcOdmcdoruwgoICbNq0CQcOHEB+fj4AwMfHBzabDZGRkfDy8jJ5QmtQZ46ysjLs3r37pp3Dw8P1qdXlRJ051Jnj9OnTWLNmDQ4cOIC8vDwA19+yZ7PZ0Lt3b/j7+5s8oXXoWsihvYNDz2cOdebQvsHz3XffIT09/YbOAQEBJk9mHdo3ONSZQ/szB7OzDtFFRERERERERERERJzQjz1ERERERERERERERJzQIboF9O/fH6NHj3Z4LD4+HgMGDDBpImtSZ45HH30UMTExDo9FR0cjIiLCpImsSZ051JljzJgxeP311x0emzNnDsaMGWPSRNalayGH9g4OPZ851JlD+wZHUFAQunXr5vBYly5d8OCDD5o0kTVp3+BQZw7tzxyMzpXL7XcS0/j7+8PX19fhMT8/P91fqZypM0fPnj3tH9bzsw4dOuDChQsmTWRN6syhzhynT5/Gr+9O9+OPP+KHH34waSLr0rWQQ3sHh57PHOrMoX2DY9q0aTd8gOjLL7+MS5cumTSRNWnf4FBnDu3PHIzOuie6iIiIiIiIiIiIiIgT+vGSiIiIiIiIiMj/QVFREYqKisweQ0REyHQ7Fxd2/vx5JCcnIz09Hfn5+QAAHx8fhISEoFevXje8jUF+H3XmOH78OJKSkm7obLPZ0K9fP9SrV8/kCa1BnTnUmaOoqAiffPLJTffniIgIeHp6mjyhdehayKG9g0PPZw515tC+wZGamorExESkp6ejoKAAAODl5QWbzYaYmBiEhYWZPKE1aN/gUGcO7c8czM66nYuLOnjwIAYNGoQqVaogLCwMNWrUAACcO3cOaWlpuHr1KpYuXYrg4GCTJ3Vt6syRkpKC4cOHo1GjRggPD3fonJqaioyMDCxcuBBt27Y1eVLXps4c6sxx6tQpxMbGIjc3F02bNnXofODAAdxzzz1YsmQJ6tSpY/Kkrk/XQg7tHRx6PnOoM4f2DY4NGzZg/Pjx6NSp0007b9u2DVOmTEGPHj3MHdTFad/gUGcO7c8c7M46RHdRf/3rXxEUFIRXXnkFbm5uDmuGYeCf//wnjh49ivfee8+kCa1BnTkef/xxRERE4IUXXrjp+vz587F9+3Zs3ryZPJm1qDOHOnPExMSgatWqmDlzJry8vBzWCgoKMGrUKFy7dg1vv/22SRNah66FHNo7OPR85lBnDu0bHJ06dcKAAQPQt2/fm66vXr0aK1aswPbt28mTWYv2DQ515tD+zMHurHuiu6jMzEwMHDjwhk0PANzc3DBw4EAcOXLEhMmsRZ05Tp48icjISKfr3bp1w6lTp4gTWZM6c6gzx9dff42///3vNxygA9ffXv3CCy/gq6++MmEy69G1kEN7B4eezxzqzKF9gyMnJwehoaFO10NDQ3H27FniRNakfYNDnTm0P3OwO+sQ3UX5+Pjg0KFDTtcPHToEHx8f4kTWpM4ctWrVQkpKitP1lJQU1KxZkziRNakzhzpzeHt748yZM07Xz5w5A29vb+JE1qVrIYf2Dg49nznUmUP7Bkf9+vWxbt06p+vJycm6t3E50L7Boc4c2p852J31waIuKjY2FhMmTMA333yD0NBQ+yaXn5+PtLQ0rF27FqNGjTJ5StenzhzPP/88Ro4ciT179iAsLOyGzrt378bs2bNNntL1qTOHOnNERUUhPj4ew4YNQ5s2bRw6f/HFF3jrrbfQr18/k6e0Bl0LObR3cOj5zKHOHNo3OOLj4xEXF4fdu3ff9B7S2dnZSEhIMHlK16d9g0OdObQ/c7A7657oLmzr1q1ITExERkYGSktLAQCVKlVCo0aNEB0dja5du5o8oTWoM8fXX3+NVatWIT09HXl5eQAAX19f2Gw2DBgwACEhISZPaA3qzKHOHAkJCVi5ciXy8/Ptb0k1DAM+Pj4YOHAgBg8ebPKE1qFrIYf2Dg49nznUmUP7Bsfp06exZs0aHDhw4IbOvXv3hr+/v8kTWoP2DQ515tD+zMHsrEN0CyguLsaFCxcAANWqVYOHh4fJE1mTOouI/DFlZ2cjPz8fwPW3qNauXdvkiaxL10KxEj2fOdRZRH4r7Rsc6izy2+gQ3SKKiooAAJ6eniZPYm3qXPEuXbrk8NND3dO4YqgzhzqLFelaWPG0d/Do+cyhzhVP+0bFKykpwfHjxx06BwQE6OCxgmjf4FDniqf9mYPRWfdEd2GpqalITExEeno6CgoKAABeXl6w2WyIiYlBWFiYyRNagzpzrF27FsuXL8eJEycAXL8lg5ubGx544AHExMQgKirK5AmtQZ051Jnj+PHjSEpKQnp6usMr0W02G/r166cP+SpHuhZyaO/g0POZQ505tG9UvLKyMsydOxfvvPMOLl265LDm7e2Nvn374vnnn4e7u7tJE1qH9g0OdebQ/szB7KxXoruoDRs2YPz48ejUqRPCw8MdPtwkNTUV27Ztw5QpU9CjRw9zB3Vx6syxdOlSLFiwAP3790d4eLjDh0GkpqZi1apVGDFiBGJjY02e1LWpM4c6c6SkpGD48OFo1KjRTffnjIwMLFy4EG3btjV5UtenayGH9g4OPZ851JlD+wbHzJkzsWHDBrzwwgs37Tx37lz07NkT//jHP0ye1LVp3+BQZw7tzxz0zoa4pI4dOxpJSUlO15OSkowOHToQJ7ImdeZ45JFHjC1btjhd37Jli9GuXTveQBalzhzqzBEZGWm88cYbTtfnzZtndO/enTiRdelayKG9g0PPZw515tC+wREWFmbs2rXL6fquXbuM0NBQ4kTWpH2DQ505tD9zsDvr/UYuKicnB6GhoU7XQ0NDcfbsWeJE1qTOHOfOnUNgYKDT9QYNGtg/8ER+P3XmUGeOkydPIjIy0ul6t27dcOrUKeJE1qVrIYf2Dg49nznUmUP7Bsfly5dx9913O1339fVFYWEhcSJr0r7Boc4c2p852J11iO6i6tevj3Xr1jldT05O1r1gy4E6cwQHByMhIQElJSU3rJWWlmLJkiUIDg42YTJrUWcOdeaoVasWUlJSnK6npKSgZs2axImsS9dCDu0dHHo+c6gzh/YNjlatWmHmzJk4f/78DWvnz5/HrFmz0KpVKxMmsxbtGxzqzKH9mYPdWfdEd1F79uxBXFwc/P39ERYW5nAfq7S0NGRnZyMhIQEtW7Y0eVLXps4cmZmZGDRoEIqLi9GyZUuHznv37oWHhweWLVuGBg0amDypa1NnDnXm+PDDDzFy5Ei0bdsWYWFhDve/S0tLw+7duzF79mx06tTJ5Eldn66FHNo7OPR85lBnDu0bHD/88AOGDBmCrKwsNGjQwKHzsWPHEBAQgMWLF+Pee+81eVLXpn2DQ505tD9zsDvrEN2FnT59GmvWrMGBAweQl5cH4PpbyWw2G3r37g1/f3+TJ7QGdeYoKCjApk2bcODAAeTn5wMAfHx8YLPZEBkZCS8vL5MntAZ15lBnjq+//hqrVq1Cenr6DfvzgAEDEBISYvKE1qFrIYf2Dg49nznUmUP7BkdZWRl27959087h4eFwd9eb/MuD9g0OdebQ/szB7KxDdBERERERERERERERJ/TjUhERERERERERERERJ3SILiIiIiIiIiIiIiLihA7RRURERERERERERESc0CG6iIiIiIiIiIiIiIgTOkQXkT+sPXv24OrVq2aPIVJu9FneIiIiIq6vqKgI33//PYqKisweRURcSElJidkjyC3QIbpF/fDDDxgzZozZY7i8uLg4bNy4UQe5JomNjcWZM2fMHuNP4/vvv8eAAQPMHsPlFRUVYcaMGejbty8SEhIAAAsXLkRISAiaNWuGl19+GQUFBSZPaT1XrlxBcnIyXn/9dSQlJeHChQtmj2Qpubm5eP/995GSknLDgcGVK1ewYMECkyazlszMTIwaNQoRERFo0qQJbDYbIiMj8cYbb2jfKCcZGRnIzs62f71x40b07t0b7dq1Q58+fbBlyxYTp7MW7Rsc2jcq3vr167F//34AwLVr1zB27FjYbDZ06tQJISEhmDhxog7Ty0lqairmzZuHtLQ0AMDevXsxaNAgDBgwAMnJySZPZx3Hjx/HpEmT0KNHD4SHhyM8PBw9evTApEmTcPz4cbPHs4Rdu3bh6NGjAICysjK8+eabaNu2LYKDg/Hwww8jISFBL7AqJ2vXrkV8fLx9j9i6dSu6dOmCiIgIzJs3r1z/LDdD/6tZUmZmJnr27IkjR46YPYpLCwoKQqVKlVC1alV069YNUVFRaNy4sdljWU7Pnj1v+viRI0dQt25d3HbbbQCADRs2MMf609G+UT6mT5+OrVu3onv37khJSUHr1q2xc+dOvPjii3B3d8e8efPw8MMPY/z48WaP6tK6du2Kd955B3fddRd++OEH9O3bFxcvXsT999+P7OxsVKpUCe+99x5q165t9qgu7+DBg4iNjUVZWRlKSkrg5+eHN998E/Xr1wcA5Ofno23btto7btHu3bsxYsQItGvXDrfddhs+/vhjPPnkk6hatSq2b98OwzDwzjvvwNfX1+xRXdrjjz+O0aNHIywsDGvXrsWUKVMQFRWFgIAAnDhxAmvXrsW4cePw1FNPmT2qS9O+waF9gyMiIgJz5sxB06ZNMWPGDGzbtg1jxoxB3bp1ceLECbz22muIiIjAqFGjzB7Vpb3//vsYO3YsAgMDceLECUyYMAHTp09Hp06dUFZWhk2bNmHWrFno3Lmz2aO6tJSUFAwfPhyNGjVCeHg4atSoAQA4d+4cUlNTkZGRgYULF6Jt27YmT+raOnfujClTpqBFixZYvHgxli1bhmeffda+byQkJGDgwIEYMmSI2aO6tMTERMydOxfh4eHYv38/+vbti8TERERHR6O0tBTLly/HqFGj8PTTT5fLn6dDdBf1//7f//uv69nZ2ZgxY4a+Mb1FQUFB+OCDD/DZZ58hOTkZx48fR4MGDRAVFYXIyEjceeedZo9oCY0aNUJoaChsNpv9McMwsHDhQvTu3dt+YR8xYoRJE1rDypUr/+v6jz/+iGXLlmnfuEWPPPIIpk2bhrCwMGRnZ6Njx46YP38+HnvsMQDXX2EzYcIEfPrppyZP6tqCgoKQmpqKGjVqYOTIkTh9+jSWLFkCb29vXL58GSNGjED16tUxe/Zss0d1eTExMbjnnnswdepUXLlyBbNmzcKHH36I5cuXo2HDhjoMKyc9evTA008/jT59+gC4vldMmTIFH374IYqLizF48GDce++9mD59usmTuramTZti69atqFWrFnr27Ik+ffrgr3/9q3198+bNWLRokV6Rfou0b3Bo3+AIDg7Gtm3bULNmTXTq1Anjxo3Dww8/bF/fu3cvRo0ahR07dpg4pevr0aMHevXqhQEDBiAtLQ1xcXF48cUXER0dDQBYtmwZPv74Y6xZs8bcQV3c448/joiICLzwwgs3XZ8/fz62b9+OzZs3kyezll/uG5GRkRg2bBi6dOliX9+5cyemTZuG7du3mzil6+vSpQuGDRuGyMhIHD58GFFRUZg0aRKioqIAXH+V+po1a7B+/fpy+fMql8vvInTDhw+Hm5vbf337h5ubG3Ei66pWrRqio6MRHR2NgwcPYt26dXjjjTfw2muv4bHHHsNTTz2F0NBQs8d0aStXrsTo0aPRpEkTjBgxAu7u1+80tWjRIvTt2xf16tUzeUJrmDZtGnx9feHh4XHT9eLiYvJE1nThwgXcf//9AIDatWujUqVKqFOnjn29Tp06OH/+vEnTWVN6ejpeeeUVeHt7AwDuuOMOPPfcc3jppZdMnswaMjIyMHHiRLi7u8PLywuTJk1CzZo1ER0djaVLl6JmzZpmj2gJWVlZDq/6+vkHcbm5ubj77rsxYsQIPPfccyZOaA1VqlTBhQsXUKtWLfz4449o0qSJw3rTpk1x+vRpk6azDu0bHNo3OHx9ffH999+jZs2aKCwsRLVq1RzWq1evjv/85z/mDGchp06dQvv27QEAoaGhKC0tRZs2bezr7dq1w+LFi80azzJOnjyJyMhIp+vdunXDkiVLiBNZ05133onc3FzUrFkT58+fd/j7IADcf//9+PHHH02azjpycnLQvHlzAEDDhg3h7u7u8OLMVq1aYebMmeX25+me6C7K19cX8+fPR2Zm5k3/pdteVIwmTZpg8uTJ2L17N/75z3/ihx9+wN/+9jezx3J5zZs3x/r163Hy5En07t0b33//vdkjWVLNmjUxZswYfPrppzf918/375Zbc++99yI9PR3A9bez//Lff/5nPz8/M0aznJ9/WHzt2rUb3qru5+enH1aUo2vXrjl8PWTIEAwdOhSxsbH2+8TKrfHz88OJEyfsX3///fcoKyvDXXfdZV+/cuWKSdNZx8MPP2x/FWPLli3x0UcfOax/+OGHuO+++8wYzXK0b1Q87RscP99j/uLFi3jiiSfw5ptv4vLlywCAwsJCzJ8/H82aNTN5StdXuXJlhxf1eHh44I477rB/7enpecO+Ir9drVq1kJKS4nQ9JSVFP+gsBx06dMCiRYtQWlqKiIgIvPPOOw4vgk1KSsKDDz5o4oTWUKVKFRQWFtq/rl69Om6//XaHX1OeH+aqV6K7qEaNGiEjI8N+e4Bf+1+vUpdbU7VqVfTq1Qu9evVy+MZVfj9vb2/MmTMHycnJeOaZZ/Dcc8/p3RTlrHHjxsjIyEDXrl1vuq59o3z07t0bo0ePxtq1a5GRkYH4+Hi8/vrryMrKgru7O9asWYOYmBizx7SEgQMHonLlyigoKMCJEyfQoEED+1pOTo79EEFuTf369bF//34EBQU5PP7z/Y71iv/y8cQTT2D8+PGIi4uDp6cnEhMT8eijj8LT0xPA9c+t8Pf3N3lK1zdy5Ej06dMH/fr1Q+PGjbF8+XJ8+eWX9nuip6en48033zR7TJenfYND+wbH8OHDcezYMTz22GNo3LgxvvrqKzz00EPw8/NDbm4u7rrrLixbtszsMV3efffdh6ysLNStWxfA9Xv+//IQPTs7Wy9EKQfPP/88Ro4ciT179iAsLAw+Pj4Arn9WRVpaGnbv3q3bIZaDl156CdHR0ejSpQtsNhs++ugjfP7557j//vtx6tQp/PTTT3j77bfNHtPl1a1bF0ePHkVAQAAA3PADoqysrHK9DuoQ3UUNGjTov76q4L777vuf9z+W/61ly5ZOb33xswceeIA0zZ/Dk08+iebNm2PkyJHl+hNDuf4N0y9/SvtrAQEB//PzFuR/i46ORo0aNZCeno4nn3wS3bt3R4MGDTBv3jwUFhYiOjoazz77rNljurxff0bCr19x8Omnn6JFixbMkSyrR48e+PLLL+333P2lwYMHwzAMvPvuuyZMZi1xcXEoLCzEwoULUVRUhPDwcIwbN86+7ufnh0mTJpk3oEX4+flh48aNSEhIwI4dO2AYBg4ePIizZ88iJCQEa9asQXBwsNljujztGxzaNzg8PT3x1ltvYdeuXdixYwfc3d1hGAZ8fX3RrFkzdO/e/YbvQ+S3i4uLc/jMMS8vL4f1b775xuGe0vL7dOnSBX5+fli1ahWWL1+OvLw8ANfvdmCz2bBq1SqEhISYPKXr8/b2xrvvvot169Zhx44dqFWrFsrKylBcXIzu3bujT58+uOeee8we0+WNHDnyv+6/OTk55fahooA+WFRE/qDKyspw+fJleHl56RXpIiIiIiIiIiJiGh2ii/wfXbp0yeGntD9/gJ2IiJivqKgIAOxvYRcRkT8GfQ/Noc4icisMw0BZWRkqVapk9iiWo/3ZOvTBoi5s7dq1iI+PR3JyMgBg69at6NKlCyIiIjBv3jyTp7OOtWvXomvXrmjVqhW6deuGbt26oVWrVujatSvWrl1r9nh/CpmZmfrQjXKSkpKCcePGYebMmfjuu+8c1n766ScMGDDApMmsRZ05UlNTMXjwYLRs2RJNmzZF06ZN0bJlSwwePBiff/652eP9aXz33XeIiIgwewzLU+fyk5mZiYULF2L16tU3fABxQUEBxowZY9Jk1qLvoTnU2Xz6u0r50ffQFa+kpASvv/46+vXrZz83Wrp0KUJCQtC0aVPEx8fbX5wit+bX+/PP/6z9mae892fdE91FJSYmYu7cuQgPD8frr7+O3NxcJCYmIjo6GqWlpVi+fDn8/PzK9d4/f0ZLly7FggUL0L9/f4SHhzt86EZqaiqmTZuGixcvIjY21uRJrU9vmrl1mzdvRnx8PNq2bYsTJ04gKSkJU6ZMweOPPw4AKC4uxt69e02e0vWpM8eGDRswfvx4dOrUCWPGjEGNGjUAAOfOnUNqaiqGDBmCKVOmoEePHuYO+idQXFyMnJwcs8ewPHUuH5999hni4uJw//334/Lly5g3bx7mzp2LNm3aAACuXr2KjRs3Yvr06SZP6tr0PTSHOv9x6O8qt07fQ3MsWLAA69atQ2RkJLZt24Zz584hJSUFkydPRllZGebMmYMVK1Zg8ODBZo/q0rQ//3GU5/6sQ3QX9d5772Hy5MmIjIzE4cOHERUVhUmTJiEqKgrA9Q+RWbNmjQ7Rb9Hq1asxbdo0dO3a1eHxgIAAtG7dGkFBQZg5c6Y2vlv06w8I/LVLly7pvujl4O2338bo0aPtr+DYunUrxo0bh2vXrtn3Drl16syxaNEijB07Fn379r1hrVevXmjWrBkWLlyoQ/Ry8L8OE3/9Sl75fdSZY8GCBYiNjcWLL74IwzCwdOlSPPvss5g7dy4efvhhs8ezDH0PzaHOHPq7Coe+h+b44IMPMGXKFLRv3x59+vRB586dMXv2bPs+8vMH6eoQ/dZof+Zg7886RHdROTk5aN68OQCgYcOGcHd3h81ms6+3atUKM2fONGk66zh37hwCAwOdrjdo0AAXLlwgTmRNO3bsQFhYmP2ns79WWlpKnsiaTp06hfbt29u/7tq1K6pXr45nn30WJSUl6NChg4nTWYc6c+Tk5CA0NNTpemhoKGbMmEGcyLpWrlyJBx98EHfcccdN169cuUKeyJrUmePbb7+1f4/s5uaGwYMH45577sELL7yAOXPmIDg42OQJrUHfQ3OoM4f+rsKh76E5cnNzERQUBACoU6cOPDw8HPaR4OBgnDlzxqzxLEP7Mwd7f9YhuouqUqUKCgsL7V9Xr14dt99+u8OvKSkpYY9lOcHBwUhISMDUqVNRubLj/11KS0uxZMkS/WWrHNStWxcdO3Z0+gqDI0eOYOfOndyhLOiOO+7AuXPnULt2bftjbdq0weLFizF06FCcPXvWxOmsQ5056tevj3Xr1mHUqFE3XU9OTka9evXIU1nTfffdh4EDB+KJJ5646fqRI0fQq1cv8lTWo84cnp6euHjxosNjkZGRcHd3x4svvoj4+HiTJrMWfQ/Noc4c+rsKh76H5vD29sbFixdx7733Arj+okwvLy/7elFRkd5ZUQ60P3Ow92cdoruounXr4ujRowgICABw/QM4fikrKwv+/v5mjGYpEyZMwKBBg/DQQw+hZcuWDvfc3bt3Lzw8PLBs2TKTp3R9jRs3xuHDh52ue3p62i/y8vs1adIEu3btcnjXCnD9nSuLFi1CXFycOYNZjDpzxMfHIy4uDrt370ZYWJjD/pyWlobs7GwkJCSYPKU1NG7cGBkZGU4Pd93c3HQv2HKgzhwPPvgg9uzZg8aNGzs83q1bNxiGgdGjR5s0mbXoe2gOdebQ31U49D00R0BAAA4fPmx/lfS7777rsH7s2DHUqVPHjNEsRfszB3t/djP03bhL2rdvH26//XannzK7evVqGIaBfv36kSeznoKCAmzatAkHDhxAfn4+AMDHxwc2mw2RkZEOP7WV36eoqAilpaWoWrWq2aNY2pdffon9+/dj6NChN13/4osv8P777+vD1G6ROvOcPn0aa9aswYEDB5CXlwcA8PX1hc1mQ+/evfXD5HKSl5eHoqIi1KpVy+xRLE2dOT7++GPs3bsXY8eOven65s2b8e9//xurVq0iT2Y9+h6aQ50rnv6uwqHvoTlOnDiBypUrO7zi/5c2b96MSpUq3XAvb/nttD9XPPb+rEN0EREREREREREREREn3M0eQMrPpEmTcP78ebPHsLwhQ4YgNzfX7DEsT5051JlDnTl0HeRRaw515lBnDl0LOdSZQ5051JlDnTnUmaMiO+sQ3UI2bdqEy5cvmz2G5e3duxfXrl0zewzLU2cOdeZQZw5dB3nUmkOdOdSZQ9dCDnXmUGcOdeZQZw515qjIzjpEtxDdmUdERP7MdB3kUWsOdeZQZxERERH5X3SILvIb1apVC5UrVzZ7DMtTZw515lBnERH5s9O1kEOdOdSZQ5051JlDnTkqsrM+WFRERERERERERERExAn9CMRCiouLcebMGdSoUQPe3t5mj2NJFy9exEcffYScnBzUqlULnTt3VusKoM4c6syhzhXjm2++QePGjc0e409BrTnUmUOdec6dO4djx46hcePG8Pb2Rn5+PjZs2ADDMNCuXTsEBgaaPaIlqDOHOnOoM4c682RnZ2Pfvn3Izc2Fu7s7ateujYceegheXl5mj2YprM56JbqLWrJkCfr3748qVaqgtLQUs2bNwqpVq1BaWgp3d3c8/vjjmDx5Mjw8PMwe1aWNGDEC3bt3R+fOnfHtt9+if//+cHNzQ+3atXHmzBm4ublhxYoVCAgIMHtUl6bOHOrMoc4cQUFBqF27Np588kn07NkTfn5+Zo9kWWrNoc4c6syxZ88exMXFobCwED4+Pli6dCmGDh2KKlWqwM3NDWfOnMFbb72F8PBws0d1aerMoc4c6syhzhxXrlzB6NGjsX37dgCAm5sbatSogfPnz+O2227DyJEj0bdvX5OndH3szronuouaM2cOLl++DABITExEcnIyXnnlFWzevBnTp09HSkoKEhMTzR3SAr788kvUr18fADBz5kw89NBDSElJwb///W/s3LkT7dq1w7Rp00ye0vWpM4c6c6gzT5s2bbBy5Uo8+uijGDp0KD755BOUlpaaPZYlqTWHOnOoc8WbO3cuevbsiX379iEmJgZDhgxBREQEtm3bho8++gj9+vXDggULzB7T5akzhzpzqDOHOnO8+uqryMvLw6ZNm7Bt2zZ06NABTzzxBPbt24dx48bhtddew+bNm80e0+XROxvikgIDA438/HzDMAyjR48exrvvvuuw/v777xvdunUzYzRLadKkiXHq1CnDMAzjoYceMjIyMhzWs7KyjObNm5sxmqWoM4c6c6gzx8/XweLiYuOjjz4yBg8ebDz44INGWFiYMXPmTCMrK8vsES1DrTnUmUOdOZo1a2a/FhYXFxsNGzY0Dh8+bF8/ceKEroXlQJ051JlDnTnUmaN169bGoUOH7F//5z//MYKDg40rV64YhmEYSUlJxhNPPGHSdNbB7qxXorswNzc3AEBOTg5CQkIc1kJCQnD69GkzxrKUwMBAfPHFFwAAX19f5OTkOKzn5OSgSpUqZoxmKerMoc4c6sxVuXJldOrUCQkJCdixYweeeeYZbNu2DV27dtVbJMuZWnOoM4c6VywPDw9cu3YNwPXPbSorK7N/DQBXr15F5cr6eK5bpc4c6syhzhzqzFFaWupwP+7bb78dpaWlKCwsBAA89NBDyMrKMms8y2B31iG6C/v3v/+NlStXwsPDAz/99JPD2uXLl+Hp6WnSZNYxbNgwzJ49G+vXr0f//v0xbdo0rF27Fl9//TWSk5Mxbtw4PP7442aP6fLUmUOdOdSZ4+cfJP+Sn58fhg8fjk8++QTLli3DPffcY8Jk1qPWHOrMoc4czZo1w+zZs7Fv3z5Mnz4dDRs2xFtvvYUrV66gsLAQCxcu1Ae8lgN15lBnDnXmUGeO4OBgrFy50v71ypUrUa1aNVSvXh3A9Xt533777WaNZxnszvpgURf16KOPOnw9YMAAREdH279esWIFtm7divfee488mfVs27YN06ZNQ25uLn75fxdPT0/07t0b8fHxqFSpkokTWoM6c6gzhzpXvKCgIKSmpqJGjRpmj2J5as2hzhzqzHHy5EkMHToUp06dQt26dbF8+XJMmjQJu3btAgD85S9/wdKlS9GoUSOTJ3Vt6syhzhzqzKHOHBkZGfjb3/4GDw8PeHh4IC8vDzNmzEC3bt0AAKtXr8bBgwcxY8YMkyd1bezOOkS3qPT0dHh6eqJhw4Zmj2IJpaWlyMjIwOnTp1FWVgZfX180atTI4W0jcuvUmUOdOdS5Yn355Zdo1qyZ3m5KoNYc6syhzlwXLlxAtWrV7F+npaXh6tWrsNlsDo/LrVFnDnXmUGcOda54ubm52LlzJ4qKitCmTRvUq1fP7JEsidlZh+giIiIiIiIiIiIiIk7oJRgurKioCJ988gnS09ORn58PAPDx8UFISAgiIiJ0T/Ryos4c6syhzhzqzKHOPGrNoc4c6syhzhzqzKHOHOrMoc4c6szB7KxXoruoU6dOITY2Frm5uWjatKn9vo7nzp3DgQMHcM8992DJkiWoU6eOyZO6NnXmUGcOdeZQZw515lFrDnXmUGcOdeZQZw515lBnDnXmUGcOdmcdoruomJgYVK1aFTNnzrzh/roFBQUYNWoUrl27hrffftukCa1BnTnUmUOdOdSZQ5151JpDnTnUmUOdOdSZQ5051JlDnTnUmYPe2RCX1KRJE+Po0aNO1zMzM40mTZoQJ7ImdeZQZw515lBnDnXmUWsOdeZQZw515lBnDnXmUGcOdeZQZw52Z/fyOYoXNm9vb5w5c8bp+pkzZ+Dt7U2cyJrUmUOdOdSZQ5051JlHrTnUmUOdOdSZQ5051JlDnTnUmUOdOdid9cGiLioqKgrx8fEYNmwY2rRpAx8fHwBAfn4+vvjiC7z11lvo16+fyVO6PnXmUGcOdeZQZw515lFrDnXmUGcOdeZQZw515lBnDnXmUGcOdmfdE92FJSQkYOXKlcjPz4ebmxsAwDAM+Pj4YODAgRg8eLDJE1qDOnOoM4c6c6gzhzrzqDWHOnOoM4c6c6gzhzpzqDOHOnOoMwezsw7RLSA7Oxv5+fkAAB8fH9SuXdvkiaxJnTnUmUOdOdSZQ5151JpDnTnUmUOdOdSZQ5051JlDnTnUmYPRWYfoFrJv3z4EBwfD09PT7FEsTZ051JlDnTnUmUOdedSaQ5051JlDnTnUmUOdOdSZQ5051JmjIjvrg0UtZPDgwfjxxx/NHsPy1JlDnTnUmUOdOdSZR6051JlDnTnUmUOdOdSZQ5051JlDnTkqsrMO0S1EbyrgUGcOdeZQZw515lBnHrXmUGcOdeZQZw515lBnDnXmUGcOdeaoyM46RBcRERERERERERERcUKH6BYyefJk1KhRw+wxLE+dOdSZQ5051JlDnXnUmkOdOdSZQ5051JlDnTnUmUOdOdSZoyI764NFLWbPnj1o2rQpqlSpYvYolqbOHOrMoc4c6syhzjxqzaHOHOrMoc4c6syhzhzqzKHOHOrMUVGd9Up0i4mNjcWZM2fMHsPy1JlDnTnUmUOdOdSZR6051JlDnTnUmUOdOdSZQ5051JlDnTkqqrNeie6ievbsedPHjxw5grp16+K2224DAGzYsIE5luWoM4c6c6gzhzpzqDOPWnOoM4c6c6gzhzpzqDOHOnOoM4c6c7A7Vy6X30Xojh07htDQUNhsNvtjhmEgMzMTrVu31n2Wyok6c6gzhzpzqDOHOvOoNYc6c6gzhzpzqDOHOnOoM4c6c6gzB72zIS7pq6++Mh577DFj7ty5Rmlpqf3xhg0bGt9++62Jk1mLOnOoM4c6c6gzhzrzqDWHOnOoM4c6c6gzhzpzqDOHOnOoMwe7s+6J7qKaN2+O9evX4+TJk+jduze+//57s0eyJHXmUGcOdeZQZw515lFrDnXmUGcOdeZQZw515lBnDnXmUGcOdmcdorswb29vzJkzB08//TSeeeYZvPfee3BzczN7LMtRZw515lBnDnXmUGceteZQZw515lBnDnXmUGcOdeZQZw515mB21j3RLeDJJ59E8+bNMXLkSJSUlJg9jmWpM4c6c6gzhzpzqDOPWnOoM4c6c6gzhzpzqDOHOnOoM4c6czA6uxmGYVTI7yx0ZWVluHz5Mry8vPTTrQqkzhzqzKHOHOrMoc48as2hzhzqzKHOHOrMoc4c6syhzhzqzFGRnXWILiIiIiIiIiIiIiLihO6J7sJWr16N6OhovPDCC0hLS3NYO3/+PCIiIkyazFrUmUOdOdSZQ5051JlHrTnUmUOdOdSZQ5051JlDnTnUmUOdOZiddYjuolauXInXXnsNdevWhaenJwYPHozFixfb18vKypCTk2PihNagzhzqzKHOHOrMoc48as2hzhzqzKHOHOrMoc4c6syhzhzqzEHvbIhL6tq1q7Fp0yb71/v27TPatGljvPHGG4ZhGEZeXp4RFBRk1niWoc4c6syhzhzqzKHOPGrNoc4c6syhzhzqzKHOHOrMoc4c6szB7ly5/I7jhen06dMICQmxf92sWTOsWLECMTExKCkpwcCBA02czjrUmUOdOdSZQ5051JlHrTnUmUOdOdSZQ5051JlDnTnUmUOdOdiddYjuoqpVq4azZ8/C39/f/liDBg2wYsUKDBw4ELm5uSZOZx3qzKHOHOrMoc4c6syj1hzqzKHOHOrMoc4c6syhzhzqzKHOHOzOuie6i2revDm2b99+w+P16tVDYmIidu3aZcJU1qPOHOrMoc4c6syhzjxqzaHOHOrMoc4c6syhzhzqzKHOHOrMwe6sV6K7qMGDByMjI+Oma/Xr18eKFStu+kSS30adOdSZQ5051JlDnXnUmkOdOdSZQ5051JlDnTnUmUOdOdSZg93ZzTAMo9x+NxERERERERERERERC9Er0V3cwYMHsX//fuTn5wMAfHx8EBISgiZNmpg8mbWoM4c6c6gzhzpzqDOPWnOoM4c6c6gzhzpzqDOHOnOoM4c6c7A665XoLurcuXMYMWIE9u/fj5o1a6JGjRr2x3NyctCsWTPMnz/f/rj8PurMoc4c6syhzhzqzKPWHOrMoc4c6syhzhzqzKHOHOrMoc4c7M46RHdRzz//PHJzczFt2jTUrVvXYS0rKwtjx47F3XffjXnz5pk0oTWoM4c6c6gzhzpzqDOPWnOoM4c6c6gzhzpzqDOHOnOoM4c6c9A7G+KSbDabkZGR4XT90KFDhs1mI05kTerMoc4c6syhzhzqzKPWHOrMoc4c6syhzhzqzKHOHOrMoc4c7M7u5XMUL2yenp4oKChwun758mV4enoSJ7ImdeZQZw515lBnDnXmUWsOdeZQZw515lBnDnXmUGcOdeZQZw52Zx2iu6iuXbti9OjR+Pjjjx2eMAUFBfj4448xZswYdO/e3cQJrUGdOdSZQ5051JlDnXnUmkOdOdSZQ5051JlDnTnUmUOdOdSZg91Z90R3UUVFRZg6dSqSk5NRWloKDw8PAEBxcTEqVaqEp556CmPHjtVPtm6ROnOoM4c6c6gzhzrzqDWHOnOoM4c6c6gzhzpzqDOHOnOoMwe7sw7RXVxBQQG++eYb5OfnAwB8fHzQuHFjeHl5mTyZtagzhzpzqDOHOnOoM49ac6gzhzpzqDOHOnOoM4c6c6gzhzpzsDrrEF1ERERERERERERExAndE92FXb16FV999RWOHz9+w9q1a9ewceNG/lAWpM4c6syhzhzqzKHOPGrNoc4c6syhzhzqzKHOHOrMoc4c6sxB7WyIS8rKyjLat29vBAYGGkFBQUbfvn2Ns2fP2tfz8vKMoKAgEye0BnXmUGcOdeZQZw515lFrDnXmUGcOdeZQZw515lBnDnXmUGcOdme9Et1FzZo1C/Xr18fnn3+Ojz76CHfccQeeeeYZ5OTkmD2apagzhzpzqDOHOnOoM49ac6gzhzpzqDOHOnOoM4c6c6gzhzpzsDvrEN1F7d+/Hy+99BKqV6+OOnXqYNGiRQgPD0ffvn2RnZ1t9niWoc4c6syhzhzqzKHOPGrNoc4c6syhzhzqzKHOHOrMoc4c6szB7qxDdBd19epVVK5c2f61m5sbXnnlFbRv3x79+vXDyZMnzRvOQtSZQ5051JlDnTnUmUetOdSZQ5051JlDnTnUmUOdOdSZQ5052J0r/+9fIn9EdevWxaFDhxAQEODw+MSJEwEAzz77rBljWY46c6gzhzpzqDOHOvOoNYc6c6gzhzpzqDOHOnOoM4c6c6gzB7uzXonuojp06IAtW7bcdG3ixIno1q0bDMMgT2U96syhzhzqzKHOHOrMo9Yc6syhzhzqzKHOHOrMoc4c6syhzhzszm6G/lcTEREREREREREREbkpvRJdRERERERERERERMQJHaKLiIiIiIiIiIiIiDihQ3QRERERERERERERESd0iC4iIiIiIiIiIiIi4oQO0UVEREREREREREREnNAhuoiIiIiIiIiIiIiIEzpEFxERERExwejRoxEYGIjAwEA0atQIYWFhiImJwbp161BWVvZ//n3Wr1+PFi1alMtM/+33CgwMxCeffFIuf46IiIiIiCvRIbqIiIiIiEnatm2Lzz77DJ9++imWLFmC1q1bY+rUqRg6dChKSkrMHo+uqKjI7BFERERERG6gQ3QREREREZN4enrC19cXfn5+aNSoEeLi4rBw4ULs2rULGzZsAAAsX74ckZGRsNlsaNeuHSZNmoTLly8DAPbs2YMxY8bg0qVL9le1z58/H8D1A+kZM2agbdu2sNlsiIqKwp49e8pt9qNHj2LAgAFo0qQJWrdujQkTJtjnAoD+/ftj6tSpDv+ZYcOGYfTo0favH330Ubz55psYNWoUmjVrhokTJ5bbfCIiIiIi5UWH6CIiIiIifyChoaEICgrC9u3bAQBubm4YN24cPvjgA7z66qv44osv8NprrwEAQkJCMHbsWHh5eeGzzz7DZ599hr/97W8AgMmTJ2P//v14/fXXsWnTJnTu3BmDBg3CyZMnb3nGK1euIDY2FnfeeSfWrVuHN954A59//jn+9a9//ebfa9myZQgKCsLGjRsxbNiwW55NRERERKS8VTZ7ABERERERcVS3bl0cPXoUABAdHW1/3N/fH3//+9/xz3/+E5MmTYKnpye8vb3h5uYGX19f+6/LycnB+vXrsWPHDvj5+QEAYmNjsXv3bqxfvx4vvfSS0z/70qVLCAkJ+a/zffDBB/ZXut9+++0AgIkTJyIuLg4jR46Ej4/P//m/a5s2bewH/yIiIiIif0Q6RBcRERER+YMxDANubm4AgM8//xyLFy9GVlYWCgoKUFpaimvXrqGwsBBVq1a96X/+2LFjKC0tRefOnR0eLyoqwl133QUADgflkZGRmDx5MgDgjjvusN9K5pc6duxo/+fvvvsOgYGB9gN0AGjWrBnKyspw4sSJ33SI3rhx4//zrxURERERMYMO0UVERERE/mC+++47+Pv74/Tp0xg6dCj69OmDF198EXfeeSf27duHcePGobi42Okh+pUrV1CpUiUkJyejUqVKDms/H3xv3LjR/piXl5f9n93d3VGnTp1b/u/g5uYGwzAcHrvZh6U6++8gIiIiIvJHoUN0EREREZE/kLS0NBw7dgzR0dHIyMiAYRgYPXo03N2vf5zRhx9+6PDrPTw8UFpa6vDYgw8+iNLSUpw/fx4tWrS46Z9zKwflAQEB2LBhA65cuWI/lP/666/h7u6OBx54AABQvXp15OXl2f8zpaWl+Pbbb9G6devf/eeKiIiIiJhBHywqIiIiImKSoqIi5OXl4ccff0RGRgYWLVqEYcOGoX379ujRowfq1KmD4uJirFq1CtnZ2di4cSPeffddh9+jVq1auHLlCtLS0nD+/HkUFhbigQceQGRkJEaNGoXt27cjOzsbBw8exOLFi7Fz585bnjsyMhKenp4YPXo0jh07hi+++AL/+te/8MQTT9hv5dKmTRukpKRg586d+O677zBp0iRcvHjxlv9sERERERE2vRJdRERERMQku3fvRnh4OCpXroy//OUvCAoKwvjx49GzZ0+4u7sjKCgIY8aMwZIlSzBnzhy0aNECL730EuLj4+2/R7NmzdC7d2/8/e9/x3/+8x+MGDECzz33HKZPn4633noLr776KnJzc3HXXXfBZrPhkUceueW5q1atirfffhtTp07FU089hapVq6Jjx44YPXq0/dc8+eSTyMzMRHx8PCpVqoTo6Gi9Cl1EREREXJKb8esbFYqIiIiIiIiIiIiICADdzkVERERERERERERExCkdoouIiIiIiIiIiIiIOKFDdBERERERERERERERJ3SILiIiIiIiIiIiIiLihA7RRURERERERERERESc0CG6iIiIiIiIiIiIiIgTOkQXEREREREREREREXFCh+giIiIiIiIiIiIiIk7oEF1ERERERERERERExAkdoouIiIiIiIiIiIiIOKFDdBERERERERERERERJ3SILiIiIiIiIiIiIiLixP8H5+D0UNy7meQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of hours: 2399\n",
      "Average clips per hour: 245.44\n",
      "Max clips in an hour: 384\n",
      "Min clips in an hour: 14\n"
     ]
    }
   ],
   "source": [
    "# filter on versions\n",
    "clip_df = total_clip_df.copy()\n",
    "total_clip_counts = clip_df.shape[0]\n",
    "print(f\"total clips: {total_clip_counts}\")\n",
    "# check the number of audio uploads\n",
    "upload_clip_df = total_clip_df[total_clip_df[\"clip_type\"] == \"upload\"].copy()\n",
    "stem_clip_df = total_clip_df[total_clip_df[\"clip_type\"] == \"stem\"].copy()\n",
    "print(\"total without model:\", (total_clip_df[\"model_name\"] == \"\").sum())\n",
    "print(\"uploads:\", upload_clip_df.shape[0])\n",
    "print(\"stems:\", stem_clip_df.shape[0])\n",
    "\n",
    "# Call the function\n",
    "plot_clip_distribution(total_clip_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proceed with feature engineering and cleaning up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "588799it [00:18, 31788.57it/s]\n"
     ]
    }
   ],
   "source": [
    "children_ids = set()\n",
    "for _, row in tqdm.tqdm(clip_df.iterrows()):\n",
    "    if concat_history_clips := row[\"metadata\"].get(\"concat_history\"):\n",
    "        # print(concat_history_clips)\n",
    "        for history_clip in concat_history_clips:\n",
    "            if isinstance(history_clip, dict):\n",
    "                children_ids.add(history_clip[\"id\"])\n",
    "            elif isinstance(history_clip, str):\n",
    "                children_ids.add(history_clip)\n",
    "            else:\n",
    "                print(\"WTF\", concat_history_clips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total childrens, 1255726, 2.132690442748714\n"
     ]
    }
   ],
   "source": [
    "print(f\"Total childrens, {len(children_ids)}, {len(children_ids) / clip_df.shape[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40523\n",
      "(588799, 31)\n",
      "(548276, 31)\n"
     ]
    }
   ],
   "source": [
    "unfinished_id_mask = clip_df[\"s3_id\"].isin(children_ids)\n",
    "print(unfinished_id_mask.sum())\n",
    "print(clip_df.shape)\n",
    "clip_df = clip_df[~unfinished_id_mask].copy()\n",
    "print(clip_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "443386"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clip_duration_mask = (clip_df[\"duration\"] >= 60) & (clip_df[\"duration\"] <= 300)\n",
    "clip_duration_mask.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(443386, 31)\n"
     ]
    }
   ],
   "source": [
    "clip_df = clip_df[clip_duration_mask].copy()\n",
    "print(clip_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(441483, 31)\n"
     ]
    }
   ],
   "source": [
    "clip_model_mask = clip_df[\"model_name\"].str.contains(\"v2\")\n",
    "clip_df = clip_df[~clip_model_mask].copy()\n",
    "print(clip_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:40.236690Z",
     "start_time": "2024-05-26T00:23:39.995713Z"
    },
    "execution": {
     "execution_failed": "2024-09-02T04:29:36.833Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    441483.000000\n",
      "mean         52.326389\n",
      "std         134.692663\n",
      "min           1.000000\n",
      "25%           5.000000\n",
      "50%          14.000000\n",
      "75%          43.000000\n",
      "max        1624.000000\n",
      "Name: user_n_clips, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# set user number of clips generated\n",
    "clip_df[\"user_n_clips\"] = clip_df[\"user_id\"].map(clip_df[\"user_id\"].value_counts())\n",
    "print(clip_df[\"user_n_clips\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tony/anaconda3/envs/suno_env/lib/python3.10/site-packages/matplotlib/axes/_axes.py:7001: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
      "  m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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rpdiMGdZf9RkP/PCHP8wjjzySyy+/PLfeemtmzZqVU045paa/qqoqJ554Yt57772MHz8+F110Ue67775cccUVxbgkPoKVjQd8B9Z/9RkP1Oe+4Mwzz8y0adMyduzYXHvttXnmmWdy7rnnFuOSaKD6jAd8B9Y/jTEeWN1nvnDhwgwbNiydO3fOvffem7POOitXXXVVfv7zn6/166tRzXrpK1/5SvXIkSNrXldVVVX36dOn+rrrritiVRTL3Llzq3faaafqp556qtil0MQWLlxYvd9++1X/5S9/qR4yZEj1BRdcUOySaEI/+tGPqg8//PBil0ERnXDCCdXf+c53arWdcsop1WeccUaRKqKp7bTTTtW///3va14XCoXqz3zmM9U33nhjTduCBQuqu3fvXv2rX/2qGCVSRMYMzceHxwMLFiyo3nXXXat/85vf1Gwzbdq06p122ql60qRJ1dXV1dWPPvpodbdu3apnz55ds80dd9xRvccee1S/++67TVo/a25l4wHfgeZhdeOB+twXLP9eTJ48uWabP/3pT9WVlZXVb7755tornkaxuvGA78D6b03GA/X5zG+//fbqT33qU7X+f/CjH/2o+otf/OLavqQanthYDy1dujRTpkxJ7969a9rKy8vTu3fvTJo0qYiVUSz/+9//ksSvtJuhUaNGpW/fvrX+e0Dz8cc//jHdu3fPqaeemr322iuHHHJI7rrrrmKXRRPq2bNnnnjiifzjH/9I8v6jws8++2z23nvvIldGscycOTOzZ8+u9f+FTTbZJLvvvrv7xGbGmKF5+fB44KWXXsp7771X6/Pv0qVLOnfunOeffz5J8vzzz2ennXZKhw4darbp06dPFi5cmGnTpjVd8XwkKxsP+A40D6sbD9TnvmDSpElp27Ztdtttt5ptevfunfLyctMXloDVjQd8B5qfxvrMn3/++ey5555p2bJlzTZ9+vTJP/7xj8yfP79JrqVFk5yFJvXWW2+lqqoq7du3r9Xevn176yw0Q4VCIT/84Q+zxx57ZKeddip2OTShX//61/nb3/6Wu+++u9ilUCT/+te/cuedd+bYY4/NSSedlBdffDEXXHBBNthggxx66KHFLo8mcMIJJ2ThwoU54IADUlFRkaqqqnzrW9/KwQcfXOzSKJLZs2cnSZ33iR+eV531mzFD81HXeGDOnDnZYIMN0rZt21rbtm/fvua/E3PmzKn1B+0kNa+Xb8O6bVXjAd+B5mF144H63BfMmTMnm2++ea3+Fi1apF27dr4HJWB14wHfgeansT7zOXPmZOutt661zfL/R8yZM6dJflwt2ID13MiRI/Pqq6/mjjvuKHYpNKH//Oc/ufDCC/Ozn/0sG264YbHLoUiqq6vTvXv3nH766UmSXXbZJa+++mrGjx8v2GgmfvOb3+SBBx7IpZdemq5du2bq1KkZPXp0OnXq5DsA0EwYDzRPxgMkxgMYD7B+MxXVemizzTZLRUXFCov+zZ07d4VfW7B+GzVqVB599NGMGzcuH/vYx4pdDk1oypQpmTt3bgYMGJBddtklu+yyS5566qnceuut2WWXXVJVVVXsEmkCHTt2TJcuXWq17bjjjnnjjTeKVBFN7ZJLLskJJ5yQAw88MJWVlTnkkENy9NFH57rrrit2aRRJx44dk8R9IsYMzcTKxgMdOnTIe++9lwULFtTafu7cuTX/nejQocMKT3Itf718G9ZdqxsP+A40D6sbD9TnvqBDhw6ZN29erf5ly5Zl/vz5vgclYHXjAd+B5qexPvNV/T+iqe4lBRvroZYtW2bXXXfNxIkTa9oKhUImTpyYnj17FrEymkp1dXVGjRqV3//+9xk3bly22WabYpdEE/v0pz+dBx54IBMmTKj5p3v37jnooIMyYcKEVFRUFLtEmsAee+xRM5fqcq+//nq22mqrIlVEU3vnnXdSVlZWq62ioiLV1dVFqohi23rrrdOxY8da94kLFy7MCy+84D6xmTFmWL+tbjzQvXv3bLDBBrU+/+nTp+eNN97IJz7xiSTJJz7xifz973+v9YePv/71r2nTpk26du3aJNfBmlvdeMB3oHlY3XigPvcFPXv2zIIFC/LSSy/VbPPEE0+kUCikR48eTXAVfBSrGw/4DjQ/jfWZf+ITn8gzzzyT9957r2abv/71r9lhhx2abI1fU1Gtp4499tiMGDEi3bt3T48ePTJu3LgsWbIkAwYMKHZpNIGRI0fmV7/6VX7605+mdevWNfPfbbLJJtloo42KXB1NoU2bNiusqbLxxhtn0003tdZKM3L00Ufn8MMPz7XXXpsDDjggkydPzl133ZVRo0YVuzSayOc///lce+216dy5c82j52PHjs3AgQOLXRpr0aJFizJjxoya1zNnzszUqVPTrl27dO7cOUOHDs0111yT7bbbLltvvXXGjBmTTp06ZZ999ili1RSDMcP6a3XjgU022SQDBw7MRRddlHbt2qVNmza54IIL0rNnz5o/avfp0yddu3bNWWedlW9/+9uZPXt2Lr/88hx55JG1Fgpl3VSf8YDvwPpvdeOBsrKy1d4XdOnSJZ/97Gfz/e9/PyNHjsx7772XH/zgBznwwAOzxRZbFPPyqIfVjQd8B9ZPH3U8UJ/P/KCDDsrVV1+dc845J8cff3xeffXV3HLLLfnOd77TZNdZVu0ne+ut2267LTfddFNmz56dnXfeOd/73vey++67F7ssmkBlZWWd7aNHjzZQbcaOOuqodOvWLeecc06xS6EJPfLII/nJT36S119/PVtvvXWOPfbYDB48uNhl0UQWLlyYMWPG5OGHH87cuXPTqVOnHHjggRk+fLg/SKzHnnzyyQwdOnSF9kMPPTQXXXRRqqurc8UVV+Suu+7KggUL8slPfjLnnXdedthhhyJUS7EZM6yf6jMeePfdd3PRRRfl17/+dZYuXZo+ffrkvPPOqzWtyL///e+cf/75eeqpp9KqVasceuihOeOMM9Kihd9IlqIPjwd8B5qH1Y0H6nNf8Pbbb+cHP/hB/vjHP6a8vDz77bdfvve976V169bFuCQaoD7jAd+B9U9jjAfq85m//PLLGTVqVF588cVsttlmGTJkSE444YQmucZEsAEAAAAAAJQQa2wAAAAAAAAlQ7ABAAAAAACUDMEGAAAAAABQMgQbAAAAAABAyRBsAAAAAAAAJUOwAQAAAAAAlAzBBgAAAAAAUDIEGwAAAAAAQMkQbACwVsycOTOVlZWZOnVqsUup8dprr2Xw4MHZbbfd8uUvf7nObY466qhceOGFTVxZ3erzHj755JOprKzMggULkiT33ntv9txzz6YqEQCA9UCp3ruvTUuWLMk3vvGN7LHHHrXut1lxzHHllVcW5TMCmjfBBsB66uyzz05lZWWuv/76Wu0PP/xwKisri1RVcV155ZVp1apVHnroodx8880r3ea0005r2sIaUf/+/fPb3/622GUAANAA7t1XVJ9797XpvvvuyzPPPJPx48fn8ccfzyabbNLkNZSK4447riifEdC8CTYA1mMbbrhhbrjhhsyfP7/YpTSapUuXrvG+M2bMyCc/+clstdVW2WyzzercZtNNN02bNm3W+BzFttFGG6V9+/bFLgMAgAZy715bfe7d12ZN//rXv9KlS5fstNNO6dixY8rKyhp8rqqqqhQKhQbvV2pat27dqJ8RQH0INgDWY717906HDh1y3XXXrXSbuh4bvvnmm9OvX7+a12effXZOPvnkXHvttendu3f23HPPXHXVVVm2bFkuvvji/N///V/23nvv3HPPPSscf/r06TnssMOy22675Utf+lKeeuqpWv1///vf87WvfS09e/ZM79698+1vfzvz5s2r6T/qqKMyatSoXHjhhenVq1eGDRtW53UUCoVcddVV2XvvvdO9e/d8+ctfzp///Oea/srKykyZMiVXX311Kisrc+WVV9Z5nA9PRXX77bdnv/32y2677ZbevXvn1FNPXel7+eHjjBo1KqNGjconP/nJ9OrVK5dffnmqq6tr1fTwww/X2m/PPffMvffeW6ttde/hB9U1FdUf//jHDBw4MLvttlt69eqV4cOH1+saAABoOu7d1+zefXX33P369cvVV1+ds846K3vssUfOPffcJMkzzzyTI444Ij169Ejfvn1zwQUXZPHixTXH/dnPfpann346lZWVOeqoo5K8H4pcfPHF+exnP5tPfOITGTRoUJ588smacy2/F//DH/6Q/v37Z7fddssbb7xR7/0ee+yxHHDAAenZs2eGDRuWWbNm1breu+++OwceeGC6d++ePn36ZNSoUTV9CxYsyDnnnJNPf/rT2WOPPTJ06NC8/PLLNf0vv/xyjjrqqPTs2TN77LFHBgwYkBdffLHO93X58c4999z07t275vvwyCOP1Lnth7+Xy7+DV111VU095557bq1Q6aGHHspBBx2UHj16pFevXjnmmGNq3n+A+hBsAKzHysvLc/rpp+e2227Lm2+++ZGO9cQTT2TWrFm57bbbcvbZZ+fKK6/MiSeemHbt2uWuu+7KYYcdlvPOO2+F81xyySU59thjM2HChHziE5/ISSedlLfeeivJ+zfLRx99dHbZZZfcfffdufHGGzN37tx885vfrHWM++67LxtssEHuvPPOjBw5ss76brnllowdOzYjRozI/fffnz59+uTkk0/O66+/niR5/PHH8/GPfzzHHXdcHn/88Rx33HGrveYXX3wxF154YU499dQ89NBDufHGGxu0fsV9992XioqK/OIXv8g555yTm2++Ob/4xS/qvf9yq3oPV+fRRx/NKaeckr59+2bChAkZN25cevTo0eAaAABYu9y7r9m9e33uuX/2s5+lW7dumTBhQk4++eTMmDEjxx9/fPbbb7/cf//9ueyyy/Lss8/mBz/4QZL3/1A/ePDg9OzZM48//nhNsDJq1KhMmjQpl112We6///7sv//++drXvlZTd5K88847ueGGG3LBBRfkV7/6Vdq3b1/v/X72s5/lkksuyW233Zb//Oc/ufjii2v677jjjowaNSqDBw/OAw88kJ/+9KfZdttta/pPO+20zJ07NzfccEPuvffe7Lrrrjn66KPz9ttvJ0nOPPPMfOxjH8vdd9+de++9N8cff3w22GCDOt/TQqGQ448/Ps8991x+9KMf5cEHH8wZZ5yR8vL6/xlx4sSJee2113LrrbfmJz/5SX7/+9/n6quvTpLMmjUrZ5xxRgYOHJgHH3wwt9xyS/bdd99agRTA6gg2ANZz++67b3beeedcccUVH+k4m266ab73ve9lxx13zFe+8pXssMMOeeedd3LSSSdl++23z4knnpgNNtggzz77bK39jjzyyHzxi19Mly5dcv7552eTTTbJ3XffnSS57bbbsssuu+T0009Ply5dsssuu+SHP/xhnnzyyfzjH/+oOcb222+fs846KzvuuGN23HHHOuu76aabcvzxx+fAAw/MjjvumG9/+9vp1q1bxo0blyTp2LFjKioqsvHGG6djx45p3br1aq/5P//5T1q1apXPfe5z2WqrrbLLLrtk6NCh9X7Pttxyy3z3u9/NjjvumIMPPjhDhgxZo7lnV/Uers61116b/v3759RTT02XLl3SrVu3nHjiiQ2uAQCAtc+9e8Pv3etzz/3pT386xx13XLbddttsu+22ue6663LQQQflmGOOyfbbb5899tgj55xzTiZMmJB33303m266aTbaaKNssMEG6dixYzbddNO88cYbuffeezNmzJjsueee2XbbbTNs2LB88pOfrPXE9XvvvZfzzz8/e+yxR3bccce89dZb9d5v5MiR2W233bLrrrvmyCOPzBNPPFHTf8011+TYY4/N0UcfnR122CE9evTIMccck+T9p08mT56cK664Irvttlu23377jBgxIm3btq1Zf++NN95I796906VLl2y//fY54IAD0q1btzrf07/+9a+ZPHlyrrzyynzmM5/JNttsk89//vPp27fvSj+HD2vZsmV++MMf5uMf/3g+97nP5dRTT80tt9ySQqGQ2bNnZ9myZdl3332z9dZbp7KyMkceeWS9xmgAy7UodgEArH1nnnlmjj766JU+Cl4fXbt2rfULnQ4dOuTjH/94zeuKiopsuummmTt3bq39evbsWfPvLVq0SPfu3TN9+vQk7z8O/eSTT9baZrkZM2Zkhx12SJLsuuuuq6xt4cKFmTVrVvbYY49a7XvssUetx68bqnfv3uncuXP22WeffPazn81nP/vZ7LvvvmnVqlW99t99991rzcX7iU98ImPHjk1VVVUqKirqXceq3sPVmTp1agYNGlTvcwEAUFzu3RumPvfc3bt3r7XPyy+/nFdeeSUPPPBATVt1dXUKhUJmzpyZLl26rHCev//976mqqsr+++9fq33p0qXZdNNNa15vsMEGtRZ8r+9+rVq1qvUERqdOnWo+n7lz52bWrFnZa6+96nwPXnnllSxevDi9evWq1f7OO+9kxowZSZJjjz023/ve9/LLX/4yvXv3zv7771/rfB80derUfOxjH6v5TNdEZWVlrXFTz549s3jx4vznP/9Jt27dstdee+Wggw5Knz590qdPn3zxi19Mu3bt1vh8QPMj2ABoBj71qU+lT58+ufTSSzNgwIBafWVlZSs88rts2bIVjtGiRe3/ZZSVldXZ1pDF8RYvXpzPf/7zOfPMM1fo69ixY82/1zdIaGxt2rTJfffdl6eeeiqPP/54rrjiilx11VW5++6707Zt2498/Pq+9x/FRhtt1KjHAwBg7XLv3vg+XNPixYtz2GGH1ayd8UFbbrllncdYvHhxKioqcs8996zwI6WNN9645t832mijWkFLffer6/NZ/llvuOGGq7q8LFq0KB07dsytt966Qt8mm2ySJPnGN76RL33pS/nTn/6UP//5z7niiity2WWXZd99911hn7U9hqioqMjYsWPz3HPP5S9/+UtuvfXWXHbZZbnrrruyzTbbrNVzA+sPU1EBNBNnnHFGHnnkkUyaNKlW++abb545c+bUGiBNnTq10c77/PPP1/z7smXLMmXKlJpH0nfddde8+uqr2WqrrbLddtvV+ueDN/mr06ZNm3Tq1CnPPfdcrfbnnnsuXbt2/Uj1t2jRIr17985ZZ52V+++/P//+979rPRK+KpMnT671+oUXXsh2221XM6DZfPPNay0I+Prrr2fJkiUrHGdV7+Hq7LTTTpk4cWK9tgUAYN3g3r3+VnfPXZdddtkl06ZNW+E6tttuu7Rs2bLOfXbeeedUVVVl3rx5K+zzwWCnsfb7oDZt2mSrrbZa6X39rrvumjlz5qSiomKFc2y++eY12+2www455phj8rOf/Sz77bdfnQvIJ+8/bfHmm2/WmmKsoV555ZW88847Na+ff/75bLzxxjXBUVlZWT75yU/m1FNPzYQJE7LBBhvk4YcfXuPzAc2PYAOgmaisrMxBBx20wq94evXqlXnz5uWGG27IjBkzcvvtt+exxx5rtPPecccd+f3vf5/XXnsto0aNyvz58zNw4MAkyRFHHJH58+fn9NNPz+TJkzNjxow89thj+c53vpOqqqoGnWfYsGG54YYb8uCDD2b69On58Y9/nJdffrlBa2J82COPPJJbbrklU6dOzb///e9MmDAhhUKh3o9kv/HGGxk9enSmT5+eX/3qV7nttttq1fPpT386t99+e/72t7/lxRdfzHnnnVfnAn6reg9X55RTTsmvf/3rXHHFFXnttdfyyiuv5Prrr6/fGwAAQFG4d6+/1d1z1+X444/PpEmTMmrUqEydOjWvv/56Hn744YwaNWql++ywww456KCDctZZZ+V3v/td/vWvf2Xy5Mm57rrr8uijjzb6fh/2jW98I2PHjs0tt9yS119/PVOmTKn5fvTu3Tuf+MQnMnz48Dz++OOZOXNmnnvuuVx22WV58cUX884772TUqFF58skn8+9//zvPPvtsXnzxxTqn3EqS//u//8uee+6ZU089NX/5y1/yr3/9q+ZJj/paunRpzjnnnEybNi1/+tOfcuWVV2bIkCEpLy/PCy+8kGuvvTYvvvhi3njjjfzud7/LvHnz6v3jLYDEVFQAzcqpp56aBx98sFZbly5dct555+W6667LNddck/322y/HHXdc7rrrrkY55xlnnJHrr78+U6dOzXbbbZdrrrmm5ldDW2yxRe688878+Mc/zrBhw7J06dJ07tw5n/3sZ2vNCVwfQ4cOzcKFC3PRRRdl3rx56dKlS376059m++23X+PaN9lkk/z+97/PVVddlXfffTfbbbddLr300lrzE6/KIYccknfeeSeDBg1KRUVFhg4dmq9+9as1/SNGjMh3v/vdHHnkkenUqVO++93vZsqUKSscZ1Xv4er06tUrY8aMyU9/+tNcf/31adOmTT71qU/V7w0AAKBo3LvXz+ruuevSrVu33Hrrrbn88stzxBFHJEm22Wab9O/ff5X7jR49Otdcc00uuuiizJo1K5tuumk+8YlP5HOf+9xa2e+DDj300Lz77ru5+eabc8kll2TTTTetWbejrKws119/fS6//PJ85zvfyVtvvZUOHTpkzz33TIcOHVJeXp633347I0aMyJw5c7LZZptlv/32y6mnnrrS81155ZW5+OKLc/rpp2fJkiXZbrvtcsYZZ9S73r322ivbbbddjjzyyCxdujRf+tKX8o1vfCPJ+0+gPP300xk3blwWLlyYzp075+yzz27Q4uQAZdUfnpwRAPjIjjrqqHTr1i3nnHNOsUsBAID1knvuddPZZ5+dBQsW/H/t3bERwyAQRUECdUFJ9EcnlARVELgCAgW250u76SWXP+Yovfd/rwI8mFNUAAAAAABADKeoAOCmtVZprR3nY4wfbgMAAADwLk5RAcBNe+8y5zzOa63lurwdAAAAAPgGYQMAAAAAAIjhjw0AAAAAACCGsAEAAAAAAMQQNgAAAAAAgBjCBgAAAAAAEEPYAAAAAAAAYggbAAAAAABADGEDAAAAAACIIWwAAAAAAAAxPpdeCZPTX/JCAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1600x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_clip_basic_distributions(clip_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-21T19:35:17.755108Z",
     "iopub.status.busy": "2024-06-21T19:35:17.754937Z",
     "iopub.status.idle": "2024-06-21T19:35:17.774581Z",
     "shell.execute_reply": "2024-06-21T19:35:17.774106Z",
     "shell.execute_reply.started": "2024-06-21T19:35:17.755091Z"
    }
   },
   "source": [
    "# Snow flake access"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_interesting_clips = clip_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "execution_failed": "2024-09-02T04:29:36.835Z"
    }
   },
   "outputs": [],
   "source": [
    "if not os.path.exists(snow_password_path):\n",
    "    raise Exception(\"you are not authorized to access snowflake -- please setup\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "execution_failed": "2024-09-02T04:29:36.835Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROD\n"
     ]
    }
   ],
   "source": [
    "snow_session = Session.builder.configs(CONNECTION_PARAMETERS).create()\n",
    "\n",
    "snow_root = Root(snow_session)\n",
    "snow_schema = snow_root.databases[\"SUNO_PROD\"].schemas[\"PROD\"]\n",
    "print(snow_schema.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "execution_failed": "2024-09-02T04:29:36.835Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(441483, 32)\n"
     ]
    }
   ],
   "source": [
    "subset_v4_clips_df = final_interesting_clips.copy()\n",
    "print(subset_v4_clips_df.shape)\n",
    "v4_clip_ids = list(str(s) for s in subset_v4_clips_df[\"id\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "execution_failed": "2024-09-02T04:29:36.835Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/5 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clip IDs in this chunk: 100000\n",
      "Length of the ID query string: 3899999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 1/5 [00:30<02:01, 30.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clip IDs in this chunk: 100000\n",
      "Length of the ID query string: 3899999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 2/5 [00:54<01:19, 26.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clip IDs in this chunk: 100000\n",
      "Length of the ID query string: 3899999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████    | 3/5 [01:23<00:55, 27.78s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clip IDs in this chunk: 100000\n",
      "Length of the ID query string: 3899999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 4/5 [01:48<00:26, 26.81s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clip IDs in this chunk: 41483\n",
      "Length of the ID query string: 1617836\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [01:59<00:00, 23.90s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "snow_batch_size = 100_000\n",
    "snow_results = []\n",
    "for clip_ids_chunk in tqdm.tqdm(\n",
    "    [v4_clip_ids[i : i + snow_batch_size] for i in range(0, len(v4_clip_ids), snow_batch_size)]\n",
    "):\n",
    "    id_query_str = \",\".join(\"'\" + x + \"'\" for x in clip_ids_chunk)\n",
    "    print(f\"Number of clip IDs in this chunk: {len(clip_ids_chunk)}\")\n",
    "    print(f\"Length of the ID query string: {len(id_query_str)}\")\n",
    "\n",
    "    session_query = snow_session.sql(\n",
    "        f\"\"\" select *\n",
    "        from ML_SONG_SUMMARY_INFO\n",
    "        where p_date = DATE(SYSDATE() - INTERVAL '2 HOUR')\n",
    "        and p_hour = hour(SYSDATE() - INTERVAL '2 HOUR')\n",
    "        and song_id in ({id_query_str})\n",
    "        order by p_hour desc;\"\"\"\n",
    "    )\n",
    "    temp_df_snow_test = pd.DataFrame(session_query.collect())\n",
    "    snow_results.append(temp_df_snow_test)\n",
    "print(len(snow_results))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "execution_failed": "2024-09-02T04:29:36.835Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of df_snow_test:\n",
      "Rows: 354315\n",
      "Columns: 10\n"
     ]
    }
   ],
   "source": [
    "df_snow_test = pd.concat(snow_results)\n",
    "df_snow_test = df_snow_test.rename(columns=lambda x: x.lower())\n",
    "df_snow_test = df_snow_test.rename(columns={\"song_id\": \"str_id\"})\n",
    "print(\"Shape of df_snow_test:\")\n",
    "print(f\"Rows: {df_snow_test.shape[0]}\")\n",
    "print(f\"Columns: {df_snow_test.shape[1]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "execution_failed": "2024-09-02T04:29:36.835Z"
    }
   },
   "outputs": [],
   "source": [
    "subset_v4_clips_df[\"str_id\"] = subset_v4_clips_df[\"id\"].astype(str)\n",
    "subset_v4_clips_df_test = subset_v4_clips_df.merge(df_snow_test, on=\"str_id\", how=\"left\")\n",
    "subset_v4_clips_df_test[\"norm_play_frac\"] = (\n",
    "    subset_v4_clips_df_test[\"total_play_time\"].fillna(0) / subset_v4_clips_df_test[\"duration\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "execution_failed": "2024-09-02T04:29:36.835Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure with two subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n",
    "\n",
    "# First subplot: Total play duration\n",
    "pos_play_time = subset_v4_clips_df_test[\"total_play_time\"]\n",
    "\n",
    "pos_play_time.hist(\n",
    "    bins=np.linspace(0, 4000, 100),\n",
    "    alpha=0.5,\n",
    "    label=f\"pos (mean={pos_play_time.mean():.2f}, median={pos_play_time.median():.2f})\",\n",
    "    ax=ax1,\n",
    ")\n",
    "ax1.legend()\n",
    "ax1.set_xlabel(\"Total play duration in seconds\")\n",
    "ax1.set_ylabel(\"counts\")\n",
    "ax1.set_title(\"Play duration comparison\")\n",
    "\n",
    "# Second subplot: Normalized play fraction\n",
    "pos_norm_play_frac = subset_v4_clips_df_test[\"norm_play_frac\"]\n",
    "\n",
    "pos_norm_play_frac.hist(\n",
    "    bins=np.linspace(0, 10, 100),\n",
    "    alpha=0.5,\n",
    "    label=f\"pos (mean={pos_norm_play_frac.mean():.2f}, median={pos_norm_play_frac.median():.2f})\",\n",
    "    ax=ax2,\n",
    ")\n",
    "ax2.legend()\n",
    "ax2.set_xlabel(\"Normalized play counts (play duration/duration)\")\n",
    "ax2.set_ylabel(\"Log counts\")\n",
    "ax2.set_yscale(\"log\")\n",
    "ax2.set_title(\"Normalized play duration comparison (Log scale)\")\n",
    "\n",
    "# Adjust layout and display the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "execution_failed": "2024-09-02T04:29:36.835Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of clips that pass the play duration cut: 0.5504\n"
     ]
    }
   ],
   "source": [
    "play_duration_mask = (\n",
    "    (subset_v4_clips_df_test[\"norm_play_frac\"] >= 0.95)\n",
    "    & (subset_v4_clips_df_test[\"total_play_time\"] >= 10)\n",
    ")\n",
    "# Calculate the fraction of clips that pass the play duration cut\n",
    "frac_pass_play_duration = play_duration_mask.sum() / subset_v4_clips_df_test.shape[0]\n",
    "\n",
    "# Print the result with a formatted string\n",
    "print(f\"Fraction of clips that pass the play duration cut: {frac_pass_play_duration:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "execution_failed": "2024-09-02T04:29:36.835Z"
    }
   },
   "outputs": [],
   "source": [
    "subset_v4_clips_df_pass_duration = subset_v4_clips_df_test[play_duration_mask].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "execution_failed": "2024-09-02T04:29:36.835Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "task\n",
       "         242998\n",
       "cover         3\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_v4_clips_df_pass_duration[\"task\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "execution_failed": "2024-09-02T04:29:36.835Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(243001, 43)\n"
     ]
    }
   ],
   "source": [
    "# subset_v4_clips_df_pass_duration.to_pickle(\n",
    "#     \"/home/tony/Data/Preference/30b_v2/concat_clips_20240908_v0.pkl\",\n",
    "# )\n",
    "print(subset_v4_clips_df_pass_duration.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import os\n",
    "\n",
    "# NPZ_DIR = \"/app/suno/data/dpo/concat_cycle_npz\"\n",
    "\n",
    "# # Find and remove files less than 1KB in size\n",
    "# for filename in os.listdir(NPZ_DIR):\n",
    "#     file_path = os.path.join(NPZ_DIR, filename)\n",
    "#     if os.path.isfile(file_path) and os.path.getsize(file_path) < 1024:\n",
    "#         # os.remove(file_path)\n",
    "#         print(f\"Removed: {file_path}\")\n",
    "\n",
    "# print(\"Finished removing files smaller than 1KB.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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