{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0d4e1a30",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T06:58:06.279935Z",
     "start_time": "2023-06-30T06:57:59.976215Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtaining file:///hhh-vol/hhh\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: coffea in /opt/conda/lib/python3.10/site-packages (from src==0.1.0) (0.7.21)\n",
      "Requirement already satisfied: spanet in /opt/conda/lib/python3.10/site-packages (from src==0.1.0) (2.1.1)\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from src==0.1.0) (1.23.5)\n",
      "Requirement already satisfied: xxhash in /opt/conda/lib/python3.10/site-packages (from src==0.1.0) (3.2.0)\n",
      "Requirement already satisfied: vector in /opt/conda/lib/python3.10/site-packages (from src==0.1.0) (1.0.0)\n",
      "Requirement already satisfied: pandas in /opt/conda/lib/python3.10/site-packages (from coffea->src==0.1.0) (2.0.0)\n",
      "Requirement already satisfied: hist>=2 in /opt/conda/lib/python3.10/site-packages (from coffea->src==0.1.0) (2.6.3)\n",
      "Requirement already satisfied: lz4 in /opt/conda/lib/python3.10/site-packages (from coffea->src==0.1.0) (4.3.2)\n",
      "Requirement already satisfied: uproot!=4.2.4,!=4.3.0,!=4.3.1,==4.*,>=4.1.6 in /opt/conda/lib/python3.10/site-packages (from coffea->src==0.1.0) (4.3.7)\n",
      "Requirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (from coffea->src==0.1.0) (23.0)\n",
      "Requirement already satisfied: toml>=0.10.2 in /opt/conda/lib/python3.10/site-packages (from coffea->src==0.1.0) (0.10.2)\n",
      "Requirement already satisfied: matplotlib>=3 in /opt/conda/lib/python3.10/site-packages (from coffea->src==0.1.0) (3.7.1)\n",
      "Requirement already satisfied: uproot3-methods>=0.10.0 in /opt/conda/lib/python3.10/site-packages (from coffea->src==0.1.0) (0.10.1)\n",
      "Requirement already satisfied: uproot3>=3.14.1 in /opt/conda/lib/python3.10/site-packages (from coffea->src==0.1.0) (3.14.4)\n",
      "Requirement already satisfied: tqdm>=4.27.0 in /opt/conda/lib/python3.10/site-packages (from coffea->src==0.1.0) (4.65.0)\n",
      "Requirement already satisfied: pyarrow>=1.0.0 in /opt/conda/lib/python3.10/site-packages (from coffea->src==0.1.0) (12.0.0)\n",
      "Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from coffea->src==0.1.0) (2023.3.0)\n",
      "Requirement already satisfied: numba>=0.56.0 in /opt/conda/lib/python3.10/site-packages (from coffea->src==0.1.0) (0.56.4)\n",
      "Requirement already satisfied: correctionlib>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from coffea->src==0.1.0) (2.2.2)\n",
      "Requirement already satisfied: scipy>=1.1.0 in /opt/conda/lib/python3.10/site-packages (from coffea->src==0.1.0) (1.10.1)\n",
      "Requirement already satisfied: mplhep>=0.1.18 in /opt/conda/lib/python3.10/site-packages (from coffea->src==0.1.0) (0.3.28)\n",
      "Requirement already satisfied: cloudpickle>=1.2.3 in /opt/conda/lib/python3.10/site-packages (from coffea->src==0.1.0) (2.2.1)\n",
      "Requirement already satisfied: awkward<2,>=1.10.3 in /opt/conda/lib/python3.10/site-packages (from coffea->src==0.1.0) (1.10.3)\n",
      "Requirement already satisfied: cachetools in /opt/conda/lib/python3.10/site-packages (from coffea->src==0.1.0) (5.3.0)\n",
      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.10/site-packages (from uproot!=4.2.4,!=4.3.0,!=4.3.1,==4.*,>=4.1.6->coffea->src==0.1.0) (67.6.1)\n",
      "Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from spanet->src==0.1.0) (1.11.1)\n",
      "Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.10/site-packages (from spanet->src==0.1.0) (1.2.2)\n",
      "Requirement already satisfied: opt-einsum in /opt/conda/lib/python3.10/site-packages (from spanet->src==0.1.0) (3.3.0)\n",
      "Requirement already satisfied: h5py>=3.7 in /opt/conda/lib/python3.10/site-packages (from spanet->src==0.1.0) (3.8.0)\n",
      "Requirement already satisfied: pytorch-lightning in /opt/conda/lib/python3.10/site-packages (from spanet->src==0.1.0) (1.8.6)\n",
      "Requirement already satisfied: pyyaml in /opt/conda/lib/python3.10/site-packages (from spanet->src==0.1.0) (6.0)\n",
      "Requirement already satisfied: torch>=1.12 in /opt/conda/lib/python3.10/site-packages (from spanet->src==0.1.0) (1.13.1)\n",
      "Requirement already satisfied: rich in /opt/conda/lib/python3.10/site-packages (from correctionlib>=2.0.0->coffea->src==0.1.0) (13.3.5)\n",
      "Requirement already satisfied: pydantic>=1.7.3 in /opt/conda/lib/python3.10/site-packages (from correctionlib>=2.0.0->coffea->src==0.1.0) (1.10.7)\n",
      "Requirement already satisfied: histoprint>=2.2.0 in /opt/conda/lib/python3.10/site-packages (from hist>=2->coffea->src==0.1.0) (2.4.0)\n",
      "Requirement already satisfied: boost-histogram~=1.3.1 in /opt/conda/lib/python3.10/site-packages (from hist>=2->coffea->src==0.1.0) (1.3.2)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3->coffea->src==0.1.0) (0.11.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3->coffea->src==0.1.0) (9.4.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3->coffea->src==0.1.0) (1.0.7)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3->coffea->src==0.1.0) (4.39.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3->coffea->src==0.1.0) (2.8.2)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3->coffea->src==0.1.0) (1.4.4)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3->coffea->src==0.1.0) (3.0.9)\n",
      "Requirement already satisfied: mplhep-data in /opt/conda/lib/python3.10/site-packages (from mplhep>=0.1.18->coffea->src==0.1.0) (0.0.3)\n",
      "Requirement already satisfied: uhi>=0.2.0 in /opt/conda/lib/python3.10/site-packages (from mplhep>=0.1.18->coffea->src==0.1.0) (0.3.3)\n",
      "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /opt/conda/lib/python3.10/site-packages (from numba>=0.56.0->coffea->src==0.1.0) (0.39.1)\n",
      "Requirement already satisfied: typing_extensions in /opt/conda/lib/python3.10/site-packages (from torch>=1.12->spanet->src==0.1.0) (4.5.0)\n",
      "Requirement already satisfied: awkward0 in /opt/conda/lib/python3.10/site-packages (from uproot3>=3.14.1->coffea->src==0.1.0) (0.15.5)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas->coffea->src==0.1.0) (2023.3)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.10/site-packages (from pandas->coffea->src==0.1.0) (2023.3)\n",
      "Requirement already satisfied: torchmetrics>=0.7.0 in /opt/conda/lib/python3.10/site-packages (from pytorch-lightning->spanet->src==0.1.0) (0.11.4)\n",
      "Requirement already satisfied: tensorboardX>=2.2 in /opt/conda/lib/python3.10/site-packages (from pytorch-lightning->spanet->src==0.1.0) (2.6)\n",
      "Requirement already satisfied: lightning-utilities!=0.4.0,>=0.3.0 in /opt/conda/lib/python3.10/site-packages (from pytorch-lightning->spanet->src==0.1.0) (0.8.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn->spanet->src==0.1.0) (3.1.0)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn->spanet->src==0.1.0) (1.2.0)\n",
      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->spanet->src==0.1.0) (1.3.0)\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from fsspec->coffea->src==0.1.0) (2.28.2)\n",
      "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /opt/conda/lib/python3.10/site-packages (from fsspec->coffea->src==0.1.0) (3.8.4)\n",
      "Requirement already satisfied: click>=7.0.0 in /opt/conda/lib/python3.10/site-packages (from histoprint>=2.2.0->hist>=2->coffea->src==0.1.0) (8.1.3)\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib>=3->coffea->src==0.1.0) (1.16.0)\n",
      "Requirement already satisfied: protobuf<4,>=3.8.0 in /opt/conda/lib/python3.10/site-packages (from tensorboardX>=2.2->pytorch-lightning->spanet->src==0.1.0) (3.20.3)\n",
      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /opt/conda/lib/python3.10/site-packages (from rich->correctionlib>=2.0.0->coffea->src==0.1.0) (2.14.0)\n",
      "Requirement already satisfied: markdown-it-py<3.0.0,>=2.2.0 in /opt/conda/lib/python3.10/site-packages (from rich->correctionlib>=2.0.0->coffea->src==0.1.0) (2.2.0)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec->coffea->src==0.1.0) (1.3.3)\n",
      "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec->coffea->src==0.1.0) (3.1.0)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec->coffea->src==0.1.0) (1.9.2)\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec->coffea->src==0.1.0) (4.0.2)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec->coffea->src==0.1.0) (22.2.0)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec->coffea->src==0.1.0) (6.0.4)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec->coffea->src==0.1.0) (1.3.1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: mdurl~=0.1 in /opt/conda/lib/python3.10/site-packages (from markdown-it-py<3.0.0,>=2.2.0->rich->correctionlib>=2.0.0->coffea->src==0.1.0) (0.1.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->fsspec->coffea->src==0.1.0) (3.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->fsspec->coffea->src==0.1.0) (2022.12.7)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->fsspec->coffea->src==0.1.0) (1.26.15)\n",
      "Installing collected packages: src\n",
      "  Attempting uninstall: src\n",
      "    Found existing installation: src 0.1.0\n",
      "    Uninstalling src-0.1.0:\n",
      "      Successfully uninstalled src-0.1.0\n",
      "  Running setup.py develop for src\n",
      "Successfully installed src-0.1.0\n"
     ]
    }
   ],
   "source": [
    "!pip install -e ../../../."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "368b55fa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T06:58:08.011750Z",
     "start_time": "2023-06-30T06:58:06.282171Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/coffea/util.py:154: FutureWarning: In coffea version v2023.3.0 (target date: 31 Mar 2023), this will be an error.\n",
      "(Set coffea.deprecations_as_errors = True to get a stack trace now.)\n",
      "ImportError: coffea.hist is deprecated\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "from pathlib import Path\n",
    "\n",
    "import awkward as ak\n",
    "import click\n",
    "import hist\n",
    "import matplotlib.pyplot as plt\n",
    "import mplhep as hep\n",
    "import numpy as np\n",
    "import uproot\n",
    "import vector\n",
    "from coffea.hist.plot import clopper_pearson_interval\n",
    "from src.data.delphes.matching import(\n",
    "    match_fjet_to_higgs,\n",
    "    match_jets_to_higgs,\n",
    "    match_higgs_to_fjet,\n",
    "    match_higgs_to_jet,\n",
    "    match_fjet_to_jet,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "455443e2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T06:58:08.021156Z",
     "start_time": "2023-06-30T06:58:08.017274Z"
    }
   },
   "outputs": [],
   "source": [
    "hep.style.use(hep.style.ROOT)\n",
    "vector.register_awkward()\n",
    "vector.register_numba()\n",
    "ak.numba.register()\n",
    "\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "\n",
    "PROJECT_DIR = Path.cwd().resolve().parents[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ba7234d1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T06:58:08.025923Z",
     "start_time": "2023-06-30T06:58:08.023622Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/hhh-vol/hhh\n"
     ]
    }
   ],
   "source": [
    "print(PROJECT_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "71d76040",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T06:58:08.044706Z",
     "start_time": "2023-06-30T06:58:08.028193Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_15.root', '/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_20.root', '/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_24.root', '/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_18.root', '/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_25.root', '/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_23.root', '/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_27.root', '/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_22.root', '/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_21.root', '/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_12.root', '/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_2.root', '/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_0.root', '/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_19.root', '/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_17.root', '/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_16.root', '/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_10.root', '/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_13.root', '/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_14.root', '/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_26.root', '/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_1.root', '/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/sample_11.root']\n"
     ]
    }
   ],
   "source": [
    "in_filenames = [str(f) for f in Path(\"/hhh-vol/data/delphes/v2/GF_HHH_SM_c3_0_d4_0_14TeV/\").glob(\"sample_*.root\")]\n",
    "print(in_filenames)\n",
    "in_filename = in_filenames[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0e13b90f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T07:13:25.585073Z",
     "start_time": "2023-06-30T07:12:39.826444Z"
    }
   },
   "outputs": [],
   "source": [
    "prefix = 0\n",
    "with uproot.open(in_filename) as in_file:\n",
    "    events = in_file[\"Delphes\"]\n",
    "    keys = (\n",
    "        [key for key in events.keys() if \"Particle/Particle.\" in key and \"fBits\" not in key]\n",
    "        + [key for key in events.keys() if \"Jet/Jet.\" in key]\n",
    "        + [key for key in events.keys() if \"FatJet/FatJet.\" in key and \"fBits\" not in key]\n",
    "    )\n",
    "    arrays = events.arrays(keys, entry_start=prefix, entry_stop=prefix+10000)  # entry_stop=10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dbf1f4b1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T07:13:45.300389Z",
     "start_time": "2023-06-30T07:13:43.358186Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3xjR5FOYm1+DBgwucaJPh08k+rfPnz0e7du3w9u1b9OrVC5cvX9ZFs0RERPQejRo1CklJSViwYIFWvapFZf78+cjMzMTw4cP1Hcp7U5x6Qw2ZobyP165dQ+PGjTFs2DDs3LkT169fx5s3b/DgwQPs2bMHU6dOxccff4ydO3fq7JoHDhzAy5cv5Z47fPgwnj9/rrNrAMD9+/fRsmVLxMXFAQAmTJigdGREVFSUTq/7vuli5AkVnE7W/jcyMkJYWBimTp2KxYsXo0GDBujZsyf69+8Pd3d3VKlSBWZmZrq4FBERERWBU6dOISQkBA0bNkT79u31HY5StWvXRpcuXfDXX39hz5496NSpk75DItJYYmIi2rZti/j4eAB5C5j269cP7u7uePr0KQ4ePIgDBw7gyZMn6NWrF06cOIGmTZtqfV3ZocC9evXCtm3bkJ2dje3bt+Prr7/Wun0AiI6Ohp+fn7gS7ffff4+ffvpJaVlp0urh4YFly5apbbdMmTI6iY+KP50krbL/oUqVKoVXr15h+/bt2L59e4HakUgkyM7O1kVIHwzu00pERLqwePFiANB7T1R+Bg8ejL/++gtLly5l0krFyqJFi8SEtVevXli/fj3Mzc3F86NHj8aWLVvwxRdfICcnB/3798eNGzdgbW1d6GumpKRgz549AIBPPvkE8+bNw7Zt2wDkDRHWRdL66NEjtGzZEo8ePQIAjB8/HgsXLlRZXpq01qtXD23bttX6+u9au3Yt1q5dq/N2iyvu0yrj7Nmzcl3ksseyW+KQ7nGfViIi0tb9+/exe/dumJmZoXfv3voOR6327dvDwcEBR48exY0bN7jdHhUbW7duBQDY2Nhg2bJlcgmrVN++ffHPP/9g1apViI6Oxu7du7WaX75z505kZGQAAAYMGIBq1aqhXr16uHz5Mk6dOoXHjx+jYsWKhW7/yZMn8PPzQ3R0NADgu+++w88//6y2jmxPKxU97tP6DkEQlD6IiIjIsC1fvhy5ubnw9/fXaDheUlISJk+ejBo1asDa2hp2dnbw8vLC7NmzkZ6ertE1b9y4gSFDhsDV1RXm5uZwcHCAn59fvivDmpiYoF+/fgCAFStWaHStd8muRPvvv/8iNzcXv/76Kxo1agQ7OzuUKlUKDRs2xJo1a8Q6OTk5WLFiBRo2bAg7OzvY2dmhUaNG+OWXX/D27Vu11zt06BC6deuGWrVqwd7eHra2tvj444/Rrl07bNq0CVlZWQp1pPFJk4Hg4GC1q0ffvn0b33zzDapXrw5ra2s4OjqiWbNmmDx5ssJ8RlVu3bqFIUOGoFKlSrCwsICbmxvat2+PgwcP5ls3KSkJs2fPRqNGjVC2bFlYW1vD09MT/fr1Q3h4eL71U1NTMX36dNStWxelSpWCra0t6tSpg5kzZyI5OVmj+JUp6PsYFxeHKVOmwMvLC/b29rCwsEDFihXRs2dPreaZPn36FI8fPwaQd+PF0dFRZdmuXbuKx5GRkYW+JvDf0GAjIyPx/430xpQgCGIiXRhxcXHw8/MTF0YbN24cFi1apLbO8+fPxZ9nUSWtsiumx8TEqCx34MAB9O3bF66urrCwsICzszO8vb3x888/IykpSaG8dIGnWbNmic9Jr6NshMqhQ4fQvXt3fPLJJ7CxsYGTkxOaNGmCCRMmIDExUSev9YMiULHk4uIiABBcXFz0HQoRkcF6k/lWcJ24V3CduFd4k/lW3+FoZdasWQIAAYAQHh6ucb3g4GCxXnBwsNIyrq6uAgBh1apV+V77xo0bgrOzs/jvdx+enp7CtWvXFNrw9fUVAAi+vr7C1q1bBXNzc5VtdOnSRXj+/LnK17Rv3z4BgODs7Kzx+yArOjpavNbFixeF9u3bq4zlu+++E968eSPGr+zRqVMnpdfJzs4W2rVrp7Ke9FG7dm3h1atXcnXVlX/3579o0SLBxMREZXk7OzvhwIEDCvG5ubkJAISAgABh+/btgqWlpco2vv/+e5Xv5+7du4XSpUurjblLly7C69evldaPjIwU3N3dVdZ1dnYWf+bqPsfKFOR93Lp1q2BlZaW2jp+fn/Dy5UuNry/7GqVtzJkzR23Za9euiWUDAwMLfC2pp0+fCsbGxgIAoVWrVuLz9+/fF9uvW7eu2jbCw8PFsrNmzRKfT0hIEGrUqCGeGzt2rEYxnTlzRqwTEREhCIIgvHjxQoiIiBCuXLkiZGRkFOKVypP9nRcdHa1wPjU1VejcubPan3OpUqWEvXv3ytULCAhQWT4gIEAsl5GRIbRq1Upt+9bW1sKJEye0fq3aKk75hE6GBxMREVHxFBUVJfZGNGnSJN/ynTp1wtOnT1G/fn107doV7u7uuHr1Knbu3Ik7d+7g5s2baNq0Ke7duwcHBweF+nfv3sWgQYOQmZmJTp06oXXr1rC3t0dERAQ2b96Mly9fIiwsDPHx8Sr3f2/cuDGAvN6rW7duoUaNGoV+/aNHj8aZM2fQpk0bdO/eHaVKlcLhw4exfv16CIKAxYsX48iRI7h69Srq16+PL774Ai4uLjh16hRWrVqF7Oxs7NmzB4cPH8Znn30m1/aMGTPEXsrKlStj6NChcHd3h5GREe7fv4/g4GDcvn0b165dw3fffYfff/9drCutN2jQICQkJKBNmzb47rvvAOQtSCW1fv16jB8/HkDeuiKDBg1Co0aN8PbtWxw9ehSbN29GcnIyvvjiC1y+fBmVKlVSeA8uXbqELVu2wMjICCNHjkTjxo2Rm5uLvXv3YseOHQDy5mN2794dn376qVzdiIgIdOvWDTk5ObCyssKQIUNQt25d2NjY4N9//8W6devw6NEjhIWFoV27djh58iSMjP4b6Pfw4UN4e3uLQ1gbN24Mf39/eHh44Nq1awgNDcXdu3fFXsKC0vR9jIiIQN++fcVRgt27d4ePjw8cHR1x5coVbN26FTExMTh27Bj69euH/fv3FygOFxcXBAcHA8jbvkmdCxcuiMfa9EZu3boVOTk5APKGBktVqVIFDRo0wMWLF3HlypUC/x96/vw5/Pz8cOvWLQDAt99+i19++UWjunfv3hWP79+/j1GjRuH8+fPicyYmJqhRowZmzZqF7t27axxTQfTp0wd79+4FkPd56969O6pWrYr4+HicPHkSoaGhSElJQefOnbF3715xYbrvvvsOffr0wcaNG7Fx40YA/32+XFxcxPa//vprHD16FABQp04dDBw4EJUrV8br169x7NgxbNy4EW/evEHfvn1x69YtlCpVqkheZ4mj76yZCqc43RkhIspPbm6u8CbzrdrHs9fpBX48SkoVe1ofJaUWqo384nqT+VbIzc0t8vdItrdz4cKFwsGDBzV6fPfdd2p7qIKCgsQ7/9nZ2fleG4AwZMgQISsrS65Mamqq0KNHD7HMyJEj5c7L9lQaGxsLv/32m8J17t27J9SsWVMst23bNpXvh4eHhwBAWL58uSZvnxzZnlYAwvTp0xXKTJ06Va7MgAEDFF7zb7/9prb3TNp76OHhISQnJyucf/PmjVCrVi0BgFC1alWlscr2hr4rLS1N/D7g5uYm3Lx5U6FMSEiIGOOwYcOUto3/7828fPmyQv05c+aIZebPny93Ljc3V/j0008FAELNmjWFp0+fKo2xf//+Yhvv/rx69+4tnhs/frzCZzAlJUXo0KGD3M+iID2t775WZe+jIAhC06ZNBQCCqampsGnTJoXzSUlJgp+fnxjDkSNHChyDJlJSUsTPjZGRkfDvv/8Wuq369esLAARLS0shJSVF7tzChQvVfv6l3u1pTUpKEmrXri3381i4cKHGMb37/0rdo2vXrkJ6enqBX7e6ntYDBw6I55YuXaq0/pkzZwRbW1sBgFCpUiXhzZs3cudlfx++KyMjQzAzMxMACC1atBDevlUc4TNv3jyx/qFDhwr8+nSpOOUT7GklIiK9S3+bA88Zh4r0Gs0XhhdZ2zfntIWV2fv7kzphwgSdtfXPP/8AyOsRMDY2zre8m5sbfvvtN5iYyL9ea2trbNy4EWfPnsXTp0+xevVqTJkyRa4HQqpv374YNmyYwvNVq1bFpk2bUK9ePQiCgJkzZ+Lzzz9XGoeXlxfu3r2L06dP45tvvtHkpSolnTf5rqFDh2LevHkAACcnJwQFBcHU1FSuzODBgxEYGIjs7GxxzqRUWloanj9/Djs7O3z99dcoXbq0wjWsrKzQsWNH3LhxQ5wXWBBLly4VF1FZtGiR0t6yXr16YfXq1Th69KjauaULFixA3bp1FZ4fN24cZs2ahdzcXIX9Nbdt24Zz587ByMgIf/31F8qXL69Q39LSEn/88QfOnz+Pu3fvYu3ateLP6+rVqwgJCQGQ1+OlbAEfW1tbbNy4Ea6urnj9+rXK+LWxd+9enD59GgAwZMgQpb26ZcqUwbp161C1alVkZWVhypQpiIiI0Gkcz549w+effy6+z71791a5Q0R+7ty5g0uXLgHImyNra2srd/7zzz8Xf49s3rwZc+bMybfNFy9eoHXr1rh27RpMTU1hZmaGN2/eYNq0aWjbtq1cz7Uqsp+hsmXLYtq0aWjevDmqV6+OBw8e4OjRo5g5cyZev36NXbt2YebMmSq3zikoQRAwbtw4AED//v0xevRopeWaNGmCpUuXYsiQIXj06BGOHj2q8Url//77rzhHvVu3bgq/JwGgX79+uHz5MgBw15QC0NlCTLIyMzOxc+dOTJgwAe3atUOjRo1QvXp1eHl5oXXr1vj222+xbds2jRdrICIioqIRGxsLAEqH8iozfvx4pV/EAMDCwkL8IpydnY1r164pLTdp0iSV7depUwddunQBkLcwUFpamtJy0ni1XfmyY8eOSpN1V1dX8djPz0/hSz8AmJqaiolabm6u3DkrKyu8ePECL1++xNixY1VeX5tE7MiRIwDybiR069ZNZblJkyZh0qRJ6N69u9JFn8qVK4f+/fsrrWttbS3eeBDeWWAzNDQUANCwYUO4u7urvL65uTk6duwIALhy5QpevXoFAHLDv6dPn66yvr29PYYPH67yvLZk45g4caLKchUqVBAT2kuXLuW7AJemBEHA+vXrUatWLXFxqNq1a2PlypWFbnPz5s3isbKfrZubGxo1agQgb5iu7JBkVVasWIHLly/D1NQUISEhWLp0KQAgKysLAwYMUPrZetfDhw9hYmICV1dXXLhwAd9++y3q168PGxsb1K5dG2PHjsW1a9dgZ2cHIG8rLmmCp61///1XHNL8xRdfqC3brVs3cRi7JguJSTk5OYnHYWFhyMzMVCjj5uaG0NBQhIaGokOHDhq3/aHT6W3hrKwszJ07F6tWrVK7Ut3x48exfPlylC5dGoGBgZg+fTrMzMx0GQoRERUjlqbGuDlH/X59aVkFvyOdnpUj9rD+M8EXlmb59yS+S5MeVEvTgrerjfDwcLRo0UKjsuvWrUNAQIDK89J9Iz/66CON2nt3TuO7vL29xeOoqChxPpiUjY0NatasmW8bu3btAgDcu3dPaQ+ONF5p/IVVtWpVpc/LzrtUVQaARr3TsnJychAbG4s7d+7gyJEjcvNYC0o6P7BOnTpy8b6rdevWaN26tcrzH3/8sdr6ql7jxYsXAeTNB5Xd7lCd3NxcJCYmonTp0nK9btJ5yqrkd14b0jhKly6NypUrqy1br149BAcHIycnB9HR0WqTdU0cO3YM48ePl0vMfHx8sG3bNjFxKwxp0urg4KAw11qqV69e4nzSzZs3o2HDhmrbzM3NFRNW6U2SXbt2Ye/evbh27RqmT5+eb6+o7PxVVdzc3DBnzhyMHj0aOTk5CA4ORr169fKtlx/p5xWAwu8ldQryO8bZ2Rm+vr4IDw/H8ePH4e7ujoEDB6JNmzZo1KgRLC0tCxQz/UdnSeuTJ0/Qtm1b3Lp1S6OtbiQSCZKTkzF//nzs3r0bhw4dkrs7QUREHw6JRJJvcliY4beyiW5ZG/P3OoS3uIiLiwOQN1RPE1WqVFF7vlq1auLxvXv3ClxfWRvKklZpvNomrZoknQVNTGXFxcVhw4YNOHXqFG7evImYmBidDAlMT08Xe8mVLa5UEIWt/+zZs0LVk/a0SpPFUqVK5XvTRJPPTWFJP6f5JaxAXkIlW6+wSeuTJ08wduxYsbcayHsf5s6di1GjRml8E0CZiIgI8TUlJiYqDGtXZuvWrVi8eLHamxfvJqwA8Pvvv6NWrVpISkrCokWL4O/vj+bNmxc6dqkePXqIw3c16QXWhLafV01t3rwZgwcPxqFDh/D48WPMmzcP8+bNg6mpKerVqwd/f3/06NGj0EO/P1Q6+ev95s0bfPbZZ7h165b4n8zY2BidOnWCp6cnXF1d4ezsjGfPnuHBgwe4desWwsLCkJ2dDUEQcP36dbRr1w5nz57lHQgiIqL3SPp3W5MbzgDUfqkF5IfJKhtSm199TdoA/ov73WG5huS3337DuHHj5IY4lytXDu7u7vDw8EDjxo1x7do1rFq1qsBtp6eniz8zc3NzreLUJKlRV69p06Zqh/e+S9pzbWFhoXEdbW4c5EfTzz4AuaHxyoZ+amL37t0ICAgQRyWam5tjxIgRmDp1qsYjHtSR7s1aEPHx8QgPD4efn5/KMmPGjFEYhu7k5IRVq1ahV69eyM3NxcCBA3Ht2jWV/2815ezsDFtbW7x+/VrpnqmFIfs5X7VqlUY3KQBotHe1rPLly+PgwYMIDw/Hjh07sHv3bjx69Ahv377F+fPncf78ecycORODBw/GsmXLYG1tXaD2P1Q6SVp//vlnuYT1yy+/xJQpU+TuRr0rJiYGc+bMwdq1awEA169fx+LFizFt2jRdhEREREQacHJywr179zT+YvjgwQO1Q/Vkh3wq64V68OBBvtfIrw0gb9sNAEoX/zEEhw8fxtdffw0gb9uSyZMno02bNgoLU82ePbtQ7ZcpUwZly5ZFUlKSuGXR++bh4YELFy4gIyMDbduqH96vjLRHPSUlBc+fP1ebsBVmoSpNubu74/Lly3j48GG+ZWXjKEwva1hYGHr27Cn2tnfu3BnLli2Tm0OtjZycHHFxK2tra2zYsEHtjaLjx4+Lc1M3b96sNmm1sbFR+vznn3+Ofv36YfPmzYiOjsaYMWOwZs0aLV5F3o0E6Q0pXW0JI7t9UIUKFQr1mS0IX19f+Pr6Yvny5Xj48CFOnTqFo0ePIiwsDMnJyVizZg2MjY2xevXqIo2jpNB6IaacnBwsW7ZMTFhHjBiB1atXq01YgbwFDv7880989dVXAPI+nEuWLDHoO6aGKCEhAZ6enkofQUFB+g6PiIgMnDTp0zRpzW+o3pkzZ8RjZXtMpqSkyO3VqK4Nc3NzlUNXpfEa6tSiFStWAMj7ov/PP/8gICBA6UrK0uS7MKTv740bN9SWO3fuHPr27Yu+ffvqNPmTXj8qKirfIc+XL1/GoUOHcPLkSfE52bnC+a3EGxkZqUWk6kmT51evXimsAv0u6dxTIyMjtXOdlTlz5gx69+6N7OxsWFhYYOPGjQgLC9NZwgoAf//9NxITEwEAXbp0Qbdu3dClSxeVj0mTJolJ7Y4dOzRaTEmZFStWiJ/vtWvXIiwsTKFMaGgoPvnkE3zyySc4e/as2vYePXqEN2/eANBur1pZsu3cvHlTbdnXr1/j0KFDOHToEJ4+farxNd68eYOkpCSF36eVK1fGgAEDEBwcjIcPH+KTTz4BAGzcuLHIc5+goCCVuUJCQkKRXluXtE5az549i+TkZAB5d5ykd2s0FRQUJP6nf/nyZb4fYpLn6OiImzdvKn0EBgbqOzwiIjJwFSpUAACNv7z8/PPPyMnJUXouLS1N3LbEwcEBderUUVpuwYIFKtuPjIzE3r17AeQtIKSql0gar7JE0BBIk8Pq1aurXJk5Ozsbhw4Vfqsn6eqvt27dwoEDB1SWW7t2LbZu3YqwsDDx560L0kW5UlJS8Ouvv6osl5KSgpYtW6Jdu3biCDsgb1ix9Oc7d+5ctfW1WUk3P7KLPC1cuFBlucePH4tDb2vXrl2g4c0AMG3aNGRmZsLY2Bj79u3LdwXbwpAdGty3b998yzs5OcHHxwcAkJycrPZzpI69vb1c7+pXX30lJs9SPj4+uHXrFm7cuCHe1FFFdoEyf3//QsX0Ljc3Nzg6OgIAli9frnJlcgD45Zdf0K5dO7Rv375Aw8BXrFiBjz76CB999JHKm0l2dnZiL296ejpSUlIK8CoKLjAwUGWuIH0/igOtk1bZu6U+Pj4FnnNgYmICX19f8d+3b9/WNiQiIiLSkHTRlGvXrqlMRmXdu3cPI0aMUOhZS01NRf/+/cWFkWbMmKFyrtaGDRuUDh+8d+8e+vfvD0EQYGRkhPnz56uMQ9rz1qxZs3xj1gdp79ndu3eVLgCTnp6OAQMGyA2FVvclWllP5uTJk8W5g2PGjFHYRxXIW601ODgYQN5wRW3nv8oaNmyY2PEwc+ZMuV52qZycHHz55ZfiYjayK1lXr14dAwYMAJDX0zp+/HiFz2BaWprc/E9tKXsfO3fuLCbgf/75J7Zt26ZQ5vnz53Lbuqi78aLM9evXcfz4cQB575u6YbiFlZaWJq66XaZMGY2Hv/bq1Us8lt0qp6A+++wzjBgxAkDeokdffvml3HlHR0dxFevNmzeLn8t37d+/X7z55e3tjd69exc6JlkmJibi3suxsbEIDAxERkaGQrkzZ85g0aJFAIAWLVqonfv67udVdmX0//3vf0rrvHr1Cvv27QOQ1/urzSrRHxKt57TK/iIu7HLUsvW0GSZDREREBdOmTRsAecParl27pvZvecWKFfHy5Uv8/vvvuHz5Mrp164Zq1arh6tWrCA0NFZMmDw8PDBs2TGkbNWrUwK1btzB06FDs2bMHrVq1QunSpXH+/Hls3LhRHL01aNAgpasGA8CLFy/Em+atWrUq7EsvUq1bt8aBAwfw+vVrNG3aFMOHD0fVqlXx6tUr3LhxA+vWrcOzZ89gY2OD1NRUAMDw4cPRq1cvdOrUSWxHuvDPqVOncObMGZiZmaFq1aqwt7eHo6Mjpk6dikmTJiEqKgoNGzZEQEAA6tevD2NjY0RERGD16tXIysqCra0tli1bptPXaGZmhsWLF6Nr16548eIFfHx8MGDAAHz66adwcHDAgwcPsGbNGnEo5siRIxW2apo9ezZCQkKQkZGBxYsX49SpU/D390f16tXx77//Ytu2bbh16xYqVqyIjIyMQq8Aq+59BPL2A23WrBmysrLQu3dvhIaGwsfHB+XKlcOVK1ewefNmPH78GADQoUMHtGvXrkDXl+1RL1++vMY97B4eHhovGBQWFiZ+lnr06KHxAls9evTAqFGjkJOTgz179uD169eFXkjp559/xuHDh3H//n3s2bMHf/75J4YOHSqeX7lyJRo1aoSkpCQMHjwY27dvR8eOHeHk5ISHDx/i5MmT4tBic3Nz/Pnnnxot3qapwYMHY+XKlYiMjERwcLA4dN7T0xNpaWk4ceIENm3ahMzMTJQpU0bpCALZxbhWr16Npk2bwtzcHNWrV0fz5s1RoUIFxMbGYs2aNYiNjUWPHj3g5OSE9PR03Lx5E+vWrRPnoUunSZIGBC0tX75ckEgkgpGRkTB58uRCtTFlyhSxjRUrVmgb0gfBxcVFACC4uLjoOxQiIoP1JvOt4Dpxr+A6ca/wJvOtvsPRyqxZswQAAgAhPDxc43rBwcFiveDgYKVl3NzcBABCUFCQ2ms3a9ZM2L9/v2Brayu2+e6jZs2aQkxMjEIbvr6+AgBh2rRpwty5cwWJRKKyjZ49ewoZGRkqX9O+ffsEAIKzs7PG74Os6OjofN8TQRDEMrNmzVJZRvreBQQEyD2fnZ0t+Pn5qXyNAIR+/foJN2/eFMzNzcXn7Ozs5Nr54osvFOrJ/vxzc3OFpUuXChYWFiqvU65cOWHPnj0ax17Qcrt27RIcHBxUXl8ikQhDhgwRsrOzldY/cuSIYGdnp7K+i4uLcP36daFatWr5/sxUye99FARB2LBhg2Bpaan2Z/b1118LqampBb7+119/rbZdVY8ff/xR42t07NhRrHfs2LECxSf7Wd2wYYP4fHh4uEb/D2SdPn1aMDIyEgAINjY2wv379+XOnzhxQu3nBYBQt25d4dKlSwV6DVKyv/Oio6MVzr948ULo06eP2utXrFhR+Oeff5S2f/jwYYXysv83Tpw4IVhbW6ttXyKRCN9++22hXp8uFad8QutbF7JzSfKbRK+K7EbDzs7O2oZEREREBdC1a1cAeYu45Kd9+/a4evUqAgMDUblyZZibm6NMmTJo3rw5goKCcPny5Xz3/Zw2bRrCw8PRo0cPODs7w8zMDOXLl0fXrl2xZ88ehIaGqh3GKo2zc+fOmr/I98zY2BiHDx/GmjVr4OPjA2dnZ5ibm6Ny5coYPHgwTp8+jU2bNqFGjRrYvn073N3dYW5uLje8EACWLFmCvn37oly5crCwsICbm5vcKq4SiQSjR4/GxYsXERAQADc3N1hYWMDJyQm+vr6YMmUKoqKidDYvUJkuXbrgxo0bGDduHGrVqoVSpUrB1tYWXl5eGDhwICIjI/Hnn3+qnELWqlUrREVFYerUqahTpw5sbW1haWmJ6tWrY+LEibh27Rpq1aqlVYz5vY8A0L9/f0RFRWHixImoU6cOSpcuDTMzM7i4uKB79+44fPgwVq1aVagtSopy9WMgb2Gyw4cPA8jryX23Rzs/uhoiDOQN6Z0wYQKAvGkDAwcOlFtsyMfHBw8fPsSiRYvg6+sLNzc3mJmZ4aOPPoKPjw+WLl2KCxcuwMvLS6s4VLG3t8eWLVuwY8cO+Pv7o3z58jAzM4OrqyvatGmDX375BVFRUSqnHrRp0wbLli1DtWrVYG5uDkdHR7l8yMfHBzExMZg+fTqaNm0KV1dXmJubw9nZGZ9++ilGjhyJW7duqRw+TMpJBKEAm1Mp8eLFCzg6OiI3NxdGRkbYvXs32rdvr3H9gwcPwt/fH7m5uTA2NkZiYqI4VKOkW7JkicpfYtIhN6pUqFABT548gYuLi7i5OBERyUvLyobnjLxheDfntIWVmU52eitxHjx4AHd3dxgbGyMuLg5ly5bVd0gqZWdno0KFCkhISMCNGzcUkjwi+rCtW7dOnDsdHR2t09WZS5rilE9o/de7TJkyaNeuHfbt24ecnBz07dsX+/fvh7e3d751IyIi0LdvX+Tm5kIikaBdu3YfTMIK5CWtqvZWs7GxUZu0EhER6UqVKlXQpUsX/PXXX9i6datBrz5/8OBBJCQkoHXr1kxYiUgBt88smXQys/mnn36CmZkZJBIJUlJS0Lx5czGRjY6OFlfWysnJQUxMDA4cOID27dvD29tbXObZ1NQUP/30ky7CKRYyMjLw+PFj+Pv7QxAEhcfr16/1HSIREX1Axo0bBwByW5IYIumKo2PGjNFvIERkkOLi4sRjiUSix0hIl3QyTsrT0xMbNmwQ94MSBAF///23OOfEyMgIZcqUwYsXL+TufkhHJhsZGWH9+vXw9PTURTjFwr1795Cbm4vq1avrOxQiIiI0a9YMffr0wdatW7F//3506NBB3yEpuH79Ov766y+0bt26SOdoElHxc/fuXdy+fRtBQUEAAAsLC66VU4LobHLP559/DgcHBwQEBCAmJgayU2VzcnJULlFeqVIlBAcHy+3VWhwlJyfj/v37SE1NhbOzM6pVq6b27o50WwAmrURERcfKzATRP3bUdxjFxrJly/D3339j8uTJaN++vcH1UkyZMgXm5uZKt6Egog/bu9+pv/rqK7ntaah4093GR8jbgPfu3bvYtGkT2rdvr3Kz3NKlS6Ndu3bYuHEj7t69a1AJ68qVKyGRSDB79myNykdFRaFz584oV64cGjRoAF9fX3h4eMDNzQ2LFy9WOa5eur/co0eP0LJlS9jb28PFxQXt27dHeHi4rl4OERGRxsqVK4elS5fi2rVrWq8gqmunT5/G3r17MWPGDFStWlXf4RCRgbGyskKZMmVQv359LF++nGvDlDBarx6cn8ePH+Ply5dITU2FjY0N7O3tUbFixaK8pFaaNm2KM2fOYNasWZg5c6basqdOnULbtm2Rlpamskznzp2xc+dOhWXev/zyS/z5558AgDp16qBOnTp48uQJTpw4gZycHCxcuBDjx49X2W5xWu2LiIiIiIgMS3HKJ3Ta06pMxYoVUbt2bXh7e6N27doGnbAGBwfjzJkzGpVNSkpCt27dkJaWBmNjY8ydOxexsbF48+YNjh8/Lu4ttXv3bsydO1ehfkxMDCwtLbF06VJcuXIF69atw5EjR3DhwgXY29tj6tSpuH37tk5fHxERERERUXFT5EmrocvIyMDp06cxdOhQDB8+XON6P/30E54/fw4gbw7QtGnT4OLiAisrK/j6+iI8PByVK1cGACxevFgsK/X3338jLS0No0ePlnu+bt26mDp1KrKysrBr1y7tXhwREREREVExVyRJa25uLjIzMxWeFwQBixYtQsOGDVGqVCk4OzujT58+uHnzZlGEkS9vb29YW1ujWbNmWLNmDbKysjSql5ubK24JUKVKFYwYMUKhjK2trTi8ODU1FSEhIRrH1axZMwDAjRs3NK5DRERERERUEuk0aV25ciUaNmwIGxsb7Ny5U+F8ly5dMHHiRERGRiI1NRXx8fEIDQ2Ft7e3uD3O+xQXF1eoDYjPnTsn9pz26dNH5eqKPXv2FFct27t3r/j827dvkZSUhDdv3iitJ62jaiErIiIiIiKiD4VOktasrCz4+/tj1KhRuHTpktJe1o0bN4qJm+zaT4IgICUlBf7+/njw4IEuwtHYnTt3kJGRIT7u3LmjUb3IyEjxuEmTJirLWVtbo06dOgp1nj17BgcHB/j5+SmtFxERAQD45JNPNIqHiIiIiIiopNJJ0vrrr79i//794r+VLUj8888/i8e9e/fG+fPnsWPHDnFPpezs7Pe+NLWZmRnMzc3lHpqQXSDJ3d1dbVnpsvyJiYlITk4GADg7O6Nly5Y4f/68wl5zt2/fxuzZs+Hk5ITevXsX4NUQERERERGVPFrvuJueno558+aJ/+7ZsyfmzJkDDw8P8bl79+7h+vXrkEgkcHNzw4YNG2BiYoIGDRqgYcOGcHd3R2ZmJoKDg7FgwQKUKlVK27CKVEJCgnjs5OSktqzs+fj4eHHI7++//46GDRtixIgRCA4ORs2aNREbG4vw8HCYmpoiJCREo+HB0p7qwipIsk5ERERERIYhMzNT6QhXTRXxzqc6pXVP682bN/Hs2TNIJBJUr14d27Ztw8cffwwjo/+a3r17t3jcp08fcc4mkLc/ULdu3QDkreR79+5dbUMqcrJzUa2srNSWlT0vW69y5cq4dOkSvvrqKyQlJWHLli149OgRevXqhevXr6Njx44axfL06VOULl260I8FCxYU8NUTEREREZG+LViwQKs84OnTp/p+CRrTuqf1/v374nGvXr2Uljl58qR4/Nlnnymcr1u3LrZu3QoAiI6ORoMGDbQNq0jJ3tEwNjZWW1Y2QU9PT5c75+rqitWrV2sVi7OzM27dulXo+uxlJSIiIiIqfiZPnoxx48YVun6NGjWKTeKqddIaExMjHlesWFFpmVOnTgEATE1N8emnnyqcd3BwEI8fPXqkbUhFTrb3NCsrCxYWFirLyia4lpaWOo9FIpEY/HBqIiIiIiLSLW2n+anaAcUQaT08uFy5cuLxq1evFM5fvXoVL168gEQiQf369ZW+sa9fvxaP1SWAhsLGxkY8zm8+qex52XpERPQeZL0BZpXOe2Qp32aMiIiIDJvWPa3S1XEB4MqVKwrnZfcnbdWqldI2ZOexVqpUSduQipxsjLGxsXI9xe+KjY0FkHcnw8XFReexJCQkwNPTU+m5wMBABAYG6vyaRERERERk+IKCghAUFKT0nOzisoZO66S1evXqMDIyQm5uLkJDQzFnzhy4ubkBANLS0uTepA4dOijUf/36tTifFVA9xNiQfPzxx+Lx9evX4eXlpbLszZs3AeS9rqLoaXV0dBSvQUREREREJKWuE6tChQp48uTJe46ocLQeHuzg4CDuJ5qVlYVmzZphxYoVCA0NxWeffYb4+HhIJBK4u7srzGc9d+4cWrRogaSkJAB5b1zNmjW1DanINWvWTDwODw9XWe7Ro0d4+PAhAMDb27uowyIiIiIiIipxtE5aAWDWrFniKrlPnz7FmDFj0KdPH5w9e1Ys8/PPP8vV8fLyQtOmTXH16lUAecNnJ0+eLLfarqFyd3dHjRo1AADbt29HcnKy0nJr1qwRj6Xb+uiadHiwsoeqoQBERFT8zJ49GxKJBBKJBCdOnNC43rp168R669aty7f8pk2bIJFIsHnzZqXXLqyWLVtCIpGgZcuWhap/6tQpSCQS/Pjjj4WOgeRVrlwZEokEgwcP1nco+YqJiSnQ55jkDR8+XHz/JBIJNm3apFG9EydOiHVmz56tUZ3bt2/DyclJrDdp0iS15Z89e4bTp08jKioKOTk5Gl2DNBcUFKQyVyhOw4N1krS6u7tj//79KFOmDIC8jWqlDwAICAhAp06d5OqkpqbK/btNmzYYOnSoLsJ5L6TLS6empmLUqFEKm/NeuXIFCxcuBJD3R6Goklbp8GBlD85nJSKignj27BnGjBmDOnXqoG/fvvoOR06zZs3QsWNHzJkzR267PaLibv/+/UV68yArKwuhoaFyz8nelNKlqKgo+Pn5icnQxIkTld5oSk1NxdSpU/HRRx/BwcEBzZo1g4eHB6ysrNC2bVtcu3at0DEMHjy4wIl2SRYYGKgyV3B0dNR3eBrTSdIKAK1bt8bFixcRGBiI+vXro1atWujevTs2bdqEP//8U6G8IAiQSCRwc3PD0qVLcfDgQZiamuoqnCIXEBCAxo0bAwA2btyIli1bIjg4GDt37sSkSZPg4+OD9PR0GBkZYdmyZcXqtRER0Ydp1KhRSEpKwoIFCwxyK4T58+cjMzMTw4cP13co701x6g01ZIb8Pmra61lYBw4cwMuXL+WeO3z4MJ4/f67T69y/fx8tW7ZEXFwcAGDChAlKE9Z79+7Bw8MD8+fPF6cISmVlZeHw4cPw8vLCkiVLdBqfruhi5AkVnE7H4rq6umL58uUald23bx8qVapULLa4UcbExARhYWHo0KEDIiMjceLECYXhWmZmZli2bBn8/f31FCUREZFmTp06hZCQEDRs2BDt27fXdzhK1a5dG126dMFff/2FPXv2KIziIipujh49ipCQkCK9hmxS3KtXL2zbtg3Z2dnYvn07vv76a51cIzo6Gn5+fuKiPt9//z1++uknhXKZmZno3bu3mNgOHDgQ3377LTw8PPDs2TPs378f06dPx4sXL/D999+jcePGaNKkiU5ipOJNZz2tBeXh4VFsE1YpR0dHnDt3DitWrIC3tzfKli0Lc3NzVK5cGV999RUuXbpU5HeDOaeViIh0YfHixQBgkD1RsqTxLV26VM+REBXO27dvERkZibFjx6Jr165FOo8zJSUFe/bsAQB88sknmDdvnnhOV0OEHz16hJYtW+LRo0cAgPHjx4tT5N61fft2REZGAgCGDRuGdevWoV69erC2toabmxtGjhyJsLAwGBkZITs7GzNmzChwPGvXrhWnKc6cObPwL6yEKClzWg1/1aP3yNXVVWFuan5MTU31uh8qt7whIiJt3b9/H7t374aZmZm4I4Chat++PRwcHHD06FHcuHEDtWrV0ndIRBrr06cPduzYgezs7PdyvZ07dyIjIwMAMGDAAFSrVg316tXD5cuXcerUKTx+/Fir7SafPHkCPz8/REdHAwC+++47hcVXZZ0/f148/vbbb5WWadasGby8vHDx4kVcvHhRnFJIhcMtb9TIzMzEzp07MWHCBLRr1w6NGjVC9erV4eXlhdatW+Pbb7/Ftm3bkJ6eXhSXJyIiogJYvnw5cnNz4e/vLy6qqE5SUhImT56MGjVqwNraGnZ2dvDy8sLs2bM1/tt+48YNDBkyBK6urjA3N4eDgwP8/PzyXRnWxMQE/fr1AwCsWLFCo2u9S3Yl2n///Re5ubn49ddf0ahRI9jZ2aFUqVJo2LCh3C4AOTk5WLFiBRo2bAg7OzvY2dmhUaNG+OWXX/D27Vu11zt06BC6deuGWrVqwd7eHra2tvj444/Rrl07bNq0CVlZWQp1pPFJk4Hg4GC1q0ffvn0b33zzDapXrw5ra2s4OjqiWbNmmDx5ssJ8RlVu3bqFIUOGiNO33Nzc0L59exw8eDDfuklJSZg9ezYaNWqEsmXLwtraGp6enujXr5/a7QGlUlNTMX36dNStWxelSpWCra0t6tSpg5kzZ6rcpUETBX0f4+LiMGXKFHh5ecHe3h4WFhaoWLEievbsiZ07dxY6DqmEhIT3lrAC/w0NNjIyEv/fSG9MCYKArVu3FrrtuLg4+Pn5iQujjRs3DosWLVJbJyoqSjxWlyy7uroCAJKTk/Hs2bMCxSW7YnpMTIzKcgcOHEDfvn3h6uoKCwsLODs7w9vbGz///LPCXFvgvwWeZs2aJT4nvY6yESqHDh1C9+7d8cknn8DGxgZOTk5o0qQJJkyYgMTExAK9JgIg6FBmZqYwbdo0oWzZsoKRkVG+D3t7e2HatGlCZmamLsP4ILi4uAgABBcXF32HQkRkuDJTBWFmqbxHZqq+o9HKrFmzBAACACE8PFzjesHBwWK94OBgpWVcXV0FAMKqVavyvfaNGzcEZ2dn8d/vPjw9PYVr164ptOHr6ysAEHx9fYWtW7cK5ubmKtvo0qWL8Pz5c5Wvad++fQIAwdnZWeP3QVZ0dLR4rYsXLwrt27dXGct3330nvHnzRoxf2aNTp05Kr5OdnS20a9dOZT3po3bt2sKrV6/k6qor/+7Pf9GiRYKJiYnK8nZ2dsKBAwcU4nNzcxMACAEBAcL27dsFS0tLlW18//33Kt/P3bt3C6VLl1Ybc5cuXYTXr18rrR8ZGSm4u7urrOvs7Cz+zNV9jpUpyPu4detWwcrKSm0dPz8/4eXLlxpf/11ZWVlCRkaG3EPadkBAQKHbVebp06eCsbGxAEBo1aqV+Pz9+/fFa9atW1dtG+Hh4WLZWbNmic8nJCQINWrUEM+NHTtWo5iGDBki1jl9+rTKch4eHgIAwczMTMjOztaobSnZ33nR0dEK51NTU4XOnTur/TmXKlVK2Lt3r1y9gIAAleVlf3YZGRlCq1at1LZvbW0tnDhxokCvqygUp3xCZ0lrbGysULNmTcHIyEiQSCT5PqTljIyMhNq1awtxcXG6CuWDIP2QmZiYCDVq1FD6WLFihb7DJCLSLyat+Satd+/eFc9fuXIl32tXrlxZACDUr19fmDt3rrB161Zh8uTJQvXq1cUytra2QkJCglwb0qTP2dlZTFg7deokLF26VFi/fr0QGBgo2Nvbi200btxY5Wt6/vy5WO7mzZsavxdSskmrt7e3AEBo06aNsGrVKmHTpk3CoEGDBIlEIpapU6eO+Jp/+eUXISQkRBg1apRconjo0CGF60yZMkXuffvhhx+EkJAQITQ0VPjxxx+Fjz/+WDz/5ZdfytU9ePCgcPDgQcHR0VGMT/rcixcvxHLr1q2T+6I9atQoYcOGDcKaNWuEL774QnwdZcqUEWJiYuSuIU1aP/nkE8Hc3FywtLQURo4cKaxbt05Yu3at0KNHD7FtiUQinD17VuE1njt3TkyMrKyshG+++Ub4448/hK1btwrTp08XKlWqJLbRtGlTIScnR67+gwcPBAsLC7mf+9y5c4WQkBBh6tSpYvIimxQXJGnV9H08d+6c3M+8e/fuwpIlS4QtW7YIEydOFG/sABDat2+v8fU1UVRJ6y+//KLyPWvQoIFG/4eUJa3Pnj0TatasKT7/7bffahyTbHtNmjQR0tPTFcosXbpULDNo0CCN25bKL2n19/eX+7z99NNPwvbt24UVK1YIvXr1Ej8HRkZGwv79+8V6169fFw4ePCj0799frC/9LF2/fl0sJ5vc1qlTR1i8eLGwc+dOYd26dcKgQYPE/y/Ozs4KN6uKwooVK1TmCtLfYR9M0pqamip4enrKJaMmJiZCt27dhKlTpwqrV68W9u7dK6xdu1aYPn260LNnT8HU1FQuia1Tp46Qlpami3A+CMXpzggRUb5yc/OSSnWP14kFfyRF/5e0JkUXro384spMzYu/iMkmjgsXLhS/LOX3+O6779R+2Q8KChLv/Kvq0ZC9NgBhyJAhQlZWllyZ1NRUuSRn5MiRcudleyqNjY2F3377TeE69+7dk/syvG3bNpXvhzSZWb58uSZvnxzZpBWAMH36dIUyU6dOlSszYMAAhdf822+/iefnzJmj0Ia099DDw0NITk5WOP/mzRuhVq1aAgChatWqSmOV7Q19V1pamvh9wM3NTWnyERISIsY4bNgwpW1Lv0BfvnxZof6cOXPEMvPnz5c7l5ubK3z66acCAKFmzZrC06dPlcYo+yX/3Z9X7969xXPjx49X+AympKQIHTp0kPtZFCRpffe1qkoMmzZtKgAQTE1NhU2bNimcT0pKEvz8/MQYjhw5UuAYVCmqpLV+/foCAMHS0lJISUmRO7dw4UK1n3+pd5PWpKQkoXbt2nI/j4ULFxYorh9++EGs6+bmJixYsEDYvn278OuvvwqdOnUSz9WrV0/p/5v8qEtaDxw4IJ5bunSp0vpnzpwRbG1tBQBCpUqVhDdv3sidl/19+K6MjAzBzMxMACC0aNFCePv2rUKZefPmqb3Z9T4Vp3xCJ0nrzJkzxYTVyMhI+Oqrr4SHDx+qrRMdHS0MGTJErud17ty5ugjng1CcPmRERPmS7REtjo/30Iv7buJYmIeyL/t9+vQRgLweR02u7ebmpvSLmCAIQnp6ujh02MTERIiNjRXPySat/fv3V3mtK1euiD0dNWrUUFlOGnefPn1UllFFNmmtU6eO0mT9wYMHYhknJyeFL/2CkDfUU9pTMWTIELlzb968Eezt7QU7Ozvhl19+URnLxIkTVX4BFgT1ydaCBQvEutu3b1d5DelQRQ8PD6VtAxDWrVuntG5qaqpgZGQkABAGDx4sd27r1q1ij9Tdu3dVXj8jI0O8yeDl5SU+f+XKFbkeL1VevHghJhFFkbTu2bNHbHv48OEq23j8+LGYkDRq1KjAMahSFEnr7du3xXb79u2rcP7hw4fieVU3TARBPmkdPXq0UK9ePTG5t7a2FoC8IbxXr14tUHw7duwQTE1NVf6uGjhwYKE7s1Qlrbm5ueKQZnW/gwRBENasWSO2sXv3brlz6pLWS5cuieeWLFmitO2HDx8KPXv2FHr27Cns27evEK9Qd4pTPqH1Qkw5OTlYtmyZuKrXiBEjsHr1ari5uamt5+rqij///BNfffUVkPdTx5IlS5Cbm6ttSERERKSh2NhYAICDg4NG5cePHw8TE+WbD1hYWGDChAkAgOzsbFy7dk1puUmTJqlsv06dOujSpQuAvIWB0tLSlJaTxqvtypcdO3aEsbGxwvPShWAAwM/PD7a2tgplTE1NUb58eQBQ+P5iZWWFFy9e4OXLlxg7dqzK679+/bqwoePIkSMAADc3N3Tr1k1luUmTJmHSpEno3r270kWfypUrh/79+yuta21tDRcXFwBQ2GEhNDQUANCwYUO4u7urvL65uTk6duwIALhy5QpevXoFADh37pxYZvr06Srr29vbF+kWgrJxTJw4UWW5ChUqiIsZXbp0Kd8FuPRJdjsbZT9bNzc3NGrUCEDe6uEXLlzIt80VK1bg8uXLMDU1RUhIiLjtVFZWFgYMGKD0s6XMhQsXMH/+fLXv3+7du/H7779r1J6m/v33X9y6dQsA8MUXX6gt261bNxgZ5aVJmiwkJuXk5CQeh4WFITMzU6GMm5sbQkNDERoaig4dOmjc9odO6y1vzp49i+TkZEgkEri7uxd437SgoCAcO3YM9+/fx8uXL3H27Fk0bdpU27CIiKg4MbUCpjxVXyZLefKSb51ltfOOR18DzKwK3oYmdUwL0a4WwsPD0aJFC43Krlu3DgEBASrPx8fHAwA++ugjjdr79NNP1Z739vYWj6OiotC+fXu58zY2NqhZs2a+bezatQsAcO/ePdSuXVuhjDReafyFVbVqVaXPS7+wqisDQGnCq05OTg5iY2Nx584dHDlyRKsv5nfv3gWQl+jLxvuu1q1bo3Xr1irPf/zxx2rrq3qNFy9eBABERERovCVJbm4uEhMTUbp0abmVZBs3bqy2Xn7ntSGNo3Tp0qhcubLasvXq1UNwcDBycnIQHR2tNlnXJ2nS6uDggM8++0xpmV69eolb0GzevBkNGzZU22Zubq6YsEpvkuzatQt79+7FtWvXMH36dPz0009q2zh+/Dj8/f2RlpaG0qVLY8KECejUqROqVKmC58+f49KlS5g7dy6uXLmCMWPG4O7du4VeJfxd0s8rAIXfS+oU5HeMs7MzfH19ER4ejuPHj8Pd3R0DBw5EmzZt0KhRI1haWhYoZvqP1kmr9BcmAPj4+BT4l7eJiQl8fX3F5bJv377NpJWI6EMjkQBm1urL5Hdemaw3/x3bfFS4Nkq4uLg4AEDZsmU1Kl+lShW156tVqyYe37t3r8D1lbWhLGmVxqtt0qrJ95aCfreRFRcXhw0bNuDUqVO4efMmYmJidLLlSXp6uthLXqlSJa3aKmz9gm5FIiXtaZUmi6VKlcr3pokmn5vCkn5O80tYAciNJLx3755BJq0RERHia0pMTISpqWm+dbZu3YrFixervXnxbsIKAL///jtq1aqFpKQkLFq0CP7+/mjevLnS+pmZmRg0aBDS0tJgZWWF06dPy93Asra2hqurK7p06YI2bdrg+PHjCAoKQqdOndC2bVtNX75K2n5eNbV582YMHjwYhw4dwuPHjzFv3jzMmzcPpqamqFevHvz9/dGjRw94enoWKp4PldZJq+wHoF69eoVqQ7be8+fPtQ3pg5KQkKDyQ69uM2EiIiIAYg/Zu0M/VVH3pRaQHyarbEhtfvU1aQP4L25Dnlb022+/Ydy4cXJDnMuVKwd3d3d4eHigcePGuHbtGlatWlXgttPT08Wfmbm5uVZxapLUqKvXtGlTtcN73yXtubawsNC4jjY3DvKj6WcfgNzQeGVDPw2BdG/WgoiPj0d4eDj8/PxUlhkzZozCMHQnJyesWrUKvXr1Qm5uLgYOHIhr164p/X978OBBPH78GAAwatQolSMujI2NERQUJH6/Xb16tU6SVtnP+apVqzS6SQFAo72rZZUvXx4HDx5EeHg4duzYgd27d+PRo0d4+/Ytzp8/j/Pnz2PmzJkYPHgwli1bBmvror2ZGhQUhKCgIKXnEhISivTauqR10ir7Rks/iAUlOx/FxsZG25A+KI6Ojrh586a+wyAiomLKyckJ9+7dQ1JSkkblHzx4oPYmteyQT2W9UA8ePMj3Gvm1Afx3k1s6p9TQHD58GF9//TUAwMPDA5MnT0abNm3E+aFSs2fPLlT7ZcqUQdmyZZGUlISYmBit4y0MDw8PXLhwARkZGYVKKqQ96ikpKXj+/Lna3lbpiLyi4O7ujsuXL+Phw4f5lpWNwxB7WXNychASEgIg7zv6hg0b1N4oOn78uDi1b/PmzWqTVlXf0T///HP069cPmzdvRnR0NMaMGYM1a9YolJP9fy2dT6vKxx9/jFKlSiElJUVuVKc2PDw8xOMKFSroJBFWx9fXF76+vli+fDkePnyIU6dO4ejRowgLC0NycjLWrFkDY2NjrF69ukjjUNeJVaFCBa3XBXhftF6ISfaXb0RERKHakI6nB/LGghMREdH7IU36NE1a81uw5cyZM+Kx7JdEKU2+hErbMDc3Vzl0VRqv7MInhkQ6D8/Gxgb//PMPAgICFBJWQLsRZtL398aNG2rLnTt3Dn379kXfvn11mvxJrx8VFZXvkOfLly/j0KFDOHnypPic7Fzh/L5DRkZGahGpetLk+dWrV4iOjlZb9vLlywDyRgyom+usL3///TcSExMBAF26dEG3bt3QpUsXlY9JkyaJSe2OHTs0XkzpXStWrBA/32vXrkVYWJhCGdkRASkpKWrby8rKQnp6ukI9bcj+Psqvw+f169c4dOgQDh06hKdP81lvQcabN2+QlJSk8Pu0cuXKGDBgAIKDg/Hw4UN88sknAICNGzca9GgRQ6J10tqiRQtxyMbJkydx4MCBAtU/ePAgjh8/nheMkRF8fX21DYmIiIg0VKFCBQCaDxP7+eefkZOTo/RcWloafv75ZwB5C8DUqVNHabkFCxaobD8yMhJ79+4FkLeAkKpeImm8yhJBQyBNDqtXr65yZebs7GwcOnSo0NeQ9lbdunVL7fevtWvXYuvWrQgLCxN/3rogXZQrJSUFv/76q8pyKSkpaNmyJdq1a4e1a9eKzzdt2lT8+c6dO1dt/ZUrV+ooakWyizwtXLhQZbnHjx+LQ29r165doOHN74vs0OC+ffvmW97JyQk+Pj4AgOTk5AJ/j5eyt7eX61396quvxORZqm7duuLxwYMH1bZ37NgxcXVhVb9HCsrNzQ2Ojo4AgOXLl6tcmRwAfvnlF7Rr1w7t27cv0DDwFStW4KOPPsJHH32k8maSnZ2d2Mubnp6ebwJPebROWsuUKYN27dpBEATk5OSgb9++cndZ1YmIiEDfvn2Rm5sLiUSCdu3awd7eXtuQiIiISEPSRVOuXbumMhmVde/ePYwYMUKhZy01NRX9+/cXF0aaMWOGyrlaGzZsUDp88N69e+jfvz8EQYCRkRHmz5+vMg5pz1uzZs3yjVkfpFvm3L17V+kCMOnp6RgwYIDckEl1X6KV9WROnjxZnDs4ZswYubakzp8/j+DgYAB5wxV11WsFAMOGDRN7G2fOnKn0+19OTg6+/PJLcTEb2ZWsq1evjgEDBgDI+044fvx4hc9gWloaAgIC8PLlS53ErOx97Ny5s5iA//nnn9i2bZtCmefPn8tt66Luxou+pKWliatulylTRuPhr7169RKPZbfKKajPPvsMI0aMAJC35s2XX34pd75p06Zi4hoSEqJy5ez79++LW2KamJjobLsjExMTzJs3D0DeVl+BgYHIyMhQKHfmzBksWrQIQF7nnLq5r+9+XmXn6f7vf/9TWufVq1fYt28fgLzeXzs7uwK9jg+V1nNaAeCnn37C33//jbdv3yIlJQXNmzdHmzZtxEnWFStWhLGxsbjM+82bN7Fs2TIcPnxYbMPU1DTfZbKJiIhIt9q0aQMgb1jbtWvX1M5XrVixIl6+fInff/8dly9fRrdu3VCtWjVcvXoVoaGhYtLk4eGBYcOGKW2jRo0auHXrFoYOHYo9e/agVatWKF26NM6fP4+NGzciOTkZADBo0CClqwYDwIsXL8Qhxq1atSrsSy9SrVu3xoEDB/D69Ws0bdoUw4cPR9WqVfHq1SvcuHED69atw7Nnz2BjY4PU1FQAwPDhw9GrVy906tRJbEe68M+pU6dw5swZmJmZoWrVqrC3t4ejoyOmTp2KSZMmISoqCg0bNkRAQADq168PY2NjREREYPXq1cjKyoKtrS2WLVum09doZmaGxYsXo2vXrnjx4gV8fHwwYMAAfPrpp3BwcMCDBw+wZs0acSjmyJEjFbZqmj17NkJCQpCRkYHFixfj1KlT8Pf3R/Xq1fHvv/9i27ZtuHXrFipWrIiMjIxCrwCr7n0EgMWLF6NZs2bIyspC7969ERoaCh8fH5QrVw5XrlzB5s2bxbVbOnTogHbt2hX2bSsyYWFh4mepR48eGi+w1aNHD4waNQo5OTnYs2cPXr9+rXIBtPz8/PPPOHz4MO7fv489e/bgzz//xNChQwHkjagMDQ2Fl5cXXr9+jWHDhmHTpk3iljeJiYm4ePEi1q9fL94cmDdvXr7zXwti8ODBWLlyJSIjIxEcHCwOnff09ERaWhpOnDiBTZs2ITMzE2XKlFE6gkB2Ma7Vq1ejadOmMDc3R/Xq1dG8eXNUqFABsbGxWLNmDWJjY9GjRw84OTkhPT0dN2/exLp168R56NLknDQg6Mi2bdsEY2NjwcjISJBIJIKRkZH4MDExERwcHAQTExO55yUSiSCRSARjY2MhJCREV6F8EFxcXAQAgomJiVCjRg2ljxUrVug7TCIi/cpMFYSZpfIeman6jkYrs2bNEgAIAITw8HCN6wUHB4v1goODlZZxc3MTAAhBQUFqr92sWTNh//79gq2trdjmu4+aNWsKMTExCm34+voKAIRp06YJc+fOFSQSico2evbsKWRkZKh8Tfv27RMACM7Ozhq/D7Kio6PzfU8EQRDLzJo1S2UZ6XsXEBAg93x2drbg5+en8jUCEPr16yfcvHlTMDc3F5+zs7OTa+eLL75QqCf788/NzRWWLl0qWFhYqLxOuXLlhD179mgce0HL7dq1S3BwcFB5fYlEIgwZMkTIzs5WWv/IkSOCnZ2dyvouLi7C9evXhWrVquX7M1Mlv/dREARhw4YNgqWlpdqf2ddffy2kpur2d4m07fx+Dvnp2LGj2NaxY8cKVFf2s7phwwbx+fDwcI3+H8g6ffq0YGRkJAAQbGxshPv378udP3HihFCzZk2177O1tbUwZ84cITc3t0CvQxDkf+dFR0crnH/x4oXQp08ftdevWLGi8M8//yht//DhwwrlZX92J06cEKytrdW2L5FIhG+//bbAr60wVqxYoTJXMDExEf+PGTqthwdLff755zh69Ki4YIIgCOIjJycHz549Q05OjtzzQN7eYEeOHJEbmkCak64erOzB7W6IiEgTXbt2BZC3iEt+2rdvj6tXryIwMBCVK1eGubk5ypQpg+bNmyMoKAiXL1/Od9/PadOmITw8HD169ICzszPMzMxQvnx5dO3aFXv27EFoaKjaYazSODt37qz5i3zPjI2NcfjwYaxZswY+Pj5wdnaGubk5KleujMGDB+P06dPYtGkTatSoge3bt8Pd3R3m5uYK24AsWbIEffv2Rbly5WBhYQE3Nze5VVwlEglGjx6NixcvIiAgAG5ubrCwsICTkxN8fX0xZcoUREVFwd/fv8hea5cuXXDjxg2MGzcOtWrVQqlSpWBrawsvLy8MHDgQkZGR+PPPP1VuW9OqVStERUVh6tSpqFOnDmxtbWFpaYnq1atj4sSJuHbtGmrVqqVVjPm9jwDQv39/REVFYeLEiahTpw5Kly4NMzMzuLi4oHv37jh8+DBWrVpV5FuUFEZSUpI4grF8+fIKPdr50dUQYQDw9vbGhAkTAORNGxg4cKDcYkM+Pj64evUqfv/9d3z++eeoXbs2rKys4Orqis8++wwTJkxAVFQUpk+fLm5tpUv29vbYsmULduzYAX9/f5QvXx5mZmZwdXVFmzZt8MsvvyAqKkrl1IM2bdpg2bJlqFatGszNzeHo6Cg3t97HxwcxMTGYPn06mjZtCldXV5ibm8PZ2RmffvopRo4ciVu3bqkcPqxrgYGBKnMF6Rzf4kAiCAXYnEoDb9++xfbt27Fx40acPXtWHOYjq3Tp0mjSpAn69++Pnj17wszMTJchfBCkS1S7uLiIm4sTEdE7st4A8/9/VfopTwEzw/uyaQgePHgAd3d3GBsbIy4uDmXLltV3SCplZ2ejQoUKSEhIwI0bN1Tu9UhEH6Z169aJc6ejo6PF+eWkqDjlEzqZ0yrL1NRUXFYdyFtp7eXLl0hNTYWNjQ3s7e1RsWJFXV+WiIiICqlKlSro0qUL/vrrL2zdutWgR+ocPHgQCQkJaN26NRNWIlLALWRKJp0ND1alYsWKqF27Nry9vVG7dm0mrERERAZo3LhxACC3JYkhkq6EO2bMGP0GQkQGKS4uTjwuiuHFpB8672mVlZOTg+joaLx8+RIZGRmwt7eHg4MDypUrV5SXJSIiogJq1qwZ+vTpg61bt2L//v3o0KGDvkNScP36dfz1119o3bp1kc7RJKLi5+7du7h9+zaCgoIAABYWFnB2dtZzVKQrOp/TmpKSgg0bNmDr1q2IjIxUuv9RhQoV4OPjg6FDh8LX11eXl/9gFKcx6EREVDw8e/YMNWrUgIuLC65cuWJwvRSdOnXC0aNHcf36dXF/UCIiQLFXddSoUTrf5qmkKU75hE6HB2/atAlVqlTB6NGjcebMGaSnp8utFix9xMbGYvPmzWjVqhWaNWuG27dv6zIMIiIiKoRy5cph6dKluHbtmtYriOra6dOnsXfvXsyYMYMJKxEpsLKyQpkyZVC/fn0sX74cixcv1ndIpEM662n98ccfMXXqVChrztjYGGXLlsXz58+VTo62trbG3r17C7w894esON0ZISIiIiIiw1Kc8gmdJK1///032rZtm9egRAJBEFC/fn1MmDABXl5ecHV1hYmJCd6+fYuHDx/iwoULWLhwIa5fvy6Wt7a2xvXr1+Hm5qZtOB8E6YfMxMQE7u7uSssEBgYa9AqQRERERERUdIKCgsR5vu+KiopCdnb2h5O01qlTB9evXwcAmJmZYePGjejZs2e+9TZs2IAhQ4YgNzcXgiCgc+fO2LVrl7bhfBCK050RIiIiIiIyLMUpn9B6TuudO3fEHlOJRIKVK1dqlLACwIABA7BkyRJxSPG+ffuQkJCgbUhERERERERUQmidtEZERIjHLVu2xJAhQwpUPzAwUJzLmpubi/DwcG1DIiIiIiIiohJC66Q1Pj5ePG7Xrl2h2ujYsaN4/OTJE21DIiIiIiIiohJC66TV2tpaPC5btmyh2ihTpox4bGJiom1IREREREREVEJonbTKrvb74MGDQrXx8OFD8bhixYrahkREREREREQlhNZJa+vWrWFnZwcA2LJlC9LT0wtUPz09HVu2bAEAWFpaonXr1tqGRERERERERCWE1kmrubk5Jk+eDEEQ8PDhQ/Tp0wcF2UWnX79+ePDgASQSCb766ivY2tpqGxIRERERERGVEFonrQDw/fff44svvoAgCNi7dy/atGmDEydOqK1z5swZtG3bFmFhYQAALy8v/PTTT7oIh4iIiIiIiEoIjVY92rBhQ75l2rRpgzt37uDixYs4fvw4jh8/jk8++QT16tWDm5sbnJ2dkZiYiIcPH+LatWu4dOkSAEAikcDHxwd//vknXr16hXLlymn3ioiIiP5f2ts0NN7cGAAQ0S8CVqZWeo6IiIiICkoiaDCW18jICBKJpEANC4Kgto6y8xKJBNnZ2QW6zoeqQoUKePLkCUxMTODu7q60TGBgIAIDA99zZEREhoNJKxERfciCgoIQFBSk9FxUVBSys7Ph4uKC2NjY9xxZwWi8v0xB5qlqWqcwbZI8R0dH3Lx5U99hEBERERGRgVHXiSXtBCsONEpaAwICijgMIiIiIiIiIkUaJa1r1qwp6jiIiIioEKpXr467d+8CyJtmExMTo9Ge55UrV0Z0dDQCAgKwdu1a8fkTJ07A19cXABAeHo4WLVoUKJ6WLVsiPDwcQMFGVA0ePBjBwcEAgOjoaLi6uqot/9VXX2H9+vW4ffs2KleuLHdtX19fHD9+vEBxS0mnLs2aNQszZ84scP0ffvgBM2bMwKlTp+Dt7V2oGIiISJ5OVg8mIiKi9+/ixYtiwgrkJYlbt27VY0Tvx7Fjx/DHH39g+PDhYsJqKMaOHQsHBwcMGzYMb9++1Xc4REQlApNWIiKiYmrTpk0Kz23evFkPkbw/aWlp+Oqrr2BjY4Np06bpOxwF1tbWmDZtGv79919u5UdEpCNMWomIiIqhnJwcsVe1XLly4pDeK1eu4NatW3qMrGj98ssvePDgAUaMGAEHBwd9h6PUsGHD4OjoiHnz5uHZs2f6DoeIqNhj0mpAnj17BicnJ3Tq1EnfoRARkYE7duwY4uPjAQB9+/bFF198IZ7bsmWLvsIqUllZWVixYgUAw14k0szMDF988QUyMjLw22+/6TscIqJij0mrAfnqq6+QkJCg7zCIiKgYkB0a3L9/f3Tr1g0mJnnrK5bUIcKbN29GQkICGjRoAE9PT32Ho9agQYMAACtXruTcViIiLTFpNRB//PEHwsLC9B0GEREVA+np6di5cycAwMPDAw0bNkTZsmXRunVrAMD9+/dx4cIFfYZYJJYsWQIAGDhwoEblb9y4gSFDhsDV1RXm5uZwcHCAn58f1q1bp/E19+3bh44dO8LJyQnm5uaoUKECevXqhVOnTqmtV7t2bdStWxdxcXHYsWOHxtcjIiJFTFoNwP379zF27FjUrVtX36EQEVExsGfPHrx+/RoAMGDAAPH5Xr16icclrbc1OjoaV69eBQC0adMm3/IhISFo0KAB1q5di0ePHiErKwvPnj3D8ePHERAQgK5duyIpKUllfUEQMHbsWPj7+2P//v1ISEhAVlYWnjx5gtDQULRo0QIzZsxATk6Oyjakce7Zs6eAr5aIiGRptE8r5S85ORn3799HamoqnJ2dUa1aNXGvN3VycnLQv39/WFhYYNWqVWjSpMl7iJaIiIoz6dBgiUQiN5e1a9euGD58ON6+fYuQkBAsXrwYRkYl4/7033//DQCws7ND9erV1Za9e/cuBg0ahMzMTHTq1AmtW7eGvb09IiIisHnzZrx8+RJhYWGIj4/HuXPnlLaxfv16PHz4EObm5hg4cCCaNm2KN2/e4NSpUwgJCUFubi7mzp2LzMxMlasEf/rppwCAo0ePavHKiYiISes7Vq5cicDAQI03FY+KisJ3332HAwcOIDs7W3y+UqVKGD16NMaOHav2C8O8efNw7tw57Ny5E+XLl9fJayAiKm4EQUB6drraMvmdz6/Oi4wXhWrD0sRSozKa3KjUhRcvXuDgwYMAgKZNm8rtU2pvb482bdpg//79iIuLQ3h4OPz8/N5LXMocOnRI47JPnjxRe16atH766af5vtdPnz6FsbExfvvtNwwbNkx8fsCAARg7diy6dOmCf//9FxEREQgNDcXnn3+u0MbDhw9Rrlw5hIWFyd1QHjlyJAYNGoTevXvj1atXWLZsGQIDA1GpUiWFNqT1EhIScOPGDdSqVUtt3EREpByT1nco2/NOlVOnTqFt27ZIS0tTOPfo0SOMHz8eJ0+exM6dO2FsbKxQ5vz585g7dy4GDx6Mbt26ISYmRqvYKX+afDEm9d7nl3P6cKRnp6Px5sZFeo32O9sXWdsR/SJgZWpVZO3LCg0NRVZWFgD5ocFSvXr1wv79+wHkDRHWZ9Larl07nbX1zz//AAC8vLw0Kt+3b1+5hFWqatWq2LRpE+rVqwdBEDBz5kylSSsALFiwQOkIqLZt22Lu3LkYPXo0MjIy8OOPP2LlypUK5cqXLw8nJyfEx8fj9OnTTFqJiAqJSauM4OBgnDlzRqOySUlJ6NatG9LS0mBsbIxZs2Zh8ODBsLe3x/nz5/Hdd98hMjISu3fvxty5czFr1iy5+mlpaejfvz8qVKiApUuXFsGrIWXexxfjku59fjknIkXSuapmZmZKk60uXbrAzMwMWVlZ2LFjB1auXAkzM7P3HaZOZWdni6vra7o366RJk1Seq1OnDrp06YJdu3bh1q1bSEtLg5WV/O81Z2dntQs+DRs2DPPnz0d8fDwuXbqkspyDgwPi4+Pz7UkmIiLVPvikNSMjA5cuXcKaNWuwceNGjev99NNPeP78OQBg2bJlGDlypHjO19cX4eHhqFOnDh4+fIjFixfjm2++wUcffSSWGTduHO7fv4/w8HDY2trq7gURERVDliaWiOgXobZMYYcHS3tYD3Q/oNFQX2Wx6aKMLjx+/FjscfT394e9vb1CGTs7O3z22WfYu3cvkpOTceDAAXTp0uW9xPcuQRA0Ljt48GAEBwcrPZeQkCC2Jfu3VBUbGxvUrFlTbRlvb2/s2rULAHDv3j3Url1b7ryXlxdMTU1V1jc3N4eXlxf279+PqKgoleWk8Ur31CUiooJ770nr9u3bERISgjt37sDGxgY+Pj749ttv4eTk9L5Dgbe3NyIiIpCbm1ugerm5uVi7di0AoEqVKhgxYoRCGVtbW8ycORMBAQFITU1FSEgIAgMDAQBHjhzBb7/9hokTJ6J58+bavxAqlPBe4e/ti2Zxl56dDt9tvvoOg0owiUSSbw9+YXr4097+N32jjEWZYj9KYPPmzWLytnPnTo2G6m/evFlvSauuxMXFicdly5bNt3yVKlXyLVOtWjXxWFnSWpA2Xr58iRcvXqBMmTIKZaTxMmklIio8nSat+/btQ3BwMK5du4bZs2ejT58+cucDAwPx66+/yj0XERGBLVu2YO/evfjkk090GU6+4uLiCpywAsC5c+fEXtY+ffqo/NLQs2dPfPnll8jOzsbevXvFpPXff/8FkNdbq2zFwb1790IikcDOzg4vX75UG4sAQe5LGakn21NjaWJZ7L/AEtGHpSDrLkjt2bMHqampsLGxKYKI3g/Zv7Oa9N5qsmKy7N9/ZSOeCtKGkZGRwvBiKWnshfm+QUREeXSStAqCgFGjRmHVqlVyz8k6cOCA3HlZjx8/RvPmzXHnzh04OjrqIiSN3LlzRy7OmJiYfJfRB4DIyEjxWN0WNdbW1qhTpw4uXbokV6dOnTr45ptvFMqnpKRg/fr1cHNzg7+/Pywt8+8FTExL5BxNIqIPwPXr13H9+nUAQKNGjTBlyhS15ZcvX46jR48iPT0du3btQv/+/d9HmEVCdjSWur1VpR48eJBvGdkhve7u7lq1UbFiRVhYWCgtI73JzR0CiIgKTydJ68aNG7Fy5UpIJBKVd0DnzJkDIO+OY/PmzfHNN9/g8ePHWLhwIRISEvD69WssXboU8+fP10VIGnl3YQpzc3ON6t2+fVs8VvaHTlbVqlVx6dIlJCYmIjk5GXZ2dvD19YWvr69C2ZiYGKxfvx61atXC8uXLNYqFiIg+DLK9rF9++WW+Q36zsrLE/UE3b95crJNWR0dHGBkZITc3V6OkNSUlBXfv3oWHh4fKMtKFF83NzZVuV3P58mVkZ2fDxET5V6WMjAxcvnwZANReRxqvPqZBERGVFFonrW/fvsWMGTPEfzdp0gTTp09H06ZNxeeePHmC8+fPQyKRwMHBAfv27YO1tTUAoE2bNqhXrx5ycnKwatUqTJ8+XaMeRn2SrmAI5P9HSPZ8fHw87OzsdBrLRxYf4W//vwtd39zcXONkvaThfFYiKi4EQcCWLVsAAKampujRo0e+dTp27AgrKyukpaXh77//xvPnzzVaxMgQmZiYwNHREXFxcXJ/g9VZsGCBuP7EuyIjI7F3714AQOvWrZUOBX78+DE2bdqEQYMGKW1j1apVSExMBAB89tlnKuOQxuvi4qJR3EREmsrMzERmZmah6xdksTx90zppvXnzJmJiYiCRSODi4oLw8HCF1fbCwsIgCAIkEgn69OkjJqwAUKtWLXTo0AF79uxBSkoK7ty5g7p162obVpF68+aNeKxqDouy87L1dCU+Lh7lPyr8kKOZM2cqbMdDRESG5Z9//sGjR48A5O0RqmzBn3dZWVnB398f27ZtQ3Z2NkJDQ5UuHFhcNG/eHNu2bZObbqPOhg0b0Lx5cwwZMkTu+Xv37qF///4QBAFGRkZqR3hNmDABNWrUQKNGjeSe379/v/i3s0KFCkqn/AB5a2dIF2Bq1qyZRnETEWlqwYIFmD17tr7DeC+0Tlrv378vHn/xxRdKl4c/ceKEeKxso/FPP/0Ue/bsAQBER0cbfNIqe0fD2NhYbVnZYUXp6eq3a3B1dS3wHY/y5cvLDVcuqA+tl1UQBKS/zdF3GMVOevZ/71lxuitHVFLIDg3u27evxvV69eqFbdu2AcgbIlyck9Y2bdpg27ZtiIiIEG+Eq1KjRg3cunULQ4cOxZ49e9CqVSuULl0a58+fx8aNG5GcnAwAGDRokMKqwe+20aJFCwwaNAhNmjRBWloa/vnnH4SEhIgLK/3www8q57OeO3cOQN7w5lq1amnx6omIFE2ePBnjxo0rdP0aNWrg6dOnOoyo6GidtD58+FA8VrU8vHRPOSMjI7lhw1KyQ2hjYmK0DanIyfaeZmVlqfxjBcgnuEUx7FliJEGpUqV03m5Jlf42B54zDuk7jOJHkgXbj/MOM7JzYW2mvjgR6U5WVha2b98OIO/vT0G2r+nQoQOsra3x5s0bnD59Go8ePVI6f7M4aNOmDQAgOTkZt27dgqenp8qyPXr0gLm5OWbMmIFdu3aJ+7HK6tmzp8oFIgFg0aJFWLlyJfbt24fffvsNv/32m9x5ExMTzJ8/X+XwYQA4e/YsAKBVq1bqXhoRUaFoO81Pk23TDEX+67nnQzZhUtaTePfuXcTHx0MikaBOnTpKl9xPS/tvy5bi8ObJvoaUlBS1ZWXPF8V2A4kJifD09FT6CAoK0vn1iIjo/Tp48CBevHgBAOjUqZPcFJv8WFpaolOnTgDyRkls3bq1SGJ8H1xdXcWRWH//nf9aDtOmTUN4eDh69OgBZ2dnmJmZoXz58ujatSv27NmD0NBQtV/2zM3NsWfPHgQHB8PHxwdly5aFubk5KleujC+//BKRkZH4/vvv1cYgjbNz586av1AiIh0KCgpSmStoukaAIdC6p1V2c+6bN28qnN+3b5943LJlS6VtyPauFoc7wLIxxsbGwsHBQWXZ2NhYABDn/Oqag6OD0ved8ndxWmtYmakf3k15ktJS0SFM31EQfZg6d+6s1bD8LVu2iIs4yZIdKSWrRYsWWl3v+PHjhaq3du1alQsnSX377bcICAjA+vXrMWbMmHyv7ePjAx8fnwLF8e5rHzRokNreVFWuX7+OK1euwNnZGd27dy9wfSIiXQgMDERgYKDScxUqVMCTJ0/ec0SFo3XSKt3yRbqy4YIFC2Bvbw8AyM7Olht6o2w+a3Z2ttyd34oVK2obUpH7+OOPxePr16/Dy8tLZVlpQlmxYsVivbF7SWRlZgwrM53s+lTipWczuSci/evbty8mTZqEyMhI3Lhxw6Dnia5btw4AMHLkSKXrfRARkea0Hh5coUIFtG/fHgCQmpoKHx8f7N27FxcuXEDfvn1x7949AHkLBvn5+cnVjYuLQ6dOncTeyDJlyqido2IoZFcADA8PV1nu0aNH4p1sb2/vIomFw4OJiOhDYWZmJvYYBAcH6zcYNd6+fYtNmzbBwsICw4YN03c4RPQB4/BgGXPmzMGBAwcA5PUsvrtIhEQiwdy5c+Xmq3bs2BEHDx6UK/Pdd98Z/B6tQF7vsnRVwe3bt+N///uf0v1X16xZIx5369atSGIp5+CAi1euqTyflpVdJNctrtKyuHIwEVFxNm7cOKxduxa//vorJkyYoHaKjr6sXr0a8fHx+OGHH1CuXDl9h0NEHzAOD5ZRv359rFmzBiNGjFC6wW3r1q0xePBgueeioqLklqyvVasWRo0apYtw3otx48bhq6++QmpqKkaNGoX169fLJeVXrlzBwoULAQCVK1cusqQ18XUGV8MlIlLBytQK1wdd13cYpENWVlb4/fff0apVK/zwww9YtmyZvkOS8+bNG8ydOxc1a9bEhAkT9B0OEVGJoPXwYKmAgACcPHkSHTt2RNmyZWFjYwMvLy/MmzdP7IVVxtTUFGPHjsWFCxcKtCKivgUEBKBx48YAgI0bN6Jly5YIDg7Gzp07MWnSJPj4+CA9PR1GRkZYtmwZ57MQERHpiJ+fH7788kv89ttvKheU0pclS5YgMTERq1ev5t9+IiIdkQjaLBGohWPHjqFChQqoWrUqjI0NY5GXmJgYuLm5AQBmzZqFmTNnqi2fkJCADh06IDIyUul5MzMzLFu2DMOHD9d1qGJ3vrGJCdyruSstM+zrrzF8xEidX7uksDQ1LhZbLBmCpLTX8A3Nm5cd/vkZlLWy1XNERERERJSfoKAglevcREVFITs7Gy4uLuIaQ4ZKb0unvrsoU3Hk6OiIc+fOYfXq1di8eTPu3LmD1NRUODs7o3Xr1hg9enSRr2zo4OCAW7e45Q0REREREcnjnNYSyNXVtcB705mamqr9MBAREREREVHh6WxOKxEREREREZGu6aSn1dXVVav6ZmZmsLa2hrW1NSpVqoTq1aujYcOG+Oyzz7iIARERERER0QdMJ0nr48ePIZFI1A6t1eQ8AJw7d058ztbWFsOHD8eMGTOK1crC79OzxER4enoqPcdhy0REREREHy51CzElJCS852gKTyerB9va2kIikSAnJwfp6eliAqpJkgrk7bmWk5ODjIwMpeWqVq2KkydPwsnJSdtQSwzpxOnyzs54WkwmUFPxxdWDiYiIiEoWaT7xwawe/Pr1a7x48QK9e/fG0aNHIQgCzMzM0LVrV9SsWRMVK1ZE+fLlkZSUhEePHuHu3bsIDQ3FmzdvIJFI0LdvX/z222/Izc1FbGwsLl26hM2bN+Ovv/4CANy7dw9du3aV64UlIiIiIiKikk8nSWtqaip8fHxw69YtAMD48eMxbtw4tT2jv/zyC5YuXYq5c+fizz//RHp6OjZs2ABXV1e4urqie/fuOH78ONq3b4+srCxcuHABYWFh6NKliy5CJiIiIiIiomJAJ0nrN998g5s3b0IikWDWrFmYMWNGvnXs7Owwc+ZM2NraYvz48di8eTMaN26Mb775RizTsmVLzJs3D99//z0AYNeuXUxaifQsPTsdaW+N9R1GsWRpYik3NYKIiIiI8qf1nNb4+HhUqlQJOTk5qFu3Li5dulTgNpo0aYKIiAiULVsWsbGxMDc3lztfpkwZvHr1Ch4eHmJv7odOOgbdxMQE7u7uSstwISbSFdk5rVR4Ef0iYGVqpe8wiIiI6AOhbiGmqKgoZGdnfxhzWsPDw5GdnQ2JRIIWLVoUqo3mzZsjIiICL168wOHDh9GpUye58zVr1sTp06fx7NkzbcMtcco5OODmzZv6DoOIiIiIiAyMuk4saSdYcaB10vr48WPxWFWPX34qV64sHj948EDhvJubG06fPo3U1NRCtU9E2rEwtsDr23MAAJemt4alKYcHayo9Ox2+23z1HQYRERFRsaV10mpr+9/WF1euXClUG/nVe/HiBQDA2dm5UO0TkXYkEgkgmAHIm5dpZaqT6fBERERERPky0rYB2d7VY8eO4e3btwWq//btW4SHh4v/rlKlitz5nJwcXL58GRKJROEcERERERERlWxaJ62+vr4oW7YsgLyhvV9++WWB6g8fPhxRUVEAAGtra7Ru3Vru/Nq1axEfHw8A8PT01DZcIiIiIiIiKka0HuNnbGyMqVOnYty4cZBIJNi4cSNev36NqVOnon79+irrXblyBfPmzcPOnTsB5A0/HDNmDCwtLQHkDQkODg7G5MmTAQBGRkYYNWqUtuGWOM8SE1Um81w9mIpCWlaOvkMoVtKz/3u/tFysnYiIiKhA1K0enJCQ8J6jKTytt7wBgNzcXPj7++PgwYNyexA2aNAAnp6ecHV1haOjIxITE/Ho0SPcunULERERYjlBENC4cWOcOHECZmZ58+aqV6+Oe/fuQRAESCQS9O/fH+vWrdM21BJDutpXeWdnPC0mq35R8ZWWlQ3PGYf0HUbxJMmC7cd5e1eHf34GZa1s86lAREREVPSk+cQHseUNkNcLumvXLgwePBhbtmwRn7948SIuXryotI5srtymTRuEhoaKCav0vLRMly5d8Ouvv+oiVCIiIiIiIipGdLYEqJmZGTZt2oR+/fphwYIFOHPmTL5D4Tw9PfH9999j0KBBioGZmMDHxwc9evTgsGAiPbM0NcbNOW31HUaxlJSWig5h+o6CiIiIqPjS+b4VHTt2RMeOHXHv3j38888/uHTpEhITE5GcnAwrKyuUKVMGtWvXRvPmzdXOeb18+TLMzc11HR4RFYJEIoGVGbe5KYz0bO5pS0RERKSNIvsWWq1aNVSrVg2DBw8uVH0mrERERERERKT1ljdERERERERERYVJKxERERERERksnQwPbtGihVb1zczMYG1tDWtra1SqVAnVq1dHw4YNUbNmTV2ER0RERERERMWUTpLWf/75R25/Vl1xd3fHmDFj8PXXXxdJ+yXBs8REeHp6Kj0XGBiIwMDA9xwREREREREZgqCgIAQFBSk9l5CQ8J6jKTyJkN++NBowMpIfZSxNMNU1XZAyTZo0wcGDB2FjY6NtqCWGdDPg8s7OePrkib7DISIVktJewzfUGwAQ/vkZlLWy1XNERERERP/lEy4uLoiNjdV3OGrppKf1+fPnyMjIwNdff429e/eKiWijRo1Qs2ZNVKxYEeXLl0dSUhIePXqEu3fv4sSJExAEARKJBKNHj8bkyZPx+vVrxMbG4tKlS9iyZQsuXboEADh79ix69+6Nffv26SJcIiIiIiIiKiZ0krSWKlUKPXr0wMmTJyGRSNCzZ09MnDgRXl5eKuvcvXsXCxYswLp167Bs2TJYWFhgwYIFqFq1Klq0aIFx48YhODgYX375JXJzc3Hw4EEcP34cLVu21EXIREREREREVAzoZPXgSZMm4cSJEwCAL7/8EiEhIWoTVgDw8PDA2rVrMXbsWAiCgIULF2L79u1yZQICAjBu3Djx3yEhIboIl4iIiIiIiIoJrZPW5ORkrFq1ChKJBNWrV8eqVasKVH/RokWoVasWBEHA2LFjkZubK3d+3rx54lzWs2fPahsuERERERERFSNaJ61HjhxBeno6AKBly5YKizLlRyKRiEN+nz59iiNHjsidNzU1RY0aNSAIAuLi4rQNl4iIiIiIiIoRrZPWmJgY8fiTTz4pVBs1atQQj+/evatw3t3dHQCQkpJSqPaJiIiIiIioeNI6abWwsBCPb968Wag2ZOtlZWUpnJcmq+XKlStU+0RERERERFQ8aZ20Vq5cWTyWLsZUUCdPnhSPXV1dFc5fv34dEokEVapUKVT7REREREREVDxpnbT6+fnB1tYWAHDjxg1MmTKlQPWnT5+Oa9euAQDMzMzQunVrufNhYWHiEGQPDw9twyUiIiIiIqJiRCfDg7/55hsIggAA+OmnnzB27Fg8ffpUbb2EhAR8//33mD9/PiQSCSQSCQICAlC6dGkAQG5uLnbu3ImBAweKdb7++mttwyUiIiIiIqJiRCJIs00tZGZmwtvbG5cvX4ZEIgGQt+pvly5d4OnpCVdXVzg6OiIxMRGPHj3CrVu3sGvXLmRmZgIABEFAtWrVcPHiRZQqVQoA4OXlhatXr0IQBEgkEnTs2BG7d+/WNtQSo0KFCnjy5AlMTEzEhareFRgYiMDAwPccGRHJSkp7Dd9QbwBA+OdnUNbKVs8RERER0YciKCgIQUFBSs9FRUUhOzsbLi4uiI2Nfc+RFYxOklYgb7/Wzp0749SpU5BIJGKyqYr0vCAIqFmzJg4cOIAKFSqI5z08PHDv3j0AQOPGjREWFgYHBwddhFoiSJPW8s7OePrkib7DISIVmLQSERGRIZLmE8UhadV6eLCUnZ0dTpw4geXLl6NixYoA8hJTVQ8AsLe3x8yZM3Hp0iW5hFWqUqVKGDt2LMLDw5mwEhERERERfYBMdNmYRCJBYGAgRo4ciWPHjuHkyZO4dOkSEhMTkZycDCsrK5QpUwa1a9dG8+bN0bFjR7ktc2QdOXIElSpV0mV4REREREREVMzoNGmVkkgkaNWqFVq1alXoNpiwElFJk56djrS3xvoOo1iyNLFUO+WEiIiISq4iSVqJiEhR+78KfyPvQxfRLwJWplb6DoOIiIj0QGdzWqlwXr58idGjR6N27dqwtrZG5cqV0aVLF1y4cEHfoREREREREemdxj2txsb/DWmTSCTIzs4W/z106FCdBCORSPDHH3/opK3iIDk5GZ988gmePHmCevXqoVevXkhMTMT+/fuxZ88e7NixA926ddN3mESkBQtjC7y+PQcAcGl6a1iacniwptKz0+G7zVffYRAREZGeaZy0ym5R8661a9fqbK7Rh5S0zpkzB0+ePMHkyZMxf/588fkLFy6gWbNmGDFiBLp27cp5XETFmEQiAQQzAHnzMq1MOSuDiIiIqCB09u1JF9u9fmjJ2bFjx2Bqaorp06fLPd+wYUO0a9cOu3fvxsOHD1GlShU9RUhEupSWlaPvEIqV9Oz/3i8dbSlORERExZDGSWuLFi1UJpXBwcG6iqdYS05Oxv3795GamgpnZ2dUq1ZNbSJuY2OD1q1bw9LSUuFc6dKlxTaJqGRo8MMRfYdQvEiyYPtx3mFGdi6szfQbDhEREemHxknr8ePHVZ4bOHCgToIxFCtXrkRgYCBmzZqFmTNn5ls+KioK3333HQ4cOCA317dSpUoYPXo0xo4dCyMjxTWvTp06pbS92NhY/P333/joo4/g6elZ+BdCRERERERUzHFylRKbNm3SuOypU6fQtm1bpKWlKZx79OgRxo8fj5MnT2Lnzp1yi1m96/Lly1iyZAni4uJw4sQJlC5dGlu2bIGFhUWhXgMRGQZLU2PcnNNW32EUS0lpqegQpu8oiIiISN+YtL4jODgYZ86c0ahsUlISunXrhrS0NBgbG2PWrFkYPHgw7O3tcf78eXz33XeIjIzE7t27MXfuXMyaNUtlWw8ePMD69evFf3t4eOCjjz7S9uUQkZ5JJBJYmfFXbWGkZ3OlZSIiIuI+rQCAjIwMnD59GkOHDsXw4cM1rvfTTz/h+fPnAIBly5Zh2rRpcHFxgZWVFXx9fREeHo7KlSsDABYvXiyWVaZHjx7Izc3FkydPsGrVKly+fBk+Pj6Ijo7W6rUREREREREVZxrd/v/nn3+KOg5R8+bN39u1AMDb2xsRERHIzc0tUL3c3FysXbsWAFClShWMGDFCoYytrS1mzpyJgIAApKamIiQkBIGBgSrblEgkcHZ2xtdff43U1FR8//332LBhg8LqwkRERERERB8KjZJWdSsH65JEIpFbyOh9iIuLK3DCCgDnzp0Te0779Omj8v3p2bMnvvzyS2RnZ2Pv3r1i0vrvv//ihx9+QMuWLTFs2DCFeg0aNAAAxMfHFzg2IiIiIiKikqJAw4MFQSiSh2zb79udO3eQkZEhPu7cuaNRvcjISPG4SZMmKstZW1ujTp06CnU++ugjbN26FSEhIUrrRUVFAQA+/vhjjeIhIiIiIiIqiTROWguSUEokkgL1zOpz03gzMzOYm5vLPTRx+/Zt8djd3V1t2apVqwIAEhMTxX1XHR0dUbduXRw/fhz79++XKx8TE4MffvgBZmZm+OyzzwrwaoiIiIiIiEoWjYYHF2QxIEEQULlyZUgkEri5uSE8PLyQoRm2hIQE8djJyUltWdnz8fHxsLOzAwD8/vvv8Pb2hr+/P3x8fFC1alUkJCTg2LFjyMjIwLJly1C9enW1bQtCLlJSUgr9OgqSqBMRERERkWHIzMxEZmZmoevrs+OwoDRKWitVqlSoxk1NTQtd19C9efNGPLayslJbVva8bL0GDRrgxo0bmDlzJi5evIiIiAi4uLigXbt2mDJlijivVZ34uHiULl26EK8gz8yZM9VuxUNERERERIZnwYIFmD17tr7DeC+4eWAhyd7VMDZWv5egicl/b3N6errcOQ8PD2zZsqXQcTiVd8Kd25rNw1WGvaxERERERMXP5MmTMW7cuELXr1GjBp4+farDiIoOk9ZCku09zcrKgoWFhcqysgmupaWlTuOQSIxQqlQpnbZJRDokCMDbNH1HUTxl/TcyBcVoCBMREdH7oO00v/exO4yuMGktJBsbG/E4JSVFbdIqO+dUtp4uPEtMhKenp9JzgYGBaveFJaL34G0aMN9Z31EUS5YSCeBWMe8fb9MA8AYdERFRQQQFBSEoKEjpOdk1egwdk9ZCkp2rGxsbCwcHB5VlY2NjAeTdzXBxcdFpHOUcHHDz5k2dtklERERERMWfuk6sChUq4MmTJ+85osJh0lpIsvunXr9+HV5eXirLSpPKihUr6rynlYiKkfH3ADP1C7fRf9JfJQD7u+g7DCIiItIzJq2F1KxZM/E4PDwcgwYNUlru0aNHePjwIQDA29tb53FweDBRMWJmBZhZ6zuK4sOUCT4REZE2ODz4A+fu7o4aNWrg1q1b2L59O/73v/+J+6/KWrNmjXjcrVs3ncfB4cFERERERKRMSRkebKTvAIoz6RLTqampGDVqlMIGvVeuXMHChQsBAJUrVy6SpJWIiIiIiKgkY0+rFgICAvDHH38gIiICGzduxOPHjxEQEIBSpUrh/PnzWLlyJdLT02FkZIRly5bB1NRU3yETEREREREVK0xatWBiYoKwsDB06NABkZGROHHiBE6cOCFXxszMDMuWLYO/v3+RxMA5rUREREREpAzntBIAwNHREefOncPq1auxefNm3LlzB6mpqXB2dkbr1q0xevRo1KpVq8iuzzmtRERERESkTEmZ06pR0rphw4ZCNZ6SklLgugMGDCjUtXTF1dVVYW5qfkxNTdmrSUREREREVAQ0SloHDRoEiURSoIYFQUBiYiICAgIKVE/fSSsREREREREZjiJZPVgikRQqySUiIiIiIiKSpfGcViaVhokLMRHRhyA9OwNpb9P0HYYoPTsdvtt8AQDhvcJhaWKp34DUsDSxLPCNZCIiKhlKykJMEoHZaLEknThd3tkZT4vJBGqiD1LWG2C+c97xlKeAmbV+4ylGkpLj4RvWRt9hFHsR/SJgZWql7zCIiMjASPMJFxcXxMbG6jsctYpkeDARERERERGRLnDLGyIiMkgWxhaIiH4MAEgfcxuW1qX0HFHxITt8mYiIqLhj0kpERAZJIpHASjqDxcSSQ1yJiIg+UExaizkuxERERERERMqUlIWYmLQWc+UcHHDz5k19h0FEVKTSsnKArGx9h1FspGfniMdcb5GI6MOlrhNLuhBTccCklYiIDF7zhceRDgt9h1F8SLJg+3HeYUZ2LqzN9BsOERGRNpi0EhGRwbNCpr5DkGOBTFy2GAEAqJexChkw13NE78r675A9rUREVMwxaSUiIoNkaWosHl/6/wTREF02wNjSJBI0RsW8f7xNA8CVl4mIqPjiPq1ERGSQJBKJvkMgIiIiA8CeViLKnyD8f2+NgcpKAxZVyzsefw8wM6CtUbIM+H0zdKZWwJSn+o6iWEp/lQDs76LvMIiIiHSCSWsxxy1v6L14mwbMd9Z3FJqRJq9U/EkkgJm1vqMonrinLRERgVvekIHgljdERERERKQMt7whog+ToQ2/LU7Y+0VERERUYExaiahgzKw4ZJOIiIiI3huuHkxEREREREQGi0krERERERERGSwmrURERERERGSwmLQSERERERGRweJCTMUc92klIiIiIiJluE8rGQTu00pERERERMqUlH1aOTyYiIiIiIiIDBaTViIiIiIiIjJYTFqJiIiIiIjIYDFpJSIiIiIiIoPFpJWIiIiIiIgMFlcPJiIiKsHSszOQ9jZN32GI0rPT4bvNFwAQ3iscliaW+g1IBUsTS0gkEn2HQUREYNJKRERUorXf10nfIagkTV4NUUS/CFiZWuk7DCIiAocHExERERERkQFjTysREVEJY2FsgYjoxwCA9DG3YWldSs8R/ceQhwfLxkZERIaDSSsREVEJI5FIYCUIef8wsTSoYa5Wpla4Pui6vsMgIqJihElrMfcsMRGenp5KzwUGBiIwMPA9R0RERAblbRqQZUB/7rPSgEXV8o7H3wPMDCehRnb6f8fSpJ+IqBgLCgpCUFCQ0nMJCQnvOZrCM6C/YlQY5RwccPPmTX2HQUREBspq6cf6DkE1afJqKCQSwK1i3nF2OmBmrd94iIi0pK4Tq0KFCnjy5Ml7jqhwuBATERERERERGSz2tBIREZU0plaokbEGAHBpWmtYmRnQn3tDHh6clgSEddR3FERE9A4D+itGREREOiGRIB0Wecdm1oAhJa1m1sCsV/qOQjnZOa1ERGQwODyYiIiIiIiIDBaTVj3KycnB0qVL0aRJE5QpUwbOzs5o27YtDhw4oO/QiIiIiIiIDAKTVj3y9/fHt99+iydPnqBdu3Zo1KgRzp49iw4dOmDKlCn6Do+IiIiIiEjvDGiSy4dlx44dOHjwIPz8/LB7925YW+ctq//06VO0adMGP/74I9q3b4/mzZvrOVIiIiIiIiL9YU+rnuzatQsA8MMPP4gJKwA4Oztj0aJFEAQBYWFheoqOiIiIiIjIMLCnVUeSk5Nx//59pKamwtnZGdWqVYNEIlFZ/v79+wCAOnXqKJzz9PQEANy+fbtogiUiIiIiIiom2NP6jpUrV0IikWD27NkalY+KikLnzp1Rrlw5NGjQAL6+vvDw8ICbmxsWL16M3NxcpfWWLFmCkydPwspKcX+6iIgIAECFChUK/0KIiIiIiIhKAPa0vmPTpk0alz116hTatm2LtLQ0hXOPHj3C+PHjcfLkSezcuRPGxsZy5xs1aqS0zUuXLmH06NEAgIEDBxYgciIiIiIiopKHPa0ygoODcebMGY3KJiUloVu3bkhLS4OxsTHmzp2L2NhYvHnzBsePH4eXlxcAYPfu3Zg7d26+7eXm5mL16tVo0aIFEhISMGbMGHh7e2v1eoiIiIiIiIq7Dz5pzcjIwOnTpzF06FAMHz5c43o//fQTnj9/DgBYtmwZpk2bBhcXF1hZWcHX1xfh4eGoXLkyAGDx4sViWWXOnj0LLy8vDB8+HFlZWZg7dy7+97//affCiIiIiIiISoAPOmn19vaGtbU1mjVrhjVr1iArK0ujerm5uVi7di0AoEqVKhgxYoRCGVtbW8ycORMAkJqaipCQEIUyb9++xffff49mzZrh6tWraNeuHSIjIzFt2jS1izgRERERERF9KD7opDUuLk7lQknqnDt3Tuw57dOnj8oEs2fPnjAxyZs2vHfvXrlzgiBg8ODBWLRoEVxcXLB//34cOHAAtWrVKnA8REREREREJdUHnbTeuXMHGRkZ4uPOnTsa1YuMjBSPmzRporKctbW1uKWNbB0A+PPPP7Fp0yY0atQIV69eRfv27QvxCoiIiIiIiEq2DzppNTMzg7m5udxDE7L7p7q7u6stW7VqVQBAYmIikpOTxedXrFgBY2NjbNu2Dfb29gUPnoiIiIiI6APALW8KISEhQTx2cnJSW1b2fHx8POzs7PD69WtcvXoVpUuXxqJFi1TW9fLywuDBg9W2Lwi5SElJ0TByRQVJ1omIiIiIyDBkZmYiMzOz0PUFQdBhNEWLSWshvHnzRjy2srJSW1b2vLTew4cPAQCvXr3CihUrVNbt06dPvklrfFw8SpcunW/MqsycOROzZs0qdH0iIiIiInr/FixYgNmzZ+s7jPeCSWshyN7RMDY2VltWuhATAKSnpwMAateurbM7G07lnXDntmZzcZVhLysREb1PaVnZ8JxxCABwc05bWJnxqwgRUWFMnjwZ48aNK3T9GjVq4OnTpzqMqOjwL0UhyPaeZmVlwcLCQmVZ2QTX0tJS57FIJEYoVaqUztslIiIiIiLDpe00v+K0xSaT1kKwsbERj1NSUtQmrbLzTWXr6cqzxER4enoqPRcY+H/s3Xl8VNX9//H3ZCUJm+wEEAIBWQXZFETBBaGKC4IVwQq4a7T96ReXKgoiVaEqisaVAipSsWALrlSFUBEBBVRo2BEQAglrIAuZLOf3R5rrhEySmZBkTpLX8/GYh3fmnnPnMzcfEz5zzz0nTnFxceX+ngCAqiPDnRvoEArJcOd43bZBpkc8VeleLwAoTnx8vOLj473u85ynx3YUrWVw9tlnO9v79u1TkyZNim27b98+SfnfZLRo0aLcY2ncpIkSExPL/bgAgOqh99SvAh1CsXpP/TrQIRQS4TqhkI7526eycxUV2HAA4IyVdBGrZcuW2r9/fyVHVDY1esmbsurYsaOzvXHjxhLbFhSUrVq1qpArrQAAAABQnXGltQwGDBjgbCckJGjs2LFe2+3du9eZKbh///4VEgvDgwEAp4sIDVbilCGBDsOrDHeOc4X1h4mXWTUR05Hjybry82cCHQYAlBuGB9dg7du3V6dOnbR582YtXLhQM2bMUP369Yu0mz17trM9fPjwComF4cEAgNO5XC6risHiRIaFWBVnZljJKwIAQFXD8OAarmB66bS0NN1///1FJmz48ccfNX36dElSTExMhRWtAABUJZFhIdr93FXa/dxVVhWsAAB78deijMaNG6dZs2ZpzZo1mjdvnn799VeNGzdOdevW1dq1a/Xaa68pMzNTQUFBmjlzpkJDQwMdMgAAAABUORStZRQSEqLFixfryiuv1Pr167VixQqtWLGiUJuwsDDNnDlTw4YNq7A4uKcVAICaISM7Q+fPP1+StGb0GkWGRpbSA0BNxz2tUNOmTbV69Wq99dZbmj9/vrZu3aq0tDRFR0fr8ssv1x//+Ed17dq1QmPgnlYAAMpfZs4pZWRnBDqMQjJzMr1u2yYiJEIulyvQYQBQ9bmnlaLVQ+vWrf1eTDw0NJQrmgAAVDO/+/TqQIdQRK28PCkofzqSoR9crFNBdk5NwlVgAOXNzt92AAAAAACIK60AAACSpFrBtbRm96+SpMx71ysiqm6AIyosM+OoBn06QpL0xdX/VERkgwBH9JvMnEwN+ueV+U/8HLUGAKWhaK3imIgJAIDy4XK5FPm/gisy/rwAR+OFyyW1aSVJiojv68RqBY/YlJMphUUFNh4AkpiICZZgIiYAAAAA3jAREwAAQHUSGqlOp2ZLktZNvFyRYXb9MynSnaGNz8fmP5mwQwqzaLKjjCPS4qsCHQWAasqu38YAAACB4nIpU7Xyt8OiJMuK1kLCIu0agmvxEjwAqj5mDwYAAAAAWMvirxDhCyZiAgCghgiLkianBjoKAFUIEzHBCkzEBAAAAMCb6jIRE8ODAQAAAADWomgFAAAAAFiLohUAAAAAYC2KVgAAAACAtShaAQAAAADWYvbgKo4lbwAAAAB4w5I3sAJL3gAAAADwhiVvAAAAAACoYBStAAAAqNYysjPU7Z1u6vZON2VkZwQ6HAB+omgFAAAAAFiLohUAAAAAYC2KVgAAAACAtZg9GAAAAOUmMydTsuy+0cyMI4W3IwMYTAkiQiLkcrkCHQZgHYrWKo51WgEAgE0G/fPKQIdQRK28PCkof4Dh0I+G6lSQnYMN14xeo8hQSytqVEms0worsE4rAAAAAG+qyzqtFK0AAAA4IxHBtbRm96/5Tx7aIVl2tTAz44gG/Sv/CvAX13+hiMiGAY7oN5k5mRr04aBAhwFYjaIVAAAAZ8TlcinSmPwneUYq2LaFRzwRxvwWqw08Y7EpLsAiFK0AAABVQIY7R52fXCpJSpwyRJFhlv4z7vnYQEdQlMsltWmVv/3yuXYVh56x5WRKYVGBjcdDRnaGzp9/viTut0Vg2XkXOgAAAIDA8pwF2rIZoVGzWPoVHQAAAKqM0EjpsaRAR1G8jCPS4qvyt//0s2TRPa2FYgPgFUUrYAt3uvRMdP72Y0lWDQ8CAKBELpfVf7ciJW38Ze//njS0K9aczEBHAFiPohUAAOA0Ge7cQIdQRIY7x+u2bSJCg+VyuQIdRpWUmZNp1TDczOzMwtsWxXa6iJAI8q4ao2gFAAA4Te+pXwU6hBL1nvp1oEMoltWTRFlu0D+vDHQIhdTKy5OC8qfAGfrRUJ0Ksnc6HCaKqt7szTwAAAAA8IVNM0Kj3PE1GAAAgPKHtSZOGRLoMIqV4c5xrrD+MPEyq65mZrhzrb86bauI4Fpas/vXQIfhVaakQf9bjueLvfsVEdhwish0uTSodcv8J5YtF4TyZc9vO5TJoZQUde7c2eu+uLg4xcXFVXJEAABUTS6Xy6pCsCSRYSFVJlYrhEVJk1MDHYVXLpdLkbZeJfS4RzRCsjdOFCs+Pl7x8fFe9yUnJ1dyNGXHb7sqrnGTJkpMTAx0GFWHMfZOIuDO8L5tA9viAQCgurB5uSCblwqSWC7IByVdxGrZsqX2799fyRGVDUUrapbsjN+WlbHZ87GBjgAAAFQGm5cL8lyOJyzSvjhZLqjGYCImAAAAAIC1uNKKmmvCjvxvDW3hzvjtCqttsXliOnkAAABUIopW1Fw2DnMpYHNsAAAAQCWiaAUAAKgCIsNCtPs5Jp1B5YkMidDGX/bmPwmxbcEb1CTc02qpI0eOKCwsTIsWLQp0KAAAAKiJCpYKmpzKCDAEFEWrpV577TVlZ2cHOgwAAAAACCiGB1vk2LFj2rRpkxYuXFjsIsAAAAAAUJNQtFrk0ksv1Y8//hjoMAAAAADAGhStFeT48ePauXOn0tLSFB0drdjYWLlcrhL7zJgxQ6mpqZKkDz74QB988EFlhApbFNw3AgAAAMDBPa2leO211+RyufTUU0/51H779u265ppr1LhxY/Xu3VuDBg1Shw4d1KZNG73wwgvKy8srtu+gQYN07bXX6tprr1XHjh3L6yMAAAAAqEQZ2Rnq9k43dXunmzKyMwIdTpVH0VqK999/3+e2K1euVI8ePfTxxx8rJyen0L69e/dqwoQJGj58uHJzc8s7TAAAAAColihaSzB37lytWrXKp7ZHjhzR8OHDlZGRoeDgYD399NPat2+f0tPTtXz5cvXs2VOStGTJEj399NMVGTYAAAAAVBsUrac5deqUvv32W91222266667fO43bdo0HT58WJI0c+ZMTZw4US1atFBkZKQGDRqkhIQExcTESJJeeOEFpy0AAAAqVoY7R20e/VRtHv1UGe6c0jsAZ8pzSDDDg88YRauH/v37KyoqSgMGDNDs2bPldrt96peXl6c5c+ZIktq2bat77rmnSJs6depo0qRJkqS0tDQtWLCg/AIHAAAAgGqKotXDgQMHSpwoqTirV692rpyOGjWq2FmCR44cqZCQ/AmbP/nkk7IHCgAAAAA1BEWrh61bt+rUqVPOY+vWrT71W79+vbPdr1+/YttFRUWpe/fuRfoAAAAAALxjnVYPYWFhhZ6Hh4f71G/Lli3Odvv27Uts265dO61bt04pKSk6fvy46tev73ecnozJ04kTJ8rcPzw83OfPCQAAAMAOWVlZysrKKnN/Y0w5RlOxKFrLQXJysrPdrFmzEtt67j948OAZF60HDxxUvXr1ytx/0qRJmjx58hnFAAAAAKByPfvss3rqqacCHUaloGgtB+np6c52ZGRkiW0993v2K6tmzZtp6xbfhjF7w1VWAAAAVHWZOZlWzdKbmZ1ZeLsCYvvThD/p7vvvLnP/nt166sCBA+UYUcWhaC0Hnpflg4ODS2xbMBGTJGVmZhbbbtKkSc5swyVxuYJUt25dH6IEAAAAqqdB/7wy0CEUUisvTwrKnz5o6EdDdSrIvqmEDp06FOgQfEbRWg48r5663W7VqlWr2LaeBW5ERMQZv/ehlBR17tzZ6764uDjFxcWd8XsAAAAAqHqOfHVER5cd9bovJ7XqrFlM0VoOateu7WyfOHGixKLVc9Ikz35l1bhJEyUmJp7xcQAAAICqJCK4ltbs/jX/yUM7pNCSb9OrTJkZRzToX/lXf7+4/gtFRDYMTCCjvb+cmZOpZi2aKedY1ShcKVrLwdlnn+1s79u3T02aNCm27b59+yRJLpdLLVq0qPDYAAAAKlOGOzfQIRSR4c7xum2biNBguVyuQIdRZbhcLkUWzICbZySbZsP1iCXCmN/itIVt8ZSCorUcdOzY0dneuHGjevbsWWzbgquirVq1KpcrrQAAADbpPfWrQIdQot5Tvw50CMVKnDJEkWH887xMno8NdASFuVxSm1b52y+fa1+RWMW+HOH/inIwYMAAZzshIUFjx4712m7v3r365ZdfJEn9+/cvl/fmnlYAAAAA3sSvdSv+e3eR142knNS8yg+ojChay0H79u3VqVMnbd68WQsXLtSMGTO8rr86e/ZsZ3v48OHl8t7c0woAAAItIjRYiVOGBDqMYmW4c5wrrD9MvMyqq5kZ7lzrr05bKzRSeiwp0FF4l3FEWnxV/vaffpYCdE9r3P8ep8vIOKJ6bdtxT2tN8+CDD+qOO+5QWlqa7r//fr377ruF7kn48ccfNX36dElSTExMuRWtAAAAgeZyuawqBEsSGRZSZWJFKVwuKSwq0FF4l+OxtGVYpH1x5hS/9KaN+D+2nIwbN06zZs3SmjVrNG/ePP36668aN26c6tatq7Vr1+q1115TZmamgoKCNHPmTIWGhgY6ZAAAAACwHkVrOQkJCdHixYt15ZVXav369VqxYoVWrFhRqE1YWJhmzpypYcOGldv7ck8rAAAAAG/i4+MVHx9f5HVjclmntaZq2rSpVq9erbfeekvz58/X1q1blZaWpujoaF1++eX64x//qK5du5bre3JPKwAAAABviruIlZFxWPVaNuee1uqgdevWMn5OTx0aGsoVTgAAAAAoJ0GBDgAAAAAAgOJwpbWK455WAAAAVIQMd446P7lUkpQ4ZQizLvshMiRCG3/Zm/8kJCJgcXBPK6zAPa0AAAAAvKku97QyPBgAAAAAYC2KVgAAAACAtRgeDAAAgGotMixEu5+7KtBhoCYJi5ImpwY6imqDorWKYyImAAAAAN4wEROswERMAAAAALxhIiYAAAAAACoYRSsAAAAAwFoUrQAAAAAAa1G0AgAAAACsRdEKAAAAALAWswdXcSx5AwAAAMAblryBFVjyBgAAAIA3LHkDAAAAAEAFo2gFAAAAAFiLohUAAAAAYC2KVgAAAAAoRxnuHLV59FO1efRTZbirxn2jNmMiJgAAAMACGe7cQIdQiGexZXvhFREaLJfLFegwUEEoWgEAAAAL9J76VaBDKFbvqV8HOoQSJU4ZosgwSpvqip9sFcc6rQAAAAC8YZ1WWIF1WgEAAKquiNBgJU4ZEugwvMpw5zhXWH+YeJl1VzIz3LlWX522QXVZp9WuzAMAAABqEJfLZV0x6E1kWEiViBPVE7MHAwAAAACsRdEKAAAAALAWRSsAAAAAwFoUrQAAAAAAa1G0AgAAAACsRdEKAAAAALAWRSsAAAAAwFoUrQAAAAAAa1G0AgAAAACsFRLoAHBmDqWkqHPnzl73xcXFKS4urpIjAgAAAGCD+Ph4xcfHF3ndmFzlpOYEIKKyoWit4ho3aaLExMRAhwEAAADAMsVdxMrIOKx6LZsr51jVKFwZHozy5U6XJtfLf7jTAx0NAAAAgCqOohUAAAAAYC2KVgAAAACAtShaAQAAAADWomgFAAAAAFiLotUC8+fP15VXXqmGDRuqa9euuuuuu5SSkhLosAAAAAAg4ChaA2zatGkaM2aMfvjhB11++eWqXbu23n77bV100UX69ddfAx0eAAAAAAQURWsA7dq1S3/+85/VoUMHbd68WQsWLNDq1as1c+ZMbdu2TU888USgQwQAAACAgKJoDaBZs2bJGKNp06apYcOGzuv33Xefunfvrn/84x86ceJEACMEAAAAgMCiaC1Hx48f17p167RixQpt375dxpgS2y9evFi1atXSFVdcUWTftddeq4yMDH311VcVFS4AAABQrMiwEO1+7irtfu4qRYaFBDoc1GAUrV689tprcrlceuqpp3xqv337dl1zzTVq3LixevfurUGDBqlDhw5q06aNXnjhBeXl5Xntl5SUpDZt2igyMrLIvk6dOjltAAAAAKCmomj14v333/e57cqVK9WjRw99/PHHysnJKbRv7969mjBhgoYPH67c3NxC+06dOqXjx4+rQYMGXo9bMFz44MGDfkYPAAAAANUHRetp5s6dq1WrVvnU9siRIxo+fLgyMjIUHBysp59+Wvv27VN6erqWL1+unj17SpKWLFmip59+ulDfY8eOSZLq1Knj9dgFrx86dKisHwUAAAAAqjyKVuVf9fz2229122236a677vK537Rp03T48GFJ0syZMzVx4kS1aNFCkZGRGjRokBISEhQTEyNJeuGFF5y2knTWWWdJUrETLaWmphZqBwAAAAA1UY0vWvv376+oqCgNGDBAs2fPltvt9qlfXl6e5syZI0lq27at7rnnniJt6tSpo0mTJkmS0tLStGDBAmdfrVq1VL9+fR09etTr8Qtej46O9uvzAAAAAEB1UuOL1gMHDhQ7UVJJVq9e7Vw5HTVqlFwul9d2I0eOVEhI/mxrn3zySaF9LVq00O7du5Wenl6kX2JioiSKVgAAAAA1W42fu3rr1q2FlqbZs2ePzjnnnFL7rV+/3tnu169fse2ioqLUvXt3rVu3rlAfKX9Zm2eeeUZLly7V9ddfX2jfkiVLFBERocsvv9zXjwIAAADUSBnu3NIbVaIMd47XbVtkWhhTSWp80RoWFlboeXh4uE/9tmzZ4my3b9++xLbt2rXTunXrlJKSouPHj6t+/fqSpNtvv13PPvusHn30UQ0aNMiZSfiVV17Rzz//rPHjxztti2NMXrH3xfoiPDzc588MAAAA2Kj31K8CHUKxek/9ukKOa3KyZXKzy9S3luuEZEpvZ4saX7SWVXJysrPdrFmzEtt67j948KBTiMbExOi5557TI488ok6dOumSSy7R7t27tXbtWp1zzjmaMmVKqXEcPHBQ9erVK9uHkDRp0iRNnjy5zP0BAAAAVL7U1R8q9du/BzqMSkHRWkae96FGRkaW2NZz/+n3rz788MNq2bKl3n33Xf373/9WdHS07rzzTk2dOlWNGjUqNY5mzZtp65atfkb/G66yAgAAoCqKCA1W4pQhgQ7Dqwx3jnOF9YeJlykyrPzLrqysQcrKerVMfY+mpqh7727KOV41hglTtJZRVlaWsx0cHFxi24KJmCQpMzOzyP7Ro0dr9OjRZYrD5QpS3bp1y9QXAAAAqKpcLleFFIPlLTIspELijAwLkepElalvaIhb8j6PrJXs/ylbyvPqqdvtVq1atYpt61ngRkRElGsch1JS1LlzZ6/74uLiFBcXV67vBwAAAKBqiI+PV3x8fJHXc/NylJNaNa6yShStZVa7dm1n+8SJEyUWrZ4TJXn2Kw+NmzRxlscBAAAAgALFXcQ6cvygmrVtpZxjVaNwrfHrtJbV2Wef7Wzv27evxLYF+10ul1q0aFGhcQEAAABAdULRWkYdO3Z0tjdu3Fhi24Iroa1atSr3K60AAAAAUJ0xPLiMBgwY4GwnJCRo7NixXtvt3btXv/zyiySpf//+5R4H97QCAAAA8IZ7Wmu49u3bq1OnTtq8ebMWLlyoGTNmOOuvepo9e7azPXz48HKPg3taAQAAAHjDPa3Qgw8+KElKS0vT/fffL2NMof0//vijpk+fLkmKiYmpkKIVAAAAAKozrrSegXHjxmnWrFlas2aN5s2bp19//VXjxo1T3bp1tXbtWr322mvKzMxUUFCQZs6cqdDQ0ECHDAAAAABVCkXrGQgJCdHixYt15ZVXav369VqxYoVWrFhRqE1YWJhmzpypYcOGVUgM3NMKAAAAwBvuaYUkqWnTplq9erXeeustzZ8/X1u3blVaWpqio6N1+eWX649//KO6du1aYe/PPa0AAAAAvKku97RStJ6mdevWRe5NLU1oaChXNQEAAACgAjAREwAAAADAWlxpreK4pxUAAACAN9zTCitwTysAAAAAb6rLPa0MDwYAAAAAWIuiFQAAAABgLYpWAAAAAIC1uKe1imMiJgAAAADeMBETrMBETAAAAAC8YSImAAAAAAAqGEUrAAAAAMBaFK0AAAAAAGtRtAIAAAAArEXRCgAAAACwFrMHV3EseQMAAADAG5a8gRVY8gYAAACANyx5AwAAAABABaNoBQAAAABYi6IVAAAAAGAtilYAAAAAgLUoWgEAAAAA1qJoBQAAAABYiyVvqjjWaQUAAADgDeu0wgqs0woAAADAG9ZpBQAAAACgglG0AgAAAACsRdEKAAAAALAWRSsAAAAAwFoUrQAAAAAAa1G0AgAAAACsRdEKAAAAALAWRSsAAAAAwFoUrQAAAAAAa1G0AgAAAACsFRLoAHBmDqWkqHPnzl73xcXFKS4urpIjAgAAAGCD+Ph4xcfHF3k9Ny9HOak5AYiobChaq7jGTZooMTEx0GEAAAAAsExxF7GOHD+oZm1bKedY1ShcGR4MAAAAALAWRWsVZ4wJdAioIbKysjR58mRlZWUFOhTUIOQdKhs5h0Ag7xAQPpQRubm5ZTp0WfsVh6K1ivqtWKVoReXIysrSU089xR9UVCryDpWNnEMgkHfVT2RYiHY/d5V2P3eVIsPsviOzpItgF1xwgcLDw5WWlubXMcvarzgUrQAAAACAQjZv3qwffvhB4eHhql27doX3KwlFKwAAAACgkMGDB0uS5s+fXyn9SkLRarEjR44oLCxMixYtCnQoAAAAAGqIJUuWaP/+/WrZsqWGDRtW4f1KQ9Fqsddee03Z2dmBDgMAAABADTJmzBhJ0tdff10p/Upj913BNdCxY8e0adMmLVy40OtCwAAAAABQUaZOnaq0tDT16dNHHTp0qPB+vqBotcyll16qH3/8MdBhAAAAAKhhcnNz9dRTT0mSli1bVuH9fEXR6qPjx49r586dSktLU3R0tGJjY+Vyucr9fWbMmKHU1FRJ0gcffKAPPvig3N8DAAAAAE43ZswY5eTk6KabbvJr5t+y9vNVjbun9bXXXpPL5XK+CSjN9u3bdc0116hx48bq3bu3Bg0apA4dOqhNmzZ64YUXlJeXV67xDRo0SNdee62uvfZadezYsVyPjd/Er3UHOoRiMSy87Gw+d8RWPdl+7myOz+bYbGfzubM5Nsn++Gxl+3mzOT6bYztdWlqaFixYoJCQEL333nsV3s8fNa5off/9931uu3LlSvXo0UMff/yxcnJyCu3bu3evJkyYoOHDhys3N7e8w0QFi/+eorU6svncEVv1ZPu5szk+m2Oznc3nzubYJPvjs5Xt583m+GyO7XSXXHKJpPx7U4ODgyu8nz9qVNE6d+5crVq1yqe2R44c0fDhw5WRkaHg4GA9/fTT2rdvn9LT07V8+XL17NlTUv60zk8//XRFhg0AAAAAFWbz5s364YcfVKdOHT3yyCMV3s9f1b5oPXXqlL799lvddtttuuuuu3zuN23aNB0+fFiSNHPmTE2cOFEtWrRQZGSkBg0apISEBMXExEiSXnjhBactAAAAAFQlgwcPliTNnz+/Uvr5q1oXrf3791dUVJQGDBig2bNny+32bUhoXl6e5syZI0lq27at7rnnniJt6tSpo0mTJkn6bRy3p4ceekgul6vER0HRCwAAAACBcOrUKe3fv18tW7bUsGHDfO63ZMmSMvUri2o9e/CBAwfKNFHS6tWrnSuno0aNKnaW4JEjR+r2229XTk6OPvnkE8XFxTn7rrnmGrVs2bLE96lbt67fsQEAAABAeTl27Jgk6euvv/ar35gxY8rUryyqddG6detWGWOc53v27NE555xTar/169c72/369Su2XVRUlLp3765169YV6iNJF110kS666KIyRA0AAAAAlcMYoz59+qhDhw4+95k6darS0tL87ldW1bpoDQsLK/Q8PDzcp35btmxxttu3b19i23bt2mndunVKSUnR8ePHVb9+fb/jLItDhw5JklKSU9SiRYsyH6f815o10omT+ZuzOkgq/7Vsz0x+fMnpRi3b2hiflJycXOpV+kAo+AKoU6dOFbJGcXmw9dxJxFZWtuedzedOsjs+W2OzPecke8+dZHdskr3x2Z53tp63AjbHV9GxeV6g80deXq5yUn9bHWXZsmU+983NzXWWD/Wn35mo1kVrWSUnJzvbzZo1K7Gt5/6DBw9WWtFasMxOXl6ekpKSKuU9/XbS0rj+Z/9+e+Pbv39/oEMolrX59j82nztiKzub8872c2dzfDbHZnPOSXafO5tjk+yOz+a8s/m8SXbHZ3NsBWrXru1z2zFjxignJ0c33XSTX/3OBEWrF+np6c52ZGRkiW0993v2q2i1atXSqVOnFBwcrMaNG5f5ODZ+mwcAAACgdGW/0pqngwcPSiq93vFUMAFtSEiI3nvvvTK9d1lQtHqRlZXlbJe2QG5IyG+nMDMzs1zjmDRpkjND8ekqs0AGAAAAUH306dNHBw8e1HPPPefX+qqXXHKJpPx7Wkurk8pTtV7ypqw8v20obZkczwI3IiKiwmICAAAAgDO1efNm/fDDD6pTp45fBWtZ+5UHilYvPMdmnzhxosS2nvsra0w3AAAAAJTF4MGDJUnz58+vlH7lgaLVi7PPPtvZ3rdvX4ltC/a7XK4zmsUXAAAAACrSkiVLtH//frVs2VLDhg2r8H7lhaLVi44dOzrbGzduLLFtYmKiJKlVq1ZcaQUAAABgrTFjxkiSvv7660rpV14oWr0YMGCAs52QkFBsu7179+qXX36RJPXv37+iwwIAAACAMpk6darS0tLUp08fdejQocL7lSeKVi/at2+vTp06SZIWLlyo48ePe203e/ZsZ3v48OGVERoAAAAA+CU3N1dPPfWUJGnZsmUV3q+8UbQW48EHH5SUvxbR/fffX2QNpB9//FHTp0+XJMXExFC0AgAAALDSmDFjlJOTo5tuusmvWxrL2q+8uUxZV6Stgvbs2aM2bdpIkiZPnlzsGqiSlJOTowEDBmjNmjWSpIEDB2rcuHGqW7eu1q5dq9dee00nT55UUFCQFi9eXKk3JB8/flw7d+5UWlqaoqOjFRsbK5fLVWnvj5olKSlJe/bskSS1bt1a0dHRfvU3xmjHjh1KSkpS7dq11a5dO9WvX78CIkV1Q+6hLHbv3q19+/apfv366tChg8LCwnzuS87BX7m5udq1a5dSUlLUvn17NWnSxK/+5BzKm7ecSktLU506dRQSEqJTp06VuL6qZ05J0qBBg3zq5ykrK0vbt2/X4cOH1ahRI8XGxqpWrVpn9sFMDbJ7924jyUgykydPLrX9wYMHTc+ePZ0+pz/CwsLMG2+8UQmR59u2bZu5+uqrTUhISKE4zj77bPP888+b3NzcSosF9jt16pSZPn26ufrqq03Hjh1NZGSkOeecc8yIESPMzJkzjdvtLrH/559/bvr06VMk73v16mU+/fTTUt8/Ly/PzJgxw7Ru3bpQ/5CQEHPVVVeZLVu2lNdHRRWQnZ1tzj//fJ9+/5J78Fdubq55+eWXTWxsbJGf+YgRI8zevXtL7E/OwV8///yzGTJkiAkPDy/0M2/QoIF59NFHzYkTJ0rsT86hNPHx8T7XLMaUnFPt27c3ksxzzz1XbP/ickqS6dChg085lZqaauLi4ky9evUK9a9bt6655557zPHjx33+/KejaC2F2+02r776qunfv79p2LChCQ8PNzExMeaOO+4wGzdurOCIf/PNN9+YyMjIYgtoSeaaa64xOTk5lRYT7JWQkOD8giru0bFjR7NixQqv/V944YUS+0oy06dPL/b9c3NzzXXXXVdi/8jIyGLfH9XPI4884tPvX3IP/jp16pS54oorSvyZR0VFmfXr13vtT87BXy+//LIJDg4u8WferFkzs23bNq/9yTn4on///j7XLL7kVHh4eLH9yyOnkpOTi3xxePqjbdu25uDBg2U6HzWqaK2qDh8+bBo1amQkmeDgYPP000+bffv2mfT0dLN8+fJCV4MnTZoU6HARYImJiSYqKsrJif79+5u//vWvZuHCheavf/2r80tQkqldu7bZvn17of7Lli0zQUFBRpJp3LixmT9/vjl27Jg5duyYef/9951cdLlc5uuvv/Yaw+TJk5336NWrl0lISDDp6elm3759ZsqUKc4f+4YNG5pDhw5VxmlBAH355ZfG5XKVWrSSeyiLW265pdCXt2vXrjXp6elmx44dZsKECU5OxcbGFrn6Rc7BX6tWrXJGvDVo0MC8+uqr5pdffjFpaWlm/fr1ZuzYsU4+nHfeeSYrK6tQf3IOvpgzZ45PX/QaU3pOef799SenoqOjjSQzevRon3LqkksucY4xevRo89///tdkZmaaTZs2mVGjRjn7Bg4cWKZzQtFaBTz00EPODzo+Pr7I/hMnTpiYmBinCOEXVM12ww03OPnywAMPFBk2npeXZ6ZNm+a06du3r8nLy3P29+3b10gyoaGhZsOGDUWOv379ehMaGmokmfPPP7/I/pSUFKdojomJMSdPnizS5pVXXnHe/5FHHjnzDw1rpaSkmGbNmhX6prW4P8DkHvy1cuVK5+c5ZsyYQr/LCjzxxBNOm7fffrvQPnIO/rr88suNJBMUFGS++eYbr23uvPNO52c+b968QvvIORQnMzPTrFy50tx6660mLCzM56K1pJxavHhxob+/vuZUQb+WLVsaY0rPqU8//dTZf8MNN3iNc+TIkU6bzz//3Mez8huKVsvl5uY637q1bdvW6x9kY4yZO3eukwivvvpqJUcJW2RkZDi/6Fq1amUyMzOLbfu73/3OyZlVq1YZY4z56aefnNfGjx9fbN9x48Y57TZt2lRo38svv+zse+edd7z2z8vLM23btjWSTJMmTbgfu5rKy8tz8qzgG9vi/gCTeyiLq666yvnHWnFf2J48edJEREQYSebqq692Xifn4K+8vDxTv3595wvf4mzatMnJiz/96U/O6+QcitOvXz/naunpj5KK1tJyqnbt2kaSGT58uF85VdBv69atxpjSc6rg+EFBQWbXrl1eY921a5dz1XfkyJF+nR9jjGHJG8utXr1ahw8fliSNGjWq2FmCR44cqZCQEEnSJ598UmnxwS4bNmyQ2+2WlJ8vJc3Udt111znb69evl1Q4d2666aZi+3ruOz3fCp6HhoZqxIgRXvu7XC7deOONkqSUlBR9//33xb4Xqq4ZM2bo888/V0REhN5+++0S25J78NfJkyf15ZdfSpKGDh2qRo0aeW1Xu3ZtrVmzRqtXr3bWGpTIOfjv0KFDOn78uCSpVatWxbZr3bq1s71t2zZnm5xDcQ4cOKC8vDy/+5WUU2lpaXK73erTp4/uvvtur308nxfklGe/Dh06SCo5p9xut/O7+IILLlBMTIzXWGNiYnTBBRdIkpYuXars7Gy/PitFq+UKiglJ6tevX7HtoqKi1L179yJ9ULMkJyc72926dSuxbfPmzZ3tzMxMSb/ljsvlcn6xeNOvXz/nC5TT863g+bnnnquoqKhij9G/f/8ifVB9rF+/Xn/+858l5RevXbp0KbW9RO7Bd//5z3+cL+l+//vfl9i2W7duOv/883Xeeec5r5Fz8FfDhg2dJZQ2btxYbLuff/7Z2fZcwoacQ3G2bt2qU6dOOY+tW7f61K+knKpdu7aysrL03Xff+ZVTnv08FZdT27ZtU1pamqSSaxXPY5w8eVI7duzw6TMWoGi13JYtW5zt9u3bl9i2Xbt2kvK/ASn4JhA1S/fu3TV37lzNnTtXV1xxRYltPb8lK/gmrSDfmjZtqjp16hTbt06dOmrcuLEkFfrFevjwYR05ckSS7/l6+jFQ9aWlpWnUqFFyu90aPny47rrrrlL7kHvw13//+19nOzY21tnevXu3vvnmGyUmJionJ6fY/uQc/BUcHKzRo0dLyv+H+ssvv1ykzalTpzRhwgTn+ZgxY5xtcg7FCQsLU3h4eKGHL3zJqeDg4DLl1OlrshaXU2WpVU4/hi9C/GqNSud55axZs2YltvXcf/DgQRaXroHatm2rtm3bltouKSlJ8fHxkvK/ibvwwgsl/ZZvpeVaQZuUlBQdPHjQee1M8hXVR1xcnLZv366WLVtq1qxZPvUh9+Avz3/wNGnSRIsWLdITTzyhzZs3O6+Hh4frggsu0EsvvaQePXoU6k/OoSxefvllbdy4UevWrdP/+3//T19//bWuuuoqNWrUSNu3b9ebb76p3bt3S5KmTJmiSy65xOlLzqG82ZBTlZWXFK2WS09Pd7YjIyNLbOu537Mf4OmXX37R1VdfraNHj0qS7r//fjVs2FDSb3lTWq55tvHMNfIV8+bN07vvvqugoCDNmzdPDRo08KkfuQd/paamOtuvv/66nn/++SJtsrKytGLFCvXp00fPPPOMHnroIWcfOYeyqFu3rr755hvdeeedmjdvnj7++GN9/PHHhdqEhYXpgw8+0PDhwwu9Ts6hvNmQU5WVlwwPtlxWVpazffpl+tMVTMQk/XaPIlDA7XbrpZde0rnnnusMqxs8eLAmT57stCnIt9JyTfot3zxzjXyt2Xbu3Kl7771XkvTYY49p4MCBPvcl9+CvkydPOtvPP/+8ateureeee04bN25UWlqaNm3apKefflrh4eHKycnRo48+WugeLXIOZfXmm28WKVQ9ud1uPfvss1q3bl2h18k5lDcbcqqy8pKi1XKe30gUTDhRHM+kiYiIqLCYUPUsWrRInTt31gMPPODcLD9ixAj985//dCaVkH7Lt9JyTfot3zxzjXytubKzszVq1CidPHlS/fr106RJk/zqT+7BX54zbdaqVUvfffedHnnkEXXt2lVRUVHq0qWLJk6cqMWLFzvtPe+vJudQFvfee68eeOABpaamqkePHlq0aJF2796ttLQ0/fzzz5o6darq1q2r77//XgMHDtSKFSucvuQcypsNOVVZeUnRarnatWs72ydOnCixred+z36oubZs2aJLL71UI0eO1M6dOyXl36w/f/58LVy4sMjMgwV5U1quebbxzDXyteZ67LHH9MMPP6hevXqaP39+oW9TfUHuwV+eP7sHHnhAXbt29dpuyJAhzsR0GzdudG6NIOfgr88++0yvv/66JOnSSy/VunXrdP3116t169aKiopSt27d9Pjjj2vVqlWKjIxUenq6brnlFucf8uQcypsNOVVZeUnRarmzzz7b2d63b1+JbQv2u1wutWjRokLjgv3eeust9ezZU8uXL5eUPxvhlClTtHPnzmLXhyvIt19//bXU4+/fv19S4bXqWrVq5Uyp7mu+nn4MVD0HDhzQCy+8ICl/6ZGtW7dq6dKlhR6eVxt27NjhvL5hwwZJ5B7853m/9MUXX1xi2/PPP9/ZLliOhJyDvzzXm46Pj1dQkPd/Rnfp0kX33XefJGnv3r1aunSpJHIO5c+GnCpLrXL6MXxB0Wq5jh07OtslrQkmSYmJiZLyk4Bv1Wq2+Ph43XXXXc79Arfddpt27dqlJ554osR13QryLS0tTXv27Cm23d69e51hxp06dXJej4yMdH4J+Zqvpx8DVY/b7ZYxRlL+P+qGDh1a5DF27Fin/bx585zXp0yZIoncg//OOeccZ7u0Cb+aNGnibKekpEgi5+C/7du3S8qfjMkz/7zp27evs71t2zZJ5BzKnw05VZZa5fRj+IKi1XIDBgxwthMSEoptt3fvXv3yyy+SCi/+i5pn4cKFuv/++yVJZ511lr788kvNmjVLjRo1KrWvr/nmue/0fCs4xu7du0v8BVrSMVDzkHvwV8+ePZ1tzzVbvfFcR7BgJBI5B38VrJ2ZmZmp7OzsEtt6zm5d0I+cQ3mzIafatGnj/F71HFXlTcH+6OhotWnTpsS2RRhYr1OnTkaSqV27tjl27JjXNpMmTTKSjCSzYMGCyg0Q1sjNzTWxsbFOvmzatMmv/klJSSYoKMhIMgMGDCi23cCBA40kExQUZA4cOFBo34IFC5xcnDhxotf+qamppm7dukaS6dSpk18xomravXu3kxeTJ08usp/cg7/cbrepX7++kWQuvPBCk5eX57VdWlqaOfvss53fi1lZWcYYcg7+u/XWW52f9+eff15i29///vdO2//85z/GGHIOvivtb2YBW3LqnnvucY7x1VdfeT3G8uXLnTb33HNPsbEWh6K1Cnj77bedH/LNN99c5A/zhg0bTEREhJFkYmJijNvtDlCkCLQlS5Y4uTJt2rQyHWPMmDHOMd58880i+998881C+Xg6t9ttYmJijCQTERFhfvzxx0L7c3Nzzc033+wcY9asWWWKE1WLL3+AyT346+GHH3Z+no8//rjJzc0ttP/UqVPmlltucdo8++yzhfaTc/DHmjVrTEhIiJFkWrZsaXbt2uW13RtvvOH8zHv06FEoL8k5+MLXotUYO3Jq8+bNJjg42EgynTt3LnKR7ejRo6ZLly5GkgkODjZbtmzx4SwU5jLmfzciwVo5OTkaMGCA1qxZI0kaOHCgxo0bp7p162rt2rV67bXXdPLkSQUFBWnx4sUaNmxYgCNGoNx3332Kj4+XlL+OXOvWrX3q1717dzVr1kyStGfPHvXq1UtHjhyRy+XS2LFj9bvf/U55eXn67LPPNG/ePBlj1KhRI61bt67QDfgFlixZouuuu07GGNWtW1f333+/evXqpaNHj2ru3LlauXKlJKlfv3765ptvfFpfDFXbnj17nKFAkydP9rokDrkHfxUsO7J7925J0gUXXKDrr79eMTEx2rZtm+bNm6fNmzdLkvr06aPvvvuu0M+cnIO/pk+frkceeURS/rDf8ePHq1evXjrrrLO0a9cuLV68WN9++62k/Htf161bp9jYWKc/OQdf+PI307OtDTk1YcIEZ1LGdu3a6f7771ebNm20Y8cOxcfHO7cxPvTQQ5o+fbr/J8XvMhcBcfDgQdOzZ0/nW47TH2FhYeaNN94IdJgIsKFDhxabIyU9Pvjgg0LHWblypWnSpEmx7Zs2bWpWrVpVYizx8fEmNDS02GP07t3bpKSkVOTpgEV8/daY3IO/tm7datq1a1fi77gRI0YUGRJXgJyDP/Ly8syUKVNMVFRUiTnXpUsXs2LFCq/HIOdQGn+utBpjR07l5OQUGtni7TF+/PgiI2J8RdFahbjdbvPqq6+a/v37m4YNG5rw8HATExNj7rjjDrNx48ZAhwcLtG/fvlyKVmOMSU5ONo8//rjp0qWLqV27tqlTp47p2rWrmThxojl06JBP8fz000/m9ttvN23atDHh4eGmYcOG5sILLzTx8fEmOzu7vD8+LObPH2ByD/7KyMgwL774ornwwgtN48aNTUREhOnZs6cZO3as+fjjj0vtT87BX0lJSebhhx82V1xxhWndurWJjIw05557rrnhhhvM22+/bXJyckrsT86hJP4WrcbYk1NLliwxV199tWnevLkJCwsz0dHR5pprrjGffvqpT/2Lw/BgAAAAAIC1WPIGAAAAAGAtilYAAAAAgLUoWgEAAAAA1qJoBQAAAABYi6IVAAAAAGAtilYAAAAAgLUoWgEAAAAA1qJoBQAAAABYi6IVAAAAAGAtilYAAAAAgLUoWgEAAAAA1qJoBQAAAABYKyTQAQAAAABAecvJyVFCQoI+++wz7dmzR0lJSTpx4oQaNGig6Oho9evXT4MHD1aXLl0CHSpKwZVWAAAAoBy5XC7n8c477/jdf8+ePYWOsWLFigqI0m6XXHJJoXNQ8DjrrLNK7Xvq1Ck99dRTatasmQYPHqwZM2boo48+0urVq5WYmKiVK1fqww8/1AMPPKCuXbvqkksu0bffflsJn6ooz8/Ztm3bMh9nxowZhc7T7t27i+SR52P8+PHl+CkqHkUrAAAAgGrh22+/VdeuXTV58mQdOXLEpz4JCQm6+OKL9cgjj8gYU8ERFjZ69Ghn+5dfftEPP/xQpuP885//dLb79u2rNm3anGloVqFoBQAAAGCtFi1aKDY2VrGxsYqJiSm23ddff60rrrhCO3fudF7r0qWLnnvuOW3YsEGHDx9Wamqq1qxZo7lz5+qSSy5x2uXl5Wn69Om65557KvSznG7kyJEKCwtzni9YsMDvYyQnJxe6UnzjjTdKkkJCQpzzFhsbqyZNmpx5wAFC0QoAAADAWu+//762b9+u7du3a/369V7b/PDDDxo2bJgyMjIkSZGRkXrppZf0888/65FHHlGPHj3UsGFD1a1bV3379tXYsWO1bNkyffzxxzr77LOd47z55puaPXt2pXwuSTrrrLM0dOhQ5/mHH37o99XexYsXKy8vT1L+0PQbbrhBUn6xX3Detm/frunTp5df4JWMohUAAABAlZWVlaVx48bp1KlTkvIL1s8++0x/+tOfFBRUcrkzbNgwffHFF6pXr57z2gMPPODz0OLycNNNNznbe/fu1Zo1a/zq7zk0uF+/fmrVqlW5xWYLilYAAAAAVdb06dP13//+13n+zjvvaODAgT7379Spk+bPn+88P3HihN5+++1yjbEk11xzjaKiopznH374oc99U1NT9fXXXzvPC4YGVzcUrQAAAACqJLfbrVdffdV5Pnz4cI0cOdLv41x55ZXq1q2b83zu3LnlEZ5PIiMjdd111znP//GPf/g8RPiTTz5Rdna2JCkoKKhMn70qYJ1WAAAAoIbZt2+fVq5cqV9//VURERFq3bq1LrnkEtWuXTvQofnlH//4h1JSUpznTz75ZJmPNXbsWE2YMEGStHXrVh04cEDNmzf3qW9KSor+85//aP/+/crJyVHbtm3VoUMHn9eAHT16tN5//31J+T+bVatW6cILLyy130cffeRsDxgwQNHR0T69X1VD0QoAAABUUy6XS5I0aNAgLV++XMnJyYqLi9O//vUv5ebmFmpbq1YtjR8/Xs8884zq168fgGj9969//cvZPv/889WjR48yH+uee+4pNCmS532uxdm4caMefvhh/fvf/3YmQ/LUq1cvPfjgg4WWtvHmiiuuUMOGDZ17aT/88MNSi9bMzEx98cUXzvPqOjRYYngwAAAAUCMkJiaqR48eWrRoUZGCVZJOnTql119/Xeedd5727NkTgAj957nUy+9+97szOlZkZKS6dOniPCIjI0tsP3fuXJ133nn64osvvBaskrRu3TqNGTNGf/jDH+R2u4s9VkhIiDPrr5R/Bbm4YxZYunSpM1tyUFCQRowYUWL7qowrrQAAAEA1l5qaquHDh+vgwYOSpMsvv1xDhw5VeHi41q5dq4SEBP3666+SpN27d2vw4MH66aefFBEREciwS7R3714dOHDAed6vX79Ke++FCxfq1ltvde49Peuss3T11VerS5cuqlWrln766SctXbpU+/fvlyTNmzdP6enphYbznm706NF64403JEkHDhzQypUrdfHFFxfb3vNYgwYNUtOmTcvjo1mJohUAAACo5jZs2CBJCg4O1quvvqq777670P6MjAyNHTtWCxculCRt375dU6dO1V/+8pdKj9VXBQVhgQ4dOlTK+x46dEh33323U7DefPPNmjFjhho1alSoXVpamp588knNmDFDUv7SNP/6178KTbrkacCAAWrVqpXz5cGCBQuKLVqzs7P1ySefOM+r89BgieHBAAAAQIUZN26cXC6XX482bdpUWDzeClYpf2jshx9+WGiI6euvv+6sfWqj09dSPeussyrlfePj4533Hjx4sN55550iBask1a5dWy+++KJuv/1257XHHnus2OO6XK5Ca7YuXLjQ6zBuSUpISNCxY8ck5Q8tvv7668v0WaoKilYAAACgBmjVqpVuu+22Yve7XC499dRTzuRNx44dc67mFbxelkdFSU1NLfTcc63T4rz//vs+xz1+/Hivx5g1a5az/frrrysoqOSS6uWXX1atWrUkSZs3by40pPl0nkVrSkqKVqxY4bWd59DgSy+91GvRXJ1QtAIAAAAVpEmTJoqNjfXr0bp16wqJ5b777lNoaGiJbbp06VJoBt3vvvuuQmIpD3Xq1Cn0/OTJkxX+njt27HCGJcfGxqpdu3al9omMjFSvXr2c5ytXriy2bY8ePdSpUyfn+YcfflikjTGm0KzJ1X1osMQ9rQAAAECFmT59usaOHetXnz179lTIEGHPwqkkPXr00Oeffy5J+umnnyRJkyZN0qRJk4q0feqppzR58mS1adNGv/zyS/kF64OGDRsWen7s2LFShwjXrl1bsbGxxe4/ceJEoXVfT7du3TpnOykpSe3bt/cp1oIJsCSVOjPz6NGj9cQTT0iSFi1apFdffVUhIb+Vbd99951zvNDQUA0fPtynGKoyilYAAACgBvC1EI6JiXG2T79v1Canz5abmJiotm3bltjn2muv1bXXXlvs/tmzZ5c4hPrQoUPOdkZGhnbs2OFjtL85evRoiftvuukmp2g9fPiwli9frsGDBzv7PYcGDx48uNLu5Q0khgcDAAAANUCLFi18ateyZUtn+/T7Rm3Srl27Qvdyrl69+oyPWVqRXh7no6T1WqX8z9W3b1/n+elDhD2L1powNFiiaAUAAABqBF+vmnoOj42MjKyocM6Yy+XSRRdd5DxfvHjxGR+ztCunnufjyiuvlDHG78fzzz9fahyjR492tj/66CNlZ2dLyh+uXTAMOywsrMSrxtUJRSsAAABQA+zevdundjt37nS2bR96OmzYMGd706ZN+vbbb8/oeF988UWJ+xs0aOBsl2VosK9uvPFGBQcHS8ofTvz1119LKnyVdejQoapXr16FxWATilYAAACgBtiyZYtP7X7++Wdnu2vXrhUVTrkYPXq0Gjdu7Dx/+OGHlZeXV6ZjLVq0SHv37i2xTbdu3ZztX375RTk5OT4dOzc313n4olmzZrrkkkuc5wsWLJBUM4cGSxStAAAAQI0QHx9faputW7fq448/dp7379+/IkM6Y7Vq1dIf//hH5/mqVas0bdo0v49z5MgRTZgwodR23bt3d5bayc7O1t///vdS++zbt0+RkZEKCQnRWWedpczMTJ9i8hwi/K9//Uv//e9/tWnTJkn5n/vqq6/26TjVAUUrAAAAUANs2LCh1Ps+J06c6FyprF27tq677rpKiOzMPPzww+rRo4fz/LHHHtOLL77oc//du3dr2LBhPg2fDg4O1k033VTovTIyMkrs85e//MWZfGnkyJGKiIjwKa7rr79e4eHhkqTjx4/r3nvvdfZdeeWVRdaprc4oWgEAAIAaYvTo0Vq0aFGR1zMyMnTjjTdq4cKFzmt33nlnlSiMwsLC9P7776tu3brOa//3f/+noUOHKjExsdh+ubm5evHFF9WlSxdn5mHP4rc4DzzwgMLCwiTlX0UdPHiwdu3aVaRdXl6enn32Wb3xxhvOa7feequvH0v16tXTlVde6Tz/z3/+42zXpKHBEuu0AgAAADVCvXr1lJqaqhtuuEGDBg1Sv3791Lx5c33//fdavny5fv31V6dt27ZtNWXKlABG65/OnTtr2bJlGjp0qA4fPixJWrp0qbp06aLu3bvr8ssvV4sWLVS/fn0dPXpUiYmJWrp0qfbv3+8cY/LkybrllltKXeu1Y8eOmjp1qh5++GFJ+UOSu3fvrquuukrnnnuuGjVqpD179mjhwoXatm1boeMPGDDAr881evRo/fOf/yz0WmRkZKEJqGoCilYAAACgBvj88891xRVXKC0tTcuXL9fy5cu9tmvdurW+/PJLRUVFVXKEZ6ZXr15atWqV7rrrrkKf7aefftJPP/1UbL/mzZvrxRdf1KhRoyTlr2frWcx689BDDyk1NVV/+ctfJElpaWlasGCBM2HS6caPH69Jkyb5+5E0bNgw1alTRydPniz0ms1LEVUEhgcDAAAANUC/fv20YcMGXXbZZV73h4eH6+6779b69etLvdpoq/bt22vZsmVauHChBg8erJCQ4q/RxcbG6oknntDWrVudglWSLr74Yp/ea+rUqfryyy/Vu3fvYtv07NlTX3/9tWbPnu37h/BQq1YtXX/99YVeq2lDgyWutAIAAADlyhhzRv1bt259xscoTmxsrL766ivt3LlT33zzjZKTk1WrVi21bt1al112WZW4h9UXI0aM0IgRI3T06FGtXbtWSUlJSk5OVp06ddSsWTN17txZnTt39tp3/vz5mj9/vk/vc/nll+vyyy/X7t279e233yo5OVlBQUHq2LGjOnbsqNatW8vlcp3RZ5k7d67mzp17Rseo6ihaAQAAgBqmXbt2ateuXaDDqHANGjTQ0KFDK/x92rRpozZt2lT4+9RUDA8GAAAAAFiLohUAAAAAYC2KVgAAAACAtbinFQAAAECZTJo0qUxLufhjzJgxioiIkCTVqVNH69evr9D3qy7279+vQYMGOc9PnDgRuGDOEEUrAAAAAGt5rplav379wAVSxeTk5GjHjh2BDqNcMDwYAAAAAGAtl6moRaAAAAAAADhDXGkFAAAAAFiLohUAAAAAYC2KVgAAAACAtShaAQAAAADWomgFAAAAAFiLohUAAAAAYC2KVgAAAACAtShaAQAAAADWomgFAAAAAFiLohUAAAAAYC2KVgAAAACAtShaAQAAAADWomgFAAAAAFiLohUAAAAAYC2KVgAAAACAtShaAQAAAADWomgFAAAAAFiLohUAAAAAYC2KVgAAAACAtShaAQAAAADWomgFAAAAAFiLohUAAAAAYC2KVgAAAACAtShaAQAAAADWomgFAAAAAFiLohUAAAAAYC2KVgAAAACAtShaAQAAAADWomgFAAAAAFiLohUAAAAAYC2KVgAAAACAtShaAQAAAADWomgFAAAAAFiLohUAAAAAYC2KVgAAAACAtShaAQAAAADWomgFAAAAAFiLohUAAAAAYC2KVgAAAACAtShaAQAAAADWCgl0ADVVWlqadu7cqWPHjqlp06Zq3769QkJ8/3FERUXp1KlTCg4OVpMmTSowUgAAAADVTUpKinJzc1WrVi2lp6cHOpwSuYwxJtBB2Oy1115TXFycJk+erEmTJp3x8Q4ePKgJEyboo48+UmZmpvN648aNdccdd+jJJ59UeHh4qccJDg5WXl7eGccDAAAAoOYKCgpSbm5uoMMoEVdaS/H++++X27G2bt2qiy66SIcOHSqy79ChQ3rmmWf01Vdfafny5YqMjCzxWAVFa1BQkJo3b15uMZaX5ORkNW3aNNBhFMvm+GyNzRijpKQkRUdHy+VyBTocr2w9dxKxlZXteWfzuZPsjs/W2GzPOcnecyfZHZtkb3y2552t562AzfHZHNuBAweUl5en4ODgQIdSOoNizZkzx0gykszkyZPP6FhZWVmmQ4cOzvH+9Kc/mZ07d5qMjAyzdu1ac/nllzv7xo4dW+rxoqOjjSQTHR19RnFVlE6dOgU6hBLZHJ+tsaWmphpJJjU1NdChFMvWc2cMsZWV7Xln87kzxu74bI3N9pwzxt5zZ4zdsRljb3y2552t562AzfHZHJvt9YQnJmI6zalTp/Ttt9/qtttu01133VVux501a5a2bdsmSXrooYf00ksvqW3btoqIiFCfPn30+eef6/zzz5ckvffee0pMTCy39wYAAACAqoqi1UP//v0VFRWlAQMGaPbs2XK73eV27FmzZkmS6tSpoyeffLLI/pCQEE2bNk2SlJeXpzlz5pTbewMAAABAVUXR6qFgXHd5S0pK0oYNGyRJV199tWrXru213cCBA537Uz/55JNyjwMAAAAAqhpritZ333034FMtb926VadOnXIeW7duLZfjrl+/3tnu169fiW379+/vxBLo8wEAAAAAgWZN0Tpu3Dg1bdpUf/jDH/Tvf/9bJgAr8YSFhSk8PLzQozxs2bLF2W7fvn2Jbdu1aycpfxa57du3l8v7AwAAAEBVZdWSN5mZmZo/f77mz5+vpk2basyYMfrDH/6gc889N9ChnZHk5GRnu1mzZiW29dx/8ODBUo+dl5enEydOlDm28izOPcXFxZX7McuTzfHZHJvtbD53xFY92X7ubI7P5thsZ/O5szk2yf74bGX7ebM5voqMLSsrS1lZWWXuXxG3RVYUlwnEJU0vYmNjtWvXLue55xpVXbt21S233KLRo0dX6pqke/bsUZs2bSRJkydP1qRJk8p0nHvvvVevv/66JGnbtm0lXm198803dffdd0uSFi5cqBEjRnht16JFCyUlJZUpHk+TJk3S5MmTz/g4qP5OnDihevXqKTU1VXXr1g10OKghyDtUNnIOgUDeoSwmT56sp5566oyPEx0drf3795dDRBXHmiutO3bs0Pfff6/58+drwYIFha4ybtq0SQ8//LAeffRRXXrppbrllls0fPhwRUZGBjBi33l+A1La4r0hIb/9SDIzM0s9drNmzc7o3tuKuMoKAAAAoGL9+c9/1oMPPljm/uecc45PIzttYE3RKkl9+vRRnz599MILL2j58uWaP3++PvroI6WmpkqScnNz9dVXX+mrr75SVFSUrr/+et1888267LLLCl2ZtY1ncV3aMjqeBW5ERESpxw4KCuIbOQAAAJQ/d7r0THT+9mNJUlhUYONBIWd6m19QkDXTG5XKykiDgoJ02WWX6W9/+5uSk5P10UcfaeTIkapVq5aMMTLGKC0tTe+9956GDBmiVq1a6dFHH9WmTZsCHbpXnkvclHb/qef+4pbGAQAAAICawsqi1VNYWJiuu+46ffjhh0pOTtbcuXM1ZMgQBQcHOwVsUlKS/vrXv6p79+4677zz9NJLLxWa/CjQzj77bGd73759Jbb13N+qVasKiwkAAAAAqgLri1ZPderU0S233KLPP/9cSUlJeuWVV9SnTx9JcgrYn3/+Wf/3f/+nVq1a6Xe/+50++OCDUofkVrSOHTs62xs3biyxbWJioqT8e1tjY2MrNC4AAAAAFcCdLk2ul/9wpwc6miqvShWtBXJycrRu3Tr9+OOP2rVrl1wul/MoKF5zcnL073//W2PGjFHbtm2d2XsDoU+fPs5484SEhGLbZWZmau3atZKk3r17KywsrDLCAwAAAABrVZmiNSsrS0uWLNHYsWPVpEkTXXXVVZo9e7aOHj0q6bcrrd26ddOwYcMUGhpaaPjwfffdp3HjxikQK/zUrl1bl156qSRpxYoV2r59u9d2CxYsUHp6/jcxw4cPr7T4AAAAAMBWVhetGRkZ+sc//qFRo0apcePGGj58uObNm6fjx487BakxRl27dtWUKVO0efNm/fTTT1qyZImSkpL04osvqlOnTpLyi9r33ntPc+bMCchnKZiO2hiju+66q8hCwHv37tWjjz4qSapXr55uv/32Eo9XMFuyzbMmo3oJDw/XpEmTWCYJlYq8Q2Uj5xAI5B0CoSrVEy4TiEuPJThx4oSWLFmiRYsW6d///rdOnTolSUWukJ577rm64YYbdMMNN6hDhw4lHvOJJ57QX/7yF7lcLp133nn64YcffIplz549atOmjaT8xXsnTZpUbNvx48dr7ty5JbYdOXKkFi1aJEnq0aOH7r77bjVt2lSbNm3SK6+8opSUFElSfHy87r333hJja9mypfbv368WLVqUOrkTAAAALGWMlJ0R6Ci8c2dIz/9vjpUJO6SwyJLbB1JopGRT8VUFlguqSvWENeu0zp49WwsXLtSyZcuUnZ0tqWih2r17d6dQbd++vc/HfuihhzRt2jTl5uYWOzS3Mrzzzjs6ceKEvvzyS/3444+6++67C+13uVx6/PHHSy1YAQAAUE1kZ/xW3NjsecsnCLW0MET5sKZovf32252JlDz16NHDKVTLOptu3bp11aBBA6WkpDhXTgMhKipKS5cu1bx58/TOO+9o48aNOn78uJo2baqLLrpI9957ry688MKAxQcAAAAAtrGmaJV+u7J63nnnOYVqu3btzvi4OTk56tSpkzp37qwrrrjC536tW7f2eeKmOXPm+HS/rMvl0h/+8Af94Q9/8DkOAAAA1AC2DcG1fXiwZ3yo1qwpWnv27OkUqm3bti3XY4eEhGj58uXlekwAAACgXIVF2jvE1ebYUO1ZU7T6OjkSAABAjVQFJnYBgIpg9ZI36enpOnTokNd93333ndauXau8vLxKjgoAAAAAUFmsK1ozMzM1ceJE9e3bV2eddZY++OADr+3+8Y9/qF+/fqpfv77Gjx+v1NTUSo4UAAAAAFDRrCpaf/rpJ/Xq1UvPPvusfvjhB+Xm5pbY3hij9PR0vfvuu+revbtWrVpVSZECAACgynCnS5Pr5T/c6YGOBoCfrClaU1JSdPHFF2vr1q3OayEhIWrYsKHX9r1791ZMTIwzu+/evXs1fPhwHTt2rFLiBQAAAABUPGuK1ieeeEInT56UJIWHh+vll1/W4cOHNXr0aK/tR48erZ07d+rLL79U8+bN5XK5dPjwYT3++OOVGTYAAAAAoAJZMXvw4cOH9be//U0ul0uS9Nlnn2nQoEE+9b3sssuUkJCgrl27yu126+2339a0adNUp06dCowYAABUO8ZI2RmBjqJ47gzv27YJjZT+9286ACgPVhStW7ZsUV5enlwul4YNG+ZzwVogNjZWN910k9555x3l5eVp8+bN6tu3b8UECwAAqqfsjN+WlLHd87GBjqB4LMcDoJxZMTzY8z7WCy64oEzHOPfcc53tzZs3n3FMAAAAAIDAs+JK69GjR53tunXrlukYISG/fZTk5OQzjqmqSE5OVufOnb3ui4uLU1xcXCVHBABANTBhhxQWGegoCnNn/HaF1bb4PGMDAsG2IfOWDOePf/1Nxb/xltd9ValmsqJobd68ubO9du3aMhVaP/zwg7PdpEmTcomrKmjatKkSExMDHQYAANVLWKTdQ1xtjw+obDZ/aRLA2OIkxd3ofV/LF3O1/2SlhlNmVgwP7tWrl7O9bNkyZxZhX508eVLLli1znnsOFQYAAAAAVF1WXGnt1KmTunTposTERCUlJemGG27QZ599pqAg32rqMWPGaP/+/XK5XDr77LPVs2fPCo4YAAAAQECFRuZP/GUjm4fzS/nxvdg00FH4zIqiVZKmTZumYcOGyeVy6csvv9T555+vhx9+WCNGjCi2eF26dKmmTp2qVatWOa9Nnjy5kiIGAAAAEDAuV9UYJs9w/jNmTdF65ZVX6sEHH9SLL74ol8ul9evXa9SoUWrcuLHOOecctWrVStHR0Tp+/Lj27Nmjbdu2ae/evYWOMXLkSI0dOzZAnwAAAAAAUN6sKVol6fnnn1e9evU0depUZWdnS5JSUlJ06NChYvsYYyRJ48eP1xtvvFEpcQIAAAAAKodVRaskPfHEExo+fLimT5+uf/zjH8rKynIKU28uvfRSPfTQQxoyZEglRgkAAACvbFt6RLJm+RGvbIsHsJB1Raskde3aVe+++65ef/11rVmzRuvWrdOhQ4d0/PhxRUREqEGDBuratav69+9faLkcAABgOXe69Ex0/vZjSdznVR3ZvPSIZH98AIqwsmgtEBUVpUsvvVSXXnppoEMBAAAAAASA1UUrAAAAqgCblx6R7F9+pECopXEBAUbRCgBAdWOMlG3pfXLcW1h2YVHS5NRAR+FdVVl6RGL5EX/YnHOoUawrWleuXKkvv/xSGzZsUHp6ut/9XS6XvvrqqwqIDACAKiI747f7Rm3GvYUAAB9YU7Tm5ubq4Ycf1ksvvVTmYxhj5HK5yi8oADhDxhhlZucGOgyvMtw56j31a0nSDxMvU2SYNX8SiogIDeb3OwAANZQ1/0KJj4/XjBkzJOVfLS1pmRsAqCoys3PV+cmlgQ6jVAXFq60Spwyxuqi2mm3373FvIQDAT1b8CyAtLU1TpkxxvkUPCgrSHXfcoYEDB6pZs2Z8uw4AQFnZfP+ezbEBAKxhRdH6448/6ujRo5LyC9bPPvtMgwcPDnBUAFC+fph4uSLDggMdhsP24cEZ7lz1nsocBQAA1HRW/Atl27ZtkvKHBV933XUUrACqpciwYKsKw8iwEO1+7qpAhwEAAFCioEAHIMm5yipJF1xwQQAjAQCgdBnuHLV59FO1efRTZbhzAh0OAADVmhVFa6tWrZzt0NDQAEYCAAAAALCJFePULrjgAgUFBckYozVr1gQ6HACAZTLcdi0b5Hl11corre4cFcx9a4yRVdMZhkVJk1MDHQUAoAqxomht3bq1rr32Wv3zn//Uv//9b+3fv18tWrQIdFgA/sfmtUZtZ1uxVVXZPCGTjcsFReiUNtfK387MzlVkeGDjAQDgTFhRtErSW2+9pZ9++km7du3SiBEjlJCQoFq1agU6LACqOmuNAgAAoPqxpmht2LChli1bplGjRmn16tVq27atnnjiCd18882qU6dOoMOzVnJysjp37ux1X1xcnOLi4io5IgAoHxGhwUqcMiTQYXhl/XJBaSekmYGOAgAQaPHx8YqPjy+6w+QpOd1UfkBlZM1f2ccff1ySdOGFF2rr1q06ePCg7rvvPt13331q3Lix2rZtq4iIiFKP43K59NVX9g4jK29NmzZVYmJioMNADWLbWqOS/QVEgYhQu86b7Vwul7U/S0+RYSH2xWnZ/6MAgMAo9iKWO10tG9XR/pNVo3C15q/ss88+K5frt6kiCraNMUpJSdGhQ4dKPYYxptAxAJQ/29YalVhvFAAAoDqz6l+exhRf6Ze0DwCAysQXJQAAVB5ritaEhIRAhwAAgG/c6dIz0fnbjyXlL+MCAAAqhDVF68UXXxzoEAAAAAAAlgkKdAAAAAAAABTHmiutAAAUYoyUnRHoKLxzZ3jftoWt5w0IlLAoaXJqoKMAUEbWFq25ublat26dvvnmG+3Zs0dHjx5Vdna2FixY4LTZv3+/WrRoEcAoAQAVJjvjt/tGbfZ8bKAjKCIy0AEAAFCOrCtajTF65ZVXNGnSJJ04caLQ657L2eTl5al169a65JJLdNttt2nUqFGBCBcAAAAAUIGsKlpPnjypwYMH6/vvv/dpiZu8vDwtW7ZMy5Yt01dffaU33nhDISFWfSQAQHmYsEMKs+j6oTvjtyustsUmKcOdo15Tv5IkrQu1KzYAAPxlTYWXm5urkSNHau3atc5rbdq00YUXXqjPPvtMx44dK9Te5XKpYcOGOnLkiCRpzpw5SkpK0meffVapcQMAKkFYpL3LylgZW44yVSt/02OUEgAAVZE1swe/9dZb+vLLL+VyuRQaGqqpU6dq586deu+999SwYcMi7V0ul/bu3atnn31WwcHBMsZo6dKl+te//lX5wQMAAAAAKoQ1RevMmTOd7aefflqPPfZYoXtYvYmIiNAjjzyiv/3tb85rjz76qE9DiwEAKLOCmUgnp1p4lRUAgOrFiuHBGzZs0NatW+VyudS+fXv93//9n1/9b7nlFr3yyitat26dtm/frl27dqldu3YVFC2qMmOMMrNzAx1GlZPh5pwBAAAgMKwoWnfu3OlsX3bZZQoODvb7GMOGDdO6deskSVu2bKFohVeZ2bnq/OTSQIcBAAAAwEdWDA/evXu3s92tW7cyHcNzvdZt27adaUgAAAAAAAtYcaU1MvK36fhPnyXYV/v373e2y3KlFjXPDxMvV2QYueKviFDOGQAAACqPFUVry5Ytne1NmzaV6Rg//fSTsx0dHX3GMaH6iwwLVmSYFf8LAAAAACiGFcODBw0apNDQUBljtHjxYv36669+9d+wYYM+/fRTSflL4Vx88cUVESYAAAAAoJJZUbTWrVtXw4cPlyRlZmbq7rvvVlZWlk99k5OTdfPNNys7O1sul0uXXnqpmjRpUpHhAgAAAAAqiRVFqyRNmzZNUVH5a9198cUXuuCCC5SQkKCcnByv7fft26e//OUvat++vbZs2SJJCgoK0rRp0yotZgAAAABAxbLmhr7WrVtr4cKFuvbaa5Wdna2ff/5Zl112mSIjIwsVrt26ddPOnTudK7HGGGffa6+9pvPOO6/SYw+k5ORkderU2eu+O+++W3fdc28lR2Q31hsFAABATREfH6/4+PiiO0yektNN0dctZU3RKklDhgzR8uXLdcsttzhrt6anp8vlcsnlckmSEhMTCxWqknTWWWfpjTfe0A033FDpMQeaqVVPmdf+1eu+lw9IL7MmKQAAAFAjxcXFKS4urugOd7paNqqj/SerRuFqzfDgAv369dN///tfzZ49WxdeeKEzQZPno0DHjh31zDPPaPv27TWyYAUAAACA6s6qK60FwsLCNG7cOI0bN06nTp3Szz//rEOHDik1NVWRkZFq0KCBunbtqgYNGgQ61IBrUreWEqcMCXQYVRLrjQIAAAD2s7Jo9VSrVi317ds30GFYy+USa40CAAAAqLaodgAAAACgPIVFSZNTAx1FtWHdPa0AAAAAABSo1CutwcG/3UPocrkKLWVz2223lct7uFwuzZo1q1yOBQAAAAAIrEotWo0xcrlcRZaskaQ5c+Y4y9qcKYpWAAAAAKgerLqn1Vsx66/yKnwBAAAAAIFXqUXrwIEDiy0q586dW5mhAAAAAACqgEotWpcvX17svltuuaUSIwEAAAAAVAXMHgwAAAAAsJb1RWtWVpbX17du3aqUlJRKjgYAAAAAUJmsLFpnzZql3//+92revLneeustr23efPNNNW/eXJ07d9ZTTz1VaPkcAAAAAED1YNXswUlJSbr55pu1YsUKn9obY7R161ZNmTJFn3zyid5//3116NChgqMEAAAAAFQWa660njx5Ur1793YKVmOMjDEKDQ312r5t27aKjIx0lslZt26dhgwZolOnTlVazAAAAACAimVN0frMM8/o4MGDzvMHHnhAiYmJuvvuu722v++++3T48GG9/fbbioqKksvl0t69e/WXv/ylskIGAAAAAFQwK4rW1NRUvfTSS84aru+9955eeOEFdezYscR+tWrV0m233aavvvpKUv7V2eeff16ZmZkVHjMAAAAAoOJZUbRu3rzZmSV44MCBGj16tF/9+/btqxEjRkiS3G63tmzZUu4xAgAAAAAqnxVF67Zt25ztQYMGlekYffr0cbY3b958piEBAAAAACxgRdHqeS9rkyZNynSM2rVrO9v79u0745gAAAAAAIFnRdHqWaiuX7++TMfYsGGDs92wYcMzjgkAAAAAEHhWrNN67rnnOtsJCQnKyclRSIjvoWVnZyshIcF53qlTp/IMz2opycnq3Lmz131xcXGKi4ur5IgAAAAA2CA+Pl7x8fFFd5g8Jaebyg+ojKwoWnv27Kk2bdpoz5492rlzp26//XbNnTvX5/5xcXHasWOHJKlx48bq169fBUVqnyZNmyoxMTHQYQAAAACwTLEXsdzpatmojvafrBqFqxXDgyXpqaeekjH5J+29997T9ddfr++//77EPps3b9aYMWP0t7/9TZLkcrn0yCOPOEvnAAAAAACqNiuutErSH/7wB3366af68MMP5XK5tHjxYi1evFjnnXeeunTpolatWik6OlrHjx/Xnj17tG3bNn3zzTdOoStJF110kf74xz8G8FMAAAAAAMqTNUWrJL3//vuKiorSnDlznNc2bNhQaJIlT54F62WXXaZFixYpODi4wuMEAAAAAFQOa4YHS1JwcLD+9re/6bPPPtOAAQMk5RemxT0kqV27dnr99df15Zdfqm7duoEMHwAAAABQzqy60lpg6NChGjp0qHbs2KH//Oc/WrdunQ4dOqTjx48rIiJCDRo0UNeuXXXhhRfqggsuCHS4AAAAAIAKYmXRWiA2NlaxsbG69dZbAx0KAAAAACAArBoeDAAAAACAJ4pWAAAAAIC1KnV4sOfMvi6XSzk5Oc7z2267rVzew+VyadasWeVyLAAAAABAYFVq0WqMkcvlKrRUTYE5c+bI5XKVy/tQtAKwhjFSdkago/DOnSE9H5u/PWGHFBYZ2HhO57b0vAEAgEpl1URM3opZf5VX4Xv8+HHt3LlTaWlpio6OVmxsbLkdG0ANkp0hPRMd6ChKV1C8AgAAWKZSi9aBAwcWW/jNnTu3MkMp1vbt2/V///d/+vzzzwsNXz777LP1xz/+UQ888ICCgsp2K/Add9zh81XgyZMna9KkSWV6HwAAAACoLiq1aF2+fHmx+2655ZZKjMS7lStXasiQIcrIKDokbe/evZowYYL+85//6KOPPip0f66vtm/fXh5hAqiqbBuCa/vwYE+hFscGAAAqlFXDgwPpyJEjGj58uDIyMhQcHKzJkydr/PjxOuuss7R27Vr93//9n9avX68lS5bo6aef1uTJk/1+j4KideTIkbr99ttLbBsby1A9oNoJi5TCogIdhXc2xwYAAGq0SitaP/74Yx0/flx16tTRddddV2T/e++9J0nF7q9o06ZN0+HDhyVJM2fO1L333uvsGzRokBISEtS9e3f98ssveuGFF3TfffepUaNGPh8/PT1dSUlJkqTLLrtMQ4YMKd8PAAD+CouSJqcGOgoAAIASVdo6rXfeeafGjRunCRMmeN0/duxYjRs3To888khlheTIy8vTnDlzJElt27bVPffcU6RNnTp1nHtM09LStGDBAr/eY8eOHc52hw4dziBaAAAAAKg5Kq1oTUtLkzFGSUlJXu8Zlcpn9uCyWL16tXOVddSoUcVOFjVy5EiFhORfnP7kk0/8eg/P+1kpWgEAAADAN5U2PLhjx45at26dsrKydMMNN+iee+5RvXr1CrVxuVzKzMzUN998c0bvddFFF/nVfv369c52v379im0XFRWl7t27a926dYX6+KKgaI2MjFSLFi1kjNGBAwe0a9cuNWvWTG3bti3zrMQAAAAAUF1VWtE6YsQIrVu3TpL0xRdf6IsvvijSxhij/fv3a9CgQWV+H5fLVWipGl9s2bLF2W7fvn2Jbdu1a6d169YpJSVFx48fV/369X16j23btknKXzrnzTff1DPPPKNff/3V2R8ZGakLLrhAM2bM0LnnnutX/AAAAABQXVVa0frQQw9p+fLl+vLLL0tsF4ghwsnJyc52s2bNSmzruf/gwYM+F60FV1q3bNni9Z7ZjIwMLVu2TL169dKkSZM0ceJEn45r8vJ04sQJn9p6Ex4ervDw8DL3BwAAAFD5srKylJWVVbbO7nQF5sbMsqm0ojU4OFhLly7VokWLlJCQoO3bt8vtdjv7ExIS5HK5FBERob59+1ZWWJLyZ/YtEBlZ8lqAnvs9+5XG857Wbt266eGHH1bfvn3VtGlTJSYm6u9//7teffVV5eTk6IknnlCvXr30u9/9rtTjHjhwoMgwa39MmjSpTMv3AAAAAAicZ599Vk899VSgw6gUlb5O64gRIzRixIgirxfcz9miRQstW7asUmPy/IYiODi4xLYFEzFJUmZmpk/HP3nypI4ePaqQkBBdcsklWrJkiWrVquXs79evn/r166dLL71Uw4cPlyTddddd2rx5s6KiSl43sXnz5oWGN/uLq6wAAABA1fPnP/9ZDz74YNk6u9PVqW0LJZ2sGtdbK71oLUmgZg/2vHrqdrsLFZSn8yxwIyIifDp+nTp1lJ2dXWq76667Ttdff70++ugj/frrr/r3v//tFLHFcQUFqW7duj7FAQAAAKB6OKPb/NzB8r5eip2sma42ISFBCQkJmjdvXqW/d+3atZ3t0u4P9dzv2a+8eF6F/v7778v9+AAAAABQlVTaldaPP/5Yx48fV506dXTdddcV2b9nzx5J+Vcl+/TpU1lhScqf0bfAvn371KRJk2Lb7tu3T1L+LMUtWrQo91g6duzobB85cqTcjw8AAAAAVUmlFa133nmnUlJSFBMT47VoHTt2rFwul2JjY73ur0ieheLGjRvVs2fPYtsmJiZKklq1alUhV1o9l+th2C8AAACAmq7ShgenpaXJGKOkpCRlZGR4bROoe1oHDBjgbCckJBTbbu/evfrll18kSf379/f5+A8//LC6deumXr16lTrj8ObNm53tDh06+PweAAAAAFAdVdqV1o4dO2rdunXKysrSDTfcoHvuuafIUi0ul0uZmZn65ptvzui9LrroIr/at2/fXp06ddLmzZu1cOFCzZgxw+v6q7Nnz3a2S5sgyVPfvn3117/+VZI0f/583XHHHV7bGWM0a9YsSfmzFA8dOtSPTwEAAAAA1U+lFa0jRozQunXrJElffPGFvvjiiyJtjDHav3+/Bg0aVOb3cblchYbY+urBBx/UHXfcobS0NN1///1699135XL9NqfWjz/+qOnTp0uSYmJi/Cpahw0bpnr16ik1NVUTJkzQ+eefr3PPPbdQm9zcXE2ZMkUrV66UJD300ENq1aqV358DAAAAAKqTShse/NBDD2nw4MEyxnh9FChuvz+Pshg3bpzOP/98SdK8efN0ySWXaO7cufroo4/06KOP6uKLL1ZmZqaCgoI0c+ZMhYaGFuo/fvx4uVwuuVyuIov81qpVS3PmzJHL5dKJEyfUp08f3X333Zo7d67+8Y9/aOrUqerbt6+mTJkiKX9Y8JNPPlmmzwEAAAAA1UmlXWkNDg7W0qVLtWjRIiUkJGj79u1yu93O/oSEBLlcLkVERKhv376VFZYjJCREixcv1pVXXqn169drxYoVWrFiRaE2YWFhmjlzpoYNG+b38YcPH64ZM2bokUceUVZWlt588029+eabRdrdcMMNeuWVV0pcKxYAAAAAaopKK1oLjBgxotBapAWCgvIv+rZo0ULLli2r7LAkSU2bNtXq1av11ltvaf78+dq6davS0tIUHR2tyy+/XH/84x/VtWvXMh//T3/6k0aOHKnnn39eq1ev1p49e3TkyBE1atRIffv21a233qqrr766HD8RAAAAAFRtlV60liRQswd7Cg0NVVxcnOLi4vzqN2fOHM2ZM6fUdi1atNCMGTPKGh4AAAAA1CjWFK0FS81EREQENhAAAAAAgDWsKVovvvjiM+p/4MABZ9ZgZt0FAAAAgOqh0orWp59+WpLUoEEDv4fe+mLgwIHauXNnmZe8AQAAAADYp9KK1kmTJsnlcik2NtanorVhw4aSpLZt2+r777/36T1suCcWAAAAAFB+KnV4sD9F5bFjxyRJJ06cqKhwAABAJctw56jzk0slSYlThigyzJo7lQAAlgqqzDdzuVwV2h4AAAAAUL1UatEKAAAAAIA/KFoBAAAAANaiaAUAAAAAWIuiFQAAoArIcOeozaOfqs2jnyrDzfJ+AGoOpuyr4lKSk9W5c2ev++Li4ipkTVwAAAAA9ouPj1d8fHzRHSZPyelVZ7lQitYqrknTpkpMTAx0GAAAS2W4cwMdQiGeVwhtvloYERrMKgYAqrxiL2K509WyUR3tP1k1CleKVgAAqrHeU78KdAjF6j3160CHUCzWkAUAe3BPKwAAAADAWnyFCABANRMRGqzEKUMCHYZXGe4c5wrrDxMvs+pqZoY71+or0yi7DHeOOj+5VBJX0YGqiP9jAQCoZlwuV5X4R3lkWEiViBMAEFgMDwYAAAAAWKvSv95MTk7WbbfdVu7tk5OTzyQsAAAAAICFKr1oPXnypObOnVtqu4Jp5n1tDwAAAACofiq9aDWmaqwFBAAAAAAIvEorWgcOHMgi3QAA1HCRYSHa/dxVgQ4DAFCFVFrRunz58sp6KwAAAABANcHswQAAAAAAa7E4GgAAwGky3LmBDqGIDHeO123bRIQGc0sYgHJF0QoAAHCa3lO/CnQIJeo99etAh1CsxClDFBnGPzEBlB+GBwMAAAAArMXXYAAAAMof1po4ZUigwyhWhjvHucL6w8TLrLqameHOtf7qNICqy57fdgAAAAHkcrmsKgRLEhkWUmViBWqiDHeOOj+5VBJD5ssDw4MBAAAAANaiaAUAAAAAWIuiFQAAAABgLYpWAAAAAIC1uCO4iktJTlbnzp297ouLi1NcXFwlRwQAAADABvHx8YqPjy+6w+QpOd1UfkBlRNFaxTVp2lSJiYmBDgMAAACAZYq9iOVOV8tGdbT/ZNUoXBkeDAAAAACwFkUrAAAAAMBaDA8GAABAuclw5wY6hCIy3Dlet20TERosl8sV6DAA61C0AgAAoNz0nvpVoEMoUe+pXwc6hGIlThmiyDD+eQ6cjuHBAAAAAABr8VUOAABAFRAZFqLdz10V6DC8iggNVuKUIYEOo1gZ7hznCusPEy+z6mpmhjvX+qvTQKDZ838sAAAAqiSXy2VVIViSyLCQKhMrgHwMDwYAAABQRIY7R20e/VRtHv3U6gmsUP1RtAIAAAAArEXRCgAAAACwFkUrAAAAAMBaFK0AAAAAAGtRtAIAAAAArEXRCgAAAACwFkUrAAAAAMBaFK0AAAAAAGtRtAIAAAAArEXRCgAAAACwFkUrAAAAAMBaFK0AAAAAAGtRtAIAAAAArBUS6ABwZlKSk9W5c2ev++Li4hQXF1fJEQEAAACwQXx8vOLj44vuMHlKTjeVH1AZUbRWcU2aNlViYmKgwwAAAABgmWIvYrnT1bJRHe0/WTUKV4YHAwAAAACsxZVWAAAAVGuRYSHa/dxVgQ4DQBlxpRUAAAAAYC2KVgAAAACAtShaAQAAAADWomgFAAAAAFiLohUAAAAAYC2KVgAAAACAtShaAQAAAADWYp1WAAAAwAIZ7txAh1BIhjvH67aNIkKD5XK5Ah0GKghFKwAAAGCB3lO/CnQIxeo99etAh1CixClDFBlGaVNdMTwYAAAAAGAtvo4AAAAAAiQiNFiJU4YEOgyvMtw5zhXWHyZeZt2VzAx3rtVXp1F+7Mo8AAAAoAZxuVzWFYPeRIaFVIk4UT0xPBgAAAAAYC2KVgAAAACAtbjGDwAAAKBKY7kgP9kYUwkoWgEAAABUaTZPyGTjckEROhXoEPzC8GAAAAAAgLW40goAAACgymG5oLLLSDuhHq8FOgrf2XX2AAAAAMAHLBd0BsKCAx2BXyw7e/BXSnKyOnfu7HVfXFyc4uLiKjkiAAAAADaIj49XfHx8kddNXp6S000AIiobitYqrknTpkpMTAx0GAAAAAAsU9xFrIy0VHWIPkv7T1aNwpWiFUDpjJGyMwIdRdXk5rwBAACcCYpWAKXLzpCeiQ50FAAAAKiBWPIGAAAAAGAtrrRWccZUjXHoqPqycoyeXZmlPy/aqfA6ZwU6nKopNDLQEVQ5WVlZevbZZ/XnP/9Z4eHhgQ4HNQA5h0Ag74CSuQxVT5XUokULJSUlqXnz5kpKSgp0OKju3Ok6Mam56j13UqmHklS3UfNAR4Qa4sSJE6pXr55SU1NVt27dQIeDGoCcQyDYmncZ7hx1fnKpJClxyhD7lm2xmO3nLiMtVe2jz1LSSaPo6Gjt378/0CGVyK6zBwAAAMAKkWEh2v3cVYEOA+CeVgAAAACAvShaAQAAAADWomgFAAAAAFiLohUAAAAAYC0mYirG8ePHtXPnTqWlpSk6OlqxsbFyuVzldvy0tDTt3LlTx44dU9OmTdW+fXuFhPDjAAAAAABPXGk9zfbt23XNNdeocePG6t27twYNGqQOHTqoTZs2euGFF5SXl3dGxz948KBuvvlmNWnSRD169NAll1yizp07Kzo6Wo8//riysrLK6ZMAAAAAQNVH0eph5cqV6tGjhz7++GPl5OQU2rd3715NmDBBw4cPV25ubpmOv3XrVp177rl6//33lZmZWWjfoUOH9Mwzz+jiiy9WRkZGmT+DLeLj4wMdQolsjs/m2Gxn87kjturJ9nNnc3w2x2Y7m8+dzbFJ9sdnK9vPm+3xVRWnTp1S06ZN9fLLL1dKP39QtP7PkSNHNHz4cGVkZCg4OFhPP/209u3bp/T0dC1fvlw9e/aUJC1ZskRPP/2038d3u9265pprdOjQIUnSn/70J+3cuVMZGRlau3atLr/8cknS2rVrde+995bfBwsQ23952ByfzbHZzuZzR2zVk+3nzub4bI7NdjafO5tjk+yPz1a2nzfb46sqbr31VqWkpKhJkyaV0s8fFK3/M23aNB0+fFiSNHPmTE2cOFEtWrRQZGSkBg0apISEBMXExEiSXnjhBaetr2bNmqVt27ZJkh566CG99NJLatu2rSIiItSnTx99/vnnOv/88yVJ7733nhITE8vx0wEAAACAd/v27dMHH3ygdu3a6aabbqrwfv6iaJWUl5enOXPmSJLatm2re+65p0ibOnXqaNKkSZLyJ1FasGCBX+8xa9Ys5zhPPvlkkf0hISGaNm1akXgAAAAAoCLdeOONMsbo73//e6X08xdFq6TVq1c7V05HjRpV7CzBI0eOdGb4/eSTT3w+flJSkjZs2CBJuvrqq1W7dm2v7QYOHKjmzZv7fXwAAAAA9ogMC9Hu565S5JKHFBlm9wohbrdbq1at0oUXXqg+ffr43G/NmjVl6lcWFK2S1q9f72z369ev2HZRUVHq3r17kT7ldXxJ6t+/v6T8SZvS09N9fg8AAAAA8NexY8fkcrn04Ycf+tVv9OjRZepXFnaX/ZVky5Ytznb79u1LbNuuXTutW7dOKSkpOn78uOrXr1/ux5ckY4y2b9+uHj16lHxwYyS3hcWtybMzrgI2x2djbO6qP6M1AAAAisrNzdXo0aMVHR3tc5/3339fu3bt8rtfWVG0SkpOTna2mzVrVmJbz/0HDx70qWg9k+MXp2AW4uTkg2rRqE6pMRTH+0DoM5ecbtTyDOKqaDbHZ2ts5n//7XRuL7mC7BykkZycrJYtWwY6DK+IrWyMyc+8Tp06FXvrRiDZfO4ku+OzNTbbc06y99xJdscm2Ruf7Xln63krYHN8FR1bQe743S8vT8lpv/X929/+5nPfvLw83XfffQoLC/Or35mgaJUKDcONjIwssa3nfl+H71bE8QvWis0zUtLJsiVrRdtvaVwFbI7P5tiSDhwIdAgl2r9/f6BDKBaxlV1SUlKgQyiW7efO5vhsjs3mnJPsPnc2xybZHZ/NeWfzeZPsjs/m2CTJ5XKpVq1aPrefPHmyjh8/rkmTJvnV70xQtErKyspytoODg0tsWzARkyRlZmYG7Pi1atXSqVOnFBwcrMaNG/sUhzc2fpsHAAAAoHRlvtJqjA7870JERESEz/1OnTqladOm6ayzzvK6IkpFoWhV4aubbre7xG8MPAtQX3/Apx+/JL4en0maAAAAAJTF6NGj9fe//10ffPCBbrzxRp/73XrrrXK73Xr33XcVVIm3i9l5Y1ol81yC5sSJEyW29dxf3NI1lX18AAAAAPDFvn379MEHH6hdu3Z+Faxl7VceKFolnX322c72vn37SmxbsN/lcqlFixYVdnxJatWqlU/HBwAAAABf3HjjjTLG6O9//3ul9CsPFK2SOnbs6Gxv3LixxLaJiYmS8gtKX6+EluX4ISEhio2N9en4AAAAAFCaNWvWaNWqVbrwwgvVp0+fIk7eUwAAIn5JREFUCu9XXihaJQ0YMMDZTkhIKLbd3r179csvv0iS+vfv7/Px+/Tpo/Dw8FKPn5mZqbVr10qSevfurbCwMJ/fAwAAAABKMnr0aLlcLn344YeV0q+8ULRKat++vTp16iRJWrhwoY4fP+613ezZs53t4cOH+3z82rVr69JLL5UkrVixQtu3b/fabsGCBc4ES/4cHwAAAABK8v7772vXrl266aabFB0dXeH9yhNF6/88+OCDkqS0tDTdf//9RaaP/vHHHzV9+nRJUkxMjN9FZcHxjTG66667Cs0SLOVfxX300UclSfXq1dPtt99eps8BAAAAAKc7fPiwmjZtqr/97W+V0q88uUxZF/epZnJycjRgwACtWbNGkjRw4ECNGzdOdevW1dq1a/Xaa6/p5MmTCgoK0uLFizVs2LBC/cePH6+5c+dKyl9wd9KkSUXeY+TIkVq0aJEkqUePHrr77rvVtGlTbdq0Sa+88opSUlIkSfHx8br33nuLjfX48ePauXOn0tLSFB0drdjYWNZbRYVJSkrSnj17JEmtW7f2+xs2Y4x27NihpKQk1a5dW+3atVP9+vUrIFJUN+QeymL37t3at2+f6tevrw4dOvh1qw05B3/l5uZq165dSklJUfv27dWkSRO/+pNzKG825FRWVpa2b9+uw4cPq1GjRoqNjS1xSVFfA8P/HDx40PTs2dNI8voICwszb7zxhte+48aNc9pNnjzZa5u0tDQzePDgYo/vcrnMxIkTi41v27Zt5uqrrzYhISGF+p199tnm+eefN7m5ueVyHlA9nDp1ykyfPt1cffXVpmPHjiYyMtKcc845ZsSIEWbmzJnG7XaX2P/zzz83ffr0KZKnvXr1Mp9++mmp75+Xl2dmzJhhWrduXah/SEiIueqqq8yWLVvK66OiCsjOzjbnn39+ib8jC5B78Fdubq55+eWXTWxsbJGf+YgRI8zevXtL7E/OwV8///yzGTJkiAkPDy/0M2/QoIF59NFHzYkTJ0rsT86hNPHx8T79zSxgQ06lpqaauLg4U69evULHqFu3rrnnnnvM8ePHffos3lC0nsbtdptXX33V9O/f3zRs2NCEh4ebmJgYc8cdd5iNGzcW28+XotWY/IR49913zWWXXWaaNGliwsLCTKtWrczo0aPNypUri+33zTffmMjIyGILXknmmmuuMTk5OWf0+VE9JCQkmPbt25eYLx07djQrVqzw2v+FF14osa8kM3369GLfPzc311x33XUl9o+MjCz2/VH9PPLIIz79jiT34K9Tp06ZK664osSfeVRUlFm/fr3X/uQc/PXyyy+b4ODgEn/mzZo1M9u2bfPan5yDL/r37+9z0WpDTiUnJxf54vD0R9u2bc3BgwfLdD4oWquAw4cPm0aNGhlJJjg42Dz99NNm3759Jj093SxfvrzQ1eFJkyYFOlwEWGJioomKinJyon///uavf/2rWbhwofnrX//q/BKUZGrXrm22b99eqP+yZctMUFCQkWQaN25s5s+fb44dO2aOHTtm3n//fScXXS6X+frrr73GMHnyZOc9evXqZRISEkx6errZt2+fmTJlivPHvmHDhubQoUOVcVoQQF9++aVxuVylFq3kHsrilltuKfTl7dq1a016errZsWOHmTBhgpNTsbGxRa5+kXPw16pVq5wRbw0aNDCvvvqq+eWXX0xaWppZv369GTt2rJMP5513nsnKyirUn5yDL+bMmePTF73G2JNTl1xyiXOM0aNHm//+978mMzPTbNq0yYwaNcrZN3DgwDKdE4rWKuChhx5yftDx8fFF9p84ccLExMQ4RQi/oGq2G264wcmXBx54oMiw8by8PDNt2jSnTd++fU1eXp6zv2/fvkaSCQ0NNRs2bChy/PXr15vQ0FAjyZx//vlF9qekpDhFc0xMjDl58mSRNq+88orz/o888siZf2hYKyUlxTRr1qzQN63F/QEm9+CvlStXOj/PMWPGFPpdVuCJJ55w2rz99tuF9pFz8Nfll19uJJmgoCDzzTffeG1z5513Oj/zefPmFdpHzqE4mZmZZuXKlebWW281YWFhPhetNuTUp59+6uy/4YYbvMY5cuRIp83nn39e4mfyhqLVcrm5uc43JG3btvX6B9kYY+bOneskwquvvlrJUcIWGRkZzi+6Vq1amczMzGLb/u53v3NyZtWqVcYYY3766SfntfHjxxfb13M4/KZNmwrte/nll51977zzjtf+eXl5pm3btkaSadKkCfdjV1N5eXlOnkVHR5f4B5jcQ1lcddVVzj/WivvC9uTJkyYiIsJIMldffbXzOjkHf+Xl5Zn69es7X/gWZ9OmTU5e/OlPf3JeJ+dQnH79+jlXS09/lFS02pJTw4cPd77M2bVrl9dj7Nq1yxl1NXLkyGJjLQ5L3lhu9erVOnz4sCRp1KhRxc4SPHLkSIWEhEiSPvnkk0qLD3bZsGGD3G63pPx8KWmmtuuuu87ZXr9+vaTCuXPTTTcV29dz3+n5VvA8NDRUI0aM8Nrf5XLpxhtvlCSlpKTo+++/L/a9UHXNmDFDn3/+uSIiIvT222+X2Jbcg79OnjypL7/8UpI0dOhQNWrUyGu72rVra82aNVq9erWeeuop53VyDv46dOiQjh8/Lklq1apVse1at27tbG/bts3ZJudQnAMHDigvL8/vfjbklNvtdn4XX3DBBYqJifF6jJiYGF1wwQWSpKVLlyo7O7vYeL2haLVcQTEhSf369Su2XVRUlLp3716kD2qW5ORkZ7tbt24ltm3evLmznZmZKem33HG5XM4vFm/69evnfIFyer4VPD/33HMVFRVV7DH69+9fpA+qj/Xr1+vPf/6zpPzitUuXLqW2l8g9+O4///mP8yXd73//+xLbduvWTeeff77OO+885zVyDv5q2LChs4TSxo0bi233888/O9uey42QcyjO1q1bderUKeexdetWn/rZkFPbtm1TWlqa8z4lKTjGyZMntWPHjhLbno6i1XJbtmxxttu3b19i23bt2knK/wak4JtA1Czdu3fX3LlzNXfuXF1xxRUltvX8lqxDhw6Sfsu3pk2bqk6dOsX2rVOnjho3bixJhX6xHj58WEeOHJHke76efgxUfWlpaRo1apTcbreGDx+uu+66q9Q+5B789d///tfZjo2NdbZ3796tb775RomJicrJySm2PzkHfwUHB2v06NGS8v+h/vLLLxdpc+rUKU2YMMF5PmbMGGebnENxwsLCFB4eXujhCxtyqiy1yunH8EWIX61R6TyvnDVr1qzEtp77Dx48yOLSNVDbtm3Vtm3bUtslJSUpPj5eUv7QuQsvvFDSb/lWWq4VtElJSdHBgwed184kX1F9xMXFafv27WrZsqVmzZrlUx9yD/7y/AdPkyZNtGjRIj3xxBPavHmz83p4eLguuOACvfTSS+rRo0eh/uQcyuLll1/Wxo0btW7dOv2///f/9PXXX+uqq65So0aNtH37dr355pvavXu3JGnKlCm65JJLnL7kHMqbDTlVWXlJ0Wq59PR0ZzsyMrLEtp77PfsBnn755RddffXVOnr0qCTp/vvvV8OGDSX9ljel5ZpnG89cI18xb948vfvuuwoKCtK8efPUoEEDn/qRe/BXamqqs/3666/r+eefL9ImKytLK1asUJ8+ffTMM8/ooYcecvaRcyiLunXr6ptvvtGdd96pefPm6eOPP9bHH39cqE1YWJg++OADDR8+vNDr5BzKmw05VVl5yfBgy2VlZTnbwcHBJbYtmIhJ+u0eRaCA2+3WSy+9pHPPPdcZVjd48GBNnjzZaVOQb6XlmvRbvnnmGvlas+3cuVP33nuvJOmxxx7TwIEDfe5L7sFfJ0+edLaff/551a5dW88995w2btyotLQ0bdq0SU8//bTCw8OVk5OjRx99VN99953Th5xDWb355ptFClVPbrdbzz77rNatW1fodXIO5c2GnKqsvKRotZznNxIFE04UxzNpIiIiKiwmVD2LFi1S586d9cADDzg3y48YMUL//Oc/nUklpN/yrbRck37LN89cI19rruzsbI0aNUonT55Uv379NGnSJL/6k3vwl+dMm7Vq1dJ3332nRx55RF27dlVUVJS6dOmiiRMnavHixU57z/uryTmUxb333qsHHnhAqamp6tGjhxYtWqTdu3crLS1NP//8s6ZOnaq6devq+++/18CBA7VixQqnLzmH8mZDTlVWXlK0Wq527drO9okTJ0ps67nfsx9qri1btujSSy/VyJEjtXPnTkn5N+vPnz9fCxcuLDJLXEHelJZrnm08c418rbkee+wx/fDDD6pXr57mz59f6NtUX5B78Jfnz+6BBx5Q165dvbYbMmSIMzHdxo0bnVsjyDn467PPPtPrr78uSbr00ku1bt06XX/99WrdurWioqLUrVs3Pf7441q1apUiIyOVnp6uW265xfmHPDmH8mZDTlVWXlK0Wu7ss892tvft21di24L9LpdLLVq0qNC4YL+33npLPXv21PLlyyXlzxw3ZcoU7dy5s9i1vAry7ddffy31+Pv375dUeK26Vq1aOVOq+5qvpx8DVc+BAwf0wgsvSMpfemTr1q1aunRpoYfn1YYdO3Y4r2/YsEESuQf/ed4vffHFF5fY9vzzz3e2C5YjIefgL8/1puPj4xUU5P2f0V26dNF9990nSdq7d6+WLl0qiZxD+bMhp8pSq5x+DF9QtFquY8eOznZJa4JJUmJioqT8JOBbtZotPj5ed911l3O/wG233aZdu3bpiSeeKHENroJ8S0tL0549e4ptt3fvXmeYcadOnZzXIyMjnV9Cvubr6cdA1eN2u2WMkZT/j7qhQ4cWeYwdO9ZpP2/ePOf1KVOmSCL34L9zzjnH2S5twq8mTZo42ykpKZLIOfhv+/btkvInY/LMP2/69u3rbG/btk0SOYfyZ0NOlaVWOf0YvqBotdyAAQOc7YSEhGLb7d27V7/88oukwov/ouZZuHCh7r//fknSWWedpS+//FKzZs1So0aNSu3ra7557js93wqOsXv37hJ/gZZ0DNQ85B781bNnT2fbc81WbzzXESwYiUTOwV8Fa2dmZmYqOzu7xLaes1sX9CPnUN5syKk2bdo4v1c9R1V5U7A/Ojpabdq0KbFtEQbW69Spk5FkateubY4dO+a1zaRJk4wkI8ksWLCgcgOENXJzc01sbKyTL5s2bfKrf1JSkgkKCjKSzIABA4ptN3DgQCPJBAUFmQMHDhTat2DBAicXJ06c6LV/amqqqVu3rpFkOnXq5FeMqJp2797t5MXkyZOL7Cf34C+3223q169vJJkLL7zQ5OXleW2XlpZmzj77bOf3YlZWljGGnIP/br31Vufn/fnnn5fY9ve//73T9j//+Y8xhpyD70r7m1nAlpy65557nGN89dVXXo+xfPlyp80999xTbKzFoWitAt5++23nh3zzzTcX+cO8YcMGExERYSSZmJgY43a7AxQpAm3JkiVOrkybNq1MxxgzZoxzjDfffLPI/jfffLNQPp7O7XabmJgYI8lERESYH3/8sdD+3Nxcc/PNNzvHmDVrVpniRNXiyx9gcg/+evjhh52f5+OPP25yc3ML7T916pS55ZZbnDbPPvtsof3kHPyxZs0aExISYiSZli1bml27dnlt98Ybbzg/8x49ehTKS3IOvvC1aDXGjpzavHmzCQ4ONpJM586di1xkO3r0qOnSpYuRZIKDg82WLVt8OAuFuYz5341IsFZOTo4GDBigNWvWSJIGDhyocePGqW7dulq7dq1ee+01nTx5UkFBQVq8eLGGDRsW4IgRKPfdd5/i4+Ml5a8j17p1a5/6de/eXc2aNZMk7dmzR7169dKRI0fkcrk0duxY/e53v1NeXp4+++wzzZs3T8YYNWrUSOvWrSt0A36BJUuW6LrrrpMxRnXr1tX999+vXr166ejRo5o7d65WrlwpSerXr5+++eYbn9YXQ9W2Z88eZyjQ5MmTvS6JQ+7BXwXLjuzevVuSdMEFF+j6669XTEyMtm3bpnnz5mnz5s2SpD59+ui7774r9DMn5+Cv6dOn65FHHpGUP+x3/Pjx6tWrl8466yzt2rVLixcv1rfffisp/97XdevWKTY21ulPzsEXvvzN9GxrQ05NmDDBmZSxXbt2uv/++9WmTRvt2LFD8fHxzm2MDz30kKZPn+7/SfG7zEVAHDx40PTs2dP5luP0R1hYmHnjjTcCHSYCbOjQocXmSEmPDz74oNBxVq5caZo0aVJs+6ZNm5pVq1aVGEt8fLwJDQ0t9hi9e/c2KSkpFXk6YBFfvzUm9+CvrVu3mnbt2pX4O27EiBFFhsQVIOfgj7y8PDNlyhQTFRVVYs516dLFrFixwusxyDmUxp8rrcbYkVM5OTmFRrZ4e4wfP77IiBhfUbRWIW6327z66qumf//+pmHDhiY8PNzExMSYO+64w2zcuDHQ4cEC7du3L5ei1RhjkpOTzeOPP266dOliateuberUqWO6du1qJk6caA4dOuRTPD/99JO5/fbbTZs2bUx4eLhp2LChufDCC018fLzJzs4u748Pi/nzB5jcg78yMjLMiy++aC688ELTuHFjExERYXr27GnGjh1rPv7441L7k3PwV1JSknn44YfNFVdcYVq3bm0iIyPNueeea2644Qbz9ttvm5ycnBL7k3Moib9FqzH25NSSJUvM1VdfbZo3b27CwsJMdHS0ueaaa8ynn37qU//iMDwYAAAAAGAtlrwBAAAAAFiLohUAAAAAYC2KVgAAAACAtShaAQAAAADWomgFAAAAAFiLohUAAAAAYC2KVgAAAACAtShaAQAAAADWomgFAADA/2/v7mOyKv84jn9ARLgFETXzIQPh1qloYjYbZQYJjgjzeSZWiLXSajXLcHMp2rQH1qA2nbaawy1sIChOp5AhaOBTKsOUMjRBRcPHQEMThN8fzPPjVh5uBOQG3q/tbOc657qu8z23/vPlfM+5AMBmkbQCAAAAAGwWSSsAAAAAwGaRtAIAAAAAbBZJKwAAAADAZjm0dgAAAAAA0NwqKiqUmZmp7du3q7CwUOfPn1dpaal69Oihfv36yc/PT0FBQfLx8WntUNEAnrQCAAAAzcjOzs7Y1q9f3+jxhYWFFnPs3r27BaK0bQEBARa/wd3N3d29wbG3bt3S8uXL1adPHwUFBSk2NlabNm3S/v37lZeXp6ysLCUmJmrBggUaPny4AgIClJ2d/RDu6n4179PLy+uB54mNjbX4nQoKCu77f1Rzi4iIaMa7aHkkrQAAAADahezsbA0fPlzLli3TlStXrBqTmZmpcePGadGiRaqqqmrhCC2FhYUZ+6dPn9ahQ4ceaJ7Nmzcb+2PGjJGnp2dTQ7MpJK0AAAAAbFb//v1lNptlNps1cODAOvulp6drwoQJOnXqlHHMx8dHX3zxhXJycnT58mWVlJTowIEDiouLU0BAgNGvsrJS0dHRmj9/fovey72mT58uR0dHo52QkNDoOYqLiy2eFM+cOVOS5ODgYPxuZrNZvXv3bnrArYSkFQAAAIDNio+PV35+vvLz83XkyJFa+xw6dEihoaEqKyuTJJlMJn399dc6evSoFi1aJF9fX/Xs2VPdunXTmDFjFB4erl27dmnr1q16/PHHjXm+/fZbrVu37qHclyS5u7srODjYaCcmJjb6ae+WLVtUWVkpqbo0fcaMGZKqk/27v1t+fr6io6ObL/CHjKQVAAAAQJv133//ac6cObp165ak6oR1+/bt+uCDD2RvX3+6ExoaqtTUVLm5uRnHFixYYHVpcXOYNWuWsX/mzBkdOHCgUeNrlgb7+flpwIABzRabrSBpBQAAANBmRUdH6/jx40Z7/fr1ev75560eP3ToUG3YsMFol5aW6rvvvmvWGOvz8ssvq2vXrkY7MTHR6rElJSVKT0832ndLg9sbklYAAAAAbdLt27e1atUqoz1lyhRNnz690fOEhIRoxIgRRjsuLq45wrOKyWTS5MmTjfbGjRutLhHetm2bysvLJUn29vYPdO9tAeu0AgAAAB3MuXPnlJWVpbNnz8rZ2VkeHh4KCAiQi4tLa4fWKBs3btTFixeN9tKlSx94rvDwcC1cuFCSdOLECV24cEF9+/a1auzFixe1Z88eFRUVqaKiQl5eXho8eLDVa8CGhYUpPj5eUvW/zd69e/Xss882OG7Tpk3G/tixY9WvXz+rrtfWkLQCAAAA7ZSdnZ0kyd/fXxkZGSouLta7776rlJQU3blzx6Kvk5OTIiIi9Nlnn6l79+6tEG3jpaSkGPtPP/20fH19H3iu+fPnW3wUqeZ7rnX57bffFBkZqZ9++sn4GFJNo0eP1ocffmixtE1tJkyYoJ49exrv0iYmJjaYtN68eVOpqalGu72WBkuUBwMAAAAdQl5ennx9fZWcnHxfwipJt27d0po1azRq1CgVFha2QoSNV3OplxdffLFJc5lMJvn4+BibyWSqt39cXJxGjRql1NTUWhNWSTp8+LBmz56t1157Tbdv365zLgcHB+Orv1L1E+S65rwrLS3N+Fqyvb29pk2bVm//townrQAAAEA7V1JSoilTpujvv/+WJAUGBio4OFhdunTRwYMHlZmZqbNnz0qSCgoKFBQUpNzcXDk7O7dm2PU6c+aMLly4YLT9/Pwe2rWTkpI0d+5c491Td3d3TZw4UT4+PnJyclJubq7S0tJUVFQkSfrhhx/077//WpTz3issLExr166VJF24cEFZWVkaN25cnf1rzuXv769HH320OW7NJpG0AgAAAO1cTk6OJKlTp05atWqV5s2bZ3G+rKxM4eHhSkpKkiTl5+drxYoVWrly5UOP1Vp3E8K7Bg8e/FCue+nSJc2bN89IWF999VXFxsaqV69eFv1u3LihpUuXKjY2VlL10jQpKSkWH12qaezYsRowYIDxx4OEhIQ6k9by8nJt27bNaLfn0mCJ8mAAAACgxcyZM0d2dnaN2jw9PVssntoSVqm6NDYxMdGixHTNmjXG2qe26N61VN3d3R/KdVevXm1cOygoSOvXr78vYZUkFxcXxcTE6M033zSOLV68uM557ezsLNZsTUpKqrWMW5IyMzN17do1SdWlxVOnTn2ge2krSFoBAACADmDAgAF644036jxvZ2en5cuXGx9vunbtmvE07+7xB9laSklJiUW75lqndYmPj7c67oiIiFrn+P777439NWvWyN6+/pTqm2++kZOTkyTp999/tyhpvlfNpPXixYvavXt3rf1qlga/8MILtSbN7QlJKwAAANBCevfuLbPZ3KjNw8OjRWJ577331Llz53r7+Pj4WHxBd9++fS0SS3NwdXW1aF+/fr3Fr3ny5EmjLNlsNsvb27vBMSaTSaNHjzbaWVlZdfb19fXV0KFDjXZiYuJ9faqqqiy+mtzeS4Ml3mkFAAAAWkx0dLTCw8MbNaawsLBFSoRrJk718fX11Y4dOyRJubm5kqSoqChFRUXd13f58uVatmyZPD09dfr06eYL1go9e/a0aF+7dq3BEmEXFxeZzeY6z5eWllqs+3qvw4cPG/vnz5/XoEGDrIr17gewJDX4ZeawsDAtWbJEkpScnKxVq1bJweH/adu+ffuM+Tp37qwpU6ZYFUNbRtIKAAAAdADWJsIDBw409u99b9SW3Pu13Ly8PHl5edU7ZtKkSZo0aVKd59etW1dvCfWlS5eM/bKyMp08edLKaP/v6tWr9Z6fNWuWkbRevnxZGRkZCgoKMs7XLA0OCgp6aO/ytibKgwEAAIAOoH///lb1e+yxx4z9e98btSXe3t4W73Lu37+/yXM2lKQ3x+9R33qtUvV9jRkzxmjfWyJcM2ntCKXBEkkrAAAA0CFY+9S0ZnmsyWRqqXCazM7OTs8995zR3rJlS5PnbOjJac3fIyQkRFVVVY3evvrqqwbjCAsLM/Y3bdqk8vJySdXl2nfLsB0dHet9atyekLQCAAAAHUBBQYFV/U6dOmXs23rpaWhoqLF/7NgxZWdnN2m+1NTUes/36NHD2H+Q0mBrzZw5U506dZJUXU6cnp4uyfIpa3BwsNzc3FosBltC0goAAAB0AH/88YdV/Y4ePWrsDx8+vKXCaRZhYWF65JFHjHZkZKQqKysfaK7k5GSdOXOm3j4jRoww9k+fPq2Kigqr5r5z546xWaNPnz4KCAgw2gkJCZI6ZmmwRNIKAAAAdAirV69usM+JEye0detWo/3MM8+0ZEhN5uTkpPfff99o7927V19++WWj57ly5YoWLlzYYL+RI0caS+2Ul5frxx9/bHDMuXPnZDKZ5ODgIHd3d928edOqmGqWCKekpOj48eM6duyYpOr7njhxolXztAckrQAAAEAHkJOT0+B7n5988onxpNLFxUWTJ09+CJE1TWRkpHx9fY324sWLFRMTY/X4goIChYaGWlU+3alTJ82aNcviWmVlZfWOWblypfHxpenTp8vZ2dmquKZOnaouXbpIkv755x+98847xrmQkJD71qltz0haAQAAgA4iLCxMycnJ9x0vKyvTzJkzlZSUZBx766232kRi5OjoqPj4eHXr1s049tFHHyk4OFh5eXl1jrtz545iYmLk4+NjfHm4ZvJblwULFsjR0VFS9VPUoKAg/fXXX/f1q6ys1Oeff661a9cax+bOnWvtbcnNzU0hISFGe8+ePcZ+RyoNllinFQAAAOgQ3NzcVFJSohkzZsjf319+fn7q27evfv31V2VkZOjs2bNGXy8vL3366aetGG3jDBs2TLt27VJwcLAuX74sSUpLS5OPj49GjhypwMBA9e/fX927d9fVq1eVl5entLQ0FRUVGXMsW7ZMr7/+eoNrvQ4ZMkQrVqxQZGSkpOqS5JEjR+qll17SE088oV69eqmwsFBJSUn6888/LeYfO3Zso+4rLCxMmzdvtjhmMpksPkDVEZC0AgAAAB3Ajh07NGHCBN24cUMZGRnKyMiotZ+Hh4d27typrl27PuQIm2b06NHau3ev3n77bYt7y83NVW5ubp3j+vbtq5iYGL3yyiuSqtezrZnM1ubjjz9WSUmJVq5cKUm6ceOGEhISjA8m3SsiIkJRUVGNvSWFhobK1dVV169ftzhmy0sRtQTKgwEAAIAOwM/PTzk5ORo/fnyt57t06aJ58+bpyJEjDT5ttFWDBg3Srl27lJSUpKCgIDk41P2Mzmw2a8mSJTpx4oSRsErSuHHjrLrWihUrtHPnTj311FN19nnyySeVnp6udevWWX8TNTg5OWnq1KkWxzpaabDEk1YAAACgWVVVVTVpvIeHR5PnqIvZbNbPP/+sU6dO6ZdfflFxcbGcnJzk4eGh8ePHt4l3WK0xbdo0TZs2TVevXtXBgwd1/vx5FRcXy9XVVX369NGwYcM0bNiwWsdu2LBBGzZssOo6gYGBCgwMVEFBgbKzs1VcXCx7e3sNGTJEQ4YMkYeHh+zs7Jp0L3FxcYqLi2vSHG0dSSsAAADQwXh7e8vb27u1w2hxPXr0UHBwcItfx9PTU56eni1+nY6K8mAAAAAAgM0iaQUAAAAA2CySVgAAAACAzeKdVgAAAAAPJCoq6oGWcmmM2bNny9nZWZLk6uqqI0eOtOj12ouioiL5+/sb7dLS0tYLpolIWgEAAADYrJprpnbv3r31AmljKioqdPLkydYOo1lQHgwAAAAAsFl2VS21CBQAAAAAAE3Ek1YAAAAAgM0iaQUAAAAA2CySVgAAAACAzSJpBQAAAADYLJJWAAAAAIDNImkFAAAAANgsklYAAAAAgM0iaQUAAAAA2CySVgAAAACAzSJpBQAAAADYrP8Bg8D0kKnSFM0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "part_pid = arrays[\"Particle/Particle.PID\"]  # PDG ID\n",
    "part_m1 = arrays[\"Particle/Particle.M1\"]\n",
    "condition_hhh6b = np.logical_and(np.abs(part_pid) == 5, part_pid[part_m1] == 25)\n",
    "# note: see some +/-15 PDG ID particles (taus) so h->tautau is turned on\n",
    "# explicitly mask these out, just keeping hhh6b events\n",
    "mask_hhh6b = ak.count(part_pid[condition_hhh6b], axis=-1) == 6\n",
    "\n",
    "particles = ak.zip(\n",
    "    {\n",
    "        \"pt\": arrays[\"Particle/Particle.PT\"],\n",
    "        \"eta\": arrays[\"Particle/Particle.Eta\"],\n",
    "        \"phi\": arrays[\"Particle/Particle.Phi\"],\n",
    "        \"mass\": arrays[\"Particle/Particle.Mass\"],\n",
    "        \"pid\": part_pid,\n",
    "        \"m1\": part_m1,\n",
    "        \"d1\": arrays[\"Particle/Particle.D1\"],\n",
    "        \"idx\": ak.local_index(part_pid),\n",
    "    },\n",
    "    with_name=\"Momentum4D\",\n",
    ")[mask_hhh6b]\n",
    "\n",
    "higgs_condition = np.logical_and(particles.pid == 25, np.abs(particles.pid[particles.d1]) == 5)\n",
    "higgses = ak.to_regular(particles[higgs_condition], axis=1)\n",
    "bquark_condition = np.logical_and(np.abs(particles.pid) == 5, particles.pid[particles.m1] == 25)\n",
    "bquarks = ak.to_regular(particles[bquark_condition], axis=1)\n",
    "\n",
    "jets = ak.zip(\n",
    "    {\n",
    "        \"pt\": arrays[\"Jet/Jet.PT\"],\n",
    "        \"eta\": arrays[\"Jet/Jet.Eta\"],\n",
    "        \"phi\": arrays[\"Jet/Jet.Phi\"],\n",
    "        \"mass\": arrays[\"Jet/Jet.Mass\"],\n",
    "        \"idx\": ak.local_index(arrays[\"Jet/Jet.PT\"]),\n",
    "        \"flavor\": arrays[\"Jet/Jet.Flavor\"],\n",
    "    },\n",
    "    with_name=\"Momentum4D\",\n",
    ")[mask_hhh6b]\n",
    "\n",
    "fjets = ak.zip(\n",
    "    {\n",
    "        \"pt\": arrays[\"FatJet/FatJet.PT\"],\n",
    "        \"eta\": arrays[\"FatJet/FatJet.Eta\"],\n",
    "        \"phi\": arrays[\"FatJet/FatJet.Phi\"],\n",
    "        \"mass\": arrays[\"FatJet/FatJet.Mass\"],\n",
    "        \"idx\": ak.local_index(arrays[\"FatJet/FatJet.PT\"]),\n",
    "    },\n",
    "    with_name=\"Momentum4D\",\n",
    ")[mask_hhh6b]\n",
    "\n",
    "fj_match = match_fjet_to_higgs(higgses, bquarks, fjets, ak.ArrayBuilder()).snapshot()\n",
    "fj_higgses = higgses[fj_match > -1]\n",
    "\n",
    "j_match = match_jets_to_higgs(higgses, bquarks, jets, ak.ArrayBuilder()).snapshot()\n",
    "match = np.logical_and(j_match[:, :, 0] != j_match[:, :, 1], ak.all(j_match > -1, axis=-1))\n",
    "j_higgses = higgses[match]\n",
    "\n",
    "higgs_pt = hist.Hist.new.Reg(10, 0, 1000, name=r\"H $p_T$ [GeV]\").Double()\n",
    "higgs_pt.fill(ak.flatten(higgses.pt))\n",
    "\n",
    "fj_higgs_pt = hist.Hist.new.Reg(10, 0, 1000, name=r\"H $p_T$ [GeV]\").Double()\n",
    "fj_higgs_pt.fill(ak.flatten(fj_higgses.pt))\n",
    "\n",
    "j_higgs_pt = hist.Hist.new.Reg(10, 0, 1000, name=r\"H $p_T$ [GeV]\").Double()\n",
    "j_higgs_pt.fill(ak.flatten(j_higgses.pt))\n",
    "\n",
    "j_ratio = j_higgs_pt / higgs_pt\n",
    "j_ratio_uncert = np.abs(clopper_pearson_interval(num=j_higgs_pt.values(), denom=higgs_pt.values()) - j_ratio)\n",
    "fj_ratio = fj_higgs_pt / higgs_pt\n",
    "fj_ratio_uncert = np.abs(clopper_pearson_interval(num=fj_higgs_pt.values(), denom=higgs_pt.values()) - fj_ratio)\n",
    "\n",
    "fig, axs = plt.subplots(2, 1, height_ratios=[2, 1])\n",
    "hep.histplot(j_higgs_pt, label=\"H(bb) matched to 2 AK5 jets\", ax=axs[0])\n",
    "hep.histplot(fj_higgs_pt, label=\"H(bb) matched to 1 AK8 jets\", ax=axs[0])\n",
    "hep.histplot(higgs_pt, label=\"All H(bb)\", ax=axs[0])\n",
    "axs[0].set_ylabel(\"Higgs bosons\")\n",
    "axs[0].set_xlim(0, 1000)\n",
    "axs[0].set_ylim(1e-1, 1e6)\n",
    "axs[0].semilogy()\n",
    "axs[0].legend(loc=\"upper right\")\n",
    "hep.histplot(\n",
    "    j_ratio,\n",
    "    yerr=j_ratio_uncert,\n",
    "    label=\"H(bb) matched to 2 AK5 jets\",\n",
    "    ax=axs[1],\n",
    ")\n",
    "hep.histplot(\n",
    "    fj_ratio,\n",
    "    yerr=fj_ratio_uncert,\n",
    "    label=\"H(bb) matched to 1 AK8 jets\",\n",
    "    ax=axs[1],\n",
    ")\n",
    "axs[1].set_ylabel(\"Efficiency\")\n",
    "axs[1].set_xlim(0, 1000)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cca76d7c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T07:13:45.762606Z",
     "start_time": "2023-06-30T07:13:45.303839Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "n_jets = hist.Hist.new.Reg(13, -0.5, 12.5, name=\"AK5 Jets\").Double()\n",
    "n_jets.fill(ak.count(jets.pt, axis=-1))\n",
    "hep.histplot(n_jets)\n",
    "plt.ylabel(\"Events\")\n",
    "plt.xlabel(\"AK5 Jets\")\n",
    "plt.xlim(-0.5, 12.5)\n",
    "plt.ylim(1, 1e5)\n",
    "plt.semilogy()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f1445fc8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T07:13:46.229215Z",
     "start_time": "2023-06-30T07:13:45.763905Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "n_fjets = hist.Hist.new.Reg(7, -0.5, 6.5, name=\"AK8 Jets\").Double()\n",
    "n_fjets.fill(ak.count(fjets.pt, axis=-1))\n",
    "hep.histplot(n_fjets)\n",
    "plt.ylabel(\"Events\")\n",
    "plt.xlabel(\"AK8 Jets\")\n",
    "plt.xlim(-0.5, 6.5)\n",
    "plt.ylim(1, 1e5)\n",
    "plt.semilogy()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07352b65",
   "metadata": {},
   "source": [
    "### After minjet condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9d0ae10f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T07:13:46.288415Z",
     "start_time": "2023-06-30T07:13:46.232241Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/awkward/_connect/_numpy.py:195: RuntimeWarning: invalid value encountered in sqrt\n",
      "  result = getattr(ufunc, method)(\n"
     ]
    }
   ],
   "source": [
    "# small-radius jet info\n",
    "pt = arrays[\"Jet/Jet.PT\"][mask_hhh6b]\n",
    "eta = arrays[\"Jet/Jet.Eta\"][mask_hhh6b]\n",
    "phi = arrays[\"Jet/Jet.Phi\"][mask_hhh6b]\n",
    "mass = arrays[\"Jet/Jet.Mass\"][mask_hhh6b]\n",
    "btag = arrays[\"Jet/Jet.BTag\"][mask_hhh6b]\n",
    "flavor = arrays[\"Jet/Jet.Flavor\"][mask_hhh6b]\n",
    "\n",
    "# large-radius jet info\n",
    "fj_pt = arrays[\"FatJet/FatJet.PT\"][mask_hhh6b]\n",
    "fj_eta = arrays[\"FatJet/FatJet.Eta\"][mask_hhh6b]\n",
    "fj_phi = arrays[\"FatJet/FatJet.Phi\"][mask_hhh6b]\n",
    "fj_mass = arrays[\"FatJet/FatJet.Mass\"][mask_hhh6b]\n",
    "fj_sdp4 = arrays[\"FatJet/FatJet.SoftDroppedP4[5]\"][mask_hhh6b]\n",
    "# first entry (i = 0) is the total SoftDropped Jet 4-momenta\n",
    "# from i = 1 to 4 are the pruned subjets 4-momenta\n",
    "fj_sdmass = np.sqrt(\n",
    "    fj_sdp4.fE[..., 0] ** 2 - fj_sdp4.fP.fX[..., 0] ** 2 - fj_sdp4.fP.fY[..., 0] ** 2 - fj_sdp4.fP.fZ[..., 0] ** 2\n",
    ")\n",
    "fj_nsub = arrays[\"FatJet/FatJet.NSubJetsSoftDropped\"][mask_hhh6b]\n",
    "fj_taus = arrays[\"FatJet/FatJet.Tau[5]\"][mask_hhh6b]\n",
    "# just saving just tau21 and tau32, can save others if useful\n",
    "fj_tau21 = fj_taus[..., 1] / fj_taus[..., 0]\n",
    "fj_tau32 = fj_taus[..., 2] / fj_taus[..., 1]\n",
    "fj_areap4 = arrays[\"FatJet/FatJet.Area\"][mask_hhh6b]\n",
    "fj_area = np.hypot(fj_areap4.fP.fX, fj_areap4.fP.fY)\n",
    "fj_charge = arrays[\"FatJet/FatJet.Charge\"][mask_hhh6b]\n",
    "fj_ptd = arrays[\"FatJet/FatJet.PTD\"][mask_hhh6b]\n",
    "fj_ehadovereem = arrays[\"FatJet/FatJet.EhadOverEem\"][mask_hhh6b]\n",
    "fj_neutralenergyfrac = arrays[\"FatJet/FatJet.NeutralEnergyFraction\"][mask_hhh6b]\n",
    "fj_chargedenergyfrac = arrays[\"FatJet/FatJet.ChargedEnergyFraction\"][mask_hhh6b]\n",
    "fj_nneutral = arrays[\"FatJet/FatJet.NNeutrals\"][mask_hhh6b]\n",
    "fj_ncharged = arrays[\"FatJet/FatJet.NCharged\"][mask_hhh6b]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f502ce26",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T07:13:46.295648Z",
     "start_time": "2023-06-30T07:13:46.290452Z"
    }
   },
   "outputs": [],
   "source": [
    "N_JETS = 10\n",
    "N_FJETS = 3\n",
    "MIN_JET_PT = 20\n",
    "MIN_FJET_PT = 200\n",
    "MIN_JETS = 6\n",
    "\n",
    "mask_minjets = ak.num(pt[pt > MIN_JET_PT]) >= MIN_JETS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fbef7cd8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T07:13:47.436190Z",
     "start_time": "2023-06-30T07:13:46.628090Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fj_match = fj_match[mask_minjets]\n",
    "fj_higgses = higgses[mask_minjets][fj_match > -1]\n",
    "\n",
    "j_match = j_match[mask_minjets]\n",
    "match = np.logical_and(j_match[:, :, 0] != j_match[:, :, 1], ak.all(j_match > -1, axis=-1))\n",
    "j_higgses = higgses[mask_minjets][match]\n",
    "\n",
    "higgs_pt = hist.Hist.new.Reg(10, 0, 1000, name=r\"H $p_T$ [GeV]\").Double()\n",
    "higgs_pt.fill(ak.flatten(higgses.pt))\n",
    "\n",
    "fj_higgs_pt = hist.Hist.new.Reg(10, 0, 1000, name=r\"H $p_T$ [GeV]\").Double()\n",
    "fj_higgs_pt.fill(ak.flatten(fj_higgses.pt))\n",
    "\n",
    "j_higgs_pt = hist.Hist.new.Reg(10, 0, 1000, name=r\"H $p_T$ [GeV]\").Double()\n",
    "j_higgs_pt.fill(ak.flatten(j_higgses.pt))\n",
    "\n",
    "j_ratio = j_higgs_pt / higgs_pt\n",
    "j_ratio_uncert = np.abs(clopper_pearson_interval(num=j_higgs_pt.values(), denom=higgs_pt.values()) - j_ratio)\n",
    "fj_ratio = fj_higgs_pt / higgs_pt\n",
    "fj_ratio_uncert = np.abs(clopper_pearson_interval(num=fj_higgs_pt.values(), denom=higgs_pt.values()) - fj_ratio)\n",
    "\n",
    "fig, axs = plt.subplots(2, 1, height_ratios=[2, 1])\n",
    "hep.histplot(j_higgs_pt, label=\"H(bb) matched to 2 AK5 jets\", ax=axs[0])\n",
    "hep.histplot(fj_higgs_pt, label=\"H(bb) matched to 1 AK8 jets\", ax=axs[0])\n",
    "hep.histplot(higgs_pt, label=\"All H(bb)\", ax=axs[0])\n",
    "axs[0].set_ylabel(\"Higgs bosons\")\n",
    "axs[0].set_xlim(0, 1000)\n",
    "axs[0].set_ylim(1e-1, 1e6)\n",
    "axs[0].semilogy()\n",
    "axs[0].legend(loc=\"upper right\")\n",
    "hep.histplot(\n",
    "    j_ratio,\n",
    "    yerr=j_ratio_uncert,\n",
    "    label=\"H(bb) matched to 2 AK5 jets\",\n",
    "    ax=axs[1],\n",
    ")\n",
    "hep.histplot(\n",
    "    fj_ratio,\n",
    "    yerr=fj_ratio_uncert,\n",
    "    label=\"H(bb) matched to 1 AK8 jets\",\n",
    "    ax=axs[1],\n",
    ")\n",
    "axs[1].set_ylabel(\"Efficiency\")\n",
    "axs[1].set_xlim(0, 1000)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91593fc8",
   "metadata": {},
   "source": [
    "### Using match_higgs_to_fjet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "451b6c7c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T07:13:47.473226Z",
     "start_time": "2023-06-30T07:13:47.438590Z"
    }
   },
   "outputs": [],
   "source": [
    "higgs_idx = match_higgs_to_jet(higgses, bquarks, jets, ak.ArrayBuilder()).snapshot()\n",
    "matched_fj_idx = match_fjet_to_jet(fjets, jets, ak.ArrayBuilder()).snapshot()\n",
    "fj_higgs_idx = match_higgs_to_fjet(higgses, bquarks, fjets, ak.ArrayBuilder()).snapshot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "61952674",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T07:13:47.550471Z",
     "start_time": "2023-06-30T07:13:47.476438Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:some Higgs bosons match to 3 small-radius jets! Check truth\n",
      "WARNING:root:some Higgs bosons match to 2 large-radius jets! Check truth\n"
     ]
    }
   ],
   "source": [
    "# when we sort by pT, are we sorting events or the particles within each events?\n",
    "# pT shape: [event, var*jet]\n",
    "# keep events with >= MIN_JETS small-radius jets\n",
    "mask_minjets = ak.num(pt[pt > MIN_JET_PT]) >= MIN_JETS\n",
    "# sort by btag first, then pt\n",
    "# argsort return the indices in the descending order, \n",
    "# axis=-1 means sorting in the most nested structure\n",
    "# so this sorted pTs in every event\n",
    "sorted_by_pt = ak.argsort(pt, ascending=False, axis=-1)\n",
    "sorted = ak.concatenate([sorted_by_pt[btag == 1], sorted_by_pt[btag == 0]], axis=-1)\n",
    "btag = btag[sorted][mask_minjets]\n",
    "pt = pt[sorted][mask_minjets]\n",
    "eta = eta[sorted][mask_minjets]\n",
    "phi = phi[sorted][mask_minjets]\n",
    "mass = mass[sorted][mask_minjets]\n",
    "flavor = flavor[sorted][mask_minjets]\n",
    "higgs_idx = higgs_idx[sorted][mask_minjets]\n",
    "matched_fj_idx = matched_fj_idx[sorted][mask_minjets]\n",
    "\n",
    "# keep only top N_JETS\n",
    "btag = btag[:, :N_JETS]\n",
    "pt = pt[:, :N_JETS]\n",
    "eta = eta[:, :N_JETS]\n",
    "phi = phi[:, :N_JETS]\n",
    "mass = mass[:, :N_JETS]\n",
    "flavor = flavor[:, :N_JETS]\n",
    "higgs_idx = higgs_idx[:, :N_JETS]\n",
    "matched_fj_idx = matched_fj_idx[:, :N_JETS]\n",
    "\n",
    "# sort by btag first, then pt\n",
    "sorted_by_fj_pt = ak.argsort(fj_pt, ascending=False, axis=-1)\n",
    "fj_higgs_idx = fj_higgs_idx[sorted_by_fj_pt][mask_minjets]\n",
    "\n",
    "# mask to define zero-padded small-radius jets\n",
    "mask = pt > MIN_JET_PT\n",
    "\n",
    "# mask to define zero-padded large-radius jets\n",
    "fj_mask = fj_pt > MIN_FJET_PT\n",
    "\n",
    "# index of small-radius jet if Higgs is reconstructed\n",
    "h1_bs = ak.local_index(higgs_idx)[higgs_idx == 1]\n",
    "h2_bs = ak.local_index(higgs_idx)[higgs_idx == 2]\n",
    "h3_bs = ak.local_index(higgs_idx)[higgs_idx == 3]\n",
    "\n",
    "# index of large-radius jet if Higgs is reconstructed\n",
    "h1_bb = ak.local_index(fj_higgs_idx)[fj_higgs_idx == 1]\n",
    "h2_bb = ak.local_index(fj_higgs_idx)[fj_higgs_idx == 2]\n",
    "h3_bb = ak.local_index(fj_higgs_idx)[fj_higgs_idx == 3]\n",
    "\n",
    "# check/fix small-radius jet truth (ensure max 2 small-radius jets per higgs)\n",
    "check = (\n",
    "    np.unique(ak.count(h1_bs, axis=-1)).to_list()\n",
    "    + np.unique(ak.count(h2_bs, axis=-1)).to_list()\n",
    "    + np.unique(ak.count(h3_bs, axis=-1)).to_list()\n",
    ")\n",
    "if 3 in check:\n",
    "    logging.warning(\"some Higgs bosons match to 3 small-radius jets! Check truth\")\n",
    "\n",
    "# check/fix large-radius jet truth (ensure max 1 large-radius jet per higgs)\n",
    "fj_check = (\n",
    "    np.unique(ak.count(h1_bb, axis=-1)).to_list()\n",
    "    + np.unique(ak.count(h2_bb, axis=-1)).to_list()\n",
    "    + np.unique(ak.count(h3_bb, axis=-1)).to_list()\n",
    ")\n",
    "if 2 in fj_check:\n",
    "    logging.warning(\"some Higgs bosons match to 2 large-radius jets! Check truth\")\n",
    "\n",
    "h1_bs = ak.fill_none(ak.pad_none(h1_bs, 2, clip=True), -1)\n",
    "h2_bs = ak.fill_none(ak.pad_none(h2_bs, 2, clip=True), -1)\n",
    "h3_bs = ak.fill_none(ak.pad_none(h3_bs, 2, clip=True), -1)\n",
    "\n",
    "h1_bb = ak.fill_none(ak.pad_none(h1_bb, 1, clip=True), -1)\n",
    "h2_bb = ak.fill_none(ak.pad_none(h2_bb, 1, clip=True), -1)\n",
    "h3_bb = ak.fill_none(ak.pad_none(h3_bb, 1, clip=True), -1)\n",
    "\n",
    "h1_b1, h1_b2 = h1_bs[:, 0], h1_bs[:, 1]\n",
    "h2_b1, h2_b2 = h2_bs[:, 0], h2_bs[:, 1]\n",
    "h3_b1, h3_b2 = h3_bs[:, 0], h3_bs[:, 1]\n",
    "\n",
    "# mask whether Higgs can be reconstructed as 2 small-radius jet\n",
    "h1_mask = ak.all(h1_bs != -1, axis=-1)\n",
    "h2_mask = ak.all(h2_bs != -1, axis=-1)\n",
    "h3_mask = ak.all(h3_bs != -1, axis=-1)\n",
    "\n",
    "# mask whether Higgs can be reconstructed as 1 large-radius jet\n",
    "h1_fj_mask = ak.all(h1_bb != -1, axis=-1)\n",
    "h2_fj_mask = ak.all(h2_bb != -1, axis=-1)\n",
    "h3_fj_mask = ak.all(h3_bb != -1, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "eb61984f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T07:13:47.922607Z",
     "start_time": "2023-06-30T07:13:47.890205Z"
    }
   },
   "outputs": [],
   "source": [
    "H_match_fj = fj_higgs_idx[fj_higgs_idx>-1]-1\n",
    "fj_higgses_v2 = higgses[mask_minjets][H_match_fj]\n",
    "\n",
    "H_match_j = higgs_idx[higgs_idx>-1]\n",
    "H1_match_2j = ak.sum(H_match_j==1, axis=-1, keepdims=True)==2\n",
    "H2_match_2j = ak.sum(H_match_j==2, axis=-1, keepdims=True)==2\n",
    "H3_match_2j = ak.sum(H_match_j==3, axis=-1, keepdims=True)==2\n",
    "H_match_j = ak.concatenate([H1_match_2j*1, H2_match_2j*2, H3_match_2j*3], axis=-1)\n",
    "H_match_j = H_match_j[H_match_j>0]-1\n",
    "j_higgses_v2 = higgses[mask_minjets][H_match_j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "97c0b9e7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T07:13:49.126692Z",
     "start_time": "2023-06-30T07:13:48.314801Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "higgs_pt = hist.Hist.new.Reg(10, 0, 1000, name=r\"H $p_T$ [GeV]\").Double()\n",
    "higgs_pt.fill(ak.flatten(higgses[mask_minjets].pt))\n",
    "\n",
    "fj_higgs_pt = hist.Hist.new.Reg(10, 0, 1000, name=r\"H $p_T$ [GeV]\").Double()\n",
    "fj_higgs_pt.fill(ak.flatten(fj_higgses_v2.pt))\n",
    "\n",
    "j_higgs_pt = hist.Hist.new.Reg(10, 0, 1000, name=r\"H $p_T$ [GeV]\").Double()\n",
    "j_higgs_pt.fill(ak.flatten(j_higgses_v2.pt))\n",
    "\n",
    "\n",
    "j_ratio = j_higgs_pt / higgs_pt\n",
    "j_ratio_uncert = np.abs(clopper_pearson_interval(num=j_higgs_pt.values(), denom=higgs_pt.values()) - j_ratio)\n",
    "fj_ratio = fj_higgs_pt / higgs_pt\n",
    "fj_ratio_uncert = np.abs(clopper_pearson_interval(num=fj_higgs_pt.values(), denom=higgs_pt.values()) - fj_ratio)\n",
    "\n",
    "fig, axs = plt.subplots(2, 1, height_ratios=[2, 1])\n",
    "hep.histplot(j_higgs_pt, label=\"H(bb) matched to 2 AK5 jets\", ax=axs[0])\n",
    "hep.histplot(fj_higgs_pt, label=\"H(bb) matched to 1 AK8 jets\", ax=axs[0])\n",
    "hep.histplot(higgs_pt, label=\"All H(bb)\", ax=axs[0])\n",
    "axs[0].set_ylabel(\"Higgs bosons\")\n",
    "axs[0].set_xlim(0, 1000)\n",
    "axs[0].set_ylim(1e-1, 1e6)\n",
    "axs[0].semilogy()\n",
    "axs[0].legend(loc=\"upper right\")\n",
    "hep.histplot(\n",
    "    j_ratio,\n",
    "    yerr=j_ratio_uncert,\n",
    "    label=\"H(bb) matched to 2 AK5 jets\",\n",
    "    ax=axs[1],\n",
    ")\n",
    "hep.histplot(\n",
    "    fj_ratio,\n",
    "    yerr=fj_ratio_uncert,\n",
    "    label=\"H(bb) matched to 1 AK8 jets\",\n",
    "    ax=axs[1],\n",
    ")\n",
    "axs[1].set_ylabel(\"Efficiency\")\n",
    "axs[1].set_xlim(0, 1000)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "5fc8b630",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T08:25:29.967237Z",
     "start_time": "2023-06-30T08:25:29.960090Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1787"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ak.sum(ak.num(fj_higgses_v2, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "84e59ad6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T09:09:28.211493Z",
     "start_time": "2023-06-30T09:09:28.203184Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_n_fj_higgs(in_file):\n",
    "    # small-radius jet info\n",
    "    part_pid = arrays[\"Particle/Particle.PID\"]  # PDG ID\n",
    "    part_m1 = arrays[\"Particle/Particle.M1\"]\n",
    "    condition_hhh6b = np.logical_and(np.abs(part_pid) == 5, part_pid[part_m1] == 25)\n",
    "    # note: see some +/-15 PDG ID particles (taus) so h->tautau is turned on\n",
    "    # explicitly mask these out, just keeping hhh6b events\n",
    "    mask_hhh6b = ak.count(part_pid[condition_hhh6b], axis=-1) == 6\n",
    "\n",
    "    particles = ak.zip(\n",
    "        {\n",
    "            \"pt\": arrays[\"Particle/Particle.PT\"],\n",
    "            \"eta\": arrays[\"Particle/Particle.Eta\"],\n",
    "            \"phi\": arrays[\"Particle/Particle.Phi\"],\n",
    "            \"mass\": arrays[\"Particle/Particle.Mass\"],\n",
    "            \"pid\": part_pid,\n",
    "            \"m1\": part_m1,\n",
    "            \"d1\": arrays[\"Particle/Particle.D1\"],\n",
    "            \"idx\": ak.local_index(part_pid),\n",
    "        },\n",
    "        with_name=\"Momentum4D\",\n",
    "    )[mask_hhh6b]\n",
    "\n",
    "    higgs_condition = np.logical_and(particles.pid == 25, np.abs(particles.pid[particles.d1]) == 5)\n",
    "    higgses = ak.to_regular(particles[higgs_condition], axis=1)\n",
    "    bquark_condition = np.logical_and(np.abs(particles.pid) == 5, particles.pid[particles.m1] == 25)\n",
    "    bquarks = ak.to_regular(particles[bquark_condition], axis=1)\n",
    "\n",
    "    \n",
    "    fjets = ak.zip(\n",
    "    {\n",
    "        \"pt\": arrays[\"FatJet/FatJet.PT\"],\n",
    "        \"eta\": arrays[\"FatJet/FatJet.Eta\"],\n",
    "        \"phi\": arrays[\"FatJet/FatJet.Phi\"],\n",
    "        \"mass\": arrays[\"FatJet/FatJet.Mass\"],\n",
    "        \"idx\": ak.local_index(arrays[\"FatJet/FatJet.PT\"]),\n",
    "    },\n",
    "        with_name=\"Momentum4D\",\n",
    "    )[mask_hhh6b]\n",
    " \n",
    "    N_JETS = 10\n",
    "    N_FJETS = 3\n",
    "    MIN_JET_PT = 20\n",
    "    MIN_FJET_PT = 200\n",
    "    MIN_JETS = 6\n",
    "    \n",
    "    fj_mask = fjets.pt > MIN_FJET_PT\n",
    "    fjets = fjets[fj_mask]\n",
    "    \n",
    "    pt = arrays[\"Jet/Jet.PT\"][mask_hhh6b]\n",
    "    mask_minjets = ak.num(pt[pt > MIN_JET_PT]) >= MIN_JETS\n",
    "    \n",
    "    fj_higgs_idx = match_higgs_to_fjet(higgses, bquarks, fjets, ak.ArrayBuilder()).snapshot()\n",
    "    sorted_by_fj_pt = ak.argsort(fjets.pt, ascending=False, axis=-1)\n",
    "    fj_higgs_idx = fj_higgs_idx[sorted_by_fj_pt][mask_minjets]\n",
    "\n",
    "    H_match_fj_idx = fj_higgs_idx[fj_higgs_idx>-1]\n",
    "    \n",
    "    n_fj_Higgs_bf = np.sum(fj_higgs_idx>0)\n",
    "    n_higgs_evt_bf = np.sum(np.sum(H_match_fj_idx>0, axis=-1)>0)\n",
    "    \n",
    "    fj_higgs_idx = fj_higgs_idx[:, :N_FJETS]\n",
    "    H_match_fj_idx = fj_higgs_idx[fj_higgs_idx>-1]\n",
    "    n_fj_Higgs_aft = np.sum(fj_higgs_idx>0)\n",
    "    n_higgs_evt_aft = np.sum(np.sum(H_match_fj_idx>0, axis=-1)>0)\n",
    "\n",
    "    return [n_fj_Higgs_bf, n_higgs_evt_bf, n_fj_Higgs_aft, n_higgs_evt_aft] # number of events that has non-zero Higgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "a4586130",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T09:12:55.119922Z",
     "start_time": "2023-06-30T09:09:59.657664Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1751, 1506, 1739, 1503]\n",
      "[1787, 1526, 1773, 1524]\n",
      "[1688, 1430, 1679, 1429]\n",
      "[1618, 1390, 1608, 1389]\n",
      "[1764, 1512, 1749, 1507]\n"
     ]
    }
   ],
   "source": [
    "in_filename = in_filenames[0]\n",
    "N_fj_higgs = []\n",
    "for i in range(5):\n",
    "    prefix = i*10000\n",
    "    with uproot.open(in_filename) as in_file:\n",
    "        events = in_file[\"Delphes\"]\n",
    "        keys = (\n",
    "            [key for key in events.keys() if \"Particle/Particle.\" in key and \"fBits\" not in key]\n",
    "            + [key for key in events.keys() if \"Jet/Jet.\" in key]\n",
    "            + [key for key in events.keys() if \"FatJet/FatJet.\" in key and \"fBits\" not in key]\n",
    "        )\n",
    "        arrays = events.arrays(keys, entry_start=prefix, entry_stop=prefix+10000)\n",
    "    n_fj_higgs = get_n_fj_higgs(in_file)\n",
    "    print(n_fj_higgs)\n",
    "    N_fj_higgs.append(n_fj_higgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "383f943c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T08:30:14.608478Z",
     "start_time": "2023-06-30T08:27:15.378231Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1708, 1442, 1695, 1439]\n",
      "[1782, 1516, 1761, 1513]\n",
      "[1710, 1447, 1695, 1446]\n",
      "[1673, 1429, 1657, 1426]\n",
      "[1726, 1483, 1710, 1480]\n"
     ]
    }
   ],
   "source": [
    "in_filename = in_filenames[-1]\n",
    "N_fj_higgs = []\n",
    "for i in range(5):\n",
    "    prefix = i*10000\n",
    "    with uproot.open(in_filename) as in_file:\n",
    "        events = in_file[\"Delphes\"]\n",
    "        keys = (\n",
    "            [key for key in events.keys() if \"Particle/Particle.\" in key and \"fBits\" not in key]\n",
    "            + [key for key in events.keys() if \"Jet/Jet.\" in key]\n",
    "            + [key for key in events.keys() if \"FatJet/FatJet.\" in key and \"fBits\" not in key]\n",
    "        )\n",
    "        arrays = events.arrays(keys, entry_start=prefix, entry_stop=prefix+10000)\n",
    "    n_fj_higgs = get_n_fj_higgs(in_file)\n",
    "    print(n_fj_higgs)\n",
    "    N_fj_higgs.append(n_fj_higgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b7b587c7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T08:33:48.746952Z",
     "start_time": "2023-06-30T08:30:14.610649Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1708, 1473, 1701, 1468]\n",
      "[1695, 1440, 1685, 1440]\n",
      "[1650, 1412, 1636, 1407]\n",
      "[1658, 1413, 1644, 1407]\n",
      "[1773, 1515, 1756, 1514]\n"
     ]
    }
   ],
   "source": [
    "in_filename = in_filenames[10]\n",
    "N_fj_higgs = []\n",
    "for i in range(5):\n",
    "    prefix = i*10000\n",
    "    with uproot.open(in_filename) as in_file:\n",
    "        events = in_file[\"Delphes\"]\n",
    "        keys = (\n",
    "            [key for key in events.keys() if \"Particle/Particle.\" in key and \"fBits\" not in key]\n",
    "            + [key for key in events.keys() if \"Jet/Jet.\" in key]\n",
    "            + [key for key in events.keys() if \"FatJet/FatJet.\" in key and \"fBits\" not in key]\n",
    "        )\n",
    "        arrays = events.arrays(keys, entry_start=prefix, entry_stop=prefix+10000)\n",
    "    n_fj_higgs = get_n_fj_higgs(in_file)\n",
    "    print(n_fj_higgs)\n",
    "    N_fj_higgs.append(n_fj_higgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "26779fee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T09:12:55.133699Z",
     "start_time": "2023-06-30T09:12:55.122034Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_n_j_higgs(in_file):\n",
    "    # small-radius jet info\n",
    "    part_pid = arrays[\"Particle/Particle.PID\"]  # PDG ID\n",
    "    part_m1 = arrays[\"Particle/Particle.M1\"]\n",
    "    condition_hhh6b = np.logical_and(np.abs(part_pid) == 5, part_pid[part_m1] == 25)\n",
    "    # note: see some +/-15 PDG ID particles (taus) so h->tautau is turned on\n",
    "    # explicitly mask these out, just keeping hhh6b events\n",
    "    mask_hhh6b = ak.count(part_pid[condition_hhh6b], axis=-1) == 6\n",
    "\n",
    "    particles = ak.zip(\n",
    "        {\n",
    "            \"pt\": arrays[\"Particle/Particle.PT\"],\n",
    "            \"eta\": arrays[\"Particle/Particle.Eta\"],\n",
    "            \"phi\": arrays[\"Particle/Particle.Phi\"],\n",
    "            \"mass\": arrays[\"Particle/Particle.Mass\"],\n",
    "            \"pid\": part_pid,\n",
    "            \"m1\": part_m1,\n",
    "            \"d1\": arrays[\"Particle/Particle.D1\"],\n",
    "            \"idx\": ak.local_index(part_pid),\n",
    "        },\n",
    "        with_name=\"Momentum4D\",\n",
    "    )[mask_hhh6b]\n",
    "\n",
    "    higgs_condition = np.logical_and(particles.pid == 25, np.abs(particles.pid[particles.d1]) == 5)\n",
    "    higgses = ak.to_regular(particles[higgs_condition], axis=1)\n",
    "    bquark_condition = np.logical_and(np.abs(particles.pid) == 5, particles.pid[particles.m1] == 25)\n",
    "    bquarks = ak.to_regular(particles[bquark_condition], axis=1)\n",
    "\n",
    "    \n",
    "    jets = ak.zip(\n",
    "        {\n",
    "            \"pt\": arrays[\"Jet/Jet.PT\"],\n",
    "            \"eta\": arrays[\"Jet/Jet.Eta\"],\n",
    "            \"phi\": arrays[\"Jet/Jet.Phi\"],\n",
    "            \"mass\": arrays[\"Jet/Jet.Mass\"],\n",
    "            \"idx\": ak.local_index(arrays[\"Jet/Jet.PT\"]),\n",
    "            \"flavor\": arrays[\"Jet/Jet.Flavor\"],\n",
    "        },\n",
    "        with_name=\"Momentum4D\",\n",
    "    )[mask_hhh6b]\n",
    " \n",
    "    N_JETS = 10\n",
    "    N_FJETS = 3\n",
    "    MIN_JET_PT = 20\n",
    "    MIN_FJET_PT = 200\n",
    "    MIN_JETS = 6\n",
    "    \n",
    "    pt = arrays[\"Jet/Jet.PT\"][mask_hhh6b]\n",
    "    btag = arrays[\"Jet/Jet.BTag\"][mask_hhh6b]\n",
    "    mask_minjets = ak.num(pt[pt > MIN_JET_PT]) >= MIN_JETS\n",
    "    \n",
    "    mask = pt > MIN_JET_PT\n",
    "    jets = jets[mask]\n",
    "    \n",
    "    higgs_idx = match_higgs_to_jet(higgses, bquarks, jets, ak.ArrayBuilder()).snapshot()\n",
    "    sorted_by_pt = ak.argsort(jets.pt, ascending=False, axis=-1)\n",
    "    sorted = ak.concatenate([sorted_by_pt[btag == 1], sorted_by_pt[btag == 0]], axis=-1)\n",
    "    higgs_idx = higgs_idx[sorted][mask_minjets]\n",
    "    higgs_idx = higgs_idx[:, :N_JETS]\n",
    "    \n",
    "    H_match_j = higgs_idx[higgs_idx>-1]\n",
    "    H1_match_2j = ak.sum(H_match_j==1, axis=-1, keepdims=True)==2\n",
    "    H2_match_2j = ak.sum(H_match_j==2, axis=-1, keepdims=True)==2\n",
    "    H3_match_2j = ak.sum(H_match_j==3, axis=-1, keepdims=True)==2\n",
    "    H_match_j_idx = ak.concatenate([H1_match_2j*1, H2_match_2j*2, H3_match_2j*3], axis=-1)\n",
    "    \n",
    "    n_j_Higgs = np.sum(H_match_j_idx>0)\n",
    "    n_higgs_evt = np.sum(np.sum(H_match_j_idx>0, axis=-1)>0)\n",
    "\n",
    "    return [n_j_Higgs, n_higgs_evt] # number of events that has non-zero Higgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "17dd17f3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-30T09:15:55.824157Z",
     "start_time": "2023-06-30T09:12:55.135664Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15509, 7958]\n",
      "[15434, 7973]\n",
      "[15384, 7978]\n",
      "[15174, 7873]\n",
      "[15453, 7968]\n"
     ]
    }
   ],
   "source": [
    "in_filename = in_filenames[0]\n",
    "N_fj_higgs = []\n",
    "for i in range(5):\n",
    "    prefix = i*10000\n",
    "    with uproot.open(in_filename) as in_file:\n",
    "        events = in_file[\"Delphes\"]\n",
    "        keys = (\n",
    "            [key for key in events.keys() if \"Particle/Particle.\" in key and \"fBits\" not in key]\n",
    "            + [key for key in events.keys() if \"Jet/Jet.\" in key]\n",
    "            + [key for key in events.keys() if \"FatJet/FatJet.\" in key and \"fBits\" not in key]\n",
    "        )\n",
    "        arrays = events.arrays(keys, entry_start=prefix, entry_stop=prefix+10000)\n",
    "    n_j_higgs = get_n_j_higgs(in_file)\n",
    "    print(n_j_higgs)\n",
    "    N_fj_higgs.append(n_j_higgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eecdbcb0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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