{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c0584767",
   "metadata": {},
   "source": [
    "# BAMBOO on charged / polar molecular dimers: why the charge-equilibrium term matters\n",
    "\n",
    "BAMBOO (`xnn.hybrid.models.bamboo.BAMBOO`, Gong *et al.* 2024) splits the atomic\n",
    "energy into a **semi-local** neural-network term (a graph equivariant\n",
    "transformer within a 5 Å cutoff) and a **charge-equilibrium electrostatic**\n",
    "term built from predicted partial charges and summed over *all* pairs. On\n",
    "molecular dimers held apart by more than the cutoff, the semi-local term is\n",
    "blind to the other monomer; only the all-pairs electrostatics can bind them.\n",
    "\n",
    "We reproduce the spirit of the LES long-range experiment (the same charged /\n",
    "polar bio-fragment dimers), but here the long-range physics is **intrinsic to\n",
    "BAMBOO**: we simply toggle `use_electrostatics` and show the electrostatic model\n",
    "extrapolates the binding curves that the short-range-only variant cannot.\n",
    "\n",
    "> **Fidelity vs. training.** The block-by-block fidelity notebook\n",
    "> (`examples/fidelity_checks/bamboo_verification.ipynb`) proves the\n",
    "> *implementation* matches `bytedance/bamboo` to machine precision. This\n",
    "> notebook is about what the *architecture* buys you.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f81a20d9",
   "metadata": {},
   "source": [
    "## 0. Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "09111c58",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:24:46.834911Z",
     "iopub.status.busy": "2026-07-20T04:24:46.834784Z",
     "iopub.status.idle": "2026-07-20T04:24:49.467149Z",
     "shell.execute_reply": "2026-07-20T04:24:49.466303Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xnn 0.1.0 | device: cuda\n"
     ]
    }
   ],
   "source": [
    "import logging, warnings\n",
    "logging.disable(logging.WARNING); warnings.filterwarnings(\"ignore\")\n",
    "import time\n",
    "import numpy as np\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import ase.io\n",
    "torch.set_default_dtype(torch.float32); torch.manual_seed(0)\n",
    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "import xnn\n",
    "print(\"xnn\", xnn.__version__, \"| device:\", DEVICE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73e4ce46",
   "metadata": {},
   "source": [
    "## 1. The three dimer classes and the train/test split\n",
    "\n",
    "Each dimer's total energy and forces are DFT references; we train on the\n",
    "*binding* energy (total minus the two isolated-monomer references), which has\n",
    "the same forces and removes the huge atomic baseline. We fit the **10 closest**\n",
    "separations and test on the **3 farthest** (pure extrapolation, well beyond the\n",
    "5 Å cutoff), the protocol of the long-range paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "363d9021",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:24:49.469238Z",
     "iopub.status.busy": "2026-07-20T04:24:49.469147Z",
     "iopub.status.idle": "2026-07-20T04:24:49.585040Z",
     "shell.execute_reply": "2026-07-20T04:24:49.584406Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6557514fd86b45bfa62e7fcb21812c5e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "lode_dimers:bio_scan:   0%|          | 0/39 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CC: 13 frames, separations 5.6-15.0 Å\n",
      "CP: 13 frames, separations 5.3-15.0 Å\n",
      "PP: 13 frames, separations 5.2-15.0 Å\n"
     ]
    }
   ],
   "source": [
    "from xnn.common.data import AtomicDataset, collate, load_dataset\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "raw = load_dataset(\"lode_dimers\", subset=\"bio_scan\", split=\"all\", return_info=True)\n",
    "CLASSES = [\"CC\", \"CP\", \"PP\"]\n",
    "\n",
    "def to_struct(d):\n",
    "    info = d[\"info\"]\n",
    "    e_bind = float(d[\"energy\"] - info[\"energyA\"] - info[\"energyB\"])\n",
    "    return {\"pos\": d[\"pos\"], \"atomic_numbers\": d[\"atomic_numbers\"].tolist(),\n",
    "            \"energy\": e_bind, \"forces\": d[\"forces\"],\n",
    "            \"distance\": float(info[\"distance\"]), \"label\": info[\"label\"]}\n",
    "\n",
    "data = {cl: sorted([to_struct(d) for d in raw if d[\"info\"][\"label\"] == cl],\n",
    "                   key=lambda s: s[\"distance\"]) for cl in CLASSES}\n",
    "splits = {cl: (data[cl][:10], data[cl][10:]) for cl in CLASSES}\n",
    "for cl in CLASSES:\n",
    "    print(f\"{cl}: {len(data[cl])} frames, separations \"\n",
    "          f\"{data[cl][0]['distance']:.1f}-{data[cl][-1]['distance']:.1f} Å\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0be85378",
   "metadata": {},
   "source": [
    "### The reference binding curves: CC has the deepest Coulomb tail, PP the weakest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a34e5ec1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:24:49.607784Z",
     "iopub.status.busy": "2026-07-20T04:24:49.607711Z",
     "iopub.status.idle": "2026-07-20T04:24:50.281108Z",
     "shell.execute_reply": "2026-07-20T04:24:50.280453Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vWaZM6rt//vkHUVFRGDFihM6f+/jx45DJZOjZs6fOn5v0V3p6Op48D8bVG3ewy+ewStdEvInSclREBcdcoR7mCvpYeno6XoSG48HjZ3jw5BkePnmOf69cV+naoporMt9vPt0ZLyUlRelmd36vUadfIGNX7wULFuTjFRBpl65GLvn6+sLT0zNrgSg8HJ6envDx8dFoYUoul8Pr+yU5F4QATJwxHyVLlIA8XY6UlFSkpP7/V0oqUtPS/nfs//9NTU1TepztscxrU1MRGxefZyEoLCISpWo00djrLiht5wrD+guwsJDLAEGu+X4lxoBx7v9JAwMDceDAgVwLe3lRpY+dO3fC2Ni4QB8yjhw5gtjYWJ0XC58+fZplQf3sfBqfJj6gNG/eHK1bt0bXrl1z/eOFCrc3UdG4evM2rt64g2u37uLazbuIT0jM+8KP2Jcto6XoSGeYK1TCXMFcoY+0sVaJTCZD0IvQjOLT42d4+PS54t8PH5LV6rOo5oqKFSsq1qf9WERERI67aatyjTr9Avnb1ZtI23Q1ckkul8PLyyv3EUNeXmjRogWSk5Px/v17vH//HklJSYrvc/rKvs07xLyNQnRsfI4xCQAio6LRrs8Qjb1OTTA1NYWpqQlMjI1hamoKE2NjmJjk9L0JTE1MYGKS9/dv3sbgn9Pn83x+becKFqV0TS4Doh4BMvX+eMiViQVQplauHzYiIyPx77//FuhpVOlj9erV2Lp1a4Ge5/Hjx3j9Oucqstg+ja9mzZqQSqUF6tPOzg716tXDkSNHtDJclfTPhw/JuHn3Pq7evKMoQoW8DMvSrlgxCzSqWwdNGrjiz70HEBsXh+y2qZBIJJBWKA+35o2A9JSsDcgwMFeojLmCuULfFHStktTUVDwNeqEY+ZRZeHr8LAipqWnZXmNmZoqa1arApUY1uNSsiprVqsBr9iK8eRud7Qe+op4rrK2t0aRJExw7dgwDBw4EkDGa6cyZM/juu+8U7V69eoWkpCRUq1ZNpWtU7fdTqu7qTaRtmhy5JJPJEBcXh5iYGMTGxiI2NlbxfUxMDO7evatU+PqUIAgICwtDhQoVCvSa1FG+bGmULlUS5mZmGV/mZjAzNYW5+f8em5uZwczMVKmN0jHzT679/+8fPH4GrzmL8ozh5L6t6Ni2FYyMjLT2OuVyOZwauiM8IlLUXMGilK4J8owPGUYmmt0hRp6a0a8gR27/WRcvXowHDx7A09MTzs7O+PHHHxEUFISNGzciMjISzZs3x+jRo2FiYoKUlBSsXr0at27dQkpKCjp37oyxY8dm28fHIiMjERISAldXV8WxnJ5j6dKlqFWrFnr37g1BEDBt2jQMGjQI5cuXx/bt25GSkoKQkBB069YNpUqVQmJiIhITE/Hff/9h8+bNuHLlCtasWQMjIyM4ODhg3LhxqFGjBgDg4MGDSExMRFxcHG7fvo327dtj8ODBipiOHz+OgwcPwsLCAl9++SXq1auX5ed14cKFbPsPDQ3NEl/58uURFRWl6Cen/g8ePIh3797h3bt3uHr1Klq3bo1Ro0YpnrNDhw44fPgwP2joMXXvgKenp+PxsyBcvXFHUYQKfPgEMplMqZ1EIoFzjapo1rAemjWqh6YN6qKOcw3FiIjWzRrB88spkEiglEAy15LxXjQnI550Db5o0i3mCuYK5gqDlJ/Faz98SMbjZ0GKwlPm1LunQS8gl2c/StLSshicq1eFc42q/1+AqgaXGtVQuZI0y4hDUxOT/88VEuaKbCxYsAA9evRAnTp10KJFC/z222+wsrLC+PHjFW3mzZuHK1eu4N69eypfo0obooLSxvS6vEYuAcD48eORlpaG+Pj4HItNmcc0uTaapaUlLC0tYWVlle1XbucU5y1M8fDqOYyb+VOez7fnd2+4t2qWZzt1tGvdHD+v3ZxnIahDm5ZaLUgBgLGxMVYu/l70XMGilFiMzTS/bXG6LM8mbdu2RXR0NAYMGIDSpUsjJCQEPXv2xNixY+Hq6oq9e/fi6dOnWLFiBZYtW4bLly9j6NChMDMzQ9WqVbPt41P37t1D9erVFY9ze44vvvgCbdu2RZ06deDj44Pg4GA0adIECQkJaNCgAWJjYzFgwABUq1YNJ06cwM8//4wJEyagV69eMDU1ReXKlTFgwAAIgoDnz5+ja9euCAwMhJWVFR49eoRly5Zh3LhxaNGiBebPn48PHz5g9OjR2LdvH77++mvMmTMH0dHRaNOmDa5evYpatWopvZac+re1tc0S34ULFxRTOXLr/9GjR1ixYgXGjx8PNzc3LFy4EGXLllWsDVKrVi2sXbtW3d8C0rL83AF/HRmFqzfv4NrNu7h68w6u37qLhMR3WfosX7YMmjWqh2YN66Fpw7poXN8VtsVtcozBo0cX+GxdnW0c3ovmZMQhT83xejIgzBXMFcwVBiOvxWsBYMSU77B1tw8ePQ1C0IvQbNsCQHEba7jUrAbn6lUVhSeXmtXgKK2g8ocU5orcde3aFUePHsWqVauwb98+uLq6wt/fHyVKlFC0qVixotL7lCrXqNKGqCA0Nb1OLpfj9evXCA0NRVhYGM6fP5/ryCUAiIqKwoABA/IVb/HixWFnZ4eSJUsq/ZuYmIi//vorz+vPnj2L9u3b5+s5syVLQcvK1vhx5RaEv36Tx8igxgV/vhzkqxCkA/qQK1iUKmJat24Nf39/eHp6AsiYw25sbAw/Pz8AGcMsDx06hBUrViAxMRFlypRB7dq1UbduXcX/JJ/28an379/D0tJS8Xjjxo05PkeVKlXw22+/4bPPPkN6ejquX89YmLN48eJwdXXF69evFc9z4sQJdOzYEYsW/W+4Y7ly5fD69WsEBAQo7igHBgaiefPmAAB3d3csXboUAFCpUiV8//33GD16NH7//XesWLFC8aYaExODbdu2Kdqq0v+n8V24cEFxXV79d+vWTTFqICoqCpcvX1Z80LC0tMT79+9V+c9JOpbXHfCF33nBwsJcUYR6GfYqSx+WlsXQqG7tj4pQ9eBQ0T7fO2Z59OiCXt06anzNEiKAuYK5gtSR1453AJD47j3+OX1B8bikXQnUrllNeeRTzWqoUL6cRnZSZK7IXdeuXdG1a9ccz2e3+Hhe16jahkgdqk6v+7Tg9Om/YWFhePXqVY6jMnNTs2ZN1KxZM0uRKfPfj78vUaJEjutGyuVy+Pv7Izw8POcCkVSKtm3b5jvGnBgbG2PlolnwHP21qAUhlQpBOiR2rmBRqoiLiIhAkyZN0K1bN8UxCwsLAMD333+PxYsXY+DAgYiNjcWKFSswaNCgPPssW7asYkvcvJ4DAJo2bYpXr16hX79+sLOzy7XvypUrKz3+7rvv8PDhQwwYMADFixfHo0eP8O7d/0aiODo6Kr6vVKmSYjveqKgoVKpUSanfO3fuZHm+vPrPSV79fzw32srKSmlBzOjoaJQrVy7P5yDdUuUO+Nyl3krHJRIJXGpWU0zDa9awHmrXqq6xXcaMjY21NrSY6GPMFf/rl7mCMqWlpeHewyeK6dinLvirdN3IgX0xrH8fuNSohjKlS2qk+JQb5gqiwkGV6XUDBw5E2bJlERERoVLBydjYGBUrVoSDgwPMzMxw/nzei15v2LAB7u7u+Y4/u+deuXIlPD09cy4QeXtrvDDi0b2TXhSExC4EfUrMXMGiVBFjYWGBlJT/LVLWoEEDHDhwQDHF4WO2trZYvnw5li9fjvPnz2PcuHEYNGhQlj4+Vb9+fQQHByM1NRVmZma5PodMJsOAAQPw66+/Yv369fD19VUMO83reQDgxo0bWLBgATp06IDIyEiEh4crnb948SLS0tJgamqKU6dOoXbt2gCA2rVr4+TJk2jRogUEQcCJEyfQqVOnfPWfW3yq9p+dW7duoUWLFiq1Jd05dPxsnnfAAaBVs0bo0akdmjWqh0b1aqO4Tc7T8Ij0FXMFcwUpEwQBL8NeKa0JeDPwvlq73g3r34dFIqIiQBPrPgmCgPDwcNy+fRsHDhzIc3pdamqqok1mwUkqlcLBwSHbf8uVK6eISS6Xw8nJKc+RS25ubvl6Dbnx8PCAj49PttMRvb29tbZuor4UhHjTIAOLUmLR9LxMFfurXbs2Hjx4gO7du6Nhw4aYO3cuTpw4gerVq6N+/fowMTFBixYt8M0332D58uW4du0a0tPTcevWLfTv3z/bPj5dvNbCwgJdu3bFqVOn0KNHD0yYMCHH55g9ezaqV6+OsWPHwt3dHR06dECDBg1QuXJlNGvWDHPnzkVYWBh69OiR7evx8PDAwIED0aRJEzx9+jTLuiVmZmZo2LAhypUrh9u3b+P06dMAgPnz56NDhw44d+4c4uLiYGZmhrFjx+ar/9ziU7X/7Bw+fBirV69WqS1pz4cPyfC7ch2nL17CqQuXcPf+I5Wum/TlYAz06Knl6KjIYK5grsgBc0Xu1N2QIlN8QiICbgcqFaEio95maWdb3AZNG9ZFs4b10LiBKyZ8Ox+v30SJtlYJEekHddZ9Sk1NxcOHD3H79m3cuXNH8W9MTEy+nnvhwoUYNWqUUsFJFaKNXPLwQK9evTS+cHteWBDSHyxK6ZrEOGM7blmySovN5ouJRUb/ubCzs8P9+/cREBAAa2trmJmZ4fjx4wgMDMTz588hk8kUW1W7ubmhSpUqMDU1xeLFi+Hs7JxtH9mZMWMG5s6dix49euT4HDKZDM2bN0eXLhlDJGvUqIGDBw8qdmpo0qQJrl+/jvv376NSpUooVqwY0tKUt0KeNm0a2rdvj7dv36Jx48a4d++eYkclAGjfvj2mTJmC+/fvo0GDBihTpgyAjLnQDx8+xPXr12FhYYGmTZsqplT16NFDsU5Hbv1/Gp+1tbXiutz679Onj9Lr6Natm2Kax507d2BlZYX69evn+t+RNC89PR137j1UFKH8rwYgJSX/BQH7cmW1EB0VOcwVzBXMFWrLz4YUQMZIvMAHjxXFp6s37+DR06AshSUTExPUq13rfzujNqyLGlUrKy08LpfJ9WbxWiIShyrrPrVt2xZ37txRKj49ePAgS/4CMoonzs7OKF++PM6cOZPn87u5uSlN/84PsUYuGRsba2RKIBkmiZDTth+ULwkJCbC1tUV8fDyKFy8OAEhOTkZwcDAqV66stC4G5LL/345bwyTGgLH+1BlPnz6Ndu3aaWz9nPxaunQp4uLisixIq88ePHgAGxsbODg4ZDmX4+9TYSdLAV4HAqaWGt+FLDQ8Aqcv+OP0xUs463cZUW+V70RJK5RHp7at0Mm9FdxbNkPTLn3z3L41+MZ5/fjAIU8F0pKA8q6AibnKl2X3Xkaaw1yRFXNF/jFXfOKjPOF7/Hy2G1JkFoX+3rIKjeu7KopP127exY2797KdhufkKFVaE7CBqwuKFcv7Z5pdUcyhor0oi9fmiblCL/Hna7gyp8DlNs3O2Ng4x/WebG1tUa9ePdSvX1/xr4uLCywsLFSeXhccHFzgv0U1MfVQ72jxM0Whp+VcoT9/lRYlxiYoCj96VdfF0JZP7zIbAhcXF7FDMAjqTstIfPcOFy5dxekLl3D64iU8ehqkdN7aygrurZqiU9tW6NyuNWpWq6K0AK0+bd9KRQBzhU4wVxQeqmxI8cVoL6Snp2c5//E0vGaN6qFpg3ooW6aUWnHoy1olRKR7fn5+ea77lFmQqlKlSpYClKOjY46bH+hyeh1HLpEuFf6/dqnIqlmzptghkBbkZ1qGTCZDwO1AnL54CacvXMLlgNuQyf43FcrIyAhNG9bNGA3VthWaNaoHM7Oc75zo2/atRFRwzBWFh9+VG3luSJGeng4jIyPUr+OsGAHVrFG9LNPwCoprlRAVLeHh4Th8+DA2bNigUvstW7bgyy+/zPfziDW9jkibWJQiIoPhe/RkttMywiMi4fnlFPhsXY16tWv9/7pQ/jjndwXxCYlKbas6OaKTe0YRql3r5rArYZuvGHgHnIhIfwiCgGdPn+Hk/n3YsveoStdsXbkEwwfwgxsR/U9+p6sJgoB79+7h0KFDOHToEAICAvL1fFWqVFE7VrEWBifSFhaldIDLdpEmFPXfI1WnZXw6R7+EbXF0cGuhKERVcXIscCy8A07aUNT/HyfNKAq/R4mJiTh//jxOnDiBkydPIigoKO+LPlLJoaKWIiMiQ6TqTnkymQz+/v6KQlRwcLDinEQiQfPmzdGzZ0+sWrUKkZG5rEEqlcLNza1AMXN6HRUmLEppkampKSQSCaKiolCmTJkc5wcT5UUQBERFRUEikcDU1FTscEShyrQMuVwOY2NjtG7WSLFAeaN6dXjniPQacwVpSmHNFYIg4M6dO4oi1KVLl5TWATM1NUXrJvXQ2b01vDftwJu3MbluSOHWvLEuwyciPZbXTnk7duyAhYUFDh06hH/++QcxMf/bFMfc3BydOnVCr1690KNHD5QvXx5AxrRwXaz7RFRYsCilRcbGxpBKpQgLC0NISIjY4ZCBy7yzUlST2M3AByq12/TrIowc5KnlaIg0h7mCNEnfcoW6Ozi9ffsWp0+fxokTJ3Dq1Cm8fq18U6JKlSro2rUrunbtinZuLWH9LhgwtUSN6tW4IQURqUQul8PLyyvXUfhDhgxROl6qVCn06NEDvXr1QufOnWFlZZXlWq77RJQ/LEppmbW1NapXr25wO/uQ/jE1NS1yf0g/f/4c+/7ajX17duL2/ScqXVO5UtYt0on0HXMFaYo+5QpVp8QAGdNirl69ipMnT+LEiRMICAhQ+qBoaWmJ9u3bo0uXLujatSuqVav20cUpwLuMb7khBRGpSpWd8gCgQoUKGDBgAHr16oWWLVvCxCTvj9Bc94lIdSxK6YCxsTHfgIhUFBISgr///ht79+7FjRs3FMeNjY1hYmKMlJTUbK/jtAwydMwVVJjkNSXGx8cHTZo0URShzpw5g/j4eKW2rq6uitFQrVq1grm5uUrPzQ0piEgVERERKrX7+eefMWjQoHz3z3WfiFTDohQRiS40NBR///039u3bh6tXryqOGxsbo307d3zRuTn69PgMF6/egueXUwCA0zKIiPSUKlNi+vfvD5lMpnTOzs4OnTt3RpcuXdC5c2dUrKj+guTckIKI8qLKiCcgY6QUEWkPi1JEJIpXr17Bx8cHe/fuxX///ac4bmRkhLZt26J///7w8PBAGbviwOtAwNSS0zKIiAyAKlNiZDIZJBIJmjVrhq5du6JLly5o0qQJbywQkdalpaVh9erVmDdvXq7tNLVTHhHljkUpIiqQ/Cxi+/r1a+zfvx979+6Fv7+/4o65RCKBm5sbvvjiC/Tt21exewmAjLVCPsJpGURE+i00NFSldhs3bsSYMWO0HA0R0f9cvHgRkyZNwv379wEANWrUwJMnT7hTHpGIWJQiIrWpsohtVFQU9u/fj3379uHixYtIT09XtG3ZsiX69++Pvn375muaBqdlEBHpF0EQcP36dezYsQPbt29X6Zrq1atrOSoiogwRERGYPn06du3aBQAoXbo0li1bhhEjRuDgwYPcKY9IRAZXlHr69Ck2b96MyMhIuLq6Yvz48dluxZnfa9Tpl6goy2sR2/Hjx+Pp06c4f/485HK54nyzZs3wxRdfoF+/fnBw4E55RESGLCQkBDt37sSOHTvw5Mn/dkk1MjJSugnxMU6JISJdkclkWLNmDebNm4fExERIJBKMHz8eixYtQsmSJQFwpzwisRlUUer27dto3bo1+vTpg+bNm2PLli3YvXs3Ll++DDMzM7WvUadfoqJMlUVs169frzjWuHFjRSHKyclJV2ESEZEWxMXF4e+//8aOHTvg5+enOF6sWDH07t0bQ4cOxbt379C/f38AOWxMwSkxRKRlfn5+mDRpEgIDAwEATZs2xdq1a9G4cdadmrlTHpF4DKooNXPmTLi5uWHHjh0AMnZucXR0xB9//IFx48apfY06/RIVZaosYgsAY8aMwXfffYeqVavqICoiItKW1NRUnDhxAjt27MCRI0eQkpKx3p9EIkG7du0wdOhQeHh4oHjx4oprjI2NOSWGiLQmp3VNIyMjMWPGDMVU4pIlS2Lp0qUYNWoUjIyMRI6aiD5lMEWplJQUnD17Fhs3blQcK126NNq3b49//vkn2+KRKteo0y9RUff06VOV2rVr144FKSIiPaPqBhWCIODatWvYsWMH/vrrL0RHRyvO1alTB0OHDsWgQYMglUqzfR5OiSEibclpXdMuXbrg77//RkJCAiQSCcaMGYOffvoJpUqVEjFaIsqNwRSlXr58CZlMBkdHR6XjlSpVwsWLF9W+Rp1+gYyCV+ZdQgBISEjI1+shMkQ3b97E2rVrFaMK82Jvb6/liIiIKD9U2aAiKCgIO3fuxM6dO5VuQpQvXx6DBg3C0KFDUa9ePcVUvNxwSgwRaVpO65qGhYVhy5YtADKWjli7di2aNm0qRohElA8GU5RKTk4GAFhbWysdt7a2VpxT5xp1+gWAJUuWYMGCBfl4BUSGKSUlBX///TfWrl2LK1euKI6bmpoiLS0t22u4iC0Rkf7Ja4OKsWPH4t69e7h06ZLinKWlJfr06YOhQ4eiQ4cOMDExmD8diagQym1d00x2dna4dOkS1wYmMhAG85eFra0tACAmJkbpeHR0NEqUKKH2Ner0CwCzZs3CtGnTFI8TEhK4kxgVKi9fvsSGDRuwefNmREVFAcgoRPXr1w+TJk1CREQE+vXrB4CL2BIR6TtVNqjIXMpAIpGgQ4cOGDp0KPr06QMbGxudxkpElBNV1jWNjY3Ff//9x1GaRAbCYFZ6c3BwQIkSJXDv3j2l44GBgXB1dVX7GnX6BQBzc3MUL15c6YvI0AmCgNOnT6N3796oXLkylixZgqioKEilUixatAihoaHYtWsXWrZsib59+8LHxwcVK1ZU6kMqlcLHx4eL2BIR6RFVN6gYN24cQkNDcfr0aQwbNowFKSLSKxERERptR0TiM5iilEQiwaBBg7BlyxbF+k3+/v64du0ahgwZomj3+++/Y/r06Spfo2q/RIVZfHw8Vq5ciVq1aqFz5844dOgQ0tPT0b59e+zfvx/BwcGYM2cOypUrp3Sdh4cHQkJCcP78eezevRvnz59HcHAwC1JkEO7fvw8vLy8MGDAAixcvVmltwKCgIMycORO9e/fOcjODSJ+p+gGtbdu2WW42EBHpC1XXK+W6pkSGw2CKUgCwePFi2NnZwcXFBZ07d0bXrl0xY8YMdOjQQdHm2rVrOH78eL6uUaUNUWEUGBiI8ePHo2LFipg6dSqePHkCGxsbTJ48GQ8ePMDZs2fh4eGR6xoimYvYDhw4EO7u7pyyRwYhICAATZo0wfv379GpUyccPnwYrVu3znUtwWXLlqFz586QyWQ4dOgQ3r59q8OIidR3/fp1rF+/XqW2/CBHRPqsZcuWsLCwyPG8RCKBg4MD1zUlMiASIbdV4vRQeno6rly5gsjISLi6uqJatWpK569fv47o6Gh07dpV5WtUbZObhIQE2NraIj4+nlP5SHS5bfedmpqKAwcOYO3atfDz81NcU7t2bUyaNAlDhgzRr+kashTgdSBgagkYc8FKlclTgbQkoLwrYGKu8mVF5b2sU6dOsLCwwJEjRwBkrD8hlUrx888/Y+LEidleExoaiooVK+LVq1dwcHDA+fPn871eRVH5+ZL4BEHA2bNnsWTJEpw7dy7P9pkbVAQHBxvezQXmCfUxV+gl/nxz5uXlhVWrVmV7LnNdUy4jQdlirlCflnOFwSx0nsnIyAgtW7bM8XyTJk3yfY2qbYgMQU7bfc+bNw9hYWH4/fff8fr1awAZo5w8PDwwadIktGnTRqXtvYkMXXJyMs6fP4/NmzcrjtnZ2aFDhw44fvx4jkUpbmZBhkAul+PAgQNYunQpbty4AQAwMTHBoEGD0KBBA8UmLdyggogMjbe3t6IgNW3aNOzbty/L37ve3t4sSBEZGIMrShFRznLa7jssLAxjx45VPC5fvjzGjRuHsWPHokKFCroOk0hUoaGhkMvlWYpMDg4OuHjxokafKyUlBSkpKYrHqqxbRaSOlJQU7NixA8uXL8fTp08BAMWKFcOYMWMwbdo0VKpUCQDg6OiY7Y0LfpAjIn3m6+urKKovW7YMM2bMwPLly3OcGUBEhoNFKaJCIrftvjOZm5tj27Zt6Nu3L0xNTXUYHZF2rVq1Ks8pShs2bED58uUVRSJLS0ul89bW1rmuKaWOJUuWYMGCBRrtk+hjiYmJ2LhxI3799VfFYuZ2dnaYMmUKpkyZgtKlSyu19/DwQK9evfhBjogMxuXLlzF48GAIgoAJEyYoNrXKXNeUiAwbi1JEhYQq232npKSgfPnyLEhRodO6dWs4Ojrm2iZzrTRbW1sAGetIfSw6Ohp2dnYajWvWrFmKO7tAxkgpTgMkTXjz5g1WrVqFtWvXIi4uDgBQsWJFfPPNNxgzZgysra1zvJYf5IjIUDx79gyff/45kpOT0b17d6xatYrLTRAVMixKERUCcrkcPj4+KrVVdVtwIkPSsGFDNGzYUKW2UqkUJUuWxJ07d/DZZ58pjt++fRv16tXTaFzm5uYwN1d9QUii3DaqAICQkBD88ssv2LJli2JkX82aNfHdd99h8ODBMDPj4q1Eqvr999+xcuVKxUZHv/zyCxo1alSgawICArBixQr8999/MDExQevWrfHjjz/meeOEsnr79i0+++wzvH37Fo0aNcJff/2V647QRGSYjMQOgIjUJwgCfH19Ua9ePaxdu1ala7jdNxV1EokEQ4YMwZYtWxSjpc6dO4cbN25g6NChinZr166Fl5eXWGFSEeTr6wsnJye0a9cOgwYNQrt27eDk5ARfX18EBgZiyJAhqFatGtauXYvk5GQ0adIEvr6+ePDgAUaOHMmCFFE+7Nq1C1999RXmzp2LGzduoHbt2ujQoQNevXql9jVyuRwTJ05E79694efnh3/++QevX79Ghw4d8P79e129tELhw4cP6NWrF54+fYpKlSrh6NGjuY4AJSLDJRFyW4CGVMatW0mXBEHA8ePHMXfuXNy8eRNAxpQkQRCQmJiY7bpSBrvdN7dvVQ+3+c5VYmIievTogYcPH6JWrVoICAjA9OnTldZ/Gj16NK5cuYJ79+4ByChcrVq1CsnJyTh58iRatWqF0qVLY8CAARgwYIBKz1tUfr6UfzltVJGdzp07Y+bMmXB3d+c0FoB5oiCKcK6oU6cO3NzcsH79egBAeno6KlasiNGjR+PHH3/U2DXBwcGoUqUKzp49i/bt26sUW2H4+RZEeno6+vfvDx8fH9ja2uK///6Di4uL2GGRoWOuUJ+WcwXHPxIZEEEQcO7cOXz//fe4cuUKgIzFmadNm4avv/4a586dg6enJyQSCbf7JsqFjY0NLly4gFu3biEyMhJ16tTJstbT5MmTMWjQIMXjatWqYcSIEQCA8ePHK47XqlVLJzFT4aXKRhUA4OnpiVmzZqk8VZWIshcXF4f79+/jhx9+UBwzMjJC+/bt4e/vr7FrgP/tuvrp5hqUsxkzZsDHxwempqY4ePAgC1JEhRyLUkQGwt/fH3PnzsWFCxcAZGz1PWXKFEyfPl2xu5KHhwd8fHy43TeRCiQSSa4f7uvXr6/02NHRkWuCkFaoslEFAEyaNIkFKSINyJxuV7ZsWaXjZcuWxY0bNzR2TXp6OqZPn47atWujcePGOcaTkpKi2BkW+F8hqyhau3YtVqxYAQD4448/uCkDURHAohSRngsICMDcuXNx4sQJAICZmRnGjx+PWbNmoXz58lnac7tvIiLDouoGFNyogihn48ePx86dO3Ntc/fuXVSpUkXx2MhIeXldIyOjPEcs5ueayZMn48aNG/D39891ge4lS5YoTR8vqg4fPoyvvvoKALB48WIMHjxY5IiISBdYlCLSU3fv3sW8efNw6NAhAICJiQlGjRqFOXPm5LmlPLf7JiIyDOnp6bhz545KbblRBVHOVq5ciV9++SXXNlZWVgD+N9opKipK6XxUVFSWkVCZ8nvN1KlT8ddff+Hs2bNwdnbONa5Zs2Zh2rRpiscJCQl5/q1X2Fy/fh0DBgxAeno6Ro8ejVmzZokdEhHpCItSRDqW13bfjx49wg8//IC9e/cCyLgDN3ToUMybN0/p7h4RERm2O3fuYNKkSbh06VKu7TI3qnBzc9NRZESGx9zcHObmqi3AW7p0aVSrVg3//vsv+vTpozh+8eJFfPHFFwW+Ztq0adi+fTvOnDmDBg0aaDT2wuDTv4WlUil69OiBDx8+oEuXLli3bh03cSAqQozybkJEmpLbdt9BQUEYPnw4ateurShIDRgwAPfv38e2bdtYkCIiKiTi4uLg5eWFhg0b4tKlS7CyssLQoUMhkUiyfBDjRhVE2jF16lRs2bIFFy9eRHJyMn788Ue8efNGaSOLyZMno2nTpvm6Zvr06di2bRvOnDnDNeCykd3fws7Oznjz5g3q16+Pv//+G6ampmKHSUQ6xJFSRDqS03bf4eHh6Nu3L4yMjJCeng4A6N27NxYsWIC6deuKESoREWmBIAjYsWMHpk+fjjdv3gAAvvjiC6xYsQJSqRS9e/fmRhVEOjJp0iRER0ejT58+iI+PR/Xq1XH48GFUrVpV0SY5ORlJSUkqXxMdHY1ffvkFJiYmaNOmjdLzrV27FsOHD9fNi9NTOf0tLJPJAGT8fG1sbMQIjYhEJBHyWs2PVJKQkABbW1vEx8ejePHiYodDekYul8PJySnP3ZW6dOmCH3/8EU2aNNFRZAZAlgK8DgRMLQFjM7GjMRzyVCAtCSjvCpioPiWA72XaxZ9v0XX37l1MmjRJsXV8zZo1sWbNGnTs2FGpXV5TvCkbzBPqY64AAKSlpWU7OiclJQXp6ekoVqyYyte8e/cu2+ewsLDIdbHzjxW2ny+Q99/CmdOUg4OD+Z5H2sFcoT4t5wqOlCLSAVW3+545cyYLUkREhUh8fDzmz5+PNWvWQC6Xw9LSEvPmzcPXX38NM7OsfxRzowoi3ctpulhu6zzldI21tbVGYips8vpbWBAEhIaGws/Pj++BREUMi1JEOsDtvomIihZBELBr1y58++23iIyMBAB4enri119/LXK7ahER8W9hIsoJi1JEOmBpaalSO273TURk+AIDAzFp0iT4+fkBAGrUqIHVq1ejc+fOIkdGRCQOVf/G5d/CREUPd98j0iJBELBz506MGjUq13YSiQQODg7c7puIyEDI5XJcuHABe/bswYULFyCXy5GQkIBp06ahQYMG8PPzQ7FixfDTTz/h7t27LEgRUZHm5uYGqVSaZYfRTPxbmKjoUnmkVK1atfLV8aNHj/IdDFFhEhQUhPHjx+P06dMAAAcHB4SGhkIikSjtOsLtvqmoYT4hQ+fr65tll7ySJUsiPT0dcXFxAAAPDw/89ttvcHR0FClKIv3FPFD0GBsbY+XKlejbt2+Wc/xbmKhoU7ko9fjxY6xevVqltlOmTFE7ICJDl5aWht9++w0//PADPnz4AHNzc8yfPx/ffvstjhw5wu2+qchjPiFDltOW5jExMQCA8uXL448//kDXrl3FCI/IIDAPFE29evWCvb19lnWj+LcwUdEmET79qyqnhp+M7tBU28KiMG7dSvl3/fp1jBkzBnfu3AEAtG/fHhs2bED16tUVbbjddz5x+1b16PE230U5nzBXGLa8tjQHMj5chYSE8H1dl5gn1CdSrijKeUAVhTVX7N27FwMGDICdnR127dqFuLg4/i1MusNcoT4t5wqVR0rFxsaq/OT5aUtUGCQmJuL777/H6tWrIQgCSpYsiV9//RXDhg3LMnee231TUcd8QoYqry3NASAsLIxbmhPlgXmg6BEEAT/99BMAYOrUqejWrZvIERGRvlB5ofMSJUqo3Gl+2ubX9u3b0ahRI0ilUnTr1g13797NtX1gYCBGjhyJmjVronbt2hg3bhxevXql1Obbb79F6dKllb64yB6p6siRI3BxccGqVasgCAKGDBmCR48eYfjw4Tku5khUlOlLPiHKL25pTqQZzANFz9GjR3H37l3Y2NhwSiYRKcnX7ns1a9bE8uXLERkZqa14crVv3z6MHj0aU6ZMwdmzZyGVStGuXbsc45HL5Rg6dCjc3d1x5MgR7Nq1C0+fPkWHDh3w4cMHRbt3796hZcuWePTokeLryJEjunpZZKAiIiLQr18/fP755wgLC0OVKlVw6tQp7NixA2XKlBE7PCK9JnY+IcqvtLQ0HDx4UKW23NKcKG/MA0WHIAhYvHgxAGDixImws7MTOSIi0if5Kkp17NgRS5YsgVQqRZ8+ffDPP/9ALpdrK7YsFi9ejJEjR2LEiBGoWbMmNmzYABMTE6xbty7b9sbGxrh16xaGDx+OGjVqoH79+ti0aRMePXqEK1euKLU1MzNTGinFuzKUk/T0dGzcuBHOzs7w8fGBsbExvvvuOwQGBqJTp05ih0dkEMTOJ0T58erVK7Rv3x779u3LtR23NCdSHfNA0XH+/HlcvXoVFhYW+Prrr8UOh4j0TL6KUmvXrkVERAS2bduG+Ph49OzZE5UqVcL333+P4OBgbcUIAIiPj8fdu3fRsWNHxTFjY2O0b98efn5+OV736fSpzBFSFhYWSsfPnz8PR0dHuLq6YuLEiYiKitJg9GRo5HI5Lly4gD179uDChQuKP5IePHiANm3aYPz48YiPj0eTJk1w48YNLF26FJaWliJHTWQ4xMwnRPlx4cIFNGzYEP7+/ihevDhmzJgBiUSS5e8LbmlOlD/MA0VH5iip0aNHo1y5ciJHQ0T6Jl9FKSCjmDN48GCcO3cOz549w8iRI/Hnn3+iatWq6NChA/bs2aONOBXrQH36RlauXDmV124QBAHfffcdatWqhcaNGyuO29vb45dffsGFCxewbt063Lp1C61atUJSUlKOfaWkpCAhIUHpiwoHX19fODk5oV27dhg0aBDatWuHSpUqoV+/fqhfvz4uXboEKysrrFy5EpcvX0a9evXEDpnIIImVT4hUIQgCli1bhg4dOiAyMhKurq4ICAjAsmXL4OPjg4oVKyq1l0ql8PHx4ZbmRPnAPFD4XblyBefOnYOJiQmmT58udjhEpIckggb2WE1PT4ePjw/Gjx+P2NhYlbdtnTJlSp7J5vr166hcuTIePHiA2rVrw9/fH61atVKc//bbb3HkyBE8fvw4z+f7+uuv8ccff+Dff/9F3bp1c2z35s0bODg4YP369fjyyy+zbfPDDz9gwYIFWY4Xtq1bixpfX194enrm+jvco0cPrF27Fo6OjjqMrAjj9q3qEWmb74JSN58YCrF/vqSauLg4jBgxAocOHQIADBs2DOvXr1caESuXy+Hn54eIiAhuaS425gn16WGuKOx5QBWFKVd8/vnnOHLkCEaOHImtW7eKHQ4VZcwV6tNyrjApaHz+/v7YunWrYp2FkSNHqnztkiVLMH/+/FzblCxZEgAUC0d/Oq0uKipKpUWlZ86cia1bt+L06dO5FqQAoGzZsnB0dMSjR49ybDNr1ixMmzZN8TghIQEODg55xkH6Sy6Xw8vLK9c/fkqVKoUDBw7AxKTA/+sQ0ScKkk+INOX27dvw9PTE8+fPYWZmhtWrV2PMmDFZpusZGxvD3d1dnCCJCinmgcLl7t27OHLkCIyMjDBz5kyxwyEiPaXWJ+uIiAhs374dW7duxZMnT9C0aVP89ttvGDBgAGxsbFTux9raGtbW1iq1LVOmDCpXrgx/f3/07t1bcfzff//Nc6j87NmzsX79epw6dQpNmzbN87mSkpLw6tWrXItd5ubmMDdXvUpI+s/Pzw9hYWG5tomOjoa/vz8/iBBpiKbyCZEm/PHHH5g4cSKSk5NRqVIl+Pj4KE33JyLNYx4ovH766ScAQL9+/VCjRg2RoyEifZWvotSBAwewdetWHD9+HCVKlMCQIUOwf/9+1KlTR1vxKfnqq6/www8/oF+/fmjUqBF+/fVXvHr1CuPGjVO0+frrrxEQEKBY/Hzu3LlYu3YtTp06hWbNmmXpMyUlBZMnT8asWbNQpUoVREVFYdKkSTAxMcHAgQN18rpIP6i6Npmq7YgoZ2LnE6KPJScnY8qUKdi8eTMAoFu3bti5c6ditDYRaR7zQOH25MkTxYi3WbNmiRwNEemzfBWl+vbtiw4dOmDXrl3o3bu3zkcKeXl54c2bN+jYsSNSUlJQsWJF+Pr6KlXeExMTERsbCyBjVMuiRYtgbm6O7t27K/Xl7e2NIUOGwNzcHK1bt0aPHj3w4sULAEDr1q3h5+cHqVSquxdHolP1w4e9vb2WIyEq/MTOJ0SZgoKC4OnpiVu3bkEikWDhwoWYPXs2jIzyvRcMEeUD80DhtmzZMgiCgB49enBTICLKVb4WOg8JCYGTk5MWw1FNeno63r9/n+2Q3nfv3kEmk6FEiRIQBAHR0dHZ9mFjY5Ml+SUnJ8PCwkKtmArTgoRFUXBwMPr27Ytbt27l2EYikUAqlSI4OJiL2eoSFyVUjx4uXvsxfcknusZcoV+OHj2KoUOHIi4uDqVLl8bu3bvRqVMnscOi/GKeUJ+IuaKo5gFVGHquePnyJapWrQqZTIbLly+jefPmYodExFxREFrOFfm6Dfhx4nj//j0OHTqE3377TXHsyZMnOtkhw8jIKMc55tbW1ihRogSAjCJC6dKls/3K7m6MugUpMmzHjh1Do0aNcOvWLcXv1acL2mY+9vb2ZkGKSAP0JZ9Q0SCXy3HhwgXs2bMHFy5cQGpqKubMmYOePXsiLi4OzZs3x82bN1mQItIh5oHC65dffoFMJkO7du1YkCKiPKk1Nv3JkyeoXbs2xo0bp7QD3Y8//oi9e/dqLDgibZLL5Zg/fz569OiB2NhYNG3aFPfv38f+/ftRsWJFpbZSqRQ+Pj55LqpPRPnDfELa5uvrCycnJ7Rr1w6DBg1Cu3btULx4ccUCvFOmTMHFixe5gy6RSJgHCpfIyEhs2rQJADBnzhyRoyEiQ6BWUWrq1Kn44osvsiz47OXlhV9++UUjgRFpU3R0NHr06IGFCxdCEARMnDgR//77LxwcHODh4YGQkBCcP38eu3fvxvnz5xEcHMyCFJEWMJ+QNvn6+sLT0zPLzqopKSkAMjZHWbVqFczMOIyfSCzMA4XLb7/9huTkZDRr1gzt27cXOxwiMgD5WlMqk52dHYKDg1GiRAlIJBLF0Np3796hVKlSij/2ihJDn/tdlAQEBMDT0xMvXrxAsWLFsHHjRgwdOlTssCgnnP+tHj1fUypTUcsnzBW6I5fL4eTklKUglYnrBBYizBPq04NcUdTygCoMNVfExsaiUqVKSExMxKFDh/D555+LHRLR/zBXqE+f1pTKlJ6ejtTUVADKa++EhITA1tZWnS6JdGLz5s1o1aoVXrx4gWrVquHKlSssSBGJiPmEtMXPzy/HghQACIKA0NBQ+Pn56TAqIvoU80DhsWbNGiQmJsLV1RU9evQQOxwiMhBqFaU6deqkGE6bmTzevn2Lr776Cl27dtVcdEQa8uHDB4waNQpjxoxBamoqevXqhevXr6Nu3bpih0ZUpDGfkLZ8OhWooO2ISDuYBwqHd+/ewdvbGwAwe/ZsGBmp9TGTiIogE3UuWrFiBdzd3XHkyBEIgoB27dohICAAZcqUwc6dOzUdI1GBBAUFwdPTE7du3YKRkREWL16MGTNmMFkS6QHmE9KW8uXLq9TO3t5ey5EQUW6YBwqHjRs3IiYmBtWqVUO/fv3EDoeIDIhaRalKlSrh7t272LlzJwICApCeng4PDw8MHz7coOY9U+F37NgxDB48GHFxcShTpgz27NmDDh06iB0WEf0/5hPSBkEQcPDgwVzbZK4p5ebmppugiChbzAOGLzk5GStWrAAAzJw5k+v0EVG+qFWUAgAbGxtMmDBBk7EQqUUul8PPzw8RERGwt7dXfMBYsGABfvzxRwBAs2bN4OPjA6lUKmaoRJQN5hPSJEEQMG3aNKxatUpx7OPFkzMfA4C3tzc/PBHpAeYBw7Zt2zZERETAwcGBa7USUb6pPH+pd+/eKnean7ZEBeHr6wsnJye0a9cOgwYNQrt27eDo6IhGjRopClKTJk3Cv//+y4IUkZ5gPiFtEQQBX3/9tWJdk99//x379+9HxYoVldpJpVL4+PjAw8NDhCiJiHmg8EhLS8OyZcsAANOnT4eZGXc1I6L8UXmk1KFDh/Du3TuV2xJpm6+vLzw9PZXufgPAq1ev8OrVK5iZmWHr1q0YPHiwSBESUXaYT0gbMgtSK1euBJBRkBozZgwAoFevXllG1HKEFJF4mAcMX+ZMhX379iEkJASlS5fGqFGjxA6LiAxQvqbv2djYaCsOonyRy+Xw8vLKUpD6WMmSJTFgwAAdRkVEqmI+IU0SBAFTp05VTNnbtGkTRo8erThvbGwMd3d3kaIjouwwDxguX19feHl5ISwsTHEsLS0NJ06c4AhUIso3lYtSly9f1mYcRPni5+enlAiz8/r1a/j5+fGDCJGeYT4hTcqrIEVE+od5wHDlNFMhISEBnp6enBpNRPmmclGqefPm2oyDKF8iIiI02o6IdIf5hDRFEAR4eXlh9erVAIDNmzdz+giRAdCXPBAQEID169cjMjISrq6u+Pbbb1GqVCmNXePr64t169ahb9++hWIh99xmKgiCAIlEgqlTp6JXr16cIk1EKlN5oXMifWJvb6/RdkREZFg+LkhJJBIWpIgoX/777z+0atUKNjY2GDp0qOLx+/fvNXJNUFAQvLy88ODBAzx+/FibL0Vn8pqpIAgCQkND4efnp8OoiMjQsShFBqlJkyawsLDI8bxEIoGDgwPc3Nx0GBURGRKZTIbz58/jr7/+wv3791W65vXr1zhy5AiOHz+ON2/eaDlCyokgCPjqq69YkCIitc2ePRvdu3eHt7c3+vfvj6NHjyI8PBybN28u8DVpaWkYOHAgFi9ejLJly2r7pegMZyoQkTawKEUGJykpCX369EFycnK25yUSCQDA29ubQ4eJKFsxMTFo1qwZvvzyS+zatQstWrTA1KlTc2wvCAJGjhyJpk2bYtOmTVixYgUqV66M9evX6y5oAvC/gtSaNWsUBakvv/xS7LCIyIB8+PABfn5+6N27t+KYjY0NOnTogFOnThX4mtmzZ8PJyQnDhg3TRvii4UwFItIGFqXIoCQmJqJbt244ffo0rKys8MMPP0AqlSq1kUqlXGSRiHI1e/ZsfPjwAYGBgThy5AhOnz6NVatW4eTJk9m2FwQBbdu2RVBQEA4fPowzZ85g1apVmDJlCoKDg3UcfdElCAKmTJmiKEht2bKFBSkiyrfQ0FCkp6dn+zdkSEhIga45ceIE9u3bhw0bNqgcT0pKChISEpS+9JGbm1uW1/8xzlQgInXkqyj16Zvr7du3s7SZOXNmgQIiyklcXBw6deqEf//9F8WLF8epU6cwf/58hISE4Pz589i9ezfOnz+P4OBgFqSI9JyY+UQQBPz111/48ssvYW1tDQBo1qwZmjVrht27d2d7jZGREUaMGAETk//tD9KzZ0/I5XI8fPhQK3GSMkEQMHnyZKxdu1ZRkBo5cqTYYRGRmjSdB37++Wd07Ngx169Xr14BAFJTUwEAxYoVU+rD0tJSce5Tqlzz+vVrjBw5En/++Sfs7OxUjn3JkiWwtbVVfDk4OKh8rS4ZGxtj5cqV2Z7jTAUiUpdEyG77hJwaSyRKuy18+jinY0VBQkICbG1tER8fj+LFi4sdTqHz9u1bdO7cGbdu3ULJkiVx6tQpNGrUSOywSBdkKcDrQMDUEjA2EzsawyFPBdKSgPKugIm5ypfp6r1MzHzy8uVLVKpUCceOHUO3bt0Ux8eMGYObN2/ixo0bKvWzfft2jBw5EsHBwXB0dMy2TUpKClJSUhSPExIS4ODgwFyhArlcDj8/P0RERKB8+fKKkQcSiQRbt27FiBEjxA6R9AXzhPpEzBWazgMPHjxQFJ1y0qpVKxQrVgzh4eGQSqU4evQounfvrjg/atQoBAYG4tq1a1muVeWatWvXYtasWWjatKni/NWrV2FnZ4caNWrg1KlTMDLKOibA0HKFs7MzHj16pHTMwcEB3t7evDFM+ou5Qn1azhUmOZ4h0hMRERHo2LEjHjx4gLJly+LMmTNwdXUVOywi0iP+/v549uxZrm369u0LGxsbxMfHAwBKlCihdL5kyZKKc3l59uwZvv76a3z11Vc5FqSAjLvfCxYsUKlP+h9fX194eXllu8sTC1JElB0XFxe4uLio1LZixYooU6YMbt68qVRgunnzJpo0aaL2Nb169ULNmjWVrhs/fjzq1auHCRMmKEYTfcrc3Bzm5qp/0BNTXFwcnj59CgDYs2cPBEGAvb093NzcOEKKiNTCohTptdDQUHTo0AFPnz5FxYoVcfbs2SzJnojo0aNH8Pf3z7XNZ599BhsbG8XUi0+38E5MTMwyLSM7L1++RMeOHeHu7o6ff/4517azZs3CtGnTFI8z735Tznx9feHp6Znj6Ah9HDVARIZn+PDh2LJlC8aNG4eyZcvi2LFjuH37NtasWaNo8+uvv+Lhw4fYtGmTStdIpdIsay5ZW1vDwcEBHTt21N2L06IzZ85ALpejVq1aGDBggNjhEFEhYJBFqejoaERFRcHJyQkWFha5tg0LC8Pbt2+VjhUrVizbwkZ++iXtCwoKQvv27fHixQs4OTnh7NmzqFKlithhEZEeGj16NEaPHq1SW0dHR5iammZZzDYkJARVq1bN9drQ0FC4u7ujQYMG+Ouvv5TWmMqOId391gdyuRxeXl45FqQkEgmmTp2KXr168Y48ERXIggULcP/+fVSvXh1Vq1bFw4cPsXz5crRq1UrR5sGDB7hy5Uq+rinsTpw4AQBK09+JiAoi30UpHx+fXB9rU1paGkaPHo2//voLZcuWRXx8PLy9vXPdeWfRokX466+/4OTkpDhWrVo1pbjV6Ze069GjR+jQoQNevXqFatWq4dy5cxxdQFTIiJVPzMzM0KlTJ+zdu1dRyIqMjMS5c+eU7pD/999/iIqKQq9evQBk3ORwd3dHvXr1sG/fPpiamuok3qLEz88v2yl7mQRBQGhoKPz8/ODu7q67wIhIK8T8XGFpaYljx47hyZMniIyMhLOzM0qXLq3U5ptvvlGa1q3KNZ/auHFjvhY912eCICiKUl27dhU5GiIqLPJdlOrXr1+uj7Vp8eLFOHXqFB4/fgwnJyfs3r0bQ4cORYMGDdCgQYMcr+vYsWOuSU7dfkk7AgMD0bFjR7x58wYuLi44c+YM7O3txQ6LiDRMzHyybNkytGrVCl988QWaN2+OP/74A/Xr18fw4cMVbbZu3YorV66gV69eSE5ORvv27fHu3Tt89tln2LVrl6Jdq1atUL16dZ3FXphFRERotB0R6Tcx80CmGjVqoEaNGtmec3Z2zvc1n2rWrJnasembe/fuITw8HMWKFUObNm3EDoeICol8FaUSExO1FYdKNm3ahNGjRytGPQ0aNAiLFi3C5s2bsXbt2hyvk8lkePLkCWxtbVGuXDmN9UuaFxAQgC5duiAmJgb169fH6dOn87wDRUSGR+x8UqdOHdy5cwdbt27F48ePMW7cOIwePVpp9FOrVq1Qvnx5ABl5pGXLlgCAS5cuKfVVpUoVFqU0RNUbELxRQWT4xM4DlH+Zo6TatWvHpU6ISGPyVZSytrbWVhx5ioiIwKtXr5S2WAWA5s2b4+bNm7lee/jwYdy9exdRUVGwt7fHxo0b0a5duwL3S5r133//oVu3bkhISECzZs1w/PjxQjPcmYiUiZlPMjk5OWHhwoU5nh85cqTie2tra2zbtk0HURVtjRs3hqmpKdLS0rI9L5FIIJVK4ebmpuPIiEjT9CEPUP4cP34cAKfuEZFmibrQ+cuXLxETE5NrGxcXF5iZmSE6OhoAUKpUKaXzpUqVyrKQ+cfc3d0xe/ZsODo6IjU1FdOmTUOvXr0QGBiISpUqqd1vSkoKUlJSFI8TEhJyfR2kTC6Xw8/PDxEREbC3t4dMJkPv3r3x/v17tGnTBkePHoWNjY3YYRIRkY4IgoCvvvpKUZCSSCRKC55nbqXu7e3NRc6JiHQsMTFRscstFzknIk0StSi1efNmHD58ONc2R44cgYODg2JKxceFoMzHuS02+/FWpWZmZvjtt9+wfft2HDx4EF5eXmr3u2TJEixYsCDX2Cl7vr6+8PLyynYx206dOuHgwYOwtLQUITIiIhLLli1b8Mcff8DIyAhz587Fli1blPKEVCqFt7c3PDw8RIySiKhoOn/+PNLS0lC1alVUq1ZN7HCIqBARtSi1cOHCXKdOfKxixYqQSCRZFjd99epVvnZlMzU1RdmyZREaGlqgfmfNmoVp06YpHickJHB3OBX4+vrC09Mzx+2+v/zySxakiIiKmBs3bmDy5MkAMnbNnTVrFubOnas0otbNzY0jpIiIRMKpe0SkLUZiB6Aqa2trNG3aFMeOHVMcS05OxtmzZ9GhQwfFsdDQUDx+/BhAxlSA1NRUpX5CQkIQEhKCWrVq5avfT5mbm6N48eJKX5Q7uVwOLy+vHAtSEokEM2bMgFwu13FkREQklpiYGHh6eiIlJQWff/45vvvuOwCAsbEx3N3dMXDgQLi7u7MgRUQkEkEQFIucc+oeEWmaWiOlbt++neM5c3NzODo6wsrKSt2YcrRw4UJ0794dzs7OaNGiBby9vWFjY4Nx48Yp2ixYsABXrlzBvXv3kJaWhsaNG2Py5MmoXbs2Xr58iQULFsDZ2RmDBw/OV79UcH5+ftlO2cskCAJCQ0Ph5+cHd3d33QVGRKIRK5+QfkhPT8eQIUMQEhKCqlWr4s8//4SRkcHcLyMiDWAe0H9PnjxBSEgIzMzM+Dc6EWmcWkWpBg0a5Hre2NgYX3zxBTZv3qzRqVidO3fGsWPHsHr1ahw+fBiurq7w9/eHra2too2joyPi4uIAZKwhdejQIfz222/YsWMH7OzsMGrUKEyZMkVpG1NV+qWC+3SKZEHbEZHhEyufkH5YvHgxjh8/DgsLC+zfvx8lSpQQOyQi0jHmAf2XOXWvTZs2LBASkcapdTty06ZNqFmzJnx9fRESEoIXL15g//79qF69OlavXo1//vkHt2/fxpw5czQdLzp16oTDhw/j6tWr2Lx5MxwdHZXOz5s3Dz4+PorHlStXxqpVq+Dn54fDhw9j+vTpSgUpVfulgrO3t9doOyIyfGLmExLXyZMnMX/+fADAhg0bUK9ePZEjIiIxMA/oP07dIyJtkgg5LfCTC1dXV+zatQt169ZVOn7nzh0MHToUd+/exbVr1zBgwAAEBQVpLFh9lpCQAFtbW8THx3N9qRwkJyejRIkSWXY6zCSRSCCVShEcHMy1Q+h/ZCnA60DA1BIwNhM7GsMhTwXSkoDyroCJucqX6fq9rKjlE+aKDC9evEDDhg0RExODsWPHYuPGjWKHRIaMeUJ9epAriloeUIU+5YqkpCSULFkSKSkpuH//PlxcXESNh0htzBXq03KuUGuk1LNnzyCVSrMcl0qlePbsGQDAxcUFb9++Vad7KoQEQcDXX3+tKEhJJBKl85mPvb29WZAiKkKYT4qelJQU9OvXDzExMWjcuDFWrlwpdkhEJCLmAf128eJFpKSkwMHBAc7OzmKHQ0SFkFpFqRo1amDRokWQyWSKYzKZDD/++CNq1KgBALh16xaaNGmimSjJ4K1duxYbNmyARCLBzJkzUbFiRaXzUqkUPj4+8PDwEClCIhID80nRM3XqVFy/fh0lS5bE33//ne2UeiIqOpgH9NvHU/c+valMRKQJai10vnbtWvTs2RN79+5F/fr1IQgC7ty5g6SkJBw9ehQAcPr0aSxbtkyjwZJhOnXqFLy8vAAAS5cuxYwZM7Bo0SL4+fkhIiIC9vb2cHNz4wgpoiKI+aRo2b59u+IGxa5du+Dk5CR2SEQkMuYB/Za5yHnXrl1FjoSICiu11pQCgLi4OPz55594+PAhJBIJatWqheHDhxfZnXP0ae63Pnn06BGaN2+O+Ph4DBs2DNu2beNdFsofzv9Wjx6sE6KqopRPinKuuHv3Lpo3b44PHz7ghx9+UCxyTlRgzBPq05NcUZTygCr0JVc8f/4c1apVg4mJCaKjo4tc3qJChrlCfVrOFWqNlAKAEiVKKEa/EGUnJiYGPXv2RHx8PFq1aoXff/+dBSkiyoL5pPCLi4tD37598eHDB3Tt2hVz584VOyQi0iPMA/rp5MmTAIBWrVqxIEVEWqN2USopKQmPHj1CTExMlnMdO3YsUFBk+NLS0tCvXz88e/YMlSpVgq+vL8zNVa+qElHRwXxSuAmCgBEjRijywc6dO2FkpNaSlkRUSDEP6CdO3SMiXVCrKHXq1CkMGjQI0dHR2Z5Xc0YgFSJeXl44d+4crKyscPjwYZQtW1bskIhIDzGfFH4///wzDh06BDMzM/j4+KBUqVJih0REeoR5QD+lpKTg3LlzADIWOSci0ha1blVOnToVo0ePRkxMDARByPJFRdvatWuxfv16SCQS7N69G3Xr1hU7JCLSU8wnhY9cLseFCxewZ88e/Pbbb5g5cyYAYPXq1WjcuLHI0RGRvmEe0E/+/v5ISkpC+fLl+bc8EWmVWiOlXrx4gblz58LKykrT8ZCBO336tGJNgCVLluDzzz8XOSIi0mfMJ4WLr68vvLy8EBYWpnTc3d0dY8aMESkqItJnzAP66eOpe1wTloi0Sa2RUrVq1UJISIiGQyFD9/jxY/Tr1w9yuRzDhg3DjBkzxA6JiPQc80nh4evrC09PzywFKQC4ePEiDhw4IEJURKTvmAf004kTJwBw6h4RaZ9aRakJEyZg+PDhOH/+PEJDQxEWFqb0RUXPxzvttWzZkjvtEZFKmE8KB7lcDi8vr1yn2kydOhVyuVyHURGRIWAe0D+hoaG4f/8+jIyMuNA8EWmdWtP3Mofgt2/fPtvznP9dtKSlpeGLL77A06dP4ejoiAMHDnCnPSJSCfNJ4eDn55frh0dBEBAaGgo/Pz+4u7vrLjAi0nvMA/onc5RU8+bNUbJkSZGjIaLCTq2i1MOHDzUdBxmwqVOn4uzZs7CyssKRI0e40x4RqYz5pHCIiIjQaDsiKjqYB/RPZlGqa9euIkdCREWBWkWpWrVqaToOMhByuRx+fn6IiIiAvb097t27h3Xr1kEikWDXrl3cnYOI8oX5pHCwt7fXaDsiKjqYB/RLWloazpw5A4BFKSLSDZWLUo8ePQKQkTgyv88Jk0vhlNOuSkDGTnu9evUSISoiMjTMJ4WPm5sb7O3tcxwJJZFIIJVK4ebmpuPIiEgfMQ/or8uXLyMhIQGlS5dGo0aNxA6HiIoAlYtSzs7OADLmdWd+nxPO/S58MndVyum/bbVq1XQcEREZKuaTwsfY2BjVq1fPtiiVuemFt7c3jI2NdR0aEekh5gH9lTl1r0uXLjAyUmtPLCKifFG5KBUaGprt91T45bWrkkQiwddff43evXvzAwcR5Yn5pPD5999/8e+//wIAypYtizdv3ijOSaVSeHt7w8PDQ6zwiEjPMA/or+PHjwPg1D0i0h2Vi1JSqTTb76nw465KRKRJzCeFS1paGiZNmgQAGDduHNauXau09qCbmxtvWBCREuYB/RQREYHbt29DIpGgS5cuYodDREWEykWp27dvq9xp/fr11QiF9BV3VSIiTWI+KVzWrl2Le/fuoVSpUli8eDGMjY15g4KIcsU8oJ9OnToFAGjUqBHKlCkjcjREVFSoXJRq0KCByp1y7nfhwl2ViEiTmE8Kj4iICMyfPx9AxoYXpUqVEjkiIjIEzAP6iVP3iEgMKq9eFxsbq/jatGkTqlatir///hshISEICQnB33//japVq2Lz5s3ajJdE4ObmluvQaolEAgcHB+6qREQqYT4pPGbMmIGEhAQ0bdoUo0aNEjscIjIQzAP6Ry6XK0ZKdevWTeRoiKgoUXmkVIkSJRTfr1y5Ej4+PkrDaStVqoRq1aph2LBhWv3D9N27dzhy5AgiIyPh6uqKDh065Nr+119/RWpqapbj9erVU7zhnjx5Erdu3VI6X6ZMGf6B/f+MjY3Rs2dPrF+/Pss57qpERPmlL/mECubff//Fzp07IZFIsHbtWu7SREQq05c8EBsbCx8fH8Xnis8//1zxt21Br7lx4wbOnz8PS0tL9O3bF+XKldPWy9CI69evIzY2FiVKlEDTpk3FDoeIihC1/oJ89uwZHBwcshx3cHDAs2fPChxUTsLDw1G3bl388ssvuH//PgYPHowBAwbkOqw3Pj4ecXFxiq+goCDMmjULDx48ULQ5cOAAtm3bptQuMTFRa6/D0ISFhWHXrl0AAFtbW6VzUqkUPj4+3FWJiNQiVj6hgvl4cfOxY8eicePGIkdERIZKrDzw4sULuLq6Yvv27YiOjsaUKVPQq1cvpKenF+gaQRAwYcIEdOjQAcHBwQgODkanTp3w5MkTrb0WTcicute5c2eYmKg8boGIqMDUesepVasWFi5ciBUrVijetGQyGRYuXIhatWppNMCPzZw5E3Z2drh8+TLMzMzw4MED1K1bF1988UWORZEFCxYoPfb29oaZmRmGDRumdLxOnTpYunSp1mI3VIIgYNy4cUhISEDz5s1x8eJF/Pfff9xViYg0Qqx8QgXz6eLmRETqEisPzJgxAw4ODjh//jxMTEwwefJk1KpVC/v27cOAAQPUvmbjxo3Ytm0bbty4ARcXFwDA7NmzkZycrLXXogknTpwAwPWkiEj31CpKrVu3Dj169FAMtRUEAbdv30Zqair++ecfTccIIGOe84EDB/DTTz/BzMwMAODi4oLWrVvj77//VnmkzpYtW9C7d+8sO0qEh4dj1apVsLW1RYsWLVCjRg2NvwZDtH37dhw7dgzm5ubYunUrzMzMuKsSEWmMGPmECoaLmxORJomRB2QyGY4cOYJffvlFUQirWrUq2rRpA19f32yLUqpes2rVKgwaNEhRkAIAOzs7rbwOTYmKisL169cBAF26dBE5GiIqatQqSrVo0QJBQUHYvn27Yhpct27dMHz4cBQvXlyjAWZ6+fIl3r9/j5o1ayodr1mzJq5evapSH9euXcO9e/fw22+/ZTmXmJiIR48eITw8HGPHjsXcuXPx/fff59hXSkoKUlJSFI8TEhJUfCWG49WrV5g6dSoA4IcffoCzs7O4ARFRoSNGPqGC4eLmRKRJYn2u+PDhA6pVq6Z0vHr16rh8+bLa1yQmJuLhw4f47rvvcOLECdy4cQMVKlTA559/nmsBX+zPFadPn4YgCKhXrx4qVKig0+cmIlJ7wrCtrS2mTJlSoCf/559/EBgYmGub8ePHo0SJEnj37p3ieT/28bm8bNmyBZUrV86yOPqkSZOwfv16xSKF+/fvR79+/dC+fXu0bNky276WLFmSZWpgYZI5Hz4uLg6NGzfGt99+K3ZIRFRIaSKfkG5wcXMi0gZN5AFfX1/cvHkz1zbTpk1DyZIl8f79ewDIUvSytbVVnPuUKtfEx8cDAFavXg0rKyu0atUKO3fuxDfffIMzZ86gYcOG2fYt9ucKTt0jIjGpXZRKSkrCo0ePEBMTk+Vcx44dVe4jLi4u1zZyuRwAYGVlBSDrnYP4+HjFubye66+//sJ3332XZYcMV1dXpceZO2ScO3cux6LUrFmzMG3aNMXjhISEbBdpNFR79uzB4cOHYWpqij/++IMLHhKR1mgin5D2yWQyTJ48GQAXNycizdJEHjA1NYWFhUWubTI/A1hbWwP4XxEpU1xcnOLcp1S5JvNfS0tLXLx4UdGmU6dOmDlzJk6dOpVt32J9rpDL5bh48SIOHjyoiJOISNfUqjScOnUKgwYNQnR0dLbnc9sN72P9+vVDv379VGrr6OgICwsLPHv2DJ07d1Ycf/bsmUrrP+3btw9JSUkYMWKESs9nbGyc6wgsc3NzmJubq9SXoXn9+rXibtW8efNQp04dkSMiosJKU/mEtG/t2rUIDAzk4uZEpFGaygM9e/ZEz549VWrr6OgIS0tLPHnyRGkNpSdPnuS4uLoq15QoUQLly5dH8+bNla5t0aIFduzYkWM8Ynyu8PX1hZeXF8LCwhTHhg8fjlWrVnFXbSLSKbXG3U+dOhWjR49GTEwMBEHI8qUNJiYm6NGjB3bu3KkYPRUUFISLFy8qvXEeP34cW7ZsyXL9li1b0L179yzzpOVyOe7fv6907OTJkwgPD0fbtm218Er0myAImDRpEmJiYtCgQQN89913YodERIWYGPmE8i8iIgLz5s0DACxdupSLmxORxoiRB4yNjdGrVy9s374daWlpAIDHjx/Dz88Pnp6einYHDx7E6tWr83VN//794e/vrxS7n58fateurZXXog5fX194enoqFaSAjPVkPT094evrK1JkRFQUSQQ13u2trKzw5s0blabNaVJwcDBatmyJmjVromnTpti3bx9cXFxw9OhRxboWo0ePxpUrV3Dv3j3FdU+ePEHNmjVx9OhRdO/eXalPmUyG5s2bw9HREbVr18bLly+xd+9ejB49GmvWrFE5toSEBNja2iI+Pt6gF+fdt28f+vfvDxMTEwQEBKBevXpih0RFnSwFeB0ImFoCxmZiR2M45KlAWhJQ3hUwUf3uq67fy8TKJ2Ix1FwxdOhQ7Ny5E02bNsXly5e5lhTpF+YJ9elBrhArD4SGhsLNzQ1ly5ZF48aNcfDgQbRs2RJ///23Yprfp58rVLkmNjYW7u7uKFasGFq2bImrV68iODgY58+fz7JhU060mSvkcjmcnJyyFKQySSQSSKVSBAcHw9jYWKPPTSQq5gr1aTlXqDV9r1atWggJCdF5xb9y5cq4d+8e9u7di8jISPzyyy/w8PBQ+uP4s88+yzLd7M2bN5gzZ062i/eZmJjg2rVrOHHiBG7duoVmzZrhm2++Qd26dbX+evRNVFQUJk2aBACYPXs2C1JEpHVi5RNSHRc3JyJtEisPODg4IDAwEAcOHEBkZCS2bt2KLl26KK0926dPHzRt2jRf19jZ2eHatWs4fPgwXrx4AS8vL3Tv3l1vbr74+fnlWJACMmZNhIaGws/PD+7u7roLjIiKLLWKUhMmTMDw4cPx888/o1q1alkWDpdKpRoJLjulSpXCxIkTczyf3Rzo1q1bo3Xr1jleY2RkhM8++wyfffaZRmI0VFOmTMHbt2/h6uqKOXPmiB0OERUBYuaTTMnJyXkujvux9PR0pKenF4kNILi4ORFpm5h5wMbGBsOGDcvx/KczLFS5BshYI0rVdXN1LSIiQqPtiIgKSq2/qMeMGQMAaN++fbbnuQ6I4fH19cXevXthbGyMbdu2wcyMQxqJSPvEzCfz58/HypUr8e7dO1SuXBmrV6/OdTvsu3fv4ocffsD58+eRlJSEqlWrYt68eRgwYIDWYhSDXC6Hn58fIiIicOnSJS5uTkRaxc8VumVvb6/RdkREBaVWUerhw4eajoNEFB0djQkTJgAAvvvuOzRs2FDkiIioqBArn6xevRre3t74559/0LRpU/zyyy/o3bs37t27h2rVqmV7zZ49ezB27Fjs2rULpqam2LBhAwYNGoRq1aoVmhFE2e3GBABffPEFFzcnIq3g5wrdcnNzg1QqRXh4eLYFv8w1pdzc3ESIjoiKIrUWOqesDHXxWgAYMmQIdu3aBRcXF9y8eVPnW9IS5YqLEqpHDxav1WdVq1ZF7969sWLFCsUxJycneHp64pdfflGpj/T0dJibm2Pjxo348ssvVbpGn3++mbsx5fQhxcfHh9uEk35inlAfc4Ve0vbPN6f3+8ypk3y/p0KJuUJ9+rLQ+aNHjwBkLEaY+X1OatWqpWq3JLLDhw9j165dMDIywh9//MGCFBFpndj55O3btwgKCspyF7hNmza4evVqrtfKZDK8e/cOCQkJ+P3331G6dOlcp/wZCrlcDi8vr1ynyUydOhW9evXibkxEVGBi54GizsPDAzt37sTgwYOVjkulUnh7e7MgRUQ6pXJRytnZGUDGvO7M73PCwVeGITY2FuPHjwcAfPPNN0q7ixARaYs28klSUhJSU1NzbVO8eHEYGRnhzZs3AIDSpUsrnS9btmyeRamLFy+ib9++SExMRPHixbFr1y5UqFAhx/YpKSlISUlRPE5ISMjrpYiCuzERkS7xc4X4nJycAGRsIrV69WrY29vDzc2NNx6ISOdULkqFhoZm+z0Zlo8XsP3zzz8RERGBmjVrYsGCBWKHRkRFhDbyybfffovdu3fn2ubWrVuoXLmy4nF6errSeZlMlmXXp0916NABcXFxSE1NxaZNm9C7d2+cPn0abdu2zbb9kiVLDOL9lbsxEZEu8XOF+G7evAkAaN68OQYOHChyNERUlKlclPp4O1ZdbNFNmpfTArbDhg1DsWLFRIqKiIoabeSTdevWYd26dSq1zRzZFBkZqXT8zZs3uY56+piZmRkmTZqEHTt2YMeOHTkWpWbNmoVp06YpHickJMDBwUGl59Al7sZERLrEzxXiyyxKcYMjIhKbWrvvAUBUVBQ2b96s2DHDxcUFo0ePzjIdgvRDbgvYfv/996hVqxbnjxORKHSdT0qUKIE6derg7Nmz6NevH4CMUVPnzp3D2LFjFe0+fPgAmUwGGxubHPtKTEyEmVnOi2Wam5sbxFp9mbsx5TSFj7sxEZE28XOF7t26dQsAi1JEJD4jdS66cOECqlSpgk2bNuHDhw/48OEDfv/9d1SpUgX//vuvpmOkAlJ1AVu5XK7DqIiIxMsns2fPxh9//IFdu3YhKCgIkyZNQkpKCiZMmKBoM2XKFLRo0QIA8P79e3Tq1AlnzpzBq1ev8ODBA0ycOBHPnj1Teec9fWZsbAxvb+9sz2VOafT29uZaI0SkcfxcoXspKSm4d+8eABaliEh8ao2UmjJlCiZOnIglS5bAyCijrpWeno5Zs2Zh8uTJuHv3rkaDpILhArZEpK/EyicDBw7Ehw8fsGzZMkRGRsLV1RXnzp1Tmp5maWmp2L7WysoK8+fPx/Lly3Hr1i1YWVmhQYMGuHLlCho0aKCVGHXN2toaQEYR6uObGNyNiYi0iZ8rdO/evXuQyWQoVaqUXk4pJ6KiRSKosaWFpaUlwsPDYWdnp3Q8JiYGUqkUSUlJGgvQUCQkJMDW1hbx8fGKDzH6Ys+ePRg0aFCe7Xbv3s2FDkn/yFKA14GAqSVgnPM0KfqEPBVISwLKuwImqk8f0/V7WVHLJ/qcK9q0aQM/Pz94eXmhd+/eiIiI4G5MZBiYJ9SnB7miqOUBVWg7V2zatAljx45Fp06dcOrUKY33T6SXmCvUp+VcodZIqRo1auDZs2do0qSJ0vHnz5+jRo0a6nRJWsQFbIlIXzGf6Ac/Pz/4+fnBzMwMM2bMUHnBdyKigmIe0D0uck5E+kTlotTH07/Gjx+Pfv36YeHChWjSpAkEQUBAQADmzZuHWbNmaSVQUp+bmxsqVqyI8PDwbM9zAVsi0iXmE/3z008/AQBGjhzJghQRaR3zgLhYlCIifaLy9L3MhU5VocaMQIOnz1MygIyEv3HjxizHM/+7+vj4cL0Q0k8caqsePZiSkZOinE/0MVfcuHEDjRs3hrGxMZ48eYIqVaqIHRJR/jBPqE+kXFGU84AqtJkr0tLSYGNjg5SUFDx9+hTVqlXTaP9Eeou5Qn36Mn0vc4tWMjxJSUk4cuQIACh+KTJxAVsi0jXmE/2SOUpq4MCBLEgRkU4wD4jn0aNHSElJQfHixfmeT0R6QeWiVK1atbQZB2nR6tWr8erVK1SqVAkPHjzAtWvXuIAtEYmG+UR/PHjwAL6+vgDAaTJEpDPMA+LJnLpXv359xW6HRERiUmuhczIcsbGxWLp0KQBg4cKFsLS0hLu7u7hBERGRXsjMDx4eHnBxcRE5GiIi0jauJ0VE+obl8UJu2bJliIuLQ506dTB48GCxwyEiIj0RFBSE3bt3AwBmz54tcjRERKQLLEoRkb5hUaoQCw8Px8qVKwFkrBnCaXpERJRp+fLlkMvl6NKlCxo1aiR2OEREpGXp6em4ffs2ABaliEh/sChViC1cuBDJyclo1aoVevToIXY4RESkJ8LDw/HHH38AAObMmSNyNEREpAvPnj3Du3fvUKxYMdSsWVPscIiIALAoVWg9fvwYW7ZsAZCxZkh+tt4lIqLC7ddff0Vqairc3Nzg5uYmdjhERKQDmVP36tWrBxMTLi1MRPqBRalCau7cuZDL5ejRowdat24tdjhERKQn3r59iw0bNgDgWlJEREUJ15MiIn1kcEWpwMBATJ48GR07dsSdO3dUuiYgIACjRo1Cjx49MGvWLERHR6vVxlAEBATg77//hkQiwU8//SR2OEREpEdWrlyJpKQkNGrUCF26dBE7HCIi0hEWpYhIHxlUUWrhwoUYNGgQSpUqhbNnzyI2NjbPa/777z+0atUKNjY2GDp0qOLx+/fv89XGkMyaNQsAMGTIELi6uoocDRER6YuEhASsXr0aQMYoKU7tJiIqGgRBYFGKiPSSQRWlxo8fj8DAQIwZM0bla2bPno3u3bvD29sb/fv3x9GjRxEeHo7Nmzfnq42hOHPmDM6cOQNTU1MsWLBA7HCIiEiPrFu3DvHx8XB2dkbv3r3FDoeIiHTkxYsXiI2NhampKWrXri12OERECgZVlCpbtmy+2n/48AF+fn5Kf3jb2NigQ4cOOHXqlMptDEV6ejpmzpwJAJgwYQIqV64sckRERKQvkpKS8OuvvwLIGFFrZGRQfwIQEVEBZI6SqlOnDszMzESOhojofwr1X6ShoaFIT0+HVCpVOi6VShESEqJym+ykpKQgISFB6Uts+/fvx40bN2Btbc0tvomISMnmzZsRFRWFypUrY+DAgWKHQ0REOsSpe0Skr0TdC/Tnn3/GyZMnc22zfft2VKhQQa3+U1NTAQDFihVTOm5paak4p0qb7CxZskSvpselpaUpClHffPNNvkeVERFR4ZWamoqff/4ZAPDdd99xK3AioiKGRSki0lei/lXavXt3NGjQINc2dnZ2avefeW1MTIzS8ejoaMU5VdpkZ9asWZg2bZricUJCAhwcHNSOtaD++OMPPH36FKVLl8Y333wjWhxERKR/duzYgbCwMNjb22P48OFih0NERDp269YtACxKEZH+EbUo5eLiAhcXF631X7FiRZQpUwY3b95E9+7dFcdv3ryJJk2aqNwmO+bm5jA3N9da7PmRlJSEH374AQDw/fffw8bGRtyAiIhIb8hkMixduhQA8O2338LCwkLkiIiISJciIiLw+vVrGBkZoW7dumKHQ0SkpNCtKfXrr78q7c43fPhwbNmyBW/evAEAHDt2DLdv31a6U6xKG322evVqREREwMnJCePHjxc7HCIi0iN///03nj17hlKlSmHcuHFih0NERDqWOXXP2dkZlpaWIkdDRKTMoBaVOHXqFJYvX46UlBQAGWsn2dnZYdiwYRg2bBgA4MGDB7hy5YrimgULFuD+/fuoXr06qlatiocPH2L58uVo1apVvtroq9jYWMUd8IULF+rN6C0iIhKPXC6Hn58fwsPD8f333wMApk6dCisrK5EjIyLSL4GBgYiMjISLi4vK69jmdU1aWhoCAwMRGxsLR0dHVK9eXdNh5wvXkyIifWZQRSlXV1fMnDkzy/EqVaoovv/mm28QHx+veGxpaYljx47hyZMniIyMhLOzM0qXLq10vSpt9NXSpUsRFxeHOnXqYNCgQWKHQ0REIvP19YWXlxfCwsIUxyQSCRwdHUWMiohIv7x79w69evXCnTt3UL16ddy+fRtz5sxRFPLVvebKlSvo168fzM3NUalSJdy8eRMNGzbEwYMHRVtig0UpItJnBlWUsre3h729fa5tnJ2dsz1eo0YN1KhRI9drVWmjDzLvgN+/fx+//fYbgIzdAI2NjUWOjIiIxOTr6wtPT08IgqB0XBAEjBgxAtbW1vDw8BApOiIi/TF37lwEBwfj8ePHKFWqFE6fPo3OnTujTZs2aNOmjdrXfPXVV2jUqBEOHDgAiUSCqKgo1KpVC2vWrMGsWbN0+RIVMotSeW0wRUQkhkK3plRh5+vrCycnJ7Rr1w6TJ09GWloazMzMFFMaiYioaJLL5fDy8spSkPrY1KlTIZfLdRgVEZH+EQQBO3bswOjRo1GqVCkAQKdOndCwYUNs3769QNckJibCxcUFEokEAFCmTBmULVsWiYmJWn5V2Xv79i1evnwJAKhfv74oMRAR5cagRkoVdTndAU9NTUW/fv3g4+PDO+BEREWUn5+f0pS9TwmCgNDQUPj5+cHd3V13gRER6Znw8HBER0ejXr16Ssfr16+PO3fuFOia5cuXY+LEiShVqhQqVaqEU6dOwdTUFFOmTMkxnpSUFKUbzAkJCeq8rGzdunULAFCtWjXY2tpqrF8iIk1hUcpAqHoHvFevXpzGR0RUBEVERGi0HRGRIblz5w5CQ0NzbdO+fXtYWloiLi4OAFCyZEml86VKlVKc+5Sq19SrVw9169bF2rVr4eTkhPv372PMmDEoU6ZMjnEtWbIECxYsyDV2dXE9KSLSdyxKGQjeASciotzkteZiftsRERmS8+fP48yZM7m2adSoESwtLWFmZgYASEpKUjqflJSkOPcpVa4RBAFdu3ZFw4YN8ezZMxgZGeHt27do2LAhBEHA4sWLs+171qxZmDZtmuJxQkICHBwccn0tqmJRioj0HYtSBoJ3wImIKDdubm6QSqUIDw/PdlStRCKBVCqFm5ubCNEREWnX1KlTMXXqVJXaOjo6wtjYOMsN39DQUFSuXFnta8LDw/Hw4UP8+uuvMDLKWLq3dOnS+Oyzz3Dy5Mkci1Lm5uYwNzdXKfb8YlGKiPQdFzo3ELwDTkREuTE2NsbKlStzLEgBgLe3N6d4E1GRZ2FhgbZt28LX11dxLC4uDmfPnkXXrl0Vx+7evYt///1X5WvKlCkDExMTPH/+XOn5nj17hgoVKmjzJWUrISEBz549A8Cd94hIf3GklIHIvAOe0xQ+3gEnIqIePXqgRIkSWdZEkUql8Pb25mYYRET/b8mSJWjTpg0mTJiAFi1aYMOGDXBycsKoUaMUbVatWoUrV67g3r17Kl1jbm4OLy8vzJ49G4mJiahSpQpOnTqFCxcu4OzZszp/jbdv3waQMcqrdOnSOn9+IiJVcKSUgTA2Nsb06dOzPcc74EREBAA+Pj6Ii4tD+fLlcerUKezevRvnz59HcHAwC1JERB9p2rQprl27BolEgkOHDqFz587w9/dHsWLFFG3q1auHtm3b5uuaX375Bdu2bUNISAj+/vtvlChRAnfv3lXqR1c4dY+IDAFHShmQzMRiYWGB5ORkxXHeASciIgBYuXIlAGDixIno1KmTyNEQEem3unXrYt26dTmenzJlSr6vAYA+ffqgT58+BY6voFiUIiJDwKKUgQgJCcHOnTsBAOfOnUNKSgoiIiJgb28PNzc3jpAiIirirly5gmvXrsHMzAzjxo0TOxwiIhJZZlGK60kRkT5jUcpALF++HHK5HJ06dUKLFi3EDoeIiPRM5iipQYMGoWzZsiJHQ0REYkpKSsLDhw8BcKQUEek3rillAF69eoWtW7cCAObMmSNyNEREpG/Cw8Ph4+MDAPDy8hI5GiIiEtvdu3eRnp6OcuXKcXduItJrLEoZgBUrViAlJQWtWrVCmzZtxA6HiIj0zLp16yCTydCmTRvUr19f7HCIiEhkH68nlbkpEhGRPmJRSs+9ffsWGzZsAJAxSopJhYiIPvbhwwds3LgRAEdJERFRBi5yTkSGgkUpPbdy5UokJSWhYcOG6Nq1q9jhEBGRntm9ezeio6NRqVIl9OrVS+xwiIhID7AoRUSGgkUpPRYfH4/Vq1cDAGbPns1RUkREpEQQBMUC55MnT+ZOrEREhNTUVNy7dw8Ai1JEpP+4+54eW7duHeLj4+Hs7Iw+ffqIHQ4REemZCxcuIDAwEJaWlhg1apTY4RARkR64f/8+0tLSYGdnh0qVKokdDum59PR0pKamih2G9slSARkApAPpcrGjMSzy9IyfXXIKYCJkOW1mZgYjI/XHO7EopaeSkpLw22+/AQBmzZpVoP/IRERUOK1atQoAMGzYMNjZ2YkcDRER6QMuck6qSk1NRXBwMNLT08UORfsEAZCbAJJ0ACliR2N4BBMgKQzI5j3FyMgIlStXhpmZmVpdsyilpzZt2oSoqChUrlwZAwcOFDscIqJCJyAgAOvXr0dkZCRcXV3x7bffolSpUipd6+vri3Xr1qFv376YMGGCliPNXnBwMA4dOgQA+Oqrr0SJgYiI9E9mUapBgwYiR0L6TBAEREREwNjYGA4ODoV/EISQDqQlAxIjACzW5o+Q8fMztfj/n9//pKen49WrV4iIiICjo6NahXAWpfRQSkoKfv75ZwDAd999BxMT/mciItKk//77D+3atcOECRPQuXNnrFu3Dq1atcKNGzdgZWWV67VBQUHw8vKCXC5HnTp1dBRxVmvWrIEgCOjcuTOcnZ1Fi4OIiPQLFzknVchkMiQlJaFChQqwtLQUOxztE9IBY4FFKbXkXJQCgDJlyuDVq1eQyWQwNTXNd++FvBxqmLZv347w8HBUqFABI0aMEDscIqJCZ/bs2ejevTu8vb3Rv39/HD16FOHh4di8eXOu16WlpWHgwIFYvHgxypYtq6Nos3r37h22bNkCAPDy8hItDiIi0i8ymQx37twBwKIU5U4uz1hXSd0pV0SZMn+HMn+n8otFKT0jk8mwdOlSAMC3334Lc3NzkSMiIipcPnz4AD8/P/Tu3VtxzMbGBh06dMCpU6dyvXb27NlwcnLCsGHDtBxl7v7880/Ex8ejRo0a6Nq1q6ixEBGR/nj8+DE+fPgAa2trVK9eXexwyAAUxXXHEhMT8fr1a7HDUMuDBw8hCFkXG9c2uVyOoKCgbM8V9HfIoIpSsbGx8Pb2Rs2aNSGRSHDhwoU8r7l48SJ69uyJUqVKoVy5cujbty+ePn2q1Gb8+PGQSCRKX2JNydi7dy+CgoJQunRpjB07VpQYiIgKs9DQUKSnp0MqlSodl0qlCAkJyfG6EydOYN++fdiwYYPKz5WSkoKEhASlr4JKT09XLHA+ZcqUwr8GBBERqSxz6l79+vWZHyj/5DJAlqL5L7lMpad//vw5kpOTC/QSVOlj4hQvPHj4qEDPc//+gwJdr66GzVogJSXvhdo/ja+gxSxjY2MMHzESwcHBaveRE4NarOi3335DfHw81qxZg86dO+fZXi6XY/78+Zg+fTr+/PNPJCUlYfLkyejYsSPu378Pa2trRdu+ffvCx8dHm+HnKT09HT/99BMAYOrUqXmua0JERBl+/vlnnDx5Mtc227dvR4UKFRTbHhcrVkzpvKWlZY5bIr9+/RojR47Enj178rXL3ZIlS7BgwQKV26vi5MmTePLkCYoXL47hw4drtG8iIjJsXE+K1CaXAVGPAFnBikLZMrEAytQCjHMvP/Tv3x8bNmxA48aN1X6qvPoICgpG4L37aN/OXe3nAIB6jZsiOTFOb9d//jS+oSNH4bLfhQJN15wwfhyWLVuWrxu0qtDPn2AOFi5cCAAICwtTqb2xsbHSaKqSJUti1apVqFSpEq5cuYKOHTtqI0y1HTp0CA8ePICtrS0mT54sdjhERAaje/fuee4ylFlMyvw3JiZG6Xx0dHSOBaf9+/fj/fv3WLRoERYtWgQg405cTEwM7t27h1OnTmV7R3rWrFmYNm2a4nFCQgIcHBxUf2HZWLlyJQDgyy+/hI2NTYH6IiKiwoVFKVKbIM8oSBmZAMYaXGdKnprRryBHbuWHN2/eIDk5Gc+fP4eFhQWqVKkCS0tLCIKA0NBQ2NrawtbWVumayMhIvH37FoIgwMXFBW/fvs22j4/t3L0HvT/vqXQsu+eIiYlBfHwCKld2AgAkJycjKCgYLi7OePnyJYCM0UjGxsZwcXHGgwcPUadObcTGxiItLQ1ly5bF48dPkJaWBhsb6yw7HN67dx916tTG27dvYWZmhuLFiyvFlJycjMjIyFx3Rsyp/+zi2/HHFqVFyHPqPzOu6OhoAFDamfrzzz/HlK+8sGbNGo0W4wyqKKUJUVFRAJDlF/rEiROwtLSEra0t3NzcsHz5cjg5OeksLkEQFB90Jk+enCU+IiLKmYuLC1xcXFRqW7FiRZQpUwY3b95E9+7dFcdv3ryJJk2aZHtNr169ULNmTaVj48ePR7169TBhwoQc59Kbm5trdG3AR48e4eTJk5BIJJgyZYrG+iUiIsOXnp6OW7duAWBRigrA2EyzRSkASM97+t7evXvx/PlzzJkzBxYWFtixYweSkpIwdOhQGBsbIzo6GgMHDsTatWshCAI8PDzg7++PcuXKAQAuX76cbR+f3rT0/+8yvpo0UfH40n//YejIUf//HDEY2P8LrF3lDblcjvZdumLf7p1o0rgxxk+agvLlymHpT4uw7JdfIZfLMXj4SADA9cv+cG3YGCOGDcVFPz98/dUUTJk0EeMmTcbbt9F4n/QeRkZGOHboIGrWrAEAcG3YGMOGDMaFf/9FVNRbLJw/F99O+xoAsHL1Gnw/fwFKlrRDWloa9u7aAbfWrbP8zHLqP7v4GjZrgbio17CwsMi1f9eGjTFq5AicPX8eUVFvsWjBfEz9KmPAjLW1NSpWrIjbt28XaDTbp4pUUUomk2HatGlo0KABGjVqpDherVo17NmzB23btkV4eDimTp0KNzc33Lt3L8fiUEpKitJczoKuE3Ly5EncvHkTlpaWmDp1aoH6IiKi3A0fPhxbtmzBuHHjULZsWRw7dgy3b9/GmjVrFG1+/fVXPHz4EJs2bYJUKs2yBpW1dcZdKV2Ous1cS6pnz56oUqWKzp6XiIj0X1BQEBITE2FhYQFnZ2exwyHKlylTpuDPP/9UTL2Ty+WoWbMmVq1aBScnJ6SmpmLIkCHw9/eHg4MDbt++jcjISKVRPp/2kZ3w8FcoVy5jB2W5XI7ho8Zg1a8r4FSpElLTUjFk+Jfwv3QJrVu1wrbNmzBo2AhMmTgBT589w+aN6wEAa1d5Y+OmzbgdcFVpxFAbt9b4Y/PviscXzpxCbGwsXr+OxK49f2GF90r8vn6t4nyzpk3w59bNCAl5gUbNW8LTwwNyuRw//LgYt69fRdWqVfDn9p34cux4PH1wL8tryan/nOIDgOfPg/Lsv13bNti8cT0ePXqM1u06KIpSAFC+fHmEhYUVnqLUiBEj8Oeff+ba5unTp6hWrVqBn0sQBIwePRqPHz+Gv7+/0i/vt99+q/i+ePHi2Lt3L+zt7bF3794cFxvX9DohixcvBgCMGzcOpUuX1li/RESU1YIFC3D//n1Ur14dVatWxcOHD7F8+XK0atVK0ebBgwe4cuWKiFEqi42NVeRMLy8vkaMhIiJ9kzl1z9XVVW/XuSFSVXBwMEJDQzFz5kzFMSMjI8TExKBZs2YoV64cGjdujHbt2mHgwIEqF0nMzEwVa4gGB4cgNDQMM+fM/eQ5YgEAbdu4oWf3zzB95mw8fRCY5/9XA/t/ofheJpOh/6AhOHPuPOzLl0dKagqqf1LXGD50CADAyakS3Fq1xM1btyCTydCqZQtUrZpx83HY0MEYP3kKYmJiULJkyXz1n50bN2/m2X//L/oBAGrVqokPHz4gNTUVZqYZrz0lJUWjswAAkYtS27Ztw7Zt27T+PIIgYOzYsTh+/DjOnz+fZ5GrRIkSkEqlWXbp+5gm1gmRy+Xw8/PDuXPn4O/vD1NTU6UCGRERaYelpSWOHTuGJ0+eIDIyEs7OzlluCHzzzTeIj4/PsY+NGzfma9FzdWXmik2bNiEpKQm1a9dGu3bttP68RERkOORyOQ4ePAggYySDXC6HsbGxuEER5dPHyyFYWVnB1NQUAQEBsLCwyNL28uXLuH79Os6fP48ePXrgn3/+QaNGjXJcUiFTzRo1EBLyAq1atoSVlWXGc1y5lO1zxMbG4vDRf+Di7Iyjx45j4vhx2caa6eM+Llz8F6FhYXgbEQZTU1Ps3fc3Nm39Q6l9XFycYnOz2P//XiaTIS4uTtHm3bt3kMlkWdbGyqv/nH4OVlZWefb/cfFNIpEgPT1d8Tg4OBi1atXKtm91Ffp9QgVBwPjx43H48GGcO3dOpTVH4uLiEBYWhgoVKuTYxtzcHMWLF1f6yg9fX184OTmhXbt2+PHHHwEAZmZmenVXnoiosKtRowbc3NyyHaHq7OyM5s2b53hts2bNUKNGDW2Gp5Qrdu/eDQB49eoVDhw4oNXnJSIiw5GZK/bs2QMAOHLkCJycnODr6ytyZET5U65cOZw+fVqxjI6bmxsGDBgAPz8/3Lt3D/fu3UNqaio+fPiA+/fvw9LSEi1btkTJkiUVG9h83EdSUlKW5+jSuSP8Lv0HALC3t4db61YYMHgo/Pz9ce/efdy7d18xkmrEqLEYPXIEjh85iGW/rMCtW7c/irUsDh85inv37isVbTJZW1sh4vVr+F/6D8dPnMQPPy7O0uarr7/B5StX4L1qNZ48fYZWLVvArXUrPHsehJ9X/IorV69izPiJ6NalS5aiWV795xSfqv1nJyQkBMWKFUPlypXzbJsfha4oNXr0aNSpU0fxeNKkSTh48CDOnTuH2rVrZ2mfkpKCPn364Nq1a3j//j0ePnyI/v37w8bGBoMHD9ZKjL6+vvD09Myyi2BSUhI8PT2ZQIiIKMdcERcXx1xBREQAcs4V4eHhzBWkHnmq5r9UNHfuXJw7dw4DBw7E48ePsW/fPtSuXRuzZ8/GgAEDMGDAAERGRuLp06cYMGAABg4ciDlz5mDcuHHo1KlTtn186gvPvjh3/gKSk5MBAPt270RtFxfMnjsfA4YMw4AhwxAZGYmDhw7DysoS303/FuXLl8eWjRuwcPFPkMkyFm3/7efl+HXlKgwYMgwpKSmo/cngl+bNmmHyhPGY9f1cbN32J76dNhWVP9lIbfjQIZj7w0KcOXcexw8fhLW1NYoXL46zJ47hesANTP1mOsqWLYPtf2xWXFPbxQVGRkZ59p9dfEZGRir1/zEX51qKpY927tyFcePGQdMkgiAIGu9VS7Zt24aRI0dmOT5//nz88MMPADKKUleuXMG9e/fw9u1blClTJtu+Nm3ahNGjRwMA/vnnHyxbtgy3bt2CnZ0d3NzcsGjRonxVABMSEmBra4v4+PhcR03J5XI4OTllSRyZJBIJpFIpgoODOeSWCABkKcDrQMDUUvM7gRRm8lQgLQko7wqYqD7vW9X3MlIPcwWRFjBPqI+5Qi8xV5AuJCcnIzg4GJUrV84YJSOXAVGPAFmy5p/MxAIoUwswFnH1ICEdSPsASIyw4ffNKFe2LPr07iVaOBKzYhBSP4j2/PkjQC5LQ7/Bw7F7954so6qy/C79P1XfywxqBbwRI0ZgxIgRubbZvPl/Vb7SpUtDlZpb9+7dlbYF1yY/P78cEweQMd0wNDQUfn5+cHd310lMRESkX5griIgoL8wVpFHGJhmFI0Gu+b4lxuIWpD4xfuwYsUPIMiJJ3xkbG8N3/35AovnJdvrzm1FEREREaLQdEREVPswVRESUF+YK0jhjE7BEoBv3bt8QOwS9UejWlNJ39vb2Gm1HRESFD3MFERHlhbmCiAoDFqV0zM3NDVKpNMctGiUSCRwcHODm5qbjyIiISF8wVxARUV6YK4ioMGBRSseMjY2xcuVKAMiSQDIfe3t7czFCIqIijLmCiIjywlxBmmBA+56Rniro7xAnjIrAw8MDPj4+8PLyUlqcUCqVwtvbGx4eHiJGR0RE+oC5goiI8sJcQeoyNTWFRCJBVFQUypQpk+OIu0JDSAfSUv5/oe5C/lo1Tsj4+cklWRY6FwQBUVFRkEgkMDU1Vat3icDSqEaoszWuXC6Hn58fIiIiYG9vDzc3N97JIPoUt/pWD7f51kvMFURawDyhPuYKvcRcQbry7t07hIWFFY3RUoKQ8Z6nhd3jigQhPSPHZlO8lEgkkEqlsLa2Vjqu6nsZR0qJyNjYmNuzEhFRrpgriIgoL8wVpA5ra2tUr14daWlpYoeifbJU4O0TwMQCMFZvRE+RJU/L+PmVrgKYZL35Y2pqWqAiOItSREREREREREWQsbFx0RhVJ5NkVD9MjYCi8Ho1yUie8a+Feb5G1arcvcZ7JCIiIiIiIiIiygNHSmlI5jzchIQEkSMhKmRkKUDiO8AomWuF5Ic8FUiXAZYJ+V4nBOBOLNrCXEGkBcwT6mOu0EvMFURawFyhPi3nChalNCQxMREA4ODgIHIkREQFl5iYCFtbW7HDKHSYK4ioMGGu0A7mCiIqTPLKFdx9T0PS09Px6tUr2NjYaG07zYSEBDg4OCA0NLTQ73RSVF4rX2fhUhhepyAISExMRIUKFWBkxBnemqaLXKGKwvC7mhu+PsNX2F+job8+5grt0pdc8TFD/53NS2F+fYX5tQF8ffpM1VzBkVIaYmRkBKlUqpPnKl68uMH9QqqrqLxWvs7CxdBfJ+96a48uc4UqDP13NS98fYavsL9GQ359zBXao2+54mOG/DurisL8+grzawP4+vSVKrmCtzaIiIiIiIiIiEjnWJQiIiIiIiIiIiKdY1HKgJibm2P+/PkwN1d9xXtDVVReK19n4VJUXicZvsL+u8rXZ/gK+2ss7K+PCp/C/jtbmF9fYX5tAF9fYcCFzomIiIiIiIiISOc4UoqIiIiIiIiIiHSORSkiIiIiIiIiItI5FqWIiIiIiIiIiEjnWJQyIHK5HIGBgQgODhY7FK0KDw9HQEAAQkNDxQ5Fo4KCguDv74/U1NQc2zx79gw3btzAhw8fdBiZZr158wb+/v6IjY3N9nx6ejoeP36Mx48fIy0tTcfRac67d+9w6dIlhIWF5douPT0dly5dwt27d3UUGVHeXr58iTt37kAmk4kdisYlJyfj/v37uHv3Lt6/fy92OBpx69Yt3Lp1K8fzMpkMt2/fxoMHD2CIS4WGhobC398/x/9eMpkM9+/fx/PnzyGXy3UcXcElJyfjv//+U+nvtytXriAgIEAHURHlT3x8PG7evIm4uDixQ9G4N2/e4MaNGwgKCjLI99CPyeVyXLt2DQ8ePMixzYcPH3Djxg08e/ZMh5Fpxtu3b+Hv74+3b99me14QBDx9+hSPHj3K9TOXvnr06BH8/f3zbPfy5Uv4+/sjJiZGB1HpgEAGYd++fUK5cuWEqlWrCrVr1xY6d+4sREdHix2WRkVGRgpubm6Cra2t0KhRI6FEiRJC8+bNhfD/a+/ew2pM9zeA3zqnpkSpUDnnNDJiHAZj22iUQ44xTntSZrDF/k1oRtgUZmizqW1fzGAMZiIjDGaza4RlKqdODimVVS2JTtJBUc/vD1drdlOEVuvVcn+uyx/reQ/rXnVZ39bzft9nKRRSR6uXU6dOieHDh4vmzZsLACIjI6PGPg8ePBADBw4UpqamomPHjsLU1FQcPnxYgrSvLy4uTkydOlVYWloKAOLnn3+usc/GjRtFq1athL29vWjfvr2wtLQUISEhEqR9fRkZGWL+/PnCyspK6OrqivXr179w/9WrVwstLS3Rr18/NSUker7U1FTxwQcfCDMzM9GnTx/RsWNHERERIXUsldm5c6cwMzMT9vb2onv37sLIyEhs2LBB6livbfPmzcLe3l40a9ZMdO/evdZ9ZDKZsLa2Fra2tsLCwkJ069ZN3L59W81JX49MJhOjR48W5ubmAoCIiYmptr28vFz4+voKc3Nz0b17d9GmTRvRrl07ER4eLk3gV5STkyO8vb1Fq1athKGhoVi0aNEL99++fbvQ0tISdnZ2aslH9DKePHkiFi5cKAwNDcV7770nbG1txZo1a6SOpRIlJSViwoQJwsjISPTu3Vu0bNlSdOnSRcTFxUkd7ZWVlpaKNWvWCDs7O2FiYiJcXFxq3e/w4cPVPm8MHDhQPHjwQM1pX92NGzfEzJkzhbW1tQAgfvzxxxr7BAUFCRsbG9GpUyfRqVMnYW5uLr7//nsJ0r66gwcPigEDBggzMzNR1xRNbm6usLOzEwBEaGioegI2MHZKNQJnzpzB1KlT8fXXX+P27du4du0ali1bhuzsbKmjqdSKFStw7949pKen4/Lly0hPT8fDhw/x5ZdfSh2tXq5du4YlS5bg0KFDz91n3rx5ePz4MRQKBZKTk+Hr64vp06dDoVCoMWn9XL9+HWPHjn1hR9DDhw8RExODxMREpKSkwNvbG9OnT29U3X8pKSno1q0bbt26hZYtW75w3/Pnz2PPnj2YMmWKmtIRPV9xcTFGjBgBS0tLKBQKXLp0CefPn3/u1cbGJicnB56enli5ciUSExNx7do1bN++HUuXLsXNmzeljvda7ty5g9DQUHz66ae1bi8uLsakSZMwefJkyOVyZGVlwcbGBtOmTVNz0teTkJAAT09PhIWF1bq9tLQU+vr6SElJwbVr1yCXy+Hq6ooJEyagsLBQzWlfXUZGBlq2bIm4uDh069bthftev34dfn5+cHd3V1M6opfzt7/9DT/99BPi4uJw9epVpKamwsLCQupYKhEUFITw8HAkJibiypUryMzMhK2tLebPny91tFf28OFDlJWV4dy5cxg1alSt+2RmZmL69OlYtWoVkpOTkZmZieLi4kbxem/evInhw4cjKSnpufvk5OQgKioKSUlJSEpKgp+fHz755JMXdo29KW7cuIGNGzdi69atde7r7u6OqVOnqiGVGkk9K0Z1Gzx4sHB2dpY6RoMbP368cHV1rTbm5uamMa/9zJkztXZK5eXlCW1tbbFv3z7lWFlZmTA1NRUbN25Ud8x6y8/Pf26n1B8VFRUJAOLAgQNqSKZ6rVu3fm6nVNVVjLNnz4pPP/2UnVIkuX/9619CT0+vUVwRfR23bt0SAERUVJRyLC0tTQAQ586dkzBZ/S1btqzWTqmDBw8KLS0tkZ2drRyLiIgQAERCQoI6I9ZLQkJCrZ1StUlMTBQAxIULFxo+mAo5Ojo+t1OqpKRE9OjRQxw6dEisWrWKnVL0xlAoFEJHR0fs2rVL6igNYsmSJaJHjx7VxpYvXy46d+4sUSLVcHNzq7VTasOGDcLMzEw8efJEOfbdd98JHR0dkZ+fr8aEr+/JkyfP7ZT6o6dPnwptbW2xc+dONSRTjb17976wU2rLli1iyJAhIjc3l51SpD6lpaX47bffMGbMGBQWFuLy5cvIysqSOlaDWLp0KSIjIxEQEIDw8HBs2rQJERERWL58udTRGlRCQgIqKirg6OioHNPT04ODg8ML1xDRBJcuXQIAdOzYUeIkqufu7o6PP/4YQ4YMkToKEQAgPDwcAwYMQPPmzREfH4+kpCSNWlOqc+fOcHd3x+LFi3H06FGcOHECHh4ecHV1xQcffCB1vAYRExMDGxubal2b77//vnKbJrp06RKaNGmC9u3bSx1FZRYvXoy+ffti4sSJUkchqubs2bN4+vQpRo8ejfT0dMTGxqKoqEjqWCozb948FBUVwcfHB2FhYfj222+xe/du+Pv7Sx2tQcTExKBnz57Q0dFRjr3//vt4+vQpEhISJEzWMGJiYlBRUaExnzNiY2Oxbt067N27F1pamjWNo1P3LiSl+/fvo6KiArGxsVi9ejWsra2RnJyMQYMG4YcffoCZmZnUEVWmV69ecHNzg7+/Pzp06IDU1FRMmzYNvXr1kjpag6paoK5FixbVxlu0aKE5i9fV4uHDh/jss8/g7OyM3r17Sx1HpQIDA6FQKBASEiJ1FCKlu3fvwsjICI6Ojnj69CkKCgqgpaWFPXv2YOjQoVLHU4k5c+bAw8MDS5Ysga6uLoqLi7Fjxw6N++OtSl5eXo3aYWhoCENDQ42sH5mZmfD29oanpyesrKykjqMShw4dQnh4OGJjY6WOQlRDVd3w9fXFyZMn0axZM6SkpOCLL77AihUrpI5Xb3Z2dpg7dy6+/vprnD59GpmZmRg8eLDG1MQ/qq1mVD3WtJpRXFwMDw8PfPjhhxg8eLDUceqtuLgYU6dOxebNm2Fra6txXzigmX+laRBdXV0AQFhYGBISEnD16lWkpaUhOTkZS5culTidanl6euLs2bO4c+cOrly5ArlcjsuXL2v8+gpVv+PHjx9XGy8tLYWenp4UkRpccXExRo8eDUNDQ+zbt0/qOCqVkZGBpUuXYt68eYiOjoZMJsO9e/fw6NEjyGSyRrEOCmkmXV1dhIeHY+3atUhISIBcLoezszOmTJlS4/2nMUpNTcWwYcPg5eWFpKQkXL9+HZs3b4azszOuXLkidbwGoaurW+N3J4RAeXm5xtWPBw8ewMnJCT169MCWLVukjqMShYWF8PDwwLx58xAbGwuZTIb09HSUlZVBJpMhNzdX6oj0lqua3BdCQC6XIyEhAaGhoVi1ahVOnToldbx68/Pzw9atW5WfsTIzMyGEgLOzs9TRGkRtNaPqG781qWY8fvwYrq6uKC8vx8GDB9GkSROpI9XbihUrYGpqChsbG8hkMkRFRQF4ttaWJnS5cVLqDWdpaQkDAwNMnDgR5ubmAABzc3NMnDgR58+flzidah0/fhzTp09Hs2bNAAAmJiaYOXMmjh07Jm2wBmZnZwcANRY1VygUsLW1lSJSgyouLoaLiwsKCwsRFhamUd1+AFBSUgJHR0fs2rULPj4+8PHxQXR0NDIyMuDj4wO5XC51RHpLtW3bFra2tso/trW0tDB37lw8ePAAiYmJEqerv//+97+orKyEp6encmz8+PGwtLTEiRMnJEzWcOzs7JCVlVXtK8yzsrJQUVGhUfUjJycHw4YNg6WlJY4dOwYDAwOpI6lEWVkZevTogdDQUGW9CAsLQ35+Pnx8fDTigwY1bm3btgXw7MJxVcepk5MT2rVrpxGfQ44fP46xY8fCxsYGwLOJGU9PT1y+fBl3796VOJ3q2dnZ1fp5A4DG1IyysjK4uroiIyMDv/76a51fStRYGBkZQVdXV1krVq9eDQDYu3cv/v3vf0ucrv54+94bTltbG8OHD6/xBpKZmakx33xRxcLCApmZmdXGMjIyNO51/lG3bt3QqlUrHDt2DP379wfw7Ip/QkKCxt3TXlJSAhcXF+Tn5yM8PLxGC7EmsLe3h0wmqzb22WefKa+CE0nFyckJR44cQWlpKQwNDQFA+Z6rCe+zFhYWePLkCe7fvw9ra2sAQFFREfLz8zXi9dVmxIgRWLJkCc6fP69cv+7o0aMwMDDQiNsVgN8npCwsLHD8+HE0bdpU6kgqY2FhUaMu/P3vf8d3333HekFvhCFDhsDAwAAKhQJ9+/YF8KwLJTc3VyPeV5/32UNbWxvNmzeXKFXDGTFiBLZt2wa5XK68KH706FG0bt0aXbt2lThd/VVNSN25cwdnzpzRmNu8gWddff+roKAAZmZmWLduHVxdXaUJpUKclGoE/Pz8MGTIEKxevRoDBgxAZGQkDhw4gNDQUKmjqZSXlxc+//xzWFpaom/fvoiJiUFgYGCN/4SNTXp6OtLT05VXPC9duoQ7d+7A3t4eFhYW0NLSwvr16+Hh4YEWLVqgXbt28PPzw6BBgzB69GiJ07+83Nxc3Lx5U7kA5o0bN9CsWTO0adMGbdu2RWVlJcaOHYv4+Hjs3r27WmdG+/bt0apVK6miv5LS0lLlrUDl5eWQy+WQyWQwMzND9+7dJU5H9Hxubm7YunUrJk6ciAULFiAvLw8rVqzA9OnT0bp1a6nj1ZuTkxM6d+4MV1dXLFu2DLq6utiyZQtMTU0xadIkqeO9lri4ODx69AiZmZkoKSlRTlT0798fOjo6cHBwwLRp0zB79mysW7cORUVFWLZsGb788kuYmJhInL5ud+/eRWpqKtLS0gBAuYhyVU0oKSnBiBEjUFBQgICAAFy9elV5bNeuXd/4CxsVFRWIjIwE8GyC9O7du5DJZDA2Ntb49TJJMzRr1gwrVqyAl5cXCgsLYW5ujm3btsHY2BgzZsyQOl69LVy4EC4uLvD29sbIkSORmpqKFStWwMPDo1F2ZEZHR+PJkyfIyclBaWkpZDIZtLW1MWDAAADAmDFjMHDgQIwfPx6+vr5ITU3Fpk2bsHPnzjd+7cWCggJcu3YNFRUVAIBbt25BJpPB2toaHTp0AABMnjwZFy5cwK5du5CSkoKUlBQAzzrEqrrh3lTJycnIzs5GUlISACjr/bvvvgtTU1Mpo6lFE/G/Pd/0xoqPj8c///lPpKenw9bWFnPnzlV21WiSEydO4MCBA8jKyoKVlRUmTZqEcePGSR2rXr755hvs2bOnxvjKlSsxcuRI5eNjx45hz549KCwsxIABA7BkyRK888476oxaLxEREfD19a0xPm3aNCxYsADl5eUYNmxYrccuWrQIkydPbuiIKpGeno6PP/64xnj//v0REBBQ6zEBAQFITk7G9u3bGzoe0Qs9evQIAQEB+O2332BqaoqRI0dizpw50NbWljqaSuTl5WHr1q24evUqKioq8O6772LRokXKzqnGxsPDo9ZbK0+cOKH8I7W8vByBgYE4ffo09PT0MHHiRPzlL39Rc9LXExISUuv6UFU1QaFQwM3NrdZj/f393/jFiIuKivDRRx/VGO/UqRN2795d6zG7du3CL7/8wi/KoDfKjz/+iAMHDqCsrAwODg74v//7P425LSoqKgrffvst5HI5WrRoAScnJ8yaNatR1sXRo0fXWAC7adOmOH36tPLxo0ePsHHjRkRGRsLExASzZ8/G2LFj1Zz01UVHR+Pzzz+vMe7q6gpvb28AwNChQ2v9VuG5c+di1qxZDZ6xPtatW4eTJ0/WGA8MDMR7771XY7yqvqxfv14jOqM5KUVERERERERERGr3ZvfpERERERERERGRRuKkFBERERERERERqR0npYiIiIiIiIiISO04KUVERERERERERGrHSSkiIiIiIiIiIlI7TkoREREREREREZHacVKKiIiIiIiIiIjUjpNSRKQSV69exZkzZyCEkDoKERG9oVgriIioLqwVbxcdqQMQUeO3Y8cOBAQEoFmzZujTpw+2bdsmdSQiInrDsFYQEVFdWCvePpyUImrk0tLSkJCQgLFjxzbo89y4cQPx8fEAAEdHR3Tq1Em5LSQkBP/5z39gZ2eHrl271jg2MzMTMpkMANC5c2f07t27QbMSEVF1rBVERFQX1gqSAm/fI2pEUlNT8fPPP1cbO3v2LObPn9/gz3348GH89a9/xZEjR5CWllZt25/+9Cd89dVXWL9+fa2FQaFQ4MiRI/D29sauXbsaPCsR0duMtYKIiOrCWkFvCnZKETUiv/76K/z9/TFmzBjlWLt27TBu3Di1PH/Hjh0RHBxcY3zOnDmwtrZGkyZNcPz48Rrb+/Xrh+DgYLi6uqohJRHR2421goiI6sJaQW8KTkrRWyU3NxdXrlyBjo4O+vTpAxMTk2rb5XI5YmNj0aJFC/Tu3RtNmzZVbktOTkZycjJGjBiBuLg4KBQK9OvXD1ZWVsp97t27h4iICACAoaEh7O3t0aVLl2rP8b/niYqKQlZWFsaPH4/c3NwXHqtQKHDp0iUUFxcr38AdHBxga2sLJyenGq81Pj4et2/fhrW1Nfr16wctrd8bI6syjBw5EvHx8cjKykKvXr3QunXr1/q57t27F127dkWvXr3w/fffY9SoUa91HiKiNwFrRfUMrBVERDWxVlTPwFpBr4uTUvTWOHLkCGbNmgVHR0fo6ekhJSUF27dvx5///GcIIbB48WLs27cP/fv3R05ODrKysvDTTz+hb9++AIBTp07B398frVu3hqGhISorKxEbG4vg4GDlfdfZ2dk4cuQIAKC4uBgXLlzAqFGjsG/fPjRp0qTaeWxtbWFgYIBWrVph9OjRdR6blZWFmJgYlJSUKPdr2rQp8vLy4Ovrq8zw9OlTTJkyBREREejfvz/i4+NhZWWFX375BRYWFsoMa9euRdu2baGvrw8AiI6OxoEDB17rHvLdu3djzpw56NWrF1xcXJCfnw8zM7PX+j0REUmJtYK1goioLqwVrBWkQoLoLdGnTx+xbt065ePc3FwREREhhBBix44dokOHDiInJ0e53d/fX3Tp0kX5ODAwUAAQGzduVI6tWrVKWFlZiZKSklqf88GDB8La2locOnSoxnl27979wry1HfvNN98IOzu7avvt3r1btG7dWvk4KChING/eXMjlciGEEIWFhaJnz57C09OzRob/Pbe3t7fo2bPnc/P4+fmJfv361RiPjIwUurq64v79+6KyslK0b99eBAUF1XqOcePGiQULFrzwdRMRSYm1grWCiKgurBWsFaQ6XOic3hqGhoZISkrC48ePAQDNmzfHhx9+CODZjHzPnj1x5swZhISE4ODBgzA2NkZiYiKysrKU59DX18fChQuVj729vXH//n2cO3dOOfbkyRNERUXh8OHDCAsLg42NDS5evFgti4mJCWbPnl0j48scW5fg4GDMmDEDtra2AIB33nkHXl5eOHDgQLX9LCwsMHHiROXjoUOH4tatW6/0XACwc+dOjBkzBhYWFmjSpAnc3d2xc+fOVz4PEdGbgLWCtYKIqC6sFawVpDq8fY/eGoGBgfD09ISFhQUGDhyIsWPHwsPDA/r6+rhz5w7Ky8tx6NChase4ubmhvLxc+dja2lrZlgoAxsbGsLCwgFwuBwAkJCTA2dlZee+2kZERsrOzcf/+/WrntbKyUrbdVnnZY+sil8sxadKkamMdOnRAYWFhtfbX5s2bV9tHX18fZWVlr/RcJSUlOHjwIGbOnKm8H93Q0BAxMTGIi4uDg4PDK52PiEhqrBWsFUREdWGtYK0g1eGkFL01HBwccPHiRWRlZSE8PByrV6/GhQsX8MMPP8DExATDhg3Dhg0bXniOgoKCao+FECgoKIC5uTkAYPny5Rg2bBj27Nmj3Oejjz6CEKLacX8sHK9ybF3Mzc2Rl5dXbSwvLw+6uro1FmCsr5CQEOjr6yMnJ0d5PzoAdOnSBTt37sTWrVtV+nxERA2NtYK1goioLqwVrBWkOrx9j94aCoUCwLOrEjNmzICXlxeioqIAPHuT3r9/Px49elTrMVUKCgqU32QBACdPnkRlZaVy0cJ79+7B3t5euf3evXu4cOHCS+V7mWONjY2VbcLPM2jQIBw5cgSVlZXKsZCQEAwYMADa2tovleVl7dq1C7NmzUJwcHC1f2vWrMH+/ftf+QoJEZHUWCtYK4iI6sJawVpBqsNOKXprTJgwAd27d0e/fv1QWlqKTZs2Ydq0aQCAlStXIjw8HH379oWnpyeaNm2K6OhopKamVruv28jICLNmzYKXlxcqKyvx1VdfwcvLCzY2NgAAV1dX/OMf/4CBgQF0dHQQGBgIHZ2X+2/2Msf27t0bDx48gL+/Pzp27FhrG6uvry9CQkLg7OyMCRMmIDIyEkePHq1W9FQhJSUF58+fx9q1a2tsGzVqFIqLi3H06FFMmTJFpc9LRNSQWCsiXvMnVzvWCiLSRKwVEa/5k6sda8XbjZNS9NaQyWTYv38/IiMjoaenhy1btmDcuHEAnt0HffHiRezbtw/R0dEwMDDA8OHDMXXq1GrnaNOmDUJCQhAcHIy7d+9i06ZN1RYW9PHxQZs2bXD27Fno6+tjy5YtSEtLg5bW702JnTt3houLS418L3vsyZMncezYMVy/fh1NmzZFu3btlK8DAFq2bInY2Fhs374dMpkMVlZWuHr1Krp06fLCDFZWVnBzc3vhzzA3NxfBwcFwdHTErVu3MHPmTAwcOLDGfsbGxvjiiy+QnZ0NAMjMzIRMJoNCoUCbNm1e+BxERFJirWCtICKqC2sFawWpThPxqjeWEr2lgoKCEBQUhMTERKmjSCI0NFT5TRvu7u4YOXLkSx8bHR2NzZs3AwCcnJzwySefNEhGIiKpsVawVhAR1YW1grWCfsdJKaKX9LYXDyIiqhtrBRER1YW1guh3XOic6CU9rz2WiIioCmsFERHVhbWC6HfslCIiIiIiIiIiIrVjpxQREREREREREakdJ6WIiIiIiIiIiEjtOClFRERERERERERqx0kpIiIiIiIiIiJSO05KERERERERERGR2nFSioiIiIiIiIiI1I6TUkREREREREREpHaclCIiIiIiIiIiIrXjpBQREREREREREand/wOZW8wIMHSAvgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x320 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 3, figsize=(12, 3.2))\n",
    "for a, cl in zip(ax, CLASSES):\n",
    "    d = [s[\"distance\"] for s in data[cl]]; e = [s[\"e_bind\"] if False else s[\"energy\"] for s in data[cl]]\n",
    "    a.plot(d, e, \"o-\", color=\"k\")\n",
    "    a.axvspan(data[cl][10][\"distance\"] - 0.2, data[cl][-1][\"distance\"] + 0.2,\n",
    "              color=\"tab:orange\", alpha=0.15, label=\"test (extrapolation)\")\n",
    "    a.set_title(f\"{cl} dimer\"); a.set_xlabel(\"separation [Å]\"); a.set_ylabel(\"binding E [eV]\")\n",
    "    a.legend(fontsize=8)\n",
    "plt.tight_layout(); plt.savefig(\"dimer_reference_curves.png\", dpi=110); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ce818c4",
   "metadata": {},
   "source": [
    "## 2. Two BAMBOO models: identical, except the electrostatic term\n",
    "\n",
    "A small BAMBOO (feature width 32, 4 heads, 2 GET layers). The only difference\n",
    "is `use_electrostatics`: the **LR** model keeps the charge-equilibrium term, the\n",
    "**SR** model drops it (pure semi-local GET)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a53589d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:24:50.283346Z",
     "iopub.status.busy": "2026-07-20T04:24:50.283228Z",
     "iopub.status.idle": "2026-07-20T04:24:50.286207Z",
     "shell.execute_reply": "2026-07-20T04:24:50.285693Z"
    }
   },
   "outputs": [],
   "source": [
    "from xnn.common.config import from_dict\n",
    "from xnn.common.models import build_model, ForceStressOutput\n",
    "\n",
    "def make(use_elec):\n",
    "    cfg = from_dict({\"model\": {\"name\": \"bamboo\", \"cutoff\": 5.0, \"n_features\": 32,\n",
    "        \"n_rbf\": 16, \"n_interactions\": 2,\n",
    "        \"extra\": {\"num_heads\": 4, \"use_electrostatics\": use_elec}}})\n",
    "    return ForceStressOutput(build_model(cfg.model)).to(DEVICE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beeed8f7",
   "metadata": {},
   "source": [
    "## 3. Train both, per class: same data, loss, optimiser, schedule\n",
    "\n",
    "BAMBOO's charge head starts near zero (a `tanh` around 0); like the reference\n",
    "long-range fits it needs **warm restarts** to escape before a low-lr polish.\n",
    "The loss weights energy heavily (the binding curve) alongside forces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "23247e52",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:24:50.288212Z",
     "iopub.status.busy": "2026-07-20T04:24:50.288098Z",
     "iopub.status.idle": "2026-07-20T04:33:06.524409Z",
     "shell.execute_reply": "2026-07-20T04:33:06.523394Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trained 6 models in 496s\n"
     ]
    }
   ],
   "source": [
    "def run_stage(m, loader, epochs, lr0, step, ew, fw):\n",
    "    opt = torch.optim.Adam(m.parameters(), lr=lr0, amsgrad=True)\n",
    "    sch = torch.optim.lr_scheduler.StepLR(opt, step, 0.9)\n",
    "    for _ in range(epochs):\n",
    "        for b in loader:\n",
    "            b = b.to(DEVICE); out = m(b)\n",
    "            loss = ew * ((out[\"energy\"] - b.energy) ** 2).mean() + \\\n",
    "                   fw * ((out[\"forces\"] - b.forces) ** 2).mean()\n",
    "            opt.zero_grad(); loss.backward()\n",
    "            torch.nn.utils.clip_grad_norm_(m.parameters(), 10.0); opt.step()\n",
    "        sch.step()\n",
    "\n",
    "def train(m, tr):\n",
    "    loader = DataLoader(AtomicDataset(tr, 5.0), batch_size=10, shuffle=True,\n",
    "                        collate_fn=collate)\n",
    "    for _ in range(6):\n",
    "        run_stage(m, loader, 150, 1e-2, 10, 100, 1000)   # warm restarts\n",
    "    run_stage(m, loader, 400, 1e-3, 40, 100, 1000)       # polish\n",
    "\n",
    "models = {}\n",
    "t0 = time.time()\n",
    "for cl in CLASSES:\n",
    "    tr, _ = splits[cl]\n",
    "    for kind, use in [(\"LR\", True), (\"SR\", False)]:\n",
    "        torch.manual_seed(0)\n",
    "        m = make(use); train(m, tr); models[(cl, kind)] = m\n",
    "print(f\"trained 6 models in {time.time() - t0:.0f}s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8a9a05b",
   "metadata": {},
   "source": [
    "## 4. Binding curves + force parity (the payoff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "88295532",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:33:06.527060Z",
     "iopub.status.busy": "2026-07-20T04:33:06.526909Z",
     "iopub.status.idle": "2026-07-20T04:33:11.558054Z",
     "shell.execute_reply": "2026-07-20T04:33:11.557260Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def bind_curve(m, S):\n",
    "    m.eval(); ds = AtomicDataset(S, 5.0)\n",
    "    d = np.array([s[\"distance\"] for s in S])\n",
    "    p = np.array([float(m(ds[i].to(DEVICE))[\"energy\"].detach()) for i in range(len(S))])\n",
    "    return d, p\n",
    "\n",
    "def force_rmse(m, S):\n",
    "    m.eval(); ds = AtomicDataset(S, 5.0)\n",
    "    diffs = [((m(ds[i].to(DEVICE))[\"forces\"].detach().cpu() -\n",
    "               torch.tensor(S[i][\"forces\"], dtype=torch.float32)) ** 2)\n",
    "             for i in range(len(S))]\n",
    "    return float(torch.cat(diffs).mean().sqrt()) * 1000\n",
    "\n",
    "fig, ax = plt.subplots(1, 3, figsize=(13, 3.6))\n",
    "results = {}\n",
    "for j, cl in enumerate(CLASSES):\n",
    "    tr, te = splits[cl]; a = ax[j]\n",
    "    a.plot([s[\"distance\"] for s in data[cl]], [s[\"energy\"] for s in data[cl]],\n",
    "           \"o\", color=\"k\", label=\"DFT\", zorder=5, ms=4)\n",
    "    for kind, col in [(\"SR\", \"tab:green\"), (\"LR\", \"tab:red\")]:\n",
    "        d, p = bind_curve(models[(cl, kind)], data[cl])\n",
    "        a.plot(d, p, \"-\", color=col, label=kind)\n",
    "        _, pe = bind_curve(models[(cl, kind)], te)\n",
    "        eR = np.sqrt(((pe - np.array([s[\"energy\"] for s in te])) ** 2).mean()) * 1000\n",
    "        results[(cl, kind)] = (eR, force_rmse(models[(cl, kind)], te))\n",
    "    a.axvspan(te[0][\"distance\"] - 0.2, te[-1][\"distance\"] + 0.2,\n",
    "              color=\"tab:orange\", alpha=0.12)\n",
    "    a.set_title(f\"{cl} dimer\"); a.set_xlabel(\"separation [Å]\")\n",
    "    a.set_ylabel(\"binding E [eV]\"); a.legend(fontsize=8)\n",
    "plt.tight_layout(); plt.savefig(\"dimer_sr_vs_lr.png\", dpi=110); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27eb104d",
   "metadata": {},
   "source": [
    "## 5. Summary: the electrostatic term extrapolates, the short-range one flattens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2cf0d139",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:33:11.560530Z",
     "iopub.status.busy": "2026-07-20T04:33:11.560408Z",
     "iopub.status.idle": "2026-07-20T04:33:11.564294Z",
     "shell.execute_reply": "2026-07-20T04:33:11.563590Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class       SR E      LR E         SR F      LR F\n",
      "      test binding-E [meV]          test F [meV/Å]\n",
      "------------------------------------------------\n",
      "CC         354.9      15.2         42.7      23.5\n",
      "CP         106.3      28.2         39.7       9.6\n",
      "PP          15.0      10.8          3.4       3.0\n",
      "\n",
      "BAMBOO's built-in charge-equilibrium electrostatics binds the monomers beyond the\n",
      "5 Å GET cutoff; the short-range-only variant cannot see the other monomer and\n",
      "flattens past the cutoff. No LES wrapper needed -- the long-range physics is\n",
      "intrinsic to the BAMBOO energy split.\n"
     ]
    }
   ],
   "source": [
    "print(f\"{'class':<6}{'SR E':>10}{'LR E':>10}   {'SR F':>10}{'LR F':>10}\")\n",
    "print(f\"{'':6}{'test binding-E [meV]':>20}   {'test F [meV/Å]':>21}\")\n",
    "print(\"-\" * 48)\n",
    "for cl in CLASSES:\n",
    "    se, sf = results[(cl, \"SR\")]; le, lf = results[(cl, \"LR\")]\n",
    "    print(f\"{cl:<6}{se:>10.1f}{le:>10.1f}   {sf:>10.1f}{lf:>10.1f}\")\n",
    "print(\"\\nBAMBOO's built-in charge-equilibrium electrostatics binds the monomers \"\n",
    "      \"beyond the\\n5 Å GET cutoff; the short-range-only variant cannot see the \"\n",
    "      \"other monomer and\\nflattens past the cutoff. No LES wrapper needed -- the \"\n",
    "      \"long-range physics is\\nintrinsic to the BAMBOO energy split.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adafe160",
   "metadata": {},
   "source": [
    "## Where the binding comes from: predicted charges and the energy split\n",
    "\n",
    "BAMBOO reports the electrostatic energy separately (`energy_elec`) and the\n",
    "per-atom partial charges. On the farthest CC frame, the binding is almost\n",
    "entirely electrostatic, and the predicted charges separate into the two\n",
    "oppositely-charged monomers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a77298b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:33:11.566052Z",
     "iopub.status.busy": "2026-07-20T04:33:11.565984Z",
     "iopub.status.idle": "2026-07-20T04:33:11.617801Z",
     "shell.execute_reply": "2026-07-20T04:33:11.616922Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "farthest CC dimer @ 15.0 Å:\n",
      "  E_nn   = -1.015 eV\n",
      "  E_elec = +1.214 eV  (the long-range binding)\n",
      "  total charge = +1.37e-06 (conserved)\n",
      "  |dipole| = 11.86\n"
     ]
    }
   ],
   "source": [
    "m = models[(\"CC\", \"LR\")].model  # the bare BAMBOO (unwrap ForceStressOutput)\n",
    "ds = AtomicDataset(data[\"CC\"], 5.0)\n",
    "far = ds[len(data[\"CC\"]) - 1].to(DEVICE)\n",
    "out = m(far)\n",
    "print(f\"farthest CC dimer @ {data['CC'][-1]['distance']:.1f} Å:\")\n",
    "print(f\"  E_nn   = {float(out['energy_nn'][0]):+.3f} eV\")\n",
    "print(f\"  E_elec = {float(out['energy_elec'][0]):+.3f} eV  (the long-range binding)\")\n",
    "print(f\"  total charge = {float(out['charges'].sum()):+.2e} (conserved)\")\n",
    "print(f\"  |dipole| = {float(out['dipole'][0].norm()):.2f}\")"
   ]
  }
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