code/Language Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Language Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Demonstration\n",
    "\n",
    "### Source Rewriter\n",
    "\n",
    "Given the following example code (taken from Nvidia’s streamcluster benchmark):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "//#define Elements\n",
      "__kernel void memset_kernel(__global char * mem_d, short val, int number_bytes){\n",
      "    const int thread_id = get_global_id(0);\n",
      "    mem_d[thread_id] = val;\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "code = \"\"\"//#define Elements\n",
    "__kernel void memset_kernel(__global char * mem_d, short val, int number_bytes){\n",
    "    const int thread_id = get_global_id(0);\n",
    "    mem_d[thread_id] = val;\n",
    "}\"\"\"\n",
    "\n",
    "print(code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We apply the rewriter. Variable and function names are normalized, comments removed, and code style enforced:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__kernel void A(__global char* a, short b, int c) {\n",
      "  const int d = get_global_id(0);\n",
      "  a[d] = b;\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "from clgen.preprocess import preprocess\n",
    "\n",
    "rewritten = preprocess(code)\n",
    "print(rewritten)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Source Encoder\n",
    "\n",
    "Deriving a 1-of-$k$ vocabulary for a piece of code, using a hybrid character and token based approach:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GreedyAtomizer[27 tokens]\n"
     ]
    }
   ],
   "source": [
    "from clgen.atomizer import GreedyAtomizer\n",
    "\n",
    "atomizer = GreedyAtomizer.from_text(rewritten)\n",
    "print(atomizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The derived vocabulary maps tokens to indices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>token</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'\\n'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>'  '</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>' '</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>'('</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>')'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>'*'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>','</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>'0'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>';'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>'='</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>'A'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>'['</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>']'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>'__global'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>'__kernel'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>'a'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>'b'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>'c'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>'char'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>'const'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>'d'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>'get_global_id'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>'int'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>'short'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>'void'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>'{'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>'}'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              token\n",
       "0              '\\n'\n",
       "1              '  '\n",
       "2               ' '\n",
       "3               '('\n",
       "4               ')'\n",
       "5               '*'\n",
       "6               ','\n",
       "7               '0'\n",
       "8               ';'\n",
       "9               '='\n",
       "10              'A'\n",
       "11              '['\n",
       "12              ']'\n",
       "13       '__global'\n",
       "14       '__kernel'\n",
       "15              'a'\n",
       "16              'b'\n",
       "17              'c'\n",
       "18           'char'\n",
       "19          'const'\n",
       "20              'd'\n",
       "21  'get_global_id'\n",
       "22            'int'\n",
       "23          'short'\n",
       "24           'void'\n",
       "25              '{'\n",
       "26              '}'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "pd.DataFrame(sorted([f\"'{k}'\" for k in atomizer.vocab]), columns=[\"token\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encoding the source using this vocabulary yields:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14  1 24  1 10  3 13  1 18  5  1 15  6  1 23  1 16  6  1 22  1 17  4  1 25\n",
      "  0  2 19  1 22  1 20  1  9  1 21  3  7  4  8  0  2 15 11 20 12  1  9  1 16\n",
      "  8  0 26]\n"
     ]
    }
   ],
   "source": [
    "encoded = atomizer.atomize(rewritten)\n",
    "print(encoded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reversing the process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<__kernel>< ><void>< ><A><(><__global>< ><char><*>< ><a><,>< ><short>< ><b><,>< ><int>< ><c><)>< ><{><\\n><  ><const>< ><int>< ><d>< ><=>< ><get_global_id><(><0><)><;><\\n><  ><a><[><d><]>< ><=>< ><b><;><\\n><}>"
     ]
    }
   ],
   "source": [
    "for i in encoded:\n",
    "    t = atomizer.deatomize([i])\n",
    "    if t == '\\n': t = '\\\\n'\n",
    "    print(f\"<{t}>\", end=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Padding\n",
    "\n",
    "Sequences are padded to a fixed length using an out-of-vocabulary token:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 14  1 24\n",
      "  1 10  3 13  1 18  5  1 15  6  1 23  1 16  6  1 22  1 17  4  1 25  0  2 19\n",
      "  1 22  1 20  1  9  1 21  3  7  4  8  0  2 15 11 20 12  1  9  1 16  8  0 26]\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "pad_val = atomizer.vocab_size\n",
    "print(pad_sequences([encoded], maxlen=len(encoded) + 22, value=pad_val)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 2. Deriving Vocabulary from Handwritten GPGPU Benchmarks\n",
    "\n",
    "For the experiments in the paper, we derived a vocabulary from 45k lines of real world handwritten GPGPU benchmarks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lines of code: 45497\n",
      "derived vocabulary: GreedyAtomizer[128 tokens]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "srcs = '\\n'.join(pd.read_csv(\"../data/case-study-a/cgo17-amd.csv\")['src'].values)\n",
    "print(\"lines of code:\", len(srcs.split('\\n')))\n",
    "\n",
    "derived_atomizer = GreedyAtomizer.from_text(srcs)\n",
    "print(\"derived vocabulary:\", derived_atomizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>token</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'\\n'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>'  '</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>' '</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>'!'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>'%'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>'y'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>'z'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>'{'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>'|'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>'}'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    token\n",
       "0    '\\n'\n",
       "1    '  '\n",
       "2     ' '\n",
       "3     '!'\n",
       "4     '%'\n",
       "..    ...\n",
       "123   'y'\n",
       "124   'z'\n",
       "125   '{'\n",
       "126   '|'\n",
       "127   '}'\n",
       "\n",
       "[128 rows x 1 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 10)\n",
    "pd.DataFrame(sorted([f\"'{k}'\" for k in derived_atomizer.vocab]), columns=[\"token\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this derived vocabulary, lets plot the first 80 tokens of 12 real world GPU benchmarks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q1YH0u4GcPXsWgEqVKpEvXz4sLbPOvnz27FmcnZ0B6NWrF82aNcPc3Jxly5Yx\nd+5cihUrRnBwMKNHjyYhIQGNRkORIkVM23g+t0XVqlXRaDS5su9CCCGEEEIIIcS/yb+qwUK1McBg\nMKDVph8CM7OcO5vodDrS0tIyLUtNTcVgMFC1alU+++wzwsPD2bVrF0ajEaPxz1vqGQwGU730er1S\n/YQQQgghhBBCiHfVv2pIyIkTJ0hNTeXBgwc8efIEGxsbIL0hIyUlBYDy5ctz/PhxatasyZEjR6hS\npcory61SpQqHDh2iWrVqrFmzhjx58hAbGwuk34kE4P79+zg4OGBra4tGo+H69esULVqUw4cPU6tW\nLVJTUzOVmbFRQwghhBBCCCGEEJn9qxosypQpw5AhQ7hy5QpDhw5l9uzZANSsWRNfX1/s7Ozw8/Nj\nwoQJaDQarK2tCQ4O5syZMy8tt2fPnowaNQofHx8sLS2ZNm0aOp2OgIAAOnfuTL58+UhLSzPdVjUw\nMJARI0ZgZmZGiRIlaN26Nd9//32mMt9//306derEhg0bcudgCCGEEEIIIYQQ/2D/mgYLDw8PPDw8\nMi1r164dAPXq1WP//v2m5aGhoZlyrq6uuLq6mp5HR0dnyS1YsCDLNoODg7Oti4uLC6tXr85Sv4yW\nL1+e474IIYQQQgghhBDvun/VHBZCCCGEEEIIIYT4d/jX9LAQ755bt5NytfwxHm5KeXMbW6X88oHL\nlPIewxoq5TUvmUj2r2BdwFwp/+zuHaX8pn2zlfITu81SyvutHqqU3xW4Xilff0Dj185+PKmbUtmH\nZ32nlFc9lv/0a9NMpzYBc25fm6rnq8NUL6V8bp+vH3fPV8ob01JfHcpg85jVrw5lsG5LoFJe1awf\ntynlu2h7KeXta1VTyuf2+S1QrqxSfu3QEKV8u7EtlfKq76+VX4Qo5T/qVUspr9HqlPK5fX67jXz9\n7yZm+Quo1eXLdUp574ltlfJ2zlWV8tN95ijlew1Ru/btXWoo5cP3zFTKq57bzl82VsqrWjZ3/6tD\nGXwyTPHaVzyeaQaDUn5LyDGlvOpnoargvkuV8gNHNVXKq3725CtcVCmvevwX7FG7fj4K7KqUfxnp\nYSGEEEIIIYQQQoi3zlvbYBEeHm6axPI5Nzc3Hj9+/NplxMXFZZk7AtLnqOjevTve3t506NCBkJAQ\npXr8FVkhhBBCCCGEEELk7J0cEvL111+zYsUKChUqxNOnT+nVqxetWrWiYMGCf3fVhBBCCCGEEEII\nwVveYBFvkRE1AAAgAElEQVQXF0e/fv24efMmPXv2NC0/f/48EyZMwMzMDK1Wy+zZs7GxsWHx4sVE\nRUWh1WoZPnw4xYsXN62zd+9ewsLC+O9//0tCQgJJSenzH+TNm5c1a9YA8PDhQ7788ksePXpEgQIF\nmDFjBgC3b99m8ODBXLp0iU8++YROnToRHR3NzJkzMTMzo1ChQlnuGLJy5UoiIiLQarU0a9aMPn36\ncPbsWSZMmIC5uTnm5ubMnDkTKyur3D6MQgghhBBCCCHEP85b3WARGxtLeHg4jx49ol27duh06ZOP\n3Lt3D39/fypVqsTs2bOJiIigQYMGREVFsW7dOq5du8aiRYsYMGAAAFeuXGHBggUsXrwYnU7HkCFD\n6NSpE7Vr16Z+/fq0adMGa2trli5dSv369enRowchISEcPHgQgGvXrrF69WquXLnCsGHD6NSpE+PG\njWPZsmUUKVKEgIAAIiIi0Gg0pnxkZKTp1qbdunWjZcuWhIeH061bN9q3b8/Bgwe5c+eONFgIIYQQ\nQgghhBDZeGvnsABwdnZGr9dja2tL/vz5SUhIAMDe3p4ZM2bg7e3NDz/8QEJCAmfPnqV69epotVpK\nlixJUFAQAE+ePGHgwIH4+/tToED6bMndu3cnMjKSFi1acODAAVq3bs3t27c5e/Yszs7OAPTq1Ytm\nzZoBUL16dXQ6HYUKFSIxMZGEhAQ0Gg1FihQBwNXVlXPnzpnq/csvv3DlyhV69OhBjx49ePz4MX/8\n8QdNmzZlwYIFzJo1C3t7e8qWVZuZWwghhBBCCCGEeFe81Q0Wz3ssvCgoKIgePXoQFhZGly5dANDp\ndKSlpWXJ3rx5k1q1arFq1SrTsqdPn+Lo6EiHDh2YP38+DRo04Oeff86xDLMXbsGn0WgwGo2m5waD\nIVNd9Xo9jRs3JjQ0lNDQUCIiIvjggw+oW7cuGzZsoEyZMowePZpDhw6pHRAhhBBCCCGEEOId8VY3\nWJw4cYLU1FTu37/PkydPsLGxASAhIQEnJyeSk5PZu3cvBoOBypUrc+zYMVJSUrh79y4DBw4EoHTp\n0owfP56rV6+yf/9+YmNj8fDwMN1tJC0tjdu3b1OiRAmqVKliakRYs2YNmzZtyrZe1tbWaDQarl+/\nDsDhw4epUqWK6fXKlSsTHR3NkydPMBqNTJw4kadPnxIWFkZCQgJt27alZ8+emXplCCGEEEIIIYQQ\n4k9v9RwWZcqUYciQIVy5coWhQ4cye/ZsALy9vRk4cCAlSpTAx8eHgIAAWrVqRbt27fD29sZoNDJs\n2DBTORqNhqCgIPr378+6devo168fvXr1Im/evBgMBtzc3HBxcaFChQqMGjUKHx8fLC0tmTZtGtu3\nb8+2boGBgYwYMQIzMzNKlChB69at+f777wEoWrQoPXr0wMvLC51OR7NmzcibNy9OTk4MGTKEAgUK\nYG5unmWiTiGEEEIIIYQQQqR7axssPDw88PDwyLSsXbt2AHTp0sU0FASgefPmAFSsWJE+ffpkWic8\nPBwAJycntm7dCkCHDh3o0KFDlm0WKFCABQsWZKnHc5aWluzevRsAFxcX06Sa2WW9vLzw8vLK9HrD\nhg1p2LBhTrsshBBCCCGEEEKI/++tHhIihBBCCCGEEEKId9Nb28NCiFcpVNAiV8s/9+t9pXyNhHil\nfM/5vZXy+4I3KuVd++Vub54CNnmV8mfXH1bKx117qJT3Wz1UKT+x26xcLX9X4PrXzjYc4q5Udu2h\n7ZTyHzp3V8ofPLFWKZ9mMCjl2zX6Qin//f55Svmjl24q5T9ycFTKb/Nfo1Z+YFel/N3DJ5Tyqp8l\niz5brJTvM12t/kl/xCnl2/h9rJRPvHRFKW/vUkMpn9vX/xZ/tfJVz2/b+oOU8qrvr4s37inl8yi+\nvzaNWqmU95rTSymv+v4ypqUq5SPHr1PKq57ftORnSnkVpYpZKeUfX1X7rM1bsLBSfti3A5Tyqu+t\nj52rKuVz+29Xbn8vKelorZS3Uzw+dw4dVco71Fb7bO40solS3rJUKaV8suL3eP81ufe9EKCp4vFX\nrb+5ja1SPjxSbSoD1e9K7efm/P6SHhZCCCGEEEIIIYR467y0wcJgMODp6Ymvr+9rF7hr1y6Sk5MJ\nDAxk/fo/W5LGjx/P1KlTTc+XL1/O9OnTsy0jLi4uy/wVGfn4+HDx4sVMy5YsWYKPjw/t2rWjTp06\n+Pj44OPjQ3Jy8mvX/XVERETg7u5OTEzMX1quEEIIIYQQQggh/vTSBos7d+6QnJzMlClTXrvAkJAQ\nDAYDrq6umX7UX758mTNnzpieHz16FFdX1zeocvb69u1LaGgoY8eOpXbt2oSGhhIaGoq5uflftg2A\nAwcOMHLkSFxcXP7ScoUQQgghhBBCCPGnl85hERwczNWrVxkzZgxxceljUlNSUpgyZQpOTk5MnDiR\n06dPk5qaSrdu3dBqtZw4cYJ+/foxZ84cU4+KhIQEzM3NSU5O5smTJ+TLl4+TJ08yefJkzp8/z4QJ\nEzAzM0Or1ZpuXfrcokWL2LFjB1qtliZNmtC/f38Atm3bRlBQEAkJCSxYsICiRYtmuw9xcXGMHDkS\nCwsLvL29SUxMJCwsDK1WS/ny5QkMDCQ8PJyjR49y//59fv/9dz755BM8PT2zbLtq1ars27eP06dP\nY2Vlxd27dwkJCUGn01G5cmX8/PyYO3cu165dIy4ujsGDBxMWFoZOp+Ps2bP079+fn376iXPnzjFq\n1CiaNWv2fz6BQgghhBBCCCHEv9FLGyx8fX35448/6NatG0lJSdSpU4cNGzawatUq+vfvz48//sjO\nnTsxGAxs2rSJzp07M2fOHBYvXoylpSWWlpbcvHmTs2fPUqNGDZ48ecLx48cpXLgwRYsWxcLCgnv3\n7uHv70+lSpWYPXs2ERERNGny56Qq3377Lfv370en02W6jai9vb1pWMn27dvp1atXjvtx7tw59uzZ\ng62tLWvXrmXJkiVYWVnh5eXFhQsXALh48SJr1qwhNjaW4cOH4+npmWXb9erVo0GDBri7u1O5cmXa\nt2/P5s2bsbS0pH///hw6dAhIH0qzatUqoqOjOXfuHJGRkRw5coQvv/ySXbt2cfLkSUJDQ6XBQggh\nhBBCCCGEyMFr3SXE0dGRiRMnMnfuXB4+fEjlypWxsbGhVKlSDBgwgJYtW9K+ffss67m6unL48GHO\nnz9PgwYNSEpKIiYmhiJFipiGg9jb2zNt2jSePn3K7du3+fjjzLOFu7u707t3b9q0aUPbtm1Ny2vV\nqgVAoUKFSEhIeGn9S5Qoga1t+kyo1tbWfP7550D6MJXn69aoUQOdTkfhwoVJTEx86bYBYmNjKVmy\nJJaWlgDUrl2bc+fOAVCtWjVTrmLFipibm+Po6EipUqWwsLDA3t7etA0hhBBCCCGEEEJk9Vp3CZkz\nZw7169dn5cqVDBw40LR8yZIlDBo0iPPnz5uGamTk6urKsWPHOHHiBNWrV8fZ2ZmTJ08SExNjarAI\nCgqiR48ehIWF0aVLlyxlTJgwgfHjx3Pnzh18fHxISUkBQKfTmTJGo/Gl9dfr9QAkJycTEBDAzJkz\nCQsLo3r16qaMmVnWtpuctg2g0WgybddgMKDRaDJt78Vys9uGEEIIIYQQQgghsnqtBov4+HicnJww\nGo3s2rULg8FAXFwcK1asoHLlyvj6+pp6Kmg0GlJT0+9Z/cEHH3DixAm0Wi0WFhbY2try+PFjLly4\ngLOzM5A+v4WTkxPJycns3bsXQ4b7mScmJjJv3jzKli3LoEGDsLa25tGjR2+8s48fP0an0+Ho6MiN\nGzc4ffp0pu1l9KptlypViitXrpiWHT58mCpVqrxx3YQQQgghhBBCCPGn1/on/y5duhAYGEixYsXw\n8fHB39+f2NhYjh8/ztatW9Hr9XTs2BFIHxrRvXt3VqxYgZ2dHVqtlsqVK5vKKl++PLGxseTJkwcA\nb29vBg4cSIkSJfDx8SEgIIBWrVoBUKBAAeLj4+nUqRMWFhbUrFkTGxubN95ZW1tb6tWrR8eOHalY\nsSJ9+/YlODiYnj17Zsm+atsWFhaMGjWKvn37otVqqVWrFi4uLhw8ePCN6yeEEEIIIYQQQoh0L22w\nKF68OOHh4QCZJsL86aefAKhfv36WdYKDgzM9f77+c4GBgZmed+nSJdNQkObNm2daz9/fP8s2QkND\nTY+9vb0zvebq6prpdqkZ9wFg8uTJmfK9e/fO9NzS0pLdu3fnuO2M67do0YIWLVpken3w4MHZ1uW9\n994z1TvjYyGEEEIIIYQQQmT1WkNChBBCCCGEEEIIIf6XZBZIIXKw+ZcTSvluZL1Tzr+Z4VnKq0MZ\n1B7aTikf0HCIUt49oaVS3m/1UKX8rsD1Svn6Axq/dlZvZaVUdsTY1a8OZXDg2Cql/McfDlDKf79/\nnlK+dolySnlVtcoVVso/u3tHKf9RYFelvOr5+nhSN6V82/qDlPKq52tit1lKedX31qZRK5Xybfw+\nfnUoA2NaqlL+Q+fuSvmDJ9Yq5dsEZp1g/GVUj7/q+V03XK3Hp+r5vXtY7W9ph6leSvlFny1WyveZ\nrvb+TfojTinfbGQrpfz9Y78o5e1darx2Ni2HedpycvDsH0r5pv6eSvmwQUuV8l5zeinl67Z/Xymv\nqk3l6q8OZZDySO2OgMOmeCjlDQ8fKuWbeb/+tQOg0epeHcrAsU4tpbzq9TlzzGalvOpnVejo8FeH\nMvjkv58o5au7l1fKq/ph0lalfKOu1V4dykDlsweg5fjOSvmXkR4WQgghhBBCCCGEeOu8cYOFwWDA\n09MTX19fwsLC3rgCYWFhzJ07l/79+2easLJfv36sXPnnv7pMmjSJNWvWvPF23NzcePz4sVJ29OjR\n7Nmzh7i4OGrWrImPjw/e3t507tyZHTt2vHFdhBBCCCGEEEII8XJv3GBx584dkpOTKV68+F9SEVdX\nV44cOQJAWloaN2/eJCYmxvR6TExMpsk0/9dKly5NaGgoYWFhLFq0iEmTJvH06dO/rT5CCCGEEEII\nIcS/2RvPYREcHMzVq1e5fv06tra2AEydOpVjx46RmpqKl5cXVatWxd/fn1Wr0sdPL1iwAEtLS8qX\nL8+kSZNwcHDA0dGREiVK4OrqarrDyIULF6hRowZnz54F4NGjR8THx1O6dGkOHDjA7Nmz0ev1WFlZ\nMWvWLI4fP863335LUlISvr6+bN68mdOnT5Oamkq3bt3w8EgfE7Zy5Ur27t1LamoqS5YsAWDEiBEk\nJSXx9OlT/P39qVbt1eN5bGxscHR05M6dOzx+/JgJEyZgZmaGVqtl9uzZWFpaMnLkSFOjzuDBg6lb\nt26WZQ0bNnzTwy+EEEIIIYQQQvyrvXEPC19fX0qXLk3RokUBOHLkCL/++itr1qxh+fLlzJs3j0KF\nCpGcnMzNmzcB+PHHH2nVqhXTp0/nP//5D8uWLSM+Ph6AihUrcuXKFZKTk4mJiaFmzZoUL16cK1eu\ncPz4cWrVSp/I5cGDB0ybNo2wsDDy58/P/v37Abh48SJLly6lePHi/Pjjj6xZs4ZVq1aRkvLnxIDl\ny5dn5cqVFC1alEOHDnHnzh08PT0JDQ1l+PDhLF78ehM3xcXFkZCQQJEiRbh37x7+/v6Ehobi7OxM\nREQEFy9eJD4+npUrV7J06VIePHiQ7TIhhBBCCCGEEEJk7y+7S8jp06f54IMPALCwsKBcuXJcuXKF\ntm3bsm3bNlq1akX+/PlxcHDgjz/+oGLFigB88MEHPHv2DK1WS9WqVfnll1+IiYlhxIgRPHr0iJiY\nGK5evWoaDmJnZ4efnx+pqalcu3aNOnXqYGlpSYUKFTA3N8fc3JxSpUoxYMAAWrZsSfv2f9654Xmj\nR6FChUhMTMTBwYFvvvmGpUuXkpycjIWFRY779/vvv+Pj44PRaCRPnjxMmTIFMzMz7O3tmTZtGk+f\nPuX27dt8/PHHlClThsePHzNy5EiaN29O69atefbsWZZlQgghhBBCCCGEyN5f1mCh0WgyPTcYDGi1\nWtq0acPgwYPJly8fbdq0AUCr/bNjh9FoND12dXXl2LFjXL16FScnJ2rVqsXq1auJjY2lU6dOAIwd\nO5ZFixZRtmxZAgICTOuam5ubHi9ZsoQzZ86wZcsWvvvuO7799lsAdLo/b89jNBpZvnw5hQoV4j//\n+Q+//PILU6dOzXH/ns9h8aKgoCD69etHw4YNWbp0KUlJSeTLl49169Zx7NgxNm3axJ49ewgODs52\nmRBCCCGEEEIIIbL6y25rWqVKFaKjowF4/PgxV69epWTJktjZ2WFtbc13331H8+bNgfQeDr/99htG\no5HDhw+bynB1dWX79u04OTkB6cNELl26RHx8PCVKlADS57MoUqQIDx8+JDo6GsML9/CNi4tjxYoV\nVK5cGV9fXxISEnKsc3x8vGlbO3fuzFLW60hISMDJyYnk5GT27t2LwWDgzJkzRERE4OLiwvjx47l8\n+XK2y4QQQgghhBBCCJG9v6yHhYuLC1WqVMHLy4uUlBRGjBhhGmLh7u7Onj17yJ8/PwBDhw5lyJAh\nFC1alMKFC5vKeO+997h27Roff/wxkN4jwtLSklKlSpky3bt3p1u3bpQqVYq+ffsyd+5chg8fbnq9\nYMGCHD9+nK1bt6LX6+nYsWOOdW7Xrh2+vr5ERkbi5eXFli1b2Lhxo9J+e3t7M3DgQEqUKIGPjw8B\nAQHUr1+f77//nrVr16LT6fjkk08oXrw4M2bMyLRMCCGEEEIIIYQQ2XvjBovixYsTHh6eadmwYcOy\nzR44cICuXbuanjds2DDbO2RoNBoOHTqUadnSpUszPR8yZAhDhgwxPe/QoQOAabiJubk5M2fOzFL2\n7t27TY99fX1Nj7dt22Z63LRpUwBTI8fkyZNNr724r8916dKFLl26mJ4/70VSu3btLNkX90UIIYQQ\nQgghhBDZ+8uGhGTn2bNndO7cmfz581OnTp3c3JQQQgghhBBCCCH+Rf6yISHZyZMnD+vWrcvNTQiR\na9pXraGUf3b3fi7VJF3DMTkPb8rOvmC14U2u/bL2enqZ2kPbKeUPz/pOKb9p32yl/PKBy5TyHsPU\n9rfB4GZKeXMb29fO7gpcr1R2i+FqdVHl16l5rpZfws5aKZ94SW3On6Yj3JXyKucKYJv/GqV8bp8v\nnVb36tD/QeUSjkr5Z3fvKOU7TPVSyzcc8upQBqqfJUMbf6SUT06IV8rrrayU8lsvHH51KAN/xevB\n8z9qx39it1lK+WHB7V8d+j+wzKNXypvlL6CUTzir9vnjULu4Ut7qvVJK+cAu0187q3rsfRf2Ucqn\nPEpUyj9+pjZXnGr5FsUclPKGhw+V8j2D1I6n6rX27Qi1vy19pnd9dSiDPA5qf3tVP9tUJV66opT3\nXzsil2qSrs/83kr53P7eqer+w6e5Wv7fKVd7WAghhBBCCCGEEEK8CWmwyCA8PJwpU6aYngcHB2d6\n/ldyc3Pj8ePHuVK2EEIIIYQQQgjxT5erQ0L+yTZu3Mi1a9eYN2/e310VIYQQQgghhBDinfOParAI\nDw/nyJEjxMfH8+uvvzJs2DC2bNnC5cuXmTZtGlOmTMHJyYnjx4/TrVs3Lly4wMmTJ/Hy8sLLy4tm\nzZrRuXNnIiMjKVmyJJUrVzY9nj79zzGBx44dY926dSxbtgytNr0TSkxMDDNmzMDMzIwiRYoQGBjI\n8ePH+fbbb0lKSsLX15dhw4bRrFkzjh07RoECBVi0aBFJSUmMHTuWBw8ekJqaip+fHxUrVvy7DqEQ\nQgghhBBCCPGP8I9qsACIjY1l1apVrF+/noULF7J582bCw8NZuHAh586dY/78+Tx48IA2bdqwa9cu\nnj17xuDBg/Hy8iItLY1KlSrRr18/GjduTIsWLdiwYQONGzfm4f+feOf69esMGjSI+fPnY2FhYdru\nxIkTCQkJwcbGhqlTpxIZGUmhQoW4ePEiUVFRmJubc+3aNdq1a4evry+dO3fmwoUL7N69mwYNGuDp\n6cmlS5cICgpi2TK1SVqEEEIIIYQQQoh3zT+uwaJKlSpoNBocHR2pUKECOp0OBwcHEhMTcXJywtbW\nFnNzc+zs7ChUqBCPHz8mMfHPWYarVauGRqPB3t6eSpUqAWBnZ2fKnDp1ik8//ZSpU6cSGhqKmZkZ\nd+/e5cqVKwwePBiApKQkbG1tKVSoEBUqVMDc3ByA/Pnzm3pPFC5cmMTERI4fP879+/f5/vvvAXjy\n5Mn/7FgJIYQQQgghhBD/VP+4BgszM7NsHxcrVizTJJYZX8tIp9Nl+9hoNALQsmVLevXqxdWrV5k7\ndy7Dhg1Dr9dTsGBBQkNDM5UVHR1taqx4sbznZer1evz9/alZs6bKbgohhBBCCCGEEO80uUtIDkaN\nGsXu3bs5ePAg1tbp9y2+dOkSAKGhoZw/f/61yqlevTo7d+40rS/DQYQQQgghhBBCiFeTBosc5M2b\nl//85z989dVX3Lt3j6CgIMaMGUP37t05evQoZcqUea1yvL29uXr1Kt27d8fPzw8XF5dcrrkQQggh\nhBBCCPHP948aEuLh4WF63KRJE5o0aZLlMYClpSW7d+/O8vj5/yH9jiMvPi5evHim7VWsWNG0jr29\nPevXr8/0uqurK66urqbn0dHRpsdz5swxPZ47d26WfclYFyGEEEIIIYQQQmQmPSyEEEIIIYQQQgjx\n1vlH9bAQQgiR+1JS0v7uKvyraM3z5Gr5tx7dUcprtLpXhzJoH9xNKe/RZJhSftO+2Ur5HnVqK+VV\nuXWtqpTXW1kp5Rd+ulgpv2/fQqX842tXlPL6Amr1/3xkU6V8/jJllfKqus/soZTv0HCIUl71+lw3\nPPTVoQw6z/BRyvcf1vi1s5alSimVvbT/UqV8AQvzV4cy+PHXX5XyH4TuU8ofO3lTKX/82h9Keb1O\n7WdT3MO7SvliVvZK+V8Uj8/OA78r5csVsVXKf/x1W6V83kJq5ScnxCvlEy+pfRbau9RQyveY01Mp\nn9t/G/Oaq/1tt3NW+1t359BRpbxDbbXj+TLSw0IIIYQQQgghhBBvnbeuh8X169e5e/cu1apVy7Q8\nPDycAgUK0Lx58yzrLFmyhL179/Lw4UNu3bpF+fLlAVi6dGmm244KIYQQQgghhBDin+Gta7A4dOgQ\nSUlJWRosMk64+aK+ffvSt29foqOjWblyZaYJL4UQQgghhBBCCPHPk6sNFuHh4Rw5coT4+Hh+/fVX\nhg0bxpYtW7h8+TLTpk1j69atnDp1imfPntGtWzeaNm3KvHnzMDMzo0iRIoSEhJh6S9ja2mJra0v+\n/Pk5deoUX3/9Nd999x1Hjx4lICAg2+3HxcUxcuRILCws8Pb2JjExkbCwMLRaLeXLlycwMJDw8HCO\nHj3K/fv3+f333/nkk0/w9PRk0aJF7NixA61WS5MmTejfvz9ubm60b9+eQ4cOodfrmTt3Lvny5ePr\nr7/m2rVrJCcn88UXX1C/fv1s14+JiWHGjBmm/QsMDJQeIEIIIYQQQgghRDZyvYdFbGwsq1atYv36\n9SxcuJDNmzcTHh7Oxo0bKVeuHGPGjOHp06c0a9YMT09POnTogK2tLU2bNjU1WHTr1s10a9D27dvz\n3XffcebMGZYvX05ISMhLt3/u3Dn27NmDra0ta9euZcmSJVhZWeHl5cWFCxcAuHjxImvWrCE2Npbh\nw4fj6enJt99+y/79+9HpdKxevdpUXtmyZfniiy+YPHkymzZtwtraGnNzc8LCwrh16xY9evQgKioq\n2/UnTpxISEgINjY2TJ06lcjISNq2VZugRgghhBBCCCGEeBfkeoNFlSpV0Gg0ODo6UqFCBXQ6HQ4O\nDhgMBh48eEDXrl3R6/XEx2c/8+uLQ0MA/Pz88PLyYvTo0Vi9YnbuEiVKYGubPguttbU1n3/+OQCX\nL18mISEBgBo1aqDT6ShcuDCJiYkAuLu707t3b9q0aZOpUaFu3bqmdQ4dOoSZmRmurq4AFCpUCHNz\ncxISErKsf/fuXa5cucLgwYMBSEpKMtVLCCGEEEIIIYQQmeV6g4WZmVm2j+Pi4rh69SqhoaHo9Xpq\n1qyZ7fp6vT7Lsvj4eCwtLbl169Yrt/98/eTkZAICAvjuu+9wdHTks88+y7Zez02YMIHLly+zbds2\nfHx8WL9+PQBGo9H0f41Gk2nZ8+1otdos6y9dupSCBQsSGqp2uyshhBBCCCGEEOJd9Lfd1vT06dMU\nLlwYvV7Prl27SE1NJTk5GY1GQ0pKSo7rpaSkMG3aNFauXMmuXbuIi4t7re09fvwYnU6Ho6MjN27c\n4PTp0xgMhmyziYmJzJs3j7JlyzJo0CCsra159OgRADExMQCcOHGCcuXKUbVqVaKjowG4ceMGWq0W\njUaTZX2tNv1QX7p0CYDQ0FDOnz//egdLCCGEEEIIIYR4x/xtdwn58MMPuXLlCt7e3jRr1ozGjRsz\nfvx4Wrduja+vL3Z2dtmuFxISQpMmTShcuDDDhg0jMDCQhQsXvnJ7tra21KtXj44dO1KxYkX69u1L\ncHAwPXv2zJItUKAA8fHxdOrUCQsLC2rWrImNjQ0AZ86cYdWqVWg0GgYPHkzevHk5fPgwPj4+GAwG\nAgICclw/KCiIMWPGoNfrKViwIF26dPm/HUQhhBBCCCGEEOJfKlcbLDLeirRJkyY0adIky+PnevXq\nZXq8f/9+gExzRzyf+yGjunXrmuaUAHB1dTXNJwFQvHhxwsPDTc8nT56caf3evXtnem5pacnu3bsB\n8Pf3z3afPvvsMywtLTMtCwoKypLLbn0XFxfT0BIhhBBCCCGEEELk7G8bEiKEEEIIIYQQQgiRk79t\nSMg/0fPeF0JkJ49D9sOYcmJuo3aXmH3BG5Xyrv0aKuXzODgq5Q/P+k4pX3toO6X88oHLlPLdgtor\n5VWP/65Atd5R9Qc0fu1sU39PpbIPTNuklP/wyw5K+V9/T1DKuyRkf5ennPSc3/vVoQy2+a9RyrsN\naW2S94AAACAASURBVKqUV5WSanx1KAP9K+5m9aIODYco5Q8cW6WUD/9yhVK+1agWSvlN+2Yr5SPG\nrn51KIMOU73U8orHU7X+64arTabdZ5racFCz/AWU8g0bfvbqUAYHT6xVyttWz6+UD+wyXSnvt3qo\nUn7VMLXr+f+1d+dhUVWNH8C/gKOWgAuJuaQZb+6Q+HMDA0HIFEVFxRXUcC0Q1FxIxS00TVNxR8UN\nEFdwwzVJ0VA0l0wNTTDDBRdUBEO2Ob8/eGdecGaAU2KDfT/Pw/OM937n3HPvuXMHjufeI9u+j85c\nlMr3WegplV8xbLVUfsTi/iXO/nmnZM95Uxn0rdx3UfqNW1L5vouHSOWVOp41p0u0xxKp/LLoaVL5\nkFEhUvlFeyZL5WXPtXdaN5fKx/1U/C30BblO61Z8qIDstDSpvMJE7rtR9nwzayl3fGTPt9mS55vs\ntWfh4OVS+XEbvaXypX2+yX5evtjylc51HGFBRERERERERHqHHRb/NWvWLLi5ueHs2bNYskR3j9m1\na9cwaNAgeHh4oGfPnpg/f36haU2JiIiIiIiI6O/jLSH/dfz4cURFRcHU1BStWrXSmQsMDMSECRNg\nZWUFpVIJb29vXLlyBc2aNXuNtSUiIiIiIiJ6s5VKh0V6ejp8fX3x4sULtG/fHtu2bUO5cuVgb28P\nMzMzuLm5YfLkycjJyYGBgQFmz54NAwMD+Pr6qmf16NmzJ5YsWYJly5bh7bffRlJSEp48eYJvvvkG\nTZo00brdpUuXIiUlBffu3cPDhw8xYcIE2Nvbo2PHjmjSpAnatWsHa2trzJo1CwYGBqhUqRLmzp2L\nbdu24cGDBxg1ahS8vLywZ88ejBs3DhMmTMDWrVtx+/ZtjBkzBlu3bkV6ejoyMjIAAIaGhli5ciUA\nIDIyEr/99hsmTZqE58+fw9XVFTExMXB2dkafPn1w8OBB1KtXD02bNlW//u47ufs6iYiIiIiIiP4t\nSuWWkF27dsHCwgIREREwMcl/WFRubi7s7e3x+eefIygoCL1790ZoaCgGDBiAZcuWFVlebm4uNmzY\nAD8/PyxfXvQDSO7fv49169ZhwYIFWLhwIQAgOTkZ3t7ecHd3x9dff41Zs2Zh48aNaNeuHcLDwzFs\n2DBUr14da9asUdf3/fffh729PXbu3IkFCxZgypQpUCgU8PHxgZ+fH7y8vBASEoIHDx4UWR+lUokm\nTZpg586dOH/+PGrXro0dO3bg3LlzePbsWUkPKREREREREdG/Sql0WCQmJqJFixYAACen/z2t3crK\nCgBw+fJltG7dGgDQpk0bXL16tcjybG1tAQDNmzfHzZs3i8za2NgAABo2bIj79+8DAN566y18+OGH\nAIBLly4hICAAnp6e2LNnD1JTU3WWNXLkSERERMDY2Bj/93//BwBwdnbG0aNH0bt3byQkJKBr165I\nSEgosk5WVlYwMDCAmZmZenRItWrVkJ6eXuT7iIiIiIiIiP6tSuWWECEEDA3z+0IMDAzUyxUKhXqZ\n6kGVOTk5MDQ0LJQD8kdVqCiVSvXrl3MvK5h9ebtAfufFpk2bii0HADIzMwGgUKfGixcvYGpqChcX\nF7i4uGDZsmX4/vvvUbt2ba11BwAjIyOtr/mwTiIiIiIiIiLtSmWERd26dXH58mUAQGxsrMZ6S0tL\nxMfHAwDOnj2LZs2awdjYGKmpqRBC4OHDh0hOTlbnz507BwC4cOECLCwsity2KpuQkIBatWpprG/U\nqJG6TtHR0Th16pTOsr777juMHj0atWrVwv79+5GRkYHOnTsXug0kJSUFderUgbGxsXq5qg5ERERE\nRERE9NeUyggLNzc3fPHFF/D09IStrS0MDQ0LjXzw9fXFlClTsG3bNigUCsyZMweVK1eGra0tevXq\nhUaNGqFx48bqfFZWFkaOHIl79+5h/vz5RW7b2NgYo0aNwp07dzB58mSN9VOmTEFAQADWrFmDChUq\n6Hzw5c8//4y7d+/C0dERzZs3h6enJ+zt7TFjxgz4+vpCoVAgNzcXVlZW6NatG/7880+sXLkSnp6e\naN++fYlGcBARERERERGRdqXSYZGZmQlvb2/Y2dnhwoULOHv2LNatW6deX6NGDaxdu1bjfd98843W\n8pycnODo6FiibTdv3hweHh6FlqlGcwCAhYUFNm/erPG+mJgYAPnP1GjTpg0AqOtYtWpV7Nu3DwDQ\nvn17tG/fXuP9xsbG6hlOAGDYsGGFygVQaH3B10RERERERERUWKl0WJiYmGDDhg3qGT2mTJnySsv3\n8fFBWlpaoWXGxsY6pzslIiIiIiIiorKlVDosTE1NERIS8krKmjt3rsay4qZBJXoVdv1yUSrf85F9\nKdUkn/1XvaTysd/slMq3GS5X/9ZjukvlzyzeLZUfvPwzqbybvZ9UftM6X6m83WhnqXz5KlVLnD36\n9Xapsj/+3EEqL+vD+lWk8jL7CpR+WxmUK5WvNrVyRnK3/OVITmEdFRsklbdtMUAqf+riVqm8MidH\nKt/tYx+p/J6Tct/pgf0XS+Vlj2fUxHCpfJ+FnlL5R2fkvlvead1cKu/RwkEqn/PsqVQ++6X/MCrO\npGAvqfzj879I5T2WDZXKy57PXuM0RwQXRfZ8fvz8T6l8OWOTEmcjp+2SKlv2XFbm5knlhVIuv857\nvVS+QU0zqfyADhOl8hPcNUdYFyVklNzfQheS70jlFUb7pfK3nz2Syv82YIlUfnBnuWvV93FFz/z4\nsv/UlPtdw7XB+1J5fbu2fRkq97uPvl3b7qW9utkwS+Whm0REREREREREfwc7LP6i69evw9NTriea\niIiIiIiIiEqGHRZEREREREREpHdK90bfMiYyMhKxsbF48OAB7OzscPz4cRgaGsLZ2RleXl5ISUmB\nn58fypcvj4YNG6rf16ZNG/VMJL6+vhg4cCAaN26M8ePHIyMjAyYmJli4cCGEEJg8eTLS0tKQl5eH\nqVOnolGjRv/U7hIRERERERHpLY6weMm9e/cwb948xMXFISIiAuHh4Th8+DDu3r2LTZs2wcXFBaGh\noTA3Ny+ynJCQEHz88cfYvHkzbGxscOrUKWzcuBF2dnbYuHEjZsyYgXnz5r2mvSIiIiIiIiIqWzjC\n4iWWlpb45ZdfcOvWLQwaNAgA8Pz5c9y5cweJiYno1KkTgPxRFSdOnNBZztWrV+Hnl/8k/CFDhgAA\ntmzZgsePH2PPnj0AgMzMzFLcEyIiIiIiIqKyix0WL1EoFFAoFHBwcMCsWbMKrVuzZg0MDfMHpSiV\nSq3vz/nvlDJGRkYaGYVCgYCAAFhbW5dCzYmIiIiIiIjeHLwlRIumTZsiPj4emZmZEEIgMDAQL168\nQP369XH58mUAUD+zAgAMDAyQmZmJzMxM/PrrrwCAZs2a4fTp0wDyR1ZERUXho48+wvfffw8AuHHj\nBtavl5tfmoiIiIiIiOjfgiMstKhVqxYGDRqEgQMHwsjICM7OzqhYsSIGDRqEMWPG4MiRI2jQoIE6\n379/f/Tp0wcWFhZo2rQpAGDw4MGYOHEiPD09UalSJSxYsAAA8NVXX2HAgAFQKpWYMmXKP7J/RERE\nRERERPqOHRYF9OzZU/164MCBGDhwYKH1tWvXxvbt2zXe5+fnp35eRUErV67UWLZ06dJXUFMiIiIi\nIiKiNxtvCSEiIiIiIiIivcMRFkQ69LBsLpWv8E41qXz5KlWl8rHf7JTKtxluL5Wv8E51qfyZxbul\n8q3HdJfKb/SWe8bL1j0zpPKyx//o15qjq4ry8ecOJc46BbhLlR23IEoqbzveTSr/282nUvmWT59I\n5aNig6TyBwK2SOU7+DlJ5WXl5gmpvMLUVCrvZq85Yq8ocec3S+Ujx2+SyrtM7CiV33NymVRe9loy\nNWKMVF72WtJ/dg+pvKyYLb9I5d0afSCV/2LtCKm8bYsBUvlTF7dK5ZX/fRh5Sa2Yf1QqPzXCUiq/\neazc+S97Pj86c1EqL3s+rxi2usTZEYv7S5X9553bUvnKkufm4/Ny5/6w1XLnsuy5dt1jiVTe+vMu\nUvnzo0Kk8suip0nlZc+1d1rL/V67euQaqbylp9zvnQ27fSSVV5jIfZc+u/67VN6spdzx0bdr2yIv\nzZH9RdG3a1tROMKCiIiIiIiIiPROqXRYhIeHo0+fPvDw8EDv3r0RFxf3t8tcunQpwsLCXkHtinfh\nwgU0bNhQPeMHEREREREREb1er/yWkNu3b2Pbtm3YsWMHFAoFfv/9d0ydOhW2travelOlZt++fahf\nvz6io6PRuHHjf7o6RERERERERP86r7zDIiMjA1lZWcjJyYFCocD777+PsLAwXL16FTNnzoSBgQGs\nra0xadIkxMXFISgoCAqFAqampli8eDEuXLiA8PBwGBgYICkpCZ9++il8fHx0bu+bb77BpUuXkJWV\nhf79+8Pd3R3+/v5QKBR4+vQp5syZA19fX7x48QLt27fHtm3bEBMTg59++gkLFy5EuXLlULNmTXz9\n9dcoX7488vLycOjQISxatAiTJk3C+PHjAaBQmY6OjoiNjcWDBw+waNEirFu3rlAdPv30U7i7u+Pg\nwYMwMDDAnj17cOXKFdjZ2WHx4sWoWLEizMzMsGDBAigUilfdBERERERERERl3iu/JaRRo0awsrKC\nk5MT/P39sX//fuTm5iIwMBAzZ87Eli1bkJqaijt37iAtLQ0LFixAWFgYjI2NcfLkSQDApUuXMHfu\nXGzZsgWhoaE6t5WVlYXatWsjIiICmzdvRlDQ/x7kVrlyZSxduhS7du2ChYUFIiIiYGJiol4fGBiI\nFStWYNOmTTAzM8PBgwcBAHFxcbCwsECrVq1QpUoVXLhwQaNMALh37x7Cw8NRpUoVjTqYmpqiYcOG\n6vcePXoUXbt2RVhYGPz9/REWFoYuXbrg6VO5B9sRERERERER/VuUyiwh3377LRITE3HixAmsXbsW\nERERSEpKQqNGjdTrgfzbR6ZOnYq8vDwkJyejbdu2qFSpEpo0aYK33nqr2O1UqFABaWlp6NevHxQK\nBZ48+d+T6q2srAAAiYmJaN26NQDAyckJISEhePToEW7duoXRo0cDAP78809UrZo/Y8C+ffvQtWtX\nAICrqyuio6NhbW1dqEwAsLS0hIGBgc46dO/eHfv370ezZs1w+/ZtWFpaolOnTpg+fTpcXV3RpUsX\nVK8uNysDERERERER0b/FK++wEEIgOzsbFhYWsLCwgKenJzp37lyoM0Fl8uTJWL16NSwsLDBr1qz/\nVapcyap15swZnD59GqGhoVAoFOqOBQDqWy2EEDA0zB9IYmBgoF5nbm6uMXojKysLMTExuHLlCsLC\nwpCTk4Nnz55h8uTJhcos+FpXHezt7REUFITTp0/D0dERANCjRw/Y2dnh+++/x+eff46goCBYWFiU\naF+JiIiIiIiI/k1e+S0hO3bsQEBAAITIn6c+PT0dSqUSrVu3xs8//wwgv6MiMTERGRkZqFmzJp49\ne4b4+HjkSM5n++TJE7z77rtQKBQ4evQo8vLykJ2dXShTt25dXL58GQAQGxsLIP/WDgC4ceMGACA0\nNBQJCQmIiYlB27ZtsW/fPuzevRv79+/HBx98gPj4eOk6KBQKtGrVCkuWLIGrqysAYPny5ShXrhz6\n9u0LFxcXJCYmSu0vERERERER0b/FKx9h0bNnTyQlJcHd3R1vv/02cnNzMXXqVNSsWRMzZswAADRv\n3hwWFhYYMGAA+vfvj/fffx/Dhg3D0qVLMW7cOJ1lb9q0CYcOHQKQ3+nwzTffYM2aNfDw8ICzszMc\nHBzU21Bxc3PDF198AU9PT9ja2qpHW8yePRtfffWVerRF3759sXTpUvTu3Vtjf6Kjo3XWydbWVmsd\n5syZg86dO+PSpUuoV68eAKBWrVr47LPPYGpqClNTU3z22Weyh5eIiIiIiIjoX+GVd1gYGRlh0qRJ\nWtdFREQU+refnx/8/PzU/3ZzcwMA9TMkAKhHN4wePVr9zImCduzYoX49ZMgQjfWZmZnw9vaGnZ0d\nLly4gLNnzwIAWrZsie3btxfKLl++XOP9PXr0QI8ePQot69mzp/q1iYmJzjrExcWhX79+hfZPtY9E\nREREREREpFupPHRTn5iYmGDDhg3qzogpU6a8lu2OGDECFStWhLe392vZHhEREREREdGb5I3vsDA1\nNUVISMhr3+7q1atf+zaJSH8Ylq9QamWP37y9+FABpyb2Lj5UwKAlg6Xysz2WSOWnRoyRysuq8I7c\nDEyPzlyUyrvO6S+VXz1yjVR++/5Aqfyfd25L5btOdZXKp9+4JZWvaP6uVL7VaLn6BPZfLJWXPd+i\nJoZL5d2+HSiV79DPUiqvMDWVyoeMkvudZ4xDZ6l8f4cvpfIT3NtL5WtWNik+VIBPl1nFhwpQGMn9\n6rvLQfetytrUNjWTyg/ufEcq/zwru/jQf+2dtUeqbNdp3aTy2WlpUnmT/9STyqf+JHdtNmvZXCo/\nJcxXKl/a155t40KLDxXQZ6GnVF72u25E8HCp/Iphcn/7jFgs911a1s+3gK1y106l5LMd21rWksrL\nit54QSo/uLXc8SnKK3/oJhERERERERHR36W3HRaRkZGYN29eoWUdOnTA8+fPS1zG7du3Cz1voqD7\n9++jcePG+P7777Wuv379Ojw9NXsufX19ER8fj8jISBw5cgQAcPDgQQD5s5Bs3ry5xPUjIiIiIiIi\nIu3e+FtCdImOjka9evUQHR0NZ2dn6ferOkKys7OxYcMGdOrUCfb29q+6mkRERERERET/SnrdYXH7\n9m0MHz4cKSkpGDz4f/dUp6SkYPLkycjJyYGBgQFmz56N9957D2vWrMGhQ4dgaGiIcePGoU6dOur3\nHD9+HGFhYVi1ahWMjIywb98+TJs2DWPHjsWff/6Jt99+GykpKfDz80P58uXRsGFD9XvXrFmD6Oho\n1KpVCxkZGQCApUuXomrVqkhMTMS1a9cwY8YMWFlZ4bfffsOkSZOwceNG7N+/HwDg5OSEESNGwN/f\nH+bm5rhy5Qru3r2LBQsWoGnTpq/paBIRERERERGVHXp7SwgA/P7771ixYgU2bdqEJUuWQAgBAAgK\nCkLv3r0RGhqKAQMGYNmyZfj9999x6NAhbNu2DfPnz8fevXvV5dy6dQsrV67EwoULYWRkhKSkJKSn\np8PW1hZt2rRBTEwMAGDTpk1wcXFBaGgozM3NAQDPnj1DREQEtm7dim+//Ra//fZboToOHToU9evX\nx4wZM9TLkpOTERUVhfDwcISHh+PAgQP4448/AOSPyAgJCcGgQYOwa9eu0jx8RERERERERGWWXndY\ntGjRAgqFAlWrVoWxsTGePn0KALh8+TJat24NAGjTpg2uXr2Kq1ev4qOPPoKhoSHq1auH2bNnAwAy\nMzPh7e2NgIAAmJjkP3l63759cHFxAQB07doV+/btAwAkJibC2tpaXS6Q39nxn//8BxUqVICxsXGJ\nRkT8+uuv+Oijj1CuXDmUK1cOLVq0QEJCAgCgZcuWAIB3331XPVqDiIiIiIiIiArT61tCDAwMdC5X\njbbIycmBoaEhjIyMoFQqNbIpKSno1q0bNm/erO7EiI6OhoGBAY4dOwalUonk5GQ8e/YMQggYGub3\n4ajKKrhM9e+S1LtgTlVHADAyMpIqi4iIiIiIiOjfSK9HWFy8eBF5eXl4/PgxMjMzUaVKFQCApaUl\n4uPjAQBnz55Fs2bN0LRpU5w/fx65ubl49OgRvL29AUB9u8Yff/yBkydP4tKlS6hUqRIOHjyI3bt3\nY+/evejcuTMOHTqE+vXr4/LlywCgLr9u3bpITExEdnY2MjIy1OtVDA0NkZeXV2hZ48aNcfHiReTm\n5iI3Nxc///wzGjduXKrHioiIiIiIiOhNotcjLD744AP4+fnh1q1bGDNmDIKCggDkTy06ZcoUbNu2\nDQqFAnPmzEGNGjXQvXt3eHh4QAiBsWPHqstRPZhz1KhR+PjjjzWmOu3VqxeWL1+OwMBAjBkzBkeO\nHEGDBg0AAFWqVEGPHj3Qr18/1KlTB5aWloXeW716deTk5MDX1xcODg4AgDp16qBv377quri7u6N2\n7dqleKSIiIiIiIiI3ix622HRs2dPjY6F7t27AwAqVaqEtWvXarzHy8sLXl5ehZZFRkYCyB8poZq1\n42UtW7bE+vXrAQDbt2/XWO/t7a0esaGiesYFAK3lDhw4EAMHDiy0bO7cuerXjo6OcHR01FofIiIi\nIiIion87vb4lhIiIiIiIiIj+nfR2hAURUVmmzM4qtbIXDHCXygtlXvGhAjb5bpTKT1w5WCpf2rIe\nPZTKv9O6uVR+7+QIqfyI4OFSeTd7P6l8VGyQXH5iuFS+61RXqbzs+daulYdU/tTFrVJ5ZU6OVP5K\nstz54yaVBmK2/CKV7yN5fnb3+j+pvOz5/2jYaqm8pae9VL5htzSp/CATU6l8+o1bUnmzlnLHR/Z8\nm+2xRCo/NWJMibPhvhukyi5nbCKVf3o1USr/Tus6Uvkdwael8l4N3pfKZ6fJnWuTgr2KDxXw+Lzc\nZ73v4iFSedlzbdWiY1L5qRFy536FckbFhwrQt/MtavUZqbxJ2EWp/K6LcufDBPf2UvlfbzyWym/u\nMksqrzCS6zbY5zBOKr/nYpjOdRxhQURERERERER6hx0W/7VmzRp07doVP/30E6ZNm6Yzl5KSguHD\nh8PDwwO9e/fGV199hezs7NdYUyIiIiIiIqI3Hzss/uvEiROYP38+WrZsiVmzdA+RCQoKQs+ePREW\nFoYdO3ZAoVDgxIkTr7GmRERERERERG++N+4ZFpGRkThx4gQyMjKQkpKCIUOGIDg4GPb29jAzM0PP\nnj0xZcoU5OTkwMjICIGBgThz5gyuXr2KqVOnYv78+Rg/fjzWrVsHT09PbNmyBXl5eRgwYAA2b96M\nZ8+eISMjQ709VedGfHw8wsPDsWRJ/r2Ibdq0QXx8PDw9PdGmTRv8+OOPMDQ0RI8ePRAVFQUjIyNs\n2LABRkZy93sRERERERER/Ru8cR0WAHDjxg1ERUXh2bNn6N69O4yMjGBvbw97e3tMnjwZXl5esLW1\nxfHjx7FixQoEBgZi586dCAgIQPny5QEAVapUwWeffYbVq1cjKysLI0eOhKmpKYYPH44vvvgCkZGR\naNeuHVxdXVGvXr0i61O9enVERESgX79+SEtLw+bNmzFgwABcv34djRs3fh2HhIiIiIiIiKhMeSM7\nLFq1aoVy5cqhWrVqqFy5MpKTk2FlZQUAuHDhAm7evImVK1ciLy8P1apV01mOm5sbhg0bBkNDQ/j7\n+wMAmjdvjqNHj+LHH39EbGwsevfujUWLFkGhUOgsR7Vtc3NzNGnSBADwzjvvID09/VXtMhERERER\nEdEb5Y3ssFAqlerXQggYGBioOxQUCgWCgoJgbm5ebDm5ubnIzMyEUqlETk4OFAoFXrx4gbfeegvO\nzs5wdnaGtbU1oqOj4ebmpvFelYK3fRR8LYT4y/tIRERERERE9CZ7Ix+6efHiReTl5eHx48d4/vw5\nqlSpol730Ucf4fvvvwcAnDp1Cnv37tVZzvr16+Hi4gJnZ2esX78eSqUSrq6uuHHjhjqTkpKCOnXq\nwNjYGA8ePAAAJCQk4Pnz56W0d0RERERERERvvjdyhEXt2rXh5+eHW7duYcyYMeoHYQKAj48PJk+e\njOjoaBgYGOCbb77RWsadO3dw+PBhbNmyBUqlEu7u7ujSpQu+++47zJgxQ52rU6cOpk2bhooVK+Lt\nt99Gv379YG1tjdq1a5f2bhIRERERERG9sd7IDou6deti0qRJ6n/36NFD/bpGjRoICQnReE9oaKj6\ndWRkJABgx44d6mW7d+8GkN8ZEhYWpnW769atU79Wbb9guQU7Tgq+JiIiIiIiIqLC3shbQoiIiIiI\niIiobDMQfPIjEREREREREekZjrAgIiIiIiIiIr3DDgsiIiIiIiIi0jvssCAiIiIiIiIivcMOCyIi\nIiIiIiLSO+ywICIiIiIiIiK9ww4LIiIiIiIiItI77LAow549e6ZznbbZalNSUkpU7uPHj6XqcerU\nqRJnc3NzcefOHeTm5pb4PbL1kVWSmX2FEHj8+DFSU1NLtS5ERERERESUjx0WZZiPj4/GsiNHjsDR\n0RE2NjaYNGkSMjIy1OsmTpyokT927Bg+/fRTDBkyBNevX0e3bt3g6emJDh064Pjx4xr5Xbt2FfqJ\niorC9OnT1f9+WWBgoPp1XFwcPvnkE4wZMwYdO3bEiRMnNPLHjx/HtGnTAOR3hDg6OmLQoEHo0KED\njh07ppFv0aIFvv766xJ3JJw8eRKdO3fGwIEDcenSJfTq1Qv29vbo1KkTzpw5o5G/efMmRo0ahW7d\nusHJyQkjR45U78P9+/dLtE0iIiIiIiKSV+6frgAVLTw8XOc6bX8wr169GlFRUTA1NcX27dsxdOhQ\nrF27FiYmJlpHEqxcuRLr16/H3bt3MWrUKKxYsQKNGjXCo0ePMGrUKLRv375Qfvny5ahSpUqh5VlZ\nWbh9+7bWOl67dq3Qezdt2oT33nsPDx8+hI+PD+zs7ArllyxZguDgYI38kydPMHLkSDg4OBTKN23a\nFJ06dcKXX36JmjVromfPnrC2tka5ctpP7eXLl2Pjxo1IS0uDp6cnNmzYgEaNGuHOnTuYMGECNm/e\nXCg/ffp0zJ49G++99x6SkpIQGhqK6dOnIzY2FuPHj0doaGihfE5ODnbu3Im4uDg8fPgQAGBubg47\nOzu4ubnByMhIa720WbBgAcaPH19oWWpqKtatW4e0tDR07doVbdu2Va+bNWuWurNH5cmTJ9i+fTtq\n1KiB7t27Izg4GOfPn0f9+vUxYsQIVKtWrdh6eHp6auynyvHjx9XnwtOnT7F06VJcv34dDRo0gLe3\nt0b56enp+Omnn+Do6Ihnz55h1apVSExM1FmfadOmwd3dHZaWlsXWU1X+li1bULVqVfTs2RPhmxYs\njQAAEq1JREFU4eG4cuUK6tWrB09PTxgbGxfKK5VKHDhwACdPnkRqaiqEEKhduzYcHR01zn1Av9qX\nbfvmti3A9mX76m/76lvbAm9e+/Kz+z9vWtsCbN+XnThxQmv72tjYlKiOKhs2bMCQIUMKLcvKykJk\nZCSePn2KTz/9FB988IF63YoVK/DFF18UymdnZ+PIkSOoUaMGWrZsiejoaJw7dw7169dHnz59UKFC\nhWLr8eWXX+K7777Tuu7q1ato0qSJelvh4eG4fv06PvzwQwwcOFCj/JycHCQkJMDS0hI5OTnYvn07\nbty4gfr168Pd3R0VK1YslF+9ejW6d++OGjVqFFtPIH/k++HDh1G1alXY2Njghx9+wOXLl1GvXj10\n6dJF6+fr4sWLWtv3P//5j9ZtvIr25QgLPbdhwwZcu3YNT5480fjRdluFkZERqlSpAkNDQ/Tt2xfD\nhw/H0KFD8fjxYxgYGGjky5cvj1q1aqFly5YwNzdHo0aNAADvvPOO1g/lvn37YGtri2vXrsHNzQ0+\nPj5499134ePjo3XER8FtVq5cGe+99x4AoHr16lo7FXJzc1GpUiUAgImJCerUqQMAqFKlitYOFwMD\nA7Rq1QobNmzAgAEDsHfvXnTt2hW9evXCiBEjNPIKhQLm5ub48MMPYWpqqt7f2rVra/1QZmdnq+v8\n/vvvqztg7O3t8eLFC438xIkTce/ePXh5eeHbb7/FvHnz4OHhgYSEBHz11Vca+czMTJ0/Fy9e1MhP\nmDABtWrVQrt27bB8+XIsX75cve7GjRta65OdnY1z587B29sb6enp8Pb2Rp06dbSOuGnUqBHs7e3h\n5OSEDh06oEOHDrh48SI6dOgAJycnjXxISIj69ddff40aNWpgxowZsLCwwOTJkzXyvr6+ePToEQBg\n5syZMDExgY+PD95//31MmjRJI3/x4kVERETA19dX6wgYbcdHqVQiKSkJAwYMwL1799C1a1cA0Fqf\nGTNm4I8//kD//v3RvHlzNG/eHM7Ozti9ezfmzZunkden9mXbvrltq6oP25ftq6JP7atvbQuU7fbV\np7YF9K99y3LbAmzf4tp35syZ2LVrF5o1a4ZevXqhV69eaNCgAUJDQ3V+3nWJiYnRWPbll18iOTkZ\nCoUCfn5+hUaDnz59WiM/ceJEnDx5EsHBwQgMDMQPP/yAVq1a4e7du1rbV9WOBdv3yJEjOtt37ty5\n6tezZ8/GrVu30KlTJ6Smpmo9nl9++aV6RHpgYCB+/fVXtG7dGg8ePNDavjt37kRAQADmzJmD5ORk\nHUfqf/z9/XH69Gls3boVvr6+2L17N8zNzXHmzBlMnTpVI79o0SLs2LEDFhYWyM3NhbGxMUxNTTFt\n2jRs2LBBI//K2leQXrt27Zrw9PQUWVlZGus8PDw0ls2bN0+MGDFCZGZmqpfFxsYKV1dXYWdnp5H3\n8/MTa9euLbTs3r17Yvbs2WLcuHE665WYmChGjBghVq5cKXr37q0z17ZtW+Hr6ytGjx4tXFxcxP79\n+4UQQoSEhIiRI0dq5Hfv3i06duwoZs6cKfz9/cWoUaNEcHCw8PLyElu3bi3RMRBCiPv374vLly9r\nLPfx8RELFy4UAQEBYujQoSIgIEAcPnxYfPvtt8LPz08jHxAQIMaOHSvWr18vvLy8xOLFi4UQQnz1\n1VfC399fIz9w4ECdx0LbuqZNmwpHR8dCPx06dBCOjo7CysqqyP3Ny8sT48aNE0uXLtVYp+Lp6SmE\nEEKpVIqOHTvqLEslNjZWeHh4iIMHD6qX9enTR+c+qcoXQohBgwYVW37Bc+Xl9f3799fIqzJJSUli\nxowZolu3bmLq1KkiLCxMfS7pqk+nTp10rtNVh8GDB2utq4o+tS/bVvs6XXUoS21bcJ/YvmxfIfSr\nffWtbYUo2+2rT21bMKMv7VuW21YItm9x7autjkWta9u2rdafNm3aiKZNmxZZn+fPn4vBgweLnTt3\naq1rwWU5OTnCwcFB5OXlqddpO98iIiKEl5eX+Pnnn9XLimrfgtt8uTxt9XF3d1e/HjBgQKF12uqj\nKiMuLk6MHDlSDB48WKxcuVL88MMPheqobZvOzs7F1uflZV5eXkIIIXJzc4Wbm5tGXrZ9deEICz3X\noEEDBAcHax2N4O/vr7Fs4sSJ8PLyKjQ6ws7ODps3b9Y6AmLu3LmoWbNmoWWpqamoVasW5syZo7Ne\nH3zwAYKDg/Huu++qR0FoExQUhIEDB8LDwwPTp09HixYtAOSPsNA2XKpbt27YunUrWrZsiQ8++ADW\n1tZ45513MGfOHPTp00cj3717d63bNTc3R9OmTTWWz5s3D+bm5mjbti3Wrl2Lli1b4scff1Rv42Uz\nZ86Ei4sLcnNzMXjwYPj5+QEABg0apDVvYGCAw4cPIycnR70sOzsbe/fuRfny5TXyEydOhKurK2Ji\nYtQ/R48eRUxMDKysrDTy5cqVw6FDhyCEgKGhIebPn4/k5GQEBATg+fPnGnnVQ04NDAwK9ZQmJCQU\nqqOKnZ0dQkJCcO3aNXh7eyM5OVnryByVJ0+e4Pjx4zh+/DjKly+PhIQEAEBycjIyMzM18nXr1sWc\nOXPwyy+/oE2bNjhw4AAePXqEyMhIVK9eXSOv2nb9+vUxffp07NixA507d0ZGRgbOnTundX9v3bqF\nCxcuIC0tTf2/KYmJiVr3VwiBkydPIi0tDbt371YPrdP2/BZVfQ4dOlSq7Xvw4MESte/rattjx45B\noVCU6bbdtWvXX27bPXv2lGrbTp069R/97B47duwvfXb379+vN+1b0s+utmvzq2xffbw2l0b76ttn\nV1Wnstq+r/t7V58+uyW9Nv8TbftPX5v/Le2rVCpx5coVjeXnz5/Xepx69eqF0aNH49SpU4V+Tp8+\nDWtra63lX758GQDw9ttvY8WKFdi7dy9WrVqldaR6Tk4Onj9/jnLlysHX1xeGhvl/Kj98+BBZWVka\n+X79+mH+/PmIiIjAzJkzkZ6eXmT7ZmZmIjExETdu3EC1atXUoyDS09O1nm+VK1fGxo0b8fjxY7Rr\n1w6XLl0CAMTHx2sdCa/ato2NDVatWoV58+bBzMwMMTExWLlypc79vXv3Lp49e6a+xf/JkyfIzs7W\nyGdnZyMpKQkA8NNPPyEvLw+A9tFIgO72PXfuXJHHSUOJuzaIqFj37t0T/v7+okOHDsLGxkbY2NgI\nZ2dnERAQIO7fv6/1PVFRUeL58+cay1etWqWx7O7du2LSpEmFRtAIkT8ypUuXLhr58+fPa4wcOXLk\niOjWrZu4dOlSkfuSlJQkRowYIT755BMhhBBpaWkaGX9//0I/p06dEkIIMXr0aHH48GGNfE5OjggP\nDxfDhg0TnTt3Fp06dRIeHh5i9erV4sGDBxp5X19fnfXTVp8zZ86Inj17imHDhokbN26IIUOGCBsb\nG9GtWzdx4cIFjXxiYqL4/PPPhYuLixg7dqy4d++eEEKIJUuWaO2JVrWvk5OTsLW1LdS+d+/e1VrP\nXbt2iYyMDI3ly5Yt01n+ixcvCi3fvXu3Rs/3hQsXdLbt2bNntdZFiPz/Fbp582ahtlXtd0GqNp04\ncaJG20ZFRWnkVW07fPhw4eLiUqhttZWvalulUqn1OLzs7NmzolevXsLLy0ujbbXtb1Ftqy1fVNs+\nfPhQIy9EftsW/OympqYKIYRYuXKlzvILfnZTU1PFnj17RN++fTXy2tp3//79wtXVVevosYJU7evk\n5KQzo+2zm5qaKnx8fER8fLxGvmD7qj67Li4uIjg4WON6JIT2z67q+Gijal/VZ3fw4MGicePGxX52\nu3TpIsaOHStSUlJEamqqCAoKEklJSRr5l9vX1ta22PZ9+docFxcnhNDdvi9fm+Pi4qTa98CBA6J7\n9+4lbl/V51cbbe0bFxcnRo8eXWT7Frw29+/fX6xevVrjeqStbVXHRpuzZ89qXJdbt24t1bZxcXFi\n6dKl4ubNm1q3UfC719bWVrRu3Vo0atRITJ48Wet3ixCF2zcnJ0fcvn1b5OTklKh9VfmoqKgStW9O\nTo5Yv369cHFxKbJ9b968KYYNGyaaNGkicnNztWZebtuTJ0+K27dvF/vZVbWts7OzcHR0FMuXLy/R\nZ7fgsdHm5fYdNGiQsLGxEV26dCnR9+7du3fF7du3xcKFC4v87KraltfmwrR9dl/1tblg+5b02qxq\n34LXZm2f319//VV4enqKDh06CDc3N9GjRw/h4OCg/q5/mVKpFMHBwVp/b/766681liUkJAgPD49C\nv4fl5uaKFStWiI8//lgjf/ToUTFkyJBCy2JjY0X79u1FbGys1mOkcvr0aTFw4ECNkSgFeXh4CE9P\nT+Hh4SE8PDzEkSNHhBBCDBkyRBw4cEAjn56eLr799lvRuXNn0apVK2FlZSU+/fRTMX36dK3tpm3U\nS1EOHTok7OzshKurq4iPjxeurq7C1dVVtG/fXhw9elQjf/78edGtWzdhY2Mj+vTpI3777TchRP7I\n83Pnzmnkf/31VzFo0CB1+7q5uRXZvrqww4LoNZG9iOhLPiUlRa/q80/mDx8+LBwcHETr1q3FxIkT\nRXp6epH5I0eOCAcHB9GmTZsS5VXla8u/PAyvqGxJyy6qbf/qvpY0/1frL1t+SfM//PCD6Nixoxg8\neLBISEgQXbt2FS4uLsLBwUEcO3ZMI3/s2DF1/tq1a8LV1VW4uLgIR0dH8cMPPxRZvirfpUsXqXxR\n9dGWVw2DLmleVb62+hTc34SEhEL7K1P+X6nPqyg/KipKREVFicjISPWPs7OzermufMH3ffLJJ3qV\nd3JyKjYfGRkpVX7B46MrrypTpu6qMlWvS3LsC26nqPKFKPyHyo8//igcHBxE7969haOjo9Y/MorK\nHz9+vER5d3d3qbyu+mjLduzYsVTr/lePzT9Rn2PHjomAgAAhRH7HmIODQ5HXzqLyMTExJcp37dpV\nKi9bH9W1ubTq/1ePzz9RH2trazFr1ixx7949cf/+fXH//n2dnWMF848ePdKZ+Tv55s2ba+SzsrJ0\ndiC+XH52drbW//AqqvxXWX8rK6u/tb9KpVI8evSo0K0wf6c+J06cEJ06dRL9+vUT58+fF05OTqJd\nu3aiY8eO4vTp0yUqQwghOEsI0SskO6sL82UrX3AWnh07dhQ7C09wcLDUrD1FzfLz8tA52RmBdOUB\nSNeluH0tybF5VfUvSb4k9Sk4Y9Lnn39e7IxJK1asKHKGpZdnNCpuRibZ/Mv1edX5l+tTcH9Lcnz+\n6fqXZIarnJwcnTNcyc6I9U/k/6n6r1ixQq/qDsjPSFZc3t7e/m/lr1+/XuL6yNa9uLJl6y5b/t+t\nj2z5srPHFZd3dHQs1bxsfUq7/vpeH9Vsf/7+/urZ/oqaaUV2dkDZfLNmzbTmdc1Go6t8XXSV/6rq\nb2Vl9Ur2V3UrzN+tj+ocePr0KTw9PbFx40Y0bNhQ5+yMurDDgugV2rBhA2xsbGBubq6xTtu9csyX\nrbxqFh4A6NOnD6pVq4ahQ4di1apVWu/FK5jv27cvzMzM/nK+NMt+1fnXfWxeRX1UMybVqlWrRDMm\nMV+28vv27cOKFStw7do1+Pv7o3bt2jhx4oTWZzsxX3Ren+qiIjsjWWnnCyouX5plv47ySzv/8uxx\ntWvXBqB79jjZ2eZed76061/Wyi84298vv/yC7du3IyAgAJUqVYKZmRlWr17NfBnOKxQKVK9eHdWr\nV4epqSkaNmwIQPfsjLqww4LoFVq+fDkCAwMxdepUjYdBxcfHM1/G8y1atMDIkSMRFBSEihUrwtnZ\nGRUqVMCQIUPw9OnT15rXp7q8CXkzMzOEhIRg6NCh2LJlCwAgJSUF69atw7vvvst8Gc9XqFABY8eO\nRVJSEmbNmgVra2solUqNHPPF5/WpLiq//fYb/Pz8IITArVu3cODAAXTu3Bnr1q2DiYmJXuf1qS76\nmB86dCh69OiBdu3aoUqVKvD29oa1tTXi4+Ph7u5ebP6LL77Qq3xp17+slV+wE8PS0hKWlpYAgAcP\nHuDhw4fMl/F85cqVsWjRIjx58gR169bFtGnTYGdnh4sXL8LMzEwjr1OJbx4hohL5888/td77peth\nTMyXrfzp06c1HhSZnp6uddrd0s7rU13Kej4zM1NER0cXWnb58mWxfv16jYcOMl/28i+LiooSY8aM\nKTbHfNmoS3x8fKEf1fN59uzZo/Whx/qU16e66GNeCCGePHkioqOjxerVq0VwcLDYuXOn+n3Ml+38\n9u3bdZbDfNnPP3/+XISFham/r3fv3i2mT58u1q1bp/XBqboYCKFlfA4RERERERER0T9I+xM1iIiI\niIiIiIj+QeywICIiIiIiIiK9ww4LIiIiIiIiItI77LAgIiIiIiIiIr3DDgsiIiIiIiIi0jv/D3zD\nh8tVNm3BAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f246463ba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "from labm8 import viz\n",
    "\n",
    "natoms = 80\n",
    "nprog = 12\n",
    "\n",
    "data = [derived_atomizer.atomize(src)[:natoms] for src in\n",
    "        pd.read_csv(\"../data/case-study-a/cgo17-amd.csv\")['src'].values[:nprog]]\n",
    "kernels = [x.split(\"-\")[-1] for x in\n",
    "           pd.read_csv(\"../data/case-study-a/cgo17-amd.csv\")['benchmark'].values[:nprog]]\n",
    "data = np.reshape(data, (nprog, natoms))\n",
    "    \n",
    "ax = sns.heatmap(data, vmin=0, vmax=derived_atomizer.vocab_size, square=True,\n",
    "                 cbar=False, yticklabels=kernels, xticklabels=[str(x) for x in range(1, natoms + 1)])\n",
    "plt.title(\"Encoded GPU benchmark sources (first 80 tokens)\")\n",
    "viz.finalise(figsize=(15, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that, as a result of the rewriting process, each of the kernels starts in an identical manner, i.e. `__kernel void A(...`\n",
    "\n",
    "# 3. Reproduce LaTeX Tables from the Paper\n",
    "\n",
    "Order the atoms by their appearance in the example code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "i = 1\n",
    "s = set()\n",
    "ordered = []\n",
    "for idx in derived_atomizer.atomize(rewritten):\n",
    "    if idx not in s:\n",
    "        t = derived_atomizer.decoder[idx]\n",
    "        ordered.append((idx, t, i))\n",
    "        i += 1\n",
    "        s.add(idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table 3c:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\footnotesize\n",
      "\\begin{tabular}{l l | l l | l l}\n",
      "  \\toprule\n",
      "  \\textbf{idx} & \\textbf{token} & \\textbf{idx} & \\textbf{token} & \\textbf{idx} & \\textbf{token} \\\\\n",
      "  \\midrule\n",
      "  \\texttt{1} & \\texttt{`\\_\\_kernel'} & \\texttt{10} & \\texttt{`,'} & \\texttt{19} & \\texttt{`const'} \\\\\n",
      "  \\texttt{2} & \\texttt{` '} & \\texttt{11} & \\texttt{`short'} & \\texttt{20} & \\texttt{`d'} \\\\\n",
      "  \\texttt{3} & \\texttt{`void'} & \\texttt{12} & \\texttt{`b'} & \\texttt{21} & \\texttt{`='} \\\\\n",
      "  \\texttt{4} & \\texttt{`A'} & \\texttt{13} & \\texttt{`int'} & \\texttt{22} & \\texttt{`get\\_global\\_id'} \\\\\n",
      "  \\texttt{5} & \\texttt{`('} & \\texttt{14} & \\texttt{`c'} & \\texttt{23} & \\texttt{`0'} \\\\\n",
      "  \\texttt{6} & \\texttt{`\\_\\_global'} & \\texttt{15} & \\texttt{`)'} & \\texttt{24} & \\texttt{`;'} \\\\\n",
      "  \\texttt{7} & \\texttt{`char'} & \\texttt{16} & \\texttt{`\\{'} & \\texttt{25} & \\texttt{`['} \\\\\n",
      "  \\texttt{8} & \\texttt{`*'} & \\texttt{17} & \\texttt{`\\textbackslash n'} & \\texttt{26} & \\texttt{`]'} \\\\\n",
      "  \\texttt{9} & \\texttt{`a'} & \\texttt{18} & \\texttt{`  '} & \\texttt{27} & \\texttt{`\\}'} \\\\\n",
      "  \\bottomrule\n",
      "\\end{tabular}\n"
     ]
    }
   ],
   "source": [
    "print(\"\"\"\n",
    "\\\\footnotesize\n",
    "\\\\begin{tabular}{l l | l l | l l}\n",
    "  \\\\toprule\\\n",
    "\"\"\")\n",
    "print(\" \", \" & \".join([\"\\\\textbf{idx} & \\\\textbf{token}\"] * 3), \"\\\\\\\\\")\n",
    "print(\"  \\\\midrule\")\n",
    "\n",
    "def escape(x):\n",
    "    import re\n",
    "    if x == '\\n':\n",
    "        return '\\\\textbackslash n'\n",
    "    elif x == '{':\n",
    "        return '\\\\{'\n",
    "    elif x == '}':\n",
    "        return '\\\\}'\n",
    "    else:\n",
    "        return re.sub(r'_', '\\\\_', x)\n",
    "\n",
    "for i, (j, k, l) in enumerate(zip(ordered[:9], ordered[9:18], ordered[18:])):\n",
    "    i1, i2, i3 = i + 1, i + 10, i + 19\n",
    "    c1, c2, c3 = escape(j[1]), escape(k[1]), escape(l[1])\n",
    "    print(f\"\"\"  \\\n",
    "\\\\texttt{{{i1}}} & \\\\texttt{{`{c1}'}} & \\\n",
    "\\\\texttt{{{i2}}} & \\\\texttt{{`{c2}'}} & \\\n",
    "\\\\texttt{{{i3}}} & \\\\texttt{{`{c3}'}} \\\\\\\\\\\n",
    "\"\"\")\n",
    "    \n",
    "print(\"\"\"\\\n",
    "  \\\\bottomrule\n",
    "\\\\end{tabular}\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table 3d:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\rowcolors{2}{white}{gray!25}\n",
      "\\footnotesize\n",
      "\\begin{tabular}{l l l l l l l l l l l}\n",
      "  \\toprule\n",
      "  \\texttt{01} & \\texttt{02} & \\texttt{03} & \\texttt{02} & \\texttt{04} & \\texttt{05} & \\texttt{06} & \\texttt{02} & \\texttt{07} & \\texttt{08} & \\texttt{02} \\\\\n",
      "  \\texttt{09} & \\texttt{10} & \\texttt{02} & \\texttt{11} & \\texttt{02} & \\texttt{12} & \\texttt{10} & \\texttt{02} & \\texttt{13} & \\texttt{02} & \\texttt{14} \\\\\n",
      "  \\texttt{15} & \\texttt{02} & \\texttt{16} & \\texttt{17} & \\texttt{18} & \\texttt{19} & \\texttt{02} & \\texttt{13} & \\texttt{02} & \\texttt{20} & \\texttt{02} \\\\\n",
      "  \\texttt{21} & \\texttt{02} & \\texttt{22} & \\texttt{05} & \\texttt{23} & \\texttt{15} & \\texttt{24} & \\texttt{17} & \\texttt{18} & \\texttt{09} & \\texttt{25} \\\\\n",
      "  \\texttt{20} & \\texttt{26} & \\texttt{02} & \\texttt{21} & \\texttt{02} & \\texttt{12} & \\texttt{24} & \\texttt{17} & \\texttt{27} & \\multicolumn{2}{l}{\\texttt{<pad\\ldots}>} \\\\\n",
      "  \\bottomrule\n",
      "\\end{tabular}\n"
     ]
    }
   ],
   "source": [
    "translator = dict((i1, i2) for i1, _, i2 in ordered)\n",
    "\n",
    "print(\"\"\"\\\n",
    "\\\\rowcolors{2}{white}{gray!25}\n",
    "\\\\footnotesize\n",
    "\\\\begin{tabular}{l l l l l l l l l l l}\n",
    "  \\\\toprule\n",
    "  \"\"\", end=\"\")\n",
    "\n",
    "for i, idx in enumerate(derived_atomizer.atomize(rewritten)):\n",
    "    t = translator[idx]\n",
    "    print(f\"\\\\texttt{{{t:02d}}}\", end=\"\")\n",
    "    if (i + 1) % 11:\n",
    "        print(\" & \", end=\"\")\n",
    "    else:\n",
    "        print(\" \\\\\\\\\\n  \", end=\"\")\n",
    "\n",
    "print(\"\"\"\\\n",
    "\\\\multicolumn{2}{l}{\\\\texttt{<pad\\\\ldots}>} \\\\\\\\\"\"\")\n",
    "print(\"\"\"\\\n",
    "  \\\\bottomrule\n",
    "\\\\end{tabular}\"\"\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "End of experiments."
   ]
  }
 ],
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