{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"/home/sg666/MDpLM/data/uniref50/200k_seqs\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(path + \"/train.csv\")\n",
    "test = pd.read_csv(path + \"/test.csv\")\n",
    "val = pd.read_csv(path + '/val.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_residues = ['A','R','N','D','C','E','Q','G','H','I','L','K','M','F','P','S','T','W','Y','V']\n",
    "\n",
    "for df in [train, test, val]:\n",
    "    df['Length'] = df['Sequence'].str.len()\n",
    "\n",
    "    for residue in valid_residues:\n",
    "        df[residue] = 0\n",
    "\n",
    "    for idx, row in df.iterrows():\n",
    "        sequence = row['Sequence']\n",
    "\n",
    "        for residue in valid_residues:\n",
    "            df.at[idx, residue] = sequence.count(residue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "amino_acid_frequencies = {}\n",
    "\n",
    "datasets = {'Train': train, 'Test': test, 'Val': val}\n",
    "\n",
    "\n",
    "for name, df in datasets.items():\n",
    "    # Count total occurrences of each amino acid in each dataset\n",
    "    amino_acid_frequencies[name] = df[valid_residues].sum() / df['Length'].sum()\n",
    "\n",
    "# Convert frequencies to a dataframe for easier manipulation\n",
    "freq_df = pd.DataFrame(amino_acid_frequencies)\n",
    "\n",
    "# Plot the histogram comparing amino acid frequencies\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Set bar width\n",
    "bar_width = 0.2\n",
    "\n",
    "# Generate positions for the bars\n",
    "amino_acids = list(valid_residues)\n",
    "x = np.arange(len(amino_acids))  # positions for the amino acids\n",
    "\n",
    "# Plot the bars for each dataset with an offset\n",
    "plt.bar(x - bar_width, freq_df['Train'], width=bar_width, label='Train', align='center')\n",
    "plt.bar(x, freq_df['Test'], width=bar_width, label='Test', align='center')\n",
    "plt.bar(x + bar_width, freq_df['Val'], width=bar_width, label='Val', align='center')\n",
    "\n",
    "plt.xticks(x, amino_acids)\n",
    "\n",
    "plt.title('UniRef50 (200k Sequences) - Train, Test, and Val Residue Frequencies')\n",
    "plt.xlabel('Amino Acid')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pqalGhQoVjODgYHNMw/jvZxwQEGC0bt3abEv7Pb75/8HOnTsbxYsXN5/v2LHDkGQMHjzYKi40NNSQZLz++utm252W/WfmM8LDw4PTA4A7YNk/gDzxzDPPWC1pb9KkiaT/Zr3uZN68eSpZsqS8vLxUr149RUVFaeTIkVZXm46IiFDlypVVqVIl/f333+bjkUcekSStXbv2juMsXbpUI0eOVMeOHdW3b1+tX79ewcHBmjZtmk6cOGHG/fvvv3J2dk73ehcXF3P77Zw/f15Fixa9Yz4HDx7UgAEDFBQUZDVTldZ/ZnLITq6TJ0/WoEGD9M4779z2qt83mj9/vmbOnKmAgAB9//33Gj58uCpXrqyWLVvq5MmTZlxERISaNGmiokWLWv2cWrVqpZSUFG3YsEHSfxecc3R01AsvvGC+tkCBAnrppZcylU9GIiIi5OHhodatW1uNXbduXRUpUiTdMVKlShXzOJX+m4WqWLGi1TH73XffqWbNmurcuXO68dKWt2f22Ey7yNUPP/yQ4YUvb5R2/Nzqrhu56cafSZpChQqZ/758+bL+/vtvNWrUSIZhaNeuXXfss1WrVlYreWrUqCF3d/dMfT5khWEY+u6779ShQwcZhmH18wgODlZ8fLy5RNjT01MnTpzQtm3bcjSHmzk7O5uriFJSUnT+/HnzlI+Mlivf6bN0+/btOnv2rPr3728Vl7b0PTO6d++uggULWi39X79+vU6ePGku+Zesf+6XLl3S33//rSZNmujKlSs6ePBgpsa6EZ8jd/85kl0xMTE6cuSIevbsqfPnz5v9X758WS1bttSGDRvSfS7dfCecJk2a6Pz580pISJAkc1n+jaedScrW+5+ZzwhPT09t3bpVp06dynL/wP2CZf8AclxG9/G+8dx06f8XLzefI5mRjh07auDAgUpOTta2bdv09ttv68qVK1bL7o8cOaIDBw7ccsnt2bNns7ILkv7bjyFDhmjVqlVat26deXXrQoUKZXiO5dWrV83td2IYxm23x8XFKSQkRB4eHvr222+tlt6m9Z+ZHLKa6/r167V8+XKNGjXqtuf538zBwUEDBgzQgAEDdP78eW3evFlz5szRihUr9OSTT5rnGx85ckR79uy548/pzz//lK+vb7qrslesWDHTOd3syJEjio+PT3fu8M1jp7n5mJX+O25vPGaPHj2qrl273nHczByb3bt31yeffKJnn31Wr7zyilq2bKkuXbqoW7du6U4xSTt+MvpdS5OcnKwLFy5YtZUsWdLqWMoqR0dHlSpVKl378ePHNXbsWC1dujTd73R8fPwd+83Me50Tzp07p4sXL2ru3LmaO3duhjFpP49Ro0Zp9erVatCggcqXL682bdqoZ8+eGZ6TfjdSU1M1Y8YMzZo1S7GxsVbny2d0ysOdPkv//PNPSUp315GCBQuqXLlymcqpePHiCg4O1vfff685c+bIxcVFCxculKOjo5544gkzbv/+/RozZozWrFljFnxpMvNzvxmfI7cfN6f/j7u5f0m3PSUiPj7e6ovr2x2L7u7u+vPPP+Xg4KCAgACruPLly2c5v8y8j5MnT1afPn1UunRp1a1bV+3bt1fv3r0zfdwD9wOKfwBZcqfZ7StXrpgxN7pVwXGnIliSSpUqpVatWkmS2rdvrxIlSmjgwIFq0aKFedG91NRUVa9eXdOmTcuwjzud53oraa+7sYjy9fXV6dOn08Wmtd18ju7NihcvftuiJj4+Xu3atdPFixe1cePGdP35+vpajXdzDsWKFTNn+319fbV27VoZhmFVKN4q16pVq+rixYv64osv9Pzzz6f7oy0zihcvrscee0yPPfaYmjdvrvXr1+vPP/+Uv7+/UlNT1bp1a40cOTLD1958W8XMsFgsGR5HN190LDU1VV5eXlqwYEGG/dz8R/XdHLM3j5uZY7NQoULasGGD1q5dq+XLl2vlypX65ptv9Mgjj+jnn3+2yift+LnxNnw327Jli1q0aGHVFhsbe1f3uL9xljpNSkqKWrdurQsXLmjUqFGqVKmSChcurJMnTyo0NPSOqxiknHuv7yQtl6eeeuqWRU6NGjUk/XctkEOHDmnZsmVauXKlvvvuO82aNUtjx47V+PHjcyynt99+W6+99pr69u2rCRMmqFixYnJwcNDgwYMzfO/y6r166qmntGzZMi1btkyPPfaYvvvuO7Vp08b8Pbl48aKaNWsmd3d3vfHGGwoMDJSLi4t27typUaNGZernfjt8jljLrf/jbuxf+u82tLVq1cow5uYvUfLqWMzsWE888YSaNGmi77//Xj///LOmTJmid955R4sXL1a7du1yPCfgXkTxDyBL0i7KdOjQoXR/bFy5ckV//fWX2rRpk6s5PP/885o+fbrGjBmjzp07y2KxKDAwULt371bLli1vOxuaVWlLCm/8g65WrVrauHGjUlNTrQqhrVu3ytXV9Y5/eFaqVEmxsbEZbrt69ao6dOigw4cPa/Xq1apSpUq6mAceeEAlS5bU9u3b02379ddfrf5wq1Wrlj755BMdOHDAqq+0i4Pd/EdeiRIl9O233+rhhx9Wy5YttWnTpjt+mXE79erV0/r163X69Gn5+/srMDBQiYmJ5pc5t+Lv76+oqCglJiZa/cF56NChdLFFixbNcHl42gxomsDAQK1evVqNGzfO1OqMzAgMDNS+ffvuGJPZY9PBwUEtW7ZUy5YtNW3aNL399tt69dVXtXbtWqv3LDY2Vg4ODrc91mrWrKnIyEirNh8fn0zsVdbs3btXhw8f1meffabevXub7TePnR+ULFlSbm5uSklJueMxKEmFCxdW9+7d1b17dyUnJ6tLly566623NHr0aLm4uOTIZ823336rFi1aaN68eVbtFy9evO2XO7eS9hl95MgRc0m4JF27dk2xsbGqWbNmpvp57LHH5ObmpoULF6pgwYL6559/rJb8r1u3TufPn9fixYut7lxyq8+2u8HnSNY+R7KbgyS5u7tn6ncjM9K+qImNjbVaiZLR3Slyap98fX314osv6sUXX9TZs2dVp04dvfXWWxT/wP/hnH8AWdKyZUs5OTlp9uzZ6WZ25s6dq+vXr+f6f7KOjo4aNmyYDhw4YN7u7IknntDJkyf18ccfp4v/999/b3vrPum/mf2bZ3iuXbumSZMmycnJyWoGtVu3bjpz5owWL15stv3999+KiIhQhw4dMjzH/kZBQUHat29fuuX4KSkp6t69u6KjoxUREaGgoKBb9tG1a1ctW7ZMf/31l9kWFRWlw4cP6/HHHzfbOnbsqIIFC2rWrFlmm2EYmjNnjh544AE1atQoXd+lSpXS6tWr9e+//6p169Y6f/78bfcnLi5Ov/32W7r25ORkRUVFycHBwVzm+cQTTyg6OlqrVq1KF3/x4kVdv35d0n8rPK5fv67Zs2eb21NSUvTBBx+ke11gYKAOHjyoc+fOmW27d+9Od5X8J554QikpKZowYUK6Pq5fv66LFy/edj8z0rVrV+3evVvff/99um1pM1KZPTZvXqIv/f8vZ24+Vnbs2KGqVave9hzuokWLqlWrVlaPjFbl3K20GbkbZ+AMw9CMGTNyfKy7VaBAAXXt2lXfffddhsXWjcfQzce9k5OTqlSpIsMwdO3aNUn/fTkgKVvHzo053TxTGhERYXWOe1bUq1dPJUuW1Jw5c5ScnGy2h4eHZynPQoUKqXPnzvrpp580e/ZsFS5cWB07drTKW7L+uScnJ1t91mQFnyM58zmSXXXr1lVgYKDeffddJSYmptt+4/uSWcHBwZKU7pjI6P2/29+llJSUdKeaeHl5yc/PL0dvhQjc65j5B5AlXl5eGjt2rMaMGaOmTZvqsccek6urq7Zs2aKvvvpKbdq0UYcOHXI9j9DQUI0dO1bvvPOOOnXqpKefflqLFi1S//79tXbtWjVu3FgpKSk6ePCgFi1apFWrVmV4a7w0S5cu1Ztvvqlu3bopICBAFy5c0MKFC7Vv3z69/fbbVjOm3bp100MPPaRnnnlGv/32m0qUKKFZs2YpJSUlU8uBO3bsqAkTJmj9+vVWqySGDRumpUuXqkOHDrpw4YK+/PJLq9elXXNAkv73v/8pIiJCLVq00KBBg5SYmKgpU6aoevXqeuaZZ8y4UqVKafDgwZoyZYquXbum+vXra8mSJdq4caMWLFhwy6WU5cuX188//6zmzZsrODhYa9asueUt5U6cOKEGDRrokUceUcuWLeXj46OzZ8/qq6++0u7duzV48GBzBnPEiBFaunSpHn30UfN2V5cvX9bevXv17bff6tixYypRooQ6dOigxo0b65VXXtGxY8dUpUoVLV68OMPziPv27atp06YpODhYYWFhOnv2rObMmaOqVatanYfcrFkzPf/885o4caJiYmLUpk0bFSxYUEeOHFFERIRmzJihbt263fHnd6MRI0bo22+/1eOPP66+ffuqbt26unDhgpYuXao5c+aoZs2amT4233jjDW3YsEEhISHy9/fX2bNnNWvWLJUqVcrqdozXrl3T+vXr011Ey1YqVaqkwMBADR8+XCdPnpS7u7u+++67HD9fX/pvdrBZs2Zat25dtvuYNGmS1q5dq4YNG+q5555TlSpVdOHCBe3cuVOrV682v4Rp06aNfHx81LhxY3l7e+vAgQOaOXOmQkJC5ObmJum/gkmSXn31VT355JMqWLCgOnTooMKFC2vcuHEaP3681q5dm+H949M8+uijeuONN/TMM8+oUaNG2rt3rxYsWJDt85QLFiyoN998U88//7weeeQRde/eXbGxsZo/f36W+3zqqaf0+eefa9WqVerVq5dZoElSo0aNVLRoUfXp00cvv/yyLBaLvvjii2wv+eZzJGc+R27n2rVrGd62tVixYnrxxRf1ySefqF27dqpataqeeeYZPfDAAzp58qTWrl0rd3d3/fjjj1nar7p166pr16567733dP78efNWf4cPH5ZkPdt/u9+lzLh06ZJKlSqlbt26qWbNmipSpIhWr16tbdu2aerUqVnKG7BreXdjAQD25MsvvzQeeugho3Dhwoazs7NRqVIlY/z48Va3dzKM/397qoiICKv22NjYdLdautWt/m51655x48YZkoy1a9cahvHfLa7eeecdo2rVqoazs7NRtGhRo27dusb48eON+Ph483UZ3epv+/btRocOHYwHHnjAcHJyMooUKWI8/PDDxqJFizIc+8KFC0ZYWJhRvHhxw9XV1WjWrJmxbdu2271lVmrUqGGEhYVZtTVr1syQdMvHzfbt22e0adPGcHV1NTw9PY1evXoZcXFx6eJSUlKMt99+2/D39zecnJyMqlWrGl9++WW6uBtv9Zdm69athpubm9G0adMMb+tlGIaRkJBgzJgxwwgODjZKlSplFCxY0HBzczOCgoKMjz/+2Oq2UYZhGJcuXTJGjx5tlC9f3nBycjJKlChhNGrUyHj33XeN5ORkM+78+fPG008/bbi7uxseHh7G008/bezatSvdcWMY/x2P5cqVM5ycnIxatWoZq1atuuXtxebOnWvUrVvXKFSokOHm5mZUr17dGDlypHHq1KnbvheGkfHtwM6fP28MHDjQPHZKlSpl9OnTx+pWcpk5NqOiooyOHTsafn5+hpOTk+Hn52f06NEj3e3MVqxYYUgyjhw5kuHPIyfc6lZ/t7pV2G+//Wa0atXKKFKkiFGiRAnjueeeM2/FdePP6la3+svod9zf39/qFn2XLl0yJBlPPvlklvbl5lv9GYZhnDlzxhgwYIBRunRpo2DBgoaPj4/RsmVLY+7cuWbMRx99ZDRt2tQoXry44ezsbAQGBhojRoyw+iwxjP9uO/fAAw8YDg4OVu/ZsGHDDIvFkuGt6m509epVY9iwYYavr69RqFAho3HjxkZ0dHS6Yy0rn6WGYRizZs0yAgICDGdnZ6NevXrGhg0bbnk7u1u5fv264evra0gyfvrpp3TbN2/ebDz00ENGoUKFDD8/P2PkyJHGqlWrrD6XDSNzt/rjcyRnPkduJe1WnRk9AgMDzbhdu3YZXbp0MY97f39/44knnjCioqLMmFvd8nf+/PnpPjcuX75sDBgwwChWrJhRpEgRo1OnTsahQ4cMScakSZOsXn+r36XMfEYkJSUZI0aMMGrWrGm4ubkZhQsXNmrWrGnMmjXrtu8LcL+xGEYuXJUDAHBbX3zxhQYMGKDjx4+bt3jDnR07dkwBAQGaP3++QkNDbZ2OTXTq1EkWiyXDJcL27KefftKjjz6q3bt3q3r16rZO544aNGggf39/RURE2DoV3ITPEduKiYlR7dq19eWXX1pdRwJA7uOcfwCwgV69eqlMmTL68MMPbZ0K7iEHDhzQsmXLMjzf2N6tXbtWTz755D1R+CckJGj37t164403bJ0KYFMZ3Rnovffek4ODg9WFIgHkDc75BwAbcHBwuOPVnYGbVa5c2byY2f1mypQptk4h09zd3bnIGCBp8uTJ2rFjh1q0aCFHR0etWLFCK1asUL9+/e769oQAso7iHwAAAECOa9SokSIjIzVhwgQlJiaqTJkyGjdunF599VVbpwbclzjnHwAAAAAAO8c5/wAAAAAA2DmKfwAAAAAA7Bzn/OeQ1NRUnTp1Sm5ubrJYLLZOBwAAAABg5wzD0KVLl+Tn5ycHh9vP7VP855BTp05x1VIAAAAAQJ7766+/VKpUqdvGUPznEDc3N0n/venu7u42zgYAAAAAYO8SEhJUunRpsx69HYr/HJK21N/d3Z3iHwAAAACQZzJz6jkX/AMAAAAAwM5R/AMAAAAAYOco/gEAAAAAsHOc8w8AAAAAyBUpKSm6du2ardO4ZxUoUECOjo45cjt5in8AAAAAQI5LTEzUiRMnZBiGrVO5p7m6usrX11dOTk531Q/FPwAAAAAgR6WkpOjEiRNydXVVyZIlc2Tm+n5jGIaSk5N17tw5xcbGqkKFCnJwyP6Z+xT/AAAAAIAcde3aNRmGoZIlS6pQoUK2TueeVahQIRUsWFB//vmnkpOT5eLiku2+uOAfAAAAACBXMON/9+5mtt+qnxzpBQAAAAAA5FsU/wAAAAAA2DnO+QcAAAAA5Imw8G15Ot680Pp5Ol5GypYtq8GDB2vw4ME2zYOZfwAAAADAfc9isdz2MW7cuGz1u23bNvXr1y9nk80GZv4BAAAAAPe906dPm//+5ptvNHbsWB06dMhsK1KkiPlvwzCUkpIiR8c7l9QlS5bM2USziZl/AAAAAMB9z8fHx3x4eHjIYrGYzw8ePCg3NzetWLFCdevWlbOzszZt2qSjR4+qY8eO8vb2VpEiRVS/fn2tXr3aqt+yZcvqvffeM59bLBZ98skn6ty5s1xdXVWhQgUtXbo01/eP4h8AAAAAgEx45ZVXNGnSJB04cEA1atRQYmKi2rdvr6ioKO3atUtt27ZVhw4ddPz48dv2M378eD3xxBPas2eP2rdvr169eunChQu5mjvFPwAAAAAAmfDGG2+odevWCgwMVLFixVSzZk09//zzqlatmipUqKAJEyYoMDDwjjP5oaGh6tGjh8qXL6+3335biYmJ+vXXX3M1d4p/AAAAAAAyoV69elbPExMTNXz4cFWuXFmenp4qUqSIDhw4cMeZ/xo1apj/Lly4sNzd3XX27NlcyTkNF/wDAAAAACATChcubPV8+PDhioyM1Lvvvqvy5curUKFC6tatm5KTk2/bT8GCBa2eWywWpaam5ni+N6L4BwAAAAAgGzZv3qzQ0FB17txZ0n8rAY4dO2bbpG6B4h92KSx8W56ONy+0fp6OBwAAAMD2KlSooMWLF6tDhw6yWCx67bXXcn0GP7so/gEAAAAAecLeJs2mTZumvn37qlGjRipRooRGjRqlhIQEW6eVIYthGIatk7AHCQkJ8vDwUHx8vNzd3W2dzn2PmX8AAADAdq5evarY2FgFBATIxcXF1unc0273XmalDrXp1f4nTpyo+vXry83NTV5eXurUqZMOHTpkFXP16lUNGDBAxYsXV5EiRdS1a1edOXPGKub48eMKCQmRq6urvLy8NGLECF2/ft0qZt26dapTp46cnZ1Vvnx5hYeHp8vnww8/VNmyZeXi4qKGDRvm+q0WAAAAAADICzYt/tevX68BAwbol19+UWRkpK5du6Y2bdro8uXLZsyQIUP0448/KiIiQuvXr9epU6fUpUsXc3tKSopCQkKUnJysLVu26LPPPlN4eLjGjh1rxsTGxiokJEQtWrRQTEyMBg8erGeffVarVq0yY7755hsNHTpUr7/+unbu3KmaNWsqODg412+3AAAAAABAbstXy/7PnTsnLy8vrV+/Xk2bNlV8fLxKliyphQsXqlu3bpKkgwcPqnLlyoqOjtZDDz2kFStW6NFHH9WpU6fk7e0tSZozZ45GjRqlc+fOycnJSaNGjdLy5cu1b98+c6wnn3xSFy9e1MqVKyVJDRs2VP369TVz5kxJUmpqqkqXLq2XXnpJr7zyyh1zZ9l//sKyfwAAAMB2WPafc+xi2f/N4uPjJUnFihWTJO3YsUPXrl1Tq1atzJhKlSqpTJkyio6OliRFR0erevXqZuEvScHBwUpISND+/fvNmBv7SItJ6yM5OVk7duywinFwcFCrVq3MmJslJSUpISHB6gEAAAAAQH6Ub4r/1NRUDR48WI0bN1a1atUkSXFxcXJycpKnp6dVrLe3t+Li4syYGwv/tO1p224Xk5CQoH///Vd///23UlJSMoxJ6+NmEydOlIeHh/koXbp09nYcAAAAAIBclm+K/wEDBmjfvn36+uuvbZ1KpowePVrx8fHm46+//rJ1SgAAAAAAZMjR1glI0sCBA7Vs2TJt2LBBpUqVMtt9fHyUnJysixcvWs3+nzlzRj4+PmbMzVflT7sbwI0xN98h4MyZM3J3d1ehQoVUoEABFShQIMOYtD5u5uzsLGdn5+ztMAAAAAAAecimM/+GYWjgwIH6/vvvtWbNGgUEBFhtr1u3rgoWLKioqCiz7dChQzp+/LiCgoIkSUFBQdq7d6/VVfkjIyPl7u6uKlWqmDE39pEWk9aHk5OT6tataxWTmpqqqKgoMwYAAAAAgHuVTWf+BwwYoIULF+qHH36Qm5ubeX69h4eHChUqJA8PD4WFhWno0KEqVqyY3N3d9dJLLykoKEgPPfSQJKlNmzaqUqWKnn76aU2ePFlxcXEaM2aMBgwYYM7M9+/fXzNnztTIkSPVt29frVmzRosWLdLy5cvNXIYOHao+ffqoXr16atCggd577z1dvnxZzzzzTN6/MQAAAAAA5CCbFv+zZ8+WJDVv3tyqff78+QoNDZUkTZ8+XQ4ODuratauSkpIUHBysWbNmmbEFChTQsmXL9MILLygoKEiFCxdWnz599MYbb5gxAQEBWr58uYYMGaIZM2aoVKlS+uSTTxQcHGzGdO/eXefOndPYsWMVFxenWrVqaeXKlekuAggAAAAAyKaF3fN2vJ7f5O14+ZjFMAzD1knYg6zcXxG5Lyx8W56ONy+0fp6OBwAAAORnt7w3fT4u/i0Wy223v/766xo3bly20rBYLPr+++/VqVOnLL/2lu+lslaH5osL/gEAAAAAYEunT582//3NN99o7NixOnTokNlWpEgRW6SVY/LNrf4AAAAAALAVHx8f8+Hh4SGLxWLV9vXXX6ty5cpycXFRpUqVrE5HT05O1sCBA+Xr6ysXFxf5+/tr4sSJkqSyZctKkjp37iyLxWI+z2vM/AMAAAAAcBsLFizQ2LFjNXPmTNWuXVu7du3Sc889Z15z7v3339fSpUu1aNEilSlTRn/99Zf++usvSdK2bdvk5eWl+fPnq23btipQoIBN9oHiHwAAAACA23j99dc1depUdenSRdJ/F5X/7bff9NFHH6lPnz46fvy4KlSooIcfflgWi0X+/v7ma0uWLClJ8vT0lI+Pj03ylyj+AQAAAAC4pcuXL+vo0aMKCwvTc889Z7Zfv35dHh4ekqTQ0FC1bt1aFStWVNu2bfXoo4+qTZs2tko5QxT/AAAAAADcQmJioiTp448/VsOGDa22pS3hr1OnjmJjY7VixQqtXr1aTzzxhFq1aqVvv/02z/O9FYp/AAAAAABuwdvbW35+fvrjjz/Uq1evW8a5u7ure/fu6t69u7p166a2bdvqwoULKlasmAoWLKiUlJQ8zDo9in8AAAAAAG5j/Pjxevnll+Xh4aG2bdsqKSlJ27dv1z///KOhQ4dq2rRp8vX1Ve3ateXg4KCIiAj5+PjI09NT0n9X/I+KilLjxo3l7OysokWL5vk+UPwDAAAAAPJGz29snUG2PPvss3J1ddWUKVM0YsQIFS5cWNWrV9fgwYMlSW5ubpo8ebKOHDmiAgUKqH79+vrpp5/k4OAgSZo6daqGDh2qjz/+WA888ICOHTuW5/tgMQzDyPNR7VBCQoI8PDwUHx8vd3d3W6dz3wsL35an480LrZ+n4wEAAAD52dWrVxUbG6uAgAC5uLjYOp172u3ey6zUoQ65mSQAAAAAALA9in8AAAAAAOwcxT8AAAAAAHaO4h8AAAAAADtH8Q8AAAAAgJ2j+AcAAAAAwM5R/AMAAAAAYOco/gEAAAAAsHMU/wAAAAAA2DlHWycAAAAAALg/DIwamKfjzWw5M0/Ha968uWrVqqX33nsvT8fNDGb+AQAAAAD3vQ4dOqht27YZbtu4caMsFov27NmTx1nlHIp/AAAAAMB9LywsTJGRkTpx4kS6bfPnz1e9evVUo0YNG2SWMyj+AQAAAAD3vUcffVQlS5ZUeHi4VXtiYqIiIiLUqVMn9ejRQw888IBcXV1VvXp1ffXVV7ZJNhso/gEAAAAA9z1HR0f17t1b4eHhMgzDbI+IiFBKSoqeeuop1a1bV8uXL9e+ffvUr18/Pf300/r1119tmHXmccE/5Imw8G22TgEAAAAAbqtv376aMmWK1q9fr+bNm0v6b8l/165d5e/vr+HDh5uxL730klatWqVFixapQYMGNso485j5BwAAAABAUqVKldSoUSN9+umnkqTff/9dGzduVFhYmFJSUjRhwgRVr15dxYoVU5EiRbRq1SodP37cxllnDsU/AAAAAAD/JywsTN99950uXbqk+fPnKzAwUM2aNdOUKVM0Y8YMjRo1SmvXrlVMTIyCg4OVnJxs65QzheIfAAAAAID/88QTT8jBwUELFy7U559/rr59+8pisWjz5s3q2LGjnnrqKdWsWVPlypXT4cOHbZ1uplH8AwAAAADwf4oUKaLu3btr9OjROn36tEJDQyVJFSpUUGRkpLZs2aIDBw7o+eef15kzZ2ybbBZwwT8AAAAAQJ6Y2XKmrVPIlLCwMM2bN0/t27eXn5+fJGnMmDH6448/FBwcLFdXV/Xr10+dOnVSfHy8jbPNHIp/AAAAAABuEBQUZHW7P0kqVqyYlixZctvXrVu3LveSukss+wcAAAAAwM5R/AMAAAAAYOco/gEAAAAAsHMU/wAAAAAA2DmKfwAAAABArrj5onnIupx6Dyn+AQAAAAA5qkCBApKk5ORkG2dy77ty5YokqWDBgnfVD7f6AwAAAADkKEdHR7m6uurcuXMqWLCgHByYd84qwzB05coVnT17Vp6enuYXKtlF8Q8AAAAAyFEWi0W+vr6KjY3Vn3/+aet07mmenp7y8fG5634o/gEAAAAAOc7JyUkVKlRg6f9dKFiw4F3P+Keh+AcAAAAA5AoHBwe5uLjYOg2IC/4BAAAAAGD3KP4BAAAAALBzNi3+N2zYoA4dOsjPz08Wi0VLliyx2m6xWDJ8TJkyxYwpW7Zsuu2TJk2y6mfPnj1q0qSJXFxcVLp0aU2ePDldLhEREapUqZJcXFxUvXp1/fTTT7myzwAAAAAA5DWbFv+XL19WzZo19eGHH2a4/fTp01aPTz/9VBaLRV27drWKe+ONN6ziXnrpJXNbQkKC2rRpI39/f+3YsUNTpkzRuHHjNHfuXDNmy5Yt6tGjh8LCwrRr1y516tRJnTp10r59+3JnxwEAAAAAyEM2veBfu3bt1K5du1tuv/l2Bj/88INatGihcuXKWbW7ubnd8tYHCxYsUHJysj799FM5OTmpatWqiomJ0bRp09SvXz9J0owZM9S2bVuNGDFCkjRhwgRFRkZq5syZmjNnzt3sIgAAAAAANnfPnPN/5swZLV++XGFhYem2TZo0ScWLF1ft2rU1ZcoUXb9+3dwWHR2tpk2bysnJyWwLDg7WoUOH9M8//5gxrVq1suozODhY0dHRt8wnKSlJCQkJVg8AAAAAAPKje+ZWf5999pnc3NzUpUsXq/aXX35ZderUUbFixbRlyxaNHj1ap0+f1rRp0yRJcXFxCggIsHqNt7e3ua1o0aKKi4sz226MiYuLu2U+EydO1Pjx43Ni1wAAAAAAyFX3TPH/6aefqlevXunuETl06FDz3zVq1JCTk5Oef/55TZw4Uc7OzrmWz+jRo63GTkhIUOnSpXNtPAAAAAAAsuueKP43btyoQ4cO6ZtvvrljbMOGDXX9+nUdO3ZMFStWlI+Pj86cOWMVk/Y87ToBt4q51XUEJMnZ2TlXv1wAAAAAACCn3BPn/M+bN09169ZVzZo17xgbExMjBwcHeXl5SZKCgoK0YcMGXbt2zYyJjIxUxYoVVbRoUTMmKirKqp/IyEgFBQXl4F4AAAAAAGAbNi3+ExMTFRMTo5iYGElSbGysYmJidPz4cTMmISFBERERevbZZ9O9Pjo6Wu+99552796tP/74QwsWLNCQIUP01FNPmYV9z5495eTkpLCwMO3fv1/ffPONZsyYYbVkf9CgQVq5cqWmTp2qgwcPaty4cdq+fbsGDhyYu28AAAAAAAB5wKbL/rdv364WLVqYz9MK8j59+ig8PFyS9PXXX8swDPXo0SPd652dnfX1119r3LhxSkpKUkBAgIYMGWJV2Ht4eOjnn3/WgAEDVLduXZUoUUJjx441b/MnSY0aNdLChQs1ZswY/e9//1OFChW0ZMkSVatWLZf2HAAAAACAvGMxDMOwdRL2ICEhQR4eHoqPj5e7u7ut08l3wsK32TqFXDUvtL6tUwAAAABwn8lKHXpPnPMPAAAAAACyj+IfAAAAAAA7R/EPAAAAAICds+kF/wB7kdfXNOAaAwAAAACygpl/AAAAAADsHMU/AAAAAAB2juIfAAAAAAA7R/EPAAAAAICdo/gHAAAAAMDOUfwDAAAAAGDnKP4BAAAAALBzFP8AAAAAANg5in8AAAAAAOwcxT8AAAAAAHaO4h8AAAAAADtH8Q8AAAAAgJ2j+AcAAAAAwM5R/AMAAAAAYOco/gEAAAAAsHMU/wAAAAAA2DmKfwAAAAAA7BzFPwAAAAAAdo7iHwAAAAAAO0fxDwAAAACAnaP4BwAAAADAzlH8AwAAAABg5yj+AQAAAACwcxT/AAAAAADYOYp/AAAAAADsHMU/AAAAAAB2juIfAAAAAAA7R/EPAAAAAICdo/gHAAAAAMDOUfwDAAAAAGDnKP4BAAAAALBzFP8AAAAAANg5in8AAAAAAOwcxT8AAAAAAHaO4h8AAAAAADtH8Q8AAAAAgJ2j+AcAAAAAwM5R/AMAAAAAYOco/gEAAAAAsHMU/wAAAAAA2DmbFv8bNmxQhw4d5OfnJ4vFoiVLllhtDw0NlcVisXq0bdvWKubChQvq1auX3N3d5enpqbCwMCUmJlrF7NmzR02aNJGLi4tKly6tyZMnp8slIiJClSpVkouLi6pXr66ffvopx/cXAAAAAABbsGnxf/nyZdWsWVMffvjhLWPatm2r06dPm4+vvvrKanuvXr20f/9+RUZGatmyZdqwYYP69etnbk9ISFCbNm3k7++vHTt2aMqUKRo3bpzmzp1rxmzZskU9evRQWFiYdu3apU6dOqlTp07at29fzu80AAAAAAB5zGIYhmHrJCTJYrHo+++/V6dOncy20NBQXbx4Md2KgDQHDhxQlSpVtG3bNtWrV0+StHLlSrVv314nTpyQn5+fZs+erVdffVVxcXFycnKSJL3yyitasmSJDh48KEnq3r27Ll++rGXLlpl9P/TQQ6pVq5bmzJmT4dhJSUlKSkoynyckJKh06dKKj4+Xu7v73bwVdiksfJutU7Ar80Lr2zoFAAAAADaWkJAgDw+PTNWh+f6c/3Xr1snLy0sVK1bUCy+8oPPnz5vboqOj5enpaRb+ktSqVSs5ODho69atZkzTpk3Nwl+SgoODdejQIf3zzz9mTKtWrazGDQ4OVnR09C3zmjhxojw8PMxH6dKlc2R/AQAAAADIafm6+G/btq0+//xzRUVF6Z133tH69evVrl07paSkSJLi4uLk5eVl9RpHR0cVK1ZMcXFxZoy3t7dVTNrzO8Wkbc/I6NGjFR8fbz7++uuvu9tZAAAAAAByiaOtE7idJ5980vx39erVVaNGDQUGBmrdunVq2bKlDTOTnJ2d5ezsbNMccP/K69MoOM0AAAAAuLfl65n/m5UrV04lSpTQ77//Lkny8fHR2bNnrWKuX7+uCxcuyMfHx4w5c+aMVUza8zvFpG0HAAAAAOBedk8V/ydOnND58+fl6+srSQoKCtLFixe1Y8cOM2bNmjVKTU1Vw4YNzZgNGzbo2rVrZkxkZKQqVqyookWLmjFRUVFWY0VGRiooKCi3dwkAAAAAgFxn0+I/MTFRMTExiomJkSTFxsYqJiZGx48fV2JiokaMGKFffvlFx44dU1RUlDp27Kjy5csrODhYklS5cmW1bdtWzz33nH799Vdt3rxZAwcO1JNPPik/Pz9JUs+ePeXk5KSwsDDt379f33zzjWbMmKGhQ4eaeQwaNEgrV67U1KlTdfDgQY0bN07bt2/XwIED8/w9AQAAAAAgp9m0+N++fbtq166t2rVrS5KGDh2q2rVra+zYsSpQoID27Nmjxx57TA8++KDCwsJUt25dbdy40epc+wULFqhSpUpq2bKl2rdvr4cfflhz5841t3t4eOjnn39WbGys6tatq2HDhmns2LHq16+fGdOoUSMtXLhQc+fOVc2aNfXtt99qyZIlqlatWt69GQAAAAAA5BKLYRiGrZOwB1m5v+L9KK8vUIecxQX/AAAAgPwnK3XoPXXOPwAAAAAAyDqKfwAAAAAA7BzFPwAAAAAAdo7iHwAAAAAAO0fxDwAAAACAnaP4BwAAAADAzlH8AwAAAABg5yj+AQAAAACwcxT/AAAAAADYOYp/AAAAAADsHMU/AAAAAAB2juIfAAAAAAA7R/EPAAAAAICdo/gHAAAAAMDOUfwDAAAAAGDnKP4BAAAAALBzFP8AAAAAANg5in8AAAAAAOwcxT8AAAAAAHaO4h8AAAAAADtH8Q8AAAAAgJ2j+AcAAAAAwM5R/AMAAAAAYOco/gEAAAAAsHMU/wAAAAAA2DmKfwAAAAAA7BzFPwAAAAAAdo7iHwAAAAAAO0fxDwAAAACAnaP4BwAAAADAzlH8AwAAAABg5yj+AQAAAACwcxT/AAAAAADYOYp/AAAAAADsHMU/AAAAAAB2juIfAAAAAAA7R/EPAAAAAICdo/gHAAAAAMDOUfwDAAAAAGDnKP4BAAAAALBzFP8AAAAAANg5in8AAAAAAOwcxT8AAAAAAHbOpsX/hg0b1KFDB/n5+clisWjJkiXmtmvXrmnUqFGqXr26ChcuLD8/P/Xu3VunTp2y6qNs2bKyWCxWj0mTJlnF7NmzR02aNJGLi4tKly6tyZMnp8slIiJClSpVkouLi6pXr66ffvopV/YZAAAAAIC8ZtPi//Lly6pZs6Y+/PDDdNuuXLminTt36rXXXtPOnTu1ePFiHTp0SI899li62DfeeEOnT582Hy+99JK5LSEhQW3atJG/v7927NihKVOmaNy4cZo7d64Zs2XLFvXo0UNhYWHatWuXOnXqpE6dOmnfvn25s+MAAAAAAOQhR1sO3q5dO7Vr1y7DbR4eHoqMjLRqmzlzpho0aKDjx4+rTJkyZrubm5t8fHwy7GfBggVKTk7Wp59+KicnJ1WtWlUxMTGaNm2a+vXrJ0maMWOG2rZtqxEjRkiSJkyYoMjISM2cOVNz5szJsN+kpCQlJSWZzxMSEjK/4wAAAAAA5KF76pz/+Ph4WSwWeXp6WrVPmjRJxYsXV+3atTVlyhRdv37d3BYdHa2mTZvKycnJbAsODtahQ4f0zz//mDGtWrWy6jM4OFjR0dG3zGXixIny8PAwH6VLl86BPQQAAAAAIOfdM8X/1atXNWrUKPXo0UPu7u5m+8svv6yvv/5aa9eu1fPPP6+3335bI0eONLfHxcXJ29vbqq+053FxcbeNSduekdGjRys+Pt58/PXXX3e9jwAAAAAA5AabLvvPrGvXrumJJ56QYRiaPXu21bahQ4ea/65Ro4acnJz0/PPPa+LEiXJ2ds61nJydnXO1fwAAAAAAckq+n/lPK/z//PNPRUZGWs36Z6Rhw4a6fv26jh07Jkny8fHRmTNnrGLSnqddJ+BWMbe6jgAAAAAAAPeSfF38pxX+R44c0erVq1W8ePE7viYmJkYODg7y8vKSJAUFBWnDhg26du2aGRMZGamKFSuqaNGiZkxUVJRVP5GRkQoKCsrBvQEAAAAAwDZsuuw/MTFRv//+u/k8NjZWMTExKlasmHx9fdWtWzft3LlTy5YtU0pKinkOfrFixeTk5KTo6Ght3bpVLVq0kJubm6KjozVkyBA99dRTZmHfs2dPjR8/XmFhYRo1apT27dunGTNmaPr06ea4gwYNUrNmzTR16lSFhITo66+/1vbt261uBwgAAAAAwL3KYhiGYavB161bpxYtWqRr79Onj8aNG6eAgIAMX7d27Vo1b95cO3fu1IsvvqiDBw8qKSlJAQEBevrppzV06FCr8/H37NmjAQMGaNu2bSpRooReeukljRo1yqrPiIgIjRkzRseOHVOFChU0efJktW/fPtP7kpCQIA8PD8XHx9/x1IT7UVj4NlungLswL7S+rVMAAAAAcJOs1KHZKv7/+OMPlStXLtsJ2iOK/9uj+L+3UfwDAAAA+U9W6tBsnfNfvnx5tWjRQl9++aWuXr2arSQBAAAAAEDeyNbMf0xMjObPn6+vvvpKycnJ6t69u8LCwtSgQYPcyPGewMz/7THzj6xitQEAAABwe7k+81+rVi3NmDFDp06d0qeffqrTp0/r4YcfVrVq1TRt2jSdO3cuW4kDAAAAAICcd1e3+nN0dFSXLl0UERGhd955R7///ruGDx+u0qVLq3fv3jp9+nRO5QkAAAAAALLpror/7du368UXX5Svr6+mTZum4cOH6+jRo4qMjNSpU6fUsWPHnMoTAAAAAABkk2N2XjRt2jTNnz9fhw4dUvv27fX555+rffv2cnD477uEgIAAhYeHq2zZsjmZKwAAAAAAyIZsFf+zZ89W3759FRoaKl9f3wxjvLy8NG/evLtKDgAAAAAA3L1sFf9Hjhy5Y4yTk5P69OmTne4BAAAAAEAOytY5//Pnz1dERES69oiICH322Wd3nRQAAAAAAMg52Sr+J06cqBIlSqRr9/Ly0ttvv33XSQEAAAAAgJyTreL/+PHjCggISNfu7++v48eP33VSAAAAAAAg52Sr+Pfy8tKePXvSte/evVvFixe/66QAAAAAAEDOyVbx36NHD7388stau3atUlJSlJKSojVr1mjQoEF68sknczpHAAAAAABwF7J1tf8JEybo2LFjatmypRwd/+siNTVVvXv35px/AAAAAADymWwV/05OTvrmm280YcIE7d69W4UKFVL16tXl7++f0/kBAAAAAIC7lK3iP82DDz6oBx98MKdyAQAAAAAAuSBbxX9KSorCw8MVFRWls2fPKjU11Wr7mjVrciQ5AAAAAABw97JV/A8aNEjh4eEKCQlRtWrVZLFYcjovAAAAAACQQ7JV/H/99ddatGiR2rdvn9P5AAAAAACAHJatW/05OTmpfPnyOZ0LAAAAAADIBdkq/ocNG6YZM2bIMIyczgcAAAAAAOSwbC3737Rpk9auXasVK1aoatWqKliwoNX2xYsX50hyAAAAAADg7mWr+Pf09FTnzp1zOhcAAAAAAJALslX8z58/P6fzAAAAAAAAuSRb5/xL0vXr17V69Wp99NFHunTpkiTp1KlTSkxMzLHkAAAAAADA3cvWzP+ff/6ptm3b6vjx40pKSlLr1q3l5uamd955R0lJSZozZ05O5wkAAAAAALIpWzP/gwYNUr169fTPP/+oUKFCZnvnzp0VFRWVY8kBAAAAAIC7l62Z/40bN2rLli1ycnKyai9btqxOnjyZI4kBAAAAAICcka2Z/9TUVKWkpKRrP3HihNzc3O46KQAAAAAAkHOyVfy3adNG7733nvncYrEoMTFRr7/+utq3b59TuQEAAAAAgByQrWX/U6dOVXBwsKpUqaKrV6+qZ8+eOnLkiEqUKKGvvvoqp3MEAAAAAAB3IVvFf6lSpbR79259/fXX2rNnjxITExUWFqZevXpZXQAQAAAAAADYXraKf0lydHTUU089lZO5AAAAAACAXJCt4v/zzz+/7fbevXtnKxkAAAAAAJDzslX8Dxo0yOr5tWvXdOXKFTk5OcnV1ZXiHwAAAACAfCRbV/v/559/rB6JiYk6dOiQHn74YS74BwAAAABAPpOt4j8jFSpU0KRJk9KtCgAAAAAAALaVY8W/9N9FAE+dOpWTXQIAAAAAgLuUrXP+ly5davXcMAydPn1aM2fOVOPGjXMkMQAAAAAAkDOyVfx36tTJ6rnFYlHJkiX1yCOPaOrUqTmRFwAAAAAAyCHZKv5TU1NzOg8AsBIWvi1Px5sXWj9PxwMAAADyUo6e8w8AAAAAAPKfbM38Dx06NNOx06ZNy84QAAAAAAAgh2Rr5n/Xrl369NNP9dFHH2ndunVat26d5s6dq3nz5mnXrl3mIyYm5rb9bNiwQR06dJCfn58sFouWLFlitd0wDI0dO1a+vr4qVKiQWrVqpSNHjljFXLhwQb169ZK7u7s8PT0VFhamxMREq5g9e/aoSZMmcnFxUenSpTV58uR0uURERKhSpUpycXFR9erV9dNPP2XnrQEAAAAAIN/JVvHfoUMHNW3aVCdOnNDOnTu1c+dO/fXXX2rRooUeffRRrV27VmvXrtWaNWtu28/ly5dVs2ZNffjhhxlunzx5st5//33NmTNHW7duVeHChRUcHKyrV6+aMb169dL+/fsVGRmpZcuWacOGDerXr5+5PSEhQW3atJG/v7927NihKVOmaNy4cZo7d64Zs2XLFvXo0UNhYWHatWuXOnXqpE6dOmnfvn3ZeXsAAAAAAMhXLIZhGFl90QMPPKCff/5ZVatWtWrft2+f2rRpo1OnTmU9EYtF33//vXknAcMw5Ofnp2HDhmn48OGSpPj4eHl7eys8PFxPPvmkDhw4oCpVqmjbtm2qV6+eJGnlypVq3769Tpw4IT8/P82ePVuvvvqq4uLi5OTkJEl65ZVXtGTJEh08eFCS1L17d12+fFnLli0z83nooYdUq1YtzZkzJ1P5JyQkyMPDQ/Hx8XJ3d8/y/tu7vL54G5BVXPAPAAAA95qs1KHZmvlPSEjQuXPn0rWfO3dOly5dyk6X6cTGxiouLk6tWrUy2zw8PNSwYUNFR0dLkqKjo+Xp6WkW/pLUqlUrOTg4aOvWrWZM06ZNzcJfkoKDg3Xo0CH9888/ZsyN46TFpI2TkaSkJCUkJFg9AAAAAADIj7JV/Hfu3FnPPPOMFi9erBMnTujEiRP67rvvFBYWpi5duuRIYnFxcZIkb29vq3Zvb29zW1xcnLy8vKy2Ozo6qlixYlYxGfVx4xi3iknbnpGJEyfKw8PDfJQuXTqruwgAAAAAQJ7IVvE/Z84ctWvXTj179pS/v7/8/f3Vs2dPtW3bVrNmzcrpHPOl0aNHKz4+3nz89ddftk4JAAAAAIAMZetWf66urpo1a5amTJmio0ePSpICAwNVuHDhHEvMx8dHknTmzBn5+vqa7WfOnFGtWrXMmLNnz1q97vr167pw4YL5eh8fH505c8YqJu35nWLStmfE2dlZzs7O2dgzAAAAAADyVrZm/tOcPn1ap0+fVoUKFVS4cGFl49qBtxQQECAfHx9FRUWZbQkJCdq6dauCgoIkSUFBQbp48aJ27NhhxqxZs0apqalq2LChGbNhwwZdu3bNjImMjFTFihVVtGhRM+bGcdJi0sYBAAAAAOBelq3i//z582rZsqUefPBBtW/fXqdPn5YkhYWFadiwYZnuJzExUTExMYqJiZH030X+YmJidPz4cVksFg0ePFhvvvmmli5dqr1796p3797y8/Mz7whQuXJltW3bVs8995x+/fVXbd68WQMHDtSTTz4pPz8/SVLPnj3l5OSksLAw7d+/X998841mzJihoUOHmnkMGjRIK1eu1NSpU3Xw4EGNGzdO27dv18CBA7Pz9gAAAAAAkK9kq/gfMmSIChYsqOPHj8vV1dVs7969u1auXJnpfrZv367atWurdu3akqShQ4eqdu3aGjt2rCRp5MiReumll9SvXz/Vr19fiYmJWrlypVxcXMw+FixYoEqVKqlly5Zq3769Hn74Yc2dO9fc7uHhoZ9//lmxsbGqW7euhg0bprFjx6pfv35mTKNGjbRw4ULNnTtXNWvW1LfffqslS5aoWrVq2Xl7AAAAAADIVyxGNtbq+/j4aNWqVapZs6bc3Ny0e/dulStXTn/88Ydq1KihxMTE3Mg1X8vK/RXvR2Hh22ydAnBb80Lr2zoFAAAAIEuyUodma+b/8uXLVjP+aS5cuMBF8AAAAAAAyGeyVfw3adJEn3/+ufncYrEoNTVVkydPVosWLXIsOQAAAAAAcPeydau/yZMnq2XLltq+fbuSk5M1cuRI7d+/XxcuXNDmzZtzOkcAAAAAAHAXsjXzX61aNR0+fFgPP/ywOnbsqMuXL6tLly7atWuXAgMDczpHAAAAAABwF7I883/t2jW1bdtWc+bM0auvvpobOQEAAAAAgByU5Zn/ggULas+ePbmRCwAAAAAAyAXZWvb/1FNPad68eTmdCwAAAAAAyAXZuuDf9evX9emnn2r16tWqW7euChcubLV92rRpOZIcAAAAAAC4e1kq/v/44w+VLVtW+/btU506dSRJhw8ftoqxWCw5lx0AAAAAALhrWSr+K1SooNOnT2vt2rWSpO7du+v999+Xt7d3riQHAAAAAADuXpbO+TcMw+r5ihUrdPny5RxNCAAAAAAA5KxsXfAvzc1fBgAAAAAAgPwnS8W/xWJJd04/5/gDAAAAAJC/Zemcf8MwFBoaKmdnZ0nS1atX1b9//3RX+1+8eHHOZQgAAAAAAO5Klor/Pn36WD1/6qmncjQZAAAAAACQ87JU/M+fPz+38gAAAAAAALnkri74BwAAAAAA8j+KfwAAAAAA7BzFPwAAAAAAdo7iHwAAAAAAO0fxDwAAAACAncvS1f4BwF6FhW/L0/HmhdbP0/EAAABwf2PmHwAAAAAAO0fxDwAAAACAnaP4BwAAAADAzlH8AwAAAABg5yj+AQAAAACwcxT/AAAAAADYOYp/AAAAAADsHMU/AAAAAAB2juIfAAAAAAA7R/EPAAAAAICdo/gHAAAAAMDOUfwDAAAAAGDnKP4BAAAAALBzFP8AAAAAANg5in8AAAAAAOwcxT8AAAAAAHaO4h8AAAAAADtH8Q8AAAAAgJ2j+AcAAAAAwM452joBALgfhYVvy9Px5oXWz9PxAAAAkL8w8w8AAAAAgJ3L98V/2bJlZbFY0j0GDBggSWrevHm6bf3797fq4/jx4woJCZGrq6u8vLw0YsQIXb9+3Spm3bp1qlOnjpydnVW+fHmFh4fn1S4CAAAAAJCr8v2y/23btiklJcV8vm/fPrVu3VqPP/642fbcc8/pjTfeMJ+7urqa/05JSVFISIh8fHy0ZcsWnT59Wr1791bBggX19ttvS5JiY2MVEhKi/v37a8GCBYqKitKzzz4rX19fBQcH58FeAgAAAACQe/J98V+yZEmr55MmTVJgYKCaNWtmtrm6usrHxyfD1//888/67bfftHr1anl7e6tWrVqaMGGCRo0apXHjxsnJyUlz5sxRQECApk6dKkmqXLmyNm3apOnTp1P8AwAAAADuefl+2f+NkpOT9eWXX6pv376yWCxm+4IFC1SiRAlVq1ZNo0eP1pUrV8xt0dHRql69ury9vc224OBgJSQkaP/+/WZMq1atrMYKDg5WdHT0LXNJSkpSQkKC1QMAAAAAgPwo38/832jJkiW6ePGiQkNDzbaePXvK399ffn5+2rNnj0aNGqVDhw5p8eLFkqS4uDirwl+S+TwuLu62MQkJCfr3339VqFChdLlMnDhR48ePz8ndAwAAAAAgV9xTxf+8efPUrl07+fn5mW39+vUz/129enX5+vqqZcuWOnr0qAIDA3Mtl9GjR2vo0KHm84SEBJUuXTrXxgMAAAAAILvumeL/zz//1OrVq80Z/Vtp2LChJOn3339XYGCgfHx89Ouvv1rFnDlzRpLM6wT4+PiYbTfGuLu7ZzjrL0nOzs5ydnbO1r4AAAAAAJCX7plz/ufPny8vLy+FhITcNi4mJkaS5OvrK0kKCgrS3r17dfbsWTMmMjJS7u7uqlKlihkTFRVl1U9kZKSCgoJycA8AAAAAALCNe6L4T01N1fz589WnTx85Ov7/xQpHjx7VhAkTtGPHDh07dkxLly5V79691bRpU9WoUUOS1KZNG1WpUkVPP/20du/erVWrVmnMmDEaMGCAOXPfv39//fHHHxo5cqQOHjyoWbNmadGiRRoyZIhN9hcAAAAAgJx0TxT/q1ev1vHjx9W3b1+rdicnJ61evVpt2rRRpUqVNGzYMHXt2lU//vijGVOgQAEtW7ZMBQoUUFBQkJ566in17t1bb7zxhhkTEBCg5cuXKzIyUjVr1tTUqVP1ySefcJs/AAAAAIBdsBiGYdg6CXuQkJAgDw8PxcfHy93d3dbp5Dth4dtsnQJwX5sXWt/WKQAAACCHZaUOvSdm/gEAAAAAQPZR/AMAAAAAYOco/gEAAAAAsHMU/wAAAAAA2DmKfwAAAAAA7BzFPwAAAAAAdo7iHwAAAAAAO0fxDwAAAACAnaP4BwAAAADAzlH8AwAAAABg5yj+AQAAAACwcxT/AAAAAADYOUdbJwAAyH1h4dvydLx5ofXzdDwAAADcHjP/AAAAAADYOYp/AAAAAADsHMU/AAAAAAB2juIfAAAAAAA7R/EPAAAAAICdo/gHAAAAAMDOUfwDAAAAAGDnKP4BAAAAALBzFP8AAAAAANg5in8AAAAAAOwcxT8AAAAAAHaO4h8AAAAAADtH8Q8AAAAAgJ2j+AcAAAAAwM5R/AMAAAAAYOco/gEAAAAAsHMU/wAAAAAA2DmKfwAAAAAA7BzFPwAAAAAAdo7iHwAAAAAAO0fxDwAAAACAnaP4BwAAAADAzjnaOgEAgP0JC9+Wp+PNC62fp+MBAADca5j5BwAAAADAzlH8AwAAAABg5yj+AQAAAACwcxT/AAAAAADYOYp/AAAAAADsHMU/AAAAAAB2juIfAAAAAAA7l6+L/3HjxslisVg9KlWqZG6/evWqBgwYoOLFi6tIkSLq2rWrzpw5Y9XH8ePHFRISIldXV3l5eWnEiBG6fv26Vcy6detUp04dOTs7q3z58goPD8+L3QMAAAAAIE/k6+JfkqpWrarTp0+bj02bNpnbhgwZoh9//FERERFav369Tp06pS5dupjbU1JSFBISouTkZG3ZskWfffaZwsPDNXbsWDMmNjZWISEhatGihWJiYjR48GA9++yzWrVqVZ7uJwAAAAAAucXR1gnciaOjo3x8fNK1x8fHa968eVq4cKEeeeQRSdL8+fNVuXJl/fLLL3rooYf0888/67ffftPq1avl7e2tWrVqacKECRo1apTGjRsnJycnzZkzRwEBAZo6daokqXLlytq0aZOmT5+u4ODgPN1XAAAAAAByQ76f+T9y5Ij8/PxUrlw59erVS8ePH5ck7dixQ9euXVOrVq3M2EqVKqlMmTKKjo6WJEVHR6t69ery9vY2Y4KDg5WQkKD9+/ebMTf2kRaT1setJCUlKSEhweoBAAAAAEB+lK+L/4YNGyo8PFwrV67U7NmzFRsbqyZNmujSpUuKi4uTk5OTPD09rV7j7e2tuLg4SVJcXJxV4Z+2PW3b7WISEhL077//3jK3iRMnysPDw3yULl36bncXAAAAAIBcka+X/bdr1878d40aNdSwYUP5+/tr0aJFKlSokA0zk0aPHq2hQ4eazxMSEvgCAAAAAACQL+Xrmf+beXp66sEHH9Tvv/8uHx8fJScn6+LFi1YxZ86cMa8R4OPjk+7q/2nP7xTj7u5+2y8YnJ2d5e7ubvUAAAAAACA/uqeK/8TERB09elS+vr6qW7euChYsqKioKHP7oUOHdPz4cQUFBUmSgoKCtHfvXp09e9aMiYyMlLu7u6pUqWLG3NhHWkxaHwAAAAAA3OvydfE/fPhwrV+/XseOHdOWLVvUuXNnFShQQD169JCHh4fCwsI0dOhQrV27Vjt27NAzzzyjoKAgPfTQQ5KkNm3aqEqVKnr66ae1e/durVq1SmPGjNGAAQPk7OwsSerfv7/++OMPjRw5UgcPHtSsWbO0aNEiDRkyxJa7DgAAAABAjsnX5/yfOHFCPXr00Pnz51WyZEk9/PDD+uWXX1SyZElJ0vTp0+Xg4KCuXbsqKSlJwcHBmjVrlvn6AgUKaNmyZXrhhRcUFBSkwoULq0+fPnrjjTfMmICAAC1fvlxDhgzRjBkzVKpUKX3yySfc5g8A7iFh4dvyfMx5ofXzfEwAAIDsshiGYdg6CXuQkJAgDw8PxcfHc/5/BmzxhzkA5CaKfwAAYGtZqUPz9bJ/AAAAAABw9yj+AQAAAACwcxT/AAAAAADYOYp/AAAAAADsHMU/AAAAAAB2juIfAAAAAAA7R/EPAAAAAICdo/gHAAAAAMDOUfwDAAAAAGDnKP4BAAAAALBzFP8AAAAAANg5in8AAAAAAOwcxT8AAAAAAHaO4h8AAAAAADtH8Q8AAAAAgJ2j+AcAAAAAwM452joBAADuRWHh2/J0vHmh9fN0PAAAYF+Y+QcAAAAAwM5R/AMAAAAAYOco/gEAAAAAsHMU/wAAAAAA2DmKfwAAAAAA7BzFPwAAAAAAdo7iHwAAAAAAO0fxDwAAAACAnaP4BwAAAADAzlH8AwAAAABg5yj+AQAAAACwcxT/AAAAAADYOUdbJwAAAO4sLHxbno43L7R+no4HAAByFzP/AAAAAADYOYp/AAAAAADsHMU/AAAAAAB2jnP+70N5fd4oAAAAAMC2mPkHAAAAAMDOUfwDAAAAAGDnKP4BAAAAALBzFP8AAAAAANg5in8AAAAAAOwcxT8AAAAAAHaO4h8AAAAAADtH8Q8AAAAAgJ1ztHUCAAAg/wkL35an480LrZ+n4wEAcL/J1zP/EydOVP369eXm5iYvLy916tRJhw4dsopp3ry5LBaL1aN///5WMcePH1dISIhcXV3l5eWlESNG6Pr161Yx69atU506deTs7Kzy5csrPDw8t3cPAAAAAIA8ka+L//Xr12vAgAH65ZdfFBkZqWvXrqlNmza6fPmyVdxzzz2n06dPm4/Jkyeb21JSUhQSEqLk5GRt2bJFn332mcLDwzV27FgzJjY2ViEhIWrRooViYmI0ePBgPfvss1q1alWe7SsAAAAAALklXy/7X7lypdXz8PBweXl5aceOHWratKnZ7urqKh8fnwz7+Pnnn/Xbb79p9erV8vb2Vq1atTRhwgSNGjVK48aNk5OTk+bMmaOAgABNnTpVklS5cmVt2rRJ06dPV3BwcO7tIHLVS2fG5Ol4H3i/mafjAQAAAEBm5euZ/5vFx8dLkooVK2bVvmDBApUoUULVqlXT6NGjdeXKFXNbdHS0qlevLm9vb7MtODhYCQkJ2r9/vxnTqlUrqz6Dg4MVHR19y1ySkpKUkJBg9QAAAAAAID/K1zP/N0pNTdXgwYPVuHFjVatWzWzv2bOn/P395efnpz179mjUqFE6dOiQFi9eLEmKi4uzKvwlmc/j4uJuG5OQkKB///1XhQoVSpfPxIkTNX78+BzdR+SsqZ7n83Q8pzwdDQAAAAAy754p/gcMGKB9+/Zp06ZNVu39+vUz/129enX5+vqqZcuWOnr0qAIDA3Mtn9GjR2vo0KHm84SEBJUuXTrXxgMAwJ5xdwEAAHLXPVH8Dxw4UMuWLdOGDRtUqlSp28Y2bNhQkvT7778rMDBQPj4++vXXX61izpw5I0nmdQJ8fHzMthtj3N3dM5z1lyRnZ2c5Oztna3/uR8lJL9g6BQAAAAC4b+Xrc/4Nw9DAgQP1/fffa82aNQoICLjja2JiYiRJvr6+kqSgoCDt3btXZ8+eNWMiIyPl7u6uKlWqmDFRUVFW/URGRiooKCiH9gQAAAAAANvJ18X/gAED9OWXX2rhwoVyc3NTXFyc4uLi9O+//0qSjh49qgkTJmjHjh06duyYli5dqt69e6tp06aqUaOGJKlNmzaqUqWKnn76ae3evVurVq3SmDFjNGDAAHPmvn///vrjjz80cuRIHTx4ULNmzdKiRYs0ZMgQm+07AAAAAAA5JV8X/7Nnz1Z8fLyaN28uX19f8/HNN99IkpycnLR69Wq1adNGlSpV0rBhw9S1a1f9+OOPZh8FChTQsmXLVKBAAQUFBempp55S79699cYbb5gxAQEBWr58uSIjI1WzZk1NnTpVn3zyCbf5AwAAAADYhXx9zr9hGLfdXrp0aa1fv/6O/fj7++unn366bUzz5s21a9euLOUHAAAAAMC9IF/P/AMAAAAAgLuXr2f+gXvJS2fG5Ol4H3i/mafjAQAAALh3UfwDOWSq5/k8Hc8pT0cDAAAAcC9j2T8AAAAAAHaO4h8AAAAAADvHsn8AAHDfCQvfludjzgutn+djAgCQhpl/AAAAAADsHDP/wD2KuwsAAAAAyCyKf+Aexd0FAAAAAGQWy/4BAAAAALBzzPwDAADkgby+yCAXGAQA3IiZfwAAAAAA7BzFPwAAAAAAdo5l/wAy5bBm5Ol4D2pQno4HAAAA2DOKfwCZUjbpYN4O6Jy3wwEAAAD2jOIfAADADnGBQQDAjTjnHwAAAAAAO0fxDwAAAACAnWPZPwAAAO4apxkAQP5G8Q8gX+LuAgAAAEDOofgHkC9xdwEAAAAg53DOPwAAAAAAdo6ZfwAAANxzuMYAAGQNxT8ASEpOeiFPx3Nynp2n4wEAAOD+xrJ/AAAAAADsHDP/AAAAwB1wmgGAex3FPwDYQF6fZiBxqgEAAMD9jGX/AAAAAADYOYp/AAAAAADsHMv+AeA+wR0NAODekdfXGJC4zgBg7yj+AQC54rBm5Ol4D2pQno4HAPaGixoC9o1l/wAAAAAA2Dlm/gEAuaJs0sG8HdA5b4cDANwdVhoAeYuZfwAAAAAA7Bwz/wAAu8AFDQEAt8NKA9zvKP4BAAAAIIfxZQPyG4p/AACygZUGAID8hNtD4k4o/u9Tef1HKwDg7rx0ZkyejveB95t5Oh4A4N7D6oZ7C8U/AAAAACDf48uGu0PxDwDAPWCq5/m8HdAGK8Q4tQEAgNxD8Q8AAPIFrqMAAEDuofgHAAD3Jb5sAADcTyj+AQAA8gBfNgAAbIni/yYffvihpkyZori4ONWsWVMffPCBGjRoYOu0AAAAssTe7+wz7GLxPB2PO2AAuNdR/N/gm2++0dChQzVnzhw1bNhQ7733noKDg3Xo0CF5eXnZOj0AAAD8n/vhIpj2ji9wgLxF8X+DadOm6bnnntMzzzwjSZozZ46WL1+uTz/9VK+88oqNswMAAADsh71/gZPXX2685F0yT8eTpLJJB/N0PE5nujsU//8nOTlZO3bs0OjRo802BwcHtWrVStHR0enik5KSlJSUZD6Pj4+XJCUkJOR+sncp+d9EJSel2DoNAAAAwG5NdD6bp+P5Xczb8SQpOa/H+7dfno6XkLA+T8fLjrT60zCMO8ZS/P+fv//+WykpKfL29rZq9/b21sGD6b/RmjhxosaPH5+uvXTp0rmWIwAAAAAgbywa4mHrFDLt0qVL8vC4fb4U/9k0evRoDR061HyempqqCxcuqHjx4rJYLDbJKSEhQaVLl9Zff/0ld3d3m+QAZIRjE/kVxybyK45N5Gccn8iv7sdj0zAMXbp0SX5+fneMpfj/PyVKlFCBAgV05swZq/YzZ87Ix8cnXbyzs7OcnZ2t2jw9PXMzxUxzd3e/bw523Fs4NpFfcWwiv+LYRH7G8Yn86n47Nu8045/GIZfzuGc4OTmpbt26ioqKMttSU1MVFRWloKAgG2YGAAAAAMDdYeb/BkOHDlWfPn1Ur149NWjQQO+9954uX75sXv0fAAAAAIB7EcX/Dbp3765z585p7NixiouLU61atbRy5cp0FwHMr5ydnfX666+nOx0BsDWOTeRXHJvIrzg2kZ9xfCK/4ti8PYuRmXsCAAAAAACAexbn/AMAAAAAYOco/gEAAAAAsHMU/wAAAAAA2DmKfwAAAAAA7BzFvx358MMPVbZsWbm4uKhhw4b69ddfbZ0S7NjEiRNVv359ubm5ycvLS506ddKhQ4esYq5evaoBAwaoePHiKlKkiLp27aozZ85YxRw/flwhISFydXWVl5eXRowYoevXr+flrsDOTZo0SRaLRYMHDzbbODZhKydPntRTTz2l4sWLq1ChQqpevbq2b99ubjcMQ2PHjpWvr68KFSqkVq1a6ciRI1Z9XLhwQb169ZK7u7s8PT0VFhamxMTEvN4V2JGUlBS99tprCggIUKFChRQYGKgJEyboxuuCc2wir2zYsEEdOnSQn5+fLBaLlixZYrU9p47FPXv2qEmTJnJxcVHp0qU1efLk3N41m6P4txPffPONhg4dqtdff107d+5UzZo1FRwcrLNnz9o6Ndip9evXa8CAAfrll18UGRmpa9euqU2bNrp8+bIZM2TIEP3444+KiIjQ+vXrderUKXXp0sXcnpKSopCQECUnJ2vLli367LPPFB4errFjx9pil2CHtm3bpo8++kg1atSwaufYhC38888/aty4sQoWLKgVK1bot99+09SpU1W0aFEzZvLkyXr//fc1Z84cbd26VYULF1ZwcLCuXr1qxvTq1Uv79+9XZGSkli1bpg0bNqhfv3622CXYiXfeeUezZ8/WzJkzdeDAAb3zzjuaPHmyPvjgAzOGYxN55fLly6pZs6Y+/PDDDLfnxLGYkJCgNm3ayN/fXzt27NCUKVM0btw4zZ07N9f3z6YM2IUGDRoYAwYMMJ+npKQYfn5+xsSJE22YFe4nZ8+eNSQZ69evNwzDMC5evGgULFjQiIiIMGMOHDhgSDKio6MNwzCMn376yXBwcDDi4uLMmNmzZxvu7u5GUlJS3u4A7M6lS5eMChUqGJGRkUazZs2MQYMGGYbBsQnbGTVqlPHwww/fcntqaqrh4+NjTJkyxWy7ePGi4ezsbHz11VeGYRjGb7/9Zkgytm3bZsasWLHCsFgsxsmTJ3Mvedi1kJAQo2/fvlZtXbp0MXr16mUYBscmbEeS8f3335vPc+pYnDVrllG0aFGr/9NHjRplVKxYMZf3yLaY+bcDycnJ2rFjh1q1amW2OTg4qFWrVoqOjrZhZrifxMfHS5KKFSsmSdqxY4euXbtmdVxWqlRJZcqUMY/L6OhoVa9eXd7e3mZMcHCwEhIStH///jzMHvZowIABCgkJsToGJY5N2M7SpUtVr149Pf744/Ly8lLt2rX18ccfm9tjY2MVFxdndWx6eHioYcOGVsemp6en6tWrZ8a0atVKDg4O2rp1a97tDOxKo0aNFBUVpcOHD0uSdu/erU2bNqldu3aSODaRf+TUsRgdHa2mTZvKycnJjAkODtahQ4f0zz//5NHe5D1HWyeAu/f3338rJSXF6o9USfL29tbBgwdtlBXuJ6mpqRo8eLAaN26satWqSZLi4uLk5OQkT09Pq1hvb2/FxcWZMRkdt2nbgOz6+uuvtXPnTm3bti3dNo5N2Moff/yh2bNna+jQofrf//6nbdu26eWXX5aTk5P69OljHlsZHXs3HpteXl5W2x0dHVWsWDGOTWTbK6+8ooSEBFWqVEkFChRQSkqK3nrrLfXq1UuSODaRb+TUsRgXF6eAgIB0faRtu/F0LHtC8Q/grg0YMED79u3Tpk2bbJ0KoL/++kuDBg1SZGSkXFxcbJ0OYEpNTVW9evX09ttvS5Jq166tffv2ac6cOerTp4+Ns8P9bNGiRVqwYIEWLlyoqlWrKiYmRoMHD5afnx/HJmBHWPZvB0qUKKECBQqku1L1mTNn5OPjY6OscL8YOHCgli1bprVr16pUqVJmu4+Pj5KTk3Xx4kWr+BuPSx8fnwyP27RtQHbs2LFDZ8+eVZ06deTo6ChHR0etX79e77//vhwdHeXt7c2xCZvw9fVVlSpVrNoqV66s48ePS/r/x9bt/j/38fFJdzHf69ev68KFCxybyLYRI0bolVde0ZNPPqnq1avr6aef1pAhQzRx4kRJHJvIP3LqWLxf/5+n+LcDTk5Oqlu3rqKiosy21NRURUVFKSgoyIaZwZ4ZhqGBAwfq+++/15o1a9Itnapbt64KFixodVweOnRIx48fN4/LoKAg7d271+oDOjIyUu7u7un+QAYyq2XLltq7d69iYmLMR7169dSrVy/z3xybsIXGjRunuyXq4cOH5e/vL0kKCAiQj4+P1bGZkJCgrVu3Wh2bFy9e1I4dO8yYNWvWKDU1VQ0bNsyDvYA9unLlihwcrMuCAgUKKDU1VRLHJvKPnDoWg4KCtGHDBl27ds2MiYyMVMWKFe12yb8krvZvL77++mvD2dnZCA8PN3777TejX79+hqenp9WVqoGc9MILLxgeHh7GunXrjNOnT5uPK1eumDH9+/c3ypQpY6xZs8bYvn27ERQUZAQFBZnbr1+/blSrVs1o06aNERMTY6xcudIoWbKkMXr0aFvsEuzYjVf7NwyOTdjGr7/+ajg6OhpvvfWWceTIEWPBggWGq6ur8eWXX5oxkyZNMjw9PY0ffvjB2LNnj9GxY0cjICDA+Pfff82Ytm3bGrVr1za2bt1qbNq0yahQoYLRo0cPW+wS7ESfPn2MBx54wFi2bJkRGxtrLF682ChRooQxcuRIM4ZjE3nl0qVLxq5du4xdu3YZkoxp06YZu3btMv7880/DMHLmWLx48aLh7e1tPP3008a+ffuMr7/+2nB1dTU++uijPN/fvETxb0c++OADo0yZMoaTk5PRoEED45dffrF1SrBjkjJ8zJ8/34z5999/jRdffNEoWrSo4erqanTu3Nk4ffq0VT/Hjh0z2rVrZxQqVMgoUaKEMWzYMOPatWt5vDewdzcX/xybsJUff/zRqFatmuHs7GxUqlTJmDt3rtX21NRU47XXXjO8vb0NZ2dno2XLlsahQ4esYs6fP2/06NHDKFKkiOHu7m4888wzxqVLl/JyN2BnEhISjEGDBhllypQxXFxcjHLlyhmvvvqq1W3QODaRV9auXZvh35h9+vQxDCPnjsXdu3cbDz/8sOHs7Gw88MADxqRJk/JqF23GYhiGYZs1BwAAAAAAIC9wzj8AAAAAAHaO4h8AAAAAADtH8Q8AAAAAgJ2j+AcAAAAAwM5R/AMAAAAAYOco/gEAAAAAsHMU/wAAAAAA2DmKfwAAAAAA7BzFPwAAQC6wWCxasmSJrdMAAEASxT8AAPnWuXPn9MILL6hMmTJydnaWj4+PgoODtXnzZlunlm/khwJ73LhxqlWrlk1zAADgThxtnQAAAMhY165dlZycrM8++0zlypXTmTNnFBUVpfPnz9s6NQAAcI9h5h8AgHzo4sWL2rhxo9555x21aNFC/v7+atCggUaPHq3HHnvMKu7ZZ59VyZIl5e7urkceeUS7d++26mvSpEny9vaWm5ubwsLC9Morr1jNVDdv3lyDBw+2ek2nTp0UGhpqPk9KStLw4cP1wAMPqHDhwmrYsKHWrVtnbg8PD5enp6dWrVqlypUrq0iRImrbtq1Onz5t1e+nn36qqlWrytnZWb6+vho4cGCW9iWrPvnkE1WuXFkuLi6qVKmSZs2aZW47duyYLBaLFi9erBYtWsjV1VU1a9ZUdHS0VR8ff/yxSpcuLVdXV3Xu3FnTpk2Tp6enud/jx4/X7t27ZbFYZLFYFB4ebr7277//VufOneXq6qoKFSpo6dKld7U/AABkF8U/AAD5UJEiRVSkSBEtWbJESUlJt4x7/PHHdfbsWa1YsUI7duxQnTp11LJlS124cEGStGjRIo0bN05vv/22tm/fLl9fX6sCOLMGDhyo6Ohoff3119qzZ48ef/xxtW3bVkeOHDFjrly5onfffVdffPGFNmzYoOPHj2v48OHm9tmzZ2vAgAHq16+f9u7dq6VLl6p8+fKZ3pesWrBggcaOHau33npLBw4c0Ntvv63XXntNn332mVXcq6++quHDhysmJkYPPvigevTooevXr0uSNm/erP79+2vQoEGKiYlR69at9dZbb5mv7d69u4YNG6aqVavq9OnTOn36tLp3725uHz9+vJ544gnt2bNH7du3V69evbK9PwAA3BUDAADkS99++61RtGhRw8XFxWjUqJExevRoY/fu3eb2jRs3Gu7u7sbVq1etXhcYGGh89NFHhmEYRlBQkPHiiy9abW/YsKFRs2ZN83mzZs2MQYMGWcV07NjR6NOnj2EYhvHnn38aBQoUME6ePGkV07JlS2P06NGGYRjG/PnzDUnG77//bm7/8MMPDW9vb/O5n5+f8eqrr2a4r5nZl4xIMr7//vsMtwUGBhoLFy60apswYYIRFBRkGIZhxMbGGpKMTz75xNy+f/9+Q5Jx4MABwzAMo3v37kZISIhVH7169TI8PDzM56+//rrV+3ljbmPGjDGfJyYmGpKMFStW3HJ/AADILcz8AwCQT3Xt2lWnTp3S0qVL1bZtW61bt0516tQxl5Xv3r1biYmJKl68uLlSoEiRIoqNjdXRo0clSQcOHFDDhg2t+g0KCspSHnv37lVKSooefPBBq3HWr19vjiNJrq6uCgwMNJ/7+vrq7NmzkqSzZ8/q1KlTatmyZYZjZGZfsuLy5cs6evSowsLCrPp788030/VXo0YNq5zT8pWkQ4cOqUGDBlbxNz+/nRv7Lly4sNzd3c2+AQDIS1zwDwCAfMzFxUWtW7dW69at9dprr+nZZ5/V66+/rtDQUCUmJsrX19fq3Ps0aeekZ4aDg4MMw7Bqu3btmvnvxMREFShQQDt27FCBAgWs4ooUKWL+u2DBglbbLBaL2W+hQoVum0NO7cuN/Un/na9/85cfN+/DjXlbLBZJUmpqapbHzEhG70lO9Q0AQFZQ/AMAcA+pUqWKeWu7OnXqKC4uTo6OjipbtmyG8ZUrV9bWrVvVu3dvs+2XX36xiilZsqTVhflSUlK0b98+tWjRQpJUu3ZtpaSk6OzZs2rSpEm28nZzc1PZsmUVFRVl9nujzOxLVnh7e8vPz09//PGHevXqle1+KlasqG3btlm13fzcyclJKSkp2R4DAIC8QPEPAEA+dP78eT3++OPq27evatSoITc3N23fvl2TJ09Wx44dJUmtWrVSUFCQOnXqpMmTJ+vBBx/UqVOntHz5cnXu3Fn16tXToEGDFBoaqnr16qlx48ZasGCB9u/fr3LlypljPfLIIxo6dKiWL1+uwMBATZs2TRcvXjS3P/jgg+rVq5d69+6tqVOnqnbt2jp37pyioqJUo0YNhYSEZGqfxo0bp/79+8vLy0vt2rXTpUuXtHnzZr300kuZ2pdbiY2NVUxMjFVbhQoVNH78eL388svy8PBQ27ZtlZSUpO3bt+uff/7R0KFDM5XzSy+9pKZNm2ratGnq0KGD1qxZoxUrVpgrBCSpbNmyZg6lSpWSm5ubnJ2dM9U/AAB5heIfAIB8qEiRImrYsKGmT5+uo0eP6tq1aypdurSee+45/e9//5P03xLyn376Sa+++qqeeeYZnTt3Tj4+PmratKm8vb0l/Xc1+qNHj2rkyJG6evWqunbtqhdeeEGrVq0yx+rbt692796t3r17y9HRUUOGDEk3Oz9//ny9+eabGjZsmE6ePKkSJUrooYce0qOPPprpferTp4+uXr2q6dOna/jw4SpRooS6deuW6X25lYwK+Y0bN+rZZ5+Vq6urpkyZohEjRqhw4cKqXr16utsa3k7jxo01Z84cjR8/XmPGjFFwcLCGDBmimTNnmjFdu3Y1bxd48eJFzZ8/3+o2iQAA5AcW4+aT/AAAgF0bN26clixZkm62HJnz3HPP6eDBg9q4caOtUwEAINOY+QcAALiNd999V61bt1bhwoW1YsUKffbZZ5o1a5at0wIAIEso/gEAAG7j119/1eTJk3Xp0iWVK1dO77//vp599llbpwUAQJaw7B8AAAAAADvnYOsEAAAAAABA7qL4BwAAAADAzlH8AwAAAABg5yj+AQAAAACwcxT/AAAAAADYOYp/AAAAAADsHMU/AAAAAAB2juIfAAAAAAA79/8ACZ13sxrznTkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. Sequence lengths histogram\n",
    "plt.figure(figsize=(12, 6))\n",
    "for name, df in datasets.items():\n",
    "    plt.hist(df['Length'], bins=30, alpha=0.7, label=name)\n",
    "\n",
    "plt.title('UniRef50 (200k Sequences) - Train, Test, and Val Sequence Lengths')\n",
    "plt.xlabel('Sequence Length')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}