{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import (\n",
    "    Callable,\n",
    "    Dict,\n",
    "    Iterable,\n",
    "    Iterator,\n",
    "    List,\n",
    "    Optional,\n",
    "    Sequence,\n",
    "    TextIO,\n",
    "    Tuple,\n",
    "    Union,\n",
    ")\n",
    "import gzip\n",
    "import json\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _close_when_exhausted(file: TextIO) -> Iterable[str]:\n",
    "    with file:\n",
    "        for line in file:\n",
    "            yield json.loads(line)\n",
    "\n",
    "def open_read_cleaned(filename) -> Iterable[str]:\n",
    "    file: TextIO = gzip.open(filename, \"rt\")  # type: ignore\n",
    "    return _close_when_exhausted(file)\n",
    "\n",
    "def get_perplexities_and_text(corpus: Iterable[str], n: float = None) -> List[float]:\n",
    "    perplexities = []\n",
    "    texts = []\n",
    "    if n is None:\n",
    "        for entry in enumerate(corpus):\n",
    "            perplexities.append(entry['perplexity'])\n",
    "            texts.append(entry['text'])\n",
    "    else:\n",
    "        for i, entry in enumerate(corpus):\n",
    "            if i >= n:\n",
    "                break\n",
    "            perplexities.append(entry['perplexity'])\n",
    "            texts.append(entry['text'])\n",
    "\n",
    "    return perplexities, texts\n",
    "\n",
    "def describe_perplexities(perplexities: List[float]):\n",
    "    print(pd.Series(perplexities).describe())\n",
    "\n",
    "def perplexity_histogram(perplexities: List[float]):\n",
    "    series = pd.Series(perplexities)\n",
    "    series.hist(bins=1000, weights=np.zeros_like(series) + 1. / len(series), cumulative=True)\n",
    "    plt.xlim(0, 5000)\n",
    "    plt.xticks(np.arange(0, 5000, 200), rotation=90)\n",
    "    plt.yticks(np.arange(0, 1.1, 0.1))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perplexity descriptive stats per language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perplexities_langs_stats(langs: list[str], n: int) -> None:\n",
    "    for lang in langs:\n",
    "        print(lang)\n",
    "        if lang == \"de\":\n",
    "            corpus = f\"/mnt/data_2/shared/tower_llm_data/{lang}/2/0000.json.gz\"\n",
    "        elif lang == \"es\":\n",
    "            corpus = f\"/mnt/data_2/shared/tower_llm_data/{lang}/3/0000.json.gz\"\n",
    "        elif lang == \"fr\":\n",
    "            corpus = f\"/mnt/data_2/shared/tower_llm_data/{lang}/1/0000.json.gz\"\n",
    "        elif lang == \"ru\":\n",
    "            corpus = f\"/mnt/data_2/shared/tower_llm_data/{lang}/6/0000.json.gz\"\n",
    "        else:\n",
    "            corpus = f\"/mnt/data_2/shared/tower_llm_data/{lang}/0000.json.gz\"\n",
    "        train_corpus = open_read_cleaned(corpus)\n",
    "        perplexities, _ = get_perplexities_and_text(train_corpus, n=n)\n",
    "        describe_perplexities(perplexities)\n",
    "        print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "de\n",
      "count    100000.000000\n",
      "mean        878.973901\n",
      "std         960.759983\n",
      "min          14.200000\n",
      "25%         395.300000\n",
      "50%         611.800000\n",
      "75%        1019.800000\n",
      "max       46451.000000\n",
      "dtype: float64\n",
      "es\n",
      "count    100000.000000\n",
      "mean        419.436802\n",
      "std         492.000302\n",
      "min           7.700000\n",
      "25%         184.800000\n",
      "50%         275.700000\n",
      "75%         470.800000\n",
      "max       28396.800000\n",
      "dtype: float64\n",
      "fr\n",
      "count    100000.000000\n",
      "mean        586.847523\n",
      "std         959.302055\n",
      "min           4.300000\n",
      "25%         189.200000\n",
      "50%         322.000000\n",
      "75%         622.300000\n",
      "max       70893.900000\n",
      "dtype: float64\n",
      "it\n",
      "count    100000.000000\n",
      "mean        522.677809\n",
      "std         725.482231\n",
      "min           1.800000\n",
      "25%         224.100000\n",
      "50%         332.900000\n",
      "75%         562.500000\n",
      "max       86453.800000\n",
      "dtype: float64\n",
      "ko\n",
      "count    100000.000000\n",
      "mean        193.209064\n",
      "std         349.575732\n",
      "min           1.500000\n",
      "25%          46.100000\n",
      "50%          72.800000\n",
      "75%         198.000000\n",
      "max       55450.000000\n",
      "dtype: float64\n",
      "nl\n",
      "count    100000.000000\n",
      "mean        920.457715\n",
      "std        1077.452949\n",
      "min           8.500000\n",
      "25%         445.000000\n",
      "50%         649.200000\n",
      "75%        1016.900000\n",
      "max       64355.100000\n",
      "dtype: float64\n",
      "pt\n",
      "count    100000.000000\n",
      "mean        393.057294\n",
      "std         476.371983\n",
      "min           4.100000\n",
      "25%         169.900000\n",
      "50%         257.500000\n",
      "75%         441.225000\n",
      "max       20750.100000\n",
      "dtype: float64\n",
      "ru\n",
      "count    100000.000000\n",
      "mean        474.163654\n",
      "std         896.298660\n",
      "min           6.100000\n",
      "25%         228.500000\n",
      "50%         334.300000\n",
      "75%         532.400000\n",
      "max      193807.100000\n",
      "dtype: float64\n",
      "zh\n",
      "count    100000.000000\n",
      "mean       2985.985015\n",
      "std        2826.177214\n",
      "min           6.800000\n",
      "25%        1488.900000\n",
      "50%        2041.600000\n",
      "75%        3437.200000\n",
      "max      121231.600000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "perplexities_langs_stats(\n",
    "    [\"en\", \"de\", \"es\", \"fr\", \"it\", \"ko\", \"nl\", \"pt\", \"ru\", \"zh\"],\n",
    "    n=100_000\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perplexity histograms per language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perplexities_langs_histograms(langs: list[str], n: int) -> None:\n",
    "    for lang in langs:\n",
    "        print(lang)\n",
    "        if lang == \"de\":\n",
    "            corpus = f\"/mnt/data_2/shared/tower_llm_data/{lang}/2/0000.json.gz\"\n",
    "        elif lang == \"es\":\n",
    "            corpus = f\"/mnt/data_2/shared/tower_llm_data/{lang}/3/0000.json.gz\"\n",
    "        elif lang == \"fr\":\n",
    "            corpus = f\"/mnt/data_2/shared/tower_llm_data/{lang}/1/0000.json.gz\"\n",
    "        elif lang == \"ru\":\n",
    "            corpus = f\"/mnt/data_2/shared/tower_llm_data/{lang}/6/0000.json.gz\"\n",
    "        else:\n",
    "            corpus = f\"/mnt/data_2/shared/tower_llm_data/{lang}/0000.json.gz\"\n",
    "        train_corpus = open_read_cleaned(corpus)\n",
    "        perplexities, _ = get_perplexities_and_text(train_corpus, n=n)\n",
    "        perplexity_histogram(perplexities)\n",
    "        print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "de\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "es\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "fr\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "it\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "ko\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "nl\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "pt\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "ru\n"
     ]
    },
    {
     "data": {
      "image/png": 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rlZ2drZqaGsXGxra6XEhnRtrr0Ucf1UknnXTAIiJJ+fn58ng8gce1tbVKSkrS6NGjFR8f3+5x+Hw+lZaWauzYsYqMjAxL1oy1Tnn9jnaPze00mpPmJ88GeXYeG3k8t+Qdmjw7j60j5DV6XUEtF1IZ6dmzp1wul6qrq5tMr66uVkJCwgHXraur07PPPqs77rjjJ7fjdrvldrubTY+MjGx3eThYeV6/Q97G9j9x5Nkvz85jI88+WeR17jw7j83Oef4gM0K6gDUqKkqpqakqKyv7fkN+v8rKypq8bdOS5557Tl6vV1dccUUomwQAAJ1cyG/TeDweTZo0SWlpaUpPT1dhYaHq6uqUm5srScrJyVFiYqLmzZvXZL1HH31UEyZMCMvbLAAAoPMIuYxMnDhRu3fv1syZM1VVVaWUlBSVlJSod+/ekqTKyko5nU1PuHzwwQd666239Morr4Rn1AAAoNNo0wWseXl5ysvLa3HeqlWrmk074YQTFMKHdgAAwGGE76YBAACWoowAAABLUUYAAIClKCMAAMBSlBEAAGApyggAALAUZQQAAFiKMgIAACxFGQEAAJaijAAAAEtRRgAAgKUoIwAAwFKUEQAAYCnKCAAAsBRlBAAAWIoyAgAALEUZAQAAlqKMAAAAS1FGAACApSgjAADAUpQRAABgKcoIAACwFGUEAABYijICAAAs1aYyUlRUpOTkZEVHRysjI0MVFRUHXP6bb77RjTfeqD59+sjtduv444/XSy+91KYBAwCAziUi1BWWLFkij8ejRYsWKSMjQ4WFhcrKytIHH3ygXr16NVu+oaFBY8eOVa9evfT8888rMTFRn376qeLi4sIxfgAA0MGFXEbmz5+vyZMnKzc3V5K0aNEirVixQosXL9a0adOaLb948WJ99dVXWr16tSIjIyVJycnJ7Rs1AADoNEIqIw0NDVq3bp3y8/MD05xOp8aMGaPy8vIW13nxxReVmZmpG2+8UcuXL9dRRx2l7Oxs3XbbbXK5XC2u4/V65fV6A49ra2slST6fTz6fL5Qht2h/Rjiz3E7T7qwf5pBnfZ6dx0aefbLI69x5dh5bR8hrdAWX4zDGBL3FnTt3KjExUatXr1ZmZmZg+tSpU/XGG29ozZo1zdYZNGiQtm/frssvv1w33HCDtm7dqhtuuEE33XSTCgoKWtzOrFmzNHv27GbTi4uLFRMTE+xwAQCAherr65Wdna2amhrFxsa2ulzIb9OEyu/3q1evXvrzn/8sl8ul1NRU7dixQ/fcc0+rZSQ/P18ejyfwuLa2VklJSRo9erTi4+PbPSafz6fS0lLNWOuU1+9oV5bbaTQnzR+WLPLslWfnsZHHc0veocmz89g6Ql6jt+V3QH4spDLSs2dPuVwuVVdXN5leXV2thISEFtfp06ePIiMjm7wlM3jwYFVVVamhoUFRUVHN1nG73XK73c2mR0ZGBq47CQev3yFvY/v/2OHOIs9eeXYeG3n2ySKvc+fZeWx2zvMHmRHSR3ujoqKUmpqqsrKy7zfk96usrKzJ2zY/dPrpp2vr1q3y+/2BaR9++KH69OnTYhEBAACHl5DvM+LxePTwww/riSee0ObNm3X99derrq4u8OmanJycJhe4Xn/99frqq690880368MPP9SKFSs0d+5c3XjjjeH7LQAAQIcV8jUjEydO1O7duzVz5kxVVVUpJSVFJSUl6t27tySpsrJSTuf3HScpKUkrV67ULbfcopNPPlmJiYm6+eabddttt4XvtwAAAB1Wmy5gzcvLU15eXovzVq1a1WxaZmam3n777bZsCgAAdHJ8Nw0AALAUZQQAAFiKMgIAACxFGQEAAJaijAAAAEtRRgAAgKUoIwAAwFKUEQAAYCnKCAAAsBRlBAAAWIoyAgAALEUZAQAAlqKMAAAAS1FGAACApSgjAADAUpQRAABgKcoIAACwFGUEAABYijICAAAsRRkBAACWoowAAABLUUYAAIClKCMAAMBSlBEAAGCpNpWRoqIiJScnKzo6WhkZGaqoqGh12ccff1wOh6PJT3R0dJsHDAAAOpeQy8iSJUvk8XhUUFCg9evXa+jQocrKytKuXbtaXSc2NlZffPFF4OfTTz9t16ABAEDnEXIZmT9/viZPnqzc3FwNGTJEixYtUkxMjBYvXtzqOg6HQwkJCYGf3r17t2vQAACg84gIZeGGhgatW7dO+fn5gWlOp1NjxoxReXl5q+t9++236t+/v/x+v0455RTNnTtXP/vZz1pd3uv1yuv1Bh7X1tZKknw+n3w+XyhDbtH+DLfTtDtrf0Y4ssizV56dx0aefbLI69x5dh5bR8hrdAWX4zDGBL3FnTt3KjExUatXr1ZmZmZg+tSpU/XGG29ozZo1zdYpLy/XRx99pJNPPlk1NTW699579eabb+q9997T0Ucf3eJ2Zs2apdmzZzebXlxcrJiYmGCHCwAALFRfX6/s7GzV1NQoNja21eVCOjPSFpmZmU2Ky/DhwzV48GA99NBDmjNnTovr5Ofny+PxBB7X1tYqKSlJo0ePVnx8fLvH5PP5VFpaqhlrnfL6He3KcjuN5qT5w5JFnr3y7Dw28nhuyTs0eXYeW0fIa/S6gloupDLSs2dPuVwuVVdXN5leXV2thISEoDIiIyM1bNgwbd26tdVl3G633G53i+tGRkaGMuQD8vod8ja2/48d7izy7JVn57GRZ58s8jp3np3HZuc8f5AZIV3AGhUVpdTUVJWVlX2/Ib9fZWVlTc5+HEhjY6Peffdd9enTJ5RNAwCATirkt2k8Ho8mTZqktLQ0paenq7CwUHV1dcrNzZUk5eTkKDExUfPmzZMk3XHHHTrttNN07LHH6ptvvtE999yjTz/9VFdffXV4fxMAANAhhVxGJk6cqN27d2vmzJmqqqpSSkqKSkpKAh/XrayslNP5/QmXr7/+WpMnT1ZVVZW6d++u1NRUrV69WkOGDAnfbwEAADqsNl3AmpeXp7y8vBbnrVq1qsnj++67T/fdd19bNgMAAA4DfDcNAACwFGUEAABYijICAAAsRRkBAACWoowAAABLUUYAAIClKCMAAMBSlBEAAGApyggAALAUZQQAAFiKMgIAACxFGQEAAJaijAAAAEtRRgAAgKUoIwAAwFKUEQAAYCnKCAAAsBRlBAAAWIoyAgAALEUZAQAAlqKMAAAAS1FGAACApSgjAADAUpQRAABgqTaVkaKiIiUnJys6OloZGRmqqKgIar1nn31WDodDEyZMaMtmAQBAJxRyGVmyZIk8Ho8KCgq0fv16DR06VFlZWdq1a9cB19u+fbt++9vf6uc//3mbBwsAADqfkMvI/PnzNXnyZOXm5mrIkCFatGiRYmJitHjx4lbXaWxs1OWXX67Zs2dr4MCB7RowAADoXCJCWbihoUHr1q1Tfn5+YJrT6dSYMWNUXl7e6np33HGHevXqpauuukr//Oc/f3I7Xq9XXq838Li2tlaS5PP55PP5Qhlyi/ZnuJ2m3Vn7M8KRRZ698uw8NvLsk0Ve586z89g6Ql6jK7gchzEm6C3u3LlTiYmJWr16tTIzMwPTp06dqjfeeENr1qxpts5bb72lSy+9VBs3blTPnj3161//Wt98842WLVvW6nZmzZql2bNnN5teXFysmJiYYIcLAAAsVF9fr+zsbNXU1Cg2NrbV5UI6MxKqPXv26Morr9TDDz+snj17Br1efn6+PB5P4HFtba2SkpI0evRoxcfHt3tcPp9PpaWlmrHWKa/f0a4st9NoTpo/LFnk2SvPzmMjj+eWvEOTZ+exdYS8Rq8rqOVCKiM9e/aUy+VSdXV1k+nV1dVKSEhotvzHH3+s7du367zzzgtM8/v93204IkIffPCBjjnmmGbrud1uud3uZtMjIyMVGRkZypAPyOt3yNvY/j92uLPIs1eencdGnn2yyOvceXYem53z/EFmhHQBa1RUlFJTU1VWVvb9hvx+lZWVNXnbZr9Bgwbp3Xff1caNGwM/559/vkaPHq2NGzcqKSkplM0DAIBOKOS3aTwejyZNmqS0tDSlp6ersLBQdXV1ys3NlSTl5OQoMTFR8+bNU3R0tE488cQm68fFxUlSs+kAAODwFHIZmThxonbv3q2ZM2eqqqpKKSkpKikpUe/evSVJlZWVcjq5sSsAAAhOmy5gzcvLU15eXovzVq1adcB1H3/88bZsEgAAdFKcwgAAAJaijAAAAEtRRgAAgKUoIwAAwFKUEQAAYCnKCAAAsBRlBAAAWIoyAgAALEUZAQAAlqKMAAAAS1FGAACApSgjAADAUpQRAABgKcoIAACwFGUEAABYijICAAAsRRkBAACWoowAAABLUUYAAIClKCMAAMBSlBEAAGApyggAALAUZQQAAFiKMgIAACxFGQEAAJZqUxkpKipScnKyoqOjlZGRoYqKilaXXbp0qdLS0hQXF6euXbsqJSVFTz31VJsHDAAAOpeQy8iSJUvk8XhUUFCg9evXa+jQocrKytKuXbtaXL5Hjx66/fbbVV5ernfeeUe5ubnKzc3VypUr2z14AADQ8UWEusL8+fM1efJk5ebmSpIWLVqkFStWaPHixZo2bVqz5UeNGtXk8c0336wnnnhCb731lrKyslrchtfrldfrDTyura2VJPl8Pvl8vlCH3Mz+DLfTtDtrf0Y4ssizV56dx0aefbLI69x5dh5bR8hrdAWX4zDGBL3FhoYGxcTE6Pnnn9eECRMC0ydNmqRvvvlGy5cvP+D6xhi99tprOv/887Vs2TKNHTu2xeVmzZql2bNnN5teXFysmJiYYIcLAAAsVF9fr+zsbNXU1Cg2NrbV5UI6M/Lll1+qsbFRvXv3bjK9d+/e2rJlS6vr1dTUKDExUV6vVy6XS3/6059aLSKSlJ+fL4/HE3hcW1urpKQkjR49WvHx8aEMuUU+n0+lpaWasdYpr9/Rriy302hOmj8sWeTZK8/OYyOP55a8Q5Nn57F1hLxGryuo5UJ+m6YtjjjiCG3cuFHffvutysrK5PF4NHDgwGZv4ezndrvldrubTY+MjFRkZGTYxuX1O+RtbP8fO9xZ5Nkrz85jI88+WeR17jw7j83Oef4gM0IqIz179pTL5VJ1dXWT6dXV1UpISGh1PafTqWOPPVaSlJKSos2bN2vevHmtlhEAAHD4COnTNFFRUUpNTVVZWVlgmt/vV1lZmTIzM4PO8fv9TS5QBQAAh6+Q36bxeDyaNGmS0tLSlJ6ersLCQtXV1QU+XZOTk6PExETNmzdPkjRv3jylpaXpmGOOkdfr1UsvvaSnnnpKDz74YHh/EwAA0CGFXEYmTpyo3bt3a+bMmaqqqlJKSopKSkoCF7VWVlbK6fz+hEtdXZ1uuOEGff755+rSpYsGDRqkp59+WhMnTgzfbwEAADqsNl3AmpeXp7y8vBbnrVq1qsnjO++8U3feeWdbNgMAAA4DfDcNAACwFGUEAABYijICAAAsRRkBAACWoowAAABLUUYAAIClKCMAAMBSlBEAAGApyggAALAUZQQAAFiKMgIAACxFGQEAAJaijAAAAEtRRgAAgKUoIwAAwFKUEQAAYCnKCAAAsBRlBAAAWIoyAgAALEUZAQAAlqKMAAAAS1FGAACApSgjAADAUpQRAABgqTaVkaKiIiUnJys6OloZGRmqqKhoddmHH35YP//5z9W9e3d1795dY8aMOeDyAADg8BJyGVmyZIk8Ho8KCgq0fv16DR06VFlZWdq1a1eLy69atUqXXXaZXn/9dZWXlyspKUlnnXWWduzY0e7BAwCAji/kMjJ//nxNnjxZubm5GjJkiBYtWqSYmBgtXry4xeWfeeYZ3XDDDUpJSdGgQYP0yCOPyO/3q6ysrN2DBwAAHV9EKAs3NDRo3bp1ys/PD0xzOp0aM2aMysvLg8qor6+Xz+dTjx49Wl3G6/XK6/UGHtfW1kqSfD6ffD5fKENu0f4Mt9O0O2t/RjiyyLNXnp3HRp59ssjr3Hl2HltHyGt0BZfjMMYEvcWdO3cqMTFRq1evVmZmZmD61KlT9cYbb2jNmjU/mXHDDTdo5cqVeu+99xQdHd3iMrNmzdLs2bObTS8uLlZMTEywwwUAABaqr69Xdna2ampqFBsb2+pyIZ0Zaa+77rpLzz77rFatWtVqEZGk/Px8eTyewOPa2lolJSVp9OjRio+Pb/c4fD6fSktLNWOtU16/o11ZbqfRnDR/WLLIs1eencdGHs8teYcmz85j6wh5jV5XUMuFVEZ69uwpl8ul6urqJtOrq6uVkJBwwHXvvfde3XXXXXr11Vd18sknH3BZt9stt9vdbHpkZKQiIyNDGfIBef0OeRvb/8cOdxZ59sqz89jIs08WeZ07z85js3OeP8iMkC5gjYqKUmpqapOLT/dfjPrDt21+7O6779acOXNUUlKitLS0UDYJAAA6uZDfpvF4PJo0aZLS0tKUnp6uwsJC1dXVKTc3V5KUk5OjxMREzZs3T5L0hz/8QTNnzlRxcbGSk5NVVVUlSerWrZu6desWxl8FAAB0RCGXkYkTJ2r37t2aOXOmqqqqlJKSopKSEvXu3VuSVFlZKafz+xMuDz74oBoaGvSrX/2qSU5BQYFmzZrVvtEDAIAOr00XsObl5SkvL6/FeatWrWryePv27W3ZBAAAOEzw3TQAAMBSh/Sjve2VMa9M+yK6tjvH7TK6Oz0MAwIAAO3GmREAAGApyggAALAUZQQAAFiKMgIAACxFGQEAAJaijAAAAEtRRgAAgKUoIwAAwFKUEQAAYCnKCAAAsBRlBAAAWIoyAgAALEUZAQAAlqKMAAAAS1FGAACApSgjAADAUpQRAABgKcoIAACwFGUEAABYijICAAAsRRkBAACWoowAAABLtamMFBUVKTk5WdHR0crIyFBFRUWry7733nu66KKLlJycLIfDocLCwraOFQAAdEIhl5ElS5bI4/GooKBA69ev19ChQ5WVlaVdu3a1uHx9fb0GDhyou+66SwkJCe0eMAAA6FxCLiPz58/X5MmTlZubqyFDhmjRokWKiYnR4sWLW1z+1FNP1T333KNLL71Ubre73QMGAACdS0QoCzc0NGjdunXKz88PTHM6nRozZozKy8vDNiiv1yuv1xt4XFtbK0lyO41cLtPufLfTNPmnXbLIs1eencdGnn2yyOvceXYeW0fIawzymO0wxgS9xZ07dyoxMVGrV69WZmZmYPrUqVP1xhtvaM2aNQdcPzk5WVOmTNGUKVMOuNysWbM0e/bsZtOLi4sVExMT7HABAICF6uvrlZ2drZqaGsXGxra6XEhnRg6V/Px8eTyewOPa2lolJSXpzg1O7Yt0tTvf7TSak+bXjLVOef0O22SRZ688O4+NPJ5b8g5Nnp3H1hHyGr3BHbNDKiM9e/aUy+VSdXV1k+nV1dVhvTjV7Xa3eH2J1+/Qvsb2/3F+mOcNU144s8izV56dx0aefbLI69x5dh6bnfP8QWaEdAFrVFSUUlNTVVZW9v2G/H6VlZU1edsGAAAgWCG/TePxeDRp0iSlpaUpPT1dhYWFqqurU25uriQpJydHiYmJmjdvnqTvLnp9//33A/++Y8cObdy4Ud26ddOxxx4bxl8FAAB0RCGXkYkTJ2r37t2aOXOmqqqqlJKSopKSEvXu3VuSVFlZKafz+xMuO3fu1LBhwwKP7733Xt17770aOXKkVq1a1f7fAAAAdGhtuoA1Ly9PeXl5Lc77ccFITk5WCB/YAQAAhxm+mwYAAFiKMgIAACxFGQEAAJaijAAAAEtRRgAAgKUoIwAAwFKUEQAAYCnKCAAAsBRlBAAAWIoyAgAALEUZAQAAlqKMAAAAS1FGAACApSgjAADAUpQRAABgKcoIAACwFGUEAABYijICAAAsRRkBAACWoowAAABLUUYAAIClKCMAAMBSlBEAAGApyggAALBUm8pIUVGRkpOTFR0drYyMDFVUVBxw+eeee06DBg1SdHS0TjrpJL300kttGiwAAOh8Qi4jS5YskcfjUUFBgdavX6+hQ4cqKytLu3btanH51atX67LLLtNVV12lDRs2aMKECZowYYL+85//tHvwAACg4wu5jMyfP1+TJ09Wbm6uhgwZokWLFikmJkaLFy9ucfkFCxbo7LPP1q233qrBgwdrzpw5OuWUU7Rw4cJ2Dx4AAHR8EaEs3NDQoHXr1ik/Pz8wzel0asyYMSovL29xnfLycnk8nibTsrKytGzZsla34/V65fV6A49ramq+G6yvLpThtirCb1Rf71eEz6lGv8M2WeTZK8/OYyOP55a8Q5Nn57F1hDz/vnpJkjHmwAuaEOzYscNIMqtXr24y/dZbbzXp6ektrhMZGWmKi4ubTCsqKjK9evVqdTsFBQVGEj/88MMPP/zw0wl+PvvsswP2i5DOjBwq+fn5Tc6mfPPNN+rfv78qKyt15JFHtju/trZWSUlJ+uyzzxQbG2ubLPLslWfnsZHHc0veocmz89g6Qp4xRnv27FHfvn0PuFxIZaRnz55yuVyqrq5uMr26uloJCQktrpOQkBDS8pLkdrvldrubTT/yyCPD8sfZLzY2Nmx54cwiz155dh4befbJIq9z59l5bHbPC+YkQkgXsEZFRSk1NVVlZWWBaX6/X2VlZcrMzGxxnczMzCbLS1JpaWmrywMAgMNLyG/TeDweTZo0SWlpaUpPT1dhYaHq6uqUm5srScrJyVFiYqLmzZsnSbr55ps1cuRI/fGPf9T48eP17LPPau3atfrzn/8c3t8EAAB0SCGXkYkTJ2r37t2aOXOmqqqqlJKSopKSEvXu3VuSVFlZKafz+xMuw4cPV3FxsaZPn67f/e53Ou6447Rs2TKdeOKJQW/T7XaroKCgxbdu2iKceXYeG3n2ySLPXnl2Hht59sqz89g6Ql6wHMb81OdtAAAADh6+mwYAAFiKMgIAACxFGQEAAJaijAAAAEtRRgAAgKVseTv4L7/8UosXL1Z5ebmqqqokfXcn1+HDh+vXv/61jjrqKItHCFhr/xdJhuvjd3bOq6mpafLfgfZ8JUQ4s2A/dn4dhzsv3K9lq/cN250Z+fe//63jjz9e999/v4488kiNGDFCI0aM0JFHHqn7779fgwYN0tq1a0PO3bdvnzZt2qSVK1dq5cqV2rRpk3w+X5vGGM4sSaqqqtLy5cv10EMP6aGHHtLy5csDLwo75EnfvVA/+OADffDBB4FvUe7MeXZUWlqqcePGqXv37oqJiVFMTIy6d++ucePG6dVXX+10eY888oiGDBmiHj16aMiQIU3+/dFHH7Us68d+/C3j7WXnPDvut3Z/Hdt5vzgYeW0WzLf1HkoZGRnmmmuuMX6/v9k8v99vrrnmGnPaaacFndfY2Ghuv/12ExcXZxwOR5OfuLg4M336dNPY2HjIs4wx5ttvvzWXX365cblcJiIiwvTq1cv06tXLREREGJfLZa644gpTV1dnWZ4xxjz88MNm8ODBxul0NvkZPHiweeSRR0LK6gh5xhjj8/nMxo0bTUlJiSkpKTEbN240DQ0NbcoKV97jjz9uIiIizKWXXmoee+wx89JLL5mXXnrJPPbYY+ayyy4zkZGR5sknn+w0eXfffbeJiYkx06ZNM6+//rp5//33zfvvv29ef/11k5+fb7p27WruueeeQ5613yuvvGLOOeccExcXF3jNxcXFmXPOOceUlpaGlNUR8uy639r9dWzn/eJg5LWH7cpIdHS02bx5c6vzN2/ebKKjo4POu/XWW81RRx1lFi1aZLZt22bq6+tNfX292bZtm3nooYdMr169zNSpUw95ljHGXHXVVea4444zJSUlZt++fYHp+/btMytXrjTHH3+8ufrqqy3Ls/sLP9x54S6b4cw77rjjzMKFC1udX1RUZI499tigx2b3vH79+pklS5a0Ov/ZZ581SUlJhzzLGPsfsA6nA6DdX8d23i8ORl572K6MJCcnmyeeeKLV+U888YTp379/0Hm9e/c2JSUlrc4vKSkxvXr1OuRZxhgTFxdn/vWvf7U6/6233jJxcXGW5dn9hR/uvHCXzXDmud1us2XLllbnb9myJaSSbve86Oho8/7777c6/7333jNdunQ55FnG2P+AdTgdAO3+OrbzfnEw8trDdmVk4cKFxu12m5tuusksX77cvP322+btt982y5cvNzfddJPp0qWLKSoqCjovJibGvPPOO63O37Rpk+nateshzzLGmNjYWPPvf/+71fkVFRUmNjbWsjy7v/DDnRfushnOvFNOOcXceuutrc6fOnWqOeWUU4Iem93zfv7zn5ucnBzj8/mazdu3b5/JyckxI0aMOORZxtj/gHU4HQDt/jq2835xMPLaw5bfTbNkyRLdd999WrdunRobGyVJLpdLqamp8ng8uuSSS4LOGj9+vPbt26dnnnlGPXv2bDLvyy+/1JVXXimXy6V//OMfhzRLki6//HJt3rxZjz76qIYNG9Zk3oYNGzR58mQNGjRITz/9tCV5I0aM0IABA/Too48qIqLpB68aGxv1m9/8Rtu3b9cbb7zRKfK6du2qt99+WyeddFKL89955x0NHz5c33777SHPW7Vqlc4991wNHDhQY8aMCXwxZXV1tcrKyvTJJ59oxYoVGjFiRFBjs3veO++8o6ysLPl8Po0YMaJJ3ptvvqmoqCi98sorQX3hZjizJCk1NVW/+MUvdPfdd7c4/7bbbtOrr76qdevWdYo8O++3dn8d23m/OBh57WHLMrKfz+fTl19+KUnq2bOnIiMjQ8747LPPNG7cOG3ZskUnnXRSkz/2u+++qyFDhugf//iHkpKSDmmWJH399dfKzs7WypUr1b17d/Xq1UuStGvXLn3zzTfKyspScXGx4uLiLMmz+ws/3HnhLpvhztu+fbsefPBBvf32200+gpeZmanrrrtOycnJQeV0lLw9e/bo6aefbjEvOztbsbGxlmTZ/YB1uB0A7f46tvN+cTDy2srWZSRc/H6/Vq5c2eIf+6yzzpLTGfwnnMOZtd/mzZtbzBs0aFDIWeHOs/sLP5x54S6b4c6Dfdj9gMUBEB3NYVFGgGCFu2yGO2/fvn167733All9+vTR4MGD23TWsCPkVVVVac2aNU3y0tPTlZCQYGkW7MXur2M77xcHI68tDpsyUlFR0eIdXU899VRLsxoaGrRs2bIW8y644AJFRUVZmifZ/4Vvhx3pYPP7/Zo5c6aKioqa3RzqyCOPVF5enmbPnh10ubF7Xl1dna699lo9++yzcjgc6tGjhyTpq6++kjFGl112mR566CHFxMQc0qwfsvsB63A4ANr9dWzn/eJg5LXLIblM1kLV1dXmjDPOMA6Hw/Tv39+kp6eb9PR0079/f+NwOMwZZ5xhqqurD3mWMcZ89NFHZuDAgSY6OtqMHDnSXHLJJeaSSy4xI0eONNHR0ebYY481H330kWV5dr8p28G4yZsxxqxZs8YUFhaaadOmmWnTppnCwkJTUVERck448+z8seODkRfOe+aE+/47dr4fzcHIs/N+a/fXsZ33i4OR1x6dvoxcdNFFJjMzs8WPum3ZssUMHz7c/OpXvzrkWcYYM2bMGHPBBReYmpqaZvNqamrMBRdcYM466yzL8uz+wg93XrjLZjjz7Pyx44ORF8575oT7/jt2P2AdTgdAu7+O7bxfHIy89uj0ZaRbt25m/fr1rc5fu3at6dat2yHPMsaYLl26mHfffbfV+e+8805In98Pd57dX/jhzgt32QxnXrjvcWP3vHDeMyfc99+x+wHrcDoA2v11bOf94mDktYftvigv3Nxut2pra1udv2fPnqC/QTGcWZIUFxen7du3tzp/+/btQX8M92Dk+f3+A15jEhUVJb/f32nyVq5cqaKiIp1wwgnN5p1wwgm6//77VVJSYkneqFGj9Nvf/jbwUfcf+vLLL3Xbbbdp1KhRQY/N7nnnnnuurrnmGm3YsKHZvA0bNuj666/Xeeedd8izpO/28759+7Y6v0+fPqqrq+s0eXbeb+3+OrbzfnEw8trlkFQeC91www2mf//+ZunSpU3evqipqTFLly41ycnJJi8v75BnGWPMjBkzTPfu3c38+fPNpk2bTFVVlamqqjKbNm0y8+fPNz169DAFBQWW5WVnZ5thw4a1eDZo/fr1JjU11Vx++eWdJi8+Pt6sWrWq1fmvv/66iY+PtySvsrLSnHjiiSYiIsIMGzbMnH322ebss882w4YNMxEREebkk082lZWVQY/N7nlfffWVOfvss43D4TA9evQwgwYNMoMGDTI9evQwTqfTnHPOOebrr78+5FnGGDNu3Dhz1llnmd27dzebt3v3bnP22Web8ePHd5o8O++3dn8d23m/OBh57dHpP03j9Xo1ZcoULV68WPv27Qs08oaGBkVEROiqq67SfffdF9QZjdayvF6vIiMjQ8ra7w9/+IMWLFigqqoqORwOSZIxRgkJCZoyZYqmTp0a0u8bzjy735Qt3Hk33nijVqxYofvuu0+/+MUvAvc6qK2tVVlZmTwej84991w98MADluTZ/WPHB+MePFu2bGn2ybC23jMnXPffsfv9aA63mzPa/XVs9/1CCv+9rtqi05eR/Wpra7Vu3bomf+zU1NQ23VyntrZWa9euVXV1tSSpd+/eSktLa9eNerZt29ZkbAMGDGhzVrjz7HxTNil8O2Y4i+vByIN92P2AxQEQHc1hU0YOpqioKG3atEmDBw+2eigIg3AW13DnhfMeN9J3B62WDkx+v1+ff/65+vXr16bc/c4880w99thj6t+/f0jreb1eOZ3OwD0xPv74Yy1evFiVlZXq37+/rrrqqpAK9qZNm7Ru3TqNGjVKAwcO1HvvvaeioiL5/X5deOGFysrKCml8sBf2i7btF5J99o3Dooz83//9n9atW6cePXpoyJAhTebt3btXf/3rX5WTk/OTOR6Pp8XpCxYs0BVXXKH4+HhJ0vz584Ma1/r169W9e/fAi+epp57SokWLAi+svLw8XXrppUFl7bdw4UJVVFRo3LhxuvTSS/XUU09p3rx58vv9+uUvf6k77rij2ZdTHcjBuIna559/rri4OHXr1q3JdJ/Pp/Ly8qC/M6M1AwcO1MqVK3Xccce1K8dOdu3apYsuukj/+te/1K9fvyan3isrK3X66afrhRdeCJzy/im1tbW6+uqr9fe//12xsbG69tprVVBQIJfLFcjt27dv4Isqf8qLL77Y4vRf/vKXWrBgQeAtgfPPPz+ovFGjRikvL0+/+tWv9K9//Uu/+MUvdMIJJ2jw4MH68MMP9cEHH+jVV19VZmbmT2YtXbpUl1xyieLi4uT1evW3v/1NF198sdLS0uRyufTqq6/qySefVHZ2dlBj248DoPUHQPaLtu8X0sHbN9rkkFyZYqEPPvggcF8Hp9NpRowYYXbs2BGYX1VVZZxOZ1BZDofDpKSkmFGjRjX5cTgc5tRTTzWjRo0yo0ePDnpsJ598siktLTXGGPPwww+bLl26mJtuusk8+OCDZsqUKaZbt27m0UcfDTpvzpw55ogjjjAXXXSRSUhIMHfddZeJj483d955p5k7d6456qijzMyZM4POC/dN1Hbu3GlOPfVU43Q6jcvlMldeeaXZs2dPYH4oz4UxxixYsKDFH5fLZfLz8wOPg/XZZ581uejvzTffNNnZ2eaMM84wl19+uVm9enXQWfv9/e9/NzNmzDBvvfWWMcaYsrIyc84555isrCzz0EMPBZ0T7o8d33TTTeb44483zz33nHn44YdN//79zfjx443X6zXGfPdcOByOoPP2718/vsnWD39CeW5jY2PNhx9+aIwxZuTIkeaWW25pMn/69Onm9NNPDyrrlFNOMXfeeacxxpi//OUvJi4uztxxxx2B+ffee69JSUkJemzhvh9NTU2Nufjii010dLTp1auXmTFjRpP7b4S6XyxfvrzFH5fLZRYuXBh4HKyRI0ea5557zhjz3cdu3W63Ofnkk83EiRPNsGHDTExMTEj7xgsvvGBcLpeJj4833bp1M6WlpSYuLs6MGTPGZGVlGZfLZZ555pmgstgv2r5fGBP+faM9On0ZmTBhghk/frzZvXu3+eijj8z48ePNgAEDzKeffmqMCW1HnzdvnhkwYIApKytrMj0iIsK89957IY+tS5cuZvv27cYYY4YNG2b+/Oc/N5n/zDPPmCFDhgSdd8wxx5gXXnjBGGPMxo0bjcvlMk8//XRg/tKlS82xxx4bdF64b6KWk5NjMjIyzL///W9TWlpqUlNTTVpamvnqq6+MMW3b0Y8++miTnJzc5MfhcJjExESTnJxsBgwYEHReenq6+fvf/26MMWbZsmXG6XSa888/39x2223mwgsvNJGRkYH5wVi0aJGJiIgwqampJjY21jz11FPmiCOOMFdffbW59tprTZcuXUxhYWFQWeG+x02/fv3M66+/Hni8e/duk56ebs466yyzd+/ekA+A+z+h8eODcFv3ja5du5rNmzcbY767b8bGjRubzN+6dWvQv2/Xrl3Ntm3bjDHG+P1+ExkZ2eTeDx9//HFIfzsOgPY5ALJftH2/2J8Xzn2jPTp9GenVq1eTP67f7zfXXXed6devn/n4449DfnFVVFSY448/3vzv//6vaWhoMMa0/YUVHx9v1q5dGxhnSy+sUG96tr9kGWNMZGSk+c9//hN4vH37dhMTExNSXjhvota3b1+zZs2awOO9e/ea8847z6SkpJj//ve/IT8X1157rUlJSTHvv/9+k+nt2dE/+eQTY4wxGRkZ5q677moy/4EHHjDDhg0LOm/IkCGBgvnaa6+Z6OhoU1RUFJj/2GOPmcGDBweVFe6PHXfp0iXwu+5XW1trMjMzzZlnnmk++eSTkJ4LY4yZP3++SUpKalLY2vpcnHnmmebuu+82xhgzfPhw88QTTzSZ//zzz5t+/foFlZWQkBDYz7766ivjcDiaHHAqKipMQkJC0GPjAGifAyD7Rdv3C2PCv2+0R6cvI0cccUSzg5Uxxtx4443m6KOPNm+++WbIL649e/aYnJwcc/LJJ5t3333XREZGtumFdcUVV5irrrrKGGPMxRdfbKZPn95k/ty5c81JJ50UdN6AAQPMyy+/bIwx5sMPPzROp9P89a9/DcxfsWKFSU5ODjqvT58+BzwT8OKLL5o+ffoEnde1a9fA/2Ht5/P5zIQJE8zJJ59s3nnnnZCfi6VLl5qkpCTzwAMPBKa1dUc/8sgjzaZNm4wx35XD/f++39atW0Mucz8uhz8sd9u2bQs6L9z3uDnhhBPMihUrmk3fs2ePyczMNEOHDg35uTDGmA0bNpghQ4aYa665xtTV1bX5uVi9erU58sgjTUFBgXnggQdMz549zfTp080zzzxjZs6caeLi4swf/vCHoLKuuOIKk5GRYZ5++mlz3nnnmaysLHPaaaeZzZs3my1btpiRI0eGdCaDA6B9DoDsF23fL4wJ/77RHp2+jJx66qnmySefbHHejTfeaOLi4tr04jLmu1OMvXv3Nk6ns00vrB07dpjk5GQzYsQI4/F4TJcuXcwZZ5xhJk+ebEaMGGGioqJa3DFaM336dHPUUUeZq6++2gwYMMBMmzbN9OvXzzz44INm0aJFJikpqdkp1gMJ903UTjrpJPP88883m76/kPTr169Nz8Xnn39uzjzzTHP22WebL774os07+vnnn2+mTZtmjDEmKyur2fUmDz/8sDnuuOOCzttfdo357rl2OBxNns9Vq1aZo48+OqisvXv3muuuu85ERUUZp9NpoqOjTXR0tHE6nSYqKspcf/31Zu/evUGP7X/+539a/Y9MbW2tycjIaPN+UV9fb6699lpz3HHHGZfL1abnwpjv/sN72mmnNXuLITExMei3t4z57m2OsWPHmm7dupmsrCzzzTffmLy8vMDbFccdd5zZunVr0HkcAO1zAGxtv3A4HOwXQQj3vtEenb6MzJ0715xzzjmtzr/++utDej/2xz777DOzbNky8+2337Zp/a+//trcdtttZsiQISY6OtpERUWZ/v37m+zs7AN+Z0BLGhsbze9//3tz7rnnmrlz5xq/32/+8pe/mKSkJBMfH29+/etfhzzOu+66y/Tp0yfw4tz/XnSfPn1C+g+QMcZMnTq11WtMfD6fOf/889v8XPj9fjN37lyTkJDQ5h39/fffN/Hx8SYnJ8fMmTPHdOvWzVxxxRXm97//vcnJyTFut9s89thjQefdeOON5rjjjjN33nmnSU9PN5MmTTKDBg0yL7/8sikpKTEnnXSS+c1vfhPSGGtqasxrr71miouLTXFxsXnttddavKbnp3z11VdN3sL7sdra2gP+338wli9fbqZMmRLSxZwt2bVrl3n77bfN6tWrA6f3w+Hjjz827777rvH5fCGtRzE8eAdAh8PRpgNgTU2NKSsrC+wXZWVlYd0v/H6/MSZ8+8VNN90U1v3ix2fW2qut+0Z7HBYf7UX7heMmavv27VN9fX2r99fYt2+fduzYEfJHD39o3bp1euutt5STk6Pu3buHvP7HH3+s6dOna8WKFfr2228lSRERETr11FN16623asKECUFn1dXV6ZZbblF5ebmGDx+uBx54QPfff79uv/12+Xw+jRw5UkuWLAn6Y4ewl3DdP+brr7/Wzp079bOf/azF+Xv27NH69es1cuTINo/1xRdf1Ouvv678/Px2vd52796tTz75RH6/X3369FFycnKbs37sk08+UX19vQYNGhTS7QdaEu57P5F38FFG0GafffaZCgoKtHjx4k6XZ4zRrl275Pf71bNnz8A9FsJh79698vl8OuKII0JaL1z3yzkc88I9tv13D91/x9AtW7ZowYIF8nq9uuKKK3TmmWcGnXWo8goLC9XQ0NCuvOHDh+uEE04I2/jamxfuez+R1768djlk52DQ6WzcuLHNp487Yl5lZaXJzc21JC+c98tpLW/nzp2dMi/cY3v55ZdNVFSU6dGjh4mOjjYvv/yyOeqoo8yYMWPMmWeeaVwuV7OP/5N3cPLCfe8n8tqX1x6UEbSqtZsn7f+57777wnIzJrvk/RQry1I475dzuOWFe2yZmZnm9ttvN8Z8dxF79+7dze9+97vA/GnTppmxY8eSdwjywn3vJ/Lal9celBG0Ktw3T7J7np3LUrjvl3M45YV7bLGxsYE7Dzc2NpqIiIgm9x159913Te/evck7RHnhvPcTee3PayvKCFrVt29fs2zZslbnb9iwIaT/iNs9z85lKdz3yzmc8sI9ttjY2Caf9ujWrZv5+OOPA4+3b99uoqOjyTtEecaE795P5IUnry1C/x5pHDZSU1O1bt26Vuc7HA6ZEK5/tntenz59tHTpUvn9/hZ/1q9fH3RWuPMGDRqktWvXNpu+cOFCXXDBBUF/0dbhmBfusSUnJ+ujjz4KPC4vL2/yJXaVlZXq06cPeYcoT5K6deumJ554Qvn5+RozZkzQX2RH3sHJawvKCFp16623avjw4a3OP/bYY/X66693mjw7l6ULL7xQf/nLX1qct3DhQl122WUhje1wygv32K6//vom/7E+8cQTm3wU9eWXXw7p0yXktS/vhy699FKtXbtWS5cubdctAsgLT14o+Ggv8P/985//VF1dnc4+++wW59fV1Wnt2rVB3+8h3HkA0FlRRgAAgKV4mwYAAFiKMgIAACxFGQEAAJaijAAAAEtRRgAAgKUoIwAAwFKUEQAAYKn/B/RbJggBMCCSAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "zh\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "perplexities_langs_histograms(\n",
    "    [\"en\", \"de\", \"es\", \"fr\", \"it\", \"ko\", \"nl\", \"pt\", \"ru\", \"zh\"],\n",
    "    n=100_000\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aggregate stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perplexities_langs_stats(langs: list[str], n: int) -> None:\n",
    "    means = []\n",
    "    maxes = []\n",
    "    mins = []\n",
    "    medians = []\n",
    "    std_devs = []\n",
    "    for lang in langs:\n",
    "        print(f\"On {lang}\")\n",
    "        if lang == \"de\":\n",
    "            corpus = f\"/mnt/data_2/shared/tower_llm_data/{lang}/2/0000.json.gz\"\n",
    "        elif lang == \"es\":\n",
    "            corpus = f\"/mnt/data_2/shared/tower_llm_data/{lang}/3/0000.json.gz\"\n",
    "        elif lang == \"fr\":\n",
    "            corpus = f\"/mnt/data_2/shared/tower_llm_data/{lang}/1/0000.json.gz\"\n",
    "        elif lang == \"ru\":\n",
    "            corpus = f\"/mnt/data_2/shared/tower_llm_data/{lang}/6/0000.json.gz\"\n",
    "        else:\n",
    "            corpus = f\"/mnt/data_2/shared/tower_llm_data/{lang}/0000.json.gz\"\n",
    "        train_corpus = open_read_cleaned(corpus)\n",
    "        perplexities, _ = get_perplexities_and_text(train_corpus, n=n)\n",
    "        means.append(np.mean(perplexities)) \n",
    "        maxes.append(np.max(perplexities))\n",
    "        mins.append(np.min(perplexities))\n",
    "        medians.append(np.median(perplexities))\n",
    "        std_devs.append(np.std(perplexities))\n",
    "    print(f\"Macro-avg mean: {np.mean(means)}\")\n",
    "    print(f\"Macro-avg max: {np.mean(maxes)}\")\n",
    "    print(f\"Macro-avg min: {np.mean(mins)}\")\n",
    "    print(f\"Macro-avg median: {np.mean(medians)}\")\n",
    "    print(f\"Macro-avg std_dev: {np.mean(std_devs)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On de\n",
      "On es\n",
      "On fr\n",
      "On it\n",
      "On ko\n",
      "On nl\n",
      "On pt\n",
      "On ru\n",
      "On zh\n",
      "Macro-avg mean: 819.4231974444444\n",
      "Macro-avg max: 76421.04444444444\n",
      "Macro-avg min: 6.111111111111111\n",
      "Macro-avg median: 544.1999999999999\n",
      "Macro-avg std_dev: 973.7085881455251\n"
     ]
    }
   ],
   "source": [
    "perplexities_langs_stats(\n",
    "    [\"en\", \"de\", \"es\", \"fr\", \"it\", \"ko\", \"nl\", \"pt\", \"ru\", \"zh\"],\n",
    "    n=100_000\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text examples with perplexity ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_text_within_perplexity_range(\n",
    "    corpus_path: str,\n",
    "    perplexity_range: Tuple[float, float],\n",
    "    n_max: int = 20\n",
    ") -> List[str]:\n",
    "    corpus = open_read_cleaned(corpus_path)\n",
    "    ranged_texts = []\n",
    "    while len(ranged_texts) < n_max:\n",
    "        entry = next(corpus)\n",
    "        if perplexity_range[0] <= entry['perplexity'] <= perplexity_range[1]:\n",
    "            ranged_texts.append(entry['text'])\n",
    "    return ranged_texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On perplexity range: (0, 50)\n",
      "ko\n",
      "On perplexity range: (50, 100)\n",
      "ko\n",
      "On perplexity range: (100, 200)\n",
      "ko\n",
      "On perplexity range: (200, 300)\n",
      "ko\n",
      "On perplexity range: (300, 400)\n",
      "ko\n",
      "On perplexity range: (400, 500)\n",
      "ko\n",
      "On perplexity range: (500, 600)\n",
      "ko\n",
      "On perplexity range: (600, 700)\n",
      "ko\n",
      "On perplexity range: (700, 800)\n",
      "ko\n"
     ]
    }
   ],
   "source": [
    "for perplexity_range in [(0, 50), (50, 100), (100, 200), (200, 300), (300, 400), (400, 500), (500, 600), (600, 700), (700, 800)]:\n",
    "    print(\"On perplexity range:\", perplexity_range)\n",
    "    for lang in [\"ko\"]:\n",
    "        print(lang)\n",
    "        if lang == \"de\":\n",
    "            path = f\"/mnt/data_2/shared/tower_llm_data/{lang}/2/0000.json.gz\"\n",
    "        elif lang == \"es\":\n",
    "            path = f\"/mnt/data_2/shared/tower_llm_data/{lang}/3/0000.json.gz\"\n",
    "        else:\n",
    "            path = f\"/mnt/data_2/shared/tower_llm_data/{lang}/0000.json.gz\"\n",
    "        texts = get_text_within_perplexity_range(\n",
    "            corpus_path=path,\n",
    "            perplexity_range=perplexity_range,\n",
    "            n_max=50\n",
    "        )\n",
    "        with open(f\"/mnt/data/jpombal/multilinguality_megatron/perplexity_texts/perplexity_texts_range_{lang}_{perplexity_range}.txt\", \"w\") as f:\n",
    "            f.write(\"\\n\".join(texts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# English"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = datasets.load_from_disk('/mnt/data_2/shared/tower_llm_data/en/data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': 'Beginners BBQ Class Taking Place in Missoula!\\nDo you want to get better at making delicious BBQ? You will have the opportunity, put this on your calendar now. Thursday, September 22nd join World Class BBQ Champion, Tony Balay from Lonestar Smoke Rangers. He will be teaching a beginner level class for everyone who wants to get better with their culinary skills.\\nHe will teach you everything you need to know to compete in a KCBS BBQ competition, including techniques, recipes, timelines, meat selection and trimming, plus smoker and fire information.\\nThe cost to be in the class is $35 per person, and for spectators it is free. Included in the cost will be either a t-shirt or apron and you will be tasting samples of each meat that is prepared.',\n",
       " 'timestamp': '2019-04-25T12:57:54Z',\n",
       " 'url': None,\n",
       " 'perplexity_score': 478.8,\n",
       " 'text_length': 747,\n",
       " 'domain': None,\n",
       " 'dup_ratio': 0.0,\n",
       " 'pairs': [],\n",
       " 'repetitions': [],\n",
       " 'included_in_dedup': True,\n",
       " 'cluster': []}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On perplexity range: (0, 50)\n",
      "On perplexity range: (50, 100)\n",
      "On perplexity range: (100, 200)\n",
      "On perplexity range: (200, 300)\n",
      "On perplexity range: (300, 400)\n",
      "On perplexity range: (400, 500)\n",
      "On perplexity range: (500, 600)\n",
      "On perplexity range: (600, 700)\n",
      "On perplexity range: (700, 800)\n"
     ]
    }
   ],
   "source": [
    "def get_text_within_perplexity_range(\n",
    "    corpus,\n",
    "    perplexity_range: Tuple[float, float],\n",
    "    n_max: int = 20\n",
    ") -> List[str]:\n",
    "    ranged_texts = []\n",
    "    i = 0\n",
    "    while len(ranged_texts) < n_max:\n",
    "        entry = corpus[i]\n",
    "        if perplexity_range[0] <= entry['perplexity_score'] <= perplexity_range[1]:\n",
    "            ranged_texts.append(entry['text'])\n",
    "        i += 1\n",
    "    return ranged_texts\n",
    "\n",
    "for perplexity_range in [(0, 50), (50, 100), (100, 200), (200, 300), (300, 400), (400, 500), (500, 600), (600, 700), (700, 800)]:\n",
    "    print(\"On perplexity range:\", perplexity_range)\n",
    "    texts = get_text_within_perplexity_range(\n",
    "        corpus=data,\n",
    "        perplexity_range=perplexity_range,\n",
    "        n_max=50\n",
    "    )\n",
    "    with open(f\"/mnt/data/jpombal/multilinguality_tower/notebooks/perplexity_texts/perplexity_texts_range_en_{perplexity_range}.txt\", \"w\") as f:\n",
    "        f.write(\"\\n\".join(texts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fPn26XnvtNa1du3af61dXVysnJ0dr165VdnZ2u8vNmTNHc+fObTW9rKxMCQkJoQ4XAABYqKmpSfn5+aqvr1diYmK7y3XpfUYefvhhHXPMMfssIpJUXFwsj8cTeNzQ0KC0tDSNHz9eycnJEY/D5/OpoqJCEydOVGxsbJdmHT1n1T7nu51G87L8mrXOKa8/8vuMkGePLPLslWfnsZFnrzw7j6075LV4XSEtF1YZ6d+/v1wul+rq6oKm19XVKSUlZZ/rNjY26umnn9Ytt9zyk8/jdrvldrtbTY+NjY24PHRWXqhZod7IzOt3RPWmZ+TZI4s8e+XZeWzk2SvPzmOzc54/xIywPsAaFxenzMxMVVZW/vBEfr8qKyuDTtu05ZlnnpHX69XFF18czlMCAIAeLuzTNB6PR1OnTlVWVpays7O1aNEiNTY2qrCwUJJUUFCg1NRULViwIGi9hx9+WJMnT47KaRYAANBzhF1GpkyZop07d2r27Nmqra1VRkaGysvLNXDgQElSTU2NnM7gAy7vv/++3njjDb300kvRGTUAAOgxOvQB1qKiIhUVFbU5b/Xq1a2mHXXUUQrjoh0AALAf4btpAACApSgjAADAUpQRAABgKcoIAACwFGUEAABYijICAAAsRRkBAACWoowAAABLUUYAAIClKCMAAMBSlBEAAGApyggAALAUZQQAAFiKMgIAACxFGQEAAJaijAAAAEtRRgAAgKUoIwAAwFKUEQAAYCnKCAAAsBRlBAAAWIoyAgAALEUZAQAAlqKMAAAAS3WojJSWlio9PV3x8fHKyclRdXX1Ppf/9ttvdc0112jQoEFyu9068sgj9cILL3RowAAAoGeJCXeFpUuXyuPxaPHixcrJydGiRYuUl5en999/XwMGDGi1fHNzsyZOnKgBAwbo2WefVWpqqj777DMlJSVFY/wAAKCbC7uMLFy4UNOmTVNhYaEkafHixVq5cqWWLFmiGTNmtFp+yZIl+vrrr7VmzRrFxsZKktLT0yMbNQAA6DHCKiPNzc1av369iouLA9OcTqcmTJigqqqqNtd5/vnnlZubq2uuuUYrVqzQQQcdpPz8fN10001yuVxtruP1euX1egOPGxoaJEk+n08+ny+cIbdpb4YVWW6X2fd8pwn6M1Lk2SOLPHvl2Xls5Nkrz85j6w55LT+xz9vLYYwJ+Rm3b9+u1NRUrVmzRrm5uYHp06dP12uvvaa1a9e2WmfYsGHaunWrLrroIl199dX66KOPdPXVV+vaa69VSUlJm88zZ84czZ07t9X0srIyJSQkhDpcAABgoaamJuXn56u+vl6JiYntLhf2aZpw+f1+DRgwQH/+85/lcrmUmZmpbdu26Y477mi3jBQXF8vj8QQeNzQ0KC0tTePHj1dycnLEY/L5fKqoqNDEiRMDp466KuvoOav2Od/tNJqX5desdU55/Y6IxkZeZHl2Hht5vLbkdU2encfWHfJavG2fAfmxsMpI//795XK5VFdXFzS9rq5OKSkpba4zaNAgxcbGBp2SGT58uGpra9Xc3Ky4uLhW67jdbrnd7lbTY2NjIy4PnZUXapa3JbQX1+t3hLwseZ2bZ+exkWefLPJ6dp6dx2bnPH+IGWFd2hsXF6fMzExVVlb+8ER+vyorK4NO2/y3E088UR999JH8fn9g2gcffKBBgwa1WUQAAMD+Jez7jHg8Hj344IN67LHHtHnzZl111VVqbGwMXF1TUFAQ9AHXq666Sl9//bWuu+46ffDBB1q5cqXmz5+va665Jnq/BQAA6LbC/szIlClTtHPnTs2ePVu1tbXKyMhQeXm5Bg4cKEmqqamR0/lDx0lLS9OqVat0ww036Nhjj1Vqaqquu+463XTTTdH7LQAAQLfVoQ+wFhUVqaioqM15q1evbjUtNzdXb775ZkeeCgAA9HB8Nw0AALAUZQQAAFiKMgIAACxFGQEAAJaijAAAAEtRRgAAgKUoIwAAwFKUEQAAYCnKCAAAsBRlBAAAWIoyAgAALEUZAQAAlqKMAAAAS1FGAACApSgjAADAUpQRAABgKcoIAACwFGUEAABYijICAAAsRRkBAACWoowAAABLUUYAAIClKCMAAMBSlBEAAGCpDpWR0tJSpaenKz4+Xjk5Oaqurm532UcffVQOhyPoJz4+vsMDBgAAPUvYZWTp0qXyeDwqKSnRhg0bNHLkSOXl5WnHjh3trpOYmKgvv/wy8PPZZ59FNGgAANBzhF1GFi5cqGnTpqmwsFAjRozQ4sWLlZCQoCVLlrS7jsPhUEpKSuBn4MCBEQ0aAAD0HDHhLNzc3Kz169eruLg4MM3pdGrChAmqqqpqd73vvvtOQ4YMkd/v13HHHaf58+frZz/7WbvLe71eeb3ewOOGhgZJks/nk8/nC2fIbdqbYUWW22X2Pd9pgv6MFHn2yCLPXnl2Hht59sqz89i6Q17LT+zz9nIYY0J+xu3btys1NVVr1qxRbm5uYPr06dP12muvae3ata3Wqaqq0ocffqhjjz1W9fX1uvPOO/X666/r3Xff1SGHHNLm88yZM0dz585tNb2srEwJCQmhDhcAAFioqalJ+fn5qq+vV2JiYrvLhXVkpCNyc3ODisvo0aM1fPhwPfDAA5o3b16b6xQXF8vj8QQeNzQ0KC0tTePHj1dycnLEY/L5fKqoqNDEiRMVGxvbpVlHz1m1z/lup9G8LL9mrXPK63dENDbyIsuz89jI47Ulr2vy7Dy27pDX4nWFtFxYZaR///5yuVyqq6sLml5XV6eUlJSQMmJjYzVq1Ch99NFH7S7jdrvldrvbXDfS8tBZeaFmeVtCe3G9fkfIy5LXuXl2Hht59skir2fn2Xlsds7zh5gR1gdY4+LilJmZqcrKyh+eyO9XZWVl0NGPfWlpadE777yjQYMGhfPUAACghwr7NI3H49HUqVOVlZWl7OxsLVq0SI2NjSosLJQkFRQUKDU1VQsWLJAk3XLLLTrhhBN0+OGH69tvv9Udd9yhzz77TJdddll0f5MoSJ+xMux13C6j27O/P/0SzVYKAMD+IuwyMmXKFO3cuVOzZ89WbW2tMjIyVF5eHrhct6amRk7nDwdcvvnmG02bNk21tbXq27evMjMztWbNGo0YMSJ6vwUAAOi2OvQB1qKiIhUVFbU5b/Xq1UGP77rrLt11110deRoAALAf4LtpAACApSgjAADAUpQRAABgKcoIAACwFGUEAABYijICAAAsRRkBAACWoowAAABLUUYAAIClKCMAAMBSlBEAAGApyggAALAUZQQAAFiKMgIAACxFGQEAAJaijAAAAEtRRgAAgKUoIwAAwFKUEQAAYCnKCAAAsBRlBAAAWIoyAgAALEUZAQAAlqKMAAAAS3WojJSWlio9PV3x8fHKyclRdXV1SOs9/fTTcjgcmjx5ckeeFgAA9EBhl5GlS5fK4/GopKREGzZs0MiRI5WXl6cdO3bsc72tW7fqt7/9rX7+8593eLAAAKDnCbuMLFy4UNOmTVNhYaFGjBihxYsXKyEhQUuWLGl3nZaWFl100UWaO3euDj300IgGDAAAepaYcBZubm7W+vXrVVxcHJjmdDo1YcIEVVVVtbveLbfcogEDBujSSy/VP//5z598Hq/XK6/XG3jc0NAgSfL5fPL5fOEMuU17M36c5XaZsLPcThP0Z6TIs0+encdGnn2yyOvZeXYeW3fIawlxv+owxoT8jNu3b1dqaqrWrFmj3NzcwPTp06frtdde09q1a1ut88Ybb+iCCy7Qxo0b1b9/f/3617/Wt99+q+XLl7f7PHPmzNHcuXNbTS8rK1NCQkKowwUAABZqampSfn6+6uvrlZiY2O5yYR0ZCdeuXbt0ySWX6MEHH1T//v1DXq+4uFgejyfwuKGhQWlpaRo/frySk5MjHpfP51NFRYUmTpyo2NjYwPSj56wKO8vtNJqX5desdU55/Y6Ix0aeffLsPDbyeG3J65o8O4+tO+S1eF0hLRdWGenfv79cLpfq6uqCptfV1SklJaXV8h9//LG2bt2qM888MzDN7/d//8QxMXr//fd12GGHtVrP7XbL7Xa3mh4bGxtUHiL14zxvS8f/4r1+R0Trk2ffPDuPjTz7ZJHXs/PsPDY75/lDzAjrA6xxcXHKzMxUZWXlD0/k96uysjLotM1ew4YN0zvvvKONGzcGfs466yyNHz9eGzduVFpaWjhPDwAAeqCwT9N4PB5NnTpVWVlZys7O1qJFi9TY2KjCwkJJUkFBgVJTU7VgwQLFx8fr6KOPDlo/KSlJklpNBwAA+6ewy8iUKVO0c+dOzZ49W7W1tcrIyFB5ebkGDhwoSaqpqZHTyY1dAQBAaDr0AdaioiIVFRW1OW/16tX7XPfRRx/tyFMCAIAeikMYAADAUpQRAABgKcoIAACwFGUEAABYijICAAAsRRkBAACWoowAAABLUUYAAIClKCMAAMBSlBEAAGApyggAALAUZQQAAFiKMgIAACxFGQEAAJaijAAAAEtRRgAAgKUoIwAAwFKUEQAAYCnKCAAAsBRlBAAAWIoyAgAALEUZAQAAlqKMAAAAS1FGAACApSgjAADAUh0qI6WlpUpPT1d8fLxycnJUXV3d7rLLli1TVlaWkpKS1Lt3b2VkZOiJJ57o8IABAEDPEnYZWbp0qTwej0pKSrRhwwaNHDlSeXl52rFjR5vL9+vXTzfffLOqqqr09ttvq7CwUIWFhVq1alXEgwcAAN1fTLgrLFy4UNOmTVNhYaEkafHixVq5cqWWLFmiGTNmtFp+3LhxQY+vu+46PfbYY3rjjTeUl5fX5nN4vV55vd7A44aGBkmSz+eTz+cLd8it7M34cZbbZcLOcjtN0J+RIs8+eXYeG3n2ySKvZ+fZeWzdIa8lxP2qwxgT8jM2NzcrISFBzz77rCZPnhyYPnXqVH377bdasWLFPtc3xuiVV17RWWedpeXLl2vixIltLjdnzhzNnTu31fSysjIlJCSEOlwAAGChpqYm5efnq76+XomJie0uF9aRka+++kotLS0aOHBg0PSBAwdqy5Yt7a5XX1+v1NRUeb1euVwu/elPf2q3iEhScXGxPB5P4HFDQ4PS0tI0fvx4JScnhzPkNvl8PlVUVGjixImKjY0NTD96TvinjtxOo3lZfs1a55TX74h4bOTZJ8/OYyOP15a8rsmz89i6Q16L1xXScmGfpumIAw44QBs3btR3332nyspKeTweHXrooa1O4ezldrvldrtbTY+NjQ0qD5H6cZ63peN/8V6/I6L1ybNvnp3HRp59ssjr2Xl2Hpud8/whZoRVRvr37y+Xy6W6urqg6XV1dUpJSWl3PafTqcMPP1ySlJGRoc2bN2vBggXtlhEAALD/COtqmri4OGVmZqqysjIwze/3q7KyUrm5uSHn+P3+oA+oAgCA/VfYp2k8Ho+mTp2qrKwsZWdna9GiRWpsbAxcXVNQUKDU1FQtWLBAkrRgwQJlZWXpsMMOk9fr1QsvvKAnnnhC999/f3R/EwAA0C2FXUamTJminTt3avbs2aqtrVVGRobKy8sDH2qtqamR0/nDAZfGxkZdffXV+uKLL9SrVy8NGzZMTz75pKZMmRK93wIAAHRbHfoAa1FRkYqKitqct3r16qDHt956q2699daOPA0AANgP8N00AADAUpQRAABgKcoIAACwFGUEAABYijICAAAsRRkBAACWoowAAABLUUYAAIClKCMAAMBSlBEAAGApyggAALAUZQQAAFiKMgIAACxFGQEAAJaijAAAAEtRRgAAgKUoIwAAwFKUEQAAYCnKCAAAsBRlBAAAWIoyAgAALEUZAQAAlqKMAAAAS1FGAACApTpURkpLS5Wenq74+Hjl5OSourq63WUffPBB/fznP1ffvn3Vt29fTZgwYZ/LAwCA/UvYZWTp0qXyeDwqKSnRhg0bNHLkSOXl5WnHjh1tLr969WpdeOGFevXVV1VVVaW0tDSdcsop2rZtW8SDBwAA3V/YZWThwoWaNm2aCgsLNWLECC1evFgJCQlasmRJm8s/9dRTuvrqq5WRkaFhw4bpoYcekt/vV2VlZcSDBwAA3V9MOAs3Nzdr/fr1Ki4uDkxzOp2aMGGCqqqqQspoamqSz+dTv3792l3G6/XK6/UGHjc0NEiSfD6ffD5fOENu096MH2e5XSbsLLfTBP0ZKfLsk2fnsZFnnyzyenaencfWHfJaQtyvOowxIT/j9u3blZqaqjVr1ig3Nzcwffr06Xrttde0du3an8y4+uqrtWrVKr377ruKj49vc5k5c+Zo7ty5raaXlZUpISEh1OECAAALNTU1KT8/X/X19UpMTGx3ubCOjETqtttu09NPP63Vq1e3W0Qkqbi4WB6PJ/C4oaFBaWlpGj9+vJKTkyMeh8/nU0VFhSZOnKjY2NjA9KPnrAo7y+00mpfl16x1Tnn9jojHRp598uw8NvJ4bcnrmjw7j6075LV4XSEtF1YZ6d+/v1wul+rq6oKm19XVKSUlZZ/r3nnnnbrtttv08ssv69hjj93nsm63W263u9X02NjYoPIQqR/neVs6/hfv9TsiWp88++bZeWzk2SeLvJ6dZ+ex2TnPH2JGWB9gjYuLU2ZmZtCHT/d+GPW/T9v82O2336558+apvLxcWVlZ4TwlAADo4cI+TePxeDR16lRlZWUpOztbixYtUmNjowoLCyVJBQUFSk1N1YIFCyRJf/jDHzR79myVlZUpPT1dtbW1kqQ+ffqoT58+UfxVAABAdxR2GZkyZYp27typ2bNnq7a2VhkZGSovL9fAgQMlSTU1NXI6fzjgcv/996u5uVm/+tWvgnJKSko0Z86cyEYPAAC6vQ59gLWoqEhFRUVtzlu9enXQ461bt3bkKQAAwH6C76YBAACWoowAAABLUUYAAIClKCMAAMBSlBEAAGApyggAALAUZQQAAFiKMgIAACxFGQEAAJaijAAAAEtRRgAAgKUoIwAAwFKUEQAAYCnKCAAAsBRlBAAAWIoyAgAALEUZAQAAlqKMAAAAS1FGAACApSgjAADAUpQRAABgKcoIAACwFGUEAABYijICAAAs1aEyUlpaqvT0dMXHxysnJ0fV1dXtLvvuu+/q3HPPVXp6uhwOhxYtWtTRsQIAgB4o7DKydOlSeTwelZSUaMOGDRo5cqTy8vK0Y8eONpdvamrSoYceqttuu00pKSkRDxgAAPQsYZeRhQsXatq0aSosLNSIESO0ePFiJSQkaMmSJW0uf/zxx+uOO+7QBRdcILfbHfGAAQBAzxITzsLNzc1av369iouLA9OcTqcmTJigqqqqqA3K6/XK6/UGHjc0NEiSfD6ffD5fxPl7M36c5XaZsLPcThP0Z6TIs0+encdGnn2yyOvZeXYeW3fIawlxv+owxoT8jNu3b1dqaqrWrFmj3NzcwPTp06frtdde09q1a/e5fnp6uq6//npdf/31+1xuzpw5mjt3bqvpZWVlSkhICHW4AADAQk1NTcrPz1d9fb0SExPbXS6sIyNdpbi4WB6PJ/C4oaFBaWlpGj9+vJKTkyPO9/l8qqio0MSJExUbGxuYfvScVWFnuZ1G87L8mrXOKa/fEfHYyLNPnp3HRh6vLXldk2fnsXWHvBavK6Tlwioj/fv3l8vlUl1dXdD0urq6qH441e12t/n5ktjY2KDyEKkf53lbOv4X7/U7IlqfPPvm2Xls5Nkni7yenWfnsdk5zx9iRlgfYI2Li1NmZqYqKyt/eCK/X5WVlUGnbQAAAEIV9mkaj8ejqVOnKisrS9nZ2Vq0aJEaGxtVWFgoSSooKFBqaqoWLFgg6fsPvb733nuB/962bZs2btyoPn366PDDD4/irwIAALqjsMvIlClTtHPnTs2ePVu1tbXKyMhQeXm5Bg4cKEmqqamR0/nDAZft27dr1KhRgcd33nmn7rzzTo0dO1arV6+O/DcAAADdWoc+wFpUVKSioqI25/24YKSnpyuMC3YAAMB+hu+mAQAAlqKMAAAAS9nyPiPtyVlQqT0xvSPOcbuMbs/+/r4i0bwUCgAAhI8jIwAAwFKUEQAAYCnKCAAAsBRlBAAAWIoyAgAALEUZAQAAlqKMAAAAS1FGAACApSgjAADAUpQRAABgKcoIAACwFGUEAABYijICAAAsRRkBAACWoowAAABLUUYAAIClKCMAAMBSlBEAAGApyggAALAUZQQAAFiKMgIAACzVoTJSWlqq9PR0xcfHKycnR9XV1ftc/plnntGwYcMUHx+vY445Ri+88EKHBgsAAHqesMvI0qVL5fF4VFJSog0bNmjkyJHKy8vTjh072lx+zZo1uvDCC3XppZfqrbfe0uTJkzV58mT95z//iXjwAACg+wu7jCxcuFDTpk1TYWGhRowYocWLFyshIUFLlixpc/m7775bp556qm688UYNHz5c8+bN03HHHaf77rsv4sEDAIDuLyachZubm7V+/XoVFxcHpjmdTk2YMEFVVVVtrlNVVSWPxxM0LS8vT8uXL2/3ebxer7xeb+BxfX3994P1NYYz3HbF+I2amvyK8TnV4nfYJos8e+XZeWzk8dqS1zV5dh5bd8jz72mSJBlj9r2gCcO2bduMJLNmzZqg6TfeeKPJzs5uc53Y2FhTVlYWNK20tNQMGDCg3ecpKSkxkvjhhx9++OGHnx7w8/nnn++zX4R1ZKSrFBcXBx1N+fbbbzVkyBDV1NTowAMPjDi/oaFBaWlp+vzzz5WYmGibLPLslWfnsZHHa0te1+TZeWzdIc8Yo127dunggw/e53JhlZH+/fvL5XKprq4uaHpdXZ1SUlLaXCclJSWs5SXJ7XbL7Xa3mn7ggQdG5S9nr8TExKjlRTOLPHvl2Xls5Nkni7yenWfnsdk9L5SDCGF9gDUuLk6ZmZmqrKwMTPP7/aqsrFRubm6b6+Tm5gYtL0kVFRXtLg8AAPYvYZ+m8Xg8mjp1qrKyspSdna1FixapsbFRhYWFkqSCggKlpqZqwYIFkqTrrrtOY8eO1R//+EdNmjRJTz/9tNatW6c///nP0f1NAABAtxR2GZkyZYp27typ2bNnq7a2VhkZGSovL9fAgQMlSTU1NXI6fzjgMnr0aJWVlWnmzJn63e9+pyOOOELLly/X0UcfHfJzut1ulZSUtHnqpiOimWfnsZFnnyzy7JVn57GRZ688O4+tO+SFymHMT11vAwAA0Hn4bhoAAGApyggAALAUZQQAAFiKMgIAACxFGQEAAJay5e3gv/rqKy1ZskRVVVWqra2V9P2dXEePHq1f//rXOuiggyweIWCtvV8kGa3L7+ycV19fH/TvQCRfCRHNLNiPnd/H0c6L9nvZ6m3DdkdG/v3vf+vII4/UPffcowMPPFBjxozRmDFjdOCBB+qee+7RsGHDtG7durBz9+zZo02bNmnVqlVatWqVNm3aJJ/P16ExRjNLkmpra7VixQo98MADeuCBB7RixYrAm8IOedL3b9T3339f77//fuBblHtynh1VVFTo9NNPV9++fZWQkKCEhAT17dtXp59+ul5++eUel/fQQw9pxIgR6tevn0aMGBH03w8//LBlWT/2428Zj5Sd8+y43dr9fWzn7aIz8joslG/r7Uo5OTnm8ssvN36/v9U8v99vLr/8cnPCCSeEnNfS0mJuvvlmk5SUZBwOR9BPUlKSmTlzpmlpaenyLGOM+e6778xFF11kXC6XiYmJMQMGDDADBgwwMTExxuVymYsvvtg0NjZalmeMMQ8++KAZPny4cTqdQT/Dhw83Dz30UFhZ3SHPGGN8Pp/ZuHGjKS8vN+Xl5Wbjxo2mubm5Q1nRynv00UdNTEyMueCCC8wjjzxiXnjhBfPCCy+YRx55xFx44YUmNjbWPP744z0m7/bbbzcJCQlmxowZ5tVXXzXvvfeeee+998yrr75qiouLTe/evc0dd9zR5Vl7vfTSS+a0004zSUlJgfdcUlKSOe2000xFRUVYWd0hz67brd3fx3beLjojLxK2KyPx8fFm8+bN7c7fvHmziY+PDznvxhtvNAcddJBZvHix+fTTT01TU5Npamoyn376qXnggQfMgAEDzPTp07s8yxhjLr30UnPEEUeY8vJys2fPnsD0PXv2mFWrVpkjjzzSXHbZZZbl2f2NH+28aJfNaOYdccQR5r777mt3fmlpqTn88MNDHpvd8wYPHmyWLl3a7vynn37apKWldXmWMfbfYe1PO0C7v4/tvF10Rl4kbFdG0tPTzWOPPdbu/Mcee8wMGTIk5LyBAwea8vLydueXl5ebAQMGdHmWMcYkJSWZf/3rX+3Of+ONN0xSUpJleXZ/40c7L9plM5p5brfbbNmypd35W7ZsCauk2z0vPj7evPfee+3Of/fdd02vXr26PMsY+++w9qcdoN3fx3beLjojLxK2KyP33Xefcbvd5tprrzUrVqwwb775pnnzzTfNihUrzLXXXmt69eplSktLQ85LSEgwb7/9drvzN23aZHr37t3lWcYYk5iYaP7973+3O7+6utokJiZalmf3N36086JdNqOZd9xxx5kbb7yx3fnTp083xx13XMhjs3vez3/+c1NQUGB8Pl+reXv27DEFBQVmzJgxXZ5ljP13WPvTDtDu72M7bxedkRcJW343zdKlS3XXXXdp/fr1amlpkSS5XC5lZmbK4/Ho/PPPDzlr0qRJ2rNnj5566in1798/aN5XX32lSy65RC6XS//4xz+6NEuSLrroIm3evFkPP/ywRo0aFTTvrbfe0rRp0zRs2DA9+eSTluSNGTNGQ4cO1cMPP6yYmOALr1paWvSb3/xGW7du1WuvvdYj8nr37q0333xTxxxzTJvz3377bY0ePVrfffddl+etXr1aZ5xxhg499FBNmDAh8MWUdXV1qqys1CeffKKVK1dqzJgxIY3N7nlvv/228vLy5PP5NGbMmKC8119/XXFxcXrppZdC+sLNaGZJUmZmpn7xi1/o9ttvb3P+TTfdpJdfflnr16/vEXl23m7t/j6283bRGXmRsGUZ2cvn8+mrr76SJPXv31+xsbFhZ3z++ec6/fTTtWXLFh1zzDFBf9nvvPOORowYoX/84x9KS0vr0ixJ+uabb5Sfn69Vq1apb9++GjBggCRpx44d+vbbb5WXl6eysjIlJSVZkmf3N36086JdNqOdt3XrVt1///168803gy7By83N1ZVXXqn09PSQcrpL3q5du/Tkk0+2mZefn6/ExERLsuy+w9rfdoB2fx/bebvojLyOsnUZiRa/369Vq1a1+Zd9yimnyOkM/QrnaGbttXnz5jbzhg0bFnZWtPPs/saPZl60y2a082Afdt9hsQNEd7NflBEgVNEum9HO27Nnj959991A1qBBgzR8+PAOHTXsDnm1tbVau3ZtUF52drZSUlIszYK92P19bOftojPyOmK/KSPV1dVt3tH1+OOPtzSrublZy5cvbzPv7LPPVlxcnKV5kv3f+HbYkDqb3+/X7NmzVVpa2urmUAceeKCKioo0d+7ckMuN3fMaGxt1xRVX6Omnn5bD4VC/fv0kSV9//bWMMbrwwgv1wAMPKCEhoUuz/pvdd1j7ww7Q7u9jO28XnZEXkS75mKyF6urqzEknnWQcDocZMmSIyc7ONtnZ2WbIkCHG4XCYk046ydTV1XV5ljHGfPjhh+bQQw818fHxZuzYseb88883559/vhk7dqyJj483hx9+uPnwww8ty7P7Tdk64yZvxhizdu1as2jRIjNjxgwzY8YMs2jRIlNdXR12TjTz7HzZcWfkRfOeOdG+/46d70fTGXl23m7t/j6283bRGXmR6PFl5NxzzzW5ubltXuq2ZcsWM3r0aPOrX/2qy7OMMWbChAnm7LPPNvX19a3m1dfXm7PPPtuccsopluXZ/Y0f7bxol81o5tn5suPOyIvmPXOiff8du++w9qcdoN3fx3beLjojLxI9voz06dPHbNiwod3569atM3369OnyLGOM6dWrl3nnnXfanf/222+Hdf1+tPPs/saPdl60y2Y086J9jxu750XznjnRvv+O3XdY+9MO0O7vYztvF52RFwnbfVFetLndbjU0NLQ7f9euXSF/g2I0syQpKSlJW7dubXf+1q1bQ74MtzPy/H7/Pj9jEhcXJ7/f32PyVq1apdLSUh111FGt5h111FG65557VF5ebkneuHHj9Nvf/jZwqft/++qrr3TTTTdp3LhxIY/N7nlnnHGGLr/8cr311lut5r311lu66qqrdOaZZ3Z5lvT9dn7wwQe3O3/QoEFqbGzsMXl23m7t/j6283bRGXkR6ZLKY6Grr77aDBkyxCxbtizo9EV9fb1ZtmyZSU9PN0VFRV2eZYwxs2bNMn379jULFy40mzZtMrW1taa2ttZs2rTJLFy40PTr18+UlJRYlpefn29GjRrV5tGgDRs2mMzMTHPRRRf1mLzk5GSzevXqdue/+uqrJjk52ZK8mpoac/TRR5uYmBgzatQoc+qpp5pTTz3VjBo1ysTExJhjjz3W1NTUhDw2u+d9/fXX5tRTTzUOh8P069fPDBs2zAwbNsz069fPOJ1Oc9ppp5lvvvmmy7OMMeb00083p5xyitm5c2ereTt37jSnnnqqmTRpUo/Js/N2a/f3sZ23i87Ii0SPv5rG6/Xq+uuv15IlS7Rnz55AI29ublZMTIwuvfRS3XXXXSEd0Wgvy+v1KjY2Nqysvf7whz/o7rvvVm1trRwOhyTJGKOUlBRdf/31mj59eli/bzTz7H5TtmjnXXPNNVq5cqXuuusu/eIXvwjc66ChoUGVlZXyeDw644wzdO+991qSZ/fLjjvjHjxbtmxpdWVYR++ZE63779j9fjT7280Z7f4+tvt2IUX/Xlcd0ePLyF4NDQ1av3590F92ZmZmh26u09DQoHXr1qmurk6SNHDgQGVlZUV0o55PP/00aGxDhw7tcFa08+x8UzYpehtmNItrZ+TBPuy+w2IHiO5mvykjnSkuLk6bNm3S8OHDrR4KoiCaxTXaedG8x430/U6rrR2T3+/XF198ocGDB3cod6+TTz5ZjzzyiIYMGRLWel6vV06nM3BPjI8//lhLlixRTU2NhgwZoksvvTSsgr1p0yatX79e48aN06GHHqp3331XpaWl8vv9Ouecc5SXlxfW+GAvbBcd2y4k+2wb+0UZ+X//7/9p/fr16tevn0aMGBE0b/fu3frrX/+qgoKCn8zxeDxtTr/77rt18cUXKzk5WZK0cOHCkMa1YcMG9e3bN/DmeeKJJ7R48eLAG6uoqEgXXHBBSFl73Xfffaqurtbpp5+uCy64QE888YQWLFggv9+vX/7yl7rllltafTnVvnTGTdS++OILJSUlqU+fPkHTfT6fqqqqQv7OjPYceuihWrVqlY444oiIcuxkx44dOvfcc/Wvf/1LgwcPDjr0XlNToxNPPFHPPfdc4JD3T2loaNBll12mv//970pMTNQVV1yhkpISuVyuQO7BBx8c+KLKn/L888+3Of2Xv/yl7r777sApgbPOOiukvHHjxqmoqEi/+tWv9K9//Uu/+MUvdNRRR2n48OH64IMP9P777+vll19Wbm7uT2YtW7ZM559/vpKSkuT1evW3v/1N5513nrKysuRyufTyyy/r8ccfV35+fkhj24sdoPU7QLaLjm8XUudtGx3SJZ9MsdD7778fuK+D0+k0Y8aMMdu2bQvMr62tNU6nM6Qsh8NhMjIyzLhx44J+HA6HOf744824cePM+PHjQx7bscceayoqKowxxjz44IOmV69e5tprrzX333+/uf76602fPn3Mww8/HHLevHnzzAEHHGDOPfdck5KSYm677TaTnJxsbr31VjN//nxz0EEHmdmzZ4ecF+2bqG3fvt0cf/zxxul0GpfLZS655BKza9euwPxwXgtjjLn77rvb/HG5XKa4uDjwOFSff/550If+Xn/9dZOfn29OOukkc9FFF5k1a9aEnLXX3//+dzNr1izzxhtvGGOMqaysNKeddprJy8szDzzwQMg50b7s+NprrzVHHnmkeeaZZ8yDDz5ohgwZYiZNmmS8Xq8x5vvXwuFwhJy3d/v68U22/vsnnNc2MTHRfPDBB8YYY8aOHWtuuOGGoPkzZ840J554YkhZxx13nLn11luNMcb85S9/MUlJSeaWW24JzL/zzjtNRkZGyGOL9v1o6uvrzXnnnWfi4+PNgAEDzKxZs4LuvxHudrFixYo2f1wul7nvvvsCj0M1duxY88wzzxhjvr/s1u12m2OPPdZMmTLFjBo1yiQkJIS1bTz33HPG5XKZ5ORk06dPH1NRUWGSkpLMhAkTTF5ennG5XOapp54KKYvtouPbhTHR3zYi0ePLyOTJk82kSZPMzp07zYcffmgmTZpkhg4daj777DNjTHgb+oIFC8zQoUNNZWVl0PSYmBjz7rvvhj22Xr16ma1btxpjjBk1apT585//HDT/qaeeMiNGjAg577DDDjPPPfecMcaYjRs3GpfLZZ588snA/GXLlpnDDz885Lxo30StoKDA5OTkmH//+9+moqLCZGZmmqysLPP1118bYzq2oR9yyCEmPT096MfhcJjU1FSTnp5uhg4dGnJedna2+fvf/26MMWb58uXG6XSas846y9x0003mnHPOMbGxsYH5oVi8eLGJiYkxmZmZJjEx0TzxxBPmgAMOMJdddpm54oorTK9evcyiRYtCyor2PW4GDx5sXn311cDjnTt3muzsbHPKKaeY3bt3h70D3HuFxo93wh3dNnr37m02b95sjPn+vhkbN24Mmv/RRx+F/Pv27t3bfPrpp8YYY/x+v4mNjQ2698PHH38c1t8dO0D77ADZLjq+XezNi+a2EYkeX0YGDBgQ9Jfr9/vNlVdeaQYPHmw+/vjjsN9c1dXV5sgjjzT/+7//a5qbm40xHX9jJScnm3Xr1gXG2dYbK9ybnu0tWcYYExsba/7zn/8EHm/dutUkJCSElRfNm6gdfPDBZu3atYHHu3fvNmeeeabJyMgw//d//xf2a3HFFVeYjIwM89577wVNj2RD/+STT4wxxuTk5JjbbrstaP69995rRo0aFXLeiBEjAgXzlVdeMfHx8aa0tDQw/5FHHjHDhw8PKSvalx336tUr8Lvu1dDQYHJzc83JJ59sPvnkk7BeC2OMWbhwoUlLSwsqbB19LU4++WRz++23G2OMGT16tHnssceC5j/77LNm8ODBIWWlpKQEtrOvv/7aOByOoB1OdXW1SUlJCXls7ADtswNku+j4dmFM9LeNSPT4MnLAAQe02lkZY8w111xjDjnkEPP666+H/ebatWuXKSgoMMcee6x55513TGxsbIfeWBdffLG59NJLjTHGnHfeeWbmzJlB8+fPn2+OOeaYkPOGDh1qXnzxRWOMMR988IFxOp3mr3/9a2D+ypUrTXp6esh5gwYN2ueRgOeff94MGjQo5LzevXsH/g9rL5/PZyZPnmyOPfZY8/bbb4f9WixbtsykpaWZe++9NzCtoxv6gQceaDZt2mSM+b4c7v3vvT766KOwy9yPy+F/l7tPP/005Lxo3+PmqKOOMitXrmw1fdeuXSY3N9eMHDky7NfCGGPeeustM2LECHP55ZebxsbGDr8Wa9asMQceeKApKSkx9957r+nfv7+ZOXOmeeqpp8zs2bNNUlKS+cMf/hBS1sUXX2xycnLMk08+ac4880yTl5dnTjjhBLN582azZcsWM3bs2LCOZLADtM8OkO2i49uFMdHfNiLR48vI8ccfbx5//PE2511zzTUmKSmpQ28uY74/xDhw4EDjdDo79Mbatm2bSU9PN2PGjDEej8f06tXLnHTSSWbatGlmzJgxJi4urs0Noz0zZ840Bx10kLnsssvM0KFDzYwZM8zgwYPN/fffbxYvXmzS0tJaHWLdl2jfRO2YY44xzz77bKvpewvJ4MGDO/RafPHFF+bkk082p556qvnyyy87vKGfddZZZsaMGcYYY/Ly8lp93uTBBx80RxxxRMh5e8uuMd+/1g6HI+j1XL16tTnkkENCytq9e7e58sorTVxcnHE6nSY+Pt7Ex8cbp9Np4uLizFVXXWV2794d8tj+53/+p91/ZBoaGkxOTk6Ht4umpiZzxRVXmCOOOMK4XK4OvRbGfP8P7wknnNDqFENqamrIp7eM+f40x8SJE02fPn1MXl6e+fbbb01RUVHgdMURRxxhPvroo5Dz2AHaZwfY3nbhcDjYLkIQ7W0jEj2+jMyfP9+cdtpp7c6/6qqrwjof+2Off/65Wb58ufnuu+86tP4333xjbrrpJjNixAgTHx9v4uLizJAhQ0x+fv4+vzOgLS0tLeb3v/+9OeOMM8z8+fON3+83f/nLX0xaWppJTk42v/71r8Me52233WYGDRoUeHPuPRc9aNCgsP4BMsaY6dOnt/sZE5/PZ84666wOvxZ+v9/Mnz/fpKSkdHhDf++990xycrIpKCgw8+bNM3369DEXX3yx+f3vf28KCgqM2+02jzzySMh511xzjTniiCPMrbfearKzs83UqVPNsGHDzIsvvmjKy8vNMcccY37zm9+ENcb6+nrzyiuvmLKyMlNWVmZeeeWVNj/T81O+/vrroFN4P9bQ0LDP//sPxYoVK8z1118f1oc527Jjxw7z5ptvmjVr1gQO70fDxx9/bN555x3j8/nCWo9i2Hk7QIfD0aEdYH19vamsrAxsF5WVlVHdLvx+vzEmetvFtddeG9Xt4sdH1iLV0W0jEvvFpb2IXDRuorZnzx41NTW1e3+NPXv2aNu2bWFfevjf1q9frzfeeEMFBQXq27dv2Ot//PHHmjlzplauXKnvvvtOkhQTE6Pjjz9eN954oyZPnhxyVmNjo2644QZVVVVp9OjRuvfee3XPPffo5ptvls/n09ixY7V06dKQLzuEvUTr/jHffPONtm/frp/97Gdtzt+1a5c2bNigsWPHdniszz//vF599VUVFxdH9H7buXOnPvnkE/n9fg0aNEjp6ekdzvqxTz75RE1NTRo2bFhYtx9oS7Tv/URe56OMoMM+//xzlZSUaMmSJT0uzxijHTt2yO/3q3///oF7LETD7t275fP5dMABB4S1XrTul7M/5kV7bHvvHrr3jqFbtmzR3XffLa/Xq4svvlgnn3xyyFldlbdo0SI1NzdHlDd69GgdddRRURtfpHnRvvcTeZHlRaTLjsGgx9m4cWOHDx93x7yamhpTWFhoSV4075fTXt727dt7ZF60x/biiy+auLg4069fPxMfH29efPFFc9BBB5kJEyaYk08+2bhcrlaX/5PXOXnRvvcTeZHlRYIygna1d/OkvT933XVXVG7GZJe8n2JlWYrm/XL2t7xojy03N9fcfPPNxpjvP8Tet29f87vf/S4wf8aMGWbixInkdUFetO/9RF5keZGgjKBd0b55kt3z7FyWon2/nP0pL9pjS0xMDNx5uKWlxcTExATdd+Sdd94xAwcOJK+L8qJ57yfyIs/rKMoI2nXwwQeb5cuXtzv/rbfeCusfcbvn2bksRft+OftTXrTHlpiYGHS1R58+fczHH38ceLx161YTHx9PXhflGRO9ez+RF528jgj/e6Sx38jMzNT69evbne9wOGTC+Pyz3fMGDRqkZcuWye/3t/mzYcOGkLOinTds2DCtW7eu1fT77rtPZ599dshftLU/5kV7bOnp6frwww8Dj6uqqoK+xK6mpkaDBg0ir4vyJKlPnz567LHHVFxcrAkTJoT8RXbkdU5eR1BG0K4bb7xRo0ePbnf+4YcfrldffbXH5Nm5LJ1zzjn6y1/+0ua8++67TxdeeGFYY9uf8qI9tquuuiroH+ujjz466FLUF198MayrS8iLLO+/XXDBBVq3bp2WLVsW0S0CyItOXji4tBf4//3zn/9UY2OjTj311DbnNzY2at26dSHf7yHaeQDQU1FGAACApThNAwAALEUZAQAAlqKMAAAAS1FGAACApSgjAADAUpQRAABgKcoIAACw1P8HhsZBdQbHzdIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def perplexity_histogram(perplexities: List[float]):\n",
    "    series = pd.Series(perplexities)\n",
    "    series.hist(bins=1000, weights=np.zeros_like(series) + 1. / len(series), cumulative=True)\n",
    "    plt.xlim(0, 5000)\n",
    "    plt.xticks(np.arange(0, 5000, 200), rotation=90)\n",
    "    plt.yticks(np.arange(0, 1.1, 0.1))\n",
    "    plt.show()\n",
    "\n",
    "def get_perplexities_and_text(corpus: Iterable[str], n: float = None) -> List[float]:\n",
    "    perplexities = []\n",
    "    texts = []\n",
    "    if n is None:\n",
    "        for entry in enumerate(corpus):\n",
    "            perplexities.append(entry['perplexity_score'])\n",
    "            texts.append(entry['text'])\n",
    "    else:\n",
    "        for i, entry in enumerate(corpus):\n",
    "            if i >= n:\n",
    "                break\n",
    "            perplexities.append(entry['perplexity_score'])\n",
    "            texts.append(entry['text'])\n",
    "\n",
    "    return perplexities, texts\n",
    "\n",
    "def describe_perplexities(perplexities: List[float]):\n",
    "    print(pd.Series(perplexities).describe())\n",
    "\n",
    "perplexities, _ = get_perplexities_and_text(data, n=100_000)\n",
    "perplexity_histogram(perplexities)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def describe_perplexities(perplexities: List[float]):\n",
    "    print(pd.Series(perplexities).describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    100000.000000\n",
      "mean        811.207134\n",
      "std        1455.270563\n",
      "min           7.200000\n",
      "25%         364.100000\n",
      "50%         516.300000\n",
      "75%         817.100000\n",
      "max      249779.100000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "describe_perplexities(perplexities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "flan_instruction_tuning_venv",
   "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"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}