002_makemore_Bigrams/bigram_exercises.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8805a252",
   "metadata": {},
   "source": [
    "# makemore: bigram exercises\n",
    "\n",
    "1. watch the \"<u>makemore: bigram</u>\" video on [YouTube](https://www.youtube.com/watch?v=PaCmpygFfXo&list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ&index=3)\n",
    "2. come back and complete these exercises to level up :)\n",
    "\n",
    "-----\n",
    "<br><br><a id=\"e1\"></a>\n",
    "# Exercises\n",
    "----\n",
    "1. Train a [**trigram**](#1) language model, i.e. take two characters as an input to predict the 3rd one. Feel free to use either [counting](#1a) or a [neural net](#1b). Evaluate the loss; Did it improve over a **bigram** model?\n",
    "2. [Split up the dataset](#2) randomly into <u>80% train set, 10% dev set, 10% test set</u>. Train the bigram and trigram models only on the training set. Evaluate them on dev and test splits. What can you see?\n",
    "3. Use the <u>[dev set to tune the strength of smoothing (or regularization)](#3)</u> for the trigram model - i.e. try many possibilities and see which one works best based on the dev set loss. What patterns can you see in the train and dev set loss as you tune this strength? Take the best setting of the smoothing and evaluate on the test set once and at the end. How good of a loss do you achieve?\n",
    "4. We saw that our 1-hot vectors merely select a row of `W`, so producing these vectors explicitly feels wasteful. Can you delete our use of `F.one_hot` in favor of [simply indexing into rows of `W`](#4)?\n",
    "5. [Look up and use `F.cross_entropy` instead](#5). You should achieve the same result. Can you think of why we'd prefer to use `F.cross_entropy` instead?\n",
    "6. [Meta-exercise!](#6) Think of a fun/interesting exercise and complete it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4c6950ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math, random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import nltk\n",
    "from collections import defaultdict\n",
    "from tqdm import tqdm\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import Normalize\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dee26305",
   "metadata": {},
   "source": [
    "-----\n",
    "<a id='1'></a>\n",
    "# 1. Trigram Language Model\n",
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5f4322c",
   "metadata": {},
   "source": [
    "<a id='1a'></a>\n",
    "# a. Counting Approach\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5ea7e117",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!wget https://raw.githubusercontent.com/karpathy/makemore/master/names.txt # use if you don't have the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "86131d51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['emma',\n",
       " 'olivia',\n",
       " 'ava',\n",
       " 'isabella',\n",
       " 'sophia',\n",
       " 'charlotte',\n",
       " 'mia',\n",
       " 'amelia',\n",
       " 'harper',\n",
       " 'evelyn']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = open('../data/names.txt', 'r').read().splitlines()\n",
    "words[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9f0589d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32033, 2, 15)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(words), min(len(w) for w in words), max(len(w) for w in words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "21775c59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['emma',\n",
       " 'olivia',\n",
       " 'ava',\n",
       " 'isabella',\n",
       " 'sophia',\n",
       " 'charlotte',\n",
       " 'mia',\n",
       " 'amelia',\n",
       " 'harper',\n",
       " 'evelyn']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "beaca697",
   "metadata": {},
   "outputs": [],
   "source": [
    "t = {}\n",
    "for w in words:\n",
    "    #chs = ['<S>'] + list(w) + ['<E>']\n",
    "    chs = ['.'] + list(w) + ['.']\n",
    "    for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n",
    "        #print(ch1, ch2, ch3)\n",
    "        trigram = (ch1, ch2, ch3)\n",
    "        #print(trigram)\n",
    "        \n",
    "        # add up all bigrams & count how often they occur\n",
    "        # if bigram is not in the dictionary, b.get(bigram,0) returns 0\n",
    "        t[trigram] = t.get(trigram, 0) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dd320c48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('.', 'e', 'm'): 288,\n",
       " ('e', 'm', 'm'): 100,\n",
       " ('m', 'm', 'a'): 72,\n",
       " ('m', 'a', '.'): 174,\n",
       " ('.', 'o', 'l'): 104,\n",
       " ('o', 'l', 'i'): 69,\n",
       " ('l', 'i', 'v'): 54,\n",
       " ('i', 'v', 'i'): 78,\n",
       " ('v', 'i', 'a'): 147,\n",
       " ('i', 'a', '.'): 903}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{key:t[key] for idx, key in enumerate(t) if idx<10}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6ea383f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6037"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sorted(t.items(), key = lambda kv: -kv[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f487f181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('a', 'h', '.'), 1714),\n",
       " (('n', 'a', '.'), 1673),\n",
       " (('a', 'n', '.'), 1509),\n",
       " (('o', 'n', '.'), 1503),\n",
       " (('.', 'm', 'a'), 1453),\n",
       " (('.', 'j', 'a'), 1255),\n",
       " (('.', 'k', 'a'), 1254),\n",
       " (('e', 'n', '.'), 1217),\n",
       " (('l', 'y', 'n'), 976),\n",
       " (('y', 'n', '.'), 953)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorts the trigram dict. items based on their counts in desc. order \n",
    "\n",
    "# Sorts trigram dict items based on the counts of the bigrams (kv[1]) in desc. order, \n",
    "# resulting in a list of tuples sorted by frequency.\n",
    "# lambda: extracts the 2nd element (kv[1]) of each key-value pair and \n",
    "        # negates it to sort in descending order.\n",
    "        # kv: represents each tuple in the list (('a', 'b', 'c'), 198)\n",
    "        # -kv[1]: returns negated value of second elem in tuple\n",
    "        # key = lambda kv: -kv[1] --> sorting in descending order of frequency\n",
    "\n",
    "sorted(t.items(), key = lambda kv: -kv[1])[:10] # 10 most common trigrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "820ed701",
   "metadata": {},
   "outputs": [],
   "source": [
    "N = torch.zeros((27, 27, 27), dtype=torch.int32) # We add one dimension compared to the bigram model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "817cbdc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "string to integer dict: {'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5, 'f': 6, 'g': 7, 'h': 8, 'i': 9, 'j': 10, 'k': 11, 'l': 12, 'm': 13, 'n': 14, 'o': 15, 'p': 16, 'q': 17, 'r': 18, 's': 19, 't': 20, 'u': 21, 'v': 22, 'w': 23, 'x': 24, 'y': 25, 'z': 26, '.': 0}\n",
      "\n",
      "integer to string dict: {1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e', 6: 'f', 7: 'g', 8: 'h', 9: 'i', 10: 'j', 11: 'k', 12: 'l', 13: 'm', 14: 'n', 15: 'o', 16: 'p', 17: 'q', 18: 'r', 19: 's', 20: 't', 21: 'u', 22: 'v', 23: 'w', 24: 'x', 25: 'y', 26: 'z', 0: '.'}\n"
     ]
    }
   ],
   "source": [
    "chars = sorted(list(set(''.join(words))))\n",
    "stoi = {s:i+1 for i,s in enumerate(chars)}\n",
    "stoi['.'] = 0\n",
    "itos = {i:s for s,i in stoi.items()}\n",
    "print(f\"string to integer dict: {stoi}\\n\")\n",
    "print(f\"integer to string dict: {itos}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "97251a88",
   "metadata": {},
   "outputs": [],
   "source": [
    "for w in words:\n",
    "    chs = ['.'] + list(w) + ['.']\n",
    "    for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n",
    "        ix1 = stoi[ch1]\n",
    "        ix2 = stoi[ch2]\n",
    "        ix3 = stoi[ch3]\n",
    "        N[ix1, ix2, ix3] +=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "af496e99",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Input</th>\n",
       "      <th>Target</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>.e</td>\n",
       "      <td>m</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>em</td>\n",
       "      <td>m</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mm</td>\n",
       "      <td>a</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ma</td>\n",
       "      <td>.</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>.o</td>\n",
       "      <td>l</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Input Target  Count\n",
       "0    .e      m    288\n",
       "1    em      m    100\n",
       "2    mm      a     72\n",
       "3    ma      .    174\n",
       "4    .o      l    104"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(list(t.items()), columns=['Trigram', 'Count'])\n",
    "#df['Trigram'] = df['Trigram'].apply(lambda x: (''.join(x[:-1]), x[-1]))\n",
    "df[['Input', 'Target']] = df['Trigram'].apply(lambda x: pd.Series([''.join(x[:2]), x[2]]))\n",
    "#df['Input'] = df['Input'].apply(lambda row: str.join(row))\n",
    "df.drop(columns=['Trigram'], inplace=True)\n",
    "df = df[['Input', 'Target', 'Count']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4a3142f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Input</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>an</th>\n",
       "      <td>5438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>.a</th>\n",
       "      <td>4410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ar</th>\n",
       "      <td>3264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>el</th>\n",
       "      <td>3248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ri</th>\n",
       "      <td>3033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sq</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jb</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wz</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mf</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>601 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Count\n",
       "Input       \n",
       "an      5438\n",
       ".a      4410\n",
       "ar      3264\n",
       "el      3248\n",
       "ri      3033\n",
       "...      ...\n",
       "sq         1\n",
       "jb         1\n",
       "wz         1\n",
       "cp         1\n",
       "mf         1\n",
       "\n",
       "[601 rows x 1 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.groupby('Input').mean().sort_values('Count', ascending=False)\n",
    "df.groupby('Input').sum().sort_values('Count', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ff8c58e",
   "metadata": {},
   "source": [
    "## detour to figure out how to visualize trigrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b69f5b96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([27, 27, 1])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N.sum(2, keepdim=True) .shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "569b1470",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([27, 27])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N.sum(2).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "36187e69",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(N.sum(axis=2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b706fe43",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 16))\n",
    "plt.imshow(N.sum(axis=2), cmap='Blues')\n",
    "for i in range(27):\n",
    "    for j in range(27):\n",
    "        chstr = itos[i] + itos[j]\n",
    "        plt.text(j, i, chstr, ha=\"center\", va=\"bottom\", color='gray')\n",
    "        plt.text(j, i, N.sum(axis=2)[i, j].item(), ha=\"center\", va=\"top\", color='gray')\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c7c648b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1C2eWmZmN68QRfh+IiBdExHOAQeCVHWiDmZlZKueWmZl1C2eWmZkBndng93OSbpL0PeAs4KSJiiRtkrRF0pYY2d/eFpqZmT0lO7f2P7arvS00MzMrZGfWjp072ttCMzNri7Zu8JM0H/gQcH5EPBf4KDDhdZ8jYnNEbIiIDeobbGczzczMgJnn1uCSFe1sppmZ2Ywza9XQqnY208zM2qTdR/iNB85OSYuA89s8fTMzsxzOLTMz6xbOLDMze1LbNvhJuhZYQLGn6Tbgy8C32zV9MzOzHM4tMzPrFs4sMzM7XF+7JhQRG8u7f1DezMzMKsu5ZWZm3cKZZWZmh+vERTvMzMzMzMzMzMysRRQRnW7DtE49bUN8/Z+/lVR7cGQsa9yD/b3JtZKSa8fG0j/X3U8cTK4FWLl4IKs+Ve53IeezHpiX/jnnGs34rHt70v+HrWpDK9vxwKMHsuqPWjZhP86zlvtdyvlttUor/4eD83RzRGzIbZN1r5zcyv3utXJ+mio3a/v7qrF/cfjQaHJtKz/nQxmf37yMz25kNH28PZnz3Z4W5VbOZwHQ19v5vIBq5FbO/xugrzf9u+TcmltOO21D/NNNW5JqHz8wkjXuhQOtWdfKyc4djw0n1wIcWZHl4+FD6b/x+RnrtLly5jU585lWtaGV7bhn576s+mOGFrSkHTnbGqB1GZ7D61qd0fYlcEl3Sxpq93TNzMxyObPMzKybOLfMzGxcNXa5m5mZmZmZmZmZWVN0aoPf70r6nqRvSTquQ20wMzNL4cwyM7Nu4twyM7OObfDbExHPBT4AvL9DbTAzM0vhzDIzqwhJl0t6WNJtDcNWSLpO0o/Kv8vL4ZL055K2SvqupFMbXnNhWf8jSRc2DD+t3Fi2tXxt5zu/yufcMjOzjm3w+1TD3xdNVCBpk6Qtkrbs3LGjfS0zMzN7umkzC5xbZmZt8gng3MOG/R5wfUQcD1xfPgZ4BXB8edsEfBiKDYTAxcALgdOBi8c3EpY1v9rwusOn1Q2y1rV27HRmmZnVUV+HphuT3H9qYMRmYDMUVztsR6PMzKqud8kxESP7k+tj/44vR0Q3rqxUybSZBc4tM7OJNDu3IuLrktYfNvg84Mzy/hXADcDbyuGfjOLyqDdKWibpqLL2uojYBSDpOuBcSTcASyLixnL4J4FXA/+Q/AaqIWtd6zRnlpkZkJ9ZUO31rU5t8Hsd8J7y7zc71AYzs64TI/sZOOG1yfUHbv2gr9Q3e84sM7MZalNuHRERD5T3HwSOKO+vAe5rqNtWDptq+LYJhncb55aZ2QzkZhZUe32rbRv8JF0LvLl8uFzSd4Fh4PXtaoOZWfcTyBdYbzVnlplZs2Tn1pCkLQ2PN5dHoyWJiJA0545Yc26ZmTVDvda12rbBLyI2lnfXl3/f1q5pm5nVhoCu7D+8uzizzMyaJD+3dkbEhsypPCTpqIh4oDxl9+Fy+HZgbUPd0eWw7Tx1CvD48BvK4UdPUF95zi0zsyao2bpWfTZdmpnNFepJv5mZmXVa63PrGmD8SrsXAp9vGP7G8mq9Z1BcvfYB4MvAOZKWlxfrOAf4cvncY5LOKK/O+8aGcZmZ2VyQk1kVX9/qVB9+2ZS4lfWx/SNZ410wkP4RFP39punpSd8q3JtR20qPPH4wq35RxmeXY//B0az6eb05n19rPusDmW1eOL81n90RSwZaMl7I+/5bi9Vor1NdjUUwfGgsqTYnL1op5zferfODquTtaMbn1zvWms+6Kv/B0cz3l/N7qci/u2VSl40roYltlfQpiqPzhiRto7ja7nuAqyRdBNwDjHfAdC2wEdgK7AN+GSAidkl6F/Dtsu6d4xfwAH6d4krAgxQX6+i2C3a01K7c9YX5C5Jrc7IlZ37el7Wu0DoP7hnOql862Jr1hScO5K0vD8zr/AaNfZnrWksGW9Pmo1cMtmS8kPf976bZ/7j8ZccOvslu/IAn0TUb/MzMDOrWr4SZmdVdc3MrIibrk+7sCWoDeMsk47kcuHyC4VuA58ymjWZm1q3qta7lDX5mZt2mRnudzMxsDnBumZlZt6hRZnmDn5lZNxG12utkZmY159wyM7NuUbPMavsGP0kvAC4FFlJcKv7siNjb7naYmXUnQU9vpxsxpzi3zMxmw7nVbs4tM7OZqldmtXWDn6R+4NPA6yLi25KWAPvb2QYzs65Xo8PMq865ZWbWBM6ttnFumZnNUo0yq91H+J0APBAR3waIiMcmK5S0CdgEsHbduva0zsys8urVkWwXmFFuHb3WuWVmVnButVlSbnldy8xsIvXKrMq+k4jYHBEbImLD0NCqTjfHzKwaRLHXKfVmbdOYWyuHhjrdHDOzanBuVVJjZq3yupaZWSE3syqeW+3e4HcHcFTZrwSSFkvyhUPMzHKoJ/1ms+XcMjObLedWOzm3zMxmIyezKp5bbWudpGuBIeB1wP+R9C/AdcD8drXBzKz7qTYBVHXOLTOzZnButYtzy8xstjIzq+K51ba9PRGxsbx7P3BGu6ZrZlY7PdU+dLwunFtmZk3i3GoL55aZWRPUKLO64vDu0bHg8QMjSbWP7T+UNe4jl6Xv8FLG+dkjo2PJtQ8/NpxcC7BsYX9WffJ4F8zLqn90X/pnPb8//dLWgxm1AGNjkVwbkV7bjZ4YTvudjFs0P30WkPP9H834nwD0VmCeenAk/TcL+d/TphGV35NkhdRfQSu7/siZ5+X8xjN/4i2TO08fyWh4X8ZPPLcdo6Pp9WM96bV7E5eVAJYO5mV+VWR9p3vS55XZ/8OM71Jvi1Yc8pdpOhS2zq1a2f3Ewaz6dUMLkmtzcihnuW37rryLFA8tHkiuzfkdrlqctw63Y2/6Z71gIH2ZfmHG8j94XatR6jaJcUsGW7OulbOtAaAvY2WrVcuOw4dy29yh3GhRZkm6G9gLjAIjEbFB0gqKq6qvB+4GXhsRu1V8sJcCG4F9wJsi4jvleC4E/qAc7SURccVU03X6mpl1m5p0ImtmZnOEc8vMzLpF6y7a8XMRcUpEbCgf/x5wfUQcD1xfPgZ4BXB8edsEfLhollYAFwMvBE4HLpa0fKoJdnSDn6QzJX2hk20wM+su7gupU5xZZmYz4dzqFOeWmVmutvbhdx4wfoTeFcCrG4Z/Mgo3AsskHQW8HLguInZFxG6KPlrPnWoCXXFKr5mZNfAREGZm1k2cW2Zm1i1ak1kBfEVSAH8REZuBIyLigfL5B4EjyvtrgPsaXrutHDbZ8Em1fYOfpHOB91Oci/yNdk/fzKzr+QiItnFmmZk1gXOrbZxbZmazlJ9ZQ5K2NDzeXG7Qa/RvImK7pGcA10n6YeOTERHlxsCmausGP0nzgY8CZwFbKTooNDOzVO7jqG2cWWZmTeDcahvnlpnZLM0ss3Y29Ms3oYjYXv59WNLnKPrge0jSURHxQHnK7sNl+XZgbcPLjy6HbQfOPGz4DVNNt927204E7oqIH0Vx+Ze/mqxQ0iZJWyRt2fXIzva10Mys6twXUrskZxY8Pbce2encMjN7knOrXWa0rrVj5472tdDMrOqa3IefpIWSFo/fB84BbgOuAS4syy4EPl/evwZ4owpnAHvKU3+/DJwjaXl5sY5zymGTqmwffuUhkJsBTn7+afW+vreZWQ4fKVFJjbl1yqnOLTOzJzm3Kqcxs047bYMzy8xsXPMz6wjgcyrG2wf8TUR8SdK3gaskXQTcA7y2rL8W2EhxpPY+4JcBImKXpHcB3y7r3hkRu6aacLs3+P0QWC/pWRHxY+D1bZ6+mVmXU9OPgJC0DPgY8ByKDmV/BbiD4lSg9cDdwGsjYreKpLqUIoT2AW+KiO+U47kQ+INytJdExBV0N2eWmdmsNT+3bFLOLTOzWWl+ZkXEncDJEwx/BDh7guEBvGWScV0OXJ467balr6RrgRXAJuCLkr7DU+com5lZqvG+JVJuaS4FvhQRJ1KE0Q+A3wOuj4jjgevLxwCvAI4vb5uADxdN0grgYuCFFH1SXFweat6VnFlmZk3U/Nyywzi3zMyaJCezKp5bbTvCLyI2lnfvp+hfwszMcomm7nWStBR4KfAmgIg4CByUdB5PdQp7BUWHsG8DzgM+We55ulHSsrKT2TOB68YPK5d0HXAu8KmmNbaNnFlmZk3S5NyyiTm3zMyaoGaZVdk+/Brds3sfmz59a1LtB89/Xta4R0bHZtKkaQ0fas14AUbH0rvZKNbJW2NkNH3cYxltznl/AH903b8m1/7Gi9cn137xjgeTa3/plLXTFzXIeY+9Pel7DW6579Gsdhy3alFy7WB/b3Lt0sF5We14bP+h5NrF89NnWzmf80N7DiTXAqxZPphV3zxNP8z8WGAH8HFJJwM3A28Fjig7hwV4kKLvCYA1wH0Nr99WDpts+Jz04N5h3vO1rUm1F52WN/9Yv2pBcm3O7HQ0Iw8fPzCSPmLy5h8HDo2mjzgz4nbsHU6uXbuyNZ8zwK4nDibXPmPJQHJtTuQPj+Qtp8yflz7fUcbe7tHM5ZTRQ+n1BzKWxRZkfEehWB/otP0HM34rwIKBTrXap/TWyTOWzs+qz1nXypmX5nz/c+ZJ0Lp1rdysOJQxn85Z1zqUuf77hk/enFz7vlc/J7n2gzfek1z7hz9/fHIttG5d6yv/mr5+CPCCNSuSaxcMpK/jrFzUn9WO3RnLHTnrcTm/72279ifXQt7ybnPVK7Pq807MzOaKvEPMh8avwlfeNh02tj7gVODDEfF84AmeOn0XeLIfCXfobWZmM1OTU6PMzGwO8Cm9ZmbWMXl7nXZGxIYpnt8GbIuIm8rHV1Ns8HtI0lER8UB5yu54P0DbgcZD0o4uh23nqVOAx4ffkNNQMzOrqRodLWFmZjVXo8yqzzsxM5srmrjHKSIeBO6TdEI56Gzg+8A1wIXlsAuBz5f3rwHeqMIZwJ7y1N8vA+dIWl5erOOccpiZmc11NTlSwszM5gAf4Tdzkk4D/gxYBOwE3tTQT5SZmU1FLelX4jeAv5bUD9wJ/DLFDqGrJF0E3AO8tqy9FtgIbAX2lbVExC5J7wK+Xda9c/wCHt3OuWVmNgutyS2bhDPLzGwWapZZbd3gJ2ke8H+A8yJih6TXAe8GfqWd7TAz62pN3pMUEbcCE532e/YEtQG8ZZLxXA5c3tTGdZhzy8ysCSp+BERdOLPMzJqgRpnV7iP8TgCeA1xXXi2pF5hwj1PZsfwmgMEVR7arfWZmlZd7tTmblRnl1uJVq9vVPjOzynNutc2MMmvtunXtap+ZWeXVKbPavcFPwO0R8aLpCiNiM7AZYNn6n/bVIc3MKGaidQqhLjCj3Dry+Oc4t8zMcG612Ywy67TTNjizzMyoX2a1++TkO4BVkl4ExWHnkk5qcxvMzLqXhHrSbzZrzi0zs9lwbrWTM8vMbDYyM6vqudW2DX6SrgWGgPOB90r6F+BW4MXtaoOZWR1ISr7ZzDm3zMyaw7nVes4sM7PmyMmsqudW207pjYiN5d37gZe2a7pmZnVT9WCpC+eWmVlzOLdaz5llZtYcdcqsdvfhZ2Zms1SnEDIzs/pzbpmZWbeoU2Z1xQa/Z65cyN9cuCGp9tDoWNa4+3pbc1Zzznif+YyFWePOOU1cPa07a3vlov7k2p6MRufUAlzyihOz6lP9yunrk2vHxvL6Os59j6le/KyVWfWt+v7nWjI4ryXj7etN/5zXr8r7HXaMyptV2uol83nXuSck1UZmV+k5CyEZPwF6M+ZLOfP/XPPn9bZs3EevGGzJeHM+u9x25Py/52XM01uVQ7ly/9+5n3WdLZqftxjfsRUY51atLFuQt8zWqmXN/r708T57zeKsced8XXtbuCx95LL5ybU58/SBnrz57tUXnZ5Vn+o9v/DTybVVWdf6xeeuyaqvyrrW8oWtWW7L+ZxPWJ33O+yYmmVWV2zwMzOzgqh+XxFmZmbjnFtmZtYt6pZZHdvkLOlnJN0u6VZJrdnlbmZWQ3XpRLbbOLfMzGbGudUZzi0zs3x1umhHJ48xfQPwxxFxSkTs72A7zMy6Sl0CqAs5t8zMZsC51THOLTOzTHXa4NeRU3olvRl4LfBySa+IiDd0oh1mZt2o6sFSR84tM7OZc261n3PLzGxm6pRZHdngFxEfk/RvgAMjmk8AACAASURBVC9ExNUT1UjaBGwCWLtuXTubZ2ZWXTXrSLZbOLfMzGbIudUR0+WWM8vMbAI1y6xqXDZmAhGxOSI2RMSGoaFVnW6OmVll1OUQ87pxbpmZTcy5VT2NmbXKmWVm9iSf0mtmZh1RtytHmZlZvTm3zMysW9Qtsyp7hJ+ZmU2sLnuczMxsbmh2bkn6r+XVZ2+T9ClJ8yUdK+kmSVslfVpSf1k7UD7eWj6/vmE8by+H3yHp5S1582Zm1lXqdIRf2zf4SbpW0up2T9fMrDaUcbNZc26Zmc1SE3NL0hrgN4ENEfEcoBe4AHgv8L6IOA7YDVxUvuQiYHc5/H1lHZKeXb7uJOBc4EOSemf/ZjvPuWVmNgs5mVXx9a22n9IbERvLu29q97TNzLqe6nXlqG7g3DIzm4XW5FYfMCjpELAAeAA4C/il8vkrgHcAHwbOK+8DXA18QEWDzgOujIhh4C5JW4HTgW82u7Ht5twyM5uhmq1rdU0ffhGRVDc2llZXJQdHxrLqFwy05t/2xPBIVv283mqcEZ763YC8H2/OeKvyrcv9+ue8x1a2o7en8zPV3M+ik0FQpxCqqwgYTpy392b+P3uq8HvpdAO6XM48sjfj3z3awmWgKnzvoHW51Y3z1W5qczPbGhHbJf0pcC+wH/gKcDPwaESML8xuA9aU99cA95WvHZG0B1hZDr+xYdSNr7FJtHI+0yoHDuWtay2an76ulTNPevxA3rpWf5/XtZ6sTa5srZHM739vT2tanvs77MtYb6/C/zt33M3WTfk6na7Z4GdmZoU6hZCZmdVfZm4NSdrS8HhzRGxuGNdyiqPzjgUeBT5DcUqumZnZrNVpXcsb/MzMukjdrhxlZmb1NoPc2hkRG6Z4/ueBuyJiB4CkzwIvAZZJ6iuP8jsa2F7WbwfWAtsk9QFLgUcaho9rfI2Zmc1BdVvXqsZxwmZmlq4mnciamdkc0dzcuhc4Q9KCsi++s4HvA18Dzi9rLgQ+X96/pnxM+fw/RnFu2TXABeVVfI8Fjge+NfM3aWZmteCLdsycpI3AnwFPAP8EPDMiXtnudpiZdaWadSRbdc4sM7NZanJuRcRNkq4GvgOMALcAm4EvAldKuqQcdln5ksuAvywvyrGL4sq8RMTtkq6i2Fg4ArwlIkab1tAOcW6Zmc1Czda12rrBT9J84C+Al0bEXZI+1c7pm5nVQZ1CqMqcWWZmzdHs3IqIi4GLDxt8J8VVdg+vPQC8ZpLxvBt4d1Mb10HOLTOz2avTula7T+k9EbgzIu4qH08aQpI2SdoiacvOHTva0zozsy4gKflms5KcWXBYbu10bpmZjXNutc2M1rV2OLPMzJ6Uk1lVz63K9uEXEZsjYkNEbBhatarTzTEzq46a9ClRN0/LrSHnlpnZk5xbldOYWaucWWZmT6lRH37t3uB3B/BMSevLx69r8/TNzLpeXfY4dQFnlplZEzi32sa5ZWY2Sz7CbwYkXQssB34d+JKkm4G9wJ52tcHMrNvVKYCqzJllZtYczq32cG6Zmc1ebmZVPbfadtGOiNgIIOmxiDhRxSfzQWBLu9pgZlYHPT2V7Y2hNpxZZmbN49xqPeeWmVlz1Cmz2nqV3tKvSroQ6AduobiS1JQe3X+Qv7vt/qSRD/b1ZjXmF046Kqu+FfYdHM2qXzDQmn/bwszxPvzYcHLtM5YMJNdGRFY7Do2m1/cob9ypRjLaANDbn/c9rYKcf8vYWN7n0ZOxY6RVe1EOjoxl1Q/M6+D/sMkfgaS7KY4CGAVGImKDpBXAp4H1wN3AayNid7kCcSmwEdgHvCkivlOO50LgD8rRXhIRVzS3pR2RnVkAQTCa+DsYI+/3Mq+vNQshI6Ppv4HM2TS9OT/yFjpwKP09DmbMp3PneTnzm76Mzy5neWLx/E4sAv6k/ZnLQPPnpX//e7LyIu9/mPMbyGlGTsYdysytvt4O/g6rMQuYK2aUW6n+6jv3ZtX/2ouf2czJPylnfWHvgZGscS/KmD/m/GYXD87Lasf2XfuTa9esGEyuzV3XysmsnLzPacbBjGUUyF+vrYKcRYnMxY6s/3mr1rWGM5bBAOZ3cn25RpnV9k2XEfG+iDglIp4dEW+IiH3tboOZWTdr0SHmP1fOmzeUj38PuD4ijgeuLx8DvAI4vrxtAj5ctmkFcDHwQuB04GJJy2f9ZjvMmWVmNnt1OTWqGzi3zMxmpxWn9ErqlXSLpC+Uj4+VdJOkrZI+Lam/HD5QPt5aPr++YRxvL4ffIenlKdNt+wY/SXdLGmr3dM3MakFtW3E6Dxg/Qu8K4NUNwz8ZhRuBZZKOAl4OXBcRuyJiN3AdcO5sGlAFziwzs1lqX24Zzi0zs1nJzKyM3Hor8IOGx+8F3hcRxwG7gYvK4RcBu8vh7yvrkPRs4ALgJIp1rA9JmvYwyPqcnGxmNgeI4vSw1FuiAL4i6WZJm8phR0TEA+X9B4EjyvtrgPsaXrutHDbZcDMzm8NalFtmZmZNl5tZKbkl6WjgF4CPlY8FnAVcXZYcfnDF+EEXVwNnl/XnAVdGxHBE3AVspTirakqd2uD3G5K+I+l7kk7sUBvMzLpQ9h6nIUlbGm6bJhjpv4mIUylO132LpJc2PhlFxx+t6QCzOzizzMxmrD5XO+wizi0zsxmZ0VV6p1vfej/wu8B4R4YrgUcjYryD0cYDJZ48iKJ8fk9ZP6ODKzrVm+XOiDhV0q8DvwO8uUPtMDPrOpnrQzsb+uWbUERsL/8+LOlzFHuLHpJ0VEQ8UJ6y+3BZvh1Y2/Dyo8th24EzDxt+Q1ZLq8uZZWY2C96O13bOLTOzGZpBZk26viXplcDDEXGzpDNn2bRsnTrC77Pl35sprgD5EyRtGt9C+tjuXW1rmJlZ1TXzSAlJCyUtHr8PnAPcBlwDXFiWXQh8vrx/DfBGFc4A9pSn/n4ZOEfSchUX6zinHFYH02YWPD23Htm5sy0NMzPrBj7Cr+2y1rV27NzRtoaZmVVdk/vwewnwKkl3A1dSnMp7KUU/6OMH4I0fQAENB1eUzy8FHmHygy6m1KkNfsPl31EmOcowIjZHxIaI2LBk+Yr2tczMrMqa3KcERd9835D0L8C3gC9GxJeA9wAvk/Qj4OfLxwDXAndS9BvxUeDXASJiF/Au4Nvl7Z3lsDqYNrPg6bm1csj9pZuZAa3ILZte1rrWqqFV7WuZmVmVZWbWdLkVEW+PiKMjYj3FRTf+MSLeAHwNOL8sO/zgivGDLs4v66McfoGKq/geCxxPse42pU6d0mtmZjMgoKeneWtEEXEncPIEwx8Bzp5geABvmWRclwOXN61xZmbW9ZqdW2ZmZq3Sxsx6G3ClpEuAW4DLyuGXAX8paSuwi2IjIRFxu6SrgO8DI8BbImJ0uom0bYOfpGtx/xFmZrPmIyBaz5llZtY8zq3Wc26ZmTVHqzIrIm6g7OO8POjiJ66yGxEHgNdM8vp3A+/OmWbbNvhFxMby7vqGYVt4eifvZmY2Dfdx1HrOLDOz5nFutZ5zy8ysOeqUWT6l18ysm7iPIzMz6ybOLTMz6xY1y6yu2OAXAaNjY0m1+w5F1rhHRtPGC/DE8LSnSD8p57TvbY/sTy8Gli/sT67dNzySXLtgIO/rsHJRejvGxtL/L09ktBng/t0HkmtXL5+fXPvNux9Jrt2wNu/CMv196dfLyZnh7Nw7PH1Rg8cPpH/Wz1gykFy7aH7ed2n4UPrvcH5/b3LtaMb37tF9h5JrAZYOZpU3jajXXqe62r3/EJ/57rak2p9esSRr3Kc/c3lybWREYs736pHH8+Y1RyxNn/fm5PKOvQez2rFwIH3+MUh67VjOBw3sP5i+PLF0wbzk2pz87OvNm48szFxGSDUvsx0jozlf6vTaeb1517HLWc5r1Tx7JCPjAHo71I+ec6teHt2fPv+CvHn6nv3p87CcWccPHnwsvRhYtTh9HSdnWTp3+fjIZenZmbPMu3d/3jLvXTv2Jdceu2pBcu3fff/+5NqNJxyVXAswOC89w3M8tCdv+efRJ9KXU9asSF+5WJaxbAB5yx052wTyltnyPrsVGdsamqlumdWpq/QCIOljkp7dyTaYmXWXpl4m3jI5t8zMcjm3OsWZZWaWKy+zqp5bHT3CLyLcsayZWaaK50qtObfMzPI5tzrDmWVmlq9OmdXpI/xukLShk20wM+s2ddnj1I2cW2Zm+ZxbneHMMjPL5yP8zMysM2rWkayZmdWcc8vMzLpFzTKrshv8JG0CNgGsPHJNh1tjZlYNdetItk4ac2vFEas73Bozs2pwblVTY2atXbeuw60xM6uGumVWR0/pnUpEbI6IDRGxYfGyvCugmpnVmZR+s/ZpzK1Fy1d2ujlmZpXh3KqexsxaNbSq080xM6uMnMyqem5V9gg/MzObWJ32OpmZWf05t8zMrFvUKbM6coSfpGsl+XwnM7MZqMsep27i3DIzmznnVns5s8zMZs5H+M1SRGws757ZiembmXUt1WuvU7dwbpmZzZBzq+2cWWZmM1SzzPIpvWZmXaToSLbTrTAzM0vj3DIzs25Rt8zqig1+yxf085qT1ybV/uD+vVnj7utNP6t56YLWnAH9rCMWZtX39qR/AxcPzkuujYisdoyOpdf3ZnzOOW0GOCGzPtU5P31kcu2efYeyxt2T8T/MsXr5YEvGm+vgyFhW/fz+3pa0I+e3csTS+S1pQ/OpVnud6mrlgn7eeNoxSbV5c968vY458/Wcb9WqxQMZ1XlycnnV4v6scbfqt5MzrwFYMpi++JXzPxzK+L/M663GfKQn93+SUT6vr3U914xlLAPlvMWc/3dfi5Ylms+5VSdnrs+7mGLOPH3lorx5eqpT1y7Lqs9p87KF6W3OmW8AjOXMD1rUZoDnZ9anetML1ifX7nr8YNa4W7WudczQgpbWpzpwaDSrfsFAazb75Hzv1q5szWfRfPXKrK7Y4GdmZk9p1UKMmZlZKzi3zMysW9Qps6bcJCupV9IPWzVxSR+T9OxWjd/MrHZq1IlsKzi3zMwqxrk1KWeWmVnFZGZW1XNryiP8ImJU0h2S1kXEvc2eeES8udnjNDOrs6JfiYonSwc5t8zMqsW5NTlnlplZtdQts1JO6V0O3C7pW8AT4wMj4lWznbikG4DfiYgtsx2XmdlcUacQahHnlplZhTi3puTMMjOrkDplVsoGv//R8laYmVmyGmVQqzi3zMwqxLk1JWeWmVmF1Cmzpt3gFxH/V9IxwPER8VVJC4DWXFKzgaRNwCaAtevWtXpyZmZdo057nVrBuWVmVi3Orck5s8zMqqVOmTXtdZQl/SpwNfAX5aA1wN+1slEAEbE5IjZExIahoVWtnpyZWXeoUSeyreLcMjOrEOfWlKqQWaucWWZmhZpdtGPaDX7AW4CXAI8BRMSPgGe0slFmZjYxIaT02xzl3DIzqwjn1rScWWZmFZGbWVXPrZQNfsMRcXD8gaQ+IGYzUUnXSlpdPpzVuMzM5pq67HFqIeeWmVmFOLem5MwyM6uQOh3hl3LRjv8r6feBQUkvA34d+PvZTDQiNgJIWgnsms24zMzmmp6qJ0vnObfMzCrEuTUlZ5aZWYXUKbNSNvj9HnAR8D3gPwHXRsRHZzthSdcB34uIu6arHYtg/6HRpPEeSKyrkr0HRrLqFw/Oa0k7Dhway6o/OJJev3RBysGkMzMymt6Ovt7WtKOvtxozhZzPAvI6JO3tSa+d18LPIyJ9R3XO+3si83e4cH7K7LM1apRBrdLx3IrImEdm/j97e9L7cu/J+N3m/LYOjeYdMNKf0Y4cuQtkwxm5Ndif/jnnns4xPJK+rNKfkVuHMt5fX8b3CFo33xkZy/su5eTLaMa4c7+irfo8cr5LI2N5mT+vr3XLYtNxbk2p45mVY8/woWaOri12P5HX5mUL+1vSjv0H89ZTD2TM01cuak2bIS9bcuYzOcsdrVy3yJG7rpUjZz01Z9kgV6vWtR7bn/c7XNKibR4p6pRZKWusvxERlwJPBo+kt5bDZiwiXjab15uZzUXFoeM1SqHWcG6ZmVVEK3JL0jLgY8BzKE5Z/RXgDuDTwHrgbuC1EbFbxcQvBTYC+4A3RcR3yvFcCPxBOdpLIuKKpjY0jTPLzKwi6raulbJp+MIJhr1pphOUdLekoZm+3sxsrutR+i2VpF5Jt0j6Qvn4WEk3Sdoq6dOS+svhA+XjreXz6xvG8fZy+B2SXt7cd53FuWVmViEtyK1LgS9FxInAycAPKI6Uuz4ijgeuLx8DvAI4vrxtAj4MIGkFcDHwQuB04GJJy5vyhvM0NbPAuWVmNhs5mdWik1iaZtIj/CS9Hvgl4FhJ1zQ8tRj3BWFm1jEt2uv0VooVpiXl4/cC74uIKyV9hOJ0ow+Xf3dHxHGSLijrXifp2cAFwEnAauCrkn4qItrWz4Jzy8ysmpqZW5KWAi+l3ChWXvDioKTzgDPLsiuAG4C3AecBn4ziPLUbJS2TdFRZe11E7CrHex1wLvCppjV26vfhzDIzq6A6HeE31Sm9/ww8AAwB/7th+F7gu7OdsKRB4LPAZ5vRT4WZ2VzR7AySdDTwC8C7gd8uT386i2JFBIoVp3dQbPA7r7wPcDXwgbL+PODKiBgG7pK0leKIiW82t7VTcm6ZmVVQk3PrWGAH8HFJJwM3U+y0OiIiHihrHgSOKO+vAe5reP22cthkw9ulpZkFzi0zs5mo0fa+yTf4RcQ9wD2S3gDcHxEH4MngOJqib4yZWgRcSbG37ZOzGI+Z2ZwiQLlXeZje+4HfpTiqAGAl8GhEjF/JpHEl6MkVpIgYkbSnrF8D3NgwznavODm3zMwqaAa5NSRpS8PjzRGxueFxH3AqRd93N0m6lKdO3wUgIkJS3hVh2qzFmQXOLTOzbC1a1+qYlD78rgIaL0czCnxmltP9PPDxqcJH0iZJWyRteWTnzllOzsysPjL7lBgan5eWt02N45L0SuDhiLi5E++lRTqeWzt37pjl5MzM6iMzt3ZGxIaG2+bDRrcN2BYRN5WPr6bYAPhQeaou5d+Hy+e3A2sbXn90OWyy4e3WisyCaXKrMbN2OLPMzJ5Upz78Ujb49ZV9YwBP9pMx2+t+/xNwrqY4OToiNo8H/coh9zlrZgaAhDJuTL/i9BLgVZLupjgS4CyKztCXSRo/CrxxJejJFaTy+aXAI1RnxQkqkFtDQ6tmOTkzs5rIz60pRcSDwH2STigHnQ18H7iGpy6AcSHFBi/K4W9U4QxgT3nq75eBcyQtLy/WcU45rN1akVkwTW41ZtYqZ5aZWSEzs6re31/KBr8dkl41/qDsEHe2h9z9IbAb+OAsx2NmNucUl4tPu00nIt4eEUdHxHqKi278Y0S8AfgacH5ZdviK0/gK1fllfZTDL1BxFd9jKa6G+K0mveVczi0zswppZm6VfgP4a0nfBU4B/ifwHuBlkn4E/Hz5GOBa4E5gK/BR4NcByot1vAv4dnl75/gFPNqsFZkFzi0zsxnJyayKb++b8qId436NIlA/QHFK833AG3MnJOla4M0Ng94KXC7pf0XE7+aOz8xsLhLQ055keRtwpaRLgFuAy8rhlwF/WV6UYxfFRkIi4nZJV1EcZTECvKWdV+g9jHPLzKwiWpFbEXErsGGCp86eoDaAt0wynsuBy5vauHxNySxwbpmZzVYb17XaYtoNfhHxY+AMSYvKx4/PZEIRsbG8u75h8C/PZFxmZnNZqzIoIm4Abijv30lxld3Daw4Ar5nk9e+muNJvRzm3zMyqpUbrTk3XrMwqX+vcMjObpTplVsoRfkj6BeAkYP74OcoR8c4WtsvMzCZR9b4iqsC5ZWZWHc6tqTmzzMyqo06ZNe0GP0kfARYAPwd8jKLPprb2y3RoJNi+a39S7W2P7Mka96nrlyXX5vzjR0bHpi8qXfW9+5NrAX7rpc/Kqk812N+bVf/3309v92tPWTt90Qz9+KEnkmuPP3JRcm1PxiV39g3nnbm4cCBpW3u2vt6UbjmfMjoWybVjGbUHDuV9HvPnpX/3cv4vObY+lLdD/eRj0ucdzdQNfUV0WhVy6+DIGPc+si+pdtH8vPnBUcvmJ9fm/MZzTl/YuXc4uRZg9fLB5Nri7Ls0IxnvD+CJ4ZHk2vnz0uenmc2gP2NenfN7v237Y8m1z8+ch/W3aN6bO9aDI+nLV/19Gf/D7JZkyPhO57RiX8b3GfI+j2Zybk2tCpmV4+t3P5pV/7ITj0iuzfmejIym/67ec8OP00cMfOQ1z02uzVk/XJiZ95de/6/Jtb9/9k8l1+bkLMBt29Kz5eR1S5Nrc5bp9x7Im98tHpyXVZ8qd10rZ5tAzjLb/oN561o56/m9Lcr7W+/Lm3e89Kc6czGhZmeWpPnA14EBiu1vV0fExWWf51cCK4Gbgf8YEQclDQCfBE6juDDi6yLi7nJcbwcuoria+29GxLQXmkr5xr44It4I7I6IPwJeBKTPUaYg6QZJE/W/YWZmk+iRkm9zlHPLzKxCnFtTcmaZmVVITmYl5NYwcFZEnExxkalzyyvGvxd4X0QcR3GBpYvK+oso8uA44H1lHZKeTdF3+knAucCHJE27JTdlg9+B8u8+SauBQ8BRCa8zM7MWUMZtjnJumZlViHNrSs4sM7MKycms6XIrCuOnks0rbwGcBVxdDr8CeHV5/7zyMeXzZ6s4lPg84MqIGI6IuyiuPP8T/a0fLuW44r+XtAz4E+A7ZeM+mvA6MzNrMtG6Q+1rxLllZlYRzq1pObPMzCqiFZlVHol3M3Ac8EHgx8CjETF+rvo2YE15fw3F1dqJiBFJeyhO+10D3Ngw2sbXTGrKDX6SeoDrI+JR4G8lfQGYHxF5HeWZmVlzSLXqSLbZnFtmZhXj3JqUM8vMrGJmlllDkrY0PN4cEZvHH0TEKHBKuXPnc8CJs29omik3+EXEmKQPAs8vHw9TnIPccpI2AZsAjlrTugs+mJl1G683Tc65ZWZWPc6tiVUls9auW9eOSZqZdYUZZNbOiJi2v9SIeFTS1yj6al0mqa88yu9oYHtZth1YC2yT1Acspbh4x/jwcY2vmVRKH37XS/r3avOuuYjYHBEbImLD8hVD7Zy0mVmlqdzzlHKbo5xbZmYV4tyaUscza9VQZ66GaWZWRTmZNd2sW9Kq8sg+JA0CLwN+AHyN4qrsABcCny/vX1M+pnz+H6O4tPY1wAWSBsor/B5PwhXdU/rw+0/AbwMjkg5QnNYcEbEk4bVmZtZEAtwV0rScW2ZmFeHcmpYzy8ysIlqQWUcBV5T9+PUAV0XEFyR9H7hS0iXALcBlZf1lwF9K2grsorgyLxFxu6SrgO8DI8BbylOFpzTtBr+IWDyDNzUlSdcCb272eM3M5oI5egREMueWmVm1OLcm58wyM6uWZmZWRHyXstuGw4bfyQRX2Y2IA8BrJhnXu4F350w/5Qg/JC2nOGRwfsPEvp4zoUYRsbG8e+ZMx2FmNld5tWl6zi0zs+pwbk3NmWVmVh11yqxpN/hJejPwVopOAW8FzgC+CZzV2qaZmdnhJOjxkRJTcm6ZmVWHc2tqziwzs+qoW2alHOH3VuAFwI0R8XOSTgT+Z2ub9XR9veKIpfOnLwR6tuf9c0bHIrm2N+USJ6W+jOILnrcmfcQV8vwjl3e6CQCcsDr9TIhDI2PJtT0ZJ+8PzMv4cgBFv5tpcg4pzvk+Q17/BDnt6O/L+zxyPutWOeGopp9R0zI1yqBW6Xhu9ff1sG7lgqTa3P9n3mkG6fOEsYz50opF/RltaJ3cWceSwXnJtTmf89hoerYA7Ds4bZcrT1q6IL3NJ2bMx+b1VmNG8sjjB7Pqj1g60JJ25H6XWnWKas7yweKM7zNAbwez1rk1pY5nVo7c71FOtvRkHFfTlzEP++9nHZdcC9U5Bf2Vxx/RkvHmvr/nr1+WXHswY12rP+O7NNjfm1wLrVvXGsnM+5zfS047BjLXtTo5/x932jHV2HaQoiKzgKZI2eB3ICIOlFcgGYiIH0o6oeUtMzOzCVVlQbTCnFtmZhXi3JqSM8vMrELqlFkpG/y2lZcR/jvgOkm7gXta2ywzM5tMjTKoVZxbZmYV4tyakjPLzKxC6pRZKVfp/cXy7jskfQ1YCnyppa0yM7MJCdWqX4lWcG6ZmVWHc2tqziwzs+qoW2YlnfwtqVfSauAuis5kj0ydgKS7Jf2xpFslbZF0qqQvS/qxpF+bYbvNzOYmFXudUm9zlXPLzKwinFvTmk1mla93bpmZNUNmZlU9t1Ku0vsbwMXAQ8B4L5UBPC9jOvdGxCmS3gd8AngJxWXnbwM+Msl0NwGbAI5euy5jUmZm9VanfiVawbllZlYtzq3JNSmzIDO3GjNr7TpnlpnZuDplVupVek+IiEdmMZ1ryr/fAxZFxF5gr6RhScsi4tHDXxARm4HNACc//7S8S4+amdVY3nW55qSO59bzT93g3DIzKzm3ptSMzILM3GrMrNNOc2aZmY2rU2albPC7D9gzy+kMl3/HGu6PP05pg5mZAaJee51axLllZlYRzq1pNSOzwLllZjZrdcusSWf+kn67vHsncIOkL9IQHhHxZy1um5mZTaCnPhnUVM4tM7Nqcm79JGeWmVk11Smzptrbs7j8e2956y9vSSRdC7x55k0zM7OJ1CmEmsy5ZWZWQc6tCc0qs8C5ZWbWCnXKrKk2+P0vYHFEPNw4UNIzgMemG3FEbCzvrm8Y9gmKTmTHH6/HzMySFVeDqlEKNZdzy8ysYpxbk5pVZoFzy8ys2eqWWVNt8LsU+BLw2cOGvwQ4B/jPrWrU4b53x32s/9n/mlT73//kt7LG3deb3iXj8KHR5NrH9o8k1z6671ByLcCRy+Yn1x44mN7m+f29We14bH96uyPS+wLOKAVgZCz9Bf196f/vb/xoZ3Ltc9csTa6F1s1EcvdGDB8am76odGg0vXbBQF5XMWMZ/8OeFu1yOZjx/gD6RjsXfUlsuwAAIABJREFUBHXa69RklcmtnfuG+cSWe5Jqz3v26qxxH7k0fT6WM6/J+Vrtenx4+qIGq5cPprcjo82jY3m/2+GR9PqcvMidL/Vl1Ofk58KB9Bw/mPFZAAzMy1tGSLV84bys+pzM78n4LuW+u5GMzMhZ1mxVGwDm9Tq3KqYymZVj+6P7s+pzvv856y27njiYXPvI4+m1AOuGFiTX7htOX+cbzFzXeuDx9M/6OWNLssadI2cZeX5GVnzuu9uSa1/6zGck10Ir17Xyxrsv4zt9KCOXF83vwnWtzOWO/t68+maqU2ZNNQc+LSIODyAi4nPAS1vXJDMzm0qx5yntNsc4t8zMKsi5NSFnlplZBeVkVtVza6pNw1Pt2qjTlYrNzLqGyN+7OIc4t8zMKsa5NSlnlplZxdQts6YKk4clnX74QEkvAHa0rklmZjaVnozbHOPcMjOrIOfWhJxZZmYVlJNZVc+tqY7w+2/AVZI+AdxcDtsAvBG4YKYTlHQ3cAXwb4F5wGsi4oczHZ+Z2VxTo51Ozdb03HJmmZnNnnNrQl7XMjOroDpl1qQbJCPiW8DpFEc1vqm8CXhhRNw0y+nujIhTgQ8DvzPLcZmZzRmS6Mm4JYxvvqRvSfoXSbdL+qNy+LGSbpK0VdKnJfWXwwfKx1vL59c3jOvt5fA7JL28RR/BpFqYW84sM7MZanZu1YXXtczMqic3s6qeW1Ne3qW8TPzFLZjueAe1NwP/bqICSZuATQDMW9SCJpiZdacmX/BxGDgrIh6XNA/4hqR/AH4beF9EXCnpI8BFFCsOFwG7I+I4SRcA7wVeJ+nZFEcknASsBr4q6aciIv3yZE3QotyaNrPg6bm1/Ii8K++amdVZiy5U3PWqsq61dt26FjTBzKw71SmzOvVWhsu/o0yy0TEiNkfEhojYoL7B9rXMzKzCxjuSbdYepyg8Xj6cV94COAu4uhx+BfDq8v555WPK58+WpHL4lRExHBF3AVspjlyog2kzC56eWwuXrWhPy8zMKq7ZuWVJsta1Vg2tal/LzMwqLDezqp5bNdp2aWY2N2ReJn5I0paG26afHJ96Jd0KPAxcB/wYeDQiRsqSbcCa8v4a4D6A8vk9wMrG4RO8xszM5rDM3DIzM+uYnMyqem5NeUpvM0m6Fnhzu6ZnZlZLgp68YNkZERumKihPuz1F0jLgc8CJM29gPTizzMyaJD+3bAacW2ZmTVCzzJp0g5+kv6c4rWtCEfGqnAlFxMby7vqGYVuAM3PGY2Y214nWpFBEPCrpa8CLgGWS+sqj+I4Gtpdl24G1wDZJfcBS4JGG4eMaX9MWzcwtZ5aZWfO0Kre6mde1zMyqqU6ZNdURfn9a/v13wJHAX5WPXw881MpGHe6knzqaz3/lT5Jqn7FkIGvcI6NjybW9GZt6ly6Yl1y7fGF6LUDEpMsGP2FgXvpZ24dG0j8LgOcfsyy5VhnHuua8P4C9B0amLyotHOhNrn1w34Hk2jMG8vrrGh1Lf48537t7du7Lasfyhf3JtTmfXe53Kec9jmV8P3LGu3Ag74DnTsVA0a9EE8cnrQIOlRv7BoGXUVyI42vA+cCVwIXA58uXXFM+/mb5/D9GREi6BvgbSX9GcdGO44FvNa+lSSqTW4v7+/jZY4aSavv7Wte7Rs5vICcPc38vOf7/9u49Xo66vv/4633OyY0kkMsJEUIQVCwqKGIqWmuLUhDiBWoVtVbRoqkttFq1VVtbWy+Vamu9tD/7i4UKrVWx2p/UxgtF8NZqjaCAtxoRioCSAA13yMn5/P6Y74ElOXvOfPfs7M7MeT/z2Ef2zH525rszs/Oe287kZEDO5wOYzFj25sj9SuZkc46JjM+3oCZXpK5y/s9Znchc9WCsovGXs76UO+5y+t1P/c6tFqlNZuV4/3MenVWfs044Nlp+Rlm9rPw67JrleduHOTm0eEH59eN7M9ePf+nwtaVrRzK+ZDnbIQA779xVunZycfl+b7up/LbWMx+Vt96Rs06Tszzf9tPbZy/qsDpj3ttvSfnPmDsv5awvjUb52pzr1y1fnDcNh3VtvLZl1kwXH/8CgKS/3OPnYP8qaWvlLTMzs2n1OYQOAM6VNEpxXdfzI+JTkr4DfETSW4HLgLNT/dnAP0jaBtxMcWdeIuLbks4HvgNMAGcM4Q69zi0zsxpq08ZTvzizzMzqqU2ZVWY361JJD4mIqwAkHQosrbZZZmbWTT/P0oiIy4HHTtP9Kqa5y25E3A08t0u/3ga8rW+N651zy8ysRoZ1dmFDOLPMzGqkTZlVZoffq4BLJF1FcYbjg4G97vJoZmbVa9tp5hVxbpmZ1YRza1bOLDOzmmhbZs24w0/SCMUF2Q/j/rs2fi8i7ul1gJKOo7hmxRjwdeA359I/M7N5pQG3fx8m55aZWc04t7pyZpmZ1UzLMmvGK1RGxCTw+xFxT0R8Kz3mEkCLgQ8Cz4uIIymC6Dd77Z+Z2Xw0IpV+zDfOLTOz+ul3bkkalXSZpE+lvw+V9DVJ2yR9VNLC1H1R+ntbev2Qjn68IXX/vqSnVfCxZ+XMMjOrn5zMqvv2Vplb0vy7pNdKWi9p1dSjx+H9DPCjiPjv9Pe5wC9MVyhpk6StkrbefNOOHgdnZtYuU6eZl33MU0PPrVtuvqnHwZmZtUtFufVK4Lsdf/858FcR8TDgFuD01P104JbU/a9SHZIeSXHTqUcBJwL/J928ahiGnlnbd2zvcXBmZu2Sm1l1394qs8PvecAZwBeBb6RH5XeOiojNEbEhIjasWj1e9eDMzBpDKv+Yp4aeWytXra56cGZmjdHP3JJ0EPB04O/S3wKeCvxzKjkXOCU9Pzn9TXr9uFR/MvCRdGbdj4BtTHOjqgEZematGV9T9eDMzBojJ7Pqvr016007IuLQPg7v+8Ahkh4WEduAFwFf6GP/zcxaToxQ82QZMueWmVmd9D233g38PrA8/b0a+N+ImEh//xhYl56vA64FiIgJSTtT/Trgqx397HzPQDmzzMzqpF3bWl13+El6akR8XtKzp3s9Ij6RMyBJW4CXAS8FPiZp6kKyf5vTHzOz+UzU/0jSsDi3zMzqp4fcGpfUeYbb5ojYDCDpGcCNEfENScf2rZFD4MwyM6uftm1rzXSG3y8CnweeOc1rAWSFUERsTE+vBx6b814zM0sacK2IIXJumZnVTX5u7YiIDV1eexLwLEkbgcXAvsB7gBWSxtJZfgcB16X664D1wI/TDrD9gJs6uk/pfM+gOLPMzOqmZdtaXXf4RcSb0v8vHVxzzMxsNnW/G9SwOLfMzOqpX7kVEW8A3gCQzvB7bUS8UNLHgOcAHwFOAz6Z3nJB+vs/0+ufj4iQdAHwT5LeBRwIHAb8V18aWf6zOLPMzGqoTdtas17DT9Ii4FeAQzrrI+LN1TXrgRaMjnDAisWlanftjqx+Lxwrc9+Sat07MZlVP1bRDBjkjbuqjGTuUl+9bGEl7XjOYw4qXTs5mTfuqlqGHLRqSVb92Gg18//YsO5zNwejDVmut+008yrUIbcWLxjl8AOXz14IROaiVxXNADnLg2WLq5sJcz7fWOYXd9niWVd7epKbW8qY5jnjY1EN1mly5c7Po2067D5HTRkXA8qt1wEfkfRW4DLg7NT9bOAfJG0Dbqa4My8R8W1J5wPfASaAMyJid+WtnEYdMivHZGZoLV5QzUphzrrm3bvyJu0ClV+W5szbuXNYVV+b3GXH2v3KbYfn+r2nPKx0bV22tQ5ZszSrvqp9DVVtw1WpKdfF63dmSVoPnAespTh7e3NEvCfdjf2jFMv+q4FTI+KWdGOp9wAbgTuBl0TEpalfpwFvTL1+a0ScyyzKrPl+EthJcceoe8p/NDMzq0KbjjpVxLllZlYjVeRWRFwCXJKeX8U0d9mNiLuB53Z5/9uAt/W9YfmcWWZmNdLnzJoAXhMRl0paDnxD0oXAS4CLIuIsSa8HXk9x8OokirPODwOOAd4PHJN2EL4J2ECx4/Abki6IiFtmGniZHX4HRcSJvX02MzPrN+/vm5Vzy8ysRpxbM3JmmZnVSD8zKyJuAG5Iz2+T9F2Ku8KfDBybys6lOID1utT9vIgI4KuSVkg6INVeGBE3F23UhcCJwIdnGn6Zc0H/Q9KReR/LzMyqIIoFd9nHPOXcMjOrCefWrJxZZmY1kZtZObkl6RCKmyp9DVibdgYC/ITiJ79Q7Ay8tuNtP07dunWfUdcz/CRdCUymmpdKuoriNHMBERGPnvUTTd/fq4HzKU5VvAv41YjY1ku/zMzmHVV3DbemqyK3nFlmZnPk3JqWt7XMzGqot8wal7S14+/NEbH5Ab2VlgEfB14VEbd2DiPdTKqSGyrM9JPedcBRVQwU2BkRR0p6MfBu4Bl7FkjaBGwCWH/wwRU1w8ysebzZ1FVVuTVrZoFzy8ysG+fWtLytZWZWQz1k1o6I2NC1f9ICip19H4qIT6TOP5V0QETckH6ye2Pqfh2wvuPtB6Vu13H/T4Cnul8yW8Nm2uH3o4i4ZrYe9OjDHf//1XQFaY/oZoCjH7ehHrePNTMbMuGbdsygqtyaNbPAuWVmNh3nVle12dZ6nDPLzAzof2alu+6eDXw3It7V8dIFwGnAWen/T3Z0P1PSRyhu2rEz7RT8LPBnklamuhOAN8w2/Jl2+O0v6dXdXtyjsbmiy3MzM5uFN5u6qiq3nFlmZnPg3JqWt7XMzGqoz5n1JOBFwBWSvpm6/QHFjr7zJZ0OXAOcml7bAmwEtgF3Ai8FiIibJb0F+Hqqe/PUDTxmMtMOv1FgGdVk9PMoPuDzgP+soP9mZq3lEyW6qiq3nFlmZnPg3JqWt7XMzGqoz3fp/TLdl/PHTVMfwBld+nUOcE7O8Gfa4XdDRLw5p2czkbQFeFn6c6WkyykuTPuCfg3DzKzthBj1llM3fcstZ5aZWX84t7rytpaZWc20LbNm2uHX108ZERvhvjuevDMiXtfP/puZzRe+22FXfRsxziwzs/5xbk3L21pmZjXUpsyaaYffXqcXNsHCsZFhNyFbXdpcl3Y00chIPRYKY6P1mIb3Tkxm1Xvey1OPua2WapNbImdloXmXV6rJIi/baE0aXtWKZBNXUOsyTZqoSdO7OS0dqNpkVo7FC0aH3YRsdWlzXdrRRHXZ1qrLNsvdu3Zn1Xvey1OPua0/uu7wK3MBwF5ExCFV9NfMbF5QszbyBqmK3HJmmZnNkXNrWt7WMjOroZZl1kxn+JmZWc0IqMexRTMzs9k5t8zMrCnallne4Wdm1jBtOupkZmbt59wyM7OmaFNmDXyHn6QTgXcDdwJfBh4SEc8YdDvMzJqqPRFUf84sM7O5c24NjnPLzGxu2pRZAz1bUdJi4APAM4HHAQ8a5PDNzNpAKv+w3jmzzMz6w7k1GM4tM7O5y8msuufWoH+efDjwo4j4QUQE8I/dCiVtkrRV0tYdO7YProVmZjVWXFdCpR82J6UzCx6YW9udW2ZmgHNrwHra1nJmmZkVcjOr7rlV2+sRRsTmiNgQERvGx9cMuzlmZrXRzyNOktZLuljSdyR9W9IrU/dVki6U9IP0/8rUXZLeK2mbpMslHd3Rr9NS/Q8knVbV56+rztxa49wyM7tPW86UaBNnlpnZ9HyGX+++Bxwi6aHp7xcMePhmZg2nrH8lTACviYhHAk8AzpD0SOD1wEURcRhwUfob4CTgsPTYBLwfih2EwJuAY4DHA2+a2knYYM4sM7M563tuWXfOLTOzOcnLrLrn1sB2+EnaAqyi2ED8N0mXAjcOavhmZm3RzyNOEXFDRFyant8GfBdYB5wMnJvKzgVOSc9PBs6LwleBFZIOAJ4GXBgRN0fELcCFwIl9/NgD5cwyM+uftpwpUWfOLTOz/mjTGX4Du0tvRGxMT6+nuL4Eko4FXjuoNpiZNd3UdSUq6bd0CPBY4GvA2oi4Ib30E2Bter4OuLbjbT9O3bp1byRnlplZf1SZW3Y/55aZ2dy1LbMGtsNvLu7etZvvXX9bqdrv3XRrVr9PObL89uhklO/v7oziK6/dWb7HwNGHlv+V3GRGOyYj4wMCf/mFH5aufd1TDytdmzPuAO66d3fp2kVj5U9qzdlbf+Ot95QvBg5cuSSrvqzb7tqVVb9owWgl7cidl3Km+ehINQvgn+68O6t+7X6LK2nHrPKPJI1L2trx9+aI2LxXb6VlwMeBV0XEreoYSESEpLyJOs/dPTHJD396e6na5Yvzonh8+aLStfdOTJauHRstP2PlLvMOWFH++5Kz+Lh3d/nPB3DPrvL1+y4pP10yY4t7dpXPrQWj5XPrljvuLV27LHO+22dRNauMkZkXO+8sn3M5bc7NlpzqnGV2zui4NTPz912yIKu+bxpwBoSV9+4vll//B3jlkx9SujZnfTCn9ovbdpSuBTj+EWtnL+qhHbnLu5d/9Fula895wVGla3O3te64p3xmLVlYftsiZ7Fw3S13ZVTDIWuWZtWX9b8ZOQt54yNnqmTOSrXY1rr2pjuz6g9aVc328qxalllD3eEXEZcAlwyzDWZmTZMZQjsiYsPM/dMCip19H4qIT6TOP5V0QETckH6yO/WzoOuA9R1vPyh1uw44do/ul2S1tOacWWZmvWnTxlOTOLfMzPK1KbNqe5deMzObXj8vIqviVL6zge9GxLs6XroAmLrT7mnAJzu6vzjdrfcJwM7009/PAidIWplu1nFC6mZmZvNcWy5+bmZm7demm3bU4ie9kkYjovy5wmZm85SAPp9p/yTgRcAVkr6Zuv0BcBZwvqTTgWuAU9NrW4CNwDbgTuClABFxs6S3AF9PdW+OiJv72tIacW6ZmZVTQW5ZJmeWmVk5bcusoe3wk3Q18FHgeOAdwEeG1RYzsybp55GkiPgy3S+jctw09QGc0aVf5wDn9K1xNePcMjPrTd3PgGgjZ5aZWW/alFnDPsPvpog4eshtMDNrlDZdV6KBnFtmZpmcW0PjzDIzy9SmzBr2Dr+PdntB0iZgE8CD1q3vVmZmNu+06ahTA5XKrQOcW2Zm93FuDU2pzFp/8MEDa5CZWd21KbOGfdOOO7q9EBGbI2JDRGxYuWr1INtkZlZbU9eVKPuwviuXW6vHB9kmM7Pacm4NVanMWjO+ZpBtMjOrrdzMqntuDfsMPzMzy1L/u0GZmZndz7llZmZN0a7MGvgZfpK2SDpw0MM1M2sFFdeVKPuwuXNumZnNgXNroJxZZmZzkJlZdc+tgZ/hFxEb09NDBj1sM7M2qHmutI5zy8xsbpxbg+PMMjObmzZlln/Sa2bWIMV1JdoUQ2Zm1mbOLTMza4q2ZVYjdvhJYuFYuV8fP3rtiux+l66NKF1btr0AD1qxuHRtrpGMq0huu+H2rH6f/rP1uKPXWMZnzBkfoxm1+ywcLV0LEBnzUs48unRR3lf69nsmStfefe/u0rUrli7MakfOuK7Kin0WDLsJpbUog1prbESsWlbue3BXxncr1+KMZdPkZPnl0rLF1a0+5CynF2ZemWTXxGTp2pxl7wjlxx1UtyKZs+4xNpo37qrKrXszpgnAfhnL6owmVyprXTNj1sj5fkPed6vfnFvt8UuH5t2UKmtZmjGfjC0ovww7bHxZ+R5nylmH/dY1O7P6/baTDi9dW9VyBmBstJppmDPulmeud+Ss0+QsG/ddkre9sPOuXaVrb7+7/HbZ/vsuympHzriuKu/XLM9rc06/+61NmdWIHX5mZna/Nl1I1szM2s+5ZWZmTdGmzPIOPzOzhmnTUSczM2s/55aZmTVFmzJr4HfpBZB0lqTvSLpc0l8Mow1mZk2ljIfNnTPLzGxunFuD5dwyM+tdTmbVPbcGfoafpNXALwOHR0RIyrvonpnZfFf3ZGkRZ5aZWR84twbGuWVmNkctyqxhnOG3E7gbOFvSs4E7pyuStEnSVklbb7lpx0AbaGZWV8WRpPL/bM5KZRY8MLducm6ZmQHOrSHI3tbavmP7QBtoZlZXuZlV99wa+A6/iJgAHg/8M/AM4DNd6jZHxIaI2LBydd7doMzMWkvFdSXKPmxuymZWqr0vt1Y7t8zMCs6tgeplW2vN+JpBNtHMrL4yM6vuuTWMn/QuA/aJiC2SvgJcNeg2mJk1Wc1zpVWcWWZmc+fcGhznlpnZ3LQpswZ6hp+kLcB+wKckXQ58GXj1INtgZtZ4bbmKbM05s8zM+qSPuSVpvaSL000pvi3plan7KkkXSvpB+n9l6i5J75W0Ld3E4uiOfp2W6n8g6bT+fujBc26ZmfVBi+7aMdAz/CJiY3r6+EEO18ysPep/rYi2cGaZmfVD33NrAnhNRFwqaTnwDUkXAi8BLoqIsyS9Hng98DrgJOCw9DgGeD9wjKRVwJuADUCk/lwQEbf0s7GD5NwyM5urdm1rDeOmHWZmNgdtuaaEmZnND/3MrYi4ISIuTc9vA74LrANOBs5NZecCp6TnJwPnReGrwApJBwBPAy6MiJvTTr4LgRP7+LHNzKyBfA2/ARsbEePLF5Wq3XnnrsraMTJSzdQcG63HftfVyxZm1Y9WND5y+xuj5euravPEZGTVq6Ilw127dmfVL19cfhEwmtHmsYrGc5V2Z07D3Pp+acCZ45ZEyVlkxT4Lsvqbs/iIso0gL+OqWpbmyl2ULhyrJm9zl+k55TnTJWexlDsNq8qt3CVpVZ8x57tS1GeVV6IpWVtlbkk6BHgs8DVgbUTckF76CbA2PV8HXNvxth+nbt262wyqWo5CddtaVbY5x9r9FmfVj2Vs4+TIXf4vzNhWzdmuzVnu3rs7b6Fb1bx0xz0TWfU563g506XK/QdV5f2u3ZNZ9WO7h5NxbdvWqsfSz8zMymvJNSXMzGyeyMutcUlbOx6bpu1lcXOKjwOviohbO1+LYk9CDXbLmplZ4/T5Gn6SzpF0o6QrO7oN5LqzQ9vhJ+lVkvYZ1vDNzJpKGf+sf5xbZma9ycytHRGxoeOxea/+SQsodvZ9KCI+kTr/NP1Ul/T/jan7dcD6jrcflLp1694Kziwzs97kZFbJ7a0PsvclI15Pcd3Zw4CL0t/wwOvObqK47iwd1509huI6rW+a2kk4k2Ge4fcqwCFkZpapLdeUaCDnlplZD/qZWyp+b3Y28N2IeFfHSxcAU2c8nAZ8sqP7i9NZE08Adqaf/n4WOEHSyrTRdELq1hbOLDOzHvT7Gn4R8UXg5j06D+S6s0O5hp+k3wEOBC6WtCMinjKMdpiZNZH34w2ec8vMrHd9zq0nAS8CrpD0zdTtD4CzgPMlnQ5cA5yaXtsCbAS2AXcCLwWIiJslvQX4eqp7c0TsuUHWSM4sM7Pe9ZBZ45K2dvy9ebqz0/cwkOvODmWHX0S8V9KrgadExI5htMHMrJF8bb6hcG6ZmfWoz7kVEV+eoY/HTVMfwBld+nUOcE7/WlcPziwzsx71llk7ImJDr4OMiJBUyXVna3vTDkmbpi7We/NNzikzsym+hl89debWTTucW2ZmU5xb9dOZWdt3bB92c8zMaqOCa/hNZyDXna3tDr+I2Dx1sd5Vq8eH3Rwzs1oQvoZfXXXm1upx55aZGTi36qozs9aMrxl2c8zMaiE3s+aQWwO57uxQftKb3AYsB3wahJlZBm8PDY1zy8ysB86toXBmmZn1oN+ZJenDwLEU1/r7McXddgdy3dmBn+EnaYukA4HNwGckXTzoNpiZNZoyHrP1SjpH0o2SruzotkrShZJ+kP5fmbpL0nslbZN0uaSjO95zWqr/gaTTphtWUzm3zMzmqI+5ZTNzZpmZzVFOZpW7S+8LIuKAiFgQEQdFxNkRcVNEHBcRh0XEL03tvEt35z0jIh4aEUdGxNaO/pwTEQ9Lj78v81EGvsMvIjZGxPUR8b6I+BnfNcrMLE+frynxQfa+pfvrgYsi4jDgovQ3wEnAYemxCXg/FDsIKY5UHQM8HnjT1E7CNnBumZnNja/hNzjOLDOzuRnQNfwGYpg/6S1tdEQsXTRaqnafheXqpuyerORmKBQ3BCtn5T4LKmlDdjuWLszq92RGv3PkTpNdu3PqJ0tXTmT0d3z5oow2VCd3ktx290Tp2qWLyi8uRkbyFnw582nO7JHTjH0yPh/ktbnf+nmNo4j4oqRD9uh8MsVp5wDnApcAr0vdz0t3PPyqpBXpIrPHAhdOHZmSdCHFTsQP96+lzSLBgtFqVgBU0UWucubpRWN5xwuranNuhA/xa/sAY6Plx99oxoJs3yUZy+marJ8uzBgXkLf8y1mfyB0fuTlXhZx5Y9h8bb72OGTN0qz6id3l171z5CzPq1xPn8xYzuy/b147qtrWyp0m906Ur89pcU47DlixOKPP1cld77jljl2la/fL2CeQu/zPWcfLyc6cdixfkrfPI+e71W9tyqxG7PAzM7P7DSCD1qaLwwL8BFibnq8Dru2o+3Hq1q27mZlZzc9/MDMzu1+bMss7/MzMmiYvhcYlbe34e3NEbC775ogISTU5L8rMzBqpTVtPZmbWbi3KLO/wMzNrEAlG8s4z3xERGzIH81NJB0TEDeknuzem7tcB6zvqDkrdruP+nwBPdb8kc5hmZtZCPeSWmZnZULQts4Zxl96rJY2n5xskXTLoNpiZNdkAbnZ4ATB1p93TgE92dH9xulvvE4Cd6ae/nwVOkLQy3azjhNSt8ZxZZmZz55v0Do5zy8xsbvp8k96h8hl+ZmZN08dkkfRhirPzxiX9mOJuu2cB50s6HbgGODWVbwE2AtuAO4GXAkTEzZLeAnw91b156gYeZmZmtd8iMjMzm9KizKrtDj9Jm4BNAOvXHzzk1piZ1UV/b/8eES/o8tJx09QGcEaX/pwDnNO3hjVQZ24d5NwyM0v6m1vWHw/Y1jrYmWVmVmhXZg38J73ARMdwu95fOyI2R8SGiNgwvmbNYFpmZtYAUvmHzVmpzIIH5tbq8fHqW2Zm1hDOrYHK3tZaM+5tLTOzKTmZVffcGsYOv6uBx6XnvzIr729wAAAgAElEQVSE4ZuZNVabrinREFfjzDIz65lza+CuxrllZtaT3Myqe24NbIefpC2SDgT+FHiPpK3A7kEN38ysNdqSQDXmzDIz6yPnVuWcW2ZmfdKiPX4Du4ZfRGxMT68HHj6o4ZqZtU2britRV84sM7P+cW5Vz7llZtYfbcqs2t60Y08q+ePonXfem9Xf1csX9dKcEsrPJLfckdfmlWMLy7ci40flub8/v+Hmu0vXrlu1pHTt6EheQ5YsHM2qL2sso7cTuycz+13NybVLF+WNi5z5o0o57RitqMl1mYZl1GSy2QwksWhBue+jJ2fvcvNiZEE139vifjZV1Zf/jHfdW/5knn0W5a0CVrXs3T2ZN+7GMhqSM3vUJQ9zZM52Q82OBo5e6+Lm2/O2Wx60YsbL3w7E9lvvyapfs2/57cORzBzKcc2Nd5auPXT/paVrc9dhq1rnXThWvr/3TuStp+f0O8fyxXnZWeX8kSMn43JyNoe3tYajMTv8zMys0KIMMjOzecC5ZWZmTdGmzPIOPzOzJmnA3aDMzMzu49wyM7OmaFlmeYefmVnjtCiFzMxsHnBumZlZU7Qnswb6w2hJJ0r6WMffx0r61CDbYGbWZKI46lT2YXPj3DIzmxvn1uA4s8zM5iY3s+qeW4O+EuK/A8dImrqq6POAjwy4DWZmjdaSu8Q3hXPLzGyOnFsD48wyM5ujnMyqe24NdIdfREwAnwGeKWkMeDrwyelqJW2StFXS1h07tg+ymWZmtdaWI05N0Gtu3bTduWVmNsW5NRi9ZtZ2b2uZmd3HZ/jNzUeAU4GnAlsj4rbpiiJic0RsiIgN4+NrBtpAM7M6U8Y/64vs3Fq9xrllZjbFuTVQ2Zm1xttaZmb3ycmsuufWMHb4fQE4Gng5PsXczCxfW84xbw7nlpnZXDi3BsmZZWY2Fy36Te/AdvhJ2iLpwIjYDXwKOCn9b2ZmGVqSP7Xn3DIz6w/nVvWcWWZm/dGi/X2D2+EXERsj4vr0/MyIWBYRdw5q+GZmbdCma0rUnXPLzGzunFuD4cwyM5u73Myqe26NDbsBZmaWp+7XijAzM+vk3DIzs6ZoU2Y1ZoffSMlxvnRRYz7SfZYvbl6bAdbut2jYTajU5GSUrh0tO4NWTHU/xFBjI00adw1q6nwlyi8Xmrj8GB3GFYBrLHfZW9XiZsnC0dK1dZnvFozlzUwR5bO57ZnYqI/XpLbajJYvad52y377LBh2E4C85RfAQauWVNSSesjZ1lowWo+FSF2Wu7nzUh3ysA5tKK1BTZ1N85bYZmbzXIsyyMzM5gHnlpmZNUWbMss7/MzMGqZJB8jMzMycW2Zm1hRtyizv8DMzaxS16roSZmbWds4tMzNrinZl1kCuwiPpZyV9Ij0/WdJdkhZKWizpqkG0wcysDUR77hpVZ84tM7P+cG5Vz5llZtYfuZlV99wa1Bl+lwFHpedPBq4EfjYN/2vTvUHSJmATwPqDDx5AE83MmqHuwdISzi0zsz5xblXOmWVm1idtyqyBnOEXERPADyU9Ang88C7gFygC6Utd3rM5IjZExIbx8TWDaKaZWSMo45/1xrllZtY/zq1qzTWz1jizzMzuk5NZdc+tgezwS74InATsAv4d+Pn0mDaEzMxsGi06xbwBnFtmZnPl3BoUZ5aZ2VxlZlbdc2uQN+34EnAecF5EbJe0GlhLccq5mZmVINp1q/iac26Zmc2Rc2tgnFlmZnPUtsyq/Aw/SVskHUhx/Yi1FEefAC4HroiIqLoNZmatooyHZXNumZn1mXOrMs4sM7M+y8msmudW5Wf4RcTGjj8XdXTfVPWwzczaqO7Ximg655aZWX85t6rjzDIz6682ZdYgf9I7JxO7yx2cWjA2yMsS9sfoSDNnKNXkB+s5By5z2jySMV2aevA0p905HzFn3FUpb96osCF91qS2zmdlvwa5y486LHtzF3k1aDJQj3EH1eX+SMbna+J8B/VpRx00aVw0qKk2i0UN3NZaMFqPGTD3OyvVY/vC21r3y52GOe2ezPiIddl/kPP5atLkUtqUWc1bYpuZzXP9PsNc0omSvi9pm6TXV9BkMzObx5xbZmbWFP3+Re8wM8s7/MzMmqaPCSRpFPgbijv7PRJ4gaRHVtFsMzObp5xbZmbWFH3c4zfszPIOPzOzhlHGvxIeD2yLiKsi4l7gI8DJlX4AMzObV5xbZmbWFDmZVSK3hppZA93hJ2m/dCrjz6S/Pyzp5YNsg5lZk4niuhJlHyWsA67t+PvHqZvh3DIzmyvn1uA4s8zM5iY3s0rk1lAza6A37YiInZLOBD4o6T3Ayoj4wCDbYGbWZJde+o3PLlmg8Yy3LJa0tePvzRGxud/taivnlpnZ3Di3BseZZWY2Nz1kFtQ4twZ+l96IuFDScyl+x/yYbnWSNgGbANavP3hArTMzq7eIOLHPvbwOWN/x90GpmyU95dbBzi0zM3BuDZozy8ysd23LrIFfw0/SCPAI4E5gZbe6iNgcERsiYsP4mjUDa5+Z2TzzdeAwSYdKWgg8H7hgyG2qlZ5ya9y5ZWZWEefWDHrJrDXOLDOzqgw1s4Zx047fBb4L/Crw95IWDKENZmYGRMQEcCbwWYpl8/kR8e3htqp2nFtmZjXh3JqVM8vMrCaGnVkD+0mvpC3AnwMvAx4fEbdJ+iLwRuBNg2qHmZk9UERsAbYMux1149wyM6sn59benFlmZvU0zMwa2A6/iNiYnj6io9urBzV8MzOzHM4tMzNrCmeWmZntaeA37ejF7XdP8JUf3lSq9p7du7P6fcIj1pau3T0ZpWsndpevLV9ZWLJwtHy/o3zfVeKe0p1yxsfoSPl+5/QX4Nqb7ixde9CqJaVrt159S+nao9avKF0LsDhjGua46968+T9nXOfMdxO7J7PakWNstJorEeTO/5OZ86nNL/fsmuQHP7m9VO3qZQuz+r16+aJemjSrnLy45c5dWf3O/Yxl3X73RFZ9zrd86eLqVpF2TZRfRubk54233lO6dlXmNFk4lreMLKvKvBjJWK5nRkB2ZlQhN4dq0GRrgX+89H+y6k/b8ODStTnrpfdmLEcz4g3IW/7nZGeuiaxtrfK1udta2356R+nah+6/tHTtZ7/3k9K1Tz1s/9K1APssqibD77gnb70jZ5/A0kXlt7V2TVS3/K9qWytX7nxq06vH1DQzMzMzMzMzM7O+8A4/MzMzMzMzMzOzFvEOPzMzMzMzMzMzsxYZ6A4/SU+V9P86/j5e0r8Msg1mZmZlObfMzKwpnFlmZtZp0Gf4XQwcLmlN+vulwDkDboOZmVlZzi0zM2sKZ5aZmd1noDv8oriN0T8AvyZpBfBE4NPT1UraJGmrpK07byl3h14zM7N+6jW3brl5xyCbaWZm1nNmbd+xfZDNNDOzAanmftUz+3vgX4G7gY9FxLT3to6IzcBmgIc/6ijfk9nMzIYlO7ce9eijnVtmZjYM2Zn1uMdtcGaZmbXQwHf4RcT1kq4H3gj80qCHb2ZmlsO5ZWZmTeHMMjOzKQP7Sa+kLZIOTH9+CLg2Ir47qOGbmZnlcG6ZmVlTOLPMzGxPAzvDLyI2dvz588AHBjVsMzOzXM4tMzNrCmeWmZntaeA/6ZX0DeAO4DWDHraZmVku55aZmTWFM8vMzKYM4xp+j8t9z7LFYzzpoatL1W6/7Z6sfksqXTs2mlNbvg133D3ttXT7IufzFTf2Km/X7snStQvHyv96fHSkfJsBDlq1pJJ+P6HkPAcwkTEuqrR4Qd6v9HPmjxyTmaNjJHOal5Xz+XKnYe58as3VS24tWjDCoWuWlqrN/RrmLKtzvgM5tfsuHsY9v/a2dFFG2AKTFV2WfjKzxxUtehlfvrB0bV2WYZmrHizIWJ+oUlXfwxyTmSNvbKQe486q1Utm5Tj2kDVZ9dVta5Wfn2+9a1fp2lw5ny83K+6dKL9uunhB+TzMGc8AD1tbbn0G8rLlGUccOHtRUpdtrX0W5q13VLX83505L1WV+Tmfb1fG/Az1WU9pOie/mZmZmZmZmZlZi3iHn5mZmZmZmZmZWYsM4xp+G4C/S3+OAkdEhM/XNDOzWnJumZlZUzizzMxsyjCu4bcVOApA0juBzwy6DWZmZmU5t8zMrCmcWWZmNmVoV92W9DzgaOCELq9vAjYBrF9/8ABbZmZmtjfnlpmZNUVWZh3szDIza6OhXMNP0hHAnwDPj4jd09VExOaI2BARG8bX5N0NyszMrJ+cW2Zm1hS5mbVm3JllZtZGA9/hJ2kF8GHgxRGxfdDDNzMzy+HcMjOzpnBmmZnZlIH+pFfSFuCjwIOBD0jF9WMj4qhBtsPMzKwM55aZmTWFM8vMzDoNdIdfRGxMT88d5HDNzMx64dwyM7OmcGaZmVmnoVzDz8zMzMzMzMzMzKqhiBh2G2YlaTtwzTQvjQM7Svamqtq6tKOJbXY7eq91O3qvHUY7HhwRviL2PNIlt+o+n9a5HU1ss9vRe63b0Xttv/rt3JpHhrCtVWW/m9iOJra5Lu1oYpvdjt5ru9U7s7qJiMY+gK3Drq1LO5rYZrej+W2uSzua2GY/5t+jqfNpHdrRxDa7Hc1vc13aUZc2+zH/Hk2cT5vYjia2uS7taGKb3Y7BtdmP8E96zczMzMzMzMzM2sQ7/MzMzMzMzMzMzFqk6Tv8Ntegti7taGKb3Y7ea92O3mvr1A6bX5o6n9ahHU1ss9vRe63b0Xtt1f22+aWJ82kT29HENtelHU1ss9vRe20v9fNaI27aYWZmZmZmZmZmZuU0/Qy/eU/SPsNug5mZWRnOLDMzaxLnlpk1mXf4DYCktZJUQX+fBbxP0sJ+97sKktZL+sCw22FmZjOrIreallng3DIzawrnljPLzPbmHX6JpJH0f7+DYh3wRuAFOf2erVbSauB3gD8HDpK0ak4N3bv/peYNSWNl+xkR1wJ/Lekhw2yHdSdpv2G3YUodpqmkB0taXLL2qKrbY9apTrnVlMxKtc6tFqlLbtVlejq3rK6qyqzUT+cW1WZWTjtsZs6tB7TBmVWxebHDT9KS2WoiYjI9fbCksTJhIekRJQZ/PfAN4LHAszNC7sA0jG5fxHuBCeBNwLuAyS51e5H0oNnaMTU+JB3WrVbSGuATJceVUn+/Bbxf0mVl2trRjlMkHdCl3+PAtn4HcZdhdR0f09RW8hOAMtNvj/ozJJ1You63gFdI2neGmoWSHpmeH9dtmnR5789L2lRyfnkS8ML0vGx9mTZsTCtwZWr3B34PmLVe0ospvguHl+m32UzKZBbULrdqn1np9XmVW3XIrNTv0rlVNrNS7Yy5VdfM6nhPmTrnltVeVdtaqd/OrRK5VWVmpdedW91rnVv31zizaqb1O/wknQm8Q9LbNc3edEk/J+n56flvA58AzgFerxmOeEj6TeCdktbOUKMo7ooyAjwSeB1wcokF9pnA30o6C/gtSYv2rImI24DPAycDl0fE/5b8cj8aeDPwK9PVTzM+/g04W9Jz9qyPiO3A84HjZ1v4R8fdYSLiacC1kr44Qzvva0fySmC0S793AL8N/IeklTO1o8uwTpb0S5KWzVJ3JjOMjz1qXwH8jaT/K+kpswWSpCdJeomkJ8wy3804/aapPxk4DvjOLHW/AZwG/FNE3DrDys/BwLsl/QPwaoqVodnaMPV5HgI8Gvi1WcadgGOAp8AD550u9cuB0yWdOkPN1Od5A/DU2dqc7KD4vL89y/BfSTF/3gZkz39mnWbLrFRTq9xqSmaldsyb3KpDZqXa0rlVNrNSbZncql1mpfc4t6w1qtrWSvXOrZK5VWVmpX72nFtN2tZKtc6tvd8zY245s+qr1Tv8VOxBfy5wFvDrFNdgOGyPspXA2yW9BXhiqv8gsAQ4a7oFgorrObwCOCMiftpt+BERkl5IMRP/AfAfFF+srgsPSacApwIvovgiPjwi7ukyiI9ShNBzJf1uiR0jzwTeBzwK2NSlHVPj40+AxwAnpXY/EXjxnvURcSfFuLpC0oqZhp/aMJLe9yzgdklf6FK6EvizjiDahxnm14j4V+B3ga05IZT6/7fA8cBnuwVpmuaPZpbxkWp/BTgDeA9wO3AiMxxtlPRzwN9RzBuvAP6yy3w3Nf2OoPv066xfB/w1cHtE/I+6HE1VcVT2JOCPgTvTytVfp+/PA0TENuByivnu0xFxk6TRWcLwoen/fwS+RHH0tdu42yfNx+8FHpaCvytJj0wrZF8C9vxudzo4/X8xcE96b7fpsU7Sz6QjnmcCa9XlaJKkpwDPoZgfPgAcOlN7zWZSMrOgRrnVtMxKn7H1uVWHzEq1pXOrbGal2lK5VbfMSu9xbllrVLWtlfrt3Lr/M5bOraoyK/UzO7eatK2Vap1be7+nTG45s+oqIlr5APal+GIfQHH9hc8A5wGfBg7bo/Z44ErgQ+nvhRQz87nAEdP0+xXAH6TnC2Zpx5uB3+vo728Dl1AEjaapPw14NvAS4HPAwtT94TMM47HAfwNnzlCzFvgycHjHZ/hr4JQ925HGxxXABzra/XzgL4Hf6NLuE4EfAitLTJuRjudbgC90qTuJ+xd4bwH2T9N1pNtw0nvKtuNg4HnAQ9PfvwZsBVbtUbcO+B/gnDLjg2Jl47UdtS8DPjI1LfeofTzFgvEJ6e9DgLcCr96jbv80/R65x/Q7ebrp0fG+ZwM/BU7t6Dbd9NsEXAZcALwd+C3gb7q0+WFpXF0KvLCj+7Iu4/hq4EXp7zGKFazPUawYqqP2KcCfAs9If58A/AnFCs50bX4icE2aBg+nWDl4+TR1RwDfp/g5xjeBrwFHU6yM7bNHG5YCf0VxRHdTqvlb4Ljpxh0wDoyn528H/rxjvD9rtnnQDz+mHmRkVqqvRW7R0MxKda3MLWqQWem17NyiZGal7qVyi5pkVqpxbvnRmgcVbmulGufW3sMolVtUmFkd7yvTjsZsa6XXnFt793/W3MKZVevH0Bsw5w8wzRe747VFFEdOLk5/i+LU0TdP88U6GbgFeF5Ht08Az52mvydRhNrPdHR7EXDKNLWnAP8PeFRHt69SXAB2+TT1v0ixAP1SR7ffAd7BDGFHcURkB/AbXV5fmb6gT0p/L0hfri8AJ05TfzJwI/D89PcIRUD+GbBfl2GcBHyPPRbiXWo7g+gTU1/eaeqenvo5SbFScEmq/zCwT6/tSOP0axSnX78MWJy6v5BpAiwtVK4vMz7SNP8kKSxSt4uAR0zTjuOB3dy/QjMGbAT+bprp91XgyXtMv69QHPWYaaffMyjC/NQZahYDPzs1zihC9uJu4zjVPBP4VppGJ1B8r8a61F0KvKCj26eBv+gcdxSnob8sTZPfpThydwlwzDT9XAgclObpS4FnUawUXAE8bs95jeLI1+EUQTE1L11I8ZOSxdOMi6Mpjur+IUWIfx1YN8s8fTLFUarjUv1eO2n88COie26RkVnp9aHnFg3OrI7x0rrcYsiZ1TENs3OLEpmV6rJyiyFmVqp3bvnRyAdD2NZKrzm3uo+XWXOLCjOrTDto2LZWxzR0bt1fXyq3cGbV+jH0Bsyp8cXM8k/AohlqDqM4/fTI9GX8KHBwl9pnAFdR7Ok+Jc3MD52mbl+KoyBvT+95QZrpHjZN7QrgbelxXKr/924zNLCMYu/4XwDHAi+muAjttEe/9njvEdO1t+P11wB/NNUv4GnApyhOq99rHKaFy+U8cMG7107KPd5zclogjJRo70j6/9fTQmGvBVh6/TiKoyFnpDYsBdb32o40bc+jOErxDorTwY+dGj7FkahDex0faZq/NU3z4ykWjl8nHZ3o0tZtpIU0xYrIf1EcZeo8IvJqitPAp6bfCcDHgXdShOdMO/1OAq4Ffnm2aQKcnub9MvPciWmcbKUjdKep25jqfp0i0D8HHNil9uFpPn0rRWB8lCKclV5/IsXRqUdRBNcFFEdpX57q/wgY7dLvJRTfr1UU1ynpGizAfmka/FEaz09M3buF/THAXRRHth7Vrb9+zO8Hs+QWGZmV6oeaWzQ8s1JdK3Or7PigosxKr/WUW5TMrI7PVSq3GEJmpRrnlh+NfDCkba1U69zqPpxSuUWFmTVTO2jotlZ6zbkVvecWzqzaPYbegDl/AFg6y+uLKC7eeiHw7Zm+KKn+FIqjAJ8EHjJD3QEUp/luoTgC8ugZag+k2Bv9+fTl61rb0e/foLho6XnAkX0aVwdRBOen0wLy+xSnOV8APKbLe6YWXs/JGM5epxvPUn9sienyNODHzHLkZLZ2cP8p42envxencfI+isCYNghzx0fHNP8cxVGyacdvR/0zgVuBfwbOZ5pTlNP0e2vq59soflpwAsWC/Sxm3xl7/EzzdKrZB3gp0xwhm+E9a4A1Jep+keIo0pYS42NRmjZvZI+fWKTx8DKK4Pv19F15dnrtdLqvOIpiBebrpCN3GZ/xD4HNs9Ssoji67KNNfsz4YIbcIjOz0nuGmls0PLPS+1qZW2XHBxVkVsc07Cm3KJFZqS4rtxhwZnWMB+eWH418MKRtrVTr3Oo+rNK5RUWZNV07aPC2Vsc0dG71kFs4s2r5mDpjptUkLQAeBExGxHUl6n8RuCYiri5RuxAgIsrcQWcpxR7r22dtNPe1m4jYVaa+ZD/3BX6O4vT7LRQLnA8Ax0eXC+JKOh74YURc1a929KJf7ZD0bIrrMbwmIj6s4q5C76A4WvHHUVwYty/tUHHHKEXEHSVqn0VxqvaHIuKdUxc6jY4vqYo7JD2R4gjjZRFxsaRRivD539mGUYZ0393O+i6Nj4iIu+baBkmPoTjqu5wiBEvdpl3S71OM4zLLAkVEpAsOv5TiZyRd2y5pUXS/8LNZKbmZld4z9NxyZlXXlrnk1jAzK9U1Nrf6mVmpzrllrVTltlaqd24NQB0yK7cdzq29+jv03HJm1cu82OFn00t3vXk7xbUovjXs9gySpKdTfPa3dwTRyojYPuR2nUBxrYPfiYhPlKgfjYjd1besniTtT/EzhFdSnP5/dYn3jEXERMYwRPHTkB9FxJW9ttXM5mY+ZxbUM7dyMyu9x7nl3DKbF+ZzbtUxs1K7nFuZcnPLmVUv3uE3j0k6gOKCutcMuy3DIOkkYDPFXZo+Nuz2TKnLUb4mkbSgn0dnzax+5ntmQT1zy5nVG+eWWfvN99yqY2aBc6tXzq1m8g4/m9e8wDczsyZxbpmZWVM4s8yGyzv8zMzMzMzMzMzMWmRk2A0wMzMzMzMzMzOz/vEOPzMzMzMzMzMzsxbxDj8zMzMzMzMzM7MW8Q4/MzMzMzMzMzOzFvEOP5sTSbslfbPjcYik/8jsx6sk7TNN939J/dwmaWfHMH6uD+1+uKQtkn4g6VJJ50taK+klkv56rv3PaMcKSb81qOGZmc13zq05t8O5ZWY2QM6tObfDuWXzlu/Sa3Mi6faIWFaibiwiJrq8djWwISJ2dHn9WOC1EfGMubS1o3+LgSuAV0fEv3YMYwewIbXlzB77PRoRuzPqDwE+FRFHZLxHFN/dyfwWmpnNb86tvfrt3DIzqzHn1l79dm6ZleQz/KzvJN2e/j9W0pckXQB8R9JSSf8m6VuSrpT0PEm/AxwIXCzp4hL9viIdpZGkmyS9OHU/T9LxkhZL+vtUd5mkp0zTm18F/nMqfAAi4pKIuDL9eaCkz6SjUe/oGPb7JW2V9G1Jf9rR/WpJfy7pUuC5kl4u6evpc3586mhaOqL1L6n7t9KRs7OAh6Yjae9Mdb+X3n/51HDSkbzvSzoPuBJYL+mDaTxeIel3S08gMzN7AOeWc8vMrEmcW84tszLGht0Aa7wlkr6Znv8oIn55j9ePBo6IiB9J+hXg+oh4OoCk/SJip6RXA0/pdsRpD18BngRcA1wFPBk4D3gi8JvAGUBExJGSDgc+J+nhEXF3Rz+OAL4xwzCOAh4L3AN8X9L7IuJa4A8j4mZJo8BFkh4dEZen99wUEUenz7U6Ij6Qnr8VOB14H/Be4AsR8cupH8uA16fxc1SqPwE4DHg8IOACSb8A/E/qflpEfFXS44B1U0eqJK0oMe7MzMy55dwyM2sW51bBuWWWyWf42VzdFRFHpcee4QPwXxHxo/T8CuD4dHTmyRGxs4fhfQn4hfR4P3CkpHXALRFxB/DzwD8CRMT3KILq4ZnDuCgidqbQ+g7w4NT91HRU6TLgUcAjO97z0Y7nR6QjbVcAL0y1AE9NbSYidnf5/Cekx2XApcDhFMEDcE1EfDU9vwp4iKT3SToRuDXzM5qZzVfOrYJzy8ysGZxbBeeWWSbv8LOq3TH1JCL+m+II1BXAWyX9cQ/9+yLFUaYnA5cA24HnUARTWd8GHjfD6/d0PN8NjEk6FHgtcFxEPBr4N2BxR90dHc8/CJwZEUcCf7pH3WwEvL0j1B8WEWfvOYyIuAV4DMU4eAXwdxnDMDOz7pxbzi0zsyZxbjm3zKblHX42MJIOBO6MiH8E3kkRRgC3AcvL9COd6j0OHBYRVwFfpgiGL6aSL1Ec5UHSw4GDge/v0Zt/An5O0tM72vYLkma6kOu+FAGwU9Ja4KQZapcDN0haMNWW5CKK0+CRNCppP/b+7J8Ffl3SslS3TtL+ew5A0jgwEhEfB97I/ePSzMz6xLnl3DIzaxLnlnPLrJOv4WeDdCTwTkmTwC7SwhjYDHxG0vURMd1FX/f0NWA0Pf8S8HaKIAL4P8D70+ndE8BLIqLzCBIRcZekZwDvlvTu1JbLgVd2G2BEfEvSZcD3gGsprm3RzR+lNm5P/08FzCuBzZJOpziS9ZsR8Z+SviLpSuDTEfF7kh4B/KckgNuBX0v1ndYBfy9paqf9G2Zoj5mZ9ca55dwyM2sS55Zzy+w+iohht8HMzMzMzMzMzMz6xD/pNTMzMzMzMzMzaxHv8DMzMzMzMzMzM2sR7/AzMzMzMzMzMzNrEe/wMzMzMzMzMzMzaxHv8DMzMzMzMzMzM2sR7/AzMzMzMzMzM4aYFIcAAAAjSURBVDNrEe/wMzMzMzMzMzMzaxHv8DMzMzMzMzMzM2uR/w/j/8XS8k0Y7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create subplots\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n",
    "\n",
    "# Iterate over alpha values\n",
    "for alpha, ax in enumerate(axes):\n",
    "    im = ax.imshow(N.sum(axis=alpha), cmap='Blues')\n",
    "    ax.set_xticks(range(27))\n",
    "    ax.set_yticks(range(27))\n",
    "    ax.set_xticklabels([itos[i] for i in range(27)], rotation=45)\n",
    "    #ax.set_yticklabels([itos[i] for i in range(27)])\n",
    "    ax.set_yticklabels([itos[j] + itos[k] for j in range(27) for k in range(27)])\n",
    "    ax.set_xlabel('First Two Characters')\n",
    "    ax.set_ylabel('Third Character')\n",
    "    ax.set_title(f'Trigram Counts (Alpha={alpha})')\n",
    "    \n",
    "    # Add colorbar\n",
    "    cbar = plt.colorbar(im, ax=ax, orientation='vertical', shrink=0.6, pad=0.05)\n",
    "    #cbar.set_label('Trigram Count')\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "835756b3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(18, 6), sharex=True, sharey=True)\n",
    "\n",
    "# Loop through each axis and plot the trigram counts\n",
    "for i, ax in enumerate(axes.flatten()):\n",
    "    ax.imshow(N.sum(axis=i), cmap='Blues')\n",
    "    ax.set_title(f'Trigram Counts (Axis {i})')\n",
    "    ax.set_xlabel('Second Character')\n",
    "    ax.set_ylabel('First Two Characters')\n",
    "    ax.set_xticks(range(27))\n",
    "    ax.set_yticks(range(27))\n",
    "    ax.set_xticklabels([itos[i] for i in range(27)])\n",
    "    ax.set_yticklabels([itos[i] for i in range(27)])\n",
    "    ax.tick_params(axis='both', labelsize=8)\n",
    "\n",
    "plt.colorbar(im, ax=axes.ravel().tolist()[0], label='Trigram Count')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "39249d8a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the trigram counts\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 6), sharex=True, sharey=True)\n",
    "\n",
    "# Loop through each axis and plot the trigram counts\n",
    "for i, ax in enumerate(axes.flatten()):\n",
    "    ax.imshow(N.sum(axis=i), cmap='Blues')\n",
    "    ax.set_title(f'Trigram Counts (Axis {i})')\n",
    "    ax.set_xlabel('Second Character')\n",
    "    ax.set_ylabel('First Two Characters')\n",
    "    ax.set_xticks(range(27))\n",
    "    ax.set_yticks(range(27))\n",
    "    ax.set_xticklabels([itos[i] for i in range(27)])\n",
    "    ax.set_yticklabels([itos[j] + itos[k] for j in range(27) for k in range(27)])\n",
    "    ax.tick_params(axis='both', labelsize=8)\n",
    "\n",
    "plt.colorbar(im, ax=axes.ravel().tolist()[0], label='Trigram Count')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "281c4493",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['..', '.a', '.b', '.c', '.d', '.e', '.f', '.g', '.h', '.i']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[itos[j] + itos[k] for j in range(27) for k in range(27)][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d15b3614",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "slice_index = N.shape[0] // 2\n",
    "slice_data = N[slice_index, :, :]\n",
    "\n",
    "# Plot the slice\n",
    "plt.imshow(slice_data, cmap='viridis')\n",
    "plt.colorbar()\n",
    "plt.title('Slice of 3D Tensor')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6231c09a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create grid coordinates\n",
    "x, y, z = np.meshgrid(np.arange(27), np.arange(27), np.arange(27))\n",
    "\n",
    "# Plot 3D tensor\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(x, y, z, c=N.flatten(), cmap='viridis')\n",
    "ax.set_title('3D Tensor Visualization')\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_zlabel('Z')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0470da0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'.emma.olivia.ava.isabella.sophia.'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "__words = '.'+'.'.join(words[:5])+'.'\n",
    "__words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "edeb0d90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('.', 'e')]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "__bigrams = [__words[i:i+2] for i in range(0, len(__words)-1)]\n",
    "__bigrams = [(s[0], s[1]) for s in __bigrams]\n",
    "__bigrams[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "29838aab",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27,\n",
       " FreqDist({('a', '.'): 5, ('i', 'a'): 2, ('.', 'e'): 1, ('e', 'm'): 1, ('m', 'm'): 1, ('m', 'a'): 1, ('.', 'o'): 1, ('o', 'l'): 1, ('l', 'i'): 1, ('i', 'v'): 1, ...}))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(nltk.FreqDist(__bigrams)), nltk.FreqDist(__bigrams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "287889c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['I', 'am', 'going', 'to', 'the', 'store']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = nltk.word_tokenize(\"I am going to the store\")\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0f701b69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('I', 'am'),\n",
       " ('am', 'going'),\n",
       " ('going', 'to'),\n",
       " ('to', 'the'),\n",
       " ('the', 'store')]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2 = nltk.ngrams(a,2)\n",
    "list(a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "011095d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('a', '.'), 5), (('i', 'a'), 2), (('.', 'e'), 1), (('e', 'm'), 1)]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.FreqDist(__bigrams).most_common(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "40895509",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('n', '.'), 6763),\n",
       " (('a', '.'), 6640),\n",
       " (('a', 'n'), 5438),\n",
       " (('.', 'a'), 4410),\n",
       " (('e', '.'), 3983)]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "__words = '.'+'.'.join(words)+'.'\n",
    "__bigrams = [__words[i:i+2] for i in range(0, len(__words)-1)]\n",
    "__bigrams = [(s[0], s[1]) for s in __bigrams]\n",
    "nltk.FreqDist(__bigrams).most_common(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3a8c76ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.emma.', '.olivia.', '.ava.', '.isabella.', '.sophia.']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names_with_periods_list = ['.' + name + '.' for name in words]\n",
    "names_with_periods_list[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f1466f63",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.em', 'emm', 'mma', 'ma.', '.ol']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = []\n",
    "for i in names_with_periods_list[:5]:\n",
    "    #print(i)\n",
    "    for j in range(len(i)-2):\n",
    "        #print(i[j:j+2])\n",
    "        x.append(i[j:j+3])\n",
    "x[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9942f8e",
   "metadata": {},
   "source": [
    "## function for making n-grams for visualization input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b58817b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_ngrams(names:list, n:int):\n",
    "    names = ['.' + name + '.' for name in names] # to keep track of start and end of names\n",
    "    ngrams_list = []\n",
    "    for name in names:\n",
    "        for i in range(len(name)-n+1):\n",
    "            ngrams_list.append(name[i:i+n])\n",
    "    return ngrams_list\n",
    "\n",
    "def find_ngrams(names, n):\n",
    "    #names = ['.' + name + '.' for name in names] \n",
    "    names = '.'.join(names)+'.'\n",
    "    return list(zip(*[names[i:] for i in range(n)]))\n",
    "\n",
    "\n",
    "def make_char_ngram(names:list, N:int):\n",
    "    data = defaultdict(int) # for speed\n",
    "    text = '.'+'.'.join(names)+'.'\n",
    "    for i in range(len(text)-N+1):\n",
    "        x = text[i:i+N] # actual N-gram\n",
    "        data[x] += 1    # add 1 to this N-gram key\n",
    "    return data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f7c5af39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(228144,\n",
       " 6631,\n",
       " [(('a', 'h', '.'), 1714),\n",
       "  (('n', 'a', '.'), 1673),\n",
       "  (('a', 'n', '.'), 1509),\n",
       "  (('o', 'n', '.'), 1503),\n",
       "  (('.', 'm', 'a'), 1453)])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(find_ngrams(words, 3)),\\\n",
    "len(nltk.FreqDist(find_ngrams(words, 3))),\\\n",
    "nltk.FreqDist(find_ngrams(words, 3)).most_common(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "46b7dede",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6631"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(make_char_ngram(words,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d6c4f71e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('ah.', 1714),\n",
       " ('na.', 1673),\n",
       " ('an.', 1509),\n",
       " ('on.', 1503),\n",
       " ('.ma', 1453),\n",
       " ('a.a', 1277),\n",
       " ('.ja', 1255),\n",
       " ('.ka', 1254),\n",
       " ('en.', 1217),\n",
       " ('lyn', 976)]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make_char_ngram(words, 3).items()\n",
    "sorted(make_char_ngram(words, 3).items(), key = lambda kv: -kv[1])[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f64904dc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(228146, 196113)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_b = extract_ngrams(words, 2)\n",
    "_t = extract_ngrams(words, 3)\n",
    "len(_b), len(_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "16fa1549",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['.e', 'em', 'mm', 'ma', 'a.'], ['.em', 'emm', 'mma', 'ma.', '.ol'])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_b[:5], _t[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6caeba4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(627, 6037)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(nltk.FreqDist(_b)), len(nltk.FreqDist(_t)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "810380d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most common bigrams are: [('n.', 6763), ('a.', 6640), ('an', 5438), ('.a', 4410), ('e.', 3983)]\n",
      "\n",
      "The most common trigrams are: [('ah.', 1714), ('na.', 1673), ('an.', 1509), ('on.', 1503), ('.ma', 1453)]\n"
     ]
    }
   ],
   "source": [
    "print(f'The most common bigrams are: {nltk.FreqDist(_b).most_common(5)}\\n')\n",
    "print(f'The most common trigrams are: {nltk.FreqDist(_t).most_common(5)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0a1cb332",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('n.', 6763), ('a.', 6640), ('an', 5438), ('.a', 4410), ('e.', 3983)]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(nltk.FreqDist(_b).items(), key=lambda kv: -kv[1])[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b906878d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('ah.', 1714), ('na.', 1673), ('an.', 1509), ('on.', 1503), ('.ma', 1453)]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(nltk.FreqDist(_t).items(), key=lambda kv: -kv[1])[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ea2b5a02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Creating FreqDist for whole BoW, keeping the 20 most common tokens\n",
    "all_fdist = nltk.FreqDist(_t).most_common(20)\n",
    "\n",
    "## Conversion to Pandas series via Python Dictionary for easier plotting\n",
    "all_fdist = pd.Series(dict(all_fdist))\n",
    "\n",
    "## Setting figure, ax into variables\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "\n",
    "## Seaborn plotting using Pandas attributes + xtick rotation for ease of viewing\n",
    "all_plot = sns.barplot(x=all_fdist.index, y=all_fdist.values, ax=ax)\n",
    "plt.xticks(rotation=30);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1c70daf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Join bigram tokens with '_' + maintain sorting\n",
    "ngram_joined = {'_'.join(k):v for k,v in sorted(all_fdist.items(), key=lambda item:-item[1])}\n",
    "\n",
    "## Convert to Pandas series for easy plotting\n",
    "ngram_freqdist = pd.Series(ngram_joined)\n",
    "ngram_freqdist_percent = 100 * ngram_freqdist / sum(ngram_freqdist)\n",
    "\n",
    "## Setting figure & ax for plots\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "\n",
    "## Setting plot to horizontal for easy viewing + setting title + display  \n",
    "bar_plot = sns.barplot(x=ngram_freqdist_percent.values, y=ngram_freqdist.index, orient='h', ax=ax)\n",
    "plt.title('Frequency Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "18381bb1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21728, 100.00000000000001)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(ngram_freqdist), sum(ngram_freqdist_percent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fb19c189",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a_h_.    7.888439\n",
       "n_a_.    7.699742\n",
       "a_n_.    6.944956\n",
       "o_n_.    6.917342\n",
       "._m_a    6.687224\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ngram_freqdist_percent.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2860e010",
   "metadata": {},
   "source": [
    "## back to counting approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "40fc08de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0], dtype=torch.int32),\n",
       " torch.Size([27]))"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N[0][0], N[0][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f379dc4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Broadcasting rules check\n",
    "# 27, 27\n",
    "# 27, 1   (column vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f328265e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(1.), torch.Size([27, 27]), torch.Size([27]), torch.Size([27, 1]))"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P = (N[0]+1).float()                  \n",
    "P /= P.sum(1, keepdim=True)          \n",
    "P[0].sum(), P.shape, P.sum(1).shape, P.sum(1, keepdim=True).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c12afe0c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([tensor(1.),\n",
       "  tensor(1.0000),\n",
       "  tensor(1.),\n",
       "  tensor(1.),\n",
       "  tensor(1.0000),\n",
       "  tensor(1.0000),\n",
       "  tensor(1.),\n",
       "  tensor(1.),\n",
       "  tensor(1.),\n",
       "  tensor(1.),\n",
       "  tensor(1.0000),\n",
       "  tensor(1.),\n",
       "  tensor(1.0000),\n",
       "  tensor(1.0000),\n",
       "  tensor(1.0000),\n",
       "  tensor(1.),\n",
       "  tensor(1.),\n",
       "  tensor(1.),\n",
       "  tensor(1.),\n",
       "  tensor(1.0000),\n",
       "  tensor(1.),\n",
       "  tensor(1.),\n",
       "  tensor(1.),\n",
       "  tensor(1.),\n",
       "  tensor(1.0000),\n",
       "  tensor(1.0000),\n",
       "  tensor(1.)],\n",
       " 27)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[P[i].sum() for i in range(P.sum(1, keepdim=True).shape[0])],\\\n",
    "len([P[i].sum() for i in range(P.sum(1, keepdim=True).shape[0])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "99b5b69b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000],\n",
       "        [1.0000]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P.sum(1, keepdim=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "69ffa6b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Broadcasting rules check\n",
    "# 27, 27, 27\n",
    "# 27, 27, 1   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fc8cd43e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(1.),\n",
       " torch.Size([27, 27, 27]),\n",
       " torch.Size([27, 27]),\n",
       " torch.Size([27, 27, 1]))"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P = (N+1).float() \n",
    "# P.sum(2) because we want the third level of the array to be normalized to get the probabilities\n",
    "P /= P.sum(2, keepdim=True) \n",
    "P[0][0].sum(), P.shape, P.sum(2).shape, P.sum(2, keepdim=True).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "78eec51a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[27, 27, 1]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(P.sum(2, keepdim=True).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2cfdd7ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(729,\n",
       " [tensor(1.),\n",
       "  tensor(1.0000),\n",
       "  tensor(1.),\n",
       "  tensor(1.),\n",
       "  tensor(1.0000),\n",
       "  tensor(1.0000),\n",
       "  tensor(1.),\n",
       "  tensor(1.),\n",
       "  tensor(1.),\n",
       "  tensor(1.)])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x,y,z] = list(P.sum(2, keepdim=True).shape)\n",
    "len([P[i][j].sum() for i in range(x) for j in range(y)]),\\\n",
    "[P[i][j].sum() for i in range(x) for j in range(y)][:10]\n",
    "# for i in range(P.sum(2, keepdim=True).shape[0]):\n",
    "#     for j in range(P.sum(2, keepdim=True).shape[1]):\n",
    "#         print(P[i][j].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3eebe3d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('c', 'e', 3, 5)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = ['.', '.', 'a', 'c', 'e']\n",
    "out[-2], out[-1], stoi[out[-2]], stoi[out[-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0592ef4e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.4066, 0.0225, 0.0035, 0.0190, 0.0173, 0.0381, 0.0017, 0.0017, 0.0017,\n",
       "        0.0121, 0.0017, 0.0035, 0.1609, 0.0087, 0.0830, 0.0225, 0.0069, 0.0017,\n",
       "        0.0536, 0.0450, 0.0190, 0.0035, 0.0035, 0.0017, 0.0017, 0.0554, 0.0035])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp = P[stoi[out[-2]], stoi[out[-1]]]\n",
    "pp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4fd464c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'t'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finally, sample from the 'neural net' model\n",
    "g = torch.Generator().manual_seed(1478)\n",
    "ix = torch.multinomial(pp, num_samples=1, replacement=True, generator=g).item()\n",
    "itos[ix]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c164def0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([12, 12,  0,  0,  0, 15, 12, 15,  0, 14,  0, 18, 14,  3, 14,  0, 19,  0,\n",
       "         5, 12, 24, 12,  0,  5, 12,  2, 18,  0,  0, 12, 25, 18, 19,  0,  0, 26,\n",
       "        18,  0, 18, 20, 14, 14, 14, 18, 14,  0,  1,  0,  0, 26, 14,  0,  0,  0,\n",
       "        12, 18,  0,  0,  0, 15,  0, 14, 13,  0,  0,  0,  0, 19,  0, 14,  0,  0,\n",
       "        25,  0, 18,  0,  0,  5,  0,  0, 12,  0,  5, 14,  0, 12,  0,  0, 12, 14,\n",
       "        16,  5,  0,  5,  0,  0,  0,  1,  0,  0])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.multinomial(pp, num_samples=100, replacement=True, generator=g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d60ee062",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "..xina.\n",
      "..josmaratresarielyn.\n",
      "..mann.\n",
      "..fiydracyr.\n",
      "..vonerrichaizi.\n",
      "..inese.\n",
      "..grani.\n",
      "...\n",
      "..ilyim.\n",
      "..dy.\n"
     ]
    }
   ],
   "source": [
    "g = torch.Generator().manual_seed(1478)\n",
    "\n",
    "for i in range(10):\n",
    "    out = ['.', '.']\n",
    "    #ix = 0\n",
    "    while True:\n",
    "        p = P[stoi[out[-2]], stoi[out[-1]]]  # Plug the last two chars into our probabilities table\n",
    "        ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n",
    "        out.append(itos[ix])\n",
    "        if ix == 0:\n",
    "            break\n",
    "    print(''.join(out))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "fe0a4cc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".em: 0.1855\n",
      "emm: 0.1269\n",
      "mma: 0.3744\n",
      "ma.: 0.0669\n",
      ".ol: 0.2494\n",
      "oli: 0.1084\n",
      "liv: 0.0219\n",
      "ivi: 0.2669\n",
      "via: 0.1578\n",
      "ia.: 0.3657\n",
      ".av: 0.0550\n",
      "ava: 0.1882\n",
      "va.: 0.1405\n"
     ]
    }
   ],
   "source": [
    "for w in words[:3]:\n",
    "    chs = ['.'] + list(w) + ['.']\n",
    "    for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n",
    "        ix1 = stoi[ch1]\n",
    "        ix2 = stoi[ch2]\n",
    "        ix3 = stoi[ch3]\n",
    "        prob = P[ix1, ix2, ix3]\n",
    "        print(f'{ch1}{ch2}{ch3}: {prob:.4f}')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bcc9b2c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".em: 0.1855\n",
      "emm: 0.1269\n",
      "mma: 0.3744\n",
      "ma.: 0.0669\n",
      ".ol: 0.2494\n",
      "oli: 0.1084\n",
      "liv: 0.0219\n",
      "ivi: 0.2669\n",
      "via: 0.1578\n",
      "ia.: 0.3657\n",
      ".av: 0.0550\n",
      "ava: 0.1882\n",
      "va.: 0.1405\n",
      ".is: 0.2023\n",
      "isa: 0.1065\n",
      "sab: 0.0627\n",
      "abe: 0.3063\n",
      "bel: 0.2962\n",
      "ell: 0.2513\n",
      "lla: 0.2464\n",
      "la.: 0.2585\n",
      ".so: 0.0735\n",
      "sop: 0.0394\n",
      "oph: 0.3115\n",
      "phi: 0.2684\n",
      "hia: 0.1085\n",
      "ia.: 0.3657\n",
      "\n",
      "log_likelihood = -50.2693\n",
      "negative log_likelihood = 50.2693\n",
      "\n",
      "average negative log_likelihood = 1.8618\n"
     ]
    }
   ],
   "source": [
    "log_likelihood = 0.0\n",
    "n = 0\n",
    "for w in words[:5]:\n",
    "    chs = ['.'] + list(w) + ['.']\n",
    "    for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n",
    "        ix1 = stoi[ch1]\n",
    "        ix2 = stoi[ch2]\n",
    "        ix3 = stoi[ch3]\n",
    "        prob = P[ix1, ix2, ix3]\n",
    "        logprob = torch.log(prob)\n",
    "        log_likelihood += logprob\n",
    "        n += 1\n",
    "        print(f'{ch1}{ch2}{ch3}: {prob:.4f}')\n",
    "        \n",
    "print(f'\\nlog_likelihood = {log_likelihood:.4f}')\n",
    "nll = -log_likelihood\n",
    "print(f'negative log_likelihood = {nll:.4f}\\n')\n",
    "avg_nll = nll/n\n",
    "print(f'average negative log_likelihood = {avg_nll:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd816052",
   "metadata": {},
   "source": [
    "## combining it all together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2cb5b1ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "N = torch.zeros((27, 27, 27), dtype=torch.int32)\n",
    "chars = sorted(list(set(''.join(words))))\n",
    "stoi = {s:i+1 for i,s in enumerate(chars)}\n",
    "stoi['.'] = 0\n",
    "itos = {i:s for s,i in stoi.items()}\n",
    "\n",
    "for w in words:\n",
    "    chs = ['.'] + list(w) + ['.']\n",
    "    for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n",
    "        ix1 = stoi[ch1]\n",
    "        ix2 = stoi[ch2]\n",
    "        ix3 = stoi[ch3]\n",
    "        N[ix1, ix2, ix3] +=1\n",
    "        \n",
    "P = (N+1).float() \n",
    "P /= P.sum(2, keepdim=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "9415bac2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "..xina.\n",
      "..josmaratresarielyn.\n",
      "..mann.\n",
      "..fiydracyr.\n",
      "..vonerrichaizi.\n",
      "..inese.\n",
      "..grani.\n",
      "...\n",
      "..ilyim.\n",
      "..dy.\n"
     ]
    }
   ],
   "source": [
    "g = torch.Generator().manual_seed(1478)\n",
    "\n",
    "for i in range(10):\n",
    "    out = ['.', '.']\n",
    "    ix = 0\n",
    "    while True:\n",
    "        p = P[stoi[out[-2]], stoi[out[-1]]]  # Plug the last two chars into our probabilities table\n",
    "        ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n",
    "        out.append(itos[ix])\n",
    "        if ix == 0:\n",
    "            break\n",
    "    print(''.join(out))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b616c69f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "log_likelihood = -410414.9688\n",
      "negative log_likelihood = 410414.9688\n",
      "\n",
      "average negative log_likelihood = 2.0927\n"
     ]
    }
   ],
   "source": [
    "log_likelihood = 0.0\n",
    "n = 0\n",
    "for w in words:\n",
    "    chs = ['.'] + list(w) + ['.']\n",
    "    for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n",
    "        ix1 = stoi[ch1]\n",
    "        ix2 = stoi[ch2]\n",
    "        ix3 = stoi[ch3]\n",
    "        prob = P[ix1, ix2, ix3]\n",
    "        logprob = torch.log(prob)\n",
    "        log_likelihood += logprob\n",
    "        n += 1\n",
    "        \n",
    "print(f'\\nlog_likelihood = {log_likelihood:.4f}')\n",
    "nll = -log_likelihood\n",
    "print(f'negative log_likelihood = {nll:.4f}\\n')\n",
    "avg_nll = nll/n\n",
    "print(f'average negative log_likelihood = {avg_nll:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "191b9bae",
   "metadata": {},
   "source": [
    "----\n",
    "<a id='1b'></a>\n",
    "# b. Neural Network Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29b48d2f",
   "metadata": {},
   "source": [
    "----\n",
    "```\n",
    "                                                                       \n",
    "    +------|------+    W0 [27, 27]     +-------|-------+               +------|------+    \n",
    "    |  2 Inputs   |   -------------->  | Hidden        |               | Output      |    \n",
    "    |             |    W1 [27, 27]     | Layer         |   -------->   |             |    \n",
    "    | 2x27 inputs |   -------------->  | 2x27 neurons  |               | 27 outputs  |    \n",
    "    +------|------+     W [54, 27]     +-------|-------+               +------|------+    \n",
    "           |                                   |                              |          \n",
    "           |                                   |                              |          \n",
    "           X [196113, 54]                     W.X                             y\n",
    "                                (no bias, no non-linearity)\n",
    "```  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b1635192",
   "metadata": {},
   "outputs": [],
   "source": [
    "xs, ys = [], []\n",
    "\n",
    "for w in words:\n",
    "    chs = ['.'] + list(w) + ['.']\n",
    "    for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n",
    "        ix1 = stoi[ch1]\n",
    "        ix2 = stoi[ch2]\n",
    "        ix3 = stoi[ch3]\n",
    "        #print(ch1, ch2, ch3)\n",
    "        xs.append((ix1, ix2))\n",
    "        ys.append(ix3)\n",
    "        \n",
    "xs = torch.tensor(xs) \n",
    "ys = torch.tensor(ys)\n",
    "\n",
    "g = torch.Generator().manual_seed(1478)\n",
    "W = torch.randn((27*2, 27), generator=g, requires_grad=True)\n",
    "\n",
    "xenc = F.one_hot(xs, num_classes=27).float()  # input to the network: one-hot encoding\n",
    "logits = xenc.view(-1, 27*2) @ W              # predict log-counts\n",
    "counts = logits.exp()                         # counts, equivalent to N\n",
    "probs = counts / counts.sum(1, keepdim=True)\n",
    "#print(xenc.shape, counts.shape, probs.shape)\n",
    "#loss = -probs[torch.arange(ys.shape[0]), ys].log().mean() # ys.shape[0] is the number of examples (allows us to avoid using a count variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ba25102a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([196113, 2]), torch.int64, torch.Size([196113]), 196113)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xs.shape, xs.dtype, ys.shape, ys.nelement()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ca9336ab",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([196113, 2, 27]) torch.Size([54, 27]) torch.Size([196113, 27]) torch.Size([196113, 27])\n"
     ]
    }
   ],
   "source": [
    "print(xenc.shape, W.shape, counts.shape, probs.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ec6b461f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([196113, 2, 27]), torch.Size([196113, 54]), torch.Size([54, 27]))"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#xs, ys\n",
    "#xs.view(-1, 27*2).shape\n",
    "xenc.shape, xenc.view(-1, 27*2).shape, W.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "cce478d6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.1189, 0.0051, 0.0123, 0.0038, 0.3363, 0.0004, 0.0195, 0.0013, 0.0135,\n",
       "        0.0617, 0.0037, 0.0161, 0.0078, 0.0083, 0.0203, 0.0176, 0.1262, 0.0057,\n",
       "        0.0155, 0.0389, 0.0120, 0.0134, 0.0027, 0.0081, 0.0130, 0.0018, 0.1161],\n",
       "       grad_fn=<SelectBackward0>)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d3a984da",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------\n",
      "\n",
      "trigram example 1: .em (indexes [0, 5],13)\n",
      "input to the neural net: [0, 5]\n",
      "output probabilities from the neural net: tensor([0.0076, 0.0152, 0.0289, 0.0012, 0.0266, 0.0049, 0.0239, 0.0417, 0.0110,\n",
      "        0.1575, 0.0446, 0.1141, 0.0203, 0.0036, 0.0024, 0.0013, 0.0078, 0.2472,\n",
      "        0.0183, 0.0078, 0.0031, 0.0197, 0.0050, 0.0279, 0.0620, 0.0048, 0.0916],\n",
      "       grad_fn=<SelectBackward0>)\n",
      "label (actual next character): 13\n",
      "probability assigned by the net to the the correct character: 0.003583\n",
      "log likelihood: -5.6314\n",
      "negative log likelihood: 5.6314\n",
      "--------\n",
      "\n",
      "trigram example 2: emm (indexes [5, 13],13)\n",
      "input to the neural net: [5, 13]\n",
      "output probabilities from the neural net: tensor([0.1189, 0.0051, 0.0123, 0.0038, 0.3363, 0.0004, 0.0195, 0.0013, 0.0135,\n",
      "        0.0617, 0.0037, 0.0161, 0.0078, 0.0083, 0.0203, 0.0176, 0.1262, 0.0057,\n",
      "        0.0155, 0.0389, 0.0120, 0.0134, 0.0027, 0.0081, 0.0130, 0.0018, 0.1161],\n",
      "       grad_fn=<SelectBackward0>)\n",
      "label (actual next character): 13\n",
      "probability assigned by the net to the the correct character: 0.008272\n",
      "log likelihood: -4.7949\n",
      "negative log likelihood: 4.7949\n",
      "--------\n",
      "\n",
      "trigram example 3: mma (indexes [13, 13],1)\n",
      "input to the neural net: [13, 13]\n",
      "output probabilities from the neural net: tensor([0.1742, 0.0041, 0.0109, 0.0006, 0.3083, 0.0055, 0.0069, 0.0041, 0.0139,\n",
      "        0.0202, 0.0037, 0.0025, 0.0301, 0.0141, 0.0050, 0.0066, 0.1897, 0.0050,\n",
      "        0.0031, 0.0042, 0.0096, 0.1362, 0.0009, 0.0072, 0.0004, 0.0068, 0.0263],\n",
      "       grad_fn=<SelectBackward0>)\n",
      "label (actual next character): 1\n",
      "probability assigned by the net to the the correct character: 0.004090\n",
      "log likelihood: -5.4992\n",
      "negative log likelihood: 5.4992\n",
      "--------\n",
      "\n",
      "trigram example 4: ma. (indexes [13, 1],0)\n",
      "input to the neural net: [13, 1]\n",
      "output probabilities from the neural net: tensor([0.1586, 0.0470, 0.0041, 0.0005, 0.0343, 0.0201, 0.0364, 0.0063, 0.0067,\n",
      "        0.0046, 0.0051, 0.0185, 0.1030, 0.0176, 0.0148, 0.0308, 0.0474, 0.2389,\n",
      "        0.0055, 0.0006, 0.0427, 0.0298, 0.0059, 0.0180, 0.0008, 0.0558, 0.0460],\n",
      "       grad_fn=<SelectBackward0>)\n",
      "label (actual next character): 0\n",
      "probability assigned by the net to the the correct character: 0.158585\n",
      "log likelihood: -1.8415\n",
      "negative log likelihood: 1.8415\n",
      "--------\n",
      "\n",
      "trigram example 5: .ol (indexes [0, 15],12)\n",
      "input to the neural net: [0, 15]\n",
      "output probabilities from the neural net: tensor([0.0015, 0.0114, 0.0107, 0.0013, 0.0155, 0.0133, 0.0090, 0.0069, 0.0031,\n",
      "        0.0385, 0.0191, 0.3142, 0.0075, 0.0022, 0.0133, 0.0039, 0.0239, 0.2257,\n",
      "        0.0097, 0.0171, 0.0057, 0.0400, 0.0031, 0.0138, 0.0391, 0.0063, 0.1443],\n",
      "       grad_fn=<SelectBackward0>)\n",
      "label (actual next character): 12\n",
      "probability assigned by the net to the the correct character: 0.007525\n",
      "log likelihood: -4.8896\n",
      "negative log likelihood: 4.8896\n",
      "\n",
      "=========\n",
      "average negative log likelihood, i.e. loss = 4.5313\n"
     ]
    }
   ],
   "source": [
    "num = 5\n",
    "nlls = torch.zeros(num)\n",
    "for i in range(num):\n",
    "    # i-th trigram:\n",
    "    x = xs.numpy().tolist()[i] \n",
    "    y = ys[i].item() \n",
    "    #print(x)\n",
    "    #print(y)\n",
    "    print('--------\\n')\n",
    "    print(f'trigram example {i+1}: {itos[x[0]]}{itos[x[1]]}{itos[y]} (indexes {x},{y})')\n",
    "    print('input to the neural net:', x)\n",
    "    print('output probabilities from the neural net:', probs[i])\n",
    "    print('label (actual next character):', y)\n",
    "    p = probs[i, y]\n",
    "    print(f'probability assigned by the net to the the correct character: {p.item():.6f}')\n",
    "    logp = torch.log(p)\n",
    "    #print('log likelihood:', logp.item()%.4f)\n",
    "    print(f'log likelihood: {logp.item():.4f}')\n",
    "\n",
    "    nll = -logp\n",
    "    print(f'negative log likelihood: {nll.item():.4f}')\n",
    "    nlls[i] = nll\n",
    "\n",
    "print('\\n=========')\n",
    "print(f'average negative log likelihood, i.e. loss = {nlls.mean().item():.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5903632",
   "metadata": {},
   "source": [
    "----\n",
    "## combining it all together\n",
    "```\n",
    "                                                                       \n",
    "    +------|------+    W0 [27, 27]     +-------|-------+               +------|------+    \n",
    "    |  2 Inputs   |   -------------->  | Hidden        |               | Output      |    \n",
    "    |             |    W1 [27, 27]     | Layer         |   -------->   |             |    \n",
    "    | 2x27 inputs |   -------------->  | 2x27 neurons  |               | 27 outputs  |    \n",
    "    +------|------+     W [54, 27]     +-------|-------+               +------|------+    \n",
    "           |                                   |                              |          \n",
    "           |                                   |                              |          \n",
    "           X [196113, 54]                     W.X                             y\n",
    "                                (no bias, no non-linearity)\n",
    "```  \n",
    "\n",
    "\n",
    "---\n",
    "We need to make sure the matrix size aligns for matrix multiplication.\n",
    "* **W.X** = `xenc.view(-1, 27x2)` @ `W` ------>  **[196113, 54] x [54, 27]**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f57bc7e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of examples:  196113\n"
     ]
    }
   ],
   "source": [
    "xs, ys = [], []\n",
    "\n",
    "for w in words:\n",
    "    chs = ['.'] + list(w) + ['.']\n",
    "    for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n",
    "        ix1 = stoi[ch1]\n",
    "        ix2 = stoi[ch2]\n",
    "        ix3 = stoi[ch3]\n",
    "        #print(ch1, ch2, ch3)\n",
    "        xs.append((ix1, ix2))\n",
    "        ys.append(ix3)\n",
    "        \n",
    "xs = torch.tensor(xs) \n",
    "ys = torch.tensor(ys)\n",
    "num = ys.nelement()\n",
    "print('number of examples: ', num)\n",
    "\n",
    "# initialize the 'network'\n",
    "g = torch.Generator().manual_seed(1478)\n",
    "W = torch.randn((27*2, 27), generator=g, requires_grad=True)\n",
    "# Got stuck a bit on this part and didn't realize how many neurons I'd be needing, \n",
    "# started with (27x27, 27) but I think this was not necessary\n",
    "# You just need 27x2, the first 27 will activate for the first input, the second 27 for the second one\n",
    "# Double the inputs, double the neurons, that makes sense! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "9470defa",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "average negative log_likelihood = 2.2573\n",
      "\n",
      "CPU times: user 51.9 s, sys: 7.93 s, total: 59.8 s\n",
      "Wall time: 26.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "n_iter = 250\n",
    "lr = 50\n",
    "C = 0.01\n",
    "losses = []\n",
    "# gradient descent\n",
    "for k in range(n_iter):\n",
    "\n",
    "    # forward pass\n",
    "    xenc = F.one_hot(xs, num_classes=27).float()       # input to the network: one-hot encoding\n",
    "    logits = xenc.view(-1, 27*2) @ W                   # predict log-counts\n",
    "    counts = logits.exp()                              # counts, equivalent to N\n",
    "    probs = counts / counts.sum(1, keepdim=True)      # probabilities for next character\n",
    "    loss = -probs[torch.arange(num), ys].log().mean() + C*(W**2).mean() \n",
    "    #print(loss.item())\n",
    "    losses.append(loss)\n",
    "\n",
    "    # backward pass\n",
    "    W.grad = None # set to zero the gradient\n",
    "    loss.backward()\n",
    "\n",
    "    # update\n",
    "    W.data += -lr * W.grad\n",
    "print(f'\\naverage negative log_likelihood = {loss.item():.4f}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c17f0e55",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_loss (final): 2.2573\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"train_loss (final): {losses[-1]:.4f}\")\n",
    "plt.plot(losses)\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.title(\"Loss on training data\")\n",
    "plt.grid() \n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c7abe20",
   "metadata": {},
   "source": [
    "---\n",
    "For the <u>trigram models</u>, the loss for <u>**counting**</u> is **`2.09`** and the loss for **<u>neural net</u>** is **`2.25`**. The counting trigram model is better than the single layer neural net. However, both trigram models are better than the <u>bigram model</u>, which has a loss of **`2.42`**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95732897",
   "metadata": {},
   "source": [
    "**Let's visualize the input, `X` and parameters/weights, `W`.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "cf74c591",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_xenc = xenc.view(-1, 27*2)\n",
    "sns.heatmap(_xenc==0, vmin=0, vmax=1, cbar=False)\n",
    "plt.title(\"Feature matrix of X_enc\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "8a468964",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(W.detach().numpy(), vmin=0, vmax=1, cbar=False)\n",
    "plt.title(\"Feature matrix of W\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "570f328f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# logits = x_enc @ w # predict log-counts\n",
    "counts = W.exp()\n",
    "probas = counts / counts.sum(1, keepdims=True)\n",
    "probas_t = probas.T \n",
    "\n",
    "plt.figure(figsize=(30, 20))\n",
    "plt.imshow(probas_t.detach().numpy())\n",
    "#plt.imshow(probas_t.tolist())\n",
    "plt.title(\"(row) normalized Exp(w.T)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f3833873",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([27, 27])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probas_t[:, 0:27].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b40ccbbe",
   "metadata": {},
   "source": [
    "-----\n",
    "<a id='2'></a>\n",
    "# 2. Validate Trigram Language Model\n",
    "-------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "1ffe7871",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(1245)\n",
    "words = open('../data/names.txt', 'r').read().splitlines()\n",
    "random.shuffle(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "cd13262a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['urban',\n",
       " 'tal',\n",
       " 'myrah',\n",
       " 'sanya',\n",
       " 'anelle',\n",
       " 'daniel',\n",
       " 'naika',\n",
       " 'paitynn',\n",
       " 'kierstin',\n",
       " 'mayah']"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "e2f29b93",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = words[ : int(len(words) * 0.8)]\n",
    "val = words[int(len(words) * 0.8) : int(len(words) * 0.9)]\n",
    "test = words[int(len(words) * 0.9) : ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b908807c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25626, 3203, 3204, 32033)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train), len(val), len(test), len(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "c679b30e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80% training set\n",
      "10% validation set\n",
      "10% test set\n"
     ]
    }
   ],
   "source": [
    "print(f'{len(train)/len(words)*100:.0f}% training set')\n",
    "print(f'{len(val)/len(words)*100:.0f}% validation set')\n",
    "print(f'{len(test)/len(words)*100:.0f}% test set')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d204b849",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of examples:  156751\n"
     ]
    }
   ],
   "source": [
    "xs_tr, ys_tr = [], []\n",
    "\n",
    "for w in train:\n",
    "    chs = ['.'] + list(w) + ['.']\n",
    "    for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n",
    "        ix1 = stoi[ch1]\n",
    "        ix2 = stoi[ch2]\n",
    "        ix3 = stoi[ch3]\n",
    "        xs_tr.append((ix1, ix2))\n",
    "        ys_tr.append(ix3)\n",
    "        \n",
    "xs_tr = torch.tensor(xs_tr) \n",
    "ys_tr = torch.tensor(ys_tr)\n",
    "num_tr = ys_tr.nelement()\n",
    "print('number of examples: ', num_tr)\n",
    "\n",
    "# initialize the 'network'\n",
    "g = torch.Generator().manual_seed(1478)\n",
    "W = torch.randn((27*2, 27), generator=g, requires_grad=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b3c6094b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "average negative log_likelihood = 2.2576\n",
      "\n",
      "train_loss (final): 2.2576\n",
      "CPU times: user 34.4 s, sys: 6.21 s, total: 40.6 s\n",
      "Wall time: 19 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "n_iter = 250\n",
    "lr = 50\n",
    "C = 0.01\n",
    "losses_tr = []\n",
    "# gradient descent\n",
    "for k in range(n_iter):\n",
    "\n",
    "    # forward pass\n",
    "    xenc_tr = F.one_hot(xs_tr, num_classes=27).float()      \n",
    "    logits = xenc_tr.view(-1, 27*2) @ W               \n",
    "    counts = logits.exp()                             \n",
    "    probs = counts / counts.sum(1, keepdim=True)      \n",
    "    loss_tr = -probs[torch.arange(num_tr), ys_tr].log().mean() + C*(W**2).mean() \n",
    "    #print(loss.item())\n",
    "    losses_tr.append(loss_tr)\n",
    "\n",
    "    # backward pass\n",
    "    W.grad = None # set to zero the gradient\n",
    "    loss_tr.backward()\n",
    "\n",
    "    # update\n",
    "    W.data += -lr * W.grad\n",
    "print(f'\\naverage negative log_likelihood = {loss_tr.item():.4f}\\n')\n",
    "\n",
    "print(f\"train_loss (final): {losses_tr[-1]:.4f}\")\n",
    "plt.plot(losses_tr)\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.title(\"Loss on training data\")\n",
    "plt.grid() \n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ac64ee3c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([156751, 2, 27]), torch.Size([54, 27]))"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xenc_tr.shape, W.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b914303a",
   "metadata": {},
   "source": [
    "## Evaluate model on validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "bf995e34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The loss on the validation set is 2.2514\n"
     ]
    }
   ],
   "source": [
    "xs_val, ys_val = [], []\n",
    "losses_val = []\n",
    "\n",
    "for w in val:\n",
    "    chs = ['.'] + list(w) + ['.']\n",
    "    for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n",
    "        ix1 = stoi[ch1]\n",
    "        ix2 = stoi[ch2]\n",
    "        ix3 = stoi[ch3]\n",
    "        xs_val.append((ix1, ix2))\n",
    "        ys_val.append(ix3)\n",
    "        \n",
    "xs_val2 = torch.tensor(xs_val) \n",
    "ys_val2 = torch.tensor(ys_val)\n",
    "num_val = ys_val2.nelement()\n",
    "\n",
    "xenc_val = F.one_hot(xs_val2, num_classes=27).float()      \n",
    "logits = xenc_val.view(-1, 27*2) @ W                   \n",
    "counts = logits.exp()                             \n",
    "probs = counts / counts.sum(1, keepdim=True)     \n",
    "loss_val = -probs[torch.arange(num_val), ys_val2].log().mean()\n",
    "    \n",
    "print(f'The loss on the validation set is {loss_val.item():.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ac13a02",
   "metadata": {},
   "source": [
    "## Evaluate model on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "bd5aefcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The loss on the test set is 2.2460\n"
     ]
    }
   ],
   "source": [
    "xs_test, ys_test = [], []\n",
    "losses_test = []\n",
    "\n",
    "for w in test:\n",
    "    chs = ['.'] + list(w) + ['.']\n",
    "    for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n",
    "        ix1 = stoi[ch1]\n",
    "        ix2 = stoi[ch2]\n",
    "        ix3 = stoi[ch3]\n",
    "        xs_test.append((ix1, ix2))\n",
    "        ys_test.append(ix3)\n",
    "        \n",
    "xs_test2 = torch.tensor(xs_test) \n",
    "ys_test2 = torch.tensor(ys_test)\n",
    "num_test = ys_test2.nelement()\n",
    "\n",
    "xenc_test = F.one_hot(xs_test2, num_classes=27).float()      \n",
    "logits = xenc_test.view(-1, 27*2) @ W                   \n",
    "counts = logits.exp()                             \n",
    "probs = counts / counts.sum(1, keepdim=True)     \n",
    "loss_test = -probs[torch.arange(num_test), ys_test2].log().mean()\n",
    "\n",
    "print(f'The loss on the test set is {loss_test.item():.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb2bc37b",
   "metadata": {},
   "source": [
    "---\n",
    "For the <u>trigram model</u>, the loss after training the model on the <u>**training set**</u> was **`2.2576`**. Evaluating the model on the **<u>validation</u>** and **<u>test</u>** set yielded losses of **`2.2514`** and **`2.2460`** respectively. The  test and validation losses are very similar so that means the model is <u>not overfitting</u> (generalizes well to new unseen data)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5dbaf7f",
   "metadata": {},
   "source": [
    "-----\n",
    "<a id='3'></a>\n",
    "# 3. Tune Regularization/Smoothing with Validation Set\n",
    "-------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "de65133a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  8%|▊         | 1/12 [00:08<01:30,  8.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For regularization parameter = 0.0010 ---> train loss: 2.2633,    validation loss: 2.2658\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 17%|█▋        | 2/12 [00:16<01:21,  8.19s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For regularization parameter = 0.0018 ---> train loss: 2.2632,    validation loss: 2.2667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 25%|██▌       | 3/12 [00:25<01:16,  8.54s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For regularization parameter = 0.0031 ---> train loss: 2.2656,    validation loss: 2.2654\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 33%|███▎      | 4/12 [00:35<01:10,  8.78s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For regularization parameter = 0.0054 ---> train loss: 2.2686,    validation loss: 2.2677\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 42%|████▏     | 5/12 [00:43<01:00,  8.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For regularization parameter = 0.0096 ---> train loss: 2.2722,    validation loss: 2.2664\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 50%|█████     | 6/12 [00:50<00:49,  8.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For regularization parameter = 0.0169 ---> train loss: 2.2779,    validation loss: 2.2674\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 58%|█████▊    | 7/12 [00:58<00:40,  8.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For regularization parameter = 0.0297 ---> train loss: 2.2877,    validation loss: 2.2672\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 67%|██████▋   | 8/12 [01:06<00:32,  8.07s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For regularization parameter = 0.0522 ---> train loss: 2.3015,    validation loss: 2.2689\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 75%|███████▌  | 9/12 [01:16<00:25,  8.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For regularization parameter = 0.0918 ---> train loss: 2.3235,    validation loss: 2.2745\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 83%|████████▎ | 10/12 [01:24<00:17,  8.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For regularization parameter = 0.1615 ---> train loss: 2.3535,    validation loss: 2.2860\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 92%|█████████▏| 11/12 [01:33<00:08,  8.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For regularization parameter = 0.2842 ---> train loss: 2.3949,    validation loss: 2.3056\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12/12 [01:41<00:00,  8.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For regularization parameter = 0.5000 ---> train loss: 2.4526,    validation loss: 2.3375\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#reg_param_list = [0, 0.001, 0.01, 0.02, 0.05, 0.1, 0.25, 0.5, 1.0]\n",
    "#reg_param_list = [0.5, 0.375, 0.25, 0.1, 0.05, 0.01, 0.005, 0.001, 0.0005, 0.0001, 0.00005, 0.00001][::-1]\n",
    "#reg_param_list = [0, 0.02, 0.05, 0.1]\n",
    "reg_param_list = np.geomspace(0.001, 0.5, 12)\n",
    "\n",
    "loss_dict = {} # <reg_strength: [final train loss, final val loss]>\n",
    "n_iter = 100\n",
    "lr = 50\n",
    "\n",
    "for C in tqdm(reg_param_list):\n",
    "    # Initialize model & losses\n",
    "    W = torch.randn((27*2, 27), generator=g, requires_grad=True)\n",
    "    #tr_losses, val_losses= [], []\n",
    "    \n",
    "    # gradient descent\n",
    "    for k in range(n_iter):\n",
    "\n",
    "        # forward pass\n",
    "        xenc = F.one_hot(xs_tr, num_classes=27).float()       \n",
    "        logits = xenc.view(-1, 27*2) @ W                 \n",
    "        counts = logits.exp()                             \n",
    "        probs = counts / counts.sum(1, keepdim=True)     \n",
    "        tr_loss = -probs[torch.arange(num_tr), ys_tr].log().mean() + C*(W**2).mean() \n",
    "        #print(loss.item())\n",
    "        #tr_losses.append(tr_loss)\n",
    "\n",
    "        # Zero grad & backward pass\n",
    "        W.grad = None \n",
    "        tr_loss.backward()\n",
    "\n",
    "        # update\n",
    "        W.data += -lr * W.grad     \n",
    "\n",
    "    for w in val:\n",
    "        chs = ['.'] + list(w) + ['.']\n",
    "        for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n",
    "            ix1 = stoi[ch1]\n",
    "            ix2 = stoi[ch2]\n",
    "            ix3 = stoi[ch3]\n",
    "            xs_val.append((ix1, ix2))\n",
    "            ys_val.append(ix3)\n",
    "\n",
    "    xs_val2 = torch.tensor(xs_val) \n",
    "    ys_val2 = torch.tensor(ys_val)\n",
    "    num_val = ys_val2.nelement()\n",
    "\n",
    "    xenc_val = F.one_hot(xs_val2, num_classes=27).float()      \n",
    "    logits = xenc_val.view(-1, 27*2) @ W                   \n",
    "    counts = logits.exp()                             \n",
    "    probs = counts / counts.sum(1, keepdim=True)     \n",
    "    val_loss = -probs[torch.arange(num_val), ys_val2].log().mean()\n",
    "\n",
    "    print(f'For regularization parameter = {C:.4f} ---> train loss: {tr_loss.item():.4f},\\\n",
    "    validation loss: {val_loss.item():.4f}')\n",
    "    \n",
    "    _tr_loss = np.round(tr_loss.item(), decimals=4)\n",
    "    _val_loss = np.round(val_loss.item(), decimals=4)\n",
    "    _C = np.round(C, decimals=4)\n",
    "    loss_dict[_C] = [_tr_loss, _val_loss]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "fd29f686",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0.001: [2.2633, 2.2658],\n",
       " 0.0018: [2.2632, 2.2667],\n",
       " 0.0031: [2.2656, 2.2654],\n",
       " 0.0054: [2.2686, 2.2677],\n",
       " 0.0096: [2.2722, 2.2664],\n",
       " 0.0169: [2.2779, 2.2674],\n",
       " 0.0297: [2.2877, 2.2672],\n",
       " 0.0522: [2.3015, 2.2689],\n",
       " 0.0918: [2.3235, 2.2745],\n",
       " 0.1615: [2.3535, 2.286],\n",
       " 0.2842: [2.3949, 2.3056],\n",
       " 0.5: [2.4526, 2.3375]}"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "e473008f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(f\"Effect of Regularization/Smoothing on Trigram Model\")\n",
    "plt.plot(list(loss_dict.keys()), [train_loss for train_loss, _ in loss_dict.values()], \"o--\", label=\"train\")\n",
    "plt.plot(list(loss_dict.keys()), [val_loss for _, val_loss in loss_dict.values()], \"o--\", label=\"val\")\n",
    "plt.xlabel(\"Regularization Strength, C\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.plot()\n",
    "#plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "61bc5f17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.001 [2.2633, 2.2658]\n",
      "0.0018 [2.2632, 2.2667]\n",
      "0.0031 [2.2656, 2.2654]\n",
      "0.0054 [2.2686, 2.2677]\n",
      "0.0096 [2.2722, 2.2664]\n",
      "0.0169 [2.2779, 2.2674]\n",
      "0.0297 [2.2877, 2.2672]\n",
      "0.0522 [2.3015, 2.2689]\n",
      "0.0918 [2.3235, 2.2745]\n",
      "0.1615 [2.3535, 2.286]\n",
      "0.2842 [2.3949, 2.3056]\n",
      "0.5 [2.4526, 2.3375]\n"
     ]
    }
   ],
   "source": [
    "#sorted_dicts_by_train_loss = dict(sorted(loss_dict.items(), key=lambda item: min(item[1])))\n",
    "#sorted_dicts_by_train_loss = dict(sorted(loss_dict.items(), key=lambda item: min(item[1][1])))\n",
    "#{key:t[key] for idx, key in enumerate(t) if idx<10}\n",
    "for k, v in loss_dict.items():\n",
    "    print(k,v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "5036e60c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loss_dict = {k:v[0] for k,v in loss_dict.items() }\n",
    "val_loss_dict = {k:v[1] for k,v in loss_dict.items() }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "8e12d1ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_loss_dict, val_loss_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "b95b6568",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.2632, 0.0018)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(train_loss_dict.values()), min(train_loss_dict, key=train_loss_dict.get)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "606dcb0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.2654, 0.0031)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(val_loss_dict.values()), min(val_loss_dict, key=val_loss_dict.get)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "199c2236",
   "metadata": {},
   "source": [
    "Increasing the **regularization parameter `C`** increases both the validation and training losses. Initially, for **`C < ~0.035`**, the <u>validation losses are greater than the training losses</u>, but for **`C > ~0.035`** the <u>training losses are greater than the validation losses and grow faster too</u>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "38f2f4a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The test loss after applying a C=0 regularization parameter: 2.3323\n"
     ]
    }
   ],
   "source": [
    "C_test = 0\n",
    "logits_test = xenc_test.view(-1, 27*2) @ W                   \n",
    "counts_test = logits_test.exp()                             \n",
    "probs_test = counts_test / counts_test.sum(1, keepdim=True)     \n",
    "loss_test2 = -probs_test[torch.arange(num_test), ys_test2].log().mean() + C_test*(W**2).mean()\n",
    "print(f'The test loss after applying a C=0 regularization parameter: {loss_test2.item():.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ad29220",
   "metadata": {},
   "source": [
    "The best regularization parameter was `C = 0`, so the model was evaluated on the test set once using `C = 0` and the loss was **`2.2710`**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d853b88",
   "metadata": {},
   "source": [
    "-----\n",
    "<a id='4'></a>\n",
    "# 4. Indexing into Rows of `W` (Discard One-Hot Encoding)\n",
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b7ddee8",
   "metadata": {},
   "source": [
    "Currently we are indexing rows by performing the following matrix multiplication.\n",
    "```\n",
    "logits = x_enc @ w\n",
    "logits = [0, 1, 0, 0, ...] @ W\n",
    "logits =  W[xs[:, 0]] + W[xs[:, 1] + 27]\n",
    "```\n",
    "Same could be achivied by simply using the value of x_i as the index of the W matrix. Let's pick one x_i value from x_enc and check if it yields the same results when using both methods to determine the logits.\n",
    "\n",
    "`x_enc(idx) @ W = W[xs[idx, 0]] + W[xs[idx, 1] + 27]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "3d20dc92",
   "metadata": {},
   "outputs": [],
   "source": [
    "xs, ys = [], []\n",
    "\n",
    "for w in words:\n",
    "    chs = ['.'] + list(w) + ['.']\n",
    "    for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n",
    "        ix1 = stoi[ch1]\n",
    "        ix2 = stoi[ch2]\n",
    "        ix3 = stoi[ch3]\n",
    "        #print(ch1, ch2, ch3)\n",
    "        xs.append((ix1, ix2))\n",
    "        ys.append(ix3)\n",
    "        \n",
    "xs = torch.tensor(xs) \n",
    "ys = torch.tensor(ys)\n",
    "num = ys.nelement()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "c3a1b149",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([196113, 2, 27])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_enc = F.one_hot(xs, num_classes=27).float()\n",
    "x_enc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "7f8a855a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_enc[100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "ed517537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(11), torch.Size([1, 2, 27]), torch.Size([1, 54]))"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x100_enc_i = torch.tensor([x_enc[100].tolist()])\n",
    "x100_enc_i.argmax(), x100_enc_i.shape, x100_enc_i.view(-1, 27*2).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "0c7fc152",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x100_enc_i.view(-1, 27*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "16d15df3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.3609,  2.0429, -0.6675, -0.4255,  0.0137,  0.3082, -0.7364, -0.7811,\n",
       "         -0.7451,  0.4934, -0.7190, -0.6170,  1.6086,  0.1379,  1.8832,  0.2683,\n",
       "         -0.6588, -0.7640,  0.5670,  0.4279, -0.1699, -0.2077, -0.4730, -0.7689,\n",
       "         -0.7119, -0.0456, -0.4161]], grad_fn=<MmBackward0>)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x100_enc_i.view(-1, 27*2) @ W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "37fafe13",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 1.3609,  2.0429, -0.6675, -0.4255,  0.0137,  0.3082, -0.7364, -0.7811,\n",
       "        -0.7451,  0.4934, -0.7190, -0.6170,  1.6086,  0.1379,  1.8832,  0.2683,\n",
       "        -0.6588, -0.7640,  0.5670,  0.4279, -0.1699, -0.2077, -0.4730, -0.7689,\n",
       "        -0.7119, -0.0456, -0.4161], grad_fn=<AddBackward0>)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xW_100_enc_i = W[xs[100, 0]] + W[xs[100, 1] + 27]\n",
    "xW_100_enc_i "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "c3022601",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([1, 27]), torch.Size([27]))"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x100_enc_i.view(-1, 27*2) @ W).shape, (W[xs[100, 0]] + W[xs[100, 1] + 27]).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "b3f32775",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([196113, 27]), torch.Size([196113, 27]))"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x_enc.view(-1, 27*2) @ W).shape, (W[xs[:, 0]] + W[xs[:, 1] + 27]).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "9ca7f440",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[True, True, True, True, True, True, True, True, True, True, True, True,\n",
       "         True, True, True, True, True, True, True, True, True, True, True, True,\n",
       "         True, True, True]])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xW_100_enc_i == x100_enc_i.view(-1, 27*2) @ W"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cef8abb",
   "metadata": {},
   "source": [
    "-----\n",
    "<a id='5'></a>\n",
    "# 5. Indexing into Rows of `W` (Discard One-Hot Encoding)\n",
    "-------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "ecc625c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Negative Log Likelihood Loss: 2.3353\n"
     ]
    }
   ],
   "source": [
    "logits = x_enc.view(-1, 2*27) @ W\n",
    "counts = logits.exp()\n",
    "probs = counts / counts.sum(1, keepdim=True)\n",
    "loss = -probs[torch.arange(x_enc.shape[0]), ys].log().mean()\n",
    "print(f\"Average Negative Log Likelihood Loss: {loss.item():.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "9b789308",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Entropy Loss: 2.3354\n"
     ]
    }
   ],
   "source": [
    "loss_ce = F.cross_entropy(logits, ys)\n",
    "print(f\"Cross Entropy Loss: {loss_ce.item():.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ef6beb",
   "metadata": {},
   "source": [
    "We get the same results using both methods. However, `F.cross_entropy` is preferable because it combines the softmax step and loss function step together at once. Basically, `F.cross_entropy` incorporates transforming the neural net outputs to probabilities (1) with calculating the loss in one step.(**`logits`** -> **`losses`**)\n",
    "\n",
    "\n",
    "- <u>NN outputs to probabilities</u>: **`logits`** -> [exp(x)] -> **`counts`** -> [normalize(x)] -> **`probs`**\n",
    "- <u>Probabilities to losses</u>: **`probs`** -> [Average negative log likelihood(x)] -> **`losses`**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b9200fa",
   "metadata": {},
   "source": [
    "-----\n",
    "<a id='6'></a>\n",
    "# 6. meta-exercise: PyTorch Base Class for all Neural Network Modules\n",
    "-------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "af90d3b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "xs, ys = [], []\n",
    "\n",
    "for w in words:\n",
    "    chs = ['.'] + list(w) + ['.']\n",
    "    for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n",
    "        ix1 = stoi[ch1]\n",
    "        ix2 = stoi[ch2]\n",
    "        ix3 = stoi[ch3]\n",
    "        #print(ch1, ch2, ch3)\n",
    "        xs.append([ix1, ix2])\n",
    "        ys.append(ix3)\n",
    "\n",
    "xs = torch.tensor(xs) \n",
    "ys = torch.tensor(ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "00f798b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MLP(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.fc1 = torch.nn.Linear(2*27, 27) # first fully connected layer of MLP\n",
    "        torch.nn.init.normal_(self.fc1.weight, mean=0, std=1)\n",
    "        #torch.nn.init.xavier_uniform(self.fc1.weight)\n",
    "\n",
    "    def forward(self, x):\n",
    "        logits = W[x[:, 0]] + W[x[:, 1] + 27]\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "2906d924",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 249 iterations, the loss is 2.3354\n"
     ]
    }
   ],
   "source": [
    "C = 0\n",
    "model = MLP()\n",
    "# gradient descent\n",
    "for k in range(250):\n",
    "\n",
    "    # forward pass\n",
    "    logits = model(xs)\n",
    "    loss = F.cross_entropy(logits, ys) \n",
    "    loss += C * (model.fc1.weight ** 2).mean() # added regularization term\n",
    "    \n",
    "#     if k % 10 == 0:\n",
    "#         print(f'After {k} iterations, the loss is {loss.item():.4f}')\n",
    "\n",
    "    # backward pass\n",
    "    model.fc1.zero_grad() # set to zero the gradient (reset gradients of the layer)\n",
    "    loss.backward()\n",
    "\n",
    "    # update\n",
    "    model.fc1.weight.data += -50 * model.fc1.weight.grad\n",
    "print(f\"After {k} iterations, the loss is {loss.item():.4f}\")"
   ]
  }
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