01a_text_embeddings_balanced_epochs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision.all import *\n",
    "from fastai.learner import *\n",
    "from fastai.data.all import *\n",
    "from fastai.callback.tracker import SaveModelCallback\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib2 import Path\n",
    "import numpy as np\n",
    "import random\n",
    "from torch.nn import MSELoss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can download the ebooks [here](https://www.openslr.org/resources/12/original-books.tar.gz).\n",
    "\n",
    "We will need the pretrained embeddings from https://github.com/iamyuanchung/speech2vec-pretrained-vectors. We will use them to compare our results and also to figure out what vocab the authors of the speech2vec paper used for training. Let us start with the latter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/deprecation.py:143: FutureWarning: The sklearn.datasets.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.datasets. Anything that cannot be imported from sklearn.datasets is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "from six import iteritems\n",
    "from web.datasets.similarity import fetch_MEN, fetch_WS353, fetch_SimLex999\n",
    "from web.embeddings import fetch_GloVe\n",
    "from web.evaluate import evaluate_similarity\n",
    "from web.embedding import Embedding, Vocabulary\n",
    "from gensim.models import Word2Vec\n",
    "from gensim.models import KeyedVectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "speech2vec = KeyedVectors.load_word2vec_format('../speech2vec-pretrained-vectors/speech2vec/50.vec', binary=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37622"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab = set(speech2vec.vocab.keys())\n",
    "len(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab = [w for w in vocab if w not in set([\"reservation\", \"cholera\", \"inviolate\", \"granite\", \"pardons\", \"uselessly\", \"apes\", \"mammoth\", \"importation\", \"cedar\", \"tonneau\", \"mason\", \"excommunication\", \"oblivious\", \"universality\", \"migrations\", \"dumont\", \"vane\", \"opprobrious\", \"uninterruptedly\", \"babble\", \"thibet\", \"jostle\", \"capture\", \"scandalized\", \"ambiguity\", \"hysterical\", \"gertrude\", \"begat\", \"parched\", \"ether\", \"scotia\", \"des\", \"magnifying\", \"shackled\", \"luxuriance\", \"unblinking\", \"shrugging\", \"trolley\", \"becomingly\", \"confederacy\", \"giantess\", \"township\", \"ritz\", \"appear\\'d\", \"resignation\", \"munchausen\", \"faithfulness\", \"papyrus\", \"buzz\", \"pines\", \"calming\", \"chemically\", \"shimmered\", \"speculated\", \"daffodil\", \"infallible\", \"grill\", \"subsisting\", \"hangs\", \"worthlessness\", \"modred\", \"injected\", \"fold\", \"levin\", \"reaper\", \"magdalene\", \"notorious\", \"agriculture\", \"overcoats\", \"proverb\", \"afterward\", \"panic\", \"bemoan\", \"broadcast\", \"requests\", \"beaches\", \"graded\", \"handel\", \"acquaintanceship\", \"heathen\", \"kidney\", \"automobile\", \"galvanic\", \"walden\", \"miller\", \"disruption\", \"candelabra\", \"accost\", \"gardeners\", \"pioneer\", \"jeered\", \"acceptance\", \"courtesy\", \"keel\", \"darrow\", \"peremptory\", \"unflagging\", \"hag\", \"vehicle\", \"phoebe\", \"persuades\", \"anarchistic\", \"threadbare\", \"unwell\", \"repining\", \"punctuated\", \"scamps\", \"fortification\", \"plastic\", \"disheartened\", \"reviving\", \"icarus\", \"blushes\", \"bain\", \"precipitous\", \"outcasts\", \"valour\", \"premium\", \"invariable\", \"inaction\", \"defies\", \"briefest\", \"din\", \"forge\", \"janet\", \"mussel\", \"slouching\", \"windless\", \"stilted\", \"participated\", \"alliances\", \"wallowing\", \"doting\", \"mightier\", \"pave\", \"confine\", \"taverns\", \"surroundings\", \"trudge\", \"arid\", \"warring\", \"formulate\", \"buffeted\", \"orchestra\", \"twin\", \"assemblies\", \"unbending\", \"soothe\", \"corduroy\", \"athirst\", \"heart\\'s\", \"d\\'urville\", \"pugnacity\", \"saucepan\", \"babes\", \"caravans\", \"sallies\", \"neatest\", \"undergraduate\", \"pill\", \"rendezvous\", \"administers\", \"scion\", \"spattered\", \"drunkenness\", \"fatherland\", \"affable\", \"protects\", \"covetous\", \"unbeknown\", \"orderlies\", \"connoisseurs\", \"convulsed\", \"numbered\", \"valet\", \"housings\", \"gladden\", \"ruffian\", \"unpractical\", \"torres\", \"affright\", \"idealism\", \"maze\", \"deprives\", \"ventilation\", \"intractable\", \"recent\", \"shelled\", \"rumblings\", \"chords\", \"quoting\", \"fraulein\", \"beetle\", \"guatemala\", \"scribble\", \"considerately\", \"caucasus\", \"clue\", \"amalgamated\", \"gloss\", \"teresa\", \"henceforth\", \"host\\'s\", \"fanned\", \"butchered\", \"sluggishly\", \"lineament\", \"hesitating\", \"redeem\", \"relates\", \"pedro\", \"depository\", \"quaver\", \"offender\", \"electing\", \"felicitous\", \"milwaukee\", \"availing\", \"emphatic\", \"combatants\", \"rarefied\", \"crape\", \"deprecating\", \"forestall\", \"hypothetical\", \"attics\", \"celebration\", \"stylish\", \"askance\", \"insoluble\", \"researches\", \"unqualified\", \"threshing\", \"pas\", \"accursed\", \"lamed\", \"mexico\", \"incisions\", \"bertrand\", \"thirsting\", \"una\", \"twopenny\", \"negatively\", \"telescopes\", \"mildew\", \"katherine\", \"madonna\", \"memorable\", \"catastrophes\", \"displace\", \"wage\", \"clipped\", \"jolt\", \"explication\", \"brooks\", \"palazzo\", \"frere\", \"quizzically\", \"drifts\", \"mobility\", \"crocus\", \"lobbies\", \"generating\", \"deservedly\", \"heredity\", \"earthen\", \"jericho\", \"renaissance\", \"cruising\", \"introductions\", \"nourish\", \"warp\", \"divorced\", \"comment\", \"ingots\", \"renouncing\", \"habitual\", \"curates\", \"bruising\", \"misjudged\", \"dews\", \"alacrity\", \"mainland\", \"myriad\", \"hinge\", \"authorized\", \"rump\", \"alluvial\", \"communicative\", \"profuse\", \"abiding\", \"bodice\", \"horizontally\", \"pelts\", \"slayers\", \"kidnap\", \"comforters\", \"simultaneous\", \"boost\", \"arrow\", \"suggestively\", \"exclamation\", \"whipped\", \"olivia\", \"imply\", \"gascon\", \"swamps\", \"spiny\", \"porpoise\", \"fir\", \"festooned\", \"leaveth\", \"directness\", \"rex\", \"seth\", \"recalls\", \"athletics\", \"investment\", \"resounding\", \"prone\", \"forswear\", \"blemish\", \"believers\", \"misunderstanding\", \"admissible\", \"insidiously\", \"watts\", \"hammer\", \"lucidity\", \"stouter\", \"animates\", \"padding\", \"fasted\", \"consents\", \"candlesticks\", \"wag\", \"poems\", \"exemplar\", \"produces\", \"concealments\", \"evading\", \"western\", \"participant\", \"cuffed\", \"surrendered\", \"gnats\", \"cruelties\", \"fancier\", \"executions\", \"orb\", \"bail\", \"flow\", \"sheltered\", \"reproduced\", \"cinders\", \"conferring\", \"surname\", \"consciences\", \"consecrated\", \"lover\\'s\", \"blackfellow\", \"richardson\", \"jaguar\", \"remissness\", \"exerts\", \"ivied\", \"justices\", \"hulls\", \"subdivided\", \"complex\", \"flip\", \"blotches\", \"vowing\", \"osiris\", \"permanence\", \"jot\", \"tracking\", \"hog\\'s\", \"mishap\", \"falcon\", \"arbiter\", \"mewed\", \"achieve\", \"incriminating\", \"cumberland\", \"mishaps\", \"slackened\", \"reptiles\", \"persevered\", \"sobs\", \"carrot\", \"intellectually\", \"clicking\", \"absurdity\", \"umbrage\", \"uniformly\", \"nunnery\", \"transcendent\", \"oration\", \"charger\", \"patriarch\", \"forbidding\", \"justin\", \"risky\", \"entrancing\", \"radius\", \"instanced\", \"amuses\", \"shallow\", \"dispirited\", \"homogeneous\", \"defenceless\", \"staples\", \"proselytes\", \"crafts\", \"wicks\", \"emolument\", \"statute\", \"speculation\", \"adroitly\", \"contradicting\", \"keepeth\", \"sycamore\", \"plotting\", \"launching\", \"skepticism\", \"polonius\", \"rumor\", \"clarion\", \"fanged\", \"makeshift\", \"hurling\", \"maximilian\", \"unfortunates\", \"carline\", \"referendum\", \"dump\", \"sprinkle\", \"baling\", \"loathed\", \"warwickshire\", \"effie\", \"discernible\", \"protested\", \"uncontrolled\", \"whaling\", \"all\\'s\", \"prim\", \"diffuse\", \"designs\", \"agatha\", \"inadequacy\", \"memorial\", \"harpooner\", \"cantered\", \"converge\", \"outset\", \"headaches\", \"supple\", \"flank\", \"ambiguous\", \"carols\", \"buenos\", \"nursing\", \"richmond\", \"raja\\'s\", \"retracing\", \"sailor\", \"appellations\", \"tierra\", \"protestants\", \"pointer\", \"outpost\", \"rama\", \"prey\", \"cornelia\", \"devise\", \"undergraduates\", \"stumbles\", \"hieroglyphic\", \"overspread\", \"damn\", \"nicolas\", \"plenitude\", \"collects\", \"ascribing\", \"regiment\", \"foretell\", \"plots\", \"cumulative\", \"egregious\", \"worshiped\", \"butlers\", \"souvenirs\", \"evans\", \"amounts\", \"misrepresented\", \"winners\", \"justify\", \"caked\", \"traditions\", \"mamelukes\", \"adjudged\", \"genera\", \"interplay\", \"juices\", \"unpack\", \"erroneous\", \"dolphins\", \"jenny\", \"enables\", \"paroxysms\", \"regal\", \"depict\", \"squall\", \"christina\", \"apologized\", \"objectively\", \"scarlatina\", \"slaveholder\", \"slang\", \"rhythmical\", \"mirthful\", \"amazement\", \"myrtles\", \"thitherward\", \"foothold\", \"rashly\", \"stanley\", \"emphatically\", \"favorably\", \"exploitation\", \"undergoing\", \"taxation\", \"lair\", \"scotchman\", \"gratifications\", \"bishoprics\", \"imprint\", \"urchins\", \"kettles\", \"chariots\", \"relentless\", \"saddened\", \"dislocated\", \"call\\'d\", \"foals\", \"dreariest\", \"exorcise\", \"scholar\", \"nations\", \"exempted\", \"quietness\", \"hedgerow\", \"flask\", \"inflicted\", \"exclaims\", \"filth\", \"bins\", \"assaulting\", \"nicknames\", \"emigrated\", \"sandal\", \"tarry\", \"player\", \"chores\", \"frieda\", \"tradesmen\", \"digestive\", \"therein\", \"laurels\", \"permanently\", \"topple\", \"wilfrid\", \"futures\", \"junk\", \"disrespectful\", \"avoids\", \"prophetess\", \"fulfilled\", \"hardening\", \"assigning\", \"outwardly\", \"disinterred\", \"kneel\", \"welding\", \"dissipating\", \"spider\", \"woodwork\", \"apathetic\", \"abdication\", \"month\\'s\", \"steer\", \"figuratively\", \"glowering\", \"skye\", \"wedded\", \"wilhelm\", \"terrier\", \"presentiment\", \"recommends\", \"delineated\", \"mine\\'s\", \"stamboul\", \"category\", \"ally\", \"teaches\", \"criticising\", \"invigorated\", \"petroleum\", \"cronies\", \"justinian\", \"nimrod\", \"georgian\", \"concede\", \"cherry\", \"plumbing\", \"hedged\", \"taps\", \"relate\", \"islets\", \"uprising\", \"bowen\", \"grayish\", \"gorgeous\", \"barometers\", \"shank\", \"undeserving\", \"compositions\", \"keith\", \"popularly\", \"authors\", \"shoemaker\", \"epoch\", \"yale\", \"displeasure\", \"saucers\", \"introspection\", \"crests\", \"diligent\", \"plaything\", \"aversion\", \"subtile\", \"acid\", \"abdominal\", \"crystallize\", \"eleanor\", \"invitations\", \"resorted\", \"inimitable\", \"liege\", \"babe\", \"dubbed\", \"mirage\", \"unreal\", \"overwhelmingly\", \"maize\", \"horseshoe\", \"deputation\", \"derivation\", \"rote\", \"hugo\", \"inbred\", \"shortening\", \"compel\", \"girlish\", \"befell\", \"insured\", \"junior\", \"keener\", \"attachment\", \"gossamer\", \"ling\", \"bryce\", \"sleight\", \"discreetly\", \"expeditions\", \"aristocrats\", \"compels\", \"languages\", \"whims\", \"intact\", \"billiards\", \"plundering\", \"solidarity\", \"pledged\", \"mee\", \"paraphernalia\", \"trading\", \"purity\", \"upland\", \"harsher\", \"dolphin\", \"redundant\", \"dromedary\", \"gorges\", \"reinstate\", \"maddest\", \"conclude\", \"tendered\", \"decade\", \"warble\", \"arresting\", \"fangled\", \"jangle\", \"stabbed\", \"twelvemonth\", \"impunity\", \"nomination\", \"predecessor\", \"latitude\", \"arras\", \"cairns\", \"transcends\", \"fingered\", \"progressive\", \"transact\", \"holders\", \"rig\", \"obviate\", \"teem\", \"glacial\", \"derisive\", \"beak\", \"refracting\", \"soaks\", \"enticing\", \"emblem\", \"depraved\", \"insensibility\", \"collectively\", \"margaret\\'s\", \"pawnbroker\", \"swamped\", \"wrists\", \"unwrapped\", \"pocketbook\", \"unfasten\", \"hypocritically\", \"uprose\", \"seducer\", \"avidity\", \"oasis\", \"amenable\", \"anciently\", \"dot\", \"rapine\", \"herbage\", \"damaging\", \"reproachfully\", \"scales\", \"bleating\", \"levying\", \"afield\", \"gunner\", \"kangaroos\", \"rebukes\", \"cain\", \"husbandmen\", \"regretful\", \"distinguish\", \"hartford\", \"grog\", \"charon\", \"cowl\", \"roamed\", \"stations\", \"managing\", \"decorative\", \"unevenly\", \"collectors\", \"handicap\", \"waterless\", \"cloudless\", \"varro\", \"sealed\", \"stanley\\'s\", \"calendar\", \"deposit\", \"peaks\", \"bengal\", \"strengthens\", \"homestead\", \"attribute\", \"saturnalia\", \"standstill\", \"holdings\", \"forasmuch\", \"beginneth\", \"admitting\", \"especial\", \"arctic\", \"unacknowledged\", \"atom\", \"lenses\", \"disarm\", \"arnica\", \"laboring\", \"promenading\", \"dessert\", \"executor\", \"acquitted\", \"handiwork\", \"enshrined\", \"estuary\", \"beetling\", \"inestimable\", \"sag\", \"doughty\", \"furred\", \"clamored\", \"advised\", \"specify\", \"citron\", \"overestimate\", \"irreconcilable\", \"correctly\", \"unerringly\", \"pouch\", \"prate\", \"disheveled\", \"treaty\", \"nutritious\", \"poesy\", \"acquaint\", \"mocked\", \"buckles\", \"thereabouts\", \"whir\", \"apennines\", \"decently\", \"vitality\", \"clamorously\", \"falseness\", \"plashing\", \"flaw\", \"orphaned\", \"gnomes\", \"massacred\", \"eloquence\", \"landmarks\", \"fissures\", \"inaccessible\", \"praiseworthy\", \"sooty\", \"ave\", \"colouring\", \"bean\", \"bulgaria\", \"episode\", \"wessex\", \"exiled\", \"incensed\", \"prescribes\", \"giggling\", \"frequented\", \"immoderately\", \"unwavering\", \"incursion\", \"solidity\", \"protector\", \"dissolves\", \"fella\", \"delicious\", \"excluding\", \"depriving\", \"mesopotamia\", \"joyousness\", \"tides\", \"hapless\", \"bountiful\", \"fireman\", \"weber\", \"jason\", \"jes\", \"descends\", \"ancestors\", \"eateth\", \"moody\", \"clarence\", \"fabrication\", \"tacit\", \"votaries\", \"response\", \"convictions\", \"prescott\", \"ragamuffin\", \"blunted\", \"encountering\", \"successor\", \"permits\", \"wharves\", \"incomparably\", \"grounded\", \"landowners\", \"rarest\", \"blush\", \"jackals\", \"spill\", \"observable\", \"yelp\", \"commonplace\", \"flowered\", \"battery\", \"shielding\", \"undulation\", \"flows\", \"mormon\", \"harped\", \"quartz\", \"eliot\", \"repairs\", \"hock\", \"abbot\\'s\", \"conformable\", \"sleepless\", \"etchings\", \"louvre\", \"watcher\", \"ironed\", \"capriciously\", \"ida\", \"hiring\", \"snubbed\", \"maternity\", \"shaven\", \"signing\", \"console\", \"apt\", \"falconer\", \"clients\", \"entanglement\", \"congressmen\", \"crackled\", \"deputy\", \"freezing\", \"ailment\", \"competitions\", \"angeles\", \"tombs\", \"melody\", \"diagrams\", \"porcupine\", \"friction\", \"afterthought\", \"relegated\", \"honours\", \"harvested\", \"tobias\", \"uninfluenced\", \"bronze\", \"discovers\", \"views\", \"hawaii\", \"sez\", \"films\", \"demure\", \"theatres\", \"precipices\", \"pristine\", \"cigars\", \"bitterest\", \"vicissitudes\", \"cupids\", \"alcestis\", \"storming\", \"unamiable\", \"closes\", \"solicitous\", \"engrossing\", \"overloaded\", \"gasoline\", \"horde\", \"ay\", \"nationalities\", \"visages\", \"orifice\", \"withers\", \"abduction\", \"garner\", \"mandates\", \"slant\", \"periodical\", \"culled\", \"sap\", \"journalism\", \"partner\\'s\", \"paralytic\", \"eulogies\", \"cathedrals\", \"presumes\", \"tolls\", \"robust\", \"wistful\", \"leghorn\", \"islander\", \"notary\", \"drumming\", \"chokes\", \"hazardous\", \"noblesse\", \"anguished\", \"bacchanalian\", \"interprets\", \"tremblingly\", \"deviations\", \"alien\", \"disturbs\", \"combustion\", \"discontinued\", \"equip\", \"dishonorable\", \"harvard\", \"outcome\", \"pedestrians\", \"squalling\", \"drowns\", \"tours\", \"voltaire\", \"bankrupt\", \"copses\", \"corrals\", \"fowl\", \"mottled\", \"manifested\", \"hairpins\", \"prongs\", \"ruining\", \"assailants\", \"retreating\", \"unpacked\", \"austere\", \"parallels\", \"glaciers\", \"speakest\", \"glutton\", \"wilson\", \"coca\", \"bella\", \"symphonic\", \"eustace\", \"epistle\", \"dangers\", \"ritual\", \"sarcasm\", \"cult\", \"calories\", \"pedestal\", \"inland\", \"dreamers\", \"scruff\", \"loudness\", \"hammond\", \"dissuade\", \"saracens\", \"fiddler\", \"jones\\'s\", \"trojans\", \"plausibility\", \"proclaims\", \"irresolute\", \"presentation\", \"laterally\", \"foresee\", \"memoir\", \"grudge\", \"shovel\", \"unveiled\", \"continent\", \"revisit\", \"qui\", \"babbling\", \"petted\", \"barnes\", \"forebore\", \"ploughboy\", \"suffocating\", \"inventions\", \"lucca\", \"endureth\", \"sanded\", \"ferdinando\", \"panel\", \"conversely\", \"earthquakes\", \"solar\", \"shakespeare\\'s\", \"immigrant\", \"transcend\", \"collation\", \"ridding\", \"delegates\", \"parasites\", \"underfoot\", \"limitless\", \"subsidiary\", \"rupert\", \"strap\", \"devil\\'s\", \"crepe\", \"critias\", \"incest\", \"farewells\", \"interwoven\", \"plants\", \"untrue\", \"credible\", \"quarreling\", \"natchez\", \"mastiff\", \"sorrel\", \"dosed\", \"elias\", \"congruity\", \"mara\", \"forgiving\", \"narrowed\", \"rivets\", \"unavoidably\", \"nation\\'s\", \"falkland\", \"flinging\", \"cunningham\", \"terminal\", \"impoverished\", \"justifies\", \"likenesses\", \"maidens\", \"signora\", \"wegg\\'s\", \"macedonian\", \"unsurpassed\", \"reconsidered\", \"inspect\", \"unnamed\", \"signals\", \"churchwarden\", \"unpractised\", \"educators\", \"docile\", \"malady\", \"commotion\", \"hyaena\", \"neighborhood\", \"hispaniola\", \"cultured\", \"breaker\", \"imposts\", \"spaniels\", \"baal\", \"numa\", \"ices\", \"onslaught\", \"labored\", \"ravens\", \"scholars\", \"cornwall\", \"cyrus\", \"swedenborg\", \"alfred\", \"familiarity\", \"retailed\", \"vacillation\", \"unconditionally\", \"cloister\", \"fraught\", \"wraps\", \"bosh\", \"inimical\", \"benign\", \"planning\", \"hewn\", \"satan\\'s\", \"pietro\", \"delegation\", \"illiberal\", \"differs\", \"carnegie\", \"gateway\", \"disconsolate\", \"mortality\", \"hordes\", \"strews\", \"armistice\", \"glut\", \"nutt\", \"argues\", \"modernized\", \"blocks\", \"nineteenth\", \"porcupines\", \"visualize\", \"furnishings\", \"formalities\", \"blackening\", \"sendeth\", \"slumbered\", \"figured\", \"impeccable\", \"hemmed\", \"scabbard\", \"cedars\", \"flirting\", \"al\", \"islet\", \"sappho\", \"automatic\", \"gerald\", \"confirming\", \"poison\", \"oilcloth\", \"tractor\", \"adela\", \"passageway\", \"forges\", \"warde\", \"compulsion\", \"poppy\", \"blankness\", \"titans\", \"ensuing\", \"dormant\", \"stoke\", \"allowances\", \"waistcoats\", \"indulgence\", \"deserved\", \"beth\", \"commoner\", \"withouten\", \"flirtation\", \"coasting\", \"domingo\", \"householder\", \"tearfully\", \"jealously\", \"revolution\", \"sunken\", \"encompass\", \"eventually\", \"keys\", \"oxford\", \"redemption\", \"pension\", \"subtler\", \"overtopped\", \"fishy\", \"cushioned\", \"scaffolding\", \"swans\", \"planet\", \"projected\", \"patroness\", \"resentments\", \"queried\", \"kai\", \"circumference\", \"mites\", \"conformity\", \"operatic\", \"forecastle\", \"masthead\", \"deprived\", \"spheres\", \"glisten\", \"guaranteed\", \"ecstatic\", \"fruits\", \"basic\", \"disembarked\", \"saps\", \"restoration\", \"prudently\", \"specifically\", \"metres\", \"smoulders\", \"lecturers\", \"bracing\", \"villains\", \"charles\\'s\", \"butted\", \"immigration\", \"dumas\", \"pounce\", \"attacked\", \"infected\", \"luckily\", \"mid\", \"bristle\", \"chum\", \"millville\", \"verily\", \"diffusing\", \"sufficed\", \"vermilion\", \"cerebral\", \"specialized\", \"award\", \"implanted\", \"homespun\", \"illusion\", \"reproduces\", \"inferred\", \"julius\", \"filigree\", \"oppressively\", \"scenery\", \"sunshiny\", \"graze\", \"beheaded\", \"tootles\", \"soudan\", \"districts\", \"mucilage\", \"provocation\", \"mansions\", \"stilton\", \"faery\", \"proprietorship\", \"southwestern\", \"equinox\", \"misuse\", \"tweed\", \"partakes\", \"propping\", \"pumice\", \"hoeing\", \"ruse\", \"acumen\", \"massed\", \"dupes\", \"ties\", \"vitriol\", \"cudgel\", \"brighton\", \"clambered\", \"reticent\", \"dorians\", \"parables\", \"aggressively\", \"seventeenth\", \"engulfed\", \"angel\\'s\", \"deploring\", \"pauline\", \"stateliness\", \"occupants\", \"loki\", \"slates\", \"cures\", \"viewpoint\", \"shelves\", \"bede\", \"purest\", \"cheerily\", \"jeopardy\", \"farmhouse\", \"vat\", \"sacraments\", \"install\", \"dislodge\", \"unprincipled\", \"hampshire\", \"felony\", \"collector\\'s\", \"impropriety\", \"telescopic\", \"conspirator\", \"survey\", \"graver\", \"promulgated\", \"turneth\", \"lairs\", \"demoralization\", \"implore\", \"humanitarian\", \"lashings\", \"canvassing\", \"untold\", \"deformities\", \"readjustment\", \"curb\", \"follows\", \"behoved\", \"volunteered\", \"poisons\", \"unfitness\", \"incisive\", \"intoxication\", \"prussians\", \"soothes\", \"maneuvered\", \"categorically\", \"supremacy\", \"inexcusable\", \"exercises\", \"mastership\", \"dory\", \"wheelbarrow\", \"concludes\", \"disjointed\", \"withdraws\", \"crochet\", \"comets\", \"highlands\", \"combated\", \"fakir\", \"revenues\", \"multiplied\", \"galore\", \"outrageously\", \"cleaves\", \"bowsprit\", \"ornament\", \"prairies\", \"feasts\", \"explosions\", \"dependency\", \"accustoming\", \"mowing\", \"mirth\", \"unequivocally\", \"pearson\", \"derogatory\", \"union\", \"exploding\", \"colloquial\", \"crawford\", 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\"thickest\", \"whelp\", \"alas\", \"ashy\", \"sorrowfully\", \"matrimony\", \"paraphrase\", \"marshy\", \"weasel\", \"boss\", \"abate\", \"arabian\", \"mouldy\", \"repel\", \"wrestle\", \"cook\\'s\", \"harbouring\", \"rot\", \"mischance\", \"snored\", \"judith\", \"reproof\", \"pore\", \"wenches\", \"samaritan\", \"hawking\", \"smock\", \"huntsman\", \"witch\", \"contemptuous\", \"hark\", \"beareth\", \"preyed\", \"magistrate\", \"cabbages\", \"cherish\", \"picks\", \"windlass\", \"jangling\", \"shove\", \"assyrian\", \"mitten\", \"whiles\", \"florins\", \"impiety\", \"afeared\", \"grovelling\", \"buck\", \"prick\", \"unborn\", \"retorts\", \"rochester\", \"clip\", \"hound\", \"rumbled\", \"warnings\", \"hive\", \"contagion\", \"chalk\", \"goldsmith\", \"chemist\", \"dun\", \"cleanly\", \"unpolished\", \"savouring\", \"strangeness\", \"punched\", \"aggravate\", \"catalogue\", \"tabernacle\", \"haply\", \"fretting\", \"fading\", \"hail\", \"personages\", \"allegory\", \"ancestor\", \"cypress\", \"coffins\", \"romulus\", \"poultry\", \"wither\", \"gentlewoman\", \"troublous\", \"wringing\", \"remonstrating\", \"chink\", \"humorously\", \"trumpeter\", \"venetian\", \"subservient\", \"chivalrous\", \"savoury\", \"hinting\", \"cramp\", \"seekest\", \"meddling\", \"quench\", \"unimaginable\", \"navy\", \"recorder\", \"servile\", \"tether\", \"flighty\", \"coop\", \"what\\'ll\", \"that\\'ll\", \"throve\", \"squirrel\", \"grumbling\", \"scapegrace\", \"slink\", \"curtained\", \"crib\", \"yarn\", \"gingerbread\", \"behindhand\", \"clamouring\", \"behaving\", \"pussy\", \"direful\", \"cat\\'s\", \"sweetened\", \"whisked\", \"disagreed\", \"busiest\", \"stocking\", \"tim\", \"hips\", \"thump\", \"plums\", \"gayest\", \"frisky\", \"seaside\", \"licked\", \"nap\", \"strayed\", \"sunning\", \"tuck\", \"tumbler\", \"enlivened\", \"harrow\", \"oaks\", \"fetching\", \"maw\", \"howls\", \"bagged\", \"fighter\", \"swirl\", \"liverpool\", \"leak\", \"furnaces\", \"zoological\", \"ivory\", \"prehistoric\", \"titanic\", \"squeeze\", \"majestically\", \"facial\", \"land\\'s\", \"body\\'s\", \"bunker\", \"roosting\", \"zoo\", \"daybreak\", \"overhauling\", \"outrun\", \"armor\", \"kicks\", \"glimpsed\", \"rammed\", \"luckier\", \"muddled\", \"bandits\", \"misplaced\", \"tighter\", \"understandable\", \"jackets\", \"releasing\", \"bile\", \"flaring\", \"disowned\", \"livers\", \"stewed\", \"preserves\", \"henri\", \"leafy\", \"oozed\", \"screwing\", \"calculation\", \"fins\", \"navigating\", \"frying\", \"prickly\", \"mushrooms\", \"who\\'ll\", \"gunpowder\", \"cork\", \"aground\", \"carpets\", \"chunks\", \"nudged\", \"fineness\", \"hug\", \"heeled\", \"skylight\", \"josephine\", \"oyster\", \"oysters\", \"reefs\", \"beefsteak\", \"cruised\", \"shoal\", \"hunk\", \"parrots\", \"lengthened\", \"whizzed\", \"loading\", \"necklaces\", \"slings\", \"cannibal\", \"satellites\", \"sling\", \"kangaroo\", \"prancing\", \"concussion\", \"snarls\", \"jointed\", \"bulges\", \"sealing\", \"pets\", \"unfurled\", \"peninsular\", \"panama\", \"manned\", \"insides\", \"ripping\", \"cloaked\", \"spiky\", \"periwinkles\", \"greedily\", \"ridiculously\", \"diver\", \"speechless\", \"fortifications\", \"mocha\", \"nowadays\", \"clogged\", \"brandished\", \"ton\", \"bottoms\", \"lookout\", \"carp\", \"hitting\", \"emptying\", \"deserting\", \"palaces\", \"enamel\", \"pits\", \"wrack\", \"hustling\", \"photographic\", \"flicker\", \"basins\", \"sizes\", \"thinned\", \"stark\", \"bank\\'s\", \"subsiding\", \"icebergs\", \"sentinels\", \"powell\", \"diamonds\", \"thinnest\", \"detaching\", \"supervised\", \"punctured\", \"dangle\", \"obstruct\", \"catches\", \"dissected\", \"swooped\", \"exploded\", \"confront\", \"bragged\", \"cheque\", \"woody\", \"kindling\", \"filing\", \"mouthful\", \"lambent\", \"stays\", \"iridescent\", \"invincible\", \"blotted\", \"dodge\", \"fooling\", \"bunched\", \"donkeys\", \"officially\", \"sneaked\", \"horsemanship\", \"ochre\", \"colourless\", \"ducked\", \"pinnacle\", \"hurting\", \"slack\", \"hogshead\", \"handcuffs\", \"awning\", \"brimming\", \"battened\", \"chant\", \"decrepit\", \"pawed\", \"pyramid\", \"hoof\", \"valorous\", \"coughing\", \"mealy\", \"predatory\", \"sneaking\", \"bicycle\", \"drip\", \"fathomless\", \"biscuit\", \"scoop\", \"pinafore\", \"steeped\", \"whoops\", \"kit\", \"rumoured\", \"dime\", \"recruits\", \"barked\", \"jam\", \"marster\", \"lingo\", \"banging\", \"bedtime\", \"kisses\", \"prosy\", \"stormed\", \"elasticity\", \"consultation\", \"cuffs\", \"crestfallen\", \"ungracious\", \"baker\\'s\", \"susan\\'s\", \"mahogany\", \"emma\", \"toned\", \"measles\", \"childishness\", \"emma\\'s\", \"behaves\", \"spinster\", \"racket\", \"reasonableness\", \"inconsiderate\", \"mustard\", \"mince\", \"fidgeted\", \"singled\", \"communing\", \"foibles\", \"flurried\", \"distresses\", \"daggers\", \"puss\", \"infancy\", \"genteel\", \"skips\", \"autumnal\", \"unclasped\", \"ajar\", \"loudest\", \"pendulum\", \"churchyard\", \"crumb\", \"quarry\", \"scalding\", \"spectre\", \"unmeaning\", \"cradles\", \"ambuscade\", \"mashed\", \"snake\\'s\", \"shroud\", \"inviolable\", \"duds\", \"seams\", \"victors\", \"smuggle\", \"daresay\", \"furtively\", \"strawberry\", \"gooseberry\", \"bang\", \"jessie\", \"turkeys\", \"creepy\", \"hisself\", \"squealing\", \"housemaid\", \"thankfulness\", \"bespoke\", \"negligently\", \"edmund\", \"aunts\", \"dummy\", \"sillier\", \"palings\", \"twopence\", \"obtrusive\", \"loophole\", \"emboldened\", \"badger\", \"concession\", \"plainer\", \"corkscrew\", \"injunction\", \"subscribed\", \"penal\", \"harboured\", \"advertise\", \"browsing\", \"dozing\", \"unsympathetic\", \"bristled\", \"extraordinarily\", \"undermined\", \"bracken\", \"parapet\", \"explosive\", \"cob\", \"favourably\", \"fume\", \"teacups\", \"inanity\", \"flavour\", \"sulkily\", \"circus\", \"silky\", \"wholesale\", \"blatant\", \"garrisoned\", \"whitewashed\", \"tranquillity\", \"eton\", \"lugged\", \"rations\", \"fiercer\", \"forefathers\", \"resigning\", \"burton\", \"sheepish\", \"grimace\", \"cornet\", \"gauze\", \"provident\", \"contrivances\", \"swum\", \"bray\", \"poke\", \"scandalous\", \"deserts\", \"hump\", \"stunted\", \"feint\", \"chatty\", \"briskness\", \"mournfully\", \"perspiring\", \"tradespeople\", \"onions\", \"halter\", \"bandit\", \"faltering\", \"oppress\", \"petersburg\", \"abhorred\", \"throngs\", \"rascality\", \"flapped\", \"torchlight\", \"cavernous\", \"undoing\", \"spoons\", \"irrevocably\", \"bead\", \"crumble\", \"umbrellas\", \"banisters\", \"counterpart\", \"muddle\", \"veterinary\", \"tick\", \"thicken\", \"tights\", \"drury\", \"birthright\", \"sorted\", \"something\\'s\", \"dickens\", \"officious\", \"outsides\", \"how\\'s\", \"d\\'ye\", \"treacle\", \"bounce\", \"currant\", \"sloppy\", \"renunciation\", \"forwards\", \"nuptial\", \"theatrical\", \"loosing\", \"submissively\", \"crossly\", \"downhill\", \"tickling\", \"toughest\", \"defenders\", \"smoothing\", \"william\\'s\", \"squared\", \"sweated\", \"mongrel\", \"manufacturer\", \"uglier\", \"shirk\", \"endurable\", \"blots\", \"trash\", \"accumulates\", \"nobody\\'s\", \"motioning\", \"wig\", \"tiara\", \"functionary\", \"swap\", \"mayflower\", \"blinked\", \"disquietude\", \"giggled\", \"flabby\", \"lineal\", \"repressing\", \"cropping\", \"yachting\", \"outnumber\", \"provisioned\", \"bangs\", \"portcullis\", \"mutely\", \"ogre\", \"hatchet\", \"lengthening\", \"wry\", \"chinks\", \"deciphered\", \"arcade\", \"caverns\", \"tearless\", \"irradiated\", \"apprentice\", \"heaves\", \"cuirass\", \"middling\", \"tanner\", \"defrauded\", \"guineas\", \"undertaker\", \"indolently\", \"highland\", \"receiver\", \"plundered\", \"computation\", \"threepence\", \"testimonial\", \"minerals\", \"ralph\", \"northumberland\", \"predilection\", \"confining\", \"burrowed\", \"elegancies\", \"pottage\", \"plainest\", \"pans\", \"smugglers\", \"debauched\", \"banditti\", \"delegate\", \"obstinately\", \"clamoured\", \"turpentine\", \"partiality\", \"whetted\", \"credulity\", \"coaches\", \"insupportable\", \"sixpences\", \"showered\", \"fainter\", \"auspicious\", \"fagged\", \"pal\", \"pitchers\", \"cuff\", \"widowhood\", \"prisoner\\'s\", \"apprised\", \"hilly\", \"excusing\", \"harvey\", \"traitors\", \"fighters\", \"tramps\", \"tombstone\", \"combustibles\", \"coaxingly\", \"cowards\", \"uncles\", \"branded\", \"monetary\", \"jailed\", \"generically\", \"dries\", \"twirl\", \"rosebuds\", \"burglars\", \"spiders\", \"pitfalls\", \"footsore\", \"freshen\", \"shrivel\", \"unseasonable\", \"fetches\", \"milkman\", \"arcadian\", \"orgies\", \"crocodiles\", \"preside\", \"thunderbolt\", \"watchman\", \"wakening\", \"squeezing\", \"ginger\", \"baseless\", \"meddled\", \"scalps\", \"minced\", \"enchanter\", \"saws\", \"ravings\", \"mummy\", \"artifice\", \"comers\", \"huntsmen\", \"shovels\", \"tyre\", \"timed\", \"sullenly\", \"irrevocable\", \"obsequies\", \"grudgingly\", \"figs\", \"rattles\", \"lagged\", \"squaws\", \"wigwam\", \"blighting\", \"capering\", \"boxwood\", \"quadrupeds\", \"downwards\", \"clawing\", \"butting\", \"ostrich\", \"pests\", \"caterpillars\", \"performer\", \"whooping\", \"pegged\", \"riven\", \"lances\", \"unchained\", \"indubitable\", \"winner\", \"custard\", \"kine\", \"thunderstorm\", \"cogent\", \"axes\", \"murdering\", \"facetious\", \"extraction\", \"stringy\", \"anear\", \"cramming\", \"shins\", \"poker\", \"telegraphic\", \"observatory\", \"stoutest\", \"mercifully\", \"enrolled\", \"besieging\", \"dismounting\", \"indescribably\", \"bellow\", \"blaming\", \"persuasions\", \"volley\", \"foil\", \"pestering\", \"bulged\", \"pikes\", \"pumping\", \"insinuated\", \"lanky\", \"nonsensical\", \"protestation\", \"clamps\", \"undiscovered\", \"crockery\", \"pianoforte\", \"forenoon\", \"hair\\'s\", \"scamp\", \"repulsed\", \"onlookers\", \"blindfold\", \"handbill\", \"loafer\", \"joker\", \"humbug\", \"craven\", \"gases\", \"fictions\", \"hybrid\", \"clod\", \"keynote\", \"creased\", \"deacon\", \"tempter\", \"jackson\\'s\", \"economist\", \"varlet\", \"underclothes\", \"thatcher\", \"sawyer\", \"budge\", \"i\\'s\", \"resk\", \"jim\\'s\", \"sawyer\\'s\", \"huck\", \"clumb\", \"drownded\", \"busted\", \"ole\", \"dey\\'s\", \"slouch\", \"looky\", \"lemme\", \"whack\", \"gimme\", \"catched\", \"cuss\", \"skillet\", \"sifted\", \"ferryboat\", \"sid\", \"missouri\", \"lolled\", \"uz\", \"b\\'lieve\", \"skift\", \"kase\", \"um\", \"granny\", \"luck\\'s\", \"stopper\", \"barlow\", \"reticule\", \"hank\", \"abner\", \"finn\", \"nigger\\'s\", \"sarah\\'s\", \"goshen\", \"snack\", \"blinders\", \"cottonwood\", \"snag\", \"we\\'s\", \"jake\", \"hatfield\", \"packard\", \"slanted\", \"unfavorable\", \"slam\", \"whacks\", \"sollermun\", \"shet\", \"doan\", \"chile\", \"bill\\'s\", \"gumption\", \"chillen\", \"jabber\", \"whooped\", \"whoop\", \"thumped\", \"wigwams\", \"nab\", \"handiest\", \"armpits\", \"scalped\", \"mort\", \"buckskin\", \"parlors\", \"peaches\", \"yellower\", \"gunn\\'s\", \"chirrup\", \"locket\", \"emmeline\", \"many\\'s\", \"decanter\", \"bitters\", \"buck\\'s\", \"peppered\", \"ornery\", \"hog\", \"woodpile\", \"lonesomeness\", \"chute\", \"mesmerism\", \"petting\", \"humbugs\", \"ike\", \"shucks\", \"armand\", \"shakespearian\", \"garrick\", \"romeo\", \"juliet\", \"juliet\\'s\", \"nightcap\", \"printers\", \"handbills\", \"lemonade\", \"heathens\", \"pranced\", \"hamlet\\'s\", \"awnings\", \"holler\", \"afire\", \"mum\", \"leastways\", \"harvey\\'s\", \"cubby\", \"frocks\", \"biscuits\", \"snuggled\", \"orphans\", \"bartley\", \"undertaker\\'s\", \"yourn\", \"tarred\", \"mumps\", \"warbling\", \"blister\", \"cravats\", \"blubber\", \"snoring\", \"deviltry\", \"sass\", \"phelps\", \"starter\", \"sawmill\", \"where\\'d\", \"wrinkling\", \"bugs\", \"hub\", \"slumped\", \"waylay\", \"injun\", \"mosey\", \"pickaxe\", \"jackass\", \"nat\", \"nat\\'s\", \"sally\\'s\", \"mooning\", \"grindstone\", \"franklin\", \"sawdust\", \"pegging\", \"camped\", \"clack\", \"mothered\", \"chalked\", \"huckleberry\", \"nipped\", \"hutch\", \"margate\", \"swishing\", \"cyril\\'s\", \"snail\\'s\", \"tubby\", \"seashore\", \"scurried\", \"radiantly\", \"tiredness\", \"anthea\\'s\", \"carrier\\'s\", \"skinny\", \"beale\", \"quailed\", \"ostler\", \"tuppence\", \"crock\", \"papa\\'s\", \"laps\", \"mopped\", \"pinafores\", \"crispin\", \"uncorked\", \"frothed\", \"kid\\'s\", \"a\\'most\", \"paraffin\", \"squeaking\", \"postman\", \"pinky\", \"scurry\", \"clergyman\\'s\", \"marlborough\", \"highwaymen\", \"candied\", \"spotty\", \"twenties\", \"vicarage\", \"someone\\'s\", \"vicar\\'s\", \"desperadoes\", \"poachers\", \"trimming\", \"lancet\", \"misdoubt\", \"enchanter\\'s\", \"urchin\", \"gatehouse\", \"drawbridge\", \"dishcloth\", \"guardroom\", \"suet\", \"sharpening\", \"fiddling\", \"dahlias\", \"marigolds\", \"asters\", \"flounced\", \"bobs\", \"wearer\\'s\", \"turkey\\'s\", \"sillies\", \"snarled\", \"haystack\", \"draper\", \"slouched\", \"sydney\", \"carton\", \"peeps\", \"waterbury\", \"hilary\", \"craning\", \"sunstroke\", \"scalping\", \"storks\", \"mush\", \"reconnoitre\", \"squinting\", \"reddest\", \"redskins\", \"aces\", \"browner\", \"redskin\", \"firewood\", \"peasmarsh\", \"beale\\'s\", \"garnet\", \"pincushion\", \"burrow\", \"behoof\", \"curtseyed\", \"wheezy\", \"morgiana\", \"manipulated\", \"sleary\", \"eyeglass\", \"brassy\", \"imbecility\", \"cot\", \"stun\", \"saunter\", \"penknife\", \"monotonously\", \"rebounded\", \"drunkards\", \"apprenticed\", \"goers\", \"tho\", \"natur\", \"bestirred\", \"unluckily\", \"prig\", \"loo\", \"wonderingly\", \"curtsey\", \"blackpool\", \"compeers\", \"unnerved\", \"submissive\", \"nowt\", \"yo\", \"wean\", \"irritably\", \"lass\", \"dunnot\", \"mutters\", \"dank\", \"brutish\", \"aw\", \"codicil\", \"blotch\", \"millers\", \"doorways\", \"managerial\", \"desks\", \"townspeople\", \"truckle\", \"armoury\", \"cutlasses\", \"tripod\", \"informer\", \"coldest\", \"enervated\", \"jem\", \"humbugging\", \"braggart\", \"unrelenting\", \"cram\", \"boredom\", \"notabilities\", \"dines\", \"capitally\", \"sagaciously\", \"iscariot\", \"bein\", \"canna\", \"lyin\", \"tak\", \"dawdled\", \"hearthrug\", \"discordantly\", \"rebels\", \"staunchly\", \"irresolution\", \"takin\", \"cherishes\", \"thinkin\", \"unselfishly\", \"pegler\", \"blundered\", \"unfrequent\", \"jocular\", \"bounderbys\", \"gusty\", \"maggot\", \"incredulously\", \"searchingly\", \"persuasively\", \"blustered\", \"domestics\", \"amiably\", \"backgammon\", \"morosely\", \"placidity\", \"intimacies\", \"insignificance\", \"fertilization\", \"acrimony\", \"sweetbreads\", \"appertaining\", \"contradistinguished\", \"sleepily\", \"gutters\", \"anatomist\", \"undeceived\", \"deeps\", \"smouldered\", \"abjectly\", \"lancashire\", \"ingenuousness\", \"crimsoned\", \"pent\", \"scroll\", \"wantin\", \"gaslight\", \"maligned\", \"larks\", \"pitman\", \"dyin\", \"bellman\", \"cauliflower\", \"oddity\", \"gig\", \"pinning\", \"polka\", \"audaciously\", \"intruded\", \"airless\", \"sissy\"])]\n",
    "vocab_set = set(vocab) # to speed things up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4612"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "starting_lines = {\n",
    "    '1004/1004.txt.utf-8': 535,\n",
    "    '10123/10123.txt.utf-8': 85,\n",
    "    '10359/10359.txt.utf-8': 76,\n",
    "    '10360/10360.txt.utf-8': 57,\n",
    "    '10378/10378.txt.utf-8': 96,\n",
    "    '10390/10390.txt.utf-8': 89,\n",
    "    '1193/1193.txt.utf-8': 272,\n",
    "    '12441/12441-0.txt': 101,\n",
    "    '1249/1249.txt.utf-8': 614,\n",
    "    '1325/1325.txt.utf-8': 434,\n",
    "    '1674/1674.txt.utf-8': 360,\n",
    "    '2046/2046.txt.utf-8': 295,\n",
    "    '2147/2147.txt.utf-8': 66,\n",
    "    '2184/2184.txt.utf-8': 302,\n",
    "    '2383/2383.txt.utf-8': 408,\n",
    "    '2486/2486.txt.utf-8': 293,\n",
    "    '2488/2488.txt.utf-8': 495,\n",
    "    '2512/2512-0.txt': 113,\n",
    "    '2515/2515.txt.utf-8': 281,\n",
    "    '2678/2678.txt.utf-8': 305,\n",
    "    '2679/2679.txt.utf-8': 308,\n",
    "    '269/269-0.txt': 67,\n",
    "    '282/282-0.txt': 43,\n",
    "    '2891/2891.txt.utf-8': 336,\n",
    "    '3053/3053.txt.utf-8': 340,\n",
    "    '3169/3169.txt.utf-8': 386,\n",
    "    '325/325.txt.utf-8': 170,\n",
    "    '3300/3300.txt.utf-8': 40,\n",
    "    '34757/34757-0.txt': 185,\n",
    "    '3604/3604.txt.utf-8': 612,\n",
    "    '3623/3623.txt.utf-8': 342,\n",
    "    '3697/3697.txt.utf-8': 449,\n",
    "    '37660/37660-0.txt': 74,\n",
    "    '4028/4028.txt.utf-8': 402,\n",
    "    '4042/4042.txt.utf-8': 438,\n",
    "    '435/435.txt.utf-8': 62,\n",
    "    '6456/6456.txt.utf-8': 53,\n",
    "    '7098/7098.txt.utf-8': 77,\n",
    "    '76/76.txt.utf-8': 579,\n",
    "    '778/778.txt.utf-8': 297,\n",
    "    '786/786-0.txt': 158,\n",
    "}   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 15.1 s, sys: 1.57 s, total: 16.6 s\n",
      "Wall time: 36.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "word_pairs = []\n",
    "\n",
    "for fn, starting_line in starting_lines.items():\n",
    "    with open(f'data/books/LibriSpeech/books/utf-8/{fn}') as file:\n",
    "        out = []\n",
    "        for i, line in enumerate(file.readlines()):\n",
    "            if i < starting_line: continue\n",
    "            line = line.strip()\n",
    "            toks = []\n",
    "\n",
    "            for tok in line.split():\n",
    "                tok = tok.lower()\n",
    "                if tok in vocab_set:\n",
    "                    toks.append(tok)\n",
    "                else:\n",
    "                    toks.append('<UNK>')\n",
    "            out += toks\n",
    "\n",
    "        for i, word in enumerate(out):\n",
    "            if word is '<UNK>': continue\n",
    "            for offset in [-2, -1, 1, 2]:\n",
    "                if i + offset < 0 or i + offset >= len(out): continue\n",
    "                target_word = out[i+offset]\n",
    "                if target_word is '<UNK>': continue\n",
    "                word_pairs.append([word, target_word])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.shuffle(word_pairs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from collections import Counter\n",
    "\n",
    "# c = Counter([w0 for w0, w1 in word_pairs])\n",
    "\n",
    "# missing = []\n",
    "# for w, count in c.items():\n",
    "#     if count < 100: missing.append(w)\n",
    "\n",
    "# ', '.join([f'\"{w}\"' for w in missing])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['body', 'things'],\n",
       " ['and', 'tell'],\n",
       " ['is', 'cents'],\n",
       " ['proved', 'wonderfully'],\n",
       " ['of', 'sake'],\n",
       " ['that', 'the'],\n",
       " ['disagreeable', 'to'],\n",
       " ['gay', 'voice'],\n",
       " ['art', 'lost'],\n",
       " ['was', 'as'],\n",
       " ['him', 'and'],\n",
       " ['determined', 'or'],\n",
       " ['that', 'follow'],\n",
       " ['of', 'great'],\n",
       " ['of', 'imposed'],\n",
       " ['passed', 'word'],\n",
       " ['has', 'never'],\n",
       " ['at', 'sitting'],\n",
       " ['time', 'first'],\n",
       " ['men', 'the']]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_pairs[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8399722"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(word_pairs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vocab = list(set(w0 for w0, w1 in word_pairs))\n",
    "word2index = {w: i for i, w in enumerate(vocab)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Dataset():\n",
    "    def __init__(self, pairs, len_mult=1000):\n",
    "        self.pairs = pairs\n",
    "        self.len_mult = len_mult\n",
    "        self.word2row_idxs = defaultdict(list)\n",
    "\n",
    "        for idx, (source_word, target_word) in enumerate(pairs):\n",
    "            if target_word in vocab_set:\n",
    "                self.word2row_idxs[source_word].append(idx)\n",
    "    def __len__(self):\n",
    "        return len(vocab) * self.len_mult\n",
    "    def __getitem__(self, idx):\n",
    "        word_idx = idx % len(vocab)\n",
    "        word = vocab[word_idx]\n",
    "        row_idx = np.random.choice(self.word2row_idxs[word])\n",
    "        source_word, target_word = self.pairs[row_idx]\n",
    "        return word2index[source_word], word2index[target_word]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ds = Dataset(word_pairs[:7_700_000])\n",
    "valid_ds = Dataset(word_pairs[7_700_000:], 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# missing = []\n",
    "# for w, idxs in train_ds.word2row_idxs.items():\n",
    "#     if len(idxs) == 0: missing.append(w)\n",
    "# for w, idxs in  valid_ds.word2row_idxs.items():\n",
    "#     if len(idxs) == 0 : missing.append(w)\n",
    "\n",
    "# print(', '.join([f'\"{w}\"' for w in missing]))\n",
    "\n",
    "# for i in range(len(vocab)):\n",
    "#     train_ds[i]\n",
    "    \n",
    "# for i in range(len(vocab)):\n",
    "#     valid_ds[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4612000, 230600)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_ds), len(valid_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "BS = 2048\n",
    "NUM_WORKERS = 8\n",
    "\n",
    "train_dl = DataLoader(train_ds, BS, NUM_WORKERS, shuffle=True)\n",
    "valid_dl = DataLoader(valid_ds, BS, NUM_WORKERS)\n",
    "\n",
    "dls = DataLoaders(train_dl, valid_dl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model(Module):\n",
    "    def __init__(self, hidden_size=50):\n",
    "        self.embeddings = nn.Embedding(len(vocab), hidden_size)\n",
    "        self.linear = nn.Linear(hidden_size, len(vocab))\n",
    "            \n",
    "    def forward(self, x):\n",
    "        x = self.embeddings(x)\n",
    "        return self.linear(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = Learner(\n",
    "    dls.cuda(),\n",
    "    Model().cuda(),\n",
    "    loss_func=CrossEntropyLossFlat(),\n",
    "    opt_func=Adam,\n",
    "    metrics=[accuracy]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5.275205</td>\n",
       "      <td>5.366485</td>\n",
       "      <td>0.112164</td>\n",
       "      <td>05:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5.193288</td>\n",
       "      <td>5.309926</td>\n",
       "      <td>0.120230</td>\n",
       "      <td>05:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5.130229</td>\n",
       "      <td>5.275091</td>\n",
       "      <td>0.123300</td>\n",
       "      <td>05:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>5.079602</td>\n",
       "      <td>5.255422</td>\n",
       "      <td>0.122580</td>\n",
       "      <td>05:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.035384</td>\n",
       "      <td>5.229244</td>\n",
       "      <td>0.123387</td>\n",
       "      <td>05:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>4.997400</td>\n",
       "      <td>5.216551</td>\n",
       "      <td>0.125069</td>\n",
       "      <td>04:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>4.950434</td>\n",
       "      <td>5.199488</td>\n",
       "      <td>0.125317</td>\n",
       "      <td>04:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>4.925382</td>\n",
       "      <td>5.190596</td>\n",
       "      <td>0.128352</td>\n",
       "      <td>04:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>4.898867</td>\n",
       "      <td>5.181565</td>\n",
       "      <td>0.129081</td>\n",
       "      <td>04:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>4.882050</td>\n",
       "      <td>5.159481</td>\n",
       "      <td>0.130121</td>\n",
       "      <td>04:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>4.856300</td>\n",
       "      <td>5.154222</td>\n",
       "      <td>0.130347</td>\n",
       "      <td>05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>4.825101</td>\n",
       "      <td>5.147906</td>\n",
       "      <td>0.132229</td>\n",
       "      <td>05:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>4.819412</td>\n",
       "      <td>5.138477</td>\n",
       "      <td>0.132806</td>\n",
       "      <td>04:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>4.797683</td>\n",
       "      <td>5.136057</td>\n",
       "      <td>0.132758</td>\n",
       "      <td>05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>4.788361</td>\n",
       "      <td>5.137747</td>\n",
       "      <td>0.133127</td>\n",
       "      <td>05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>4.768821</td>\n",
       "      <td>5.127964</td>\n",
       "      <td>0.133461</td>\n",
       "      <td>05:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>4.765866</td>\n",
       "      <td>5.134545</td>\n",
       "      <td>0.133786</td>\n",
       "      <td>05:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>4.753489</td>\n",
       "      <td>5.119493</td>\n",
       "      <td>0.134536</td>\n",
       "      <td>05:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>4.734592</td>\n",
       "      <td>5.119400</td>\n",
       "      <td>0.134224</td>\n",
       "      <td>05:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>4.725729</td>\n",
       "      <td>5.133537</td>\n",
       "      <td>0.131878</td>\n",
       "      <td>05:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>4.712478</td>\n",
       "      <td>5.139512</td>\n",
       "      <td>0.133057</td>\n",
       "      <td>05:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>4.708564</td>\n",
       "      <td>5.123372</td>\n",
       "      <td>0.134185</td>\n",
       "      <td>05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>4.696864</td>\n",
       "      <td>5.120550</td>\n",
       "      <td>0.133786</td>\n",
       "      <td>04:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>4.699534</td>\n",
       "      <td>5.129873</td>\n",
       "      <td>0.134076</td>\n",
       "      <td>04:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>4.684427</td>\n",
       "      <td>5.131408</td>\n",
       "      <td>0.134133</td>\n",
       "      <td>04:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>4.678338</td>\n",
       "      <td>5.138782</td>\n",
       "      <td>0.133426</td>\n",
       "      <td>05:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>4.675047</td>\n",
       "      <td>5.135251</td>\n",
       "      <td>0.134918</td>\n",
       "      <td>05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>4.670907</td>\n",
       "      <td>5.124706</td>\n",
       "      <td>0.135009</td>\n",
       "      <td>04:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>4.667906</td>\n",
       "      <td>5.138563</td>\n",
       "      <td>0.133461</td>\n",
       "      <td>04:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>4.669808</td>\n",
       "      <td>5.143836</td>\n",
       "      <td>0.132840</td>\n",
       "      <td>05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>4.660567</td>\n",
       "      <td>5.131320</td>\n",
       "      <td>0.134241</td>\n",
       "      <td>05:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>4.658246</td>\n",
       "      <td>5.142937</td>\n",
       "      <td>0.134271</td>\n",
       "      <td>04:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>4.661414</td>\n",
       "      <td>5.135188</td>\n",
       "      <td>0.134822</td>\n",
       "      <td>04:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>4.659635</td>\n",
       "      <td>5.136370</td>\n",
       "      <td>0.134818</td>\n",
       "      <td>05:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>4.650879</td>\n",
       "      <td>5.130359</td>\n",
       "      <td>0.134258</td>\n",
       "      <td>05:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>4.653129</td>\n",
       "      <td>5.138649</td>\n",
       "      <td>0.133738</td>\n",
       "      <td>05:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>4.646383</td>\n",
       "      <td>5.141904</td>\n",
       "      <td>0.135186</td>\n",
       "      <td>05:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>4.638758</td>\n",
       "      <td>5.149887</td>\n",
       "      <td>0.134198</td>\n",
       "      <td>05:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>4.640759</td>\n",
       "      <td>5.142891</td>\n",
       "      <td>0.134549</td>\n",
       "      <td>05:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>4.638592</td>\n",
       "      <td>5.144846</td>\n",
       "      <td>0.134896</td>\n",
       "      <td>05:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>4.636355</td>\n",
       "      <td>5.137404</td>\n",
       "      <td>0.135447</td>\n",
       "      <td>05:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>4.631473</td>\n",
       "      <td>5.152006</td>\n",
       "      <td>0.133838</td>\n",
       "      <td>05:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>4.633555</td>\n",
       "      <td>5.146039</td>\n",
       "      <td>0.133140</td>\n",
       "      <td>05:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>4.639254</td>\n",
       "      <td>5.152481</td>\n",
       "      <td>0.133725</td>\n",
       "      <td>05:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>4.633391</td>\n",
       "      <td>5.147282</td>\n",
       "      <td>0.135108</td>\n",
       "      <td>05:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>4.630073</td>\n",
       "      <td>5.145233</td>\n",
       "      <td>0.133964</td>\n",
       "      <td>05:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>4.622160</td>\n",
       "      <td>5.151458</td>\n",
       "      <td>0.132463</td>\n",
       "      <td>05:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>4.625717</td>\n",
       "      <td>5.150868</td>\n",
       "      <td>0.133435</td>\n",
       "      <td>05:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>4.631735</td>\n",
       "      <td>5.151288</td>\n",
       "      <td>0.133786</td>\n",
       "      <td>05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>4.622919</td>\n",
       "      <td>5.154229</td>\n",
       "      <td>0.133057</td>\n",
       "      <td>05:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>4.625145</td>\n",
       "      <td>5.149819</td>\n",
       "      <td>0.134258</td>\n",
       "      <td>05:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>4.622703</td>\n",
       "      <td>5.162801</td>\n",
       "      <td>0.134701</td>\n",
       "      <td>05:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>4.613230</td>\n",
       "      <td>5.156139</td>\n",
       "      <td>0.134328</td>\n",
       "      <td>05:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>4.620250</td>\n",
       "      <td>5.158879</td>\n",
       "      <td>0.134133</td>\n",
       "      <td>05:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>4.631753</td>\n",
       "      <td>5.166248</td>\n",
       "      <td>0.134458</td>\n",
       "      <td>05:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>4.612599</td>\n",
       "      <td>5.158644</td>\n",
       "      <td>0.134363</td>\n",
       "      <td>05:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>4.616346</td>\n",
       "      <td>5.156932</td>\n",
       "      <td>0.134783</td>\n",
       "      <td>05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>4.607988</td>\n",
       "      <td>5.175451</td>\n",
       "      <td>0.133365</td>\n",
       "      <td>05:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>4.619757</td>\n",
       "      <td>5.162996</td>\n",
       "      <td>0.134939</td>\n",
       "      <td>05:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>4.622205</td>\n",
       "      <td>5.166253</td>\n",
       "      <td>0.133920</td>\n",
       "      <td>05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>4.616467</td>\n",
       "      <td>5.166188</td>\n",
       "      <td>0.133010</td>\n",
       "      <td>05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>4.615540</td>\n",
       "      <td>5.160424</td>\n",
       "      <td>0.133534</td>\n",
       "      <td>04:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>4.618485</td>\n",
       "      <td>5.162355</td>\n",
       "      <td>0.134219</td>\n",
       "      <td>05:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>4.622671</td>\n",
       "      <td>5.155396</td>\n",
       "      <td>0.134887</td>\n",
       "      <td>05:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>4.603102</td>\n",
       "      <td>5.163652</td>\n",
       "      <td>0.134232</td>\n",
       "      <td>05:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>4.601651</td>\n",
       "      <td>5.162756</td>\n",
       "      <td>0.134332</td>\n",
       "      <td>05:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>4.612507</td>\n",
       "      <td>5.161337</td>\n",
       "      <td>0.135247</td>\n",
       "      <td>04:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>4.603437</td>\n",
       "      <td>5.156661</td>\n",
       "      <td>0.133621</td>\n",
       "      <td>04:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>4.613223</td>\n",
       "      <td>5.169813</td>\n",
       "      <td>0.134501</td>\n",
       "      <td>04:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>4.613356</td>\n",
       "      <td>5.159193</td>\n",
       "      <td>0.134150</td>\n",
       "      <td>05:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>4.612409</td>\n",
       "      <td>5.158749</td>\n",
       "      <td>0.134965</td>\n",
       "      <td>05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>4.603409</td>\n",
       "      <td>5.171458</td>\n",
       "      <td>0.134324</td>\n",
       "      <td>04:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>4.605336</td>\n",
       "      <td>5.159513</td>\n",
       "      <td>0.134983</td>\n",
       "      <td>04:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>4.605320</td>\n",
       "      <td>5.172830</td>\n",
       "      <td>0.134276</td>\n",
       "      <td>05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>4.605912</td>\n",
       "      <td>5.172896</td>\n",
       "      <td>0.134913</td>\n",
       "      <td>05:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>4.605249</td>\n",
       "      <td>5.174880</td>\n",
       "      <td>0.134245</td>\n",
       "      <td>05:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>4.609533</td>\n",
       "      <td>5.174242</td>\n",
       "      <td>0.133677</td>\n",
       "      <td>05:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>4.600833</td>\n",
       "      <td>5.175335</td>\n",
       "      <td>0.133257</td>\n",
       "      <td>05:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>4.608615</td>\n",
       "      <td>5.168785</td>\n",
       "      <td>0.133951</td>\n",
       "      <td>05:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>4.604119</td>\n",
       "      <td>5.175068</td>\n",
       "      <td>0.134363</td>\n",
       "      <td>05:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>4.606126</td>\n",
       "      <td>5.168785</td>\n",
       "      <td>0.134579</td>\n",
       "      <td>05:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>4.596734</td>\n",
       "      <td>5.167299</td>\n",
       "      <td>0.135312</td>\n",
       "      <td>05:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>4.608734</td>\n",
       "      <td>5.173178</td>\n",
       "      <td>0.133946</td>\n",
       "      <td>05:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>4.605975</td>\n",
       "      <td>5.162995</td>\n",
       "      <td>0.134024</td>\n",
       "      <td>05:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>4.611616</td>\n",
       "      <td>5.158595</td>\n",
       "      <td>0.134718</td>\n",
       "      <td>05:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>4.605959</td>\n",
       "      <td>5.173126</td>\n",
       "      <td>0.135252</td>\n",
       "      <td>05:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>4.606497</td>\n",
       "      <td>5.177045</td>\n",
       "      <td>0.133209</td>\n",
       "      <td>05:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>4.609554</td>\n",
       "      <td>5.169492</td>\n",
       "      <td>0.134536</td>\n",
       "      <td>05:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>4.596308</td>\n",
       "      <td>5.162471</td>\n",
       "      <td>0.134337</td>\n",
       "      <td>05:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>4.603715</td>\n",
       "      <td>5.177938</td>\n",
       "      <td>0.134480</td>\n",
       "      <td>05:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>4.599377</td>\n",
       "      <td>5.172542</td>\n",
       "      <td>0.133890</td>\n",
       "      <td>05:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>4.594715</td>\n",
       "      <td>5.191763</td>\n",
       "      <td>0.132931</td>\n",
       "      <td>05:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>4.603384</td>\n",
       "      <td>5.172720</td>\n",
       "      <td>0.133677</td>\n",
       "      <td>05:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>4.612012</td>\n",
       "      <td>5.176952</td>\n",
       "      <td>0.133773</td>\n",
       "      <td>05:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>4.601963</td>\n",
       "      <td>5.172427</td>\n",
       "      <td>0.135234</td>\n",
       "      <td>05:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>4.598845</td>\n",
       "      <td>5.168215</td>\n",
       "      <td>0.133920</td>\n",
       "      <td>05:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>4.595694</td>\n",
       "      <td>5.180325</td>\n",
       "      <td>0.133747</td>\n",
       "      <td>05:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>4.598853</td>\n",
       "      <td>5.181450</td>\n",
       "      <td>0.134245</td>\n",
       "      <td>05:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>4.602789</td>\n",
       "      <td>5.184424</td>\n",
       "      <td>0.134601</td>\n",
       "      <td>05:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>4.605288</td>\n",
       "      <td>5.178628</td>\n",
       "      <td>0.134879</td>\n",
       "      <td>05:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>4.602851</td>\n",
       "      <td>5.176650</td>\n",
       "      <td>0.133638</td>\n",
       "      <td>05:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>4.601348</td>\n",
       "      <td>5.175569</td>\n",
       "      <td>0.134406</td>\n",
       "      <td>05:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>4.598460</td>\n",
       "      <td>5.183992</td>\n",
       "      <td>0.131392</td>\n",
       "      <td>05:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>4.602215</td>\n",
       "      <td>5.167121</td>\n",
       "      <td>0.133669</td>\n",
       "      <td>05:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>4.601404</td>\n",
       "      <td>5.186110</td>\n",
       "      <td>0.134068</td>\n",
       "      <td>05:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>4.599029</td>\n",
       "      <td>5.184690</td>\n",
       "      <td>0.132979</td>\n",
       "      <td>05:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>4.603448</td>\n",
       "      <td>5.180229</td>\n",
       "      <td>0.133621</td>\n",
       "      <td>05:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>4.592659</td>\n",
       "      <td>5.182448</td>\n",
       "      <td>0.133586</td>\n",
       "      <td>05:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>4.600913</td>\n",
       "      <td>5.167016</td>\n",
       "      <td>0.135113</td>\n",
       "      <td>05:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>4.598020</td>\n",
       "      <td>5.176447</td>\n",
       "      <td>0.134415</td>\n",
       "      <td>05:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>4.593395</td>\n",
       "      <td>5.169193</td>\n",
       "      <td>0.134258</td>\n",
       "      <td>05:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>4.595388</td>\n",
       "      <td>5.180669</td>\n",
       "      <td>0.134003</td>\n",
       "      <td>05:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>4.597306</td>\n",
       "      <td>5.173533</td>\n",
       "      <td>0.134124</td>\n",
       "      <td>05:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>4.595091</td>\n",
       "      <td>5.181830</td>\n",
       "      <td>0.133651</td>\n",
       "      <td>05:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>4.589716</td>\n",
       "      <td>5.187234</td>\n",
       "      <td>0.134462</td>\n",
       "      <td>05:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>4.600167</td>\n",
       "      <td>5.190926</td>\n",
       "      <td>0.134107</td>\n",
       "      <td>04:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>4.592661</td>\n",
       "      <td>5.182637</td>\n",
       "      <td>0.134475</td>\n",
       "      <td>04:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>117</td>\n",
       "      <td>4.601406</td>\n",
       "      <td>5.173690</td>\n",
       "      <td>0.134159</td>\n",
       "      <td>05:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>4.594600</td>\n",
       "      <td>5.178669</td>\n",
       "      <td>0.132862</td>\n",
       "      <td>05:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>4.600182</td>\n",
       "      <td>5.168757</td>\n",
       "      <td>0.135451</td>\n",
       "      <td>04:58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NUM_EPOCHS = 120\n",
    "learn.fit(NUM_EPOCHS, lr=1e-3, cbs=SaveModelCallback(fname='text_embeddings', every_epoch=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[0;32m/home/radek/workspace/audio-embeddings/mtrand.pyx\u001b[0m(900)\u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n",
      "\n",
      "ipdb> exit\n",
      "--KeyboardInterrupt--\n",
      "ipdb> exit\n"
     ]
    }
   ],
   "source": [
    "%debug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "2+2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evalute embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = learn.model.embeddings.weight.cpu().detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "e = Embeddings(\n",
    "    embeddings,\n",
    "    vocab\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fast: ['fast', 'soon', 'just', 'well', 'quietly']\n",
      "lost: ['lost', 'left', 'won', 'changed', 'caught']\n",
      "small: ['small', 'large', 'smaller', 'larger', 'considerable']\n",
      "true: ['true', 'truth', 'this', 'simple', 'clear']\n",
      "nice: ['nice', 'little', 'clever', 'pleasant', 'good']\n",
      "slow: ['slow', 'steady', 'rapid', 'quick', 'apparent']\n"
     ]
    }
   ],
   "source": [
    "for w in ['fast', 'lost', 'small', 'true', 'nice', 'slow']:\n",
    "    print(f'{w}: {e.nn_words_to(e[w])}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating embeddings using [word-embeddings-benchmarks](https://github.com/kudkudak/word-embeddings-benchmarks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "from six import iteritems\n",
    "from web.datasets.similarity import fetch_MEN, fetch_WS353, fetch_SimLex999\n",
    "from web.embeddings import fetch_GloVe\n",
    "from web.evaluate import evaluate_similarity\n",
    "from web.embedding import Embedding, Vocabulary\n",
    "from gensim.models import Word2Vec\n",
    "from gensim.models import KeyedVectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "tasks = {\n",
    "    \"MEN\": fetch_MEN(),\n",
    "    \"WS353\": fetch_WS353(),\n",
    "    \"SIMLEX999\": fetch_SimLex999()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "our_embeddings = Embedding(\n",
    "    Vocabulary(vocab),\n",
    "    embeddings\n",
    ")\n",
    "\n",
    "speech2vec = KeyedVectors.load_word2vec_format('../speech2vec-pretrained-vectors/word2vec/50.vec', binary=False) \n",
    "speech2vec_embeddings = Embedding(Vocabulary(list(speech2vec.vocab.keys())), speech2vec.vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Missing 2897 words. Will replace them with mean vector\n",
      "/opt/conda/lib/python3.7/site-packages/web-0.0.1-py3.7.egg/web/evaluate.py:336: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
      "  A = np.vstack(w.get(word, mean_vector) for word in X[:, 0])\n",
      "/opt/conda/lib/python3.7/site-packages/web-0.0.1-py3.7.egg/web/evaluate.py:337: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
      "  B = np.vstack(w.get(word, mean_vector) for word in X[:, 1])\n",
      "Missing 339 words. Will replace them with mean vector\n",
      "Missing 676 words. Will replace them with mean vector\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spearman correlation of scores on MEN 0.10638617248045758\n",
      "Spearman correlation of scores on WS353 0.22700063451645328\n",
      "Spearman correlation of scores on SIMLEX999 0.1048960986334698\n"
     ]
    }
   ],
   "source": [
    "for name, data in iteritems(tasks):\n",
    "    print(\"Spearman correlation of scores on {} {}\".format(name, evaluate_similarity(our_embeddings, data.X, data.y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Missing 392 words. Will replace them with mean vector\n",
      "Missing 61 words. Will replace them with mean vector\n",
      "Missing 24 words. Will replace them with mean vector\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spearman correlation of scores on MEN 0.6056592803599269\n",
      "Spearman correlation of scores on WS353 0.43349390636024643\n",
      "Spearman correlation of scores on SIMLEX999 0.25938770901422736\n"
     ]
    }
   ],
   "source": [
    "for name, data in iteritems(tasks):\n",
    "    print(\"Spearman correlation of scores on {} {}\".format(name, evaluate_similarity(speech2vec_embeddings, data.X, data.y)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loss decrease and improvements on semantic tasks as training progresses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "val_losses, accuracies, task_perf = [], [], []\n",
    "for i in range(NUM_EPOCHS):\n",
    "    learn.load(f'text_embeddings_{i}')\n",
    "    loss, accuracy = learn.validate()\n",
    "    val_losses.append(loss)\n",
    "    accuracies.append(accuracy)\n",
    "    \n",
    "    embeddings = learn.model.embeddings.weight.cpu().detach().numpy()\n",
    "    our_embeddings = Embedding(\n",
    "        Vocabulary([w.lower() for w in vocab]),\n",
    "        embeddings\n",
    "    )\n",
    "\n",
    "    task_perf.append([evaluate_similarity(our_embeddings, data.X, data.y) for name, data in iteritems(tasks)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "men, ws353, simlex999 = list(zip(*task_perf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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spPGbRoJnBaMP0VP4SCFo8HT0/bKhs8i04PcFtG5rpXFNI5kfZWLKNNH0TRPWHCvhl4fjMrto/raZlKdSEFqB/zh/Ri0bhbvd3VlCEDIrhOTHkqn/tJ64O+OIuCaClg0tFPyhgJxf5YALYm6JQbok+6/fT93yOrT+WsIuDSNgagDaAC2GGAOhs0M7g56UkoaVDdStqKPh8wbQwPSi6afU/el49avOpSNATJZS1vWw3h+wSCllx0Qzy6SUI/uTtjvHW+eypaCe6xZvZsmt0zgrtft/2MLCQgICAggLCzslfoHSJdk6Ziu2Uhujl4/GUeMge142GUsycFld5P46l1HLRhF5TeSJz0vHfC6tra0nbT6Xsn+WkX+3pyl04sOJJP0hyXM9ym1Iu2TCdxMIOsvT/NfV5gI3aE2HnwSrl1TTtK6J5L8l4xPhg3RLSv5eQuHDhSQ/nkzSH5LYc+kezNvNTC+eTsUrFZ7jaWHStkmdQaK/9l29j6avmxj9/mj8x/uzedhmQi8KZfSyw83a7dV2Kl6toOHzBlJfSO1SRt9W0Ia92o7L7KLw4UJat7eSuigV/7H+1H9Wj8vsYsT/G4HoqDNsWN3A3kv3Ip2e/1NjipFp+dP6/bdrybZgiDf0+tbbE3u1HZfV1fk2B55ivr0X7cVR7yBtcRrRP/f0w2pc28jeS/diiDWQuSITU8bxF/1LKcm9LRefCB/i7ojr8UneZXWxIWIDWpMWR4MDXBA8K5iR/x7JtgnbCPlJCKOXjaZlWws7puwg9rex1H9aj73KjkavwWU+XJylj9TjMrs4o+wM9CEDa+LudnqGSmpa46l3rPuojrLnyki4LwFns5O65XU46g4/8EbOjWTk2yMRWkH+vfmU/7McbYCWkAtCCL0olOiboo8p2u6PU63OZVCCy1HbnoFnnoCMgaY95HiDS3ZlCxc9/y0vz5vIRWNiut3mVJuJsv3Ldprvbibo2SCMs41It6ThqgakRSLbJNpELSHvhJy0QHiyZ6JsL2tnc8JmTGNMTNo6CY1BQ8NXDey5YA++6b5MzZ464HOXUrJ/7n7qPqxj1LJRZF2VRdIjSST/NRkpJfl35+OX7kfcb3t/G+yOJdvCvp/toy23Db9RflizrUzZO+WYVnL94bK42H/Dfuo/7mjSrQHckPlRJuFzPA9HO8/dSXtJOxlvZ2DZayHvjjxPUJzUd1BsL21nS8oWfFN9yfwkE79UP0/T7tcrcVld+I30w3+8P77Dui/S3HHWDmylNqYXTu980t57+V5aNrUw9suxxwTm5g3N7LloD65WF36j/Ii4OoKkh5PQ+AzsRtnwZQN7LtwDePpcRV0fRdilYZ63kyPGs6t5v4b91+xn3NfjCJgUgDXXiv8EfzQ6DXn35FHxcgVnVpzJwQcPUvPfGs6sOBO3w03BAwUIvSD8inD80vyoWVpD9TvVhF8ZTspjKQPK6yGOJgc7pu/AVmLD3eYm7q44Rjw/AvCMauFsduJqdVG9pJrChwoJvTgUfaie6neqib83npS/pwz4Oh3tVAsuSCn7/AIKgR3AdmB+D9tcARwAGoAzBpL26C8/Pz95PMoarTLpwU/l0u+Ljyv9UNhxzg65adgm6Xa6O5fVrayTa1gj17BGNm9uHsLcnRzVy6ql9aC1y7Kyl8pk/Zf1x71Pe71dbojZINdo1sg1mjWyraTth2azk9PqlPn358s1mjUy6/qsH7Qvt9MtK96qkDUf1kh7g11uTNood5yzQ0opZeuuVrmGNbLk6RIppeec1urWyvz78/u178K/Fso1rJHfhn4rvw39VhY+Wig3xG7o/Ntawxq5RqyRllzLMWmbNjZ1btPwdcPh4+vXyrz/y+vxmG2lbbJ0UancOWunXMMambMgp9vtKt+ulEWPF0m3233Muj2X7ZHfRX4nW/e2ypzbcuR6//WdeS18tLBzu33X7pPfRXwnXQ7XMfs4dO0K/1oo1/mtkwduPdDX5frBLHkW+V3Ed3LvFXu7/D8frfzVcrlGeK5t0d+6vwbHA0/pUb/u6Sfjq7/BJbbjeySwGzi3l23PBb4aaFpgPrAN2Obj43NcF7e13SGTHvxUvrquf/98Q61lW4vn5vFMSZflbrdbZt2Q1es/sdK3+tX1cg1r5J45e07I/tuK2qTT6hzUfZY8W+J5qPi+WWbfki3X+a2T9gZ75/rdF+2WG5M29nlDcrvcctOwTXLneTulNd8qt4zcItewRm6fvl02ftsoHc0OWf+F5/qULio9Jv2+q/fJb4O/lesD1svsX2VLKaUsX1wu17BGtmxr6de55D+YL9ewRla8XnE4X263LPxLYWfgyr8vv8u5WA9a5RqxRhY8UtC5zGV3yabvmuTuS3bLtfq10lpglU6L0xM0ftNz0Ng6aatco10zoDz/UE6rs1/Bou7zOln9v+pBPfapFlz6VRArpazo+F4jhFgOTAXW97DteiHEcCFEuJSyrr9ppZSLgcXgKRbrT76OZvLRohGe1mI/BmXPl6H11xJzS9ciPCHEMYNCKgMXemEo474ah2nMiSkpMCYZ+95ogGJuiaFoYRGFfyykeV0zUb+M6lIHEHldJAduOkDr960ETuu5v0XjN420F7WT/HgyvsN9mbhlIubdZoLODuosZgy9IBTfNF8aVjcQf/fhxiBthW3UflhL4gOJ2Kvt1H5Qy4gXR1Dz3xp803zxn+jf02G7SPlbCuadZnJvzwUN6AJ1NHzRQOXiSqJ+GYXWpKX06VK0QVqGPTIMgPIXyxFaQeyC2M79aPQags4KIv3VdLakbqHoT0WE/ywct9Xda11kzK9iyLsjj4DJAf0qRhwM/W0Z1p++WT92fRbyCSFMQoiAQz8DFwL7jtomVXT8xQohJgI+QH1/0g4mIYRn8MofQT8XW6WNmqU1RP8qGl2QV7UIP6WEnBeCT+SPp2mnLlBH7PxYGlc34m53E3dH13qhsDlhCB9BzbKaXvdT9UYVuhAd4VeEd+43+JzgY+qvQn8aStPaJlzthyu3y54vQ2gEcXfEETUvCleLi8rFlTStbSLy+sh+14EJrWDUf0dhiDd09hmpXFxJ/P/FM/LNkYx4YQRRv4ii6I9FZF2TRdO3TVS+UUn4VeEY4o5tcWaIMxB3dxzVS6opfqIYfbieoBk9j/UWeUMkhiQDCQ/23mlYOTH6c1eLApZ3/EHpgHellKuEEAugc/7lq4BfCCEcQBtwnZRSCiG6TXsCzqNTkK+e5h/BEDAlfy9BOiVxdw68UlnxbnF3xlH6XCnB5wYf0xxaH6wn9Keh1C6rZfhTwztblR3JUe+g9sNaYn8Ti9bY+5N0yIUhlL9QTsuGFkLOC8HR5KDqjSoir4/EEGfAJ9oHn1gfCn5fABKirh9YJ1x9qJ7JuyfTlteG0Al0gboub3zpb6RjTDRS9s8yat/3DAUUf2fPTeoTH0ykcnEl5u1mYubH9NqqSh+i54yiMwaUX2Xw9BlcpJQFwLhulr9yxM9PAk/2N+2JFGg89QevbNnSQvkL5cTeFovfCL+hzo5yijEmekYg8E3tvhVX5HWR1H9ST9W/qoi5+dhWkVX/rkLaJTG3dt9i8kjBM4MRek9foJDzQih9shSX2UXCfZ6nfaEVRN0QRenTpfhP8McvfeB/rzp/XWdH1KNpdBqSH00m4f4EKhZX4GxwEnhmz8V9+hA9iQ8lUnB/AZHXnfjm+crx86rhX8AzeOWp/ObitrvJuTUHQ5yBlCeOr9mj4v3CZofhl9r9jTzimgiCfxJMzq9zqPlf1+KxtqI2ihYWEXxeMP5j+64b0fnrCDo7yNPRtKydskVlRM6L7JL20JBBUfMGZ+igbvMRqCPxvkRSHk/ps9gt4d4Exn87nuBZPU/TrQw9ryvsD/LVU91yak2vW/lGJZVvVhI8IxhnkxPLPguZn2SiC/S6y6+cBBofDZkrMtkzew/ZN2QjtIKIKyOQbknOzTkgIf219H7vL/SnoRT8voDcBblItyT5sa4daP3H+jNp2yRMY0+NLhRCKzrnEVJOXV53dzvVisVcVhcFvy9ASknLlhZwQcS1EYRf2vuQH4rSG52/jrGfjWX3BbvJuiqL0Is90x00rWki7bW0Lr3q+xJyYQj8Hho+ayD+d/Hddqo8Wa2tFO/hfcHlFKvQr3qrCkedg/Hrx+M/zp+W71sInH5qDtmt/LjognSMXz+e8v9XTsnfSmhY2UDoJaHHNG3vi/84f/SRetw2N0kPJZ2g3CqnG68LLkG+emxON+0OF0b90M7b7na4KXmqhMAzAzv7FxwaBE9RBoPWqCXxvkRibo6h+t1qIuf2v6nwIUIjSHs1DY1Bgz7s5Az7o3g/rwsuh0ZGbm13DnlwqV1Wi63Yxoh/Ht888YrSX/pQ/Q8aFTviZz1Pa60ox8MLW4t5nryGumhMSknJkyX4jfLrnFhLURTldOG1wWWoe+k3ftGIZa+FhPsTuu3opiiK4s28L7gYD81GObTBpWxRGT7RPgPu0awoiuINvC64BPl66lyGsljMkm2hYVUDsbfHojF43SVWFOVHSggxWwiRI4TIF0L8vpv184QQezq+NgohxvU37dG87s53uFhs6EZGLltUhjB0HdlVURRlKAkhtMCLwEXAKOB6IcTRw68XAjOklGPxTF2/eABpu/C+4DLExWKOegfVb1cTfWM0PhE/ntF4FUXxelOBfCllgZTSDiwF5hy5gZRyo5SysePjZiC+v2mP5nXBxajXYtBphiy4VLxa4Rkq/W412rGiKKeUOKD0iM9lHct6cgvw+XGm9b5+LsCQzulS814NQecEHTNUuqIoygmmE0JsO+Lz4o5JGA/prtlqtxMzCiFm4QkuZw80bWdmelv5YxVo1A3JbJT2WjuWPRaS/5bc98aKoiiDyymlnNzL+jLgyJnT4oGKozcSQowFXgcuklLWDyTtkbyuWAyGbsKwpjVNAAT/RI3YqijKKWcrMEIIkSyE8AHmAh8fuYEQIhH4ELhRSpk7kLRH8843F189DRb7ST9u4zeNaAO0BExWI8gqinJqkVI6hRB3AKsBLfCmlDLrqFmF/wSEAS91DFnllFJO7iltb8fzzuBi1FNYZznpx236uongGcG9Tr2qKIoyVKSUK4GVRy07clbhW4Fb+5u2N155FwzyPflzurSXtNOW30bweapITFEUxSuDS6CvjpZ2J1L22phhUDV+42kaHvKTkJN2TEVRlFOVdwYXox6XW2Kxu07aMZu+aUIfoceUeWpMBasoijKUvDK4hPh5esY3nqRKfSkljV83EjwrWI2ArCiKgpcGl8hAAwDVLe0n5XjW/VbsFXZCzlNFYoqiKOClwSU6yAhA1QkILrYqGyX/KMFtcwOet5aDDxxE46dRk4IpiqJ08M7gEtgRXJoHP7hUv1NNwYMFZP8iG+mW1CytoWFlA8l/S8YQaxj04ymKovwYeWU/lyBfPQad5oQUi5l3mRE6Qe2yWnICcqhfUU/AtADi7zz++csVRVG8jVcGFyEE0UFGqlpsg75v8y4zobND8U3zpezZMoROkP56OkKrKvIVRVEO8crgAhAVaKR6kIvFXO0urAesRFwRwbC/DENj0OA73FeNgKwoinIUrw0u0YFGdpU2Deo+rVlWcIH/eH+ERpDyeMqg7l9RFMVbeGWFPtBRLNY+qL30zbvMAJjGqY6SiqIovfHa4BIVaMTudA/q0PvmXWa0/lp8U3wHbZ+KoijeqF/BRQhRJITYK4TYddRMZ4fWzxFC7Dm0Xghx9hHrZgshcoQQ+UKI3w9m5nvT2Rx5EFuMmXeZMY0zqV74iqIofRjIm8ssKeX4HmY6+xoYJ6UcD9yMZxYzhBBa4EXgImAUcL0QYtQPzHO/RAd5+pwMVl8X6ZaYd5vxH6cq7xVFUfoyKMViUkqzPFy5YeLw3MpTgXwpZYGU0g4sBeYMxjH7EhngeXMZrL4u7UXtuFpd+I9XwUVRFKUv/Q0uEvhCCLFdCDG/uw2EEFcIIQ4An+F5ewGIA0qP2KysY9kJF9XZS39w+rocqsxXwUVRFKVv/Q0uZ0kpJ+Ip3vqtEOLcozeQUi6XUo4EfgY82rG4u8qJbptvCSHmd9TXbHM6nf3MVs98dBrCTD6DVudi3mUGDUjdfQ0AACAASURBVGpIfUVRlH7oV3CRUlZ0fK8BluMp7upp2/XAcCFEOJ43lYQjVscDFT2kW9wxV/NknW5wut9EBRoHrVjMvNuMX7ofWl/toOxPURTFm/UZXIQQJiFEwKGfgQuBfUdtkyqEEB0/TwR8gHpgKzBCCJEshPAB5gIfD+4p9Cw6yPiDK/QtBywUP1FM07omVSSmKIrST/15RYgClnfEDh3wrpRylRBiAYCU8hXgKuAXQggH0AZc11HB7xRC3AGsBrTAm1LKrBNwHt1nPNDI7h/QS7/qP1Uc+MUBAAKmBpBwX0IfKRRFURToR3CRUhYA47pZ/soRPz8JPNlD+pXAyh+Qx+MWHWik3mLH5nRh0A28OKtueR2GJAMTvpuAMd54AnKoKIrinby2hz4c7utSc5yjI7dubSXozCAVWBRFUQbIq4PLoebI/anUd7W5sNfZOz/bqmzYymwETAk4YflTFEXxVl4dXAYy3XH2vGy2T9qOdHtaSrdubQVQwUVRFOU4eHdw6ed0x82bm6lbXoetxEbrNk9Qad3aChoImKCCi6IoykB5dXDp73THhQ8XogvTgQbqP6kHPMHFNNqE1qT6tSiKogyUVwcXIQQxfUx33Ph1I03fNJH0SBJBZwZR/2k9UkpatraoIjFFUZTj5NXBBXqf7lhKScHDBRjiDcQuiCXssjDMu8w0f9eMs96pgouiKMpx8vrgEhNkpLKlrdt1JY+X0LqllaQ/J6E1agm7LAyAoj8XARA4JfBkZVNRFMWreH1wiQ7ypbrZhtvddbzMqneqKHykkKifRxFzSwwAfiP9MA430rSmCeEjMI1Rg1QqiuI9+pq8UQgxUgixSQhhE0Lcd9S6XieNPJrXB5eYICN2l5sG6+E+LI1rGsm5OYfgWcGkv5FOx9A2CCEIvywc8Aytr/Hx+sujKMppop+TNzYAdwFP97Cb3iaN7MLr756dfV066l2kW5K7IBdjipHRH44+JoAcKhpT9S2KoniZPidvlFLWSCm3Ao4fejCvDC5uhxvLAQtwbF+Xxm8aacttY9gfh6EP1h+TNuicICKuiyBqXtTJy7CiKMqJ90Mnb+xz0sgjDc7EKacQKSXZP8+mdlkt49aMI2aiLwCVHX1dKl6sQB+uJ+LqiG7Ta/QaRi8dfdLyqyiKMkh0R9WFLJZSLj7ic78nb+zBWVLKCiFEJPClEOJAx/xd3WdmADv+USh5vITaZbVofDXk35XPhG2T0GkEVc1ttJe2U/dxHYkPJKIxeOVLm6Iopy9nH3Uh/Z68sTtHThophDg0aWSPwcWr7rB1K+o6W4CNfHsklr0Wql+vJCrQSGVzO5WLK0FC7ILYoc6qoijKyXbckzf2Z9LIo3nNm4uj3kH2jdkETAkgbXEaGqOG4FnBFP6xkGEP+GA5aKXiNQthl4ZhTFJD6CuKcnqRUnY7eeOREz8KIaKBbUAg4BZC3IOnZVk43Uwa2dvxhGfCyFOLyWSSFotlwOnqPq0jYEIAhjjPPC7mvWa2TdgGrsPbjP1iLKEXhA5WVhVFUU4JQgirlPKU6ZznVcGlO9VLqln9cTHrrc08+6dpqte9oihe6VQLLl5TLNaTqHlR2BMtfPVZLTLTd6izoyiKclrwqgr9nhzdkVJRFEU5sU6P4NLRkbKyufsBLBVFUZTBdXoEF/XmoiiKclKdFsElMsCIEFCpgouiKMpJcVoEFx+dhnB/Q5/THSuKoiiD47QILtAxaZh6c1EURTkpTpvgEhVoVHUuiqIoJ8lpE1w8by6qtZiiKMrJ4JXBxeVqp6Vla5dl0UFGWtqdWGzOIcqVoijK6cPrgovb7SQr60p27JhKbe0HnctjDjVHVpX6iqIoJ5xXBRcpJfn5d9HQ8Dk+PjHk5i7Abq8BIC7YD4CSButQZlFRFOW04FXBpazsWSoqXiYh4QHGjfsSp7OV3NzfIKUkNdIfgIM15iHOpaIoivfrV3ARQhQJIfYKIXYdNY3mofXzhBB7Or42CiHG9TftYHE46ikufoyIiGtISXkCk2k0ycmPUVf3EdXV/yHU5EOIn56DtSq4KIqinGgDGRV5lpSyrod1hcAMKWWjEOIiYDEwrZ9pB4VeH8bEiZsxGBIRwhMzExLupb5+BXl5dxAUdDapkf7kqzcXRVGUE25QisWklBullI0dHzfjmZv5pPPzS0erPTysvhBaMjLeATRkZ89jRKRRBRdFUZSToL/BRQJfCCG2CyHm97HtLcDnA00rhJgvhNgmhNjmdA5ec2GjMYm0tFdoadnM5LC3aLQ6qDfbBm3/iqIoyrH6Wyx2lpSyQggRCXwphDggpVx/9EZCiFl4gsvZA00rpVyMpzgNk8k0qNNjRkXNpbFxNbLqJRID0jhYayHM3zCYh1AURVGO0K83FyllRcf3GmA5MPXobYQQY4HXgTlSyvqBpD0ZUlMXodUEcNnw91TRmKIoygnWZ3ARQpiEEAGHfgYuBPYdtU0i8CFwo5QydyBpTxadLoj4+DuYFLWRirpdQ5EFRVGU00Z/3lyigO+EELuB74HPpJSrhBALhBALOrb5ExAGvHRUk+Nu0w7yOfRbfPw9OKWBYPfLQ5UFRVGU04KQclCrNwaFyWSSFovlhOz7tU9vJMX0LmdOz8XXd/gJOYaiKMrJJoSwSilNQ52PQ7yqh35/OHx/g9utJTf/Qdxux1BnR1EUxSuddsFlWORwPj54HY31H7B79/nYbFVDnSVliO2v3c+cpXO47dPbhjoriuI1BtJD3yukRvrzScFcZo+biqb1QbZvn8j48Wvx80sb6qwpJ4HT7WRDyQaKm4tpc7Sxq2oXr+14DSEETreTOSPnMDt19nHvf0/Zpxj1EaRFTet746OsOLCCJzc8SXxgPOlh6cwbO4+R4SOPOy+KMpROuzoXm9NFxh9X8dtZqfzmTDu7ds3A338848Z9jRDihBzTW7ncLlrtrbilG4PWgMnH1GXd+uL1ZERkEO0fPeB9tzvb8dH6oBF9v1xLKakyV7G/dj8FjQUUNRXhcDs4L/k8ZgybQWNbI98UfsOqg6v4LPczGtsbO9NqhIb5E+fzxxl/ZNa/ZyGlZO9tezHouu8H1dDWQEFjAZNjJx9xfDfZxa+yK+/PxOprqbXB19ZLufvsf5ARkdHtftzSjd1lx6jzTAWRXZvN5NcmE2mKRKfRUdBYgK/Ol7eveJsrM64cyKXrltVhJbs2m4kxE7v8ndvttfj4RBz3ft3STX5DPmUtZcT4x5AQlIC/j3+P20spqW+rp6CxAK3QMjZqLHqtvjOPLreLAENA5/ar81fz1q63uGf6PUyPn37M/t7e/TZaoWXe2HnHfQ7e4lSrczntggvArKfXMjI6gJd/Pony8lfIy7uNjIx3iYq6/oQd09u02Fo4682z2FfjaVmuFVrumnYXC2cupN5az43Lb2RD6QYEgmnx0/jtlN/y87E/70z/9IaneGrjk8QExJMUnMTYyLGcGT8Nfyr5NHc5Xxd8jUOXwP+75C3OSTqn2zzUW+t5+JuHWZa1rEvA0AotWo0Wu8uOj9YHu8sOQJhvGJemXcrl6ZczNmosfno/Ag2BnTfDLw5+wU/f+SmP/+RxfnfG7/ji4BfYXXZ+NvJnaDVaChoLuPA/F1LQWMC2+duYGDMRKSXfbLsEreVzqtoFFp+zSBDbabW3c+9uePqSpVw7+tou+c6p/o4vt15GkLYFXfjvuHzcQqa9Po0aSw27FuwiNiCWitYKfv6/yxim3cFliYlcMO1r/P1SAbDYLXyc8zEhviEMDxlOckgyOk3PhRAtthZmvzObTWWbOC/5PJ664CkmxEygrOwF8vPvIi3tVWJj+xp447Cm9ibe3/8+S/ctZUv5Fsz2rv3GjDojQYYgTD4m7C47NqcNh9uBlBK7y06b8/CMsH56PyZET6DGUkN+Qz5GnZGFMxdy7/R7eWvXW9z+2e24pRuJ5KqMq/j7+X8nNdRzHd7d+y7zPpyHTqNj6y0bCHWuIybm1+j1wf0+l1ONW7r79UDVHRVc+uFEB5db/72VkgYrX9w7Ayld7NgxHZutnKlTD6DTBZ6w43qTmz66if/s+Q8LZywkyBjEnuo9vLnzTaL8o7DYLQgh+Pt5f6fOWsey/cvIqsli1c9XceHwC9ldtZsXvpjAFXFaVjZNZl11KyWN2fxplJvJIYeP0ezQ8Gyum1FJC1g4cyFR/lEAOFwO3t79Ng9+9SBN7U3cMOYGJsdOZnTEaFJDU4kLjMPusrOuaB1fF35NpCmS81POZ3z0+D7/ca9870o+z/8cg9ZAs60ZgPHR47lr6l089M1D2Jw2NELDqIhRrLtpHbkHH6Gy7HG+qAvjN+dvJSk4GbN5Lzt3zaKxvYUPy9zcdMYrnJVyNW3tRXyR9TRG8xL0AixuA6F6G2vrfHk2p40Pr/+S81POx+12UFT0F8rKFuF2W7C5oMFpICF9BQ02G3d+ficlzSWdeU4OTuatOW8xY9gMADaXbSanLoeZw2YSbAxm9pLZbKvYxu2Tb2fJ3iU0tDVwVdoMbovZhFYIpLQxcuS/iY6+EfAE7U9zP+WjnI8oaS4h1DeUYGMwze3NVFuqyanLweayMTJsBOelXMDEmEkMCx5GZWslpS2l1FvrabG1YHaYMWgNGLQG9Fo9GqFBK7QkBCWQHDQMm6udjWWb2V65nUhTJGMix7CrahcrclaQGJRISXMJF6VexJtz3mTx9sU8tfEpnG4nj816jMmxk7nwnQuZEjuFvIY8bhqm46LwCoYNe5Rhwx6hzdHGc5ufY9HmRUyKncRDZz/U40MKwLfF32JxWJg1bFaPb60AS/YsYWfVTv4y8y9d3tR/KCklT218ijVFa/jk+k96fVjoiQou/XCig8sTK7N5a0MR2Y/ORqsRtLRsZceOacTGLmDEiBdV8VgflmUt47r3r+NP5/6Jv8z6S+fyreVbuXvV3fjqfXnj8jcYFjwM8BR3TH1tKrXWWrb+eis3/u9iHk7JwkerAylJSfkbldXvYrXso8XvBiYlXYVRq6Gg8M9YLbvYUAdbG/VMSLySYB8NFTUriDFYybOn8Ztz/8fY6LFIKWlt3U5Dw0oaGr7A7W4nLe0VAgMn93AW3StqLOT3n55PckgyM5Ivp9nlz4Pf/JXi5mLiA+P59KpFHCx9lfdzvmRe5hWYrMtZXQVzztrE9ITDxTZm8z72Z9+E1bL92GO0B3HuxE9ICJ3Ix1uuIti+GqcIYtr4jzGZMsnKupampq+JjJxLYuLDrD34IcaGP7OzER7JgllxKdw39ZcYAs4hr6GQJ757guLGfP4yZQZZjTUszc/G1fFvHeATQJuzjWVXL+OKjCtoam/imY3PENjyHGMDLNy+S8s/JsQTpiklJe1NXszaz3Obn8PhdhAfGM+YyDE0tjfS1N5EoCGQKFMUaWFpXJs+E0fZzUjpJjBwOn5+I3E6G7DbazAahxEWdinBwTPRao3HnL/L1UZW1jW0tx9k7NhVGI1JXdYvz17OfV/ex+zhs3n+ouc7b7QVrRXc9tltfJzzMQBpYWlsumUT3+Qtw1R7G75a0PsksFv3AP/Y+DTFzcX8X+ZI9jdUsK66hTHR05iTPoeLRlzEuKhxuN1WWlt3sqm2lcv+exku6SLAJ4CzE8+moa2BwqZC0sPSef3y10kLS+PVba+y4DNP175REaN4/5r3eyz27E1TexPXf3A9Fa0VLDz3z0wP1/Cf7X+jqG4b6eEjmH/+bnz1vn3v6CgquPTDiQ4u720t4cEP9rLu/pkkhXl+F3l5d1Fe/gKhoReTnv46BkPMCTv+qazF1sKr217l+4rv2Vu9F4lk/sT53DLxFrRCy7cl3zLvw3mkh6Xz3c3f9fsJa3/tfiYvnoyv3pcb4xqYE6dlyuTtFBQ8TEPDZ2g0JkaPfp+wsMOV6W63k9LSpygqfgzpPjyDqENq0Ooi0biqCAu7jLCwy6moeAWzeTsgCAiYjN1ehd1exfDhzxAXd0e3DwyNjWspKfkbMTHziYi4GqezmQMHbqS+/tPObbTaIJJTX+KrajtnhOuoLliA290OuADY0gDVfnfy7Ox/dnveB2u38tBn54OrhRD/kVyY/gsuH3Mfuo56BoDm5i0cOPBz2toO4uMTjcNRT1raq8TE3NS5TVHpixQdvAOJBoEbgJCQn5KZ+QFWRzsffzeJOF0xAE78MJomUt/WSFN7I/Emf3xFK1K6iIi4Bj+/keTn34lvxB28mm/hv3ve4vkJBlJNNhYXgG/4L7l98m+ZFDMJh6Mam60Uh6OO4OAZaLUmpHSxe/f5tLRsJTLyGlpavqet7SB6fTh6fThtbXm43Va0Wn9iYn5NfPy9GI0JALhc7WRlXUFDw2q0WhM6XSjjx3/Ta58zp7MVrdaEEBqklLyX9R5v7XqLFy9+kdTQVLKzb6K86j+8XQw3D3Pzf7sB34k8fealiLq/AiDRsN/sz0O7W2hxwlnxU3k80427bRuL8n0ocY1m4cyFfJ67gqamr2gglfjAYSw/sByby8bc0XN5c9ebzBkxm/mTbuZXn9yB2W7m8vTLuCCskWhtAQfsk9nWJPD38Sc1NJVwv3AKGgvIrc8lLiCOX47/JSHGEC5+92Ly6vNICkpgTngBl8Z6ztOFjiD/MUyevKPHa9Gb/gQXIcRs4HlAC7wupfz7UetHAm8BE4GHpZRP9zftMcc6HYPL1qIGrnllE2/dNIVZIyMBT6VseflLFBTcj0ZjYty4LwkImHDC8nAqaHe2syp/FcHGYFJCUvjy4Jc89M1D1FhqGB4ynDFRY2hoa2B98XqMOiN2lx23dBPqG8qWW7d0ln33xOVqQ6Mxdt7Y39r5Fn9YfTNLpmlIiJtPWtrLSOmiouI1goLOwN9/XLf7kdJNe3sRpXXrkehJjb0SjcaHsrLnKSx8GLe7HT+/DOLi7iIy8hr0+jAcjgYOHPgl9fWfEhFxNenpb3Qp8qyoeI28vNsBLVLaCAo6B5utAputmJSUfxAYOBW7vYaSkr/T2vo9YWGX09CwEpMpkzFjPmdT+U7mLrsYX2Mie2/P6rUSu6yljKb2JjIjM3vcxuk0k59/D01NX5OR8Q5BQWcds01l5VtYLPs68lZLfv7dBARMQacLpLHxS6ITHyfEP52G+o+wWA6PsqTXR2AwxONymamrW4GUNnx905kyZTcajYH1xeu5e+UCfhFXxYTARiIirkMILY2NX+Fw1HTux9c3nVGjltLY+AUFBQ+Snv4GMTE3d/N7b6epaS3V1e9QU7MUIQRBQefi6zuctrY8mprWkp7+Bv7+49m9+0I0Gh/S098gNHR2l4cAt9tBXt7tVFa+jhA6fHyiiY6+hWHD/ty5XUvLNnbsmEJI1G95ZEced8WuxxQ0k2njPmTr1lFotYGkpi6isfELysoWodXHsl/zS2rKHuesMBtlbYJIA6RnfkVc6GT27r2c5uZ1hIVdyqhRy6iyNHDTipv4vuQrfj82jXNC6nC5zARF/JIXc6oYIb5marAVsxP8dbCv1UipVRDp04ZRC68XajBrUihpLumsA/TT+7H8mvcIs75Cfd1y1jTGkpn6KFdn/uoHlZr0FVyEEFogF7gAKAO2AtdLKfcfsU0kkAT8DGg8FFz6k/aY452OwaXebGPSY1/xyCUZ3HpOSpd1FssBduyYQlTUjaSlvXTC8nCy7ajcwTt73mFq3FRmp85mffF67l19LwWNBV22OyvhLBbNXtSlNdTOyp28ufNNQn1DOSfpHM6IP6PX8marNYeior9SU/NfNBoDBkMifn4jCQo6m4KqTxBt3zN9+kEMhrgffF7t7SXYbOUEBk4/5h9TSjelpc9QUPAHfH2Hk5b2KnZ7BfX1n1JT819CQ2eTkbGE2tr3KSx8GCH0jB79PkFBZ3buw+22U1j4MKWlTxMcPJPMzI/Q6YIA+PeufzMxZiJjosb84PM4HrW1H7F//1ykdHTc6G/qM43D0URd3UcEBk7DZDpcpCOlREo3xcV/obj4UfT6KEJCzicwcDpG4zCktJOXdycORx0gCQu7jNGj3+/zZtjeXkxZ2fM0N2+kvb0Al6uV1NR/Ehv7a8BTfLh37yXYbCWYTJnExt5OcPBMDIY4srKupbFxNbGxC9DpQjCbd9LQsIr4+HsZPvwZWlo2k509D5fLyrRpueh0geTl3U1FxSvEx99FaenTjBv3NSEhPwGgqek79u27DLe7Hbe7nRzXDN4tauDR9BoM+gC02iAslt1ER99EZeUbBAWdTWLig1RVL6G29gOQdsLC5qDXh1JV9W/ADWiIT3oUffA10PoxpSVPIKUdg286NnsVLnsFSUmPoPc/i025z+Ns20G8nw/SWYfbbSU1dRHx8Xcf519AV/0ILmcAC6WUP+34/AcAKeUT3Wy7EDAfEVz6nbZzH6djcJFSMuHRL7l4TAyPX3HsjWHPnotpby9i6tQeg/KPhtPt5MnvnmThuoW43C4kEo3Q4JZuMsIzePL8J/HV+3Kw4SDR/tFcnn55v56e3G4HZWXP4eeXTnj4nM5l+fn3UlHxMhqNLzExtyCEHputGLN5D21tnjFN4+PvJTX12RN63kdqalrP/v3XYbd7OsxqtYHExs4nOfkJNB3Fei6XFSnd6HTdv4G0tR3EYEhEo9F3u36otLbuwOlsISRk5qDt0+lsRqsNPObvwG6vIyfnFqzW/UycuBm9PmzA+5bShech+DC3205NzVJKS5/GYtkLgBA6pJSkp79KTMwtHWkl+fn3UF7+T4KCZtDc/C1GYyIZGf8lKMhT32U272HbNs8bcFjYHMaM+ajLsSyWLPbtu5Lw8DmkpDyJEIKmpu/YtWsmGo2+o2j2Empq3iM7+0akdKDThRAZOZe4uDs7A7LFsp+ysueJiLiG0NDzu5wfaDz9ppyt5OXdQXX12x1rPUW2vr7D8fGJJiTkQsLCLhrwNexJP4LL1cBsKeWtHZ9vBKZJKe/oZtuFdA0u/U57yGnXiRJACEFKuImC2u6H3g8OnkFBwefY7TX4+ESe5Nz9cC63i81lm/k8/3OWH1jO/tr9zM2cywsXvUBufS4r81YS4x/D/EnzO/sYnJ9yfh97PaytrYj9++fS2roFgMTEh0hIuJ/9+6+lsfFL4uLuJCnpj8f0n7DZqjCbdxAcPGvwTrYfgoPPZfLkXTQ0rMJkGou//9hjbnBarV+v+zhVx6ELCJg46Ps89GZ2NB+fcMaMWYGU8riLb46+7gAajQ/R0b8gKupGrNYcWlo2YjbvJDz8Z4SEnHdEWkFq6iKE0FJW9hzR0TeTmvpcl+JOf/+xBARMxWzeyfDhTx1zLJNpNNOm5XRZFhx8NuPGrUanCyMgYDwAkZHXYTAkYrdXEBp6yTENE0ymUaSnv9rr+el0AWRk/JvIyLk4nU2Ehl54XAF5AHRHDBoMsLhjnqzO7HWTpr9vFwNOe1oGF4CUCH/W59Z2uy44eCYATU3riIy85iTm6ofZWbmTf+36F8v2L6PKXIVWaDkj4QyWXrWU6zKvAyDcL5wzE87sY09dSSkpLn4Ui2UfLpeF5uYNgCQjYwlNTWspKXmc8vIXcLvbSE9/k5iYX3W7H4MhGoPh4h96msfFxyeK6OhfDsmxvc2Jak0phMBkGonJ1POoBEIIhg9/hoSE+3tsdJOe/gY2Wyl+fiP6fewjg9ghQUFn9Dt9bwbz7aQPTillb80jy4CEIz7HAxX93PeA057GwcXE+9vLaG13EGDsWtTh7z8Rrdb/RxNcpJQ8v+V5frf6d/hofbh4xMVcN/o6fpr6U4KNA+tQ5nJZyMn5DYmJD+Lv7ykybG3dTlHRnzEYktDrwwkJ+QnDhz+Nr28KkZHX4+8/gbKyRYwY8QKhoReeiFNUlE5CiF5bc/r7Z+Lv33PjidPYVmCEECIZKAfmAjecqLSnbXAZHuEpWy+otTAuoesNWKPRExh4Fk1Na4cgZwPjdDu5+/O7eWnbS1yZcSVvXP7GgAPKkcrLX6amZgkuVwtjxnj6E1RWLkaj8WPKlN3HFJkIIYiLu424ODXoo6KcyqSUTiHEHcBqPM2J35RSZh2al0tK+YoQIhrYBgQCbiHEPcAoKWVLd2l7O95pHFw89V4FdeZjggt4isYKC//wg8deOlGklKzIWcGf1/6ZPdV7eODMB3ji/Cf6PXSEp3jrO8zmvcTGzkenC8TlslJa+hRCGKiv/wSLJRuDIZ7q6neJjLyux7J4RVF+HKSUK4GVRy175Yifq/AUefUrbW9O2+CSGGpCqxEU1HbfKi042DOURnPzeiIirjqZWeui1dbK0xufpsZSg8PtwOKw0GJroaCxgAN1BxgROoJlVy/jmtH9K75zu23k5Mynpua/SOmZz6axcTVjxqykouJVHI4aRo9eTnb2DZSWPk3g/2/vvMOjqvI+/jlTkkkmvVc6IY0QuhQFaYJSLCiK4opt3cWCq2vdddXV1Xffd3ftfa2LiLoWBEWRIqKg1ATSIIQQQhLSe5ty3j9mMiSkkEBC2vk8T57M3HvOvb+Tcr9zzvkVj4lYrVUEB7c/95RCoVD0W3Fx0mkI93ZpVVzc3ceh0bhSWrq1W8Xl3m/v5e19b+Pn6oeT1gkXvQuezp6EeYTx4JQHuSHuBnQam9tmfv6HuLmNxmiMbvFatrQbV1JcvIHQ0Dvx9V1AbW0mhw79lrS0Wygp2YiX18X4+19OScnN5Oa+SVnZTxiNsXh4dDyFvEKh6L/0W3EBm8fYkVbckTUaPZ6eUykp2XxOrpfnwteHv+bf+/7Nw1Mf5m8z/9Zm2+zs5zhy5A8IoWfgwMcYMODBJjEZFksVBw4ssEdHv+WIHQCoq8vh2DFbjrDo6I8ACA//Azk5r1JTk8awYS+ofGsKhaJD9LtKlI0Z6m/kaGEVVmvL7tq+vguork4mKekqTKbS82pbSU0Jt311GzH+Mfxl2l+anS8v/wWTyZZmvrj4W44cQnRjGwAAIABJREFUuR9f34X4+V1BZuaf2bdvMvX1NldrKSWpqcspLf2BqKj/NBEWgEGD/kJY2L0EBv7GsRzo4jIEf/+r0WhcCQy8AYVCoegI/X7mUme2cqK0hnCf5kF0oaErkNJERsYD7NkzhpiYzxxBVl1BvaWerw9/zb7cfXyT/g0nK0+y9tq1zVKAN9ThEMIJX99LKSnZgtEYS1TUKnQ6N/LzryY1dRmJiXMYNWozubn/pqDgE4YM+TuBgc29B23Bac0j5keMeJ26ur+g13s3O6dQKBRt0b/Fxa/BY6yqRXERQhAefi+enpNJSlpMQsJM4uN/6BIf+qT8JG74/Ab25+1HIzRE+EbwymWvMDZkbJN2hYVfkp5+Dz4+l+HqGmHP32Vg5Mi1jtQlAQGL0WrdOHhwIfv2TaW6OhV//8WEh9/fIZt0Ok/lIaZQKM6KfplbrIGSqnpG/3UjD8+L5LfT2k7vUVNzhH37LgKsxMdv61D0b1tYpZUXfnmBh75/CA9nD1657BUuHX4prnqb2NXV5ZGefg96vQ9OTiFkZT2D0TiS+PgtaLWuSGnBaq1Hq21e/6Gg4AuSkhbj6hrBmDG/oNO5N2ujUCj6Bj2tnku/nrl4G50I8TSQnFt+xrYuLkMZNep79u+/iISEmcTE/BcPj/HndP+ssiyWf7mczUc3syBiAW8tfIsAY9NcZkePPkxh4WdotR6YzcW4uAxj5Mi1jlxYQmhbFBYAf//LGTduL05OwUpYFArFeaVfiwtAdIgnSTlnFhcAozGKuLiN9uWmyQwa9CQDBjzQYjK+M/Ft+rdc8+k1WKWVtxa8xc2jb27mkVVRsY+8vPcID7+PoUP/F5OpFK3W2KHMvG5ucR22TaFQKM6Vfu0tBhAd4kFGQSU19ZZ2tXd3j2fcuAT8/K7k6NFH2LNnPAUFnyGltd333JOzh6s+vopBXoNIuCOBW8bc0kItEsmRI/eh0/kwYMCjAOj1Xj0u5btCoVC0RL8Xl5gQD6wSUvPaN3sB0Ou9iY7+iKioVVgsFSQlXcWuXXFUVx8+Y99jpceYv3o+vq6+bLh+A0O8h7TYrqjoK0pLtzB48BPo9WefK0yhUCi6AyUuIbZaEO3Zd2mMEILAwKWMH59CVNSHmEwnSUycQ11dy1mopZQcz/svG36KIdZYztdLvybYvWlmV6vVTFVVEmlpd5CcfC0uLiNU2hWFQtEr6fd7LqFeLni66Nu973I6Go2OwMDrcHEZTkLCxSQmXkJ8/LYmsSHV1Yc5dOh3lJZuYpgrPBLtwXAvWzJMk6mYxMS5VFTswVY2FYRwJihoGQMGPKKWwRQ9FpPJRHZ2NrW1td1tSp/GYDAQFhaGXt+7ngX9XlyEEEQHe5B8luLSgIfHOGJiPufAgUtJSrqKUaM2IoQWq7Wegwcvp7QqndePwOWjHmNA9bMcPnwXUVEfkJR0FZWVCYSH/xGt1g293ht//2t6ZCZmhaIx2dnZuLu7M2jQIJUeqIuQUlJUVER2djaDBw/ubnM6RLvERQiRCVQAFlqodiaEuB540P62EvidlDLBfm4u8Dy2GgBvSSmf7RzTO4/oEA9W/XIMi1Wi1Zz9P4mPzywiIt4gLW05x449g1/ISrYl3IprdTJ/OQjToh5k2fgnOHbMiaNH/0RdXRbl5TuJivoPgYHXd+KIFIqup7a2VglLFyOEwNfXl4KClqvm9mQ6sudysZQyvpUymkeBaVLKOOCvwBsAwuaj+zIwD4gGrhNCtJyytxuJCfGg1mQlo5Uklh0hKOg3BAQsJTPzcX7zgR+6ijVsK9QzKeJeR/LJ8PAHcHMbTXn5TgYO/LMSFkWvRQlL19Nbf8adsiwmpfy50dudnCo2MwFIl1JmAAghPgIWAcmdcd/OIrrRpv7wwHMLNhRC4BTwACezPuKuoXVIjQf3XZaM0SXU0Uaj0RMT8xklJd8RHHzrOd1PoVAoeiLtnblI4DshxB4hxJncl24BvrG/DgWONzqXbT/Woxjq74aTTnPWm/qNKakpYcGaa/lnuhGN1ovoEa83EZYGXFwGERJyO6KdlSMVCkVzhBAsW7bM8d5sNuPv78/8+fMBePfdd/H39yc+Pt7xlZycTGZmJkIIXnzxRUffO++8k3ffffd8D6HP0t6ZyxQpZY4QIgDYKIRIlVJuO72REOJibOIyteFQC9dqMZmZXbRuB3BycmqnWZ2DXqshMsj9nDf1pZTc8PkNHCk+wvc3fs+FA6Yq8VAouhCj0cjBgwepqanBxcWFjRs3Ehra9MPckiVLeOmll5ocy8zMJCAggOeff57f/va35/2Z0x9o15NPSplj/54PfI5tuasJQog44C1gkZSyyH44Gwhv1CwMaDEQREr5hpRynJRynE53/p3YooM9SMop41wSeb6z/x2+Pvw1/5jzDy4aeJESFoXiPDBv3jzWr18PwOrVq7nuuuva1c/f35+ZM2fy3nvvdaV5/ZYzPsWFEEZAI6WssL+eAzx5WpsBwGfAMinloUandgHDhRCDgRPAtUDzgiI9gMggdz7adZyCijoCPAwd7n+87Dj3fnsv0wZOY8WEFV1goULRc1m5YSX78/Z36jXjg+J5bu5zZ2x37bXX8uSTTzJ//nwSExO5+eab+fHHHx3n16xZw/bt2x3vd+zY4Xj90EMPMW/ePG6++eZOtV3RvmWxQOBzu8eCDvhQSrlBCHEHgJTyNeAxwBd4xd7ObJ+FmIUQdwLfYnNFfltKmdQF4zhnRgTZNvVT8yo6LC5SSm5fdztmq5l/L/w3GjVjUSjOG3FxcWRmZrJ69WouvfTSZudbWhZrYPDgwUyYMIEPP/ywq83sd5xRXOyeXqNaOP5ao9e3Ai26PUkpvwa+PgcbzwuRQTYvsdS8ci6K6FgA48aMjWxI38Dzc59nqE/bdWEUir5Ie2YYXcnChQu5//772bp1K0VFRWfu0IhHHnmExYsXc9FFF3WRdf0T9RHbjrfRiUAPZ1LzKjrc94vULzDqjdw+VuUBUyi6g5tvvpnHHnuMkSNHdrhvZGQk0dHRrFu3rgss678ocWlEZJAHqbkdExcpJV8d+orZQ2dj0HV8r0ahUJw7YWFh3HPPPS2eW7NmTRNX5J9//rlZm0cffZTs7OyuNrNf0e9zizUmMsidHUeKMFms6LXt0939efvJLs/mielPdLF1CoXidCorm2fVmD59OtOnTwfgpptu4qabbmqx78GDBx2vR40ahdXa/ppMijOjZi6NiAx2p95iJbOwqt19vjr0FQLBZcMv60LLFAqFonehxKURIwJtHmMpHdh3+erQV0wMm0igW2BXmaVQKBS9DiUujRgaYESnEaS1syplTkUOu3N2syBiQRdbplAoFL0LJS6NcNZpGeJvbPem/vpDtqhgJS4KheJMmCxWskuqMVv6x96OEpfTiAzyaJc7ssliYtWBVQzyGkRsQOx5sEyhUHQG5bUmak2W837fnNIaiqvqKaqqP+/37g6UuJzGiCB3TpTWUF5rarVNflU+M9+fyQ/HfmDlxJW9tt6CQtHdmC1WckprqDefn4e9xSo5VljFkfxKKtr4H2+MlJLqevM55R2sqDVRVmNCIwRFlfVYz+Fa54IQYq4QIk0IkS6EeKiF80II8YL9fKIQYkyjc5lCiANCiP1CiN1nupcSl9OICrZF6qe1MnvJKMlg7Btj2Z2zmw+v/JB7LmjZt16hULRNrclCekElhZV1FFae+jQvpSQ9v4K88to2+997770899ypzACXXHIJt956KlHIfffdxz//+U/uvvtuYmNjGTlyJOPHj+d41jGEEMydO4/YkXFER8dw+29/S229iep6M6+/+e8mafqff/k10vMr2Zt8iLFjxxIfH09MTAyvvWZLUlJntlDcxmzEapXklNbgrNMywMcVs9VKaXX7hK0zaWfxxnnAcPvX7cCrp51vq2hkE1Scy2lE2nOMJZ0oY/wgn2bnX/jlBQqqCthxyw5GB48+3+YpFF3CppSTOOu0TB3u16X3sVit1Jis1NSbyS+vQwiBQa+lvNZEsDQghKCqzkx1vYXqegtOWoGP0dnRX0pJTb2F8loTkXFj+erLz1l+++9xN+goLCykvPyUM87PP//M3LlzycnJITExEY1Gw76UdKosOoYFuPHK2+8jnFyRUnLfb3/D82++z7xFV5FfUcf8y6/i7Tdepd5i5fDJSqxSojX68OP2n3B1MVBZWUlsbCwLFiyg1smTqjoz7gZdi/FxBZV11JmtDPYz4uasw6DXUlRZh7ervtmqh5SyK1dC2lO8cRHwvrRN03YKIbyEEMFSytyO3kzNXE4j2NNAmLcL29MLm52zWC18nPQx84bPU8Ki6DNIKXn4swPc/0kCFuup5ZryWhMFFXVn7F9ZayajoBKrte2lnlqThZTcCjIKKsktq8VJr2FYgBu+RifqzVbqzLaN7rJaMxohcHPWcaKklvJaE1V1Zgoq6kjPryS9oJKCinpGjBrP7l92klVcw579icTGxuLu7k5JSQl1dXWkpKTg4uJCcHAwGo3tUeflH0yAvy9OOg0jBwczxM9IsIcTWmnB182ZQb5G3Aw6akwW8sprOV5cgxAw0NcVjU5PpX3CUVdXh9VqparOTFWdGcDxvTFlNSbyy2vxcnHC3WATEz83J2pMFsprTBRW1pFRUElaXgVJOWVnlX6qETohxO5GX6fno2pP8ca22nSkaKSauZyOEIIZkQF8sjubWpMFg17rOLc9azu5lblcG3NtN1qoUHQuRwuryLeLyLZDBVwcGQDA7e/vpqLWzPq7L2yzf0l1PScfzqT4UD1aTeufuuvNVkwWiUGvQSMElQKSACmhut5Mok6LXiuorregEQKncR44PxzUJKjZoNcS6uWCl6sebZgnBmc9J3Oz2fHDNi644AJycnLYsWMHnp6exMXFsXTpUqZOncqPP/7IjBkzmDz3ci4Yb1vR0QjBVYvm8+uvvzJv3jyW33AdWq0WLxc9WzZ8xbQLfmbgkGH861//xDNkKO4GE4lpR/jDLdeRnp7O3//+dzRuvugtVixWSXW9BS/XU+OtqjNzvLgaFycdod4ujuNeLk7kldVyrLjaMSYXvQatVodee06zFvMZlqvaU7yxrTbtKhrZgJq5tMDFIwKoMVnYmdE0u+qapDW46l2ZHzG/myxTKDqfHfa/cxe9lo92ZQGwM6OInRnFpOZVUG9u23W24RO7+QwzF7NVotMItBpB45UfIWwPeovVitUqkdLWTggY7Gck2NOFQb5GooI9iAh0x9fNGa19JjJlyhSOJe9j1y87iIofx6RJk/j555/5+eefmTx5MmFhYaSlpfHMM88gheCWaxax+6dTz8Nvv/2W3Nxc6urq2Lx5M2DLsHwsM5OtO3Yzc+ZM7r7jNgACPZwJCA7lux9/5fDhw7zz7nscP5FDoIczrk7aJjOXOpOFzKIq9FoNg3xdm4iuRiMI9XYlwN3A8EB3IgLdGeBrJNTLhQD3Ls1P2J7ija22aU/RyMaomUsLTBrqi0GvYWtaAdNH2D7Fma1mPk3+lPkR8zE6GbvZQkV/ZM+xYjxdnBgW4Nap191xpIhAD2cWjgrhnZ8yKaio48XNhwG7d1VRFcMD3VvsW2+2UG+x4v5EOBohiAp2b3HPoLS6nqziagb7GXE36Judzy2robCiHh+jnuIqE1HB7ujs+xf+7s7N2jcwefJk9u/+hYy0FLxDh+IbEMzW//k7Xp6e/PY22+a+s7Mz8+bNY8JFM9C4ePHdN+u4fP5cxzUMBgMLFy7kyy+/ZPbs2fj6+gIQYoA/3rOCZ574MwCuTjo8DHryK2rRatwYMHQEiXt2Mi1+OPUWSUF5LRarFa1GQ2FlHVLCYD9Xxzga4+mix9Ol+c+hi2lP8ca1wJ32/ZiJQJmUMrc9RSNPR81cWsCg1zJ5qB+bU/Md7odbjm6hoLpALYkpuoU1u7JY/NoO7vu4c6s9SinZmVHMpCG+LBk/ALNV8tiXB/kpvYgrRtuW2g/nVzZp33gmU1lncyH2c3fGbLVSVd+yS3FJtQm9VoObc8ufZz0MeiSS4qp6jM7aFh/ILTFlyhTWr19PgL8fbgYn9K4eFJeUsnPnTiZOvIC9e/eSk2P7cF5ZYyI9NZnBgwZSWVlJbq5tj9psNvP1118TGRkJ4DgOsHbtWqKiogDIzs7Gz0UwwMcVWVfJ/t07mTAqFo0QGJ20SKC63oJVSkprTHgY9DjptPQUpJRmoKF4YwrwsZQySQhxR0PxR2y1tzKAdOBN4Pf244HAdiFEAvArsF5KuaGt+6mZSytcHBnA5tR8jhRUMSzAjY8OfoS7kzvzhs/rbtMU/Yy3fszgqfUpeLnqScguI7+89qxKcbdEer7NFXjSUF+GBbgxbqA33xzMw8foxJ/nR/PF/hMcPlkJ9jIpL29J58Nfsvj+vmmAbUlMp9Hg7+ZEQUUd5TWmZgJSb7ZSUWsiwN3QqieUq5MWrUZgscoOfaIfOXIkhYWFLF26lKH2Gd3oUXH8VFWFwcOL/Pw93HbbbdTV1VFvthI/Zhx33XUXZWVlLFy4kLq6OiwWCzNmzOCOO2zP1xdeeIG1a9ei0+nw8fHh3XffBSAlJYX77rsPIYTNCeLBB5g0fozdfh0CQVWdBatVYrFKvI3nfWZyRloq3nha4UcJNKvT3lrRyLZQ4tIKMyID+DOwJTWfEG8Nn6V+xqLIRapmSx+nrNrEig/38sSiGIb6d+7y09mwLjGHp9ancOnIIO6YNpSFL/3ElrR8lowfAMBfvjzIF/tzGOjryhA/Iw/MjSTEy+UMVz1Fw77ipCE2F+Rrxoez+1gJt0wdjI/RiXBvVw7nn/Jg2ppWQE5ZLWt2HecCH5u4GJ21aDUa3J11lNeYCPZsKiKl1bYYkLYetkII3J31lNbU49EBcdFqtU3cjwE+eO89knPLqagxM3fuXObOnYvZYiU5t5wgTwMGg+1r165dLV7zmWee4Zlnnml2fPbs2SQmJrZsh0ZgcNJQVW+m1iTanKX1F/r36Nsg1MuFEYHubE7NR+f+A6W1pdw6usVKzoo+xI/pBWxPL+T75JMMndZ14rLnWAnrEnMI9jQQ6uXKxZH+uDo1/Xc0W6z847tDRAa588K1o9FqBCGeBr5PsYlLQUUdH/6aRXSIJx4GHWsTcgj1duGPl0S2244dGUWEerkQ7mMTpCtGh2K1Si63L4kND3Aj3b4sVm+2kniiDIA3t2UwdkEQWKz4Odv2RDxcdI7UKi72sVitkqKqetycdTifYYko0NMZD5eWY0U6gkZjc2OuqDUh7bEz1fblOqNT1z3yjE46W2oXCX7uTv0+c4fac2mDWdEB7DxaxOOb/0W0XzQXDVQ1tvsSaXkVTP2fzWQVVTuO7TpaDMDBnPZlxj5bnlyXzDs/ZfK3r1NZ8eFeXtlypFmbL/bncLSwintnR6DTahBCMDMqkO2HC6k1Wfjo1yxMFsm/rhnFB7dMZOJgXzYmn2y3DVarbb9l4hAfx4NQr9Vw7YQBDhf8YYFuZBRUOT7515utXDMujJyyWkeUecMndA+DHgGU1pyKPi+qqsdksbbLC8pZp8XL1and9reFh4uOeouV2obYmRoTQghc9F23B2J01iGlRCLx7qRx9GaUuLTBiouHMXFEKVmVB3CzXEpZzflP2aDoOjalniS7pIZvDp7awP01swSApJyyLrtvWl4FCcdL+dNlUSQ+PodJQ3xZl5jTJHeVyWLlhU2HiQ31YE70qVpBM6NsbvLbDxey6pcsLhzuxxD78t3s6EAOnazkWFHbxe6klBwvrmbN7uMUV9UzaYhvq22HB9gK6GUVV7P3mO1nc+/sCKKCPagzW9FpNDjrbI8RnVaDp6sThRV1VNaasFitFFTU4m7Q42Y4v4skDR5pFTW2AMyS6nr83JzQtBGHc64YnWzC5aLXNomP668ocWkDVycdrt6bcNK6kJ83gee+P9zdJik6kYaH5da0AsD26TY1rxx3Zx1HC6uaxC2sT8ylsPLM0eoNSCk5eKKsxfTqH+8+jl4ruGJ0KB4GPYviQ8gsqiap0Wzps73ZZBVX84fZEU2WVy4Y4ourk5a/rk8mr7yWGycNcpybbRehtmYvZouVxa/t4MK/b+Hhzw5gdNJyUYR/q+2H2zfJD+dXsjerhBBPA8GeLvxu+lAAjM7aJvaFerngpNOSVVxDblktZqskyKN1V+KuQq/V4OKkpbzWTE5pDXqtpqtjSNBpNfi7OxPkqfZlQYlLm5TUlPDRwY+4adQypg4d0CyoUtF7kVKy51gJGgG7MoupqDWx91gJUto2taWEVHvRuIyCSlZ8uJc/fJzQ7sy46xJzmf/idmb98wf+uyfbITL1Ziuf7zvBrKhAfN1sD905MUFoNYL1B2wzqFqThRc2pTMq3IuL7XFWDRj0WqYO8+NYUTWhXi7MiDx1PtzHlcggd75Lal1c1ibksOdYCXdePIwvV0xhz59nE9iG51mDB1Z6fiX7skoZPdAbgMtGBuPqpMXH2HT5R6sRDPR1xSptbsWeLnrH/sv5xsOgp7reTI3JQrCnoc3sAZ1FsKdLi3E8/RElLm3wfsL71Jhr+N343zF2oDdpJyvaTMWv6D0cLayipNrE5fGhmK2Sn9IL+TWzGJ1GcP1EmyfWwRM2cdlin9lsO1TA2oTTA5pb5u2fjhLq5YKrk477Pklg4Us/cby4mk0pJymuquea8aeCoH2MTkwe6svXB3KRUvL6DxmcKK3hobmRLW4Kz4yyCcoNFwxs9sCcEx3I7mPFLWbpNVusvLg5ncggd/4wO4JR4V5nXL5xc9YR6uXC9sOFnCitYcwAm7hoNQIfo1OLD1KDXku4jyvOOi1BneQyfTa425fi3Jx13RGw2O9R4tIG7ye+z9jgscQHxTNuoA9Swr6s0u42S3EWmC1WDp885VK7x74kduuFQ3A36NiSWsCuo8XEhnoy2M+Ir9HJse+yNS2fIX5GRoV58td1yZSdIV36/uOl7Msq5bYLB7P+7qm8vHQM2SXVLHhpO89vOkyQh4GLhjddipofF8yxomq+Sz7JK1vTuSwumElDW94LWTAqhJWzhrNs0sBm5+bEBGGVtizHYKsj0pBQ8ku7g8DKWREd2nsYFuDmSBEzZoBXu/p4uugZEeSO83nYe3j66aeJiYkhLi6O+Ph4fvnlF6ZPn05Swj5CvFyYMT6Wiy5q6owTHx9PbKytyN/WrVuZP795Sqfp06czYsQIR+r9xYsXA3D33Xfz17/+tcn9V6ywhYYkJCQwadIkRo4cyYIFCxxu0vX19SxfvpyRI0cyatQotm7d6ui/Zs0a4uLiiImJ4YEHHujUn013osSlFZILktmbu5dlccsAiB/ghUaceigpehfv/pzJnOe2OQRjb1YpHgYdkUHuXDTcn81p+SRmlzFhsM1zKjrEg6SccqrrzfySUcyMyAD+duVISuxxMPd8tI/5L/7Ii5sON1sqe+/nTNycdVw1NgwhBJfFBbP2zqkEuDuTmlfB4rFhLcw4bEtj93y0DyHgkUujWh2Lq5OOlbMiWoyjiAnxIMTTwOvbMlj40nZGPv4dc57bxmd7s3lx82Gigz24JCawhau2TkO6GSedhpgQzw717Wp27NjBunXr2Lt3L4mJiXz//feEh9tmhbYMxLalx4qKCo4ftyX7TUlJaff1V61axf79+9m/fz+ffvopAE899RTvvPMOGRkZHD16lLfeeounn34agFtvvZVnn32WAwcOcMUVV/C///u/ALz55psAHDhwgI0bN3LfffdhtVopKirij3/8I5s2bSIpKYmTJ0+yadOmzvnhdDNKXFrhP4n/QSu0XBtrS/fi5qwjMsiDPceKu9myvktKbjl//CQBUxfUGP983wmktMVngG0zf/QAbzQawbQR/hRU1FFvsTpq+MSEeHLoZAXbDhVQb7EyfUQAMSGe3HbhELanF/Lr0WI0QvCPjYe475MER0qU/Ipa1iXmsHhsWJMlo0F+Rj7//RQeXxDNbRcNaWaft9GJKcP8qDVZ+d20YYR2IBCyMUIIFowK4UhBJVqN4PfTh6IVgj98nEBmUTUrZw3vcPxFw6b+yFBPnHQ965GRm5uLn58fzvZYGz8/P0JCQpq1u+aaa1izZg0Aq1ev5rrrrjvre3p4ePD0009z5513smLFCp588km8vGwzurS0NMcsafbs2fz3v/8FIDk5mZkzZwIQEBCAl5cXu3fvJiMjg4iICPz9bTPZWbNmOfr0dlQQZQtYpZVVB1YxZ+gcAt1OfcobN8ibT+2bs+3NfaRoP1/sO8Ene7JZPDaMiW24x3aUIwWVJOWUE+DuzFeJudwxfSiH8iu4LC4YgOmNvKXG2TesY0I8MFkkr2/LwNVJy/jBtuMPzh3BiouH4m7QI6Xkxc3p/HPjIY4VVTN1mB9HCioxWSS/mTyomR1GZx03TRncqp23TB2MTiP47bTm4tMRHpgbyZ0zhjnE7f45I/g+5SQZhVUOj7KOMDzQJi5tLYkdPrySysrOzXvm5hbP8OHPtdlmzpw5PPnkk0RERDBr1iyWLFnCtGnTmrVbvHgxN910E/fffz9fffUVq1at4oMPPjijDddffz0uLjahnz17tmMmct111/HCCy+g1WpZtmyZo31sbCxr165l0aJFfPLJJ47Z0qhRo/jyyy+59tprOX78OHv27OH48ePMmDGD1NRUMjMzCQsL44svvqC+vvWqlr0JJS4tsD1rO1llWTwzs2kKiLEDvXl/xzFS8yqIDe1ZywN9gQP26O/t6YWdKi5r9+cgBLy2bCxXv7aDP6xJQErb7xMgwMPAyFBPTBYr3nbvp4bf776sUmZFBTqiy4UQjoe2EIK7Zw4nxMuF//s2jRc2H0ZKmBUVyGC/jmfOnhbhz7Q23ILbi1YjmsyaNBrBnJigs75edLAnk4f6smBU8xlBd+Pm5saePXv48ccf2bKvXSBkAAAZZklEQVRlC0uWLOHZZ59t1s7Hxwdvb28++ugjoqKicHV1beFqzVm1ahXjxjUvkZKdnU1eXh5CCCorK3Fzswnw22+/zd13382TTz7JwoULcXKy/T3dfPPNpKSkMG7cOAYOHMjkyZPR6XR4e3vz6quvsmTJEjQaDZMnTyYjI+McfiI9ByUuLfBBwgcY9UYWjVjU5HjDw2jPsRIlLh3gtR+OkHC8lJeXjml1I7khLgRs4nLfnBHndM+GcrFSSr5KzGHiYB/GDPBmflwwX+7PQSNgVPipT+IvXjcaS6O9k4E+rrg566isM3NxZNsP/MVjw1g8Nox6s5X8ilrHOn9fwcVJy4e3XdBmmzPNMLoSrVbL9OnTmT59OiNHjuS9995rsd2SJUtYsWKFIxHluXDPPffw+OOPk5KSwhNPPOGY0URGRvLdd98BcOjQIdavXw+ATqfjX//6l6P/5MmTGT58OAALFixgwYIFALzxxhtotX0jAFOt7ZxGrbmWT5I/4aroq5rVbQn1ciHIw8Bu+6Z+dkn1GT2H+jtWq+Sdn47yzcE8Pt2T3Wq748U1lNeaCfY0kHC8tMVsCMeLq3l16xHuXr2Pxa/+TEZBZbM2OzOKWPrmTiY/u5k9x0pIyikno6CKhaNsubJuu9C25BQZ5NFkQ3yQn7FJokqNxlabBHDU9DkTTjoNYd6uKjr7PJKWlsbhw6eCm/fv38/Agc296ACuuOIKHnjgAS655JJzuuc333xDfn4+N954I3/+85/5/PPPSU62laHPz88HwGq18tRTTzkyLVdXV1NVZcucsHHjRnQ6HdHR0U36lJSU8Morr3DrrX0jh2G7Zi5CiEygArDQQilNIUQk8A4wBnhUSvl/7e3b09iQvoGyujKWxp5eQ8e2DDJ2kDc7jhRy87u72Jyaz8Uj/HlneZsF2fo1iSfKOFleh7uzjmc3pDInJrDF/FENS2K3TB3MU+tT2JlRxCWNlnLMFitL39rJ8eIaQr1cyC2r4bO9J7j/EtsMx2Sxcvv7u9mSVoC/uzPOOg3XvbGTkWGe6DSCebG2a8WGenLT5EGOfYS2mB8Xgq/R+aw31xVdT2VlJXfddRelpaXodDqGDRvGG2+84XAbboy7uzsPPvhgi9fZtGkTYWFhjveffPIJ0HTPxc/Pj3Xr1rFy5Uo+/fRThBAYjUb+/ve/c+edd7J582ZWr17Nyy+/DMCVV17J8uXLAZuAXHLJJWg0GkJDQ5vs99xzzz0kJCQA8NhjjxEREdEJP5nuR7Qn4tguEOOklIWtnA8ABgKXAyUtiEurfVvCaDTKBpU/3yz5dAlbjm4h574cdJrm2vv+jkwe+zIJX6MTA31dScgu49dHZjqirRVN+Z8Nqby5LYNVt05k6Vu/sGR8OH+7YmSr7fb/ZQ4Tnv6eq8aE8dfLYx3nP9+Xzb1rEnh92VguiQnimtd3UFFr5pt7bPXdt6Tls/ydXdw1YxgrLh5GrcnCig/38lN6ETMiA3j7pvHnbcz9hZSUFEchLUXX0p6ftRCiWkrZY8rkdsqymJQyX0q5C+jVa0SV9ZV8lfYVV0df3aKwACwZH847y8fz00Mz+OvlsViskm/bSLfR3/k2KY8LhvgycYgvN00exOpfs9iSlt+s3cETZUQEuuPmrGPiYB9+Sj/1WcRqlbyy5QiRQe7MjrJ5O82KCiAlt5wTpTUAfJWQg7tBx50zhmHQ27Lrvrt8Ao/Nj+ahee1PQa9QKDqH9oqLBL4TQuwRQtzewXu0q68Q4nYhxG4hxG6z2dxasy7lq7SvqDHXOGJbWsJZp+XiEQEY9Fqigz0Y4mdkXWL7UoL0N9LzK8goqHIE7a2cNZyIAHdueXcXL20+7Igcb9jMH2l3kpg63J+MwiqHcHyXfJLD+ZX8bvpQh0PAjEjbNTen5lNrsrAx6SRzY4Ka1AzRazXcPHUwEa3Uf1coFF1He8VlipRyDDAPWCGE6Ehhk3b1lVK+IaUcJ6Ucp9N1jxPb6oOrCfMIY8qAKe1q3xB9vTOjiIKK9mfM7S80zOhmR9v2O9wNej77/WTmx4Xwf98d4ner9mC1Sk6U1lBSbSI2zC4uw2xVEdcl5FBYWccrW9MZ5OvK/LhTrrBD/Y0M8nVlU8pJfjhUQEWduUe6yvZ12pvIU3H29NafcbvERUqZY/+eD3wOtHsH+1z6nk9KakrYkL6BJTFL0Ij2rxbOjwvBKmFDo5og/ZmCijqSc8qpM1v4LimPUeFeTVKQG511PH9tPA/OjeTbpJO8vyPTkSCyYeYSEehGkIeBZ75JZdxT35OYXcYd04Y2SZkihGBGZCA/Hynik93HHckfFecPg8FAUVFRr3349QaklBQVFWEw9L40/mecIgghjIBGSllhfz0HeLI9Fz+Xvuebz1I+w2Q1tbkk1hIRgW4MC3BjXWIuyxrV1ugPWK0SIWiSTmTFqr2O7MJmq+SBuc3jVYQQ3DFtCL8cLeJ/NqQxJyYQrUYQGeTuOP/hbRM5cKKMshpb4sWrxoY1u86sqADe/uko36fkc/3EASprwnkmLCyM7OxsCgoKutuUPo3BYGjiydZbaM/6UyDwuf0BogM+lFJuEELcASClfE0IEQTsBjwAqxBiJRAN+LXUt/OHce6sOrCKYT7DGBs8tkP9hBDMjwvm+U2HeXxtEoN8Xbkwwr9JzERfIL+8Fm+jk6O+uZSS+S9uZ9oIfx6ca9swN1usJGSXcvEIf6KCPcgprWFxC6IAtp/b364YyZx/bePL/TlEBrk3iQ8Z4u/mqLDYGuMG+eDurKOiztxkyUxxftDr9Qwe3Ho6G0X/5oziIqXMAEa1cPy1Rq/zgJaeIuUt9e1pZJVlsTVzK49Pf7zDSf3AFqG9OTWfj3cfp7reQkyIB+vvvrALLO0eak0WZv7jB5ZNGsgDdiFJyC4jOde2nNUgLkcLq6gzW1kwKoQrx5z5k1aIlwuPXBrFI58fcCyJdQQnnYaZUQHsyixhwmCfDvdXKBRdh0r/AqxKXIVEckPcDWfVP8zblbV3TkVKyVPrU/hgx7E+ldwyObecijoza3YdZ+WsCJx0GtbZi2alnayg1mTBoNc6xCY6xKPd175uQjj5FbVNKip2hKevGEl1veW8VBlUKBTtp288/c4BKSXvJ77P1AFTGeJ9btlohRBEBXtQb7FyrLi6kyzsfhKP2wqkFVXVszn1JFarZF1iLh4GHRardNR+T84px0mr6dCSoBCClbMiiAtrXxGq0zE66/B3VwGsCkVPo9+Ly57cPaQWpnJj3I2dcr0Ie1qRxlUPewtSSvIrajmQXYbFesoDKCG7DD83Z4I9DXy06zi7j5WQV17LnTOGAXAg2yY+ybnlRAS5OfZlFApF/6XfL4u9n/A+zlpnro65ulOu11C179DJSubGnqFxN/HxruM46zUsig91HHt5Szqvbj1CZZ0tgPWpy2O54QJbAsCE7FLiw72ICnbn5S3p6DQaXPRarp84kDe2HSUxuwwpJck55cyK6ni9EIVC0ffo1x8xTRYTqw+uZuGIhXgZzm5Z5nRcnXSEebtw6DzPXKxWyZ++OHDGMswny2v50xcHuf+TBNLzbTYm5ZTxj+/SiA/34i8Lohno68qGg3kAlNWYyCioYlSYJ1ePDccq4fuUk8yMCsDorGNUmCcJ2aXkV9RRVFXfof0WhULRd+nX4vLiry9SWF3IzaNv7tTrRgS6c/hk83TwXcnBnDL+szOLL/adaLPd2z8dxWy1YtBreeSzg1iskse+TMLb1YmXl45h+ZTBzIu1ZR0oqzE5aqyMCvdigK8rU4bZAhUbouHjwrzIKKxiZ0YR0LHNfIVC0Xfpt+KSVZbFY1se47Lhl3HJ0HOr73A6wwPdyCisxNwFteBb4/sUWzLItDZmTOW1Jj7cmcWlI4P582XR/JpZzK3v7WLPsRIenBeJp6uteuHs6EDMVsnWtHz22zfz4+ypWVZMH8bUYX6Oiolx4Z5ICR/vtpVzbQiEVCgU/Zt+Ky53fXMXVmnlpUtfOqvYlraICHDHZJFkFnWNx1hJVT0X/n0z2w6diozenGrL43X4ZEWr6Tg+/CWLijozd0wbytXjwpgw2IctaQWMGeDF4kZxKaPDvfBzc+a75JMkZpcyyNfVUYNl8jA//nPrREfA4yi7l9dP6UUM8nVtUl5XoVD0LIQQc4UQaUKIdCHEQy2cF0KIF+znE4UQY9rb93T6pbh8kfoFa9PW8sT0JxjkNajTr9+QhberPMZ2ZhRxvLiGFzbZKvDlldVy8EQ5oV4ulFSbKKhsnkSzzmzh7e1HmTrMj9hQT4QQPHPlSCYN8eVvV45sUn5YoxHMigrgh7QC9maVNikHfDo+RifCvG3FlNSSmELRcxFCaIGXsSURjgauE0JEn9ZsHjDc/nU78GoH+jahX4rLs9ufJcovipUXrOyS6w8NsNXrOdTGvsvG5JPc+Pav1Js7vnTWUGZ597ESEo6XsjnVtiR224W2VByH8pret85s4cFPE8mvqOOOaUNP2envxurbLyAyqLkozI4OpLLOTEFF3RljUBpmL9HBSlwUih7MBCBdSpkhpawHPgIWndZmEfC+tLET8BJCBLezbxP6nbiU1payK2cXi6MXo9d2zRKOq5OOcB8XDuW3PHMxW6w8tT6ZbYcK2JTS8UJju4+VEBvqgbuzjn9vP8rm1JOE+7hwmT2/VuN9l7JqE795+1e+2J/D/XMimDrcr133mDLMDxf70ld8eNupWRr2Y9TMRaHoVnQNNbHsX6fXzwoFjjd6n20/1p427enb1JiOWN4X+CHzB6zSyozBM7r0PhEB7q0ui32+7wTHiqpx1mn48Ncs5o0Mbvd1a+otJJ0o4/aLhlA/2Mo7P2ei0wiumzAAf3dnfI1OHMo7dd+b3v2VgyfKeG5JPJePbvNvoQkGvZZpEf5sTDlJdHDb4nLpyGD2ZpUwfpDK76VQdCNmKeW4Ns63tLl8+gZta23a07cJ/W7msunoJlx0LkwKm9Sl9xke6M7RwipMp3mMmS1WXtqSTkyIB7+fPowfDxeSZd/433GkiCte+Ym3fsygorblitH7j5ditkrGDfLmN5MHIaWkzmx15OaKCHR3zFzS8yvYl1XKw/OiOiQsDTw4L5KXl47GxUnbZrtwH1deXzZObeYrFD2bbCC80fsw4PQyuq21aU/fJvQ7cdl8dDNTB0zFWde1+agiAt0wWSQf7z7OP79L45mvU9h/vNQxa7ln5nCWjA9HI+CjXVnkl9dy1+q9pOVV8NT6FCY9s5l/bTzUTJx2ZxYDMHaAD+E+rswbGYyHQcfEIbZZw4gg24zJapWOSpCXdmBm1JjBfkbmxp5dX4VC0ePYBQwXQgwWQjgB1wJrT2uzFrjR7jV2AVAmpcxtZ98m9KllsezybELdQ1t1Lc6rzCOpIIllccu63JYR9niPRz8/iEaAViN4fVsGGmHb+J4dHeiopvjx7mz2HCuhqs7C2junUGuy8tq2Izy/6TA/HCrghWtHM8DXFbDtt0QEujliUp65ciTFlfWO2vERge5U1Vs4UVrDty1UglQoFP0TKaVZCHEn8C2gBd6WUiY1rs0FfA1cCqQD1cDytvq2db8+Iy6ltaVMeHMCo4NH8+aCNwlxb148avPRzQDMHDKzy+2JDvbgxetG4+vmxKgwL8xWybcH8/g+5SS3XTTEIYBLJ4bzfcpJCivr+MfVoxhud2N+eekY5sXm8PBnB7j0hR95d/l4Rg/wZm9WSZPCWB4GPR6NlqNGBNlym/1wqIDE7LIWK0EqFIr+iZTya2wC0vhY49pcEljR3r5t0WeWxTycPXho6kNsObqF2FdiWX1gdbM2mzI24WXwYnTQ6C63RwjBglEhTB7qh9FZh6eLnmvGh/PGjeOabHxPiwggLsyTmyYPalbKd35cCBtWXoS/uzO3vb+bjcl5VNSaGT/Iu9X7NojTaz8cAeCSmKAuGJ1CoVC0TZ8RF43QcPfEu9l/x35G+I1g6WdLeXrb003abM7czMWDLkaraXuD+nyi1Qi+XDGFxxfGtHg+1MuFd24ajxCCOz/cB8C4ga17ZXkY9IR4GsguqWGov7HPlVtWKBS9gz4jLg1E+Ebw4/IfWRa3jD9t+ROPbnqU8rpy/pP4HzJLM7vcBflsOFP6mUF+Rt68cRwajcDf3ZlwH5c220fY93vUrEWhUHQXfWbPpTE6jY53L38Xg87A37b/jWd/ehartBLkFsSiEW0GlfZYxg705r3lE6g1Wc4oRiMC3dmaVsAcJS4KhaKb6JPiArZlstfnv05sQCx5lXnMHTaXSWGTuiwq/3wwaahvu9pdPS4MZ72WuNC2gx8VCoWiqxCtZdDtToxGo6yqqupuMxQKhaLXIISollIau9uOBvrcnotCoVAouh8lLgqFQqHodJS4KBQKhaLTUeKiUCgUik5HiYtCoVAoOh0lLgqFQqHodJS4KBQKhaLTUeKiUCgUik6nRwZRCiGsQM1ZdtcB5k40pztRY+mZqLH0XPrSeDo6FhcpZY+ZMPRIcTkXhBC7z1BHutegxtIzUWPpufSl8fT2sfQYlVMoFApF30GJi0KhUCg6nb4oLm90twGdiBpLz0SNpefSl8bTq8fS5/ZcFAqFQtH99MWZi0KhUCi6mT4jLkKIuUKINCFEuhDioe62pyMIIcKFEFuEEClCiCQhxD324z5CiI1CiMP2797dbWt7EUJohRD7hBDr7O9781i8hBCfCiFS7b+jSb11PEKIe+1/YweFEKuFEIbeMhYhxNtCiHwhxMFGx1q1XQjxsP15kCaEuKR7rG6ZVsbyv/a/sUQhxOdCCK9G53rsWFqjT4iLEEILvAzMA6KB64QQ0d1rVYcwA/dJKaOAC4AVdvsfAjZJKYcDm+zvewv3ACmN3vfmsTwPbJBSRgKjsI2r141HCBEK3A2Mk1LGAlrgWnrPWN4F5p52rEXb7f8/1wIx9j6v2J8TPYV3aT6WjUCslDIOOAQ8DL1iLC3SJ8QFmACkSykzpJT1wEfAom62qd1IKXOllHvtryuwPbxCsY3hPXuz94DLu8fCjiGECAMuA95qdLi3jsUDuAj4N4CUsl5KWUovHQ+2wDwXIYQOcAVy6CVjkVJuA4pPO9ya7YuAj6SUdVLKo0A6tudEj6ClsUgpv5NSNgRN7gTC7K979Fhao6+ISyhwvNH7bPuxXocQYhAwGvgFCJRS5oJNgICA7rOsQzwHPABYGx3rrWMZAhQA79iX+d4SQhjpheORUp4A/g/IAnKBMinld/TCsTSiNdt7+zPhZuAb++teOZa+Ii6ihWO9zg1OCOEG/BdYKaUs7257zgYhxHwgX0q5p7tt6SR0wBjgVSnlaKCKnrts1Cb2/YhFwGAgBDAKIW7oXqu6jF77TBBCPIptqXxVw6EWmvX4sfQVcckGwhu9D8M23e81CCH02IRllZTyM/vhk0KIYPv5YCC/u+zrAFOAhUKITGzLkzOEEP+hd44FbH9b2VLKX+zvP8UmNr1xPLOAo1LKAimlCfgMmEzvHEsDrdneK58JQojfAPOB6+WpOJFeOZa+Ii67gOFCiMFCCCdsm19ru9mmdiOEENjW9FOklP9sdGot8Bv7698AX55v2zqKlPJhKWWYlHIQtt/DZinlDfTCsQBIKfOA40KIEfZDM4Fkeud4soALhBCu9r+5mdj293rjWBpozfa1wLVCCGchxGBgOPBrN9jXboQQc4EHgYVSyupGp3rdWACQUvaJL+BSbB4WR4BHu9ueDto+Fds0NxHYb/+6FPDF5gFz2P7dp7tt7eC4pgPr7K977ViAeGC3/ffzBeDdW8cDPAGkAgeBDwDn3jIWYDW2vSITtk/zt7RlO/Co/XmQBszrbvvbMZZ0bHsrDc+A13rDWFr7UhH6CoVCoeh0+sqymEKhUCh6EEpcFAqFQtHpKHFRKBQKRaejxEWhUCgUnY4SF4VCoVB0OkpcFIpOQAgxvSEDtEKhUOKiUCgUii5AiYuiXyGEuEEI8asQYr8Q4nV73ZlKIcQ/hBB7hRCbhBD+9rbxQoidjepreNuPDxNCfC+ESLD3GWq/vFujui+r7FHwCCGeFUIk26/zf900dIXivKLERdFvEEJEAUuAKVLKeMACXA8Ygb1SyjHAD8Bf7F3eBx6UtvoaBxodXwW8LKUchS03V679+GhgJbaaQkOAKUIIH+AKIMZ+nae6dpQKRc9AiYuiPzETGAvsEkLst78fgq00wBp7m/8AU4UQnoCXlPIH+/H3gIuEEO5AqJTycwApZa08lQfqVylltpTSii19xyCgHKgF3hJCXAk0zhmlUPRZlLgo+hMCeE9KGW//GiGlfLyFdm3lRGop/XkDdY1eWwCdtBV/moAt4/XlwIYO2qxQ9EqUuCj6E5uAxUKIAHDUXx+I7f9gsb3NUmC7lLIMKBFCXGg/vgz4Qdrq7GQLIS63X8NZCOHa2g3tNXo8pZRfY1syi++KgSkUPQ1ddxugUJwvpJTJQog/Ad8JITTYMtKuwFYALEYIsQcow7YvA7YU7q/ZxSMDWG4/vgx4XQjxpP0aV7dxW3fgSyGEAdus595OHpZC0SNRWZEV/R4hRKWU0q277VAo+hJqWUyhUCgUnY6auSgUCoWi01EzF4VCoVB0OkpcFAqFQtHpKHFRKBQKRaejxEWhUCgUnY4SF4VCoVB0OkpcFAqFQtHp/D/FCZXlHdIwEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.plot(val_losses, label='val loss')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(men, label='MEN', c='g')\n",
    "ax2.plot(ws353, label='WS353', c='m')\n",
    "ax2.plot(simlex999, label='SIMLEX999', c='y')\n",
    "\n",
    "ax1.legend(loc=[0.07, 0.9])\n",
    "ax2.legend(loc=[0.7, 0.15])\n",
    "\n",
    "ax1.set_xlabel('epochs');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}