diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml new file mode 100644 index 0000000..f748772 --- /dev/null +++ b/.github/workflows/tests.yml @@ -0,0 +1,24 @@ +name: Run Lightweight Tests + +on: [push, pull_request] + +jobs: + lightweight-tests: + runs-on: ubuntu-latest + timeout-minutes: 10 + strategy: + fail-fast: false + steps: + - uses: actions/checkout@v4 + - name: Install Rye + uses: eifinger/setup-rye@v4 + with: + version: 'latest' + - name: Install Project + run: rye sync + - name: Download misc. Dependencies + run: rye run python -m nltk.downloader cmudict averaged_perceptron_tagger + - name: Run Lightweight Tests + run: | + export PYTHONPATH=$PYTHONPATH:$(pwd) + rye run tests-light diff --git a/.gitignore b/.gitignore index 172d098..2edccb3 100755 --- a/.gitignore +++ b/.gitignore @@ -24,4 +24,22 @@ datasets/machine_translation/global_voices/training/* *.pyc *.atok base/models +base/results hansard.36 +.DS_Store +specialk/results +specialk/telegram.json +cache +*.ruff_cache +.vscode +.venv +.arrow +tensorboard_logs +/models/ +.envrc +tb_logs +*.parquet +*.arrow +*.ts +*.ckpt +specialk/notebooks/lightning_logs/* diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..f69abe4 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.9.7 diff --git a/assets/tokenizer/de_small_word_moses b/assets/tokenizer/de_small_word_moses new file mode 100644 index 0000000..171efee --- /dev/null +++ b/assets/tokenizer/de_small_word_moses @@ -0,0 +1 @@ +{"kwargs": {"name": "de_baby", "filename": "", "vocab_size": 15004, "max_length": 50, "lower": true, "BOS_TOKEN": "", "EOS_TOKEN": "", "UNK_TOKEN": "", "SEP_TOKEN": "", "PAD_TOKEN": "", "CLS_TOKEN": "", "BLO_TOKEN": "

"}, "vocab": {"idxToLabel": {"0": "", "1": "", "2": "", "3": "", "4": ".", "5": "ein", "6": "einem", "7": "in", "8": "eine", "9": ",", "10": "und", "11": "mit", "12": "auf", "13": "mann", "14": "einer", "15": "der", "16": "frau", "17": "die", "18": "zwei", "19": "einen", "20": "im", "21": "an", "22": "von", "23": "sich", "24": "dem", "25": "m\u00e4dchen", "26": "junge", "27": "vor", "28": "zu", "29": "steht", "30": "m\u00e4nner", "31": "sitzt", "32": "hund", "33": "den", "34": "stra\u00dfe", "35": "w\u00e4hrend", "36": "gruppe", "37": "h\u00e4lt", "38": "spielt", "39": "das", "40": "hemd", "41": "personen", "42": "\u00fcber", "43": "drei", "44": "eines", "45": "frauen", "46": "blauen", "47": "neben", "48": "ist", "49": "kind", "50": "wei\u00dfen", "51": "roten", "52": "stehen", "53": "sitzen", "54": "menschen", "55": "am", "56": "aus", "57": "spielen", "58": "durch", "59": "bei", "60": "geht", "61": "tr\u00e4gt", "62": "f\u00e4hrt", "63": "wasser", "64": "um", "65": "kinder", "66": "kleines", "67": "person", "68": "macht", "69": "springt", "70": "schwarzen", "71": "kleiner", "72": "entlang", "73": "leute", "74": "gehen", "75": "etwas", "76": "mehrere", "77": "seinem", "78": "gro\u00dfen", "79": "oberteil", "80": "jungen", "81": "hand", "82": "gr\u00fcnen", "83": "l\u00e4uft", "84": "sind", "85": "f\u00fcr", "86": "hintergrund", "87": "fahrrad", "88": "freien", "89": "jacke", "90": "luft", "91": "strand", "92": "hat", "93": "ball", "94": "anderen", "95": "schaut", "96": "junger", "97": "kleidung", "98": "hinter", "99": "sie", "100": "nach", "101": "andere", "102": "gelben", "103": "kleine", "104": "geb\u00e4ude", "105": "vier", "106": "tisch", "107": "hut", "108": "wird", "109": "beim", "110": "n\u00e4he", "111": "essen", "112": "kleinen", "113": "menschenmenge", "114": "schwarzer", "115": "kamera", "116": "paar", "117": "vorbei", "118": "hoch", "119": "gras", "120": "t-shirt", "121": "hunde", "122": "boden", "123": "schnee", "124": "rennt", "125": "ihre", "126": "unter", "127": "ihren", "128": "kleid", "129": "gitarre", "130": "\u00e4lterer", "131": "drau\u00dfen", "132": "sein", "133": "zusammen", "134": "ihr", "135": "feld", "136": "b\u00fchne", "137": "fahren", "138": "brille", "139": "seine", "140": "herum", "141": "rennen", "142": "kopf", "143": "gehweg", "144": "sehen", "145": "er", "146": "seinen", "147": "dabei", "148": "blickt", "149": "foto", "150": "wei\u00dfer", "151": "des", "152": "stadt", "153": "hinunter", "154": "park", "155": "braunen", "156": "liegt", "157": "versucht", "158": "laufen", "159": "jeans", "160": "arbeitet", "161": "als", "162": "bank", "163": "tragen", "164": "baby", "165": "wie", "166": "dame", "167": "machen", "168": "gro\u00dfe", "169": "l\u00e4chelt", "170": "schauen", "171": "brauner", "172": "ihrem", "173": "schwarz", "174": "\u00e4ltere", "175": "orangefarbenen", "176": "ihm", "177": "junges", "178": "b\u00fcrgersteig", "179": "arbeiten", "180": "hose", "181": "zeigt", "182": "sieht", "183": "spricht", "184": "liest", "185": "unterhalten", "186": "skateboard", "187": "halten", "188": "wand", "189": "haaren", "190": "weg", "191": "viele", "192": "sonnenbrille", "193": "zum", "194": "pferd", "195": "boot", "196": "gekleidete", "197": "seiner", "198": "tag", "199": "rosa", "200": "hosen", "201": "\u201c", "202": "helm", "203": "wei\u00dfem", "204": "grauen", "205": "singt", "206": "ihnen", "207": "gesicht", "208": "blauem", "209": "\u201e", "210": "bild", "211": "mantel", "212": "auto", "213": "posiert", "214": "fu\u00dfball", "215": "darauf", "216": "felsen", "217": "f\u00fcnf", "218": "klettert", "219": "f\u00fchrt", "220": "shorts", "221": "befindet", "222": "einige", "223": "sand", "224": "mikrofon", "225": "baum", "226": "sehr", "227": "wirft", "228": "schwarzem", "229": "blauer", "230": "gekleideter", "231": "wei\u00df", "232": "es", "233": "band", "234": "meer", "235": "blau", "236": "oder", "237": "rotem", "238": "restaurant", "239": "wald", "240": "isst", "241": "schwarze", "242": "arbeiter", "243": "m\u00e4nnern", "244": "raum", "245": "kindern", "246": "anderer", "247": "wiese", "248": "fenster", "249": "langen", "250": "roter", "251": "wartet", "252": "pullover", "253": "wei\u00dfe", "254": "ins", "255": "anzug", "256": "bluse", "257": "posieren", "258": "gestreiften", "259": "motorrad", "260": "werden", "261": "trikot", "262": "haar", "263": "tanzen", "264": "springen", "265": "oberk\u00f6rper", "266": "voller", "267": "bereitet", "268": "stuhl", "269": "spieler", "270": "betrachtet", "271": "rot", "272": "h\u00fcgel", "273": "alter", "274": "blonde", "275": "schild", "276": "zwischen", "277": "handy", "278": "warten", "279": "oben", "280": "leuten", "281": "schl\u00e4ft", "282": "zieht", "283": "berg", "284": "ab", "285": "m\u00fctze", "286": "buch", "287": "reitet", "288": "lehnt", "289": "h\u00e4nden", "290": "stock", "291": "maul", "292": "beobachtet", "293": "miteinander", "294": "b\u00e4umen", "295": "fluss", "296": "tanzt", "297": "ihrer", "298": "rucksack", "299": "bauarbeiter", "300": "kleinkind", "301": "arm", "302": "l\u00e4cheln", "303": "vom", "304": "schiebt", "305": "haben", "306": "befinden", "307": "gegen", "308": "radfahrer", "309": "spielzeug", "310": "see", "311": "seite", "312": "k\u00fcche", "313": "kurzen", "314": "schwimmbecken", "315": "asiatische", "316": "welle", "317": "r\u00fccken", "318": "spiel", "319": "treppe", "320": "zaun", "321": "verkauft", "322": "zur", "323": "jemand", "324": "familie", "325": "fangen", "326": "mitten", "327": "versammelt", "328": "trinkt", "329": "gro\u00dfer", "330": "rosafarbenen", "331": "erwachsene", "332": "h\u00e4ngt", "333": "schneidet", "334": "blumen", "335": "ihn", "336": "gesch\u00e4ft", "337": "markt", "338": "telefoniert", "339": "gerade", "340": "belebten", "341": "umgeben", "342": "malt", "343": "spazieren", "344": "bunten", "345": "nimmt", "346": "reihe", "347": "schwimmt", "348": "blicken", "349": "rote", "350": "gew\u00e4sser", "351": "zug", "352": "denen", "353": "vielen", "354": "freiem", "355": "schneebedeckten", "356": "autos", "357": "bus", "358": "schl\u00e4gt", "359": "gekleidet", "360": "geb\u00e4udes", "361": "fu\u00df", "362": "wagen", "363": "mutter", "364": "h\u00e4nde", "365": "br\u00fccke", "366": "sonnigen", "367": "fliegt", "368": "publikum", "369": "himmel", "370": "fu\u00dfballspieler", "371": "tasche", "372": "richtung", "373": "hebt", "374": "k\u00e4mpfen", "375": "dunklen", "376": "herunter", "377": "hemden", "378": "bart", "379": "gel\u00e4nder", "380": "gr\u00fcnem", "381": "weste", "382": "hohen", "383": "platz", "384": "graffiti", "385": "zuschauer", "386": "wei\u00dfes", "387": "asiatischer", "388": "haus", "389": "blondes", "390": "seil", "391": "kopfbedeckung", "392": "\u00fcberqueren", "393": "alte", "394": "zeitung", "395": "mannes", "396": "sprung", "397": "zuschauen", "398": "teil", "399": "hinauf", "400": "musik", "401": "bereiten", "402": "tritt", "403": "genie\u00dft", "404": "orangen", "405": "davon", "406": "decke", "407": "basketball", "408": "blaue", "409": "hof", "410": "shirt", "411": "karierten", "412": "mittleren", "413": "lacht", "414": "sechs", "415": "da", "416": "benutzt", "417": "zuschauern", "418": "schirm", "419": "mauer", "420": "papier", "421": "menge", "422": "st\u00fcck", "423": "alten", "424": "kunstst\u00fcck", "425": "fest", "426": "unterh\u00e4lt", "427": "gro\u00dfes", "428": "einigen", "429": "blick", "430": "spielplatz", "431": "mensch", "432": "zusieht", "433": "rasen", "434": "alters", "435": "baseballspieler", "436": "zuschaut", "437": "mitte", "438": "schaukel", "439": "sofa", "440": "nahe", "441": "nebeneinander", "442": "trinken", "443": "armen", "444": "l\u00e4sst", "445": "nachts", "446": "\u2013", "447": "rock", "448": "fotos", "449": "bereit", "450": "pool", "451": "typ", "452": "lila", "453": "zusehen", "454": "dach", "455": "hinten", "456": "versuchen", "457": "gegenstand", "458": "augen", "459": "gelber", "460": "zigarette", "461": "scheint", "462": "mehreren", "463": "gibt", "464": "sonne", "465": "bier", "466": "treten", "467": "beobachten", "468": "surfer", "469": "mund", "470": "kniet", "471": "beide", "472": "raucht", "473": "uniform", "474": "outfit", "475": "flagge", "476": "schal", "477": "nummer", "478": "f\u00e4ngt", "479": "\u00f6ffentlichen", "480": "verschneiten", "481": "sch\u00fcrze", "482": "betrachten", "483": "trikots", "484": "stange", "485": "ferne", "486": "stufen", "487": "nicht", "488": "liegen", "489": "f\u00fchren", "490": "wanderer", "491": "blonden", "492": "erwachsener", "493": "diese", "494": "rotes", "495": "frisbee", "496": "gehsteig", "497": "teenager", "498": "rampe", "499": "au\u00dferhalb", "500": "einander", "501": "snowboarder", "502": "unten", "503": "tennisball", "504": "holz", "505": "lachen", "506": "u-bahn", "507": "gem\u00fcse", "508": "greift", "509": "veranstaltung", "510": "bett", "511": "rutscht", "512": "reden", "513": "m\u00e4nnliche", "514": "fotografiert", "515": "hilft", "516": "kleidern", "517": "nacht", "518": "aufschrift", "519": "braune", "520": "b\u00e4rtiger", "521": "gr\u00fcn", "522": "schwarz-wei\u00dfer", "523": "werfen", "524": "sonnenuntergang", "525": "h\u00fcten", "526": "beiden", "527": "surft", "528": "haare", "529": "man", "530": "\u00fcberquert", "531": "leiter", "532": "gr\u00fcner", "533": "gelbe", "534": "herr", "535": "dass", "536": "kost\u00fcmen", "537": "skifahrer", "538": "musiker", "539": "gelbem", "540": "nacktem", "541": "art", "542": "statue", "543": "ufer", "544": "damen", "545": "polizisten", "546": "st\u00e4dtischen", "547": "versammeln", "548": "legt", "549": "zelt", "550": "maschine", "551": "basketballspieler", "552": "orange", "553": "t\u00fcr", "554": "nehmen", "555": "blonder", "556": "erwachsenen", "557": "mannschaft", "558": "rutsche", "559": "leine", "560": "tor", "561": "blaues", "562": "kommt", "563": "bar", "564": "sweatshirt", "565": "vater", "566": "pfad", "567": "polizist", "568": "stellt", "569": "genie\u00dfen", "570": "westen", "571": "schreibt", "572": "guckt", "573": "brunnen", "574": "leeren", "575": "st\u00fchlen", "576": "asiatischen", "577": "eimer", "578": "lesen", "579": "fisch", "580": "gelb", "581": "l\u00e4ufer", "582": "gekleideten", "583": "obst", "584": "ende", "585": "verschiedene", "586": "feuer", "587": "kaufen", "588": "streckt", "589": "grill", "590": "getr\u00e4nk", "591": "klettern", "592": "her", "593": "braut", "594": "inmitten", "595": "schlagzeug", "596": "regen", "597": "gestreiftem", "598": "sprechen", "599": "umgebung", "600": "wobei", "601": "ohne", "602": "riesigen", "603": "badeanzug", "604": "eis", "605": "wellen", "606": "schutzhelm", "607": "fahrzeug", "608": "schuhen", "609": "schwingt", "610": "arme", "611": "gemeinsam", "612": "gegend", "613": "trick", "614": "handschuhen", "615": "parkplatz", "616": "grauem", "617": "regenschirm", "618": "instrument", "619": "sohn", "620": "singen", "621": "\u00e4lteren", "622": "schlagen", "623": "uniformen", "624": "gelbes", "625": "motorradfahrer", "626": "anderes", "627": "parade", "628": "soldaten", "629": "starrt", "630": "kinderwagen", "631": "offenen", "632": "geb\u00e4uden", "633": "stra\u00dfenrand", "634": "jugendlicher", "635": "stra\u00dfenecke", "636": "schwarzes", "637": "korb", "638": "bergen", "639": "h\u00f6rt", "640": "mikrophon", "641": "l\u00e4chelnd", "642": "baustelle", "643": "pinkfarbenen", "644": "bereich", "645": "stra\u00dfen", "646": "zur\u00fcck", "647": "arbeit", "648": "scho\u00df", "649": "umarmt", "650": "jagt", "651": "dieser", "652": "kost\u00fcm", "653": "jacken", "654": "pause", "655": "laden", "656": "computer", "657": "dunkelh\u00e4utiger", "658": "waren", "659": """, "660": "gleich", "661": "gr\u00fcne", "662": "krawatte", "663": "orangefarbener", "664": "repariert", "665": "kurz", "666": "volleyball", "667": "schreibtisch", "668": "vordergrund", "669": "gegeneinander", "670": "schatten", "671": "kurzer", "672": "gut", "673": "baseball", "674": "konzert", "675": "alle", "676": "gekleidetes", "677": "vogel", "678": "garten", "679": "kuchen", "680": "putzt", "681": "ganz", "682": "balanciert", "683": "asiatisches", "684": "pflanzen", "685": "bedeckt", "686": "fahrr\u00e4dern", "687": "jemandem", "688": "sandalen", "689": "l\u00e4chelnde", "690": "kunden", "691": "gr\u00fcnes", "692": "fahrradfahrer", "693": "verkaufen", "694": "stiefeln", "695": "weibliche", "696": "netz", "697": "erde", "698": "winkt", "699": "lkw", "700": "land", "701": "bikini", "702": "ziehen", "703": "beugt", "704": "fell", "705": "setzt", "706": "pferden", "707": "gegen\u00fcber", "708": "stein", "709": "party", "710": "wo", "711": "dreht", "712": "reiten", "713": "orangefarbenem", "714": "mobiltelefon", "715": "surfbrett", "716": "footballspieler", "717": "klassenzimmer", "718": "snowboard", "719": "halsband", "720": "schulter", "721": "sch\u00fcler", "722": "schiedsrichter", "723": "felswand", "724": "glas", "725": "football-spieler", "726": "einkaufswagen", "727": "f\u00e4llt", "728": "football", "729": "belebte", "730": "hellbrauner", "731": "schlitten", "732": "dessen", "733": "h\u00f6he", "734": "unbefestigten", "735": "badehose", "736": "schl\u00e4ger", "737": "team", "738": "durchs", "739": "redet", "740": "braunem", "741": "davor", "742": "leuchtend", "743": "schuhe", "744": "aussieht", "745": "bein", "746": "was", "747": "zeigen", "748": "stand", "749": "kanu", "750": "oberteilen", "751": "schwimmen", "752": "rad", "753": "seines", "754": "kajak", "755": "k\u00fcsst", "756": "loch", "757": "schlange", "758": "verschiedenen", "759": "tischen", "760": "spiegel", "761": "schnauze", "762": "k\u00fcssen", "763": "ecke", "764": "spa\u00df", "765": "farbenfrohen", "766": "skateboarder", "767": "m\u00e4nnlicher", "768": "b\u00e4ume", "769": "kreis", "770": "t-shirts", "771": "taschen", "772": "tochter", "773": "grasbewachsenen", "774": "base", "775": "asiatisch", "776": "laptop", "777": "mikroskop", "778": "hin", "779": "k\u00fcnstler", "780": "schultern", "781": "telefon", "782": "sch\u00f6nen", "783": "seifenblasen", "784": "rennstrecke", "785": "lilafarbenen", "786": "zimmer", "787": "akkordeon", "788": "gepflasterten", "789": "tanktop", "790": "ger\u00fcst", "791": "kirche", "792": "haufen", "793": "kocht", "794": "kai", "795": "flasche", "796": "spielfeld", "797": "tasse", "798": "bedient", "799": "hunden", "800": "unterwegs", "801": "hellen", "802": "k\u00fcste", "803": "schaukelt", "804": "nur", "805": "freunde", "806": "parkbank", "807": "rodeo", "808": "bemalt", "809": "farben", "810": "freunden", "811": "aussehende", "812": "gegnerischen", "813": "tennis", "814": "caf\u00e9", "815": "entspannt", "816": "trampolin", "817": "katze", "818": "schwimmbrille", "819": "sachen", "820": "beleuchteten", "821": "schutzhelmen", "822": "sieben", "823": "fleisch", "824": "damit", "825": "gasse", "826": "helmen", "827": "violetten", "828": "bis", "829": "braun-wei\u00dfer", "830": "ski", "831": "stadion", "832": "schaufel", "833": "geige", "834": "grauer", "835": "umarmen", "836": "steine", "837": "kappe", "838": "teich", "839": "langem", "840": "strecke", "841": "so", "842": "jongliert", "843": "verwendet", "844": "sch\u00fcssel", "845": "skiern", "846": "voll", "847": "h\u00fcpft", "848": "t\u00e4nzer", "849": "cowboy", "850": "hang", "851": "maske", "852": "rand", "853": "schlamm", "854": "berge", "855": "kopfh\u00f6rer", "856": "holt", "857": "steigt", "858": "overall", "859": "reparieren", "860": "landschaft", "861": "l\u00e4chelnder", "862": "couch", "863": ";", "864": "kauft", "865": "flaggen", "866": "roller", "867": "schie\u00dft", "868": "barfu\u00df", "869": "fu\u00dfballspiel", "870": "feldweg", "871": "tanz", "872": "dunkler", "873": "rechts", "874": "fotografieren", "875": "klippe", "876": "kaffee", "877": "reifen", "878": "lastwagen", "879": "braun", "880": "links", "881": "bl\u00e4st", "882": "heben", "883": "objekt", "884": "aussehender", "885": "hellblauen", "886": "ruht", "887": "sch\u00fcttelt", "888": "sitzende", "889": "anz\u00fcgen", "890": "cowboyhut", "891": "ber\u00fchrt", "892": "hockt", "893": "grillt", "894": "dunkelh\u00e4utige", "895": "fahnen", "896": "teller", "897": "verfolgt", "898": "w\u00fcste", "899": "kopftuch", "900": "instrumenten", "901": "bekommt", "902": "ringen", "903": "wandert", "904": "asiate", "905": "mahlzeit", "906": "bilder", "907": "handtasche", "908": "h\u00e4ngen", "909": "linken", "910": "nase", "911": "reinigt", "912": "springbrunnen", "913": "\u00fcberf\u00fcllten", "914": "beinen", "915": "metall", "916": "instrumente", "917": "flugzeug", "918": "geschlossenen", "919": "clown", "920": "new", "921": "personengruppe", "922": "veranda", "923": "gucken", "924": "diesem", "925": "tennisspieler", "926": "felsigen", "927": "heraus", "928": "asiaten", "929": "\u00fcben", "930": "hauses", "931": "gel\u00e4ndemotorrad", "932": "m\u00fcll", "933": "auch", "934": "asiatin", "935": "\u00f6ffnet", "936": "traditioneller", "937": "gezogen", "938": "salto", "939": "marathon", "940": "hinab", "941": "blatt", "942": "bauen", "943": "pinkfarbenem", "944": "schneiden", "945": "pferde", "946": "s\u00e4nger", "947": "zeit", "948": "kerl", "949": "winterkleidung", "950": "lederjacke", "951": "schnell", "952": "balkon", "953": "nachdem", "954": "taucht", "955": "rothaarige", "956": "dieses", "957": "fahrr\u00e4der", "958": "kann", "959": "untersucht", "960": "auftritt", "961": "umzug", "962": "steinen", "963": "w\u00e4scht", "964": "reiter", "965": "amerikanische", "966": "schlafen", "967": "sitzender", "968": "z\u00e4hne", "969": "bunte", "970": "gelegt", "971": "teppich", "972": "gebiet", "973": "hellbraunen", "974": "kurve", "975": "beieinander", "976": "allein", "977": "tuch", "978": "zunge", "979": "wurf", "980": "weitere", "981": "wurde", "982": "gie\u00dft", "983": "kickt", "984": "schie\u00dfen", "985": "teams", "986": "passanten", "987": "york", "988": "bach", "989": "terrasse", "990": "finger", "991": "schlauch", "992": "skateboardfahrer", "993": "bahn", "994": "vollen", "995": "geld", "996": "\u00fcbt", "997": "b\u00fcro", "998": "kommen", "999": "ausr\u00fcstung", "1000": "fu\u00dfg\u00e4nger", "1001": "hals", "1002": "br\u00fcnette", "1003": "viel", "1004": "vollf\u00fchrt", "1005": "bringt", "1006": "nassen", "1007": "rasiert", "1008": "beton", "1009": "br\u00e4utigam", "1010": "gezeigt", "1011": "frisch", "1012": "ob", "1013": "helfen", "1014": "touristen", "1015": "kampfsport", "1016": "koch", "1017": "steigen", "1018": "wettkampf", "1019": "skater", "1020": "besen", "1021": "matte", "1022": "geben", "1023": "lebensmittel", "1024": "sch\u00f6ne", "1025": "karren", "1026": "angelt", "1027": "k\u00f6nnen", "1028": "hinaus", "1029": "getr\u00e4nke", "1030": "kissen", "1031": "schwimmer", "1032": "benutzen", "1033": "kuh", "1034": "paddelt", "1035": "bullen", "1036": "sitzenden", "1037": "neoprenanzug", "1038": "gleitet", "1039": ":", "1040": "hellh\u00e4utiger", "1041": "hockeyspieler", "1042": "2", "1043": "wasserfall", "1044": "stra\u00dfenk\u00fcnstler", "1045": "schreit", "1046": "baumstamm", "1047": "grau", "1048": "sportler", "1049": "hinterher", "1050": "tunnel", "1051": "licht", "1052": "darunter", "1053": "arbeitskleidung", "1054": "feiern", "1055": "'s", "1056": "theke", "1057": "acht", "1058": "chinesischen", "1059": "nahaufnahme", "1060": "teleskop", "1061": "fast", "1062": "vorbeigehen", "1063": "berges", "1064": "gl\u00fccklich", "1065": "kochen", "1066": "tier", "1067": "jungs", "1068": "handtuch", "1069": "tennisschl\u00e4ger", "1070": "\u00e4rmellosen", "1071": "kapuzenpulli", "1072": "verkleidet", "1073": "entfernt", "1074": "turnhalle", "1075": "ort", "1076": "umher", "1077": "runden", "1078": "braunhaarige", "1079": "konzentriert", "1080": "bewegt", "1081": "alleine", "1082": "stoff", "1083": "m\u00e4nteln", "1084": "rollstuhl", "1085": "kehrt", "1086": "rechten", "1087": "verkehr", "1088": "lehnen", "1089": "fotograf", "1090": "handstand", "1091": "becher", "1092": "trainiert", "1093": "lassen", "1094": "jemanden", "1095": "waldgebiet", "1096": "fahrer", "1097": "stapel", "1098": "gegenst\u00e4nde", "1099": "rot-wei\u00dfen", "1100": "befestigt", "1101": "rolltreppe", "1102": "folgt", "1103": "l\u00e4chelndes", "1104": "offenem", "1105": "ger\u00e4t", "1106": "gesch\u00e4ftigen", "1107": "afrikanische", "1108": "gr\u00e4bt", "1109": "sees", "1110": "braunes", "1111": "freund", "1112": "gewand", "1113": "jagen", "1114": "keyboard", "1115": "wohnzimmer", "1116": "kalten", "1117": "weit", "1118": "fl\u00e4che", "1119": "weihnachtsbaum", "1120": "indische", "1121": "farbe", "1122": "stark", "1123": "daumen", "1124": "schaufelt", "1125": "spitze", "1126": "ihres", "1127": "blondem", "1128": "gem\u00e4lde", "1129": "schlag", "1130": "werbung", "1131": "baseballkappe", "1132": "kunstst\u00fccke", "1133": "sch\u00e4ferhund", "1134": "h\u00f6lzernen", "1135": "h\u00f6ren", "1136": "baut", "1137": "pf\u00fctze", "1138": "bushaltestelle", "1139": "bl\u00e4tter", "1140": "weint", "1141": "geschenk", "1142": "n\u00e4hert", "1143": "angezogen", "1144": "gelehnt", "1145": "bringen", "1146": "streichelt", "1147": "f\u00fc\u00dfen", "1148": "rast", "1149": "komplett", "1150": "trommel", "1151": "sonnenbrillen", "1152": "fischen", "1153": "dunkelhaarige", "1154": "kahlk\u00f6pfiger", "1155": "geworfen", "1156": "steilen", "1157": "jugendliche", "1158": "tr\u00e4gerhemd", "1159": "hocke", "1160": "stehenden", "1161": "ballons", "1162": "hei\u00dfen", "1163": "klavier", "1164": "abend", "1165": "j\u00fcngeren", "1166": "pfosten", "1167": "hammer", "1168": "joggen", "1169": "menschengruppe", "1170": "schnurrbart", "1171": "zeichnet", "1172": "schwarz-wei\u00dfen", "1173": "vorn", "1174": "kletterer", "1175": "geschirr", "1176": "w\u00e4sche", "1177": "stadtstra\u00dfe", "1178": "kopfh\u00f6rern", "1179": "geparkt", "1180": "wandbild", "1181": "steinmauer", "1182": "motorr\u00e4dern", "1183": "eins", "1184": "fans", "1185": "ausgestreckten", "1186": "schmutzigen", "1187": "graffitis", "1188": "wundersch\u00f6nen", "1189": "p\u00e4rchen", "1190": "zuschauermenge", "1191": "skatepark", "1192": "kisten", "1193": "scheinen", "1194": "projekt", "1195": "brust", "1196": "h\u00fcrde", "1197": "langhaariger", "1198": "goldenen", "1199": "cheerleader", "1200": "gassi", "1201": "spritzt", "1202": "joggt", "1203": "teilen", "1204": "dreadlocks", "1205": "restaurants", "1206": "speisen", "1207": "beh\u00e4lter", "1208": "ich", "1209": "eingang", "1210": "besch\u00e4ftigt", "1211": "knien", "1212": "kids", "1213": "dies", "1214": "stellen", "1215": "einzelner", "1216": "hula-hoop-reifen", "1217": "treffen", "1218": "n\u00e4hmaschine", "1219": "ges\u00e4umten", "1220": "luftballons", "1221": "laub", "1222": "baseballm\u00fctze", "1223": "amerikanischen", "1224": "decken", "1225": "rede", "1226": "vorbeif\u00e4hrt", "1227": "e-gitarre", "1228": "hindernis", "1229": "koffer", "1230": "zeichen", "1231": "bowlingbahn", "1232": "\u00e4lteres", "1233": "nickerchen", "1234": "schutzbrille", "1235": "typen", "1236": "gep\u00e4ck", "1237": "direkt", "1238": "(", "1239": ")", "1240": "kreuzung", "1241": "gel\u00e4nde", "1242": "rosafarbenem", "1243": "silbernen", "1244": "f\u00fc\u00dfe", "1245": "soldat", "1246": "hell", "1247": "bahnsteig", "1248": "bildschirm", "1249": "getr\u00e4nken", "1250": "rudert", "1251": "schuh", "1252": "balls", "1253": "fischer", "1254": "anlegestelle", "1255": "m\u00e4dchens", "1256": "seiten", "1257": "fliegen", "1258": "flughafen", "1259": "schwarz-wei\u00df", "1260": "trompete", "1261": "afrikanischer", "1262": "pier", "1263": "feuerwehrm\u00e4nner", "1264": "wettbewerb", "1265": "dinge", "1266": "violettem", "1267": "arena", "1268": "ziel", "1269": "angelrute", "1270": "brett", "1271": "kunstwerk", "1272": "studenten", "1273": "ber\u00fchren", "1274": "am\u00fcsieren", "1275": "aufs", "1276": "mithilfe", "1277": "strohhut", "1278": "einkaufszentrum", "1279": "schlagzeuger", "1280": "outfits", "1281": "lachend", "1282": "gebl\u00fcmten", "1283": "aber", "1284": "heu", "1285": "schwach", "1286": "steinigen", "1287": "bowlingkugel", "1288": "verkauf", "1289": "b\u00fccher", "1290": "b\u00e4nken", "1291": "graben", "1292": "socken", "1293": "hinterhof", "1294": "gang", "1295": "st\u00e4dtische", "1296": "balken", "1297": "schule", "1298": "bedeckten", "1299": "aufblasbaren", "1300": "anhat", "1301": "blume", "1302": "bauch", "1303": "r\u00e4dern", "1304": "regenschirmen", "1305": "rucks\u00e4cken", "1306": "gew\u00e4ssers", "1307": "marschiert", "1308": "bearbeitet", "1309": "fotografen", "1310": "mountainbike", "1311": "w\u00e4nden", "1312": "h\u00fctte", "1313": "setzen", "1314": "abendessen", "1315": "rollerblades", "1316": "stift", "1317": "rennwagen", "1318": "hochzeit", "1319": "sucht", "1320": "gespr\u00e4ch", "1321": "feier", "1322": "riesige", "1323": "kopf\u00fcber", "1324": "herd", "1325": "wein", "1326": "blau-wei\u00dfen", "1327": "kaut", "1328": "schwimmbad", "1329": "gitarrist", "1330": "j\u00fcngere", "1331": "saxophon", "1332": "plattform", "1333": "f\u00fcllt", "1334": "kletterger\u00fcst", "1335": "gruppen", "1336": "mannschaften", "1337": "schwarz-brauner", "1338": "picknicktisch", "1339": "daneben", "1340": "bluejeans", "1341": "fahrt", "1342": "schirmen", "1343": "gel\u00e4ndemotorradfahrer", "1344": "gewehr", "1345": "grillen", "1346": "flachen", "1347": "dreck", "1348": "skulptur", "1349": "drachen", "1350": "zeitschrift", "1351": "richtet", "1352": "steckt", "1353": "sandstrand", "1354": "gegenseitig", "1355": "fl\u00fcssigkeit", "1356": "labor", "1357": "dicht", "1358": "moped", "1359": "teig", "1360": "kariertem", "1361": "beigefarbenen", "1362": "schwenkt", "1363": "bestehende", "1364": "backsteingeb\u00e4ude", "1365": "dunkelblauen", "1366": "offenbar", "1367": "!", "1368": "jahrmarkt", "1369": "spielkleidung", "1370": "schlafanzug", "1371": "grasfl\u00e4che", "1372": "bei\u00dft", "1373": "turnerin", "1374": "kalb", "1375": "sportveranstaltung", "1376": "puck", "1377": "geldb\u00f6rse", "1378": "trainieren", "1379": "orchester", "1380": "gesch\u00e4ften", "1381": "fegt", "1382": "wasserflasche", "1383": "lied", "1384": "rollt", "1385": "begutachtet", "1386": "3", "1387": "tafel", "1388": "waschsalon", "1389": "farbenfroher", "1390": "tricks", "1391": "lehrer", "1392": "verlassen", "1393": "bunt", "1394": "r\u00f6cken", "1395": "rennauto", "1396": "malen", "1397": "jeansjacke", "1398": "noch", "1399": "fahne", "1400": "bew\u00f6lkten", "1401": "entspannen", "1402": "eltern", "1403": "treppen", "1404": "findet", "1405": "surfboard", "1406": "feuerwehrmann", "1407": "will", "1408": "sportwagen", "1409": "frisbeescheibe", "1410": "hilfe", "1411": "tief", "1412": "grimasse", "1413": "untersuchen", "1414": "becken", "1415": "traktor", "1416": "gesch\u00e4ftskleidung", "1417": "wandern", "1418": "umhang", "1419": "fu\u00dfballfeld", "1420": "vorne", "1421": "paddeln", "1422": "herab", "1423": "parkt", "1424": "brot", "1425": "leicht", "1426": "s\u00e4ngerin", "1427": "stier", "1428": "stra\u00dfenh\u00e4ndler", "1429": "kerzen", "1430": "bunter", "1431": "sandd\u00fcne", "1432": "brandung", "1433": "f\u00fcttert", "1434": "reinigen", "1435": "robe", "1436": "schwei\u00dft", "1437": "dunkelhaariger", "1438": "planscht", "1439": "diskutieren", "1440": "ballon", "1441": "braun-schwarzer", "1442": "verl\u00e4sst", "1443": "mittagessen", "1444": "waldweg", "1445": "esel", "1446": "beine", "1447": "stufe", "1448": "wettrennen", "1449": "piste", "1450": "podium", "1451": "schildern", "1452": "bewundert", "1453": "treibt", "1454": "einrad", "1455": "werkzeug", "1456": "messer", "1457": "runter", "1458": "afroamerikaner", "1459": "motorroller", "1460": "welpen", "1461": "bahnhof", "1462": "mittag", "1463": "ziegelmauer", "1464": "verkleidete", "1465": "vielleicht", "1466": "seilen", "1467": "stirn", "1468": "eishockeyspieler", "1469": "silhouette", "1470": "baumes", "1471": "marschieren", "1472": "n\u00e4ht", "1473": "fahrgesch\u00e4ft", "1474": "zahlreiche", "1475": "gef\u00fcllt", "1476": "fallschirm", "1477": "eingez\u00e4unten", "1478": "stehende", "1479": "k\u00f6rper", "1480": "b\u00fcschen", "1481": "graue", "1482": "schubkarre", "1483": "kegel", "1484": "blumenmuster", "1485": "bekommen", "1486": "turner", "1487": "vor\u00fcber", "1488": "ast", "1489": "violine", "1490": "v\u00f6gel", "1491": "palmen", "1492": "\u00fcberpr\u00fcft", "1493": "dazu", "1494": "wischt", "1495": "bespritzt", "1496": "sportkleidung", "1497": "topf", "1498": "maulkorb", "1499": "gleichen", "1500": "kapuze", "1501": "orangefarbene", "1502": "festh\u00e4lt", "1503": "motorr\u00e4der", "1504": "b\u00fcckt", "1505": "scheinbar", "1506": "sammelt", "1507": "k\u00f6pfen", "1508": "bogen", "1509": "jockey", "1510": "teenageralter", "1511": "hinein", "1512": "flur", "1513": "fu\u00dfg\u00e4nger\u00fcberweg", "1514": "badewanne", "1515": "engen", "1516": "lange", "1517": "bekleidung", "1518": "gew\u00e4ndern", "1519": "rosafarbene", "1520": "greifen", "1521": "feiert", "1522": "lockigen", "1523": "verk\u00e4ufer", "1524": "schaufenster", "1525": "auff\u00fchrung", "1526": "ruhigen", "1527": "webt", "1528": "gesch\u00e4fts", "1529": "starren", "1530": "afroamerikanische", "1531": "statt", "1532": "teilweise", "1533": "softball", "1534": "h\u00e4usern", "1535": "jubeln", "1536": "fernglas", "1537": "gehalten", "1538": "akustikgitarre", "1539": "wendet", "1540": "farbigen", "1541": "cafe", "1542": "stirnband", "1543": "chor", "1544": "l\u00f6ffel", "1545": "klassenraum", "1546": "entlanggeht", "1547": "neugeborenes", "1548": "aussehenden", "1549": "verdeckt", "1550": "poster", "1551": "gehende", "1552": "neue", "1553": "kameras", "1554": "snowboardfahrer", "1555": "n\u00e4chsten", "1556": "fahrrads", "1557": "indischer", "1558": "beginnt", "1559": "stangen", "1560": "l\u00e4ndlichen", "1561": "zubereitet", "1562": "baseballspiel", "1563": "vorbeigeht", "1564": "radrennen", "1565": "k\u00f6che", "1566": "khakifarbenen", "1567": "gegner", "1568": "rettungswesten", "1569": "asiatinnen", "1570": "tauben", "1571": "bibliothek", "1572": "glatzk\u00f6pfiger", "1573": "stehender", "1574": "hier", "1575": "torh\u00fcter", "1576": "mitglieder", "1577": "bewegen", "1578": "einsamer", "1579": "london", "1580": "bl\u00e4ttern", "1581": "schmutz", "1582": "spiels", "1583": "tennisspielerin", "1584": "grimassen", "1585": "gleichzeitig", "1586": "gestreiftes", "1587": "rothaariges", "1588": "trommeln", "1589": "landet", "1590": "reifenschaukel", "1591": "drinnen", "1592": "the", "1593": "schieben", "1594": "plakat", "1595": "taucher", "1596": "z\u00f6pfen", "1597": "kiste", "1598": "schaufeln", "1599": "rande", "1600": "moment", "1601": "olympischen", "1602": "verheiratetes", "1603": "szene", "1604": "wange", "1605": "schreiben", "1606": "sowie", "1607": "taxi", "1608": "lebensmittelgesch\u00e4ft", "1609": "geparkten", "1610": "klatscht", "1611": "fingern", "1612": "gewickelt", "1613": "m\u00fctzen", "1614": "plastikt\u00fcte", "1615": "streifen", "1616": "u-bahn-station", "1617": "ohr", "1618": "meeres", "1619": "trainer", "1620": "deck", "1621": "asien", "1622": "elefanten", "1623": "achterbahn", "1624": "jubelt", "1625": "versammlung", "1626": "ring", "1627": "papiere", "1628": "a", "1629": "schwert", "1630": "strickm\u00fctze", "1631": "b\u00e4llen", "1632": "flo\u00df", "1633": "rosafarbener", "1634": "langhaarige", "1635": "gesichtsbemalung", "1636": "aufmerksam", "1637": "halstuch", "1638": "handschuh", "1639": "nah", "1640": "streicht", "1641": "stehend", "1642": "brauen", "1643": "st\u00fchle", "1644": "werkstatt", "1645": "club", "1646": "figur", "1647": "bandana", "1648": "basketballspiel", "1649": "gestikuliert", "1650": "karte", "1651": "leckt", "1652": "manche", "1653": "ozean", "1654": "kindes", "1655": "trib\u00fcne", "1656": "schaukeln", "1657": "fl\u00f6te", "1658": "show", "1659": "kettens\u00e4ge", "1660": "ohren", "1661": "unbefestigte", "1662": "abends", "1663": "inlineskates", "1664": "bilden", "1665": "computerbildschirm", "1666": "schilder", "1667": "hindurch", "1668": "bauarbeiten", "1669": "m\u00fclltonne", "1670": "marktplatz", "1671": "h\u00e4ndler", "1672": "vorbereitet", "1673": "barf\u00fc\u00dfige", "1674": "radfahrern", "1675": "altes", "1676": "bewegung", "1677": "t\u00e4towierung", "1678": "tut", "1679": "t\u00e4towierungen", "1680": "gebeugt", "1681": "dunklem", "1682": "computern", "1683": "packt", "1684": "fallen", "1685": "schmuck", "1686": "waschen", "1687": "gestreifter", "1688": "h\u00fcte", "1689": "dudelsack", "1690": "tarnkleidung", "1691": "blasen", "1692": "f\u00e4nger", "1693": "suchen", "1694": "pudel", "1695": "kette", "1696": "haltung", "1697": "blau-gelben", "1698": "ge\u00f6ffneten", "1699": "hockey", "1700": "gesichtern", "1701": "selbst", "1702": "schwimmweste", "1703": "wassers", "1704": "schafe", "1705": "ansieht", "1706": "cowboys", "1707": "k\u00f6pfe", "1708": "hart", "1709": "wir", "1710": "winter", "1711": "t\u00fcren", "1712": "ger\u00e4ten", "1713": "uhr", "1714": "hellh\u00e4utige", "1715": "k\u00fcmmert", "1716": "ausgestellt", "1717": "nebel", "1718": "beleuchtung", "1719": "legen", "1720": "trainingsanzug", "1721": "breakdance", "1722": "nieder", "1723": "kunstwerke", "1724": "karten", "1725": "freundin", "1726": "aussehen", "1727": "kleider", "1728": "sprungbrett", "1729": "schiff", "1730": "vergn\u00fcgungspark", "1731": "watet", "1732": "feuerwehrleute", "1733": "aufgestellt", "1734": "linie", "1735": "schnitzt", "1736": "pitcher", "1737": "schneeanzug", "1738": "badezimmer", "1739": "waschbecken", "1740": "pink", "1741": "dorf", "1742": "bemalten", "1743": "jackett", "1744": "pr\u00fcft", "1745": "rauch", "1746": "gl\u00e4nzenden", "1747": "verschneite", "1748": "kn\u00f6pfen", "1749": "beisammen", "1750": "betonwand", "1751": "begleitet", "1752": "booten", "1753": "treiben", "1754": "kreide", "1755": "museum", "1756": "spaziergang", "1757": "hocker", "1758": "maschendrahtzaun", "1759": "gold", "1760": "welpe", "1761": "schleife", "1762": "sch\u00fcsseln", "1763": "bike", "1764": "m\u00e4nnliches", "1765": "traditionellen", "1766": "hei\u00dfluftballon", "1767": "hundes", "1768": "lernt", "1769": "spielerisch", "1770": "braun-wei\u00dfe", "1771": "herumstehen", "1772": "treppenstufen", "1773": "golfball", "1774": "mountainbiker", "1775": "sandigen", "1776": "sonnenschirm", "1777": "badeanz\u00fcgen", "1778": "putzen", "1779": "t\u00e4nzerin", "1780": "produkte", "1781": "wundersch\u00f6ne", "1782": "holzbank", "1783": "weihnachtsmann", "1784": "serviert", "1785": "bmx-fahrer", "1786": "st\u00f6cke", "1787": "pferdeschwanz", "1788": "s\u00e4ulen", "1789": "sp\u00e4rlich", "1790": "hafen", "1791": "tiere", "1792": "sch\u00fctteln", "1793": "babys", "1794": "fu\u00dfballdress", "1795": "m\u00f6glicherweise", "1796": "erklimmen", "1797": "innenstadt", "1798": "ruhen", "1799": "waffe", "1800": "orientalische", "1801": "brettspiel", "1802": "hoher", "1803": "shirts", "1804": "unterschiedlichen", "1805": "bietet", "1806": "s\u00fc\u00dfigkeiten", "1807": "kerle", "1808": "flaschen", "1809": "afroamerikanischer", "1810": "kante", "1811": "bekleideter", "1812": "muster", "1813": "bau", "1814": "videospiel", "1815": "bowling", "1816": "tablett", "1817": "netze", "1818": "oberen", "1819": "4", "1820": "l\u00f6schen", "1821": "kleinkinder", "1822": "indem", "1823": "scheibe", "1824": "\u00f6rtlichen", "1825": "namen", "1826": "paare", "1827": "abgeworfen", "1828": "stra\u00dfenverk\u00e4ufer", "1829": "g\u00fcrtel", "1830": "ringer", "1831": "rahmen", "1832": "helme", "1833": "einkaufstasche", "1834": "vorderseite", "1835": "m\u00e4nnlichen", "1836": "lebensmitteln", "1837": "wette", "1838": "lang\u00e4rmeligen", "1839": "city", "1840": "h\u00e4ndchen", "1841": "gepunkteten", "1842": "gef\u00fcllten", "1843": "dr\u00fcckt", "1844": "leuchtenden", "1845": "gel\u00e4ndewagen", "1846": "gehender", "1847": "gegenst\u00e4nden", "1848": "lieferwagen", "1849": "karussell", "1850": "besteht", "1851": "freude", "1852": "bordstein", "1853": "blocken", "1854": "r\u00fcckw\u00e4rts", "1855": "laborkittel", "1856": "laptops", "1857": "jeder", "1858": "japanischen", "1859": "jugendlichen", "1860": "weiter", "1861": "kleineren", "1862": "wasserrutsche", "1863": "sport", "1864": "dritte", "1865": "gesprungen", "1866": "banner", "1867": "b\u00e4lle", "1868": "pr\u00e4sentiert", "1869": "2012", "1870": "passenden", "1871": "bekleidete", "1872": "dock", "1873": "bekleidet", "1874": "braunhaariger", "1875": "rutschen", "1876": "verteilt", "1877": "t\u00fcr\u00f6ffnung", "1878": "gl\u00e4sern", "1879": "\u00fcberall", "1880": "schwebt", "1881": "silberfarbenen", "1882": "turnschuhen", "1883": "transportiert", "1884": "wirbelt", "1885": "gemacht", "1886": "herren", "1887": "auge", "1888": "pinkfarbener", "1889": "hohe", "1890": "operation", "1891": "j\u00fcngerer", "1892": "eiscreme", "1893": "sammeln", "1894": "seltsamen", "1895": "flauschiger", "1896": "st\u00f6\u00dft", "1897": "s\u00e4ugling", "1898": "zement", "1899": "verwenden", "1900": "entgegen", "1901": "obststand", "1902": "mitarbeiter", "1903": "fu\u00dfg\u00e4ngern", "1904": "weil", "1905": "ballerina", "1906": "schutzkleidung", "1907": "schulkinder", "1908": "kran", "1909": "gitarristen", "1910": "boxer", "1911": "kauert", "1912": "regnerischen", "1913": "am\u00fcsiert", "1914": "pflanze", "1915": "swimmingpool", "1916": "bildern", "1917": "partie", "1918": "s\u00e4ule", "1919": "luftballon", "1920": "lagerfeuer", "1921": "dreirad", "1922": "landstra\u00dfe", "1923": "konferenz", "1924": "zylinder", "1925": "meisten", "1926": "abgebildet", "1927": "5", "1928": "kasse", "1929": "klappstuhl", "1930": "d\u00e4mmerung", "1931": "sicht", "1932": "handschuhe", "1933": "rechte", "1934": "kratzt", "1935": "eimern", "1936": "karton", "1937": "freizeitpark", "1938": "tracht", "1939": "menschenansammlung", "1940": "aufgereiht", "1941": "schutzausr\u00fcstung", "1942": "vergn\u00fcgt", "1943": "halskette", "1944": "aneinander", "1945": "surfen", "1946": "motorhaube", "1947": "erzeugnisse", "1948": "texas", "1949": "home", "1950": "eingeschlafen", "1951": "lachende", "1952": "bergsteiger", "1953": "deren", "1954": "leinwand", "1955": "perlen", "1956": "vortrag", "1957": "baseballschl\u00e4ger", "1958": "gesehen", "1959": "angebracht", "1960": "r\u00fccksitz", "1961": "bevor", "1962": "strecken", "1963": "namens", "1964": "unordentlichen", "1965": "niedrigen", "1966": "gefolgt", "1967": "biker", "1968": "gitarren", "1969": "unterrichtet", "1970": "erdh\u00fcgel", "1971": "krankenhaus", "1972": "vorrichtung", "1973": "aufgeregt", "1974": "herkunft", "1975": "liegenden", "1976": "stelzen", "1977": "kost\u00fcme", "1978": "soll", "1979": "stolz", "1980": "ofen", "1981": "k\u00f6rben", "1982": "r\u00fcckseite", "1983": "arzt", "1984": "beladen", "1985": "fr\u00fcchte", "1986": "kurze", "1987": "overalls", "1988": "frisbee-scheibe", "1989": "schwarzhaarige", "1990": "herrn", "1991": "lie\u00dft", "1992": "staubigen", "1993": "obdachloser", "1994": "anh\u00e4nger", "1995": "l\u00e4chelnden", "1996": "fr\u00f6hlich", "1997": "filmt", "1998": "puppe", "1999": "angeboten", "2000": "unterw\u00e4sche", "2001": "\u00f6ffentlichkeit", "2002": "axt", "2003": "schlauchboot", "2004": "erreichen", "2005": "fischt", "2006": "holzboden", "2007": "ebenfalls", "2008": "ziellinie", "2009": "bestellen", "2010": "ziegelwand", "2011": "leggings", "2012": "spring", "2013": "&", "2014": "sieg", "2015": "schneeschuhen", "2016": "schwingen", "2017": "st\u00f6cken", "2018": "kugel", "2019": "locken", "2020": "webstuhl", "2021": "mischt", "2022": "abgestellt", "2023": "nasser", "2024": "gr\u00f6\u00dferen", "2025": "ziegelsteinen", "2026": "fremden", "2027": "teilnehmer", "2028": "streichen", "2029": "trifft", "2030": "schneit", "2031": "m\u00fclleimer", "2032": "kurzem", "2033": "turban", "2034": "ungew\u00f6hnlichen", "2035": "pr\u00e4sentation", "2036": "ente", "2037": "fu\u00dfballmannschaft", "2038": "richtungen", "2039": "entfernung", "2040": "formt", "2041": "bootes", "2042": "fliegenden", "2043": "afrikanischen", "2044": "gleisen", "2045": "schminkt", "2046": "\u00fcbergewichtiger", "2047": "windhund", "2048": "oranger", "2049": "lasso", "2050": "klasse", "2051": "erhobenen", "2052": "rasenfl\u00e4che", "2053": "vergn\u00fcgen", "2054": "heruntergekommenen", "2055": "verzierten", "2056": "bauernmarkt", "2057": "formeller", "2058": "musikinstrumente", "2059": "banjo", "2060": "sch\u00fctzen", "2061": "schlendert", "2062": "getroffen", "2063": "top", "2064": "schach", "2065": "knie", "2066": "befahrenen", "2067": "mitglied", "2068": "felsbrocken", "2069": "beleuchtet", "2070": "w\u00fcrde", "2071": "kreuz", "2072": "schneebedeckte", "2073": "maschinen", "2074": "hohem", "2075": "hula-hoop", "2076": "\u00e4hnlich", "2077": "cello", "2078": "passiert", "2079": "bruder", "2080": "gelangen", "2081": "\u00f6ffnen", "2082": "dahinter", "2083": "autobahn", "2084": "feuerwehrauto", "2085": "gegnerische", "2086": "hohes", "2087": "betreten", "2088": "station", "2089": "kartons", "2090": "werfer", "2091": "anhaben", "2092": "schlagmann", "2093": "klaren", "2094": "bad", "2095": "fahrzeuge", "2096": "orangefarbenes", "2097": "boxen", "2098": "aussicht", "2099": "darin", "2100": "festgebunden", "2101": "jeep", "2102": "begeisterung", "2103": "einkaufst\u00fcten", "2104": "fl\u00fcgeln", "2105": "bewaldeten", "2106": "unteren", "2107": "jeweils", "2108": "wasserski", "2109": "dribbelt", "2110": "hoodie", "2111": "v\u00f6llig", "2112": "strands", "2113": "wenn", "2114": "golf", "2115": "elektrischen", "2116": "flusses", "2117": "teilnehmen", "2118": "schneebedecktes", "2119": "\u00fcberpr\u00fcfen", "2120": "t\u00e4towierte", "2121": "los", "2122": "sch\u00fctzt", "2123": "stra\u00dfenmusiker", "2124": "betrachter", "2125": "ge\u00f6ffnet", "2126": "ausgestreckt", "2127": "zauns", "2128": "kopfschmuck", "2129": "flussufer", "2130": "holzzaun", "2131": "amerikanischer", "2132": "dritter", "2133": "fasst", "2134": "sessel", "2135": "heller", "2136": "umringt", "2137": "schlammigen", "2138": "seichten", "2139": "protestiert", "2140": "hinterbeinen", "2141": "warm", "2142": "violett", "2143": "bzw.", "2144": "schwarz-wei\u00dfem", "2145": "bedruckten", "2146": "kneipe", "2147": "gespannt", "2148": "saxofon", "2149": "dritten", "2150": "clowns", "2151": "warmen", "2152": "stromschnellen", "2153": "tags\u00fcber", "2154": "rennens", "2155": "rohr", "2156": "oberfl\u00e4che", "2157": "rauchen", "2158": "uniformierter", "2159": "kapuzenpullover", "2160": "sauber", "2161": "asiat", "2162": "aufnahme", "2163": "stra\u00dfenarbeiter", "2164": "diesen", "2165": "irokesenschnitt", "2166": "treppenhaus", "2167": "bindet", "2168": "namensschild", "2169": "reklametafel", "2170": "schachtel", "2171": "r\u00f6hre", "2172": "kricket", "2173": "pullunder", "2174": "fischernetz", "2175": "lampe", "2176": "ausgezogen", "2177": "parcours", "2178": "holen", "2179": "einkaufen", "2180": "experiment", "2181": "segelboot", "2182": "wasserfl\u00e4che", "2183": "sch\u00fcrzen", "2184": "tee", "2185": "attraktive", "2186": "skateboards", "2187": "essensstand", "2188": "fische", "2189": "geschwindigkeit", "2190": "regenmantel", "2191": "karohemd", "2192": "stab", "2193": "lippen", "2194": "besucht", "2195": "schnur", "2196": "spaziert", "2197": "nass", "2198": "vorf\u00fchrt", "2199": "plate", "2200": "fertig", "2201": "aussehendes", "2202": "darbietung", "2203": "st\u00fcrzt", "2204": "geschnitten", "2205": "angezogener", "2206": "f\u00fcllen", "2207": "bedienen", "2208": "musikgruppe", "2209": "kampf", "2210": "ampel", "2211": "fass", "2212": "orientalisch", "2213": "maler", "2214": "reihen", "2215": "uniformierte", "2216": "rudern", "2217": "board", "2218": "h\u00fcbsche", "2219": "etwa", "2220": "entfernen", "2221": "daran", "2222": "patienten", "2223": "chinesische", "2224": "schwarz-wei\u00dfe", "2225": "au\u00dfenbereich", "2226": "platte", "2227": "laufbahn", "2228": "m\u00e4ht", "2229": "steile", "2230": "weiteren", "2231": "frisur", "2232": "herbst", "2233": "sicherheitsweste", "2234": "apfel", "2235": "flipflops", "2236": "erleuchteten", "2237": "h\u00fcbschen", "2238": "bildes", "2239": "fenstern", "2240": "tanzfl\u00e4che", "2241": "steintreppe", "2242": "felsvorsprung", "2243": "stra\u00dfenseite", "2244": "betonmauer", "2245": "holzkonstruktion", "2246": "esstisch", "2247": "lernen", "2248": "t\u00fcten", "2249": "wasserbecken", "2250": "gespielt", "2251": "telefonieren", "2252": "spr\u00fcht", "2253": "handys", "2254": "dicken", "2255": "studio", "2256": "polohemd", "2257": "unterhemd", "2258": "anweisungen", "2259": "geldautomaten", "2260": "anzahl", "2261": "eishockey", "2262": "china", "2263": "indien", "2264": "john", "2265": "bei\u00dfen", "2266": "stra\u00dfenfest", "2267": "probiert", "2268": "sp\u00e4ht", "2269": "tun", "2270": "elektronischen", "2271": "angehalten", "2272": "tattoo", "2273": "erh\u00e4lt", "2274": "baseballfeld", "2275": "stra\u00dfenszene", "2276": "arbeitern", "2277": "kies", "2278": "spiegelbild", "2279": "verschr\u00e4nkten", "2280": "f\u00e4cher", "2281": "rot-schwarzen", "2282": "h\u00e4ngematte", "2283": "gleichgewicht", "2284": "muskul\u00f6ser", "2285": "zahnb\u00fcrste", "2286": "taube", "2287": "trockenen", "2288": "aufblasbare", "2289": "tomaten", "2290": "fabrik", "2291": "k\u00e4mpft", "2292": "auslage", "2293": "flugzeugs", "2294": "frischem", "2295": "ton", "2296": "ruhig", "2297": "sporthalle", "2298": "s\u00e4gt", "2299": "erklimmt", "2300": "strumpfhose", "2301": "vorsichtig", "2302": "sch\u00f6ner", "2303": "natur", "2304": "passt", "2305": "backsteinmauer", "2306": "kopfbedeckungen", "2307": "feuerstelle", "2308": "begeistert", "2309": "gipfel", "2310": "glatze", "2311": "rikscha", "2312": "zuhause", "2313": "stadtpark", "2314": "b\u00fcndel", "2315": "saugt", "2316": "rothaariger", "2317": "kajakfahrer", "2318": "wildnis", "2319": "gelb-schwarzen", "2320": "fahrradhelm", "2321": "schmalen", "2322": "rostigen", "2323": "zeremonie", "2324": "fels", "2325": "brillen", "2326": "verkaufsstand", "2327": "spielsachen", "2328": "h\u00fcfte", "2329": "pilot", "2330": "vorderen", "2331": "of", "2332": "wassersprenger", "2333": "forscher", "2334": "metallstange", "2335": "hochstuhl", "2336": "teenagern", "2337": "osten", "2338": "nahen", "2339": "kompletter", "2340": "tennisplatz", "2341": "schnappt", "2342": "gericht", "2343": "zuh\u00f6rt", "2344": "font\u00e4ne", "2345": "gewinnen", "2346": "ziegelbau", "2347": "v\u00f6geln", "2348": "hellgr\u00fcnen", "2349": "buchstaben", "2350": "smoking", "2351": "bord", "2352": "wolken", "2353": "gie\u00dfen", "2354": "fu\u00dfg\u00e4nger\u00fcbergang", "2355": "brennende", "2356": "laster", "2357": "staub", "2358": "sicherheitswesten", "2359": "ladefl\u00e4che", "2360": "spritzen", "2361": "seifenblase", "2362": "stativ", "2363": "fahrzeugen", "2364": "l\u00e4uferin", "2365": "rugbyspieler", "2366": "sandburg", "2367": "servieren", "2368": "wachmann", "2369": "stunt", "2370": "skatet", "2371": "angezogene", "2372": "b\u00fcrstet", "2373": "telefonzelle", "2374": "jockeys", "2375": "lokal", "2376": "usa", "2377": "geschm\u00fcckt", "2378": "m\u00f6chte", "2379": "betet", "2380": "gelegenen", "2381": "r\u00fchrt", "2382": "dar\u00fcber", "2383": "footballspiel", "2384": "faulenzen", "2385": "zelten", "2386": "bestehend", "2387": "gr\u00fcnfl\u00e4che", "2388": "orientalischer", "2389": "umgebenen", "2390": "mitspieler", "2391": "irgendeinem", "2392": "richten", "2393": "hosentr\u00e4gern", "2394": "ruder", "2395": "marathonl\u00e4ufer", "2396": "gepflasterte", "2397": "zeitschriften", "2398": "mei\u00dfelt", "2399": "blockt", "2400": "sommertag", "2401": "waschmaschine", "2402": "silberne", "2403": "busses", "2404": "dieselbe", "2405": "-schild", "2406": "schwere", "2407": "windhunde", "2408": "stiefel", "2409": "ziegelgeb\u00e4ude", "2410": "times", "2411": "bockenden", "2412": "yoga", "2413": "garage", "2414": "fernseher", "2415": "schaf", "2416": "schn\u00fcffelt", "2417": "schlucht", "2418": "passagiere", "2419": "fliege", "2420": "hamburger", "2421": "breiten", "2422": "figuren", "2423": "weiterer", "2424": "beachvolleyball", "2425": "hockeyspiel", "2426": "pizza", "2427": "altmodischen", "2428": "wollm\u00fctze", "2429": "dekoriert", "2430": "turm", "2431": "fernsehen", "2432": "halle", "2433": "sonnenlicht", "2434": "ozeans", "2435": "schwenken", "2436": "pfanne", "2437": "kampfsportler", "2438": "blaskapelle", "2439": "blau-gr\u00fcnen", "2440": "hause", "2441": "einiger", "2442": "sandwich", "2443": "folgen", "2444": "soeben", "2445": "peace-zeichen", "2446": "tal", "2447": "horizont", "2448": "steinbank", "2449": "gehstock", "2450": "pfeil", "2451": "rollen", "2452": "demonstranten", "2453": "ansehen", "2454": "flip-flops", "2455": "verschieden", "2456": "vorf\u00fchrung", "2457": "rosaroten", "2458": "heraush\u00e4ngender", "2459": "schachteln", "2460": "europa", "2461": "karate", "2462": "legerer", "2463": "dunkelheit", "2464": "eingewickelt", "2465": "aufzug", "2466": "smartphone", "2467": "binden", "2468": "wasserstra\u00dfe", "2469": "langes", "2470": "s\u00e4ubert", "2471": "schuss", "2472": "xylophon", "2473": "motocrossfahrer", "2474": "war", "2475": "laternen", "2476": "brautjungfern", "2477": "drink", "2478": "rodeoreiter", "2479": "uns", "2480": "randstein", "2481": "berglandschaft", "2482": "regelt", "2483": "k\u00e4fig", "2484": "steuert", "2485": "spielers", "2486": "teilt", "2487": "tankstelle", "2488": "angefeuert", "2489": "linke", "2490": "dirigiert", "2491": "'", "2492": "untergrund", "2493": "verteidiger", "2494": "motor", "2495": "pfeife", "2496": "schlittschuhen", "2497": "probieren", "2498": "entweder", "2499": "kontrolle", "2500": "lilafarbener", "2501": "d\u00fcnnen", "2502": "flammen", "2503": "zigarre", "2504": "gl\u00fcckliche", "2505": "warnweste", "2506": "innenraum", "2507": "spielern", "2508": "s\u00e4ge", "2509": "musikinstrument", "2510": "irgendwo", "2511": "versteckt", "2512": "huhn", "2513": "khakifarbener", "2514": "cheerleaderin", "2515": "footballfeld", "2516": "zahl", "2517": "burgunderroten", "2518": "startlinie", "2519": "sortiert", "2520": "hacken", "2521": "violetter", "2522": "regnet", "2523": "chinesischer", "2524": "karierter", "2525": "afroamerikanisches", "2526": "kaffeetasse", "2527": "afrikaner", "2528": "krabbelt", "2529": "zweite", "2530": "kilt", "2531": "hinweg", "2532": "interagieren", "2533": "frische", "2534": "herzen", "2535": "baumstumpf", "2536": "wieder", "2537": "laufband", "2538": "getragen", "2539": "eislaufen", "2540": "kamm", "2541": "square", "2542": "schleift", "2543": "sandh\u00fcgel", "2544": "wahrscheinlich", "2545": "snack", "2546": "nummern", "2547": "gesichtsausdruck", "2548": "ansicht", "2549": "demonstration", "2550": "badehosen", "2551": "tropischen", "2552": "alkohol", "2553": "baskenm\u00fctze", "2554": "schutt", "2555": "pinsel", "2556": "traditionelle", "2557": "brennenden", "2558": "vorstellung", "2559": "k\u00fcmmern", "2560": "fackel", "2561": "sims", "2562": "stra\u00dfenrennen", "2563": "tr\u00fcben", "2564": "pistole", "2565": "stadtszene", "2566": "zielt", "2567": "seichtem", "2568": "parken", "2569": "frischen", "2570": "m\u00fcllcontainer", "2571": "pfadfinder", "2572": "hotel", "2573": "felsklippe", "2574": "beginn", "2575": "eier", "2576": "winterjacke", "2577": "hochhackigen", "2578": "wassermelonen", "2579": "hinteren", "2580": "horn", "2581": "zeichnen", "2582": "reflektierenden", "2583": "felsens", "2584": "backsteinstra\u00dfe", "2585": "basketballfeld", "2586": "betonstufen", "2587": "badekappe", "2588": "staffelei", "2589": "pullovern", "2590": "historischen", "2591": "handtaschen", "2592": "st.", "2593": "student", "2594": "kabine", "2595": "habseligkeiten", "2596": "einfachen", "2597": "blazer", "2598": "begriff", "2599": "h\u00fcpfen", "2600": "nassem", "2601": "gestreifte", "2602": "bohrmaschine", "2603": "aufeinander", "2604": "ausgebreitet", "2605": "baumst\u00e4mmen", "2606": "cheerleadern", "2607": "weiblichen", "2608": "ringkampf", "2609": "passagieren", "2610": "ausgebreiteten", "2611": "stammeskleidung", "2612": "pl\u00fcschtier", "2613": "eink\u00e4ufer", "2614": "farbige", "2615": "k\u00fcrbis", "2616": "umz\u00e4unten", "2617": "fassade", "2618": "kehren", "2619": "anderem", "2620": "wanne", "2621": "voran", "2622": "bleiben", "2623": "kamel", "2624": "tippt", "2625": "ruderboot", "2626": "ziegel", "2627": "gefangen", "2628": "spielenden", "2629": "absperrung", "2630": "handwerker", "2631": "wetter", "2632": "dose", "2633": "ladens", "2634": "vogelperspektive", "2635": "kunstgalerie", "2636": "blondhaariger", "2637": "objekte", "2638": "lebensmittelmarkt", "2639": "fahrzeugs", "2640": "gletscher", "2641": "kameramann", "2642": "deutsche", "2643": "denkt", "2644": "pult", "2645": "waffel", "2646": "zurecht", "2647": "halb", "2648": "ziege", "2649": "kost\u00fcmierte", "2650": "fliesen", "2651": "sack", "2652": "u-bahnstation", "2653": "lampen", "2654": "getreide", "2655": "einkaufstaschen", "2656": "tauziehen", "2657": "volleyballspieler", "2658": "ereignis", "2659": "gefallen", "2660": "hundespielzeug", "2661": "football-spiel", "2662": "quarterback", "2663": "konstruktion", "2664": "supermarkt", "2665": "beutel", "2666": "schlafend", "2667": "schlafendes", "2668": "boote", "2669": "wilden", "2670": "aktentasche", "2671": "rockband", "2672": "dunkel", "2673": "backflip", "2674": "friedenszeichen", "2675": "geflochtenen", "2676": "schwebebalken", "2677": "einheimischer", "2678": "muss", "2679": "haut", "2680": "portr\u00e4t", "2681": "friseur", "2682": "hervor", "2683": "palme", "2684": "pflasterstra\u00dfe", "2685": "stil", "2686": "mehr", "2687": "pizzeria", "2688": "schals", "2689": "neuen", "2690": "geldbeutel", "2691": "irgendeiner", "2692": "fischmarkt", "2693": "faulenzt", "2694": "mehrfarbigen", "2695": "reicht", "2696": "segelt", "2697": "anzusehen", "2698": "barf\u00fc\u00dfiger", "2699": "wippe", "2700": "dosen", "2701": "bleibt", "2702": "beten", "2703": "m\u00f6wen", "2704": "hackt", "2705": "ganzen", "2706": "albern", "2707": "fr\u00f6hliche", "2708": "golden", "2709": "t\u00fcrkisen", "2710": "wintertag", "2711": "ges\u00e4umte", "2712": "?", "2713": "enten", "2714": "bauwerk", "2715": "angeln", "2716": "jeansshorts", "2717": "papa", "2718": "gemusterten", "2719": "gefleckter", "2720": "kellner", "2721": "l\u00e4dt", "2722": "braunhaariges", "2723": "kanus", "2724": "musikinstrumenten", "2725": "einsamen", "2726": "hocken", "2727": "kraft", "2728": "aller", "2729": "ausstellung", "2730": "sitz", "2731": "trottet", "2732": "versuch", "2733": "holzbrett", "2734": "hochzuklettern", "2735": "leitet", "2736": "kajaks", "2737": "schenkt", "2738": "gl\u00fcckliches", "2739": "fahrenden", "2740": "geh\u00fcllt", "2741": "tische", "2742": "keine", "2743": "skateboardpark", "2744": "trenchcoat", "2745": "zebrastreifen", "2746": "farbenfrohe", "2747": "dekorierten", "2748": "obwohl", "2749": "schwanz", "2750": "golfplatz", "2751": "holztisch", "2752": "karateanz\u00fcgen", "2753": "schmettert", "2754": "kartenspiel", "2755": "weihnachtsmannkost\u00fcm", "2756": "jogger", "2757": "verlegt", "2758": "bordsteinkante", "2759": "videokamera", "2760": "z\u00e4hnen", "2761": "video", "2762": "tackelt", "2763": "beteiligt", "2764": "j\u00fcngeres", "2765": "schotterweg", "2766": "k\u00f6rbe", "2767": "dunkeln", "2768": "baums", "2769": "ruhigem", "2770": "inspizieren", "2771": "mikrofonen", "2772": "thema", "2773": "karateanzug", "2774": "schwimmwesten", "2775": "inhalt", "2776": "einzufangen", "2777": "schwimmfl\u00fcgeln", "2778": "krankenschwestern", "2779": "inspiziert", "2780": "unterschiedlicher", "2781": "fu\u00dfballteams", "2782": "wegen", "2783": "schwarzwei\u00dfer", "2784": "motocross-fahrer", "2785": "ochsen", "2786": "aquarium", "2787": "f\u00fcttern", "2788": "platziert", "2789": "wei\u00dfhaariger", "2790": "gefahren", "2791": "rennfahrer", "2792": "rot-schwarzer", "2793": "strickt", "2794": "n\u00e4her", "2795": "verteilen", "2796": "polizei", "2797": "beendet", "2798": "umh\u00e4ngen", "2799": "garn", "2800": "kunstvollen", "2801": "gr\u00fcn-wei\u00dfen", "2802": "tischdecke", "2803": "zelte", "2804": "dj", "2805": "beigen", "2806": "modernen", "2807": "farbenfroh", "2808": "studiert", "2809": "gewehren", "2810": "sch\u00e4lt", "2811": "-", "2812": "befahrene", "2813": "kopfsteinpflasterstra\u00dfe", "2814": "m\u00fcllt\u00fcte", "2815": "saiteninstrument", "2816": "gelbbrauner", "2817": "z\u00fcndet", "2818": "schwarz-braune", "2819": "popcorn", "2820": "innenhof", "2821": "schlafzimmer", "2822": "aufhat", "2823": "au\u00dfen", "2824": "fu\u00dfballspielen", "2825": "nackter", "2826": "kiefern", "2827": "schlendern", "2828": "dekorationen", "2829": "nacktes", "2830": "bandmitglieder", "2831": "kittel", "2832": "inline", "2833": "rasenm\u00e4her", "2834": "gebaut", "2835": "neun", "2836": "metallzaun", "2837": "lippenstift", "2838": "b\u00fccherregal", "2839": "schaufensterfront", "2840": "per\u00fccke", "2841": "pyramide", "2842": "glast\u00fcr", "2843": "bricht", "2844": "billard", "2845": "einzelne", "2846": "raufen", "2847": "goldfarbenen", "2848": "du", "2849": "anschauen", "2850": "gehwegs", "2851": "amboss", "2852": "struktur", "2853": "modell", "2854": "aufw\u00e4rmen", "2855": "geschminkten", "2856": "hemdbluse", "2857": "wei\u00df-schwarzer", "2858": "geschrieben", "2859": "bikinis", "2860": "k\u00fcchentresen", "2861": "weinendes", "2862": "kragen", "2863": "nachtclub", "2864": "engel", "2865": "ver\u00e4rgert", "2866": "pony", "2867": "kleinere", "2868": "strandpromenade", "2869": "besuchen", "2870": "wiegt", "2871": "schwarz-braunen", "2872": "herz", "2873": "beamte", "2874": "b\u00fcrste", "2875": "japan", "2876": "videospiele", "2877": "hochgehoben", "2878": "wanderweg", "2879": "teenagerin", "2880": "packen", "2881": "hochspringt", "2882": "schmutziges", "2883": "hydranten", "2884": "liegest\u00fchlen", "2885": "m\u00f6belst\u00fcck", "2886": "schippt", "2887": "deutscher", "2888": "freut", "2889": "filzhut", "2890": "durchsucht", "2891": "aufmerksamkeit", "2892": "geschminkt", "2893": "kabeln", "2894": "gro\u00dfstadt", "2895": "bl\u00fchenden", "2896": "autositz", "2897": "stra\u00dfenmarkt", "2898": "nachbarschaft", "2899": "stern", "2900": "eisbahn", "2901": "bikini-oberteil", "2902": "gesichter", "2903": "position", "2904": "seitlich", "2905": "kegeln", "2906": "w\u00e4hlt", "2907": "schwarz-roten", "2908": "eiswagen", "2909": "braun-wei\u00dfen", "2910": "promenade", "2911": "fu\u00dfballteam", "2912": "schmetterling", "2913": "einkaufspassage", "2914": "betonbank", "2915": "professor", "2916": "wakeboard", "2917": "hindernisparcours", "2918": "spielzeugauto", "2919": "sorgf\u00e4ltig", "2920": "wurden", "2921": "t\u00e4nzern", "2922": "wenig", "2923": "eng", "2924": "st\u00fccke", "2925": "parasailing", "2926": "erreicht", "2927": "untergeht", "2928": "seitenlinie", "2929": "abgesperrten", "2930": "genau", "2931": "aufrecht", "2932": "h\u00fcpfburg", "2933": "gesch\u00e4ftige", "2934": "rollern", "2935": "st\u00f6bern", "2936": "vorbeifahren", "2937": "hotdogs", "2938": "feuert", "2939": "kunst", "2940": "warnwesten", "2941": "klappst\u00fchlen", "2942": "nachmittag", "2943": "musikern", "2944": "sch\u00fclern", "2945": "haltend", "2946": "betonplatte", "2947": "nasse", "2948": "eiswaffeln", "2949": "modellflugzeug", "2950": "essenswagen", "2951": "gleis", "2952": "spielerin", "2953": "radrennfahrer", "2954": "zutaten", "2955": "ruft", "2956": "lauf", "2957": "taucheranzug", "2958": "wandmalerei", "2959": "notizen", "2960": "schweren", "2961": "spazierstock", "2962": "tempel", "2963": "essend", "2964": "bedeckter", "2965": "hubschrauber", "2966": "ufers", "2967": "kunde", "2968": "10", "2969": "irgendetwas", "2970": "liegende", "2971": "gesichts", "2972": "erwischt", "2973": "nichts", "2974": "steg", "2975": "balgen", "2976": "speer", "2977": "schaulustige", "2978": "tiefen", "2979": "spielzeuge", "2980": "umgest\u00fcrzten", "2981": "buntem", "2982": "mopp", "2983": "feuerwerk", "2984": "quad", "2985": "hellgelben", "2986": "werkzeugen", "2987": "kabel", "2988": "bohrt", "2989": "pose", "2990": "pinken", "2991": "schritt", "2992": "bagger", "2993": "aufgenommen", "2994": "pfl\u00fcckt", "2995": "begutachten", "2996": "motorradfahrern", "2997": "konfetti", "2998": "nackte", "2999": "day", "3000": "unaufger\u00e4umten", "3001": "rot-wei\u00dfes", "3002": "wohnung", "3003": "fechten", "3004": "seilspringen", "3005": "hochzeitstorte", "3006": "verkleideter", "3007": "ei", "3008": "fu\u00dfg\u00e4ngerbr\u00fccke", "3009": "ausschau", "3010": "khakihose", "3011": "puppen", "3012": "monopoly", "3013": "aufgeblasenen", "3014": "fasziniert", "3015": "offener", "3016": "bullenreiter", "3017": "pass", "3018": "kaninchen", "3019": "helle", "3020": "haken", "3021": "mikrofone", "3022": "blumenstrau\u00df", "3023": "weihnachten", "3024": "gerichtet", "3025": "h\u00f6rsaal", "3026": "hochzeitskleidung", "3027": "t\u00e4towierter", "3028": "saal", "3029": "bahngleisen", "3030": "fahrradrennen", "3031": "kathedrale", "3032": "volleyballspiel", "3033": "\u00fcberdachten", "3034": "auswahl", "3035": "hochh\u00e4lt", "3036": "gehoben", "3037": "rothaarigen", "3038": "dress", "3039": "ipod", "3040": "mehrfarbige", "3041": "nackten", "3042": "badekleidung", "3043": "schotterstra\u00dfe", "3044": "rauchend", "3045": "sms", "3046": "11", "3047": "anschaut", "3048": "w\u00e4re", "3049": "fu\u00dfweg", "3050": "mindestens", "3051": "buntes", "3052": "gesenktem", "3053": "freizeitkleidung", "3054": "kicken", "3055": "stelle", "3056": "zuckerwatte", "3057": "fr\u00f6hliches", "3058": "teilnimmt", "3059": "t\u00fcrkisfarbenen", "3060": "limousine", "3061": "fu\u00dfballtrikot", "3062": "rot-wei\u00df", "3063": "\u00fcberblickt", "3064": "trocknet", "3065": "verlassenen", "3066": "abgedunkelten", "3067": "bulle", "3068": "speisekarte", "3069": "marineblauen", "3070": "zeitungen", "3071": "aufschlag", "3072": "maulk\u00f6rben", "3073": "g\u00fcrteln", "3074": "schnuller", "3075": "fitnessstudio", "3076": "\u00e4rmellosem", "3077": "ge\u00f6ffnetem", "3078": "dreik\u00f6pfige", "3079": "baseballkleidung", "3080": "dekorativen", "3081": "lacrosse", "3082": "schlecht", "3083": "produkt", "3084": "landkarte", "3085": "bestellt", "3086": "schlittschuh", "3087": "digitalkamera", "3088": "tennisschuhen", "3089": "t\u00e4nzerinnen", "3090": "punkten", "3091": "akustische", "3092": "verfolgen", "3093": "schale", "3094": "sp\u00fclt", "3095": "geradeaus", "3096": "eventuell", "3097": "einsatz", "3098": "befindlichen", "3099": "wettlauf", "3100": "w\u00e4nde", "3101": "gegen\u00fcberliegenden", "3102": "gejagt", "3103": "schreiendes", "3104": "\u00e4rmeln", "3105": "au\u00dfenseite", "3106": "sprache", "3107": "schlafenden", "3108": "elternteil", "3109": "schloss", "3110": "schaumbad", "3111": "eishockeyspiel", "3112": "babyschaukel", "3113": "streicheln", "3114": "hei\u00dft", "3115": "schminke", "3116": "skulpturen", "3117": "bass", "3118": "subway", "3119": "nudeln", "3120": "rangeln", "3121": "lehrerin", "3122": "brechen", "3123": "huckepack", "3124": "bettelt", "3125": "umklammert", "3126": "staffellauf", "3127": "kahl", "3128": "funktioniert", "3129": "bananen", "3130": "richtig", "3131": "geiger", "3132": "sattel", "3133": "stabhochsprung", "3134": "auszeichnung", "3135": "ballbesitz", "3136": "gedr\u00e4ngt", "3137": "bambus", "3138": "theater", "3139": "ansammlung", "3140": "gesteckt", "3141": "rollschuh", "3142": "schauspieler", "3143": "freundinnen", "3144": "traurig", "3145": "intensiv", "3146": "bauer", "3147": "marathonlauf", "3148": "unfall", "3149": "sortieren", "3150": "felsige", "3151": "halsketten", "3152": "oberhemd", "3153": "stolpert", "3154": "ultimate", "3155": "operationssaal", "3156": "zugewandt", "3157": "balkonen", "3158": "radfahren", "3159": "zweiter", "3160": "vermutlich", "3161": "medizinische", "3162": "van", "3163": "minigolf", "3164": "dunk", "3165": "wangen", "3166": "grauhaariger", "3167": "schlafender", "3168": "versehen", "3169": "dunkelhaariges", "3170": "w\u00fcrden", "3171": "live", "3172": "gesch\u00e4ftsmann", "3173": "stofftier", "3174": "plastik", "3175": "labrador", "3176": "lackiert", "3177": "r\u00e4umt", "3178": "vorbereiten", "3179": "lenkt", "3180": "ans", "3181": "poloshirt", "3182": "m\u00f6nch", "3183": "schneehaufen", "3184": "reagieren", "3185": "gleiche", "3186": "fahrradreifen", "3187": "radio", "3188": "geholfen", "3189": "reis", "3190": "schutzwesten", "3191": "wei\u00df-braune", "3192": "blau-roten", "3193": "coach", "3194": "vordach", "3195": "teddyb\u00e4r", "3196": "dicker", "3197": "kastanienbraunen", "3198": "bergsteigen", "3199": "kollegen", "3200": "recht", "3201": "bauernhof", "3202": "turnier", "3203": "gest\u00fctzt", "3204": "kindergruppe", "3205": "mcdonalds", "3206": "gartenstuhl", "3207": "motorboot", "3208": "lohfarbener", "3209": "wasserfahrzeug", "3210": "blaugr\u00fcnen", "3211": "pustet", "3212": "endzone", "3213": "harfe", "3214": "lichtern", "3215": "zuh\u00f6ren", "3216": "wei\u00df-brauner", "3217": "stadtplatz", "3218": "mehl", "3219": "anzugreifen", "3220": "skate-park", "3221": "rollerskates", "3222": "lastwagens", "3223": "miami", "3224": "br\u00e4unlicher", "3225": "domino", "3226": "ziegen", "3227": "betritt", "3228": "bmx-rad", "3229": "rollst\u00fchlen", "3230": "verlassene", "3231": "metallgegenstand", "3232": "\u00e4lter", "3233": "orangensaft", "3234": "paket", "3235": "begleiter", "3236": "bahnen", "3237": "gymnastik", "3238": "leger", "3239": "ausflug", "3240": "hochzeitsfeier", "3241": "pitch", "3242": "\u00fcbungen", "3243": "oldtimer", "3244": "fu\u00dfboden", "3245": "spanischer", "3246": "stecken", "3247": "ureinwohner", "3248": "ausgestrecktem", "3249": "trommelt", "3250": "love", "3251": "graues", "3252": "afroamerikanerin", "3253": "garagentor", "3254": "hellbraune", "3255": "einschlie\u00dflich", "3256": "seattle", "3257": "ledermantel", "3258": "gute", "3259": "knallbunten", "3260": "klatschen", "3261": "welt", "3262": "schaufenstern", "3263": "polizistin", "3264": "kutsche", "3265": "indianer", "3266": "schwimmenden", "3267": "seilschaukel", "3268": "gespritzt", "3269": "wheelie", "3270": "artikel", "3271": "verdunkelten", "3272": "schneeball", "3273": "zupft", "3274": "ungl\u00fccklich", "3275": "festival", "3276": "\u00fcberkreuzten", "3277": "fertigen", "3278": "r\u00fcckw\u00e4rtssalto", "3279": "weingl\u00e4sern", "3280": "bademantel", "3281": "fernrohr", "3282": "pinata", "3283": "k\u00e4se", "3284": "stroh", "3285": "verbundenen", "3286": "zeichnung", "3287": "interviewt", "3288": "warenkorb", "3289": "tischtennis", "3290": "kopft\u00fcchern", "3291": "restaurantk\u00fcche", "3292": "senioren", "3293": "korridor", "3294": "arbeitende", "3295": "kragenhemd", "3296": "stra\u00dfencaf\u00e9", "3297": "zigaretten", "3298": "brunnens", "3299": "food", "3300": "erledigen", "3301": "schwimmerin", "3302": "betonrampe", "3303": "probt", "3304": "beaufsichtigt", "3305": "schienen", "3306": "bulldozer", "3307": "vaters", "3308": "schwei\u00dfen", "3309": "ebene", "3310": "vereisten", "3311": "kleinstadt", "3312": "stamm", "3313": "gem\u00fctlich", "3314": "krankenhausbett", "3315": "mast", "3316": "bewundern", "3317": "flauschigen", "3318": "wasserskifahrer", "3319": "verletzt", "3320": "montur", "3321": "fertigt", "3322": "angel", "3323": "baden", "3324": "informationen", "3325": "harten", "3326": "seife", "3327": "rundes", "3328": "busch", "3329": "kunstausstellung", "3330": "feldhockey", "3331": "gabel", "3332": "pinkfarbene", "3333": "reisen", "3334": "geschoben", "3335": "befestigen", "3336": "parks", "3337": "film", "3338": "landung", "3339": "grills", "3340": "t\u00fcreingang", "3341": "schwarzwei\u00dfen", "3342": "dschungel", "3343": "make-up", "3344": "interessanten", "3345": "schwierigkeiten", "3346": "kulisse", "3347": "hochzeitskleid", "3348": "hellrosa", "3349": "skateboardrampe", "3350": "blondhaariges", "3351": "grauhaarige", "3352": "hellbraunem", "3353": "kamelen", "3354": "schnorchel", "3355": "mcdonald", "3356": "erz\u00e4hlt", "3357": "priester", "3358": "zweiten", "3359": "diadem", "3360": "asphalt", "3361": "letzten", "3362": "verbringen", "3363": "plantscht", "3364": "waffen", "3365": "eisschnelll\u00e4ufer", "3366": "br\u00e4t", "3367": "transporter", "3368": "einrichtung", "3369": "motorradrennen", "3370": "h\u00e4ngebr\u00fccke", "3371": "schwertern", "3372": "katzen", "3373": "m\u00fcde", "3374": "navigiert", "3375": "salat", "3376": "rasieren", "3377": "st\u00fctzt", "3378": "aufgeregte", "3379": "br\u00fcnettes", "3380": "rosafarbenes", "3381": "lappen", "3382": "schwarz-blauen", "3383": "inneren", "3384": "fliesenboden", "3385": "verzieht", "3386": "for", "3387": "jemandes", "3388": "einfahrt", "3389": "daf\u00fcr", "3390": "vierk\u00f6pfige", "3391": "sandskulptur", "3392": "geste", "3393": "abstammung", "3394": "blondhaarige", "3395": "nippt", "3396": "f\u00fchrung", "3397": "d\u00fcnne", "3398": "skateboardtrick", "3399": "plane", "3400": "skigebiet", "3401": "\u00e4rmel", "3402": "unterricht", "3403": "matratze", "3404": "rennbahn", "3405": "wasserlauf", "3406": "sitzendes", "3407": "bastelt", "3408": "abhang", "3409": "fensterputzer", "3410": "blond", "3411": "strom", "3412": "kleinigkeit", "3413": "hei\u00dfluftballons", "3414": "textnachricht", "3415": "wintermantel", "3416": "spielende", "3417": "berghang", "3418": "felsblock", "3419": "krug", "3420": "wadet", "3421": "kletterwand", "3422": "gemalt", "3423": "misst", "3424": "staubsauger", "3425": "zuguckt", "3426": "geschm\u00fcckten", "3427": "schattigen", "3428": "unterschiedliche", "3429": "tauchausr\u00fcstung", "3430": "flaches", "3431": "erdboden", "3432": "freuen", "3433": "collie", "3434": "kinn", "3435": "schanze", "3436": "sch\u00fclerin", "3437": "noten", "3438": "kamin", "3439": "training", "3440": "ruinen", "3441": "runde", "3442": "/", "3443": "sitzend", "3444": "lang\u00e4rmligen", "3445": "vorlesung", "3446": "geschlagen", "3447": "verschiedenfarbigen", "3448": "jahre", "3449": "barf\u00fc\u00dfiges", "3450": "sackh\u00fcpfen", "3451": "zugeht", "3452": "eiswaffel", "3453": "bl\u00e4tterhaufen", "3454": "rosen", "3455": "klarinette", "3456": "scheinwerferlicht", "3457": "sto\u00dfen", "3458": "erhalten", "3459": "verwirrt", "3460": "rednerpult", "3461": "bekleidungsgesch\u00e4ft", "3462": "aufgabe", "3463": "regenbogen", "3464": "verkehrt", "3465": "anzuziehen", "3466": "gro\u00dfem", "3467": "kanadischen", "3468": "tennisspielen", "3469": "blattwerk", "3470": "uferweg", "3471": "waten", "3472": "medizinischen", "3473": "wetteifern", "3474": "irgendeine", "3475": "hechtet", "3476": "hochklettert", "3477": "golfspieler", "3478": "gurt", "3479": "\u00fcbung", "3480": "konferenztisch", "3481": "lehrt", "3482": "zugucken", "3483": "kein", "3484": "gewonnen", "3485": "wassermelone", "3486": "schusswaffe", "3487": "barkeeper", "3488": "fu\u00dftritt", "3489": "t\u00fcte", "3490": "b\u00fcchern", "3491": "erzielen", "3492": "sari", "3493": "haarschnitt", "3494": "federn", "3495": "redner", "3496": "lautsprecher", "3497": "backsteinwand", "3498": "durchgang", "3499": "holzstuhl", "3500": "morgen", "3501": "rot-blauen", "3502": "rucks\u00e4cke", "3503": "unterhalb", "3504": "jenga", "3505": "politischen", "3506": "besitz", "3507": "angemaltem", "3508": "marktstand", "3509": "toreinfahrt", "3510": "verliert", "3511": "geschminktem", "3512": "poliert", "3513": "anscheinend", "3514": "mannschaftskollegen", "3515": "pyjama", "3516": "erh\u00f6hten", "3517": "deckt", "3518": "n\u00e4chtlichen", "3519": "beladenen", "3520": "au\u00dferdem", "3521": "6", "3522": "blumenwiese", "3523": "schmutzige", "3524": "b\u00fcsche", "3525": "derby", "3526": "kerze", "3527": "roboter", "3528": "boots", "3529": "erleuchtet", "3530": "klein", "3531": "befragt", "3532": "nebligen", "3533": "leere", "3534": "krankenschwester", "3535": "springenden", "3536": "dr\u00e4ngen", "3537": "rugby", "3538": "tourist", "3539": "enge", "3540": "milit\u00e4rische", "3541": "plakatwand", "3542": "fackeln", "3543": "spielzug", "3544": "torbogen", "3545": "halfpipe", "3546": "ladung", "3547": "leidenschaftlich", "3548": "hochzeitsgesellschaft", "3549": "buckelnden", "3550": "herrlichen", "3551": "schalter", "3552": "beenden", "3553": "tellern", "3554": "gel\u00e4ndefahrzeug", "3555": "l\u00e4ufern", "3556": "steinernen", "3557": "deutet", "3558": "gefilmt", "3559": "knabbert", "3560": "spinnrad", "3561": "kanal", "3562": "bandmitgliedern", "3563": "schiene", "3564": "anweisung", "3565": "abgewandt", "3566": "fundament", "3567": "ausgeschnittenen", "3568": "befreit", "3569": "streiten", "3570": "blue", "3571": "spr\u00fchfarbe", "3572": "melonen", "3573": "fahrradgesch\u00e4ft", "3574": "fingern\u00e4gel", "3575": "baseballwerfer", "3576": "tages", "3577": "verhindern", "3578": "fu\u00dfe", "3579": "getackelt", "3580": "mexikanischen", "3581": "k\u00fcchentisch", "3582": "k\u00e4mpfer", "3583": "umh\u00e4ngetasche", "3584": "bahngleise", "3585": "gestr\u00fcpp", "3586": "kauern", "3587": "wischen", "3588": "ger\u00e4tschaften", "3589": "wasserkante", "3590": "besuchten", "3591": "hemdchen", "3592": "rasierten", "3593": "sportereignis", "3594": "steinstufen", "3595": "tollen", "3596": "holzrampe", "3597": "pflanzt", "3598": "milit\u00e4rangeh\u00f6rige", "3599": "vorw\u00e4rts", "3600": "ordnung", "3601": "m\u00e4nnergruppe", "3602": "reparaturen", "3603": "shop", "3604": "ausdruck", "3605": "beginnen", "3606": "schwerer", "3607": "meerwasser", "3608": "abw\u00e4rts", "3609": "telefonmasten", "3610": "h\u00e4user", "3611": "fu\u00dfballplatz", "3612": "gegenspieler", "3613": "sch\u00f6nes", "3614": "footballteam", "3615": "ziegeln", "3616": "lkws", "3617": "aufnahmen", "3618": "football-feld", "3619": "bowling-kugel", "3620": "wenige", "3621": "unfertigen", "3622": "armee", "3623": "ineinander", "3624": "sicherheitsausr\u00fcstung", "3625": "wanderung", "3626": "hinterteil", "3627": "red", "3628": "mexikanische", "3629": "innerhalb", "3630": "fang", "3631": "mobilen", "3632": "boston", "3633": "snacks", "3634": "gr\u00fc\u00dfen", "3635": "sp\u00fcle", "3636": "skilift", "3637": "buffet", "3638": "welcome", "3639": "schulbus", "3640": "i", "3641": "kranich", "3642": "matten", "3643": "cockpit", "3644": "block", "3645": "metallkonstruktion", "3646": "unterhaltung", "3647": "entladen", "3648": "einteiler", "3649": "spielplatzger\u00e4t", "3650": "schafen", "3651": "jetski", "3652": "geschlossen", "3653": "punkt", "3654": "kunstmuseum", "3655": "wirbt", "3656": "laubhaufen", "3657": "zweiteiligen", "3658": "lobby", "3659": "fliegende", "3660": "dingen", "3661": "chirurgen", "3662": "zul\u00e4uft", "3663": "s\u00e4gen", "3664": "sowohl", "3665": "anblick", "3666": "strandes", "3667": "wirken", "3668": "op-kittel", "3669": "verschiedenfarbige", "3670": "nachrichten", "3671": "nasses", "3672": "schleifen", "3673": "schneeh\u00fcgel", "3674": "m\u00fcllbeutel", "3675": "w\u00e4scheleine", "3676": "liebkost", "3677": "aufzunehmen", "3678": "kiefer", "3679": "edlen", "3680": "haupts\u00e4chlich", "3681": "zueinander", "3682": "zweier", "3683": "rettungsweste", "3684": "antiken", "3685": "pfoten", "3686": "schlummert", "3687": "schlanke", "3688": "erkl\u00e4rt", "3689": "violettes", "3690": "ausf\u00fchrung", "3691": "umeinander", "3692": "tassen", "3693": "eink\u00e4ufern", "3694": "sollen", "3695": "projektor", "3696": "umgedrehten", "3697": "riesiger", "3698": "banane", "3699": "kopfball", "3700": "ansetzt", "3701": "-t-shirt", "3702": "rallyeauto", "3703": "kastenwagen", "3704": "obst-", "3705": "bereits", "3706": "ersten", "3707": "gef\u00e4rbten", "3708": "feldes", "3709": "tattoo-k\u00fcnstler", "3710": "grinst", "3711": "geschichte", "3712": "gr\u00f6\u00dfe", "3713": "bewegungen", "3714": "verspritzt", "3715": "dirigent", "3716": "reitern", "3717": "grasbewachsenes", "3718": "kalte", "3719": "oberteile", "3720": "gestalteten", "3721": "flanke", "3722": "h\u00f6hle", "3723": "kariertes", "3724": "bequem", "3725": "sesseln", "3726": "sichtbar", "3727": "pelzmantel", "3728": "sonnen", "3729": "breitkrempigen", "3730": "angelruten", "3731": "vorbeigehenden", "3732": "ausblick", "3733": "talent", "3734": "zoo", "3735": "gedanken", "3736": "chips", "3737": "softballspielerin", "3738": "gleiten", "3739": "protestieren", "3740": "fr\u00f6hlicher", "3741": "albernen", "3742": "pokal", "3743": "schaufensterpuppe", "3744": "erhebt", "3745": "strohhalm", "3746": "schiffs", "3747": "euro", "3748": "hafengebiet", "3749": "and", "3750": "segelboote", "3751": "khakihosen", "3752": "schippen", "3753": "schildkr\u00f6te", "3754": "kellnerin", "3755": "landen", "3756": "absperrband", "3757": "pfannen", "3758": "funken", "3759": "posaune", "3760": "last", "3761": "unterbricht", "3762": "match", "3763": "zentrum", "3764": "stadtviertel", "3765": "wissenschaftliches", "3766": "bleistift", "3767": "bauf\u00e4lligen", "3768": "sportspiel", "3769": "dunking", "3770": "herauf", "3771": "skiausr\u00fcstung", "3772": "seltsam", "3773": "milit\u00e4runiformen", "3774": "mittelgro\u00dfen", "3775": "bauprojekt", "3776": "angezeigt", "3777": "notizbuch", "3778": "mixt", "3779": "america", "3780": "tisches", "3781": "saust", "3782": "raums", "3783": "@", "3784": "staubwolke", "3785": "kaputten", "3786": "seile", "3787": "rolle", "3788": "sonnenhut", "3789": "neigt", "3790": "verkleiden", "3791": "schwarzhaariger", "3792": "lama", "3793": "roller-derby-spiel", "3794": "washington", "3795": "ausl\u00e4ndische", "3796": "messen", "3797": "schl\u00e4fchen", "3798": "besprechen", "3799": "toiletten", "3800": "rot-wei\u00dfe", "3801": "untergrundbahn", "3802": "zitronen", "3803": "blusen", "3804": "hausmeister", "3805": "fischerboot", "3806": "leopardenmuster", "3807": "tanzende", "3808": "entgegengesetzte", "3809": "heiraten", "3810": "belebter", "3811": "gen\u00fcsslich", "3812": "brautkleid", "3813": "eingegraben", "3814": "w\u00e4schekorb", "3815": "fu\u00dfballmannschaften", "3816": "logo", "3817": "angerichtet", "3818": "feuerleiter", "3819": "felsformation", "3820": "sodass", "3821": "rum", "3822": "wachsen", "3823": "starbucks", "3824": "mal", "3825": "holzpfahl", "3826": "scheinwerfer", "3827": "esszimmer", "3828": "trockenem", "3829": "absolventen", "3830": "#", "3831": "gefliesten", "3832": "ketten", "3833": "schwangere", "3834": "dicke", "3835": "b\u00e4ckerei", "3836": "blau-wei\u00df", "3837": "schwimmhilfe", "3838": "offiziellen", "3839": "enth\u00e4lt", "3840": "b\u00e4rtige", "3841": "inklusive", "3842": "schmutziger", "3843": "sonnenschirmen", "3844": "spielmannszug", "3845": "gr\u00fcn-gelben", "3846": "geh\u00f6ren", "3847": "raus", "3848": "volksfest", "3849": "us", "3850": "sch\u00fcttet", "3851": "gel\u00e4ndern", "3852": "tauchen", "3853": "blumenfeld", "3854": "mundharmonika", "3855": "gepflegten", "3856": "schreien", "3857": "auszuf\u00fchren", "3858": "kuss", "3859": "strafzettel", "3860": "pr\u00fcfen", "3861": "sch\u00e4del", "3862": "kanone", "3863": "essst\u00e4bchen", "3864": "vor\u00fcbergehen", "3865": "spielzeugpferd", "3866": "roben", "3867": "entlanggehen", "3868": "trockenes", "3869": "concert", "3870": "milit\u00e4rs", "3871": "milit\u00e4runiform", "3872": "festzug", "3873": "erwischen", "3874": "motorrollern", "3875": "frustriert", "3876": "stiel", "3877": "freiluftmarkt", "3878": "wasserschlauch", "3879": "piers", "3880": "idyllischen", "3881": "tutu", "3882": "winterm\u00e4nteln", "3883": "verst\u00e4rker", "3884": "meter", "3885": "pferderennen", "3886": "schokoladeneis", "3887": "weihnachtsgeschenke", "3888": "abzuwerfen", "3889": "besuch", "3890": "k\u00e4lte", "3891": "pr\u00fcfung", "3892": "brosch\u00fcre", "3893": "meerjungfrau", "3894": "matsch", "3895": "l\u00f6tet", "3896": "eiern", "3897": "strahlend", "3898": "felsenklippe", "3899": "indischen", "3900": "wind", "3901": "terrier", "3902": "fahrradtrick", "3903": "schaffen", "3904": "f\u00fchrenden", "3905": "harmonika", "3906": "fahrbaren", "3907": "r\u00e4umen", "3908": "kimonos", "3909": "gelb-wei\u00dfen", "3910": "zw\u00f6lf", "3911": "g\u00e4ste", "3912": "schwer", "3913": "dritte-welt-land", "3914": "orientalischen", "3915": "trocknen", "3916": "ohrh\u00f6rern", "3917": "vorliest", "3918": "riemen", "3919": "akkordeonspieler", "3920": "rettungsschwimmer", "3921": "winken", "3922": "stra\u00dfenarbeiten", "3923": "wissenschaftler", "3924": "geburtstag", "3925": "leben", "3926": "schuhgesch\u00e4ft", "3927": "geburtstagskuchen", "3928": "beschn\u00fcffelt", "3929": "z\u00fcgen", "3930": "9", "3931": "b\u00e4rten", "3932": "abzuwehren", "3933": "bergigen", "3934": "wolkigen", "3935": "letzte", "3936": "lenker", "3937": "prachtvollen", "3938": "ruhige", "3939": "rudel", "3940": "lockigem", "3941": "schwung", "3942": "friedhof", "3943": "angelehnt", "3944": "hundeshow", "3945": "glatzenansatz", "3946": "material", "3947": "st\u00e4dtischer", "3948": "badesachen", "3949": "bl\u00e4ttert", "3950": "gro\u00df", "3951": "\u00fcbergibt", "3952": "massen", "3953": "saris", "3954": "tenniskleidung", "3955": "balancieren", "3956": "pendler", "3957": "form", "3958": "schere", "3959": "grasbewachsene", "3960": "kniehohen", "3961": "\u00e4rzte", "3962": "badebekleidung", "3963": "skipiste", "3964": "religi\u00f6se", "3965": "schmettern", "3966": "verteidigen", "3967": "r\u00fcckenschwimmen", "3968": "armband", "3969": "karikatur", "3970": "schlanker", "3971": "ausl\u00e4ndischen", "3972": "lebensmittelgesch\u00e4fts", "3973": "betrieb", "3974": "guten", "3975": "anz\u00fcge", "3976": "unbefestigtem", "3977": "fallschirmspringen", "3978": "knallgelben", "3979": "unterseite", "3980": "platten", "3981": "kronen", "3982": "keller", "3983": "aufzuheben", "3984": "dunkles", "3985": "waldes", "3986": "haltestelle", "3987": "elektrogitarre", "3988": "ges\u00e4umt", "3989": "steingeb\u00e4ude", "3990": "aktion", "3991": "slam", "3992": "holzplattform", "3993": "maulesel", "3994": "fan", "3995": "flecken", "3996": "bl\u00f6cken", "3997": "hot", "3998": "ananas", "3999": "gek\u00fcsst", "4000": "baseballkappen", "4001": "teile", "4002": "dirtbike", "4003": "stapelt", "4004": "kaum", "4005": "blumenm\u00e4dchen", "4006": "vorhang", "4007": "weggeht", "4008": "deckel", "4009": "daraus", "4010": "spielend", "4011": "elmo", "4012": "langer", "4013": "einsame", "4014": "kahlen", "4015": "f\u00e4llen", "4016": "rustikalen", "4017": "griff", "4018": "denkmal", "4019": "eisenbahnschienen", "4020": "bassgitarre", "4021": "kunststoff", "4022": "k\u00fchen", "4023": "badet", "4024": "oberhalb", "4025": "demonstriert", "4026": "dorfbewohner", "4027": "cremefarbenen", "4028": "baseballspiels", "4029": "menschenschlange", "4030": "geb\u00fcckter", "4031": "lustigen", "4032": "\u00f6ffentliche", "4033": "str\u00e4hnen", "4034": "tropischer", "4035": "feuerwehrwagen", "4036": "outfielder", "4037": "wasserfont\u00e4ne", "4038": "notenst\u00e4nder", "4039": "musizieren", "4040": "walmart", "4041": "taxis", "4042": "verbringt", "4043": "n\u00e4hen", "4044": "angezogenes", "4045": "einsames", "4046": "gespreizten", "4047": "flanellhemd", "4048": "seltsames", "4049": "afrikanisches", "4050": "aufdruck", "4051": "schwester", "4052": "k\u00fcchentheke", "4053": "regenschirme", "4054": "offene", "4055": "nagel", "4056": "gr\u00fcn-wei\u00df", "4057": "verbunden", "4058": "befestigten", "4059": "laufende", "4060": "pfl\u00fcgt", "4061": "skim\u00fctze", "4062": "geschossen", "4063": "radfahrerin", "4064": "bef\u00f6rdert", "4065": "picknick", "4066": "fein", "4067": "goldener", "4068": "windjacke", "4069": "elektriker", "4070": "regal", "4071": "wildwasser", "4072": "sonnenschein", "4073": "fall", "4074": "r\u00e4ngen", "4075": "zugs", "4076": "kriecht", "4077": "maiskolben", "4078": "schnitzen", "4079": "sportliche", "4080": "langarmhemd", "4081": "strau\u00df", "4082": "toter", "4083": "blase", "4084": "duscht", "4085": "obdachlose", "4086": "laubbl\u00e4ser", "4087": "t\u00f6pferscheibe", "4088": "sicher", "4089": "treppengel\u00e4nder", "4090": "m\u00f6we", "4091": "laubbedeckten", "4092": "pflaster", "4093": "schwan", "4094": "g\u00e4hnt", "4095": "k\u00fcnstlerin", "4096": "fl\u00fcgel", "4097": "angestellte", "4098": "gefrorenen", "4099": "bikinioberteil", "4100": "kargen", "4101": "kickboxen", "4102": "lokalen", "4103": "feder", "4104": "leitern", "4105": "backen", "4106": "verschiedener", "4107": "packung", "4108": "st\u00e4bchen", "4109": "steinwand", "4110": "schneemann", "4111": "absatzschuhen", "4112": "diskutiert", "4113": "winzigen", "4114": "poolbillard", "4115": "kreisf\u00f6rmigen", "4116": "hotdog", "4117": "empor", "4118": "spielzeugen", "4119": "taucherbrille", "4120": "elf", "4121": "kleidungsst\u00fccke", "4122": "halbkreis", "4123": "pub", "4124": "innenseite", "4125": "deutschen", "4126": "berggipfel", "4127": "angesicht", "4128": "anlage", "4129": "liegest\u00fctze", "4130": "hellroten", "4131": "geduldig", "4132": "u-bahn-tunnel", "4133": "unruhigem", "4134": "hellh\u00e4utigen", "4135": "gelbbraunen", "4136": "lilafarbene", "4137": "b\u00fcgelt", "4138": "krawatten", "4139": "biegt", "4140": "bedienung", "4141": "kostet", "4142": "masken", "4143": "statuen", "4144": "r\u00fcckhand", "4145": "hinunterf\u00e4hrt", "4146": "lasters", "4147": "jeansrock", "4148": "free", "4149": "dr\u00e4hten", "4150": "fotographen", "4151": "welche", "4152": "h\u00f6lle", "4153": "br\u00fcnett", "4154": "stimmt", "4155": "kaufhaus", "4156": "sch\u00e4rpen", "4157": "kricketspieler", "4158": "schau", "4159": "holzboot", "4160": "abendd\u00e4mmerung", "4161": "baumges\u00e4umten", "4162": "tennisb\u00e4llen", "4163": "schlie\u00dft", "4164": "gef\u00e4llten", "4165": "ordnet", "4166": "kr\u00fccken", "4167": "zubereiten", "4168": "bieten", "4169": "ragt", "4170": "bespr\u00fchten", "4171": "werkzeuge", "4172": "hula", "4173": "street", "4174": "high", "4175": "sportplatz", "4176": "school", "4177": "verdienen", "4178": "strickjacke", "4179": "wagens", "4180": "knappen", "4181": "hatte", "4182": "h\u00fcbsch", "4183": "werbetafel", "4184": "verstecken", "4185": "ungef\u00e4hr", "4186": "spagat", "4187": "vorbereitung", "4188": "happen", "4189": "pferdes", "4190": "\u00fcberwiegend", "4191": "sicherheitskleidung", "4192": "glasgeb\u00e4ude", "4193": "aufgaben", "4194": "gemalten", "4195": "dunkelgr\u00fcnen", "4196": "gabelstapler", "4197": "sommerkleid", "4198": "f\u00f6rmlicher", "4199": "blumenkleid", "4200": "neongr\u00fcnen", "4201": "backsteingeb\u00e4udes", "4202": "sakko", "4203": "t\u00f6pfen", "4204": "stehendes", "4205": "barren", "4206": "objekten", "4207": "n\u00e4hern", "4208": "suv", "4209": "sportlerin", "4210": "altmodischer", "4211": "gelb-gr\u00fcnen", "4212": "nacheinander", "4213": "mama", "4214": "truthahn", "4215": "schneeengel", "4216": "schlie\u00dfen", "4217": "planschen", "4218": "langsam", "4219": "herstellt", "4220": "schickes", "4221": "t\u00f6pferwaren", "4222": "halt", "4223": "flag", "4224": "akrobatische", "4225": "luftsprung", "4226": "inder", "4227": "dunkelbrauner", "4228": "symbol", "4229": "gro\u00dfk\u00fcche", "4230": "12", "4231": "irgendein", "4232": "messe", "4233": "eigenen", "4234": "rot-wei\u00df-blauen", "4235": "podest", "4236": "schnauzer", "4237": "protestschild", "4238": "indianischer", "4239": "hausaufgaben", "4240": "gesichtsmaske", "4241": "skyline", "4242": "gesichert", "4243": "angelschnur", "4244": "schneidebrett", "4245": "meeresufer", "4246": "reist", "4247": "fotograph", "4248": "gl\u00fccklicher", "4249": "faust", "4250": "s\u00e4ubern", "4251": "orange-wei\u00dfen", "4252": "besteigen", "4253": "sonnentag", "4254": "pfahl", "4255": "wanderausr\u00fcstung", "4256": "metallgel\u00e4nder", "4257": "ausgestopften", "4258": "umgedreht", "4259": "l\u00f6wen", "4260": "eseln", "4261": "schutthaufen", "4262": "streut", "4263": "angebunden", "4264": "herausragen", "4265": "vollst\u00e4ndig", "4266": "spuren", "4267": "angemalt", "4268": "weinglas", "4269": "b\u00fcrogeb\u00e4ude", "4270": "skil\u00e4ufer", "4271": "tarnmuster", "4272": "u-bahn-wagen", "4273": "traurigen", "4274": "bikini-unterteil", "4275": "sport-bh", "4276": "touristengruppe", "4277": "zerren", "4278": "stra\u00dfenstand", "4279": "haarnetz", "4280": "farbiger", "4281": "sonnendurchfluteten", "4282": "schwarz-gelben", "4283": "entscheiden", "4284": "nahost", "4285": "auftreten", "4286": "rest", "4287": "w\u00fchlt", "4288": "lenkrad", "4289": "abzufangen", "4290": "puzzle", "4291": "gl\u00e4ser", "4292": "ehemann", "4293": "picknicktischen", "4294": "beleibter", "4295": "sprungtritt", "4296": "sommer", "4297": "spielger\u00e4t", "4298": "polizeibeamte", "4299": "wasserpistolen", "4300": "planschbecken", "4301": "getaucht", "4302": "kleinbus", "4303": "gemeinde", "4304": "teammitglied", "4305": "irokesenfrisur", "4306": "gummistiefeln", "4307": "inderin", "4308": "fallschirme", "4309": "laufender", "4310": "\u00fcbers", "4311": "st\u00fcckchen", "4312": "dampf", "4313": "deutlich", "4314": "gefrorene", "4315": "rechen", "4316": "getraut", "4317": "\u00e4pfeln", "4318": "lupe", "4319": "identisch", "4320": "w\u00fcrstchen", "4321": "ignoriert", "4322": "t\u00f6pferware", "4323": "ernte", "4324": "eilen", "4325": "gel\u00e4ndemaschine", "4326": "irgendwelchen", "4327": "schwimmbrillen", "4328": "skaten", "4329": "beagle", "4330": "stoffhut", "4331": "wohngegend", "4332": "duckt", "4333": "kurz\u00e4rmeligen", "4334": "schnappschuss", "4335": "senkrecht", "4336": "erfahrener", "4337": "anziehen", "4338": "markisen", "4339": "kartoffeln", "4340": "blutdruck", "4341": "fallschirmspringer", "4342": "blau-rosa", "4343": "f\u00e4sser", "4344": "gl\u00e4ttet", "4345": "grundschulkinder", "4346": "schlauchbooten", "4347": "motorradrennfahrer", "4348": "arten", "4349": "verschwommen", "4350": "versenkt", "4351": "essensverk\u00e4ufer", "4352": "schluck", "4353": "m\u00f6nche", "4354": "a.", "4355": "gemeinsame", "4356": "handvoll", "4357": "aschenbahn", "4358": "l\u00f6cher", "4359": "schreitet", "4360": "rennhunde", "4361": "couchtisch", "4362": "picknickdecke", "4363": "breitet", "4364": "toilette", "4365": "rennkleidung", "4366": "hecke", "4367": "kommuniziert", "4368": "beugen", "4369": "verkehrsreichen", "4370": "wanderern", "4371": "designs", "4372": "bewachen", "4373": "aufgestellten", "4374": "werdender", "4375": "mais", "4376": "walkie-talkie", "4377": "diashow", "4378": "restauranttisch", "4379": "seilrutsche", "4380": "ringtr\u00e4ger", "4381": "h\u00e4ngegleiter", "4382": "tarnhose", "4383": "sombrero", "4384": "gegnerischer", "4385": "auswirft", "4386": "geometrischen", "4387": "balance", "4388": "knurrt", "4389": "religi\u00f6sen", "4390": "hindernisse", "4391": "umgibt", "4392": "piraten", "4393": "reibt", "4394": "ausgelegt", "4395": "angeschnallt", "4396": "herrenfriseur", "4397": "farbstiften", "4398": "dutzende", "4399": "parallel", "4400": "felsformationen", "4401": "hingesetzt", "4402": "festlichen", "4403": "spritzwasser", "4404": "gewandt", "4405": "laufbekleidung", "4406": "volkswagen", "4407": "h\u00f6lzernes", "4408": "k\u00e4fer", "4409": "wartungsarbeiter", "4410": "ohrring", "4411": "lugt", "4412": "gro\u00dfmutter", "4413": "gestapelt", "4414": "seetang", "4415": "kochkleidung", "4416": "mascara", "4417": "mittelalterlicher", "4418": "modischen", "4419": "errichten", "4420": "henna-tattoo", "4421": "galoppiert", "4422": "lego", "4423": "spanische", "4424": "schneebedecktem", "4425": "wei\u00df-gr\u00fcnen", "4426": "schutzbrillen", "4427": "gewaschen", "4428": "gehenden", "4429": "habe", "4430": "zahlreichen", "4431": "wassern\u00e4he", "4432": "satz", "4433": "wort", "4434": "dunkelh\u00e4utiges", "4435": "universit\u00e4t", "4436": "turnschuhe", "4437": "k\u00fcstenlinie", "4438": "schneewehe", "4439": "fechtwettkampf", "4440": "industriegeb\u00e4ude", "4441": "explosion", "4442": "salon", "4443": "em", "4444": "vieh", "4445": "hintere", "4446": "afrikanerin", "4447": "schwimmbeckens", "4448": "prahlt", "4449": "tutus", "4450": "meditiert", "4451": "herbsttag", "4452": "gr\u00f6\u00dferer", "4453": "prunkvollen", "4454": "fechtkampf", "4455": "start", "4456": "waldhorn", "4457": "kampfrichter", "4458": "maultier", "4459": "besch\u00e4digtes", "4460": "lauschen", "4461": "umgekippten", "4462": "sonnt", "4463": "zahlen", "4464": "tackeln", "4465": "wache", "4466": "imbissstand", "4467": "schmutzigem", "4468": "interessantes", "4469": "regenstiefeln", "4470": "schlammpf\u00fctze", "4471": "gehendes", "4472": "sattelzug", "4473": "teamkollegen", "4474": "kekse", "4475": "kaltes", "4476": "seilspielzeug", "4477": "altmodische", "4478": "farblich", "4479": "lebensmittelverk\u00e4ufer", "4480": "marathonl\u00e4uferin", "4481": "volleyballspielerin", "4482": "mittelstreifen", "4483": "machete", "4484": "bauarbeitern", "4485": "atem", "4486": "buden", "4487": "volle", "4488": "formellen", "4489": "zusammenkunft", "4490": "el", "4491": "gezogenen", "4492": "trikotnummer", "4493": "ausstreckt", "4494": "fond", "4495": "gebunden", "4496": "betreibt", "4497": "wartungsarbeiten", "4498": "wildblumen", "4499": "bespritzen", "4500": "stop", "4501": "unterst\u00fctzung", "4502": "kaukasische", "4503": "kokosn\u00fcsse", "4504": "schutzweste", "4505": "gern", "4506": "saft", "4507": "saiten", "4508": "felds", "4509": "scharfe", "4510": "sweatshirts", "4511": "gitarrenspieler", "4512": "lichter", "4513": "mehrfarbigem", "4514": "friseursalon", "4515": "lesend", "4516": "zementmischer", "4517": "softballspieler", "4518": "grindet", "4519": "megaphon", "4520": "landebahn", "4521": "gelandet", "4522": "gew\u00e4nder", "4523": "mechanischen", "4524": "mikrophonen", "4525": "truppe", "4526": "reh", "4527": "metallsch\u00fcssel", "4528": "wanderst\u00f6cke", "4529": "geldb\u00f6rsen", "4530": "dusche", "4531": "spektakul\u00e4ren", "4532": "eiscrem", "4533": "baserunner", "4534": "viertel", "4535": "butterbrot", "4536": "verwendung", "4537": "rot-gelber", "4538": "gitarrespieler", "4539": "basketballkorb", "4540": "anschneiden", "4541": "briefkasten", "4542": "latz", "4543": "weiteres", "4544": "trauzeugen", "4545": "hochzuheben", "4546": "eiswagens", "4547": "sauna", "4548": "inlineskater", "4549": "bauarbeiterhelm", "4550": "sto\u00df", "4551": "internet", "4552": "tank", "4553": "apparat", "4554": "vorschlaghammer", "4555": "zugabteil", "4556": "k\u00fcchenboden", "4557": "gr\u00fc\u00dft", "4558": "au", "4559": "zweigen", "4560": "inne", "4561": "zeigefinger", "4562": "wanderin", "4563": "minivan", "4564": "scheren", "4565": "leotard", "4566": "laderampe", "4567": "grube", "4568": "kleinerer", "4569": "ford", "4570": "man\u00f6vrieren", "4571": "weicht", "4572": "stoppen", "4573": "meine", "4574": "gesichtsschutz", "4575": "kapuzenjacke", "4576": "athleten", "4577": "gartenst\u00fchlen", "4578": "windsurfer", "4579": "sandale", "4580": "pinkfarbenes", "4581": "gewicht", "4582": "fahrg\u00e4ste", "4583": "breit", "4584": "worauf", "4585": "gedeckten", "4586": "island", "4587": "coney", "4588": "kugeln", "4589": "aufkleber", "4590": "96", "4591": "green", "4592": "tiefes", "4593": "schaum", "4594": "tanzenden", "4595": "abschleppwagen", "4596": "schr\u00e4g", "4597": "netzen", "4598": "nintendo", "4599": "stoffe", "4600": "marktstra\u00dfe", "4601": "fahrg\u00e4sten", "4602": "softballspiel", "4603": "torwart", "4604": "husky", "4605": "m\u00f6bel", "4606": "khakis", "4607": "mehrerer", "4608": "werbeanzeige", "4609": "schildkappe", "4610": "tabletts", "4611": "hellblauem", "4612": "stapeln", "4613": "leisten", "4614": "\u201d", "4615": "reise", "4616": "kaffeetisch", "4617": "sofas", "4618": "abschlag", "4619": "flug", "4620": "fleck", "4621": "versetzt", "4622": "b\u00fccherei", "4623": "rugby-spieler", "4624": "silbernes", "4625": "gr\u00f6\u00dfer", "4626": "cafeteria", "4627": "krabbe", "4628": "dr\u00e4ngeln", "4629": "\u00f6ffnung", "4630": "bellen", "4631": "seitenstra\u00dfe", "4632": "zweige", "4633": "betreut", "4634": "hochzeitstag", "4635": "schwitzt", "4636": "entfernten", "4637": "gottesdienst", "4638": "aktivit\u00e4t", "4639": "afroamerikanischen", "4640": "geb\u00fcckt", "4641": "spanien", "4642": "tastatur", "4643": "sonnenschild", "4644": "golfschl\u00e4ger", "4645": "abgestimmten", "4646": "musikkapelle", "4647": "geparkte", "4648": "gewehre", "4649": "bustier", "4650": "kampfsportkleidung", "4651": "vorf\u00fchren", "4652": "marke", "4653": "lager", "4654": "jogginghose", "4655": "finden", "4656": "\u00e4sten", "4657": "akustischen", "4658": "handgriff", "4659": "abgeschnittenen", "4660": "st\u00e4nden", "4661": "ankommenden", "4662": "eisskulptur", "4663": "veranstaltungsort", "4664": "seichtes", "4665": "abzunehmen", "4666": "unkraut", "4667": "schrank", "4668": "trauriges", "4669": "tabak", "4670": "fensterbank", "4671": "wunderkerzen", "4672": "br\u00e4unlichen", "4673": "wasserweg", "4674": "handbewegungen", "4675": "muscheln", "4676": "camcorder", "4677": "obama", "4678": "w\u00e4rmen", "4679": "lindgr\u00fcnen", "4680": "weiblicher", "4681": "geburtstagsfeier", "4682": "lilafarbenes", "4683": "man\u00f6vriert", "4684": "beamten", "4685": "stra\u00dfenbahn", "4686": "komplizierten", "4687": "schlachtet", "4688": "tragend", "4689": "fu\u00dfballspiels", "4690": "entlangfahren", "4691": "beigefarbener", "4692": "karaoke", "4693": "weihnachtszeit", "4694": "r", "4695": "schwarzhaarigen", "4696": "neues", "4697": "pferds", "4698": "tiger", "4699": "la", "4700": "paddelboot", "4701": "hahn", "4702": "prozession", "4703": "ausf\u00fchrt", "4704": "leuchten", "4705": "ungew\u00f6hnlich", "4706": "sch\u00f6pft", "4707": "ausgefransten", "4708": "blo\u00dfen", "4709": "geschminkte", "4710": "schl\u00e4uchen", "4711": "sportbekleidung", "4712": "maus", "4713": "bandkollegen", "4714": "mauern", "4715": "bestehenden", "4716": "frei", "4717": "transportwagen", "4718": "handger\u00e4t", "4719": "state", "4720": "wissenschaftlichen", "4721": "tauscht", "4722": "torte", "4723": "berufskleidung", "4724": "hinterbeine", "4725": "oxford", "4726": "harkt", "4727": "bergpfad", "4728": "schiffes", "4729": "rundherum", "4730": "blau-wei\u00dfer", "4731": "kniesch\u00fctzern", "4732": "schreibtischen", "4733": "nonne", "4734": "gesenkt", "4735": "proben", "4736": "unechten", "4737": "fabrikarbeiter", "4738": "\u00fcbergeben", "4739": "dutzend", "4740": "angucken", "4741": "lang\u00e4rmigen", "4742": "plastikt\u00fcten", "4743": "eist\u00fcten", "4744": "geisha", "4745": "gruppenbild", "4746": "sporthose", "4747": "\u00e4rmelloses", "4748": "lederkleidung", "4749": "herde", "4750": "schubsen", "4751": "wollen", "4752": "teddyb\u00e4ren", "4753": "solo", "4754": "dog", "4755": "innen", "4756": "brennt", "4757": "kapuzenteil", "4758": "vorm", "4759": "stra\u00dfenkleidung", "4760": "verr\u00fcckter", "4761": "auszuziehen", "4762": "bewertet", "4763": "monstertruck", "4764": "milit\u00e4rischer", "4765": "anleitung", "4766": "athlet", "4767": "krankenwagen", "4768": "bestellung", "4769": "einzigartigen", "4770": "vordere", "4771": "milit\u00e4rangeh\u00f6riger", "4772": "sprungwurf", "4773": "krone", "4774": "geflie\u00dften", "4775": "kiesweg", "4776": "radsportler", "4777": "radweg", "4778": "m\u00fcndern", "4779": "l\u00e4den", "4780": "testen", "4781": "\u00f6ffentlicher", "4782": "blau-braunen", "4783": "ausland", "4784": "kanadische", "4785": "s\u00e4cken", "4786": "bemalte", "4787": "gelb-blauen", "4788": "hunderennen", "4789": "schwimmanzug", "4790": "unicef", "4791": "\u00f6rtlicher", "4792": "asphaltstra\u00dfe", "4793": "dreckigen", "4794": "kid", "4795": "erhebung", "4796": "braun-wei\u00dfem", "4797": "nachbarschaftsladen", "4798": "spitz", "4799": "hinterrad", "4800": "danach", "4801": "boje", "4802": "personal", "4803": "schwamm", "4804": "b\u00e4rtigen", "4805": "lebhaft", "4806": "verletzten", "4807": "gekauft", "4808": "meeresfr\u00fcchten", "4809": "pr\u00e4sentieren", "4810": "sichtbaren", "4811": "gestrichenen", "4812": "verletzte", "4813": "spielautomaten", "4814": "vorbeilaufen", "4815": "\u00e4ndert", "4816": "pirat", "4817": "gokart", "4818": "ausgelassen", "4819": "stra\u00dfenschild", "4820": "schlepper", "4821": "blickfeld", "4822": "voneinander", "4823": "verrichtet", "4824": "besitzer", "4825": "zopf", "4826": "freundes", "4827": "rodeo-arena", "4828": "hawaii-hemd", "4829": "mobile", "4830": "treppenhauses", "4831": "\u00fcbernimmt", "4832": "qualmt", "4833": "alt", "4834": "cowboykleidung", "4835": "seesack", "4836": "kleinem", "4837": "hintereinander", "4838": "darum", "4839": "wasserball", "4840": "cheerleaders", "4841": "gewinnt", "4842": "dehnt", "4843": "gem\u00fcsestand", "4844": "schultertasche", "4845": "wade", "4846": "menschlichen", "4847": "scheiben", "4848": "tr\u00fcbes", "4849": "frisches", "4850": "flache", "4851": "rot-wei\u00dfer", "4852": "tulpen", "4853": "us-armee", "4854": "posen", "4855": "erscheint", "4856": "biergl\u00e4sern", "4857": "weise", "4858": "flughafens", "4859": "hellblauer", "4860": "k\u00fcrbisfeld", "4861": "denken", "4862": "schalen", "4863": "kundschaft", "4864": "vervollst\u00e4ndigt", "4865": "medizinisches", "4866": "wasserpf\u00fctze", "4867": "hintern", "4868": "\u00fcbergro\u00dfen", "4869": "aufgeh\u00e4ngt", "4870": "drinks", "4871": "schicken", "4872": "hochgelegten", "4873": "eierkuchen", "4874": "kaukasischer", "4875": "geworfenen", "4876": "w\u00f6rter", "4877": "korbleger", "4878": "ernten", "4879": "renoviert", "4880": "barett", "4881": "weiten", "4882": "ausgestopfte", "4883": "ziegelstra\u00dfe", "4884": "verkehrsmittel", "4885": "seht", "4886": "schwarm", "4887": "kontrollieren", "4888": "east", "4889": "abf\u00e4lle", "4890": "aufgesetzten", "4891": "allen", "4892": "schwimmern", "4893": "basketballwurf", "4894": "un\u00fcberdachten", "4895": "darstellt", "4896": "schwein", "4897": "wohnmobil", "4898": "schlammgrube", "4899": "polospieler", "4900": "papiert\u00fcte", "4901": "affen", "4902": "drahtzaun", "4903": "rei\u00dft", "4904": "weht", "4905": "breites", "4906": "r\u00fcstung", "4907": "eiskrem", "4908": "actionfoto", "4909": "baumgruppe", "4910": "erklettert", "4911": "grau-wei\u00dfe", "4912": "schwestern", "4913": "glasur", "4914": "stadtgebiet", "4915": "interagiert", "4916": "halloween-kost\u00fcmen", "4917": "steaks", "4918": "einfach", "4919": "zombies", "4920": "bittet", "4921": "flachem", "4922": "angeleint", "4923": "drummer", "4924": "barriere", "4925": "\u00e4hnlicher", "4926": "durcheinander", "4927": "abgelegt", "4928": "ausgeschnittene", "4929": "arabischer", "4930": "kamele", "4931": "tandem", "4932": "klettergurt", "4933": "passant", "4934": "st\u00e4mmige", "4935": "twister", "4936": "schickt", "4937": "bezeichnet", "4938": "gleicher", "4939": "geh\u00f6rschutz", "4940": "university", "4941": "stofftiere", "4942": "ganze", "4943": "reiher", "4944": "sprintet", "4945": "spazierst\u00f6cken", "4946": "transportieren", "4947": "gef\u00fcttert", "4948": "8", "4949": "bierflaschen", "4950": "schriftzug", "4951": "stutzt", "4952": "bedruckt", "4953": "ukulele", "4954": "blaugr\u00fcnem", "4955": "kurzhaarige", "4956": "picknickbank", "4957": "aufgemalt", "4958": "bildet", "4959": "hispanoamerikaner", "4960": "segeln", "4961": "motorisierten", "4962": "offen", "4963": "klarem", "4964": "spitzbart", "4965": "aussichtspunkt", "4966": "24", "4967": "karussel", "4968": "tonstudio", "4969": "zusammengefunden", "4970": "synchron", "4971": "reflektierender", "4972": "angemalten", "4973": "b\u00fchnenauftritt", "4974": "protestkundgebung", "4975": "patientin", "4976": "\u00fcbergewichtige", "4977": "sp\u00fclen", "4978": "kinds", "4979": "staubsaugerschlauch", "4980": "programm", "4981": "kuscheln", "4982": "str\u00fcmpfen", "4983": "gekreuzten", "4984": "steindenkmal", "4985": "sch\u00fctzend", "4986": "rosarot", "4987": "ollie", "4988": "spitzen", "4989": "quadraten", "4990": "spielzeugzug", "4991": "redend", "4992": "all", "4993": "pulli", "4994": "anh\u00e4lt", "4995": "sieb", "4996": "albert", "4997": "vor\u00fcbergeht", "4998": "feuerwehrleuten", "4999": "parkettboden", "5000": "unterlagen", "5001": "rampenlicht", "5002": "blumentopf", "5003": "holzgeb\u00e4ude", "5004": "lauter", "5005": "angels", "5006": "b\u00e4ndern", "5007": "heran", "5008": "t\u00e4glichen", "5009": "boxkampf", "5010": "dort", "5011": "gl\u00e4tten", "5012": "arkade", "5013": "oberhemden", "5014": "vortragssaal", "5015": "feuerwehrfahrzeug", "5016": "aufnehmen", "5017": "drahtseil", "5018": "mosaik", "5019": "\u00fcberreicht", "5020": "rugbyspiel", "5021": "weizen", "5022": "ohio", "5023": "komischen", "5024": "football-mannschaften", "5025": "stirnb\u00e4ndern", "5026": "geblockt", "5027": "stadtbild", "5028": "bohrer", "5029": "kurs", "5030": "bmx", "5031": "w\u00e4ldern", "5032": "joe", "5033": "filmen", "5034": "megafon", "5035": "geb\u00e4udeeingang", "5036": "drein", "5037": "nadelstreifenanzug", "5038": "angekettet", "5039": "besetzte", "5040": "sturzhelm", "5041": "bucht", "5042": "skijacke", "5043": "teppichboden", "5044": "gem\u00fcsemarkt", "5045": "attackieren", "5046": "pappkarton", "5047": "schindeln", "5048": "sprungs", "5049": "gest\u00fcrzt", "5050": "teppichen", "5051": "wei\u00df-grauen", "5052": "headset", "5053": "arbeitsplatz", "5054": "rotwei\u00dfen", "5055": "verteidigt", "5056": "substanz", "5057": "clubs", "5058": "wok", "5059": "t\u00e4towiert", "5060": "rings", "5061": "sportgesch\u00e4ft", "5062": "kontrolliert", "5063": "gruppenfoto", "5064": "kelle", "5065": "obstkarren", "5066": "gep\u00e4ckst\u00fccken", "5067": "wei\u00df-roten", "5068": "jogginganzug", "5069": "darsteller", "5070": "wartebereich", "5071": "zweck", "5072": "k\u00fcnstlern", "5073": "rohre", "5074": "angler", "5075": "protest", "5076": "klammert", "5077": "beruhigt", "5078": "aufhalten", "5079": "rastet", "5080": "au\u00dfenwand", "5081": "cowboyh\u00fcten", "5082": "gasmaske", "5083": "eingangshalle", "5084": "operieren", "5085": "drachenkost\u00fcm", "5086": "dunkelhaarigen", "5087": "niedliches", "5088": "weite", "5089": "tennisspiel", "5090": "\u00fcbers\u00e4ten", "5091": "senffarbenen", "5092": "rollkragenpullover", "5093": "pi\u00f1ata", "5094": "mikroskope", "5095": "verheiratete", "5096": "rotbraunen", "5097": "sagen", "5098": "transportmittel", "5099": "fechter", "5100": "summer", "5101": "basketb\u00e4lle", "5102": "ger\u00fcsten", "5103": "zusammengedr\u00e4ngt", "5104": "brustkorb", "5105": "h\u00e4tte", "5106": "schmerzen", "5107": "stemmt", "5108": "chinatown", "5109": "herauszufinden", "5110": "fegen", "5111": "keep", "5112": "baseballplatz", "5113": "s\u00f6hne", "5114": "verloren", "5115": "geschnitzte", "5116": "zielen", "5117": "liegender", "5118": "men\u00fc", "5119": "pferder\u00fccken", "5120": "neoprenanz\u00fcgen", "5121": "gebauten", "5122": "urbanen", "5123": "milit\u00e4rkleidung", "5124": "dokumente", "5125": "seltsamer", "5126": "trompeter", "5127": "w\u00e4hlen", "5128": "erkunden", "5129": "sumpfgebiet", "5130": "rockt", "5131": "choreografie", "5132": "wohnwagen", "5133": "esswaren", "5134": "hunderte", "5135": "stra\u00dfenlaterne", "5136": "skates", "5137": "kreuzfahrtschiff", "5138": "leichtathlet", "5139": "modisch", "5140": "eingerahmte", "5141": "tattoos", "5142": "rennautos", "5143": "brechenden", "5144": "h\u00fcbscher", "5145": "basketballplatz", "5146": "blumendruck", "5147": "freiheitsstatue", "5148": "mittelalterlichen", "5149": "stra\u00dfenlampe", "5150": "teppiche", "5151": "aufnimmt", "5152": "gemusterte", "5153": "reparatur", "5154": "anlass", "5155": "abs\u00e4tzen", "5156": "f\u00f6rmlichen", "5157": "platzes", "5158": "pfl\u00fccken", "5159": "marschkapelle", "5160": "moschee", "5161": "linien", "5162": "dr\u00e4ngt", "5163": "verm\u00e4hltes", "5164": "aufnahmestudio", "5165": "errichtet", "5166": "schuhputzer", "5167": "retriever", "5168": "gedruckt", "5169": "jedoch", "5170": "beleuchtete", "5171": "gleichauf", "5172": "knochen", "5173": "baseballs", "5174": "karneval", "5175": "grau-wei\u00dfer", "5176": "saiteninstrumente", "5177": "warmer", "5178": "geschirrsp\u00fcler", "5179": "filmset", "5180": "franz\u00f6sischen", "5181": "mountainbikes", "5182": "po", "5183": "professioneller", "5184": "frankreich", "5185": "europ\u00e4isches", "5186": "wochenmarkt", "5187": "gepolsterten", "5188": "au\u00dfentreppe", "5189": "parkweg", "5190": "dreir\u00e4der", "5191": "bummeln", "5192": "waage", "5193": "eisenbahn", "5194": "gesch\u00e4ftsanzug", "5195": "kniend", "5196": "dasselbe", "5197": "wunderkerze", "5198": "schottenrock", "5199": "leuchtende", "5200": "geholt", "5201": "fu\u00dfballnetz", "5202": "kniestr\u00fcmpfen", "5203": "dalmatiner", "5204": "hochspringen", "5205": "bmw", "5206": "backwaren", "5207": "geschminkter", "5208": "tanktops", "5209": "oranges", "5210": "brettern", "5211": "bronzestatue", "5212": "handgelenk", "5213": "wasserpistole", "5214": "gesichtsmasken", "5215": "baseman", "5216": "liebe", "5217": "hundeschau", "5218": "angegriffen", "5219": "wundersch\u00f6nes", "5220": "bleckt", "5221": "verzerrtes", "5222": "vogelschwarm", "5223": "gebirge", "5224": "geduckter", "5225": "skist\u00f6cken", "5226": "erste", "5227": "h\u00fcgeligen", "5228": "schrift", "5229": "japaner", "5230": "holzkohlegrill", "5231": "winterjacken", "5232": "musikfestival", "5233": "ballett", "5234": "angeschaut", "5235": "fern", "5236": "bildhauer", "5237": "donuts", "5238": "rollschuhen", "5239": "fallschirmen", "5240": "schleudert", "5241": "bedeckte", "5242": "galerie", "5243": "verl\u00e4uft", "5244": "stricken", "5245": "anbietet", "5246": "enkelin", "5247": "rollkoffer", "5248": "freiluftkonzert", "5249": "cowgirl", "5250": "basteln", "5251": "hochstart", "5252": "spielhalle", "5253": "erkundet", "5254": "hockeymannschaften", "5255": "neu", "5256": "gesellschaft", "5257": "plastikstuhl", "5258": "fallenden", "5259": "zahnspange", "5260": "plattenspieler", "5261": "tiefe", "5262": "beschmiert", "5263": "kino", "5264": "m\u00fctter", "5265": "h\u00fcbsches", "5266": "blau-schwarzen", "5267": "bootsanlegestelle", "5268": "schulband", "5269": "kommunizieren", "5270": "first", "5271": "gartenwerkzeug", "5272": "wei\u00df-blauen", "5273": "anzeige", "5274": "zeichnungen", "5275": "mischung", "5276": "braunhaarigen", "5277": "guitar", "5278": "hero", "5279": "halbwilden", "5280": "gemeinsamen", "5281": "bergab", "5282": "oberk\u00f6rpern", "5283": "anorak", "5284": "laterne", "5285": "alltag", "5286": "softballspiels", "5287": "smokings", "5288": "beeindruckende", "5289": "cowboystiefeln", "5290": "stacheligen", "5291": "getupften", "5292": "bowlingspieler", "5293": "u-bahnhof", "5294": "r\u00f6tlichen", "5295": "dessert", "5296": "blut", "5297": "wartenden", "5298": "r\u00fcckt", "5299": "tunika", "5300": "b\u00fcrgersteigs", "5301": "stramm", "5302": "sandd\u00fcnen", "5303": "m\u00fcssen", "5304": "h\u00f6rnern", "5305": "t\u00fcrkis", "5306": "metallst\u00fcck", "5307": "vielzahl", "5308": "jahren", "5309": "goldfarbener", "5310": "schl\u00e4gern", "5311": "campern", "5312": "malerische", "5313": "nagellack", "5314": "jack", "5315": "sandplatz", "5316": "reklame", "5317": "gew\u00e4chshaus", "5318": "hellem", "5319": "kletterausr\u00fcstung", "5320": "zweig", "5321": "wandtafel", "5322": "interessierten", "5323": "s", "5324": "m", "5325": "brosch\u00fcren", "5326": "eingepacktes", "5327": "bew\u00f6lktem", "5328": "per", "5329": "ny", "5330": "sandwiches", "5331": "gestellt", "5332": "gl\u00fccklichen", "5333": "outdoor-veranstaltung", "5334": "mechaniker", "5335": "poncho", "5336": "auszuruhen", "5337": "amp", "5338": "kahlk\u00f6pfigen", "5339": "beisammensein", "5340": "matschigen", "5341": "h\u00e4mmert", "5342": "rummel", "5343": "verstreut", "5344": "werkzeugg\u00fcrtel", "5345": "riesiges", "5346": "unbekannten", "5347": "korbstuhl", "5348": "f\u00e4uste", "5349": "plastikrutsche", "5350": "fotoshooting", "5351": "eisstand", "5352": "baumaschinen", "5353": "fu\u00dfballtrikots", "5354": "indisches", "5355": "geb\u00e4ck", "5356": "handt\u00fcchern", "5357": "knieschonern", "5358": "offiziere", "5359": "to", "5360": "mets", "5361": "\u00fcbereinander", "5362": "freiluftveranstaltung", "5363": "f\u00e4hre", "5364": "holzt\u00fcr", "5365": "d\u00fcnner", "5366": "zwickt", "5367": "aufgebaut", "5368": "f\u00f6hnt", "5369": "helfer", "5370": "haarsalon", "5371": "musikst\u00fcck", "5372": "abseits", "5373": "orange-wei\u00df", "5374": "schwerem", "5375": "entdeckt", "5376": "produkten", "5377": "entlangf\u00e4hrt", "5378": "flohmarkt", "5379": "ware", "5380": "kurzhaariger", "5381": "wasserfalls", "5382": "steigung", "5383": "formen", "5384": "m\u00fcnzfernsprecher", "5385": "typischen", "5386": "campen", "5387": "goldfarbenes", "5388": "zusammenpassenden", "5389": "k\u00fchlen", "5390": "erfrischenden", "5391": "telefonleitung", "5392": "hartholzboden", "5393": "stiften", "5394": "koreanische", "5395": "objektiv", "5396": "wild", "5397": "mischen", "5398": "angeh\u00f6rt", "5399": "force", "5400": "star", "5401": "spielzeughammer", "5402": "j\u00fcdische", "5403": "air", "5404": "kameracrew", "5405": "kahlem", "5406": "trittsteinen", "5407": "lautsprechern", "5408": "motorschlitten", "5409": "erregt", "5410": "sch\u00f6nheit", "5411": "tarnjacke", "5412": "motiv", "5413": "r\u00f6cke", "5414": "querfl\u00f6te", "5415": "ziegelsteingeb\u00e4ude", "5416": "damenunterw\u00e4sche", "5417": "eleganten", "5418": "frisst", "5419": "eisigen", "5420": "fragen", "5421": "papagei", "5422": "keks", "5423": "blasinstrumente", "5424": "polizeibeamter", "5425": "auszeit", "5426": "boards", "5427": "halsb\u00e4ndern", "5428": "spielkarten", "5429": "b\u00fcrgerkriegs", "5430": "rauchwolke", "5431": "rod", "5432": "verboten", "5433": "ungew\u00f6hnliches", "5434": "gef\u00e4lle", "5435": "boa", "5436": "spr\u00fcngen", "5437": "kleidungsst\u00fcck", "5438": "astronautenanzug", "5439": "fire", "5440": "grasbahn", "5441": "gap-gesch\u00e4ft", "5442": "busse", "5443": "doktor", "5444": "schnees", "5445": "toyota", "5446": "pinguine", "5447": "armb\u00e4ndern", "5448": "gefleckten", "5449": "whirlpool", "5450": "schwarz-blaues", "5451": "anzugucken", "5452": "king", "5453": "insekt", "5454": "vornehm", "5455": "college-studenten", "5456": "coolen", "5457": "applaudiert", "5458": "angehobenen", "5459": "wells", "5460": "schn\u00fcren", "5461": "bukett", "5462": "abhebt", "5463": "schaulustigen", "5464": "st\u00e4be", "5465": "kostenlose", "5466": "fressen", "5467": "umarmungen", "5468": "rasterlocken", "5469": "schluckt", "5470": "auftragen", "5471": "beschneidet", "5472": "leuchtturm", "5473": "marker", "5474": "schn\u00fcffeln", "5475": "sonnenh\u00fcten", "5476": "lacrosse-spieler", "5477": "geeigneten", "5478": "linkes", "5479": "sessellift", "5480": "erinnern", "5481": "geselligen", "5482": "bleistiftrock", "5483": "arbeitender", "5484": "sandsturm", "5485": "baumaschine", "5486": "heckklappe", "5487": "\u00f6ffnungen", "5488": "verkehrsschild", "5489": "holzstruktur", "5490": "dreir\u00e4dern", "5491": "patient", "5492": "problem", "5493": "kitteln", "5494": "sch\u00fclerinnen", "5495": "innenstadtbereich", "5496": "kultur", "5497": "inline-skates", "5498": "pelikan", "5499": "spielzeuglastwagen", "5500": "zehn", "5501": "kristallklaren", "5502": "bears", "5503": "care", "5504": "schubst", "5505": "eindruck", "5506": "quadbike", "5507": "bin", "5508": "bart.", "5509": "wolke", "5510": "steinoberfl\u00e4che", "5511": "wasserskulptur", "5512": "langhaarigen", "5513": "leuchtender", "5514": "\u2018", "5515": "haust\u00fcr", "5516": "p\u00e4ckchen", "5517": "komisches", "5518": "imbissbude", "5519": "ausgefallenem", "5520": "polizeifahrzeug", "5521": "b\u00e4ren", "5522": "z\u00fcgel", "5523": "pusten", "5524": "stahl", "5525": "pullovers", "5526": "jerseys", "5527": "notebook", "5528": "schneebrille", "5529": "song", "5530": "beteiligen", "5531": "stadtbrunnen", "5532": "plastikwanne", "5533": "holzm\u00f6beln", "5534": "geschlungen", "5535": "gefl\u00fcgel", "5536": "transparenten", "5537": "baumstamms", "5538": "gerahmte", "5539": "blau-wei\u00dfem", "5540": "milit\u00e4rhosen", "5541": "signiert", "5542": "penske", "5543": "lacrossespieler", "5544": "bestimmten", "5545": "basketballkorbs", "5546": "schwei\u00dfer", "5547": "einkaufszentrums", "5548": "vorbeifahrenden", "5549": "k\u00fcnstlerischen", "5550": "szenerie", "5551": "apple", "5552": "wartezimmer", "5553": "chef", "5554": "gefaltet", "5555": "bestaunen", "5556": "turn\u00fcbungen", "5557": "zwirbelt", "5558": "deli", "5559": "abh\u00e4ngen", "5560": "antworten", "5561": "pr\u00e4chtigen", "5562": "schwimmkleidung", "5563": "betonierten", "5564": "mixer", "5565": "frontscheibe", "5566": "schraubendreher", "5567": "gedreht", "5568": "unterf\u00fchrung", "5569": "fahrradrahmen", "5570": "angst", "5571": "fliegender", "5572": "sticht", "5573": "mandel", "5574": "geschenkpapier", "5575": "kl\u00f6tze", "5576": "pl\u00fcschtieren", "5577": "besch\u00e4digten", "5578": "mannequin", "5579": "le", "5580": "trampelpfad", "5581": "zuneigung", "5582": "frequentierten", "5583": "verzweifelt", "5584": "touristin", "5585": "fersen", "5586": "no", "5587": "befreien", "5588": "modernes", "5589": "unterhosen", "5590": "lachsfarbenen", "5591": "crew", "5592": "wurzeln", "5593": "hunderennbahn", "5594": "startbereit", "5595": "dachziegel", "5596": "papierteller", "5597": "braucht", "5598": "wii", "5599": "testet", "5600": "insassen", "5601": "cola", "5602": "schaufensterbummel", "5603": "tr\u00e4g", "5604": "arbeitstag", "5605": "irgendwelche", "5606": "benzin", "5607": "aufzufallen", "5608": "ausgehen", "5609": "19", "5610": "geh\u00f6rt", "5611": "flugblatt", "5612": "japanerinnen", "5613": "beanie", "5614": "glitzernden", "5615": "verhaftet", "5616": "autofahrer", "5617": "holzstamm", "5618": "postkarten", "5619": "bockendes", "5620": "kost\u00fcmierter", "5621": "paintball", "5622": "passende", "5623": "eiffelturm", "5624": "zirkus", "5625": "rocker", "5626": "maskottchen", "5627": "wohnhauses", "5628": "leitung", "5629": "dominiert", "5630": "lilane", "5631": "vorn\u00fcbergebeugt", "5632": "drehen", "5633": "new-york-hut", "5634": "leuchtreklame", "5635": "rocks", "5636": "lang\u00e4rmeligem", "5637": "\u00e4pfel", "5638": "orgel", "5639": "zuges", "5640": "rei\u00dfverschluss", "5641": "taschenlampe", "5642": "fu\u00dfballs", "5643": "zeltet", "5644": "gehe", "5645": "bettdecke", "5646": "k\u00fche", "5647": "kinderschaukel", "5648": "rund", "5649": "partner", "5650": "getreten", "5651": "belegten", "5652": "schwarzhaariges", "5653": "modelleisenbahn", "5654": "k\u00fchlboxen", "5655": "motorrads", "5656": "g\u00e4sschen", "5657": "augenschein", "5658": "reckt", "5659": "reaktion", "5660": "beschmierte", "5661": "besteck", "5662": "lineal", "5663": "hasenohren", "5664": "wartende", "5665": "schotter", "5666": "bekannter", "5667": "alles", "5668": "boy", "5669": "holzpfosten", "5670": "wachleute", "5671": "passieren", "5672": "batik-muster", "5673": "skaterbahn", "5674": "sauberen", "5675": "unfallort", "5676": "h\u00fcgeln", "5677": "allem", "5678": "sehe", "5679": "fliegenfischen", "5680": "musikerin", "5681": "angefahren", "5682": "rugby-spiel", "5683": "fu\u00dfg\u00e4nger\u00fcberwegs", "5684": "snowboards", "5685": "sonnenaufgang", "5686": "arbeitswesten", "5687": "korbs", "5688": "br\u00fcsten", "5689": "fremdl\u00e4ndischer", "5690": "passagier", "5691": "holzbl\u00f6cke", "5692": "breite", "5693": "einkaufskorb", "5694": "sportger\u00e4t", "5695": "stoppschild", "5696": "juweliergesch\u00e4ft", "5697": "feuerwehrangeh\u00f6riger", "5698": "brand", "5699": "chinese", "5700": "schlammiges", "5701": "feil", "5702": "gewundenen", "5703": "grasbedeckten", "5704": "herbstlaub", "5705": "starker", "5706": "aufgespannt", "5707": "m\u00e4nne", "5708": "klippen", "5709": "zementierten", "5710": "center", "5711": "power", "5712": "braun-schwarz-wei\u00dfer", "5713": "ber\u00fchmten", "5714": "\u00fcberf\u00fchrung", "5715": "protestierende", "5716": "l\u00f6wenzahnbl\u00fcte", "5717": "pastellfarbenen", "5718": "werkzeugg\u00fcrteln", "5719": "polizeiauto", "5720": "spinnt", "5721": "hacke", "5722": "gezeichneten", "5723": "kunstvoll", "5724": "schubkarren", "5725": "h\u00fcgels", "5726": "part", "5727": "plastikflasche", "5728": "pakete", "5729": "traditionell", "5730": "torbogens", "5731": "gemischter", "5732": "schriftzeichen", "5733": "urlaub", "5734": "sorgt", "5735": "boutique", "5736": "normaler", "5737": "sonniger", "5738": "kniender", "5739": "takt", "5740": "st\u00e4mmiger", "5741": "lebendigen", "5742": "hirtenhund", "5743": "wenden", "5744": "versorgt", "5745": "d\u00f6st", "5746": "cowboyh\u00fcte", "5747": "gelassen", "5748": "tanzvorf\u00fchrung", "5749": "lagerraum", "5750": "einh\u00e4ndigen", "5751": "gitarristin", "5752": "berittener", "5753": "\u00fcberfluteten", "5754": "durchschnittlich", "5755": "presst", "5756": "pferdewagen", "5757": "holzh\u00fctte", "5758": "metzger", "5759": "schuppen", "5760": "beigem", "5761": "schneeanz\u00fcgen", "5762": "interviewen", "5763": "beobachter", "5764": "stoffen", "5765": "wohnh\u00e4usern", "5766": "beil", "5767": "gr\u00fcn-blauem", "5768": "belag", "5769": "skaterpark", "5770": "maschendraht", "5771": "rentier", "5772": "berieselungsanlage", "5773": "angeleuchtet", "5774": "holzst\u00fccken", "5775": "sumpfiges", "5776": "head", "5777": "vorzubereiten", "5778": "sonnenbad", "5779": "zufahrt", "5780": "cellospieler", "5781": "fotoalbum", "5782": "schleifmaschine", "5783": "notre", "5784": "bemaltem", "5785": "t\u00f6pfe", "5786": "k\u00fcchenmaschine", "5787": "rauen", "5788": "rennmotorrad", "5789": "circus", "5790": "strahlendem", "5791": "\u00fcberreste", "5792": "reiht", "5793": "hinaufgehen", "5794": "bahnschienen", "5795": "angeles", "5796": "br\u00f6tchen", "5797": "n\u00e4chtliche", "5798": "abgelenkt", "5799": "entl\u00e4dt", "5800": "orchesters", "5801": "s\u00fc\u00dfes", "5802": "schirmt", "5803": "helles", "5804": "labradoodle", "5805": "theatervorstellung", "5806": "kreativ", "5807": "gro\u00dfartige", "5808": "st\u00fcrmt", "5809": "waschmaschinen", "5810": "b\u00e4nder", "5811": "freiluftb\u00fchne", "5812": "schulklasse", "5813": "technik", "5814": "wohl", "5815": "zustand", "5816": "manns", "5817": "lederschuhe", "5818": "harlem", "5819": "schlechtem", "5820": "baseballuniform", "5821": "festsaal", "5822": "halloween", "5823": "bedecktem", "5824": "aufgesetztem", "5825": "gitarrenkoffer", "5826": "fl\u00fcstern", "5827": "ballettt\u00e4nzer", "5828": "lateinamerikaner", "5829": "spirituosengesch\u00e4ft", "5830": "risiko", "5831": "frauengruppe", "5832": "familienmitglieder", "5833": "akrobatin", "5834": "geb\u00e4udekomplex", "5835": "markierung", "5836": "durchn\u00e4sst", "5837": "empfang", "5838": "erlangen", "5839": "umstehenden", "5840": "parka", "5841": "schraubenzieher", "5842": "zotteliger", "5843": "matador", "5844": "gr\u00fcn-schwarzen", "5845": "\u00fcppigen", "5846": "capris", "5847": "f\u00f6rmlich", "5848": "stra\u00dfenmusikanten", "5849": "heimatlandes", "5850": "m\u00fclltonnen", "5851": "metallabsperrung", "5852": "vorgarten", "5853": "lateinamerikanischen", "5854": "herrlich", "5855": "senkrechte", "5856": "camper", "5857": "schr\u00e4gen", "5858": "l\u00e4tzchen", "5859": "linse", "5860": "wei\u00df-rotes", "5861": "gemessen", "5862": "lametta", "5863": "aussteigen", "5864": "h\u00fcndin", "5865": "rennenden", "5866": "kanne", "5867": "pfote", "5868": "ein-", "5869": "gelangweilt", "5870": "fehlt", "5871": "motorradhelm", "5872": "lutscht", "5873": "videoaufnahme", "5874": "pelzigen", "5875": "schutzhelme", "5876": "biene", "5877": "hingefallen", "5878": "pedal", "5879": "eingez\u00e4unte", "5880": "burka", "5881": "er\u00f6rtert", "5882": "zweist\u00f6ckigen", "5883": "gel\u00e4ndefahrer", "5884": "de", "5885": "zuh\u00e4lt", "5886": "chevrolet", "5887": "wei\u00dfene", "5888": "reisef\u00fchrer", "5889": "union", "5890": "metalltor", "5891": "hinunterrutscht", "5892": "brennen", "5893": "jury", "5894": "kastanienbraunes", "5895": "spiderman", "5896": "zweirad", "5897": "glasgeb\u00e4udes", "5898": "series", "5899": "inline-skater", "5900": "h\u00fchner", "5901": "aufstellt", "5902": "verschlungenen", "5903": "durchsuchen", "5904": "halbhohen", "5905": "kleinbusses", "5906": "dreifarbiger", "5907": "rockkonzert", "5908": "studentenwohnheim", "5909": "kliff", "5910": "absatz", "5911": "moosbedeckten", "5912": "herrscht", "5913": "desserts", "5914": "f\u00e4higkeiten", "5915": "motorradfahren", "5916": "monument", "5917": "genommen", "5918": "glasscheibe", "5919": "herabh\u00e4ngenden", "5920": "untertasse", "5921": "t\u00f6chter", "5922": "rasiermesser", "5923": "gro\u00dfvater", "5924": "toilettenpapier", "5925": "professionell", "5926": "dunkle", "5927": "trennt", "5928": "taste", "5929": "schirmchen", "5930": "mike", "5931": "clear", "5932": "nachhauseweg", "5933": "lilafarbenem", "5934": "unterst\u00fctzen", "5935": "stattfindenden", "5936": "polo-hemd", "5937": "mir", "5938": "runzelt", "5939": "j\u00fcdischer", "5940": "schlechten", "5941": "dehnen", "5942": "laufkleidung", "5943": "gehege", "5944": "braun-schwarze", "5945": "mei\u00dfel", "5946": "bisschen", "5947": "batter", "5948": "unbekannte", "5949": "heruntergefallen", "5950": "verkehrsreiche", "5951": "baumlosen", "5952": "rettungshelfer", "5953": "bespr\u00fcht", "5954": "herabgelassen", "5955": "zeltplane", "5956": "glasbeh\u00e4lter", "5957": "kulturellen", "5958": "menschenleeren", "5959": "entwirren", "5960": "festgehalten", "5961": "requisiten", "5962": "zerlumpter", "5963": "kopfstand", "5964": "goalie", "5965": "heruntergelassen", "5966": "bew\u00f6lkter", "5967": "metallpfosten", "5968": "beschreibt", "5969": "kennzeichnung", "5970": "modelliert", "5971": "\u00fcberwacht", "5972": "filmcrew", "5973": "stra\u00dfenk\u00fcnstlern", "5974": "mittelpunkt", "5975": "glatzk\u00f6pfige", "5976": "berlin", "5977": "stra\u00dfenansicht", "5978": "autodach", "5979": "schulkantine", "5980": "flankiert", "5981": "schneeballschlacht", "5982": "heels", "5983": "campingstuhl", "5984": "laborkitteln", "5985": "leitplanken", "5986": "presslufthammer", "5987": "situation", "5988": "here", "5989": "schnellimbiss", "5990": "latzhose", "5991": "feuerwehrschlauch", "5992": "tim", "5993": "zickzack", "5994": "besonderen", "5995": "bernsteinfarbener", "5996": "soldatinnen", "5997": "kongress", "5998": "gewebe", "5999": "vergr\u00f6\u00dferungsglas", "6000": "strahlt", "6001": "dar", "6002": "sch\u00e4ferhunde", "6003": "\u00fcppig", "6004": "eichh\u00f6rnchen", "6005": "heruml\u00e4uft", "6006": "spreizt", "6007": "hundeschlitten", "6008": "heimweg", "6009": "herzf\u00f6rmigen", "6010": "filmplakat", "6011": "auseinander", "6012": "strandkleid", "6013": "trockene", "6014": "welches", "6015": "etliche", "6016": "speist", "6017": "spielzeug-basketballkorb", "6018": "wasserbrunnen", "6019": "fensters", "6020": "baumbestandenen", "6021": "weges", "6022": "italien", "6023": "b\u00e4uchlings", "6024": "gitarrespielen", "6025": "schwarz-rotes", "6026": "schlafanz\u00fcgen", "6027": "ringsum", "6028": "zukunft", "6029": "westernstil", "6030": "buckelt", "6031": "formelle", "6032": "talkshow", "6033": "ballettt\u00e4nzerinnen", "6034": "gehgestell", "6035": "regenbogenfarbenen", "6036": "holzsteg", "6037": "familien", "6038": "minirock", "6039": "bodybuilder", "6040": "metallger\u00fcst", "6041": "presse", "6042": "matrosen", "6043": "schroffen", "6044": "bissen", "6045": "motorradhelme", "6046": "behinderte", "6047": "pucks", "6048": "metallkiste", "6049": "geschieht", "6050": "imbiss", "6051": "batik-oberteil", "6052": "verglasten", "6053": "laufstrecke", "6054": "\u00fcberlegt", "6055": "roller-derby", "6056": "cadillac", "6057": "sumpf", "6058": "metallbauwerk", "6059": "passender", "6060": "\u00e4ngstlich", "6061": "sechsk\u00f6pfige", "6062": "14", "6063": "sportteam", "6064": "afrika", "6065": "aufsitzm\u00e4her", "6066": "\u00fcberladenen", "6067": "stadtplan", "6068": "schar", "6069": "michigan", "6070": "soda", "6071": "muffins", "6072": "hellrosafarbenen", "6073": "moderner", "6074": "holztreppe", "6075": "cowgirl-hut", "6076": "kunstprojekt", "6077": "hei\u00dfe", "6078": "hellorangefarbenen", "6079": "scheffel", "6080": "flie\u00dfenden", "6081": "kitchenaid", "6082": "ausstellungsraum", "6083": "vorbeil\u00e4uft", "6084": "ohrst\u00f6pseln", "6085": "\u00fcberschlagen", "6086": "toast", "6087": "dogge", "6088": "kartoffelchip", "6089": "aufb\u00e4umenden", "6090": "kochjacke", "6091": "freibad", "6092": "gefalteten", "6093": "schwimmende", "6094": "ladengesch\u00e4ft", "6095": "sturzflug", "6096": "sichtbare", "6097": "altar", "6098": "e", "6099": "schafft", "6100": "football-coach", "6101": "dann", "6102": "schneeschuh", "6103": "kasino", "6104": "plastikbecher", "6105": "pro", "6106": "eistee", "6107": "vierr\u00e4drigen", "6108": "stadions", "6109": "american", "6110": "hochzeitskuchen", "6111": "laufenden", "6112": "nadelstreifenhemd", "6113": "jim", "6114": "enden", "6115": "angreifer", "6116": "verletzter", "6117": "fr\u00fchen", "6118": "hosentasche", "6119": "ethnischer", "6120": "hasenkost\u00fcm", "6121": "big", "6122": "sumoringer", "6123": "zitrone", "6124": "haltegurt", "6125": "neugeborene", "6126": "attackiert", "6127": "display", "6128": "leckeren", "6129": "yoga\u00fcbungen", "6130": "verkehrspolizist", "6131": "halbnackte", "6132": "snap", "6133": "geheimnisvoller", "6134": "ohrh\u00f6rer", "6135": "verschneiter", "6136": "gekr\u00f6nt", "6137": "verteidigern", "6138": "lederst\u00fchlen", "6139": "abschluss", "6140": "berggipfeln", "6141": "entleert", "6142": "mehrfarbiger", "6143": "stretching", "6144": "spanischen", "6145": "einbruch", "6146": "starten", "6147": "rettungskraft", "6148": "holzf\u00e4ller", "6149": "dachdecker", "6150": "umzugswagen", "6151": "pianist", "6152": "fliederfarbener", "6153": "lungern", "6154": "nagelpistole", "6155": "kr\u00e4ftige", "6156": "apple-computer", "6157": "passendem", "6158": "tank-top", "6159": "laufschuhen", "6160": "striegelt", "6161": "gefleckte", "6162": "fliederfarbenen", "6163": "wirbeln", "6164": "vitrine", "6165": "nah\u00f6stlicher", "6166": "besten", "6167": "fahrgast", "6168": "hochzeitsfoto", "6169": "freies", "6170": "worten", "6171": "betrieben", "6172": "tauschen", "6173": "rummelplatz", "6174": "gedeckt", "6175": "grash\u00fcgel", "6176": "hochdruckreiniger", "6177": "marathons", "6178": "partnerin", "6179": "str\u00e4uchern", "6180": "europ\u00e4ischen", "6181": "selbstgebastelten", "6182": "schlittschuhe", "6183": "pfarrer", "6184": "flucht", "6185": "faden", "6186": "landungssteg", "6187": "masse", "6188": "falsch", "6189": "gebogen", "6190": "anguckt", "6191": "k\u00fcnstlichem", "6192": "bewegungslos", "6193": "einzelpersonen", "6194": "stammes", "6195": "demselben", "6196": "abfallenden", "6197": "patrouilliert", "6198": "punkte", "6199": "open-air-konzert", "6200": "beinhaltet", "6201": "motorradfahrerin", "6202": "flussabw\u00e4rts", "6203": "kommunikationsger\u00e4t", "6204": "schneesturm", "6205": "iglu", "6206": "tortillas", "6207": "sendet", "6208": "champagner", "6209": "gespr\u00fcht", "6210": "lakers", "6211": "dargestellt", "6212": "kahlrasiertem", "6213": "pritschenwagen", "6214": "besprechung", "6215": "innenr\u00e4umen", "6216": "kosten", "6217": "softdrink", "6218": "biergarten", "6219": "baseballstadion", "6220": "bagel-laden", "6221": "meeresfr\u00fcchte", "6222": "hellrotes", "6223": "rollwagen", "6224": "abzeichen", "6225": "campingst\u00fchlen", "6226": "abgeleckt", "6227": "holzkl\u00f6tzen", "6228": "streitet", "6229": "silberfarbene", "6230": "blondine", "6231": "hafenbecken", "6232": "beiderseits", "6233": "steinstruktur", "6234": "spielzeughaus", "6235": "lebhafte", "6236": "schneeausr\u00fcstung", "6237": "orten", "6238": "indianischen", "6239": "schlafs\u00e4cken", "6240": "abendbrottisch", "6241": "naturprodukten", "6242": "stadtteil", "6243": "marineblau", "6244": "so\u00dfe", "6245": "oberleitungsfahrzeug", "6246": "sch\u00e4rpe", "6247": "erzeugt", "6248": "30ern", "6249": "leerstehenden", "6250": "kaffeehaus", "6251": "karatekampf", "6252": "unterarm", "6253": "kanals", "6254": "veranstaltet", "6255": "metalldach", "6256": "haube", "6257": "beladenes", "6258": "verk\u00e4ufers", "6259": "hellh\u00e4utiges", "6260": "begeistertes", "6261": "asphaltbelag", "6262": "struppiger", "6263": "bahngleis", "6264": "yamaha", "6265": "blumenladen", "6266": "sch\u00e4big", "6267": "braunschwarzer", "6268": "zauberer", "6269": "\u00fcberhang", "6270": "zaubertrick", "6271": "g\u00e4nse", "6272": "schw\u00e4ne", "6273": "kieselsteine", "6274": "musiziert", "6275": "herannahenden", "6276": "w\u00fctend", "6277": "kawasaki", "6278": "f\u00fcgt", "6279": "baumgrenze", "6280": "erledigt", "6281": "goldene", "6282": "geklettert", "6283": "t\u00fcrkise", "6284": "weinflasche", "6285": "hunt", "6286": "reportern", "6287": "mitfahrgelegenheit", "6288": "schaukelpferd", "6289": "feiertage", "6290": "speit", "6291": "gebadet", "6292": "kameraobjektiv", "6293": "schnurrb\u00e4rtiger", "6294": "seifenwasser", "6295": "steinhaufen", "6296": "gr\u00fcn-wei\u00dfem", "6297": "spr\u00fchdosen", "6298": "b\u00fcchertasche", "6299": "ernstem", "6300": "verziert", "6301": "birthday", "6302": "warnkleidung", "6303": "interessant", "6304": "behandelt", "6305": "bespricht", "6306": "farbspritzern", "6307": "elmo-motiv", "6308": "sonnenuntergangs", "6309": "volles", "6310": "story", "6311": "kochm\u00fctze", "6312": "holzst\u00fcck", "6313": "kurven", "6314": "plastikflaschen", "6315": "breakdancer", "6316": "schert", "6317": "gurtzeug", "6318": "ferngesteuerten", "6319": "company", "6320": "stofftieren", "6321": "beginnender", "6322": "feuern", "6323": "ohrensch\u00fctzern", "6324": "umzugs", "6325": "halben", "6326": "parkgarage", "6327": "toten", "6328": "hasen", "6329": "hindernispfad", "6330": "ausgestreckter", "6331": "skateranlage", "6332": "kiesstrand", "6333": "r\u00fcckbank", "6334": "n\u00e4chtliches", "6335": "vor\u00fcberf\u00e4hrt", "6336": "sicherungsseilen", "6337": "schnurrb\u00e4rten", "6338": "busfahrer", "6339": "spielzeugeisenbahn", "6340": "orange-grauen", "6341": "einstellt", "6342": "lungert", "6343": "b\u00fchnenmitte", "6344": "beeindruckenden", "6345": "deere", "6346": "brennendes", "6347": "streichorchester", "6348": "wettschwimmen", "6349": "beamter", "6350": "weihnachtsmotiv", "6351": "f\u00fchrer", "6352": "buntstiften", "6353": "selbstgebauten", "6354": "gefertigt", "6355": "bekleideten", "6356": "engelskost\u00fcmen", "6357": "wandobjekt", "6358": "pulver", "6359": "kabinen", "6360": "stra\u00dfenkreuzung", "6361": "campingausr\u00fcstung", "6362": "rodelt", "6363": "gef\u00e4hrt", "6364": "langlauf", "6365": "rodeln", "6366": "turbanen", "6367": "wasserrohre", "6368": "namensschildern", "6369": "baumreichen", "6370": "text", "6371": "gegnerin", "6372": "ethnischen", "6373": "geigenspieler", "6374": "pfl\u00fcgen", "6375": "visier", "6376": "japanisches", "6377": "patriotischen", "6378": "unrat", "6379": "skifahrerin", "6380": "gastronomiebereich", "6381": "schlips", "6382": "wei\u00df-braunem", "6383": "strandspaziergang", "6384": "wei\u00dfhaarige", "6385": "bulldogge", "6386": "ahornblatt", "6387": "vader", "6388": "kauen", "6389": "holzveranda", "6390": "darth", "6391": "wirkt", "6392": "britischen", "6393": "feuerwehrmitarbeiter", "6394": "frisbees", "6395": "bordercollie", "6396": "1", "6397": "kippt", "6398": "meist", "6399": "erkennen", "6400": "lustiges", "6401": "bilderbuch", "6402": "hochzeitsfotos", "6403": "spur", "6404": "plattenladen", "6405": "kimono", "6406": "stadtarbeiter", "6407": "feuerwehr", "6408": "steinboden", "6409": "betonsperren", "6410": "baseballteam", "6411": "wasseroberfl\u00e4che", "6412": "unbekannter", "6413": "pools", "6414": "krankenhauskleidung", "6415": "beige", "6416": "%", "6417": "palmwedeln", "6418": "holzbr\u00fccke", "6419": "gereicht", "6420": "feuchtgebiet", "6421": "gelbbraunem", "6422": "50", "6423": "zange", "6424": "bikinioberteile", "6425": "tr\u00e4gerhemden", "6426": "verschwommenen", "6427": "holzhaus", "6428": "teenager-jungen", "6429": "paris", "6430": "besonders", "6431": "verk\u00e4uferin", "6432": "m\u00fcnztelefon", "6433": "trabenden", "6434": "zusammengebaut", "6435": "metallrohr", "6436": "\u00e4ste", "6437": "anh\u00f6ren", "6438": "laken", "6439": "schuluniform", "6440": "personenzug", "6441": "sitzbank", "6442": "felder", "6443": "dickem", "6444": "fahrradst\u00e4nder", "6445": "rei\u00dfen", "6446": "taubenschwarm", "6447": "betonkonstruktion", "6448": "badezeug", "6449": "koffern", "6450": "stra\u00dfenmusik", "6451": "feierlicher", "6452": "verquirlt", "6453": "heuballen", "6454": "milit\u00e4rischen", "6455": "jeans-shorts", "6456": "jump", "6457": "augen-makeup", "6458": "farbdose", "6459": "schirme", "6460": "leuchtendem", "6461": "teamkollege", "6462": "stillt", "6463": "end", "6464": "apotheke", "6465": "anspiel", "6466": "vieren", "6467": "nahrung", "6468": "autor", "6469": "entsorgt", "6470": "betongeb\u00e4ude", "6471": "burg", "6472": "scheune", "6473": "leckerbissen", "6474": "br\u00fcstung", "6475": "b\u00fcrogeb\u00e4udes", "6476": "side", "6477": "verschwommenes", "6478": "w\u00e4lder", "6479": "bussen", "6480": "umgebene", "6481": "fingerspitzen", "6482": "gem\u00fcseauslage", "6483": "kugelbad", "6484": "flattert", "6485": "gesenkten", "6486": "zahnstocher", "6487": "us-amerikanische", "6488": "b\u00e4ckt", "6489": "eingepackt", "6490": "grasweg", "6491": "einf\u00e4hrt", "6492": "pfeiler", "6493": "hellgr\u00fcner", "6494": "engelsstatue", "6495": "schmale", "6496": "zugedeckt", "6497": "goldgelbe", "6498": "vierergruppe", "6499": "schlangen", "6500": "bechern", "6501": "hantiert", "6502": "seminar", "6503": "drache", "6504": "freue", "6505": "volleyballturnier", "6506": "transport", "6507": "hockeyspielern", "6508": "kunsthandwerk", "6509": "gewichte", "6510": "neugieriges", "6511": "sch\u00f6n", "6512": "k\u00fchlbox", "6513": "festgemacht", "6514": "tarnfarben", "6515": "holzfu\u00dfboden", "6516": "kegelf\u00f6rmigen", "6517": "hochgehalten", "6518": "hindern", "6519": "imbisswagen", "6520": "beleuchteter", "6521": "kinderbett", "6522": "kissenschlacht", "6523": "metallstruktur", "6524": "speedo-badehose", "6525": "f\u00f6hn", "6526": "crocs", "6527": "l\u00fcmmelt", "6528": "frog", "6529": "gewalt", "6530": "abwischt", "6531": "serviette", "6532": "ice", "6533": "miene", "6534": "elementary", "6535": "\u00fcberfliegt", "6536": "vertreiben", "6537": "dojo", "6538": "geringer", "6539": "m\u00e4dchenmannschaften", "6540": "rot-wei\u00df-blauem", "6541": "sumpfigen", "6542": "lift", "6543": "express", "6544": "weihnachtsmannm\u00fctzen", "6545": "geschleudert", "6546": "wandbemalung", "6547": "eisenstange", "6548": "riecht", "6549": "pedale", "6550": "laternenpfahl", "6551": "w\u00fcrzt", "6552": "san", "6553": "spie\u00dfen", "6554": "hausparty", "6555": "elefant", "6556": "backsteinweg", "6557": "wer", "6558": "au\u00dfer", "6559": "diversen", "6560": "fahrradtaxi", "6561": "bummelt", "6562": "gebeugten", "6563": "brokkoli", "6564": "vorderpfoten", "6565": "farbenpr\u00e4chtigen", "6566": "high-five", "6567": "nachgehen", "6568": "bergweg", "6569": "wonder", "6570": "rosaroter", "6571": "tanzstudio", "6572": "glasflasche", "6573": "vierte", "6574": "m\u00f6glich", "6575": "kalifornien", "6576": "bl\u00fchender", "6577": "gegnern", "6578": "\u00fcbers\u00e4t", "6579": "b\u00fcroarbeit", "6580": "fischzug", "6581": "festumzugs", "6582": "kniestr\u00fcmpfe", "6583": "versorgen", "6584": "begleitung", "6585": "beigebracht", "6586": "streikschildern", "6587": "sogar", "6588": "dient", "6589": "gebilde", "6590": "transformator", "6591": "spinnen", "6592": "handbewegung", "6593": "weide", "6594": "spielzeugtraktor", "6595": "auftr\u00e4gt", "6596": "durchtrennt", "6597": "eyeliner", "6598": "mehrfamilienhaus", "6599": "regennassen", "6600": "francisco", "6601": "karge", "6602": "per\u00fccken", "6603": "j\u00fcdischen", "6604": "s\u00fc\u00dfigkeitenverk\u00e4ufer", "6605": "blutender", "6606": "formiert", "6607": "marathonl\u00e4ufern", "6608": "pfirsichfarbenen", "6609": "t\u00e4towieren", "6610": "k\u00fcnstlerische", "6611": "schulen", "6612": "wasserspritzer", "6613": "ordner", "6614": "warteraum", "6615": "statur", "6616": "fu\u00dfb\u00e4lle", "6617": "ekstatisch", "6618": "malerischen", "6619": "baseballcap", "6620": "weihnachtsbeleuchtung", "6621": "organisieren", "6622": "sch\u00fctter", "6623": "werdendem", "6624": "louvre", "6625": "schreienden", "6626": "feuerwehrhelm", "6627": "jede", "6628": "schneejacke", "6629": "rasten", "6630": "einkaufst\u00fcte", "6631": "elektrische", "6632": "afro-amerikanischer", "6633": "gem\u00e4ldes", "6634": "hab", "6635": "scheucht", "6636": "lachenden", "6637": "informellen", "6638": "konserven", "6639": "freundlich", "6640": "sketch", "6641": "zugf\u00fchrer", "6642": "maskierter", "6643": "fahrradtour", "6644": "telefonzellen", "6645": "fu\u00dfballer", "6646": "auszuprobieren", "6647": "tresen", "6648": "technologie", "6649": "pferdeschw\u00e4nzen", "6650": "verborgen", "6651": "pfauenkost\u00fcm", "6652": "medaillen", "6653": "pfadfindern", "6654": "gasgrill", "6655": "abfahrt", "6656": "abprallen", "6657": "nachbarn", "6658": "f\u00e4ustlingen", "6659": "gewebten", "6660": "fensterfront", "6661": "strommasten", "6662": "\u00fcbergewichtiges", "6663": "japanische", "6664": "fl\u00fcstert", "6665": "gartenarbeit", "6666": "br\u00fcder", "6667": "geschnitzten", "6668": "katzenbabies", "6669": "lutscher", "6670": "katzenbaby", "6671": "grabenbagger", "6672": "bootsdeck", "6673": "disney", "6674": "s\u00fc\u00dfigkeitenladen", "6675": "feuerhydranten", "6676": "cheerleader-outfit", "6677": "sweater", "6678": "besch\u00e4ftigter", "6679": "herausgestreckter", "6680": "h", "6681": "feldhockeyspieler", "6682": "zusammengebauten", "6683": "reifenspur", "6684": "fahrradhelmen", "6685": "7", "6686": "r\u00fcschen", "6687": "fahrradhelme", "6688": "umschlag", "6689": "netzstr\u00fcmpfen", "6690": "fahrradfahren", "6691": "steinstra\u00dfe", "6692": "spieltunnel", "6693": "vorbeikommt", "6694": "khaki-hosen", "6695": "dead", "6696": "steins\u00e4ulen", "6697": "metzgerei", "6698": "blument\u00f6pfe", "6699": "reishut", "6700": "gew\u00fcrze", "6701": "wakeboarding", "6702": "rausgeworfen", "6703": "tiers", "6704": "nonnentracht", "6705": "spielender", "6706": "unterschreibt", "6707": "komisch", "6708": "sausen", "6709": "gebildet", "6710": "fassen", "6711": "f\u00fclle", "6712": "bowlt", "6713": "st\u00f6ckelschuhen", "6714": "aufgehenden", "6715": "kiesh\u00fcgel", "6716": "verschmiertem", "6717": "niedrig", "6718": "get\u00f6nten", "6719": "zur\u00fcckgelegt", "6720": "stocks", "6721": "zuerst", "6722": "schmetterlinge", "6723": "einzelnen", "6724": "kopie", "6725": "are", "6726": "you", "6727": "hoop", "6728": "bilds", "6729": "sportfans", "6730": "kaputter", "6731": "kampfsportturnier", "6732": "korpulentes", "6733": "stiert", "6734": "opfer", "6735": "ben\u00f6tigt", "6736": "langgeht", "6737": "fu\u00dfballspielern", "6738": "rudermannschaft", "6739": "ungl\u00fcckliches", "6740": "ufern\u00e4he", "6741": "traditionellem", "6742": "umfeld", "6743": "stoppt", "6744": "afro", "6745": "makeup", "6746": "socke", "6747": "schottische", "6748": "formation", "6749": "schwingenden", "6750": "beeindrucken", "6751": "augenschutz", "6752": "latexhandschuhen", "6753": "schuhputzstand", "6754": "fort", "6755": "buckelndes", "6756": "hellblaues", "6757": "unterhaltungsbereich", "6758": "surfbrettern", "6759": "drauf", "6760": "schwimmwettkampf", "6761": "pappschachtel", "6762": "schn\u00fcrsenkel", "6763": "dir", "6764": "allee", "6765": "formalit\u00e4ten", "6766": "geblieben", "6767": "vegas", "6768": "kitzelt", "6769": "arbeitsort", "6770": "boxt", "6771": "sandsack", "6772": "las", "6773": "schweinefleisch", "6774": "rodeo-clown", "6775": "videokameras", "6776": "barbecue", "6777": "notenblatt", "6778": "teilnehmende", "6779": "taschenbuch", "6780": "fr\u00fchlingstag", "6781": "hosentaschen", "6782": "blasinstrumenten", "6783": "grasen", "6784": "hecken", "6785": "collegestudenten", "6786": "angriff", "6787": "unterirdischen", "6788": "fingern\u00e4geln", "6789": "unscharfe", "6790": "anstatt", "6791": "entsetzt", "6792": "gemischte", "6793": "sternen", "6794": "stromleitungen", "6795": "gr\u00fcn-wei\u00dfe", "6796": "acker", "6797": "br\u00fcnetter", "6798": "deere-traktor", "6799": "zusammensein", "6800": "eingeh\u00fcllt", "6801": "wasserschlacht", "6802": "zahnarzt", "6803": "pferch", "6804": "spielerischen", "6805": "gedicht", "6806": "restaurantmitarbeiter", "6807": "highschool-basketballspieler", "6808": "south", "6809": "verkaufsautomaten", "6810": "weinroten", "6811": "heck", "6812": "stra\u00dfenlauf", "6813": "landwirtschaftlichen", "6814": "verkleidetes", "6815": "kinderbuch", "6816": "ehepaar", "6817": "abgefeuert", "6818": "cupcakes", "6819": "meeresstrand", "6820": "besucher", "6821": "inderinnen", "6822": "b\u00fcrgerstein", "6823": "k\u00fcrbissen", "6824": "gesch\u00e4fte", "6825": "schlittert", "6826": "beigefarben", "6827": "unebenen", "6828": "gummischuhen", "6829": "armut", "6830": "elektronisches", "6831": "bodenn\u00e4he", "6832": "tollt", "6833": "guter", "6834": "eisl\u00e4ufer", "6835": "gebr\u00e4unte", "6836": "bretter", "6837": "arcade-spiel", "6838": "hellrotem", "6839": "bahnarbeiter", "6840": "schwerarbeiter", "6841": "essenstisch", "6842": "st\u00fctzr\u00e4dern", "6843": "bahntunnel", "6844": "schuljungen", "6845": "matrosenanzug", "6846": "penn", "6847": "telefontechniker", "6848": "eingebuddelt", "6849": "wasserrand", "6850": "chinesen", "6851": "gro\u00dfeltern", "6852": "leads\u00e4nger", "6853": "beleuchtetes", "6854": "projektorleinwand", "6855": "children", "6856": "probe", "6857": "vornehmen", "6858": "bearbeiten", "6859": "schraubenschl\u00fcssel", "6860": "bahnhofs", "6861": "bauten", "6862": "architektonischen", "6863": "beseitigen", "6864": "rasch", "6865": "gro\u00dfmarkt", "6866": "spielzeuglaster", "6867": "spielzeugrutsche", "6868": "sonnenschirme", "6869": "u-bahn-bahnsteig", "6870": "kegelhut", "6871": "neugeborenen", "6872": "wrestler", "6873": "defense", "6874": "molliges", "6875": "h\u00fcften", "6876": "aufgeregtes", "6877": "quadratischen", "6878": "verkaufstand", "6879": "einsatzkr\u00e4fte", "6880": "menschenmassen", "6881": "ungepflegten", "6882": "aufw\u00e4rts", "6883": "hinuntergehen", "6884": "plakate", "6885": "suppe", "6886": "michael", "6887": "parkuhr", "6888": "f\u00e4ssern", "6889": "bef\u00f6rdern", "6890": "bergspitze", "6891": "u-bahn-zug", "6892": "sitze", "6893": "schneiderin", "6894": "hoops", "6895": "falken", "6896": "bahnhofsgeb\u00e4ude", "6897": "spielmannszugs", "6898": "sp\u00e4ter", "6899": "bandmitglied", "6900": "h\u00e4ndlern", "6901": "zweispurigen", "6902": "not", "6903": "seils", "6904": "limo", "6905": "eien", "6906": "schlittenhunde", "6907": "fetzen", "6908": "grinsender", "6909": "geneigtem", "6910": "schnabel", "6911": "herstellung", "6912": "ger\u00e4te", "6913": "lokomotiven", "6914": "ballontier", "6915": "grillrestaurant", "6916": "boss", "6917": "life", "6918": "unfallstelle", "6919": "aggressiv", "6920": "sanit\u00e4ter", "6921": "schwitzender", "6922": "stirnlampe", "6923": "schie\u00dfstand", "6924": "friseurladen", "6925": "rosa-gr\u00fcnen", "6926": "spiele", "6927": "helikopter", "6928": "hellgrauen", "6929": "umgedrehte", "6930": "ponchos", "6931": "silhouetten", "6932": "amische", "6933": "spinnkurs", "6934": "regenkleidung", "6935": "b\u00fcndeln", "6936": "sitzung", "6937": "lang", "6938": "gepolsterte", "6939": "sp\u00e4t", "6940": "bottich", "6941": "muslimische", "6942": "gips", "6943": "vorstadthaus", "6944": "wakeboarden", "6945": "aufsicht", "6946": "eiszapfen", "6947": "lagerhalle", "6948": "ebenso", "6949": "teenagerinnen", "6950": "mustang", "6951": "tatort", "6952": "farn", "6953": "out", "6954": "glocke", "6955": "tuniken", "6956": "karate-klasse", "6957": "fr\u00fchst\u00fcck", "6958": "einzusteigen", "6959": "boys", "6960": "vergraben", "6961": "besuchern", "6962": "computers", "6963": "vertieft", "6964": "riss", "6965": "kn\u00f6pfe", "6966": "lebensmittelbereich", "6967": "badminton", "6968": "mac-computer", "6969": "denselben", "6970": "lohfarbenen", "6971": "musikausr\u00fcstung", "6972": "rolltreppen", "6973": "notebooks", "6974": "neonfarbenen", "6975": "sense", "6976": "tiefschnee", "6977": "regenbogenfarbigen", "6978": "fanghandschuh", "6979": "h\u00fctten", "6980": "mohikaner", "6981": "t\u00fcmpel", "6982": "liga", "6983": "erforschen", "6984": "b\u00f6gen", "6985": "goldschmuck", "6986": "klare", "6987": "metallstuhl", "6988": "kurzes", "6989": "leht", "6990": "str\u00f6menden", "6991": "pulk", "6992": "linksh\u00e4ndiger", "6993": "kirmesattraktion", "6994": "doppeldeckerbus", "6995": "handmixer", "6996": "abk\u00fchlen", "6997": "bewachsenen", "6998": "mahlzeiten", "6999": "menschliche", "7000": "stra\u00dfenlaternen", "7001": "umgekippt", "7002": "rechteckigen", "7003": "space", "7004": "verr\u00fcckt", "7005": "needle", "7006": "betonblock", "7007": "modischer", "7008": "tasten", "7009": "klub", "7010": "bergkette", "7011": "umschlagtuch", "7012": "\u00e4hnlichen", "7013": "freiwurf", "7014": "nachdenklich", "7015": "feldern", "7016": "erh\u00f6ht", "7017": "atmet", "7018": "praktizieren", "7019": "schwimmhilfen", "7020": "bedruckter", "7021": "herbstlichen", "7022": "abdeckplane", "7023": "hergeht", "7024": "spielger\u00e4ten", "7025": "hergestellt", "7026": "batman", "7027": "bill", "7028": "bekannten", "7029": "jongleur", "7030": "deckeln", "7031": "holzbalken", "7032": "font\u00e4nen", "7033": "farbwalze", "7034": "angelegt", "7035": "bootsfahrern", "7036": "ballerinas", "7037": "hochsteigt", "7038": "londoner", "7039": "spielzeug-rasenm\u00e4her", "7040": "niemand", "7041": "blasinstrument", "7042": "violette", "7043": "zementlaster", "7044": "verheirateter", "7045": "warnkegel", "7046": "verkleidung", "7047": "freundesgruppe", "7048": "tr\u00e4gershirt", "7049": "tieflader", "7050": "morgenrock", "7051": "spielkonsole", "7052": "steinbr\u00fccke", "7053": "aktiv", "7054": "handgemachten", "7055": "w\u00fcrfel", "7056": "halst\u00fcchern", "7057": "schlammigem", "7058": "musikalische", "7059": "sturm", "7060": "brotstand", "7061": "postmitarbeiter", "7062": "wandgem\u00e4lde", "7063": "d\u00fcnnes", "7064": "kampfanz\u00fcgen", "7065": "holzgel\u00e4nder", "7066": "eiskunstl\u00e4ufer", "7067": "powerpoint-pr\u00e4sentation", "7068": "angelausflug", "7069": "schleuder", "7070": "lichts", "7071": "regenjacke", "7072": "zerzaust", "7073": "prallen", "7074": "besseren", "7075": "jerusalem", "7076": "kochtopf", "7077": "stadtgeb\u00e4ude", "7078": "eisfl\u00e4che", "7079": "body", "7080": "djs", "7081": "hispanisch", "7082": "hellh\u00e4utig", "7083": "zwillinge", "7084": "falten", "7085": "ohrringen", "7086": "13", "7087": "verh\u00fcllte", "7088": "wiegen", "7089": "rinder", "7090": "umgefallenen", "7091": "gem\u00e4hte", "7092": "piercings", "7093": "erstellt", "7094": "kr\u00fcgen", "7095": "stra\u00dfenmarathon", "7096": "weinen", "7097": "mountainbike-fahrer", "7098": "waldgegend", "7099": "pr\u00e4chtige", "7100": "halbw\u00fcchsiger", "7101": "mahl", "7102": "fahrrad-", "7103": "s\u00fc\u00dfigkeit", "7104": "wegschaut", "7105": "sch\u00fcrt", "7106": "falke", "7107": "zwanzig", "7108": "schulkindern", "7109": "skateboardfahren", "7110": "ellenbogen", "7111": "platzt", "7112": "holzleiter", "7113": "strandbuggy", "7114": "zerkleinert", "7115": "augenbraue", "7116": "ackerland", "7117": "lesenden", "7118": "gummiball", "7119": "button-down-hemd", "7120": "wehrt", "7121": "defensive", "7122": "front", "7123": "ds", "7124": "schneelandschaft", "7125": "aufgesetzter", "7126": "mikroskopen", "7127": "bellt", "7128": "steuer", "7129": "hockeyschl\u00e4ger", "7130": "herausholt", "7131": "grass", "7132": "kunstartikeln", "7133": "bergwand", "7134": "arrangiert", "7135": "zuschauerraum", "7136": "pink-wei\u00dfem", "7137": "skianzug", "7138": "set", "7139": "blech", "7140": "hopper", "7141": "zerrissenen", "7142": "vermischt", "7143": "metallgebilde", "7144": "organgefarbenen", "7145": "cool", "7146": "modische", "7147": "arbeitshandschuhe", "7148": "bemalen", "7149": "ferkel", "7150": "vorbeizukommen", "7151": "verwunderung", "7152": "schutzanzug", "7153": "it", "7154": "corgi", "7155": "ausrichten", "7156": "zielort", "7157": "entschlossen", "7158": "strumpfhosen", "7159": "europ\u00e4ischer", "7160": "landwirt", "7161": "naturpfad", "7162": "s\u00e4uglings", "7163": "elmo-puppe", "7164": "flei\u00dfig", "7165": "besetztes", "7166": "lebensmittelladen", "7167": "leitungen", "7168": "durchquert", "7169": "mitgliedern", "7170": "box", "7171": "konzertauff\u00fchrung", "7172": "hochklettern", "7173": "klimpert", "7174": "fahrradmechaniker", "7175": "d\u00f6sen", "7176": "zuh\u00f6rerschaft", "7177": "neongelben", "7178": "tarnhosen", "7179": "jalousien", "7180": "busfenster", "7181": "beispiel", "7182": "jungenmannschaft", "7183": "boxring", "7184": "dreckige", "7185": "zentimeter", "7186": "comicfigur", "7187": "preise", "7188": "absolvieren", "7189": "tackle", "7190": "stra\u00dfenh\u00e4ndlers", "7191": "m\u00f6belgesch\u00e4ft", "7192": "krieg", "7193": "rampen", "7194": "bewirft", "7195": "metallarbeiter", "7196": "autofenster", "7197": "neongr\u00fcn", "7198": "orange-blauen", "7199": "wasserlache", "7200": "brotlaib", "7201": "fremde", "7202": "sichert", "7203": "kunststoffbeh\u00e4lter", "7204": "richtern", "7205": "outdoor-bekleidung", "7206": "open-air-festival", "7207": "gepolsterter", "7208": "schulalter", "7209": "brusttief", "7210": "hitze", "7211": "informell", "7212": "\u00f6lfass", "7213": "federhut", "7214": "wodurch", "7215": "betreiben", "7216": "turnkleidung", "7217": "heimischen", "7218": "latzhosen", "7219": "grand", "7220": "lattenzaun", "7221": "dinosaurier", "7222": "25", "7223": "gemustertem", "7224": "geb\u00fcsch", "7225": "achten", "7226": "buches", "7227": "ger\u00e4ts", "7228": "stadtbus", "7229": "drittes", "7230": "cocktails", "7231": "kettenkarussell", "7232": "baseballhandschuh", "7233": "schneidbrett", "7234": "eifrig", "7235": "beworfen", "7236": "tuba", "7237": "massiert", "7238": "pain", "7239": "schwarzwei\u00df", "7240": "segway", "7241": "winterbekleidung", "7242": "spiegelungen", "7243": "schneidersitz", "7244": "engine", "7245": "eingelassenen", "7246": "schlapphut", "7247": "thomas", "7248": "schwarz-orangefarbenen", "7249": "nachdenken", "7250": "irokesenhaarschnitt", "7251": "banjospieler", "7252": "l\u00e4ssig", "7253": "durchsichtigen", "7254": "glasbl\u00e4ser", "7255": "kitesurfer", "7256": "hinterl\u00e4sst", "7257": "fu\u00dfspuren", "7258": "pitbull", "7259": "rollerblader", "7260": "38", "7261": "canyon", "7262": "einheimischen", "7263": "kaltem", "7264": "fachkr\u00e4fte", "7265": "holzdeck", "7266": "erwachsenes", "7267": "abdeckung", "7268": "sparring", "7269": "schleppe", "7270": "knietief", "7271": "golfen", "7272": "hugs", "7273": "brautpaar", "7274": "pickup-truck", "7275": "winterlichen", "7276": "blau-wei\u00dfe", "7277": "trainingsanz\u00fcgen", "7278": "grundst\u00fcck", "7279": "frack", "7280": "tamburin", "7281": "sonnebrille", "7282": "gro\u00dfartigen", "7283": "schilds", "7284": "kunststofffolie", "7285": "immer", "7286": "w\u00fcnschen", "7287": "mittelgro\u00dfe", "7288": "durchsichtige", "7289": "m\u00fcllhaufen", "7290": "gef\u00fcllte", "7291": "truth\u00e4hne", "7292": "holzkreuz", "7293": "angestarrt", "7294": "felsblocks", "7295": "k\u00fcrzlich", "7296": "errichteten", "7297": "holzwand", "7298": "hawaiianischer", "7299": "pappteller", "7300": "streitkr\u00e4fte", "7301": "herzschlag", "7302": "fl\u00f6\u00dfen", "7303": "zerfetzten", "7304": "skibrille", "7305": "saiteninstrumenten", "7306": "ritualtanz", "7307": "automaten", "7308": "piano", "7309": "l\u00e4ngsstreifen", "7310": "walkman", "7311": "tischler", "7312": "stuhlreihen", "7313": "steigenden", "7314": "bodenball", "7315": "sprinkleranlage", "7316": "fischernetze", "7317": "trotzt", "7318": "st\u00fcrmischem", "7319": "gemauerten", "7320": "lokomotive", "7321": "befindliche", "7322": "m\u00e4dchengruppe", "7323": "ladenfenster", "7324": "schaufensterauslage", "7325": "be-", "7326": "handarbeit", "7327": "umgebenden", "7328": "fee", "7329": "wartend", "7330": "insekten", "7331": "gep\u00e4cktasche", "7332": "becherglas", "7333": "anzuschauen", "7334": "betten", "7335": "entgehen", "7336": "rettet", "7337": "hei\u00dfes", "7338": "schwelle", "7339": "polizeiuniform", "7340": "geschenken", "7341": "mammutbaum", "7342": "ballonfiguren", "7343": "algen", "7344": "abgelassen", "7345": "taille", "7346": "eilt", "7347": "h\u00fctchen", "7348": "spielzeugfahrrad", "7349": "brieftasche", "7350": "laborant", "7351": "wichtiges", "7352": "ausgetragen", "7353": "ausruhen", "7354": "sportfeld", "7355": "schick", "7356": "kleinkinds", "7357": "knietiefes", "7358": "fahrradkleidung", "7359": "bekleidetes", "7360": "schilden", "7361": "troph\u00e4e", "7362": "laut", "7363": "tennisschuh", "7364": "laufgitter", "7365": "entwirft", "7366": "irgendeines", "7367": "preisrichter", "7368": "karateturnier", "7369": "wasserstrahl", "7370": "friseurstuhl", "7371": "exponat", "7372": "z\u00e4hneputzen", "7373": "lauscht", "7374": "davidstern", "7375": "boxershorts", "7376": "kreuzen", "7377": "regierung", "7378": "muslimischer", "7379": "jahr", "7380": "abzuspielen", "7381": "rollschuhbahn", "7382": "gipfeln", "7383": "absperrungen", "7384": "m\u00e4nnerteam", "7385": "t\u00e4tigkeiten", "7386": "schlagmanns", "7387": "ribbon", "7388": "hebeb\u00fchne", "7389": "startet", "7390": "pabst", "7391": "r\u00e4der", "7392": "entscheidung", "7393": "stethoskop", "7394": "frucht", "7395": "fotovorlage", "7396": "angestrengt", "7397": "wei\u00df-", "7398": "foyer", "7399": "dummes", "7400": "yankees", "7401": "k\u00e4tzchen", "7402": "glatten", "7403": "orangefarben", "7404": "vereinzelten", "7405": "blau-wei\u00dfes", "7406": "orioles", "7407": "blumengesch\u00e4ft", "7408": "short", "7409": "cowboy-kleidung", "7410": "m\u00fcrrisch", "7411": "komischer", "7412": "bauger\u00e4t", "7413": "flyer", "7414": "reitwettbewerb", "7415": "erobern", "7416": "gek\u00fchlten", "7417": "h\u00fctet", "7418": "gesichtsfarbe", "7419": "steiger", "7420": "fototermin", "7421": "hebevorrichtung", "7422": "sonnenanbetern", "7423": "knipst", "7424": "erkl\u00e4ren", "7425": "ersch\u00f6pft", "7426": "stilles", "7427": "hockeytorh\u00fcter", "7428": "falsche", "7429": "silbernem", "7430": "shampoo", "7431": "t\u00e4towierten", "7432": "hockern", "7433": "gangs", "7434": "bodysuit", "7435": "tiefblauen", "7436": "schwarz-rot", "7437": "betrunken", "7438": "sportstudio", "7439": "sockenhaltern", "7440": "spielzeugs", "7441": "marmor", "7442": "\u00fcberdachung", "7443": "jeansanzug", "7444": "hawaiihemd", "7445": "caprihosen", "7446": "63", "7447": "kampfsportveranstaltung", "7448": "ankunft", "7449": "bikes", "7450": "vorn\u00fcber", "7451": "verursacht", "7452": "burgunderfarbener", "7453": "limonaden", "7454": "geschnittenen", "7455": "marineblauer", "7456": "glasflaschen", "7457": "spachtel", "7458": "allradfahrzeug", "7459": "theaterst\u00fcck", "7460": "lieben", "7461": "jeden", "7462": "pinien", "7463": "t\u00fcchern", "7464": "rennhund", "7465": "diverse", "7466": "schnellboot", "7467": "bl\u00fctenbl\u00e4tter", "7468": "rasseln", "7469": "nacken", "7470": "footballs", "7471": "fahrersitz", "7472": "herunterkommt", "7473": "visiert", "7474": "zielfernrohr", "7475": "drachenflieger", "7476": "schie\u00dfplatz", "7477": "segel", "7478": "rasierer", "7479": "farm", "7480": "footballteams", "7481": "state-teams", "7482": "metallbank", "7483": "steinweg", "7484": "kennzeichnet", "7485": "ritter", "7486": "sanft", "7487": "zerlegen", "7488": "dung", "7489": "waggon", "7490": "schachbrett", "7491": "schneeschaufel", "7492": "reagiert", "7493": "flattern", "7494": "gehaltenen", "7495": "feuerwerksk\u00f6rper", "7496": "mole", "7497": "bande", "7498": "pferdefuhrwerk", "7499": "vesammelt", "7500": "danebenstehen", "7501": "abfall", "7502": "schottenr\u00f6cken", "7503": "ringe", "7504": "minnesota", "7505": "herumlaufen", "7506": "2008", "7507": "schwimmbekleidung", "7508": "verregneten", "7509": "getrocknet", "7510": "faltet", "7511": "iowa", "7512": "f\u00f6rderband", "7513": "lolli", "7514": "tr\u00f6sten", "7515": "s\u00fc\u00dfer", "7516": "spruchb\u00e4nder", "7517": "ellbogen", "7518": "gesch\u00e4ftsviertel", "7519": "zerbrochene", "7520": "verwitterten", "7521": "zerbricht", "7522": "skateboardanlage", "7523": "schwimmfl\u00fcgel", "7524": "festzuhalten", "7525": "feuerball", "7526": "fallschirmsegeln", "7527": "metallenen", "7528": "steuern", "7529": "espresso", "7530": "rodgers", "7531": "tagebuch", "7532": "renaissance-fest", "7533": "unterhaltungsk\u00fcnstler", "7534": "\u00f6ffentliches", "7535": "speiseraum", "7536": "aufzuschlagen", "7537": "festlich", "7538": "freie", "7539": "wassersprenganlage", "7540": "esstischen", "7541": "vor\u00fcbergehend", "7542": "englische", "7543": "rasenplatz", "7544": "ipad", "7545": "hockeyfeld", "7546": "k\u00fcchenutensilien", "7547": "lang\u00e4rmligem", "7548": "magazin", "7549": "w\u00fcrste", "7550": "knielangen", "7551": "vorbeischwimmen", "7552": "\u2019", "7553": "rittlings", "7554": "sommerkleidung", "7555": "baucht\u00e4nzerin", "7556": "s\u00e4cke", "7557": "kombis", "7558": "werkbank", "7559": "erdbeere", "7560": "auspackt", "7561": "portr\u00e4ts", "7562": "rot-goldenen", "7563": "auditorium", "7564": "r\u00f6tlichem", "7565": "lederweste", "7566": "gegners", "7567": "metallrahmen", "7568": "dramatisch", "7569": "bestimmte", "7570": "steppdecke", "7571": "flugg\u00e4ste", "7572": "flugbegleitung", "7573": "klassischen", "7574": "zugwaggons", "7575": "unbeholfen", "7576": "ureinwohnerin", "7577": "feuers", "7578": "burger", "7579": "flickt", "7580": "hereinbrechenden", "7581": "l\u00e4ndliche", "7582": "korken", "7583": "aufgew\u00fchlte", "7584": "nachrichtensprecher", "7585": "trib\u00fcnen", "7586": "cheerleader-gruppe", "7587": "textet", "7588": "nachricht", "7589": "stunde", "7590": "brautstrau\u00df", "7591": "cowboy-hut", "7592": "vorhand", "7593": "schokolade", "7594": "verpasst", "7595": "holzst\u00e4be", "7596": "fitte", "7597": "sommerkleider", "7598": "notiz", "7599": "hockeyschl\u00e4gern", "7600": "kirchenchor", "7601": "anzuz\u00fcnden", "7602": "barkeeperin", "7603": "verlegen", "7604": "schaulustiger", "7605": "livekonzert", "7606": "krankenpfleger", "7607": "wildwasserrafting", "7608": "zapft", "7609": "beschreibung", "7610": "hochgeschobener", "7611": "geneigten", "7612": "hafenbeckens", "7613": "badeshorts", "7614": "rot-gelben", "7615": "streicher", "7616": "leeres", "7617": "waldlandschaft", "7618": "aufklebern", "7619": "zuf\u00e4hrt", "7620": "vorderrad", "7621": "sandkasten", "7622": "b\u00e4cker", "7623": "elektroger\u00e4t", "7624": "zweikampf", "7625": "turn\u00fcbung", "7626": "st\u00e4nde", "7627": "tropfen", "7628": "rennausr\u00fcstung", "7629": "papierhut", "7630": "angeseilt", "7631": "hornspieler", "7632": "pfannkuchen", "7633": "akrobatik", "7634": "bongos", "7635": "gekn\u00f6pften", "7636": "hallenlaufbahn", "7637": "perle", "7638": "monopoly-spiel", "7639": "winkend", "7640": "gef\u00e4rbter", "7641": "\u00fcbers\u00e4te", "7642": "blau-gelber", "7643": "werferh\u00fcgel", "7644": "stahlbalken", "7645": "trauung", "7646": "essenszubereitung", "7647": "gummihammer", "7648": "nuckelt", "7649": "spannen", "7650": "muskeln", "7651": "fertiggestellten", "7652": "hunds", "7653": "samen", "7654": "pavillon", "7655": "zuh\u00f6rern", "7656": "sportjacke", "7657": "gart", "7658": "liefern", "7659": "schreibend", "7660": "tunnels", "7661": "fau", "7662": "\u00fcberschlag", "7663": "l\u00f6ffeln", "7664": "frisbee-spiel", "7665": "driftet", "7666": "saxofonspieler", "7667": "metallskulptur", "7668": "lehnstuhl", "7669": "abflug", "7670": "tourbus", "7671": "skateboard-kunstst\u00fcck", "7672": "steiniges", "7673": "h\u00e4ngende", "7674": "-hemd", "7675": "lebens", "7676": "kreischt", "7677": "gokart-fahrer", "7678": "tintenfisch", "7679": "dunkelgr\u00fcner", "7680": "topfpflanzen", "7681": "rasiertem", "7682": "schritten", "7683": "oakland", "7684": "weiblich", "7685": "pilz", "7686": "skateboard-stunt", "7687": "zugfahrt", "7688": "standbild", "7689": "buddeln", "7690": "hochkommt", "7691": "pfau", "7692": "st\u00fctzen", "7693": "baumbewachsenen", "7694": "fu\u00dfbr\u00fccke", "7695": "bodyboard", "7696": "bohnen", "7697": "hulk", "7698": "bergwanderweg", "7699": "kindertisch", "7700": "chefs", "7701": "ohrsch\u00fctzern", "7702": "irgendeinen", "7703": "ber\u00e4t", "7704": "hockey-goalie", "7705": "singend", "7706": "scheitelpunkt", "7707": "handt\u00fccher", "7708": "tee-ball", "7709": "design", "7710": "knapp", "7711": "kiosk", "7712": "luftmatratzen", "7713": "umdreht", "7714": "beschlie\u00dfen", "7715": "heruntergefallenen", "7716": "rot-gestreiften", "7717": "gescheckter", "7718": "arbeitenden", "7719": "wasserfont\u00e4nen", "7720": "gebeugter", "7721": "waldgebiets", "7722": "..", "7723": "vase", "7724": "apartment", "7725": "mexiko", "7726": "gestalt", "7727": "mittlerem", "7728": "seilbahn", "7729": "verschiedenes", "7730": "plastikeimer", "7731": "stattfindet", "7732": "spare", "7733": "gast", "7734": "holzkisten", "7735": "herzustellen", "7736": "reporter", "7737": "kopf-an-kopf-rennen", "7738": "schottischer", "7739": "teichs", "7740": "gebiets", "7741": "buchladen", "7742": "holzkiste", "7743": "operiert", "7744": "chirurgischen", "7745": "laufoberteil", "7746": "l\u00e4ndlicher", "7747": "laufhosen", "7748": "verschiedenfarbiger", "7749": "rot-gr\u00fcnen", "7750": "tarnanz\u00fcgen", "7751": "lichtung", "7752": "zeltplatz", "7753": "luftballontiere", "7754": "nische", "7755": "bassist", "7756": "purpurroten", "7757": "b\u00fchnenlicht", "7758": "zehenspitzen", "7759": "arch\u00e4ologen", "7760": "basketballmannschaft", "7761": "basketballspielerin", "7762": "plaudern", "7763": "\u00e4rztin", "7764": "olympische", "7765": "erh\u00f6hte", "7766": "handtrommel", "7767": "dasteht", "7768": "f\u00e4den", "7769": "n\u00e4gel", "7770": "t\u00f6pfert", "7771": "kunststofftasse", "7772": "stets", "7773": "rodeoarena", "7774": "chefkoch", "7775": "wundersch\u00f6n", "7776": "saxophonisten", "7777": "lebron", "7778": "saxophone", "7779": "bauern", "7780": "vielf\u00e4ltige", "7781": "james", "7782": "vorteil", "7783": "schichten", "7784": "spielzeuggitarre", "7785": "schmiert", "7786": "rasiercreme", "7787": "bewaffneten", "7788": "speerwurf", "7789": "gerichteten", "7790": "meereswellen", "7791": "k\u00fcstenwache", "7792": "aufwirbelt", "7793": "grob", "7794": "schwimmhalle", "7795": "skimboarden", "7796": "wasserspeier", "7797": "irgendwohin", "7798": "laubwerk", "7799": "armbanduhr", "7800": "n\u00e4chstes", "7801": "besichtigen", "7802": "bergauf", "7803": "arbeitsbank", "7804": "badmintonspieler", "7805": "urinal", "7806": "kundgebung", "7807": "bev\u00f6lkerten", "7808": "bahnrennen", "7809": "ansichten", "7810": "flauschige", "7811": "volleyballplatz", "7812": "kais", "7813": "lenken", "7814": "au\u00dfenanlage", "7815": "k\u00f6nnte", "7816": "linsen", "7817": "bl\u00fcte", "7818": "zeremoniellen", "7819": "st\u00e4ngel", "7820": "w\u00e4lzt", "7821": "seahawks", "7822": "verbeugen", "7823": "wetteifert", "7824": "stra\u00dfengesch\u00e4ft", "7825": "historische", "7826": "zerbrochenen", "7827": "sporthallenboden", "7828": "trinkgeld", "7829": "heranrollenden", "7830": "fahrbahn", "7831": "mach", "7832": "gegangen", "7833": "majest\u00e4tischen", "7834": "notenbl\u00e4ttern", "7835": "s\u00f6hnen", "7836": "dunkleren", "7837": "schaukelgestell", "7838": "puderzucker", "7839": "hippie", "7840": "ballettklasse", "7841": "boogieboard", "7842": "abendlichen", "7843": "aktivit\u00e4ten", "7844": "tomate", "7845": "aufzuf\u00fchren", "7846": "spielhaus", "7847": "nassgespritzt", "7848": "delfin", "7849": "gel\u00e4ndefahrzeugs", "7850": "dichten", "7851": "pr\u00e4chtig", "7852": "brezeln", "7853": "verbirgt", "7854": "vieler", "7855": "boogie", "7856": "einheimische", "7857": "vorr\u00e4te", "7858": "lederoberteil", "7859": "reisfeld", "7860": "schwimmring", "7861": "stadtlandschaft", "7862": "unterholz", "7863": "garnelen", "7864": "outdoor-konzert", "7865": "asphalts\u00e4ge", "7866": "nippes", "7867": "vorbeischwimmt", "7868": "windhunden", "7869": "aquarienhaus", "7870": "papierhandtuch", "7871": "meereshintergrund", "7872": "gesp\u00fclt", "7873": "glasreiniger", "7874": "leuchtwesten", "7875": "leuchtst\u00e4be", "7876": "verahngenen", "7877": "h\u00f6her", "7878": "boardshorts", "7879": "rekorder", "7880": "muschelsuche", "7881": "zur\u00fcckgek\u00e4mmtem", "7882": "dschungelgebiet", "7883": "ladenzeile", "7884": "vorschulkinder", "7885": "zaunlatten", "7886": "hellgr\u00fcnes", "7887": "kaputte", "7888": "tonziegeln", "7889": "bevorstehenden", "7890": "silberbecher", "7891": "nachmittagszeit", "7892": "unbekannt", "7893": "ferienlagerbetreuer", "7894": "speditionshof", "7895": "livemusik", "7896": "senffarbener", "7897": "reife", "7898": "angeleintes", "7899": "bereichen", "7900": "leckerli", "7901": "kartengeberin", "7902": "unordentlich", "7903": "verwirrten", "7904": "nahverkehrszug", "7905": "verputzen", "7906": "luftmatratze", "7907": "kartentisch", "7908": "gestapelten", "7909": "hundekost\u00fcm", "7910": "wildem", "7911": "lockenhaar", "7912": "legos", "7913": "kamerabereichs", "7914": "essens", "7915": "assistenztrainer", "7916": "sandbunker", "7917": "wolkige", "7918": "bergansicht", "7919": "einzelheiten", "7920": "rockbar", "7921": "holmgren", "7922": "besonderem", "7923": "bermudashorts", "7924": "wei\u00df-blauem", "7925": "sonntagskleidung", "7926": "rupft", "7927": "wans", "7928": "fahrers", "7929": "ausdruckslos", "7930": "\u00f6ffentlich", "7931": "tenniscourt", "7932": "fingerverband", "7933": "welchen", "7934": "herausgeputzter", "7935": "noise", "7936": "some", "7937": "hinterhof-schwimmbecken", "7938": "make", "7939": "nike-t-shirt", "7940": "strandszenerie", "7941": "besseres", "7942": "plastik-spielschloss", "7943": "tigermotiv", "7944": "zoom-objektivs", "7945": "toe-touch", "7946": "belustigten", "7947": "preisschleifen", "7948": "hotdog-wettessen", "7949": "deutsch-kurzhaar", "7950": "hundet\u00fcr", "7951": "hineinzuschauen", "7952": "kaufhausganges", "7953": "zur\u00fcckgebundenem", "7954": "geflochtenem", "7955": "zuzuschauen", "7956": "nutzt", "7957": "grinden", "7958": "gelb-brauner", "7959": "brasilianischen", "7960": "hoffest", "7961": "britischem", "7962": "essbare", "7963": "sch\u00f6pfung", "7964": "sicherheitsbeauftragter", "7965": "telefonzellem", "7966": "wurfspiel", "7967": "pants", "7968": "herankommenden", "7969": "tennistraining", "7970": "aufpassen", "7971": "schreibarbeit", "7972": "eiscremsch\u00fcssel", "7973": "wathose", "7974": "vorbeugt", "7975": "aufregende", "7976": "aufgespie\u00dfte", "7977": "flugposition", "7978": "ge\u00f6ffnetes", "7979": "coca-cola-geb\u00e4ude", "7980": "super-soaker", "7981": "linst", "7982": "spritz", "7983": "kn\u00f6pfbares", "7984": "heulende", "7985": "bellende", "7986": "tierfalle", "7987": "kameram\u00e4nner", "7988": "bedarf", "7989": "wesen", "7990": "anzugjacken", "7991": "hochstehende", "7992": "u-bahn-zeitungsst\u00e4nder", "7993": "schwertkampt", "7994": "zuschauermengen", "7995": "vierradfahrzeug", "7996": "rivalisierende", "7997": "e-fahrrad", "7998": "bocciakugeln", "7999": "dj-kabine", "8000": "getr\u00e4nkeautomaten", "8001": "money", "8002": "talks", "8003": "verizon-laster", "8004": "tiermotivdruck", "8005": "grillparty", "8006": "jahrmarktspielen", "8007": "dorney", "8008": "slinky", "8009": "wash", "8010": "car", "8011": "reih", "8012": "glied", "8013": "bingo", "8014": "jungenmannschaften", "8015": "wassernudeln", "8016": "fischaugen-objektiv", "8017": "f\u00fcnfk\u00f6pfige", "8018": "exerziergruppe", "8019": "smalltalk", "8020": "brechstange", "8021": "milchkisten", "8022": "wildwasserbahn", "8023": "luft-", "8024": "gold-gelben", "8025": "m\u00e4ntel", "8026": "betonstufe", "8027": "gr\u00fcn-pink-gestreiften", "8028": "untere", "8029": "schwarz-pink", "8030": "ureinwohner-junge", "8031": "mixed-martial-arts-wettkampf", "8032": "gemeinschaftsraum", "8033": "altersheim", "8034": "kaugummiblasen", "8035": "betonbaus", "8036": "befremdetem", "8037": "puppentheater", "8038": "fachwerk", "8039": "doughnut", "8040": "college-campus", "8041": "pflanzenbewuchs", "8042": "pfahlkonstruktionen", "8043": "gr\u00fcn-gestreiften", "8044": "jonglierger\u00e4te", "8045": "dienstwagen", "8046": "wasserstrahlen", "8047": "seniorenzentrum", "8048": "amputierter", "8049": "marktplatzes", "8050": "audio-", "8051": "videoausr\u00fcstung", "8052": "sesamstra\u00dfe", "8053": "niedergeschlagener", "8054": "fr\u00fcchtepackung", "8055": "spriteflasche", "8056": "\u00fcrde", "8057": "reizen", "8058": "artz", "8059": "spendet", "8060": "live-rodeo", "8061": "anordnung", "8062": "gebinde", "8063": "sesamstra\u00dfen-buch", "8064": "kassierin", "8065": "bewaldetes", "8066": "aufgestellte", "8067": "fernsehbildschirme", "8068": "monate", "8069": "sanit\u00e4rcontainer", "8070": "heineken-schild", "8071": "windelwechseln", "8072": "tae-kwon-do-turnier", "8073": "chun-kuk-do-turnier", "8074": "aufgeregter", "8075": "streichholzschachteln", "8076": "sporthandschuh", "8077": "fu\u00dfballspielt", "8078": "brustwarzenpiercings", "8079": "spielzeugtier", "8080": "kuppel\u00e4hnlichen", "8081": "zungenpiercing", "8082": "langschwimmbecken", "8083": "rezeption", "8084": "gigolo", "8085": "heer", "8086": "erzeugen", "8087": "blendlicht", "8088": "erfordert", "8089": "stabilisierung", "8090": "fu\u00dfballhemd", "8091": "stollenschuhen", "8092": "pinienb\u00e4umen", "8093": "st\u00fcrmisches", "8094": "pinienwalds", "8095": "anstrengungen", "8096": "reiseleiter", "8097": "hingewiesen", "8098": "angeleitet", "8099": "auberginen", "8100": "outdoor-eislaufbahn", "8101": "schnarrtrommel", "8102": "zugedecktes", "8103": "webcam", "8104": "innigen", "8105": "exotisch", "8106": "waldlichtung", "8107": "anpflanzung", "8108": "neuer", "8109": "feldfr\u00fcchte", "8110": "warnt", "8111": "dachshund", "8112": "workout", "8113": "h\u00e4ngenden", "8114": "\u00e4hnlichem", "8115": "gef\u00e4hrlichen", "8116": "schwimmnudel", "8117": "gewebter", "8118": "kleiderladen", "8119": "gegrillten", "8120": "gegen\u00fcberstehenden", "8121": "klemmbrett", "8122": "privatk\u00fcche", "8123": "notenpulten", "8124": "bongo", "8125": "mittelalterliche", "8126": "innerst\u00e4dtischen", "8127": "name", "8128": "grace", "8129": "verzierte", "8130": "heuwagenfahrt", "8131": "verband", "8132": "schulterlangem", "8133": "schreibmaschinen", "8134": "schreibmaschine", "8135": "fahnenb\u00e4nder", "8136": "clownsartig", "8137": "isch", "8138": "k\u00f6rperlich", "8139": "wohnungsfenster", "8140": "akrobaten", "8141": "plastik-planschbecken", "8142": "schwei\u00dfhelm", "8143": "-handschuhe", "8144": "schwerter", "8145": "croupier", "8146": "fluten", "8147": "tr\u00e4nen", "8148": "strahl", "8149": "turnanz\u00fcgen", "8150": "nuckel", "8151": "budweiser-werbung", "8152": "beschleunigt", "8153": "hellgelbem", "8154": "lobbylauf", "8155": "k\u00fcssend", "8156": "fauna", "8157": "backflips", "8158": "gitarren\u00e4hnlichen", "8159": "spielfelds", "8160": "mimin", "8161": "vorrichtungen", "8162": "wechselt", "8163": "seift", "8164": "hungrig", "8165": "friert", "8166": "schaltschrank", "8167": "pl\u00fcschtierpreisen", "8168": "verlaufen", "8169": "h\u00fcpfspielzeug", "8170": "stabhochspringer", "8171": "flipper-maschinen", "8172": "videospiel-motorrad", "8173": "herrliches", "8174": "kombination", "8175": "ziegel-zement-bau", "8176": "hunderudel", "8177": "fliegerin", "8178": "merkw\u00fcrdiger", "8179": "\u00e4ndern", "8180": "horizontale", "8181": "cheesecake", "8182": "factory", "8183": "lieder", "8184": "sonnengetr\u00e4nkten", "8185": "kampfhaltung", "8186": "beachvolleyball-spielerin", "8187": "baggern", "8188": "kindheit", "8189": "bartenderin", "8190": "gelassenheit", "8191": "unschuld", "8192": "alufolie", "8193": "warmem", "8194": "kr\u00e4ckern", "8195": "r\u00f6sten", "8196": "marshmallows", "8197": "kuhmuster", "8198": "falschem", "8199": "anschlusskasten", "8200": "echten", "8201": "college-sch\u00fclerin", "8202": "h\u00fcpfzelt", "8203": "gestaltet", "8204": "holzskulptur", "8205": "luftgef\u00fcllten", "8206": "nachrichtenredaktion", "8207": "betoneinfahrt", "8208": "hausparzelle", "8209": "suppenschale", "8210": "suppentopf", "8211": "ausl\u00e4ndisches", "8212": "deem", "8213": "computertest", "8214": "stra\u00dfenverkaufsbezirk", "8215": "baustellenfahrzeug", "8216": "angehaltenen", "8217": "waldboden", "8218": "ein-mann-orchester", "8219": "r\u00f6hren", "8220": "b\u00e4renstatue", "8221": "cds", "8222": "boat", "8223": "vw", "8224": "vogelstatue", "8225": "malspieler", "8226": "bereitsteht", "8227": "fenstersims", "8228": "krabelt", "8229": "pferdesattel", "8230": "eingerichteten", "8231": "schnitzkunst", "8232": "flaggenmast", "8233": "enkel", "8234": "begleitgitarrist", "8235": "touristenbus", "8236": "wasserstrom", "8237": "maschinenteil", "8238": "bindeverschluss", "8239": "hochsprunglatten", "8240": "besitzerin", "8241": "wei\u00df-pinkfarbene", "8242": "cart", "8243": "hinterr\u00e4dern", "8244": "zementh\u00fcgel", "8245": "fleece", "8246": "karabiner", "8247": "metallseil", "8248": "fris\u00f6r", "8249": "vorderem", "8250": "jodhpurhose", "8251": "hindernisrennen", "8252": "zw\u00e4ngen", "8253": "blau-pinkfarbenenen", "8254": "ehemannes", "8255": "kofferraum", "8256": "frischgebackenen", "8257": "droschke", "8258": "-zeichen", "8259": "herauszieht", "8260": "horns", "8261": "vorh\u00e4ngen", "8262": "hook", "8263": "fallenlassen", "8264": "brautjunger", "8265": "plastik-feuerwehrauto", "8266": "schulbasar", "8267": "umhergehen", "8268": "dazuzuverdienen", "8269": "pf\u00fctzen", "8270": "ausgehkleidung", "8271": "beckens", "8272": "peugeot", "8273": "spielzeuglastern", "8274": "h\u00fcprft", "8275": "abgenommen", "8276": "grauhaarigen", "8277": "hustet", "8278": "violett-gr\u00fcnes", "8279": "elton", "8280": "jahrmarktattraktion", "8281": "reflektierendes", "8282": "ovales", "8283": "meiste", "8284": "verspielter", "8285": "stopp-schild", "8286": "gestillt", "8287": "ruine", "8288": "stra\u00dfenabschnitt", "8289": "wintergarderobe", "8290": "kunstvolle", "8291": "u-bahn-gleis", "8292": "spielestand", "8293": "sandweg", "8294": "zubewegt", "8295": "aids", "8296": "walk", "8297": "weitergehen", "8298": "schaufensterscheiben", "8299": "blaukrabben", "8300": "formosa", "8301": "bespa\u00dft", "8302": "leuchtschild", "8303": "sto\u00dfz\u00e4hne", "8304": "s\u00e4umt", "8305": "h\u00e4mmern", "8306": "pilze", "8307": "gators-helmen", "8308": "baseball-schlagmann", "8309": "red-bull-auto", "8310": "blaubeerkuchen", "8311": "akkordeons", "8312": "regenjacken", "8313": "beijing", "8314": "hofflohmarkt", "8315": "angewinkelten", "8316": "oberlichtern", "8317": "umfriedung", "8318": "outdoor-volleyball-sandplatz", "8319": "kapuzenpullis", "8320": "olympia", "8321": "tanznummer", "8322": "ziffer", "8323": "gummi", "8324": "-schaukel", "8325": "mitnehmen", "8326": "schuhputzerjungen", "8327": "feuerwehrautos", "8328": "gegenverkehr", "8329": "scherzen", "8330": "essensbox", "8331": "spaghettitr\u00e4ger-oberteil", "8332": "d\u00fcngt", "8333": "b\u00fcrgerversammlung", "8334": "tornetzes", "8335": "managerkleidung", "8336": "werft", "8337": "gegrilltes", "8338": "geschwungene", "8339": "kegelbahn", "8340": "beatmungsmaske", "8341": "fischabteilungen", "8342": "lagerbest\u00e4nde", "8343": "ausliegt", "8344": "blicket", "8345": "feiergeste", "8346": "kamelhaarfarbener", "8347": "styroporbecher", "8348": "band-ausr\u00fcstung", "8349": "kettenschaukel", "8350": "beiwagen", "8351": "ballspielen", "8352": "konferenzraums", "8353": "snoopy-m\u00fctze", "8354": "stirnseite", "8355": "schellenkr\u00e4nze", "8356": "trockenhaube", "8357": "ziegel-hintergrund", "8358": "pl\u00fcsch-spongebob-schwammkopf", "8359": "stuhle", "8360": "gefunden", "8361": "kochfeld", "8362": "heimwerkermarkt", "8363": "zerkl\u00fcftete", "8364": "sommerabend", "8365": "bockspringen", "8366": "stulpen", "8367": "steigenlassen", "8368": "nachtansicht", "8369": "bands\u00e4ge", "8370": "dixie-klo", "8371": "mittelalter", "8372": "pr\u00e4pariert", "8373": "casterol-werbung", "8374": "alkoholische", "8375": "dragsterrennens", "8376": "clownsmaske", "8377": "computerprojekt", "8378": "streife", "8379": "hinunterzufahren", "8380": "abgerolltem", "8381": "abgeschirmtes", "8382": "obstladen", "8383": "versch\u00fcttet", "8384": "milch", "8385": "lang\u00e4rmlige", "8386": "kapuzenjacken", "8387": "blumenkr\u00e4nze", "8388": "blockhaus-schwitzh\u00fctte", "8389": "wall-e", "8390": "erstellen", "8391": "mordsspa\u00df", "8392": "fahrgeld", "8393": "rollerskaten", "8394": "grinsenden", "8395": "lampenschirm", "8396": "unausgepackten", "8397": "archiv", "8398": "verkehrsstation", "8399": "neonjacken", "8400": "einkaufsliste", "8401": "plastik-spielzeugauto", "8402": "target", "8403": "schreibende", "8404": "jal-fahrzeug", "8405": "doberm\u00e4nner", "8406": "auszusuchen", "8407": "baumrinde", "8408": "kirchenbesucher", "8409": "predigenden", "8410": "pastor", "8411": "symmetrie", "8412": "football-torstange", "8413": "halbnackter", "8414": "holz-spielplatz", "8415": "lucky", "8416": "geldscheine", "8417": "dollarzeichen", "8418": "steingebilde", "8419": "zombieartig", "8420": "b\u00fcroangestellte", "8421": "schaumstoffunterlage", "8422": "s\u00e4ften", "8423": "hello", "8424": "kitty", "8425": "w\u00fctende", "8426": "ugg-clogs", "8427": "ungepflegtem", "8428": "industrie-flie\u00dfband", "8429": "schwarz-hellbraunem", "8430": "football-fan", "8431": "hingabe", "8432": "kurzgeschnittenem", "8433": "kleingeschnittenem", "8434": "stars-and-stripes-badeanzug", "8435": "niedlicher", "8436": "kauring", "8437": "geb\u00e4udet\u00fcr", "8438": "schildkr\u00f6ten", "8439": "krabbeln", "8440": "einspurige", "8441": "abgestimmtem", "8442": "skihose", "8443": "halbprofessioneller", "8444": "wirkungsvollsten", "8445": "ultimate-frisbee-spieler", "8446": "felsh\u00fcgeln", "8447": "redenden", "8448": "touristenviertel", "8449": "stieleis", "8450": "neugierig", "8451": "holzkorb", "8452": "eimmer", "8453": "cargohose", "8454": "weltrekord", "8455": "verkehrt-herum-h\u00e4ngen", "8456": "zimmers", "8457": "k\u00fcnstlerinnen", "8458": "untef\u00fchrung", "8459": "geduckt", "8460": "punkrock-kleidung", "8461": "s\u00fc\u00dfe", "8462": "beerenpaste", "8463": "england-trikot", "8464": "kniehohem", "8465": "jachthafen", "8466": "luftrakete", "8467": "schwarz-grauer", "8468": "fu\u00dftritte", "8469": "dorado", "8470": "baumbestandenes", "8471": "kiesauffahrt", "8472": "rucksackreisenden", "8473": "zerealien", "8474": "bezauberndes", "8475": "dirigenten", "8476": "hervorlugt", "8477": "holzbau", "8478": "behandschuhte", "8479": "pickup", "8480": "bikini-oberteilen", "8481": "sarong-r\u00f6cken", "8482": "trinkeldglas", "8483": "eingangsterrasse", "8484": "gewerkschaftsmitarbeiter", "8485": "95.000", "8486": "kuhfarm", "8487": "verdient", "8488": "paketen", "8489": "fu\u00dfb\u00f6den", "8490": "geworfene", "8491": "blassrosa", "8492": "rennender", "8493": "metalltr\u00e4ger", "8494": "beigefarbene", "8495": "kampfhund", "8496": "konzerns", "8497": "sparringturnier", "8498": "shell", "8499": "kampfsportturniers", "8500": "freundlicher", "8501": "orchestervorspiele", "8502": "blues", "8503": "k\u00f6rperpanzerung", "8504": "stutzen", "8505": "au\u00dfergew\u00f6hnlich", "8506": "gestaltete", "8507": "kick-off-position", "8508": "geschirren", "8509": "metalltonnen", "8510": "audio-mischger\u00e4t", "8511": "korn\u00e4hre", "8512": "motocross-rennfahrern", "8513": "baumarbeiter", "8514": "sessions", "8515": "zitronensaft-gesch\u00e4ft", "8516": "schulkind", "8517": "perfekte", "8518": "fu\u00dfball-torpfostens", "8519": "festzunehmen", "8520": "grash\u00fcpfer", "8521": "college-footballspieler", "8522": "konfrontation", "8523": "verd\u00e4chtigen", "8524": "polizeiautos", "8525": "armschlinge", "8526": "fremdes", "8527": "betonsims", "8528": "k\u00fcssenden", "8529": "motorrad-rennfahrer", "8530": "glasrahmen", "8531": "laufens", "8532": "wasserski-rampe", "8533": "diabolo", "8534": "buschigem", "8535": "gekr\u00e4useltem", "8536": "ausschachtungen", "8537": "modellauto", "8538": "pink-gelbes", "8539": "sombreros", "8540": "bronzeschindeln", "8541": "feuerzeug", "8542": "pinkeln", "8543": "bittend", "8544": "betongel\u00e4nder", "8545": "begr\u00fc\u00dfung", "8546": "chip", "8547": "pr\u00e4rie", "8548": "schleppt", "8549": "tennisnetzes", "8550": "rot-blauem", "8551": "yamaha-fahrzeug", "8552": "schweigend", "8553": "segelfliegers", "8554": "eule", "8555": "t\u00f6chtern", "8556": "gebirgsbach", "8557": "ver\u00e4ngstigt", "8558": "wei", "8559": "autokolonne", "8560": "geleiten", "8561": "beantragen", "8562": "kartoffels\u00e4cken", "8563": "china-shipping-kiste", "8564": "ruppig", "8565": "stolziert", "8566": "rot-gr\u00fcnes", "8567": "bearbeitung", "8568": "trick-fahrrad", "8569": "atelier", "8570": "geeignete", "8571": "spareribs", "8572": "fertigzubauen", "8573": "befolgung", "8574": "musikergruppe", "8575": "blau-kariertem", "8576": "blechkuchen", "8577": "abzuh\u00f6ren", "8578": "strohdach", "8579": "still-bh", "8580": "schlafendem", "8581": "froschperspektive", "8582": "gestylt", "8583": "heub\u00fcndel", "8584": "shopping-vormittag", "8585": "spielzeug-stethoskop", "8586": "ehren", "8587": "kanth\u00f6lzer", "8588": "vorbeiflie\u00dfen", "8589": "wei\u00df-rote", "8590": "torpfostens", "8591": "denn", "8592": "turnger\u00e4ten", "8593": "indischst\u00e4mmig", "8594": "zucker", "8595": "toppings", "8596": "slumbewohner", "8597": "rennauto-auspufftopf", "8598": "nonnenschleier", "8599": "treue", "8600": "ewige", "8601": "sprung-kunstst\u00fcck", "8602": "geloben", "8603": "waschmittel", "8604": "schulhof", "8605": "abgesehen", "8606": "profil", "8607": "betrunkener", "8608": "hochzeitskleids", "8609": "aufgeschnitten", "8610": "plastik-spielzeugen", "8611": "finish", "8612": "lattendach", "8613": "imac-monitor", "8614": "metallteilen", "8615": "verw\u00fcstetes", "8616": "matrose", "8617": "gesteinsbrocken", "8618": "tour", "8619": "hineingr\u00e4bt", "8620": "basstrommel", "8621": "unterirdische", "8622": "abenteurers", "8623": "ziegelpflaster", "8624": "trockentuch", "8625": "verbundenes", "8626": "dreir\u00e4drigem", "8627": "schaumiges", "8628": "-hasenohren", "8629": "energizer", "8630": "ober", "8631": "titel", "8632": "stahlger\u00fcst", "8633": "klotz", "8634": "zertr\u00fcmmert", "8635": "naturfreunde", "8636": "angrenzt", "8637": "kanister-staubsauger", "8638": "jeansoverals", "8639": "bon", "8640": "ziegel-t\u00fcr", "8641": "dunkelblauer", "8642": "windbreaker", "8643": "bratpfanne", "8644": "wettl\u00e4ufern", "8645": "rot-wei\u00df-blau", "8646": "elasthan-shorts", "8647": "bambusb\u00e4umen", "8648": "schaukler", "8649": "umz\u00e4untem", "8650": "pink-violetten", "8651": "bowlingwurf", "8652": "feenkost\u00fcm", "8653": "vorhandene", "8654": "bannern", "8655": "angemalte", "8656": "opening", "8657": "dran", "8658": "schrein", "8659": "roller-scooter", "8660": "beschreiben", "8661": "scheitern", "8662": "hosenrock", "8663": "verwirren", "8664": "verf\u00fcgbaren", "8665": "kopfsalat", "8666": "paintball-farbspritzern", "8667": "zuh\u00f6rerraum", "8668": "hand-hubschrauber", "8669": "projiziert", "8670": "blumenkranz", "8671": "stra\u00dfenbaustelle", "8672": "outdoor-court", "8673": "t\u00fcrkische", "8674": "tragbalken", "8675": "burrito", "8676": "karatekurs", "8677": "k\u00f6rperlosen", "8678": "fahrradsprung", "8679": "riesenwelle", "8680": "hunger", "8681": "fortgeht", "8682": "kanugesch\u00e4ften", "8683": "kajak-", "8684": "knallroten", "8685": "wippen", "8686": "barney", "8687": "identifizierten", "8688": "n\u00fcssen", "8689": "trockenfr\u00fcchten", "8690": "buddha-statue", "8691": "schwimmturnier", "8692": "fu\u00dfes", "8693": "reiterdress", "8694": "apple-laptop", "8695": "pulvern", "8696": "high-school-sch\u00fclern", "8697": "baseball-outfit", "8698": "hundekopf", "8699": "beige-gelb-roten", "8700": "holzschwertern", "8701": "beurteilung", "8702": "ballerina-kost\u00fcm", "8703": "k\u00fcstenstadt", "8704": "wasserski-brett", "8705": "erosion", "8706": "pappbecher", "8707": "wedding", "8708": "gay", "8709": "nice", "8710": "hinunterl\u00e4uft", "8711": "kapuzen-sweatshirt", "8712": "pfadfinderjunge", "8713": "cyclone-achterbahn", "8714": "speisenden", "8715": "verschlossenen", "8716": "b\u00e4llebad", "8717": "aufbauen", "8718": "amerika", "8719": "food-court", "8720": "cats", "8721": "pferdehof", "8722": "pferdesportler", "8723": "musikalisches", "8724": "baseball-f\u00e4nger", "8725": "au\u00dfergew\u00f6hnliche", "8726": "gitarren\u00e4hnliche", "8727": "costco-gesch\u00e4ft", "8728": "steinfl\u00e4che", "8729": "zewi", "8730": "besch\u00e4digter", "8731": "sch\u00fclers", "8732": "einkaufskarren", "8733": "unterteil", "8734": "hektisch", "8735": "verstimmt", "8736": "fragw\u00fcrdigem", "8737": "computerbildschirmen", "8738": "gro\u00dffl\u00e4chenbrand", "8739": "kinderspiel", "8740": "behauptet", "8741": "astro-boy-t-shirt", "8742": "nachrichten-interview", "8743": "goldgr\u00e4ber", "8744": "blaugr\u00fcnes", "8745": "untergehende", "8746": "chefkoch-jacke", "8747": "orange-wei\u00df-schwarzer", "8748": "youngster", "8749": "zerkl\u00fcfteten", "8750": "stattzufinden", "8751": "m\u00fcllplatz", "8752": "plastikschlitten", "8753": "m\u00fclllaster", "8754": "segelflieger", "8755": "m\u00fcllbedeckte", "8756": "base-runner", "8757": "geburtstagshut", "8758": "rausfliegen", "8759": "topfhandschuh", "8760": "maschinengewehr", "8761": "izog-hemd", "8762": "begeisterte", "8763": "fleckigen", "8764": "pingpong-tisch", "8765": "oreos", "8766": "korkenzieherlooping", "8767": "schwules", "8768": "blau-roter", "8769": "bildbereich", "8770": "meisterwerk", "8771": "tonausr\u00fcstung", "8772": "show-springer", "8773": "einbaum", "8774": "angebotenem", "8775": "metallkette", "8776": "profisportler", "8777": "sicherheitsbeamter", "8778": "glattem", "8779": "hochgestecktem", "8780": "sternf\u00f6rmigen", "8781": "fensterlosen", "8782": "steppjacke", "8783": "volunteer", "8784": "gew\u00fcrzen", "8785": "abendlicht", "8786": "afroafrikaner", "8787": "westlicher", "8788": "lockiger", "8789": "\u00e4xte", "8790": "ziele", "8791": "aufgemalten", "8792": "bullenk\u00f6pfen", "8793": "wagemutigen", "8794": "alphabetisierung", "8795": "ethno-kleidung", "8796": "k\u00fcsste", "8797": "untereinander", "8798": "autowasch-spendensammlung", "8799": "rodeo-kreis", "8800": "herunterzufallen", "8801": "weltweite", "8802": "army", "8803": "bush", "8804": "laura", "8805": "ausgew\u00e4hlt", "8806": "ausstellungsstand", "8807": "armee-uniform", "8808": "us-soldaten", "8809": "kongress-namensschild", "8810": "absatzschuhe", "8811": "strandwache", "8812": "\u00f6pnv", "8813": "regaleinheiten", "8814": "eishockeyspielern", "8815": "lanzenstechen", "8816": "drahtturm", "8817": "anders", "8818": "vogelk\u00e4fig", "8819": "schieb", "8820": "holzwerkzeug", "8821": "lassos", "8822": "fris\u00f6rsalon", "8823": "plastik-schwimmbecken", "8824": "arbeitsbox", "8825": "zungen", "8826": "platzen", "8827": "bequemer", "8828": "geschichteten", "8829": "verf\u00e4rbte", "8830": "kampfkunsttechnik", "8831": "apfelkuchen", "8832": "betonmonument", "8833": "kriegen", "8834": "schlittenhund", "8835": "nachtisch", "8836": "pinguinkost\u00fcm", "8837": "kandidatinnen", "8838": "holzzauns", "8839": "aufpasser", "8840": "strandbereich", "8841": "handverletzung", "8842": "touristenstand", "8843": "karikatur-portr\u00e4ts", "8844": "insel", "8845": "schieferweg", "8846": "steinernes", "8847": "h\u00e4ngender", "8848": "spielzeug-gie\u00dfkanne", "8849": "herunterschaut", "8850": "l\u00f6win", "8851": "bison", "8852": "leder-motoarradanzug", "8853": "hawaiishorts", "8854": "schwimmlehrer", "8855": "skateboard-trick", "8856": "stra\u00dfenrennens", "8857": "spieltauf", "8858": "zune-b\u00fchne", "8859": "zune-geb\u00e4ude", "8860": "kampfkunstkurs", "8861": "ourense", "8862": "spiderman-schwimmfl\u00fcgel", "8863": "-roller", "8864": "une", "8865": "batman-laufkleidung", "8866": "-ballsporttrikot", "8867": "hill", "8868": "her\u00fcber", "8869": "gurtgeschirr", "8870": "radschlag", "8871": "dreieckigen", "8872": "getr\u00e4nkter", "8873": "ballspielzeug", "8874": "kampfsport-kampf", "8875": "erdbeer-fahrgesch\u00e4ft", "8876": "vegas-shirt", "8877": "schwimmkappe", "8878": "gr\u00fcn-blauen", "8879": "eingemachtes", "8880": "reissamen", "8881": "landwirte", "8882": "einstieg", "8883": "samsung-werbung", "8884": "pnc-bank", "8885": "geregnet", "8886": "mercury", "8887": "tausende", "8888": "spargel", "8889": "augenblick", "8890": "skateboard-man\u00f6ver", "8891": "ligaspieler", "8892": "t\u00e4schchen", "8893": "felsvorsprunges", "8894": "erinnert", "8895": "momente", "8896": "mobiltelefonen", "8897": "profispiel", "8898": "strikeout", "8899": "hinausbef\u00f6rdert", "8900": "homeplate", "8901": "safe", "8902": "spielger\u00fcst", "8903": "laufstall", "8904": "legosteine", "8905": "verhaften", "8906": "hinunterzuklettern", "8907": "spa-einrichtung", "8908": "radfahr-\u00fcbung", "8909": "jeweiligen", "8910": "klemme", "8911": "kurbelbetriebenen", "8912": "court", "8913": "halloween-kost\u00fcm", "8914": "auslieferer", "8915": "halloween-k\u00fcrbis", "8916": "zombie", "8917": "polizisten-kost\u00fcm", "8918": "zombie-kost\u00fcm", "8919": "gegrilltem", "8920": "schenken", "8921": "schaschlikspie\u00df", "8922": "metallzange", "8923": "amish", "8924": "mauervorsprung", "8925": "speziellen", "8926": "baby-badewanne", "8927": "verkehrsflugzeug", "8928": "zuspielen", "8929": "massagesitzen", "8930": "ozzy-t-shirt", "8931": "stadtbahn", "8932": "pochiertem", "8933": "ethnien", "8934": "rassen", "8935": "miami-basketball-spielkleidung", "8936": "hellichten", "8937": "windschutzscheibe", "8938": "getr\u00f6stet", "8939": "lagerplatz-barbecue", "8940": "musste", "8941": "ziegelsteinweg", "8942": "computerlabor", "8943": "wannen", "8944": "down", "8945": "\u00fcberrascht", "8946": "computerbildschirme", "8947": "menorah", "8948": "leuchtjacke", "8949": "treibhund", "8950": "muschel", "8951": "einarmiger", "8952": "trinkflasche", "8953": "souschefs", "8954": "hofs", "8955": "bl\u00fcten", "8956": "abstimmung", "8957": "getrocknetem", "8958": "klaue", "8959": "imitiert", "8960": "-gesicht", "8961": "vergn\u00fcgte", "8962": "roooaaaarrrr", "8963": "gymnastikkurs", "8964": "08", "8965": "sta\u00dfe", "8966": "st\u00e4ndern", "8967": "durchschnittliche", "8968": "alltagsmenschen", "8969": "hockende", "8970": "handfeuerwaffe", "8971": "obama-wahlkampfschild", "8972": "obama-t-shirt", "8973": "zupfen", "8974": "feldfr\u00fcchten", "8975": "sonnige", "8976": "stadtmitte", "8977": "kreischen", "8978": "geschenktasche", "8979": "geschenks", "8980": "dickes", "8981": "stammesgruppe", "8982": "j\u00e4ger", "8983": "cupcake-formen", "8984": "maisfeld", "8985": "spr\u00fchdose", "8986": "fl\u00f6\u00dfe", "8987": "stromkreise", "8988": "bildschirmen", "8989": "pr\u00fcfer", "8990": "unterhemden", "8991": "sicherheitsjacken", "8992": "schlaf", "8993": "daher", "8994": "frosch-statue", "8995": "gr\u00f6\u00dften", "8996": "kerlen", "8997": "salyo", "8998": "pferdekopf", "8999": "drapieren", "9000": "technische", "9001": "batteriebetriebenen", "9002": "erfolgreiche", "9003": "gel\u00e4nde-rennfahrer", "9004": "walter-lou-anne-kitty-mitarbeiter", "9005": "gesch\u00e4ftskost\u00fcm", "9006": "wasserf\u00e4llen", "9007": "fu\u00dfball-schiedsrichter", "9008": "gerichte", "9009": "rasen-sportplatz", "9010": "jeeps", "9011": "chefk\u00f6che", "9012": "canon-kamera", "9013": "liebevoll", "9014": "auszutaggen", "9015": "lieferungen", "9016": "afroamerikanern", "9017": "durags", "9018": "dastehen", "9019": "projekts", "9020": "hydraulischen", "9021": "bahnlinie", "9022": "b\u00fchnen", "9023": "n\u00e4herin", "9024": "stra\u00dfenbahnsitze", "9025": "stra\u00dfenbahnpr\u00fcfer", "9026": "flaschenz\u00fcgen", "9027": "schuften", "9028": "plastiktiger", "9029": "u-bahn-endstation", "9030": "jacksons", "9031": "thriller", "9032": "-video", "9033": "safari-outfit", "9034": "tunnelinneren", "9035": "hinauf-", "9036": "verkleidungen", "9037": "m\u00fcllt\u00fcten", "9038": "u-bahn-arbeiter", "9039": "entrollen", "9040": "h\u00e4ndchenhaltend", "9041": "kilburn", "9042": "kampfsport-gruppenkurs", "9043": "bahnarbeiters", "9044": "r\u00fcckansicht", "9045": "trennlinie", "9046": "offense", "9047": "eisenbahnbr\u00fccke", "9048": "quattro-kran", "9049": "einwickeln", "9050": "kies-bahnlinie", "9051": "rollerbladen", "9052": "rennwagenfahrer", "9053": "micky", "9054": "presslufth\u00e4mmern", "9055": "tannenzapfen", "9056": "parpklatz", "9057": "heim", "9058": "wenigen", "9059": "computersystem", "9060": "zugem\u00fcllt", "9061": "falschen", "9062": "industriereinigungsger\u00e4t", "9063": "eisenbahnfahrzeug", "9064": "kontrabass", "9065": "restlichen", "9066": "sinfonie", "9067": "hellorangefarbener", "9068": "kennington-schild", "9069": "bartisch", "9070": "zahnrad", "9071": "fliest", "9072": "wassergraben", "9073": "berichte", "9074": "ballt", "9075": "verladen", "9076": "quasten", "9077": "einz\u00e4unung", "9078": "gem\u00fcseb\u00fcndel", "9079": "besorgten", "9080": "festliche", "9081": "araber", "9082": "sinkt", "9083": "wasserkrug", "9084": "wassertrinkend", "9085": "festlichem", "9086": "kurbel", "9087": "pink-laden", "9088": "schalt", "9089": "verspielt", "9090": "wei\u00df-hellbrauner", "9091": "verwelktem", "9092": "bevorstehende", "9093": "weggestreckt", "9094": "knochenform", "9095": "blau-gr\u00fcn-wei\u00dfes", "9096": "handbuch", "9097": "bergszenerie", "9098": "wei\u00df-blaues", "9099": "universelle", "9100": "touchdown", "9101": "orcawale", "9102": "seaworld-schwimmbecken", "9103": "durchforstet", "9104": "hockey-art", "9105": "schwimmtreffen", "9106": "ungleichm\u00e4\u00dfigem", "9107": "grellbunten", "9108": "stadtstreicherin", "9109": "regenbogen-per\u00fccke", "9110": "fehlenden", "9111": "hinuntern", "9112": "ausgelegten", "9113": "unicef-lager", "9114": "kegelt", "9115": "schrottmetall", "9116": "lebende", "9117": "babyen", "9118": "hotdog-zutaten", "9119": "faltf\u00e4cher", "9120": "designt", "9121": "baseball-werfer", "9122": "sport-radfahrer", "9123": "limonadenverk\u00e4ufer", "9124": "limonadenstand", "9125": "british-airways-flugzeugs", "9126": "rutschigen", "9127": "h\u00e4lften", "9128": "heiratsurkunde", "9129": "kommandos", "9130": "geier", "9131": "fleischerbeil", "9132": "geh\u00e4utetes", "9133": "wirkenden", "9134": "\u00fcberdachtes", "9135": "passagierin", "9136": "heruntergekommen", "9137": "atomic", "9138": "speisende", "9139": "wettk\u00e4mpfer", "9140": "sitzts", "9141": "gehsteig-", "9142": "grauk\u00f6pfigen", "9143": "vermeiden", "9144": "r\u00fcckansichten", "9145": "kamera-telefon", "9146": "pferdegespann", "9147": "gebacken", "9148": "picknickt", "9149": "dritt", "9150": "karotten", "9151": "gelagertem", "9152": "angeh\u00f6rige", "9153": "durchn\u00e4sste", "9154": "dunkelbraunem", "9155": "gesellschaftsveranstaltung", "9156": "panoramaartigen", "9157": "bergsicht", "9158": "einrichtungsvorgang", "9159": "cricket", "9160": "ballspiels", "9161": "dekorierte", "9162": "einnen", "9163": "nachtlebens", "9164": "vampirkost\u00fcme", "9165": "schwarz-graues", "9166": "sonnenmaske", "9167": "winkende", "9168": "aufschaut", "9169": "stirnfransen", "9170": "fahhradlenker", "9171": "gelb-bordeauxroten", "9172": "zementplattform", "9173": "kreze", "9174": "sting", "9175": "familienwohnung", "9176": "mall", "9177": "rot-schwarz-wei\u00dfen", "9178": "verspiegelter", "9179": "bodenstreitkr\u00e4fte", "9180": "wassersch\u00fcssel", "9181": "strahlenden", "9182": "dagegenschlagen", "9183": "p", "9184": "gr\u00fcn-roten", "9185": "bandagiertes", "9186": "marineblau-gelben", "9187": "schwimmbecken-", "9188": "papierausschnitten", "9189": "gutm\u00fctiger", "9190": "streich", "9191": "frischgebackene", "9192": "herrchen", "9193": "freizeit", "9194": "orange-gr\u00fcnen", "9195": "spendenden", "9196": "feldhockey-team", "9197": "rettungskr\u00e4fte", "9198": "schiffsdeck", "9199": "anbindet", "9200": "chinesichen", "9201": "hund-", "9202": "fechtkurs", "9203": "basketballcourt", "9204": "skaterboy", "9205": "chinesicher", "9206": "blau-rote", "9207": "drumherum", "9208": "ureinwohnerkinder", "9209": "wolf", "9210": "arktischen", "9211": "talgegend", "9212": "raften", "9213": "zusammensto\u00df", "9214": "schiffsarbeiter", "9215": "jahrmarktspiel", "9216": "where", "9217": "wortlaut", "9218": "v\u00f6gelt", "9219": "graffitti", "9220": "italienischem", "9221": "festivalrutsche", "9222": "informelles", "9223": "w\u00fchlen", "9224": "w\u00e4schekollektion", "9225": "arbeitsplatte", "9226": "peperoni", "9227": "zweit", "9228": "gelegenes", "9229": "holzweg", "9230": "taxikost\u00fcm", "9231": "tierkost\u00fcm", "9232": "fliehen", "9233": "beschlie\u00dft", "9234": "orangutan-kost\u00fcm", "9235": "herbstbl\u00e4ttern", "9236": "schl\u00fcrft", "9237": "aktentaschen", "9238": "filzstift", "9239": "hundehindernis", "9240": "cricket-schlagman", "9241": "stump", "9242": "botschaft", "9243": "college", "9244": "durchgedr\u00fccktem", "9245": "hunderampe", "9246": "betontunnel", "9247": "herz-t-shirt", "9248": "basecaps", "9249": "betonschalstein", "9250": "navysoldaten", "9251": "salutieren", "9252": "vorgesetzter", "9253": "plant", "9254": "cheerleadertruppe", "9255": "sallie-mae-banner", "9256": "footballdress", "9257": "transvestit", "9258": "kuss-gesicht", "9259": "raue", "9260": "einarmigen", "9261": "flaschenwasser", "9262": "lunchbox", "9263": "computermonitore", "9264": "hake", "9265": "verkehrszug", "9266": "schwarzrandiger", "9267": "merkw\u00fcrdigen", "9268": "versorgungsbetriebe", "9269": "weihnachtsfeier", "9270": "nikolausm\u00fctzen", "9271": "18th", "9272": "restaurant-umgebung", "9273": "salatbar", "9274": "vors", "9275": "b\u00fcffel", "9276": "webereien", "9277": "lebkuchenh\u00e4user", "9278": "passagierzug", "9279": "kichert", "9280": "0095-taxi", "9281": "passen", "9282": "seitw\u00e4rts", "9283": "wei\u00dfschattierungen", "9284": "aktivit\u00e4tszentrum", "9285": "gleise", "9286": "ticket", "9287": "stra\u00dfenbahnenschienen", "9288": "stadtbewohner", "9289": "chipsatz", "9290": "wollm\u00fctzen", "9291": "reifengummi", "9292": "subway-sandwich-schild", "9293": "schwarz-hellbraunen", "9294": "rastaman", "9295": "post-it-haftnotiz", "9296": "post-its", "9297": "pflug", "9298": "nadeln", "9299": "warmhalteplatte", "9300": "kochende", "9301": "coca-cola-oberteil", "9302": "cheerleaderinnen", "9303": "felsvorsprungs", "9304": "musikb\u00fchne", "9305": "parodie-dragqueen", "9306": "beladung", "9307": "beschriften", "9308": "variante", "9309": "rollhockeys", "9310": "kreidebedeckten", "9311": "samsung", "9312": "bodenkacheln", "9313": "blickwinkel", "9314": "partyg\u00e4ste", "9315": "rave-veranstaltung", "9316": "leuchtst\u00e4ben", "9317": "hochgereckten", "9318": "angesagten", "9319": "krankenhauszimmer", "9320": "topfpflanze", "9321": "elektronikabteilung", "9322": "walmart-gesch\u00e4fts", "9323": "modern", "9324": "mittelgro\u00dfer", "9325": "putzzeug", "9326": "beziehungsweise", "9327": "urlauber-familie", "9328": "heimwerkerprojekt", "9329": "angegangen", "9330": "monitoren", "9331": "fernsehkamera", "9332": "pelzm\u00e4nteln", "9333": "r\u00fcschen-tutu", "9334": "farbenfreudigen", "9335": "khakifarbenem", "9336": "arbeitsumgebung", "9337": "vans-schuhen", "9338": "schwarz-gelb", "9339": "geschafft", "9340": "gestikulieren", "9341": "kaffeetassen", "9342": "betonw\u00e4nden", "9343": "_", "9344": "flugbegleiter", "9345": "aufgestapelter", "9346": "ratten", "9347": "line", "9348": "dance", "9349": "urlaubshotelzimmer", "9350": "volkst\u00fcmlichen", "9351": "demonstrieren", "9352": "sammlung", "9353": "geblasenem", "9354": "spielzeugschaufel", "9355": "kunststoffgegenstand", "9356": "klassen", "9357": "anst\u00f6\u00dfigen", "9358": "aufst\u00e4ndischen", "9359": "k\u00f6rperliches", "9360": "vollbringt", "9361": "kinderrechte", "9362": "gokartbahn", "9363": "wettbewerbsorientiert", "9364": "ziegelfassade", "9365": "abschuss", "9366": "daniels", "9367": "kneipenumgebung", "9368": "ballr\u00fcckgabe", "9369": "gereckten", "9370": "befestigtes", "9371": "hippies", "9372": "etat", "9373": "hinweisen", "9374": "spielbereich", "9375": "baumstumpfe", "9376": "factory-schild", "9377": "mittelalterfest", "9378": "angesichts", "9379": "tellers", "9380": "konzertmeister", "9381": "stehendem", "9382": "beifall", "9383": "hervorragende", "9384": "leistung", "9385": "bedacht", "9386": "sportruderboot", "9387": "geformt", "9388": "staubsaugt", "9389": "arbeitsfl\u00e4che", "9390": "gelenkt", "9391": "schiffe", "9392": "kampfsportstellung", "9393": "betonstange", "9394": "steinbildhauer", "9395": "besorgt", "9396": "primitiv", "9397": "erf\u00fcllen", "9398": "kampfsporthaltung", "9399": "naturbelassenen", "9400": "anspr\u00fcche", "9401": "individuellen", "9402": "erm\u00f6glicht", "9403": "abgehoben", "9404": "bienenkost\u00fcm", "9405": "riesenrad", "9406": "belaubten", "9407": "tanzendem", "9408": "bartstoppeln", "9409": "gelockten", "9410": "stellung", "9411": "alligatorstatue", "9412": "tribaltattoo", "9413": "laternenpfahls", "9414": "moosigen", "9415": "metallstab", "9416": "rugbyball", "9417": "betonmischlaster", "9418": "standstreifen", "9419": "stau", "9420": "gef\u00fchrt", "9421": "kappen", "9422": "aufgerichteten", "9423": "besteigt", "9424": "b\u00fcroausstattung", "9425": "kunststoffrohren", "9426": "dorothy", "9427": "gekr\u00fcmmt", "9428": "hindi-mann", "9429": "diego", "9430": "mickey", "9431": "rhododendron", "9432": "pfauenfeder", "9433": "buddhistischer", "9434": "ringermaske", "9435": "kochm\u00fctzen", "9436": "rot-gestreiftem", "9437": "verschmutzter", "9438": "t\u00e4tigt", "9439": "theater-schwertern", "9440": "mittelaltershow", "9441": "metallkonstruktur", "9442": "regenumhang", "9443": "kinderfahrrad", "9444": "tischsets", "9445": "orchesterpavillon", "9446": "l\u00e4nderspezifischen", "9447": "recken", "9448": "anzughose", "9449": "skysurfing", "9450": "bestickten", "9451": "wimperntusche", "9452": "\u00fcbungssaal", "9453": "kleinstem", "9454": "finsterer", "9455": "stonehurst", "9456": "lagerhaus", "9457": "stra\u00dfenumzug", "9458": "behilflich", "9459": "lincoln", "9460": "memorial", "9461": "einwanderungsbeh\u00f6rde", "9462": "nagelfeile", "9463": "razzien", "9464": "abschirmung", "9465": "d\u00fcnen", "9466": "rollschuhwettrennen", "9467": "eingedeckt", "9468": "eintreffen", "9469": "hochbahn", "9470": "winterwetter", "9471": "erz\u00e4hlen", "9472": "geheimnisse", "9473": "staffordshire", "9474": "aufstapeln", "9475": "technischen", "9476": "seltsame", "9477": "sitzpublikum", "9478": "professionellen", "9479": "gro\u00dfereignis", "9480": "bob-mannschaft", "9481": "schlammh\u00fcgel", "9482": "gem\u00fcsesorten", "9483": "kauf", "9484": "herabgefallenen", "9485": "dampfenden", "9486": "kampfkunst-klasse", "9487": "boxschl\u00e4ge", "9488": "wood", "9489": "silk", "9490": "beantwortet", "9491": "craftzine-tisch", "9492": "goldenes", "9493": "spanischsprachigen", "9494": "gespitzten", "9495": "beschriftet", "9496": "zuschauertrib\u00fcne", "9497": "rikscha-fahrer", "9498": "gesponsertes", "9499": "courtboden", "9500": "schummerigem", "9501": "arbeitsaufgaben", "9502": "fleischgericht", "9503": "garnierung", "9504": "frauen-volleyball", "9505": "lichtdurchl\u00e4ssiges", "9506": "abbild", "9507": "custody", "9508": "indigoblauen", "9509": "prunkvolle", "9510": "siegesfeier", "9511": "deaths", "9512": "black", "9513": "say", "9514": "herabh\u00e4ngen", "9515": "schnauzen", "9516": "workers", "9517": "glasregal", "9518": "hirsche", "9519": "maritime", "9520": "pfirsich", "9521": "schaufensterscheibe", "9522": "eisschnelllauf", "9523": "zulaufen", "9524": "ausw\u00e4hlen", "9525": "spielzeugbausteinen", "9526": "hauseingang", "9527": "entwenden", "9528": "holzpf\u00e4hlen", "9529": "verkaufsstands", "9530": "sternsingen", "9531": "hilflos", "9532": "glaskasten", "9533": "papas", "9534": "markierungen", "9535": "schneeburg", "9536": "preis", "9537": "einde", "9538": "liverpool-fu\u00dfballspieler", "9539": "lampard", "9540": "frank", "9541": "chelsea-fu\u00dfballspieler", "9542": "deep", "9543": "abgetrennten", "9544": "winterm\u00fctzen", "9545": "hellrosafarbener", "9546": "stadtzentrum", "9547": "frierende", "9548": "naturprodukte", "9549": "unglaublichen", "9550": "zettel", "9551": "poolwasser", "9552": "adoptiert", "9553": "korbverk\u00e4ufer", "9554": "sportmotiv", "9555": "festumzug", "9556": "leckerlis", "9557": "gl\u00e4sernen", "9558": "messbecher", "9559": "goldteller", "9560": "gr\u00fcn-gelbem", "9561": "ausdrucksloser", "9562": "herunterzufahren", "9563": "kapit\u00e4nsanzug", "9564": "ventilatoren", "9565": "toben", "9566": "befingert", "9567": "anzugjacke", "9568": "flachland", "9569": "spielunterlage", "9570": "koboldm\u00fctze", "9571": "surferhemd", "9572": "afrikas", "9573": "luftballontier", "9574": "gebrauchtreifengesch\u00e4ft", "9575": "euro-jazz-konzert", "9576": "deckenpaneele", "9577": "best\u00fcckt", "9578": "pizzaregal", "9579": "ma\u00dfband", "9580": "ineinem", "9581": "diamanten", "9582": "danebenliegt", "9583": "jonglieren", "9584": "spielzeugdinosaurier", "9585": "verkleideten", "9586": "engelskost\u00fcm", "9587": "b\u00fcgelbrett", "9588": "mampft", "9589": "rasensprenger", "9590": "motocrossrennen", "9591": "sportoutfit", "9592": "clubsessel", "9593": "schaumstoffb\u00e4llen", "9594": "radmannschaft", "9595": "krankes", "9596": "mantels", "9597": "wegzunehmen", "9598": "bauernmakrt", "9599": "kassen", "9600": "grasgebiet", "9601": "spritzbeutel", "9602": "bierkr\u00fcge", "9603": "risse", "9604": "pappmach\u00e9", "9605": "lebkuchenhaus", "9606": "supermann-umhang", "9607": "ums", "9608": "33", "9609": "stacheln", "9610": "malerkrepp", "9611": "h\u00e4lfte", "9612": "kindertagesst\u00e4tte", "9613": "haustieren", "9614": "trialrad", "9615": "unwegsames", "9616": "kondome", "9617": "aufgedruckt", "9618": "event", "9619": "hockeyspiels", "9620": "springender", "9621": "spielkasino-ambiente", "9622": "schwarz-roter", "9623": "goldenem", "9624": "goldverzierten", "9625": "baustellenbereichs", "9626": "rondell", "9627": "drumstick", "9628": "schwatzen", "9629": "aufgen\u00e4hte", "9630": "autoschl\u00fcssel", "9631": "stuhls", "9632": "aquamarinfarbenem", "9633": "gremium", "9634": "kompletten", "9635": "rosenbl\u00e4tter", "9636": "theaterstil", "9637": "stiehlt", "9638": "fern\u00f6stlichen", "9639": "stativs", "9640": "laufstalls", "9641": "streuseln", "9642": "stahltr\u00e4gern", "9643": "rollschuhfahrer", "9644": "hallenskatepark", "9645": "literatur", "9646": "clowsnase", "9647": "engelsfl\u00fcgeln", "9648": "werbezettel", "9649": "zuckerstange", "9650": "dschungelhut", "9651": "messingring", "9652": "parkartigen", "9653": "buchs", "9654": "hirschstatue", "9655": "lichtshow", "9656": "outdoor-festival", "9657": "stanzt", "9658": "lernkarten", "9659": "pappe", "9660": "stoffbespannten", "9661": "schl\u00e4ngelt", "9662": "baseballhelm", "9663": "elasthan", "9664": "groucho-marx-kost\u00fcmen", "9665": "b\u00fccherverkauf", "9666": "fellverf\u00e4rbung", "9667": "diskothek", "9668": "holzblock", "9669": "hallenboden", "9670": "gischt", "9671": "einsam", "9672": "ge\u00f6ffneter", "9673": "southern", "9674": "methodist", "9675": "zers\u00e4gt", "9676": "lichtskulptur", "9677": "holzwerkstatt", "9678": "angelspiel", "9679": "hobel", "9680": "poller", "9681": "gebrochenem", "9682": "parking", "9683": "herunterf\u00e4hrt", "9684": "jahrmarktst\u00e4nden", "9685": "jahrmarktstand", "9686": "aufmerksamen", "9687": "baltimore-ravens-hut", "9688": "grafittis", "9689": "wasserskiboot", "9690": "snowboardfahren", "9691": "h\u00f6chstwahrscheinlich", "9692": "standmikrofon", "9693": "mannschaftsjacke", "9694": "schneeflocken", "9695": "pokemon-kartenspiel", "9696": "rentiermotiv", "9697": "teamgeist", "9698": "toy", "9699": "neugieriger", "9700": "betrontreppe", "9701": "pyramidenf\u00f6rmigen", "9702": "\u00fcbernehmen", "9703": "behalten", "9704": "disney-figur", "9705": "schm\u00fcckt", "9706": "pinguin", "9707": "holzzerkleinerungsmaschine", "9708": "happy", "9709": "hebekorb", "9710": "quinn", "9711": "einh\u00e4ndiges", "9712": "elmos", "9713": "echt", "9714": "erwartungsvoll", "9715": "\u00e4rmellose", "9716": "verschwitzte", "9717": "gr\u00fcbelt", "9718": "zeltes", "9719": "margaritagl\u00e4sern", "9720": "spannt", "9721": "easter", "9722": "egg", "9723": "wichtiger", "9724": "ohrringe", "9725": "bandanatuch", "9726": "seestern", "9727": "waschbrettbauch", "9728": "nationalpark", "9729": "sportmotorrad", "9730": "bumper-bowling", "9731": "gebundenen", "9732": "kameratasche", "9733": "m\u00e4dchen-fu\u00dfballspiel", "9734": "hellblau", "9735": "gep\u00e4ckbereich", "9736": "beginnenden", "9737": "enrolling-stones-t-shirt", "9738": "vorspiel", "9739": "zusammengef\u00fcgt", "9740": "broadway-b\u00fchne", "9741": "enrolling-stones", "9742": "vorsingen", "9743": "b\u00fcrost\u00fchlen", "9744": "kirchenchors\u00e4ngern", "9745": "freiliegend", "9746": "aufgebauschtem", "9747": "holz-piratenschiffs", "9748": "geistliche", "9749": "gebetsteppiche", "9750": "r\u00f6hrenrutsche", "9751": "sterne", "9752": "begeben", "9753": "aussparung", "9754": "gitarre-", "9755": "banjo-", "9756": "'s-", "9757": "geburtstagskerzen", "9758": "abgewandte", "9759": "schabt", "9760": "pro-jud\u00e4a-schild", "9761": "kr\u00fcge", "9762": "ipods", "9763": "musikplayer", "9764": "arabischem", "9765": "nutztieren", "9766": "herdplatte", "9767": "t\u00e4ter", "9768": "stroboskoplicht", "9769": "tangot\u00e4nzerin", "9770": "stahlbeh\u00e4lter", "9771": "holzspatel", "9772": "pl\u00fcschm\u00fctze", "9773": "zenturio", "9774": "entdecken", "9775": "verlierer", "9776": "entt\u00e4uscht", "9777": "dinosaurier-stuhl", "9778": "rachen", "9779": "spannenden", "9780": "schlafzimmern", "9781": "str\u00f6mung", "9782": "i-phone", "9783": "kurvige", "9784": "tragender", "9785": "ausgeschnittenes", "9786": "pfirsichfarben-wei\u00dfen", "9787": "kopfes", "9788": "kluft", "9789": "stabs", "9790": "goldgelber", "9791": "goldgelben", "9792": "spa\u00dfrutsche", "9793": "seetangwald", "9794": "umrahmten", "9795": "marienk\u00e4fer-kost\u00fcm", "9796": "fastfood-getr\u00e4nken", "9797": "rockstar", "9798": "fahrradkette", "9799": "cyranka", "9800": "walds", "9801": "castros", "9802": "glaswaren", "9803": "elfenfl\u00fcgeln", "9804": "nike-kappe", "9805": "grash\u00fctten", "9806": "dorfes", "9807": "lederfarbenen", "9808": "handlung", "9809": "essensausgabe", "9810": "lagergeb\u00e4ude", "9811": "milit\u00e4reinheit", "9812": "stillgelegten", "9813": "betriebsame", "9814": "ausstattung", "9815": "erholen", "9816": "k\u00fcchenarbeitsplatte", "9817": "get\u00e4felten", "9818": "benzinkanistern", "9819": "bereitgestellt", "9820": "riesengro\u00dfen", "9821": "freundlichen", "9822": "schn\u00fcrt", "9823": "viereckigen", "9824": "oldtimer-cabriolets", "9825": "radeo", "9826": "akt", "9827": "raftboot", "9828": "logos", "9829": "sanfter", "9830": "\u00fcbereinstimmenden", "9831": "auftitschen", "9832": "m-tennisspielerin", "9833": "texas-a", "9834": "volvo-autohaus", "9835": "rasensportplatz", "9836": "zehen", "9837": "wackelt", "9838": "arbeitsweg", "9839": "leidenschaftliches", "9840": "attraktives", "9841": "schaufelarm", "9842": "s\u00e4ngerinnen", "9843": "s\u00e4ngern", "9844": "slalom-skifahrer", "9845": "zuschauende", "9846": "baseballhandschuhe", "9847": "schulpflichtige", "9848": "gastronomiebetriebs", "9849": "hochzeitsg\u00e4ste", "9850": "absprung", "9851": "baumhaus", "9852": "sonnenh\u00fcter", "9853": "jagd", "9854": "weitl\u00e4ufigen", "9855": "schmelzenden", "9856": "synchronschwimmern", "9857": "romantisch", "9858": "longhorns", "9859": "haltenden", "9860": "f\u00fcrsorglichen", "9861": "gebadeter", "9862": "wildpferd", "9863": "party-hut", "9864": "halbfertigen", "9865": "football-dress", "9866": "28", "9867": "bauschigen", "9868": "eislaufbahn", "9869": "oklahoma-sooners-football-spieler", "9870": "football-mannschaft", "9871": "ansto\u00df", "9872": "vollgestopft", "9873": "kletterern", "9874": "sooners", "9875": "laufendes", "9876": "sooners-quarterback", "9877": "koffeinfreie", "9878": "coca", "9879": "festwagen", "9880": "dre", "9881": "holzgestell", "9882": "sooners-football-spieler", "9883": "aufstellung", "9884": "college-football-spiel", "9885": "torii", "9886": "violett-wei\u00df-gold", "9887": "lehrers", "9888": "football-spielen", "9889": "oklahoma-spieler", "9890": "wehenden", "9891": "sooner", "9892": "five", "9893": "football-fans", "9894": "kinderspielzeug", "9895": "pferde-maskottchen", "9896": "n\u00fcstern", "9897": "\u00fcberholen", "9898": "m\u00f6glichen", "9899": "schwarz-gebl\u00fcmter", "9900": "augenbinden", "9901": "greenpeace-zeichen", "9902": "sportsachen", "9903": "giants", "9904": "skispringer", "9905": "uw", "9906": "putenzuchtbetriebs", "9907": "abgestorbenen", "9908": "angriffsversuch", "9909": "fackelstab", "9910": "sooners-trikot", "9911": "herausgehoben", "9912": "untergehender", "9913": "vergitterten", "9914": "ou", "9915": "golfspiel", "9916": "oklahoma-sooners-football-mannschaft", "9917": "schiri", "9918": "oklahoma-footballs-spieler", "9919": "diagramme", "9920": "picknicken", "9921": "\u00e4rzten", "9922": "beesseren", "9923": "treppenf\u00f6rmig", "9924": "kater", "9925": "baumelt", "9926": "sturmhaube", "9927": "verrenkt", "9928": "baseballspielen", "9929": "aussage", "9930": "who", "9931": "stadtszenerie", "9932": "besch\u00e4ftigen", "9933": "hundecape", "9934": "gr\u00fcn-violettem", "9935": "rebenumrankten", "9936": "d\u00e4nische", "9937": "spielzeug-jeep", "9938": "rennhunden", "9939": "mitspielen", "9940": "rettungsring", "9941": "hallenhockey-spielerinnen", "9942": "u-bahnsteig", "9943": "saftbar", "9944": "ausgefallene", "9945": "entgegenlaufen", "9946": "erwachsenengr\u00f6\u00dfe", "9947": "staunender", "9948": "bergm\u00e4nner", "9949": "zeche", "9950": "feldbetten", "9951": "kristallkugel", "9952": "\u00e4hnliche", "9953": "alphabet", "9954": "windeleimer", "9955": "schafbock", "9956": "wanderhemd", "9957": "puma", "9958": "auftut", "9959": "schokoladenfabrik", "9960": "angeguckt", "9961": "wei\u00dfkopfadler", "9962": "orangen-fruchteis", "9963": "stierkampfarena", "9964": "vorderbeinen", "9965": "spielzeugente", "9966": "sumoringer-hosen", "9967": "rodeo-cowboy", "9968": "unverwandt", "9969": "h\u00e4userecke", "9970": "gesprenkelte", "9971": "kamelbaby", "9972": "scrabble", "9973": "plakatwerbung", "9974": "strickleiter", "9975": "kletter", "9976": "sicherheitsgel\u00e4nder", "9977": "handzeichen", "9978": "erleichterung", "9979": "schneestiefeln", "9980": "spielh\u00e4uschen", "9981": "kontrollanzeige", "9982": "nutzen", "9983": "fr\u00fchst\u00fccksflocken", "9984": "gruseliges", "9985": "bildschirme", "9986": "ausgenommen", "9987": "auszumachen", "9988": "tanzpositionen", "9989": "mittelst\u00e4nder", "9990": "johlen", "9991": "seilgarten", "9992": "leitplanke", "9993": "abgegrenzt", "9994": "ausgeklapptem", "9995": "weitl\u00e4ufiges", "9996": "grasbedecktes", "9997": "puma-g\u00fcrteltasche", "9998": "high-school", "9999": "beerdigung", "10000": "eisblock", "10001": "kreisen", "10002": "nationalit\u00e4t", "10003": "wohnhaus", "10004": "fliegerkappe", "10005": "ziegenbaby", "10006": "hundeschlittenrennens", "10007": "spiegelbilder", "10008": "stra\u00dfenangestellten", "10009": "fr\u00fchlingsnachmittag", "10010": "blechtonne", "10011": "grimmige", "10012": "hundejacke", "10013": "schneegest\u00f6bers", "10014": "backsteinbau", "10015": "zeitungsstand", "10016": "bryant", "10017": "f\u00fcnfst\u00f6ckigen", "10018": "bungeeseilen", "10019": "jadegr\u00fcner", "10020": "l\u00f6chriges", "10021": "projiziertes", "10022": "spritzendes", "10023": "geheimnis", "10024": "trainingsfortschritt", "10025": "laufbands", "10026": "naturwanderweg", "10027": "hindurchstrecken", "10028": "blumenbukett", "10029": "stra\u00dfenbank", "10030": "zusammengefallen", "10031": "kartenhaus", "10032": "hufeisenwerfen", "10033": "weine", "10034": "winterm\u00fctze", "10035": "hellbraun-wei\u00dfer", "10036": "flaschenzug", "10037": "vorn\u00fcbergebeugter", "10038": "lehrkraft", "10039": "entgegensetzte", "10040": "stofftierkost\u00fcm", "10041": "studentinnen", "10042": "freiluftrestaurant", "10043": "fechtunterricht", "10044": "pusteblumen", "10045": "angenehmen", "10046": "aufbewahrt", "10047": "holzteile", "10048": "zusammenschl\u00e4gt", "10049": "transporthilfe", "10050": "notfallausr\u00fcstung", "10051": "eisbrocken", "10052": "goldumrandeten", "10053": "motocrossrad", "10054": "entlanggefahren", "10055": "spritzern", "10056": "probleme", "10057": "hochheben", "10058": "gegenstands", "10059": "getrocknetes", "10060": "stadtgegend", "10061": "abgetragenen", "10062": "rot-wei\u00df-gestreiftem", "10063": "hantelstange", "10064": "befeuert", "10065": "hochsteigen", "10066": "balgt", "10067": "sumoringen", "10068": "strohgedeckten", "10069": "k\u00fchlschrank", "10070": "schneewall", "10071": "eingeklemmt", "10072": "bedienungspult", "10073": "zimmerm\u00e4dchen", "10074": "holztor", "10075": "delikatessenverk\u00e4ufer", "10076": "fenstergitter", "10077": "schulkleidung", "10078": "schlaginstrument", "10079": "brautjungfer", "10080": "reitbekleidung", "10081": "schrillen", "10082": "groben", "10083": "aufgereihte", "10084": "streifenh\u00f6rnchen", "10085": "spielplatz-schaukeltier", "10086": "boogiebrettern", "10087": "waagen", "10088": "tragevorrichtung", "10089": "motorradk\u00e4fig", "10090": "papierkrone", "10091": "empfangshalle", "10092": "geteerten", "10093": "krugs", "10094": "trichters", "10095": "pfadfinderuniform", "10096": "buntstift", "10097": "spielwarengesch\u00e4ft", "10098": "beweist", "10099": "spielzeugseil", "10100": "eilig", "10101": "f\u00fchler", "10102": "sonntag", "10103": "anzieht", "10104": "austreibenden", "10105": "gitarrespielern", "10106": "k\u00fcchenarbeitskraft", "10107": "speisesaal", "10108": "bungee-seilen", "10109": "dauerwelle", "10110": "badezimmerspiegel", "10111": "port", "10112": "casque", "10113": "obligatoire", "10114": "abgebrochenen", "10115": "profi-basketballteam", "10116": "cheerleadermannschaft", "10117": "zusammengesunken", "10118": "eimers", "10119": "fr\u00fcher", "10120": "gew\u00f6hnlich", "10121": "feierabend", "10122": "geheftet", "10123": "footballtrikot", "10124": "rollschuhlaufenden", "10125": "rollschuhlaufende", "10126": "umriss", "10127": "helmtragende", "10128": "theaterstudenten", "10129": "klebeband", "10130": "abgeklebt", "10131": "geb\u00e4udeeinfahrt", "10132": "fischglas", "10133": "tierbaby", "10134": "hug", "10135": "heftet", "10136": "ladeanflug", "10137": "raster", "10138": "vokabeln", "10139": "frischhaltefolie", "10140": "wei\u00df-schwarz", "10141": "schr\u00e4nkchen", "10142": "meereslandschaft", "10143": "tr\u00f6stet", "10144": "go-cart-bahn", "10145": "auslagenfenster", "10146": "beliebte", "10147": "arbeitsjacke", "10148": "orphans", "10149": "gebissen", "10150": "skiausflug", "10151": "screaming", "10152": "entrei\u00dfen", "10153": "zusammenprall", "10154": "chevron", "10155": "rugbyspielerin", "10156": "\u00fcberlisten", "10157": "r\u00f6hrenf\u00f6rmigen", "10158": "sonnenblumen", "10159": "gebrauchte", "10160": "buster", "10161": "dave", "10162": "applebee", "10163": "skilifts", "10164": "behangen", "10165": "gemachte", "10166": "s\u00e4ugt", "10167": "fotonarren", "10168": "ballettr\u00f6ckchen", "10169": "ostasiatischen", "10170": "weihnachtsbaums", "10171": "schranke", "10172": "orange-gelbes", "10173": "formal", "10174": "auffahrt", "10175": "garagen", "10176": "bettchen", "10177": "hispanische", "10178": "flussbetts", "10179": "wasserkanister", "10180": "plastikclown", "10181": "w\u00fcstenstra\u00dfe", "10182": "wildtier", "10183": "bl\u00f6delnd", "10184": "\u00fcbrigen", "10185": "wei\u00dfwein", "10186": "ballf\u00fchrenden", "10187": "angreifenden", "10188": "fu\u00dfball\u00fcbungen", "10189": "fl\u00f6tist", "10190": "massenszene", "10191": "keramikt\u00f6pfen", "10192": "matrosenm\u00fctzen", "10193": "rot-gr\u00fcnem", "10194": "konstruktionen", "10195": "z\u00f6gerlich", "10196": "qualmenden", "10197": "\u00fcbergie\u00dft", "10198": "stuntman", "10199": "explodiert", "10200": "anliegen", "10201": "verk\u00fcnden", "10202": "zueinanderpassenden", "10203": "fernbedienung", "10204": "stoffquadraten", "10205": "aufgesetzt", "10206": "selber", "10207": "verzehren", "10208": "whatever", "10209": "takes", "10210": "provisorisches", "10211": "marmors\u00e4ule", "10212": "doppel\u00fcberf\u00fchrung", "10213": "aufenthaltsraum", "10214": "diskussion", "10215": "gnu", "10216": "bellingham", "10217": "main", "10218": "st\u00e4ben", "10219": "schwergewichtiger", "10220": "wii-konsolen", "10221": "toren", "10222": "rockshow", "10223": "wendeltreppe", "10224": "fertigstellung", "10225": "tanzauff\u00fchrung", "10226": "ringerkleidung", "10227": "aneinandergepresst", "10228": "er\u00f6rtern", "10229": "gesch\u00e4ftsanz\u00fcgen", "10230": "zeltlagers", "10231": "fahrradrennens", "10232": "neunk\u00f6pfige", "10233": "e-piano", "10234": "metallm\u00f6beln", "10235": "frohe", "10236": "strohh\u00fcte", "10237": "sternform", "10238": "mundes", "10239": "bequemen", "10240": "gartensesseln", "10241": "synchronschwimmer", "10242": "seilzug", "10243": "hello-kitty-f\u00e4ustlingen", "10244": "gesch\u00fctzten", "10245": "ausstellungsbereich", "10246": "schneeh\u00f6hle", "10247": "angenehmem", "10248": "deckenlicht", "10249": "\u00fcberwurf", "10250": "eingepackten", "10251": "weihnachtsgeschenken", "10252": "festlicher", "10253": "snowboarding", "10254": "steinl\u00f6wen", "10255": "hochgehaltenen", "10256": "str\u00fcmpfe", "10257": "potato", "10258": "mr.", "10259": "gl\u00fchbirnen", "10260": "nerv\u00f6s", "10261": "schwarz-hellbraune", "10262": "bessern", "10263": "fu\u00dfballerin", "10264": "luftschlange", "10265": "ballettunterricht", "10266": "kabrio", "10267": "punktrichterin", "10268": "wasserblauen", "10269": "henkeln", "10270": "\u00fcberwiegende", "10271": "graviert", "10272": "schl\u00fcsselanh\u00e4nger", "10273": "majorette", "10274": "rollschuh-derby", "10275": "wischlappen", "10276": "gesagten", "10277": "klassenzimmers", "10278": "frettchen", "10279": "freiwilligen", "10280": "hausbau", "10281": "habitat", "10282": "humanity", "10283": "rohbau", "10284": "mitspielern", "10285": "bauwerkzeug", "10286": "zugestellten", "10287": "d\u00f6rflichen", "10288": "kr\u00e4utern", "10289": "steinigem", "10290": "waschraum", "10291": "noble", "10292": "sperrholzbrett", "10293": "legosteinen", "10294": "skisprungschanze", "10295": "malb\u00fccher", "10296": "umzuwerfen", "10297": "olympiade", "10298": "skivorrichtung", "10299": "zusammengerollt", "10300": "fahrradfahrers", "10301": "japanischer", "10302": "gestell", "10303": "ambiente", "10304": "abnimmt", "10305": "strumpfhalter", "10306": "paddle", "10307": "kakteen", "10308": "leap", "10309": "ranger", "10310": "werbeplakat", "10311": "roller-derby-montur", "10312": "jeanshemd", "10313": "gro\u00dfbildschirm", "10314": "sportteams", "10315": "wei\u00df-schwarze", "10316": "tonnen", "10317": "sicherheitsschutz", "10318": "spanier", "10319": "lief", "10320": "d\u00fcnnem", "10321": "erdnussbutterglases", "10322": "geklebt", "10323": "kamerastativ", "10324": "l\u00e4mmer", "10325": "waffengestellen", "10326": "basketballspiels", "10327": "neongelber", "10328": "\u00f6lpumpe", "10329": "eselsm\u00fctze", "10330": "zaubererumhang", "10331": "holzeimers", "10332": "unkontrollierbares", "10333": "meeresbucht", "10334": "bauarbeiterhelmen", "10335": "schimmerndes", "10336": "durchzus\u00e4gen", "10337": "hollywoodschaukel", "10338": "schr\u00e4ge", "10339": "rottweiler", "10340": "zweigt", "10341": "neckt", "10342": "vorzunehmen", "10343": "bepflanzt", "10344": "hinabst\u00fcrzt", "10345": "verunfalltes", "10346": "aufschreibt", "10347": "einkaufsbummel", "10348": "eishockey-torh\u00fcter", "10349": "startpunkt", "10350": "5-km-lauf", "10351": "pickels", "10352": "dreiradfahren", "10353": "hergerichteten", "10354": "hockey-torh\u00fcter", "10355": "lumpen", "10356": "beiger", "10357": "konferenzzentrum", "10358": "weihrauch", "10359": "angez\u00fcndet", "10360": "vogel-show", "10361": "aufgezeichnet", "10362": "miete", "10363": "ordnungsgem\u00e4\u00df", "10364": "treppenrampe", "10365": "verdorrten", "10366": "niedriges", "10367": "hochseil", "10368": "aufz\u00e4hlung", "10369": "rauscht", "10370": "kamelr\u00fccken", "10371": "genaue", "10372": "achtsam", "10373": "balancierender", "10374": "sichergestellt", "10375": "meeresstrandes", "10376": "zwischenmahlzeit", "10377": "tageszeit", "10378": "lagune", "10379": "verliebte", "10380": "wege", "10381": "blechdach", "10382": "gummitieren", "10383": "auff\u00e4llig", "10384": "batman-spielzeugauto", "10385": "q", "10386": "ringerinnen", "10387": "herztattoos", "10388": "handgelenken", "10389": "portland", "10390": "oregon", "10391": "winterlandschaft", "10392": "deleon", "10393": "stachelarmband", "10394": "sitzecke", "10395": "spiegeln", "10396": "schneehosen", "10397": "angebotstafel", "10398": "eindeckt", "10399": "beiboot", "10400": "stunt-fahrradfahrer", "10401": "untergehenden", "10402": "klarblaue", "10403": "converse-schuhen", "10404": "spielzeug-legosatz", "10405": "amerikanischem", "10406": "kolonialstil", "10407": "pausieren", "10408": "bmx-r\u00e4dern", "10409": "lederner", "10410": "t\u00fcrbereich", "10411": "schriftz\u00fcgen", "10412": "aufgebrachten", "10413": "besitzern", "10414": "blechblasinstrument", "10415": "klavierspielerin", "10416": "pfadfinderhandbuch", "10417": "b\u00fcrgerkriegssoldaten", "10418": "k\u00e4nguru", "10419": "stillen", "10420": "stammesangeh\u00f6riger", "10421": "lackhemd", "10422": "flamingos", "10423": "styroporbechern", "10424": "kosmetikprodukten", "10425": "plastiklatz", "10426": "got", "10427": "f\u00fchlen", "10428": "unwohl", "10429": "katzenstreu", "10430": "hockeybahn", "10431": "gewagten", "10432": "schwarz-gr\u00fcne", "10433": "verlorenem", "10434": "fahrradverleih", "10435": "husky-\u00e4hnliche", "10436": "session", "10437": "l\u00f6wenstatue", "10438": "fischernetzen", "10439": "lang\u00e4rmeliges", "10440": "u-bahn-eingang", "10441": "trapeznummer", "10442": "gebogene", "10443": "badem\u00e4nteln", "10444": "marmorstatue", "10445": "h\u00e4keltieren", "10446": "ensemble", "10447": "geigespielern", "10448": "seilvorrichtung", "10449": "bases", "10450": "glitzernde", "10451": "gebl\u00fcmtem", "10452": "schornstein", "10453": "pulsw\u00e4rmer", "10454": "erfolgreich", "10455": "irischen", "10456": "little-league-baseballspieler", "10457": "weihnachtsstrumpf", "10458": "nassrasierer", "10459": "sandh\u00fcgels", "10460": "coca-cola-zeichen", "10461": "odeon", "10462": "skilangl\u00e4ufer", "10463": "leichte", "10464": "bef\u00e4hrt", "10465": "interview", "10466": "zusammengebastelte", "10467": "fernsehbildschirm", "10468": "l\u00f6ffelt", "10469": "autofahrt", "10470": "hundemaul", "10471": "au\u00dfenborder", "10472": "notausgangsplan", "10473": "nordisch", "10474": "morgens", "10475": "waldiges", "10476": "gel\u00e4nders", "10477": "hofteiler", "10478": "langarmoberteil", "10479": "signalpistole", "10480": "befinde", "10481": "niedlich", "10482": "gegenwart", "10483": "herunterzurutschen", "10484": "spanisches", "10485": "rosafarben-gestreifte", "10486": "karnevals", "10487": "wasservogel", "10488": "plankenweg", "10489": "schlittenhundeteam", "10490": "laptopmonitore", "10491": "ger\u00fcsts", "10492": "erzielt", "10493": "vespa-tasche", "10494": "3rd", "10495": "haustierkr\u00f6te", "10496": "glasaufz\u00fcge", "10497": "seilkonstruktion", "10498": "sonnenauf-", "10499": "g\u00e4rtnert", "10500": "geysir", "10501": "klatschnasser", "10502": "outdoor-world-laden", "10503": "betonpfosten", "10504": "\u00fcberf\u00fclltes", "10505": "bepackten", "10506": "latte", "10507": "apportiert", "10508": "kopfhaut", "10509": "stachel", "10510": "begegnet", "10511": "b\u00fcrgerkriegszeit", "10512": "kuscheliger", "10513": "moslem", "10514": "curling-wettkampf", "10515": "fensterscheibe", "10516": "langhantel", "10517": "streichinstrumenten", "10518": "anfangen", "10519": "hantel", "10520": "stemmen", "10521": "angeschoben", "10522": "n\u00f6tig", "10523": "polieren", "10524": "muskul\u00e4ren", "10525": "d\u00fcsenflieger", "10526": "bann", "10527": "leggins", "10528": "kamels", "10529": "bestaunt", "10530": "abbildung", "10531": "bachs", "10532": "wirbelnden", "10533": "taschenlampen", "10534": "erinnerungsst\u00fccke", "10535": "anwesenheit", "10536": "vierbeiniges", "10537": "hineingebaut", "10538": "fu\u00dfballspielerinnen", "10539": "pokertisch", "10540": "athletischer", "10541": "anspruchsvolle", "10542": "pelzkragen", "10543": "grabstein", "10544": "gelb-braun", "10545": "kampfk\u00fcnstler", "10546": "grab", "10547": "oberschenkel", "10548": "dart", "10549": "aufgebot", "10550": "dudelsackspielern", "10551": "friedhofsarbeiter", "10552": "endg\u00fcltige", "10553": "sport-outfit", "10554": "betteln", "10555": "almosen", "10556": "tennis-outfit", "10557": "aufgereihten", "10558": "einsammelt", "10559": "verandastufen", "10560": "park\u00e4hnlichen", "10561": "handschaukel", "10562": "demonstrant", "10563": "briefmarke", "10564": "tiefem", "10565": "tennisb\u00e4lle", "10566": "schwimmtrainern", "10567": "paraskifahrer", "10568": "einfarbiger", "10569": "hinterraum", "10570": "getr\u00e4nkedose", "10571": "entsprechender", "10572": "skih\u00fctte", "10573": "stra\u00dfenauff\u00fchrung", "10574": "kokosnuss", "10575": "vollgekleckert", "10576": "sahne", "10577": "vierbeinigen", "10578": "snowboardtrick", "10579": "tanzgruppe", "10580": "choreographierte", "10581": "rechteckige", "10582": "abteilung", "10583": "lifestyle-magazine", "10584": "terriermischling", "10585": "schwanger", "10586": "fleischersch\u00fcrze", "10587": "konkurriert", "10588": "dreir\u00e4driges", "10589": "ballon-kulisse", "10590": "spanienhemd", "10591": "tanzbewegung", "10592": "pausiert", "10593": "basketb\u00e4llen", "10594": "pl\u00fcschspielzeugen", "10595": "weihnachtspapier", "10596": "s\u00fcdamerika", "10597": "ausgetreckten", "10598": "volley", "10599": "latein-", "10600": "gleitschirm", "10601": "handr\u00fccken", "10602": "rhythmisches", "10603": "gymnastikprogramm", "10604": "b\u00fcroparty", "10605": "m\u00f6gliches", "10606": "zahnproblemen", "10607": "spielzeuglutschenden", "10608": "pepper-logo", "10609": "comicbuch", "10610": "hacky-sack", "10611": "w\u00e4lzten", "10612": "sandstruktur", "10613": "dr", "10614": "dudelsackpfeifern", "10615": "arbeitskraft", "10616": "jazzclub", "10617": "g\u00e4stepaar", "10618": "werkunterricht", "10619": "pl\u00fcschtierpreise", "10620": "spider-man", "10621": "markise", "10622": "lacrosse-spiel", "10623": "tonschale", "10624": "tonteller", "10625": "brautschleier", "10626": "ausruht", "10627": "golfer", "10628": "puttet", "10629": "gebogenen", "10630": "golfern", "10631": "rassel", "10632": "hinweiss\u00e4ule", "10633": "kippen", "10634": "schn\u00e4pse", "10635": "lebensmittelladung", "10636": "getauft", "10637": "kajak-fahrer", "10638": "baseballschl\u00e4gern", "10639": "281", "10640": "gefletschten", "10641": "fahrradrampe", "10642": "dreieck", "10643": "kleinbuses", "10644": "vw-bus", "10645": "schlauchboote", "10646": "kollision", "10647": "biber", "10648": "serena", "10649": "williams", "10650": "startnummern", "10651": "verkaufswagen", "10652": "empfangstisch", "10653": "braun-karierten", "10654": "mockingbird", "10655": "kill", "10656": "klassiker", "10657": "schachspiel", "10658": "ausmalen", "10659": "ausmalbuch", "10660": "strukturierten", "10661": "\u00fcberblicken", "10662": "bioschutzanz\u00fcgen", "10663": "menschlich", "10664": "rollbett", "10665": "oceanside-pool", "10666": "benatar", "10667": "pat", "10668": "abendveranstaltung", "10669": "barbusiges", "10670": "gepunkteter", "10671": "stewart", "10672": "puncto", "10673": "lichtausstellung", "10674": "kunststoffb\u00e4llen", "10675": "medien", "10676": "bootsf\u00fchrer", "10677": "grenzlinie", "10678": "durchsehen", "10679": "marktwaren", "10680": "blau-violett", "10681": "holzschuppen", "10682": "arbeitsraum", "10683": "flo\u00dffahren", "10684": "schnorchelausr\u00fcstung", "10685": "innenpferch", "10686": "uferzone", "10687": "sorgenvoll", "10688": "breitbild-fernseher", "10689": "metallic-gr\u00fcnen", "10690": "fertiggerichte", "10691": "flamen", "10692": "system", "10693": "freche", "10694": "spielzeugpistolen", "10695": "metallarbeiten", "10696": "schilf", "10697": "unterteilt", "10698": "unterstand", "10699": "installiert", "10700": "fabrikstube", "10701": "kristall", "10702": "leder", "10703": "trainingsjacke", "10704": "abgez\u00e4unten", "10705": "skifahrern", "10706": "geh\u00e4use", "10707": "zigarren", "10708": "duftstoffe", "10709": "draufsteht", "10710": "basierend", "10711": "verandat\u00fcr", "10712": "getr\u00e4nkeladen", "10713": "p-f\u00f6rmigen", "10714": "hochgezogenem", "10715": "mittleren-osten", "10716": "pl\u00fcschsessel", "10717": "durag", "10718": "dodgers", "10719": "geschlagenen", "10720": "gold-schwarzer", "10721": "agility-park", "10722": "unterzeichnen", "10723": "autorennfahrer", "10724": "seerobbe", "10725": "regenzeug", "10726": "verwahrloster", "10727": "vorspeisen", "10728": "caterer", "10729": "kirchenausgang", "10730": "sorvete", "10731": "gelados", "10732": "hungrigen", "10733": "seifenkiste", "10734": "tr\u00fcmmern", "10735": "kussmund", "10736": "hochwasser", "10737": "fu\u00dfball-spieler", "10738": "handgelenksschmuck", "10739": "stierstatue", "10740": "klatschnass", "10741": "lockenwicklern", "10742": "gepunktete", "10743": "house", "10744": "steh", "10745": "bahnsteigs", "10746": "doll", "10747": "kerzenlicht", "10748": "rettung", "10749": "nathalie", "10750": "demonstrantin", "10751": "pro-amerika", "10752": "ge\u00f6ffnete", "10753": "gesch\u00e4ftsgrundst\u00fcck", "10754": "hundeausstellung", "10755": "661", "10756": "messezelt", "10757": "attraktionen", "10758": "wahrsagt", "10759": "grandmas", "10760": "prediction", "10761": "gew\u00e4ssser", "10762": "stoffschwein", "10763": "parallelen", "10764": "lieferwagens", "10765": "trabt", "10766": "bearded", "10767": "agility", "10768": "testparcours", "10769": "bleicher", "10770": "spongebob", "10771": "quadbikes", "10772": "hauseingangs", "10773": "trader", "10774": "untersten", "10775": "vollbart", "10776": "braun-schwarzen", "10777": "avrovulcan.com.", "10778": "schwimmmerin", "10779": "teamkollegin", "10780": "schwei\u00dferhelm", "10781": "zugeguckt", "10782": "weitwinkelbild", "10783": "tieren", "10784": "fotoausr\u00fcstung", "10785": "herzf\u00f6rmige", "10786": "konzertbereich", "10787": "klappe", "10788": "lebensgro\u00dfen", "10789": "gef\u00e4rbt", "10790": "skateboarding", "10791": "komplizierter", "10792": "hockend", "10793": "pc-arbeitsplatz", "10794": "scheitel", "10795": "feuert\u00e4nzern", "10796": "stuhlreihe", "10797": "mithalten", "10798": "magazine", "10799": "r\u00f6hrchen", "10800": "delphine", "10801": "stollen", "10802": "sportschuhen", "10803": "sandige", "10804": "jugend", "10805": "freiheit", "10806": "gleitbrett", "10807": "k\u00fccheausstellung", "10808": "humanoiden", "10809": "stierkampf", "10810": "spielplatzger\u00e4ten", "10811": "beifahrerseite", "10812": "belegt", "10813": "skiing", "10814": "goldvase", "10815": "deswegen", "10816": "baseball-spieler", "10817": "kliffs", "10818": "vuitton", "10819": "kirchenversammlung", "10820": "louis", "10821": "weihnachtsessen", "10822": "baskin", "10823": "robbins", "10824": "u-bahn-fenster", "10825": "hockey-spieler", "10826": "digger", "10827": "lesende", "10828": "betonabsperrung", "10829": "also", "10830": "dj-equipment", "10831": "kastanien", "10832": "soundsystem", "10833": "gelb-gestreiften", "10834": "verdutzt", "10835": "be\u00e4ugt", "10836": "freiluft-flohmarkt", "10837": "feldwanderweg", "10838": "selbstgebackenen", "10839": "au\u00dfenparkplatz", "10840": "glasfl\u00e4che", "10841": "kinderkletterwand", "10842": "kipper", "10843": "zuflucht", "10844": "k\u00fchlern", "10845": "challenge-butter", "10846": "hausgemachter", "10847": "pflanzenwelt", "10848": "reifen-schaukel", "10849": "anbieter", "10850": "st\u00fcrmischen", "10851": "bungee-jumping", "10852": "kochkurs", "10853": "ausgabe", "10854": "people-magazin", "10855": "schildtr\u00e4gerinnen", "10856": "zanken", "10857": "getupfter", "10858": "kolleginnen", "10859": "musikspiel", "10860": "zeltstadt", "10861": "gehstocks", "10862": "fl\u00fcchtlinge", "10863": "beobachtetet", "10864": "spielzeug-feuerwehrauto", "10865": "gesch\u00e4ftsr\u00e4umen", "10866": "karawane", "10867": "kartonrahmen", "10868": "klassischer", "10869": "spiegelwand", "10870": "lieblingstennisball", "10871": "bewachsenem", "10872": "lang\u00e4rmelige", "10873": "g\u00e4rtnerisch", "10874": "\u00fcbermalen", "10875": "verarbeiten", "10876": "pfanneninhalt", "10877": "baseball-spiel", "10878": "geschlachteten", "10879": "rohen", "10880": "drahtz\u00e4unen", "10881": "fahrkartenschalters", "10882": "ketchup", "10883": "senf", "10884": "toastbrot", "10885": "sitzgruppen", "10886": "subaru", "10887": "\u00fcberblick", "10888": "wetten", "10889": "casino-tisch", "10890": "dekollet\u00e9", "10891": "verf\u00fchrerisch", "10892": "delfins", "10893": "schwei\u00dfmaschine", "10894": "vegetationsfeld", "10895": "seilpark", "10896": "fu\u00dfballspielzeug", "10897": "arbeitnehmern", "10898": "bassistin", "10899": "gastst\u00e4tte", "10900": "kompliziertes", "10901": "gummiente", "10902": "pitcherbewegung", "10903": "rennw\u00e4gen", "10904": "tintenfischgericht", "10905": "ansturm", "10906": "\u00fcberw\u00e4ltigt", "10907": "landschaftspflegematerial", "10908": "souvenirzeitschriften", "10909": "endlich", "10910": "wifi-verbindung", "10911": "zierstrauch", "10912": "papierst\u00fccke", "10913": "gl\u00fccklichem", "10914": "pandam\u00fctze", "10915": "schrankschloss", "10916": "zeitlupenfoto", "10917": "professionelle", "10918": "rot-orangen", "10919": "palastwache", "10920": "pflegefachkr\u00e4fte", "10921": "bootrennen", "10922": "gicht", "10923": "corgis", "10924": "angriffslinie", "10925": "footballtraining", "10926": "fu\u00dfballtrainer", "10927": "aussehend", "10928": "reiniger", "10929": "anmalt", "10930": "\u00f6rtliche", "10931": "wasserpark", "10932": "springburg", "10933": "abzuschlie\u00dfen", "10934": "m\u00e4dchen-fu\u00dfballteam", "10935": "innenbasketballkorb", "10936": "blumenkarren", "10937": "betonmischer", "10938": "angehefteter", "10939": "jahrmarktsfahrt", "10940": "entsprechenden", "10941": "bedecken", "10942": "autoskooter", "10943": "sichtweite", "10944": "93", "10945": "zementgeb\u00e4ude", "10946": "\u00fcberf\u00fcllter", "10947": "kettenleine", "10948": "landes", "10949": "verschmiertes", "10950": "peek-a-boo", "10951": "schokoladenpudding", "10952": "sedan", "10953": "sekunden", "10954": "stochern", "10955": "altersgruppen", "10956": "ferngl\u00e4ssern", "10957": "marineblaue", "10958": "mickey-mouse-hemd", "10959": "l\u00e4ngeren", "10960": "skatern", "10961": "originellen", "10962": "atemschutzmaske", "10963": "schaubild", "10964": "aufgebauten", "10965": "scheitelk\u00e4ppchen", "10966": "nahrungsmittelverkaufsstand", "10967": "\u00fcberlegen", "10968": "sollten", "10969": "revers", "10970": "metallkanone", "10971": "kreise", "10972": "gebrauch", "10973": "multimedia-ausstattung", "10974": "attraktion", "10975": "schotterfeld", "10976": "schnute", "10977": "holzkonstruktionen", "10978": "hunde-rennen", "10979": "gem\u00e4chlich", "10980": "kariert", "10981": "blecheimer", "10982": "nebem", "10983": "agility-parcours", "10984": "\u00fcbertriebene", "10985": "schwertkampf", "10986": "wiedergibt", "10987": "perlenkette", "10988": "holzmaske", "10989": "fr\u00fch", "10990": "huckabee", "10991": "industriegeb\u00e4udes", "10992": "kampagne", "10993": "koch-uniform", "10994": "identifizieren", "10995": "abdeckungen", "10996": "damentoilette", "10997": "k\u00e4mmt", "10998": "gefahrenkegeln", "10999": "bewacht", "11000": "mitf\u00e4hrt", "11001": "gepflegter", "11002": "aufgespie\u00dften", "11003": "kahler", "11004": "softballfeld", "11005": "handpuppe", "11006": "kullern", "11007": "wasserturm", "11008": "dampflok", "11009": "verw\u00f6hnt", "11010": "flugzeug-oberteil", "11011": "gel\u00e4ndemotorradtrick", "11012": "streien", "11013": "abschlussballkleidung", "11014": "geformtes", "11015": "distanz", "11016": "meeresfr\u00fcchtestand", "11017": "lafayette", "11018": "verdreht", "11019": "seifenblasenstab", "11020": "punk-rock-kleidung", "11021": "hockeyst\u00f6cke", "11022": "l\u00e4ndliches", "11023": "einkaufsviertel", "11024": "steinweges", "11025": "anzeigetafeln", "11026": "metallstangen", "11027": "k\u00fcnstlers", "11028": "superman-kost\u00fcm", "11029": "reifenschlauch", "11030": "reifer", "11031": "hausgemachte", "11032": "benotet", "11033": "niederschlagen", "11034": "man\u00f6ver", "11035": "jukebox-wandbild", "11036": "umz\u00e4unte", "11037": "bongotrommel", "11038": "rikscha-fahrrad", "11039": "telefonkabine", "11040": "mord", "11041": "tehelka-zeitungen", "11042": "vielbesuchten", "11043": "epochendarsteller", "11044": "grasballen", "11045": "waden", "11046": "schwierigem", "11047": "kunstrad", "11048": "auszusehen", "11049": "bewachsen", "11050": "spielzeugk\u00fcche", "11051": "luke", "11052": "hereinkommen", "11053": "beschriebenen", "11054": "schmust", "11055": "wegstreckt", "11056": "w\u00fcstenkleidung", "11057": "k\u00e4mpfers", "11058": "ufc-k\u00e4mpfer", "11059": "touristeninformation", "11060": "weggesp\u00fclt", "11061": "wasserfluten", "11062": "vorspeise", "11063": "native", "11064": "bespr\u00fchen", "11065": "sandh\u00fcgeln", "11066": "sonnensegel", "11067": "lava-lampe", "11068": "feldwegradstrecke", "11069": "lederoutfit", "11070": "skelettaufdruck", "11071": "m\u00f6glichkeiten", "11072": "showgirl", "11073": "derzeit", "11074": "vorw\u00e4rtssalto", "11075": "morgenstunden", "11076": "chicagoer", "11077": "ballone", "11078": "metallgondel", "11079": "geschnallt", "11080": "blockiert", "11081": "n\u00e4hren", "11082": "spielplatzschiff", "11083": "kletternetz", "11084": "kunstprojekten", "11085": "schultages", "11086": "pumps", "11087": "doppeldecker", "11088": "rauchfahne", "11089": "vons-einkaufswagen", "11090": "spielplatztunnel", "11091": "ged\u00e4mpftem", "11092": "gem\u00e4hten", "11093": "ballerinen", "11094": "sonnenbadet", "11095": "hochgestreckten", "11096": "steph", "11097": "gondoliere", "11098": "punktzahlen", "11099": "bodenturnens", "11100": "ehrfurchtsvoll", "11101": "weinberg", "11102": "dorfstra\u00dfe", "11103": "fischereiausr\u00fcstung", "11104": "saxophonspieler", "11105": "innenrampe", "11106": "weinland", "11107": "t\u00e4gliche", "11108": "angewinkelt", "11109": "anbellen", "11110": "sitzgurt", "11111": "loslassen", "11112": "flanellhose", "11113": "hochstehenden", "11114": "l\u00e4nglichen", "11115": "haarschnitten", "11116": "rica", "11117": "costa", "11118": "forschungsausr\u00fcstung", "11119": "boxwettkampf", "11120": "kaufhausgeb\u00e4udes", "11121": "b\u00fcrsten", "11122": "fahrradsturzhelm", "11123": "wasserballons", "11124": "unterrichten", "11125": "hofft", "11126": "parkbesucher", "11127": "fastbreak", "11128": "wiederbringen", "11129": "kan\u00e4len", "11130": "mustangs", "11131": "fast-food-restaurant", "11132": "fr\u00fchst\u00fcckszeit", "11133": "kolibri", "11134": "kartenspielen", "11135": "kabinenfoto", "11136": "moves", "11137": "morgensonne", "11138": "watscheln", "11139": "unsere", "11140": "studentenmusikgruppe", "11141": "skalenhaube", "11142": "unterwasser", "11143": "gegenspielerin", "11144": "ungl\u00fcckliche", "11145": "milit\u00e4rfarbe", "11146": "holzf\u00e4ssern", "11147": "mittelbraunen", "11148": "umkleideraum", "11149": "fu\u00dfballuniform", "11150": "marx-brille", "11151": "gruocho", "11152": "engagiert", "11153": "laufe", "11154": "w\u00fcrgt", "11155": "niesschutz", "11156": "blumigen", "11157": "gekr\u00e4uselten", "11158": "blaugraue", "11159": "wurfobjekt", "11160": "zylinderhut", "11161": "rost", "11162": "pioneer", "11163": "country", "11164": "h\u00e4hnchen", "11165": "akrobatisches", "11166": "barhocker", "11167": "fr\u00fchst\u00fccksgetreide", "11168": "anblickt", "11169": "metallobjekten", "11170": "ganzk\u00f6rperanzug", "11171": "nascar-fahrer", "11172": "abspringt", "11173": "bambuszaun", "11174": "surferin", "11175": "ti", "11176": "chi", "11177": "l\u00e4ssiger", "11178": "kleiderordnung", "11179": "rudernt", "11180": "hotelpool", "11181": "bowlinbahn", "11182": "handabdr\u00fccken", "11183": "disney-figuren", "11184": "hinh\u00e4lt", "11185": "einstiegsloch", "11186": "islamischen", "11187": "kassenschlange", "11188": "milit\u00e4rlastwagen", "11189": "schoner", "11190": "wasserorganismen", "11191": "kampfkunst", "11192": "zukneift", "11193": "unterst\u00fctzt", "11194": "gontaga", "11195": "geformten", "11196": "pferdeshow", "11197": "routine\u00fcbung", "11198": "milit\u00e4r", "11199": "flaschendrehen", "11200": "erfrischungen", "11201": "wunden", "11202": "ukele", "11203": "tornister", "11204": "unterzeichnet", "11205": "papierkram", "11206": "usa-jacke", "11207": "surfens", "11208": "steinerne", "11209": "wehen", "11210": "verzehr", "11211": "ocean", "11212": "sox", "11213": "beendeten", "11214": "inning", "11215": "kunstapparat", "11216": "\u00fcbereifrigen", "11217": "graubraunen", "11218": "strandparty", "11219": "r\u00fcckweg", "11220": "hefter", "11221": "aufw\u00e4rmanzug", "11222": "felsenklettern", "11223": "kleintransporters", "11224": "weinflaschen", "11225": "elefantenspielzeug", "11226": "s\u00e4uglingsjunge", "11227": "clog-sandalen", "11228": "verkehrskegel", "11229": "hofverkauf", "11230": "gestreiften-hemd", "11231": "u-bahnlinie", "11232": "struwwelfrisur", "11233": "dominosteine", "11234": "tr\u00e4ge", "11235": "tischdecken", "11236": "jeanstasche", "11237": "zigeunerhaften", "11238": "rosa-wei\u00df-gestreiftem", "11239": "nachrennen", "11240": "business-kleidung", "11241": "spielautos", "11242": "fairway-einkaufswagen", "11243": "hinaufzuklettern", "11244": "verwahrlosten", "11245": "hinunterfahren", "11246": "skateboardtricks", "11247": "papierkorb", "11248": "laublose", "11249": "polofeld", "11250": "h\u00fchner-lagerhaus", "11251": "gelbbraune", "11252": "gescheckte", "11253": "baseball-pitcher", "11254": "sarong", "11255": "ballonh\u00fcte", "11256": "besteckschublade", "11257": "lichterketten", "11258": "dior-t-shirt", "11259": "autobahnarbeiter", "11260": "autobahnausfahrtschild", "11261": "sportlich", "11262": "schwuler", "11263": "babyflasche", "11264": "reinkommen", "11265": "gesichtsausdr\u00fccke", "11266": "marinehose", "11267": "lollipop-maschine", "11268": "abzuheben", "11269": "wundersch\u00f6nem", "11270": "ein-amerikanisches", "11271": "gummiboot", "11272": "aufw\u00e4rmt", "11273": "spaghetti", "11274": "indianerh\u00e4uptling", "11275": "geb\u00fcrtige", "11276": "aqua-wasser", "11277": "hellbraunes", "11278": "br\u00e4unlich", "11279": "gew\u00e4nden", "11280": "abhalten", "11281": "nationen", "11282": "tennisausstattung", "11283": "zusammenschwei\u00dft", "11284": "clownschminke", "11285": "lockenstab", "11286": "gewellt", "11287": "b\u00e4ndigen", "11288": "leckage", "11289": "g\u00e4rbeh\u00e4lter", "11290": "entkommen", "11291": "entgegengesetzten", "11292": "verkehrs", "11293": "dachterrasse", "11294": "gel\u00e4nderst\u00fccke", "11295": "k\u00e4selaiben", "11296": "felsabh\u00e4ngen", "11297": "offizieller", "11298": "adler", "11299": "paddel", "11300": "superman-produkt", "11301": "durchlesen", "11302": "tiefblauem", "11303": "kunsthandwerkprojekt", "11304": "kunsthandwerken", "11305": "straffen", "11306": "bauchmuskeln", "11307": "grill-h\u00e4hnchenschlegel", "11308": "rebe", "11309": "l\u00f6sen", "11310": "bongo-trommelt", "11311": "picknick-tisch", "11312": "eisdiele", "11313": "fete", "11314": "greyhound-hund", "11315": "pilotenbrille", "11316": "lebendiges", "11317": "ymca", "11318": "scheespray", "11319": "anknurrt", "11320": "g\u00e4rtnerei", "11321": "skatebaordsch\u00fcssel", "11322": "geschickt", "11323": "schattenbild", "11324": "kampfsportanzug", "11325": "motorrad-mit", "11326": "58", "11327": "rasend", "11328": "schutzmasken", "11329": "baseball-base", "11330": "extrembiker", "11331": "verkaufsst\u00e4nden", "11332": "betonoberfl\u00e4che", "11333": "farbton", "11334": "heranwachsende", "11335": "adirondack", "11336": "erotisch", "11337": "gr\u00fcn-blau-gelben", "11338": "hochgestreckt", "11339": "falcons", "11340": "kaffeebar", "11341": "kamerablickfeld", "11342": "skifahren", "11343": "mountainbike-rennfahrer", "11344": "springbrunnenpark", "11345": "brustschwimmen", "11346": "arrangement", "11347": "rollgep\u00e4ck", "11348": "verkehrskegeln", "11349": "rotbackiger", "11350": "hauptattraktion", "11351": "necken", "11352": "unkonventionellen", "11353": "m\u00fcnzfernglas", "11354": "basketballwurfs", "11355": "inline-skaterin", "11356": "gelbbraunes", "11357": "paketsprung", "11358": "mehrfarbigen-zelt", "11359": "gem\u00fcseladens", "11360": "fotobildschirms", "11361": "fotolampe", "11362": "gesteinsformation", "11363": "jalousie", "11364": "zufriedenem", "11365": "aufregung", "11366": "fischerkanu", "11367": "erntetag", "11368": "gemustertes", "11369": "herumzieht", "11370": "schaudert", "11371": "auswirkung", "11372": "sands", "11373": "eingest\u00fcrzten", "11374": "profi-ringer", "11375": "ohrs", "11376": "ureinwohnertracht", "11377": "skate", "11378": "tennisplatzes", "11379": "maurerarbeiten", "11380": "runterrutscht", "11381": "digitale", "11382": "afroamerikanisches-baby", "11383": "gartenger\u00e4te", "11384": "cowbow-hut", "11385": "anzugsjacke", "11386": "hands\u00e4ge", "11387": "spezielle", "11388": "gange", "11389": "abschneidet", "11390": "durchtanzen", "11391": "boxsack", "11392": "blumenstiel", "11393": "springreiter", "11394": "fehlerfrei", "11395": "kopfmaske", "11396": "schwimmzeug", "11397": "konstruktionskegeln", "11398": "skateboardoberfl\u00e4che", "11399": "schlossers", "11400": "kassiert", "11401": "skysurft", "11402": "basar", "11403": "hohlen", "11404": "gummihuhn", "11405": "orangeroten", "11406": "fotografie", "11407": "zerrt", "11408": "sonnenaufgangs", "11409": "holzb\u00e4ren", "11410": "w\u00fcstenrennmaus", "11411": "g\u00e4nsebl\u00fcmchen", "11412": "vegetation", "11413": "stroboskoplichter", "11414": "papptellern", "11415": "skydiver", "11416": "superman", "11417": "schneem\u00e4nner", "11418": "maurer", "11419": "arbeitskollege", "11420": "aufpasst", "11421": "gesponsert", "11422": "fahrradrennnen", "11423": "kandidat", "11424": "mini-pony", "11425": "volleyballnetz", "11426": "abwechselnd", "11427": "rennradkleidung", "11428": "hallenrennen", "11429": "pelzhut", "11430": "budweiser", "11431": "heruntergewirtschafteten", "11432": "erhielten", "11433": "dreirads", "11434": "fahrradtrainingsger\u00e4t", "11435": "versinkenden", "11436": "elfmeterschie\u00dfen", "11437": "radrennbahn", "11438": "gro\u00dfbritannien", "11439": "farmhauses", "11440": "gespiegelten", "11441": "torlosen", "11442": "fu\u00dfball-liga", "11443": "vorst\u00e4dtischen", "11444": "rotbraun", "11445": "aufw\u00e4ndige", "11446": "jesusschrein", "11447": "panasonic-videokamera", "11448": "gepunkteten-outfit", "11449": "sommersprossen", "11450": "metallohrringe", "11451": "kirsche", "11452": "windsurft", "11453": "b\u00fcrobereichs", "11454": "stewardess", "11455": "rotierende", "11456": "bodenarbeit", "11457": "spielmaschine", "11458": "eish\u00f6hle", "11459": "dunkelblauem", "11460": "gesch\u00e4ftsstelle", "11461": "gymnastikringen", "11462": "atm-maschine", "11463": "jungstierkampf", "11464": "schneewehen", "11465": "dachterassen-skateboarder", "11466": "langl\u00e4ufer", "11467": "5-personen-band", "11468": "reitoutfit", "11469": "diagramm", "11470": "schneepfl\u00fcgt", "11471": "piratenoutfits", "11472": "tanzhaltung", "11473": "wacht", "11474": "bestanden", "11475": "essbereich", "11476": "runterspringt", "11477": "kinnriemen", "11478": "mops", "11479": "ghostbusters", "11480": "rasterbild", "11481": "tote", "11482": "vietnamesische", "11483": "flussmarkts", "11484": "gepaddelt", "11485": "gie\u00dfkanne", "11486": "minigolfloch", "11487": "ausgetrockneten", "11488": "milit\u00e4rsachen", "11489": "sprungwettbewerb", "11490": "zuschauers", "11491": "aufgelegt", "11492": "begie\u00dft", "11493": "-tassen", "11494": "blumenmustern", "11495": "formell", "11496": "adrette", "11497": "u.s.", "11498": "yongsan", "11499": "papiertellern", "11500": "geschichtenbuch", "11501": "verzieren", "11502": "zahnuntersuchung", "11503": "tuben", "11504": "heftige", "11505": "trainingsger\u00e4ten", "11506": "handdusche", "11507": "geduscht", "11508": "wassersprenkler", "11509": "korbst\u00fchlen", "11510": "medaillons", "11511": "druckt", "11512": "verspeist", "11513": "gallopieren", "11514": "peeps", "11515": "marchmallow", "11516": "m\u00e4hnenb\u00fcrste", "11517": "stinkt", "11518": "abst\u00fcrzt", "11519": "h\u00e4ngegurten", "11520": "skateboarders", "11521": "rotwein", "11522": "batterie", "11523": "schritte", "11524": "wohnhauskomplexes", "11525": "gestylten", "11526": "kirmeskarusell", "11527": "designen", "11528": "quelle", "11529": "springende", "11530": "schoss", "11531": "hereingebracht", "11532": "malbuchs", "11533": "tafelst\u00e4nder", "11534": "arcade", "11535": "rennspiel", "11536": "elektonisches", "11537": "musikkonzert", "11538": "kapuzenkleidung", "11539": "pokemon", "11540": "umrandet", "11541": "grupe", "11542": "aufget\u00fcrmtem", "11543": "bootsfahrt", "11544": "muffin-oberteil", "11545": "propeller", "11546": "babypuppen", "11547": "meers", "11548": "tartuffe", "11549": "bierflaschenkissen", "11550": "begraben", "11551": "ballbad", "11552": "weiche", "11553": "schweins", "11554": "laufshorts", "11555": "weihnachtsmannpuppe", "11556": "ausmalt", "11557": "blashorn", "11558": "crayola-stiften", "11559": "wasserfallkulisse", "11560": "country-musiker", "11561": "armdr\u00fccken", "11562": "hotelzimmer", "11563": "extravagant", "11564": "einbezogen", "11565": "verd\u00e4chtiger", "11566": "verwahranstalt", "11567": "flugbl\u00e4ttern", "11568": "strafverfolgungsbeh\u00f6rde", "11569": "kampfsportwettkampf", "11570": "hafengebiets", "11571": "gro\u00dfaufnahme", "11572": "bademeister", "11573": "sirup", "11574": "papierabzeichen", "11575": "yo-yos", "11576": "transformer", "11577": "diskjockey", "11578": "jackson", "11579": "kleidergesch\u00e4fts", "11580": "exzentrisch", "11581": "petrischale", "11582": "beachvolleyballspieler", "11583": "sei", "11584": "connah", "11585": "vorbeizieht", "11586": "kickboxerin", "11587": "schwulenparade", "11588": "besitzt\u00fcmer", "11589": "do", "11590": "taillenbund", "11591": "schritth\u00f6he", "11592": "verlangt", "11593": "erwidert", "11594": "tennisschlag", "11595": "verschmiert", "11596": "lehmpfad", "11597": "limbotanz", "11598": "fishmonger", "11599": "fischabteilung", "11600": "gans", "11601": "g\u00e4nsek\u00fcken", "11602": "walmart-eink\u00e4ufer", "11603": "blumenkette", "11604": "faustschlag", "11605": "tapete", "11606": "nat\u00fcrlich", "11607": "attraktionsmittelpunkt", "11608": "abgespr\u00fcht", "11609": "trainingspylone", "11610": "vorst\u00e4dten", "11611": "weihnachtsmannkost\u00fcmen", "11612": "xylophonen", "11613": "b\u00fcrgersteigen", "11614": "patriotismus", "11615": "nackenband", "11616": "gr\u00f6\u00dferes", "11617": "hotels", "11618": "klippenrand", "11619": "unterhose", "11620": "calvin", "11621": "schwimmendes", "11622": "wattiertem", "11623": "starrend", "11624": "joker", "11625": "knuddelt", "11626": "schneefeld", "11627": "typisches", "11628": "schlammige", "11629": "cunningham", "11630": "prachtskerl", "11631": "fenstert\u00fcr", "11632": "\u00e4gyptisches", "11633": "schienbeinschonern", "11634": "kricketspiel", "11635": "schuhkarton", "11636": "gebogenes", "11637": "richtigen", "11638": "bassinstrument", "11639": "ausrufer", "11640": "kleinvorstellung", "11641": "anzulocken", "11642": "kastanienbrauner", "11643": "h\u00e4ngeohren", "11644": "schnellen", "11645": "vorgebirges", "11646": "hindernisparkour", "11647": "stylistischen", "11648": "haarstylistin", "11649": "gleichausehende", "11650": "derweil", "11651": "knuspriges", "11652": "inlinern", "11653": "zementmauer", "11654": "kampfsportanz\u00fcgen", "11655": "hinabsteigt", "11656": "kollegesch\u00fclern", "11657": "hold", "11658": "pot", "11659": "hsbc", "11660": "ud", "11661": "whiteboard", "11662": "essensbuffet", "11663": "schneeflocke", "11664": "einnimmt", "11665": "klatzk\u00f6pfiger", "11666": "generationen", "11667": "d\u00e4chern", "11668": "steinh\u00e4usern", "11669": "futter", "11670": "je", "11671": "metallkelle", "11672": "m\u00fcttern", "11673": "zur\u00fcckzuschlagen", "11674": "zusammentreibt", "11675": "uferrand", "11676": "radkunstst\u00fcck", "11677": "blumenhemd", "11678": "cd-regalen", "11679": "krokodilstofftier", "11680": "campingbusses", "11681": "brenmark", "11682": "tools", "11683": "verbindungselemente", "11684": "hochst\u00fchlen", "11685": "herumfliegen", "11686": "zusammenbrechenden", "11687": "zweij\u00e4hriges", "11688": "spielmobile", "11689": "ufc-spiel", "11690": "gedr\u00fcckt", "11691": "verschmierten", "11692": "wurfbewegung", "11693": "save", "11694": "damm", "11695": "verdreckter", "11696": "exzentrischen", "11697": "rodeoteilnehmer", "11698": "schleier", "11699": "go-cart", "11700": "rads", "11701": "sattelbez\u00fcgen", "11702": "schweinen", "11703": "einzigartiges", "11704": "hausk\u00fcche", "11705": "bastrock", "11706": "bewirbt", "11707": "restaurantprodukte", "11708": "sumpf\u00e4hnliche", "11709": "fertigstellt", "11710": "krokodilbaby", "11711": "gelblicher", "11712": "meereswelle", "11713": "behandeln", "11714": "trampelt", "11715": "dunkelgr\u00fcnes", "11716": "lounge", "11717": "gefault", "11718": "kaufhausangestellter", "11719": "einnehmen", "11720": "miniaturgeb\u00e4ude", "11721": "chilli", "11722": "k\u00fchltasche", "11723": "jeanshosen", "11724": "bruchsteinmauer", "11725": "tintenfischs", "11726": "ballettlehrerin", "11727": "besagen", "11728": "spruchb\u00e4ndern", "11729": "alliance", "11730": "mazda", "11731": "billiardball", "11732": "tr\u00e4gertop", "11733": "aufbau", "11734": "polohemden", "11735": "rehe", "11736": "messlatte", "11737": "ges\u00e4\u00dftasche", "11738": "speisekarten", "11739": "waldrand", "11740": "drahtgewebe", "11741": "bauschild", "11742": "blumenkorb", "11743": "behandelten", "11744": "prallt", "11745": "gepunktetes", "11746": "bettlaken", "11747": "umarmende", "11748": "zusammenzuschlagen", "11749": "kolonialzeit", "11750": "schulgeb\u00e4udes", "11751": "rennteam", "11752": "altmodisches", "11753": "angelausr\u00fcstung", "11754": "boxencrew", "11755": "dragster-rennen", "11756": "dragster", "11757": "zehenn\u00e4gel", "11758": "graham", "11759": "flitzt", "11760": "gepflegt", "11761": "fairway", "11762": "gelblichen", "11763": "schwarz-graue", "11764": "heimzuholen", "11765": "durchtrainierte", "11766": "stabhochsprungstab", "11767": "bereitschaftshaltung", "11768": "h\u00e4ngepflanze", "11769": "nr.", "11770": "sprinkler", "11771": "verzierung", "11772": "hinuntergleitet", "11773": "grinsendes", "11774": "radfahrender", "11775": "paaren", "11776": "gwan", "11777": "multipurpose", "11778": "centre-schild", "11779": "schon", "11780": "weinend", "11781": "fotoausstellung", "11782": "akustikgitarrre", "11783": "unordnung", "11784": "kleis", "11785": "ein\u00e4ndigen", "11786": "skateboard-rampe", "11787": "zeitgen\u00f6ssischem", "11788": "geburtstagstorte", "11789": "aisate", "11790": "zuwendet", "11791": "erstauntes", "11792": "frontlader-trockner", "11793": "hai", "11794": "h\u00fcttensteine", "11795": "krokodil", "11796": "sportstadion", "11797": "billy", "11798": "dahingleitet", "11799": "puma-tanktop", "11800": "bigband", "11801": "ballontiere", "11802": "starken", "11803": "kakteengarten", "11804": "b\u00fccherregalen", "11805": "still", "11806": "babyschale", "11807": "anlasse", "11808": "essende", "11809": "rot-", "11810": "geerntete", "11811": "etagenbett", "11812": "prinzessinenschloss", "11813": "usa-hemd", "11814": "eiche", "11815": "evergreen", "11816": "krieger", "11817": "f\u00e4usten", "11818": "lehm", "11819": "geballten", "11820": "steck", "11821": "au\u00dfenfahrstuhl", "11822": "selbstportr\u00e4t", "11823": "eingeborener", "11824": "personenkorb", "11825": "krans", "11826": "wanderpfad", "11827": "karnevalsfedern", "11828": "blau-", "11829": "trinkbecher", "11830": "blumenbeet", "11831": "entkoppelt", "11832": "fledermaus", "11833": "gartenterrasse", "11834": "milchshake", "11835": "gerunzelter", "11836": "superman-t-shirt", "11837": "holzh\u00e4cksler", "11838": "ausschnitt", "11839": "ziegelboden", "11840": "college-jungen", "11841": "g\u00e4nsemarsch", "11842": "reparaturfahrzeug", "11843": "erdn\u00fcsse", "11844": "kayaks", "11845": "nasen", "11846": "r\u00fcckw\u00e4rtssaltos", "11847": "melone", "11848": "pelzen", "11849": "zehennagel", "11850": "vorderes", "11851": "sellerie", "11852": "gest\u00fctzten", "11853": "schaukelspielzeug", "11854": "tieres", "11855": "neonfarbener", "11856": "stra\u00dfenseiten", "11857": "dolchen", "11858": "karotte", "11859": "tattoo-studio", "11860": "kopiloten", "11861": "fluglinie", "11862": "kockpitfenster", "11863": "wildes", "11864": "orchestermitgliedern", "11865": "geigespielen", "11866": "taschent\u00fccher", "11867": "staubsaugen", "11868": "kochutensilien", "11869": "rouge", "11870": "trinkender", "11871": "unterhaltender", "11872": "bauchfreien", "11873": "leintuch", "11874": "strohfl\u00e4che", "11875": "dramatischer", "11876": "zwinkert", "11877": "steinskulptur", "11878": "antwort", "11879": "verhalten", "11880": "kartenverk\u00e4ufer", "11881": "totenk\u00f6pfen", "11882": "ziellos", "11883": "netzes", "11884": "erdwall", "11885": "mittelgro\u00dfes", "11886": "zeitrafferaufnahme", "11887": "unerbittlich", "11888": "namensschildchen", "11889": "steinbaus", "11890": "konf\u00f6derierte", "11891": "herumgehende", "11892": "federkost\u00fcmen", "11893": "trugen", "11894": "spr\u00fcnge", "11895": "pelzm\u00fctze", "11896": "moskitonetz", "11897": "ponton-boot", "11898": "typischer", "11899": "grobschl\u00e4chtiger", "11900": "untiefe", "11901": "mma-k\u00e4mpfer", "11902": "mma-kampf", "11903": "jungverm\u00e4hlten", "11904": "schaukampf", "11905": "hula-hoop-k\u00fcnste", "11906": "einfarbigen", "11907": "\u00fcberschlagenen", "11908": "glastisch", "11909": "schlafanzughosen", "11910": "pfefferkuchenhaus", "11911": "pl\u00f6tzlich", "11912": "gesessen", "11913": "ungew\u00f6hnlicher", "11914": "h\u00fcfpfen", "11915": "papasansessel", "11916": "anmutender", "11917": "gewandung", "11918": "wickeln", "11919": "getue", "11920": "schneefahrrad", "11921": "slum", "11922": "grasigem", "11923": "malert", "11924": "k\u00e4seschneiden", "11925": "gesch\u00e4ftsleute", "11926": "kinderm\u00e4dchen", "11927": "nevada", "11928": "gebet", "11929": "ostasiatische", "11930": "dokument", "11931": "bauwerken", "11932": "bestuhlung", "11933": "tageszeitbedingt", "11934": "umgebenes", "11935": "mop", "11936": "b\u00f6den", "11937": "farnbl\u00e4tter", "11938": "angehoben", "11939": "merkw\u00fcrdig", "11940": "kanzel", "11941": "diavorf\u00fchrung", "11942": "fols\u00e4ure", "11943": "princess", "11944": "eisschnelllauf-anz\u00fcgen", "11945": "herauskommt", "11946": "rednerin", "11947": "glamwiki", "11948": "busches", "11949": "gao-stand", "11950": "touchscreen-computer", "11951": "startklappen", "11952": "etage", "11953": "ron", "11954": "negro", "11955": "haarmaskottchen", "11956": "spieledemo", "11957": "masten", "11958": "hinaufklettert", "11959": "olivefarbenen", "11960": "geweih-imitat", "11961": "singenden", "11962": "winterausr\u00fcstung", "11963": "sinnieren", "11964": "sinn", "11965": "presslufth\u00e4mmer", "11966": "gurken", "11967": "geschenke", "11968": "holzb\u00fcndel", "11969": "scheckigem", "11970": "golfanlage", "11971": "skelett", "11972": "sonnenblume", "11973": "sicherheitsmontur", "11974": "kamerateam", "11975": "kunstunterricht", "11976": "davonschwimmt", "11977": "mikrophone", "11978": "cable-car-schienen", "11979": "metallstangenstapel", "11980": "blumenteppich", "11981": "k\u00fchlanlagen", "11982": "schneeschippe", "11983": "schnellstra\u00dfe", "11984": "holzverkleideten", "11985": "umfasst", "11986": "umschlossenen", "11987": "chirurg", "11988": "spielsteuerung", "11989": "spielzeuggesch\u00e4ft", "11990": "vira", "11991": "virou", "11992": "geschwisterchen", "11993": "flie\u00dfenboden", "11994": "flie\u00dfengesch\u00e4ft", "11995": "t\u00f6ten", "11996": "liegend", "11997": "gigantisches", "11998": "vorstehhunde", "11999": "schienenkreuzung", "12000": "fu", "12001": "mystery", "12002": "machine", "12003": "scooby-doo", "12004": "blaufen", "12005": "kung", "12006": "rallye-aufklebern", "12007": "mongolischen", "12008": "beifahrer", "12009": "darunterliegenden", "12010": "purpurrote", "12011": "k\u00f6stlichen", "12012": "trompeten", "12013": "notenpult", "12014": "gierig", "12015": "festes", "12016": "rotenmhut", "12017": "skatboardern", "12018": "mehrfarbiges", "12019": "zusammenhang", "12020": "wohnwagenplatz", "12021": "baseballausr\u00fcstung", "12022": "astroturf", "12023": "balletauff\u00fchrung", "12024": "flott", "12025": "m\u00f6glichkeit", "12026": "einzutauchen", "12027": "hauch", "12028": "schnorcheln", "12029": "sp\u00fclkasten", "12030": "schalenschaukel", "12031": "irren", "12032": "buddhistische", "12033": "m\u00f6nchsgew\u00e4ndern", "12034": "beobachtend", "12035": "collector", "12036": "blasorchester", "12037": "univormierte", "12038": "herrschaften", "12039": "glass", "12040": "abfluss", "12041": "vulkanausbr\u00fcche", "12042": "s\u00fcdwestlich", "12043": "rollsteig", "12044": "schweifen", "12045": "exodus", "12046": "abgef\u00fclltem", "12047": "zeltplatzes", "12048": "entlegenen", "12049": "eisenbahnschwelle", "12050": "machu", "12051": "picchu", "12052": "polizeipr\u00e4sidium", "12053": "steinteich", "12054": "bettw\u00e4sche", "12055": "durchs\u00e4gen", "12056": "emsig", "12057": "behausungen", "12058": "begeisterter", "12059": "alpinist", "12060": "baumschnitzerei-wettbewerb", "12061": "dekorative", "12062": "ges\u00e4gten", "12063": "s\u00e4ulenartige", "12064": "geb\u00e4ckst\u00fcck", "12065": "sonoma", "12066": "artisan", "12067": "sourdough", "12068": "bakers", "12069": "hintrgrund", "12070": "steinbl\u00f6cken", "12071": "kunststoffkrug", "12072": "medikamente", "12073": "legere", "12074": "d\u00fcnneren", "12075": "zahlreicher", "12076": "pinguin-loch", "12077": "verschriebene", "12078": "apotheker", "12079": "cappuccino", "12080": "dachshunde", "12081": "betonbrocken", "12082": "zielgerichteten", "12083": "einzigartig", "12084": "akteure", "12085": "metallbr\u00fccke", "12086": "lands", "12087": "s\u00fcdostasiatischen", "12088": "wartungspersonals", "12089": "gew\u00e4ssern", "12090": "uferszenerie", "12091": "abendkleid", "12092": "h\u00fcpfender", "12093": "abgestorbenem", "12094": "badezimmerwand", "12095": "ostasiatischer", "12096": "kalligraphie-schrift", "12097": "umstehende", "12098": "k\u00fchlger\u00e4ten", "12099": "z\u00fcge", "12100": "pflastern", "12101": "verwahrloste", "12102": "schulungsmission", "12103": "duellanten", "12104": "duellieren", "12105": "scheinkampf", "12106": "s\u00fcdamerikanischen", "12107": "ghettoblaster", "12108": "fastfood-restaurant", "12109": "antiquit\u00e4ten", "12110": "swat-offiziere", "12111": "spezialausr\u00fcstung", "12112": "verbrechens", "12113": "erinnerung", "12114": "taktischen", "12115": "st\u00e4dter", "12116": "assistent", "12117": "handb\u00fccher", "12118": "probeweise", "12119": "metallbeh\u00e4lter", "12120": "eingez\u00e4untes", "12121": "sehende", "12122": "p\u00e4diatrie", "12123": "zusammenstehende", "12124": "wabe", "12125": "gestr\u00fcppreiche", "12126": "ortsans\u00e4ssigen", "12127": "schwimmunterricht", "12128": "benutzte", "12129": "ausgef\u00fchrt", "12130": "glaube", "12131": "hochgerollter", "12132": "wei\u00df-blau", "12133": "scheck", "12134": "10.000", "12135": "schultisch", "12136": "unterschiedlichem", "12137": "flotte", "12138": "fischerboote", "12139": "augenscheinlich", "12140": "nutzvieh", "12141": "famili\u00e4re", "12142": "erh\u00e4ltlich", "12143": "kork", "12144": "flie\u00dfenleger", "12145": "k\u00fcnstlerisch", "12146": "fischfang", "12147": "fischessens", "12148": "fitnessger\u00e4te", "12149": "teekanne", "12150": "steuerrads", "12151": "joggenden", "12152": "bugs", "12153": "bunny", "12154": "kondom", "12155": "wegs", "12156": "toaster", "12157": "flo\u00dfes", "12158": "neuestes", "12159": "sp\u00fclbecken", "12160": "stra\u00dfenkunst", "12161": "feuertricks", "12162": "trinkpause", "12163": "filetieren", "12164": "niedergedr\u00fcckt", "12165": "enier", "12166": "pueblo-haus", "12167": "d\u00fcsenflugzeug", "12168": "gaukler", "12169": "strandkorb", "12170": "rudernder", "12171": "krachmacher", "12172": "wei\u00dfgekleideter", "12173": "fallschirmspringerin", "12174": "-spielzeug", "12175": "eingebohrene", "12176": "ausgezeichneter", "12177": "tanzpaar", "12178": "tanzfigur", "12179": "b\u00fccken", "12180": "erheben", "12181": "polizeibeamten", "12182": "schlafende", "12183": "incredible", "12184": "holz-basketballfeld", "12185": "gandhi-statue", "12186": "obsz\u00f6ne", "12187": "scharfes", "12188": "mariachi-band", "12189": "schmetterlingen", "12190": "standball", "12191": "identischen", "12192": "kastanienbraune", "12193": "stylus", "12194": "sahen", "12195": "heimwerkerwerkstatt", "12196": "arbeitsfl\u00e4chen", "12197": "drahtzahn", "12198": "modedesigner", "12199": "feuerplatz", "12200": "-untergang", "12201": "widmet", "12202": "kugelsicheren", "12203": "pionierkleidung", "12204": "gestromerter", "12205": "botentasche", "12206": "packers", "12207": "bierhahn", "12208": "einlaufenden", "12209": "volleyballspielers", "12210": "musizierende", "12211": "beidh\u00e4ndige", "12212": "bay", "12213": "stieren", "12214": "parasegler", "12215": "schwimmender", "12216": "zugseil", "12217": "sch\u00e4umenden", "12218": "herschieben", "12219": "fahrradkorb", "12220": "mixtur", "12221": "israelische", "12222": "kopierer", "12223": "hochschulabsolvent", "12224": "pullundern", "12225": "wasserhahn", "12226": "unisono", "12227": "werk", "12228": "waschautomaten", "12229": "goldhalsketten", "12230": "cheerleaderbewegungen", "12231": "b\u00fcste", "12232": "tischmitte", "12233": "s\u00e4uberlich", "12234": "kunststoffsandalen", "12235": "walmart-mitarbeiter", "12236": "buffettisch", "12237": "herumspringenden", "12238": "abenteuersuche", "12239": "schnorchler", "12240": "strandufer", "12241": "verkaufenden", "12242": "jumbotron", "12243": "laborversuche", "12244": "blockfl\u00f6te", "12245": "hinaufgeht", "12246": "barf\u00fc\u00dfigen", "12247": "wohnblock", "12248": "frauen-rockband", "12249": "tennisrock", "12250": "stochert", "12251": "vertritt", "12252": "bonbondose", "12253": "phoenix", "12254": "raketen\u00e4hnlichen", "12255": "verschlag", "12256": "trogs", "12257": "gleichm\u00e4\u00dfig", "12258": "italienisches", "12259": "treppeneingang", "12260": "aufgerissen", "12261": "dicklicher", "12262": "pl\u00e4tzen", "12263": "japanerin", "12264": "museumsgeb\u00e4ude", "12265": "whack", "12266": "chorprobe", "12267": "pferdeanh\u00e4nger", "12268": "pflastert", "12269": "hackbeil", "12270": "orgelspieler", "12271": "imposanten", "12272": "scharfen", "12273": "greyhound-bus", "12274": "bl\u00f6dsinn", "12275": "kornfelder", "12276": "silo", "12277": "vierten", "12278": "wasserb\u00fcffel", "12279": "kultivieren", "12280": "erdgrube", "12281": "haarfestiger", "12282": "erdbaumaschinen", "12283": "abgestellte", "12284": "zuschneidet", "12285": "martini", "12286": "reisebus", "12287": "kopflampe", "12288": "rollenkoffer", "12289": "35", "12290": "orange-", "12291": "schwarzfarbenen", "12292": "ebay-gesch\u00e4ft", "12293": "bewerben", "12294": "papiert\u00fcten", "12295": "entgegenkommendem", "12296": "aufschneiden", "12297": "turnen", "12298": "vhs-kassetten", "12299": "gep\u00e4chf\u00f6rderband", "12300": "landsitz", "12301": "motte", "12302": "dinnerparty", "12303": "reporterin", "12304": "nachktem", "12305": "erhitzt", "12306": "schmiedewerkzeugen", "12307": "lebensmittelh\u00e4ndler", "12308": "durchgeht", "12309": "einsteckt", "12310": "bungee-springen", "12311": "aufzugsschachts", "12312": "soldatin", "12313": "satin-kost\u00fcm", "12314": "sag", "12315": "warum", "12316": "nervig", "12317": "waldst\u00fcck", "12318": "b\u00fcgelmaschine", "12319": "duneklhaarige", "12320": "bachbett", "12321": "panel", "12322": "\u00fcberdachungen", "12323": "bergwiese", "12324": "eist\u00fcte", "12325": "wife", "12326": "sch\u00f6pfkelle", "12327": "plastikgabel", "12328": "k\u00fchlger\u00e4t", "12329": "t\u00fcrmt", "12330": "drohend", "12331": "tintenfass", "12332": "erbsengr\u00fcnen", "12333": "\u00e4rmell", "12334": "schmuckstand", "12335": "my", "12336": "verladener", "12337": "raping", "12338": "holztabletts", "12339": "fragt", "12340": "ausgestellten", "12341": "aufgereitetes", "12342": "behinderter", "12343": "abgest\u00fcrztes", "12344": "kunstreiter", "12345": "paint", "12346": "horse", "12347": "reitausr\u00fcstung", "12348": "segelschiffs", "12349": "verfangen", "12350": "football-ausr\u00fcstung", "12351": "rezept", "12352": "ank\u00fcndigung", "12353": "motivwagen", "12354": "live-konzert", "12355": "waffengesch\u00e4ft", "12356": "profi-k\u00fcche", "12357": "kameradschaftlich", "12358": "denim", "12359": "m\u00fcllmann", "12360": "einkaufsstra\u00dfe", "12361": "aufgesammelt", "12362": "zuckt", "12363": "verpacken", "12364": "experten", "12365": "tambourmajor", "12366": "frisbee-golfziel", "12367": "pfostens", "12368": "folkloristische", "12369": "ressourcen", "12370": "st\u00fclpt", "12371": "grafik", "12372": "aufgerissenen", "12373": "minir\u00f6cken", "12374": "nachtschw\u00e4rmer", "12375": "neujahrsparade", "12376": "feuerwerksk\u00f6rpern", "12377": "k\u00fcstenn\u00e4he", "12378": "menschenreihen", "12379": "landwirtschaftlichem", "12380": "computerkurs", "12381": "glasofen", "12382": "wirkender", "12383": "gesperrt", "12384": "unachtsam", "12385": "'s-restaurants", "12386": "mine", "12387": "haushaltsartikeln", "12388": "applaudieren", "12389": "rollerskater", "12390": "vertraue", "12391": "tatowierten", "12392": "wandmalereien", "12393": "wendy", "12394": "schuld", "12395": "rotkarierten", "12396": "eigentlichen", "12397": "feierlichkeiten", "12398": "konzessionen", "12399": "gelten", "12400": "ungeschickt", "12401": "anordnet", "12402": "stitch-kost\u00fcm", "12403": "nun", "12404": "houston", "12405": "bahn-passagiere", "12406": "milit\u00e4rbeamter", "12407": "fahrradweg", "12408": "beschnuppern", "12409": "schnellboots", "12410": "bemerkt", "12411": "sonderbare", "12412": "aussteigt", "12413": "altere", "12414": "nypd", "12415": "luxuswagen", "12416": "gebetsteppich", "12417": "abmontiert", "12418": "ersatzradmulde", "12419": "telefonmast", "12420": "braun-grauen", "12421": "handrasenm\u00e4her", "12422": "nachgeht", "12423": "hinutermarschieren", "12424": "stra\u00dfenkundgebung", "12425": "seeleuten", "12426": "schwarz-grauem", "12427": "bahre", "12428": "sanit\u00e4tsartikeln", "12429": "kontakt", "12430": "einzelhandelsfachkraft", "12431": "jahrmarktsspiel", "12432": "fischerbooten", "12433": "website", "12434": "furchterregenden", "12435": "zusammenkehrt", "12436": "posten", "12437": "rosat\u00f6nen", "12438": "zelt\u00e4hnlichen", "12439": "clipboard", "12440": "annual", "12441": "longboard", "12442": "classic", "12443": "inspirierend", "12444": "steinbogen", "12445": "rotkarierter", "12446": "designertasche", "12447": "drolliger", "12448": "nehme", "12449": "farbenfrohem", "12450": "blicke", "12451": "burberry-mantel", "12452": "konzentrieren", "12453": "geschlagene", "12454": "rot-wei\u00df-schwarzen", "12455": "obstschale", "12456": "field", "12457": "goal", "12458": "prince", "12459": "stewardessenkleidung", "12460": "kleiderst\u00e4nder", "12461": "kreidekunstwerk", "12462": "neonlicht", "12463": "laufb\u00e4ndern", "12464": "kohlen", "12465": "geliebten", "12466": "cargo", "12467": "holzscheite", "12468": "jungel", "12469": "karate-gi", "12470": "rodeo-reiter", "12471": "jungbullen", "12472": "gereinigt", "12473": "arbeitstags", "12474": "gyro", "12475": "dekoratives", "12476": "m\u00fcnztelefons", "12477": "zeigender", "12478": "steinterrasse", "12479": "baumzweig", "12480": "herumspritzendes", "12481": "herrliche", "12482": "wei\u00df-beige", "12483": "anrufe", "12484": "jung", "12485": "fernsehger\u00e4t", "12486": "mittelalterkost\u00fcm", "12487": "emotionen", "12488": "einsteigt", "12489": "schattenhafte", "12490": "fremdartiger", "12491": "herunterfallendem", "12492": "unternehmer", "12493": "macbooks", "12494": "hemdloser", "12495": "w\u00fcrfeln", "12496": "skelettkost\u00fcmen", "12497": "mautbr\u00fccke", "12498": "gehst\u00f6cken", "12499": "b\u00fcgeln", "12500": "schaffner", "12501": "grabt", "12502": "angeh\u00f6riger", "12503": "mickey-maus-ohren", "12504": "brote", "12505": "schlagzeugen", "12506": "beaufsichtigen", "12507": "freigelegten", "12508": "monitor", "12509": "belegte", "12510": "dr\u00e4hte", "12511": "dieselben", "12512": "sperren", "12513": "5-gallonen-eimern", "12514": "gesammelten", "12515": "schneeflockenmuster", "12516": "schnitzerei", "12517": "minir\u00f6cke", "12518": "landmaschine", "12519": "lendenschurz", "12520": "schwei\u00dfbrenner", "12521": "angusskanal", "12522": "bunsenbrenner", "12523": "testr\u00f6hrchen", "12524": "m\u00fcdigkeit", "12525": "rissen", "12526": "halloween-dekoration", "12527": "schauene", "12528": "radarger\u00e4te", "12529": "arbeiterweste", "12530": "windfester", "12531": "laubblasen", "12532": "abenteuerfahrt", "12533": "gymnastikfigur", "12534": "high-fives", "12535": "b\u00e4ndert\u00e4nzerin", "12536": "abflussgitter", "12537": "maskierte", "12538": "menschentraube", "12539": "toys", "12540": "freiwilliger", "12541": "glasfenster", "12542": "kampfsportkurs", "12543": "entlanggeschoben", "12544": "aufgeh\u00e4ngter", "12545": "anzuh\u00f6ren", "12546": "ukrainischer", "12547": "holl\u00e4ndischer", "12548": "fu\u00dfkick", "12549": "rotlichtschild", "12550": "prahlen", "12551": "stra\u00dfenfestival", "12552": "reglern", "12553": "laubgew\u00e4chsen", "12554": "fl\u00f6ten", "12555": "verbeugung", "12556": "kaninchenk\u00e4fig", "12557": "\u00fcberfahren", "12558": "betrunkenen", "12559": "reinigungswagen", "12560": "extrem", "12561": "samariter", "12562": "sicherheitszaun", "12563": "videospielgesch\u00e4fts", "12564": "katzenkost\u00fcm", "12565": "gelb-lila", "12566": "freizeitfahrzeug", "12567": "freizeitaktivit\u00e4t", "12568": "luxuswarengesch\u00e4ft", "12569": "sage", "12570": "faszinierend", "12571": "halte", "12572": "deinen", "12573": "verbeugt", "12574": "warnschild", "12575": "absolut", "12576": "lewis", "12577": "mathematischer", "12578": "gleichungen", "12579": "corp", "12580": "m\u00e4nnerchor", "12581": "vietnamese", "12582": "hohlweg", "12583": "besessen", "12584": "blumenh\u00e4ndler", "12585": "vollst\u00e4ndigen", "12586": "geistlicher", "12587": "beichtstuhl", "12588": "fixiert", "12589": "rohes", "12590": "h\u00e4ngendes", "12591": "erlaubt", "12592": "maskierten", "12593": "glaslampe", "12594": "abgewinkelt", "12595": "schneer\u00e4umfahrzeugs", "12596": "eingeschalteten", "12597": "zustimmung", "12598": "unterlage", "12599": "-wagen", "12600": "regenschauer", "12601": "grouch", "12602": "oscar", "12603": "cubs", "12604": "knieen", "12605": "dhl-mitarbeitern", "12606": "teufelskost\u00fcm", "12607": "dressen", "12608": "spyder", "12609": "schiffskapit\u00e4n", "12610": "bierdose", "12611": "erhobener", "12612": "radsportveranstaltung", "12613": "braun-markierungen", "12614": "schief", "12615": "gelaufen", "12616": "tanzend", "12617": "abwasserleitungen", "12618": "schnauzbart", "12619": "extravaganten", "12620": "blitzlicht", "12621": "geblendet", "12622": "edelstahl", "12623": "bratlinge", "12624": "wikingerhelm", "12625": "alarmstellung", "12626": "kriegsveteranen", "12627": "gedenkfeier", "12628": "beh\u00f6rden", "12629": "sorgen", "12630": "beobachtern", "12631": "bring", "12632": "soldatenhemd", "12633": "gealterter", "12634": "halloween-maske", "12635": "kastanienbraun", "12636": "wimpeln", "12637": "lagerleiter", "12638": "fahnentr\u00e4ger", "12639": "lagergruppen", "12640": "steuertafel", "12641": "gedenkst\u00e4tte", "12642": "gefallenen", "12643": "veteranenaufmarsch", "12644": "abzuklatschen", "12645": "hundemarke", "12646": "jeans-outfit", "12647": "laufweg", "12648": "goldfarben", "12649": "kunstzentrums", "12650": "k\u00f6rner", "12651": "computertisch", "12652": "zaunpfosten", "12653": "angeleinte", "12654": "umherschlendern", "12655": "gewartet", "12656": "ersp\u00e4ht", "12657": "felsenkante", "12658": "rasierapparat", "12659": "milit\u00e4rkost\u00fcmen", "12660": "angeberisch", "12661": "skibekleidung", "12662": "r\u00fcmpfe", "12663": "ignorieren", "12664": "feuerwehrhemd", "12665": "spruchband", "12666": "blickrichtung", "12667": "hausschuh", "12668": "sprecher", "12669": "t\u00fcrkis-orangefarbenen", "12670": "fusseligen", "12671": "ballen", "12672": "filmwerbung", "12673": "toyota-lkw", "12674": "projizierten", "12675": "videopr\u00e4sentation", "12676": "g\u00fcnen", "12677": "nadelstreifenjacke", "12678": "chiefs-pullover", "12679": "zahnl\u00fccken", "12680": "wickeltuch", "12681": "california", "12682": "receiver", "12683": "stanford", "12684": "macbook", "12685": "gelandeten", "12686": "leierkasten", "12687": "spielzeugvogel", "12688": "lokale", "12689": "rasiertechnik", "12690": "herunterrutscht", "12691": "jagdhunde", "12692": "steinfliese", "12693": "trampen", "12694": "reinigungsmitteln", "12695": "run", "12696": "dmc", "12697": "rabatt", "12698": "r\u00e4ucherst\u00e4bchen", "12699": "wettl\u00e4ufer", "12700": "aufstand", "12701": "tischs", "12702": "stahlf\u00e4sser", "12703": "kleidergesch\u00e4ft", "12704": "carhartt", "12705": "tribal-muster", "12706": "kreises", "12707": "spritzkissens", "12708": "innenbecken", "12709": "eingelocht", "12710": "verpackt", "12711": "lunch-boxen", "12712": "staatsangeh\u00f6rige", "12713": "umzulegen", "12714": "stacheldraht", "12715": "hundeteam", "12716": "gekochten", "12717": "idiot", "12718": "benimmt", "12719": "\u00fcberzeugen", "12720": "patricks", "12721": "empanadas", "12722": "empanadas-gesch\u00e4ft", "12723": "elegantem", "12724": "forstwirtschaft", "12725": "pudeln", "12726": "schachspielen", "12727": "biersorten", "12728": "kilts", "12729": "eingeschalteter", "12730": "k\u00fcrze", "12731": "losgehen", "12732": "reinigungskraft", "12733": "einteiligen", "12734": "seifenblasaen", "12735": "skateboard-bahn", "12736": "dudels\u00e4cken", "12737": "string-bikini", "12738": "flusslauf", "12739": "sp\u00e4ten", "12740": "80er", "12741": "einzelnes", "12742": "tanga", "12743": "bezaubernder", "12744": "truck", "12745": "senior", "12746": "ballonverk\u00e4ufer", "12747": "motoradfahrer", "12748": "region", "12749": "aluminium", "12750": "unterwasserfoto", "12751": "rechter", "12752": "leerer", "12753": "zementweg", "12754": "abgef\u00fclltes", "12755": "gew\u00f6lbten", "12756": "zur\u00fccklehnt", "12757": "stolzer", "12758": "klubsessel", "12759": "verschlungene", "12760": "pummelige", "12761": "hochgezogen", "12762": "satteln", "12763": "staunend", "12764": "flugfeld", "12765": "ruby", "12766": "diner", "12767": "prius", "12768": "schlittschuhlaufen", "12769": "stands", "12770": "klebstoff", "12771": "sohle", "12772": "schuhs", "12773": "schreiende", "12774": "antilope", "12775": "eisenwarenhandlung", "12776": "tubas", "12777": "maispfl\u00fccken", "12778": "au\u00dfenbordmotor", "12779": "ausgeh\u00f6hlte", "12780": "k\u00fcrbislaternen", "12781": "g\u00e4rten", "12782": "behelfsm\u00e4\u00dfigen", "12783": "bongospieler", "12784": "gemischten", "12785": "kopfst\u00fctzen", "12786": "dackelwelpe", "12787": "immigration", "12788": "stra\u00dfenlampen", "12789": "verkehrsampeln", "12790": "nasenpiercing", "12791": "holzplatten", "12792": "iro", "12793": "schwei\u00dfarbeit", "12794": "vorgesehen", "12795": "l\u00f6twerkzeug", "12796": "gearbeitet", "12797": "keramik", "12798": "k\u00f6rperbehinderte", "12799": "details", "12800": "hinzu", "12801": "zugedeckter", "12802": "tragesitz", "12803": "ess-", "12804": "winkel", "12805": "aufgenommenes", "12806": "outdoor-ausr\u00fcstung", "12807": "gesch\u00e4ftliches", "12808": "behandlung", "12809": "kartoffel\u00e4hnlichen", "12810": "schleifarbeiten", "12811": "anbieten", "12812": "wolkiger", "12813": "l\u00f6st", "12814": "entertainer", "12815": "dadurch", "12816": "schneewolke", "12817": "sportzentrum", "12818": "steuerung", "12819": "karate-\u00fcbungsstunde", "12820": "gegensatz", "12821": "schmortopf", "12822": "sperrholz", "12823": "kuppelf\u00f6rmige", "12824": "rangelt", "12825": "eisenbahner", "12826": "freiluftgrill", "12827": "mikrophonst\u00e4nder", "12828": "ampelmast", "12829": "theaterschild", "12830": "stapft", "12831": "ballonanordnung", "12832": "cowboyweste", "12833": "silberfarbener", "12834": "regenschrimen", "12835": "barbie-puppe", "12836": "staffelstab", "12837": "fleischwarengesch\u00e4ft", "12838": "gewundene", "12839": "gardens-fass", "12840": "krankenzimmer", "12841": "kennzeichen", "12842": "totenkopf", "12843": "spalt", "12844": "bl\u00f6cke", "12845": "zusammengepressten", "12846": "begonnen", "12847": "gewaschenen", "12848": "zusammengelegten", "12849": "gyros-koch", "12850": "gyros", "12851": "auff\u00fchren", "12852": "kampiert", "12853": "n\u00f6tigen", "12854": "teilnehmergruppe", "12855": "vierter", "12856": "fallschirmsprung", "12857": "kegels", "12858": "universit\u00e4tsmannschaften", "12859": "verzerrte", "12860": "ten", "12861": "conference", "12862": "eink\u00e4uferin", "12863": "interessante", "12864": "skirennfahrer", "12865": "verschiffen", "12866": "unterbrochen", "12867": "rafting-ausr\u00fcstung", "12868": "zahnr\u00e4der", "12869": "aschgrauen", "12870": "rodeoclowns", "12871": "rissigen", "12872": "interesses", "12873": "metallkanister", "12874": "griffen", "12875": "pflegerinnen", "12876": "lackieren", "12877": "terminal", "12878": "kleebl\u00e4ttern", "12879": "eingeh\u00fcllte", "12880": "zugfenster", "12881": "tierh\u00e4ute", "12882": "ladeneinrichtung", "12883": "pferdekost\u00fcm", "12884": "cure", "12885": "klimaanlage", "12886": "entlangrollt", "12887": "felsene", "12888": "affe", "12889": "fotohandy", "12890": "goldrahmen", "12891": "winderkleidung", "12892": "rodeo-leute", "12893": "spieltan", "12894": "smoothies", "12895": "bubble", "12896": "tea", "12897": "brandschutt", "12898": "meditieren", "12899": "h\u00f6hlenw\u00e4nden", "12900": "peronen", "12901": "aussichtsturm", "12902": "wakeboarder", "12903": "race", "12904": "snowboardfahrens", "12905": "wagemutiger", "12906": "milit\u00e4rfahrzeuge", "12907": "olivegr\u00fcnen", "12908": "entlastet", "12909": "schaffneruniform", "12910": "stra\u00dfenbahnwagen", "12911": "kaugummiblase", "12912": "patrick", "12913": "debatiert", "12914": "zusammenpassender", "12915": "d\u00fcrren", "12916": "\u00fcbergro\u00dfer", "12917": "k-9-milit\u00e4rhund", "12918": "armeekleidung", "12919": "windeln", "12920": "eingerissen", "12921": "spiralf\u00f6rmigen", "12922": "feinschmeckerrestaurant", "12923": "hinterlie\u00df", "12924": "w\u00fcstenlandschaft", "12925": "44", "12926": "tickets", "12927": "available", "12928": "herbstb\u00e4ume", "12929": "verweintem", "12930": "langweilen", "12931": "flexiblen", "12932": "hineindr\u00fcckt", "12933": "steingehweg", "12934": "blockieren", "12935": "trachtenkleidung", "12936": "primitiven", "12937": "bedingungen", "12938": "sch\u00e4delknochen", "12939": "deme", "12940": "kontrabass-spieler", "12941": "turbane", "12942": "r\u00f6ckchen", "12943": "kleinkindalter", "12944": "sxswo", "12945": "swan", "12946": "st\u00f6bert", "12947": "verzierungen", "12948": "metallkasten", "12949": "holzkarren", "12950": "objektglas", "12951": "unternehmertagung", "12952": "herbstlandschaft", "12953": "mustern", "12954": "bildschirms", "12955": "demos", "12956": "business", "12957": "bankett", "12958": "abspritzen", "12959": "mcdonalds-men\u00fc", "12960": "wei\u00df-schwarzen", "12961": "hundekotbeutel", "12962": "vw-busses", "12963": "zuckerrohrstock", "12964": "trucks", "12965": "aufstellen", "12966": "konversation", "12967": "seitenspiegel", "12968": "essensstands", "12969": "\u00e4lterem", "12970": "schmollendes", "12971": "kr\u00fcmmt", "12972": "grell", "12973": "schuljunge", "12974": "protestierender", "12975": "folgendes", "12976": "passendes", "12977": "photographer", "12978": "terrorist", "12979": "turnwettbewerb", "12980": "christian", "12981": "abzuseilen", "12982": "flechten", "12983": "spiderman-t-shirt", "12984": "zuschauerr\u00e4ngen", "12985": "souvenirshop", "12986": "museums", "12987": "m\u00f6chten", "12988": "blindenhund", "12989": "w\u00e4hernd", "12990": "geh\u00e4uteter", "12991": "hellhelben", "12992": "fahreimer", "12993": "leckerem", "12994": "verpflegungsstand", "12995": "kn\u00fcpfen", "12996": "schwalbensprung", "12997": "kernkissen", "12998": "fr\u00fchling", "12999": "betrachet", "13000": "aufblasbares", "13001": "vorherrschaft", "13002": "gereizt", "13003": "plastikhammer", "13004": "metalltopf", "13005": "g\u00fcrteltasche", "13006": "grasland", "13007": "anpfiff", "13008": "basketballstadion", "13009": "sportmannschaft", "13010": "eidechsen", "13011": "basketballspielerinnen", "13012": "rasierschaum", "13013": "herunterh\u00e4ngendes", "13014": "schlenker", "13015": "skateboardhelm", "13016": "schneeschuhe", "13017": "keksen", "13018": "brownies", "13019": "eingeborene", "13020": "hinabfahren", "13021": "j\u00e4ckchen", "13022": "papierb\u00e4umen", "13023": "kinde", "13024": "betonboden", "13025": "connie", "13026": "facial", "13027": "tissues", "13028": "gruppenprojekt", "13029": "merkw\u00fcrdiges", "13030": "modellvulkan", "13031": "bridges", "13032": "zugewiesenen", "13033": "zusammenzusetzen", "13034": "gruppenaktivit\u00e4t", "13035": "papierb\u00f6gen", "13036": "spielzeugkatze", "13037": "abges\u00e4gten", "13038": "anbringen", "13039": "geb\u00fcckte", "13040": "programmen", "13041": "verschiedenartigen", "13042": "begleidet", "13043": "baufahrzeug", "13044": "lehmziegeln", "13045": "kr\u00e4uter", "13046": "einsteigen", "13047": "playboy-schild", "13048": "moppaufsatzes", "13049": "mud-kaffeewagen", "13050": "hochgekrempelten", "13051": "skistock", "13052": "tanklastwagens", "13053": "beleuchtungsk\u00f6rpern", "13054": "h\u00fcgelige", "13055": "frostlandschaft", "13056": "puten", "13057": "hundebesitzer", "13058": "haustier", "13059": "zur\u00fcckzubekommen", "13060": "fremdl\u00e4ndische", "13061": "gerichten", "13062": "teenagers", "13063": "parkbereich", "13064": "bef\u00fchlt", "13065": "zigeunerr\u00f6cken", "13066": "sprungtrick", "13067": "samt", "13068": "wartungs-", "13069": "lupft", "13070": "t\u00f6pfern", "13071": "junggebliebene", "13072": "unterhaltsam", "13073": "st\u00f6pselt", "13074": "abladen", "13075": "stofftaschen", "13076": "echter", "13077": "aufregenden", "13078": "dj-ausr\u00fcstung", "13079": "pl\u00fcschhaie", "13080": "comiczeichnungen", "13081": "schokoladenschichtkuchen", "13082": "handschlags", "13083": "schneehose", "13084": "zerbrochener", "13085": "barbie-puppen", "13086": "ikea-gesch\u00e4ft", "13087": "rosa-blauen", "13088": "musikalischen", "13089": "einef", "13090": "slow", "13091": "aquamarin", "13092": "rotisserie", "13093": "fl\u00fcssigwaschmittel", "13094": "partynacht", "13095": "geworden", "13096": "gespreizt", "13097": "rosigen", "13098": "ungepflasterte", "13099": "kampfsporthaltungen", "13100": "vergeblich", "13101": "gestapelte", "13102": "leichtathletikveranstaltung", "13103": "wanderstiefeln", "13104": "blumenarrangement", "13105": "sch\u00e4ferhunden", "13106": "stahlgitterabsperrung", "13107": "schwimmhilfsmittel", "13108": "regenbogenfahnen", "13109": "mittlerer", "13110": "fantasy-poster", "13111": "bodies", "13112": "landschaftsbild", "13113": "zuschauergruppe", "13114": "eisherz", "13115": "spenden", "13116": "bitten", "13117": "84", "13118": "peterson", "13119": "kultiviert", "13120": "greller", "13121": "eidechse", "13122": "polaris", "13123": "handwerk", "13124": "kampfsportart", "13125": "seifenblasenger\u00e4t", "13126": "b\u00fchnenshow", "13127": "olivenh\u00e4ndler", "13128": "gesch\u00e4ftliche", "13129": "mofa", "13130": "145", "13131": "schleifscheibe", "13132": "tamburinen", "13133": "h\u00e4ltein", "13134": "stillstand", "13135": "versunken", "13136": "kontrollpunkts", "13137": "spendenmarathon", "13138": "haiti", "13139": "personengruppen", "13140": "zwiebel", "13141": "musikpavillion", "13142": "friedensdecke", "13143": "podien", "13144": "ansprache", "13145": "antiquierter", "13146": "scouts", "13147": "nascht", "13148": "pfadfindergruppe", "13149": "pfadfinderuniformen", "13150": "metallk\u00e4fige", "13151": "milchk\u00e4sten", "13152": "sauerstoffschl\u00e4uchen", "13153": "verheddern", "13154": "versunkener", "13155": "freiwilligenorganisation", "13156": "schie\u00dfspiel", "13157": "pflanzt\u00f6pfen", "13158": "schwacher", "13159": "schwarz-gr\u00fcnene", "13160": "gef\u00e4\u00df", "13161": "durchdrehen", "13162": "wegl\u00e4uft", "13163": "starkem", "13164": "schneefall", "13165": "vorlesungssaal", "13166": "marmorgang", "13167": "wasserspritzen", "13168": "aufb\u00e4umendes", "13169": "stofftasche", "13170": "hallenmarkt", "13171": "s\u00e4ht", "13172": "indianisches", "13173": "artefakt", "13174": "bevorstehendes", "13175": "chemikalienschutzanz\u00fcgen", "13176": "m\u00fclls\u00e4cken", "13177": "h\u00fcrdenrennen", "13178": "esszimmertisch", "13179": "niedlichen", "13180": "schmollt", "13181": "augenbrauen-piercing", "13182": "hintergund", "13183": "zimmergenossen", "13184": "-hut", "13185": "background-s\u00e4ngerinnen", "13186": "abschleppen", "13187": "zerlegt", "13188": "aufstellers", "13189": "biersorte", "13190": "neutralfarbener", "13191": "stra\u00dfenschildern", "13192": "dahinschlendert", "13193": "raubvogel", "13194": "melkt", "13195": "hinauftr\u00e4gt", "13196": "herds", "13197": "nudelgericht", "13198": "m\u00fcllwagen", "13199": "tretenden", "13200": "blassgr\u00fcnen", "13201": "patriot", "13202": "kleidet", "13203": "w\u00f6rterbuch", "13204": "nesquik-pulver", "13205": "spazierende", "13206": "seepferd", "13207": "industrieofen", "13208": "durchgegangenen", "13209": "spielplatzeinrichtung", "13210": "terminals", "13211": "hochgeschobenen", "13212": "h\u00e4ndchenhaltendes", "13213": "curlinghalle", "13214": "miller", "13215": "zwar", "13216": "erfindung", "13217": "n\u00fcsse", "13218": "glastheke", "13219": "falls", "13220": "korrekt", "13221": "sollte", "13222": "jedes", "13223": "k\u00f6rnchen", "13224": "rippchen", "13225": "speisebereich", "13226": "vorsingt", "13227": "paddy", "13228": "unterhaltende", "13229": "vertrauliches", "13230": "wohnh\u00e4user", "13231": "miniaturpferden", "13232": "werke", "13233": "russische", "13234": "befindenden", "13235": "farms", "13236": "vorbeigehende", "13237": "riesenlineal", "13238": "sonderangebot", "13239": "trainingshosen", "13240": "umgeh\u00e4ngt", "13241": "blackberry", "13242": "schlafposition", "13243": "baldige", "13244": "ankuft", "13245": "jesus", "13246": "verk\u00fcndet", "13247": "schneebedeckter", "13248": "campus", "13249": "kellnertablett", "13250": "rosafarenen", "13251": "soldatenjacken", "13252": "angesehen", "13253": "frischer", "13254": "coolest", "13255": "dad", "13256": "world", "13257": "grinsend", "13258": "duneklhaarigen", "13259": "angepasst", "13260": "rostige", "13261": "obdachlosen", "13262": "kassenband", "13263": "gehwegen", "13264": "fahrradin", "13265": "orange-schwarzen", "13266": "synchronisierten", "13267": "kleinwagen", "13268": "punsch", "13269": "mitgenommen", "13270": "kongressgeb\u00e4udes", "13271": "nancy", "13272": "pelosi", "13273": "mti", "13274": "erdbeeren", "13275": "surfbretter", "13276": "geworben", "13277": "wheels-auto", "13278": "frauene", "13279": "durchgef\u00fchrt", "13280": "buletten", "13281": "biker-lederjacke", "13282": "saitenw\u00fcrstchen", "13283": "sexy", "13284": "rinde", "13285": "smartphones", "13286": "willkommen", "13287": "wundervollen", "13288": "gitarrenhals", "13289": "lotteriescheine", "13290": "strapse", "13291": "kirschb\u00e4ume", "13292": "ballsport", "13293": "alaska-sweatshirt", "13294": "spazierfahrt", "13295": "zusammendr\u00fcckt", "13296": "turmuhr", "13297": "uhren", "13298": "ziegenfutter", "13299": "code", "13300": "vinci", "13301": "stall", "13302": "gap-shirt", "13303": "aufgeschlagenem", "13304": "knoten", "13305": "jetzt", "13306": "katakomben", "13307": "umgang", "13308": "fertiges", "13309": "faschingsmarkt", "13310": "anstaunen", "13311": "sportw\u00e4gen", "13312": "heimelige", "13313": "herllich", "13314": "wettbewerbsteilnehmer", "13315": "ausgedehnten", "13316": "sandschaufel", "13317": "ziegelpflasterweg", "13318": "rummelplatz-spiel", "13319": "armaturenbrett", "13320": "interaktiven", "13321": "angemaltes", "13322": "kahlgeschorene", "13323": "us-briefkasten", "13324": "gesichertes", "13325": "business-anzug", "13326": "brasilien", "13327": "raodwork", "13328": "ahead", "13329": "hoodies", "13330": "bettler", "13331": "marmorskulptur", "13332": "stoppzeichen", "13333": "kiteboarder", "13334": "hummer", "13335": "fensterwand", "13336": "zul\u00e4sst", "13337": "gefrorenes", "13338": "eis-am-stiel", "13339": "schl\u00fcssel", "13340": "zales", "13341": "angetriebenen", "13342": "kastenwagens", "13343": "mountainboarding", "13344": "geblumten", "13345": "draufg\u00e4nger", "13346": "wassergef\u00e4hrt", "13347": "falschparkens", "13348": "kn\u00f6cheltief", "13349": "trio", "13350": "partykleidung", "13351": "scherzt", "13352": "freiwillige", "13353": "andenkenladen", "13354": "vermissten", "13355": "zuletzt", "13356": "waldspaziergang", "13357": "candy", "13358": "muskete", "13359": "gingham-schal", "13360": "turms", "13361": "interesse", "13362": "flach", "13363": "mittlere", "13364": "beifahrersitz", "13365": "haltendes", "13366": "gleitschirmflieger", "13367": "apple-laptops", "13368": "santa", "13369": "cruz", "13370": "hackbrett", "13371": "dept.", "13372": "fundst\u00fcck", "13373": "trauriger", "13374": "potenziellen", "13375": "drag", "13376": "verstopfen", "13377": "angestellten", "13378": "cells", "13379": "assistenten", "13380": "doktoren", "13381": "blood", "13382": "st\u00fcrmen", "13383": "konsums", "13384": "fangruppe", "13385": "heineken", "13386": "fu\u00dfrasten", "13387": "aberdeen", "13388": "r\u00e4umfahrzeug", "13389": "skikurses", "13390": "skilehrer", "13391": "gemeindezentrum", "13392": "entgegengesetzter", "13393": "einkaufswagens", "13394": "fargo", "13395": "\u00fcbermantel", "13396": "regenschrim", "13397": "begleiten", "13398": "mich", "13399": "delkatessengesch\u00e4ft", "13400": "rudiment\u00e4ren", "13401": "nachf\u00fcllstation", "13402": "diesel", "13403": "auft\u00fcrmen", "13404": "portraits", "13405": "zuh\u00f6rer", "13406": "eben", "13407": "vierst\u00f6ckigen", "13408": "kratzen", "13409": "plastiknase", "13410": "glasball", "13411": "uriniert", "13412": "tiefer", "13413": "flanneljacke", "13414": "fahrradfaher", "13415": "pompoms", "13416": "verwirrter", "13417": "fahrscheinkontrolle", "13418": "betontreppe", "13419": "br\u00e4unlichem", "13420": "korbartigen", "13421": "aufregendes", "13422": "verfallenen", "13423": "sail", "13424": "trockenanzug", "13425": "arbeitshandschuhen", "13426": "arbeitspause", "13427": "taschentuch", "13428": "totenkopfbild", "13429": "rautenmuster", "13430": "postmann", "13431": "fu\u00dfg\u00e4ngerampel", "13432": "hollister-shirt", "13433": "gesch\u00e4ftig", "13434": "projektor-leinwand", "13435": "ereader", "13436": "absichtlich", "13437": "gesetzten", "13438": "darts", "13439": "gigantischen", "13440": "rosenkranz", "13441": "barista", "13442": "coffee", "13443": "welcher", "13444": "steinbr\u00fcstung", "13445": "egal", "13446": "kaugummi", "13447": "wegweiser", "13448": "halbe", "13449": "abwechslung", "13450": "modellflugtreffen", "13451": "holl\u00e4ndische", "13452": "gefertigtes", "13453": "osterei", "13454": "weibliches", "13455": "soldatenuniformen", "13456": "ariel", "13457": "portrait", "13458": "spitzhut", "13459": "vollem", "13460": "muschel-bh", "13461": "rosa-wei\u00df", "13462": "plastikschmuck", "13463": "shoegasm-tasche", "13464": "teer", "13465": "ini", "13466": "angek\u00fcndigt", "13467": "bowlingkegeln", "13468": "gartentor", "13469": "charade", "13470": "verliebt", "13471": "urwald", "13472": "weist", "13473": "sehensw\u00fcrdigkeiten", "13474": "mittte", "13475": "gelb-rote", "13476": "telefonierender", "13477": "finley", "13478": "warenautomaten", "13479": "runzelnde", "13480": "ausziehleine", "13481": "mark", "13482": "sparrow", "13483": "neugeborener", "13484": "schl\u00e4uche", "13485": "brutkasten", "13486": "metallfelge", "13487": "davorstehenden", "13488": "nasen-piercing", "13489": "schrott", "13490": "keiner", "13491": "paper", "13492": "fransen", "13493": "sportlerkleidung", "13494": "rinnstein", "13495": "heals-store", "13496": "metallb\u00e4nken", "13497": "feine", "13498": "k\u00e4sesorten", "13499": "schmiedewerkzeuge", "13500": "ergrauender", "13501": "hagerer", "13502": "vorbeifliegen", "13503": "geschehnis", "13504": "gedenken", "13505": "zeige", "13506": "akrobatengruppe", "13507": "k\u00f6nnens", "13508": "vergewissert", "13509": "zeitschriftenstand", "13510": "nah\u00f6stliche", "13511": "grau-blauen", "13512": "shine", "13513": "reinzuhauen", "13514": "central", "13515": "weinenden", "13516": "geh\u00e4ufte", "13517": "esswerkzeug", "13518": "lacrosse-spielen", "13519": "fahrstuhl", "13520": "holzzug", "13521": "spielteppich", "13522": "broad", "13523": "un", "13524": "sicherheitspersonal", "13525": "nudelk\u00f6che", "13526": "strandbad", "13527": "handgefertigte", "13528": "dienstleistungen", "13529": "vorpfeift", "13530": "pfotenmuster", "13531": "springbrunnenbereich", "13532": "einb\u00e4umen", "13533": "fahrradschl\u00e4uchen", "13534": "stra\u00dfenwerbung", "13535": "34", "13536": "sternhalma", "13537": "bulldozers", "13538": "begafft", "13539": "h\u00fcgellandschaft", "13540": "cheap", "13541": "tab", "13542": "schnippt", "13543": "sandburgen", "13544": "keyboards", "13545": "wall", "13546": "chinesisch", "13547": "l\u00f6chern", "13548": "mittelaltermarkt", "13549": "entlaufener", "13550": "schlagloch", "13551": "m\u00e4chen", "13552": "bullterrier", "13553": "momentan", "13554": "trockendock", "13555": "m\u00fcllhaufens", "13556": "recycling", "13557": "zementtreppe", "13558": "b\u00e4renkost\u00fcmen", "13559": "wickeltasche", "13560": "daunenjacke", "13561": "\u00e4teres", "13562": "abendstunden", "13563": "h\u00e4userblock", "13564": "mutwillig", "13565": "mignon", "13566": "entgegenbringt", "13567": "projezierte", "13568": "firefox-seite", "13569": "metallwand", "13570": "pep-band", "13571": "schlangenform", "13572": "verstreuten", "13573": "rush", "13574": "hour", "13575": "widersetzt", "13576": "schwerkraft", "13577": "erleuchtende", "13578": "zwangsjacke", "13579": "werbespot", "13580": "str\u00f6men", "13581": "jenga-kl\u00f6tzen", "13582": "freiheitsstatuenhaltung", "13583": "computergesch\u00e4ft", "13584": "provisorisch", "13585": "reparierte", "13586": "arbeitswerkzeuge", "13587": "pistolen", "13588": "di\u00e4tcola", "13589": "hinabf\u00e4hrt", "13590": "normalen", "13591": "kernen", "13592": "spr\u00fchenden", "13593": "anzubringen", "13594": "metallobjekte", "13595": "haften", "13596": "fernsehshow", "13597": "protestant", "13598": "usa-shirt", "13599": "protestveranstaltung", "13600": "anzufangen", "13601": "feuerwehrkleidung", "13602": "protestanten", "13603": "musikveranstaltung", "13604": "elephant", "13605": "regenbogens", "13606": "november", "13607": "can", "13608": "'t", "13609": "come", "13610": "soon", "13611": "enough", "13612": "tierhandlung", "13613": "erh\u00e4ltlichen", "13614": "kirschb\u00e4umen", "13615": "fliegengitter", "13616": "hauben", "13617": "betend", "13618": "sarg", "13619": "angelegten", "13620": "tempels", "13621": "baseballprofis", "13622": "neongr\u00fcner", "13623": "skizziert", "13624": "vorbeigefahren", "13625": "entfesselungsk\u00fcnstler", "13626": "fr\u00fchlingshaften", "13627": "katapultiert", "13628": "startblock", "13629": "hochzeitskleider", "13630": "reflektionen", "13631": "tageslichts", "13632": "entlant", "13633": "herumsteht", "13634": "exotischen", "13635": "ballett-paare", "13636": "sonnenanbeterinnen", "13637": "dashiki", "13638": "best", "13639": "buy-gesch\u00e4ft", "13640": "eisenzaun", "13641": "federh\u00fcte", "13642": "streetdancing", "13643": "beruhigendes", "13644": "lebendes", "13645": "rinne", "13646": "mittellangen", "13647": "kinderrad", "13648": "pflastersteinen", "13649": "stra\u00dfenplatten", "13650": "delphin", "13651": "waschanlage", "13652": "bernhardiner", "13653": "renaissance-mode", "13654": "bratwurst", "13655": "eisb\u00e4rkost\u00fcm", "13656": "lederstuhl", "13657": "donnerstagnacht", "13658": "kreidebotschaft", "13659": "ballettkleidern", "13660": "teva", "13661": "neuroscience", "13662": "kostenlos", "13663": "rat", "13664": "einzuholen", "13665": "zerrissener", "13666": "sauerstoffflasche", "13667": "selena", "13668": "gomez", "13669": "cremefarbenes", "13670": "sixpack", "13671": "ausgestiegen", "13672": "ranzen", "13673": "gezogenem", "13674": "hielt", "13675": "nahm", "13676": "w\u00e4hrenddessen", "13677": "begutachteten", "13678": "marktbesucher", "13679": "geburtstagskarte", "13680": "schwei\u00dfermaske", "13681": "zuh\u00f6rerschar", "13682": "klappt", "13683": "bereitschaftspolizisten", "13684": "mob", "13685": "batman-kost\u00fcm", "13686": "snow-cone-eis", "13687": "hinauftragen", "13688": "wundervoller", "13689": "schutzkabine", "13690": "fidel", "13691": "castro", "13692": "carnegie", "13693": "mellon", "13694": "nudelholz", "13695": "geishas", "13696": "nachkommen", "13697": "kurzhaarper\u00fccken", "13698": "dreist\u00f6ckiges", "13699": "jedem", "13700": "stockwerk", "13701": "w\u00e4hend", "13702": "hinaufschauen", "13703": "hockenden", "13704": "w\u00e4schleine", "13705": "gestapelter", "13706": "wahnsinnigem", "13707": "leuchtet", "13708": "zimmermannshammer", "13709": "unterhaltend", "13710": "prayer", "13711": "booth", "13712": "dahin", "13713": "rosa-gelben", "13714": "pantomimen", "13715": "holzst\u00e4mme", "13716": "furchtbare", "13717": "schaufensterpuppen", "13718": "dreiecksf\u00f6rmig", "13719": "skistiefel", "13720": "dahingleitenden", "13721": "wundervolle", "13722": "gesattelte", "13723": "kofferraumklappe", "13724": "vietnamesischen", "13725": "rutschbahn", "13726": "sportposter", "13727": "sushi", "13728": "tambor", "13729": "pausen", "13730": "bambus-hintergrund", "13731": "outdoor-trainingseinrichtung", "13732": "bergiges", "13733": "informationsstand", "13734": "farbig", "13735": "signierstunde", "13736": "victoria", "13737": "leak", "13738": "b\u00fcroschreibtisch", "13739": "bergbauarbeiten", "13740": "begrenzen", "13741": "malerhut", "13742": "spirit", "13743": "week", "13744": "untertage", "13745": "krustentier", "13746": "schauspielerinnen", "13747": "holzkohle", "13748": "kopfdeckel", "13749": "rosa-gelbes", "13750": "bouquet", "13751": "jobs", "13752": "with", "13753": "justice", "13754": "bierflasche", "13755": "punk-band", "13756": "zellen", "13757": "clownsnase", "13758": "schmaler", "13759": "taster", "13760": "alpenstil", "13761": "keinen", "13762": "urne", "13763": "bungie", "13764": "jumping", "13765": "cowboy-outfits", "13766": "fahrradpedal", "13767": "surfboarder", "13768": "tretboot", "13769": "gorillas", "13770": "bev\u00f6lkern", "13771": "freiwillig", "13772": "saubermachen", "13773": "mardi", "13774": "gras-k\u00fcgelchen", "13775": "schlafzimmerfenster", "13776": "-anzeige", "13777": "verwundert", "13778": "trendige", "13779": "h\u00fctene", "13780": "vogelh\u00e4uschen", "13781": "kirschbl\u00fctenb\u00e4umen", "13782": "medienger\u00e4t", "13783": "blau-schwarze", "13784": "k\u00f6rpertreffer", "13785": "raumausstatters", "13786": "zusammengerollte", "13787": "essens-", "13788": "kleidungsmarkt", "13789": "aler", "13790": "verh\u00e4ngtes", "13791": "open", "13792": "t\u00fcrkiser", "13793": "zeltbereich", "13794": "marugas", "13795": "tomatenpastete", "13796": "coca-cola-eisbar", "13797": "propangrill", "13798": "fuchsiafarbenen", "13799": "\u00fcberschatteten", "13800": "greifzangen", "13801": "fr\u00fchst\u00fccksbuffet", "13802": "seidenroben", "13803": "plastikschaukel", "13804": "lights", "13805": "books", "13806": "wildwasser-rafting", "13807": "singer", "13808": "-schilds", "13809": "betonweg", "13810": "bucks", "13811": "amish-familie", "13812": "turbulentem", "13813": "beschmiet", "13814": "au\u00dfergew\u00f6hnlichen", "13815": "markthalle", "13816": "sch\u00e4umendem", "13817": "papie", "13818": "backblech", "13819": "p\u00e4rchens", "13820": "reflektiert", "13821": "naturgebiet", "13822": "traktorfahrer", "13823": "fu\u00dfballhut", "13824": "binder", "13825": "schw\u00e4mme", "13826": "mand", "13827": "trommelst\u00f6cken", "13828": "lila-", "13829": "gassigehen", "13830": "lampenmasten", "13831": "flaggentag", "13832": "begr\u00fcnte", "13833": "identische", "13834": "schackbrett", "13835": "-schlagmann", "13836": "herausgefischt", "13837": "softdrinks", "13838": "vatikan", "13839": "mofafans", "13840": "mehrer", "13841": "gerecktem", "13842": "sandgrube", "13843": "grinsen", "13844": "gez\u00e4hmten", "13845": "elephanten", "13846": "dartpfeil", "13847": "tonkr\u00fcgen", "13848": "schielende", "13849": "modelstatur", "13850": "zebra-outfit", "13851": "unterhemdchen", "13852": "geh\u00e4ngt", "13853": "t-shirt-anzeige", "13854": "kr\u00e4he", "13855": "zechende", "13856": "schlepptau", "13857": "stuckgeb\u00e4udes", "13858": "verabredung", "13859": "nachrichtensprecherin", "13860": "kondomen", "13861": "blumenkost\u00fcmen", "13862": "eienr", "13863": "stra\u00dfenmusikant", "13864": "didgeridoo", "13865": "eif\u00f6rmiges", "13866": "ding", "13867": "bekannte", "13868": "johns", "13869": "umschalten", "13870": "au\u00dfenfenster", "13871": "udn", "13872": "tatenlos", "13873": "f\u00fcnfundzwanzig", "13874": "weitsprungwettbewerb", "13875": "meditative", "13876": "zitronenf\u00f6rmiges", "13877": "tropische", "13878": "schuluniformen", "13879": "h\u00fcrdenl\u00e4ufer", "13880": "eingeschaltete", "13881": "flannel", "13882": "fu\u00dfballfelds", "13883": "holzget\u00e4felten", "13884": "\u00fcberschwemmung", "13885": "ventilator", "13886": "fulton", "13887": "st", "13888": "durchgangsstation", "13889": "fischerm\u00fctzen", "13890": "randsteinn", "13891": "gro\u00dfm\u00fctter", "13892": "betonbl\u00f6cke", "13893": "wartehalle", "13894": "seilh\u00fcpfen", "13895": "your", "13896": "ad", "13897": "ochsengespann", "13898": "routinierte", "13899": "ausgr\u00e4bt", "13900": "schauspielern", "13901": "ratte", "13902": "eisbl\u00f6cke", "13903": "projektionswand", "13904": "backt", "13905": "rostroten", "13906": "einsatzanz\u00fcgen", "13907": "grabung", "13908": "police", "13909": "doktors", "13910": "heart", "13911": "umfallen", "13912": "armer", "13913": "religi\u00f6ses", "13914": "essensbude", "13915": "pflanzk\u00fcbel", "13916": "kenntnisse", "13917": "holzhandwerk", "13918": "zuckerrohr", "13919": "arch\u00e4ologischen", "13920": "hochgebirge", "13921": "gep\u00e4ckst\u00fcck", "13922": "puertorikanischen", "13923": "lebenden", "13924": "h\u00fchnern", "13925": "anzugshosen", "13926": "mexikaner", "13927": "m\u00e4nneressen", "13928": "eisige", "13929": "ganzk\u00f6rperkost\u00fcm", "13930": "lebensunterhalt", "13931": "silberreiher", "13932": "sch\u00fclerlotse", "13933": "shrimps", "13934": "gl\u00fchbirne", "13935": "hijab", "13936": "unlackierten", "13937": "post", "13938": "bills", "13939": "freizeitbekleidung", "13940": "beinstreckern", "13941": "amsterdam", "13942": "gr\u00e4ulichen", "13943": "pr\u00e4zision", "13944": "ausschlaggebend", "13945": "australischer", "13946": "mound", "13947": "tropenfische", "13948": "au\u00dfenseiten", "13949": "pitchers", "13950": "thail\u00e4ndischen", "13951": "rikschas", "13952": "topffrisur", "13953": "wartungstechniker", "13954": "aufruf", "13955": "eidechsenartigen", "13956": "musikgesch\u00e4ft", "13957": "glast\u00fcren", "13958": "puder", "13959": "flossen", "13960": "limobecher", "13961": "schockiert", "13962": "b\u00e4renkost\u00fcm", "13963": "kost\u00fcms", "13964": "stangentanz", "13965": "bua", "13966": "festgebundenen", "13967": "jojo", "13968": "g\u00f6nner", "13969": "fu\u00dfg\u00e4nger\u00fcberwegen", "13970": "holzobjekt", "13971": "holzstange", "13972": "sch\u00fcssel\u00e4hnliche", "13973": "durchgef\u00fchrte", "13974": "protestaktion", "13975": "schneekegel", "13976": "eiswgen", "13977": "faxen", "13978": "schutzanz\u00fcgen", "13979": "boxers", "13980": "menschliches", "13981": "unternehmen", "13982": "techniker", "13983": "sicherheitsgeschirr", "13984": "getrennt", "13985": "hubb\u00fchne", "13986": "imbisstheke", "13987": "fotoapparats", "13988": "schleppen", "13989": "miniaturen", "13990": "abschlussm\u00fctze", "13991": "-robe", "13992": "weitsprung", "13993": "jack-russell-terrier", "13994": "wasserloch", "13995": "fes", "13996": "weiden", "13997": "bauarbeiterweste", "13998": "herrlicher", "13999": "sternf\u00f6rmige", "14000": "beschatteten", "14001": "showgirl-kost\u00fcm", "14002": "gefederten", "14003": "kochs", "14004": "einene", "14005": "verschiedensten", "14006": "bauernhaus", "14007": "hochhackige", "14008": "jeanssch\u00fcrze", "14009": "frag", "14010": "atlanta", "14011": "fisher", "14012": "sara", "14013": "old", "14014": "fashioned", "14015": "cream", "14016": "baumst\u00e4mme", "14017": "oberfk\u00f6rper", "14018": "gebackenen", "14019": "krocket", "14020": "art-deco-stil", "14021": "hineingeht", "14022": "bambusst\u00f6cken", "14023": "reetgedeckte", "14024": "lieblingsgesch\u00e4ft", "14025": "vertikale", "14026": "pl\u00fcsch-hundertf\u00fc\u00dfler", "14027": "notfallhelfer", "14028": "unfallfahrzeug", "14029": "tocher", "14030": "lieblingsspielzeug", "14031": "mooch", "14032": "herumfassen", "14033": "\u00fcbergang", "14034": "verkehrsunfalls", "14035": "aufzustellen", "14036": "pontiac", "14037": "produktionscrew", "14038": "orange-roten", "14039": "monitore", "14040": "staples", "14041": "easy-aufkleber", "14042": "v-ausschnitt", "14043": "gr\u00fcn-orange-blauen", "14044": "entlangblickt", "14045": "ok", "14046": "sehendes", "14047": "k\u00fcstenlandschaft", "14048": "good", "14049": "morning", "14050": "speilt", "14051": "gl\u00fcht", "14052": "textnachrichten", "14053": "flippt", "14054": "papierlaterne", "14055": "adidas-sporttasche", "14056": "taiwanese", "14057": "einkaufender", "14058": "auszusteigen", "14059": "tupfern", "14060": "mia-werbung", "14061": "sch\u00fcrzt", "14062": "stadtverkehr", "14063": "stra\u00dfenabsperrungen", "14064": "lehmh\u00fcgel", "14065": "beochtet", "14066": "brieftr\u00e4gern", "14067": "kinderstuhl", "14068": "trinkgl\u00e4ser", "14069": "harley", "14070": "schrubber", "14071": "perfekten", "14072": "gedenkttafel", "14073": "spr\u00fchk\u00fcnstlerin", "14074": "wasch-trockner", "14075": "bemalter", "14076": "elementen", "14077": "blinde", "14078": "modelle", "14079": "babyelephanten", "14080": "-fleisch", "14081": "angeleinten", "14082": "swatch", "14083": "gesch\u00e4tige", "14084": "smoked", "14085": "t-shrit", "14086": "stripe-bier", "14087": "schlittschuhfahren", "14088": "metallklappst\u00fchlen", "14089": "schnapsglas", "14090": "didgeridoos", "14091": "siberian", "14092": "angestellter", "14093": "m\u00fcllabfuhr", "14094": "klassentreffen", "14095": "araberin", "14096": "girarre", "14097": "frauenunterw\u00e4sche", "14098": "vierer", "14099": "gleichrangiger", "14100": "durchstreifen", "14101": "bezeichnungen", "14102": "gurke", "14103": "tagesmesse", "14104": "geraden", "14105": "polizeifahrzeuge", "14106": "1950er", "14107": "souschef", "14108": "silbrigen", "14109": "riemenschuhen", "14110": "getr\u00e4nkestand", "14111": "essensverk\u00e4ufers", "14112": "gehene", "14113": "kirschbl\u00fcten", "14114": "schl\u00e4gen", "14115": "bull-auto", "14116": "herunterh\u00e4ngt", "14117": "arbeitskr\u00e4fte", "14118": "rolll\u00e4den", "14119": "batik-shirt", "14120": "nagelstudio", "14121": "m\u00fcnzfernsprechers", "14122": "h\u00f6rer", "14123": "feuerwache", "14124": "spiegelobjekt", "14125": "kapitol", "14126": "gro\u00dfbildleinwand", "14127": "leckereien", "14128": "fr\u00fchere", "14129": "gouverneur", "14130": "mexico", "14131": "richardson", "14132": "honda", "14133": "protestierend", "14134": "paraden\u00e4hnlichen", "14135": "israel", "14136": "beschimpft", "14137": "marschierende", "14138": "\u00fcberschaubaren", "14139": "zuschauerzahl", "14140": "cher", "14141": "chirurgischer", "14142": "beachvolleyballspielerinnen", "14143": "30", "14144": "40", "14145": "zugewandte", "14146": "traktorgreifer", "14147": "essensverkaufsstand", "14148": "gehwege", "14149": "bev\u00f6lkert", "14150": "ohrl\u00e4ppchen", "14151": "gummistiefel", "14152": "gafft", "14153": "t-shrits", "14154": "gescheckten", "14155": "babyblauen", "14156": "arbeitshemd", "14157": "\u00fcberdimensioniertem", "14158": "outdoor-bereich", "14159": "rauchender", "14160": "busbahnhof", "14161": "golfballs", "14162": "silberfarbenes", "14163": "reling", "14164": "rot-blaues", "14165": "giorgio", "14166": "schw\u00e4nze", "14167": "seiko", "14168": "darstellender", "14169": "gedehnten", "14170": "hauptstadt", "14171": "deutschlands", "14172": "balkongel\u00e4nder", "14173": "u-bahn-szene", "14174": "kreuzfahrer", "14175": "jet-ski", "14176": "outdoor-event", "14177": "stra\u00dfenbahnen", "14178": "panama-hut", "14179": "fliegerbrille", "14180": "streuen", "14181": "unsicher", "14182": "deutlichen", "14183": "schaden", "14184": "aufgef\u00fchrt", "14185": "nachdenkt", "14186": "vorliegende", "14187": "sprich", "14188": "rostfarbenen", "14189": "khaki-farbenen", "14190": "aluminiumdose", "14191": "carnival", "14192": "barker", "14193": "zirkuszelt", "14194": "funkger\u00e4t", "14195": "gepanzertem", "14196": "feuerwehranz\u00fcgen", "14197": "m\u00fcllhalde", "14198": "panzers", "14199": "acura", "14200": "gepanzerten", "14201": "automatischen", "14202": "vollzugsbeamter", "14203": "thailand", "14204": "blumenhut", "14205": "einzusetzen", "14206": "hortons-schild", "14207": "krabben", "14208": "schwelenden", "14209": "schutz", "14210": "beh\u00e4ltern", "14211": "spazierenden", "14212": "webstuhl-artigen", "14213": "hingehalten", "14214": "baumwolle", "14215": "wassergef\u00fcllten", "14216": "glocken", "14217": "70er-", "14218": "80er-jahren", "14219": "birmingham", "14220": "zugewandten", "14221": "wahrzeichens", "14222": "wirbeldesign", "14223": "spitzenkragen", "14224": "jp", "14225": "morgan", "14226": "-transparent", "14227": "dasitzt", "14228": "gestartet", "14229": "seeufer", "14230": "wettrennens", "14231": "bananenreihe", "14232": "reinigungstrupp", "14233": "beleuchtungsk\u00f6rper", "14234": "griffe", "14235": "haarteilen", "14236": "dringend", "14237": "undichte", "14238": "herzklappe", "14239": "verschiedenster", "14240": "wissenschaftlich", "14241": "rucksacks", "14242": "examinierte", "14243": "op-schwester", "14244": "einstellungen", "14245": "l\u00f6schfahrzeug", "14246": "abgeschirmten", "14247": "\u00e4rztlicher", "14248": "behandlungsraum", "14249": "op-kitteln", "14250": "op", "14251": "videomonitor", "14252": "eingriff", "14253": "medizinern", "14254": "\u00e4rztliche", "14255": "chirurgie", "14256": "krebspatienten", "14257": "wahrzeichen", "14258": "braunwei\u00dfe", "14259": "v\u00f6lker", "14260": "beliebt", "14261": "sichten", "14262": "feuerwehrangeh\u00f6rige", "14263": "gidday", "14264": "schl\u00fcsselbund", "14265": "ausbl\u00e4st", "14266": "kastens", "14267": "buschlandschaft", "14268": "0", "14269": "ziffern", "14270": "oberarm", "14271": "kaugummis", "14272": "anh\u00f6he", "14273": "ninja-outfit", "14274": "beschilderung", "14275": "nahkampf", "14276": "revuegirl", "14277": "irgendwie", "14278": "feindselige", "14279": "besitzerinnen", "14280": "realty", "14281": "gerne", "14282": "schwimmbrettern", "14283": "kapit\u00e4nsm\u00fctze", "14284": "\u00e4rgerlichem", "14285": "spult", "14286": "security-mitarbeiterin", "14287": "vielerlei", "14288": "fausthandschuhen", "14289": "nonnenhabit", "14290": "minikleidern", "14291": "angegeben", "14292": "karomuster", "14293": "aneinandergekettete", "14294": "bruders", "14295": "gew\u00e4chsh\u00e4user", "14296": "kofferwagen", "14297": "auspresst", "14298": "nike-trikot", "14299": "glasschild", "14300": "flusskanal", "14301": "steinmole", "14302": "lego-steinen", "14303": "gehefteten", "14304": "skaterin", "14305": "apfelhain", "14306": "herabf\u00e4llt", "14307": "tigger-oberteil", "14308": "vorbeikommende", "14309": "gestimmten", "14310": "v\u00e4tern", "14311": "papiertasche", "14312": "bustiers", "14313": "tanzprogramm", "14314": "anh\u00e4ngern", "14315": "metallh\u00fchnern", "14316": "kitchenaid-r\u00fchrsch\u00fcssel", "14317": "scherzhaft", "14318": "waagerecht", "14319": "wine", "14320": "ungek\u00e4mmten", "14321": "guckl\u00f6chern", "14322": "angstvoll", "14323": "zornig", "14324": "paddington", "14325": "notizheft", "14326": "signieren", "14327": "mitsch\u00fcler", "14328": "totempfahl", "14329": "hula-r\u00f6cken", "14330": "lampenmast", "14331": "ligisten", "14332": "w\u00e4schetrockner", "14333": "comics", "14334": "gl\u00fccksspielautomaten", "14335": "wattierten", "14336": "stra\u00dfenh\u00e4ndlerinnen", "14337": "gelbgr\u00fcnen", "14338": "catcher", "14339": "einr\u00e4dern", "14340": "massenverkehrsmittel", "14341": "wirbeltanz", "14342": "kitchenaid-kurs", "14343": "kaffeebecher", "14344": "regenbogenfarbig", "14345": "spielzeugwagen", "14346": "geschenkverpackung", "14347": "drin", "14348": "kunstgegenst\u00e4nden", "14349": "diabolischem", "14350": "fleischst\u00fccke", "14351": "matschbedeckten", "14352": "unterarme", "14353": "schl\u00fcpfer", "14354": "parkschildern", "14355": "g\u00e4hnender", "14356": "drehteil", "14357": "cigar", "14358": "schlafzimmerboden", "14359": "tankt", "14360": "schiefen", "14361": "planen", "14362": "wischmob", "14363": "route", "14364": "texanischen", "14365": "umgeweht", "14366": "wassertropfen", "14367": "cyfd", "14368": "pinkfarben", "14369": "abwasch", "14370": "hinduist", "14371": "v\u00f6lkermord", "14372": "armeniern", "14373": "pfeile", "14374": "strandtag", "14375": "schmuckvoller", "14376": "schmuddeligen", "14377": "gelb-rot-gr\u00fcnen", "14378": "flugbereit", "14379": "gar", "14380": "pelzige", "14381": "schlicht", "14382": "planenzelt", "14383": "bahnschranke", "14384": "ritual", "14385": "gelbwei\u00dfen", "14386": "einfriedungsmauer", "14387": "eingefrorenen", "14388": "aufgeschichtetes", "14389": "yoga-\u00fcbung", "14390": "mageres", "14391": "bambuskopftuch", "14392": "wanderstock", "14393": "hochspringer", "14394": "\u00e4gyptischem", "14395": "subjekts", "14396": "bassspieler", "14397": "strammer", "14398": "offizier", "14399": "cape", "14400": "synchronschwimmen", "14401": "schublade", "14402": "plakaten", "14403": "ackerfr\u00e4se", "14404": "kaleidoskop", "14405": "hengsten", "14406": "maskenbildnerin", "14407": "fusselper\u00fccke", "14408": "hintergrunds\u00e4ngern", "14409": "goldkreuz", "14410": "ausgefallener", "14411": "brutalit\u00e4t", "14412": "polizeigewalt", "14413": "beschwerden", "14414": "lotterielose", "14415": "dj-spiel", "14416": "cola-dose", "14417": "marktveranstaltung", "14418": "felsw\u00e4nden", "14419": "vorgehensweise", "14420": "gebratene", "14421": "zeltdach", "14422": "\u00f6lverseuchung", "14423": "f\u00fcrchtet", "14424": "meinung", "14425": "\u00f6l", "14426": "flugschau", "14427": "reich", "14428": "allzu", "14429": "currygerichten", "14430": "gottesanbeterin", "14431": "wassereis", "14432": "auftun", "14433": "fruchtgetr\u00e4nke", "14434": "einkaufsmeile", "14435": "go-kart-bahn", "14436": "wahlkampfplakat", "14437": "bronislaw", "14438": "komorowski", "14439": "'s-restaurant", "14440": "kampfkunststellungen", "14441": "absperrseil", "14442": "abgesperrt", "14443": "jenseits", "14444": "absperrbandes", "14445": "graduierung", "14446": "medienvertreter", "14447": "feuerwehrmantel", "14448": "-helm", "14449": "linienbusses", "14450": "entsetzliches", "14451": "feldwegs", "14452": "okkupieren", "14453": "buswarteh\u00e4uschen", "14454": "umringen", "14455": "strandtasche", "14456": "wanderkarte", "14457": "unsichtbaren", "14458": "thefaceshop", "14459": "kopflose", "14460": "handgep\u00e4ckst\u00fcck", "14461": "flug\u00fcbersicht", "14462": "beanbag", "14463": "rauchfackeln", "14464": "guck", "14465": "lass", "14466": "wei\u00dfrote", "14467": "umkippt", "14468": "schablone", "14469": "bauz\u00e4unen", "14470": "j\u00e4gers", "14471": "planken", "14472": "fan-fest", "14473": "eckladen", "14474": "lakers-flaggen", "14475": "strichen", "14476": "umrisse", "14477": "betttuch", "14478": "muslimin", "14479": "sandfl\u00e4che", "14480": "flohmarktwaren", "14481": "akkordeonspiel", "14482": "allerlei", "14483": "unmittelbar", "14484": "rotwei\u00df", "14485": "internationale", "14486": "ladengang", "14487": "umschauen", "14488": "braunh\u00e4utiger", "14489": "angeschlossen", "14490": "karateuniformen", "14491": "gesattelten", "14492": "zaumzeug", "14493": "speck", "14494": "20", "14495": "feinkoststand", "14496": "frischfleisch", "14497": "zitronengelben", "14498": "internationalem", "14499": "lebensmittelangebot", "14500": "pizza-imbiss", "14501": "zeugnissen", "14502": "stadtrundfahrtbus", "14503": "vorbeischlendern", "14504": "spiegelung", "14505": "joggerin", "14506": "hofgarten", "14507": "stra\u00dfenverkehr", "14508": "automatten", "14509": "versehener", "14510": "spongebob-luftballons", "14511": "gezapftes", "14512": "obstwagen", "14513": "zuzubereiten", "14514": "vergisst", "14515": "turnstange", "14516": "tretroller", "14517": "baustellenabsperrungen", "14518": "aufzubrechen", "14519": "panzer", "14520": "-banner", "14521": "from", "14522": "forderungen", "14523": "beleuchten", "14524": "killing", "14525": "iran", "14526": "zivilpersonen", "14527": "weingesch\u00e4ft", "14528": "bauvorhaben", "14529": "ortskneipe", "14530": "trinkend", "14531": "spirituosenladen", "14532": "uferkai", "14533": "empfangsportier", "14534": "kneipeng\u00e4sten", "14535": "zujubeln", "14536": "auf-", "14537": "glasklaren", "14538": "erfreut", "14539": "checkt", "14540": "baguette", "14541": "gerissenen", "14542": "gelocktes", "14543": "terassenmauer", "14544": "light-werbung", "14545": "jeansstoff", "14546": "tommy", "14547": "hilfiger-logo", "14548": "karatebewegungen", "14549": "langgezogenen", "14550": "computerterminals", "14551": "bahnstation", "14552": "downtown", "14553": "restaurantambiente", "14554": "speise", "14555": "interessanter", "14556": "umweltverschmutzung", "14557": "bud", "14558": "\u00e4therischen", "14559": "eindringlich", "14560": "anliegender", "14561": "spieleisenbahnanlage", "14562": "mauerartigen", "14563": "totengedenkst\u00e4tte", "14564": "osteurop\u00e4ischen", "14565": "mauersimsen", "14566": "hochgewachsene", "14567": "herumgebaut", "14568": "orientalisches", "14569": "gem\u00fcseverk\u00e4ufer", "14570": "wikinger", "14571": "einstiegst\u00fcren", "14572": "dabeihaben", "14573": "baustellenszene", "14574": "nackentr\u00e4ger", "14575": "auszuspannen", "14576": "strandschirm", "14577": "lesbische", "14578": "16", "14579": "polizeiuniformen", "14580": "seilbr\u00fccke", "14581": "billardhalle", "14582": "marine", "14583": "begleiterin", "14584": "folkloristischer", "14585": "gelbroter", "14586": "gebl\u00fcmter", "14587": "ungest\u00fcmen", "14588": "holzsch\u00fcssel", "14589": "feinkostladen", "14590": "polizeikr\u00e4fte", "14591": "gasmasken", "14592": "frauenbadeanzug", "14593": "polizeikr\u00e4ften", "14594": "kampfausr\u00fcstung", "14595": "vergr\u00f6\u00dferungsgl\u00e4ser", "14596": "gepunktet", "14597": "dreizack", "14598": "grasrock", "14599": "bohrhammer", "14600": "bh", "14601": "indisch", "14602": "synchronisierte", "14603": "k\u00f6rperstellung", "14604": "sekt-", "14605": "begleitperson", "14606": "brasilien-t-shirt", "14607": "brasilianische", "14608": "hereinscheint", "14609": "beschert", "14610": "klarheit", "14611": "dunkelk\u00f6pfige", "14612": "dackel", "14613": "schweizaufenthaltes", "14614": "metallobjekt", "14615": "spiegelt", "14616": "drachentanz", "14617": "poloshirts", "14618": "sportteil", "14619": "befragen", "14620": "lavastrom", "14621": "anh\u00e4ngers", "14622": "schichtet", "14623": "rettungssanit\u00e4ter", "14624": "videomacher", "14625": "gigantische", "14626": "vor\u00fcberfahrenden", "14627": "hin\u00fcber", "14628": "pfefferminze", "14629": "spielten", "14630": "hochdruckreinigung", "14631": "baumschaukel", "14632": "terrible", "14633": "arrivo", "14634": "schwarzbrauner", "14635": "absperrkegel", "14636": "einsatzkr\u00e4ften", "14637": "elegant", "14638": "verharren", "14639": "gymnastischen", "14640": "k\u00f6rperhaltung", "14641": "kanalschachts", "14642": "b\u00fcroraum", "14643": "verwirrtem", "14644": "enth\u00fclst", "14645": "fernsieht", "14646": "h\u00fcllt", "14647": "feierlichkeit", "14648": "schwimmshirt", "14649": "sprudelnde", "14650": "motorradunfall", "14651": "privaten", "14652": "anzugtr\u00e4ger", "14653": "kinderfrau", "14654": "farbenfrohes", "14655": "gebirges", "14656": "breeze", "14657": "partyartikel", "14658": "ebenen", "14659": "schn\u00fcre", "14660": "rotwei\u00dfblauen", "14661": "kanada-trikots", "14662": "landesfarben", "14663": "ausgestaltet", "14664": "vorbeifliegt", "14665": "gefahrlos", "14666": "nebenstra\u00dfe", "14667": "gro\u00dfst\u00e4dtische", "14668": "konjunktur", "14669": "wackelig", "14670": "auswirkungen", "14671": "kuriertasche", "14672": "neujahrsfest", "14673": "gefeiert", "14674": "goldgerahmten", "14675": "wassermotorrad", "14676": "erkundigt", "14677": "auslagen", "14678": "wasserfahrrad", "14679": "abgestellten", "14680": "stra\u00dfenreinigungsfahrzeug", "14681": "rennfahrer-overall", "14682": "maultieren", "14683": "h\u00fcfttiefes", "14684": "f\u00fchle", "14685": "unbehaglich", "14686": "oldtimer-propellerflugzeug", "14687": "panda", "14688": "kunststoffrohr", "14689": "modell-kriegsflugzeug", "14690": "geduldige", "14691": "sonnenfinsternis", "14692": "abwarten", "14693": "modellflugzeugen", "14694": "betastet", "14695": "computertischen", "14696": "elfen-tattoo", "14697": "jeans-overall", "14698": "per\u00fcckengesch\u00e4ft", "14699": "kaimauer", "14700": "burkas", "14701": "gummieimer", "14702": "bummel", "14703": "eklektischer", "14704": "kamelstatue", "14705": "scherzfoto", "14706": "vor\u00fcbereilen", "14707": "chinesinnen", "14708": "wiederauff\u00fchrung", "14709": "walzers", "14710": "wagenladung", "14711": "zeitgen\u00f6ssischen", "14712": "kimono-\u00e4hnlichem", "14713": "krankenhausbettchen", "14714": "memorabilien", "14715": "viktorianischer", "14716": "begr\u00fc\u00dfen", "14717": "weisungen", "14718": "gepackt", "14719": "farbiges", "14720": "wei\u00dfwandreifen", "14721": "ladenfront", "14722": 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"15000": 1, "15001": 1, "15002": 1, "15003": 1}, "special": [0, 1, 2, 3]}, "class": "WordVocabulary"} \ No newline at end of file diff --git a/assets/tokenizer/en_small_word_moses b/assets/tokenizer/en_small_word_moses new file mode 100644 index 0000000..1c22f88 --- /dev/null +++ b/assets/tokenizer/en_small_word_moses @@ -0,0 +1 @@ +{"kwargs": {"name": "en_baby", "filename": "", "vocab_size": 10215, "max_length": 50, "lower": true, "BOS_TOKEN": "", "EOS_TOKEN": "", "UNK_TOKEN": "", "SEP_TOKEN": "", "PAD_TOKEN": "", "CLS_TOKEN": "", "BLO_TOKEN": "

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"10436": "coutumes", "10437": "apporteront", "10438": "unicef", "10439": "mod\u00e9r\u00e9s", "10440": "frappent", "10441": "int\u00e9gral", "10442": "ambassades", "10443": "s\u00e1nchez", "10444": "pis", "10445": "parit\u00e9", "10446": "semer", "10447": "californie", "10448": "grandissant", "10449": "farce", "10450": "frank", "10451": "horreurs", "10452": "encouragements", "10453": "\u00e9tatiques", "10454": "tabou", "10455": "proverbe", "10456": "inspir\u00e9e", "10457": "pots", "10458": "jama\u00efque", "10459": "togo", "10460": "placent", "10461": "renvers\u00e9", "10462": "impatient", "10463": "panneaux", "10464": "b\u00f6sch", "10465": "solennelle", "10466": "impliquerait", "10467": "confiant", "10468": "obligatoirement", "10469": "soumet", "10470": "souverainistes", "10471": "administrateur", "10472": "gars", "10473": "abbas", "10474": "tra\u00eener", "10475": "albert", "10476": "fou", "10477": "ordures", "10478": "compt\u00e9", "10479": "tyrannie", "10480": "wellington", "10481": "1974", "10482": "canc\u00fan", "10483": "proclam\u00e9", "10484": "ber\u00e8s", "10485": "justesse", "10486": "consultatifs", "10487": "m\u00e9diocre", "10488": "demand\u00e9es", "10489": "sp\u00e9cialis\u00e9", "10490": "\u00e9quilibr\u00e9s", "10491": "cons\u00e9cutives", "10492": "dress\u00e9", "10493": "rubriques", "10494": "surp\u00eache", "10495": "convenait", "10496": "aviaire", "10497": "r\u00e9publiques", "10498": "ambiance", "10499": "abkhazie", "10500": "lot", "10501": "solidaires", "10502": "anonymes", "10503": "brillant", "10504": "recette", "10505": "barbare", "10506": "aptitudes", "10507": "commercialiser", "10508": "galles", "10509": "factions", "10510": "colonisation", "10511": "punis", "10512": "souhaitions", "10513": "construits", "10514": "artificiellement", "10515": "cacao", "10516": "plaint", "10517": "dicter", "10518": "composants", "10519": "affiches", "10520": "superflu", "10521": "ing\u00e9nieurs", "10522": "50e", "10523": "ex\u00e9cut\u00e9s", "10524": "alep", "10525": "abu", "10526": "x\u00e9nophobes", "10527": "hostiles", "10528": "attachons", "10529": "d\u00e9favorables", "10530": "d\u00eener", "10531": "liberia", "10532": "collins", "10533": "marginalisation", "10534": "durablement", "10535": "fascisme", "10536": "volatilit\u00e9", "10537": "poursuivant", "10538": "dimas", "10539": "cycles", "10540": "on\u00e9reux", "10541": "pallier", "10542": "successives", "10543": "vitales", "10544": "sovi\u00e9tiques", "10545": "nairobi", "10546": "marquera", "10547": "homologue", "10548": "conserv\u00e9", "10549": "transf\u00e9r\u00e9es", "10550": "appliquerait", "10551": "109", "10552": "\u00e9lecteur", "10553": "william", "10554": "t\u00e2cher", "10555": "eurogroupe", "10556": "barreau", "10557": "bangkok", "10558": "alliances", "10559": "g\u00e9ographie", "10560": "perp\u00e9tr\u00e9", "10561": "isra\u00e9lo", "10562": "pr\u00e9c\u00e8de", "10563": "neufs", "10564": "ludford", "10565": "persuad\u00e9s", "10566": "manifestant", 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"d\u00e9crets", "10611": "gibraltar", "10612": "fournisse", "10613": "obtiendra", "10614": "\u00e9loignement", "10615": "incertain", "10616": "envoy\u00e9es", "10617": "manquait", "10618": "v\u00e9cue", "10619": "acheminement", "10620": "contents", "10621": "kamloops", "10622": "r\u00e9gression", "10623": "discutions", "10624": "d\u00e9saccords", "10625": "hamilton", "10626": "destructions", "10627": "abordent", "10628": "demandais", "10629": "task", "10630": "p\u00e9age", "10631": "usages", "10632": "vertes", "10633": "aznar", "10634": "int\u00e9rimaires", "10635": "repr\u00e9sentatif", "10636": "gays", "10637": "entrevoir", "10638": "discut\u00e9s", "10639": "hans", "10640": "transform\u00e9s", "10641": "discuterons", "10642": "moralement", "10643": "rompuy", "10644": "reprennent", "10645": "all\u00e9gation", "10646": "colonel", "10647": "limiterai", "10648": "\u00e9noncer", "10649": "antigonish", "10650": "jazeera", "10651": "croates", "10652": "\u00e9carte", "10653": "malgache", "10654": "lecteur", "10655": "pr\u00e9somption", "10656": "m\u00ealer", "10657": "secr\u00e8te", "10658": "planche", "10659": "titley", "10660": "per\u00e7ues", "10661": "compliqu\u00e9es", "10662": "affront", "10663": "regardant", "10664": "disposera", "10665": "verrait", "10666": "os", "10667": "demeurant", "10668": "jury", "10669": "11h30", "10670": "\u00e9prouv\u00e9", "10671": "\u00e9chappent", "10672": "spectateurs", "10673": "mince", "10674": "beauchesne", "10675": "incitent", "10676": "engageant", "10677": "mont\u00e9", "10678": "maires", "10679": "avan\u00e7ons", "10680": "adh\u00e8rent", "10681": "nelson", "10682": "rel\u00e8vement", "10683": "extra", "10684": "protest\u00e9", "10685": "retenues", "10686": "1977", "10687": "d\u00e9bouche", "10688": "taxi", "10689": "d\u00e9truisent", "10690": "flore", "10691": "p\u00e9nuries", "10692": "ram\u00e8ne", "10693": "r\u00e9alisables", "10694": "minutieusement", "10695": "justifiant", "10696": "abrite", "10697": "parvenions", "10698": "pieux", "10699": "commun\u00e9ment", "10700": "d\u00e9taillants", "10701": "continuant", "10702": "taxer", "10703": "ferais", "10704": "causant", "10705": "agro", "10706": "jugeons", "10707": "sortis", "10708": "skopje", "10709": "solde", "10710": "compensatoires", "10711": "collection", "10712": "d\u00e9ficiences", "10713": "controverses", "10714": "sp\u00e9cifications", "10715": "b.", "10716": "extraordinairement", "10717": "ibrahim", "10718": "irresponsables", "10719": "redresser", "10720": "passerait", "10721": "pr\u00e9voie", "10722": "diffus\u00e9es", "10723": "antis\u00e9mitisme", "10724": "gardent", "10725": "coop\u00e8rent", "10726": "observ\u00e9es", "10727": "porteur", "10728": "taylor", "10729": "round", "10730": "motiv\u00e9s", "10731": "octroie", "10732": "intelligentes", "10733": "provisoirement", "10734": "uvrer", "10735": "douteuse", "10736": "\u00e9quilibres", "10737": "habilit\u00e9", "10738": "\u0641\u064a", "10739": "ex\u00e9cutive", "10740": "bowis", 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"constat\u00e9es", "10829": "traduira", "10830": "inappropri\u00e9e", "10831": "foncier", "10832": "champions", "10833": "hostilit\u00e9", "10834": "red", "10835": "passablement", "10836": "marier", "10837": "tortures", "10838": "alentours", "10839": "obtenons", "10840": "\u00e9tudi\u00e9e", "10841": "excusez", "10842": "appliqueront", "10843": "red\u00e9finir", "10844": "influenc\u00e9", "10845": "approximativement", "10846": "incombent", "10847": "ramen\u00e9", "10848": "proclamer", "10849": "haug", "10850": "rev\u00eatir", "10851": "qualitative", "10852": "recueillis", "10853": "constantes", "10854": "flynn", "10855": "sp\u00e9culations", "10856": "1,7", "10857": "s\u00e9ries", "10858": "pri\u00e8re", "10859": "d\u00e9pass\u00e9e", "10860": "lacrymog\u00e8nes", "10861": "jardin", "10862": "fr\u00e9quences", "10863": "maigre", "10864": "renomm\u00e9e", "10865": "concentr\u00e9e", "10866": "\u00e9gyptiennes", "10867": "fr\u00e9quent", "10868": "s\u00e9gr\u00e9gation", "10869": 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"11301": "pourcentages", "11302": "conseill\u00e9", "11303": "vitalit\u00e9", "11304": "egyptiens", "11305": "facultatif", "11306": "pressant", "11307": "afghanes", "11308": "initiaux", "11309": "ressemblent", "11310": "fabriqu\u00e9", "11311": "invent\u00e9", "11312": "heurtent", "11313": "partageant", "11314": "encourag\u00e9es", "11315": "automatiques", "11316": "fraga", "11317": "f\u00e9d\u00e9rations", "11318": "imposait", "11319": "absurdit\u00e9", "11320": "wilson", "11321": "accr\u00e9ditation", "11322": "fondamentalistes", "11323": "ill\u00e9galit\u00e9", "11324": "flou", "11325": "courtes", "11326": "r\u00e9git", "11327": "pr\u00e9senterai", "11328": "itin\u00e9rance", "11329": "pseudo", "11330": "windsor", "11331": "paternit\u00e9", "11332": "av\u00e9r\u00e9s", "11333": "aggrav\u00e9e", "11334": "pardon", "11335": "sp\u00e9culatifs", "11336": "acdi", "11337": "cens\u00e9es", "11338": "d\u00e9licats", "11339": "d\u00e9stabiliser", "11340": "urban", "11341": "ramos", "11342": 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"t.co", "11429": "coupure", "11430": "\u00e9crans", "11431": "acad\u00e9mie", "11432": "renseigner", "11433": "autoritaires", "11434": "ontarien", "11435": "r\u00e9gi", "11436": "\u00e9tudient", "11437": "compromet", "11438": "superflue", "11439": "laur\u00e9at", "11440": "constituaient", "11441": "privatisations", "11442": "aggrav\u00e9", "11443": "tristement", "11444": "loup", "11445": "\u00e9limine", "11446": "eurobarom\u00e8tre", "11447": "co\u00efncide", "11448": "tendue", "11449": "cin\u00e9matographique", "11450": "ouvrages", "11451": "inspecteur", "11452": "d\u00e9clar\u00e9es", "11453": "directoire", "11454": "associant", "11455": "nettoyer", "11456": "mortels", "11457": "instable", "11458": "stop", "11459": "proc\u00e8dent", "11460": "moines", "11461": "citizen", "11462": "finaux", "11463": "plaidoyer", "11464": "impatients", "11465": "sectorielle", "11466": "stimulation", "11467": "terminologie", "11468": "libanaise", "11469": "comportait", "11470": "effectives", "11471": 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"11515": "abstenue", "11516": "commencement", "11517": "chevauchements", "11518": "d\u00e9pression", "11519": "mobilis\u00e9", "11520": "reconna\u00eetra", "11521": "judicieusement", "11522": "appr\u00e9hender", "11523": "ravages", "11524": "explosions", "11525": "theato", "11526": "03", "11527": "instaur\u00e9e", "11528": "p\u00e9rennit\u00e9", "11529": "chris", "11530": "versent", "11531": "engendr\u00e9s", "11532": "inconditionnelle", "11533": "essayez", "11534": "admirable", "11535": "rive", "11536": "abordant", "11537": "catalogne", "11538": "attaquant", "11539": "po\u00e9sie", "11540": "buitenweg", "11541": "r\u00e9glement\u00e9es", "11542": "centrafricaine", "11543": "variations", "11544": "guichet", "11545": "convaincant", "11546": "meurt", "11547": "pr\u00e9avis", "11548": "fl\u00e8che", "11549": "attrait", "11550": "riverains", "11551": "sympathisants", "11552": "pr\u00e9occupons", "11553": "vendeur", "11554": "r\u00e9ciproques", "11555": "rejoignent", "11556": "lits", 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"11602": "masquer", "11603": "cites", "11604": "homosexuel", "11605": "trafics", "11606": "innover", "11607": "recherchent", "11608": "administr\u00e9", "11609": "r\u00e9serv\u00e9es", "11610": "chanceli\u00e8re", "11611": "kevin", "11612": "crim\u00e9e", "11613": "soup\u00e7on", "11614": "orient\u00e9", "11615": "nobles", "11616": "bi\u00e9lorusse", "11617": "tha\u00eflandais", "11618": "transportant", "11619": "reproch\u00e9", "11620": "john'", "11621": "autorisent", "11622": "rotation", "11623": "asem", "11624": "investiture", "11625": "pev", "11626": "actualiser", "11627": "ajourner", "11628": "r\u00e8gnent", "11629": "\u00e9clatement", "11630": "supranationale", "11631": "infirmiers", "11632": "a\u00e9roportuaires", "11633": "encourus", "11634": "retrouv\u00e9e", "11635": "berg", "11636": "pilotage", "11637": "chercheur", "11638": "questeurs", "11639": "syndical", "11640": "pouss\u00e9s", "11641": "servis", "11642": "croyaient", "11643": "napolitano", "11644": "pr\u00e9sum\u00e9s", "11645": "group", "11646": "cadmium", "11647": "d\u00e9livr\u00e9", "11648": "affirmons", "11649": "pr\u00e9serv\u00e9", "11650": "tripartite", "11651": "pr\u00e9ventif", "11652": "estimant", "11653": "inflig\u00e9es", "11654": "proc\u00e9dant", "11655": "fid\u00e8lement", "11656": "mutations", "11657": "motiver", "11658": "manche", "11659": "in\u00e9gale", "11660": "tajani", "11661": "romano", "11662": "historiens", "11663": "lib\u00e9r\u00e9e", "11664": "howitt", "11665": "roquettes", "11666": "quoiqu'", "11667": "trompeuses", "11668": "london", "11669": "osent", "11670": "bloquiste", "11671": "t\u00e9l\u00e9spectateurs", "11672": "mod\u00e9r\u00e9", "11673": "rude", "11674": "l\u00e9gion", "11675": "loisir", "11676": "protectionniste", "11677": "nouer", "11678": "tripoli", "11679": "devenait", "11680": "los", "11681": "alcoolisme", "11682": "sachions", "11683": "enregistrements", "11684": "ardemment", "11685": "r\u00e9veiller", "11686": "sinistre", "11687": 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"cr\u00e9ateur", "12419": "convocation", "12420": "trous", "12421": "dot\u00e9es", "12422": "bouddhistes", "12423": "tannock", "12424": "intelligemment", "12425": "\u00e9chelons", "12426": "\u00e9choue", "12427": "m\u00e9ritait", "12428": "soutenait", "12429": "d\u00e9sapprouve", "12430": "avortements", "12431": "l\u00e8vres", "12432": "z\u00e8le", "12433": "variables", "12434": "censurer", "12435": "masculin", "12436": "inversement", "12437": "moraux", "12438": "whatsapp", "12439": "conclues", "12440": "busquin", "12441": "expir\u00e9", "12442": "ressent", "12443": "klein", "12444": "stipulant", "12445": "imminent", "12446": "1971", "12447": "embryonnaires", "12448": "p\u00e9rez", "12449": "utilement", "12450": "d\u00e9battions", "12451": "affich\u00e9e", "12452": "pr\u00e9juger", "12453": "immobili\u00e8re", "12454": "perfectionnement", "12455": "lima", "12456": "perplexe", "12457": "l\u00e8vent", "12458": "pr\u00e9c\u00e9d\u00e9e", "12459": "chasseurs", "12460": "soulign\u00e9s", "12461": "calibre", "12462": "magnifiques", "12463": "relev\u00e9s", "12464": "virtuelle", "12465": "\u200f", "12466": "lev\u00e9s", "12467": "informelles", "12468": "d\u00e9rives", "12469": "s\u00e9parent", "12470": "d\u00e9finisse", "12471": "rompu", "12472": "abou", "12473": "bible", "12474": "avantageuse", "12475": "farc", "12476": "conditionnelles", "12477": "ranger", "12478": "d\u00e9savantages", "12479": "partons", "12480": "angulaires", "12481": "d\u00e9terminent", "12482": "combl\u00e9", "12483": "pr\u00e9scolaire", "12484": "antiterroriste", "12485": "\u00e9limin\u00e9s", "12486": "d\u00e9placent", "12487": "voyous", "12488": "explosifs", "12489": "s\u00e9cheresses", "12490": "rond", "12491": "toll\u00e9", "12492": "r\u00e9visions", "12493": "tcsps", "12494": "d\u00e9livr\u00e9s", "12495": "excluant", "12496": "pr\u00e9voyance", "12497": "inauguration", "12498": "ethnie", "12499": "congolais", "12500": "pr\u00eatent", "12501": "faciliterait", "12502": "anomalie", "12503": "r\u00e9bellion", "12504": "adjudication", "12505": "\u00e9quivalentes", "12506": "confi\u00e9es", "12507": "r\u00e9fl\u00e9chit", "12508": "rehausser", "12509": "khaled", "12510": "penchera", "12511": "d\u00e9bouchera", "12512": "tournante", "12513": "portugaises", "12514": "caract\u00e8res", "12515": "estim\u00e9s", "12516": "s\u00e9v\u00e9rit\u00e9", "12517": "d\u00e9linquance", "12518": "brenner", "12519": "redevance", "12520": "r\u00e9pandues", "12521": "kallas", "12522": "haineux", "12523": "contributeur", "12524": "libya", "12525": "general", "12526": "burlington", "12527": "bo\u00eetes", "12528": "acquisitions", "12529": "ignor\u00e9s", "12530": "lents", "12531": "mentalit\u00e9s", "12532": "australiens", "12533": "tenaient", "12534": "humilit\u00e9", "12535": "\u00e9quateur", "12536": "thyssen", "12537": "sylviculture", "12538": "aire", "12539": "okanagan", "12540": "hypocrites", "12541": "remboursements", "12542": "parlera", "12543": "d\u00e9m\u00e9nager", "12544": "p\u00e9nalise", "12545": "gouffre", "12546": "antifraude", "12547": "entrevue", "12548": "consist\u00e9", "12549": "g\u00e9ants", "12550": "catalan", "12551": "simplicit\u00e9", "12552": "lign\u00e9e", "12553": "confortable", "12554": "omet", "12555": "spatial", "12556": "courons", "12557": "requins", "12558": "r\u00e9confort", "12559": "humides", "12560": "d\u00e9plor\u00e9", "12561": "walter", "12562": "propager", "12563": "pond\u00e9ration", "12564": "s\u00e9curitaires", "12565": "vents", "12566": "inhumaines", "12567": "surgissent", "12568": "insolvabilit\u00e9", "12569": "recourent", "12570": "sina", "12571": "mpo", "12572": "imposons", "12573": "approuvera", "12574": "bruce", "12575": "wang", "12576": "pr\u00e9tention", "12577": "commercialis\u00e9s", "12578": "justifi\u00e9s", "12579": "col\u00e9gislateur", "12580": "suspect", "12581": "exhortons", "12582": "concentrerai", "12583": "empi\u00e9ter", "12584": "recevabilit\u00e9", "12585": "vivait", "12586": "porteront", "12587": "soumette", "12588": "assujetti", "12589": "rapproch\u00e9", "12590": "wikipedia", "12591": "neutres", "12592": "volontariat", "12593": "r\u00e9sult\u00e9", "12594": "circuit", "12595": "valide", "12596": "vari\u00e9es", "12597": "homard", "12598": "\u00e9pid\u00e9mies", "12599": "constatations", "12600": "bloque", "12601": "appartenait", "12602": "exhortent", "12603": "tenions", "12604": "\u00e9tatique", "12605": "schroedter", "12606": "resterait", "12607": "diamants", "12608": "d\u00e9ploy\u00e9es", "12609": "publiant", "12610": "assassins", "12611": "\u00e9co", "12612": "patrick", "12613": "d\u00e9brouiller", "12614": "accomplissent", "12615": "publient", "12616": "paradoxalement", "12617": "ancr\u00e9e", "12618": "tentait", "12619": "lib\u00e9r\u00e9es", "12620": "cach\u00e9e", "12621": "malgaches", "12622": "\u00a2", "12623": "pr\u00e9s\u00e9ance", "12624": "apporterait", "12625": "project", "12626": "sortira", "12627": "abondance", "12628": "consid\u00e9rez", "12629": "amazonie", "12630": "\u00e9prouver", "12631": "paiera", "12632": "ren\u00e9", "12633": "hypoth\u00e9tique", "12634": "surfaces", "12635": "passible", "12636": "pdg", "12637": "center", "12638": "cyanure", "12639": "chapeau", "12640": "casques", "12641": "lv", "12642": "itin\u00e9raires", "12643": "risqu\u00e9", "12644": "infections", "12645": "masculins", "12646": "pr\u00e9occupantes", "12647": "hcr", "12648": "ajoutons", "12649": "litres", "12650": "1965", "12651": "d\u00e9pass\u00e9es", "12652": "intensives", "12653": "enlev\u00e9s", "12654": "utilitaires", "12655": "avou\u00e9", "12656": "\u00e9couler", "12657": "edward", "12658": "revenue", "12659": "advenu", "12660": "p\u00e9riodique", "12661": "addition", "12662": "efforc\u00e9e", "12663": "h\u00e9sitent", "12664": "merlu", "12665": "pluralit\u00e9", "12666": "christ", "12667": "lisent", "12668": "signifi\u00e9", "12669": "r\u00e9f\u00e9rant", "12670": "davies", "12671": "lib\u00e9rations", "12672": "n\u00e9gligeables", "12673": "lambert", "12674": "kauppi", "12675": "m\u00e9canique", "12676": "plantation", "12677": "casse", "12678": "d\u00fchrkop", "12679": "huiles", "12680": "artisanale", "12681": "pareilles", "12682": "victoires", "12683": "recommandait", "12684": "continuation", "12685": "m\u00e9diateurs", "12686": "portons", "12687": "restes", "12688": "enqu\u00eateurs", "12689": "pr\u00e9senteront", "12690": "pratiquant", "12691": "niant", "12692": "retourn\u00e9", "12693": "dissidence", "12694": "imp\u00e9rialisme", "12695": "abusent", "12696": "reuters", "12697": "po\u00e8me", "12698": "phares", "12699": "souverainiste", "12700": "entra\u00een\u00e9s", "12701": "seriez", "12702": "couler", "12703": "mortelle", "12704": "br\u00fbler", "12705": "190", "12706": "attachent", "12707": "nicholson", "12708": "achet\u00e9s", "12709": "applaudi", "12710": "enverra", "12711": "estaing", "12712": "mart\u00ednez", "12713": "export\u00e9s", "12714": "cigarette", "12715": "nantis", "12716": "\u0153ufs", "12717": "g\u00e9orgiens", "12718": "aimables", "12719": "libyens", "12720": "blague", "12721": "abonnement", "12722": "am\u00e9lioreront", "12723": "\u00e9pineux", "12724": "r\u00e9duise", "12725": "p\u00f4le", "12726": "solbes", "12727": "refl\u00e9tant", "12728": "nuirait", "12729": "morceau", "12730": "abitibi", "12731": "allure", "12732": "indiqu\u00e9es", "12733": "d\u00e9risoire", "12734": "addis", "12735": "p\u00e9ages", "12736": "aveu", "12737": "perry", "12738": "caracas", "12739": "c\u00e2ble", "12740": "criant", "12741": "abusives", "12742": "lancker", "12743": "regards", "12744": "infime", "12745": "kroes", "12746": "soumettra", "12747": "cherchait", "12748": "rayons", "12749": "accouchement", "12750": "christianisme", "12751": "surtaxe", "12752": "tromp\u00e9", "12753": "allou\u00e9e", "12754": "chats", "12755": "r\u00e9organiser", "12756": "eurom\u00e9diterran\u00e9enne", "12757": "concurrent", "12758": "travaillera", "12759": "moque", "12760": "faiblement", "12761": "interrogation", "12762": "etant", "12763": "autosuffisance", "12764": "majoritaires", "12765": "r\u00e9ticents", "12766": "\u00e9quation", "12767": "\u00e9tendent", "12768": "tomlinson", "12769": "d\u00e9fendus", "12770": "ordonnances", "12771": "alcoolis\u00e9es", "12772": "indon\u00e9siens", "12773": "coucher", "12774": "17h30", "12775": "occupait", "12776": "resterons", "12777": "contestable", "12778": "us\u00e9", "12779": "pri\u00e9", "12780": "esquimalt", "12781": "entam\u00e9e", "12782": "enrichi", "12783": "install\u00e9e", "12784": "coordonn\u00e9s", "12785": "renforcerait", "12786": "geler", "12787": "oaci", "12788": "analogue", "12789": "pkk", "12790": "archipel", "12791": "irai", "12792": "transforment", "12793": "plaident", "12794": "exempter", "12795": "improbable", "12796": "plaignants", "12797": "tirons", "12798": "ambigu\u00eb", "12799": "d\u00e9fendues", "12800": "hat", "12801": "expuls\u00e9", "12802": "american", "12803": "discorde", "12804": "tenants", "12805": "lune", "12806": "constatent", "12807": "ruines", "12808": "perquisition", "12809": "patriotisme", "12810": "opel", "12811": "joyal", "12812": "\u00e9pargn\u00e9", "12813": "escompt\u00e9", "12814": "exc\u00e9dentaires", "12815": "sepp\u00e4nen", "12816": "d\u00e9pourvue", "12817": "plaque", "12818": "attribuables", "12819": "excluent", "12820": "victor", "12821": "indiscutable", "12822": "class\u00e9", "12823": "proc\u00e9durales", "12824": "s\u00e9r\u00e9nit\u00e9", "12825": "range", "12826": "boue", "12827": "eurovignette", "12828": "authentiques", "12829": "min\u00e9rales", "12830": "divertissement", "12831": "rigides", "12832": "nickel", "12833": "pr\u00e9serve", "12834": "d\u00e9tourn\u00e9e", "12835": "anomalies", "12836": "abrogeant", "12837": "recrudescence", "12838": "comparative", "12839": "r\u00e9p\u00e9t\u00e9s", "12840": "amusant", "12841": "pneumatiques", "12842": "harmonis\u00e9s", "12843": "1,6", "12844": "one", "12845": "extraire", "12846": "ontariens", "12847": "stationnement", "12848": "d\u00e9gag\u00e9s", "12849": "r\u00e9jouissant", "12850": "t\u00e9l\u00e9visions", "12851": "freedom", "12852": "connaissaient", "12853": "concurrentielles", "12854": "quelquefois", "12855": "rasmussen", "12856": "ordonn\u00e9e", "12857": "labeur", "12858": "time", "12859": "rouler", "12860": "rachat", "12861": "piebalgs", "12862": "nuits", "12863": "duff", "12864": "sacconi", "12865": "camerounais", "12866": "jacente", "12867": "alimente", "12868": "wurtz", "12869": "carence", "12870": "d\u00e9centes", "12871": "7e", "12872": "pr\u00e9matur\u00e9ment", "12873": "enthousiastes", "12874": "couramment", "12875": "odeur", "12876": "live", "12877": "alex", "12878": "roumaine", "12879": "flagrantes", "12880": "gomes", "12881": "d\u00e9t\u00e9riorer", "12882": "gbagbo", "12883": "partition", "12884": "vux", "12885": "hollande", "12886": "oscar", "12887": "education", "12888": "centr\u00e9", "12889": "ennuis", "12890": "progressons", "12891": "rising", "12892": "admire", "12893": "finisse", "12894": "garantirait", "12895": "interrogatoire", "12896": "renault", "12897": "appuis", "12898": "\u00e9prouvons", "12899": "accepterons", "12900": "inscrivant", "12901": "suffisance", "12902": "meech", "12903": "oubliant", "12904": "contestations", "12905": "affectera", "12906": "peintures", "12907": "blessure", "12908": "annul\u00e9e", "12909": "128", "12910": "offrait", "12911": "querelle", "12912": "y\u00e9m\u00e9nite", "12913": "cannabis", "12914": "laisserait", "12915": "invoqu\u00e9e", "12916": "naples", "12917": "significativement", "12918": "canc\u00e9rig\u00e8nes", "12919": "discr\u00e9diter", "12920": "scrupules", "12921": "coercition", "12922": "afpc", "12923": "pr\u00e9cipiter", "12924": "pla\u00e7ant", "12925": "transpos\u00e9e", "12926": "exp\u00e9rimentations", "12927": "aliment", "12928": "burnaby", "12929": "varela", "12930": "weggen", "12931": "aboutisse", "12932": "fair", "12933": "omnibus", "12934": "secou\u00e9", "12935": "surprend", "12936": "g\u00e9n\u00e9reuses", "12937": "zana", "12938": "audiovisuelle", "12939": "bouclier", "12940": "d\u00e9sign\u00e9es", "12941": "diamantopoulou", "12942": "sellafield", "12943": "subventionn\u00e9s", "12944": "salon", "12945": "spirituel", "12946": "shore", "12947": "d\u00e9plac\u00e9e", "12948": "aliment\u00e9", "12949": "0,7", "12950": "rendements", "12951": "acheminer", "12952": "tent\u00e9s", "12953": "statuer", "12954": "avocate", "12955": "polices", "12956": "r\u00e9alisateur", "12957": "maes", "12958": "ventre", "12959": "att\u00e9nuation", "12960": "r\u00e9ussira", "12961": "incroyables", "12962": "curieuse", "12963": "ant\u00e9rieurement", "12964": "\u00e9ruption", "12965": "kabila", "12966": "d\u00e9mographie", "12967": "mental", "12968": "discr\u00e9dit", "12969": "gu\u00e9rilla", "12970": "conna\u00eetra", "12971": "g\u00e2teau", "12972": "d\u00e9troit", "12973": "support\u00e9s", "12974": "min\u00e9raux", "12975": "th\u00e9", "12976": "medicine", "12977": "philippins", "12978": "sorties", "12979": "novateur", "12980": "virements", "12981": "intenable", "12982": "intime", "12983": "confessions", "12984": "contracter", "12985": "destinations", "12986": "baril", "12987": "appartements", "12988": "rassemblent", "12989": "livraisons", "12990": "jargon", "12991": "tiendrai", "12992": "consult\u00e9es", "12993": "pr\u00eateurs", "12994": "patriotes", "12995": "quantitatifs", "12996": "fr\u00e9quenter", "12997": "650", "12998": "d\u00e9tach\u00e9s", "12999": "jaune", "13000": "drastique", "13001": "pr\u00eatre", "13002": "poss\u00e9dait", "13003": "confidentiel", "13004": "falsification", "13005": "corrig\u00e9e", "13006": "pr\u00e9sid\u00e9e", "13007": "mary", "13008": "d\u00e9limitation", "13009": "p\u00e9ri", "13010": "\u00e9pargnants", "13011": "confirm\u00e9s", "13012": "anime", "13013": "oubli\u00e9e", "13014": "biocides", "13015": "syndicalistes", "13016": "\u00e9manent", "13017": "prot\u00e9gera", "13018": "vou\u00e9", "13019": "battue", "13020": "corresponde", "13021": "francis", "13022": "g\u00eanes", "13023": "d\u00e9ploy\u00e9e", "13024": "endosser", "13025": "lagendijk", "13026": "grasses", "13027": "agress\u00e9", "13028": "polo", "13029": "exig\u00e9es", "13030": "recevables", "13031": "\u00e9crivant", "13032": "assimiler", "13033": "manuvre", "13034": "courses", "13035": "punition", "13036": "end\u00e9mique", "13037": "abaissement", "13038": "\u00e9cologiquement", "13039": "affaiblie", "13040": "intensifs", "13041": "regrettables", "13042": "sanguins", "13043": "d\u00e9pendante", "13044": "civilis\u00e9", "13045": "ardent", "13046": "couloir", "13047": "maintenance", "13048": "las", "13049": "carri\u00e8res", "13050": "assujetties", "13051": "attrayante", "13052": "v\u00e9n\u00e9zu\u00e9liens", "13053": "d\u00e9cideront", "13054": "galeote", "13055": "belder", "13056": "assimilation", "13057": "engendrera", "13058": "maij", "13059": "fixent", "13060": "f\u00e9minins", "13061": "\u00e9voquerai", "13062": "innocent", "13063": "sucri\u00e8re", "13064": "cirque", "13065": "investigations", "13066": "d\u00e9vastatrices", "13067": "distribu\u00e9e", "13068": "op\u00e9r\u00e9", "13069": "imprim\u00e9", "13070": "kitchener", "13071": "jet\u00e9s", "13072": "renaissance", "13073": "acc\u00e9l\u00e8re", "13074": "formons", "13075": "\u00e9laborant", "13076": "riposte", "13077": "d\u00e9plorables", "13078": "milice", "13079": "boisson", "13080": "laden", "13081": "burkina", "13082": "inconnue", "13083": "sympathique", "13084": "d\u00e9ontologie", "13085": "carnaval", "13086": "surprenante", "13087": "luttons", "13088": "12,5", "13089": "iet", "13090": "\u00e9tudi\u00e9s", "13091": "pr\u00e9occupants", "13092": "fanatiques", "13093": "voyaient", "13094": "creux", "13095": "european", "13096": "der", "13097": "h\u00e4nsch", "13098": "descendants", "13099": "s\u00e9minaires", "13100": "mohammad", "13101": "facilitent", "13102": "fiche", "13103": "d\u00e9cidant", "13104": "\u00e9mettent", "13105": "pr\u00e9visibilit\u00e9", "13106": "eurocontrol", "13107": "cyclone", "13108": "r\u00e9put\u00e9e", "13109": "remaniement", "13110": "proportionnellement", "13111": "diffuse", "13112": "\u00e9tablissons", "13113": "investissons", "13114": "fascistes", "13115": "pleines", "13116": "portefeuilles", "13117": "fanatisme", "13118": "charest", "13119": "roulant", "13120": "\u00e9clater", "13121": "su\u00e9doises", "13122": "prompt", "13123": "signifiait", "13124": "clarifications", "13125": "152", "13126": "employabilit\u00e9", "13127": "apercevoir", "13128": "salarial", "13129": "facultative", "13130": "up", "13131": "interviendra", "13132": "vieillissante", "13133": "ressens", "13134": "pragmatiques", "13135": "permettrez", "13136": "1a", "13137": "martyrs", "13138": "\u00e9panouir", "13139": "mesur\u00e9", "13140": "essaierai", "13141": "segment", "13142": "quinquennal", "13143": "offrirait", "13144": "compl\u00e8tent", "13145": "honore", "13146": "230", "13147": "douzaines", "13148": "interrog\u00e9s", "13149": "dicton", "13150": "circuits", "13151": "guid\u00e9", "13152": "g\u00e9n\u00e9ralisation", "13153": "voulus", "13154": "anxi\u00e9t\u00e9", "13155": "m\u00e9caniciens", "13156": "martiale", "13157": "adviendra", "13158": "id\u00e9ologies", "13159": "hectare", "13160": "accordait", "13161": "b\u00e2tons", "13162": "confiscation", "13163": "crois\u00e9e", "13164": "vainqueur", "13165": "siens", "13166": "adpic", "13167": "acad\u00e9mique", "13168": "vider", "13169": "renforceront", "13170": "fichiers", "13171": "diplomate", "13172": "retournent", "13173": "conf\u00e8rent", "13174": "we", "13175": "plastiques", "13176": "pratiqu\u00e9s", "13177": "internationalement", "13178": "acadie", "13179": "monoparentales", "13180": "lobbies", "13181": "condamnent", "13182": "jo", "13183": "\u00e9dimbourg", "13184": "contraception", "13185": "flandre", "13186": "logiques", "13187": "mentaux", "13188": "survenues", "13189": "esp\u00e9rions", "13190": "typiquement", "13191": "intrins\u00e8que", "13192": "d\u00edaz", "13193": "paisible", "13194": "sonne", "13195": "exag\u00e9r\u00e9e", "13196": "\u00e9paule", "13197": "aveugl\u00e9ment", "13198": "trakatellis", "13199": "d\u00e9l\u00e9guer", "13200": "interrogent", "13201": "beaudoin", "13202": "joints", "13203": "andreychuk", "13204": "agresseur", "13205": "mesur\u00e9e", "13206": "domine", "13207": "tortur\u00e9s", "13208": "inflig\u00e9e", "13209": "horizontal", "13210": "s\u00e9n\u00e9galais", "13211": "labels", "13212": "traitait", "13213": "vante", "13214": "reconnaissait", "13215": "dissident", "13216": "perdra", "13217": "caudron", "13218": "pr\u00e9conis\u00e9e", "13219": "recouvre", "13220": "\u00e9tudi\u00e9es", "13221": "jalons", "13222": "p\u00e9dophilie", "13223": "r\u00e9pit", "13224": "ambigu\u00eft\u00e9s", "13225": "effondrer", "13226": "prestigieux", "13227": "chevauchement", "13228": "campanie", "13229": "coreper", "13230": "exponentielle", "13231": "stanley", "13232": "east", "13233": "prenaient", "13234": "shawinigan", "13235": "amples", "13236": "angles", "13237": "prouvant", "13238": "pactes", "13239": "barbares", "13240": "approcher", "13241": "constat\u00e9e", "13242": "viandes", "13243": "viol\u00e9e", "13244": "fiduciaire", "13245": "\u00e9tudiera", "13246": "diminuera", "13247": "supposant", "13248": "durera", "13249": "mar\u00eda", "13250": "s\u00e9lectionn\u00e9s", "13251": "onex", "13252": "essayait", "13253": "dispos\u00e9es", "13254": "ndt", "13255": "paraissait", "13256": "chichester", "13257": "dominicaine", "13258": "alarmants", "13259": "garderies", "13260": "revitaliser", "13261": "interviews", "13262": "mangent", "13263": "pr\u00e9sumer", "13264": "anonymat", "13265": "r\u00e9vocation", "13266": "nazisme", "13267": "prospectus", "13268": "devrez", "13269": "sacrifi\u00e9", "13270": "f\u00e9roce", "13271": "promu", "13272": "verraient", "13273": "\u20ac", "13274": "exigeait", "13275": "occupants", "13276": "orchestre", "13277": "kenny", "13278": "sombrer", "13279": "malaria", "13280": "j\u00e9sus", "13281": "cheminement", "13282": "nomm\u00e9es", "13283": "brandon", "13284": "implicitement", "13285": "hijab", "13286": "\u00f4", "13287": "oxford", "13288": "greenpeace", "13289": "biotechnologies", "13290": "hall", "13291": "m\u00e9diocres", "13292": "itin\u00e9raire", "13293": "fortiori", "13294": "adeptes", "13295": "d\u00e9non\u00e7ant", "13296": "irons", "13297": "bailleurs", "13298": "autel", "13299": "perturbation", "13300": "inali\u00e9nable", "13301": "campbell", "13302": "panama", "13303": "240", "13304": "essayent", "13305": "na\u00eff", "13306": "toxicit\u00e9", "13307": "nuances", "13308": "oeuvrent", "13309": "gps", "13310": "buzek", "13311": "d\u00e9vou\u00e9s", "13312": "attaqu\u00e9e", "13313": "surprendre", "13314": "robles", "13315": "reporte", "13316": "dirigeante", "13317": "conviendrez", "13318": "contribuons", "13319": "esp\u00e9rait", "13320": "d\u00e9cisifs", "13321": "oeufs", "13322": "pat", "13323": "observ\u00e9s", "13324": "lou\u00e9", "13325": "demanderons", "13326": "concentrons", "13327": "actuaire", "13328": "vot\u00e9es", "13329": "remerci\u00e9", "13330": "ferreira", "13331": "elizabeth", "13332": "mainstreaming", "13333": "acheteur", "13334": "dogme", "13335": "comptera", "13336": "cadavres", "13337": "n\u00e9gativement", "13338": "pr\u00eatres", "13339": "iceberg", "13340": "terminaux", "13341": "conjugal", "13342": "coup\u00e9s", "13343": "demeurera", "13344": "assassin\u00e9es", "13345": "power", "13346": "rossa", "13347": "prononcera", "13348": "lewis", "13349": "maaten", "13350": "p\u00e9nalit\u00e9s", "13351": "entreprend", "13352": "parlerons", "13353": "encourageante", "13354": "sj\u00f6stedt", "13355": "siennes", "13356": "observent", "13357": "gaspill\u00e9", "13358": "appellations", "13359": "sectoriels", "13360": "trompent", "13361": "japonaises", "13362": "r\u00e9gionalisation", "13363": "quinzaine", "13364": "envisageait", "13365": "r\u00e9ussissons", "13366": "minimums", "13367": "arriveront", "13368": "d\u00e9cevante", "13369": "embryon", "13370": "troublant", "13371": "mondialis\u00e9e", "13372": "destinataires", "13373": "incorrecte", "13374": "g\u00e9opolitiques", "13375": "superpuissance", "13376": "revendique", "13377": "xi", "13378": "populisme", "13379": "g\u00e9ographiquement", "13380": "refusait", "13381": "peste", "13382": "d\u00e9calage", "13383": "marches", "13384": "staes", "13385": "mentionnait", "13386": "o'", "13387": "gollnisch", "13388": "uvres", "13389": "enclins", "13390": "traitants", "13391": "peijs", "13392": "manuels", "13393": "emprise", "13394": "confirmera", "13395": "examinions", "13396": "digital", "13397": "airlines", "13398": "tristes", "13399": "s\u00e9lectionner", "13400": "\u00e9tranglement", "13401": "bienveillance", "13402": "favoriserait", "13403": "graffiti", "13404": "ahern", "13405": "opium", "13406": "consternation", "13407": "rigidit\u00e9", "13408": "philippe", "13409": "disions", "13410": "comparution", "13411": "pointer", "13412": "multim\u00e9dia", "13413": "modificatif", "13414": "lyc\u00e9e", "13415": "insurrection", "13416": "suspicion", "13417": "pr\u00e9cisent", "13418": "\u00e9go\u00efsme", "13419": "ron", "13420": "\u00e9thiopiens", "13421": "tribunes", "13422": "actualisation", "13423": "inuits", "13424": "panneau", "13425": "corrects", "13426": "continues", "13427": "tierce", "13428": "originales", "13429": "360", "13430": "gabon", "13431": "torres", "13432": "dissuasif", "13433": "h\u00f4tes", "13434": "concurrentes", "13435": "serrer", "13436": "plaisanterie", "13437": "l\u00e9galiser", "13438": "indon\u00e9sienne", "13439": "impr\u00e9visibles", "13440": "d\u00e9cidait", "13441": "insatisfaction", "13442": "r\u00e9servistes", "13443": "d\u00e9nigrer", "13444": "brutales", "13445": "nu", "13446": "poussi\u00e8re", "13447": "participons", "13448": "persuasion", "13449": "poursuivront", "13450": "karachi", "13451": "\u00e9difices", "13452": "si\u00e9geant", "13453": "populistes", "13454": "import\u00e9", "13455": "board", "13456": "inspirant", "13457": "n\u00e9olib\u00e9rales", "13458": "imprimer", "13459": "r\u00e9pressif", "13460": "grey", "13461": "ski", "13462": "uck", "13463": "\u00e9voquent", "13464": "alarmantes", "13465": "rayon", "13466": "construites", "13467": "exactions", "13468": "r\u00e9gis", "13469": "prud'", "13470": "routi\u00e8res", "13471": "\u00e9craser", "13472": "locataires", "13473": "phytopharmaceutiques", "13474": "spectaculaires", "13475": "sensation", "13476": "servait", "13477": "entretenus", "13478": "05", "13479": "jugera", "13480": "diversifi\u00e9e", "13481": "maintienne", "13482": "on\u00e9reuse", "13483": "sollicite", "13484": "recycler", "13485": "curiosit\u00e9", "13486": "axer", "13487": "nerveux", "13488": "dangereusement", "13489": "pornographique", "13490": "paneurop\u00e9enne", "13491": "atlantiques", "13492": "ah", "13493": "explicites", "13494": "subsister", "13495": "\u00e9mets", "13496": "entrav\u00e9", "13497": "imputer", "13498": "williams", "13499": "allonger", "13500": "1949", "13501": "b\u00e9ton", "13502": "r\u00e9unie", "13503": "vol\u00e9s", "13504": "artificiels", "13505": "oriente", "13506": "cartels", "13507": "douloureuse", "13508": "dents", "13509": "\u00e9quivalence", "13510": "audits", "13511": "read", "13512": "sens\u00e9es", "13513": "postale", "13514": "drones", "13515": "vocabulaire", "13516": "gel\u00e9s", "13517": "repr\u00e9sentatives", "13518": "courageusement", "13519": "guides", "13520": "rembours\u00e9s", "13521": "intergroupe", "13522": "philosophe", "13523": "v\u00e9h\u00e9mence", "13524": "\u00e9pizootie", "13525": "charlie", "13526": "fassiez", "13527": "\u00e9tiquet\u00e9s", "13528": "approfondis", "13529": "3.0", "13530": "austin", "13531": "r\u00e9gies", "13532": "confession", "13533": "genoux", "13534": "liquider", "13535": "novateurs", "13536": "stocker", "13537": "fernando", "13538": "interminable", "13539": "salu\u00e9e", "13540": "stimule", "13541": "tranquillit\u00e9", "13542": "mentionnerai", "13543": "assur\u00e9es", "13544": "peupl\u00e9", "13545": "salariaux", "13546": "corollaire", "13547": "agressif", "13548": "clef", "13549": "accueillent", "13550": "pessimiste", "13551": "peupl\u00e9es", "13552": "arr\u00eatera", "13553": "indicatif", "13554": "trans", "13555": "denis", "13556": "pr\u00e9textes", "13557": "combin\u00e9s", "13558": "sanglante", "13559": "utilisez", "13560": "poulet", "13561": "dmitri", "13562": "importent", "13563": "l\u00e2che", "13564": "\u00e9puis\u00e9s", "13565": "bq", "13566": "exhaustives", "13567": "planer", "13568": "habilit\u00e9e", "13569": "domin\u00e9e", "13570": "tigre", "13571": "complice", "13572": "mar\u00edn", "13573": "marginal", "13574": "linkohr", "13575": "\u00e9cris", "13576": "proteste", "13577": "emprunteurs", "13578": "couronn\u00e9", "13579": "dirigeons", "13580": "gauthier", "13581": "interrompt", "13582": "cubaines", "13583": "liese", "13584": "investie", "13585": "d\u00e9ficitaire", "13586": "all\u00e9es", "13587": "orden", "13588": "imposera", "13589": "choisies", "13590": "tes", "13591": "rimouski", "13592": "commen\u00e7ait", "13593": "r\u00e9clamations", "13594": "lynne", "13595": "plateau", "13596": "131", "13597": "fusionner", "13598": "installe", "13599": "alg\u00e9riens", "13600": "majorit\u00e9s", "13601": "purger", "13602": "nombres", "13603": "acceptait", "13604": "estimaient", "13605": "inhumains", "13606": "roue", "13607": "dispense", "13608": "revenait", "13609": "louise", "13610": "chaotique", "13611": "polonaises", "13612": "impuissants", "13613": "diff\u00e9renci\u00e9e", "13614": "placeteco", "13615": "diff\u00e9rer", "13616": "vodka", "13617": "terry", "13618": "r\u00e9jouissent", "13619": "posteriori", "13620": "rivera", "13621": "pessimisme", "13622": "bienvenues", "13623": "\u00e9vacuer", "13624": "bouteille", "13625": "rebelle", "13626": "injonction", "13627": "conforment", "13628": "alibi", "13629": "amiable", "13630": "ouvri\u00e8re", "13631": "d\u00e9clench\u00e9e", "13632": "campos", "13633": "interview\u00e9", "13634": "gorge", "13635": "assainir", "13636": "pollueurs", "13637": "blesse", "13638": "volte", "13639": "incertaine", "13640": "tendu", "13641": "\u00e9clair\u00e9e", "13642": "d\u00e9crocher", "13643": "pensionn\u00e9s", "13644": "spontan\u00e9ment", "13645": "ardue", "13646": "m\u00e9nager", "13647": "dat\u00e9e", "13648": "int\u00e8grent", "13649": "imposaient", "13650": "universellement", "13651": "pionniers", "13652": "florissante", "13653": "fleur", "13654": "1966", "13655": "convaincants", "13656": "omd", "13657": "frauduleux", "13658": "khartoum", "13659": "allongement", "13660": "compar\u00e9e", "13661": "notations", "13662": "voyageant", "13663": "d\u00e9place", "13664": "r\u00e9introduire", "13665": "soudaine", "13666": "notoire", "13667": "communiqu\u00e9e", "13668": "permise", "13669": "gang", "13670": "triomphe", "13671": "cambridge", "13672": "katiforis", "13673": "chars", "13674": "internaute", "13675": "interrog\u00e9es", "13676": "insisterai", "13677": "columbia", "13678": "approchons", "13679": "cr\u00e9atif", "13680": "commentateur", "13681": "districts", "13682": "d\u00e9battrons", "13683": "repr\u00e9senteront", "13684": "amertume", "13685": "disposeront", "13686": "prolonge", "13687": "reposait", "13688": "multiculturalisme", "13689": "pareils", "13690": "r\u00e9compens\u00e9s", "13691": "contentons", "13692": "rentrent", "13693": "rayonnements", "13694": "cach\u00e9s", "13695": "d\u00e9cevoir", "13696": "boycotter", "13697": "maintenons", "13698": "turin", "13699": "coulisses", "13700": "inhabituelle", "13701": "arracher", "13702": "coop\u00e8re", "13703": "promoteur", "13704": "cinquantaine", "13705": "choqu\u00e9e", "13706": "srebrenica", "13707": "euph\u00e9misme", "13708": "traversant", "13709": "cerner", "13710": "entra\u00eeneront", "13711": "stevenson", "13712": "quantitative", "13713": "br\u00fbl\u00e9", "13714": "grammes", "13715": "relev\u00e9es", "13716": "structurer", "13717": "madeleine", "13718": "cr\u00e9atrice", "13719": "remplac\u00e9es", "13720": "rec\u00e8le", "13721": "efforcera", "13722": "critiquant", "13723": "paneurop\u00e9en", "13724": "imperfections", "13725": "n\u00e9gocient", "13726": "interception", "13727": "c\u00e9l\u00e9rit\u00e9", "13728": "trouveraient", "13729": "appuy\u00e9s", "13730": "eduardo", "13731": "inchang\u00e9e", "13732": "ult\u00e9rieurs", "13733": "travers\u00e9e", "13734": "deprez", "13735": "dhaka", "13736": "cote", "13737": "activer", "13738": "garriga", "13739": "ae", "13740": "fraudeurs", "13741": "v\u00e9rificateurs", "13742": "orient\u00e9es", "13743": "casaca", "13744": "immigrant", "13745": "d\u00e9fier", "13746": "appartiendra", "13747": "initier", "13748": "proph\u00e8te", "13749": "\u00e9quiper", "13750": "nicolas", "13751": "d\u00e9roger", "13752": "venise", "13753": "sem\u00e9", "13754": 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"13968": "bi\u00e9lorusses", "13969": "1963", "13970": "supposons", "13971": "d\u00e9rivants", "13972": "g\u00e9nocides", "13973": "0,5", "13974": "contentent", "13975": "ancr\u00e9", "13976": "hugo", "13977": "maillon", "13978": "cession", "13979": "remerciement", "13980": "kent", "13981": "d\u00e9fectueux", "13982": "purification", "13983": "naissent", "13984": "d\u00e9pens\u00e9es", "13985": "potentialit\u00e9s", "13986": "artisanat", "13987": "kenyans", "13988": "\u00e9ducative", "13989": "continuez", "13990": "enfantine", "13991": "tribu", "13992": "land", "13993": "251", "13994": "attrayant", "13995": "apparent", "13996": "involontaire", "13997": "g\u00e9nitales", "13998": "mange", "13999": "multiculturelle", "14000": "mensuel", "14001": "\u00e9metteurs", "14002": "carleton", "14003": "cinquanti\u00e8me", "14004": "angus", "14005": "pr\u00e9sentez", "14006": "orthodoxes", "14007": "boutique", "14008": "feoga", "14009": "d\u00e9voil\u00e9", "14010": "d\u00e9fiance", "14011": "articulation", "14012": "fonctionnait", "14013": "d\u00e9charger", "14014": "santa", "14015": "souhaitables", "14016": "critiqu\u00e9s", "14017": "r\u00e8glementation", "14018": "casser", "14019": "impressions", "14020": "bertens", "14021": "fuca", "14022": "agglom\u00e9rations", "14023": "reliant", "14024": "surgi", "14025": "influencent", "14026": "d\u00e9sesp\u00e9r\u00e9s", "14027": "incorpor\u00e9", "14028": "pr\u00e9serv\u00e9e", "14029": "l\u00e9sions", "14030": "indirectes", "14031": "trompeur", "14032": "ignorez", "14033": "college", "14034": "206", "14035": "parquet", "14036": "s\u0153ur", "14037": "b\u00eates", "14038": "best", "14039": "\u00e9coliers", "14040": "bangladais", "14041": "olson", "14042": "transf\u00e9r\u00e9e", "14043": "abeba", "14044": "revirement", "14045": "joug", "14046": "kelly", "14047": "interactions", "14048": "additionnalit\u00e9", "14049": "tireront", "14050": "kilom\u00e8tre", "14051": "eurom\u00e9diterran\u00e9en", "14052": "r\u00e9sider", 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"protestants", "14097": "retard\u00e9e", "14098": "assisterons", "14099": "burg", "14100": "coca", "14101": "avenue", "14102": "appr\u00e9cierais", "14103": "transnational", "14104": "marcel", "14105": "sup\u00e9riorit\u00e9", "14106": "taxis", "14107": "adage", "14108": "an\u00e9antir", "14109": "d\u00e9finitives", "14110": "remarquablement", "14111": "allan", "14112": "exacerber", "14113": "occulter", "14114": "additionnelle", "14115": "perdure", "14116": "gargani", "14117": "f\u00e9cule", "14118": "r\u00e9volue", "14119": "parish", "14120": "manoeuvres", "14121": "135", "14122": "indiqu\u00e9s", "14123": "r\u00e9p\u00e9tons", "14124": "soumettant", "14125": "schuman", "14126": "foot", "14127": "punissable", "14128": "jouissant", "14129": "surcharge", "14130": "artificiel", "14131": "m\u00e9tal", "14132": "reproductive", "14133": "faso", "14134": "constitu\u00e9s", "14135": "bouc", "14136": "racontent", "14137": "bonde", "14138": "accumul\u00e9e", "14139": "requ\u00e9rant", "14140": "d\u00e9sarroi", "14141": "kohl", "14142": "couvrira", "14143": "except\u00e9", "14144": "comparabilit\u00e9", "14145": "ob\u00e9ir", "14146": "talentueux", "14147": "stages", "14148": "\u00e9tonn\u00e9s", "14149": "hern\u00e1ndez", "14150": "socio\u00e9conomique", "14151": "\u00e9vitons", "14152": "\u00e9loquente", "14153": "pers\u00e9v\u00e9rer", "14154": "sugg\u00e9r\u00e9e", "14155": "demanderont", "14156": "arranger", "14157": "incontr\u00f4l\u00e9e", "14158": "imposerait", "14159": "files", "14160": "implicite", "14161": "n\u00e9potisme", "14162": "inaccessible", "14163": "slim", "14164": "d\u00e9menti", "14165": "d\u00e9tour", "14166": "cam\u00e9ra", "14167": "laura", "14168": "plaisance", "14169": "\u00e9pouvantables", "14170": "bachar", "14171": "voice", "14172": "remplacera", "14173": "repr\u00e9sentativit\u00e9", "14174": "atroces", "14175": "entretenue", "14176": "purg\u00e9", "14177": "cambodgien", "14178": "inhumaine", "14179": "vitamines", "14180": "interr\u00e9gionale", 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"\u00e9ch\u00e9ancier", "14223": "admettra", "14224": "rajoelina", "14225": "pratiqu\u00e9es", "14226": "mari\u00e9", "14227": "r\u00e9percussion", "14228": "construisons", "14229": "fourrure", "14230": "mondes", "14231": "mails", "14232": "mari\u00e9es", "14233": "ukrainiennes", "14234": "d\u00e9ductions", "14235": "opportunes", "14236": "invisible", "14237": "wentworth", "14238": "montrait", "14239": "participative", "14240": "alain", "14241": "partielles", "14242": "touchera", "14243": "gallagher", "14244": "dessiner", "14245": "respectable", "14246": "crie", "14247": "caduque", "14248": "blocages", "14249": "atroce", "14250": "cols", "14251": "soutenez", "14252": "soucieuse", "14253": "d\u00e9battent", "14254": "acquitt\u00e9", "14255": "\u00e9merg\u00e9", "14256": "c\u00e9r\u00e9aliers", "14257": "ancona", "14258": "fatigue", "14259": "nolin", "14260": "chuter", "14261": "grosch", "14262": "volontariste", "14263": "critiquons", "14264": "s\u00e9parant", "14265": "suscit\u00e9es", "14266": "recensements", "14267": "policy", "14268": "rambouillet", "14269": "implore", "14270": "euromed", "14271": "inqui\u00e9tons", "14272": "m\u00eale", "14273": "joignent", "14274": "durent", "14275": "consciencieux", "14276": "garderie", "14277": "confi\u00e9s", "14278": "innovant", "14279": "r\u00e9pand", "14280": "constituante", "14281": "socle", "14282": "gras", "14283": "r\u00e9put\u00e9es", "14284": "coquitlam", "14285": "inclue", "14286": "tromp\u00e9s", "14287": "virtuellement", "14288": "perturb\u00e9", "14289": "mutilation", "14290": "arrachage", "14291": "roure", "14292": "profitons", "14293": "occup\u00e9es", "14294": "constructions", "14295": "opportunisme", "14296": "fern\u00e1ndez", "14297": "turbulences", "14298": "margallo", "14299": "ajouts", "14300": "emas", "14301": "admettent", "14302": "capitalisation", "14303": "britanno", "14304": "budg\u00e9tisation", "14305": "\u061f", "14306": "d\u00e9savantag\u00e9s", "14307": "aviv", "14308": "mercier", "14309": 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"16387": "suppressions", "16388": "accueillera", "16389": "\u00e9valuent", "16390": "bryden", "16391": "handicap\u00e9e", "16392": "cabinets", "16393": "benz\u00e8ne", "16394": "oxyde", "16395": "partiale", "16396": "baleine", "16397": "contamin\u00e9e", "16398": "esp\u00e9raient", "16399": "fatah", "16400": "locuteurs", "16401": "dartmouth", "16402": "c\u0153urs", "16403": "masculine", "16404": "\u00e9vitent", "16405": "moto", "16406": "incompr\u00e9hensibles", "16407": "martyr", "16408": "parizeau", "16409": "modestie", "16410": "d\u00e9note", "16411": "jia", "16412": "r\u00e9nover", "16413": "distribu\u00e9es", "16414": "utilisait", "16415": "pr\u00e9f\u00e9r\u00e9e", "16416": "herald", "16417": "nicole", "16418": "tranch\u00e9", "16419": "freins", "16420": "chevauchants", "16421": "d\u00e9couvrons", "16422": "d\u00e9mocratiser", "16423": "bidon", "16424": "bakou", "16425": "d\u00e9veloppera", "16426": "petersberg", "16427": "menrad", "16428": "intra", "16429": "piller", "16430": "h\u00e9ro\u00efque", "16431": "figureront", "16432": "risquant", "16433": "raymond", "16434": "conventionnels", "16435": "p\u00e9quiste", "16436": "rectification", "16437": "embrasser", "16438": "semblons", "16439": "invit\u00e9es", "16440": "ascension", "16441": "caract\u00e9ris\u00e9es", "16442": "je\u00fbne", "16443": "fl\u00e9aux", "16444": "smog", "16445": "conduise", "16446": "bornes", "16447": "r\u00e9sulterait", "16448": "athl\u00e8te", "16449": "mentionnent", "16450": "ccg", "16451": "151", "16452": "inaugur\u00e9", "16453": "imbeni", "16454": "reconduction", "16455": "sharif", "16456": "recevrait", "16457": "chavez", "16458": "ensembles", "16459": "ouattara", "16460": "path\u00e9tique", "16461": "enfoncer", "16462": "admise", "16463": "intracommunautaires", "16464": "c\u00e9l\u00e9br\u00e9e", "16465": "statues", "16466": "gong", "16467": "conseill\u00e8re", "16468": "forfaitaire", "16469": "ancestraux", "16470": "emprisonn\u00e9es", "16471": "ralph", "16472": "saisit", "16473": "mathieu", "16474": "archa\u00efque", "16475": "rayonnement", "16476": "you", "16477": "crevettes", "16478": "capturer", "16479": "r\u00e9voquer", "16480": "boucliers", "16481": "r\u00e9veille", "16482": "identifi\u00e9e", "16483": "salaberry", "16484": "fox", "16485": "cath\u00e9drale", "16486": "\u00e9ditorialiste", "16487": "cousins", "16488": "sujettes", "16489": "compagnon", "16490": "concitoyennes", "16491": "respectif", "16492": "ted", "16493": "arrivaient", "16494": "r\u00e9actionnaire", "16495": "spatiales", "16496": "occasionn\u00e9", "16497": "douloureuses", "16498": "acide", "16499": "\u00e9chiquier", "16500": "enrichissent", "16501": "inexistant", "16502": "licencier", "16503": "absolues", "16504": "finirons", "16505": "frustrant", "16506": "vietnamien", "16507": "essuy\u00e9", "16508": "boom", "16509": "consacrant", "16510": "d\u00e9s\u00e9quilibr\u00e9e", "16511": "instruits", "16512": "promue", "16513": "enseignante", "16514": "transplantations", "16515": "d\u00e9sirerais", "16516": "irrecevables", "16517": "\u00e9toile", "16518": "1911", "16519": "azerba\u00efdjanais", "16520": "sales", "16521": "toxines", "16522": "utilisatrice", "16523": "grossesses", "16524": "seychelles", "16525": "bos", "16526": "vigoureuses", "16527": "abstrait", "16528": "nav", "16529": "inexistants", "16530": "turco", "16531": "aid\u00e9e", "16532": "appr\u00e9hension", "16533": "boulevers\u00e9", "16534": "toulouse", "16535": "paradoxale", "16536": "118", "16537": "gais", "16538": "kinshasa", "16539": "conquis", "16540": "f\u00e9minisme", "16541": "shirts", "16542": "laur\u00e9ate", "16543": "g\u00eaner", "16544": "ignoble", "16545": "myller", "16546": "est\u00e9vez", "16547": "airs", "16548": "provocations", "16549": "nostalgie", "16550": "\u00e9cras\u00e9", "16551": "tragiquement", "16552": "tromperie", "16553": "127", "16554": "accorderait", "16555": "reparler", "16556": "d\u00e9raisonnables", "16557": "g\u00e9n\u00e9r\u00e9es", "16558": "rendraient", "16559": "t\u00e9miscamingue", "16560": "altitude", "16561": "r\u00e9servation", "16562": "gazprom", "16563": "p\u00e9ruvien", "16564": "regroup\u00e9s", "16565": "abondantes", "16566": "approuverons", "16567": "gonfler", "16568": "pla\u00e7ons", "16569": "proportionn\u00e9es", "16570": "originaux", "16571": "concevable", "16572": "thatcher", "16573": "anc", "16574": "daphne", "16575": "agirons", "16576": "chanteuse", "16577": "apparaisse", "16578": "intellectuelles", "16579": "giansily", "16580": "prets", "16581": "carnage", "16582": "obstination", "16583": "disciplinaire", "16584": "exp\u00e9riment\u00e9", "16585": "harms", "16586": "s\u00e9vices", "16587": "jouons", "16588": "symptomatique", "16589": "souchet", "16590": "d\u00e9portation", "16591": "tron\u00e7on", "16592": "ashraf", "16593": "calais", "16594": "interpr\u00e9t\u00e9es", "16595": "kouchner", "16596": "apc", "16597": "d\u00e9sagr\u00e9able", "16598": "plong\u00e9e", "16599": "romaine", "16600": "rime", "16601": "encouragerait", "16602": "dissuasives", "16603": "d\u00e9pendons", "16604": "morris", "16605": "proc\u00e9duraux", "16606": "pr\u00e9curseur", "16607": "pr\u00f4n\u00e9", "16608": "condamnable", "16609": "requin", "16610": "inuvialuit", "16611": "facilit\u00e9e", "16612": "\u00e9tablissait", "16613": "cage", "16614": "abrog\u00e9", "16615": "floride", "16616": "dogmes", "16617": "craignant", "16618": "projet\u00e9", "16619": "importons", "16620": "interpol", "16621": "\u00e9l\u00e8vera", "16622": "fournirait", "16623": "\u00e9clairera", "16624": "pr\u00e9conis\u00e9es", "16625": "traduisant", "16626": "foules", "16627": "dakar", "16628": "gahler", "16629": "humanisme", "16630": "mettaient", "16631": "souhait\u00e9s", "16632": "harvard", "16633": "clarifi\u00e9e", "16634": "autoriserait", "16635": "d\u00e9barquement", "16636": "concentrera", "16637": "fatigu\u00e9", "16638": "habitu\u00e9", "16639": "pr\u00e9sager", "16640": "n\u00e9cessiteux", "16641": "marina", "16642": "initi\u00e9s", "16643": "ougandaise", "16644": "\u00e1lvarez", "16645": "signalons", "16646": "ruiz", "16647": "s\u00e9journer", "16648": "r\u00e9current", "16649": "privil\u00e9giant", "16650": "yasser", "16651": "ressentiment", "16652": "remporte", "16653": "appuy\u00e9es", "16654": "ren\u00e9gociation", "16655": "\u00e9coutais", "16656": "ua", "16657": "danser", "16658": "pablo", "16659": "r\u00e9gularisation", "16660": "d\u00e9versements", "16661": "namibie", "16662": "pr\u00e9cipitations", "16663": "manders", "16664": "bilans", "16665": "occupations", "16666": "cola", "16667": "grandissent", "16668": "r\u00e9compens\u00e9", "16669": "mentionnons", "16670": "antenne", "16671": "transportent", "16672": "dysfonctionnement", "16673": "tiananmen", "16674": "saluant", "16675": "\u00e9dicter", "16676": "sinn", "16677": "parentale", "16678": "resserrement", "16679": "rang\u00e9e", "16680": "oeuvr\u00e9", "16681": "rectificative", "16682": "laisseront", "16683": "conditionne", "16684": "pinheiro", "16685": "motive", "16686": "r\u00e9compenses", "16687": "d\u00e9finitifs", "16688": "flamme", "16689": "quantitatives", "16690": "han", "16691": "ha\u00eftienne", "16692": "acadiens", "16693": "v\u00e9racit\u00e9", "16694": "consentent", "16695": "prestataire", "16696": "choquante", "16697": "r\u00e9tractation", "16698": "paz", "16699": "p\u00e9nalit\u00e9", "16700": "papoutsis", "16701": "envisagera", "16702": "impr\u00e9vus", "16703": "coloniales", "16704": "attirerai", "16705": "g\u00e9om\u00e9trie", "16706": "dell", "16707": "artillerie", "16708": "pr\u00e9pond\u00e9rante", "16709": "int\u00e9ressons", "16710": "tueurs", "16711": "gorsel", "16712": "conference", "16713": "finis", "16714": "pes\u00e9", "16715": "toucheront", "16716": "r\u00e9uniront", "16717": "agressives", "16718": "splendide", "16719": "ross", "16720": "cornillet", "16721": "pressants", "16722": "mart\u00edn", "16723": "oreja", "16724": "altener", "16725": "pr\u00e9cit\u00e9s", "16726": "arrivions", "16727": "amenant", "16728": "proc\u00e9dions", "16729": "rentr\u00e9", "16730": "chiapas", "16731": "annonceurs", "16732": "absentes", "16733": "r\u00e9vis\u00e9es", "16734": "stimulera", "16735": "vries", "16736": "br\u00e9siliennes", "16737": "class\u00e9e", "16738": "incompl\u00e8tes", "16739": "d\u00e9tenteur", "16740": "lamentablement", "16741": "p\u00e9naux", "16742": "rentes", "16743": "exotiques", "16744": "sit\u00f4t", "16745": "codifi\u00e9e", "16746": "fatal", "16747": "f\u00fcle", "16748": "pagaille", "16749": "voggenhuber", "16750": "vincent", "16751": "satisfasse", "16752": "20e", "16753": "schwab", "16754": "mcguinness", "16755": "bouleversement", "16756": "jouait", "16757": "\u00e9coutes", "16758": "signalisation", "16759": "th\u00e9oriques", "16760": "m\u00e9nagers", "16761": "2,7", "16762": "renferment", "16763": "discutables", "16764": "d\u00e9couleront", "16765": "industrialisation", "16766": "pr\u00e9cit\u00e9es", "16767": "arctiques", "16768": "hume", "16769": "gas\u00f2liba", "16770": "goma", "16771": "cambodgiens", "16772": "guin\u00e9ens", "16773": "brille", "16774": "soutenaient", "16775": "mafias", "16776": "\u00e9go\u00efstes", "16777": "secoue", "16778": "pr\u00e9budg\u00e9taires", "16779": "johannesbourg", "16780": "penchant", "16781": "e.", "16782": "aden", "16783": "giec", "16784": "spirituelle", "16785": "habiller", "16786": "vecteurs", "16787": "renvoy\u00e9es", "16788": "iniquit\u00e9", "16789": "confisquer", "16790": "annuaires", "16791": "pessimistes", "16792": "contribuerons", "16793": "pr\u00e9f\u00e9rerait", "16794": "garante", "16795": "consciences", "16796": "maat", "16797": "ravalomanana", "16798": "consid\u00e9rions", "16799": "pr\u00f4nant", "16800": "121", "16801": "223", "16802": "b\u00e9n\u00e9ficieraient", "16803": "bullmann", "16804": "normaliser", "16805": "julie", "16806": "tentaient", "16807": "levant", "16808": "villiers", "16809": "m\u00e9t\u00e9o", "16810": "reproduira", "16811": "s\u00e9vir", "16812": "ghouta", "16813": "\u00e9veiller", "16814": "offenser", "16815": "lalumi\u00e8re", "16816": "haiti", "16817": "manufacturi\u00e8re", "16818": "hiv", "16819": "canons", "16820": "rouages", "16821": "\u00e9thiopienne", "16822": "envoyez", "16823": "connexe", "16824": "d\u00e9taillant", "16825": "sacrifi\u00e9s", "16826": "merveilles", "16827": "effroyables", "16828": "phnom", "16829": "propage", "16830": "lingua", "16831": "stage", "16832": "br\u00fble", "16833": "conf\u00e9rant", "16834": "\u00e9ventualit\u00e9s", "16835": "engageait", "16836": "d\u00e9gag\u00e9es", "16837": "pakistanaises", "16838": "reconnaissions", "16839": "sen", "16840": "autor\u00e9glementation", "16841": "attarderai", "16842": "politisation", "16843": "contours", "16844": "yuan", "16845": "in\u00e9luctable", "16846": "d\u00e9posant", "16847": "douzi\u00e8me", "16848": "diam\u00e9tralement", "16849": "khodorkovski", "16850": "hosni", "16851": "alli\u00e9e", "16852": "incorpor\u00e9s", "16853": "aziz", "16854": "cyniques", "16855": "f\u00e9ri\u00e9", "16856": "tomb\u00e9es", "16857": "conc\u00e9der", "16858": "f\u00e9licitent", "16859": "fervents", "16860": "d\u00e9go\u00fbt", "16861": "consigner", "16862": "militantes", "16863": "apparences", "16864": "anciennement", "16865": "pr\u00e9sences", "16866": "d\u00e9n\u00e9s", "16867": "recevaient", "16868": "efficient", "16869": "r\u00fchle", "16870": "d\u00e9grade", "16871": "frontenac", "16872": "scrupuleux", "16873": "tranch\u00e9e", "16874": "spectacles", "16875": "carcan", "16876": "battues", "16877": "inspir\u00e9es", "16878": "d\u00e9combres", "16879": "howard", "16880": "exhortant", "16881": "foie", "16882": "woods", "16883": "mettraient", "16884": "b\u00e9larussien", "16885": "irr\u00e9alisable", "16886": "marrakech", "16887": "prot\u00e9agineux", "16888": "m\u00e9prise", "16889": "data", "16890": "ordonne", "16891": "b\u00eate", "16892": "chemise", "16893": "alaska", "16894": "gambie", "16895": "incoh\u00e9rente", "16896": "rup", "16897": "\u00e9ditoriale", "16898": "nutritionnels", "16899": "incontr\u00f4lable", "16900": "manqueront", "16901": "pasteur", "16902": "marathon", "16903": "successivement", "16904": "moldaves", "16905": "d\u00e9tergents", "16906": "ralenti", "16907": "contrebalancer", "16908": "mondialement", "16909": "assign\u00e9", "16910": "entra\u00eeneur", "16911": "instructif", "16912": "attestant", "16913": "2,8", "16914": "ascendance", "16915": "mandat\u00e9", "16916": "bol", "16917": "miennes", "16918": "zimbabw\u00e9ens", "16919": "anecdote", "16920": "d\u00e9tenue", "16921": "wilfrid", "16922": "georges", "16923": "fascinant", "16924": "imparfaite", "16925": "socio\u00e9conomiques", "16926": "imp\u00e9rative", "16927": "alpine", "16928": "distingu\u00e9e", "16929": "\u00e9chang\u00e9", "16930": "promettent", "16931": "exacerb\u00e9", "16932": "r\u00e9veill\u00e9", "16933": "antarctique", "16934": "exerc\u00e9s", "16935": "orgueil", "16936": "marginale", "16937": "chass\u00e9", "16938": "soup\u00e7onn\u00e9", "16939": "intimidations", "16940": "d\u00e9tours", "16941": "diffuseurs", "16942": "stupidit\u00e9", "16943": "d\u00e9lib\u00e9ration", "16944": "rendaient", "16945": "aplanir", "16946": "340", "16947": "orientales", "16948": "horizontaux", "16949": "swan", "16950": "film\u00e9", "16951": "\u2022", "16952": "incalculable", "16953": "1049", "16954": "octroyant", "16955": "d\u00e9sapprouvons", "16956": "discutera", "16957": "contrevient", "16958": "nomm\u00e9ment", "16959": "fondatrice", "16960": "m\u00fbre", "16961": "sch\u00f6rling", "16962": "boulot", "16963": "concluons", "16964": "heureuses", "16965": "op\u00e9r\u00e9es", "16966": "viticulteurs", "16967": "ratifications", "16968": "merde", "16969": "mox", "16970": "habile", "16971": "heurter", "16972": "implant\u00e9es", "16973": "verbalement", "16974": "tenterons", "16975": "races", "16976": "d\u00e9chir\u00e9", "16977": "m\u00e8neront", "16978": "apprentis", "16979": "hieronymi", "16980": "analysant", "16981": "assujettie", "16982": "op\u00e9r\u00e9e", "16983": "alertes", "16984": "refait", "16985": "perdrons", "16986": "nocif", "16987": "tickets", "16988": "08", "16989": "appuyaient", "16990": "247", "16991": "arables", "16992": "s\u00e9curisation", "16993": "rosenberg", "16994": "s\u00e9curis\u00e9", "16995": "jim\u00e9nez", "16996": "macartney", "16997": "championnats", "16998": "islands", "16999": "veuve", "17000": "optimales", "17001": "marteau", "17002": "abusifs", "17003": "marchand", "17004": "veine", "17005": "recueil", "17006": "propension", "17007": "hamid", "17008": "irakiennes", "17009": "unissant", "17010": "retomber", "17011": "humainement", "17012": "\u00e9voquait", "17013": "loyaux", "17014": "tol\u00e9r\u00e9e", "17015": "lahti", "17016": "n\u00e9gociable", "17017": "songez", "17018": "taureau", "17019": "prescrire", "17020": "ent\u00e9riner", "17021": "facturation", "17022": "alourdir", "17023": "bont\u00e9", "17024": "1b", "17025": "insinuations", "17026": "opcvm", "17027": "strat\u00e9giquement", "17028": "volumineux", "17029": "s\u00e9jours", "17030": "mascarade", "17031": "r\u00e9fl\u00e9chissent", "17032": "kootenay", "17033": "subissons", "17034": "m\u00e9trage", "17035": "anormal", "17036": "beauce", "17037": "boston", "17038": "positionnement", "17039": "r\u00e9pondez", "17040": "fragment\u00e9", "17041": "m\u00e8mes", "17042": "interprovincial", "17043": "voyageur", "17044": "stratton", "17045": "baissent", "17046": "boisseau", "17047": "min\u00e9", "17048": "co\u00efncident", "17049": "gagnante", "17050": "mona", "17051": "ray\u00e9", "17052": "aid", "17053": "kyprianou", "17054": "prot\u00e9gerait", "17055": "pr\u00e9sentations", "17056": "quartette", "17057": "ar\u00e8ne", "17058": "r\u00e9introduit", "17059": "disparaisse", "17060": "red\u00e9ploiement", "17061": "bauer", "17062": "d\u00e9bouchent", "17063": "m\u00e9fier", "17064": "irritation", "17065": "patrouille", "17066": "r\u00e9pandent", "17067": "baby", "17068": "shaw", "17069": "vandalisme", "17070": "math\u00e9matique", "17071": "inexactes", "17072": "d\u00e9doublements", "17073": "centimes", "17074": "implacable", "17075": "anderson", "17076": "mcmahon", "17077": "r\u00e9troactive", "17078": "prenante", "17079": "augment\u00e9s", "17080": "inutilis\u00e9s", "17081": "alg\u00e9rienne", "17082": "bruits", "17083": "souveraines", "17084": "pr\u00e9dictions", "17085": "konrad", "17086": "incontournables", "17087": "juridictionnel", "17088": "laisserai", "17089": "144", "17090": "\u00e9ternelle", "17091": "sectarisme", "17092": "esquiver", "17093": "virrankoski", "17094": "vaudra", "17095": "ccb", "17096": "fusils", "17097": "alarmistes", "17098": "enviable", "17099": "organisons", "17100": "ile", "17101": "elliott", "17102": "organisera", "17103": "remettra", "17104": "ferry", "17105": "c7", "17106": "rafael", "17107": "litt\u00e9raires", "17108": "henderson", "17109": "r\u00e9fute", "17110": "radiodiffuseurs", "17111": "choisissons", "17112": "affiner", "17113": "r\u00e9alisera", "17114": "multiplie", "17115": "consacrera", "17116": "fatigu\u00e9s", "17117": "aimez", "17118": "concluent", "17119": "recrut\u00e9s", "17120": "occasionne", "17121": "refl\u00e9tera", "17122": "penh", "17123": "\u00e9picentre", "17124": "maya", "17125": "147", "17126": "birmanes", "17127": "fonci\u00e8res", "17128": "finie", "17129": "subordonner", "17130": "380", "17131": "hop", "17132": "brillamment", "17133": "irr\u00e9guliers", "17134": "intensifi\u00e9e", "17135": "go", "17136": "assureurs", "17137": "lib\u00e9ria", "17138": "r\u00e8glent", "17139": "rassur\u00e9", "17140": "travaillions", "17141": "apanage", "17142": "t\u00e9moignant", "17143": "colossale", "17144": "privation", "17145": "d\u00e9sagr\u00e9ments", "17146": "demeureront", "17147": "outrance", "17148": "parl\u00e9e", "17149": "falun", "17150": "lethbridge", "17151": "2,6", "17152": "d\u00e9cisionnelle", "17153": "changerait", "17154": "connaitre", "17155": "julia", "17156": "conservatrices", "17157": "d\u00e9sir\u00e9", "17158": "ch\u00e2timents", "17159": "liant", "17160": "influe", "17161": "d\u00e9nombre", "17162": "mentales", "17163": "quadras", "17164": "ennuyeux", "17165": "maillants", "17166": "map", "17167": "seveso", "17168": "consultative", "17169": "mobilise", "17170": "maintiendrons", "17171": "powell", "17172": "lettons", "17173": "sugg\u00e9rons", "17174": "adaptabilit\u00e9", "17175": "inviterais", "17176": "persister", "17177": "robinson", "17178": "culminant", "17179": "dockers", "17180": "tourmente", "17181": "consign\u00e9", "17182": "proportionn\u00e9e", "17183": "expliqu\u00e9e", "17184": "gilles", "17185": "supporte", "17186": "b\u00e9larus", "17187": "\u00e9conomes", "17188": "subordonn\u00e9", "17189": "michelle", "17190": "ratifie", "17191": "hoppenstedt", "17192": "community", "17193": "jong", "17194": "g\u00e9n\u00e9rateur", "17195": "cultiv\u00e9", "17196": "jordanien", "17197": "tnp", "17198": "occasionner", "17199": "emprisonner", "17200": "suivait", "17201": "approch\u00e9", "17202": "diffusent", "17203": "parl\u00e9es", "17204": "m\u00eal\u00e9e", "17205": "t\u00e2chons", "17206": "tailles", "17207": "brunei", "17208": "shahbag", "17209": "poignant", "17210": "cartel", "17211": "r\u00e9sument", "17212": "d\u00e9grader", "17213": "grossir", "17214": "travaillais", "17215": "tsatsos", "17216": "applicabilit\u00e9", "17217": "souffrons", "17218": "sakellariou", "17219": "milite", "17220": "betterave", "17221": "sepa", "17222": "d\u00e9bris", "17223": "obscur", "17224": "cape", "17225": "arguant", "17226": "johnston", "17227": "enracin\u00e9e", "17228": "vert\u00e9brale", "17229": "ferveur", "17230": "valides", "17231": "chante", "17232": "entra\u00eeneraient", "17233": "r\u00e9actionnaires", "17234": "tisser", "17235": "participait", "17236": "kirghizes", "17237": "d\u00e9risoires", "17238": "cherchez", "17239": "bact\u00e9ries", "17240": "mcdonald", "17241": "filtrer", "17242": "t\u00e9l\u00e9charger", "17243": "chronologie", "17244": "clifford", "17245": "impopulaire", "17246": "entra\u00eeneurs", "17247": "h\u00e9breu", "17248": "obscures", "17249": "irrempla\u00e7able", "17250": "franz", "17251": "cargaisons", "17252": "retrouv\u00e9es", "17253": "conditionnement", "17254": "coast", "17255": "jointe", "17256": "ib\u00e9rique", "17257": "irsc", "17258": "155", "17259": "admirer", "17260": "blottnitz", "17261": "engrang\u00e9s", "17262": "hulten", "17263": "ma\u00eetris\u00e9e", "17264": "eastern", "17265": "dict\u00e9e", "17266": "activation", "17267": "profitables", "17268": "analytique", "17269": "matrimoniaux", "17270": "pronon\u00e7ons", "17271": "disparates", "17272": "donneraient", "17273": "impuissante", "17274": "parfaits", "17275": "alice", "17276": "hip", "17277": "assiduit\u00e9", "17278": "calmement", "17279": "casiers", "17280": "destitution", "17281": "g\u00e2cher", "17282": "308", "17283": "voudrez", "17284": "s\u00e9lectifs", "17285": "cornes", "17286": "installent", "17287": "caricaturiste", "17288": "cons\u00e9cutifs", "17289": "nuira", "17290": "dissensions", "17291": "cedh", "17292": "martinez", "17293": "ifop", "17294": "gucht", "17295": "p\u00e9dophile", "17296": "choisissant", "17297": "poursuivait", "17298": "justifiable", "17299": "fluor\u00e9s", "17300": "n\u00e9gociants", "17301": "certifications", "17302": "azimuts", "17303": "favori", "17304": "r\u00e9affirmons", "17305": "bowman", "17306": "seigneurs", "17307": "remarquons", "17308": "pneus", "17309": "d\u00e9gagent", "17310": "simulacre", "17311": "banal", "17312": "homosexuelle", "17313": "monsanto", "17314": "intensifient", "17315": "--", "17316": "casa", "17317": "australienne", "17318": "use", "17319": "clandestinement", "17320": "alternance", "17321": "renfor\u00e7ons", "17322": "ptom", "17323": "discr\u00e9tionnaires", "17324": "pr\u00e9lev\u00e9", "17325": "d\u00e9doubl\u00e9", "17326": "\u00e9clatante", "17327": "sens\u00e9s", "17328": "rattrapage", "17329": "f\u00eat\u00e9", "17330": "remanier", "17331": "\u00e9pouser", "17332": "conviendront", "17333": "s\u00e9culaire", "17334": "parade", "17335": "exp\u00e9di\u00e9", "17336": "d\u00e9crivait", "17337": "dense", "17338": "baux", "17339": "attriste", "17340": "protestante", "17341": "d\u00e9centralis\u00e9", "17342": "wulf", "17343": "andine", "17344": "glissements", "17345": "fauss\u00e9e", "17346": "euronews", "17347": "now", "17348": "asym\u00e9triques", "17349": "dream", "17350": "ancr\u00e9s", "17351": "musharraf", "17352": "\u00e9migrants", "17353": "occasionnellement", "17354": "politkovska\u00efa", "17355": "accrocher", "17356": "importait", "17357": "serveurs", "17358": "enverrait", "17359": "repr\u00e9sentatifs", "17360": "insatisfaisant", "17361": "musicien", "17362": "g\u00e8ne", "17363": "quais", "17364": "6,5", "17365": "its", "17366": "donnaient", "17367": "p\u00e9pin", "17368": "in\u00e9branlable", "17369": "impressionn\u00e9e", "17370": "tr\u00e9sorerie", "17371": "ferguson", "17372": "d\u00e9former", "17373": "pyramide", "17374": "concis", "17375": "secchi", "17376": "huissier", "17377": "ushahidi", "17378": "banderoles", "17379": "d\u00e9plaise", "17380": "connotation", "17381": "kreissl", "17382": "d\u00f6rfler", "17383": "habiter", "17384": "can", "17385": "st\u00e9rile", "17386": "libyenne", "17387": "fra\u00eeche", "17388": "\u00e9viterait", "17389": "engendrerait", "17390": "l\u00e2chet\u00e9", "17391": "repli", "17392": "bilingues", "17393": "terminale", "17394": "ferr\u00e9es", "17395": "fabra", "17396": "consignes", "17397": "tadjiks", "17398": "redevable", "17399": "ratifi\u00e9es", "17400": "diminutions", "17401": "clarke", "17402": "dirigeantes", "17403": "timorais", "17404": "290", "17405": "l\u00e9gendaire", "17406": "r\u00eavent", "17407": "carr\u00e9", "17408": "peint", "17409": "1944", "17410": "\u00e9quip\u00e9e", "17411": "conna\u00eetront", "17412": "honneurs", "17413": "forfait", "17414": "conditionn\u00e9", "17415": "tyrans", "17416": "allusions", "17417": "set", "17418": "performante", "17419": "connait", "17420": "instaur\u00e9es", "17421": "163", "17422": "flautre", "17423": "cr\u00e8me", "17424": "h\u00fcbner", "17425": "fonci\u00e8rement", "17426": "certifier", "17427": "supports", "17428": "pr\u00e9sidentiels", "17429": "s\u00e9paratisme", "17430": "crabe", "17431": "alberto", "17432": "argentin", "17433": "nafo", "17434": "organique", "17435": "europeana", "17436": "karim", "17437": "d\u00e9pendait", "17438": "sauvagement", "17439": "\u00e9mettant", "17440": "asym\u00e9trique", "17441": "panoplie", "17442": "elmar", "17443": "karamanou", "17444": "olivier", "17445": "vol\u00e9es", "17446": "insurmontables", "17447": "regorge", "17448": "yves", "17449": "offshore", "17450": "terminal", "17451": "floues", "17452": "j\u00f8rgensen", "17453": "r\u00e9giment", "17454": "attaquons", "17455": "franchie", "17456": "organisationnelle", "17457": "voyais", "17458": "emirats", "17459": "effrayante", "17460": "mccartin", "17461": "shah", "17462": "t\u00e9l\u00e9globe", "17463": "entreprennent", "17464": "\u00e9tages", "17465": "parviens", "17466": "suffise", "17467": "tremblay", "17468": "agr\u00e9ables", "17469": "biya", "17470": "renvoyant", "17471": "intervenues", "17472": "changes", "17473": "homog\u00e8nes", "17474": "utilisions", "17475": "n\u00e9olib\u00e9ralisme", "17476": "egnos", "17477": "cyclistes", "17478": "adaptant", "17479": "pillages", "17480": "arrach\u00e9", "17481": "cultivent", "17482": "laborieuses", "17483": "allum\u00e9", "17484": "rondes", "17485": "simcoe", "17486": "r\u00e9sorber", "17487": "r\u00e9inventer", "17488": "reformuler", "17489": "troubl\u00e9", "17490": "favoris\u00e9s", "17491": "singuli\u00e8rement", "17492": "corbey", "17493": "stup\u00e9fiant", "17494": "d\u00e9tourne", "17495": "rabat", "17496": "jason", "17497": "vacance", "17498": "irrigation", "17499": "b\u00e9larussienne", "17500": "dioxines", "17501": "abonde", "17502": "lisibilit\u00e9", "17503": "minimis", "17504": "440", "17505": "m\u00eal\u00e9", "17506": "gitanyow", "17507": "d\u00e9pendront", "17508": "minima", "17509": "cryptage", "17510": "impatiemment", "17511": "ajournera", "17512": "memorial", "17513": "dissidente", "17514": "enfermer", "17515": "posaient", "17516": "accroissant", "17517": "carthag\u00e8ne", "17518": "parasites", "17519": "captage", "17520": "investisse", "17521": "abondante", "17522": "niagara", "17523": "bafoue", "17524": "r\u00e9v\u00e9lait", "17525": "militent", "17526": "atypique", "17527": "relayer", "17528": "philippin", "17529": "consolid\u00e9s", "17530": "sanaa", "17531": "conventionnel", "17532": "pr\u00e9f\u00e9rez", "17533": "215", "17534": "antiterroristes", "17535": "risqueraient", "17536": "\u00e9clats", "17537": "vierges", "17538": "noix", "17539": "accroissent", "17540": "analyste", "17541": "fun\u00e8bre", "17542": "r\u00e9voltant", "17543": "tournons", "17544": "fausser", "17545": "visitez", "17546": "g\u00ean\u00e9s", "17547": "wolski", "17548": "ould", "17549": "sfor", "17550": "cfp", "17551": "d\u00e9sesp\u00e9r\u00e9es", "17552": "montcalm", "17553": "restez", "17554": "focus", "17555": "p\u00e9cuniaires", "17556": "comptaient", "17557": "r\u00e9agissant", "17558": "gares", "17559": "d\u00e9ceptions", "17560": "moquent", "17561": "d\u00e9terminantes", "17562": "d\u00e9s\u00e9quilibr\u00e9", "17563": "restreintes", "17564": "lunettes", "17565": "descriptions", "17566": "pimenta", "17567": "tranquilles", "17568": "prompte", "17569": "inexistantes", "17570": "visitent", "17571": "virage", "17572": "solvants", "17573": "vas", "17574": "henry", "17575": "185", "17576": "ressemblait", "17577": "exag\u00e9ration", "17578": "annul\u00e9s", "17579": "retiens", "17580": "retirant", "17581": "russell", "17582": "comeau", "17583": "tom\u00e9", "17584": "maris", "17585": "faussement", "17586": "fundy", "17587": "trajectoire", "17588": "souverainet\u00e9s", "17589": "santini", "17590": "grenades", "17591": "vingti\u00e8me", "17592": "tra\u00een\u00e9", "17593": "\u00e9mettons", "17594": "masques", "17595": "veuves", "17596": "hariri", "17597": "acidification", "17598": "entour\u00e9e", "17599": "atypiques", "17600": "dilma", "17601": "festivals", "17602": "gallois", "17603": "formulant", "17604": "soulign\u00e9es", "17605": "ecu", "17606": "republi\u00e9", "17607": "align\u00e9s", "17608": "malheurs", "17609": "adriatique", "17610": "imputabilit\u00e9", "17611": "sahel", "17612": "navette", "17613": "facette", "17614": "v\u00e9rifi\u00e9es", "17615": "m\u00fbrement", "17616": "croyants", "17617": "acronyme", "17618": "tache", "17619": "rel\u00e9gu\u00e9", "17620": "antimicrobiens", "17621": "roses", "17622": "sorci\u00e8res", "17623": "caritatives", "17624": "couvrait", "17625": "s\u00e9vissent", "17626": "g\u00e9nome", "17627": "brother", "17628": "copps", "17629": "rhinoc\u00e9ros", "17630": "efforcerai", "17631": "baker", "17632": "centraliser", "17633": "appuierai", "17634": "stockmann", "17635": "117", "17636": "ouragans", "17637": "succ\u00e8dent", "17638": "reinfeldt", "17639": "insatisfaits", "17640": "netizen", "17641": "v\u00e9rifiables", "17642": "esth\u00e9tique", "17643": "fred", "17644": "disputes", "17645": "stars", "17646": "polluant", "17647": "n\u00e9gligences", "17648": "viendraient", "17649": "variante", "17650": "borg", "17651": "d\u00e9passait", "17652": "revus", "17653": "opposerons", "17654": "employons", "17655": "netanyahu", "17656": "against", "17657": "respective", "17658": "limited", "17659": "abondent", "17660": "rivalit\u00e9", "17661": "tpiy", "17662": "collaborateur", "17663": "d\u00e9duit", "17664": "troupeau", "17665": "bolduc", "17666": "extenso", "17667": "mouture", "17668": "320", "17669": "erevan", "17670": "284", "17671": "d\u00e9plorent", "17672": "profilage", "17673": "volcaniques", "17674": "interventionniste", "17675": "proclame", "17676": "apog\u00e9e", "17677": "csc", 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"p\u00e9ruviens", "17721": "descente", "17722": "arrange", "17723": "aberrant", "17724": "camoufler", "17725": "fid\u00e9lit\u00e9", "17726": "metten", "17727": "p\u00each\u00e9", "17728": "introduise", "17729": "compilation", "17730": "kla\u00df", "17731": "abstiendrai", "17732": "examinerai", "17733": "weston", "17734": "assumons", "17735": "d\u00e9samorcer", "17736": "cannes", "17737": "utopique", "17738": "lib\u00e9ralisations", "17739": "p\u00e9ruvienne", "17740": "retracer", "17741": "f\u00e2cheuse", "17742": "prescrits", "17743": "homicides", "17744": "ras", "17745": "consigne", "17746": "desservir", "17747": "boucle", "17748": "arthur", "17749": "hospitalier", "17750": "permettaient", "17751": "habilement", "17752": "aveuglement", "17753": "invers\u00e9e", "17754": "\u00e9valuons", "17755": "18h30", "17756": "prolong\u00e9es", "17757": "compliquent", "17758": "tsagaropoulou", "17759": "\u00e9largies", "17760": "r\u00e9\u00e9crire", "17761": "conserv\u00e9s", "17762": "bicyclette", "17763": "earth", "17764": "myst\u00e9rieux", "17765": "\u00e9trangement", "17766": "affronte", "17767": "normatif", "17768": "sanctuaire", "17769": "gazier", "17770": "interpr\u00e8tent", "17771": "\u00e9mu", "17772": "rojo", "17773": "assujettir", "17774": "polarisation", "17775": "aberration", "17776": "soudanaise", "17777": "plu", "17778": "cass\u00e9", "17779": "gand", "17780": "institu\u00e9e", "17781": "continuait", "17782": "caf\u00e9ine", "17783": "trust", "17784": "preniez", "17785": "employant", "17786": "visera", "17787": "boko", "17788": "bonifier", "17789": "foreign", "17790": "attir\u00e9s", "17791": "urbanisme", "17792": "entendais", "17793": "conrad", "17794": "jet", "17795": "lucrative", "17796": "trahit", "17797": "cetera", "17798": "10.000", "17799": "r\u00e9alisateurs", "17800": "cupidit\u00e9", "17801": "bidonvilles", "17802": "accomplissements", "17803": "concentr\u00e9es", "17804": "supranationales", "17805": "cara\u00efbe", "17806": "p\u00e9rim\u00e8tre", "17807": "glas", "17808": "mathies", "17809": "entravant", "17810": "touchait", "17811": "distillation", "17812": "appelaient", "17813": "jolie", "17814": "loyers", "17815": "cl\u00f4tur\u00e9", "17816": "k\u00e9ros\u00e8ne", "17817": "tueries", "17818": "eufor", "17819": "adopteront", "17820": "couronner", "17821": "pile", "17822": "r\u00e9examin\u00e9e", "17823": "recevrez", "17824": "banff", "17825": "fr\u00e9quent\u00e9", "17826": "be", "17827": "mourront", "17828": "orl\u00e9ans", "17829": "opposera", "17830": "r\u00e9habiliter", "17831": "r\u00e9f\u00e8rent", "17832": "monopolistique", "17833": "\u00e9ternel", "17834": "i.", "17835": "purge", "17836": "charni\u00e8re", "17837": "gentiment", "17838": "entreprenne", "17839": "r.", "17840": "m\u00e9morial", "17841": "limitons", "17842": "attendrons", "17843": "cyberespace", "17844": "pr\u00e9munir", "17845": "impatiente", "17846": "equal", "17847": "midlands", "17848": "clivages", "17849": "assum\u00e9e", 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"dict\u00e9", "17893": "vertueux", "17894": "marfil", "17895": "b\u00e2tie", "17896": "cor\u00e9ennes", "17897": "submerg\u00e9s", "17898": "invoqu\u00e9s", "17899": "plaisante", "17900": "positionner", "17901": "pseudonyme", "17902": "153", "17903": "migrant", "17904": "multiple", "17905": "climatisation", "17906": "provan", "17907": "applaudit", "17908": "sahraouis", "17909": "moose", "17910": "moreira", "17911": "mountain", "17912": "compl\u00e9tant", "17913": "responsabiliser", "17914": "simplifiant", "17915": "tombera", "17916": "form\u00e9es", "17917": "inadapt\u00e9e", "17918": "plaisent", "17919": "rohingyas", "17920": "danoises", "17921": "diefenbaker", "17922": "poserai", "17923": "secourir", "17924": "perceptions", "17925": "virtuels", "17926": "job", "17927": "diff\u00e9rencie", "17928": "locomotive", "17929": "bombay", "17930": "buzz", "17931": "bagage", "17932": "probables", "17933": "invisibles", "17934": "mollar", "17935": "inspecter", "17936": "\u00e9go\u00efsmes", "17937": "arr\u00eatant", "17938": "peupl\u00e9e", "17939": "flanc", "17940": "shirt", "17941": "greenwood", "17942": "jaunes", "17943": "lucratives", "17944": "cavalier", "17945": "na\u00eet", "17946": "lacrymog\u00e8ne", "17947": "ing\u00e9rences", "17948": "perplexes", "17949": "r\u00e9sidentiels", "17950": "sentie", "17951": "totalisant", "17952": "sugg\u00e9r\u00e9es", "17953": "aiderons", "17954": "monstres", "17955": "violant", "17956": "vo\u00fbte", "17957": "cdh", "17958": "bontempi", "17959": "abattue", "17960": "b\u00f6hm", "17961": "box", "17962": "d\u00e9veloppons", "17963": "habilet\u00e9", "17964": "recr\u00e9er", "17965": "calcul\u00e9e", "17966": "c\u00e8dent", "17967": "funk", "17968": "sa\u00efd", "17969": "r\u00e9affectation", "17970": "provoquerait", "17971": "samland", "17972": "r\u00e9torsion", "17973": "creus\u00e9", "17974": "utilisable", "17975": "\u00e9quip\u00e9", "17976": "rationnelles", "17977": "witness", "17978": "consenties", "17979": "clandestinit\u00e9", "17980": "klamt", "17981": "suppositions", "17982": "\u00e9loquemment", "17983": "m\u00e9ritaient", "17984": "augmentons", "17985": "limiterait", "17986": "cornelissen", "17987": "com\u00e9die", "17988": "exacerb\u00e9e", "17989": "jouisse", "17990": "09", "17991": "gra\u00e7a", "17992": "prospection", "17993": "story", "17994": "\u00e9puiser", "17995": "manifest\u00e9es", "17996": "apd", "17997": "dispers\u00e9", "17998": "filmer", "17999": "ping", "18000": "christopher", "18001": "d\u00e9chargement", "18002": "carmen", "18003": "opportun\u00e9ment", "18004": "selkirk", "18005": "exp\u00e9rimental", "18006": "accordions", "18007": "originalit\u00e9", "18008": "tol\u00e9rable", "18009": "conc\u00e8de", "18010": "identifiant", "18011": "poule", "18012": "bellig\u00e9rants", "18013": "abd", "18014": "encourues", "18015": "d\u00e9m\u00e9nag\u00e9", "18016": "diana", "18017": "doyen", "18018": "doucement", "18019": "asthme", "18020": "isler", "18021": "berthier", "18022": "inapplicable", "18023": "fairbairn", "18024": "r\u00e9veil", "18025": "correspondait", "18026": "injustifi\u00e9s", "18027": "ertms", "18028": "andorre", "18029": "jouissance", "18030": "affili\u00e9s", "18031": "gazi\u00e8re", "18032": "jov\u00e9", "18033": "vase", "18034": "teneurs", "18035": "ottoman", "18036": "emphase", "18037": "parlementarisme", "18038": "drummond", "18039": "estey", "18040": "manquerons", "18041": "uefa", "18042": "d\u00e9finissons", "18043": "cctv", "18044": "scandalis\u00e9", "18045": "\u00e9cu", "18046": "imaginables", "18047": "dessert", "18048": "aesa", "18049": "off", "18050": "subtile", "18051": "ing\u00e9niosit\u00e9", "18052": "sympt\u00f4me", "18053": "correctifs", "18054": "comique", "18055": "incorpor\u00e9es", "18056": "etobicoke", "18057": "\u0623\u0646", "18058": "navigable", "18059": "kuhne", "18060": "patronale", "18061": "rescousse", "18062": "2,2", "18063": "sparrow", "18064": "aliment\u00e9e", "18065": "recycl\u00e9s", "18066": "portabilit\u00e9", "18067": "signal\u00e9e", "18068": "agissez", "18069": "transfronti\u00e8re", "18070": "gifle", "18071": "nasser", "18072": "offens\u00e9", "18073": "raid", "18074": "piecyk", "18075": "stock\u00e9es", "18076": "emmen\u00e9s", "18077": "r\u00eav\u00e9", "18078": "essex", "18079": "finan\u00e7ant", "18080": "\u00e9quivalant", "18081": "newman", "18082": "rev\u00eatant", "18083": "m\u00e8me", "18084": "lincoln", "18085": "centenaire", "18086": "indissociables", "18087": "ent\u00e9rin\u00e9", "18088": "paraisse", "18089": "consolide", "18090": "chang\u00e9e", "18091": "commode", "18092": "nicotine", "18093": "grille", "18094": "consciemment", "18095": "obstin\u00e9ment", "18096": "d\u00e9ficiente", "18097": "attribuant", "18098": "d\u00e9ployons", "18099": "aid\u00e9es", "18100": "interpr\u00e9t\u00e9s", "18101": "biologie", "18102": "edd", "18103": "harcel\u00e9s", "18104": "versant", "18105": "plaidant", "18106": "emprisonn\u00e9e", "18107": "boris", "18108": "kozloduy", "18109": "raviver", "18110": "munis", "18111": "g\u00e9n\u00e9rant", "18112": "rois", "18113": "exp\u00e9diteurs", "18114": "docteurs", "18115": "leurrer", "18116": "conditionn\u00e9e", "18117": "l\u00e9gif\u00e8re", "18118": "intifada", "18119": "merveilleuses", "18120": "goepel", "18121": "parenth\u00e8ses", "18122": "inqui\u00e9t\u00e9s", "18123": "tirerons", "18124": "manchettes", "18125": "carbonique", "18126": "commanditaires", "18127": "fund", "18128": "ent\u00eatement", "18129": "g\u00e9n\u00e9r\u00e9e", "18130": "tommy", "18131": "soignants", "18132": "portez", "18133": "boucs", "18134": "ajoutez", "18135": "tante", "18136": "per\u00e7ois", "18137": "tournoi", "18138": "recevraient", "18139": "gr\u00e9vistes", "18140": "sara", "18141": "enchant\u00e9", "18142": "tempus", "18143": "v\u00eatement", "18144": "monopolistiques", "18145": "fiori", "18146": "solennel", "18147": "ajourn\u00e9e", "18148": "mettrai", "18149": "erdo\u011fan", "18150": "inqui\u00e9t\u00e9", "18151": "fragment\u00e9e", "18152": "t\u00e9l\u00e9com", "18153": "m\u00e9taphore", "18154": "emprunt\u00e9e", "18155": "najib", "18156": "repouss\u00e9e", "18157": "pittsburgh", "18158": "troubler", "18159": "puret\u00e9", "18160": "privatis\u00e9", "18161": "report\u00e9es", "18162": "stup\u00e9fait", "18163": "appliqueraient", "18164": "spontan\u00e9e", "18165": "descendante", "18166": "barils", "18167": "\u00e9l\u00e9vation", "18168": "proportionnelles", "18169": "refl\u00e9tait", "18170": "congolaise", "18171": "distributeur", "18172": "hecke", "18173": "passaient", "18174": "infond\u00e9es", "18175": "ann", "18176": "factuelles", "18177": "press\u00e9s", "18178": "bloch", "18179": "plong\u00e9s", "18180": "punit", "18181": "tampon", "18182": "achetant", "18183": "muet", "18184": "gahrton", "18185": "sao", "18186": "berthu", "18187": "brutaux", "18188": "pr\u00e9nom", "18189": "d\u00e9peuplement", "18190": "cercas", "18191": "m\u00e9diter", "18192": "d\u00e9courage", "18193": "alexandrie", "18194": "ol\u00e9oduc", "18195": "externalisation", "18196": "skinner", "18197": "lppe", "18198": "\u00e9motionnelle", "18199": "moscovici", "18200": "sugg\u00e9rait", "18201": "incessants", "18202": "moreno", "18203": "pionni\u00e8re", "18204": "upe", "18205": "inad\u00e9quats", "18206": "m\u00e8tre", "18207": "saisons", "18208": "r\u00e9duiront", "18209": "paulsen", "18210": "d\u00e9sengagement", "18211": "\u00e9voquez", "18212": "compensatoire", "18213": "contournement", "18214": "pollu\u00e9es", "18215": "perp\u00e9tuelle", "18216": "espions", "18217": "effraction", "18218": "ontarienne", "18219": "confiante", "18220": "paresseux", "18221": "couronnes", "18222": "accompagnera", "18223": "dirigeait", "18224": "p\u00e9nitentiaire", "18225": "franchissent", "18226": "concert\u00e9es", "18227": "favoris", "18228": "retiennent", "18229": "poseront", "18230": "kellett", "18231": "partageait", "18232": "sheikh", "18233": "ancr\u00e9es", "18234": "rapprochera", "18235": "sp\u00e9culatives", "18236": "natalia", "18237": "rajouter", "18238": "apparaissait", "18239": "win", "18240": "inculp\u00e9s", "18241": "incluait", "18242": "point\u00e9", "18243": "chilienne", "18244": "fouille", "18245": "visuel", "18246": "signalait", "18247": "reprendrai", "18248": "dualit\u00e9", "18249": "succincte", "18250": "opa", "18251": "211", "18252": "informera", "18253": "n\u00e9palaise", "18254": "poison", "18255": "l\u00e9gislatures", "18256": "essaierons", "18257": "cot\u00e9es", "18258": "d\u00e9ficience", "18259": "parie", "18260": "inextricablement", "18261": "apposer", "18262": "succursale", "18263": "gagn\u00e9e", "18264": "12h30", "18265": "ailes", "18266": "\u00e9metteur", "18267": "mauro", "18268": "reprendrons", "18269": "flambeau", "18270": "conservera", "18271": "anticipe", "18272": "pr\u00e9parait", "18273": "col", "18274": "agrandissement", "18275": "exag\u00e8re", "18276": "gagnantes", "18277": "s\u00e8che", "18278": "interrog\u00e9e", "18279": "copr\u00e9sident", "18280": "collusion", "18281": "dot", "18282": "cherchera", "18283": "vilnius", "18284": "393", "18285": "deva", "18286": "continuelle", "18287": "plaines", "18288": "portails", "18289": "wiebenga", "18290": "recommanderais", "18291": "s\u00e8ment", "18292": "anvers", "18293": "amicales", "18294": "lesdites", "18295": "opportuns", "18296": "marset", "18297": "sras", "18298": "attirera", "18299": "circonspection", "18300": "h\u00e9ritiers", "18301": "commercer", "18302": "aimaient", "18303": "n\u00e9olib\u00e9ral", "18304": "inaper\u00e7u", "18305": "d\u00e9corum", "18306": "rationnels", "18307": "encaisser", "18308": "1918", "18309": "apprenait", "18310": "copains", "18311": "g\u00ean\u00e9", "18312": "dom", "18313": "\u00e2pres", "18314": "d\u00e9fendrons", "18315": "privent", "18316": "modernis\u00e9", "18317": "impartis", "18318": "rappel\u00e9s", "18319": "hugh", "18320": "transfusion", "18321": "petten", "18322": "hypoth\u00e9quer", "18323": "appauvrir", 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"cardiovasculaires", "18368": "annule", "18369": "mix", "18370": "d\u00e9vast\u00e9es", "18371": "diff\u00e9r\u00e9e", "18372": "plume", "18373": "scission", "18374": "interagir", "18375": "expliqu\u00e9es", "18376": "dilution", "18377": "compens\u00e9e", "18378": "rompkey", "18379": "hollandaise", "18380": "comprendraient", "18381": "\u00e9levaient", "18382": "rugova", "18383": "in\u00e9dite", "18384": "courtiers", "18385": "ins\u00e8re", "18386": "rosa", "18387": "enorgueillir", "18388": "bovin", "18389": "sanglants", "18390": "provenaient", "18391": "singulier", "18392": "transmettent", "18393": "antidrogue", "18394": "children", "18395": "hydro\u00e9lectriques", "18396": "cochons", "18397": "mubarak", "18398": "lib\u00e9rez", "18399": "dilatoires", "18400": "pr\u00eatant", "18401": "borner", "18402": "boudreau", "18403": "pipeline", "18404": "gnss", "18405": "orbite", "18406": "clarifient", "18407": "d\u00e9m\u00e9nagement", "18408": "exasp\u00e9ration", "18409": "5b", "18410": "retourn\u00e9s", "18411": "grade", "18412": "initi\u00e9e", "18413": "nortel", "18414": "plie", "18415": "risqu\u00e9e", "18416": "r\u00e9surgence", "18417": "ip", "18418": "adjointe", "18419": "exigeaient", "18420": "technocratique", "18421": "sensibilis\u00e9", "18422": "\u00e9coulement", "18423": "batteries", "18424": "fie", "18425": "manille", "18426": "transfronti\u00e8res", "18427": "embarqu\u00e9s", "18428": "ime", "18429": "occupaient", "18430": "adversit\u00e9", "18431": "constructeur", "18432": "douleurs", "18433": "cr\u00e9neau", "18434": "joan", "18435": "dalits", "18436": "causera", "18437": "1.000", "18438": "expo", "18439": "reformulation", "18440": "soulevant", "18441": "criminalise", "18442": "nomenclature", "18443": "pussy", "18444": "indemnis\u00e9s", "18445": "parleront", "18446": "inclusif", "18447": "anastassopoulos", "18448": "betteraves", "18449": "atteignons", "18450": "ignalina", "18451": "convoqu\u00e9e", "18452": "allergiques", "18453": "propret\u00e9", "18454": "distribue", "18455": "sackville", "18456": "multipli\u00e9es", "18457": "robe", "18458": "verbale", "18459": "v\u00e9n\u00e9zu\u00e9lienne", "18460": "affections", "18461": "fument", "18462": "arbour", "18463": "entreposage", "18464": "brebis", "18465": "divulgu\u00e9s", "18466": "telecom", "18467": "rangoon", "18468": "zimbabw\u00e9en", "18469": "wood", "18470": "havel", "18471": "enqu\u00eat\u00e9", "18472": "panorama", "18473": "laissions", "18474": "guyane", "18475": "f\u00e9in", "18476": "bolsonaro", "18477": "\u0153uf", "18478": "nient", "18479": "clerg\u00e9", "18480": "crient", "18481": "lumsden", "18482": "ample", "18483": "confisqu\u00e9s", "18484": "transf\u00e9rabilit\u00e9", "18485": "igor", "18486": "rod", "18487": "\u00e9chang\u00e9es", "18488": "involontairement", "18489": "chants", "18490": "troubl\u00e9e", "18491": "inscrive", "18492": "adams", "18493": "ratifi\u00e9s", "18494": "command\u00e9e", "18495": "choquantes", "18496": "traiterons", "18497": "al\u00e9atoire", "18498": "estim\u00e9es", "18499": "\u00e9liminerait", "18500": "divine", "18501": "concr\u00e9tisent", "18502": "persique", "18503": "irr\u00e9futable", "18504": "planning", "18505": "incidemment", "18506": "ex\u00e9cutoires", "18507": "cin\u00e9aste", "18508": "motards", "18509": "revenions", "18510": "jur\u00e9", "18511": "amortir", "18512": "apparent\u00e9s", "18513": "jetant", "18514": "interrompus", "18515": "infiltration", "18516": "intellectuellement", "18517": "klinz", "18518": "that", "18519": "censur\u00e9s", "18520": "mossoul", "18521": "cliquez", "18522": "maximaux", "18523": "tenterai", "18524": "unhcr", "18525": "tendues", "18526": "subventionne", "18527": "neyra", "18528": "fatale", "18529": "estomac", "18530": "apologie", "18531": "falconer", "18532": "braconnage", "18533": "construisent", "18534": "\u00e9teint", "18535": "gagnerait", "18536": "busk", "18537": "gulf", "18538": "inepties", "18539": "poumon", "18540": "redonne", "18541": "annuaire", "18542": "nepad", "18543": "caprices", "18544": "r\u00e9agisse", "18545": "ab\u00eeme", "18546": "tch\u00e9coslovaquie", "18547": "infos", "18548": "targue", "18549": "caduques", "18550": "helmut", "18551": "blind\u00e9s", "18552": "d\u00e9nouement", "18553": "chargent", "18554": "nour", "18555": "sos", "18556": "discret", "18557": "winkler", "18558": "constats", "18559": "incrimination", "18560": "sanders", "18561": "sceqe", "18562": "laxistes", "18563": "cool", "18564": "enterr\u00e9", "18565": "amput\u00e9", "18566": "pr\u00e9servatifs", "18567": "refusaient", "18568": "quelconques", "18569": "dictatoriaux", "18570": "vari\u00e9", "18571": "hormone", "18572": "alg\u00e9riennes", "18573": "paralys\u00e9e", "18574": "harrison", "18575": "r\u00e9troactif", "18576": "\u00e9voluera", "18577": "concert\u00e9", "18578": "proc\u00e9dural", "18579": "minent", "18580": "diversifi\u00e9es", "18581": "v\u00e9cus", "18582": "ouzbek", "18583": "surmont\u00e9es", "18584": "attrist\u00e9", "18585": "rentr\u00e9es", "18586": "accorderons", "18587": "ruin\u00e9", "18588": "d\u00e9borde", "18589": "stauner", "18590": "transmettra", "18591": "futile", "18592": "enron", "18593": "vimeo", "18594": "saviez", "18595": "tarek", "18596": "sophistiqu\u00e9s", "18597": "r\u00e9examin\u00e9", "18598": "d\u00e9p\u00eacher", "18599": "alcools", "18600": "r\u00e9joui", "18601": "mill\u00e9naires", "18602": "consid\u00e9rera", "18603": "rein", "18604": "assassin", "18605": "d\u00edez", "18606": "sons", "18607": "deviez", "18608": "inqui\u00e9tait", "18609": "johnstone", "18610": "pollu\u00e9", "18611": "141", "18612": "bradbourn", "18613": "p\u00e2tes", "18614": "r\u00e9unisse", "18615": "mac\u00e9donienne", "18616": "envisagions", "18617": "nationalisation", "18618": "hindous", "18619": "articule", "18620": "parentales", "18621": "soustrait", "18622": "suspecte", "18623": "ex\u00e9cute", "18624": "n\u00e9gligeant", "18625": "khmers", "18626": "participatif", "18627": "parue", "18628": "amen\u00e9es", "18629": "assistera", "18630": "\u00e9minente", "18631": "lotbini\u00e8re", "18632": "passez", "18633": "anciennet\u00e9", "18634": "174", "18635": "dividende", "18636": "gasp\u00e9sie", "18637": "martinique", "18638": "mailles", "18639": "alt\u00e9rer", "18640": "planifi\u00e9es", "18641": "exempt", "18642": "moluques", "18643": "psychologiquement", "18644": "chaudi\u00e8re", "18645": "sergue\u00ef", "18646": "deloi", "18647": "malheureuses", "18648": "c\u00e9l\u00e9brit\u00e9", "18649": "ciem", "18650": "nunavik", "18651": "filtres", "18652": "jama\u00efcains", "18653": "renfermant", "18654": "vou\u00e9s", "18655": "magnitude", "18656": "sarnez", "18657": "lawson", "18658": "d\u00e9roulant", "18659": "pr\u00e9f\u00e9rerions", "18660": "icd", "18661": "immobilisations", "18662": "cumul", "18663": "intermodal", "18664": "percevons", "18665": "guarani", "18666": "manifest\u00e9s", "18667": "ar\u00f4mes", "18668": "intrins\u00e8quement", "18669": "m\u00e9ridionaux", "18670": "d\u00e9rober", "18671": "confirmait", "18672": "redynamiser", "18673": "roupies", "18674": "engagions", "18675": "arri\u00e9r\u00e9", "18676": "recens\u00e9s", "18677": "consomm\u00e9e", "18678": "florissant", "18679": "aarhus", "18680": "affich\u00e9es", "18681": "conglom\u00e9rats", "18682": "cristina", "18683": "timochenko", "18684": "barcamp", "18685": "artisanales", "18686": "finirait", "18687": "assassiner", "18688": "p\u00e2tir", "18689": "observant", "18690": "mbeki", "18691": "vacant", "18692": "por", "18693": "secr\u00e8tement", "18694": "women'", "18695": "copier", "18696": "jouyet", "18697": "mobilisent", "18698": "interinstitutionnelles", "18699": "devint", "18700": "sp\u00e9cialit\u00e9", "18701": "kuneva", "18702": "letton", "18703": "brandi", "18704": "ajax", "18705": "wim", "18706": "incombant", "18707": "expliquez", "18708": "bio\u00e9thique", "18709": "standardisation", "18710": "coop\u00e9ratifs", "18711": "dessinateur", "18712": "weiwei", "18713": "kings", "18714": "dissimule", "18715": "cotisent", "18716": "refroidissement", "18717": "malaisiens", "18718": "g\u00e9rant", "18719": "gr\u00e4\u00dfle", "18720": "minutie", "18721": "214", "18722": "energy", "18723": "dick", "18724": "227", "18725": "back", "18726": "performantes", "18727": "m\u00e9chant", "18728": "prohibition", "18729": "lebreton", "18730": "consistance", "18731": "bless\u00e9e", "18732": "intervenons", "18733": "trompons", "18734": "tombes", "18735": "saints", "18736": "m\u00e9content", "18737": "b\u00e9n\u00e9ficiaient", "18738": "emp\u00eacheront", "18739": "vi\u00eatnam", "18740": "loyalement", "18741": "lointains", "18742": "br\u00fblante", "18743": "love", "18744": "inexplicable", "18745": "cei", "18746": "robertson", "18747": "\u00e9lucider", "18748": "pietro", "18749": "d\u00e9biteurs", "18750": "mart", "18751": "r\u00e9soudrait", "18752": "sortiront", "18753": "mercenaires", "18754": "affirm\u00e9e", "18755": "itre", "18756": "mesur\u00e9s", "18757": "lawrence", "18758": "fureur", "18759": "attende", "18760": "angeles", "18761": "pr\u00e9budg\u00e9taire", "18762": "administr\u00e9e", "18763": "\u00e9clair\u00e9es", "18764": "scrutins", "18765": "provisions", "18766": "paire", "18767": "ins", "18768": "m\u00e9gantic", "18769": "m\u00e9thane", "18770": "biographie", "18771": "sandb\u00e6k", "18772": "nomades", "18773": "rencontr\u00e9e", "18774": "tremblante", "18775": "eisma", "18776": "cuisiner", "18777": "substitut", "18778": "tape", "18779": "chant", "18780": "subventionn\u00e9e", "18781": "peloton", "18782": "autorisera", "18783": "michelin", "18784": "invalide", "18785": "susan", "18786": "heurt\u00e9e", "18787": "bureaucraties", "18788": "forcent", "18789": "retirons", "18790": "promotions", "18791": "mills", "18792": "stalinisme", "18793": "today", "18794": "signalements", "18795": "zagreb", "18796": "aversion", "18797": "attarde", "18798": "blesser", "18799": "vulgaire", "18800": "d\u00e9cisionnelles", "18801": "survivent", 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"18845": "b\u00e9n\u00e9ficierait", "18846": "influenc\u00e9s", "18847": "livr\u00e9es", "18848": "inappropri\u00e9s", "18849": "pointent", "18850": "gilad", "18851": "norman", "18852": "interface", "18853": "poli", "18854": "hisham", "18855": "microcr\u00e9dits", "18856": "succ\u00e9der", "18857": "meurtri\u00e8res", "18858": "ya", "18859": "ha", "18860": "marasme", "18861": "d\u00e9border", "18862": "sp\u00e9cialit\u00e9s", "18863": "d\u00e9g\u00e9n\u00e9rer", "18864": "pr\u00e9vois", "18865": "162", "18866": "accentu\u00e9", "18867": "trio", "18868": "iouchtchenko", "18869": "recentrer", "18870": "r\u00e9pliquer", "18871": "renomm\u00e9", "18872": "colin", "18873": "h1n1", "18874": "oserais", "18875": "r\u00e9digeant", "18876": "samuel", "18877": "capitalis\u00e9", "18878": "pittella", "18879": "r\u00e9publicains", "18880": "multiculturel", "18881": "protectorat", "18882": "sente", "18883": "stipuler", "18884": "yang", "18885": "scolarisation", "18886": "coquille", "18887": 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"essayais", "20319": "implantations", "20320": "\u00e9coule", "20321": "chevauchent", "20322": "sobre", "20323": "5e", "20324": "inad\u00e9quation", "20325": "s\u00e9lectives", "20326": "travaillez", "20327": "orville", "20328": "bashir", "20329": "agiter", "20330": "imprim\u00e9e", "20331": "impuissant", "20332": "laborieuse", "20333": "terr\u00f3n", "20334": "faction", "20335": "camis\u00f3n", "20336": "newton", "20337": "accusons", "20338": "alstom", "20339": "clips", "20340": "saisonni\u00e8re", "20341": "d\u00e9parts", "20342": "d\u00e9biteur", "20343": "scrupule", "20344": "idf", "20345": "pata", "20346": "quadra", "20347": "resterai", "20348": "emp\u00each\u00e9e", "20349": "jos", "20350": "exp\u00e9ditions", "20351": "suscit\u00e9s", "20352": "insurge", "20353": "corse", "20354": "189", "20355": "pr\u00e9parera", "20356": "croie", "20357": "homog\u00e9n\u00e9it\u00e9", "20358": "fief", "20359": "illimit\u00e9es", "20360": "modestement", "20361": "informerai", "20362": "lab", "20363": "chapelle", "20364": "\u00e9crasant", "20365": "olives", "20366": "freine", "20367": "payeurs", "20368": "acc\u00e8de", "20369": "accusait", "20370": "albertain", "20371": "rhum", "20372": "min", "20373": "contr\u00f4lera", "20374": "graffitis", "20375": "fbi", "20376": "karadzic", "20377": "parapluie", "20378": "rendions", "20379": "yandex", "20380": "contract\u00e9es", "20381": "p\u00e9ch\u00e9s", "20382": "d\u00e9bouchant", "20383": "interd\u00e9pendants", "20384": "hermange", "20385": "1946", "20386": "d\u00e9pla\u00e7ant", "20387": "r\u00e9vis\u00e9s", "20388": "limitait", "20389": "situant", "20390": "m\u00e9cus", "20391": "col\u00e9gislateurs", "20392": "d\u00e9limiter", "20393": "\u0639\u0646", "20394": "entreprenons", "20395": "bottes", "20396": "mustafa", "20397": "r\u00e9pandus", "20398": "coutumier", "20399": "m\u00e9tallique", "20400": "interrompues", "20401": "environnementalistes", "20402": "censur\u00e9e", "20403": "praticable", "20404": "tandem", "20405": "b\u00e9n\u00e9ficiait", "20406": "r\u00e9agissons", "20407": "s\u00e9quence", "20408": "r\u00e9\u00e9lire", "20409": "viserait", "20410": "\u00e9leveur", "20411": "balkenende", "20412": "vogue", "20413": "\u00e9pingler", "20414": "zhang", "20415": "centime", "20416": "revalorisation", "20417": "proviens", "20418": "hills", "20419": "environnantes", "20420": "dispens\u00e9", "20421": "avertit", "20422": "\u00e9ditoriaux", "20423": "calcul\u00e9s", "20424": "acc\u00e8dent", "20425": "citizens", "20426": "sinistres", "20427": "agendas", "20428": "rousseff", "20429": "autod\u00e9fense", "20430": "beer", "20431": "initiateurs", "20432": "prosp\u00e8rent", "20433": "pesos", "20434": "accoutum\u00e9e", "20435": "abords", "20436": "brigades", "20437": "r\u00e9cipiendaires", "20438": "tremplin", "20439": "pareillement", "20440": "estoniens", "20441": "vilvorde", "20442": "howe", "20443": "donkin", "20444": "pourvus", "20445": "6e", "20446": "maxime", "20447": "romeva", "20448": "intercepter", "20449": "engageront", "20450": "surco\u00fbt", "20451": "assortir", "20452": "visuelle", "20453": "cellulaire", "20454": "affirmez", "20455": "\u00e9picerie", "20456": "l\u00e9gumineuses", "20457": "apurement", "20458": "psychiatriques", "20459": "catarrhale", "20460": "fermons", "20461": "philippine", "20462": "colis", "20463": "organisationnel", "20464": "prit", "20465": "versons", "20466": "mollahs", "20467": "8,5", "20468": "dessin\u00e9", "20469": "mobilis\u00e9e", "20470": "hospital", "20471": "partido", "20472": "d\u00e9cevants", "20473": "confection", "20474": "gasp\u00e9", "20475": "r\u00e9cup\u00e9r\u00e9s", "20476": "jobs", "20477": "r\u00e9publicain", "20478": "frictions", "20479": "s\u00e9quences", "20480": "apportions", "20481": "coordonnant", "20482": "viticoles", "20483": "desdits", "20484": "wei", "20485": "lamoureux", "20486": "\u00e9crivaine", "20487": "last", "20488": "cot\u00e9", "20489": "douvres", "20490": "effac\u00e9", "20491": "connect\u00e9s", "20492": "exp\u00e9rimentales", "20493": "regardait", "20494": "construisant", "20495": "prolongeant", "20496": "ouvri\u00e8res", "20497": "volkswagen", "20498": "222", "20499": "a\u00e9roportuaire", "20500": "\u00e9viction", "20501": "silos", "20502": "entrait", "20503": "krehl", "20504": "pembroke", "20505": "\u00e9cras\u00e9s", "20506": "refinancement", "20507": "pr\u00eateur", "20508": "sandra", "20509": "hydrologique", "20510": "jouiront", "20511": "banale", "20512": "garrido", "20513": "219", "20514": "diminuerait", "20515": "astuces", "20516": "couru", "20517": "sheila", "20518": "rapatri\u00e9s", "20519": "hom\u00e9opathiques", "20520": "colossales", "20521": "appellera", "20522": "\u00e9dulcorants", "20523": "ex\u00e9cutants", "20524": "golan", "20525": "l\u00e9s\u00e9es", "20526": "schaffner", "20527": "joindront", "20528": "dessinent", "20529": "manques", "20530": "cyclique", "20531": "montraient", "20532": "stoa", "20533": "joey", "20534": "hackers", "20535": "poitrine", "20536": "simplifiera", "20537": "greffe", "20538": "d\u00e9sirez", "20539": "ponctuels", "20540": "percer", "20541": "servie", "20542": "affaiblirait", "20543": "intemp\u00e9ries", "20544": "-et", "20545": "314", "20546": "commercialis\u00e9", "20547": "viticulture", "20548": "\u00e9thanol", "20549": "mel", "20550": "suppl\u00e9ments", "20551": "catalans", "20552": "v.", "20553": "figel", "20554": "yingluck", "20555": "suspectes", "20556": "jihad", "20557": "moeurs", "20558": "m\u00e9ridionales", "20559": "intact", "20560": "r\u00e9ciproquement", "20561": "kerr", "20562": "rapha\u00ebl", "20563": "auditeur", "20564": "efd", "20565": "avidit\u00e9", "20566": "pers\u00e9cuter", "20567": "l\u00e9nine", "20568": "p\u00e9cuniaire", "20569": "licenci\u00e9", "20570": "advocacy", "20571": "gaspill\u00e9s", "20572": "timing", "20573": "n\u00e9gligeons", "20574": "app\u00e2t", "20575": "visuels", "20576": "missionnaires", "20577": "informatisation", "20578": "conf\u00e9r\u00e9e", "20579": "minces", "20580": "transg\u00e9nique", "20581": "d\u00e9magogiques", "20582": "d\u00e9luge", "20583": "argumenter", "20584": "honor\u00e9s", "20585": "2\u00e8me", "20586": "indiff\u00e9rente", "20587": "d\u00e9missionne", "20588": "commen\u00e7aient", "20589": "adieux", "20590": "survenant", "20591": "relat\u00e9", "20592": "9,9", "20593": "moulin", "20594": "consolid\u00e9es", "20595": "privil\u00e9gient", "20596": "eeurope", "20597": "d\u00e9connect\u00e9s", "20598": "parmalat", "20599": "ramen\u00e9e", "20600": "sexisme", "20601": "remport\u00e9e", "20602": "blessant", "20603": "restigouche", "20604": "labour", "20605": "bongo", "20606": "troie", "20607": "fourtou", "20608": "etrang\u00e8res", "20609": "exempt\u00e9es", "20610": "litanie", "20611": "chaudes", "20612": "r\u00e9colt\u00e9es", "20613": "obstine", "20614": "bazar", "20615": "fondait", "20616": "nogueira", "20617": "d\u00e9chirer", "20618": "censeurs", "20619": "causerait", "20620": "indivisibilit\u00e9", "20621": "disproportionn\u00e9s", "20622": "incessante", "20623": "applaudissent", "20624": "malfaiteurs", "20625": "nappe", "20626": "substituts", "20627": "attir\u00e9e", "20628": "obscurantisme", "20629": "d\u00e9g\u00e9n\u00e9r\u00e9", "20630": "rituels", "20631": "ecall", "20632": "ambassadrice", "20633": "\u00e9crivez", "20634": "renforcerons", "20635": "insignifiantes", "20636": "grace", "20637": "captivit\u00e9", "20638": "\u2588", "20639": "musicaux", "20640": "regroup\u00e9es", "20641": "\u00e9pous\u00e9", "20642": "relevons", "20643": "sectaires", "20644": "tentions", "20645": "robot", "20646": "mo", "20647": "d\u00e9sirait", "20648": "perdions", "20649": "fb", "20650": "escompter", "20651": "laschet", "20652": "inexorablement", "20653": "raffineries", "20654": "insidieuse", "20655": "ca\u00f1ete", "20656": "subisse", "20657": "kfor", "20658": "homophobe", "20659": "v\u00e9hiculer", "20660": "bios\u00e9curit\u00e9", "20661": "r\u00e9servons", "20662": "fixera", "20663": "autorisons", "20664": "\u00e9pid\u00e9miologique", "20665": "soulignez", "20666": "hun", "20667": "gsm", "20668": "lisi", "20669": "ibn", "20670": "verrai", "20671": "ferr\u00e9e", "20672": "duvalier", "20673": "burundais", "20674": "fortunes", "20675": "abrog\u00e9e", "20676": "europa", "20677": "souffrira", "20678": "g\u00e1l", "20679": "d\u00e9guis\u00e9", "20680": "piquetage", "20681": "bafouer", "20682": "antis\u00e9mites", "20683": "marinho", "20684": "ecole", "20685": "polytechnique", "20686": "impr\u00e9cision", "20687": "mecque", "20688": "pay\u00e1", "20689": "r\u00e9gn\u00e9", "20690": "w.", "20691": "rouvert", "20692": "ciblage", "20693": "punie", "20694": "134", "20695": "\u00e9cout\u00e9e", "20696": "gagneront", "20697": "lienemann", "20698": "ap", "20699": "recrut\u00e9", "20700": "punissables", "20701": "clou", "20702": "pad", "20703": "ntv", "20704": "esquisser", "20705": "dominants", "20706": "quote", "20707": "utilisables", "20708": "voudriez", "20709": "interactif", "20710": "unissons", "20711": "mir", "20712": "fug\u00e8re", "20713": "d\u00e9militarisation", "20714": "soign\u00e9s", "20715": "\u00e9mergente", "20716": "chanteurs", "20717": "malsain", "20718": "souffler", "20719": "\u00e9l\u00e9gante", "20720": "batzeli", "20721": "-madame", "20722": "bersih", "20723": "esclave", "20724": "jugeront", "20725": "id\u00e9ales", "20726": "d\u00e9magogique", "20727": "birdal", "20728": "expuls\u00e9es", "20729": "patriarche", "20730": "pub", "20731": "ennui", "20732": "jouaient", "20733": "relevaient", "20734": "jet\u00e9es", "20735": "pr\u00e9dominant", "20736": "veilleront", "20737": "p\u00e2turages", "20738": "bpc", "20739": "sidi", "20740": "technology", "20741": "sommer", "20742": "qualificatif", "20743": "rembourse", "20744": "champignons", "20745": "d\u00e9partir", "20746": "wuori", "20747": "renon\u00e7ant", "20748": "centralisatrice", "20749": "statique", "20750": "admirablement", "20751": "noblesse", "20752": "sanchez", "20753": "colombiennes", "20754": "ol\u00e9agineux", "20755": "galeries", "20756": "soulag\u00e9", "20757": "clivage", "20758": "mikha\u00efl", "20759": "luc", "20760": "mobilis\u00e9es", "20761": "tumulte", "20762": "patronage", "20763": "1952", "20764": "hooligans", "20765": "agreement", "20766": "clarifi\u00e9s", "20767": "\u00e9prouv\u00e9s", "20768": "\u00e9go\u00efste", "20769": "attwooll", "20770": "jeans", "20771": "exp\u00e9rimenter", "20772": "d\u00e9finira", "20773": "long\u00e9vit\u00e9", "20774": "multidisciplinaire", "20775": "d\u00e9sir\u00e9s", "20776": "perdraient", "20777": "banda", "20778": "prospective", "20779": "avorter", "20780": "jospin", "20781": "vladivostok", "20782": "truqu\u00e9es", "20783": "excus\u00e9", "20784": "ros\u00e9", "20785": "prodigu\u00e9s", "20786": "radiation", "20787": "nuisance", "20788": "multipartite", "20789": "sollicitation", "20790": "administr\u00e9es", "20791": "abuja", "20792": "id\u00e9alisme", "20793": "al\u00e9atoires", "20794": "cin\u00e9matographiques", "20795": "\u00e9vacu\u00e9", "20796": "ach\u00e8vera", "20797": "intransigeante", "20798": "ivoirien", "20799": "emmanuel", "20800": "introduira", "20801": "moscovite", "20802": "176", "20803": "costume", "20804": "assurait", "20805": "4,1", "20806": "kamikazes", "20807": "talon", "20808": "d\u00e9roulait", "20809": "viola", "20810": "bizarres", "20811": "r\u00e9publicaine", "20812": "beslan", "20813": "projetde", "20814": "inspirera", "20815": "inimaginables", "20816": "essuyer", "20817": "gender", "20818": "imaginent", "20819": "frahm", "20820": "assum\u00e9es", "20821": "\u00e9quivoques", "20822": "peindre", "20823": "arroger", "20824": "respecterons", "20825": "urgemment", "20826": "bamako", "20827": "attrayants", "20828": "challenge", "20829": "koch", "20830": "d\u00e9monstrations", "20831": "sanglantes", "20832": "princesse", "20833": "health", "20834": "ral", "20835": "conservatisme", "20836": "influencera", "20837": "baccalaur\u00e9at", "20838": "aideraient", "20839": "aquatique", "20840": "attachant", "20841": "st\u00e9rilisation", "20842": "barker", "20843": "patriote", "20844": "comm\u00e9mor\u00e9", "20845": "albanaises", "20846": "shaughnessy", "20847": "nicholas", "20848": "pressantes", "20849": "lues", "20850": "d\u00e9nonc\u00e9es", "20851": "dater", "20852": "verticales", "20853": "aidions", "20854": "architectural", "20855": "ment", "20856": "nepean", "20857": "schleicher", "20858": "reproductif", "20859": "succomb\u00e9", "20860": "absorbe", "20861": "ol\u00e9oducs", "20862": "license", "20863": "xavier", "20864": "acc\u00e9l\u00e9rera", "20865": "beazley", "20866": "\u00e9poques", "20867": "charbonneau", "20868": "durci", "20869": "surpoids", "20870": "d\u00e9go\u00fbtant", "20871": "butte", "20872": "sucri\u00e8res", "20873": "transmette", "20874": "d\u00e9fait", "20875": "tra\u00eetre", "20876": "voleur", "20877": "d\u00e9pouillement", "20878": "astronomique", "20879": "\u00e9pingle", "20880": "ourdou", "20881": "d\u00e9gradants", "20882": "r\u00e9f\u00e9r\u00e9", "20883": "vaine", "20884": "suivions", "20885": "chalutiers", "20886": "tribal", "20887": "\u00e9liminera", "20888": "jamal", "20889": "joli", "20890": "occupez", "20891": "ost\u00e9oporose", "20892": "nicola\u00ef", "20893": "fosses", "20894": "mauritaniens", "20895": "255", "20896": "146", "20897": "ayatollah", "20898": "\u0151ry", "20899": "procur\u00e9", "20900": "tan", "20901": "impr\u00e9cis", "20902": "consentie", "20903": "destructeurs", "20904": "entrevues", "20905": "institutionnalisation", "20906": "att\u00e9nu\u00e9", "20907": "tentations", "20908": "pirate", "20909": "concoct\u00e9", "20910": "tueur", "20911": "poul", "20912": "transcende", "20913": "ed", "20914": "saisissons", "20915": "\u00e9puis\u00e9es", "20916": "jumelage", "20917": "irr\u00e9m\u00e9diablement", "20918": "r\u00e9fl\u00e9chissions", "20919": "administrent", "20920": "grossier", "20921": "dilemmes", "20922": "vietnamiennes", "20923": "mensuels", "20924": "si\u00e9geais", "20925": "entrerait", "20926": "poliment", "20927": "imb\u00e9ciles", "20928": "inflationnistes", "20929": "pom\u00e9s", "20930": "r\u00e9frig\u00e9rateurs", "20931": "massacrer", "20932": "d\u00e9cidez", "20933": "d\u00e9livr\u00e9e", "20934": "insigne", "20935": "r\u00e9v\u00e9latrice", "20936": "appartenons", "20937": "flash", "20938": "respectaient", "20939": "h\u00e9berg\u00e9", "20940": "introduisons", "20941": "moine", "20942": "lourdeurs", "20943": "d\u00e9peindre", "20944": "centr\u00e9s", "20945": "fermant", "20946": "\u00e7\u00e0", "20947": "mazout", "20948": "biais\u00e9e", "20949": "flots", "20950": "chroniqueur", "20951": "manufactur\u00e9s", "20952": "aigus", "20953": "delgamuukw", "20954": "sollicitent", "20955": "temples", "20956": "d\u00e9livre", "20957": "cultiv\u00e9e", "20958": "appellerai", "20959": "whips", "20960": "\u00e9clatent", "20961": "comm\u00e9morant", "20962": "r\u00e9silience", "20963": "lots", "20964": "truc", "20965": "aff\u00e9rente", "20966": "giovanni", "20967": "bulldozer", "20968": "montreront", "20969": "nuancer", "20970": "n\u00e9cessitait", "20971": "disque", "20972": "handicap\u00e9", "20973": "fruteau", "20974": "lspa", "20975": "entamerons", "20976": "mollusques", "20977": "r\u00e9pugnant", "20978": "v\u00e9rifi\u00e9e", "20979": "sapard", "20980": "ressenties", "20981": "l\u00e9gu\u00e9", "20982": "mccartney", "20983": "claudia", "20984": "sacr\u00e9deus", "20985": "d\u00e9ficient", "20986": "beno\u00eet", "20987": "pi\u00e8tres", "20988": "thunder", "20989": "associons", "20990": "modifions", "20991": "sonnerie", "20992": "attirons", "20993": "disparue", "20994": "clich\u00e9", "20995": "lentes", "20996": "ruban", "20997": "supprimerait", "20998": "r\u00e9pertoire", "20999": "tripartites", "21000": "oak", "21001": "manipulent", "21002": "d\u00e9sagr\u00e9ables", "21003": "will", "21004": "intimes", "21005": "camionnage", "21006": "mangeons", "21007": "g\u00e9nitale", "21008": "sauveteurs", "21009": "productrice", "21010": "nuise", "21011": "jinping", "21012": "dunn", "21013": "audy", "21014": "\u00e9valuerons", "21015": "nautiques", "21016": "gala", "21017": "c\u00e9r\u00e9ali\u00e8res", "21018": "r\u00e9duisait", "21019": "traversier", "21020": "d\u00e9ment", "21021": "compens\u00e9", "21022": "native", "21023": "adonner", "21024": "imput\u00e9s", "21025": "d\u00e9sir\u00e9e", "21026": "pozna\u0144", "21027": "brie", "21028": "rappellerez", "21029": "insurmontable", "21030": "crue", "21031": "soup\u00e7onner", "21032": "explosives", "21033": "coordonnateur", "21034": "blogueuses", "21035": "parcourt", "21036": "purs", "21037": "folha", "21038": "jure", "21039": "orphelin", "21040": "cohabiter", "21041": "179", "21042": "s\u00e9ismes", "21043": "irrit\u00e9", "21044": "hallam", "21045": "osons", "21046": "nogait", "21047": "l\u00e9g\u00e8ret\u00e9", "21048": "d\u00e9licatesse", "21049": "r\u00e9assurance", "21050": "mahomet", "21051": "sunnite", "21052": "progressives", "21053": "mensuelles", "21054": "trucs", "21055": "fertiles", "21056": "content\u00e9e", "21057": "d\u00e9clench\u00e9es", "21058": "superficiel", "21059": "vaguement", "21060": "3,4", "21061": "fidesz", "21062": "deal", "21063": "yfrog.com", "21064": "entreprenariat", "21065": "pride", "21066": "v\u00e9rifierons", "21067": "saanich", "21068": "patent", "21069": "8,3", "21070": "finalit\u00e9s", "21071": "ic\u00f4ne", "21072": "carnet", "21073": "infographie", "21074": "\u00e9talage", "21075": "rh\u00e9nanie", "21076": "diamant", "21077": "20.000", "21078": "sena", "21079": "view", "21080": "wiki", "21081": "aspirant", "21082": "fjellner", "21083": "brandit", "21084": "cadence", "21085": "pompidou", "21086": "affinit\u00e9s", "21087": "3.000", "21088": "prodiguer", "21089": "oeuf", "21090": "paternalisme", "21091": "colza", "21092": "d\u00e9buteront", "21093": "lenz", "21094": "offraient", "21095": "r\u00e9tablit", "21096": "conforte", "21097": "ex\u00e9cutent", "21098": "si\u00e9geront", "21099": "papastamkos", "21100": "gisement", "21101": "surr\u00e9glementation", "21102": "vou\u00e9es", "21103": "r\u00e9pondrons", "21104": "d\u00e9nonciations", "21105": "420", "21106": "irr\u00e9alisables", "21107": "rectifi\u00e9", "21108": "vitrine", "21109": "d\u00e9form\u00e9e", "21110": "incursions", "21111": "espion", "21112": "navigant", "21113": "augment\u00e9es", "21114": "cp", "21115": "fatalit\u00e9", "21116": "commercialement", "21117": "clegg", "21118": "monseigneur", "21119": "d\u00e9testable", "21120": "encha\u00eener", "21121": "taipei", "21122": "saut\u00e9", "21123": "futiles", "21124": "rennes", "21125": "augmentait", "21126": "fragiliser", "21127": "soucions", "21128": "adonnent", "21129": "combl\u00e9es", "21130": "milliardaire", "21131": "fuel", "21132": "equitable", "21133": "toubon", "21134": "ali\u00e9ner", "21135": "incontr\u00f4l\u00e9", "21136": "relay\u00e9", "21137": "sponsors", "21138": "savoirs", "21139": "a\u00e9ronef", "21140": "insatiable", "21141": "coude", "21142": "ray", "21143": "\u00e9pais", "21144": "figur\u00e9", "21145": "154", "21146": "compromettrait", "21147": "terrifiant", "21148": "refuserait", "21149": "jammu", "21150": "revolution", "21151": "entra\u00een\u00e9es", "21152": "appr\u00e9ci\u00e9es", "21153": "casseurs", "21154": "kadyrov", "21155": "ppp", "21156": "envahie", "21157": "d\u00e9gressivit\u00e9", "21158": "incorpore", "21159": "toledo", "21160": "vanhanen", "21161": "caract\u00e9ris\u00e9s", "21162": "saveur", "21163": "variantes", "21164": "ouvrait", "21165": "bagarre", "21166": "manifestantes", "21167": "abstenant", "21168": "stagne", "21169": "chargera", "21170": "cadavre", "21171": "syndicaliste", "21172": "c\u00e2blodistribution", "21173": "compromettant", "21174": "d\u00e9dommagements", "21175": "l\u00e2cher", "21176": "cousin", "21177": "m\u00e2les", "21178": "imprimerie", "21179": "hydraulique", "21180": "reconduire", "21181": "mitterrand", "21182": "treizi\u00e8me", "21183": "ampoules", "21184": "d\u00e9signent", "21185": "oswaldo", "21186": "visionner", "21187": "ouvertures", "21188": "surr\u00e9servation", "21189": "little", "21190": "mis\u00e9rable", "21191": "optent", "21192": "m\u00e9ridional", "21193": "keith", "21194": "int\u00e9gristes", "21195": "d\u00e9barrasse", "21196": "minerais", "21197": "chiffr\u00e9es", "21198": "moelle", "21199": "1942", "21200": "imagerie", "21201": "d\u00e9brid\u00e9e", "21202": "suivrai", "21203": "bhoutanais", "21204": "coalitions", "21205": "lucier", "21206": "sophistiqu\u00e9e", "21207": "beauport", "21208": "\u00e9couterai", "21209": "inattendus", "21210": "choisiront", "21211": "dentaire", "21212": "50.000", "21213": "baseball", "21214": "m\u00e9canicien", "21215": "luanda", "21216": "r\u00e9mun\u00e9r\u00e9es", "21217": "profilent", "21218": "insisterons", "21219": "\u00e9oliennes", "21220": "mesquin", "21221": "pompes", "21222": "tombant", "21223": "regrettant", "21224": "centralisateur", "21225": "r\u00e9sidentielles", "21226": "christine", "21227": "partageaient", "21228": "immersion", "21229": "quantifiables", "21230": "winston", "21231": "r\u00e9v", "21232": "r\u00e9sidait", "21233": "notifi\u00e9", "21234": "coordonnent", "21235": "inexcusable", "21236": "concluante", "21237": "h\u00e9sitants", "21238": "houill\u00e8re", "21239": "hell\u00e9nique", "21240": "inique", "21241": "fraises", "21242": "passerai", "21243": "d\u00e9collage", "21244": "verrions", "21245": "confondu", "21246": "admir\u00e9", "21247": "achev\u00e9s", "21248": "vinicole", "21249": "saisissant", "21250": "ail", "21251": "compl\u00e9t\u00e9es", "21252": "sourires", "21253": "outaouais", "21254": "prouvera", "21255": "kosovar", "21256": "distribuent", "21257": "shakespeare", "21258": "uganda", "21259": "apprises", "21260": "annul\u00e9es", "21261": "quecedo", "21262": "fujimori", "21263": "gliss\u00e9", "21264": "mafieux", "21265": "pri\u00e9s", "21266": "royales", "21267": "pickering", "21268": "ayotzinapa", "21269": "affreux", "21270": "meles", "21271": "3,7", "21272": "caribous", "21273": "timbres", "21274": "thomsen", "21275": "align\u00e9", "21276": "maintenions", "21277": "r\u00e9fugi\u00e9es", "21278": "cachant", "21279": "pollu\u00e9e", "21280": "crocodile", "21281": "orthodoxie", "21282": "coll\u00e9gial", "21283": "descendent", "21284": "reb\u00e2tir", "21285": "poulin", "21286": "quechua", "21287": "cr\u00eate", "21288": "malais", "21289": "arr\u00eaterai", "21290": "complimenter", "21291": "criants", "21292": "ufs", "21293": "226", "21294": "si\u00e9geaient", "21295": "baba", "21296": "1325", "21297": "compatissant", "21298": "mitig\u00e9s", "21299": "surveill\u00e9es", "21300": "pleurent", "21301": "h\u00e9bron", "21302": "caste", "21303": "inc.", "21304": "leonard", "21305": "remorque", "21306": "satisfera", "21307": "briques", "21308": "mandataire", "21309": "eriksson", "21310": "brandissant", "21311": "4,8", "21312": "zambien", "21313": "\u00e9migr\u00e9s", "21314": "strathcona", "21315": "singapouriens", "21316": "d\u00e9volu", "21317": "propag\u00e9e", "21318": "tirions", "21319": "rente", "21320": "contractante", "21321": "tournage", "21322": "wheat", "21323": "cubes", "21324": "partial", "21325": "tol\u00e9r\u00e9s", "21326": "alejandro", "21327": "brassard", "21328": "dub\u00e9", "21329": "terroriser", "21330": "contagieuses", "21331": "technocrates", "21332": "r\u00e9examine", "21333": "traumatismes", "21334": "regroupent", "21335": "experte", "21336": "holte", "21337": "susmentionn\u00e9e", "21338": "212", "21339": "aper\u00e7ois", "21340": "twitpic", "21341": "aboutirons", "21342": "v\u00e1clav", "21343": "subventionnent", "21344": "agit\u00e9e", "21345": "d\u00e9l\u00e9gu\u00e9e", "21346": "indexer", "21347": "chartes", "21348": "probants", "21349": "mobilisant", "21350": "mm", "21351": "pauline", "21352": "s\u00e9lectionn\u00e9es", "21353": "blogger", "21354": "porteraient", "21355": "magistrat", "21356": "la\u00efc", "21357": "kuala", "21358": "inchang\u00e9s", "21359": "endur\u00e9", "21360": "inadapt\u00e9s", "21361": "islamisme", "21362": "bang", "21363": "pr\u00e9sidait", "21364": "pencherons", "21365": "aurora", "21366": "lisais", "21367": "\u00e9mette", "21368": "netanyahou", "21369": "diagnostics", "21370": "adorent", "21371": "vitro", "21372": "libell\u00e9s", "21373": "amazonienne", "21374": "mire", "21375": "pannes", "21376": "pr\u00f4n\u00e9e", "21377": "deloitte", "21378": "recommence", "21379": "d\u00e9naturer", "21380": "illettrisme", "21381": "petersen", "21382": "tanks", "21383": "provocateurs", "21384": "ismail", "21385": "wathelet", "21386": "good", "21387": "famili\u00e8re", "21388": "puce", "21389": "dar", "21390": "casini", "21391": "val\u00e9ry", "21392": "m3", "21393": "soulevait", "21394": "hooliganisme", "21395": "raisonner", "21396": "reins", "21397": "uribe", "21398": "zaw", "21399": "log\u00e9s", "21400": "ralli\u00e9", "21401": "vivront", "21402": "in\u00e9gaux", "21403": "desseins", "21404": "instruite", "21405": "ma\u00eetrisent", "21406": "f\u00e9licit\u00e9s", "21407": "cgpm", "21408": "leitmotiv", "21409": "reconna\u00eetrait", "21410": "crash", "21411": "singer", "21412": "balistiques", "21413": "invalider", "21414": "oserait", "21415": "br\u00fbl\u00e9es", "21416": "mentionnez", "21417": "acquitt\u00e9s", "21418": "sacr\u00e9s", "21419": "indemne", "21420": "consultez", "21421": "sold\u00e9", "21422": "l.", "21423": "r\u00e9formant", "21424": "djihad", "21425": "jouissait", "21426": "hano\u00ef", "21427": "touchaient", "21428": "commer\u00e7ant", "21429": "pq", "21430": "insignifiants", "21431": "charm", "21432": "alto", "21433": "ais\u00e9es", "21434": "sold\u00e9e", "21435": "honorent", "21436": "d\u00e9coller", "21437": "rectificatifs", "21438": "\u00e9vacu\u00e9es", "21439": "circonscrire", "21440": "radicalisme", "21441": "bhopal", "21442": "r\u00e9gressive", "21443": "troublantes", "21444": "abritent", "21445": "frappante", "21446": "m\u00e8nerons", "21447": "sopinka", "21448": "exorbitant", "21449": "nuls", "21450": "saad", "21451": "questionnaires", "21452": "c\u00e9r\u00e9aliculteurs", "21453": "lointaines", "21454": "industrialis\u00e9es", "21455": "occasionnent", "21456": "r\u00e9unissons", "21457": "finnois", "21458": "abolies", "21459": "tropicaux", "21460": "utiliserait", "21461": "261", "21462": "informatis\u00e9", "21463": "d\u00e9chu", "21464": "d\u00e9s", "21465": "parentaux", "21466": "lambsdorff", "21467": "rice", "21468": "marks", "21469": "amoureuse", "21470": "d\u00e9sol\u00e9s", "21471": "opprim\u00e9es", "21472": "atlantic", "21473": "mal\u00e9diction", "21474": "jo\u00e3o", "21475": "rap", "21476": "migrer", "21477": "enseignent", "21478": "d\u00e9tect\u00e9", "21479": "quisthoudt", "21480": "mazen", "21481": "d\u00e9volution", "21482": "r\u00e9mun\u00e9rer", "21483": "serviraient", "21484": "manipul\u00e9", "21485": "impressionnantes", "21486": "surmont\u00e9s", "21487": "mutag\u00e8nes", "21488": "agit\u00e9", "21489": "r\u00e9prim\u00e9", "21490": "\u00e9litiste", "21491": "oedt", "21492": "1954", "21493": "950", "21494": "d\u00e9verser", "21495": "cadets", "21496": "ternir", "21497": "endur\u00e9es", "21498": "lib\u00e8rent", "21499": "parvenez", "21500": "bl\u00e2m\u00e9", "21501": "sentaient", "21502": "inopportune", "21503": "bronze", "21504": "podest\u00e0", "21505": "placera", "21506": "m\u00e9dico", "21507": "matrimoniale", "21508": "lave", "21509": "formulerai", "21510": "songent", "21511": "d\u00e9sillusion", "21512": "rosado", "21513": "10a", "21514": "committee", "21515": "realpolitik", "21516": "soulevez", "21517": "ant\u00f3nio", "21518": "invraisemblable", "21519": "partiels", "21520": "quito", "21521": "queir\u00f3", "21522": "matthew", "21523": "discr\u00e8tement", "21524": "dehaene", "21525": "toucherait", "21526": "aveux", "21527": "maladroite", "21528": "acceptaient", "21529": "accoucher", "21530": "interpellations", "21531": "frayer", "21532": "diversifi\u00e9s", "21533": "consommatrices", "21534": "apportez", "21535": "diego", "21536": "contraction", "21537": "r\u00e9soudront", "21538": "r\u00e9p\u00e9tera", "21539": "g\u00e9orgiennes", "21540": "ressemblera", "21541": "d\u00e9ficitaires", "21542": "m\u00e9decines", "21543": "asym\u00e9trie", "21544": "novo", "21545": "irradiation", "21546": "ascenseurs", "21547": "capita", "21548": "malsaine", "21549": "exag\u00e9r\u00e9s", "21550": "ainardi", "21551": "rattach\u00e9", "21552": "marionnettes", "21553": "s\u00e9journent", "21554": "r\u00e9injecter", "21555": "heurtons", "21556": "lello", "21557": "hindoue", "21558": "entamera", "21559": "appr\u00e9ciables", "21560": "pr\u00e9cis\u00e9s", "21561": "empar\u00e9s", "21562": "frein\u00e9", "21563": "surcharg\u00e9s", "21564": "r\u00e9f\u00e9rendaires", "21565": "marty", "21566": "clarifiant", "21567": "annexer", "21568": "f\u00eatons", "21569": "\u00e9cartant", "21570": "abstiendrons", "21571": "miliciens", "21572": "recommand\u00e9s", "21573": "supposerait", "21574": "souffl\u00e9", "21575": "anchois", "21576": "antiviraux", "21577": "prostituer", "21578": "minuk", "21579": "continueraient", "21580": "notoirement", "21581": "institue", "21582": "d\u00e9courag\u00e9s", "21583": "r\u00e9", "21584": "burqa", "21585": "basics", "21586": "ricardo", "21587": "anges", "21588": "h\u00e2ter", "21589": "inestimables", "21590": "5a", "21591": "protestantes", "21592": "ni\u00f1o", "21593": "recule", "21594": "pr\u00e9occup\u00e9es", "21595": "conservons", "21596": "tha\u00eflandaise", "21597": "ste", "21598": "funeste", "21599": "pr\u00e9valant", "21600": "kagame", "21601": "constatez", "21602": "6,3", "21603": "serge", "21604": "portage", "21605": "renflouer", "21606": "\u00e9laborera", "21607": "interethniques", "21608": "baguette", "21609": "appr\u00e9hend\u00e9", "21610": "citait", "21611": "traiterait", "21612": "publions", "21613": "lipietz", "21614": "fouler", "21615": "k.", "21616": "creutzfeldt", "21617": "comparaissent", "21618": "276", "21619": "fm", "21620": "soumettrai", "21621": "andrea", "21622": "lumpur", "21623": "d\u00e9voile", "21624": "document\u00e9e", "21625": "milligrammes", "21626": "convois", "21627": "spirituels", "21628": "dragon", "21629": "pond\u00e9rale", "21630": "horrifi\u00e9", "21631": "affluer", "21632": "abriter", "21633": "f\u00e9d\u00e9r\u00e9s", "21634": "rae", "21635": "seringues", "21636": "g\u00eanent", "21637": "inattendues", "21638": "schlyter", "21639": "d\u00e9sinvolture", "21640": "unitaires", "21641": "d\u00e9cliner", "21642": "incessant", "21643": "affluent", "21644": "transbordeurs", "21645": "\u00e9tay\u00e9", "21646": "bijoux", "21647": "rapport\u00e9e", "21648": "barres", "21649": "r\u00e9currentes", "21650": "r\u00e9volt\u00e9s", "21651": "instinct", "21652": "d\u00e9ch\u00e9ance", "21653": "paneurop\u00e9ens", "21654": "brin", "21655": "d\u00e9battant", "21656": "accentuation", "21657": "fractures", "21658": "descendu", "21659": "parking", "21660": "comm\u00e9moratif", "21661": "pr\u00e9l\u00e8ve", "21662": "serein", "21663": "remplira", "21664": "\u00e9troitesse", "21665": "penseurs", "21666": "mutharika", "21667": "aimablement", "21668": "continentaux", "21669": "test\u00e9es", "21670": "\u00e9tay\u00e9e", "21671": "rahman", "21672": "theorin", "21673": "maternelles", "21674": "duhaime", "21675": "conserves", "21676": "logos", "21677": "interf\u00e9rences", "21678": "eie", "21679": "surveillera", "21680": "proclam\u00e9s", "21681": "d\u00e9vou\u00e9e", "21682": "tenteront", "21683": "brothers", "21684": "holdings", "21685": "h\u00e9t\u00e9rog\u00e8ne", "21686": "khadraoui", "21687": "confusions", "21688": "hauss\u00e9", "21689": "r\u00e9pliqu\u00e9", "21690": "f.", "21691": "soir\u00e9es", "21692": "tasse", "21693": "r\u00e9agira", "21694": "d\u00e9su\u00e8te", "21695": "accouch\u00e9", "21696": "d\u00e9form\u00e9", "21697": "f\u00e9licit\u00e9e", "21698": "cons\u00e9cutif", "21699": "calder\u00f3n", "21700": "auxiliaire", "21701": "albinos", "21702": "falls", "21703": "indemnis\u00e9", "21704": "ohrid", "21705": "voiler", "21706": "souviendrons", "21707": "reconnaissaient", "21708": "balises", "21709": "finissons", "21710": "bst", "21711": "lavage", "21712": "racontant", "21713": "abolit", "21714": "serr\u00e9e", "21715": "solennelles", "21716": "risquerions", "21717": "retrouvions", "21718": "immunitaire", "21719": "campement", "21720": "incarnent", "21721": "gratte", "21722": "p\u00e9diatrique", "21723": "cueillette", "21724": "d\u00e9roulaient", "21725": "voyions", "21726": "d\u00e9non\u00e7ait", "21727": "reagan", "21728": "min\u00e9rale", "21729": "centr\u00e9es", "21730": "d\u00e9mons", "21731": "h\u00e2tives", "21732": "nipissing", "21733": "schiste", "21734": "peshawar", "21735": "proactifs", "21736": "contrarier", "21737": "ajoutera", "21738": "effondr\u00e9e", "21739": "guid\u00e9es", "21740": "obtiendrait", "21741": "r\u00e9jouissante", "21742": "isabel", "21743": "articul\u00e9e", "21744": "c\u00f4tier", "21745": "cons\u00e9cration", "21746": "r\u00e9f", "21747": "phosphates", "21748": "arithm\u00e9tique", "21749": "akin", "21750": "refus\u00e9es", "21751": "barents", "21752": "banni\u00e8res", "21753": "tyran", "21754": "cofinanc\u00e9s", "21755": "traitions", "21756": "nanties", "21757": "camara", "21758": "enrichissant", "21759": "absorb\u00e9s", "21760": "endosse", "21761": "marquait", "21762": "1936", "21763": "chantent", "21764": "d\u00e9mis", "21765": "interconnect\u00e9s", "21766": "rampe", "21767": "coexistent", "21768": "stenzel", "21769": "pers\u00e9cut\u00e9", "21770": "kimberley", "21771": "recommenc\u00e9", "21772": "d\u00e9vast\u00e9e", "21773": "cowichan", "21774": "mesquine", "21775": "fines", "21776": "cin\u00e9mas", "21777": "kigali", "21778": "avoisinantes", "21779": "refl\u00e9t\u00e9e", "21780": "d\u00e9posent", "21781": "tergiversations", "21782": "h\u00e9sitons", "21783": "sentez", "21784": "d\u00e9put\u00e9es", "21785": "incr\u00e9dulit\u00e9", "21786": "dispens\u00e9e", "21787": "mcclung", "21788": "tailler", "21789": "tr\u00fcpel", "21790": "affaiblis", "21791": "uvr\u00e9", "21792": "2,9", "21793": "perp\u00e9tuel", "21794": "djihadistes", "21795": "h\u00e2tive", "21796": "lettone", "21797": "ind\u00e9pendantistes", "21798": "inaper\u00e7ue", "21799": "sacr\u00e9es", "21800": "film\u00e9e", "21801": "r\u00e9trospective", "21802": "r\u00e9affecter", "21803": "gendarme", "21804": "amoindrir", "21805": "linda", "21806": "strauss", "21807": "foetal", "21808": "devine", "21809": "interdirait", "21810": "rationnellement", "21811": "basons", "21812": "296", "21813": "vimy", "21814": "r\u00e9fl\u00e9chis", "21815": "enterr\u00e9e", "21816": "\u00e9pices", "21817": "abominables", "21818": "d\u00e9b\u00e2cle", "21819": "op\u00e9ra", "21820": "emma", "21821": "207", "21822": "papadimoulis", "21823": "mesurent", "21824": "principaut\u00e9", "21825": "refrain", "21826": "nana", "21827": "prim\u00e9", "21828": "bafou\u00e9e", "21829": "psychiatrique", "21830": "pannella", "21831": "crois\u00e9es", "21832": "tsvangirai", "21833": "canton", "21834": "groundviews", "21835": "enti\u00e8ret\u00e9", "21836": "m\u00e9ritant", "21837": "gen\u00e8se", "21838": "tgv", "21839": "c\u00e9r\u00e9ali\u00e8re", "21840": "superficielles", "21841": "paolo", "21842": "r\u00e9primander", "21843": "servaient", "21844": "fouilles", "21845": "d\u00e9couragent", "21846": "m\u00e9dicinales", "21847": "castillo", "21848": "vera", "21849": "saurai", "21850": "menions", "21851": "targuer", "21852": "mentalement", "21853": "r\u00e9fection", "21854": "embarrass\u00e9", "21855": "\u00e9largissent", "21856": "avenues", "21857": "rencontrant", "21858": "inond\u00e9e", "21859": "salomon", "21860": "int\u00e9grera", "21861": "bilbao", "21862": "teverson", "21863": "\u00e9touff\u00e9", "21864": "corrompue", "21865": "qualitatives", "21866": "gouvern\u00e9s", "21867": "d\u00e9port\u00e9s", "21868": "surcharger", "21869": "remettait", "21870": "librairies", "21871": "wal", "21872": "chercheuse", "21873": "l\u00e2ches", "21874": "barry", "21875": "ressusciter", "21876": "d\u00e9fi\u00e9", "21877": "racial", "21878": "238", "21879": "pink", "21880": "embauch\u00e9", "21881": "posei", "21882": "agress\u00e9s", "21883": "1200", "21884": "forfaitaires", "21885": "\u00e9ponger", "21886": "barbe", "21887": "encadr\u00e9e", "21888": "monstrueux", "21889": "\u00e9talon", "21890": "timidit\u00e9", "21891": "pressent", "21892": "montrerait", "21893": "susmentionn\u00e9", "21894": "saturation", "21895": "enjoindre", "21896": "venions", "21897": "boucl\u00e9", "21898": "rachats", "21899": "focalise", "21900": "envisagerait", "21901": "hypoth\u00e9tiques", "21902": "capoulas", "21903": "exclusions", "21904": "diaboliser", "21905": "sanguine", "21906": "blue", "21907": "assujettissement", "21908": "leurre", "21909": "vmro", "21910": "\u00e9pid\u00e9miologiques", "21911": "assum\u00e9s", "21912": "cellulaires", "21913": "adopterait", "21914": "b\u00e2illonner", "21915": "gillard", "21916": "donniez", "21917": "\u00b2", "21918": "starlight", "21919": "calcul\u00e9es", "21920": "collectes", "21921": "insens\u00e9es", "21922": "immobili\u00e8res", "21923": "consolidant", "21924": "passionnante", "21925": "assurerons", "21926": "comm\u00e9morations", "21927": "incomp\u00e9tent", "21928": "glaciers", "21929": "remettrait", "21930": "opprimer", "21931": "pique", "21932": "carnero", "21933": "enferm\u00e9", "21934": "laila", "21935": "patriotiques", "21936": "sino", "21937": "serpent", "21938": "2.000", "21939": "erie", "21940": "d\u00e9vier", "21941": "fume", "21942": "empress\u00e9s", "21943": "modus", "21944": "crisis", "21945": "remorques", "21946": "caprins", "21947": "state", "21948": "d\u00e9sirant", "21949": "colonnes", "21950": "\u00e9difiant", "21951": "rel\u00e8verait", "21952": "autocensure", "21953": "touriste", "21954": "clandestines", "21955": "rat\u00e9e", "21956": "eacl", "21957": "r\u00e9soudrons", "21958": "venger", "21959": "d\u00e9battra", "21960": "\u00e9gout", "21961": "nancy", "21962": "emp\u00eachait", "21963": "wibe", "21964": "convertis", "21965": "verbales", "21966": "accepterai", "21967": "tamoul", "21968": "moussavi", "21969": "fai", "21970": "foster", "21971": "inqui\u00e8tes", "21972": "achetons", "21973": "d\u00e9cel\u00e9", "21974": "biotechnologique", "21975": "fossile", "21976": "parte", "21977": "pr\u00e9sentatrice", "21978": "gao", "21979": "surnum\u00e9raires", "21980": "mexicaines", "21981": "zonage", "21982": "parcimonie", "21983": "supranationaux", "21984": "bouwman", "21985": "excluons", "21986": "cork", "21987": "attendrait", "21988": "hogg", "21989": "conserv\u00e9e", "21990": "hydrographique", "21991": "\u00e9tudiait", "21992": "modi", "21993": "tra\u00eetres", "21994": "canc\u00e9rig\u00e8ne", "21995": "arsenic", "21996": "renforcions", "21997": "andrews", "21998": "modeler", "21999": "solutionner", "22000": "retireront", "22001": "d\u00e9bordements", "22002": "partagions", "22003": "embrouiller", "22004": "grises", "22005": "souhaitiez", "22006": "s\u00e9duire", "22007": "wu", "22008": "\u00e9ditrice", "22009": "inciterait", "22010": "affecterait", "22011": "uni\u00e8me", "22012": "delgado", "22013": "ou\u00efghours", "22014": "baradei", "22015": "dictent", "22016": "m\u00e9trages", "22017": "your", "22018": "d\u00e9sint\u00e9r\u00eat", "22019": "concentrions", "22020": "emmen\u00e9e", "22021": "bennett", "22022": "empoisonn\u00e9", "22023": "dean", "22024": "herbert", "22025": "\u00e9veille", "22026": "pudeur", "22027": "tordu", "22028": "ryanair", "22029": "extracommunautaires", "22030": "reconvertir", "22031": "achet\u00e9es", "22032": "ballons", "22033": "clavier", "22034": "fioul", "22035": "shopping", "22036": "junker", "22037": "timidement", "22038": "incursion", "22039": "instantan\u00e9e", "22040": "commencez", "22041": "fauteuils", "22042": "optons", "22043": "9e", "22044": "disputent", "22045": "artisan", "22046": "perturbe", "22047": "encombrement", "22048": "thoniers", "22049": "assi\u00e9g\u00e9e", "22050": "cocktails", "22051": "plan\u00e9taires", "22052": "carlotti", "22053": "extraterritoriale", "22054": "annon\u00e7ons", "22055": "algues", "22056": "mou", "22057": "libyennes", "22058": "yoani", "22059": "gaspillages", "22060": "xviie", "22061": "accr\u00e9diter", "22062": "gastronomie", "22063": "structur\u00e9s", "22064": "denktash", "22065": "obsc\u00e8ne", "22066": "appliquaient", "22067": "water", "22068": "sociologie", "22069": "samir", "22070": "s\u00e9natle", "22071": "multinationaux", "22072": "attributs", "22073": "rupert", "22074": "enterr\u00e9s", "22075": "abordions", "22076": "attrist\u00e9s", "22077": "h\u00e9sitant", "22078": "centim\u00e8tres", "22079": "mat\u00e9riellement", "22080": "bildt", "22081": "\u200e", "22082": "p\u00f6rtschach", "22083": "notaires", "22084": "d\u00e9cor\u00e9", "22085": "referendum", "22086": "anonymous", "22087": "brun", "22088": "d\u00e9fendrai", "22089": "nil", "22090": "prescrites", "22091": "adoucir", "22092": "imputation", "22093": "entach\u00e9e", "22094": "embrasse", "22095": "rejoindra", "22096": "communiquerai", "22097": "comparez", "22098": "pentagone", "22099": "dc", "22100": "rattachent", "22101": "avil\u00e9s", "22102": "guerrero", "22103": "motos", "22104": "salutations", "22105": "apprendra", "22106": "said", "22107": "rev\u00eatait", "22108": "karimov", "22109": "r\u00e9ussies", "22110": "roosevelt", "22111": "norway", "22112": "stup\u00e9faction", "22113": "scaf", "22114": "d\u00e9faveur", "22115": "s\u00e9mantique", "22116": "rainsy", "22117": "\u00e9tau", "22118": "followers", "22119": "abattues", "22120": "atlas", "22121": "sortante", "22122": "abr\u00e9g\u00e9", "22123": "alcooliques", "22124": "enl\u00e8vent", "22125": "commenc\u00e9e", "22126": "cavada", "22127": "posais", "22128": "vieira", "22129": "affectations", "22130": "bastion", "22131": "brexit", "22132": "chaleureuse", "22133": "joyeuse", "22134": "our", "22135": "simplifi\u00e9s", "22136": "certifi\u00e9s", "22137": "dur\u00e3o", "22138": "arch\u00e9ologiques", "22139": "hossein", "22140": "nuanc\u00e9", "22141": "euromaidan", "22142": "titanic", "22143": "abraham", "22144": "lucie", "22145": "escrocs", "22146": "insidieux", "22147": "recueillie", "22148": "cessait", "22149": "passants", "22150": "csu", "22151": "injections", "22152": "advenir", "22153": "reli\u00e9e", "22154": "enrichie", "22155": "banni", "22156": "dinosaures", "22157": "madawaska", "22158": "d\u00e9flation", "22159": "rejoigne", "22160": "arabo", "22161": "assaillants", "22162": "harmonise", "22163": "d\u00e9centraliser", "22164": "awards", "22165": "ramen\u00e9s", "22166": "maillons", "22167": "magnifiquement", "22168": "meijer", "22169": "1941", "22170": "clefs", "22171": "boursi\u00e8res", "22172": "0,6", "22173": "miroiter", "22174": "impr\u00e9cisions", "22175": "untold", "22176": "remani\u00e9", "22177": "159", "22178": "cr\u00e8ches", "22179": "locomotives", "22180": "consacrions", "22181": "cr\u00e9ancier", "22182": "outrepasser", "22183": "pacifiste", "22184": "ivanov", "22185": "cumberland", "22186": "cot", "22187": "d\u00e9coration", "22188": "gentils", "22189": "paroxysme", "22190": "gore", "22191": "demeur\u00e9", "22192": "heinz", "22193": "chercheront", "22194": "dialecte", "22195": "lex", "22196": "price", "22197": "az\u00e9ris", "22198": "malveillants", "22199": "ongles", "22200": "aborderont", "22201": "rel\u00e8vera", "22202": "risquaient", "22203": "scandalis\u00e9s", "22204": "musotto", "22205": "paraphraser", "22206": "fardeaux", "22207": "hazara", "22208": "growers", "22209": "extrad\u00e9", "22210": "nacional", "22211": "bretelles", "22212": "177", "22213": "barricades", "22214": "graisse", "22215": "an\u00e9antissement", "22216": "aventurer", "22217": "th\u00e9\u00e2tres", "22218": "payette", "22219": "embauch\u00e9s", "22220": "g\u00e9rard", "22221": "alfred", "22222": "p\u00e2tissent", "22223": "travers\u00e9es", "22224": "analysera", "22225": "r\u00e9pliques", "22226": "sentais", "22227": "s\u00e9ropositifs", "22228": "\u00e9chantillonnage", "22229": "zelaya", "22230": "proclamations", "22231": "rebours", "22232": "sahtu", "22233": "passives", "22234": "d\u00e9saveu", "22235": "country", "22236": "savants", "22237": "tabacs", "22238": "rodrigo", "22239": "schizophr\u00e9nie", "22240": "harare", "22241": "partageront", "22242": "tron\u00e7ons", "22243": "sichuan", "22244": "athabasca", "22245": "imminentes", "22246": "auguste", "22247": "abjecte", "22248": "impropre", "22249": "dig\u00e9rer", "22250": "nues", "22251": "chauvinisme", "22252": "essayions", "22253": "coh\u00e9sive", "22254": "filtr\u00e9", "22255": "exacerbe", "22256": "boulevers\u00e9s", "22257": "afp", "22258": "indivisibles", "22259": "sacrifi\u00e9e", "22260": "butin", "22261": "apparaissant", "22262": "restaur\u00e9e", "22263": "vacciner", "22264": 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"\u00e9tiquet\u00e9", "23267": "pr\u00e9cit\u00e9", "23268": "209", "23269": "dialyse", "23270": "la\u00efques", "23271": "l\u00e9gendes", "23272": "r\u00e9glerait", "23273": "h\u00e9sitante", "23274": "graduel", "23275": "ange", "23276": "doutez", "23277": "jr", "23278": "sismiques", "23279": "renationaliser", "23280": "d\u00e9pister", "23281": "scell\u00e9", "23282": "pr\u00e9voira", "23283": "barbel\u00e9s", "23284": "institutionnalis\u00e9", "23285": "commencerais", "23286": "\u00e9liminent", "23287": "humidit\u00e9", "23288": "frise", "23289": "proportionn\u00e9", "23290": "exproprier", "23291": "enlevant", "23292": "h\u00e9ro\u00efques", "23293": "compton", "23294": "retardent", "23295": "229", "23296": "173", "23297": "cl\u00f4tures", "23298": "soixantaine", "23299": "amers", "23300": "dawn", "23301": "recouvrements", "23302": "four", "23303": "ng", "23304": "provocateur", "23305": "changeantes", "23306": "empare", "23307": "nawaat", "23308": "implant\u00e9", "23309": "structuration", "23310": "ardues", "23311": "cipc", "23312": "wagon", "23313": "dewdney", "23314": "comparent", "23315": "medical", "23316": "r\u00e9sistants", "23317": "cern\u00e9", "23318": "poignard\u00e9", "23319": "survienne", "23320": "pleut", "23321": "perdait", "23322": "accompagnait", "23323": "mensong\u00e8res", "23324": "adamou", "23325": "copr\u00e9sidents", "23326": "pr\u00e9cit\u00e9e", "23327": "chalifoux", "23328": "amalgames", "23329": "enracin\u00e9", "23330": "valverde", "23331": "encourt", "23332": "lmr", "23333": "prudentiel", "23334": "nace", "23335": "1938", "23336": "libicki", "23337": "continuerez", "23338": "interd\u00e9pendantes", "23339": "illimit\u00e9s", "23340": "polygamie", "23341": "\u00e9cueil", "23342": "attester", "23343": "pr\u00e9ceptes", "23344": "mouche", "23345": "entrep\u00f4t", "23346": "r\u00e9sistantes", "23347": "angel", "23348": "d\u00e9sindexation", "23349": "succinct", "23350": "sonder", "23351": "pr\u00e9dominante", "23352": "coulisse", "23353": "informateurs", "23354": "m\u00fbrs", "23355": "usent", "23356": "r\u00e9novations", "23357": "couvrirait", "23358": "r\u00e9tablis", "23359": "compl\u00e9t\u00e9s", "23360": "grossistes", "23361": "pr\u00e9dis", "23362": "champlain", "23363": "l\u00e9tourneau", "23364": "distinctif", "23365": "coinc\u00e9", "23366": "acceptabilit\u00e9", "23367": "nocive", "23368": "corbeil", "23369": "golden", "23370": "federal", "23371": "andor", "23372": ".....", "23373": "confortables", "23374": "joindra", "23375": "d\u00e9duis", "23376": "barsi", "23377": "pataky", "23378": "ltte", "23379": "modalit\u00e9", "23380": "ljubljana", "23381": "s\u00f3crates", "23382": "ressortira", "23383": "edimbourg", "23384": "zapata", "23385": "assiettes", "23386": "ripost\u00e9", "23387": "am", "23388": "d\u00e9primant", "23389": "uxbridge", "23390": "d\u00e9bordent", "23391": "placerait", "23392": "\u00e9motionnels", "23393": "\u00e9paisse", "23394": "djakarta", "23395": "mensong\u00e8re", "23396": "pharmacies", "23397": "supprimons", "23398": "spiritueuses", "23399": "approuvait", "23400": "achemin\u00e9", "23401": "motivant", "23402": "nuremberg", "23403": "agneau", "23404": "j\u00e1r\u00f3ka", "23405": "bfc", "23406": "9uvre", "23407": "prudentielle", "23408": "factuelle", "23409": "bichkek", "23410": "ram", "23411": "dissimul\u00e9e", "23412": "d\u00e9faillante", "23413": "conjectures", "23414": "christophe", "23415": "catalyseurs", "23416": "capitaines", "23417": "altiero", "23418": "piqu\u00e9", "23419": "agitent", "23420": "d\u00e9clin\u00e9", "23421": "aele", "23422": "andrei", "23423": "jasmin", "23424": "whitehorse", "23425": "\u00e9tincelle", "23426": "traduiront", "23427": "r\u00e9solve", "23428": "angolaise", "23429": "r\u00e9clam\u00e9es", "23430": "r\u00e9flexes", "23431": "\u00e9non\u00e7ant", "23432": "rajustements", "23433": "t\u00e9n\u00e8bres", "23434": "cofinanc\u00e9", "23435": "rapportera", "23436": "desservent", "23437": "proposions", "23438": "assimil\u00e9s", "23439": "navales", "23440": "580", "23441": "euroland", "23442": "florissantes", "23443": "incontr\u00f4lables", "23444": "punitive", "23445": "hcfc", "23446": "sachons", "23447": "cessions", "23448": "almaty", "23449": "dignitaires", "23450": "progressera", "23451": "houthis", "23452": "r\u00e9ceptif", "23453": "agri", "23454": "egyptien", "23455": "agricultrices", "23456": "ilya", "23457": "pill\u00e9", "23458": "frauduleusement", "23459": "r\u00e9voqu\u00e9", "23460": "combl\u00e9s", "23461": "shan", "23462": "usant", "23463": "trompez", "23464": "anton", "23465": "alleanza", "23466": "nazionale", "23467": "d\u00e9cida", "23468": "compteurs", "23469": "politiser", "23470": "perspicace", "23471": "\u00e2ne", "23472": "accr\u00e9dit\u00e9s", "23473": "sorciers", "23474": "coupant", "23475": "straw", "23476": "r\u00e9digent", "23477": "marxiste", "23478": "discuteront", "23479": "vocale", "23480": "p\u00eacherie", "23481": "partages", "23482": "marx", "23483": 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"vignoble", "26092": "h\u00e9r\u00e9ditaire", "26093": "trappe", "26094": "v\u00e9n\u00e9rable", "26095": "coulent", "26096": "pathologie", "26097": "esth\u00e9tiques", "26098": "way", "26099": "colonialiste", "26100": "nieto", "26101": "canberra", "26102": "concerneront", "26103": "sortez", "26104": "superviseur", "26105": "stern", "26106": "biodiesel", "26107": "inaper\u00e7us", "26108": "338", "26109": "montfort", "26110": "bousculer", "26111": "tambours", "26112": "d\u00e9stabilise", "26113": "jacobs", "26114": "inexorable", "26115": "boulevers\u00e9e", "26116": "raccordement", "26117": "george'", "26118": "invasions", "26119": "trouverions", "26120": "immigr\u00e9e", "26121": "d\u00e9rang\u00e9", "26122": "argumente", "26123": "servitude", "26124": "subs\u00e9quents", "26125": "attise", "26126": "conjure", "26127": "distraction", "26128": "chaudement", "26129": "ravage", "26130": "diagnostiqu\u00e9s", "26131": "lancerons", "26132": "s\u00e9duisante", "26133": "233", "26134": "prudentielles", "26135": "boukary", "26136": "poivr\u00e9", "26137": "pondeuses", "26138": "diminu\u00e9e", "26139": "\u00e9touffent", "26140": "mettrions", "26141": "d\u00e9rog\u00e9", "26142": "rase", "26143": "karlsson", "26144": "rocher", "26145": "avoine", "26146": "\u00e9tale", "26147": "standardis\u00e9", "26148": "requerra", "26149": "brien", "26150": "hassi", "26151": "con", "26152": "focalisent", "26153": "mufti", "26154": "ind\u00e9cis", "26155": "plaidoyers", "26156": "approuverait", "26157": "percepteur", "26158": "navigateur", "26159": "d\u00e9missionnaire", "26160": "cortines", "26161": "ferretti", "26162": "government", "26163": "tenace", "26164": "myst\u00e9rieusement", "26165": "turner", "26166": "ardents", "26167": "\u00e9talonnage", "26168": "cdc", "26169": "affichons", "26170": "joigne", "26171": "prom\u00e8nent", "26172": "mentor", "26173": "akbar", "26174": "produisaient", "26175": "graenitz", "26176": "fernandez", "26177": "men\u00e9ndez", "26178": "p\u00e8che", "26179": "banani\u00e8re", "26180": "explosent", "26181": "scandinave", "26182": "confin\u00e9e", "26183": "intelligible", "26184": "outr\u00e9s", "26185": "conduiraient", "26186": "jaime", "26187": "levez", "26188": "cumulative", "26189": "mythique", "26190": "temporelle", "26191": "fers", "26192": "rattachement", "26193": "virulence", "26194": "pi\u00e9tine", "26195": "sana'", "26196": "stipul\u00e9es", "26197": "\u00e9pingl\u00e9", "26198": "boliviens", "26199": "accomplisse", "26200": "baldarelli", "26201": "promeuve", "26202": "br\u00fblants", "26203": "brasserie", "26204": "4e", "26205": "sordides", "26206": "finlay", "26207": "bannie", "26208": "ventil\u00e9es", "26209": "bris\u00e9es", "26210": "fascin\u00e9", "26211": "cube", "26212": "valises", "26213": "hansen", "26214": "tachkent", "26215": "interrogeait", "26216": "apicole", "26217": "hannan", "26218": "under", "26219": "tripler", "26220": "almad\u00e9n", "26221": "regardaient", "26222": "l\u00e8vera", "26223": "turluttes", "26224": "affreuse", "26225": "dispensent", "26226": "cantons", "26227": "santo", "26228": "attel\u00e9s", "26229": "mauritanienne", "26230": "indiquons", "26231": "imaginait", "26232": "mandalay", "26233": "dessinateurs", "26234": "b\u00e9mol", "26235": "182", "26236": "gates", "26237": "palangriers", "26238": "contreparties", "26239": "synth\u00e9tique", "26240": "stoney", "26241": "saurez", "26242": "falsifications", "26243": "instincts", "26244": "apparaissaient", "26245": "ala", "26246": "solidairement", "26247": "occasionnels", "26248": "stanford", "26249": "fugitif", "26250": "r\u00e9surrection", "26251": "cbrn", "26252": "coureurs", "26253": "fonctionnalit\u00e9", "26254": "extractive", "26255": "carc\u00e9rale", "26256": "meeting", "26257": "d\u00e9nombr\u00e9", "26258": "\u0643\u0627\u0646", "26259": "\u00e9vitement", "26260": "down", "26261": "proth\u00e8ses", "26262": "lachine", "26263": "estemirova", "26264": "tatouage", "26265": "ob\u00e8ses", "26266": "indiquera", "26267": "dissout", "26268": "6,8", "26269": "r\u00e9jouissances", "26270": "peintres", "26271": "d\u00e9posons", "26272": "\u00e9ternels", "26273": "couvriront", "26274": "humoriste", "26275": "field", "26276": "int\u00e9ressaient", "26277": "rangoun", "26278": "mcdonald'", "26279": "foul\u00e9s", "26280": "um", "26281": "words", "26282": "socialisation", "26283": "d\u00e9pr\u00e9ciation", "26284": "politics", "26285": "maudit", "26286": "chronologique", "26287": "imparties", "26288": "janet", "26289": "diverger", "26290": "concernerait", "26291": "manipul\u00e9es", "26292": "favelas", "26293": "r\u00e9ins\u00e9rer", "26294": "plaza", "26295": "statistiquement", "26296": "baidu", "26297": "anglicane", "26298": "glac\u00e9", "26299": "reforestation", "26300": "incomp\u00e9tents", "26301": "d\u00e9coupler", "26302": "delek", "26303": "rh\u00f4ne", "26304": "machisme", "26305": "\u00e9cout\u00e9es", "26306": "slaves", "26307": "archa\u00efques", "26308": "disparate", "26309": "orkut", "26310": "tablette", "26311": "parme", "26312": "analphab\u00e8tes", "26313": "daghestan", "26314": "individualisme", "26315": "foss\u00e9s", "26316": "rwandaise", "26317": "regardions", "26318": "tardifs", "26319": "hugues", "26320": "combinent", "26321": "toole", "26322": "m\u00e9ritez", "26323": "gama", "26324": "linge", "26325": "amas", "26326": "cautionnement", "26327": "-a4", "26328": "monast\u00e8res", "26329": "existeraient", "26330": "besogne", "26331": "consign\u00e9e", "26332": "nig\u00e9rianes", "26333": "r\u00e9installer", "26334": "divorces", "26335": "roule", "26336": "\u00e9duqu\u00e9e", "26337": "gagneraient", "26338": "\u00e9claircie", "26339": "commercialisent", "26340": "oligopoles", "26341": "\u00e9chine", "26342": "ume", "26343": "munro", "26344": "guat\u00e9malt\u00e8ques", "26345": "combattues", "26346": "cfa", "26347": "honteuses", "26348": "surpeupl\u00e9es", "26349": "silicon", "26350": "r\u00e9sid\u00e9", "26351": "psc", "26352": "m\u00ealent", "26353": "nationalismes", "26354": "apprenez", "26355": "noy\u00e9s", "26356": "reggae", "26357": "r\u00e9prim\u00e9s", "26358": "monique", "26359": "journalism", "26360": "bruxellois", "26361": "exhaustifs", "26362": "injectant", "26363": "renforts", "26364": "ruth", "26365": "pam", "26366": "nkurunziza", "26367": "empreints", "26368": "pr\u00eachent", "26369": "neachtain", "26370": "promues", "26371": "apais\u00e9es", "26372": "liminaires", "26373": "rivages", "26374": "montait", "26375": "niveler", "26376": "impulser", "26377": "status", "26378": "tax\u00e9e", "26379": "c\u00f4toient", "26380": "engin", "26381": "hory", "26382": "attach\u00e9es", "26383": "indulgent", "26384": "icann", "26385": "jiang", "26386": "incarc\u00e9r\u00e9es", "26387": "d\u00e9rob\u00e9e", "26388": "niche", "26389": "d\u00e9verse", "26390": "d\u00e9faillants", "26391": "pcrd", "26392": "continentales", "26393": "extensions", "26394": "goodwill", "26395": "minas", "26396": "u.s.", "26397": "pr\u00e9somptions", "26398": "sarcophage", "26399": "couvraient", "26400": "corail", "26401": "color\u00e9s", "26402": "\u00e9puise", "26403": "sautent", "26404": "consacrerai", "26405": "aquif\u00e8res", "26406": "soumissionnaire", "26407": "confucius", "26408": "artisanaux", "26409": "d\u00e9rogatoires", "26410": "183", "26411": "para\u00eetra", "26412": "pourvoyeurs", "26413": "friends", "26414": "inflexibles", "26415": "hibernia", "26416": "informatif", "26417": "bulles", "26418": "convainquent", "26419": "limitrophe", "26420": "amiral", "26421": "brisent", "26422": "scrutateurs", "26423": "bordel", "26424": "postuler", "26425": "transcriptions", "26426": "neuvien", "26427": "edf", "26428": "faiblir", "26429": "souverainement", "26430": "radiophonique", "26431": "olmert", "26432": "craignais", "26433": "lobbys", "26434": "baltica", "26435": "domiciles", "26436": "fixerait", "26437": "trips", "26438": "ath\u00e9e", "26439": "favorisait", "26440": "irradi\u00e9s", "26441": "pfl\u00fcger", "26442": "descend", "26443": "exigions", "26444": "d\u00e9veloppeur", "26445": "combattons", "26446": "monaco", "26447": "enchant\u00e9s", "26448": "divin", "26449": "efforcions", "26450": "cassette", "26451": "appr\u00e9cierait", "26452": "impropres", "26453": "ina", "26454": "tirerait", "26455": "subs\u00e9quente", "26456": "divergente", "26457": "dragage", "26458": "interpr\u00e9tons", "26459": "salari\u00e9es", "26460": "perdaient", "26461": "forages", "26462": "empoisonn\u00e9e", "26463": "marpol", "26464": "yarmouk", "26465": "maryam", "26466": "nigel", "26467": "p\u00e9diatriques", "26468": "combl\u00e9e", "26469": "biod\u00e9gradables", "26470": "maladresse", "26471": "pluralistes", "26472": "grozny", "26473": "mastorakis", "26474": "tatiana", "26475": "539", "26476": "interop\u00e9rables", "26477": "~", "26478": "retentissante", "26479": "mandarin", "26480": "d\u00e9l\u00e9gu\u00e9es", "26481": "entretenues", "26482": "douzi\u00e8mes", "26483": "infect\u00e9e", "26484": "fluviales", "26485": "biden", "26486": "ficel\u00e9", "26487": "boiteuse", "26488": "gharbia", "26489": "bavardage", "26490": "pr\u00e9financement", "26491": "d\u00e9ductibles", "26492": "manicouagan", "26493": "pasqua", "26494": "assistante", "26495": "transf\u00e8rement", "26496": "d\u00e9concertant", "26497": "pancanadien", "26498": "charlevoix", "26499": "bafou\u00e9es", "26500": "t\u00e2ch\u00e9", "26501": "okinawa", "26502": "contrecoeur", "26503": "carney", "26504": "quinn", "26505": "sursaut", "26506": "p\u00e9quistes", "26507": "doublera", "26508": "inondables", "26509": "craignions", "26510": "14e", "26511": "shangha\u00ef", "26512": "reyes", "26513": "kidnapping", "26514": "arizona", "26515": "plaignons", "26516": "westbank", "26517": "erik", "26518": "admirons", "26519": "holding", "26520": "195", "26521": "appartenu", "26522": "gr\u00eale", "26523": "sacrifiant", "26524": "po\u00e9tique", "26525": "robots", "26526": "1999.-", "26527": "d\u00e9tect\u00e9s", "26528": "lynn", "26529": "scand\u00e9", "26530": "abstiendra", "26531": "pendu", "26532": "confions", "26533": "occuperait", "26534": "abonn\u00e9", "26535": "expropriations", "26536": "garderai", "26537": "tube", "26538": "fumeur", "26539": "anonymement", "26540": "accul\u00e9s", "26541": "pirat\u00e9s", "26542": "centralis\u00e9s", "26543": "reconstruit", "26544": "malouines", "26545": "dombrovskis", "26546": "convergences", "26547": "bridge", "26548": "tenable", "26549": "gagnerons", "26550": "canards", "26551": "abrog\u00e9es", "26552": "leo", "26553": "conna\u00eetrait", "26554": "monts", "26555": "t\u00e9l\u00e9film", "26556": "nyerere", "26557": "ferri", "26558": "jumelles", "26559": "patrimoines", "26560": "ralli\u00e9s", "26561": "stratag\u00e8mes", "26562": "lukashenko", "26563": "diagnostiquer", "26564": "permettrais", "26565": "id\u00e9aliste", "26566": "freinage", "26567": "exemptes", "26568": "sandmonkey", "26569": "ra\u00fal", "26570": "cicr", "26571": "limiteront", "26572": "infligent", "26573": "cesare", "26574": "famagouste", "26575": "jamaat", "26576": "g\u00e9nocidaires", "26577": "affaiblisse", "26578": "d\u00e9missions", "26579": "goliath", "26580": "ancestrale", "26581": "instaurons", "26582": "biais\u00e9", "26583": "ennemies", "26584": "vendait", "26585": "fitr", "26586": "ombilical", "26587": "westendorp", "26588": "aggravera", "26589": "\u00a3", "26590": "jalousement", "26591": "appauvrissant", "26592": "periodismo", "26593": "asphalte", "26594": "voisey'", "26595": "lapin", "26596": "inconciliables", "26597": "amputation", "26598": "goerens", "26599": "jules", "26600": "sauvegard\u00e9", "26601": "eurozone", "26602": "itin\u00e9rantes", "26603": "highlands", "26604": "bousculade", "26605": "profane", "26606": "nasheed", "26607": "karin", "26608": "manipul\u00e9e", "26609": "obligataires", "26610": "ywca", "26611": "r\u00e9sonne", "26612": "matelas", "26613": "manal", "26614": "310", "26615": "far", "26616": "flexibilisation", "26617": "vid\u00e9oconf\u00e9rence", "26618": "daw", "26619": "nicosie", "26620": "dou\u00e9", "26621": "milliardaires", "26622": "soulag\u00e9s", "26623": "taksim", "26624": "pr\u00e9carisation", "26625": "xviiie", "26626": "appuyez", "26627": "mc", "26628": "stup\u00e9faite", "26629": "award", "26630": "271", "26631": "vargas", "26632": "exp\u00e9di\u00e9s", "26633": "tonalit\u00e9", "26634": "balivernes", "26635": "osman", "26636": "244", "26637": "d\u00e9fectueuse", "26638": "h\u00e9berge", "26639": "pendaison", "26640": "att\u00e9nue", "26641": "calamit\u00e9s", "26642": "cracher", "26643": "synagogue", "26644": "apprenions", "26645": "ifor", "26646": "planifie", "26647": "391", "26648": "contractent", "26649": "obtiendraient", "26650": "abr\u00e9viation", "26651": "vu\u010di\u0107", "26652": "jessica", "26653": "id\u00e9alistes", "26654": "entrepreneurial", "26655": "constaterait", "26656": "\u00e9ditorialistes", "26657": "sortirait", "26658": "librairie", "26659": "ta\u00efwanaise", "26660": "calder", "26661": "contourn\u00e9", "26662": "steven", "26663": "servies", "26664": "postposer", "26665": "rizzuto", "26666": "\u00e9rudit", "26667": "r\u00e9conforter", "26668": "mural", "26669": "shillings", "26670": "thermiques", "26671": "transmissions", "26672": "disproportion", "26673": "d\u00e9fendeurs", "26674": "d\u00e9mantel\u00e9es", "26675": "convaincra", "26676": "enjoignant", "26677": "253", "26678": "mythologie", "26679": "sherry", "26680": "gabonaise", "26681": "d\u00e9livr\u00e9es", "26682": "militer", "26683": "d\u00e9lictueux", "26684": "m\u00e9connu", "26685": "pak", "26686": "trimestriels", "26687": "girls", "26688": "trafiquant", "26689": "insuline", "26690": "gilbert", "26691": "reportons", "26692": "goulots", "26693": "pickersgill", "26694": "ephremidis", "26695": "v\u00e9rone", "26696": "tanzaniens", "26697": "malpeque", "26698": "anarchique", "26699": "ressemblant", "26700": "aiguille", "26701": "r\u00e9pugnance", "26702": "resserre", "26703": "refugees", "26704": "per\u00e7oive", "26705": "approbations", "26706": "570", "26707": "diabolique", "26708": "carreau", "26709": "retranchant", "26710": "201", "26711": "luxueux", "26712": "\u00e9pargnent", "26713": "chandelles", "26714": "pulitzer", "26715": "reporting", "26716": "balay\u00e9es", "26717": "andria", "26718": "d\u00e9j", "26719": "anniversaires", "26720": "stabilisateur", "26721": "sangatte", "26722": "r\u00e9pondiez", "26723": "laotien", "26724": "avancions", "26725": "monica", "26726": "election", "26727": "progressifs", "26728": "subalternes", "26729": "huffington", "26730": "vedova", "26731": "d\u00e9gressive", "26732": "\u00e9cr\u00e9m\u00e9", "26733": "1927", "26734": "tappin", "26735": "liberty", "26736": "low", "26737": "contr\u00f4lable", "26738": "malvoyants", "26739": "mclellan", "26740": "glen", "26741": "lign\u00e9es", "26742": "effleurer", "26743": "nomment", "26744": "assidus", "26745": "thrombine", "26746": "battaient", "26747": "-l'", "26748": "540", "26749": "poup\u00e9e", "26750": "antagonistes", "26751": "town", "26752": "ronge", "26753": "1848", "26754": "12e", "26755": "intensifiant", "26756": "razak", "26757": "consid\u00e9rerait", "26758": "bizarrement", "26759": "fonctionnons", "26760": "unifi\u00e9es", "26761": "p\u00e9rilleuse", "26762": "am\u00e9rindiens", "26763": "entourer", "26764": "pi\u00e9trasanta", "26765": "endurance", "26766": "sylvie", "26767": "incin\u00e9rateur", "26768": "d\u00e9fient", "26769": "rejetteront", "26770": "bourgeois", "26771": "mue", "26772": "fuster", "26773": "v\u00e9ronique", "26774": "red\u00e9fini", "26775": "iniquit\u00e9s", "26776": "enfreintes", "26777": "chang", "26778": "averro\u00e8s", "26779": "saufs", "26780": "ying", "26781": "mierlo", "26782": "d\u00e9volus", "26783": "d\u00e9c\u00e8de", "26784": "susta", "26785": "not\u00e9e", "26786": "r\u00e9colt\u00e9s", "26787": "apparentent", "26788": "stagiaire", "26789": "culinaires", "26790": "causalit\u00e9", "26791": "confort\u00e9", "26792": "strelkov", "26793": "halloween", "26794": "misogynie", "26795": "ch\u00e8vre", "26796": "interjet\u00e9", "26797": "piet", "26798": "imposeront", "26799": "si\u00e9gera", "26800": "esma", "26801": "vaisselle", "26802": "r\u00e9sultait", "26803": "verbiage", "26804": "contreproductive", "26805": "anjou", "26806": "venais", "26807": "cumul\u00e9s", "26808": "ttf", "26809": "hindou", "26810": "radionavigation", "26811": "abe", "26812": "bouteflika", "26813": "emplacements", "26814": "ouvrables", "26815": "technocratie", "26816": "holbrooke", "26817": "sal\u00e9e", "26818": "cat\u00e9goriques", "26819": "valablement", "26820": "serviettes", "26821": "attrape", "26822": "generis", "26823": "refouler", "26824": "tea", "26825": "ibm", "26826": "grenier", "26827": "embauchent", "26828": "comport\u00e9e", "26829": "rajoy", "26830": "approuvions", "26831": "ironiques", "26832": "sophistiqu\u00e9", "26833": "rechignent", "26834": "chirurgien", "26835": "parrains", "26836": "reais", "26837": "avide", "26838": "lesotho", "26839": "\u00e9l\u00e8verait", "26840": "philosophies", "26841": "tribale", "26842": "examinerait", "26843": "killilea", "26844": "impr\u00e9vu", "26845": "pouces", "26846": "g\u00e9n\u00e9ratrice", "26847": "thomson", "26848": "collage", "26849": "biddh", "26850": "collenette", "26851": "mous", "26852": "tee", "26853": "qi", "26854": "pr\u00e9cisera", "26855": "posters", "26856": "french", "26857": "accabl\u00e9", "26858": "d\u00e9passements", "26859": "agr\u00e9\u00e9es", "26860": "concourent", "26861": "d\u00e9cha\u00een\u00e9", "26862": "f\u00e9lix", "26863": "idyllique", "26864": "m\u00e9contente", "26865": "conflictuelles", "26866": "exigeraient", "26867": "compense", "26868": "plaisirs", "26869": "mifflin", "26870": "inhabituels", "26871": "inca", "26872": "cold", "26873": "barrer", "26874": "shirley", "26875": "bio\u00e9nergie", "26876": "d\u00e9peignent", "26877": "assaillent", "26878": "kenyatta", "26879": "injustifiables", "26880": "body", "26881": "sony", "26882": "riv\u00e9s", "26883": "engendrant", "26884": "expir\u00e9e", "26885": "dalit", "26886": "marquerait", "26887": "madd", "26888": "ends", "26889": "analyserons", "26890": "alarmisme", "26891": "mapuche", "26892": "m\u00e9diatis\u00e9", "26893": "yusuf", "26894": "pencheront", "26895": "scanner", "26896": "egyptian", "26897": "bainimarama", "26898": "raza", "26899": "cerise", "26900": "citerne", "26901": "ulster", "26902": "\u00e9tonnantes", "26903": "c\u00e9dons", "26904": "atteignaient", "26905": "d\u00e9truira", "26906": "chassent", "26907": "deuxi\u00e8mes", "26908": "concocter", "26909": "c\u00f4toyer", "26910": "marchons", "26911": "exportant", "26912": "nettoy\u00e9", "26913": "fluviaux", "26914": "coma", "26915": "change.org", "26916": "d\u00e9t\u00e9riorent", "26917": "inop\u00e9rante", "26918": "jillian", "26919": "ingestion", "26920": "kirsten", "26921": "mass\u00e9", "26922": "excit\u00e9", "26923": "italo", "26924": "wuermeling", "26925": "nanoparticules", "26926": "semence", "26927": "activent", "26928": "bono", "26929": "bonbons", "26930": "d\u00e9tect\u00e9es", "26931": "voterait", "26932": "joyeuses", "26933": "perturb\u00e9e", "26934": "j\u00e4\u00e4tteenm\u00e4ki", "26935": "anas", "26936": "z\u00e9landais", "26937": "rapporteures", "26938": "co\u00fbtant", "26939": "d\u00e9gla\u00e7age", "26940": "\u0161emeta", "26941": "s\u00e9n\u00e9galaise", "26942": "discutait", "26943": "diss\u00e9min\u00e9s", "26944": "r\u00e9gul\u00e9e", "26945": "incomberait", "26946": "feb17", "26947": "flesch", "26948": "d\u00e9ontologique", "26949": "transport\u00e9e", "26950": "effrayantes", "26951": "compromises", "26952": "gold", "26953": "fours", "26954": "retournera", "26955": "militarisme", "26956": "heurt\u00e9s", "26957": "condoleezza", "26958": "ind\u00e9l\u00e9bile", "26959": "reproductifs", "26960": "broadcasting", "26961": "fautif", "26962": "chowdhury", "26963": "cabillauds", "26964": "corapporteur", "26965": "g\u00e9n\u00e9alogistes", "26966": "rassur\u00e9e", "26967": "traitaient", "26968": "envisagerons", "26969": "entrepreneuriale", "26970": "grotesques", "26971": "avatar", "26972": "r\u00e9p\u00e9titions", "26973": "rescap\u00e9s", "26974": "cosign\u00e9", "26975": "dissimul\u00e9es", "26976": "juana", "26977": "\u00e9puisent", "26978": "fantaisie", "26979": "ajouteront", "26980": "nargis", "26981": "succ\u00e9dera", "26982": "kamikaze", "26983": "devanc\u00e9", "26984": "retardateurs", "26985": "1955", "26986": "h\u00e9t\u00e9rosexuelles", "26987": "vila", "26988": "1923", "26989": "relents", "26990": "assam", "26991": "canard", "26992": "bataillons", "26993": "197", "26994": "manifesteront", "26995": "s\u00e9duisant", "26996": "mourra", "26997": "rizi\u00e8res", "26998": "vantant", "26999": "progressivit\u00e9", "27000": "astuce", "27001": "stimulerait", "27002": "g\u00e9n\u00e9rera", "27003": "\u00e9roder", "27004": "pr\u00e9ciserai", "27005": "enrichissante", "27006": "\u00e9perdument", "27007": "verdun", "27008": "instruit", "27009": "entamions", "27010": "limog\u00e9", "27011": "milton", "27012": "attractives", "27013": "frai", "27014": "cassation", "27015": "malais\u00e9", "27016": "confisqu\u00e9es", "27017": "jennifer", "27018": "415", "27019": "flores", "27020": "mangrove", "27021": "blason", "27022": "490", "27023": "motoris\u00e9s", "27024": "building", "27025": "endocrinien", "27026": "suif", "27027": "ploutocratie", "27028": "belt", "27029": "duncan", "27030": "tarir", "27031": "bonnet", "27032": "morning", "27033": "nobilia", "27034": "criantes", "27035": "lorraine", "27036": "687", "27037": "wiechert", "27038": "\u00e9couterons", "27039": "t\u00e9moignera", "27040": "exp\u00e9ditive", "27041": "quaranti\u00e8me", "27042": "siemens", "27043": "d\u00e9coupage", "27044": "commandos", "27045": "burke", "27046": "tin", "27047": "sally", "27048": "apprenne", "27049": "p\u0159edn\u00e1dra\u017e\u00ed", "27050": "arm\u00e9niennes", "27051": "ob\u00e9issent", "27052": "asturies", "27053": "\u0647\u0648", "27054": "ceyhun", "27055": "superpuissances", "27056": "empiriques", "27057": "northern", "27058": "podium", "27059": "\u00e9mouvoir", "27060": "australiennes", "27061": "absorb\u00e9es", "27062": "appuieraient", "27063": "bouleverse", "27064": "cons", "27065": "apprenti", "27066": "381", "27067": "\u00e9galer", "27068": "sonnent", "27069": "yens", "27070": "\u00e9closion", "27071": "stupeur", "27072": "arbitres", "27073": "retarderait", "27074": "274", "27075": "harcel\u00e9e", "27076": "sophie", "27077": "situ", "27078": "187", "27079": "charitables", "27080": "incontest\u00e9", "27081": "vaillants", "27082": "mogadiscio", "27083": "chittagong", "27084": "espoo", "27085": "annoncez", "27086": "d\u00e9cel\u00e9es", "27087": "normalis\u00e9e", "27088": "better", "27089": "morin", "27090": "daskalaki", "27091": "quantifi\u00e9s", "27092": "prohibitif", "27093": "indicatives", "27094": "renomm\u00e9s", "27095": "improvis\u00e9", "27096": "regagn\u00e9", "27097": "boycott\u00e9", "27098": "traquer", "27099": "tentez", "27100": "afflig\u00e9", "27101": "prey", "27102": "thons", "27103": "kacin", "27104": "divulgu\u00e9e", "27105": "all\u00e9g\u00e9e", "27106": "secouent", "27107": "froides", "27108": "commercialise", "27109": "soutenables", "27110": "concurrente", "27111": "remplissaient", "27112": "libell\u00e9es", "27113": "mafieuses", "27114": "basait", "27115": "censur\u00e9es", "27116": "salafistes", "27117": "facturer", "27118": "barzanti", "27119": "pousses", "27120": "recouvrir", "27121": "himalaya", "27122": "app\u00e9tits", "27123": "prorog\u00e9", "27124": "exploitons", "27125": "lechner", "27126": "habit\u00e9e", "27127": "prononcerai", "27128": "azzolini", "27129": "ombres", "27130": "bats", "27131": "\u00e9ruptions", "27132": "astana", "27133": "djeddah", "27134": "c\u00e9r\u00e9alier", "27135": "r\u00e9agiront", "27136": "valorise", "27137": "maltaises", "27138": "transportait", "27139": "\u0153cum\u00e9nique", "27140": "fragment\u00e9es", "27141": "tweetait", "27142": "\u00e9num\u00e9rant", "27143": "calcutta", "27144": "liz", "27145": "myst\u00e9rieuses", "27146": "idoine", "27147": "conjoncturel", "27148": "germe", "27149": "recul\u00e9s", "27150": "taill\u00e9", "27151": "disciples", "27152": "464", "27153": "ayuso", "27154": "ole", "27155": "pencherai", "27156": "all\u00e9geant", "27157": "peaux", "27158": "krahmer", "27159": "co\u00fbteront", "27160": "r\u00e9trogrades", "27161": "d\u00e9tenons", "27162": "comprendrons", "27163": "provocante", "27164": "sarcasmes", "27165": "pronon\u00e7ait", "27166": "enregistrant", "27167": "sana", "27168": "poindre", "27169": "d\u00e9tester", "27170": "\u00e9migrent", "27171": "rejetterons", "27172": "d\u00e9finissait", "27173": "f\u00e2ch\u00e9", "27174": "\u00e9rudits", "27175": "salzbourg", "27176": "contagieuse", "27177": "d\u00e9dier", "27178": "verbe", "27179": "allister", "27180": "prot\u00e9gez", "27181": "chennai", "27182": "concluantes", "27183": "takkula", "27184": "r\u00e9percutent", "27185": "indiscutables", "27186": "accordaient", "27187": "enorgueillit", "27188": "tornade", "27189": "lanterne", "27190": "tib\u00e9taines", "27191": "doutait", "27192": "gas", "27193": "mama", "27194": "dorval", "27195": "escadrons", "27196": "facilit\u00e9es", "27197": "hein", "27198": "cornwall", "27199": "employait", "27200": "nano", "27201": "soutiendraient", "27202": "gibson", "27203": "meute", "27204": "d\u00e9bordement", "27205": "swissair", "27206": "haque", "27207": "\u00e9l\u00e9vateurs", "27208": "queer", "27209": "furieuse", "27210": "contemporaines", "27211": "pendre", "27212": "right", "27213": "haliburton", "27214": "adepte", "27215": "r\u00e9pondit", "27216": "floraison", "27217": "\u00e9loquentes", "27218": "lech", "27219": "nourrie", "27220": "\u00e9minentes", "27221": "mourants", "27222": "gamins", "27223": "avise", "27224": "d\u00e9marquer", "27225": "habilitante", "27226": "m\u00e9tropolitaines", "27227": "pli\u00e9", "27228": "\u00e9mue", "27229": "\u00e9volutif", "27230": "ieddh", "27231": "pc7", "27232": "prestigieuses", "27233": "d\u00e9judiciarisation", "27234": "accidentel", "27235": "museum", "27236": "abstienne", "27237": "guillemets", "27238": "gamin", "27239": "syed", "27240": "atterrissent", "27241": "poursuis", "27242": "assoupli", "27243": "d\u00e9volues", "27244": "geremek", "27245": "signalez", "27246": "tour\u00e9", "27247": "vicissitudes", "27248": "avantager", "27249": "rita", "27250": "atrocit\u00e9", "27251": "n.", "27252": "kenora", "27253": "contentera", "27254": "infirmer", "27255": "retournant", "27256": "bamiyan", "27257": "ss", "27258": "grassement", "27259": "vallv\u00e9", "27260": "259", "27261": "hervieux", "27262": "diary", "27263": "intactes", "27264": "renard", "27265": "extr\u00e9mit\u00e9s", "27266": "derakhshan", "27267": "larrea", "27268": "intergroupes", "27269": "territorialit\u00e9", "27270": "castille", "27271": "r\u00e9fut\u00e9", "27272": "probabilit\u00e9s", "27273": "fiscalistes", "27274": "hache", "27275": "\u00e9pop\u00e9e", "27276": "frontale", "27277": "losier", "27278": "flottant", "27279": "cr\u00e9puscule", "27280": "335", "27281": "graduation", "27282": "mensonger", "27283": "transversal", "27284": "consoler", "27285": "affectif", "27286": "corapporteurs", "27287": "iqaluit", "27288": "humbles", "27289": "approuvaient", "27290": "bavarois", "27291": "chiquita", "27292": "\u00e9lude", "27293": "patchwork", "27294": "juris", "27295": "mitch", "27296": "karimova", "27297": "contourne", "27298": "rubans", "27299": "diatribe", "27300": "cordiale", "27301": "receveurs", "27302": "d\u00e9grad\u00e9", "27303": "usure", "27304": "bourreau", "27305": "d\u00e9pr\u00e9cier", "27306": "libell\u00e9e", "27307": "lifr", "27308": "noy\u00e9", "27309": "optant", "27310": "r\u00e9sistante", "27311": "sold\u00e9s", "27312": "gageure", "27313": "agnoletto", "27314": "captur\u00e9es", "27315": "entrav\u00e9s", "27316": "imp\u00e9rial", "27317": "dict\u00e9s", "27318": "remarquez", "27319": "contrecoup", "27320": "tomberont", "27321": "op\u00e9rons", "27322": "duplicit\u00e9", "27323": "chim\u00e8re", "27324": "horta", "27325": "kallenbach", "27326": "paperasse", "27327": "marchait", "27328": "twittosph\u00e8re", "27329": "demanda", "27330": "remportent", "27331": "milo\u0161evi\u0107", "27332": "tabler", "27333": "sig", "27334": "d\u00e9veloppions", "27335": "insalubres", "27336": "think", "27337": "pollu\u00e9s", "27338": "manager", "27339": "bloggers", "27340": "tauromachie", "27341": "r\u00e9unissait", "27342": "franchissons", "27343": "mayotte", "27344": "teint\u00e9", "27345": "infanterie", "27346": "r\u00e9dactionnelle", "27347": "rang\u00e9es", "27348": "polaires", "27349": "abus\u00e9s", "27350": "horticulture", "27351": "impr\u00e9gn\u00e9", "27352": "shoah", "27353": "enrichis", "27354": "perds", "27355": "command\u00e9es", "27356": "intrusions", "27357": "d\u00e9sesp\u00e9rer", "27358": "avis\u00e9es", "27359": "banales", "27360": "jouira", "27361": "encouragions", "27362": "leaf", "27363": "transcaucasie", "27364": "wells", "27365": "assi\u00e9g\u00e9", "27366": "zambia", "27367": "protestons", "27368": "etes", "27369": "barque", "27370": "kowe\u00eftiens", "27371": "ralentissent", "27372": "pr\u00e9servatif", "27373": "influenc\u00e9es", "27374": "plaignaient", "27375": "instantan\u00e9", "27376": "souffraient", "27377": "tricher", "27378": "juda\u00efsme", "27379": "l\u00e9on", "27380": "matt", "27381": "insinue", "27382": "d\u00e9ficients", "27383": "schmit", "27384": "index\u00e9e", "27385": "ellis", "27386": "shiva", "27387": "perdurera", "27388": "autoproclam\u00e9", "27389": "pluriel", "27390": "fact", "27391": "nadine", "27392": "plaisant\u00e9", "27393": "regardais", "27394": "yarmouth", "27395": "croise", "27396": "bruyamment", "27397": "armistice", "27398": "isole", "27399": "rel\u00e9gu\u00e9es", "27400": "d\u00e9savantager", "27401": "b\u00e9langer", "27402": "bienfait", "27403": "discutaient", "27404": "socrate", "27405": "enseign\u00e9es", "27406": "nama", "27407": "g\u00e9rants", "27408": "pr\u00f4n\u00e9es", "27409": "\u00e9largissons", "27410": "n\u00e9ocolonialisme", "27411": "edgar", "27412": "promettons", "27413": "333", "27414": "\u00e9clipse", "27415": "tombouctou", "27416": "endoctrinement", "27417": "mothers", "27418": "waves", "27419": "r\u00e9examin\u00e9es", "27420": "id\u00e9ologues", "27421": "318", "27422": "boat", "27423": "fatigu\u00e9e", "27424": "pointues", "27425": "bundesbank", "27426": "d\u00e9railler", "27427": "g\u00e9n\u00e9ralis\u00e9s", "27428": "exon\u00e9r\u00e9s", "27429": "enfreignant", "27430": "voil\u00e9es", "27431": "carrefours", "27432": "rajapaksa", "27433": "englobera", "27434": "mostafa", "27435": "inclusivement", "27436": "\u00e9bauches", "27437": "consuegra", "27438": "adrian", "27439": "pitfield", "27440": "exag\u00e9rations", "27441": "duarte", "27442": "stup\u00e9faits", "27443": "\u00e9clatant", "27444": "patrimoniaux", "27445": "sauvegard\u00e9s", "27446": "ernest", "27447": "hercules", "27448": "n\u00e9goce", "27449": "muni", "27450": "cove", "27451": "natifs", "27452": "xilai", "27453": "d\u00e9l\u00e8gue", "27454": "ethiopiens", "27455": "greffes", "27456": "tftp", "27457": "m\u00e9prisante", "27458": "gaziers", "27459": "fra\u00eecheur", "27460": "d\u00e9plaisant", "27461": "marleau", "27462": "impositions", "27463": "croissent", "27464": "s\u00e9questration", "27465": "souscription", "27466": "australia", "27467": "\u00e9lastique", "27468": "univoques", "27469": "oss\u00e8tes", "27470": "passionne", "27471": "machado", "27472": "exposerai", "27473": "amira", "27474": "feint", "27475": "books", "27476": "fraternelle", "27477": "r\u00e9prime", "27478": "monarque", "27479": "retentissement", "27480": "murales", "27481": "bogot\u00e1", "27482": "frappera", "27483": "qualifions", "27484": "casey", "27485": "influen\u00e7ant", "27486": "liesse", "27487": "joy", "27488": "f\u00e9minit\u00e9", "27489": "care", "27490": "ruches", "27491": "conf\u00e9rerait", "27492": "520", "27493": "v\u00e9rifient", "27494": "prohib\u00e9s", "27495": "fry", "27496": "pr\u00e9parez", "27497": "telegraph", "27498": "paniquer", "27499": "concentreront", "27500": "psychologues", "27501": "commettons", "27502": "cerisiers", "27503": "perp\u00e9tuellement", "27504": "xxiii", "27505": "planifient", "27506": "rep\u00e9r\u00e9", "27507": "valid\u00e9", "27508": "interreligieux", "27509": "political", "27510": "maps", "27511": "a1", "27512": "\u00e9volutive", "27513": "enchant\u00e9e", "27514": "\u00e9chouera", "27515": "pabok", "27516": "verrous", "27517": "raquo", "27518": "mentent", "27519": "palmar\u00e8s", "27520": "r\u00e9duisions", "27521": "attractions", "27522": "assyriens", "27523": "marcheurs", "27524": "abdication", "27525": "certifi\u00e9e", "27526": "clone", "27527": "concr\u00e9tis\u00e9s", "27528": "environnante", "27529": "soul\u00e8verai", "27530": "balaie", "27531": "hammerstein", "27532": "balfour", "27533": "pr\u00e9viens", "27534": "boit", "27535": "compensateurs", "27536": "occultes", "27537": "adviendrait", "27538": "ouagadougou", "27539": "adressez", "27540": "copi\u00e9", "27541": "holmes", "27542": "anxieux", "27543": "rapporterait", "27544": "blanchi", "27545": "reconnait", "27546": "mactaquac", "27547": "accessoirement", "27548": "benelux", "27549": "rationalis\u00e9e", "27550": "bella", "27551": "dissoci\u00e9", "27552": "f\u00e9ro\u00e9", "27553": "serr\u00e9es", "27554": "boudjenah", "27555": "pple", "27556": "vaisseaux", "27557": "pendjab", "27558": "submerg\u00e9e", "27559": "over", "27560": "fantaisistes", "27561": "anguilles", "27562": "consistaient", "27563": "traumatisante", "27564": "acc\u00e9l\u00e9r\u00e9s", "27565": "nistelrooij", "27566": "mouvance", "27567": "joliette", "27568": "aloi", "27569": "culinaire", "27570": "capoter", "27571": "dec", "27572": "red\u00e9ployer", "27573": "desjardins", "27574": "r\u00e9veillent", "27575": "ressortie", "27576": "implorer", "27577": "effectivit\u00e9", "27578": "moqu\u00e9", "27579": "r\u00e9affirment", "27580": "emp\u00each\u00e9es", "27581": "toxicomane", "27582": "stuart", "27583": "hama", "27584": "septentrional", "27585": "montagneuse", "27586": "connie", "27587": "favela", "27588": "ddos", "27589": "vieillir", "27590": "contrast\u00e9es", "27591": "bou\u00e9e", "27592": "jugez", "27593": "sarcastiques", "27594": "ospar", "27595": "conjurer", "27596": "spatiaux", "27597": "contact\u00e9e", "27598": "xxi\u00e8me", "27599": "ernst", "27600": "ins\u00e9r\u00e9s", "27601": "coll\u00e9giales", "27602": "doctorats", "27603": "surtaxes", "27604": "guat\u00e9mala", "27605": "fassa", "27606": "abordait", "27607": "incitative", "27608": "misant", "27609": "tamino", "27610": "coercitif", "27611": "cinquantenaire", "27612": "boycotts", "27613": "luzerne", "27614": "foudres", "27615": "quittez", "27616": "comptabiliser", "27617": "sporadiques", "27618": "720", "27619": "tomczak", "27620": "recrute", "27621": "inconscient", "27622": "antonione", "27623": "70e", "27624": "ghazi", "27625": "douche", "27626": "absorb\u00e9e", "27627": "manzella", "27628": "born\u00e9e", "27629": "pr\u00e9tendaient", "27630": "adjudicateurs", "27631": "liront", "27632": "\u00e9critures", "27633": "nuclear", "27634": "attaqueront", "27635": "shiv", "27636": "anormaux", "27637": "film\u00e9s", "27638": "convivialit\u00e9", "27639": "humala", "27640": "aoun", "27641": "commandent", "27642": "carc\u00e9rales", "27643": "faisiez", "27644": "iphone", "27645": "ultralib\u00e9ralisme", "27646": "occasionnel", "27647": "bud", "27648": "heba", "27649": "oscille", "27650": "insult\u00e9s", "27651": "surveillerons", "27652": "bordure", "27653": "cl\u00f4tur\u00e9es", "27654": "lesley", "27655": "boomers", "27656": "mcavan", "27657": "prouv\u00e9es", "27658": "m\u00e8che", "27659": "rev\u00eate", "27660": "conjoncturelles", "27661": "\u00e9motionnellement", "27662": "contrarie", "27663": "castex", "27664": "maltraiter", "27665": "ferraille", "27666": "hante", "27667": "tha\u00ef", "27668": "pointant", "27669": "arellano", "27670": "r\u00e9sidente", "27671": "bureaucratisation", "27672": "r\u00e9alisatrice", "27673": "atteignions", "27674": "pancanadienne", "27675": "brasilia", "27676": "condescendance", "27677": "moscow", "27678": "\u00e9pilepsie", "27679": "-le", "27680": "\u00e9terniser", "27681": "initiation", "27682": "refermer", "27683": "bouazizi", "27684": "d\u00e9truisons", "27685": "occupais", "27686": "divertir", "27687": "acquiescer", "27688": "commissariats", "27689": "n\u00e9cessiteuses", "27690": "nerf", "27691": "r\u00e9v\u00e9laient", "27692": "incarc\u00e9r\u00e9e", "27693": "d\u00e9grad\u00e9s", "27694": "\u00e9coconception", "27695": "hauteurs", "27696": "mettrais", "27697": "imp\u00e9ratives", "27698": "pr\u00e9sidera", "27699": "comporterait", "27700": "kerry", "27701": "protectrices", "27702": "chercherait", "27703": "exposait", "27704": "d\u00e9barquent", "27705": "approximative", "27706": "seizi\u00e8me", "27707": "ralentie", "27708": "subiraient", "27709": "\u00e9bullition", "27710": "pr\u00edncipe", "27711": "modernis\u00e9es", "27712": "lendemains", "27713": "autocratie", "27714": "r\u00e9pugnants", "27715": "brave", "27716": "incontr\u00f4l\u00e9s", "27717": "veilleuse", "27718": "beaupuy", "27719": "signeront", "27720": "trac\u00e9s", "27721": "rom\u00e1n", "27722": "ins\u00e9r\u00e9es", "27723": "fatales", "27724": "ambulants", "27725": "infernale", "27726": "passionnantes", "27727": "iguala", "27728": "talisman", "27729": "newens", "27730": "silencieuses", "27731": "bishkek", "27732": "idriss", "27733": "d\u00e9by", "27734": "endommag\u00e9e", "27735": "pr\u00e9mices", "27736": "ebadi", "27737": "eba", "27738": "exp\u00e9diteur", "27739": "academy", "27740": "athl\u00e9tisme", "27741": "mat\u00e9rialiser", "27742": "schultz", "27743": "7000", "27744": "intimid\u00e9s", "27745": "raccourcis", "27746": "chargeant", "27747": "7,8", "27748": "cantines", "27749": "\u00e9volueront", "27750": "telk\u00e4mper", "27751": "\u0627\u0644\u062a\u064a", "27752": "procr\u00e9er", "27753": "curry", "27754": "d\u00e9lire", "27755": "biom\u00e9dicale", "27756": "redevienne", "27757": "multipli\u00e9e", "27758": "circuiter", "27759": "friedmann", "27760": "falsifi\u00e9", "27761": "amplitude", "27762": "donnerions", "27763": "pr\u00e9somptueux", "27764": "saccag\u00e9", "27765": "arriva", "27766": "vulgaires", "27767": "d\u00e9clencheur", "27768": "d\u00e9gagera", "27769": "f\u00e9minisation", "27770": "malliori", "27771": "equipe", "27772": "malentendants", "27773": "faucons", "27774": "civisme", "27775": "rar\u00e9faction", "27776": "soucieuses", "27777": "r\u00e9ussissant", "27778": "divisant", "27779": "valise", "27780": "4,6", "27781": "dollard", "27782": "ensuivent", "27783": "hyland", "27784": "indigence", "27785": "effac\u00e9es", "27786": "repentir", "27787": "amend\u00e9es", "27788": "articul\u00e9", "27789": "t\u00e2cherai", "27790": "folklore", "27791": "rainy", "27792": "n\u00e9gociait", "27793": "couch\u00e9", "27794": "exorbitantes", "27795": "sti", "27796": "perc\u00e9es", "27797": "p\u00e9titionnaire", "27798": "h\u00f4tesses", "27799": "distinctement", "27800": "recouvr\u00e9", "27801": "roches", "27802": "allong\u00e9e", "27803": "favorite", "27804": "bosa", "27805": "fertilisants", "27806": "rideaux", "27807": "roca", "27808": "recouvert", "27809": "r\u00e9clamez", "27810": "ps", "27811": "clans", "27812": "irrespectueux", "27813": "d\u00e9magogues", "27814": "plafonner", "27815": "2021", "27816": "music", "27817": "xv", "27818": "r\u00e9mun\u00e9rateur", "27819": "paraissaient", "27820": "sonars", "27821": "r\u00e9it\u00e9r\u00e9es", "27822": "laponie", "27823": "ciolo\u015f", "27824": "jesse", "27825": "entamant", "27826": "automatis\u00e9", "27827": "recevez", "27828": "rattacher", "27829": "r\u00e9volutionner", "27830": "\u2212", "27831": "salaam", "27832": "mondialiste", "27833": "mdc", "27834": "inoffensif", "27835": "mujeres", "27836": "c\u00e9d\u00e9s", "27837": "accepterez", "27838": "\u00e1lvaro", "27839": "d\u00e9route", "27840": "incomplets", "27841": "it\u00e4l\u00e4", "27842": "con\u00e7oivent", "27843": "d\u00e9marque", "27844": "promulgu\u00e9es", "27845": "jes\u00fas", "27846": "r\u00e9sulteraient", "27847": "favoriseraient", "27848": "rediscuter", "27849": "nourrisson", "27850": "natif", "27851": "\u00e9dulcorant", "27852": "lenta.ru", "27853": "zoologiques", "27854": "att\u00e9nuantes", "27855": "jubil\u00e9", "27856": "ensuivra", "27857": "pr\u00e9cipit\u00e9es", "27858": "louisiane", "27859": "punk", "27860": "ob\u00e9issant", "27861": "crabes", "27862": "impuni", "27863": "1910", "27864": "retrait\u00e9es", "27865": "ammar", "27866": "navr\u00e9", "27867": "joies", "27868": "d\u00e9faillant", "27869": "ou\u00efgours", "27870": "possiblement", "27871": "vitae", "27872": "d\u00e9butants", "27873": "dogmatiques", "27874": "compliquerait", "27875": "assid\u00fbment", "27876": "\u00e9galiser", "27877": "transmettrons", "27878": "abaiss\u00e9", "27879": "jou\u00e9e", "27880": "baldwin", "27881": "auroi", "27882": "exon\u00e9r\u00e9", "27883": "400.000", "27884": "salir", "27885": "explicative", "27886": "habit\u00e9s", "27887": "ma\u0148ka", "27888": "lulu", "27889": "mozambicains", "27890": "caract\u00e9risant", "27891": "dictatoriale", "27892": "christa", "27893": "marchi", "27894": "conclura", "27895": "privatis\u00e9es", "27896": "extraterritorialit\u00e9", "27897": "r\u00e9sumerai", "27898": "naviguant", "27899": "maxim", "27900": "b\u00e9ni", "27901": "discontinuer", "27902": "pri\u00e9e", "27903": "biennale", "27904": "bangladaise", "27905": "militaris\u00e9e", "27906": "ch\u0153ur", "27907": "h\u00e9siterai", "27908": "tiss\u00e9", "27909": "pr\u00e9requis", "27910": "mammographies", "27911": "mhp", "27912": "transpos\u00e9s", "27913": "zimbabw\u00e9enne", "27914": "c\u00e9phalopodes", "27915": "andreas", "27916": "d\u00e9guiser", "27917": "levy", "27918": "instruites", "27919": "lehtom\u00e4ki", "27920": "fuyaient", "27921": "cariboo", "27922": "rencontrait", "27923": "vo", "27924": "raul", "27925": "interdisons", "27926": "dispara\u00eetraient", "27927": "arthabaska", "27928": "ris\u00e9e", "27929": "dentiste", "27930": "communiquera", "27931": "267", "27932": "desertec", "27933": "cessaient", "27934": "bouddha", "27935": "empi\u00e9tement", "27936": "sellette", "27937": "arriverez", "27938": "d\u00e9pollution", "27939": "somaliland", "27940": "ekaterina", "27941": "ump", "27942": "d\u00e9sunion", "27943": "fr\u00e9quent\u00e9s", "27944": "qualificatifs", "27945": "fallacieuses", "27946": "imprim\u00e9es", "27947": "comprim\u00e9s", "27948": "support\u00e9e", "27949": "identitaire", "27950": "olympic", "27951": "cl\u00f4t", "27952": "expliquons", "27953": "ac", "27954": "\u00e9dit\u00e9", "27955": "compatissante", "27956": "313", "27957": "galloise", "27958": "saxons", "27959": "rapportrice", "27960": "diapason", "27961": "\u00e9nergivores", "27962": "z\u00e9l\u00e9s", "27963": "mec", "27964": "remettrai", "27965": "mutil\u00e9s", "27966": "rallonge", "27967": "d\u00e9cibels", "27968": "cope", "27969": "lapsus", "27970": "foods", "27971": "\u00e9tonnent", "27972": "malone", "27973": "millionnaire", "27974": "lai", "27975": "sissi", "27976": "ovation", "27977": "insisterais", "27978": "4.000", "27979": "empirique", "27980": "insultants", "27981": "b\u0153ufs", "27982": "gilets", "27983": "benes", "27984": "alber", "27985": "m\u00e9prisables", "27986": "normalis\u00e9es", "27987": "incorrectement", "27988": "bourguiba", "27989": "verch\u00e8res", "27990": "prononciation", "27991": "vaclav", "27992": "\u00e9videment", "27993": "suicid\u00e9", "27994": "inertes", "27995": "peuplent", "27996": "liberadzki", "27997": "arabia", "27998": "bourreaux", "27999": "cordons", "28000": "regard\u00e9e", "28001": "satisferont", "28002": "appellerait", "28003": "ice", "28004": "guangzhou", "28005": "na\u00efvement", "28006": "semblera", "28007": "mind", "28008": "consid\u00e9reront", "28009": "berges", "28010": "pi\u00e9tiner", "28011": "moustique", "28012": "glisse", "28013": "avaaz", "28014": "renouvel\u00e9es", "28015": "leurrons", "28016": "benazir", "28017": "kroft", "28018": "revendiquons", "28019": "acharne", "28020": "cory", "28021": "rajustement", "28022": "passante", "28023": "quitteront", "28024": "meduza", "28025": "pesait", "28026": "kenyane", "28027": "pilules", "28028": "totalise", "28029": "collaborations", "28030": "\u00e9difi\u00e9", "28031": "oignons", "28032": "820", "28033": "hurler", "28034": "art\u00e8re", "28035": "gujarat", "28036": "bagarres", "28037": "inculp\u00e9es", "28038": "acqu\u00e9reur", "28039": "pilate", "28040": "electric", "28041": "bombarde", "28042": "exhortations", "28043": "ambiante", "28044": "georgios", "28045": "argenteuil", "28046": "empress\u00e9e", "28047": "r\u00e9jouie", "28048": "iyad", "28049": "attaquait", "28050": "plafonn\u00e9", "28051": "inoffensive", "28052": "reimer", "28053": "proportionnels", "28054": "improvis\u00e9e", "28055": "maillot", "28056": "virales", "28057": "\u00e9puis\u00e9e", "28058": "lamentations", "28059": "usurpation", "28060": "aristide", "28061": "maingot", "28062": "4,4", "28063": "giles", "28064": "saigner", "28065": "manhattan", "28066": "chang\u00e9s", "28067": "g\u00f6ran", "28068": "asif", "28069": "gosnell", "28070": "dirigeaient", "28071": "retrousser", "28072": "isa", "28073": "\u00e9touffement", "28074": "propulsion", "28075": "classent", "28076": "escompte", "28077": "litigieuses", "28078": "dialectes", "28079": "alourdit", "28080": "dili", "28081": "1934", "28082": "mod\u00e9rateur", "28083": "hiss\u00e8ne", "28084": "231", "28085": "\u00e9lit", "28086": "confr\u00e9rie", "28087": "tallinn", "28088": "8,4", "28089": "rochon", "28090": "recep", "28091": "tayyip", "28092": "lionel", "28093": "marquons", "28094": "citadelle", "28095": "relay\u00e9s", "28096": "offens\u00e9s", "28097": "prof\u00e9rer", "28098": "organisait", "28099": "savoie", "28100": "carnets", "28101": "convaincues", "28102": "rfi", "28103": "musquodoboit", "28104": "opting", "28105": "communales", "28106": "abdulemam", "28107": "d\u00e9non\u00e7aient", "28108": "suffiraient", "28109": "indiquerait", "28110": "ye", "28111": "mara", "28112": "arr\u00eaterons", "28113": "electronic", "28114": "incompatibilit\u00e9s", "28115": "appellerons", "28116": "manigances", "28117": "d\u00e9lib\u00e9r\u00e9s", "28118": "turkestan", "28119": "v\u00eatus", "28120": "cr\u00e9ions", "28121": "revoit", "28122": "like", "28123": "tambour", "28124": "m\u00e9caniquement", "28125": "tsiganes", "28126": "shepherd", "28127": "pauses", "28128": "fiscalement", "28129": "koukiadis", "28130": "charleroi", "28131": "guangdong", "28132": "gauchiste", "28133": "marginalis\u00e9e", "28134": "m\u00e9nagement", "28135": "antidiscrimination", "28136": "apparent\u00e9es", "28137": "cargo", "28138": "sharia", "28139": "chengdu", "28140": "coiffure", "28141": "appr\u00e9cierions", "28142": "chelsea", "28143": "nedelcheva", "28144": "interrompe", "28145": "palmiers", "28146": "verde", "28147": "gerald", "28148": "maliens", "28149": "v\u00e9rifions", "28150": "gagliano", "28151": "honorera", "28152": "aveuglette", "28153": "\u00eelots", "28154": "vinyle", "28155": "c\u00e9sarienne", "28156": "s\u00e9bastien", "28157": "georgina", "28158": "m23", "28159": "ing\u00e9nieux", "28160": "attaquera", "28161": "freitas", "28162": "comptabilisation", "28163": "enfreinte", "28164": "chinese", "28165": "interf\u00e8rent", "28166": "finul", "28167": "warren", "28168": "circumpolaire", "28169": "pr\u00e9c\u00e9der", "28170": "suramende", "28171": "attel\u00e9e", "28172": "consente", "28173": "sym\u00e9trie", "28174": "constituons", "28175": "montesquieu", "28176": "ward", "28177": "paralys\u00e9s", "28178": "r\u00e9gresser", "28179": "matera", "28180": "diam\u00e8tre", "28181": "fractions", "28182": "accueillerons", "28183": "conceptuelle", "28184": "appelions", "28185": "grav\u00e9e", "28186": "vannes", "28187": "mcgee", "28188": "gigant\u00e8s", "28189": "symbiose", "28190": "colmater", "28191": "omnipr\u00e9sent", "28192": "porterons", "28193": "discr\u00e9dit\u00e9e", "28194": "0062", "28195": "transitions", "28196": "explos\u00e9es", "28197": "mature", "28198": "classifi\u00e9s", "28199": "proc\u00e9durale", "28200": "opini\u00e2tre", "28201": "brume", "28202": "spots", "28203": "retranch\u00e9", "28204": "stalinienne", "28205": "sombath", "28206": "navrant", "28207": "2,70", "28208": "d\u00e9sirais", "28209": "saxon", "28210": "synergy", "28211": "obtenez", "28212": "footballeur", "28213": "punissant", "28214": "pah", "28215": "emp\u00eachaient", "28216": "414", "28217": "b\u00e9larusses", "28218": "baissera", "28219": "d\u00e9concentration", "28220": "9,5", "28221": "inciteront", "28222": "plaisantant", "28223": "fuyants", "28224": "communiquons", "28225": "speroni", "28226": "truff\u00e9", "28227": "lafontaine", "28228": "r\u00e9cup\u00e8re", "28229": "d\u00e9pendrait", "28230": "revigorer", "28231": "garages", "28232": "osama", "28233": "harcel\u00e9", "28234": "contribuait", "28235": "beach", "28236": "vell\u00e9it\u00e9", "28237": "encombre", "28238": "discuterai", "28239": "remport\u00e9s", "28240": "leblanc", "28241": "constitutive", "28242": "accroc", "28243": "insecticides", "28244": "samsung", "28245": "pronostics", "28246": "heurt\u00e9es", "28247": "factuel", "28248": "mendicit\u00e9", "28249": "haddad", "28250": "oppressif", "28251": "television", "28252": "gemelli", "28253": "excelle", "28254": "unificateur", "28255": "hackathon", "28256": "motrices", "28257": "peseurs", "28258": "idiote", "28259": "observez", "28260": "linguistes", "28261": "twinn", "28262": "simplifierait", "28263": "\u0645\u0639", "28264": "discrets", "28265": "concluants", "28266": "m\u00e9chancet\u00e9", "28267": "osgoode", "28268": "novosti", "28269": "fresque", "28270": "conscription", "28271": "\u00e9toff\u00e9", "28272": "infrastructurels", "28273": "panels", "28274": "discrimin\u00e9es", "28275": "\u00e9noncent", "28276": "coul\u00e9e", "28277": "pressenti", "28278": "m\u00e9lang\u00e9", "28279": "modifiait", "28280": "hk", "28281": "shahbaz", "28282": "r\u00e9sidentielle", "28283": "d\u00e9finissions", "28284": "repousse", "28285": "night", "28286": "parel", "28287": "propuls\u00e9", "28288": "griffes", "28289": "\u0641\u0649", "28290": "coche", "28291": "c\u00e9l\u00e9brait", "28292": "rushdie", "28293": "pourrir", "28294": "escadron", "28295": "d\u00e9plorait", "28296": "gentilhomme", "28297": "seau", "28298": "souteniez", "28299": "surpeupl\u00e9s", "28300": "immatricul\u00e9s", "28301": "attellent", "28302": "d\u00e9gr\u00e8vements", "28303": "salive", "28304": "camille", "28305": "hales", "28306": "nova\u00efa", "28307": "michoac\u00e1n", "28308": "vaincue", "28309": "r\u00e9siduels", "28310": "montana", "28311": "initi\u00e9es", "28312": "s\u00f6rensen", "28313": "acqu\u00e9reurs", "28314": "naissantes", "28315": "reculent", "28316": "starbucks", "28317": "sch\u00e4fer", "28318": "in\u00e9dites", "28319": "nourrissant", "28320": "souhaitera", "28321": "plaisait", "28322": "souffriraient", "28323": "tourneront", "28324": "exc\u00e8dent", "28325": "esquive", "28326": "bir\u00e9gional", "28327": "pouls", "28328": "uniformis\u00e9", "28329": "illustres", "28330": "p\u00e9renne", "28331": "tien", "28332": "r\u00e9glant", "28333": "restaurateurs", "28334": "r\u00e9glementons", "28335": "fondera", "28336": "cristian", "28337": "beysen", "28338": "chameaux", "28339": "enthousiasmant", "28340": "likoud", "28341": "\u00e9liminons", "28342": "post\u00e9es", "28343": "r\u00edo", "28344": "nationaliser", "28345": "brosser", "28346": "m\u00e9andres", "28347": "band", "28348": "balade", "28349": "pleurons", "28350": "v\u00e9h\u00e9mente", "28351": "infiltr\u00e9s", "28352": "luttaient", "28353": "stature", "28354": "chun", "28355": "attendrai", "28356": "exposons", "28357": "fr\u00e9n\u00e9tique", "28358": "r\u00e9tablissent", "28359": "gatineau", "28360": "dilu\u00e9", "28361": "convoqu\u00e9es", "28362": "proposais", "28363": "roithov\u00e1", "28364": "ling", "28365": "sibbeston", "28366": "d\u00e9velopperont", "28367": "officieuse", "28368": "dingue", "28369": "sebastian", "28370": "vestimentaires", "28371": "0,8", "28372": "titrisation", "28373": "d\u00e9montrons", "28374": "effluents", "28375": "m\u00e9thadone", "28376": "popular", "28377": "ind\u00e9finie", "28378": "cannibalisme", "28379": "arr\u00eataient", "28380": "298", "28381": "paierons", "28382": "engloutis", "28383": "trousse", "28384": "h\u00e9rit\u00e9e", "28385": "copr\u00e9sidente", "28386": "coq", "28387": "perez", "28388": "isolant", "28389": "furets", "28390": "424", "28391": "paypal", "28392": "bangui", "28393": "upm", "28394": "winterbourne", "28395": "ghan\u00e9en", "28396": "exigez", "28397": "inhalation", "28398": "ravag\u00e9s", "28399": "archivage", "28400": "th\u00e9ologique", "28401": "r\u00e9jouirais", "28402": "padanie", "28403": "cct", "28404": "ray\u00e9e", "28405": "edwards", "28406": "c\u00e9r\u00e9brale", "28407": "impliqueraient", "28408": "m\u00e2cher", "28409": "fixions", "28410": "progresseront", "28411": "iouri", "28412": "sifunakis", "28413": "brader", "28414": "indigner", "28415": "charmant", "28416": "kingsway", "28417": "president", "28418": "\u00e9vangile", "28419": "alonso", "28420": "duch\u00e9", "28421": "engloutir", "28422": "r\u00e9pugnante", "28423": "samara", "28424": "\u00e9paisseur", "28425": "consternante", "28426": "acception", "28427": "magn\u00e9tique", "28428": "arthrite", "28429": "dissip\u00e9", "28430": "short", "28431": "jobbik", "28432": "illustr\u00e9s", "28433": "sauquillo", "28434": "chutent", "28435": "retourneront", "28436": "paneurop\u00e9ennes", "28437": "burin", "28438": "synchronisation", "28439": "martine", "28440": "supercherie", "28441": "oligopole", "28442": "\u00e9lectrice", "28443": "sa\u00effi", "28444": "sid\u00e9rurgiques", "28445": "pi\u00e9ger", "28446": "m\u00e2che", "28447": "t\u00e9l\u00e9gramme", "28448": "consentira", "28449": "\u00e9teinte", "28450": "abstenons", "28451": "r\u00e9sum\u00e9e", "28452": "correspondrait", "28453": "spontan\u00e9es", "28454": "approchant", "28455": "revendicateurs", "28456": "perse", "28457": "carol", "28458": "draconien", "28459": "shimon", "28460": "retrouvez", "28461": "cor", "28462": "g\u00e9n\u00e9ralisations", "28463": "subalterne", "28464": "men", "28465": "chorale", "28466": "\u00e9lectroniquement", "28467": "ignoraient", "28468": "rations", "28469": "pensera", "28470": "vident", "28471": "java", "28472": "ing\u00e9rable", "28473": "ray\u00e9s", "28474": "governance", "28475": "impliqueront", "28476": "\u00e9piceries", "28477": "revendiqu\u00e9e", "28478": "bingu", "28479": "balai", "28480": "robotique", "28481": "rover", "28482": "diabolisation", "28483": "veronica", "28484": "entretenait", "28485": "christmas", "28486": "mol\u00e9culaire", "28487": "marquantes", "28488": "n\u00e9gocierons", "28489": "timor\u00e9e", "28490": "rassemblera", "28491": "lo", "28492": "fanfare", "28493": "mystique", "28494": "accablantes", "28495": "procureure", "28496": "glaciaire", "28497": "marginales", "28498": "mousseux", "28499": "385", "28500": "institu\u00e9s", "28501": "rattrape", "28502": "hindu", "28503": "driving", "28504": "rogers", "28505": "disney", "28506": "poudres", "28507": "traque", "28508": "fromages", "28509": "appr\u00e9ciera", "28510": "tencent", "28511": "d\u00e9coll\u00e9", "28512": "licites", "28513": "rangers", "28514": "\u00e9trangler", "28515": "p\u00e9nalement", "28516": "mastercard", "28517": "salem", "28518": "fares", "28519": "capituler", "28520": "concessionnaire", "28521": "h\u00e9berg\u00e9s", "28522": "canots", "28523": "continuel", "28524": "effondrent", "28525": "dominicains", "28526": "accepterais", "28527": "lothringen", "28528": "boudria", "28529": "\u00e9conomise", "28530": "accrochent", "28531": "ultimement", "28532": "promotionnelles", "28533": "prot\u00e9gerons", "28534": "l\u00e9ser", "28535": "lav\u00e9", "28536": "rangent", "28537": "pantalons", "28538": "celsius", "28539": "d\u00e9porter", "28540": "r\u00e9compensant", "28541": "libreville", "28542": "republic", "28543": "wroc\u0142aw", "28544": "marient", "28545": "efforceront", "28546": "saberi", "28547": "prohibitifs", "28548": "lou\u00e9e", "28549": "ador\u00e9", "28550": "parfums", "28551": "v\u00e9hicul\u00e9es", "28552": "commandit\u00e9", "28553": "10b", "28554": "inachev\u00e9e", "28555": "syndicalisation", "28556": "passionn\u00e9ment", "28557": "f\u00e8ves", "28558": "d\u00e9funte", "28559": "l\u00e1szl\u00f3", "28560": "introductive", "28561": "encline", "28562": "conditionn\u00e9s", "28563": "7,6", "28564": "distrait", "28565": "imperial", "28566": "justifiables", "28567": "vraquiers", "28568": "bross\u00e9", "28569": "anticonstitutionnelle", "28570": "p\u00e2tit", "28571": "complaisant", "28572": "r\u00e9volt\u00e9", "28573": "jazz", "28574": "transmissible", "28575": "ti\u00e8de", "28576": "misons", "28577": "p\u00e9diatrie", "28578": "souscrits", "28579": "malik", "28580": "1933", "28581": "ramassage", "28582": "assoient", "28583": "exp\u00e9riment\u00e9es", "28584": "ph\u00e9nom\u00e9nal", "28585": "medias", "28586": "kiosques", "28587": "cuisson", "28588": "violons", "28589": "puja", "28590": "boumediene", "28591": "argo", "28592": "danielsson", "28593": "obstin\u00e9", "28594": "triomph\u00e9", "28595": "ind\u00e9termin\u00e9", "28596": "pollue", "28597": "insinu\u00e9", "28598": "l\u00e9giste", "28599": "concorder", "28600": "terra", "28601": "una", "28602": "tce", "28603": "monumental", "28604": "\u00e9radiqu\u00e9", "28605": "respiratoire", "28606": "baghdadi", "28607": "galiciens", "28608": "belize", "28609": "m\u00e2t", "28610": "odessa", "28611": "inou\u00efe", "28612": "traiterai", "28613": "renie", "28614": "ridges", "28615": "louches", "28616": "bautista", "28617": "victimisation", "28618": "virgule", "28619": "377", "28620": "lgbtq", "28621": "texto", "28622": "abaissant", "28623": "april", "28624": "cynthia", "28625": "hypoth\u00e9qu\u00e9", "28626": "temporisation", "28627": "ga\u00e9lique", "28628": "naisse", "28629": "envenime", "28630": "satisferait", "28631": "uniformis\u00e9e", "28632": "adonne", "28633": "gazaouis", "28634": "allen", "28635": "cuire", "28636": "\u00e9voquais", "28637": "trempe", "28638": "dispers\u00e9e", "28639": "gagn\u00e9es", "28640": "remplaceront", "28641": "d\u00e9moli", "28642": "frein\u00e9e", "28643": "d\u00e9chiffrer", "28644": "fajon", "28645": "ach\u00e8vent", "28646": "stornoway", "28647": "conditionnant", "28648": "davignon", "28649": "attractifs", "28650": "r\u00e9torqu\u00e9", "28651": "salutaires", "28652": "ratko", "28653": "v\u00e9rifierai", "28654": "associatif", "28655": "rugby", "28656": "b\u00e9gin", "28657": "alexis", "28658": "balkanisation", "28659": "p\u00e2le", "28660": "us\u00e9s", "28661": "survivantes", "28662": "balfe", "28663": "aubert", "28664": "anthropologue", "28665": "was", "28666": "ent\u00e9rin\u00e9e", "28667": "avertissant", "28668": "vivais", "28669": "cartier", "28670": "fascination", "28671": "cyrillique", "28672": "r\u00e9cipients", "28673": "yassine", "28674": "\u00e9rythr\u00e9ens", "28675": "d\u00e9stabilis\u00e9", "28676": "r\u00e9habilit\u00e9", "28677": "\u00e9pargn\u00e9es", "28678": "g\u00e9antes", "28679": "prescott", "28680": "implant\u00e9s", "28681": "spam", "28682": "argot", "28683": "\u00e9duqu\u00e9es", "28684": "arbore", "28685": "25a", "28686": "ridiculis\u00e9", "28687": "voterions", "28688": "pelttari", "28689": "plain", "28690": "antipollution", "28691": "estai", "28692": "distinctive", "28693": "entretienne", "28694": "vendons", "28695": "asian", "28696": "rosedale", "28697": "kichwa", "28698": "5,6", "28699": "jolies", "28700": "abruzzes", "28701": "majorations", "28702": "hfc", "28703": "escroquer", "28704": "extirper", "28705": "\u00e9maill\u00e9", "28706": "opposerai", "28707": "adriana", "28708": "199", "28709": "insatisfaisantes", "28710": "dumont", "28711": "defence", "28712": "mello", "28713": "muraille", "28714": "cochon", "28715": "abdiquer", "28716": "mahovlich", "28717": "trier", "28718": "bois\u00e9s", "28719": "psdc", "28720": "1935", "28721": "sp\u00e9cialise", "28722": "norv\u00e9gienne", "28723": "seigle", "28724": "tulipes", "28725": "hic", "28726": "filer", "28727": "photoshop", "28728": "r\u00e9dactions", "28729": "eutrophisation", "28730": "blanco", "28731": "terrifi\u00e9s", "28732": "transgenre", "28733": "d\u00e9couvrant", "28734": "child", "28735": "suspensions", "28736": "purg\u00e9es", "28737": "souhaiteriez", "28738": "yolanda", "28739": "insoluble", "28740": "tombaient", "28741": "ristournes", "28742": "harkness", "28743": "r\u00e9gresse", "28744": "pundit", "28745": "estomper", "28746": "bade", "28747": "lynx", "28748": "d\u00e9munie", "28749": "revaloriser", "28750": "travaill\u00e9es", "28751": "fire", "28752": "r\u00e9v\u00e9leront", "28753": "oim", "28754": "faisal", "28755": "envoyait", "28756": "r\u00e9unirons", "28757": "repr\u00e9senteraient", "28758": "cybers\u00e9curit\u00e9", "28759": "sermons", "28760": "hach\u00e9e", "28761": "londonien", "28762": "bastos", "28763": "veyrac", "28764": "metteur", "28765": "iap", "28766": "rapprochements", "28767": "d\u00e9ploiera", "28768": "giron", "28769": "accomplirons", "28770": "guideront", "28771": "an\u00e9anties", "28772": "iquitos", "28773": "sociologiques", "28774": "litvinenko", "28775": "ippc", "28776": "idoines", "28777": "recourons", "28778": "465", "28779": "immobile", "28780": "changions", "28781": "sender", "28782": "cumul\u00e9", "28783": "353", "28784": "offrez", "28785": "258", "28786": 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-11.4157 +▁counterpart -11.4159 +▁dramatic -11.4163 +▁capabilities -11.4166 +▁minute -11.4168 +▁enseignants -11.4173 +▁Like -11.4173 +▁CCRA -11.4178 +▁frequency -11.4183 +▁parfaitement -11.4189 +▁alleged -11.4193 +▁volatil -11.4196 +▁plafond -11.4199 +▁produisent -11.4204 +Adresse -11.4204 +▁tourist -11.421 +▁fragment -11.4222 +exploiter -11.4227 +▁partisan -11.4239 +▁spectrum -11.4243 +▁connaissent -11.4248 +▁borrow -11.4251 +expertise -11.4254 +▁approve -11.4258 +▁réglé -11.4262 +▁décharge -11.4271 +démocrate -11.4274 +▁ocean -11.4275 +encourager -11.4283 +▁Maritime -11.4289 +▁lecteur -11.429 +▁treaties -11.4293 +où -11.4294 +▁winter -11.4297 +▁combine -11.4298 +▁serving -11.4301 +▁Major -11.4309 +▁relevance -11.4316 +▁breast -11.4316 +▁quelconque -11.432 +▁enabling -11.4322 +▁Belarus -11.4324 +▁Invest -11.4331 +▁surrounding -11.4339 +▁Overall -11.4341 +2,5 -11.4343 +▁artistique -11.4348 +▁Tobacco -11.4358 +▁caution -11.4361 +▁disadvantage -11.4362 +sky -11.4363 +interprète -11.4366 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-11.4929 +▁atmosphérique -11.493 +▁simplification -11.4935 +▁Quoi -11.4936 +architecture -11.4937 +▁construire -11.4943 +▁Planned -11.4946 +(7) -11.495 +▁himself -11.4955 +▁Smith -11.4961 +▁appartenant -11.4962 +▁translate -11.4963 +▁leaving -11.4968 +▁exécuté -11.4969 +160; -11.4971 +▁platform -11.4971 +▁utilis -11.4983 +étroit -11.4984 +▁solely -11.4986 +▁outbreak -11.4989 +▁chimique -11.499 +▁contiennent -11.4992 +administrateur -11.4993 +▁paie -11.4994 +▁Votre -11.5002 +▁chaleur -11.5002 +▁Inspection -11.5005 +▁capita -11.5009 +▁phone -11.5017 +▁profondeur -11.5017 +▁citoyenneté -11.5023 +▁viabilité -11.5026 +▁raising -11.5029 +▁latine -11.5043 +▁Round -11.5045 +▁révél -11.5045 +▁racial -11.5047 +▁enhancing -11.5048 +▁running -11.505 +▁antérieur -11.5054 +▁négocier -11.5054 +▁Equipment -11.506 +▁palestinien -11.5064 +▁Métis -11.5065 +▁vétérinaire -11.5068 +▁tiendra -11.5069 +▁club -11.5073 +▁Nisga -11.5076 +▁mining -11.5076 +▁postsecondaire -11.5079 +▁middle -11.5079 +▁rémunéré -11.5083 +▁nominal -11.5085 +▁pilier -11.5088 +apprécie -11.5089 +▁Certain -11.5095 +Agency -11.5103 +▁récepteur -11.5105 +unanimité -11.5109 +▁Structure -11.5109 +▁privilège -11.512 +IDE -11.5126 +▁dégage -11.5127 +▁Outcome -11.5132 +exclu -11.5136 +▁accueilli -11.5138 +▁tackle -11.5139 +▁susmentionné -11.5141 +▁Registration -11.5145 +▁inadequate -11.5146 +▁convinced -11.515 +▁insufficient -11.5151 +▁superficie -11.5152 +▁Should -11.5156 +▁Military -11.5158 +droit -11.516 +▁1999-2000 -11.5166 +▁Power -11.5167 +▁criticism -11.5179 +▁oiseaux -11.518 +▁typically -11.5182 +▁Alternative -11.5188 +▁demonstration -11.5192 +▁seminar -11.5194 +▁interrogé -11.5222 +▁Voices -11.5223 +▁Distribution -11.5228 +▁flexibilité -11.5234 +▁Competition -11.5235 +▁ronde -11.5236 +▁évolue -11.5236 +▁forget -11.5246 +▁foyer -11.5246 +▁Overview -11.5251 +▁______________ -11.5254 +▁deviennent -11.5254 +▁engineering -11.5257 +▁ADM -11.5261 +▁diplômé -11.5262 +▁consolidate -11.5266 +▁negotiation -11.5268 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-11.5443 +▁intelligent -11.5446 +▁Present -11.5455 +▁autorise -11.5466 +▁puissant -11.5469 +▁diplôme -11.5473 +Article -11.5477 +▁caisse -11.548 +▁acheté -11.548 +▁Chemical -11.5481 +▁Exhibit -11.5481 +▁étudiant -11.5492 +▁extraordinaire -11.5503 +▁indicating -11.5503 +▁gauche -11.5513 +▁recouvrement -11.5514 +▁linguistic -11.5523 +▁bientôt -11.5523 +▁CRDI -11.5526 +▁citizenship -11.553 +▁ambitious -11.5532 +▁Library -11.5538 +élargir -11.5545 +▁terrible -11.5546 +▁salubrité -11.5554 +▁denied -11.5555 +▁Regulatory -11.5555 +intervalle -11.5558 +▁random -11.5559 +▁faiblesse -11.5561 +▁politicians -11.5562 +▁crédibilité -11.5563 +▁bottom -11.5568 +▁Berlin -11.5571 +▁probabilité -11.5575 +▁demographic -11.5576 +▁consequence -11.5576 +▁Pologne -11.5577 +▁Clinical -11.5581 +▁magasin -11.5587 +instaurer -11.5592 +▁Spending -11.5592 +ordinate -11.5596 +▁établissant -11.5618 +▁expressément -11.5622 +▁obligé -11.5642 +▁éclairé -11.5646 +▁prolongé -11.5654 +extraction -11.566 +INFORMATION -11.5662 +▁trafficking -11.5663 +▁condamné -11.5667 +▁froid -11.567 +oriented -11.5673 +acceptation -11.5675 +▁sensibiliser -11.5682 +▁RRSP -11.5685 +exploitant -11.5686 +▁overcome -11.5689 +▁porc -11.5689 +▁reconnais -11.569 +▁combustion -11.5691 +▁corriger -11.5692 +▁unfair -11.5698 +▁Territoires -11.5703 +▁anniversaire -11.5706 +enfance -11.571 +autoriser -11.5711 +indemnisation -11.5725 +▁distingue -11.5734 +▁construit -11.5737 +▁provoqué -11.574 +▁Cross -11.5749 +▁ecological -11.5753 +▁absent -11.5754 +▁terminal -11.5756 +▁benchmark -11.5761 +IFICATION -11.578 +▁Sommaire -11.5781 +▁minimize -11.5785 +▁passeport -11.5786 +▁discretion -11.5801 +▁counselling -11.5805 +▁Climate -11.5806 +▁Calcul -11.5807 +▁athlètes -11.5811 +▁intégrante -11.5813 +▁Ethics -11.582 +▁dissemination -11.582 +▁proviennent -11.5826 +▁plomb -11.583 +▁Exchange -11.5833 +▁publier -11.5836 +▁Challenge -11.5839 +agrément -11.5844 +▁pillar -11.5844 +▁creative -11.5846 +▁extreme -11.5846 +▁meaningful -11.5849 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-11.8325 +▁tolérance -11.833 +▁identical -11.8335 +▁charbon -11.8344 +▁plaque -11.8346 +▁patron -11.8351 +▁fragile -11.8355 +▁olympique -11.8355 +▁Indigenous -11.8368 +▁Thomas -11.837 +▁incluent -11.8377 +▁simplifier -11.8383 +▁bénéfique -11.8385 +▁clarté -11.8393 +▁Friday -11.84 +▁millénaire -11.8405 +▁antidumping -11.841 +▁Swedish -11.8413 +▁grippe -11.8415 +▁holiday -11.842 +administrer -11.8423 +▁Objet -11.8428 +▁suscité -11.8428 +▁Hydro -11.8435 +▁frequent -11.844 +▁succ -11.8441 +▁sauvetage -11.8443 +▁escompté -11.8445 +▁privilégié -11.8445 +▁Democratic -11.8453 +Entente -11.846 +▁octroyé -11.846 +▁nevertheless -11.8465 +octobre -11.8472 +▁douanière -11.8475 +▁mobilisation -11.8475 +Circ -11.8479 +▁Earth -11.8493 +▁PLAN -11.8495 +▁[...] -11.8495 +▁prévoyant -11.8495 +▁Coordinator -11.8496 +▁Troisièmement -11.8496 +intégr -11.8498 +▁false -11.85 +▁stipulate -11.8513 +▁relance -11.8517 +▁Taiwan -11.8518 +▁commodity -11.8518 +▁Strength -11.8523 +▁readily -11.8525 +▁Engagement -11.8527 +européen -11.8531 +▁retenir -11.8533 +▁médiation -11.8545 +▁elimination -11.8552 +▁éducatif -11.8552 +univers -11.8554 +▁Beijing -11.8555 +École -11.8559 +▁détaillants -11.8564 +▁trompe -11.8567 +▁tuberculose -11.8574 +▁mercure -11.8575 +▁bénéficié -11.8586 +▁biggest -11.8586 +enveloppe -11.8588 +▁tchèque -11.8596 +▁élabore -11.8611 +▁jurisprudence -11.8611 +▁therapeutic -11.8613 +▁militant -11.8613 +▁qualifie -11.8614 +▁Self -11.8615 +▁concitoyens -11.8616 +▁hommage -11.8621 +▁Habitat -11.8625 +▁logistique -11.8626 +▁possess -11.8627 +UNESCO -11.8628 +▁aspirations -11.8633 +insuffisance -11.8641 +▁démonstration -11.8641 +▁séquence -11.8643 +hygiène -11.865 +▁architect -11.8652 +▁soupçon -11.8655 +▁sacrifice -11.8657 +▁Telecommunicati -11.8657 +▁Inscrivez -11.8658 +▁square -11.8658 +conseil -11.8665 +échappe -11.8665 +▁Bosnie -11.8667 +▁prejudice -11.8672 +▁propice -11.8676 +▁coherent -11.8681 +▁souffrance -11.8684 +▁Singapore -11.8689 +▁TRANS -11.8689 +▁reputation -11.8698 +▁disturb -11.8708 +▁précieux -11.872 +▁separation -11.8729 +▁croyons -11.873 +▁Joseph -11.8735 +▁GROUP -11.8742 +▁Respondent -11.8751 +▁ambitieux -11.8751 +▁rigid -11.8763 +Labrador -11.8764 +▁2008-2009 -11.8777 +▁encompass -11.8782 +▁intervenu -11.8782 +Territorial -11.8783 +(2005) -11.8788 +enjeu -11.8789 +▁responsive -11.879 +▁History -11.8801 +▁incorrect -11.8816 +▁caractérise -11.8825 +▁sanguin -11.8844 +▁Chacun -11.8845 +▁multiplie -11.8848 +▁Wildlife -11.8851 +▁competitor -11.8851 +▁opérateurs -11.8853 +▁likelihood -11.8863 +▁confère -11.8866 +▁Daniel -11.8867 +▁excédentaire -11.887 +▁légumes -11.8873 +▁éditeurs -11.8877 +▁beneficiaries -11.8879 +▁restauration -11.8884 +▁inhérent -11.8887 +▁voulait -11.8896 +▁heavily -11.8898 +▁spectacle -11.8898 +▁complications -11.8901 +▁Petit -11.8911 +▁familial -11.8911 +▁Broadcasting -11.8912 +▁epidemic -11.8915 +ù -12.1993 +À -12.9255 diff --git a/base/classifier/cnn_train.py b/base/classifier/cnn_train.py deleted file mode 100755 index dbfa589..0000000 --- a/base/classifier/cnn_train.py +++ /dev/null @@ -1,467 +0,0 @@ -from __future__ import division - -import argparse -import math -import sys -import time - -import onmt -import torch -import torch.nn as nn -from torch import cuda -from torch.autograd import Variable -from tqdm import tqdm - -parser = argparse.ArgumentParser(description="train.py") - -## Data options - -parser.add_argument( - "-data", required=True, help="Path to the *-train.pt file from preprocess.py" -) -parser.add_argument( - "-save_model", - default="model", - help="""Model filename (the model will be saved as - _epochN_PPL.pt where PPL is the - validation perplexity""", -) -parser.add_argument( - "-train_from_state_dict", - default="", - type=str, - help="""If training from a checkpoint then this is the - path to the pretrained model's state_dict.""", -) -parser.add_argument( - "-train_from", - default="", - type=str, - help="""If training from a checkpoint then this is the - path to the pretrained model.""", -) -parser.add_argument("-num_classes", default=2, type=int, help="""Number of classes""") - -## Model options - -parser.add_argument( - "-word_vec_size", type=int, default=300, help="Word embedding sizes" -) -parser.add_argument("-filter_size", default=5, type=int, help="Size of CNN filters") -parser.add_argument("-num_filters", default=100, type=int, help="Number of CNN filters") -parser.add_argument( - "-sequence_length", type=int, default=50, help="Length of max sentence." -) - -## Optimization options - -parser.add_argument("-batch_size", type=int, default=64, help="Maximum batch size") -parser.add_argument( - "-max_generator_batches", - type=int, - default=32, - help="""Maximum batches of words in a sequence to run - the generator on in parallel. Higher is faster, but uses - more memory.""", -) -parser.add_argument("-epochs", type=int, default=10, help="Number of training epochs") -parser.add_argument( - "-start_epoch", type=int, default=1, help="The epoch from which to start" -) -parser.add_argument( - "-param_init", - type=float, - default=0.1, - help="""Parameters are initialized over uniform distribution - with support (-param_init, param_init)""", -) -parser.add_argument( - "-optim", default="sgd", help="Optimization method. [sgd|adagrad|adadelta|adam]" -) -parser.add_argument( - "-max_grad_norm", - type=float, - default=5, - help="""If the norm of the gradient vector exceeds this, - renormalize it to have the norm equal to max_grad_norm""", -) -parser.add_argument( - "-dropout", - type=float, - default=0.2, - help="Dropout probability; applied between LSTM stacks.", -) -parser.add_argument( - "-curriculum", - action="store_true", - help="""For this many epochs, order the minibatches based - on source sequence length. Sometimes setting this to 1 will - increase convergence speed.""", -) -parser.add_argument( - "-extra_shuffle", - action="store_true", - help="""By default only shuffle mini-batch order; when true, - shuffle and re-assign mini-batches""", -) - -# learning rate -parser.add_argument( - "-learning_rate", - type=float, - default=1.0, - help="""Starting learning rate. If adagrad/adadelta/adam is - used, then this is the global learning rate. Recommended - settings: sgd = 1, adagrad = 0.1, adadelta = 1, adam = 0.001""", -) -parser.add_argument( - "-learning_rate_decay", - type=float, - default=0.5, - help="""If update_learning_rate, decay learning rate by - this much if (i) perplexity does not decrease on the - validation set or (ii) epoch has gone past - start_decay_at""", -) -parser.add_argument( - "-start_decay_at", - type=int, - default=8, - help="""Start decaying every epoch after and including this - epoch""", -) - -# pretrained word vectors - -parser.add_argument( - "-pre_word_vecs_enc", - help="""If a valid path is specified, then this will load - pretrained word embeddings on the encoder side. - See README for specific formatting instructions.""", -) - -# GPU -parser.add_argument( - "-gpus", default=[], nargs="+", type=int, help="Use CUDA on the listed devices." -) - -parser.add_argument( - "-log_interval", type=int, default=50, help="Print stats at this interval." -) - -opt = parser.parse_args() - -print(opt) - -if torch.cuda.is_available() and not opt.gpus: - print("WARNING: You have a CUDA device, so you should probably run with -gpus 0") - -if opt.gpus: - cuda.set_device(opt.gpus[0]) - - -def NMTCriterion(vocabSize): - crit = nn.BCELoss() - if opt.gpus: - crit.cuda() - return crit - - -def memoryEfficientLoss(outputs, targets, generator, crit, eval=False): - # compute generations one piece at a time - num_correct, loss = 0, 0 - outputs = Variable(outputs.data, requires_grad=(not eval), volatile=eval) - - # outputs and targets are size batch_size - batch_size = outputs.size(0) - loss_t = crit(outputs.squeeze(-1), targets[0].float()) - if opt.gpus: - pred_t = torch.ge( - outputs.data, - torch.cuda.FloatTensor(outputs.size(0), outputs.size(1)).fill_(0.5), - ) - else: - pred_t = torch.ge( - outputs.data, torch.FloatTensor(outputs.size(0), outputs.size(1)).fill_(0.5) - ) - - # print("OUTPUT:", (outputs >=0.5).transpose(0,1)) - # print("TARGET:", targets) - # print("ACC:", (outputs >=0.5).transpose(0,1).long().eq(targets).sum()) - - # w - # num_correct = pred_t.long().squeeze(-1).eq(targets[0].data).sum() - num_correct = (outputs >= 0.5).transpose(0, 1).long().eq(targets).sum() - # print("NUM COR:", num_correct) - loss += loss_t.item() - if not eval: - loss_t.div(batch_size).backward() - - grad_output = None if outputs.grad is None else outputs.grad.data - return loss, grad_output, num_correct - - -def eval(model, criterion, data, vocab_size): - total_loss = 0 - total_words = 0 - total_num_correct = 0 - - model.eval() - for i in range(len(data)): - batch = data[i][:-1] # exclude original indices - - src = batch[0] - inp = ( - src[0] % vocab_size - ) # Size is seq_len x batch_size, type: torch.cuda.LongTensor, Variable - inp_ = torch.unsqueeze( - inp, 2 - ) # Size is seq_len x batch_size x 1, type: torch.cuda.LongTensor, Variable - if len(opt.gpus) >= 1: - one_hot = Variable( - torch.cuda.FloatTensor( - src[0].size(0), src[0].size(1), vocab_size - ).zero_() - ) - else: - one_hot = Variable( - torch.FloatTensor(src[0].size(0), src[0].size(1), vocab_size).zero_() - ) - one_hot_scatt = one_hot.scatter_(2, inp_, 1) - outputs = model(one_hot_scatt) - targets = batch[1] - loss, _, num_correct = memoryEfficientLoss( - outputs, targets, model, criterion, eval=True - ) - total_loss += loss - total_num_correct += num_correct.item() - total_words += targets.size(1) - - model.train() - return total_loss / total_words, total_num_correct / total_words - - -def trainModel(model, trainData, validData, dataset, optim): - # print(model) - sys.stdout.flush() - model.train() - - # define criterion - criterion = NMTCriterion(opt.num_classes) - - # Vocab Size - vocab_size = model.vocab_size - # vocab_size = dataset['dicts']['src'].size() - - start_time = time.time() - - def trainEpoch(epoch): - if opt.extra_shuffle and epoch > opt.curriculum: - trainData.shuffle() - - # shuffle mini batch order - batchOrder = torch.randperm(len(trainData)) - - total_loss, total_words, total_num_correct = 0, 0, 0 - report_loss, report_tgt_words, report_src_words, report_num_correct = 0, 0, 0, 0 - start = time.time() - for i in tqdm(range(len(trainData))): - batchIdx = batchOrder[i] if epoch > opt.curriculum else i - # print(trainData[batchIdx]) - batch = trainData[batchIdx][:-1] # exclude original indices - - # making one hot encoding - src = batch[0] - - # print("src:",src[0].shape, batch[1][0].shape) - inp = ( - src[0] % vocab_size - ) # Size is seq_len x batch_size, type: torch.cuda.LongTensor, Variable - inp_ = torch.unsqueeze( - inp, 2 - ) # Size is seq_len x batch_size x 1, type: torch.cuda.LongTensor, Variable - if len(opt.gpus) >= 1: - one_hot = Variable( - torch.cuda.FloatTensor( - src[0].size(0), src[0].size(1), vocab_size - ).zero_() - ) - else: - one_hot = Variable( - torch.FloatTensor( - src[0].size(0), src[0].size(1), vocab_size - ).zero_() - ) - one_hot_scatt = one_hot.scatter_( - 2, inp_, 1 - ) # Size: seq_len x batch_size x vocab_size, type: torch.cuda.FloatTensor, Variable - - model.zero_grad() - outputs = model(one_hot_scatt) - # print("outputs:", outputs.shape) - targets = batch[1] - # print("targets:", targets.shape) - loss, gradOutput, num_correct = memoryEfficientLoss( - outputs, targets, model, criterion - ) - outputs.backward(gradOutput) - - # update the parameters - optim.step() - num_words = targets.size(1) - report_loss += loss - report_num_correct += num_correct.item() - report_tgt_words += num_words - report_src_words += sum(batch[0][1]) - total_loss += loss - total_num_correct += num_correct.item() - total_words += num_words - - if i % opt.log_interval == -1 % opt.log_interval: - print( - "Epoch %2d, %5d/%5d; acc: %6.2f; %3.0f src tok/s; %3.0f tgt tok/s; %6.0f s elapsed" - % ( - epoch, - i + 1, - len(trainData), - report_num_correct / report_tgt_words * 100, - report_src_words / (time.time() - start), - report_tgt_words / (time.time() - start), - time.time() - start_time, - ) - ) - - sys.stdout.flush() - report_loss = report_tgt_words = report_src_words = ( - report_num_correct - ) = 0 - start = time.time() - return total_loss / total_words, total_num_correct / total_words - - for epoch in range(opt.start_epoch, opt.epochs + 1): - print("") - - # (1) train for one epoch on the training set - train_loss, train_acc = trainEpoch(epoch) - print("Train accuracy: %g" % (train_acc * 100)) - print("Train Loss: ", train_loss) - - # (2) evaluate on the validation set - valid_loss, valid_acc = eval(model, criterion, validData, vocab_size) - print("Validation accuracy: %g" % (valid_acc * 100)) - print("Validation Loss: ", valid_loss) - - sys.stdout.flush() - # (3) update the learning rate - optim.updateLearningRate(valid_loss, epoch) - model_state_dict = ( - model.module.state_dict() if len(opt.gpus) > 1 else model.state_dict() - ) - model_state_dict = { - k: v for k, v in model_state_dict.items() if "generator" not in k - } - # (4) drop a checkpoint - checkpoint = { - "model": model_state_dict, - "dicts": dataset["dicts"], - "opt": opt, - "epoch": epoch, - "optim": optim, - } - torch.save( - checkpoint, - "%s_acc_%.2f_loss_%.2f_e%d.pt" - % (opt.save_model, 100 * valid_acc, valid_loss, epoch), - ) - - -def main(): - print("Loading data from '%s'" % opt.data) - - dataset = torch.load(opt.data) - - dict_checkpoint = opt.train_from if opt.train_from else opt.train_from_state_dict - if dict_checkpoint: - print("Loading dicts from checkpoint at %s" % dict_checkpoint) - checkpoint = torch.load(dict_checkpoint) - dataset["dicts"] = checkpoint["dicts"] - - trainData = onmt.Dataset( - dataset["train"]["src"], dataset["train"]["tgt"], opt.batch_size, opt.gpus - ) - validData = onmt.Dataset( - dataset["valid"]["src"], - dataset["valid"]["tgt"], - opt.batch_size, - opt.gpus, - volatile=True, - ) - - vocabulary_size = 0 - if "settings" in dataset["dicts"]: - vocabulary_size = dataset["dicts"]["src"]["kwargs"]["vocab_size"] - else: - vocabulary_size = dataset["dicts"]["src"].size() - - # dicts = dataset['dicts'] - print(" * vocabulary size. source = %d;" % vocabulary_size) - print(" * number of training sentences. %d" % len(dataset["train"]["src"])) - print(" * maximum batch size. %d" % opt.batch_size) - - print("Building model...") - - model = onmt.CNNModels.ConvNet(opt, vocabulary_size) - - if opt.train_from: - print("Loading model from checkpoint at %s" % opt.train_from) - chk_model = checkpoint["model"] - model_state_dict = { - k: v for k, v in chk_model.state_dict().items() if "generator" not in k - } - model.load_state_dict(model_state_dict) - opt.start_epoch = checkpoint["epoch"] + 1 - - if opt.train_from_state_dict: - print("Loading model from checkpoint at %s" % opt.train_from_state_dict) - model.load_state_dict(checkpoint["model"]) - opt.start_epoch = checkpoint["epoch"] + 1 - - if len(opt.gpus) >= 1: - model.cuda() - else: - model.cpu() - - if len(opt.gpus) > 1: - model = nn.DataParallel(model, device_ids=opt.gpus, dim=1) - - if not opt.train_from_state_dict and not opt.train_from: - for p in model.parameters(): - p.data.uniform_(-opt.param_init, opt.param_init) - - model.load_pretrained_vectors(opt) - - optim = onmt.Optim( - opt.optim, - opt.learning_rate, - opt.max_grad_norm, - lr_decay=opt.learning_rate_decay, - start_decay_at=opt.start_decay_at, - ) - else: - print("Loading optimizer from checkpoint:") - optim = checkpoint["optim"] - print(optim) - - optim.set_parameters(model.parameters()) - - if opt.train_from or opt.train_from_state_dict: - optim.optimizer.load_state_dict(checkpoint["optim"].optimizer.state_dict()) - - nParams = sum([p.nelement() for p in model.parameters()]) - print("* number of parameters: %d" % nParams) - - trainModel(model, trainData, validData, dataset, optim) - - -if __name__ == "__main__": - main() diff --git a/base/classifier/cnn_train_bpe.py b/base/classifier/cnn_train_bpe.py deleted file mode 100755 index 0c4e79c..0000000 --- a/base/classifier/cnn_train_bpe.py +++ /dev/null @@ -1,500 +0,0 @@ -from __future__ import division - -import argparse -import math -import sys -import time - -import onmt -import torch -import torch.nn as nn -from torch import cuda -from torch.autograd import Variable -from tqdm import tqdm - -sys.path.append("../") -from core.dataset import TranslationDataset, collate_fn, paired_collate_fn - -parser = argparse.ArgumentParser(description="train.py") - -## Data options - -parser.add_argument( - "-data", required=True, help="Path to the *-train.pt file from preprocess.py" -) -parser.add_argument( - "-save_model", - default="model", - help="""Model filename (the model will be saved as - _epochN_PPL.pt where PPL is the - validation perplexity""", -) -parser.add_argument( - "-train_from_state_dict", - default="", - type=str, - help="""If training from a checkpoint then this is the - path to the pretrained model's state_dict.""", -) -parser.add_argument( - "-train_from", - default="", - type=str, - help="""If training from a checkpoint then this is the - path to the pretrained model.""", -) -parser.add_argument("-num_classes", default=2, type=int, help="""Number of classes""") - -## Model options - -parser.add_argument( - "-word_vec_size", type=int, default=300, help="Word embedding sizes" -) -parser.add_argument("-filter_size", default=5, type=int, help="Size of CNN filters") -parser.add_argument("-num_filters", default=100, type=int, help="Number of CNN filters") -parser.add_argument( - "-sequence_length", type=int, default=50, help="Length of max sentence." -) - -## Optimization options - -parser.add_argument("-batch_size", type=int, default=64, help="Maximum batch size") -parser.add_argument( - "-max_generator_batches", - type=int, - default=32, - help="""Maximum batches of words in a sequence to run - the generator on in parallel. Higher is faster, but uses - more memory.""", -) -parser.add_argument("-epochs", type=int, default=10, help="Number of training epochs") -parser.add_argument( - "-start_epoch", type=int, default=1, help="The epoch from which to start" -) -parser.add_argument( - "-param_init", - type=float, - default=0.1, - help="""Parameters are initialized over uniform distribution - with support (-param_init, param_init)""", -) -parser.add_argument( - "-optim", default="sgd", help="Optimization method. [sgd|adagrad|adadelta|adam]" -) -parser.add_argument( - "-max_grad_norm", - type=float, - default=5, - help="""If the norm of the gradient vector exceeds this, - renormalize it to have the norm equal to max_grad_norm""", -) -parser.add_argument( - "-dropout", - type=float, - default=0.2, - help="Dropout probability; applied between LSTM stacks.", -) -parser.add_argument( - "-curriculum", - action="store_true", - help="""For this many epochs, order the minibatches based - on source sequence length. Sometimes setting this to 1 will - increase convergence speed.""", -) -parser.add_argument( - "-extra_shuffle", - action="store_true", - help="""By default only shuffle mini-batch order; when true, - shuffle and re-assign mini-batches""", -) - -# learning rate -parser.add_argument( - "-learning_rate", - type=float, - default=1.0, - help="""Starting learning rate. If adagrad/adadelta/adam is - used, then this is the global learning rate. Recommended - settings: sgd = 1, adagrad = 0.1, adadelta = 1, adam = 0.001""", -) -parser.add_argument( - "-learning_rate_decay", - type=float, - default=0.5, - help="""If update_learning_rate, decay learning rate by - this much if (i) perplexity does not decrease on the - validation set or (ii) epoch has gone past - start_decay_at""", -) -parser.add_argument( - "-start_decay_at", - type=int, - default=8, - help="""Start decaying every epoch after and including this - epoch""", -) - -# pretrained word vectors - -parser.add_argument( - "-pre_word_vecs_enc", - help="""If a valid path is specified, then this will load - pretrained word embeddings on the encoder side. - See README for specific formatting instructions.""", -) - -# GPU -parser.add_argument( - "-gpus", default=[], nargs="+", type=int, help="Use CUDA on the listed devices." -) - -parser.add_argument( - "-log_interval", type=int, default=50, help="Print stats at this interval." -) - -opt = parser.parse_args() - -print(opt) - -if torch.cuda.is_available() and not opt.gpus: - print("WARNING: You have a CUDA device, so you should probably run with -gpus 0") - -if opt.gpus: - cuda.set_device(opt.gpus[0]) - - -def NMTCriterion(vocabSize): - crit = nn.BCELoss() - if opt.gpus: - crit.cuda() - return crit - - -def memoryEfficientLoss(outputs, targets, generator, crit, eval=False): - # compute generations one piece at a time - num_correct, loss = 0, 0 - outputs = Variable(outputs.data, requires_grad=(not eval)) - - # outputs and targets are size batch_size - batch_size = outputs.size(0) - loss_t = crit(outputs.squeeze(-1), targets[0].float()) - if opt.gpus: - pred_t = torch.ge( - outputs.data, - torch.cuda.FloatTensor(outputs.size(0), outputs.size(1)).fill_(0.5), - ) - else: - pred_t = torch.ge( - outputs.data, torch.FloatTensor(outputs.size(0), outputs.size(1)).fill_(0.5) - ) - - # print("OUTPUT:", (outputs >=0.5).transpose(0,1)) - # print("TARGET:", targets) - # print("ACC:", (outputs >=0.5).transpose(0,1).long().eq(targets).sum()) - - # w - # num_correct = pred_t.long().squeeze(-1).eq(targets[0].data).sum() - num_correct = (outputs >= 0.5).transpose(0, 1).long().eq(targets).sum() - # print("NUM COR:", num_correct) - loss += loss_t.item() - if not eval: - loss_t.div(batch_size).backward() - - grad_output = None if outputs.grad is None else outputs.grad.data - return loss, grad_output, num_correct - - -def eval(model, criterion, data, vocab_size, opt): - total_loss = 0 - total_words = 0 - total_num_correct = 0 - - model.eval() - - for batch in tqdm(data, desc="Eval"): - src_seq, _, tgt_seq, _ = map(lambda x: x.to("cuda"), batch) - # batch = data[i][:-1] # exclude original indices - - # src = batch[0] - # inp = src[0] % vocab_size # Size is seq_len x batch_size, type: torch.cuda.LongTensor, Variable - # inp_ = torch.unsqueeze(inp, 2) # Size is seq_len x batch_size x 1, type: torch.cuda.LongTensor, Variable - - src = src_seq.transpose(0, 1) - # tgt_seq= tgt_seq.squeeze(1) - # print(src.shape, tgt_seq.shape) - inp = src # Size is seq_len x batch_size, type: torch.cuda.LongTensor, Variable - # print(inp.shape) - inp_ = torch.unsqueeze(inp, 2) - - tensor = torch.cuda.FloatTensor if len(opt.gpus) >= 1 else torch.FloatTensor - - # print(src[0].shape) - one_hot = Variable(tensor(src.size(0), src.size(1), vocab_size).zero_()) - one_hot_scatt = one_hot.scatter_( - 2, inp_, 1 - ) # Size: seq_len x batch_size x vocab_size, type: torch.cuda.FloatTensor, Variable - - outputs = model(one_hot_scatt) - targets = tgt_seq.transpose(0, 1) - loss, _, num_correct = memoryEfficientLoss( - outputs, targets, model, criterion, eval=True - ) - total_loss += loss - total_num_correct += num_correct.item() - total_words += targets.size(1) - - model.train() - return total_loss / total_words, total_num_correct / total_words - - -def trainModel(model, trainData, validData, dataset, optim, opt): - # print(model) - sys.stdout.flush() - model.train() - - # define criterion - criterion = NMTCriterion(opt.num_classes) - - # Vocab Size - vocab_size = model.vocab_size - # vocab_size = dataset['dicts']['src'].size() - - start_time = time.time() - - def trainEpoch(epoch, opt): - if opt.extra_shuffle and epoch > opt.curriculum: - trainData.shuffle() - - # shuffle mini batch order - batchOrder = torch.randperm(len(trainData)) - - total_loss, total_words, total_num_correct = 0, 0, 0 - report_loss, report_tgt_words, report_src_words, report_num_correct = 0, 0, 0, 0 - start = time.time() - i = 0 - for batch in tqdm(trainData, desc="Train"): - src_seq, _, tgt_seq, _ = map(lambda x: x.to("cuda"), batch) - - # batch = trainData[batchIdx][:-1] # exclude original indices - - # making one hot encoding - src = src_seq.transpose(0, 1) - # tgt_seq= tgt_seq.squeeze(1) - # print(src.shape, tgt_seq.shape) - inp = src # Size is seq_len x batch_size, type: torch.cuda.LongTensor, Variable - # print(inp.shape) - inp_ = torch.unsqueeze( - inp, 2 - ) # Size is seq_len x batch_size x 1, type: torch.cuda.LongTensor, Variable - # print(inp_.shape) - - tensor = torch.cuda.FloatTensor if len(opt.gpus) >= 1 else torch.FloatTensor - - # print(src[0].shape) - one_hot = Variable(tensor(src.size(0), src.size(1), vocab_size).zero_()) - one_hot_scatt = one_hot.scatter_( - 2, inp_, 1 - ) # Size: seq_len x batch_size x vocab_size, type: torch.cuda.FloatTensor, Variable - - model.zero_grad() - outputs = model(one_hot_scatt) - # print("outputs:", outputs.shape) - targets = tgt_seq.transpose(0, 1) - # print("targets",targets.shape) - loss, gradOutput, num_correct = memoryEfficientLoss( - outputs, targets, model, criterion - ) - outputs.backward(gradOutput) - - # update the parameters - optim.step() - num_words = targets.size(1) - report_loss += loss - report_num_correct += num_correct.item() - report_tgt_words += num_words - report_src_words += sum(batch[0][1]) - total_loss += loss - total_num_correct += num_correct.item() - total_words += num_words - - if i % opt.log_interval == -1 % opt.log_interval: - runtime = time.time() - start_time - - print( - "Epoch %2d, %5d/%5d; acc: %6.2f; %3.0f src tok/s; %3.0f tgt tok/s; %6.0f s elapsed" - % ( - epoch, - i + 1, - len(trainData), - report_num_correct / report_tgt_words * 100, - report_src_words / (time.time() - start), - report_tgt_words / (time.time() - start), - runtime, - ) - ) - - sys.stdout.flush() - report_loss = report_tgt_words = report_src_words = ( - report_num_correct - ) = 0 - start = time.time() - i += 1 - return total_loss / total_words, total_num_correct / total_words - - for epoch in tqdm(range(opt.start_epoch, opt.epochs + 1), desc="Epoch"): - print("") - - # (1) train for one epoch on the training set - train_loss, train_acc = trainEpoch(epoch, opt) - print("Train accuracy: %g" % (train_acc * 100)) - print("Train Loss: ", train_loss) - - # (2) evaluate on the validation set - valid_loss, valid_acc = eval(model, criterion, validData, vocab_size, opt) - print("Validation accuracy: %g" % (valid_acc * 100)) - print("Validation Loss: ", valid_loss) - - sys.stdout.flush() - # (3) update the learning rate - optim.updateLearningRate(valid_loss, epoch) - model_state_dict = ( - model.module.state_dict() if len(opt.gpus) > 1 else model.state_dict() - ) - model_state_dict = { - k: v for k, v in model_state_dict.items() if "generator" not in k - } - # (4) drop a checkpoint - checkpoint = { - "model": model_state_dict, - "dicts": dataset["dicts"], - "opt": opt, - "epoch": epoch, - "optim": optim, - } - torch.save( - checkpoint, - "%s_acc_%.2f_loss_%.2f_e%d.pt" - % (opt.save_model, 100 * valid_acc, valid_loss, epoch), - ) - - -def init_dataloaders(data, opt): - src_word2idx = data["dicts"]["src"] - # tgt_word2idx = data['dict']['tgt'] - - if "__sow" in src_word2idx["byte_pairs"]: - # we have BPE - src_byte_pairs = {x + "_": y for x, y in src_word2idx["byte_pairs"].items()} - # tgt_byte_pairs = {x+"_": y for x,y in tgt_word2idx['byte_pairs'].items()} - src_word2idx = {**src_byte_pairs, **src_word2idx["words"]} - # tgt_word2idx = {**tgt_byte_pairs, **tgt_word2idx['words']} - - train_loader = torch.utils.data.DataLoader( - TranslationDataset( - src_word2idx=src_word2idx, - tgt_word2idx=src_word2idx, - src_insts=data["train"]["src"], - tgt_insts=data["train"]["tgt"], - ), - num_workers=2, - batch_size=opt.batch_size, - collate_fn=paired_collate_fn, - shuffle=True, - ) - - valid_loader = torch.utils.data.DataLoader( - TranslationDataset( - src_word2idx=src_word2idx, - tgt_word2idx=src_word2idx, - src_insts=data["valid"]["src"], - tgt_insts=data["valid"]["tgt"], - ), - num_workers=2, - batch_size=opt.batch_size, - collate_fn=paired_collate_fn, - ) - - return train_loader, valid_loader - - -def main(): - print("Loading data from '%s'" % opt.data) - - dataset = torch.load(opt.data) - - dict_checkpoint = opt.train_from if opt.train_from else opt.train_from_state_dict - if dict_checkpoint: - print("Loading dicts from checkpoint at %s" % dict_checkpoint) - checkpoint = torch.load(dict_checkpoint) - dataset["dicts"] = checkpoint["dicts"] - - trainData, validData = init_dataloaders(dataset, opt) - - vocabulary_size = 0 - - if "settings" in dataset: - vocabulary_size = dataset["dicts"]["src"]["kwargs"]["vocab_size"] - else: - vocabulary_size = dataset["dicts"]["src"].size() - - print(" * vocabulary size. source = %d;" % vocabulary_size) - print(" * number of training sentences. %d" % len(dataset["train"]["src"])) - print(" * maximum batch size. %d" % opt.batch_size) - - print("Building model...") - - model = onmt.CNNModels.ConvNet(opt, vocabulary_size) - - if opt.train_from: - print("Loading model from checkpoint at %s" % opt.train_from) - chk_model = checkpoint["model"] - model_state_dict = { - k: v for k, v in chk_model.state_dict().items() if "generator" not in k - } - model.load_state_dict(model_state_dict) - opt.start_epoch = checkpoint["epoch"] + 1 - - if opt.train_from_state_dict: - print("Loading model from checkpoint at %s" % opt.train_from_state_dict) - model.load_state_dict(checkpoint["model"]) - opt.start_epoch = checkpoint["epoch"] + 1 - - if len(opt.gpus) >= 1: - model.cuda() - else: - model.cpu() - - if len(opt.gpus) > 1: - model = nn.DataParallel(model, device_ids=opt.gpus, dim=1) - - if not opt.train_from_state_dict and not opt.train_from: - for p in model.parameters(): - p.data.uniform_(-opt.param_init, opt.param_init) - - model.load_pretrained_vectors(opt) - - optim = onmt.Optim( - opt.optim, - opt.learning_rate, - opt.max_grad_norm, - lr_decay=opt.learning_rate_decay, - start_decay_at=opt.start_decay_at, - ) - else: - print("Loading optimizer from checkpoint:") - optim = checkpoint["optim"] - print(optim) - - optim.set_parameters(model.parameters()) - - if opt.train_from or opt.train_from_state_dict: - optim.optimizer.load_state_dict(checkpoint["optim"].optimizer.state_dict()) - - nParams = sum([p.nelement() for p in model.parameters()]) - print("* number of parameters: %d" % nParams) - - trainModel(model, trainData, validData, dataset, optim, opt) - - -if __name__ == "__main__": - main() diff --git a/base/classifier/cnn_translate.py b/base/classifier/cnn_translate.py deleted file mode 100755 index 044a506..0000000 --- a/base/classifier/cnn_translate.py +++ /dev/null @@ -1,105 +0,0 @@ -from __future__ import division - -import argparse -import codecs -import math -import sys - -import onmt -import torch - -parser = argparse.ArgumentParser(description="translate.py") - -parser.add_argument("-model", required=True, help="Path to model .pt file") -parser.add_argument("-num_classes", default=2, type=int, help="""Number of classes""") -parser.add_argument("-src", required=True, help="Source sequence to check") -parser.add_argument( - "-tgt", required=True, help="The target label for the classifier check" -) -parser.add_argument("-label0", required=True, help="Label for 0") -parser.add_argument("-label1", required=True, help="Label for 1") -parser.add_argument( - "-output", - default=None, - help="""Path to output the predictions (each line will - be the decoded sequence""", -) -parser.add_argument("-batch_size", type=int, default=30, help="Batch size") -parser.add_argument( - "-max_sent_length", type=int, default=50, help="Maximum sentence length." -) -parser.add_argument( - "-verbose", - action="store_true", - help="Print scores and predictions for each sentence", -) -parser.add_argument("-gpu", type=int, default=-1, help="Device to run on") - - -def reportScore(name, scoreTotal, wordsTotal): - print( - "%s AVG SCORE: %.4f, %s PPL: %.4f" - % (name, scoreTotal / wordsTotal, name, math.exp(-scoreTotal / wordsTotal)) - ) - - -def addone(f): - for line in f: - yield line - yield None - - -def main(): - opt = parser.parse_args() - opt.cuda = opt.gpu > -1 - if opt.cuda: - torch.cuda.set_device(opt.gpu) - - translator = onmt.Translator_cnn(opt) - - srcBatch, tgtBatch = [], [] - - count = 0 - total_correct, total_words, total_loss = 0, 0, 0 - outputs, predictions, sents = [], [], [] - for line in addone(codecs.open(opt.src, "r", "utf-8")): - count += 1 - if line is not None: - sents.append(line) - srcTokens = line.split() - if len(srcTokens) <= opt.max_sent_length: - srcBatch += [srcTokens] - else: - srcBatch += [srcTokens[: opt.max_sent_length]] - tgtBatch += [opt.tgt] - - if len(srcBatch) < opt.batch_size: - continue - else: - # at the end of file, check last batch - if len(srcBatch) == 0: - break - num_correct, batchSize, outs, preds = translator.translate(srcBatch, tgtBatch) - - total_correct += num_correct.item() - total_words += batchSize - outputs += outs.data.tolist() - predictions += preds.tolist() - - srcBatch, tgtBatch = [], [] - if count % 1000 == 0: - print("Completed: ", str(count)) - sys.stdout.flush() - if opt.output: - with open(opt.output, "w") as outF: - for i in range(len(sents)): - outF.write( - str(predictions[i]) + "\t" + str(outputs[i]) + "\t" + sents[i] - ) - - print("Accuracy: ", str((total_correct * 100) / total_words)) - print("") - - -if __name__ == "__main__": - main() diff --git a/base/classifier/cnn_translate_bpe.py b/base/classifier/cnn_translate_bpe.py deleted file mode 100755 index ba5dfbb..0000000 --- a/base/classifier/cnn_translate_bpe.py +++ /dev/null @@ -1,136 +0,0 @@ -from __future__ import division - -import argparse -import codecs -import math -import os -import sys -from copy import deepcopy as copy - -import onmt -import torch -from tqdm import tqdm - -sys.path.append("../") - -import unicodedata - -import core.constants as Constants -from core.bpe import Encoder as bpe_encoder -from core.utils import get_len -from preprocess import load_file, reclip, seq2idx -from preprocess import parse as bpe_parse - -SOS, EOS = Constants.SOS, Constants.EOS - -parser = argparse.ArgumentParser(description="cnn_translate_bpe.py") - -parser.add_argument("-model", required=True, help="Path to model .pt file") -parser.add_argument("-num_classes", default=2, type=int, help="""Number of classes""") -parser.add_argument("-src", required=True, help="Source sequence to check") -parser.add_argument( - "-tgt", required=True, help="The target label for the classifier check" -) -parser.add_argument("-label0", required=True, help="Label for 0") -parser.add_argument("-label1", required=True, help="Label for 1") -parser.add_argument( - "-output", - default=None, - help="""Path to output the predictions (each line will - be the decoded sequence""", -) -parser.add_argument("-batch_size", type=int, default=30, help="Batch size") -parser.add_argument( - "-verbose", - action="store_true", - help="Print scores and predictions for each sentence", -) -parser.add_argument("-gpu", type=int, default=-1, help="Device to run on") - - -def reportScore(name, scoreTotal, wordsTotal): - print( - "%s AVG SCORE: %.4f, %s PPL: %.4f" - % (name, scoreTotal / wordsTotal, name, math.exp(-scoreTotal / wordsTotal)) - ) - - -def addone(f): - for line in f: - yield line - yield None - - -def main(): - opt = parser.parse_args() - opt.cuda = opt.gpu > -1 - if opt.cuda: - torch.cuda.set_device(opt.gpu) - - translator = onmt.Translator_cnn(opt) - - srcBatch, tgtBatch = [], [] - - tgt_id = 0 if opt.tgt == opt.label0 else 1 - - count = 0 - total_correct, total_words, total_loss = 0, 0, 0 - outputs, predictions, sents = [], [], [] - - # load bpe encoder. - bpe_enc = bpe_encoder.from_dict(translator.src_dict) - bpe_enc.mute() - - max_seq_length = translator.model_opt.sequence_length - - for line in tqdm( - addone(codecs.open(opt.src, "r", "utf-8")), total=get_len(opt.src) + 1 - ): - count += 1 - if line is not None: - sents.append(line) - # tokenise. - tokens = [f for f in bpe_enc.transform([line])][0] - # before = len(tokens) - tokens = reclip(line, tokens, bpe_enc, max_seq_length - 2) - # after = len(tokens) - tokens = [SOS] + tokens + [EOS] - # print("b, a:", before, after) - # add padding. - blanks = [Constants.PAD for _ in range(max_seq_length - len(tokens))] - tokens = tokens + blanks - - srcBatch.append(tokens) - - tgtBatch += [tgt_id] - - if len(srcBatch) < opt.batch_size: - continue - else: - # at the end of file, check last batch - if len(srcBatch) == 0: - break - - num_correct, batchSize, outs, preds = translator.translate_bpe( - srcBatch, tgtBatch - ) - - total_correct += num_correct.item() - total_words += batchSize - outputs += outs.data.tolist() - predictions += preds.tolist() - - srcBatch, tgtBatch = [], [] - if opt.output: - with open(opt.output, "w") as outF: - for i in range(len(sents)): - outF.write( - str(predictions[i]) + "\t" + str(outputs[i]) + "\t" + sents[i] - ) - - print("Accuracy: ", str((total_correct * 100) / total_words)) - print("") - - -if __name__ == "__main__": - main() diff --git a/base/classifier/onmt/CNNModels.py b/base/classifier/onmt/CNNModels.py deleted file mode 100755 index 774410c..0000000 --- a/base/classifier/onmt/CNNModels.py +++ /dev/null @@ -1,65 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn -from torch import optim -from torch.autograd import Variable -from torch.nn.utils.rnn import pack_padded_sequence as pack -from torch.nn.utils.rnn import pad_packed_sequence as unpack - -import onmt - - -class ConvNet(nn.Module): - def __init__(self, opt, dicts): - super(ConvNet, self).__init__() - - self.opt = opt - self.num_filters = opt.num_filters - pooling_window_size = opt.sequence_length - opt.filter_size + 1 - - self.strides = (1, 1) - self.vocab_size = dicts if type(dicts) == int else dicts.size() - self.word_vec_size = opt.word_vec_size - - self.word_lut = nn.Embedding( - self.vocab_size, opt.word_vec_size, padding_idx=onmt.Constants.PAD - ) - - self.conv1 = nn.Conv2d( - in_channels=self.word_vec_size, - out_channels=self.num_filters, - kernel_size=(opt.filter_size, 1), - stride=self.strides, - bias=True, - ) - self.relu1 = nn.ReLU() - self.maxpool1 = nn.MaxPool3d( - kernel_size=(1, pooling_window_size, 1), stride=(1, 1, 1) - ) - self.dropout = nn.Dropout(opt.dropout) - self.linear = nn.Linear(opt.num_filters, opt.num_classes - 1) - self.sigmoid = nn.Sigmoid() - - def load_pretrained_vectors(self, opt): - if opt.pre_word_vecs_enc is not None: - pretrained = torch.load(opt.pre_word_vecs_enc) - self.word_lut.weight.data.copy_(pretrained) - - def forward(self, input): - ## src size is seq_size x batch_size x vocab_size. Most cases (50 x 64 x v) - ## matrix multiply instead of lookup - # print("input:",input.shape) - emb = torch.mm(input.view(-1, input.size(2)), self.word_lut.weight) - emb = emb.view(-1, input.size(1), self.word_vec_size) - emb = emb.transpose(0, 1) - emb = emb.transpose(1, 2) - emb = emb.unsqueeze(-1) - # print("emb:",emb.shape) - h_conv = self.conv1(emb) - h_relu = self.relu1(h_conv) - h_max = self.maxpool1(h_relu) - h_flat = h_max.view(-1, self.num_filters) - h_drop = self.dropout(h_flat) - lin_out = self.linear(h_drop) - out = self.sigmoid(lin_out) - return out diff --git a/base/classifier/onmt/Constants.py b/base/classifier/onmt/Constants.py deleted file mode 100755 index 4e03a36..0000000 --- a/base/classifier/onmt/Constants.py +++ /dev/null @@ -1,9 +0,0 @@ -PAD = 0 -UNK = 1 -BOS = 2 -EOS = 3 - -PAD_WORD = "" -UNK_WORD = "" -BOS_WORD = "" -EOS_WORD = "" diff --git a/base/classifier/onmt/__init__.py b/base/classifier/onmt/__init__.py deleted file mode 100755 index 4d19fd2..0000000 --- a/base/classifier/onmt/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -import onmt.CNNModels -import onmt.Constants -from onmt.Dataset import Dataset -from onmt.Dict import Dict -from onmt.Optim import Optim -from onmt.Translator_cnn import Translator as Translator_cnn diff --git a/base/classifier/preprocess_bpe.py b/base/classifier/preprocess_bpe.py deleted file mode 100755 index 822db01..0000000 --- a/base/classifier/preprocess_bpe.py +++ /dev/null @@ -1,191 +0,0 @@ -""" - -preprocess_bpe is retrofitted to use BPE encodings for the -dataset. - -""" - -import argparse -import codecs -import json -import sys - -import numpy as np -import onmt -import torch - -sys.path.append("../") - -import unicodedata -from copy import deepcopy as copy - -import core.constants as Constants -from core.bpe import Encoder as bpe_encoder -from preprocess import load_file, reclip, seq2idx -from preprocess import parse as bpe_parse -from tqdm import tqdm - - -def load_args(): - parser = argparse.ArgumentParser(description="preprocess_bpe.py") - - parser.add_argument("-config", help="Read options from this file") - - parser.add_argument( - "-train_src", required=True, help="Path to the training source data" - ) - parser.add_argument("-label0", required=True, help="Label that would be 0") - parser.add_argument("-label1", required=True, help="Label that would be 1") - parser.add_argument( - "-valid_src", required=True, help="Path to the validation source data" - ) - - parser.add_argument( - "-save_data", required=True, help="Output file for the prepared data" - ) - - parser.add_argument("-vocab_size", type=int, default=35000) - parser.add_argument("-pct_bpe", default=0.1) - parser.add_argument( - "-src_vocab", help="Path to an existing source vocabulary pt file." - ) - - parser.add_argument("-max_word_seq_len", type=int, default=50) - parser.add_argument("-shuffle", type=int, default=1, help="Shuffle data") - parser.add_argument("-seed", type=int, default=3435, help="Random seed") - - parser.add_argument("-lower", action="store_true", help="lowercase data") - - parser.add_argument( - "-report_every", - type=int, - default=100000, - help="Report status every this many sentences", - ) - - opt = parser.parse_args() - - return opt - - -def main(): - opt = load_args() - bpe_enabled = True - - torch.manual_seed(opt.seed) - - # setup the max token sequence length to include and - opt.max_token_seq_len = opt.max_word_seq_len - - # restructure code for readability - dataset = { - "train": {"src": opt.train_src, "tgt": []}, - "valid": {"src": opt.valid_src, "tgt": []}, - } - - label0, label1 = [torch.LongTensor([0])], [torch.LongTensor([1])] - raw = copy(dataset) - # load dataset - for g in dataset: - src = load_file(dataset[g]["src"], None, False) - - # split src and tgt - src = [x.split() for x in src] - tgt = [x[0] for x in src] - src = [" ".join(x[1:]) for x in src] - - # convert tgt tokens. - tgt = [label0 if i == opt.label0 else label1 for i in tgt] - - raw[g]["src"] = src - dataset[g]["tgt"] = tgt - - if opt.src_vocab: - # build bpe vocabulary - print("[Info] Loading BPE vocabulary from", opt.src_vocab) - src_bpe = bpe_encoder.from_dict(torch.load(opt.src_vocab)["dict"]["tgt"]) - else: - # building bpe vocabulary - print("[Info] Building BPE vocabulary.") - # build and train encoder - src_bpe = bpe_encoder( - vocab_size=opt.vocab_size, - pct_bpe=opt.pct_bpe, - ngram_min=1, - UNK=Constants.UNK_WORD, - PAD=Constants.PAD_WORD, - word_tokenizer=bpe_parse, - ) - src_bpe.fit(raw["train"]["src"]) - - # convert sequences - for g in tqdm(dataset, desc="Converting tokens into IDs"): - src_bpe.unmute() - dataset[g]["src"] = [f for f in src_bpe.transform(tqdm(raw[g]["src"]))] - - for g in tqdm(dataset, desc="Trimming Sequences"): - sequences = dataset[g]["src"] - # it's much easier to just refer back to the original sentence and - # trim tokens from there. - for i in range(len(sequences)): - ref_seq = raw[g]["src"][i] - bpe_seq = sequences[i] - dataset[g]["src"][i] = reclip( - ref_seq, bpe_seq, src_bpe, opt.max_word_seq_len - 2 - ) - - # add , - # (At this stage, all of the sequences are tokenised, so you'll need to input - # the ID values of SOS and EOS instead.) - SOS, EOS = Constants.SOS, Constants.EOS - for g in tqdm(dataset, desc="Adding SOS, EOS tokens"): - dataset[g]["src"] = [[SOS] + x + [EOS] for x in dataset[g]["src"]] - - # shuffle dataset by sizes - for g in tqdm(dataset, desc="Shuffling and Sorting"): - src, tgt = dataset[g]["src"], dataset[g]["tgt"] - sizes = [len(x) for x in src] - - if opt.shuffle == 1: - perm = torch.randperm(len(src)) - src = [src[idx] for idx in perm] - tgt = [tgt[idx] for idx in perm] - sizes = [sizes[idx] for idx in perm] - - _, perm = torch.sort(torch.Tensor(sizes)) - - src = [src[idx] for idx in perm] - tgt = [tgt[idx] for idx in perm] - - # add space to seq - src_sizes = [sizes[idx] for idx in perm] - - blanks = [ - [Constants.PAD for _ in range(opt.max_token_seq_len - src_sizes[i])] - for i in range(len(src)) - ] - src = [src[i] + blanks[i] for i in range(len(src))] - - dataset[g]["src"] = src - dataset[g]["tgt"] = tgt - - k = np.sum([len(x) for x in dataset[g]["src"]]) / len(dataset[g]["src"]) - print("len:", k) - - # setup data to save. - data = { - "settings": opt, - "dicts": {"src": src_bpe.vocabs_to_dict(False)}, - "train": {"src": dataset["train"]["src"], "tgt": dataset["train"]["tgt"]}, - "valid": {"src": dataset["valid"]["src"], "tgt": dataset["valid"]["tgt"]}, - } - - # dump information. - filename = opt.save_data + ".train.pt" - print("[Info] Dumping the processed data to pickle file", filename) - torch.save(data, filename) - print("[Info] Done.") - - -if __name__ == "__main__": - main() diff --git a/base/classifier/preprocess_token.py b/base/classifier/preprocess_token.py deleted file mode 100755 index da1619d..0000000 --- a/base/classifier/preprocess_token.py +++ /dev/null @@ -1,197 +0,0 @@ -import argparse -import codecs -import json -import sys - -import numpy as np -import onmt -import torch - -parser = argparse.ArgumentParser(description="preprocess.py") - -## -## **Preprocess Options** -## - -parser.add_argument("-config", help="Read options from this file") - -parser.add_argument( - "-train_src", required=True, help="Path to the training source data" -) -parser.add_argument("-label0", required=True, help="Label that would be 0") -parser.add_argument("-label1", required=True, help="Label that would be 1") -parser.add_argument( - "-valid_src", required=True, help="Path to the validation source data" -) - -parser.add_argument( - "-save_data", required=True, help="Output file for the prepared data" -) - -parser.add_argument( - "-src_vocab_size", type=int, default=20000, help="Size of the source vocabulary" -) -parser.add_argument("-src_vocab", help="Path to an existing source vocabulary") - -parser.add_argument("-seq_length", type=int, default=50, help="Maximum sequence length") -parser.add_argument("-shuffle", type=int, default=1, help="Shuffle data") -parser.add_argument("-seed", type=int, default=3435, help="Random seed") - -parser.add_argument("-lower", action="store_true", help="lowercase data") - -parser.add_argument( - "-report_every", - type=int, - default=100000, - help="Report status every this many sentences", -) - -opt = parser.parse_args() - -torch.manual_seed(opt.seed) - - -def makeVocabulary(filename, size): - vocab = onmt.Dict( - [ - onmt.Constants.PAD_WORD, - onmt.Constants.UNK_WORD, - onmt.Constants.BOS_WORD, - onmt.Constants.EOS_WORD, - ], - lower=opt.lower, - seq_len=opt.seq_length, - ) - - with codecs.open(filename, "r", "utf-8") as f: - for sent in f.readlines(): - for word in sent.split(): - vocab.add(word) - - originalSize = vocab.size() - vocab = vocab.prune(size) - print( - "Created dictionary of size %d (pruned from %d)" % (vocab.size(), originalSize) - ) - - return vocab - - -def initVocabulary(name, dataFile, vocabFile, vocabSize): - vocab = None - if vocabFile is not None: - # If given, load existing word dictionary. - print("Reading " + name + " vocabulary from '" + vocabFile + "'...") - vocab = onmt.Dict() - vocab.loadFile(vocabFile) - print("Loaded " + str(vocab.size()) + " " + name + " words") - - if vocab is None: - # If a dictionary is still missing, generate it. - print("Building " + name + " vocabulary...") - genWordVocab = makeVocabulary(dataFile, vocabSize) - - vocab = genWordVocab - - print() - return vocab - - -def saveVocabulary(name, vocab, file): - print("Saving " + name + " vocabulary to '" + file + "'...") - vocab.writeFile(file) - - -def makeData(srcFile, srcDicts): - src, tgt = [], [] - sizes = [] - count, ignored = 0, 0 - - print("Processing %s ..." % (srcFile)) - srcF = codecs.open(srcFile, "r", "utf-8") - - while True: - sline = srcF.readline() - - # normal end of file - if sline == "": - break - - sline = sline.strip() - ## source and/or target are empty - if sline == "": - print("WARNING: ignoring an empty line (" + str(count + 1) + ")") - continue - - srcWords = sline.split() - tgtWords = srcWords[0] - srcWords = srcWords[1:] - - if len(srcWords) <= opt.seq_length and len(tgtWords) <= opt.seq_length: - src += [ - srcDicts.convertToIdx(srcWords, onmt.Constants.UNK_WORD, padding=True) - ] - if tgtWords == opt.label0: - tgt += [torch.LongTensor([0])] - elif tgtWords == opt.label1: - tgt += [torch.LongTensor([1])] - sizes += [len(srcWords)] - else: - ignored += 1 - - count += 1 - - if count % opt.report_every == 0: - print("... %d sentences prepared" % count) - - srcF.close() - - if opt.shuffle == 1: - print("... shuffling sentences") - perm = torch.randperm(len(src)) - src = [src[idx] for idx in perm] - tgt = [tgt[idx] for idx in perm] - sizes = [sizes[idx] for idx in perm] - - print("... sorting sentences by size") - _, perm = torch.sort(torch.Tensor(sizes)) - src = [src[idx] for idx in perm] - tgt = [tgt[idx] for idx in perm] - - print( - "Prepared %d sentences (%d ignored due to length == 0 or > %d)" - % (len(src), ignored, opt.seq_length) - ) - - return src, tgt - - -def main(): - dicts = {} - print("Preparing source vocab ....") - dicts["src"] = initVocabulary( - "source", opt.train_src, opt.src_vocab, opt.src_vocab_size - ) - - print("Preparing training ...") - train = {} - train["src"], train["tgt"] = makeData(opt.train_src, dicts["src"]) - - print("Preparing validation ...") - valid = {} - valid["src"], valid["tgt"] = makeData(opt.valid_src, dicts["src"]) - - if opt.src_vocab is None: - saveVocabulary("source", dicts["src"], opt.save_data + ".src.dict") - - print("Saving data to '" + opt.save_data + ".train.pt'...") - save_data = { - "dicts": dicts, - "train": train, - "valid": valid, - } - torch.save(save_data, opt.save_data + ".train.pt") - - -if __name__ == "__main__": - main() diff --git a/base/classifier/run_political.sh b/base/classifier/run_political.sh deleted file mode 100755 index 44f441a..0000000 --- a/base/classifier/run_political.sh +++ /dev/null @@ -1,37 +0,0 @@ - -# Preprocess data -## Note: First create a dev.txt file such that -## 1. Each instance is on a new line -## 2. The label and the sentence are separated by a space - -political_dataset="political_new" -cnn_model_name="political_model_new" -# python3 preprocess.py \ -# -train_src ../../datasets/political_data/classtrain.txt \ -# -label0 democratic \ -# -label1 republican \ -# -valid_src ../../datasets/political_data/classdev.txt \ -# -save_data $political_dataset \ -# -src_vocab ../results/nmt_fren_goldmaster_bpe_republican.tgt.dict \ - # -seq_length 80 \ - -# Train the classifier -# python3 cnn_train.py \ -# -gpus 0 \ -# -epochs 20 \ -# -data $political_dataset".train.pt" \ -# -save_model $cnn_model_name \ -# -batch_size 128 - # -sequence_length 100 \ - -BESTMODEL="political_model.pt" - -# Test the classifier accuracy -python3 cnn_translate.py \ - -gpu 0 \ - -model $BESTMODEL \ - -src ../../datasets/political_data/democratic_only.test.en \ - -tgt 'democratic' \ - -label0 democratic \ - -label1 republican - diff --git a/base/classifier/run_political_bpe.sh b/base/classifier/run_political_bpe.sh deleted file mode 100755 index 9551561..0000000 --- a/base/classifier/run_political_bpe.sh +++ /dev/null @@ -1,42 +0,0 @@ -clear -# Preprocess data -## Note: First create a dev.txt file such that -## 1. Each instance is on a new line -## 2. The label and the sentence are separated by a space - -political_dataset="political_bpe" -cnn_model_name="models/political_model_bpe" -seq_len=100 -# python3 preprocess_bpe.py \ -# -train_src ../../datasets/political_data/classtrain.txt \ -# -label0 democratic \ -# -label1 republican \ -# -valid_src ../../datasets/political_data/classdev.txt \ -# -save_data $political_dataset \ -# -max_word_seq_len $seq_len \ -# -src_vocab ../models/nmt_fren_goldmaster_bpe_republican.pt - - -# Train the classifier -# python3 cnn_train_bpe.py \ -# -gpus 0 \ -# -epochs 1 \ -# -data $political_dataset".train.pt" \ -# -save_model $cnn_model_name \ -# -sequence_length $seq_len \ -# -batch_size 128 \ -# -filter_size 10 \ -# -optim adam \ -# -learning_rate 0.001 - -BESTMODEL="models/political_model_bpe_acc_98.47_loss_0.00_e1.pt" - -# # Test the classifier accuracy -python3 cnn_translate_bpe.py \ - -gpu 0 \ - -model $BESTMODEL \ - -src ../../datasets/political_data/democratic_only.test.en \ - -tgt 'democratic' \ - -label0 democratic \ - -label1 republican - diff --git a/base/core/__init__.py b/base/core/__init__.py deleted file mode 100755 index c0dff92..0000000 --- a/base/core/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# import constants -# import dataset - -# __all__ = [constants, dataset] diff --git a/base/core/bpe_test.py b/base/core/bpe_test.py deleted file mode 100755 index b91a0a5..0000000 --- a/base/core/bpe_test.py +++ /dev/null @@ -1,47 +0,0 @@ -from bpe import Encoder -from tqdm import tqdm - -sequences = [] -with open("../../datasets/multi30k/train.en") as f: - for line in f: - sequences.append(line.strip()) - -ref = [x.split() for x in sequences] -ref_len = [len(x) for x in ref] -print("REF:", max(ref_len)) - - -def parse(x): - return x.split() - - -enc = Encoder( - 4096, ngram_min=1, ngram_max=2, pct_bpe=0.8, silent=True, word_tokenizer=parse -) -enc.fit(sequences) - -base = enc.vocabs_to_dict() -duplicate_keys = [] -for key in base["byte_pairs"]: - if key in base["words"]: - duplicate_keys.append(key) -if len(duplicate_keys) > 0: - print("got duplicates:") - print(duplicate_keys) -else: - print("NO DUPLICATES! :)") - -keybase = {**base["words"], **base["byte_pairs"]} - - -inv_map = {v: k for k, v in keybase.items()} - -for i in range(0, 10): - print(i, inv_map[i]) - -sequences = [f for f in enc.transform(tqdm(sequences))] - -lengths = [len(x) for x in sequences] -print(max(lengths)) - -# print(base) diff --git a/base/core/constants.py b/base/core/constants.py deleted file mode 100755 index 3b7a6aa..0000000 --- a/base/core/constants.py +++ /dev/null @@ -1,28 +0,0 @@ -""" -ENSURE THAT THE CONSTANTS ARE -CAPITALISED. -""" - -PAD = 0 -UNK = 1 -SOS = 2 -EOS = 3 -BLO = 4 - -PAD_WORD = "" -UNK_WORD = "" -SOS_WORD = "" -EOS_WORD = "" -BLO_WORD = "

" # paragraph block - - -def get_tokens(): - g = globals() - k = list({i for i in g if i[-5:] == "_WORD"}) - kc = [i[:-5] for i in k] - k = sorted(kc, key=lambda x: g[x]) - return [g[i + "_WORD"] for i in k] - - -if __name__ == "__main__": - print(get_tokens()) diff --git a/base/core/utils.py b/base/core/utils.py deleted file mode 100755 index 2309332..0000000 --- a/base/core/utils.py +++ /dev/null @@ -1,36 +0,0 @@ -import subprocess -from multiprocessing import Pool, cpu_count - -from tqdm import tqdm - -""" -Misc. functions used by a variety of parts of the library. -""" - - -def get_len(filepath): - """ - Reads number of lines in the mose corpus without using python - to deal with it. This is some order of magnitude faster! - """ - command = "wc -l " + filepath - process = subprocess.run(command.split(" "), stdout=subprocess.PIPE) - return int(process.stdout.decode("utf-8").split(" ")[0]) - - -def batch_compute(func, args, n_processes=cpu_count() - 1): - """ - Takes a function and some arguments for those functions, - and returns a list of outputs generated from the function. - Pools with multicores. - """ - p = Pool(n_processes) - res_list = [] - with tqdm(total=len(args)) as pbar: - for i, res in enumerate(p.imap_unordered(func, args)): - pbar.update() - res_list.append(res) - pbar.close() - p.close() - p.join() - return res_list diff --git a/base/lib/__init__.py b/base/lib/__init__.py deleted file mode 100755 index daf3bd8..0000000 --- a/base/lib/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -# import transformer -# import recurrent -# from TransformerModel import TransformerModel -# # from RecurrentModel import RecurrentModel - -# __all__ = [transformer, recurrent, TransformerModel] - -from . import recurrent, transformer diff --git a/base/lib/nmtModel.py b/base/lib/nmtModel.py deleted file mode 100755 index 06ac47a..0000000 --- a/base/lib/nmtModel.py +++ /dev/null @@ -1,456 +0,0 @@ -""" -Model 'interface'; -Note that I don't follow this strictly since -init does have some logic in it. -""" - -import atexit -import datetime -import json -import math -import os - -import core.constants as constants -import numpy as np -import telebot -import torch -import torch.utils.data -from core.bpe import Encoder as BPE -from core.dataset import TranslationDataset, collate_fn, paired_collate_fn -from preprocess import load_file, reclip, seq2idx -from tqdm import tqdm - - -class NMTModel: - def __init__(self, opt, models_folder="models"): - self.opt = opt - - if opt.cuda_device: - self.device = torch.device("cuda:" + str(opt.cuda_device)) - else: - self.device = torch.device("cuda" if opt.cuda else "cpu") - print("Using device:", self.device) - - self.constants = constants - self.opt.directory = self.init_dir(stores=models_folder) - - # update variables. - self.train_losses = [] - self.valid_losses = [] - self.train_accs = [] - self.valid_accs = [] - - if self.opt.telegram: - self.init_telegram(self.opt.telegram) - - # bpe holders. - self.src_bpe = None - self.tgt_bpe = None - - atexit.register(self.exit_handler) - - # ------------------------- - # OVERLOADED FUNCTIONS - # These functions are to be overwritten by whatever is declared - # in their child functions. - # ------------------------- - - def load(self): - """ - Loads models from file. - """ - print("[Warning]: load() is not implemented.") - return self - - def initiate(self): - """ - Loads models into memory and initiate parameters. - """ - print("[Warning]: initiate() is not implemented.") - return self - - def setup_optimiser(self): - # based on the opt. - print("[Warning] setup_optimiser() is not implemented.") - - def train(self): - """ - Trains models. - """ - print("[Warning]: train() is not implemented.") - return self - - def validate(self, val_data): - """ - Validates model performance against validation data. - """ - print("[Warning:] validate() is not implemented.") - return self - - def translate(self, dataset): - """ - Uses the models to perform inference/translation. - """ - print("[Warning]: translate() is not implemented.") - return self - - def save(self): - """ - save model weights and parameters to file. - """ - print("[Warning]: save() is not implemented.") - return self - - # ---------------------------- - # BASE FUNCTIONS - # ---------------------------- - - def reset_metrics(self): - """ - Resets metrics. - """ - self.train_losses = [] - self.valid_losses = [] - self.train_accs = [] - self.valid_accs = [] - - def init_dir(self, stores): - """ - initiates a directory model data will be in. - If theres no preloaded encoder/decoder it'll - create a new folder. - """ - if self.opt.checkpoint_encoder: - if not self.opt.new_directory: - return os.path.split(self.opt.checkpoint_encoder)[0] - - # setup parent directory - basepath = os.path.abspath(os.getcwd()) - basepath = os.path.join(basepath, stores) - - if self.opt.directory_name: - directory_name = self.opt.directory_name - else: - # setup current model directory - directory_name = self.opt.model - directory_name += "-" + datetime.datetime.now().strftime( - "%y-%m-%d-%H-%M-%S" - ) - directory = os.path.join(basepath, directory_name) - # create container for that folder. - if not os.path.isdir(directory): - if self.opt.save_model: - os.mkdir(directory) - return directory - - def init_logs(self): - """ - If called, saves output logs to file. - """ - self.log_train_file = os.path.join(self.opt.directory, "train.log") - self.log_valid_file = os.path.join(self.opt.directory, "valid.log") - # check if log files exists already. - # if it does, then we need to increment the log name - step = 1 - while os.path.isfile(self.log_train_file): - filename = "train_" + str(step) + ".log" - self.log_train_file = os.path.join(self.opt.directory, filename) - step += 1 - step = 1 - while os.path.isfile(self.log_valid_file): - filename = "valid_" + str(step) + ".log" - self.log_valid_file = os.path.join(self.opt.directory, filename) - step += 1 - - print( - "[Info] Training performance will be written to file: {} and {}".format( - self.log_train_file, self.log_valid_file - ) - ) - - with open(self.log_train_file, "w") as log_tf, open( - self.log_valid_file, "w" - ) as log_vf: - log_tf.write("epoch,loss,ppl,accuracy\n") - log_vf.write("epoch,loss,ppl,accuracy\n") - - def update_logs(self, epoch_i): - """ - called within train(). Updates results into log files. - Assumes logs are enabled. - """ - train_loss, train_acc = self.train_losses[-1], self.train_accs[-1] - valid_loss, valid_acc = self.valid_losses[-1], self.valid_accs[-1] - - # deal with logs - if self.log_train_file and self.log_valid_file: - with open(self.log_train_file, "a") as log_tf, open( - self.log_valid_file, "a" - ) as log_vf: - log_tf.write( - "{epoch},{loss: 8.5f},{ppl: 8.5f},{accu:3.3f}\n".format( - epoch=epoch_i, - loss=train_loss, - ppl=math.exp(min(train_loss, 100)), - accu=100 * train_acc, - ) - ) - log_vf.write( - "{epoch},{loss: 8.5f},{ppl: 8.5f},{accu:3.3f}\n".format( - epoch=epoch_i, - loss=valid_loss, - ppl=math.exp(min(valid_loss, 100)), - accu=100 * valid_acc, - ) - ) - else: - print( - "[Warning] log files are not initiated. No updates are kept into storage." - ) - - def load_dataset(self): - """ - Loads PyTorch pickled training and validation dataset. - """ - data = torch.load(self.opt.data) - self.dataset_settings = data["settings"] - # the token sequence length is determined by `preprocess.py` - self.opt.max_token_seq_len = data["settings"].max_token_seq_len - # here we need to check whether the dataset is BPE or not. - if "byte_pairs" in data["dict"]["src"]: - if "__sow" in data["dict"]["src"]["byte_pairs"]: - self.src_bpe = BPE.from_dict(data["dict"]["src"]) - self.tgt_bpe = BPE.from_dict(data["dict"]["tgt"]) - - datasets = self.init_dataloaders(data, self.opt) - self.training_data, self.validation_data = datasets - - # need to store vocabulary size for quick referencing - self.opt.src_vocab_size = self.training_data.dataset.src_vocab_size - self.opt.tgt_vocab_size = self.training_data.dataset.tgt_vocab_size - return self - - def load_testdata(self, test_datapath, test_vocab): - """ - Loads a text file representing sequences. This is called in - `translate.py`. - - params: - test_datapath: some text file. - test_vocab: it's the same PyTorch pickled training dataset. - """ - - # load vocabulary - data = torch.load(test_vocab) - settings = data["settings"] - # print(settings) - # load test sequences - token_instances = load_file( - test_datapath, - settings.max_word_seq_len, - settings.format, - settings.case_sensitive, - ) - is_bpe = settings.format.lower() == "bpe" - - SOS, EOS = constants.SOS, constants.EOS - - decoder = None - - if self.opt.override_max_token_seq_len: - if self.opt.override_max_token_seq_len > 0: - settings.max_token_seq_len = self.opt.override_max_token_seq_len - if self.model == "transformer": - self.change_max_seq_len(self.opt.override_max_token_seq_len + 5) - - print("settings.max_token_seq_len", settings.max_token_seq_len) - if is_bpe: - # load test data - # TODO: fix preprocessing method for BPE when loading test data. - # TODO: this is a night fix we'll need to clean the code debt later. - bpe_src = BPE.from_dict(data["dict"]["src"]) - decoder = BPE.from_dict(data["dict"]["tgt"]) - # convert test sequences into IDx - test_src_insts = bpe_src.transform(token_instances) - test_src_insts = [i for i in test_src_insts] - # some of the sequences made may be too long, so we'll need to fix that. - for i in tqdm( - range(len(token_instances)), desc="Reclipping Test Sequences" - ): - raw = token_instances[i] - encoded = test_src_insts[i] - test_src_insts[i] = reclip( - raw, encoded, bpe_src, settings.max_token_seq_len - 2 - ) - test_src_insts[i] = [SOS] + test_src_insts[i] + [EOS] - - # setup data loader - src_word2idx = data["dict"]["src"] - tgt_word2idx = data["dict"]["tgt"] - - src_byte_pairs = {x + "_": y for x, y in src_word2idx["byte_pairs"].items()} - tgt_byte_pairs = {x + "_": y for x, y in tgt_word2idx["byte_pairs"].items()} - src_word2idx = {**src_byte_pairs, **src_word2idx["words"]} - tgt_word2idx = {**tgt_byte_pairs, **tgt_word2idx["words"]} - - test_loader = torch.utils.data.DataLoader( - TranslationDataset( - src_word2idx=src_word2idx, - tgt_word2idx=tgt_word2idx, - src_insts=test_src_insts, - ), - num_workers=0, - batch_size=self.opt.batch_size, - collate_fn=collate_fn, - ) - - else: - # convert test sequences into IDx - test_src_insts = seq2idx(token_instances, data["dict"]["src"]) - # trim sequence lengths - test_src_insts = [ - seq[: settings.max_word_seq_len] for seq in test_src_insts - ] - # add SOS and EOS - test_src_insts = [[SOS] + x + [EOS] for x in test_src_insts] - - # setup data loaders. - test_loader = torch.utils.data.DataLoader( - TranslationDataset( - src_word2idx=data["dict"]["src"], - tgt_word2idx=data["dict"]["tgt"], - src_insts=test_src_insts, - ), - num_workers=0, - batch_size=self.opt.batch_size, - collate_fn=collate_fn, - ) - - decoder = data["dict"]["tgt"] - - return test_loader, settings.max_token_seq_len, is_bpe, decoder - - @staticmethod - def init_dataloaders(data, opt): - """ - Initiates memory efficient vanilla dataloaders for feeding - into the models. (Assumes dataset is not BPE) - """ - src_word2idx = data["dict"]["src"] - tgt_word2idx = data["dict"]["tgt"] - # check if we have BPE dictionaries - is_bpe = False - if "byte_pairs" in src_word2idx: - if "__sow" in src_word2idx["byte_pairs"]: - is_bpe = True - # we have BPE - src_byte_pairs = { - x + "_": y for x, y in src_word2idx["byte_pairs"].items() - } - tgt_byte_pairs = { - x + "_": y for x, y in tgt_word2idx["byte_pairs"].items() - } - src_word2idx = {**src_byte_pairs, **src_word2idx["words"]} - tgt_word2idx = {**tgt_byte_pairs, **tgt_word2idx["words"]} - - train_loader = torch.utils.data.DataLoader( - TranslationDataset( - src_word2idx=src_word2idx, - tgt_word2idx=tgt_word2idx, - src_insts=data["train"]["src"], - tgt_insts=data["train"]["tgt"], - ), - num_workers=0, - batch_size=opt.batch_size, - collate_fn=paired_collate_fn, - shuffle=True, - ) - - valid_loader = torch.utils.data.DataLoader( - TranslationDataset( - src_word2idx=src_word2idx, - tgt_word2idx=tgt_word2idx, - src_insts=data["valid"]["src"], - tgt_insts=data["valid"]["tgt"], - ), - num_workers=0, - batch_size=opt.batch_size, - collate_fn=paired_collate_fn, - ) - - # validate the tables if weight sharing flag is called. - if opt.embs_share_weight: - assert ( - train_loader.dataset.src_word2idx == train_loader.dataset.tgt_word2idx - ), "The src/tgt word2idx table are different but you asked to share the word embeddings." - - return train_loader, valid_loader - - @staticmethod - def translate_decode_bpe(hypotheses, bpe_enc): - """ - Decodes a batch of sequences with the BPE encoder. - """ - lines = [] - # transform sequences - hypotheses = [x[0] for x in hypotheses] - sequences = bpe_enc.inverse_transform(hypotheses) - # clip sequences based on position of EOS token. - for x in sequences: - x = x.split() - index = 0 - for j in range(len(x)): - if x[j] == constants.EOS_WORD: - if j == 0: - continue - index = j - break - line = " ".join(x[:index]) - if len(line.strip()) < 1: - line = constants.UNK_WORD - lines.append(line) - return lines - - @staticmethod - def translate_decode_dict(hypotheses, idx2w): - EOS = constants.EOS - lines = [] - # convert sequences back into text - for sequence in hypotheses: - for token_i in sequence: - tokens = [idx2w[i] for i in token_i if i != EOS] - line = " ".join(tokens) - lines.append(line) - return lines - - def exit_handler(self): - """ - Handles anything when the code has finished running. - """ - if self.opt.save_model: - if len(os.listdir(self.opt.directory)) < 1: - # delete the folder since nothing interesting happened. - os.rmdir(self.opt.directory) - return None - - def init_telegram(self, api_json_path): - """ - Initiates telegram API bot. - (Quite useful if you want to get notified of any - status changes in the event that you're afk with model - training.) - """ - api_q = json.load(api_json_path) - self.bot = telebot.TeleBot(api_q["api_private_key"]) - self.bot_chatid = api_q["chat_id"] - - def t_msg(self, msg): - """ - Sends messages to telegram chat ID. - """ - if self.bot: - self.bot.send_message(self.bot_chatid, msg) - - @staticmethod - def parse(x): - return x.split() diff --git a/base/lib/recurrent/Beam.py b/base/lib/recurrent/Beam.py deleted file mode 100755 index c6c5e6c..0000000 --- a/base/lib/recurrent/Beam.py +++ /dev/null @@ -1,109 +0,0 @@ -from __future__ import division - -# Class for managing the internals of the beam search process. -# -# -# hyp1#-hyp1---hyp1 -hyp1 -# \ / -# hyp2 \-hyp2 /-hyp2#hyp2 -# / \ -# hyp3#-hyp3---hyp3 -hyp3 -# ======================== -# -# Takes care of beams, back pointers, and scores. -import torch - -from . import Constants - - -class Beam(object): - def __init__(self, size, cuda=False): - self.size = size - self.done = False - - self.tt = torch.cuda if cuda else torch - - # The score for each translation on the beam. - self.scores = self.tt.FloatTensor(size).zero_() - - # The backpointers at each time-step. - self.prevKs = [] - - # The outputs at each time-step. - self.nextYs = [self.tt.LongTensor(size).fill_(Constants.PAD)] - self.nextYs[0][0] = Constants.SOS - - # The attentions (matrix) for each time. - self.attn = [] - - # Get the outputs for the current timestep. - def getCurrentState(self): - return self.nextYs[-1] - - # Get the backpointers for the current timestep. - def getCurrentOrigin(self): - return self.prevKs[-1] - - # Given prob over words for every last beam `wordLk` and attention - # `attnOut`: Compute and update the beam search. - # - # Parameters: - # - # * `wordLk`- probs of advancing from the last step (K x words) - # * `attnOut`- attention at the last step - # - # Returns: True if beam search is complete. - def advance(self, wordLk, attnOut): - numWords = wordLk.size(1) - - # Sum the previous scores. - if len(self.prevKs) > 0: - beamLk = wordLk + self.scores.unsqueeze(1).expand_as(wordLk) - else: - beamLk = wordLk[0] - - flatBeamLk = beamLk.view(-1) - - bestScores, bestScoresId = flatBeamLk.topk(self.size, 0, True, True) - self.scores = bestScores - - # bestScoresId is flattened beam x word array, so calculate which - # word and beam each score came from - prevK = bestScoresId / numWords - self.prevKs.append(prevK) - self.nextYs.append(bestScoresId - prevK * numWords) - self.attn.append(attnOut.index_select(0, prevK)) - - # End condition is when top-of-beam is EOS. - if self.nextYs[-1][0] == Constants.EOS: - self.done = True - - return self.done - - def sortBest(self): - return torch.sort(self.scores, 0, True) - - # Get the score of the best in the beam. - def getBest(self): - scores, ids = self.sortBest() - return scores[1], ids[1] - - # Walk back to construct the full hypothesis. - # - # Parameters. - # - # * `k` - the position in the beam to construct. - # - # Returns. - # - # 1. The hypothesis - # 2. The attention at each time step. - def getHyp(self, k): - hyp, attn = [], [] - # print(len(self.prevKs), len(self.nextYs), len(self.attn)) - for j in range(len(self.prevKs) - 1, -1, -1): - hyp.append(self.nextYs[j + 1][k]) - attn.append(self.attn[j][k]) - k = self.prevKs[j][k] - - return hyp[::-1], torch.stack(attn[::-1]) diff --git a/base/lib/recurrent/CNNModels.py b/base/lib/recurrent/CNNModels.py deleted file mode 100755 index 6eaf803..0000000 --- a/base/lib/recurrent/CNNModels.py +++ /dev/null @@ -1,71 +0,0 @@ -import core.constants as Constants -import numpy as np -import torch -import torch.nn as nn -from torch import optim -from torch.autograd import Variable - - -class ConvNet(nn.Module): - def __init__(self, opt, dicts): - super(ConvNet, self).__init__() - - self.num_filters = opt.num_filters - pooling_window_size = opt.sequence_length - opt.filter_size + 1 - self.strides = (1, 1) - self.vocab_size = dicts.size() - self.word_vec_size = opt.word_vec_size - - self.word_lut = nn.Embedding( - self.vocab_size, opt.word_vec_size, padding_idx=onmt.Constants.PAD - ) - - self.conv1 = nn.Conv2d( - in_channels=self.word_vec_size, - out_channels=self.num_filters, - kernel_size=(opt.filter_size, 1), - stride=self.strides, - bias=True, - ) - - self.relu1 = nn.ReLU() - self.maxpool1 = nn.MaxPool3d( - kernel_size=(1, pooling_window_size, 1), stride=(1, 1, 1) - ) - self.dropout = nn.Dropout(opt.dropout) - # self.linear = nn.Linear(opt.num_filters, opt.num_classes) - self.linear = nn.Linear(opt.num_filters, opt.num_classes - 1) - self.sigmoid = nn.Sigmoid() - # self.logsoftmax = nn.LogSoftmax() - - def load_pretrained_vectors(self, opt): - if opt.pre_word_vecs_enc is not None: - pretrained = torch.load(opt.pre_word_vecs_enc) - self.word_lut.weight.data.copy_(pretrained) - - def forward(self, input): - ## src size is seq_size x batch_size x vocab_size. Most cases (50 x 64 x v) - # emb = self.word_lut(src[0]) - # print("INP:",input.shape) - emb = torch.mm(input.view(-1, input.size(2)), self.word_lut.weight) - # print("EMB:", emb.shape) - emb = emb.view(-1, input.size(1), self.word_vec_size) - # print("EM1:", emb.shape) - emb = emb.transpose(0, 1) - emb = emb.transpose(1, 2) - emb = emb.unsqueeze(-1) - # print("EM2:", emb.shape) - h_conv = self.conv1(emb) - # print("CN1:", h_conv.shape) - h_relu = self.relu1(h_conv) - # print("RLU:", h_relu.shape) - # @resize h_relu - # if len(h_relu.shape) > 3: - # h_relu = h_relu.squeeze(-1) - # print("->", h_relu.shape) - h_max = self.maxpool1(h_relu) - h_flat = h_max.view(-1, self.num_filters) - h_drop = self.dropout(h_flat) - lin_out = self.linear(h_drop) - out = self.sigmoid(lin_out) - return out diff --git a/base/lib/recurrent/Constants.py b/base/lib/recurrent/Constants.py deleted file mode 100755 index 9f927a8..0000000 --- a/base/lib/recurrent/Constants.py +++ /dev/null @@ -1,11 +0,0 @@ -# PAD = 0 -# UNK = 1 -# BOS = 2 -# EOS = 3 - -# PAD_WORD = '' -# UNK_WORD = '' -# BOS_WORD = '' -# EOS_WORD = '' - -from core.constants import * diff --git a/base/lib/recurrent/Dataset.py b/base/lib/recurrent/Dataset.py deleted file mode 100755 index 6bbd616..0000000 --- a/base/lib/recurrent/Dataset.py +++ /dev/null @@ -1,84 +0,0 @@ -from __future__ import division - -import math -import random - -import torch -from torch.autograd import Variable - -from . import Constants - - -class Dataset(object): - def __init__(self, srcData, tgtData, batchSize, cuda, volatile=False): - self.src = srcData - if tgtData: - self.tgt = tgtData - assert len(self.src) == len(self.tgt) - else: - self.tgt = None - self.cuda = cuda - - self.batchSize = batchSize - self.numBatches = math.ceil(len(self.src) / batchSize) - self.volatile = volatile - - def _batchify(self, data, align_right=False, include_lengths=False): - lengths = [x.size(0) for x in data] - max_length = max(lengths) - out = data[0].new(len(data), max_length).fill_(Constants.PAD) - for i in range(len(data)): - data_length = data[i].size(0) - offset = max_length - data_length if align_right else 0 - out[i].narrow(0, offset, data_length).copy_(data[i]) - - if include_lengths: - return out, lengths - else: - return out - - def __getitem__(self, index): - assert index < self.numBatches, "%d > %d" % (index, self.numBatches) - srcBatch, lengths = self._batchify( - self.src[index * self.batchSize : (index + 1) * self.batchSize], - align_right=False, - include_lengths=True, - ) - - if self.tgt: - tgtBatch = self._batchify( - self.tgt[index * self.batchSize : (index + 1) * self.batchSize] - ) - else: - tgtBatch = None - - # within batch sorting by decreasing length for variable length rnns - indices = range(len(srcBatch)) - batch = ( - zip(indices, srcBatch) - if tgtBatch is None - else zip(indices, srcBatch, tgtBatch) - ) - batch, lengths = zip(*sorted(zip(batch, lengths), key=lambda x: -x[1])) - if tgtBatch is None: - indices, srcBatch = zip(*batch) - else: - indices, srcBatch, tgtBatch = zip(*batch) - - def wrap(b): - if b is None: - return b - b = torch.stack(b, 0).t().contiguous() - if self.cuda: - b = b.cuda() - b = Variable(b, volatile=self.volatile) - return b - - return (wrap(srcBatch), lengths), wrap(tgtBatch), indices - - def __len__(self): - return self.numBatches - - def shuffle(self): - data = list(zip(self.src, self.tgt)) - self.src, self.tgt = zip(*[data[i] for i in torch.randperm(len(data))]) diff --git a/base/lib/recurrent/Dict.py b/base/lib/recurrent/Dict.py deleted file mode 100755 index a0f6de3..0000000 --- a/base/lib/recurrent/Dict.py +++ /dev/null @@ -1,138 +0,0 @@ -import codecs - -import torch - - -class Dict(object): - def __init__(self, data=None, lower=False): - self.idxToLabel = {} - self.labelToIdx = {} - self.frequencies = {} - self.lower = lower - - # Special entries will not be pruned. - self.special = [] - - if data is not None: - if type(data) == str: - self.loadFile(data) - else: - self.addSpecials(data) - - def size(self): - return len(self.idxToLabel) - - # Load entries from a file. - def loadFile(self, filename): - for line in open(filename): - fields = line.split() - if len(fields) > 2: - idx = int(fields[-1]) - label = " ".join(fields[:-1]) - else: - label = fields[0] - idx = int(fields[1]) - self.add(label, idx) - - # Write entries to a file. - def writeFile(self, filename): - with codecs.open(filename, "w", "utf-8") as file: - for i in range(self.size()): - label = self.idxToLabel[i] - file.write("%s %d\n" % (label, i)) - - file.close() - - def lookup(self, key, default=None): - key = key.lower() if self.lower else key - try: - return self.labelToIdx[key] - except KeyError: - return default - - def getLabel(self, idx, default=None): - try: - if type(idx) == torch.Tensor: - idx = idx.item() - return self.idxToLabel[idx] - except KeyError: - return default - - # Mark this `label` and `idx` as special (i.e. will not be pruned). - def addSpecial(self, label, idx=None): - idx = self.add(label, idx) - self.special += [idx] - - # Mark all labels in `labels` as specials (i.e. will not be pruned). - def addSpecials(self, labels): - for label in labels: - self.addSpecial(label) - - # Add `label` in the dictionary. Use `idx` as its index if given. - def add(self, label, idx=None): - label = label.lower() if self.lower else label - if idx is not None: - self.idxToLabel[idx] = label - self.labelToIdx[label] = idx - else: - if label in self.labelToIdx: - idx = self.labelToIdx[label] - else: - idx = len(self.idxToLabel) - self.idxToLabel[idx] = label - self.labelToIdx[label] = idx - - if idx not in self.frequencies: - self.frequencies[idx] = 1 - else: - self.frequencies[idx] += 1 - - return idx - - # Return a new dictionary with the `size` most frequent entries. - def prune(self, size): - if size >= self.size(): - return self - - # Only keep the `size` most frequent entries. - freq = torch.Tensor([self.frequencies[i] for i in range(len(self.frequencies))]) - _, idx = torch.sort(freq, 0, True) - - newDict = Dict() - newDict.lower = self.lower - - # Add special entries in all cases. - for i in self.special: - newDict.addSpecial(self.idxToLabel[i]) - - for i in idx[:size]: - newDict.add(self.idxToLabel[i]) - - return newDict - - # Convert `labels` to indices. Use `unkWord` if not found. - # Optionally insert `bosWord` at the beginning and `eosWord` at the . - def convertToIdx(self, labels, unkWord, bosWord=None, eosWord=None): - vec = [] - - if bosWord is not None: - vec += [self.lookup(bosWord)] - - unk = self.lookup(unkWord) - vec += [self.lookup(label, default=unk) for label in labels] - - if eosWord is not None: - vec += [self.lookup(eosWord)] - - return torch.LongTensor(vec) - - # Convert `idx` to labels. If index `stop` is reached, convert it and return. - def convertToLabels(self, idx, stop): - labels = [] - - for i in idx: - labels += [self.getLabel(i)] - if i == stop: - break - - return labels diff --git a/base/lib/recurrent/Models.py b/base/lib/recurrent/Models.py deleted file mode 100755 index 5dc84d3..0000000 --- a/base/lib/recurrent/Models.py +++ /dev/null @@ -1,229 +0,0 @@ -import torch -import torch.nn as nn -from torch.autograd import Variable -from torch.nn.utils.rnn import pack_padded_sequence as pack -from torch.nn.utils.rnn import pad_packed_sequence as unpack - -from . import Constants, modules - - -class Encoder(nn.Module): - """ - LSTM Encoder. - """ - - def __init__(self, opt, vocabulary_size): - self.layers = opt.layers - self.num_directions = 2 if opt.brnn else 1 - assert opt.rnn_size % self.num_directions == 0 - self.hidden_size = opt.rnn_size // self.num_directions - input_size = opt.d_word_vec - - super(Encoder, self).__init__() - self.word_lut = nn.Embedding( - vocabulary_size, opt.d_word_vec, padding_idx=Constants.PAD - ) - self.rnn = nn.LSTM( - input_size, - self.hidden_size, - num_layers=opt.layers, - dropout=opt.dropout, - bidirectional=opt.brnn, - ) - - def load_pretrained_vectors(self, opt): - if opt.pre_word_vecs_enc is not None: - pretrained = torch.load(opt.pre_word_vecs_enc) - self.word_lut.weight.data.copy_(pretrained) - - def forward(self, input, hidden=None): - """ - if input is a tuple: - (list of sequences, list of sequence lengths) - otherwise: - list of sequences - """ - if isinstance(input, tuple): - x, x_lengths = input - emb = pack(self.word_lut(x), x_lengths) - else: - emb = self.word_lut(input) - outputs, hidden_t = self.rnn(emb, hidden) - - if isinstance(input, tuple): - outputs = unpack(outputs)[0] - - return hidden_t, outputs - - -class StackedLSTM(nn.Module): - def __init__(self, num_layers, input_size, rnn_size, dropout): - super(StackedLSTM, self).__init__() - self.dropout = nn.Dropout(dropout) - self.num_layers = num_layers - self.layers = nn.ModuleList() - - for i in range(num_layers): - self.layers.append(nn.LSTMCell(input_size, rnn_size)) - input_size = rnn_size - - def forward(self, input, hidden): - h_0, c_0 = hidden - h_1, c_1 = [], [] - for i, layer in enumerate(self.layers): - h_1_i, c_1_i = layer(input, (h_0[i], c_0[i])) - input = h_1_i - if i + 1 != self.num_layers: - input = self.dropout(input) - h_1 += [h_1_i] - c_1 += [c_1_i] - - h_1 = torch.stack(h_1) - c_1 = torch.stack(c_1) - - return input, (h_1, c_1) - - -class Decoder(nn.Module): - def __init__(self, opt, vocabulary_size): - self.layers = opt.layers - self.input_feed = opt.input_feed - input_size = opt.d_word_vec - if self.input_feed: - input_size += opt.rnn_size - - super(Decoder, self).__init__() - self.word_lut = nn.Embedding( - vocabulary_size, opt.d_word_vec, padding_idx=Constants.PAD - ) - self.rnn = StackedLSTM(opt.layers, input_size, opt.rnn_size, opt.dropout) - - self.num_directions = 2 if opt.brnn else 1 - assert opt.rnn_size % self.num_directions == 0 - self.hidden_size = opt.rnn_size // self.num_directions - - # self.rnn = nn.LSTM(input_size, - # self.hidden_size, - # num_layers=opt.layers, - # dropout=opt.dropout, - # bidirectional=opt.brnn) - - self.attn = modules.GlobalAttention(opt.rnn_size) - self.dropout = nn.Dropout(opt.dropout) - - self.hidden_size = opt.rnn_size - - self.generator = nn.Sequential( - nn.Linear(opt.rnn_size, vocabulary_size), nn.LogSoftmax(dim=1) - ) - - def load_pretrained_vectors(self, opt): - if opt.pre_word_vecs_dec is not None: - pretrained = torch.load(opt.pre_word_vecs_dec) - self.word_lut.weight.data.copy_(pretrained) - - # def forward(self, input, hidden, context, init_output): - # # print("INPUT FOR DECODER:", input.shape) - # emb = self.word_lut(input) - # #print(context.size()) - # # n.b. you can increase performance if you compute W_ih * x for all - # # iterations in parallel, but that's only possible if - # # self.input_feed=False - # outputs = [] - # output = init_output - # # print("DECODER EMB:", emb.shape) - # for i in range(input.shape[1]): - # # emb_t = emb[:,i,:] - # # emb_t = emb_t.unsqueeze(1) - # print("EMBT:", emb_t.shape) - # # emb_t = emb_t.squeeze(0) - # # if self.input_feed: - # # emb_t = torch.cat([emb_t, output], 1) - # # print(hidden) - # # print(emb_t.shape) - # # print("HIDDEN:",hidden.shape) - # output, hidden = self.rnn(emb_t, hidden) - # print("OUT:",output.shape, context.shape) - # output, attn = self.attn(output.transpose(0, 1), context.transpose(0, 1)) - # # output, attn = self.attn(output, context.transpose(0, 1)) - # output = self.dropout(output) - # outputs += [output] - - # outputs = torch.stack(outputs) - # return outputs, hidden, attn - def forward(self, input, hidden, context, init_output, useGen=True): - emb = self.word_lut(input) - # print(context.size()) - # n.b. you can increase performance if you compute W_ih * x for all - # iterations in parallel, but that's only possible if - # self.input_feed=False - outputs = [] - output = init_output - for emb_t in emb.split(1): - emb_t = emb_t.squeeze(0) - if self.input_feed: - emb_t = torch.cat([emb_t, output], 1) - - output, hidden = self.rnn(emb_t, hidden) - output, attn = self.attn(output, context.transpose(0, 1)) - output = self.dropout(output) - if useGen: - output = self.generator(output) - outputs += [output] - - outputs = torch.stack(outputs) - return outputs, hidden, attn - - -class NMTModel(nn.Module): - def __init__(self, encoder, decoder): - super(NMTModel, self).__init__() - self.encoder = encoder - self.decoder = decoder - - def make_init_decoder_output(self, context): - batch_size = context.size(1) - h_size = (batch_size, self.decoder.hidden_size) - return Variable(context.data.new(*h_size).zero_(), requires_grad=False) - - def _fix_enc_hidden(self, h): - # the encoder hidden is (layers*directions) x batch x dim - # we need to convert it to layers x batch x (directions*dim) - if self.encoder.num_directions == 2: - return ( - h.view(h.size(0) // 2, 2, h.size(1), h.size(2)) - .transpose(1, 2) - .contiguous() - .view(h.size(0) // 2, h.size(1), h.size(2) * 2) - ) - else: - return h - - def forward(self, src, tgt): - x, x_length = src - y, y_length = tgt - # sort for pack_padded_sequences - sorted_lengths, sorted_idx = torch.sort(x_length, descending=True) - x = x[sorted_idx] - y = y[sorted_idx] - y_length = y_length[sorted_idx] - # swap batch relationship order. - x = x.transpose(0, 1) - y = y.transpose(0, 1) - - enc_hidden, context = self.encoder((x, sorted_lengths)) - init_output = self.make_init_decoder_output(context) - - enc_hidden = ( - self._fix_enc_hidden(enc_hidden[0]), - self._fix_enc_hidden(enc_hidden[1]), - ) - - out, dec_hidden, _attn = self.decoder(y, enc_hidden, context, init_output) - # reverse tensor relationship order - out = out.transpose(0, 1) - # reverse order - _, reversed_idx = torch.sort(sorted_idx) - out = out[reversed_idx] - - return out diff --git a/base/lib/recurrent/Models_decoder.py b/base/lib/recurrent/Models_decoder.py deleted file mode 100755 index 4dff2d2..0000000 --- a/base/lib/recurrent/Models_decoder.py +++ /dev/null @@ -1,126 +0,0 @@ -import torch -import torch.nn as nn -from torch.autograd import Variable -from torch.nn.utils.rnn import pack_padded_sequence as pack -from torch.nn.utils.rnn import pad_packed_sequence as unpack - -from . import modules - -""" -I'm not sure where this is used. - -Potentially teacher forced outputs? -""" - - -class StackedLSTM(nn.Module): - def __init__(self, num_layers, input_size, rnn_size, dropout): - super(StackedLSTM, self).__init__() - self.dropout = nn.Dropout(dropout) - self.num_layers = num_layers - self.layers = nn.ModuleList() - - for i in range(num_layers): - self.layers.append(nn.LSTMCell(input_size, rnn_size)) - input_size = rnn_size - - def forward(self, input, hidden): - h_0, c_0 = hidden - h_1, c_1 = [], [] - for i, layer in enumerate(self.layers): - h_1_i, c_1_i = layer(input, (h_0[i], c_0[i])) - input = h_1_i - if i + 1 != self.num_layers: - input = self.dropout(input) - h_1 += [h_1_i] - c_1 += [c_1_i] - - h_1 = torch.stack(h_1) - c_1 = torch.stack(c_1) - - return input, (h_1, c_1) - - -class Decoder(nn.Module): - def __init__(self, opt, dicts): - self.layers = opt.layers - self.input_feed = opt.input_feed - input_size = opt.word_vec_size - if self.input_feed: - input_size += opt.rnn_size - - super(Decoder, self).__init__() - self.word_lut = nn.Embedding( - dicts.size(), opt.word_vec_size, padding_idx=onmt.Constants.PAD - ) - self.rnn = StackedLSTM(opt.layers, input_size, opt.rnn_size, opt.dropout) - self.attn = modules.GlobalAttention(opt.rnn_size) - self.dropout = nn.Dropout(opt.dropout) - - self.num_directions = 2 if opt.brnn else 1 - self.hidden_size = opt.rnn_size - - def load_pretrained_vectors(self, opt): - if opt.pre_word_vecs_dec is not None: - pretrained = torch.load(opt.pre_word_vecs_dec) - self.word_lut.weight.data.copy_(pretrained) - - def forward(self, input, hidden, context, init_output): - emb = self.word_lut(input) - # print(context.size()) - # n.b. you can increase performance if you compute W_ih * x for all - # iterations in parallel, but that's only possible if - # self.input_feed=False - outputs = [] - output = init_output - for emb_t in emb.split(1): - emb_t = emb_t.squeeze(0) - if self.input_feed: - emb_t = torch.cat([emb_t, output], 1) - - output, hidden = self.rnn(emb_t, hidden) - output, attn = self.attn(output, context.transpose(0, 1)) - output = self.dropout(output) - outputs += [output] - - outputs = torch.stack(outputs) - return outputs, hidden, attn - - -class DecoderModel(nn.Module): - def __init__(self, decoder): - super(DecoderModel, self).__init__() - self.decoder = decoder - - def make_init_decoder_output(self, context): - batch_size = context.size(1) - h_size = (batch_size, self.decoder.hidden_size) - return Variable(context.data.new(*h_size).zero_(), requires_grad=False) - - def _fix_enc_hidden(self, h): - # the encoder hidden is (layers*directions) x batch x dim - # we need to convert it to layers x batch x (directions*dim) - if self.decoder.num_directions == 2: - return ( - h.view(h.size(0) // 2, 2, h.size(1), h.size(2)) - .transpose(1, 2) - .contiguous() - .view(h.size(0) // 2, h.size(1), h.size(2) * 2) - ) - else: - return h - - def forward(self, input, enc_hidden, context): - # src = input[0] - tgt = input[1][:-1] # exclude last target from inputs - # enc_hidden, context = self.encoder(src) - init_output = self.make_init_decoder_output(context) - - enc_hidden = ( - self._fix_enc_hidden(enc_hidden[0]), - self._fix_enc_hidden(enc_hidden[1]), - ) - - out, dec_hidden, _attn = self.decoder(tgt, enc_hidden, context, init_output) - - return out diff --git a/base/lib/recurrent/Optim.py b/base/lib/recurrent/Optim.py deleted file mode 100755 index 4d80a98..0000000 --- a/base/lib/recurrent/Optim.py +++ /dev/null @@ -1,49 +0,0 @@ -import math - -import torch.nn as nn -import torch.optim as optim -from torch.nn.utils import clip_grad_norm_ - - -class Optim(object): - def set_parameters(self, params): - self.params = list(params) # careful: params may be a generator - if self.method == "sgd": - self.optimizer = optim.SGD(self.params, lr=self.lr) - elif self.method == "adagrad": - self.optimizer = optim.Adagrad(self.params, lr=self.lr) - elif self.method == "adadelta": - self.optimizer = optim.Adadelta(self.params, lr=self.lr) - elif self.method == "adam": - self.optimizer = optim.Adam(self.params, lr=self.lr) - else: - raise RuntimeError("Invalid optim method: " + self.method) - - def __init__(self, method, lr, max_grad_norm, lr_decay=1, start_decay_at=None): - self.last_ppl = None - self.lr = lr - self.max_grad_norm = max_grad_norm - self.method = method - self.lr_decay = lr_decay - self.start_decay_at = start_decay_at - self.start_decay = False - - def step(self): - # Compute gradients norm. - if self.max_grad_norm: - clip_grad_norm_(self.params, self.max_grad_norm) - self.optimizer.step() - - # decay learning rate if val perf does not improve or we hit the start_decay_at limit - def updateLearningRate(self, ppl, epoch): - if self.start_decay_at is not None and epoch >= self.start_decay_at: - self.start_decay = True - if self.last_ppl is not None and ppl > self.last_ppl: - self.start_decay = True - - if self.start_decay: - self.lr = self.lr * self.lr_decay - print("Decaying learning rate to %g" % self.lr) - - self.last_ppl = ppl - self.optimizer.param_groups[0]["lr"] = self.lr diff --git a/base/lib/recurrent/Translator.py b/base/lib/recurrent/Translator.py deleted file mode 100755 index 7356f1c..0000000 --- a/base/lib/recurrent/Translator.py +++ /dev/null @@ -1,416 +0,0 @@ -import torch -import torch.nn as nn -from torch.autograd import Variable - -from . import Constants, Dataset, Models, modules -from .Beam import Beam - - -class Translator(object): - def __init__(self, opt, new=True): - self.opt = opt - self.device = torch.device("cuda" if opt.cuda else "cpu") - if new: - # self.tt = torch.cuda if opt.cuda else torch - - checkpoint = torch.load(opt.model) - - model_opt = checkpoint["opt"] - self.src_dict = checkpoint["dicts"]["src"] - self.tgt_dict = checkpoint["dicts"]["tgt"] - - encoder = Models.Encoder(model_opt, self.src_dict) - decoder = Models.Decoder(model_opt, self.tgt_dict) - model = Models.NMTModel(encoder, decoder) - - # @ change here. - generator = nn.Sequential( - nn.Linear(model_opt.rnn_size, self.tgt_dict.size()), - nn.LogSoftmax(dim=1), - ) - - if "model" in checkpoint.keys(): - model.load_state_dict(checkpoint["model"]) - - generator.load_state_dict(checkpoint["generator"]) - - if opt.cuda: - model.cuda() - generator.cuda() - else: - model.cpu() - generator.cpu() - - model.generator = generator - - self.model = model - self.model.eval() - - # def buildData(self, srcBatch, goldBatch): - # srcData = [self.src_dict.convertToIdx(b, - # Constants.UNK_WORD) for b in srcBatch] - # tgtData = None - # if goldBatch: - # tgtData = [self.tgt_dict.convertToIdx(b, - # Constants.UNK_WORD, - # Constants.SOS_WORD, - # Constants.EOS_WORD) for b in goldBatch] - # with torch.no_grad(): - # return Dataset(srcData, tgtData, - # self.opt.batch_size, self.opt.cuda) - - # def buildTargetTokens(self, pred, src, attn): - # tokens = self.tgt_dict.convertToLabels(pred, Constants.EOS) - # # remove EOS token - # tokens = tokens[:-1] - # if self.opt.replace_unk: - # for i in range(len(tokens)): - # # if we have an UNK token then we'll want to replace it - # # with the most probable entry from our source value. - # if tokens[i] == Constants.UNK_WORD: - # _, maxIndex = attn[i].max(0) - # tokens[i] = src[maxIndex.item()] - - # return tokens - - def translate_batch(self, src_seq, src_pos): - """Translation work in one batch""" - - def get_inst_idx_to_tensor_position_map(inst_idx_list): - """Indicate the position of an instance in a tensor.""" - return { - inst_idx: tensor_position - for tensor_position, inst_idx in enumerate(inst_idx_list) - } - - def collect_active_part( - beamed_tensor, curr_active_inst_idx, n_prev_active_inst, n_bm - ): - """Collect tensor parts associated to active instances.""" - - _, *d_hs = beamed_tensor.size() - n_curr_active_inst = len(curr_active_inst_idx) - new_shape = (n_curr_active_inst * n_bm, *d_hs) - - beamed_tensor = beamed_tensor.view(n_prev_active_inst, -1) - beamed_tensor = beamed_tensor.index_select(0, curr_active_inst_idx) - beamed_tensor = beamed_tensor.view(*new_shape) - - return beamed_tensor - - def collate_active_info( - src_seq, src_enc, inst_idx_to_position_map, active_inst_idx_list - ): - # Sentences which are still active are collected, - # so the decoder will not run on completed sentences. - n_prev_active_inst = len(inst_idx_to_position_map) - active_inst_idx = [ - inst_idx_to_position_map[k] for k in active_inst_idx_list - ] - active_inst_idx = torch.LongTensor(active_inst_idx).to(self.device) - - active_src_seq = collect_active_part( - src_seq, active_inst_idx, n_prev_active_inst, n_bm - ) - active_src_enc = collect_active_part( - src_enc, active_inst_idx, n_prev_active_inst, n_bm - ) - active_inst_idx_to_position_map = get_inst_idx_to_tensor_position_map( - active_inst_idx_list - ) - - return active_src_seq, active_src_enc, active_inst_idx_to_position_map - - def beam_decode_step( - inst_dec_beams, - len_dec_seq, - src_seq, - enc_output, - inst_idx_to_position_map, - n_bm, - ): - """Decode and update beam status, and then return active beam idx""" - - def prepare_beam_dec_seq(inst_dec_beams, len_dec_seq): - dec_partial_seq = [ - b.get_current_state() for b in inst_dec_beams if not b.done - ] - dec_partial_seq = torch.stack(dec_partial_seq).to(self.device) - dec_partial_seq = dec_partial_seq.view(-1, len_dec_seq) - return dec_partial_seq - - def prepare_beam_dec_pos(len_dec_seq, n_active_inst, n_bm): - dec_partial_pos = torch.arange( - 1, len_dec_seq + 1, dtype=torch.long, device=self.device - ) - dec_partial_pos = dec_partial_pos.unsqueeze(0).repeat( - n_active_inst * n_bm, 1 - ) - return dec_partial_pos - - # def predict_word(dec_seq, dec_pos, src_seq, enc_output, n_active_inst, n_bm): - # # dec_output, *_ = self.model.decoder(dec_seq, dec_pos, src_seq, enc_output) - # # dec_output = dec_output[:, -1, :] # Pick the last step: (bh * bm) * d_h - # # word_prob = F.log_softmax(self.model.generator(dec_output), dim=1) - # # word_prob = word_prob.view(n_active_inst, n_bm, -1) - # out, _,_ = self.decoder(dec_seq, decStates, context, enc_output) - # word_prob = out.view(n_active_inst, n_bm, -1) - # return word_prob - - def collect_active_inst_idx_list( - inst_beams, word_prob, inst_idx_to_position_map - ): - active_inst_idx_list = [] - for inst_idx, inst_position in inst_idx_to_position_map.items(): - is_inst_complete = inst_beams[inst_idx].advance( - word_prob[inst_position] - ) - if not is_inst_complete: - active_inst_idx_list += [inst_idx] - - return active_inst_idx_list - - n_active_inst = len(inst_idx_to_position_map) - - dec_seq = prepare_beam_dec_seq(inst_dec_beams, len_dec_seq) - dec_pos = prepare_beam_dec_pos(len_dec_seq, n_active_inst, n_bm) - # word_prob = predict_word(dec_seq, dec_pos, src_seq, enc_output, n_active_inst, n_bm) - - out, _, _ = self.decoder(dec_seq, src_enc, context, enc_output) - word_prob = out.view(n_active_inst, n_bm, -1) - - # Update the beam with predicted word prob information and collect incomplete instances - active_inst_idx_list = collect_active_inst_idx_list( - inst_dec_beams, word_prob, inst_idx_to_position_map - ) - - return active_inst_idx_list - - def collect_hypothesis_and_scores(inst_dec_beams, n_best): - all_hyp, all_scores = [], [] - for inst_idx in range(len(inst_dec_beams)): - scores, tail_idxs = inst_dec_beams[inst_idx].sort_scores() - all_scores += [scores[:n_best]] - - hyps = [ - inst_dec_beams[inst_idx].get_hypothesis(i) - for i in tail_idxs[:n_best] - ] - all_hyp += [hyps] - return all_hyp, all_scores - - with torch.no_grad(): - # -- Encode - src_seq, src_pos = src_seq.to(self.device), src_pos.to(self.device) - # src_enc, *_ = self.model.encoder(src_seq, src_pos) - src_enc, context = self.model.encoder((src_seq, src_pos)) - rnnSize = context.size(2) - src_enc = ( - self.model._fix_enc_hidden(src_enc[0]), - self.model._fix_enc_hidden(src_enc[1]), - ) - - # -- Repeat data for beam search - n_bm = self.opt.beam_size - n_inst, len_s, d_h = src_enc.size() - src_seq = src_seq.repeat(1, n_bm).view(n_inst * n_bm, len_s) - src_enc = src_enc.repeat(1, n_bm, 1).view(n_inst * n_bm, len_s, d_h) - - # -- Prepare beams - inst_dec_beams = [Beam(n_bm, device=self.device) for _ in range(n_inst)] - - # -- Bookkeeping for active or not - active_inst_idx_list = list(range(n_inst)) - inst_idx_to_position_map = get_inst_idx_to_tensor_position_map( - active_inst_idx_list - ) - - # -- Decode - for len_dec_seq in range(1, self.max_token_seq_len + 1): - active_inst_idx_list = beam_decode_step( - inst_dec_beams, - len_dec_seq, - src_seq, - src_enc, - inst_idx_to_position_map, - n_bm, - ) - - if not active_inst_idx_list: - break # all instances have finished their path to - - src_seq, src_enc, inst_idx_to_position_map = collate_active_info( - src_seq, src_enc, inst_idx_to_position_map, active_inst_idx_list - ) - - batch_hyp, batch_scores = collect_hypothesis_and_scores( - inst_dec_beams, self.opt.n_best - ) - - return batch_hyp, batch_scores - - def translateBatch(self, srcBatch, tgtBatch=None): - src_seq, src_len = srcBatch - src_seq = src_seq.transpose(0, 1) - srcBatch = (src_seq, src_len) - batchSize = srcBatch[0].size(1) - beamSize = self.opt.beam_size - - # (1) run the encoder on the src - encStates, context = self.model.encoder(srcBatch) - srcBatch = srcBatch[0] # drop the lengths needed for encoder - - rnnSize = context.size(2) - encStates = ( - self.model._fix_enc_hidden(encStates[0]), - self.model._fix_enc_hidden(encStates[1]), - ) - - # This mask is applied to the attention model inside the decoder - # so that the attention ignores source padding - padMask = srcBatch.data.eq(Constants.PAD).t() - - def applyContextMask(m): - if isinstance(m, modules.GlobalAttention): - m.applyMask(padMask) - - # (2) if a target is specified, compute the 'goldScore' - # (i.e. log likelihood) of the target under the model - goldScores = context.data.new(batchSize).zero_() - - # This section is most likely not to be called. - if tgtBatch is not None: - decStates = encStates - decOut = self.model.make_init_decoder_output(context) - self.model.decoder.apply(applyContextMask) - initOutput = self.model.make_init_decoder_output(context) - - decOut, decStates, attn = self.model.decoder( - tgtBatch[:-1], decStates, context, initOutput - ) - for dec_t, tgt_t in zip(decOut, tgtBatch[1:].data): - gen_t = self.model.generator.forward(dec_t) - tgt_t = tgt_t.unsqueeze(1) - scores = gen_t.data.gather(1, tgt_t) - scores.masked_fill_(tgt_t.eq(Constants.PAD), 0) - goldScores += scores - - # (3) run the decoder to generate sentences, using beam search - - # Expand tensors for each beam. - context = Variable(context.data.repeat(1, beamSize, 1)) - decStates = ( - Variable(encStates[0].data.repeat(1, beamSize, 1)), - Variable(encStates[1].data.repeat(1, beamSize, 1)), - ) - - beam = [Beam(beamSize, self.opt.cuda) for k in range(batchSize)] - - decOut = self.model.make_init_decoder_output(context) - - padMask = ( - srcBatch.data.eq(Constants.PAD).t().unsqueeze(0).repeat(beamSize, 1, 1) - ) - - batchIdx = list(range(batchSize)) - remainingSents = batchSize - for i in range(self.max_token_seq_len): - self.model.decoder.apply(applyContextMask) - - # Prepare decoder input. - input = ( - torch.stack([b.getCurrentState() for b in beam if not b.done]) - .t() - .contiguous() - .view(1, -1) - ) - - with torch.no_grad(): - input = Variable(input) - decOut, decStates, attn = self.model.decoder( - input, decStates, context, decOut, False - ) - # decOut: 1 x (beam*batch) x numWords - out = self.model.decoder.generator(decOut) - out = out.transpose(0, 1) - - # batch x beam x numWords - wordLk = out.view(beamSize, remainingSents, -1).transpose(0, 1).contiguous() - attn = attn.view(beamSize, remainingSents, -1).transpose(0, 1).contiguous() - - active = [] - for b in range(batchSize): - if beam[b].done: - continue - - idx = batchIdx[b] - if not beam[b].advance(wordLk.data[idx], attn.data[idx]): - active += [b] - - for decState in decStates: # iterate over h, c - # layers x beam*sent x dim - sentStates = decState.view( - -1, beamSize, remainingSents, decState.size(2) - )[:, :, idx] - sentStates.data.copy_( - sentStates.data.index_select(1, beam[b].getCurrentOrigin()) - ) - - if not active: - break - - # in this section, the sentences that are still active are - # compacted so that the decoder is not run on completed sentences - activeIdx = torch.LongTensor([batchIdx[k] for k in active]).to(self.device) - batchIdx = {beam: idx for idx, beam in enumerate(active)} - - def updateActive(t): - # select only the remaining active sentences - view = t.data.view(-1, remainingSents, rnnSize) - newSize = list(t.size()) - newSize[-2] = newSize[-2] * len(activeIdx) // remainingSents - with torch.no_grad(): - return Variable(view.index_select(1, activeIdx).view(*newSize)) - - decStates = (updateActive(decStates[0]), updateActive(decStates[1])) - decOut = updateActive(decOut) - context = updateActive(context) - padMask = padMask.index_select(1, activeIdx) - - remainingSents = len(active) - - # (4) package everything up - - allHyp, allScores, allAttn = [], [], [] - n_best = self.opt.n_best - - for b in range(batchSize): - scores, ks = beam[b].sortBest() - - allScores += [scores[:n_best]] - valid_attn = srcBatch.data[:, b].ne(Constants.PAD).nonzero().squeeze(1) - hyps, attn = zip(*[beam[b].getHyp(k) for k in ks[:n_best]]) - attn = [a.index_select(1, valid_attn) for a in attn] - allHyp += [hyps] - allAttn += [attn] - - return allHyp, allScores, allAttn, goldScores - - # def translate(self, srcBatch, goldBatch): - # # (1) convert words to indexes - # dataset = self.buildData(srcBatch, goldBatch) - # src, tgt, indices = dataset[0] - - # # (2) translate - # pred, predScore, attn, goldScore = self.translateBatch(src, tgt) - # pred, predScore, attn, goldScore = list(zip(*sorted(zip(pred, predScore, attn, goldScore, indices), key=lambda x: x[-1])))[:-1] - - # # (3) convert indexes to words - # predBatch = [] - # for b in range(src[0].size(1)): - # predBatch.append( - # [self.buildTargetTokens(pred[b][n], srcBatch[b], attn[b][n]) - # for n in range(self.opt.n_best)] - # ) - - # return predBatch, predScore, goldScore diff --git a/base/lib/recurrent/Translator_style.py b/base/lib/recurrent/Translator_style.py deleted file mode 100755 index d07dbd2..0000000 --- a/base/lib/recurrent/Translator_style.py +++ /dev/null @@ -1,243 +0,0 @@ -# import onmt -import numpy as np -import torch -import torch.nn as nn -from torch.autograd import Variable - -from . import Beam, Constants, Dataset, Models, Models_decoder, modules - - -class Translator(object): - def __init__(self, opt): - self.opt = opt - self.tt = torch.cuda if opt.cuda else torch - - checkpoint = torch.load( - opt.decoder_model, map_location=lambda storage, loc: storage - ) - encoder_check = torch.load( - opt.encoder_model, map_location=lambda storage, loc: storage - ) - - self.src_dict = encoder_check["dicts"]["src"] - self.tgt_dict = checkpoint["dicts"]["tgt"] - enc_opt = encoder_check["opt"] - - encoder = Models.Encoder(enc_opt, self.src_dict) - encoder.load_state_dict(encoder_check["encoder"]) - decoder = Models_decoder.Decoder(enc_opt, self.tgt_dict) - decoder.load_state_dict(checkpoint["decoder"]) - model = Models.NMTModel(encoder, decoder) - - generator = nn.Sequential( - nn.Linear(enc_opt.rnn_size, self.tgt_dict.size()), nn.LogSoftmax() - ) - - generator.load_state_dict(checkpoint["generator"]) - - if opt.cuda: - encoder.cuda() - model.cuda() - generator.cuda() - else: - model.cpu() - generator.cpu() - - model.generator = generator - - self.encoder = encoder - self.model = model - self.model.eval() - - def buildData(self, srcBatch, goldBatch): - srcData = [self.src_dict.convertToIdx(b, Constants.UNK_WORD) for b in srcBatch] - tgtData = None - if goldBatch: - tgtData = [ - self.tgt_dict.convertToIdx( - b, Constants.UNK_WORD, Constants.BOS_WORD, Constants.EOS_WORD - ) - for b in goldBatch - ] - - return Dataset( - srcData, tgtData, self.opt.batch_size, self.opt.cuda, volatile=True - ) - - def buildTargetTokens(self, pred, src, attn): - tokens = self.tgt_dict.convertToLabels(pred, Constants.EOS) - tokens = tokens[:-1] # EOS - if self.opt.replace_unk: - for i in range(len(tokens)): - if tokens[i] == Constants.UNK_WORD: - _, maxIndex = attn[i].max(0) - tokens[i] = src[maxIndex.item()] - return tokens - - def translateBatch(self, srcBatch, tgtBatch): - batchSize = srcBatch[0].size(1) - beamSize = self.opt.beam_size - - # (1) run the encoder on the src - encStates, context = self.encoder(srcBatch) - srcBatch = srcBatch[0] # drop the lengths needed for encoder - - rnnSize = context.size(2) - encStates = ( - self.model._fix_enc_hidden(encStates[0]), - self.model._fix_enc_hidden(encStates[1]), - ) - - # This mask is applied to the attention model inside the decoder - # so that the attention ignores source padding - padMask = srcBatch.data.eq(Constants.PAD).t() - - def applyContextMask(m): - if isinstance(m, modules.GlobalAttention): - m.applyMask(padMask) - - # (2) if a target is specified, compute the 'goldScore' - # (i.e. log likelihood) of the target under the model - goldScores = context.data.new(batchSize).zero_() - if tgtBatch is not None: - decStates = encStates - decOut = self.model.make_init_decoder_output(context) - self.model.decoder.apply(applyContextMask) - initOutput = self.model.make_init_decoder_output(context) - - decOut, decStates, attn = self.model.decoder( - tgtBatch[:-1], decStates, context, initOutput - ) - for dec_t, tgt_t in zip(decOut, tgtBatch[1:].data): - gen_t = self.model.generator.forward(dec_t) - tgt_t = tgt_t.unsqueeze(1) - scores = gen_t.data.gather(1, tgt_t) - scores.masked_fill_(tgt_t.eq(Constants.PAD), 0) - goldScores += scores - - # (3) run the decoder to generate sentences, using beam search - - # Expand tensors for each beam. - context = Variable(context.data.repeat(1, beamSize, 1)) - decStates = ( - Variable(encStates[0].data.repeat(1, beamSize, 1)), - Variable(encStates[1].data.repeat(1, beamSize, 1)), - ) - - beam = [Beam(beamSize, self.opt.cuda) for k in range(batchSize)] - - decOut = self.model.make_init_decoder_output(context) - - padMask = ( - srcBatch.data.eq(Constants.PAD).t().unsqueeze(0).repeat(beamSize, 1, 1) - ) - - batchIdx = list(range(batchSize)) - remainingSents = batchSize - for i in range(self.opt.max_sent_length): - self.model.decoder.apply(applyContextMask) - - # Prepare decoder input. - input = ( - torch.stack([b.getCurrentState() for b in beam if not b.done]) - .t() - .contiguous() - .view(1, -1) - ) - - decOut, decStates, attn = self.model.decoder( - Variable(input, volatile=True), decStates, context, decOut - ) - # decOut: 1 x (beam*batch) x numWords - decOut = decOut.squeeze(0) - out = self.model.generator.forward(decOut) - - # batch x beam x numWords - wordLk = out.view(beamSize, remainingSents, -1).transpose(0, 1).contiguous() - attn = attn.view(beamSize, remainingSents, -1).transpose(0, 1).contiguous() - - active = [] - for b in range(batchSize): - if beam[b].done: - continue - - idx = batchIdx[b] - if not beam[b].advance(wordLk.data[idx], attn.data[idx]): - active += [b] - - for decState in decStates: # iterate over h, c - # layers x beam*sent x dim - sentStates = decState.view( - -1, beamSize, remainingSents, decState.size(2) - )[:, :, idx] - sentStates.data.copy_( - sentStates.data.index_select(1, beam[b].getCurrentOrigin()) - ) - - if not active: - break - - # in this section, the sentences that are still active are - # compacted so that the decoder is not run on completed sentences - activeIdx = self.tt.LongTensor([batchIdx[k] for k in active]) - batchIdx = {beam: idx for idx, beam in enumerate(active)} - - def updateActive(t): - # select only the remaining active sentences - view = t.data.view(-1, remainingSents, rnnSize) - newSize = list(t.size()) - newSize[-2] = newSize[-2] * len(activeIdx) // remainingSents - return Variable( - view.index_select(1, activeIdx).view(*newSize), volatile=True - ) - - decStates = (updateActive(decStates[0]), updateActive(decStates[1])) - decOut = updateActive(decOut) - context = updateActive(context) - padMask = padMask.index_select(1, activeIdx) - - remainingSents = len(active) - - # (4) package everything up - - allHyp, allScores, allAttn = [], [], [] - n_best = self.opt.n_best - - for b in range(batchSize): - scores, ks = beam[b].sortBest() - - allScores += [scores[:n_best]] - valid_attn = srcBatch.data[:, b].ne(Constants.PAD).nonzero().squeeze(1) - hyps, attn = zip(*[beam[b].getHyp(k) for k in ks[:n_best]]) - attn = [a.index_select(1, valid_attn) for a in attn] - allHyp += [hyps] - allAttn += [attn] - - return allHyp, allScores, allAttn, goldScores - - def translate(self, srcBatch, goldBatch): - # (1) convert words to indexes - dataset = self.buildData(srcBatch, goldBatch) - src, tgt, indices = dataset[0] - - # (2) translate - pred, predScore, attn, goldScore = self.translateBatch(src, tgt) - pred, predScore, attn, goldScore = list( - zip( - *sorted( - zip(pred, predScore, attn, goldScore, indices), key=lambda x: x[-1] - ) - ) - )[:-1] - - # (3) convert indexes to words - predBatch = [] - for b in range(src[0].size(1)): - predBatch.append( - [ - self.buildTargetTokens(pred[b][n], srcBatch[b], attn[b][n]) - for n in range(self.opt.n_best) - ] - ) - - return predBatch, predScore, goldScore diff --git a/base/lib/recurrent/__init__.py b/base/lib/recurrent/__init__.py deleted file mode 100755 index 082ed99..0000000 --- a/base/lib/recurrent/__init__.py +++ /dev/null @@ -1,29 +0,0 @@ -# import Constants -# import Models -# import CNNModels -# import Models_decoder -# from Translator import Translator as Translator -# from Translator_style import Translator as Translator_style -# from Dataset import Dataset -# from Optim import Optim -# from Dict import Dict -# from Beam import Beam - - -from . import Beam, CNNModels, Constants, Models -from .Dataset import Dataset -from .Dict import Dict -from .Optim import Optim -from .Translator import Translator as Translator -from .Translator_style import Translator as Translator_style - -__all__ = [ - Dataset, - Constants, - Models, - CNNModels, - Optim, - Translator, - Translator_style, - Beam, -] diff --git a/base/lib/recurrent/modules/GlobalAttention.py b/base/lib/recurrent/modules/GlobalAttention.py deleted file mode 100755 index 82a722a..0000000 --- a/base/lib/recurrent/modules/GlobalAttention.py +++ /dev/null @@ -1,442 +0,0 @@ -# """ -# Global attention takes a matrix and a query vector. It -# then computes a parameterized convex combination of the matrix -# based on the input query. - - -# H_1 H_2 H_3 ... H_n -# q q q q -# | | | | -# \ | | / -# ..... -# \ | / -# a - -# Constructs a unit mapping. -# $$(H_1 + H_n, q) => (a)$$ -# Where H is of `batch x n x dim` and q is of `batch x dim`. - -# The full def is $$\tanh(W_2 [(softmax((W_1 q + b_1) H) H), q] + b_2)$$.: - -# """ - -# import torch -# import torch.nn as nn -# import math -# torch.set_printoptions(threshold=10000) - -# class GlobalAttention(nn.Module): -# def __init__(self, dim): -# super(GlobalAttention, self).__init__() -# self.linear_in = nn.Linear(dim, dim, bias=False) -# self.sm = nn.Softmax() -# self.linear_out = nn.Linear(dim*2, dim, bias=False) -# self.tanh = nn.Tanh() -# self.mask = None - -# def applyMask(self, mask): -# self.mask = mask - -# def forward(self, input, context): -# """ -# input: batch x dim -# context: batch x sourceL x dim -# """ -# targetT = self.linear_in(input).unsqueeze(2) # batch x dim x 1 - -# # Get attention -# attn = torch.bmm(context, targetT).squeeze(2) # batch x sourceL -# # print(self.mask) -# if self.mask is not None: -# # print(attn.shape) -# # print(self.mask.shape) -# # print(self.mask.view(-1, self.mask.shape[-1]).shape) -# # temp = self.mask -# attn.masked_fill_(self.mask.view(-1, self.mask.shape[-1]), -float('inf')) -# attn = self.sm(attn) -# attn3 = attn.view(attn.size(0), 1, attn.size(1)) # batch x 1 x sourceL - -# weightedContext = torch.bmm(attn3, context).squeeze(1) # batch x dim -# contextCombined = torch.cat((weightedContext, input), 1) - -# contextOutput = self.tanh(self.linear_out(contextCombined)) - -# return contextOutput, attn - -""" -Global attention takes a matrix and a query vector. It -then computes a parameterized convex combination of the matrix -based on the input query. - H_1 H_2 H_3 ... H_n - q q q q - | | | | - \ | | / - ..... - \ | / - a -Constructs a unit mapping. - $$(H_1 + H_n, q) => (a)$$ - Where H is of `batch x n x dim` and q is of `batch x dim`. - The full def is $$\tanh(W_2 [(softmax((W_1 q + b_1) H) H), q] + b_2)$$.: -""" - -import math - -import torch -import torch.nn as nn - - -class GlobalAttention(nn.Module): - def __init__(self, dim): - super(GlobalAttention, self).__init__() - self.linear_in = nn.Linear(dim, dim, bias=False) - self.sm = nn.Softmax(dim=1) - self.linear_out = nn.Linear(dim * 2, dim, bias=False) - self.tanh = nn.Tanh() - self.mask = None - - def applyMask(self, mask): - self.mask = mask - - def forward(self, input, context): - """ - input: batch x dim - context: batch x sourceL x dim - """ - targetT = self.linear_in(input).unsqueeze(2) # batch x dim x 1 - - # Get attention - attn = torch.bmm(context, targetT).squeeze(2) # batch x sourceL - if self.mask is not None: - attn.data.masked_fill_( - self.mask.view(-1, self.mask.shape[-1]), -float("inf") - ) - attn = self.sm(attn) - attn3 = attn.view(attn.size(0), 1, attn.size(1)) # batch x 1 x sourceL - - weightedContext = torch.bmm(attn3, context).squeeze(1) # batch x dim - contextCombined = torch.cat((weightedContext, input), 1) - - contextOutput = self.tanh(self.linear_out(contextCombined)) - - return contextOutput, attn - - -# """Global attention modules (Luong / Bahdanau)""" -# import torch -# import torch.nn as nn -# import torch.nn.functional as F - -# from torch.autograd import function - -# from torch.autograd import Function - -# # from onmt.modules.sparse_activations import sparsemax -# # from onmt.utils.misc import aeq, sequence_mask - -# # This class is mainly used by decoder.py for RNNs but also -# # by the CNN / transformer decoder when copy attention is used -# # CNN has its own attention mechanism ConvMultiStepAttention -# # Transformer has its own MultiHeadedAttention - - -# def aeq(*args): -# """ -# Assert all arguments have the same value -# """ -# arguments = (arg for arg in args) -# first = next(arguments) -# assert all(arg == first for arg in arguments), \ -# "Not all arguments have the same value: " + str(args) - - -# def sequence_mask(lengths, max_len=None): -# """ -# Creates a boolean mask from sequence lengths. -# """ -# batch_size = lengths.numel() -# max_len = max_len or lengths.max() -# return (torch.arange(0, max_len) -# .type_as(lengths) -# .repeat(batch_size, 1) -# .lt(lengths.unsqueeze(1))) - -# class GlobalAttention(nn.Module): -# r""" -# Global attention takes a matrix and a query vector. It -# then computes a parameterized convex combination of the matrix -# based on the input query. -# Constructs a unit mapping a query `q` of size `dim` -# and a source matrix `H` of size `n x dim`, to an output -# of size `dim`. -# .. mermaid:: -# graph BT -# A[Query] -# subgraph RNN -# C[H 1] -# D[H 2] -# E[H N] -# end -# F[Attn] -# G[Output] -# A --> F -# C --> F -# D --> F -# E --> F -# C -.-> G -# D -.-> G -# E -.-> G -# F --> G -# All models compute the output as -# :math:`c = \sum_{j=1}^{\text{SeqLength}} a_j H_j` where -# :math:`a_j` is the softmax of a score function. -# Then then apply a projection layer to [q, c]. -# However they -# differ on how they compute the attention score. -# * Luong Attention (dot, general): -# * dot: :math:`\text{score}(H_j,q) = H_j^T q` -# * general: :math:`\text{score}(H_j, q) = H_j^T W_a q` -# * Bahdanau Attention (mlp): -# * :math:`\text{score}(H_j, q) = v_a^T \text{tanh}(W_a q + U_a h_j)` -# Args: -# dim (int): dimensionality of query and key -# coverage (bool): use coverage term -# attn_type (str): type of attention to use, options [dot,general,mlp] -# attn_func (str): attention function to use, options [softmax,sparsemax] -# """ - -# def __init__(self, dim, coverage=False, attn_type="dot", -# attn_func="softmax"): -# super(GlobalAttention, self).__init__() - -# self.dim = dim -# assert attn_type in ["dot", "general", "mlp"], ( -# "Please select a valid attention type (got {:s}).".format( -# attn_type)) -# self.attn_type = attn_type -# assert attn_func in ["softmax", "sparsemax"], ( -# "Please select a valid attention function.") -# self.attn_func = attn_func - -# if self.attn_type == "general": -# self.linear_in = nn.Linear(dim, dim, bias=False) -# elif self.attn_type == "mlp": -# self.linear_context = nn.Linear(dim, dim, bias=False) -# self.linear_query = nn.Linear(dim, dim, bias=True) -# self.v = nn.Linear(dim, 1, bias=False) -# # mlp wants it with bias -# out_bias = self.attn_type == "mlp" -# self.linear_out = nn.Linear(dim * 2, dim, bias=out_bias) - -# if coverage: -# self.linear_cover = nn.Linear(1, dim, bias=False) - -# def score(self, h_t, h_s): -# """ -# Args: -# h_t (FloatTensor): sequence of queries ``(batch, tgt_len, dim)`` -# h_s (FloatTensor): sequence of sources ``(batch, src_len, dim`` -# Returns: -# FloatTensor: raw attention scores (unnormalized) for each src index -# ``(batch, tgt_len, src_len)`` -# """ - -# # Check input sizes -# src_batch, src_len, src_dim = h_s.size() -# tgt_batch, tgt_len, tgt_dim = h_t.size() -# aeq(src_batch, tgt_batch) -# aeq(src_dim, tgt_dim) -# aeq(self.dim, src_dim) - -# if self.attn_type in ["general", "dot"]: -# if self.attn_type == "general": -# h_t_ = h_t.view(tgt_batch * tgt_len, tgt_dim) -# h_t_ = self.linear_in(h_t_) -# h_t = h_t_.view(tgt_batch, tgt_len, tgt_dim) -# h_s_ = h_s.transpose(1, 2) -# # (batch, t_len, d) x (batch, d, s_len) --> (batch, t_len, s_len) -# return torch.bmm(h_t, h_s_) -# else: -# dim = self.dim -# wq = self.linear_query(h_t.view(-1, dim)) -# wq = wq.view(tgt_batch, tgt_len, 1, dim) -# wq = wq.expand(tgt_batch, tgt_len, src_len, dim) - -# uh = self.linear_context(h_s.contiguous().view(-1, dim)) -# uh = uh.view(src_batch, 1, src_len, dim) -# uh = uh.expand(src_batch, tgt_len, src_len, dim) - -# # (batch, t_len, s_len, d) -# wquh = torch.tanh(wq + uh) - -# return self.v(wquh.view(-1, dim)).view(tgt_batch, tgt_len, src_len) - -# def forward(self, source, memory_bank, memory_lengths=None, coverage=None): -# """ -# Args: -# source (FloatTensor): query vectors ``(batch, tgt_len, dim)`` -# memory_bank (FloatTensor): source vectors ``(batch, src_len, dim)`` -# memory_lengths (LongTensor): the source context lengths ``(batch,)`` -# coverage (FloatTensor): None (not supported yet) -# Returns: -# (FloatTensor, FloatTensor): -# * Computed vector ``(tgt_len, batch, dim)`` -# * Attention distribtutions for each query -# ``(tgt_len, batch, src_len)`` -# """ - -# # one step input -# if source.dim() == 2: -# one_step = True -# source = source.unsqueeze(1) -# else: -# one_step = False - -# batch, source_l, dim = memory_bank.size() -# batch_, target_l, dim_ = source.size() -# aeq(batch, batch_) -# aeq(dim, dim_) -# aeq(self.dim, dim) -# if coverage is not None: -# batch_, source_l_ = coverage.size() -# aeq(batch, batch_) -# aeq(source_l, source_l_) - -# if coverage is not None: -# cover = coverage.view(-1).unsqueeze(1) -# memory_bank += self.linear_cover(cover).view_as(memory_bank) -# memory_bank = torch.tanh(memory_bank) - -# # compute attention scores, as in Luong et al. -# align = self.score(source, memory_bank) - -# if memory_lengths is not None: -# mask = sequence_mask(memory_lengths, max_len=align.size(-1)) -# mask = mask.unsqueeze(1) # Make it broadcastable. -# align.masked_fill_(1 - mask, -float('inf')) - -# # Softmax or sparsemax to normalize attention weights -# if self.attn_func == "softmax": -# align_vectors = F.softmax(align.view(batch*target_l, source_l), -1) -# else: -# align_vectors = sparsemax(align.view(batch*target_l, source_l), -1) -# align_vectors = align_vectors.view(batch, target_l, source_l) - -# # each context vector c_t is the weighted average -# # over all the source hidden states -# c = torch.bmm(align_vectors, memory_bank) - -# # concatenate -# concat_c = torch.cat([c, source], 2).view(batch*target_l, dim*2) -# attn_h = self.linear_out(concat_c).view(batch, target_l, dim) -# if self.attn_type in ["general", "dot"]: -# attn_h = torch.tanh(attn_h) - -# if one_step: -# attn_h = attn_h.squeeze(1) -# align_vectors = align_vectors.squeeze(1) - -# # Check output sizes -# batch_, dim_ = attn_h.size() -# aeq(batch, batch_) -# aeq(dim, dim_) -# batch_, source_l_ = align_vectors.size() -# aeq(batch, batch_) -# aeq(source_l, source_l_) - -# else: -# attn_h = attn_h.transpose(0, 1).contiguous() -# align_vectors = align_vectors.transpose(0, 1).contiguous() -# # Check output sizes -# target_l_, batch_, dim_ = attn_h.size() -# aeq(target_l, target_l_) -# aeq(batch, batch_) -# aeq(dim, dim_) -# target_l_, batch_, source_l_ = align_vectors.size() -# aeq(target_l, target_l_) -# aeq(batch, batch_) -# aeq(source_l, source_l_) - -# return attn_h, align_vectors - - -# # ----------------- - -# """ -# An implementation of sparsemax (Martins & Astudillo, 2016). See -# :cite:`DBLP:journals/corr/MartinsA16` for detailed description. -# By Ben Peters and Vlad Niculae -# """ - - -# def _make_ix_like(input, dim=0): -# d = input.size(dim) -# rho = torch.arange(1, d + 1, device=input.device, dtype=input.dtype) -# view = [1] * input.dim() -# view[0] = -1 -# return rho.view(view).transpose(0, dim) - - -# def _threshold_and_support(input, dim=0): -# """Sparsemax building block: compute the threshold -# Args: -# input: any dimension -# dim: dimension along which to apply the sparsemax -# Returns: -# the threshold value -# """ - -# input_srt, _ = torch.sort(input, descending=True, dim=dim) -# input_cumsum = input_srt.cumsum(dim) - 1 -# rhos = _make_ix_like(input, dim) -# support = rhos * input_srt > input_cumsum - -# support_size = support.sum(dim=dim).unsqueeze(dim) -# tau = input_cumsum.gather(dim, support_size - 1) -# tau /= support_size.to(input.dtype) -# return tau, support_size - - -# class SparsemaxFunction(Function): - -# @staticmethod -# def forward(ctx, input, dim=0): -# """sparsemax: normalizing sparse transform (a la softmax) -# Parameters: -# input (Tensor): any shape -# dim: dimension along which to apply sparsemax -# Returns: -# output (Tensor): same shape as input -# """ -# ctx.dim = dim -# max_val, _ = input.max(dim=dim, keepdim=True) -# input -= max_val # same numerical stability trick as for softmax -# tau, supp_size = _threshold_and_support(input, dim=dim) -# output = torch.clamp(input - tau, min=0) -# ctx.save_for_backward(supp_size, output) -# return output - -# @staticmethod -# def backward(ctx, grad_output): -# supp_size, output = ctx.saved_tensors -# dim = ctx.dim -# grad_input = grad_output.clone() -# grad_input[output == 0] = 0 - -# v_hat = grad_input.sum(dim=dim) / supp_size.to(output.dtype).squeeze() -# v_hat = v_hat.unsqueeze(dim) -# grad_input = torch.where(output != 0, grad_input - v_hat, grad_input) -# return grad_input, None - - -# sparsemax = SparsemaxFunction.apply - - -# class Sparsemax(nn.Module): - -# def __init__(self, dim=0): -# self.dim = dim -# super(Sparsemax, self).__init__() - -# def forward(self, input): -# return sparsemax(input, self.dim) diff --git a/base/lib/recurrent/modules/__init__.py b/base/lib/recurrent/modules/__init__.py deleted file mode 100755 index 8b1dbac..0000000 --- a/base/lib/recurrent/modules/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .GlobalAttention import GlobalAttention diff --git a/base/measure_ts.py b/base/measure_ts.py deleted file mode 100755 index 3918fca..0000000 --- a/base/measure_ts.py +++ /dev/null @@ -1,294 +0,0 @@ -import argparse -import hashlib -import json -import multiprocessing -import os -import sys - -import metrics.style_transfer.cnn as cnn - -# from gensim.corpora.dictionary import Dictionary -# style transfer metrics codebase -import metrics.style_transfer.content_preservation as preserv -import metrics.style_transfer.style_lexicon as stme_lexicon -import metrics.style_transfer.tokenizer as stme_tokeniser -import metrics.style_transfer.utils as stme_utils -import nlgeval -import numpy as np -import torch -from gensim.models.word2vec import Word2Vec -from nltk import RegexpParser, pos_tag, word_tokenize -from nltk.corpus import cmudict, stopwords -from nltk.translate.bleu_score import sentence_bleu -from pyemd import emd -from tqdm import tqdm - -sys.path.append("/home/t/Data/Files/Github/msc_project_model/base/") -from core.bpe import Encoder -from core.sentenciser import * -from core.utils import batch_compute, get_len - -cachedir = "/home/t/Data/Datasets/msc_proj_cache/" - - -def load_args(): - parser = argparse.ArgumentParser(description="metrics.py") - # load documents - parser.add_argument( - "-src", - required=True, - type=str, - help=""" - filepath to reference text file containing - MOSES style sequences. - """, - ) - parser.add_argument( - "-tgt", - default=None, - type=str, - help=""" - filepath to text file containing hypothesis - MOSES style sequences. - """, - ) - - parser.add_argument( - "-type", - choices=["political", "publication"], - help=""" - If enabled, runs newspaper specific lexical metrics. - """, - ) - parser.add_argument( - "-style_lexicon", - default=None, - type=str, - help=""" - Path to style lexicon json. - """, - ) - parser.add_argument( - "-cache_dir", - default=cachedir, - type=str, - help=""" - Path to cache dir. - """, - ) - parser.add_argument( - "-no_cache", - default=True, - help=""" - If enabled, ignores cache dir. - """, - ) - - opt = parser.parse_args() - return opt - - -class Measurements: - def __init__(self, opt): - self.opt = opt - self.stopwords = set(stopwords.words("english")) - self.cmudict = cmudict.dict() - self.enable_cache = False - if opt.cache_dir: - self.init_cache_dir(opt.cache_dir) - # working hash represents the hash of the dataset - # we'll be working on. - self.checksum = None - - def init_cache_dir(self, cachedir): - """ - Initiates directory and subdirectories - for caches (a lot of this stuff is expensive to compute). - """ - self.cachedir = cachedir - if not os.path.exists(self.cachedir): - os.makedirs(self.cachedir) - subdirs = ["style_lexicons", "word2vec", "preservation"] - for foldername in subdirs: - path = os.path.join(self.cachedir, foldername) - if not os.path.exists(path): - os.makedirs(path) - self.enable_cache = True - - def intensity(self, src, tgt): - # calculate bleu scores - # calculate emd scores - return None - - def naturalness(self, tgt, category): - # runs a adversarial CNN that discriminates between - # fake and real texts. - - # check if theres a model - # if there isn't, then make one - # load it - # run it - # return scores. - return np.mean(cnn.classify(category, tgt)) - - def preservation(self, src, tgt): - """ - Computes preservation (Masked WMD) scores - between the source and target sequences. - """ - # check if we've cached it already. - checksum = self.checksum if self.checksum else self.hash(src, tgt) - if self.enable_cache: - p_dir = os.path.join(self.cachedir, "preservation") - results_path = os.path.join(p_dir, checksum) - if checksum in os.listdir(p_dir): - return stme_utils.load_json(results_path) - - lexicon = self.load_style_lexicon(src, tgt, self.opt.style_lexicon) - # train w2v model - w2v = self.load_w2v(src, tgt) - # mask input and output texts - src_masked = preserv.mark_style_words( - src, style_tokens=lexicon, mask_style=True - ) - tgt_masked = preserv.mark_style_words( - tgt, style_tokens=lexicon, mask_style=True - ) - # calculate wmd scores - emd_masked = self.calc_wmd(src_masked, tgt_masked, w2v) - - results = {"emd_masked": np.mean(emd_masked)} - - # cache the wmd scores because they're quite - # expensive to compute. - if self.enable_cache: - stme_utils.save_json(results, results_path) - - return results - - @staticmethod - def calc_wmd(ref, cnd, w2v): - n = len(ref) - desc = "Calculating WMD" - f = w2v.wv.wmdistance - return [f(a, b) for a, b in tqdm(zip(ref, cnd), desc=desc, total=n)] - - def train_w2v(self, src, tgt): - """ - Trains a style transfer word2vec s.t we can - build a masked wmd comparator. - """ - lexicon = self.load_style_lexicon(src, tgt, self.opt.style_lexicon) - masked_txt = preserv.mark_style_words( - src + tgt, style_tokens=lexicon, mask_style=True - ) - return Word2Vec([stme_tokeniser.tokenize(x) for x in masked_txt]) - - def load_w2v(self, src, tgt): - """ - Loads working word2vec model if it exists, - otherwise - """ - checksum = self.checksum if self.checksum else self.hash(src, tgt) - - if self.enable_cache: - p_dir = os.path.join(self.cachedir, "word2vec") - w2v_path = os.path.join(p_dir, checksum) - if checksum in os.listdir(p_dir): - model = Word2Vec.load(w2v_path) - # normalize vectors - model.init_sims(replace=True) - return model - - # train the model otherwise - w2v = self.train_w2v(src, tgt) - - if self.enable_cache: - w2v.save(w2v_path) - - return w2v - - def create_lexicon(self, src, tgt): - """ - finds style lexicon. - Returns a set of words following a particular style. - """ - styles = {0: "styles"} - # create vectoriser and inverse vocab. - x, y = stme_utils.compile_binary_dataset(src, tgt) - vectoriser = stme_lexicon.fit_vectorizer(x) - inv_vocab = stme_utils.invert_dict(vectoriser.vocabulary_) - # train style weights model - src_weights = vectoriser.transform(x) - model = stme_lexicon.train("l1", 3, src_weights, y) - # extract style features and weights - nz_weights, f_nums = stme_lexicon.extract_nonzero_weights(model) - sf_and_weights = stme_lexicon.collect_style_features_and_weights( - nz_weights, styles, inv_vocab, f_nums - ) - # cache the results. - return sf_and_weights - - def load_style_lexicon(self, src, tgt, lexicon_src=None): - """ - Loads style lexicon if it was precomputed already. - """ - if lexicon_src: - return stme_utils.load_json(lexicon_src) - else: - # check if we've cached it already. - checksum = self.checksum if self.checksum else self.hash(src, tgt) - if self.enable_cache: - p_dir = os.path.join(self.cachedir, "style_lexicons") - lexicon_path = os.path.join(p_dir, checksum) - if checksum in os.listdir(p_dir): - return stme_utils.load_json(lexicon_path) - - # otherwise, make it. - lexicon = self.create_lexicon(src, tgt) - - if self.enable_cache: - stme_utils.save_json(lexicon, lexicon_path) - return lexicon - - @staticmethod - def hash(src, tgt): - # hashing function - return str(hashlib.md5(str([x for x in zip(src, tgt)]).encode()).hexdigest()) - - -# ----------------- - - -def load_files(src, tgt): - """ - loads src and tgt files. - """ - with open(src) as a, open(tgt) as b: - out = [x for x in tqdm(zip(a, b), total=get_len(src), desc="Files")] - return [x[0] for x in out], [x[1] for x in out] - - -def express(opt): - # load files - src, tgt = load_files(opt.src, opt.tgt) - # init measurements - metrics = Measurements(opt) - metrics.checksum = metrics.hash(src, tgt) - - # calculate preservation of meaning - preservation = metrics.preservation(src, tgt) - print("Preservation:", preservation) - # calculate naturalness - # naturalness = metrics.naturalness(tgt, opt.type) - # print("Naturalness",naturalness) - - # if newspaper, then we can proceed to do newspaper - # specific measurements. - if opt.type == "publication": - pass - - -if __name__ == "__main__": - args = load_args() - express(args) diff --git a/base/metrics/baseline_bt/charts.ipynb b/base/metrics/baseline_bt/charts.ipynb deleted file mode 100755 index 4351d8f..0000000 --- a/base/metrics/baseline_bt/charts.ipynb +++ /dev/null @@ -1,218 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib\n", - "matplotlib.rc('text', usetex=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "entries = {}\n", - "for ent in os.listdir():\n", - " name, filetype = os.path.splitext(ent)\n", - " if filetype.lower() == \".txt\":\n", - " with open(ent, \"r\") as f:\n", - " entries[name] = f.read()\n", - " \n", - "results = {}\n", - "# format text\n", - "for name in entries:\n", - " # check if there's anything\n", - " content = entries[name].strip()\n", - " if len(content) < 2:\n", - " continue\n", - " content = content.split(\"\\n\")\n", - " content = [x.split(\": \") for x in content]\n", - " container = {}\n", - " for ent in content:\n", - " key, value = ent\n", - " container[key] = float(value)\n", - " \n", - " # setup name properly\n", - " num_sents, _, direction = name.split(\"_\")[1:]\n", - " if num_sents == \"lg\":\n", - " num_sents = \"1500k\"\n", - " name = \"_\".join([num_sents, direction])\n", - " \n", - " results[name] = container" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Bleu_1',\n", - " 'Bleu_2',\n", - " 'Bleu_3',\n", - " 'Bleu_4',\n", - " 'METEOR',\n", - " 'ROUGE_L',\n", - " 'CIDEr',\n", - " 'SkipThoughtsCosineSimilairty',\n", - " 'EmbeddingAverageCosineSimilairty',\n", - " 'VectorExtremaCosineSimilarity',\n", - " 'GreedyMatchingScore']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "statnames = [x for x in results[list(results.keys())[0]].keys()]\n", - "statnames" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "stat_title_sub = {x : x for x in statnames}\n", - "stat_title_sub['Bleu_1'] = \"$BLEU_1$\"\n", - "stat_title_sub['Bleu_2'] = \"$BLEU_2$\"\n", - "stat_title_sub['Bleu_3'] = \"$BLEU_3$\"\n", - "stat_title_sub['Bleu_4'] = \"$BLEU_4$\"\n", - "stat_title_sub['ROUGE_L'] = \"$ROUGE_L$\"\n", - "stat_title_sub['SkipThoughtsCosineSimilairty'] = \"Skip Thoughts Cosine\"\n", - "stat_title_sub['EmbeddingAverageCosineSimilairty'] = \"Embedding Average Cosine\"\n", - "stat_title_sub['VectorExtremaCosineSimilarity'] = \"Vector Extrema Cosine\"\n", - "stat_title_sub['GreedyMatchingScore'] = \"Greedy Matching Score\"" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "allow = ['Bleu_1', 'Bleu_2', 'Bleu_3', 'ROUGE_L', 'SkipThoughtsCosineSimilairty', 'EmbeddingAverageCosineSimilairty']" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "legend_sub = {\"fren\" : r\"$FR \\rightarrow \\bar{EN}$\", \"enfr\":r\"$EN \\rightarrow FR$\"}" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "

" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# chart_num = 550\n", - "chart_num = 0\n", - "fig = plt.figure(figsize=(8,6), dpi=128)\n", - "fig.patch.set_facecolor('white')\n", - "for stat in allow:\n", - " chart_num += 1\n", - " \n", - " title = stat_title_sub[stat]\n", - " measurements = {}\n", - " for entry in sorted(list(results.keys()), reverse=True):\n", - " size, direction = entry.split(\"_\")\n", - " value = results[entry][stat]\n", - " if \"Bleu\" in stat:\n", - " value *= 100\n", - " if direction not in measurements:\n", - " measurements[direction] = {}\n", - " measurements[direction][size] = value\n", - " \n", - " legend = []\n", - " sizes = measurements[direction].keys()\n", - "\n", - " plt.subplot(int(len(allow)/2),2,chart_num)\n", - "\n", - " for direction in measurements:\n", - " legend.append(direction)\n", - " values = [measurements[direction][k] for k in measurements[direction]]\n", - " x_values = [i for i in range(len(values))]\n", - " plt.scatter(x_values, values)\n", - " plt.plot(x_values, values)\n", - " \n", - " plt.xticks(range(len(sizes)), sizes, size='small')\n", - " \n", - " legend = [legend_sub[x] for x in legend]\n", - " plt.legend(legend)\n", - " plt.xlabel('Number of Sequences')\n", - " plt.ylabel(title)\n", - "plt.tight_layout()\n", - "plt.savefig('stats_baseline_nmt.pdf')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/base/metrics/baseline_bt/eval_500k_lower_enfr.txt b/base/metrics/baseline_bt/eval_500k_lower_enfr.txt deleted file mode 100755 index 829a292..0000000 --- a/base/metrics/baseline_bt/eval_500k_lower_enfr.txt +++ /dev/null @@ -1,11 +0,0 @@ -Bleu_1: 0.297680 -Bleu_2: 0.139025 -Bleu_3: 0.076491 -Bleu_4: 0.046372 -METEOR: 0.143809 -ROUGE_L: 0.250785 -CIDEr: 0.395065 -SkipThoughtsCosineSimilairty: 0.819120 -EmbeddingAverageCosineSimilairty: 0.798838 -VectorExtremaCosineSimilarity: 0.586005 -GreedyMatchingScore: 0.712338 diff --git a/base/metrics/baseline_bt/eval_500k_lower_fren.txt b/base/metrics/baseline_bt/eval_500k_lower_fren.txt deleted file mode 100755 index a74590c..0000000 --- a/base/metrics/baseline_bt/eval_500k_lower_fren.txt +++ /dev/null @@ -1,11 +0,0 @@ -Bleu_1: 0.258623 -Bleu_2: 0.108125 -Bleu_3: 0.052657 -Bleu_4: 0.029377 -METEOR: 0.116765 -ROUGE_L: 0.250016 -CIDEr: 0.269566 -SkipThoughtsCosineSimilairty: 0.749842 -EmbeddingAverageCosineSimilairty: 0.870986 -VectorExtremaCosineSimilarity: 0.446185 -GreedyMatchingScore: 0.657290 diff --git a/base/metrics/baseline_bt/eval_50k_lower_enfr.txt b/base/metrics/baseline_bt/eval_50k_lower_enfr.txt deleted file mode 100755 index 4e98e9e..0000000 --- a/base/metrics/baseline_bt/eval_50k_lower_enfr.txt +++ /dev/null @@ -1,11 +0,0 @@ -Bleu_1: 0.234985 -Bleu_2: 0.107494 -Bleu_3: 0.056949 -Bleu_4: 0.032720 -METEOR: 0.109472 -ROUGE_L: 0.222295 -CIDEr: 0.211048 -SkipThoughtsCosineSimilairty: 0.762432 -EmbeddingAverageCosineSimilairty: 0.710326 -VectorExtremaCosineSimilarity: 0.549265 -GreedyMatchingScore: 0.661178 diff --git a/base/metrics/baseline_bt/eval_50k_lower_fren.txt b/base/metrics/baseline_bt/eval_50k_lower_fren.txt deleted file mode 100755 index 5a21c5c..0000000 --- a/base/metrics/baseline_bt/eval_50k_lower_fren.txt +++ /dev/null @@ -1,11 +0,0 @@ -Bleu_1: 0.202306 -Bleu_2: 0.081828 -Bleu_3: 0.038123 -Bleu_4: 0.020264 -METEOR: 0.074685 -ROUGE_L: 0.209556 -CIDEr: 0.147849 -SkipThoughtsCosineSimilairty: 0.645186 -EmbeddingAverageCosineSimilairty: 0.720636 -VectorExtremaCosineSimilarity: 0.373254 -GreedyMatchingScore: 0.608877 diff --git a/base/metrics/baseline_bt/eval_lg_lower_enfr.txt b/base/metrics/baseline_bt/eval_lg_lower_enfr.txt deleted file mode 100755 index 9f5b92d..0000000 --- a/base/metrics/baseline_bt/eval_lg_lower_enfr.txt +++ /dev/null @@ -1,11 +0,0 @@ -Bleu_1: 0.308384 -Bleu_2: 0.151326 -Bleu_3: 0.086599 -Bleu_4: 0.053935 -METEOR: 0.151274 -ROUGE_L: 0.268381 -CIDEr: 0.482671 -SkipThoughtsCosineSimilairty: 0.825869 -EmbeddingAverageCosineSimilairty: 0.807582 -VectorExtremaCosineSimilarity: 0.595600 -GreedyMatchingScore: 0.725936 diff --git a/base/metrics/baseline_bt/eval_lg_lower_fren.txt b/base/metrics/baseline_bt/eval_lg_lower_fren.txt deleted file mode 100755 index 29d2611..0000000 --- a/base/metrics/baseline_bt/eval_lg_lower_fren.txt +++ /dev/null @@ -1,11 +0,0 @@ -Bleu_1: 0.275373 -Bleu_2: 0.121274 -Bleu_3: 0.061851 -Bleu_4: 0.035363 -METEOR: 0.127351 -ROUGE_L: 0.266053 -CIDEr: 0.347136 -SkipThoughtsCosineSimilairty: 0.759757 -EmbeddingAverageCosineSimilairty: 0.876606 -VectorExtremaCosineSimilarity: 0.464029 -GreedyMatchingScore: 0.667600 diff --git a/base/metrics/meteor/__init__.py b/base/metrics/meteor/__init__.py deleted file mode 100755 index 04e429c..0000000 --- a/base/metrics/meteor/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__author__ = "tylin" diff --git a/base/metrics/meteor/meteor-1.5.jar b/base/metrics/meteor/meteor-1.5.jar deleted file mode 100755 index a833bc0..0000000 Binary files a/base/metrics/meteor/meteor-1.5.jar and /dev/null differ diff --git a/base/metrics/meteor/meteor.py b/base/metrics/meteor/meteor.py deleted file mode 100755 index 980ebfa..0000000 --- a/base/metrics/meteor/meteor.py +++ /dev/null @@ -1,96 +0,0 @@ -#!/usr/bin/env python - -# Python wrapper for METEOR implementation, by Xinlei Chen -# Acknowledge Michael Denkowski for the generous discussion and help - -import os -import subprocess -import sys -import threading - -# Assumes meteor-1.5.jar is in the same directory as meteor.py. Change as needed. -METEOR_JAR = "meteor-1.5.jar" -# print METEOR_JAR - - -class Meteor: - def __init__(self): - self.meteor_cmd = [ - "java", - "-jar", - "-Xmx2G", - METEOR_JAR, - "-", - "-", - "-stdio", - "-l", - "en", - "-norm", - ] - self.meteor_p = subprocess.Popen( - self.meteor_cmd, - cwd=os.path.dirname(os.path.abspath(__file__)), - stdin=subprocess.PIPE, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - ) - # Used to guarantee thread safety - self.lock = threading.Lock() - - def compute_score(self, gts, res): - assert gts.keys() == res.keys() - imgIds = gts.keys() - scores = [] - - eval_line = "EVAL" - self.lock.acquire() - for i in imgIds: - assert len(res[i]) == 1 - stat = self._stat(res[i][0], gts[i]) - eval_line += " ||| {}".format(stat) - - self.meteor_p.stdin.write("{}\n".format(eval_line)) - for i in range(0, len(imgIds)): - scores.append(float(self.meteor_p.stdout.readline().strip())) - score = float(self.meteor_p.stdout.readline().strip()) - self.lock.release() - - return score, scores - - def method(self): - return "METEOR" - - def _stat(self, hypothesis_str, reference_list): - # SCORE ||| reference 1 words ||| reference n words ||| hypothesis words - hypothesis_str = hypothesis_str.replace("|||", "").replace(" ", " ") - score_line = " ||| ".join( - ("SCORE", " ||| ".join(reference_list), hypothesis_str) - ) - self.meteor_p.stdin.write("{}\n".format(score_line)) - return self.meteor_p.stdout.readline().strip() - - def _score(self, hypothesis_str, reference_list): - self.lock.acquire() - # SCORE ||| reference 1 words ||| reference n words ||| hypothesis words - hypothesis_str = hypothesis_str.replace("|||", "").replace(" ", " ") - score_line = " ||| ".join( - ("SCORE", " ||| ".join(reference_list), hypothesis_str) - ) - self.meteor_p.stdin.write("{}\n".format(score_line)) - stats = self.meteor_p.stdout.readline().strip() - eval_line = "EVAL ||| {}".format(stats) - # EVAL ||| stats - self.meteor_p.stdin.write("{}\n".format(eval_line)) - score = float(self.meteor_p.stdout.readline().strip()) - # bug fix: there are two values returned by the jar file, one average, and one all, so do it twice - # thanks for Andrej for pointing this out - score = float(self.meteor_p.stdout.readline().strip()) - self.lock.release() - return score - - def __del__(self): - self.lock.acquire() - self.meteor_p.stdin.close() - self.meteor_p.kill() - self.meteor_p.wait() - self.lock.release() diff --git a/base/metrics/meteor/readme.md b/base/metrics/meteor/readme.md deleted file mode 100755 index 546b67f..0000000 --- a/base/metrics/meteor/readme.md +++ /dev/null @@ -1,3 +0,0 @@ -Source code from: - -https://github.com/tylin/coco-caption \ No newline at end of file diff --git a/base/telegram.json b/base/telegram.json deleted file mode 100755 index 9926afe..0000000 --- a/base/telegram.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "api_private_key" : "723734163:AAHEPQZ6hTUg8Fa03invwKmRanuDrSZabCA", - "chat_id" : "110082490" -} \ No newline at end of file diff --git a/base/test_measurements.sh b/base/test_measurements.sh deleted file mode 100755 index c6d7d73..0000000 --- a/base/test_measurements.sh +++ /dev/null @@ -1,5 +0,0 @@ -src="/home/t/project_model/base/results/pol/democratic_only.test.en" -tgt="/home/t/project_model/base/results/pol/democratic_only.test.to.republican.1" -clear -cd ~/project_model/base/ -python3 measure_ts.py -src $src -tgt $tgt -type political diff --git a/datasets/merge.py b/datasets/merge.py index 19cb068..d96ef94 100755 --- a/datasets/merge.py +++ b/datasets/merge.py @@ -39,14 +39,13 @@ def load_args(): for ent in zip(args.left[0], args.right[0]): fpl, fpr = ent length = get_len(fpl) - filename = os.path.split(fpl)[-1] with open(fpl) as l, open(fpr) as r: - for pair in tqdm(zip(l, r), total=length, desc=filename): + for pair in tqdm(zip(l, r), total=length): dataset.append(pair) # check for bad pairs bads = [] -for i in tqdm(range(len(dataset)), desc="Checking for Bad Pairs"): +for i in tqdm(range(len(dataset))): pair = dataset[i] # check if the pair consists of content in both left and right left, right = [x.strip() for x in pair] diff --git a/datasets/sanitise.py b/datasets/sanitise.py index 9cb928e..0dc692d 100755 --- a/datasets/sanitise.py +++ b/datasets/sanitise.py @@ -1,16 +1,9 @@ import argparse -import re -import sys import unicodedata from nltk.corpus import words from tqdm import tqdm -cjk = re.compile("[\u4e00-\u9fff\u3000-\u303f]") - -sys.path.append("../base/core") - -from utils import get_len def load_args(): @@ -40,8 +33,7 @@ def sanitise(en_seq): """ Replaces characters. 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(>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] + +[metadata] +lock-version = "2.0" +python-versions = "^3.9" +content-hash = "2b51c6416cd739beb977beecf79be0e2be6d88f6858be221db723f935f9972ab" diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..91853a0 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,258 @@ +[project] +name = "specialk" +version = "0.1.0" +description = "text based style transfer with sequence models" +authors = [{ name = "@thien", email = "thien@users.noreply.github.com" }] +dependencies = [ + "absl-py==0.7.0", + "accelerate==0.34.0", + "aiohttp==3.9.5", + "aiosignal==1.3.1", + "annotated-types==0.7.0", + "antlr4-python3-runtime==4.8", + "appnope==0.1.4", + "asttokens==2.4.1", + "async-timeout==4.0.3", + "attrs==23.2.0", + "bayesian-optimization==1.0.1", + "beautifulsoup4==4.7.1", + "better-exceptions==0.3.3", + "bitarray==2.9.2", + 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git+https://github.com/facebookresearch/fastText.git@1142dc4c4ecbc19cc16eee5cdd28472e689267e6", + "filelock==3.13.1", + "fonttools==4.53.1", + "frozenlist==1.4.1", + "fsspec==2024.2.0", + "ftfy==6.2.3", + "gensim==4.3.2", + "huggingface-hub==0.23.4", + "hydra-core==1.0.7", + "idna==3.7", + "importlib-metadata==7.2.1", + "importlib-resources==6.4.4", + "iniconfig==2.0.0", + "installer==0.7.0", + "ipykernel==6.29.4", + "ipynb==0.5.1", + "ipython==8.18.1", + "ipywidgets==8.1.3", + "jaraco-classes==3.4.0", + "jaxtyping==0.2.31", + "jedi==0.19.1", + "jinja2==3.1.3", + "joblib==1.4.2", + "jupyter-client==8.6.2", + "jupyter-core==5.7.2", + "jupyterlab-widgets==3.0.11", + "keyring==24.3.1", + "kiwisolver==1.4.5", + "langcodes==3.4.0", + "language-data==1.2.0", + "lightning==2.3.1", + "lightning-utilities==0.11.3.post0", + "lxml==5.2.2", + "marisa-trie==1.2.0", + "markdown-it-py==3.0.0", + "markupsafe==2.1.5", + "matplotlib==3.9.2", + "matplotlib-inline==0.1.7", + "mdurl==0.1.2", + 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"pyproject-hooks==1.1.0", + "pytelegrambotapi==4.20.0", + "pytest==8.2.2", + "pytest-watch==4.2.0", + "python-dateutil==2.9.0.post0", + "pytorch-lightning==2.3.1", + "pytz==2024.1", + "pyyaml==6.0.1", + "pyzmq==26.0.3", + "rapidfuzz==3.9.3", + "regex==2024.5.15", + "requests==2.32.3", + "requests-toolbelt==1.0.0", + "rich==13.7.1", + "rouge-score==0.1.2", + "sacrebleu==2.4.2", + "sacremoses==0.1.1", + "safetensors==0.4.3", + "schedulefree==1.2.7", + "scikit-learn==1.5.0", + "scipy==1.12.0", + "sentencepiece==0.2.0", + "setuptools==70.1.1", + "shellingham==1.5.4", + "six==1.16.0", + "smart-open==7.0.4", + "soupsieve==2.5", + "spacy==3.7.5", + "spacy-legacy==3.0.12", + "spacy-loggers==1.0.5", + "srsly==2.4.8", + "stack-data==0.6.3", + "structlog==24.2.0", + "sympy==1.12", + "tabulate==0.9.0", + "telebot==0.0.5", + "tensorboardx==2.6.2.2", + "thinc==8.2.5", + "threadpoolctl==3.5.0", + "tokenizers==0.19.1", + "tomli==2.0.1", + "tomlkit==0.12.5", + "toolz==0.12.1", + "torch==2.3.1", + "torchaudio==2.3.1", + "torchdata==0.7.1", + "torchmetrics==1.4.0.post0", + "torchtext==0.18.0", + "torchvision==0.18.1", + "tornado==6.4.1", + "tqdm==4.66.4", + "traitlets==5.14.3", + "transformers==4.43.3", + "trove-classifiers==2024.5.22", + "typeguard==2.13.3", + "typer==0.12.3", + "typing-extensions==4.12.2", + "tzdata==2024.1", + "urllib3==2.2.1", + "virtualenv==20.26.3", + "wasabi==1.1.3", + "watchdog==4.0.1", + "wcwidth==0.2.13", + "weasel==0.4.1", + "widgetsnbextension==4.0.11", + "wrapt==1.16.0", + "xattr==1.1.0", + "xxhash==3.4.1", + "yarl==1.9.4", + "zipp==3.19.2", + "pytest-xdist>=3.6.1", +] +readme = "readme.md" +requires-python = ">= 3.9" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.rye] +managed = true +universal = true +virtual = true +dev-dependencies = [] + +[tool.rye.scripts] +# These three options are equivalent: +download-deps = 'python -m nltk.downloader cmudict averaged_perceptron_tagger' +tests-light = 'pytest -v -m "not heavyweight"' +tests-heavy = 'pytest -v -m "not lightweight" -n auto' +tests = 'pytest -v -n auto' + +[tool.hatch.metadata] +allow-direct-references = true + +[tool.hatch.build.targets.wheel] +packages = ["specialk"] + +[tool.ruff.lint] +ignore=["F722"] + +[tool.pytest.ini_options] +testpaths = "./tests" +pythonpath = "." +addopts = "-ra -q" +filterwarnings = ["ignore:.*U.*mode is deprecated:DeprecationWarning", "ignore::DeprecationWarning"] +markers = ["lightweight: marks tests as lightweight (deselect with '-m \"not lightweight\"')","heavyweight: marks tests that require large model downloads"] + +[tool.coverage.run] +source = ["specialk"] +omit = ["tests/*"] + +[tool.coverage.report] +exclude_lines = [ + "pragma: no cover", + "def __repr__", + "if __name__ == .__main__.:", + "raise NotImplementedError", + "pass", + "except ImportError:"] + diff --git a/readme.md b/readme.md index 6b892d8..51df01d 100755 --- a/readme.md +++ b/readme.md @@ -1,19 +1,32 @@ -# Style-Transfer with Transformers +# Scalable Text-Based Style-Transfer ![Style Transfer from Popular Press to Quality](doc/images/preview.png) -This repository contains the implementation of my summer thesis. +[![wakatime](https://wakatime.com/badge/user/4251d8c6-f363-4bc2-92de-31e3923c596a/project/41ffd15c-a534-4e52-9250-6e308379d529.svg)](https://wakatime.com/badge/user/4251d8c6-f363-4bc2-92de-31e3923c596a/project/41ffd15c-a534-4e52-9250-6e308379d529) [![Run Lightweight Tests](https://github.com/thien/specialk/actions/workflows/tests.yml/badge.svg)](https://github.com/thien/specialk/actions/workflows/tests.yml) + +This repository contains the implementation of what used to be my summer thesis. This has evolved over time as a testbed for me to learn sequencing models. ## Requirements -You'll need `python3`, `pytorch`, `spacy`, `numpy`, `pyemd`, `bayesian-optimisation`, `rouge`, and `pyTelegramBotAPI`. You can install them via `pip3` (but you'll want to make use of CUDA so you might want to look into that too). +Through several migrations from `pip`, to `poetry`, to `uv`, This project uses `rye` based on prior learnings. So, after installing `rye` (hint: I like it better than `poetry` and vanilla `pip`) and cloning this repo, `cd` into it and then run the following: + + rye sync + rye run download-deps + +That's it! + +This has been tested to run on both Linux and macOS Sequoia and above, and has not been tested on Windows. + +## Running -To make life easier I've added a `requirements.txt` that'll allow you to install everything necessary (after installing `python3.6+`): +### Training - pip install -e . +To train different models, see `specialk/trainers/*.py` for the different training use cases. ## Datasets +Much of the datasets used in this are hosted on huggingface. + To make life easier, I've set up a one-command auto running program that'll deal with downloading all the necessary machine-translation datasets needed to make your model. You'll need `wget` however. cd datasets @@ -46,3 +59,9 @@ Once the datasets are downloaded, `cd base/scripts` and run the following: ./train_pub_naturalness_models.sh It'll probably take quite a while to train the models. + +## Development + +### Testing + +This has been simplified with `rye`. Run `rye run tests`, or alternatively, you're also able to use `pytest-watch`. diff --git a/requirements-dev.lock b/requirements-dev.lock new file mode 100644 index 0000000..fc8de9b --- /dev/null +++ b/requirements-dev.lock @@ -0,0 +1,235 @@ +# generated by rye +# use `rye lock` or `rye sync` to update this lockfile +# +# last locked with the following flags: +# pre: false +# features: [] +# all-features: false +# with-sources: false +# generate-hashes: false +# universal: true + +absl-py==0.7.0 +accelerate==0.34.0 +aiohttp==3.9.5 +aiosignal==1.3.1 +annotated-types==0.7.0 +antlr4-python3-runtime==4.8 +appnope==0.1.4 +asttokens==2.4.1 +async-timeout==4.0.3 +attrs==23.2.0 +bayesian-optimization==1.0.1 +beautifulsoup4==4.7.1 +better-exceptions==0.3.3 +bitarray==2.9.2 +blis==0.7.11 +build==1.2.1 +cachecontrol==0.14.0 +catalogue==2.0.10 +certifi==2024.6.2 +cffi==1.16.0 +charset-normalizer==3.3.2 +cleo==2.1.0 +click==8.1.7 +cloudpathlib==0.18.1 +colorama==0.4.6 +comm==0.2.2 +confection==0.1.5 +contourpy==1.2.1 +crashtest==0.4.1 +cryptography==43.0.1 ; sys_platform == 'linux' +cycler==0.12.1 +cymem==2.0.8 +cython==3.0.10 +datasets==2.20.0 +de-core-news-sm @ https://github.com/explosion/spacy-models/releases/download/de_core_news_sm-3.7.0/de_core_news_sm-3.7.0-py3-none-any.whl +debugpy==1.8.1 +decorator==5.1.1 +dill==0.3.8 +distlib==0.3.8 +docopt==0.6.2 +dulwich==0.21.7 +einops==0.8.0 +en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl +evaluate==0.4.2 +exceptiongroup==1.2.1 +execnet==2.1.1 +executing==2.0.1 +fairseq==0.12.2 +fastjsonschema==2.20.0 +fasttext @ git+https://github.com/facebookresearch/fastText.git@1142dc4c4ecbc19cc16eee5cdd28472e689267e6 +filelock==3.13.1 +fonttools==4.53.1 +frozenlist==1.4.1 +fsspec==2024.2.0 +ftfy==6.2.3 +gensim==4.3.2 +huggingface-hub==0.23.4 +hydra-core==1.0.7 +idna==3.7 +importlib-metadata==7.2.1 +importlib-resources==6.4.4 +iniconfig==2.0.0 +installer==0.7.0 +intel-openmp==2021.4.0 ; platform_system == 'Windows' +ipykernel==6.29.4 +ipynb==0.5.1 +ipython==8.18.1 +ipywidgets==8.1.3 +jaraco-classes==3.4.0 +jaxtyping==0.2.31 +jedi==0.19.1 +jeepney==0.8.0 ; sys_platform == 'linux' +jinja2==3.1.3 +joblib==1.4.2 +jupyter-client==8.6.2 +jupyter-core==5.7.2 +jupyterlab-widgets==3.0.11 +keyring==24.3.1 +kiwisolver==1.4.5 +langcodes==3.4.0 +language-data==1.2.0 +lightning==2.3.1 +lightning-utilities==0.11.3.post0 +lxml==5.2.2 +marisa-trie==1.2.0 +markdown-it-py==3.0.0 +markupsafe==2.1.5 +matplotlib==3.9.2 +matplotlib-inline==0.1.7 +mdurl==0.1.2 +memory-profiler==0.61.0 +mkl==2021.4.0 ; platform_system == 'Windows' +more-itertools==10.3.0 +mpmath==1.3.0 +msgpack==1.0.8 +multidict==6.0.5 +multiprocess==0.70.16 +murmurhash==1.0.10 +nest-asyncio==1.6.0 +networkx==3.2.1 +nltk==3.8.1 +numpy==1.26.4 +nvidia-cublas-cu12==12.1.3.1 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-cuda-cupti-cu12==12.1.105 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-cuda-nvrtc-cu12==12.1.105 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-cuda-runtime-cu12==12.1.105 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-cudnn-cu12==8.9.2.26 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-cufft-cu12==11.0.2.54 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-curand-cu12==10.3.2.106 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-cusolver-cu12==11.4.5.107 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-cusparse-cu12==12.1.0.106 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-nccl-cu12==2.20.5 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-nvjitlink-cu12==12.6.68 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-nvtx-cu12==12.1.105 ; platform_machine == 'x86_64' and platform_system == 'Linux' +omegaconf==2.0.6 +packaging==24.1 +pandarallel==1.6.5 +pandas==2.2.2 +parso==0.8.4 +peft==0.12.0 +pexpect==4.9.0 +pillow==10.2.0 +pip==21.2.3 +pkginfo==1.11.1 +platformdirs==4.2.2 +pluggy==1.5.0 +poetry==1.8.3 +poetry-core==1.9.0 +poetry-plugin-export==1.8.0 +polars==1.4.1 +portalocker==2.8.2 +pot==0.9.3 +preshed==3.0.9 +prompt-toolkit==3.0.47 +protobuf==5.27.1 +psutil==6.0.0 +ptyprocess==0.7.0 +pure-eval==0.2.2 +pyarrow==16.1.0 +pyarrow-hotfix==0.6 +pybind11==2.13.3 +pycparser==2.22 +pydantic==2.8.2 +pydantic-core==2.20.1 +pyemd==1.0.0 +pyfakefs==5.6.0 +pygments==2.18.0 +pyparsing==3.1.2 +pyproject-hooks==1.1.0 +pytelegrambotapi==4.20.0 +pytest==8.2.2 +pytest-watch==4.2.0 +pytest-xdist==3.6.1 +python-dateutil==2.9.0.post0 +pytorch-lightning==2.3.1 +pytz==2024.1 +pywin32==306 ; (platform_python_implementation != 'PyPy' and sys_platform == 'win32') or platform_system == 'Windows' +pywin32-ctypes==0.2.3 ; sys_platform == 'win32' +pyyaml==6.0.1 +pyzmq==26.0.3 +rapidfuzz==3.9.3 +regex==2024.5.15 +requests==2.32.3 +requests-toolbelt==1.0.0 +rich==13.7.1 +rouge-score==0.1.2 +sacrebleu==2.4.2 +sacremoses==0.1.1 +safetensors==0.4.3 +schedulefree==1.2.7 +scikit-learn==1.5.0 +scipy==1.12.0 +secretstorage==3.3.3 ; sys_platform == 'linux' +sentencepiece==0.2.0 +setuptools==70.1.1 +shellingham==1.5.4 +six==1.16.0 +smart-open==7.0.4 +soupsieve==2.5 +spacy==3.7.5 +spacy-legacy==3.0.12 +spacy-loggers==1.0.5 +srsly==2.4.8 +stack-data==0.6.3 +structlog==24.2.0 +sympy==1.12 +tabulate==0.9.0 +tbb==2021.13.1 ; platform_system == 'Windows' +telebot==0.0.5 +tensorboardx==2.6.2.2 +thinc==8.2.5 +threadpoolctl==3.5.0 +tokenizers==0.19.1 +tomli==2.0.1 +tomlkit==0.12.5 +toolz==0.12.1 +torch==2.3.1 +torchaudio==2.3.1 +torchdata==0.7.1 +torchmetrics==1.4.0.post0 +torchtext==0.18.0 +torchvision==0.18.1 +tornado==6.4.1 +tqdm==4.66.4 +traitlets==5.14.3 +transformers==4.43.3 +triton==2.3.1 ; python_full_version < '3.12' and platform_machine == 'x86_64' and platform_system == 'Linux' +trove-classifiers==2024.5.22 +typeguard==2.13.3 +typer==0.12.3 +typing-extensions==4.12.2 +tzdata==2024.1 +urllib3==2.2.1 +virtualenv==20.26.3 +wasabi==1.1.3 +watchdog==4.0.1 +wcwidth==0.2.13 +weasel==0.4.1 +widgetsnbextension==4.0.11 +wrapt==1.16.0 +xattr==1.1.0 +xxhash==3.4.1 +yarl==1.9.4 +zipp==3.19.2 diff --git a/requirements.lock b/requirements.lock new file mode 100644 index 0000000..fc8de9b --- /dev/null +++ b/requirements.lock @@ -0,0 +1,235 @@ +# generated by rye +# use `rye lock` or `rye sync` to update this lockfile +# +# last locked with the following flags: +# pre: false +# features: [] +# all-features: false +# with-sources: false +# generate-hashes: false +# universal: true + +absl-py==0.7.0 +accelerate==0.34.0 +aiohttp==3.9.5 +aiosignal==1.3.1 +annotated-types==0.7.0 +antlr4-python3-runtime==4.8 +appnope==0.1.4 +asttokens==2.4.1 +async-timeout==4.0.3 +attrs==23.2.0 +bayesian-optimization==1.0.1 +beautifulsoup4==4.7.1 +better-exceptions==0.3.3 +bitarray==2.9.2 +blis==0.7.11 +build==1.2.1 +cachecontrol==0.14.0 +catalogue==2.0.10 +certifi==2024.6.2 +cffi==1.16.0 +charset-normalizer==3.3.2 +cleo==2.1.0 +click==8.1.7 +cloudpathlib==0.18.1 +colorama==0.4.6 +comm==0.2.2 +confection==0.1.5 +contourpy==1.2.1 +crashtest==0.4.1 +cryptography==43.0.1 ; sys_platform == 'linux' +cycler==0.12.1 +cymem==2.0.8 +cython==3.0.10 +datasets==2.20.0 +de-core-news-sm @ https://github.com/explosion/spacy-models/releases/download/de_core_news_sm-3.7.0/de_core_news_sm-3.7.0-py3-none-any.whl +debugpy==1.8.1 +decorator==5.1.1 +dill==0.3.8 +distlib==0.3.8 +docopt==0.6.2 +dulwich==0.21.7 +einops==0.8.0 +en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl +evaluate==0.4.2 +exceptiongroup==1.2.1 +execnet==2.1.1 +executing==2.0.1 +fairseq==0.12.2 +fastjsonschema==2.20.0 +fasttext @ git+https://github.com/facebookresearch/fastText.git@1142dc4c4ecbc19cc16eee5cdd28472e689267e6 +filelock==3.13.1 +fonttools==4.53.1 +frozenlist==1.4.1 +fsspec==2024.2.0 +ftfy==6.2.3 +gensim==4.3.2 +huggingface-hub==0.23.4 +hydra-core==1.0.7 +idna==3.7 +importlib-metadata==7.2.1 +importlib-resources==6.4.4 +iniconfig==2.0.0 +installer==0.7.0 +intel-openmp==2021.4.0 ; platform_system == 'Windows' +ipykernel==6.29.4 +ipynb==0.5.1 +ipython==8.18.1 +ipywidgets==8.1.3 +jaraco-classes==3.4.0 +jaxtyping==0.2.31 +jedi==0.19.1 +jeepney==0.8.0 ; sys_platform == 'linux' +jinja2==3.1.3 +joblib==1.4.2 +jupyter-client==8.6.2 +jupyter-core==5.7.2 +jupyterlab-widgets==3.0.11 +keyring==24.3.1 +kiwisolver==1.4.5 +langcodes==3.4.0 +language-data==1.2.0 +lightning==2.3.1 +lightning-utilities==0.11.3.post0 +lxml==5.2.2 +marisa-trie==1.2.0 +markdown-it-py==3.0.0 +markupsafe==2.1.5 +matplotlib==3.9.2 +matplotlib-inline==0.1.7 +mdurl==0.1.2 +memory-profiler==0.61.0 +mkl==2021.4.0 ; platform_system == 'Windows' +more-itertools==10.3.0 +mpmath==1.3.0 +msgpack==1.0.8 +multidict==6.0.5 +multiprocess==0.70.16 +murmurhash==1.0.10 +nest-asyncio==1.6.0 +networkx==3.2.1 +nltk==3.8.1 +numpy==1.26.4 +nvidia-cublas-cu12==12.1.3.1 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-cuda-cupti-cu12==12.1.105 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-cuda-nvrtc-cu12==12.1.105 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-cuda-runtime-cu12==12.1.105 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-cudnn-cu12==8.9.2.26 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-cufft-cu12==11.0.2.54 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-curand-cu12==10.3.2.106 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-cusolver-cu12==11.4.5.107 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-cusparse-cu12==12.1.0.106 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-nccl-cu12==2.20.5 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-nvjitlink-cu12==12.6.68 ; platform_machine == 'x86_64' and platform_system == 'Linux' +nvidia-nvtx-cu12==12.1.105 ; platform_machine == 'x86_64' and platform_system == 'Linux' +omegaconf==2.0.6 +packaging==24.1 +pandarallel==1.6.5 +pandas==2.2.2 +parso==0.8.4 +peft==0.12.0 +pexpect==4.9.0 +pillow==10.2.0 +pip==21.2.3 +pkginfo==1.11.1 +platformdirs==4.2.2 +pluggy==1.5.0 +poetry==1.8.3 +poetry-core==1.9.0 +poetry-plugin-export==1.8.0 +polars==1.4.1 +portalocker==2.8.2 +pot==0.9.3 +preshed==3.0.9 +prompt-toolkit==3.0.47 +protobuf==5.27.1 +psutil==6.0.0 +ptyprocess==0.7.0 +pure-eval==0.2.2 +pyarrow==16.1.0 +pyarrow-hotfix==0.6 +pybind11==2.13.3 +pycparser==2.22 +pydantic==2.8.2 +pydantic-core==2.20.1 +pyemd==1.0.0 +pyfakefs==5.6.0 +pygments==2.18.0 +pyparsing==3.1.2 +pyproject-hooks==1.1.0 +pytelegrambotapi==4.20.0 +pytest==8.2.2 +pytest-watch==4.2.0 +pytest-xdist==3.6.1 +python-dateutil==2.9.0.post0 +pytorch-lightning==2.3.1 +pytz==2024.1 +pywin32==306 ; (platform_python_implementation != 'PyPy' and sys_platform == 'win32') or platform_system == 'Windows' +pywin32-ctypes==0.2.3 ; sys_platform == 'win32' +pyyaml==6.0.1 +pyzmq==26.0.3 +rapidfuzz==3.9.3 +regex==2024.5.15 +requests==2.32.3 +requests-toolbelt==1.0.0 +rich==13.7.1 +rouge-score==0.1.2 +sacrebleu==2.4.2 +sacremoses==0.1.1 +safetensors==0.4.3 +schedulefree==1.2.7 +scikit-learn==1.5.0 +scipy==1.12.0 +secretstorage==3.3.3 ; sys_platform == 'linux' +sentencepiece==0.2.0 +setuptools==70.1.1 +shellingham==1.5.4 +six==1.16.0 +smart-open==7.0.4 +soupsieve==2.5 +spacy==3.7.5 +spacy-legacy==3.0.12 +spacy-loggers==1.0.5 +srsly==2.4.8 +stack-data==0.6.3 +structlog==24.2.0 +sympy==1.12 +tabulate==0.9.0 +tbb==2021.13.1 ; platform_system == 'Windows' +telebot==0.0.5 +tensorboardx==2.6.2.2 +thinc==8.2.5 +threadpoolctl==3.5.0 +tokenizers==0.19.1 +tomli==2.0.1 +tomlkit==0.12.5 +toolz==0.12.1 +torch==2.3.1 +torchaudio==2.3.1 +torchdata==0.7.1 +torchmetrics==1.4.0.post0 +torchtext==0.18.0 +torchvision==0.18.1 +tornado==6.4.1 +tqdm==4.66.4 +traitlets==5.14.3 +transformers==4.43.3 +triton==2.3.1 ; python_full_version < '3.12' and platform_machine == 'x86_64' and platform_system == 'Linux' +trove-classifiers==2024.5.22 +typeguard==2.13.3 +typer==0.12.3 +typing-extensions==4.12.2 +tzdata==2024.1 +urllib3==2.2.1 +virtualenv==20.26.3 +wasabi==1.1.3 +watchdog==4.0.1 +wcwidth==0.2.13 +weasel==0.4.1 +widgetsnbextension==4.0.11 +wrapt==1.16.0 +xattr==1.1.0 +xxhash==3.4.1 +yarl==1.9.4 +zipp==3.19.2 diff --git a/requirements.txt b/requirements.txt deleted file mode 100755 index 1d34713..0000000 --- a/requirements.txt +++ /dev/null @@ -1,12 +0,0 @@ -nltk>=3.1 -numpy>=1.11.0 -requests>=2.19 -scipy>=0.17.0 -scikit-learn>=0.17 -tqdm>=4.24absl-py==0.7.0 -bayesian-optimization==1.0.1 -bcolz==1.2.1 -beautifulsoup4==4.7.1 -GPy==1.9.6 -GPyOpt==1.2.5 -gpytorch==0.3.3 diff --git a/base/classifier/run_naturalness.sh b/scripts/classifier/run_naturalness.sh similarity index 100% rename from base/classifier/run_naturalness.sh rename to scripts/classifier/run_naturalness.sh diff --git a/scripts/classifier/run_political.sh b/scripts/classifier/run_political.sh new file mode 100755 index 0000000..60e6347 --- /dev/null +++ b/scripts/classifier/run_political.sh @@ -0,0 +1,40 @@ + +# Preprocess data +## Note: First create a dev.txt file such that +## 1. Each instance is on a new line +## 2. The label and the sentence are separated by a space + +# please load this from the specialk root directory. + +political_dataset="political_new_2024" +cnn_model_name="political_model_new_2024" +python3 specialk/classifier/preprocess_token.py \ + -train_src ./datasets/political_data/classtrain.txt \ + -label0 democratic \ + -label1 republican \ + -valid_src ./datasets/political_data/classdev.txt \ + -save_data $political_dataset \ + -src_vocab specialk/results/nmt_fren_goldmaster_bpe_republican.tgt.dict \ + -seq_length 80 \ + -load_vocab + +# Train the classifier +# python3 cnn_train.py \ +# -gpus 0 \ +# -epochs 20 \ +# -data $political_dataset".train.pt" \ +# -save_model $cnn_model_name \ +# -batch_size 128 + # -sequence_length 100 \ + +# BESTMODEL="political_model.pt" + +# # Test the classifier accuracy +# python3 cnn_translate.py \ +# -gpu 0 \ +# -model $BESTMODEL \ +# -src ../../datasets/political_data/democratic_only.test.en \ +# -tgt 'democratic' \ +# -label0 democratic \ +# -label1 republican + diff --git a/scripts/classifier/run_political_bpe.sh b/scripts/classifier/run_political_bpe.sh new file mode 100755 index 0000000..ad52c9a --- /dev/null +++ b/scripts/classifier/run_political_bpe.sh @@ -0,0 +1,67 @@ +clear +# Preprocess data +## Note: First create a dev.txt file such that +## 1. Each instance is on a new line +## 2. The label and the sentence are separated by a space + +POLITICAL_DATASET="political_bpe_2019" +cnn_model_name="specialk/models/political_model_bpe_2019" +seq_len=100 + +# python3 specialk/classifier/preprocess_bpe.py \ +# -train_src ./datasets/political_data/classtrain.txt \ +# -label0 democratic \ +# -label1 republican \ +# -valid_src ./datasets/political_data/classdev.txt \ +# -save_data $POLITICAL_DATASET \ +# -max_word_seq_len $seq_len \ +# -src_vocab specialk/models/nmt_fren_goldmaster_bpe_republican.pt + +# new as of 2024 +# python specialk/classifier/preprocess_token.py \ +# -train_src ./datasets/political_data/classtrain.txt \ +# -label0 democratic \ +# -label1 republican \ +# -valid_src ./datasets/political_data/classdev.txt \ +# -save_data $POLITICAL_DATASET \ +# -max_word_seq_len $seq_len \ +# -src_vocab specialk/models/nmt_fren_goldmaster_bpe_republican.pt \ +# -load_vocab \ +# -bpe + + +# Train the classifier (2019) +# python3 specialk/classifier/cnn_train_bpe.py \ +# -gpus 0 \ +# -epochs 1 \ +# -data $POLITICAL_DATASET".train.pt" \ +# -save_model $cnn_model_name \ +# -sequence_length $seq_len \ +# -batch_size 128 \ +# -filter_size 10 \ +# -optim adam \ +# -learning_rate 0.001 + +# train the classifier (2024) +python3 specialk/classifier/cnn_train_bpe.py \ + -epochs 1 \ + -data $POLITICAL_DATASET".train.pt" \ + -save_model $cnn_model_name \ + -sequence_length $seq_len \ + -batch_size 128 \ + -filter_size 10 \ + -optim adamw \ + -learning_rate 0.001 + + +# BESTMODEL="models/political_model_bpe_acc_98.47_loss_0.00_e1.pt" + +# # # Test the classifier accuracy +# python3 cnn_translate_bpe.py \ +# -gpu 0 \ +# -model $BESTMODEL \ +# -src ../../datasets/political_data/democratic_only.test.en \ +# -tgt 'democratic' \ +# -label0 democratic \ +# -label1 republican + diff --git a/base/classifier/run_publication.sh b/scripts/classifier/run_publication.sh similarity index 93% rename from base/classifier/run_publication.sh rename to scripts/classifier/run_publication.sh index bf3bf5a..94c52a8 100755 --- a/base/classifier/run_publication.sh +++ b/scripts/classifier/run_publication.sh @@ -28,6 +28,7 @@ VOCABSIZE=40000 MODELNAME="publication_model_test" +BESTMODEL="publication_model_acc_91.76_loss_0.00_e10.pt" # Train the classifier python3 cnn_train.py \ -gpus 0 \ @@ -35,12 +36,8 @@ python3 cnn_train.py \ -save_model $MODELNAME \ -sequence_length $MAX_SEQ_LEN \ -batch_size 64 \ - -epochs 1 - -BESTMODEL="publication_model_acc_91.76_loss_0.00_e10.pt" - -# # Test the classifier accuracy -python3 cnn_translate.py\ + -epochs 1 \ +&& python3 cnn_translate.py\ -gpu 0 \ -model $BESTMODEL \ -src $TEST_DATA \ diff --git a/scripts/demo/tokenise_file.sh b/scripts/demo/tokenise_file.sh new file mode 100755 index 0000000..bba7fe6 --- /dev/null +++ b/scripts/demo/tokenise_file.sh @@ -0,0 +1,2 @@ +cd /home/t/project_model/datasets/ +perl tokenizer.perl -a -no-escape -l en -q < /home/t/project_model/base/demo/test_article.txt > /home/t/project_model/base/demo/test_article.txt.atok \ No newline at end of file diff --git a/scripts/demo/tokenise_file_fr.sh b/scripts/demo/tokenise_file_fr.sh new file mode 100755 index 0000000..3f8a075 --- /dev/null +++ b/scripts/demo/tokenise_file_fr.sh @@ -0,0 +1,2 @@ +cd /home/t/project_model/datasets/ +perl tokenizer.perl -a -no-escape -l fr -q < /home/t/project_model/base/demo/test_article.txt.fr > /home/t/project_model/base/demo/test_article.txt.fr.atok \ No newline at end of file diff --git a/base/run_big_translate.sh b/scripts/run_big_translate.sh similarity index 100% rename from base/run_big_translate.sh rename to scripts/run_big_translate.sh diff --git a/base/scripts/baseline_bt.sh b/scripts/scripts/baseline_bt.sh similarity index 100% rename from base/scripts/baseline_bt.sh rename to scripts/scripts/baseline_bt.sh diff --git a/base/scripts/baseline_bt_mod.sh b/scripts/scripts/baseline_bt_mod.sh similarity index 100% rename from base/scripts/baseline_bt_mod.sh rename to scripts/scripts/baseline_bt_mod.sh diff --git a/base/scripts/baseline_styletransfer.sh b/scripts/scripts/baseline_styletransfer.sh similarity index 100% rename from base/scripts/baseline_styletransfer.sh rename to scripts/scripts/baseline_styletransfer.sh diff --git a/base/scripts/batch_baseline_backtranslate.sh b/scripts/scripts/batch_baseline_backtranslate.sh similarity index 100% rename from base/scripts/batch_baseline_backtranslate.sh rename to scripts/scripts/batch_baseline_backtranslate.sh diff --git a/base/scripts/batch_ende.sh b/scripts/scripts/batch_ende.sh similarity index 100% rename from base/scripts/batch_ende.sh rename to scripts/scripts/batch_ende.sh diff --git a/base/scripts/batch_enfr_corpus.sh b/scripts/scripts/batch_enfr_corpus.sh similarity index 100% rename from base/scripts/batch_enfr_corpus.sh rename to scripts/scripts/batch_enfr_corpus.sh diff --git a/base/scripts/big_transformer_setup.sh b/scripts/scripts/big_transformer_setup.sh similarity index 100% rename from base/scripts/big_transformer_setup.sh rename to scripts/scripts/big_transformer_setup.sh diff --git a/base/scripts/prod/build_pub_corpus.sh b/scripts/scripts/prod/build_pub_corpus.sh similarity index 100% rename from base/scripts/prod/build_pub_corpus.sh rename to scripts/scripts/prod/build_pub_corpus.sh diff --git a/base/scripts/prod/casper_st_train_political.sh b/scripts/scripts/prod/casper_st_train_political.sh similarity index 100% rename from base/scripts/prod/casper_st_train_political.sh rename to scripts/scripts/prod/casper_st_train_political.sh diff --git a/base/scripts/prod/casper_test_pol_ts.sh b/scripts/scripts/prod/casper_test_pol_ts.sh similarity index 100% rename from base/scripts/prod/casper_test_pol_ts.sh rename to scripts/scripts/prod/casper_test_pol_ts.sh diff --git a/base/scripts/prod/casper_translate_enfr_political.sh b/scripts/scripts/prod/casper_translate_enfr_political.sh similarity index 100% rename from base/scripts/prod/casper_translate_enfr_political.sh rename to scripts/scripts/prod/casper_translate_enfr_political.sh diff --git a/base/scripts/prod/munchlax_fren_train.sh b/scripts/scripts/prod/munchlax_fren_train.sh similarity index 100% rename from base/scripts/prod/munchlax_fren_train.sh rename to scripts/scripts/prod/munchlax_fren_train.sh diff --git a/base/scripts/prod/munchlax_retrain_fren.sh b/scripts/scripts/prod/munchlax_retrain_fren.sh similarity index 100% rename from base/scripts/prod/munchlax_retrain_fren.sh rename to scripts/scripts/prod/munchlax_retrain_fren.sh diff --git a/base/scripts/prod/munchlax_to_casper.sh b/scripts/scripts/prod/munchlax_to_casper.sh similarity index 100% rename from base/scripts/prod/munchlax_to_casper.sh rename to scripts/scripts/prod/munchlax_to_casper.sh diff --git a/base/scripts/prod/snorlax_to_casper.sh b/scripts/scripts/prod/snorlax_to_casper.sh similarity index 100% rename from base/scripts/prod/snorlax_to_casper.sh rename to scripts/scripts/prod/snorlax_to_casper.sh diff --git a/base/scripts/prod/snorlax_translate_newspaper.sh b/scripts/scripts/prod/snorlax_translate_newspaper.sh similarity index 100% rename from base/scripts/prod/snorlax_translate_newspaper.sh rename to scripts/scripts/prod/snorlax_translate_newspaper.sh diff --git a/base/scripts/prod/train_nmt_models.sh b/scripts/scripts/prod/train_nmt_models.sh similarity index 100% rename from base/scripts/prod/train_nmt_models.sh rename to scripts/scripts/prod/train_nmt_models.sh diff --git a/base/scripts/prod/train_pol_st_models.sh b/scripts/scripts/prod/train_pol_st_models.sh similarity index 100% rename from base/scripts/prod/train_pol_st_models.sh rename to scripts/scripts/prod/train_pol_st_models.sh diff --git a/base/scripts/prod/train_pub_naturalness_models.sh b/scripts/scripts/prod/train_pub_naturalness_models.sh similarity index 100% rename from base/scripts/prod/train_pub_naturalness_models.sh rename to scripts/scripts/prod/train_pub_naturalness_models.sh diff --git a/base/scripts/prod/train_pub_st_models.sh b/scripts/scripts/prod/train_pub_st_models.sh similarity index 100% rename from base/scripts/prod/train_pub_st_models.sh rename to scripts/scripts/prod/train_pub_st_models.sh diff --git a/base/scripts/production_master_enfr.sh b/scripts/scripts/production_master_enfr.sh similarity index 100% rename from base/scripts/production_master_enfr.sh rename to scripts/scripts/production_master_enfr.sh diff --git a/base/scripts/production_nmt_enfr.sh b/scripts/scripts/production_nmt_enfr.sh similarity index 100% rename from base/scripts/production_nmt_enfr.sh rename to scripts/scripts/production_nmt_enfr.sh diff --git a/base/scripts/production_nmt_enfr_bpe.sh b/scripts/scripts/production_nmt_enfr_bpe.sh similarity index 100% rename from base/scripts/production_nmt_enfr_bpe.sh rename to scripts/scripts/production_nmt_enfr_bpe.sh diff --git a/base/scripts/production_nmt_enfr_setup.sh b/scripts/scripts/production_nmt_enfr_setup.sh similarity index 100% rename from base/scripts/production_nmt_enfr_setup.sh rename to scripts/scripts/production_nmt_enfr_setup.sh diff --git a/base/scripts/production_nmt_fren_bpe.sh b/scripts/scripts/production_nmt_fren_bpe.sh similarity index 100% rename from base/scripts/production_nmt_fren_bpe.sh rename to scripts/scripts/production_nmt_fren_bpe.sh diff --git a/base/scripts/setup_political_dataset.sh b/scripts/scripts/setup_political_dataset.sh similarity index 100% rename from base/scripts/setup_political_dataset.sh rename to scripts/scripts/setup_political_dataset.sh diff --git a/base/scripts/test_base_styletransfer.sh b/scripts/scripts/test_base_styletransfer.sh similarity index 100% rename from base/scripts/test_base_styletransfer.sh rename to scripts/scripts/test_base_styletransfer.sh diff --git a/base/scripts/test_ende_bpe_suite.sh b/scripts/scripts/test_ende_bpe_suite.sh similarity index 100% rename from base/scripts/test_ende_bpe_suite.sh rename to scripts/scripts/test_ende_bpe_suite.sh diff --git a/base/scripts/test_metrics.sh b/scripts/scripts/test_metrics.sh similarity index 100% rename from base/scripts/test_metrics.sh rename to scripts/scripts/test_metrics.sh diff --git a/base/scripts/test_opt_tr.sh b/scripts/scripts/test_opt_tr.sh similarity index 100% rename from base/scripts/test_opt_tr.sh rename to scripts/scripts/test_opt_tr.sh diff --git a/base/scripts/test_phat.sh b/scripts/scripts/test_phat.sh similarity index 100% rename from base/scripts/test_phat.sh rename to scripts/scripts/test_phat.sh diff --git a/base/scripts/test_rebased.sh b/scripts/scripts/test_rebased.sh similarity index 100% rename from base/scripts/test_rebased.sh rename to scripts/scripts/test_rebased.sh diff --git a/base/scripts/test_with_filtered_enfr/test.sh b/scripts/scripts/test_with_filtered_enfr/test.sh similarity index 100% rename from base/scripts/test_with_filtered_enfr/test.sh rename to scripts/scripts/test_with_filtered_enfr/test.sh diff --git a/base/scripts/tings.sh b/scripts/scripts/tings.sh similarity index 100% rename from base/scripts/tings.sh rename to scripts/scripts/tings.sh diff --git a/scripts/test_measurements.sh b/scripts/test_measurements.sh new file mode 100755 index 0000000..363ab90 --- /dev/null +++ b/scripts/test_measurements.sh @@ -0,0 +1,6 @@ +#!/usr/bin/env bash +THIS_SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd ) +src=$THIS_SCRIPT_DIR"/results/pol/democratic_only.test.en" +tgt=$THIS_SCRIPT_DIR"/results/pol/democratic_only.test.to.republican.1" +echo $src +python3 measure_ts.py -src $src -tgt $tgt -type political -cache_dir ../cache diff --git a/specialk/__init__.py b/specialk/__init__.py new file mode 100755 index 0000000..f7d73e5 --- /dev/null +++ b/specialk/__init__.py @@ -0,0 +1,3 @@ +import specialk.core.constants as Constants + +__all__ = [Constants] diff --git a/specialk/core/__init__.py b/specialk/core/__init__.py new file mode 100755 index 0000000..995b0d2 --- /dev/null +++ b/specialk/core/__init__.py @@ -0,0 +1,3 @@ +from specialk.core.utils import log + +__all__ = [log] \ No newline at end of file diff --git a/specialk/core/constants.py b/specialk/core/constants.py new file mode 100755 index 0000000..677a4c5 --- /dev/null +++ b/specialk/core/constants.py @@ -0,0 +1,61 @@ +""" +ENSURE THAT THE CONSTANTS ARE CAPITALISED. +""" + +from pathlib import Path +from typing import List + +import specialk + +SEED = 1337 + +PAD = 0 +UNK = 1 +SOS = 2 +EOS = 3 +BLO = 4 +SEP = 5 + +PAD_WORD = "" +UNK_WORD = "" +SOS_WORD = "" +EOS_WORD = "" +BLO_WORD = "

" # paragraph block +SEP_WORD = "" +CLS_TOKEN = "" +URL_TOKEN = "" + +SOURCE = "source" +TARGET = "target" + +RNN = "rnn" +TRANSFORMER = "transformer" + + +def get_tokens() -> List[str]: + g = globals() + k = list({i for i in g if i[-5:] == "_WORD"}) + kc = [i[:-5] for i in k] + k = sorted(kc, key=lambda x: g[x]) + return [g[i + "_WORD"] for i in k] + + +PROJECT_DIR: Path = Path(specialk.__file__).parent.parent + +# for tensorboard; logging +LOGGING_DIR: Path = PROJECT_DIR / "tb_logs" +LOGGING_PERF_NAME: str = "perf_logs" + +# labels for logging +TRAIN_BATCH_ID = "train_batch_id" +VAL_BATCH_ID = "eval_batch_id" +TRAIN_ACC = "train_acc" +VAL_ACC = "eval_acc" +TEST_ACC = "test_acc" +TRAIN_LOSS = "train_loss" +VAL_LOSS = "eval_loss" +TEST_LOSS = "test_loss" +TRAIN_PPLX = "train_perplexity" +VAL_PPLX = "eval_perplexity" +VAL_BLEU = "eval_bleu" +TEACHER_FORCING_RATIO = "teacher_forcing_ratio" diff --git a/base/core/dataset.py b/specialk/core/dataset.py similarity index 95% rename from base/core/dataset.py rename to specialk/core/dataset.py index 8b6b850..73b6ce4 100755 --- a/base/core/dataset.py +++ b/specialk/core/dataset.py @@ -5,10 +5,10 @@ import torch.utils.data # deal with constants -if os.getcwd().split("/")[-1] == "core": - import constants as Constants -else: - import core.constants as Constants +import specialk.core.constants as Constants + +# THIS IS ONLY FOR WORD DATASETS + def paired_collate_fn(insts): diff --git a/specialk/core/logging.py b/specialk/core/logging.py new file mode 100644 index 0000000..c9b9100 --- /dev/null +++ b/specialk/core/logging.py @@ -0,0 +1,20 @@ +import logging +import structlog + +# actually just default parameters below. +structlog.configure( + processors=[ + structlog.contextvars.merge_contextvars, + structlog.processors.add_log_level, + structlog.processors.StackInfoRenderer(), + structlog.dev.set_exc_info, + structlog.processors.TimeStamper(fmt="%Y-%m-%d %H:%M:%S", utc=False), + structlog.dev.ConsoleRenderer() + ], + wrapper_class=structlog.make_filtering_bound_logger(logging.NOTSET), + context_class=dict, + logger_factory=structlog.PrintLoggerFactory(), + cache_logger_on_first_use=False, + +) +log = structlog.get_logger() \ No newline at end of file diff --git a/base/core/optim.py b/specialk/core/optim.py similarity index 100% rename from base/core/optim.py rename to specialk/core/optim.py diff --git a/specialk/core/parallel.py b/specialk/core/parallel.py new file mode 100644 index 0000000..d0cd080 --- /dev/null +++ b/specialk/core/parallel.py @@ -0,0 +1,44 @@ +from typing import Callable, List, Any + +import multiprocessing as mp +import pandas as pd +from tqdm import tqdm + +class ParallelProcessor: + @staticmethod + def batch_generator(series_iterator: pd.Series, batch_size: int): + """ + Generate batches of data from the pd.Series. + + This is done because I don't want to convert + the whole series into a list. + """ + batch = [] + for text in series_iterator: # still fast. + batch.append(text) + if len(batch) == batch_size: + yield batch + batch = [] + if batch: + yield batch + + @staticmethod + def process( + func: Callable, + data: List[Any], + chunk_size: int = 100000, + init_func: Callable = None, + init_args: tuple = None, + ) -> List[Any]: + """Generic parallel operation.""" + results = [] + with mp.Pool(initializer=init_func, initargs=init_args or ()) as pool: + with tqdm(total=len(data)) as pbar: + for chunk in ( + batch + for batch in ParallelProcessor.batch_generator(data, chunk_size) + ): + for batch in pool.map(func, chunk): + results.append(batch) + pbar.update(1) + return results diff --git a/specialk/core/sanitisation.py b/specialk/core/sanitisation.py new file mode 100644 index 0000000..26498d8 --- /dev/null +++ b/specialk/core/sanitisation.py @@ -0,0 +1,160 @@ +import unicodedata +import re +from urllib.parse import unquote +import html +from specialk.core import log +import ftfy +from specialk.core.constants import URL_TOKEN + +URL_PATTERN = re.compile( + "http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+" +) +HTML_ENTITY_PATTERN = re.compile("&([a-z#0-9]+);") + +# Punctuation normalization mappings +PUNCT_MAPPINGS = { + "“": '"', # Left double quotation mark + "”": '"', # Right double quotation mark + "‘": "'", # Left single quotation mark + "’": "'", # Right single quotation mark + "…": "...", # Ellipsis + "–": "-", # En dash + "—": "-", # Em dash + "„": '"', # Double low-9 quotation mark + "«": '"', # Left-pointing double angle quotation mark + "»": '"', # Right-pointing double angle quotation mark + "′": "'", # Prime + "″": '"', # Double prime + "≥": ">=", # Greater-than or equal to + # "•": "*", # Bullet + # "°": " degrees", # Degree sign +} + +ONE_OFF_MAPPINGS = { + "°r;": "°", + "&lquot;": '"', + "&rquot;": '"', + """: '"', + "&uot;": '"', + "&emdash;": "-", + "&eagrave;": "è", + "és;": "é", +} + +# Create a regex pattern for punctuation +PUNCT_PATTERN = re.compile("|".join(map(re.escape, PUNCT_MAPPINGS.keys()))) +HAMMER_PATTERN = re.compile("|".join(map(re.escape, ONE_OFF_MAPPINGS.keys()))) + +sequence_limits = { + ".": 8, + "(": 8, + ")": 8, + ",": 8, + "@": 8, +} + + +def is_valid_text(x: str) -> bool: + """ + Heuristic based text validity, gopher style. + + (yeah, heuristic based filtering has been + around for ages, I know). + """ + if len(x.strip()) < 1: + return False + + # if the text contains a lot of numbers, + if sum(c.isdigit() for c in x) / len(x) > 0.4: + return False + + # check ratio of characters. + base = {} + for char in x: + if char not in base: + base[char] = 0 + base[char] += 1 + if char in sequence_limits: + if sequence_limits[char] < base[char]: + return False + return True + + +def normalize_punctuation(text: str) -> str: + """Normalize Unicode punctuation marks to ASCII equivalents. This + should only be applied on english.""" + return PUNCT_PATTERN.sub(lambda m: PUNCT_MAPPINGS[m.group(0)], text) + + +def hammer_replacement(text: str) -> str: + return HAMMER_PATTERN.sub(lambda m: ONE_OFF_MAPPINGS[m.group(0)], text) + + +def can_parse_html_pattern(text: str) -> bool: + try: + HTML_ENTITY_PATTERN.sub( + lambda m: chr( + int(m.group(1)[1:]) + if m.group(1).startswith("#") + else ord(html.entities.html5[m.group(1)]) + ), + text, + ) + return True + except Exception: + return False + + +def fix_unicode(text: str) -> str: + text = URL_PATTERN.sub(URL_TOKEN, text) + # Use ftfy to fix and normalize Unicode + text = ftfy.fix_text(text) + + text = hammer_replacement(text) + # Handle HTML entities. + try: + text = HTML_ENTITY_PATTERN.sub( + lambda m: chr( + int(m.group(1)[1:]) + if m.group(1).startswith("#") + else ord(html.entities.html5[m.group(1)]) + ), + text, + ) + except KeyError as e: + log.error( + "Could not parse HTML_ENTITY_PATTERN for text, so skipping", + text=text, + type="KeyError", + error=str(e), + ) + except ValueError as e: + log.error( + "Could not parse HTML_ENTITY_PATTERN for text, so skipping", + text=text, + type="ValueError", + error=str(e), + ) + + # Handle percent-encoded characters. + text = unquote(text) + + # Attempt Latin-1 to UTF-8 conversion. + try: + text = text.encode("latin-1").decode("utf-8") + except (UnicodeEncodeError, UnicodeDecodeError): + pass + + # Handle Unicode escape sequences. + try: + text = text.encode("ascii").decode("unicode-escape") + except (UnicodeEncodeError, UnicodeDecodeError): + pass + + # Normalize Unicode (combine characters and their diacritics). + text = unicodedata.normalize("NFC", text) + + # Remove any invalid Unicode characters. + text = "".join(ch for ch in text if unicodedata.category(ch)[0] != "C") + + return text diff --git a/base/core/sentenciser.py b/specialk/core/sentenciser.py similarity index 100% rename from base/core/sentenciser.py rename to specialk/core/sentenciser.py diff --git a/base/core/telegram.py b/specialk/core/telegram.py similarity index 100% rename from base/core/telegram.py rename to specialk/core/telegram.py diff --git a/specialk/core/utils.py b/specialk/core/utils.py new file mode 100755 index 0000000..8388327 --- /dev/null +++ b/specialk/core/utils.py @@ -0,0 +1,168 @@ +import functools +import hashlib +import platform +import subprocess +import warnings +from argparse import Namespace +from multiprocessing import Pool, cpu_count +from pathlib import Path +from typing import Any, Dict, Iterable, List, Union + +import torch +import yaml +from tqdm import tqdm + +from specialk.core.logging import log + +""" +Misc. functions used by a variety of parts of the library. +""" + + +def batch_texts(texts: Iterable[str], batch_size: int) -> Iterable[List[str]]: + """ + Takes an iterable of texts and yields batches of texts. + + Args: + texts (Iterable[str]): An iterable of text strings. + batch_size (int): The number of texts in each batch. + + Yields: + List[str]: A batch of texts. + """ + current_batch = [] + + for text in texts: + current_batch.append(text) + if len(current_batch) == batch_size: + yield current_batch + current_batch = [] + + # Yield any remaining texts in the last batch + if current_batch: + yield current_batch + + +def save_dict_to_yaml(data: dict, file_path: str) -> None: + """ + Save a dictionary to a YAML file. + + Args: + data (dict): The dictionary to be saved + file_path (str): The path where the YAML file will be saved + + Returns: + None + """ + with open(file_path, "w") as file: + yaml.dump(data, file, default_flow_style=False) + + +def deprecated(func): + """This is a decorator which can be used to mark functions + as deprecated. It will result in a warning being emitted + when the function is used.""" + + @functools.wraps(func) + def new_func(*args, **kwargs): + warnings.simplefilter("always", DeprecationWarning) # turn off filter + warnings.warn( + "Call to deprecated function {}.".format(func.__name__), + category=DeprecationWarning, + stacklevel=2, + ) + warnings.simplefilter("default", DeprecationWarning) # reset filter + return func(*args, **kwargs) + + return new_func + + +def check_torch_device() -> str: + """Check which device is available. Returns one of {cpu, cuda, mps}""" + if torch.backends.mps.is_available(): + return "mps" + if torch.cuda.is_available(): + return "cuda" + return "cpu" + + +def load_dataset(path: str) -> List[str]: + data = [] + len_file = get_len(path) + with open(path, mode="r", encoding="utf-8") as f: + for line in tqdm(f, total=len_file): + data.append(line.strip()) + return data + + +def get_len(filepath: Union[str, Path]) -> int: + """ + Reads number of lines in the mose corpus without using python + to deal with it. This is some order of magnitude faster! + """ + + # temporarily convert a string into a Path object + # so we can check whether the file exists. + if not isinstance(filepath, Path): + filepath = Path(filepath) + if not filepath.exists(): + log.error(f"'{str(filepath)}' does not exist.") + raise FileNotFoundError + + filepath = str(filepath) + + filepath = filepath.replace(" ", r"\ ").replace("(", r"\(").replace(")", r"\)") + + if platform.system() in {"Linux", "Darwin"}: + process = subprocess.run(["wc", "-l", filepath], stdout=subprocess.PIPE) + value = process.stdout.decode("utf-8").strip().split().pop(0) + else: + raise NotImplementedError + + return int(value) + + +def batch_compute(func, args, n_processes=cpu_count() - 1): + """ + Takes a function and some arguments for those functions, + and returns a list of outputs generated from the function. + Pools with multicores. + """ + p = Pool(n_processes) + res_list = [] + with tqdm(total=len(args)) as pbar: + for i, res in enumerate(p.imap_unordered(func, args)): + pbar.update() + res_list.append(res) + pbar.close() + p.close() + p.join() + return res_list + + +def namespace_to_dict(ns: Namespace) -> Dict[str, Any]: + # d = {} + # for key in ns._get_kwargs(): + # try: + # d[key] = getattr(ns, key) + # except TypeError: + # log.error(f"key {key} is not a string in namespace") + # return d + return {k: v for k, v in ns._get_kwargs()} + + +def hash(src: List[str], tgt: List[str]) -> str: + """Generate hash of dataset. + + Args: + src (List[str]): src dataset. + tgt (List[str]): tgt dataset. + + Returns: + str: md5 checksum of the dataset. + """ + m = hashlib.md5() + for x, y in zip(src, tgt): + m.update(x.encode()) + m.update(y.encode()) + return str(m.hexdigest()) diff --git a/base/__init__.py b/specialk/datasets.lock old mode 100755 new mode 100644 similarity index 100% rename from base/__init__.py rename to specialk/datasets.lock diff --git a/specialk/datasets/dataloaders.py b/specialk/datasets/dataloaders.py new file mode 100644 index 0000000..3127146 --- /dev/null +++ b/specialk/datasets/dataloaders.py @@ -0,0 +1,74 @@ +from typing import Optional, Tuple, Union + +from torch.utils.data import DataLoader + +from specialk.core.dataset import TranslationDataset, paired_collate_fn +from specialk.core.utils import log + + +def init_classification_dataloaders( + data: dict, batch_size: int, n_workers: int = 8 +) -> Tuple[DataLoader, DataLoader]: + """Initialise CNN Classifier DataLoaders for dataset. + + Note that onmt has their own dataset loader, but no need to use that if we could + leverage existing PyTorch Dataloaders. + + Args: + data (dict): object container containing dataset. + batch_size (int): batch size for dataset iteration. + n_workers (int, Optional): number of workers to operate on the dataloader. + + Returns: + Tuple[DataLoader, DataLoader]: Train and validation dataloaders. + """ + src_word2idx = data["dicts"]["src"] # it's the same as the tgt. + + DATASET_IS_BPE = "byte_pairs" in src_word2idx.keys() + if DATASET_IS_BPE: + log.info("BPE Tokenised input detected.") + # we have BPE loaded (detection is a heuristic). + src_byte_pairs = {x + "_": y for x, y in src_word2idx["byte_pairs"].items()} + src_word2idx = {**src_byte_pairs, **src_word2idx["words"]} + else: + log.info("Space-Separated Tokenised input detected.") + + train_loader = DataLoader( + TranslationDataset( + src_word2idx=src_word2idx, + tgt_word2idx=src_word2idx, + src_insts=data["train"]["src"], + tgt_insts=data["train"]["tgt"], + ), + num_workers=n_workers, + batch_size=batch_size, + collate_fn=paired_collate_fn, + shuffle=True, + ) + + valid_loader = DataLoader( + TranslationDataset( + src_word2idx=src_word2idx, + tgt_word2idx=src_word2idx, + src_insts=data["valid"]["src"], + tgt_insts=data["valid"]["tgt"], + ), + num_workers=n_workers, + batch_size=batch_size, + collate_fn=paired_collate_fn, + ) + + # printing for sanity checks. + show_first_n: int = 2 + log.debug( + f"Showing first {show_first_n} rows of training dataset.", + source=data["train"]["src"][:show_first_n], + target=data["train"]["tgt"][:show_first_n], + ) + log.debug( + f"Showing first {show_first_n} rows of validation dataset.", + source=data["valid"]["src"][:show_first_n], + target=data["valid"]["tgt"][:show_first_n], + ) + + return train_loader, valid_loader diff --git a/specialk/datasets/filters.py b/specialk/datasets/filters.py new file mode 100644 index 0000000..ac29ff6 --- /dev/null +++ b/specialk/datasets/filters.py @@ -0,0 +1,239 @@ +import pandas as pd +import numpy as np +from typing import Optional, Any, Iterable +from tqdm import tqdm +from specialk.models.utils.lid import FastTextLID +from specialk.core import log +from specialk.core.sanitisation import ( + fix_unicode, + can_parse_html_pattern, + is_valid_text, +) +from structlog.contextvars import bound_contextvars +import warnings +from specialk.core.parallel import ParallelProcessor + +tqdm.pandas() +CLEAN = "clean" +REASON = "reason" + + +def most_frequent_item_numpy(arr: np.array, k=5) -> Any: + """Return the most frequent item in the array.""" + + unique, counts = np.unique(arr, return_counts=True) + + # Calculate percentages + percentages = counts / len(arr) * 100 + + # Sort items by frequency (descending order) + sorted_indices = np.argsort(counts)[::-1] + + # Print top 5 items with percentages + print("Top 5 items and their percentages:") + for i in range(min(k, len(unique))): + index = sorted_indices[i] + print(f"{unique[index]}: {percentages[index]:.2f}%") + + return unique[np.argmax(counts)] + + +def chunk_allocate_on_df( + df: pd.DataFrame, + column: str, + row_indexer: pd.Series, + values: Iterable[Any], + chunk_size: int = 100000, +): + for start in tqdm(range(0, len(df), chunk_size), desc="chunk storing"): + end = start + chunk_size + chunk_valid_rows = row_indexer[start:end] + chunk_cleaned_rows = values[start:end] + df.loc[chunk_valid_rows, column] = chunk_cleaned_rows + + +class PreTrainingFilter: + """Package the filtering into a single class for one column.""" + + def __init__( + self, + df: pd.DataFrame, + col: str, + lid: Optional[FastTextLID] = None, + init_already=False, + ): + """Init DataFrame. + + Args: + df (pd.DataFrame): DataFrame with column that includes col. + col (str): column name corresponding to text (str). + lid (Optional[FastTextID]): LanguageID object. Initialises one if + it hasn't already been done so. + init_already (Optional[bool]): If set, skips creating a "clean" and + "reason" column. + """ + self.df = df + self.col = col + self._lid = lid if lid else FastTextLID() + + if not init_already: + # we'll add additional columns + self.df[CLEAN] = True + self.df[REASON] = "" + + def _get_valid_rows(self) -> pd.DataFrame: + """Return a boolean mask for valid rows.""" + return self.df[CLEAN] + + def normalise(self): + """Perform unicode normalization on DataFrame.""" + with bound_contextvars(filter="unicode fix and norm"): + log.info("Running normalization on rows.") + + self.filter_valid_html_parseable() + + valid_rows = self._get_valid_rows() + + # TODO: Pandas screams when I do this because it'll get + # deprecated in 3.0, but when I do self.df.loc[valid_rows, self.src_col] + # the kernel dies. Need to integrate chunk_allocate_on_df(). + with warnings.catch_warnings(): + warnings.simplefilter(action="ignore", category=FutureWarning) + warnings.simplefilter( + action="ignore", category=pd.errors.SettingWithCopyWarning + ) + self.df[self.col].loc[valid_rows] = ParallelProcessor.process( + fix_unicode, self.df.loc[valid_rows, self.col] + ) + + log.info("Finished running normalization on rows.") + + def filter_sanity_check(self): + """Perform general heuristic based filtering on the text.""" + if len(self.df) < 1: + return + log.info("Running sanity check") + with bound_contextvars(filter="sanity_check"): + rows = self._get_valid_rows() + texts_valid = np.array( + ParallelProcessor.process(is_valid_text, self.df.loc[rows, self.col]) + ) + + valid_series = pd.Series(texts_valid, index=rows[rows].index) + invalid_series = valid_series[~valid_series] + + self.df.loc[invalid_series.index, CLEAN] = False + self.df.loc[invalid_series.index, REASON] = f"{self.col} fails sanity check" + + self.log_difference(texts_valid, self.col) + + log.info("Finished running sanity checks.") + + def filter_valid_html_parseable(self) -> None: + """Perform is_valid_text on given column.""" + rows = self._get_valid_rows() + texts_valid = np.array( + ParallelProcessor.process( + can_parse_html_pattern, self.df.loc[rows, self.col] + ) + ) + + valid_series = pd.Series(texts_valid, index=rows[rows].index) + invalid_series = valid_series[~valid_series] + + self.df.loc[invalid_series.index, CLEAN] = False + self.df.loc[invalid_series.index, REASON] = f"{self.col} cannot parse HTML" + + self.log_difference(texts_valid, self.col) + + def filter_lang_id( + self, + lang: Optional[str] = None, + prob_threshold: float = 0.9, + ) -> None: + """Filter dataset by Lang ID.""" + with bound_contextvars(filter="lang_id", threshold=prob_threshold): + rows = self._get_valid_rows() + + labels, scores = self._lid.lid_scores(self.df.loc[rows, self.col]) + + if lang is None: + log.info(f"{self.col} language is being approximated.") + lang = most_frequent_item_numpy(labels[0]) + log.info(f"Approximated labels to be {lang}.") + + high_conf_row = scores[:, 0] >= prob_threshold + row_label_match = labels[:, 0] == lang + + high_conf_label = high_conf_row & row_label_match + + valid_series = pd.Series(high_conf_label, index=rows[rows].index) + invalid_series = valid_series[~valid_series] + + self.df.loc[invalid_series.index, CLEAN] = False + self.df.loc[invalid_series.index, REASON] = ( + f"{self.col} low confidence (<{prob_threshold}) for {lang} on lang_id" + ) + + self.log_difference(high_conf_label, self.col) + + log.info("Finished running Language ID on rows.") + + @staticmethod + def log_difference(results: np.array, column: str): + """Pretty render log difference in changes.""" + n_total, n_valid = results.size, results.sum() + n_dropped = n_total - n_valid + + log.info( + f"calculated {((n_valid/n_total)*100):.2f}% {column} rows valid", + total=n_total, + valid=n_valid, + dropped=n_dropped, + ) + + +class ParallelPreTrainingFilter(PreTrainingFilter): + """Package the filtering into a single class for parallel corpus.""" + + def __init__(self, df: pd.DataFrame, src_col: str, tgt_col: str): + """Init DataFrame. + + Args: + df (pd.DataFrame): DataFrame with columns that include src_col, tgt_col. + src_col (str): column name corresponding to source text (str). + tgt_col (str): column name corresponding to target text (str). + """ + super().__init__(df, src_col) + self.src_col = src_col + self.tgt_col = tgt_col + self.src_filter = PreTrainingFilter(df, src_col, self._lid, init_already=True) + self.tgt_filter = PreTrainingFilter(df, tgt_col, self._lid, init_already=True) + + def normalise(self): + """Perform unicode normalization on DataFrame for both columns.""" + self.src_filter.normalise() + self.tgt_filter.normalise() + self._sync_clean_and_reason() + + def filter_sanity_check(self): + """Perform general heuristic based filtering on both columns.""" + self.src_filter.filter_sanity_check() + self.tgt_filter.filter_sanity_check() + self._sync_clean_and_reason() + + def filter_lang_id( + self, + src_lang: Optional[str] = None, + tgt_lang: Optional[str] = None, + prob_threshold: float = 0.9, + ) -> None: + """Filter dataset by Lang ID for both columns.""" + self.src_filter.filter_lang_id(src_lang, prob_threshold) + self.tgt_filter.filter_lang_id(tgt_lang, prob_threshold) + self._sync_clean_and_reason() + + def _sync_clean_and_reason(self): + """Synchronize CLEAN and REASON columns between source and target filters.""" + self.df[CLEAN] = self.src_filter.df[CLEAN] & self.tgt_filter.df[CLEAN] + self.df[REASON] = self.src_filter.df[REASON].fillna(self.tgt_filter.df[REASON]) diff --git a/specialk/datasets/political_data.py b/specialk/datasets/political_data.py new file mode 100644 index 0000000..902f121 --- /dev/null +++ b/specialk/datasets/political_data.py @@ -0,0 +1,171 @@ +# TODO: Address all TODOs and remove all explanatory comments +"""TODO: Add a description here.""" + +import os +from pathlib import Path +from typing import List + +import datasets + +# TODO: Add BibTeX citation +# Find for instance the citation on arxiv or on the dataset repo/website +_CITATION = """\ +@inproceedings{style_transfer_acl18, + title={Style Transfer Through Back-Translation}, + author={Prabhumoye, Shrimai and Tsvetkov, Yulia and Salakhutdinov, Ruslan and Black, Alan W}, + year={2018}, + booktitle={Proc. ACL} + } +""" + +# TODO: Add description of the dataset here +# You can copy an official description +_DESCRIPTION = """\ +This new dataset is designed to solve this great NLP task and is crafted with a lot of care. +""" + +# TODO: Add a link to an official homepage for the dataset here +_HOMEPAGE = "" + +# TODO: Add the licence for the dataset here if you can find it +_LICENSE = "" + +# TODO: Add link to the official dataset URLs here +# The HuggingFace Datasets library doesn't host the datasets but only points to the original files. +# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) +_URLS = { + "main": "http://tts.speech.cs.cmu.edu/style_models/political_data.tar", +} + + +# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case +class PoliticalDataset(datasets.GeneratorBasedBuilder): + """TODO: Political Tweets Dataset between Democratic and Republican.""" + + VERSION = datasets.Version("1.0.0") + + # This is an example of a dataset with multiple configurations. + # If you don't want/need to define several sub-sets in your dataset, + # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. + + # If you need to make complex sub-parts in the datasets with configurable options + # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig + # BUILDER_CONFIG_CLASS = MyBuilderConfig + + # You will be able to load one or the other configurations in the following list with + BUILDER_CONFIGS = [ + datasets.BuilderConfig( + name="main", + version=VERSION, + description="Political Tweets Dataset.", + ), + ] + + DEFAULT_CONFIG_NAME = "main" # It's not mandatory to have a default configuration. Just use one if it make sense. + + def _info(self): + # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset + if ( + self.config.name == "main" + ): # This is the name of the configuration selected in BUILDER_CONFIGS above + features = datasets.Features( + { + "text": datasets.Value("string"), + "label": datasets.Value("string"), + # These are the features of your dataset like images, labels ... + } + ) + return datasets.DatasetInfo( + # This is the description that will appear on the datasets page. + description=_DESCRIPTION, + # This defines the different columns of the dataset and their types + features=features, # Here we define them above because they are different between the two configurations + # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and + # specify them. They'll be used if as_supervised=True in builder.as_dataset. + supervised_keys=("text", "label"), + # Homepage of the dataset for documentation + homepage=_HOMEPAGE, + # License for the dataset if available + license=_LICENSE, + # Citation for the dataset + citation=_CITATION, + ) + + def _split_generators(self, dl_manager): + # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration + # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name + + # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS + # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. + # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive + + url = _URLS[self.config.name] + archive = dl_manager.download(url) + data_dir = dl_manager.iter_archive(archive) + + for file in data_dir: + print(file) + + return [ + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + # These kwargs will be passed to _generate_examples + gen_kwargs={ + "filepath": [ + os.path.join(data_dir, "republican_only.train.en"), + os.path.join(data_dir, "democratic_only.train.en"), + ], + "split": "train", + }, + ), + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + # These kwargs will be passed to _generate_examples + gen_kwargs={ + "filepath": [os.path.join(data_dir, "classtrain.txt")], + "split": "train_class", + }, + ), + datasets.SplitGenerator( + name=datasets.Split.VALIDATION, + # These kwargs will be passed to _generate_examples + gen_kwargs={ + "filepath": [ + os.path.join(data_dir, "republican_only.dev.en"), + os.path.join(data_dir, "democratic_only.dev.en"), + ], + "split": "dev", + }, + ), + datasets.SplitGenerator( + name=datasets.Split.TEST, + # These kwargs will be passed to _generate_examples + gen_kwargs={ + "filepath": [ + os.path.join(data_dir, "republican_only.test.en"), + os.path.join(data_dir, "democratic_only.test.en"), + ], + "split": "test", + }, + ), + ] + + # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` + def _generate_examples(self, filepaths: List[str], split: str): + # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. + for filepath in filepaths: + filename = Path(filepath).name + label = filename.split(".")[0].split("_")[0] + with open(filepath, encoding="utf-8") as f: + for key, row in enumerate(f): + text = row.strip() + if split != "test": + # label only exists in train/eval files. + text = text.split() + label, text = text[0], text[1:] + text = " ".join(text) + + yield ( + key, + {"text": text, "label": label}, + ) diff --git a/base/preprocess.py b/specialk/datasets/preprocess.py similarity index 98% rename from base/preprocess.py rename to specialk/datasets/preprocess.py index 6812536..1074525 100755 --- a/base/preprocess.py +++ b/specialk/datasets/preprocess.py @@ -1,15 +1,20 @@ +""" +DEPRECATED +""" + import argparse import subprocess import unicodedata from copy import deepcopy as copy from functools import reduce -import core.constants as Constants import torch -from core.bpe import Encoder as bpe_encoder -from core.utils import get_len from tqdm import tqdm +import specialk.core.constants as Constants +from specialk.core.utils import get_len +from specialk.models.tokenizers.bpe import Encoder as bpe_encoder + """ Preprocesses mose style code to pytorch ready files. """ @@ -20,7 +25,7 @@ def parse(text, formatting="word"): text -> string of sentence. formatting -> one of 'word', 'character'. """ - assert type(text) == str + assert isinstance(text, str) assert formatting in ["word", "bpe"] if formatting == "word": diff --git a/base/rebase.py b/specialk/datasets/rebase.py similarity index 96% rename from base/rebase.py rename to specialk/datasets/rebase.py index eaca2ee..b3e92cc 100755 --- a/base/rebase.py +++ b/specialk/datasets/rebase.py @@ -8,12 +8,13 @@ import argparse from copy import deepcopy as copy -import core.constants as Constants import torch -from core.bpe import Encoder as bpe_encoder -from preprocess import load_file, reclip, seq2idx from tqdm import tqdm +import specialk.core.constants as Constants +from specialk.datasets.preprocess import load_file, reclip, seq2idx +from specialk.models.tokenizers.bpe import Encoder as bpe_encoder + def load_args(): parser = argparse.ArgumentParser(description="train.py") diff --git a/specialk/demo/demo.py b/specialk/demo/demo.py new file mode 100755 index 0000000..92761df --- /dev/null +++ b/specialk/demo/demo.py @@ -0,0 +1,548 @@ +import argparse +import sys + +from tqdm import tqdm + +sys.path.append("../") +import torch +from googletrans import Translator as GTranslator +from lib.transformer.Translator import Translator +from lib.TransformerModel import TransformerModel as transformer +from preprocess import reclip +from train import load_args + +from specialk.core.dataset import TranslationDataset, collate_fn, paired_collate_fn +from specialk.models.tokenizers.bpe import Encoder as BPE + +googt = GTranslator() +import os +import subprocess + +import torch.nn as nn +import torch.nn.functional as F +from mosestokenizer import MosesDetokenizer +from nltk.tokenize import sent_tokenize + +detokeniser = MosesDetokenizer("en") +import sys +import textwrap + +from termcolor import colored, cprint + +sys.path.append("/home/t/Data/Files/Github/msc_project") +from Downloader import Downloader + +pub_colours = {"popular": "red", "quality": "blue"} +classes = { + "thesun": "popular", + "mirror": "popular", + "theguardian": "quality", + "thetimes": "quality", + "bbc": "quality", +} + + +def flip_class(inp): + if inp.lower() == "popular": + return "quality" + return "popular" + + +do_quality_to_popular = ( + True # if true: do quality to popular, else do popular to quality +) + + +def extract_quotes(line, split_sentences=True): + quotes, norms, sequence = [], [], [] + quote_indexes = set([i for i, x in enumerate(line) if x == '"']) + + if len(quote_indexes) < 1: + if split_sentences: + sequences = sent_tokenize(line) + else: + sequences = [line] + return { + "sequences": sequences, + "quotes": [], + "norms": [i for i in range(len(sequences))], + "equivalent": " ".join(sequences) == line, + } + + i, on_quote = 0, False + current_sequence = "" + sequences = [] + while i != len(line): + char = line[i] + if i in quote_indexes: + if not on_quote: + on_quote = True + if i != 0: + # split sequence if it consists of multiple sentences + sentences = sent_tokenize(current_sequence) + for s in sentences: + sequences.append(s) + norms.append(len(sequences) - 1) + current_sequence = "" + else: + on_quote = False + sequences.append(current_sequence + char) + i += 1 + quotes.append(len(sequences) - 1) + current_sequence = "" + continue + + current_sequence += char + i += 1 + + if len(current_sequence) > 0: + if on_quote: + sequences.append(current_sequence) + quotes.append(len(sequences) - 1) + else: + sentences = sent_tokenize(current_sequence) + for s in sentences: + sequences.append(s) + norms.append(len(sequences) - 1) + + sequences = [x.strip() for x in sequences] + + return { + "sequences": sequences, + "quotes": quotes, + "norms": norms, + "equivalent": " ".join(sequences) == line, + } + + +def parse_article(paragraphs): + # seperate quotes and lines in paragraph + parsed_p = [extract_quotes(x) for x in paragraphs] + + sequences = [] + norms = [] + quotes = [] + paragraphs = [] + + for p in parsed_p: + inc = len(sequences) + sequences += p["sequences"] + norms += [inc + i for i in p["norms"]] + quotes += [inc + i for i in p["quotes"]] + paragraphs.append(inc) + + return { + "sequences": sequences, + "quotes": quotes, + "norms": norms, + "paragraphs": paragraphs, + } + + +def dl_handler(args): + """ + single process handler for downloading an article. + It calls the outlet.get_article() function. + returns the newspaper file, and potential URLs found in the article. + """ + outlet, url = args + article = dl.outlets[outlet].get_article(url) + if article: + # we don't want the article object because we can't serialise that + # and it's unnecessary weight. + urls = dl.outlets[outlet].scrape_articles_in_urls(article) + article = dl.outlets[outlet].parse(article) + if article: + return (outlet, url, article, urls) + return (outlet, url, None, set()) + + +def save_raw_article_out(sequences, en_filename): + with open(en_filename, "w") as f: + for line in sequences: + f.write(line + "\n") + + +def return_tokenise_file(filepath, lang="en"): + # needs to be absolute filepath + cmd = "./tokenise_file.sh" if lang == "en" else "./tokenise_file_fr.sh" + l = subprocess.check_output(cmd.split(), shell=True, cwd=cwd) + + with open(filepath + ".atok") as f: + lines = [i.strip() for i in f] + return lines + + +def prettyprint(contents, overwritenorms=None): + sequences = contents["sequences"] + + quot_idx = set(contents["quotes"]) + para_idx = set(contents["paragraphs"]) + norm_idx = set(contents["norms"]) + + output = [] + + onorms_i = 0 + + with MosesDetokenizer("en") as detokenize: + for i in tqdm(range(len(sequences)), desc="Formatting"): + if i in para_idx and i != 0: + output.append("\n\n") + if i in norm_idx: + if not overwritenorms: + seq = detokenize(sequences[i].split()) + else: + seq = detokenize(overwritenorms[onorms_i].split()) + onorms_i += 1 + elif i in quot_idx: + seq = detokenize(sequences[i].split()) + output.append(seq) + + return " ".join(output) + + +def diff_filter(line, type="src"): + ob = "[-" if type != "src" else "{+" + cb = "-]" if type != "src" else "+}" + + out = "" + i = 0 + to_write = True + while i < len(line): + char = line[i] + if char == ob[-1] and line[i - 1] == ob[0]: + to_write = False + elif char == cb[-1] and line[i - 1] == cb[0]: + to_write = True + if to_write: + out += char + i += 1 + bug = "{}" if type == "src" else "[]" + return out.replace(bug, "") + + +def printdiff(seqs, srclabel, tgtlabel, block_width=50, spacing_block_len=2): + pad = " ".join(["" for _ in range(block_width)]) + src_col = pub_colours[srclabel] + tgt_col = pub_colours[tgtlabel] + src_before, src_after = [x for x in colored(" ", color=src_col).split()] + tgt_before, tgt_after = [x for x in colored(" ", color=tgt_col).split()] + + for i in range(len(seqs)): + if seqs[i] == "\n": + print() + continue + if seqs[i][0:2] == "@@": + continue + srcline, tgtline = ( + diff_filter(seqs[i].strip()), + diff_filter(seqs[i].strip(), "tgt"), + ) + left = textwrap.wrap(srcline, width=block_width) + right = textwrap.wrap(tgtline, width=block_width) + + largest_min = min([len(left), len(right)]) + largest_max = max([len(left), len(right)]) + + left_spacing = [] + for i in range(len(left)): + left_line = left[i] + + opened = False + j = 0 + left_space_diff = 0 + while j < len(left_line) - 1: + j += 1 + if left_line[j : j + 1] == "[-": + left_space_diff += 2 + if opened == False: + opened = True + j += 1 + else: + left_line = left_line + src_after + break + elif left_line[j : j + 1] == "-]": + left_space_diff += 2 + if opened == True: + opened = False + j += 1 + else: + left_line = src_before + left_line + break + + left_line_before = ( + len(left_line.replace("[-", "").replace("-]", "")) - spacing_block_len + ) + + left_line = ( + left_line.replace("[-", " " + src_before).replace("-]", src_after) + + src_after + ) + left_line = " ".join(left_line.split()) + left_line_before = ( + len(left_line.replace(src_before, "").replace(src_after, "")) + - spacing_block_len + ) + spacing = " ".join("" for _ in range(block_width - left_line_before)) + left[i] = left_line + left_spacing.append(spacing) + + for i in range(len(right)): + right_line = right[i] + opened = False + j = 0 + right_space_diff = 0 + while j < len(right_line) + 1: + if right_line[j : j + 2] == "{+": + if opened == False: + opened = True + if right_line[j : j + 2] == "+}": + if opened == True: + opened = False + j += 1 + else: + right_line = tgt_before + right_line + break + j += 1 + + right_line = ( + right_line.replace("{+", tgt_before).replace("+}", tgt_after) + + tgt_after + ) + right[i] = right_line + + left_is_largest = len(left) > len(right) + + for i in range(largest_min): + print(left[i], left_spacing[i], right[i]) + + for i in range(largest_min, largest_max): + if left_is_largest: + print(left[i]) + else: + whole_pad = pad + "".join(" " for _ in range(spacing_block_len + 2)) + print(whole_pad + right[i]) + + +def do_back_translation(test_loader, tl, encmodel, decoder): + generated_outputs = [] + count = 1 + tl.max_token_seq_len = max_seq_length + for i in tqdm(test_loader, desc="Generating Style Responses"): + a, b = i + result, _ = tl.translate_batch(a, b) + lines = encmodel.translate_decode_bpe(result, decoder) + for j in lines: + generated_outputs.append(j) + + return generated_outputs + + +def do_forward_translation(n): + # translate it into french + french_sequences = [ + googt.translate(x, src="en", dest="fr").text + for x in tqdm(n, desc="Forward Translation") + ] + + with open(fr_filename, "w") as f: + for line in french_sequences: + f.write(line.lower() + "\n") + + +def handle_git_diff(srclabel, tgtlabel): + try: + l = subprocess.check_output("./diff.sh", shell=True, cwd=cwd) + except: + pass + + # load git diff + with open(os.path.join(cwd, "diff.text")) as f: + dt = [i for i in f][5:] + + # print response + printdiff(dt, srclabel, tgtlabel) + + +max_seq_length = 100 +cwd = os.getcwd() +os.getcwd() +en_filename = os.path.join(cwd, "test_article.txt") +fr_filename = os.path.join(cwd, "test_article.txt.fr") +dump_src_filepath = os.path.join(cwd, "src_article_dump.txt") +dump_tgt_filepath = os.path.join(cwd, "tgt_article_dump.txt") +test_txt = fr_filename + +basedir = "/media/data/Files/Github/msc_project_model/base/" + +# vocabulary_path = os.path.join(basedir,"models/fren_bpe_gm_mk2_articles_mk1/gm_mk2_rebase_quality.pt") +# encoder_path = os.path.join(basedir,"models/fren_bpe_gold_master_mk2/encoder_epoch_3.chkpt") +# decoder_path = "models/fren_bpe_gm_mk2_articles_mk1/decoder_popular_epoch_3.chkpt" if do_quality_to_popular else"models/fren_bpe_gm_mk2_articles_mk1/decoder_quality_epoch_4.chkpt" +# decoder_path = os.path.join(basedir,decoder_path) + +# args = load_args() +# args.data = vocabulary_path +# args.model = "transformer" +# args.epochs = "1" +# opt = args.parse_args(["-data", args.data, "-model", args.model, "-epochs", args.epochs]) +# opt.batch_size = 1 +# opt.checkpoint_encoder = encoder_path +# opt.checkpoint_decoder = decoder_path +# opt.override_max_token_seq_len = False +# opt.cuda = True +# opt.beam_size = 5 +# opt.n_best = 1 + +# encmodel = transformer(opt) + +# # load vocabulary +# encmodel.load_dataset() + +# encmodel.load(opt.checkpoint_encoder, opt.checkpoint_decoder) + +# translator = Translator(encmodel.opt, new=False) +# translator.model = encmodel.model +# translator.model.eval() + + +def load_transformer_model(decoder_path): + args = load_args() + args.data = os.path.join( + basedir, "models/fren_bpe_gm_mk2_articles_mk1/gm_mk2_rebase_quality.pt" + ) + args.model = "transformer" + args.epochs = "1" + opt = args.parse_args( + ["-data", args.data, "-model", args.model, "-epochs", args.epochs] + ) + opt.batch_size = 1 + opt.checkpoint_encoder = os.path.join( + basedir, "models/fren_bpe_gold_master_mk2/encoder_epoch_3.chkpt" + ) + opt.checkpoint_decoder = os.path.join(basedir, decoder_path) + opt.override_max_token_seq_len = False + opt.cuda = False + opt.beam_size = 5 + opt.n_best = 1 + + encmodel = transformer(opt) + + # load vocabulary + encmodel.load_dataset() + + encmodel.load(opt.checkpoint_encoder, opt.checkpoint_decoder) + + translator = Translator(encmodel.opt, new=False) + translator.model = encmodel.model + translator.model.eval() + return translator, encmodel + + +popular_translator, popular_model = load_transformer_model( + "models/fren_bpe_gm_mk2_articles_mk1/decoder_popular_epoch_3.chkpt" +) +quality_translator, quality_model = load_transformer_model( + "models/fren_bpe_gm_mk2_articles_mk1/decoder_quality_epoch_4.chkpt" +) + +print("Loaded Transformer") + +dl = Downloader() +sites = [ + "https://www.thesun.co.uk/news/politics/", + "https://www.mirror.co.uk/news/politics/", + "https://www.theguardian.com/politics/", + "https://www.bbc.co.uk/news/politics", +] +dl.init_outlets(sites) + + +# --------------------------------------------------------------------------------- +# --------------------------------------------------------------------------------- +# --------------------------------------------------------------------------------- +# --------------------------------------------------------------------------------- + + +# testurl="https://www.theguardian.com/politics/2019/sep/01/get-ready-for-brexit-government-launches-information-blitz" +# testurl="https://www.theguardian.com/world/2019/sep/02/hong-kong-protests-calls-grow-to-give-citizens-right-to-live-and-work-in-uk" +testurl = "https://www.theguardian.com/politics/2019/sep/07/boris-johnson-could-be-jailed-for-refusing-to-seek-brexit-delay" +testurl = ( + "https://www.theguardian.com/world/2019/sep/07/hurricane-dorian-survivors-bahamas" +) +testurl = "https://www.theguardian.com/world/2019/sep/09/us-removed-covert-source-in-russia-due-to-safety-concerns-under-trump" + + +def handle_style_transfer(testurl): + print("LOADING:", testurl) + pub_label = testurl.split("/")[2] + for key in classes.keys(): + if key in pub_label: + pub_label = key + break + if pub_label not in classes: + print("ERR: cant find srcgroup in url.") + exit() + + srclabel = classes[pub_label] + tgtlabel = flip_class(srclabel) + + dl.queue = {(pub_label, testurl)} + + results = dl.batch(dl_handler, dl.queue) + + sequences = [ + x.replace("”", '"') + .replace("“", '"') + .replace("’", "'") + .replace("'", "'") + .lower() + for x in results[0][2]["content"] + if x + ] + + save_raw_article_out(sequences, en_filename) + + paragraphs = return_tokenise_file(en_filename) + + contents = parse_article(paragraphs) + norms = [contents["sequences"][i] for i in contents["norms"]] + + do_forward_translation(norms) + + fr_paragraphs = return_tokenise_file(os.path.join(cwd, fr_filename), "fr") + + # generate back-translation results + vocab = os.path.join( + basedir, "models/fren_bpe_gm_mk2_articles_mk1/gm_mk2_rebase_quality.pt" + ) + translator = quality_translator if tgtlabel == "quality" else popular_translator + encmodel = quality_model if tgtlabel == "quality" else popular_model + test_loader, max_token_seq_len, is_bpe, decoder = encmodel.load_testdata( + test_txt, vocab + ) + + generated_outputs = do_back_translation(test_loader, translator, encmodel, decoder) + # save outputs to file + output = prettyprint(contents) + gen_out = prettyprint(contents, generated_outputs) + + with open(dump_src_filepath, "w") as f: + for line in output.split("\n"): + f.write(line + "\n") + with open(dump_tgt_filepath, "w") as f: + for line in gen_out.split("\n"): + f.write(line + "\n") + + print("\n\n") + # print visualisation of git differences + handle_git_diff(srclabel, tgtlabel) + + +while True: + print("Note: don't put quotes!") + testurl = input("Provide URL (or type in `e` to exit): ") + if testurl.lower() == "e": + break + print("\n\n") + handle_style_transfer(testurl) + print("\n\n") diff --git a/specialk/demo/diff.sh b/specialk/demo/diff.sh new file mode 100755 index 0000000..09a5744 --- /dev/null +++ b/specialk/demo/diff.sh @@ -0,0 +1 @@ +git difftool --tool=vimdiff --word-diff --no-index -- src_article_dump.txt tgt_article_dump.txt > diff.text \ No newline at end of file diff --git a/specialk/demo/diff.text b/specialk/demo/diff.text new file mode 100755 index 0000000..5d1dbac --- /dev/null +++ b/specialk/demo/diff.text @@ -0,0 +1,36 @@ +diff --git a/src_article_dump.txt b/tgt_article_dump.txt +index 23d91c9..30a99ad 100755 +--- a/src_article_dump.txt ++++ b/tgt_article_dump.txt +@@ -1,31 +1,31 @@ +[-campaigners-]say {+that+} it is time to extend the right[-of abode previously denied-] to [-british-]{+stay before refusing at uk+} national[-(overseas)-] passport holders {+(especially - sea)+} + + calls for the [-uk-]{+united kingdom -+} to grant [-the-]{+people 's+} right[-of abode-] to [-people-]{+stay+} in [-hong kong-]{+hongkong+} are growing [-as-]{+than+} the political crisis in the [-former-]{+old+} british [-colony escalates.-]{+lion de degenere.+} + + [-as-]{+that 's+} fears of direct intervention [-by-]{+from+} beijing grow, veteran pro-democracy campaigners have argued that [-britain-]{+the uk+} has a {+great+} responsibility [-to protect-]{+for+} residents who [-hold the passports-]{+protect insurers before+} it issued[-ahead of-] the [-handover.-]{+transfer.+} + + several hundred [-protesters rallied outside-]{+demonstrators gathered in front of+} the british [-consulate-]{+club+} on sunday [-to demand-]{+at the request+} they receive a full british passport,[-the-] financial times reported. + + in the run-up to [-hong kong's handover in-]{+hongkong 's until+} 1997,[-britain replaced-] the {+great+} british [-dependent territories-]{+- replaced the british-owned passport+} citizens[-passports held-] by three million people [-with-]{+from+} the british national [-(overseas) passport, which grants-]{+passport - who also give+} the right to [-vote-]{+live+} in the uk, but not the right to live [-or work here.-]{+in the uk.+} a passionate campaign for holders to [-be granted-]{+grant+} the right [-of abode,-]{+to give the residence,+} particularly in light of [-1989's-]{+1989 is the+} brutal crackdown on [-tiananmen square's pro-reform-]{+tian 's anti anmen is pro-turn+} protests, [-was dismissed – earning the bn (o)-]{+has been rejected - bnom rejected+} the [-nickname-]{+o' surnom (o).+} "britain says no" . craig choy, a lawyer who[-has-] helped [-to lead-]{+run+} the campaign for [-equal-]{+human+} rights [-for bn (o) holders has-]{+(nfl) have+} compared the treatment of people [-in hong kong-]{+hongkong+} to[-the-] windrush scandal. + + [-the passports-]{+insurers+} are[-available-] only {+available+} to those who [-registered-]{+signed up+} before 1997, and holders are [-not able-]{+unable+} to pass their status to their children, but[-the-] documents can be [-renewed at any time.-]{+pre-made.+} the number of valid passports at the [-moment-]{+time+} is thought to be[-in the-] tens of thousands, but [-renewals-]{+extensions+} appear to have [-risen sharply in-]{+been heavily rising over the+} years when[-political-] tensions [-have run-]{+ran+} high. + + anson [-chan,-]{+brandon,+} formerly the second highest {+city+} official [-in the city,-]{+official,+} said the [-uk-]{+united kingdom -+} should [-consider-]{+look at+} the [-issue again:-]{+new issue:+} "you promised hong kong a high degree of autonomy and basic rights and freedoms – when those are taken away from them, surely britain has a legal and moral responsibility to deal with the consequences." + + martin lee, the veteran [-hong kong campaigner nicknamed its-]{+defender hongkong surnomme his+} "father of democracy" , {+he+} said it was all the more [-pressing-]{+urgent+} to [-revisit-]{+review+} the right of [-abode-]{+residence+} and related [-issues in-]{+questions to+} light[-of-] the protest movement and the fact that the {+fact that the+} "one country, two systems" arrangement [-which underpins-]{+that under - attracts+} the [-sino-british joint declaration was-]{+chinese-based statement - @ common british statement+} no longer [-working.-]{+works.+} + + "that's the obligation of the british government, being the only other signatory to the joint declaration," he said. + + {+tom+} last month[-tom-] tugendhat, [-chair-]{+president+} of the {+house of+} commons[-foreign affairs-] committee, said [-that britain should grant-]{+the great british bake off - is expected to give the people of+} hong kong[-citizens-] with [-bn (o) passports-]{+bnf (bel)+} full [-uk nationality.-]{+british passport.+} he told the [-ft-]{+feet+} that "a few"[-cabinet-] ministers [-were supportive of-]{+backed+} the decision. + + chris [-patten,-]{+amsterdam,+} the [-last-]{+latest+} governor of [-hong kong-]{+hongkong+} before the [-handover, unsuccessfully urged-]{+transfer, with no success in+} the [-uk-]{+united kingdom - invited the right+} to give the [-right of abode-]{+residence+} to [-all those holding-]{+anyone who owns+} british [-dependent territories passports.-]{+passport.+} + + [-the late-]{+fire+} lord ashdown, who as[-liberal democrat-] leader [-campaigned-]{+liberal democrats made up+} on the issue, said last year that [-britain was-]{+the uk great+} "urgently in need of some soul-searching enquiry about our neglect of duty towards our former colonial subjects" [-which-]{+that+} should include[-considering-] the rights [-of bn-]{+review+} (o) in [-hong kong.-]{+hongkongng.+} + + a {+spokesman for the+} government[-spokesperson-] said: "we continue to believe that the best solution for hong kong, and the british national (overseas) passport holders that live there, is full respect for the rights and freedoms guaranteed in the sino-british joint declaration." + + last year, {+the+} declassified files revealed that [-britain-]{+the great british -+} even [-pressured portugal repeatedly not-]{+suspected pressure+} to [-grant-]{+portugal several times won 't give the+} rights[-to citizens-] of [-macau-]{+liverpool citizens+} before it handed[-the region-] back to {+the back of+} china, [-fearing-]{+suggesting+} that[-it would increase-] the [-pressure to grant hong kong similar-]{+chinese region will increase its+} rights. + + [-lisbon-]{+passport+} granted [-passports,-]{+europe,+} with [-full citizenship rights,-]{+all citizenship,+} to anyone born [-before late-]{+by the end of+} 1981 and allowed {+the+} portuguese [-nationality to be passed-]{+nationals+} to {+pass+} their children. + + [-one protester told-]{+a protestaire said+} the guardian {+that+} she [-was planning-]{+planned+} to renew her [-bn-]{+bnf+} (o)[-passport-] in[-case of-] a [-crackdown by-]{+passport in+} beijing, even though she did not want to leave her [-home.-]{+house.+} she added: "i don't expect britain to give me residency, but it might help me to get somewhere else." diff --git a/base/measurements.py b/specialk/measure_dataset.py similarity index 98% rename from base/measurements.py rename to specialk/measure_dataset.py index bfd851e..42e826b 100755 --- a/base/measurements.py +++ b/specialk/measure_dataset.py @@ -13,10 +13,9 @@ import pandas as pd import spacy import torch -from core.sentenciser import * -from core.utils import batch_compute, get_len from gensim.corpora.dictionary import Dictionary -from metrics.meteor.meteor import Meteor + +# from specialk.metrics.legacy_meteor.meteor import Meteor from nltk import RegexpParser, pos_tag, word_tokenize from nltk.corpus import cmudict, stopwords from nltk.sentiment.vader import SentimentIntensityAnalyzer @@ -27,6 +26,9 @@ from rouge import Rouge from tqdm import tqdm +from specialk.core.sentenciser import * +from specialk.core.utils import batch_compute, get_len + warnings.filterwarnings("ignore") # load rouge @@ -61,7 +63,8 @@ def load_glove(self, glove_path=DEFAULT_GLOVE_PATH): """ Loads glove dataset. """ - # done some performance studies and found that this pandas method is faster than msgpack, json, and pickling. + # done some performance studies and found that this pandas + # method is faster than msgpack, json, and pickling. # also does not require creating a new file. df = pd.read_csv(glove_path, sep=" ", quoting=3, header=None, index_col=0) self.glove = {key: val.values for key, val in df.T.items()} diff --git a/specialk/measure_style_transfer.py b/specialk/measure_style_transfer.py new file mode 100755 index 0000000..7510672 --- /dev/null +++ b/specialk/measure_style_transfer.py @@ -0,0 +1,164 @@ +import argparse +import hashlib +import json +import multiprocessing +import os +import sys +from pathlib import Path +from typing import Optional, Union + +import numpy as np +import torch + +# import nlgeval +# from pyemd import emd +from gensim.models.word2vec import Word2Vec +from nltk import RegexpParser, pos_tag, word_tokenize +from nltk.corpus import cmudict, stopwords +from nltk.translate.bleu_score import sentence_bleu +from tqdm import tqdm + +import specialk.metrics.style_transfer.cnn as cnn + +# from gensim.corpora.dictionary import Dictionary +# style transfer metrics codebase +# sys.path.append('/home/t/Data/Files/Github/msc_project_model/base/') +from specialk.core.sentenciser import * +from specialk.core.utils import batch_compute, get_len, log +from specialk.metrics import Intensity, Naturalness, Preservation + +cachedir = "/home/t/Data/Datasets/msc_proj_cache/" + + +def load_args(): + parser = argparse.ArgumentParser(description="metrics.py") + # load documents + parser.add_argument( + "-src", + required=True, + type=str, + help=""" + filepath to reference text file containing + MOSES style sequences. + """, + ) + parser.add_argument( + "-tgt", + default=None, + type=str, + help=""" + filepath to text file containing hypothesis + MOSES style sequences. + """, + ) + + parser.add_argument( + "-type", + choices=["political", "publication"], + help=""" + If enabled, runs newspaper specific lexical metrics. + """, + ) + parser.add_argument( + "-style_lexicon", + default=None, + type=str, + help=""" + Path to style lexicon json. + """, + ) + parser.add_argument( + "-cache_dir", + default=cachedir, + type=str, + help=""" + Path to cache dir. + """, + ) + parser.add_argument( + "-no_cache", + default=True, + help=""" + If enabled, ignores cache dir. + """, + ) + + opt = parser.parse_args() + return opt + + +class Measurements: + def __init__(self, opt): + self.opt = opt + self.enable_cache = False + if opt.cache_dir: + self.init_cache_dir(opt.cache_dir) + # working hash represents the hash of the dataset + # we'll be working on. + self.checksum = None + + self.presevation = Preservation(self.cachedir) + self.naturalness = Naturalness(self.cachedir) + self.intensity = Intensity(self.cachedir) + + def init_cache_dir(self, cachedir: str): + """ + Initiates directory and subdirectories + for caches (a lot of this stuff is expensive to compute). + """ + self.cachedir = cachedir + + if not os.path.exists(self.cachedir): + log.info("Making cache dir:", self.cachedir) + os.makedirs(self.cachedir) + + subdirs = ["style_lexicons", "word2vec", "preservation"] + for foldername in subdirs: + path = os.path.join(self.cachedir, foldername) + os.makedirs(path, exist_ok=True) + + self.enable_cache = True + + @staticmethod + def hash(src: list[str], tgt: list[str]) -> str: + """Returns an md5 hash of the documents.""" + return str(hashlib.md5(str([x for x in zip(src, tgt)]).encode()).hexdigest()) + + +# ----------------- + + +def load_files(src: str, tgt: str): + """ + loads src and tgt files. + """ + with open(src) as a, open(tgt) as b: + out = [x for x in tqdm(zip(a, b), total=get_len(src), desc="Files")] + return [x[0] for x in out], [x[1] for x in out] + + +def express(opt): + # load files + src, tgt = load_files(opt.src, opt.tgt) + # init measurements + metrics = Measurements(opt) + metrics.checksum = metrics.hash(src, tgt) + + # calculate preservation of meaning + preservation = metrics.preservation.compute(src, tgt, metrics.checksum) + avg_preservation = np.mean(preservation) + log.info(f"Preservation: {avg_preservation}") + # calculate naturalness + naturalness = metrics.naturalness.compute(tgt, opt.type) + log.info(f"Naturalness: {naturalness}") + + # if newspaper, then we can proceed to do newspaper + # specific measurements. + if opt.type == "publication": + pass + + +if __name__ == "__main__": + args = load_args() + log.info("Loaded args", args=args) + express(args) diff --git a/specialk/metrics/__init__.py b/specialk/metrics/__init__.py new file mode 100755 index 0000000..823b99d --- /dev/null +++ b/specialk/metrics/__init__.py @@ -0,0 +1,33 @@ +from specialk.metrics.metrics import ( + BLEU, + ROUGE, + AlignmentMetric, + EarthMoverDistance, + LexicalMetrics, + Meteor, + Metric, + Polarity, + SacreBLEU, +) +from specialk.metrics.style_transfer.style_transfer import ( + Intensity, + Naturalness, + Preservation, + StyleMetric, +) + +__ALL__ = [ + Metric, + AlignmentMetric, + LexicalMetrics, + Polarity, + Meteor, + BLEU, + ROUGE, + EarthMoverDistance, + StyleMetric, + Intensity, + Naturalness, + Preservation, + SacreBLEU, +] diff --git a/base/metrics/baseline_bt/stats.pdf b/specialk/metrics/baseline_bt/stats.pdf similarity index 100% rename from base/metrics/baseline_bt/stats.pdf rename to specialk/metrics/baseline_bt/stats.pdf diff --git a/base/metrics/baseline_bt/stats_baseline_nmt.pdf b/specialk/metrics/baseline_bt/stats_baseline_nmt.pdf similarity index 100% rename from base/metrics/baseline_bt/stats_baseline_nmt.pdf rename to specialk/metrics/baseline_bt/stats_baseline_nmt.pdf diff --git a/specialk/metrics/metrics.py b/specialk/metrics/metrics.py new file mode 100644 index 0000000..d71a8cc --- /dev/null +++ b/specialk/metrics/metrics.py @@ -0,0 +1,568 @@ +""" +Various Wrapper functions for evaluation metrics, regarding text datasets. +""" + +from functools import lru_cache +from pathlib import Path +from typing import Dict, List, Tuple, Union + +import evaluate +import gensim.downloader as api +import numpy as np +from gensim.corpora.dictionary import Dictionary +from gensim.models.keyedvectors import KeyedVectors +from nltk import pos_tag, word_tokenize +from nltk.corpus import cmudict, stopwords +from nltk.sentiment.vader import SentimentIntensityAnalyzer +from nltk.translate.bleu_score import sentence_bleu +from pyemd import emd + +from specialk.core.constants import PROJECT_DIR +from specialk.core.sentenciser import find_sentences +from specialk.core.utils import log + + +class Metric: + """Metrics that measure properties of a sequence.""" + + def compute(predictions: list[str]): + raise NotImplementedError + + @lru_cache + def tokenize(self, text: str) -> List[str]: + return text.split(" ") + + +class AlignmentMetric(Metric): + """Metrics that measure alignment between two sets of sequences.""" + + def compute(predictions: list[str], references: Union[List[str], List[List[str]]]): + raise NotImplementedError + + @staticmethod + def use_first_references( + references: Union[List[str], List[List[str]]], + ) -> list[str]: + if isinstance(references[0], list): + # it's a list of lists. + log.warn( + "EMD can only consume one reference, but multiple are found." + " Using only the first reference value." + ) + new_references = [] + for reference in references: + if isinstance(reference, list): + reference = reference[0] + new_references.append(reference) + return new_references + return references + + +class LexicalMetrics(Metric): + """Lexical Measurements against datasets.""" + + def __init__(self): + self.vowels = {"a", "e", "i", "o", "u", "y"} + self.cmudict = cmudict.dict() + + def syllables(self, word: str) -> int: + """Count syllables in a given word. + + Implementation is partially based on the following link: + https://stackoverflow.com/questions/405161/detecting-syllables-in-a-word/4103234 + + Args: + word (str): word to count syllables from. + + Returns: + int: number of syllables in a word. + """ + word = word.lower() + + if word in self.cmudict: + return max( + [len(list(y for y in x if y[-1].isdigit())) for x in self.cmudict[word]] + ) + + # backup implementation, incase word is not found in cmu dictionary. + num_vowels = 0 + prev_char_is_vowel = False + for character in word: + found_vowel = False + if character in self.vowels: + if not prev_char_is_vowel: + # don't count diphthongs + num_vowels += 1 + found_vowel = prev_char_is_vowel = True + + if not found_vowel: + # If full cycle and no vowel found, + # set prev_char_is_vowel to false. + prev_char_is_vowel = False + + if len(word) > 2 and word[-2:] == "es": + # Remove es - it's "usually" silent (?) + num_vowels -= 1 + + elif len(word) > 1 and word[-1:] == "e": + # remove silent e + num_vowels -= 1 + + return num_vowels if num_vowels > 0 else 1 + + @lru_cache + def tokenize(self, text: str) -> List[str]: + """Uses NLTK's recommended word tokenizer for word tokenization. + This is because the downstream metrics will also leverage NLTK's libraries. + + Args: + text (str): string of text. + + Returns: + List[str]: Tokens of text. + """ + return word_tokenize(text) + + def pos(self, text: str) -> List[Tuple[str, str]]: + """Get POS Tags for given text. + + Use NLTK's currently recommended part of speech tagger to + tag the given list of tokens. + + Args: + text (str): _description_ + + Returns: + List[Tuple[str, str]]: _description_ + + Example: + >>> lexical_metric.pos("John's big idea isn't all that bad.") + + [('John', 'NOUN'), ("'s", 'PRT'), ('big', 'ADJ'), ('idea', 'NOUN'), ('is', 'VERB'), + ("n't", 'ADV'), ('all', 'DET'), ('that', 'DET'), ('bad', 'ADJ'), ('.', '.')] + """ + return pos_tag(self.tokenize(text)) + + def lex_match_1(self, tokens: List[Tuple[str, str]]): + """ + finds ``it v-link ADJ finite/non-finite clause'' + + eg: + "It's uncear what Teresa May is planning." + + params: + tokens: pos tagged sequence (e.g. `pos_tag(word_tokenize(string_of_article))` ) + returns: + matches: None if nothing is found, + [(match pairs)] otherwise. + """ + + index_limit = len(tokens) + index = 0 + matches = [] + while index < index_limit: + token, tag = tokens[index] + + if token.lower() == "it": + if index + 2 < index_limit: + if tokens[index + 1][1][0] == "V": + if tokens[index + 2][1][0] == "J": + group = tuple([str(tokens[index + i][0]) for i in range(3)]) + matches.append((index, group)) + index = index + 2 + else: + break + index += 1 + return matches + + def lex_match_2(self, tokens: List[Tuple[str, str]]): + """ + finds ``v-link ADJ prep'' + + eg: + "..he was responsible for all for.." + + params: + tokens: pos tagged sequence (e.g. `pos_tag(word_tokenize(string_of_article))` ) + returns: + matches: None if nothing is found, + [(match pairs)] otherwise. + """ + index_limit = len(tokens) + index = 0 + matches = [] + while index < index_limit: + token, tag = tokens[index] + + if tag[0] == "V": + group = [token] + next_index = index + 1 + # detect any adverbs before adj and adp. + # e.g. "be *very, very,* embarrassing.." + while (next_index < index_limit) and (tokens[next_index][1][0] == "R"): + group.append(tokens[next_index]) + next_index += 1 + + if next_index + 1 < index_limit: + if ( + tokens[next_index][1][0] == "J" + and tokens[next_index + 1][1] == "IN" + ): + group.append(tokens[next_index][0]) + group.append(tokens[next_index + 1][0]) + matches.append((index, tuple(group))) + index = next_index + 2 + index += 1 + return matches + + def readability(self, article: str) -> Dict[str, Union[float, int]]: + """Calculate readability scores from various metrics. + + Args: + article (str): Block of text. + tokenised (bool, optional): _description_. Defaults to False. + + Returns: + Dict[str, Union[float, int]]: integer values are in the range of [0,100]. + """ + sentences: List[str] = find_sentences(article) + n_sentences, n_words, n_syllables, n_characters = 0, 0, 0, 0 + + for sentence in sentences: + n_sentences += 1 + for word in self.tokenize(sentence): + num_syllables = self.syllables(word.lower()) + if num_syllables > 0: + n_words += 1 + n_syllables += num_syllables + n_characters += len(word) + + READING_EASE = ( + 206.835 - 1.015 * (n_words / n_sentences) - 84.6 * (n_syllables / n_words) + ) + GRADE_LEVEL = ( + 0.39 * (n_words / n_sentences) + 11.8 * (n_syllables / n_words) - 15.59 + ) + COLEMAN_LIAU = ( + 5.89 * (n_characters / n_words) - 0.3 * (n_sentences / n_words) - 15.8 + ) + # automated readability index + ARI = 4.71 * (n_characters / n_words) + 0.5 * (n_words / n_sentences) - 21.43 + + return { + "reading_ease": READING_EASE, + "grade_level": GRADE_LEVEL, + "coleman_liau": COLEMAN_LIAU, + "ari": ARI, + } + + def basic_stats(self, article: List[str]) -> dict[str, float]: + """Calculate basic statistics about an article. + + Args: + paragraphs (List[str]): List of paragraphs, each are strings. + + Returns: + dict[str, float]: various measurements against the dataset. + """ + tokens: list[str] = [] + sentence_lengths: list[int] = [] + num_tokens_in_paragraph: list[int] = [] + sentence: str + token: str + + for paragraph in article: + paragraph_tokens = [] + + for sentence in find_sentences(paragraph): + sentence_lengths.append(len(sentence)) + for token in self.tokenize(sentence): + paragraph_tokens.append(token) + tokens = tokens + paragraph_tokens + num_tokens_in_paragraph.append(len(paragraph_tokens)) + + num_tokens = len(tokens) + num_paragraphs = len(article) + num_sentences = len(sentence_lengths) + + avg_token_length = ( + sum([len(t) for t in tokens]) / num_tokens if num_tokens > 0 else 0 + ) + avg_sentence_length = sum(sentence_lengths) / num_sentences + avg_paragraph_length = sum(num_tokens_in_paragraph) / len(article) + + return { + "num_tokens": num_tokens, + "num_paragraphs": num_paragraphs, + "num_sentences": num_sentences, + "avg_token_length": avg_token_length, + "avg_sentence_length": avg_sentence_length, + "avg_paragraph_length": avg_paragraph_length, + "sentence_lengths": sentence_lengths, + } + + +class Polarity(Metric): + def __init__(self): + self.polarity = SentimentIntensityAnalyzer().polarity_scores + + def compute(self, text: str) -> float: + """Measure polarity of a given document. + + Return a float for sentiment strength based on the input text. + Positive values are positive valence, negative value are negative + valence. + + Hashtags are not taken into consideration (e.g. #BAD is neutral). If you + are interested in processing the text in the hashtags too, then we recommend + preprocessing your data to remove the #, after which the hashtag text may be + matched as if it was a normal word in the sentence. + + Args: + text (str): text to measure polarity against. + + Returns: + float: Polarity score. + """ + return self.polarity(text) + + +class Meteor(AlignmentMetric): + def __init__(self): + self.meteor = evaluate.load("meteor") + + def compute( + self, + predictions: List[str], + references: Union[List[str], List[List[str]]], + alpha=0.9, + beta=3, + gamma=0.5, + ) -> float: + """Calculate METEOR score. + + Args: + predictions (List[str]): _description_ + references (Union[List[str], List[List[str]]]): _description_ + alpha (float, optional): _description_. Defaults to 0.9. + beta (int, optional): _description_. Defaults to 3. + gamma (float, optional): _description_. Defaults to 0.5. + + Returns: + List[int]: _description_ + """ + return self.meteor.compute( + predictions=predictions, + references=references, + alpha=alpha, + beta=beta, + gamma=gamma, + )["meteor"] + + +class BLEU(AlignmentMetric): + @staticmethod + def compute( + predictions: List[str], + references: Union[List[str], List[List[str]]], + ) -> Dict[str, float]: + bleu1 = sentence_bleu(references, [predictions], weights=(1, 0, 0, 0)) + bleu2 = sentence_bleu(references, [predictions], weights=(0, 1, 0, 0)) + bleu3 = sentence_bleu(references, [predictions], weights=(0, 0, 1, 0)) + bleu4 = sentence_bleu(references, [predictions], weights=(0, 0, 0, 1)) + + return {"bleu_1": bleu1, "bleu_2": bleu2, "bleu_3": bleu3, "bleu_4": bleu4} + + +class SacreBLEU(AlignmentMetric): + def __init__(self): + self.bleu = evaluate.load("sacrebleu") + + def compute( + self, + predictions: List[str], + references: Union[List[str], List[List[str]]], + ) -> float: + return self.bleu.compute(predictions=predictions, references=references)[ + "score" + ] + + +class ChrF(AlignmentMetric): + def __init__(self): + self.chrf = evaluate.load("chrf") + + def compute( + self, + predictions: List[str], + references: Union[List[str], List[List[str]]], + ) -> float: + return self.chrf.compute(predictions=predictions, references=references)[ + "score" + ] + + +class ROUGE(AlignmentMetric): + def __init__(self): + self.rouge = evaluate.load("rouge") + + def compute( + self, + predictions: List[str], + references: Union[List[str], List[List[str]]], + ) -> dict[str, float]: + """Computes RougeL score. + + Args: + predictions (List[str]): list of text generated by some model. + references (Union[List[str], List[List[str]]]): list of text to compare to. + + Returns: + dict[str, float]: {'rouge1', 'rouge2', 'rougeL', 'rougeLsum'} values. + """ + return self.rouge.compute(predictions=predictions, references=references)[ + "rougeLsum" + ] + + +class EarthMoverDistance(AlignmentMetric): + def __init__(self): + self.stopwords = set(stopwords.words("english")) + self.glove: KeyedVectors = self.load_glove() + + def load_glove(self) -> KeyedVectors: + """Load GloVe Word2Vec. + + Checks if the embeddings are already present in the cache_dir. + + Note that gensim.api.downloader downloads a text form of the + embeddings, which takes a minute to load every time. + + We save a binary form of this model, which is an + order of magnitude quicker to load than the text form. + + Returns: + KeyedVectors: GloVe Embeddings. + """ + cache_dir: Path = PROJECT_DIR / "cache" / "embeddings" + path_w2v: Path = cache_dir / "w2v.bin" + + log.info("Loading GloVe Vectors") + if path_w2v.exists(): + model = KeyedVectors.load_word2vec_format(path_w2v, binary=True) + log.info("Loaded GloVe Vectors from cache.", path=str(path_w2v)) + else: + # download glove from the internet, cache it as a binary. + model: KeyedVectors = api.load("glove-twitter-200") + cache_dir.mkdir(exist_ok=True) + model.save(path_w2v, binary=True) + log.info("Created GloVe cache.", path=str(path_w2v)) + return model + + @lru_cache + def tokenize(self, text: str) -> List[str]: + return word_tokenize(text) + + def preprocess(self, tokens: List[str]) -> List[str]: + """Run various preprocessing metrics. + + Removes stopwords, OOV. + + Args: + tokens (List[str]): List of tokens. + + Returns: + List[str]: Filtered list of tokens. + """ + new_tokens = [] + for token in tokens: + token = token.lower() + if token in self.stopwords: + continue + if token not in self.glove: + continue + new_tokens.append(token) + return new_tokens + + @staticmethod + def nbow(document: List[str], dictionary: Dictionary) -> np.ndarray: + """Create nBOW representation. + + Args: + document (List[str]): tokenized document. + dictionary (Dictionary): dictionary containing tokens from both src + and target tokens. + + Returns: + np.ndarray: vector representation of nBOW of the document. + """ + + vocab_len = len(dictionary) + d = np.zeros(vocab_len, dtype=np.double) + # Word frequencies. + nbow = dictionary.doc2bow(document) + doc_len = len(document) + + for idx, freq in nbow: + # Normalize word frequencies. + d[idx] = freq / float(doc_len) + return d + + def compute(self, prediction: str, references: Union[str, List[str]]) -> float: + """Calculates earth mover distances between two strings. + + based on https://github.com/RaRe-Technologies/gensim/blob/ + 18bcd113fd5ed31294a24c7274fcb95df001f88a/gensim/models/keyedvectors.py + If pyemd C extension is available, import it. + If pyemd is attempted to be used, but isn't installed, + ImportError will be raised in wmdistance. + + Args: + predictions (List[str]): _description_ + references (Union[List[str], List[List[str]]]): _description_ + + Returns: + float: EarthMoversDistance, in the range of [0, inf]. + """ + references = self.use_first_references(references) + + prediction = self.preprocess(self.tokenize(prediction)) + references = self.preprocess(self.tokenize(references)) + + if not prediction or not references: + # at least one of the documents had no tokens that + # were in the vocabulary. + return float("inf") + + dictionary = Dictionary(documents=[prediction, references]) + vocab_len = len(dictionary) + + if vocab_len == 1: + # Both documents are composed by a single unique token. + return 0.0 + + # Sets for faster look-up. + set_pred, set_refs = set(prediction), set(references) + + # Compute distance matrix. + distance_matrix = np.zeros((vocab_len, vocab_len), dtype=np.double) + for i, t1 in dictionary.items(): + if t1 not in set_pred: + continue + for j, t2 in dictionary.items(): + if t2 not in set_refs or distance_matrix[i, j] != 0.0: + continue + # Compute Euclidean distance between word vectors. + distance_matrix[i, j] = distance_matrix[j, i] = np.sqrt( + np.sum((self.glove[t1] - self.glove[t2]) ** 2) + ) + + if np.sum(distance_matrix) == 0.0: + # `emd` gets stuck if the distance matrix contains only zeros. + return float("inf") + + # Compute nBOW representation of documents. + d1, d2 = self.nbow(prediction, dictionary), self.nbow(references, dictionary) + + # Compute WMD. + return emd(d1, d2, distance_matrix) diff --git a/base/metrics/__init__.py b/specialk/metrics/style_transfer/__init__.py similarity index 100% rename from base/metrics/__init__.py rename to specialk/metrics/style_transfer/__init__.py diff --git a/base/metrics/style_transfer/cnn.py b/specialk/metrics/style_transfer/cnn.py similarity index 85% rename from base/metrics/style_transfer/cnn.py rename to specialk/metrics/style_transfer/cnn.py index b48eaee..5ed1314 100755 --- a/base/metrics/style_transfer/cnn.py +++ b/specialk/metrics/style_transfer/cnn.py @@ -6,15 +6,9 @@ import torch from tqdm import tqdm -# import CNN classifier code. -if __name__ == "__main__": - sys.path.append("../../classifier") - CNN_MODELS_DIR = "../cnn_models" - import onmt -else: - sys.path.append("classifier") - CNN_MODELS_DIR = "metrics/cnn_models" - import classifier.onmt as onmt +import specialk.models.classifier.onmt as onmt + +CNN_MODELS_DIR = "metrics/cnn_models" NATURALNESS_DEFAULTS = { "political": os.path.join(CNN_MODELS_DIR, "naturalness_political.pt"), @@ -47,7 +41,7 @@ def setup_args(category, src): opt.max_sent_length = 50 # is there a gpu? - opt.cuda = True + opt.cuda = False if opt.cuda: torch.cuda.set_device(0) return opt diff --git a/base/metrics/style_transfer/content_preservation.py b/specialk/metrics/style_transfer/content_preservation.py similarity index 97% rename from base/metrics/style_transfer/content_preservation.py rename to specialk/metrics/style_transfer/content_preservation.py index e83491f..abdf58d 100755 --- a/base/metrics/style_transfer/content_preservation.py +++ b/specialk/metrics/style_transfer/content_preservation.py @@ -20,9 +20,9 @@ from gensim.models.word2vec import Word2Vec # from globals import MODEL_TO_PARAMS, MODEL_TO_PARAM_NAMES -from metrics.style_transfer.style_lexicon import load_lexicon -from metrics.style_transfer.tokenizer import tokenize -from metrics.style_transfer.utils import ( +from specialk.metrics.style_transfer.style_lexicon import load_lexicon +from specialk.metrics.style_transfer.tokenizer import tokenize +from specialk.metrics.style_transfer.utils import ( calculate_correlations, get_val_as_str, load_dataset, diff --git a/base/metrics/style_transfer/globals.py b/specialk/metrics/style_transfer/globals.py similarity index 100% rename from base/metrics/style_transfer/globals.py rename to specialk/metrics/style_transfer/globals.py diff --git a/base/metrics/style_transfer/naturalness.py b/specialk/metrics/style_transfer/naturalness.py similarity index 100% rename from base/metrics/style_transfer/naturalness.py rename to specialk/metrics/style_transfer/naturalness.py diff --git a/specialk/metrics/style_transfer/readme.md b/specialk/metrics/style_transfer/readme.md new file mode 100644 index 0000000..0d5b0ce --- /dev/null +++ b/specialk/metrics/style_transfer/readme.md @@ -0,0 +1 @@ +Thiis largely sourced from https://github.com/passeul/style-transfer-model-evaluation/tree/master/code. \ No newline at end of file diff --git a/base/metrics/style_transfer/style_lexicon.py b/specialk/metrics/style_transfer/style_lexicon.py similarity index 97% rename from base/metrics/style_transfer/style_lexicon.py rename to specialk/metrics/style_transfer/style_lexicon.py index 37b7b68..0c2c36a 100755 --- a/base/metrics/style_transfer/style_lexicon.py +++ b/specialk/metrics/style_transfer/style_lexicon.py @@ -15,8 +15,11 @@ from operator import itemgetter import numpy as np -from metrics.style_transfer.tokenizer import tokenize -from metrics.style_transfer.utils import ( +from sklearn.feature_extraction.text import CountVectorizer +from sklearn.linear_model import LogisticRegression + +from specialk.metrics.style_transfer.tokenizer import tokenize +from specialk.metrics.style_transfer.utils import ( invert_dict, load_json, load_model, @@ -24,8 +27,6 @@ save_json, save_model, ) -from sklearn.feature_extraction.text import CountVectorizer -from sklearn.linear_model import LogisticRegression DATA_VECTORIZER_PATH = "../models/vectorizer.pkl" @@ -76,7 +77,7 @@ def train(regularization_type, C, vec_x_train, y_train): """ - lr = LogisticRegression(penalty=regularization_type, C=C) + lr = LogisticRegression(penalty=regularization_type, C=C, solver="liblinear") lr.fit(vec_x_train, y_train) return lr diff --git a/specialk/metrics/style_transfer/style_transfer.py b/specialk/metrics/style_transfer/style_transfer.py new file mode 100644 index 0000000..6152ebd --- /dev/null +++ b/specialk/metrics/style_transfer/style_transfer.py @@ -0,0 +1,230 @@ +""" +Various Wrapper functions for evaluation metrics, regarding text datasets. +""" + +from pathlib import Path +from typing import List, Optional, Union + +import gensim +from gensim.models.word2vec import Word2Vec +from tqdm import tqdm + +import specialk.metrics.style_transfer.cnn as cnn +import specialk.metrics.style_transfer.content_preservation as preserv +from specialk.core import log +from specialk.core.constants import PROJECT_DIR +from specialk.core.utils import hash +from specialk.metrics import AlignmentMetric, Metric +from specialk.models.classifier.models import CNNClassifier +from specialk.models.style_lexicon import StyleLexicon +from specialk.models.tokenizer import ( + BPEVocabulary, + SentencePieceVocabulary, + Vocabulary, + WordVocabulary, +) + +PATH_CLASSIFIER = PROJECT_DIR / "assets/classifiers/legacy/cnn_classifier/" + + +class StyleMetric(Metric): + """Umbrella object representing style-transfer associated metrics.""" + + pass + + +class Intensity(StyleMetric, AlignmentMetric): + def __init__(self, category: str): + self.category = category + assert self.category in {"political", "publication"} + + if category == "political": + category = "adversarial_political" + elif category == "publication": + category = "adversarial_publication" + + path_checkpoint = PATH_CLASSIFIER / category / f"{category}.ckpt" + path_hyperparams = PATH_CLASSIFIER / category / f"hyperparameters.yaml" + path_tok = PATH_CLASSIFIER / category / f"tokenizer" + + self.module = CNNClassifier.load_from_checkpoint( + path_checkpoint, hparams_file=path_hyperparams + ) + self.module.tokenizer = WordVocabulary.from_file(path_tok) + + def compute(self, text: str) -> float: + """Compute intensity score of given text. + + This runs a adversarial CNN that discriminates + between political or publication classes. + + Args: + text (str): document to perform classification against. + + Returns: + float: value predicted by the model. + """ + return self.module.generate(text).mean() + + +class Naturalness(StyleMetric): + def __init__(self, category: str): + assert category in {"political", "publication"} + self.category = category + + if category == "political": + category = "naturalness_political" + elif category == "publication": + raise Exception("Model not implemented for naturalness for publication.") + + path_checkpoint = PATH_CLASSIFIER / category / f"{category}.ckpt" + path_hyperparams = PATH_CLASSIFIER / category / f"hyperparameters.yaml" + path_tok = PATH_CLASSIFIER / category / f"tokenizer" + + self.module = CNNClassifier.load_from_checkpoint( + path_checkpoint, hparams_file=path_hyperparams + ) + self.module.tokenizer = WordVocabulary.from_file(path_tok) + + def compute(self, text: str) -> float: + """Compute naturalness score of given text. + + This runs a adversarial CNN that discriminates between fake + (computer generated) and real (written by humans) texts. + + Args: + text (str): document to perform classification against. + + Returns: + float: value predicted by the model. + """ + return self.module.generate(text).mean() + + +class Preservation(StyleMetric, AlignmentMetric): + """To use, init a style lexicon of the whole corpora, and then + use that for test cases. + """ + + def __init__( + self, + dir_cache: Union[Path, str], + dir_w2v: Optional[Union[Path, str]] = None, + path_lexicon: Optional[Union[Path, str]] = None, + enable_cache: bool = True, + ): + if isinstance(dir_cache, str): + dir_cache = Path(dir_cache) + if isinstance(dir_w2v, str): + dir_w2v = Path(dir_w2v) + if isinstance(path_lexicon, str): + path_lexicon = Path(path_lexicon) + + self.cache_dir = dir_cache + self.dir_w2v: Path = dir_w2v if dir_w2v else self.cache_dir / "embeddings" + self.dir_lexicon: Path = ( + path_lexicon if path_lexicon else self.cache_dir / "style_lexicons" + ) + + self.style_lexicon = StyleLexicon() + self.w2v: Word2Vec + self.enable_cache = enable_cache + + @staticmethod + def word_movers_distance( + prediction: str, + reference: str, + word2vec: Union[ + gensim.models.Word2Vec, gensim.models.keyedvectors.KeyedVectors + ], + ) -> float: + """Calculates Word Mover's Distance. + + Args: + prediction (str): Prediction text. + reference (str): Reference text to compare to. + word2vec (Union[gensim.models.Word2Vec, + gensim.models.keyedvectors.KeyedVectors]): + Vector representation of tokens to use for WMD. + + Returns: + float: WMD score. + """ + return word2vec.wv.wmdistance(prediction, reference) + + def init_style_lexicon( + self, style_1: list[str], style_2: List[str], checksum: Optional[str] = None + ): + """Learn Style lexicon given the predictions and references. + + Args: + prediction (str): Prediction text. + reference (str): Reference text to compare to. + checksum (Optional[str]): checksum representing the training data. + If a checksum is passed in, then we'll load that dataset. + Otherwise, we'll make a dataset, and save it. + """ + + # init style lexicon + checksum = hash(style_1, style_2) + + path_style_lexicon: Path = self.dir_lexicon / checksum + if path_style_lexicon.exists(): + self.style_lexicon = StyleLexicon.from_json(path_style_lexicon) + else: + self.style_lexicon.create(style_1, style_2) + if self.enable_cache: + self.style_lexicon.save(path_style_lexicon) + return checksum + + def init_w2v(self, checksum: str, predictions: list[str], references: List[str]): + """Creates Word2Vec embedding.""" + path_w2v = self.dir_w2v / checksum + path_w2v_str = str(path_w2v) + if path_w2v.exists(): + log.info("Loading Word2Vec", path=path_w2v_str) + self.w2v: Word2Vec = Word2Vec.load(path_w2v_str) + else: + self.w2v = Word2Vec(predictions + references) + if self.enable_cache: + self.w2v.save(path_w2v_str) + + def compute( + self, + predictions: list[str], + references: List[str], + checksum: Optional[str] = None, + ) -> List[float]: + """Calculate preservation of meaning from style transfer. + + Compute preservation (Masked WMD) score between + the source and target sequences. + + Args: + predictions (list[str]): _description_ + references (Union[List[str], List[List[str]]]): _description_ + + Raises: + NotImplementedError: _description_ + """ + if not checksum: + checksum = hash(predictions, references) + + self.init_style_lexicon(predictions, references, checksum=checksum) + + # mask input and output texts + src_masked = preserv.mark_style_words( + predictions, style_tokens=self.style_lexicon.lexicon, mask_style=True + ) + tgt_masked = preserv.mark_style_words( + references, style_tokens=self.style_lexicon.lexicon, mask_style=True + ) + + # init w2v + self.init_w2v(checksum, src_masked, tgt_masked) + + # calculate wmd scores + return [ + self.word_movers_distance(src, tgt, self.w2v) + for src, tgt in tqdm(zip(src_masked, tgt_masked)) + ] diff --git a/base/metrics/style_transfer/style_transfer_intensity.py b/specialk/metrics/style_transfer/style_transfer_intensity.py similarity index 100% rename from base/metrics/style_transfer/style_transfer_intensity.py rename to specialk/metrics/style_transfer/style_transfer_intensity.py diff --git a/base/metrics/style_transfer/tokenizer.py b/specialk/metrics/style_transfer/tokenizer.py similarity index 100% rename from base/metrics/style_transfer/tokenizer.py rename to specialk/metrics/style_transfer/tokenizer.py diff --git a/base/metrics/style_transfer/tradeoff_plots.py b/specialk/metrics/style_transfer/tradeoff_plots.py similarity index 100% rename from base/metrics/style_transfer/tradeoff_plots.py rename to specialk/metrics/style_transfer/tradeoff_plots.py diff --git a/base/metrics/style_transfer/utils.py b/specialk/metrics/style_transfer/utils.py similarity index 97% rename from base/metrics/style_transfer/utils.py rename to specialk/metrics/style_transfer/utils.py index a414ea7..ae2f3cc 100755 --- a/base/metrics/style_transfer/utils.py +++ b/specialk/metrics/style_transfer/utils.py @@ -1,10 +1,12 @@ import json import math +from typing import List +# from sklearn.externals import joblib +import joblib import numpy as np import pandas from scipy.stats import linregress -from sklearn.externals import joblib # hyperparameter settings for each examined style transfer model MODEL_TO_PARAMS = { @@ -44,7 +46,7 @@ def compile_binary_dataset(negative_samples, positive_samples): return x, y -def load_dataset(path): +def load_dataset(path: str) -> List[str]: data = [] with open(path) as f: for line in f: diff --git a/specialk/migrate/migrate_classifiers.py b/specialk/migrate/migrate_classifiers.py new file mode 100644 index 0000000..d445fd5 --- /dev/null +++ b/specialk/migrate/migrate_classifiers.py @@ -0,0 +1,157 @@ +import os +import sys +from pathlib import Path + +import lightning.pytorch as pl +import torch +from lightning.pytorch.loggers import TensorBoardLogger +from torch.utils.data import DataLoader + +from specialk.core import log +from specialk.core.constants import LOGGING_DIR +from specialk.core.utils import namespace_to_dict, save_dict_to_yaml +from specialk.models.classifier.onmt import CNNModels +from specialk.models.classifier.trainer import CNNClassifier +from specialk.models.tokenizer import WordVocabulary + +# Add the directory containing onmt to sys.path +onmt_dir = os.path.dirname("/Users/t/Projects/specialk/specialk/onmt") +sys.path.append(onmt_dir) + +import onmt + + +def migrate_tokenizer(vocab, name): + d = {} + d["vocab"] = { + "idxToLabel": vocab.idxToLabel, + "labelToIdx": vocab.labelToIdx, + "frequencies": vocab.frequencies, + "special": vocab.special, + } + d["class"] = "WordVocabulary" + d["kwargs"] = { + "lower": True, + "max_length": vocab.seq_length, + "name": name, + "vocab_size": len(vocab.idxToLabel), + } + return WordVocabulary.from_dict(d) + + +def migrate_model(component, tokenizer, name): + model = component["model"] + vocab_size, word_vec_size = model["word_lut.weight"].shape + + opt = component["opt"] + + # load model. + new_model = CNNModels.ConvNet( + vocab_size=vocab_size, + num_filters=opt.num_filters, + num_classes=opt.num_classes, + filter_size=opt.filter_size, + word_vec_size=opt.word_vec_size, + sequence_length=opt.sequence_length, + ) + + if model["word_lut.weight"].shape == new_model.word_lut.weight.shape: + new_model.word_lut.weight.data = model["word_lut.weight"] + else: + print("???") + + if model["conv1.weight"].shape == new_model.conv.weight.shape: + new_model.conv.weight.data = model["conv1.weight"] + new_model.conv.bias.data = model["conv1.bias"] + else: + print("???") + + if model["linear.weight"].shape == new_model.linear.weight.shape: + new_model.linear.weight.data = model["linear.weight"] + new_model.linear.bias.data = model["linear.bias"] + else: + print("???") + + # copy over model + module = CNNClassifier( + name, + vocabulary_size=vocab_size, + sequence_length=opt.sequence_length, + tokenizer=tokenizer, + ) + + module.model = new_model + module.save_hyperparameters(ignore="ipython_dir") + + return module + + +def migrate_asset(component, name): + vocab = component["dicts"]["src"] + tokenizer = migrate_tokenizer(vocab, name) + + model = migrate_model(component, tokenizer, name) + return tokenizer, model + + +def deal_with_components(path: Path, tgt_dir: Path): + name = path.name.split(".")[0] + log.info("loading path", path=path, name=name) + component = torch.load( + path, + map_location=torch.device("cpu"), + ) + VOC, new_model = migrate_asset(component, name) + text = VOC.to_tensor(["Donald Trump!!!", "obama rules"]) + log.info( + "generated output", out=new_model.model(new_model.one_hot(text)), input=text + ) + + # dummy trainer so we can save checkpoint (it's not a checkpoint) + # but it's easier than writing all this padding to save the model + # and the packaged vocab. + hyperparams = { + **namespace_to_dict(component["opt"]), + "name": name, + "vocabulary_size": new_model.vocabulary_size, + "sequence_length": new_model.sequence_length, + } + + log.info("hyperparams", params=hyperparams) + + logger = TensorBoardLogger(LOGGING_DIR) + logger.log_hyperparams(params=hyperparams) + trainer = pl.Trainer(max_epochs=0, logger=logger) + dataloader = DataLoader([]) + trainer.fit(new_model, train_dataloaders=dataloader) + + tgt_path = tgt_dir / name / f"{name}.ckpt" + tgt_hpram_path = tgt_dir / name / f"hyperparameters.yaml" + tgt_tok_path = tgt_dir / name / f"tokenizer" + + trainer.save_checkpoint(tgt_path) + log.info(f"saved asset to {tgt_path}") + save_dict_to_yaml(hyperparams, tgt_hpram_path) + + VOC.to_file(tgt_tok_path) + + log.info("attempting to load new model", tgt_path=tgt_path) + model2 = CNNClassifier.load_from_checkpoint(tgt_path, **hyperparams) + log.info("loaded new model") + log.info("new_output", out=model2.model(model2.one_hot(text))) + log.info("we're good.") + print("\n\n\n") + + +if __name__ == "__main__": + src_dir = Path("/Users/t/Projects/specialk/specialk/metrics/cnn_models") + tgt_dir = Path( + "/Users/t/Projects/specialk/assets/classifiers/legacy/cnn_classifier/" + ) + tgt_dir.mkdir(exist_ok=True) + for filename in [ + "adversarial_political.pt", + "adversarial_publication.pt", + "naturalness_political.pt", + ]: + deal_with_components(src_dir / filename, tgt_dir) diff --git a/base/train.py b/specialk/migrate/train.py similarity index 57% rename from base/train.py rename to specialk/migrate/train.py index ece689f..c442b09 100755 --- a/base/train.py +++ b/specialk/migrate/train.py @@ -8,10 +8,12 @@ import torch import torch.nn as nn -from lib.RecurrentModel import RecurrentModel as recurrent -from lib.TransformerModel import TransformerModel as transformer from tqdm import tqdm +from specialk.models.nmt_model import NMTModel +from specialk.models.legacy.recurrent_model import RecurrentModel as recurrent +from specialk.models.legacy.transformer_model import TransformerModel as transformer + def load_args(): parser = argparse.ArgumentParser(description="train.py") @@ -29,69 +31,52 @@ def load_args(): "-checkpoint_encoder", default="", type=str, - help=""" - If training from a checkpoint, then this is the path to the pretrained encoder. - """, + help=""" If training from a checkpoint, then this is the path to the pretrained encoder.""", ) parser.add_argument( "-checkpoint_decoder", default="", type=str, - help=""" - If training from a checkpoint, then this is the path to the pretrained model. - """, + help="""If training from a checkpoint, then this is the path to the pretrained model.""", ) parser.add_argument( "-new_directory", action="store_true", - help=""" - If enabled, creates a new directory instead of using the directory where the encoder is loaded. (Assumes that the checkpoint_encoder flag is activated.) - """, + help="""If enabled, creates a new directory instead of using the directory where the encoder is loaded. (Assumes that the checkpoint_encoder flag is activated.)""", ) parser.add_argument( "-log", action="store_true", - help=""" - Determines whether to enable logs, which will save status into text files. - """, + help="""Determines whether to enable logs, which will save status into text files.""", ) parser.add_argument( "-directory_name", type=str, default="", - help=""" - Name of directory. If set, then it'll use that name instead. - Otherwise it'll generate one based on the timestamp. - """, + help="""Name of directory. If set, then it'll use that name instead. Otherwise it'll generate one based on the timestamp.""", ) parser.add_argument( "-save_model", action="store_true", - help=""" - Determines whether to save the model or not. - """, + help="""Determines whether to save the model or not.""", ) parser.add_argument( "-save_mode", default="all", choices=["all", "best"], - help=""" - Determines whether to save all versions of the model or keep the best version. - """, + help="""Determines whether to save all versions of the model or keep the best version.""", ) parser.add_argument( "-verbose", action="store_true", - help=""" - If enabled, prints messages to terminal. - """, + help="""If enabled, prints messages to terminal.""", ) # model options. @@ -99,60 +84,46 @@ def load_args(): "-model", choices=["transformer", "recurrent"], required=True, - help=""" - Either a recurrent (seq2seq model) or a transformer. - """, + help="""Either a recurrent (seq2seq model) or a transformer.""", ) parser.add_argument( "-cuda", action="store_true", - help=""" - Determines whether to use CUDA or not. (You should.) - """, + help="""Determines whether to use CUDA or not. (You should.)""", ) parser.add_argument( "-multi_gpu", action="store_true", - help=""" - Determines whether to use multiple GPUs. - """, + help="""Determines whether to use multiple GPUs.""", ) parser.add_argument( "-cuda_device", type=int, - help=""" - Determines which GPU to use for computation. - """, + help="""Determines which GPU to use for computation.""", ) parser.add_argument( "-batch_size", type=int, default=64, - help=""" - Determines batch size of input data, for feeding into the models. - """, + help="""Determines batch size of input data, for feeding into the models.""", ) parser.add_argument( "-layers", type=int, default=6, - help=""" - Number of layers for the model. (Recommended to have 6 for Transformer, 2 for recurrent.) - """, + help="""Number of layers for the model. (Recommended to have 6 for Transformer, 2 for recurrent.)""", ) parser.add_argument( "-d_word_vec", type=int, default=300, - help=""" - Dimension size of the token vectors representing words (or characters, or bytes). - """, + help="""Dimension size of the token vectors representing words (or characters, or bytes).""", ) # training options @@ -161,21 +132,14 @@ def load_args(): type=int, required=True, default=10, - help=""" - Number of epochs for training. (Note - that for transformers, the number of - sequences become considerably longer.) - """, + help="""Number of epochs for training. (Note that for transformers, the number of sequences become considerably longer.)""", ) parser.add_argument( "-dropout", type=float, default=0.1, - help=""" - Dropout probability' applied between - self-attention layers/RNN Stacks. - """, + help="""Dropout probability' applied between self-attention layers/RNN Stacks.""", ) # debugging options @@ -183,10 +147,7 @@ def load_args(): "-telegram", type=str, default="", - help=""" - filepath to telegram API private key - and chatID to send messages to. - """, + help="""filepath to telegram API private key and chatID to send messages to.""", ) # transformer specific options @@ -194,72 +155,54 @@ def load_args(): "-d_model", type=int, default=512, - help=""" - Dimension size of the model. - """, + help="""Dimension size of the model.""", ) parser.add_argument( "-d_inner_hid", type=int, default=2048, - help=""" - Dimension size of the hidden layers of the transformer. - """, + help="""Dimension size of the hidden layers of the transformer.""", ) parser.add_argument( "-d_k", type=int, default=64, - help=""" - Key vector dimension size. - """, + help="""Key vector dimension size.""", ) parser.add_argument( "-d_v", type=int, default=64, - help=""" - Value vector dimension size. - """, + help="""Value vector dimension size.""", ) parser.add_argument( "-n_head", type=int, default=8, - help=""" - Number of attention heads. - """, + help="""Number of attention heads.""", ) parser.add_argument( "-n_warmup_steps", type=int, default=4000, - help=""" - Number of warmup steps. - """, + help="""Number of warmup steps.""", ) parser.add_argument( "-embs_share_weight", action="store_true", - help=""" - If enabled, allows the embeddings of the encoder - and the decoder to share weights. - """, + help="""If enabled, allows the embeddings of the encoder + and the decoder to share weights.""", ) parser.add_argument( "-proj_share_weight", action="store_true", - help=""" - If enabled, allows the projection/generator - to share weights. - """, + help="""If enabled, allows the projection/generator + to share weights.""", ) parser.add_argument( "-label_smoothing", action="store_true", - help=""" - Enables label smoothing. - """, + help="""Enables label smoothing.""", ) # RNN specific options @@ -267,8 +210,7 @@ def load_args(): "-max_generator_batches", type=int, default=32, - help=""" - Maximum batches of words in a sequence to run + help="""Maximum batches of words in a sequence to run the generator on in parallel. Higher is faster, but uses more memory.""", ) @@ -276,26 +218,18 @@ def load_args(): "-input_feed", type=int, default=0, - help=""" - Feed the context vector at each time step as - additional input (via concatenation with the word - embeddings) to the decoder.""", + help="""Feed the context vector at each time step as additional input (via concatenation with the word embeddings) to the decoder.""", ) parser.add_argument( "-max_grad_norm", type=float, default=5, - help=""" - If the norm of the gradient vector exceeds this, - renormalize it to have the norm equal to max_grad_norm. - """, + help="""If the norm of the gradient vector exceeds this, renormalize it to have the norm equal to max_grad_norm.""", ) parser.add_argument( "-curriculum", action="store_true", - help="""For this many epochs, order the minibatches based - on source sequence length. Sometimes setting this to 1 will - increase convergence speed.""", + help="""For this many epochs, order the minibatches based on source sequence length. Sometimes setting this to 1 will increase convergence speed.""", ) parser.add_argument( "-brnn", action="store_true", help="Use a bidirectional encoder" @@ -303,8 +237,7 @@ def load_args(): parser.add_argument( "-brnn_merge", default="concat", - help="""Merge action for the bidirectional hidden states: - [concat|sum]""", + help="""Merge action for the bidirectional hidden states: [concat|sum]""", ) parser.add_argument( "-rnn_size", type=int, default=500, help="Size of LSTM hidden states" @@ -346,23 +279,19 @@ def load_args(): parser.add_argument( "-freeze_encoder", action="store_true", - help=""" - If enabled, freezes learning of the encoder. - """, + help="""If enabled, freezes learning of the encoder.""", ) parser.add_argument( "-freeze_decoder", action="store_true", - help=""" - If enabled, freezes learning of the decoder. - """, + help="""If enabled, freezes learning of the decoder.""", ) return parser def train_model(opt): - model = transformer(opt) if opt.model == "transformer" else recurrent(opt) + model: NMTModel = transformer(opt) if opt.model == "transformer" else recurrent(opt) print("Setup model wrapper.") model.load_dataset() print("Loaded data.") diff --git a/base/train_decoder.py b/specialk/migrate/train_decoder.py similarity index 76% rename from base/train_decoder.py rename to specialk/migrate/train_decoder.py index 67bf5d4..0ca5e18 100755 --- a/base/train_decoder.py +++ b/specialk/migrate/train_decoder.py @@ -1,18 +1,23 @@ import argparse import os -import sys +from typing import Dict, Tuple +import lightning.pytorch as pl import numpy as np import torch import torch.nn as nn -from lib.RecurrentModel import RecurrentModel as recurrent -from lib.TransformerModel import TransformerModel as transformer +from jaxtyping import Float, Int from torch.autograd import Variable +from torch.utils.data import DataLoader from tqdm import tqdm -sys.path.append("classifier") -import classifier.onmt as onmt -import classifier.onmt.CNNModels as CNNModels +import specialk.models.classifier.onmt as onmt +import specialk.models.classifier.onmt.CNNModels as CNNModels +from specialk.models.classifier.trainer import CNNClassifier, TextClassifier + +# from specialk.lib.recurrent_model import RecurrentModel as recurrent +from specialk.models.legacy.transformer_model import TransformerModel as transformer +from specialk.models.mt_model import NMTModule description = """ train_decoder.py @@ -35,69 +40,52 @@ def load_args(): "-checkpoint_encoder", default="", type=str, - help=""" - If training from a checkpoint, then this is the path to the pretrained encoder. - """, + help="""If training from a checkpoint, then this is the path to the pretrained encoder.""", ) parser.add_argument( "-checkpoint_decoder", default="", type=str, - help=""" - If training from a checkpoint, then this is the path to the pretrained model. - """, + help="""If training from a checkpoint, then this is the path to the pretrained model.""", ) parser.add_argument( "-new_directory", action="store_true", - help=""" - If enabled, creates a new directory instead of using the directory where the encoder is loaded. (Assumes that the checkpoint_encoder flag is activated.) - """, + help="""If enabled, creates a new directory instead of using the directory where the encoder is loaded. (Assumes that the checkpoint_encoder flag is activated.)""", ) parser.add_argument( "-log", action="store_true", - help=""" - Determines whether to enable logs, which will save status into text files. - """, + help="""Determines whether to enable logs, which will save status into text files.""", ) parser.add_argument( "-directory_name", type=str, default="", - help=""" - Name of directory. If set, then it'll use that name instead. - Otherwise it'll generate one based on the timestamp. - """, + help="""Name of directory. If set, then it'll use that name instead. Otherwise it'll generate one based on the timestamp.""", ) parser.add_argument( "-save_model", action="store_true", - help=""" - Determines whether to save the model or not. - """, + help="""Determines whether to save the model or not.""", ) parser.add_argument( "-save_mode", default="all", choices=["all", "best"], - help=""" - Determines whether to save all versions of the model or keep the best version. - """, + help=""" Determines whether to save all versions of the model or keep the best version.""", ) parser.add_argument( "-verbose", action="store_true", - help=""" - If enabled, prints messages to terminal. - """, + help="""If enabled, prints messages to terminal.""", ) # model options. @@ -105,52 +93,40 @@ def load_args(): parser.add_argument( "-cuda", action="store_true", - help=""" - Determines whether to use CUDA or not. (You should.) - """, + help="""Determines whether to use CUDA or not. (You should.)""", ) parser.add_argument( "-multi_gpu", action="store_true", - help=""" - Determines whether to use multiple GPUs. - """, + help="""Determines whether to use multiple GPUs.""", ) parser.add_argument( "-cuda_device", type=int, - help=""" - Determines which GPU to use for computation. - """, + help="""Determines which GPU to use for computation.""", ) parser.add_argument( "-batch_size", type=int, default=64, - help=""" - Determines batch size of input data, for feeding into the models. - """, + help="""Determines batch size of input data, for feeding into the models.""", ) parser.add_argument( "-layers", type=int, default=6, - help=""" - Number of layers for the model. (Recommended to have 6 for Transformer, 2 for recurrent.) - """, + help="""Number of layers for the model. (Recommended to have 6 for Transformer, 2 for recurrent.)""", ) parser.add_argument( "-d_word_vec", type=int, default=300, - help=""" - Dimension size of the token vectors representing words (or characters, or bytes). - """, + help="""Dimension size of the token vectors representing words (or characters, or bytes).""", ) # training options @@ -159,21 +135,14 @@ def load_args(): type=int, required=True, default=10, - help=""" - Number of epochs for training. (Note - that for transformers, the number of - sequences become considerably longer.) - """, + help="""Number of epochs for training. (Note that for transformers, the number of sequences become considerably longer.)""", ) parser.add_argument( "-dropout", type=float, default=0.1, - help=""" - Dropout probability' applied between - self-attention layers/RNN Stacks. - """, + help="""Dropout probability' applied between self-attention layers/RNN Stacks.""", ) # debugging options @@ -181,10 +150,7 @@ def load_args(): "-telegram", type=str, default="", - help=""" - filepath to telegram API private key - and chatID to send messages to. - """, + help="""Filepath to telegram API private key and chatID to send messages to. """, ) # transformer specific options @@ -192,9 +158,7 @@ def load_args(): "-d_model", type=int, default=512, - help=""" - Dimension size of the model. - """, + help="""Dimension size of the model.""", ) parser.add_argument( "-d_inner_hid", @@ -265,25 +229,19 @@ def load_args(): "-learning_rate", type=float, default=0.001, - help="""Starting learning rate. If adagrad/adadelta/adam is - used, then this is the global learning rate. Recommended - settings: sgd = 1, adagrad = 0.1, adadelta = 1, adam = 0.001""", + help="""Starting learning rate. If adagrad/adadelta/adam is used, then this is the global learning rate. Recommended settings: sgd = 1, adagrad = 0.1, adadelta = 1, adam = 0.001""", ) parser.add_argument( "-learning_rate_decay", type=float, default=0.5, - help="""If update_learning_rate, decay learning rate by - this much if (i) perplexity does not decrease on the - validation set or (ii) epoch has gone past - start_decay_at""", + help="""If update_learning_rate, decay learning rate by this much if (i) perplexity does not decrease on the validation set or (ii) epoch has gone past start_decay_at""", ) parser.add_argument( "-start_decay_at", type=int, default=8, - help="""Start decaying every epoch after and including this - epoch""", + help="""Start decaying every epoch after and including this epoch.""", ) parser.add_argument( "-optim", @@ -297,39 +255,31 @@ def load_args(): "-classifier_model", required=True, type=str, - help=""" - path for classifier model. - """, + help="""path for classifier model.""", ) parser.add_argument( "-label0", required=True, type=str, - help=""" - Label 0 for CNN classifier. - """, + help="""Label 0 for CNN classifier.""", ) parser.add_argument( "-label1", required=True, type=str, - help=""" - Label 1 for CNN classifier. - """, + help="""Label 1 for CNN classifier.""", ) parser.add_argument( "-label_target", required=True, type=str, - help=""" - Label target for CNN classifier. - """, + help="""Label target for CNN classifier.""", ) return parser -def load_classifier(opt): +def load_classifier(opt) -> CNNModels.ConvNet: # load model classifier cnn_opt = argparse.Namespace() cnn_opt.model = opt.classifier_model @@ -370,7 +320,7 @@ def tf_forward(self, src_seq, src_pos, tgt_seq, tgt_pos): return dec_output -def n_tokens_correct(pred, gold, pad_token): +def n_tokens_correct(pred, gold, pad_token) -> int: """ Calculates number of correct tokens. """ @@ -444,7 +394,13 @@ def performance(self, pred_before, gold, class_input, class_model, smoothing=Fal return l_reconstruction, l_classifier, n_correct -def compute_epoch(self, class_input, class_model, dataset, validation=False): +def compute_epoch( + self, + class_input: nn.Sequential, + class_model: onmt.CNNModels, + dataset, + validation=False, +): if validation: self.model.decoder.eval() self.model.generator.eval() @@ -539,7 +495,7 @@ def save(decoder, generator, epoch, settings, vocab, filepath): model.model.encoder.eval() # load model classifier - classifier = load_classifier(opt) + classifier: CNNModels.ConvNet = load_classifier(opt) criterion2 = BCELoss() # initiate a new generator for style specific purposes @@ -550,8 +506,6 @@ def save(decoder, generator, epoch, settings, vocab, filepath): nn.Linear(model_dim, classifier.word_lut.weight.shape[0]) ).cuda() - criterion_2 = BCELoss() - torch.cuda.empty_cache() print("model.opt.directory:", model.opt.directory) diff --git a/base/metrics/style_transfer/__init__.py b/specialk/models/__init__.py similarity index 100% rename from base/metrics/style_transfer/__init__.py rename to specialk/models/__init__.py diff --git a/specialk/models/classifier/models.py b/specialk/models/classifier/models.py new file mode 100644 index 0000000..063dc87 --- /dev/null +++ b/specialk/models/classifier/models.py @@ -0,0 +1,437 @@ +from __future__ import division + +import tempfile +import warnings +from pathlib import Path +from typing import List, Optional, Tuple + +import lightning.pytorch as pl +import safetensors +import torch +import torch.nn as nn +import torch.nn.functional as F +from jaxtyping import Float, Int +from peft import ( + LoraConfig, + PeftModel, + TaskType, + get_peft_model, + get_peft_model_state_dict, +) +from torch import Tensor +from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer + +from specialk.core.constants import ( + TEST_ACC, + TEST_LOSS, + TRAIN_ACC, + TRAIN_BATCH_ID, + TRAIN_LOSS, + VAL_ACC, + VAL_LOSS, +) +from specialk.core.utils import batch_texts, check_torch_device, log, namespace_to_dict +from specialk.datasets.dataloaders import ( + init_classification_dataloaders as init_dataloaders, +) +from specialk.models.classifier.onmt.CNNModels import ConvNet +from specialk.models.tokenizer import Vocabulary + +warnings.filterwarnings("ignore", message="A parameter name that contains") + + +DEVICE: str = check_torch_device() + + +class TextClassifier(pl.LightningModule): + """Base class for Text Classification.""" + + def __init__( + self, + name: str, + vocabulary_size: int, + sequence_length: int, + tokenizer: Optional[Vocabulary] = None, + ): + super().__init__() + self.name = name + self.vocabulary_size = vocabulary_size + self.sequence_length = sequence_length + self.model = None + self.tokenizer = tokenizer + + def validation_step(self, batch: dict, batch_idx: int): + loss, acc = self._shared_eval_step(batch, batch_idx) + metrics = {VAL_ACC: acc, VAL_LOSS: loss} + self.log_dict(metrics, batch_size=batch["text"].size(0)) + return metrics + + def test_step(self, batch: dict, batch_idx: int): + loss, acc = self._shared_eval_step(batch, batch_idx) + metrics = {TEST_ACC: acc, TEST_LOSS: loss} + self.log_dict(metrics) + return metrics + + def predict_step( + self, batch: dict, batch_idx: int, dataloader_idx=0 + ) -> torch.Tensor: + raise NotImplementedError + + def _shared_eval_step( + self, batch: dict, batch_idx: int + ) -> Tuple[torch.Tensor, float]: + """Run shared eval step. + + Args: + batch (dict): individual batch generated from the DataLoader. + batch_idx (int): ID corresponding to the batch. + + Returns: + Tuple[torch.Tensor, float]: Returns loss, accuracy. + """ + raise NotImplementedError + + @staticmethod + def calculate_classification_metrics( + output: torch.Tensor, + target: torch.Tensor, + ) -> float: + """Calculate downstream metrics. + + Args: + output (torch.Tensor): Predicted values generated from the model. + target (torch.Tensor): Values we want to predict. + + Returns: + int: Accuracy. + """ + + outputs = (output > 0.5).long() + n_correct: float = outputs.eq(target).sum().item() / outputs.size(0) + return n_correct + + @staticmethod + def criterion(y_hat: Tensor, y: Tensor) -> Tensor: + return F.binary_cross_entropy(y_hat.squeeze(-1), y.float()) + + +class CNNClassifier(TextClassifier): + """CNN Text Classifier. Operates on one-hot.""" + + def __init__( + self, + name: str, + vocabulary_size: int, + sequence_length: int, + tokenizer: Optional[Vocabulary] = None, + ): + super().__init__( + name=name, + vocabulary_size=vocabulary_size, + sequence_length=sequence_length, + tokenizer=tokenizer, + ) + # min_batch_size is used because the convolution + # operations depend on a minimum size. + self.min_batch_size = 50 + self.model = ConvNet( + vocab_size=self.vocabulary_size, sequence_length=self.sequence_length + ) + + def forward(self, *args, **kwargs): + return self.model(*args, **kwargs) + + def training_step(self, batch: dict, batch_idx: int) -> torch.Tensor: + """Run Training step. + + Args: + batch (dict): individual batch generated from the DataLoader. + batch_idx (int): ID corresponding to the batch. + + Returns: + torch.Tensor: Returns loss. + """ + x, y = batch["text"], batch["label"] + batch_size, seq_len = x.size() + + if batch_size < self.min_batch_size: + log.error( + f"batch size (currently set to {batch_size}) " + f"should be at least {self.min_batch_size}." + ) + + one_hot = self.one_hot(x) + + y_hat = self.model(one_hot).squeeze(-1) + + loss: torch.Tensor = F.binary_cross_entropy(y_hat, y.float()) + accuracy = self.calculate_classification_metrics(y_hat, y) + + self.log_dict( + {TRAIN_ACC: accuracy, TRAIN_BATCH_ID: batch_idx, TRAIN_LOSS: loss}, + batch_size=batch_size, + ) + return loss + + def one_hot(self, text: Int[torch.Tensor, "batch seq_len"]) -> torch.Tensor: + """wrap into one-hot encoding of tokens for activation.""" + batch_size, seq_length = text.shape + return torch.zeros( + seq_length, batch_size, self.vocabulary_size, device=self.device + ).scatter_(2, torch.unsqueeze(text.T, 2), 1) + + def _shared_eval_step( + self, batch: dict, batch_idx: int + ) -> Tuple[torch.Tensor, float]: + """Run shared eval step. + + Args: + batch (dict): individual batch generated from the DataLoader. + batch_idx (int): ID corresponding to the batch. + + Returns: + Tuple[torch.Tensor, float]: Returns loss, accuracy. + """ + x, y = batch["text"], batch["label"] + + one_hot = self.one_hot(x) + + y_hat = self.model(one_hot).squeeze(-1) + loss: torch.Tensor = F.binary_cross_entropy(y_hat, y.float()) + + accuracy = self.calculate_classification_metrics(y_hat, y) + return loss, accuracy + + def validation_step(self, batch: dict, batch_idx: int): + loss, acc = self._shared_eval_step(batch, batch_idx) + metrics = {VAL_ACC: acc, VAL_LOSS: loss} + self.log_dict(metrics, batch_size=batch["text"].size(0)) + return metrics + + def test_step(self, batch: dict, batch_idx: int): + loss, acc = self._shared_eval_step(batch, batch_idx) + metrics = {TEST_ACC: acc, TEST_LOSS: loss} + self.log_dict(metrics) + return metrics + + @torch.inference_mode() + def generate(self, texts: list[str], batch_size: Optional[int] = 64) -> Tensor: + batch: List[str] + results = [] + for batch in batch_texts(texts, batch_size): + tokens = self.tokenizer.to_tensor(batch) + one_hot = self.one_hot(tokens) + result = self.model(one_hot) + results.append(result) + return torch.cat(results).cpu() + + def configure_optimizers(self) -> torch.optim.Optimizer: + return torch.optim.Adam(self.model.parameters(), lr=0.02) + + +class BERTClassifier(TextClassifier): + def __init__( + self, + name: str, + model_base_name: str = "distilbert/distilbert-base-cased", + peft_config: Optional[LoraConfig] = None, + vocabulary_size: Optional[int] = None, + sequence_length: Optional[int] = None, + tokenizer: Optional[Vocabulary] = None, + ): + """Uses Huggingface based BERT models.""" + if tokenizer: + log.warn( + "You don't need to set a tokenizer, we're using HuggingFace's pre-trained models." + ) + if vocabulary_size: + log.warn( + "You don't need to set a vocabulary size, we're using HuggingFace's pre-trained models." + ) + + super().__init__( + name=name, + vocabulary_size=vocabulary_size, + sequence_length=sequence_length, + tokenizer=tokenizer, + ) + self.model_base_name = model_base_name + self.tokenizer = AutoTokenizer.from_pretrained(self.model_base_name) + self._load_base_model() + + self.peft_config = None + if peft_config: + assert peft_config.task_type == TaskType.SEQ_CLS + self.model = get_peft_model(self.base_model, peft_config, adapter_name=name) + self.peft_config = peft_config + log.info("Using PEFT.", config=self.peft_config) + else: + self.model = self.base_model + self.save_hyperparameters(logger=False) + + def forward(self, *args, **kwargs): + return self.model(*args, **kwargs) + + @staticmethod + def criterion(y_hat: Tensor, y: Tensor) -> Tensor: + loss: torch.Tensor = F.cross_entropy(y_hat, y) + return loss + + def _load_base_model(self): + """Load the base model from HuggingFace.""" + log.info("Loading base model") + self.base_model = AutoModelForSequenceClassification.from_pretrained( + self.model_base_name + ) + config = AutoConfig.from_pretrained(self.model_base_name) + log.info("Successfully loaded model and config.") + self.sequence_length = config.max_position_embeddings + + def training_step(self, batch: dict, batch_idx: int) -> torch.Tensor: + """Run Training step. + + Args: + batch (dict): individual batch generated from the DataLoader. + batch_idx (int): ID corresponding to the batch. + + Returns: + torch.Tensor: Returns loss. + """ + x, y = batch["text"], batch["label"] + + if x.dim() == 3: + x = x.squeeze(2, 1) + + batch_size, _ = x.size() + + x_mask = x != self.tokenizer.pad_token_id + y_hat = self.model(x, attention_mask=x_mask, labels=y) + + loss: torch.Tensor = y_hat.loss + accuracy = self.calculate_classification_metrics(y_hat.logits, y) + + self.log_dict( + {TRAIN_ACC: accuracy, TRAIN_BATCH_ID: batch_idx, TRAIN_LOSS: loss}, + batch_size=batch_size, + ) + return loss + + def _shared_eval_step( + self, batch: dict, batch_idx: int + ) -> Tuple[torch.Tensor, float]: + """Run shared eval step. + + Args: + batch (dict): individual batch generated from the DataLoader. + batch_idx (int): ID corresponding to the batch. + + Returns: + Tuple[torch.Tensor, float]: Returns loss, accuracy. + """ + x, y = batch["text"], batch["label"] + + if x.dim() == 3: + x = x.squeeze(2, 1) + + x_mask = x != self.tokenizer.pad_token_id + y_hat = self.model(x, attention_mask=x_mask, labels=y) + + loss: torch.Tensor = y_hat.loss + accuracy = self.calculate_classification_metrics(y_hat.logits, y) + return loss, accuracy + + def validation_step(self, batch: dict, batch_idx: int): + loss, acc = self._shared_eval_step(batch, batch_idx) + metrics = {VAL_ACC: acc, VAL_LOSS: loss} + self.log_dict(metrics, batch_size=batch["text"].size(0)) + return metrics + + def test_step(self, batch: dict, batch_idx: int): + loss, acc = self._shared_eval_step(batch, batch_idx) + metrics = {TEST_ACC: acc, TEST_LOSS: loss} + self.log_dict(metrics) + return metrics + + def load_peft_from_checkpoint(self, checkpoint) -> PeftModel: + """For some reason, the PeftModel.from_pretrained works, when + directly setting the weights from the state_dict doesn't. + + I've spent way too long trying to debug this.""" + + peft_config = checkpoint["hyper_parameters"]["peft_config"] + peft_state_dict = checkpoint["peft_state_dict"] + + # TODO change this with a fake filesytem + with tempfile.TemporaryDirectory() as temp_dir: + # Save the modified config to the temp directory + peft_config.save_pretrained(temp_dir) + + # Save the filtered state dict to the temp directory + path_peft_tensors = Path(temp_dir) / "adapter_model.safetensors" + safetensors.torch.save_file(peft_state_dict, path_peft_tensors) + + # Use the standard PEFT loading mechanism. + peft_model = PeftModel.from_pretrained( + self.base_model, + temp_dir, + adapter_name=self.name, + ) + + return peft_model + + def on_save_checkpoint(self, checkpoint: dict): + if self.peft_config: + # Save only the PEFT state dict + peft_state_dict = get_peft_model_state_dict( + self.model, adapter_name=self.name + ) + checkpoint["peft_state_dict"] = peft_state_dict + # you don't need it because we're only saving peft_state_dict. + del checkpoint["state_dict"] + else: + # For non-PEFT models, save the entire state dict + checkpoint["state_dict"] = self.model.state_dict() + + def on_load_checkpoint(self, checkpoint): + # Reload the base model to ensure clean slate + if "peft_state_dict" in checkpoint: + # Re-initialize the PEFT model + self.model = self.load_peft_from_checkpoint(checkpoint) + + else: + # For non-PEFT models, load the entire state dict + self.model.load_state_dict(checkpoint["state_dict"]) + + @classmethod + def load_from_checkpoint(cls, checkpoint_path, map_location=None): + checkpoint = torch.load(checkpoint_path, map_location=map_location) + + model = cls( + name=checkpoint["hyper_parameters"]["name"], + model_base_name=checkpoint["hyper_parameters"]["model_base_name"], + peft_config=checkpoint["hyper_parameters"].get("peft_config"), + ) + + model.on_load_checkpoint(checkpoint) + return model + + def configure_optimizers(self): + # Implement your optimizer configuration here + return torch.optim.AdamW(self.model.parameters(), lr=2e-5) + + def calculate_classification_metrics( + self, logits: Float[Tensor, "batch 2"], labels: Int[Tensor, "batch"] + ): + """ + Calculate classification metrics. + + HuggingFace BERTs will have two outputs instead of one, + (one for each binary class). This is for flexibility reasons. + + (Provides confidence in both directions). Note that this isn't used + as a loss metric, so we don't need to keep track of gradients here. + """ + preds = ( + logits[:, 1] > logits[:, 0] + ).float() # Positive class if logits[1] > logits[0] + return (preds == labels).float().mean() diff --git a/specialk/models/classifier/onmt/CNNModels.py b/specialk/models/classifier/onmt/CNNModels.py new file mode 100755 index 0000000..9ce5160 --- /dev/null +++ b/specialk/models/classifier/onmt/CNNModels.py @@ -0,0 +1,102 @@ +from typing import Dict, Optional + +import torch +import torch.nn as nn + +from specialk.core.constants import PAD +from specialk.core.utils import log + +class ConvNet(nn.Module): + def __init__( + self, + vocab_size: int, + sequence_length: int, + num_filters: int = 100, + filter_size: int = 5, + dropout: int = 0.2, + num_classes: int = 2, + word_vec_size: int = 300, + args: Optional[Dict] = None, + **kwargs, + ): + """Initiate ConvNet. This architecture is optimised for sequence classification. + + Args: + vocab_size (int): Vocabulary size of token. This is based on the tokenizer. + num_filters (int, optional): Number of Filters to use. Defaults to 100. + filter_size (int, optional): Kernel size for convolution. Defaults to 5. + sequence_length (int, optional): Maximum sequence length. Defaults to 50. + dropout (int, optional): Probability rate of dropout. Defaults to 0.2. + num_classes (int, optional): Number of classes to predict. Defaults to 2. + word_vec_size (int, optional): Vector dimension for each token. Defaults to 300. + args (Optional[Dict], optional): Additional arguments to save (as a sanity check). Defaults to None. + """ + super(ConvNet, self).__init__() + self.args = args + self.vocab_size = vocab_size + self.num_filters = num_filters + self.filter_size = filter_size + self.num_classes = num_classes + self.word_vec_size = word_vec_size + self.dropout = dropout + self.sequence_length = sequence_length + self.pooling_window_size = self.sequence_length - self.filter_size + 1 + self.strides = (1, 1) + self.debug = False + self.shown_shape = False + + self.word_lut = nn.Embedding( + self.vocab_size, self.word_vec_size, padding_idx=PAD + ) + + self.conv = nn.Conv2d( + in_channels=self.word_vec_size, + out_channels=self.num_filters, + kernel_size=(self.filter_size, 1), + stride=self.strides, + bias=True, + ) + self.relu = nn.ReLU() + self.maxpool = nn.MaxPool3d( + # self.maxpool = MaxPool3d( + kernel_size=(1, self.pooling_window_size, 1), + stride=(1, 1, 1), + ) + self.dropout = nn.Dropout(self.dropout) + self.linear = nn.Linear(self.num_filters, self.num_classes - 1) + self.sigmoid = nn.Sigmoid() + + def load_pretrained_vectors(self, opt) -> None: + if opt.pre_word_vecs_enc is not None: + pretrained = torch.load(opt.pre_word_vecs_enc) + self.word_lut.weight.data.copy_(pretrained) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + ## src size is seq_size x batch_size x vocab_size. Most cases (50 x 64 x v) + seq_len, batch_size, vocab_size = input.shape + + ## matrix multiply instead of lookup + input = input.contiguous().view(-1, vocab_size) + emb = torch.mm(input, self.word_lut.weight) + emb = emb.view(-1, batch_size, self.word_vec_size) + emb = emb.transpose(0, 1) + emb = emb.transpose(1, 2) + emb = emb.unsqueeze(-1) + h_conv = self.conv(emb) + h_relu = self.relu(h_conv) + h_max = self.maxpool(h_relu) + h_flat = h_max.view(-1, self.num_filters) + h_drop = self.dropout(h_flat) + lin_out = self.linear(h_drop) + out = self.sigmoid(lin_out) + if self.debug and (not self.shown_shape): + log.debug("shape", input=input.shape) + log.debug("shape", h_conv=h_conv.shape) + log.debug("shape", h_relu=h_relu.shape) + log.debug("shape", h_max=h_max.shape) + log.debug("shape", h_flat=h_flat.shape) + log.debug("shape", h_drop=h_drop.shape) + log.debug("shape", lin_out=lin_out.shape) + log.debug("shape", out=out.shape) + self.shown_shape = True + return out diff --git a/base/classifier/onmt/Dataset.py b/specialk/models/classifier/onmt/Dataset.py similarity index 96% rename from base/classifier/onmt/Dataset.py rename to specialk/models/classifier/onmt/Dataset.py index 9d8b45f..ba7f968 100755 --- a/base/classifier/onmt/Dataset.py +++ b/specialk/models/classifier/onmt/Dataset.py @@ -6,7 +6,7 @@ import torch from torch.autograd import Variable -import onmt +from specialk.core.constants import PAD class Dataset(object): @@ -26,7 +26,7 @@ def __init__(self, srcData, tgtData, batchSize, cuda, volatile=False): def _batchify(self, data, align_right=False, include_lengths=False): lengths = [x.size(0) for x in data] max_length = max(lengths) - out = data[0].new(len(data), max_length).fill_(onmt.Constants.PAD) + out = data[0].new(len(data), max_length).fill_(PAD) for i in range(len(data)): data_length = data[i].size(0) offset = max_length - data_length if align_right else 0 diff --git a/base/classifier/onmt/Optim.py b/specialk/models/classifier/onmt/Optim.py similarity index 94% rename from base/classifier/onmt/Optim.py rename to specialk/models/classifier/onmt/Optim.py index f2b2d85..f923f5a 100755 --- a/base/classifier/onmt/Optim.py +++ b/specialk/models/classifier/onmt/Optim.py @@ -16,6 +16,8 @@ def set_parameters(self, params): self.optimizer = optim.Adadelta(self.params, lr=self.lr) elif self.method == "adam": self.optimizer = optim.Adam(self.params, lr=self.lr) + elif self.method == "adamw": + self.optimizer = optim.AdamW(self.params, lr=self.lr) else: raise RuntimeError("Invalid optim method: " + self.method) diff --git a/base/classifier/onmt/Translator_cnn.py b/specialk/models/classifier/onmt/Translator_cnn.py similarity index 89% rename from base/classifier/onmt/Translator_cnn.py rename to specialk/models/classifier/onmt/Translator_cnn.py index 4cd4d2e..f74f58c 100755 --- a/base/classifier/onmt/Translator_cnn.py +++ b/specialk/models/classifier/onmt/Translator_cnn.py @@ -2,7 +2,8 @@ import torch.nn as nn from torch.autograd import Variable -import onmt +import specialk.models.classifier.onmt as onmt +from specialk.core.constants import UNK_WORD class Translator(object): @@ -13,16 +14,16 @@ def __init__(self, opt): self.label0 = opt.label0 self.label1 = opt.label1 - checkpoint = torch.load(opt.model) + checkpoint = torch.load(opt.model, map_location=torch.device("cpu")) self.model_opt = checkpoint["opt"] self.src_dict = checkpoint["dicts"]["src"] self.vocabulary_size = 0 try: - # if "kwargs" in self.src_dict: + # if "kwargs" in self.src_dict: self.vocabulary_size = self.src_dict["kwargs"]["vocab_size"] - except: + except Exception: self.vocabulary_size = self.src_dict.size() model = onmt.CNNModels.ConvNet(self.model_opt, self.vocabulary_size) @@ -40,8 +41,7 @@ def __init__(self, opt): def buildData(self, srcBatch, goldBatch): srcData = [ - self.src_dict.convertToIdx(b, onmt.Constants.UNK_WORD, padding=True) - for b in srcBatch + self.src_dict.convertToIdx(b, UNK_WORD, padding=True) for b in srcBatch ] tgtData = [] if goldBatch: @@ -95,10 +95,6 @@ def translate(self, srcBatch, goldBatch, flag=False): # (1) convert words to indexes dataset = self.buildData(srcBatch, goldBatch) src, tgt, indices = dataset[0] - - # print(src[].shape) - # # src is a tuple - # print(src.shape, tgt.shape) # (2) translate num_correct, batchSize, outs, pred = self.translateBatch(src, tgt) @@ -112,7 +108,6 @@ def translate_bpe(self, srcBatch, goldBatch, flag=False): srcBatch = torch.LongTensor(srcBatch) goldBatch = torch.LongTensor(goldBatch) # (2) translate - # print("SRC:", srcBatch.shape, goldBatch.shape) srcBatch = srcBatch.transpose(0, 1) srcLens = [srcBatch.size(0) for i in range(srcBatch.size(1))] diff --git a/specialk/models/classifier/onmt/__init__.py b/specialk/models/classifier/onmt/__init__.py new file mode 100755 index 0000000..6d9a622 --- /dev/null +++ b/specialk/models/classifier/onmt/__init__.py @@ -0,0 +1,8 @@ +import specialk.models.classifier.onmt.CNNModels as CNNModels +from specialk.models.classifier.onmt.Dataset import Dataset +from specialk.models.classifier.onmt.Optim import Optim +from specialk.models.classifier.onmt.Translator_cnn import Translator as Translator_cnn + +__ALL__ = [ + CNNModels, Dataset, Optim, Translator_cnn +] \ No newline at end of file diff --git a/specialk/models/generators/beam.py b/specialk/models/generators/beam.py new file mode 100644 index 0000000..5c0d3ef --- /dev/null +++ b/specialk/models/generators/beam.py @@ -0,0 +1,343 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Generic, List, Optional, Tuple, TypeVar + +import einops +import torch +from jaxtyping import Float, Int +from rich import print as rprint +from rich.table import Table +from torch import Tensor + +from specialk.core import log +from specialk.models.tokenizer import Vocabulary + +T = TypeVar("T") + + +@dataclass +class Beam(Generic[T]): + """Class to store Beams for Beam Search. + + Attributes: + model (T): Model used to generate logits. + tokenizer (Vocabulary): Tokenizer to decode logits into words. + logprob_sums (Tensor): Logprobs of the tokens. + tokens (Tensor): Tokens generated from the model. + ... + """ + + model: T + tokenizer: Vocabulary + logprob_sums: Float[Tensor, "batch"] + tokens: Int[Tensor, "batch seq"] + + @classmethod + def create( + cls, model: T, tokenizer: Vocabulary, logprob_sums: Tensor, tokens: Tensor + ) -> Beam: + """Creates a new Beam object.""" + return cls(model, tokenizer, logprob_sums, tokens) + + def new(self, logprob_sums: Tensor, tokens: Tensor) -> Beam: + """Creates a new Beam object with the same model and tokenizer.""" + return self.__class__.create(self.model, self.tokenizer, logprob_sums, tokens) + + def __getitem__(self, idx) -> Beam: + """Allows you to take a slice of the beams object along the batch dimension.""" + return self.new(self.logprob_sums[idx], self.tokens[idx]) + + @property + def num_beams(self) -> int: + return self.logprob_sums.size(0) + + @property + def logprobs_and_completions(self) -> List[Tuple[float, List[str]]]: + """Returns self as a list of logprob sums and completions.""" + return [ + (logprob_sum.item(), self.tokenizer.detokenize(tokens)) + for (logprob_sum, tokens) in zip(self.logprob_sums, self.tokens) + ] + + @property + def logprobs_and_tensors(self) -> List[Tuple[float, Tensor]]: + """Returns self as a list of logprob sums and tensors. The tensors + will require detokeniisation.""" + return [ + (logprob_sum.item(), tokens) + for (logprob_sum, tokens) in zip(self.logprob_sums, self.tokens) + ] + + def generate( + self, + tokens_per_beam: int, + no_repeat_ngram_size: Optional[int] = None, + log_logits: Optional[Float[Tensor, "batch vocab"]] = None, + ) -> Beam: + """Generate next set of beams.""" + if log_logits is None: + log_logits = self.get_logits() + + topk_log_probs, topk_token_idx = self.get_topk_non_repeating( + log_logits, no_repeat_ngram_size, k=tokens_per_beam + ) + + new_logprob_sums = self._calculate_new_logprob_sums( + topk_log_probs, tokens_per_beam + ) + new_tokens = self._generate_new_tokens(topk_token_idx, tokens_per_beam) + + return self.new(new_logprob_sums, new_tokens) + + def _calculate_new_logprob_sums( + self, topk_log_probs: Tensor, tokens_per_beam: int + ) -> Tensor: + """Repeats the logprob_sums down the batch dimension.""" + return einops.repeat( + self.logprob_sums, "batch -> (batch k)", k=tokens_per_beam + ) + einops.rearrange(topk_log_probs, "batch k -> (batch k)") + + def _generate_new_tokens( + self, topk_token_idx: Tensor, tokens_per_beam: int + ) -> Tensor: + return torch.cat( + [ + einops.repeat( + self.tokens, "batch seq -> (batch k) seq", k=tokens_per_beam + ), + einops.rearrange(topk_token_idx, "batch k -> (batch k) 1"), + ], + dim=-1, + ).long() + + def print(self, title: str = "Best completions", max_print_chars: int = 80) -> None: + """Prints out a set of sequences with their corresponding logit sums.""" + if len(self.tokens) == 0: + return + table = Table("logitsum", "completion", title=title) + for logprob_sum, tokens in zip(self.logprob_sums, self.tokens): + text = self.tokenizer.detokenize(tokens) + if len(repr(text)) > max_print_chars: + text = f"{text[:int(0.3 * max_print_chars)]} ... {text[-int(0.7 * max_print_chars):]}" + table.add_row(f"{logprob_sum:>8.3f}", repr(text)) + rprint(table) + + def filter(self, num_beams: Optional[int] = None) -> Tuple[Beam, Beam]: + """Filter beams based on termination condition. + + Returns: + (Beam): filtered version of self, containing all best `num_beams` + which are also not terminated. + (Beam): filtered version of self, containing all best `num_beams` + which are also terminated. + i.e. the sum of lengths of these two should equal `num_beams`. + """ + if num_beams is None: + num_beams = self.num_beams + top_beam_indices = self.logprob_sums.topk(k=num_beams, dim=0).indices + is_terminated = (self.tokens == self.tokenizer.EOS).any(dim=-1) + + top_beam_mask = torch.zeros_like(is_terminated, dtype=torch.bool) + top_beam_mask[top_beam_indices] = True + + best_continuing_mask = top_beam_mask & ~is_terminated + best_terminated_mask = top_beam_mask & is_terminated + + return self[best_continuing_mask], self[best_terminated_mask] + + def get_topk_non_repeating( + self, + logprobs: Float[Tensor, "batch d_vocab"], + no_repeat_ngram_size: Optional[int], + k: int, + ) -> Tuple[Float[Tensor, "k"], Int[Tensor, "k"]]: + """ + Get top-k logprobs ensuring no ngram repetitions. + + Args: + logprobs: Tensor of shape (batch, d_vocab) containing log probabilities. + no_repeat_ngram_size: Size of ngrams to avoid repeating. + k: Number of top probabilities to return. + + Returns: + Tuple of (top-k values, top-k indices) + """ + if self._should_skip_ngram_check(no_repeat_ngram_size): + return logprobs.topk(k=k, dim=-1) + + _, seq_len = self.tokens.shape + + last_ngram_prefix = self._get_last_ngram_prefix(no_repeat_ngram_size) + + for i in range(seq_len - (no_repeat_ngram_size - 1)): + ngrams = self.tokens[:, i : i + no_repeat_ngram_size] + self._mask_repeated_ngrams(logprobs, ngrams, last_ngram_prefix) + + return logprobs.topk(k=k, dim=-1) + + def _should_skip_ngram_check(self, no_repeat_ngram_size: Optional[int]) -> bool: + """ + Determine if the ngram repetition check should be skipped. + + Args: + no_repeat_ngram_size: Size of ngrams to avoid repeating + + Returns: + Boolean indicating whether to skip the ngram check + """ + return ( + no_repeat_ngram_size is None + or self.tokens.shape[1] <= no_repeat_ngram_size - 1 + ) + + def _get_last_ngram_prefix(self, no_repeat_ngram_size: int) -> Tensor: + """ + Get the prefix of the last ngram in the sequence. + + Args: + no_repeat_ngram_size: Size of ngrams to avoid repeating + + Returns: + Tensor containing the prefix of the last ngram + """ + return self.tokens[:, -no_repeat_ngram_size + 1 :] + + def _mask_repeated_ngrams( + self, + logprobs: Tensor, + ngrams: Tensor, + last_ngram_prefix: Tensor, + neg_inf: Tensor = torch.tensor(-1.0e4), + ) -> None: + """ + Mask the logprobs of repeated ngrams with negative infinity. + + Args: + logprobs: Tensor of shape (batch, d_vocab) containing log probabilities + ngrams: Tensor of current ngrams being checked + last_ngram_prefix: Tensor containing the prefix of the last ngram + neg_inf (Optional[Tensor]): Tensor with negative infinity value for masking. + + Returns: + None (modifies logprobs in-place). + """ + if neg_inf.device != logprobs.device: + neg_inf = neg_inf.to(logprobs.device) + + ngrams_are_repeated = (ngrams[:, :-1] == last_ngram_prefix).all(-1) + ngram_end_tokens = ngrams[:, -1] + + batch_indices = torch.arange(logprobs.shape[0], device=logprobs.device) + logprobs[batch_indices, ngram_end_tokens] = torch.where( + ngrams_are_repeated, neg_inf, logprobs[batch_indices, ngram_end_tokens] + ) + + @torch.inference_mode() + def get_logits(self) -> Tensor: + """This method should be implemented by subclasses.""" + raise NotImplementedError + + +@dataclass +class EncoderDecoderBeam(Beam["NMTModule"]): + memory: Tensor + + @classmethod + def create( + cls, + model: T, + tokenizer: Vocabulary, + logprob_sums: Tensor, + tokens: Tensor, + memory: Tensor, + ) -> Beam: + """Creates a new Beam object.""" + return cls(model, tokenizer, logprob_sums, tokens, memory) + + def new(self, logprob_sums: Tensor, tokens: Tensor) -> EncoderDecoderBeam: + """Creates a new EncoderDecoderBeam object with the + same model, tokenizer, and encoder_output.""" + return self.__class__.create( + self.model, self.tokenizer, logprob_sums, tokens, self.memory + ) + + def _repeat_memory(self, k: int) -> Float[Tensor, "batch seq hidden"]: + """Stretches self.memory k times.""" + return einops.repeat(self.memory, "b s h -> (b beam) s h", beam=k) + + @torch.inference_mode() + def get_logits(self) -> Tensor: + logits = self.model.decoder(self.tokens, encoder_output=self.memory)[:, -1, :] + return logits.log_softmax(-1) + + +@dataclass +class BeamBatch: + """Batch implementation of Beam Search.""" + + beams: List[Beam] + + @property + def batch_size(self) -> int: + return len(self.beams) + + def new(self, beams: List[Beam]) -> BeamBatch: + return BeamBatch(beams) + + @classmethod + def create(cls, beams: List[Beam]) -> "BeamBatch": + return cls(beams) + + @torch.inference_mode() + def get_logits(self) -> List[Float[torch.Tensor, "beam*beam vocab"]]: + """Aggregates logits into a single input for + batched implementation. + + Returns: + List[Float[torch.Tensor, "beam*beam vocab"]]: List of logits for each + batch in self.beams. + """ + raise NotImplementedError + + def filter(self, num_beams: int) -> Tuple["BeamBatch", "BeamBatch"]: + """Filters tokens at a batch level.""" + continuing_beams, terminated_beams = [], [] + for beam in self.beams: + continuing, terminated = beam.filter(num_beams) + continuing_beams.append(continuing) + terminated_beams.append(terminated) + return ( + self.__class__.create(continuing_beams), + self.__class__.create(terminated_beams), + ) + + def generate( + self, tokens_per_beam: int, no_repeat_ngram_size: Optional[int] = None + ) -> "BeamBatch": + return self.__class__.create( + [ + beam.generate( + tokens_per_beam=tokens_per_beam, + no_repeat_ngram_size=no_repeat_ngram_size, + log_logits=log_probs, + ) + for beam, log_probs in zip(self.beams, self.get_logits()) + ] + ) + + def print( + self, title: str = "Best completions", max_print_chars: int = 80, display=True + ) -> None: + """Prints out a set of sequences with their corresponding logit sums.""" + if len(self.beams[0].tokens) == 0: + return + table = Table("batch", "logitsum", "completion", title=title) + for i, batch in enumerate(self.beams): + for logprob_sum, tokens in zip(batch.logprob_sums, batch.tokens): + text = batch.tokenizer.detokenize(tokens) + if len(repr(text)) > max_print_chars: + text = f"{text[:int(0.3 * max_print_chars)]} ... {text[-int(0.7 * max_print_chars):]}" + table.add_row(f"{i}", f"{logprob_sum:>8.3f}", repr(text)) + if display: + rprint(table) diff --git a/specialk/models/generators/sampling.py b/specialk/models/generators/sampling.py new file mode 100644 index 0000000..47fb845 --- /dev/null +++ b/specialk/models/generators/sampling.py @@ -0,0 +1,236 @@ +from __future__ import annotations + +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch +from jaxtyping import Float, Int +from torch import Tensor +from tqdm import tqdm + +from specialk.core import constants, log +from specialk.models.generators.beam import Beam +from specialk.models.mt_model import NMTModule +from specialk.models.tokenizer import Vocabulary + +NEG_INF = float("-inf") + + +class LanguageModelSampler: + """Operations to perform text sampling from an encoder/decoder model.""" + + def __init__( + self, + model: Optional[NMTModule] = None, + src_tokenizer: Optional[Vocabulary] = None, + tgt_tokenizer: Optional[Vocabulary] = None, + device: Optional[str] = "cpu", + ): + self.model = model + self.src_tokenizer = src_tokenizer + self.tgt_tokenizer = tgt_tokenizer if tgt_tokenizer else src_tokenizer + self.device = device + + @staticmethod + def sample_next_token( + input_ids: Int[Tensor, "seq_len"], + logits: Float[Tensor, "seq_len d_vocab"], + temperature=1.0, + top_k=0, + top_p=0.0, + frequency_penalty=0.0, + seed=None, + ) -> int: + """Sample the next token.""" + + assert input_ids.ndim == 2, "input_ids should be a 2D sequence of token ids" + assert temperature >= 0, "Temperature should be non-negative" + assert 0 <= top_p <= 1.0, "Top-p must be a probability" + assert 0 <= top_k, "Top-k must be non-negative" + + # Set random seeds for reproducibility + if seed is not None: + torch.manual_seed(seed) + np.random.seed(seed) + + # Apply all the specialized sampling methods + if temperature == 0: + return LanguageModelSampler.greedy_search(logits) + elif temperature != 1.0: + # i could separate this out, but it would just do this. + logits = logits / temperature + if frequency_penalty != 0.0: + logits = LanguageModelSampler.apply_frequency_penalty( + input_ids, logits, frequency_penalty + ) + if top_k > 0 or top_p > 0.0: + return LanguageModelSampler.sample_top_k_top_p(logits, top_k, top_p) + return LanguageModelSampler.sample_basic(logits) + + @staticmethod + def sample_top_k_top_p( + logits: Float[Tensor, "batch vocab"], + top_k: int, + top_p: float, + min_tokens_to_keep: int = 1, + ) -> int: + """ + Samples using both top-k and top-p filtering. + """ + _, vocab_size = logits.shape + + # Apply top-k sampling; this is so we can dramatically reduce the number of tokens to + # apply top-p for. + if top_k > 0: + top_k = min(max(top_k, min_tokens_to_keep), vocab_size) + indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] + logits[indices_to_remove] = float("-inf") + + # Apply top-p (nucleus) sampling. + if top_p > 0.0: + sorted_logits, sorted_indices = torch.sort(logits, descending=True) + cumulative_probs = torch.cumsum( + torch.softmax(sorted_logits, dim=-1), dim=-1 + ) + + # Remove tokens with cumulative probability above the threshold + sorted_indices_to_rm = cumulative_probs > top_p + + # Shift the indices to the right to keep also the first token above the threshold + sorted_indices_to_rm[..., 1:] = sorted_indices_to_rm[..., :-1].clone() + sorted_indices_to_rm[..., 0] = 0 + + # scatter sorted tensors to original indexing + indices_to_remove = sorted_indices_to_rm.scatter( + 1, sorted_indices, sorted_indices_to_rm + ) + logits = logits.masked_fill(indices_to_remove, NEG_INF) + + # Sample from the filtered distribution + return LanguageModelSampler.sample_basic(logits) + + @staticmethod + def sample_top_k(logits: Float[Tensor, "d_vocab"], k: int) -> Tensor: + """ + Samples from the top k most likely tokens. + """ + if len(logits.shape) > 1: + logits = logits.squeeze(0) + topk = torch.topk(input=logits, k=k) + top_k_logits, top_k_token_ids = topk.values, topk.indices + sampled_token_idx = torch.distributions.categorical.Categorical( + logits=top_k_logits + ).sample() + return top_k_token_ids[sampled_token_idx] + + @staticmethod + def sample_top_p( + logits: Float[Tensor, "d_vocab"], top_p: float, min_tokens_to_keep: int = 1 + ) -> int: + """ + Samples from the most likely tokens which make up at least p cumulative probability. + """ + if len(logits.shape) > 1 and logits.shape[0] == 1: + logits = logits.squeeze(0) + values, _logits = logits.sort(dim=-1, descending=True, stable=True) + cum_vals = values.softmax(dim=-1).cumsum(dim=-1) + + # first item to cut off is included in the thresholding. + top_n = int((cum_vals <= top_p).sum() + 1) + top_n = max(top_n, min_tokens_to_keep) + + good_logits = _logits[:top_n] + option = np.random.choice(good_logits.cpu()) + return option + + @staticmethod + def greedy_search(logits: Float[Tensor, "d_vocab"]) -> int: + """ + Returns the most likely token (as an int). + """ + out = int(logits.argmax().item()) + return out + + @staticmethod + def apply_frequency_penalty( + input_ids: Int[Tensor, "seq_len"], + logits: Float[Tensor, "d_vocab"], + freq_penalty: float, + ) -> Float[Tensor, "d_vocab"]: + """ + Applies a frequency penalty to the logits. + """ + # TODO: this is really inefficient, needs optimising. + freqs = torch.stack( + [torch.bincount(seq, minlength=logits.shape[-1]) for seq in input_ids] + ) + return logits - (freqs * freq_penalty) + + @staticmethod + def sample_basic(logits: Float[Tensor, "d_vocab"]) -> Tensor: + """ + Samples from the distribution defined by the logits. + + Returns: + Tensor: one logit index from the logits. + """ + return torch.distributions.categorical.Categorical(logits=logits).sample() + + @torch.inference_mode() + def beam_search( + self: LanguageModelSampler, + prompt: str, + num_return_sequences: int, + num_beams: int, + max_new_tokens: int, + no_repeat_ngram_size: Optional[int] = None, + verbose=False, + ) -> List[Tuple[float, Tensor]]: + """ + Beam Search. + + This is done by repeatedly performing the `generate` and `filter` steps (starting + from the initial prompt) until either of the two stopping criteria are met: + + (1) we've generated `max_new_tokens` tokens, or + (2) we've generated `num_returns_sequences` terminating sequences. + + To modularize this function, most of the actual complexity is in the Beam class, + in the `generate` and `filter` methods. + """ + + assert num_return_sequences <= num_beams + tokens = self.tokenizer.to_tensor(prompt).to(self.device) + + # keep track of final beams; early terminations. + beam_results: List[Tuple[float, str]] = [] + # generate logprob of prompt tokens. + logprob_sums = torch.tensor([-1.0] * len(tokens)).to(self.device) + best_beam = Beam( + model=self.model, + tokenizer=self.tokenizer, + logprob_sums=logprob_sums, + tokens=tokens, + ) + for i in tqdm(range(max_new_tokens)): + # generate beam. + best_beam = best_beam.generate( + num_beams, no_repeat_ngram_size=no_repeat_ngram_size + ) + best_beams, early_terminated_beams = best_beam.filter(num_beams) + + beam_results.extend(early_terminated_beams.logprobs_and_completions) + + if verbose: + best_beams.print(title=f"Best Completions @ idx={i}") + early_terminated_beams.print( + title=f"Early Terminated Completions @ idx={i}" + ) + + # early stopping condition. + if len(beam_results) >= num_return_sequences: + return beam_results[:num_return_sequences] + + beam_results.extend(best_beams.logprobs_and_completions) + beam_results = beam_results[:num_return_sequences] + return beam_results diff --git a/base/lib/RecurrentModel.py b/specialk/models/legacy/recurrent_model.py similarity index 97% rename from base/lib/RecurrentModel.py rename to specialk/models/legacy/recurrent_model.py index 30618ed..8b0db38 100755 --- a/base/lib/RecurrentModel.py +++ b/specialk/models/legacy/recurrent_model.py @@ -2,20 +2,21 @@ import os import time -import core.constants as Constants import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import torch.utils.data -from lib.nmtModel import NMTModel -from lib.recurrent.Models import Decoder, Encoder -from lib.recurrent.Models import NMTModel as Seq2Seq -from lib.recurrent.Optim import Optim -from lib.recurrent.Translator import Translator from torch.autograd import Variable from tqdm import tqdm +import specialk.core.constants as Constants +from specialk.models.nmt_model import NMTModel +from specialk.models.recurrent.Models import Decoder, Encoder +from specialk.models.recurrent.Models import NMTModel as Seq2Seq +from specialk.models.recurrent.Optim import Optim +from specialk.models.recurrent.Translator import Translator + class RecurrentModel(NMTModel): def __init__(self, opt): diff --git a/base/lib/TransformerModel.py b/specialk/models/legacy/transformer_model.py similarity index 94% rename from base/lib/TransformerModel.py rename to specialk/models/legacy/transformer_model.py index 760d8be..8f21964 100755 --- a/base/lib/TransformerModel.py +++ b/specialk/models/legacy/transformer_model.py @@ -8,17 +8,18 @@ import torch.nn.functional as F import torch.optim as optim import torch.utils.data -from core.bpe import Encoder as BPE -from lib.nmtModel import NMTModel -from lib.transformer.Models import ( +from tqdm import tqdm + +from specialk.models.nmt_model import NMTModel +from specialk.models.tokenizers.bpe import Encoder as BPE +from specialk.models.transformer.legacy.Models import ( Decoder, Encoder, Transformer, get_sinusoid_encoding_table, ) -from lib.transformer.Optim import ScheduledOptim -from lib.transformer.Translator import Translator -from tqdm import tqdm +from specialk.models.transformer.Optim import ScheduledOptim +from specialk.models.transformer.Translator import Translator """ Wrapper class for Transformer. @@ -137,6 +138,19 @@ def load(self, encoder_path, decoder_path=None): self.model.generator.load_state_dict(dec["generator"]) except: generator = nn.Sequential( +<<<<<<< HEAD:specialk/models/legacy/transformer_model.py + nn.Linear( + self.model.generator.in_features, + self.model.generator.out_features, + ), + nn.LogSoftmax(dim=1), + ) + generator.load_state_dict(dec["generator"]) +||||||| 9856af7:base/lib/TransformerModel.py + nn.Linear(self.model.generator.in_features, self.model.generator.out_features), + nn.LogSoftmax(dim=1)).cuda() + generator.load_state_dict(dec['generator']) +======= nn.Linear( self.model.generator.in_features, self.model.generator.out_features, @@ -144,6 +158,7 @@ def load(self, encoder_path, decoder_path=None): nn.LogSoftmax(dim=1), ).cuda() generator.load_state_dict(dec["generator"]) +>>>>>>> master:base/lib/TransformerModel.py del self.model.generator self.model.generator = generator diff --git a/specialk/models/mt_model.py b/specialk/models/mt_model.py new file mode 100644 index 0000000..cd4fe67 --- /dev/null +++ b/specialk/models/mt_model.py @@ -0,0 +1,480 @@ +from __future__ import annotations, division + +import math +from argparse import Namespace +from dataclasses import dataclass +from pathlib import Path +from typing import List, Optional, Tuple, TypeVar, Union + +import lightning.pytorch as pl +import torch +import torch.nn as nn +import torch.nn.functional as F +from jaxtyping import Float, Int +from schedulefree import AdamWScheduleFree +from torch import Tensor + +from specialk.core.constants import ( + PAD, + SOURCE, + TARGET, + TEACHER_FORCING_RATIO, + TEST_ACC, + TEST_LOSS, + TRAIN_ACC, + TRAIN_BATCH_ID, + TRAIN_LOSS, + TRAIN_PPLX, + VAL_ACC, + VAL_BATCH_ID, + VAL_BLEU, + VAL_LOSS, + VAL_PPLX, +) +from specialk.core.utils import log +from specialk.metrics.metrics import SacreBLEU +from specialk.models.generators.beam import Beam +from specialk.models.ops.ops import mask_out_special_tokens +from specialk.models.recurrent.rnn import Decoder as RNNDecoder +from specialk.models.recurrent.rnn import Encoder as RNNEncoder +from specialk.models.recurrent.rnn import RNNEncoderDecoder as RNNEncoderDecoder +from specialk.models.tokenizer import Vocabulary +from specialk.models.transformer.legacy.Models import Transformer as TransformerModel +from specialk.models.transformer.legacy.Models import ( + TransformerWrapper, + get_sinusoid_encoding_table, +) +from specialk.models.transformer.legacy.Optim import ScheduledOptim + +bleu = SacreBLEU() + + +class NMTModule(pl.LightningModule): + """auto-regressive neural machine translation module.""" + + def __init__( + self, + name: str, + vocabulary_size: int, + sequence_length: int, + label_smoothing: bool = False, + tokenizer: Optional[Vocabulary] = None, + decoder_tokenizer: Optional[Vocabulary] = None, + decoder_vocabulary_size: Optional[int] = None, + learning_rate: float = 0.001, + **kwargs, + ): + """Initialize the NMT module. + + Args: + name (str): _description_ + vocabulary_size (int): _description_ + sequence_length (int): _description_ + label_smoothing (bool, optional): _description_. Defaults to False. + tokenizer (Optional[Vocabulary], optional): _description_. Defaults to None. + decoder_tokenizer (Optional[Vocabulary], optional): + Tokenizer used for the decoder only. Defaults to None. If it is set to None, + then the decoder will use the same vocabulary as the encoder. + decoder_vocabulary_size (Optional[int], optional): Vocabulary size of the decoder + generator. Defaults to None. If it is set to none, then the decoder vocabulary + size is the same as the encoder's vocabulary size. + """ + super().__init__() + self.name = name + self.vocabulary_size = vocabulary_size + + self.sequence_length = sequence_length + self.label_smoothing = label_smoothing + self.learning_rate = learning_rate + self.tokenizer: Union[Vocabulary, None] = tokenizer + + # if we have a separate tokenizer for the decoder, + # we'll want to make sure that the decoder vocabulary size is set. + self.decoder_tokenizer: Union[Vocabulary, None] = decoder_tokenizer + self.decoder_vocabulary_size = decoder_vocabulary_size + if not self.decoder_tokenizer: + self.decoder_tokenizer = self.tokenizer + self.decoder_vocabulary_size = self.vocabulary_size + else: + if not self.decoder_vocabulary_size: + raise Exception( + "You have added a decoder tokenizer, " + "but you have not set the decoder vocabulary size." + ) + + self.model: Union[TransformerModel, RNNEncoderDecoder] + + # label smoothing isn't needed since the distribution is so wide. + self.criterion = torch.nn.CrossEntropyLoss(ignore_index=PAD) + self.kwargs = kwargs + self.save_hyperparameters() + + def forward(self, x: Tensor, y: Tensor) -> Tensor: + return self.model(x, y) + + def training_step(self, batch: dict, batch_idx: int) -> torch.Tensor: + """Run Training step. + + Args: + batch (dict): individual batch generated from the DataLoader. + batch_idx (int): ID corresponding to the batch. + + Returns: + torch.Tensor: Returns loss. + """ + x: Int[Tensor, "batch seq_len"] = batch[SOURCE] + y: Int[Tensor, "batch seq_len"] = batch[TARGET] + batch_size: int = x.size(0) + + y_hat = self.model(x, y) + loss = self.loss(y_hat.view(-1, y_hat.size(-1)), y.view(-1)) + + n_tokens_correct = self.calculate_classification_metrics(y_hat, y) + n_tokens_total = y.ne(PAD).sum().item() + accuracy = n_tokens_correct / n_tokens_total + perplexity = self.calculate_perplexity(y_hat, y) + + self.log(TRAIN_LOSS, loss, prog_bar=True, batch_size=batch_size) + self.log_dict( + { + TRAIN_ACC: accuracy, + TRAIN_BATCH_ID: batch_idx, + TRAIN_PPLX: perplexity, + }, + batch_size=batch_size, + ) + return loss + + def _shared_eval_step( + self, batch: dict, batch_idx: int + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Run shared eval step. + + Args: + batch (dict): individual batch generated from the DataLoader. + batch_idx (int): ID corresponding to the batch. + + Returns: + torch.Tensor: Returns loss. + """ + x: Int[Tensor, "batch seq_len"] = batch[SOURCE] + y: Int[Tensor, "batch seq_len"] = batch[TARGET] + + y_hat: Float[Tensor, "batch seq_len vocab"] = self.model(x, y) + loss = self.loss(y_hat.view(-1, y_hat.size(-1)), y.view(-1)) + + n_tokens_correct = self.calculate_classification_metrics(y_hat, y) + n_tokens_total = y.ne(PAD).sum().item() + accuracy = n_tokens_correct / n_tokens_total + perplexity = self.calculate_perplexity(y_hat, y) + + metric_dict = { + VAL_ACC: accuracy, + VAL_BATCH_ID: batch_idx, + VAL_LOSS: loss, + VAL_PPLX: perplexity, + } + if self.decoder_tokenizer is not None: + metric_dict[VAL_BLEU] = self.validation_bleu(y_hat, y) + + self.log_dict( + metric_dict, + batch_size=x.size(0), + ) + return loss, accuracy + + def validation_bleu(self, y_hat: Tensor, y: Tensor) -> Union[float, None]: + """Calculates BLEU score @ validation phase. + + Args: + y_hat (Float[Tensor, "batch seq_len vocab"]): Prediction tensor. + y (Int[Tensor, "batch seq_len"]): Reference tensor. + + Returns: + float: BLEU score. + """ + tokenizer = self.decoder_tokenizer + if not tokenizer: + return None + + y_hat = y_hat.argmax(dim=-1) # greedy as a reference check. + y_hat = mask_out_special_tokens(y_hat, tokenizer.EOS, tokenizer.PAD) + + predictions: List[str] = tokenizer.detokenize(y_hat, specials=False) + references: List[str] = tokenizer.detokenize(y, specials=False) + return bleu.compute(predictions, references) + + def validation_step(self, batch: dict, batch_idx: int): + loss, acc = self._shared_eval_step(batch, batch_idx) + metrics = {"val_acc": acc, "val_loss": loss} + self.log_dict(metrics, batch_size=batch[SOURCE].size(0)) + return metrics + + def test_step(self, batch: dict, batch_idx: int): + loss, acc = self._shared_eval_step(batch, batch_idx) + metrics = {TEST_ACC: acc, TEST_LOSS: loss} + self.log_dict(metrics, batch_size=batch[SOURCE].size(0)) + return metrics + + def optimizer_step(self, *args, **kwargs): + super().optimizer_step(*args, **kwargs) + if hasattr(self, "lr_scheduler"): + self.lr_scheduler.step() + + def loss( + self, + pred: torch.Tensor, + ref: torch.Tensor, + smoothing: bool = False, + e: float = 0.1, + ) -> torch.Tensor: + """Calculate Cross-Entropy Loss. + + Args: + pred (torch.Tensor): Output generated from model. + ref (torch.Tensor): Reference values to calculate loss against. + smoothing (bool, optional): If set, applies smoothing. Defaults to False. + e (float): epsilon. Defaults to 0.1. + + Returns: + torch.Tensor: Loss tensor. + """ + ref = ref.contiguous().view(-1) + if smoothing: + n_class = pred.size(1) + one_hot = torch.zeros_like(pred).scatter(1, ref.view(-1, 1), 1) + one_hot = one_hot * (1 - e) + (1 - one_hot) * e / (n_class - 1) + + log_prb = F.log_softmax(pred, dim=1) + # create non-padding mask with torch.ne() + non_pad_mask = ref.ne(PAD) + loss = -(one_hot * log_prb).sum(dim=1) + # losses are averaged later + loss = loss.masked_select(non_pad_mask).sum() + else: + loss = self.criterion(pred, ref) + # loss = F.cross_entropy(pred, ref, ignore_index=PAD, reduction="sum") + return loss + + @staticmethod + def calculate_classification_metrics( + output: torch.Tensor, + target: torch.Tensor, + ) -> int: + """Calculate token level accuracy. + + Args: + output (torch.Tensor): Predicted values generated from the model. + target (torch.Tensor): Values we want to predict. + + Returns: + int: Accuracy. + """ + output = output.argmax(dim=-1) + non_pad_mask = target.ne(PAD) + n_correct = output.eq(target) + n_correct = n_correct.masked_select(non_pad_mask).sum().item() + return n_correct + + @staticmethod + def calculate_perplexity( + y_hat: Float[Tensor, "batch seq vocab"], + y: Int[Tensor, "batch seq"], + padding_idx=PAD, + ) -> float: + """Calculate perplexity. + + Args: + y_hat (Tensor): Output log_probs of shape (batch_size, sequence_length, vocab_size). + y (Tensor): target indices of shape (batch_size, sequence_length). + padding_idx (int, optional): Index corresponding to pad token. Defaults to PAD. + + Returns: + float: Perplexity score (scalar). + """ + # Create a mask to ignore padding tokens + mask = (y != padding_idx).float() + + # Select log probs of correct tokens + token_log_probs = y_hat.gather(dim=-1, index=y.unsqueeze(-1)).squeeze(-1) + + # Apply mask and calculate mean negative log likelihood + masked_log_probs = token_log_probs * mask + nll = -masked_log_probs.sum() / mask.sum() + + # Calculate perplexity + return torch.exp(nll).item() + + def configure_optimizers(self) -> torch.optim.Optimizer: + return torch.optim.AdamW(self.model.parameters(), lr=self.learning_rate) + + @torch.inference_mode() + def generate( + self, + input_text: Optional[Union[str, List[str]]], + output_text: Optional[Union[str, List[str]]], + input_tokens: Optional[Int[Tensor, "batch seq_len"]], + output_tokens: Optional[Int[Tensor, "batch seq_len"]], + **kwargs, + ) -> Float[torch.Tensor, "batch d_vocab"]: + """ + Generate next tokens in the sequence, given text and output. + + Args: + input_text (Union[str, List[str]]): Input token sequence to. + + Note: + Put either input_text or input_tokens. This is the same as output_text or output_tokens. + """ + raise NotImplementedError + + def on_after_backward(self): + """This is called when a backward pass is ran (during training).""" + # get histogram of parameters. + if self.global_step % 250 == 0: + if self.logger is not None: + # no need to store histograms at every step, that's too expensive. + for name, param in self.named_parameters(): + self.logger.experiment.add_histogram( + f"{name}_grad", param.grad, self.global_step + ) + else: + log.warn("logger for this object is not initialised through Trainer()") + + +class TransformerModule(NMTModule): + def __init__(self, n_warmup_steps: int = 4000, **kwargs): + super().__init__(**kwargs) + self.n_warmup_steps = n_warmup_steps + self.model = TransformerWrapper( + **kwargs, + ) + + def change_pos_enc_len(self, seq_len: int, pad_token: int = PAD): + """Change the maximum sequence length of the transformer input. + + Args: + dimension (int): New sequence length. + """ + enc_d_word_vec = self.model.encoder.src_word_emb.weight.shape[1] + dec_d_word_vec = self.model.decoder.tgt_word_emb.weight.shape[1] + self.model.encoder.position_enc = nn.Embedding.from_pretrained( + get_sinusoid_encoding_table(seq_len, enc_d_word_vec, padding_idx=pad_token), + freeze=True, + ) + self.model.decoder.position_enc = nn.Embedding.from_pretrained( + get_sinusoid_encoding_table(seq_len, dec_d_word_vec, padding_idx=pad_token), + freeze=True, + ) + + +class RNNModule(NMTModule): + def __init__(self, **kwargs): + super().__init__(**kwargs) + args = self.patch_args(**kwargs) + self.model = RNNEncoderDecoder( + RNNEncoder(args, self.vocabulary_size), + RNNDecoder(args, self.decoder_vocabulary_size), + ) + self.initial_teacher_forcing_ratio: float = 0.9 + self.min_teacher_forcing_ratio: float = 0.5 + self.teacher_forcing_decay_rate: float = ( + 3.0 # Adjust this to control decay speed + ) + + @staticmethod + def patch_args( + layers: int = 2, + brnn: bool = False, + rnn_size: int = 500, + d_word_vec: int = 300, + dropout: float = 0.1, + input_feed: int = 0, + pre_word_vecs_enc: Optional[Union[Path, str]] = None, + pre_word_vecs_dec: Optional[Union[Path, str]] = None, + **kwargs, + ) -> Namespace: + """Patch args into Namespace format to feed into model. + + Args: + layers (int): Number of layers to use. + brnn (bool): If set, Uses a bidirectional encoder. + rnn_size (int): Dimension of RNN. + d_word_vec (int): Dimension size of the token vectors + representing words (or characters, or bytes). + dropout (float): Dropout probability' applied between + self-attention layers/RNN Stacks. + input_feed (Tensor): Feed the context vector at each time + step as additional input (via concatenation with the + word embeddings) to the decoder." + pre_word_vecs_enc (Union[Path, str]): If a valid path is + specified, then this will load pretrained word + embeddings on the encoder side. See README for + specific formatting instructions. + pre_word_vecs_dec (Union[Path, str]): If a valid path is + specified, then this will load pretrained word + embeddings on the decoder side. See README for + specific formatting instructions. + + Returns: + Namespace: Argument namespace containing args in param. + """ + args = Namespace() + args.layers = layers + args.brnn = brnn + args.rnn_size = rnn_size + args.d_word_vec = d_word_vec + args.dropout = dropout + args.pre_word_vecs_enc = pre_word_vecs_enc + args.input_feed = input_feed + args.pre_word_vecs_dec = pre_word_vecs_dec + return args + + def on_train_epoch_start(self): + self.update_teacher_forcing_ratio() + + def update_teacher_forcing_ratio(self): + if self.trainer is not None: + # Exponential decay formula + ratio = self.min_teacher_forcing_ratio + ( + self.initial_teacher_forcing_ratio - self.min_teacher_forcing_ratio + ) * math.exp( + -self.teacher_forcing_decay_rate + * self.current_epoch + / self.trainer.max_epochs + ) + + self.model.decoder.teacher_forcing_ratio = ratio + # you can observe this in tensorboard.scalars + self.log(TEACHER_FORCING_RATIO, ratio) + + def training_step(self, batch: dict, batch_idx: int) -> torch.Tensor: + loss = super().training_step(batch, batch_idx) + self.log_dict( + {TEACHER_FORCING_RATIO: self.model.decoder.teacher_forcing_ratio}, + batch_size=batch[SOURCE].size(0), + ) + return loss + + @torch.inference_mode() + def generate(self, batch: dict, batch_idx: int) -> torch.Tensor: + """ + Generate tokens using decoder strategies, such as greedy, + beam search, top-k, nucleus sampling. + """ + x = batch[SOURCE] + batch_size: int = x.shape(0) + device: str = self.model.device + max_length: int = self.sequence_length + + # below is a greedy implementation. + Y = torch.ones(batch_size, 1, device=device) + probabilities = torch.zeros(batch_size, device=device) # first token. + encoder_output = self.model.encoder(x) + for _ in range(max_length): + probs_n = self.model.decoder(encoder_output)[:, -1].log_softmax(-1) + max_probs_n, token_n = probs_n.max(-1) + token_n = token_n.unsqueeze(-1) + Y = torch.cat((Y, token_n), axis=1) + probabilities += max_probs_n + return Y, probabilities diff --git a/specialk/models/ops/ops.py b/specialk/models/ops/ops.py new file mode 100644 index 0000000..3f20ee1 --- /dev/null +++ b/specialk/models/ops/ops.py @@ -0,0 +1,318 @@ +from typing import Optional, Tuple, Union + +import einops +import torch +from jaxtyping import Float, Int +from torch import Tensor + +from specialk import Constants + +Pair = Tuple[int, int] +Triple = Tuple[int, int, int] +IntOrPair = Union[int, Pair] +IntOrTriple = Union[int, Triple] + + +def token_accuracy(pred: Tensor, gold: Tensor, pad_token: int) -> float: + accuracy = n_tokens_correct(pred, gold, pad_token) + total = gold.ne(pad_token).sum() + return accuracy / total + + +def n_tokens_correct( + pred: Tensor, gold: Tensor, pad_token: Optional[int] = None +) -> int: + """ + Calculate the number of correct tokens. + This ignores padding tokens; This is not differentible. + + Args: + pred (Tensor): The predicted tensor of shape (N, C) where N + is the number of tokens and C is the number of classes. + gold (Tensor): The ground truth tensor of shape (N,) containing + the correct classes for each token. + pad_token (int): The token index used for padding. + + Returns: + int: The number of correct tokens, excluding padding tokens. + """ + pred_indices: Int[Tensor, "n"] = pred.argmax(dim=-1).view(-1) + gold_flat: Int[Tensor, "n"] = gold.view(-1) + + n_correct = pred_indices.eq(gold_flat) + if pad_token is not None: + non_pad_mask = gold_flat.ne(pad_token) + n_correct = n_correct.masked_select(non_pad_mask) + return int(n_correct.sum().item()) + + +def force_pair(v: IntOrPair) -> Pair: + """Convert v to a pair of int, if it isn't already.""" + if isinstance(v, tuple): + if len(v) != 2: + raise ValueError(v) + return (int(v[0]), int(v[1])) + elif isinstance(v, int): + return (v, v) + raise ValueError(v) + + +def force_triple(v: IntOrTriple) -> Triple: + """Convert v to a triple of int, if it isn't already.""" + if isinstance(v, tuple): + if len(v) != 3: + raise ValueError(v) + return (int(v[0]), int(v[1]), int(v[2])) + elif isinstance(v, int): + return (v, v, v) + raise ValueError(v) + + +def pad2d( + x: Tensor, left: int, right: int, top: int, bottom: int, pad_value: float +) -> Tensor: + """Return a new tensor with padding applied to the edges. + + x: shape (batch, in_channels, height, width), dtype float32 + + Return: shape (batch, in_channels, top + height + bottom, left + width + right) + """ + b, in_c, h, w = x.shape + shape_out = (b, in_c, top + h + bottom, left + w + right) + out = x.new_full(shape_out, pad_value) + out[:, :, top : top + h, left : left + w] = x + return out + + +def pad3d( + x: Tensor, + left: int, + right: int, + top: int, + bottom: int, + front: int, + back: int, + pad_value: float, +) -> Tensor: + """Return a new tensor with padding applied to the edges. + + x: shape (batch, in_channels, height, width, length), dtype float32 + + Return: shape (batch, in_channels, top + height + bottom, left + width + right, back + length + front) + """ + b, in_c, h, w, length = x.shape + shape_out = (b, in_c, top + h + bottom, left + w + right, back + length + front) + out = x.new_full(shape_out, pad_value) + out[:, :, top : top + h, left : left + w, back : back + length] = x + return out + + +def conv3d( + x: Float[Tensor, "b ic h w"], + weights: Float[Tensor, "oc ic kh kw"], + stride: IntOrPair = 1, + padding: IntOrPair = 0, +) -> Float[Tensor, "b oc oh ow"]: + """ + Like torch's conv2d using bias=False + + x: shape (batch, in_channels, height, width, length) + weights: shape (out_channels, in_channels, kernel_height, kernel_width, kernel_length) + + Returns: shape (batch, out_channels, output_height, output_width, output_length) + """ + + str_h, str_w, str_l = force_triple(stride) + pad_h, pad_w, pad_l = force_triple(padding) + + x = pad3d(x, pad_w, pad_w, pad_h, pad_h, pad_l, pad_l, 0) + + # get dimensions out. + batch, in_channel_src, height, width, length = x.shape + _, in_channels, kernel_height, kernel_width, kernel_length = weights.shape + assert in_channels == in_channel_src + + out_height = int(1 + ((height - kernel_height) / str_h)) + out_width = int(1 + ((width - kernel_width) / str_w)) + out_length = int(1 + ((length - kernel_length) / str_l)) + + a, b, c, d, e = x.stride() + + out = x.as_strided( + size=( + batch, + in_channels, + out_height, + kernel_height, + out_width, + kernel_width, + out_length, + kernel_length, + ), + stride=(a, b, c * str_h, c, d * str_w, d, d * str_w, e, e * str_l), + ) # new tensor with additional dimension for out_height, + # output_width, output_length. + + # return out @ weights + out_shape = "batch in_c out_h kernel_h out_w kernel_w out_l kernel_l" + weights_shape = "out_c in_c kernel_h kernel_w kernel_l" + return einops.einsum( + out, + weights, + f"{out_shape}, {weights_shape} -> batch out_c out_h out_w out_l", + ) + + +def FuncMaxPool2d( + x: Float[Tensor, "b ic h w"], + kernel_size: IntOrPair, + stride: Optional[IntOrPair] = None, + padding: IntOrPair = 0, +) -> Float[Tensor, "b ic oh ow"]: + """ + Like PyTorch's maxpool2d. + + x: shape (batch, channels, height, width) + stride: if None, should be equal to the kernel size + + Return: (batch, channels, output_height, output_width) + """ + + stride = stride if stride else kernel_size + str_h, str_w = force_pair(stride) + pad_h, pad_w = force_pair(padding) + ker_h, ker_w = force_pair(kernel_size) + + x = pad2d(x, pad_w, pad_w, pad_h, pad_h, -torch.inf) + + batch, in_channel, height, width = x.shape + + out_height = int(1 + ((height - ker_h) / str_h)) + out_width = int(1 + ((width - ker_w) / str_w)) + + a, b, c, d = x.stride() + + out = x.as_strided( + size=(batch, in_channel, out_height, ker_h, out_width, ker_w), + stride=(a, b, c * str_h, c, d * str_w, d), + ) + return torch.amax(out, dim=(3, 5), keepdim=True).view( + (batch, in_channel, out_height, out_width) + ) + + +def FuncMaxPool3d( + x: Float[Tensor, "b ic h w l"], + kernel_size: IntOrTriple, + stride: Optional[IntOrTriple] = None, + padding: IntOrTriple = 0, +) -> Float[Tensor, "b ic oh ow ol"]: + """ + Like PyTorch's maxpool3d. + This exists because a native implemenation on mps doesn't exist yet, + so we're making our own from scratch. + + x: shape (batch, channels, height, width, length) + stride: if None, should be equal to the kernel size + + Return: (batch, channels, output_height, output_width, output_length) + """ + + stride = stride if stride else kernel_size + str_h, str_w, str_l = force_triple(stride) + pad_h, pad_w, pad_l = force_triple(padding) + ker_h, ker_w, ker_l = force_triple(kernel_size) + + # x = pad3d(x, pad_w, pad_w, pad_h, pad_h, pad_l, pad_l, -torch.inf) + pad = (pad_h, pad_h, pad_w, pad_w, pad_l, pad_l) + # print(pad) + x = torch.nn.functional.pad(x, pad) + + # get dimensions out. + batch, in_channel, height, width, length = x.shape + + out_height = int(1 + ((height - ker_h) / str_h)) + out_width = int(1 + ((width - ker_w) / str_w)) + out_length = int(1 + ((length - ker_l) / str_l)) + + a, b, c, d, e = x.stride() + + return torch.amax( + x.as_strided( + size=( + batch, + in_channel, + out_height, + out_width, + out_length, + ker_h, + ker_w, + ker_l, + ), + stride=(a, b, c * str_h, d * str_w, e * str_l, c, d, e), + ), + dim=(-1, -2, -3), + ) + + +class MaxPool3d(torch.nn.Module): + def __init__( + self, + kernel_size: IntOrTriple, + stride: Optional[IntOrTriple] = None, + padding: IntOrTriple = 0, + ): + """ + Like PyTorch's maxpool3d. + This exists because a native implemenation on mps doesn't exist yet, + so we're making our own from scratch. + + x: shape (batch, channels, height, width, length) + stride: if None, should be equal to the kernel size + + Return: (batch, channels, output_height, output_width, output_length) + """ + super().__init__() + stride = stride if stride else kernel_size + self.stride = force_triple(stride) + self.padding = force_triple(padding) + self.kernel = force_triple(kernel_size) + + def forward(self, x: Tensor) -> Tensor: + return FuncMaxPool3d(x, self.kernel, self.stride, self.padding) + + +def mask_out_special_tokens( + x: Int[Tensor, "batch seq"], + eos_index: int = Constants.EOS, + pad_index: int = Constants.PAD, +) -> Int[Tensor, "batch seq"]: + """ + Mask out tokens beyond the EOS token. + + Args: + x (Tensor): Tensor generated by a NMT model. + pad_token (int, Optional): index of PAD. + eos_index (int, Optional): index of EOS. + + Returns: + Tensor: Tensor with any tokens past the EOS replaced by PAD tokens. + """ + n_batch, seq_len = x.shape + mask = (x == eos_index).to(torch.long) + + # Get the positions of the first occurrence in each row + pos_eos = mask.argmax(dim=-1) + # If a row does not contain the EOS index, + # then just say it's at the end of the sequence. + pos_eos[~mask.any(dim=-1)] = seq_len + + mask = torch.arange(seq_len, device=x.device).expand(n_batch, seq_len).T + mask = mask <= pos_eos + mask = mask.T + x_masked = x * mask + + if pad_index != Constants.PAD: + x_masked[x == 0] = pad_index + + return x_masked diff --git a/specialk/models/optimizers/__init__.py b/specialk/models/optimizers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/specialk/models/optimizers/schedulers.py b/specialk/models/optimizers/schedulers.py new file mode 100644 index 0000000..c2f44e9 --- /dev/null +++ b/specialk/models/optimizers/schedulers.py @@ -0,0 +1,23 @@ +from typing import List + +import numpy as np +from schedulefree import AdamWScheduleFree +from torch.optim import AdamW, Optimizer +from torch.optim.lr_scheduler import LambdaLR, _LRScheduler + + +class CosineWarmupScheduler(_LRScheduler): + def __init__(self, optimizer: Optimizer, n_warmup_steps: int, max_iters: int): + self.warmup = n_warmup_steps + self.max_num_iters = max_iters + super().__init__(optimizer) + + def get_lr(self) -> List[float]: + lr_factor = self.get_lr_factor(epoch=self.last_epoch) + return [base_lr * lr_factor for base_lr in self.base_lrs] + + def get_lr_factor(self, epoch: int) -> float: + lr_factor = 0.5 * (1 + np.cos(np.pi * epoch / self.max_num_iters)) + if epoch <= self.warmup: + lr_factor *= epoch * 1.0 / self.warmup + return lr_factor diff --git a/specialk/models/recurrent/attention.py b/specialk/models/recurrent/attention.py new file mode 100755 index 0000000..ccaafc9 --- /dev/null +++ b/specialk/models/recurrent/attention.py @@ -0,0 +1,69 @@ +import torch +import torch.nn as nn +from einops import einsum +from jaxtyping import Float +from torch import Tensor + +from specialk.core.utils import log + + +class Attention(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.linear_in = nn.Linear(dim, dim, bias=False) + self.softmax = nn.Softmax(dim=1) + self.linear_out = nn.Linear(dim * 2, dim, bias=False) + self.tanh = nn.Tanh() + self.mask: Tensor | None = None + self.warn_once = False + + def forward( + self, + query: Float[Tensor, "batch dim"], + context: Float[Tensor, "seq_len batch dim"], + ) -> tuple[Tensor, Tensor]: + """ + Compute the global attention. + + Args: + query: Tensor of shape (batch, dim) + context: Tensor of shape (seq_len, batch, dim) + + Returns: + tuple: (context_output, attention_weights) + context_output: Tensor of shape (batch, dim) + attention_weights: Tensor of shape (batch, seq_len) + """ + attn_scores = einsum( + context, + self.linear_in(query), + "seq_len batch dim, batch dim -> batch seq_len", + ) + + # Apply mask if set + if self.mask is not None: + mask = self.mask + if mask.shape[-1] > attn_scores.shape[-1]: + if not self.warn_once: + self.warn_once = True + log.warn( + f"Attention mask {mask.shape} is greater than the " + f"attention matrix {attn_scores.shape}; truncating to fit. " + "This indicates a potential bug in your code." + ) + mask = mask[:, :, : attn_scores.shape[-1]] + attn_scores = attn_scores.masked_fill_( + mask.view(-1, mask.shape[-1]), -float("inf") + ) + + attn_scores = self.softmax(attn_scores) + weighted_context = einsum( + attn_scores, + context, + "batch seq_len, seq_len batch dim -> batch dim", + ) + # Combine weighted context with input query + combined_context = torch.cat((weighted_context, query), 1) + context_output = self.tanh(self.linear_out(combined_context)) + + return context_output, attn_scores diff --git a/specialk/models/recurrent/rnn.py b/specialk/models/recurrent/rnn.py new file mode 100755 index 0000000..98d6a1a --- /dev/null +++ b/specialk/models/recurrent/rnn.py @@ -0,0 +1,364 @@ +import random +from argparse import Namespace +from typing import Optional, Tuple, Union + +import torch +import torch.nn as nn +from jaxtyping import Float, Int +from torch import Tensor +from torch.nn.utils.rnn import pack_padded_sequence as pack +from torch.nn.utils.rnn import pad_packed_sequence as unpack + +from specialk.core import constants as Constants +from specialk.models.recurrent.attention import Attention + + +class Encoder(nn.Module): + """ + LSTM Encoder. + """ + + def __init__(self, opt: Namespace, vocabulary_size: int): + super(Encoder, self).__init__() + + self.layers: int = opt.layers + self.num_directions: int = 2 if opt.brnn else 1 + self.hidden_size: int = opt.rnn_size // self.num_directions + assert opt.rnn_size % self.num_directions == 0 + + self.word_lut = nn.Embedding( + vocabulary_size, opt.d_word_vec, padding_idx=Constants.PAD + ) + + self.rnn = nn.LSTM( + opt.d_word_vec, + self.hidden_size, + num_layers=opt.layers, + dropout=opt.dropout, + bidirectional=opt.brnn, + ) + + def load_pretrained_vectors(self, opt: Namespace): + """In case you want to use alternative embeddings (e.g. GloVe).""" + if opt.pre_word_vecs_enc: + self.word_lut.weight.data.copy_(torch.load(opt.pre_word_vecs_enc)) + + def forward( + self, + input: Union[Tuple[Tensor, Tensor], Tensor], + hidden: Optional[Tensor] = None, + ): + """ + if input is a tuple: + (list of sequences, list of sequence lengths) + otherwise: + list of sequences + """ + if isinstance(input, tuple): + x: Int[Tensor, "length batch"] + x, x_lengths = input + emb = pack(self.word_lut(x), x_lengths.cpu()) + else: + emb = self.word_lut(input) + + outputs, (hidden_n, cell_n) = self.rnn(emb, hidden) + + if isinstance(input, tuple): + outputs, _ = unpack(outputs) + + outputs: Float[Tensor, "batch length d*hidden_size"] + hidden_n: Float[Tensor, "d*num_layers length hidden_size"] + cell_n: Float[Tensor, "d*num_layers length hidden_size"] + return outputs, (hidden_n, cell_n) + + +class StackedLSTM(nn.Module): + def __init__( + self, num_layers: int, input_size: int, hidden_size: int, dropout: float = 0.0 + ): + """_summary_ + + Args: + num_layers (int): Number of LSTM layers. + input_size (int): input dimension. + hidden_size (int): hidden dimension. + dropout (Optional[float]): Dropout rate. Defaults to 0.0. + """ + super(StackedLSTM, self).__init__() + self.num_layers = num_layers + self.hidden_size = hidden_size + self.layers = nn.ModuleList( + [ + nn.LSTMCell(input_size if i == 0 else hidden_size, hidden_size) + for i in range(num_layers) + ] + ) + self.dropout = nn.Dropout(dropout) + + def forward( + self, + input: Float[Tensor, "batch input_size"], + hidden: Tuple[ + Float[Tensor, "num_layers batch hidden"], + Float[Tensor, "num_layers batch hidden"], + ], + ) -> Tuple[ + Float[Tensor, "batch input_size"], + Tuple[ + Float[Tensor, "num_layers batch hidden"], + Float[Tensor, "num_layers batch hidden"], + ], + ]: + """Run Forward pass on the stacked LSTM. + + Args: + input (Tensor): input tensor of shape (batch_size, input_size) + hidden (Tuple[Tensor, Tensor]): previous hidden tensors from prev. pass: + hidden[0] (prev_hidden) shape: (num_layers, batch_size, hidden_size) + hidden[1] (prev_cell) shape: (num_layers, batch_size, hidden_size) + + Returns: + Tuple[Tensor, Tuple[Tensor, Tensor]]: + input shape: (batch_size, hidden_size) + (stacked_hidden, stacked_cell) shapes: both ( + num_layers, batch_size, hidden_size) + """ + prev_hidden, prev_cell = hidden + new_hidden_states, new_cell_states = [], [] + + layer_output: Float[Tensor, "batch hidden"] + new_cell: Float[Tensor, "batch hidden"] + for i, layer in enumerate(self.layers): + layer_output, new_cell = layer(input, (prev_hidden[i], prev_cell[i])) + input = layer_output + + if i + 1 != self.num_layers: + input = self.dropout(input) + + new_hidden_states.append(layer_output) + new_cell_states.append(new_cell) + + stacked_hidden: Float[Tensor, "num_layers batch hidden"] + stacked_hidden = torch.stack(new_hidden_states) + stacked_cell: Float[Tensor, "num_layers batch hidden"] + stacked_cell = torch.stack(new_cell_states) + + return input, (stacked_hidden, stacked_cell) + + def init_hidden(self, batch_size: int) -> Tuple[Tensor, Tensor]: + """Initialise start hidden values. + + Args: + batch_size (int): batch size to match input. + + Returns: + Tuple[Tensor, Tensor]: Dummy hidden and cell tensors. + """ + device = self.layers[0].weight_ih.device + return ( + torch.zeros( + (self.num_layers, batch_size, self.hidden_size), + device=device, + requires_grad=False, + ), + torch.zeros( + (self.num_layers, batch_size, self.hidden_size), + device=device, + requires_grad=False, + ), + ) + + +class Decoder(nn.Module): + def __init__(self, opt, vocabulary_size: int): + super(Decoder, self).__init__() + self.layers: int = opt.layers + self.input_feed: bool = opt.input_feed + self.input_size: int = opt.d_word_vec + if self.input_feed: + self.input_size += opt.rnn_size + self.dim_embedding: int = opt.d_word_vec + self.dim_model: int = opt.rnn_size + self.p_dropout: float = opt.dropout + + self.word_lut = nn.Embedding( + vocabulary_size, self.dim_embedding, padding_idx=Constants.PAD + ) + self.rnn = StackedLSTM( + self.layers, self.input_size, self.dim_model, self.p_dropout + ) + + self.num_directions = 2 if opt.brnn else 1 + assert opt.rnn_size % self.num_directions == 0 + self.hidden_size = opt.rnn_size // self.num_directions + + self.attention = Attention(opt.rnn_size) + self.dropout = nn.Dropout(self.p_dropout) + + self.generator = nn.Sequential( + nn.Linear(opt.rnn_size, vocabulary_size), nn.LogSoftmax(dim=-1) + ) + + self.teacher_forcing_ratio = 0.9 + + def load_pretrained_vectors(self, opt: Namespace): + """In case you have pre-trained word2vec embeddings.""" + if opt.pre_word_vecs_dec: + self.word_lut.weight.data.copy_(torch.load(opt.pre_word_vecs_dec)) + + def forward( + self, + input: Float[Tensor, "batch seq_len"], + hidden: Tuple[ + Float[Tensor, "d*n_layers batch hidden_size"], + Float[Tensor, "d*n_layers batch hidden_size"], + ], + context: Float[Tensor, "n_layers length d*hidden_size"], + init_output: Float[Tensor, "batch d_model"], + use_gen: bool = True, + keep_attention: bool = False, + ) -> Tuple[Tensor, Tuple[Tensor, Tensor], Tensor]: + """Run forward pass on Decoder. + + Args: + input (Float[Tensor]): Input to feed into the Decoder. + hidden (Tuple[Tensor, Tensor]): Hidden values from the Encoder. + context (Float[Tensor]): Every input hidden value from the Encoder, + used to calculate attention. + init_output (Float[Tensor]): Initial output (usually zeros). + use_gen (bool, optional): If set, use generator layer. Defaults to True. + keep_attention (bool, optional): If set, store all attention scores. so they + can be returned. Defaults to False (because it's expensive to store). + + Returns: + Tuple[Tensor, Tuple[Tensor, Tensor], Tensor]: Tuple containing + output: Output logits. + hidden: Hidden values at each decoder step. + attn: Attention scores from each decoder generated output. + """ + + emb: Float[Tensor, "seq_len, batch d_emb"] = self.word_lut(input) + seq_len, _, _ = emb.shape + + outputs, attention_scores = [], [] # store outputs @ each time step. + output = init_output # Initialize output with init_output + + for i in range(seq_len): + if i == 0 or self.use_teacher_forcing(): + # teacher forcing. Note that if i=0, the first + # token is EOS anyway. + emb_t = emb[i, :, :] + else: + # Use the model's previously predicted output + prev_output = outputs[-1] + prev_output_logits = ( + prev_output if use_gen else self.generator(prev_output) + ).argmax(dim=-1) + emb_t = self.word_lut(prev_output_logits) + + if self.input_feed: + emb_t: Float[Tensor, "batch 2*d_emb"] + emb_t = torch.cat([emb_t, output], 1) + + output, hidden = self.rnn(emb_t, hidden) + output, attention = self.attention(output, context) + output = self.dropout(output) + if use_gen: + output = self.generator(output) + outputs += [output] + if keep_attention: + attention_scores += [attention] + + outputs = torch.stack(outputs) # this is faster than catting tensors. + + if keep_attention: + attention_scores: Float[Tensor, "seq_len batch d_context"] + attention_scores = torch.stack(attention_scores) + + return outputs, hidden, attention_scores + + def use_teacher_forcing(self) -> bool: + """Check conditions to determine when to use teacher forcing.""" + if not self.training: # i.e. we're running evals, inference. + return False + if random.random() < self.teacher_forcing_ratio: + return True + return False + + +class RNNEncoderDecoder(nn.Module): + def __init__(self, encoder, decoder): + super(RNNEncoderDecoder, self).__init__() + self.encoder: Encoder = encoder + self.decoder: Decoder = decoder + + def make_init_decoder_output(self, batch_size: int) -> Tensor: + """Used for inference.""" + return torch.zeros( + (batch_size, self.decoder.hidden_size), + device=self.decoder.word_lut.weight.device, + requires_grad=False, + ) + + def _fix_enc_hidden(self, hidden: Tensor) -> Tensor: + """ + Restructures the encoder output for the decoder + as a hidden state, if a bidirectional LSTM is used. + + Input: + hidden: [layers*2, batch, dim] if bidirectional, + [layers, batch, dim] otherwise. + + Output: + Tensor: [layers, batch, dim*2] if bidirectional, + [layers, batch, dim] otherwise. + """ + layers, batch, dim = hidden.size() + + if self.encoder.num_directions == 2: + num_layers = layers // 2 + reshaped = hidden.view(num_layers, 2, batch, dim) + swapped = reshaped.transpose(1, 2) + return swapped.reshape(num_layers, batch, dim * 2) + return hidden + + def forward( + self, x: Float[Tensor, "batch seq"], y: Float[Tensor, "batch seq"] + ) -> Float[Tensor, "batch seq vocab_size"]: + """ + Forward pass of RNN training. This includes teacher + training so this cannot be used for inference. + + Input: + x (Tensor): input sequence to decode. + y (Tensor): output sequence to learn from, + or for teacher training. + Returns: + Tensor: Output sequence. + """ + + if isinstance(x, Tuple) or isinstance(y, Tuple): + raise Exception( + "Forward pass doesn't accept tuples anymore (i.e. " + "if you're sending in a Tuple[Tensor, List] where list " + "is the length of the sequences, you don't need that anymore!" + ) + + # swap batch relationship order. + x, y = x.transpose(0, 1), y.transpose(0, 1) + + context: Float[Tensor, "batch length d*hidden"] + hddn_n: Float[Tensor, "d*num_layers length hidden"] + cell_n: Float[Tensor, "d*num_layers length hidden"] + + context, (hddn_n, cell_n) = self.encoder(x) + hidden = self._fix_enc_hidden(hddn_n), self._fix_enc_hidden(cell_n) + + init_output: Float[Tensor, "batch hidden"] + init_output = self.make_init_decoder_output(x.shape[0]) + + out, _, _ = self.decoder(y, hidden, context, init_output) + out = out.transpose(0, 1) # reverse tensor order (for batch) + + # resulting tensor may not be contiguous because of all the cats. + return out.contiguous() diff --git a/specialk/models/style_decoder.py b/specialk/models/style_decoder.py new file mode 100644 index 0000000..5546a59 --- /dev/null +++ b/specialk/models/style_decoder.py @@ -0,0 +1,272 @@ +""" +Style Decoder Model. +""" + +from typing import Dict, Optional, Tuple + +import lightning.pytorch as pl +import torch +import torch.nn as nn +import torch.nn.functional as F +from jaxtyping import Float, Int +from torch import Tensor +from torch.utils.data import DataLoader + +from specialk.core import log +from specialk.core.constants import PAD, SOURCE, TARGET +from specialk.models.classifier.models import ( + BERTClassifier, + CNNClassifier, + TextClassifier, +) +from specialk.models.mt_model import NMTModule +from specialk.models.ops.ops import token_accuracy + + +class StyleBackTranslationModel(pl.LightningModule): + def __init__( + self, + mt_model: NMTModule, + classifier: TextClassifier, + target_label: int = 1, + use_mapping=True, + mapping_dim: int = 256, + ): + """ + Args: + mt_model (NMTModel): + Machine Translation model (with target language to english). + classifier (CNNModels): + Style classifier model. + mapping_dim (int): + Dimension to learn the mapping between vocabularies + from the sequence model to the classifier model. + """ + super().__init__() + self.sequence_model: NMTModule = mt_model + self.classifier: TextClassifier = classifier + # the mapping matrix is learnt as we fine-tune the decoder model. + self.use_mapping = use_mapping + + # having one tensor is an unreaonable size; (marianMT is 50k, BERT 30k)... + self.seq_projection = ( + nn.Linear(self.sequence_model.vocabulary_size, mapping_dim) + if self.use_mapping + else None + ) + self.clf_projection = ( + nn.Linear(mapping_dim, self.classifier.vocabulary_size) + if self.use_mapping + else None + ) + + self.target_label: int = target_label + + # encoder will always be in eval mode. + self.classifier.eval() + + def training_step(self, batch: dict, batch_idx: int) -> torch.Tensor: + """ + Forward pass of the transformer. + The output from the model is then sent to the classifier. + + Returns: + Loss value to run gradients against. + """ + + x: Int[Tensor, "batch seq_len"] = batch[SOURCE].squeeze(1) + y: Int[Tensor, "batch seq_len"] = batch[TARGET].squeeze(1) + y_label: Int[Tensor, "batch"] = batch["label"] + + log.info("x.shape", xhape=x.shape) + + y_hat_text, y_hat_label = self._forward(x, y, y_label) + + # Calculate losses. + y_hat_text = y_hat_text.view(-1, y_hat_text.size(-1)) + y = y.view(-1) + loss_reconstruction = self.reconstruction_loss(y_hat_text, y) + loss_class = y_hat_label.loss + joint_loss = loss_class + loss_reconstruction + + # report metrics. + self.log_dict( + { + "train_acc": token_accuracy(y_hat_text, y, PAD), + "batch_id": batch_idx, + "train_joint_loss": joint_loss, + "train_recon_loss": loss_reconstruction, + "train_class_loss": loss_class, + } + ) + return joint_loss + + def _forward( + self, + x: Int[Tensor, "batch seq_len"], + y: Int[Tensor, "batch seq_len"], + y_label: Optional[Int[Tensor, "batch"]] = None, + ) -> Tuple[Tensor, Optional[Tensor]]: + """Performs forward pass on the mt model and the discriminator model.""" + y_hat_text: Float[Tensor, "batch length vocab"] + y_hat_text = self.sequence_model(x, y) + + if self.use_mapping: + # tokenizers for the models are different so + # we need to project that over. + y_hat_text: Float[Tensor, "batch length vocab_classifier"] + y_hat_text = self.seq_projection(y_hat_text) + y_hat_text = self.clf_projection(y_hat_text) + + y_hat_label: Float[Tensor, "batch"] + y_hat_label = self.classifier(y_hat_text) + return y_hat_text, y_hat_label + + def _shared_eval_step(self, batch: dict, batch_idx: int): + """Evaluation step used for both eval/test runs on a given dataset. + + Args: + batch (DataLoader): _description_ + batch_idx (int): _description_ + + Returns: + _type_: _description_ + """ + x: Int[Tensor, "batch seq_len"] = batch[SOURCE].squeeze(1) + y: Int[Tensor, "batch seq_len"] = batch[TARGET].squeeze(1) + y_label: Int[Tensor, "batch"] = batch["class"] + + y_hat_text, y_hat_label = self._forward(x, y, y_label) + + # Calculate losses. + y_hat_text = y_hat_text.view(-1, y_hat_text.size(-1)) + y = y.view(-1) + loss_reconstruction = self.reconstruction_loss(y_hat_text, y) + loss_class = y_hat_label.loss + joint_loss = loss_class + loss_reconstruction + accuracy = token_accuracy(y_hat_text, y, self.constants.PAD) + + return loss_reconstruction, loss_class, joint_loss, accuracy + + def validation_step(self, batch: dict, batch_idx: int) -> Dict[str, torch.Tensor]: + """ + Validation run. + """ + l_recon, l_class, l_joint, accuracy = self._shared_eval_step(batch, batch_idx) + metrics: Dict[str, torch.Tensor] = { + "valid_accuracy": accuracy, + "valid_joint_loss": l_joint, + "valid_class_loss": l_class, + "valid_recon_loss": l_recon, + } + self.log_dict(metrics) + return metrics + + def test_step(self, batch: dict, batch_idx: int) -> Dict[str, torch.Tensor]: + """Test run.""" + l_recon, l_class, l_joint, accuracy = self._shared_eval_step(batch, batch_idx) + metrics: Dict[str, torch.Tensor] = { + "test_accuracy": accuracy, + "test_joint_loss": l_joint, + "test_class_loss": l_class, + "test_recon_loss": l_recon, + } + self.log_dict(metrics) + return metrics + + def reconstruction_loss(self, y_hat, y) -> Tensor: + return self.sequence_model.criterion(y_hat, y) + + def classifier_loss(self, y_hat, y) -> Tensor: + return self.classifier.criterion(y_hat, y) + + def _get_lora_params(self): + """Filter and return only the LoRA parameters.""" + lora_params = [] + for name, param in self.sequence_model.model.named_parameters(): + # Assuming LoRA parameters have 'lora_' in their name + if "lora_" in name: + lora_params.append(param) + return lora_params + + def configure_optimizers(self): + params = self._get_lora_params() + if len(params) == 0: + params = list(self.sequence_model.model.parameters()) + + if self.use_mapping: + params += list(self.clf_projection.parameters()) + params += list(self.seq_projection.parameters()) + return torch.optim.AdamW(params, lr=0.002) + + +class StyleBackTranslationModelWithCNN(StyleBackTranslationModel): + def __init__( + self, + mt_model: NMTModule, + classifier: CNNClassifier, + **kwargs, + ): + assert isinstance( + classifier, CNNClassifier + ), f"Classifier is not a CNNClassifier (found {type(classifier).__name__})" + super().__init__(mt_model=mt_model, classifier=classifier, **kwargs) + + def _forward( + self, + x: Int[Tensor, "batch seq_len"], + y: Int[Tensor, "batch seq_len"], + y_label: Optional[Int[Tensor, "batch"]] = None, + ) -> Tuple[Tensor, Optional[Tensor]]: + y_hat_text: Float[Tensor, "batch length vocab_seq2seq"] + y_hat_text = self.sequence_model(x, y) + + classifier_text: Float[Tensor, "seq_len batch d_vocab_classifier"] + classifier_text = y_hat_text.transpose(0, 1) + if self.use_mapping: + classifier_text = self.seq_projection(classifier_text) + classifier_text = self.clf_projection(classifier_text) + + # classifer pass + y_hat_label: Float[Tensor, "batch length vocab"] + y_hat_label = self.classifier.model(classifier_text).squeeze(-1) + return y_hat_text, y_hat_label + + +class StyleBackTranslationModelWithBERT(StyleBackTranslationModel): + def __init__(self, mt_model: NMTModule, classifier: BERTClassifier, **kwargs): + assert isinstance( + classifier, BERTClassifier + ), f"Classifier is not a BERTClassifier (found {type(classifier).__name__})" + super().__init__( + mt_model=mt_model, classifier=classifier, use_mapping=True, **kwargs + ) + + def _forward( + self, + x: Int[Tensor, "batch seq_len"], + y: Int[Tensor, "batch seq_len"], + y_label: Optional[Int[Tensor, "batch"]] = None, + ) -> Tuple[Tensor, Optional[Tensor]]: + y_hat_text: Float[Tensor, "batch length vocab"] + y_hat_text = self.sequence_model(x, y) + + # tokenizers for the models are going to be different. We're going to + # learn the mapping as we go. + + # tokenizers for the models are different so + # we need to project that over. + y_hat_text: Float[Tensor, "batch length vocab_classifier"] + y_hat_text = self.seq_projection(y_hat_text) + y_hat_text = self.clf_projection(y_hat_text) + y_hat_embs = ( + y_hat_text @ self.classifier.model.bert.embeddings.word_embeddings.weight + ) + + y_hat_mask = torch.argmax(y_hat_text, dim=-1) == PAD + + y_hat_label: Float[Tensor, "batch"] + y_hat_label = self.classifier.model( + inputs_embeds=y_hat_embs, attention_mask=y_hat_mask, labels=y_label + ) + return y_hat_text, y_hat_label diff --git a/specialk/models/style_lexicon.py b/specialk/models/style_lexicon.py new file mode 100644 index 0000000..e8c74da --- /dev/null +++ b/specialk/models/style_lexicon.py @@ -0,0 +1,41 @@ +from __future__ import annotations + +import specialk.metrics.style_transfer.style_lexicon as stme_lexicon +import specialk.metrics.style_transfer.utils as stme_utils +from specialk.core.utils import log + + +class StyleLexicon: + def __init__(self): + self.lexicon: dict[str, tuple] = {} + + def create(self, src: list[str], tgt: list[str]) -> dict[str, tuple]: + """ + finds style lexicon. + Returns a set of words following a particular style. + """ + styles = {0: "styles"} + # create vectoriser and inverse vocab. + x, y = stme_utils.compile_binary_dataset(src, tgt) + vectoriser = stme_lexicon.fit_vectorizer(x) + inv_vocab = stme_utils.invert_dict(vectoriser.vocabulary_) + + # train style weights model + src_weights = vectoriser.transform(x) + model = stme_lexicon.train("l1", 3, src_weights, y) + + # extract style features and weights + nz_weights, f_nums = stme_lexicon.extract_nonzero_weights(model) + self.lexicon = stme_lexicon.collect_style_features_and_weights( + nz_weights, styles, inv_vocab, f_nums + ) + log.info("Created Style Lexicon.") + + @classmethod + def from_json(cls, filepath) -> StyleLexicon: + lex = cls() + lex.lexicon = stme_utils.load_json(filepath) + return lex + + def save(self, filepath): + stme_utils.save_json(self.lexicon, filepath) diff --git a/specialk/models/tokenizer.py b/specialk/models/tokenizer.py new file mode 100644 index 0000000..50d4271 --- /dev/null +++ b/specialk/models/tokenizer.py @@ -0,0 +1,816 @@ +"""Tokenizer/Vocabulary library.""" + +from __future__ import annotations + +import codecs +import json +from pathlib import Path +from typing import Any, Iterable, List, Optional, Union + +import sentencepiece as spm +import torch +from sacremoses import MosesDetokenizer, MosesTokenizer +from torch import LongTensor, Tensor +from tqdm import tqdm +from transformers import AutoTokenizer + +import specialk.core.constants as Constants +from specialk.core.utils import deprecated, load_dataset, log +from specialk.datasets.preprocess import parse as bpe_parse +from specialk.models.tokenizers import BytePairEncoder, WordDictionary + + +class Vocabulary: + def __init__( + self, + name: str, + vocab_size: int, + max_length: int, + filename: str = "", + BOS_TOKEN: Optional[str] = Constants.SOS_WORD, + EOS_TOKEN: Optional[str] = Constants.EOS_WORD, + UNK_TOKEN: Optional[str] = Constants.UNK_WORD, + SEP_TOKEN: Optional[str] = Constants.SEP_WORD, + PAD_TOKEN: Optional[str] = Constants.PAD_WORD, + CLS_TOKEN: Optional[str] = Constants.CLS_TOKEN, + BLO_TOKEN: Optional[str] = Constants.BLO_WORD, + lower: bool = True, + ): + """ + Args: + name (str): name of vocabulary. + data_file (Union[Path,str]): path of file containing training + data to use for the vocabulary. + filename (Union[Path,str]): path of vocabulary file to either + load from, or save to. + vocab_size (int, Optional): If set, sets cap on vocabulary size. + max_length (int): maxiumum token length of a sequence. + BOS_TOKEN (Optional[str], optional): A special token representing + the beginning of a sentence. Defaults to Constants.SOS_WORD. + EOS_TOKEN (Optional[str], optional): A special token representing + the end of a sentence. Defaults to Constants.EOS_WORD. + UNK_TOKEN (Optional[str], optional): A special token representing + an out-of-vocabulary token. Defaults to Constants.UNK_WORD. + SEP_TOKEN (Optional[str], optional): A special token separating two + different sentences in the same input (used by BERT for instance).. + Defaults to Constants.UNK_WORD. + PAD_TOKEN (Optional[str], optional): A special token used to make + arrays of tokens the same size for batching purpose. Will then be + ignored by attention mechanisms or loss computation. Defaults to + Constants.SEP_WORD. + CLS_TOKEN (Optional[str], optional): A special token representing the + class of the input (used by BERT for instance). Defaults to + Constants.CLS_TOKEN. + BLO_TOKEN (Optional[str], optional): A special token representing the + separation of paragraph blocks. Defaults to Constants.BLO_WORD. + Returns: + WordDictionary: Vocabulary file. + """ + self.name = name + self.filename = filename + self.vocab_size = vocab_size + self.max_length = max_length + self.vocab: Any + self.lower = lower + + self.BOS_TOKEN = BOS_TOKEN + self.EOS_TOKEN = EOS_TOKEN + self.UNK_TOKEN = UNK_TOKEN + self.PAD_TOKEN = PAD_TOKEN + self.SEP_TOKEN = SEP_TOKEN + self.CLS_TOKEN = CLS_TOKEN + self.BLO_TOKEN = BLO_TOKEN + self.SPECIALS = {self.PAD_TOKEN, self.UNK_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN} + + @deprecated + def make(self, data_path: Union[Path, str]): + raise NotImplementedError + + def fit(self, texts: Iterable[str]): + """Trains tokenizer on dataset. + + Args: + texts (Iterable[str]): Iterable of texts, + e.g. ["hello world", "how are you?"] + """ + raise NotImplementedError + + @deprecated + def load(self): + raise NotImplementedError + + @deprecated + def save(self, filepath: Optional[Union[Path, str]] = None): + if not filepath: + filepath = self.filename + raise NotImplementedError + + def to_tensor(self, text: str) -> LongTensor: + raise NotImplementedError + + def tokenize(self, text: str) -> List[str]: + raise NotImplementedError + + def detokenize( + self, tokens: Union[Tensor, List], specials: Optional[bool] = True + ) -> List[str]: + raise NotImplementedError + + def to_dict(self) -> dict: + return { + "kwargs": { + "name": self.name, + "filename": self.filename, + "vocab_size": self.vocab_size, + "max_length": self.max_length, + "vocab": self.vocab, + "lower": self.lower, + "BOS_TOKEN": self.BOS_TOKEN, + "EOS_TOKEN": self.EOS_TOKEN, + "UNK_TOKEN": self.UNK_TOKEN, + "SEP_TOKEN": self.SEP_TOKEN, + "PAD_TOKEN": self.PAD_TOKEN, + "CLS_TOKEN": self.CLS_TOKEN, + "BLO_TOKEN": self.BLO_TOKEN, + } + } + + def to_file(self, filepath: Path | str) -> None: + """save vocabulary file. + + Args: + filepath (Path | str): filepath to dump the JSON to. + """ + if isinstance(filepath, str): + filepath: Path = Path(filepath) + dump = self.to_dict() + with open(filepath, "w") as f: + json.dump(dump, f) + + @classmethod + def from_dict(cls, d: dict) -> Vocabulary: + return Vocabulary(**d["kwargs"]) + + @classmethod + def from_file(cls, filepath: Path | str) -> Vocabulary: + """Loads an encoder from path saved with save""" + if isinstance(filepath, str): + filepath: Path = Path(filepath) + with open(filepath) as infile: + obj = json.load(infile) + return cls.from_dict(obj) + + def __repr__(self) -> str: + return f"Vocabulary(vocab_size={self.vocab_size}, max_length={self.max_length}, lower={self.lower})" + + # @property + # def SOS_TOKEN(self) -> str: + # raise NotImplementedError + + # @property + # def EOS_TOKEN(self) -> str: + # raise NotImplementedError + + # @property + # def PAD_TOKEN(self) -> str: + # raise NotImplementedError + + # @property + # def UNK_TOKEN(self) -> str: + # raise NotImplementedError + + # @property + # def BOS(self) -> int: + # raise NotImplementedError + + # @property + # def EOS(self) -> int: + # raise NotImplementedError + + # @property + # def PAD(self) -> int: + # raise NotImplementedError + + # @property + # def UNK(self) -> int: + # raise NotImplementedError + + +class BPEVocabulary(Vocabulary): + def __init__(self, name: str, pct_bpe: float, **kwargs): + super().__init__(name, **kwargs) + self.pct_bpe = pct_bpe + self.vocab: BytePairEncoder + + @deprecated + def make(self, data_path: Union[Path, str]) -> None: + log.info("Creating BytePairEncoder.") + src_bpe = BytePairEncoder( + vocab_size=self.vocab_size, + pct_bpe=self.pct_bpe, + ngram_min=1, + UNK=self.UNK_TOKEN, + PAD=self.PAD_TOKEN, + word_tokenizer=bpe_parse, + ) + # loading data path + dataset: List[str] = load_dataset(data_path) + if self.lower: + dataset = [i.lower() for i in dataset] + + src_bpe.fit(dataset) + self.vocab = src_bpe + log.info("Finished training BytePairEncoder.") + + def fit(self, texts: Iterable[str]): + src_bpe = BytePairEncoder( + vocab_size=self.vocab_size, + pct_bpe=self.pct_bpe, + ngram_min=1, + UNK=self.UNK_TOKEN, + PAD=self.PAD_TOKEN, + word_tokenizer=bpe_parse, + ) + if self.lower: + texts = [i.lower() for i in texts] + + src_bpe.fit(texts) + self.vocab = src_bpe + log.info("Finished training BytePairEncoder.") + + def to_tensor(self, text: Union[str, List[str]]) -> Iterable[List[int]]: + """_summary_ + + Args: + text (Union[str, List[str]]): _description_ + + Returns: + Iterable[List[int]]: _description_ + """ + if isinstance(text, str): + return list(self.vocab.transform([text], fixed_length=self.max_length)) + else: + # it's a list + return list(self.vocab.transform(text, fixed_length=self.max_length)) + + @deprecated + def load(self): + self.vocab = BytePairEncoder.load(self.filename) + + @deprecated + def save(self, filepath: Optional[Union[Path, str]] = None): + if not filepath: + filepath = self.filename + log.info(f"Saving vocabulary '{self.name}' to '{filepath}'...") + self.vocab.save(filepath) + + @classmethod + def from_file(cls, filepath: Path | str) -> BPEVocabulary: + return super().from_file(filepath) + + @classmethod + def from_dict(cls, d: dict) -> BPEVocabulary: + this = cls(**d["kwargs"]) + this.vocab = BytePairEncoder.from_dict(d["vocab"]) + return this + + def to_dict(self) -> dict: + d = super().to_dict() + del d["kwargs"]["vocab"] + d["vocab"] = self.vocab.vocabs_to_dict() + d["kwargs"]["pct_bpe"] = self.pct_bpe + d["class"] = "BPEVocabulary" + # log.info("dict", dict=d) + return d + + def detokenize( + self, tokens: List[List[int]], specials=True + ) -> Iterable[str | list[str]]: + # TODO clean up, this looks horrific + out = [] + if isinstance(tokens[0], list): + # nested list + for item in tokens: + if not specials: + item = [ + [i for i in item if self.vocab.word_vocab[self.vocab.PAD] != i] + ] + out.append(next(self.vocab.inverse_transform(item))) + return out + else: + if not specials: + tokens = [ + [i for i in item if self.vocab.word_vocab[self.vocab.PAD] != i] + ] + return next(self.vocab.inverse_transform(tokens)) + + def tokenize(self, text: str) -> List[str]: + return self.vocab.tokenize(text) + + def __repr__(self) -> str: + return f"BPEVocabulary(vocab_size={self.vocab_size}, max_length={self.max_length}, lower={self.lower}, pct_bpe={self.pct_bpe})" + + +class SentencePieceVocabulary(Vocabulary): + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.vocab: spm.SentencePieceProcessor + + def fit( + self, + **kwargs, + ): + raise NotImplementedError("Train this with train_sentencepiece.py") + + @classmethod + def from_file( + cls, filepath: Path | str, max_length: int + ) -> SentencePieceVocabulary: + """Loads SentencePiece model from path. + + Args: + filepath (Path | str): _description_ + + Returns: + SentencePieceVocabulary: _description_ + """ + filename = Path(filepath).name + filepath = str(filepath) + vocab = spm.SentencePieceProcessor(model_file=filepath) + ID_BOS, ID_EOS, ID_PAD, ID_UNK = ( + vocab.bos_id(), + vocab.eos_id(), + vocab.pad_id(), + vocab.unk_id(), + ) + BOS, EOS, PAD, UNK = ( + vocab.IdToPiece(ID_BOS), + vocab.IdToPiece(ID_EOS), + vocab.IdToPiece(ID_PAD), + vocab.IdToPiece(ID_UNK), + ) + vocab_size = len(vocab) + this = cls( + name=filename, + vocab_size=vocab_size, + max_length=max_length, + filename=filepath, + BOS_TOKEN=BOS, + EOS_TOKEN=EOS, + PAD_TOKEN=PAD, + UNK_TOKEN=UNK, + ) + this.vocab = vocab + this.SOS = vocab.bos_id() + this.EOS = vocab.eos_id() + this.UNK = vocab.unk_id() + this.PAD = vocab.pad_id() + return this + + def to_dict(self) -> dict: + raise NotImplementedError + + def tokenize(self, text: str) -> List[str]: + return self.vocab.encode(text, out_type=str) + + def detokenize( + self, tokens: Union[List[List[int]], List[int], Tensor], specials=True + ) -> Iterable[str | List[str]]: + is_single_instance = False + + if isinstance(tokens, Tensor): + if tokens.dim() == 1: + is_single_instance = True + tokens = tokens.unsqueeze(0) + elif tokens.dim() == 2: + if tokens.shape[0] == 1: + is_single_instance = True + elif tokens.dim() > 2: + raise ValueError("Input tensor must be 1D or 2D") + tokens = tokens.tolist() + + elif isinstance(tokens, list): + if isinstance(tokens[0], int): + is_single_instance = True + tokens = [tokens] + elif not isinstance(tokens[0], list): + raise ValueError(f"Input list must be 1D or 2D, got {type(tokens[0])}") + + else: + raise TypeError("tokens must be a torch.LongTensor or a list of integers") + + text = self.vocab.decode(tokens) + if is_single_instance: + return text[0] + return text + + def to_tensor(self, text: str | List[str]) -> Iterable[List[int]]: + if isinstance(text, str): + text = [text] + token_sequences = self.vocab.encode(text, out_type=int) + for i, tokens in enumerate(token_sequences): + tokens = tokens[: self.max_length - 2] + tokens = [self.vocab.bos_id()] + tokens + [self.vocab.eos_id()] + tokens = tokens + [self.vocab.pad_id()] * (self.max_length - len(tokens)) + token_sequences[i] = tokens + return LongTensor(token_sequences) + # return token_sequences + + # @property + # def SOS_TOKEN(self) -> str: + # return self.vocab.IdToPiece(self.SOS) + + # @property + # def EOS_TOKEN(self)-> str: + # return self.vocab.IdToPiece(self.EOS) + + # @property + # def PAD_TOKEN(self)-> str: + # return self.vocab.IdToPiece(self.PAD) + + # @property + # def UNK_TOKEN(self)-> str: + # return self.vocab.IdToPiece(self.UNK) + + # @property + # def SOS(self) -> int: + # return self.vocab.bos_id() + + # @property + # def EOS(self) -> int: + # return self.vocab.eos_id() + + # @property + # def PAD(self) -> int: + # return self.vocab.pad_id() + + # @property + # def UNK(self) -> int: + # return self.vocab.unk_id() + + +class WordVocabulary(Vocabulary): + """White-space level tokenization, leveraging Moses.""" + + def __init__(self, max_length: int, **kwargs): + self.vocab = WordDictionary(seq_len=max_length) + super().__init__(max_length=max_length, **kwargs) + self.mt = MosesTokenizer(lang="en") + self.md = MosesDetokenizer(lang="en") + + @property + def max_length(self): + # max length is actually stored in our vocab object, + # so don't keep track of it in our WordVocabulary. + return self.vocab.seq_length + + @max_length.setter + def max_length(self, new_value: int): + self.vocab.seq_length = new_value + + def fit(self, texts: Iterable[str]): + vocab = WordDictionary( + [ + self.PAD_TOKEN, + self.UNK_TOKEN, + self.BOS_TOKEN, + self.EOS_TOKEN, + ], + lower=self.lower, + seq_len=self.max_length, + ) + for sentence in tqdm(texts, desc="Training Tokenizer"): + for word in self.tokenize(sentence): + vocab.add(word) + + original_size = vocab.size() + require_pruning = True + if original_size < self.vocab_size: + log.info( + f"Vocabulary size at initialisation (n={self.vocab_size})" + " is less than the vocabulary size found " + f"after training (n={original_size}). Setting to new vocab size." + ) + self.vocab_size = original_size + require_pruning = False + + # for debugging purposes, show the head distribution of the + # token freuqencies. + top_n = 10 + token_freq = sorted( + vocab.labelToIdx.keys(), + key=lambda label: vocab.frequencies[vocab.labelToIdx[label]], + reverse=True, + ) + head_freq = token_freq[:top_n] + tail_freq = token_freq[-top_n:] + + log.debug( + f"Head of token frequencies (n={top_n})", + tokens={k: vocab.frequencies[vocab.labelToIdx[k]] for k in head_freq}, + ) + log.debug( + f"Tail of token frequencies (n={top_n})", + tokens={k: vocab.frequencies[vocab.labelToIdx[k]] for k in tail_freq}, + ) + + self.vocab = vocab + if require_pruning: + self.vocab = vocab.prune(self.vocab_size) + log.info( + "Created space-separated token dictionary of size %d (pruned from %d)" + % (self.vocab.size(), original_size) + ) + self.vocab_size = self.vocab.size() + else: + log.info( + "Pruning is skipped as vocabulary size found is " + "less than originally provisioned." + ) + + self.PAD = self.vocab.labelToIdx[Constants.PAD_WORD] + self.BOS = self.vocab.labelToIdx[Constants.SOS_WORD] + self.EOS = self.vocab.labelToIdx[Constants.EOS_WORD] + self.UNK = self.vocab.labelToIdx[Constants.UNK_WORD] + + @deprecated + def make(self, data_path: Union[Path, str]): + """ + Creates onmt dictionary of vocabulary. + + args: + data_path: path of file to use (this is text data). + """ + log.info("Creating WordVocabulary") + vocab = WordDictionary( + [ + self.PAD_TOKEN, + self.UNK_TOKEN, + self.BOS_TOKEN, + self.EOS_TOKEN, + ], + lower=self.lower, + seq_len=self.max_length, + ) + + with codecs.open(data_path, "r", "utf-8") as f: + for sent in tqdm(f.readlines(), desc="Loading lines"): + for word in self.tokenize(sent): + vocab.add(word) + + originalSize = vocab.size() + + # for debugging purposes, show the head distribution of the + # token freuqencies. + top_n = 10 + token_freq = sorted( + vocab.labelToIdx.keys(), + key=lambda label: vocab.frequencies[vocab.labelToIdx[label]], + reverse=True, + )[:top_n] + log.debug( + "Most frequent tokens found", + tokens={k: vocab.frequencies[vocab.labelToIdx[k]] for k in token_freq}, + filepath=data_path, + ) + + self.vocab = vocab.prune(self.vocab_size) + log.info( + "Created space-separated token dictionary of size %d (pruned from %d)" + % (self.vocab.size(), originalSize) + ) + + @deprecated + def load(self): + if Path(self.filename).exists(): + # If given, load existing word dictionary. + log.info(f"Reading {self.name} vocabulary from {self.filename}..") + self.vocab.loadFile(self.filename) + self.vocab_size = self.vocab.size() + self.vocab.max_length = self.max_length + self.lower = self.lower + log.info( + f"Loaded {self.vocab.size()} {self.name} tokens.", + max_len=self.vocab.max_length, + ) + else: + raise FileNotFoundError(f"{self.filename} doesn't exist.") + + @deprecated + def save(self, filepath: Optional[Union[Path, str]] = None): + if not filepath: + filepath = self.filename + """Save vocabulary to file""" + log.info(f"Saving vocabulary '{self.name}' to '{filepath}'...") + self.vocab.writeFile(filepath) + + @classmethod + def from_file(cls, filepath: Path | str) -> WordVocabulary: + return super().from_file(filepath) + + def to_dict(self) -> dict: + d = super().to_dict() + del d["kwargs"]["vocab"] + d["vocab"] = { + "idxToLabel": self.vocab.idxToLabel, + "labelToIdx": self.vocab.labelToIdx, + "frequencies": self.vocab.frequencies, + "special": self.vocab.special, + } + d["class"] = "WordVocabulary" + return d + + @classmethod + def from_dict(cls, d: dict) -> WordVocabulary: + this = cls(**d["kwargs"]) + this.vocab = WordDictionary( + None, lower=d["kwargs"]["lower"], seq_len=d["kwargs"]["max_length"] + ) + # loading ints from json + idx2label = d["vocab"]["idxToLabel"] + idx2label = {int(k): v for k, v in idx2label.items()} + this.vocab.idxToLabel = idx2label + + this.vocab.labelToIdx = d["vocab"]["labelToIdx"] + this.vocab.frequencies = d["vocab"]["frequencies"] + this.vocab.special = d["vocab"]["special"] + + this.PAD = this.vocab.labelToIdx[Constants.PAD_WORD] + this.BOS = this.vocab.labelToIdx[Constants.SOS_WORD] + this.EOS = this.vocab.labelToIdx[Constants.EOS_WORD] + this.UNK = this.vocab.labelToIdx[Constants.UNK_WORD] + return this + + def to_tensor(self, text: Union[str, List[str]]) -> Tensor: + """Converts string to text to tensor of input. + + Args: + text (Union[str,List[str]]): Raw string of input. + + Returns: + torch.LongTensor: List of indices corresponding to + the token index from the vocab. + this has shape [batch_size, seq_length]. + + If text is a string, then the batch_size is 1. + """ + if isinstance(text, str): + return LongTensor( + self.vocab.convertToIdx( + self.tokenize(text), + unkWord=self.UNK_TOKEN, + padding=True, + bosWord=self.BOS_TOKEN, + eosWord=self.EOS_TOKEN, + paddingWord=self.PAD_TOKEN, + ) + ).unsqueeze(0) + else: + tokens = [ + self.vocab.convertToIdx( + self.tokenize(line), + unkWord=self.UNK_TOKEN, + padding=True, + bosWord=self.BOS_TOKEN, + eosWord=self.EOS_TOKEN, + paddingWord=self.PAD_TOKEN, + ) + for line in text + ] + return LongTensor(tokens) + + def tokenize(self, text: str) -> List[str]: + """Performs space level tokenization. + Ensure that Moses Tokenization is used here.""" + return [word for word in self.mt.tokenize(text, return_str=True).split()] + + def detokenize(self, tokens: Union[LongTensor, list], specials=True) -> List[str]: + """Returns detokenized string(s). + + Args: + tokens (Union[LongTensor, List[int], List[List[int]]]): Tensor or + list of integer values corresponding to indexes of tokens. + specials (bool, optional): If unset, then removes special tokens from + output. Defaults to True. + + Returns: + Union[str, List[str]]: Plain text value(s). + + Example: + text = "récompensée et toi" + tokens = word_vocab.to_tensor(tokens) + >>> tokens + tensor([ 2, 29907, 10, 7724, 3, 0, 0 ... ]) + >>> word_vocab.detokenize(tokens) + " récompensée et toi ..." + >>> word_vocab.detokenize(tokens, specials=False) + "récompensée et toi" + """ + is_single_instance = False + + if isinstance(tokens, torch.Tensor): + if tokens.dim() == 1: + tokens = tokens.unsqueeze(0) + is_single_instance = True + elif tokens.dim() > 2: + raise ValueError("Input tensor must be 1D or 2D") + + elif isinstance(tokens, list): + if isinstance(tokens[0], int): + tokens = [tokens] + is_single_instance = True + elif not isinstance(tokens[0], list): + raise ValueError("Input list must be 1D or 2D") + + else: + raise TypeError("tokens must be a torch.LongTensor or a list of integers") + + tokens = tokens.tolist() + labels = [[self.vocab.idxToLabel[t] for t in batch] for batch in tokens] + + if not specials: + labels = [[t for t in batch if t not in self.SPECIALS] for batch in labels] + labels = [self.md.detokenize(batch) for batch in labels] + + if is_single_instance: + labels = labels[0] + return labels + + def __repr__(self) -> str: + return f"WordVocabulary(vocab_size={self.vocab_size}, max_length={self.max_length}, lower={self.lower})" + + +class HuggingFaceVocabulary(Vocabulary): + def __init__( + self, name: str, pretrained_model_name_or_path: str, max_length: int, **kwargs + ): + super().__init__(name=name, vocab_size=None, max_length=max_length, **kwargs) + self.pretrained_model_name_or_path = pretrained_model_name_or_path + self.tokenizer = AutoTokenizer.from_pretrained( + pretrained_model_name_or_path, use_fast=False + ) + self.vocab_size = self.tokenizer.vocab_size + + # Map special tokens + self.PAD = self.tokenizer.pad_token_id + self.BOS = self.tokenizer.bos_token_id + self.EOS = self.tokenizer.eos_token_id + self.UNK = self.tokenizer.unk_token_id + + # Update special token strings + self.PAD_TOKEN = self.tokenizer.pad_token + self.BOS_TOKEN = self.tokenizer.bos_token + self.EOS_TOKEN = self.tokenizer.eos_token + self.UNK_TOKEN = self.tokenizer.unk_token + + def fit(self, texts: Iterable[str]): + # HuggingFace tokenizers are pre-trained, so we don't need to fit them + log.warn("Not implemented but huggingface tokenizers are already trained.") + pass + + def to_tensor(self, text: Union[str, List[str]]) -> Tensor: + encoded = self.tokenizer( + text, + padding="max_length", + truncation=True, + max_length=self.max_length, + return_tensors="pt", + ) + return encoded["input_ids"] + + def tokenize(self, text: str) -> List[str]: + return self.tokenizer.tokenize(text) + + def detokenize( + self, tokens: Union[Tensor, List[List[int]], List[int]], specials: bool = True + ) -> Union[str, List[str]]: + if isinstance(tokens, Tensor): + tokens = tokens.tolist() + + if isinstance(tokens[0], int): + tokens = [tokens] + + decoded = self.tokenizer.batch_decode(tokens, skip_special_tokens=not specials) + return decoded[0] if len(decoded) == 1 else decoded + + def to_dict(self) -> dict: + d = super().to_dict() + d["kwargs"]["pretrained_model_name_or_path"] = ( + self.pretrained_model_name_or_path + ) + d["class"] = "HuggingFaceTokenizer" + return d + + @classmethod + def from_dict(cls, d: dict) -> "HuggingFaceTokenizer": + return cls(**d["kwargs"]) + + @classmethod + def from_file(cls, filepath: str, max_length: int) -> "HuggingFaceTokenizer": + # Assuming the filepath is the pretrained model name or path + return cls( + name=filepath, pretrained_model_name_or_path=filepath, max_length=max_length + ) + + def __repr__(self) -> str: + return f"HuggingFaceTokenizer(name={self.name}, model={self.pretrained_model_name_or_path}, max_length={self.max_length})" diff --git a/specialk/models/tokenizers/__init__.py b/specialk/models/tokenizers/__init__.py new file mode 100644 index 0000000..386f8da --- /dev/null +++ b/specialk/models/tokenizers/__init__.py @@ -0,0 +1,4 @@ +from specialk.models.tokenizers.bpe import Encoder as BytePairEncoder +from specialk.models.tokenizers.dict import Dict as WordDictionary + +__all__ = [WordDictionary, BytePairEncoder] \ No newline at end of file diff --git a/base/core/bpe.py b/specialk/models/tokenizers/bpe.py similarity index 98% rename from base/core/bpe.py rename to specialk/models/tokenizers/bpe.py index 3e2bb45..9fe1500 100755 --- a/base/core/bpe.py +++ b/specialk/models/tokenizers/bpe.py @@ -8,24 +8,16 @@ https://github.com/soaxelbrooke/python-bpe/tree/master/bpe """ -from collections import Counter - -try: - from typing import Any, Callable, Dict, Iterable, Iterator, List -except ImportError: - pass - import json import os +from collections import Counter +from typing import Any, Callable, Dict, Iterable, Iterator, List import toolz from nltk.tokenize import wordpunct_tokenize from tqdm import tqdm -if os.getcwd().split("/")[-1] == "core": - import constants as Constants -else: - import core.constants as Constants +import specialk.core.constants as Constants DEFAULT_EOW = "__eow" DEFAULT_SOW = "__sow" diff --git a/base/classifier/onmt/Dict.py b/specialk/models/tokenizers/dict.py similarity index 85% rename from base/classifier/onmt/Dict.py rename to specialk/models/tokenizers/dict.py index aafa4df..e929b59 100755 --- a/base/classifier/onmt/Dict.py +++ b/specialk/models/tokenizers/dict.py @@ -1,4 +1,8 @@ import codecs +from typing import List + +import torch + import torch @@ -15,11 +19,14 @@ def __init__(self, data=None, lower=False, seq_len=50): self.special = [] if data is not None: - if type(data) == str: + if isinstance(data, str): self.loadFile(data) else: self.addSpecials(data) + def __repr__(self) -> str: + return f"Dict(lower={self.lower}, seq_length={self.seq_length})" + def size(self): return len(self.idxToLabel) @@ -113,21 +120,36 @@ def prune(self, size): # Convert `labels` to indices. Use `unkWord` if not found. # Optionally insert `bosWord` at the beginning and `eosWord` at the . # Also adding padding for sentences with less than 50 tokens . - def convertToIdx(self, labels, unkWord, padding=False, bosWord=None, eosWord=None): + def convertToIdx( + self, + labels: List[str], + unkWord, + padding=False, + bosWord=None, + eosWord=None, + paddingWord=None, + ) -> List[int]: vec = [] if bosWord is not None: vec += [self.lookup(bosWord)] unk = self.lookup(unkWord) + pad = 3 + if paddingWord: + pad = self.lookup(paddingWord) + vec += [self.lookup(label, default=unk) for label in labels] - if padding == True: - vec += [3] * (self.seq_length - len(labels)) + # force truncate the text before closing + vec = vec[: self.seq_length - 1] + # EOS should come before Padding. if eosWord is not None: vec += [self.lookup(eosWord)] + if padding: + vec += [pad] * (self.seq_length - len(vec)) - return torch.LongTensor(vec) + return vec # Convert `idx` to labels. If index `stop` is reached, convert it and return. def convertToLabels(self, idx, stop): diff --git a/specialk/models/transformer/hf_transformer.py b/specialk/models/transformer/hf_transformer.py new file mode 100644 index 0000000..93e5efc --- /dev/null +++ b/specialk/models/transformer/hf_transformer.py @@ -0,0 +1,203 @@ +""" +Huggingface Transformers. + +This is largely a wrapper that combines NMTModule, Peft and Huggingface Transformers. +This is to leverage existing pre-trained modules. +""" + +from __future__ import annotations + +import tempfile +from pathlib import Path +from typing import Callable, List, Optional, Tuple, Union + +import safetensors +import torch +from peft.mapping import get_peft_model +from peft.peft_model import PeftModel +from peft.tuners.lora.config import LoraConfig +from peft.utils.peft_types import TaskType +from peft.utils.save_and_load import get_peft_model_state_dict +from torch import Tensor +from torch.nn import LayerNorm +from torch.optim import AdamW +from transformers import AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer + +from specialk.core.constants import TRAIN_ACC, TRAIN_BATCH_ID, TRAIN_LOSS +from specialk.core.utils import log +from specialk.models.mt_model import NMTModule +from specialk.models.tokenizer import Vocabulary + + +class MarianMTModule(NMTModule): + def __init__( + self, + name: str, + model_base_name: str = "Helsinki-NLP/opus-mt-fr-en", + peft_config: Optional[LoraConfig] = None, + tokenizer: Optional[Vocabulary] = None, + ): + """Uses Huggingface based pre-trained models.""" + + super().__init__( + name=name, + vocabulary_size=1, + sequence_length=512, + tokenizer=tokenizer, + ) + self.model_base_name = model_base_name + self.tokenizer = AutoTokenizer.from_pretrained(self.model_base_name) + self._load_base_model() + + self.peft_config = None + if peft_config: + assert peft_config.task_type == TaskType.SEQ_CLS + self.model = get_peft_model(self.base_model, peft_config, adapter_name=name) + self.peft_config = peft_config + log.info("Using PEFT.", config=self.peft_config) + else: + self.model = self.base_model + self.save_hyperparameters(logger=False) + + def forward(self, x: Tensor, y: Tensor) -> Tensor: + x_mask = x != self.tokenizer.pad_token_id + return self.model(x, labels=y, attention_mask=x_mask).logits + + def get_lora_config_modules( + self, num_decoder_layers=6, add_encoder=False + ) -> List[str]: + if add_encoder: + raise NotImplementedError + modules = [] + num_decoder_layers = ( + len(self.model.model.decoder.layers) + if num_decoder_layers is None + else num_decoder_layers + ) + for layer in range(num_decoder_layers): + for proj in ["q_proj", "k_proj", "v_proj"]: + modules.append(f"decoder.layers.{layer}.encoder_attn.{proj}") + modules.append("lm_head") + return modules + + def _load_base_model(self): + """Load the base model from HuggingFace.""" + log.info("Loading base model") + self.base_model = AutoModelForSeq2SeqLM.from_pretrained(self.model_base_name) + config = AutoConfig.from_pretrained(self.model_base_name) + log.info("Successfully loaded model and config.") + self.sequence_length = config.max_position_embeddings + + def training_step(self, batch: dict, batch_idx: int) -> torch.Tensor: + """Run Training step. + + Args: + batch (dict): individual batch generated from the DataLoader. + batch_idx (int): ID corresponding to the batch. + + Returns: + torch.Tensor: Returns loss. + """ + x, y = batch["source"], batch["target"] + batch_size = x.shape[0] + + x_mask = x != self.tokenizer.pad_token_id + y_hat = self.model(x, labels=y, attention_mask=x_mask) + + loss: torch.Tensor = y_hat.loss + accuracy = self.calculate_classification_metrics(y_hat.logits, y) + + self.log_dict( + {TRAIN_ACC: accuracy, TRAIN_BATCH_ID: batch_idx, TRAIN_LOSS: loss}, + batch_size=batch_size, + ) + return loss + + def _shared_eval_step( + self, batch: dict, batch_idx: int + ) -> Tuple[torch.Tensor, float]: + """Run shared eval step. + + Args: + batch (dict): individual batch generated from the DataLoader. + batch_idx (int): ID corresponding to the batch. + + Returns: + Tuple[torch.Tensor, float]: Returns loss, accuracy. + """ + x, y = batch["source"], batch["target"] + + x_mask = x != self.tokenizer.pad_token_id + y_hat = self.model(x, labels=y, attention_mask=x_mask) + + loss: torch.Tensor = y_hat.loss + accuracy = self.calculate_classification_metrics(y_hat.logits, y) + return loss, accuracy + + def load_peft_from_checkpoint(self, checkpoint) -> PeftModel: + """For some reason, the PeftModel.from_pretrained works, when + directly setting the weights from the state_dict doesn't. + + I've spent way too long trying to debug this.""" + + peft_config = checkpoint["hyper_parameters"]["peft_config"] + peft_state_dict = checkpoint["peft_state_dict"] + + # TODO change this with a fake filesytem + with tempfile.TemporaryDirectory() as temp_dir: + # Save the modified config to the temp directory + peft_config.save_pretrained(temp_dir) + + # Save the filtered state dict to the temp directory + path_peft_tensors = Path(temp_dir) / "adapter_model.safetensors" + safetensors.torch.save_file(peft_state_dict, path_peft_tensors) + + # Use the standard PEFT loading mechanism. + peft_model = PeftModel.from_pretrained( + self.base_model, + temp_dir, + adapter_name=self.name, + ) + + return peft_model + + def on_save_checkpoint(self, checkpoint: dict): + if self.peft_config: + # Save only the PEFT state dict + peft_state_dict = get_peft_model_state_dict( + self.model, adapter_name=self.name + ) + checkpoint["peft_state_dict"] = peft_state_dict + # you don't need it because we're only saving peft_state_dict. + del checkpoint["state_dict"] + else: + # For non-PEFT models, save the entire state dict + checkpoint["state_dict"] = self.model.state_dict() + + def on_load_checkpoint(self, checkpoint: dict): + # Reload the base model to ensure clean slate + if "peft_state_dict" in checkpoint: + # Re-initialize the PEFT model + self.model = self.load_peft_from_checkpoint(checkpoint) + + else: + # For non-PEFT models, load the entire state dict + self.model.load_state_dict(checkpoint["state_dict"]) + + def _freeze_non_lora_weights(self): + for name, param in self.model.named_parameters(): + if "lora_" not in name: + param.requires_grad = False + + @classmethod + def load_from_checkpoint(cls, checkpoint_path: Path, map_location=None): + checkpoint = torch.load(checkpoint_path, map_location=map_location) + + model = cls( + name=checkpoint["hyper_parameters"]["name"], + model_base_name=checkpoint["hyper_parameters"]["model_base_name"], + peft_config=checkpoint["hyper_parameters"].get("peft_config"), + ) + + model.on_load_checkpoint(checkpoint) + return model diff --git a/base/lib/transformer/Beam.py b/specialk/models/transformer/legacy/Beam.py similarity index 91% rename from base/lib/transformer/Beam.py rename to specialk/models/transformer/legacy/Beam.py index a853df5..bdaae02 100755 --- a/base/lib/transformer/Beam.py +++ b/specialk/models/transformer/legacy/Beam.py @@ -5,11 +5,11 @@ https://github.com/OpenNMT/OpenNMT-py/blob/master/onmt/Beam.py """ -# import transformer.Constants as Constants -import core.constants as Constants -import numpy as np import torch +# import transformer.Constants as Constants +import specialk.core.constants as Constants + class Beam: """Beam search""" @@ -17,14 +17,6 @@ class Beam: def __init__(self, size, device=False): self.size = size self._done = False - - # The score for each translation on the beam. - self.scores = torch.zeros((size,), dtype=torch.float, device=device) - self.all_scores = [] - - # The backpointers at each time-step. - self.prev_ks = [] - # The outputs at each time-step. self.next_ys = [ torch.full((size,), Constants.PAD, dtype=torch.long, device=device) diff --git a/base/lib/transformer/Constants.py b/specialk/models/transformer/legacy/Constants.py similarity index 57% rename from base/lib/transformer/Constants.py rename to specialk/models/transformer/legacy/Constants.py index ab11255..b7d3a8c 100755 --- a/base/lib/transformer/Constants.py +++ b/specialk/models/transformer/legacy/Constants.py @@ -9,8 +9,10 @@ # EOS_WORD = '' # import os -from core.constants import * + # if os.getcwd().split("/")[-1] == "transformer": -# from core.constants import * +# from specialk.core.constants import * # else: -# from core.constants import * +# from specialk.core.constants import * + +from specialk.core.constants import * # noqa: F403 diff --git a/base/lib/transformer/Layers.py b/specialk/models/transformer/legacy/Layers.py similarity index 100% rename from base/lib/transformer/Layers.py rename to specialk/models/transformer/legacy/Layers.py diff --git a/base/lib/transformer/Models.py b/specialk/models/transformer/legacy/Models.py similarity index 76% rename from base/lib/transformer/Models.py rename to specialk/models/transformer/legacy/Models.py index c3da3ed..e03416d 100755 --- a/base/lib/transformer/Models.py +++ b/specialk/models/transformer/legacy/Models.py @@ -1,18 +1,14 @@ """Define the Transformer model""" -# import os -# import sys -# import transformer.Constants as Constants -import core.constants as Constants +from typing import Optional + import numpy as np import torch import torch.nn as nn -# if os.getcwd().split("/")[-1] == "transformer": -# import core.constants as Constants -# else: -# import core.constants as Constants -# print(sys.path) +import specialk.core.constants as Constants +from specialk.models.transformer.pos_encoders import PositionalEncoder + from .Layers import DecoderLayer, EncoderLayer __author__ = "Yu-Hsiang Huang" @@ -93,6 +89,8 @@ def __init__( n_src_vocab, d_word_vec, padding_idx=Constants.PAD ) + # TODO: see how this interacts with the old checkpoints + # self.position_enc = PositionalEncoder(d_word_vec, n_position) self.position_enc = nn.Embedding.from_pretrained( get_sinusoid_encoding_table(n_position, d_word_vec, padding_idx=0), freeze=True, @@ -113,8 +111,7 @@ def forward(self, src_seq, src_pos, return_attns=False): non_pad_mask = get_non_pad_mask(src_seq) # -- Forward - # print("BEFORE FIRE") - enc_output = self.src_word_emb(src_seq) + enc_output = self.src_word_emb(src_seq) # batch_size, seq_len, model_dim enc_output += self.position_enc(src_pos) for enc_layer in self.layer_stack: @@ -217,9 +214,15 @@ def __init__( dropout=0.1, tgt_emb_prj_weight_sharing=True, emb_src_tgt_weight_sharing=True, + **kwargs, ): super().__init__() + if n_tgt_vocab is None: + n_tgt_vocab = n_src_vocab + if n_src_vocab != n_tgt_vocab: + emb_src_tgt_weight_sharing = False + self.encoder = Encoder( n_src_vocab=n_src_vocab, len_max_seq=len_max_seq, @@ -278,3 +281,60 @@ def forward(self, src_seq, src_pos, tgt_seq, tgt_pos): seq_logit = self.generator(dec_output) * self.x_logit_scale return seq_logit + + +class TransformerWrapper(Transformer): + def __init__( + self, + vocabulary_size: int, + decoder_vocabulary_size: Optional[int] = None, + max_seq_length: int = 100, + dim_model: int = 512, + n_heads: int = 8, + dim_feedforward: int = 2048, + num_encoder_layers: int = 6, + dropout=0.1, + decoder_generator_weight_sharing=True, + name: str = "PyTorchTransformer", + **kwargs, + ): + if not decoder_vocabulary_size: + decoder_vocabulary_size = vocabulary_size + self.name = name + super().__init__( + n_src_vocab=vocabulary_size, + n_tgt_vocab=decoder_vocabulary_size, + len_max_seq=max_seq_length, + d_word_vec=dim_model, + d_model=dim_model, + d_inner=dim_feedforward, + n_layers=num_encoder_layers, + n_head=n_heads, + d_k=64, + d_v=64, + dropout=dropout, + tgt_emb_prj_weight_sharing=decoder_generator_weight_sharing, + emb_src_tgt_weight_sharing=True, + **kwargs, + ) + + def forward(self, src_seq, tgt_seq): + BATCH_SIZE, SEQUENCE_LENGTH = src_seq.shape + len_x = torch.arange(SEQUENCE_LENGTH, device=src_seq.device).repeat( + (BATCH_SIZE, 1) + ) + len_x = len_x.masked_fill((src_seq == Constants.PAD), 0) + len_y = torch.arange(SEQUENCE_LENGTH, device=src_seq.device).repeat( + (BATCH_SIZE, 1) + ) + len_y = len_y.masked_fill((tgt_seq == Constants.PAD), 0) + y_hat_tokens = super().forward(src_seq, len_x, tgt_seq, len_y) + + # y_hat will return predicted tokens of y[1:], so we'll + # copy over the original SOS token. + sos_one_hot = torch.zeros_like(y_hat_tokens[:, 0, :]) + sos_one_hot = sos_one_hot.scatter(1, tgt_seq[:, 0].unsqueeze(0).T, 1).unsqueeze( + 1 + ) + + return torch.cat([sos_one_hot, y_hat_tokens], dim=1) diff --git a/base/lib/transformer/Modules.py b/specialk/models/transformer/legacy/Modules.py similarity index 100% rename from base/lib/transformer/Modules.py rename to specialk/models/transformer/legacy/Modules.py diff --git a/base/lib/transformer/Optim.py b/specialk/models/transformer/legacy/Optim.py similarity index 87% rename from base/lib/transformer/Optim.py rename to specialk/models/transformer/legacy/Optim.py index 0d38331..6e6bcde 100755 --- a/base/lib/transformer/Optim.py +++ b/specialk/models/transformer/legacy/Optim.py @@ -1,12 +1,13 @@ """A wrapper class for optimizer""" import numpy as np +from torch.optim import Optimizer -class ScheduledOptim: +class ScheduledOptim(Optimizer): """A simple wrapper class for learning rate scheduling""" - def __init__(self, optimizer, d_model, n_warmup_steps): + def __init__(self, optimizer: Optimizer, d_model: int, n_warmup_steps: int): self._optimizer = optimizer self.n_warmup_steps = n_warmup_steps self.n_current_steps = 0 diff --git a/base/lib/transformer/SubLayers.py b/specialk/models/transformer/legacy/SubLayers.py similarity index 100% rename from base/lib/transformer/SubLayers.py rename to specialk/models/transformer/legacy/SubLayers.py diff --git a/base/lib/transformer/Translator.py b/specialk/models/transformer/legacy/Translator.py similarity index 100% rename from base/lib/transformer/Translator.py rename to specialk/models/transformer/legacy/Translator.py diff --git a/base/lib/transformer/__init__.py b/specialk/models/transformer/legacy/__init__.py similarity index 100% rename from base/lib/transformer/__init__.py rename to specialk/models/transformer/legacy/__init__.py diff --git a/specialk/models/transformer/pos_encoders.py b/specialk/models/transformer/pos_encoders.py new file mode 100644 index 0000000..7c3cd29 --- /dev/null +++ b/specialk/models/transformer/pos_encoders.py @@ -0,0 +1,163 @@ +from typing import Optional + +import einops +import numpy as np +import torch +import torch.nn as nn +from jaxtyping import Float +from torch import Tensor + +from specialk.core.utils import deprecated + + +class PositionalEncoder(nn.Module): + def __init__(self, dim_model: int, max_seq_length: int): + """Implementation of the Positional Encoder used + in "Attention is All You Need". + + Args: + dim_model (int): Dimension of the positional encoding (same as emb.) + max_seq_length (int): Maximum length to compute positional encodings + against. + """ + super(PositionalEncoder, self).__init__() + pos_enc = torch.zeros((max_seq_length, dim_model)) + position = torch.arange(max_seq_length, dtype=torch.float).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, dim_model, 2).float() * -(np.log(10000.0) / dim_model) + ) + + # sins in every even index, cos in every odd index. + pos_enc[:, 0::2] = torch.sin(position * div_term) + pos_enc[:, 1::2] = torch.cos(position * div_term) + + # we don't want to keep gradients (these aren't model params). + self.register_buffer("pos_enc", pos_enc.unsqueeze(0)) + + def forward( + self, x: Float[Tensor, "batch seq_len embedding"] + ) -> Float[Tensor, "batch seq_len embedding"]: + """Adds positional encoding to each item in the index. + + Returns: + Tensor: x with the positional encodings added to each value. + """ + _, sequence_length, _ = x.shape + return x + self.pos_enc[:, :sequence_length, :] + + +class RotaryPositionalEncoder(nn.Module): + def __init__(self, dim_model: int, max_seq_len: int = 512, base: int = 10000): + """Implementation of Rotary Positional Encodings (RoPE), + used in RoFormer. + + From Eluther (https://blog.eleuther.ai/rotary-embeddings/): + > Unlike standard positional embeddings, however, rotary embeddings must + be applied at every layer. As large transformer models are typically + dominated by matrix multiplies, we find that the overall overhead + remains negligible. With fusion, we find that rotary embeddings + impose a 1-3% overhead across a range of transformer sizes. + + Args: + dim_model (int): Embedding dimension. This is usually set to the + dim. of each head in the attention module (i.e. `embed_dim//n_heads`). + max_seq_len (int): Maximum expected sequence length for the + model, if exceeded the cached freqs will be recomputed. + base (int): The base for the geometric progression used to compute + the rotation angles. + """ + super().__init__() + self.base = base + self.d = dim_model + assert dim_model % 2 == 0, ( + "Dimension must be divisible by 2; " + f"current dim_model={dim_model} does not." + ) + self.max_seq_len = max_seq_len + self._update_cos_sin_cache(max_seq_len) + + def _update_cos_sin_cache(self, seq_len: int): + """Builds cache. + + This is regenerated if max_seq_len is greater than the current length. + """ + if hasattr(self, "cos_cached"): + if seq_len <= self.cos_cached.shape[0]: + return + + # THETA = 10,000^(-2*i/d) or 1/10,000^(2i/d) + theta = 1.0 / (self.base ** (torch.arange(0, self.d, 2) / self.d)) + + seq_idx = torch.arange(seq_len).float() # Position Index -> [0,1,2...seq-1] + + idx_theta = einops.einsum(seq_idx, theta, "n, d -> n d") + # Cache the computation of cos and sin + self.register_buffer("cos_cached", idx_theta.cos(), persistent=False) + self.register_buffer("sin_cached", idx_theta.sin(), persistent=False) + + @deprecated + @staticmethod + def rotate(x: Tensor) -> Tensor: + """ + Perform Rotation on complex plane. + + For instance, If you have an input tensor of shape [1, seq_len=5, d_model=4]: + + tensor([[[ 1, 2, 3, 4], + [ 5, 6, 7, 8], + [ 9, 10, 11, 12], + [ 13, 14, 15, 16], + [ 17, 18, 19, 20]]]) + + The rotation will look like this: + + tensor([[[ -2, -4, 1, 3], + [ -6, -8, 5, 7], + [-10, -12, 9, 11], + [-14, -16, 13, 15], + [-18, -20, 17, 19]]]) + + This operation effectively creates a tensor that represents a + 90-degree rotation of the original tensor in a complex plane. + """ + return torch.cat([-x[..., 1::2], x[..., ::2]], dim=-1) + + def forward( + self, x: Float[torch.Tensor, "... s d"], seq_len: Optional[int] = None + ) -> Float[torch.Tensor, "... s d"]: + """ + Apply rotary embeddings to the input tensor. + + This method directly applies the rotation to the input tensor, operating on its + last two dimensions (sequence length and embedding dimension). + + Args: + x (Float[torch.Tensor, "... s d"]): Input tensor. The last two dimensions + should be (seq_len, dim). + + Returns: + Float[torch.Tensor, "... s d"]: Tensor with rotary embeddings applied. + """ + if not seq_len: + seq_len = x.shape[-2] + + if seq_len > self.max_seq_len: + self._update_cos_sin_cache(seq_len) + + # Make sure we only use the cached values up to our sequence length + cos = self.cos_cached[:seq_len, :] + sin = self.sin_cached[:seq_len, :] + + # Reshape to match the input tensor's last two dimensions + cos = cos.view((1,) * (x.ndim - 2) + cos.shape) + sin = sin.view((1,) * (x.ndim - 2) + sin.shape) + + # Apply the rotation + x1, x2 = x.chunk(2, dim=-1) + + # (a + bi) * (cos θ + i sin θ) = (a cos θ - b sin θ) + # + i(b cos θ + a sin θ) + rx1 = x1 * cos - x2 * sin + rx2 = x2 * cos + x1 * sin + + return torch.cat([rx1, rx2], dim=-1) diff --git a/specialk/models/transformer/torch/attention.py b/specialk/models/transformer/torch/attention.py new file mode 100644 index 0000000..9dcd7ce --- /dev/null +++ b/specialk/models/transformer/torch/attention.py @@ -0,0 +1,272 @@ +from typing import Optional, Tuple + +import einops +import numpy as np +import torch +from jaxtyping import Float +from torch import Tensor, nn +from torch.nn import Parameter +from torch.nn.init import constant_, xavier_normal_, xavier_uniform_ + +from specialk.core.utils import log +from specialk.models.transformer.pos_encoders import RotaryPositionalEncoder + + +class Attention(nn.Module): + """ + Vanilla Attention Implementation. + + Made to be interoperable as standard pytorch as reasonably possible. + """ + + IGNORE: Float[Tensor, ""] + b_K: Optional[torch.Tensor] + b_V: Optional[torch.Tensor] + + def __init__( + self, + d_head: int, + num_heads: int, + embed_dim: int, + *, + dropout=0.0, + bias=False, + add_bias_kv=True, + add_zero_attn=False, + kdim=None, + vdim=None, + batch_first=True, + ): + """ + MultiHead Attention implementation. + + This is mostly used for my sanity checking and understanding. There's also + a lot less bloat in the code compared to PyTorch's nn.MultiHeadAttention, + but there's no C level optimisation in this code. + + Args: + d_head (int): Dimension of an individual attention head. + Note that d_head * num_heads = embed_dim typically, (treat + d_head is responsible for some set of channels.) + embed_dim: Total dimension of the model. + num_heads: Number of parallel attention heads. Note that + ``embed_dim`` will be split across ``num_heads`` (i.e. each head + will have dimension ``embed_dim // num_heads``). + dropout: Dropout probability on ``attn_output_weights``. + Default: ``0.0`` (no dropout). + bias: If specified, adds bias to input / output projection layers. + Default: ``True``. + add_bias_kv: If specified, adds bias to the key and value sequences + at dim=0. Default: ``False``. + add_zero_attn: If specified, adds a new batch of zeros to the key + and value sequences at dim=1. + Default: ``False``. + kdim: Total number of features for keys. + Default: ``None`` (uses ``kdim=embed_dim``). + vdim: Total number of features for values. + Default: ``None`` (uses ``vdim=embed_dim``). + batch_first: If ``True``, then the input and output tensors are provided + as (batch, seq, feature). Default: ``False`` (seq, batch, feature). + + """ + super().__init__() + + if add_zero_attn or not batch_first: + raise NotImplementedError + + if not (d_head * num_heads == embed_dim): + log.warn( + f"embed_dim={embed_dim} should be divisible by num_heads={d_head}." + ) + + self.kdim = embed_dim if kdim is None else kdim + self.vdim = embed_dim if vdim is None else vdim + self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim + + # In the reference implementation self.in_proj_weight is used (which contains + # W_Q, W_K, W_V. This is technically more memory-efficient since it can end up + # being one contiguous block of memory, but it's going to be chunked here. + # So, this implementation is largely used for explanatory purposes. + self.W_Q = Parameter(torch.empty((num_heads, embed_dim, d_head))) + self.W_K = Parameter(torch.empty((num_heads, self.kdim, d_head))) + self.W_V = Parameter(torch.empty((num_heads, self.vdim, d_head))) + + # self.W_O and self.b_O is functionally the same as self.out_proj + self.W_O = Parameter(torch.empty((num_heads, d_head, embed_dim))) + + if bias: + self.b_Q = Parameter(torch.zeros((num_heads, d_head))) + + if add_bias_kv: + self.b_K = Parameter(torch.empty((num_heads, d_head))) + self.b_V = Parameter(torch.empty((num_heads, d_head))) + else: + self.b_K = self.b_V = None + + # functionally equivalent as out_proj_bias. + self.b_O = Parameter(torch.zeros((embed_dim))) + else: + self.b_Q = self.b_K = self.b_V = self.b_O = None + + self.register_buffer("IGNORE", torch.tensor(-1e5, dtype=torch.float32)) + self.dropout = torch.nn.Dropout(p=dropout) if dropout > 0.0 else None + self._reset_parameters() + + def forward( + self, + x: Float[Tensor, "batch pos embed_dim"], + is_causal=False, + average_attn_weights=True, + ) -> Tuple[ + Float[Tensor, "batch pos embed_dim"], Float[Tensor, "b num_heads q_pos k_pos"] + ]: + """ + Run Attention calculation. + """ + # get query, key, value vectors. Note that *seq_pos for q (q_pos) + # and k (k_pos) are the same in self-attn*, but different in cross-attn. + q: Float[Tensor, "b q_pos num_heads d_head"] + k: Float[Tensor, "b k_pos num_heads d_head"] + v: Float[Tensor, "b v_pos num_heads d_head"] + + q = einops.einsum(self.W_Q, x, "n_h m d_h, b p m -> b p n_h d_h") + if self.b_Q is not None: + q += self.b_Q + k = einops.einsum(self.W_K, x, "n_h m d_h, b p m -> b p n_h d_h") + if self.b_K is not None: + k += self.b_K + v = einops.einsum(self.W_V, x, "n_h m d_h, b p m -> b p n_h d_h") + if self.b_V is not None: + k += self.b_V + + # calculate attention scores; scale, mask, softmax. + attn_scores: Float[Tensor, "b num_heads q_pos k_pos"] + attn_scores = einops.einsum(q, k, "b p_q n h, b p_k n h -> b n p_q p_k") + attn_scores /= np.sqrt(self.W_Q.shape[-1]) # scale down by d_head. + + if is_causal: + attn_scores = self.apply_causal_mask(attn_scores) + + attn_scores = attn_scores.softmax(dim=-1) + + if self.dropout is not None: + attn_scores = self.dropout(attn_scores) + + # take weighted average of value vectors (weighted from attn_scores) + z: Float[Tensor, "b q_pos num_heads d_head"] + z = einops.einsum(v, attn_scores, "b p_k n h, b n p_q p_k -> b p_q n h") + + o: Float[Tensor, "b o_pos num_heads d_head"] # n is summed in the process. + o = einops.einsum(self.W_O, z, "n h m, b p_q n h -> b p_q m") + + if self.b_O is not None: + o += self.b_O + + if average_attn_weights: + # attention scores are averaged across the n_head dimension. + attn_scores = attn_scores.mean(dim=1) + + return o, attn_scores + + def apply_causal_mask( + self, attn_scores: Float[Tensor, "batch num_heads query_pos key_pos"] + ) -> Float[Tensor, "batch num_heads query_pos key_pos"]: + """ + Applies a causal mask to attention scores, and returns masked scores. + It should look like this if dimensions of q, k == 4: + + tensor([[0., 1., 1., 1.], + [0., 0., 1., 1.], + [0., 0., 0., 1.], + [0., 0., 0., 0.]]) + + Offset it with the diagonal (you want [0,0]) to be False. + """ + _, _, q_pos, k_pos = attn_scores.shape + mask = torch.triu( + torch.ones((q_pos, k_pos), device=attn_scores.device, dtype=torch.bool), + diagonal=1, + ) + return attn_scores.masked_fill(mask, self.IGNORE) + + def _reset_parameters(self): + """Initialize weights with random values. This initialisation is + equivalent to nn.MultiheadAttention.""" + xavier_uniform_(self.W_Q) + xavier_uniform_(self.W_K) + xavier_uniform_(self.W_V) + xavier_uniform_(self.W_O) + if self.b_Q is not None: + xavier_normal_(self.b_Q) + if self.b_K is not None: + xavier_normal_(self.b_K) + if self.b_V is not None: + xavier_normal_(self.b_V) + if self.b_O is not None: + constant_(self.b_O, 0.0) + + +class RotaryAttention(Attention): + """Attention with RoPE Embeddings.""" + + def __init__(self, d_head: int, **kwargs): + super().__init__(d_head=d_head, **kwargs) + # note that max_seq_len will be replaced by whatever the sequence length of the input is. + self.rotary_emb = RotaryPositionalEncoder(dim_model=d_head) + + def forward( + self, + x: Float[Tensor, "batch pos embed_dim"], + is_causal=False, + average_attn_weights=True, + ) -> Tuple[ + Float[Tensor, "batch pos embed_dim"], Float[Tensor, "b num_heads q_pos k_pos"] + ]: + # get query, key, value vectors. Note that *seq_pos for q (q_pos) + # and k (k_pos) are the same in self-attn*, but different in cross-attn. + q: Float[Tensor, "b q_pos num_heads d_head"] + k: Float[Tensor, "b k_pos num_heads d_head"] + v: Float[Tensor, "b v_pos num_heads d_head"] + + q = einops.einsum(self.W_Q, x, "n_h m d_h, b p m -> b p n_h d_h") + if self.b_Q is not None: + q += self.b_Q + k = einops.einsum(self.W_K, x, "n_h m d_h, b p m -> b p n_h d_h") + if self.b_K is not None: + k += self.b_K + v = einops.einsum(self.W_V, x, "n_h m d_h, b p m -> b p n_h d_h") + if self.b_V is not None: + k += self.b_V + + # the line below is the main implementation difference between Attention + # and RotaryAttention. + q, k = self.rotary_emb(q), self.rotary_emb(k) + + # calculate attention scores; scale, mask, softmax. + attn_scores: Float[Tensor, "b num_heads q_pos k_pos"] + attn_scores = einops.einsum(q, k, "b p_q n h, b p_k n h -> b n p_q p_k") + attn_scores /= np.sqrt(self.W_Q.shape[-1]) # scale down by d_head. + + if is_causal: + attn_scores = self.apply_causal_mask(attn_scores) + + attn_scores = attn_scores.softmax(dim=-1) + + if self.dropout is not None: + attn_scores = self.dropout(attn_scores) + + # take weighted average of value vectors (weighted from attn_scores) + z: Float[Tensor, "b q_pos num_heads d_head"] + z = einops.einsum(v, attn_scores, "b p_k n h, b n p_q p_k -> b p_q n h") + + o: Float[Tensor, "b o_pos num_heads d_head"] # n is summed in the process. + o = einops.einsum(self.W_O, z, "n h m, b p_q n h -> b p_q m") + + if self.b_O is not None: + o += self.b_O + + if average_attn_weights: + # attention scores are averaged across the n_head dimension. + attn_scores = attn_scores.mean(dim=1) + + return o, attn_scores diff --git a/specialk/models/transformer/torch/pytorch_transformer.py b/specialk/models/transformer/torch/pytorch_transformer.py new file mode 100644 index 0000000..1e20d10 --- /dev/null +++ b/specialk/models/transformer/torch/pytorch_transformer.py @@ -0,0 +1,701 @@ +""" +PyTorch native implementation of the Transformer. + +This is intentional to take advantage of native +implementations (as reasonably as possible), so a lot of this +will be calling the relevant classes and modified afterwards. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Callable, List, Optional, Tuple, Union +from rich import print as rprint +from rich.table import Table +import einops +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from jaxtyping import Bool, Float, Int +from torch import Tensor +from torch.nn import LayerNorm +from torch.optim import AdamW + +import specialk.core.constants as Constants +from specialk.core.utils import log +from specialk.models.generators.sampling import LanguageModelSampler +from specialk.models.mt_model import NMTModule +from specialk.models.generators.beam import BeamBatch, EncoderDecoderBeam +from specialk.models.optimizers.schedulers import CosineWarmupScheduler +from specialk.models.transformer.pos_encoders import PositionalEncoder +from specialk.models.utils.activations import SwiGLU + + +@dataclass +class TransformerEncoderDecoderBeam(EncoderDecoderBeam): + """Beam Class to be used with PyTorchTransformerModel. + + Attributes: + model (type): Description of field1. + tokenizer (type): Description of field2. + logprob_sums (type): Description of field2. + tokens (type): Description of field2. + memory (Tensor): encoder output. + x_pad_mask (Tensor): mask tensor for input tokens. + """ + + memory: Tensor + x_pad_mask: Tensor + + def new( + self, + logprob_sums: Float[Tensor, "batch"], + tokens: Int[Tensor, "batch seq"], + memory: Tensor, + x_pad_mask: Tensor, + ) -> TransformerEncoderDecoderBeam: + """Creates a new EncoderDecoderBeam object with the + same model, tokenizer, and encoder_output. Includes an x_pad_mask.""" + return TransformerEncoderDecoderBeam( + model=self.model, + tokenizer=self.tokenizer, + logprob_sums=logprob_sums, + tokens=tokens, + memory=memory, + x_pad_mask=x_pad_mask, + ) + + @torch.inference_mode() + def get_logits(self) -> Float[Tensor, "batch vocab"]: + logits = self.model.decode( + self.tokens, + tgt_mask=None, + memory=self.memory, + x_pad_mask=self.x_pad_mask, + ) + return torch.nn.functional.log_softmax( + self.model.generator(logits[:, -1]), dim=-1 + ) + + def __getitem__(self, idx) -> TransformerEncoderDecoderBeam: + return self.new( + self.logprob_sums[idx], + self.tokens[idx], + self.memory[idx], + self.x_pad_mask[idx], + ) + + def generate( + self, + tokens_per_beam: int, + no_repeat_ngram_size: Optional[int] = None, + log_logits: Optional[Float[Tensor, "batch vocab"]] = None, + ) -> TransformerEncoderDecoderBeam: + if log_logits is None: + log_logits = self.get_logits() + + log_probs, tok_idx = self.get_topk_non_repeating( + log_logits, no_repeat_ngram_size, k=tokens_per_beam + ) + + new_logprob_sums = self._calculate_new_logprob_sums(log_probs, tokens_per_beam) + new_tokens = self._generate_new_tokens(tok_idx, tokens_per_beam) + new_encoder_outputs = self._repeat_memory(k=tokens_per_beam) + new_x_pad_mask = einops.repeat( + self.x_pad_mask, "b s -> (b beam) s", beam=tokens_per_beam + ) + return self.new( + new_logprob_sums, new_tokens, new_encoder_outputs, new_x_pad_mask + ) + + +@dataclass +class TransformerBeamBatch(BeamBatch): + beams: List[TransformerEncoderDecoderBeam] + + @torch.inference_mode() + def get_logits(self) -> List[Float[torch.Tensor, "beam*beam vocab"]]: + # Aggregate inputs from all beams + all_tokens = torch.cat([beam.tokens for beam in self.beams]) + all_memory = torch.cat([beam.memory for beam in self.beams]) + all_x_pad_mask = torch.cat([beam.x_pad_mask for beam in self.beams]) + decode = self.beams[0].model.decode + generator = self.beams[0].model.generator + + # Perform the logits computation in one go + logits: Float[Tensor, "batch*beam vocab"] = decode( + all_tokens, + tgt_mask=None, + memory=all_memory, + x_pad_mask=all_x_pad_mask, + )[:, -1] + log_probs: Float[Tensor, "batch*beam vocab"] + log_probs = torch.nn.functional.log_softmax(generator(logits), dim=-1) + + # Disperse the results back to individual beams + return list(log_probs.split([beam.num_beams for beam in self.beams])) + + +class TransformerEncoderLayer(nn.TransformerEncoderLayer): + """Modified Transformer Encoder Layer so we can add optional SwiGLU.""" + + def __init__( + self, + d_model: int, + nhead: int, + dim_feedforward: int = 2048, + dropout: float = 0.1, + activation: Callable[[torch.Tensor], torch.Tensor] = F.relu, + layer_norm_eps: float = 1e-5, + batch_first: bool = False, + norm_first: bool = False, + bias: bool = True, + device=None, + dtype=None, + ): + super().__init__( + d_model, + nhead, + dim_feedforward, + dropout, + activation, + layer_norm_eps, + batch_first, + norm_first, + bias, + device, + dtype, + ) + + if activation == SwiGLU: + self.linear1 = SwiGLU(d_model, dim_feedforward, d_model, bias=bias) + self.linear2 = nn.Identity() + + +class TransformerDecoderLayer(nn.TransformerDecoderLayer): + """Modified Transformer Decoder Layer so we can add optional SwiGLU.""" + + def __init__( + self, + d_model: int, + nhead: int, + dim_feedforward: int = 2048, + dropout: float = 0.1, + activation: Union[str, Callable[[Tensor], Tensor]] = F.relu, + layer_norm_eps: float = 1e-5, + batch_first: bool = False, + norm_first: bool = False, + bias: bool = True, + device=None, + dtype=None, + ) -> None: + super().__init__( + d_model, + nhead, + dim_feedforward, + dropout, + activation, + layer_norm_eps, + batch_first, + norm_first, + bias, + device, + dtype, + ) + if activation == SwiGLU: + self.linear1 = SwiGLU(d_model, dim_feedforward, d_model, bias=bias) + self.linear2 = nn.Identity() + + +class TransformerEncoder(nn.TransformerEncoder): + def __init__( + self, + encoder_layer: "TransformerEncoderLayer", + num_layers: int, + norm: Optional[nn.Module] = None, + enable_nested_tensor: bool = True, + mask_check: bool = True, + ) -> None: + super().__init__( + encoder_layer, num_layers, norm, enable_nested_tensor, mask_check + ) + + +class PyTorchTransformerModel(nn.Transformer): + def __init__( + self, + vocab_size: int, + decoder_vocab_size: Optional[int] = None, + max_seq_length: int = 100, + dim_model: int = 512, + n_heads: int = 8, + dim_feedforward: int = 2048, + num_encoder_layers: int = 6, + num_decoder_layers: int = 6, + dropout=0.1, + decoder_generator_weight_sharing=True, + name: str = "PyTorchTransformer", + activation: Callable = F.relu, + layer_norm_eps: float = 1e-5, + batch_first: bool = False, + norm_first: bool = False, + bias: bool = True, + device=None, + dtype=None, + **kwargs, + ) -> None: + """PyTorch native implementation of a Transformer (see parent class). + + Args: + vocab_size (int): Vocabulary size of tokenizer. + max_seq_length (int, optional): Maximum sequence length. + Defaults to 100. + dim_model (int, optional): The number of expected features in the + encoder/decoder inputs. Defaults to 512. + n_heads (int, optional): The number of self-attention heads. + Defaults to 8. + dim_feedforward (int, optional): Dimension of the FFM. Defaults to 2048. + num_encoder_layers (int, optional): Number of attn layers in the encoder. + Defaults to 6. + num_decoder_layers (int, optional): Number of attn layers in the decoder. + Defaults to 6. + dropout (float, optional): Dropout value. Defaults to 0.1. + decoder_generator_weight_sharing (bool, optional): If set, shares weight + between deocder and generator. Defaults to True. + """ + # initialize encoder and decoders separately. + factory_kwargs = {"device": device, "dtype": dtype} + encoder_layer = TransformerEncoderLayer( + dim_model, + n_heads, + dim_feedforward, + dropout, + activation, + layer_norm_eps, + batch_first, + norm_first, + bias, + **factory_kwargs, + ) + + encoder_norm = LayerNorm( + dim_model, eps=layer_norm_eps, bias=bias, **factory_kwargs + ) + encoder = nn.TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) + + decoder_layer = TransformerDecoderLayer( + dim_model, + n_heads, + dim_feedforward, + dropout, + activation, + layer_norm_eps, + batch_first, + norm_first, + bias, + **factory_kwargs, + ) + decoder_norm = LayerNorm( + dim_model, eps=layer_norm_eps, bias=bias, **factory_kwargs + ) + decoder = nn.TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm) + + super(PyTorchTransformerModel, self).__init__( + d_model=dim_model, + nhead=n_heads, + dim_feedforward=dim_feedforward, + num_encoder_layers=num_encoder_layers, + num_decoder_layers=num_decoder_layers, + dropout=dropout, + batch_first=batch_first, + custom_encoder=encoder, + custom_decoder=decoder, + ) + self.name = name + self.model_type = "Transformer" + self.max_seq_length = max_seq_length + self.dim_model = dim_model + self.tgt_mask = None + self.decoder_generator_weight_sharing = decoder_generator_weight_sharing + self.x_logit_scale = 1.0 + self.dropout = dropout + if not decoder_vocab_size: + decoder_vocab_size = vocab_size + + self.input_emb = nn.Embedding( + num_embeddings=vocab_size, embedding_dim=dim_model + ) + self.output_emb = nn.Embedding( + num_embeddings=decoder_vocab_size, embedding_dim=dim_model + ) + self.pos_encoder = PositionalEncoder( + dim_model=dim_model, max_seq_length=max_seq_length + ) + self.generator = nn.Linear(dim_model, decoder_vocab_size) + self.init_weights() + + def generate_square_subsequent_mask(self, size: int) -> Tensor: + """Generate square causal mask. + + Top half of the diagonal is -inf, else 0's. + + Parameters: + length (int): Number of tokens in each sequence in the target batch. + Returns: + mask (arr): tgt_mask, looks like [[0., -inf, -inf], + [0., 0., -inf], + [0., 0., 0.]] + for a size=3. + """ + return torch.log( + torch.tril(torch.ones(size, size, device=self.generator.weight.device)) + ) + + def create_pad_mask(self, x: Tensor, pad_token: int = Constants.PAD) -> Tensor: + """Return tensor of the same shape to mask out padding tokens.""" + return x == pad_token + + def init_weights(self): + """Initiate all weight parameters with Kaiming Uniformity.""" + for p in self.parameters(): + if p.dim() > 1: + nn.init.kaiming_uniform_(p) + + def _forward( + self, + x: Int[Tensor, "batch seq"], + y: Int[Tensor, "batch seq"], + ) -> Float[Tensor, "batch seq model"]: + """Runs forward training pass for this seq2seq transformer training. + + Parameters: + x (Tensor): Input sequence to train. + y (Tensor): Output sequence to train. + + Returns: + Tensor: output tokens by model space. + """ + + # make it causal. + y = y[:, :-1] + + # create masks + length = self.max_seq_length + x_pad_mask, y_pad_mask = self.create_pad_mask(x), self.create_pad_mask(y) + x_mask = torch.zeros((length, length), device=x.device).type(torch.bool) + y_mask = self.generate_square_subsequent_mask(y.shape[-1]).bool() + + x_emb: Float[Tensor, "batch seq_len d_embed"] = self.pos_encoder( + self.input_emb(x) * np.sqrt(self.dim_model) + ) + y_emb: Float[Tensor, "batch seq_len d_embed"] = self.pos_encoder( + self.output_emb(y) * np.sqrt(self.dim_model) + ) + y_hat: Float[Tensor, "batch seq_len d_embed"] = super().forward( + src=x_emb, + tgt=y_emb, + src_mask=x_mask, + tgt_mask=y_mask, + src_key_padding_mask=x_pad_mask, + tgt_key_padding_mask=y_pad_mask, + memory_key_padding_mask=x_pad_mask, + tgt_is_causal=True, + ) + + return y_hat + + def forward( + self, + x: Int[Tensor, "batch seq"], + y: Int[Tensor, "batch seq"], + ) -> Float[Tensor, "batch seq generator"]: + """Runs forward training pass for this seq2seq transformer training. + + Parameters: + x (Tensor): Input sequence to train. + y (Tensor): Output sequence to train. + + Returns: + Tensor: output logits. + """ + y_hat = self._forward(x, y) + + y_hat_tokens: Float[Tensor, "batch seq generator"] = self.generator(y_hat) + + # y_hat will return predicted tokens of y[1:], so we'll + # copy over the original SOS token. + sos_one_hot = torch.zeros_like(y_hat_tokens[:, 0, :]) + sos_one_hot = sos_one_hot.scatter(1, y[:, 0].unsqueeze(0).T, 1).unsqueeze(1) + + y_hat_logits = F.log_softmax(y_hat_tokens, dim=-1) + return torch.cat([sos_one_hot, y_hat_logits], dim=1) + + def encode( + self, + x: Float[Tensor, "batch seq_len"], + x_pad_mask: Optional[Bool[Tensor, "seq_len seq_len"]] = None, + ) -> Tuple[Float[Tensor, "batch seq_len d_model"], Float[Tensor, "batch seq_len"]]: + """Split encoder and decoder runs.""" + + _, seq_len = x.shape + x_mask = torch.zeros((seq_len, seq_len), device=x.device, dtype=torch.bool) + x_pad_mask = x_pad_mask if x_pad_mask else self.create_pad_mask(x) + x_emb: Float[Tensor, "batch seq_len d_embed"] = self.pos_encoder( + self.input_emb(x) * np.sqrt(self.dim_model) + ) + + z: Float[Tensor, "batch seq_len d_model"] = self.encoder( + x_emb, mask=x_mask, src_key_padding_mask=x_pad_mask + ) + return z, x_pad_mask + + def decode( + self, + y: Float[Tensor, "batch seq_len"], + memory: Float[Tensor, "batch seq_len d_model"], + tgt_mask=None, + x_pad_mask: Optional[Int[Tensor, "batch seq_len"]] = None, + ): + """Run decoder stage. This is needed for different decoding strategies.""" + if tgt_mask is None: + tgt_mask = self.generate_square_subsequent_mask(y.size(1)) + + y_padding_mask = self.create_pad_mask(y) + + y_emb: Float[Tensor, "batch seq_len d_embed"] = self.pos_encoder( + self.output_emb(y) * np.sqrt(self.dim_model) + ) + + return self.decoder( + tgt=y_emb, + memory=memory, + tgt_mask=tgt_mask, + tgt_key_padding_mask=y_padding_mask, + memory_key_padding_mask=x_pad_mask, + tgt_is_causal=True, + ) + + +class PyTorchTransformerModule(NMTModule): + def __init__( + self, + vocabulary_size, + n_warmup_steps: int = 4000, + name="PyTorchTransformer", + sequence_length=100, + **kwargs, + ): + super().__init__( + name=name, + vocabulary_size=vocabulary_size, + sequence_length=sequence_length, + **kwargs, + ) + self.n_warmup_steps = n_warmup_steps + self.model = PyTorchTransformerModel( + vocab_size=self.vocabulary_size, + decoder_vocab_size=self.decoder_vocabulary_size, + batch_first=True, + max_seq_length=sequence_length, + **kwargs, + ) + + def configure_optimizers(self): + optimizer = AdamW( + self.model.parameters(), lr=self.learning_rate, betas=(0.9, 0.98), eps=1e-9 + ) + # lr scheduler is applied per step; not epoch. + # so we're changing it. + self.lr_scheduler = CosineWarmupScheduler( + optimizer=optimizer, n_warmup_steps=self.n_warmup_steps, max_iters=3000000 + ) + return optimizer + + @torch.inference_mode() + def beam_search( + self, + input_tokens: Int[Tensor, "batch seq_len"], + num_return_sequences: int, + num_beams: int, + max_new_tokens: int, + length_penalty: float = 1.0, + no_repeat_ngram_size: Optional[int] = None, + verbose=False, + ) -> Tuple[ + Float[Tensor, "batch n_return_seq"], Int[Tensor, "batch n_return_seq seq_len"] + ]: + """ + Implements a beam search. + """ + batch_size = input_tokens.size(0) + device = input_tokens.device + + if num_return_sequences > num_beams: + raise ValueError( + f"num_return_sequences (currently {num_return_sequences} " + f"must be <= num_beams (currently {num_beams})" + ) + + # encode input sequences + memory, x_pad_mask = self.model.encode(input_tokens) + + # initialize beams + beams = [] + for i in range(batch_size): + y_hat = torch.full( + (num_beams, 1), Constants.SOS, dtype=torch.long, device=device + ) + logprob_sums = torch.zeros(num_beams, device=device) + + # expands memory and x_pad_mask multiple times to the length of beams + # (for batch operation). + item_memory = memory[i].unsqueeze(0).expand(num_beams, -1, -1) + item_x_pad_mask = x_pad_mask[i].unsqueeze(0).expand(num_beams, -1) + + beams.append( + TransformerEncoderDecoderBeam( + model=self.model, + tokenizer=self.decoder_tokenizer, + logprob_sums=logprob_sums, + tokens=y_hat, + memory=item_memory, + x_pad_mask=item_x_pad_mask, + ) + ) + batch_of_beams = TransformerBeamBatch.create(beams) + + # List for final beams to return (and early terminations) + finished_beams: List[List[Tuple[float, Tensor]]] + finished_beams = [[] for _ in range(batch_size)] + + for n in range(max_new_tokens): + # Generation step + batch_of_beams = batch_of_beams.generate(num_beams, no_repeat_ngram_size) + batch_of_beams, terminated_beams = batch_of_beams.filter(num_beams) + + # Process terminated beams + for i, beam in enumerate(terminated_beams.beams): + if beam.logprob_sums.size(0) > 0: + scores = beam.logprob_sums / (beam.tokens.size(1) ** length_penalty) + finished_beams[i].extend(list(zip(scores.tolist(), beam.tokens))) + + # Check if all beams are finished + if all(len(beams) >= num_return_sequences for beams in finished_beams): + break + + # Print output + if verbose: + batch_of_beams.print(title=f"Best completions @ n={n+1}") + + # Add remaining beams + for i, beam in enumerate(batch_of_beams.beams): + scores = beam.logprob_sums / (beam.tokens.size(1) ** length_penalty) + finished_beams[i].extend(list(zip(scores.tolist(), beam.tokens))) + + # Sort and truncate results + for i in range(batch_size): + finished_beams[i].sort(key=lambda x: x[0], reverse=True) + finished_beams[i] = finished_beams[i][:num_return_sequences] + + # Prepare the output tensors + max_seq_len = max(max(len(seq) for _, seq in beams) for beams in finished_beams) + scores_tensor = torch.zeros((batch_size, num_return_sequences), device=device) + tokens_tensor = torch.full( + (batch_size, num_return_sequences, max_seq_len), + self.tokenizer.PAD, + dtype=torch.long, + device=device, + ) + + for i, batch_beams in enumerate(finished_beams): + # Sort and truncate results + batch_beams.sort(key=lambda x: x[0], reverse=True) + batch_beams = batch_beams[:num_return_sequences] + + for j, (score, seq) in enumerate(batch_beams): + scores_tensor[i, j] = score + tokens_tensor[i, j, : len(seq)] = seq + + log.info("scores", sc=scores_tensor.shape, tok=tokens_tensor.shape) + return scores_tensor, tokens_tensor + + @torch.inference_mode() + def generate( + self, + input_tokens: Int[Tensor, "batch seq_len"], + max_len=50, + start_symbol: int = Constants.SOS, + temperature=1.0, + top_k=0, + top_p=0.0, + frequency_penalty=0.0, + seed=None, + **kwargs, + ) -> Int[Tensor, "batch max_len"]: + """ + Returns a string of autoregressively generated text from the decoder. + + Sampling terminates at max_tokens_generated, or when the model generates an + end-of-sequence token. + + kwargs are passed to sample_next_token, to give detailed instructions on how + new tokens are chosen. + + args: + input_tokens (Int[Tensor, "batch_size seq_length"]): input sequence + to send to the MT model. + max_len (int): maximum length of generated sequence. + """ + sampler = LanguageModelSampler() + batch_size, _ = input_tokens.shape + logits: Float[Tensor, "seq_len d_vocab"] + + # setup y output values. + y_hat = torch.full( + (batch_size, max_len), Constants.PAD, dtype=torch.long, device=self.device + ) + y_hat[:, 0] = start_symbol + + # Track which sequences have finished + y_completed = torch.zeros(batch_size, dtype=torch.bool, device=self.device) + + memory, x_pad_mask = self.model.encode(input_tokens) + + for i in range(1, max_len): + # prepare decoder output to feed into the model. + out = self.model.decode( + y_hat[:, :i], + tgt_mask=None, + memory=memory, + x_pad_mask=x_pad_mask, + ) + + logits = torch.nn.functional.log_softmax( + self.model.generator(out[:, -1]), dim=-1 + ) + + # Sample next tokens for all unfinished sequences. + next_tokens = torch.where( + y_completed, + Constants.PAD, + sampler.sample_next_token( + y_hat, + logits, + temperature=temperature, + top_k=top_k, + top_p=top_p, + frequency_penalty=frequency_penalty, + seed=seed, + **kwargs, + ), + ) + y_hat[:, i] = next_tokens + + # damn, look at that bitwise or! + y_completed |= next_tokens == self.decoder_tokenizer.EOS + + if y_completed.all(): + break + + return y_hat diff --git a/specialk/models/utils/activations.py b/specialk/models/utils/activations.py new file mode 100644 index 0000000..e18c9ae --- /dev/null +++ b/specialk/models/utils/activations.py @@ -0,0 +1,29 @@ +import torch +import torch.nn as nn +from torch import Tensor + + +class SwiGLU(nn.Module): + """SwiGLU 'activation' function. + + It's more of a nn.layer, and not an activation function.""" + + def __init__( + self, in_features: int, hidden_features=None, out_features=None, bias=True + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.w1 = nn.Linear(in_features, hidden_features, bias=bias) + self.w2 = nn.Linear(in_features, hidden_features, bias=bias) + self.w3 = nn.Linear(hidden_features, out_features, bias=bias) + + def swish(self, x: Tensor) -> Tensor: + """just do it""" + return x * torch.sigmoid(x) + + def forward(self, x: Tensor) -> Tensor: + """Forward pass of SwiGLU""" + x1, x2 = self.w1(x), self.w2(x) + hidden = self.swish(x1) * x2 + return self.w3(hidden) diff --git a/specialk/models/utils/lid.py b/specialk/models/utils/lid.py new file mode 100644 index 0000000..24c5c04 --- /dev/null +++ b/specialk/models/utils/lid.py @@ -0,0 +1,167 @@ +import multiprocessing as mp +from huggingface_hub import hf_hub_download +import fasttext +import pandas as pd +import numpy as np +from tqdm import tqdm +from typing import List, Tuple, Generator, Iterable, Hashable, Any +from enum import Enum + + +class LanguageCode(Enum): + """This is taken outside of FastTextLID.languages so I can use it + for type hinting.""" + + @classmethod + def from_string(cls, code: str): + # Handle both formats: with and without "__label__" prefix + clean_code = code.replace("__label__", "") + for member in cls: + if member.value == clean_code: + return member + raise ValueError(f"'{code}' is not a valid LanguageCode") + + +class FastTextLID: + """FastText wrapper.""" + + def __init__( + self, repo_id="facebook/fasttext-language-identification", filename="model.bin" + ): + """Loads huggingface repo; downloads model asset. Initiates with this model.""" + self.model_path = hf_hub_download(repo_id=repo_id, filename=filename) + self.model = fasttext.load_model(self.model_path) + self.pbar_label = "FastText LID" + + @property + def language(self) -> Enum: + """Generate Enum for {ISO 639-3}_{ISO 15924} code.""" + language_dict = {} + for code in self.model.get_labels(): + # Strip the "__label__" prefix and split the remaining code + clean_code = code.replace("__label__", "") + lang, script = clean_code.split("_") + enum_name = f"{lang.upper()}_{script}" + enum_value = clean_code + language_dict[enum_name] = enum_value + + return LanguageCode("LanguageCode", language_dict) + + + def process_batch( + self, batch: List[str], k=1, threshold=0.0, on_unicode_error="strict" + ) -> Tuple[np.ndarray, np.ndarray]: + """Generate FastText LID labels and probs. + + From the FastText lib: + Given a string, get a list of labels and a list of + corresponding probabilities. k controls the number + of returned labels. A choice of 5, will return the 5 + most probable labels. By default this returns only + the most likely label and probability. threshold filters + the returned labels by a threshold on probability. A + choice of 0.5 will return labels with at least 0.5 + probability. k and threshold will be applied together to + determine the returned labels. + + Args: + batch (List[str]): list of text to get labels for. + k (int, optional): number of labels to return for each text. + Defaults to 1. + threshold (float, optional): Minimum threshold for label probs. + Defaults to 0.0. + on_unicode_error (str, optional): Not sure. + Defaults to "strict". + + Returns: + Tuple[np.ndarray, np.ndarray]: Tuple of label and scores. + """ + labels, probs = self.model.f.multilinePredict( + batch, k, threshold, on_unicode_error + ) + return np.array(labels).squeeze(), np.array(probs).squeeze() + + def lid_scores( + self, + series: pd.Series, + batch_size: int = 1000, + k: int = 2, + threshold: float = 0.0, + on_unicode_error: str = "strict", + ) -> Tuple[np.ndarray, np.ndarray]: + """Multiprocessing version of process_batch. + + args: + series (pd.series): list of text to get labels for. + batch_size (int, optional): batch of text to send to fasttext. + defaults to 1000. + k (int, optional): number of labels to return for each text. + defaults to 1. + threshold (float, optional): Minimum threshold for label probs. + Defaults to 0.0. + on_unicode_error (str, optional): Not sure. + Defaults to "strict". + + Returns: + Tuple[np.ndarray, np.ndarray]: Labels, scores. + + """ + total_rows = len(series) + if total_rows < 1: + return np.array([]), np.array([]) + + def batch_generator( + series_iterator: Iterable[tuple[Hashable, Any]], batch_size: int + ): + """ + Generate batches of data from the pd.Series. + + This is done because I don't want to convert + the whole series into a list. + """ + batch = [] + for _, text in series_iterator: # still fast. + batch.append(text) + if len(batch) == batch_size: + yield batch + batch = [] + if batch: + yield batch + + with mp.Pool( + initializer=self._init_worker, initargs=(self.model_path,) + ) as pool: + labels, scores = [], [] + with tqdm(total=total_rows, desc=self.pbar_label) as pbar: + for batch_labels, batch_scores in pool.imap( + self._process_batch_wrapper, + ( + (batch, k, threshold, on_unicode_error) + for batch in batch_generator(series.items(), batch_size) + ), + ): + labels.append(batch_labels) + scores.append(batch_scores) + pbar.update(len(batch_labels)) + + return np.concatenate(labels), np.concatenate(scores) + + @staticmethod + def _init_worker(model_path): + global worker_model # to avoid pickling. + worker_model = fasttext.load_model(model_path) + + @staticmethod + def _process_batch_wrapper(args): + """ + Function to call during multiprocessing. + + This is separate to process_batch since + if we called process_batch() we would have to pickle + the LID. + """ + batch, k, threshold, on_unicode_error = args + labels, probs = worker_model.f.multilinePredict( + batch, k, threshold, on_unicode_error + ) + return np.array(labels).squeeze(), np.array(probs).squeeze() diff --git a/specialk/notebooks/try_batched_data.py b/specialk/notebooks/try_batched_data.py new file mode 100644 index 0000000..f78066d --- /dev/null +++ b/specialk/notebooks/try_batched_data.py @@ -0,0 +1,49 @@ +from pathlib import Path + +import pandas as pd +from torch.utils.data import DataLoader +from tqdm import tqdm + +import datasets +from datasets import Dataset +from specialk.core.utils import load_dataset + +parent_dir = Path("/Users/t/Projects/specialk/datasets/machine_translation/") + +from specialk.models.tokenizer import BPEEncoder, BPEVocabulary, WordVocabulary + +word_vocab = WordVocabulary.from_file( + "/Users/t/Projects/specialk/assets/tokenizer/fr_en_word_moses" +) + + +def tokenize(example): + # perform tokenization at this stage. + example["source"] = word_vocab.to_tensor(example["source"]) + example["target"] = word_vocab.to_tensor(example["target"]) + return example + + +batched_data = Dataset.load_from_disk( + "/Users/t/Projects/specialk/datasets/machine_translation/huggingface/corpus_enfr", + keep_in_memory=True, +) + + +def collator(batch): + src = [i["source"] for i in batch] + tgt = [i["target"] for i in batch] + return {"source": word_vocab.to_tensor(src), "target": word_vocab.to_tensor(tgt)} + + +torchdata = DataLoader( + batched_data.with_format("torch"), batch_size=3, collate_fn=collator, num_workers=2 +) + +j = 100 +k = 0 +for i in tqdm(torchdata): + k += 1 + if k == j: + break + continue diff --git a/specialk/trainers/train_classifier.py b/specialk/trainers/train_classifier.py new file mode 100755 index 0000000..2f5bbc5 --- /dev/null +++ b/specialk/trainers/train_classifier.py @@ -0,0 +1,291 @@ +""" +CNN Training Runner. + +Trains classifier used for style transfer. +Modified version of the following code: +https://github.com/shrimai/Style-Transfer-Through-Back-Translation/blob/master/classifier/cnn_train.py. + + +TODO: Check this works on both BPE and word-separated tokenized datasets from the huggingface datasets. +""" + +from __future__ import division + +import argparse +from pathlib import Path +from typing import List, Optional, Tuple, Union + +import lightning.pytorch as pl +from lightning.pytorch.loggers import TensorBoardLogger +from lightning.pytorch.profilers import AdvancedProfiler +from peft import LoraConfig, TaskType +from torch.utils.data import DataLoader + +from datasets import Dataset, load_dataset +from specialk.core.constants import LOGGING_DIR, LOGGING_PERF_NAME, PROJECT_DIR +from specialk.core.utils import check_torch_device, log, namespace_to_dict +from specialk.models.classifier.models import ( + BERTClassifier, + CNNClassifier, + TextClassifier, +) +from specialk.models.tokenizer import ( + BPEVocabulary, + HuggingFaceVocabulary, + SentencePieceVocabulary, + Vocabulary, + WordVocabulary, +) + +DEVICE: str = check_torch_device() + + +def init_dataloader( + dataset: Dataset, + tokenizer: Vocabulary, + batch_size: int, + shuffle=False, + n_workers: int = 8, + persistent_workers: bool = True, + cache_path: Optional[Union[Path, str]] = None, +) -> DataLoader: + def tokenize(example): + # perform tokenization at this stage. + example["text"] = tokenizer.to_tensor(example["text"]) + return example + + if cache_path: + if isinstance(cache_path, Path): + cache_path = str(cache_path) + if not cache_path.lower().endswith(".parquet"): + cache_path = f"{cache_path}.parquet" + + tokenized_dataset = dataset.with_format("torch").map( + tokenize, batched=True, num_proc=n_workers, cache_file_name=cache_path + ) + dataloader = DataLoader( + tokenized_dataset, + batch_size=batch_size, + persistent_workers=persistent_workers, + shuffle=shuffle, + num_workers=n_workers, + ) + + return dataloader + + +def get_tokenizer(tokenizer_option: str = "spm") -> Vocabulary: + # interestingly the bpe tokenizer is a lot slwoer to run instead of the word tokenizer. + # we should see how well this performs with sentencepience. + dir_tokenizer = PROJECT_DIR / "assets" / "tokenizer" + if tokenizer_option == "bpe": + tokenizer_filepath = dir_tokenizer / "fr_en_bpe" + tokenizer = BPEVocabulary.from_file(tokenizer_filepath) + tokenizer.vocab._progress_bar = iter + elif tokenizer_option == "word": + # word option. + tokenizer_filepath = dir_tokenizer / "fr_en_word_moses" + tokenizer = WordVocabulary.from_file(tokenizer_filepath) + else: + # sentencepiece + tokenizer_filepath = str(dir_tokenizer / "sentencepiece" / "enfr.model") + tokenizer = SentencePieceVocabulary.from_file(tokenizer_filepath, max_length=72) + return tokenizer + + +def get_model(model_option: str, tokenizer: Vocabulary) -> TextClassifier: + if model_option == "cnn": + task = CNNClassifier( + "political", + vocabulary_size=tokenizer.vocab_size, + sequence_length=tokenizer.max_length, + tokenizer=tokenizer, + ) + elif model_option == "bert": + peft_config = LoraConfig( + task_type=TaskType.SEQ_CLS, + r=8, + lora_alpha=32, + lora_dropout=0.1, + bias="none", + target_modules=["q_lin", "k_lin", "v_lin", "out_lin"], + ) + + model_base_name = "distilbert/distilbert-base-cased" + task = BERTClassifier( + name="test_distilbert", + model_base_name=model_base_name, + peft_config=peft_config, + ) + else: + raise Exception("Model not chosen") + + return task + + +def main_new(args): + BATCH_SIZE = 128 if DEVICE == "mps" else 680 + + # tokenizer + tokenizer = get_tokenizer(args.tokenizer) + task = get_model(args.model, tokenizer) + + log.info("Loaded tokenizer", tokenizer=tokenizer) + + """ + TODO we should add a wrapper function so we can intelligently describe the hf dataset + so we know which columns correspond to the label and the dataset also. + """ + hf_dataset_name = "thien/political" + hf_dataset_name = "thien/publications" + + dataset: Dataset = load_dataset(hf_dataset_name) + dataset = dataset.class_encode_column("label") + + train_dataloader = init_dataloader( + dataset["train"], tokenizer, BATCH_SIZE, shuffle=True + ) + val_dataloader = init_dataloader( + dataset["eval"], tokenizer, BATCH_SIZE, shuffle=False + ) + + project_name = f"{hf_dataset_name}/{task.name}" + + logger = TensorBoardLogger(LOGGING_DIR, name=project_name) + logger.log_hyperparams( + params={ + "batch_size": BATCH_SIZE, + "tokenizer": tokenizer.__class__.__name__, + "dataset": hf_dataset_name, + "tokenizer_path": None, + } + ) + REVIEW_RATE = len(train_dataloader) // 32 # for debugging purposes. + + profiler = AdvancedProfiler(dirpath=logger.log_dir, filename=LOGGING_PERF_NAME) + trainer = pl.Trainer( + accelerator=DEVICE, + max_epochs=2, + log_every_n_steps=20, + logger=logger, + profiler=profiler, + precision="16-mixed", + val_check_interval=REVIEW_RATE, + gradient_clip_val=0.5, + gradient_clip_algorithm="norm", + ) + + trainer.fit( + task, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader + ) + + +def main_distilbert_peft(): + BATCH_SIZE = 32 if DEVICE == "mps" else 32 + peft_config = LoraConfig( + task_type=TaskType.SEQ_CLS, + r=8, + lora_alpha=32, + lora_dropout=0.1, + bias="none", + target_modules=["q_lin", "k_lin", "v_lin", "out_lin"], + ) + + model_base_name = "distilbert/distilbert-base-cased" + task = BERTClassifier( + name="test_distilbert", + model_base_name=model_base_name, + peft_config=peft_config, + ) + tokenizer = HuggingFaceVocabulary( + name=model_base_name, + pretrained_model_name_or_path=model_base_name, + max_length=512, + ) + + """ + TODO we should add a wrapper function so we can intelligently describe the hf dataset + so we know which columns correspond to the label and the dataset also. + """ + hf_dataset_name = "thien/political" + hf_dataset_name = "thien/publications" + + dataset: Dataset = load_dataset(hf_dataset_name) + dataset = dataset.class_encode_column("label") + + train_dataloader = init_dataloader( + dataset["train"], tokenizer, BATCH_SIZE, shuffle=True + ) + val_dataloader = init_dataloader( + dataset["eval"], tokenizer, BATCH_SIZE, shuffle=False + ) + + project_name = f"{hf_dataset_name}/{task.name}" + + logger = TensorBoardLogger(LOGGING_DIR, name=project_name) + logger.log_hyperparams( + params={ + "batch_size": BATCH_SIZE, + "tokenizer": tokenizer.__class__.__name__, + "dataset": hf_dataset_name, + "model_base_name": model_base_name, + "tokenizer_path": None, + } + ) + REVIEW_RATE = len(train_dataloader) // 32 # for debugging purposes. + + profiler = AdvancedProfiler(dirpath=logger.log_dir, filename=LOGGING_PERF_NAME) + trainer = pl.Trainer( + accelerator=DEVICE, + max_epochs=2, + log_every_n_steps=20, + logger=logger, + profiler=profiler, + precision="16-mixed", + val_check_interval=REVIEW_RATE, + gradient_clip_val=0.5, + gradient_clip_algorithm="norm", + ) + + trainer.fit( + task, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader + ) + + +def get_args(): + parser = argparse.ArgumentParser(description="CNN Training Runner") + parser.add_argument( + "--model_type", + choices=["cnn", "bert"], + default="cnn", + help="Type of model to use", + ) + parser.add_argument( + "--model_name", type=str, default="political", help="Name of the model" + ) + parser.add_argument( + "--tokenizer", + choices=["bpe", "word", "spm"], + default="spm", + help="Type of tokenizer to use (for CNN model)", + ) + parser.add_argument( + "--dataset_name", + type=str, + default="thien/political", + help="Name of the dataset to use", + ) + parser.add_argument( + "--batch_size", type=int, default=128, help="Batch size for training" + ) + parser.add_argument( + "--epochs", type=int, default=2, help="Number of epochs for training" + ) + + args = parser.parse_args() + return args + + +if __name__ == "__main__": + # main_new() + main_distilbert_peft() diff --git a/specialk/trainers/train_model_decoder.py b/specialk/trainers/train_model_decoder.py new file mode 100644 index 0000000..e41fcc9 --- /dev/null +++ b/specialk/trainers/train_model_decoder.py @@ -0,0 +1,355 @@ +from __future__ import division + +from pathlib import Path +from typing import Optional, Tuple, Union + +import lightning.pytorch as pl +import numpy as np +import pandas as pd +import torch +from lightning.pytorch.callbacks import LearningRateMonitor, ModelCheckpoint +from lightning.pytorch.loggers import TensorBoardLogger +from lightning.pytorch.profilers import AdvancedProfiler +from torch.utils.data import DataLoader + +import datasets +from datasets import Dataset, concatenate_datasets, load_dataset +from specialk.core.constants import ( + LOGGING_DIR, + LOGGING_PERF_NAME, + PROJECT_DIR, + RNN, + SEED, + SOURCE, + TARGET, + TRANSFORMER, +) +from specialk.core.utils import check_torch_device, log +from specialk.models.mt_model import RNNModule, TransformerModule +from specialk.models.tokenizer import ( + BPEVocabulary, + SentencePieceVocabulary, + Vocabulary, + WordVocabulary, +) +from specialk.models.transformer.torch.pytorch_transformer import ( + PyTorchTransformerModule, +) + +DEVICE: str = check_torch_device() + +np.random.seed(SEED) # if you're using numpy +torch.manual_seed(SEED) +if DEVICE == "cuda": + torch.cuda.manual_seed_all(SEED) +torch.use_deterministic_algorithms(True) + + +def load_dataset(num_proc=8) -> Dataset: + """Datasets have been uploaded to huggingface.""" + + ds = load_dataset("thien/publications", num_proc=num_proc, keep_in_memory=True) + ds = ( + ds.rename_column("fr_text", SOURCE) + .rename_column("text", TARGET) + .remove_columns(["__index_level_0__"]) + ) + return ds + + +def init_dataloader( + dataset: Dataset, + tokenizer: Vocabulary, + batch_size: int, + shuffle: bool, + decoder_tokenizer: Optional[Vocabulary] = None, + n_workers: int = 8, + persistent_workers: bool = True, + cache_path: Optional[Union[Path, str]] = None, +) -> Tuple[DataLoader, Tuple[Vocabulary, Vocabulary]]: + """ + Prepare dataset into model interpretable data. + + In other words, performs tokenization, shuffling (if needed), + and creates DataLoaders for the datasets. + """ + if decoder_tokenizer is None: + decoder_tokenizer = tokenizer + + def tokenize(example): + example[SOURCE] = tokenizer.to_tensor(example[SOURCE]).squeeze(0) + example[TARGET] = decoder_tokenizer.to_tensor(example[TARGET]).squeeze(0) + return example + + if cache_path: + if isinstance(cache_path, Path): + cache_path = str(cache_path) + if not cache_path.lower().endswith(".parquet"): + cache_path = f"{cache_path}.parquet" + + tokenized_dataset = dataset.with_format("torch").map( + tokenize, batched=True, num_proc=n_workers, cache_file_name=cache_path + ) + dataloader = DataLoader( + tokenized_dataset, + batch_size=batch_size, + persistent_workers=persistent_workers, + shuffle=shuffle, + num_workers=n_workers, + ) + return dataloader, (tokenizer, decoder_tokenizer) + + +def load_tokenizer( + option: str = "word", max_length: int = 100 +) -> Tuple[Union[BPEVocabulary, WordVocabulary, SentencePieceVocabulary], Path]: + """Load tokenizer. + + Args: + option (str, optional): _description_. Defaults to "word". + + Returns: + Union[BPEVocabulary, WordVocabulary, SentencePieceVocabulary]: Returns tokenizer. + """ + WORD, BPE, SPM = "word", "bpe", "spm" + assert option in {WORD, BPE, SPM} + + dir_tokenizer = PROJECT_DIR / "assets" / "tokenizer" + tokenizer_filepath: Path = dir_tokenizer + tokenizer = None + if option == BPE: + tokenizer_filepath = dir_tokenizer / "fr_en_bpe" + tokenizer = BPEVocabulary.from_file(tokenizer_filepath) + tokenizer.vocab._progress_bar = iter + elif option == WORD: + tokenizer_filepath = dir_tokenizer / "fr_en_word_moses" + tokenizer = WordVocabulary.from_file(tokenizer_filepath) + elif option == SPM: + tokenizer_filepath = dir_tokenizer / "sentencepiece" / "enfr.model" + tokenizer = SentencePieceVocabulary.from_file( + tokenizer_filepath, max_length=max_length + ) + else: + raise Exception("valid option not selected.") + log.info("Loaded tokenizer", tokenizer=tokenizer, filepath=str(tokenizer_filepath)) + return tokenizer, tokenizer_filepath + + +def load_mono_tokenizer() -> Tuple[WordVocabulary, WordVocabulary, Path, Path]: + dir_tokenizer = PROJECT_DIR / "assets" / "tokenizer" + src_tokenizer_filepath: Path = dir_tokenizer / "de_small_word_moses" + src_tokenizer = WordVocabulary.from_file(src_tokenizer_filepath) + tgt_tokenizer_filepath: Path = dir_tokenizer / "en_small_word_moses" + tgt_tokenizer = WordVocabulary.from_file(tgt_tokenizer_filepath) + return src_tokenizer, tgt_tokenizer, src_tokenizer_filepath, tgt_tokenizer_filepath + + +def invert_df_columns(df: pd.DataFrame) -> pd.DataFrame: + """This is for backtranslation.""" + df[SOURCE], df[TARGET] = df[TARGET], df[SOURCE] + df["source_lang"], df["target_lang"] = df["target_lang"], df["source_lang"] + return df + + +def main(): + PROD = DEVICE == "cuda" + MODEL = TRANSFORMER + TRANSFORMER_LEGACY = False + DATASET_DIR = PROJECT_DIR / "datasets" / "machine_translation" / "parquets" + # make cache dir + CACHE_DIR = PROJECT_DIR / "cache" + DATASET_CACHE_DIR = CACHE_DIR / "datasets" + DATASET_CACHE_DIR.mkdir(exist_ok=True) + N_WORKERS: int = 7 + if PROD: + TASK_NAME = "nmt_model" + # dataset configs + PATH_VALID = Path("wmt15") + PATH_TRAIN = Path("thien_mt_datasets") + BACK_TRANSLATION = True + + BATCH_SIZE = 64 + MAX_SEQ_LEN = 100 + if MODEL == RNN: + BATCH_SIZE = 192 + MAX_SEQ_LEN = 75 + N_EPOCHS = 6 + LOG_EVERY_N_STEPS = 20 + # model configs + RNN_CONFIG = { + "name": "lstm", + "brnn": True, + "learning_rate": 0.001, + "accumulate_grad_batches": 1, + } + TRANSFORMER_CONFIG = { + "name": "transformer", + "num_encoder_layers": 6, + "num_decoder_layers": 6, + "n_heads": 8, + "dim_model": 512, + "n_warmup_steps": 4000, + "learning_rate": 0.001, + "accumulate_grad_batches": 10, # multiply by batch_size to get eff. batch size + } + src_tokenizer, src_tokenizer_filepath = load_tokenizer("spm", MAX_SEQ_LEN) + tgt_tokenizer, tgt_tokenizer_filepath = src_tokenizer, src_tokenizer_filepath + + TOKENIZER_CONFIG = { + "vocabulary_size": src_tokenizer.vocab_size, + "decoder_vocabulary_size": tgt_tokenizer.vocab_size, + "sequence_length": src_tokenizer.max_length, + "tokenizer": src_tokenizer, + "decoder_tokenizer": tgt_tokenizer, + } + else: + TASK_NAME = "nmt_model_dummy" + PATH_VALID = DATASET_DIR / "de_en_30k_valid.parquet" + PATH_TRAIN = DATASET_DIR / "de_en_30k_train.parquet" + BACK_TRANSLATION = False + BATCH_SIZE = 64 if DEVICE == "mps" else 32 + BATCH_SIZE = 192 + MAX_SEQ_LEN = 30 + src_tokenizer, tgt_tokenizer, src_tokenizer_filepath, tgt_tokenizer_filepath = ( + load_mono_tokenizer() + ) + RNN_CONFIG = { + "name": "lstm_smol", + "rnn_size": 192, + "d_word_vec": 128, + "brnn": True, + "learning_rate": 0.001, + "accumulate_grad_batches": 1, + } + TRANSFORMER_CONFIG = { + "name": "transformer_smol", + "num_encoder_layers": 3, + "num_decoder_layers": 3, + "n_heads": 4, + "dim_model": 128, + "n_warmup_steps": 80, + "learning_rate": 0.001, + "accumulate_grad_batches": 4, + } + if TRANSFORMER_LEGACY: + TRANSFORMER_CONFIG["name"] += "_legacy" + N_EPOCHS = 60 + LOG_EVERY_N_STEPS = 20 + + TOKENIZER_CONFIG = { + "tokenizer": src_tokenizer, + "decoder_tokenizer": tgt_tokenizer, + "vocabulary_size": src_tokenizer.vocab_size, + "decoder_vocabulary_size": tgt_tokenizer.vocab_size, + "sequence_length": src_tokenizer.max_length, + } + RNN_CONFIG = {**RNN_CONFIG, **TOKENIZER_CONFIG} + TRANSFORMER_CONFIG = {**TRANSFORMER_CONFIG, **TOKENIZER_CONFIG} + + # load dataset + log.info("Loading datasets", train=str(PATH_TRAIN), valid=str(PATH_VALID)) + if not PROD: + df_train = pd.read_parquet(PATH_TRAIN).sample(frac=1) # shuffle. + df_valid = pd.read_parquet(PATH_VALID) + if BACK_TRANSLATION: + log.info("Inverting columns to allow for backtranslation.") + df_train = invert_df_columns(df_train) + df_valid = invert_df_columns(df_valid) + log.info("Backtranslation flag enabled.") + else: + log.info("Backtranslation is disabled.") + + train_dataset: Dataset = Dataset.from_pandas(df_train) + valid_dataset: Dataset = Dataset.from_pandas(df_valid) + else: + train_dataset = load_training_dataset( + src_lang="fr", tgt_lang="en", num_proc=N_WORKERS + ) + valid_dataset = load_validation_dataset( + src_lang="fr", tgt_lang="en", num_proc=N_WORKERS + ) + log.info("Loaded dataset", train=train_dataset.shape, valid=valid_dataset.shape) + + # create dataloaders + train_dataloader, _ = init_dataloader( + train_dataset, + src_tokenizer, + BATCH_SIZE, + decoder_tokenizer=tgt_tokenizer, + shuffle=True, + cache_path=DATASET_CACHE_DIR / PATH_TRAIN.name, + n_workers=N_WORKERS, + ) + val_dataloader, _ = init_dataloader( + valid_dataset, + src_tokenizer, + BATCH_SIZE, + decoder_tokenizer=tgt_tokenizer, + shuffle=False, + cache_path=DATASET_CACHE_DIR / PATH_VALID.name, + n_workers=N_WORKERS, + ) + log.info("Created dataset dataloaders.") + + if MODEL == RNN: + task = RNNModule(**RNN_CONFIG) + # is this needed? + task.model.PAD = src_tokenizer.PAD + else: + if TRANSFORMER_LEGACY and not PROD: + task = TransformerModule(**TRANSFORMER_CONFIG) + else: + task = PyTorchTransformerModule(**TRANSFORMER_CONFIG) + + log.info("model initialised.", model=task) + + hyperparams = { + "batch_size": BATCH_SIZE, + "src_tokenizer": src_tokenizer.__class__.__name__, + "tgt_tokenizer": tgt_tokenizer.__class__.__name__, + "dataset": "machine_translation", + "src_tokenizer_path": src_tokenizer_filepath, + "tgt_tokenizer_path": tgt_tokenizer_filepath, + "max_sequence_length": src_tokenizer.max_length, + "dataset_train_path": PATH_TRAIN, + "dataset_valid_path": PATH_VALID, + } + log.info("Hyperparameters initialised.", hyperparams=hyperparams) + + logger = TensorBoardLogger(LOGGING_DIR, name=f"{TASK_NAME}/{task.name}") + logger.log_hyperparams(params=hyperparams) + + checkpoint_callback = ModelCheckpoint(save_top_k=3, monitor="val_loss") + + profiler = None + if PROD: + # profiling will slow prod down. + profiler = AdvancedProfiler(dirpath=logger.log_dir, filename=LOGGING_PERF_NAME) + + REVIEW_RATE = len(train_dataloader) // 4 # for debugging purposes. + if PROD: + REVIEW_RATE = len(train_dataloader) // 70 # takes around 24 hours per epoch + + lr_monitor = LearningRateMonitor(logging_interval="step") + + trainer = pl.Trainer( + accelerator=DEVICE, + max_epochs=N_EPOCHS, + log_every_n_steps=LOG_EVERY_N_STEPS, + logger=logger, + profiler=profiler, + precision="16-mixed", + val_check_interval=REVIEW_RATE, + gradient_clip_val=0.5, + gradient_clip_algorithm="norm", + accumulate_grad_batches=task.kwargs["accumulate_grad_batches"], + callbacks=[lr_monitor, checkpoint_callback], + ) + + trainer.fit( + task, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader + ) + + +if __name__ == "__main__": + main() diff --git a/specialk/trainers/train_nmt.py b/specialk/trainers/train_nmt.py new file mode 100644 index 0000000..e14d072 --- /dev/null +++ b/specialk/trainers/train_nmt.py @@ -0,0 +1,388 @@ +from __future__ import division + +from pathlib import Path +from typing import Optional, Tuple, Union + +import lightning.pytorch as pl +import numpy as np +import pandas as pd +import torch +from lightning.pytorch.callbacks import LearningRateMonitor, ModelCheckpoint +from lightning.pytorch.loggers import TensorBoardLogger +from lightning.pytorch.profilers import AdvancedProfiler +from torch.utils.data import DataLoader + +import datasets +from datasets import Dataset, concatenate_datasets, load_dataset +from specialk.core.constants import ( + LOGGING_DIR, + LOGGING_PERF_NAME, + PROJECT_DIR, + RNN, + SEED, + SOURCE, + TARGET, + TRANSFORMER, +) +from specialk.core.utils import check_torch_device, log +from specialk.models.mt_model import RNNModule, TransformerModule +from specialk.models.tokenizer import ( + BPEVocabulary, + SentencePieceVocabulary, + Vocabulary, + WordVocabulary, +) +from specialk.models.transformer.torch.pytorch_transformer import ( + PyTorchTransformerModule, +) + +DEVICE: str = check_torch_device() + +np.random.seed(SEED) # if you're using numpy +torch.manual_seed(SEED) +if DEVICE == "cuda": + torch.cuda.manual_seed_all(SEED) +torch.use_deterministic_algorithms(True) + + +def load_validation_dataset(src_lang: str, tgt_lang: str, num_proc=1) -> Dataset: + ds = load_dataset( + "wmt/wmt15", "fr-en", split=datasets.Split.VALIDATION, num_proc=num_proc + ) + + # Define a function to restructure the data + def restructure(examples): + return { + SOURCE: [x[src_lang] for x in examples["translation"]], + TARGET: [x[tgt_lang] for x in examples["translation"]], + } + + # Apply the function to the dataset + restructured_ds = ds.map(restructure, batched=True, remove_columns=ds.column_names) + + return restructured_ds + + +def load_training_dataset(src_lang: str, tgt_lang: str, num_proc=8) -> Dataset: + """Datasets have been uploaded to huggingface.""" + + dataset_sources = [ + "thien/gigatext", + "thien/cc", + "thien/un2k", + "thien/europarl", + "thien/news-commentary", + "thien/globalvoices", + ] + ds: Dataset = concatenate_datasets( + [ + load_dataset(d, num_proc=num_proc, keep_in_memory=True)["train"] + for d in dataset_sources + ] + ) + ds = ds.shuffle(seed=SEED) + ds = ( + ds.rename_column(src_lang, SOURCE) + .rename_column(tgt_lang, TARGET) + .remove_columns(["__index_level_0__"]) + ) + + return ds + + +def init_dataloader( + dataset: Dataset, + tokenizer: Vocabulary, + batch_size: int, + shuffle: bool, + decoder_tokenizer: Optional[Vocabulary] = None, + n_workers: int = 8, + persistent_workers: bool = True, + cache_path: Optional[Union[Path, str]] = None, +) -> Tuple[DataLoader, Tuple[Vocabulary, Vocabulary]]: + """ + Prepare dataset into model interpretable data. + + In other words, performs tokenization, shuffling (if needed), + and creates DataLoaders for the datasets. + """ + if decoder_tokenizer is None: + decoder_tokenizer = tokenizer + + def tokenize(example): + example[SOURCE] = tokenizer.to_tensor(example[SOURCE]).squeeze(0) + example[TARGET] = decoder_tokenizer.to_tensor(example[TARGET]).squeeze(0) + return example + + if cache_path: + if isinstance(cache_path, Path): + cache_path = str(cache_path) + if not cache_path.lower().endswith(".parquet"): + cache_path = f"{cache_path}.parquet" + + tokenized_dataset = dataset.with_format("torch").map( + tokenize, batched=True, num_proc=n_workers, cache_file_name=cache_path + ) + dataloader = DataLoader( + tokenized_dataset, + batch_size=batch_size, + persistent_workers=persistent_workers, + shuffle=shuffle, + num_workers=n_workers, + ) + return dataloader, (tokenizer, decoder_tokenizer) + + +def load_tokenizer( + option: str = "word", max_length: int = 100 +) -> Tuple[Union[BPEVocabulary, WordVocabulary, SentencePieceVocabulary], Path]: + """Load tokenizer. + + Args: + option (str, optional): _description_. Defaults to "word". + + Returns: + Union[BPEVocabulary, WordVocabulary, SentencePieceVocabulary]: Returns tokenizer. + """ + WORD, BPE, SPM = "word", "bpe", "spm" + assert option in {WORD, BPE, SPM} + + dir_tokenizer = PROJECT_DIR / "assets" / "tokenizer" + tokenizer_filepath: Path = dir_tokenizer + tokenizer = None + if option == BPE: + tokenizer_filepath = dir_tokenizer / "fr_en_bpe" + tokenizer = BPEVocabulary.from_file(tokenizer_filepath) + tokenizer.vocab._progress_bar = iter + elif option == WORD: + tokenizer_filepath = dir_tokenizer / "fr_en_word_moses" + tokenizer = WordVocabulary.from_file(tokenizer_filepath) + elif option == SPM: + tokenizer_filepath = dir_tokenizer / "sentencepiece" / "enfr.model" + tokenizer = SentencePieceVocabulary.from_file( + tokenizer_filepath, max_length=max_length + ) + else: + raise Exception("valid option not selected.") + log.info("Loaded tokenizer", tokenizer=tokenizer, filepath=str(tokenizer_filepath)) + return tokenizer, tokenizer_filepath + + +def load_mono_tokenizer() -> Tuple[WordVocabulary, WordVocabulary, Path, Path]: + dir_tokenizer = PROJECT_DIR / "assets" / "tokenizer" + src_tokenizer_filepath: Path = dir_tokenizer / "de_small_word_moses" + src_tokenizer = WordVocabulary.from_file(src_tokenizer_filepath) + tgt_tokenizer_filepath: Path = dir_tokenizer / "en_small_word_moses" + tgt_tokenizer = WordVocabulary.from_file(tgt_tokenizer_filepath) + return src_tokenizer, tgt_tokenizer, src_tokenizer_filepath, tgt_tokenizer_filepath + + +def invert_df_columns(df: pd.DataFrame) -> pd.DataFrame: + """This is for backtranslation.""" + df[SOURCE], df[TARGET] = df[TARGET], df[SOURCE] + df["source_lang"], df["target_lang"] = df["target_lang"], df["source_lang"] + return df + + +def main(): + PROD = DEVICE == "cuda" + MODEL = TRANSFORMER + TRANSFORMER_LEGACY = False + DATASET_DIR = PROJECT_DIR / "datasets" / "machine_translation" / "parquets" + # make cache dir + CACHE_DIR = PROJECT_DIR / "cache" + DATASET_CACHE_DIR = CACHE_DIR / "datasets" + DATASET_CACHE_DIR.mkdir(exist_ok=True) + N_WORKERS: int = 7 + if PROD: + TASK_NAME = "nmt_model" + # dataset configs + PATH_VALID = Path("wmt15") + PATH_TRAIN = Path("thien_mt_datasets") + BACK_TRANSLATION = True + + BATCH_SIZE = 64 + MAX_SEQ_LEN = 100 + if MODEL == RNN: + BATCH_SIZE = 192 + MAX_SEQ_LEN = 75 + N_EPOCHS = 6 + LOG_EVERY_N_STEPS = 20 + # model configs + RNN_CONFIG = { + "name": "lstm", + "brnn": True, + "learning_rate": 0.001, + "accumulate_grad_batches": 1, + } + TRANSFORMER_CONFIG = { + "name": "transformer", + "num_encoder_layers": 6, + "num_decoder_layers": 6, + "n_heads": 8, + "dim_model": 512, + "n_warmup_steps": 4000, + "learning_rate": 0.001, + "accumulate_grad_batches": 10, # multiply by batch_size to get eff. batch size + } + src_tokenizer, src_tokenizer_filepath = load_tokenizer("spm", MAX_SEQ_LEN) + tgt_tokenizer, tgt_tokenizer_filepath = src_tokenizer, src_tokenizer_filepath + + TOKENIZER_CONFIG = { + "vocabulary_size": src_tokenizer.vocab_size, + "decoder_vocabulary_size": tgt_tokenizer.vocab_size, + "sequence_length": src_tokenizer.max_length, + "tokenizer": src_tokenizer, + "decoder_tokenizer": tgt_tokenizer, + } + else: + TASK_NAME = "nmt_model_dummy" + PATH_VALID = DATASET_DIR / "de_en_30k_valid.parquet" + PATH_TRAIN = DATASET_DIR / "de_en_30k_train.parquet" + BACK_TRANSLATION = False + BATCH_SIZE = 64 if DEVICE == "mps" else 32 + BATCH_SIZE = 192 + MAX_SEQ_LEN = 30 + src_tokenizer, tgt_tokenizer, src_tokenizer_filepath, tgt_tokenizer_filepath = ( + load_mono_tokenizer() + ) + RNN_CONFIG = { + "name": "lstm_smol", + "rnn_size": 192, + "d_word_vec": 128, + "brnn": True, + "learning_rate": 0.001, + "accumulate_grad_batches": 1, + } + TRANSFORMER_CONFIG = { + "name": "transformer_smol", + "num_encoder_layers": 3, + "num_decoder_layers": 3, + "n_heads": 4, + "dim_model": 128, + "n_warmup_steps": 80, + "learning_rate": 0.001, + "accumulate_grad_batches": 4, + } + if TRANSFORMER_LEGACY: + TRANSFORMER_CONFIG["name"] += "_legacy" + N_EPOCHS = 60 + LOG_EVERY_N_STEPS = 20 + + TOKENIZER_CONFIG = { + "tokenizer": src_tokenizer, + "decoder_tokenizer": tgt_tokenizer, + "vocabulary_size": src_tokenizer.vocab_size, + "decoder_vocabulary_size": tgt_tokenizer.vocab_size, + "sequence_length": src_tokenizer.max_length, + } + RNN_CONFIG = {**RNN_CONFIG, **TOKENIZER_CONFIG} + TRANSFORMER_CONFIG = {**TRANSFORMER_CONFIG, **TOKENIZER_CONFIG} + + # load dataset + log.info("Loading datasets", train=str(PATH_TRAIN), valid=str(PATH_VALID)) + if not PROD: + df_train = pd.read_parquet(PATH_TRAIN).sample(frac=1) # shuffle. + df_valid = pd.read_parquet(PATH_VALID) + if BACK_TRANSLATION: + log.info("Inverting columns to allow for backtranslation.") + df_train = invert_df_columns(df_train) + df_valid = invert_df_columns(df_valid) + log.info("Backtranslation flag enabled.") + else: + log.info("Backtranslation is disabled.") + + train_dataset: Dataset = Dataset.from_pandas(df_train) + valid_dataset: Dataset = Dataset.from_pandas(df_valid) + else: + train_dataset = load_training_dataset( + src_lang="fr", tgt_lang="en", num_proc=N_WORKERS + ) + valid_dataset = load_validation_dataset( + src_lang="fr", tgt_lang="en", num_proc=N_WORKERS + ) + log.info("Loaded dataset", train=train_dataset.shape, valid=valid_dataset.shape) + + # create dataloaders + train_dataloader, _ = init_dataloader( + train_dataset, + src_tokenizer, + BATCH_SIZE, + decoder_tokenizer=tgt_tokenizer, + shuffle=True, + cache_path=DATASET_CACHE_DIR / PATH_TRAIN.name, + n_workers=N_WORKERS, + ) + val_dataloader, _ = init_dataloader( + valid_dataset, + src_tokenizer, + BATCH_SIZE, + decoder_tokenizer=tgt_tokenizer, + shuffle=False, + cache_path=DATASET_CACHE_DIR / PATH_VALID.name, + n_workers=N_WORKERS, + ) + log.info("Created dataset dataloaders.") + + if MODEL == RNN: + task = RNNModule(**RNN_CONFIG) + # is this needed? + task.model.PAD = src_tokenizer.PAD + else: + if TRANSFORMER_LEGACY and not PROD: + task = TransformerModule(**TRANSFORMER_CONFIG) + else: + task = PyTorchTransformerModule(**TRANSFORMER_CONFIG) + + log.info("model initialised.", model=task) + + hyperparams = { + "batch_size": BATCH_SIZE, + "src_tokenizer": src_tokenizer.__class__.__name__, + "tgt_tokenizer": tgt_tokenizer.__class__.__name__, + "dataset": "machine_translation", + "src_tokenizer_path": src_tokenizer_filepath, + "tgt_tokenizer_path": tgt_tokenizer_filepath, + "max_sequence_length": src_tokenizer.max_length, + "dataset_train_path": PATH_TRAIN, + "dataset_valid_path": PATH_VALID, + } + log.info("Hyperparameters initialised.", hyperparams=hyperparams) + + logger = TensorBoardLogger(LOGGING_DIR, name=f"{TASK_NAME}/{task.name}") + logger.log_hyperparams(params=hyperparams) + + checkpoint_callback = ModelCheckpoint(save_top_k=3, monitor="val_loss") + + profiler = None + if PROD: + # profiling will slow prod down. + profiler = AdvancedProfiler(dirpath=logger.log_dir, filename=LOGGING_PERF_NAME) + + REVIEW_RATE = len(train_dataloader) // 4 # for debugging purposes. + if PROD: + REVIEW_RATE = len(train_dataloader) // 70 # takes around 24 hours per epoch + + lr_monitor = LearningRateMonitor(logging_interval="step") + + trainer = pl.Trainer( + accelerator=DEVICE, + max_epochs=N_EPOCHS, + log_every_n_steps=LOG_EVERY_N_STEPS, + logger=logger, + profiler=profiler, + precision="16-mixed", + val_check_interval=REVIEW_RATE, + gradient_clip_val=0.5, + gradient_clip_algorithm="norm", + accumulate_grad_batches=task.kwargs["accumulate_grad_batches"], + callbacks=[lr_monitor, checkpoint_callback], + ) + + trainer.fit( + task, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader + ) + + +if __name__ == "__main__": + main() diff --git a/specialk/trainers/train_sentencepiece.py b/specialk/trainers/train_sentencepiece.py new file mode 100644 index 0000000..824c500 --- /dev/null +++ b/specialk/trainers/train_sentencepiece.py @@ -0,0 +1,33 @@ +import sentencepiece as spm + +import specialk.core.constants as Constants +from specialk.core.utils import log + +special_tokens = ["", "", "", "", "

", "", ""] +special_tokens = [i for i in special_tokens if i not in {""}] + +inp = f"{Constants.PROJECT_DIR}/datasets/machine_translation/corpus_enfr_final.en,{Constants.PROJECT_DIR}/datasets/machine_translation/corpus_enfr_final.fr" +log.info("Setting up SPM", src_data=inp) + +DEFAULT_VOCAB_SIZE = 35000 +SMALL_VOCAB_SIZE = 10000 +spm.SentencePieceTrainer.train( + input=inp, + model_prefix="frenidentity_small", + vocab_size=SMALL_VOCAB_SIZE, + accept_language="en,fr", + num_threads=14, + control_symbols=special_tokens, + bos_id=Constants.SOS, + eos_id=Constants.EOS, + unk_id=Constants.UNK, + unk_piece=Constants.UNK_WORD, + bos_piece=Constants.SOS_WORD, + eos_piece=Constants.EOS_WORD, + pad_piece=Constants.PAD_WORD, + shuffle_input_sentence=True, + train_extremely_large_corpus=True, + normalization_rule_name="identity", +) + +print("hello") diff --git a/base/translate.py b/specialk/translate.py similarity index 97% rename from base/translate.py rename to specialk/translate.py index bc480c9..44c99fa 100755 --- a/base/translate.py +++ b/specialk/translate.py @@ -8,10 +8,11 @@ import argparse import torch -from lib.RecurrentModel import RecurrentModel as recurrent -from lib.TransformerModel import TransformerModel as transformer from tqdm import tqdm +from models.recurrent import RecurrentModel as recurrent +from models.transformer import TransformerModel as transformer + def load_args(): parser = argparse.ArgumentParser(description="train.py") diff --git a/base/big_transformer_translate.py b/specialk/translate_fairseq.py similarity index 99% rename from base/big_transformer_translate.py rename to specialk/translate_fairseq.py index 533a9ce..da942b6 100755 --- a/base/big_transformer_translate.py +++ b/specialk/translate_fairseq.py @@ -9,11 +9,12 @@ from collections import namedtuple import torch -from core.utils import get_len from fairseq import checkpoint_utils, options, tasks, utils from fairseq.data import encoders from tqdm import tqdm +from specialk.core.utils import get_len + # In[2]: diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..21b405d --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1 @@ +import os diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..e357417 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,5 @@ +import sys + +from specialk.core.constants import PROJECT_DIR + +sys.path.insert(0, str(PROJECT_DIR)) diff --git a/tests/datasets/test_classifier_dataset.py b/tests/datasets/test_classifier_dataset.py new file mode 100644 index 0000000..6fa392e --- /dev/null +++ b/tests/datasets/test_classifier_dataset.py @@ -0,0 +1,109 @@ +from pathlib import Path + +import pytest +import torch +from torch.utils.data import DataLoader + +from datasets import Dataset, load_dataset +from datasets.exceptions import DatasetGenerationError +from specialk.core.utils import log +from specialk.models.tokenizer import BPEVocabulary, WordVocabulary + +dirpath = "tests/tokenizer/test_files" +dev_path = "/Users/t/Projects/datasets/political/political_data/*" + +BATCH_SIZE = 2 + +torch.manual_seed(1337) + + +@pytest.fixture(scope="module", autouse=True) +def dataset() -> Dataset: + try: + dataset = load_dataset("thien/political", split="eval[:5%]") + except DatasetGenerationError: + dataset = Dataset.from_parquet(dev_path) + dataset = dataset.class_encode_column("label") + return dataset + + +@pytest.fixture(scope="module") +def bpe_tokenizer() -> BPEVocabulary: + tokenizer_filepath = Path(dirpath) / "bpe_tokenizer" + tokenizer = BPEVocabulary.from_file(tokenizer_filepath) + return tokenizer + + +@pytest.fixture(scope="module") +def word_tokenizer() -> WordVocabulary: + tokenizer_filepath = Path(dirpath) / "word_tokenizer" + word_tokenizer = WordVocabulary.from_file(tokenizer_filepath) + return word_tokenizer + + +@pytest.fixture(scope="module") +def bpe_dataloader(dataset: Dataset, bpe_tokenizer: BPEVocabulary): + def tokenize(example): + # perform tokenization at this stage. + example["text"] = bpe_tokenizer.to_tensor(example["text"]) + return example + + tokenized_dataset = dataset.with_format("torch").map(tokenize) + dataloader = DataLoader(tokenized_dataset, batch_size=BATCH_SIZE, shuffle=True) + return dataloader + + +@pytest.mark.lightweight +def test_make_dataloader(dataset): + dataloader = DataLoader(dataset, batch_size=BATCH_SIZE) + batch = next(iter(dataloader)) + assert isinstance(batch["text"][0], str) + + +@pytest.mark.heavyweight +def test_dataloader_tokenized_bpe(dataset: Dataset, bpe_tokenizer: BPEVocabulary): + def tokenize(example): + # perform tokenization at this stage. + example["text"] = bpe_tokenizer.to_tensor(example["text"]) + return example + + tokenized_dataset = dataset.with_format("torch").map(tokenize) + dataloader = DataLoader(tokenized_dataset, batch_size=BATCH_SIZE, shuffle=False) + batch = next(iter(dataloader)) + # number chosen by the seed. + batch_idx = batch["id"] + expected_idx = torch.Tensor([0, 1]) + assert torch.all(batch_idx.eq(expected_idx)) + assert isinstance(batch["text"], torch.Tensor) + batch["text"] = batch["text"].reshape(2, -1) + assert batch["text"].shape == torch.Size([BATCH_SIZE, bpe_tokenizer.max_length]) + log.debug("batch shape", shape=batch["text"].shape) + assert isinstance(batch["text"][0][0].item(), int) + + +@pytest.mark.heavyweight +def test_dataloader_tokenized_word(dataset: Dataset, word_tokenizer: WordVocabulary): + def tokenize(example): + # perform tokenization at this stage. + example["text"] = word_tokenizer.to_tensor(example["text"]) + return example + + tokenized_dataset = dataset.with_format("torch").map(tokenize) + dataloader = DataLoader(tokenized_dataset, batch_size=BATCH_SIZE, shuffle=False) + batch = next(iter(dataloader)) + # number chosen by the seed. + batch_idx = batch["id"] + expected_idx = torch.Tensor([0, 1]) + assert torch.all(batch_idx.eq(expected_idx)) + assert isinstance(batch["text"], torch.Tensor) + batch["text"] = batch["text"].reshape(2, -1) + assert batch["text"].shape == torch.Size([BATCH_SIZE, word_tokenizer.max_length]) + log.debug("batch shape", shape=batch["text"].shape) + assert isinstance(batch["text"][0][0].item(), int) + + +@pytest.mark.heavyweight +def test_dataloader_class_label(dataset: Dataset): + torch_dataset = dataset.with_format("torch") + log.info("features", features=dataset.features) + assert isinstance(torch_dataset["label"][0].item(), int) diff --git a/tests/datasets/test_newspaper_dataset.py b/tests/datasets/test_newspaper_dataset.py new file mode 100644 index 0000000..cc136b6 --- /dev/null +++ b/tests/datasets/test_newspaper_dataset.py @@ -0,0 +1,91 @@ +from pathlib import Path + +import pytest +import torch +from torch.utils.data import DataLoader + +from datasets import Dataset, load_dataset +from specialk.core.utils import log +from specialk.models.tokenizer import BPEVocabulary, WordVocabulary + +dirpath = "tests/tokenizer/test_files" + +BATCH_SIZE = 2 + +torch.manual_seed(1337) + + +@pytest.fixture(scope="session", autouse=True) +def dataset() -> Dataset: + dataset = load_dataset("thien/publications", split="eval[:5%]") + dataset = dataset.class_encode_column("label") + return dataset + + +@pytest.fixture(scope="session") +def bpe_tokenizer() -> BPEVocabulary: + tokenizer_filepath = Path(dirpath) / "bpe_tokenizer" + tokenizer = BPEVocabulary.from_file(tokenizer_filepath) + return tokenizer + + +@pytest.fixture(scope="session") +def word_tokenizer() -> WordVocabulary: + tokenizer_filepath = Path(dirpath) / "word_tokenizer" + word_tokenizer = WordVocabulary.from_file(tokenizer_filepath) + return word_tokenizer + + +@pytest.mark.heavyweight +def test_make_dataloader(dataset): + dataloader = DataLoader(dataset, batch_size=BATCH_SIZE) + batch = next(iter(dataloader)) + assert isinstance(batch["text"][0], str) + + +@pytest.mark.heavyweight +def test_dataloader_tokenized_bpe(dataset: Dataset, bpe_tokenizer: BPEVocabulary): + def tokenize(example): + # perform tokenization at this stage. + example["text"] = bpe_tokenizer.to_tensor(example["text"]) + return example + + tokenized_dataset = dataset.with_format("torch").map(tokenize) + dataloader = DataLoader( + tokenized_dataset, batch_size=BATCH_SIZE, shuffle=False + ) # don't shuffle for testing purposes + batch = next(iter(dataloader)) + # number chosen by the seed. + print(batch) + assert isinstance(batch["text"], torch.Tensor) + batch["text"] = batch["text"].reshape(2, -1) + assert batch["text"].shape == torch.Size([BATCH_SIZE, bpe_tokenizer.max_length]) + log.debug("batch shape", shape=batch["text"].shape) + assert isinstance(batch["text"][0][0].item(), int) + + +@pytest.mark.heavyweight +def test_dataloader_tokenized_word(dataset: Dataset, word_tokenizer: WordVocabulary): + def tokenize(example): + # perform tokenization at this stage. + example["text"] = word_tokenizer.to_tensor(example["text"]) + return example + + tokenized_dataset = dataset.with_format("torch").map(tokenize) + dataloader = DataLoader( + tokenized_dataset, batch_size=BATCH_SIZE, shuffle=False + ) # don't shuffle for testing purposes + batch = next(iter(dataloader)) + # number chosen by the seed. + assert isinstance(batch["text"], torch.Tensor) + batch["text"] = batch["text"].reshape(2, -1) + assert batch["text"].shape == torch.Size([BATCH_SIZE, word_tokenizer.max_length]) + log.debug("batch shape", shape=batch["text"].shape) + assert isinstance(batch["text"][0][0].item(), int) + + +@pytest.mark.heavyweight +def test_dataloader_class_label(dataset: Dataset): + torch_dataset = dataset.with_format("torch") + log.info("features", features=dataset.features) + assert isinstance(torch_dataset["label"][0].item(), int) diff --git a/tests/metrics/style_transfer/test_intensity.py b/tests/metrics/style_transfer/test_intensity.py new file mode 100644 index 0000000..a8ec058 --- /dev/null +++ b/tests/metrics/style_transfer/test_intensity.py @@ -0,0 +1,27 @@ +import pytest + +from specialk.metrics import Intensity + + +@pytest.fixture +def intensity(): + return Intensity("political") + + +@pytest.mark.heavyweight +def test_intensity(intensity): + test_democrat_tweets = [ + "lets 'federalize all gun crimes.", + "assault weapons make no sense except in the armed services.", + ] + + avg_score = intensity.compute(test_democrat_tweets) + assert avg_score < 0.5 + + test_republican_tweets = [ + "he just came out of the communist closet.", + "he's a true patriot!", + ] + + avg_score = intensity.compute(test_republican_tweets) + assert avg_score > 0.1 diff --git a/tests/metrics/style_transfer/test_naturalness.py b/tests/metrics/style_transfer/test_naturalness.py new file mode 100644 index 0000000..3599e70 --- /dev/null +++ b/tests/metrics/style_transfer/test_naturalness.py @@ -0,0 +1,29 @@ +import pytest + +from specialk.metrics import Naturalness + + +@pytest.fixture +def naturalness(): + return Naturalness("political") + +@pytest.mark.heavyweight +def test_naturalness(naturalness): + test_natural_tweets = [ + "let 'federalize all gun crimes.", + "assault weapons make no sense except in the armed services.", + "he just came out of the communist closet.", + "he's a true patriot!", + ] + + avg_score = naturalness.compute(test_natural_tweets) + assert avg_score > 0.85 + + test_unnatural_tweets = [ + "aaaaaaa asdfasdf a8e4fa weif asdfjo", + "the the the the the", + "asdf 123 123 123 2 3 4 1 5 2" "hello", + ] + + avg_score = naturalness.compute(test_unnatural_tweets) + assert avg_score <= 1 # TODO this is temporary to check if we can do inference. diff --git a/tests/metrics/style_transfer/test_preservation.py b/tests/metrics/style_transfer/test_preservation.py new file mode 100644 index 0000000..69fdb6e --- /dev/null +++ b/tests/metrics/style_transfer/test_preservation.py @@ -0,0 +1,22 @@ +import pytest + +from specialk.core.constants import PROJECT_DIR +from specialk.metrics import Preservation + + +@pytest.fixture +def preservation(): + return Preservation( + dir_cache=PROJECT_DIR / "cache", + dir_w2v=PROJECT_DIR / "cache" / "embeddings", + path_lexicon=PROJECT_DIR / "cache" / "style_lexicons", + ) + + +@pytest.mark.heavyweight +def test_preservation(preservation): + text_close = ("the fox was sleeping", "the fox slept") + text_far = ("the fox was sleeping", "chicken wings are gerat") + score_close = preservation.compute(text_close[0], text_close[1]) + score_far = preservation.compute(text_far[0], text_far[1]) + assert score_close < score_far diff --git a/tests/metrics/test_emd.py b/tests/metrics/test_emd.py new file mode 100644 index 0000000..7d537df --- /dev/null +++ b/tests/metrics/test_emd.py @@ -0,0 +1,37 @@ +import pytest + +from specialk.metrics import EarthMoverDistance + + +@pytest.fixture(scope="module") +def emd(): + return EarthMoverDistance() + + +@pytest.mark.heavyweight +def test_emd_perfect(emd): + assert emd.compute(prediction="hello world", references="hello world") == 0.0 + + +@pytest.mark.heavyweight +def test_emd_great(emd): + assert ( + emd.compute(prediction="hello world", references="hi world") + == 2.2670567114660742 + ) + + +@pytest.mark.heavyweight +def test_emd_poor(emd): + assert ( + emd.compute(prediction="bye world", references="hello world") + == 2.8166197714643477 + ) + + +@pytest.mark.heavyweight +def test_emd_horror(emd): + assert ( + emd.compute(prediction="chicken wings", references="hello world") + == 8.018476176153182 + ) diff --git a/tests/metrics/test_metrics.py b/tests/metrics/test_metrics.py new file mode 100644 index 0000000..4e8895c --- /dev/null +++ b/tests/metrics/test_metrics.py @@ -0,0 +1,107 @@ +from typing import List, Tuple + +import numpy as np +import pytest + +from specialk.metrics import ROUGE, LexicalMetrics, Meteor, Polarity, SacreBLEU + + +@pytest.fixture +def lexical(): + return LexicalMetrics() + + +@pytest.fixture +def polarity(): + return Polarity() + + +@pytest.fixture +def rouge(): + return ROUGE() + + +@pytest.fixture +def meteor(): + return Meteor() + + +@pytest.fixture +def sacrebleu(): + return SacreBLEU() + + +@pytest.fixture +def pos_sequence_1(lexical) -> List[Tuple[str, str]]: + sequence = "It's uncear what Teresa May is planning." + pos_tokens = lexical.pos(sequence) + return pos_tokens + + +@pytest.fixture +def pos_sequence_2(lexical) -> List[Tuple[str, str]]: + sequence = "he was responsible for all for." + pos_tokens = lexical.pos(sequence) + return pos_tokens + +@pytest.mark.lightweight +def test_meteor(meteor): + test_case = [ + (["hello world"], ["who are you"], 0), + (["hello world"], ["hello world"], 0.9375), + (["hello"], ["hello world"], 0.2631578947368421), + (["hello world", "dog"], ["hello world", "cat"], 0.46875), + ] + for pred, actual, expected_score in test_case: + assert meteor.compute(pred, actual) == expected_score + +@pytest.mark.lightweight +def test_rouge(rouge): + rouge: ROUGE + test_case = [ + (["this is a test"], ["this is a test"], 1.0), + ( + ["this is a test", "hello world"], + [["this is a test"], ["hello world hi"]], + 0.9, + ), + ] + for pred, actual, score in test_case: + pred_score = rouge.compute(pred, actual) + assert np.isclose(pred_score, score, rtol=1e-02, atol=1e-03) + +@pytest.mark.lightweight +def test_sacrebleu(sacrebleu): + test_case = [ + (["this is a test"], ["this is a test"], 100.0), + ( + ["this is a test", "hello world"], + [["this is a test"], ["hello world hi"]], + 84.64, + ), + ] + for pred, actual, score in test_case: + pred_score = sacrebleu.compute(pred, actual) + assert np.isclose(pred_score, score, rtol=1e-02, atol=1e-03) + + +@pytest.mark.lightweight +def test_syllables(lexical): + assert lexical.syllables("word") > 0 + + +@pytest.mark.lightweight +def test_lexical_lex_match_1(lexical, pos_sequence_1): + scores = lexical.lex_match_1(pos_sequence_1) + assert len(scores) > 0 + +@pytest.mark.lightweight +def test_lexical_lex_match_2(lexical, pos_sequence_2): + scores = lexical.lex_match_2(pos_sequence_2) + assert len(scores) > 0 + + +@pytest.mark.lightweight +def test_lexical_basic_stats(lexical): + article = "The quick brown fox jumped over the lazy dog. He moved and jumped." + lexical.basic_stats(article) diff --git a/tests/models/classifier/test_bert_classifier.py b/tests/models/classifier/test_bert_classifier.py new file mode 100644 index 0000000..b064e90 --- /dev/null +++ b/tests/models/classifier/test_bert_classifier.py @@ -0,0 +1,230 @@ +import lightning.pytorch as pl +import pytest +import torch +from lightning.pytorch import Trainer +from peft import LoraConfig, PeftModel +from peft.utils.peft_types import TaskType +from transformers import AutoModelForSequenceClassification + +from specialk.core.constants import PROJECT_DIR +from specialk.core.utils import log +from specialk.models.classifier.models import BERTClassifier, CNNClassifier +from tests.models.fixtures import dataset # noqa: F401; noqa: F402 +from tests.models.fixtures import hf_bert_dataloader # noqa: F401; noqa: F402 +from tests.models.fixtures import hf_bert_tokenizer # noqa: F401; noqa: F402 +from tests.models.fixtures import hf_distilbert_dataloader # noqa: F401; noqa: F402 +from tests.models.fixtures import hf_distilbert_tokenizer # noqa: F401; noqa: F402 + +dirpath = "tests/tokenizer/test_files" +ref_model_base_name = "bert-base-uncased" +ref_distil_model_base_name = "distilbert/distilbert-base-cased" + + +import logging + +# supress bert renaming warnings (re. gamma, beta). +# see https://github.com/huggingface/transformers/pull/31654 +loggers = [logging.getLogger(name) for name in logging.root.manager.loggerDict] +for logger in loggers: + if "transformers" in logger.name.lower(): + logger.setLevel(logging.ERROR) + +torch.manual_seed(1337) +torch.use_deterministic_algorithms(True) + + +@pytest.fixture(scope="session", autouse=True) +def pretrained_bert() -> BERTClassifier: + return BERTClassifier(name="test", model_base_name=ref_model_base_name) + + +@pytest.fixture(scope="session", autouse=True) +def pretrained_peft_bert() -> BERTClassifier: + peft_config = LoraConfig( + task_type=TaskType.SEQ_CLS, + r=8, + lora_alpha=32, + lora_dropout=0.1, + bias="none", + target_modules=["query", "key", "value"], + ) + return BERTClassifier( + name="test", model_base_name=ref_model_base_name, peft_config=peft_config + ) + + +@pytest.fixture(scope="session", autouse=True) +def pretrained_distilbert() -> BERTClassifier: + return BERTClassifier( + name="test_distilbert", + model_base_name=ref_distil_model_base_name, + ) + + +@pytest.fixture(scope="session", autouse=True) +def pretrained_peft_distilbert() -> BERTClassifier: + peft_config = LoraConfig( + task_type=TaskType.SEQ_CLS, + r=8, + lora_alpha=32, + lora_dropout=0.1, + bias="none", + target_modules=["q_lin", "k_lin", "v_lin", "out_lin"], + ) + return BERTClassifier( + name="test_distilbert", + model_base_name=ref_distil_model_base_name, + peft_config=peft_config, + ) + + +@pytest.mark.heavyweight +def test_model_inference(hf_bert_dataloader, pretrained_bert): # noqa: F811 + model = pretrained_bert + model.eval() + + batch: dict = next(iter(hf_bert_dataloader)) + x: torch.Tensor + y: torch.Tensor + x, y = batch["text"], batch["label"] + + x = x.squeeze(2, 1) + + x_mask = x != model.tokenizer.pad_token_id + log.info("input", x=x.shape) + y_hat = model.model(x, attention_mask=x_mask, labels=y) + log.info(y_hat) + output = y_hat.logits + + +@pytest.mark.heavyweight +def test_model_inference_peft(hf_bert_dataloader, pretrained_peft_bert): # noqa: F811 + model = pretrained_peft_bert + model.eval() + + batch: dict = next(iter(hf_bert_dataloader)) + x: torch.Tensor + y: torch.Tensor + x, y = batch["text"], batch["label"] + + x = x.squeeze(2, 1) + + x_mask = x != model.tokenizer.pad_token_id + log.info("input", x=x.shape) + y_hat = model.model(x, attention_mask=x_mask, labels=y) + log.info(y_hat) + output = y_hat.logits + + +@pytest.mark.heavyweight +def test_model_inference_peft_distilbert( + hf_distilbert_dataloader, pretrained_peft_distilbert +): # noqa: F811 + model = pretrained_peft_distilbert + model.eval() + + batch: dict = next(iter(hf_distilbert_dataloader)) + x: torch.Tensor + y: torch.Tensor + x, y = batch["text"], batch["label"] + + x = x.squeeze(2, 1) + + x_mask = x != model.tokenizer.pad_token_id + log.info("input", x=x.shape) + y_hat = model.model(x, attention_mask=x_mask, labels=y) + log.info(y_hat) + output = y_hat.logits + + +@pytest.mark.heavyweight +def test_save_load_peft_distilbert_checkpoint( + hf_distilbert_dataloader, pretrained_peft_distilbert, tmp_path +): + checkpoint_path = tmp_path / "peft_distilbert_checkpoint.ckpt" + module = pretrained_peft_distilbert + + # Save initial state of PEFT parameters + initial_peft_state = { + name: param.clone() + for name, param in module.model.named_parameters() + if "lora_" in name + } + + trainer = Trainer(max_epochs=1, logger=False, limit_train_batches=1) + trainer.fit(module, train_dataloaders=hf_distilbert_dataloader) + + # Save checkpoint + trainer.save_checkpoint(checkpoint_path) + + # Load checkpoint + ckpt_module = BERTClassifier.load_from_checkpoint(checkpoint_path) + + # Check PEFT weights + for name, param in ckpt_module.model.named_parameters(): + if "lora_" in name: + if name not in initial_peft_state: + raise ValueError( + f"Unexpected LoRA parameter {name} after loading checkpoint" + ) + if not torch.allclose( + param, initial_peft_state[name], rtol=1e-4, atol=1e-4 + ): + err_msg = f"LoRA parameter {name} changed unexpectedly after loading checkpoint" + log.error(err_msg, initial=initial_peft_state[name], ckpt=param) + + raise ValueError(err_msg) + + +@pytest.mark.heavyweight +def test_save_load_distilbert_checkpoint_cpu( + hf_distilbert_dataloader, pretrained_distilbert, tmp_path +): + checkpoint_path = tmp_path / "checkpoint.ckpt" + module = pretrained_distilbert + trainer = Trainer(max_epochs=0, accelerator="cpu", logger=False) + trainer.fit(module, train_dataloaders=hf_distilbert_dataloader) + + m = module.model + + # generate temporary path to save checkpoint and load from. + trainer.save_checkpoint(checkpoint_path) + + ckpt_module = BERTClassifier.load_from_checkpoint(checkpoint_path) + m2 = ckpt_module.model + torch.testing.assert_close( + m.distilbert.embeddings.word_embeddings.weight, + m2.distilbert.embeddings.word_embeddings.weight, + ) + + +@pytest.mark.heavyweight +def test_save_load_distilbert_checkpoint( + hf_distilbert_dataloader, pretrained_distilbert, tmp_path +): + checkpoint_path = tmp_path / "distilbert_checkpoint.ckpt" + + module = pretrained_distilbert + + # Save initial state of PEFT parameters + trainer = Trainer(max_epochs=1, logger=False, limit_train_batches=1) + trainer.fit(module, train_dataloaders=hf_distilbert_dataloader) + + # Save checkpoint + trainer.save_checkpoint(checkpoint_path) + + # Load checkpoint + ckpt_module = BERTClassifier.load_from_checkpoint(checkpoint_path) + + batch: dict = next(iter(hf_distilbert_dataloader)) + x: torch.Tensor + y: torch.Tensor + x, y = batch["text"], batch["label"] + + x = x.squeeze(2, 1) + + x_mask = x != module.tokenizer.pad_token_id + log.info("input", x=x.shape) + y_old = module.model(x, attention_mask=x_mask, labels=y) + y_new = ckpt_module.model(x, attention_mask=x_mask, labels=y) + torch.testing.assert_close(y_old, y_new) diff --git a/tests/models/classifier/test_classifier.py b/tests/models/classifier/test_classifier.py new file mode 100644 index 0000000..1e5859b --- /dev/null +++ b/tests/models/classifier/test_classifier.py @@ -0,0 +1,113 @@ +from pathlib import Path + +import pytest +import torch +from jaxtyping import Float +from torch import Tensor, nn +from torchmetrics.functional import accuracy + +from specialk.core.utils import log +from specialk.models.classifier.models import CNNClassifier +from specialk.models.classifier.onmt.CNNModels import ConvNet +from specialk.models.tokenizer import WordVocabulary +from tests.models.fixtures import bpe_dataloader # noqa: F401; noqa: F402 +from tests.models.fixtures import bpe_tokenizer # noqa: F401; noqa: F402 +from tests.models.fixtures import dataset # noqa: F401; noqa: F402 +from tests.models.fixtures import word_dataloader # noqa: F401; noqa: F402 +from tests.models.fixtures import word_tokenizer # noqa: F401; noqa: F402 +from tests.tokenizer.test_tokenizer import VOCABULARY_SIZE + +dirpath = "tests/tokenizer/test_files" +dev_path = ( + "/Users/t/Projects/datasets/political/political_data/democratic_only.dev.en.parquet" +) + +BATCH_SIZE = 128 +SEQUENCE_LENGTH = 100 + +torch.manual_seed(1337) + + +@pytest.mark.heavyweight +def test_model_inference(bpe_dataloader): # noqa: F811 + model = ConvNet( + vocab_size=VOCABULARY_SIZE, + sequence_length=SEQUENCE_LENGTH, + ) + model.eval() + criterion = nn.BCELoss() + + batch: dict = next(iter(bpe_dataloader)) + x: torch.Tensor + y: torch.Tensor + x, y = batch["text"], batch["label"] + + x = x.squeeze(2, 1) + seq_len: int = x.size(1) + batch_size: int = x.size(0) + vocab_size: int = model.vocab_size + + assert x.shape == (BATCH_SIZE, SEQUENCE_LENGTH) + assert vocab_size == VOCABULARY_SIZE + assert batch_size == BATCH_SIZE + assert ( + batch_size >= 50 + ), f"Batch size needs to be greater than 50 (currently {batch_size})." + + one_hot = torch.zeros(seq_len, batch_size, vocab_size).scatter_( + 2, torch.unsqueeze(x.T, 2), 1 + ) + + # log.info("one hot",one_hot=one_hot_old[:, 0, :]) + + # one_hot = torch.nn.functional.one_hot(x.T, num_classes=VOCABULARY_SIZE).float() + + # assert not torch.equal(one_hot, one_hot_old) + + # log.info("one_hots", old=one_hot_old, new=one_hot) + + assert one_hot.shape == (SEQUENCE_LENGTH, BATCH_SIZE, VOCABULARY_SIZE) + + y_hat = model(one_hot).squeeze(-1) + + assert y_hat.shape[0] == BATCH_SIZE + assert y.shape[0] == BATCH_SIZE + + log.info("shapes", y=y.shape, y_hat=y_hat.shape) + + _ = criterion(y_hat, y.float()) + + +@pytest.mark.lightweight +def test_accuracy(): + y = torch.LongTensor([0, 0, 1, 1, 1, 1]).float() + y_pred = torch.LongTensor([0, 0, 0, 1, 1, 1]) + assert y.shape == y_pred.shape + acc = accuracy(y_pred, y, "binary") + assert acc == pytest.approx(0.833, 0.1) + + +@pytest.mark.heavyweight +def test_load_model_from_checkpoint(): + path_classifier = Path( + "/Users/t/Projects/specialk/assets/classifiers/legacy/cnn_classifier/" + ) + for category in [ + "adversarial_political", + "adversarial_publication", + "naturalness_political", + ]: + path_checkpoint = path_classifier / category / f"{category}.ckpt" + path_hyperparams = path_classifier / category / f"hyperparameters.yaml" + path_tok = path_classifier / category / f"tokenizer" + + module = CNNClassifier.load_from_checkpoint( + path_checkpoint, hparams_file=path_hyperparams + ) + module.tokenizer = WordVocabulary.from_file(path_tok) + + text = ["Donald Trump!!!", "obama rules"] * 10 + batch_size = 3 + output: Float[Tensor, "batch 1"] = module.generate(text, batch_size) + log.info("output", out=output, shape=output.shape) + assert output.shape == torch.Size((len(text), 1)) diff --git a/tests/models/fixtures.py b/tests/models/fixtures.py new file mode 100644 index 0000000..202c68b --- /dev/null +++ b/tests/models/fixtures.py @@ -0,0 +1,150 @@ +from pathlib import Path + +import pandas as pd +import pytest +import torch +from torch import nn +from torch.utils.data import DataLoader +from torchmetrics.functional import accuracy + +from datasets import Dataset, load_dataset +from datasets.exceptions import DatasetGenerationError +from specialk.core.constants import PROJECT_DIR, SOURCE, TARGET +from specialk.core.utils import log +from specialk.models.classifier.onmt.CNNModels import ConvNet +from specialk.models.tokenizer import ( + BPEVocabulary, + HuggingFaceVocabulary, + WordVocabulary, +) +from tests.tokenizer.test_tokenizer import VOCABULARY_SIZE + +dirpath = "tests/tokenizer/test_files" +dev_path = ( + "/Users/t/Projects/datasets/political/political_data/democratic_only.dev.en.parquet" +) + +BATCH_SIZE = 128 +SEQUENCE_LENGTH = 100 + +torch.manual_seed(1337) + + +@pytest.fixture(scope="session", autouse=True) +def dataset() -> Dataset: + try: + dataset = load_dataset("thien/political", split="eval") + except DatasetGenerationError: + dataset = Dataset.from_parquet(dev_path) + dataset = dataset.class_encode_column("label") + return dataset + + +@pytest.fixture(scope="session") +def bpe_tokenizer() -> BPEVocabulary: + return BPEVocabulary.from_file(Path(dirpath) / "bpe_tokenizer") + + +@pytest.fixture(scope="session") +def word_tokenizer() -> WordVocabulary: + return WordVocabulary.from_file(Path(dirpath) / "word_tokenizer") + + +@pytest.fixture(scope="session") +def hf_bert_tokenizer() -> HuggingFaceVocabulary: + return HuggingFaceVocabulary( + name="bert-base-uncased", + pretrained_model_name_or_path="bert-base-uncased", + max_length=512, + ) + + +@pytest.fixture(scope="session") +def hf_distilbert_tokenizer() -> HuggingFaceVocabulary: + model_name = "distilbert/distilbert-base-cased" + return HuggingFaceVocabulary( + name=model_name, + pretrained_model_name_or_path=model_name, + max_length=512, + ) + + +@pytest.fixture(scope="session", autouse=True) +def mt_dataset() -> Dataset: + dataset_path = PROJECT_DIR / "tests/test_files/datasets/en_fr.parquet" + return Dataset.from_pandas(pd.read_parquet(dataset_path)[:10]) + + +@pytest.fixture(scope="session") +def hf_marianmt_tokenizer() -> HuggingFaceVocabulary: + model_name = "Helsinki-NLP/opus-mt-fr-en" + return HuggingFaceVocabulary( + name=model_name, + pretrained_model_name_or_path=model_name, + max_length=100, + ) + + +@pytest.fixture(scope="session") +def hf_marianmt_dataloader( + mt_dataset: Dataset, hf_marianmt_tokenizer: HuggingFaceVocabulary +): + def tokenize(example): + # perform tokenization at this stage. + example[SOURCE] = hf_marianmt_tokenizer.to_tensor(example[SOURCE]).squeeze(0) + example[TARGET] = hf_marianmt_tokenizer.to_tensor(example[TARGET]).squeeze(0) + return example + + tokenized_dataset = mt_dataset.with_format("torch").map(tokenize) + dataloader = DataLoader(tokenized_dataset, batch_size=BATCH_SIZE, shuffle=True) + return dataloader + + +@pytest.fixture(scope="session") +def hf_bert_dataloader(dataset: Dataset, hf_bert_tokenizer: HuggingFaceVocabulary): + def tokenize(example): + # perform tokenization at this stage. + example["text"] = hf_bert_tokenizer.to_tensor(example["text"]) + return example + + tokenized_dataset = dataset.with_format("torch").map(tokenize) + dataloader = DataLoader(tokenized_dataset, batch_size=BATCH_SIZE, shuffle=True) + return dataloader + + +@pytest.fixture(scope="session") +def hf_distilbert_dataloader( + dataset: Dataset, hf_distilbert_tokenizer: HuggingFaceVocabulary +): + def tokenize(example): + # perform tokenization at this stage. + example["text"] = hf_distilbert_tokenizer.to_tensor(example["text"]) + return example + + tokenized_dataset = dataset.with_format("torch").map(tokenize) + dataloader = DataLoader(tokenized_dataset, batch_size=BATCH_SIZE, shuffle=True) + return dataloader + + +@pytest.fixture(scope="session") +def word_dataloader(dataset: Dataset, word_tokenizer: WordVocabulary): + def tokenize(example): + # perform tokenization at this stage. + example["text"] = word_tokenizer.to_tensor(example["text"]) + return example + + tokenized_dataset = dataset.with_format("torch").map(tokenize) + dataloader = DataLoader(tokenized_dataset, batch_size=BATCH_SIZE, shuffle=True) + return dataloader + + +@pytest.fixture(scope="session") +def bpe_dataloader(dataset: Dataset, bpe_tokenizer: BPEVocabulary): + def tokenize(example): + # perform tokenization at this stage. + example["text"] = bpe_tokenizer.to_tensor(example["text"]) + return example + + tokenized_dataset = dataset.with_format("torch").map(tokenize) + dataloader = DataLoader(tokenized_dataset, batch_size=BATCH_SIZE, shuffle=True) + return dataloader diff --git a/tests/models/generators/test_beam_components.py b/tests/models/generators/test_beam_components.py new file mode 100644 index 0000000..6bd9164 --- /dev/null +++ b/tests/models/generators/test_beam_components.py @@ -0,0 +1,249 @@ +from dataclasses import dataclass +from typing import Optional + +import einops +import pytest +import torch +from jaxtyping import Int + +from specialk.core import constants +from specialk.core.utils import log + +# Import the class to be tested +from specialk.models.transformer.torch.pytorch_transformer import ( + PyTorchTransformerModule, + TransformerEncoderDecoderBeam, +) + +torch.manual_seed(constants.SEED) + + +# Mock classes and functions to simulate the behavior of the actual classes +@dataclass +class MockModel: + def encode(self, input_tokens): + batch_size, seq_len = input_tokens.shape + return torch.randn(batch_size, seq_len, 512), torch.randint( + 0, 2, (batch_size, seq_len) + ).float() + + def decode(self, tokens, tgt_mask, memory, x_pad_mask): + return torch.randn(tokens.shape[0], tokens.shape[1], 512) + + def generator(self, input_tensor): + return torch.randn(input_tensor.shape[0], 1000) # Assuming vocab size of 1000 + + +@dataclass +class MockTokenizer: + EOS: int = constants.EOS + SOS: int = constants.SOS + PAD: int = constants.PAD + + +@pytest.fixture +def mock_pytorch_transformer_module(): + class MockPyTorchTransformerModule(PyTorchTransformerModule): + def __init__(self): + self.model = MockModel() + self.tokenizer = MockTokenizer() + self.decoder_tokenizer = MockTokenizer() + + return MockPyTorchTransformerModule() + + +@pytest.fixture +def mock_beam(): + model = MockModel() + tokenizer = MockTokenizer() + batch_size = 10 + beam_size = 3 + seq_len = 10 + enc_seq = 8 + hidden_size = 512 + vocab_size = 1000 + + return TransformerEncoderDecoderBeam( + model=model, + tokenizer=tokenizer, + logprob_sums=torch.randn(batch_size * beam_size), + tokens=torch.randint(0, vocab_size, (batch_size * beam_size, seq_len)), + memory=torch.randn(batch_size * beam_size, enc_seq, hidden_size), + x_pad_mask=torch.randint(0, 2, (batch_size * beam_size, enc_seq)).float(), + ) + + +def test_filter(mock_beam): + num_beams = 2 + batch_size = mock_beam.logprob_sums.shape[0] // mock_beam.num_beams + continuing, terminated = mock_beam.filter(num_beams) + + assert isinstance(continuing, TransformerEncoderDecoderBeam) + assert isinstance(terminated, TransformerEncoderDecoderBeam) + assert ( + continuing.logprob_sums.shape[0] + terminated.logprob_sums.shape[0] + == num_beams * batch_size + ) + + +@pytest.fixture +def mock_beam_semi_completed(): + model = MockModel() + tokenizer = MockTokenizer() + batch_size = 1 + beam_size = 5 + seq_len = 10 + enc_seq = 8 + hidden_size = 512 + vocab_size = 1000 + + # Create a tensor where some sequences contain the EOS token + tokens: Int[torch.Tensor, "batch*beam seq"] + tokens = torch.randint( + low=10, high=vocab_size, size=(batch_size * beam_size, seq_len) + ) + + # Set EOS token for some sequences + tokens[0, -1] = tokenizer.EOS # First sequence in first batch + tokens[2, -2] = tokenizer.EOS # Last sequence in first batch + tokens[4, -3] = tokenizer.EOS # Middle sequence in second batch + + return TransformerEncoderDecoderBeam( + model=model, + tokenizer=tokenizer, + logprob_sums=torch.randn(batch_size * beam_size), + tokens=tokens, + memory=torch.randn(batch_size * beam_size, enc_seq, hidden_size), + x_pad_mask=torch.randint(0, 2, (batch_size * beam_size, enc_seq)).float(), + ) + + +def test_filter_with_terminated_sequences(mock_beam_semi_completed): + beam = mock_beam_semi_completed + # Modify the tokens to include some terminated sequences + batch_size = beam.logprob_sums.shape[0] // beam.num_beams + + continuing, terminated = beam.filter() + + assert isinstance(continuing, TransformerEncoderDecoderBeam) + assert isinstance(terminated, TransformerEncoderDecoderBeam) + + # Check that we have both continuing and terminated beams + assert continuing.logprob_sums.shape[0] > 0 + assert terminated.logprob_sums.shape[0] > 0 + + log.info( + "n_beams", + continuing=continuing.logprob_sums.shape[0], + terminated=terminated.logprob_sums.shape[0], + ) + + # The total number of beams should be preserved + assert ( + continuing.logprob_sums.shape[0] + terminated.logprob_sums.shape[0] + == beam.num_beams + ) + + # Check that all sequences in the terminated beam actually contain the EOS token + assert (terminated.tokens == beam.tokenizer.EOS).any(dim=1).all() + + # Check that none of the continuing sequences contain the EOS token + assert not (continuing.tokens == beam.tokenizer.EOS).any() + + # Verify that we have the correct number of continuing beams per batch + assert continuing.logprob_sums.shape[0] != batch_size * beam.num_beams + + +def test_new_beams(mock_beam): + new_beam = mock_beam.new( + mock_beam.logprob_sums, mock_beam.tokens, mock_beam.memory, mock_beam.x_pad_mask + ) + assert isinstance(new_beam, TransformerEncoderDecoderBeam) + assert torch.equal(new_beam.logprob_sums, mock_beam.logprob_sums) + assert torch.equal(new_beam.tokens, mock_beam.tokens) + assert torch.equal(new_beam.memory, mock_beam.memory) + assert torch.equal(new_beam.x_pad_mask, mock_beam.x_pad_mask) + + +def test_get_logits(mock_beam): + logits = mock_beam.get_logits() + assert logits.shape == ( + mock_beam.tokens.shape[0], + 1000, + ) # Assuming vocab size of 1000 + + +def test_calculate_new_logprob_sums(mock_beam): + tokens_per_beam = 2 + topk_log_probs = torch.randn(mock_beam.logprob_sums.shape[0], tokens_per_beam) + new_logprob_sums = mock_beam._calculate_new_logprob_sums( + topk_log_probs, tokens_per_beam + ) + assert new_logprob_sums.shape == ( + mock_beam.logprob_sums.shape[0] * tokens_per_beam, + ) + + +def test_generate_new_tokens(mock_beam): + tokens_per_beam = 2 + topk_token_idx = torch.randint( + 0, 1000, (mock_beam.tokens.shape[0], tokens_per_beam) + ) + new_tokens = mock_beam._generate_new_tokens(topk_token_idx, tokens_per_beam) + assert new_tokens.shape == ( + mock_beam.tokens.shape[0] * tokens_per_beam, + mock_beam.tokens.shape[1] + 1, + ) + + +def test_getitem(mock_beam): + idx = 1 + sub_beam = mock_beam[idx] + assert isinstance(sub_beam, TransformerEncoderDecoderBeam) + assert torch.equal(sub_beam.logprob_sums, mock_beam.logprob_sums[idx]) + assert torch.equal(sub_beam.tokens, mock_beam.tokens[idx]) + assert torch.equal(sub_beam.memory, mock_beam.memory[idx]) + assert torch.equal(sub_beam.x_pad_mask, mock_beam.x_pad_mask[idx]) + + +def test_generate(mock_beam): + tokens_per_beam = 2 + new_beam = mock_beam.generate(tokens_per_beam) + assert isinstance(new_beam, TransformerEncoderDecoderBeam) + assert ( + new_beam.logprob_sums.shape[0] + == mock_beam.logprob_sums.shape[0] * tokens_per_beam + ) + assert new_beam.tokens.shape[0] == mock_beam.tokens.shape[0] * tokens_per_beam + assert new_beam.tokens.shape[1] == mock_beam.tokens.shape[1] + 1 + + +# New test for beam_search method in PyTorchTransformerModule +def test_beam_search(mock_pytorch_transformer_module): + # Set up input parameters + batch_size = 2 + seq_len = 10 + input_tokens = torch.randint(0, 1000, (batch_size, seq_len)) + num_return_sequences = 2 + num_beams = 3 + max_new_tokens = 5 + length_penalty = 1.0 + no_repeat_ngram_size = None + verbose = False + + # Perform beam search + logprobs, logits = mock_pytorch_transformer_module.beam_search( + input_tokens, + num_return_sequences, + num_beams, + max_new_tokens, + length_penalty, + no_repeat_ngram_size, + verbose, + ) + + assert logprobs.shape == torch.Size((batch_size, num_return_sequences)) + # it's +1 because of the BOS token. + assert logits.shape == torch.Size( + (batch_size, num_return_sequences, max_new_tokens + 1) + ) diff --git a/tests/models/generators/test_generator.py b/tests/models/generators/test_generator.py new file mode 100644 index 0000000..78ab4c6 --- /dev/null +++ b/tests/models/generators/test_generator.py @@ -0,0 +1,119 @@ +from pathlib import Path + +import pytest +import torch +from jaxtyping import Int +from torch import Tensor + +from specialk.core.constants import EOS, PAD, PROJECT_DIR, SEED, SOS +from specialk.core.utils import log +from specialk.models.transformer.torch.pytorch_transformer import ( + PyTorchTransformerModule, +) + +torch.manual_seed(SEED) + + +@pytest.fixture(scope="session", autouse=True) +def module() -> PyTorchTransformerModule: + checkpoint_path = next( + Path( + f"{PROJECT_DIR}/tb_logs/nmt_model_dummy/transformer_smol/version_0/checkpoints/" + ).iterdir() + ) + + module = PyTorchTransformerModule.load_from_checkpoint( + checkpoint_path=checkpoint_path + ) + return module + + +@pytest.mark.heavyweight +def test_generator_sampling(module): + test_src_text = "Eine Gruppe von Männern lädt Baumwolle auf einen Lastwagen" + tokens = module.tokenizer.to_tensor(test_src_text).to("mps") + log.info("Tokens:", tokens=tokens, shape=tokens.shape) + y_tokens = module.generate(tokens, top_p=0.2, seed=SEED) + text = module.decoder_tokenizer.detokenize(y_tokens, specials=False) + assert text[0].lower().startswith("a group of men") + + +@pytest.mark.heavyweight +def test_generator_sampling_batch(module): + test_src = [ + "Eine Gruppe von Männern lädt Baumwolle auf einen Lastwagen", + "Ein Mann schläft in einem grünen Raum auf einem Sofa.", + ] + test_tgt = [ + "A group of men", + "A man is sleeping", + ] + + tokens = module.tokenizer.to_tensor(test_src).to("mps") + + log.info("Tokens:", tokens=tokens, shape=tokens.shape) + + y_tokens = module.generate(tokens, top_p=0.2, seed=SEED) + generated_text = module.decoder_tokenizer.detokenize(y_tokens, specials=False) + + for expected, actual in zip(generated_text, test_tgt): + assert expected.lower().startswith(actual.lower()) + + +@pytest.mark.heavyweight +def test_generator_beam(module): + test_src_text = "Eine Gruppe von Männern lädt Baumwolle auf einen Lastwagen" + tokens: Int[Tensor, "batch seq_len"] + tokens = module.tokenizer.to_tensor(test_src_text).to("mps") + log.info("Tokens:", tokens=tokens, shape=tokens.shape) + y_hat = module.beam_search( + tokens, + num_return_sequences=1, + num_beams=10, + max_new_tokens=50, + no_repeat_ngram_size=3, + verbose=True, + ) + log.info("y_hat", y_hat=y_hat) + _, output_tokens = y_hat + text = module.decoder_tokenizer.detokenize(output_tokens[:, 0, :], specials=False) + log.info("output", y_hat=text) + assert text[0].lower().startswith("a group of men") + + +@pytest.mark.heavyweight +def test_generator_beam_batch(module): + test_src_text = [ + "Eine Gruppe von Männern lädt Baumwolle auf einen Lastwagen", + "Ein Mann schläft in einem grünen Raum auf einem Sofa.", + ] + test_tgt_text = [ + "A group of men", + "A man is", + ] + tokens: Int[Tensor, "batch seq_len"] + tokens = module.tokenizer.to_tensor(test_src_text).to("mps") + log.info("Tokens:", tokens=tokens, shape=tokens.shape) + + n_return_seq = 2 + y_hat = module.beam_search( + tokens, + num_return_sequences=n_return_seq, + num_beams=5, + max_new_tokens=30, + no_repeat_ngram_size=5, + verbose=True, + ) + y_hat_scores, y_hat_tensors = y_hat + + assert y_hat_scores.shape[0] == y_hat_tensors.shape[0] + assert y_hat_scores.shape[0] == len(test_src_text) + assert y_hat_scores.shape[1] == y_hat_tensors.shape[1] + assert y_hat_scores.shape[1] == n_return_seq + + log.info("y_hat", y_hat=y_hat) + text = module.decoder_tokenizer.detokenize(y_hat_tensors[:, 0, :], specials=False) + + log.info("output", y_hat=text) + for pred, actual in zip(text, test_tgt_text): + assert pred.lower().startswith(actual.lower()) diff --git a/tests/models/ops/test_activations.py b/tests/models/ops/test_activations.py new file mode 100644 index 0000000..0da0533 --- /dev/null +++ b/tests/models/ops/test_activations.py @@ -0,0 +1,18 @@ +import pytest +import torch + +from specialk.models.utils.activations import SwiGLU + + +@pytest.mark.lightweight +def test_swiglu(): + # Example usage + batch_size = 16 + input_dim = 21 + hidden_dim = 12 + output_dim = 5 + + swiglu = SwiGLU(input_dim, hidden_dim, output_dim) + input_tensor = torch.randn(batch_size, input_dim) # Batch size of 32 + output = swiglu(input_tensor) + assert output.shape == torch.Size((batch_size, output_dim)) diff --git a/tests/models/ops/test_maxpool.py b/tests/models/ops/test_maxpool.py new file mode 100644 index 0000000..ac1c606 --- /dev/null +++ b/tests/models/ops/test_maxpool.py @@ -0,0 +1,42 @@ +import numpy as np +import pytest +import torch + +from specialk.models.ops.ops import FuncMaxPool3d + + +@pytest.mark.skip(reason="Not using implementation; not pre-training on MPS for now.") +def test_maxpool3d(n_tests=20): + for _ in range(n_tests): + b = np.random.randint(2, 10) + h = np.random.randint(10, 50) + w = np.random.randint(10, 50) + l = np.random.randint(10, 50) + ci = np.random.randint(1, 20) + stride = ( + None + if np.random.random() < 0.5 + else tuple(np.random.randint(1, 5, size=(3,))) + ) + kernel_size = tuple(np.random.randint(1, 10, size=(3,))) + kH, kW, kL = kernel_size + padding = ( + np.random.randint(0, 1 + kH // 2), + np.random.randint(0, 1 + kW // 2), + np.random.randint(0, 1 + kL // 2), + ) + x = torch.randn((b, ci, h, w, l)) + my_output = FuncMaxPool3d( + x, + kernel_size, + stride=stride, + padding=padding, + ) + torch_output = torch.max_pool3d( + x, + kernel_size, + stride=stride, # type: ignore (None actually is allowed) + padding=padding, + ) + torch.testing.assert_close(my_output, torch_output) + print("All tests in `test_maxpool3d` passed!") diff --git a/tests/models/ops/test_ops.py b/tests/models/ops/test_ops.py new file mode 100644 index 0000000..55e7858 --- /dev/null +++ b/tests/models/ops/test_ops.py @@ -0,0 +1,144 @@ +import pytest +import torch +from torch import LongTensor + +from specialk.core.constants import EOS, PAD +from specialk.models.ops.ops import mask_out_special_tokens, n_tokens_correct + + +@pytest.mark.lightweight +def test_n_tokens_correct_2d(): + # Test Case 1: Basic test case with no padding + pred1 = torch.tensor([[0.1, 0.9], [0.8, 0.2], [0.4, 0.6]]) + gold1 = torch.tensor([1, 0, 1]) + pad_token1 = -1 + assert n_tokens_correct(pred1, gold1, pad_token1) == 3 + + # Test Case 2: 2 predictions are correct. + pred2 = torch.tensor([[0.1, 0.9], [0.8, 0.2], [0.4, 0.6]]) + gold2 = torch.tensor([1, 0, 0]) + pad_token2 = -1 + assert n_tokens_correct(pred2, gold2, pad_token2) == 2 + + # Test Case 3: All predictions are wrong + pred3 = torch.tensor([[0.9, 0.1], [0.2, 0.8], [0.6, 0.4]]) + gold3 = torch.tensor([1, 0, 1]) + pad_token3 = -1 + assert n_tokens_correct(pred3, gold3, pad_token3) == 0 + + # Test Case 4: Including padding tokens + pred4 = torch.tensor([[0.1, 0.9], [0.8, 0.2], [0.4, 0.6], [0.7, 0.3]]) + gold4 = torch.tensor([1, 0, -1, 1]) + pad_token4 = -1 + assert n_tokens_correct(pred4, gold4, pad_token4) == 2 + + # Test Case 5: All tokens are padding + pred5 = torch.tensor([[0.1, 0.9], [0.8, 0.2], [0.4, 0.6]]) + gold5 = torch.tensor([-1, -1, -1]) + pad_token5 = -1 + assert n_tokens_correct(pred5, gold5, pad_token5) == 0 + + +@pytest.mark.lightweight +def test_n_tokens_correct_3d(): + # Test Case 1: Basic test case with no padding + pred1 = torch.tensor([[[0.1, 0.9], [0.8, 0.2], [0.4, 0.6]]]) + gold1 = torch.tensor([[1, 0, 1]]) + pad_token1 = -1 + assert n_tokens_correct(pred1, gold1, pad_token1) == 3 + + # Test Case 2: 2 predictions are correct + pred2 = torch.tensor([[[0.1, 0.9], [0.8, 0.2], [0.4, 0.6]]]) + gold2 = torch.tensor([[1, 0, 0]]) + pad_token2 = -1 + assert n_tokens_correct(pred2, gold2, pad_token2) == 2 + + # Test Case 3: All predictions are wrong + pred3 = torch.tensor([[[0.9, 0.1], [0.2, 0.8], [0.6, 0.4]]]) + gold3 = torch.tensor([[1, 0, 1]]) + pad_token3 = -1 + assert n_tokens_correct(pred3, gold3, pad_token3) == 0 + + # Test Case 4: Including padding tokens + pred4 = torch.tensor([[[0.1, 0.9], [0.8, 0.2], [0.4, 0.6], [0.7, 0.3]]]) + gold4 = torch.tensor([[1, 0, -1, 1]]) + pad_token4 = -1 + assert n_tokens_correct(pred4, gold4, pad_token4) == 2 + + # Test Case 5: All tokens are padding + pred5 = torch.tensor([[[0.1, 0.9], [0.8, 0.2], [0.4, 0.6]]]) + gold5 = torch.tensor([[-1, -1, -1]]) + pad_token5 = -1 + assert n_tokens_correct(pred5, gold5, pad_token5) == 0 + + # Test Case 6: Batch processing with padding + pred6 = torch.tensor( + [ + [ + [0.1, 0.9], # 1 (correct) + [0.8, 0.2], # 0 (correct) + [0.4, 0.6], # 1 skip because we'll pad + ], + [ + [0.7, 0.3], # 0 (we get this wrong) + [0.2, 0.8], # 1 (we'll get this wrong) + [0.6, 0.4], # 0 (we'll get this wrong) + ], + ] + ) # we should only get 2 correct items. + gold6 = torch.tensor([[1, 0, -1], [1, 0, 1]]) + pad_token6 = -1 + assert n_tokens_correct(pred6, gold6, pad_token6) == 2 + + +@pytest.mark.lightweight +def test_mask_out_special_tokens(): + """Mostly for my sanity checking to see if masking is actually happening.""" + test_cases = [ + ( + LongTensor([[20, 44, 55, 245, 62626, EOS, 222, 222, 222]]), + LongTensor([[20, 44, 55, 245, 62626, EOS, PAD, PAD, PAD]]), + ), + ( + LongTensor([[20, 44, 55, 245, 62626, 222, 222, 222, EOS]]), + LongTensor([[20, 44, 55, 245, 62626, 222, 222, 222, EOS]]), + ), + ( + LongTensor([[20, 44, 55, 245, 62626, 222, 222, 222, 222]]), + LongTensor([[20, 44, 55, 245, 62626, 222, 222, 222, 222]]), + ), + ( + LongTensor([[EOS, 222, 222, 222, 222, 222, 222, 222]]), + LongTensor([[EOS, PAD, PAD, PAD, PAD, PAD, PAD, PAD]]), + ), + ( # combines all of the above. + LongTensor( + [ + [20, 44, 55, 245, 62626, EOS, 222, 222, 222], + [20, 44, 55, 245, 62626, 222, 222, 222, EOS], + [20, 44, 55, 245, 62626, 222, 222, 222, 222], + [EOS, 222, 222, 222, 222, 222, 222, 222, 22], + ] + ), + LongTensor( + [ + [20, 44, 55, 245, 62626, EOS, PAD, PAD, PAD], + [20, 44, 55, 245, 62626, 222, 222, 222, EOS], + [20, 44, 55, 245, 62626, 222, 222, 222, 222], + [EOS, PAD, PAD, PAD, PAD, PAD, PAD, PAD, PAD], + ] + ), + ), + ] + devices = ["cpu"] + if torch.backends.mps.is_available(): + devices.append("mps") + if torch.cuda.is_available(): + devices.append("cuda") + + for device in devices: + for x, y in test_cases: + x = x.to(device) + y = y.to(device) + y_hat = mask_out_special_tokens(x, EOS, PAD) + assert torch.equal(y, y_hat) diff --git a/tests/models/test_attention.py b/tests/models/test_attention.py new file mode 100644 index 0000000..2652278 --- /dev/null +++ b/tests/models/test_attention.py @@ -0,0 +1,416 @@ +import numpy as np +import pytest +import torch +import torch.nn as nn +from jaxtyping import Float +from torch import Tensor + +from specialk.core import constants +from specialk.core.utils import log +from specialk.models.transformer.torch.attention import Attention, RotaryAttention + +torch.manual_seed(constants.SEED) +np.random.seed(constants.SEED) + + +@pytest.mark.lightweight +@torch.inference_mode() +def test_reference_attention_n_heads_1(): + embed_dim = 8 + num_heads = 1 + seq_len = 3 + head_dim = embed_dim // num_heads + + # These are the default values, but w + # kdim = embed_dim + # vdim = embed_dim + + mha = nn.MultiheadAttention( + embed_dim=embed_dim, + num_heads=num_heads, + dropout=0, + bias=False, + add_bias_kv=False, + add_zero_attn=False, + ) # kdim=kdim, vdim=vdim + + x = torch.rand(seq_len, embed_dim) + + # Self-attention + # These are the reference tensors I want to obtain + + attn_output, attn_output_weights = mha(x, x, x) + + assert mha.in_proj_weight.shape == torch.Size([embed_dim * 3, embed_dim]) + assert mha.out_proj.weight.shape == torch.Size([embed_dim, embed_dim]) + + wq, wk, wv = torch.split( + mha.in_proj_weight, [embed_dim, embed_dim, embed_dim], dim=0 + ) + + q = torch.matmul(x, wq.T) + k = torch.matmul(x, wk.T) + v = torch.matmul(x, wv.T) + + assert k.shape == torch.Size([seq_len, embed_dim]) + # x*W_q.T -> q, x*W_k.T -> k, x*W_v.T -> v + # q @ k^T -> softmax -> attn_output_weights + # (attn_output_weights * v)*out_proj.T -> attn_output + + scale = 1.0 / head_dim**0.5 + s = torch.softmax(q @ k.T * scale, dim=-1) + + torch.allclose(attn_output_weights, s) + + y = attn_output_weights @ v + y = y.reshape(seq_len * 1, embed_dim) + y = y @ mha.out_proj.weight.T + y = y.reshape(seq_len, 1, embed_dim) + y = y.squeeze(1) + + torch.allclose(attn_output, y) + + +@pytest.mark.lightweight +@torch.inference_mode() +def test_reference_attention_n_heads_4_and_batched(): + embed_dim = 20 + num_heads = 4 + seq_len = 3 + bs = 2 + head_dim = embed_dim // num_heads + + # These are the default values, but w + # kdim = embed_dim + # vdim = embed_dim + + mha = nn.MultiheadAttention( + embed_dim=embed_dim, + num_heads=num_heads, + dropout=0, + bias=False, + add_bias_kv=False, + add_zero_attn=False, + ) # kdim=kdim, vdim=vdim + + assert mha.in_proj_weight.shape == torch.Size([embed_dim * 3, embed_dim]) + assert mha.out_proj.weight.shape == torch.Size([embed_dim, embed_dim]) + + x = torch.rand(seq_len, bs, embed_dim) + + attn_output, attn_output_weights = mha(x, x, x) + + assert attn_output.shape == torch.Size([seq_len, bs, embed_dim]) + assert attn_output_weights.shape == torch.Size([bs, seq_len, seq_len]) + + wq, wk, wv = torch.split( + mha.in_proj_weight, [embed_dim, embed_dim, embed_dim], dim=0 + ) + + q = torch.matmul(x, wq.T) + k = torch.matmul(x, wk.T) + v = torch.matmul(x, wv.T) + + assert k.shape == torch.Size([seq_len, bs, embed_dim]) + + # It is not necessary to reshape the input, this is already + # how matmul works for higher dimensions + torch.allclose( + x @ wq.T, + (x.reshape(seq_len * bs, embed_dim) @ wq.T).reshape(seq_len, bs, embed_dim), + ) + + q = q.reshape(seq_len, num_heads * bs, head_dim).transpose(0, 1) + k = k.reshape(seq_len, num_heads * bs, head_dim).transpose(0, 1) + v = v.reshape(seq_len, num_heads * bs, head_dim).transpose(0, 1) + + scale = 1.0 / head_dim**0.5 + + # This is equivalent to the s=torch.bmm(q, k.transpose(1, 2)) + s = torch.zeros(bs * num_heads, seq_len, seq_len) + for i in range(num_heads * bs): + s[i] = q[i] @ k[i].T + + s = torch.softmax(s * scale, dim=-1) + + s_reshaped = s.reshape(bs, num_heads, seq_len, seq_len) + + s_avr = s_reshaped.sum(1) / num_heads # if average_attn_weights is True + # s_avr is what is returned by the function, but the it uses the full s + + torch.allclose(attn_output_weights, s_avr) + y = torch.bmm(s, v) + y = y.transpose(0, 1).reshape(seq_len * bs, embed_dim) + y = y @ mha.out_proj.weight.T + y = y.reshape(seq_len, bs, embed_dim) + + torch.allclose(attn_output, y) + + +@pytest.mark.lightweight +@torch.inference_mode() +def test_reference_attention_n_heads_4_and_batch_first(): + embed_dim = 128 + num_heads = 8 + seq_len = 10 + bs = 4 + head_dim = embed_dim // num_heads + + # These are the default values, but with kdim = vdim = embed_dim + mha = nn.MultiheadAttention( + embed_dim=embed_dim, + num_heads=num_heads, + dropout=0, + bias=False, + add_bias_kv=False, + add_zero_attn=False, + batch_first=True, + ) + + assert mha.in_proj_weight.shape == torch.Size([embed_dim * 3, embed_dim]) + assert mha.out_proj.weight.shape == torch.Size([embed_dim, embed_dim]) + + x = torch.rand(bs, seq_len, embed_dim) + + attn_output, attn_output_weights = mha(x, x, x) + + assert attn_output.shape == torch.Size([bs, seq_len, embed_dim]) + assert attn_output_weights.shape == torch.Size([bs, seq_len, seq_len]) + + wq, wk, wv = torch.split( + mha.in_proj_weight, [embed_dim, embed_dim, embed_dim], dim=0 + ) + + q = torch.matmul(x, wq.T) + k = torch.matmul(x, wk.T) + v = torch.matmul(x, wv.T) + + assert k.shape == torch.Size([bs, seq_len, embed_dim]) + + # It is not necessary to reshape the input, this is already + # how matmul works for higher dimensions + torch.allclose( + x @ wq.T, + (x.reshape(seq_len * bs, embed_dim) @ wq.T).reshape(bs, seq_len, embed_dim), + ) + + q = q.reshape(num_heads * bs, seq_len, head_dim) + k = k.reshape(num_heads * bs, seq_len, head_dim) + v = v.reshape(num_heads * bs, seq_len, head_dim) + + scale = 1.0 / head_dim**0.5 + + s_slow = torch.zeros(bs * num_heads, seq_len, seq_len) + for i in range(num_heads * bs): + s_slow[i] = q[i] @ k[i].T + + # functionally equivalent to the below. + s = torch.bmm(q, k.transpose(1, 2)) + assert torch.allclose(s, s_slow) + + s = torch.softmax(s * scale, dim=-1) + + s_reshaped = s.reshape(bs, num_heads, seq_len, seq_len) + + s_avr = s_reshaped.sum(1) / num_heads # if average_attn_weights is True + # s_avr is what is returned by the function, but the it uses the full s + + torch.allclose(attn_output_weights, s_avr) + y = torch.bmm(s, v) + y = y.transpose(0, 1).reshape(seq_len * bs, embed_dim) + y = y @ mha.out_proj.weight.T + y = y.reshape(bs, seq_len, embed_dim) + + torch.allclose(attn_output, y) + + +@pytest.mark.lightweight +@torch.inference_mode() +def test_reference_attention_batch_first(): + embed_dim = 128 + num_heads = 8 + seq_len = 10 + bs = 4 + head_dim = embed_dim // num_heads + print("head_dim:", head_dim) + + # These are the default values, but with kdim = vdim = embed_dim + mha = nn.MultiheadAttention( + embed_dim=embed_dim, + num_heads=num_heads, + dropout=0, + bias=False, + add_bias_kv=False, + add_zero_attn=False, + batch_first=True, + ) + + assert mha.in_proj_weight.shape == torch.Size([embed_dim * 3, embed_dim]) + assert mha.out_proj.weight.shape == torch.Size([embed_dim, embed_dim]) + + x = torch.rand(bs, seq_len, embed_dim) + attn_output, attn_output_weights = mha(x, x, x) + + assert attn_output.shape == torch.Size([bs, seq_len, embed_dim]) + assert attn_output_weights.shape == torch.Size([bs, seq_len, seq_len]) + + # all of which are of shape [embed_dim, embed_dim] + # my implementation is like [n_heads embed_dim d_head] + # (gets einoped out anyway) + wq, wk, wv = torch.split( + mha.in_proj_weight, [embed_dim, embed_dim, embed_dim], dim=0 + ) + + assert wq.shape == torch.Size([embed_dim, embed_dim]) # same for wk, wv + + q = torch.matmul(x, wq.T) + k = torch.matmul(x, wk.T) + v = torch.matmul(x, wv.T) + + # same shape for Q, V. + assert k.shape == torch.Size([bs, seq_len, embed_dim]) + # in my implementation it's [bs, seq_len, n_head, d_head] (n_head*d_head=embed_dim) + + # It is not necessary to reshape the input, this is already + # how matmul works for higher dimensions + torch.allclose( + x @ wq.T, + (x.reshape(seq_len * bs, embed_dim) @ wq.T).reshape(bs, seq_len, embed_dim), + ) + + q = q.reshape(num_heads * bs, seq_len, head_dim) + k = k.reshape(num_heads * bs, seq_len, head_dim) + v = v.reshape(num_heads * bs, seq_len, head_dim) + + scale = 1.0 / head_dim**0.5 + + s = torch.bmm(q, k.transpose(1, 2)) + s = torch.softmax(s * scale, dim=-1) + + # if average_attn_weights is True, + # s_avr is what is returned by the function, but the it uses the full score. + s_avr = s.reshape(bs, num_heads, seq_len, seq_len).sum(1) / num_heads + torch.allclose(attn_output_weights, s_avr) + + y: Float[Tensor, "bs*n_heads seq_len d_head"] + y = torch.bmm(s, v) + y: Float[Tensor, "bs*seq_len n_heads*d_head"] + y = y.transpose(0, 1).reshape(seq_len * bs, embed_dim) + print("y", y.shape) + # [embed_dim, embed_dim] @ [embed_dim, embed_dim] + y = y @ mha.out_proj.weight.T # out_proj is of shape [embed_dim, embed_dim] + y = y.reshape(bs, seq_len, embed_dim) + + torch.allclose(attn_output, y) + + +@pytest.mark.lightweight +@torch.inference_mode() +def test_attention_equivalence_no_bias(): + # Set up parameters + batch_size = 4 + seq_len = 10 + embed_dim = 32 + num_heads = 4 + head_dim = embed_dim // num_heads + + custom_attn = Attention( + d_head=head_dim, + num_heads=num_heads, + embed_dim=embed_dim, + dropout=0.0, + bias=False, + add_bias_kv=False, + add_zero_attn=False, + batch_first=True, + ) + + # Initialize nn.MultiheadAttention + mha = nn.MultiheadAttention( + embed_dim=embed_dim, + num_heads=num_heads, + dropout=0.0, + bias=False, + add_bias_kv=False, + add_zero_attn=False, + batch_first=True, + ) + + # Set the weights of nn.MultiheadAttention to match your custom Attention + with torch.no_grad(): + # Set in_proj_weight + mha.in_proj_weight.copy_( + torch.cat( + [ + custom_attn.W_Q.transpose(1, 2).reshape(embed_dim, embed_dim), + custom_attn.W_K.transpose(1, 2).reshape(embed_dim, embed_dim), + custom_attn.W_V.transpose(1, 2).reshape(embed_dim, embed_dim), + ] + ) + ) + + # Set out_proj weight + mha.out_proj.weight.copy_(custom_attn.W_O.reshape(embed_dim, embed_dim).T) + + # Generate random input + x = torch.rand(batch_size, seq_len, embed_dim) + + # Run both attention mechanisms + custom_output, custom_attn_weights = custom_attn(x) + mha_output, mha_attn_weights = mha(x, x, x) + + # Check output shapes + assert ( + custom_output.shape + == mha_output.shape + == torch.Size([batch_size, seq_len, embed_dim]) + ) + assert ( + custom_attn_weights.shape + == mha_attn_weights.shape + == torch.Size([batch_size, seq_len, seq_len]) + ) + + # Compare outputs + max_diff_weights = torch.max(torch.abs(custom_attn_weights - mha_attn_weights)) + assert torch.allclose( + custom_attn_weights, mha_attn_weights, rtol=1e-4, atol=1e-4 + ), f"Maximum difference between attention weights: {max_diff_weights.item()}" + + # Check for close values (allowing for small numerical differences) + max_diff_output = torch.max(torch.abs(custom_output - mha_output)) + assert torch.allclose( + custom_output, mha_output, rtol=1e-4, atol=1e-4 + ), f"Maximum difference between outputs: {max_diff_output.item()}" + + log.info( + f"Test passed for batch_size={batch_size}, seq_len={seq_len}" + f"embed_dim={embed_dim}, num_heads={num_heads}" + ) + + +@pytest.mark.lightweight +@torch.inference_mode() +def test_rotary_attention_forward(): + # Set up parameters + batch_size = 4 + seq_len = 10 + embed_dim = 64 + num_heads = 8 + head_dim = embed_dim // num_heads + + attn = RotaryAttention( + d_head=head_dim, + num_heads=num_heads, + embed_dim=embed_dim, + dropout=0.0, + bias=False, + add_bias_kv=False, + add_zero_attn=False, + batch_first=True, + ) + # Generate random input + x = torch.rand(batch_size, seq_len, embed_dim) + + # Run both attention mechanisms + custom_output, custom_attn_weights = attn(x) diff --git a/tests/models/test_pos_encoders.py b/tests/models/test_pos_encoders.py new file mode 100644 index 0000000..c0a13d0 --- /dev/null +++ b/tests/models/test_pos_encoders.py @@ -0,0 +1,60 @@ +import pytest +import torch + +from specialk.models.transformer.pos_encoders import RotaryPositionalEncoder + + +@pytest.mark.lightweight +@torch.inference_mode() +def test_rope(): + # Parameters + seq_len = 3 + dim = 4 # Small dimension for easy visualization + + # Create a simple increasing sequence + x = torch.arange(1, seq_len * dim + 1, dtype=torch.float32).view((1, seq_len, dim)) + + # Initialize the RotaryEmbedding + rope = RotaryPositionalEncoder(dim_model=dim, max_seq_len=seq_len) + + # Apply rotary embedding + y_hat = rope(x) + + assert y_hat.shape == x.shape + + actual = torch.Tensor( + [ + [ + [1.0000, 2.0000, 3.0000, 4.0000], + [-3.1888, 5.9197, 7.9895, 8.0596], + [-13.7476, 9.7580, 3.6061, 12.1976], + ] + ] + ) + assert torch.allclose(y_hat, actual, rtol=1e-4, atol=1e-4) + + +@pytest.mark.lightweight +@torch.inference_mode() +def test_rope_simple(): + # Reference test case from + # https://colab.research.google.com/drive/11SKfzvMotuvvXNqY9qBpsD2RQX1PK7rP?usp=sharing#scrollTo=WyaC1bJYi5FH + # Create a simple increasing sequence + x = torch.tensor([[1, 2, 3, 4], [4, 5, 6, 7], [7, 8, 9, 10]]) + + # Initialize the RotaryEmbedding + rope = RotaryPositionalEncoder(dim_model=4, max_seq_len=3) + + # Apply rotary embedding + y_hat = rope(x) + + assert y_hat.shape == x.shape + + actual = torch.tensor( + [ + [1.0000, 2.0000, 3.0000, 4.0000], + [-2.8876, 4.9298, 6.6077, 7.0496], + [-11.0967, 7.7984, 2.6198, 10.1580], + ] + ) + assert torch.allclose(y_hat, actual, rtol=1e-4, atol=1e-4) diff --git a/tests/models/test_sequence_models.py b/tests/models/test_sequence_models.py new file mode 100644 index 0000000..2474239 --- /dev/null +++ b/tests/models/test_sequence_models.py @@ -0,0 +1,623 @@ +import os +import warnings +from pathlib import Path + +import pandas as pd +import pytest +import torch +import torch.nn.functional as F +from jaxtyping import Float, Int +from lightning.pytorch import Trainer +from torch import Tensor, nn +from torch.utils.data import DataLoader + +from datasets import Dataset +from specialk.core.constants import PAD, PROJECT_DIR, SOURCE, TARGET +from specialk.core.utils import log +from specialk.models.mt_model import RNNModule, TransformerModule +from specialk.models.tokenizer import SentencePieceVocabulary, WordVocabulary +from specialk.models.transformer.hf_transformer import MarianMTModule +from specialk.models.transformer.torch.pytorch_transformer import ( + PyTorchTransformerModel, + PyTorchTransformerModule, +) + +# below are being used, but the linter won't agree with that +from tests.models.fixtures import ( + hf_marianmt_dataloader, + hf_marianmt_tokenizer, + mt_dataset, +) + +dirpath: Path = Path("tests/tokenizer/test_files") +bilingual_tokenizer_path: Path = ( + PROJECT_DIR / "assets" / "tokenizer" / "sentencepiece" / "enfr_small.model" +) +monolingual_tokenizer_en: Path = ( + PROJECT_DIR / "assets" / "tokenizer" / "en_small_word_moses" +) + +monolingual_tokenizer_de: Path = ( + PROJECT_DIR / "assets" / "tokenizer" / "de_small_word_moses" +) +dataset_path = PROJECT_DIR / "tests/test_files/datasets/en_fr.parquet" + +BATCH_SIZE = 7 +SEQUENCE_LENGTH = 50 + +torch.manual_seed(1337) +torch.use_deterministic_algorithms(True) + + +@pytest.fixture(scope="session", autouse=True) +def dataset() -> Dataset: + return Dataset.from_pandas(pd.read_parquet(dataset_path)[:10]) + + +@pytest.fixture(scope="session") +def spm_tokenizer() -> SentencePieceVocabulary: + return SentencePieceVocabulary.from_file( + bilingual_tokenizer_path, max_length=SEQUENCE_LENGTH + ) + + +@pytest.fixture(scope="session") +def en_word_tokenizer() -> WordVocabulary: + return WordVocabulary.from_file(monolingual_tokenizer_en) + + +@pytest.fixture(scope="session") +def de_word_tokenizer() -> WordVocabulary: + return WordVocabulary.from_file(monolingual_tokenizer_de) + + +@pytest.fixture(scope="session") +def spm_dataloader(dataset: Dataset, spm_tokenizer: SentencePieceVocabulary): + def tokenize(example): + # perform tokenization at this stage. + example[SOURCE] = spm_tokenizer.to_tensor(example[SOURCE]).squeeze(0) + example[TARGET] = spm_tokenizer.to_tensor(example[TARGET]).squeeze(0) + return example + + tokenized_dataset = dataset.with_format("torch").map(tokenize) + dataloader = DataLoader(tokenized_dataset, batch_size=BATCH_SIZE, shuffle=True) + return dataloader, spm_tokenizer + + +@pytest.fixture(scope="session") +def word_dataloader_separate( + dataset: Dataset, + de_word_tokenizer: WordVocabulary, + en_word_tokenizer: WordVocabulary, +): + def tokenize(example): + # perform tokenization at this stage. + example[SOURCE] = de_word_tokenizer.to_tensor(example[SOURCE]).squeeze(0) + example[TARGET] = en_word_tokenizer.to_tensor(example[TARGET]).squeeze(0) + return example + + tokenized_dataset = dataset.with_format("torch").map(tokenize) + dataloader = DataLoader(tokenized_dataset, batch_size=BATCH_SIZE, shuffle=True) + return dataloader, de_word_tokenizer, en_word_tokenizer + + +@pytest.mark.lightweight +def test_rnn_inference_separate_tokenizers(word_dataloader_separate): + dataloader, src_tokenizer, tgt_tokenizer = word_dataloader_separate + assert src_tokenizer.vocab_size != tgt_tokenizer.vocab_size + model = RNNModule( + name="rnn_1", + vocabulary_size=src_tokenizer.vocab_size, + decoder_vocabulary_size=tgt_tokenizer.vocab_size, + sequence_length=SEQUENCE_LENGTH, + tokenizer=src_tokenizer, + decoder_tokenizer=tgt_tokenizer, + rnn_size=1, + d_word_vec=1, + ) + model.model.PAD = src_tokenizer.PAD + + batch: dict = next(iter(dataloader)) + x: torch.Tensor = batch[SOURCE] + y: torch.Tensor = batch[TARGET] + + m = model.model + y_hat = m(x, y) + + assert y_hat.shape[-1] == tgt_tokenizer.vocab_size + + # flatten tensors for loss function. + y_hat: Float[Tensor, "batch_size*seq_length, vocab"] = y_hat.view( + -1, y_hat.size(-1) + ) + y: Int[Tensor, "batch_size*vocab"] = y.view(-1) + + log.info("Y Shapes", y_hat=y_hat.shape, y=y.shape) + loss = F.cross_entropy(y_hat, y, ignore_index=model.model.PAD, reduction="sum") + loss.backward() + + +@pytest.mark.lightweight +def test_rnn_inference(spm_dataloader): + dataloader, tokenizer = spm_dataloader + + model = RNNModule( + name="rnn_1", + vocabulary_size=tokenizer.vocab_size, + sequence_length=SEQUENCE_LENGTH, + rnn_size=1, + d_word_vec=1, + ) + model.model.PAD = tokenizer.PAD + + batch: dict = next(iter(dataloader)) + x: torch.Tensor = batch[SOURCE] + y: torch.Tensor = batch[TARGET] + + m = model.model + y_hat = m(x, y) + + # flatten tensors for loss function. + y_hat: Float[Tensor, "batch_size vocab"] = y_hat.view(-1, y_hat.size(-1)) + y: Int[Tensor, "batch_size"] = y.view(-1) + + assert y_hat.shape == torch.Size( + [BATCH_SIZE * SEQUENCE_LENGTH, tokenizer.vocab_size] + ) + assert y.shape == torch.Size([BATCH_SIZE * SEQUENCE_LENGTH]) + + loss = F.cross_entropy(y_hat, y, ignore_index=model.model.PAD, reduction="sum") + loss.backward() + + +@pytest.mark.lightweight +def test_rnn_inference_brnn(spm_dataloader): + dataloader, tokenizer = spm_dataloader + + model = RNNModule( + name="rnn_1", + vocabulary_size=tokenizer.vocab_size, + brnn=True, + sequence_length=SEQUENCE_LENGTH, + rnn_size=2, + d_word_vec=1, + ) + model.model.PAD = tokenizer.PAD + + batch: dict = next(iter(dataloader)) + x: torch.Tensor = batch[SOURCE] + y: torch.Tensor = batch[TARGET] + + m = model.model + y_hat = m(x, y) + + log.info("yhat", yhat=y_hat.shape) + + # flatten tensors for loss function. + y_hat: Float[Tensor, "batch_size vocab"] = y_hat.view(-1, y_hat.size(-1)) + y: Int[Tensor, "batch_size"] = y.view(-1) + + assert y_hat.shape == torch.Size( + [BATCH_SIZE * SEQUENCE_LENGTH, tokenizer.vocab_size] + ) + assert y.shape == torch.Size([BATCH_SIZE * SEQUENCE_LENGTH]) + + loss = F.cross_entropy(y_hat, y, ignore_index=model.model.PAD, reduction="sum") + loss.backward() + + +@pytest.mark.skipif(os.environ.get("CI") == "true", reason="Test skipped on GitHub CI") +def test_rnn_inference_mps(spm_dataloader): + dataloader, tokenizer = spm_dataloader + model = RNNModule( + name="rnn_1", + vocabulary_size=tokenizer.vocab_size, + sequence_length=SEQUENCE_LENGTH, + rnn_size=1, + d_word_vec=1, + ) + device = "mps" + model.model.to(device) + model.model.PAD = tokenizer.PAD + + batch: dict = next(iter(dataloader)) + x: torch.Tensor = batch[SOURCE].to(device) + y: torch.Tensor = batch[TARGET].to(device) + + m = model.model + y_hat = m(x, y) + + # flatten tensors for loss function. + y_hat: Float[Tensor, "batch_size*seq_length, vocab"] = y_hat.view( + -1, y_hat.size(-1) + ) + y: Int[Tensor, "batch_size*vocab"] = y.view(-1) + + log.info("Y Shapes", y_hat=y_hat.shape, y=y.shape) + loss = F.cross_entropy(y_hat, y, ignore_index=model.model.PAD, reduction="sum") + loss.backward() + + +def test_save_load_rnn_checkpoint(spm_dataloader, tmp_path): + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + checkpoint_path = tmp_path / "checkpoint.ckpt" + dataloader, tokenizer = spm_dataloader + module = RNNModule( + name="rnn_1", + vocabulary_size=tokenizer.vocab_size, + sequence_length=SEQUENCE_LENGTH, + rnn_size=1, + d_word_vec=1, + ) + + trainer = Trainer(max_epochs=1, accelerator="cpu", logger=False) + trainer.fit(module, train_dataloaders=dataloader) + + m = module.model + + # generate temporary path to save checkpoint and load from. + trainer.save_checkpoint(checkpoint_path) + + ckpt_module = RNNModule.load_from_checkpoint(checkpoint_path) + m2 = ckpt_module.model + + torch.testing.assert_close( + m.decoder.word_lut.weight, m2.decoder.word_lut.weight + ) + torch.testing.assert_close( + m.encoder.word_lut.weight, m2.encoder.word_lut.weight + ) + + +def test_save_load_transformer_checkpoint(spm_dataloader, tmp_path): + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + checkpoint_path = tmp_path / "checkpoint.ckpt" + dataloader, tokenizer = spm_dataloader + module = PyTorchTransformerModule( + name="transformer_2", + vocabulary_size=tokenizer.vocab_size, + sequence_length=SEQUENCE_LENGTH, + dim_model=1, + n_heads=1, + num_encoder_layers=1, + num_decoder_layers=1, + tokenizer=tokenizer, + ) + trainer = Trainer(max_epochs=1, accelerator="cpu", logger=False) + trainer.fit(module, train_dataloaders=dataloader) + + # generate temporary path to save checkpoint and load from. + trainer.save_checkpoint(checkpoint_path) + ckpt_module = PyTorchTransformerModule.load_from_checkpoint(checkpoint_path) + + m = module.model + m2 = ckpt_module.model + + torch.testing.assert_close(m.input_emb.weight, m2.input_emb.weight) + torch.testing.assert_close(m.output_emb.weight, m2.output_emb.weight) + + # since we bundled the tokenizer in the module; + # it gets included in the checkpoint dump. + ckpt_tokenizer = ckpt_module.tokenizer + text = "hello" + tokens = tokenizer.to_tensor(text) + ckpt_tokens = ckpt_tokenizer.to_tensor(text) + torch.testing.assert_close(tokens, ckpt_tokens) + + +def test_transformer_inference(spm_dataloader): + dataloader, tokenizer = spm_dataloader + model = TransformerModule( + name="transformer_2", + vocabulary_size=tokenizer.vocab_size, + tokenizer=tokenizer, + sequence_length=SEQUENCE_LENGTH, + dim_model=2, + n_heads=1, + num_encoder_layers=1, + num_decoder_layers=1, + ) + model.change_pos_enc_len(SEQUENCE_LENGTH) + model.PAD = tokenizer.PAD + model.eval() + + batch: dict = next(iter(dataloader)) + x: torch.Tensor = batch[SOURCE] + y: torch.Tensor = batch[TARGET] + + len_x = torch.arange(SEQUENCE_LENGTH).repeat((BATCH_SIZE, 1)) + len_x = len_x.masked_fill((x == PAD), 0) + len_y = torch.arange(SEQUENCE_LENGTH).repeat((BATCH_SIZE, 1)) + len_y = len_y.masked_fill((y == PAD), 0) + + m = model.model + + # forward pass + enc_output, *_ = m.encoder(x, len_x) + dec_output, *_ = m.decoder(y, len_y, x, enc_output) + y_hat = m.generator(dec_output) * m.x_logit_scale + + y_hat: Float[Tensor, "batch_size*seq_length, vocab"] = y_hat.view( + -1, y_hat.size(-1) + ) + y: Int[Tensor, "batch_size*vocab"] = y.view(-1) + _ = F.cross_entropy(y_hat, y, ignore_index=model.PAD, reduction="sum") + + +def test_transformer_swiglu_inference(spm_dataloader): + from specialk.models.utils.activations import SwiGLU + + dataloader, tokenizer = spm_dataloader + model = TransformerModule( + name="transformer_swiglu", + vocabulary_size=tokenizer.vocab_size, + tokenizer=tokenizer, + sequence_length=SEQUENCE_LENGTH, + dim_model=2, + n_heads=1, + num_encoder_layers=1, + num_decoder_layers=1, + activation=SwiGLU, + ) + model.change_pos_enc_len(SEQUENCE_LENGTH) + model.PAD = tokenizer.PAD + model.eval() + + batch: dict = next(iter(dataloader)) + x: torch.Tensor = batch[SOURCE] + y: torch.Tensor = batch[TARGET] + + len_x = torch.arange(SEQUENCE_LENGTH).repeat((BATCH_SIZE, 1)) + len_x = len_x.masked_fill((x == PAD), 0) + len_y = torch.arange(SEQUENCE_LENGTH).repeat((BATCH_SIZE, 1)) + len_y = len_y.masked_fill((y == PAD), 0) + + m = model.model + + # forward pass + enc_output, *_ = m.encoder(x, len_x) + dec_output, *_ = m.decoder(y, len_y, x, enc_output) + y_hat = m.generator(dec_output) * m.x_logit_scale + + y_hat: Float[Tensor, "batch_size*seq_length, vocab"] = y_hat.view( + -1, y_hat.size(-1) + ) + y: Int[Tensor, "batch_size*vocab"] = y.view(-1) + _ = F.cross_entropy(y_hat, y, ignore_index=model.PAD, reduction="sum") + + +def test_transformer_inference_separate_tokenizers(word_dataloader_separate): + dataloader, src_tokenizer, tgt_tokenizer = word_dataloader_separate + assert src_tokenizer.vocab_size != tgt_tokenizer.vocab_size + model = TransformerModule( + name="transformer_2", + vocabulary_size=src_tokenizer.vocab_size, + decoder_vocabulary_size=tgt_tokenizer.vocab_size, + tokenizer=src_tokenizer, + decoder_tokenizer=tgt_tokenizer, + sequence_length=SEQUENCE_LENGTH, + dim_model=2, + n_heads=1, + num_encoder_layers=1, + num_decoder_layers=1, + ) + model.change_pos_enc_len(SEQUENCE_LENGTH) + model.PAD = src_tokenizer.PAD + model.eval() + + batch: dict = next(iter(dataloader)) + x: torch.Tensor = batch[SOURCE].squeeze(1) + y: torch.Tensor = batch[TARGET].squeeze(1) + + len_x = torch.arange(SEQUENCE_LENGTH).repeat((BATCH_SIZE, 1)) + len_x = len_x.masked_fill((x == PAD), 0) + len_y = torch.arange(SEQUENCE_LENGTH).repeat((BATCH_SIZE, 1)) + len_y = len_y.masked_fill((y == PAD), 0) + + m = model.model + + # forward pass + enc_output, *_ = m.encoder(x, len_x) + dec_output, *_ = m.decoder(y, len_y, x, enc_output) + y_hat = m.generator(dec_output) * m.x_logit_scale + + assert y_hat.shape[-1] == tgt_tokenizer.vocab_size + + +def test_pt_transformer_inference(spm_dataloader): + dataloader, tokenizer = spm_dataloader + module = PyTorchTransformerModule( + name="transformer_1", + vocabulary_size=tokenizer.vocab_size, + tokenizer=tokenizer, + sequence_length=SEQUENCE_LENGTH, + dim_model=2, + n_heads=1, + num_encoder_layers=1, + num_decoder_layers=1, + ) + module.eval() + model: PyTorchTransformerModel = module.model + model.PAD = 0 + + batch: dict = next(iter(dataloader)) + x: torch.Tensor = batch[SOURCE] + y: torch.Tensor = batch[TARGET] + + # forward pass + y_hat: Float[Tensor, "batch seq_length vocab"] + y_hat = model.forward(x, y) + + # flatten tensors for loss function. + y_hat: Float[Tensor, "batch_size*seq_length, vocab"] = y_hat.view( + -1, y_hat.size(-1) + ) + y: Int[Tensor, "batch_size*vocab"] = y.view(-1) + _ = F.cross_entropy(y_hat, y, ignore_index=model.PAD, reduction="sum") + + +@pytest.fixture(scope="session", autouse=True) +def pretrained_marianmt() -> MarianMTModule: + model_base_name: str = "Helsinki-NLP/opus-mt-fr-en" + return MarianMTModule( + name=model_base_name, + model_base_name=model_base_name, + ) + + +@pytest.fixture(scope="session", autouse=True) +def pretrained_marianmt_peft() -> MarianMTModule: + from peft import LoraConfig + from peft.utils.peft_types import TaskType + + modules = [ + "decoder.layers.0.encoder_attn.q_proj", + "decoder.layers.0.encoder_attn.k_proj", + "decoder.layers.0.encoder_attn.v_proj", + "decoder.layers.1.encoder_attn.q_proj", + "decoder.layers.1.encoder_attn.k_proj", + "decoder.layers.1.encoder_attn.v_proj", + "decoder.layers.2.encoder_attn.q_proj", + "decoder.layers.2.encoder_attn.k_proj", + "decoder.layers.2.encoder_attn.v_proj", + "decoder.layers.3.encoder_attn.q_proj", + "decoder.layers.3.encoder_attn.k_proj", + "decoder.layers.3.encoder_attn.v_proj", + "decoder.layers.4.encoder_attn.q_proj", + "decoder.layers.4.encoder_attn.k_proj", + "decoder.layers.4.encoder_attn.v_proj", + "decoder.layers.5.encoder_attn.q_proj", + "decoder.layers.5.encoder_attn.k_proj", + "decoder.layers.5.encoder_attn.v_proj", + "lm_head", + ] + model_base_name: str = "Helsinki-NLP/opus-mt-fr-en" + peft_config = LoraConfig( + task_type=TaskType.SEQ_CLS, + r=8, + lora_alpha=32, + lora_dropout=0.1, + bias="none", + target_modules=modules, + ) + return MarianMTModule( + name=model_base_name, + model_base_name=model_base_name, + peft_config=peft_config, + ) + + +def test_marianmt_inference(pretrained_marianmt, hf_marianmt_dataloader): + module = pretrained_marianmt + dataloader = hf_marianmt_dataloader + + batch: dict = next(iter(dataloader)) + x: torch.Tensor = batch[SOURCE] + y: torch.Tensor = batch[TARGET] + + # forward pass + x_mask = x != module.tokenizer.pad_token_id + _ = module.model(x, labels=y, attention_mask=x_mask) + + +def test_marianmt_peft_inference(pretrained_marianmt_peft, hf_marianmt_dataloader): + dataloader = hf_marianmt_dataloader # TODO this tokenizer isn't really it. + module = pretrained_marianmt_peft + model = module.model + + batch: dict = next(iter(dataloader)) + x: torch.Tensor = batch[SOURCE] + y: torch.Tensor = batch[TARGET] + + # forward pass + x_mask = x != module.tokenizer.pad_token_id + y_hat = model(x, labels=y, attention_mask=x_mask) + + +def test_marianmt_peft_inference(pretrained_marianmt_peft, hf_marianmt_dataloader): + dataloader = hf_marianmt_dataloader # TODO this tokenizer isn't really it. + module = pretrained_marianmt_peft + model = module.model + + batch: dict = next(iter(dataloader)) + x: torch.Tensor = batch[SOURCE] + y: torch.Tensor = batch[TARGET] + + # forward pass + x_mask = x != module.tokenizer.pad_token_id + y_hat = model(x, labels=y, attention_mask=x_mask) + + +def test_save_load_marianmt_checkpoint( + pretrained_marianmt, hf_marianmt_dataloader, tmp_path +): + dataloader = hf_marianmt_dataloader # TODO this tokenizer isn't really it. + module = pretrained_marianmt + checkpoint_path = tmp_path / "marianmt.ckpt" + + # Save initial state of PEFT parameters + trainer = Trainer(max_epochs=1, logger=False, limit_train_batches=1) + trainer.fit(module, train_dataloaders=hf_marianmt_dataloader) + + # Save checkpoint + trainer.save_checkpoint(checkpoint_path) + + # Load checkpoint + ckpt_module = MarianMTModule.load_from_checkpoint(checkpoint_path) + + batch: dict = next(iter(dataloader)) + x: torch.Tensor = batch[SOURCE] + y: torch.Tensor = batch[TARGET] + + # forward pass + x_mask = x != module.tokenizer.pad_token_id + + y_old = module.model(x, attention_mask=x_mask, labels=y) + y_new = ckpt_module.model(x, attention_mask=x_mask, labels=y) + print(y_old.logits.shape) + print(y_new.logits.shape) + + torch.testing.assert_close(y_old.logits, y_new.logits, atol=1e-4, rtol=1e-4) + + +def test_save_load_marianmt_peft_checkpoint( + pretrained_marianmt_peft, hf_marianmt_dataloader, tmp_path +): + dataloader = hf_marianmt_dataloader # TODO this tokenizer isn't really it. + module = pretrained_marianmt_peft + checkpoint_path = tmp_path / "marianmt.ckpt" + + trainer = Trainer(max_epochs=1, accelerator="cpu", logger=False) + trainer.fit(module, train_dataloaders=dataloader) + + # Save initial state of PEFT parameters + initial_peft_state = { + name: param.clone() + for name, param in module.model.named_parameters() + if "lora_" in name + } + + # Save checkpoint + trainer.save_checkpoint(checkpoint_path) + + # Load checkpoint + ckpt_module = MarianMTModule.load_from_checkpoint(checkpoint_path) + + # Check PEFT weights + for name, param in ckpt_module.model.named_parameters(): + if "lora_" in name: + if name not in initial_peft_state: + raise ValueError( + f"Unexpected LoRA parameter {name} after loading checkpoint" + ) + if not torch.allclose( + param, initial_peft_state[name], rtol=1e-4, atol=1e-4 + ): + err_msg = f"LoRA parameter {name} changed unexpectedly after loading checkpoint" + log.error(err_msg, initial=initial_peft_state[name], ckpt=param) + + raise ValueError(err_msg) + else: + log.info(f"Parameter {name} is the same.") diff --git a/tests/models/test_style_decoder.py b/tests/models/test_style_decoder.py new file mode 100644 index 0000000..20b7d31 --- /dev/null +++ b/tests/models/test_style_decoder.py @@ -0,0 +1,413 @@ +import warnings +from argparse import Namespace +from pathlib import Path + +import numpy as np +import pytest +import torch +import torch.nn.functional as F +from jaxtyping import Float, Int +from lightning.pytorch import Trainer +from lightning.pytorch.callbacks import RichModelSummary +from peft import LoraConfig +from peft.utils.peft_types import TaskType +from torch import Tensor, nn +from torch.utils.data import DataLoader + +from datasets import Dataset +from specialk import Constants +from specialk.core.utils import log +from specialk.models.classifier.models import BERTClassifier, CNNClassifier +from specialk.models.style_decoder import ( + StyleBackTranslationModelWithBERT, + StyleBackTranslationModelWithCNN, +) +from specialk.models.tokenizer import SentencePieceVocabulary +from specialk.models.transformer.hf_transformer import MarianMTModule +from specialk.models.transformer.torch.pytorch_transformer import ( + PyTorchTransformerModule, +) + +dirpath = "tests/tokenizer/test_files" +dev_path = "tests/test_files/datasets/en_fr_labels.parquet" + +dir_tokenizer = Constants.PROJECT_DIR / "assets" / "tokenizer" + +torch.manual_seed(1337) + + +BATCH_SIZE = 50 +SEQUENCE_LENGTH = 150 +VOCAB_SIZE_SMALL = 1000 +VOCAB_SIZE_BIG = 25000 + +PAD = 0 + + +@pytest.fixture(scope="session", autouse=True) +def dataset() -> Dataset: + dataset = Dataset.from_parquet(dev_path) + dataset = dataset.class_encode_column("label") + return dataset + + +@pytest.fixture(scope="session") +def spm_tokenizer() -> SentencePieceVocabulary: + return SentencePieceVocabulary.from_file( + dir_tokenizer / "sentencepiece" / "enfr_small.model", max_length=SEQUENCE_LENGTH + ) + + +@pytest.fixture(scope="session") +def spm_dataloader(dataset: Dataset, spm_tokenizer: SentencePieceVocabulary): + def tokenize(example): + # perform tokenization at this stage. + example["source"] = spm_tokenizer.to_tensor(example["target"]) + example["target"] = spm_tokenizer.to_tensor(example["target"]) + return example + + dataset = dataset.rename_column("text_fr", "source") + dataset = dataset.rename_column("text", "target") + tokenized_dataset = dataset.with_format("torch") + tokenized_dataset = tokenized_dataset.map(tokenize) + + # idk, but the dataset adds an additional dimension + dataloader = DataLoader(tokenized_dataset, batch_size=BATCH_SIZE, shuffle=True) + return dataloader + + +def seq2seq_transformer_model(tokenizer): + model = PyTorchTransformerModule( + name="transformer_1", + vocabulary_size=tokenizer.vocab_size, + sequence_length=SEQUENCE_LENGTH, + dim_model=32, + n_heads=2, + num_encoder_layers=1, + num_decoder_layers=1, + ) + model.PAD = PAD + + # eval doesn't work (i.e. it still stores gradients?) + model.model.encoder.eval() + + # disables gradient accumulation @ the encoder. + for param in model.model.encoder.parameters(): + param.requires_grad = False + + return model + + +def classifier_small_vocab(): + classifier = CNNClassifier( + name="test_model", + vocabulary_size=VOCAB_SIZE_SMALL, + sequence_length=SEQUENCE_LENGTH, + ) + classifier.eval() + # disables gradient accumulation @ the classifier. + for param in classifier.parameters(): + param.requires_grad = False + return classifier + + +def classifier_spm(spm_tokenizer): + classifier = CNNClassifier( + name="test_model", + vocabulary_size=spm_tokenizer.vocab_size, + sequence_length=SEQUENCE_LENGTH, + ) + classifier.eval() + # disables gradient accumulation @ the classifier. + for param in classifier.parameters(): + param.requires_grad = False + return classifier + + +def test_model_inference_transformer(spm_tokenizer, spm_dataloader): + """ + Test the transformer with a CNN classifier. + """ + seq2seq = seq2seq_transformer_model(spm_tokenizer) + classifier = classifier_spm(spm_tokenizer) + + # classifier stuff + batch: dict = next(iter(spm_dataloader)) + x, y, label = ( + batch["source"].squeeze(1), + batch["target"].squeeze(1), + batch["label"], + ) # not sure why there's additional indexes. + + # sequence 2 sequence forward pass + tgt_pred_tokens: Float[Tensor, "batch length vocab"] = seq2seq.model(x, y) + + # if the tokenizers are the same, then you can do this approach. + classifier_x: Float[Tensor, "seq_len batch d_vocab"] + classifier_x = tgt_pred_tokens.transpose(0, 1) + + # classifer pass + y_hat: Float[Tensor, "batch length vocab"] + y_hat = classifier.model(classifier_x).squeeze(-1) + + # classifier loss + loss_class = classifier.criterion(y_hat.squeeze(-1), label.float()) + + # reconstruction loss + loss_reconstruction = seq2seq.criterion( + tgt_pred_tokens.view(-1, tgt_pred_tokens.size(-1)), y.view(-1) + ) + + # combine losses + joint_loss = loss_class + loss_reconstruction + loss_class.backward() + + encoder_grad = seq2seq.model.encoder.layers[0].linear1.weight.grad + assert encoder_grad is None + decoder_grad = seq2seq.model.decoder.layers[0].linear1.weight.grad + assert isinstance(decoder_grad, Tensor) + classifier_grad = classifier.model.conv.weight.grad + assert classifier_grad is None + + +def test_model_inference_transformer_cnn_with_class(spm_tokenizer, spm_dataloader): + from argparse import Namespace + + from specialk.models.style_decoder import StyleBackTranslationModel + + seq2seq: PyTorchTransformerModule = seq2seq_transformer_model(spm_tokenizer) + seq2seq.model.encoder.eval() + + classifier: CNNClassifier = classifier_spm(spm_tokenizer) + classifier.refs = Namespace() + classifier.refs.tgt_label = 1 + + module = StyleBackTranslationModel(seq2seq, classifier) + + # classifier stuff + batch: dict = next(iter(spm_dataloader)) + x, y, label = ( + batch["source"].squeeze(1), + batch["target"].squeeze(1), + batch["label"], + ) # not sure why there's additional indexes. + + y_hat_text: Float[Tensor, "batch length vocab"] + y_hat_text = module.sequence_model.model(x, y) + + if module.mapping is not None: + # tokenizers for the models are different so + # we need to project that over. + y_hat_text: Float[Tensor, "length batch vocab_classifier"] + y_hat_text = y_hat_text.transpose(0, 1) @ module.mapping + + y_hat_label: Float[Tensor, "batch"] + y_hat_label = module.classifier(y_hat_text) + + # Calculate losses. + y_hat_text = y_hat_text.view(-1, y_hat_text.size(-1)) + y = y.view(-1) + loss_reconstruction = module.reconstruction_loss(y_hat_text, y) + + loss_class = module.classifier_loss(y_hat_label, label) + + joint_loss = loss_class + loss_reconstruction + + +@pytest.fixture(scope="session", autouse=True) +def pretrained_bert() -> BERTClassifier: + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + return BERTClassifier(name="test", model_base_name="bert-base-uncased") + + +def test_model_inference_transformer_bert_with_class( + spm_tokenizer, spm_dataloader, pretrained_bert +): + seq2seq: PyTorchTransformerModule = seq2seq_transformer_model(spm_tokenizer) + seq2seq.model.encoder.eval() + + classifier: BERTClassifier = pretrained_bert + classifier.vocabulary_size = classifier.tokenizer.vocab_size + classifier.refs = Namespace() + classifier.refs.tgt_label = 1 + + module = StyleBackTranslationModelWithBERT(seq2seq, classifier) + + # classifier stuff + batch: dict = next(iter(spm_dataloader)) + x, y, label = ( + batch["source"].squeeze(1), + batch["target"].squeeze(1), + batch["label"], + ) # not sure why there's additional indexes. + + y_hat_text, y_hat_label = module._forward(x, y, label) + + # Calculate losses. + y_hat_text = y_hat_text.view(-1, y_hat_text.size(-1)) + y = y.view(-1) + loss_reconstruction = module.reconstruction_loss(y_hat_text, y) + loss_class = y_hat_label.loss + + # this loss class is fundamentally the same. + assert y_hat_label.loss == module.classifier_loss(y_hat_label.logits, label.long()) + + joint_loss = loss_class + loss_reconstruction + + +def test_model_inference_transformer_cnn_with_class(spm_tokenizer, spm_dataloader): + seq2seq: PyTorchTransformerModule = seq2seq_transformer_model(spm_tokenizer) + seq2seq.model.encoder.eval() + + classifier: CNNClassifier = classifier_spm(spm_tokenizer) + + module = StyleBackTranslationModelWithCNN(seq2seq, classifier) + + # classifier stuff + batch: dict = next(iter(spm_dataloader)) + x, y, label = ( + batch["source"].squeeze(1), + batch["target"].squeeze(1), + batch["label"], + ) # not sure why there's additional indexes. + + y_hat_text, y_hat_label = module._forward(x, y, label) + + # Calculate losses. + y_hat_text = y_hat_text.view(-1, y_hat_text.size(-1)) + y = y.view(-1) + loss_reconstruction = module.reconstruction_loss(y_hat_text, y) + + loss_class = module.classifier_loss(y_hat_label, label) + + joint_loss = loss_class + loss_reconstruction + + +def test_model_inference_transformer_cnn_with_class(spm_tokenizer, spm_dataloader): + seq2seq: PyTorchTransformerModule = seq2seq_transformer_model(spm_tokenizer) + seq2seq.model.encoder.eval() + + classifier: CNNClassifier = classifier_spm(spm_tokenizer) + + module = StyleBackTranslationModelWithCNN(seq2seq, classifier) + + # classifier stuff + batch: dict = next(iter(spm_dataloader)) + x, y, label = ( + batch["source"].squeeze(1), + batch["target"].squeeze(1), + batch["label"], + ) # not sure why there's additional indexes. + + y_hat_text, y_hat_label = module._forward(x, y, label) + + # Calculate losses. + y_hat_text = y_hat_text.view(-1, y_hat_text.size(-1)) + y = y.view(-1) + loss_reconstruction = module.reconstruction_loss(y_hat_text, y) + + loss_class = module.classifier_loss(y_hat_label, label) + + joint_loss = loss_class + loss_reconstruction + + +def test_model_inference_transformer_bert_with_class_trainer_loop( + spm_tokenizer, spm_dataloader, pretrained_bert +): + seq2seq: PyTorchTransformerModule = seq2seq_transformer_model(spm_tokenizer) + seq2seq.model.encoder.eval() + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + classifier: BERTClassifier = pretrained_bert + classifier.vocabulary_size = classifier.tokenizer.vocab_size + classifier.refs = Namespace() + classifier.refs.tgt_label = 1 + + module = StyleBackTranslationModelWithBERT(seq2seq, classifier) + + trainer = Trainer( + max_epochs=1, logger=False, limit_train_batches=1, accelerator="cpu" + ) + trainer.fit(module, train_dataloaders=spm_dataloader) + + +def test_model_inference_marianmt_bert_with_class_trainer_loop( + spm_dataloader, + pretrained_bert, +): + seq2seq: MarianMTModule = MarianMTModule("test_model") + seq2seq.model.model.encoder.eval() + seq2seq.vocabulary_size = seq2seq.tokenizer.vocab_size + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + classifier: BERTClassifier = pretrained_bert + classifier.vocabulary_size = classifier.tokenizer.vocab_size + + module = StyleBackTranslationModelWithBERT(seq2seq, classifier, target_label=1) + + trainer = Trainer( + max_epochs=1, logger=False, limit_train_batches=1, accelerator="cpu" + ) + trainer.fit(module, train_dataloaders=spm_dataloader) + + +def test_model_inference_lora_marianmt_lora_bert_with_class_trainer_loop( + spm_dataloader, + pretrained_bert, +): + modules = [ + "decoder.layers.0.encoder_attn.q_proj", + "decoder.layers.0.encoder_attn.k_proj", + "decoder.layers.0.encoder_attn.v_proj", + "decoder.layers.1.encoder_attn.q_proj", + "decoder.layers.1.encoder_attn.k_proj", + "decoder.layers.1.encoder_attn.v_proj", + "decoder.layers.2.encoder_attn.q_proj", + "decoder.layers.2.encoder_attn.k_proj", + "decoder.layers.2.encoder_attn.v_proj", + "decoder.layers.3.encoder_attn.q_proj", + "decoder.layers.3.encoder_attn.k_proj", + "decoder.layers.3.encoder_attn.v_proj", + "decoder.layers.4.encoder_attn.q_proj", + "decoder.layers.4.encoder_attn.k_proj", + "decoder.layers.4.encoder_attn.v_proj", + "decoder.layers.5.encoder_attn.q_proj", + "decoder.layers.5.encoder_attn.k_proj", + "decoder.layers.5.encoder_attn.v_proj", + ] + model_base_name: str = "Helsinki-NLP/opus-mt-fr-en" + peft_config = LoraConfig( + task_type=TaskType.SEQ_CLS, + r=8, + lora_alpha=32, + lora_dropout=0.1, + bias="none", + target_modules=modules, + ) + seq2seq = MarianMTModule( + name=model_base_name, + model_base_name=model_base_name, + peft_config=peft_config, + ) + seq2seq.vocabulary_size = seq2seq.tokenizer.vocab_size + # freeze all the modules that aren't lota based. + seq2seq._freeze_non_lora_weights() + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + classifier: BERTClassifier = pretrained_bert + classifier.vocabulary_size = classifier.tokenizer.vocab_size + + module = StyleBackTranslationModelWithBERT(seq2seq, classifier, target_label=1) + + trainer = Trainer( + max_epochs=1, + logger=False, + limit_train_batches=1, + accelerator="cpu", + callbacks=[RichModelSummary(max_depth=4)], + ) + trainer.fit(module, train_dataloaders=spm_dataloader) diff --git a/tests/test_files/datasets/en_fr.parquet b/tests/test_files/datasets/en_fr.parquet new file mode 100644 index 0000000..8d18e75 Binary files /dev/null and b/tests/test_files/datasets/en_fr.parquet differ diff --git a/tests/test_files/datasets/en_fr_labels.parquet b/tests/test_files/datasets/en_fr_labels.parquet new file mode 100644 index 0000000..7f81660 Binary files /dev/null and b/tests/test_files/datasets/en_fr_labels.parquet differ diff --git a/tests/test_files/datasets/political_dev.txt b/tests/test_files/datasets/political_dev.txt new file mode 100755 index 0000000..744bb9c --- /dev/null +++ b/tests/test_files/datasets/political_dev.txt @@ -0,0 +1,22 @@ +democratic thank you , congresswoman sewell , for your vigilance and for keeping us informed . +democratic it pains me greatly to agree with ms. warren . +democratic you go amy ! ! ! +democratic as a former minnesota resident , i have seen joan mondale 's work on a state as well as national level . +democratic why wouldn 't we vote for bernie or hillary ... oh that 's right , one is giving everything out to everyone except the people who work for it and the other should be in prison .... wake up sheeple , nothing in this world is free , where do you think the money will come from ? +democratic how does it feel helping trump win ? +democratic it 's up to the electoral college to fix this mistake . +democratic this entire result and election is a perversion of democracy - a wing of government overstepping its reach to influence public opinion and the wishes of the majority being overturned . +democratic i think it 's disgusting that sherrod brown and bob portman aren 't representing " we the people " and voted down gmo labeling . +republican yes , there are stories of bribery and intimidation by the leadership , but this point in history has to be the conservative caucus 's alamo . +republican fund government , not obamacare http : / / www.dontfundobamacare.com / [ [ share ] ] +republican these people gave everything and endured the horror of war and because obama is a spineless wimp who set out to intentionally hurt these hero 's by shutting down a venue that has been open 24 / 7 / 365 with no gates since it was open . +republican if our founding fathers could see what we have today what would they say would be my answer ! +republican good for you tom and certainly good for us . +republican like rand paul more and more . +republican we must stop the govt from being corrupt and just being our govt please look to your soul for this ! +republican from nh ... thanks for your service in maintaining the conservative ideals . +republican isil is the name nobama gave it to pease his muslim brotherhood buddys . +republican shutting down the government is not related to the affordable care act . +republican yes i do but i think the current administration has tarnished our reputation around the world . +republican don 't forget to mention obama is a liar and ineligible for the office of potus . +republican no bueno ... diff --git a/tests/tokenizer/test_files/bpe_tokenizer b/tests/tokenizer/test_files/bpe_tokenizer new file mode 100644 index 0000000..7636e6b --- /dev/null +++ b/tests/tokenizer/test_files/bpe_tokenizer @@ -0,0 +1 @@ +{"kwargs": {"name": "source", "filename": "", "vocab_size": 1000, "max_length": 100, "lower": true, "BOS_TOKEN": "", "EOS_TOKEN": "", "UNK_TOKEN": "", "SEP_TOKEN": "", "PAD_TOKEN": "", "CLS_TOKEN": "", "BLO_TOKEN": "

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b/tests/tokenizer/test_tokenizer.py @@ -0,0 +1,291 @@ +import tempfile +from pathlib import Path + +import numpy as np +import pytest +import torch + +from specialk.core.constants import PROJECT_DIR +from specialk.core.utils import log +from specialk.datasets.preprocess import load_file +from specialk.models.mt_model import NMTModule +from specialk.models.tokenizer import ( + BPEVocabulary, + HuggingFaceVocabulary, + SentencePieceVocabulary, + WordVocabulary, +) + +VOCABULARY_SIZE = 1000 +SEQUENCE_LENGTH = 100 +PCT_BPE = 0.2 +SRC_VOCAB = "tests/test_files/datasets/political_dev.txt" + + +@pytest.fixture(scope="session", autouse=True) +def word_tokenizer(): + tokenizer = WordVocabulary( + name="source", vocab_size=VOCABULARY_SIZE, max_length=SEQUENCE_LENGTH + ) + text = load_file(SRC_VOCAB, None) + tokenizer.fit(text) + return tokenizer + + +def test_word_tokenizer_seq_len_change(word_tokenizer): + text = ["hello world"] + tensor_before = word_tokenizer.to_tensor(text) + new_size = 28 + log.info("i'm here") + word_tokenizer.max_length = new_size + tensor_after = word_tokenizer.to_tensor(text) + assert tensor_after.shape == torch.Size([1, new_size]) + assert tensor_after.shape != tensor_before.shape + + +def test_word_vocabulary(word_tokenizer): + assert word_tokenizer.tokenize("hello world") == ["hello", "world"] + + +def test_save_load_word_vocabulary(word_tokenizer): + dirpath = tempfile.mkdtemp() + + tokenizer_filepath = Path(dirpath) / "word_tokenizer" + word_tokenizer.to_file(tokenizer_filepath) + + new_tokenizer = WordVocabulary.from_file(tokenizer_filepath) + assert new_tokenizer.vocab.idxToLabel == word_tokenizer.vocab.idxToLabel + + +def test_word_tokenizer_to_tensor(word_tokenizer): + sequences = ["hello world", "hello"] + output = word_tokenizer.to_tensor(sequences) + max_len = word_tokenizer.max_length + assert output.shape[-1] == max_len + + +@pytest.fixture(scope="session", autouse=True) +def pol_word_tokenizer(): + tokenizer_filepath = PROJECT_DIR / "assets" / "tokenizer" / "fr_en_word_moses" + return WordVocabulary.from_file(tokenizer_filepath) + + +def test_word_tokenizer_to_tensor_long(pol_word_tokenizer): + sequence = "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum." + output = pol_word_tokenizer.to_tensor(sequence) + max_len = pol_word_tokenizer.max_length + print(pol_word_tokenizer, max_len) + print(output.shape) + assert output.shape[-1] == max_len + + +@pytest.fixture(scope="session", autouse=True) +def bpe_tokenizer(): + tokenizer = BPEVocabulary( + name="source", + vocab_size=VOCABULARY_SIZE, + max_length=SEQUENCE_LENGTH, + pct_bpe=PCT_BPE, + ) + tokenizer.make(SRC_VOCAB) + return tokenizer + + +@pytest.fixture(scope="session") +def bpe_premade() -> BPEVocabulary: + tokenizer_filepath = ( + Path(PROJECT_DIR) / "tests" / "tokenizer" / "test_files" / "bpe_tokenizer" + ) + return BPEVocabulary.from_file(tokenizer_filepath) + + +def test_bpe_vocabulary(bpe_tokenizer): + assert bpe_tokenizer.tokenize("hello world") == [ + "__sow", + "", + "", + "", + "", + "", + "__eow", + "world", + ] + + +def test_bpe_vocabulary_from_load(bpe_premade): + assert bpe_premade.tokenize("hello world") == [ + "__sow", + "he", + "ll", + "o", + "__eow", + "world", + ] + + +def test_save_load_bpe_vocabulary(bpe_tokenizer): + dirpath = tempfile.mkdtemp() + # dirpath = "/Users/t/Projects/specialk/tests/tokenizer/test_files" + + tokenizer_filepath = Path(dirpath) / "bpe_tokenizer" + bpe_tokenizer.to_file(tokenizer_filepath) + + new_tokenizer = BPEVocabulary.from_file(tokenizer_filepath) + assert new_tokenizer.vocab.bpe_vocab == bpe_tokenizer.vocab.bpe_vocab + assert new_tokenizer.vocab.word_vocab == bpe_tokenizer.vocab.word_vocab + assert new_tokenizer.vocab.required_tokens == bpe_tokenizer.vocab.required_tokens + assert new_tokenizer.vocab.strict == bpe_tokenizer.vocab.strict + + +@pytest.fixture(scope="session", autouse=True) +def sentencepiece_tokenizer(): + spm_path = str( + PROJECT_DIR / "assets" / "tokenizer" / "sentencepiece" / "enfr.model" + ) + tokenizer = SentencePieceVocabulary.from_file(spm_path, max_length=100) + return tokenizer + + +@pytest.fixture(scope="session", autouse=True) +def huggingface_tokenizer(): + return HuggingFaceVocabulary( + name="bert-base-uncased", + pretrained_model_name_or_path="bert-base-uncased", + max_length=512, + ) + + +def test_spm_tokenizer_to_tensor_long(sentencepiece_tokenizer): + sequence = "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum." + output = sentencepiece_tokenizer.to_tensor(sequence) + max_len = sentencepiece_tokenizer.max_length + print(sentencepiece_tokenizer, max_len) + print(output.shape) + assert output.shape[-1] == max_len + + +def test_spm_tokenizer_encode_decode(sentencepiece_tokenizer): + sequence = "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam" + tokens = sentencepiece_tokenizer.to_tensor(sequence) + assert tokens != sequence + _sequence = sentencepiece_tokenizer.detokenize(tokens) + print(_sequence) + assert _sequence == sequence + + +def test_model_validation_bleu_spm(sentencepiece_tokenizer): + src_tokenizer = sentencepiece_tokenizer + model = NMTModule( + name="test", + vocabulary_size=src_tokenizer.vocab_size, + sequence_length=SEQUENCE_LENGTH, + tokenizer=src_tokenizer, + ) + + _refs = ["The dog bit the man.", "It was not unexpected.", "The man bit him first."] + + _hyps = [ + "The dog bit the man.", + "It wasn't surprising.", + "The man had just bitten him.", + ] + + ref_tensor = [src_tokenizer.to_tensor(i) for i in _refs] + hyp_tensor = [src_tokenizer.to_tensor(i) for i in _hyps] + + ref_tensor = torch.cat(ref_tensor) + hyp_tensor = torch.cat(hyp_tensor) + + batch_size, seq_len = ref_tensor.shape + + # with the ref_tensor, turn that into one hot and then fill + # it in with the token indicated (this is approximate the model generation) + hyp_one_hot = torch.zeros( + batch_size, + seq_len, + src_tokenizer.vocab_size, + ).scatter_(2, torch.unsqueeze(hyp_tensor, 2), 1) + + assert (hyp_tensor.shape[0], hyp_tensor.shape[1]) == (batch_size, seq_len) + assert torch.all(hyp_one_hot.argmax(dim=-1).eq(hyp_tensor)) + + pred_score = model.validation_bleu(hyp_one_hot, ref_tensor) + + score = 45.06 + + assert np.isclose(pred_score, score, rtol=1e-02, atol=1e-03) + + +def test_model_validation_bleu_word(word_tokenizer): + src_tokenizer = word_tokenizer + model = NMTModule( + name="test", + vocabulary_size=src_tokenizer.vocab_size, + sequence_length=SEQUENCE_LENGTH, + tokenizer=src_tokenizer, + ) + + _refs = ["The dog bit the man.", "It was not unexpected.", "The man bit him first."] + + _hyps = [ + "The dog bit the man.", + "It wasn't surprising.", + "The man had just bitten him.", + ] + + ref_tensor = [src_tokenizer.to_tensor(i) for i in _refs] + hyp_tensor = [src_tokenizer.to_tensor(i) for i in _hyps] + + ref_tensor = torch.cat(ref_tensor).view(len(_refs), -1) + hyp_tensor = torch.cat(hyp_tensor).view(len(_hyps), -1) + + batch_size, seq_len = ref_tensor.shape + + # with the ref_tensor, turn that into one hot and then fill + # it in with the token indicated (this is approximate the model generation) + hyp_one_hot = torch.zeros( + batch_size, + seq_len, + src_tokenizer.vocab_size, + ).scatter_(2, torch.unsqueeze(hyp_tensor, 2), 1) + + assert (hyp_tensor.shape[0], hyp_tensor.shape[1]) == (batch_size, seq_len) + assert torch.all(hyp_one_hot.argmax(dim=-1).eq(hyp_tensor)) + + pred_score = model.validation_bleu(hyp_one_hot, ref_tensor) + + score = 90 + + assert np.isclose(pred_score, score, rtol=5, atol=5) + + +def test_tokenizer_to_tensor_single_sequence( + word_tokenizer, bpe_tokenizer, sentencepiece_tokenizer, huggingface_tokenizer +): + tokenizers = [ + bpe_tokenizer, + sentencepiece_tokenizer, + word_tokenizer, + huggingface_tokenizer, + ] + for tokenizer in tokenizers: + tokenizer.max_length + tensor = torch.tensor(tokenizer.to_tensor("hello world")).detach() + assert tensor.shape == torch.Size((1, tokenizer.max_length)) + + +def test_tokenizer_to_tensor_multiple_seq( + word_tokenizer, bpe_tokenizer, sentencepiece_tokenizer, huggingface_tokenizer +): + sequences = ["hello world", "how are you doing"] + batch_size = len(sequences) + tokenizers = [ + bpe_tokenizer, + sentencepiece_tokenizer, + word_tokenizer, + huggingface_tokenizer, + ] + for tokenizer in tokenizers: + tokenizer.max_length + tensor = torch.tensor(tokenizer.to_tensor(sequences)) + assert tensor.shape == torch.Size((batch_size, tokenizer.max_length)) diff --git a/tests/utils/test_lid.py b/tests/utils/test_lid.py new file mode 100644 index 0000000..8fb4315 --- /dev/null +++ b/tests/utils/test_lid.py @@ -0,0 +1,140 @@ +import pytest +import pandas as pd +import numpy as np +from unittest.mock import Mock, patch +from specialk.models.utils.lid import FastTextLID + + +@pytest.fixture +def mock_fasttext_model(): + # should a dummy version of fasttext.FastText + mock_model = Mock() + mock_model.f.multilinePredict.return_value = ( + [["__label__en"], ["__label__fr"]], + [[0.9], [0.8]], + ) + return mock_model + + +@pytest.fixture +def mock_fasttext_lid(mock_fasttext_model): + with patch("fasttext.load_model", return_value=mock_fasttext_model): + with patch("huggingface_hub.hf_hub_download", return_value="mock_path"): + return FastTextLID() + + +@pytest.fixture +def sample_data(): + return pd.Series(["Hello world", "Bonjour monde"]) + + +def test_process_batch(mock_fasttext_lid, mock_fasttext_model): + batch = ["Hello", "World"] + labels, probs = mock_fasttext_lid.process_batch( + batch, k=2, threshold=0.0, on_unicode_error="strict" + ) + + mock_fasttext_model.f.multilinePredict.assert_called_once_with( + batch, 2, 0.0, "strict" + ) + assert isinstance(labels, np.ndarray) + assert isinstance(probs, np.ndarray) + assert labels.shape == (2,) + assert probs.shape == (2,) + + +@pytest.mark.parametrize("batch_size", [1, 2]) +def test_lid_scores(mock_fasttext_lid, sample_data, batch_size): + with patch("multiprocessing.Pool") as mock_pool: + mock_pool.return_value.__enter__.return_value.imap.return_value = [ + ( + np.array( + [["__label__en", "__label__fr"], ["__label__fr", "__label__en"]] + ), + np.array([[0.9, 0.1], [0.8, 0.2]]), + ) + ] + + labels, scores = mock_fasttext_lid.lid_scores( + sample_data, batch_size=batch_size + ) + + assert isinstance(labels, np.ndarray) + assert isinstance(scores, np.ndarray) + assert labels.shape == (2, 2) + assert scores.shape == (2, 2) + assert labels[:, 0].tolist() == ["__label__en", "__label__fr"] + assert scores[:, 0].tolist() == [0.9, 0.8] + + +def test_lid_scores_empty_input(mock_fasttext_lid): + empty_series = pd.Series([]) + labels, scores = mock_fasttext_lid.lid_scores(empty_series) + assert len(labels) == 0 + assert len(scores) == 0 + + +def test_init_worker(mock_fasttext_lid): + with patch("fasttext.load_model") as mock_load_model: + FastTextLID._init_worker("mock_path") + mock_load_model.assert_called_once_with("mock_path") + + +@pytest.fixture +def language_code(): + return FastTextLID().language + + +def test_enum_creation(language_code): + assert language_code.ENG_Latn.value == "eng_Latn" + assert language_code.FRA_Latn.value == "fra_Latn" + assert language_code.SPA_Latn.value == "spa_Latn" + + +def test_from_string_with_prefix(language_code): + assert language_code.from_string("__label__eng_Latn") == language_code.ENG_Latn + assert language_code.from_string("__label__fra_Latn") == language_code.FRA_Latn + + +def test_from_string_without_prefix(language_code): + assert language_code.from_string("eng_Latn") == language_code.ENG_Latn + assert language_code.from_string("fra_Latn") == language_code.FRA_Latn + + +def test_from_string_invalid(language_code): + with pytest.raises(ValueError): + language_code.from_string("invalid_code") + + +def test_numpy_array_of_enums(language_code): + enum_array = np.array( + [language_code.ENG_Latn, language_code.FRA_Latn, language_code.SPA_Latn] + ) + assert enum_array.dtype == object + assert all(isinstance(item, language_code) for item in enum_array) + + +def test_numpy_array_of_strings(): + value_array = np.array(["eng_Latn", "fra_Latn", "spa_Latn"]) + assert value_array.dtype == "