diff --git a/config/gpt2_nano_ul2r.yaml b/config/gpt2_nano_ul2r.yaml new file mode 100644 index 000000000..68b43f3d9 --- /dev/null +++ b/config/gpt2_nano_ul2r.yaml @@ -0,0 +1,70 @@ +#data: +# id: dlwh/wikitext_103_detokenized + +#disable_jit: true +#data_only: true + +model: + type: gpt2 + # hidden_dim: 32 + # num_heads: 4 + # num_layers: 2 + +initialize_from_hf: openai-community/gpt2 +use_hf_model_config: true + +trainer: + # checkpointer: + # base_path: "checkpoints" + # keep: + # - every: 50 + # save_interval: 5m + + # per_device_parallelism: -1 + batch_axis: "batch" + fsdp_axis: "embed" + mp: f32 + num_train_steps: 100000 + tensor_parallel_axes: ["mlp", "heads"] + train_batch_size: 32 # what's good? + +data: + id: dlwh/wikitext_103_detokenized + # id: openai/gsm8k # This is super tiny. It's just for smoke tests. + # name: socratic + cache_dir: /tmp/marin_cache + tokenizer: ./gpt2_ul2r_DEV + format: + type: ul2r + #text_key: text # only for gsm8k + #text_key: question + task_configs: + r: + type: rx + mask_prob: 0.15 + mean_span_length: 3.0 + random_roll: true + task_token_id: 50257 + x1: + type: rx + mask_prob: 0.15 + mean_span_length: 32.0 + random_roll: true + task_token_id: 50258 + x2: + type: rx + mask_prob: 0.5 + mean_span_length: 3.0 + random_roll: true + task_token_id: 50258 + s: + type: s + task_token_id: 50259 + task_probs: + r: 0.5 + x1: 0.125 + x2: 0.125 + s: 0.25 + rng_seed: 42 + sentinel_token_id_start: 50260 + sentinel_token_id_count: 100 diff --git a/config/llama2_7b_ul2r.yaml b/config/llama2_7b_ul2r.yaml new file mode 100644 index 000000000..2ea496424 --- /dev/null +++ b/config/llama2_7b_ul2r.yaml @@ -0,0 +1,70 @@ +data: + tokenizer: ./llama_2_7b_hf_ul2r/ + configs: + pile: + id: dlwh/wikitext_103_detokenized + cache_dir: /tmp/marin_cache_pile + ul2r: + id: dlwh/wikitext_103_detokenized + cache_dir: /tmp/marin_cache_ul2r + format: + type: ul2r + task_configs: + r: + type: rx + mask_prob: 0.15 + mean_span_length: 3.0 + random_roll: true + task_token_id: 32000 + x1: + type: rx + mask_prob: 0.15 + mean_span_length: 32.0 + random_roll: true + task_token_id: 32001 + x2: + type: rx + mask_prob: 0.5 + mean_span_length: 3.0 + random_roll: true + task_token_id: 32001 + s: + type: s + task_token_id: 32002 + task_probs: + r: 0.5 + x1: 0.125 + x2: 0.125 + s: 0.25 + rng_seed: 42 + sentinel_token_id_start: 32003 + sentinel_token_id_count: 100 + train_weights: + pile: 0 # only use Pile for evaluation; UL2R says loss should decrease + ul2r: 1 + +model: + type: llama +initialize_from_hf: true +use_hf_model_config: true + +# From config/llam2_7b_continued.yaml + +trainer: + tracker: + type: wandb + project: "levanter" + tags: ["ul2r", "llama2"] + + mp: p=f32,c=bfloat16 + + model_axis_size: 1 + per_device_eval_parallelism: 4 + + train_batch_size: 1024 + num_train_steps: 10000 + steps_per_eval: 500 + +optimizer: + learning_rate: 1.2e-4 + weight_decay: 0.0 diff --git a/gpt2_ul2r_DEV/added_tokens.json b/gpt2_ul2r_DEV/added_tokens.json new file mode 100644 index 000000000..855d7b991 --- /dev/null +++ b/gpt2_ul2r_DEV/added_tokens.json @@ -0,0 +1,105 @@ +{ + "<|mask_0|>": 50260, + "<|mask_10|>": 50270, + "<|mask_11|>": 50271, + "<|mask_12|>": 50272, + "<|mask_13|>": 50273, + "<|mask_14|>": 50274, + "<|mask_15|>": 50275, + "<|mask_16|>": 50276, + "<|mask_17|>": 50277, + "<|mask_18|>": 50278, + "<|mask_19|>": 50279, + "<|mask_1|>": 50261, + "<|mask_20|>": 50280, + "<|mask_21|>": 50281, + "<|mask_22|>": 50282, + "<|mask_23|>": 50283, + "<|mask_24|>": 50284, + "<|mask_25|>": 50285, + "<|mask_26|>": 50286, + "<|mask_27|>": 50287, + "<|mask_28|>": 50288, + "<|mask_29|>": 50289, + "<|mask_2|>": 50262, + "<|mask_30|>": 50290, + "<|mask_31|>": 50291, + "<|mask_32|>": 50292, + "<|mask_33|>": 50293, + "<|mask_34|>": 50294, + "<|mask_35|>": 50295, + "<|mask_36|>": 50296, + "<|mask_37|>": 50297, + "<|mask_38|>": 50298, + "<|mask_39|>": 50299, + "<|mask_3|>": 50263, + "<|mask_40|>": 50300, + "<|mask_41|>": 50301, + "<|mask_42|>": 50302, + "<|mask_43|>": 50303, + "<|mask_44|>": 50304, + "<|mask_45|>": 50305, + "<|mask_46|>": 50306, + "<|mask_47|>": 50307, + "<|mask_48|>": 50308, + "<|mask_49|>": 50309, + "<|mask_4|>": 50264, + "<|mask_50|>": 50310, + "<|mask_51|>": 50311, + "<|mask_52|>": 50312, + "<|mask_53|>": 50313, + "<|mask_54|>": 50314, + "<|mask_55|>": 50315, + "<|mask_56|>": 50316, + "<|mask_57|>": 50317, + "<|mask_58|>": 50318, + "<|mask_59|>": 50319, + "<|mask_5|>": 50265, + "<|mask_60|>": 50320, + "<|mask_61|>": 50321, + "<|mask_62|>": 50322, + "<|mask_63|>": 50323, + "<|mask_64|>": 50324, + "<|mask_65|>": 50325, + "<|mask_66|>": 50326, + "<|mask_67|>": 50327, + "<|mask_68|>": 50328, + "<|mask_69|>": 50329, + "<|mask_6|>": 50266, + "<|mask_70|>": 50330, + "<|mask_71|>": 50331, + "<|mask_72|>": 50332, + "<|mask_73|>": 50333, + "<|mask_74|>": 50334, + "<|mask_75|>": 50335, + "<|mask_76|>": 50336, + "<|mask_77|>": 50337, + "<|mask_78|>": 50338, + "<|mask_79|>": 50339, + "<|mask_7|>": 50267, + "<|mask_80|>": 50340, + "<|mask_81|>": 50341, + "<|mask_82|>": 50342, + "<|mask_83|>": 50343, + "<|mask_84|>": 50344, + "<|mask_85|>": 50345, + "<|mask_86|>": 50346, + "<|mask_87|>": 50347, + "<|mask_88|>": 50348, + "<|mask_89|>": 50349, + "<|mask_8|>": 50268, + "<|mask_90|>": 50350, + "<|mask_91|>": 50351, + "<|mask_92|>": 50352, + "<|mask_93|>": 50353, + "<|mask_94|>": 50354, + "<|mask_95|>": 50355, + "<|mask_96|>": 50356, + "<|mask_97|>": 50357, + "<|mask_98|>": 50358, + "<|mask_99|>": 50359, + "<|mask_9|>": 50269, + "<|r|>": 50257, + "<|s|>": 50258, + "<|x|>": 50259 +} diff --git a/gpt2_ul2r_DEV/merges.txt b/gpt2_ul2r_DEV/merges.txt new file mode 100644 index 000000000..226b0752c --- /dev/null +++ b/gpt2_ul2r_DEV/merges.txt @@ -0,0 +1,50001 @@ +#version: 0.2 +Ġ t +Ġ a +h e +i n +r e +o n +Ġt he +e r +Ġ s +a t +Ġ w +Ġ o +e n +Ġ c +i t +i s +a n +o r +e s +Ġ b +e d +Ġ f +in g +Ġ p +o u +Ġa n +a l +a r +Ġt o +Ġ m +Ġo f +Ġ in +Ġ d +Ġ h +Ġan d +i c +a s +l e +Ġt h +i on +o m +l l +en t +Ġ n +Ġ l +s t +Ġ re +v e +Ġ e +r o +l y +Ġb e +Ġ g +Ġ T +c t +Ġ S +i d +o t +Ġ I +u t +e t +Ġ A +Ġ is +Ġ on +i m +a m +o w +a y +a d +s e +Ġth at +Ġ C +i g +Ġf or +a c +Ġ y +v er +u r +Ġ u +l d +Ġs t +Ġ M +' s +Ġ he +Ġ it +at ion +it h +i r +c e +Ġy ou +i l +Ġ B +Ġw h +o l +Ġ P +Ġw ith +Ġ 1 +t er +c h +Ġa s +Ġw e +Ġ ( +n d +i ll +Ġ D +i f +Ġ 2 +a g +er s +k e +Ġ " +Ġ H +e m +Ġc on +Ġ W +Ġ R +he r +Ġw as +Ġ r +o d +Ġ F +u l +at e +Ġa t +r i +p p +o re +ĠT he +Ġs e +u s +Ġp ro +Ġh a +u m +Ġa re +Ġd e +a in +an d +Ġo r +ig h +es t +is t +a b +r om +Ġ N +t h +Ġc om +Ġ G +u n +o p +0 0 +Ġ L +Ġn ot +es s +Ġe x +Ġ v +re s +Ġ E +e w +it y +an t +Ġb y +e l +o s +or t +o c +q u +Ġf rom +Ġha ve +Ġs u +i ve +ou ld +Ġs h +Ġth is +n t +r a +p e +igh t +ar t +m ent +Ġa l +u st +en d +- - +al l +Ġ O +ac k +Ġc h +Ġ le +i es +re d +ar d +â Ģ +ou t +Ġ J +Ġa b +e ar +i v +al ly +ou r +o st +g h +p t +Ġp l +as t +Ġc an +a k +om e +u d +T he +Ġh is +Ġd o +Ġg o +Ġh as +g e +' t +Ġ U +r ou +Ġs a +Ġ j +Ġb ut +Ġw or +Ġa ll +e ct +Ġ k +am e +Ġw ill +o k +Ġw he +Ġthe y +id e +0 1 +f f +ic h +p l +t her +Ġt r +. . +Ġin t +i e +u re +ag e +Ġn e +i al +a p +in e +ic e +Ġm e +Ġo ut +an s +on e +on g +ion s +Ġwh o +Ġ K +Ġu p +Ġthe ir +Ġa d +Ġ 3 +Ġu s +at ed +ou s +Ġm ore +u e +o g +ĠS t +in d +i ke +Ġs o +im e +p er +. 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11245, + "some": 11246, + "Ġlimitations": 11247, + "Ġmeth": 11248, + "build": 11249, + "config": 11250, + "NT": 11251, + "rell": 11252, + "blem": 11253, + "ĠMom": 11254, + "Ġveterans": 11255, + "ĠHu": 11256, + "Ġtrends": 11257, + "arer": 11258, + "ĠGiven": 11259, + "ĠCaption": 11260, + "may": 11261, + "AST": 11262, + "Ġwondering": 11263, + "ĠClark": 11264, + "normal": 11265, + "Ġseparated": 11266, + "Ġdesp": 11267, + "stic": 11268, + "brew": 11269, + "Ġrelating": 11270, + "ĠNik": 11271, + "ĠFarm": 11272, + "Ġenthusi": 11273, + "good": 11274, + "deb": 11275, + "Ġactivist": 11276, + "Ġmart": 11277, + "Ġexplosion": 11278, + "ĠEconomic": 11279, + "Link": 11280, + "Ġinsight": 11281, + "Ġconvenient": 11282, + "Ġcounterpart": 11283, + "support": 11284, + "ĠVirt": 11285, + "agen": 11286, + "ĠTennessee": 11287, + "ĠSimon": 11288, + "ĠAward": 11289, + "OCK": 11290, + "ĠFigure": 11291, + "Ġoverseas": 11292, + "Ġpride": 11293, + "ĠCas": 11294, + "note": 11295, + "mg": 11296, + "Current": 11297, + "Ġdisplays": 11298, + "content": 11299, + "Ġtraveling": 11300, + "Ġhospitals": 11301, + "ĠFinancial": 11302, + "ĠPast": 11303, + "Ġdefendant": 11304, + "Ġstreaming": 11305, + "mble": 11306, + "ĠBerlin": 11307, + "uki": 11308, + "Ġdistribut": 11309, + "Ġantib": 11310, + "Ġchocolate": 11311, + "ĠCastle": 11312, + "Ġinterrupt": 11313, + "ĠRow": 11314, + "Ġconversion": 11315, + "Ġbugs": 11316, + "ĠRather": 11317, + "liest": 11318, + "LY": 11319, + "ĠJean": 11320, + "common": 11321, + "akh": 11322, + "Ġ130": 11323, + "otton": 11324, + "ĠDean": 11325, + "Ġamendment": 11326, + "Ġgameplay": 11327, + "ĠWarren": 11328, + "oda": 11329, + "Ġhighlights": 11330, + "Ġirre": 11331, + "ĠNATO": 11332, + "Ġballs": 11333, + "Ġdemanding": 11334, + "URE": 11335, + "ĠLuke": 11336, + "Figure": 11337, + "stop": 11338, + "onia": 11339, + "zone": 11340, + "izers": 11341, + "ĠWR": 11342, + "Ġawarded": 11343, + "Ġregulatory": 11344, + "ĠHart": 11345, + "ĠSN": 11346, + "pling": 11347, + "Ġsour": 11348, + "ĠPixel": 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11451, + "Ġsubscrib": 11452, + "Ġhosts": 11453, + "Ġerr": 11454, + "Ġgrants": 11455, + "antom": 11456, + "Ġsummon": 11457, + "early": 11458, + "ĠClear": 11459, + "ĠPrim": 11460, + "Ġsuspension": 11461, + "Ġguaranteed": 11462, + "apper": 11463, + "Ġrice": 11464, + "ĠSean": 11465, + "ĠShin": 11466, + "Ġreferendum": 11467, + "Ġfled": 11468, + "rust": 11469, + "Ġ360": 11470, + "tery": 11471, + "Ġshocked": 11472, + "BR": 11473, + "ĠOil": 11474, + "ĠAllah": 11475, + "Ġpartly": 11476, + "Ġignor": 11477, + "Ġtransmission": 11478, + "Ġhomosexual": 11479, + "iversal": 11480, + "Ġhopefully": 11481, + "ãĤ¤": 11482, + "Ġlesson": 11483, + "Leg": 11484, + "Ġ..": 11485, + "Yet": 11486, + "table": 11487, + "appropri": 11488, + "rett": 11489, + "Ġboards": 11490, + "Ġincorrect": 11491, + "Ġbacteria": 11492, + "aru": 11493, + "amac": 11494, + "Ġsnap": 11495, + ".'\"": 11496, + "Ġparad": 11497, + "tem": 11498, + "heart": 11499, + "Ġavailability": 11500, + "Ġwisdom": 11501, + "Ġ(+": 11502, + "Ġpriest": 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"Ġmanufacturer": 11554, + "Ġvel": 11555, + "ĠNi": 11556, + "Ġisolated": 11557, + "ĠMedicine": 11558, + "Ġbackup": 11559, + "Ġpromoting": 11560, + "Ġcommander": 11561, + "Ġflee": 11562, + "ĠRussell": 11563, + "Ġforgotten": 11564, + "ĠMissouri": 11565, + "Ġresidence": 11566, + "mons": 11567, + "Ġresemb": 11568, + "Ġwand": 11569, + "Ġmeaningful": 11570, + "PT": 11571, + "Ġbol": 11572, + "Ġhelic": 11573, + "Ġwealthy": 11574, + "Ġrifle": 11575, + "strong": 11576, + "rowing": 11577, + "plan": 11578, + "asury": 11579, + "âĢ¦.": 11580, + "Ġexpanding": 11581, + "ĠHamilton": 11582, + "Ġreceives": 11583, + "SI": 11584, + "eatures": 11585, + "ĠAnim": 11586, + "REE": 11587, + "Put": 11588, + "Ġbriefly": 11589, + "rive": 11590, + "Ġstimul": 11591, + "Ġ``(": 11592, + "Ġ__": 11593, + "Ġchip": 11594, + "Ġhaz": 11595, + "Ġprize": 11596, + "ĠThings": 11597, + "ACE": 11598, + "ulin": 11599, + "dict": 11600, + "oku": 11601, + "Ġassociate": 11602, + "ockets": 11603, + "youtube": 11604, + "Story": 11605, + "ategory": 11606, + "Ġmild": 11607, + "ailing": 11608, + "ĠYe": 11609, + "Orig": 11610, + "ĠKa": 11611, + "orig": 11612, + "Ġpropaganda": 11613, + "Ġanonymous": 11614, + "Ġstruggled": 11615, + "Ġoutrage": 11616, + "ATED": 11617, + "ĠBeijing": 11618, + "rary": 11619, + "Ġleather": 11620, + "Ġworlds": 11621, + "Ġbroader": 11622, + "125": 11623, + "idal": 11624, + "ĠBetter": 11625, + "Ġtear": 11626, + "Ext": 11627, + "Ġproposals": 11628, + "Ġiter": 11629, + "ĠSquad": 11630, + "Ġvolunt": 11631, + "mi": 11632, + "Did": 11633, + "ĠPu": 11634, + "pin": 11635, + "Ġspeakers": 11636, + "Ġborders": 11637, + "Ġfigured": 11638, + "='": 11639, + "Ġsimultaneously": 11640, + "aeda": 11641, + "Ġcharging": 11642, + "Ġurged": 11643, + "Ġconj": 11644, + "256": 11645, + "ĠGordon": 11646, + "merce": 11647, + "Ġdocumentary": 11648, + "Share": 11649, + "itol": 11650, + "ONE": 11651, + "ĠGarden": 11652, + "hatt": 11653, + "ĠThompson": 11654, + "aneous": 11655, + "apore": 11656, + "Ġtanks": 11657, + "Ġlessons": 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"ĠLiverpool": 11761, + "Ġgrip": 11762, + "ĠBuy": 11763, + "Ġopenly": 11764, + "Ġreviewed": 11765, + "pert": 11766, + "Ġverify": 11767, + "ĠCole": 11768, + "ĠWales": 11769, + "MO": 11770, + "Ġunpre": 11771, + "Ġshelter": 11772, + "ĠImperial": 11773, + "Ġgui": 11774, + "ĠDak": 11775, + "Ġsuggestions": 11776, + "Ġexplicitly": 11777, + "Ġslave": 11778, + "Ġblockchain": 11779, + "Ġcompeting": 11780, + "Ġpromising": 11781, + "SON": 11782, + "Ġsoccer": 11783, + "Ġconstitution": 11784, + "429": 11785, + "Ġdistract": 11786, + "ĠUser": 11787, + "esides": 11788, + "ĠMethod": 11789, + "ĠTokyo": 11790, + "Ġaccompanied": 11791, + "Client": 11792, + "sur": 11793, + "alog": 11794, + "Ġidentification": 11795, + "Ġinvasion": 11796, + "asma": 11797, + "Ġindustries": 11798, + "ppers": 11799, + "Ġsubtle": 11800, + "ĠUnit": 11801, + "natural": 11802, + "Ġsurvived": 11803, + "Ġflaw": 11804, + "ĺħ": 11805, + "ĠHoll": 11806, + "Ġdeficit": 11807, + "Ġtutorial": 11808, + "ĠChance": 11809, + "Ġarguing": 11810, + 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"§": 16273, + "Ġdigest": 16274, + "know": 16275, + "ĠXL": 16276, + "Ġpredictions": 16277, + "Ġdin": 16278, + "WAR": 16279, + "Ġaftermath": 16280, + "Example": 16281, + "ĠSuccess": 16282, + "ĠThr": 16283, + "IGN": 16284, + "Ġminer": 16285, + "Bus": 16286, + "Ġclarity": 16287, + "heimer": 16288, + "ĠOUT": 16289, + "ĠSend": 16290, + "ĠCircle": 16291, + "ĠDiet": 16292, + "Ġpronounced": 16293, + "Ġcreators": 16294, + "Ġearthquake": 16295, + "attery": 16296, + "geons": 16297, + "Ġod": 16298, + "Ġlaying": 16299, + "orp": 16300, + "Ult": 16301, + "project": 16302, + "Ġundermin": 16303, + "Ġsequel": 16304, + "Sam": 16305, + "ĠDarkness": 16306, + "Ġreception": 16307, + "bull": 16308, + "YS": 16309, + "ĠVir": 16310, + "Ġsequences": 16311, + "ĠCoin": 16312, + "Ġoutfit": 16313, + "ĠWait": 16314, + "119": 16315, + "Ġdelivers": 16316, + "......": 16317, + "Ġblown": 16318, + "ĠEsc": 16319, + "ĠMath": 16320, + "perm": 16321, + "ĠUl": 16322, + "Ġglim": 16323, + "Ġfacial": 16324, + "Ġgreenhouse": 16325, 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"ERT": 17395, + "zel": 17396, + "Ġdivisions": 17397, + "hots": 17398, + "ĠAlb": 17399, + "ĠDS": 17400, + "Loader": 17401, + "Washington": 17402, + "atisf": 17403, + "ĠCreative": 17404, + "\\.": 17405, + "ĠAutom": 17406, + "redict": 17407, + "Ġreceptor": 17408, + "ĠCarlos": 17409, + "Method": 17410, + "oka": 17411, + "Ġmalicious": 17412, + "Ġstepping": 17413, + ",[": 17414, + "ĠDad": 17415, + "Ġattraction": 17416, + "ĠEffects": 17417, + "ĠPirate": 17418, + "ĠCer": 17419, + "ĠIndustry": 17420, + "ĠRud": 17421, + "Ġcharter": 17422, + "Ġdining": 17423, + "Ġinsists": 17424, + "Ġconfigure": 17425, + "Ġ(#": 17426, + "ĠSimple": 17427, + "ĠScroll": 17428, + "UTC": 17429, + "175": 17430, + "ĠKon": 17431, + "Ġmarketplace": 17432, + "ĠãĤ": 17433, + "Ġrefres": 17434, + "Ġgates": 17435, + "erred": 17436, + "ĠPod": 17437, + "Ġbehave": 17438, + "Frank": 17439, + "node": 17440, + "Ġendorsed": 17441, + "hett": 17442, + "asive": 17443, + "ĠHomeland": 17444, + "Ġrides": 17445, + "ĠLeave": 17446, + 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"Ġcelebrating": 17499, + "osc": 17500, + "Ġ135": 17501, + "Ġlobbying": 17502, + "Ġincomplete": 17503, + "Ġrestriction": 17504, + "Ġincap": 17505, + "itus": 17506, + "Ġexpectation": 17507, + "ĠApollo": 17508, + "Ġintens": 17509, + "Ġsync": 17510, + "GH": 17511, + "Ġmanipulation": 17512, + "BY": 17513, + "Ġspear": 17514, + "Ġbreasts": 17515, + "Ġvolcan": 17516, + "ilia": 17517, + "Material": 17518, + "Ġformats": 17519, + "ĠBast": 17520, + "Ġparliamentary": 17521, + "Ġsnake": 17522, + "Ġservants": 17523, + "ĠTrudeau": 17524, + "ĠGrim": 17525, + "ĠArabic": 17526, + "ĠSCP": 17527, + "ĠBoys": 17528, + "station": 17529, + "Ġprospective": 17530, + "orde": 17531, + "initialized": 17532, + "Ġbored": 17533, + "ABLE": 17534, + "Ġaccessed": 17535, + "Ġtaxi": 17536, + "ĠShell": 17537, + "aiden": 17538, + "ursed": 17539, + "inates": 17540, + "ĠInsurance": 17541, + "ĠPete": 17542, + "September": 17543, + "650": 17544, + "Ġadventures": 17545, + "ĠCover": 17546, + "Ġtribute": 17547, + "Ġsketch": 17548, 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17600, + "oulder": 17601, + "tx": 17602, + "Ġreminds": 17603, + "Ġpublishers": 17604, + "Ġnort": 17605, + "Ġgit": 17606, + "Ġflies": 17607, + "ĠEmily": 17608, + "Ġflowing": 17609, + "ĠAlien": 17610, + "ĠStrateg": 17611, + "Ġhardest": 17612, + "Ġmodification": 17613, + "API": 17614, + "ĠMY": 17615, + "Ġcrashes": 17616, + "stairs": 17617, + "number": 17618, + "Ġurging": 17619, + "channel": 17620, + "ĠFalcon": 17621, + "Ġinhabitants": 17622, + "Ġterrifying": 17623, + "Ġutilize": 17624, + "Ġbanner": 17625, + "Ġcigarettes": 17626, + "Ġsenses": 17627, + "ĠHolmes": 17628, + "Ġpractition": 17629, + "ĠPhillips": 17630, + "otto": 17631, + "Ġcompile": 17632, + "Model": 17633, + "ĠKo": 17634, + "Ġ[]": 17635, + "Americans": 17636, + "ĠTerms": 17637, + "Ġmedications": 17638, + "ĠAna": 17639, + "Ġfundamentally": 17640, + "ĠNotice": 17641, + "Ġweaker": 17642, + "Ġ0000": 17643, + "Ġgarlic": 17644, + "Ġoutbreak": 17645, + "Ġeconomist": 17646, + "ĠBirth": 17647, + "Ġobstacles": 17648, + "arcer": 17649, + "ĠOrthodox": 17650, + "Ġplacebo": 17651, + "ĠCrew": 17652, + "aspberry": 17653, + "ĠAngels": 17654, + "Ġdischarge": 17655, + "Ġdestructive": 17656, + "117": 17657, + "ĠRising": 17658, + "Ġdairy": 17659, + "late": 17660, + "Ġcollision": 17661, + "ĠTigers": 17662, + "eanor": 17663, + "ocumented": 17664, + "ĠInvalid": 17665, + "Ġdont": 17666, + "ĠLiter": 17667, + "ĠVa": 17668, + "Ġhydrogen": 17669, + "Ġvariants": 17670, + "ĠBrowns": 17671, + "Ġ1965": 17672, + "Ġindigenous": 17673, + "Ġtrades": 17674, + "Ġremainder": 17675, + "Ġswept": 17676, + "ĠImpact": 17677, + "Ġredist": 17678, + "Ġunint": 17679, + "graduate": 17680, + "ãĥķ": 17681, + "ĠWILL": 17682, + "ãģ®ç": 17683, + "ĠCritical": 17684, + "Ġfisher": 17685, + "Ġvicious": 17686, + "Ġreversed": 17687, + "Year": 17688, + "ĠSox": 17689, + "Ġshootings": 17690, + "Ġfilming": 17691, + "Ġtouchdowns": 17692, + "aires": 17693, + "mel": 17694, + "Ġgrandfather": 17695, + "Ġaffection": 17696, + "ingle": 17697, + "Ġoverly": 17698, + "Additional": 17699, + "Ġsupreme": 17700, + "ĠGrad": 17701, + "Ġsporting": 17702, + "Ġmercy": 17703, + "ĠBrooks": 17704, + "ounty": 17705, + "Ġperforms": 17706, + "Ġtightly": 17707, + "Ġdemons": 17708, + "Ġkillings": 17709, + "Ġfaction": 17710, + "ĠNova": 17711, + "auts": 17712, + "Ġundoubtedly": 17713, + "arin": 17714, + "Ġunderway": 17715, + "rak": 17716, + "Ġliv": 17717, + "ĠRegion": 17718, + "Ġbriefing": 17719, + "sers": 17720, + "cloud": 17721, + "ĠMik": 17722, + "usp": 17723, + "Ġprediction": 17724, + "azor": 17725, + "Ġportable": 17726, + "ĠGand": 17727, + "Ġpresenting": 17728, + "Ġ1080": 17729, + "»": 17730, + "ushi": 17731, + "ĠSpark": 17732, + "thereum": 17733, + "Ġjustification": 17734, + "ĠNy": 17735, + "Ġcontractors": 17736, + "mingham": 17737, + "ĠStyle": 17738, + "åħ": 17739, + "ĠChronicles": 17740, + "ĠPicture": 17741, + "Ġproving": 17742, + "Ġwives": 17743, + "sett": 17744, + "Ġmolecules": 17745, + "ĠFairy": 17746, + "Ġconsisting": 17747, + "Ġpier": 17748, + "alone": 17749, + "inition": 17750, + "Ġnucle": 17751, + "json": 17752, + "Ġgotta": 17753, + "Ġmobil": 17754, + "Ġverbal": 17755, + "arium": 17756, + "Ġmonument": 17757, + "ucked": 17758, + "Ġ256": 17759, + "Tech": 17760, + "minecraft": 17761, + "ĠTrack": 17762, + "Ġtile": 17763, + "Ġcompatibility": 17764, + "asis": 17765, + "Ġsadd": 17766, + "Ġinstructed": 17767, + "ĠMueller": 17768, + "Ġlethal": 17769, + "Ġhormone": 17770, + "Ġorche": 17771, + "else": 17772, + "Ġskelet": 17773, + "Ġentertaining": 17774, + "Ġminimize": 17775, + "again": 17776, + "Ġundergo": 17777, + "Ġconstraints": 17778, + "Ġcigarette": 17779, + "ĠIslamist": 17780, + "Ġtravels": 17781, + "ĠPanthers": 17782, + "lings": 17783, + "Care": 17784, + "Ġlawsuits": 17785, + "uras": 17786, + "Ġcryst": 17787, + "Ġlowered": 17788, + "Ġaerial": 17789, + "Ġcombinations": 17790, + "Ġhaun": 17791, + "Ġcha": 17792, + "Ġvine": 17793, + "Ġquantities": 17794, + "Ġlinking": 17795, + "bank": 17796, + "Ġsoy": 17797, + "Bill": 17798, + "ĠAngela": 17799, + "Ġrecipient": 17800, + "ĠProtest": 17801, + "Ġsocket": 17802, + "Ġsolidarity": 17803, + "ĠâĨ": 17804, + "mill": 17805, + "Ġvaries": 17806, + "ĠPakistani": 17807, + "Dragon": 17808, + "Ġune": 17809, + "Ġhorizon": 17810, + "³³³³³³³³": 17811, + "Ġprovinces": 17812, + "Ġfrankly": 17813, + "Ġenacted": 17814, + "notes": 17815, + "['": 17816, + "Ġ192": 17817, + "ocracy": 17818, + "Ġendorsement": 17819, + "Ġovertime": 17820, + "True": 17821, + "Lab": 17822, + "licted": 17823, + "ĠDNC": 17824, + "Ġbeats": 17825, + "ĠJamie": 17826, + "152": 17827, + "ĠINT": 17828, + "Contact": 17829, + "Ġaccounted": 17830, + "hash": 17831, + "ĠPackers": 17832, + "pires": 17833, + "Ġlesbian": 17834, + "Ġamendments": 17835, + "Ġhopeful": 17836, + "ĠFinland": 17837, + "Ġspotlight": 17838, + "Ġconfigured": 17839, + "Ġtroubled": 17840, + "Ġgaze": 17841, + "ĠCalgary": 17842, + "Ġreliability": 17843, + "Ġinsurg": 17844, + "swer": 17845, + "buy": 17846, + "ĠSkin": 17847, + "Ġpixels": 17848, + "Ġhandgun": 17849, + "Ġparas": 17850, + "Ġcategor": 17851, + "ĠEL": 17852, + "ĠRex": 17853, + "Indeed": 17854, + "Ġkinda": 17855, + "Ġconjunction": 17856, + "ĠBryan": 17857, + "ĠManufact": 17858, + "yang": 17859, + "Plus": 17860, + "SQL": 17861, + "ishment": 17862, + "Ġdominate": 17863, + "Ġnail": 17864, + "Ġoath": 17865, + "Ġerupt": 17866, + "ĠFine": 17867, + "itbart": 17868, + "ĠChip": 17869, + "ĠAbd": 17870, + "ĠNam": 17871, + "Ġbuyer": 17872, + "Ġdissent": 17873, + "Leaks": 17874, + "Contin": 17875, + "Ġrider": 17876, + "ĠSomeone": 17877, + "Ġillusion": 17878, + "cin": 17879, + "ĠBoeing": 17880, + "Ġinadequ": 17881, + "ovation": 17882, + "iants": 17883, + "Ġrebuild": 17884, + "450": 17885, + "ĠDestiny": 17886, + "SW": 17887, + "ĠTill": 17888, + "Hit": 17889, + "iaz": 17890, + "ĠBangl": 17891, + "achers": 17892, + "ĠReform": 17893, + "Ġsegments": 17894, + "Ġsystematic": 17895, + "dc": 17896, + "ĠConservatives": 17897, + "Ġportal": 17898, + "hor": 17899, + "ĠDragonbound": 17900, + "Ġdragged": 17901, + "omo": 17902, + "Ġthee": 17903, + "advert": 17904, + "ĠReports": 17905, + "ĠEt": 17906, + "Ġbarrels": 17907, + "August": 17908, + "Ġcomparisons": 17909, + "Ġhex": 17910, + "Ġanthrop": 17911, + "\"[": 17912, + "borough": 17913, + "abi": 17914, + "Ġpictured": 17915, + "playing": 17916, + "ĠAddress": 17917, + "ĠMirror": 17918, + "Smith": 17919, + "Ġtires": 17920, + "ĠNPR": 17921, + "AAAA": 17922, + "Ġclassification": 17923, + "ĠThan": 17924, + "ĠHarm": 17925, + "ĠRA": 17926, + "Ġrejection": 17927, + "mination": 17928, + "Ġranged": 17929, + "ĠFalls": 17930, + "DI": 17931, + "Host": 17932, + "ãĤ´": 17933, + "ĠExample": 17934, + "listed": 17935, + "thirds": 17936, + "Ġsafegu": 17937, + "brand": 17938, + "Ġprobable": 17939, + "Canada": 17940, + "ITION": 17941, + "ĠQaeda": 17942, + "Ġchick": 17943, + "Ġimports": 17944, + "hit": 17945, + "loc": 17946, + "WW": 17947, + "Ġblew": 17948, + "Ġanytime": 17949, + "Ġwholes": 17950, + "iked": 17951, + "Ġcalculation": 17952, + "create": 17953, + "ĠOri": 17954, + "Ġupgraded": 17955, + "Ġappar": 17956, + "utory": 17957, + "ĠMol": 17958, + "Brit": 17959, + "ĠJong": 17960, + "INAL": 17961, + "ĠStarting": 17962, + "Ġdice": 17963, + "urtle": 17964, + "Ġrelying": 17965, + "closure": 17966, + "Ġprofitable": 17967, + "Ġslaughter": 17968, + "ĠManual": 17969, + "caster": 17970, + "Ġ\"$": 17971, + "Ġfeather": 17972, + "ĠSimply": 17973, + "ieves": 17974, + "Ġdeterior": 17975, + "ĠPCI": 17976, + "Ġstamp": 17977, + "Ġflaws": 17978, + "Ġshade": 17979, + "hammer": 17980, + "Ġpassport": 17981, + "Ġconting": 17982, + "amel": 17983, + "Ġobservers": 17984, + "Ġneglect": 17985, + "ĠRB": 17986, + "ĠBrotherhood": 17987, + "Ġskeptical": 17988, + "family": 17989, + "usk": 17990, + "Ġemotionally": 17991, + "âĻ": 17992, + "ĠBeta": 17993, + "asonable": 17994, + "idity": 17995, + "ĠMul": 17996, + "Ġkicking": 17997, + "ĠCarm": 17998, + "ollah": 17999, + "VERTIS": 18000, + "ĠAthen": 18001, + "Ġladder": 18002, + "ĠBullet": 18003, + "å£": 18004, + "0001": 18005, + "ĠWildlife": 18006, + "ĠMask": 18007, + "ĠNan": 18008, + "Rev": 18009, + "Ġunacceptable": 18010, + "legal": 18011, + "Ġcrowded": 18012, + "agi": 18013, + "ĠCox": 18014, + "je": 18015, + "Ġmorality": 18016, + "Ġfuels": 18017, + "Ġcables": 18018, + "Ġmankind": 18019, + "ĠCaribbean": 18020, + "Ġanchor": 18021, + "Ġbyte": 18022, + "ĠOften": 18023, + "ĠOz": 18024, + "Ġcrafted": 18025, + "Ġhistorian": 18026, + "ĠWu": 18027, + "Ġtowers": 18028, + "ĠCitizens": 18029, + "Ġhelm": 18030, + "Ġcredentials": 18031, + "Ġsingular": 18032, + "ĠJesse": 18033, + "Ġtackles": 18034, + "Ġcontempt": 18035, + "Ġafore": 18036, + "ĠShadows": 18037, + "Ġnil": 18038, + "Ġurgent": 18039, + "apple": 18040, + "blood": 18041, + "Ġvon": 18042, + "Ġoffline": 18043, + "Ġbreathe": 18044, + "Ġjumps": 18045, + "Ġirrelevant": 18046, + "oxic": 18047, + "omal": 18048, + "important": 18049, + "Jim": 18050, + "Ġgloves": 18051, + "arming": 18052, + "depth": 18053, + "Ġtalents": 18054, + "ookie": 18055, + "ĠSB": 18056, + "Ġpalm": 18057, + "uffs": 18058, + "esta": 18059, + "IGH": 18060, + "Ġcanon": 18061, + "ĠVerizon": 18062, + "ĠPle": 18063, + "Ġcoupled": 18064, + "velt": 18065, + "Ġfundraising": 18066, + "ĠGetting": 18067, + "ĠDLC": 18068, + "Ġmathematical": 18069, + "ĠHS": 18070, + "ĠCardinals": 18071, + "telling": 18072, + "Ġsponsors": 18073, + "ĠÏ": 18074, + "ĠBulls": 18075, + "option": 18076, + "Ġpropose": 18077, + "Ġmemorable": 18078, + "Ġembraced": 18079, + "Ġdeclining": 18080, + "Health": 18081, + "eda": 18082, + "Ġ};": 18083, + "Ġspam": 18084, + "mile": 18085, + "Ġpitcher": 18086, + "ĠEight": 18087, + "Ġcaring": 18088, + "utic": 18089, + "role": 18090, + "Ġairline": 18091, + "ernandez": 18092, + "ĠAthlet": 18093, + "Ġcertification": 18094, + "uxe": 18095, + "riger": 18096, + "Ġempir": 18097, + "Ġsensation": 18098, + "Ġdism": 18099, + "Ġbolt": 18100, + "Ġevolve": 18101, + "House": 18102, + "Ġconsultation": 18103, + "ĠDuty": 18104, + "Ġtouches": 18105, + "ĠNathan": 18106, + "Ġfaint": 18107, + "had": 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18158, + "Ġmasters": 18159, + "heastern": 18160, + ".\"[": 18161, + "ĠRunning": 18162, + "Ġsuscept": 18163, + "ĠFeng": 18164, + "America": 18165, + "prises": 18166, + "stitial": 18167, + "ĠWeekly": 18168, + "ĠGreater": 18169, + "modules": 18170, + "ifter": 18171, + "Graphics": 18172, + "uler": 18173, + "Ġwholly": 18174, + "Ġsuppress": 18175, + "Ġconcealed": 18176, + "Ġhappily": 18177, + "Ġaccepts": 18178, + "ĠEnjoy": 18179, + "Ġrivers": 18180, + "ĠExcept": 18181, + "225": 18182, + "ĠNHS": 18183, + "ĠMcConnell": 18184, + "Ġpussy": 18185, + "ferred": 18186, + "utable": 18187, + "Ġattain": 18188, + "Ġ>=": 18189, + "Ġdeposits": 18190, + "rophic": 18191, + "Ġnotorious": 18192, + "ĠShaw": 18193, + "ilitation": 18194, + "Ġepidemic": 18195, + "allic": 18196, + "Ġsmallest": 18197, + "ovich": 18198, + "Ġaccessories": 18199, + "perties": 18200, + "Ġsurplus": 18201, + "ĠMech": 18202, + "Ġambig": 18203, + "ĠImmigration": 18204, + "Ġchim": 18205, + "eval": 18206, + "Ġpracticing": 18207, + "ĠMystery": 18208, + "Ġdomains": 18209, + "ĠSilicon": 18210, + "apps": 18211, + "Ġkilometers": 18212, + "ea": 18213, + "ĠSmash": 18214, + "Ġwarranty": 18215, + "Ġnost": 18216, + "sil": 18217, + "rev": 18218, + "Jon": 18219, + "ĠDublin": 18220, + "Ġtastes": 18221, + "Ġbout": 18222, + "great": 18223, + "error": 18224, + "Ġswitches": 18225, + "ĠBapt": 18226, + "DO": 18227, + "oki": 18228, + "Ġsourced": 18229, + "produ": 18230, + "Ġattachment": 18231, + "ĠIssue": 18232, + "ĠQuestion": 18233, + "Join": 18234, + "Ġfitted": 18235, + "Ġunlawful": 18236, + "^^": 18237, + "erek": 18238, + "Ġauthentication": 18239, + "Ġstole": 18240, + "Ġaccountability": 18241, + "label": 18242, + "Search": 18243, + "Ġalbeit": 18244, + "atican": 18245, + "funded": 18246, + "ĠAdding": 18247, + "ĠIQ": 18248, + "Ġsubmar": 18249, + "lit": 18250, + "aque": 18251, + "ĠLearning": 18252, + "Ġinteger": 18253, + "Master": 18254, + "ĠChrom": 18255, + "Ġpremier": 18256, + "Op": 18257, + "ĠLiu": 18258, + "Ġblessed": 18259, + "ĠGlobe": 18260, + "ĠResponse": 18261, + "Ġlegitim": 18262, + "ĠMerkel": 18263, + "Ġdisposal": 18264, + "´": 18265, + "Ġgauge": 18266, + "peat": 18267, + "Ġinduced": 18268, + "Ġquestionable": 18269, + "arthy": 18270, + "ĠVit": 18271, + "ĠFeed": 18272, + "Until": 18273, + "Ut": 18274, + "worthy": 18275, + "RY": 18276, + "ĠHerald": 18277, + "ĠHammer": 18278, + "Ġmedal": 18279, + "ĠRivers": 18280, + "ĠHack": 18281, + "Ġclarify": 18282, + "Ġtracked": 18283, + "Ġautonomous": 18284, + "Ġtenant": 18285, + "ĠQatar": 18286, + "erie": 18287, + "Ġgrim": 18288, + "ĠMonitor": 18289, + "Ġresistant": 18290, + "ĠSpec": 18291, + "ĠWells": 18292, + "NAS": 18293, + "148": 18294, + "Ġminers": 18295, + "iotics": 18296, + "Ġmisses": 18297, + "116": 18298, + "gian": 18299, + "git": 18300, + "ĠEyes": 18301, + "pres": 18302, + "Ġgraduated": 18303, + "Ġangel": 18304, + "Ġsynchron": 18305, + "Ġefficiently": 18306, + "Ġtransmitted": 18307, + "Harry": 18308, + "Ġglobally": 18309, + "ENCE": 18310, + "ĠMontana": 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18362, + "Ġcurric": 18363, + "Ġmarker": 18364, + "ĠMcL": 18365, + "Ġextensions": 18366, + "ĠPv": 18367, + "ĠArms": 18368, + "Ġofferings": 18369, + "Ġdefenses": 18370, + "Ġvendor": 18371, + "Ġcontradict": 18372, + "ĠColin": 18373, + "Ġreddit": 18374, + "Ġperipher": 18375, + "122": 18376, + "Ġsins": 18377, + "Edit": 18378, + "ICT": 18379, + "Soft": 18380, + "ĠShah": 18381, + "Ġadministrator": 18382, + "ĠTrip": 18383, + "Ġpornography": 18384, + "Ġtuition": 18385, + "inence": 18386, + "ĠProgress": 18387, + "Ġcatalog": 18388, + "Ġsuite": 18389, + "Ġhike": 18390, + "Ġreproductive": 18391, + "engine": 18392, + "Ġdrought": 18393, + "ĠNoah": 18394, + "Ġ230": 18395, + "Ġdude": 18396, + "Ġrelaxed": 18397, + "Ġpartition": 18398, + "Ġparticipant": 18399, + "Ġtelesc": 18400, + "Ġfeas": 18401, + "ĠFF": 18402, + "owner": 18403, + "Ġsweeping": 18404, + "Ġlenses": 18405, + "Ġmatchup": 18406, + "ĠRepl": 18407, + "ournals": 18408, + "Ġcredible": 18409, + "Ġgrandmother": 18410, + "Ġthermal": 18411, + "Ġsubscribing": 18412, + "Ġidentities": 18413, + "colm": 18414, + "UCT": 18415, + "Ġreluctant": 18416, + "users": 18417, + "ĠCort": 18418, + "Ġassisted": 18419, + "OSS": 18420, + "ATIONS": 18421, + "ISH": 18422, + "Ġpharmaceutical": 18423, + "icable": 18424, + "adian": 18425, + "ĠSonic": 18426, + "ĠFury": 18427, + "ĠMong": 18428, + "AH": 18429, + "ĠPsychology": 18430, + "Ġphosph": 18431, + "Ġtreats": 18432, + "ŃĶ": 18433, + "Ġsteadily": 18434, + "ĠHello": 18435, + "Ġrelates": 18436, + "Ġclue": 18437, + "Expl": 18438, + "auth": 18439, + "Ġrevision": 18440, + "Ġeld": 18441, + "osion": 18442, + "Ġbron": 18443, + "144": 18444, + "rikes": 18445, + "Ġmines": 18446, + "Ġblanket": 18447, + "ĠFail": 18448, + "eled": 18449, + "ĠImagine": 18450, + "ĠPlanned": 18451, + "aic": 18452, + "Request": 18453, + "Mad": 18454, + "ĠHorse": 18455, + "ĠEagle": 18456, + "Ġcapac": 18457, + "157": 18458, + "Ġling": 18459, + "ĠNice": 18460, + "ĠParenthood": 18461, + "minster": 18462, + "ogs": 18463, + "ensitive": 18464, + "Nothing": 18465, + "Ġcarn": 18466, + "Fin": 18467, + "ĠPE": 18468, + "Ġrifles": 18469, + "ĠLP": 18470, + "Sand": 18471, + "ĠguiActive": 18472, + "Ġtourist": 18473, + "CNN": 18474, + "Ġunveiled": 18475, + "Ġpredecessor": 18476, + "}{": 18477, + "uber": 18478, + "Ġoffshore": 18479, + "Ġoptical": 18480, + "ĠRot": 18481, + "ĠPearl": 18482, + "eton": 18483, + "Ġstared": 18484, + "Ġfarther": 18485, + "atility": 18486, + "contin": 18487, + "ĠGy": 18488, + "ĠFoster": 18489, + "ĠCoc": 18490, + "rients": 18491, + "Ġdesigning": 18492, + "ĠEconomy": 18493, + "ONG": 18494, + "Women": 18495, + "ĠNancy": 18496, + "erver": 18497, + "Ġmascul": 18498, + "Ġcasualties": 18499, + "Ġ225": 18500, + "ĠSullivan": 18501, + "ĠChoice": 18502, + "Ġaster": 18503, + "ws": 18504, + "Ġhotels": 18505, + "Ġconsiderations": 18506, + "Ġcouch": 18507, + "ĠStrip": 18508, + "ĠGn": 18509, + "Ġmanipulate": 18510, + "lied": 18511, + "Ġsynthetic": 18512, + "Ġassaulted": 18513, + "Ġoffenses": 18514, + "ĠDrake": 18515, + "Ġimpe": 18516, + "October": 18517, + "ĠHeritage": 18518, + "hl": 18519, + "ĠBlair": 18520, + "Unlike": 18521, + "Ġgrief": 18522, + "Ġ450": 18523, + "Ġopted": 18524, + "Ġresignation": 18525, + "ilo": 18526, + "Ġverse": 18527, + "ĠTomb": 18528, + "Ġupt": 18529, + "Ġaired": 18530, + "ĠHook": 18531, + "ĠMLB": 18532, + "Ġassumes": 18533, + "outed": 18534, + "ĠVers": 18535, + "Ġinferior": 18536, + "Ġbundle": 18537, + "ĠDNS": 18538, + "ographer": 18539, + "Ġmultip": 18540, + "ĠSouls": 18541, + "Ġillustrated": 18542, + "Ġtactic": 18543, + "Ġdressing": 18544, + "Ġduo": 18545, + "Conf": 18546, + "Ġrelent": 18547, + "Ġcant": 18548, + "Ġscarce": 18549, + "Ġcandy": 18550, + "ĠCF": 18551, + "Ġaffiliated": 18552, + "Ġsprint": 18553, + "ylan": 18554, + "ĠGarcia": 18555, + "Ġjunk": 18556, + "Print": 18557, + "exec": 18558, + "Crit": 18559, + "Ġportrait": 18560, + "iries": 18561, + "ĠOFF": 18562, + "Ġdisputes": 18563, + "WR": 18564, + "Love": 18565, + "ãģĦ": 18566, + "ĠReyn": 18567, + "Ġhipp": 18568, + "opath": 18569, + "Ġfloors": 18570, + "ĠFeel": 18571, + "Ġworries": 18572, + "Ġsettlements": 18573, + "ĠPos": 18574, + "Ġmosque": 18575, + "Ġfinals": 18576, + "Ġcrushed": 18577, + "ĠProbably": 18578, + "ĠBot": 18579, + "ĠMans": 18580, + "ĠPeriod": 18581, + "Ġsovereignty": 18582, + "Ġseller": 18583, + "Ġapost": 18584, + "Ġamateur": 18585, + "Ġdorm": 18586, + "Ġconsuming": 18587, + "Ġarmour": 18588, + "ĠRoose": 18589, + "Ġintensive": 18590, + "Ġeliminating": 18591, + "ĠSunni": 18592, + "ĠAleppo": 18593, + "jin": 18594, + "Ġadvise": 18595, + "pal": 18596, + "ĠHalo": 18597, + "Ġdescent": 18598, + "Ġsimpler": 18599, + "Ġbooth": 18600, + "STR": 18601, + "Later": 18602, + "ĠCave": 18603, + "===": 18604, + "Ġmol": 18605, + "Ġfist": 18606, + "Ġshotgun": 18607, + "supp": 18608, + "Ġrobbery": 18609, + "Effect": 18610, + "Ġobscure": 18611, + "ĠProfessional": 18612, + "Ġembassy": 18613, + "Ġmilitant": 18614, + "Ġincarcer": 18615, + "Ġgenerates": 18616, + "Ġlaunches": 18617, + "Ġadministrators": 18618, + "Ġshaft": 18619, + "Ġcircular": 18620, + "Ġfreshman": 18621, + "ĠWes": 18622, + "ĠJoel": 18623, + "ĠDrew": 18624, + "ĠDuncan": 18625, + "ĠApparently": 18626, + "sight": 18627, + "ĠInternal": 18628, + "ĠIndividual": 18629, + "ĠFE": 18630, + "Ġbore": 18631, + "ĠMt": 18632, + "Ġbroadly": 18633, + "ĠOptions": 18634, + "ountain": 18635, + "ipes": 18636, + "ĠVideos": 18637, + "204": 18638, + "Ġhills": 18639, + "Ġsimulation": 18640, + "Ġdisappointment": 18641, + "itan": 18642, + "ĠLaboratory": 18643, + "Ġupward": 18644, + "Ġboundary": 18645, + "Ġdarker": 18646, + "hart": 18647, + "Ġdominance": 18648, + "Cong": 18649, + "ĠOracle": 18650, + "ĠLords": 18651, + "Ġscholarship": 18652, + "ĠVincent": 18653, + "ede": 18654, + "ĠRah": 18655, + "Ġencourages": 18656, + "rov": 18657, + "Ġquo": 18658, + "Ġpremise": 18659, + "ĠCrisis": 18660, + "ĠHolocaust": 18661, + "Ġrhythm": 18662, + "Ġmetric": 18663, + "club": 18664, + "Ġtransported": 18665, + "Ġnod": 18666, + "ĠPist": 18667, + "Ġancestors": 18668, + "ĠFreder": 18669, + "thumbnails": 18670, + "ĠCE": 18671, + "OND": 18672, + "Phil": 18673, + "venge": 18674, + "ĠProducts": 18675, + "castle": 18676, + "Ġqualifying": 18677, + "ĠKaren": 18678, + "VERTISEMENT": 18679, + "Ġmighty": 18680, + "Ġexplanations": 18681, + "Ġfixing": 18682, + "Di": 18683, + "Ġdeclaring": 18684, + "Ġanonymity": 18685, + "Ġjuven": 18686, + "ĠNord": 18687, + "ĠDoom": 18688, + "ĠActually": 18689, + "Ok": 18690, + "phis": 18691, + "ĠDesert": 18692, + "Ġ116": 18693, + "IK": 18694, + "ĠFM": 18695, + "Ġincomes": 18696, + "VEL": 18697, + "okers": 18698, + "Ġpecul": 18699, + "Ġlightweight": 18700, + "gue": 18701, + "Ġaccent": 18702, + "Ġincrement": 18703, + "ĠChan": 18704, + "Ġcomplaining": 18705, + "ĠBaghd": 18706, + "Ġmidfielder": 18707, + "Ġoverhaul": 18708, + "Process": 18709, + "ĠHollow": 18710, + "ĠTitans": 18711, + "Small": 18712, + "manuel": 18713, + "ĠUnity": 18714, + "ĠEvents": 18715, + "Sty": 18716, + "Ġdisproportion": 18717, + "nesty": 18718, + "enes": 18719, + "ĠCod": 18720, + "Ġdemonstrations": 18721, + "ĠCrimson": 18722, + "ĠOH": 18723, + "Ġenrolled": 18724, + "Ġcel": 18725, + "ĠBrett": 18726, + "Ġaide": 18727, + "Ġheels": 18728, + "Ġbroadband": 18729, + "Ġmarking": 18730, + "Ġwizard": 18731, + "ĠNJ": 18732, + "ĠChiefs": 18733, + "Ġingredient": 18734, + "Ġdug": 18735, + "ĠShut": 18736, + "urchase": 18737, + "endor": 18738, + "Ġfarmer": 18739, + "ĠGoldman": 18740, + "129": 18741, + "155": 18742, + "Order": 18743, + "Ġlion": 18744, + "iably": 18745, + "Ġstain": 18746, + "array": 18747, + "ilitary": 18748, + "ĠFAQ": 18749, + "Ġexploded": 18750, + "ĠMcCarthy": 18751, + "ĠTweet": 18752, + "ĠGreens": 18753, + "eking": 18754, + "ln": 18755, + "ensen": 18756, + "Ġmotorcycle": 18757, + "Ġparticle": 18758, + "Ġcholesterol": 18759, + "Bron": 18760, + "Ġstair": 18761, + "Ġoxid": 18762, + "Ġdesirable": 18763, + "ibles": 18764, + "Ġtheor": 18765, + "forcing": 18766, + "Ġpromotional": 18767, + "ovo": 18768, + "boot": 18769, + "ĠBonus": 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"Ġpotatoes": 18821, + "Ġfinest": 18822, + "%).": 18823, + "Ġcrap": 18824, + "ĠZach": 18825, + "abases": 18826, + "ĠGoth": 18827, + "Ġbillionaire": 18828, + "wolf": 18829, + "Ġsanction": 18830, + "SK": 18831, + "Ġlogged": 18832, + "Po": 18833, + "eyed": 18834, + "unal": 18835, + "Ġcricket": 18836, + "Ġarmies": 18837, + "Ġuncovered": 18838, + "Cloud": 18839, + "ón": 18840, + "Ġrebounds": 18841, + "Ġmes": 18842, + "Oper": 18843, + "Pac": 18844, + "Ġnationally": 18845, + "Ġinserted": 18846, + "pict": 18847, + "Ġgovernance": 18848, + "и": 18849, + "Ġprivileges": 18850, + "GET": 18851, + "Ġfavorites": 18852, + "imity": 18853, + "Ġlover": 18854, + "them": 18855, + "empl": 18856, + "Ġgorgeous": 18857, + "Ann": 18858, + "Ġslipped": 18859, + "Ġveto": 18860, + "Bob": 18861, + "Ġslim": 18862, + "ucc": 18863, + "ĠFame": 18864, + "uddenly": 18865, + "Ġdenies": 18866, + "ĠMaur": 18867, + "Ġdistances": 18868, + "Ġwanna": 18869, + "tar": 18870, + "ĠSER": 18871, + "ĠâĪ": 18872, + "Ġlemon": 18873, + 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"Ġsums": 21784, + "ĠEnemy": 21785, + "Ġenvision": 21786, + "Ġgranting": 21787, + "ĠHoney": 21788, + "ĠSkyrim": 21789, + "Ġsocio": 21790, + "graded": 21791, + "Ġselective": 21792, + "WASHINGTON": 21793, + "Ġ1948": 21794, + "ĠSirius": 21795, + "ĠGross": 21796, + "activity": 21797, + "ĠIvan": 21798, + "Ġfurious": 21799, + "BSD": 21800, + "ĠPrevious": 21801, + "Ġresponsive": 21802, + "Ġcharitable": 21803, + "Ġleaning": 21804, + "ĠPew": 21805, + "Ġviolates": 21806, + "\\\\\\\\\\\\\\\\": 21807, + "ĠComing": 21808, + "wire": 21809, + "Ġpoet": 21810, + "Ġresolutions": 21811, + "command": 21812, + "ĠPortuguese": 21813, + "Ġnickname": 21814, + "Ġdeaf": 21815, + "February": 21816, + "Ġrecognise": 21817, + "Ġentirety": 21818, + "Ġseasonal": 21819, + "placed": 21820, + "ĠTelegraph": 21821, + "Ġmicrophone": 21822, + "ouring": 21823, + "Ġgrains": 21824, + "Ġgoverned": 21825, + "Ġpostp": 21826, + "ĠWaters": 21827, + "inement": 21828, + "Ġundocumented": 21829, + "ĠComcast": 21830, + "Ġfox": 21831, + "Ġassaults": 21832, + "reon": 21833, + "many": 21834, + "ĠJenkins": 21835, + "ĠAnyway": 21836, + "Ġassessments": 21837, + "Ġdowns": 21838, + "ĠMouse": 21839, + "Ġsuperb": 21840, + "kt": 21841, + "ĠDow": 21842, + "Ġtaxation": 21843, + "401": 21844, + "Ġsmiles": 21845, + "Ġundertaken": 21846, + "Ġexh": 21847, + "Ġenthusiastic": 21848, + "Ġtwent": 21849, + "Ġgovernmental": 21850, + "Ġautonomy": 21851, + "ĠTechnologies": 21852, + "ĠChain": 21853, + "Ġprevalent": 21854, + "fb": 21855, + "Ġnicotine": 21856, + "ogram": 21857, + "job": 21858, + "Ġawaiting": 21859, + "ĠMenu": 21860, + "Ġdeputies": 21861, + "kov": 21862, + "ishops": 21863, + "Button": 21864, + "ĠShanghai": 21865, + "Ġdiesel": 21866, + "ĠDuck": 21867, + "Ryan": 21868, + "ĠPCs": 21869, + "NF": 21870, + "jury": 21871, + "ente": 21872, + "Ġinaccurate": 21873, + "eddy": 21874, + "Whatever": 21875, + "Ġshowc": 21876, + "ĠNad": 21877, + "odus": 21878, + "etr": 21879, + "Ġplaintiffs": 21880, + "ĠWOR": 21881, + "ĠAssange": 21882, + "Ġprivat": 21883, + "Ġpremiums": 21884, + "Ġtam": 21885, + "URL": 21886, + "Ġelites": 21887, + "ĠRanger": 21888, + "ottenham": 21889, + "ĠHoff": 21890, + "ĠAthens": 21891, + "Ġdefinite": 21892, + "Ġsighed": 21893, + "Ġevenly": 21894, + "211": 21895, + "ĠAmber": 21896, + "akia": 21897, + "Ġmailing": 21898, + "Ġcrashing": 21899, + "ĠConfederate": 21900, + "rugged": 21901, + "Wal": 21902, + "ĠDepths": 21903, + "Ġjuvenile": 21904, + "Ġreactor": 21905, + "Introduction": 21906, + "ĠDeluxe": 21907, + "1995": 21908, + "ĠSanchez": 21909, + "ĠMead": 21910, + "ivable": 21911, + ":-": 21912, + "ĠPlanning": 21913, + "ĠTrap": 21914, + "quin": 21915, + "ĠProtect": 21916, + "vered": 21917, + "Information": 21918, + "Ġkidney": 21919, + "innamon": 21920, + "las": 21921, + "Ġpolicing": 21922, + "Ġtolerate": 21923, + "ĠQi": 21924, + "Ġbiased": 21925, + "Fort": 21926, + "ĠKi": 21927, + "save": 21928, + "Ġprivileged": 21929, + "Ġbeasts": 21930, + "ĠGlas": 21931, + "ĠCinem": 21932, + "Ġcomeback": 21933, + 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"Ġinfringement": 23059, + "SY": 23060, + "Listen": 23061, + "inning": 23062, + "ku": 23063, + "Ġrotate": 23064, + "program": 23065, + "arah": 23066, + "Design": 23067, + "Ġ(£": 23068, + "Ġstoring": 23069, + "Ġwarrants": 23070, + "Ġjudgement": 23071, + "ĠBrist": 23072, + "usually": 23073, + "photo": 23074, + "ĠRan": 23075, + "ĠPine": 23076, + "Ġoutrageous": 23077, + "ĠValentine": 23078, + "luence": 23079, + "ĠEverybody": 23080, + "Altern": 23081, + "Ġrelevance": 23082, + "Ġterminated": 23083, + "Ġdessert": 23084, + "Ġfulfilled": 23085, + "Ġprosecuted": 23086, + "ĠWords": 23087, + "Ġmigrant": 23088, + "Ġcultivation": 23089, + "ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ": 23090, + "idelity": 23091, + "ĠVern": 23092, + "ĠLogin": 23093, + "Ġmetaphor": 23094, + "ĠTip": 23095, + "Ġrecruits": 23096, + "ĠPig": 23097, + "ribing": 23098, + "Ġenthusiasts": 23099, + "exper": 23100, + "Ġfrightening": 23101, + "ĠHair": 23102, + "anson": 23103, + "strate": 23104, + "Ġhi": 23105, 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"ĠPosition": 23158, + "Adam": 23159, + "lb": 23160, + "ubs": 23161, + "Ġimperfect": 23162, + "Ġclusters": 23163, + "ĠEngineer": 23164, + "ĠCherry": 23165, + "Ġinauguration": 23166, + "ĠSau": 23167, + "Ġembodiment": 23168, + "ĠUncle": 23169, + "Ġoverr": 23170, + "Ġexplosions": 23171, + "cule": 23172, + "ĠPrinceton": 23173, + "ĠAndrea": 23174, + "Ġincorrectly": 23175, + "Ġearnest": 23176, + "Ġpilgr": 23177, + "ĠSprint": 23178, + "Ġsleeve": 23179, + "Ġhears": 23180, + "ĠAmazing": 23181, + "Ġbrowsing": 23182, + "agin": 23183, + "Ġhomeland": 23184, + "Ġhaw": 23185, + "Ġdiving": 23186, + "istered": 23187, + "178": 23188, + "Ġbargaining": 23189, + "ĠArcade": 23190, + "Ġdelegate": 23191, + "terson": 23192, + "................................................................": 23193, + "ĠJacksonville": 23194, + "275": 23195, + "Ġstagn": 23196, + "Ġadam": 23197, + "ĠSherman": 23198, + "CB": 23199, + "Ġsuburb": 23200, + "ĠFoods": 23201, + "Ġconverting": 23202, + "ĠArist": 23203, + "Ġchambers": 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23308, + "Ġpossessions": 23309, + "Ġinviting": 23310, + "Ġpredators": 23311, + "Ġaccelerated": 23312, + "Ġ157": 23313, + "uterte": 23314, + "ĠCube": 23315, + "east": 23316, + "account": 23317, + "Give": 23318, + "Ġtransplant": 23319, + "redients": 23320, + "idable": 23321, + "Ġscreenshots": 23322, + "ĠGund": 23323, + "ĠFS": 23324, + "Ġtravelers": 23325, + "Ġsensory": 23326, + "ĠFiat": 23327, + "ĠRockets": 23328, + "İĭ": 23329, + "_{": 23330, + "Friend": 23331, + "Ġcharming": 23332, + "ALS": 23333, + "Ġenjoyment": 23334, + "mph": 23335, + "Ġ5000": 23336, + "ĠREG": 23337, + "ÙĨ": 23338, + "bia": 23339, + "Ġcompilation": 23340, + "rost": 23341, + "ĠVP": 23342, + "ĠSchne": 23343, + "2019": 23344, + "Ġcopying": 23345, + "MORE": 23346, + "ĠFlore": 23347, + "falls": 23348, + "215": 23349, + "total": 23350, + "Ġdisciples": 23351, + "double": 23352, + "Ġexceeding": 23353, + "Ġsmashed": 23354, + "Ġconceptual": 23355, + "ĠRomania": 23356, + "ĠBrent": 23357, + "ĠICE": 23358, + "ĠTou": 23359, + "Ġgrap": 23360, + "Ġnails": 23361, + "189": 23362, + "ãĥĺ": 23363, + "Ġprocure": 23364, + "eur": 23365, + "Ġconfirming": 23366, + "ĠCec": 23367, + "awi": 23368, + "ĠEden": 23369, + "Ġng": 23370, + "Ġengineered": 23371, + "atics": 23372, + "Ġhooked": 23373, + "Ġdisgusting": 23374, + "ĠMurder": 23375, + "ãĤ¿": 23376, + "Library": 23377, + "Ġ168": 23378, + "Almost": 23379, + "hematic": 23380, + "Menu": 23381, + "ĠNotre": 23382, + "ĠJur": 23383, + "Ġkidnapped": 23384, + "Ġhacker": 23385, + "ĠJade": 23386, + "Ġcreepy": 23387, + "Ġdrawings": 23388, + "ĠSponsor": 23389, + "Ġcyclists": 23390, + "ĠGoblin": 23391, + "Ġoptimized": 23392, + "Ġstaged": 23393, + "ĠMcD": 23394, + "between": 23395, + "Age": 23396, + "eno": 23397, + "Sex": 23398, + "ĠWide": 23399, + "nings": 23400, + "avis": 23401, + "Ġincapable": 23402, + "ĠKob": 23403, + "Ġrewarding": 23404, + "ĠLone": 23405, + "olescent": 23406, + "Ġcontracted": 23407, + "Ġsticky": 23408, + "Jose": 23409, + "Ball": 23410, + "fest": 23411, + "ĠInput": 23412, + "ĠRecently": 23413, + "Ġtomat": 23414, + "square": 23415, + "Application": 23416, + "Ġnitrogen": 23417, + "Ġduplicate": 23418, + "ĠRecon": 23419, + "ĠDear": 23420, + "London": 23421, + "Ġintra": 23422, + "Ġdock": 23423, + "Ġoutreach": 23424, + "ĠMillion": 23425, + "Ġmammals": 23426, + "ampton": 23427, + "VAL": 23428, + "Ġsnaps": 23429, + "Ġdos": 23430, + "ĠWhole": 23431, + "ĠReady": 23432, + "Try": 23433, + "ĠWinnipeg": 23434, + "earance": 23435, + "Ġincurred": 23436, + "renched": 23437, + "ĠNSW": 23438, + "ilot": 23439, + "raine": 23440, + "Ġcube": 23441, + "got": 23442, + "Ġrunway": 23443, + "etermined": 23444, + "ĠHawks": 23445, + "Ġsurvivor": 23446, + "ĠWish": 23447, + "ĠDin": 23448, + "ĠDEF": 23449, + "ĠVault": 23450, + "187": 23451, + "Ġmushrooms": 23452, + "Ġcrisp": 23453, + "bey": 23454, + "ĠDiscovery": 23455, + "Ġdevelopmental": 23456, + "Ġparadigm": 23457, + "Ġchaotic": 23458, + "ĠTsu": 23459, + "Ġ333": 23460, + "bons": 23461, + "Ġbacterial": 23462, + 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"SpaceEngineers": 31032, + "OURCE": 31033, + "Ġ(~": 31034, + "Ġunsur": 31035, + "Ġaffirmed": 31036, + "Ġfascism": 31037, + "Ġresolving": 31038, + "ĠChavez": 31039, + "ĠCyn": 31040, + "Ġdetract": 31041, + "Lost": 31042, + "Ġrigged": 31043, + "Ġhomage": 31044, + "ĠBruno": 31045, + "555": 31046, + "eca": 31047, + "Ġpresses": 31048, + "Ġhumour": 31049, + "Ġspacing": 31050, + "Ġ'/": 31051, + "olkien": 31052, + "Coun": 31053, + "OPER": 31054, + "Tre": 31055, + "Son": 31056, + "ĠCambodia": 31057, + "ierre": 31058, + "mong": 31059, + "ozy": 31060, + "Ġliquidity": 31061, + "ĠSoviets": 31062, + "ĠFernando": 31063, + "Ġ229": 31064, + "Ġslug": 31065, + "ĠCatalan": 31066, + "electric": 31067, + "Ġscenery": 31068, + "ĠHearth": 31069, + "Ġconstrained": 31070, + "Ġgoalie": 31071, + "ĠGuidelines": 31072, + "ĠAmmo": 31073, + "ĠPearson": 31074, + "Ġtaxed": 31075, + "Ġfetus": 31076, + "Response": 31077, + "ĠAlexis": 31078, + "thia": 31079, + "Guy": 31080, + "Ġreconstruct": 31081, + "Ġextremes": 31082, + "Ġconcluding": 31083, + "ĠPeg": 31084, + "ooks": 31085, + "Ġdeductions": 31086, + "Rose": 31087, + "Ġgroundbreaking": 31088, + "ĠTarg": 31089, + "ãĥģ": 31090, + "ĠReve": 31091, + "resource": 31092, + "Ġmoons": 31093, + "Ġelectromagnetic": 31094, + "Ġamidst": 31095, + "ĠViktor": 31096, + "NESS": 31097, + "BACK": 31098, + "Ġcommute": 31099, + "ĠAnaheim": 31100, + "Ġfluctuations": 31101, + "640": 31102, + "Ġnoodles": 31103, + "ĠCopenhagen": 31104, + "ĠTide": 31105, + "ĠGrizz": 31106, + "ĠSEE": 31107, + "Ġpipelines": 31108, + "Ġscars": 31109, + "endo": 31110, + "agus": 31111, + "ĠETF": 31112, + "/#": 31113, + "ĠBecome": 31114, + "448": 31115, + "Ġvisc": 31116, + "ĠRecommended": 31117, + "Ġjumper": 31118, + "Ġcognition": 31119, + "Ġassassin": 31120, + "Ġwitnessing": 31121, + "ĠSetup": 31122, + "Ġlac": 31123, + "vim": 31124, + "ISM": 31125, + "pages": 31126, + "SSL": 31127, + "358": 31128, + "Ġadject": 31129, + "industrial": 31130, + "lore": 31131, + "chery": 31132, + "Ġglitter": 31133, + "Ġcalf": 31134, + "Florida": 31135, + "Ġspoilers": 31136, + "Ġsucceeds": 31137, + "Ġchanting": 31138, + "Ġslogans": 31139, + "ĠTracy": 31140, + "Visit": 31141, + "rology": 31142, + "Ġmornings": 31143, + "Ġlineage": 31144, + "Ġsip": 31145, + "Ġintensely": 31146, + "Ġflourish": 31147, + "ĠSleeping": 31148, + "ĠFem": 31149, + "orpor": 31150, + "ĠKlan": 31151, + "ĠDarth": 31152, + "hack": 31153, + "ĠNielsen": 31154, + "Ġtumors": 31155, + "Ġprocurement": 31156, + "ĠYorkshire": 31157, + "Ġraided": 31158, + "KY": 31159, + "Anna": 31160, + "Ġ//[": 31161, + "ĠDisorder": 31162, + "ĠMustang": 31163, + "ĠWen": 31164, + "ĠTrying": 31165, + "sq": 31166, + "Ġdeliveries": 31167, + "Ġshutter": 31168, + "Ġcerebral": 31169, + "Ġbipolar": 31170, + "ĠCN": 31171, + "lass": 31172, + "jet": 31173, + "Ġdebating": 31174, + ">:": 31175, + "Ġeagle": 31176, + "grades": 31177, + "ĠDixon": 31178, + "UGC": 31179, + "MAS": 31180, + "ĠDraco": 31181, + "ĠMachines": 31182, + "affer": 31183, + "Ġeman": 31184, + "²": 31185, + "pron": 31186, + "ĠGym": 31187, + "Ġcomparatively": 31188, + "ĠTribunal": 31189, + "PRO": 31190, + "Ġlex": 31191, + "Ġfertile": 31192, + "Ġdepressing": 31193, + "Ġsuperficial": 31194, + "essential": 31195, + "ĠHunters": 31196, + "gp": 31197, + "Ġprominence": 31198, + "Liber": 31199, + "ĠAncest": 31200, + "otechnology": 31201, + "Ġmocking": 31202, + "ĠTraff": 31203, + "ĸļ": 31204, + "Medium": 31205, + "Iraq": 31206, + "Ġpsychiatrist": 31207, + "Quantity": 31208, + "ĠLect": 31209, + "Ġnoisy": 31210, + "520": 31211, + "GY": 31212, + "Ġslapped": 31213, + "ĠMTV": 31214, + "Ġpara": 31215, + "pull": 31216, + "Multiple": 31217, + "asher": 31218, + "Ġnour": 31219, + "ĠSeg": 31220, + "Spell": 31221, + "vous": 31222, + "ordial": 31223, + "Senior": 31224, + "ĠGoldberg": 31225, + "ĠPlasma": 31226, + "need": 31227, + "Ġmessenger": 31228, + "eret": 31229, + "Ġteamed": 31230, + "Ġliteracy": 31231, + "ĠLeah": 31232, + "ĠDoyle": 31233, + "Ġemitted": 31234, + "UX": 31235, + "Ġevade": 31236, + "Ġmaze": 31237, + "Ġwrongly": 31238, + "ĠLars": 31239, + "Ġstereotype": 31240, + "Ġpledges": 31241, + "Ġaroma": 31242, + "ĠMET": 31243, + "Ġacre": 31244, + "ĠOD": 31245, + "Ġff": 31246, + "Ġbreweries": 31247, + "ĠHilton": 31248, + "undle": 31249, + "ĠKak": 31250, + "ĠThankfully": 31251, + "ĠCanucks": 31252, + "inctions": 31253, + "ĠAppears": 31254, + "Ġcoer": 31255, + "Ġundermined": 31256, + "rovers": 31257, + "Andre": 31258, + "Ġblaze": 31259, + "umers": 31260, + "Ġfamine": 31261, + "amphetamine": 31262, + "ulkan": 31263, + "Amount": 31264, + "Ġdesperation": 31265, + "wikipedia": 31266, + "development": 31267, + "ĠCorinth": 31268, + "ussia": 31269, + "Jackson": 31270, + "LI": 31271, + "Native": 31272, + "Rs": 31273, + "Ohio": 31274, + "ĠKathleen": 31275, + "Fortunately": 31276, + "Ġattendant": 31277, + "ĠPreferred": 31278, + "ĠDidn": 31279, + "ĠVs": 31280, + "Mis": 31281, + "Ġrespondent": 31282, + "Ġboun": 31283, + "stable": 31284, + "Ġpaved": 31285, + "Ġunexpl": 31286, + "ĠCheney": 31287, + "LM": 31288, + "ĠCull": 31289, + "blown": 31290, + "Ġconfronting": 31291, + "ocese": 31292, + "serving": 31293, + "Wi": 31294, + "ĠLithuania": 31295, + "anni": 31296, + "Ġstalk": 31297, + "hd": 31298, + "Ġvener": 31299, + "APH": 31300, + "ynchronous": 31301, + "URR": 31302, + "umably": 31303, + "historic": 31304, + "Half": 31305, + "Hay": 31306, + "Ġresilience": 31307, + "spection": 31308, + "Ġabandoning": 31309, + "Obs": 31310, + "ĠDebbie": 31311, + "Ġgradient": 31312, + "ĠPlaint": 31313, + "ĠCanal": 31314, + "ARCH": 31315, + "Ġexpansive": 31316, + "Ġfung": 31317, + "Ġbounced": 31318, + "Und": 31319, + "Ġprecautions": 31320, + "Ġclarification": 31321, + "Ġdagger": 31322, + "Ġgrips": 31323, + "Ġµ": 31324, + "ĠRivera": 31325, + "ĠUndead": 31326, + "isites": 31327, + "ĠFIRST": 31328, + "ño": 31329, + "audi": 31330, + "Ġhostages": 31331, + "Ġcompliant": 31332, + "Ġalumni": 31333, + "Seven": 31334, + "Ġcybersecurity": 31335, + "either": 31336, + "Collect": 31337, + "Ġinvariably": 31338, + "ĠSoci": 31339, + "Ġlawmaker": 31340, + "Ġale": 31341, + "ĠPersonally": 31342, + "Nazi": 31343, + "Ġcustomization": 31344, + "ĠProc": 31345, + "ĠSaskatchewan": 31346, + "eaturing": 31347, + "Ġspared": 31348, + "Ġdiscontinued": 31349, + "Ġcomputational": 31350, + "ĠMotorola": 31351, + "Ġsupremacist": 31352, + "governmental": 31353, + "Ġparadise": 31354, + "ĠDowning": 31355, + "ĠNikon": 31356, + "Ġcatalyst": 31357, + "berra": 31358, + "Toronto": 31359, + "875": 31360, + "beta": 31361, + "ĠMacron": 31362, + "Ġunrealistic": 31363, + "vector": 31364, + "ĠVehicles": 31365, + "itiveness": 31366, + "ĠRV": 31367, + "ĠColbert": 31368, + "sin": 31369, + "oji": 31370, + "entin": 31371, + "ĠKrish": 31372, + "hello": 31373, + "ffield": 31374, + "oky": 31375, + "ĠTate": 31376, + "Ġmaple": 31377, + "Ġaids": 31378, + "chemical": 31379, + "334": 31380, + "nuts": 31381, + "ĠWarp": 31382, + "Ġxx": 31383, + "ĠRobb": 31384, + "umerous": 31385, + "_-_": 31386, + "ftime": 31387, + "ĠVW": 31388, + "Ġwinger": 31389, + "ĠDome": 31390, + "tools": 31391, + "ĠPV": 31392, + "ĠGeorgetown": 31393, + "Ġgeared": 31394, + "Ġjihadists": 31395, + "Ġcp": 31396, + "Ġsteroids": 31397, + "Mother": 31398, + "clerosis": 31399, + "ĠDRM": 31400, + "nesia": 31401, + "Ġlinger": 31402, + "Ġimmersive": 31403, + "ĠCOUN": 31404, + "Ġoutweigh": 31405, + "ensual": 31406, + "Band": 31407, + "Ġtransforms": 31408, + "matched": 31409, + "psons": 31410, + "ĠJudicial": 31411, + "factor": 31412, + "Ġreferral": 31413, + "Ġoddly": 31414, + "ĠWenger": 31415, + "Bring": 31416, + "ĠBows": 31417, + "602": 31418, + "ICLE": 31419, + "Ġlions": 31420, + "ĠAcademic": 31421, + "ĠThorn": 31422, + "ĠRaider": 31423, + "kefeller": 31424, + "Storage": 31425, + "Lower": 31426, + "ĠOrt": 31427, + "ĠEquality": 31428, + "ALT": 31429, + "ĠSOC": 31430, + "Types": 31431, + "Ġlyn": 31432, + "ĠAsset": 31433, + "coat": 31434, + "TPP": 31435, + "CVE": 31436, + "ĠPioneer": 31437, + "application": 31438, + "Modern": 31439, + "ĠHK": 31440, + "Environment": 31441, + "Alright": 31442, + "Rain": 31443, + "IPP": 31444, + "ĠShiite": 31445, + "Ġmound": 31446, + "ĠAbilities": 31447, + "condition": 31448, + "Staff": 31449, + "Ġcompetence": 31450, + "ĠMoor": 31451, + "ĠDiablo": 31452, + "Ġwithheld": 31453, + "Ġostensibly": 31454, + "ĠBrom": 31455, + "Ġmsg": 31456, + "Ġdenomin": 31457, + "ĠReferences": 31458, + "ĠFP": 31459, + "Ġplunged": 31460, + "Ġpamph": 31461, + "moving": 31462, + "central": 31463, + "Ġdownright": 31464, + "Ġfading": 31465, + "Tal": 31466, + "Typ": 31467, + "ĠThy": 31468, + "ukes": 31469, + "ithe": 31470, + "Ġove": 31471, + "Ġbattled": 31472, + "Ġseafood": 31473, + "Ġfigur": 31474, + "ĠRD": 31475, + "crop": 31476, + "Ġsquads": 31477, + "{\\": 31478, + "à¹": 31479, + "ĠEh": 31480, + "Ġinterviewing": 31481, + "ĠQin": 31482, + "Ġaspiring": 31483, + "PLIC": 31484, + "Ġclauses": 31485, + "ĠGast": 31486, + "ĠNir": 31487, + "Ġluggage": 31488, + "Ġhose": 31489, + "Ġsystemd": 31490, + "Ġdescending": 31491, + "ĠRevised": 31492, + "ĠRails": 31493, + "align": 31494, + "709": 31495, + "337": 31496, + "Ġfug": 31497, + "charging": 31498, + "tags": 31499, + "Ġuter": 31500, + "kish": 31501, + "WARNING": 31502, + "490": 31503, + "profits": 31504, + "Ġvoyage": 31505, + "Ġace": 31506, + "ĠVanguard": 31507, + "ĠTanks": 31508, + "ĠMuk": 31509, + "Ġ226": 31510, + "Safe": 31511, + "Armor": 31512, + "Ġvolcanic": 31513, + "Ġwomb": 31514, + "ĠMIL": 31515, + "Ġbeginner": 31516, + "ĠRecogn": 31517, + "ĠAAP": 31518, + "PLAY": 31519, + ")!": 31520, + "Ġdetecting": 31521, + "cn": 31522, + "Ġbreaches": 31523, + "Basically": 31524, + "ĠPag": 31525, + "ĠMunicipal": 31526, + "ĠIndie": 31527, + "ĠLaf": 31528, + "ĠDisable": 31529, + "ĠOlson": 31530, + "Ġrestrained": 31531, + "Ġrulings": 31532, + "Ġhumane": 31533, + "events": 31534, + "ĠCinema": 31535, + "displayText": 31536, + "ĠHatch": 31537, + "actionDate": 31538, + "onnaissance": 31539, + "Ġassaulting": 31540, + "ĠLug": 31541, + "CHAT": 31542, + "Ġvigorous": 31543, + "ĠPerse": 31544, + "Ġintolerance": 31545, + "ĠSnapchat": 31546, + "ĠSharks": 31547, + "Ġdummy": 31548, + "ĠDiagn": 31549, + "ĠGuitar": 31550, + "imeters": 31551, + "403": 31552, + "REG": 31553, + "Ax": 31554, + "Ġseparates": 31555, + "ĠMahm": 31556, + "Ġtv": 31557, + "jah": 31558, + "OOL": 31559, + "Circ": 31560, + "ĠWindsor": 31561, + "ussian": 31562, + "Ġintuition": 31563, + "Ġdisdain": 31564, + "ĠDonovan": 31565, + "Ġ221": 31566, + "Emb": 31567, + "Ġcondemning": 31568, + "Ġgenerosity": 31569, + "zzy": 31570, + "Ġpanties": 31571, + "ĠPrevent": 31572, + "ActionCode": 31573, + "ANA": 31574, + "342": 31575, + "externalActionCode": 31576, + "Ġspecifying": 31577, + "Ġcrystall": 31578, + "Jere": 31579, + "Ġrupt": 31580, + "ĠApprentice": 31581, + "Ġprofiling": 31582, + "к": 31583, + "Strike": 31584, + "Ġsideline": 31585, + "Ġobligated": 31586, + "Ġoccult": 31587, + "Ġbureaucratic": 31588, + "antically": 31589, + "rupted": 31590, + "negative": 31591, + "ĠEthiopia": 31592, + "ĠCivic": 31593, + "Ġinsiders": 31594, + "eligible": 31595, + "ĠTVs": 31596, + "ĠBAR": 31597, + "ĠTI": 31598, + "iologist": 31599, + "ĠAIR": 31600, + "Ġsubstituted": 31601, + "Arab": 31602, + "ĠSaul": 31603, + "ĠYog": 31604, + "prem": 31605, + "Ġbuilders": 31606, + "Ġstationary": 31607, + "Ġdoubtful": 31608, + "Ġvigorously": 31609, + "Ġthrilling": 31610, + "Physical": 31611, + "ĠCarey": 31612, + "ĠHydra": 31613, + "geoning": 31614, + "ĠSly": 31615, + "yton": 31616, + "Ġborrowers": 31617, + "ĠParkinson": 31618, + "Ġë": 31619, + "ĠJamaica": 31620, + "Ġsatir": 31621, + "Ġinsurgents": 31622, + "ĠFirm": 31623, + "Ġisot": 31624, + "ĠKarn": 31625, + "ourning": 31626, + "akens": 31627, + "docs": 31628, + "little": 31629, + "ĠMonaco": 31630, + "CLASS": 31631, + "Turkey": 31632, + "Ly": 31633, + "ĠConan": 31634, + "assic": 31635, + "Ġstarred": 31636, + "ĠPacers": 31637, + "eties": 31638, + "Ġtipping": 31639, + "Moon": 31640, + "ĠRw": 31641, + "same": 31642, + "Ġcavity": 31643, + "Ġgoof": 31644, + "ĠZo": 31645, + "Shock": 31646, + "ummer": 31647, + "Ġemphasizes": 31648, + "Ġregrett": 31649, + "Ġnovelty": 31650, + "Ġenvy": 31651, + "ĠPassive": 31652, + "rw": 31653, + "505": 31654, + "Ġindifferent": 31655, + "ĠRica": 31656, + "ĠHimself": 31657, + "ĠFreddie": 31658, + "Ġadip": 31659, + "ä¸Ģ": 31660, + "Ġbreakout": 31661, + "Ġhurried": 31662, + "ĠHuang": 31663, + "ĠDisk": 31664, + "Ġroaming": 31665, + "?????-?????-": 31666, + "UV": 31667, + "ĠRicky": 31668, + "ĠSigma": 31669, + "Ġmarginalized": 31670, + "Ġedits": 31671, + "Ġ304": 31672, + "memory": 31673, + "Ġspecimen": 31674, + "293": 31675, + "ãģ¯": 31676, + "Ġvertically": 31677, + "Ġaudition": 31678, + "ĠHeck": 31679, + "Ġcaster": 31680, + "ĠHoldings": 31681, + "adal": 31682, + "ĠCron": 31683, + "ĠLiam": 31684, + "Ġdeflect": 31685, + "Pick": 31686, + "ĠDebug": 31687, + "REF": 31688, + "Ġversatility": 31689, + "othes": 31690, + "classified": 31691, + "ĠMahar": 31692, + "ĠHort": 31693, + "Counter": 31694, + "stasy": 31695, + "noticed": 31696, + 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34833, + "Ġventilation": 34834, + "ĠSchwarz": 34835, + "Ġwizards": 34836, + "Ġominous": 34837, + "ĠHeads": 34838, + "ĠBG": 34839, + "Ġlumber": 34840, + "ĠSpiel": 34841, + "ĠisEnabled": 34842, + "Ġancestral": 34843, + "ĠShips": 34844, + "Ġwrestler": 34845, + "phi": 34846, + "Ġyuan": 34847, + "ĠRebellion": 34848, + "Ġiceberg": 34849, + "Ġmagically": 34850, + "Ġdiversion": 34851, + "arro": 34852, + "ythm": 34853, + "ĠRiders": 34854, + "ĠRobbie": 34855, + "ĠKara": 34856, + "ĠMaintenance": 34857, + "ĠHerb": 34858, + "Ġharms": 34859, + "packed": 34860, + "ĠFeinstein": 34861, + "Ġmarrying": 34862, + "Ġblending": 34863, + "ĠRates": 34864, + "Ġ1880": 34865, + "Ġwrink": 34866, + "ĠUnch": 34867, + "ĠTorch": 34868, + "described": 34869, + "Ġhumanoid": 34870, + "ilitating": 34871, + "ĠConv": 34872, + "ĠFeld": 34873, + "IGHTS": 34874, + "Ġwhistleblower": 34875, + "ortmund": 34876, + "etsy": 34877, + "arrett": 34878, + "ĠMono": 34879, + "ĠIke": 34880, + "ĠCNBC": 34881, + "ĠWAY": 34882, + "ĠMDMA": 34883, + "ĠIndividuals": 34884, + "Ġsupplemental": 34885, + "Ġpowerhouse": 34886, + "ĠStru": 34887, + "Focus": 34888, + "aphael": 34889, + "ĠColleg": 34890, + "atti": 34891, + "ZA": 34892, + "Ġperenn": 34893, + "ĠSignature": 34894, + "ĠRodney": 34895, + "Ġcubes": 34896, + "iddled": 34897, + "ĠDante": 34898, + "ĠINV": 34899, + "ilingual": 34900, + "ĠCth": 34901, + "Ġsofa": 34902, + "Ġintimidate": 34903, + "ĠRoe": 34904, + "ĠDiplom": 34905, + "ĠCountries": 34906, + "ayson": 34907, + "Ġextradition": 34908, + "Ġdisabling": 34909, + "ĠCardiff": 34910, + "Ġmemorandum": 34911, + "ĠTrace": 34912, + "Ġ???": 34913, + "sector": 34914, + "ĠRouhani": 34915, + "ĠYates": 34916, + "ĠFreeze": 34917, + "Ġbladder": 34918, + "Motor": 34919, + "ĠPromise": 34920, + "antasy": 34921, + "Ġforeseeable": 34922, + "ĠCologne": 34923, + "container": 34924, + "ĠTrees": 34925, + "ĠGors": 34926, + "ĠSinclair": 34927, + "Ġbarring": 34928, + "keye": 34929, + "Ġslashed": 34930, + "ĠStatistical": 34931, + "éĩ": 34932, + "Ġâĸº": 34933, + "Allows": 34934, + "Ġhumility": 34935, + "Ġdrilled": 34936, + "ĠFurn": 34937, + "443": 34938, + "Ġsewage": 34939, + "Ġhomepage": 34940, + "Ġcourtyard": 34941, + "Ġvile": 34942, + "Ġsubsidiaries": 34943, + "ajo": 34944, + "directory": 34945, + "Ġammon": 34946, + "Vers": 34947, + "charges": 34948, + "Ġ}}": 34949, + "ĠChains": 34950, + "Ġ246": 34951, + "nob": 34952, + "Ġpercept": 34953, + "Ġgrit": 34954, + "Ġfishermen": 34955, + "ĠIraqis": 34956, + "ĠDISTR": 34957, + "ĠFULL": 34958, + "ĠEvaluation": 34959, + "graph": 34960, + "atial": 34961, + "Ġcooperating": 34962, + "Ġmelan": 34963, + "Ġenlightened": 34964, + "Ġali": 34965, + "tailed": 34966, + "Ġsalute": 34967, + "Ġweakest": 34968, + "ĠBulldogs": 34969, + "UA": 34970, + "ĠAlloy": 34971, + "Ġsemen": 34972, + "ocene": 34973, + "ĠWilliamson": 34974, + "spr": 34975, + ",âĢĶ": 34976, + "ĠGF": 34977, + "ittens": 34978, + "Beat": 34979, + "ĠJunk": 34980, + "iphate": 34981, + "ĠFarmers": 34982, + "ĠBitcoins": 34983, + "igers": 34984, + "dh": 34985, + "ĠLoyal": 34986, + "payer": 34987, + "Ġentertained": 34988, + "Ġpenned": 34989, + "Ġcoupon": 34990, + "Queue": 34991, + "Ġweakening": 34992, + "carry": 34993, + "Ġunderestimate": 34994, + "Ġshootout": 34995, + "Ġcharismatic": 34996, + "ĠProcedure": 34997, + "Ġprudent": 34998, + "inances": 34999, + "Ġriches": 35000, + "Ġcortical": 35001, + "Ġstrides": 35002, + "Ġdrib": 35003, + "ĠOilers": 35004, + "540": 35005, + "ĠPerform": 35006, + "ĠBangkok": 35007, + "Ġeuth": 35008, + "SER": 35009, + "Ġsimplistic": 35010, + "tops": 35011, + "campaign": 35012, + "Quality": 35013, + "Ġimpoverished": 35014, + "ĠEisenhower": 35015, + "Ġaugment": 35016, + "ĠHarden": 35017, + "Ġintervened": 35018, + "Ġlistens": 35019, + "ĠKok": 35020, + "Ġsage": 35021, + "Ġrubbish": 35022, + "ĠDed": 35023, + "Ġmull": 35024, + "pelling": 35025, + "Ġvideot": 35026, + "Production": 35027, + "DJ": 35028, + "miah": 35029, + "Ġadaptations": 35030, + "Ġmedically": 35031, + "Ġboarded": 35032, + 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35184, + "Ġhalftime": 35185, + "Ġresidue": 35186, + "Ġindign": 35187, + "ĠShaun": 35188, + "ĠElm": 35189, + "auri": 35190, + "Aff": 35191, + "WATCH": 35192, + "ĠLyon": 35193, + "helps": 35194, + "361": 35195, + "Ġlobbyist": 35196, + "Ġdiminishing": 35197, + "Ġoutbreaks": 35198, + "Ġgoats": 35199, + "favorite": 35200, + "ĠNah": 35201, + "sonian": 35202, + "ĠBooster": 35203, + "Ġsandbox": 35204, + "ĠFare": 35205, + "ĠMalta": 35206, + "ĠattRot": 35207, + "ĠMOR": 35208, + "lde": 35209, + "Ġnavigating": 35210, + "Touch": 35211, + "Ġuntrue": 35212, + "ĠDisaster": 35213, + "Ġludicrous": 35214, + "Password": 35215, + "ĠJFK": 35216, + "blogspot": 35217, + "416": 35218, + "ĠUNDER": 35219, + "ernal": 35220, + "Ġdelaying": 35221, + "TOP": 35222, + "Ġimplants": 35223, + "ĠAVG": 35224, + "ĠHuge": 35225, + "attr": 35226, + "Ġjournalistic": 35227, + "ĠPeyton": 35228, + "ĠIA": 35229, + "Rap": 35230, + "goal": 35231, + "ĠProgramme": 35232, + "Ġsmashing": 35233, + "wives": 35234, + "println": 35235, + 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"Ġuptake": 35340, + "Ġpredatory": 35341, + "ULTS": 35342, + "/**": 35343, + "Ġintoxicated": 35344, + "ĠWestbrook": 35345, + "odder": 35346, + "hement": 35347, + "Ġbaseman": 35348, + "APD": 35349, + "storage": 35350, + "ĠFifty": 35351, + "editor": 35352, + "GEN": 35353, + "UTION": 35354, + "irting": 35355, + "Ġsewing": 35356, + "rift": 35357, + "Ġagony": 35358, + "ĠSands": 35359, + "Ġ254": 35360, + "Cash": 35361, + "Ġlodge": 35362, + "Ġpunt": 35363, + "Natural": 35364, + "ĠIdeas": 35365, + "Ġerroneous": 35366, + "ĠSensor": 35367, + "ĠHannity": 35368, + "Ġ1921": 35369, + "Ġmould": 35370, + "ĠGon": 35371, + "kaya": 35372, + "Ġanonymously": 35373, + "ĠKEY": 35374, + "Ġsimulator": 35375, + "Winter": 35376, + "Ġstreamed": 35377, + "507": 35378, + "?\",": 35379, + "Ġteased": 35380, + "Ġcoefficient": 35381, + "Ġwartime": 35382, + "ĠTHR": 35383, + "''.": 35384, + "ĠBanking": 35385, + "mpire": 35386, + "Ġfandom": 35387, + "Ġlia": 35388, + "Ga": 35389, + "Ġdownhill": 35390, + "Ġinterpreting": 35391, + "Individual": 35392, + "Norm": 35393, + "Ġjealousy": 35394, + "bitcoin": 35395, + "Ġpleasures": 35396, + "ĠToys": 35397, + "ĠChevrolet": 35398, + "ĠAdvisor": 35399, + "IZE": 35400, + "Ġreceptions": 35401, + "706": 35402, + "Cro": 35403, + "Ġ262": 35404, + "Ġcitrus": 35405, + "iru": 35406, + "Reviewer": 35407, + "jected": 35408, + "UES": 35409, + "anz": 35410, + "1981": 35411, + "ĠWorker": 35412, + "Ġcomplied": 35413, + "orescent": 35414, + "continental": 35415, + "Ton": 35416, + "ĠPrism": 35417, + "ĠSheep": 35418, + "Ġ288": 35419, + "nox": 35420, + "ĠVog": 35421, + "Ord": 35422, + "Ġrealms": 35423, + "tek": 35424, + "Ġirrigation": 35425, + "Ġbicycles": 35426, + "Ġelectronically": 35427, + "poly": 35428, + "tall": 35429, + "());": 35430, + "Ġaesthetics": 35431, + "ĠIntegrated": 35432, + "Explore": 35433, + "Ġdunk": 35434, + "476": 35435, + "pain": 35436, + "ĠJacques": 35437, + "ĠDmit": 35438, + "Frames": 35439, + "Ġreunited": 35440, + "Ġhumid": 35441, + "Dro": 35442, + "Political": 35443, + "Ġyouthful": 35444, + "Ġentails": 35445, + "Ġmosquito": 35446, + "363": 35447, + "species": 35448, + "Ġcoordinating": 35449, + "ĠMayhem": 35450, + "ĠMagnus": 35451, + "Mount": 35452, + "Improved": 35453, + "ĠSTATE": 35454, + "ATTLE": 35455, + "Ġflowed": 35456, + "Ġtackled": 35457, + "Ġfashioned": 35458, + "Ġreorgan": 35459, + "ivari": 35460, + "finger": 35461, + "Ġreluctantly": 35462, + "etting": 35463, + "ĠVand": 35464, + "young": 35465, + "ĠGarland": 35466, + "Ġpresumption": 35467, + "Ġamenities": 35468, + "ĠPleasant": 35469, + "onential": 35470, + "ĠOxy": 35471, + "Ġmorals": 35472, + "ĠYah": 35473, + "Ready": 35474, + "Simon": 35475, + "Enh": 35476, + "Demon": 35477, + "Ġclich": 35478, + "Monitor": 35479, + "ĠDU": 35480, + "Ġwelcomes": 35481, + "Ġstandout": 35482, + "Ġdreadful": 35483, + "Ġbananas": 35484, + "Ġballoons": 35485, + "hooting": 35486, + "basic": 35487, + "Ġsuffix": 35488, + "Ġduly": 35489, + "cano": 35490, + "Chain": 35491, + "atos": 35492, + 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"Ġknack": 47868, + "Ġinsulated": 47869, + "ĠTile": 47870, + "ĠExtrem": 47871, + "Ġuntold": 47872, + "Ġconverge": 47873, + "Ġrefuel": 47874, + "igroup": 47875, + "Ġdistortions": 47876, + "Ġravaged": 47877, + "Ġmechanically": 47878, + "ĠReilly": 47879, + "ĠNose": 47880, + "ĠIncarnation": 47881, + "ĠBecky": 47882, + "abbling": 47883, + "Ġtaco": 47884, + "Ġrake": 47885, + "Ġmelancholy": 47886, + "Ġillustrious": 47887, + "ĠDartmouth": 47888, + "Guide": 47889, + "ĠRazer": 47890, + "ĠBenz": 47891, + "Ultimate": 47892, + "ĠSurprise": 47893, + "Ġpageant": 47894, + "offer": 47895, + "Whoever": 47896, + "Ġwiser": 47897, + "Ġchemist": 47898, + "ĠHELL": 47899, + "ĠBulk": 47900, + "Ġplutonium": 47901, + "ĠCOVER": 47902, + "Ö¼": 47903, + "failed": 47904, + "Ġtirelessly": 47905, + "Ġinfertility": 47906, + "ĠTrident": 47907, + "ĠShowtime": 47908, + "ĠCiv": 47909, + "Vice": 47910, + "requires": 47911, + "ittance": 47912, + "Ġuncontrolled": 47913, + "interesting": 47914, + "561": 47915, + "Ġinnovate": 47916, + "ategic": 47917, + "Lie": 47918, + "ĠSelling": 47919, + "Ul": 47920, + "Ġsavior": 47921, + "ĠTosh": 47922, + "Ġswast": 47923, + "PASS": 47924, + "Ġrink": 47925, + "Ġcardio": 47926, + "ĠIro": 47927, + "udi": 47928, + "Ġvantage": 47929, + "Ġvans": 47930, + "ĠNiño": 47931, + "+=": 47932, + "Ġpropagate": 47933, + "": 49029, + "Ġleukemia": 49030, + "Ġeluc": 49031, + "Ġannouncer": 49032, + "ĠLithuan": 49033, + "ĠArmageddon": 49034, + "åĩ": 49035, + "Lenin": 49036, + "ĠRuk": 49037, + "Ġpepp": 49038, + "ĠRomantic": 49039, + "ĠPIT": 49040, + "ĠInterstellar": 49041, + "ĠAtkinson": 49042, + "Raid": 49043, + "Js": 49044, + "Goal": 49045, + "Course": 49046, + "Ġvanishing": 49047, + "esley": 49048, + "ĠRounds": 49049, + "Elsa": 49050, + "593": 49051, + "Ġredundancy": 49052, + "ĠSTAND": 49053, + "Ġprophetic": 49054, + "Ġhabitable": 49055, + "ryu": 49056, + "Ġfaintly": 49057, + "MODE": 49058, + "Ġflanked": 49059, + "IRC": 49060, + "Awesome": 49061, + "Ġspurious": 49062, + "ĠZah": 49063, + 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"urus": 17142, + "hed": 17143, + "Pattern": 17144, + "▁година": 17145, + "▁Philadel": 17146, + "tikzpicture": 17147, + "▁coal": 17148, + "▁sede": 17149, + "▁satisfies": 17150, + "▁trim": 17151, + "▁bat": 17152, + "▁américain": 17153, + "▁luglio": 17154, + "▁поча": 17155, + "ffff": 17156, + "▁Target": 17157, + "generate": 17158, + "▁Zie": 17159, + "ția": 17160, + "▁gard": 17161, + "▁workers": 17162, + "▁Job": 17163, + "▁urban": 17164, + "ahlen": 17165, + "▁Building": 17166, + "▁neu": 17167, + "▁chron": 17168, + "▁Earl": 17169, + "gro": 17170, + "USE": 17171, + "▁XII": 17172, + "▁wealth": 17173, + "inae": 17174, + "▁Бра": 17175, + "▁libert": 17176, + "iros": 17177, + ":$": 17178, + "lee": 17179, + "ieves": 17180, + "▁Justice": 17181, + "▁oil": 17182, + "▁Athlet": 17183, + "▁clo": 17184, + "Scale": 17185, + "▁lips": 17186, + "▁april": 17187, + "▁impression": 17188, + "▁perce": 17189, + "▁участи": 17190, + "vil": 17191, + "éch": 17192, + "▁equality": 17193, + "▁мет": 17194, + "▁annotation": 17195, + "ernal": 17196, + "▁Mach": 17197, + "▁intitul": 17198, + "problem": 17199, + "ющих": 17200, + "oplus": 17201, + "▁thousands": 17202, + "▁calculations": 17203, + "umps": 17204, + "▁triangle": 17205, + "phal": 17206, + "▁Dorf": 17207, + "▁dollars": 17208, + "▁denen": 17209, + "lès": 17210, + "olid": 17211, + "▁Results": 17212, + "▁Stadium": 17213, + "▁Desp": 17214, + "▁Eisen": 17215, + "imir": 17216, + "▁sotto": 17217, + "▁či": 17218, + "atable": 17219, + "orum": 17220, + "▁convergence": 17221, + "▁jeune": 17222, + "oking": 17223, + "▁живо": 17224, + "aining": 17225, + "pointer": 17226, + "culo": 17227, + "▁jsou": 17228, + "▁grab": 17229, + "akte": 17230, + "▁hoping": 17231, + "▁Mak": 17232, + "▁sag": 17233, + "origine": 17234, + "▁послед": 17235, + "▁Veg": 17236, + "▁theoret": 17237, + "▁Tru": 17238, + "nement": 17239, + "▁faces": 17240, + "Hor": 17241, + "Join": 17242, + "arel": 17243, + "▁около": 17244, + "However": 17245, + "▁catal": 17246, + "bourg": 17247, + 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17300, + "zahl": 17301, + "▁Among": 17302, + "Qt": 17303, + "▁togg": 17304, + "▁variant": 17305, + "▁hill": 17306, + "писи": 17307, + "colon": 17308, + "▁dicembre": 17309, + "гор": 17310, + "▁Wind": 17311, + "ünstler": 17312, + "▁=\\": 17313, + "saved": 17314, + "▁nej": 17315, + "unte": 17316, + "utto": 17317, + "▁recens": 17318, + "▁sick": 17319, + "▁desen": 17320, + "UST": 17321, + "▁worst": 17322, + "▁Angel": 17323, + "odox": 17324, + "▁Province": 17325, + "▁Maz": 17326, + "▁agreement": 17327, + "▁Bass": 17328, + "▁segunda": 17329, + "onces": 17330, + "▁Linki": 17331, + "▁CL": 17332, + "▁já": 17333, + "itement": 17334, + "▁área": 17335, + "▁scalar": 17336, + "▁Рес": 17337, + "awt": 17338, + "sieme": 17339, + "▁juni": 17340, + "▁худож": 17341, + "ikus": 17342, + "▁lid": 17343, + "ppel": 17344, + "avi": 17345, + "▁balance": 17346, + "ipping": 17347, + "cussion": 17348, + "ческих": 17349, + "(\".": 17350, + "Also": 17351, + "▁whis": 17352, + "HOME": 17353, + "▁brown": 17354, + "▁día": 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+ "▁altre": 17410, + "▁'-": 17411, + "▁Weg": 17412, + "NotFound": 17413, + "йской": 17414, + "▁converting": 17415, + "phabet": 17416, + "atrice": 17417, + "bourne": 17418, + "alom": 17419, + "▁comparing": 17420, + "▁Zo": 17421, + "▁fla": 17422, + "вая": 17423, + "▁entra": 17424, + "▁charset": 17425, + "developers": 17426, + "ística": 17427, + "}>": 17428, + "▁Jazz": 17429, + "▁Howard": 17430, + "шта": 17431, + "▁clone": 17432, + "door": 17433, + "▁Pin": 17434, + "***": 17435, + "▁silent": 17436, + "ecycle": 17437, + "isce": 17438, + "▁mud": 17439, + "▁Display": 17440, + "▁lip": 17441, + "▁использова": 17442, + "▁characteristic": 17443, + "▁sb": 17444, + "firebase": 17445, + "▁Bew": 17446, + "Calendar": 17447, + "▁uso": 17448, + "èse": 17449, + "▁Rat": 17450, + "▁esper": 17451, + "▁throwing": 17452, + "▁rodz": 17453, + "▁yards": 17454, + "▁grass": 17455, + "▁marker": 17456, + "▁Kos": 17457, + "Theta": 17458, + "▁organis": 17459, + "kernel": 17460, + "▁personas": 17461, + "keep": 17462, + "▁exclaimed": 17463, + "oslav": 17464, + "▁Entertain": 17465, + "нер": 17466, + "▁inwon": 17467, + "▁Rand": 17468, + "reduce": 17469, + "fac": 17470, + "expression": 17471, + "yj": 17472, + "▁differenti": 17473, + "aglia": 17474, + "▁templates": 17475, + "▁mű": 17476, + "▁prv": 17477, + "▁mois": 17478, + "▁gewann": 17479, + "▁була": 17480, + "bibli": 17481, + "demo": 17482, + "▁Anderson": 17483, + "▁ред": 17484, + "▁porque": 17485, + "▁Pologne": 17486, + "▁trip": 17487, + "▁exemple": 17488, + "▁Internacional": 17489, + "▁као": 17490, + "Insert": 17491, + "general": 17492, + "SESSION": 17493, + "berga": 17494, + "hält": 17495, + "unas": 17496, + "мира": 17497, + "▁yields": 17498, + "mapsto": 17499, + "spot": 17500, + "▁+\\": 17501, + "лла": 17502, + "▁precisely": 17503, + "▁член": 17504, + "shadow": 17505, + "Are": 17506, + "unal": 17507, + "▁dispar": 17508, + "▁título": 17509, + "nest": 17510, + "▁Low": 17511, + "▁prot": 17512, + "▁Costa": 17513, + "named": 17514, + "▁gained": 17515, + "lesia": 17516, + "▁administration": 17517, + "Import": 17518, + "branch": 17519, + "▁sympath": 17520, + "voj": 17521, + "▁EC": 17522, + "▁municipio": 17523, + "▁animated": 17524, + "▁directories": 17525, + "▁roof": 17526, + "ząd": 17527, + "imet": 17528, + "proto": 17529, + "bla": 17530, + ":]": 17531, + "have": 17532, + "atem": 17533, + "▁ns": 17534, + "▁sector": 17535, + "three": 17536, + "owane": 17537, + "wers": 17538, + "ових": 17539, + "rence": 17540, + "▁extr": 17541, + "igten": 17542, + "▁occident": 17543, + "ță": 17544, + "▁eat": 17545, + "▁hydro": 17546, + "ubernetes": 17547, + "[@": 17548, + "▁Moon": 17549, + "▁Sho": 17550, + "▁elsewhere": 17551, + "üller": 17552, + "Upload": 17553, + "ланд": 17554, + "▁För": 17555, + "wissenschaft": 17556, + "KS": 17557, + "▁physics": 17558, + "tz": 17559, + "▁серед": 17560, + "▁Arbeit": 17561, + "▁мест": 17562, + "▁Gebiet": 17563, + "▁insect": 17564, + "Ah": 17565, + "izado": 17566, + "▁temple": 17567, + "▁annual": 17568, + "stad": 17569, + "▁habitat": 17570, + "▁AB": 17571, + "wort": 17572, + "▁repos": 17573, + "▁Neu": 17574, + "▁$(\".": 17575, + "Vorlage": 17576, + "▁reprezent": 17577, + "estanden": 17578, + "Intern": 17579, + ".`": 17580, + "▁failing": 17581, + "▁Material": 17582, + "▁effectively": 17583, + "телем": 17584, + "▁гла": 17585, + "▁nahm": 17586, + "▁differently": 17587, + "extension": 17588, + "▁Verm": 17589, + "enabled": 17590, + "configure": 17591, + "nio": 17592, + "ciones": 17593, + "▁Beach": 17594, + "сона": 17595, + "▁copying": 17596, + "▁україн": 17597, + "▁призна": 17598, + "zh": 17599, + "Desktop": 17600, + "▁sost": 17601, + "▁subsequently": 17602, + "▁Lehr": 17603, + "▁ó": 17604, + "lär": 17605, + "odor": 17606, + "phon": 17607, + "nc": 17608, + "iterator": 17609, + "▁эти": 17610, + "▁europé": 17611, + "▁Toronto": 17612, + "ódigo": 17613, + "▁posto": 17614, + "ffe": 17615, + "▁crew": 17616, + "▁Schwar": 17617, + "Sa": 17618, + "square": 17619, + "▁beside": 17620, + "▁Мі": 17621, + "▁ath": 17622, + "▁advent": 17623, + "cji": 17624, + "written": 17625, + "▁russ": 17626, + "rost": 17627, + "HI": 17628, + "▁dice": 17629, + "cca": 17630, + "▁dép": 17631, + "ply": 17632, + "bigg": 17633, + "ział": 17634, + "ütt": 17635, + "▁одно": 17636, + "JECT": 17637, + "ському": 17638, + "nos": 17639, + "mock": 17640, + "Launch": 17641, + "same": 17642, + "▁jobs": 17643, + "▁widely": 17644, + "▁defines": 17645, + "▁Pse": 17646, + "▁neighbour": 17647, + "ющие": 17648, + "▁closer": 17649, + "▁располо": 17650, + "▁clubs": 17651, + "fly": 17652, + "шим": 17653, + "▁suffered": 17654, + "▁nar": 17655, + "▁lavor": 17656, + "Extension": 17657, + "itionally": 17658, + "▁grace": 17659, + "▁Campeonato": 17660, + "▁Christmas": 17661, + "middle": 17662, + "othek": 17663, + "elements": 17664, + "▁sondern": 17665, + "▁tarde": 17666, + "▁permanent": 17667, + "▁conclude": 17668, + "Seg": 17669, + "▁акаде": 17670, + "}\",": 17671, + "▁февраля": 17672, + "řed": 17673, + "▁IL": 17674, + "jud": 17675, + "▁USS": 17676, + "▁Nature": 17677, + "ifference": 17678, + "Serializer": 17679, + "▁twelve": 17680, + "tid": 17681, + "мия": 17682, + "ческого": 17683, + "▁calendar": 17684, + "concat": 17685, + "▁intersection": 17686, + "▁PA": 17687, + "azure": 17688, + "▁située": 17689, + "▁kinds": 17690, + "▁ausge": 17691, + "▁rural": 17692, + "Theme": 17693, + "▁tale": 17694, + "noindent": 17695, + "going": 17696, + "rx": 17697, + "agi": 17698, + "wrapper": 17699, + "▁Coast": 17700, + "mbH": 17701, + "▁перед": 17702, + "spre": 17703, + "▁}\\": 17704, + "▁LI": 17705, + "znam": 17706, + "itled": 17707, + "Sample": 17708, + "uliar": 17709, + "*\\": 17710, + "▁resistance": 17711, + "stock": 17712, + "ked": 17713, + "▁HE": 17714, + "▁possession": 17715, + "▁Ring": 17716, + "▁magyar": 17717, + "outs": 17718, + "▁Secretary": 17719, + "nde": 17720, + "▁Wald": 17721, + "-(": 17722, + "▁ISO": 17723, + "▁afternoon": 17724, + "ionen": 17725, + "▁stops": 17726, + "▁constants": 17727, + "guard": 17728, + "bow": 17729, + "▁ers": 17730, + "▁Firebase": 17731, + "▁Clear": 17732, + "▁Holy": 17733, + "Win": 17734, + "▁titles": 17735, + "▁трав": 17736, + "▁contrib": 17737, + "häng": 17738, + "▁photograph": 17739, + "▁Distribution": 17740, + "ifts": 17741, + "▁aunque": 17742, + "comb": 17743, + "ADD": 17744, + "▁publication": 17745, + "▁служ": 17746, + "▁кня": 17747, + "▁ayant": 17748, + "▁restore": 17749, + "▁belief": 17750, + "▁vég": 17751, + "▁extensions": 17752, + "▁decom": 17753, + "вший": 17754, + "WT": 17755, + "▁parti": 17756, + "▁gioc": 17757, + "▁мира": 17758, + "▁issu": 17759, + "pipe": 17760, + "▁props": 17761, + "▁willing": 17762, + "▁nest": 17763, + "aso": 17764, + "pot": 17765, + "▁handles": 17766, + "▁фо": 17767, + "▁moder": 17768, + "▁ebenfalls": 17769, + "▁fighting": 17770, + "umbn": 17771, + "▁transparent": 17772, + "▁Krist": 17773, + "▁homes": 17774, + "▁voyage": 17775, + "Failed": 17776, + "▁Bird": 17777, + "▁Heart": 17778, + "Counter": 17779, + "▁Scottish": 17780, + "ática": 17781, + "▁arbeit": 17782, + "^{-\\": 17783, + "▁Sor": 17784, + "▁engaged": 17785, + "▁aside": 17786, + "▁Fou": 17787, + "▁wiel": 17788, + "▁reconst": 17789, + "ousin": 17790, + "▁hosted": 17791, + "▁classe": 17792, + "▁contest": 17793, + "...\"": 17794, + "мом": 17795, + "▁bean": 17796, + "gem": 17797, + "▁consultato": 17798, + "▁bio": 17799, + "▁subjects": 17800, + "boBox": 17801, + "▁Schrift": 17802, + "▁dinner": 17803, + "ăr": 17804, + "▁równ": 17805, + "▁%%": 17806, + "bage": 17807, + "▁veröff": 17808, + "▁detected": 17809, + "ienn": 17810, + "rose": 17811, + "▁Ton": 17812, + "Complete": 17813, + "▁proto": 17814, + "ichts": 17815, + "STAT": 17816, + "Checked": 17817, + "▁inten": 17818, + "▁smile": 17819, + "▁strip": 17820, + "neut": 17821, + "');\r": 17822, + "four": 17823, + "▁todas": 17824, + "Controls": 17825, + "▁thorough": 17826, + "rup": 17827, + "▁држави": 17828, + "ită": 17829, + "Protocol": 17830, + "Ка": 17831, + "▁expanded": 17832, + "extra": 17833, + "oport": 17834, + "▁Станов": 17835, + "leases": 17836, + "▁notion": 17837, + "▁guest": 17838, + "▁Islands": 17839, + "icked": 17840, + "▁Dave": 17841, + "▁reflection": 17842, + "liv": 17843, + "ální": 17844, + "▁revealed": 17845, + "▁sog": 17846, + "▁Tax": 17847, + "▁periodo": 17848, + "▁Weltkrie": 17849, + "catalina": 17850, + "qué": 17851, + "▁Father": 17852, + "▁Bir": 17853, + "expect": 17854, + "▁regression": 17855, + "iné": 17856, + "▁dabei": 17857, + "perm": 17858, + "мене": 17859, + "▁Abd": 17860, + "▁CF": 17861, + "arks": 17862, + "resolve": 17863, + "wedge": 17864, + "▁initialization": 17865, + "▁Véase": 17866, + "▁приня": 17867, + "stmt": 17868, + "▁income": 17869, + "MY": 17870, + "▁odkazy": 17871, + "▁Siehe": 17872, + "▁bodies": 17873, + "▁soc": 17874, + "Random": 17875, + "▁senza": 17876, + "ablo": 17877, + "▁regarded": 17878, + "onCreate": 17879, + "▁Magazine": 17880, + "▁Raf": 17881, + "▁Buenos": 17882, + "ил": 17883, + ")));": 17884, + "capt": 17885, + "redirect": 17886, + "▁petit": 17887, + "▁farm": 17888, + "▁rôle": 17889, + "▁статьи": 17890, + "    ": 17891, + "subfigure": 17892, + "èces": 17893, + "ziel": 17894, + "▁окон": 17895, + "EE": 17896, + "mee": 17897, + "▁perten": 17898, + "▁représent": 17899, + "▁LA": 17900, + "?'": 17901, + "▁тру": 17902, + "▁rational": 17903, + "osof": 17904, + "▁kne": 17905, + "▁artists": 17906, + "Flow": 17907, + "▁Аль": 17908, + "izard": 17909, + "▁numero": 17910, + "actic": 17911, + "▁destruct": 17912, + "▁Пра": 17913, + "onsieur": 17914, + "qt": 17915, + "abestanden": 17916, + "ność": 17917, + "Connect": 17918, + "▁oracle": 17919, + "▁Stockholm": 17920, + "sizeof": 17921, + "▁gemäß": 17922, + "ACT": 17923, + "▁expert": 17924, + "utions": 17925, + "▁hacia": 17926, + "▁logger": 17927, + "▁fool": 17928, + "rypto": 17929, + "ær": 17930, + "▁cidade": 17931, + "▁составе": 17932, + "oker": 17933, + "▁Transfer": 17934, + "▁denied": 17935, + "Track": 17936, + "▁radi": 17937, + "zec": 17938, + "▁Historic": 17939, + "▁Einwohner": 17940, + "кою": 17941, + "▁хра": 17942, + "▁Category": 17943, + "▁Disney": 17944, + "▁swap": 17945, + "Begin": 17946, + "▁mientras": 17947, + "▁dance": 17948, + "▁tête": 17949, + "▁droit": 17950, + "erta": 17951, + "▁birds": 17952, + "▁convin": 17953, + "parator": 17954, + "дра": 17955, + "▁ES": 17956, + "▁Ressources": 17957, + "EGIN": 17958, + "ücke": 17959, + "▁Cruz": 17960, + "abling": 17961, + "▁\"@": 17962, + "▁metres": 17963, + "▁Beg": 17964, + "▁Gründ": 17965, + "▁Boh": 17966, + "▁mile": 17967, + "▁Technology": 17968, + "\"+": 17969, + "acco": 17970, + "▁ss": 17971, + "▁Fed": 17972, + "▁Hend": 17973, + "usch": 17974, + "itä": 17975, + "folk": 17976, + "▁absor": 17977, + "antal": 17978, + "odge": 17979, + "▁WHEN": 17980, + "▁Externí": 17981, + "▁Regiment": 17982, + "▁evaluation": 17983, + "▁Tai": 17984, + "▁vocals": 17985, + "▁experimental": 17986, + "embed": 17987, + "▁Minn": 17988, + "▁вме": 17989, + "prec": 17990, + "every": 17991, + "▁hoof": 17992, + "▁Fernando": 17993, + "▁Bibliographie": 17994, + "▁nag": 17995, + "amerikanischer": 17996, + "▁marks": 17997, + "▁UTC": 17998, + "▁uncertain": 17999, + "дия": 18000, + "olia": 18001, + "▁cup": 18002, + "▁fille": 18003, + "▁dok": 18004, + "useppe": 18005, + "esterd": 18006, + "▁Brand": 18007, + "▁Third": 18008, + "PP": 18009, + "nodes": 18010, + "▁Pad": 18011, + "▁loved": 18012, + "swing": 18013, + "▁surprised": 18014, + "ardi": 18015, + "▁GR": 18016, + "]\"": 18017, + "▁equally": 18018, + "ihe": 18019, + "care": 18020, + "писок": 18021, + "lijk": 18022, + "rinn": 18023, + "▁\\[\\": 18024, + "▁sons": 18025, + "▁tät": 18026, + "icamente": 18027, + "▁listing": 18028, + "iellement": 18029, + "▁nyelven": 18030, + "▁ds": 18031, + "▁agricult": 18032, + "▁Hermann": 18033, + "▁besides": 18034, + "progress": 18035, + "▁peculiar": 18036, + "focus": 18037, + "cn": 18038, + "-$": 18039, + "ственный": 18040, + "ourg": 18041, + "▁wyn": 18042, + "▁conducted": 18043, + "▁Становништво": 18044, + "connected": 18045, + "▁bott": 18046, + "▁смер": 18047, + "▁Poz": 18048, + "unct": 18049, + "conda": 18050, + "▁савезној": 18051, + "▁havet": 18052, + "ligt": 18053, + "orted": 18054, + "▁entering": 18055, + "multip": 18056, + "▁Temple": 18057, + "▁Plant": 18058, + "typeof": 18059, + "▁Vlad": 18060, + "▁qued": 18061, + "▁reste": 18062, + "▁май": 18063, + "▁Very": 18064, + "ambiguation": 18065, + "▁challeng": 18066, + "▁respective": 18067, + "▁тор": 18068, + "Ctrl": 18069, + "▁absence": 18070, + "aru": 18071, + "вое": 18072, + "▁först": 18073, + "▁sq": 18074, + "▁Emperor": 18075, + "▁Ign": 18076, + "▁това": 18077, + ":`": 18078, + "adoop": 18079, + "▁Madame": 18080, + "▁gruppo": 18081, + "stud": 18082, + "▁externas": 18083, + "▁Александр": 18084, + "▁dign": 18085, + "▁живе": 18086, + "Amount": 18087, + "▁correlate": 18088, + "▁Fant": 18089, + "▁rails": 18090, + "fp": 18091, + "министратив": 18092, + "▁bought": 18093, + "▁filters": 18094, + "▁ancora": 18095, + "▁partner": 18096, + "▁quand": 18097, + "symbol": 18098, + "ulating": 18099, + "▁zd": 18100, + "awn": 18101, + "▁Grant": 18102, + "because": 18103, + "rable": 18104, + "\\}": 18105, + "ísticas": 18106, + "▁уче": 18107, + "▁période": 18108, + "▁ske": 18109, + "▁Anyway": 18110, + "▁indexes": 18111, + "▁directions": 18112, + "▁RAM": 18113, + "chrome": 18114, + "▁apost": 18115, + "▁warnings": 18116, + "▁Airport": 18117, + "VI": 18118, + "abile": 18119, + "▁lord": 18120, + "provider": 18121, + "▁Ji": 18122, + "ostream": 18123, + "▁gemeente": 18124, + "tableView": 18125, + "Extra": 18126, + "cursor": 18127, + "eground": 18128, + "▁Moz": 18129, + "▁rib": 18130, + "▁morph": 18131, + "loads": 18132, + "elsk": 18133, + "▁MAX": 18134, + "▁Santiago": 18135, + "▁Him": 18136, + "codes": 18137, + "▁lanz": 18138, + "▁counts": 18139, + "rinningsområ": 18140, + "щё": 18141, + "▁spé": 18142, + "▁pierws": 18143, + "▁Sver": 18144, + "▁acknow": 18145, + "Boolean": 18146, + "▁фамили": 18147, + "▁Senate": 18148, + "шов": 18149, + "agers": 18150, + "▁Nueva": 18151, + "bil": 18152, + "kiem": 18153, + "▁Mey": 18154, + "wij": 18155, + "▁GmbH": 18156, + "validation": 18157, + "▁ensuite": 18158, + "inking": 18159, + "▁campion": 18160, + "▁financial": 18161, + "izon": 18162, + "Headers": 18163, + "▁deprecated": 18164, + "▁fonction": 18165, + "REG": 18166, + "▁volumes": 18167, + "▁Chi": 18168, + "▁encountered": 18169, + "lak": 18170, + "рая": 18171, + "▁continues": 18172, + "▁~[": 18173, + "uerte": 18174, + "▁\\;": 18175, + "▁Dok": 18176, + "▁weights": 18177, + "▁rh": 18178, + "▁Napole": 18179, + "▁naturally": 18180, + "sku": 18181, + "pas": 18182, + "▁gegründ": 18183, + "etr": 18184, + "▁Ku": 18185, + "icted": 18186, + "▁fabric": 18187, + "▁ASC": 18188, + "▁Entertainment": 18189, + "▁energ": 18190, + "клад": 18191, + "omon": 18192, + "theme": 18193, + "▁харак": 18194, + "▁draft": 18195, + "▁channels": 18196, + "▁desert": 18197, + "▁través": 18198, + "▁Lock": 18199, + "▁siendo": 18200, + "фек": 18201, + "même": 18202, + "▁packet": 18203, + 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"▁Références": 18578, + "▁ogni": 18579, + "▁curs": 18580, + "▁SK": 18581, + "они": 18582, + "▁destac": 18583, + "\");\r": 18584, + "▁Sure": 18585, + "▁partido": 18586, + "▁Folge": 18587, + "▁Moore": 18588, + "▁wz": 18589, + "скус": 18590, + "ltre": 18591, + "ondo": 18592, + "▁pose": 18593, + "imos": 18594, + "бой": 18595, + "ципа": 18596, + "jus": 18597, + ".....": 18598, + "▁época": 18599, + "▁quanto": 18600, + "▁Support": 18601, + "geschichte": 18602, + "SERVER": 18603, + "▁Georges": 18604, + "enum": 18605, + "▁herm": 18606, + "▁nebo": 18607, + "▁Chr": 18608, + "character": 18609, + "▁***": 18610, + "▁Forsch": 18611, + "iami": 18612, + "▁¿": 18613, + "cych": 18614, + "▁fifth": 18615, + "sent": 18616, + "▁anderem": 18617, + "▁proportion": 18618, + "▁prest": 18619, + "▁Girl": 18620, + "▁drama": 18621, + "wand": 18622, + "▁Mail": 18623, + "▁Lux": 18624, + "▁který": 18625, + "▁Gesellschaft": 18626, + "▁Hinweis": 18627, + "nisse": 18628, + "▁mondo": 18629, + "Eq": 18630, + "▁perí": 18631, 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"▁encode": 19750, + "Modal": 19751, + "▁forb": 19752, + "▁dynamics": 19753, + "▁depos": 19754, + "arde": 19755, + "▁streets": 19756, + "▁Komm": 19757, + "=$(": 19758, + "▁повер": 19759, + "▁dois": 19760, + "▁vitt": 19761, + "▁automatisch": 19762, + "▁reload": 19763, + "▁Verwalt": 19764, + "bero": 19765, + "▁hub": 19766, + "▁mos": 19767, + "▁tutto": 19768, + "▁Frederick": 19769, + "łow": 19770, + "antages": 19771, + "aque": 19772, + "paper": 19773, + "▁einige": 19774, + "`),": 19775, + "dj": 19776, + "▁Ple": 19777, + "▁%,": 19778, + "▁Bitmap": 19779, + "▁friendly": 19780, + "▁truly": 19781, + "▁stroke": 19782, + "roph": 19783, + "▁engl": 19784, + "▁coff": 19785, + "▁dust": 19786, + "▁Jahres": 19787, + "ppi": 19788, + "▁wys": 19789, + "factor": 19790, + "schluss": 19791, + "▁деревня": 19792, + "▁Past": 19793, + "▁дома": 19794, + "COM": 19795, + "▁pueden": 19796, + "▁gift": 19797, + "▁Gla": 19798, + "▁triggered": 19799, + "ély": 19800, + "ülés": 19801, + "▁Oliv": 19802, + "▁verso": 19803, 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"management": 21895, + "▁lod": 21896, + "uffle": 21897, + "Texture": 21898, + "arga": 21899, + "átum": 21900, + "▁DDR": 21901, + "нії": 21902, + "▁Société": 21903, + "▁domains": 21904, + "▁permitted": 21905, + "▁externe": 21906, + "▁quelque": 21907, + "vt": 21908, + "yman": 21909, + "▁Ward": 21910, + "▁agli": 21911, + "▁andra": 21912, + "Snapshot": 21913, + "▁må": 21914, + "▁yeah": 21915, + "дена": 21916, + "ępu": 21917, + "askell": 21918, + "▁République": 21919, + "inject": 21920, + "▁';": 21921, + "änn": 21922, + "▁zelf": 21923, + "▁Entwicklung": 21924, + "ária": 21925, + "onomy": 21926, + "▁svil": 21927, + "iese": 21928, + "▁conser": 21929, + "▁nim": 21930, + "▁rész": 21931, + "▁Итали": 21932, + "▁partici": 21933, + "▁Lion": 21934, + "sr": 21935, + "always": 21936, + "▁Владимир": 21937, + "ческие": 21938, + "[,": 21939, + "▁Definition": 21940, + "nant": 21941, + "oem": 21942, + "Ids": 21943, + "▁вне": 21944, + "▁[...]": 21945, + "▁направ": 21946, + "▁GO": 21947, + "▁års": 21948, + 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25745, + "▁Bootstrap": 25746, + "éné": 25747, + "▁derivatives": 25748, + "▁Detroit": 25749, + "▁provincial": 25750, + "onomie": 25751, + "EB": 25752, + "▁cuer": 25753, + "▁относи": 25754, + "▁ней": 25755, + ")».": 25756, + "▁Ciudad": 25757, + "IAL": 25758, + "zyst": 25759, + ")\")": 25760, + "▁Alc": 25761, + "blogs": 25762, + "▁parmi": 25763, + "▁Albums": 25764, + "▁Boliv": 25765, + "▁clés": 25766, + "Products": 25767, + "uerdo": 25768, + "▁gelang": 25769, + "znik": 25770, + "hagen": 25771, + "anonymous": 25772, + "▁svg": 25773, + "▁Conseil": 25774, + "▁Ari": 25775, + "coli": 25776, + "▁czy": 25777, + "▁CV": 25778, + "▁ford": 25779, + "▁Außer": 25780, + "▁CI": 25781, + "▁tempt": 25782, + "▁Organisation": 25783, + "áš": 25784, + "▁cycles": 25785, + "▁geslacht": 25786, + "▁людей": 25787, + "ými": 25788, + "▁Spieler": 25789, + "efe": 25790, + "▁Marvel": 25791, + "▁portal": 25792, + "▁Серг": 25793, + "▁grado": 25794, + "▁handlers": 25795, + "▁Interface": 25796, + "AME": 25797, + 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"CONFIG": 25903, + "▁quadratic": 25904, + "onneur": 25905, + "▁Giorg": 25906, + "▁identific": 25907, + "éricaine": 25908, + "▁UIView": 25909, + "▁Liberal": 25910, + "▁Koch": 25911, + "▁Berliner": 25912, + "▁notifications": 25913, + "▁Susan": 25914, + "▁cadre": 25915, + "▁Kloster": 25916, + "▁examine": 25917, + "▁един": 25918, + "▁UNION": 25919, + "▁alten": 25920, + "▁finit": 25921, + "▁pedig": 25922, + "cyk": 25923, + "▁mouvement": 25924, + "IOS": 25925, + "▁британ": 25926, + "▁bout": 25927, + "▁автор": 25928, + "ництво": 25929, + "ето": 25930, + "lera": 25931, + "cls": 25932, + "▁Ley": 25933, + "amy": 25934, + "agens": 25935, + "ashed": 25936, + "▁okrę": 25937, + "гро": 25938, + "ellett": 25939, + "▁Fellow": 25940, + "▁manifold": 25941, + "$),": 25942, + "lder": 25943, + "▁voz": 25944, + "▁begg": 25945, + "▁baron": 25946, + "▁fid": 25947, + "▁firing": 25948, + "ilda": 25949, + "dek": 25950, + "AU": 25951, + "itare": 25952, + "▁Ara": 25953, + "▁Exit": 25954, + "▁cinemat": 25955, + 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26166, + "sci": 26167, + "▁hibernate": 26168, + "dog": 26169, + "▁восто": 26170, + "▁intensity": 26171, + "legend": 26172, + "▁Wille": 26173, + "▁szerint": 26174, + "gesellschaft": 26175, + "▁Living": 26176, + "allo": 26177, + "▁Split": 26178, + "dru": 26179, + "need": 26180, + "▁Джон": 26181, + "▁Swiss": 26182, + "▁spraw": 26183, + "▁beho": 26184, + "▁fotograf": 26185, + "▁rencontre": 26186, + "▁kis": 26187, + "▁signing": 26188, + "akult": 26189, + "▁indexing": 26190, + "apor": 26191, + "▁conception": 26192, + "aggreg": 26193, + "▁Савез": 26194, + "▁affair": 26195, + "ění": 26196, + "August": 26197, + "▁секре": 26198, + "▁mieszkań": 26199, + "UIImage": 26200, + "▁bishop": 26201, + "▁servants": 26202, + "▁trail": 26203, + "digit": 26204, + "▁joins": 26205, + "▁Near": 26206, + "öffentlich": 26207, + ">{": 26208, + "▁skład": 26209, + "geführt": 26210, + "▁Holz": 26211, + "▁Militär": 26212, + "achi": 26213, + "Upper": 26214, + "pine": 26215, + "utzt": 26216, + "▁nuova": 26217, + "ibration": 26218, + "▁Bien": 26219, + "▁первый": 26220, + "▁Creating": 26221, + "Once": 26222, + "▁einmal": 26223, + "▁geometric": 26224, + "stvo": 26225, + "▁kW": 26226, + "▁decomposition": 26227, + "▁comedy": 26228, + "▁activation": 26229, + "▁angry": 26230, + "illeurs": 26231, + "▁instantly": 26232, + "▁suggesting": 26233, + "▁Clay": 26234, + "cot": 26235, + "▁Gén": 26236, + "($(": 26237, + "unwrap": 26238, + "▁lifted": 26239, + "▁Kit": 26240, + "▁linea": 26241, + "ок": 26242, + "hart": 26243, + "->_": 26244, + "▁nuit": 26245, + "▁Issue": 26246, + "лии": 26247, + "▁röm": 26248, + "Tasks": 26249, + "▁Sr": 26250, + "▁seis": 26251, + "asia": 26252, + "}}$.": 26253, + ":{": 26254, + "controls": 26255, + "▁Stim": 26256, + "▁Recht": 26257, + "ociación": 26258, + "▁Natal": 26259, + "▁Philippines": 26260, + "ulen": 26261, + "Fixed": 26262, + "▁switched": 26263, + "Zip": 26264, + "ospel": 26265, + "▁начале": 26266, + "▁Blan": 26267, + "urst": 26268, + "▁autour": 26269, + "Ca": 26270, + "▁latitude": 26271, + "▁Frei": 26272, + "▁Musée": 26273, + "▁Kurz": 26274, + "▁região": 26275, + "swap": 26276, + "▁hate": 26277, + "▁modifications": 26278, + "▁Ком": 26279, + "▁Antoine": 26280, + "uga": 26281, + "RECT": 26282, + "éter": 26283, + "GROUP": 26284, + "▁sacrific": 26285, + "▁Whe": 26286, + "▁Stevens": 26287, + "ologische": 26288, + "Summary": 26289, + "obs": 26290, + "hnen": 26291, + "<%=": 26292, + "dienst": 26293, + "remark": 26294, + "▁veröffentlicht": 26295, + "ел": 26296, + "▁Mock": 26297, + "▁Льв": 26298, + "▁três": 26299, + "gb": 26300, + "▁celebrated": 26301, + "▁Eb": 26302, + "▁costa": 26303, + "▁Geographic": 26304, + "▁attachment": 26305, + "mannschaft": 26306, + "▁dependence": 26307, + "��": 26308, + "▁attitude": 26309, + "etal": 26310, + "vic": 26311, + "baut": 26312, + "▁дов": 26313, + "▁interven": 26314, + "▁Gü": 26315, + "ónica": 26316, + "▁Pon": 26317, + "▁disponible": 26318, + "▁Feb": 26319, + "▁worship": 26320, + "▁Specifically": 26321, + "Hy": 26322, + "iju": 26323, + "▁cb": 26324, + "▁spac": 26325, + "leveland": 26326, + "▁localidad": 26327, + "▁preceding": 26328, + "▁Hessen": 26329, + "xp": 26330, + "▁Wein": 26331, + "▁Româ": 26332, + "▁giorno": 26333, + "▁квітня": 26334, + "llaços": 26335, + "▁Academia": 26336, + "▁kül": 26337, + "▁Års": 26338, + "▁нај": 26339, + "uclide": 26340, + "Internet": 26341, + "orton": 26342, + "▁corn": 26343, + "ями": 26344, + "▁\"*": 26345, + "▁Felix": 26346, + "apat": 26347, + "▁свои": 26348, + "MIT": 26349, + "made": 26350, + "▁locomot": 26351, + "хода": 26352, + "FP": 26353, + "▁pm": 26354, + ".*;": 26355, + "▁Hamm": 26356, + "`}": 26357, + "LayoutInflater": 26358, + "==\"": 26359, + "▁Eur": 26360, + "▁dogs": 26361, + "жении": 26362, + "▁azon": 26363, + "▁emulator": 26364, + "▁ricon": 26365, + "beeld": 26366, + "▁ну": 26367, + "▁approximate": 26368, + "LM": 26369, + "▁Bond": 26370, + "▁enh": 26371, + "ędz": 26372, + "▁solit": 26373, + "RelativeLayout": 26374, + "eteor": 26375, + "amentos": 26376, + "▁indirect": 26377, + "iből": 26378, + "▁gros": 26379, + "▁Originals": 26380, + "commands": 26381, + "Export": 26382, + "▁Avec": 26383, + "▁solemn": 26384, + "▁correction": 26385, + "▁проводи": 26386, + "▁Mosk": 26387, + "▁подо": 26388, + "▁gebied": 26389, + "▁następ": 26390, + "▁Driver": 26391, + "▁Ook": 26392, + "▁Vec": 26393, + "▁lungo": 26394, + "ficos": 26395, + "▁svol": 26396, + "▁kid": 26397, + "nja": 26398, + "▁Hr": 26399, + "▁поддер": 26400, + "▁visibility": 26401, + "▁Méd": 26402, + "▁cpu": 26403, + "discussion": 26404, + "Asset": 26405, + "▁defense": 26406, + "▁Anyone": 26407, + "▁Justin": 26408, + "iszt": 26409, + "▁Collins": 26410, + "▁Valent": 26411, + "▁Pale": 26412, + "▁fuel": 26413, + "▁nose": 26414, + "ríguez": 26415, + "▁Schles": 26416, + "▁Malays": 26417, + "▁commut": 26418, + "dro": 26419, + "uing": 26420, + "▁Rico": 26421, + "▁Emma": 26422, + "orp": 26423, + "▁Kirk": 26424, + "▁Quando": 26425, + "▁Neue": 26426, + "▁demande": 26427, + "▁Cover": 26428, + "▁rescue": 26429, + "▁gewählt": 26430, + "▁Calendar": 26431, + "▁Madonna": 26432, + "WP": 26433, + "oshi": 26434, + "▁Maven": 26435, + "▁belle": 26436, + "▁wx": 26437, + "▁sugar": 26438, + "▁Betrieb": 26439, + "▁equilibrium": 26440, + "EAR": 26441, + "▁texts": 26442, + "слов": 26443, + "▁czerwca": 26444, + "▁Düsseld": 26445, + "▁ELSE": 26446, + "▁amery": 26447, + "▁ani": 26448, + "▁obey": 26449, + "▁Nell": 26450, + "▁inne": 26451, + "▁тро": 26452, + "FD": 26453, + "cco": 26454, + "▁Zob": 26455, + "alette": 26456, + "▁május": 26457, + "ected": 26458, + "▁Turkey": 26459, + "▁Whether": 26460, + "qi": 26461, + "▁што": 26462, + "▁headquarters": 26463, + "endi": 26464, + "arus": 26465, + "opus": 26466, + "▁золо": 26467, + "▁destru": 26468, + "▁Lok": 26469, + "▁satisfaction": 26470, + "()\r": 26471, + "▁Тер": 26472, + "Jose": 26473, + "▁conquer": 26474, + "▁Effect": 26475, + "LayoutParams": 26476, + "iez": 26477, + "▁externs": 26478, + "▁gegenüber": 26479, + "▁ESP": 26480, + "olta": 26481, + "processor": 26482, + "▁Kult": 26483, + "▁Atlanta": 26484, + "▁tier": 26485, + "Operator": 26486, + "▁диа": 26487, + "▁пись": 26488, + "▁groß": 26489, + "▁hearts": 26490, + "▁millimeter": 26491, + "although": 26492, + "alles": 26493, + "▁Magic": 26494, + "training": 26495, + "oline": 26496, + "▁органі": 26497, + ">\\<^": 26498, + "ціаль": 26499, + "exports": 26500, + "Workbook": 26501, + "▁вересня": 26502, + "▁teles": 26503, + "▁economy": 26504, + "▁trap": 26505, + "▁refuse": 26506, + "▁stranger": 26507, + "▁instinct": 26508, + "пода": 26509, + "olan": 26510, + "▁ning": 26511, + "inflate": 26512, + "itatea": 26513, + "acks": 26514, + "▁Joy": 26515, + "FLAG": 26516, + "ailand": 26517, + "▁sorti": 26518, + "▁впер": 26519, + "▁pén": 26520, + "Nothing": 26521, + "▁száz": 26522, + "▁Áng": 26523, + "▁AUT": 26524, + "Actions": 26525, + "Every": 26526, + "▁червня": 26527, + "▁автомо": 26528, + "▁routine": 26529, + "▁estruct": 26530, + "▁Gang": 26531, + "▁holes": 26532, + "thesis": 26533, + "▁concl": 26534, + "▁pé": 26535, + "riers": 26536, + "ровой": 26537, + "adic": 26538, + "Speed": 26539, + "▁commanded": 26540, + "▁Nazionale": 26541, + "Managed": 26542, + "▁DECLARE": 26543, + "▁sedan": 26544, + "Strings": 26545, + "▁sacred": 26546, + "tersuch": 26547, + "▁abitanti": 26548, + "brit": 26549, + "▁NCAA": 26550, + "▁СП": 26551, + "▁aged": 26552, + "▁Chiesa": 26553, + "▁revision": 26554, + "opro": 26555, + "▁overwrite": 26556, + "embros": 26557, + "▁sortie": 26558, + "▁otten": 26559, + "xiv": 26560, + "▁deli": 26561, + "▁Asp": 26562, + "▁balls": 26563, + "kaf": 26564, + "▁brave": 26565, + "▁всего": 26566, + "egn": 26567, + "jpeg": 26568, + "▁Osten": 26569, + "Constants": 26570, + "▁Infantry": 26571, + "▁Nev": 26572, + "▁яких": 26573, + "▁муниципа": 26574, + "cija": 26575, + "▁poem": 26576, + "▁negro": 26577, + "хар": 26578, + "▁Ask": 26579, + "▁avo": 26580, + "▁Meyer": 26581, + "▁Westen": 26582, + "▁oko": 26583, + "agin": 26584, + "▁Süden": 26585, + "entries": 26586, + "▁Republik": 26587, + "CollectionView": 26588, + "-------": 26589, + "▁firefox": 26590, + "▁alcune": 26591, + "▁фото": 26592, + "▁отрима": 26593, + "~~~~~~~~": 26594, + "▁Раз": 26595, + "▁Complex": 26596, + "▁pia": 26597, + "▁publicada": 26598, + "wei": 26599, + "cedure": 26600, + "occupation": 26601, + "▁medicine": 26602, + "▁drove": 26603, + "Problem": 26604, + "▁beginner": 26605, + "▁thoroughly": 26606, + "uria": 26607, + "avant": 26608, + "ucha": 26609, + "▁lever": 26610, + "▁teatro": 26611, + "AVA": 26612, + "squ": 26613, + "trat": 26614, + "ivatal": 26615, + "▁dirty": 26616, + "▁seconde": 26617, + "▁gravit": 26618, + "▁proposition": 26619, + "hbar": 26620, + "omini": 26621, + "▁”": 26622, + "▁Camil": 26623, + "▁queen": 26624, + "modifier": 26625, + "Jan": 26626, + "▁lyr": 26627, + "ComboBox": 26628, + "ionic": 26629, + "▁holy": 26630, + "▁Sebastian": 26631, + "|_{": 26632, + "▁{@": 26633, + "▁можно": 26634, + "▁Creative": 26635, + "▁interess": 26636, + "▁CT": 26637, + "ições": 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"▁mutable": 26691, + "▁siempre": 26692, + "▁Montreal": 26693, + "▁locate": 26694, + "▁traces": 26695, + "▁insgesamt": 26696, + "▁Nil": 26697, + "▁прода": 26698, + "▁Warner": 26699, + "▁Nau": 26700, + "triangle": 26701, + "▁concentration": 26702, + "▁gentlemen": 26703, + "ächt": 26704, + "filters": 26705, + "incipal": 26706, + "VALID": 26707, + "▁депута": 26708, + "adó": 26709, + "▁konst": 26710, + "gså": 26711, + "agas": 26712, + "▁meilleur": 26713, + "▁данным": 26714, + "єдна": 26715, + "encoded": 26716, + "<'": 26717, + "▁sheets": 26718, + "cuador": 26719, + "▁використову": 26720, + "▁Deput": 26721, + "▁manière": 26722, + "ąg": 26723, + "csol": 26724, + ")$-": 26725, + "UIView": 26726, + "▁millones": 26727, + "▁Ehren": 26728, + "Sil": 26729, + "▁atac": 26730, + "▁Cold": 26731, + "\"\\": 26732, + "▁approached": 26733, + "▁Årsmed": 26734, + "WM": 26735, + "▁Deport": 26736, + "mis": 26737, + "andbox": 26738, + "observ": 26739, + "setting": 26740, + "ható": 26741, + "▁strat": 26742, + 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28422, + "▁profesor": 28423, + "▁repeatedly": 28424, + "▁invasion": 28425, + "programming": 28426, + "├──": 28427, + "▁Lip": 28428, + "вшие": 28429, + "▁keen": 28430, + "▁critics": 28431, + "▁Nicola": 28432, + "▁Cand": 28433, + "▁distint": 28434, + "▁heading": 28435, + "pragma": 28436, + "{|": 28437, + "ymen": 28438, + "▁terrain": 28439, + "iedenis": 28440, + "▁besonders": 28441, + "▁nominated": 28442, + "BOOL": 28443, + "▁Kay": 28444, + "cian": 28445, + "stelle": 28446, + "▁dispute": 28447, + "▁щ": 28448, + "DataSet": 28449, + "nothing": 28450, + "Autom": 28451, + "hören": 28452, + "▁shed": 28453, + "▁paused": 28454, + "san": 28455, + "▁nunca": 28456, + "!(\"": 28457, + "▁położ": 28458, + "Secret": 28459, + "▁Domain": 28460, + "▁возмож": 28461, + "XV": 28462, + "lv": 28463, + "ikh": 28464, + "▁Sony": 28465, + "mq": 28466, + "otrop": 28467, + "▁Logger": 28468, + "▁threat": 28469, + "asted": 28470, + "зько": 28471, + "▁freely": 28472, + "▁improvements": 28473, + "istema": 28474, + "▁illustrate": 28475, + "▁tact": 28476, + "▁figur": 28477, + "ués": 28478, + "riminal": 28479, + "odon": 28480, + "intendo": 28481, + "▁influenced": 28482, + "FFER": 28483, + "▁Ghost": 28484, + "▁совер": 28485, + "nad": 28486, + "ioned": 28487, + "▁Events": 28488, + "▁wrapping": 28489, + "---------+": 28490, + "fif": 28491, + "▁(**": 28492, + "={{": 28493, + "маль": 28494, + "▁losses": 28495, + "▁Galerie": 28496, + "tel": 28497, + "▁лютого": 28498, + "▁Kru": 28499, + "▁Polen": 28500, + "нім": 28501, + "near": 28502, + "▁shame": 28503, + "▁moyenne": 28504, + "▁CP": 28505, + "preis": 28506, + "▁passenger": 28507, + "lek": 28508, + "ionales": 28509, + "kafka": 28510, + "▁participe": 28511, + "▁membership": 28512, + "[_": 28513, + "lando": 28514, + "stelling": 28515, + "Sem": 28516, + "gon": 28517, + "▁Correct": 28518, + "▁valle": 28519, + "▁readily": 28520, + "▁Dokument": 28521, + "honneur": 28522, + "▁testim": 28523, + "ulative": 28524, + "doFilter": 28525, + "▁dominant": 28526, + "ammer": 28527, + "▁која": 28528, + "▁Monsieur": 28529, + "zeg": 28530, + "▁війни": 28531, + "▁Fo": 28532, + "▁Amy": 28533, + "▁¡": 28534, + "▁február": 28535, + "▁downloading": 28536, + "▁leng": 28537, + "\\}$,": 28538, + "▁neat": 28539, + "▁Cache": 28540, + "ICATION": 28541, + "▁deve": 28542, + "▁sorrow": 28543, + "slow": 28544, + "▁hinaus": 28545, + "▁reconoc": 28546, + "▁Linked": 28547, + "▁Shaw": 28548, + "market": 28549, + "▁Dic": 28550, + "▁Ski": 28551, + "▁delimiter": 28552, + "▁MainActivity": 28553, + "▁Musical": 28554, + "▁Reyn": 28555, + "ScrollView": 28556, + "▁conventional": 28557, + "ença": 28558, + "▁refactor": 28559, + "'-": 28560, + "▁Hed": 28561, + "sprech": 28562, + "▁athlet": 28563, + "▁especies": 28564, + "▁Schön": 28565, + "▁kleinen": 28566, + "шко": 28567, + "▁Йо": 28568, + "▁Happy": 28569, + "multirow": 28570, + "▁augusti": 28571, + "▁Gand": 28572, + "▁appointment": 28573, + "▁Mediabestanden": 28574, + "Three": 28575, + "▁Kenneth": 28576, + "NEW": 28577, + "▁Notification": 28578, + "▁Marx": 28579, + "▁insc": 28580, + "Mor": 28581, + "вый": 28582, + "väst": 28583, + "vidia": 28584, + "▁demonstrated": 28585, + "fonts": 28586, + "▁kamen": 28587, + "▁Ster": 28588, + "▁mieszkańców": 28589, + "▁Koh": 28590, + "~$\\": 28591, + "»).": 28592, + "rene": 28593, + "insic": 28594, + "ická": 28595, + "xygen": 28596, + "▁mn": 28597, + "▁sched": 28598, + "ASC": 28599, + "Ig": 28600, + "▁Constant": 28601, + "▁opportun": 28602, + "▁MyClass": 28603, + "sef": 28604, + "oped": 28605, + "▁injured": 28606, + "VIS": 28607, + "▁Pero": 28608, + "▁Until": 28609, + "▁flesh": 28610, + "orphism": 28611, + "▁Portal": 28612, + "▁gminy": 28613, + "▁власти": 28614, + "▁Nä": 28615, + "ктиче": 28616, + "▁hrab": 28617, + "▁Cub": 28618, + "avoir": 28619, + "▁Lars": 28620, + "▁Бело": 28621, + "▁seizoen": 28622, + "▁Genomsnitt": 28623, + "▁Lil": 28624, + "▁Pool": 28625, + "▁Dios": 28626, + "TX": 28627, + "aes": 28628, + "autore": 28629, + "Alpha": 28630, + "states": 28631, + "Lab": 28632, + "nederbörd": 28633, + "erton": 28634, + "▁brid": 28635, + "▁richt": 28636, + "▁Ela": 28637, + "▁сла": 28638, + "▁weapon": 28639, + "▁combatt": 28640, + "agar": 28641, + "▁regnig": 28642, + "▁utilisé": 28643, + "▁servir": 28644, + "▁brick": 28645, + "▁gateway": 28646, + "▁torraste": 28647, + "▁procedures": 28648, + "▁årsnederbörd": 28649, + "▁Genomsnittlig": 28650, + "чёт": 28651, + "▁områ": 28652, + "▁regnigaste": 28653, + "▁честь": 28654, + "▁amid": 28655, + "▁grateful": 28656, + "▁DIS": 28657, + "DAY": 28658, + "▁ору": 28659, + "▁rivière": 28660, + "heure": 28661, + "▁Richmond": 28662, + "▁Compar": 28663, + "▁Нор": 28664, + "DOC": 28665, + "esia": 28666, + "calc": 28667, + "▁IU": 28668, + "▁vorg": 28669, + "▁habían": 28670, + "çoit": 28671, + "▁arist": 28672, + "▁кли": 28673, + "▁Sue": 28674, + "▁Touch": 28675, + "▁Writing": 28676, + "ifiable": 28677, + "▁wc": 28678, + "▁withdraw": 28679, + "зар": 28680, + "▁presently": 28681, + "▁FK": 28682, + "▁prakt": 28683, + "▁colored": 28684, + "usb": 28685, + "▁Perú": 28686, + "▁plata": 28687, + "▁wishes": 28688, + "▁кам": 28689, + "azar": 28690, + "ável": 28691, + "▁lamp": 28692, + "bishop": 28693, + "▁inclusion": 28694, + "jq": 28695, + "arth": 28696, + "▁Flag": 28697, + "▁нор": 28698, + "ædia": 28699, + "UNCTION": 28700, + "▁Bahnhof": 28701, + "▁approaching": 28702, + "▁Gött": 28703, + "▁cube": 28704, + "▁argued": 28705, + "▁Things": 28706, + "Gui": 28707, + "дови": 28708, + "▁recre": 28709, + "▁réseau": 28710, + "▁significa": 28711, + "Git": 28712, + "gebracht": 28713, + "▁liga": 28714, + "▁assured": 28715, + "alus": 28716, + "рит": 28717, + "▁энциклопеди": 28718, + "▁%).": 28719, + "▁Première": 28720, + "▁declarations": 28721, + "▁tricky": 28722, + "▁profiles": 28723, + "▁Fon": 28724, + "▁Jas": 28725, + "âr": 28726, + "babel": 28727, + "▁Friday": 28728, + "▁június": 28729, + "▁cols": 28730, + "▁EXISTS": 28731, + "▁Italiana": 28732, + "▁authorization": 28733, + "▁sulle": 28734, + "▁Emb": 28735, + "▁Variable": 28736, + "trees": 28737, + "▁Fly": 28738, + "riors": 28739, + "▁damals": 28740, + "▁findet": 28741, + "▁Sept": 28742, + "▁mundial": 28743, + "▁removal": 28744, + "▁longitude": 28745, + "clic": 28746, + "▁fade": 28747, + "▁gradle": 28748, + "▁zák": 28749, + "▁timing": 28750, + "trightarrow": 28751, + "atia": 28752, + "-.": 28753, + "uche": 28754, + "▁serialize": 28755, + "▁Hmm": 28756, + "▁Representatives": 28757, + "bah": 28758, + "rend": 28759, + "assador": 28760, + "▁shield": 28761, + "ucion": 28762, + "▁américaine": 28763, + "zę": 28764, + "villa": 28765, + "▁hombre": 28766, + "áss": 28767, + "▁SF": 28768, + "▁repeating": 28769, + "▁criter": 28770, + "▁Struct": 28771, + "???": 28772, + "▁cheap": 28773, + "▁rings": 28774, + "abhäng": 28775, + "▁corte": 28776, + "▁administ": 28777, + "ixon": 28778, + "gypt": 28779, + "▁puntos": 28780, + "▁mezi": 28781, + "▁pochod": 28782, + "isko": 28783, + "nię": 28784, + "▁осу": 28785, + "▁ár": 28786, + "тельной": 28787, + "▁Metropolitan": 28788, + "jin": 28789, + "zess": 28790, + "▁віці": 28791, + "▁conflicts": 28792, + "ijst": 28793, + "▁Market": 28794, + "стров": 28795, + "▁\",\"": 28796, + "▁Scroll": 28797, + "gun": 28798, + "тара": 28799, + "▁amateur": 28800, + "▁róż": 28801, + "poss": 28802, + "▁generalized": 28803, + "▁Harm": 28804, + "cita": 28805, + "▁Switzerland": 28806, + "icola": 28807, + "▁muit": 28808, + "located": 28809, + "▁có": 28810, + "▁arose": 28811, + "▁communauté": 28812, + "})^": 28813, + "visibility": 28814, + "ída": 28815, + "▁FB": 28816, + "▁Freund": 28817, + "gat": 28818, + "\":{\"": 28819, + "intellij": 28820, + "ifie": 28821, + "hmen": 28822, + "▁édition": 28823, + "▁које": 28824, + "▁інших": 28825, + "oming": 28826, + "▁arquitect": 28827, + "▁Presidente": 28828, + "▁Під": 28829, + "▁cabin": 28830, + "Theorem": 28831, + "▁Gay": 28832, + "ifice": 28833, + "▁hect": 28834, + "lą": 28835, + "irmingham": 28836, + "▁semantic": 28837, + "▁Louisiana": 28838, + "▁sacrifice": 28839, + "▁Christoph": 28840, + "▁Executive": 28841, + "_+": 28842, + "ják": 28843, + "▁seria": 28844, + "▁Overflow": 28845, + "▁Lucy": 28846, + "▁melhor": 28847, + "▁voices": 28848, + "cza": 28849, + "▁капи": 28850, + "▁университета": 28851, + "INCT": 28852, + "▁coloc": 28853, + "▁prue": 28854, + "▁geomet": 28855, + "▁diretto": 28856, + "reso": 28857, + "▁Akt": 28858, + "▁unh": 28859, + "▁сери": 28860, + "▁Alert": 28861, + "Wel": 28862, + "audi": 28863, + "äler": 28864, + "▁guests": 28865, + "▁иде": 28866, + "Studio": 28867, + "▁кате": 28868, + "▁exponent": 28869, + "rze": 28870, + "pmod": 28871, + "rolle": 28872, + "▁Limited": 28873, + "Allemagne": 28874, + "▁pity": 28875, + "▁lä": 28876, + "▁runner": 28877, + "kende": 28878, + "EQ": 28879, + "▁MM": 28880, + "szág": 28881, + "поді": 28882, + "▁regret": 28883, + "▁publié": 28884, + "▁departamento": 28885, + "▁accused": 28886, + "hp": 28887, + "▁Pfl": 28888, + "▁Sint": 28889, + "▁ekonom": 28890, + "ractor": 28891, + "▁Пів": 28892, + "▁awful": 28893, + "ować": 28894, + "]->": 28895, + "▁Fine": 28896, + "Са": 28897, + "tis": 28898, + "éta": 28899, + "▁Роди": 28900, + "▁Düsseldorf": 28901, + "LOB": 28902, + "osas": 28903, + "werke": 28904, + "▁lance": 28905, + "▁листопада": 28906, + "▁incomplete": 28907, + "▁Picture": 28908, + "('\\": 28909, + "esters": 28910, + "▁belonged": 28911, + "▁Sank": 28912, + "ammed": 28913, + "▁repositories": 28914, + "▁addr": 28915, + "Collect": 28916, + "Hot": 28917, + "▁tyl": 28918, + "▁instanceof": 28919, + "▁bonus": 28920, + "ový": 28921, + "▁моря": 28922, + "▁interactive": 28923, + "▁Mys": 28924, + "▁Edmund": 28925, + "fileName": 28926, + "emor": 28927, + "▁Три": 28928, + "▁Rosen": 28929, + "▁Prima": 28930, + "▁voting": 28931, + "▁XP": 28932, + "▁Zero": 28933, + "▁Led": 28934, + "amsung": 28935, + "▁enables": 28936, + "▁redirects": 28937, + "AST": 28938, + "Paint": 28939, + "acker": 28940, + "lecht": 28941, + "▁chairman": 28942, + "▁Aven": 28943, + "▁Sach": 28944, + "(\"<": 28945, + "кер": 28946, + "▁mistakes": 28947, + "▁Weit": 28948, + "▁prowad": 28949, + "▁didnt": 28950, + "énario": 28951, + "unless": 28952, + "▁backwards": 28953, + "boa": 28954, + "duino": 28955, + "```": 28956, + "stor": 28957, + "Completion": 28958, + "puesta": 28959, + "▁dinast": 28960, + "últ": 28961, + "▁SY": 28962, + "ifolia": 28963, + "œuvres": 28964, + "▁racing": 28965, + "▁cabinet": 28966, + "▁cutting": 28967, + "▁thumb": 28968, + "▁Кара": 28969, + "highlight": 28970, + "куп": 28971, + "▁sd": 28972, + "▁національ": 28973, + "▁campagne": 28974, + "▁registers": 28975, + "▁educational": 28976, + "▁pesar": 28977, + "üge": 28978, + "▁oro": 28979, + "burgo": 28980, + "▁Athletics": 28981, + "▁MTV": 28982, + "getMessage": 28983, + "▁Hyp": 28984, + "▁victim": 28985, + "))\\": 28986, + "▁drums": 28987, + "hostname": 28988, + "tał": 28989, + "making": 28990, + "▁powiat": 28991, + "őd": 28992, + "threads": 28993, + "▁absolv": 28994, + "▁люди": 28995, + "▁stepped": 28996, + "exist": 28997, + "▁NK": 28998, + "▁ves": 28999, + "istiche": 29000, + "%'": 29001, + "ativos": 29002, + "▁такой": 29003, + "▁MongoDB": 29004, + "▁Ung": 29005, + "▁Рус": 29006, + "▁elim": 29007, + "▁Fif": 29008, + "icación": 29009, + "▁Tennis": 29010, + "▁Jefferson": 29011, + "ján": 29012, + "fog": 29013, + "anha": 29014, + "zor": 29015, + "▁університе": 29016, + "ahu": 29017, + "iada": 29018, + "Sdk": 29019, + "Setting": 29020, + "▁Kill": 29021, + "▁Wend": 29022, + "▁bald": 29023, + "▁Kub": 29024, + "▁visto": 29025, + "▁jeunes": 29026, + "collections": 29027, + "ací": 29028, + "вропей": 29029, + "▁arise": 29030, + "оні": 29031, + "MAIN": 29032, + "доступ": 29033, + "▁berg": 29034, + "▁criticism": 29035, + "▁Torre": 29036, + "▁descript": 29037, + "ières": 29038, + "▁estudio": 29039, + "▁ili": 29040, + "▁militare": 29041, + "▁Clara": 29042, + "▁Ellen": 29043, + "limited": 29044, + "лм": 29045, + "▁Españ": 29046, + "▁infinitely": 29047, + "America": 29048, + "ouc": 29049, + "glass": 29050, + "▁rud": 29051, + "▁zat": 29052, + "▁rin": 29053, + "▁Bibliografía": 29054, + "▁merchant": 29055, + "tensorflow": 29056, + "▁dér": 29057, + "▁ActiveRecord": 29058, + "IES": 29059, + "▁linker": 29060, + "▁estudios": 29061, + "cdnjs": 29062, + "▁Государ": 29063, + "ánchez": 29064, + "appe": 29065, + "club": 29066, + "▁další": 29067, + "▁Algorithm": 29068, + "dfs": 29069, + "▁Bac": 29070, + "▁кафе": 29071, + "▁&=\\": 29072, + "▁ат": 29073, + "▁Глав": 29074, + "▁Mou": 29075, + "Machine": 29076, + "(...)": 29077, + "▁compart": 29078, + "▁augusztus": 29079, + "avan": 29080, + "▁rolled": 29081, + "▁еди": 29082, + "Scan": 29083, + "▁регі": 29084, + "▁świata": 29085, + "▁mines": 29086, + "},{": 29087, + "▁Tier": 29088, + "Cannot": 29089, + "мін": 29090, + "▁NEW": 29091, + "▁Вол": 29092, + "▁Manh": 29093, + "▁Gregory": 29094, + "▁principe": 29095, + "ISO": 29096, + "prog": 29097, + "▁Fail": 29098, + "▁aa": 29099, + "▁fecha": 29100, + "▁WCF": 29101, + "▁magistr": 29102, + "▁Zach": 29103, + "▁unicode": 29104, + 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30903, + "五": 30904, + "چ": 30905, + "로": 30906, + "ョ": 30907, + "重": 30908, + "于": 30909, + "发": 30910, + "史": 30911, + "ظ": 30912, + "ช": 30913, + "え": 30914, + "國": 30915, + "ĭ": 30916, + "ப": 30917, + "인": 30918, + "你": 30919, + "駅": 30920, + "‒": 30921, + "♥": 30922, + "多": 30923, + "ħ": 30924, + "Қ": 30925, + "ồ": 30926, + "士": 30927, + "四": 30928, + "┴": 30929, + "ம": 30930, + "司": 30931, + "ে": 30932, + "ὰ": 30933, + "∂": 30934, + "╬": 30935, + "次": 30936, + "Ľ": 30937, + "⟶": 30938, + "立": 30939, + "点": 30940, + "音": 30941, + "⠀": 30942, + "器": 30943, + "하": 30944, + "井": 30945, + "存": 30946, + "ֹ": 30947, + "当": 30948, + "Ë": 30949, + "★": 30950, + "寺": 30951, + "性": 30952, + "也": 30953, + "め": 30954, + "だ": 30955, + "位": 30956, + "ങ": 30957, + "ہ": 30958, + "值": 30959, + "古": 30960, + "გ": 30961, + "ব": 30962, + "院": 30963, + "േ": 30964, + "▶": 30965, + "ர": 30966, + "界": 30967, + "語": 30968, + "സ": 30969, + "수": 30970, + "ǒ": 30971, + "愛": 30972, + "✔": 30973, + "時": 30974, + "ọ": 30975, + "റ": 30976, + "մ": 30977, + "ケ": 30978, + "东": 30979, + "同": 30980, + "주": 30981, + "保": 30982, + "Õ": 30983, + "ố": 30984, + "ἰ": 30985, + "青": 30986, + "ゴ": 30987, + "体": 30988, + "清": 30989, + "相": 30990, + "จ": 30991, + "ء": 30992, + "情": 30993, + "𝕜": 30994, + "ক": 30995, + "ḫ": 30996, + "ờ": 30997, + "将": 30998, + "族": 30999, + "동": 31000, + "Υ": 31001, + "┌": 31002, + "ボ": 31003, + "宮": 31004, + "』": 31005, + "ম": 31006, + "『": 31007, + "ļ": 31008, + "श": 31009, + "ป": 31010, + "Ա": 31011, + "ब": 31012, + "자": 31013, + "政": 31014, + "ா": 31015, + "间": 31016, + "fi": 31017, + "松": 31018, + "ṃ": 31019, + "始": 31020, + "息": 31021, + "少": 31022, + "教": 31023, + "获": 31024, + "列": 31025, + "开": 31026, + "ტ": 31027, + "ワ": 31028, + "კ": 31029, + "科": 31030, + "春": 31031, + "治": 31032, + "吉": 31033, + "ས": 31034, + "ศ": 31035, + "ɒ": 31036, + "台": 31037, + "ネ": 31038, + "း": 31039, + "ĩ": 31040, + "工": 31041, + "ά": 31042, + "知": 31043, + "八": 31044, + "場": 31045, + "画": 31046, + "百": 31047, + "☆": 31048, + "記": 31049, + "得": 31050, + "ソ": 31051, + "氏": 31052, + "ာ": 31053, + "에": 31054, + "ল": 31055, + "ṛ": 31056, + "关": 31057, + "ġ": 31058, + "έ": 31059, + "∑": 31060, + "ベ": 31061, + "标": 31062, + "니": 31063, + "ὴ": 31064, + "ֵ": 31065, + "外": 31066, + "♠": 31067, + "わ": 31068, + "間": 31069, + "ภ": 31070, + "校": 31071, + "制": 31072, + "แ": 31073, + "力": 31074, + "門": 31075, + "好": 31076, + "ғ": 31077, + "Ù": 31078, + "ℓ": 31079, + "ֶ": 31080, + "는": 31081, + "┐": 31082, + "∗": 31083, + "指": 31084, + "色": 31085, + "返": 31086, + "馬": 31087, + "请": 31088, + "≫": 31089, + "風": 31090, + "ό": 31091, + "接": 31092, + "서": 31093, + "↳": 31094, + "せ": 31095, + "志": 31096, + "̲": 31097, + "魔": 31098, + "ң": 31099, + "更": 31100, + "程": 31101, + "김": 31102, + "郡": 31103, + "ོ": 31104, + "ũ": 31105, + "ച": 31106, + "利": 31107, + "県": 31108, + "周": 31109, + "そ": 31110, + "や": 31111, + "谷": 31112, + "香": 31113, + "♯": 31114, + "じ": 31115, + "،": 31116, + "期": 31117, + "∅": 31118, + "┘": 31119, + "初": 31120, + "福": 31121, + "片": 31122, + "ザ": 31123, + "動": 31124, + "参": 31125, + "성": 31126, + "Ə": 31127, + "╦": 31128, + "어": 31129, + "ხ": 31130, + "義": 31131, + "च": 31132, + "象": 31133, + "功": 31134, + "♂": 31135, + "도": 31136, + "고": 31137, + "过": 31138, + "վ": 31139, + "皇": 31140, + "特": 31141, + "ậ": 31142, + "长": 31143, + "英": 31144, + "ấ": 31145, + "ണ": 31146, + "Ъ": 31147, + "স": 31148, + "其": 31149, + "ত": 31150, + "流": 31151, + "除": 31152, + "일": 31153, + "ু": 31154, + "្": 31155, + "永": 31156, + "直": 31157, + "상": 31158, + "千": 31159, + "ắ": 31160, + "館": 31161, + "Ť": 31162, + "朝": 31163, + "ட": 31164, + "ɣ": 31165, + "单": 31166, + "ʀ": 31167, + "格": 31168, + "德": 31169, + "전": 31170, + "☺": 31171, + "ピ": 31172, + "歌": 31173, + "进": 31174, + "限": 31175, + "夫": 31176, + "트": 31177, + "⊢": 31178, + "園": 31179, + "量": 31180, + "土": 31181, + "放": 31182, + "码": 31183, + "等": 31184, + "系": 31185, + "∼": 31186, + "華": 31187, + "↵": 31188, + "소": 31189, + "常": 31190, + "否": 31191, + "見": 31192, + "源": 31193, + "ׁ": 31194, + "实": 31195, + "博": 31196, + "라": 31197, + "원": 31198, + "보": 31199, + "⊕": 31200, + "解": 31201, + "〜": 31202, + "男": 31203, + "দ": 31204, + "ポ": 31205, + "ろ": 31206, + "나": 31207, + "ག": 31208, + "無": 31209, + "Û": 31210, + "̥": 31211, + "ұ": 31212, + "查": 31213, + "̣": 31214, + "╗": 31215, + "╩": 31216, + "条": 31217, + "য": 31218, + "ὁ": 31219, + "後": 31220, + "他": 31221, + "网": 31222, + "ல": 31223, + "≃": 31224, + "화": 31225, + "ە": 31226, + "阿": 31227, + "ေ": 31228, + "户": 31229, + "∫": 31230, + "구": 31231, + "ར": 31232, + "မ": 31233, + "▸": 31234, + "լ": 31235, + "○": 31236, + "命": 31237, + "就": 31238, + "龍": 31239, + "君": 31240, + "夏": 31241, + "": 31242, + "言": 31243, + "先": 31244, + "➜": 31245, + "შ": 31246, + "ძ": 31247, + "ਾ": 31248, + "வ": 31249, + "ど": 31250, + "ヒ": 31251, + "ไ": 31252, + "ன": 31253, + "ば": 31254, + "ギ": 31255, + "գ": 31256, + "ἄ": 31257, + "ヤ": 31258, + "典": 31259, + "府": 31260, + "̄": 31261, + "신": 31262, + "组": 31263, + "改": 31264, + "ὲ": 31265, + "华": 31266, + "与": 31267, + "调": 31268, + "╝": 31269, + "ヴ": 31270, + "ქ": 31271, + "由": 31272, + "修": 31273, + "學": 31274, + "♣": 31275, + "消": 31276, + "符": 31277, + "ʌ": 31278, + "부": 31279, + "ớ": 31280, + "‾": 31281, + "▲": 31282, + "录": 31283, + "ള": 31284, + "연": 31285, + "을": 31286, + "ひ": 31287, + "영": 31288, + "┤": 31289, + "已": 31290, + "陽": 31291, + "င": 31292, + "국": 31293, + "容": 31294, + "未": 31295, + "宗": 31296, + "ᴇ": 31297, + "び": 31298, + "장": 31299, + "龙": 31300, + "්": 31301, + "提": 31302, + "ĝ": 31303, + "六": 31304, + "形": 31305, + "제": 31306, + "Հ": 31307, + "伊": 31308, + "ϵ": 31309, + "ข": 31310, + "Ű": 31311, + "ゃ": 31312, + "火": 31313, + "Ṣ": 31314, + "佐": 31315, + "⊥": 31316, + "̪": 31317, + "ứ": 31318, + "□": 31319, + "结": 31320, + "九": 31321, + "雄": 31322, + "թ": 31323, + "ា": 31324, + "而": 31325, + "བ": 31326, + "우": 31327, + "张": 31328, + "ट": 31329, + "ष": 31330, + "向": 31331, 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": 31353, + "은": 31354, + "ʷ": 31355, + "那": 31356, + "線": 31357, + "务": 31358, + "基": 31359, + "属": 31360, + "配": 31361, + "미": 31362, + "軍": 31363, + "โ": 31364, + "津": 31365, + "完": 31366, + "研": 31367, + "注": 31368, + "失": 31369, + "应": 31370, + "က": 31371, + "╚": 31372, + "友": 31373, + "章": 31374, + "Ψ": 31375, + "求": 31376, + "ण": 31377, + "경": 31378, + "‬": 31379, + "भ": 31380, + "们": 31381, + "模": 31382, + "需": 31383, + "ச": 31384, + "電": 31385, + "প": 31386, + "դ": 31387, + "へ": 31388, + "此": 31389, + "夜": 31390, + "或": 31391, + "橋": 31392, + "根": 31393, + "Ī": 31394, + "玉": 31395, + "ู": 31396, + "ṅ": 31397, + "交": 31398, + "品": 31399, + "良": 31400, + "ང": 31401, + "ォ": 31402, + "则": 31403, + "開": 31404, + "Ζ": 31405, + "문": 31406, + "被": 31407, + "조": 31408, + "株": 31409, + "记": 31410, + "會": 31411, + "经": 31412, + "ू": 31413, + "ょ": 31414, + "转": 31415, + "崎": 31416, + "마": 31417, + "⌘": 31418, + "比": 31419, + "造": 31420, + "ܐ": 31421, + "ื": 31422, + "没": 31423, + "现": 31424, + "七": 31425, + "Ά": 31426, + "商": 31427, + "ை": 31428, + "机": 31429, + "阳": 31430, + "ĉ": 31431, + "角": 31432, + "站": 31433, + "բ": 31434, + "해": 31435, + "及": 31436, + "ध": 31437, + "術": 31438, + "认": 31439, + "‘": 31440, + "创": 31441, + "編": 31442, + "ղ": 31443, + "ḩ": 31444, + "伝": 31445, + "岡": 31446, + "ड": 31447, + "ホ": 31448, + "港": 31449, + "任": 31450, + "登": 31451, + "ི": 31452, + "็": 31453, + "布": 31454, + "究": 31455, + "帝": 31456, + "여": 31457, + "산": 31458, + "န": 31459, + "◦": 31460, + "密": 31461, + "变": 31462, + "序": 31463, + "♀": 31464, + "∣": 31465, + "计": 31466, + "曲": 31467, + "Ă": 31468, + "ύ": 31469, + "ʋ": 31470, + "传": 31471, + "】": 31472, + "包": 31473, + "意": 31474, + "去": 31475, + "沙": 31476, + "⸮": 31477, + "【": 31478, + "写": 31479, + "超": 31480, + "ய": 31481, + "今": 31482, + "┈": 31483, + "森": 31484, + "ි": 31485, + "⊗": 31486, + "비": 31487, + "հ": 31488, + "Ḩ": 31489, + "ǫ": 31490, + "黄": 31491, + "∙": 31492, + "드": 31493, + "🌍": 31494, + "景": 31495, 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31567, + "视": 31568, + "秋": 31569, + "因": 31570, + "년": 31571, + "ے": 31572, + "输": 31573, + "̱": 31574, + "Մ": 31575, + "∆": 31576, + "康": 31577, + "세": 31578, + "思": 31579, + "死": 31580, + "聖": 31581, + "민": 31582, + "－": 31583, + "头": 31584, + "ർ": 31585, + "∉": 31586, + "車": 31587, + "┃": 31588, + "▇": 31589, + "按": 31590, + "⍵": 31591, + "夢": 31592, + "汉": 31593, + "从": 31594, + "ী": 31595, + "题": 31596, + "ˆ": 31597, + "ἡ": 31598, + "展": 31599, + "省": 31600, + "ུ": 31601, + "葉": 31602, + "호": 31603, + "ਰ": 31604, + "素": 31605, + "関": 31606, + "그": 31607, + "；": 31608, + "න": 31609, + "页": 31610, + "共": 31611, + "宿": 31612, + "态": 31613, + "ན": 31614, + "技": 31615, + "乐": 31616, + "控": 31617, + "移": 31618, + "影": 31619, + "ụ": 31620, + "ゆ": 31621, + "ご": 31622, + "್": 31623, + "管": 31624, + "ൾ": 31625, + "╣": 31626, + "戸": 31627, + "⇔": 31628, + "函": 31629, + "ẓ": 31630, + "尾": 31631, + "场": 31632, + "介": 31633, + "": 31634, + "育": 31635, + "ර": 31636, + "泉": 31637, + "ൽ": 31638, 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"content": "<|mask_97|>", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false, + "special": false + }, + "32101": { + "content": "<|mask_98|>", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false, + "special": false + }, + "32102": { + "content": "<|mask_99|>", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false, + "special": false + } + }, + "bos_token": "", + "clean_up_tokenization_spaces": false, + "eos_token": "", + "extra_special_tokens": {}, + "legacy": true, + "model_max_length": 1000000000000000019884624838656, + "tokenizer_class": "LlamaTokenizerFast", + "unk_token": "", + "use_default_system_prompt": false +} diff --git a/src/levanter/compat/hf_checkpoints.py b/src/levanter/compat/hf_checkpoints.py index 5bdc5add7..4b02771fb 100644 --- a/src/levanter/compat/hf_checkpoints.py +++ b/src/levanter/compat/hf_checkpoints.py @@ -680,6 +680,9 @@ def load_pretrained( tokenizer_Vocab = self.Vocab Vocab = tokenizer_Vocab.resize(hf_config.vocab_size) + # print(f"tokenizer_Vocab.size={tokenizer_Vocab.size}") + # print(f"hf_config.vocab_size={hf_config.vocab_size}") + # TODO: in an ideal world, we would only load the part of the array we needed, but # AFAICT neither torch state dicts nor safetensors support this. state_dict = self.load_state_dict(ref, dtype) diff --git a/src/levanter/data/packing.py b/src/levanter/data/packing.py index 192ac5edb..ffabf6255 100644 --- a/src/levanter/data/packing.py +++ b/src/levanter/data/packing.py @@ -432,7 +432,7 @@ def pack_documents( class GreedyPrepackedDataset(AsyncDataset[tuple[T, T]]): """ - Prepacks a dataset into a new dataset where examples are packed into a single example. + Prepacks a dataset into a new dataset where a contiguous range of examples is packed into a single example. As per usual, I can't help but make this generic. @@ -454,6 +454,7 @@ def __init__( max_segments_per_example: int | None = None, pad_with_zeros: bool = True, slice_strategy: Literal["left", "right", "raise"] = "raise", + packing_lengths: PyTree[np.ndarray] | None = None, ): """ Args: @@ -462,6 +463,16 @@ def __init__( max_segments_per_example: Maximum number of documents that can be packed into a single example. pad_with_zeros: If True, pad examples to max_length with zeros. If False, return examples as-is. slice_strategy: One of "left", "right", or "raise". Determines how to handle examples that exceed max_length. + packing_lengths: An (optional) PyTree of numpy arrays. Each array should be of + length `n_docs`, the number of documents. + If supplied, when computing whether we can pack the i-th document into a batch, we + will use the length `packing_lengths[j][i]` instead of the true length. + If None, we just use the true lengths computed from `offsets`. + Added for UL2R because when we construct denoising data the length of each example + increases by an independent amount. But we want to turn one batch of original data + into one batch of denoising data so we need to reserve some space in the batch. + We DO NOT insert padding corresponding to `packing_lengths`; documents are still + contiguous. """ super().__init__() @@ -477,14 +488,19 @@ def __init__( _offsets = jax.tree.map(lambda store: store.offsets[0 : store.num_rows + 1].read(), self.dataset) self._offsets = jax.tree.map(lambda fut: fut.result(), _offsets) - def diff_offsets(offsets: np.ndarray): - # fine to mutate since we have a copy - # the array store has the number of rows in the 0th offset - offsets[0] = 0 - return offsets[1:] - offsets[:-1] - # Convert offsets to lengths - self._lengths = jax.tree.map(diff_offsets, self._offsets) + if packing_lengths is not None: + self._lengths = packing_lengths + else: + def diff_offsets(offsets: np.ndarray): + # fine to mutate since we have a copy + # the array store has the number of rows in the 0th offset + offsets[0] = 0 + return offsets[1:] - offsets[:-1] + + # Convert offsets to lengths + self._lengths = jax.tree.map(diff_offsets, self._offsets) + # Build pack indices self._pack_indices: list[range] = pack_documents( @@ -513,11 +529,13 @@ async def get_batch(self, indices: Sequence[int]) -> Sequence[tuple[PyTree[np.nd Returns a list of tuples (data, segment_ids), where each is a PyTree (with the same structure as self.dataset), and each leaf is a numpy array representing the data or segment IDs for that packed example. + + Segment IDs index into the original `dataset`. """ pack_doc_ranges = [self._pack_indices[i] for i in indices] - async def get_data_for_leaf(store, offsets, allowed: int) -> tuple[list[np.ndarray], list[np.ndarray]]: + async def get_data_for_leaf(store, offsets, lengths, allowed: int) -> tuple[list[np.ndarray], list[np.ndarray]]: out_data = [] out_segment_ids = [] # Using ts.Batch to group reads. @@ -577,7 +595,7 @@ async def get_data_for_leaf(store, offsets, allowed: int) -> tuple[list[np.ndarr # Use tree.map to combine the leaves from: dataset, max_length and, for each pack, its doc_range. # Note: jax.tree.map will map over each pack in parallel across the leaves. max_length_tree = tree_broadcast_to(self.max_length, self._offsets) - leaf_batch_futures = jax.tree.map(get_data_for_leaf, self.dataset, self._offsets, max_length_tree) + leaf_batch_futures = jax.tree.map(get_data_for_leaf, self.dataset, self._offsets, self._lengths, max_length_tree) # Flatten the resulting PyTree: each leaf is now an Awaitable returning a tuple of lists of np.ndarray—one per requested pack. leaves, treedef = jax.tree.flatten(leaf_batch_futures) diff --git a/src/levanter/data/text.py b/src/levanter/data/text.py index 71027722a..825daae5e 100644 --- a/src/levanter/data/text.py +++ b/src/levanter/data/text.py @@ -74,6 +74,7 @@ UrlDataSource, WrappedHFDataSource, ) +from levanter.data.ul2r import SENTINEL_TOKEN_IDS, DenoisingConfig, Ul2rDataset # noqa from levanter.shapes import NamedShapeSpec, ShapeSpec # noqa from levanter.store.cache import build_or_load_cache # noqa from levanter.utils.jax_utils import key_iterator, use_cpu_device # noqa @@ -438,6 +439,15 @@ class SupervisedLmDatasetFormat(LmDatasetFormatBase): pack: bool = True mask_inputs: bool = True +@LmDatasetFormatBase.register_subclass("ul2r") +@dataclass(frozen=True) +class Ul2rDatasetFormat(TextLmDatasetFormat): + task_configs: Dict[str, DenoisingConfig] = field(default_factory=dict) + task_probs: Dict[str, float] = field(default_factory=dict) + rng_seed: int = 0 + sentinel_token_id_start: int = 0 + sentinel_token_id_count: int = 0 + @dataclass(frozen=True) class LmDatasetSourceConfigBase(abc.ABC): @@ -606,7 +616,7 @@ def preprocessor_for_format( format: LmDatasetFormatBase, tokenizer: HfTokenizer, *, enforce_eos: bool = True, enforce_bos: bool = True ) -> BatchProcessor[dict, dict]: match format: - case TextLmDatasetFormat(text_key=key): + case TextLmDatasetFormat(text_key=key) | Ul2rDatasetFormat(text_key=key): return BatchTokenizer(tokenizer, enforce_bos=enforce_bos, enforce_eos=enforce_eos, text_field=key) case ChatLmDatasetFormat(messages_field=m, single_turn=s_turn, chat_template=ct, mask_user_turns=mt): if s_turn: @@ -630,6 +640,17 @@ def dataset_for_format( ignore_index: int | None, ) -> AsyncDataset[LmExample]: match format: + case Ul2rDatasetFormat(task_configs=task_configs, task_probs=task_probs, rng_seed=rng_seed, sentinel_token_id_start=sentinel_token_id_start, sentinel_token_id_count=sentinel_token_id_count): + key = jax.random.PRNGKey(rng_seed) + # TODO Get actual pad_token_id. Currently we only use this in ul2r_loss_mask. + pad_token_id = 0 + max_segments_per_example = 64 + slice_strategy = "left" + # TODO Figure out whether to pass this in. Needs a lot of piping thru train_set etc. + # Seems weird that only UL2R needs it. + KPos = Pos.alias("key_position") + sentinel_token_ids = jnp.arange(sentinel_token_id_start, sentinel_token_id_start + sentinel_token_id_count, dtype=jnp.int32) + return Ul2rDataset(cache, Pos, KPos, task_configs, task_probs, key, pad_token_id, sentinel_token_ids, max_segments_per_example, slice_strategy) case TextLmDatasetFormat(): return CausalLmDataset(TokenSeqDataset(cache, Pos.size), Pos, eos_id=eos_id, ignore_index=ignore_index) case ChatLmDatasetFormat(single_turn=single_turn, pack=pack, mask_user_turns=mask_user_turns): diff --git a/src/levanter/data/ul2r.py b/src/levanter/data/ul2r.py new file mode 100644 index 000000000..b00213f7d --- /dev/null +++ b/src/levanter/data/ul2r.py @@ -0,0 +1,882 @@ +from dataclasses import dataclass +from haliax import Axis +from jaxtyping import PRNGKeyArray +from typing import Literal, Optional, Dict +import dataclasses +import draccus +import equinox as eqx +import functools +import jax +import jax.numpy as jnp +import numpy as np +import typing +import haliax as hax + +from levanter.data.dataset import MappedAsyncDataset +from levanter.data.packing import GreedyPrepackedDataset +from levanter.layers.attention import AttentionMask +from levanter.models.lm_model import LmExample +from levanter.store.cache import TreeCache + + +# From https://huggingface.co/stanford-crfm/marin-tokenizer/blob/main/tokenizer.json +R_TASK_TOKEN_ID = 128011 +R_TASK_TOKEN = "<|reserved_special_token_3|>" +X_TASK_TOKEN_ID = 128012 +X_TASK_TOKEN = "<|reserved_special_token_4|>" +S_TASK_TOKEN_ID = 128013 +S_TASK_TOKEN = "<|reserved_special_token_5|>" + + +# Use the last 100 reserved token IDs as sentinel tokens (equivalent to T5's +# extra_ids); <|reserved_special_token_148|> to 247 +# https://github.com/google-research/text-to-text-transfer-transformer/blob/e081111ccd51df425aa7124e8e311ed6d403d767/t5/models/gin/objectives/span.gin#L46-L47 +NUM_TOKEN_IDS = 128256 +NUM_SENTINELS = 100 +SENTINEL_TOKEN_IDS = jnp.arange(NUM_TOKEN_IDS - NUM_SENTINELS, NUM_TOKEN_IDS, dtype=jnp.int32) + +RX_TASK_KIND = 0 +S_TASK_KIND = 1 + +MIN_S_LENGTH = 3 + + +@dataclass(frozen=True) +class DenoisingConfig(draccus.ChoiceRegistry): + task_token_id: int + + def with_task_token_id(self, task_token_id: int) -> "DenoisingConfig": + return dataclasses.replace(self, task_token_id=task_token_id) + + @staticmethod + def ul2_configs( + r_task_token_id: int = R_TASK_TOKEN_ID, + x_task_token_id: int = X_TASK_TOKEN_ID, + s_task_token_id: int = S_TASK_TOKEN_ID, + ) -> typing.Dict[str, "DenoisingConfig"]: + # Table 1 https://arxiv.org/pdf/2205.05131 + return { + "r1": RXDenoisingConfig(r_task_token_id, 0.15, 3.0, False), + "r2": RXDenoisingConfig(r_task_token_id, 0.15, 8.0, False), + "x1": RXDenoisingConfig(x_task_token_id, 0.5, 3.0, False), + "x2": RXDenoisingConfig(x_task_token_id, 0.5, 8.0, False), + "x3": RXDenoisingConfig(x_task_token_id, 0.15, 64.0, False), + "x4": RXDenoisingConfig(x_task_token_id, 0.5, 64.0, False), + "s": SDenoisingConfig(s_task_token_id), + } + + @staticmethod + def ul2r_configs( + r_task_token_id: int = R_TASK_TOKEN_ID, + x_task_token_id: int = X_TASK_TOKEN_ID, + s_task_token_id: int = S_TASK_TOKEN_ID, + ) -> Dict[str, "DenoisingConfig"]: + # Section 3.3 Loss Objectives https://arxiv.org/pdf/2210.11399v2 + return { + "r": RXDenoisingConfig(r_task_token_id, 0.15, 3.0, False), + "x1": RXDenoisingConfig(x_task_token_id, 0.15, 32.0, False), + "x2": RXDenoisingConfig(x_task_token_id, 0.5, 3.0, False), + "s": SDenoisingConfig(s_task_token_id), + } + + def to_task_params(self) -> jnp.ndarray: + raise NotImplementedError + + @staticmethod + def task_kind_from_task_params(task_params: jnp.ndarray) -> jnp.ndarray: + return task_params[0] + + @staticmethod + def task_token_id_from_task_params(task_params: jnp.ndarray) -> jnp.ndarray: + return task_params[1] + + +@DenoisingConfig.register_subclass("rx") +@dataclass(frozen=True) +class RXDenoisingConfig(DenoisingConfig): + mask_prob: float # r in the paper + mean_span_length: float # mu in the paper + random_roll: bool + + def to_task_params(self) -> jnp.ndarray: + args = [ + RX_TASK_KIND, + self.task_token_id, + self.mask_prob, + self.mean_span_length, + int(self.random_roll), + ] + return jnp.array(args) + + @staticmethod + def mask_prob_from_task_params(task_params: jnp.ndarray) -> jnp.ndarray: + return task_params[2] + + @staticmethod + def mean_span_length_from_task_params(task_params: jnp.ndarray) -> jnp.ndarray: + return task_params[3] + + @staticmethod + def random_roll_from_task_params(task_params: jnp.ndarray) -> jnp.ndarray: + return task_params[4].astype(jnp.bool_) + + +@DenoisingConfig.register_subclass("s") +@dataclass(frozen=True) +class SDenoisingConfig(DenoisingConfig): + def to_task_params(self) -> jnp.ndarray: + # should match size of RXDenoisingConfig + return jnp.array([S_TASK_KIND, self.task_token_id, 0, 0, 0]) + + +@functools.partial(jax.jit, static_argnames=["padded_length"]) +def random_segmentation_1(num_items: int, padded_length: int) -> jnp.ndarray: + out = jnp.zeros((padded_length,), dtype=jnp.int32) + return out.at[0].set(num_items) + + +@functools.partial(jax.jit, static_argnames=["padded_length"]) +def random_segmentation_2(num_items: int, num_segments: int, key: PRNGKeyArray, padded_length: int) -> jnp.ndarray: + """ + Generates a random partition of `num_items` items into `num_segments` + segments described by their lengths. The length of the returned tensor is + `padded_length`. + + Based on `_random_segmentation` in `random_spans_noise_mask` in T5: + https://github.com/google-research/text-to-text-transfer-transformer/blob/e081111ccd51df425aa7124e8e311ed6d403d767/t5/data/preprocessors.py#L2992-L3010 + + + Precondition: + 1 <= num_segments < num_items + + Returns: + A tensor of segment lengths with shape `(padded_length,)` where the + first `num_segments` values sum to `num_items` and all other values are + zero. + """ + + indices = jnp.arange(padded_length) + + perm = jax.random.permutation(key, indices) + + # Threshold is the (num_segments - 1)th smallest value in the first + # num_items - 1 elements + perm_masked = jnp.where(indices < num_items - 1, perm, padded_length) + sorted_perm = jnp.sort(perm_masked) + threshold = sorted_perm[num_segments - 1] + + # 1 where perm < threshold; so num_segments-1 random 1s out of the first + # num_items-1 elements, everything else is 0 + first_in_segment = jnp.where(perm_masked < threshold, 1, 0) + + # Roll right by 1; guarantees first element is 0 + # Now there are num_segments-1 random 1s out of the first num_items elements + # (perm_masked[padded_length-1] = padded_length because + # padded_length-1 >= num_items-1 and + # padded_length >= max perm_masked >= threshold) + first_in_segment = jnp.roll(first_in_segment, 1) + + # Set position num_items to 1 (marks end of all segments) + first_in_segment = first_in_segment.at[num_items].set(1) + + # The 1s mark the ends of the num_segments random segments, so segment_sum + # on the cumsum gives us the segment lengths + segment_id = jnp.cumsum(first_in_segment) + segment_length = jax.ops.segment_sum(jnp.ones_like(segment_id), segment_id, padded_length) + + return segment_length + + +@functools.partial(jax.jit, static_argnames=["padded_length"]) +def random_segmentation(num_items: int, num_segments: int, key: PRNGKeyArray, padded_length: int) -> jnp.ndarray: + return jax.lax.cond( + num_segments > 1, + lambda: random_segmentation_2(num_items, num_segments, key, padded_length), + lambda: random_segmentation_1(num_items, padded_length), + ) + + +@jax.jit +def num_noise_spans_tokens_and_spans( + length: int, noise_density: float, mean_noise_span_length: float +) -> tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]: + adjusted_length = jnp.maximum(length, 2) + + num_noise_tokens = jnp.round(adjusted_length * noise_density).astype(jnp.int32) + num_noise_tokens = jnp.clip(num_noise_tokens, 1, adjusted_length - 1) + num_noise_spans = jnp.maximum(jnp.round(num_noise_tokens / mean_noise_span_length).astype(jnp.int32), 1) + num_nonnoise_tokens = adjusted_length - num_noise_tokens + + return num_noise_tokens, num_noise_spans, num_nonnoise_tokens + + +@functools.partial(jax.jit, static_argnames=["padded_length"]) +def random_spans_noise_mask( + length: int, + noise_density: float, + key: PRNGKeyArray, + mean_noise_span_length: float, + random_roll: bool, + padded_length: int, +) -> jnp.ndarray: + """ + Generates a random 1D Boolean mask tensor where `noise_density` gives the + fraction of tokens that are 1s, occurring in runs of length + `mean_noise_span_length`. + + TODO Implement random_roll! This implementation has a bug because it turns + input noise masks like 010 into target noise masks like 101, which will + create one masked span in the input but two in the target. + + You must use `random_roll` to make each mask equally likely; otherwise the + distribution is not uniform and the mask will have a prefix of 0s. + + Only the first `length` tokens describe the mask; the contents of the + remaining `padded_length - length` tokens are 0s. + + Based on `random_spans_noise_mask` in T5: + https://github.com/google-research/text-to-text-transfer-transformer/blob/e081111ccd51df425aa7124e8e311ed6d403d767/t5/data/preprocessors.py#L2930-L3039 + + Returns: + Boolean mask tensor of shape (padded_length,). + """ + num_noise_tokens, num_noise_spans, num_nonnoise_tokens = num_noise_spans_tokens_and_spans( + length, noise_density, mean_noise_span_length + ) + + key1, key2, key3 = jax.random.split(key, 3) + + noise_span_lengths = random_segmentation(num_noise_tokens, num_noise_spans, key1, padded_length) + nonnoise_span_lengths = random_segmentation(num_nonnoise_tokens, num_noise_spans, key2, padded_length) + + # Interleave using reshape + interleaved_span_lengths = jnp.reshape( + jnp.stack([nonnoise_span_lengths, noise_span_lengths], axis=1), + [padded_length * 2], + )[:padded_length] + + # Create span_start_indicator using bincount + span_starts = jnp.cumsum(interleaved_span_lengths) + span_start_indicator = jnp.bincount(span_starts, length=padded_length) + + span_num = jnp.cumsum(span_start_indicator) + # Only odd spans less than 2*num_noise_spans are noise + is_noise = ((span_num % 2) == 1) & (span_num < 2 * num_noise_spans) + is_noise = is_noise.astype(jnp.bool_) + + # Zero everything at length and after + indices = jnp.arange(padded_length) + is_noise = jnp.where(indices < length, is_noise, False) + is_noise = typing.cast(jnp.ndarray, is_noise) + + def apply_roll(m): + offset = jax.random.randint(key3, (), 0, length, dtype=jnp.int32) + # Roll the mask + rolled = jnp.roll(m, offset) + # We want to roll within [0, length) so we need to overwrite values that + # came from the end + rolled = jnp.where( + indices < offset, + jnp.roll(m, offset - length), + rolled, + ) + rolled = typing.cast(jnp.ndarray, rolled) + rolled = jnp.where(indices < length, rolled, False) + return rolled + + mask = jax.lax.cond(random_roll, apply_roll, lambda m: m, is_noise) + return mask + + +@functools.partial(jax.jit, static_argnames=["force_initial_sentinel"]) +def noise_span_to_unique_sentinel( + tokens: jnp.ndarray, + length: int, + noise_mask: jnp.ndarray, + sentinel_tokens: jnp.ndarray, + force_initial_sentinel: bool, +) -> jnp.ndarray: + """ + Replace each run of consecutive noise tokens with a different sentinel. + `length` must be the un-padded length of `tokens`. + + For example: + + tokens = "The longest river in the world is the Amazon" + noise_mask = [0, 1, 0, ...] + noise_span_to_unique_sentinel(...) = + "The river in the world is the Amazon Amazon" + + Based on `noise_span_to_unique_sentinel` in T5: + https://github.com/google-research/text-to-text-transfer-transformer/blob/e081111ccd51df425aa7124e8e311ed6d403d767/t5/data/preprocessors.py#L3141 + + Returns: + A tensor with the same shape and dtype as `tokens`. + """ + + # Identify first noise tokens in each span + prev_token_is_noise = jnp.roll(noise_mask, 1) + prev_token_is_noise = prev_token_is_noise.at[0].set(False) + first_noise_tokens = noise_mask & ~prev_token_is_noise + subsequent_noise_tokens = noise_mask & prev_token_is_noise + + # max_segments = jnp.max(segments) + 1 # Number of unique noise spans + + # If max_segments > len(sentinel_tokens) we will reuse sentinel tokens which + # isn't good. Ideally we'd log a warning but we can't do that inside of + # jax.jit. + # TODO Warn in the non-JIT wrapper? + # checkify.checkify( + # lambda: checkify.check( + # max_segments <= len(sentinel_tokens), + # f"Too many noise spans: {max_segments} > {len(sentinel_tokens)}", + # ), + # errors=checkify.index_checks, + # )() + + def loop_body(read_pos, state): + out, write_pos, sentinel_count = state + # Advance the read position from left to right in `tokens`. + # When we see the first noise token for a span, write the sentinel for + # the span. + # When we see a subsequent noise token, advance the read position + # without writing anything. + # Otherwise just copy from `tokens`. + sentinel_id = sentinel_tokens[sentinel_count % len(sentinel_tokens)] + is_first_noise_token = first_noise_tokens[read_pos] + is_subsequent_noise_token = subsequent_noise_tokens[read_pos] + + should_write_sentinel = (read_pos == -1) | is_first_noise_token + should_write = (read_pos == -1) | ~is_subsequent_noise_token + token_to_write = jax.lax.select(should_write_sentinel, sentinel_id, tokens[read_pos]) + sentinel_count = jax.lax.select(should_write_sentinel, sentinel_count + 1, sentinel_count) + + out, write_pos = jax.lax.cond( + ~should_write, + lambda: (out, write_pos), + lambda: (out.at[write_pos].set(token_to_write), write_pos + 1), + ) + + return out, write_pos, sentinel_count + + result = jnp.zeros_like(tokens) + i0 = jax.lax.select(force_initial_sentinel & ~first_noise_tokens[0], -1, 0) + result, _, _ = jax.lax.fori_loop(i0, length, loop_body, (result, 0, 0)) + + return result + + +@jax.jit +def to_ul2r_rx_tokens( + key: PRNGKeyArray, + tokens: jnp.ndarray, + length: int, + mask_prob: float, + mean_noise_span_length: float, + random_roll: bool, + pad_token_id: int, + sentinel_token_ids: jnp.ndarray, + max_length: int, +) -> tuple[jnp.ndarray, jnp.ndarray]: + """ + Create a UL2R R/X-denoising to the first `length` elements of `tokens`. + + Based on `span_corruption` in T5: + https://github.com/google-research/text-to-text-transfer-transformer/blob/e081111ccd51df425aa7124e8e311ed6d403d767/t5/data/preprocessors.py#L1931 + + Does not set task tokens. + + Returns: + - The length of `inputs` (before `targets`). + For use when generating the loss mask / PrefixLM attention mask. + - A tensor with the same shape as `tokens` containing + `inputs targets 0...` where `inputs targets` is truncated to fit `max_length`. + There is no leading padding. + """ + + padded_length = tokens.shape[0] + noise_mask = random_spans_noise_mask( + length, + mask_prob, + key, + mean_noise_span_length, + random_roll, + padded_length, + ) + + # When random_roll is True we can get noise masks that end with non-noise. + # 010 as noise_mask for inputs -> 101 as noise_mask for targets + # foo buzz (inputs) -> bar (ouputs -- wrong) + # want: bar + # So for targets we must not read past the last noised part of inputs. + target_in_len = jnp.where(noise_mask, jnp.arange(noise_mask.shape[0]), 0).max() + 1 + + inputs = noise_span_to_unique_sentinel( + tokens, + length, + noise_mask, + sentinel_token_ids, + force_initial_sentinel=False, + ) + targets = noise_span_to_unique_sentinel( + tokens, + target_in_len, + ~noise_mask, + sentinel_token_ids, + force_initial_sentinel=True, + ) + + indices = jnp.arange(padded_length) + input_len = jnp.where(inputs == 0, indices, padded_length).min() + target_len = jnp.where(targets == 0, indices, padded_length).min() + + # If `inputs + targets` exceed available `max_length`, truncate both proportionally + combined_len = input_len + target_len + overflow = jnp.maximum(combined_len - max_length, 0) + + # Distribute overflow proportionally between `inputs` and `targets` + drop_inputs = jnp.where( + combined_len > 0, + (overflow * input_len) // jnp.maximum(combined_len, 1), + 0, + ) + drop_targets = overflow - drop_inputs + + trunc_input_len = jnp.maximum(input_len - drop_inputs, 0) + trunc_target_len = jnp.maximum(target_len - drop_targets, 0) + + # Truncate `targets` to the new length; `inputs` are gated by `new_input_len` below + targets = jnp.where(indices < trunc_target_len, targets, pad_token_id) + targets = typing.cast(jnp.ndarray, targets) + + targets = jnp.roll(targets, trunc_input_len) + result = jnp.where(indices < trunc_input_len, inputs, targets) + result = typing.cast(jnp.ndarray, result) + return trunc_input_len, result + + +@jax.jit +def to_ul2r_s_tokens( + key: PRNGKeyArray, + tokens: jnp.ndarray, + length: int, + sentinel_token_id: int, +) -> tuple[jnp.ndarray, jnp.ndarray]: + """ + Create a S-denoising example from the first `length` elements of `tokens`. + + Unlike the T5 version, all start positions for the continuation are equally + likely given that at least one token is included in the prefix. + + In Tay et al 2022 they mention reusing random_spans_noise_mask and setting + noise_density to 0.25 and `mean_noise_span_length` to `length / 4`. + for S-denoising. https://arxiv.org/abs/2210.11399 + But given their code I think this will deterministically just make the + last quarter of the input noise?! + https://github.com/google-research/text-to-text-transfer-transformer/blob/e081111ccd51df425aa7124e8e311ed6d403d767/t5/data/preprocessors.py#L2929-L3039 + Here we instead choose a random pivot, including at least one token from + the beginning of the input. + + Does not set task tokens. + + Returns: + - The length of `inputs` (before `targets`). + For use when generating the loss mask / PrefixLM attention mask. + - A tensor with the same shape as `tokens` containing + `inputs prefix s[0] continuation 0...`. + """ + + # TODO support parameters? + + # I'm not sure whether S-denoising examples look like + # [S] + # or + # [S] + # The latter would be identical to what we had as CausalLmConfig. + # The code in UL2 mentions noise_span_to_unique_sentinel for R/X-denoising + # but not S-denoising (see Section 9.2) and a figure in UL2R says + # "For PaLM and U-PaLM default, we pass the input as-is to the model. + # For the rest, we prepend one of [S2S], [NLU], or [NLG] to the + # beginning of # the input, and in the case of [NLU] and [NLG], we add + # the infill token at the end of the input, as typical for these modes." + # (implying there is no infill token for S-denoising) but I'm not sure. + # https://github.com/google-research/text-to-text-transfer-transformer/blob/e081111ccd51df425aa7124e8e311ed6d403d767/t5/data/preprocessors.py#L2033 + + pivot = jax.random.randint(key, (), 1, length - 1) + n_tokens = tokens.shape[0] + tokens = jnp.where(jnp.arange(n_tokens) < length, tokens, 0) + targets = jnp.roll(tokens, 1) + indices = jnp.arange(n_tokens) + result = jnp.where(indices < pivot, tokens, targets) + result = result.at[pivot].set(sentinel_token_id) + return pivot, result + + +@jax.jit +def to_ul2r_tokens( + key: PRNGKeyArray, + task_params: jnp.ndarray, + tokens: jnp.ndarray, + length: int, + pad_token_id: int, + sentinel_token_ids: jnp.ndarray, + # TODO maybe we don't actually need the truncation logic in + # to_ul2r_rx_tokens given that we truncate while packing + # See slice_strategy. + # However that slices using offsets[stop] - offsets[start], which can be + # less than the manually-specified lengths. + max_length: int, +) -> tuple[jnp.ndarray, jnp.ndarray]: + """ + Applies UL2R denoising to the first `length` `tokens`. + + Returns `(inputs_len, denoising_tokens)` where + `denoising_tokens = task_token inputs targets 0...`. + + Loss should not be computed on inputs; inputs may have PrefixLM attention. + If the example given is too short, we return `(inputs_len = length, tokens)` + with `tokens` unmodified. The caller should ignore this example and not + compute loss on `tokens[:length]` at all (which is what should happen + anyways for input tokens). + """ + + task_kind = DenoisingConfig.task_kind_from_task_params(task_params) + task_token_id = DenoisingConfig.task_token_id_from_task_params(task_params) + + def rx_tokens(): + noise_density = RXDenoisingConfig.mask_prob_from_task_params(task_params) + mean_noise_span_length = RXDenoisingConfig.mean_span_length_from_task_params(task_params) + random_roll = RXDenoisingConfig.random_roll_from_task_params(task_params) + inputs_len, out = to_ul2r_rx_tokens( + key, + tokens[:-1], + length, + noise_density, + mean_noise_span_length, + random_roll, + pad_token_id, + sentinel_token_ids, + max_length - 1, + ) + # Add 1 to inputs_len for the task token. + return inputs_len + 1, jnp.concatenate([jnp.array([task_token_id], dtype=jnp.int32), out]) + + def s_tokens(): + # TODO Do we ensure that the prefix/continuation isn't just <{begin,end}_of_text>? + inputs_len, out = to_ul2r_s_tokens(key, tokens[:-1], length, sentinel_token_ids[0]) + return inputs_len + 1, jnp.concatenate([jnp.array([task_token_id], dtype=jnp.int32), out]) + + def too_short(): + return length, tokens + + # Should probably remove short examples from the dataset beforehand? + return jax.lax.cond( + length < MIN_S_LENGTH, + too_short, + lambda: jax.lax.cond(task_kind == RX_TASK_KIND, rx_tokens, s_tokens), + ) + + +@jax.jit +def ul2r_loss_mask( + input_mask: jnp.ndarray, + segment_ids: jnp.ndarray, + tokens: jnp.ndarray, + pad_token_id: int, +) -> jnp.ndarray: + """ + Creates a loss mask for UL2R training. + + Loss is computed only on output tokens (where input_mask is 0), + excluding the last token in each segment and padding tokens. + + - input_mask: Binary mask indicating input positions + positions (0) + - segment_ids: Segment IDs for packed sequences (-1 for padding) + - tokens: Token IDs + - pad_token_id: Padding token ID + + Returns: + Loss mask array of same shape as inputs + """ + # aaaabbb segment_ids + # 1100110 input_mask + # → 0110010 + # || ^ no loss on last output (would predict the next segment) + # |^ loss on last input / first output + # ^ no loss on inputs / task token + + loss_mask = jnp.logical_not(input_mask) + loss_mask = jnp.roll(loss_mask, -1) + + # Don't compute loss across segment boundaries + segment_continues = jnp.concatenate([segment_ids[1:] == segment_ids[:-1], jnp.array([True])]) + loss_mask = loss_mask & segment_continues + + # Don't compute loss on padding + valid_mask = tokens != pad_token_id + loss_mask = loss_mask & valid_mask + + return loss_mask + + +@jax.jit +def compute_denoising_length( + task_params: jnp.ndarray, + length: jnp.ndarray, +) -> jnp.ndarray: + def _rx_length() -> jnp.ndarray: + noise_density = RXDenoisingConfig.mask_prob_from_task_params(task_params) + mean_noise_span_length = RXDenoisingConfig.mean_span_length_from_task_params(task_params) + random_roll = RXDenoisingConfig.random_roll_from_task_params(task_params) + _num_noise_tokens, num_noise_spans, _num_nonnoise_tokens = num_noise_spans_tokens_and_spans( + length, noise_density, mean_noise_span_length + ) + # When random_roll is True, we might create an additional noise span by + # rolling a noise span so that it is cut by the beginning/end. Reserve + # space for it. + num_noise_spans = jax.lax.select(random_roll, num_noise_spans + 1, num_noise_spans) + # [task_token] one three two + return 1 + 2 * num_noise_spans + length + + def _s_length() -> jnp.ndarray: + # [task_token] one two three + return jax.lax.select(length < MIN_S_LENGTH, length, 2 + length) + + task_kind = DenoisingConfig.task_kind_from_task_params(task_params) + return jax.lax.cond(task_kind == RX_TASK_KIND, _rx_length, _s_length) + + +@functools.partial(jax.jit, static_argnames=("max_segments_per_example", "QPos", "KPos")) +def create_ul2r_example( + key: PRNGKeyArray, + QPos: Axis, + KPos: Axis, + pad_token_id: int, + sentinel_token_ids: jnp.ndarray, + max_segments_per_example: int, + task_params: jnp.ndarray, + task_indices: jnp.ndarray, + tokens: hax.NamedArray, + segment_ids: hax.NamedArray, +) -> LmExample: + # jax.debug.print("create_ul2r_example start") + + # TODO Use NamedArrays more idiomatically + # `unique_seg_ids = [3, 4, ..., -1, ...]` + # Valid segment IDs come first, padded with -1. + # Sorted; assumes `segment_ids` is also sorted in ascending order. + # We use the same ordering for `out_starts` etc. + max_seg_id = jnp.max(segment_ids.array) + seg_ids = jnp.where(segment_ids.array == -1, max_seg_id, segment_ids.array) + unique_seg_ids = jnp.unique(seg_ids, size=max_segments_per_example, fill_value=-1) + + def _prepare_segment(id: int) -> tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]: + """ + Returns `(in_start, in_length, out_length)` for segment `id`. + The result is undefined for ids not in `segment_ids` or the id -1. + """ + + mask = segment_ids.array == id + n = mask.shape[0] + idx = jnp.arange(n) + + in_start = jnp.min(jnp.where(mask, idx, n)) + in_end = jnp.max(jnp.where(mask, idx + 1, 0)) + in_length = in_end - in_start + + # Eventually we don't want to have to instantiate all of `offsets` + # and `task_indices` (because they could take up a lot of + # space). We can generate `task_params` / `task_idx` for each + # batch independently (but in a way that matches how we computed + # lengths for packing). + task_idx = task_indices[id] + out_length = compute_denoising_length(task_params[task_idx], in_length) + + return in_start, in_length, out_length + + in_starts, in_lengths, out_lengths = jax.vmap(_prepare_segment)(unique_seg_ids) + + # `out_starts[i]` is the offset of the beginning of the i-th output segment. + # Segment lengths increase when we turn them into denoising examples. + out_starts = jax.lax.cumsum(out_lengths) - out_lengths + out_starts = jnp.where(unique_seg_ids == -1, -1, out_starts) + out_starts = typing.cast(jnp.ndarray, out_starts) + + def _process_segment(key: PRNGKeyArray, id: int) -> tuple[jnp.ndarray, jnp.ndarray, int, int]: + """ + Applies UL2R denoising to a single segment. + Returns `(input_mask, denoising_tokens, out_seg_ids)`. + `input_mask` is true when the token is an input given to the LLM + (i.e. one where we don't compute loss). + `denoising_tokens` is `0... to_ul2r_tokens(...) 0...` where the + prefix is `out_starts[idx]` zeros. + We'll or all the segments together at the end; the nonzero parts + shouldn't overlap. + """ + + task_idx = task_indices[id] + + idx = jnp.nonzero(unique_seg_ids == id, size=1)[0] + in_start = typing.cast(int, jnp.squeeze(in_starts[idx])) + in_length = typing.cast(int, jnp.squeeze(in_lengths[idx])) + out_length = typing.cast(int, jnp.squeeze(out_lengths[idx])) + out_start = typing.cast(int, jnp.squeeze(out_starts[idx])) + + segment = jnp.roll(tokens.array, -in_start) + # TODO this should return the actual length, not just out_length which + # might include an extra token? Or we could just use padding. Loss + # shouldn't be compute don padding anyways. + inputs_len, denoising_tokens = to_ul2r_tokens( + key, task_params[task_idx], segment, in_length, pad_token_id, sentinel_token_ids, QPos.size + ) + + n_tokens = tokens.array.shape[0] + input_mask = jnp.arange(n_tokens) < inputs_len + input_mask = jnp.roll(input_mask, out_start) + denoising_tokens = jnp.roll(denoising_tokens, out_start) + return (input_mask, denoising_tokens, out_start, out_length) + + def _should_loop( + acc: tuple[PRNGKeyArray, int, jnp.ndarray, jnp.ndarray, jnp.ndarray], + ) -> bool: + (_, i, _, _, _) = acc + return (i < max_segments_per_example) & (unique_seg_ids[i] != -1) + + def _loop( + acc: tuple[PRNGKeyArray, int, jnp.ndarray, jnp.ndarray, jnp.ndarray], + ) -> tuple[PRNGKeyArray, int, jnp.ndarray, jnp.ndarray, jnp.ndarray]: + (key, i, input_mask, denoising_tokens, out_seg_ids) = acc + process_key, key = jax.random.split(key) + (seg_input_mask, seg_denoising_tokens, out_start, out_length) = _process_segment(process_key, unique_seg_ids[i]) # type: ignore + input_mask = input_mask | seg_input_mask + denoising_tokens = denoising_tokens | seg_denoising_tokens + + indices = jnp.arange(out_seg_ids.shape[0]) + seg_mask = (indices >= out_start) & (indices < out_start + out_length) + out_seg_ids = jnp.where(seg_mask, unique_seg_ids[i], out_seg_ids) + out_seg_ids = typing.cast(jnp.ndarray, out_seg_ids) + + return (key, i + 1, input_mask, denoising_tokens, out_seg_ids) + + input_mask = jnp.zeros_like(tokens.array, dtype=jnp.bool_) + denoising_tokens = jnp.zeros_like(tokens.array) + out_seg_ids = jnp.full(tokens.array.shape, -1) + acc = (key, 0, input_mask, denoising_tokens, out_seg_ids) + + # jax.debug.print("create_ul2r_example loop") + + (_, _, input_mask, denoising_tokens, out_seg_ids) = jax.lax.while_loop(_should_loop, _loop, acc) + + # jax.debug.print("create_ul2r_example loss_mask") + # TODO GreedyPrepackedDataset pads w/ zeros so can we end up with two + # padding token IDs? + loss_mask = ul2r_loss_mask(input_mask, out_seg_ids, denoising_tokens, pad_token_id) + loss_mask = hax.named(loss_mask, QPos) + + # TODO Do we not need KPos? LmExample.causal() just reuses the QPos seg_ids... + out_seg_ids = hax.named(out_seg_ids, [QPos]) + attn_mask = AttentionMask( + is_causal=True, + is_prefix=True, + input_mask=hax.named(input_mask, [QPos]), + segment_ids=(out_seg_ids, out_seg_ids), + ) + + denoising_tokens = hax.named(denoising_tokens, QPos) + return LmExample(tokens=denoising_tokens, loss_mask=loss_mask, attn_mask=attn_mask) + + +TokenizedDict = typing.TypedDict("TokenizedDict", {"input_ids": np.ndarray}) + + +# [example, seg_ids]: tuple[TokenizedDict, TokenizedDict] +class Ul2rDataset(MappedAsyncDataset[tuple[TokenizedDict, TokenizedDict], LmExample]): + def __init__( + self, + cache: TreeCache[TokenizedDict], + QPos: Axis, + KPos: Axis, + task_configs: typing.Dict[str, DenoisingConfig], + task_probs: Dict[str, float], + key: PRNGKeyArray, + pad_token_id: int, + sentinel_token_ids: jnp.ndarray, + max_segments_per_example: int = 64, + slice_strategy: Literal["left", "right", "raise"] = "left", + ): + cache.await_finished() + + # Copied from GreedyPrepackedDataset.__init__ + # TODO factor out? + # TODO avoid reading store.offsets twice (here and in GreedyPrepackedDataset) + offsets = jax.tree.map(lambda store: store.offsets[0 : store.num_rows + 1].read(), cache.store.tree) + offsets = jax.tree.map(lambda fut: fut.result(), offsets) + + def _diff_offsets(offsets: np.ndarray): + # fine to mutate since we have a copy + # the array store has the number of rows in the 0th offset + offsets[0] = 0 + return offsets[1:] - offsets[:-1] + + in_lengths = jax.tree.map(_diff_offsets, offsets) + in_token_counts = jnp.array(in_lengths["input_ids"]) + n_docs = in_token_counts.shape[0] + + # print("Ul2rDataset after in_lengths") + + task_items = [(config, task_probs[name]) for name, config in task_configs.items()] + n_tasks = len(task_items) + task_probs_arr = jnp.array([prob for _, prob in task_items]) + task_indices_key, key = jax.random.split(key) + task_indices = jax.random.choice(task_indices_key, n_tasks, shape=(n_docs,), p=task_probs_arr) + + # shape (n_tasks, A) + # where A = max(num_args(to_ul2r_rx_tokens), num_args(to_ul2r_s_tokens)) + # e.g. [[task_kind, pad_token_id, mask_prob, mean_span_length]] + # task_kind = 0 for R/X-denoising, 1 for S-denoising + task_params = jnp.array([task.to_task_params() for task, _prob in task_items]) + + # We compute the length of the tokens after denoising because we want + # to turn each input batch into a denoising batch while still staying + # under the max sequence length for the model. + def _compute_length(task_idx: jnp.ndarray, length: jnp.ndarray) -> int: + return compute_denoising_length(task_params[task_idx], length) + + out_token_counts = jax.vmap(_compute_length)(task_indices, in_token_counts) + out_lengths = {**in_lengths, "input_ids": out_token_counts} + + # NB the GreedyPackedDataset returns a tuple, where the first has the + # packed leaves and the second has the segment ids + self.packed: GreedyPrepackedDataset[TokenizedDict] = GreedyPrepackedDataset( + cache.store.tree, + QPos.size, + max_segments_per_example=max_segments_per_example, + slice_strategy=slice_strategy, + packing_lengths=out_lengths, + # Reserve space for UL2R; denoising examples increase in length. + pad_with_zeros=True, + ) + self.QPos = QPos + self.KPos = KPos + self.pad_token_id = pad_token_id + + @functools.partial(eqx.filter_jit) + def _create_lm_example(e: tuple[TokenizedDict, TokenizedDict]) -> LmExample: + example, seg_ids = e + tokens = hax.named(example["input_ids"], self.QPos) + segment_ids = hax.named(seg_ids["input_ids"], self.QPos) + return create_ul2r_example( + key, + self.QPos, + self.KPos, + self.pad_token_id, + sentinel_token_ids, + max_segments_per_example, + task_params, + task_indices, + tokens, + segment_ids, + ) + + super().__init__(self.packed, _create_lm_example) diff --git a/src/levanter/layers/attention.py b/src/levanter/layers/attention.py index 60ed64695..f9d20acaa 100644 --- a/src/levanter/layers/attention.py +++ b/src/levanter/layers/attention.py @@ -154,6 +154,8 @@ def dot_product_attention( attention_out = None + # jax.debug.print("attn_backend={}", attn_backend) + match attn_backend: case AttentionBackend.NVTE: attention_out = _try_te_attention( @@ -279,6 +281,7 @@ def dot_product_attention( else: # local import to avoid circular imports from levanter.models.flash_attention import flash_attention + # jax.debug.print("flash_attention") return flash_attention( QPos, @@ -379,6 +382,7 @@ def simple_attention_with_dropout( QPos = query.resolve_axis(QPos) KPos = key.resolve_axis(KPos) m = materialize_mask(mask, QPos, KPos) + # jax.debug.print("mask={} QPos={} KPos={}", mask, QPos, KPos) orig_dtype = query.dtype if scaling_factor is None: @@ -530,6 +534,9 @@ def _te_flash_attention( fused_attn, # noqa: F401 ) + if isinstance(mask, AttentionMask) and mask.is_prefix: + raise NotImplementedError("is_prefix not supported for NVTE fused attention") + if logits_soft_cap is not None: raise NotImplementedError( "logits_soft_cap is not supported for NVTE fused attention. " @@ -792,6 +799,7 @@ def _materialize_segment_mask( kv_segment_ids = segment_ids.rename({QPos.name: KPos.name})[KPos.name, k_slice] q_segment_ids = segment_ids[QPos.name, q_slice] + # TODO Does this need to worry about padding segments? return q_segment_ids.broadcast_axis(kv_segment_ids.axes) == kv_segment_ids @@ -837,12 +845,16 @@ class AttentionMask(eqx.Module): # we apply a shifted causal mask such that a query at position *i* can attend to key *j* whenever # ``j <= i + causal_offset``. A ``None`` offset means a static offset of 0 (i.e., standard causal masking). is_causal: bool = eqx.field(default=False, static=True) + is_prefix: bool = eqx.field(default=False, static=True) causal_offset: None | NamedArray = None explicit_mask: Optional[NamedArray] = None segment_ids: tuple[NamedArray, NamedArray] | None = None sliding_window: Optional[int] = eqx.field(default=None, static=True) + # `input_mask` is used when `is_prefix`. Queries at input positions can + # attend to keys from all other input positions in the same segment in a + # non-causal way. Requires `segment_ids`. + input_mask: Optional[NamedArray] = None # CF https://github.com/jax-ml/jax/blob/47858c4ac2fd4757a3b6fc5bb2981b71a71f00c2/jax/experimental/pallas/ops/tpu/flash_attention.py#L34 - # TODO: add prefixlm # cf https://github.com/google-research/t5x/blob/51a99bff8696c373cc03918707ada1e98cbca407/t5x/examples/decoder_only/layers.py#L978 def materialize( @@ -867,6 +879,8 @@ def materialize( shifted_k_start = k_slice.start - offset if isinstance(shifted_k_start, NamedArray): # need to vmap + # TODO I think the mask is nonzero even for padding positions. + # Is that OK? Could check using segment_ids. causal = hax.vmap(causal_mask, shifted_k_start.axes)( QPos.resize(q_slice.size), KPos.resize(k_slice.size), @@ -896,10 +910,21 @@ def materialize( ) mask = combine_masks_and(mask, sw_mask) + segment_mask = None if self.segment_ids is not None: segment_mask = _materialize_segment_mask(self.segment_ids, QPos, KPos, q_slice, k_slice) mask = combine_masks_and(mask, segment_mask) + if self.is_prefix and self.input_mask is not None and segment_mask is not None: + # TODO is this right? we need to rename so QPos is the outer axis to + # match `mask`. + input_attn_mask = self.input_mask.rename({QPos.name: KPos.name}) + input_attn_mask = input_attn_mask[KPos, k_slice].broadcast_axis(QPos) + input_attn_mask_sliced = input_attn_mask[QPos, q_slice] + prefix_mask = combine_masks_and(segment_mask, input_attn_mask_sliced) + # prefix_mask = segment_mask & input_attn_mask_sliced + mask = combine_masks_or(mask, prefix_mask) + return mask # Static constructors -------------------------------------------------- @@ -932,6 +957,9 @@ def __post_init__(self): warnings.warn("Storing segment_ids as a single NamedArray is deprecated. Use a tuple instead.") object.__setattr__(self, "segment_ids", (self.segment_ids, self.segment_ids)) + if self.is_prefix and (self.input_mask is None or self.segment_ids is None): + raise ValueError("is_prefix requires input_mask and segment_ids") + def with_segment_ids(self, segment_ids: NamedArray, kv_segment_ids: NamedArray | None = None) -> "AttentionMask": """Attach segment ids to the mask. @@ -947,20 +975,24 @@ def with_segment_ids(self, segment_ids: NamedArray, kv_segment_ids: NamedArray | return AttentionMask( is_causal=self.is_causal, + is_prefix=self.is_prefix, causal_offset=self.causal_offset, explicit_mask=self.explicit_mask, segment_ids=seg_field, sliding_window=self.sliding_window, + input_mask=self.input_mask, ) def with_sliding_window(self, sliding_window: int | None) -> "AttentionMask": """Return a copy of this mask with ``sliding_window`` applied.""" return AttentionMask( is_causal=self.is_causal, + is_prefix=self.is_prefix, causal_offset=self.causal_offset, explicit_mask=self.explicit_mask, segment_ids=self.segment_ids, sliding_window=sliding_window, + input_mask=self.input_mask, ) def __and__(self, other) -> "AttentionMask": @@ -996,10 +1028,12 @@ def __and__(self, other) -> "AttentionMask": return AttentionMask( is_causal=is_causal, + is_prefix=self.is_prefix, causal_offset=causal_offset, explicit_mask=explicit_mask, segment_ids=segment_ids, sliding_window=sliding_window, + input_mask=self.input_mask, ) def __or__(self, other) -> "AttentionMask": @@ -1025,10 +1059,12 @@ def __or__(self, other) -> "AttentionMask": sliding_window = max(self.sliding_window, other.sliding_window) return AttentionMask( is_causal=is_causal, + is_prefix=self.is_prefix, causal_offset=causal_offset, explicit_mask=explicit_mask, segment_ids=segment_ids, sliding_window=sliding_window, + input_mask=self.input_mask, ) def _check_for_same_segment_ids(self, other): @@ -1205,6 +1241,9 @@ def _tpu_splash_attention( splash_attention_mask, ) + if isinstance(mask, AttentionMask) and mask.is_prefix: + raise NotImplementedError("Splash attention does not support is_prefix") + # Splash attention requires BHSD format # We need to reshape the input to match this format if dropout != 0.0: diff --git a/src/levanter/main/cache_dataset.py b/src/levanter/main/cache_dataset.py index 2767bd6a4..d8614e898 100644 --- a/src/levanter/main/cache_dataset.py +++ b/src/levanter/main/cache_dataset.py @@ -7,7 +7,7 @@ import levanter from levanter.data.metrics_monitor import LoggingMetricsMonitor -from levanter.data.text import BatchTokenizer, SingleDatasetLMConfigBase +from levanter.data.text import BatchTokenizer, SingleDatasetLMConfig, SingleDatasetLMConfigBase from levanter.distributed import RayConfig from levanter.store.cache import build_or_load_cache from levanter.tracker import NoopConfig, TrackerConfig @@ -18,7 +18,8 @@ @dataclass(frozen=True) -class RayCachedLMDatasetConfig(SingleDatasetLMConfigBase, RayConfig): +class RayCachedLMDatasetConfig(RayConfig): + data: SingleDatasetLMConfig = field(default_factory=SingleDatasetLMConfigBase) tracker: TrackerConfig = field(default_factory=NoopConfig) @@ -28,14 +29,15 @@ def main(args: RayCachedLMDatasetConfig): init_logging(".", "cache_dataset.log") args.initialize() - tokenizer = args.the_tokenizer + data = args.data + tokenizer = data.the_tokenizer for split in ["train", "validation"]: - print(f"Caching {split} to {args.cache_dir}.") + print(f"Caching {split} to {data.cache_dir}.") # connect or start the actor - batch_tokenizer = BatchTokenizer(tokenizer, enforce_eos=args.enforce_eos) - split_cache_dir = os.path.join(args.cache_dir, split) # type: ignore - source = args.get_shard_source(split) + batch_tokenizer = BatchTokenizer(tokenizer, enforce_eos=data.enforce_eos) + split_cache_dir = os.path.join(data.cache_dir, split) # type: ignore + source = data.get_shard_source(split) if source is None: logger.warning(f"Skipping {split} because it is empty.") diff --git a/src/levanter/main/inference_repl.py b/src/levanter/main/inference_repl.py index e6225d05a..7595b21aa 100644 --- a/src/levanter/main/inference_repl.py +++ b/src/levanter/main/inference_repl.py @@ -79,6 +79,7 @@ def _load_model( model_config: LmConfig, hf_checkpoint: str | None, levanter_checkpoint: str | None, + tokenizer_path: str | None = None, *, key, ) -> tuple[LmHeadModel, HfTokenizer]: @@ -90,7 +91,7 @@ def _load_model( raise ValueError("Specify only one of checkpoint_path or hf_checkpoint") mp = trainer_config.mp - tokenizer = load_tokenizer(hf_checkpoint) + tokenizer = load_tokenizer(tokenizer_path or hf_checkpoint) vocab_size = len(tokenizer) with trainer_config.use_device_mesh(), hax.axis_mapping(trainer_config.compute_axis_mapping): @@ -236,6 +237,7 @@ def load(self, path: str, tokenizer: Optional[str] = None, **kwargs): model_config=self.config.model, hf_checkpoint=path, levanter_checkpoint=None, + tokenizer_path=tokenizer, key=jrandom.PRNGKey(self.config.server.seed), ) else: @@ -247,6 +249,7 @@ def load(self, path: str, tokenizer: Optional[str] = None, **kwargs): model_config=self.config.model, hf_checkpoint=None, levanter_checkpoint=path, + tokenizer_path=tokenizer, key=jrandom.PRNGKey(self.config.server.seed), ) diff --git a/src/levanter/main/train_lm.py b/src/levanter/main/train_lm.py index a5231fc56..a6ff4f4ec 100644 --- a/src/levanter/main/train_lm.py +++ b/src/levanter/main/train_lm.py @@ -7,7 +7,10 @@ import logging import os from dataclasses import dataclass, field +import itertools from typing import Optional, Union +import jax +from tqdm_loggable.auto import tqdm import jax.numpy as jnp import jax.random as jrandom @@ -18,6 +21,7 @@ import levanter import levanter.callbacks +from levanter.data.loader import DataLoader import levanter.eval import levanter.eval_harness from levanter import callbacks @@ -70,6 +74,9 @@ class TrainLmConfig: # TODO: really need to add callback framework log_entropy: bool = False + data_only: bool = False + disable_jit: bool = False + def main(config: TrainLmConfig): tokenizer = config.data.the_tokenizer @@ -132,9 +139,11 @@ def main(config: TrainLmConfig): # For most things, we just insist you specify the config right, but tokenizers often have strange numbers of # tokens: gpt-2 has 50257, for example. So we round up. vocab_size = len(tokenizer) + # print(f"vocab_size={vocab_size}") Vocab = round_axis_for_partitioning(Axis("vocab", vocab_size), parameter_axis_mapping) if vocab_size != Vocab.size: logger.info(f"Rounding vocab size from {vocab_size} to {Vocab.size} for partitioning") + # print(f"Vocab.size={Vocab.size}") # Get the training dataset train_dataset = config.data.train_set( @@ -144,6 +153,50 @@ def main(config: TrainLmConfig): epochs=config.epoch, ) + if config.data_only: + # batch_size = 512 # was hitting OOM + batch_size = 1 + data_loader = DataLoader(train_dataset, batch_size=batch_size) + it = data_loader.iter_from_step(0) + for batch in it: + # DataLoaderIterator returns + # LmExample( + # tokens=NamedArray( + # array=i32[512,131072], + # axes=(Axis(name='batch', size=512), Axis(name='position', size=131072)) + # ), + # loss_mask=NamedArray( + # array=i32[512,131072], + # axes=(Axis(name='batch', size=512), Axis(name='position', size=131072)) + # ), + # attn_mask=AttentionMask( + # is_causal=True, + # segment_ids=( + # NamedArray( + # array=i32[512,131072], + # axes=(Axis(name='batch', size=512), Axis(name='position', size=131072)) + # ), + # NamedArray( + # array=i32[512,131072], + # axes=(Axis(name='batch', size=512), Axis(name='position', size=131072)) + # ) + # ) + # ) + # ) + # TODO Why do we only have is_causal=True and segment_ids but not prefix_mask? + print(batch.attn_mask) + for i in range(batch_size): + n = 512 + print(batch.tokens["batch", i].array[:n]) + print(tokenizer.decode(batch.tokens["batch", i].array[:n])) + print(batch.loss_mask["batch", i].array[:n]) + print(batch.attn_mask.segment_ids[0]["batch", i, "position", :n].array) + print(batch.attn_mask.materialize(Pos, Pos)["batch", i].array[:n, :n]) + # TODO print more from batch + break + logger.info("Exiting early because data_only is True") + return + # Get the tagged evaluation datasets tagged_eval_datasets = config.data.tagged_eval_sets(Pos) @@ -152,7 +205,12 @@ def main(config: TrainLmConfig): if int(state.step) == 0 and config.initialize_from_checkpoint_path is not None: state = load_checkpoint(state, config.initialize_from_checkpoint_path) + print(f"int(state.step)={int(state.step)}") + + print(f"int(state.step)={int(state.step)}") + if int(state.step) == 0: + print("true") # TODO: I don't love that we init the model twice, but it's not a big deal i think? if config.initialize_from_hf: # initialize from an hf pretrained model @@ -163,6 +221,7 @@ def main(config: TrainLmConfig): # this is a bit gross, but we want to free up the memory from the model we just built state = dataclasses.replace(state, model=None) gc.collect() + print("load_pretrained") model = converter.load_pretrained( config.model.model_type, config=config.model if not config.use_hf_model_config else None, @@ -174,6 +233,7 @@ def main(config: TrainLmConfig): else: logger.info("No checkpoint found. Starting from scratch.") + print("after check") levanter.tracker.log_summary({"parameter_count": parameter_count(state.model)}) max_eval_examples_per_ds = config.trainer.max_eval_batches @@ -265,7 +325,10 @@ def compute_logits(model: LmHeadModel, example: LmExample): train_loader = train_loader.iter_from_step(0) ## OK, actually run training! - last_info = trainer.train(state, train_loader) + disable_jit = config.disable_jit + last_info = None + with jax.disable_jit(disable_jit): + last_info = trainer.train(state, train_loader) # If running EpochDataset save latest checkpoint by default if trainer.config.checkpointer is not None and config.epoch > 0: diff --git a/src/levanter/models/gpt2.py b/src/levanter/models/gpt2.py index cfb378e5d..2031a4285 100644 --- a/src/levanter/models/gpt2.py +++ b/src/levanter/models/gpt2.py @@ -4,8 +4,10 @@ import dataclasses from dataclasses import dataclass from typing import Callable, Dict, Optional, Type, Union +import sys import equinox as eqx +import jax import jax.numpy as jnp import jax.random as jrandom from jaxtyping import PRNGKeyArray @@ -181,7 +183,12 @@ def init(config: Gpt2Config, *, key) -> "Gpt2Attention": @named_call def __call__(self, x: NamedArray, mask: Optional[AttentionMask | NamedArray], layer_idx, *, key): k_drop, k_attn, k_out = hax.jax_utils.maybe_rng_split(key, 3) + qkv_out = self.c_attn(x, key=k_attn).rearrange((..., "qkv", "heads", "position", "head_size")) + qkv_norm = jnp.linalg.norm(qkv_out.array) + qkv_has_nan = jnp.any(jnp.isnan(qkv_out.array)) + # jax.debug.print("Attn layer {layer} qkv: norm={norm}, has_nan={nan}", layer=layer_idx, norm=qkv_norm, nan=qkv_has_nan) + q, k, v = qkv_out.unbind("qkv") # Rename k and v's Pos as haliax doesn't support unnamed axes or duplicate axes @@ -237,14 +244,45 @@ def init(config: Gpt2Config, *, key) -> "Gpt2Block": def __call__(self, x: NamedArray, mask: Optional[AttentionMask | NamedArray], layer_idx, *, key): k1, k2, k3, k4 = haliax.jax_utils.maybe_rng_split(key, 4) - attn_output = self.attn(self.ln_1(x), mask=mask, layer_idx=layer_idx, key=k1) + # Debug: input to block + x_norm = jnp.linalg.norm(x.array) + x_has_nan = jnp.any(jnp.isnan(x.array)) + # jax.debug.print("Block {layer} input: norm={norm}, has_nan={nan}", layer=layer_idx, norm=x_norm, nan=x_has_nan) + + ln1_out = self.ln_1(x) + ln1_norm = jnp.linalg.norm(ln1_out.array) + ln1_has_nan = jnp.any(jnp.isnan(ln1_out.array)) + # jax.debug.print("Block {layer} ln_1: norm={norm}, has_nan={nan}", layer=layer_idx, norm=ln1_norm, nan=ln1_has_nan) + + attn_output = self.attn(ln1_out, mask=mask, layer_idx=layer_idx, key=k1) + attn_norm = jnp.linalg.norm(attn_output.array) + attn_has_nan = jnp.any(jnp.isnan(attn_output.array)) + # jax.debug.print("Block {layer} attn: norm={norm}, has_nan={nan}", layer=layer_idx, norm=attn_norm, nan=attn_has_nan) + attn_output = self.resid_dropout(attn_output, key=k2) x = x + attn_output - ff_output = self.mlp(self.ln_2(x), key=k3) + post_attn_norm = jnp.linalg.norm(x.array) + post_attn_has_nan = jnp.any(jnp.isnan(x.array)) + # jax.debug.print("Block {layer} post_attn: norm={norm}, has_nan={nan}", layer=layer_idx, norm=post_attn_norm, nan=post_attn_has_nan) + + ln2_out = self.ln_2(x) + ln2_norm = jnp.linalg.norm(ln2_out.array) + ln2_has_nan = jnp.any(jnp.isnan(ln2_out.array)) + # jax.debug.print("Block {layer} ln_2: norm={norm}, has_nan={nan}", layer=layer_idx, norm=ln2_norm, nan=ln2_has_nan) + + ff_output = self.mlp(ln2_out, key=k3) + mlp_norm = jnp.linalg.norm(ff_output.array) + mlp_has_nan = jnp.any(jnp.isnan(ff_output.array)) + # jax.debug.print("Block {layer} mlp: norm={norm}, has_nan={nan}", layer=layer_idx, norm=mlp_norm, nan=mlp_has_nan) + ff_output = self.resid_dropout(ff_output, key=k4) x = x + ff_output + output_norm = jnp.linalg.norm(x.array) + output_has_nan = jnp.any(jnp.isnan(x.array)) + # jax.debug.print("Block {layer} output: norm={norm}, has_nan={nan}", layer=layer_idx, norm=output_norm, nan=output_has_nan) + return x @@ -268,8 +306,17 @@ def init(config: Gpt2Config, *, key): def __call__(self, x: NamedArray, attn_mask: Optional[AttentionMask | NamedArray], *, key=None) -> NamedArray: keys = hax.jax_utils.maybe_rng_split(key, self.config.num_layers) if key is not None else None x = self.blocks.fold(x, attn_mask, hax.arange(self.config.Layers), key=keys) + + x_norm = jnp.linalg.norm(x.array) + x_has_nan = jnp.any(jnp.isnan(x.array)) + # jax.debug.print("Before final ln: norm={norm}, has_nan={nan}", norm=x_norm, nan=x_has_nan) + x = self.ln_f(x) + final_norm = jnp.linalg.norm(x.array) + final_has_nan = jnp.any(jnp.isnan(x.array)) + # jax.debug.print("After final ln: norm={norm}, has_nan={nan}", norm=final_norm, nan=final_has_nan) + return x def _state_dict_key_map(self) -> Dict[str, Optional[str]]: @@ -300,10 +347,28 @@ def init(Vocab: Axis, config: Gpt2Config, *, key) -> "Gpt2Embeddings": @named_call def embed(self, input_ids, *, key, pos_ids: NamedArray): + # jax.debug.print("input_ids: min={min}, max={max}, has_nan={nan}", min=jnp.min(input_ids.array), max=jnp.max(input_ids.array), nan=jnp.any(jnp.isnan(input_ids.array))) + # jax.debug.print("token_embeddings.weight: has_nan={nan}", nan=jnp.any(jnp.isnan(self.token_embeddings.weight.array))) + input_embeds = self.token_embeddings(input_ids) + input_embeds_norm = jnp.linalg.norm(input_embeds.array) + input_embeds_has_nan = jnp.any(jnp.isnan(input_embeds.array)) + # jax.debug.print("input_embeds: norm={norm}, has_nan={nan}", norm=input_embeds_norm, nan=input_embeds_has_nan) + position_embeds = self.position_embeddings.embed(pos_ids) + pos_embeds_norm = jnp.linalg.norm(position_embeds.array) + pos_embeds_has_nan = jnp.any(jnp.isnan(position_embeds.array)) + # jax.debug.print("position_embeds: norm={norm}, has_nan={nan}", norm=pos_embeds_norm, nan=pos_embeds_has_nan) + x = input_embeds + position_embeds + x_norm = jnp.linalg.norm(x.array) + x_has_nan = jnp.any(jnp.isnan(x.array)) + # jax.debug.print("embeds_sum: norm={norm}, has_nan={nan}", norm=x_norm, nan=x_has_nan) + x = self.dropout(x, key=key) + x_dropout_norm = jnp.linalg.norm(x.array) + x_dropout_has_nan = jnp.any(jnp.isnan(x.array)) + # jax.debug.print("embeds_after_dropout: norm={norm}, has_nan={nan}", norm=x_dropout_norm, nan=x_dropout_has_nan) return x @@ -339,6 +404,8 @@ def init(cls, Vocab: Axis, config: Gpt2Config, *, key) -> "Gpt2LMHeadModel": k_t, k_embeddings = jrandom.split(key, 2) transformer = Gpt2Transformer.init(config, key=k_t) embeddings = Gpt2Embeddings.init(Vocab, config, key=k_embeddings) + # jax.debug.print("embeddings Vocab.size={}", Vocab.size) + # jax.debug.print("embeddings token_embeddings.weight.shape={}", embeddings.token_embeddings.weight.shape) return Gpt2LMHeadModel(transformer, embeddings) @@ -353,7 +420,11 @@ def activations( k_embed, k_transformer = haliax.jax_utils.maybe_rng_split(key, 2) if pos_ids is None: pos_ids = hax.arange(input_ids.resolve_axis("position"), dtype=jnp.int32) + # jax.debug.print(f"input_ids={input_ids.array}") + # jax.debug.print(f"pos_ids={pos_ids.array}") x = self.embeddings.embed(input_ids, pos_ids=pos_ids, key=k_embed) + # jax.debug.print(f"embedded={x.array}") + # jax.debug.print(f"attn_mask={attn_mask}") x = self.transformer(x, attn_mask, key=k_transformer) return x diff --git a/src/levanter/trainer.py b/src/levanter/trainer.py index b44b22ea2..f987bf85e 100644 --- a/src/levanter/trainer.py +++ b/src/levanter/trainer.py @@ -483,6 +483,16 @@ def train_step(self, state: S, *batch: X, **batch_kwargs) -> StepInfo[S]: loss = result.loss.item() if self.config.crash_on_nan and jnp.isnan(loss): + jnp.set_printoptions(threshold=sys.maxsize, linewidth=sys.maxsize) + # jax.debug.print(f"tokens={batch[0].tokens.array.astype(dtype=jnp.int32)}") + # jax.debug.print(f"loss_mask={batch[0].loss_mask.array.astype(dtype=jnp.int32)}") + # print(f"batch={batch}") + # jax.debug.print("{result}", result=result) + # jax.debug.print(f"attn_mask={batch[0].attn_mask}") + # jax.debug.print("input_mask={x}", x=batch[0].attn_mask.input_mask.array) + # jax.debug.print("segment_ids={x}", x=batch[0].attn_mask.segment_ids[0].array) + # materialized = batch[0].attn_mask.materialize(hax.Axis(name='position', size=1024), hax.Axis(name='key_position', size=1024)) + # jax.debug.print(f"attn_mask={materialized.array}") raise RuntimeError("Loss is NaN") if self.config.crash_on_inf and jnp.isinf(loss): @@ -880,7 +890,7 @@ def initialize(self): """Initializes jax, logging, setting the run name/id in the process""" self._initialize_jax_config() # Can't do full logging setup until we've initialized jax b/c we use jax for rank id - pylogging.basicConfig(level=pylogging.WARNING) + pylogging.basicConfig(level=pylogging.INFO) self.distributed.initialize() self._validate_and_set_defaults() diff --git a/tests/test_attention.py b/tests/test_attention.py index 6c2e7e781..87d67f2b6 100644 --- a/tests/test_attention.py +++ b/tests/test_attention.py @@ -783,3 +783,108 @@ def test_attention_equivalence_jax_flash( o2 = sink_attention_ref_gpt_oss(q, k, v, sinks, sm_scale, sliding_window, start_q) torch.testing.assert_close(o1, o2) + + +def test_attention_mask_with_prefix(): + """Test that AttentionMask correctly handles is_prefix for UL2R-style attention.""" + L = 8 + Pos = Axis("Pos", L) + KPos = Pos.alias("KPos") + + # Create a prefix mask pattern for UL2R + # input_masks: 1 1 0 0 1 0 0 0 (segments 0 and 1) + # segment_ids: 0 0 0 0 1 1 -1 -1 + input_masks = jnp.array([1, 1, 0, 0, 1, 0, 0, 0], dtype=jnp.bool_) + segment_ids = jnp.array([0, 0, 0, 0, 1, 1, -1, -1]) + + # Create AttentionMask with causal, segment_ids, and is_prefix + input_masks_named = hax.named(input_masks, Pos) + segment_ids_named = hax.named(segment_ids, Pos) + mask = AttentionMask( + is_causal=True, + is_prefix=True, + input_mask=input_masks_named, + segment_ids=(segment_ids_named, segment_ids_named.rename({Pos.name: KPos.name})) + ) + + # Materialize the mask + materialized = mask.materialize(Pos, KPos) + + print(materialized) + + # Check the materialized mask + # The mask should allow: + # - Causal attention for all positions + # - Additional bidirectional attention for input positions within same segment + + # For position 0 (input in segment 0): + # Should see position 0 (causal) + position 1 (prefix - same segment input) + assert materialized.array[0, 0] == True # Can see self (causal) + assert materialized.array[0, 1] == True # Can see position 1 (prefix) + assert materialized.array[0, 2] == False # Cannot see position 2 (future + not input) + + # For position 1 (input in segment 0): + # Should see positions 0-1 (causal + prefix) + assert materialized.array[1, 0] == True # Can see position 0 (causal + prefix) + assert materialized.array[1, 1] == True # Can see self (causal) + assert materialized.array[1, 2] == False # Cannot see position 2 (future) + + # For position 2 (output in segment 0): + # Should only follow causal pattern + assert materialized.array[2, 0] == True # Can see position 0 (causal) + assert materialized.array[2, 1] == True # Can see position 1 (causal) + assert materialized.array[2, 2] == True # Can see self (causal) + assert materialized.array[2, 3] == False # Cannot see position 3 (future) + + # For position 4 (input in segment 1): + # Should only see itself (different segment, no cross-segment attention) + assert materialized.array[4, 0] == False # Cannot see segment 0 + assert materialized.array[4, 4] == True # Can see self (causal + prefix) + assert materialized.array[4, 5] == False # Cannot see position 5 (future) + + # For positions 6-7 (padding): + # Should only follow causal pattern (is_prefix doesn't affect padding) + assert materialized.array[6, 6] == True # Can see self (causal) + assert materialized.array[6, 7] == False # Cannot see future + + +def test_attention_mask_is_prefix_with_batch(): + """Test that prefix attention masks work correctly with a batch axis.""" + B, L = 2, 4 + Batch = Axis("Batch", B) + Pos = Axis("Pos", L) + KPos = Pos.alias("KPos") + + # Batch 0: [0, 0, 0, 0] with segment [0, 0, -1, -1] + # Batch 1: [1, 1, 0, 0] with segment [0, 0, 0, 0] + input_masks = jnp.array([[1, 0, 0, 0], [1, 1, 0, 0]], dtype=jnp.bool_) + segment_ids = jnp.array([[0, 0, -1, -1], [0, 0, 0, 0]]) + + input_masks_named = hax.named(input_masks, (Batch, Pos)) + segment_ids_named = hax.named(segment_ids, (Batch, Pos)) + + mask = AttentionMask( + is_causal=True, + is_prefix=True, + input_mask=input_masks_named, + segment_ids=(segment_ids_named, segment_ids_named.rename({Pos.name: KPos.name})) + ) + + materialized = mask.materialize(Pos, KPos) + + # Batch 0: only pos 0 is input, so only causal attention + # Batch 1: pos 0,1 are input, so they can attend bidirectionally to each other + expected = jnp.array([ + [[True, False, False, False], # Batch 0 + [True, True, False, False], + [False, False, True, False], + [False, False, True, True]], + [[True, True, False, False], # Batch 1: positions 0-1 see each other + [True, True, False, False], + [True, True, True, False], + [True, True, True, True]] + ]) + + # TODO Right now the batch axis isn't the outer axis. Is that right? + assert jnp.all(materialized[Batch, 0].array == expected[0]) + assert jnp.all(materialized[Batch, 1].array == expected[1]) diff --git a/tests/test_ul2r.py b/tests/test_ul2r.py new file mode 100644 index 000000000..3776015e8 --- /dev/null +++ b/tests/test_ul2r.py @@ -0,0 +1,1073 @@ +import json +from pathlib import Path +import sys +import tempfile +import typing +from haliax import Axis, NamedArray +from haliax.nn import hax +import jax +import jax.numpy as jnp +from jaxtyping import PRNGKeyArray +import numpy as np +import pytest + +from levanter.data.text import ( + TextLmDatasetFormat, + UrlSingleDatasetLMConfig, + build_lm_dataset_cache, + preprocessor_for_format, +) +from levanter.data.ul2r import ( + RX_TASK_KIND, + TokenizedDict, + compute_denoising_length, + noise_span_to_unique_sentinel, + num_noise_spans_tokens_and_spans, + to_ul2r_tokens, + ul2r_loss_mask, + random_segmentation, + random_spans_noise_mask, + to_ul2r_rx_tokens, + to_ul2r_s_tokens, + SENTINEL_TOKEN_IDS, + create_ul2r_example, + RXDenoisingConfig, + SDenoisingConfig, + Ul2rDataset, + R_TASK_TOKEN_ID, + X_TASK_TOKEN_ID, + S_TASK_TOKEN_ID, +) +from levanter.layers.attention import AttentionMask +from levanter.models.lm_model import LmExample +from levanter.store.cache import TreeCache + + +def test_random_segmentation(): + padded_length = 100 + + test_cases = [ + (6, 2), + (14, 2), + (5, 2), + (30, 1), + ] + + for num_items, num_segments in test_cases: + key = jax.random.PRNGKey(35) + + segment_lengths = random_segmentation(num_items, num_segments, key, padded_length) + + relevant_lengths = segment_lengths[:num_segments] + + assert ( + jnp.sum(relevant_lengths) == num_items + ), f"Sum of segments {jnp.sum(relevant_lengths)} != num_items {num_items}" + + assert jnp.all( + relevant_lengths > 0 + ), f"Found zero-length segments for num_items={num_items}, num_segments={num_segments}" + + # Check determinism - same key should give same result + segment_lengths2 = random_segmentation(num_items, num_segments, key, padded_length) + np.testing.assert_array_equal(segment_lengths, segment_lengths2) + + # Different key should give different result (usually) + key2 = jax.random.PRNGKey(43) + segment_lengths3 = random_segmentation(num_items, num_segments, key2, padded_length) + # For edge cases like single segment, result might be same + if num_segments > 1 and num_segments < num_items: + assert not np.array_equal(segment_lengths[:num_segments], segment_lengths3[:num_segments]) + + +def test_random_spans_noise_mask(): + padded_length = 256 + # NOTE At short lengths, it can be deterministic because the num_segments = + # 1 for noise/non-noise. + test_cases = [ + (20, 0.3, 3.0, False), + (100, 0.15, 3.0, False), + # (100, 0.15, 3.0, True), + (200, 0.5, 10.0, False), + (10, 0.3, 3.0, True), + ] + + for length, noise_density, mean_span_length, random_roll in test_cases: + key = jax.random.PRNGKey(37) + + mask = random_spans_noise_mask(length, noise_density, key, mean_span_length, random_roll, padded_length) + print(mask) + + assert mask.shape == (padded_length,), f"Expected shape ({padded_length},), got {mask.shape}" + assert mask.dtype == jnp.bool_, f"Expected bool dtype, got {mask.dtype}" + + assert jnp.all(mask[length:] == False), "Mask should be False after length" + + # Check noise density approximately matches (for non-zero cases) + actual_density = jnp.sum(mask[:length]) / length + # Allow for some variance due to rounding + tolerance = 0.2 + assert ( + abs(actual_density - noise_density) < tolerance + ), f"Noise density {actual_density} too far from target {noise_density}" + + # Test that the function is deterministic with same key + mask2 = random_spans_noise_mask(length, noise_density, key, mean_span_length, random_roll, padded_length) + np.testing.assert_array_equal(mask, mask2, "Function should be deterministic with same key") + + # Test that different keys produce different results + keyb = jax.random.PRNGKey(38) + mask3 = random_spans_noise_mask( + length, + noise_density, + keyb, + mean_span_length, + random_roll, + padded_length, + ) + + # DEBUG START ONE + + num_noise_tokens, num_noise_spans, num_nonnoise_tokens = num_noise_spans_tokens_and_spans( + length, noise_density, mean_span_length + ) + + keyu, keyv, keyw = jax.random.split(key, 3) + + noise_span_lengths4 = random_segmentation(num_noise_tokens, num_noise_spans, keyu, padded_length) + nonnoise_span_lengths4 = random_segmentation(num_nonnoise_tokens, num_noise_spans, keyv, padded_length) + + print(f"noise_span_lengths4 {noise_span_lengths4}") + print(f"nonnoise_span_lengths4 {nonnoise_span_lengths4}") + + # Interleave using reshape + interleaved_span_lengths = jnp.reshape( + jnp.stack([nonnoise_span_lengths4, noise_span_lengths4], axis=1), + [padded_length * 2], + )[:padded_length] + + # Create span_start_indicator using bincount + span_starts = jnp.cumsum(interleaved_span_lengths) + span_start_indicator = jnp.bincount(span_starts, length=padded_length) + + print(f"span_starts4 {span_starts}") + print(f"span_start_indicator4 {span_start_indicator}") + + span_num = jnp.cumsum(span_start_indicator) + # Only odd spans less than 2*num_noise_spans are noise + is_noise = ((span_num % 2) == 1) & (span_num < 2 * num_noise_spans) + is_noise = is_noise.astype(jnp.bool_) + + print(f"span_num4 {span_num}") + print(f"is_noise4 {is_noise}") + + # Zero everything at length and after + indices = jnp.arange(padded_length) + is_noise = jnp.where(indices < length, is_noise, False) + is_noise = typing.cast(jnp.ndarray, is_noise) + + print(f"length4={length}") + print("is_noise4") + print(is_noise) + + offset = jax.random.randint(keyw, (), 0, length, dtype=jnp.int32) + print(f"keyw={keyw} offset={offset}") + # Roll the mask + rolled = jnp.roll(is_noise, offset) + # We want to roll within [0, length) so we need to overwrite values that + # came from the end + rolled = jnp.where( + indices < offset, + jnp.roll(is_noise, offset - length), + rolled, + ) + rolled = typing.cast(jnp.ndarray, rolled) + mask_debug4 = jnp.where(indices < length, rolled, False) + + print("mask4 rolled") + print(mask_debug4) + + # DEBUG START + + num_noise_tokens, num_noise_spans, num_nonnoise_tokens = num_noise_spans_tokens_and_spans( + length, noise_density, mean_span_length + ) + + key1, key2, key3 = jax.random.split(keyb, 3) + + noise_span_lengths = random_segmentation(num_noise_tokens, num_noise_spans, key1, padded_length) + nonnoise_span_lengths = random_segmentation(num_nonnoise_tokens, num_noise_spans, key2, padded_length) + + print(f"noise_span_lengths {noise_span_lengths}") + print(f"nonnoise_span_lengths {nonnoise_span_lengths}") + + keyx, keyy, keyz = jax.random.split(key, 3) + + noise_span_lengths2 = random_segmentation(num_noise_tokens, num_noise_spans, keyx, padded_length) + nonnoise_span_lengths2 = random_segmentation(num_nonnoise_tokens, num_noise_spans, keyy, padded_length) + + print(f"noise_span_lengths2 {noise_span_lengths2}") + print(f"nonnoise_span_lengths2 {nonnoise_span_lengths2}") + + # Interleave using reshape + interleaved_span_lengths = jnp.reshape( + jnp.stack([nonnoise_span_lengths, noise_span_lengths], axis=1), + [padded_length * 2], + )[:padded_length] + + # Create span_start_indicator using bincount + span_starts = jnp.cumsum(interleaved_span_lengths) + span_start_indicator = jnp.bincount(span_starts, length=padded_length) + + print(f"span_starts {span_starts}") + print(f"span_start_indicator {span_start_indicator}") + + span_num = jnp.cumsum(span_start_indicator) + # Only odd spans less than 2*num_noise_spans are noise + is_noise = ((span_num % 2) == 1) & (span_num < 2 * num_noise_spans) + is_noise = is_noise.astype(jnp.bool_) + + print(f"span_num {span_num}") + print(f"is_noise {is_noise}") + + # Zero everything at length and after + indices = jnp.arange(padded_length) + is_noise = jnp.where(indices < length, is_noise, False) + is_noise = typing.cast(jnp.ndarray, is_noise) + + print(f"length={length}") + print("is_noise") + print(is_noise) + + offset = jax.random.randint(key3, (), 0, length, dtype=jnp.int32) + print(f"key3={key3} offset={offset}") + # Roll the mask + rolled = jnp.roll(is_noise, offset) + # We want to roll within [0, length) so we need to overwrite values that + # came from the end + rolled = jnp.where( + indices < offset, + jnp.roll(is_noise, offset - length), + rolled, + ) + rolled = typing.cast(jnp.ndarray, rolled) + mask_debug = jnp.where(indices < length, rolled, False) + + print("mask rolled") + print(mask_debug) + print("mask") + print(mask) + print("mask3") + print(mask3) + + # DEBUG END + + # NOTE This test could fail spuriously because of randomness. Inspect + # the results. + assert not jnp.array_equal(mask, mask3), "Different keys should produce different masks" + + +def test_noise_span_to_unique_sentinel(): + """Test that noise_span_to_unique_sentinel works correctly with static shapes.""" + padded_length = 256 + pad_token_id = 0 + sentinel_tokens = jnp.array([100, 101, 102, 103, 104]) + + # Test case 1: First token is a noise span (single span) + tokens = jnp.arange(10, 20) # [10, 11, 12, ..., 19] + tokens = jnp.pad(tokens, (0, padded_length - 10), constant_values=pad_token_id) + noise_mask = jnp.array( + [ + True, + True, + True, + False, + False, + False, + False, + False, + False, + False, # Span 1: [10, 11, 12] + ] + ) + noise_mask = jnp.pad(noise_mask, (0, padded_length - 10), constant_values=False) + + result = noise_span_to_unique_sentinel(tokens, 10, noise_mask, sentinel_tokens, force_initial_sentinel=False) + + expected = jnp.array([100, 13, 14, 15, 16, 17, 18, 19]) + np.testing.assert_array_equal(result[:8], expected) + assert jnp.all(result[8:] == pad_token_id) + + # Test case 2: First token is NOT a noise span + # force_initial_sentinel=True (for use w/ targets, which must always start + # with a sentinel even if the noise mask is 010) + tokens = jnp.arange(10, 25) # [10, 11, 12, ..., 24] + tokens = jnp.pad(tokens, (0, padded_length - 15), constant_values=pad_token_id) + noise_mask = jnp.array( + [ + False, # Does not start with noise span + True, + True, + False, + False, + False, + True, + False, + False, + False, + True, + True, + True, + False, + False, + ] + ) + noise_mask = jnp.pad(noise_mask, (0, padded_length - 15), constant_values=False) + + result = noise_span_to_unique_sentinel(tokens, 15, noise_mask, sentinel_tokens, force_initial_sentinel=True) + + # Should still start with sentinel + expected = jnp.array([100, 10, 101, 13, 14, 15, 102, 17, 18, 19, 103, 23, 24]) + np.testing.assert_array_equal(result[:13], expected) + assert jnp.all(result[13:] == pad_token_id) + + # Test case 3: Empty noise mask (no noise) + tokens = jnp.arange(10, 20) + tokens = jnp.pad(tokens, (0, padded_length - 10), constant_values=pad_token_id) + noise_mask = jnp.zeros(padded_length, dtype=jnp.bool_) + + result = noise_span_to_unique_sentinel(tokens, 10, noise_mask, sentinel_tokens, force_initial_sentinel=False) + + # Should be unchanged except for padding + np.testing.assert_array_equal(result[:10], jnp.arange(10, 20)) + assert jnp.all(result[10:] == pad_token_id) + + +def test_to_ul2r_rx_tokens(): + max_length = 256 + pad_token_id = 0 + sentinel_tokens = jnp.array([100, 101, 102, 103, 104]) + + # Test case: Simple sequence with known noise pattern + tokens = jnp.arange(10, 20) + tokens = jnp.pad(tokens, (0, max_length - tokens.shape[0]), constant_values=pad_token_id) + length = 10 + + key = jax.random.PRNGKey(38) + + # DEBUG + + padded_length = tokens.shape[0] + noise_mask = random_spans_noise_mask( + length, + 0.3, + key, + 3.0, + True, + padded_length, + ) + + print(noise_mask) + print(noise_mask[:length]) + + num_noise_tokens, num_noise_spans, num_nonnoise_tokens = num_noise_spans_tokens_and_spans(length, 0.3, 3.0) + + key1, key2, key3 = jax.random.split(key, 3) + + print(num_noise_tokens) + print(num_nonnoise_tokens) + print(num_noise_spans) + + noise_span_lengths = random_segmentation(num_noise_tokens, num_noise_spans, key1, padded_length) + nonnoise_span_lengths = random_segmentation(num_nonnoise_tokens, num_noise_spans, key2, padded_length) + + print(noise_span_lengths) + print(nonnoise_span_lengths) + + # Interleave using reshape + interleaved_span_lengths = jnp.reshape( + jnp.stack([nonnoise_span_lengths, noise_span_lengths], axis=1), + [2 * padded_length], + )[:padded_length] + + # Create span_start_indicator using bincount + span_starts = jnp.cumsum(interleaved_span_lengths) + span_start_indicator = jnp.bincount(span_starts, length=padded_length) + + span_num = jnp.cumsum(span_start_indicator) + # Only odd spans less than 2*num_noise_spans are noise + is_noise = ((span_num % 2) == 1) & (span_num < 2 * num_noise_spans) + is_noise = is_noise.astype(jnp.bool_) + + # Zero everything at length and after + indices = jnp.arange(padded_length) + is_noise = jnp.where(indices < length, is_noise, False) + is_noise = typing.cast(jnp.ndarray, is_noise) + + inputs = noise_span_to_unique_sentinel( + tokens, + length, + noise_mask, + sentinel_tokens, + force_initial_sentinel=False, + ) + targets = noise_span_to_unique_sentinel( + tokens, + length, + ~noise_mask, + sentinel_tokens, + force_initial_sentinel=True, + ) + + print(inputs) + print(targets) + + # END DEBUG + + input_length, result = to_ul2r_rx_tokens( + key, + tokens, + length, + mask_prob=0.3, + mean_noise_span_length=3.0, + random_roll=False, + pad_token_id=pad_token_id, + sentinel_token_ids=sentinel_tokens, + max_length=max_length, + ) + + print(tokens) + print(result) + + assert result.shape == (max_length,) + + assert input_length.shape == () + assert input_length > 0 + assert input_length <= length + + inputs_part = result[:input_length] + contains_sentinels = jnp.any(jnp.isin(inputs_part, sentinel_tokens)) + assert contains_sentinels, "Inputs should contain sentinel tokens" + + # Find where all padding starts (after both inputs and targets) + # Look for continuous padding at the end + is_pad = result == pad_token_id + # Find the last non-padding position + non_pad_positions = jnp.where(~is_pad, jnp.arange(max_length), -1) + last_non_pad = jnp.max(non_pad_positions) + + if last_non_pad < max_length - 1: + assert jnp.all(result[last_non_pad + 1 :] == pad_token_id), "Should have continuous padding at the end" + + # Test with random_roll=True + # input_length_roll, result_roll = to_ul2r_rx_tokens( + # key, + # tokens, + # length, + # mask_prob=0.3, + # mean_noise_span_length=3.0, + # random_roll=True, + # sentinel_token_ids=sentinel_tokens, + # max_length=max_length, + # ) + + # assert result_roll.shape == (max_length,) + # assert input_length_roll.shape == () + + # is_pad_roll = result_roll == pad_token_id + # non_pad_positions_roll = jnp.where(~is_pad_roll, jnp.arange(max_length), -1) + # last_non_pad_roll = jnp.max(non_pad_positions_roll) + # if last_non_pad_roll < max_length - 1: + # assert jnp.all( + # result_roll[last_non_pad_roll + 1 :] == pad_token_id + # ), "Should have continuous padding at the end with roll" + + +def test_to_ul2r_rx_tokens_roll(): + max_length = 256 + pad_token_id = 0 + min_sentinel_id = 100 + num_sentinels = 5 + sentinel_ids = jnp.arange(min_sentinel_id, min_sentinel_id + num_sentinels) + + tokens = jnp.arange(10, 20) + tokens = jnp.pad(tokens, (0, max_length - tokens.shape[0]), constant_values=pad_token_id) + length = 10 + + for i in range(5): + key = jax.random.PRNGKey(i) + + input_length, result = to_ul2r_rx_tokens( + key, + tokens, + length, + mask_prob=0.3, + mean_noise_span_length=3.0, + random_roll=True, + pad_token_id=pad_token_id, + sentinel_token_ids=sentinel_ids, + max_length=max_length, + ) + + print(tokens) + print(result) + + assert result.shape == (max_length,) + + assert input_length.shape == () + assert input_length > 0 + assert input_length <= length + + inputs = result[:input_length] + targets = result[input_length:] + + # The same sentinels should exist in inputs/targets. + # We previously had a bug where a noise mask of 010 (created from + # random_roll) would produce a target noise mask of 101, which would create + # 2 sentinels in the target vs. only 1 in the input. + + print("inputs", inputs) + print("targets", targets) + np.testing.assert_array_equal(jnp.isin(sentinel_ids, inputs), jnp.isin(sentinel_ids, targets)) + assert jnp.any(jnp.isin(sentinel_ids, inputs)) + assert jnp.any(jnp.isin(sentinel_ids, targets)) + + +def test_compute_denoising_length_rx_random_roll(): + """ + Test that compute_denoising_length with random_roll=True reserves enough + space and sets pad_token_id when it doesn't create an extra span. + + When random_roll=True, we reserve space for an extra span. However, rolling doesn't + always create an additional span, so we should see both cases: + - no pad_token_id (rolling created an extra span) + - 1 pad_token_id (rolling created an extra span) + """ + max_length = 16 + pad_token_id = 999 # Use non-zero pad token to verify it's actually being used + sentinel_ids = jnp.arange(100, 120) + + length = 12 + tokens = jnp.arange(1, length + 1) + tokens = jnp.pad(tokens, (0, max_length - length), constant_values=pad_token_id) + + mask_prob = 0.3 + mean_noise_span_length = 3.0 + random_roll = True + task_params = jnp.array([RX_TASK_KIND, R_TASK_TOKEN_ID, mask_prob, mean_noise_span_length, int(random_roll)]) + + predicted_length = compute_denoising_length(task_params, length) + + padding_counts = [] + for i in range(16): + key = jax.random.PRNGKey(i) + _input_length, result = to_ul2r_rx_tokens( + key, tokens, length, mask_prob, mean_noise_span_length, True, + pad_token_id, sentinel_ids, max_length + ) + + # Subtract 1 because `result` doesn't include the task token. + # print(result[:predicted_length - 1]) + num_padding = jnp.sum(result[:predicted_length - 1] == pad_token_id) + padding_counts.append(int(num_padding)) + + actual_length = jnp.sum(result != pad_token_id) + assert actual_length <= predicted_length + + assert any(p == 0 for p in padding_counts) + assert any(p == 2 for p in padding_counts) + assert all(p == 0 or p == 2 for p in padding_counts) + + +def test_ul2r_loss_mask(): + # Test case 1: Simple single segment + input_masks = jnp.array([1, 1, 0, 0]) # First 2 are inputs + segment_ids = jnp.array([0, 0, 0, 0]) # All in same segment + tokens = jnp.array([10, 11, 12, 13]) # No padding + pad_token_id = 0 + + mask = ul2r_loss_mask(input_masks, segment_ids, tokens, pad_token_id) + + # Expected: loss on outputs, shifted by 1 + # Original: 1 1 0 0 (input_masks) + # Inverted: 0 0 1 1 + # Shifted: 0 1 1 0 (roll -1) + # No boundaries or padding to worry about + expected = jnp.array([False, True, True, False]) + + np.testing.assert_array_equal(mask, expected) + + # Test case 2: Multiple segments with padding + input_masks = jnp.array([1, 1, 0, 0, 1, 0, 0, 0]) + tokens = jnp.array([10, 11, 12, 13, 14, 15, 0, 0]) # Last 2 are padding tokens + segment_ids = jnp.array([0, 0, 0, 0, 1, 1, -1, -1]) # Two segments plus padding + + mask = ul2r_loss_mask(input_masks, segment_ids, tokens, pad_token_id) + + # Expected: no loss across segment boundaries or on padding + # Original: 1 1 0 0 | 1 0 | 0 0 (padding) + # Inverted: 0 0 1 1 | 0 1 | 1 1 + # Shifted: 0 1 1 0 | 1 1 | 1 0 + # Segment cont: T T T F | T F | F F (boundaries and padding) + # Valid mask: T T T T | T T | F F (padding tokens are 0) + # Final: 0 1 1 0 | 1 0 | 0 0 + expected = jnp.array([False, True, True, False, True, False, False, False]) + + np.testing.assert_array_equal(mask, expected) + + +def test_to_ul2r_rx_tokens_truncates_both_sections_and_contains_sentinels(): + """ + Ensure we truncate from both inputs and outputs and retain sentinels in each section. + + We create a long padded input (padded_length > max_length) and compare the + pre-truncation lengths to the output of to_ul2r_rx_tokens with a small + max_length to force truncation. + """ + padded_length = 512 + max_length = 400 + pad_token_id = 0 + sentinel_tokens = jnp.arange(100, 140) # plenty of sentinels + + length = 450 # true sequence length (greater than max_length) + tokens = jnp.arange(1, length + 1) + tokens = jnp.pad(tokens, (0, padded_length - length), constant_values=pad_token_id) + + key = jax.random.PRNGKey(123) + mask_prob = 0.3 + mean_noise_span_length = 3.0 + random_roll = False + + # First, run with a large max_length to observe untruncated section lengths + input_len_full, result_full = to_ul2r_rx_tokens( + key, + tokens, + length, + mask_prob=mask_prob, + mean_noise_span_length=mean_noise_span_length, + random_roll=random_roll, + pad_token_id=pad_token_id, + sentinel_token_ids=sentinel_tokens, + max_length=padded_length, + ) + # Compute outputs length (non-padding after inputs) + outputs_full = result_full[input_len_full:] + outputs_nonpad_full = jnp.sum(outputs_full != pad_token_id) + + # Now run with a smaller max_length to force truncation + input_len_small, result_small = to_ul2r_rx_tokens( + key, + tokens, + length, + mask_prob=mask_prob, + mean_noise_span_length=mean_noise_span_length, + random_roll=random_roll, + pad_token_id=pad_token_id, + sentinel_token_ids=sentinel_tokens, + max_length=max_length, + ) + + assert result_small.shape == (padded_length,) + assert input_len_small > 0 + assert input_len_small < input_len_full # inputs truncated + + # Outputs section length and checks + outputs_slice = result_small[input_len_small:] + outputs_nonpad = jnp.sum(outputs_slice != pad_token_id) + assert outputs_nonpad > 0 + assert outputs_nonpad < outputs_nonpad_full # outputs truncated + + # Sentinel presence in both sections + inputs_slice = result_small[:input_len_small] + has_sentinel_inputs = jnp.any(jnp.isin(inputs_slice, sentinel_tokens)) + has_sentinel_outputs = jnp.any(jnp.isin(outputs_slice[:outputs_nonpad], sentinel_tokens)) + assert has_sentinel_inputs + assert has_sentinel_outputs + + +def test_to_ul2r_s_tokens(): + # Test case 1: Basic functionality with simple sequence + tokens = jnp.arange(10, 20) # [10, 11, 12, ..., 19] + padded_tokens = jnp.pad(tokens, (0, 256 - 10), constant_values=0) + length = 10 + key = jax.random.PRNGKey(42) + + pivot, result = to_ul2r_s_tokens(key, padded_tokens, length, SENTINEL_TOKEN_IDS[0]) + + assert result.shape == padded_tokens.shape + assert pivot.shape == () + assert 1 <= pivot < length - 1, f"Pivot {pivot} should be between 1 and {length-2}" + assert result[pivot] == SENTINEL_TOKEN_IDS[0] + + # Check prefix is unchanged (before pivot) + np.testing.assert_array_equal(result[:pivot], padded_tokens[:pivot]) + + # Check continuation (after pivot) is shifted from original + # The continuation should be tokens[pivot:] starting at position pivot+1 + expected_continuation = padded_tokens[pivot:] + # Shift by 1 to account for sentinel at pivot + np.testing.assert_array_equal(result[pivot + 1 :], expected_continuation[:-1]) + + # Test case 2: Determinism - same key should give same result + pivot2, result2 = to_ul2r_s_tokens(key, padded_tokens, length, SENTINEL_TOKEN_IDS[0]) + np.testing.assert_array_equal(result, result2) + assert pivot == pivot2 + + # Test case 3: Different keys should give different pivots (usually) + key2 = jax.random.PRNGKey(43) + pivot3, result3 = to_ul2r_s_tokens(key2, padded_tokens, length, SENTINEL_TOKEN_IDS[0]) + assert pivot != pivot3, "Different keys should produce different pivots" + + +def test_create_ul2r_example(): + QPos = Axis("QPos", 128) + KPos = Axis("KPos", 128) + pad_token_id = 0 + max_segments_per_example = 8 + + task_configs = [ + RXDenoisingConfig(R_TASK_TOKEN_ID, 0.15, 3.0, False), + RXDenoisingConfig(X_TASK_TOKEN_ID, 0.5, 3.0, False), + SDenoisingConfig(S_TASK_TOKEN_ID), + ] + task_params = jnp.array([cfg.to_task_params() for cfg in task_configs]) + task_indices = jnp.array([0, 1, 2]) + + in_len_r = 10 + in_len_x = 8 + in_len_s = 5 + in_len = in_len_r + in_len_x + in_len_s + + out_len_r = compute_denoising_length(task_params[0], in_len_r) + out_len_x = compute_denoising_length(task_params[1], in_len_x) + out_len_s = compute_denoising_length(task_params[2], in_len_s) + + tokens = jnp.concatenate( + [ + jnp.arange(10, 10 + in_len_r), # 0-10 segment 0 [R] + # When we pack examples together, we compute the amount of padding + # we need to reserve using `compute_denoising_length`. Here we just + # manually add padding. + jnp.zeros(10, dtype=jnp.int32), # 10-20 padding + jnp.arange(20, 20 + in_len_x), # 20-28 segment 1 [X] + jnp.zeros(10, dtype=jnp.int32), # 28-38 padding + jnp.arange(30, 30 + in_len_s), # 38-43 segment 2 [S] + jnp.zeros(128 - in_len - 20, dtype=jnp.int32), # 43-128 padding + ] + ) + tokens = hax.named(tokens, QPos) + + print(tokens.array) + + # The segment_ids need to + segment_ids = jnp.concatenate( + [ + jnp.full(in_len_r, 0), + jnp.full(10, -1), + jnp.full(in_len_x, 1), + jnp.full(10, -1), + jnp.full(in_len_s, 2), + jnp.full(128 - in_len - 20, -1), + ] + ) + segment_ids = hax.named(segment_ids, QPos) + + key = jax.random.PRNGKey(37) + + # DEBUG START + + # TODO Use NamedArrays more idiomatically + # `unique_seg_ids = [3, 4, ..., -1, ...]` + # Valid segment IDs come first, padded with -1. + # Sorted; assumes `segment_ids` is also sorted in ascending order. + # We use the same ordering for `out_starts` etc. + max_seg_id = jnp.max(segment_ids.array) + seg_ids = jnp.where(segment_ids.array == -1, max_seg_id, segment_ids.array) + unique_seg_ids = jnp.unique(seg_ids, size=max_segments_per_example, fill_value=-1) + + def prepare_segment(id: int) -> tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]: + """ + Returns `(in_start, in_length, out_length)` for segment `id`. + The result is undefined for ids not in `segment_ids` or the id -1. + """ + + mask = segment_ids.array == id + n = mask.shape[0] + idx = jnp.arange(n) + + in_start = jnp.min(jnp.where(mask, idx, n)) + in_end = jnp.max(jnp.where(mask, idx + 1, 0)) + in_length = in_end - in_start + + # Eventually we don't want to have to instantiate all of `offsets` + # and `task_indices` (because they could take up a lot of + # space). We can generate `task_params` / `task_idx` for each + # batch independently (but in a way that matches how we computed + # lengths for packing). + task_idx = task_indices[id] + out_length = compute_denoising_length(task_params[task_idx], in_length) + + return in_start, in_length, out_length + + in_starts, in_lengths, out_lengths = jax.vmap(prepare_segment)(unique_seg_ids) + + # `out_starts[i]` is the offset of the beginning of the i-th output segment. + # Segment lengths increase when we turn them into denoising examples. + out_starts = jax.lax.cumsum(out_lengths) - out_lengths + out_starts = jnp.where(unique_seg_ids == -1, -1, out_starts) + out_starts = typing.cast(jnp.ndarray, out_starts) + + print(out_starts) + + def process_segment(key, id: int) -> tuple[jnp.ndarray, jnp.ndarray, int, int]: + """ + Applies UL2R denoising to a single segment. + Returns `(input_mask, denoising_tokens, start, length)`. + `input_mask` is true when the token is an input given to the LLM + (i.e. one where we don't compute loss). + `denoising_tokens` is `0... to_ul2r_tokens(...) 0...` where the + prefix is `out_starts[idx]` zeros. + We'll or all the segments together at the end; the nonzero parts + shouldn't overlap. + """ + + task_idx = task_indices[id] + + idx = jnp.nonzero(unique_seg_ids == id, size=1)[0] + in_start = typing.cast(int, jnp.squeeze(in_starts[idx])) + in_length = typing.cast(int, jnp.squeeze(in_lengths[idx])) + out_length = typing.cast(int, jnp.squeeze(out_lengths[idx])) + out_start = typing.cast(int, jnp.squeeze(out_starts[idx])) + + segment = jnp.roll(tokens.array, -in_start) + print(key, task_params[task_idx], segment, in_length, QPos.size) + inputs_len, denoising_tokens = to_ul2r_tokens(key, task_params[task_idx], segment, in_length, pad_token_id, SENTINEL_TOKEN_IDS, QPos.size) + + n_tokens = tokens.array.shape[0] + input_mask = jnp.arange(n_tokens) < inputs_len + input_mask = jnp.roll(input_mask, out_start) + denoising_tokens = jnp.roll(denoising_tokens, out_start) + return (input_mask, denoising_tokens, out_start, out_length) + + process_key, _key = jax.random.split(key) + print(process_key) + print(process_segment(process_key, 0)) + + def loop( + acc: tuple[PRNGKeyArray, int, jnp.ndarray, jnp.ndarray, jnp.ndarray], + ) -> tuple[PRNGKeyArray, int, jnp.ndarray, jnp.ndarray, jnp.ndarray]: + (key, i, input_mask, denoising_tokens, out_seg_ids) = acc + process_key, key = jax.random.split(key) + id = unique_seg_ids[i] + (seg_input_mask, seg_denoising_tokens, start, length) = process_segment(process_key, id) + input_mask = input_mask | seg_input_mask + denoising_tokens = denoising_tokens | seg_denoising_tokens + indices = jnp.arange(out_seg_ids.shape[0]) + out_seg_ids = jnp.where((indices >= start) & (indices < start + length), id, out_seg_ids) + out_seg_ids = typing.cast(jnp.ndarray, out_seg_ids) + return (key, i + 1, input_mask, denoising_tokens, out_seg_ids) + + key2, i2, input_mask2, denoising_tokens2, out_seg_ids2 = loop( + ( + key, + 0, + jnp.zeros_like(tokens.array, dtype=jnp.bool_), + jnp.zeros_like(tokens.array), + jnp.full(tokens.array.shape, -1), + ) + ) + key3, i3, input_mask3, denoising_tokens3, out_seg_ids3 = loop( + (key2, i2, input_mask2, denoising_tokens2, out_seg_ids2) + ) + + jnp.set_printoptions(threshold=sys.maxsize, linewidth=sys.maxsize) + + input_attn_mask = jnp.outer(input_mask3, input_mask3) + print("input_attn_mask") + print(input_attn_mask.astype(jnp.int32)) + print("out_seg_ids3") + print(out_seg_ids3) + segment_mask = out_seg_ids3 == out_seg_ids3[:, jnp.newaxis] + print("segment_mask") + print(segment_mask.astype(jnp.int32)) + prefix_mask = segment_mask & input_attn_mask + print("prefix_mask") + print(prefix_mask.astype(jnp.int32)) + + # DEBUG END + + example = create_ul2r_example( + key, + QPos, + KPos, + pad_token_id, + SENTINEL_TOKEN_IDS, + max_segments_per_example, + task_params, + task_indices, + tokens, + segment_ids, + ) + + # Basic smoke checks + assert example.tokens.array.shape == (QPos.size,) + assert example.loss_mask.array.shape == (QPos.size,) + assert example.attn_mask.is_causal + + # Should contain sentinel tokens after denoising + contains_sentinels = jnp.any(jnp.isin(example.tokens.array, SENTINEL_TOKEN_IDS)) + assert contains_sentinels + + # Should have some loss positions + assert jnp.any(example.loss_mask.array) + + # No loss on padding tokens + is_padding = example.tokens.array == pad_token_id + assert not jnp.any(example.loss_mask.array & is_padding) + + print(example.tokens.array) + print(example.loss_mask.array.astype(jnp.int32)) + print(example.attn_mask.segment_ids[0].array) + print(example.attn_mask.materialize(QPos, KPos).array.astype(jnp.int32)) + + # The R-denoising example should contain exactly 2 of the first sentinel + # (one to identify the masked span, one to identify the output span) + assert jnp.sum(example.tokens.array[0:out_len_r] == SENTINEL_TOKEN_IDS[0]) == 2 + + +@pytest.fixture +def dummy_text_data(): + texts = [ + {"text": "The quick brown fox jumps over the lazy dog."}, + {"text": "Machine learning is a subset of artificial intelligence."}, + {"text": "Python is a popular programming language."}, + {"text": "Data science combines statistics and computer science."}, + ] + with tempfile.TemporaryDirectory() as tmpdir: + path = Path(tmpdir) / "text.jsonl" + with path.open("w") as f: + for text in texts: + f.write(json.dumps(text) + "\n") + yield str(path) + + +@pytest.fixture(scope="module") +def hf_tokenizer(): + from transformers import AutoTokenizer + + return AutoTokenizer.from_pretrained( + "stanford-crfm/marin-tokenizer", + revision="49a09e626c220e9daae74124ea41be1bf5cd331d", + ) + + +@pytest.mark.ray +def test_ul2r_dataset_build(dummy_text_data, hf_tokenizer): + with tempfile.TemporaryDirectory() as tmpdir: + tokenizer = hf_tokenizer + + config = UrlSingleDatasetLMConfig(train_urls=[dummy_text_data], format=TextLmDatasetFormat()) + + processor = preprocessor_for_format(config.format, tokenizer) + + # Test the processor + source = config.get_shard_source("train") + assert source is not None + assert source.shard_names is not None + processed = [] + for doc in source.open_shard(source.shard_names[0]): + processed += processor([doc]) + + assert len(processed) == 4 + + # Build cache + cache = build_lm_dataset_cache(tmpdir, source, config.format, tokenizer) + cache.await_finished() + cache_sync = cache.as_sync_dataset() + assert len(cache_sync) == 4 + + sample = next(iter(cache)) + assert sample["input_ids"].shape[0] > 0 # pyright: ignore + cache = typing.cast(TreeCache[TokenizedDict], cache) + + # Test Ul2rDataset + QPos = hax.Axis("QPos", 128) + KPos = hax.Axis("KPos", 128) + task_configs = { + "r": RXDenoisingConfig(R_TASK_TOKEN_ID, 0.15, 3.0, False), + "x": RXDenoisingConfig(X_TASK_TOKEN_ID, 0.5, 3.0, False), + "s": SDenoisingConfig(S_TASK_TOKEN_ID), + } + + dataset = Ul2rDataset( + cache=cache, + QPos=QPos, + KPos=KPos, + task_configs=task_configs, + task_probs={"r": 0.33, "x": 0.33, "s": 0.34}, + key=jax.random.PRNGKey(123), + pad_token_id=tokenizer.pad_token_id or 0, + sentinel_token_ids=SENTINEL_TOKEN_IDS, + max_segments_per_example=4, + ) + dataset_sync = dataset.as_sync_dataset() + + assert len(dataset_sync) > 0 + + ex = dataset_sync[0] + + # Structure checks + assert isinstance(ex, LmExample) + assert ex.tokens.axes == (QPos,) + assert ex.loss_mask.axes == (QPos,) + assert isinstance(ex.attn_mask, AttentionMask) + assert ex.attn_mask.is_causal + + # Content checks + pad_id = tokenizer.pad_token_id or 0 + non_padding = ex.tokens.array != pad_id + num_loss = jnp.sum(ex.loss_mask.array) + + assert jnp.sum(non_padding) > 0 + assert num_loss > 0 + assert num_loss < jnp.sum(non_padding) # Loss < content (some tokens are inputs) + assert not jnp.any(ex.loss_mask.array & ~non_padding) # No loss on padding + assert jnp.any(jnp.isin(ex.tokens.array, SENTINEL_TOKEN_IDS)) # Has sentinels from denoising + + # Collect all original input tokens from the cache + original_tokens = set() + for item in cache_sync: + original_tokens.update(int(t) for t in item["input_ids"] if t != pad_id) + + # Check that all output tokens (except pad, sentinels, and task tokens) were present in the input + # This helps verify we're not creating gibberish by overlapping spans + ul2r_special_tokens = set(SENTINEL_TOKEN_IDS.tolist()) | { + R_TASK_TOKEN_ID, + X_TASK_TOKEN_ID, + S_TASK_TOKEN_ID, + pad_id, + } + allowed_tokens = original_tokens | ul2r_special_tokens + output_tokens = set(int(t) for t in ex.tokens.array) + unexpected_tokens = output_tokens - allowed_tokens + assert len(unexpected_tokens) == 0, f"Found unexpected tokens not in input: {unexpected_tokens}" + + # Attention mask checks + input_mask = typing.cast(NamedArray, ex.attn_mask.input_mask) + assert input_mask.array.shape == (QPos.size,) + # Materialize full attention mask (causal + prefix) + materialized = ex.attn_mask.materialize(QPos, KPos) + assert materialized is not None + # Diagonal should be True for all non-padding (tokens attend to themselves) + diag = jnp.diag(materialized.array) + assert jnp.all(diag[non_padding]) + # Some off-diagonal should be True (bidirectional attention on input positions) + off_diag_sum = jnp.sum(materialized.array) - jnp.sum(diag) + assert off_diag_sum > 0, "Expected some bidirectional attention for input positions" + + # Check consistency across multiple examples + for ex_i in [dataset_sync[i] for i in range(min(3, len(dataset_sync)))]: + assert ex_i.tokens.axes == (QPos,) and ex_i.loss_mask.axes == (QPos,) + non_pad_i = jnp.sum(ex_i.tokens.array != pad_id) + loss_i = jnp.sum(ex_i.loss_mask.array) + assert 0 < loss_i < non_pad_i