import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase_     :   Dict 				=				'▁'

lowercase_     :   Union[str, Any] 				=				get_tests_dir('fixtures/test_sentencepiece.model')






@require_sentencepiece
class 		_lowerCamelCase		(   UpperCamelCase_    ,     unittest.TestCase    ):
    __a  =      BertGenerationTokenizer
    __a  =      False
    __a  =      True
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     super().setUp()

     SCREAMING_SNAKE_CASE__:   Tuple=      BertGenerationTokenizer(lowerCAmelCase , keep_accents=lowerCAmelCase   )
     tokenizer.save_pretrained(self.tmpdirname   )
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      '''<s>'''
     SCREAMING_SNAKE_CASE__:   str=      1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase   ) , lowerCAmelCase   )
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase   ) , lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   int=      list(self.get_tokenizer().get_vocab().keys()   )

     self.assertEqual(vocab_keys[0] , '''<unk>'''   )
     self.assertEqual(vocab_keys[1] , '''<s>'''   )
     self.assertEqual(vocab_keys[-1] , '''<pad>'''   )
     self.assertEqual(len(lowerCAmelCase   ) , 1002   )
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     self.assertEqual(self.get_tokenizer().vocab_size , 1000   )
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   List[Any]=      BertGenerationTokenizer(lowerCAmelCase , keep_accents=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   List[str]=      tokenizer.tokenize('''This is a test'''   )
     self.assertListEqual(lowerCAmelCase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']   )

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(lowerCAmelCase   ) , [285, 46, 10, 170, 382] , )

     SCREAMING_SNAKE_CASE__:   Dict=      tokenizer.tokenize('''I was born in 92000, and this is falsé.'''   )
     self.assertListEqual(
         lowerCAmelCase , [
             SPIECE_UNDERLINE + '''I''',
             SPIECE_UNDERLINE + '''was''',
             SPIECE_UNDERLINE + '''b''',
             '''or''',
             '''n''',
             SPIECE_UNDERLINE + '''in''',
             SPIECE_UNDERLINE + '''''',
             '''9''',
             '''2''',
             '''0''',
             '''0''',
             '''0''',
             ''',''',
             SPIECE_UNDERLINE + '''and''',
             SPIECE_UNDERLINE + '''this''',
             SPIECE_UNDERLINE + '''is''',
             SPIECE_UNDERLINE + '''f''',
             '''al''',
             '''s''',
             '''é''',
             '''.''',
         ] , )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      tokenizer.convert_tokens_to_ids(lowerCAmelCase   )
     self.assertListEqual(
         lowerCAmelCase , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )

     SCREAMING_SNAKE_CASE__:   Tuple=      tokenizer.convert_ids_to_tokens(lowerCAmelCase   )
     self.assertListEqual(
         lowerCAmelCase , [
             SPIECE_UNDERLINE + '''I''',
             SPIECE_UNDERLINE + '''was''',
             SPIECE_UNDERLINE + '''b''',
             '''or''',
             '''n''',
             SPIECE_UNDERLINE + '''in''',
             SPIECE_UNDERLINE + '''''',
             '''<unk>''',
             '''2''',
             '''0''',
             '''0''',
             '''0''',
             ''',''',
             SPIECE_UNDERLINE + '''and''',
             SPIECE_UNDERLINE + '''this''',
             SPIECE_UNDERLINE + '''is''',
             SPIECE_UNDERLINE + '''f''',
             '''al''',
             '''s''',
             '''<unk>''',
             '''.''',
         ] , )
    @cached_property
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''   )
    @slow
    def 						UpperCamelCase_				(  self   )       ->					int:
     SCREAMING_SNAKE_CASE__:   str=      '''Hello World!'''
     SCREAMING_SNAKE_CASE__:   int=      [18536, 2260, 101]

     self.assertListEqual(lowerCAmelCase , self.big_tokenizer.encode(lowerCAmelCase   )   )
    @slow
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   int=      (
         '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
         ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
     )
     SCREAMING_SNAKE_CASE__:   Dict=      [
         871,
         419,
         358,
         946,
         991,
         2521,
         452,
         358,
         1357,
         387,
         7751,
         3536,
         112,
         985,
         456,
         126,
         865,
         938,
         5400,
         5734,
         458,
         1368,
         467,
         786,
         2462,
         5246,
         1159,
         633,
         865,
         4519,
         457,
         582,
         852,
         2557,
         427,
         916,
         508,
         405,
         34324,
         497,
         391,
         408,
         11342,
         1244,
         385,
         100,
         938,
         985,
         456,
         574,
         362,
         12597,
         3200,
         3129,
         1172,
     ]

     self.assertListEqual(lowerCAmelCase , self.big_tokenizer.encode(lowerCAmelCase   )   )
    @require_torch
    @slow
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     import torch

     from transformers import BertGenerationConfig, BertGenerationEncoder

     # Build sequence
     SCREAMING_SNAKE_CASE__:   List[Any]=      list(self.big_tokenizer.get_vocab().keys()   )[:10]
     SCREAMING_SNAKE_CASE__:   Tuple=      ''' '''.join(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      self.big_tokenizer.encode_plus(lowerCAmelCase , return_tensors='''pt''' , return_token_type_ids=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Tuple=      self.big_tokenizer.batch_encode_plus(
         [sequence + ''' ''' + sequence] , return_tensors='''pt''' , return_token_type_ids=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   List[str]=      BertGenerationConfig()
     SCREAMING_SNAKE_CASE__:   List[str]=      BertGenerationEncoder(lowerCAmelCase   )

     assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

     with torch.no_grad():
      model(**lowerCAmelCase   )
      model(**lowerCAmelCase   )



    @slow
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     # fmt: off
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      {'''input_ids''': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=lowerCAmelCase , model_name='''google/bert_for_seq_generation_L-24_bbc_encoder''' , revision='''c817d1fd1be2ffa69431227a1fe320544943d4db''' , )


from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)

