from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


a_							=      logging.get_logger(__name__)


a_							=      OrderedDict(
    [
        # Base model mapping
        ("""albert""", """FlaxAlbertModel"""),
        ("""bart""", """FlaxBartModel"""),
        ("""beit""", """FlaxBeitModel"""),
        ("""bert""", """FlaxBertModel"""),
        ("""big_bird""", """FlaxBigBirdModel"""),
        ("""blenderbot""", """FlaxBlenderbotModel"""),
        ("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
        ("""clip""", """FlaxCLIPModel"""),
        ("""distilbert""", """FlaxDistilBertModel"""),
        ("""electra""", """FlaxElectraModel"""),
        ("""gpt-sw3""", """FlaxGPT2Model"""),
        ("""gpt2""", """FlaxGPT2Model"""),
        ("""gpt_neo""", """FlaxGPTNeoModel"""),
        ("""gptj""", """FlaxGPTJModel"""),
        ("""longt5""", """FlaxLongT5Model"""),
        ("""marian""", """FlaxMarianModel"""),
        ("""mbart""", """FlaxMBartModel"""),
        ("""mt5""", """FlaxMT5Model"""),
        ("""opt""", """FlaxOPTModel"""),
        ("""pegasus""", """FlaxPegasusModel"""),
        ("""regnet""", """FlaxRegNetModel"""),
        ("""resnet""", """FlaxResNetModel"""),
        ("""roberta""", """FlaxRobertaModel"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
        ("""roformer""", """FlaxRoFormerModel"""),
        ("""t5""", """FlaxT5Model"""),
        ("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
        ("""vit""", """FlaxViTModel"""),
        ("""wav2vec2""", """FlaxWav2Vec2Model"""),
        ("""whisper""", """FlaxWhisperModel"""),
        ("""xglm""", """FlaxXGLMModel"""),
        ("""xlm-roberta""", """FlaxXLMRobertaModel"""),
    ]
)

a_							=      OrderedDict(
    [
        # Model for pre-training mapping
        ("""albert""", """FlaxAlbertForPreTraining"""),
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""bert""", """FlaxBertForPreTraining"""),
        ("""big_bird""", """FlaxBigBirdForPreTraining"""),
        ("""electra""", """FlaxElectraForPreTraining"""),
        ("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""mt5""", """FlaxMT5ForConditionalGeneration"""),
        ("""roberta""", """FlaxRobertaForMaskedLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
        ("""roformer""", """FlaxRoFormerForMaskedLM"""),
        ("""t5""", """FlaxT5ForConditionalGeneration"""),
        ("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
        ("""whisper""", """FlaxWhisperForConditionalGeneration"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
    ]
)

a_							=      OrderedDict(
    [
        # Model for Masked LM mapping
        ("""albert""", """FlaxAlbertForMaskedLM"""),
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""bert""", """FlaxBertForMaskedLM"""),
        ("""big_bird""", """FlaxBigBirdForMaskedLM"""),
        ("""distilbert""", """FlaxDistilBertForMaskedLM"""),
        ("""electra""", """FlaxElectraForMaskedLM"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""roberta""", """FlaxRobertaForMaskedLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
        ("""roformer""", """FlaxRoFormerForMaskedLM"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
    ]
)

a_							=      OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
        ("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
        ("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
        ("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
        ("""marian""", """FlaxMarianMTModel"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""mt5""", """FlaxMT5ForConditionalGeneration"""),
        ("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
        ("""t5""", """FlaxT5ForConditionalGeneration"""),
    ]
)

a_							=      OrderedDict(
    [
        # Model for Image-classsification
        ("""beit""", """FlaxBeitForImageClassification"""),
        ("""regnet""", """FlaxRegNetForImageClassification"""),
        ("""resnet""", """FlaxResNetForImageClassification"""),
        ("""vit""", """FlaxViTForImageClassification"""),
    ]
)

a_							=      OrderedDict(
    [
        ("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
    ]
)

a_							=      OrderedDict(
    [
        # Model for Causal LM mapping
        ("""bart""", """FlaxBartForCausalLM"""),
        ("""bert""", """FlaxBertForCausalLM"""),
        ("""big_bird""", """FlaxBigBirdForCausalLM"""),
        ("""electra""", """FlaxElectraForCausalLM"""),
        ("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
        ("""gpt2""", """FlaxGPT2LMHeadModel"""),
        ("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
        ("""gptj""", """FlaxGPTJForCausalLM"""),
        ("""opt""", """FlaxOPTForCausalLM"""),
        ("""roberta""", """FlaxRobertaForCausalLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
        ("""xglm""", """FlaxXGLMForCausalLM"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
    ]
)

a_							=      OrderedDict(
    [
        # Model for Sequence Classification mapping
        ("""albert""", """FlaxAlbertForSequenceClassification"""),
        ("""bart""", """FlaxBartForSequenceClassification"""),
        ("""bert""", """FlaxBertForSequenceClassification"""),
        ("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
        ("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
        ("""electra""", """FlaxElectraForSequenceClassification"""),
        ("""mbart""", """FlaxMBartForSequenceClassification"""),
        ("""roberta""", """FlaxRobertaForSequenceClassification"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
        ("""roformer""", """FlaxRoFormerForSequenceClassification"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
    ]
)

a_							=      OrderedDict(
    [
        # Model for Question Answering mapping
        ("""albert""", """FlaxAlbertForQuestionAnswering"""),
        ("""bart""", """FlaxBartForQuestionAnswering"""),
        ("""bert""", """FlaxBertForQuestionAnswering"""),
        ("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
        ("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
        ("""electra""", """FlaxElectraForQuestionAnswering"""),
        ("""mbart""", """FlaxMBartForQuestionAnswering"""),
        ("""roberta""", """FlaxRobertaForQuestionAnswering"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
        ("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
    ]
)

a_							=      OrderedDict(
    [
        # Model for Token Classification mapping
        ("""albert""", """FlaxAlbertForTokenClassification"""),
        ("""bert""", """FlaxBertForTokenClassification"""),
        ("""big_bird""", """FlaxBigBirdForTokenClassification"""),
        ("""distilbert""", """FlaxDistilBertForTokenClassification"""),
        ("""electra""", """FlaxElectraForTokenClassification"""),
        ("""roberta""", """FlaxRobertaForTokenClassification"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
        ("""roformer""", """FlaxRoFormerForTokenClassification"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
    ]
)

a_							=      OrderedDict(
    [
        # Model for Multiple Choice mapping
        ("""albert""", """FlaxAlbertForMultipleChoice"""),
        ("""bert""", """FlaxBertForMultipleChoice"""),
        ("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
        ("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
        ("""electra""", """FlaxElectraForMultipleChoice"""),
        ("""roberta""", """FlaxRobertaForMultipleChoice"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
        ("""roformer""", """FlaxRoFormerForMultipleChoice"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
    ]
)

a_							=      OrderedDict(
    [
        ("""bert""", """FlaxBertForNextSentencePrediction"""),
    ]
)

a_							=      OrderedDict(
    [
        ("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
        ("""whisper""", """FlaxWhisperForConditionalGeneration"""),
    ]
)

a_							=      OrderedDict(
    [
        ("""whisper""", """FlaxWhisperForAudioClassification"""),
    ]
)

a_							=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
a_							=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
a_							=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
a_							=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
a_							=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
a_							=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
a_							=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
a_							=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
a_							=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
a_							=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
a_							=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
a_							=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
a_							=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
a_							=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)


class 			UpperCAmelCase__    (		_BaseAutoModelClass					):



  """simple docstring"""






  lowerCAmelCase__		:	Any    		= FLAX_MODEL_MAPPING






a_							=      auto_class_update(FlaxAutoModel)


class 			UpperCAmelCase__    (		_BaseAutoModelClass					):



  """simple docstring"""






  lowerCAmelCase__		:	Tuple    		= FLAX_MODEL_FOR_PRETRAINING_MAPPING






a_							=      auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")


class 			UpperCAmelCase__    (		_BaseAutoModelClass					):



  """simple docstring"""






  lowerCAmelCase__		:	List[str]    		= FLAX_MODEL_FOR_CAUSAL_LM_MAPPING






a_							=      auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")


class 			UpperCAmelCase__    (		_BaseAutoModelClass					):



  """simple docstring"""






  lowerCAmelCase__		:	Any    		= FLAX_MODEL_FOR_MASKED_LM_MAPPING






a_							=      auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")


class 			UpperCAmelCase__    (		_BaseAutoModelClass					):



  """simple docstring"""






  lowerCAmelCase__		:	Any    		= FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






a_							=      auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)


class 			UpperCAmelCase__    (		_BaseAutoModelClass					):



  """simple docstring"""






  lowerCAmelCase__		:	List[Any]    		= FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING






a_							=      auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)


class 			UpperCAmelCase__    (		_BaseAutoModelClass					):



  """simple docstring"""






  lowerCAmelCase__		:	List[Any]    		= FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING






a_							=      auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")


class 			UpperCAmelCase__    (		_BaseAutoModelClass					):



  """simple docstring"""






  lowerCAmelCase__		:	Union[str, Any]    		= FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING






a_							=      auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)


class 			UpperCAmelCase__    (		_BaseAutoModelClass					):



  """simple docstring"""






  lowerCAmelCase__		:	Optional[int]    		= FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING






a_							=      auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")


class 			UpperCAmelCase__    (		_BaseAutoModelClass					):



  """simple docstring"""






  lowerCAmelCase__		:	int    		= FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING






a_							=      auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)


class 			UpperCAmelCase__    (		_BaseAutoModelClass					):



  """simple docstring"""






  lowerCAmelCase__		:	Tuple    		= FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






a_							=      auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="""image classification"""
)


class 			UpperCAmelCase__    (		_BaseAutoModelClass					):



  """simple docstring"""






  lowerCAmelCase__		:	str    		= FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING






a_							=      auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")


class 			UpperCAmelCase__    (		_BaseAutoModelClass					):



  """simple docstring"""






  lowerCAmelCase__		:	List[str]    		= FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING






a_							=      auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)





import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


a_							=      get_tests_dir("""fixtures/spiece.model""")


@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase__    (		snake_case		,	unittest.TestCase					):



  """simple docstring"""






  lowerCAmelCase__		:	List[Any]    		= AlbertTokenizer
  lowerCAmelCase__		:	int    		= AlbertTokenizerFast
  lowerCAmelCase__		:	str    		= True
  lowerCAmelCase__		:	Union[str, Any]    		= True
  lowerCAmelCase__		:	int    		= True






  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> str:




    '''simple docstring'''




    super().setUp()

    # We have a SentencePiece fixture for testing
    __UpperCAmelCase						    =       AlbertTokenizer(__lowerCAmelCase   )
    tokenizer.save_pretrained(self.tmpdirname   )






  def 	_UpperCAmelCase				(				self:			Any				,				__lowerCAmelCase:			Tuple   ) -> Union[str, Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       "this is a test"
    __UpperCAmelCase						    =       "this is a test"
    return input_text, output_text






  def 	_UpperCAmelCase				(				self:			Dict   ) -> List[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       "<pad>"
    __UpperCAmelCase						    =       0

    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:			str   ) -> Optional[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       list(self.get_tokenizer().get_vocab().keys()   )

    self.assertEqual(vocab_keys[0]				,				"<pad>"   )
    self.assertEqual(vocab_keys[1]				,				"<unk>"   )
    self.assertEqual(vocab_keys[-1]				,				"▁eloquent"   )
    self.assertEqual(len(__lowerCAmelCase   )				,				30_000   )






  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> List[str]:




    '''simple docstring'''




    self.assertEqual(self.get_tokenizer().vocab_size				,				30_000   )






  def 	_UpperCAmelCase				(				self:			str   ) -> int:




    '''simple docstring'''




    if not self.test_rust_tokenizer:
      return

    __UpperCAmelCase						    =       self.get_tokenizer()
    __UpperCAmelCase						    =       self.get_rust_tokenizer()

    __UpperCAmelCase						    =       "I was born in 92000, and this is falsé."

    __UpperCAmelCase						    =       tokenizer.tokenize(__lowerCAmelCase   )
    __UpperCAmelCase						    =       rust_tokenizer.tokenize(__lowerCAmelCase   )
    self.assertListEqual(__lowerCAmelCase				,				__lowerCAmelCase   )

    __UpperCAmelCase						    =       tokenizer.encode(__lowerCAmelCase				,				add_special_tokens=__lowerCAmelCase   )
    __UpperCAmelCase						    =       rust_tokenizer.encode(__lowerCAmelCase				,				add_special_tokens=__lowerCAmelCase   )
    self.assertListEqual(__lowerCAmelCase				,				__lowerCAmelCase   )

    __UpperCAmelCase						    =       self.get_rust_tokenizer()
    __UpperCAmelCase						    =       tokenizer.encode(__lowerCAmelCase   )
    __UpperCAmelCase						    =       rust_tokenizer.encode(__lowerCAmelCase   )
    self.assertListEqual(__lowerCAmelCase				,				__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			Optional[Any]   ) -> Any:




    '''simple docstring'''




    __UpperCAmelCase						    =       AlbertTokenizer(__lowerCAmelCase				,				keep_accents=__lowerCAmelCase   )

    __UpperCAmelCase						    =       tokenizer.tokenize("This is a test"   )
    self.assertListEqual(__lowerCAmelCase				,				["▁this", "▁is", "▁a", "▁test"]   )

    self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase   )				,				[48, 25, 21, 1_289]   )

    __UpperCAmelCase						    =       tokenizer.tokenize("I was born in 92000, and this is falsé."   )
    self.assertListEqual(
        __lowerCAmelCase				,				["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]   )
    __UpperCAmelCase						    =       tokenizer.convert_tokens_to_ids(__lowerCAmelCase   )
    self.assertListEqual(__lowerCAmelCase				,				[31, 23, 386, 19, 561, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]   )

    __UpperCAmelCase						    =       tokenizer.convert_ids_to_tokens(__lowerCAmelCase   )
    self.assertListEqual(
        __lowerCAmelCase				,				["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]				,				)






  def 	_UpperCAmelCase				(				self:			str   ) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       AlbertTokenizer(__lowerCAmelCase   )

    __UpperCAmelCase						    =       tokenizer.encode("sequence builders"   )
    __UpperCAmelCase						    =       tokenizer.encode("multi-sequence build"   )

    __UpperCAmelCase						    =       tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase   )
    __UpperCAmelCase						    =       tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase				,				__lowerCAmelCase   )

    assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
    assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
        tokenizer.sep_token_id
    ]






  @slow
  def 	_UpperCAmelCase				(				self:			Optional[int]   ) -> str:




    '''simple docstring'''




    __UpperCAmelCase						    =       {"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, 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], [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, 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]], "input_ids": [[2, 21_970, 13, 5, 6_092, 167, 28, 7_103, 2_153, 673, 8, 7_028, 12_051, 18, 17, 7_103, 2_153, 673, 8, 3_515, 18_684, 8, 4_461, 6, 1_927, 297, 8, 12_060, 2_607, 18, 13, 5, 4_461, 15, 10_538, 38, 8, 135, 15, 822, 58, 15, 993, 10_363, 15, 1_460, 8_005, 4_461, 15, 993, 255, 2_328, 9, 9, 9, 6, 26, 1_112, 816, 3_260, 13, 5, 103, 2_377, 6, 17, 1_112, 816, 2_782, 13, 5, 103, 10_641, 6, 29, 84, 2_512, 2_430, 782, 18_684, 2_761, 19, 808, 2_430, 2_556, 17, 855, 1_480, 9_477, 4_091, 128, 11_712, 15, 7_103, 2_153, 673, 17, 24_883, 9_990, 9, 3], [2, 11_502, 25, 1_006, 20, 782, 8, 11_809, 855, 1_732, 19_393, 18_667, 37, 367, 21_018, 69, 1_854, 34, 11_860, 19_124, 27, 156, 225, 17, 193, 4_141, 19, 65, 9_124, 9, 3, 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], [2, 14, 2_231, 886, 2_385, 17_659, 84, 14, 16_792, 1_952, 9, 3, 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, 0, 0, 0, 0, 0]], "token_type_ids": [[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, 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, 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, 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="albert-base-v2"				,				revision="6b6560eaf5ff2e250b00c50f380c5389a9c2d82e"				,				)



