






'''simple docstring'''





import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class       snake_case__	( UpperCamelCase			,							unittest.TestCase):
					a_								= BarthezTokenizer
					a_								= BarthezTokenizerFast
					a_								= True
					a_								= True




					def 						A	(   self   :						Optional[Any]	)  ->				List[str]:
								super().setUp()

								UpperCAmelCase_	:    Tuple	  =    BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''	)
								tokenizer.save_pretrained(self.tmpdirname	)
								tokenizer.save_pretrained(self.tmpdirname							,      legacy_format=_A	)
								UpperCAmelCase_	:    str	  =    tokenizer




					def 						A	(   self   :						Union[str, Any]	)  ->				Any:
								UpperCAmelCase_	:    int	  =    '''<pad>'''
								UpperCAmelCase_	:    int	  =    1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A	)							,      _A	)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A	)							,      _A	)




					def 						A	(   self   :						int	)  ->				Dict:
								UpperCAmelCase_	:    Tuple	  =    list(self.get_tokenizer().get_vocab().keys()	)

								self.assertEqual(vocab_keys[0]							,      '''<s>'''	)
								self.assertEqual(vocab_keys[1]							,      '''<pad>'''	)
								self.assertEqual(vocab_keys[-1]							,      '''<mask>'''	)
								self.assertEqual(len(_A	)							,      10_11_22	)




					def 						A	(   self   :						int	)  ->				Any:
								self.assertEqual(self.get_tokenizer().vocab_size							,      10_11_22	)




					@require_torch
					def 						A	(   self   :						Tuple	)  ->				Any:
								UpperCAmelCase_	:    Union[str, Any]	  =    ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								UpperCAmelCase_	:    int	  =    [0, 57, 30_18, 7_03_07, 91, 2]

								UpperCAmelCase_	:    Optional[int]	  =    self.tokenizer(
								    _A							,      max_length=len(_A	)							,      padding=_A							,      truncation=_A							,      return_tensors='''pt'''	)
								self.assertIsInstance(_A							,      _A	)

								self.assertEqual((2, 6)							,      batch.input_ids.shape	)
								self.assertEqual((2, 6)							,      batch.attention_mask.shape	)
								UpperCAmelCase_	:    Optional[int]	  =    batch.input_ids.tolist()[0]
								self.assertListEqual(_A							,      _A	)




					def 						A	(   self   :						Any	)  ->				Tuple:






								if not self.test_rust_tokenizer:
											return

								UpperCAmelCase_	:    List[Any]	  =    self.get_tokenizer()
								UpperCAmelCase_	:    Optional[Any]	  =    self.get_rust_tokenizer()

								UpperCAmelCase_	:    Dict	  =    '''I was born in 92000, and this is falsé.'''

								UpperCAmelCase_	:    Union[str, Any]	  =    tokenizer.tokenize(_A	)
								UpperCAmelCase_	:    Optional[Any]	  =    rust_tokenizer.tokenize(_A	)
								self.assertListEqual(_A							,      _A	)

								UpperCAmelCase_	:    Tuple	  =    tokenizer.encode(_A							,      add_special_tokens=_A	)
								UpperCAmelCase_	:    Union[str, Any]	  =    rust_tokenizer.encode(_A							,      add_special_tokens=_A	)
								self.assertListEqual(_A							,      _A	)

								UpperCAmelCase_	:    Tuple	  =    self.get_rust_tokenizer()
								UpperCAmelCase_	:    List[Any]	  =    tokenizer.encode(_A	)
								UpperCAmelCase_	:    Optional[int]	  =    rust_tokenizer.encode(_A	)
								self.assertListEqual(_A							,      _A	)





					@slow
					def 						A	(   self   :						Tuple	)  ->				Tuple:
								UpperCAmelCase_	:    Optional[Any]	  =    {'''input_ids''': [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 2, 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, 1_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 5, 2]], '''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, 0, 0, 0, 0, 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, 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]]}  # noqa: E501
								# fmt: on

								# moussaKam/mbarthez is a french model. So we also use french texts.
								UpperCAmelCase_	:    Dict	  =    [
								    '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
								    '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
								    '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
								    '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
								    '''telles que la traduction et la synthèse de texte.''',
								]

								self.tokenizer_integration_test_util(
								    expected_encoding=_A							,      model_name='''moussaKam/mbarthez'''							,      revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''							,      sequences=_A							,      )





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_									=   {'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   ['XLNetTokenizer']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   ['XLNetTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   [
      'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
      'XLNetForMultipleChoice',
      'XLNetForQuestionAnswering',
      'XLNetForQuestionAnsweringSimple',
      'XLNetForSequenceClassification',
      'XLNetForTokenClassification',
      'XLNetLMHeadModel',
      'XLNetModel',
      'XLNetPreTrainedModel',
      'load_tf_weights_in_xlnet',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   [
      'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFXLNetForMultipleChoice',
      'TFXLNetForQuestionAnsweringSimple',
      'TFXLNetForSequenceClassification',
      'TFXLNetForTokenClassification',
      'TFXLNetLMHeadModel',
      'TFXLNetMainLayer',
      'TFXLNetModel',
      'TFXLNetPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  lowercase_									=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


