


'''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     _lowerCamelCase       (     snake_case_     ,		unittest.TestCase					):




 '''simple docstring'''
 __lowercase					:		Union[str, Any]     				=    BarthezTokenizer
 __lowercase					:		List[Any]     				=    BarthezTokenizerFast
 __lowercase					:		Tuple     				=    True
 __lowercase					:		Tuple     				=    True


 def 			snake_case__ (   self    ):

  """simple docstring"""







  super().setUp()

  __A	:		int	  =      BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'    )
  tokenizer.save_pretrained(self.tmpdirname    )
  tokenizer.save_pretrained(self.tmpdirname   ,   legacy_format=__lowercase    )
  __A	:		Union[str, Any]	  =      tokenizer


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[str]	  =      '<pad>'
  __A	:		Dict	  =      1

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


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      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(__lowercase    )   ,   101_122    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  self.assertEqual(self.get_tokenizer().vocab_size   ,   101_122    )


 @require_torch
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      ['A long paragraph for summarization.', 'Another paragraph for summarization.']
  __A	:		int	  =      [0, 57, 3_018, 70_307, 91, 2]

  __A	:		Tuple	  =      self.tokenizer(
      __lowercase   ,   max_length=len(__lowercase    )   ,   padding=__lowercase   ,   truncation=__lowercase   ,   return_tensors='pt'    )
  self.assertIsInstance(__lowercase   ,   __lowercase    )

  self.assertEqual((2, 6)   ,   batch.input_ids.shape    )
  self.assertEqual((2, 6)   ,   batch.attention_mask.shape    )
  __A	:		List[Any]	  =      batch.input_ids.tolist()[0]
  self.assertListEqual(__lowercase   ,   __lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  if not self.test_rust_tokenizer:
   return

  __A	:		Any	  =      self.get_tokenizer()
  __A	:		Union[str, Any]	  =      self.get_rust_tokenizer()

  __A	:		Tuple	  =      'I was born in 92000, and this is falsé.'

  __A	:		List[Any]	  =      tokenizer.tokenize(__lowercase    )
  __A	:		Optional[int]	  =      rust_tokenizer.tokenize(__lowercase    )
  self.assertListEqual(__lowercase   ,   __lowercase    )

  __A	:		int	  =      tokenizer.encode(__lowercase   ,   add_special_tokens=__lowercase    )
  __A	:		Union[str, Any]	  =      rust_tokenizer.encode(__lowercase   ,   add_special_tokens=__lowercase    )
  self.assertListEqual(__lowercase   ,   __lowercase    )

  __A	:		Optional[int]	  =      self.get_rust_tokenizer()
  __A	:		Optional[Any]	  =      tokenizer.encode(__lowercase    )
  __A	:		int	  =      rust_tokenizer.encode(__lowercase    )
  self.assertListEqual(__lowercase   ,   __lowercase    )







 @slow
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		int	  =      {'input_ids': [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 173, 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, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 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.
  __A	:		Optional[int]	  =      [
      '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=__lowercase   ,   model_name='moussaKam/mbarthez'   ,   revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'   ,   sequences=__lowercase   ,   )










'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase_			:  Optional[Any]										=   {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Any										=   [
     'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'AdaptiveEmbedding',
     'TransfoXLForSequenceClassification',
     'TransfoXLLMHeadModel',
     'TransfoXLModel',
     'TransfoXLPreTrainedModel',
     'load_tf_weights_in_transfo_xl',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Optional[Any]										=   [
     'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFAdaptiveEmbedding',
     'TFTransfoXLForSequenceClassification',
     'TFTransfoXLLMHeadModel',
     'TFTransfoXLMainLayer',
     'TFTransfoXLModel',
     'TFTransfoXLPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
 from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_transfo_xl import (
      TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      AdaptiveEmbedding,
      TransfoXLForSequenceClassification,
      TransfoXLLMHeadModel,
      TransfoXLModel,
      TransfoXLPreTrainedModel,
      load_tf_weights_in_transfo_xl,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_transfo_xl import (
      TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFAdaptiveEmbedding,
      TFTransfoXLForSequenceClassification,
      TFTransfoXLLMHeadModel,
      TFTransfoXLMainLayer,
      TFTransfoXLModel,
      TFTransfoXLPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase_			:  Optional[int]										=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






