

"""simple docstring"""




import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCAmelCase =				get_tests_dir('fixtures/test_sentencepiece.model')
__UpperCAmelCase =				get_tests_dir('fixtures/test_sentencepiece_bpe.model')

__UpperCAmelCase =				'pt' if is_torch_available() else 'tf'







@require_sentencepiece
@require_tokenizers
class      __lowercase			(    __lowerCamelCase	,			unittest.TestCase					):
  snake_case_				       =       CamembertTokenizer
  snake_case_				       =       CamembertTokenizerFast
  snake_case_				       =       True
  snake_case_				       =       True

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   super().setUp()

   # We have a SentencePiece fixture for testing
   UpperCAmelCase__					:  Optional[int]					   =					CamembertTokenizer(A    )
   tokenizer.save_pretrained(self.tmpdirname    )

  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					"""<pad>"""
   UpperCAmelCase__					:  Union[str, Any]					   =					1

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

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					list(self.get_tokenizer().get_vocab().keys()    )

   self.assertEqual(vocab_keys[0]					,"""<s>NOTUSED"""    )
   self.assertEqual(vocab_keys[1]					,"""<pad>"""    )
   self.assertEqual(vocab_keys[-1]					,"""<mask>"""    )
   self.assertEqual(len(A    )					,1_004    )

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   self.assertEqual(self.get_tokenizer().vocab_size					,1_005    )

  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					CamembertTokenizer(A    )
   tokenizer.save_pretrained(self.tmpdirname    )
   UpperCAmelCase__					:  List[Any]					   =					CamembertTokenizerFast.from_pretrained(self.tmpdirname    )

   UpperCAmelCase__					:  Any					   =					"""I was born in 92000, and this is falsé."""

   UpperCAmelCase__					:  Tuple					   =					tokenizer.encode(A    )
   UpperCAmelCase__					:  Any					   =					rust_tokenizer.encode(A    )
   self.assertListEqual(A					,A    )

   UpperCAmelCase__					:  Dict					   =					tokenizer.encode(A					,add_special_tokens=A    )
   UpperCAmelCase__					:  Tuple					   =					rust_tokenizer.encode(A					,add_special_tokens=A    )
   self.assertListEqual(A					,A    )

   # <unk> tokens are not the same for `rust` than for `slow`.
   # Because spm gives back raw token instead of `unk` in EncodeAsPieces
   # tokens = tokenizer.tokenize(sequence)
   UpperCAmelCase__					:  Optional[Any]					   =					tokenizer.convert_ids_to_tokens(A    )
   UpperCAmelCase__					:  List[Any]					   =					rust_tokenizer.tokenize(A    )
   self.assertListEqual(A					,A    )

  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   if not self.test_rust_tokenizer:
    return

   UpperCAmelCase__					:  List[str]					   =					self.get_tokenizer()
   UpperCAmelCase__					:  Optional[int]					   =					self.get_rust_tokenizer()

   UpperCAmelCase__					:  int					   =					"""I was born in 92000, and this is falsé."""

   UpperCAmelCase__					:  Optional[int]					   =					tokenizer.tokenize(A    )
   UpperCAmelCase__					:  Tuple					   =					rust_tokenizer.tokenize(A    )
   self.assertListEqual(A					,A    )

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

   UpperCAmelCase__					:  Dict					   =					self.get_rust_tokenizer()
   UpperCAmelCase__					:  Union[str, Any]					   =					tokenizer.encode(A    )
   UpperCAmelCase__					:  Tuple					   =					rust_tokenizer.encode(A    )
   self.assertListEqual(A					,A    )




  @slow
  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   # fmt: off
   UpperCAmelCase__					:  List[str]					   =					{"""input_ids""": [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 8, 930, 9, 6]], """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, 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]]}  # noqa: E501
   # fmt: on

   # camembert is a french model. So we also use french texts.
   UpperCAmelCase__					:  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=A					,model_name="""camembert-base"""					,revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""					,sequences=A					,)








"""simple docstring"""




from typing import TYPE_CHECKING

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


__UpperCAmelCase =				{
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RobertaForCausalLM',
          'RobertaForMaskedLM',
          'RobertaForMultipleChoice',
          'RobertaForQuestionAnswering',
          'RobertaForSequenceClassification',
          'RobertaForTokenClassification',
          'RobertaModel',
          'RobertaPreTrainedModel',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRobertaForCausalLM',
          'TFRobertaForMaskedLM',
          'TFRobertaForMultipleChoice',
          'TFRobertaForQuestionAnswering',
          'TFRobertaForSequenceClassification',
          'TFRobertaForTokenClassification',
          'TFRobertaMainLayer',
          'TFRobertaModel',
          'TFRobertaPreTrainedModel',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'FlaxRobertaForCausalLM',
          'FlaxRobertaForMaskedLM',
          'FlaxRobertaForMultipleChoice',
          'FlaxRobertaForQuestionAnswering',
          'FlaxRobertaForSequenceClassification',
          'FlaxRobertaForTokenClassification',
          'FlaxRobertaModel',
          'FlaxRobertaPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

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





