
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


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

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



@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__  (       __lowercase    ,unittest.TestCase    ):
   _SCREAMING_SNAKE_CASE      :							str	       =      CamembertTokenizer
   _SCREAMING_SNAKE_CASE      :							Optional[int]	       =      CamembertTokenizerFast
   _SCREAMING_SNAKE_CASE      :							str	       =      True
   _SCREAMING_SNAKE_CASE      :							Optional[int]	       =      True
   def       lowerCAmelCase			(self :					List[str]   ):
    super().setUp()

    # We have a SentencePiece fixture for testing
    __a :  Dict						=  CamembertTokenizer(snake_case_   )
    tokenizer.save_pretrained(self.tmpdirname   )
   def       lowerCAmelCase			(self :					Union[str, Any]   ):
    __a :  List[Any]						=  '''<pad>'''
    __a :  List[str]						=  1

    self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_   )					,				snake_case_   )
    self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_   )					,				snake_case_   )
   def       lowerCAmelCase			(self :					int   ):
    __a :  str						=  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(snake_case_   )					,				1_0_0_4   )
   def       lowerCAmelCase			(self :					Union[str, Any]   ):
    self.assertEqual(self.get_tokenizer().vocab_size					,				1_0_0_5   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    __a :  str						=  CamembertTokenizer(snake_case_   )
    tokenizer.save_pretrained(self.tmpdirname   )
    __a :  Tuple						=  CamembertTokenizerFast.from_pretrained(self.tmpdirname   )

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

    __a :  List[str]						=  tokenizer.encode(snake_case_   )
    __a :  Tuple						=  rust_tokenizer.encode(snake_case_   )
    self.assertListEqual(snake_case_					,				snake_case_   )

    __a :  List[Any]						=  tokenizer.encode(snake_case_					,				add_special_tokens=snake_case_   )
    __a :  Optional[Any]						=  rust_tokenizer.encode(snake_case_					,				add_special_tokens=snake_case_   )
    self.assertListEqual(snake_case_					,				snake_case_   )

    # <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)
    __a :  Union[str, Any]						=  tokenizer.convert_ids_to_tokens(snake_case_   )
    __a :  int						=  rust_tokenizer.tokenize(snake_case_   )
    self.assertListEqual(snake_case_					,				snake_case_   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    if not self.test_rust_tokenizer:
     return

    __a :  List[Any]						=  self.get_tokenizer()
    __a :  Tuple						=  self.get_rust_tokenizer()

    __a :  Any						=  '''I was born in 92000, and this is falsé.'''

    __a :  Optional[int]						=  tokenizer.tokenize(snake_case_   )
    __a :  Dict						=  rust_tokenizer.tokenize(snake_case_   )
    self.assertListEqual(snake_case_					,				snake_case_   )

    __a :  Union[str, Any]						=  tokenizer.encode(snake_case_					,				add_special_tokens=snake_case_   )
    __a :  Optional[Any]						=  rust_tokenizer.encode(snake_case_					,				add_special_tokens=snake_case_   )
    self.assertListEqual(snake_case_					,				snake_case_   )

    __a :  Any						=  self.get_rust_tokenizer()
    __a :  int						=  tokenizer.encode(snake_case_   )
    __a :  int						=  rust_tokenizer.encode(snake_case_   )
    self.assertListEqual(snake_case_					,				snake_case_   )







   @slow
   def       lowerCAmelCase			(self :					int   ):
    # fmt: off
    __a :  List[Any]						=  {'''input_ids''': [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 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.
    __a :  List[str]						=  [
        '''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=snake_case_					,				model_name='''camembert-base'''					,				revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''					,				sequences=snake_case_					,				)


from typing import TYPE_CHECKING

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


lowercase__       ={
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RoFormerForCausalLM',
          'RoFormerForMaskedLM',
          'RoFormerForMultipleChoice',
          'RoFormerForQuestionAnswering',
          'RoFormerForSequenceClassification',
          'RoFormerForTokenClassification',
          'RoFormerLayer',
          'RoFormerModel',
          'RoFormerPreTrainedModel',
          'load_tf_weights_in_roformer',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRoFormerForCausalLM',
          'TFRoFormerForMaskedLM',
          'TFRoFormerForMultipleChoice',
          'TFRoFormerForQuestionAnswering',
          'TFRoFormerForSequenceClassification',
          'TFRoFormerForTokenClassification',
          'TFRoFormerLayer',
          'TFRoFormerModel',
          'TFRoFormerPreTrainedModel',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'FlaxRoFormerForMaskedLM',
          'FlaxRoFormerForMultipleChoice',
          'FlaxRoFormerForQuestionAnswering',
          'FlaxRoFormerForSequenceClassification',
          'FlaxRoFormerForTokenClassification',
          'FlaxRoFormerModel',
          'FlaxRoFormerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

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