


"""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      ):
   __UpperCAmelCase					:			str				    =    BarthezTokenizer
   __UpperCAmelCase					:			List[Any]				    =    BarthezTokenizerFast
   __UpperCAmelCase					:			List[Any]				    =    True
   __UpperCAmelCase					:			Optional[Any]				    =    True







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




       super().setUp()

       snake_case       : Tuple    =       BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez"		)
       tokenizer.save_pretrained(self.tmpdirname		)
       tokenizer.save_pretrained(self.tmpdirname		,      legacy_format=UpperCamelCase__		)
       snake_case       : List[str]    =       tokenizer







   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       snake_case       : int    =       "<pad>"
       snake_case       : List[str]    =       1

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







   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       snake_case       : int    =       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(UpperCamelCase__		)		,      10_1122		)







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       self.assertEqual(self.get_tokenizer().vocab_size		,      10_1122		)







   @require_torch
   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       snake_case       : str    =       ["A long paragraph for summarization.", "Another paragraph for summarization."]
       snake_case       : Any    =       [0, 57, 3018, 7_0307, 91, 2]

       snake_case       : Union[str, Any]    =       self.tokenizer(
           UpperCamelCase__		,      max_length=len(UpperCamelCase__		)		,      padding=UpperCamelCase__		,      truncation=UpperCamelCase__		,      return_tensors="pt"		)
       self.assertIsInstance(UpperCamelCase__		,      UpperCamelCase__		)

       self.assertEqual((2, 6)		,      batch.input_ids.shape		)
       self.assertEqual((2, 6)		,      batch.attention_mask.shape		)
       snake_case       : Optional[Any]    =       batch.input_ids.tolist()[0]
       self.assertListEqual(UpperCamelCase__		,      UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       if not self.test_rust_tokenizer:
           return

       snake_case       : str    =       self.get_tokenizer()
       snake_case       : Dict    =       self.get_rust_tokenizer()

       snake_case       : List[Any]    =       "I was born in 92000, and this is falsé."

       snake_case       : Tuple    =       tokenizer.tokenize(UpperCamelCase__		)
       snake_case       : List[str]    =       rust_tokenizer.tokenize(UpperCamelCase__		)
       self.assertListEqual(UpperCamelCase__		,      UpperCamelCase__		)

       snake_case       : Optional[int]    =       tokenizer.encode(UpperCamelCase__		,      add_special_tokens=UpperCamelCase__		)
       snake_case       : List[Any]    =       rust_tokenizer.encode(UpperCamelCase__		,      add_special_tokens=UpperCamelCase__		)
       self.assertListEqual(UpperCamelCase__		,      UpperCamelCase__		)

       snake_case       : Union[str, Any]    =       self.get_rust_tokenizer()
       snake_case       : Tuple    =       tokenizer.encode(UpperCamelCase__		)
       snake_case       : List[Any]    =       rust_tokenizer.encode(UpperCamelCase__		)
       self.assertListEqual(UpperCamelCase__		,      UpperCamelCase__		)







   @slow
   def 					lowerCamelCase						(      self		)      ->						List[Any]:

       '''simple docstring'''




       snake_case       : Union[str, Any]    =       {"input_ids": [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 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, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 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.
       snake_case       : 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=UpperCamelCase__		,      model_name="moussaKam/mbarthez"		,      revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"		,      sequences=UpperCamelCase__		,      )









"""simple docstring"""



from typing import TYPE_CHECKING

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


__snake_case       =       {
    """configuration_poolformer""": [
        """POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """PoolFormerConfig""",
        """PoolFormerOnnxConfig""",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case       =       ["""PoolFormerFeatureExtractor"""]
  __snake_case       =       ["""PoolFormerImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case       =       [
      """POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """PoolFormerForImageClassification""",
      """PoolFormerModel""",
      """PoolFormerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_poolformer import (
      POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      PoolFormerConfig,
      PoolFormerOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_poolformer import PoolFormerFeatureExtractor
    from .image_processing_poolformer import PoolFormerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_poolformer import (
        POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        PoolFormerForImageClassification,
        PoolFormerModel,
        PoolFormerPreTrainedModel,
    )


else:
  import sys

  __snake_case       =       _LazyModule(__name__, globals()["""__file__"""], _import_structure)





