


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


SCREAMING_SNAKE_CASE       :      Dict												= get_tests_dir("fixtures/test_sentencepiece.model")
SCREAMING_SNAKE_CASE       :      List[str]												= get_tests_dir("fixtures/test_sentencepiece_bpe.model")

SCREAMING_SNAKE_CASE       :      Optional[Any]												= "pt" if is_torch_available() else "tf"






@require_sentencepiece
@require_tokenizers
class _lowerCamelCase(						_a, unittest.TestCase   ):
       lowercase_ :	List[str]     =			CamembertTokenizer
       lowercase_ :	int     =			CamembertTokenizerFast
       lowercase_ :	Optional[Any]     =			True
       lowercase_ :	Any     =			True
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           super().setUp()

           # We have a SentencePiece fixture for testing
           _lowercase       :							Tuple      		=		CamembertTokenizer(__UpperCAmelCase)
           tokenizer.save_pretrained(self.tmpdirname)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							List[str]      		=		'<pad>'
           _lowercase       :							int      		=		1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase),      __UpperCAmelCase)
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase),      __UpperCAmelCase)
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		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(__UpperCAmelCase),      10_04)
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           self.assertEqual(self.get_tokenizer().vocab_size,      10_05)
       def 			UpperCamelCase     (					self)       ->   Dict:




           """simple docstring"""

           _lowercase       :							str      		=		CamembertTokenizer(__UpperCAmelCase)
           tokenizer.save_pretrained(self.tmpdirname)
           _lowercase       :							Dict      		=		CamembertTokenizerFast.from_pretrained(self.tmpdirname)

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

           _lowercase       :							List[str]      		=		tokenizer.encode(__UpperCAmelCase)
           _lowercase       :							Dict      		=		rust_tokenizer.encode(__UpperCAmelCase)
           self.assertListEqual(__UpperCAmelCase,      __UpperCAmelCase)

           _lowercase       :							List[Any]      		=		tokenizer.encode(__UpperCAmelCase,      add_special_tokens=__UpperCAmelCase)
           _lowercase       :							str      		=		rust_tokenizer.encode(__UpperCAmelCase,      add_special_tokens=__UpperCAmelCase)
           self.assertListEqual(__UpperCAmelCase,      __UpperCAmelCase)

           # <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)
           _lowercase       :							Tuple      		=		tokenizer.convert_ids_to_tokens(__UpperCAmelCase)
           _lowercase       :							Union[str, Any]      		=		rust_tokenizer.tokenize(__UpperCAmelCase)
           self.assertListEqual(__UpperCAmelCase,      __UpperCAmelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           if not self.test_rust_tokenizer:
               return

           _lowercase       :							Optional[Any]      		=		self.get_tokenizer()
           _lowercase       :							int      		=		self.get_rust_tokenizer()

           _lowercase       :							Optional[Any]      		=		'I was born in 92000, and this is falsé.'

           _lowercase       :							Union[str, Any]      		=		tokenizer.tokenize(__UpperCAmelCase)
           _lowercase       :							str      		=		rust_tokenizer.tokenize(__UpperCAmelCase)
           self.assertListEqual(__UpperCAmelCase,      __UpperCAmelCase)

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

           _lowercase       :							Dict      		=		self.get_rust_tokenizer()
           _lowercase       :							Any      		=		tokenizer.encode(__UpperCAmelCase)
           _lowercase       :							int      		=		rust_tokenizer.encode(__UpperCAmelCase)
           self.assertListEqual(__UpperCAmelCase,      __UpperCAmelCase)




       @slow
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		{'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 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_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 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.
           _lowercase       :							Any      		=		[
               '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='camembert-base',      revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf',      sequences=__UpperCAmelCase,      )









"""simple docstring"""





from typing import TYPE_CHECKING

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


__A											=		{
    """configuration_owlvit""": [
        """OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """OwlViTConfig""",
        """OwlViTOnnxConfig""",
        """OwlViTTextConfig""",
        """OwlViTVisionConfig""",
    ],
    """processing_owlvit""": ["""OwlViTProcessor"""],
}


try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		["""OwlViTFeatureExtractor"""]
  __A											=		["""OwlViTImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		[
      """OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """OwlViTModel""",
      """OwlViTPreTrainedModel""",
      """OwlViTTextModel""",
      """OwlViTVisionModel""",
      """OwlViTForObjectDetection""",
  ]

if TYPE_CHECKING:
  from .configuration_owlvit import (
      OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      OwlViTConfig,
      OwlViTOnnxConfig,
      OwlViTTextConfig,
      OwlViTVisionConfig,
  )
  from .processing_owlvit import OwlViTProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_owlvit import OwlViTFeatureExtractor
    from .image_processing_owlvit import OwlViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_owlvit import (
        OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OwlViTForObjectDetection,
        OwlViTModel,
        OwlViTPreTrainedModel,
        OwlViTTextModel,
        OwlViTVisionModel,
    )

else:
  import sys

  __A											=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
