





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


__lowerCamelCase			:	Optional[Any]					    =	get_tests_dir("""fixtures/test_sentencepiece.model""")
__lowerCamelCase			:	Dict					    =	get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

__lowerCamelCase			:	str					    =	"""pt""" if is_torch_available() else """tf"""



@require_sentencepiece
@require_tokenizers
class    A__  (			__snake_case			,  unittest.TestCase     ):
      _UpperCAmelCase      :Dict		       =  CamembertTokenizer
      _UpperCAmelCase      :int		       =  CamembertTokenizerFast
      _UpperCAmelCase      :int		       =  True
      _UpperCAmelCase      :List[str]		       =  True





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       super().setUp()

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





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[Any]			      =  "<pad>"
       UpperCamelCase      :   int			      =  1

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





      def 			__UpperCamelCase(					self  ):
       '''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_  )			,				1004  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       self.assertEqual(self.get_tokenizer().vocab_size			,				1005  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  CamembertTokenizer(A_  )
       tokenizer.save_pretrained(self.tmpdirname  )
       UpperCamelCase      :   Optional[int]			      =  CamembertTokenizerFast.from_pretrained(self.tmpdirname  )

       UpperCamelCase      :   str			      =  "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      :   str			      =  tokenizer.encode(A_			,				add_special_tokens=A_  )
       UpperCamelCase      :   int			      =  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      :   List[str]			      =  tokenizer.convert_ids_to_tokens(A_  )
       UpperCamelCase      :   List[str]			      =  rust_tokenizer.tokenize(A_  )
       self.assertListEqual(A_			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       if not self.test_rust_tokenizer:
        return

       UpperCamelCase      :   Tuple			      =  self.get_tokenizer()
       UpperCamelCase      :   Tuple			      =  self.get_rust_tokenizer()

       UpperCamelCase      :   Optional[int]			      =  "I was born in 92000, and this is falsé."

       UpperCamelCase      :   Optional[Any]			      =  tokenizer.tokenize(A_  )
       UpperCamelCase      :   Any			      =  rust_tokenizer.tokenize(A_  )
       self.assertListEqual(A_			,				A_  )

       UpperCamelCase      :   List[Any]			      =  tokenizer.encode(A_			,				add_special_tokens=A_  )
       UpperCamelCase      :   Any			      =  rust_tokenizer.encode(A_			,				add_special_tokens=A_  )
       self.assertListEqual(A_			,				A_  )

       UpperCamelCase      :   int			      =  self.get_rust_tokenizer()
       UpperCamelCase      :   Union[str, Any]			      =  tokenizer.encode(A_  )
       UpperCamelCase      :   int			      =  rust_tokenizer.encode(A_  )
       self.assertListEqual(A_			,				A_  )





      @slow
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[Any]			      =  {"input_ids": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 2_7575, 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, 9088, 20, 1517, 8, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 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      :   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=A_			,				model_name="camembert-base"			,				revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"			,				sequences=A_			,				)







from typing import TYPE_CHECKING

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


__lowerCamelCase			:	List[Any]					    =	{
    """configuration_chinese_clip""": [
        """CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ChineseCLIPConfig""",
        """ChineseCLIPOnnxConfig""",
        """ChineseCLIPTextConfig""",
        """ChineseCLIPVisionConfig""",
    ],
    """processing_chinese_clip""": ["""ChineseCLIPProcessor"""],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase			:	Tuple					    =	["""ChineseCLIPFeatureExtractor"""]
    __lowerCamelCase			:	List[str]					    =	["""ChineseCLIPImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase			:	str					    =	[
        """CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ChineseCLIPModel""",
        """ChineseCLIPPreTrainedModel""",
        """ChineseCLIPTextModel""",
        """ChineseCLIPVisionModel""",
    ]

if TYPE_CHECKING:
    from .configuration_chinese_clip import (
        CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
        ChineseCLIPConfig,
        ChineseCLIPOnnxConfig,
        ChineseCLIPTextConfig,
        ChineseCLIPVisionConfig,
    )
    from .processing_chinese_clip import ChineseCLIPProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_chinese_clip import (
            CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            ChineseCLIPModel,
            ChineseCLIPPreTrainedModel,
            ChineseCLIPTextModel,
            ChineseCLIPVisionModel,
        )

else:
    import sys

    __lowerCamelCase			:	Any					    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
