

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


__snake_case							:	Any											=       get_tests_dir("""fixtures/test_sentencepiece.model""")
__snake_case							:	List[Any]											=       get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

__snake_case							:	List[Any]											=       """pt""" if is_torch_available() else """tf"""



@require_sentencepiece
@require_tokenizers
class     A__(__SCREAMING_SNAKE_CASE,   unittest.TestCase				):
						"""simple docstring"""






						_A  :							str     =       CamembertTokenizer
						_A  :							str     =       CamembertTokenizerFast
						_A  :							Tuple     =       True
						_A  :							Dict     =       True





						def        UpperCamelCase__ (  self  )			->      int:
									super().setUp()

									# We have a SentencePiece fixture for testing
									a_		:		Optional[Any]      				=      CamembertTokenizer(_lowercase  )
									tokenizer.save_pretrained(self.tmpdirname  )





						def        UpperCamelCase__ (  self  )			->      Optional[Any]:
									a_		:		Any      				=      """<pad>"""
									a_		:		Union[str, Any]      				=      1

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





						def        UpperCamelCase__ (  self  )			->      List[Any]:
									a_		:		List[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(_lowercase  )   ,	1_004  )





						def        UpperCamelCase__ (  self  )			->      str:
									self.assertEqual(self.get_tokenizer().vocab_size   ,	1_005  )





						def        UpperCamelCase__ (  self  )			->      Optional[int]:
									a_		:		List[str]      				=      CamembertTokenizer(_lowercase  )
									tokenizer.save_pretrained(self.tmpdirname  )
									a_		:		Dict      				=      CamembertTokenizerFast.from_pretrained(self.tmpdirname  )

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

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

									a_		:		Dict      				=      tokenizer.encode(_lowercase   ,	add_special_tokens=_lowercase  )
									a_		:		str      				=      rust_tokenizer.encode(_lowercase   ,	add_special_tokens=_lowercase  )
									self.assertListEqual(_lowercase   ,	_lowercase  )

									# <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_		:		Any      				=      tokenizer.convert_ids_to_tokens(_lowercase  )
									a_		:		str      				=      rust_tokenizer.tokenize(_lowercase  )
									self.assertListEqual(_lowercase   ,	_lowercase  )





						def        UpperCamelCase__ (  self  )			->      Optional[Any]:
									if not self.test_rust_tokenizer:
												return

									a_		:		str      				=      self.get_tokenizer()
									a_		:		List[str]      				=      self.get_rust_tokenizer()

									a_		:		str      				=      """I was born in 92000, and this is falsé."""

									a_		:		List[str]      				=      tokenizer.tokenize(_lowercase  )
									a_		:		Optional[Any]      				=      rust_tokenizer.tokenize(_lowercase  )
									self.assertListEqual(_lowercase   ,	_lowercase  )

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

									a_		:		Tuple      				=      self.get_rust_tokenizer()
									a_		:		List[Any]      				=      tokenizer.encode(_lowercase  )
									a_		:		Tuple      				=      rust_tokenizer.encode(_lowercase  )
									self.assertListEqual(_lowercase   ,	_lowercase  )





						@slow
						def        UpperCamelCase__ (  self  )			->      Optional[int]:
									# fmt: off
									a_		:		Dict      				=      {"""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.
									a_		:		Union[str, 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=_lowercase   ,	model_name="""camembert-base"""   ,	revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""   ,	sequences=_lowercase   ,	)




import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


_lowerCamelCase					   =logging.get_logger(__name__)





class 							A__		(	__SCREAMING_SNAKE_CASE):
      def __init__(      self    ,  *__magic_name__    ,  **__magic_name__      ):
       warnings.warn(
           """The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
           """ use OwlViTImageProcessor instead."""    ,  __magic_name__    ,  )
       super().__init__(*__magic_name__    ,  **__magic_name__      )




