


"""simple docstring"""







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[int]      						=  get_tests_dir("""fixtures/test_sentencepiece.model""")
lowerCamelCase_       :       Tuple      						=  get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

lowerCamelCase_       :       Tuple      						=  """pt""" if is_torch_available() else """tf"""






@require_sentencepiece
@require_tokenizers
class 				__A    (	_SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  CamembertTokenizer
 __lowerCAmelCase			 =  CamembertTokenizerFast
 __lowerCAmelCase			 =  True
 __lowerCAmelCase			 =  True


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

     # We have a SentencePiece fixture for testing
     a					=CamembertTokenizer(__A						)
     tokenizer.save_pretrained(self.tmpdirname						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     a					='''<pad>'''
     a					=1

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


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     a					=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 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     self.assertEqual(self.get_tokenizer().vocab_size			,   1005						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     a					=CamembertTokenizer(__A						)
     tokenizer.save_pretrained(self.tmpdirname						)
     a					=CamembertTokenizerFast.from_pretrained(self.tmpdirname						)

     a					='''I was born in 92000, and this is falsé.'''

     a					=tokenizer.encode(__A						)
     a					=rust_tokenizer.encode(__A						)
     self.assertListEqual(__A			,   __A						)

     a					=tokenizer.encode(__A			,   add_special_tokens=__A						)
     a					=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)
     a					=tokenizer.convert_ids_to_tokens(__A						)
     a					=rust_tokenizer.tokenize(__A						)
     self.assertListEqual(__A			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     if not self.test_rust_tokenizer:
         return

     a					=self.get_tokenizer()
     a					=self.get_rust_tokenizer()

     a					='''I was born in 92000, and this is falsé.'''

     a					=tokenizer.tokenize(__A						)
     a					=rust_tokenizer.tokenize(__A						)
     self.assertListEqual(__A			,   __A						)

     a					=tokenizer.encode(__A			,   add_special_tokens=__A						)
     a					=rust_tokenizer.encode(__A			,   add_special_tokens=__A						)
     self.assertListEqual(__A			,   __A						)

     a					=self.get_rust_tokenizer()
     a					=tokenizer.encode(__A						)
     a					=rust_tokenizer.encode(__A						)
     self.assertListEqual(__A			,   __A						)





 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     # fmt: off
     a					={'''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.
     a					=[
         '''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			,   )



"""simple docstring"""







from ...processing_utils import ProcessorMixin






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  "WhisperFeatureExtractor"
 __lowerCAmelCase			 =  "WhisperTokenizer"


 def __init__(		self			,   __A			,   __A						) ->					Dict:
     super().__init__(__A			,   __A						)
     a					=self.feature_extractor
     a					=False


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A=None			,   __A=None			,   __A=True						) ->					int:
     return self.tokenizer.get_decoder_prompt_ids(task=__A			,   language=__A			,   no_timestamps=__A						)


 def __call__(		self			,   *__A			,   **__A						) ->					Tuple:
     # For backward compatibility
     if self._in_target_context_manager:
         return self.current_processor(*__A			,   **__A						)

     a					=kwargs.pop('''audio'''			,   __A						)
     a					=kwargs.pop('''sampling_rate'''			,   __A						)
     a					=kwargs.pop('''text'''			,   __A						)
     if len(__A						) > 0:
         a					=args[0]
         a					=args[1:]

     if audio is None and text is None:
         raise ValueError('''You need to specify either an `audio` or `text` input to process.'''						)

     if audio is not None:
         a					=self.feature_extractor(__A			,   *__A			,   sampling_rate=__A			,   **__A						)
     if text is not None:
         a					=self.tokenizer(__A			,   **__A						)

     if text is None:
         return inputs

     elif audio is None:
         return encodings
     else:
         a					=encodings['''input_ids''']
         return inputs


 def 				SCREAMING_SNAKE_CASE							(		self			,   *__A			,   **__A						) ->					Optional[Any]:
     return self.tokenizer.batch_decode(*__A			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   *__A			,   **__A						) ->					Union[str, Any]:
     return self.tokenizer.decode(*__A			,   **__A						)





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A="np"						) ->					Optional[Any]:
     return self.tokenizer.get_prompt_ids(__A			,   return_tensors=__A						)