

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


A_	:			Optional[Any]  	=      get_tests_dir('fixtures/test_sentencepiece.model')
A_	:			List[str]  	=      get_tests_dir('fixtures/test_sentencepiece_bpe.model')

A_	:			Optional[Any]  	=      'pt' if is_torch_available() else 'tf'



@require_sentencepiece
@require_tokenizers
class    _a						(__magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Optional[Any]  							=	CamembertTokenizer
					UpperCAmelCase__:       Union[str, Any]  							=	CamembertTokenizerFast
					UpperCAmelCase__:       Optional[Any]  							=	True
					UpperCAmelCase__:       Dict  							=	True






					def   __A      (			self      ):
						super().setUp()

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






					def   __A      (			self      ):
						A__							:   List[str]					       =		"""<pad>"""
						A__							:   Optional[Any]					       =		1

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






					def   __A      (			self      ):
						A__							:   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   __A      (			self      ):
						self.assertEqual(self.get_tokenizer().vocab_size   ,	1005      )






					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		CamembertTokenizer(A__      )
						tokenizer.save_pretrained(self.tmpdirname      )
						A__							:   Tuple					       =		CamembertTokenizerFast.from_pretrained(self.tmpdirname      )

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

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

						A__							:   Optional[Any]					       =		tokenizer.encode(A__   ,	add_special_tokens=A__      )
						A__							:   str					       =		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__							:   int					       =		tokenizer.convert_ids_to_tokens(A__      )
						A__							:   Dict					       =		rust_tokenizer.tokenize(A__      )
						self.assertListEqual(A__   ,	A__      )






					def   __A      (			self      ):
						if not self.test_rust_tokenizer:
							return

						A__							:   Optional[Any]					       =		self.get_tokenizer()
						A__							:   List[Any]					       =		self.get_rust_tokenizer()

						A__							:   Union[str, Any]					       =		"""I was born in 92000, and this is falsé."""

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

						A__							:   List[str]					       =		tokenizer.encode(A__   ,	add_special_tokens=A__      )
						A__							:   Tuple					       =		rust_tokenizer.encode(A__   ,	add_special_tokens=A__      )
						self.assertListEqual(A__   ,	A__      )

						A__							:   Optional[Any]					       =		self.get_rust_tokenizer()
						A__							:   Tuple					       =		tokenizer.encode(A__      )
						A__							:   int					       =		rust_tokenizer.encode(A__      )
						self.assertListEqual(A__   ,	A__      )






					@slow
					def   __A      (			self      ):
						# fmt: off
						A__							:   Optional[int]					       =		{"""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__							:   Dict					       =		[
						    """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__   ,	)






import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Optional[Any]  							=	'''MCTCTFeatureExtractor'''
					UpperCAmelCase__:       Optional[int]  							=	'''AutoTokenizer'''






					def __init__(			self   ,	A__   ,	A__      ):
						super().__init__(A__   ,	A__      )
						A__							:   List[str]					       =		self.feature_extractor
						A__							:   Optional[int]					       =		False






					def __call__(			self   ,	*A__   ,	**A__      ):
						# For backward compatibility
						if self._in_target_context_manager:
							return self.current_processor(*A__   ,	**A__      )

						if "raw_speech" in kwargs:
							warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""      )
							A__							:   Dict					       =		kwargs.pop("""raw_speech"""      )
						else:
							A__							:   Tuple					       =		kwargs.pop("""audio"""   ,	A__      )
						A__							:   Union[str, Any]					       =		kwargs.pop("""sampling_rate"""   ,	A__      )
						A__							:   int					       =		kwargs.pop("""text"""   ,	A__      )
						if len(A__      ) > 0:
							A__							:   Optional[int]					       =		args[0]
							A__							:   Dict					       =		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__							:   List[str]					       =		self.feature_extractor(A__   ,	*A__   ,	sampling_rate=A__   ,	**A__      )
						if text is not None:
							A__							:   Optional[Any]					       =		self.tokenizer(A__   ,	**A__      )

						if text is None:
							return inputs
						elif audio is None:
							return encodings
						else:
							A__							:   List[Any]					       =		encodings["""input_ids"""]
							return inputs






					def   __A      (			self   ,	*A__   ,	**A__      ):
						return self.tokenizer.batch_decode(*A__   ,	**A__      )






					def   __A      (			self   ,	*A__   ,	**A__      ):
						# For backward compatibility
						if self._in_target_context_manager:
							return self.current_processor.pad(*A__   ,	**A__      )

						A__							:   Optional[Any]					       =		kwargs.pop("""input_features"""   ,	A__      )
						A__							:   Union[str, Any]					       =		kwargs.pop("""labels"""   ,	A__      )
						if len(A__      ) > 0:
							A__							:   List[Any]					       =		args[0]
							A__							:   Optional[int]					       =		args[1:]

						if input_features is not None:
							A__							:   Union[str, Any]					       =		self.feature_extractor.pad(A__   ,	*A__   ,	**A__      )
						if labels is not None:
							A__							:   List[Any]					       =		self.tokenizer.pad(A__   ,	**A__      )

						if labels is None:
							return input_features
						elif input_features is None:
							return labels
						else:
							A__							:   Dict					       =		labels["""input_ids"""]
							return input_features






					def   __A      (			self   ,	*A__   ,	**A__      ):
						return self.tokenizer.decode(*A__   ,	**A__      )






					@contextmanager
					def   __A      (			self      ):
						warnings.warn(
						    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
						    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
						    """your audio inputs, or in a separate call."""      )
						A__							:   int					       =		True
						A__							:   List[Any]					       =		self.tokenizer
						yield
						A__							:   Tuple					       =		self.feature_extractor
						A__							:   Dict					       =		False



