






import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 				lowerCAmelCase_	(		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       List[str]							       = BarthezTokenizer
				_UpperCamelCase					:       List[str]							       = BarthezTokenizerFast
				_UpperCamelCase					:       List[Any]							       = True
				_UpperCamelCase					:       Tuple							       = True


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

											_lowercase							:   int 							=   BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'      )
											tokenizer.save_pretrained(self.tmpdirname      )
											tokenizer.save_pretrained(self.tmpdirname ,						legacy_format=_lowerCAmelCase      )
											_lowercase							:   Union[str, Any] 							=   tokenizer


				def 						__a					(					self      ):
											_lowercase							:   Dict 							=   '<pad>'
											_lowercase							:   Any 							=   1

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


				def 						__a					(					self      ):
											_lowercase							:   Dict 							=   list(self.get_tokenizer().get_vocab().keys()      )

											self.assertEqual(vocab_keys[0] ,						'<s>'      )
											self.assertEqual(vocab_keys[1] ,						'<pad>'      )
											self.assertEqual(vocab_keys[-1] ,						'<mask>'      )
											self.assertEqual(len(_lowerCAmelCase      ) ,						1_0_1_1_2_2      )


				def 						__a					(					self      ):
											self.assertEqual(self.get_tokenizer().vocab_size ,						1_0_1_1_2_2      )


				@require_torch
				def 						__a					(					self      ):
											_lowercase							:   Union[str, Any] 							=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											_lowercase							:   Optional[Any] 							=   [0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

											_lowercase							:   Any 							=   self.tokenizer(
											    _lowerCAmelCase ,						max_length=len(_lowerCAmelCase      ) ,						padding=_lowerCAmelCase ,						truncation=_lowerCAmelCase ,						return_tensors='pt'      )
											self.assertIsInstance(_lowerCAmelCase ,						_lowerCAmelCase      )

											self.assertEqual((2, 6) ,						batch.input_ids.shape      )
											self.assertEqual((2, 6) ,						batch.attention_mask.shape      )
											_lowercase							:   Optional[int] 							=   batch.input_ids.tolist()[0]
											self.assertListEqual(_lowerCAmelCase ,						_lowerCAmelCase      )


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

											_lowercase							:   int 							=   self.get_tokenizer()
											_lowercase							:   Tuple 							=   self.get_rust_tokenizer()

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

											_lowercase							:   str 							=   tokenizer.tokenize(_lowerCAmelCase      )
											_lowercase							:   Optional[int] 							=   rust_tokenizer.tokenize(_lowerCAmelCase      )
											self.assertListEqual(_lowerCAmelCase ,						_lowerCAmelCase      )

											_lowercase							:   Optional[Any] 							=   tokenizer.encode(_lowerCAmelCase ,						add_special_tokens=_lowerCAmelCase      )
											_lowercase							:   Tuple 							=   rust_tokenizer.encode(_lowerCAmelCase ,						add_special_tokens=_lowerCAmelCase      )
											self.assertListEqual(_lowerCAmelCase ,						_lowerCAmelCase      )

											_lowercase							:   Dict 							=   self.get_rust_tokenizer()
											_lowercase							:   Optional[int] 							=   tokenizer.encode(_lowerCAmelCase      )
											_lowercase							:   int 							=   rust_tokenizer.encode(_lowerCAmelCase      )
											self.assertListEqual(_lowerCAmelCase ,						_lowerCAmelCase      )


				@slow
				def 						__a					(					self      ):
											# fmt: off
											_lowercase							:   List[str] 							=   {'input_ids': [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 2, 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, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], '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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
											# fmt: on

											# moussaKam/mbarthez is a french model. So we also use french texts.
											_lowercase							:   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=_lowerCAmelCase ,						model_name='moussaKam/mbarthez' ,						revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6' ,						sequences=_lowerCAmelCase ,						)








"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[str]							 ="""Speech2TextFeatureExtractor"""
							__UpperCAmelCase		:       Union[str, Any]							 ="""Speech2TextTokenizer"""

							def __init__(	self       ,  __a       ,  __a      ):
										super().__init__(__a       ,  __a      )
										__lowerCAmelCase							       =     self.feature_extractor
										__lowerCAmelCase							       =     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."      )
													__lowerCAmelCase							       =     kwargs.pop("raw_speech"      )
										else:
													__lowerCAmelCase							       =     kwargs.pop("audio"       ,  __a      )
										__lowerCAmelCase							       =     kwargs.pop("sampling_rate"       ,  __a      )
										__lowerCAmelCase							       =     kwargs.pop("text"       ,  __a      )
										if len(__a      ) > 0:
													__lowerCAmelCase							       =     args[0]
													__lowerCAmelCase							       =     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:
													__lowerCAmelCase							       =     self.feature_extractor(__a       ,  *__a       ,  sampling_rate=__a       ,  **__a      )
										if text is not None:
													__lowerCAmelCase							       =     self.tokenizer(__a       ,  **__a      )

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

							def      snake_case							(	self       ,  *__a       ,  **__a      ):
										return self.tokenizer.batch_decode(*__a       ,  **__a      )

							def      snake_case							(	self       ,  *__a       ,  **__a      ):
										return self.tokenizer.decode(*__a       ,  **__a      )



							@contextmanager
							def      snake_case							(	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."      )
										__lowerCAmelCase							       =     True
										__lowerCAmelCase							       =     self.tokenizer
										yield
										__lowerCAmelCase							       =     self.feature_extractor
										__lowerCAmelCase							       =     False




