

'''simple docstring'''

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     """Wav2Vec2FeatureExtractor"""
				_a    	=     """AutoTokenizer"""



				def __init__(    self,     _lowercase,     _lowercase       )     ->							Dict:
											super().__init__(snake_case_,     snake_case_       )
											SCREAMING_SNAKE_CASE_				=				self.feature_extractor
											SCREAMING_SNAKE_CASE_				=				False



				@classmethod
				def   a__					(    cls,     _lowercase,     **_lowercase       )     ->							str:
											try:
																		return super().from_pretrained(snake_case_,     **snake_case_       )
											except OSError:
																		warnings.warn(
																		    f"""Loading a tokenizer inside {cls.__name__} from a config that does not"""
																		    ' include a `tokenizer_class` attribute is deprecated and will be '
																		    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
																		    ' attribute to either your `config.json` or `tokenizer_config.json` '
																		    'file to suppress this warning: ',     snake_case_,     )

																		SCREAMING_SNAKE_CASE_				=				WavaVecaFeatureExtractor.from_pretrained(snake_case_,     **snake_case_       )
																		SCREAMING_SNAKE_CASE_				=				WavaVecaCTCTokenizer.from_pretrained(snake_case_,     **snake_case_       )

																		return cls(feature_extractor=snake_case_,     tokenizer=snake_case_       )



				def __call__(    self,     *_lowercase,     **_lowercase       )     ->							Optional[int]:
											# For backward compatibility
											if self._in_target_context_manager:
																		return self.current_processor(*snake_case_,     **snake_case_       )

											if "raw_speech" in kwargs:
																		warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'       )
																		SCREAMING_SNAKE_CASE_				=				kwargs.pop('raw_speech'       )
											else:
																		SCREAMING_SNAKE_CASE_				=				kwargs.pop('audio',     snake_case_       )
											SCREAMING_SNAKE_CASE_				=				kwargs.pop('sampling_rate',     snake_case_       )
											SCREAMING_SNAKE_CASE_				=				kwargs.pop('text',     snake_case_       )
											if len(snake_case_       ) > 0:
																		SCREAMING_SNAKE_CASE_				=				args[0]
																		SCREAMING_SNAKE_CASE_				=				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:
																		SCREAMING_SNAKE_CASE_				=				self.feature_extractor(snake_case_,     *snake_case_,     sampling_rate=snake_case_,     **snake_case_       )
											if text is not None:
																		SCREAMING_SNAKE_CASE_				=				self.tokenizer(snake_case_,     **snake_case_       )

											if text is None:
																		return inputs
											elif audio is None:
																		return encodings
											else:
																		SCREAMING_SNAKE_CASE_				=				encodings['input_ids']
																		return inputs



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Tuple:
											# For backward compatibility
											if self._in_target_context_manager:
																		return self.current_processor.pad(*snake_case_,     **snake_case_       )

											SCREAMING_SNAKE_CASE_				=				kwargs.pop('input_features',     snake_case_       )
											SCREAMING_SNAKE_CASE_				=				kwargs.pop('labels',     snake_case_       )
											if len(snake_case_       ) > 0:
																		SCREAMING_SNAKE_CASE_				=				args[0]
																		SCREAMING_SNAKE_CASE_				=				args[1:]

											if input_features is not None:
																		SCREAMING_SNAKE_CASE_				=				self.feature_extractor.pad(snake_case_,     *snake_case_,     **snake_case_       )
											if labels is not None:
																		SCREAMING_SNAKE_CASE_				=				self.tokenizer.pad(snake_case_,     **snake_case_       )

											if labels is None:
																		return input_features
											elif input_features is None:
																		return labels
											else:
																		SCREAMING_SNAKE_CASE_				=				labels['input_ids']
																		return input_features



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Any:
											return self.tokenizer.batch_decode(*snake_case_,     **snake_case_       )



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Optional[int]:
											return self.tokenizer.decode(*snake_case_,     **snake_case_       )





				@contextmanager
				def   a__					(    self       )     ->							Union[str, Any]:
											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.'       )
											SCREAMING_SNAKE_CASE_				=				True
											SCREAMING_SNAKE_CASE_				=				self.tokenizer
											yield
											SCREAMING_SNAKE_CASE_				=				self.feature_extractor
											SCREAMING_SNAKE_CASE_				=				False










def        __lowerCAmelCase		(	__lowerCamelCase						:     int					, __lowerCamelCase						:     int						)	->    bool:
					return numa ^ numa < 0


if __name__ == "__main__":
				import doctest

				doctest.testmod()




