




from __future__ import annotations

from math import pi

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):

							if (inductance, frequency, reactance).count(0     ) != 1:
														raise ValueError('''One and only one argument must be 0'''     )
							if inductance < 0:
														raise ValueError('''Inductance cannot be negative'''     )
							if frequency < 0:
														raise ValueError('''Frequency cannot be negative'''     )
							if reactance < 0:
														raise ValueError('''Inductive reactance cannot be negative'''     )
							if inductance == 0:
														return {"inductance": reactance / (2 * pi * frequency)}
							elif frequency == 0:
														return {"frequency": reactance / (2 * pi * inductance)}
							elif reactance == 0:
														return {"reactance": 2 * pi * frequency * inductance}
							else:
														raise ValueError('''Exactly one argument must be 0'''     )


if __name__ == "__main__":
			import doctest

			doctest.testmod()








import warnings
from contextlib import contextmanager

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






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							List[str]	      =					"Wav2Vec2FeatureExtractor"
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					"AutoTokenizer"


				def __init__(							self:   Tuple		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Optional[Any]		):
											super().__init__(_lowerCamelCase		,					_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.feature_extractor
											SCREAMING_SNAKE_CASE_   	=    False


				@classmethod
				def     _A							(							cls:   List[Any]		,					_lowerCamelCase:   Tuple		,					**_lowerCamelCase:   List[str]		):
											try:
																		return super().from_pretrained(_lowerCamelCase		,					**_lowerCamelCase		)
											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: '''		,					_lowerCamelCase		,					)

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

																		return cls(feature_extractor=_lowerCamelCase		,					tokenizer=_lowerCamelCase		)


				def __call__(							self:   Union[str, Any]		,					*_lowerCamelCase:   List[Any]		,					**_lowerCamelCase:   str		):
											# For backward compatibility
											if self._in_target_context_manager:
																		return self.current_processor(*_lowerCamelCase		,					**_lowerCamelCase		)

											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'''		,					_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    kwargs.pop('''sampling_rate'''		,					_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    kwargs.pop('''text'''		,					_lowerCamelCase		)
											if len(_lowerCamelCase		) > 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(_lowerCamelCase		,					*_lowerCamelCase		,					sampling_rate=_lowerCamelCase		,					**_lowerCamelCase		)
											if text is not None:
																		SCREAMING_SNAKE_CASE_   	=    self.tokenizer(_lowerCamelCase		,					**_lowerCamelCase		)

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


				def     _A							(							self:   Optional[int]		,					*_lowerCamelCase:   str		,					**_lowerCamelCase:   List[Any]		):
											# For backward compatibility
											if self._in_target_context_manager:
																		return self.current_processor.pad(*_lowerCamelCase		,					**_lowerCamelCase		)

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

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

											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:   str		,					*_lowerCamelCase:   Dict		,					**_lowerCamelCase:   Dict		):
											return self.tokenizer.batch_decode(*_lowerCamelCase		,					**_lowerCamelCase		)


				def     _A							(							self:   Optional[int]		,					*_lowerCamelCase:   Optional[int]		,					**_lowerCamelCase:   Tuple		):
											return self.tokenizer.decode(*_lowerCamelCase		,					**_lowerCamelCase		)





				@contextmanager
				def     _A							(							self:   Tuple		):
											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


