

import warnings
from contextlib import contextmanager

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







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: List[str]       			=			'''Wav2Vec2FeatureExtractor'''
				__SCREAMING_SNAKE_CASE					: List[Any]       			=			'''AutoTokenizer'''




				def __init__(					self      ,   snake_case      ,   snake_case       ):
											super().__init__(snake_case      ,   snake_case       )
											snake_case_							=    self.feature_extractor
											snake_case_							=    False




				@classmethod
				def     a							(					cls      ,   snake_case      ,   **snake_case       ):
											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      ,   )

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

																		return cls(feature_extractor=snake_case      ,   tokenizer=snake_case       )




				def __call__(					self      ,   *snake_case      ,   **snake_case       ):
											# 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.'       )
																		snake_case_							=    kwargs.pop('raw_speech'       )
											else:
																		snake_case_							=    kwargs.pop('audio'      ,   snake_case       )
											snake_case_							=    kwargs.pop('sampling_rate'      ,   snake_case       )
											snake_case_							=    kwargs.pop('text'      ,   snake_case       )
											if len(snake_case       ) > 0:
																		snake_case_							=    args[0]
																		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:
																		snake_case_							=    self.feature_extractor(snake_case      ,   *snake_case      ,   sampling_rate=snake_case      ,   **snake_case       )
											if text is not None:
																		snake_case_							=    self.tokenizer(snake_case      ,   **snake_case       )

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




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

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

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

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




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




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




				@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.'       )
											snake_case_							=    True
											snake_case_							=    self.tokenizer
											yield
											snake_case_							=    self.feature_extractor
											snake_case_							=    False







def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							if edge <= 0 or not isinstance(UpperCamelCase__   ,							UpperCamelCase__      ):
														raise ValueError('Length must be a positive.'      )
							return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							if edge <= 0 or not isinstance(UpperCamelCase__   ,							UpperCamelCase__      ):
														raise ValueError('Length must be a positive.'      )
							return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




