




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Optional[Any]											=      """Speech2TextFeatureExtractor"""
				SCREAMING_SNAKE_CASE_ :							Tuple											=      """Speech2TextTokenizer"""





				def __init__(   self       :  List[Any]      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Tuple							)      ->  Dict:
							super().__init__(__lowerCamelCase      ,   __lowerCamelCase							)
							a					=    self.feature_extractor
							a					=    False





				def __call__(   self       :  str      ,   *__lowerCamelCase       :  Optional[int]      ,   **__lowerCamelCase       :  Dict							)      ->  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."							)
										a					=    kwargs.pop("raw_speech"							)
							else:
										a					=    kwargs.pop("audio"      ,   __lowerCamelCase							)
							a					=    kwargs.pop("sampling_rate"      ,   __lowerCamelCase							)
							a					=    kwargs.pop("text"      ,   __lowerCamelCase							)
							if len(__lowerCamelCase							) > 0:
										a					=    args[0]
										a					=    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					=    self.feature_extractor(__lowerCamelCase      ,   *__lowerCamelCase      ,   sampling_rate=__lowerCamelCase      ,   **__lowerCamelCase							)
							if text is not None:
										a					=    self.tokenizer(__lowerCamelCase      ,   **__lowerCamelCase							)

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





				def 							__UpperCAmelCase							(   self       :  Tuple      ,   *__lowerCamelCase       :  int      ,   **__lowerCamelCase       :  List[str]							)      ->  Dict:
							return self.tokenizer.batch_decode(*__lowerCamelCase      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  List[Any]      ,   *__lowerCamelCase       :  Dict      ,   **__lowerCamelCase       :  int							)      ->  Tuple:
							return self.tokenizer.decode(*__lowerCamelCase      ,   **__lowerCamelCase							)





				@contextmanager
				def 							__UpperCAmelCase							(   self       :  int							)      ->  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."							)
							a					=    True
							a					=    self.tokenizer
							yield
							a					=    self.feature_extractor
							a					=    False












def     __magic_name__       (				A	:      list     ):






			'''simple docstring'''







			for i in range(len(A     ) - 1,						0,						-1     ):
						a					=    False

						for j in range(A,						0,						-1     ):
									if unsorted[j] < unsorted[j - 1]:
												a					,	a					=    unsorted[j - 1], unsorted[j]
												a					=    True

						for j in range(A     ):
									if unsorted[j] > unsorted[j + 1]:
												a					,	a					=    unsorted[j + 1], unsorted[j]
												a					=    True

						if not swapped:
									break
			return unsorted


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		__lowerCAmelCase	:       Tuple     =	input('Enter numbers separated by a comma:\n').strip()
		__lowerCAmelCase	:       List[Any]     =	[int(item) for item in user_input.split(',')]
		print(F'''{cocktail_shaker_sort(unsorted) = }''')






