


"""simple docstring"""




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Optional[Any]										=				'Speech2TextFeatureExtractor'
		__UpperCAmelCase		:			Any										=				'Speech2TextTokenizer'

		def __init__(  self							, _a							, _a      ):
				super().__init__(_a							, _a      )
				__a           =			self.feature_extractor
				__a           =			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.'''      )
						__a           =			kwargs.pop('''raw_speech'''      )
				else:
						__a           =			kwargs.pop('''audio'''							, _a      )
				__a           =			kwargs.pop('''sampling_rate'''							, _a      )
				__a           =			kwargs.pop('''text'''							, _a      )
				if len(_a      ) > 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(_a							, *_a							, sampling_rate=_a							, **_a      )
				if text is not None:
						__a           =			self.tokenizer(_a							, **_a      )

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

		def 			__UpperCAmelCase       (  self							, *_a							, **_a      ):
				return self.tokenizer.batch_decode(*_a							, **_a      )

		def 			__UpperCAmelCase       (  self							, *_a							, **_a      ):
				return self.tokenizer.decode(*_a							, **_a      )





		@contextmanager
		def 			__UpperCAmelCase       (  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.'''      )
				__a           =			True
				__a           =			self.tokenizer
				yield
				__a           =			self.feature_extractor
				__a           =			False











import random






def     a(      A       :    Optional[Any] ,     A       :    Optional[Any] ,     A       :    str       )		->       List[Any]:
					"""simple docstring"""





					a					     =   a[left_index]
					a					     =   left_index + 1
					for j in range(left_index + 1 ,     A       ):
										if a[j] < pivot:
															a     ,				a					     =   a[i], a[j]
															i += 1
					a     ,				a					     =   a[i - 1], a[left_index]
					return i - 1






def     a(      A       :    List[Any] ,     A       :    List[Any] ,     A       :    Union[str, Any]       )		->       List[Any]:
					"""simple docstring"""





					if left < right:
										a					     =   random.randint(A ,     right - 1       )
										a     ,				a					     =   (
										    a[left],
										    a[pivot],
										)  # switches the pivot with the left most bound
										a					     =   partition(A ,     A ,     A       )
										quick_sort_random(
										    A ,     A ,     A       )  # recursive quicksort to the left of the pivot point
										quick_sort_random(
										    A ,     pivot_index + 1 ,     A       )  # recursive quicksort to the right of the pivot point






def     a(      )		->       Any:
					"""simple docstring"""





					a					     =   input("Enter numbers separated by a comma:\n"       ).strip()
					a					     =   [int(A       ) for item in user_input.split(","       )]

					quick_sort_random(A ,     0 ,     len(A       )       )

					print(A       )


if __name__ == "__main__":
				main()
