
from ...processing_utils import ProcessorMixin





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  Any				   =					['''image_processor''', '''feature_extractor''']
						UpperCamelCase      :  Optional[int]				   =					'''TvltImageProcessor'''
						UpperCamelCase      :  Union[str, Any]				   =					'''TvltFeatureExtractor'''





						def __init__(			self							:int		,    lowerCAmelCase__							:int		,    lowerCAmelCase__							:Union[str, Any]     )  ->Union[str, Any]:
										super().__init__(image_processor=lowerCAmelCase__		,    feature_extractor=lowerCAmelCase__     )

										lowercase         =      image_processor
										lowercase         =      feature_extractor





						def __call__(			self							:Any		,    lowerCAmelCase__							:Union[str, Any]=None		,    lowerCAmelCase__							:Union[str, Any]=None		,    lowerCAmelCase__							:Union[str, Any]=None		,    lowerCAmelCase__							:Any=None		,    lowerCAmelCase__							:Any=False		,    lowerCAmelCase__							:str=False		,    *lowerCAmelCase__							:Optional[Any]		,    **lowerCAmelCase__							:Dict		,    )  ->List[Any]:

										if images is None and audio is None:
														raise ValueError("You need to specify either an `images` or `audio` input to process."     )

										lowercase         =      None
										if images is not None:
														lowercase         =      self.image_processor(lowerCAmelCase__		,    mask_pixel=lowerCAmelCase__		,    *lowerCAmelCase__		,    **lowerCAmelCase__     )
										if images_mixed is not None:
														lowercase         =      self.image_processor(lowerCAmelCase__		,    is_mixed=lowerCAmelCase__		,    *lowerCAmelCase__		,    **lowerCAmelCase__     )
										if audio is not None:
														lowercase         =      self.feature_extractor(
														    lowerCAmelCase__		,    *lowerCAmelCase__		,    sampling_rate=lowerCAmelCase__		,    mask_audio=lowerCAmelCase__		,    **lowerCAmelCase__     )

										lowercase         =      {}
										if audio is not None:
														output_dict.update(lowerCAmelCase__     )
										if images is not None:
														output_dict.update(lowerCAmelCase__     )
										if images_mixed_dict is not None:
														output_dict.update(lowerCAmelCase__     )
										return output_dict





						@property
						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Dict:
										lowercase         =      self.image_processor.model_input_names
										lowercase         =      self.feature_extractor.model_input_names
										return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names     )     )


from __future__ import annotations

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				if len(__magic_name__						) == 0:
								return []
				lowercase     ,	lowercase         =      min(__magic_name__						), max(__magic_name__						)
				lowercase         =      int(max_value - min_value						) + 1
				lowercase         =      [[] for _ in range(__magic_name__						)]

				for i in my_list:
								buckets[int(i - min_value						)].append(__magic_name__						)

				return [v for bucket in buckets for v in sorted(__magic_name__						)]


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
						assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]
