



"""simple docstring"""
from ...processing_utils import ProcessorMixin



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   List[str]						=						['image_processor', 'feature_extractor']
   __lowerCAmelCase  :   List[str]						=						'TvltImageProcessor'
   __lowerCAmelCase  :   List[str]						=						'TvltFeatureExtractor'

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      int:





    '''simple docstring'''




    super().__init__(image_processor=_SCREAMING_SNAKE_CASE      ,     feature_extractor=_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Optional[int]	=	image_processor
    UpperCAmelCase	:					List[str]	=	feature_extractor

   def __call__(   self      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE=False      ,     *_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      Any:





    '''simple docstring'''




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

    UpperCAmelCase	:					Union[str, Any]	=	None
    if images is not None:
     UpperCAmelCase	:					Optional[Any]	=	self.image_processor(_SCREAMING_SNAKE_CASE      ,     mask_pixel=_SCREAMING_SNAKE_CASE      ,     *_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)
    if images_mixed is not None:
     UpperCAmelCase	:					Tuple	=	self.image_processor(_SCREAMING_SNAKE_CASE      ,     is_mixed=_SCREAMING_SNAKE_CASE      ,     *_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)
    if audio is not None:
     UpperCAmelCase	:					Union[str, Any]	=	self.feature_extractor(
         _SCREAMING_SNAKE_CASE      ,     *_SCREAMING_SNAKE_CASE      ,     sampling_rate=_SCREAMING_SNAKE_CASE      ,     mask_audio=_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Optional[Any]	=	{}
    if audio is not None:
     output_dict.update(_SCREAMING_SNAKE_CASE						)
    if images is not None:
     output_dict.update(_SCREAMING_SNAKE_CASE						)
    if images_mixed_dict is not None:
     output_dict.update(_SCREAMING_SNAKE_CASE						)
    return output_dict




   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	self.image_processor.model_input_names
    UpperCAmelCase	:					Optional[Any]	=	self.feature_extractor.model_input_names
    return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names						)						)

'''simple docstring'''




def 							lowerCamelCase	(      UpperCAmelCase__       :					list )			->      list:
							if any(not isinstance(UpperCAmelCase__      ,      UpperCAmelCase__ ) or x < 0 for x in sequence ):
														raise TypeError("""Sequence must be list of non-negative integers""" )
							for _ in range(len(UpperCAmelCase__ ) ):
														for i, (rod_upper, rod_lower) in enumerate(zip(UpperCAmelCase__      ,      sequence[1:] ) ):
																					if rod_upper > rod_lower:
																												sequence[i] -= rod_upper - rod_lower
																												sequence[i + 1] += rod_upper - rod_lower
							return sequence


if __name__ == "__main__":
						assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
						assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]





