





from ...processing_utils import ProcessorMixin



class    _SCREAMING_SNAKE_CASE						(	UpperCAmelCase__    ):







 '''simple docstring'''
 lowercase_		=			['image_processor', 'feature_extractor']
 lowercase_		=			'TvltImageProcessor'
 lowercase_		=			'TvltFeatureExtractor'

 def __init__(self				:   List[str]       , UpperCAmelCase_				:   Union[str, Any]       , UpperCAmelCase_				:   Any)       ->int:



   '''simple docstring'''



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

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

 def __call__(self				:   List[Any]       , UpperCAmelCase_				:   Optional[int]=None       , UpperCAmelCase_				:   List[str]=None       , UpperCAmelCase_				:   Optional[Any]=None       , UpperCAmelCase_				:   Optional[int]=None       , UpperCAmelCase_				:   Dict=False       , UpperCAmelCase_				:   Tuple=False       , *UpperCAmelCase_				:   Optional[int]       , **UpperCAmelCase_				:   int       , )       ->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.")

   lowerCamelCase__:      Union[str, Any]			  =None
   if images is not None:
     lowerCamelCase__:      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:
     lowerCamelCase__:      Tuple			  =self.image_processor(_SCREAMING_SNAKE_CASE       , is_mixed=_SCREAMING_SNAKE_CASE       , *_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE)
   if audio is not None:
     lowerCamelCase__:      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)

   lowerCamelCase__:      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				:   Optional[Any])       ->Any:



   '''simple docstring'''



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






"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt

def       _snake_case						(						UpperCamelCase						:		int = 1000000			,		UpperCamelCase						:		int = 10						):
 UpperCAmelCase	:					defaultdict	=	defaultdict(UpperCamelCase						)

 for outer_width in range(3			,		(t_limit // 4) + 2						):
  if outer_width * outer_width > t_limit:
   UpperCAmelCase	:					str	=	max(
       ceil(sqrt(outer_width * outer_width - t_limit						)						)			,		1						)
  else:
   UpperCAmelCase	:					Optional[Any]	=	1

  hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

  for hole_width in range(UpperCamelCase			,		outer_width - 1			,		2						):
   count[outer_width * outer_width - hole_width * hole_width] += 1

 return sum(1 for n in count.values() if 1 <= n <= 10						)


if __name__ == "__main__":
      print(f"""{solution() = }""")
