'''simple docstring'''





from ...processing_utils import ProcessorMixin




class        __A      ( UpperCamelCase__	):
    a__       :						Dict          =  ["""image_processor""", """feature_extractor"""]
    a__       :						List[str]          =  """TvltImageProcessor"""
    a__       :						Tuple          =  """TvltFeatureExtractor"""



    def __init__(self    :    Tuple   ,			__a    :    List[str]   ,			__a    :    List[Any] ):
         super().__init__(image_processor=__a   ,			feature_extractor=__a )

         UpperCAmelCase_           =   image_processor
         UpperCAmelCase_           =   feature_extractor



    def __call__(self    :    Dict   ,			__a    :    Dict=None   ,			__a    :    Union[str, Any]=None   ,			__a    :    str=None   ,			__a    :    List[str]=None   ,			__a    :    Tuple=False   ,			__a    :    Dict=False   ,			*__a    :    Optional[int]   ,			**__a    :    List[Any]   ,			):

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

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

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






    @property
    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   self.image_processor.model_input_names
         UpperCAmelCase_           =   self.feature_extractor.model_input_names
         return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names ) )






from queue import PriorityQueue
from typing import Any

import numpy as np




def  __magic_name__ (   __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						set		,  __lowerCAmelCase	:						set		,  __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						PriorityQueue		,  __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						float | int		,  )			->   float | int:
				for nxt, d in graph[v]:
								if nxt in visited_forward:
												continue
								__lowerCamelCase    					=	cst_fwd.get(__lowerCAmelCase		,  np.inf							)
								__lowerCamelCase    					=	cst_fwd[v] + d
								if new_cost_f < old_cost_f:
												queue.put((new_cost_f, nxt)							)
												__lowerCamelCase    					=	new_cost_f
												__lowerCamelCase    					=	v
								if nxt in visited_backward:
												if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																__lowerCamelCase    					=	cst_fwd[v] + d + cst_bwd[nxt]
				return shortest_distance




def  __magic_name__ (   __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						dict							)			->   int:
				__lowerCamelCase    					=	-1

				__lowerCamelCase    					=	set()
				__lowerCamelCase    					=	set()
				__lowerCamelCase    					=	{source: 0}
				__lowerCamelCase    					=	{destination: 0}
				__lowerCamelCase    					=	{source: None}
				__lowerCamelCase    					=	{destination: None}
				__lowerCamelCase    					=	PriorityQueue()
				__lowerCamelCase    					=	PriorityQueue()

				__lowerCamelCase    					=	np.inf

				queue_forward.put((0, source)							)
				queue_backward.put((0, destination)							)

				if source == destination:
								return 0

				while not queue_forward.empty() and not queue_backward.empty():
								__lowerCamelCase					,    __lowerCamelCase    					=	queue_forward.get()
								visited_forward.add(__lowerCAmelCase							)

								__lowerCamelCase					,    __lowerCamelCase    					=	queue_backward.get()
								visited_backward.add(__lowerCAmelCase							)

								__lowerCamelCase    					=	pass_and_relaxation(
								    __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  )

								__lowerCamelCase    					=	pass_and_relaxation(
								    __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  )

								if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
												break

				if shortest_distance != np.inf:
								__lowerCamelCase    					=	shortest_distance
				return shortest_path_distance


SCREAMING_SNAKE_CASE__   :			List[Any]												=    {
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}
SCREAMING_SNAKE_CASE__   :			Optional[int]												=    {
    "B": [["E", 1]],
    "C": [["B", 1]],
    "D": [["C", 1]],
    "F": [["D", 1], ["G", 1]],
    "E": [[None, np.inf]],
    "G": [["E", 2]],
}

if __name__ == "__main__":
					import doctest

					doctest.testmod()



