
from ...processing_utils import ProcessorMixin

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						Dict					      = ["""image_processor""", """feature_extractor"""]
			__UpperCamelCase      :						Optional[int]					      = """TvltImageProcessor"""
			__UpperCamelCase      :						Tuple					      = """TvltFeatureExtractor"""

			def __init__(       self :       int						,	snake_case_ :       str						,	snake_case_ :       Dict      ):
							super().__init__(image_processor=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							Tuple	=						image_processor
							UpperCamelCase_:							Union[str, Any]	=						feature_extractor

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

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

							UpperCamelCase_:							int	=						None
							if images is not None:
											UpperCamelCase_:							str	=						self.image_processor(snake_case_						,	mask_pixel=snake_case_						,	*snake_case_						,	**snake_case_      )
							if images_mixed is not None:
											UpperCamelCase_:							Dict	=						self.image_processor(snake_case_						,	is_mixed=snake_case_						,	*snake_case_						,	**snake_case_      )
							if audio is not None:
											UpperCamelCase_:							Optional[Any]	=						self.feature_extractor(
											    snake_case_						,	*snake_case_						,	sampling_rate=snake_case_						,	mask_audio=snake_case_						,	**snake_case_      )

							UpperCamelCase_:							List[str]	=						{}
							if audio is not None:
											output_dict.update(snake_case_      )
							if images is not None:
											output_dict.update(snake_case_      )
							if images_mixed_dict is not None:
											output_dict.update(snake_case_      )
							return output_dict




			@property
			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Optional[int]	=						self.image_processor.model_input_names
							UpperCamelCase_:							Dict	=						self.feature_extractor.model_input_names
							return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names      )      )








import random







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = False						)  ->	dict:
				UpperCamelCase_:							dict	=						{i: [] for i in range(lowerCamelCase						)}

				# if probability is greater or equal than 1, then generate a complete graph
				if probability >= 1:
								return complete_graph(lowerCamelCase						)
				# if probability is lower or equal than 0, then return a graph without edges
				if probability <= 0:
								return graph

				# for each couple of nodes, add an edge from u to v
				# if the number randomly generated is greater than probability probability
				for i in range(lowerCamelCase						):
								for j in range(i + 1     ,    lowerCamelCase						):
												if random.random() < probability:
																graph[i].append(lowerCamelCase						)
																if not directed:
																				# if the graph is undirected, add an edge in from j to i, either
																				graph[j].append(lowerCamelCase						)
				return graph







def        A__					(						lowerCamelCase						)  ->	dict:
				return {
				    i: [j for j in range(lowerCamelCase						) if i != j] for i in range(lowerCamelCase						)
				}


if __name__ == "__main__":
							import doctest

							doctest.testmod()






