

from ...processing_utils import ProcessorMixin





class     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       =["""image_processor""", """feature_extractor"""]
							__UpperCAmelCase       ="""TvltImageProcessor"""
							__UpperCAmelCase       ="""TvltFeatureExtractor"""




							def __init__( self					,						_A					,						_A							):
												super().__init__(image_processor=_A					,						feature_extractor=_A							)

												__lowerCAmelCase					=							image_processor
												__lowerCAmelCase					=							feature_extractor




							def __call__( self					,						_A=None					,						_A=None					,						_A=None					,						_A=None					,						_A=False					,						_A=False					,						*_A					,						**_A					,						):

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

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

												__lowerCAmelCase					=							{}
												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        A__  ( self							):
												__lowerCAmelCase					=							self.image_processor.model_input_names
												__lowerCAmelCase					=							self.feature_extractor.model_input_names
												return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names							)							)









from graphs.minimum_spanning_tree_kruskal import kruskal


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							9
					__lowerCAmelCase					=							[
					    [0, 1, 4],
					    [0, 7, 8],
					    [1, 2, 8],
					    [7, 8, 7],
					    [7, 6, 1],
					    [2, 8, 2],
					    [8, 6, 6],
					    [2, 3, 7],
					    [2, 5, 4],
					    [6, 5, 2],
					    [3, 5, 14],
					    [3, 4, 9],
					    [5, 4, 10],
					    [1, 7, 11],
					]

					__lowerCAmelCase					=							kruskal(UpperCAmelCase__		,					UpperCAmelCase__      )

					__lowerCAmelCase					=							[
					    [7, 6, 1],
					    [2, 8, 2],
					    [6, 5, 2],
					    [0, 1, 4],
					    [2, 5, 4],
					    [2, 3, 7],
					    [0, 7, 8],
					    [3, 4, 9],
					]

					assert sorted(UpperCAmelCase__      ) == sorted(UpperCAmelCase__      )






