

from __future__ import annotations

lowerCAmelCase__								=		{
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}







class 	lowercase    :


		"""simple docstring"""




		def __init__(				self	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Tuple			=						graph
			# mapping node to its parent in resulting breadth first tree
			_UpperCamelCase   :		dict[str, str | None]			=						{}
			_UpperCamelCase   :		Any			=						source_vertex


		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						{self.source_vertex}
			_UpperCamelCase   :		List[str]			=						None
			_UpperCamelCase   :		Union[str, Any]			=						[self.source_vertex]  # first in first out queue

			while queue:
				_UpperCamelCase   :		Any			=						queue.pop(0)
				for adjacent_vertex in self.graph[vertex]:
					if adjacent_vertex not in visited:
						visited.add(__snake_case)
						_UpperCamelCase   :		Any			=						vertex
						queue.append(__snake_case)






		def 		A__					(				self	,       __snake_case):
			if target_vertex == self.source_vertex:
				return self.source_vertex

			_UpperCamelCase   :		Optional[Any]			=						self.parent.get(__snake_case)
			if target_vertex_parent is None:
				_UpperCamelCase   :		Optional[int]			=						(
				    f'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
				)
				raise ValueError(__snake_case)

			return self.shortest_path(__snake_case) + f'''->{target_vertex}'''


if __name__ == "__main__":
							lowerCAmelCase__								=		Graph(graph, """G""")
							g.breath_first_search()
							print(g.shortest_path("""D"""))
							print(g.shortest_path("""G"""))
							print(g.shortest_path("""Foo"""))







from ...processing_utils import ProcessorMixin







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




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


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

			_UpperCamelCase   :		List[str]			=						image_processor
			_UpperCamelCase   :		Dict			=						feature_extractor


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

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

			_UpperCamelCase   :		Tuple			=						{}
			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 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						self.image_processor.model_input_names
			_UpperCamelCase   :		List[Any]			=						self.feature_extractor.model_input_names
			return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names))




