





'''simple docstring'''






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






def      __A						(							a_						:  dict      ,a_						:  Optional[int]      ,a_						:  Optional[Any]	):
		lowerCAmelCase     :    Optional[Any]						=   set()
		# keep track of all the paths to be checked
		lowerCAmelCase     :    str						=   [[start]]

		# return path if start is goal
		if start == goal:
				return [start]

		# keeps looping until all possible paths have been checked
		while queue:
				# pop the first path from the queue
				lowerCAmelCase     :    Optional[Any]						=   queue.pop(0	)
				# get the last node from the path
				lowerCAmelCase     :    Tuple						=   path[-1]
				if node not in explored:
						lowerCAmelCase     :    Union[str, Any]						=   graph[node]
						# go through all neighbour nodes, construct a new path and
						# push it into the queue
						for neighbour in neighbours:
								lowerCAmelCase     :    Tuple						=   list(a_	)
								new_path.append(a_	)
								queue.append(a_	)
								# return path if neighbour is goal
								if neighbour == goal:
										return new_path

            # mark node as explored
						explored.add(a_	)

    # in case there's no path between the 2 nodes
		return []






def      __A						(							a_						:  dict      ,a_						:  Optional[int]      ,a_						:  Optional[Any]	):
		if not graph or start not in graph or target not in graph:
				return -1
		if start == target:
				return 0
		lowerCAmelCase     :    Optional[int]						=   [start]
		lowerCAmelCase     :    Any						=   set(a_	)
		# Keep tab on distances from `start` node.
		lowerCAmelCase     :    Any						=   {start: 0, target: -1}
		while queue:
				lowerCAmelCase     :    str						=   queue.pop(0	)
				if node == target:
						lowerCAmelCase     :    List[Any]						=   (
						    dist[node] if dist[target] == -1 else min(dist[target]      ,dist[node]	)
						)
				for adjacent in graph[node]:
						if adjacent not in visited:
								visited.add(a_	)
								queue.append(a_	)
								lowerCAmelCase     :    List[Any]						=   dist[node] + 1
		return dist[target]


if __name__ == "__main__":
					print(bfs_shortest_path(demo_graph, """G""", """D"""))  # returns ['G', 'C', 'A', 'B', 'D']
					print(bfs_shortest_path_distance(demo_graph, """G""", """D"""))  # returns 4










'''simple docstring'''






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
					from .pipeline_kandinsky import KandinskyPipeline
					from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
					from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
					from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
					from .text_encoder import MultilingualCLIP



