



'''simple docstring'''




UpperCamelCase_       :  List[str]					=  {
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}






def 		_lowerCAmelCase				(_lowercase   ,					_lowercase   ,					_lowercase			):

       """simple docstring"""

       a__								= set()
       # keep track of all the paths to be checked
       a__								= [[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
              a__								= queue.pop(0			)
              # get the last node from the path
              a__								= path[-1]
              if node not in explored:
                     a__								= graph[node]
                     # go through all neighbour nodes, construct a new path and
                     # push it into the queue
                     for neighbour in neighbours:
                            a__								= list(lowercase__			)
                            new_path.append(lowercase__			)
                            queue.append(lowercase__			)
                            # return path if neighbour is goal
                            if neighbour == goal:
                                   return new_path

            # mark node as explored
                     explored.add(lowercase__			)

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






def 		_lowerCAmelCase				(_lowercase   ,					_lowercase   ,					_lowercase			):

       """simple docstring"""

       if not graph or start not in graph or target not in graph:
              return -1
       if start == target:
              return 0
       a__								= [start]
       a__								= set(lowercase__			)
       # Keep tab on distances from `start` node.
       a__								= {start: 0, target: -1}
       while queue:
              a__								= queue.pop(0			)
              if node == target:
                     a__								= (
                         dist[node] if dist[target] == -1 else min(dist[target]   ,					dist[node]			)
                     )
              for adjacent in graph[node]:
                     if adjacent not in visited:
                            visited.add(lowercase__			)
                            queue.append(lowercase__			)
                            a__								= 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() and is_transformers_version(""">=""", """4.25.0""")):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ...utils.dummy_torch_and_transformers_objects import (
			    VersatileDiffusionDualGuidedPipeline,
			    VersatileDiffusionImageVariationPipeline,
			    VersatileDiffusionPipeline,
			    VersatileDiffusionTextToImagePipeline,
			)
else:
			from .modeling_text_unet import UNetFlatConditionModel
			from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
			from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
			from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
			from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline