"""simple docstring"""




_a   :	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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: dict    ,_lowerCamelCase		: Any    ,_lowerCamelCase		: Tuple			) ->   list[str]:
  _lowerCAmelCase		: str						    =  set()
  # keep track of all the paths to be checked
  _lowerCAmelCase		: int						    =  [[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[int]						    =  queue.pop(0			)
    # get the last node from the path
    _lowerCAmelCase		: Union[str, Any]						    =  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		: Optional[int]						    =  list(_lowerCamelCase			)
        new_path.append(_lowerCamelCase			)
        queue.append(_lowerCamelCase			)
        # return path if neighbour is goal
        if neighbour == goal:
          return new_path

            # mark node as explored
      explored.add(_lowerCamelCase			)

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





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: dict    ,_lowerCamelCase		: int    ,_lowerCamelCase		: List[str]			) ->   int:
  if not graph or start not in graph or target not in graph:
    return -1
  if start == target:
    return 0
  _lowerCAmelCase		: Tuple						    =  [start]
  _lowerCAmelCase		: Any						    =  set(_lowerCamelCase			)
  # Keep tab on distances from `start` node.
  _lowerCAmelCase		: int						    =  {start: 0, target: -1}
  while queue:
    _lowerCAmelCase		: Dict						    =  queue.pop(0			)
    if node == target:
      _lowerCAmelCase		: Optional[int]						    =  (
          dist[node] if dist[target] == -1 else min(dist[target]    ,dist[node]			)
      )
    for adjacent in graph[node]:
      if adjacent not in visited:
        visited.add(_lowerCamelCase			)
        queue.append(_lowerCamelCase			)
        _lowerCAmelCase		: List[str]						    =  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 UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
     from .pipeline_unclip import UnCLIPPipeline
     from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
     from .text_proj import UnCLIPTextProjModel




