





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





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a						)	->						list[str]:







  """simple docstring"""





  lowerCamelCase__: Optional[int]         =set()
  # keep track of all the paths to be checked
  lowerCamelCase__: Tuple         =[[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__: Any         =path[-1]
    if node not in explored:
      lowerCamelCase__: Tuple         =graph[node]
      # go through all neighbour nodes, construct a new path and
      # push it into the queue
      for neighbour in neighbours:
        lowerCamelCase__: Any         =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 	lowerCAmelCase_					(			__a      ,       __a      ,       __a						)	->						int:







  """simple docstring"""





  if not graph or start not in graph or target not in graph:
    return -1
  if start == target:
    return 0
  lowerCamelCase__: Tuple         =[start]
  lowerCamelCase__: str         =set(__a						)
  # Keep tab on distances from `start` node.
  lowerCamelCase__: Any         ={start: 0, target: -1}
  while queue:
    lowerCamelCase__: List[Any]         =queue.pop(0						)
    if node == target:
      lowerCamelCase__: List[str]         =(
          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__: Optional[int]         =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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase__      			=						{
    '''configuration_poolformer''': [
        '''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''PoolFormerConfig''',
        '''PoolFormerOnnxConfig''',
    ]
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__      			=						['''PoolFormerFeatureExtractor''']
			UpperCAmelCase__      			=						['''PoolFormerImageProcessor''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__      			=						[
			    '''POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''PoolFormerForImageClassification''',
			    '''PoolFormerModel''',
			    '''PoolFormerPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_poolformer import (
			    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    PoolFormerConfig,
			    PoolFormerOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_poolformer import PoolFormerFeatureExtractor
						from .image_processing_poolformer import PoolFormerImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_poolformer import (
						    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PoolFormerForImageClassification,
						    PoolFormerModel,
						    PoolFormerPreTrainedModel,
						)


else:
			import sys

			UpperCAmelCase__      			=						_LazyModule(__name__, globals()['''__file__'''], _import_structure)





