


"""simple docstring"""







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


def      lowercase			(				A_  ,   A_  ,   A_       )->      list[str]:
  '''simple docstring'''







  a						:		List[str]      							=			set()
  # keep track of all the paths to be checked
  a						:		Union[str, Any]      							=			[[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						:		Tuple      							=			queue.pop(0       )
    # get the last node from the path
    a						:		Optional[int]      							=			path[-1]
    if node not in explored:
      a						:		List[str]      							=			graph[node]
      # go through all neighbour nodes, construct a new path and
      # push it into the queue
      for neighbour in neighbours:
        a						:		Optional[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      lowercase			(				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
  a						:		Tuple      							=			[start]
  a						:		Union[str, Any]      							=			set(A__       )
  # Keep tab on distances from `start` node.
  a						:		Optional[Any]      							=			{start: 0, target: -1}
  while queue:
    a						:		str      							=			queue.pop(0       )
    if node == target:
      a						:		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__       )
        a						:		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


lowercase      :       List[Any]									=  {
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase      :       Dict									=  [
        """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """NllbMoeForConditionalGeneration""",
        """NllbMoeModel""",
        """NllbMoePreTrainedModel""",
        """NllbMoeTop2Router""",
        """NllbMoeSparseMLP""",
    ]


if TYPE_CHECKING:
    from .configuration_nllb_moe import (
        NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
        NllbMoeConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_nllb_moe import (
            NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
            NllbMoeForConditionalGeneration,
            NllbMoeModel,
            NllbMoePreTrainedModel,
            NllbMoeSparseMLP,
            NllbMoeTopaRouter,
        )


else:
    import sys

    lowercase      :       List[Any]									=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


