





'''simple docstring'''





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




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






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











__lowercase  :   Dict						      ="""0.21.0"""

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
     from .utils import rich




