






'''simple docstring'''





import torch

from accelerate import Accelerator, DistributedType
class 					lowercase							:


    '''simple docstring'''

    def __enter__(       self  :  Tuple  )				->				str:

     '''simple docstring'''




     if self.enabled:
      lowerCamelCase__	=  self.model.no_sync()
      self.model_sync_obj.__enter__()

     return self

    def __exit__(       self  :  Optional[Any]					,  __lowerCamelCase  :  List[str]					,  __lowerCamelCase  :  Union[str, Any]					,  __lowerCamelCase  :  List[str]  )				->				Optional[Any]:

     '''simple docstring'''




     if self.enabled:
      # Average all models on exit
      self._sync_and_avg_model_params()
      self.model_sync_obj.__exit__(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase  )

    def __init__(       self  :  Union[str, Any]					,  __lowerCamelCase  :  Accelerator					,  __lowerCamelCase  :  torch.nn.Module					,  __lowerCamelCase  :  int					,  __lowerCamelCase  :  bool = True  )				->				str:

     '''simple docstring'''




     if accelerator.distributed_type not in [
         DistributedType.NO,
         DistributedType.MULTI_CPU,
         DistributedType.MULTI_GPU,
     ]:
      raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"  )
     lowerCamelCase__	=  enabled and accelerator.distributed_type != DistributedType.NO
     lowerCamelCase__	=  0
     if self.enabled:
      lowerCamelCase__	=  accelerator
      lowerCamelCase__	=  model
      lowerCamelCase__	=  local_sgd_steps

    def  a__					(       self  :  Union[str, Any]  )				->				Any:

     '''simple docstring'''




     self.num_steps += 1
     if not self.enabled:
      return

     if self.num_steps % self.local_sgd_steps == 0:
      self._sync_and_avg_model_params()




    def  a__					(       self  :  Any  )				->				str:

     '''simple docstring'''




     self.accelerator.wait_for_everyone()
     with self.accelerator.autocast():
      for param in self.model.parameters():
       lowerCamelCase__	=  self.accelerator.reduce(param.data					,  reduction="mean"  )









'''simple docstring'''





from __future__ import annotations

from queue import Queue
class 					lowercase							:


    '''simple docstring'''

    def __init__(       self  :  Optional[int]  )				->				None:

     '''simple docstring'''




     lowerCamelCase__	=  {}

    def  a__					(       self  :  Optional[int]  )				->				None:

     '''simple docstring'''




     for i in self.vertices:
      print(__lowerCamelCase					,  " : "					,  " -> ".join([str(__lowerCamelCase  ) for j in self.vertices[i]]  )  )

    def  a__					(       self  :  Optional[Any]					,  __lowerCamelCase  :  int					,  __lowerCamelCase  :  int  )				->				None:

     '''simple docstring'''




     if from_vertex in self.vertices:
      self.vertices[from_vertex].append(__lowerCamelCase  )
     else:
      lowerCamelCase__	=  [to_vertex]




    def  a__					(       self  :  Optional[int]					,  __lowerCamelCase  :  int  )				->				set[int]:

     '''simple docstring'''




     lowerCamelCase__	=  set()

     # create a first in first out queue to store all the vertices for BFS
     lowerCamelCase__	=  Queue()

     # mark the source node as visited and enqueue it
     visited.add(__lowerCamelCase  )
     queue.put(__lowerCamelCase  )

     while not queue.empty():
      lowerCamelCase__	=  queue.get()

      # loop through all adjacent vertex and enqueue it if not yet visited
      for adjacent_vertex in self.vertices[vertex]:
       if adjacent_vertex not in visited:
        queue.put(__lowerCamelCase  )
        visited.add(__lowerCamelCase  )
     return visited


if __name__ == "__main__":
 from doctest import testmod

 testmod(verbose=True)

 __A				:				Optional[Any]					=		Graph()
 g.add_edge(0, 1)
 g.add_edge(0, 2)
 g.add_edge(1, 2)
 g.add_edge(2, 0)
 g.add_edge(2, 3)
 g.add_edge(3, 3)

 g.print_graph()
 # 0  :  1 -> 2
 # 1  :  2
 # 2  :  0 -> 3
 # 3  :  3

 assert sorted(g.bfs(2)) == [0, 1, 2, 3]

