"""simple docstring"""







import torch

from accelerate import Accelerator, DistributedType






class     _SCREAMING_SNAKE_CASE       :



   '''simple docstring'''



   def __enter__(  self  ):


      '''simple docstring'''

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

      return self







   def __exit__(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

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







   def __init__(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE = True  ):


      '''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  ):


      '''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  ):


      '''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"""







_SCREAMING_SNAKE_CASE  :		Optional[Any]        =      {
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

_SCREAMING_SNAKE_CASE  :		List[str]        =      [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_SCREAMING_SNAKE_CASE  :		Optional[Any]        =      [0 for x in range(len(graph))]
_SCREAMING_SNAKE_CASE  :		List[Any]        =      []



def 			A__						(  lowercase_    :  Optional[int]			,   lowercase_    :  List[str]	)     ->       List[str]:
   __lowerCamelCase								=				1
   while stack:
      __lowerCamelCase								=				stack.pop()
      print(lowercase_			,   clothes[current_clothing]	)
      order += 1



def 			A__						(  lowercase_    :  List[Any]			,   lowercase_    :  List[str]			,   lowercase_    :  Union[str, Any]	)     ->       List[Any]:
   __lowerCamelCase								=				1
   for v in graph[u]:
      if not visited[v]:
         depth_first_search(lowercase_			,   lowercase_			,   lowercase_	)

   stack.append(lowercase_	)



def 			A__						(  lowercase_    :  Dict			,   lowercase_    :  Optional[Any]	)     ->       Union[str, Any]:
   for v in range(len(lowercase_	)	):
      if not visited[v]:
         depth_first_search(lowercase_			,   lowercase_			,   lowercase_	)


if __name__ == "__main__":
     topological_sort(graph, visited)
     print(stack)
     print_stack(stack, clothes)






