






"""simple docstring"""





import torch

from accelerate import Accelerator, DistributedType







class 		_a       :





   '''simple docstring'''






   def __enter__(   self)	-> int:




          '''simple docstring'''




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

          return self





   def __exit__(   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> List[Any]:




          '''simple docstring'''




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





   def __init__(   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_ = True)	-> Optional[Any]:




          '''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)")
          lowercase__:    Any        =      enabled and accelerator.distributed_type != DistributedType.NO
          lowercase__:    Tuple        =      0
          if self.enabled:
                 lowercase__:    List[Any]        =      accelerator
                 lowercase__:    int        =      model
                 lowercase__:    Optional[Any]        =      local_sgd_steps





   def __lowercase    (   self)	-> Optional[int]:




          '''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 __lowercase    (   self)	-> Any:




          '''simple docstring'''




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














"""simple docstring"""





class 		_a       :





   '''simple docstring'''






   def __init__(   self			,				UpperCAmelCase_=None)	-> List[Any]:




          '''simple docstring'''




          lowercase__:    str        =      data
          lowercase__:    Any        =      None





   def __repr__(   self)	-> Optional[Any]:




          '''simple docstring'''




          lowercase__:    Any        =      ""
          lowercase__:    Dict        =      self
          while temp:
                 string_rep += F"""<{temp.data}> ---> """
                 lowercase__:    Tuple        =      temp.next
          string_rep += "<END>"
          return string_rep







def     A(  snake_case_		):
       """simple docstring"""





       if not elements_list:
              raise Exception("The Elements List is empty"		)

       # Set first element as Head
       lowercase__:    Any        =      Node(elements_list[0]		)
       lowercase__:    Optional[int]        =      head
       # Loop through elements from position 1
       for data in elements_list[1:]:
              lowercase__:    Dict        =      Node(snake_case_		)
              lowercase__:    Dict        =      current.next
       return head


UpperCamelCase			=      [1, 3, 5, 32, 44, 12, 43]
print(F"List: {list_data}")
print("""Creating Linked List from List.""")
UpperCamelCase			=      make_linked_list(list_data)
print("""Linked List:""")







print(linked_list)






