'''simple docstring'''

class    lowerCamelCase__  :



   def __init__(    self					,      lowerCAmelCase__=None     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =data
        _UpperCamelCase  :Optional[int]       =None




   def __repr__(    self     )				->     Union[str, Any]:
        """simple docstring"""





        _UpperCamelCase  :Any       =""""""
        _UpperCamelCase  :Optional[int]       =self
        while temp:
             string_rep += f'''<{temp.data}> ---> '''
             _UpperCamelCase  :int       =temp.next
        string_rep += "<END>"
        return string_rep




def  _lowerCAmelCase				(				__a      )  ->      List[str]:
     '''simple docstring'''







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

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


_lowerCamelCase :				str														= [1, 3, 5, 32, 44, 12, 43]
print(f"List: {list_data}")
print("""Creating Linked List from List.""")
_lowerCamelCase :				int														= make_linked_list(list_data)
print("""Linked List:""")
print(linked_list)
import torch

from accelerate import Accelerator, DistributedType




class 						__snake_case						:





      '''simple docstring'''






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


          """simple docstring"""


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

          return self



      def __exit__(       self  :					List[str]     ,	_UpperCamelCase  :					Any     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					int)					->Optional[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  :					Union[str, Any]     ,	_UpperCamelCase  :					Accelerator     ,	_UpperCamelCase  :					torch.nn.Module     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					bool = True)					->int:


          """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  :		str			    =    enabled and accelerator.distributed_type != DistributedType.NO
          _lowerCamelCase  :		Optional[int]			    =    0
          if self.enabled:
              _lowerCamelCase  :		Union[str, Any]			    =    accelerator
              _lowerCamelCase  :		int			    =    model
              _lowerCamelCase  :		int			    =    local_sgd_steps



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


          """simple docstring"""


          self.accelerator.wait_for_everyone()
          with self.accelerator.autocast():
              for param in self.model.parameters():
                  _lowerCamelCase  :		Union[str, Any]			    =    self.accelerator.reduce(param.data     ,	reduction="""mean""")




