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""")





from __future__ import annotations







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		List[str]			    =    [None] * (len(__A						) + len(__A						))

    _lowerCamelCase  :		Optional[int]			    =    0  # pointer to current index for left Half
    _lowerCamelCase  :		List[str]			    =    0  # pointer to current index for the right Half
    _lowerCamelCase  :		List[str]			    =    0  # pointer to current index for the sorted array Half

    while pointera < len(__A						) and pointera < len(__A						):
        if left_half[pointera] < right_half[pointera]:
            _lowerCamelCase  :		Optional[Any]			    =    left_half[pointera]
            pointera += 1
            index += 1
        else:
            _lowerCamelCase  :		List[Any]			    =    right_half[pointera]
            pointera += 1
            index += 1
    while pointera < len(__A						):
        _lowerCamelCase  :		int			    =    left_half[pointera]
        pointera += 1
        index += 1

    while pointera < len(__A						):
        _lowerCamelCase  :		Optional[Any]			    =    right_half[pointera]
        pointera += 1
        index += 1

    return sorted_array







def     A__	(       __A						):







    '''simple docstring'''
    if len(__A						) <= 1:
        return array
    # the actual formula to calculate the middle element = left + (right - left) // 2
    # this avoids integer overflow in case of large N
    _lowerCamelCase  :		int			    =    0 + (len(__A						) - 0) // 2

    # Split the array into halves till the array length becomes equal to One
    # merge the arrays of single length returned by mergeSort function and
    # pass them into the merge arrays function which merges the array
    _lowerCamelCase  :		List[str]			    =    array[:middle]
    _lowerCamelCase  :		Optional[int]			    =    array[middle:]

    return merge(merge_sort(__A						) ,			merge_sort(__A						)						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()




