
'''simple docstring'''





import torch

from accelerate import Accelerator, DistributedType




class     UpperCamelCase					:



  def __enter__( self   ):







      '''simple docstring'''


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

      return self



  def __exit__( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   ):







      '''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		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase = 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)"""   )
      _a							:       int       					=			enabled and accelerator.distributed_type != DistributedType.NO
      _a							:       List[Any]       					=			0
      if self.enabled:
          _a							:       Optional[int]       					=			accelerator
          _a							:       int       					=			model
          _a							:       Optional[Any]       					=			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():
              _a							:       List[Any]       					=			self.accelerator.reduce(param.data		,    reduction="""mean"""   )








'''simple docstring'''





from __future__ import annotations

from enum import Enum




class     UpperCamelCase					(  snake_case__					):
  a    :Optional[int]      					=				2_4
  a    :Dict      					=				2_1
  a    :Dict      					=				1_8
  a    :int      					=				1_5
  a    :List[str]      					=				1_2
  a    :Tuple      					=				9
  a    :Optional[int]      					=				6
  a    :str      					=				3
  a    :Dict      					=				2
  a    :int      					=				1
  a    :List[Any]      					=				-1
  a    :Optional[Any]      					=				-2
  a    :Union[str, Any]      					=				-3
  a    :int      					=				-6
  a    :Union[str, Any]      					=				-9
  a    :Dict      					=				-1_2
  a    :Any      					=				-1_5
  a    :Tuple      					=				-1_8
  a    :Optional[Any]      					=				-2_1
  a    :Union[str, Any]      					=				-2_4






class     UpperCamelCase					(  snake_case__					):
  a    :Optional[Any]      					=				8
  a    :Optional[int]      					=				7
  a    :Optional[int]      					=				6
  a    :List[str]      					=				5
  a    :Union[str, Any]      					=				4
  a    :Union[str, Any]      					=				3
  a    :str      					=				2
  a    :Optional[int]      					=				1






def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  ):
    if isinstance(UpperCAmelCase_      ,  UpperCAmelCase_	):
        _a							:       int       					=			SIUnit[known_prefix.lower()]
    if isinstance(UpperCAmelCase_      ,  UpperCAmelCase_	):
        _a							:       Optional[Any]       					=			SIUnit[unknown_prefix.lower()]
    _a							:       float       					=			known_amount * (
        10 ** (known_prefix.value - unknown_prefix.value)
    )
    return unknown_amount





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  ):
    if isinstance(UpperCAmelCase_      ,  UpperCAmelCase_	):
        _a							:       Optional[Any]       					=			BinaryUnit[known_prefix.lower()]
    if isinstance(UpperCAmelCase_      ,  UpperCAmelCase_	):
        _a							:       Dict       					=			BinaryUnit[unknown_prefix.lower()]
    _a							:       float       					=			known_amount * (
        2 ** ((known_prefix.value - unknown_prefix.value) * 10)
    )
    return unknown_amount


if __name__ == "__main__":
  import doctest

  doctest.testmod()


