

from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
_SCREAMING_SNAKE_CASE							 =			TypeVar('''T''', bound='''Enum''')


@unique
class      lowerCamelCase_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	80
 _lowerCAmelCase									=	70
 _lowerCAmelCase									=	60
 _lowerCAmelCase									=	50
 _lowerCAmelCase									=	40
 _lowerCAmelCase									=	30
 _lowerCAmelCase									=	20
 _lowerCAmelCase									=	10



@unique
class      lowerCamelCase_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	24
 _lowerCAmelCase									=	21
 _lowerCAmelCase									=	18
 _lowerCAmelCase									=	15
 _lowerCAmelCase									=	12
 _lowerCAmelCase									=	9
 _lowerCAmelCase									=	6
 _lowerCAmelCase									=	3
 _lowerCAmelCase									=	2
 _lowerCAmelCase									=	1
 _lowerCAmelCase									=	-1
 _lowerCAmelCase									=	-2
 _lowerCAmelCase									=	-3
 _lowerCAmelCase									=	-6
 _lowerCAmelCase									=	-9
 _lowerCAmelCase									=	-12
 _lowerCAmelCase									=	-15
 _lowerCAmelCase									=	-18
 _lowerCAmelCase									=	-21
 _lowerCAmelCase									=	-24




 @classmethod
 def     a       (			cls							):
   return {unit.name: unit.value for unit in cls if unit.value > 0}




 @classmethod
 def     a       (			cls							):
   return {unit.name: unit.value for unit in cls if unit.value < 0}





def  _snake_case		(snake_case_					:     float  )							->		str:
  __UpperCAmelCase	:  Dict		=     SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
  for name_prefix, value_prefix in prefixes.items():
    __UpperCAmelCase	:  Optional[int]		=     value / (10**value_prefix)
    if numerical_part > 1:
      return F"{numerical_part!s} {name_prefix}"
  return str(snake_case_  )





def  _snake_case		(snake_case_					:     float  )							->		str:
  for prefix in BinaryUnit:
    __UpperCAmelCase	:  Union[str, Any]		=     value / (2**prefix.value)
    if numerical_part > 1:
      return F"{numerical_part!s} {prefix.name}"
  return str(snake_case_  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()






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""")




