code: from __future__ import annotations
  
  from enum import Enum, unique
  from typing import TypeVar
  
  # Create a generic variable that can be 'Enum', or any subclass.
  lowercase__    :       str						=			TypeVar('''T''', bound='''Enum''')
  
  
  
  
  @unique
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      Any     =       80
  	UpperCAmelCase_						:      Union[str, Any]     =       70
  	UpperCAmelCase_						:      List[str]     =       60
  	UpperCAmelCase_						:      Dict     =       50
  	UpperCAmelCase_						:      Any     =       40
  	UpperCAmelCase_						:      int     =       30
  	UpperCAmelCase_						:      Optional[Any]     =       20
  	UpperCAmelCase_						:      Optional[int]     =       10
  
  
  
  
  @unique
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      Optional[int]     =       24
  	UpperCAmelCase_						:      Dict     =       21
  	UpperCAmelCase_						:      Dict     =       18
  	UpperCAmelCase_						:      Optional[int]     =       15
  	UpperCAmelCase_						:      Any     =       12
  	UpperCAmelCase_						:      Tuple     =       9
  	UpperCAmelCase_						:      List[str]     =       6
  	UpperCAmelCase_						:      str     =       3
  	UpperCAmelCase_						:      Optional[Any]     =       2
  	UpperCAmelCase_						:      int     =       1
  	UpperCAmelCase_						:      Optional[int]     =       -1
  	UpperCAmelCase_						:      Dict     =       -2
  	UpperCAmelCase_						:      Optional[Any]     =       -3
  	UpperCAmelCase_						:      Dict     =       -6
  	UpperCAmelCase_						:      List[str]     =       -9
  	UpperCAmelCase_						:      str     =       -12
  	UpperCAmelCase_						:      Any     =       -15
  	UpperCAmelCase_						:      str     =       -18
  	UpperCAmelCase_						:      Dict     =       -21
  	UpperCAmelCase_						:      Optional[int]     =       -24
  
  
  	@classmethod
  	def     SCREAMING_SNAKE_CASE_       (							cls       )		->dict:
  								return {unit.name: unit.value for unit in cls if unit.value > 0}
  
  
  
  
  	@classmethod
  	def     SCREAMING_SNAKE_CASE_       (							cls       )		->dict:
  								return {unit.name: unit.value for unit in cls if unit.value < 0}
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			str:
  							lowerCAmelCase				=	SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
  							for name_prefix, value_prefix in prefixes.items():
  														lowerCAmelCase				=	value / (1_0**value_prefix)
  														if numerical_part > 1:
  																					return f"{numerical_part!s} {name_prefix}"
  							return str(snake_case__							)
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			str:
  							for prefix in BinaryUnit:
  														lowerCAmelCase				=	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()
  
  
  
  
style_context: import torch
  
  from accelerate import Accelerator, DistributedType
  
  
  
  
  class      lowercase_						:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	def __enter__(							self       )		->Dict:
  								if self.enabled:
  															lowerCAmelCase				=	self.model.no_sync()
  															self.model_sync_obj.__enter__()
  
  								return self
  
  
  	def __exit__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->Any:
  								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	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = True       )		->Tuple:
  								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				=	enabled and accelerator.distributed_type != DistributedType.NO
  								lowerCAmelCase				=	0
  								if self.enabled:
  															lowerCAmelCase				=	accelerator
  															lowerCAmelCase				=	model
  															lowerCAmelCase				=	local_sgd_steps
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->List[Any]:
  								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[int]:
  								self.accelerator.wait_for_everyone()
  								with self.accelerator.autocast():
  															for param in self.model.parameters():
  																						lowerCAmelCase				=	self.accelerator.reduce(param.data	,    reduction='''mean'''       )
  
  
  
  