






import torch

from accelerate import Accelerator, DistributedType
class        __snake_case:



 def __enter__(	self   :   Optional[Any]	):

    """simple docstring"""






    if self.enabled:
       lowercase__						:	Optional[int]         = self.model.no_sync()
       self.model_sync_obj.__enter__()

    return self



 def __exit__(	self   :   Dict ,		UpperCAmelCase__   :   str ,		UpperCAmelCase__   :   Dict ,		UpperCAmelCase__   :   str	):

    """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   :   Optional[int] ,		UpperCAmelCase__   :   Accelerator ,		UpperCAmelCase__   :   torch.nn.Module ,		UpperCAmelCase__   :   int ,		UpperCAmelCase__   :   bool = 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)"""	)
    lowercase__						:	Tuple         = enabled and accelerator.distributed_type != DistributedType.NO
    lowercase__						:	Tuple         = 0
    if self.enabled:
       lowercase__						:	str         = accelerator
       lowercase__						:	List[Any]         = model
       lowercase__						:	Optional[Any]         = local_sgd_steps



 def 						UpperCAmelCase    (	self   :   Optional[int]	):

    """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 						UpperCAmelCase    (	self   :   Union[str, Any]	):

    """simple docstring"""






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










from dataclasses import dataclass
from typing import Callable, Optional

import datasets
@dataclass
class        __snake_case(					datasets.BuilderConfig      ):
 lowercase :       Optional[Callable]		      =		None
 lowercase :       Optional[dict]		      =		None
 lowercase :       Optional[datasets.Features]		      =		None



 def 						UpperCAmelCase    (	self   :   int	):

    """simple docstring"""






    assert self.generator is not None, "generator must be specified"

    if self.gen_kwargs is None:
       lowercase__						:	List[Any]         = {}
class        __snake_case(					datasets.GeneratorBasedBuilder      ):
 lowercase :       Dict		      =		GeneratorConfig



 def 						UpperCAmelCase    (	self   :   Any	):

    """simple docstring"""






    return datasets.DatasetInfo(features=self.config.features	)



 def 						UpperCAmelCase    (	self   :   List[str] ,		UpperCAmelCase__   :   Optional[int]	):

    """simple docstring"""






    return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,		gen_kwargs=self.config.gen_kwargs	)]







 def 						UpperCAmelCase    (	self   :   Dict ,		**UpperCAmelCase__   :   List[str]	):

    """simple docstring"""






    for idx, ex in enumerate(self.config.generator(**UpperCAmelCase__	)	):
       yield idx, ex


