




import torch

from accelerate import Accelerator, DistributedType




class  __lowerCAmelCase							:






       def __enter__( self:       int					):
         if self.enabled:
           lowercase     :str   						=    self.model.no_sync()
           self.model_sync_obj.__enter__()

         return self






       def __exit__( self:       Tuple							,       _lowerCAmelCase:       int							,       _lowerCAmelCase:       int							,       _lowerCAmelCase:       Dict					):
         if self.enabled:
           # Average all models on exit
           self._sync_and_avg_model_params()
           self.model_sync_obj.__exit__(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)






       def __init__( self:       int							,       _lowerCAmelCase:       Accelerator							,       _lowerCAmelCase:       torch.nn.Module							,       _lowerCAmelCase:       int							,       _lowerCAmelCase:       bool = True					):
         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     :str   						=    enabled and accelerator.distributed_type != DistributedType.NO
         lowercase     :int   						=    0
         if self.enabled:
           lowercase     :List[Any]   						=    accelerator
           lowercase     :Dict   						=    model
           lowercase     :Union[str, Any]   						=    local_sgd_steps






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









import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


_UpperCAmelCase						:    List[str]      =  2 << 20  # 2mb
_UpperCAmelCase						:    Optional[int]      =  get_logger(__name__)




class  __lowerCAmelCase							(				lowerCAmelCase):






       def        SCREAMING_SNAKE_CASE     ( self:       str					):
         lowercase     :str   						=    self._options.get_all_options().get("runner"					)
         return runner in [None, "DirectRunner", "PortableRunner"]







def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase,      lowerCamelCase=False ):
  lowercase     :str   						=    FileSystems
  if fs.exists(lowerCamelCase ):
    if force_upload:
      logger.info(F"Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True." )
    else:
      logger.info(F"Remote path already exist: {remote_file_path}. Skipping it as force_upload=False." )
      return
  with fs.create(lowerCamelCase ) as remote_file:
    with open(lowerCamelCase,      "rb" ) as local_file:
      lowercase     :int   						=    local_file.read(lowerCamelCase )
      while chunk:
        remote_file.write(lowerCamelCase )
        lowercase     :List[Any]   						=    local_file.read(lowerCamelCase )







def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase,      lowerCamelCase=False ):
  lowercase     :Union[str, Any]   						=    FileSystems
  if os.path.exists(lowerCamelCase ):
    if force_download:
      logger.info(F"Local path already exist: {remote_file_path}. Overwriting it as force_upload=True." )
    else:
      logger.info(F"Local path already exist: {remote_file_path}. Skipping it as force_upload=False." )
      return
  with fs.open(lowerCamelCase ) as remote_file:
    with open(lowerCamelCase,      "wb" ) as local_file:
      lowercase     :Optional[Any]   						=    remote_file.read(lowerCamelCase )
      while chunk:
        local_file.write(lowerCamelCase )
        lowercase     :Dict   						=    remote_file.read(lowerCamelCase )



