


import torch

from accelerate import Accelerator, DistributedType
class      UpperCAmelCase_ :

   '''simple docstring'''




   def __enter__(		self			):






     """simple docstring"""





     if self.enabled:
       lowerCamelCase			: str   =    self.model.no_sync()
       self.model_sync_obj.__enter__()

     return self






   def __exit__(		self						,    __A						,    __A						,    __A			):






     """simple docstring"""





     if self.enabled:
       # Average all models on exit
       self._sync_and_avg_model_params()
       self.model_sync_obj.__exit__(__A						,    __A						,    __A			)






   def __init__(		self						,    __A						,    __A						,    __A						,    __A = 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)"			)
     lowerCamelCase			: Optional[Any]   =    enabled and accelerator.distributed_type != DistributedType.NO
     lowerCamelCase			: Any   =    0
     if self.enabled:
       lowerCamelCase			: List[str]   =    accelerator
       lowerCamelCase			: str   =    model
       lowerCamelCase			: List[Any]   =    local_sgd_steps






   def _snake_case   (		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 _snake_case   (		self			):






     """simple docstring"""





     self.accelerator.wait_for_everyone()
     with self.accelerator.autocast():
       for param in self.model.parameters():
         lowerCamelCase			: Tuple   =    self.accelerator.reduce(param.data						,    reduction="mean"			)








import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase			: str   =    ArgumentParser(
      description=(
          "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
      )     )

  # Optional arguments for the launch helper
  parser.add_argument("--num_cores"				,						type=SCREAMING_SNAKE_CASE_				,						default=1				,						help="Number of TPU cores to use (1 or 8)."     )

  # positional
  parser.add_argument(
      "training_script"				,						type=SCREAMING_SNAKE_CASE_				,						help=(
          "The full path to the single TPU training "
          "program/script to be launched in parallel, "
          "followed by all the arguments for the "
          "training script"
      )				,						)

  # rest from the training program
  parser.add_argument("training_script_args"				,						nargs=SCREAMING_SNAKE_CASE_     )

  return parser.parse_args()






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase			: Optional[Any]   =    parse_args()

  # Import training_script as a module.
  lowerCamelCase			: Tuple   =    Path(args.training_script     )
  sys.path.append(str(script_fpath.parent.resolve()     )     )
  lowerCamelCase			: int   =    script_fpath.stem
  lowerCamelCase			: Tuple   =    importlib.import_module(SCREAMING_SNAKE_CASE_     )

  # Patch sys.argv
  lowerCamelCase			: Union[str, Any]   =    [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores     )]

  xmp.spawn(mod._mp_fn				,						args=()				,						nprocs=args.num_cores     )


if __name__ == "__main__":
   main()



