



import torch

from accelerate import Accelerator, DistributedType

class 		UpperCAmelCase__      :




			'''simple docstring'''






			def __enter__(       self							:			Tuple			):
					'''simple docstring'''





					if self.enabled:
							__UpperCAmelCase   :					Optional[Any]													=     self.model.no_sync()
							self.model_sync_obj.__enter__()

					return self




			def __exit__(       self							:			Tuple	,	a_							:			Union[str, Any]	,	a_							:			Tuple	,	a_							:			int			):
					'''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[Any]	,	a_							:			Optional[Any]	,	a_							:			Union[str, Any]	,	a_							:			str	,	a_							:			List[Any] = 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)'''			)
					__UpperCAmelCase   :					Optional[Any]													=     enabled and accelerator.distributed_type != DistributedType.NO
					__UpperCAmelCase   :					List[Any]													=     0
					if self.enabled:
							__UpperCAmelCase   :					int													=     accelerator
							__UpperCAmelCase   :					Dict													=     model
							__UpperCAmelCase   :					Any													=     local_sgd_steps




			def 			snake_case__					(       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 			snake_case__					(       self							:			Union[str, Any]			):
					'''simple docstring'''





					self.accelerator.wait_for_everyone()
					with self.accelerator.autocast():
							for param in self.model.parameters():
									__UpperCAmelCase   :					List[Any]													=     self.accelerator.reduce(param.data	,	reduction='''mean'''			)










import json
import logging
import os
import subprocess
from argparse import ArgumentParser


__A             =logging.getLogger(__name__)

def 	a						(				):







		'''simple docstring'''





		__UpperCAmelCase   :					Union[str, Any]													=     ArgumentParser()
		__UpperCAmelCase							, __UpperCAmelCase   :					Optional[int]													=     parser.parse_known_args()
		for arg in unknown:
				if arg.startswith(('''-''', '''--''')						):
						parser.add_argument(arg.split('''='''						)[0]						)

		return parser.parse_args()

def 	a						(				):







		'''simple docstring'''





		__UpperCAmelCase   :					int													=     parse_args()
		__UpperCAmelCase   :					int													=     88_88
		__UpperCAmelCase   :					Union[str, Any]													=     int(os.environ['''SM_NUM_GPUS''']						)
		__UpperCAmelCase   :					Any													=     json.loads(os.environ['''SM_HOSTS''']						)
		__UpperCAmelCase   :					int													=     len(_UpperCAmelCase						)
		__UpperCAmelCase   :					Tuple													=     os.environ['''SM_CURRENT_HOST''']
		__UpperCAmelCase   :					Union[str, Any]													=     hosts.index(_UpperCAmelCase						)
		__UpperCAmelCase   :					Tuple													=     '''INFO'''

		if num_nodes > 1:
				__UpperCAmelCase   :					int													=     f'python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]						)}'
		else:
				__UpperCAmelCase   :					Optional[Any]													=     f'python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]						)}'
		try:
				subprocess.run(_UpperCAmelCase			,       shell=_UpperCAmelCase						)
		except Exception as e:
				logger.info(_UpperCAmelCase						)


if __name__ == "__main__":
				main()





