






"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp





def 							lowercase__		(				):
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						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=lowerCamelCase,	default=1,	help='Number of TPU cores to use (1 or 8).'					)

				# positional
				parser.add_argument(
				    'training_script',	type=lowerCamelCase,	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=lowerCamelCase					)

				return parser.parse_args()





def 							lowercase__		(				):
				_SCREAMING_SNAKE_CASE      :		Any							=						parse_args()

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

				# Patch sys.argv
				_SCREAMING_SNAKE_CASE      :		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()









"""simple docstring"""
import json
import logging
import os
import subprocess
from argparse import ArgumentParser


lowerCAmelCase__							= logging.getLogger(__name__)





def 							lowercase__		(				):
				_SCREAMING_SNAKE_CASE      :		Tuple							=						ArgumentParser()
				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						parser.parse_known_args()
				for arg in unknown:
								if arg.startswith(('-', '--')					):
												parser.add_argument(arg.split('='					)[0]					)

				return parser.parse_args()





def 							lowercase__		(				):
				_SCREAMING_SNAKE_CASE      :		Tuple							=						parse_args()
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						8_888
				_SCREAMING_SNAKE_CASE      :		Optional[int]							=						int(os.environ['SM_NUM_GPUS']					)
				_SCREAMING_SNAKE_CASE      :		Any							=						json.loads(os.environ['SM_HOSTS']					)
				_SCREAMING_SNAKE_CASE      :		List[str]							=						len(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		Dict							=						os.environ['SM_CURRENT_HOST']
				_SCREAMING_SNAKE_CASE      :		List[str]							=						hosts.index(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		Optional[int]							=						'INFO'

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


if __name__ == "__main__":
			main()

