

"""simple docstring"""






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

import torch_xla.distributed.xla_multiprocessing as xmp
def 		A_	(   ):
		lowercase										=							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=__UpperCamelCase       ,	default=1       ,	help='''Number of TPU cores to use (1 or 8).'''				)

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

		return parser.parse_args()
def 		A_	(   ):
		lowercase										=							parse_args()

		# Import training_script as a module.
		lowercase										=							Path(args.training_script				)
		sys.path.append(str(script_fpath.parent.resolve()				)				)
		lowercase										=							script_fpath.stem
		lowercase										=							importlib.import_module(__UpperCamelCase				)

		# Patch sys.argv
		lowercase										=							[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"""






from typing import TYPE_CHECKING

from ...utils import _LazyModule


__lowerCAmelCase				      =   {'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
							from .tokenization_phobert import PhobertTokenizer

else:
							import sys

							__lowerCAmelCase				      =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)