





"""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__					(					):




		"""simple docstring"""


		UpperCamelCase   =	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       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	parse_args()

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

		# Patch sys.argv
		UpperCamelCase   =	[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_bertweet''': ['''BertweetTokenizer''']}


if TYPE_CHECKING:
						from .tokenization_bertweet import BertweetTokenizer

else:
						import sys

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






