

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

import torch_xla.distributed.xla_multiprocessing as xmp


def 							__lowerCAmelCase     (							)		->				str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      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 							__lowerCAmelCase     (							)		->				str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      parse_args()

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

 # Patch sys.argv
 SCREAMING_SNAKE_CASE		    =      [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()




import re

from filelock import FileLock


try:
      import nltk

      a_     :    Optional[Any]     		=   True
except (ImportError, ModuleNotFoundError):
      a_     :    Union[str, Any]     		=   False

if NLTK_AVAILABLE:
      with FileLock(".lock") as lock:
            nltk.download("punkt", quiet=True)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				str:
 '''simple docstring'''


 re.sub('<n>'		,				''		,				_UpperCamelCase						)  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(_UpperCamelCase						)						)

