






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

import torch_xla.distributed.xla_multiprocessing as xmp
def       a__						(  ):
   lowercase__						:	Tuple         = 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=A		,				default=1		,				help="""Number of TPU cores to use (1 or 8)."""					)

   # positional
   parser.add_argument(
       """training_script"""		,				type=A		,				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=A					)

   return parser.parse_args()
def       a__						(  ):
   lowercase__						:	Optional[int]         = parse_args()

   # Import training_script as a module.
   lowercase__						:	List[str]         = Path(args.training_script					)
   sys.path.append(str(script_fpath.parent.resolve()					)					)
   lowercase__						:	str         = script_fpath.stem
   lowercase__						:	Optional[int]         = importlib.import_module(A					)

   # Patch sys.argv
   lowercase__						:	Optional[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()










from dataclasses import dataclass
from typing import Callable, Optional

import datasets
@dataclass
class        __snake_case(					datasets.BuilderConfig      ):
 lowercase :       Optional[Callable]		      =		None
 lowercase :       Optional[dict]		      =		None
 lowercase :       Optional[datasets.Features]		      =		None



 def 						UpperCAmelCase    (	self   :   int	):

    """simple docstring"""






    assert self.generator is not None, "generator must be specified"

    if self.gen_kwargs is None:
       lowercase__						:	List[Any]         = {}
class        __snake_case(					datasets.GeneratorBasedBuilder      ):
 lowercase :       Dict		      =		GeneratorConfig



 def 						UpperCAmelCase    (	self   :   Any	):

    """simple docstring"""






    return datasets.DatasetInfo(features=self.config.features	)



 def 						UpperCAmelCase    (	self   :   List[str] ,		UpperCAmelCase__   :   Optional[int]	):

    """simple docstring"""






    return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,		gen_kwargs=self.config.gen_kwargs	)]







 def 						UpperCAmelCase    (	self   :   Dict ,		**UpperCAmelCase__   :   List[str]	):

    """simple docstring"""






    for idx, ex in enumerate(self.config.generator(**UpperCAmelCase__	)	):
       yield idx, ex


