




from dataclasses import dataclass
from typing import Callable, Optional

import datasets




@dataclass
class       UpperCAmelCase_   ( datasets.BuilderConfig							):




							'''simple docstring'''



							a__    	=  None
							a__    	=  None
							a__    	=  None





							def  _UpperCAmelCase  (       self							:						Optional[int]      )      ->       Optional[int]:
										assert self.generator is not None, "generator must be specified"

										if self.gen_kwargs is None:
													SCREAMING_SNAKE_CASE     =       {}





class       UpperCAmelCase_   ( datasets.GeneratorBasedBuilder							):




							'''simple docstring'''



							a__    	=  GeneratorConfig





							def  _UpperCAmelCase  (       self							:						Optional[Any]      )      ->       str:
										return datasets.DatasetInfo(features=self.config.features      )





							def  _UpperCAmelCase  (       self							:						Tuple							,  a							:						str      )      ->       List[str]:
										return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,  gen_kwargs=self.config.gen_kwargs      )]







							def  _UpperCAmelCase  (       self							:						int							,  **a							:						List[str]      )      ->       Tuple:
										for idx, ex in enumerate(self.config.generator(**lowerCamelCase_      )      ):
													yield idx, ex










"""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=_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__			(							):





						"""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(_UpperCamelCase			)

						# 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()