
"""simple docstring"""







from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader




class  A__       (	_lowerCamelCase):

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = True       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = False       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = True       ,		_SCREAMING_SNAKE_CASE = "arrow"       ,		**_SCREAMING_SNAKE_CASE       ,		):
			super().__init__(
			    split=_SCREAMING_SNAKE_CASE       ,		features=_SCREAMING_SNAKE_CASE       ,		cache_dir=_SCREAMING_SNAKE_CASE       ,		keep_in_memory=_SCREAMING_SNAKE_CASE       ,		streaming=_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE       ,		)
			__lowerCAmelCase			:						List[str]									=      load_from_cache_file
			__lowerCAmelCase			:						Any									=      file_format
			__lowerCAmelCase			:						Dict									=      Spark(
			    df=_SCREAMING_SNAKE_CASE       ,		features=_SCREAMING_SNAKE_CASE       ,		cache_dir=_SCREAMING_SNAKE_CASE       ,		working_dir=_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE       ,		)







		def 							__lowerCamelCase							(      self ):
			if self.streaming:
				return self.builder.as_streaming_dataset(split=self.split )
			__lowerCAmelCase			:						Union[str, Any]									=      None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
			self.builder.download_and_prepare(
			    download_mode=_SCREAMING_SNAKE_CASE       ,		file_format=self._file_format       ,		)
			return self.builder.as_dataset(split=self.split )




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

import torch_xla.distributed.xla_multiprocessing as xmp


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     List[str]									= 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 			UpperCamelCase(  ):
		lowerCAmelCase_			:     str									= parse_args()

		# Import training_script as a module.
		lowerCAmelCase_			:     str									= Path(args.training_script )
		sys.path.append(str(script_fpath.parent.resolve() ) )
		lowerCAmelCase_			:     Tuple									= script_fpath.stem
		lowerCAmelCase_			:     Union[str, Any]									= importlib.import_module(__UpperCamelCase )

		# Patch sys.argv
		lowerCAmelCase_			:     Optional[int]									= [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()





