




"""simple docstring"""






import random





def 							UpperCAmelCase		(		snake_case			:					list      ,       snake_case			:					Optional[int]    ):
					_lowerCAmelCase:str									=  [], [], []
					for element in data:
										if element < pivot:
															less.append(snake_case    )
										elif element > pivot:
															greater.append(snake_case    )
										else:
															equal.append(snake_case    )
					return less, equal, greater





def 							UpperCAmelCase		(		snake_case			:					list      ,       snake_case			:					int    ):
					# index = len(items) // 2 when trying to find the median
					#   (value of index when items is sorted)

					# invalid input
					if index >= len(snake_case    ) or index < 0:
										return None

					_lowerCAmelCase:Optional[Any]									=  items[random.randint(0      ,       len(snake_case    ) - 1    )]
					_lowerCAmelCase:List[Any]									=  0
					_lowerCAmelCase:Optional[int]									=  _partition(snake_case      ,       snake_case    )
					_lowerCAmelCase:Union[str, Any]									=  len(snake_case    )
					_lowerCAmelCase:Union[str, Any]									=  len(snake_case    )

					# index is the pivot
					if m <= index < m + count:
										return pivot
					# must be in smaller
					elif m > index:
										return quick_select(snake_case      ,       snake_case    )
					# must be in larger
					else:
										return quick_select(snake_case      ,       index - (m + count)    )












"""simple docstring"""






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

import torch_xla.distributed.xla_multiprocessing as xmp





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:Optional[int]									=  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=snake_case      ,       default=1      ,       help='''Number of TPU cores to use (1 or 8).'''    )

					# positional
					parser.add_argument(
					    '''training_script'''      ,       type=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=snake_case    )

					return parser.parse_args()





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:int									=  parse_args()

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

					# Patch sys.argv
					_lowerCAmelCase:Dict									=  [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()






