





class SCREAMING_SNAKE_CASE_    :






						def __init__(   self   :   str						)				->     Dict:







							"""simple docstring"""


							snake_case_		:      int           =						[0]
							snake_case_		:      List[Any]           =						0






						def UpperCAmelCase_					(   self   :   int		, _A   :   int						)				->     None:







							"""simple docstring"""


							snake_case_		:      Dict           =						self.__heap[i]
							while i // 2 > 0:
								if self.__heap[i] > self.__heap[i // 2]:
									snake_case_		:      List[Any]           =						self.__heap[i // 2]
									snake_case_		:      List[Any]           =						temporary
								i //= 2






						def UpperCAmelCase_					(   self   :   Optional[Any]		, _A   :   int						)				->     None:







							"""simple docstring"""


							self.__heap.append(_A						)
							self.__size += 1
							self.__swap_up(self.__size						)






						def UpperCAmelCase_					(   self   :   Any		, _A   :   int						)				->     None:







							"""simple docstring"""


							while self.__size >= 2 * i:
								if 2 * i + 1 > self.__size:
									snake_case_		:      str           =						2 * i
								else:
									if self.__heap[2 * i] > self.__heap[2 * i + 1]:
										snake_case_		:      Dict           =						2 * i
									else:
										snake_case_		:      int           =						2 * i + 1
								snake_case_		:      Optional[Any]           =						self.__heap[i]
								if self.__heap[i] < self.__heap[bigger_child]:
									snake_case_		:      int           =						self.__heap[bigger_child]
									snake_case_		:      Optional[int]           =						temporary
								snake_case_		:      Tuple           =						bigger_child






						def UpperCAmelCase_					(   self   :   int						)				->     int:







							"""simple docstring"""


							snake_case_		:      List[Any]           =						self.__heap[1]
							snake_case_		:      List[str]           =						self.__heap[self.__size]
							self.__size -= 1
							self.__heap.pop()
							self.__swap_down(1						)
							return max_value






						@property
						def UpperCAmelCase_					(   self   :   str						)				->     List[str]:







							"""simple docstring"""


							return self.__heap[1:]






						def __len__(   self   :   List[str]						)				->     Dict:







							"""simple docstring"""


							return self.__size


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						# create an instance of BinaryHeap
						_SCREAMING_SNAKE_CASE   						=		BinaryHeap()
						binary_heap.insert(6)
						binary_heap.insert(10)
						binary_heap.insert(15)
						binary_heap.insert(12)
						# pop root(max-values because it is max heap)
						print(binary_heap.pop())  # 15
						print(binary_heap.pop())  # 12
						# get the list and size after operations
						print(binary_heap.get_list)
						print(len(binary_heap))


"""simple docstring"""







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

import torch_xla.distributed.xla_multiprocessing as xmp




def 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					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=_SCREAMING_SNAKE_CASE  ,       default=1  ,       help='Number of TPU cores to use (1 or 8).'						)

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

     return parser.parse_args()




def 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					List[str]								=		parse_args()

     # Import training_script as a module.
     __A					:					Dict								=		Path(args.training_script						)
     sys.path.append(str(script_fpath.parent.resolve()						)						)
     __A					:					List[Any]								=		script_fpath.stem
     __A					:					Optional[Any]								=		importlib.import_module(_SCREAMING_SNAKE_CASE						)

     # Patch sys.argv
     __A					:					Union[str, 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()



