"""simple docstring"""


from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

lowerCAmelCase_:       List[str]			     =       TypeVar("T", bound=bool)






class  a__     ( Generic[T]						):


   def __init__(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				value
        lowercase__								=				None
        lowercase__								=				None


   @property
   def 						snake_case__						(  self	):
        '''simple docstring'''



        return self._value


   @staticmethod
   def 						snake_case__						(  _UpperCAmelCase,					_UpperCAmelCase	):
        '''simple docstring'''



        if not roota:
             return roota

        if not roota:
             return roota

        if roota.value > roota.value:
             lowercase__								=				roota, roota

        if random.choice([True, False]	):
             lowercase__								=				roota.right, roota.left

        lowercase__								=				RandomizedHeapNode.merge(roota.left,					__lowerCamelCase	)

        return roota






class  a__     ( Generic[T]						):


   def __init__(  self,					_UpperCAmelCase = ()	):
        '''simple docstring'''



        lowercase__								=				None

        if data:
             for item in data:
                  self.insert(__lowerCamelCase	)


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				RandomizedHeapNode.merge(self._root,					RandomizedHeapNode(__lowerCamelCase	)	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.top()

        if self._root is None:
             return None

        lowercase__								=				RandomizedHeapNode.merge(self._root.left,					self._root.right	)

        return result


   def 						snake_case__						(  self	):
        '''simple docstring'''



        if not self._root:
             raise IndexError("Can\'t get top element for the empty heap."	)
        return self._root.value


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				None


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				[]
        while self:
             result.append(self.pop()	)

        return result


   def __bool__(  self	):
        '''simple docstring'''



        return self._root is not None


if __name__ == "__main__":
 import doctest

 doctest.testmod()





"""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()



