



class 						UpperCAmelCase__     :




			'''simple docstring'''






			def __init__(  self				:					List[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      [0]
					__UpperCAmelCase       :	List[str]						=      0




			def 					snake_case__	(  self				:					Dict			,			a_				:					int					):
					'''simple docstring'''





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




			def 					snake_case__	(  self				:					str			,			a_				:					int					):
					'''simple docstring'''





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




			def 					snake_case__	(  self				:					str			,			a_				:					int					):
					'''simple docstring'''





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




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





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




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





					return self.__heap[1:]




			def __len__(  self				:					Tuple					):
					'''simple docstring'''





					return self.__size


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# create an instance of BinaryHeap
				__A       =BinaryHeap()
				binary_heap.insert(6)
				binary_heap.insert(1_0)
				binary_heap.insert(1_5)
				binary_heap.insert(1_2)
				# 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))










import json
import logging
import os
import subprocess
from argparse import ArgumentParser


__A       =logging.getLogger(__name__)

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	Dict						=      ArgumentParser()
		__UpperCAmelCase				,   __UpperCAmelCase       :	int						=      parser.parse_known_args()
		for arg in unknown:
				if arg.startswith(('''-''', '''--''')		):
						parser.add_argument(arg.split('''='''		)[0]		)

		return parser.parse_args()

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      parse_args()
		__UpperCAmelCase       :	Tuple						=      88_88
		__UpperCAmelCase       :	Any						=      int(os.environ['''SM_NUM_GPUS''']		)
		__UpperCAmelCase       :	Optional[int]						=      json.loads(os.environ['''SM_HOSTS''']		)
		__UpperCAmelCase       :	int						=      len(_UpperCAmelCase		)
		__UpperCAmelCase       :	Optional[Any]						=      os.environ['''SM_CURRENT_HOST''']
		__UpperCAmelCase       :	List[str]						=      hosts.index(_UpperCAmelCase		)
		__UpperCAmelCase       :	Dict						=      '''INFO'''

		if num_nodes > 1:
				__UpperCAmelCase       :	Tuple						=      f'python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]		)}'
		else:
				__UpperCAmelCase       :	Dict						=      f'python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]		)}'
		try:
				subprocess.run(_UpperCAmelCase					,  shell=_UpperCAmelCase		)
		except Exception as e:
				logger.info(_UpperCAmelCase		)


if __name__ == "__main__":
				main()





