
"""simple docstring"""




from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
					import torch

UpperCAmelCase__				:   List[Any]   							=			logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
					from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):

					def lowercase_  (       cls						):
												return cls

def lowercase_  (       _snake_case       ,_snake_case = None       ,_snake_case = None       ,_snake_case = None       ,_snake_case = None       ,):
							SCREAMING_SNAKE_CASE__						:	str									=       device
							SCREAMING_SNAKE_CASE__						:	int									=       shape[0]

							SCREAMING_SNAKE_CASE__						:	int									=       layout or torch.strided
							SCREAMING_SNAKE_CASE__						:	Tuple									=       device or torch.device("""cpu"""						)

							if generator is not None:
														SCREAMING_SNAKE_CASE__						:	List[Any]									=       generator.device.type if not isinstance(_snake_case       ,_snake_case						) else generator[0].device.type
														if gen_device_type != device.type and gen_device_type == "cpu":
																					SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       """cpu"""
																					if device != "mps":
																												logger.info(
																												    f'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
																												    f''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
																												    f''' slighly speed up this function by passing a generator that was created on the {device} device.'''						)
														elif gen_device_type != device.type and gen_device_type == "cuda":
																					raise ValueError(f'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''						)

							if isinstance(_snake_case       ,_snake_case						):
														SCREAMING_SNAKE_CASE__						:	str									=       (1,) + shape[1:]
														SCREAMING_SNAKE_CASE__						:	List[Any]									=       [
														    torch.randn(_snake_case       ,generator=generator[i]       ,device=_snake_case       ,dtype=_snake_case       ,layout=_snake_case						)
														    for i in range(_snake_case						)
														]
														SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.cat(_snake_case       ,dim=0						).to(_snake_case						)
							else:
														SCREAMING_SNAKE_CASE__						:	Dict									=       torch.randn(_snake_case       ,generator=_snake_case       ,device=_snake_case       ,dtype=_snake_case       ,layout=_snake_case						).to(_snake_case						)

							return latents

def lowercase_  (       _snake_case						):
							if is_torch_version("""<"""       ,"""2.0.0"""						) or not hasattr(_snake_case       ,"""_dynamo"""						):
														return False
							return isinstance(_snake_case       ,torch._dynamo.eval_frame.OptimizedModule						)






"""simple docstring"""




def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:	Any									=       index
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       2 * index + 1
							SCREAMING_SNAKE_CASE__						:	Any									=       2 * index + 2
							if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
														SCREAMING_SNAKE_CASE__						:	str									=       left_index

							if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
														SCREAMING_SNAKE_CASE__						:	Any									=       right_index

							if largest != index:
														SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Tuple									=       unsorted[index], unsorted[largest]
														heapify(_snake_case       ,_snake_case       ,_snake_case						)

def lowercase_  (       _snake_case						):
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       len(_snake_case						)
							for i in range(n // 2 - 1       ,-1       ,-1						):
														heapify(_snake_case       ,_snake_case       ,_snake_case						)
							for i in range(n - 1       ,0       ,-1						):
														SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Any									=       unsorted[i], unsorted[0]
														heapify(_snake_case       ,0       ,_snake_case						)
							return unsorted


if __name__ == "__main__":
					UpperCAmelCase__				:   Optional[Any]   							=			input('Enter numbers separated by a comma:\n').strip()
					UpperCAmelCase__				:   Optional[int]   							=			[int(item) for item in user_input.split(',')]
					print(heap_sort(unsorted))




