def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase						)       ->       int:

						'''simple docstring'''



						_snake_case						:     Tuple				=    index
						_snake_case						:     Optional[Any]				=    2 * index + 1
						_snake_case						:     str				=    2 * index + 2
						if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
												_snake_case						:     int				=    left_index

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

						if largest != index:
												_snake_case				,_snake_case						:     List[str]				=    unsorted[index], unsorted[largest]
												heapify(__lowercase					,			__lowercase					,			__lowercase						)
def 					snake_case						(__lowercase						)       ->       Union[str, Any]:

						'''simple docstring'''



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


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



"""simple docstring"""


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import CamembertModel
@require_torch
@require_sentencepiece
@require_tokenizers
class snake_case					(     unittest.TestCase):







		@slow
		def        a_       (    self					:				int						)				->  Tuple:





						'''simple docstring'''







						_A         =	CamembertModel.from_pretrained("camembert-base"						)
						model.to(a__						)

						_A         =	torch.tensor(
						    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]			,      device=a__			,      dtype=torch.long			,      )  # J'aime le camembert !
						with torch.no_grad():
										_A         =	model(a__						)["last_hidden_state"]
						_A         =	torch.Size((1, 10, 7_68)						)
						self.assertEqual(output.shape			,      a__						)
						# compare the actual values for a slice.
						_A         =	torch.tensor(
						    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]			,      device=a__			,      dtype=torch.float			,      )
						# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
						# camembert.eval()
						# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

						self.assertTrue(torch.allclose(output[:, :3, :3]			,      a__			,      atol=1E-4						)						)