




class 		A__    :






	def __init__(     self						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							List[str]		       =							[0]
						__magic_name__ :							Optional[Any]		       =							0






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       None:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							self.__heap[i]
						while i // 2 > 0:
											if self.__heap[i] > self.__heap[i // 2]:
																__magic_name__ :							List[str]		       =							self.__heap[i // 2]
																__magic_name__ :							Dict		       =							temporary
											i //= 2






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       None:




						"""simple docstring"""






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






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       None:




						"""simple docstring"""






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






	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						__magic_name__ :							Any		       =							self.__heap[1]
						__magic_name__ :							Union[str, Any]		       =							self.__heap[self.__size]
						self.__size -= 1
						self.__heap.pop()
						self.__swap_down(1						)
						return max_value






	@property
	def 			lowercase				(     self						)				->       Dict:




						"""simple docstring"""






						return self.__heap[1:]






	def __len__(     self						)				->       Union[str, Any]:




						"""simple docstring"""






						return self.__size


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# create an instance of BinaryHeap
				lowercase_		=							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))









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 		A__    (       unittest.TestCase			):






	@slow
	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							CamembertModel.from_pretrained('''camembert-base'''						)
						model.to(lowerCamelCase						)

						__magic_name__ :							Dict		       =							torch.tensor(
						    [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]    ,					device=lowerCamelCase    ,					dtype=torch.long    ,					)  # J'aime le camembert !
						with torch.no_grad():
											__magic_name__ :							Tuple		       =							model(lowerCamelCase						)['''last_hidden_state''']
						__magic_name__ :							int		       =							torch.Size((1, 10, 768)						)
						self.assertEqual(output.shape    ,					lowerCamelCase						)
						# compare the actual values for a slice.
						__magic_name__ :							Tuple		       =							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=lowerCamelCase    ,					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]    ,					lowerCamelCase    ,					atol=1e-4						)						)



