



"""simple docstring"""
import unittest

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


if is_torch_available():
			import torch

			from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer


@require_torch
class 							lowerCamelCase_(      unittest.TestCase			):





					'''simple docstring'''



					@slow
					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = XLMProphetNetForConditionalGeneration.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''      )
									model.to(lowerCamelCase__      )

									# encoder-decoder outputs
									_lowerCamelCase					     = torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]      ).to(lowerCamelCase__      )
									_lowerCamelCase					     = torch.tensor(
									    [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]      ).to(lowerCamelCase__      )
									_lowerCamelCase					     = model(
									    input_ids=lowerCamelCase__ ,				attention_mask=lowerCamelCase__ ,				encoder_outputs=lowerCamelCase__ ,				decoder_input_ids=lowerCamelCase__      )
									_lowerCamelCase					     = output[0]
									_lowerCamelCase					     = torch.Size((1, 1_4, 2_5_0_0_1_2)      )
									self.assertEqual(output_predited_logis.shape ,				lowerCamelCase__      )
									_lowerCamelCase					     = torch.tensor(
									    [[[-6.3_9_8_6, -8.2_3_9_1, 1_2.5_1_8_9], [-6.3_2_8_9, -8.0_8_6_4, 1_2.6_2_1_1], [-6.2_4_1_8, -8.0_4_4_5, 1_2.7_9_6_8]]]      ).to(lowerCamelCase__      )
									self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3] ,				lowerCamelCase__ ,				atol=1e-4      )      )

									# encoder outputs
									_lowerCamelCase					     = model.prophetnet.encoder(lowerCamelCase__      )[0]
									_lowerCamelCase					     = torch.tensor(
									    [[[-1.4_2_6_0, -0.7_6_2_8, 0.8_4_5_3], [-1.4_7_1_9, -0.1_3_9_1, 0.7_8_0_7], [-1.7_6_7_8, 0.0_1_1_4, 0.4_6_4_6]]]      ).to(lowerCamelCase__      )
									_lowerCamelCase					     = torch.Size((1, 4, 1_0_2_4)      )
									self.assertEqual(encoder_outputs.shape ,				lowerCamelCase__      )
									self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3] ,				lowerCamelCase__ ,				atol=1e-4      )      )

									# decoder outputs
									_lowerCamelCase					     = model.prophetnet.decoder(
									    lowerCamelCase__ ,				encoder_hidden_states=lowerCamelCase__ ,				)
									_lowerCamelCase					     = decoder_outputs[1].view(1 ,				model.config.ngram ,				1_4 ,				-1      )
									_lowerCamelCase					     = model.lm_head(lowerCamelCase__      )
									_lowerCamelCase					     = predicting_streams_logits[:, 0]
									self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3] ,				lowerCamelCase__ ,				atol=1e-4      )      )

					@slow
					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = XLMProphetNetForConditionalGeneration.from_pretrained(
									    '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''      )
									model.to(lowerCamelCase__      )

									_lowerCamelCase					     = torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]      ).to(lowerCamelCase__      )
									_lowerCamelCase					     = torch.tensor(
									    [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]      ).to(lowerCamelCase__      )
									_lowerCamelCase					     = model(
									    input_ids=lowerCamelCase__ ,				attention_mask=lowerCamelCase__ ,				encoder_outputs=lowerCamelCase__ ,				decoder_input_ids=lowerCamelCase__      )
									_lowerCamelCase					     = output[0]
									_lowerCamelCase					     = torch.Size((1, 1_4, 2_5_0_0_1_2)      )
									self.assertEqual(output_predited_logis.shape ,				lowerCamelCase__      )
									# compare the actual values for a slice.
									_lowerCamelCase					     = torch.tensor(
									    [[[-9.2_2_5_3, -9.7_1_7_3, -6.3_5_2_9], [-7.6_7_0_1, -9.0_1_4_5, -1.9_3_8_2], [-8.0_1_9_5, -7.0_0_0_4, -0.1_5_2_3]]]      ).to(lowerCamelCase__      )

									self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3] ,				lowerCamelCase__ ,				atol=1e-4      )      )





					@slow
					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = XLMProphetNetForConditionalGeneration.from_pretrained(
									    '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''      )
									model.to(lowerCamelCase__      )
									_lowerCamelCase					     = 5_1_2

									_lowerCamelCase					     = XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''      )

									_lowerCamelCase					     = (
									    '''Microsoft Corporation intends to officially end free support for the Windows 7 operating system after'''
									    ''' January 14, 2020, according to the official portal of the organization. From that day, users of this'''
									    ''' system will not be able to receive security updates, which could make their computers vulnerable to'''
									    ''' cyber attacks.'''
									)
									_lowerCamelCase					     = (
									    '''орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7'''
									    ''' после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи'''
									    ''' этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми'''
									    ''' к кибератакам.'''
									)
									_lowerCamelCase					     = (
									    '''根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。'''
									)

									_lowerCamelCase					     = tokenizer(
									    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE] ,				padding=lowerCamelCase__ ,				max_length=2_5_5 ,				return_tensors='''pt'''      ).input_ids
									_lowerCamelCase					     = input_ids.to(lowerCamelCase__      )

									_lowerCamelCase					     = model.generate(
									    lowerCamelCase__ ,				num_beams=1_0 ,				length_penalty=1.0 ,				no_repeat_ngram_size=3 ,				early_stopping=lowerCamelCase__      )
									_lowerCamelCase					     = [tokenizer.decode(lowerCamelCase__ ,				skip_special_tokens=lowerCamelCase__      ) for g in summary_ids]
									_lowerCamelCase					     = '''Microsoft to end Windows 7 free support after January 14, 2020'''
									_lowerCamelCase					     = '''Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'''
									_lowerCamelCase					     = '''微软打算终止对Windows 7操作系统的免费支持'''
									self.assertListEqual(
									    [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH] ,				lowerCamelCase__ ,				)

									_lowerCamelCase					     = model.generate(
									    lowerCamelCase__ ,				num_beams=1 ,				length_penalty=1.0 ,				no_repeat_ngram_size=3 ,				early_stopping=lowerCamelCase__      )
									_lowerCamelCase					     = [
									    tokenizer.convert_ids_to_tokens(lowerCamelCase__ ,				skip_special_tokens=lowerCamelCase__      ) for g in summary_ids_beama
									]
									_lowerCamelCase					     = '''▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7'''.split(''' '''      )
									_lowerCamelCase					     = '''▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года'''.split(
									    ''' '''      )
									_lowerCamelCase					     = '''微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持'''.split(''' '''      )
									self.assertListEqual(
									    [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK] ,				lowerCamelCase__ ,				)






'''simple docstring'''


def 		snake_case_    (UpperCamelCase       :  List[str] ,						UpperCamelCase       :  List[Any] ,						UpperCamelCase       :  Union[str, Any]      ):





							'''simple docstring'''


							_a	     =				index
							_a	     =				2 * index + 1
							_a	     =				2 * index + 2
							if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
														_a	     =				left_index

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

							if largest != index:
														_a     ,							_a	     =				unsorted[index], unsorted[largest]
														heapify(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      )
def 		snake_case_    (UpperCamelCase       :  Union[str, Any]      ):





							'''simple docstring'''


							_a	     =				len(UpperCamelCase      )
							for i in range(n // 2 - 1 ,						-1 ,						-1      ):
														heapify(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      )
							for i in range(n - 1 ,						0 ,						-1      ):
														_a     ,							_a	     =				unsorted[i], unsorted[0]
														heapify(UpperCamelCase ,						0 ,						UpperCamelCase      )
							return unsorted


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

