






"""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 ):






    @slow
    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""					)
          model.to(snake_case_					)

          # encoder-decoder outputs
          UpperCAmelCase							=       torch.tensor([[17, 96_208, 103_471, 2]]					).to(snake_case_					)
          UpperCAmelCase							=       torch.tensor(
              [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]					).to(snake_case_					)
          UpperCAmelCase							=       model(
              input_ids=snake_case_	,attention_mask=snake_case_	,encoder_outputs=snake_case_	,decoder_input_ids=snake_case_					)
          UpperCAmelCase							=       output[0]
          UpperCAmelCase							=       torch.Size((1, 14, 250_012)					)
          self.assertEqual(output_predited_logis.shape	,snake_case_					)
          UpperCAmelCase							=       torch.tensor(
              [[[-6.3986, -8.2391, 12.5189], [-6.3289, -8.0864, 12.6211], [-6.2418, -8.0445, 12.7968]]]					).to(snake_case_					)
          self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]	,snake_case_	,atol=1e-4					)					)

          # encoder outputs
          UpperCAmelCase							=       model.prophetnet.encoder(snake_case_					)[0]
          UpperCAmelCase							=       torch.tensor(
              [[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]]					).to(snake_case_					)
          UpperCAmelCase							=       torch.Size((1, 4, 1_024)					)
          self.assertEqual(encoder_outputs.shape	,snake_case_					)
          self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]	,snake_case_	,atol=1e-4					)					)

          # decoder outputs
          UpperCAmelCase							=       model.prophetnet.decoder(
              snake_case_	,encoder_hidden_states=snake_case_	,)
          UpperCAmelCase							=       decoder_outputs[1].view(1	,model.config.ngram	,14	,-1					)
          UpperCAmelCase							=       model.lm_head(snake_case_					)
          UpperCAmelCase							=       predicting_streams_logits[:, 0]
          self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]	,snake_case_	,atol=1e-4					)					)






    @slow
    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       XLMProphetNetForConditionalGeneration.from_pretrained(
              """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""					)
          model.to(snake_case_					)

          UpperCAmelCase							=       torch.tensor([[17, 96_208, 103_471, 2]]					).to(snake_case_					)
          UpperCAmelCase							=       torch.tensor(
              [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]					).to(snake_case_					)
          UpperCAmelCase							=       model(
              input_ids=snake_case_	,attention_mask=snake_case_	,encoder_outputs=snake_case_	,decoder_input_ids=snake_case_					)
          UpperCAmelCase							=       output[0]
          UpperCAmelCase							=       torch.Size((1, 14, 250_012)					)
          self.assertEqual(output_predited_logis.shape	,snake_case_					)
          # compare the actual values for a slice.
          UpperCAmelCase							=       torch.tensor(
              [[[-9.2253, -9.7173, -6.3529], [-7.6701, -9.0145, -1.9382], [-8.0195, -7.0004, -0.1523]]]					).to(snake_case_					)

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






    @slow
    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       XLMProphetNetForConditionalGeneration.from_pretrained(
              """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""					)
          model.to(snake_case_					)
          UpperCAmelCase							=       512

          UpperCAmelCase							=       XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""					)

          UpperCAmelCase							=       (
              """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."""
          )
          UpperCAmelCase							=       (
              """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."""
          )
          UpperCAmelCase							=       (
              """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。"""
          )

          UpperCAmelCase							=       tokenizer(
              [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]	,padding=snake_case_	,max_length=255	,return_tensors="""pt"""					).input_ids
          UpperCAmelCase							=       input_ids.to(snake_case_					)

          UpperCAmelCase							=       model.generate(
              snake_case_	,num_beams=10	,length_penalty=1.0	,no_repeat_ngram_size=3	,early_stopping=snake_case_					)
          UpperCAmelCase							=       [tokenizer.decode(snake_case_	,skip_special_tokens=snake_case_					) for g in summary_ids]
          UpperCAmelCase							=       """Microsoft to end Windows 7 free support after January 14, 2020"""
          UpperCAmelCase							=       """Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"""
          UpperCAmelCase							=       """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]	,snake_case_	,)

          UpperCAmelCase							=       model.generate(
              snake_case_	,num_beams=1	,length_penalty=1.0	,no_repeat_ngram_size=3	,early_stopping=snake_case_					)
          UpperCAmelCase							=       [
              tokenizer.convert_ids_to_tokens(snake_case_	,skip_special_tokens=snake_case_					) for g in summary_ids_beama
          ]
          UpperCAmelCase							=       """▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7""".split(""" """					)
          UpperCAmelCase							=       """▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года""".split(
              """ """					)
          UpperCAmelCase							=       """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]	,snake_case_	,)





def 				A__    (  lowerCamelCase  )     ->		Optional[Any]:
				UpperCamelCase_:							Optional[int]		=  len(lowerCamelCase  )
				for _ in range(
				    0   ,			int(((no_of_elements - 1) / 2) + 1  )  ):  # we don't need to traverse to end of list as
								for j in range(0   ,			no_of_elements - 1  ):
												if (
												    lst[j + 1] < lst[j]
												):  # applying bubble sort algorithm from left to right (or forwards)
																UpperCamelCase_:							str		=  lst[j + 1]
																UpperCamelCase_:							Tuple		=  lst[j]
																UpperCamelCase_:							str		=  temp
												if (
												    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
												):  # applying bubble sort algorithm from right to left (or backwards)
																UpperCamelCase_:							Union[str, Any]		=  lst[no_of_elements - 1 - j]
																UpperCamelCase_:							Dict		=  lst[no_of_elements - 2 - j]
																UpperCamelCase_:							List[Any]		=  temp
				return lst


if __name__ == "__main__":
							print("""enter the list to be sorted""")
							lowerCamelCase_  :       Union[str, Any]					      =     [int(x) for x in input().split()]  # inputing elements of the list in one line
							lowerCamelCase_  :       Union[str, Any]					      =     double_sort(lst)
							print("""the sorted list is""")
							print(sorted_lst)






