



from __future__ import annotations






def 					a(   A :					list[list[int]]				,  A :					int				,  A :					int				,  A :					int | float )	-> None:
					"""simple docstring"""





					a  ,       a        =     m - 1, 0
					while i >= 0 and j < n:
										if key == mat[i][j]:
															print(f'''Key {key} found at row- {i + 1} column- {j + 1}''' )
															return
										if key < mat[i][j]:
															i -= 1
										else:
															j += 1
					print(f'''Key {key} not found''' )






def 					a(   )	-> None:
					"""simple docstring"""





					a        =     [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
					a        =     int(input("Enter the element to be searched:" ) )
					print(A )
					search_in_a_sorted_matrix(A				,  len(A )				,  len(mat[0] )				,  A )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()





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 			_lowercase							(     unittest.TestCase			):





	"""simple docstring"""




	@slow
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"	)
						model.to(lowerCamelCase_	)

						# encoder-decoder outputs
						a        =     torch.tensor([[17, 96208, 103471, 2]]	).to(lowerCamelCase_	)
						a        =     torch.tensor(
						    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]	).to(lowerCamelCase_	)
						a        =     model(
						    input_ids=lowerCamelCase_	, attention_mask=lowerCamelCase_	, encoder_outputs=lowerCamelCase_	, decoder_input_ids=lowerCamelCase_	)
						a        =     output[0]
						a        =     torch.Size((1, 14, 250012)	)
						self.assertEqual(output_predited_logis.shape	, lowerCamelCase_	)
						a        =     torch.tensor(
						    [[[-6.3986, -8.2391, 12.5189], [-6.3289, -8.0864, 12.6211], [-6.2418, -8.0445, 12.7968]]]	).to(lowerCamelCase_	)
						self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]	, lowerCamelCase_	, atol=1E-4	)	)

						# encoder outputs
						a        =     model.prophetnet.encoder(lowerCamelCase_	)[0]
						a        =     torch.tensor(
						    [[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]]	).to(lowerCamelCase_	)
						a        =     torch.Size((1, 4, 1024)	)
						self.assertEqual(encoder_outputs.shape	, lowerCamelCase_	)
						self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]	, lowerCamelCase_	, atol=1E-4	)	)

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




	@slow
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     XLMProphetNetForConditionalGeneration.from_pretrained(
						    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"	)
						model.to(lowerCamelCase_	)

						a        =     torch.tensor([[17, 96208, 103471, 2]]	).to(lowerCamelCase_	)
						a        =     torch.tensor(
						    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]	).to(lowerCamelCase_	)
						a        =     model(
						    input_ids=lowerCamelCase_	, attention_mask=lowerCamelCase_	, encoder_outputs=lowerCamelCase_	, decoder_input_ids=lowerCamelCase_	)
						a        =     output[0]
						a        =     torch.Size((1, 14, 250012)	)
						self.assertEqual(output_predited_logis.shape	, lowerCamelCase_	)
						# compare the actual values for a slice.
						a        =     torch.tensor(
						    [[[-9.2253, -9.7173, -6.3529], [-7.6701, -9.0145, -1.9382], [-8.0195, -7.0004, -0.1523]]]	).to(lowerCamelCase_	)

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




	@slow
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     XLMProphetNetForConditionalGeneration.from_pretrained(
						    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"	)
						model.to(lowerCamelCase_	)
						a        =     512

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

						a        =     (
						    "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."
						)
						a        =     (
						    "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."
						)
						a        =     (
						    "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。"
						)

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

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

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