


'''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 _snake_case   ( unittest.TestCase   ):
							"""simple docstring"""





							@slow
							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:



											"""simple docstring"""







											__lowerCamelCase		:      List[Any]											=					XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"	)
											model.to(__A	)

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

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

											# decoder outputs
											__lowerCamelCase		:      Tuple											=					model.prophetnet.decoder(
											    __A			,				encoder_hidden_states=__A			,				)
											__lowerCamelCase		:      List[str]											=					decoder_outputs[1].view(1			,				model.config.ngram			,				14			,				-1	)
											__lowerCamelCase		:      Any											=					model.lm_head(__A	)
											__lowerCamelCase		:      int											=					predicting_streams_logits[:, 0]
											self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]			,				__A			,				atol=1E-4	)	)







							@slow
							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:



											"""simple docstring"""







											__lowerCamelCase		:      Any											=					XLMProphetNetForConditionalGeneration.from_pretrained(
											    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"	)
											model.to(__A	)

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

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







							@slow
							def 		__SCREAMING_SNAKE_CASE(   self	)     ->str:



											"""simple docstring"""







											__lowerCamelCase		:      List[Any]											=					XLMProphetNetForConditionalGeneration.from_pretrained(
											    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"	)
											model.to(__A	)
											__lowerCamelCase		:      List[Any]											=					512

											__lowerCamelCase		:      Optional[int]											=					XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"	)

											__lowerCamelCase		:      int											=					(
											    "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		:      str											=					(
											    "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		:      Optional[int]											=					(
											    "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		:      Tuple											=					tokenizer(
											    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]			,				padding=__A			,				max_length=255			,				return_tensors="pt"	).input_ids
											__lowerCamelCase		:      Optional[Any]											=					input_ids.to(__A	)

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

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









'''simple docstring'''




def 							a_       (						lowercase    )			->str:






				'''simple docstring'''

				return " ".join(sorted(set(sentence.split()    )    )    )


if __name__ == "__main__":
				import doctest

				doctest.testmod()





