




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 	UpperCamelCase__			(unittest.TestCase  ):

    '''simple docstring'''



    @slow
    def 					_lowercase  (					self  )  ->   Optional[int]:
           lowerCamelCase     :   List[str]    				=      XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"  )
           model.to(UpperCamelCase__  )

           # encoder-decoder outputs
           lowerCamelCase     :   Optional[int]    				=      torch.tensor([[17, 9_6208, 10_3471, 2]]  ).to(UpperCamelCase__  )
           lowerCamelCase     :   Union[str, Any]    				=      torch.tensor(
               [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]  ).to(UpperCamelCase__  )
           lowerCamelCase     :   Tuple    				=      model(
               input_ids=UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						encoder_outputs=UpperCamelCase__  ,						decoder_input_ids=UpperCamelCase__  )
           lowerCamelCase     :   Optional[int]    				=      output[0]
           lowerCamelCase     :   List[str]    				=      torch.Size((1, 14, 25_0012)  )
           self.assertEqual(output_predited_logis.shape  ,						UpperCamelCase__  )
           lowerCamelCase     :   Optional[Any]    				=      torch.tensor(
               [[[-6.3986, -8.2391, 12.5189], [-6.3289, -8.0864, 12.6211], [-6.2418, -8.0445, 12.7968]]]  ).to(UpperCamelCase__  )
           self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]  ,						UpperCamelCase__  ,						atol=1e-4  )  )

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

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







    @slow
    def 					_lowercase  (					self  )  ->   List[Any]:
           lowerCamelCase     :   int    				=      XLMProphetNetForConditionalGeneration.from_pretrained(
               "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"  )
           model.to(UpperCamelCase__  )

           lowerCamelCase     :   List[str]    				=      torch.tensor([[17, 9_6208, 10_3471, 2]]  ).to(UpperCamelCase__  )
           lowerCamelCase     :   Union[str, Any]    				=      torch.tensor(
               [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]  ).to(UpperCamelCase__  )
           lowerCamelCase     :   Optional[Any]    				=      model(
               input_ids=UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						encoder_outputs=UpperCamelCase__  ,						decoder_input_ids=UpperCamelCase__  )
           lowerCamelCase     :   Union[str, Any]    				=      output[0]
           lowerCamelCase     :   Union[str, Any]    				=      torch.Size((1, 14, 25_0012)  )
           self.assertEqual(output_predited_logis.shape  ,						UpperCamelCase__  )
           # compare the actual values for a slice.
           lowerCamelCase     :   Union[str, Any]    				=      torch.tensor(
               [[[-9.2253, -9.7173, -6.3529], [-7.6701, -9.0145, -1.9382], [-8.0195, -7.0004, -0.1523]]]  ).to(UpperCamelCase__  )

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







    @slow
    def 					_lowercase  (					self  )  ->   List[Any]:
           lowerCamelCase     :   Optional[Any]    				=      XLMProphetNetForConditionalGeneration.from_pretrained(
               "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"  )
           model.to(UpperCamelCase__  )
           lowerCamelCase     :   Tuple    				=      512

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

           lowerCamelCase     :   List[str]    				=      (
               "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     :   Optional[int]    				=      (
               "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     :   List[Any]    				=      (
               "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=UpperCamelCase__  ,						max_length=255  ,						return_tensors="pt"  ).input_ids
           lowerCamelCase     :   List[Any]    				=      input_ids.to(UpperCamelCase__  )

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

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












from __future__ import annotations

def   A   (   _SCREAMING_SNAKE_CASE					)			->				list[int]:
       assert (
           isinstance(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					) and mask > 0
       ), f'''mask needs to be positive integer, your input {mask}'''
       lowerCamelCase     :   str    				=      []
       lowerCamelCase     :   Any    				=      mask

       while submask:
              all_submasks.append(_SCREAMING_SNAKE_CASE					)
              lowerCamelCase     :   Union[str, Any]    				=      (submask - 1) & mask

       return all_submasks


if __name__ == "__main__":
       import doctest

       doctest.testmod()






