code: 
  
  
  
  
  
  
  """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 					UpperCamelCase__   (							unittest.TestCase):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      @slow
      def 							a__ (		self   :  int   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'   )
             model.to(UpperCamelCase_   )
  
             # encoder-decoder outputs
             __magic_name__										=						torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]   ).to(UpperCamelCase_   )
             __magic_name__										=						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(UpperCamelCase_   )
             __magic_name__										=						model(
                 input_ids=UpperCamelCase_							,						attention_mask=UpperCamelCase_							,						encoder_outputs=UpperCamelCase_							,						decoder_input_ids=UpperCamelCase_   )
             __magic_name__										=						output[0]
             __magic_name__										=						torch.Size((1, 1_4, 2_5_0_0_1_2)   )
             self.assertEqual(output_predited_logis.shape							,						UpperCamelCase_   )
             __magic_name__										=						torch.tensor(
                 [[[-6.3_986, -8.2_391, 12.5_189], [-6.3_289, -8.0_864, 12.6_211], [-6.2_418, -8.0_445, 12.7_968]]]   ).to(UpperCamelCase_   )
             self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]							,						UpperCamelCase_							,						atol=1e-4   )   )
  
             # encoder outputs
             __magic_name__										=						model.prophetnet.encoder(UpperCamelCase_   )[0]
             __magic_name__										=						torch.tensor(
                 [[[-1.4_260, -0.7_628, 0.8_453], [-1.4_719, -0.1_391, 0.7_807], [-1.7_678, 0.0_114, 0.4_646]]]   ).to(UpperCamelCase_   )
             __magic_name__										=						torch.Size((1, 4, 1_0_2_4)   )
             self.assertEqual(encoder_outputs.shape							,						UpperCamelCase_   )
             self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]							,						UpperCamelCase_							,						atol=1e-4   )   )
  
             # decoder outputs
             __magic_name__										=						model.prophetnet.decoder(
                 UpperCamelCase_							,						encoder_hidden_states=UpperCamelCase_							,						)
             __magic_name__										=						decoder_outputs[1].view(1							,						model.config.ngram							,						1_4							,						-1   )
             __magic_name__										=						model.lm_head(UpperCamelCase_   )
             __magic_name__										=						predicting_streams_logits[:, 0]
             self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]							,						UpperCamelCase_							,						atol=1e-4   )   )
  
  
  
  
  
  
  
      @slow
      def 							a__ (		self   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						XLMProphetNetForConditionalGeneration.from_pretrained(
                 'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'   )
             model.to(UpperCamelCase_   )
  
             __magic_name__										=						torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]   ).to(UpperCamelCase_   )
             __magic_name__										=						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(UpperCamelCase_   )
             __magic_name__										=						model(
                 input_ids=UpperCamelCase_							,						attention_mask=UpperCamelCase_							,						encoder_outputs=UpperCamelCase_							,						decoder_input_ids=UpperCamelCase_   )
             __magic_name__										=						output[0]
             __magic_name__										=						torch.Size((1, 1_4, 2_5_0_0_1_2)   )
             self.assertEqual(output_predited_logis.shape							,						UpperCamelCase_   )
             # compare the actual values for a slice.
             __magic_name__										=						torch.tensor(
                 [[[-9.2_253, -9.7_173, -6.3_529], [-7.6_701, -9.0_145, -1.9_382], [-8.0_195, -7.0_004, -0.1_523]]]   ).to(UpperCamelCase_   )
  
             self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]							,						UpperCamelCase_							,						atol=1e-4   )   )
  
  
  
  
  
  
  
      @slow
      def 							a__ (		self   :  Tuple   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						XLMProphetNetForConditionalGeneration.from_pretrained(
                 'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'   )
             model.to(UpperCamelCase_   )
             __magic_name__										=						5_1_2
  
             __magic_name__										=						XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'   )
  
             __magic_name__										=						(
                 '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.'
             )
             __magic_name__										=						(
                 '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.'
             )
             __magic_name__										=						(
                 '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。'
             )
  
             __magic_name__										=						tokenizer(
                 [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]							,						padding=UpperCamelCase_							,						max_length=2_5_5							,						return_tensors='pt'   ).input_ids
             __magic_name__										=						input_ids.to(UpperCamelCase_   )
  
             __magic_name__										=						model.generate(
                 UpperCamelCase_							,						num_beams=1_0							,						length_penalty=1.0							,						no_repeat_ngram_size=3							,						early_stopping=UpperCamelCase_   )
             __magic_name__										=						[tokenizer.decode(UpperCamelCase_							,						skip_special_tokens=UpperCamelCase_   ) for g in summary_ids]
             __magic_name__										=						'Microsoft to end Windows 7 free support after January 14, 2020'
             __magic_name__										=						'Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'
             __magic_name__										=						'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_							,						)
  
             __magic_name__										=						model.generate(
                 UpperCamelCase_							,						num_beams=1							,						length_penalty=1.0							,						no_repeat_ngram_size=3							,						early_stopping=UpperCamelCase_   )
             __magic_name__										=						[
                 tokenizer.convert_ids_to_tokens(UpperCamelCase_							,						skip_special_tokens=UpperCamelCase_   ) for g in summary_ids_beama
             ]
             __magic_name__										=						'▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7'.split(' '   )
             __magic_name__										=						'▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года'.split(
                 ' '   )
             __magic_name__										=						'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_							,						)
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  snake_case   :   str    		=		{"""configuration_sew_d""": ["""SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWDConfig"""]}
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   snake_case   :   Union[str, Any]    		=		[
       """SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """SEWDForCTC""",
       """SEWDForSequenceClassification""",
       """SEWDModel""",
       """SEWDPreTrainedModel""",
   ]
  
  if TYPE_CHECKING:
   from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_sew_d import (
        SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWDForCTC,
        SEWDForSequenceClassification,
        SEWDModel,
        SEWDPreTrainedModel,
    )
  
  
  else:
   import sys
  
   snake_case   :   Union[str, Any]    		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)