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  lowerCAmelCase__   (       unittest.TestCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    @slow
    def 		_lowerCamelCase	(self  : Dict    )   ->       Union[str, Any]:
          snake_case_						       =							XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"    )
          model.to(lowerCAmelCase_    )
  
          # encoder-decoder outputs
          snake_case_						       =							torch.tensor([[17, 9_62_08, 10_34_71, 2]]    ).to(lowerCAmelCase_    )
          snake_case_						       =							torch.tensor(
              [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]]    ).to(lowerCAmelCase_    )
          snake_case_						       =							model(
              input_ids=lowerCAmelCase_	, attention_mask=lowerCAmelCase_	, encoder_outputs=lowerCAmelCase_	, decoder_input_ids=lowerCAmelCase_    )
          snake_case_						       =							output[0]
          snake_case_						       =							torch.Size((1, 14, 25_00_12)    )
          self.assertEqual(output_predited_logis.shape	, lowerCAmelCase_    )
          snake_case_						       =							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(lowerCAmelCase_    )
          self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]	, lowerCAmelCase_	, atol=1E-4    )    )
  
          # encoder outputs
          snake_case_						       =							model.prophetnet.encoder(lowerCAmelCase_    )[0]
          snake_case_						       =							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(lowerCAmelCase_    )
          snake_case_						       =							torch.Size((1, 4, 10_24)    )
          self.assertEqual(encoder_outputs.shape	, lowerCAmelCase_    )
          self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]	, lowerCAmelCase_	, atol=1E-4    )    )
  
          # decoder outputs
          snake_case_						       =							model.prophetnet.decoder(
              lowerCAmelCase_	, encoder_hidden_states=lowerCAmelCase_	, )
          snake_case_						       =							decoder_outputs[1].view(1	, model.config.ngram	, 14	, -1    )
          snake_case_						       =							model.lm_head(lowerCAmelCase_    )
          snake_case_						       =							predicting_streams_logits[:, 0]
          self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]	, lowerCAmelCase_	, atol=1E-4    )    )
  
    @slow
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       str:
          snake_case_						       =							XLMProphetNetForConditionalGeneration.from_pretrained(
              "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"    )
          model.to(lowerCAmelCase_    )
  
          snake_case_						       =							torch.tensor([[17, 9_62_08, 10_34_71, 2]]    ).to(lowerCAmelCase_    )
          snake_case_						       =							torch.tensor(
              [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]]    ).to(lowerCAmelCase_    )
          snake_case_						       =							model(
              input_ids=lowerCAmelCase_	, attention_mask=lowerCAmelCase_	, encoder_outputs=lowerCAmelCase_	, decoder_input_ids=lowerCAmelCase_    )
          snake_case_						       =							output[0]
          snake_case_						       =							torch.Size((1, 14, 25_00_12)    )
          self.assertEqual(output_predited_logis.shape	, lowerCAmelCase_    )
          # compare the actual values for a slice.
          snake_case_						       =							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(lowerCAmelCase_    )
  
          self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]	, lowerCAmelCase_	, atol=1E-4    )    )
  
  
    @slow
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       Union[str, Any]:
          snake_case_						       =							XLMProphetNetForConditionalGeneration.from_pretrained(
              "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"    )
          model.to(lowerCAmelCase_    )
          snake_case_						       =							5_12
  
          snake_case_						       =							XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"    )
  
          snake_case_						       =							(
              "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."
          )
          snake_case_						       =							(
              "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."
          )
          snake_case_						       =							(
              "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。"
          )
  
          snake_case_						       =							tokenizer(
              [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]	, padding=lowerCAmelCase_	, max_length=2_55	, return_tensors="pt"    ).input_ids
          snake_case_						       =							input_ids.to(lowerCAmelCase_    )
  
          snake_case_						       =							model.generate(
              lowerCAmelCase_	, num_beams=10	, length_penalty=1.0	, no_repeat_ngram_size=3	, early_stopping=lowerCAmelCase_    )
          snake_case_						       =							[tokenizer.decode(lowerCAmelCase_	, skip_special_tokens=lowerCAmelCase_    ) for g in summary_ids]
          snake_case_						       =							"Microsoft to end Windows 7 free support after January 14, 2020"
          snake_case_						       =							"Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"
          snake_case_						       =							"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_	, )
  
          snake_case_						       =							model.generate(
              lowerCAmelCase_	, num_beams=1	, length_penalty=1.0	, no_repeat_ngram_size=3	, early_stopping=lowerCAmelCase_    )
          snake_case_						       =							[
              tokenizer.convert_ids_to_tokens(lowerCAmelCase_	, skip_special_tokens=lowerCAmelCase_    ) for g in summary_ids_beama
          ]
          snake_case_						       =							"▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7".split(" "    )
          snake_case_						       =							"▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года".split(
              " "    )
          snake_case_						       =							"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_	, )
  
  
style_context: 
  '''simple docstring'''
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  A           =   {
      "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
  }
  
  try:
       if not is_vision_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   ["DetaImageProcessor"]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   [
           "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DetaForObjectDetection",
           "DetaModel",
           "DetaPreTrainedModel",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig
  
       try:
            if not is_vision_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .image_processing_deta import DetaImageProcessor
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_deta import (
                DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                DetaForObjectDetection,
                DetaModel,
                DetaPreTrainedModel,
            )
  
  else:
       import sys
  
       A           =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  