code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  @dataclass
  class    A					(     A_				):
  							UpperCamelCase_     :							Optional[int] ={
  							    '''sld_warmup_steps''': 15,
  							    '''sld_guidance_scale''': 20,
  							    '''sld_threshold''': 0.0,
  							    '''sld_momentum_scale''': 0.0,
  							    '''sld_mom_beta''': 0.0,
  							}
  							UpperCamelCase_     :							Tuple ={
  							    '''sld_warmup_steps''': 10,
  							    '''sld_guidance_scale''': 1_000,
  							    '''sld_threshold''': 0.01,
  							    '''sld_momentum_scale''': 0.3,
  							    '''sld_mom_beta''': 0.4,
  							}
  							UpperCamelCase_     :							Union[str, Any] ={
  							    '''sld_warmup_steps''': 7,
  							    '''sld_guidance_scale''': 2_000,
  							    '''sld_threshold''': 0.025,
  							    '''sld_momentum_scale''': 0.5,
  							    '''sld_mom_beta''': 0.7,
  							}
  							UpperCamelCase_     :							List[Any] ={
  							    '''sld_warmup_steps''': 0,
  							    '''sld_guidance_scale''': 5_000,
  							    '''sld_threshold''': 1.0,
  							    '''sld_momentum_scale''': 0.5,
  							    '''sld_mom_beta''': 0.7,
  							}
  
  
  
  
  
  @dataclass
  class    A					(     A_				):
  							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
  							UpperCamelCase_     :							Optional[List[bool]]
  							UpperCamelCase_     :							Optional[Union[List[PIL.Image.Image], np.ndarray]]
  							UpperCamelCase_     :							Optional[str]
  
  
  if is_transformers_available() and is_torch_available():
  	from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
  	from .safety_checker import SafeStableDiffusionSafetyChecker
  
  
  
  
  
  
  
style_context: 
  
  
  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    A					(     unittest.TestCase				):
  
  
  							@slow
  							def     _A (self      ):
  									__lowercase=    XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'      )
  									model.to(lowerCAmelCase      )
  
  									# encoder-decoder outputs
  									__lowercase=    torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]      ).to(lowerCAmelCase      )
  									__lowercase=    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(lowerCAmelCase      )
  									__lowercase=    model(
  									    input_ids=lowerCAmelCase   ,       attention_mask=lowerCAmelCase   ,       encoder_outputs=lowerCAmelCase   ,       decoder_input_ids=lowerCAmelCase      )
  									__lowercase=    output[0]
  									__lowercase=    torch.Size((1, 1_4, 2_5_0_0_1_2)      )
  									self.assertEqual(output_predited_logis.shape   ,       lowerCAmelCase      )
  									__lowercase=    torch.tensor(
  									    [[[-6.39_86, -8.23_91, 12.51_89], [-6.32_89, -8.08_64, 12.62_11], [-6.24_18, -8.04_45, 12.79_68]]]      ).to(lowerCAmelCase      )
  									self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]   ,       lowerCAmelCase   ,       atol=1E-4      )      )
  
  									# encoder outputs
  									__lowercase=    model.prophetnet.encoder(lowerCAmelCase      )[0]
  									__lowercase=    torch.tensor(
  									    [[[-1.42_60, -0.76_28, 0.84_53], [-1.47_19, -0.13_91, 0.78_07], [-1.76_78, 0.01_14, 0.46_46]]]      ).to(lowerCAmelCase      )
  									__lowercase=    torch.Size((1, 4, 1_0_2_4)      )
  									self.assertEqual(encoder_outputs.shape   ,       lowerCAmelCase      )
  									self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]   ,       lowerCAmelCase   ,       atol=1E-4      )      )
  
  									# decoder outputs
  									__lowercase=    model.prophetnet.decoder(
  									    lowerCAmelCase   ,       encoder_hidden_states=lowerCAmelCase   ,       )
  									__lowercase=    decoder_outputs[1].view(1   ,       model.config.ngram   ,       1_4   ,       -1      )
  									__lowercase=    model.lm_head(lowerCAmelCase      )
  									__lowercase=    predicting_streams_logits[:, 0]
  									self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]   ,       lowerCAmelCase   ,       atol=1E-4      )      )
  
  
  							@slow
  							def     _A (self      ):
  									__lowercase=    XLMProphetNetForConditionalGeneration.from_pretrained(
  									    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'      )
  									model.to(lowerCAmelCase      )
  
  									__lowercase=    torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]      ).to(lowerCAmelCase      )
  									__lowercase=    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(lowerCAmelCase      )
  									__lowercase=    model(
  									    input_ids=lowerCAmelCase   ,       attention_mask=lowerCAmelCase   ,       encoder_outputs=lowerCAmelCase   ,       decoder_input_ids=lowerCAmelCase      )
  									__lowercase=    output[0]
  									__lowercase=    torch.Size((1, 1_4, 2_5_0_0_1_2)      )
  									self.assertEqual(output_predited_logis.shape   ,       lowerCAmelCase      )
  									# compare the actual values for a slice.
  									__lowercase=    torch.tensor(
  									    [[[-9.22_53, -9.71_73, -6.35_29], [-7.67_01, -9.01_45, -1.93_82], [-8.01_95, -7.00_04, -0.15_23]]]      ).to(lowerCAmelCase      )
  
  									self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]   ,       lowerCAmelCase   ,       atol=1E-4      )      )
  
  
  
  
  
  
  
  							@slow
  							def     _A (self      ):
  									__lowercase=    XLMProphetNetForConditionalGeneration.from_pretrained(
  									    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'      )
  									model.to(lowerCAmelCase      )
  									__lowercase=    5_1_2
  
  									__lowercase=    XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'      )
  
  									__lowercase=    (
  									    '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.'
  									)
  									__lowercase=    (
  									    '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.'
  									)
  									__lowercase=    (
  									    '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。'
  									)
  
  									__lowercase=    tokenizer(
  									    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]   ,       padding=lowerCAmelCase   ,       max_length=2_5_5   ,       return_tensors='pt'      ).input_ids
  									__lowercase=    input_ids.to(lowerCAmelCase      )
  
  									__lowercase=    model.generate(
  									    lowerCAmelCase   ,       num_beams=1_0   ,       length_penalty=1.0   ,       no_repeat_ngram_size=3   ,       early_stopping=lowerCAmelCase      )
  									__lowercase=    [tokenizer.decode(lowerCAmelCase   ,       skip_special_tokens=lowerCAmelCase      ) for g in summary_ids]
  									__lowercase=    'Microsoft to end Windows 7 free support after January 14, 2020'
  									__lowercase=    'Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'
  									__lowercase=    '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   ,       )
  
  									__lowercase=    model.generate(
  									    lowerCAmelCase   ,       num_beams=1   ,       length_penalty=1.0   ,       no_repeat_ngram_size=3   ,       early_stopping=lowerCAmelCase      )
  									__lowercase=    [
  									    tokenizer.convert_ids_to_tokens(lowerCAmelCase   ,       skip_special_tokens=lowerCAmelCase      ) for g in summary_ids_beama
  									]
  									__lowercase=    '▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7'.split(' '      )
  									__lowercase=    '▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года'.split(
  									    ' '      )
  									__lowercase=    '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   ,       )
  
  
  
  
  
  
  