code: 
  
  # limitations under the License.
  
  
  from typing import Optional, Tuple, Union
  
  import torch
  
  from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
  
  
  
  
  
      def __init__(   self		,     a		,     a):
           super().__init__()
           self.register_modules(unet=a		,     scheduler=a)
  
  
  
  
  
      @torch.no_grad()
      def __call__(   self		,     a = 1		,     a = None		,     a = 50		,     a = "pil"		,     a = True		,     **a		,     ):
           lowercase__					:     str   = torch.randn(
               (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,     generator=a		,     )
           lowercase__					:     Optional[Any]   = image.to(self.device)
  
           # set step values
           self.scheduler.set_timesteps(a)
  
           for t in self.progress_bar(self.scheduler.timesteps):
                # 1. predict noise model_output
                lowercase__					:     int   = self.unet(a		,     a).sample
  
                # 2. predict previous mean of image x_t-1 and add variance depending on eta
                # eta corresponds to η in paper and should be between [0, 1]
                # do x_t -> x_t-1
                lowercase__					:     Dict   = self.scheduler.step(a		,     a		,     a).prev_sample
  
           lowercase__					:     Any   = (image / 2 + 0.5).clamp(0		,     1)
           lowercase__					:     Tuple   = image.cpu().permute(0		,     2		,     3		,     1).numpy()
           if output_type == "pil":
                lowercase__					:     Dict   = self.numpy_to_pil(a)
  
           if not return_dict:
                return (image,), "This is a local test"
  
           return ImagePipelineOutput(images=a), "This is a local test"
  
  
style_context: 
  
  from transformers.models.cpm.tokenization_cpm import CpmTokenizer
  from transformers.testing_utils import custom_tokenizers
  
  from ..xlnet.test_modeling_xlnet import XLNetModelTest
  
  
  @custom_tokenizers
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a):
           return True
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     Tuple   = CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate')
           lowercase__					:     str   = 'Hugging Face大法好，谁用谁知道。'
           lowercase__					:     Optional[int]   = 'Hugging Face大法好,谁用谁知道。<unk>'
           lowercase__					:     Any   = '▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()
  
           lowercase__					:     Any   = tokenizer.tokenize(a)
           self.assertListEqual(a		,     a)
  
           lowercase__					:     List[Any]   = tokens + [tokenizer.unk_token]
  
           lowercase__					:     Any   = [1_3789, 1_3283, 1421, 8, 10, 1164, 1_3608, 1_6528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(a)		,     a)
  
           lowercase__					:     Optional[Any]   = tokenizer.decode(a)
           self.assertEqual(a		,     a)
  
  