code: 
  
  
  import glob
  import os
  
  from get_test_info import get_tester_classes
  
  
  if __name__ == "__main__":
     UpperCamelCase__				       =			[]
  
     UpperCamelCase__				       =			os.path.join('tests', 'models', '**', 'test_modeling_*.py')
     UpperCamelCase__				       =			glob.glob(pattern)
     # TODO: deal with TF/Flax too
     UpperCamelCase__				       =			[
         x for x in test_files if not (x.startswith('test_modeling_tf_') or x.startswith('test_modeling_flax_'))
     ]
  
     for test_file in test_files:
        UpperCamelCase__				       =			get_tester_classes(test_file)
        for tester_class in tester_classes:
           # A few tester classes don't have `parent` parameter in `__init__`.
           # TODO: deal this better
           try:
              UpperCamelCase__				       =			tester_class(parent=None)
           except Exception:
              continue
           if hasattr(tester, 'get_config'):
              UpperCamelCase__				       =			tester.get_config()
              for k, v in config.to_dict().items():
                 if isinstance(v, int):
                    UpperCamelCase__				       =			None
                    if k in ["vocab_size"]:
                       UpperCamelCase__				       =			100
                    elif k in ["max_position_embeddings"]:
                       UpperCamelCase__				       =			128
                    elif k in ["hidden_size", "d_model"]:
                       UpperCamelCase__				       =			40
                    elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
                       UpperCamelCase__				       =			5
                    if target is not None and v > target:
                       failures.append(
                           F"""{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"""
                           F""" with config[\"{k}\"] = {v} which is too large for testing! Set its value to be smaller"""
                           F""" than {target}."""
                       )
  
     if len(failures) > 0:
        raise Exception(F"""There were {len(failures)} failures:\n""" + '\n'.join(failures))
style_context: 
  
  
  # limitations under the License.
  
  
  from typing import Optional, Tuple, Union
  
  import torch
  
  from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       List[str]	,      UpperCamelCase       :       Union[str, Any]	,      UpperCamelCase       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
              self.register_modules(unet=UpperCamelCase	,      scheduler=UpperCamelCase		)
  
  
  
  
       @torch.no_grad()
       def __call__( self       :       Any	,      UpperCamelCase       :       int = 1	,      UpperCamelCase       :       Optional[torch.Generator] = None	,      UpperCamelCase       :       int = 50	,      UpperCamelCase       :       Optional[str] = "pil"	,      UpperCamelCase       :       bool = True	,      **UpperCamelCase       :       int	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			torch.randn(
                  (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)	,      generator=UpperCamelCase	,      )
              _lowercase					:				Dict           =			image.to(self.device		)
  
              # set step values
              self.scheduler.set_timesteps(UpperCamelCase		)
  
              for t in self.progress_bar(self.scheduler.timesteps		):
                     # 1. predict noise model_output
                     _lowercase					:				Dict           =			self.unet(UpperCamelCase	,      UpperCamelCase		).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					:				Tuple           =			self.scheduler.step(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		).prev_sample
  
              _lowercase					:				Optional[int]           =			(image / 2 + 0.5).clamp(0	,      1		)
              _lowercase					:				int           =			image.cpu().permute(0	,      2	,      3	,      1		).numpy()
              if output_type == "pil":
                     _lowercase					:				List[Any]           =			self.numpy_to_pil(UpperCamelCase		)
  
              if not return_dict:
                     return (image,), "This is a local test"
  
              return ImagePipelineOutput(images=UpperCamelCase		), "This is a local test"