code: # limitations under the License.
  
  
  from typing import Optional, Tuple, Union
  
  import torch
  
  from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  
  
  
  class    __lowerCAmelCase  (       UpperCAmelCase__						):
  
  
  
   '''simple docstring'''
  
   def __init__(			self       :		List[Any]       ,			lowerCamelCase       :		Optional[Any]       ,			lowerCamelCase       :		Any  ) ->							List[Any]:
          super().__init__()
          self.register_modules(unet=lowerCamelCase__       ,			scheduler=lowerCamelCase__  )
  
   @torch.no_grad()
   def __call__(			self       :		str       ,			lowerCamelCase       :		Tuple = 1       ,			lowerCamelCase       :		List[Any] = None       ,			lowerCamelCase       :		str = 5_0       ,			lowerCamelCase       :		List[str] = "pil"       ,			lowerCamelCase       :		str = True       ,			**lowerCamelCase       :		str       ,			) ->							int:
          _UpperCamelCase     :       Union[str, Any]  						=  torch.randn(
              (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,			generator=lowerCamelCase__       ,			)
          _UpperCamelCase     :       List[str]  						=  image.to(self.device  )
  
          # set step values
          self.scheduler.set_timesteps(lowerCamelCase__  )
  
          for t in self.progress_bar(self.scheduler.timesteps  ):
                 # 1. predict noise model_output
                 _UpperCamelCase     :       str  						=  self.unet(lowerCamelCase__       ,			lowerCamelCase__  ).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
                 _UpperCamelCase     :       Any  						=  self.scheduler.step(lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__  ).prev_sample
  
          _UpperCamelCase     :       List[str]  						=  (image / 2 + 0.5).clamp(0       ,			1  )
          _UpperCamelCase     :       str  						=  image.cpu().permute(0       ,			2       ,			3       ,			1  ).numpy()
          if output_type == "pil":
                 _UpperCamelCase     :       int  						=  self.numpy_to_pil(lowerCamelCase__  )
  
          if not return_dict:
                 return (image,), "This is a local test"
  
          return ImagePipelineOutput(images=lowerCamelCase__  ), "This is a local test"
  
  
  
  
style_context: from .chunk_utils import chunk_layer
  from .data_transforms import make_atomaa_masks
  from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
  from .loss import compute_predicted_aligned_error, compute_tm
  from .protein import Protein as OFProtein
  from .protein import to_pdb
  from .rigid_utils import Rigid, Rotation
  from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims
  
  
  
  