





from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments



def 			_lowerCAmelCase						(							)->List[str]:
   '''simple docstring'''






   snake_case_					    =					HfArgumentParser(__lowerCAmelCase							)
   try:
      snake_case_					    =					parser.parse_args_into_dataclasses()[0]
   except ValueError as e:
      snake_case_					    =					"Arg --no_{0} is no longer used, please use --no-{0} instead."
      snake_case_					    =					" ".join(str(__lowerCAmelCase							).split(" "							)[:-1]							)
      snake_case_					    =					""
      snake_case_					    =					eval(str(__lowerCAmelCase							).split(" "							)[-1]							)
      snake_case_					    =					[]
      for arg in depreciated_args:
         # arg[2:] removes '--'
         if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
            # arg[5:] removes '--no_'
            full_error_msg += arg_error_msg.format(arg[5:]							)
         else:
            wrong_args.append(__lowerCAmelCase							)
      if len(__lowerCAmelCase							) > 0:
         snake_case_					    =					full_error_msg + begin_error_msg + str(__lowerCAmelCase							)
      raise ValueError(__lowerCAmelCase							)

   snake_case_					    =					PyTorchBenchmark(args=__lowerCAmelCase							)
   benchmark.run()


if __name__ == "__main__":
   main()








from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  def __init__(     self       :       List[str]    ,				_lowerCAmelCase       :       Optional[Any]    ,				_lowerCAmelCase       :       Tuple  )   -> str:




     """simple docstring"""
     super().__init__()
     self.register_modules(unet=_lowerCAmelCase    ,				scheduler=_lowerCAmelCase  )



  @torch.no_grad()
  def __call__(     self       :       Tuple    ,				_lowerCAmelCase       :       int = 1    ,				_lowerCAmelCase       :       Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,				_lowerCAmelCase       :       int = 1_0_0_0    ,				_lowerCAmelCase       :       Optional[str] = "pil"    ,				_lowerCAmelCase       :       bool = True    ,				)   -> Union[ImagePipelineOutput, Tuple]:




     """simple docstring"""
     # Sample gaussian noise to begin loop
     if isinstance(self.unet.config.sample_size    ,				_lowerCAmelCase  ):
        snake_case_					    =					(
            batch_size,
            self.unet.config.in_channels,
            self.unet.config.sample_size,
            self.unet.config.sample_size,
        )
     else:
        snake_case_					    =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

     if self.device.type == "mps":
        # randn does not work reproducibly on mps
        snake_case_					    =					randn_tensor(_lowerCAmelCase    ,				generator=_lowerCAmelCase  )
        snake_case_					    =					image.to(self.device  )
     else:
        snake_case_					    =					randn_tensor(_lowerCAmelCase    ,				generator=_lowerCAmelCase    ,				device=self.device  )

     # set step values
     self.scheduler.set_timesteps(_lowerCAmelCase  )

     for t in self.progress_bar(self.scheduler.timesteps  ):
        # 1. predict noise model_output
        snake_case_					    =					self.unet(_lowerCAmelCase    ,				_lowerCAmelCase  ).sample

        # 2. compute previous image: x_t -> x_t-1
        snake_case_					    =					self.scheduler.step(_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase    ,				generator=_lowerCAmelCase  ).prev_sample

     snake_case_					    =					(image / 2 + 0.5).clamp(0    ,				1  )
     snake_case_					    =					image.cpu().permute(0    ,				2    ,				3    ,				1  ).numpy()
     if output_type == "pil":
        snake_case_					    =					self.numpy_to_pil(_lowerCAmelCase  )

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_lowerCAmelCase  )




