





"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class  __lowercase	(	lowercase__							):







					'''simple docstring'''




					__lowerCAmelCase     =     42
					__lowerCAmelCase     =     42







					def __init__(  self		,	_UpperCAmelCase		,	_UpperCAmelCase   ):
									super().__init__()
									self.register_modules(unet=SCREAMING_SNAKE_CASE__		,	scheduler=SCREAMING_SNAKE_CASE__   )







					@torch.no_grad()
					def __call__(  self		,	_UpperCAmelCase = 1		,	_UpperCAmelCase = 2000		,	_UpperCAmelCase = None		,	_UpperCAmelCase = "pil"		,	_UpperCAmelCase = True		,	**_UpperCAmelCase		,	):
									__a       :  Tuple					=     self.unet.config.sample_size
									__a       :  Optional[Any]					=     (batch_size, 3, img_size, img_size)

									__a       :  Optional[Any]					=     self.unet

									__a       :  List[str]					=     randn_tensor(SCREAMING_SNAKE_CASE__		,	generator=SCREAMING_SNAKE_CASE__   ) * self.scheduler.init_noise_sigma
									__a       :  Tuple					=     sample.to(self.device   )

									self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )
									self.scheduler.set_sigmas(SCREAMING_SNAKE_CASE__   )

									for i, t in enumerate(self.progress_bar(self.scheduler.timesteps   )   ):
													__a       :  Dict					=     self.scheduler.sigmas[i] * torch.ones(shape[0]		,	device=self.device   )

													# correction step
													for _ in range(self.scheduler.config.correct_steps   ):
																	__a       :  Tuple					=     self.unet(SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__   ).sample
																	__a       :  Optional[Any]					=     self.scheduler.step_correct(SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	generator=SCREAMING_SNAKE_CASE__   ).prev_sample

													# prediction step
													__a       :  Optional[int]					=     model(SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__   ).sample
													__a       :  Tuple					=     self.scheduler.step_pred(SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	generator=SCREAMING_SNAKE_CASE__   )

													__a       :  Union[str, Any]					=     output.prev_sample, output.prev_sample_mean

									__a       :  Dict					=     sample_mean.clamp(0		,	1   )
									__a       :  Optional[Any]					=     sample.cpu().permute(0		,	2		,	3		,	1   ).numpy()
									if output_type == "pil":
													__a       :  List[Any]					=     self.numpy_to_pil(SCREAMING_SNAKE_CASE__   )

									if not return_dict:
													return (sample,)

									return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__   )



from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments







def        SCREAMING_SNAKE_CASE_						(			)	->		Any:

       """simple docstring"""

       a_					:		Optional[Any]							=  HfArgumentParser(__A				)
       a_					:		Optional[int]							=  parser.parse_args_into_dataclasses()[0]
       a_					:		List[Any]							=  TensorFlowBenchmark(args=__A				)
       try:
              a_					:		List[str]							=  parser.parse_args_into_dataclasses()[0]
       except ValueError as e:
              a_					:		Dict							=  'Arg --no_{0} is no longer used, please use --no-{0} instead.'
              a_					:		Dict							=  ' '.join(str(__A				).split(' '				)[:-1]				)
              a_					:		int							=  ''
              a_					:		int							=  eval(str(__A				).split(' '				)[-1]				)
              a_					:		Any							=  []
              for arg in depreciated_args:
                     # arg[2:] removes '--'
                     if arg[2:] in TensorFlowBenchmark.deprecated_args:
                            # arg[5:] removes '--no_'
                            full_error_msg += arg_error_msg.format(arg[5:]				)
                     else:
                            wrong_args.append(__A				)
              if len(__A				) > 0:
                     a_					:		str							=  full_error_msg + begin_error_msg + str(__A				)
              raise ValueError(__A				)
       benchmark.run()


if __name__ == "__main__":
   main()


