





from typing import List, Optional, Tuple, Union

import torch

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





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						UNetaDModel
  __lowerCAmelCase   :						KarrasVeScheduler


  def __init__(							self ,__snake_case ,__snake_case ):



       """simple docstring"""





       super().__init__()
       self.register_modules(unet=__snake_case ,scheduler=__snake_case )




  @torch.no_grad()
  def __call__(							self ,__snake_case = 1 ,__snake_case = 5_0 ,__snake_case = None ,__snake_case = "pil" ,__snake_case = True ,**__snake_case ,):



       """simple docstring"""





       A_          =       self.unet.config.sample_size
       A_          =       (batch_size, 3, img_size, img_size)

       A_          =       self.unet

       # sample x_0 ~ N(0, sigma_0^2 * I)
       A_          =       randn_tensor(__snake_case ,generator=__snake_case ,device=self.device ) * self.scheduler.init_noise_sigma

       self.scheduler.set_timesteps(__snake_case )

       for t in self.progress_bar(self.scheduler.timesteps ):
            # here sigma_t == t_i from the paper
            A_          =       self.scheduler.schedule[t]
            A_          =       self.scheduler.schedule[t - 1] if t > 0 else 0

            # 1. Select temporarily increased noise level sigma_hat
            # 2. Add new noise to move from sample_i to sample_hat
            A_   ,    A_          =       self.scheduler.add_noise_to_input(__snake_case ,__snake_case ,generator=__snake_case )

            # 3. Predict the noise residual given the noise magnitude `sigma_hat`
            # The model inputs and output are adjusted by following eq. (213) in [1].
            A_          =       (sigma_hat / 2) * model((sample_hat + 1) / 2 ,sigma_hat / 2 ).sample

            # 4. Evaluate dx/dt at sigma_hat
            # 5. Take Euler step from sigma to sigma_prev
            A_          =       self.scheduler.step(__snake_case ,__snake_case ,__snake_case ,__snake_case )

            if sigma_prev != 0:
                 # 6. Apply 2nd order correction
                 # The model inputs and output are adjusted by following eq. (213) in [1].
                 A_          =       (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2 ,sigma_prev / 2 ).sample
                 A_          =       self.scheduler.step_correct(
                     __snake_case ,__snake_case ,__snake_case ,__snake_case ,step_output.prev_sample ,step_output['''derivative'''] ,)
            A_          =       step_output.prev_sample

       A_          =       (sample / 2 + 0.5).clamp(0 ,1 )
       A_          =       sample.cpu().permute(0 ,2 ,3 ,1 ).numpy()
       if output_type == "pil":
            A_          =       self.numpy_to_pil(__snake_case )

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=__snake_case )










from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def      UpperCAmelCase_    (    )	->				Optional[Any]:
     '''simple docstring'''


     A_          =       HfArgumentParser(_UpperCAmelCase				)
     A_          =       parser.parse_args_into_dataclasses()[0]
     A_          =       TensorFlowBenchmark(args=_UpperCAmelCase				)
     try:
          A_          =       parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          A_          =       '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
          A_          =       ''' '''.join(str(_UpperCAmelCase				).split(''' '''				)[:-1]				)
          A_          =       ''''''
          A_          =       eval(str(_UpperCAmelCase				).split(''' '''				)[-1]				)
          A_          =       []
          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(_UpperCAmelCase				)
          if len(_UpperCAmelCase				) > 0:
               A_          =       full_error_msg + begin_error_msg + str(_UpperCAmelCase				)
          raise ValueError(_UpperCAmelCase				)
     benchmark.run()


if __name__ == "__main__":
      main()



