


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   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    42
       snake_case      =    42



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Optional[int]:




         '''simple docstring'''





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







       @torch.no_grad()
       def __call__(      self				,       _SCREAMING_SNAKE_CASE = 1				,       _SCREAMING_SNAKE_CASE = 50				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = "pil"				,       _SCREAMING_SNAKE_CASE = True				,       **_SCREAMING_SNAKE_CASE				,       )->Union[Tuple, ImagePipelineOutput]:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					self.unet.config.sample_size
         A_      :				List[str]		     =					(batch_size, 3, img_size, img_size)

         A_      :				Any		     =					self.unet

         # sample x_0 ~ N(0, sigma_0^2 * I)
         A_      :				List[Any]		     =					randn_tensor(_SCREAMING_SNAKE_CASE				,       generator=_SCREAMING_SNAKE_CASE				,       device=self.device		) * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)

         for t in self.progress_bar(self.scheduler.timesteps		):
           # here sigma_t == t_i from the paper
           A_      :				Optional[Any]		     =					self.scheduler.schedule[t]
           A_      :				Optional[int]		     =					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_      :				Tuple		     =					self.scheduler.add_noise_to_input(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       generator=_SCREAMING_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_      :				List[Any]		     =					(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_      :				Union[str, Any]		     =					self.scheduler.step(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_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_      :				List[str]		     =					(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2				,       sigma_prev / 2		).sample
             A_      :				Tuple		     =					self.scheduler.step_correct(
                 _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       step_output.prev_sample				,       step_output['''derivative''']				,       )
           A_      :				int		     =					step_output.prev_sample

         A_      :				Union[str, Any]		     =					(sample / 2 + 0.5).clamp(0				,       1		)
         A_      :				Union[str, Any]		     =					sample.cpu().permute(0				,       2				,       3				,       1		).numpy()
         if output_type == "pil":
           A_      :				List[str]		     =					self.numpy_to_pil(_SCREAMING_SNAKE_CASE		)

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE		)








def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if not nums:  # Makes sure that the list is not empty
    raise ValueError('''List is empty'''       )

  A_      :				Optional[int]		     =					sum(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )  # Calculate the average
  return sum(abs(x - average       ) for x in nums       ) / len(SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




