

'''simple docstring'''

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 				_snake_case			(							_snake_case    ):
   _A							:  UNetaDModel
   _A							:  KarrasVeScheduler
   def __init__(			self      :   Tuple  ,SCREAMING_SNAKE_CASE__      :   UNetaDModel  ,SCREAMING_SNAKE_CASE__      :   KarrasVeScheduler				):
     super().__init__()
     self.register_modules(unet=SCREAMING_SNAKE_CASE__  ,scheduler=SCREAMING_SNAKE_CASE__				)







   @torch.no_grad()
   def __call__(			self      :   Union[str, Any]  ,SCREAMING_SNAKE_CASE__      :   int = 1  ,SCREAMING_SNAKE_CASE__      :   int = 50  ,SCREAMING_SNAKE_CASE__      :   Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,SCREAMING_SNAKE_CASE__      :   Optional[str] = "pil"  ,SCREAMING_SNAKE_CASE__      :   bool = True  ,**SCREAMING_SNAKE_CASE__      :   Tuple  ,):
     SCREAMING_SNAKE_CASE:Optional[int]       =       self.unet.config.sample_size
     SCREAMING_SNAKE_CASE:Optional[int]       =       (batch_size, 3, img_size, img_size)

     SCREAMING_SNAKE_CASE:str       =       self.unet

     # sample x_0 ~ N(0, sigma_0^2 * I)
     SCREAMING_SNAKE_CASE:List[str]       =       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
       SCREAMING_SNAKE_CASE:Tuple       =       self.scheduler.schedule[t]
       SCREAMING_SNAKE_CASE:Union[str, Any]       =       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
       SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE:List[Any]       =       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].
       SCREAMING_SNAKE_CASE:int       =       (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
       SCREAMING_SNAKE_CASE: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].
         SCREAMING_SNAKE_CASE:Optional[Any]       =       (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2  ,sigma_prev / 2				).sample
         SCREAMING_SNAKE_CASE:Optional[Any]       =       self.scheduler.step_correct(
             SCREAMING_SNAKE_CASE__  ,SCREAMING_SNAKE_CASE__  ,SCREAMING_SNAKE_CASE__  ,SCREAMING_SNAKE_CASE__  ,step_output.prev_sample  ,step_output["derivative"]  ,)
       SCREAMING_SNAKE_CASE:Tuple       =       step_output.prev_sample

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

     if not return_dict:
       return (image,)

     return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__				)













'''simple docstring'''

from PIL import Image

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Image:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							(259 * (level + 255)) / (255 * (259 - level))

   def contrast(__UpperCAmelCase   ) -> int:
      return int(128 + factor * (c - 128)   )

   return img.point(__UpperCAmelCase   )


if __name__ == "__main__":
    # Load image
    with Image.open("image_data/lena.jpg") as img:
        # Change contrast to 170
        a   	=    change_contrast(img, 170)
        cont_img.save("image_data/lena_high_contrast.png", format="png")





