






'''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 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					=42
      __a  					=42




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         super().__init__()
         self.register_modules(unet=lowerCamelCase ,   scheduler=lowerCamelCase      )






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



         '''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(lowerCamelCase ,   generator=lowerCamelCase ,   device=self.device      ) * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(lowerCamelCase      )

         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(lowerCamelCase ,   lowerCamelCase ,   generator=lowerCamelCase      )

            # 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(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )

            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(
                   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   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(lowerCamelCase      )

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=lowerCamelCase      )







'''simple docstring'''



from maths.prime_factors import prime_factors



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int    )  ->			int:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    ):
      __a				     =    f"""Input value of [number={number}] must be an integer"""
      raise TypeError(SCREAMING_SNAKE_CASE__    )
   if number < 1:
      raise ValueError('Input must be a positive integer'    )
   return -1 if len(prime_factors(SCREAMING_SNAKE_CASE__    )    ) % 2 else 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()