


"""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 				__A    (	_SCREAMING_SNAKE_CASE							):






 """simple docstring"""




 __lowerCAmelCase			 =  42
 __lowerCAmelCase			 =  42


 def __init__(		self			,   __A			,   __A						) ->					Union[str, Any]:
     super().__init__()
     self.register_modules(unet=__A			,   scheduler=__A						)





 @torch.no_grad()
 def __call__(		self			,   __A = 1			,   __A = 2000			,   __A = None			,   __A = "pil"			,   __A = True			,   **__A			,   ) ->					Union[ImagePipelineOutput, Tuple]:
     a					=self.unet.config.sample_size
     a					=(batch_size, 3, img_size, img_size)

     a					=self.unet

     a					=randn_tensor(__A			,   generator=__A						) * self.scheduler.init_noise_sigma
     a					=sample.to(self.device						)

     self.scheduler.set_timesteps(__A						)
     self.scheduler.set_sigmas(__A						)

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

         # correction step
         for _ in range(self.scheduler.config.correct_steps						):
             a					=self.unet(__A			,   __A						).sample
             a					=self.scheduler.step_correct(__A			,   __A			,   generator=__A						).prev_sample

         # prediction step
         a					=model(__A			,   __A						).sample
         a					=self.scheduler.step_pred(__A			,   __A			,   __A			,   generator=__A						)

         a		,				a					=output.prev_sample, output.prev_sample_mean

     a					=sample_mean.clamp(0			,   1						)
     a					=sample.cpu().permute(0			,   2			,   3			,   1						).numpy()
     if output_type == "pil":
         a					=self.numpy_to_pil(__A						)

     if not return_dict:
         return (sample,)

     return ImagePipelineOutput(images=__A						)



"""simple docstring"""







def _A						(						lowercase = 2_00_00_00		):







    """simple docstring"""
    a					=[0 for i in range(n + 1		)]
    a					=1
    a					=1

    for i in range(2	,   int(n**0.5		) + 1		):
        if primality_list[i] == 0:
            for j in range(i * i	,   n + 1	,   lowercase		):
                a					=1
    a					=0
    for i in range(lowercase		):
        if primality_list[i] == 0:
            sum_of_primes += i
    return sum_of_primes


if __name__ == "__main__":
       print(F'{solution() = }')