





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 			__UpperCamelCase    (							A__    ):
     __A	:  UNetaDModel
     __A	:  ScoreSdeVeScheduler





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase					):
        super().__init__()
        self.register_modules(unet=_UpperCamelCase				,							scheduler=_UpperCamelCase					)





     @torch.no_grad()
     def __call__(     self				,							_UpperCamelCase = 1				,							_UpperCamelCase = 2000				,							_UpperCamelCase = None				,							_UpperCamelCase = "pil"				,							_UpperCamelCase = True				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   self.unet.config.sample_size
        _UpperCAmelCase           =   (batch_size, 3, img_size, img_size)

        _UpperCAmelCase           =   self.unet

        _UpperCAmelCase           =   randn_tensor(_UpperCamelCase				,							generator=_UpperCamelCase					) * self.scheduler.init_noise_sigma
        _UpperCAmelCase           =   sample.to(self.device					)

        self.scheduler.set_timesteps(_UpperCamelCase					)
        self.scheduler.set_sigmas(_UpperCamelCase					)

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

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

           # prediction step
           _UpperCAmelCase           =   model(_UpperCamelCase				,							_UpperCamelCase					).sample
           _UpperCAmelCase           =   self.scheduler.step_pred(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							generator=_UpperCamelCase					)

           _UpperCAmelCase       ,      _UpperCAmelCase           =   output.prev_sample, output.prev_sample_mean

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

        if not return_dict:
           return (sample,)

        return ImagePipelineOutput(images=_UpperCamelCase					)






def      A__       (    SCREAMING_SNAKE_CASE_   :      int				,	SCREAMING_SNAKE_CASE_   :      int						)  ->  str:






   """simple docstring"""







   if a < 0 or b < 0:
      raise ValueError('''the value of both inputs must be positive'''						)

   _UpperCAmelCase           =   str(bin(SCREAMING_SNAKE_CASE_						)						)[2:]  # remove the leading "0b"
   _UpperCAmelCase           =   str(bin(SCREAMING_SNAKE_CASE_						)						)[2:]  # remove the leading "0b"

   _UpperCAmelCase           =   max(len(SCREAMING_SNAKE_CASE_						)				,	len(SCREAMING_SNAKE_CASE_						)						)

   return "0b" + "".join(
       str(int(char_a == '''1''' and char_b == '''1'''						)						)
       for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE_						)				,	b_binary.zfill(SCREAMING_SNAKE_CASE_						)						)						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()