
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						:   List[Any]       ,		snake_case__						:   UNetaDModel       ,		snake_case__						:   ScoreSdeVeScheduler					)						->      List[str]:


          '''simple docstring'''


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



   @torch.no_grad()
   def __call__(self						:   Optional[int]       ,		snake_case__						:   int = 1       ,		snake_case__						:   int = 20_00       ,		snake_case__						:   Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,		snake_case__						:   Optional[str] = "pil"       ,		snake_case__						:   bool = True       ,		**snake_case__						:   Any       ,		)						->      Union[ImagePipelineOutput, Tuple]:


          '''simple docstring'''


          snake_case			:  Dict     							= self.unet.config.sample_size
          snake_case			:  List[str]     							= (batch_size, 3, img_size, img_size)

          snake_case			:  Any     							= self.unet

          snake_case			:  List[str]     							= randn_tensor(snake_case__       ,		generator=snake_case__					) * self.scheduler.init_noise_sigma
          snake_case			:  List[str]     							= sample.to(self.device					)

          self.scheduler.set_timesteps(snake_case__					)
          self.scheduler.set_sigmas(snake_case__					)

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

                 # correction step
                 for _ in range(self.scheduler.config.correct_steps					):
                        snake_case			:  List[str]     							= self.unet(snake_case__       ,		snake_case__					).sample
                        snake_case			:  Tuple     							= self.scheduler.step_correct(snake_case__       ,		snake_case__       ,		generator=snake_case__					).prev_sample

                 # prediction step
                 snake_case			:  Union[str, Any]     							= model(snake_case__       ,		snake_case__					).sample
                 snake_case			:  Tuple     							= self.scheduler.step_pred(snake_case__       ,		snake_case__       ,		snake_case__       ,		generator=snake_case__					)

                 snake_case							,   snake_case			:  List[Any]     							= output.prev_sample, output.prev_sample_mean

          snake_case			:  str     							= sample_mean.clamp(0       ,		1					)
          snake_case			:  Dict     							= sample.cpu().permute(0       ,		2       ,		3       ,		1					).numpy()
          if output_type == "pil":
                 snake_case			:  Optional[Any]     							= self.numpy_to_pil(snake_case__					)

          if not return_dict:
                 return (sample,)

          return ImagePipelineOutput(images=snake_case__					)







def      UpperCamelCase   (       __lowerCamelCase	:					int = 1							,   __lowerCamelCase	:					int = 1000       ):
       snake_case			:  int     							= 1
       snake_case			:  int     							= 0

       for divide_by_number in range(__lowerCamelCase							,   digit + 1       ):
              snake_case			:  list[int]     							= []
              snake_case			:  Optional[int]     							= numerator
              for _ in range(1							,   digit + 1       ):
                     if now_divide in has_been_divided:
                            if longest_list_length < len(__lowerCamelCase       ):
                                   snake_case			:  List[Any]     							= len(__lowerCamelCase       )
                                   snake_case			:  List[str]     							= divide_by_number
                     else:
                            has_been_divided.append(__lowerCamelCase       )
                            snake_case			:  Any     							= now_divide * 10 % divide_by_number

       return the_digit


# Tests
if __name__ == "__main__":
   import doctest

   doctest.testmod()





