





from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCamelCase__  (    __UpperCamelCase				):



   def __init__(       self      ,   snake_case      ,   snake_case	) ->    Dict:







      """simple docstring"""
      super().__init__()
      self.register_modules(unet=snake_case      ,   scheduler=snake_case	)





   @torch.no_grad()
   def __call__(       self      ,   snake_case = 1      ,   snake_case = None      ,   snake_case = 1_0_0_0      ,   snake_case = "pil"      ,   snake_case = True      ,   ) ->    Union[ImagePipelineOutput, Tuple]:







      """simple docstring"""
      # Sample gaussian noise to begin loop
      if isinstance(self.unet.config.sample_size      ,   snake_case	):
         lowercase    :			List[Any]  =       (
             batch_size,
             self.unet.config.in_channels,
             self.unet.config.sample_size,
             self.unet.config.sample_size,
         )
      else:
         lowercase    :			int  =       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

      if self.device.type == "mps":
         # randn does not work reproducibly on mps
         lowercase    :			Union[str, Any]  =       randn_tensor(snake_case      ,   generator=snake_case	)
         lowercase    :			Optional[Any]  =       image.to(self.device	)
      else:
         lowercase    :			Dict  =       randn_tensor(snake_case      ,   generator=snake_case      ,   device=self.device	)

      # set step values
      self.scheduler.set_timesteps(snake_case	)

      for t in self.progress_bar(self.scheduler.timesteps	):
         # 1. predict noise model_output
         lowercase    :			Any  =       self.unet(snake_case      ,   snake_case	).sample

         # 2. compute previous image: x_t -> x_t-1
         lowercase    :			Union[str, Any]  =       self.scheduler.step(snake_case      ,   snake_case      ,   snake_case      ,   generator=snake_case	).prev_sample

      lowercase    :			int  =       (image / 2 + 0.5).clamp(0      ,   1	)
      lowercase    :			Dict  =       image.cpu().permute(0      ,   2      ,   3      ,   1	).numpy()
      if output_type == "pil":
         lowercase    :			List[str]  =       self.numpy_to_pil(snake_case	)

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=snake_case	)









"""simple docstring"""
import math




def 			__snake_case						(      __A    )		->				float:
   lowercase    :			List[Any]  =       math.log(2 ,10    ) * number
   lowercase    :			Tuple  =       round((log_number - int(__A    )) ,15    )

   return difference




def 			__snake_case						(      __A = 678910    )		->				int:
   lowercase    :			Any  =       90
   lowercase    :			Optional[Any]  =       0

   lowercase    :			List[str]  =       math.log(1.23 ,10    )
   lowercase    :			str  =       math.log(1.24 ,10    )
   lowercase    :			str  =       0

   while position < number:
      lowercase    :			Optional[Any]  =       log_difference(__A    )

      if difference >= upper_limit:
         power_iterator += 93

      elif difference < lower_limit:
         power_iterator += 196

      else:
         lowercase    :			List[Any]  =       power_iterator
         power_iterator += 196
         position += 1

   return previous_power


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(F'{solution() = }')


