





"""simple docstring"""





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

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






       @torch.no_grad()
       def __call__(       self       ,					__UpperCAmelCase = 1       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = 5_0       ,					__UpperCAmelCase = "pil"       ,					__UpperCAmelCase = True       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				torch.randn(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,					generator=__UpperCAmelCase       ,					)
         lowerCAmelCase__     :Tuple											=				image.to(self.device )

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

         for t in self.progress_bar(self.scheduler.timesteps ):
           # 1. predict noise model_output
           lowerCAmelCase__     :Union[str, Any]											=				self.unet(__UpperCAmelCase       ,					__UpperCAmelCase ).sample

           # 2. predict previous mean of image x_t-1 and add variance depending on eta
           # eta corresponds to η in paper and should be between [0, 1]
           # do x_t -> x_t-1
           lowerCAmelCase__     :int											=				self.scheduler.step(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase ).prev_sample

         lowerCAmelCase__     :int											=				(image / 2 + 0.5).clamp(0       ,					1 )
         lowerCAmelCase__     :Tuple											=				image.cpu().permute(0       ,					2       ,					3       ,					1 ).numpy()
         if output_type == "pil":
           lowerCAmelCase__     :Union[str, Any]											=				self.numpy_to_pil(__UpperCAmelCase )

         if not return_dict:
           return (image,), "This is a local test"

         return ImagePipelineOutput(images=__UpperCAmelCase ), "This is a local test"







"""simple docstring"""





def 					__A  (_SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :List[Any]											=				int(_SCREAMING_SNAKE_CASE     )
  if decimal in (0, 1):  # Exit cases for the recursion
    return str(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__				,							lowerCAmelCase__     :int											=				divmod(_SCREAMING_SNAKE_CASE	,  2     )
  return binary_recursive(_SCREAMING_SNAKE_CASE     ) + str(_SCREAMING_SNAKE_CASE     )
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :str											=				str(_SCREAMING_SNAKE_CASE     ).strip()
  if not number:
    raise ValueError('No input value was provided'     )
  lowerCAmelCase__     :Dict											=				'-' if number.startswith('-'     ) else ''
  lowerCAmelCase__     :int											=				number.lstrip('-'     )
  if not number.isnumeric():
    raise ValueError('Input value is not an integer'     )
  return F"{negative}0b{binary_recursive(int(_SCREAMING_SNAKE_CASE     )     )}"


if __name__ == "__main__":
  from doctest import testmod

  testmod()
