





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):


  def __init__(							self ,__snake_case ,__snake_case ):



       """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 = 5_0 ,__snake_case = "pil" ,__snake_case = True ,**__snake_case ,):



       """simple docstring"""





       A_          =       torch.randn(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,generator=__snake_case ,)
       A_          =       image.to(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
            A_          =       self.unet(__snake_case ,__snake_case ).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
            A_          =       self.scheduler.step(__snake_case ,__snake_case ,__snake_case ).prev_sample

       A_          =       (image / 2 + 0.5).clamp(0 ,1 )
       A_          =       image.cpu().permute(0 ,2 ,3 ,1 ).numpy()
       if output_type == "pil":
            A_          =       self.numpy_to_pil(__snake_case )

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

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










from __future__ import annotations

def      UpperCAmelCase_    (    _UpperCAmelCase							:list[float]  ,						_UpperCAmelCase							:list[float]				)	->				float:
     '''simple docstring'''


     A_          =       sorted(numsa + numsa				)
     A_   ,    A_          =       divmod(len(_UpperCAmelCase				)  ,						2				)
     if mod == 1:
          return all_numbers[div]
     else:
          return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      a__      :     Optional[Any]         =		[float(x) for x in input('Enter the elements of first array: ').split()]
      a__      :     int         =		[float(x) for x in input('Enter the elements of second array: ').split()]
      print(f'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')



