





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class  __UpperCamelCase						(       A__						):





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





     @torch.no_grad()
     def __call__(    self							,       _UpperCamelCase = 1							,       _UpperCamelCase = None							,       _UpperCamelCase = 50							,       _UpperCamelCase = "pil"							,       _UpperCamelCase = True							,       **_UpperCamelCase							,       ):
        _UpperCAmelCase											=      torch.randn(
            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,       generator=_UpperCamelCase							,       )
        _UpperCAmelCase											=      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
           _UpperCAmelCase											=      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
           _UpperCAmelCase											=      self.scheduler.step(_UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase				).prev_sample

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






def     A__					(						SCREAMING_SNAKE_CASE_      :			list	)     ->     bool:






   """simple docstring"""







   if not isinstance(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	):
      raise ValueError('''Input series is not valid, valid series - [1, 2/3, 2]'''	)
   if len(SCREAMING_SNAKE_CASE_	) == 0:
      raise ValueError('''Input list must be a non empty list'''	)
   if len(SCREAMING_SNAKE_CASE_	) == 1 and series[0] != 0:
      return True
   _UpperCAmelCase											=      []
   _UpperCAmelCase											=      len(SCREAMING_SNAKE_CASE_	)
   for i in range(0							,  SCREAMING_SNAKE_CASE_	):
      if series[i] == 0:
         raise ValueError('''Input series cannot have 0 as an element'''	)
      rec_series.append(1 / series[i]	)
   _UpperCAmelCase											=      rec_series[1] - rec_series[0]
   for index in range(2							,  SCREAMING_SNAKE_CASE_	):
      if rec_series[index] - rec_series[index - 1] != common_diff:
         return False
   return True
def     A__					(						SCREAMING_SNAKE_CASE_      :			list	)     ->     float:






   """simple docstring"""







   if not isinstance(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	):
      raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''	)
   if len(SCREAMING_SNAKE_CASE_	) == 0:
      raise ValueError('''Input list must be a non empty list'''	)
   _UpperCAmelCase											=      0
   for val in series:
      answer += 1 / val
   return len(SCREAMING_SNAKE_CASE_	) / answer


if __name__ == "__main__":
   import doctest

   doctest.testmod()