





"""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"






def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			list:



       """simple docstring"""

       if len(lowercase_		) <= 1:
              return [tuple(lowercase_		)]

       A__ 					=       []

       def generate(lowercase_					,							lowercase_		):
              if k == 1:
                     res.append(tuple(arr[:]		)		)
                     return

              generate(k - 1					,							lowercase_		)

              for i in range(k - 1		):
                     if k % 2 == 0:  # k is even
                            A__					,				A__ 					=       arr[k - 1], arr[i]
                     else:  # k is odd
                            A__					,				A__ 					=       arr[k - 1], arr[0]
                     generate(k - 1					,							lowercase_		)

       generate(len(lowercase_		)					,							lowercase_		)
       return res


if __name__ == "__main__":
  _lowerCamelCase      :						int							=  input("""Enter numbers separated by a comma:\n""").strip()
  _lowerCamelCase      :						str							=  [int(item) for item in user_input.split(""",""")]
  print(heaps(arr))


