"""simple docstring"""





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput


class      UpperCAmelCase_	(    _a):


      def __init__(   self		,			a		,			a							)       ->					List[Any]:
             super().__init__()
             self.register_modules(unet=a		,			scheduler=a							)


      @torch.no_grad()
      def __call__(   self		,			a = 1		,			a = None		,			a = 5_0		,			a = "pil"		,			a = True		,			**a		,			)       ->					Union[ImagePipelineOutput, Tuple]:
             lowercase__			:		Union[str, Any]       =	torch.randn(
                 (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,			generator=a		,			)
             lowercase__			:		Tuple       =	image.to(self.device							)

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

             for t in self.progress_bar(self.scheduler.timesteps							):
                    # 1. predict noise model_output
                    lowercase__			:		Dict       =	self.unet(a		,			a							).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
                    lowercase__			:		Optional[int]       =	self.scheduler.step(a		,			a		,			a							).prev_sample

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

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

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









"""simple docstring"""





from typing import Any






class      a						:






    def __init__(       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		List[Any]:
      lowerCamelCase_         =						data
      lowerCamelCase_         =						None






class      a						:






    def __init__(       self     :							Union[str, Any]					)     ->		List[Any]:
      lowerCamelCase_         =						None






    def    UpperCamelCase       (       self     :							Dict					)     ->		Optional[int]:
      lowerCamelCase_         =						self.head
      while temp is not None:
        print(temp.data       ,						end=' '					)
        lowerCamelCase_         =						temp.next
      print()






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		Union[str, Any]:
      lowerCamelCase_         =						Node(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						self.head
      lowerCamelCase_         =						new_node






    def    UpperCamelCase       (       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							int					)     ->		Union[str, Any]:
      if node_data_a == node_data_a:
        return
      else:
        lowerCamelCase_         =						self.head
        while node_a is not None and node_a.data != node_data_a:
          lowerCamelCase_         =						node_a.next

        lowerCamelCase_         =						self.head
        while node_a is not None and node_a.data != node_data_a:
          lowerCamelCase_         =						node_a.next

        if node_a is None or node_a is None:
          return

        lowerCamelCase_     ,	lowerCamelCase_         =						node_a.data, node_a.data


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				Optional[int]					    =						LinkedList()
  for i in range(5, 0, -1):
    ll.push(i)

  ll.print_list()

  ll.swap_nodes(1, 4)
  print('''After swapping''')
  ll.print_list()




