




from __future__ import annotations





class  __lowerCamelCase       :




    """simple docstring"""

    def __init__(       self     :     int   ,	SCREAMING_SNAKE_CASE__     :     int   )      ->       None:
         lowerCAmelCase__		= data
         lowerCAmelCase__		= None






class  __lowerCamelCase       :




    """simple docstring"""

    def __init__(       self     :     Optional[Any]   )      ->       List[str]:
         lowerCAmelCase__		= None


    def    a      (       self     :     Tuple   ,	SCREAMING_SNAKE_CASE__     :     int   )      ->       int:
         lowerCAmelCase__		= Node(SCREAMING_SNAKE_CASE__   )
         lowerCAmelCase__		= self.head
         lowerCAmelCase__		= new_node
         return self.head.data


    def    a      (       self     :     str   )      ->       int | None:
         lowerCAmelCase__		= self.head
         lowerCAmelCase__		= self.head
         if self.head:
              while fast_pointer and fast_pointer.next:
                   lowerCAmelCase__		= fast_pointer.next.next
                   lowerCAmelCase__		= slow_pointer.next
              return slow_pointer.data
         else:
              print("No element found."   )
              return None


if __name__ == "__main__":
     UpperCamelCase								=     LinkedList()
     for _ in range(int(input().strip())):
          UpperCamelCase								=     int(input().strip())
          link.push(data)
     print(link.middle_element())






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class  __lowerCamelCase       (    UpperCamelCase__    ):




    """simple docstring"""

    def __init__(       self     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Any   )      ->       Union[str, Any]:
         super().__init__()
         self.register_modules(unet=SCREAMING_SNAKE_CASE__   ,	scheduler=SCREAMING_SNAKE_CASE__   )


    @torch.no_grad()
    def __call__(       self     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     int = 1   ,	SCREAMING_SNAKE_CASE__     :     Optional[torch.Generator] = None   ,	SCREAMING_SNAKE_CASE__     :     int = 50   ,	SCREAMING_SNAKE_CASE__     :     Optional[str] = "pil"   ,	SCREAMING_SNAKE_CASE__     :     bool = True   ,	**SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	)      ->       Union[ImagePipelineOutput, Tuple]:
         lowerCAmelCase__		= torch.randn(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,	generator=SCREAMING_SNAKE_CASE__   ,	)
         lowerCAmelCase__		= image.to(self.device   )

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

         for t in self.progress_bar(self.scheduler.timesteps   ):
              # 1. predict noise model_output
              lowerCAmelCase__		= self.unet(SCREAMING_SNAKE_CASE__   ,	SCREAMING_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
              lowerCAmelCase__		= self.scheduler.step(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ).prev_sample

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

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

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