




'''simple docstring'''





from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


UpperCamelCase_		:     Optional[Any]          =		transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)





def       __a      (  _UpperCamelCase:       Dict       )		->					Any:

     """simple docstring"""




     if isinstance(_UpperCamelCase   ,		torch.Tensor       ):
          return image
     elif isinstance(_UpperCamelCase   ,		PIL.Image.Image       ):
          _snake_case				= [image]

     _snake_case				= [trans(img.convert("RGB"       )       ) for img in image]
     _snake_case				= torch.stack(_UpperCamelCase       )
     return image





class   _a							(		__lowerCAmelCase				):






     def __init__(					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     str:
          super().__init__()

          # make sure scheduler can always be converted to DDIM
          _snake_case				= DDIMScheduler.from_config(scheduler.config       )

          self.register_modules(unet=_SCREAMING_SNAKE_CASE   ,scheduler=_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     Optional[int]:
          if strength < 0 or strength > 1:
               raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}"""       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Union[str, Any]:
          # get the original timestep using init_timestep
          _snake_case				= min(int(num_inference_steps * strength       )   ,_SCREAMING_SNAKE_CASE       )

          _snake_case				= max(num_inference_steps - init_timestep   ,0       )
          _snake_case				= self.scheduler.timesteps[t_start:]

          return timesteps, num_inference_steps - t_start






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=None       )				->     Dict:
          if not isinstance(_SCREAMING_SNAKE_CASE   ,(torch.Tensor, PIL.Image.Image, list)       ):
               raise ValueError(
                   f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_SCREAMING_SNAKE_CASE       )}"""       )

          _snake_case				= image.to(device=_SCREAMING_SNAKE_CASE   ,dtype=_SCREAMING_SNAKE_CASE       )

          if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ) and len(_SCREAMING_SNAKE_CASE       ) != batch_size:
               raise ValueError(
                   f"""You have passed a list of generators of length {len(_SCREAMING_SNAKE_CASE       )}, but requested an effective batch"""
                   f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""       )

          _snake_case				= init_latents.shape
          _snake_case				= randn_tensor(_SCREAMING_SNAKE_CASE   ,generator=_SCREAMING_SNAKE_CASE   ,device=_SCREAMING_SNAKE_CASE   ,dtype=_SCREAMING_SNAKE_CASE       )

          # get latents
          print("add noise to latents at timestep"   ,_SCREAMING_SNAKE_CASE       )
          _snake_case				= self.scheduler.add_noise(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
          _snake_case				= init_latents

          return latents






     @torch.no_grad()
     def __call__(					self   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = 0.8   ,_SCREAMING_SNAKE_CASE = 1   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = 0.0   ,_SCREAMING_SNAKE_CASE = 50   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = "pil"   ,_SCREAMING_SNAKE_CASE = True   ,)				->     Union[ImagePipelineOutput, Tuple]:
          self.check_inputs(_SCREAMING_SNAKE_CASE       )

          # 2. Preprocess image
          _snake_case				= preprocess(_SCREAMING_SNAKE_CASE       )

          # 3. set timesteps
          self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE   ,device=self.device       )
          _snake_case				,   _snake_case				= self.get_timesteps(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,self.device       )
          _snake_case				= timesteps[:1].repeat(_SCREAMING_SNAKE_CASE       )

          # 4. Prepare latent variables
          _snake_case				= self.prepare_latents(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,self.unet.dtype   ,self.device   ,_SCREAMING_SNAKE_CASE       )
          _snake_case				= latents

          # 5. Denoising loop
          for t in self.progress_bar(_SCREAMING_SNAKE_CASE       ):
               # 1. predict noise model_output
               _snake_case				= 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
               _snake_case				= self.scheduler.step(
                   _SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,eta=_SCREAMING_SNAKE_CASE   ,use_clipped_model_output=_SCREAMING_SNAKE_CASE   ,generator=_SCREAMING_SNAKE_CASE   ,).prev_sample

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

          if not return_dict:
               return (image, latent_timestep.item())

          return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE       )








'''simple docstring'''





from typing import Any

class   _a							:






     def __init__(					self   ,_SCREAMING_SNAKE_CASE       )				->     List[str]:
          _snake_case				= data
          _snake_case				= None







class   _a							:






     def __init__(					self       )				->     List[Any]:
          _snake_case				= None






     def 							_lowercase      (					self       )				->     str:
          _snake_case				= self.head
          while temp is not None:
               print(temp.data   ,end=" "       )
               _snake_case				= temp.next
          print()






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     Dict:
          _snake_case				= Node(_SCREAMING_SNAKE_CASE       )
          _snake_case				= self.head
          _snake_case				= new_node






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Tuple:
          if node_data_a == node_data_a:
               return
          else:
               _snake_case				= self.head
               while node_a is not None and node_a.data != node_data_a:
                    _snake_case				= node_a.next

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

               if node_a is None or node_a is None:
                    return

               _snake_case				,   _snake_case				= node_a.data, node_a.data


if __name__ == "__main__":
   UpperCamelCase_		:     str          =		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()


