




'''simple docstring'''






import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   List[Any]:
      snake_case__      ,							snake_case__       :   List[Any]	      =    image.size
      snake_case__      ,							snake_case__       :   Any	      =    (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
      snake_case__       :   str	      =    image.resize((w, h)				,      resample=PIL_INTERPOLATION['lanczos']		)
      snake_case__       :   List[Any]	      =    np.array(__SCREAMING_SNAKE_CASE		).astype(np.floataa		) / 255.0
      snake_case__       :   str	      =    image[None].transpose(0				,      3				,      1				,      2		)
      snake_case__       :   int	      =    torch.from_numpy(__SCREAMING_SNAKE_CASE		)
      return 2.0 * image - 1.0






class        lowercase_      (						lowerCAmelCase_							):





     def __init__(     self     :					Union[str, Any]     ,							__lowerCamelCase     :					VQModel     ,							__lowerCamelCase     :					UNetaDModel     ,							__lowerCamelCase     :					Union[
             DDIMScheduler,
             PNDMScheduler,
             LMSDiscreteScheduler,
             EulerDiscreteScheduler,
             EulerAncestralDiscreteScheduler,
             DPMSolverMultistepScheduler,
         ]     ,							):
           super().__init__()
           self.register_modules(vqvae=__lowerCamelCase     ,							unet=__lowerCamelCase     ,							scheduler=__lowerCamelCase			)





     @torch.no_grad()
     def __call__(     self     :					str     ,							__lowerCamelCase     :					Union[torch.Tensor, PIL.Image.Image] = None     ,							__lowerCamelCase     :					Optional[int] = 1     ,							__lowerCamelCase     :					Optional[int] = 100     ,							__lowerCamelCase     :					Optional[float] = 0.0     ,							__lowerCamelCase     :					Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,							__lowerCamelCase     :					Optional[str] = "pil"     ,							__lowerCamelCase     :					bool = True     ,							):
           if isinstance(__lowerCamelCase     ,							PIL.Image.Image			):
                 snake_case__       :   str	      =    1
           elif isinstance(__lowerCamelCase     ,							torch.Tensor			):
                 snake_case__       :   str	      =    image.shape[0]
           else:
                 raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__lowerCamelCase			)}"			)

           if isinstance(__lowerCamelCase     ,							PIL.Image.Image			):
                 snake_case__       :   List[Any]	      =    preprocess(__lowerCamelCase			)

           snake_case__      ,							snake_case__       :   Union[str, Any]	      =    image.shape[-2:]

           # in_channels should be 6: 3 for latents, 3 for low resolution image
           snake_case__       :   Optional[Any]	      =    (batch_size, self.unet.config.in_channels // 2, height, width)
           snake_case__       :   List[Any]	      =    next(self.unet.parameters()			).dtype

           snake_case__       :   int	      =    randn_tensor(__lowerCamelCase     ,							generator=__lowerCamelCase     ,							device=self.device     ,							dtype=__lowerCamelCase			)

           snake_case__       :   List[Any]	      =    image.to(device=self.device     ,							dtype=__lowerCamelCase			)

           # set timesteps and move to the correct device
           self.scheduler.set_timesteps(__lowerCamelCase     ,							device=self.device			)
           snake_case__       :   Tuple	      =    self.scheduler.timesteps

           # scale the initial noise by the standard deviation required by the scheduler
           snake_case__       :   str	      =    latents * self.scheduler.init_noise_sigma

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
           # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
           # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
           # and should be between [0, 1]
           snake_case__       :   Tuple	      =    'eta' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
           snake_case__       :   Union[str, Any]	      =    {}
           if accepts_eta:
                 snake_case__       :   List[Any]	      =    eta

           for t in self.progress_bar(__lowerCamelCase			):
                 # concat latents and low resolution image in the channel dimension.
                 snake_case__       :   str	      =    torch.cat([latents, image]     ,							dim=1			)
                 snake_case__       :   Union[str, Any]	      =    self.scheduler.scale_model_input(__lowerCamelCase     ,							__lowerCamelCase			)
                 # predict the noise residual
                 snake_case__       :   str	      =    self.unet(__lowerCamelCase     ,							__lowerCamelCase			).sample
                 # compute the previous noisy sample x_t -> x_t-1
                 snake_case__       :   Optional[int]	      =    self.scheduler.step(__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							**__lowerCamelCase			).prev_sample

           # decode the image latents with the VQVAE
           snake_case__       :   List[Any]	      =    self.vqvae.decode(__lowerCamelCase			).sample
           snake_case__       :   int	      =    torch.clamp(__lowerCamelCase     ,							-1.0     ,							1.0			)
           snake_case__       :   Union[str, Any]	      =    image / 2 + 0.5
           snake_case__       :   List[str]	      =    image.cpu().permute(0     ,							2     ,							3     ,							1			).numpy()

           if output_type == "pil":
                 snake_case__       :   Tuple	      =    self.numpy_to_pil(__lowerCamelCase			)

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=__lowerCamelCase			)






'''simple docstring'''






from typing import Any






class        lowercase_      :





     def __init__(     self     :					Dict     ,							__lowerCamelCase     :					Any			):
           snake_case__       :   str	      =    data
           snake_case__       :   Dict	      =    None






class        lowercase_      :





     def __init__(     self     :					int			):
           snake_case__       :   List[Any]	      =    None





     def 			_lowerCAmelCase      (     self     :					str			):
           snake_case__       :   Any	      =    self.head
           while temp is not None:
                 print(temp.data     ,							end=' '			)
                 snake_case__       :   int	      =    temp.next
           print()





     def 			_lowerCAmelCase      (     self     :					Optional[Any]     ,							__lowerCamelCase     :					Any			):
           snake_case__       :   Union[str, Any]	      =    Node(__lowerCamelCase			)
           snake_case__       :   List[str]	      =    self.head
           snake_case__       :   Any	      =    new_node





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					Tuple			):
           if node_data_a == node_data_a:
                 return
           else:
                 snake_case__       :   List[Any]	      =    self.head
                 while node_a is not None and node_a.data != node_data_a:
                       snake_case__       :   List[str]	      =    node_a.next

                 snake_case__       :   Union[str, Any]	      =    self.head
                 while node_a is not None and node_a.data != node_data_a:
                       snake_case__       :   str	      =    node_a.next

                 if node_a is None or node_a is None:
                       return

                 snake_case__      ,							snake_case__       :   Dict	      =    node_a.data, node_a.data


if __name__ == "__main__":
      A_			      =		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()
