






"""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 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a      ,  _a  :       List[str]             =	image.size
  _a      ,  _a  :       Optional[Any]             =	(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
  _a  :       str             =	image.resize((w, h)     ,   resample=PIL_INTERPOLATION["""lanczos"""]				)
  _a  :       List[str]             =	np.array(UpperCamelCase__				).astype(np.floataa				) / 255.0
  _a  :       Dict             =	image[None].transpose(0     ,   3     ,   1     ,   2				)
  _a  :       List[Any]             =	torch.from_numpy(UpperCamelCase__				)
  return 2.0 * image - 1.0






class        UpperCamelCase			(	snake_case_      ):


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







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

   if isinstance(UpperCAmelCase__							,						PIL.Image.Image							):
     _a  :       List[Any]             =	preprocess(UpperCAmelCase__							)

   _a      ,  _a  :       Tuple             =	image.shape[-2:]

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

   _a  :       List[str]             =	randn_tensor(UpperCAmelCase__							,						generator=UpperCAmelCase__							,						device=self.device							,						dtype=UpperCAmelCase__							)

   _a  :       Union[str, Any]             =	image.to(device=self.device							,						dtype=UpperCAmelCase__							)

   # set timesteps and move to the correct device
   self.scheduler.set_timesteps(UpperCAmelCase__							,						device=self.device							)
   _a  :       int             =	self.scheduler.timesteps

   # scale the initial noise by the standard deviation required by the scheduler
   _a  :       Any             =	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]
   _a  :       Optional[Any]             =	"""eta""" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
   _a  :       Optional[Any]             =	{}
   if accepts_eta:
     _a  :       Dict             =	eta

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

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

   if output_type == "pil":
     _a  :       Optional[int]             =	self.numpy_to_pil(UpperCAmelCase__							)

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=UpperCAmelCase__							)









"""simple docstring"""




from collections.abc import Iterable
from typing import Generic, TypeVar

_snake_case          =   TypeVar('_T')






class        UpperCamelCase			(	Generic[_T]      ):


 def __init__(     self		:						Optional[int]							,						UpperCAmelCase__		:						Iterable[_T] | None = None							)					->       None:
   _a  :       list[_T]             =	list(iterable or []							)
   _a  :       list[_T]             =	[]


 def __len__(     self		:						str							)					->       int:
   return len(self._stacka							) + len(self._stacka							)


 def __repr__(     self		:						List[str]							)					->       str:
   return f"""Queue({tuple(self._stacka[::-1] + self._stacka							)})"""


 def _lowercase	(     self		:						Union[str, Any]							,						UpperCAmelCase__		:						_T							)					->       None:
   self._stacka.append(UpperCAmelCase__							)







 def _lowercase	(     self		:						Optional[Any]							)					->       _T:
   _a  :       Any             =	self._stacka.pop
   _a  :       Union[str, Any]             =	self._stacka.append

   if not self._stacka:
     while self._stacka:
       stacka_append(stacka_pop()							)

   if not self._stacka:
     raise IndexError("""Queue is empty"""							)
   return self._stacka.pop()


if __name__ == "__main__":
 from doctest import testmod

 testmod()

