





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(       _SCREAMING_SNAKE_CASE				)							-> Union[str, Any]:




    """simple docstring"""




    _A,			_A     =							image.size
    _A,			_A     =							(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    _A     =							image.resize((w, h)							,     resample=PIL_INTERPOLATION['lanczos']				)
    _A     =							np.array(_SCREAMING_SNAKE_CASE				).astype(np.floataa				) / 255.0
    _A     =							image[None].transpose(0							,     3							,     1							,     2				)
    _A     =							torch.from_numpy(_SCREAMING_SNAKE_CASE				)
    return 2.0 * image - 1.0







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     def __init__(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     ):
         super().__init__()
         self.register_modules(vqvae=snake_case_				,     unet=snake_case_				,     scheduler=snake_case_							)




     @torch.no_grad()
     def __call__(    self				,     snake_case_ = None				,     snake_case_ = 1				,     snake_case_ = 100				,     snake_case_ = 0.0				,     snake_case_ = None				,     snake_case_ = "pil"				,     snake_case_ = True				,     ):
         if isinstance(snake_case_				,     PIL.Image.Image							):
             _A     =							1
         elif isinstance(snake_case_				,     torch.Tensor							):
             _A     =							image.shape[0]
         else:
             raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(snake_case_							)}"							)

         if isinstance(snake_case_				,     PIL.Image.Image							):
             _A     =							preprocess(snake_case_							)

         _A,			_A     =							image.shape[-2:]

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

         _A     =							randn_tensor(snake_case_				,     generator=snake_case_				,     device=self.device				,     dtype=snake_case_							)

         _A     =							image.to(device=self.device				,     dtype=snake_case_							)

         # set timesteps and move to the correct device
         self.scheduler.set_timesteps(snake_case_				,     device=self.device							)
         _A     =							self.scheduler.timesteps

         # scale the initial noise by the standard deviation required by the scheduler
         _A     =							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     =							'eta' in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
         _A     =							{}
         if accepts_eta:
             _A     =							eta

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

         # decode the image latents with the VQVAE
         _A     =							self.vqvae.decode(snake_case_							).sample
         _A     =							torch.clamp(snake_case_				,     -1.0				,     1.0							)
         _A     =							image / 2 + 0.5
         _A     =							image.cpu().permute(0				,     2				,     3				,     1							).numpy()

         if output_type == "pil":
             _A     =							self.numpy_to_pil(snake_case_							)

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=snake_case_							)












import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__A							:			List[Any]         =			"python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
 pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
 pkgs_to_check_at_runtime.append("importlib_metadata")

for pkg in pkgs_to_check_at_runtime:
 if pkg in deps:
  if pkg == "tokenizers":
   # must be loaded here, or else tqdm check may fail
   from .utils import is_tokenizers_available

   if not is_tokenizers_available():
    continue  # not required, check version only if installed

  require_version_core(deps[pkg])
 else:
  raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=None				)							-> Union[str, Any]:




    """simple docstring"""




    require_version(deps[pkg]							,     _SCREAMING_SNAKE_CASE				)





