




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






class  _lowerCAmelCase     (  __lowercase	):

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







      @torch.no_grad()
      def __call__(   self      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = 1      ,				__UpperCAmelCase = 100      ,				__UpperCAmelCase = 0.0      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = "pil"      ,				__UpperCAmelCase = True      ,				):
        if isinstance(__UpperCAmelCase      ,				PIL.Image.Image		):
          lowerCAmelCase__				:  str		       =     1
        elif isinstance(__UpperCAmelCase      ,				torch.Tensor		):
          lowerCAmelCase__				:  Any		       =     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		):
          lowerCAmelCase__				:  List[str]		       =     preprocess(__UpperCAmelCase		)

        lowerCAmelCase__				:  str		       =     image.shape[-2:]

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

        lowerCAmelCase__				:  int		       =     randn_tensor(__UpperCAmelCase      ,				generator=__UpperCAmelCase      ,				device=self.device      ,				dtype=__UpperCAmelCase		)

        lowerCAmelCase__				:  Optional[int]		       =     image.to(device=self.device      ,				dtype=__UpperCAmelCase		)

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

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

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

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

        if output_type == "pil":
          lowerCAmelCase__				:  Optional[int]		       =     self.numpy_to_pil(__UpperCAmelCase		)

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=__UpperCAmelCase		)












lowerCAmelCase_					    = 6_55_21



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	int:
  lowerCAmelCase__				:  List[str]		       =     1
  lowerCAmelCase__				:  List[Any]		       =     0
  for plain_chr in plain_text:
    lowerCAmelCase__				:  Union[str, Any]		       =     (a + ord(UpperCamelCase      )) % MOD_ADLER
    lowerCAmelCase__				:  Optional[Any]		       =     (b + a) % MOD_ADLER
  return (b << 16) | a

