






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   _A   (				SCREAMING_SNAKE_CASE__  :	int       ):
    UpperCamelCase       :List[str]								=   image.size
    UpperCamelCase       :Optional[Any]								=   (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    UpperCamelCase       :str								=   image.resize((w, h)     ,  resample=PIL_INTERPOLATION['''lanczos''']       )
    UpperCamelCase       :List[str]								=   np.array(UpperCamelCase__       ).astype(np.floataa       ) / 2_55.0
    UpperCamelCase       :Dict								=   image[None].transpose(0     ,  3     ,  1     ,  2       )
    UpperCamelCase       :List[Any]								=   torch.from_numpy(UpperCamelCase__       )
    return 2.0 * image - 1.0







class 			UpperCAmelCase_						(					snake_case_ ):



      """simple docstring"""
      def __init__(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				)				->      str:
          super().__init__()
          self.register_modules(vqvae=UpperCAmelCase__    ,				unet=UpperCAmelCase__    ,				scheduler=UpperCAmelCase__ )




      @torch.no_grad()
      def __call__(				self    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = 1    ,				SCREAMING_SNAKE_CASE_ = 100    ,				SCREAMING_SNAKE_CASE_ = 0.0    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = "pil"    ,				SCREAMING_SNAKE_CASE_ = True    ,				)				->      Union[Tuple, ImagePipelineOutput]:
          if isinstance(UpperCAmelCase__    ,				PIL.Image.Image ):
              UpperCamelCase       :str								=   1
          elif isinstance(UpperCAmelCase__    ,				torch.Tensor ):
              UpperCamelCase       :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 ):
              UpperCamelCase       :List[Any]								=   preprocess(UpperCAmelCase__ )

          UpperCamelCase       :Tuple								=   image.shape[-2:]

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

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

          UpperCamelCase       :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 )
          UpperCamelCase       :int								=   self.scheduler.timesteps

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

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

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

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

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=UpperCAmelCase__ )













"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_snake_case          =   logging.get_logger(__name__)






class        UpperCamelCase			(	snake_case_      ):


 def __init__(     self		:						Any							,						*UpperCAmelCase__		:						Optional[Any]							,						**UpperCAmelCase__		:						Tuple							)					->       None:
   warnings.warn(
       """The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
       """ use CLIPImageProcessor instead."""							,						UpperCAmelCase__							,						)
   super().__init__(*UpperCAmelCase__							,						**UpperCAmelCase__							)

