

'''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 		_A     (    A__      ):






 """simple docstring"""



 __lowercase  ,	__lowercase							  = image.size
 __lowercase  ,	__lowercase							  = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
 __lowercase							  = image.resize((w, h)     ,				resample=PIL_INTERPOLATION['''lanczos''']      )
 __lowercase							  = np.array(A__      ).astype(np.floataa      ) / 2_5_5.0
 __lowercase							  = image[None].transpose(0     ,				3     ,				1     ,				2      )
 __lowercase							  = torch.from_numpy(A__      )
 return 2.0 * image - 1.0






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




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






  @torch.no_grad()
  def __call__(      self   :     Dict					,lowercase__   :     Union[torch.Tensor, PIL.Image.Image] = None					,lowercase__   :     Optional[int] = 1					,lowercase__   :     Optional[int] = 1_0_0					,lowercase__   :     Optional[float] = 0.0					,lowercase__   :     Optional[Union[torch.Generator, List[torch.Generator]]] = None					,lowercase__   :     Optional[str] = "pil"					,lowercase__   :     bool = True					,):
   if isinstance(lowercase__					,PIL.Image.Image			):
    __lowercase							  = 1
   elif isinstance(lowercase__					,torch.Tensor			):
    __lowercase							  = image.shape[0]
   else:
    raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowercase__			)}"			)

   if isinstance(lowercase__					,PIL.Image.Image			):
    __lowercase							  = preprocess(lowercase__			)

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

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

   __lowercase							  = randn_tensor(lowercase__					,generator=lowercase__					,device=self.device					,dtype=lowercase__			)

   __lowercase							  = image.to(device=self.device					,dtype=lowercase__			)

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

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

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

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

   if output_type == "pil":
    __lowercase							  = self.numpy_to_pil(lowercase__			)

   if not return_dict:
    return (image,)

   return ImagePipelineOutput(images=lowercase__			)


"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowercase__					       =	{'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}


if TYPE_CHECKING:
 from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
 import sys

 lowercase__					       =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


