






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 :List[str] )							->			str:
 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(a ).astype(np.floataa ) / 255.0
 a		  =      image[None].transpose(0	,							3	,							1	,							2 )
 a		  =      torch.from_numpy(a )
 return 2.0 * image - 1.0






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def __init__(      self      :						Optional[Any] ,					__UpperCAmelCase      :						VQModel ,					__UpperCAmelCase      :						UNetaDModel ,					__UpperCAmelCase      :						Union[
              DDIMScheduler,
              PNDMScheduler,
              LMSDiscreteScheduler,
              EulerDiscreteScheduler,
              EulerAncestralDiscreteScheduler,
              DPMSolverMultistepScheduler,
          ] ,					)    ->Dict:




       """simple docstring"""


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







      @torch.no_grad()
      def __call__(      self      :						str ,					__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]:




       """simple docstring"""


       if isinstance(__UpperCAmelCase ,					PIL.Image.Image  ):
        a		  =      1
       elif isinstance(__UpperCAmelCase ,					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(__UpperCAmelCase  )}"""  )

       if isinstance(__UpperCAmelCase ,					PIL.Image.Image  ):
        a		  =      preprocess(__UpperCAmelCase  )

       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(__UpperCAmelCase ,					generator=__UpperCAmelCase ,					device=self.device ,					dtype=__UpperCAmelCase  )

       a		  =      image.to(device=self.device ,					dtype=__UpperCAmelCase  )

       # set timesteps and move to the correct device
       self.scheduler.set_timesteps(__UpperCAmelCase ,					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(__UpperCAmelCase  ):
        # concat latents and low resolution image in the channel dimension.
        a		  =      torch.cat([latents, image] ,					dim=1  )
        a		  =      self.scheduler.scale_model_input(__UpperCAmelCase ,					__UpperCAmelCase  )
        # predict the noise residual
        a		  =      self.unet(__UpperCAmelCase ,					__UpperCAmelCase  ).sample
        # compute the previous noisy sample x_t -> x_t-1
        a		  =      self.scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase  ).prev_sample

       # decode the image latents with the VQVAE
       a		  =      self.vqvae.decode(__UpperCAmelCase  ).sample
       a		  =      torch.clamp(__UpperCAmelCase ,					-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(__UpperCAmelCase  )

       if not return_dict:
        return (image,)

       return ImagePipelineOutput(images=__UpperCAmelCase  )










def 						_a    (      a :int )							->			bool:
 a		  =      n ** (1 / 3)
 return (val * val * val) == n


if __name__ == "__main__":
     print(perfect_cube(27))
     print(perfect_cube(4))


