


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_						:		Dict						):




      """simple docstring"""





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







class 		_snake_case  (      _snake_case							):




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




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

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

       a							,				a						:Optional[int] 							=       image.shape[-2:]

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

       a						:Tuple 							=       randn_tensor(_lowerCamelCase					,  generator=_lowerCamelCase					,  device=self.device					,  dtype=_lowerCamelCase    )

       a						:Optional[Any] 							=       image.to(device=self.device					,  dtype=_lowerCamelCase    )

       # set timesteps and move to the correct device
       self.scheduler.set_timesteps(_lowerCamelCase					,  device=self.device    )
       a						:Optional[int] 							=       self.scheduler.timesteps

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

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

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

       if output_type == "pil":
             a						:int 							=       self.numpy_to_pil(_lowerCamelCase    )

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_lowerCamelCase    )










"""simple docstring"""
A:   Union[str, Any]								=		{
    0: "0",
    1: "1",
    2: "2",
    3: "3",
    4: "4",
    5: "5",
    6: "6",
    7: "7",
    8: "8",
    9: "9",
    1_0: "a",
    1_1: "b",
    1_2: "c",
    1_3: "d",
    1_4: "e",
    1_5: "f",
}

def       _snake_case						(						UpperCamelCase						:		float						):
 assert type(UpperCamelCase						) in (int, float) and decimal == int(UpperCamelCase						)
 UpperCAmelCase	:					str	=	int(UpperCamelCase						)
 UpperCAmelCase	:					Optional[int]	=	""""""
 UpperCAmelCase	:					List[str]	=	False
 if decimal < 0:
  UpperCAmelCase	:					Any	=	True
  decimal *= -1
 while decimal > 0:
  UpperCAmelCase	,		UpperCAmelCase	:					Dict	=	divmod(UpperCamelCase			,		16						)
  UpperCAmelCase	:					Union[str, Any]	=	values[remainder] + hexadecimal
 UpperCAmelCase	:					int	=	"""0x""" + hexadecimal
 if negative:
  UpperCAmelCase	:					Optional[int]	=	"""-""" + hexadecimal
 return hexadecimal


if __name__ == "__main__":
      import doctest

      doctest.testmod()
