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

class  lowerCAmelCase__							(		UpperCamelCase_      ):







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







     @torch.no_grad()
     def __call__(				self     :     int       , _lowerCamelCase     :     Union[torch.Tensor, PIL.Image.Image] = None       , _lowerCamelCase     :     Optional[int] = 1       , _lowerCamelCase     :     Optional[int] = 100       , _lowerCamelCase     :     Optional[float] = 0.0       , _lowerCamelCase     :     Optional[Union[torch.Generator, List[torch.Generator]]] = None       , _lowerCamelCase     :     Optional[str] = "pil"       , _lowerCamelCase     :     bool = True       , ):

       if isinstance(a__       , PIL.Image.Image     ):
         _snake_case   	=			1
       elif isinstance(a__       , torch.Tensor     ):
         _snake_case   	=			image.shape[0]
       else:
         raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(a__     )}'''     )

       if isinstance(a__       , PIL.Image.Image     ):
         _snake_case   	=			preprocess(a__     )

       _snake_case   	=			image.shape[-2:]

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

       _snake_case   	=			randn_tensor(a__       , generator=a__       , device=self.device       , dtype=a__     )

       _snake_case   	=			image.to(device=self.device       , dtype=a__     )

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

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

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

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

       if output_type == "pil":
         _snake_case   	=			self.numpy_to_pil(a__     )

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=a__     )





'''simple docstring'''

def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   str     		=	len(lowerCamelCase_			)
 lowerCAmelCase__     :   Optional[Any]     		=	len(matrix[0]			)
 lowerCAmelCase__     :   Any     		=	min(lowerCamelCase_      ,			lowerCamelCase_			)

 for row in range(lowerCamelCase_			):
  # Check if diagonal element is not zero
  if matrix[row][row] != 0:
   # Eliminate all the elements below the diagonal
   for col in range(row + 1      ,			lowerCamelCase_			):
    lowerCAmelCase__     :   Tuple     		=	matrix[col][row] / matrix[row][row]
    for i in range(lowerCamelCase_      ,			lowerCamelCase_			):
     matrix[col][i] -= multiplier * matrix[row][i]
  else:
   # Find a non-zero diagonal element to swap rows
   lowerCAmelCase__     :   Dict     		=	True
   for i in range(row + 1      ,			lowerCamelCase_			):
    if matrix[i][row] != 0:
     lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[Any]     		=	matrix[i], matrix[row]
     lowerCAmelCase__     :   Optional[Any]     		=	False
     break
   if reduce:
    rank -= 1
    for i in range(lowerCamelCase_			):
     lowerCAmelCase__     :   Union[str, Any]     		=	matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
   row -= 1

 return rank


if __name__ == "__main__":
     import doctest

     doctest.testmod()





