




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       _SCREAMING_SNAKE_CASE      (							lowercase     :	int					):


      '''simple docstring'''



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







class   A(						UpperCamelCase       ):



 '''simple docstring'''


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

       """simple docstring"""






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





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

       """simple docstring"""






       if isinstance(A_						,							PIL.Image.Image  ):
             lowerCamelCase_							=   1
       elif isinstance(A_						,							torch.Tensor  ):
             lowerCamelCase_							=   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  ):
             lowerCamelCase_							=   preprocess(A_  )

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

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

       lowerCamelCase_							=   randn_tensor(A_						,							generator=A_						,							device=self.device						,							dtype=A_  )

       lowerCamelCase_							=   image.to(device=self.device						,							dtype=A_  )

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

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

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

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

       if output_type == "pil":
             lowerCamelCase_							=   self.numpy_to_pil(A_  )

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=A_  )









import math

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float     ,							lowercase     :	float					):


      '''simple docstring'''



      return math.pow(lowercase     ,							2					) - a

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float					):


      '''simple docstring'''



      return 2 * x

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float					):


      '''simple docstring'''



      lowerCamelCase_							=   2.0

      while start <= a:
            lowerCamelCase_							=   math.pow(lowercase     ,							2					)

      return start

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float     ,							lowercase     :	int = 99_99     ,							lowercase     :	float = 0.00_0000_0000_0001					):


      '''simple docstring'''



      if a < 0:
            raise ValueError('math domain error'					)

      lowerCamelCase_							=   get_initial_point(lowercase					)

      for _ in range(lowercase					):
            lowerCamelCase_							=   value
            lowerCamelCase_							=   value - fx(lowercase     ,							lowercase					) / fx_derivative(lowercase					)
            if abs(prev_value - value					) < tolerance:
                  return value

      return value


if __name__ == "__main__":
 from doctest import testmod

 testmod()



