


'''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			(lowerCAmelCase__					:int						)				->					str:




     '''simple docstring'''



     _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(lowerCAmelCase__						).astype(np.floataa						) / 2_5_5.0
     _a      =							image[None].transpose(0			,  3			,  1			,  2						)
     _a      =							torch.from_numpy(lowerCAmelCase__						)
     return 2.0 * image - 1.0






class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			)   ->							List[Any]:
      super().__init__()
      self.register_modules(vqvae=__magic_name__ ,			unet=__magic_name__ ,			scheduler=__magic_name__  )




 @torch.no_grad()
 def __call__(	self ,			__magic_name__ = None ,			__magic_name__ = 1 ,			__magic_name__ = 1_00 ,			__magic_name__ = 0.0 ,			__magic_name__ = None ,			__magic_name__ = "pil" ,			__magic_name__ = True ,			)   ->							Union[Tuple, ImagePipelineOutput]:
      if isinstance(__magic_name__ ,			PIL.Image.Image  ):
           _a      =							1
      elif isinstance(__magic_name__ ,			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(__magic_name__  )}'  )

      if isinstance(__magic_name__ ,			PIL.Image.Image  ):
           _a      =							preprocess(__magic_name__  )

      _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(__magic_name__ ,			generator=__magic_name__ ,			device=self.device ,			dtype=__magic_name__  )

      _a      =							image.to(device=self.device ,			dtype=__magic_name__  )

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

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

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=__magic_name__  )





'''simple docstring'''






import colorsys

from PIL import Image  # type: ignore






def 		_A			(lowerCAmelCase__					:float			,  lowerCAmelCase__					:float			,  lowerCAmelCase__					:int						)				->					float:




     '''simple docstring'''



     _a      =							x
     _a      =							y
     for step in range(lowerCAmelCase__						):  # noqa: B007
          _a      =							a * a - b * b + x
          _a      =							2 * a * b + y
          _a      =							a_new

          # divergence happens for all complex number with an absolute value
          # greater than 4
          if a * a + b * b > 4:
               break
     return step / (max_step - 1)






def 		_A			(lowerCAmelCase__					:float						)				->					tuple:




     '''simple docstring'''



     if distance == 1:
          return (0, 0, 0)
     else:
          return (2_55, 2_55, 2_55)






def 		_A			(lowerCAmelCase__					:float						)				->					tuple:




     '''simple docstring'''



     if distance == 1:
          return (0, 0, 0)
     else:
          return tuple(round(i * 2_55						) for i in colorsys.hsv_to_rgb(lowerCAmelCase__			,  1			,  1						)						)






def 		_A			(lowerCAmelCase__					:int = 8_00			,  lowerCAmelCase__					:int = 6_00			,  lowerCAmelCase__					:float = -0.6			,  lowerCAmelCase__					:float = 0			,  lowerCAmelCase__					:float = 3.2			,  lowerCAmelCase__					:int = 50			,  lowerCAmelCase__					:bool = True			,  )				->					Image.Image:




     '''simple docstring'''



     _a      =							Image.new('RGB'			,  (image_width, image_height)						)
     _a      =							img.load()

     # loop through the image-coordinates
     for image_x in range(lowerCAmelCase__						):
          for image_y in range(lowerCAmelCase__						):
               # determine the figure-coordinates based on the image-coordinates
               _a      =							figure_width / image_width * image_height
               _a      =							figure_center_x + (image_x / image_width - 0.5) * figure_width
               _a      =							figure_center_y + (image_y / image_height - 0.5) * figure_height

               _a      =							get_distance(lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__						)

               # color the corresponding pixel based on the selected coloring-function
               if use_distance_color_coding:
                    _a      =							get_color_coded_rgb(lowerCAmelCase__						)
               else:
                    _a      =							get_black_and_white_rgb(lowerCAmelCase__						)

     return img


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # colored version, full figure
 a_   :      Optional[Any]				    =      get_image()

 # uncomment for colored version, different section, zoomed in
 # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
 # figure_width = 0.8)

 # uncomment for black and white version, full figure
 # img = get_image(use_distance_color_coding = False)

 # uncomment to save the image
 # img.save("mandelbrot.png")

 img.show()

