






"""simple docstring"""





import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowerCamelCase      =		logging.get_logger(__name__)  # pylint: disable=invalid-name
def      a__			(   lowerCAmelCase__   ):
      warnings.warn(
          "The preprocess method is deprecated and will be removed in a future version. Please"
          " use VaeImageProcessor.preprocess instead"			,    lowerCAmelCase__			,    )
      if isinstance(lowerCAmelCase__			,    torch.Tensor   ):
            return image
      elif isinstance(lowerCAmelCase__			,    PIL.Image.Image   ):
            UpperCAmelCase_    				=							[image]

      if isinstance(image[0]			,    PIL.Image.Image   ):
            UpperCAmelCase_				,					UpperCAmelCase_    				=							image[0].size
            UpperCAmelCase_				,					UpperCAmelCase_    				=							(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

            UpperCAmelCase_    				=							[np.array(i.resize((w, h)			,    resample=PIL_INTERPOLATION["lanczos"]   )   )[None, :] for i in image]
            UpperCAmelCase_    				=							np.concatenate(lowerCAmelCase__			,    axis=0   )
            UpperCAmelCase_    				=							np.array(lowerCAmelCase__   ).astype(np.floataa   ) / 255.0
            UpperCAmelCase_    				=							image.transpose(0			,    3			,    1			,    2   )
            UpperCAmelCase_    				=							2.0 * image - 1.0
            UpperCAmelCase_    				=							torch.from_numpy(lowerCAmelCase__   )
      elif isinstance(image[0]			,    torch.Tensor   ):
            UpperCAmelCase_    				=							torch.cat(lowerCAmelCase__			,    dim=0   )
      return image
def      a__			(   lowerCAmelCase__   ):

      if isinstance(lowerCAmelCase__			,    torch.Tensor   ):
            return mask
      elif isinstance(lowerCAmelCase__			,    PIL.Image.Image   ):
            UpperCAmelCase_    				=							[mask]

      if isinstance(mask[0]			,    PIL.Image.Image   ):
            UpperCAmelCase_				,					UpperCAmelCase_    				=							mask[0].size
            UpperCAmelCase_				,					UpperCAmelCase_    				=							(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
            UpperCAmelCase_    				=							[np.array(m.convert("L"   ).resize((w, h)			,    resample=PIL_INTERPOLATION["nearest"]   )   )[None, :] for m in mask]
            UpperCAmelCase_    				=							np.concatenate(lowerCAmelCase__			,    axis=0   )
            UpperCAmelCase_    				=							mask.astype(np.floataa   ) / 255.0
            UpperCAmelCase_    				=							0
            UpperCAmelCase_    				=							1
            UpperCAmelCase_    				=							torch.from_numpy(lowerCAmelCase__   )
      elif isinstance(mask[0]			,    torch.Tensor   ):
            UpperCAmelCase_    				=							torch.cat(lowerCAmelCase__			,    dim=0   )
      return mask


class    lowercase__     (					_a   ):







       '''simple docstring'''


       UpperCamelCase		=  42
       UpperCamelCase		=  42



       def __init__(							self    :						Optional[int]	,       _UpperCAmelCase    :						Tuple	,       _UpperCAmelCase    :						Optional[int]					)				->		Union[str, Any]:


             '''simple docstring'''




             super().__init__()
             self.register_modules(unet=snake_case_	,       scheduler=snake_case_					)







       @torch.no_grad()
       def __call__(							self    :						Union[str, Any]	,       _UpperCAmelCase    :						int	,       _UpperCAmelCase    :						List[Any]	,       _UpperCAmelCase    :						Tuple = 250	,       _UpperCAmelCase    :						Dict = 0.0	,       _UpperCAmelCase    :						List[str] = 10	,       _UpperCAmelCase    :						Optional[int] = 10	,       _UpperCAmelCase    :						Dict = None	,       _UpperCAmelCase    :						Union[str, Any] = "pil"	,       _UpperCAmelCase    :						List[str] = True	,       )				->		Union[ImagePipelineOutput, Tuple]:


             '''simple docstring'''




             UpperCAmelCase_    				=							image

             UpperCAmelCase_    				=							_preprocess_image(snake_case_					)
             UpperCAmelCase_    				=							original_image.to(device=self.device	,       dtype=self.unet.dtype					)
             UpperCAmelCase_    				=							_preprocess_mask(snake_case_					)
             UpperCAmelCase_    				=							mask_image.to(device=self.device	,       dtype=self.unet.dtype					)

             UpperCAmelCase_    				=							original_image.shape[0]

             # sample gaussian noise to begin the loop
             if isinstance(snake_case_	,       snake_case_					) and len(snake_case_					) != batch_size:
                   raise ValueError(
                       F"""You have passed a list of generators of length {len(snake_case_					)}, but requested an effective batch"""
                       F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""					)

             UpperCAmelCase_    				=							original_image.shape
             UpperCAmelCase_    				=							randn_tensor(snake_case_	,       generator=snake_case_	,       device=self.device	,       dtype=self.unet.dtype					)

             # set step values
             self.scheduler.set_timesteps(snake_case_	,       snake_case_	,       snake_case_	,       self.device					)
             UpperCAmelCase_    				=							eta

             UpperCAmelCase_    				=							self.scheduler.timesteps[0] + 1
             UpperCAmelCase_    				=							generator[0] if isinstance(snake_case_	,       snake_case_					) else generator
             for i, t in enumerate(self.progress_bar(self.scheduler.timesteps					)					):
                   if t < t_last:
                         # predict the noise residual
                         UpperCAmelCase_    				=							self.unet(snake_case_	,       snake_case_					).sample
                         # compute previous image: x_t -> x_t-1
                         UpperCAmelCase_    				=							self.scheduler.step(snake_case_	,       snake_case_	,       snake_case_	,       snake_case_	,       snake_case_	,       snake_case_					).prev_sample

                   else:
                         # compute the reverse: x_t-1 -> x_t
                         UpperCAmelCase_    				=							self.scheduler.undo_step(snake_case_	,       snake_case_	,       snake_case_					)
                   UpperCAmelCase_    				=							t

             UpperCAmelCase_    				=							(image / 2 + 0.5).clamp(0	,       1					)
             UpperCAmelCase_    				=							image.cpu().permute(0	,       2	,       3	,       1					).numpy()
             if output_type == "pil":
                   UpperCAmelCase_    				=							self.numpy_to_pil(snake_case_					)

             if not return_dict:
                   return (image,)

             return ImagePipelineOutput(images=snake_case_					)









"""simple docstring"""





import colorsys

from PIL import Image  # type: ignore
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							x
      UpperCAmelCase_    				=							y
      for step in range(lowerCAmelCase__   ):  # noqa: B007
            UpperCAmelCase_    				=							a * a - b * b + x
            UpperCAmelCase_    				=							2 * a * b + y
            UpperCAmelCase_    				=							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__   ):
      if distance == 1:
            return (0, 0, 0)
      else:
            return (255, 255, 255)
def      a__			(   lowerCAmelCase__   ):
      if distance == 1:
            return (0, 0, 0)
      else:
            return tuple(round(i * 255   ) for i in colorsys.hsv_to_rgb(lowerCAmelCase__			,    1			,    1   )   )
def      a__			(   lowerCAmelCase__ = 800			,    lowerCAmelCase__ = 600			,    lowerCAmelCase__ = -0.6			,    lowerCAmelCase__ = 0			,    lowerCAmelCase__ = 3.2			,    lowerCAmelCase__ = 50			,    lowerCAmelCase__ = True			,    ):
      UpperCAmelCase_    				=							Image.new("RGB"			,    (image_width, image_height)   )
      UpperCAmelCase_    				=							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
                  UpperCAmelCase_    				=							figure_width / image_width * image_height
                  UpperCAmelCase_    				=							figure_center_x + (image_x / image_width - 0.5) * figure_width
                  UpperCAmelCase_    				=							figure_center_y + (image_y / image_height - 0.5) * figure_height

                  UpperCAmelCase_    				=							get_distance(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )

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

      return img


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # colored version, full figure
  lowerCamelCase      =		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()




