
import math
from typing import Callable, List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler




def    __UpperCamelCase     (				lowerCAmelCase__							:				Any  ,   lowerCAmelCase__							:				List[str]  ,   lowerCAmelCase__							:				Optional[Any]=[]  ):
 __a :  Optional[Any]						=  size[0] - overlap_pixels * 2
 __a :  int						=  size[1] - overlap_pixels * 2
 for letter in ["l", "r"]:
  if letter in remove_borders:
   size_x += overlap_pixels
 for letter in ["t", "b"]:
  if letter in remove_borders:
   size_y += overlap_pixels
 __a :  Dict						=  np.ones((size_y, size_x)  ,   dtype=np.uinta  ) * 2_5_5
 __a :  Tuple						=  np.pad(lowerCAmelCase__  ,   mode='''linear_ramp'''  ,   pad_width=lowerCAmelCase__  ,   end_values=0  )

 if "l" in remove_borders:
  __a :  Dict						=  mask[:, overlap_pixels : mask.shape[1]]
 if "r" in remove_borders:
  __a :  Union[str, Any]						=  mask[:, 0 : mask.shape[1] - overlap_pixels]
 if "t" in remove_borders:
  __a :  Tuple						=  mask[overlap_pixels : mask.shape[0], :]
 if "b" in remove_borders:
  __a :  int						=  mask[0 : mask.shape[0] - overlap_pixels, :]
 return mask




def    __UpperCamelCase     (				lowerCAmelCase__							:				Tuple  ,   lowerCAmelCase__							:				List[Any]  ,   lowerCAmelCase__							:				Tuple  ):
 return max(lowerCAmelCase__  ,   min(lowerCAmelCase__  ,   lowerCAmelCase__  )  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				[int]  ,   lowerCAmelCase__							:				[int]  ,   lowerCAmelCase__							:				[int]  ):
 return (
     clamp(rect[0]  ,   min[0]  ,   max[0]  ),
     clamp(rect[1]  ,   min[1]  ,   max[1]  ),
     clamp(rect[2]  ,   min[0]  ,   max[0]  ),
     clamp(rect[3]  ,   min[1]  ,   max[1]  ),
 )




def    __UpperCamelCase     (				lowerCAmelCase__							:				[int]  ,   lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				[int]  ):
 __a :  Optional[int]						=  list(lowerCAmelCase__  )
 rect[0] -= overlap
 rect[1] -= overlap
 rect[2] += overlap
 rect[3] += overlap
 __a :  Any						=  clamp_rect(lowerCAmelCase__  ,   [0, 0]  ,   [image_size[0], image_size[1]]  )
 return rect




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				Union[str, Any]  ,   lowerCAmelCase__							:				Union[str, Any]  ,   lowerCAmelCase__							:				Tuple  ):
 __a :  List[str]						=  Image.new('''RGB'''  ,   (tile.size[0] + original_slice, tile.size[1])  )
 result.paste(
     original_image.resize((tile.size[0], tile.size[1])  ,   Image.BICUBIC  ).crop(
         (slice_x, 0, slice_x + original_slice, tile.size[1])  )  ,   (0, 0)  ,   )
 result.paste(lowerCAmelCase__  ,   (original_slice, 0)  )
 return result




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				Union[str, Any]  ):
 __a :  Tuple						=  (original_image_slice * 4, 0, tile.size[0], tile.size[1])
 __a :  Dict						=  tile.crop(lowerCAmelCase__  )
 return tile




def    __UpperCamelCase     (				lowerCAmelCase__							:				Tuple  ,   lowerCAmelCase__							:				Optional[Any]  ):
 __a :  List[str]						=  n % d
 return n - divisor



class 		UpperCamelCase__  (       __lowercase    ):
   def __init__(self :					str					,				snake_case_ :					AutoencoderKL					,				snake_case_ :					CLIPTextModel					,				snake_case_ :					CLIPTokenizer					,				snake_case_ :					UNetaDConditionModel					,				snake_case_ :					DDPMScheduler					,				snake_case_ :					Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					,				snake_case_ :					int = 3_5_0					,				):
    super().__init__(
        vae=snake_case_					,				text_encoder=snake_case_					,				tokenizer=snake_case_					,				unet=snake_case_					,				low_res_scheduler=snake_case_					,				scheduler=snake_case_					,				max_noise_level=snake_case_					,				)
   def       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					int					,				snake_case_ :					List[Any]					,				snake_case_ :					int					,				snake_case_ :					Union[str, Any]					,				snake_case_ :					Any					,				snake_case_ :					List[str]					,				snake_case_ :					List[str]					,				**snake_case_ :					Dict   ):
    torch.manual_seed(0   )
    __a :  str						=  (
        min(image.size[0] - (tile_size + original_image_slice)					,				x * tile_size   ),
        min(image.size[1] - (tile_size + original_image_slice)					,				y * tile_size   ),
        min(image.size[0]					,				(x + 1) * tile_size   ),
        min(image.size[1]					,				(y + 1) * tile_size   ),
    )
    __a :  Dict						=  add_overlap_rect(snake_case_					,				snake_case_					,				image.size   )
    __a :  Dict						=  image.crop(snake_case_   )
    __a :  Any						=  ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
    __a :  Union[str, Any]						=  translated_slice_x - (original_image_slice / 2)
    __a :  int						=  max(0					,				snake_case_   )
    __a :  Dict						=  squeeze_tile(snake_case_					,				snake_case_					,				snake_case_					,				snake_case_   )
    __a :  Union[str, Any]						=  to_input.size
    __a :  Optional[int]						=  to_input.resize((tile_size, tile_size)					,				Image.BICUBIC   )
    __a :  str						=  super(snake_case_					,				self   ).__call__(image=snake_case_					,				**snake_case_   ).images[0]
    __a :  List[Any]						=  upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)					,				Image.BICUBIC   )
    __a :  Optional[int]						=  unsqueeze_tile(snake_case_					,				snake_case_   )
    __a :  str						=  upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)					,				Image.BICUBIC   )
    __a :  Tuple						=  []
    if x == 0:
     remove_borders.append('''l'''   )
    elif crop_rect[2] == image.size[0]:
     remove_borders.append('''r'''   )
    if y == 0:
     remove_borders.append('''t'''   )
    elif crop_rect[3] == image.size[1]:
     remove_borders.append('''b'''   )
    __a :  Dict						=  Image.fromarray(
        make_transparency_mask(
            (upscaled_tile.size[0], upscaled_tile.size[1])					,				tile_border * 4					,				remove_borders=snake_case_   )					,				mode='''L'''					,				)
    final_image.paste(
        snake_case_					,				(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)					,				snake_case_   )







   @torch.no_grad()
   def __call__(self :					Tuple					,				snake_case_ :					Union[str, List[str]]					,				snake_case_ :					Union[PIL.Image.Image, List[PIL.Image.Image]]					,				snake_case_ :					int = 7_5					,				snake_case_ :					float = 9.0					,				snake_case_ :					int = 5_0					,				snake_case_ :					Optional[Union[str, List[str]]] = None					,				snake_case_ :					Optional[int] = 1					,				snake_case_ :					float = 0.0					,				snake_case_ :					Optional[torch.Generator] = None					,				snake_case_ :					Optional[torch.FloatTensor] = None					,				snake_case_ :					Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,				snake_case_ :					int = 1					,				snake_case_ :					int = 1_2_8					,				snake_case_ :					int = 3_2					,				snake_case_ :					int = 3_2					,				):
    __a :  Any						=  Image.new('''RGB'''					,				(image.size[0] * 4, image.size[1] * 4)   )
    __a :  str						=  math.ceil(image.size[0] / tile_size   )
    __a :  Dict						=  math.ceil(image.size[1] / tile_size   )
    __a :  List[Any]						=  tcx * tcy
    __a :  List[str]						=  0
    for y in range(snake_case_   ):
     for x in range(snake_case_   ):
      self._process_tile(
          snake_case_					,				snake_case_					,				snake_case_					,				snake_case_					,				snake_case_					,				snake_case_					,				snake_case_					,				prompt=snake_case_					,				num_inference_steps=snake_case_					,				guidance_scale=snake_case_					,				noise_level=snake_case_					,				negative_prompt=snake_case_					,				num_images_per_prompt=snake_case_					,				eta=snake_case_					,				generator=snake_case_					,				latents=snake_case_					,				)
      current_count += 1
      if callback is not None:
       callback({'''progress''': current_count / total_tile_count, '''image''': final_image}   )
    return final_image





def    __UpperCamelCase     (				):
 # Run a demo
 __a :  List[Any]						=  '''stabilityai/stable-diffusion-x4-upscaler'''
 __a :  Dict						=  StableDiffusionTiledUpscalePipeline.from_pretrained(lowerCAmelCase__  ,   revision='''fp16'''  ,   torch_dtype=torch.floataa  )
 __a :  List[Any]						=  pipe.to('''cuda'''  )
 __a :  Union[str, Any]						=  Image.open('''../../docs/source/imgs/diffusers_library.jpg'''  )

 def callback(lowerCAmelCase__							:				List[Any]  ):
  print(f"progress: {obj['progress']:.4f}"  )
  obj["image"].save('''diffusers_library_progress.jpg'''  )

 __a :  str						=  pipe(image=lowerCAmelCase__  ,   prompt='''Black font, white background, vector'''  ,   noise_level=4_0  ,   callback=lowerCAmelCase__  )
 final_image.save('''diffusers_library.jpg'''  )


if __name__ == "__main__":
      main()


import colorsys

from PIL import Image  # type: ignore




def    __UpperCamelCase     (				lowerCAmelCase__							:				float  ,   lowerCAmelCase__							:				float  ,   lowerCAmelCase__							:				int  ):
 __a :  Any						=  x
 __a :  List[Any]						=  y
 for step in range(lowerCAmelCase__  ):  # noqa: B007
  __a :  List[Any]						=  a * a - b * b + x
  __a :  Tuple						=  2 * a * b + y
  __a :  Optional[int]						=  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    __UpperCamelCase     (				lowerCAmelCase__							:				float  ):
 if distance == 1:
  return (0, 0, 0)
 else:
  return (2_5_5, 2_5_5, 2_5_5)




def    __UpperCamelCase     (				lowerCAmelCase__							:				float  ):
 if distance == 1:
  return (0, 0, 0)
 else:
  return tuple(round(i * 2_5_5  ) for i in colorsys.hsv_to_rgb(lowerCAmelCase__  ,   1  ,   1  )  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				int = 8_0_0  ,   lowerCAmelCase__							:				int = 6_0_0  ,   lowerCAmelCase__							:				float = -0.6  ,   lowerCAmelCase__							:				float = 0  ,   lowerCAmelCase__							:				float = 3.2  ,   lowerCAmelCase__							:				int = 5_0  ,   lowerCAmelCase__							:				bool = True  ,   ):
 __a :  int						=  Image.new('''RGB'''  ,   (image_width, image_height)  )
 __a :  Dict						=  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 :  Optional[Any]						=  figure_width / image_width * image_height
   __a :  str						=  figure_center_x + (image_x / image_width - 0.5) * figure_width
   __a :  str						=  figure_center_y + (image_y / image_height - 0.5) * figure_height

   __a :  Tuple						=  get_distance(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )

   # color the corresponding pixel based on the selected coloring-function
   if use_distance_color_coding:
    __a :  Optional[int]						=  get_color_coded_rgb(lowerCAmelCase__  )
   else:
    __a :  Optional[Any]						=  get_black_and_white_rgb(lowerCAmelCase__  )

 return img


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      # colored version, full figure
      lowercase__       =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()
