




import colorsys

from PIL import Image  # type: ignore







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Union[str, Any]  =     x
     UpperCAmelCase_     :				List[str]  =     y
     for step in range(_lowercase ):  # noqa: B007
          UpperCAmelCase_     :				Union[str, Any]  =     a * a - b * b + x
          UpperCAmelCase_     :				List[Any]  =     2 * a * b + y
          UpperCAmelCase_     :				List[Any]  =     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 lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if distance == 1:
          return (0, 0, 0)
     else:
          return (255, 255, 255)







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if distance == 1:
          return (0, 0, 0)
     else:
          return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(_lowercase					,		1					,		1 ) )







def lowerCamelCase__  (     _lowercase = 800					,		_lowercase = 600					,		_lowercase = -0.6					,		_lowercase = 0					,		_lowercase = 3.2					,		_lowercase = 50					,		_lowercase = True					,		):

     '''simple docstring'''


     UpperCAmelCase_     :				int  =     Image.new('''RGB'''					,		(image_width, image_height) )
     UpperCAmelCase_     :				List[str]  =     img.load()

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

               UpperCAmelCase_     :				int  =     get_distance(_lowercase					,		_lowercase					,		_lowercase )

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

     return img


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # colored version, full figure
     __a							     =					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()



'''simple docstring'''
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		__snake_case				 =	argparse.ArgumentParser()

		parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

		parser.add_argument(
		    '''--txt2img_unclip''',
		    default='''kakaobrain/karlo-v1-alpha''',
		    type=str,
		    required=False,
		    help='''The pretrained txt2img unclip.''',
		)

		__snake_case				 =	parser.parse_args()

		__snake_case				 =	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		__snake_case				 =	CLIPImageProcessor()
		__snake_case				 =	CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

		__snake_case				 =	UnCLIPImageVariationPipeline(
		    decoder=txtaimg.decoder,
		    text_encoder=txtaimg.text_encoder,
		    tokenizer=txtaimg.tokenizer,
		    text_proj=txtaimg.text_proj,
		    feature_extractor=feature_extractor,
		    image_encoder=image_encoder,
		    super_res_first=txtaimg.super_res_first,
		    super_res_last=txtaimg.super_res_last,
		    decoder_scheduler=txtaimg.decoder_scheduler,
		    super_res_scheduler=txtaimg.super_res_scheduler,
		)

		imgaimg.save_pretrained(args.dump_path)