
"""simple docstring"""






import colorsys

from PIL import Image  # type: ignore



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       x
			__lowercase       =       y
			for step in range(__magic_name__      ):  # noqa: B007
						__lowercase       =       a * a - b * b + x
						__lowercase       =       2 * a * b + y
						__lowercase       =       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__			(		__magic_name__      )						->tuple:
			if distance == 1:
						return (0, 0, 0)
			else:
						return (2_5_5, 2_5_5, 2_5_5)



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



def      lowerCAmelCase__			(		__magic_name__ = 8_0_0			,   __magic_name__ = 6_0_0			,   __magic_name__ = -0.6			,   __magic_name__ = 0			,   __magic_name__ = 3.2			,   __magic_name__ = 5_0			,   __magic_name__ = True			,   )						->Image.Image:
			__lowercase       =       Image.new("RGB"			,   (image_width, image_height)      )
			__lowercase       =       img.load()

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

									__lowercase       =       get_distance(__magic_name__			,   __magic_name__			,   __magic_name__      )

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

			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()







"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase							=				{
    '''configuration_mask2former''': [
        '''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Mask2FormerConfig''',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				['''Mask2FormerImageProcessor''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''Mask2FormerForUniversalSegmentation''',
					    '''Mask2FormerModel''',
					    '''Mask2FormerPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					_lowercase							=				_LazyModule(__name__, globals()['''__file__'''], _import_structure)





