
import colorsys

from PIL import Image  # type: ignore


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  float:
				__a										=    x
				__a										=    y
				for step in range(a__  ):  # 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     __lowerCAmelCase			(			a__  )       ->  tuple:
				if distance == 1:
								return (0, 0, 0)
				else:
								return (255, 255, 255)


def     __lowerCAmelCase			(			a__  )       ->  tuple:
				if distance == 1:
								return (0, 0, 0)
				else:
								return tuple(round(i * 255  ) for i in colorsys.hsv_to_rgb(a__						,    1						,    1  )  )


def     __lowerCAmelCase			(			a__ = 800						,    a__ = 600						,    a__ = -0.6						,    a__ = 0						,    a__ = 3.2						,    a__ = 50						,    a__ = True						,    )       ->  Image.Image:
				__a										=    Image.new('''RGB'''						,    (image_width, image_height)  )
				__a										=    img.load()

				# loop through the image-coordinates
				for image_x in range(a__  ):
								for image_y in range(a__  ):
												# 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(a__						,    a__						,    a__  )

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

				return img


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					# colored version, full figure
					A     :	Tuple    = 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()

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A     :	List[str]    = logging.get_logger(__name__)

A     :	Optional[int]    = {
    'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class 			__A( a			):
				snake_case_						   =    '''levit'''




				def __init__(     self		,     _snake_case=224		,     _snake_case=3		,     _snake_case=3		,     _snake_case=2		,     _snake_case=1		,     _snake_case=16		,     _snake_case=[128, 256, 384]		,     _snake_case=[4, 8, 12]		,     _snake_case=[4, 4, 4]		,     _snake_case=[16, 16, 16]		,     _snake_case=0		,     _snake_case=[2, 2, 2]		,     _snake_case=[2, 2, 2]		,     _snake_case=0.02		,     **_snake_case		,     )    ->      Optional[Any]:


								'''simple docstring'''




								super().__init__(**_snake_case				)
								__a										=    image_size
								__a										=    num_channels
								__a										=    kernel_size
								__a										=    stride
								__a										=    padding
								__a										=    hidden_sizes
								__a										=    num_attention_heads
								__a										=    depths
								__a										=    key_dim
								__a										=    drop_path_rate
								__a										=    patch_size
								__a										=    attention_ratio
								__a										=    mlp_ratio
								__a										=    initializer_range
								__a										=    [
								    ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
								    ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
								]







class 			__A( a			):
				snake_case_						   =    version.parse('''1.11'''			)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Mapping[str, Mapping[int, str]]:


								'''simple docstring'''




								return OrderedDict(
								    [
								        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
								    ]				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      float:


								'''simple docstring'''




								return 1E-4