





"""simple docstring"""





import colorsys

from PIL import Image  # type: ignore



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							float    ,						SCREAMING_SNAKE_CASE       :							float    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							__lowerCamelCase      :      List[Any]         =x
							__lowerCamelCase      :      Any         =y
							for step in range(SCREAMING_SNAKE_CASE   ):  # noqa: B007
														__lowerCamelCase      :      List[str]         =a * a - b * b + x
														__lowerCamelCase      :      List[Any]         =2 * a * b + y
														__lowerCamelCase      :      Tuple         =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_			(      SCREAMING_SNAKE_CASE       :							float   ):


							'''simple docstring'''







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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							float   ):


							'''simple docstring'''







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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int = 800    ,						SCREAMING_SNAKE_CASE       :							int = 600    ,						SCREAMING_SNAKE_CASE       :							float = -0.6    ,						SCREAMING_SNAKE_CASE       :							float = 0    ,						SCREAMING_SNAKE_CASE       :							float = 3.2    ,						SCREAMING_SNAKE_CASE       :							int = 50    ,						SCREAMING_SNAKE_CASE       :							bool = True    ,						):


							'''simple docstring'''







							__lowerCamelCase      :      Dict         =Image.new('''RGB'''    ,						(image_width, image_height)   )
							__lowerCamelCase      :      Optional[int]         =img.load()

							# loop through the image-coordinates
							for image_x in range(SCREAMING_SNAKE_CASE   ):
														for image_y in range(SCREAMING_SNAKE_CASE   ):
																					# determine the figure-coordinates based on the image-coordinates
																					__lowerCamelCase      :      Optional[Any]         =figure_width / image_width * image_height
																					__lowerCamelCase      :      Tuple         =figure_center_x + (image_x / image_width - 0.5) * figure_width
																					__lowerCamelCase      :      str         =figure_center_y + (image_y / image_height - 0.5) * figure_height

																					__lowerCamelCase      :      Optional[Any]         =get_distance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

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

							return img


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# colored version, full figure
							_UpperCamelCase							=      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 .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments




