





import colorsys

from PIL import Image  # type: ignore


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


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


def _A       (   __magic_name__ = 800  , __magic_name__ = 600  , __magic_name__ = -0.6  , __magic_name__ = 0  , __magic_name__ = 3.2  , __magic_name__ = 50  , __magic_name__ = True  , ):
   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
  _snake_case           =			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 transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments


def _A       (   ):
   lowercase__			       =  HfArgumentParser(__magic_name__	)
   lowercase__			       =  parser.parse_args_into_dataclasses()[0]
   lowercase__			       =  TensorFlowBenchmark(args=__magic_name__	)
   try:
      lowercase__			       =  parser.parse_args_into_dataclasses()[0]
   except ValueError as e:
      lowercase__			       =  "Arg --no_{0} is no longer used, please use --no-{0} instead."
      lowercase__			       =  " ".join(str(__magic_name__	).split(" "	)[:-1]	)
      lowercase__			       =  ""
      lowercase__			       =  eval(str(__magic_name__	).split(" "	)[-1]	)
      lowercase__			       =  []
      for arg in depreciated_args:
         # arg[2:] removes '--'
         if arg[2:] in TensorFlowBenchmark.deprecated_args:
            # arg[5:] removes '--no_'
            full_error_msg += arg_error_msg.format(arg[5:]	)
         else:
            wrong_args.append(__magic_name__	)
      if len(__magic_name__	) > 0:
         lowercase__			       =  full_error_msg + begin_error_msg + str(__magic_name__	)
      raise ValueError(__magic_name__	)
   benchmark.run()


if __name__ == "__main__":
  main()






