import colorsys

from PIL import Image  # type: ignore



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: float				,  __UpperCamelCase					: float				,  __UpperCamelCase					: int			)							->      float:







      """simple docstring"""





      SCREAMING_SNAKE_CASE__    							=   x
      SCREAMING_SNAKE_CASE__    							=   y
      for step in range(__UpperCamelCase			):  # noqa: B007
            SCREAMING_SNAKE_CASE__    							=   a * a - b * b + x
            SCREAMING_SNAKE_CASE__    							=   2 * a * b + y
            SCREAMING_SNAKE_CASE__    							=   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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: float			)							->      tuple:







      """simple docstring"""





      if distance == 1:
            return (0, 0, 0)
      else:
            return (2_55, 2_55, 2_55)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: float			)							->      tuple:







      """simple docstring"""





      if distance == 1:
            return (0, 0, 0)
      else:
            return tuple(round(i * 2_55			) for i in colorsys.hsv_to_rgb(__UpperCamelCase				,  1				,  1			)			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int = 8_00				,  __UpperCamelCase					: int = 6_00				,  __UpperCamelCase					: float = -0.6				,  __UpperCamelCase					: float = 0				,  __UpperCamelCase					: float = 3.2				,  __UpperCamelCase					: int = 50				,  __UpperCamelCase					: bool = True				,  )							->      Image.Image:







      """simple docstring"""





      SCREAMING_SNAKE_CASE__    							=   Image.new("""RGB"""				,  (image_width, image_height)			)
      SCREAMING_SNAKE_CASE__    							=   img.load()

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

                  SCREAMING_SNAKE_CASE__    							=   get_distance(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase			)

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

      return img


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # colored version, full figure
   __lowerCamelCase    :				str							     =	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 typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase    :				List[Any]							     =	{
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase    :				Union[str, Any]							     =	[
       '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''InformerForPrediction''',
       '''InformerModel''',
       '''InformerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_informer import (
          INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          InformerForPrediction,
          InformerModel,
          InformerPreTrainedModel,
      )

else:
   import sys

   __lowerCamelCase    :				Dict							     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
