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			: Union[str, Any]							   =		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()

import warnings

from ..trainer import Trainer
from ..utils import logging


__lowerCamelCase			: List[Any]							   =		logging.get_logger(__name__)







class __snake_case				(						lowerCamelCase_		):




   def __init__(    self			:				Tuple       ,	_lowercase			:				Optional[int]=None       ,	**_lowercase			:				List[Any]      ):



     """simple docstring"""






     warnings.warn(
         """`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
         """instead."""       ,	_lowercase       ,	)
     super().__init__(args=_lowercase       ,	**_lowercase      )
