


'''simple docstring'''



import colorsys

from PIL import Image  # type: ignore







def 			_UpperCamelCase					(						UpperCamelCase__       ,					UpperCamelCase__       ,					UpperCamelCase__    ):
			UpperCAmelCase__     :    Dict         =   x
			UpperCAmelCase__     :    int         =   y
			for step in range(A__    ):  # noqa: B007
						UpperCAmelCase__     :    Union[str, Any]         =   a * a - b * b + x
						UpperCAmelCase__     :    Tuple         =   2 * a * b + y
						UpperCAmelCase__     :    Any         =   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 			_UpperCamelCase					(						UpperCamelCase__    ):



			if distance == 1:
						return (0, 0, 0)
			else:
						return (2_5_5, 2_5_5, 2_5_5)







def 			_UpperCamelCase					(						UpperCamelCase__    ):



			if distance == 1:
						return (0, 0, 0)
			else:
						return tuple(round(i * 2_5_5    ) for i in colorsys.hsv_to_rgb(A__       ,					1       ,					1    )    )







def 			_UpperCamelCase					(						UpperCamelCase__ = 8_0_0       ,					UpperCamelCase__ = 6_0_0       ,					UpperCamelCase__ = -0.6       ,					UpperCamelCase__ = 0       ,					UpperCamelCase__ = 3.2       ,					UpperCamelCase__ = 5_0       ,					UpperCamelCase__ = True       ,					):
			UpperCAmelCase__     :    str         =   Image.new("""RGB"""       ,					(image_width, image_height)    )
			UpperCAmelCase__     :    List[str]         =   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
									UpperCAmelCase__     :    str         =   figure_width / image_width * image_height
									UpperCAmelCase__     :    Any         =   figure_center_x + (image_x / image_width - 0.5) * figure_width
									UpperCAmelCase__     :    Union[str, Any]         =   figure_center_y + (image_y / image_height - 0.5) * figure_height

									UpperCAmelCase__     :    int         =   get_distance(A__       ,					A__       ,					A__    )

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

			return img


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# colored version, full figure
		__A		=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 typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase__											=	{
    '''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
    '''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase__											=	[
     '''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''GPTNeoXJapaneseForCausalLM''',
     '''GPTNeoXJapaneseLayer''',
     '''GPTNeoXJapaneseModel''',
     '''GPTNeoXJapanesePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
 from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox_japanese import (
      GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXJapaneseForCausalLM,
      GPTNeoXJapaneseLayer,
      GPTNeoXJapaneseModel,
      GPTNeoXJapanesePreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase__											=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

