



'''simple docstring'''




import colorsys

from PIL import Image  # type: ignore



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    float:
							UpperCAmelCase  :	List[Any]							  =	x
							UpperCAmelCase  :	List[str]							  =	y
							for step in range(_lowercase	):  # noqa: B007
														UpperCAmelCase  :	Optional[Any]							  =	a * a - b * b + x
														UpperCAmelCase  :	Union[str, Any]							  =	2 * a * b + y
														UpperCAmelCase  :	List[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    __lowerCamelCase						(      _lowercase	)   ->    tuple:
							if distance == 1:
														return (0, 0, 0)
							else:
														return (2_5_5, 2_5_5, 2_5_5)



def    __lowerCamelCase						(      _lowercase	)   ->    tuple:
							if distance == 1:
														return (0, 0, 0)
							else:
														return tuple(round(i * 2_5_5	) for i in colorsys.hsv_to_rgb(_lowercase			,       1			,       1	)	)



def    __lowerCamelCase						(      _lowercase = 8_0_0			,       _lowercase = 6_0_0			,       _lowercase = -0.6			,       _lowercase = 0			,       _lowercase = 3.2			,       _lowercase = 5_0			,       _lowercase = True			,       )   ->    Image.Image:
							UpperCAmelCase  :	Optional[int]							  =	Image.new("""RGB"""			,       (image_width, image_height)	)
							UpperCAmelCase  :	Any							  =	img.load()

							# loop through the image-coordinates
							for image_x in range(_lowercase	):
														for image_y in range(_lowercase	):
																					# determine the figure-coordinates based on the image-coordinates
																					UpperCAmelCase  :	Dict							  =	figure_width / image_width * image_height
																					UpperCAmelCase  :	Dict							  =	figure_center_x + (image_x / image_width - 0.5) * figure_width
																					UpperCAmelCase  :	List[Any]							  =	figure_center_y + (image_y / image_height - 0.5) * figure_height

																					UpperCAmelCase  :	Optional[Any]							  =	get_distance(_lowercase			,       _lowercase			,       _lowercase	)

																					# color the corresponding pixel based on the selected coloring-function
																					if use_distance_color_coding:
																												UpperCAmelCase  :	Dict							  =	get_color_coded_rgb(_lowercase	)
																					else:
																												UpperCAmelCase  :	int							  =	get_black_and_white_rgb(_lowercase	)

							return img


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# colored version, full figure
							a     :    Optional[int]      						=					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 ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a     :    Any      						=					{
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    Any      						=					[
							    """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """NezhaForNextSentencePrediction""",
							    """NezhaForMaskedLM""",
							    """NezhaForPreTraining""",
							    """NezhaForMultipleChoice""",
							    """NezhaForQuestionAnswering""",
							    """NezhaForSequenceClassification""",
							    """NezhaForTokenClassification""",
							    """NezhaModel""",
							    """NezhaPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nezha import (
														    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NezhaForMaskedLM,
														    NezhaForMultipleChoice,
														    NezhaForNextSentencePrediction,
														    NezhaForPreTraining,
														    NezhaForQuestionAnswering,
														    NezhaForSequenceClassification,
														    NezhaForTokenClassification,
														    NezhaModel,
														    NezhaPreTrainedModel,
														)


else:
							import sys

							a     :    Union[str, Any]      						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

