

import colorsys

from PIL import Image  # type: ignore



def   lowercase_	(    _lowercase       :		float							,      _lowercase       :		float							,      _lowercase       :		int	):




					'''simple docstring'''






					UpperCAmelCase			:   Dict      =				x
					UpperCAmelCase			:   Dict      =				y
					for step in range(_lowercase	):  # noqa: B007
										UpperCAmelCase			:   List[str]      =				a * a - b * b + x
										UpperCAmelCase			:   Dict      =				2 * a * b + y
										UpperCAmelCase			:   Optional[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   lowercase_	(    _lowercase       :		float	):




					'''simple docstring'''






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



def   lowercase_	(    _lowercase       :		float	):




					'''simple docstring'''






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



def   lowercase_	(    _lowercase       :		int = 8_00							,      _lowercase       :		int = 6_00							,      _lowercase       :		float = -0.6							,      _lowercase       :		float = 0							,      _lowercase       :		float = 3.2							,      _lowercase       :		int = 50							,      _lowercase       :		bool = True							,      ):




					'''simple docstring'''






					UpperCAmelCase			:   Dict      =				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			:   List[str]      =				figure_width / image_width * image_height
															UpperCAmelCase			:   Tuple      =				figure_center_x + (image_x / image_width - 0.5) * figure_width
															UpperCAmelCase			:   Any      =				figure_center_y + (image_y / image_height - 0.5) * figure_height

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

															# color the corresponding pixel based on the selected coloring-function
															if use_distance_color_coding:
																				UpperCAmelCase			:   Optional[Any]      =				get_color_coded_rgb(_lowercase	)
															else:
																				UpperCAmelCase			:   Any      =				get_black_and_white_rgb(_lowercase	)

					return img


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	# colored version, full figure
	snake_case_   :   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()





"""simple docstring"""






from typing import TYPE_CHECKING

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


snake_case_   :   Dict        =       {
    """configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
    """tokenization_mvp""": ["""MvpTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_   :   List[Any]        =       ["""MvpTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_   :   Any        =       [
	    """MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MvpForCausalLM""",
	    """MvpForConditionalGeneration""",
	    """MvpForQuestionAnswering""",
	    """MvpForSequenceClassification""",
	    """MvpModel""",
	    """MvpPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

	snake_case_   :   List[str]        =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


