
'''simple docstring'''





import colorsys

from PIL import Image  # type: ignore

def      A (       A_							:					float    ,							A_							:					float    ,							A_							:					int       ):
						snake_case       :     List[Any] =    x
						snake_case       :     Dict =    y
						for step in range(snake_case_       ):  # noqa: B007
												snake_case       :     Optional[int] =    a * a - b * b + x
												snake_case       :     Tuple =    2 * a * b + y
												snake_case       :     List[str] =    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      A (       A_							:					float       ):
						if distance == 1:
												return (0, 0, 0)
						else:
												return (255, 255, 255)

def      A (       A_							:					float       ):
						if distance == 1:
												return (0, 0, 0)
						else:
												return tuple(round(i * 255       ) for i in colorsys.hsv_to_rgb(snake_case_    ,							1    ,							1       )       )

def      A (       A_							:					int = 800    ,							A_							:					int = 600    ,							A_							:					float = -0.6    ,							A_							:					float = 0    ,							A_							:					float = 3.2    ,							A_							:					int = 50    ,							A_							:					bool = True    ,							):
						snake_case       :     List[Any] =    Image.new('''RGB'''    ,							(image_width, image_height)       )
						snake_case       :     Tuple =    img.load()

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

																		snake_case       :     str =    get_distance(snake_case_    ,							snake_case_    ,							snake_case_       )

																		# color the corresponding pixel based on the selected coloring-function
																		if use_distance_color_coding:
																								snake_case       :     int =    get_color_coded_rgb(snake_case_       )
																		else:
																								snake_case       :     Tuple =    get_black_and_white_rgb(snake_case_       )

						return img


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						# colored version, full figure
						UpperCAmelCase    					=   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'''





import os
import sys

UpperCAmelCase    					=   os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


UpperCAmelCase    					=   [
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]

@add_start_docstrings(AutoConfig.__doc__       )
def      A (       *A_							:					Optional[Any]    ,							**A_							:					List[str]       ):
						return AutoConfig.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoTokenizer.__doc__       )
def      A (       *A_							:					Dict    ,							**A_							:					str       ):
						return AutoTokenizer.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModel.__doc__       )
def      A (       *A_							:					Union[str, Any]    ,							**A_							:					Optional[Any]       ):
						return AutoModel.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModelForCausalLM.__doc__       )
def      A (       *A_							:					str    ,							**A_							:					Optional[Any]       ):
						return AutoModelForCausalLM.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModelForMaskedLM.__doc__       )
def      A (       *A_							:					List[str]    ,							**A_							:					Optional[Any]       ):
						return AutoModelForMaskedLM.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModelForSequenceClassification.__doc__       )
def      A (       *A_							:					Tuple    ,							**A_							:					List[str]       ):
						return AutoModelForSequenceClassification.from_pretrained(*A_    ,							**A_       )





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__       )
def      A (       *A_							:					Tuple    ,							**A_							:					Any       ):
						return AutoModelForQuestionAnswering.from_pretrained(*A_    ,							**A_       )

