





import colorsys

from PIL import Image  # type: ignore






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					float					, _lowercase		:					float					, _lowercase		:					int      )				->Dict:






	'''simple docstring'''






	a   :	Any			=       x
	a   :	int			=       y
	for step in range(_lowercase      ):  # noqa: B007
		a   :	str			=       a * a - b * b + x
		a   :	Tuple			=       2 * a * b + y
		a   :	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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					float      )				->Tuple:






	'''simple docstring'''






	if distance == 1:
		return (0, 0, 0)
	else:
		return (255, 255, 255)






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					float      )				->int:






	'''simple docstring'''






	if distance == 1:
		return (0, 0, 0)
	else:
		return tuple(round(i * 255      ) for i in colorsys.hsv_to_rgb(_lowercase					, 1					, 1      )      )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int = 800					, _lowercase		:					int = 600					, _lowercase		:					float = -0.6					, _lowercase		:					float = 0					, _lowercase		:					float = 3.2					, _lowercase		:					int = 50					, _lowercase		:					bool = True					, )				->List[str]:






	'''simple docstring'''






	a   :	Any			=       Image.new("RGB"					, (image_width, image_height)      )
	a   :	str			=       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
			a   :	str			=       figure_width / image_width * image_height
			a   :	Dict			=       figure_center_x + (image_x / image_width - 0.5) * figure_width
			a   :	Union[str, Any]			=       figure_center_y + (image_y / image_height - 0.5) * figure_height

			a   :	List[str]			=       get_distance(_lowercase					, _lowercase					, _lowercase      )

			# color the corresponding pixel based on the selected coloring-function
			if use_distance_color_coding:
				a   :	Tuple			=       get_color_coded_rgb(_lowercase      )
			else:
				a   :	List[str]			=       get_black_and_white_rgb(_lowercase      )

	return img


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# colored version, full figure
		a		:				Optional[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()









"""simple docstring"""




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]      )				->int:






	'''simple docstring'''






	a   :	int			=       {}
	a   :	Union[str, Any]			=       tokenizer(example["content"]					, truncation=_lowercase      )["input_ids"]
	a   :	Any			=       len(example["content"]      ) / len(output["input_ids"]      )
	return output


a		:				int  			=    HfArgumentParser(PretokenizationArguments)
a		:				Optional[int]  			=    parser.parse_args()
if args.num_workers is None:
		a		:				Tuple  			=    multiprocessing.cpu_count()
a		:				Optional[int]  			=    AutoTokenizer.from_pretrained(args.tokenizer_dir)

a		:				Dict  			=    time.time()
a		:				Tuple  			=    load_dataset(args.dataset_name, split='''train''')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

a		:				Dict  			=    time.time()
a		:				Tuple  			=    ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        '''repo_name''',
        '''path''',
        '''copies''',
        '''size''',
        '''content''',
        '''license''',
        '''hash''',
        '''line_mean''',
        '''line_max''',
        '''alpha_frac''',
        '''autogenerated''',
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

a		:				Tuple  			=    time.time()
ds.push_to_hub(args.tokenized_data_repo)







print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')


