


import colorsys

from PIL import Image  # type: ignore





def   _lowerCAmelCase				(   UpperCamelCase__:     float							,					UpperCamelCase__:     float							,					UpperCamelCase__:     int						) ->   float:





	"""simple docstring"""







	A								=				x
	A								=				y
	for step in range(UpperCamelCase__						):  # noqa: B007
		A								=				a * a - b * b + x
		A								=				2 * a * b + y
		A								=				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				(   UpperCamelCase__:     float						) ->   tuple:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     float						) ->   tuple:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 8_00							,					UpperCamelCase__:     int = 6_00							,					UpperCamelCase__:     float = -0.6							,					UpperCamelCase__:     float = 0							,					UpperCamelCase__:     float = 3.2							,					UpperCamelCase__:     int = 50							,					UpperCamelCase__:     bool = True							,					) ->   Image.Image:





	"""simple docstring"""







	A								=				Image.new("""RGB"""							,					(image_width, image_height)						)
	A								=				img.load()

	# loop through the image-coordinates
	for image_x in range(UpperCamelCase__						):
		for image_y in range(UpperCamelCase__						):
			# determine the figure-coordinates based on the image-coordinates
			A								=				figure_width / image_width * image_height
			A								=				figure_center_x + (image_x / image_width - 0.5) * figure_width
			A								=				figure_center_y + (image_y / image_height - 0.5) * figure_height

			A								=				get_distance(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)

			# color the corresponding pixel based on the selected coloring-function
			if use_distance_color_coding:
				A								=				get_color_coded_rgb(UpperCamelCase__						)
			else:
				A								=				get_black_and_white_rgb(UpperCamelCase__						)

	return img


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# colored version, full figure
				_lowercase       :      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()



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= ['image_processor', 'tokenizer']
				lowerCAmelCase 		= 'ViltImageProcessor'
				lowerCAmelCase 		= ('BertTokenizer', 'BertTokenizerFast')

				def __init__(						self				,   a__=None				,   a__=None				,   **a__   )	->				Any:
					A								=				None
					if "feature_extractor" in kwargs:
						warnings.warn(
						    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
						    """ instead."""				,   a__				,   )
						A								=				kwargs.pop("""feature_extractor"""   )

					A								=				image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
						raise ValueError("""You need to specify an `image_processor`."""   )
					if tokenizer is None:
						raise ValueError("""You need to specify a `tokenizer`."""   )

					super().__init__(a__				,   a__   )
					A								=				self.image_processor

				def __call__(						self				,   a__				,   a__ = None				,   a__ = True				,   a__ = False				,   a__ = None				,   a__ = None				,   a__ = 0				,   a__ = None				,   a__ = None				,   a__ = None				,   a__ = False				,   a__ = False				,   a__ = False				,   a__ = False				,   a__ = True				,   a__ = None				,   **a__				,   )	->				BatchEncoding:
					A								=				self.tokenizer(
					    text=a__				,   add_special_tokens=a__				,   padding=a__				,   truncation=a__				,   max_length=a__				,   stride=a__				,   pad_to_multiple_of=a__				,   return_token_type_ids=a__				,   return_attention_mask=a__				,   return_overflowing_tokens=a__				,   return_special_tokens_mask=a__				,   return_offsets_mapping=a__				,   return_length=a__				,   verbose=a__				,   return_tensors=a__				,   **a__				,   )
					# add pixel_values + pixel_mask
					A								=				self.image_processor(a__				,   return_tensors=a__   )
					encoding.update(a__   )

					return encoding

				def      _UpperCAmelCase					(						self				,   *a__				,   **a__   )	->				Union[str, Any]:
					return self.tokenizer.batch_decode(*a__				,   **a__   )

				def      _UpperCAmelCase					(						self				,   *a__				,   **a__   )	->				Any:
					return self.tokenizer.decode(*a__				,   **a__   )

				@property
				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				self.tokenizer.model_input_names
					A								=				self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )

				@property
				def      _UpperCAmelCase					(						self   )	->				str:
					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,   a__				,   )
					return self.image_processor_class

				@property
				def      _UpperCAmelCase					(						self   )	->				str:
					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""				,   a__				,   )
					return self.image_processor



