






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


A        =			logging.get_logger(__name__)


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				Optional[int]				    =						["""pixel_values"""]


		def __init__(						self ,    UpperCamelCase__ = True ,    UpperCamelCase__ = None ,    UpperCamelCase__ = PIL.Image.BICUBIC ,    UpperCamelCase__ = True ,    UpperCamelCase__ = None ,    UpperCamelCase__ = 1 / 255 ,    UpperCamelCase__ = True ,    UpperCamelCase__ = True ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						super().__init__(**UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   size if size is not None else {'height': 256, 'width': 256}
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   get_size_dict(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   crop_size if crop_size is not None else {'height': 224, 'width': 224}
						SCREAMING_SNAKE_CASE_   :	str													=   get_size_dict(UpperCamelCase__ ,    param_name='crop_size'       )

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   do_resize
						SCREAMING_SNAKE_CASE_   :	Tuple													=   size
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   resample
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   do_center_crop
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   crop_size
						SCREAMING_SNAKE_CASE_   :	Tuple													=   do_rescale
						SCREAMING_SNAKE_CASE_   :	List[str]													=   rescale_factor
						SCREAMING_SNAKE_CASE_   :	str													=   do_normalize
						SCREAMING_SNAKE_CASE_   :	List[str]													=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   image_std if image_std is not None else IMAGENET_STANDARD_STD


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = PIL.Image.BICUBIC ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   get_size_dict(UpperCamelCase__       )
						if "height" not in size or "width" not in size:
										raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''       )
						return resize(
						    UpperCamelCase__ ,    size=(size['height'], size['width']) ,    resample=UpperCamelCase__ ,    data_format=UpperCamelCase__ ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	Any													=   get_size_dict(UpperCamelCase__       )
						if "height" not in size or "width" not in size:
										raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''       )
						return center_crop(UpperCamelCase__ ,    size=(size['height'], size['width']) ,    data_format=UpperCamelCase__ ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						return rescale(UpperCamelCase__ ,    scale=UpperCamelCase__ ,    data_format=UpperCamelCase__ ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						return normalize(UpperCamelCase__ ,    mean=UpperCamelCase__ ,    std=UpperCamelCase__ ,    data_format=UpperCamelCase__ ,    **UpperCamelCase__       )






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__=None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = ChannelDimension.FIRST ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	Any													=   do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   resample if resample is not None else self.resample
						SCREAMING_SNAKE_CASE_   :	Tuple													=   do_center_crop if do_center_crop is not None else self.do_center_crop
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   do_rescale if do_rescale is not None else self.do_rescale
						SCREAMING_SNAKE_CASE_   :	Dict													=   rescale_factor if rescale_factor is not None else self.rescale_factor
						SCREAMING_SNAKE_CASE_   :	Tuple													=   do_normalize if do_normalize is not None else self.do_normalize
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   image_mean if image_mean is not None else self.image_mean
						SCREAMING_SNAKE_CASE_   :	int													=   image_std if image_std is not None else self.image_std

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   size if size is not None else self.size
						SCREAMING_SNAKE_CASE_   :	Tuple													=   get_size_dict(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   crop_size if crop_size is not None else self.crop_size
						SCREAMING_SNAKE_CASE_   :	Tuple													=   get_size_dict(UpperCamelCase__ ,    param_name='crop_size'       )

						SCREAMING_SNAKE_CASE_   :	Dict													=   make_list_of_images(UpperCamelCase__       )

						if not valid_images(UpperCamelCase__       ):
										raise ValueError(
										    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
										    'torch.Tensor, tf.Tensor or jax.ndarray.'       )

						if do_resize and size is None or resample is None:
										raise ValueError('Size and resample must be specified if do_resize is True.'       )

						if do_center_crop and crop_size is None:
										raise ValueError('Crop size must be specified if do_center_crop is True.'       )

						if do_rescale and rescale_factor is None:
										raise ValueError('Rescale factor must be specified if do_rescale is True.'       )

						if do_normalize and (image_mean is None or image_std is None):
										raise ValueError('Image mean and std must be specified if do_normalize is True.'       )

						# All transformations expect numpy arrays.
						SCREAMING_SNAKE_CASE_   :	Any													=   [to_numpy_array(UpperCamelCase__       ) for image in images]

						if do_resize:
										SCREAMING_SNAKE_CASE_   :	Tuple													=   [self.resize(image=UpperCamelCase__ ,    size=UpperCamelCase__ ,    resample=UpperCamelCase__       ) for image in images]

						if do_center_crop:
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [self.center_crop(image=UpperCamelCase__ ,    size=UpperCamelCase__       ) for image in images]

						if do_rescale:
										SCREAMING_SNAKE_CASE_   :	Tuple													=   [self.rescale(image=UpperCamelCase__ ,    scale=UpperCamelCase__       ) for image in images]

						if do_normalize:
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [self.normalize(image=UpperCamelCase__ ,    mean=UpperCamelCase__ ,    std=UpperCamelCase__       ) for image in images]

						SCREAMING_SNAKE_CASE_   :	Dict													=   [to_channel_dimension_format(UpperCamelCase__ ,    UpperCamelCase__       ) for image in images]

						SCREAMING_SNAKE_CASE_   :	Any													=   {'pixel_values': images}
						return BatchFeature(data=UpperCamelCase__ ,    tensor_type=UpperCamelCase__       )







import colorsys

from PIL import Image  # type: ignore



def 					_lowerCamelCase(			lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







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





				'''simple docstring'''







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



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







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



def 					_lowerCamelCase(			lowerCAmelCase__				:		int = 800    ,    lowerCAmelCase__				:		int = 600    ,    lowerCAmelCase__				:		float = -0.6    ,    lowerCAmelCase__				:		float = 0    ,    lowerCAmelCase__				:		float = 3.2    ,    lowerCAmelCase__				:		int = 50    ,    lowerCAmelCase__				:		bool = True    ,    ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   Image.new('RGB'    ,    (image_width, image_height)      )
				SCREAMING_SNAKE_CASE_   :	List[str]													=   img.load()

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

												SCREAMING_SNAKE_CASE_   :	str													=   get_distance(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

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

				return img


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# colored version, full figure
				A        =			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()