
"""simple docstring"""



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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a					:	Tuple							=		logging.get_logger(__name__)




class 		a_						( _UpperCAmelCase	):
    a      :				Union[str, Any]     =				['pixel_values']







    def __init__(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       bool = True     ,					__UpperCamelCase			:       Optional[Dict[str, int]] = None     ,					__UpperCamelCase			:       PILImageResampling = PILImageResampling.BICUBIC     ,					__UpperCamelCase			:       bool = True     ,					__UpperCamelCase			:       bool = True     ,					__UpperCamelCase			:       Union[int, float] = 1 / 2_55     ,					__UpperCamelCase			:       Dict[str, int] = None     ,					__UpperCamelCase			:       bool = True     ,					__UpperCamelCase			:       Optional[Union[float, List[float]]] = None     ,					__UpperCamelCase			:       Optional[Union[float, List[float]]] = None     ,					**__UpperCamelCase			:       Any     ,					)		->None:






        '''simple docstring'''






        super().__init__(**__UpperCamelCase     )
        _UpperCAmelCase          =   size if size is not None else {"""height""": 2_24, """width""": 2_24}
        _UpperCAmelCase          =   get_size_dict(__UpperCamelCase     )
        _UpperCAmelCase          =   crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
        _UpperCAmelCase          =   get_size_dict(__UpperCamelCase     ,					default_to_square=__UpperCamelCase     ,					param_name="""crop_size"""     )

        _UpperCAmelCase          =   do_resize
        _UpperCAmelCase          =   do_rescale
        _UpperCAmelCase          =   do_normalize
        _UpperCAmelCase          =   do_center_crop
        _UpperCAmelCase          =   crop_size
        _UpperCAmelCase          =   size
        _UpperCAmelCase          =   resample
        _UpperCAmelCase          =   rescale_factor
        _UpperCAmelCase          =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        _UpperCAmelCase          =   image_std if image_std is not None else IMAGENET_DEFAULT_STD







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       np.ndarray     ,					__UpperCamelCase			:       Dict[str, int]     ,					__UpperCamelCase			:       PILImageResampling = PILImageResampling.BILINEAR     ,					__UpperCamelCase			:       Optional[Union[str, ChannelDimension]] = None     ,					**__UpperCamelCase			:       str     ,					)		->np.ndarray:






        '''simple docstring'''






        _UpperCAmelCase          =   get_size_dict(__UpperCamelCase     )

        if "shortest_edge" in size:
            _UpperCAmelCase          =   get_resize_output_image_size(__UpperCamelCase     ,					size=size["""shortest_edge"""]     ,					default_to_square=__UpperCamelCase     )
            # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
        elif "height" in size and "width" in size:
            _UpperCAmelCase          =   (size["""height"""], size["""width"""])
        else:
            raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""     )
        return resize(__UpperCamelCase     ,					size=__UpperCamelCase     ,					resample=__UpperCamelCase     ,					data_format=__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       np.ndarray     ,					__UpperCamelCase			:       Dict[str, int]     ,					__UpperCamelCase			:       Optional[Union[str, ChannelDimension]] = None     ,					**__UpperCamelCase			:       str     ,					)		->np.ndarray:






        '''simple docstring'''






        _UpperCAmelCase          =   get_size_dict(__UpperCamelCase     )
        if "height" not in size or "width" not in size:
            raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""     )
        return center_crop(__UpperCamelCase     ,					size=(size["""height"""], size["""width"""])     ,					data_format=__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       np.ndarray     ,					__UpperCamelCase			:       float     ,					__UpperCamelCase			:       Optional[Union[str, ChannelDimension]] = None     ,					**__UpperCamelCase			:       List[str]     )		->np.ndarray:






        '''simple docstring'''






        return rescale(__UpperCamelCase     ,					scale=__UpperCamelCase     ,					data_format=__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       np.ndarray     ,					__UpperCamelCase			:       Union[float, List[float]]     ,					__UpperCamelCase			:       Union[float, List[float]]     ,					__UpperCamelCase			:       Optional[Union[str, ChannelDimension]] = None     ,					**__UpperCamelCase			:       Dict     ,					)		->np.ndarray:






        '''simple docstring'''






        return normalize(__UpperCamelCase     ,					mean=__UpperCamelCase     ,					std=__UpperCamelCase     ,					data_format=__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       Any     ,					__UpperCamelCase			:       ImageInput     ,					__UpperCamelCase			:       Optional[bool] = None     ,					__UpperCamelCase			:       Dict[str, int] = None     ,					__UpperCamelCase			:       PILImageResampling = None     ,					__UpperCamelCase			:       bool = None     ,					__UpperCamelCase			:       int = None     ,					__UpperCamelCase			:       Optional[bool] = None     ,					__UpperCamelCase			:       Optional[float] = None     ,					__UpperCamelCase			:       Optional[bool] = None     ,					__UpperCamelCase			:       Optional[Union[float, List[float]]] = None     ,					__UpperCamelCase			:       Optional[Union[float, List[float]]] = None     ,					__UpperCamelCase			:       Optional[Union[str, TensorType]] = None     ,					__UpperCamelCase			:       Union[str, ChannelDimension] = ChannelDimension.FIRST     ,					**__UpperCamelCase			:       Dict     ,					)		->BatchFeature:






        '''simple docstring'''






        _UpperCAmelCase          =   do_resize if do_resize is not None else self.do_resize
        _UpperCAmelCase          =   do_rescale if do_rescale is not None else self.do_rescale
        _UpperCAmelCase          =   do_normalize if do_normalize is not None else self.do_normalize
        _UpperCAmelCase          =   do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCAmelCase          =   crop_size if crop_size is not None else self.crop_size
        _UpperCAmelCase          =   get_size_dict(__UpperCamelCase     ,					param_name="""crop_size"""     ,					default_to_square=__UpperCamelCase     )
        _UpperCAmelCase          =   resample if resample is not None else self.resample
        _UpperCAmelCase          =   rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCAmelCase          =   image_mean if image_mean is not None else self.image_mean
        _UpperCAmelCase          =   image_std if image_std is not None else self.image_std

        _UpperCAmelCase          =   size if size is not None else self.size
        _UpperCAmelCase          =   get_size_dict(__UpperCamelCase     )

        if not is_batched(__UpperCamelCase     ):
            _UpperCAmelCase          =   [images]

        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:
            raise ValueError("""Size 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."""     )

        # All transformations expect numpy arrays.
        _UpperCAmelCase          =   [to_numpy_array(__UpperCamelCase     ) for image in images]

        if do_resize:
            _UpperCAmelCase          =   [self.resize(image=__UpperCamelCase     ,					size=__UpperCamelCase     ,					resample=__UpperCamelCase     ) for image in images]

        if do_center_crop:
            _UpperCAmelCase          =   [self.center_crop(image=__UpperCamelCase     ,					size=__UpperCamelCase     ) for image in images]

        if do_rescale:
            _UpperCAmelCase          =   [self.rescale(image=__UpperCamelCase     ,					scale=__UpperCamelCase     ) for image in images]

        if do_normalize:
            _UpperCAmelCase          =   [self.normalize(image=__UpperCamelCase     ,					mean=__UpperCamelCase     ,					std=__UpperCamelCase     ) for image in images]

        _UpperCAmelCase          =   [to_channel_dimension_format(__UpperCamelCase     ,					__UpperCamelCase     ) for image in images]

        _UpperCAmelCase          =   {"""pixel_values""": images}
        return BatchFeature(data=__UpperCamelCase     ,					tensor_type=__UpperCamelCase     )

"""simple docstring"""



import colorsys

from PIL import Image  # type: ignore


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  float:





    """simple docstring"""

    _UpperCAmelCase          =   x
    _UpperCAmelCase          =   y
    for step in range(_A      ):  # noqa: B007
        _UpperCAmelCase          =   a * a - b * b + x
        _UpperCAmelCase          =   2 * a * b + y
        _UpperCAmelCase          =   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  _UpperCamelCase	(						_A      )    ->  tuple:





    """simple docstring"""

    if distance == 1:
        return (0, 0, 0)
    else:
        return (2_5_5, 2_5_5, 2_5_5)


def  _UpperCamelCase	(						_A      )    ->  tuple:





    """simple docstring"""

    if distance == 1:
        return (0, 0, 0)
    else:
        return tuple(round(i * 2_5_5      ) for i in colorsys.hsv_to_rgb(_A					,      1					,      1      )      )


def  _UpperCamelCase	(						_A = 8_0_0					,      _A = 6_0_0					,      _A = -0.6					,      _A = 0					,      _A = 3.2					,      _A = 5_0					,      _A = True					,      )    ->  Image.Image:





    """simple docstring"""

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

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

            _UpperCAmelCase          =   get_distance(_A					,      _A					,      _A      )

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

    return img


if __name__ == "__main__":
 import doctest

 doctest.testmod()

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