






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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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:     Dict   					=					logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  ['''pixel_values''']


  def __init__(      self		:			Any       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Dict[str, int] = None       ,				lowerCamelCase		:			PILImageResampling = PILImageResampling.BICUBIC       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Union[int, float] = 1 / 255       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Optional[Union[float, List[float]]] = None       ,				lowerCamelCase		:			Optional[Union[float, List[float]]] = None       ,				lowerCamelCase		:			bool = True       ,				**lowerCamelCase		:			List[Any]       ,				)						->	None:


         """simple docstring"""
         super().__init__(**lowerCamelCase							)
         _UpperCAmelCase							=    size if size is not None else {"""height""": 384, """width""": 384}
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase       ,				default_to_square=lowerCamelCase							)

         _UpperCAmelCase							=    do_resize
         _UpperCAmelCase							=    size
         _UpperCAmelCase							=    resample
         _UpperCAmelCase							=    do_rescale
         _UpperCAmelCase							=    rescale_factor
         _UpperCAmelCase							=    do_normalize
         _UpperCAmelCase							=    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         _UpperCAmelCase							=    image_std if image_std is not None else OPENAI_CLIP_STD
         _UpperCAmelCase							=    do_convert_rgb


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


         """simple docstring"""
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase       ,				default_to_square=lowerCamelCase							)
         if "height" not in size or "width" not in size:
                raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""							)
         _UpperCAmelCase							=    (size["""height"""], size["""width"""])
         return resize(lowerCamelCase       ,				size=lowerCamelCase       ,				resample=lowerCamelCase       ,				data_format=lowerCamelCase       ,				**lowerCamelCase							)


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


         """simple docstring"""
         return rescale(lowerCamelCase       ,				scale=lowerCamelCase       ,				data_format=lowerCamelCase       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			np.ndarray       ,				lowerCamelCase		:			Union[float, List[float]]       ,				lowerCamelCase		:			Union[float, List[float]]       ,				lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None       ,				**lowerCamelCase		:			List[Any]       ,				)						->	np.ndarray:


         """simple docstring"""
         return normalize(lowerCamelCase       ,				mean=lowerCamelCase       ,				std=lowerCamelCase       ,				data_format=lowerCamelCase       ,				**lowerCamelCase							)





  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			ImageInput       ,				lowerCamelCase		:			Optional[bool] = None       ,				lowerCamelCase		:			Optional[Dict[str, int]] = None       ,				lowerCamelCase		:			PILImageResampling = None       ,				lowerCamelCase		:			Optional[bool] = None       ,				lowerCamelCase		:			Optional[float] = None       ,				lowerCamelCase		:			Optional[bool] = None       ,				lowerCamelCase		:			Optional[Union[float, List[float]]] = None       ,				lowerCamelCase		:			Optional[Union[float, List[float]]] = None       ,				lowerCamelCase		:			Optional[Union[str, TensorType]] = None       ,				lowerCamelCase		:			bool = None       ,				lowerCamelCase		:			ChannelDimension = ChannelDimension.FIRST       ,				**lowerCamelCase		:			List[str]       ,				)						->	PIL.Image.Image:


         """simple docstring"""
         _UpperCAmelCase							=    do_resize if do_resize is not None else self.do_resize
         _UpperCAmelCase							=    resample if resample is not None else self.resample
         _UpperCAmelCase							=    do_rescale if do_rescale is not None else self.do_rescale
         _UpperCAmelCase							=    rescale_factor if rescale_factor is not None else self.rescale_factor
         _UpperCAmelCase							=    do_normalize if do_normalize is not None else self.do_normalize
         _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							=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         _UpperCAmelCase							=    size if size is not None else self.size
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase       ,				default_to_square=lowerCamelCase							)

         _UpperCAmelCase							=    make_list_of_images(lowerCamelCase							)

         if not valid_images(lowerCamelCase							):
                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_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."""							)

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
                _UpperCAmelCase							=    [convert_to_rgb(lowerCamelCase							) for image in images]

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

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

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

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

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

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

         return encoded_outputs

'''simple docstring'''
import colorsys

from PIL import Image  # type: ignore







def 		A_(     A						:		float		,     A						:		float		,     A						:		int):
							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 		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(A		,     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		,     ):
							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
						lowerCAmelCase					:    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()




