
"""simple docstring"""



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import LevitImageProcessor




class 		a_						( unittest.TestCase	):







    def __init__(			self			:       int     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Union[str, Any]=7     ,					__UpperCamelCase			:       Dict=3     ,					__UpperCamelCase			:       int=18     ,					__UpperCamelCase			:       int=30     ,					__UpperCamelCase			:       Dict=4_00     ,					__UpperCamelCase			:       int=True     ,					__UpperCamelCase			:       Optional[Any]=None     ,					__UpperCamelCase			:       List[Any]=True     ,					__UpperCamelCase			:       Any=None     ,					__UpperCamelCase			:       Optional[int]=True     ,					__UpperCamelCase			:       str=[0.5, 0.5, 0.5]     ,					__UpperCamelCase			:       Tuple=[0.5, 0.5, 0.5]     ,					)		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   size if size is not None else {"""shortest_edge""": 18}
        _UpperCAmelCase          =   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
        _UpperCAmelCase          =   parent
        _UpperCAmelCase          =   batch_size
        _UpperCAmelCase          =   num_channels
        _UpperCAmelCase          =   image_size
        _UpperCAmelCase          =   min_resolution
        _UpperCAmelCase          =   max_resolution
        _UpperCAmelCase          =   do_resize
        _UpperCAmelCase          =   size
        _UpperCAmelCase          =   do_center_crop
        _UpperCAmelCase          =   crop_size
        _UpperCAmelCase          =   do_normalize
        _UpperCAmelCase          =   image_mean
        _UpperCAmelCase          =   image_std







    def      _snake_case					(			self			:       int     )		->Any:






        '''simple docstring'''






        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "do_center_crop": self.do_center_crop,
            "size": self.size,
            "crop_size": self.crop_size,
        }







@require_torch
@require_vision
class 		a_						( _UpperCAmelCase     ,						unittest.TestCase	):
    a      :				Optional[Any]     =				LevitImageProcessor if is_vision_available() else None







    def      _snake_case					(			self			:       Union[str, Any]     )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   LevitImageProcessingTester(self     )







    @property
    def      _snake_case					(			self			:       Optional[int]     )		->List[Any]:






        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()







    def      _snake_case					(			self			:       Optional[int]     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.image_processing_class(**self.image_processor_dict     )
        self.assertTrue(hasattr(__UpperCamelCase     ,					"""image_mean"""     )     )
        self.assertTrue(hasattr(__UpperCamelCase     ,					"""image_std"""     )     )
        self.assertTrue(hasattr(__UpperCamelCase     ,					"""do_normalize"""     )     )
        self.assertTrue(hasattr(__UpperCamelCase     ,					"""do_resize"""     )     )
        self.assertTrue(hasattr(__UpperCamelCase     ,					"""do_center_crop"""     )     )
        self.assertTrue(hasattr(__UpperCamelCase     ,					"""size"""     )     )







    def      _snake_case					(			self			:       Tuple     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   self.image_processing_class.from_dict(self.image_processor_dict     )
        self.assertEqual(image_processor.size     ,					{"""shortest_edge""": 18}     )
        self.assertEqual(image_processor.crop_size     ,					{"""height""": 18, """width""": 18}     )

        _UpperCAmelCase          =   self.image_processing_class.from_dict(self.image_processor_dict     ,					size=42     ,					crop_size=84     )
        self.assertEqual(image_processor.size     ,					{"""shortest_edge""": 42}     )
        self.assertEqual(image_processor.crop_size     ,					{"""height""": 84, """width""": 84}     )







    def      _snake_case					(			self			:       Optional[int]     )		->Optional[Any]:






        '''simple docstring'''






        pass







    def      _snake_case					(			self			:       str     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.image_processing_class(**self.image_processor_dict     )
        # create random PIL images
        _UpperCAmelCase          =   prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=__UpperCamelCase     )
        for image in image_inputs:
            self.assertIsInstance(__UpperCamelCase     ,					Image.Image     )

        # Test not batched input
        _UpperCAmelCase          =   image_processing(image_inputs[0]     ,					return_tensors="""pt"""     ).pixel_values
        self.assertEqual(
            encoded_images.shape     ,					(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,					)

        # Test batched
        _UpperCAmelCase          =   image_processing(__UpperCamelCase     ,					return_tensors="""pt"""     ).pixel_values
        self.assertEqual(
            encoded_images.shape     ,					(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,					)







    def      _snake_case					(			self			:       Dict     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   self.image_processing_class(**self.image_processor_dict     )
        # create random numpy tensors
        _UpperCAmelCase          =   prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=__UpperCamelCase     ,					numpify=__UpperCamelCase     )
        for image in image_inputs:
            self.assertIsInstance(__UpperCamelCase     ,					np.ndarray     )

        # Test not batched input
        _UpperCAmelCase          =   image_processing(image_inputs[0]     ,					return_tensors="""pt"""     ).pixel_values
        self.assertEqual(
            encoded_images.shape     ,					(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,					)

        # Test batched
        _UpperCAmelCase          =   image_processing(__UpperCamelCase     ,					return_tensors="""pt"""     ).pixel_values
        self.assertEqual(
            encoded_images.shape     ,					(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,					)







    def      _snake_case					(			self			:       List[str]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   self.image_processing_class(**self.image_processor_dict     )
        # create random PyTorch tensors
        _UpperCAmelCase          =   prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=__UpperCamelCase     ,					torchify=__UpperCamelCase     )
        for image in image_inputs:
            self.assertIsInstance(__UpperCamelCase     ,					torch.Tensor     )

        # Test not batched input
        _UpperCAmelCase          =   image_processing(image_inputs[0]     ,					return_tensors="""pt"""     ).pixel_values
        self.assertEqual(
            encoded_images.shape     ,					(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,					)

        # Test batched
        _UpperCAmelCase          =   image_processing(__UpperCamelCase     ,					return_tensors="""pt"""     ).pixel_values
        self.assertEqual(
            encoded_images.shape     ,					(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,					)

"""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()