






"""simple docstring"""
import unittest

import numpy as np

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

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 DPTImageProcessor






class  _SCREAMING_SNAKE_CASE(       unittest.TestCase  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=7		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=18		,SCREAMING_SNAKE_CASE__=30		,SCREAMING_SNAKE_CASE__=4_00		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=[0.5, 0.5, 0.5]		,SCREAMING_SNAKE_CASE__=[0.5, 0.5, 0.5]		,)      ->				Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  size if size is not None else {'''height''': 18, '''width''': 18}
             __SCREAMING_SNAKE_CASE       :Tuple		    =  parent
             __SCREAMING_SNAKE_CASE       :int		    =  batch_size
             __SCREAMING_SNAKE_CASE       :str		    =  num_channels
             __SCREAMING_SNAKE_CASE       :List[str]		    =  image_size
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  min_resolution
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  max_resolution
             __SCREAMING_SNAKE_CASE       :Dict		    =  do_resize
             __SCREAMING_SNAKE_CASE       :str		    =  size
             __SCREAMING_SNAKE_CASE       :str		    =  do_normalize
             __SCREAMING_SNAKE_CASE       :List[str]		    =  image_mean
             __SCREAMING_SNAKE_CASE       :str		    =  image_std




       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






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






@require_torch
@require_vision
class  _SCREAMING_SNAKE_CASE(       A   ,     unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  DPTImageProcessor if is_vision_available() else None




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  DPTImageProcessingTester(self							)




       @property
       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             return self.image_processor_tester.prepare_image_processor_dict()




       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :str		    =  self.image_processing_class(**self.image_processor_dict							)
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__		,'''image_mean'''							)							)
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__		,'''image_std'''							)							)
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__		,'''do_normalize'''							)							)
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__		,'''do_resize'''							)							)
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__		,'''size'''							)							)




       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  self.image_processing_class.from_dict(self.image_processor_dict							)
             self.assertEqual(image_processor.size		,{'''height''': 18, '''width''': 18}							)

             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.image_processing_class.from_dict(self.image_processor_dict		,size=42							)
             self.assertEqual(image_processor.size		,{'''height''': 42, '''width''': 42}							)




       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.image_processing_class(**self.image_processor_dict							)
             # create random PIL images
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  prepare_image_inputs(self.image_processor_tester		,equal_resolution=SCREAMING_SNAKE_CASE__							)
             for image in image_inputs:
                   self.assertIsInstance(SCREAMING_SNAKE_CASE__		,Image.Image							)

             # Test not batched input
             __SCREAMING_SNAKE_CASE       :List[str]		    =  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.size['''height'''],
                     self.image_processor_tester.size['''width'''],
                 )		,)

             # Test batched
             __SCREAMING_SNAKE_CASE       :str		    =  image_processing(SCREAMING_SNAKE_CASE__		,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.size['''height'''],
                     self.image_processor_tester.size['''width'''],
                 )		,)




       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.image_processing_class(**self.image_processor_dict							)
             # create random numpy tensors
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  prepare_image_inputs(self.image_processor_tester		,equal_resolution=SCREAMING_SNAKE_CASE__		,numpify=SCREAMING_SNAKE_CASE__							)
             for image in image_inputs:
                   self.assertIsInstance(SCREAMING_SNAKE_CASE__		,np.ndarray							)

             # Test not batched input
             __SCREAMING_SNAKE_CASE       :Tuple		    =  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.size['''height'''],
                     self.image_processor_tester.size['''width'''],
                 )		,)

             # Test batched
             __SCREAMING_SNAKE_CASE       :Dict		    =  image_processing(SCREAMING_SNAKE_CASE__		,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.size['''height'''],
                     self.image_processor_tester.size['''width'''],
                 )		,)




       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.image_processing_class(**self.image_processor_dict							)
             # create random PyTorch tensors
             __SCREAMING_SNAKE_CASE       :Tuple		    =  prepare_image_inputs(self.image_processor_tester		,equal_resolution=SCREAMING_SNAKE_CASE__		,torchify=SCREAMING_SNAKE_CASE__							)
             for image in image_inputs:
                   self.assertIsInstance(SCREAMING_SNAKE_CASE__		,torch.Tensor							)

             # Test not batched input
             __SCREAMING_SNAKE_CASE       :Tuple		    =  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.size['''height'''],
                     self.image_processor_tester.size['''width'''],
                 )		,)

             # Test batched
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  image_processing(SCREAMING_SNAKE_CASE__		,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.size['''height'''],
                     self.image_processor_tester.size['''width'''],
                 )		,)






"""simple docstring"""
import colorsys

from PIL import Image  # type: ignore



def 					__lowerCamelCase       (							a_					:		float			,       a_					:		float			,       a_					:		int							)      ->				float:
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  x
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  y
      for step in range(a_							):  # noqa: B007
            __SCREAMING_SNAKE_CASE       :Dict		    =  a * a - b * b + x
            __SCREAMING_SNAKE_CASE       :Tuple		    =  2 * a * b + y
            __SCREAMING_SNAKE_CASE       :Dict		    =  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       (							a_					:		float							)      ->				tuple:
      if distance == 1:
            return (0, 0, 0)
      else:
            return (2_55, 2_55, 2_55)



def 					__lowerCamelCase       (							a_					:		float							)      ->				tuple:
      if distance == 1:
            return (0, 0, 0)
      else:
            return tuple(round(i * 2_55							) for i in colorsys.hsv_to_rgb(a_			,       1			,       1							)							)



def 					__lowerCamelCase       (							a_					:		int = 8_00			,       a_					:		int = 6_00			,       a_					:		float = -0.6			,       a_					:		float = 0			,       a_					:		float = 3.2			,       a_					:		int = 50			,       a_					:		bool = True			,       )      ->				Image.Image:
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  Image.new('''RGB'''			,       (image_width, image_height)							)
      __SCREAMING_SNAKE_CASE       :Tuple		    =  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
                  __SCREAMING_SNAKE_CASE       :Dict		    =  figure_width / image_width * image_height
                  __SCREAMING_SNAKE_CASE       :str		    =  figure_center_x + (image_x / image_width - 0.5) * figure_width
                  __SCREAMING_SNAKE_CASE       :Tuple		    =  figure_center_y + (image_y / image_height - 0.5) * figure_height

                  __SCREAMING_SNAKE_CASE       :List[Any]		    =  get_distance(a_			,       a_			,       a_							)

                  # color the corresponding pixel based on the selected coloring-function
                  if use_distance_color_coding:
                        __SCREAMING_SNAKE_CASE       :Optional[int]		    =  get_color_coded_rgb(a_							)
                  else:
                        __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  get_black_and_white_rgb(a_							)

      return img


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # colored version, full figure
  lowerCamelCase_       =		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()