






"""simple docstring"""





import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor
class    lowercase__     (					unittest.TestCase   ):







       '''simple docstring'''


       def __init__(							self    :						Optional[Any]	,       _UpperCAmelCase    :						Any	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Dict[str, int] = None	,       _UpperCAmelCase    :						int = 32	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Union[int, float] = 1 / 255	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = [0.4814_5466, 0.457_8275, 0.4082_1073]	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = [0.2686_2954, 0.2613_0258, 0.2757_7711]	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Optional[int]=7	,       _UpperCAmelCase    :						Any=30	,       _UpperCAmelCase    :						Optional[Any]=400	,       _UpperCAmelCase    :						int=3	,       )				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							parent
             UpperCAmelCase_    				=							do_resize
             UpperCAmelCase_    				=							size if size is not None else {"""shortest_edge""": 288}
             UpperCAmelCase_    				=							size_divisor
             UpperCAmelCase_    				=							do_rescale
             UpperCAmelCase_    				=							rescale_factor
             UpperCAmelCase_    				=							do_normalize
             UpperCAmelCase_    				=							do_center_crop
             UpperCAmelCase_    				=							image_mean
             UpperCAmelCase_    				=							image_std
             UpperCAmelCase_    				=							do_pad
             UpperCAmelCase_    				=							batch_size
             UpperCAmelCase_    				=							num_channels
             UpperCAmelCase_    				=							min_resolution
             UpperCAmelCase_    				=							max_resolution



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


             '''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,
                 "size_divisor": self.size_divisor,
             }







       def   lowercase__       (							self    :						List[str]	,       _UpperCAmelCase    :						Tuple	,       _UpperCAmelCase    :						List[str]=False					)				->		Optional[int]:


             '''simple docstring'''




             if not batched:
                   UpperCAmelCase_    				=							self.size["""shortest_edge"""]
                   UpperCAmelCase_    				=							image_inputs[0]
                   if isinstance(__a	,       Image.Image					):
                         UpperCAmelCase_    				=							image.size
                   else:
                         UpperCAmelCase_    				=							image.shape[1], image.shape[2]
                   UpperCAmelCase_    				=							size / min(__a	,       __a					)
                   if h < w:
                         UpperCAmelCase_    				=							size, scale * w
                   else:
                         UpperCAmelCase_    				=							scale * h, size

                   UpperCAmelCase_    				=							int((1333 / 800) * size					)
                   if max(__a	,       __a					) > max_size:
                         UpperCAmelCase_    				=							max_size / max(__a	,       __a					)
                         UpperCAmelCase_    				=							newh * scale
                         UpperCAmelCase_    				=							neww * scale

                   UpperCAmelCase_    				=							int(newh + 0.5					), int(neww + 0.5					)
                   UpperCAmelCase_    				=							(
                       newh // self.size_divisor * self.size_divisor,
                       neww // self.size_divisor * self.size_divisor,
                   )

             else:
                   UpperCAmelCase_    				=							[]
                   for image in image_inputs:
                         UpperCAmelCase_    				=							self.get_expected_values([image]					)
                         expected_values.append((expected_height, expected_width)					)
                   UpperCAmelCase_    				=							max(__a	,       key=lambda _UpperCAmelCase					: item[0]					)[0]
                   UpperCAmelCase_    				=							max(__a	,       key=lambda _UpperCAmelCase					: item[1]					)[1]

             return expected_height, expected_width



@require_torch
@require_vision
class    lowercase__     (					__lowercase       ,      unittest.TestCase   ):







       '''simple docstring'''


       UpperCamelCase		=  BridgeTowerImageProcessor if is_vision_available() else None



       def   lowercase__       (							self    :						Any					)				->		Dict:


             '''simple docstring'''




             UpperCAmelCase_    				=							BridgeTowerImageProcessingTester(self					)



       @property
       def   lowercase__       (							self    :						List[str]					)				->		Optional[int]:


             '''simple docstring'''




             return self.image_processor_tester.prepare_image_processor_dict()



       def   lowercase__       (							self    :						Optional[int]					)				->		Dict:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.image_processing_class(**self.image_processor_dict					)
             self.assertTrue(hasattr(__a	,       "image_mean"					)					)
             self.assertTrue(hasattr(__a	,       "image_std"					)					)
             self.assertTrue(hasattr(__a	,       "do_normalize"					)					)
             self.assertTrue(hasattr(__a	,       "do_resize"					)					)
             self.assertTrue(hasattr(__a	,       "size"					)					)
             self.assertTrue(hasattr(__a	,       "size_divisor"					)					)



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


             '''simple docstring'''




             pass



       def   lowercase__       (							self    :						List[Any]					)				->		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=__a					)
             for image in image_inputs:
                   self.assertIsInstance(__a	,       Image.Image					)

             # Test not batched input
             UpperCAmelCase_    				=							image_processing(image_inputs[0]	,       return_tensors="pt"					).pixel_values

             UpperCAmelCase_    				=							self.image_processor_tester.get_expected_values(__a					)
             self.assertEqual(
                 encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

             # Test batched
             UpperCAmelCase_    				=							image_processing(__a	,       return_tensors="pt"					).pixel_values

             UpperCAmelCase_    				=							self.image_processor_tester.get_expected_values(__a	,       batched=__a					)
             self.assertEqual(
                 encoded_images.shape	,       (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )	,       )



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


             '''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=__a	,       numpify=__a					)
             for image in image_inputs:
                   self.assertIsInstance(__a	,       np.ndarray					)

             # Test not batched input
             UpperCAmelCase_    				=							image_processing(image_inputs[0]	,       return_tensors="pt"					).pixel_values

             UpperCAmelCase_    				=							self.image_processor_tester.get_expected_values(__a					)
             self.assertEqual(
                 encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

             # Test batched
             UpperCAmelCase_    				=							image_processing(__a	,       return_tensors="pt"					).pixel_values

             UpperCAmelCase_    				=							self.image_processor_tester.get_expected_values(__a	,       batched=__a					)
             self.assertEqual(
                 encoded_images.shape	,       (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )	,       )







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


             '''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=__a	,       torchify=__a					)
             for image in image_inputs:
                   self.assertIsInstance(__a	,       torch.Tensor					)

             # Test not batched input
             UpperCAmelCase_    				=							image_processing(image_inputs[0]	,       return_tensors="pt"					).pixel_values

             UpperCAmelCase_    				=							self.image_processor_tester.get_expected_values(__a					)
             self.assertEqual(
                 encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

             # Test batched
             UpperCAmelCase_    				=							image_processing(__a	,       return_tensors="pt"					).pixel_values

             UpperCAmelCase_    				=							self.image_processor_tester.get_expected_values(__a	,       batched=__a					)
             self.assertEqual(
                 encoded_images.shape	,       (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )	,       )









"""simple docstring"""





import colorsys

from PIL import Image  # type: ignore
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							x
      UpperCAmelCase_    				=							y
      for step in range(lowerCAmelCase__   ):  # 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__			(   lowerCAmelCase__   ):
      if distance == 1:
            return (0, 0, 0)
      else:
            return (255, 255, 255)
def      a__			(   lowerCAmelCase__   ):
      if distance == 1:
            return (0, 0, 0)
      else:
            return tuple(round(i * 255   ) for i in colorsys.hsv_to_rgb(lowerCAmelCase__			,    1			,    1   )   )
def      a__			(   lowerCAmelCase__ = 800			,    lowerCAmelCase__ = 600			,    lowerCAmelCase__ = -0.6			,    lowerCAmelCase__ = 0			,    lowerCAmelCase__ = 3.2			,    lowerCAmelCase__ = 50			,    lowerCAmelCase__ = True			,    ):
      UpperCAmelCase_    				=							Image.new("RGB"			,    (image_width, image_height)   )
      UpperCAmelCase_    				=							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
                  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(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )

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

      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()




