

"""simple docstring"""




import unittest

import numpy as np
import requests

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

      from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
      __UpperCAmelCase			=					False

if is_vision_available():
      from PIL import Image

      from transformers import PixaStructImageProcessor







class   __lowercase      (     unittest.TestCase       ):

  def __init__(	self		:      str       ,A		:      str       ,A		:      int=7       ,A		:      Tuple=3       ,A		:      Dict=18       ,A		:      Dict=30       ,A		:      Optional[int]=400       ,A		:      Dict=None       ,A		:      str=True       ,A		:      Any=True       ,A		:      str=None       ,):






   '''simple docstring'''
   UpperCAmelCase__       :	str        =       size if size is not None else {"""height""": 20, """width""": 20}
   UpperCAmelCase__       :	Union[str, Any]        =       parent
   UpperCAmelCase__       :	Optional[Any]        =       batch_size
   UpperCAmelCase__       :	str        =       num_channels
   UpperCAmelCase__       :	List[str]        =       image_size
   UpperCAmelCase__       :	Tuple        =       min_resolution
   UpperCAmelCase__       :	Any        =       max_resolution
   UpperCAmelCase__       :	Any        =       size
   UpperCAmelCase__       :	int        =       do_normalize
   UpperCAmelCase__       :	Any        =       do_convert_rgb
   UpperCAmelCase__       :	Optional[int]        =       [512, 1_024, 2_048, 4_096]
   UpperCAmelCase__       :	List[str]        =       patch_size if patch_size is not None else {"""height""": 16, """width""": 16}

  def  __lowercase							(	self		:      Union[str, Any]     ):






   '''simple docstring'''
   return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




  def  __lowercase							(	self		:      Dict     ):






   '''simple docstring'''
   UpperCAmelCase__       :	int        =       """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
   UpperCAmelCase__       :	List[str]        =       Image.open(requests.get(A       ,stream=A     ).raw     ).convert("""RGB"""     )
   return raw_image







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11						,      reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""						,      )
@require_torch
@require_vision
class   __lowercase      (     __lowerCamelCase						,      unittest.TestCase       ):
  snake_case_												=							PixaStructImageProcessor if is_vision_available() else None

  def  __lowercase							(	self		:      List[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	str        =       PixaStructImageProcessingTester(self     )

  @property
  def  __lowercase							(	self		:      List[Any]     ):






   '''simple docstring'''
   return self.image_processor_tester.prepare_image_processor_dict()

  def  __lowercase							(	self		:      Tuple     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Dict        =       self.image_processing_class(**self.image_processor_dict     )
   self.assertTrue(hasattr(A       ,"""do_normalize"""     )     )
   self.assertTrue(hasattr(A       ,"""do_convert_rgb"""     )     )

  def  __lowercase							(	self		:      List[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[Any]        =       self.image_processor_tester.prepare_dummy_image()

   UpperCAmelCase__       :	Any        =       self.image_processing_class(**self.image_processor_dict     )
   UpperCAmelCase__       :	Tuple        =       2_048

   UpperCAmelCase__       :	Union[str, Any]        =       image_processor(A       ,return_tensors="""pt"""       ,max_patches=A     )
   self.assertTrue(torch.allclose(inputs.flattened_patches.mean()       ,torch.tensor(0.0_6_0_6     )       ,atol=1e-3       ,rtol=1e-3     )     )

  def  __lowercase							(	self		:      Tuple     ):






   '''simple docstring'''
   # Initialize image_processor
   UpperCAmelCase__       :	List[str]        =       self.image_processing_class(**self.image_processor_dict     )
   # create random PIL images
   UpperCAmelCase__       :	Optional[int]        =       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__       :	Optional[Any]        =       (
       (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
       * self.image_processor_tester.num_channels
   ) + 2

   for max_patch in self.image_processor_tester.max_patches:
    # Test not batched input
    UpperCAmelCase__       :	Union[str, Any]        =       image_processor(
        image_inputs[0]       ,return_tensors="""pt"""       ,max_patches=A     ).flattened_patches
    self.assertEqual(
        encoded_images.shape       ,(1, max_patch, expected_hidden_dim)       ,)

    # Test batched
    UpperCAmelCase__       :	Any        =       image_processor(
        A       ,return_tensors="""pt"""       ,max_patches=A     ).flattened_patches
    self.assertEqual(
        encoded_images.shape       ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,)

  def  __lowercase							(	self		:      Optional[Any]     ):






   '''simple docstring'''
   # Initialize image_processor
   UpperCAmelCase__       :	Union[str, Any]        =       self.image_processing_class(**self.image_processor_dict     )
   # create random PIL images
   UpperCAmelCase__       :	Any        =       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__       :	Tuple        =       (
       (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
       * self.image_processor_tester.num_channels
   ) + 2

   UpperCAmelCase__       :	str        =       True

   for max_patch in self.image_processor_tester.max_patches:
    # Test not batched input
    with self.assertRaises(A     ):
     UpperCAmelCase__       :	Any        =       image_processor(
         image_inputs[0]       ,return_tensors="""pt"""       ,max_patches=A     ).flattened_patches

    UpperCAmelCase__       :	List[str]        =       """Hello"""

    UpperCAmelCase__       :	List[Any]        =       image_processor(
        image_inputs[0]       ,return_tensors="""pt"""       ,max_patches=A       ,header_text=A     ).flattened_patches
    self.assertEqual(
        encoded_images.shape       ,(1, max_patch, expected_hidden_dim)       ,)

    # Test batched
    UpperCAmelCase__       :	Optional[int]        =       image_processor(
        A       ,return_tensors="""pt"""       ,max_patches=A       ,header_text=A     ).flattened_patches
    self.assertEqual(
        encoded_images.shape       ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,)

  def  __lowercase							(	self		:      List[Any]     ):






   '''simple docstring'''
   # Initialize image_processor
   UpperCAmelCase__       :	List[Any]        =       self.image_processing_class(**self.image_processor_dict     )
   # create random numpy tensors
   UpperCAmelCase__       :	int        =       prepare_image_inputs(self.image_processor_tester       ,equal_resolution=A       ,numpify=A     )
   for image in image_inputs:
    self.assertIsInstance(A       ,np.ndarray     )

   UpperCAmelCase__       :	Any        =       (
       (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
       * self.image_processor_tester.num_channels
   ) + 2

   for max_patch in self.image_processor_tester.max_patches:
    # Test not batched input
    UpperCAmelCase__       :	int        =       image_processor(
        image_inputs[0]       ,return_tensors="""pt"""       ,max_patches=A     ).flattened_patches
    self.assertEqual(
        encoded_images.shape       ,(1, max_patch, expected_hidden_dim)       ,)

    # Test batched
    UpperCAmelCase__       :	Union[str, Any]        =       image_processor(
        A       ,return_tensors="""pt"""       ,max_patches=A     ).flattened_patches
    self.assertEqual(
        encoded_images.shape       ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,)




  def  __lowercase							(	self		:      Tuple     ):






   '''simple docstring'''
   # Initialize image_processor
   UpperCAmelCase__       :	int        =       self.image_processing_class(**self.image_processor_dict     )
   # create random PyTorch tensors
   UpperCAmelCase__       :	Tuple        =       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__       :	Tuple        =       (
       (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
       * self.image_processor_tester.num_channels
   ) + 2

   for max_patch in self.image_processor_tester.max_patches:
    # Test not batched input
    UpperCAmelCase__       :	Optional[int]        =       image_processor(
        image_inputs[0]       ,return_tensors="""pt"""       ,max_patches=A     ).flattened_patches
    self.assertEqual(
        encoded_images.shape       ,(1, max_patch, expected_hidden_dim)       ,)

    # Test batched
    UpperCAmelCase__       :	Union[str, Any]        =       image_processor(
        A       ,return_tensors="""pt"""       ,max_patches=A     ).flattened_patches
    self.assertEqual(
        encoded_images.shape       ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,)







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11						,      reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""						,      )
@require_torch
@require_vision
class   __lowercase      (     __lowerCamelCase						,      unittest.TestCase       ):
  snake_case_												=							PixaStructImageProcessor if is_vision_available() else None

  def  __lowercase							(	self		:      Tuple     ):






   '''simple docstring'''
   UpperCAmelCase__       :	List[str]        =       PixaStructImageProcessingTester(self       ,num_channels=4     )
   UpperCAmelCase__       :	List[Any]        =       3

  @property
  def  __lowercase							(	self		:      Optional[int]     ):






   '''simple docstring'''
   return self.image_processor_tester.prepare_image_processor_dict()

  def  __lowercase							(	self		:      Any     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Tuple        =       self.image_processing_class(**self.image_processor_dict     )
   self.assertTrue(hasattr(A       ,"""do_normalize"""     )     )
   self.assertTrue(hasattr(A       ,"""do_convert_rgb"""     )     )




  def  __lowercase							(	self		:      int     ):






   '''simple docstring'''
   # Initialize image_processor
   UpperCAmelCase__       :	Optional[Any]        =       self.image_processing_class(**self.image_processor_dict     )
   # create random PIL images
   UpperCAmelCase__       :	str        =       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__       :	str        =       (
       (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
       * (self.image_processor_tester.num_channels - 1)
   ) + 2

   for max_patch in self.image_processor_tester.max_patches:
    # Test not batched input
    UpperCAmelCase__       :	List[str]        =       image_processor(
        image_inputs[0]       ,return_tensors="""pt"""       ,max_patches=A     ).flattened_patches
    self.assertEqual(
        encoded_images.shape       ,(1, max_patch, expected_hidden_dim)       ,)

    # Test batched
    UpperCAmelCase__       :	Optional[Any]        =       image_processor(
        A       ,return_tensors="""pt"""       ,max_patches=A     ).flattened_patches
    self.assertEqual(
        encoded_images.shape       ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,)









'''simple docstring'''
import colorsys

from PIL import Image  # type: ignore

def 					lowerCamelCase_ ( __UpperCamelCase					:       float							,    __UpperCamelCase					:       float							,    __UpperCamelCase					:       int		)					->	float:







     """simple docstring"""
     _A				=							x
     _A				=							y
     for step in range(__UpperCamelCase		):  # noqa: B007
          _A				=							a * a - b * b + x
          _A				=							2 * a * b + y
          _A				=							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_ ( __UpperCamelCase					:       float		)					->	tuple:







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

def 					lowerCamelCase_ ( __UpperCamelCase					:       float		)					->	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(__UpperCamelCase							,    1							,    1		)		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       int = 8_0_0							,    __UpperCamelCase					:       int = 6_0_0							,    __UpperCamelCase					:       float = -0.6							,    __UpperCamelCase					:       float = 0							,    __UpperCamelCase					:       float = 3.2							,    __UpperCamelCase					:       int = 5_0							,    __UpperCamelCase					:       bool = True							,    )					->	Image.Image:







     """simple docstring"""
     _A				=							Image.new('RGB'							,    (image_width, image_height)		)
     _A				=							img.load()

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

               _A				=							get_distance(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase		)

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

     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()





