code: 
  
  
  
  
  """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
  
  
  if is_torch_available():
         import torch
  
  if is_vision_available():
         from PIL import Image
  
         from transformers import BlipImageProcessor
  class 			lowerCamelCase__    (  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=400				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=False				, SCREAMING_SNAKE_CASE=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]				, SCREAMING_SNAKE_CASE=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]				, SCREAMING_SNAKE_CASE=True				, ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Optional[Any]								=				size if size is not None else {"height": 20, "width": 20}
       snake_case		:						Tuple								=				parent
       snake_case		:						List[Any]								=				batch_size
       snake_case		:						Tuple								=				num_channels
       snake_case		:						Union[str, Any]								=				image_size
       snake_case		:						Tuple								=				min_resolution
       snake_case		:						str								=				max_resolution
       snake_case		:						Any								=				do_resize
       snake_case		:						Optional[Any]								=				size
       snake_case		:						int								=				do_normalize
       snake_case		:						str								=				image_mean
       snake_case		:						Optional[Any]								=				image_std
       snake_case		:						int								=				do_pad
       snake_case		:						int								=				do_convert_rgb
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_convert_rgb": self.do_convert_rgb,
           "do_pad": self.do_pad,
       }
  
  
  
     def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE=False				, SCREAMING_SNAKE_CASE=False				, SCREAMING_SNAKE_CASE=False							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
       if equal_resolution:
         snake_case		:						Dict								=				[]
         for i in range(self.batch_size							):
           image_inputs.append(
               np.random.randint(
                   255				, size=(self.num_channels, self.max_resolution, self.max_resolution)				, dtype=np.uinta							)							)
       else:
         snake_case		:						Optional[Any]								=				[]
         for i in range(self.batch_size							):
           snake_case		:						Any								=				np.random.choice(np.arange(self.min_resolution				, self.max_resolution							)				, 2							)
           image_inputs.append(np.random.randint(255				, size=(self.num_channels, width, height)				, dtype=np.uinta							)							)
  
       if not numpify and not torchify:
         # PIL expects the channel dimension as last dimension
         snake_case		:						Optional[int]								=				[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE				, 0				, -1							)							) for x in image_inputs]
  
       if torchify:
         snake_case		:						Optional[Any]								=				[torch.from_numpy(SCREAMING_SNAKE_CASE							) for x in image_inputs]
  
       return image_inputs
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 			lowerCamelCase__    (  lowerCamelCase_				,  unittest.TestCase			):
     a__ :      Dict								=     BlipImageProcessor if is_vision_available() else None
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						str								=				BlipImageProcessingTester(self							)
  
  
     @property
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       return self.image_processor_tester.prepare_image_processor_dict()
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Optional[int]								=				self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "do_resize"							)							)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "size"							)							)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "do_normalize"							)							)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "image_mean"							)							)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "image_std"							)							)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "do_convert_rgb"							)							)
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       pass
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Any								=				self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       snake_case		:						List[str]								=				self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE							)
       for image in image_inputs:
         self.assertIsInstance(SCREAMING_SNAKE_CASE				, Image.Image							)
  
       # Test not batched input
       snake_case		:						Union[str, Any]								=				image_processor(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
       snake_case		:						Dict								=				image_processor(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 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Union[str, Any]								=				self.image_processing_class(**self.image_processor_dict							)
       # create random numpy tensors
       snake_case		:						Dict								=				self.image_processor_tester.prepare_inputs(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
       snake_case		:						Any								=				image_processor(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
       snake_case		:						List[str]								=				image_processor(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 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						int								=				self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       snake_case		:						Union[str, Any]								=				self.image_processor_tester.prepare_inputs(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
       snake_case		:						str								=				image_processor(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
       snake_case		:						List[Any]								=				image_processor(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"],
           )				, )
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 			lowerCamelCase__    (  lowerCamelCase_				,  unittest.TestCase			):
     a__ :      Union[str, Any]								=     BlipImageProcessor if is_vision_available() else None
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Dict								=				BlipImageProcessingTester(self				, num_channels=4							)
       snake_case		:						Optional[int]								=				3
  
  
     @property
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       return self.image_processor_tester.prepare_image_processor_dict()
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Tuple								=				self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "do_resize"							)							)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "size"							)							)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "do_normalize"							)							)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "image_mean"							)							)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "image_std"							)							)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "do_convert_rgb"							)							)
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       pass
  
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						List[Any]								=				self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       snake_case		:						List[str]								=				self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE							)
       for image in image_inputs:
         self.assertIsInstance(SCREAMING_SNAKE_CASE				, Image.Image							)
  
       # Test not batched input
       snake_case		:						Any								=				image_processor(image_inputs[0]				, return_tensors="pt"							).pixel_values
       self.assertEqual(
           encoded_images.shape				, (
               1,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )				, )
  
       # Test batched
       snake_case		:						List[str]								=				image_processor(SCREAMING_SNAKE_CASE				, return_tensors="pt"							).pixel_values
       self.assertEqual(
           encoded_images.shape				, (
               self.image_processor_tester.batch_size,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )				, )
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  import numpy as np
  from cva import destroyAllWindows, imread, imshow, waitKey
  class 			lowerCamelCase__    :
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				0
  					# max greyscale value for #FFFFFF
  					snake_case		:						List[str]								=				int(self.get_greyscale(255				, 255				, 255							)							)
  
  					if not self.min_threshold < threshold < self.max_threshold:
  							snake_case		:						Union[str, Any]								=				F'''Factor value should be from 0 to {self.max_threshold}'''
  							raise ValueError(SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				input_img
  					snake_case		:						Optional[Any]								=				threshold
  					snake_case			,				snake_case		:						List[str]								=				self.input_img.shape[1], self.input_img.shape[0]
  
  					# error table size (+4 columns and +1 row) greater than input image because of
  					# lack of if statements
  					snake_case		:						Union[str, Any]								=				[
  					    [0 for _ in range(self.height + 4							)] for __ in range(self.width + 1							)
  					]
  					snake_case		:						Tuple								=				np.ones((self.width, self.height, 3)				, np.uinta							) * 255
  
  
  			@classmethod
  			def 							lowerCamelCase_				(     cls				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return 0.1_14 * blue + 0.5_87 * green + 0.21_26 * red
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					for y in range(self.height							):
  							for x in range(self.width							):
  									snake_case		:						Dict								=				int(self.get_greyscale(*self.input_img[y][x]							)							)
  									if self.threshold > greyscale + self.error_table[y][x]:
  											snake_case		:						List[str]								=				(0, 0, 0)
  											snake_case		:						List[Any]								=				greyscale + self.error_table[x][y]
  									else:
  											snake_case		:						int								=				(255, 255, 255)
  											snake_case		:						Tuple								=				greyscale + self.error_table[x][y] - 255
  									self.error_table[y][x + 1] += int(8 / 32 * current_error							)
  									self.error_table[y][x + 2] += int(4 / 32 * current_error							)
  									self.error_table[y + 1][x] += int(8 / 32 * current_error							)
  									self.error_table[y + 1][x + 1] += int(4 / 32 * current_error							)
  									self.error_table[y + 1][x + 2] += int(2 / 32 * current_error							)
  									self.error_table[y + 1][x - 1] += int(4 / 32 * current_error							)
  									self.error_table[y + 1][x - 2] += int(2 / 32 * current_error							)
  
  
  if __name__ == "__main__":
  							# create Burke's instances with original images in greyscale
  							__A 		=			[
  							    Burkes(imread("image_data/lena.jpg", 1), threshold)
  							    for threshold in (1, 126, 130, 140)
  							]
  
  							for burkes in burkes_instances:
  														burkes.process()
  
  							for burkes in burkes_instances:
  														imshow(
  														    f'Original image with dithering threshold: {burkes.threshold}',
  														    burkes.output_img,
  														)
  
  							waitKey(0)
  							destroyAllWindows()
  
  