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    __A       ( unittest.TestCase    ):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  	def __init__(	self				,					__A				,					__A=7				,					__A=3				,					__A=18				,					__A=30				,					__A=400				,					__A=True				,					__A=None				,					__A=True				,					__A=False				,					__A=[0.48_145_466, 0.4_578_275, 0.40_821_073]				,					__A=[0.26_862_954, 0.26_130_258, 0.27_577_711]				,					__A=True				,					)      ->  List[str]:
  					a					=size if size is not None else {'''height''': 20, '''width''': 20}
  					a					=parent
  					a					=batch_size
  					a					=num_channels
  					a					=image_size
  					a					=min_resolution
  					a					=max_resolution
  					a					=do_resize
  					a					=size
  					a					=do_normalize
  					a					=image_mean
  					a					=image_std
  					a					=do_pad
  					a					=do_convert_rgb
  
  
  	def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
  					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    SCREAMING_SNAKE_CASE							(	self				,					__A=False				,					__A=False				,					__A=False		)      ->  str:
  					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
  					if equal_resolution:
  									a					=[]
  									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:
  									a					=[]
  									for i in range(self.batch_size		):
  													a	,   a					=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
  									a					=[Image.fromarray(np.moveaxis(__A				,					0				,					-1		)		) for x in image_inputs]
  
  					if torchify:
  									a					=[torch.from_numpy(__A		) for x in image_inputs]
  
  					return image_inputs
  
  
  
  
  
  
  @require_torch
  @require_vision
  class    __A       ( A_,					unittest.TestCase    ):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  	__lowerCAmelCase				      =						BlipImageProcessor if is_vision_available() else None
  
  
  	def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
  					a					=BlipImageProcessingTester(self		)
  
  
  	@property
  	def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
  					return self.image_processor_tester.prepare_image_processor_dict()
  
  
  	def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
  					a					=self.image_processing_class(**self.image_processor_dict		)
  					self.assertTrue(hasattr(__A				,					'''do_resize'''		)		)
  					self.assertTrue(hasattr(__A				,					'''size'''		)		)
  					self.assertTrue(hasattr(__A				,					'''do_normalize'''		)		)
  					self.assertTrue(hasattr(__A				,					'''image_mean'''		)		)
  					self.assertTrue(hasattr(__A				,					'''image_std'''		)		)
  					self.assertTrue(hasattr(__A				,					'''do_convert_rgb'''		)		)
  
  
  	def    SCREAMING_SNAKE_CASE							(	self		)      ->  Any:
  					pass
  
  
  	def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
  					# Initialize image_processor
  					a					=self.image_processing_class(**self.image_processor_dict		)
  					# create random PIL images
  					a					=self.image_processor_tester.prepare_inputs(equal_resolution=__A		)
  					for image in image_inputs:
  									self.assertIsInstance(__A				,					Image.Image		)
  
  					# Test not batched input
  					a					=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
  					a					=image_processor(__A				,					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    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
  					# Initialize image_processor
  					a					=self.image_processing_class(**self.image_processor_dict		)
  					# create random numpy tensors
  					a					=self.image_processor_tester.prepare_inputs(equal_resolution=__A				,					numpify=__A		)
  					for image in image_inputs:
  									self.assertIsInstance(__A				,					np.ndarray		)
  
  					# Test not batched input
  					a					=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
  					a					=image_processor(__A				,					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    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
  					# Initialize image_processor
  					a					=self.image_processing_class(**self.image_processor_dict		)
  					# create random PyTorch tensors
  					a					=self.image_processor_tester.prepare_inputs(equal_resolution=__A				,					torchify=__A		)
  					for image in image_inputs:
  									self.assertIsInstance(__A				,					torch.Tensor		)
  
  					# Test not batched input
  					a					=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
  					a					=image_processor(__A				,					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    __A       ( A_,					unittest.TestCase    ):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  	__lowerCAmelCase				      =						BlipImageProcessor if is_vision_available() else None
  
  
  	def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
  					a					=BlipImageProcessingTester(self				,					num_channels=4		)
  					a					=3
  
  
  	@property
  	def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
  					return self.image_processor_tester.prepare_image_processor_dict()
  
  
  	def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[Any]:
  					a					=self.image_processing_class(**self.image_processor_dict		)
  					self.assertTrue(hasattr(__A				,					'''do_resize'''		)		)
  					self.assertTrue(hasattr(__A				,					'''size'''		)		)
  					self.assertTrue(hasattr(__A				,					'''do_normalize'''		)		)
  					self.assertTrue(hasattr(__A				,					'''image_mean'''		)		)
  					self.assertTrue(hasattr(__A				,					'''image_std'''		)		)
  					self.assertTrue(hasattr(__A				,					'''do_convert_rgb'''		)		)
  
  
  	def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
  					pass
  
  
  
  
  
  	def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
  					# Initialize image_processor
  					a					=self.image_processing_class(**self.image_processor_dict		)
  					# create random PIL images
  					a					=self.image_processor_tester.prepare_inputs(equal_resolution=__A		)
  					for image in image_inputs:
  									self.assertIsInstance(__A				,					Image.Image		)
  
  					# Test not batched input
  					a					=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
  					a					=image_processor(__A				,					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"""
  
  
  
  
  
  
  
  from cva import destroyAllWindows, imread, imshow, waitKey
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a	,   a					=img.shape[0], img.shape[1]
  
      def to_grayscale(lowercase ,					lowercase ,					lowercase	):
          return 0.2126 * red + 0.587 * green + 0.114 * blue
  
      def normalize(lowercase	):
          return min(lowercase ,					2_55	)
  
      for i in range(lowercase	):
          for j in range(lowercase	):
              a					=int(to_grayscale(*img[i][j]	)	)
              a					=[
                  normalize(lowercase	),
                  normalize(greyscale + factor	),
                  normalize(greyscale + 2 * factor	),
              ]
  
      return img
  
  
  if __name__ == "__main__":
         # read original image
         lowerCamelCase_     :     Any    		=						{
             percentage: imread("""image_data/lena.jpg""", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
         }
  
         for percentage, img in images.items():
                make_sepia(img, percentage)
  
         for percentage, img in images.items():
                imshow(F'Original image with sepia (factor: {percentage})', img)
  
         waitKey(0)
         destroyAllWindows()