

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, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor



class  __snake_case					(      unittest.TestCase    ):

  def __init__(   self  ,						_A  ,						_A=7  ,						_A=3  ,						_A=18  ,						_A=30  ,						_A=400  ,						_A=True  ,						_A=None  ,						_A=True  ,						_A=[0.5, 0.5, 0.5]  ,						_A=[0.5, 0.5, 0.5]  ,						):
      SCREAMING_SNAKE_CASE_													=  size if size is not None else {'height': 18, 'width': 18}
      SCREAMING_SNAKE_CASE_													=  parent
      SCREAMING_SNAKE_CASE_													=  batch_size
      SCREAMING_SNAKE_CASE_													=  num_channels
      SCREAMING_SNAKE_CASE_													=  image_size
      SCREAMING_SNAKE_CASE_													=  min_resolution
      SCREAMING_SNAKE_CASE_													=  max_resolution
      SCREAMING_SNAKE_CASE_													=  do_resize
      SCREAMING_SNAKE_CASE_													=  size
      SCREAMING_SNAKE_CASE_													=  do_normalize
      SCREAMING_SNAKE_CASE_													=  image_mean
      SCREAMING_SNAKE_CASE_													=  image_std


  def   lowerCAmelCase__       (   self):
      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  __snake_case					(      lowerCAmelCase__       ,	unittest.TestCase    ):
  __lowerCAmelCase       :		List[str]  	=					ViTImageProcessor if is_vision_available() else None

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  ViTImageProcessingTester(self)

  @property
  def   lowerCAmelCase__       (   self):
      return self.image_processor_tester.prepare_image_processor_dict()

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  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'))

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size  ,						{'height': 18, 'width': 18})

      SCREAMING_SNAKE_CASE_													=  self.image_processing_class.from_dict(self.image_processor_dict  ,						size=42)
      self.assertEqual(image_processor.size  ,						{'height': 42, 'width': 42})

  def   lowerCAmelCase__       (   self):
      pass

  def   lowerCAmelCase__       (   self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_													=  self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      SCREAMING_SNAKE_CASE_													=  prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=_A)
      for image in image_inputs:
          self.assertIsInstance(_A  ,						Image.Image)

      # Test not batched input
      SCREAMING_SNAKE_CASE_													=  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_													=  image_processing(_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   lowerCAmelCase__       (   self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_													=  self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      SCREAMING_SNAKE_CASE_													=  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
      SCREAMING_SNAKE_CASE_													=  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_													=  image_processing(_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   lowerCAmelCase__       (   self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_													=  self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE_													=  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
      SCREAMING_SNAKE_CASE_													=  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_													=  image_processing(_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'],
          )  ,						)





import cva
import numpy as np


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						np.ndarray	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    try:
        return int(image[x_coordinate][y_coordinate] >= center    )
    except (IndexError, TypeError):
        return 0


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						np.ndarray	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  image[x_coordinate][y_coordinate]
    SCREAMING_SNAKE_CASE_													=  [1, 2, 4, 8, 16, 32, 64, 128]

    # skip get_neighbors_pixel if center is null
    if center is None:
        return 0

    # Starting from the top right, assigning value to pixels clockwise
    SCREAMING_SNAKE_CASE_													=  [
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate - 1	,				y_coordinate + 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				y_coordinate + 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate - 1	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate + 1	,				y_coordinate + 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate + 1	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate + 1	,				y_coordinate - 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				y_coordinate - 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate - 1	,				y_coordinate - 1	,				_SCREAMING_SNAKE_CASE    ),
    ]

    # Converting the binary value to decimal.
    return sum(
        binary_value * power for binary_value, power in zip(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )    )


if __name__ == "__main__":
       # Reading the image and converting it to grayscale.
       UpperCamelCase__					: Optional[int]								=      cva.imread(
           "digital_image_processing/image_data/lena.jpg", cva.IMREAD_GRAYSCALE
       )

       # Create a numpy array as the same height and width of read image
       UpperCamelCase__					: Dict								=      np.zeros((image.shape[0], image.shape[1]))

       # Iterating through the image and calculating the
       # local binary pattern value for each pixel.
       for i in range(0, image.shape[0]):
              for j in range(0, image.shape[1]):
                     UpperCamelCase__					: Any								=      local_binary_value(image, i, j)

       cva.imshow("local binary pattern", lbp_image)
       cva.waitKey(0)
       cva.destroyAllWindows()


