


"""simple docstring"""


import cva
import numpy as np


def 					UpperCAmelCase			(				a__    ,			a__    ,			a__    ,			a__   ):





						'''simple docstring'''






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


def 					UpperCAmelCase			(				a__    ,			a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Optional[int]				  = image[x_coordinate][y_coordinate]
						lowerCAmelCase      :Tuple				  = [1, 2, 4, 8, 16, 32, 64, 1_28]

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

						# Starting from the top right, assigning value to pixels clockwise
						lowerCAmelCase      :Tuple				  = [
						    get_neighbors_pixel(a__    ,			x_coordinate - 1    ,			y_coordinate + 1    ,			a__   ),
						    get_neighbors_pixel(a__    ,			a__    ,			y_coordinate + 1    ,			a__   ),
						    get_neighbors_pixel(a__    ,			x_coordinate - 1    ,			a__    ,			a__   ),
						    get_neighbors_pixel(a__    ,			x_coordinate + 1    ,			y_coordinate + 1    ,			a__   ),
						    get_neighbors_pixel(a__    ,			x_coordinate + 1    ,			a__    ,			a__   ),
						    get_neighbors_pixel(a__    ,			x_coordinate + 1    ,			y_coordinate - 1    ,			a__   ),
						    get_neighbors_pixel(a__    ,			a__    ,			y_coordinate - 1    ,			a__   ),
						    get_neighbors_pixel(a__    ,			x_coordinate - 1    ,			y_coordinate - 1    ,			a__   ),
						]

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


if __name__ == "__main__":
		# Reading the image and converting it to grayscale.
		__SCREAMING_SNAKE_CASE      =      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
		__SCREAMING_SNAKE_CASE      =      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]):
						__SCREAMING_SNAKE_CASE      =      local_binary_value(image, i, j)

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







"""simple docstring"""


import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir




@dataclass
class 	__UpperCamelCase    :
					lowercase_						:							str




class 	__UpperCamelCase    (    unittest.TestCase	):
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					Optional[Any]:
											lowerCAmelCase      :List[str]				  = get_tests_dir('fixtures/test_sentencepiece.model'      )
											lowerCAmelCase      :Tuple				  = get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model'      )

											lowerCAmelCase      :List[str]				  = FakeOriginalTokenizer(vocab_file=UpperCAmelCase      )

											with warnings.catch_warnings(record=UpperCAmelCase      ) as w:
																	lowerCAmelCase      :Dict				  = SpmConverter(UpperCAmelCase      )
											self.assertEqual(len(UpperCAmelCase      )    ,   0      )

											lowerCAmelCase      :int				  = FakeOriginalTokenizer(vocab_file=UpperCAmelCase      )

											with warnings.catch_warnings(record=UpperCAmelCase      ) as w:
																	lowerCAmelCase      :List[str]				  = SpmConverter(UpperCAmelCase      )
											self.assertEqual(len(UpperCAmelCase      )    ,   1      )

											self.assertIn(
											    'The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'
											    ' which is not implemented in the fast tokenizers.'    ,   str(w[0].message      )    ,   )