


import cva
import numpy as np






def 	a__ (							A__,			A__,			A__,			A__							):

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






def 	a__ (							A__,			A__,			A__							):
			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				image[x_coordinate][y_coordinate]
			SCREAMING_SNAKE_CASE_		:    str 		=				[1, 2, 4, 8, 1_6, 3_2, 6_4, 1_2_8]

			# 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_		:    Optional[int] 		=				[
			    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.
				lowerCAmelCase__ :			List[Any]         =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
				lowerCAmelCase__ :			List[Any]         =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]):
												lowerCAmelCase__ :			Optional[Any]         =local_binary_value(image, i, j)

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









from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest







@custom_tokenizers
class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										return True






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[str] 		=				CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate'					)
										SCREAMING_SNAKE_CASE_		:    int 		=				'Hugging Face大法好，谁用谁知道。'
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				'Hugging Face大法好,谁用谁知道。<unk>'
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				'▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				tokenizer.tokenize(lowerCAmelCase__					)
										self.assertListEqual(lowerCAmelCase__ ,	lowerCAmelCase__					)

										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				tokens + [tokenizer.unk_token]

										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				[1_3_7_8_9, 1_3_2_8_3, 1_4_2_1, 8, 1_0, 1_1_6_4, 1_3_6_0_8, 1_6_5_2_8, 6_3, 8, 9, 4_4_0, 1_0_8, 4_4_0, 1_2_1, 9_0, 8, 1_2, 0]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__					) ,	lowerCAmelCase__					)

										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				tokenizer.decode(lowerCAmelCase__					)
										self.assertEqual(lowerCAmelCase__ ,	lowerCAmelCase__					)





