

"""simple docstring"""






import cva
import numpy as np
def  UpperCamelCase   (     _lowerCAmelCase       :  np.ndarray,					_lowerCAmelCase       :  int,					_lowerCAmelCase       :  int,					_lowerCAmelCase       :  int	)			->     int:

	try:
		return int(image[x_coordinate][y_coordinate] >= center	)
	except (IndexError, TypeError):
		return 0
def  UpperCamelCase   (     _lowerCAmelCase       :  np.ndarray,					_lowerCAmelCase       :  int,					_lowerCAmelCase       :  int	)			->     int:
	_UpperCAmelCase      :							int       =   image[x_coordinate][y_coordinate]
	_UpperCAmelCase      :							str       =   [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
	_UpperCAmelCase      :							List[str]       =   [
	    get_neighbors_pixel(_lowerCAmelCase,					x_coordinate - 1,					y_coordinate + 1,					_lowerCAmelCase	),
	    get_neighbors_pixel(_lowerCAmelCase,					_lowerCAmelCase,					y_coordinate + 1,					_lowerCAmelCase	),
	    get_neighbors_pixel(_lowerCAmelCase,					x_coordinate - 1,					_lowerCAmelCase,					_lowerCAmelCase	),
	    get_neighbors_pixel(_lowerCAmelCase,					x_coordinate + 1,					y_coordinate + 1,					_lowerCAmelCase	),
	    get_neighbors_pixel(_lowerCAmelCase,					x_coordinate + 1,					_lowerCAmelCase,					_lowerCAmelCase	),
	    get_neighbors_pixel(_lowerCAmelCase,					x_coordinate + 1,					y_coordinate - 1,					_lowerCAmelCase	),
	    get_neighbors_pixel(_lowerCAmelCase,					_lowerCAmelCase,					y_coordinate - 1,					_lowerCAmelCase	),
	    get_neighbors_pixel(_lowerCAmelCase,					x_coordinate - 1,					y_coordinate - 1,					_lowerCAmelCase	),
	]

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


if __name__ == "__main__":
						# Reading the image and converting it to grayscale.
						lowerCamelCase__			:					Union[str, 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__			:					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__			:					List[str]							  =					local_binary_value(image, i, j)

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








"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__			:					int							  =					{'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			:					Optional[int]							  =					[
						    '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''QDQBertForMaskedLM''',
						    '''QDQBertForMultipleChoice''',
						    '''QDQBertForNextSentencePrediction''',
						    '''QDQBertForQuestionAnswering''',
						    '''QDQBertForSequenceClassification''',
						    '''QDQBertForTokenClassification''',
						    '''QDQBertLayer''',
						    '''QDQBertLMHeadModel''',
						    '''QDQBertModel''',
						    '''QDQBertPreTrainedModel''',
						    '''load_tf_weights_in_qdqbert''',
						]


if TYPE_CHECKING:
						from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_qdqbert import (
												    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    QDQBertForMaskedLM,
												    QDQBertForMultipleChoice,
												    QDQBertForNextSentencePrediction,
												    QDQBertForQuestionAnswering,
												    QDQBertForSequenceClassification,
												    QDQBertForTokenClassification,
												    QDQBertLayer,
												    QDQBertLMHeadModel,
												    QDQBertModel,
												    QDQBertPreTrainedModel,
												    load_tf_weights_in_qdqbert,
												)


else:
						import sys

						lowerCamelCase__			:					Optional[Any]							  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





