

import cva
import numpy as np



def  __lowerCAmelCase (  A    ,       A    ,       A    ,       A      ):

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



def  __lowerCAmelCase (  A    ,       A    ,       A      ):
	UpperCAmelCase_    			=     image[x_coordinate][y_coordinate]
	UpperCAmelCase_    			=     [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_    			=     [
	    get_neighbors_pixel(lowercase_    ,       x_coordinate - 1    ,       y_coordinate + 1    ,       lowercase_      ),
	    get_neighbors_pixel(lowercase_    ,       lowercase_    ,       y_coordinate + 1    ,       lowercase_      ),
	    get_neighbors_pixel(lowercase_    ,       x_coordinate - 1    ,       lowercase_    ,       lowercase_      ),
	    get_neighbors_pixel(lowercase_    ,       x_coordinate + 1    ,       y_coordinate + 1    ,       lowercase_      ),
	    get_neighbors_pixel(lowercase_    ,       x_coordinate + 1    ,       lowercase_    ,       lowercase_      ),
	    get_neighbors_pixel(lowercase_    ,       x_coordinate + 1    ,       y_coordinate - 1    ,       lowercase_      ),
	    get_neighbors_pixel(lowercase_    ,       lowercase_    ,       y_coordinate - 1    ,       lowercase_      ),
	    get_neighbors_pixel(lowercase_    ,       x_coordinate - 1    ,       y_coordinate - 1    ,       lowercase_      ),
	]

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


if __name__ == "__main__":
				# Reading the image and converting it to grayscale.
				_a:      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
				_a:      Optional[int]								=     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]):
												_a:      Any								=     local_binary_value(image, i, j)

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


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_a:      Tuple								=     {
    """configuration_segformer""": ["""SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SegformerConfig""", """SegformerOnnxConfig"""]
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      List[str]								=     ["""SegformerFeatureExtractor"""]
				_a:      Union[str, Any]								=     ["""SegformerImageProcessor"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      Any								=     [
				    """SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """SegformerDecodeHead""",
				    """SegformerForImageClassification""",
				    """SegformerForSemanticSegmentation""",
				    """SegformerLayer""",
				    """SegformerModel""",
				    """SegformerPreTrainedModel""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      Optional[Any]								=     [
				    """TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFSegformerDecodeHead""",
				    """TFSegformerForImageClassification""",
				    """TFSegformerForSemanticSegmentation""",
				    """TFSegformerModel""",
				    """TFSegformerPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_segformer import SegformerFeatureExtractor
								from .image_processing_segformer import SegformerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_segformer import (
								    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SegformerDecodeHead,
								    SegformerForImageClassification,
								    SegformerForSemanticSegmentation,
								    SegformerLayer,
								    SegformerModel,
								    SegformerPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_segformer import (
								    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSegformerDecodeHead,
								    TFSegformerForImageClassification,
								    TFSegformerForSemanticSegmentation,
								    TFSegformerModel,
								    TFSegformerPreTrainedModel,
								)

else:
				import sys

				_a:      Optional[int]								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)