


import cva
import numpy as np






def 	lowerCamelCase_       (			_a      ,  _a      ,  _a      ,  _a			):




		"""simple docstring"""






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






def 	lowerCamelCase_       (			_a      ,  _a      ,  _a			):




		"""simple docstring"""






		lowerCAmelCase__  :     Dict       	=   image[x_coordinate][y_coordinate]
		lowerCAmelCase__  :     List[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
		lowerCAmelCase__  :     List[Any]       	=   [
		    get_neighbors_pixel(UpperCamelCase__      ,  x_coordinate - 1      ,  y_coordinate + 1      ,  UpperCamelCase__			),
		    get_neighbors_pixel(UpperCamelCase__      ,  UpperCamelCase__      ,  y_coordinate + 1      ,  UpperCamelCase__			),
		    get_neighbors_pixel(UpperCamelCase__      ,  x_coordinate - 1      ,  UpperCamelCase__      ,  UpperCamelCase__			),
		    get_neighbors_pixel(UpperCamelCase__      ,  x_coordinate + 1      ,  y_coordinate + 1      ,  UpperCamelCase__			),
		    get_neighbors_pixel(UpperCamelCase__      ,  x_coordinate + 1      ,  UpperCamelCase__      ,  UpperCamelCase__			),
		    get_neighbors_pixel(UpperCamelCase__      ,  x_coordinate + 1      ,  y_coordinate - 1      ,  UpperCamelCase__			),
		    get_neighbors_pixel(UpperCamelCase__      ,  UpperCamelCase__      ,  y_coordinate - 1      ,  UpperCamelCase__			),
		    get_neighbors_pixel(UpperCamelCase__      ,  x_coordinate - 1      ,  y_coordinate - 1      ,  UpperCamelCase__			),
		]

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


if __name__ == "__main__":
		# Reading the image and converting it to grayscale.
		lowerCamelCase		=						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		=						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		=						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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase				:     Dict								=  {
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     Optional[Any]								=  ["""CamembertTokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     Optional[Any]								=  ["""CamembertTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     int								=  [
        """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """CamembertForCausalLM""",
        """CamembertForMaskedLM""",
        """CamembertForMultipleChoice""",
        """CamembertForQuestionAnswering""",
        """CamembertForSequenceClassification""",
        """CamembertForTokenClassification""",
        """CamembertModel""",
        """CamembertPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     str								=  [
        """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFCamembertForCausalLM""",
        """TFCamembertForMaskedLM""",
        """TFCamembertForMultipleChoice""",
        """TFCamembertForQuestionAnswering""",
        """TFCamembertForSequenceClassification""",
        """TFCamembertForTokenClassification""",
        """TFCamembertModel""",
        """TFCamembertPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_camembert import CamembertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_camembert_fast import CamembertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_camembert import (
            CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CamembertForCausalLM,
            CamembertForMaskedLM,
            CamembertForMultipleChoice,
            CamembertForQuestionAnswering,
            CamembertForSequenceClassification,
            CamembertForTokenClassification,
            CamembertModel,
            CamembertPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_camembert import (
            TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCamembertForCausalLM,
            TFCamembertForMaskedLM,
            TFCamembertForMultipleChoice,
            TFCamembertForQuestionAnswering,
            TFCamembertForSequenceClassification,
            TFCamembertForTokenClassification,
            TFCamembertModel,
            TFCamembertPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase				:     List[Any]								=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
