




import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_										=       logging.get_logger(__name__)

a_										=       {
    "facebook/hubert-base-ls960": "https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json",
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}

class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      lowerCAmelCase__     :	int      =   "hubert"







      def __init__(       self   :	List[str]	,    snake_case   :	Dict=32	,    snake_case   :	int=768	,    snake_case   :	Optional[int]=12	,    snake_case   :	List[str]=12	,    snake_case   :	str=3072	,    snake_case   :	Tuple="gelu"	,    snake_case   :	Union[str, Any]=0.1	,    snake_case   :	Optional[Any]=0.1	,    snake_case   :	Optional[int]=0.1	,    snake_case   :	Optional[Any]=True	,    snake_case   :	Union[str, Any]=0.0	,    snake_case   :	List[str]=0.1	,    snake_case   :	str=0.1	,    snake_case   :	List[str]=0.02	,    snake_case   :	Union[str, Any]=1E-5	,    snake_case   :	int="group"	,    snake_case   :	int="gelu"	,    snake_case   :	Tuple=(512, 512, 512, 512, 512, 512, 512)	,    snake_case   :	str=(5, 2, 2, 2, 2, 2, 2)	,    snake_case   :	List[str]=(10, 3, 3, 3, 3, 2, 2)	,    snake_case   :	Optional[Any]=False	,    snake_case   :	List[str]=128	,    snake_case   :	List[Any]=16	,    snake_case   :	Union[str, Any]=False	,    snake_case   :	int=True	,    snake_case   :	Optional[Any]=0.05	,    snake_case   :	List[Any]=10	,    snake_case   :	Optional[Any]=2	,    snake_case   :	Tuple=0.0	,    snake_case   :	Optional[int]=10	,    snake_case   :	Any=0	,    snake_case   :	int="sum"	,    snake_case   :	List[Any]=False	,    snake_case   :	Optional[int]=False	,    snake_case   :	List[str]=256	,    snake_case   :	Any=0	,    snake_case   :	str=1	,    snake_case   :	Tuple=2	,    **snake_case   :	List[Any]	,    ):
          super().__init__(**snake_case	,    pad_token_id=snake_case	,    bos_token_id=snake_case	,    eos_token_id=snake_case			)
          __UpperCamelCase											=  hidden_size
          __UpperCamelCase											=  feat_extract_norm
          __UpperCamelCase											=  feat_extract_activation
          __UpperCamelCase											=  list(snake_case			)
          __UpperCamelCase											=  list(snake_case			)
          __UpperCamelCase											=  list(snake_case			)
          __UpperCamelCase											=  conv_bias
          __UpperCamelCase											=  num_conv_pos_embeddings
          __UpperCamelCase											=  num_conv_pos_embedding_groups
          __UpperCamelCase											=  len(self.conv_dim			)
          __UpperCamelCase											=  num_hidden_layers
          __UpperCamelCase											=  intermediate_size
          __UpperCamelCase											=  hidden_act
          __UpperCamelCase											=  num_attention_heads
          __UpperCamelCase											=  hidden_dropout
          __UpperCamelCase											=  attention_dropout
          __UpperCamelCase											=  activation_dropout
          __UpperCamelCase											=  feat_proj_layer_norm
          __UpperCamelCase											=  feat_proj_dropout
          __UpperCamelCase											=  final_dropout
          __UpperCamelCase											=  layerdrop
          __UpperCamelCase											=  layer_norm_eps
          __UpperCamelCase											=  initializer_range
          __UpperCamelCase											=  vocab_size
          __UpperCamelCase											=  do_stable_layer_norm
          __UpperCamelCase											=  use_weighted_layer_sum
          __UpperCamelCase											=  classifier_proj_size

          if (
              (len(self.conv_stride			) != self.num_feat_extract_layers)
              or (len(self.conv_kernel			) != self.num_feat_extract_layers)
              or (len(self.conv_dim			) != self.num_feat_extract_layers)
          ):
              raise ValueError(
                  '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                  ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                  F" {len(self.conv_dim			)}`, `len(config.conv_stride) = {len(self.conv_stride			)}`,"
                  F" `len(config.conv_kernel) = {len(self.conv_kernel			)}`."			)

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          __UpperCamelCase											=  apply_spec_augment
          __UpperCamelCase											=  mask_time_prob
          __UpperCamelCase											=  mask_time_length
          __UpperCamelCase											=  mask_time_min_masks
          __UpperCamelCase											=  mask_feature_prob
          __UpperCamelCase											=  mask_feature_length
          __UpperCamelCase											=  mask_feature_min_masks

          # ctc loss
          __UpperCamelCase											=  ctc_loss_reduction
          __UpperCamelCase											=  ctc_zero_infinity







      @property
      def  snake_case		(       self   :	Optional[Any]			):
          return functools.reduce(operator.mul	,    self.conv_stride	,    1			)












import cva
import numpy as np




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


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




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


    __UpperCamelCase											=  image[x_coordinate][y_coordinate]
    __UpperCamelCase											=  [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
    __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_										=       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_										=       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_										=       local_binary_value(image, i, j)

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






