




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_										=       logging.get_logger(__name__)

a_										=       {
    "mnaylor/mega-base-wikitext": "https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json",
}

class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      lowerCAmelCase__     :	List[Any]      =   "mega"







      def __init__(       self   :	Union[str, Any]	,    snake_case   :	Optional[Any]=30522	,    snake_case   :	List[Any]=128	,    snake_case   :	Tuple=4	,    snake_case   :	Optional[Any]=256	,    snake_case   :	Tuple=16	,    snake_case   :	Union[str, Any]=True	,    snake_case   :	Tuple=64	,    snake_case   :	List[str]=False	,    snake_case   :	Dict=-1	,    snake_case   :	Union[str, Any]=None	,    snake_case   :	List[str]=True	,    snake_case   :	Any="scalenorm"	,    snake_case   :	str=True	,    snake_case   :	Optional[int]="silu"	,    snake_case   :	Any="softmax"	,    snake_case   :	List[str]=0.1	,    snake_case   :	Tuple=0.1	,    snake_case   :	str=0.1	,    snake_case   :	str=False	,    snake_case   :	Union[str, Any]=True	,    snake_case   :	Tuple=256	,    snake_case   :	Tuple=True	,    snake_case   :	Optional[Any]=0.1	,    snake_case   :	Optional[int]=2048	,    snake_case   :	Optional[Any]=False	,    snake_case   :	int=2	,    snake_case   :	Union[str, Any]=0.02	,    snake_case   :	Optional[Any]=0.2	,    snake_case   :	int=0.02	,    snake_case   :	Dict=1.0	,    snake_case   :	int=1	,    snake_case   :	Any=0	,    snake_case   :	Dict=2	,    snake_case   :	Dict="rotary"	,    snake_case   :	Optional[int]=None	,    snake_case   :	Optional[Any]=True	,    snake_case   :	Dict=True	,    **snake_case   :	Any	,    ):
          super().__init__(pad_token_id=snake_case	,    bos_token_id=snake_case	,    eos_token_id=snake_case	,    **snake_case			)

          __UpperCamelCase											=  vocab_size
          __UpperCamelCase											=  hidden_size
          __UpperCamelCase											=  num_hidden_layers
          __UpperCamelCase											=  activation
          __UpperCamelCase											=  attention_activation
          __UpperCamelCase											=  intermediate_size
          __UpperCamelCase											=  ema_projection_size
          __UpperCamelCase											=  bidirectional
          __UpperCamelCase											=  shared_representation_size
          __UpperCamelCase											=  use_chunking
          __UpperCamelCase											=  chunk_size
          __UpperCamelCase											=  truncation
          __UpperCamelCase											=  normalize_before_mega
          __UpperCamelCase											=  normalization_type
          __UpperCamelCase											=  norm_affine
          __UpperCamelCase											=  dropout_prob
          __UpperCamelCase											=  hidden_dropout_prob
          __UpperCamelCase											=  attention_probs_dropout_prob
          __UpperCamelCase											=  use_feature_dropout
          __UpperCamelCase											=  use_normalized_ffn
          __UpperCamelCase											=  nffn_hidden_size
          __UpperCamelCase											=  normalize_before_ffn
          __UpperCamelCase											=  nffn_activation_dropout_prob
          __UpperCamelCase											=  max_positions
          __UpperCamelCase											=  add_token_type_embeddings
          __UpperCamelCase											=  type_vocab_size
          __UpperCamelCase											=  initializer_range
          __UpperCamelCase											=  ema_delta_alpha_range
          __UpperCamelCase											=  ema_beta_range
          __UpperCamelCase											=  ema_gamma_omega_range
          __UpperCamelCase											=  relative_positional_bias
          __UpperCamelCase											=  use_cache
          __UpperCamelCase											=  classifier_dropout
          __UpperCamelCase											=  add_lm_hidden_dense_layer
          __UpperCamelCase											=  1  # not used but required by Hugging Face







class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      @property
      def  snake_case		(       self   :	str			):
          if self.task == "multiple-choice":
              __UpperCamelCase											=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
              __UpperCamelCase											=  {0: '''batch''', 1: '''sequence'''}
          return OrderedDict(
              [
                  ('''input_ids''', dynamic_axis),
                  ('''attention_mask''', dynamic_axis),
              ]			)












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()






