


import cva
import numpy as np






def 		lowercase_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  lowerCamelCase			: Tuple   =    image[x_coordinate][y_coordinate]
  lowerCamelCase			: Optional[int]   =    [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			: Optional[Any]   =    [
      get_neighbors_pixel(SCREAMING_SNAKE_CASE_				,						x_coordinate - 1				,						y_coordinate + 1				,						SCREAMING_SNAKE_CASE_     ),
      get_neighbors_pixel(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						y_coordinate + 1				,						SCREAMING_SNAKE_CASE_     ),
      get_neighbors_pixel(SCREAMING_SNAKE_CASE_				,						x_coordinate - 1				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ),
      get_neighbors_pixel(SCREAMING_SNAKE_CASE_				,						x_coordinate + 1				,						y_coordinate + 1				,						SCREAMING_SNAKE_CASE_     ),
      get_neighbors_pixel(SCREAMING_SNAKE_CASE_				,						x_coordinate + 1				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ),
      get_neighbors_pixel(SCREAMING_SNAKE_CASE_				,						x_coordinate + 1				,						y_coordinate - 1				,						SCREAMING_SNAKE_CASE_     ),
      get_neighbors_pixel(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						y_coordinate - 1				,						SCREAMING_SNAKE_CASE_     ),
      get_neighbors_pixel(SCREAMING_SNAKE_CASE_				,						x_coordinate - 1				,						y_coordinate - 1				,						SCREAMING_SNAKE_CASE_     ),
  ]

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


if __name__ == "__main__":
   # Reading the image and converting it to grayscale.
   _snake_case					=					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
   _snake_case					=					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]):
         _snake_case					=					local_binary_value(image, i, j)

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







from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
class      UpperCAmelCase_ ( UpperCamelCase   ):

   '''simple docstring'''




   __A			:				Union[str, Any]							=		"MarkupLMFeatureExtractor"
   __A			:				List[str]							=		("MarkupLMTokenizer", "MarkupLMTokenizerFast")
   __A			:				str							=		True






   def __call__(		self						,    __A=None						,    __A=None						,    __A=None						,    __A=None						,    __A=None						,    __A = True						,    __A = False						,    __A = None						,    __A = None						,    __A = 0						,    __A = None						,    __A = None						,    __A = None						,    __A = False						,    __A = False						,    __A = False						,    __A = False						,    __A = True						,    __A = None						,    **__A						,    ):






     """simple docstring"""





     if self.parse_html:
       if html_strings is None:
         raise ValueError("Make sure to pass HTML strings in case `parse_html` is set to `True`"			)

       if nodes is not None or xpaths is not None or node_labels is not None:
         raise ValueError(
             "Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"			)

       lowerCamelCase			: Any   =    self.feature_extractor(__A			)
       lowerCamelCase			: Optional[int]   =    features["nodes"]
       lowerCamelCase			: Any   =    features["xpaths"]
     else:
       if html_strings is not None:
         raise ValueError("You have passed HTML strings but `parse_html` is set to `False`."			)
       if nodes is None or xpaths is None:
         raise ValueError("Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"			)

        # # second, apply the tokenizer
     if questions is not None and self.parse_html:
       if isinstance(__A						,    __A			):
         lowerCamelCase			: Tuple   =    [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

     lowerCamelCase			: Tuple   =    self.tokenizer(
         text=questions if questions is not None else nodes						,    text_pair=nodes if questions is not None else None						,    xpaths=__A						,    node_labels=__A						,    add_special_tokens=__A						,    padding=__A						,    truncation=__A						,    max_length=__A						,    stride=__A						,    pad_to_multiple_of=__A						,    return_token_type_ids=__A						,    return_attention_mask=__A						,    return_overflowing_tokens=__A						,    return_special_tokens_mask=__A						,    return_offsets_mapping=__A						,    return_length=__A						,    verbose=__A						,    return_tensors=__A						,    **__A						,    )

     return encoded_inputs






   def _snake_case   (		self						,    *__A						,    **__A			):






     """simple docstring"""





     return self.tokenizer.batch_decode(*__A						,    **__A			)






   def _snake_case   (		self						,    *__A						,    **__A			):






     """simple docstring"""





     return self.tokenizer.decode(*__A						,    **__A			)






   @property
   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Dict   =    self.tokenizer.model_input_names
     return tokenizer_input_names



