

import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter



class  __snake_case					(      TensorFormatter[Mapping, np.ndarray, Mapping]    ):

  def __init__(   self  ,						_A=None  ,						**_A):
      super().__init__(features=_A)
      SCREAMING_SNAKE_CASE_													=  np_array_kwargs

  def   lowerCAmelCase__       (   self  ,						_A):
      if isinstance(_A  ,						_A):
          if column and all(
              isinstance(_A  ,						np.ndarray) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column):
              return np.stack(_A)
          else:
              # don't use np.array(column, dtype=object)
              # since it fails in certain cases
              # see https://stackoverflow.com/q/51005699
              SCREAMING_SNAKE_CASE_													=  np.empty(len(_A)  ,						dtype=_A)
              SCREAMING_SNAKE_CASE_													=  column
              return out
      return column

  def   lowerCAmelCase__       (   self  ,						_A):
      if isinstance(_A  ,						(str, bytes, type(_A))):
          return value
      elif isinstance(_A  ,						(np.character, np.ndarray)) and np.issubdtype(value.dtype  ,						np.character):
          return value
      elif isinstance(_A  ,						np.number):
          return value

      SCREAMING_SNAKE_CASE_													=  {}

      if isinstance(_A  ,						np.ndarray) and np.issubdtype(value.dtype  ,						np.integer):
          SCREAMING_SNAKE_CASE_													=  {'dtype': np.intaa}
      elif isinstance(_A  ,						np.ndarray) and np.issubdtype(value.dtype  ,						np.floating):
          SCREAMING_SNAKE_CASE_													=  {'dtype': np.floataa}
      elif config.PIL_AVAILABLE and "PIL" in sys.modules:
          import PIL.Image

          if isinstance(_A  ,						PIL.Image.Image):
              return np.asarray(_A  ,						**self.np_array_kwargs)

      return np.asarray(_A  ,						**{**default_dtype, **self.np_array_kwargs})

  def   lowerCAmelCase__       (   self  ,						_A):
      # support for torch, tf, jax etc.
      if config.TORCH_AVAILABLE and "torch" in sys.modules:
          import torch

          if isinstance(_A  ,						torch.Tensor):
              return self._tensorize(data_struct.detach().cpu().numpy()[()])
      if hasattr(_A  ,						'__array__') and not isinstance(_A  ,						(np.ndarray, np.character, np.number)):
          SCREAMING_SNAKE_CASE_													=  data_struct.__array__()
      # support for nested types like struct of list of struct
      if isinstance(_A  ,						np.ndarray):
          if data_struct.dtype == object:
              return self._consolidate([self.recursive_tensorize(_A) for substruct in data_struct])
      if isinstance(_A  ,						(list, tuple)):
          return self._consolidate([self.recursive_tensorize(_A) for substruct in data_struct])
      return self._tensorize(_A)

  def   lowerCAmelCase__       (   self  ,						_A):
      return map_nested(self._recursive_tensorize  ,						_A  ,						map_list=_A)

  def   lowerCAmelCase__       (   self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  self.numpy_arrow_extractor().extract_row(_A)
      SCREAMING_SNAKE_CASE_													=  self.python_features_decoder.decode_row(_A)
      return self.recursive_tensorize(_A)

  def   lowerCAmelCase__       (   self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  self.numpy_arrow_extractor().extract_column(_A)
      SCREAMING_SNAKE_CASE_													=  self.python_features_decoder.decode_column(_A  ,						pa_table.column_names[0])
      SCREAMING_SNAKE_CASE_													=  self.recursive_tensorize(_A)
      SCREAMING_SNAKE_CASE_													=  self._consolidate(_A)
      return column


  def   lowerCAmelCase__       (   self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  self.numpy_arrow_extractor().extract_batch(_A)
      SCREAMING_SNAKE_CASE_													=  self.python_features_decoder.decode_batch(_A)
      SCREAMING_SNAKE_CASE_													=  self.recursive_tensorize(_A)
      for column_name in batch:
          SCREAMING_SNAKE_CASE_													=  self._consolidate(batch[column_name])
      return batch





import cva
import numpy as np


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						np.ndarray	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


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


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						np.ndarray	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  image[x_coordinate][y_coordinate]
    SCREAMING_SNAKE_CASE_													=  [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
    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	,				_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.
       UpperCamelCase__					: Optional[int]								=      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
       UpperCamelCase__					: Dict								=      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]):
                     UpperCamelCase__					: Any								=      local_binary_value(image, i, j)

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


