code: 
  
  
  
  
  
  '''simple docstring'''
  
  import cva
  import numpy as np
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      str		, _lowerCamelCase      :      List[Any]		, _lowerCamelCase      :      Any		, _lowerCamelCase      :      List[str] ):
  
  
  
  
  
  
      try:
          return int(image[x_coordinate][y_coordinate] >= center )
      except (IndexError, TypeError):
          return 0
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      str		, _lowerCamelCase      :      List[str]		, _lowerCamelCase      :      Optional[Any] ):
      A__       =    image[x_coordinate][y_coordinate]
      A__       =    [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
      A__       =    [
          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							:			str				=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							:			str				=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							:			Tuple				=local_binary_value(image, i, j)
  
    cva.imshow("local binary pattern", lbp_image)
    cva.waitKey(0)
    cva.destroyAllWindows()
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import bisect
  import copy
  from collections import defaultdict
  
  import numpy as np
  from torch.utils.data import BatchSampler, Sampler
  
  from utils import logger
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      str		, _lowerCamelCase      :      Optional[Any] ):
      A__       =    copy.deepcopy(_lowerCamelCase )
      A__       =    sorted(_lowerCamelCase )
      A__       =    [bisect.bisect_right(_lowerCamelCase		, _lowerCamelCase ) for y in x]
      return quantized
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int]		, _lowerCamelCase      :      Tuple=0 ):
      A__       =    np.arange(start=3		, stop=_lowerCamelCase		, step=4 ).tolist() if k > 0 else [10]
      A__       =    _quantize(_lowerCamelCase		, _lowerCamelCase )
      # count number of elements per group
      A__       =    np.unique(_lowerCamelCase		, return_counts=_lowerCamelCase )[1]
      A__       =    [0] + bins + [np.inf]
      logger.info("Using {} as bins for aspect lengths quantization".format(_lowerCamelCase ) )
      logger.info("Count of instances per bin: {}".format(_lowerCamelCase ) )
      return groups
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   def __init__(       self   :Optional[Any]		, lowercase_   :List[Any]		, lowercase_   :Optional[int]		, lowercase_   :str							)->							Dict:
       if not isinstance(lowercase_		, lowercase_							):
           raise ValueError(
               "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(lowercase_							)							)
       A__       =    sampler
       A__       =    group_ids
       A__       =    batch_size
   def __iter__(       self   :int							)->							Tuple:
       A__       =    defaultdict(lowercase_							)
       A__       =    defaultdict(lowercase_							)
  
       A__       =    0
       for idx in self.sampler:
           A__       =    self.group_ids[idx]
           buffer_per_group[group_id].append(lowercase_							)
           samples_per_group[group_id].append(lowercase_							)
           if len(buffer_per_group[group_id]							) == self.batch_size:
               yield buffer_per_group[group_id]  # TODO
               num_batches += 1
               del buffer_per_group[group_id]
           assert len(buffer_per_group[group_id]							) < self.batch_size
  
       # now we have run out of elements that satisfy
       # the group criteria, let's return the remaining
       # elements so that the size of the sampler is
       # deterministic
       A__       =    len(self							)
       A__       =    expected_num_batches - num_batches
       if num_remaining > 0:
           # for the remaining batches, group the batches by similar lengths
           A__       =    []
           for group_id, idxs in sorted(buffer_per_group.items()		, key=lambda lowercase_							: x[0]							):
               batch_idx.extend(lowercase_							)
               if len(lowercase_							) >= self.batch_size:
                   yield batch_idx[: self.batch_size]
                   A__       =    batch_idx[self.batch_size :]
                   num_remaining -= 1
           if len(lowercase_							) > 0:
               yield batch_idx
               num_remaining -= 1
       assert num_remaining == 0
  
  
  
   def __len__(       self   :int							)->							Tuple:
       return (len(self.sampler							) + self.batch_size - 1) // self.batch_size
  
  
  
  