





'''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()








'''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



