






'''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							(     snake_case__							,			snake_case__							):
      __A		   =      copy.deepcopy(lowercase_							)
      __A		   =      sorted(lowercase_							)
      __A		   =      [bisect.bisect_right(lowercase_							,			lowercase_							) for y in x]
      return quantized


def 						_UpperCamelCase							(     snake_case__							,			snake_case__=0							):
      __A		   =      np.arange(start=3							,			stop=lowercase_							,			step=4							).tolist() if k > 0 else [10]
      __A		   =      _quantize(lowercase_							,			lowercase_							)
      # count number of elements per group
      __A		   =      np.unique(lowercase_							,			return_counts=lowercase_							)[1]
      __A		   =      [0] + bins + [np.inf]
      logger.info("Using {} as bins for aspect lengths quantization".format(lowercase_							)							)
      logger.info("Count of instances per bin: {}".format(lowercase_							)							)
      return groups






class 					a__    (			__UpperCAmelCase						):
     '''simple docstring'''







     def __init__(self    :	List[str]				,				lowercase__    :	Dict				,				lowercase__    :	Optional[int]				,				lowercase__    :	str					)  ->       Any:

           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    :	List[str]					)  ->       Any:
           __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    :	Optional[int]					)  ->       int:
           return (len(self.sampler					) + self.batch_size - 1) // self.batch_size










'''simple docstring'''







from cva import destroyAllWindows, imread, imshow, waitKey


def 						_UpperCamelCase							(     snake_case__							,			snake_case__							):
      __A   ,				__A		   =      img.shape[0], img.shape[1]

      def to_grayscale(snake_case__							,			snake_case__							,			snake_case__							):
            return 0.2126 * red + 0.587 * green + 0.114 * blue

      def normalize(snake_case__							):
            return min(snake_case__							,			255							)

      for i in range(snake_case__							):
            for j in range(snake_case__							):
                  __A		   =      int(to_grayscale(*img[i][j]							)							)
                  __A		   =      [
                      normalize(snake_case__							),
                      normalize(greyscale + factor							),
                      normalize(greyscale + 2 * factor							),
                  ]

      return img


if __name__ == "__main__":
       # read original image
       __A      :       Dict      =	{
           percentage: imread('image_data/lena.jpg', 1) for percentage in (1_0, 2_0, 3_0, 4_0)
       }

       for percentage, img in images.items():
              make_sepia(img, percentage)

       for percentage, img in images.items():
              imshow(F"""Original image with sepia (factor: {percentage})""", img)

       waitKey(0)
       destroyAllWindows()


