code: 
  
  
  
  
  
  '''simple docstring'''
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      int		, _lowerCamelCase      :      Dict		, _lowerCamelCase      :      Any ):
      if start == end:
          return start if lst[start] > item else start + 1
      if start > end:
          return start
  
      A__       =    (start + end) // 2
      if lst[mid] < item:
          return binary_search(_lowerCamelCase		, _lowerCamelCase		, mid + 1		, _lowerCamelCase )
      elif lst[mid] > item:
          return binary_search(_lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase		, mid - 1 )
      else:
          return mid
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      int ):
      A__       =    len(_lowerCamelCase )
  
      for index in range(1		, _lowerCamelCase ):
          A__       =    lst[index]
          A__       =    binary_search(_lowerCamelCase		, _lowerCamelCase		, 0		, index - 1 )
          A__       =    lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :]
  
      return lst
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Dict		, _lowerCamelCase      :      List[Any] ):
      if not left:
          return right
  
      if not right:
          return left
  
      if left[0] < right[0]:
          return [left[0], *merge(left[1:]		, _lowerCamelCase )]
  
      return [right[0], *merge(_lowerCamelCase		, right[1:] )]
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Any ):
      A__       =    len(_lowerCamelCase )
      A__,			A__       =    [], []
      A__       =    [lst[0]]
      A__       =    []
      A__       =    1
      while i < length:
          if lst[i] < lst[i - 1]:
              runs.append(_lowerCamelCase )
              A__       =    [lst[i]]
          else:
              new_run.append(lst[i] )
          i += 1
      runs.append(_lowerCamelCase )
  
      for run in runs:
          sorted_runs.append(insertion_sort(_lowerCamelCase ) )
      for run in sorted_runs:
          A__       =    merge(_lowerCamelCase		, _lowerCamelCase )
  
      return sorted_array
  
  
  
  
  
  def 		UpperCamelCase       (							):
      A__       =    [5, 9, 10, 3, -4, 5, 1_78, 92, 46, -18, 0, 7]
      A__       =    tim_sort(_lowerCamelCase )
      print(_lowerCamelCase )
  
  
  if __name__ == "__main__":
    main()
  
  
  
  
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
  
  
  
  