code: 
  
  
  
  
  
  import bisect
  import copy
  from collections import defaultdict
  
  import numpy as np
  from torch.utils.data import BatchSampler, Sampler
  
  from utils import logger
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						Optional[Any]:
         _snake_case      :							Optional[int]     				=						copy.deepcopy(lowercase_  )
         _snake_case      :							str     				=						sorted(lowercase_  )
         _snake_case      :							Union[str, Any]     				=						[bisect.bisect_right(lowercase_      ,		lowercase_  ) for y in x]
         return quantized
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_=0  )			->						Optional[Any]:
         _snake_case      :							Dict     				=						np.arange(start=3      ,		stop=lowercase_      ,		step=4  ).tolist() if k > 0 else [10]
         _snake_case      :							List[Any]     				=						_quantize(lowercase_      ,		lowercase_  )
         # count number of elements per group
         _snake_case      :							int     				=						np.unique(lowercase_      ,		return_counts=lowercase_  )[1]
         _snake_case      :							Union[str, Any]     				=						[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				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               if not isinstance(lowercase_     ,  lowercase_					):
                      raise ValueError(
                          '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(lowercase_					)					)
               _snake_case      :							Any     				=						sampler
               _snake_case      :							Tuple     				=						group_ids
               _snake_case      :							int     				=						batch_size
  
  
  
        def __iter__(					self					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						defaultdict(lowercase_					)
               _snake_case      :							int     				=						defaultdict(lowercase_					)
  
               _snake_case      :							Union[str, Any]     				=						0
               for idx in self.sampler:
                      _snake_case      :							int     				=						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
               _snake_case      :							List[Any]     				=						len(self					)
               _snake_case      :							List[str]     				=						expected_num_batches - num_batches
               if num_remaining > 0:
                      # for the remaining batches, group the batches by similar lengths
                      _snake_case      :							Optional[int]     				=						[]
                      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]
                                    _snake_case      :							Any     				=						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:
  
  
  
               '''simple docstring'''
  
               return (len(self.sampler					) + self.batch_size - 1) // self.batch_size
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  def 						A_			(  lowercase_  )			->						list:
         if len(lowercase_  ) <= 1:
                return data
         else:
                return [
                    *quick_sort([e for e in data[1:] if e <= data[0]]  ),
                    data[0],
                    *quick_sort([e for e in data[1:] if e > data[0]]  ),
                ]
  
  
  if __name__ == "__main__":
     import doctest
  
     doctest.testmod()
  
  
  
  
  
  
  