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    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> int:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Union[str, Any]										= copy.deepcopy(__lowerCamelCase					)
       UpperCAmelCase__			:     Any										= sorted(__lowerCamelCase					)
       UpperCAmelCase__			:     Optional[int]										= [bisect.bisect_right(__lowerCamelCase   ,       __lowerCamelCase					) for y in x]
       return quantized
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase=0					)   -> int:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Dict										= np.arange(start=3   ,       stop=__lowerCamelCase   ,       step=4					).tolist() if k > 0 else [10]
       UpperCAmelCase__			:     Union[str, Any]										= _quantize(__lowerCamelCase   ,       __lowerCamelCase					)
       # count number of elements per group
       UpperCAmelCase__			:     Any										= np.unique(__lowerCamelCase   ,       return_counts=__lowerCamelCase					)[1]
       UpperCAmelCase__			:     List[Any]										= [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_     (		__lowerCamelCase		):
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            if not isinstance(_lowerCAmelCase  ,	_lowerCAmelCase		):
                 raise ValueError(
                     """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(_lowerCAmelCase		)		)
            UpperCAmelCase__			:     List[str]										= sampler
            UpperCAmelCase__			:     Any										= group_ids
            UpperCAmelCase__			:     List[str]										= batch_size
  
  
  
  
       def __iter__(							self		):
            UpperCAmelCase__			:     List[str]										= defaultdict(_lowerCAmelCase		)
            UpperCAmelCase__			:     str										= defaultdict(_lowerCAmelCase		)
  
            UpperCAmelCase__			:     int										= 0
            for idx in self.sampler:
                 UpperCAmelCase__			:     Union[str, Any]										= self.group_ids[idx]
                 buffer_per_group[group_id].append(_lowerCAmelCase		)
                 samples_per_group[group_id].append(_lowerCAmelCase		)
                 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
            UpperCAmelCase__			:     Union[str, Any]										= len(self		)
            UpperCAmelCase__			:     List[Any]										= expected_num_batches - num_batches
            if num_remaining > 0:
                 # for the remaining batches, group the batches by similar lengths
                 UpperCAmelCase__			:     List[Any]										= []
                 for group_id, idxs in sorted(buffer_per_group.items()  ,	key=lambda _lowerCAmelCase		: x[0]		):
                      batch_idx.extend(_lowerCAmelCase		)
                      if len(_lowerCAmelCase		) >= self.batch_size:
                           yield batch_idx[: self.batch_size]
                           UpperCAmelCase__			:     Tuple										= batch_idx[self.batch_size :]
                           num_remaining -= 1
                 if len(_lowerCAmelCase		) > 0:
                      yield batch_idx
                      num_remaining -= 1
            assert num_remaining == 0
  
  
  
  
       def __len__(							self		):
            return (len(self.sampler		) + self.batch_size - 1) // self.batch_size
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from ..utils import requires_backends
  
  
  SCREAMING_SNAKE_CASE__		:       int   							=							None
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
  
  
  
  
       def __init__(							self  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
            requires_backends(self  ,	["""detectron2"""]		)
  
  
  
  
       @classmethod
       def      __UpperCAmelCase     (							cls  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
            requires_backends(cls  ,	["""detectron2"""]		)
  
  
  
  
  
  
  