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 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> Tuple:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase    			=      copy.deepcopy(_UpperCAmelCase				)
      lowerCAmelCase    			=      sorted(_UpperCAmelCase				)
      lowerCAmelCase    			=      [bisect.bisect_right(_UpperCAmelCase,      _UpperCAmelCase				) for y in x]
      return quantized
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase=0				)					-> Dict:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase    			=      np.arange(start=3,      stop=_UpperCAmelCase,      step=4				).tolist() if k > 0 else [10]
      lowerCAmelCase    			=      _quantize(_UpperCAmelCase,      _UpperCAmelCase				)
      # count number of elements per group
      lowerCAmelCase    			=      np.unique(_UpperCAmelCase,      return_counts=_UpperCAmelCase				)[1]
      lowerCAmelCase    			=      [0] + bins + [np.inf]
      logger.info('Using {} as bins for aspect lengths quantization'.format(_UpperCAmelCase				)				)
      logger.info('Count of instances per bin: {}'.format(_UpperCAmelCase				)				)
      return groups
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase ):
  
  
  
  
  
  
      def __init__(	self			:    Union[str, Any]    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    int    ):
          if not isinstance(UpperCAmelCase_    ,				UpperCAmelCase_    ):
              raise ValueError(
                  'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(UpperCAmelCase_    )    )
          lowerCAmelCase    			=      sampler
          lowerCAmelCase    			=      group_ids
          lowerCAmelCase    			=      batch_size
  
  
  
  
  
  
      def __iter__(	self			:    Union[str, Any]    ):
          lowerCAmelCase    			=      defaultdict(UpperCAmelCase_    )
          lowerCAmelCase    			=      defaultdict(UpperCAmelCase_    )
  
          lowerCAmelCase    			=      0
          for idx in self.sampler:
              lowerCAmelCase    			=      self.group_ids[idx]
              buffer_per_group[group_id].append(UpperCAmelCase_    )
              samples_per_group[group_id].append(UpperCAmelCase_    )
              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
          lowerCAmelCase    			=      len(self    )
          lowerCAmelCase    			=      expected_num_batches - num_batches
          if num_remaining > 0:
              # for the remaining batches, group the batches by similar lengths
              lowerCAmelCase    			=      []
              for group_id, idxs in sorted(buffer_per_group.items()    ,				key=lambda UpperCAmelCase_    : x[0]    ):
                  batch_idx.extend(UpperCAmelCase_    )
                  if len(UpperCAmelCase_    ) >= self.batch_size:
                      yield batch_idx[: self.batch_size]
                      lowerCAmelCase    			=      batch_idx[self.batch_size :]
                      num_remaining -= 1
              if len(UpperCAmelCase_    ) > 0:
                  yield batch_idx
                  num_remaining -= 1
          assert num_remaining == 0
  
  
  
  
  
  
      def __len__(	self			:    Optional[int]    ):
          return (len(self.sampler    ) + self.batch_size - 1) // self.batch_size
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  __A   :     Optional[int]      =			{
      '''configuration_splinter''': ['''SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SplinterConfig'''],
      '''tokenization_splinter''': ['''SplinterTokenizer'''],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     Optional[int]      =			['''SplinterTokenizerFast''']
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     Union[str, Any]      =			[
       '''SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SplinterForQuestionAnswering''',
       '''SplinterForPreTraining''',
       '''SplinterLayer''',
       '''SplinterModel''',
       '''SplinterPreTrainedModel''',
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
   from .tokenization_splinter import SplinterTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_splinter_fast import SplinterTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_splinter import (
        SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SplinterForPreTraining,
        SplinterForQuestionAnswering,
        SplinterLayer,
        SplinterModel,
        SplinterPreTrainedModel,
    )
  
  
  else:
   import sys
  
   __A   :     Optional[Any]      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  