code: 
  '''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 						snake_case__	(      UpperCamelCase			,UpperCamelCase	) ->      str:
  		_UpperCamelCase       : Optional[Any]    =      copy.deepcopy(UpperCamelCase	)
  		_UpperCamelCase       : List[str]    =      sorted(UpperCamelCase	)
  		_UpperCamelCase       : List[str]    =      [bisect.bisect_right(UpperCamelCase			,UpperCamelCase	) for y in x]
  		return quantized
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase=0	) ->      Optional[Any]:
  		_UpperCamelCase       : Optional[Any]    =      np.arange(start=3			,stop=UpperCamelCase			,step=4	).tolist() if k > 0 else [10]
  		_UpperCamelCase       : str    =      _quantize(UpperCamelCase			,UpperCamelCase	)
  		# count number of elements per group
  		_UpperCamelCase       : str    =      np.unique(UpperCamelCase			,return_counts=UpperCamelCase	)[1]
  		_UpperCamelCase       : Dict    =      [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 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		def __init__(    self  ,					_snake_case  ,					_snake_case  ,					_snake_case     )				->    Union[str, Any]:
  				if not isinstance(_snake_case  ,					_snake_case     ):
  						raise ValueError(
  						    '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(_snake_case     )     )
  				_UpperCamelCase       : int    =      sampler
  				_UpperCamelCase       : Dict    =      group_ids
  				_UpperCamelCase       : Optional[int]    =      batch_size
  		def __iter__(    self     )				->    Optional[Any]:
  				_UpperCamelCase       : Optional[int]    =      defaultdict(_snake_case     )
  				_UpperCamelCase       : Optional[Any]    =      defaultdict(_snake_case     )
  
  				_UpperCamelCase       : Any    =      0
  				for idx in self.sampler:
  						_UpperCamelCase       : str    =      self.group_ids[idx]
  						buffer_per_group[group_id].append(_snake_case     )
  						samples_per_group[group_id].append(_snake_case     )
  						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       : str    =      len(self     )
  				_UpperCamelCase       : Union[str, Any]    =      expected_num_batches - num_batches
  				if num_remaining > 0:
  						# for the remaining batches, group the batches by similar lengths
  						_UpperCamelCase       : int    =      []
  						for group_id, idxs in sorted(buffer_per_group.items()  ,					key=lambda _snake_case     : x[0]     ):
  								batch_idx.extend(_snake_case     )
  								if len(_snake_case     ) >= self.batch_size:
  										yield batch_idx[: self.batch_size]
  										_UpperCamelCase       : Union[str, Any]    =      batch_idx[self.batch_size :]
  										num_remaining -= 1
  						if len(_snake_case     ) > 0:
  								yield batch_idx
  								num_remaining -= 1
  				assert num_remaining == 0
  
  
  
  
  		def __len__(    self     )				->    Union[str, Any]:
  				return (len(self.sampler     ) + self.batch_size - 1) // self.batch_size
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  import warnings
  
  from ...utils import logging
  from .image_processing_mobilenet_va import MobileNetVaImageProcessor
  
  
  _UpperCAmelCase   :      Optional[int] 						=  logging.get_logger(__name__)
  
  class 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		def __init__(    self  ,					*_snake_case  ,					**_snake_case     )				->    None:
  				warnings.warn(
  				    '''The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
  				    ''' Please use MobileNetV2ImageProcessor instead.'''  ,					_snake_case  ,					)
  				super().__init__(*_snake_case  ,					**_snake_case     )
  
  
  
  
  
  