code: 
  
  
  
  
  
  '''simple docstring'''
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  __lowerCAmelCase							:			int				=logging.get_logger(__name__)
  
  __lowerCAmelCase							:			Dict				={
      "google/rembert": "https://huggingface.co/google/rembert/resolve/main/config.json",
      # See all RemBERT models at https://huggingface.co/models?filter=rembert
  }
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   __lowercase     	=       """rembert"""
   def __init__(       self   :Union[str, Any]		, lowercase_   :Tuple=25_03_00		, lowercase_   :str=11_52		, lowercase_   :Dict=32		, lowercase_   :List[str]=18		, lowercase_   :List[Any]=2_56		, lowercase_   :Optional[int]=16_64		, lowercase_   :Tuple=46_08		, lowercase_   :Tuple="gelu"		, lowercase_   :Optional[int]=0.0		, lowercase_   :Tuple=0.0		, lowercase_   :Dict=0.1		, lowercase_   :Tuple=5_12		, lowercase_   :Tuple=2		, lowercase_   :Tuple=0.0_2		, lowercase_   :Dict=1E-12		, lowercase_   :Any=True		, lowercase_   :str=0		, lowercase_   :str=3_12		, lowercase_   :Optional[Any]=3_13		, **lowercase_   :List[str]		, )->							Any:
       super().__init__(pad_token_id=lowercase_		, bos_token_id=lowercase_		, eos_token_id=lowercase_		, **lowercase_							)
  
       A__       =    vocab_size
       A__       =    input_embedding_size
       A__       =    output_embedding_size
       A__       =    max_position_embeddings
       A__       =    hidden_size
       A__       =    num_hidden_layers
       A__       =    num_attention_heads
       A__       =    intermediate_size
       A__       =    hidden_act
       A__       =    hidden_dropout_prob
       A__       =    attention_probs_dropout_prob
       A__       =    classifier_dropout_prob
       A__       =    initializer_range
       A__       =    type_vocab_size
       A__       =    layer_norm_eps
       A__       =    use_cache
       A__       =    False
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   @property
   def   UpperCAmelCase_							(       self   :str							)->							Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
           A__       =    {0: "batch", 1: "choice", 2: "sequence"}
       else:
           A__       =    {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
               ("token_type_ids", dynamic_axis),
           ]							)
  
  
  
   @property
   def   UpperCAmelCase_							(       self   :Tuple							)->							float:
       return 1E-4
  
  
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
  
  
  
  