






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_	:	Optional[Any]				=				logging.get_logger(__name__)

A_	:	List[str]				=				{
    'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/config.json',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}




class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       a       :	List[Any]								='''rembert'''





       def __init__(      self						,    _lowerCamelCase=2_5_0_3_0_0						,    _lowerCamelCase=1_1_5_2						,    _lowerCamelCase=3_2						,    _lowerCamelCase=1_8						,    _lowerCamelCase=2_5_6						,    _lowerCamelCase=1_6_6_4						,    _lowerCamelCase=4_6_0_8						,    _lowerCamelCase="gelu"						,    _lowerCamelCase=0.0						,    _lowerCamelCase=0.0						,    _lowerCamelCase=0.1						,    _lowerCamelCase=5_1_2						,    _lowerCamelCase=2						,    _lowerCamelCase=0.0_2						,    _lowerCamelCase=1e-12						,    _lowerCamelCase=True						,    _lowerCamelCase=0						,    _lowerCamelCase=3_1_2						,    _lowerCamelCase=3_1_3						,    **_lowerCamelCase						,    ):
         super().__init__(pad_token_id=_lowerCamelCase						,    bos_token_id=_lowerCamelCase						,    eos_token_id=_lowerCamelCase						,    **_lowerCamelCase							)

         UpperCamelCase_:      str					   =       vocab_size
         UpperCamelCase_:      Tuple					   =       input_embedding_size
         UpperCamelCase_:      Tuple					   =       output_embedding_size
         UpperCamelCase_:      str					   =       max_position_embeddings
         UpperCamelCase_:      List[Any]					   =       hidden_size
         UpperCamelCase_:      Any					   =       num_hidden_layers
         UpperCamelCase_:      Optional[Any]					   =       num_attention_heads
         UpperCamelCase_:      int					   =       intermediate_size
         UpperCamelCase_:      Optional[Any]					   =       hidden_act
         UpperCamelCase_:      Optional[int]					   =       hidden_dropout_prob
         UpperCamelCase_:      Optional[Any]					   =       attention_probs_dropout_prob
         UpperCamelCase_:      Any					   =       classifier_dropout_prob
         UpperCamelCase_:      str					   =       initializer_range
         UpperCamelCase_:      List[str]					   =       type_vocab_size
         UpperCamelCase_:      Optional[int]					   =       layer_norm_eps
         UpperCamelCase_:      List[str]					   =       use_cache
         UpperCamelCase_:      List[Any]					   =       False







class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       @property
       def     _a					(      self							):
         if self.task == "multiple-choice":
           UpperCamelCase_:      Union[str, Any]					   =       {0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
           UpperCamelCase_:      str					   =       {0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
                 ('token_type_ids', dynamic_axis),
             ]							)





       @property
       def     _a					(      self							):
         return 1e-4







def 						snake_case      (UpperCAmelCase__ = 4_0_0_0_0_0_0						) ->       int:

  if n <= 1:
    return 0
  UpperCamelCase_:      Dict					   =       0
  UpperCamelCase_:      Union[str, Any]					   =       2
  UpperCamelCase_:      int					   =       0
  while 4 * b + a <= n:
    UpperCamelCase_   ,UpperCamelCase_:      List[str]					   =       b, 4 * b + a
    count += a
  return count + b


if __name__ == "__main__":
   print(F'''{solution() = }''')