






import logging

from transformers.configuration_utils import PretrainedConfig


lowerCamelCase     :							str    =logging.getLogger(__name__)





class       __a  (   A__	):
 _lowerCAmelCase	:	List[str]				=			'''masked_bert'''







 def __init__(		self :       Optional[int]							,       SCREAMING_SNAKE_CASE :       Dict=3_05_22							,       SCREAMING_SNAKE_CASE :       int=7_68							,       SCREAMING_SNAKE_CASE :       str=12							,       SCREAMING_SNAKE_CASE :       List[Any]=12							,       SCREAMING_SNAKE_CASE :       Optional[int]=30_72							,       SCREAMING_SNAKE_CASE :       Union[str, Any]="gelu"							,       SCREAMING_SNAKE_CASE :       str=0.1							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=0.1							,       SCREAMING_SNAKE_CASE :       str=5_12							,       SCREAMING_SNAKE_CASE :       Optional[Any]=2							,       SCREAMING_SNAKE_CASE :       Any=0.0_2							,       SCREAMING_SNAKE_CASE :       Any=1e-1_2							,       SCREAMING_SNAKE_CASE :       Any=0							,       SCREAMING_SNAKE_CASE :       List[Any]="topK"							,       SCREAMING_SNAKE_CASE :       List[str]="constant"							,       SCREAMING_SNAKE_CASE :       Optional[Any]=0.0							,       **SCREAMING_SNAKE_CASE :       int							,       ):


      '''simple docstring'''






      super().__init__(pad_token_id=SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       int	    =  vocab_size
      UpperCamelCase__      :       List[Any]	    =  hidden_size
      UpperCamelCase__      :       List[str]	    =  num_hidden_layers
      UpperCamelCase__      :       Dict	    =  num_attention_heads
      UpperCamelCase__      :       List[str]	    =  hidden_act
      UpperCamelCase__      :       Dict	    =  intermediate_size
      UpperCamelCase__      :       Tuple	    =  hidden_dropout_prob
      UpperCamelCase__      :       Optional[int]	    =  attention_probs_dropout_prob
      UpperCamelCase__      :       Union[str, Any]	    =  max_position_embeddings
      UpperCamelCase__      :       List[str]	    =  type_vocab_size
      UpperCamelCase__      :       List[Any]	    =  initializer_range
      UpperCamelCase__      :       int	    =  layer_norm_eps
      UpperCamelCase__      :       Any	    =  pruning_method
      UpperCamelCase__      :       Union[str, Any]	    =  mask_init
      UpperCamelCase__      :       List[str]	    =  mask_scale







from __future__ import annotations







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase = None ,   __lowerCAmelCase = None      )			->     None:
     if start is None:
          UpperCamelCase__      :       Union[str, Any]	    =  0

     if end is None:
          UpperCamelCase__      :       List[Any]	    =  len(__lowerCAmelCase      ) - 1

     if start >= end:
          return

     UpperCamelCase__      :       Union[str, Any]	    =  (start + end) // 2

     slowsort(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )
     slowsort(__lowerCAmelCase ,   mid + 1 ,   __lowerCAmelCase      )

     if sequence[end] < sequence[mid]:
          UpperCamelCase__    ,					UpperCamelCase__      :       Optional[int]	    =  sequence[mid], sequence[end]

     slowsort(__lowerCAmelCase ,   __lowerCAmelCase ,   end - 1      )


if __name__ == "__main__":
 from doctest import testmod

 testmod()