





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__      =logging.get_logger(__name__)

UpperCamelCase__      ={
    'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    'roc_bert'




       def __init__(      self    ,						__lowerCamelCase=3_0_5_2_2    ,						__lowerCamelCase=7_6_8    ,						__lowerCamelCase=1_2    ,						__lowerCamelCase=1_2    ,						__lowerCamelCase=3_0_7_2    ,						__lowerCamelCase="gelu"    ,						__lowerCamelCase=0.1    ,						__lowerCamelCase=0.1    ,						__lowerCamelCase=5_1_2    ,						__lowerCamelCase=2    ,						__lowerCamelCase=0.02    ,						__lowerCamelCase=1E-12    ,						__lowerCamelCase=True    ,						__lowerCamelCase=0    ,						__lowerCamelCase="absolute"    ,						__lowerCamelCase=None    ,						__lowerCamelCase=True    ,						__lowerCamelCase=True    ,						__lowerCamelCase=7_6_8    ,						__lowerCamelCase=9_1_0    ,						__lowerCamelCase=5_1_2    ,						__lowerCamelCase=2_4_8_5_8    ,						__lowerCamelCase=True    ,						**__lowerCamelCase    ,						)			->     int:
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							vocab_size
            _SCREAMING_SNAKE_CASE       :	int						    =							max_position_embeddings
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							hidden_size
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							num_hidden_layers
            _SCREAMING_SNAKE_CASE       :	Dict						    =							num_attention_heads
            _SCREAMING_SNAKE_CASE       :	int						    =							intermediate_size
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							hidden_act
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							hidden_dropout_prob
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							attention_probs_dropout_prob
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							initializer_range
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							type_vocab_size
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							layer_norm_eps
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							use_cache
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							enable_pronunciation
            _SCREAMING_SNAKE_CASE       :	int						    =							enable_shape
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							pronunciation_embed_dim
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							pronunciation_vocab_size
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							shape_embed_dim
            _SCREAMING_SNAKE_CASE       :	str						    =							shape_vocab_size
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							concat_input
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							position_embedding_type
            _SCREAMING_SNAKE_CASE       :	str						    =							classifier_dropout
            super().__init__(pad_token_id=__lowerCamelCase    ,						**__lowerCamelCase							)






def     lowerCamelCase__			(__lowerCamelCase = 10**9	):
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							1
     _SCREAMING_SNAKE_CASE       :	Any						    =							2

     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							0
     _SCREAMING_SNAKE_CASE       :	Dict						    =							0
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							0
     while perimeter <= max_perimeter:
          perimeters_sum += perimeter

          prev_value += 2 * value
          value += prev_value

          _SCREAMING_SNAKE_CASE       :	Tuple						    =							2 * value + 2 if i % 2 == 0 else 2 * value - 2
          i += 1

     return perimeters_sum


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