




'''simple docstring'''






from ... import PretrainedConfig


A_			      =		{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}






class        lowercase_      (						lowerCAmelCase_							):
     A_											=			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
     A_											=			"nezha"





     def __init__(     self     :					Dict     ,							__lowerCamelCase     :					Union[str, Any]=21128     ,							__lowerCamelCase     :					List[Any]=768     ,							__lowerCamelCase     :					Optional[int]=12     ,							__lowerCamelCase     :					Union[str, Any]=12     ,							__lowerCamelCase     :					Union[str, Any]=3072     ,							__lowerCamelCase     :					List[Any]="gelu"     ,							__lowerCamelCase     :					Any=0.1     ,							__lowerCamelCase     :					int=0.1     ,							__lowerCamelCase     :					List[str]=512     ,							__lowerCamelCase     :					Optional[int]=64     ,							__lowerCamelCase     :					int=2     ,							__lowerCamelCase     :					Dict=0.0_2     ,							__lowerCamelCase     :					Union[str, Any]=1E-12     ,							__lowerCamelCase     :					int=0.1     ,							__lowerCamelCase     :					int=0     ,							__lowerCamelCase     :					int=2     ,							__lowerCamelCase     :					Dict=3     ,							__lowerCamelCase     :					int=True     ,							**__lowerCamelCase     :					Any     ,							):
           super().__init__(pad_token_id=__lowerCamelCase     ,							bos_token_id=__lowerCamelCase     ,							eos_token_id=__lowerCamelCase     ,							**__lowerCamelCase			)

           snake_case__       :   Optional[Any]	      =    vocab_size
           snake_case__       :   Tuple	      =    hidden_size
           snake_case__       :   Dict	      =    num_hidden_layers
           snake_case__       :   Optional[int]	      =    num_attention_heads
           snake_case__       :   Optional[Any]	      =    hidden_act
           snake_case__       :   Optional[Any]	      =    intermediate_size
           snake_case__       :   List[str]	      =    hidden_dropout_prob
           snake_case__       :   Dict	      =    attention_probs_dropout_prob
           snake_case__       :   Dict	      =    max_position_embeddings
           snake_case__       :   Optional[Any]	      =    max_relative_position
           snake_case__       :   int	      =    type_vocab_size
           snake_case__       :   List[str]	      =    initializer_range
           snake_case__       :   int	      =    layer_norm_eps
           snake_case__       :   List[Any]	      =    classifier_dropout
           snake_case__       :   Optional[Any]	      =    use_cache






'''simple docstring'''






import requests
from bsa import BeautifulSoup






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   str:
      snake_case__       :   List[Any]	      =    BeautifulSoup(requests.get(__SCREAMING_SNAKE_CASE				,      params=__SCREAMING_SNAKE_CASE		).content				,      'html.parser'		)
      snake_case__       :   Optional[int]	      =    soup.find('div'				,      attrs={'class': 'gs_ri'}		)
      snake_case__       :   Dict	      =    div.find('div'				,      attrs={'class': 'gs_fl'}		).find_all('a'		)
      return anchors[2].get_text()


if __name__ == "__main__":
      A_			      =		{
          "title": (
              "Precisely geometry controlled microsupercapacitors for ultrahigh areal "
              "capacitance, volumetric capacitance, and energy density"
          ),
          "journal": "Chem. Mater.",
          "volume": 30,
          "pages": "3979-3990",
          "year": 2018,
          "hl": "en",
      }
      print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
