





from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase :    Tuple  							= logging.get_logger(__name__)

_lowerCamelCase :    int  							= {
    """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json""",
    """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json""",
    """junnyu/roformer_chinese_char_small""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"""
    ),
    """junnyu/roformer_chinese_char_base""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_discriminator""": (
        """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_generator""": (
        """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"""
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}


class 						lowerCamelCase       (__A      ):







       """simple docstring"""




       UpperCAmelCase_      						=   'roformer'






       def __init__( self     :					List[str],			_UpperCAmelCase     :					int=5_0_0_0_0,			_UpperCAmelCase     :					List[str]=None,			_UpperCAmelCase     :					Any=7_6_8,			_UpperCAmelCase     :					Tuple=1_2,			_UpperCAmelCase     :					Dict=1_2,			_UpperCAmelCase     :					Dict=3_0_7_2,			_UpperCAmelCase     :					Optional[Any]="gelu",			_UpperCAmelCase     :					Any=0.1,			_UpperCAmelCase     :					List[str]=0.1,			_UpperCAmelCase     :					Any=1_5_3_6,			_UpperCAmelCase     :					Union[str, Any]=2,			_UpperCAmelCase     :					List[Any]=0.02,			_UpperCAmelCase     :					str=1E-12,			_UpperCAmelCase     :					Union[str, Any]=0,			_UpperCAmelCase     :					str=False,			_UpperCAmelCase     :					Dict=True,			**_UpperCAmelCase     :					int,			)				->			Dict:





              """simple docstring"""
              super().__init__(pad_token_id=__lowercase,			**__lowercase						)

              SCREAMING_SNAKE_CASE__       :      Tuple     =						vocab_size
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						hidden_size if embedding_size is None else embedding_size
              SCREAMING_SNAKE_CASE__       :      List[str]     =						hidden_size
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						num_hidden_layers
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						num_attention_heads
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_act
              SCREAMING_SNAKE_CASE__       :      Dict     =						intermediate_size
              SCREAMING_SNAKE_CASE__       :      Dict     =						hidden_dropout_prob
              SCREAMING_SNAKE_CASE__       :      Tuple     =						attention_probs_dropout_prob
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						max_position_embeddings
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						type_vocab_size
              SCREAMING_SNAKE_CASE__       :      int     =						initializer_range
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						layer_norm_eps
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						rotary_value
              SCREAMING_SNAKE_CASE__       :      str     =						use_cache


class 						lowerCamelCase       (__A      ):







       """simple docstring"""




       @property
       def  A_     ( self     :					List[str]						)				->			str:





              """simple docstring"""
              if self.task == "multiple-choice":
                     SCREAMING_SNAKE_CASE__       :      int     =						{0: "batch", 1: "choice", 2: "sequence"}
              else:
                     SCREAMING_SNAKE_CASE__       :      List[str]     =						{0: "batch", 1: "sequence"}
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						{0: "batch", 1: "sequence"}
              return OrderedDict(
                  [
                      ("input_ids", dynamic_axis),
                      ("attention_mask", dynamic_axis),
                      ("token_type_ids", dynamic_axis),
                  ]						)













import requests
from bsa import BeautifulSoup




def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  dict       )	->    str:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Any     =						BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE__			,				params=SCREAMING_SNAKE_CASE__       ).content			,				"html.parser"       )
							SCREAMING_SNAKE_CASE__       :      str     =						soup.find("div"			,				attrs={"class": "gs_ri"}       )
							SCREAMING_SNAKE_CASE__       :      int     =						div.find("div"			,				attrs={"class": "gs_fl"}       ).find_all("a"       )
							return anchors[2].get_text()


if __name__ == "__main__":
		_lowerCamelCase :    Any  							= {
		    '''title''': (
		        '''Precisely geometry controlled microsupercapacitors for ultrahigh areal '''
		        '''capacitance, volumetric capacitance, and energy density'''
		    ),
		    '''journal''': '''Chem. Mater.''',
		    '''volume''': 3_0,
		    '''pages''': '''3979-3990''',
		    '''year''': 2_0_1_8,
		    '''hl''': '''en''',
		}
		print(get_citation('''https://scholar.google.com/scholar_lookup''', params=params))






