import torch

from transformers import AutoModel


class 			lowerCamelCase_  (     torch.nn.Module				):






    '''simple docstring'''






    def __init__(			self				,				__lowercase="sayef/fsner-bert-base-uncased") ->			List[str]:
        super(UpperCAmelCase__				,				self).__init__()

        __UpperCamelCase  :str    						=	AutoModel.from_pretrained(UpperCAmelCase__				,				return_dict=UpperCAmelCase__)
        __UpperCamelCase  :Dict    						=	torch.nn.CosineSimilarity(3				,				1E-0_8)
        __UpperCamelCase  :Optional[int]    						=	torch.nn.Softmax(dim=1)







    def     UpperCamelCase__     (			self				,				**__lowercase) ->			Optional[int]:
        return self.bert(**UpperCAmelCase__).last_hidden_state







    def     UpperCamelCase__     (			self				,				__lowercase) ->			Tuple:
        return token_embeddings.sum(2				,				keepdim=UpperCAmelCase__)







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase=1) ->			int:
        return self.softmax(T * self.cos(UpperCAmelCase__				,				UpperCAmelCase__))







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase) ->			Tuple:
        __UpperCamelCase  :Union[str, Any]    						=	W_supports['''sizes'''].tolist()
        __UpperCamelCase  :Optional[Any]    						=	W_supports['''start_token_id'''].item()
        __UpperCamelCase  :int    						=	W_supports['''end_token_id'''].item()

        del W_supports["sizes"]
        del W_supports["start_token_id"]
        del W_supports["end_token_id"]

        __UpperCamelCase  :Tuple    						=	self.BERT(**UpperCAmelCase__)
        __UpperCamelCase  :Dict    						=	self.BERT(**UpperCAmelCase__)

        __UpperCamelCase  :Optional[Any]    						=	None
        __UpperCamelCase  :str    						=	None

        __UpperCamelCase  :int    						=	W_supports['''input_ids'''] == start_token_id
        __UpperCamelCase  :int    						=	W_supports['''input_ids'''] == end_token_id

        for i, size in enumerate(UpperCAmelCase__):
            if i == 0:
                __UpperCamelCase  :Tuple    						=	0
            else:
                __UpperCamelCase  :int    						=	support_sizes[i - 1]

            __UpperCamelCase  :List[Any]    						=	S[s : s + size][start_token_masks[s : s + size]]
            __UpperCamelCase  :Tuple    						=	S[s : s + size][end_token_masks[s : s + size]]

            __UpperCamelCase  :int    						=	torch.matmul(q[i]				,				s_start.T).sum(1).softmax(0)
            __UpperCamelCase  :Tuple    						=	torch.matmul(q[i]				,				s_end.T).sum(1).softmax(0)

            if p_starts is not None:
                __UpperCamelCase  :Tuple    						=	torch.vstack((p_starts, p_start))
                __UpperCamelCase  :Union[str, Any]    						=	torch.vstack((p_ends, p_end))
            else:
                __UpperCamelCase  :Optional[Any]    						=	p_start
                __UpperCamelCase  :Optional[Any]    						=	p_end

        return p_starts, p_ends






import requests
from bsa import BeautifulSoup



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







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


if __name__ == "__main__":
    __lowercase											=      {
        '''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))





