import requests
from bsa import BeautifulSoup







def 			__UpperCamelCase     (    A			,				A				):
     UpperCamelCase__					    =   BeautifulSoup(requests.get(snake_case_			,				params=snake_case_				).content			,				'''html.parser'''				)
     UpperCamelCase__					    =   soup.find('''div'''			,				attrs={'''class''': '''gs_ri'''}				)
     UpperCamelCase__					    =   div.find('''div'''			,				attrs={'''class''': '''gs_fl'''}				).find_all('''a'''				)
     return anchors[2].get_text()


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










"""simple docstring"""



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       from transformers import AutoModelForSeqaSeqLM, AutoTokenizer






@require_torch
@require_sentencepiece
@require_tokenizers
class 						__snake_case   (						unittest.TestCase	):





 """simple docstring"""


 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Any							=	AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''   ,		return_dict=__lowerCamelCase    ).to(__lowerCamelCase    )
     __A       :		Optional[Any]							=	AutoTokenizer.from_pretrained('''google/mt5-small'''    )

     __A       :		Optional[int]							=	tokenizer('''Hello there'''   ,		return_tensors='''pt'''    ).input_ids
     __A       :		str							=	tokenizer('''Hi I am'''   ,		return_tensors='''pt'''    ).input_ids

     __A       :		Any							=	model(input_ids.to(__lowerCamelCase    )   ,		labels=labels.to(__lowerCamelCase    )    ).loss
     __A       :		Tuple							=	-(labels.shape[-1] * loss.item())

     __A       :		int							=	-8_4.9_1_2_7
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE    ) < 1e-4    )




