"""simple docstring"""




import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


_a      :		Optional[int]           =   get_tests_dir('fixtures/test_sentencepiece_bpe.model')
class 						__A							( __UpperCAmelCase    ,	unittest.TestCase		):
     _UpperCamelCase							:			List[Any]							=		BartphoTokenizer
     _UpperCamelCase							:			Union[str, Any]							=		False
     _UpperCamelCase							:			Dict							=		True






     def     __A  (		self  ):
       super().setUp()

       _lowerCAmelCase						:       Dict  			=     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]
       _lowerCAmelCase						:       List[str]  			=     dict(zip(lowerCamelCase__				,     range(len(lowerCamelCase__  )  )  )  )
       _lowerCAmelCase						:       Tuple  			=     {"""unk_token""": """<unk>"""}

       _lowerCAmelCase						:       int  			=     os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES["""monolingual_vocab_file"""]  )
       with open(self.monolingual_vocab_file				,     """w"""				,     encoding="""utf-8"""  ) as fp:
         for token in vocab_tokens:
           fp.write(F"{token} {vocab_tokens[token]}\n"  )

       _lowerCAmelCase						:       List[str]  			=     BartphoTokenizer(lowerCamelCase__				,     self.monolingual_vocab_file				,     **self.special_tokens_map  )
       tokenizer.save_pretrained(self.tmpdirname  )






     def     __A  (		self				,     **a__  ):
       kwargs.update(self.special_tokens_map  )
       return BartphoTokenizer.from_pretrained(self.tmpdirname				,     **lowerCamelCase__  )






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[Any]  			=     """This is a là test"""
       _lowerCAmelCase						:       List[str]  			=     """This is a<unk><unk> test"""
       return input_text, output_text






     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     BartphoTokenizer(lowerCamelCase__				,     self.monolingual_vocab_file				,     **self.special_tokens_map  )
       _lowerCAmelCase						:       Optional[int]  			=     """This is a là test"""
       _lowerCAmelCase						:       Union[str, Any]  			=     """▁This ▁is ▁a ▁l à ▁t est""".split()
       _lowerCAmelCase						:       int  			=     tokenizer.tokenize(lowerCamelCase__  )
       self.assertListEqual(lowerCamelCase__				,     lowerCamelCase__  )

       _lowerCAmelCase						:       Dict  			=     tokens + [tokenizer.unk_token]
       _lowerCAmelCase						:       Optional[int]  			=     [4, 5, 6, 3, 3, 7, 8, 3]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__  )				,     lowerCamelCase__  )





"""simple docstring"""






import requests
from bsa import BeautifulSoup



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							BeautifulSoup(requests.get(lowerCamelCase					,			params=lowerCamelCase       ).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__":
						lowerCAmelCase__  :  Optional[int]						     =	{
						    'title': (
						        'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
						        'capacitance, volumetric capacitance, and energy density'
						    ),
						    'journal': 'Chem. Mater.',
						    'volume': 30,
						    'pages': '3979-3990',
						    'year': 2_018,
						    'hl': 'en',
						}
						print(get_citation('https://scholar.google.com/scholar_lookup', params=params))



