



'''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


_UpperCamelCase							 =	get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')



class _A     (			UpperCAmelCase__		, unittest.TestCase			):
		_SCREAMING_SNAKE_CASE    :			Dict  					=	BartphoTokenizer
		_SCREAMING_SNAKE_CASE    :			Union[str, Any]  					=	False
		_SCREAMING_SNAKE_CASE    :			Optional[int]  					=	True





		def 							__A				(   self )				->   Union[str, Any]:

					'''simple docstring'''
					super().setUp()

					__UpperCAmelCase      :					List[Any]   =   ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]
					__UpperCAmelCase      :					Dict   =   dict(zip(UpperCAmelCase__       ,				range(len(UpperCAmelCase__ ) ) ) )
					__UpperCAmelCase      :					Union[str, Any]   =   {"""unk_token""": """<unk>"""}

					__UpperCAmelCase      :					Any   =   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' )

					__UpperCAmelCase      :					Union[str, Any]   =   BartphoTokenizer(UpperCAmelCase__       ,				self.monolingual_vocab_file       ,				**self.special_tokens_map )
					tokenizer.save_pretrained(self.tmpdirname )





		def 							__A				(   self       ,				**__UpperCAmelCase )				->   Any:

					'''simple docstring'''
					kwargs.update(self.special_tokens_map )
					return BartphoTokenizer.from_pretrained(self.tmpdirname       ,				**UpperCAmelCase__ )





		def 							__A				(   self       ,				__UpperCAmelCase )				->   Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase      :					Any   =   """This is a là test"""
					__UpperCAmelCase      :					List[Any]   =   """This is a<unk><unk> test"""
					return input_text, output_text





		def 							__A				(   self )				->   int:

					'''simple docstring'''
					__UpperCAmelCase      :					Optional[Any]   =   BartphoTokenizer(UpperCAmelCase__       ,				self.monolingual_vocab_file       ,				**self.special_tokens_map )
					__UpperCAmelCase      :					Dict   =   """This is a là test"""
					__UpperCAmelCase      :					Union[str, Any]   =   """▁This ▁is ▁a ▁l à ▁t est""".split()
					__UpperCAmelCase      :					Tuple   =   tokenizer.tokenize(UpperCAmelCase__ )
					self.assertListEqual(UpperCAmelCase__       ,				UpperCAmelCase__ )

					__UpperCAmelCase      :					Dict   =   tokens + [tokenizer.unk_token]
					__UpperCAmelCase      :					str   =   [4, 5, 6, 3, 3, 7, 8, 3]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__ )       ,				UpperCAmelCase__ )










import requests
from bsa import BeautifulSoup







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       str:



       """simple docstring"""

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


if __name__ == "__main__":
  _lowerCamelCase	:		Optional[Any]							       =   {
      """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))


