

"""simple docstring"""


import requests
from bsa import BeautifulSoup



def   _A					(	_a	:						str     ,   _a	:						dict				):



      """simple docstring"""






      A				  =		BeautifulSoup(requests.get(_a     ,   params=_a				).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__":
  UpperCAmelCase													={
      "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))







"""simple docstring"""


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


UpperCAmelCase													=logging.get_logger(__name__)

UpperCAmelCase													={"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase													=[
    "small",
    "small-base",
    "medium",
    "medium-base",
    "intermediate",
    "intermediate-base",
    "large",
    "large-base",
    "xlarge",
    "xlarge-base",
]

UpperCAmelCase													={
    "vocab_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt",
        "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt",
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt",
        "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt",
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json",
        "funnel-transformer/small-base": (
            "https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json",
        "funnel-transformer/large-base": (
            "https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"
        ),
    },
}
UpperCAmelCase													={f"""funnel-transformer/{name}""": 512 for name in _model_names}
UpperCAmelCase													={f"""funnel-transformer/{name}""": {"do_lower_case": True} for name in _model_names}





class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    _lowerCamelCase					=   VOCAB_FILES_NAMES
    _lowerCamelCase					=   PRETRAINED_VOCAB_FILES_MAP
    _lowerCamelCase					=   PRETRAINED_INIT_CONFIGURATION
    _lowerCamelCase					=   FunnelTokenizer
    _lowerCamelCase					=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _lowerCamelCase					=   2




    def __init__( self ,lowerCamelCase_=None ,lowerCamelCase_=None ,lowerCamelCase_=True ,lowerCamelCase_="<unk>" ,lowerCamelCase_="<sep>" ,lowerCamelCase_="<pad>" ,lowerCamelCase_="<cls>" ,lowerCamelCase_="<mask>" ,lowerCamelCase_="<s>" ,lowerCamelCase_="</s>" ,lowerCamelCase_=True ,lowerCamelCase_=True ,lowerCamelCase_=None ,lowerCamelCase_="##" ,**lowerCamelCase_ ,)    ->      List[Any]:
          super().__init__(
              lowerCamelCase_ ,tokenizer_file=lowerCamelCase_ ,do_lower_case=lowerCamelCase_ ,unk_token=lowerCamelCase_ ,sep_token=lowerCamelCase_ ,pad_token=lowerCamelCase_ ,cls_token=lowerCamelCase_ ,mask_token=lowerCamelCase_ ,bos_token=lowerCamelCase_ ,eos_token=lowerCamelCase_ ,clean_text=lowerCamelCase_ ,tokenize_chinese_chars=lowerCamelCase_ ,strip_accents=lowerCamelCase_ ,wordpieces_prefix=lowerCamelCase_ ,**lowerCamelCase_ ,)

          A				  =		json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
          if (
              normalizer_state.get("""lowercase""" ,lowerCamelCase_    ) != do_lower_case
              or normalizer_state.get("""strip_accents""" ,lowerCamelCase_    ) != strip_accents
              or normalizer_state.get("""handle_chinese_chars""" ,lowerCamelCase_    ) != tokenize_chinese_chars
          ):
                A				  =		getattr(lowerCamelCase_ ,normalizer_state.pop("""type"""    )    )
                A				  =		do_lower_case
                A				  =		strip_accents
                A				  =		tokenize_chinese_chars
                A				  =		normalizer_class(**lowerCamelCase_    )

          A				  =		do_lower_case




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_=None    )    ->      List[Any]:
          A				  =		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
                output += token_ids_a + [self.sep_token_id]

          return output




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ = None    )    ->      List[int]:
          A				  =		[self.sep_token_id]
          A				  =		[self.cls_token_id]
          if token_ids_a is None:
                return len(cls    ) * [self.cls_token_type_id] + len(token_ids_a + sep    ) * [0]
          return len(cls    ) * [self.cls_token_type_id] + len(token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ = None    )    ->      Tuple[str]:
          A				  =		self._tokenizer.model.save(lowerCamelCase_ ,name=lowerCamelCase_    )
          return tuple(lowerCamelCase_    )




