"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_a      :		Any           =   {
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a      :		Any           =   ['SqueezeBertTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a      :		Optional[Any]           =   [
         'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'SqueezeBertForMaskedLM',
         'SqueezeBertForMultipleChoice',
         'SqueezeBertForQuestionAnswering',
         'SqueezeBertForSequenceClassification',
         'SqueezeBertForTokenClassification',
         'SqueezeBertModel',
         'SqueezeBertModule',
         'SqueezeBertPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_squeezebert import (
         SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SqueezeBertConfig,
         SqueezeBertOnnxConfig,
     )
     from .tokenization_squeezebert import SqueezeBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_squeezebert import (
              SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              SqueezeBertForMaskedLM,
              SqueezeBertForMultipleChoice,
              SqueezeBertForQuestionAnswering,
              SqueezeBertForSequenceClassification,
              SqueezeBertForTokenClassification,
              SqueezeBertModel,
              SqueezeBertModule,
              SqueezeBertPreTrainedModel,
          )

else:
     import sys

     _a      :		Union[str, Any]           =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






'''simple docstring'''




import requests
from bsa import BeautifulSoup







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   str					=						BeautifulSoup(requests.get(snake_case__		,				params=snake_case__						).content		,				'''html.parser'''						)
    A       :   Dict					=						soup.find('''div'''		,				attrs={'''class''': '''gs_ri'''}						)
    A       :   Optional[int]					=						div.find('''div'''		,				attrs={'''class''': '''gs_fl'''}						).find_all('''a'''						)
    return anchors[2].get_text()


if __name__ == "__main__":
      lowercase			:				str    		=			{
          'title': (
              'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
              'capacitance, volumetric capacitance, and energy density'
          ),
          'journal': 'Chem. Mater.',
          'volume': 30,
          'pages': '3979-3990',
          'year': 20_18,
          'hl': 'en',
      }
      print(get_citation('https://scholar.google.com/scholar_lookup', params=params))




