





from typing import TYPE_CHECKING

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


_A					:				str				=			{
    '''configuration_transfo_xl''': ['''TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TransfoXLConfig'''],
    '''tokenization_transfo_xl''': ['''TransfoXLCorpus''', '''TransfoXLTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Optional[int]				=			[
      '''TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''AdaptiveEmbedding''',
      '''TransfoXLForSequenceClassification''',
      '''TransfoXLLMHeadModel''',
      '''TransfoXLModel''',
      '''TransfoXLPreTrainedModel''',
      '''load_tf_weights_in_transfo_xl''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				str				=			[
      '''TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFAdaptiveEmbedding''',
      '''TFTransfoXLForSequenceClassification''',
      '''TFTransfoXLLMHeadModel''',
      '''TFTransfoXLMainLayer''',
      '''TFTransfoXLModel''',
      '''TFTransfoXLPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
  from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_transfo_xl import (
        TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
        AdaptiveEmbedding,
        TransfoXLForSequenceClassification,
        TransfoXLLMHeadModel,
        TransfoXLModel,
        TransfoXLPreTrainedModel,
        load_tf_weights_in_transfo_xl,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_transfo_xl import (
        TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFAdaptiveEmbedding,
        TFTransfoXLForSequenceClassification,
        TFTransfoXLLMHeadModel,
        TFTransfoXLMainLayer,
        TFTransfoXLModel,
        TFTransfoXLPreTrainedModel,
    )

else:
  import sys

  _A					:				str				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







'''simple docstring'''




import requests
from bsa import BeautifulSoup




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->str:
	snake_case__	       =   BeautifulSoup(requests.get(UpperCAmelCase_					,				params=UpperCAmelCase_							).content					,				'html.parser'							)
	snake_case__	       =   soup.find('div'					,				attrs={'class': 'gs_ri'}							)
	snake_case__	       =   div.find('div'					,				attrs={'class': 'gs_fl'}							).find_all('a'							)
	return anchors[2].get_text()


if __name__ == "__main__":
			a__					:			int          =     {
			    '''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))



