



from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'ClapFeatureExtractor'
							lowerCamelCase__         =	('RobertaTokenizer', 'RobertaTokenizerFast')







							def __init__(     self,    __a,    __a):

														'''simple docstring'''

														super().__init__(__a,    __a)







							def __call__(     self,    __a=None,    __a=None,    __a=None,    **__a):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					kwargs.pop("sampling_rate",    __a)

														if text is None and audios is None:
																					raise ValueError("You have to specify either text or audios. Both cannot be none.")

														if text is not None:
																					_lowerCAmelCase  :							List[str]				     =					self.tokenizer(__a,    return_tensors=__a,    **__a)

														if audios is not None:
																					_lowerCAmelCase  :							Dict				     =					self.feature_extractor(
																					    __a,    sampling_rate=__a,    return_tensors=__a,    **__a)

														if text is not None and audios is not None:
																					_lowerCAmelCase  :							int				     =					audio_features.input_features
																					return encoding
														elif text is not None:
																					return encoding
														else:
																					return BatchEncoding(data=dict(**__a),    tensor_type=__a)







							def      snake_case__   (     self,    *__a,    **__a):

														'''simple docstring'''

														return self.tokenizer.batch_decode(*__a,    **__a)







							def      snake_case__   (     self,    *__a,    **__a):

														'''simple docstring'''

														return self.tokenizer.decode(*__a,    **__a)







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					self.tokenizer.model_input_names
														_lowerCAmelCase  :							str				     =					self.feature_extractor.model_input_names
														return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))





import requests
from bsa import BeautifulSoup






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							List[str]				     =					BeautifulSoup(requests.get(_lowerCamelCase       ,				params=_lowerCamelCase						).content       ,				"html.parser"						)
							_lowerCAmelCase  :							Any				     =					soup.find("div"       ,				attrs={"class": "gs_ri"}						)
							_lowerCAmelCase  :							str				     =					div.find("div"       ,				attrs={"class": "gs_fl"}						).find_all("a"						)
							return anchors[2].get_text()


if __name__ == "__main__":
						_snake_case	=						{
						    "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))
