
"""simple docstring"""





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = ['image_processor', 'tokenizer']
		lowercase__         = 'AutoImageProcessor'
		lowercase__         = 'AutoTokenizer'



		def __init__(   self							,						__a=None							,						__a=None							,						**__a)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''							,						__a							,						)
												_UpperCamelCase									=    kwargs.pop('''feature_extractor''')

							_UpperCamelCase									=    image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
												raise ValueError('''You need to specify an `image_processor`.''')
							if tokenizer is None:
												raise ValueError('''You need to specify a `tokenizer`.''')

							super().__init__(__a							,						__a)
							_UpperCamelCase									=    self.image_processor
							_UpperCamelCase									=    False



		def __call__(   self							,						*__a							,						**__a)			->			Optional[int]:






							'''simple docstring'''
							# For backward compatibility
							if self._in_target_context_manager:
												return self.current_processor(*__a							,						**__a)

							_UpperCamelCase									=    kwargs.pop('''images'''							,						__a)
							_UpperCamelCase									=    kwargs.pop('''text'''							,						__a)
							if len(__a) > 0:
												_UpperCamelCase									=    args[0]
												_UpperCamelCase									=    args[1:]

							if images is None and text is None:
												raise ValueError('''You need to specify either an `images` or `text` input to process.''')

							if images is not None:
												_UpperCamelCase									=    self.image_processor(__a							,						*__a							,						**__a)
							if text is not None:
												_UpperCamelCase									=    self.tokenizer(__a							,						**__a)

							if text is None:
												return inputs
							elif images is None:
												return encodings
							else:
												_UpperCamelCase									=    encodings['''input_ids''']
												return inputs



		def 		UpperCAmelCase				(   self							,						*__a							,						**__a)			->			List[Any]:






							'''simple docstring'''
							return self.tokenizer.batch_decode(*__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						*__a							,						**__a)			->			List[str]:






							'''simple docstring'''
							return self.tokenizer.decode(*__a							,						**__a)



		@contextmanager
		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							warnings.warn(
							    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
							    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
							    '''your images inputs, or in a separate call.''')
							_UpperCamelCase									=    True
							_UpperCamelCase									=    self.tokenizer
							yield
							_UpperCamelCase									=    self.image_processor
							_UpperCamelCase									=    False



		def 		UpperCAmelCase				(   self							,						__a							,						__a=False							,						__a=None)			->			Any:






							'''simple docstring'''
							if added_vocab is None:
												_UpperCamelCase									=    self.tokenizer.get_added_vocab()

							_UpperCamelCase									=    {}

							while tokens:
												_UpperCamelCase									=    re.search(R'''<s_(.*?)>'''							,						__a							,						re.IGNORECASE)
												if start_token is None:
																	break
												_UpperCamelCase									=    start_token.group(1)
												_UpperCamelCase									=    re.search(RF'''</s_{key}>'''							,						__a							,						re.IGNORECASE)
												_UpperCamelCase									=    start_token.group()
												if end_token is None:
																	_UpperCamelCase									=    tokens.replace(__a							,						'''''')
												else:
																	_UpperCamelCase									=    end_token.group()
																	_UpperCamelCase									=    re.escape(__a)
																	_UpperCamelCase									=    re.escape(__a)
																	_UpperCamelCase									=    re.search(F'''{start_token_escaped}(.*?){end_token_escaped}'''							,						__a							,						re.IGNORECASE)
																	if content is not None:
																						_UpperCamelCase									=    content.group(1).strip()
																						if r"<s_" in content and r"</s_" in content:  # non-leaf node
																											_UpperCamelCase									=    self.tokenajson(__a							,						is_inner_value=__a							,						added_vocab=__a)
																											if value:
																																if len(__a) == 1:
																																					_UpperCamelCase									=    value[0]
																																_UpperCamelCase									=    value
																						else:  # leaf nodes
																											_UpperCamelCase									=    []
																											for leaf in content.split(R'''<sep/>'''):
																																_UpperCamelCase									=    leaf.strip()
																																if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																					_UpperCamelCase									=    leaf[1:-2]  # for categorical special tokens
																																output[key].append(__a)
																											if len(output[key]) == 1:
																																_UpperCamelCase									=    output[key][0]

																	_UpperCamelCase									=    tokens[tokens.find(__a) + len(__a) :].strip()
																	if tokens[:6] == r"<sep/>":  # non-leaf nodes
																						return [output] + self.tokenajson(tokens[6:]							,						is_inner_value=__a							,						added_vocab=__a)

							if len(__a):
												return [output] if is_inner_value else output
							else:
												return [] if is_inner_value else {"text_sequence": tokens}



		@property
		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,						__a							,						)
							return self.image_processor_class




		@property
		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,						__a							,						)
							return self.image_processor













'''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))



