"""simple docstring"""






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = ["image_processor", "tokenizer"]
					snake_case__				    = "BlipImageProcessor"
					snake_case__				    = "AutoTokenizer"







					def __init__(				self   :	Dict    ,lowerCamelCase__   :	Any    ,lowerCamelCase__   :	Optional[Any]			):
									UpperCAmelCase__             =							False
									super().__init__(lowerCamelCase__    ,lowerCamelCase__			)
									UpperCAmelCase__             =							self.image_processor







					def __call__(				self   :	str    ,lowerCamelCase__   :	ImageInput = None    ,lowerCamelCase__   :	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Union[bool, str, PaddingStrategy] = False    ,lowerCamelCase__   :	Union[bool, str, TruncationStrategy] = None    ,lowerCamelCase__   :	Optional[int] = None    ,lowerCamelCase__   :	int = 0    ,lowerCamelCase__   :	Optional[int] = None    ,lowerCamelCase__   :	Optional[bool] = None    ,lowerCamelCase__   :	bool = False    ,lowerCamelCase__   :	bool = False    ,lowerCamelCase__   :	bool = False    ,lowerCamelCase__   :	bool = False    ,lowerCamelCase__   :	bool = False    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Optional[Union[str, TensorType]] = None    ,**lowerCamelCase__   :	Optional[int]    ,):
									if images is None and text is None:
													raise ValueError('You have to specify either images or text.'			)

									# Get only text
									if images is None:
													UpperCAmelCase__             =							self.tokenizer
													UpperCAmelCase__             =							self.tokenizer(
													    text=lowerCamelCase__    ,add_special_tokens=lowerCamelCase__    ,padding=lowerCamelCase__    ,truncation=lowerCamelCase__    ,max_length=lowerCamelCase__    ,stride=lowerCamelCase__    ,pad_to_multiple_of=lowerCamelCase__    ,return_attention_mask=lowerCamelCase__    ,return_overflowing_tokens=lowerCamelCase__    ,return_special_tokens_mask=lowerCamelCase__    ,return_offsets_mapping=lowerCamelCase__    ,return_token_type_ids=lowerCamelCase__    ,return_length=lowerCamelCase__    ,verbose=lowerCamelCase__    ,return_tensors=lowerCamelCase__    ,**lowerCamelCase__    ,)
													return text_encoding

									# add pixel_values
									UpperCAmelCase__             =							self.image_processor(lowerCamelCase__    ,return_tensors=lowerCamelCase__			)

									if text is not None:
													UpperCAmelCase__             =							self.tokenizer(
													    text=lowerCamelCase__    ,add_special_tokens=lowerCamelCase__    ,padding=lowerCamelCase__    ,truncation=lowerCamelCase__    ,max_length=lowerCamelCase__    ,stride=lowerCamelCase__    ,pad_to_multiple_of=lowerCamelCase__    ,return_attention_mask=lowerCamelCase__    ,return_overflowing_tokens=lowerCamelCase__    ,return_special_tokens_mask=lowerCamelCase__    ,return_offsets_mapping=lowerCamelCase__    ,return_token_type_ids=lowerCamelCase__    ,return_length=lowerCamelCase__    ,verbose=lowerCamelCase__    ,return_tensors=lowerCamelCase__    ,**lowerCamelCase__    ,)
									else:
													UpperCAmelCase__             =							None

									if text_encoding is not None:
													encoding_image_processor.update(lowerCamelCase__			)

									return encoding_image_processor







					def 		__lowerCAmelCase						(				self   :	Any    ,*lowerCamelCase__   :	Tuple    ,**lowerCamelCase__   :	Optional[Any]			):
									return self.tokenizer.batch_decode(*lowerCamelCase__    ,**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]    ,*lowerCamelCase__   :	List[Any]    ,**lowerCamelCase__   :	Dict			):
									return self.tokenizer.decode(*lowerCamelCase__    ,**lowerCamelCase__			)







					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									UpperCAmelCase__             =							self.tokenizer.model_input_names
									UpperCAmelCase__             =							self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)




"""simple docstring"""






import requests
from bsa import BeautifulSoup



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							BeautifulSoup(requests.get(lowerCamelCase					,			params=lowerCamelCase       ).content					,			'html.parser'       )
				UpperCAmelCase__             =							soup.find('div'					,			attrs={'class': 'gs_ri'}       )
				UpperCAmelCase__             =							div.find('div'					,			attrs={'class': 'gs_fl'}       ).find_all('a'       )
				return anchors[2].get_text()


if __name__ == "__main__":
						lowerCAmelCase__  :  Optional[int]						     =	{
						    '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))



