
"""simple docstring"""



import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
 import torch

class  lowerCAmelCase__							(		_lowercase      ):
     __a         =   '''char'''
     __a         =   '''bpe'''
     __a         =   '''wp'''






UpperCAmelCase__        =					(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)

class  lowerCAmelCase__							(		_lowercase      ):
     __a         =   ['''image_processor''', '''char_tokenizer''']
     __a         =   '''ViTImageProcessor'''
     __a         =   '''MgpstrTokenizer'''







     def __init__(				self     :     Dict       , _lowerCamelCase     :     Dict=None       , _lowerCamelCase     :     Union[str, Any]=None       , **_lowerCamelCase     :     Optional[int]     ):
       _snake_case   	=			None
       if "feature_extractor" in kwargs:
         warnings.warn(
             '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
             ''' instead.'''       , _lowerCamelCase       , )
         _snake_case   	=			kwargs.pop('''feature_extractor'''     )

       _snake_case   	=			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`.'''     )

       _snake_case   	=			tokenizer
       _snake_case   	=			AutoTokenizer.from_pretrained('''gpt2'''     )
       _snake_case   	=			AutoTokenizer.from_pretrained('''bert-base-uncased'''     )

       super().__init__(_lowerCamelCase       , _lowerCamelCase     )







     def __call__(				self     :     int       , _lowerCamelCase     :     Any=None       , _lowerCamelCase     :     List[Any]=None       , _lowerCamelCase     :     str=None       , **_lowerCamelCase     :     List[Any]     ):
       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:
         _snake_case   	=			self.image_processor(_lowerCamelCase       , return_tensors=_lowerCamelCase       , **_lowerCamelCase     )
       if text is not None:
         _snake_case   	=			self.char_tokenizer(_lowerCamelCase       , return_tensors=_lowerCamelCase       , **_lowerCamelCase     )

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







     def 	lowercase      (				self     :     List[str]       , _lowerCamelCase     :     Tuple     ):
       _snake_case	,   _snake_case	,   _snake_case   	=			sequences
       _snake_case   	=			char_preds.size(0     )

       _snake_case	,   _snake_case   	=			self._decode_helper(_lowerCamelCase       , '''char'''     )
       _snake_case	,   _snake_case   	=			self._decode_helper(_lowerCamelCase       , '''bpe'''     )
       _snake_case	,   _snake_case   	=			self._decode_helper(_lowerCamelCase       , '''wp'''     )

       _snake_case   	=			[]
       _snake_case   	=			[]
       for i in range(_lowerCamelCase     ):
         _snake_case   	=			[char_scores[i], bpe_scores[i], wp_scores[i]]
         _snake_case   	=			[char_strs[i], bpe_strs[i], wp_strs[i]]
         _snake_case   	=			scores.index(max(_lowerCamelCase     )     )
         final_strs.append(strs[max_score_index]     )
         final_scores.append(scores[max_score_index]     )

       _snake_case   	=			{}
       _snake_case   	=			final_strs
       _snake_case   	=			final_scores
       _snake_case   	=			char_strs
       _snake_case   	=			bpe_strs
       _snake_case   	=			wp_strs
       return out







     def 	lowercase      (				self     :     str       , _lowerCamelCase     :     Tuple       , _lowerCamelCase     :     List[str]     ):
       if format == DecodeType.CHARACTER:
         _snake_case   	=			self.char_decode
         _snake_case   	=			1
         _snake_case   	=			'''[s]'''
       elif format == DecodeType.BPE:
         _snake_case   	=			self.bpe_decode
         _snake_case   	=			2
         _snake_case   	=			'''#'''
       elif format == DecodeType.WORDPIECE:
         _snake_case   	=			self.wp_decode
         _snake_case   	=			102
         _snake_case   	=			'''[SEP]'''
       else:
         raise ValueError(f'''Format {format} is not supported.'''     )

       _snake_case	,   _snake_case   	=			[], []
       _snake_case   	=			pred_logits.size(0     )
       _snake_case   	=			pred_logits.size(1     )
       _snake_case	,   _snake_case   	=			pred_logits.topk(1       , dim=-1       , largest=_lowerCamelCase       , sorted=_lowerCamelCase     )
       _snake_case   	=			preds_index.view(-1       , _lowerCamelCase     )[:, 1:]
       _snake_case   	=			decoder(_lowerCamelCase     )
       _snake_case	,   _snake_case   	=			torch.nn.functional.softmax(_lowerCamelCase       , dim=2     ).max(dim=2     )
       _snake_case   	=			preds_max_prob[:, 1:]

       for index in range(_lowerCamelCase     ):
         _snake_case   	=			preds_str[index].find(_lowerCamelCase     )
         _snake_case   	=			preds_str[index][:pred_eos]
         _snake_case   	=			preds_index[index].cpu().tolist()
         _snake_case   	=			pred_index.index(_lowerCamelCase     ) if eos_token in pred_index else -1
         _snake_case   	=			preds_max_prob[index][: pred_eos_index + 1]
         _snake_case   	=			pred_max_prob.cumprod(dim=0     )[-1] if pred_max_prob.nelement() != 0 else 0.0
         dec_strs.append(_lowerCamelCase     )
         conf_scores.append(_lowerCamelCase     )

       return dec_strs, conf_scores







     def 	lowercase      (				self     :     Tuple       , _lowerCamelCase     :     Optional[Any]     ):
       _snake_case   	=			[seq.replace(''' '''       , ''''''     ) for seq in self.char_tokenizer.batch_decode(_lowerCamelCase     )]
       return decode_strs







     def 	lowercase      (				self     :     str       , _lowerCamelCase     :     Optional[Any]     ):
       return self.bpe_tokenizer.batch_decode(_lowerCamelCase     )







     def 	lowercase      (				self     :     Tuple       , _lowerCamelCase     :     int     ):
       _snake_case   	=			[seq.replace(''' '''       , ''''''     ) for seq in self.wp_tokenizer.batch_decode(_lowerCamelCase     )]
       return decode_strs








import requests
from bsa import BeautifulSoup






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


if __name__ == "__main__":
     a_      :      Optional[Any]    =     {
         '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))





