


'''simple docstring'''

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase				: Union[str, Any]							    =				logging.get_logger(__name__)


_UpperCAmelCase				: Dict							    =				"""▁"""

_UpperCAmelCase				: Any							    =				{"""vocab_file""": """spiece.model"""}

_UpperCAmelCase				: str							    =				{
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

_UpperCAmelCase				: List[str]							    =				{
    """google/reformer-crime-and-punishment""": 5_2_4_2_8_8,
}


class       a__				( __A      ):







				"""simple docstring"""



				__UpperCamelCase    :		List[Any]	=	VOCAB_FILES_NAMES
				__UpperCamelCase    :		List[str]	=	PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase    :		List[Any]	=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase    :		Any	=	['input_ids', 'attention_mask']




				def __init__(self       ,			__lowercase       ,			__lowercase="</s>"       ,			__lowercase="<unk>"       ,			__lowercase=[]       ,			__lowercase = None       ,			**__lowercase       ,			):
										__lowerCAmelCase							       =				{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    eos_token=__lowercase       ,			unk_token=__lowercase       ,			additional_special_tokens=__lowercase       ,			sp_model_kwargs=self.sp_model_kwargs       ,			**__lowercase       ,			)

										__lowerCAmelCase							       =				vocab_file
										__lowerCAmelCase							       =				spm.SentencePieceProcessor(**self.sp_model_kwargs		)
										self.sp_model.Load(__lowercase		)




				@property
				def 					_snake_case  (self		):
										return self.sp_model.get_piece_size()




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				{self.convert_ids_to_tokens(__lowercase		): i for i in range(self.vocab_size		)}
										vocab.update(self.added_tokens_encoder		)
										return vocab




				def __getstate__(self		):
										__lowerCAmelCase							       =				self.__dict__.copy()
										__lowerCAmelCase							       =				None
										return state




				def __setstate__(self       ,			__lowercase		):
										__lowerCAmelCase							       =				d

										# for backward compatibility
										if not hasattr(self       ,			'''sp_model_kwargs'''		):
																__lowerCAmelCase							       =				{}

										__lowerCAmelCase							       =				spm.SentencePieceProcessor(**self.sp_model_kwargs		)
										self.sp_model.Load(self.vocab_file		)




				def 					_snake_case  (self       ,			__lowercase		):
										return self.sp_model.encode(__lowercase       ,			out_type=__lowercase		)




				def 					_snake_case  (self       ,			__lowercase		):
										return self.sp_model.piece_to_id(__lowercase		)




				def 					_snake_case  (self       ,			__lowercase		):
										if index < self.sp_model.get_piece_size():
																__lowerCAmelCase							       =				self.sp_model.IdToPiece(__lowercase		)
										return token




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				[]
										__lowerCAmelCase							       =				''''''
										for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																						out_string += self.sp_model.decode(__lowercase		) + token
																						__lowerCAmelCase							       =				[]
																else:
																						current_sub_tokens.append(__lowercase		)
										out_string += self.sp_model.decode(__lowercase		)
										return out_string.strip()




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase = None		):
										if not os.path.isdir(__lowercase		):
																logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
																return
										__lowerCAmelCase							       =				os.path.join(
										    __lowercase       ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

										if os.path.abspath(self.vocab_file		) != os.path.abspath(__lowercase		) and os.path.isfile(self.vocab_file		):
																copyfile(self.vocab_file       ,			__lowercase		)
										elif not os.path.isfile(self.vocab_file		):
																with open(__lowercase       ,			'''wb'''		) as fi:
																						__lowerCAmelCase							       =				self.sp_model.serialized_model_proto()
																						fi.write(__lowercase		)

										return (out_vocab_file,)





'''simple docstring'''

import requests
from bsa import BeautifulSoup

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


if __name__ == "__main__":
	_UpperCAmelCase				: Union[str, Any]							    =				{
	    """title""": (
	        """Precisely geometry controlled microsupercapacitors for ultrahigh areal """
	        """capacitance, volumetric capacitance, and energy density"""
	    ),
	    """journal""": """Chem. Mater.""",
	    """volume""": 3_0,
	    """pages""": """3979-3990""",
	    """year""": 2_0_1_8,
	    """hl""": """en""",
	}
	print(get_citation("""https://scholar.google.com/scholar_lookup""", params=params))

