






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


a__       =     logging.get_logger(__name__)

a__       =     """▁"""

a__       =     {"""vocab_file""": """sentencepiece.bpe.model"""}

a__       =     {
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

a__       =     {
    """facebook/xglm-564M""": 2_0_4_8,
}


class    _lowerCAmelCase			(		__UpperCAmelCase      ):



    """simple docstring"""



    _lowercase				:   Optional[Any]      =  VOCAB_FILES_NAMES
    _lowercase				:   Optional[Any]      =  PRETRAINED_VOCAB_FILES_MAP
    _lowercase				:   Optional[Any]      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _lowercase				:   str      =  ["""input_ids""", """attention_mask"""]



    def __init__(      self    :    List[str]			,		UpperCamelCase__    :    Optional[Any]			,		UpperCamelCase__    :    int="<s>"			,		UpperCamelCase__    :    Optional[int]="</s>"			,		UpperCamelCase__    :    str="</s>"			,		UpperCamelCase__    :    int="<s>"			,		UpperCamelCase__    :    Tuple="<unk>"			,		UpperCamelCase__    :    Any="<pad>"			,		UpperCamelCase__    :    Optional[Any] = None			,		**UpperCamelCase__    :    Tuple			,		):






           '''simple docstring'''







           snake_case__	  =       {} if sp_model_kwargs is None else sp_model_kwargs

           # Compatibility with the original tokenizer
           snake_case__	  =       7
           snake_case__	  =       [F'''<madeupword{i}>''' for i in range(self.num_madeup_words)]

           snake_case__	  =       kwargs.get("""additional_special_tokens"""			,		[])
           kwargs["additional_special_tokens"] += [
               word for word in madeup_words if word not in kwargs["additional_special_tokens"]
           ]

           super().__init__(
               bos_token=UpperCAmelCase_			,		eos_token=UpperCAmelCase_			,		unk_token=UpperCAmelCase_			,		sep_token=UpperCAmelCase_			,		cls_token=UpperCAmelCase_			,		pad_token=UpperCAmelCase_			,		sp_model_kwargs=self.sp_model_kwargs			,		**UpperCAmelCase_			,		)

           snake_case__	  =       spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(str(UpperCAmelCase_))
           snake_case__	  =       vocab_file

           # Original fairseq vocab and spm vocab must be "aligned":
           # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
           # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
           # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
           # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

           # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
           snake_case__	  =       1

           # Mimic fairseq token-to-id alignment for the first 4 token
           snake_case__	  =       {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

           snake_case__	  =       len(self.sp_model)
           snake_case__	  =       {F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words)}
           self.fairseq_tokens_to_ids.update(UpperCAmelCase_)

           snake_case__	  =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}



    def __getstate__(      self    :    Dict):






           '''simple docstring'''







           snake_case__	  =       self.__dict__.copy()
           snake_case__	  =       None
           snake_case__	  =       self.sp_model.serialized_model_proto()
           return state



    def __setstate__(      self    :    str			,		UpperCamelCase__    :    List[Any]):






           '''simple docstring'''







           snake_case__	  =       d

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

           snake_case__	  =       spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.LoadFromSerializedProto(self.sp_model_proto)



    def 					__magic_name__							(      self    :    Optional[Any]			,		UpperCamelCase__    :    Optional[Any]			,		UpperCamelCase__    :    Union[str, Any] = None):






           '''simple docstring'''







           if token_ids_a is None:
                  return [self.sep_token_id] + token_ids_a
           snake_case__	  =       [self.sep_token_id]
           return sep + token_ids_a + sep + sep + token_ids_a



    def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    Any			,		UpperCamelCase__    :    Any = None			,		UpperCamelCase__    :    List[str] = False):






           '''simple docstring'''







           if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=UpperCAmelCase_			,		token_ids_a=UpperCAmelCase_			,		already_has_special_tokens=UpperCAmelCase_)

           if token_ids_a is None:
                  return [1] + ([0] * len(UpperCAmelCase_))
           return [1] + ([0] * len(UpperCAmelCase_)) + [1, 1] + ([0] * len(UpperCAmelCase_))



    def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    Union[str, Any]			,		UpperCamelCase__    :    Any = None):






           '''simple docstring'''







           snake_case__	  =       [self.sep_token_id]

           if token_ids_a is None:
                  return len(sep + token_ids_a) * [0]
           return len(sep + token_ids_a + sep + sep + token_ids_a) * [0]



    @property
    def 					__magic_name__							(      self    :    Optional[int]):






           '''simple docstring'''







           return len(self.sp_model) + self.fairseq_offset + self.num_madeup_words



    def 					__magic_name__							(      self    :    int):






           '''simple docstring'''







           snake_case__	  =       {self.convert_ids_to_tokens(UpperCAmelCase_): i for i in range(self.vocab_size)}
           vocab.update(self.added_tokens_encoder)
           return vocab



    def 					__magic_name__							(      self    :    Optional[Any]			,		UpperCamelCase__    :    Union[str, Any]):






           '''simple docstring'''







           return self.sp_model.encode(UpperCAmelCase_			,		out_type=UpperCAmelCase_)



    def 					__magic_name__							(      self    :    Optional[Any]			,		UpperCamelCase__    :    Tuple):






           '''simple docstring'''







           if token in self.fairseq_tokens_to_ids:
                  return self.fairseq_tokens_to_ids[token]
           snake_case__	  =       self.sp_model.PieceToId(UpperCAmelCase_)

           # Need to return unknown token if the SP model returned 0
           return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



    def 					__magic_name__							(      self    :    Optional[Any]			,		UpperCamelCase__    :    Tuple):






           '''simple docstring'''







           if index in self.fairseq_ids_to_tokens:
                  return self.fairseq_ids_to_tokens[index]
           return self.sp_model.IdToPiece(index - self.fairseq_offset)



    def 					__magic_name__							(      self    :    Optional[Any]			,		UpperCamelCase__    :    Union[str, Any]):






           '''simple docstring'''







           snake_case__	  =       """""".join(UpperCAmelCase_).replace(UpperCAmelCase_			,		""" """).strip()
           return out_string



    def 					__magic_name__							(      self    :    Tuple			,		UpperCamelCase__    :    Optional[Any]			,		UpperCamelCase__    :    Optional[Any] = None):






           '''simple docstring'''







           if not os.path.isdir(UpperCAmelCase_):
                  logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
                  return
           snake_case__	  =       os.path.join(
               UpperCAmelCase_			,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

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

           return (out_vocab_file,)








import requests
from bsa import BeautifulSoup

def _UpperCAmelCase   (	a						:	str  ,					a						:	dict ):
							snake_case__	  =       BeautifulSoup(requests.get(a  ,					params=a ).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__       =     {
	    """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))
