






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__       =     {"""vocab_file""": """spm_char.model"""}

a__       =     {
    """vocab_file""": {
        """microsoft/speecht5_asr""": """https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model""",
        """microsoft/speecht5_tts""": """https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model""",
        """microsoft/speecht5_vc""": """https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model""",
    }
}

a__       =     {
    """microsoft/speecht5_asr""": 1_0_2_4,
    """microsoft/speecht5_tts""": 1_0_2_4,
    """microsoft/speecht5_vc""": 1_0_2_4,
}


class    _lowerCAmelCase			(		lowercase_      ):



    """simple docstring"""



    _lowercase				:   Optional[int]      =  VOCAB_FILES_NAMES
    _lowercase				:   List[str]      =  PRETRAINED_VOCAB_FILES_MAP
    _lowercase				:   List[str]      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _lowercase				:   Any      =  ['''input_ids''', '''attention_mask''']



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






           '''simple docstring'''







           snake_case__	  =       {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               bos_token=UpperCamelCase__			,		eos_token=UpperCamelCase__			,		unk_token=UpperCamelCase__			,		pad_token=UpperCamelCase__			,		sp_model_kwargs=self.sp_model_kwargs			,		**UpperCamelCase__			,		)

           snake_case__	  =       vocab_file

           snake_case__	  =       spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(UpperCamelCase__)



    @property
    def 					__magic_name__							(      self    :    Tuple):






           '''simple docstring'''







           return self.sp_model.get_piece_size()



    def 					__magic_name__							(      self    :    Tuple):






           '''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 __getstate__(      self    :    List[Any]):






           '''simple docstring'''







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



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






           '''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.Load(self.vocab_file)



    def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    str):






           '''simple docstring'''







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



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






           '''simple docstring'''







           return self.sp_model.piece_to_id(UpperCamelCase__)



    def 					__magic_name__							(      self    :    int			,		UpperCamelCase__    :    List[str]):






           '''simple docstring'''







           snake_case__	  =       self.sp_model.IdToPiece(UpperCamelCase__)
           return token



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






           '''simple docstring'''







           snake_case__	  =       []
           snake_case__	  =       """"""
           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(UpperCamelCase__) + token
                         snake_case__	  =       []
                  else:
                         current_sub_tokens.append(UpperCamelCase__)
           out_string += self.sp_model.decode(UpperCamelCase__)
           return out_string.strip()



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






           '''simple docstring'''







           if token_ids_a is None:
                  return token_ids_a + [self.eos_token_id]
           # We don't expect to process pairs, but leave the pair logic for API consistency
           return token_ids_a + token_ids_a + [self.eos_token_id]



    def 					__magic_name__							(      self    :    List[str]			,		UpperCamelCase__    :    List[int]			,		UpperCamelCase__    :    Optional[List[int]] = None			,		UpperCamelCase__    :    bool = 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__)

           snake_case__	  =       [1]
           if token_ids_a is None:
                  return ([0] * len(UpperCamelCase__)) + suffix_ones
           return ([0] * len(UpperCamelCase__)) + ([0] * len(UpperCamelCase__)) + suffix_ones



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