


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

import sentencepiece as spm

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


lowerCamelCase__							:						int							     =   logging.get_logger(__name__)

lowerCamelCase__							:						List[Any]							     =   {"""vocab_file""": """sentencepiece.model"""}

lowerCamelCase__							:						Dict							     =   {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

lowerCamelCase__							:						List[str]							     =   {
    """google/rembert""": 2_5_6,
}
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							List[Any]	   =       VOCAB_FILES_NAMES
     __lowercase				:							Tuple	   =       PRETRAINED_VOCAB_FILES_MAP
     __lowercase				:							Union[str, Any]	   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

     def __init__(					self:Union[str, Any]     ,		_a:Union[str, Any]     ,		_a:Tuple=False     ,		_a:Dict=True     ,		_a:int=True     ,		_a:Optional[Any]="[CLS]"     ,		_a:int="[SEP]"     ,		_a:Dict="[UNK]"     ,		_a:Optional[int]="[SEP]"     ,		_a:Any="[PAD]"     ,		_a:Union[str, Any]="[CLS]"     ,		_a:Dict="[MASK]"     ,		**_a:str     ,		):
       super().__init__(
           do_lower_case=_a     ,		remove_space=_a     ,		keep_accents=_a     ,		bos_token=_a     ,		eos_token=_a     ,		unk_token=_a     ,		sep_token=_a     ,		pad_token=_a     ,		cls_token=_a     ,		mask_token=_a     ,		**_a     ,		)

       snake_case__						= do_lower_case
       snake_case__						= remove_space
       snake_case__						= keep_accents
       snake_case__						= vocab_file

       snake_case__						= spm.SentencePieceProcessor()
       self.sp_model.Load(_a   )

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       return len(self.sp_model   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       snake_case__						= {self.convert_ids_to_tokens(_a   ): i for i in range(self.vocab_size   )}
       vocab.update(self.added_tokens_encoder   )
       return vocab

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

     def __setstate__(					self:List[Any]     ,		_a:Optional[int]   ):
       snake_case__						= d
       snake_case__						= spm.SentencePieceProcessor()
       self.sp_model.Load(self.vocab_file   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:Tuple     ,		_a:Any=False   ):
       snake_case__						= self.sp_model.EncodeAsPieces(_a   )
       return pieces

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:Optional[int]   ):
       return self.sp_model.PieceToId(_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:Optional[int]   ):
       return self.sp_model.IdToPiece(_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]     ,		_a:Union[str, Any]   ):
       snake_case__						= self.sp_model.decode_pieces(_a   )
       return out_string

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:List[int]     ,		_a:Optional[List[int]] = None   ):
       snake_case__						= [self.sep_token_id]
       snake_case__						= [self.cls_token_id]
       if token_ids_a is None:
         return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep

     def 	SCREAMING_SNAKE_CASE__						(					self:Any     ,		_a:List[int]     ,		_a:Optional[List[int]] = None     ,		_a:bool = False   ):

       if already_has_special_tokens:
         if token_ids_a is not None:
           raise ValueError(
               '''You should not supply a second sequence if the provided sequence of '''
               '''ids is already formatted with special tokens for the model.'''   )
         return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

       if token_ids_a is not None:
         return [1] + ([0] * len(_a   )) + [1] + ([0] * len(_a   )) + [1]
       return [1] + ([0] * len(_a   )) + [1]

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]     ,		_a:List[int]     ,		_a:Optional[List[int]] = None   ):
       snake_case__						= [self.sep_token_id]
       snake_case__						= [self.cls_token_id]

       if token_ids_a is None:
         return len(cls + token_ids_a + sep   ) * [0]
       return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]







     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		_a:str     ,		_a:Optional[str] = None   ):
       if not os.path.isdir(_a   ):
         logger.error('''Vocabulary path ({}) should be a directory'''.format(_a   )   )
         return
       snake_case__						= os.path.join(
           _a     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

       if os.path.abspath(self.vocab_file   ) != os.path.abspath(_a   ):
         copyfile(self.vocab_file     ,		_a   )

       return (out_vocab_file,)







from bisect import bisect
from itertools import accumulate





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				sorted(zip(__lowerCamelCase				,     __lowerCamelCase       )				,     key=lambda __lowerCamelCase       : x[0] / x[1]				,     reverse=__lowerCamelCase       )
	a							,					a									=				[i[0] for i in r], [i[1] for i in r]
	a									=				list(accumulate(__lowerCamelCase       )       )
	a									=				bisect(__lowerCamelCase				,     __lowerCamelCase       )
	return (
	    0
	    if k == 0
	    else sum(vl[:k]       ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
	    if k != n
	    else sum(vl[:k]       )
	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

