






from math import factorial, pi






def 	UpperCamelCase	(							snake_case__  : float						,snake_case__  : int = 30	):




     '''simple docstring'''






     if not isinstance(snake_case__						,(int, float)	):
          raise ValueError("""maclaurin_sin() requires either an int or float for theta"""	)

     if not isinstance(snake_case__						,snake_case__	) or accuracy <= 0:
          raise ValueError("""maclaurin_sin() requires a positive int for accuracy"""	)

     __snake_case      :int 							=    float(snake_case__	)
     __snake_case      :int 							=    theta // (2 * pi)
     theta -= 2 * div * pi
     return sum(
         (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1	) for r in range(snake_case__	)	)






def 	UpperCamelCase	(							snake_case__  : float						,snake_case__  : int = 30	):




     '''simple docstring'''






     if not isinstance(snake_case__						,(int, float)	):
          raise ValueError("""maclaurin_cos() requires either an int or float for theta"""	)

     if not isinstance(snake_case__						,snake_case__	) or accuracy <= 0:
          raise ValueError("""maclaurin_cos() requires a positive int for accuracy"""	)

     __snake_case      :List[str] 							=    float(snake_case__	)
     __snake_case      :List[Any] 							=    theta // (2 * pi)
     theta -= 2 * div * pi
     return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r	) for r in range(snake_case__	)	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     print(maclaurin_sin(10))
     print(maclaurin_sin(-10))
     print(maclaurin_sin(10, 15))
     print(maclaurin_sin(-10, 15))

     print(maclaurin_cos(5))
     print(maclaurin_cos(-5))
     print(maclaurin_cos(10, 15))
     print(maclaurin_cos(-10, 15))










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__       		=  logging.get_logger(__name__)

lowerCamelCase__       		=  {"""vocab_file""": """sentencepiece.model"""}

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

lowerCamelCase__       		=  {
    """google/rembert""": 256,
}




class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					str								=			VOCAB_FILES_NAMES
       lowerCamelCase				:					Tuple								=			PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase				:					Optional[int]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



       def __init__(				self      ,    a__      ,    a__=False      ,    a__=True      ,    a__=True      ,    a__="[CLS]"      ,    a__="[SEP]"      ,    a__="[UNK]"      ,    a__="[SEP]"      ,    a__="[PAD]"      ,    a__="[CLS]"      ,    a__="[MASK]"      ,    **a__      ,    )			->   List[str]:






            '''simple docstring'''





            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      :List[Any] 							=    do_lower_case
            __snake_case      :Dict 							=    remove_space
            __snake_case      :int 							=    keep_accents
            __snake_case      :Optional[int] 							=    vocab_file

            __snake_case      :int 							=    spm.SentencePieceProcessor()
            self.sp_model.Load(a__     )



       @property
       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            return len(self.sp_model     )



       def        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :int 							=    {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:






            '''simple docstring'''





            __snake_case      :str 							=    self.__dict__.copy()
            __snake_case      :Dict 							=    None
            return state



       def __setstate__(				self      ,    a__     )			->   str:






            '''simple docstring'''





            __snake_case      :Dict 							=    d
            __snake_case      :Dict 							=    spm.SentencePieceProcessor()
            self.sp_model.Load(self.vocab_file     )



       def        __lowercase    (				self      ,    a__      ,    a__=False     )			->   Tuple:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    self.sp_model.EncodeAsPieces(a__     )
            return pieces



       def        __lowercase    (				self      ,    a__     )			->   str:






            '''simple docstring'''





            return self.sp_model.PieceToId(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Any:






            '''simple docstring'''





            return self.sp_model.IdToPiece(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    self.sp_model.decode_pieces(a__     )
            return out_string



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   List[int]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    [self.sep_token_id]
            __snake_case      :List[Any] 							=    [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        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = False     )			->   List[int]:






            '''simple docstring'''





            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        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   List[int]:






            '''simple docstring'''





            __snake_case      :Any 							=    [self.sep_token_id]
            __snake_case      :Any 							=    [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        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   Tuple[str]:






            '''simple docstring'''





            if not os.path.isdir(a__     ):
                 logger.error("""Vocabulary path ({}) should be a directory""".format(a__     )     )
                 return
            __snake_case      :Optional[int] 							=    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,)


