


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


snake_case							: Optional[int]      =   logging.get_logger(__name__)


snake_case							: Any      =   '''▁'''

snake_case							: Tuple      =   {'''vocab_file''': '''spiece.model'''}

snake_case							: Tuple      =   {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

snake_case							: Optional[int]      =   {
    '''google/reformer-crime-and-punishment''': 52_42_88,
}







class  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					List[Any]					   =    VOCAB_FILES_NAMES
					UpperCAmelCase__      :					Union[str, Any]					   =    PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__      :					Optional[Any]					   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__      :					Union[str, Any]					   =    ['''input_ids''', '''attention_mask''']
					def __init__(   self     :List[str]    ,__snake_case     :Dict    ,__snake_case     :Dict="</s>"    ,__snake_case     :Union[str, Any]="<unk>"    ,__snake_case     :List[Any]=[]    ,__snake_case     :Optional[Dict[str, Any]] = None    ,**__snake_case     :Dict    ,)      ->					None:
												a__    =  {} if sp_model_kwargs is None else sp_model_kwargs

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

												a__    =  vocab_file
												a__    =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
												self.sp_model.Load(__snake_case   )
					@property
					def 				lowerCamelCase__(   self     :str   )      ->					Tuple:
												return self.sp_model.get_piece_size()
					def 				lowerCamelCase__(   self     :Any   )      ->					Dict[str, int]:
												a__    =  {self.convert_ids_to_tokens(__snake_case   ): i for i in range(self.vocab_size   )}
												vocab.update(self.added_tokens_encoder   )
												return vocab
					def __getstate__(   self     :Union[str, Any]   )      ->					str:
												a__    =  self.__dict__.copy()
												a__    =  None
												return state
					def __setstate__(   self     :Optional[Any]    ,__snake_case     :Any   )      ->					Optional[Any]:
												a__    =  d

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

												a__    =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
												self.sp_model.Load(self.vocab_file   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :str   )      ->					List[str]:
												return self.sp_model.encode(__snake_case    ,out_type=__snake_case   )
					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :List[Any]   )      ->					Optional[int]:
												return self.sp_model.piece_to_id(__snake_case   )
					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :Dict   )      ->					str:
												if index < self.sp_model.get_piece_size():
																			a__    =  self.sp_model.IdToPiece(__snake_case   )
												return token
					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :Union[str, Any]   )      ->					List[str]:
												a__    =  []
												a__    =  ''
												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(__snake_case   ) + token
																										a__    =  []
																			else:
																										current_sub_tokens.append(__snake_case   )
												out_string += self.sp_model.decode(__snake_case   )
												return out_string.strip()



					def 				lowerCamelCase__(   self     :Optional[int]    ,__snake_case     :str    ,__snake_case     :Optional[str] = None   )      ->					Tuple[str]:
												if not os.path.isdir(__snake_case   ):
																			logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
																			return
												a__    =  os.path.join(
												    __snake_case    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

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

												return (out_vocab_file,)








from math import pi






def 							__lowercase     (				__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       int     ):
							return 2 * pi * radius * (angle / 3_6_0)


if __name__ == "__main__":
						print(arc_length(90, 10))




