





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


lowerCAmelCase_        =							logging.get_logger(__name__)


lowerCAmelCase_        =							'''▁'''

lowerCAmelCase_        =							{'''vocab_file''': '''spiece.model'''}

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

lowerCAmelCase_        =							{
    '''google/reformer-crime-and-punishment''': 5_2_4_2_8_8,
}



class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	Optional[int]			     = VOCAB_FILES_NAMES
			lowerCamelCase_	:	Optional[Any]			     = PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_	:	Union[str, Any]			     = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase_	:	List[Any]			     = ['''input_ids''', '''attention_mask''']






			def __init__(self   ,				__magic_name__   ,				__magic_name__="</s>"   ,				__magic_name__="<unk>"   ,				__magic_name__=[]   ,				__magic_name__ = None   ,				**__magic_name__   ,				)	->  None:


							'''simple docstring'''



							snake_case_		:       Optional[Any]           =    {} if sp_model_kwargs is None else sp_model_kwargs

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

							snake_case_		:       int           =    vocab_file
							snake_case_		:       Tuple           =    spm.SentencePieceProcessor(**self.sp_model_kwargs       )
							self.sp_model.Load(__magic_name__       )






			@property
			def      lowerCamelCase    (self       )	->  Any:


							'''simple docstring'''



							return self.sp_model.get_piece_size()






			def      lowerCamelCase    (self       )	->  Dict[str, int]:


							'''simple docstring'''



							snake_case_		:       Union[str, Any]           =    {self.convert_ids_to_tokens(__magic_name__       ): i for i in range(self.vocab_size       )}
							vocab.update(self.added_tokens_encoder       )
							return vocab






			def __getstate__(self       )	->  Dict:


							'''simple docstring'''



							snake_case_		:       Any           =    self.__dict__.copy()
							snake_case_		:       Any           =    None
							return state






			def __setstate__(self   ,				__magic_name__       )	->  str:


							'''simple docstring'''



							snake_case_		:       Tuple           =    d

							# for backward compatibility
							if not hasattr(self   ,				'''sp_model_kwargs'''       ):
											snake_case_		:       Optional[Any]           =    {}

							snake_case_		:       Dict           =    spm.SentencePieceProcessor(**self.sp_model_kwargs       )
							self.sp_model.Load(self.vocab_file       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  List[str]:


							'''simple docstring'''



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






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Tuple:


							'''simple docstring'''



							return self.sp_model.piece_to_id(__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Dict:


							'''simple docstring'''



							if index < self.sp_model.get_piece_size():
											snake_case_		:       List[Any]           =    self.sp_model.IdToPiece(__magic_name__       )
							return token






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Dict:


							'''simple docstring'''



							snake_case_		:       Dict           =    []
							snake_case_		:       Optional[int]           =    ''''''
							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(__magic_name__       ) + token
															snake_case_		:       str           =    []
											else:
															current_sub_tokens.append(__magic_name__       )
							out_string += self.sp_model.decode(__magic_name__       )
							return out_string.strip()






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__ = None       )	->  Tuple[str]:


							'''simple docstring'''



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

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

							return (out_vocab_file,)


from math import loga





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if a < 0:
        raise ValueError("Input value must be a positive integer"				)
    elif isinstance(__lowerCAmelCase     ,		__lowerCAmelCase				):
        raise TypeError("Input value must be a 'int' type"				)
    return 0 if (a == 0) else int(loga(a & -a				)				)


if __name__ == "__main__":
     import doctest

     doctest.testmod()



