"""simple docstring"""





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''': 524288,
}




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						VOCAB_FILES_NAMES
				a			=						PRETRAINED_VOCAB_FILES_MAP
				a			=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				a			=						["input_ids", "attention_mask"]





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



											"""simple docstring"""







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

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

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    vocab_file
											_SCREAMING_SNAKE_CASE		:						str			  =    spm.SentencePieceProcessor(**self.sp_model_kwargs)
											self.sp_model.Load(_A)





				@property
				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											return self.sp_model.get_piece_size()





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    {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							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.__dict__.copy()
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    None
											return state





				def __setstate__(      self							:							Dict      ,    _A							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    d

											# for backward compatibility
											if not hasattr(self      ,    """sp_model_kwargs"""):
																		_SCREAMING_SNAKE_CASE		:						int			  =    {}

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    spm.SentencePieceProcessor(**self.sp_model_kwargs)
											self.sp_model.Load(self.vocab_file)





				def 		_lowerCAmelCase		(      self							:							Optional[int]      ,    _A							:							str):



											"""simple docstring"""







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





				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							Optional[int]):



											"""simple docstring"""







											return self.sp_model.piece_to_id(_A)





				def 		_lowerCAmelCase		(      self							:							Any      ,    _A							:							Union[str, Any]):



											"""simple docstring"""







											if index < self.sp_model.get_piece_size():
																		_SCREAMING_SNAKE_CASE		:						str			  =    self.sp_model.IdToPiece(_A)
											return token





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    []
											_SCREAMING_SNAKE_CASE		:						Any			  =    """"""
											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(_A) + token
																									_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    []
																		else:
																									current_sub_tokens.append(_A)
											out_string += self.sp_model.decode(_A)
											return out_string.strip()





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							str      ,    _A							:							Optional[str] = None):



											"""simple docstring"""







											if not os.path.isdir(_A):
																		logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
																		return
											_SCREAMING_SNAKE_CASE		:						Any			  =    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) and os.path.isfile(self.vocab_file):
																		copyfile(self.vocab_file      ,    _A)
											elif not os.path.isfile(self.vocab_file):
																		with open(_A      ,    """wb""") as fi:
																									_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.sp_model.serialized_model_proto()
																									fi.write(_A)

											return (out_vocab_file,)



"""simple docstring"""





def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    int:

							if not isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														raise TypeError("""only integers accepted as input"""  )
							else:
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    str(abs(__SCREAMING_SNAKE_CASE  )  )
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    [list(__SCREAMING_SNAKE_CASE  ) for char in range(len(__SCREAMING_SNAKE_CASE  )  )]
														for index in range(len(__SCREAMING_SNAKE_CASE  )  ):
																					num_transpositions[index].pop(__SCREAMING_SNAKE_CASE  )
														return max(
														    int("""""".join(list(__SCREAMING_SNAKE_CASE  )  )  ) for transposition in num_transpositions  )


if __name__ == "__main__":
			__import__('''doctest''').testmod()


