


'''simple docstring'''


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


UpperCAmelCase_		:   str							   =		logging.get_logger(__name__)

UpperCAmelCase_		:   Optional[int]							   =		{'vocab_file': 'sentencepiece.model'}

UpperCAmelCase_		:   str							   =		{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

UpperCAmelCase_		:   Union[str, Any]							   =		{
    'google/rembert': 256,
}





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      List[str]      	=  VOCAB_FILES_NAMES
							A_							:      Optional[int]      	=  PRETRAINED_VOCAB_FILES_MAP
							A_							:      Union[str, Any]      	=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






							def __init__(    self    ,					__snake_case    ,					__snake_case=False    ,					__snake_case=True    ,					__snake_case=True    ,					__snake_case="[CLS]"    ,					__snake_case="[SEP]"    ,					__snake_case="[UNK]"    ,					__snake_case="[SEP]"    ,					__snake_case="[PAD]"    ,					__snake_case="[CLS]"    ,					__snake_case="[MASK]"    ,					**__snake_case    ,					):
											super().__init__(
											    do_lower_case=__snake_case    ,					remove_space=__snake_case    ,					keep_accents=__snake_case    ,					bos_token=__snake_case    ,					eos_token=__snake_case    ,					unk_token=__snake_case    ,					sep_token=__snake_case    ,					pad_token=__snake_case    ,					cls_token=__snake_case    ,					mask_token=__snake_case    ,					**__snake_case    ,					)

											_SCREAMING_SNAKE_CASE :       List[str]     		=      do_lower_case
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      remove_space
											_SCREAMING_SNAKE_CASE :       List[str]     		=      keep_accents
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      vocab_file

											_SCREAMING_SNAKE_CASE :       List[str]     		=      spm.SentencePieceProcessor()
											self.sp_model.Load(__snake_case  )






							@property
							def UpperCAmelCase_	(    self  ):
											return len(self.sp_model  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       int     		=      {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  ):
											_SCREAMING_SNAKE_CASE :       List[str]     		=      self.__dict__.copy()
											_SCREAMING_SNAKE_CASE :       Any     		=      None
											return state






							def __setstate__(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       List[str]     		=      d
											_SCREAMING_SNAKE_CASE :       int     		=      spm.SentencePieceProcessor()
											self.sp_model.Load(self.vocab_file  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case=False  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.sp_model.EncodeAsPieces(__snake_case  )
											return pieces






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											return self.sp_model.PieceToId(__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											return self.sp_model.IdToPiece(__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.sp_model.decode_pieces(__snake_case  )
											return out_string






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case = None  ):
											_SCREAMING_SNAKE_CASE :       List[str]     		=      [self.sep_token_id]
											_SCREAMING_SNAKE_CASE :       Dict     		=      [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 UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case = None    ,					__snake_case = 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(__snake_case  )) + [1] + ([0] * len(__snake_case  )) + [1]
											return [1] + ([0] * len(__snake_case  )) + [1]






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case = None  ):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      [self.sep_token_id]
											_SCREAMING_SNAKE_CASE :       Union[str, 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 UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case = None  ):
											if not os.path.isdir(__snake_case  ):
															logger.error("""Vocabulary path ({}) should be a directory""".format(__snake_case  )  )
															return
											_SCREAMING_SNAKE_CASE :       Tuple     		=      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  ):
															copyfile(self.vocab_file    ,					__snake_case  )

											return (out_vocab_file,)








'''simple docstring'''


def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       int     		=      0
				while len(SCREAMING_SNAKE_CASE__     ) > 1:
								_SCREAMING_SNAKE_CASE :       Any     		=      0
								# Consider two files with minimum cost to be merged
								for _ in range(2     ):
												_SCREAMING_SNAKE_CASE :       Optional[int]     		=      files.index(min(SCREAMING_SNAKE_CASE__     )     )
												temp += files[min_index]
												files.pop(SCREAMING_SNAKE_CASE__     )
								files.append(SCREAMING_SNAKE_CASE__     )
								optimal_merge_cost += temp
				return optimal_merge_cost


if __name__ == "__main__":
				import doctest

				doctest.testmod()




