
'''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__      :		Dict       							=  logging.get_logger(__name__)

UpperCamelCase__      :		Optional[Any]       							=  {"vocab_file": "sentencepiece.model"}

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

UpperCamelCase__      :		List[str]       							=  {
    "google/rembert": 256,
}

class    _a				(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE      =  PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE      =  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__       ,							)    ->			Optional[int]:
          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       ,							)

          _SCREAMING_SNAKE_CASE      =				do_lower_case
          _SCREAMING_SNAKE_CASE      =				remove_space
          _SCREAMING_SNAKE_CASE      =				keep_accents
          _SCREAMING_SNAKE_CASE      =				vocab_file

          _SCREAMING_SNAKE_CASE      =				spm.SentencePieceProcessor()
          self.sp_model.Load(__a      )






       @property
       def        UpperCamelCase				(							self      )    ->			Dict:
          return len(self.sp_model      )






       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				{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      )    ->			Union[str, Any]:
          _SCREAMING_SNAKE_CASE      =				self.__dict__.copy()
          _SCREAMING_SNAKE_CASE      =				None
          return state






       def __setstate__(							self       ,							A__      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				d
          _SCREAMING_SNAKE_CASE      =				spm.SentencePieceProcessor()
          self.sp_model.Load(self.vocab_file      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__=False      )    ->			Union[str, Any]:
          _SCREAMING_SNAKE_CASE      =				self.sp_model.EncodeAsPieces(__a      )
          return pieces






       def        UpperCamelCase				(							self       ,							A__      )    ->			Union[str, Any]:
          return self.sp_model.PieceToId(__a      )






       def        UpperCamelCase				(							self       ,							A__      )    ->			Dict:
          return self.sp_model.IdToPiece(__a      )






       def        UpperCamelCase				(							self       ,							A__      )    ->			Tuple:
          _SCREAMING_SNAKE_CASE      =				self.sp_model.decode_pieces(__a      )
          return out_string






       def        UpperCamelCase				(							self       ,							A__       ,							A__ = None      )    ->			List[int]:
          _SCREAMING_SNAKE_CASE      =				[self.sep_token_id]
          _SCREAMING_SNAKE_CASE      =				[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       ,							A__       ,							A__ = None       ,							A__ = False      )    ->			List[int]:

          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        UpperCamelCase				(							self       ,							A__       ,							A__ = None      )    ->			List[int]:
          _SCREAMING_SNAKE_CASE      =				[self.sep_token_id]
          _SCREAMING_SNAKE_CASE      =				[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       ,							A__       ,							A__ = None      )    ->			Tuple[str]:
          if not os.path.isdir(__a      ):
             logger.error("""Vocabulary path ({}) should be a directory""".format(__a      )      )
             return
          _SCREAMING_SNAKE_CASE      =				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,)



'''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


UpperCamelCase__      :		List[Any]       							=  logging.get_logger(__name__)


UpperCamelCase__      :		Any       							=  "▁"

UpperCamelCase__      :		Any       							=  {"vocab_file": "spiece.model"}

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

UpperCamelCase__      :		Optional[int]       							=  {
    "google/reformer-crime-and-punishment": 524_288,
}

class    _a				(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE      =  PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE      =  ['input_ids', 'attention_mask']






       def __init__(							self       ,							A__       ,							A__="</s>"       ,							A__="<unk>"       ,							A__=[]       ,							A__ = None       ,							**A__       ,							)    ->			None:
          _SCREAMING_SNAKE_CASE      =				{} 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      =				vocab_file
          _SCREAMING_SNAKE_CASE      =				spm.SentencePieceProcessor(**self.sp_model_kwargs      )
          self.sp_model.Load(A__      )






       @property
       def        UpperCamelCase				(							self      )    ->			Any:
          return self.sp_model.get_piece_size()






       def        UpperCamelCase				(							self      )    ->			Dict[str, int]:
          _SCREAMING_SNAKE_CASE      =				{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      )    ->			int:
          _SCREAMING_SNAKE_CASE      =				self.__dict__.copy()
          _SCREAMING_SNAKE_CASE      =				None
          return state






       def __setstate__(							self       ,							A__      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				d

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

          _SCREAMING_SNAKE_CASE      =				spm.SentencePieceProcessor(**self.sp_model_kwargs      )
          self.sp_model.Load(self.vocab_file      )






       def        UpperCamelCase				(							self       ,							A__      )    ->			List[str]:
          return self.sp_model.encode(A__       ,							out_type=A__      )






       def        UpperCamelCase				(							self       ,							A__      )    ->			Union[str, Any]:
          return self.sp_model.piece_to_id(A__      )






       def        UpperCamelCase				(							self       ,							A__      )    ->			List[Any]:
          if index < self.sp_model.get_piece_size():
             _SCREAMING_SNAKE_CASE      =				self.sp_model.IdToPiece(A__      )
          return token






       def        UpperCamelCase				(							self       ,							A__      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				[]
          _SCREAMING_SNAKE_CASE      =				""""""
          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      =				[]
             else:
                current_sub_tokens.append(A__      )
          out_string += self.sp_model.decode(A__      )
          return out_string.strip()






       def        UpperCamelCase				(							self       ,							A__       ,							A__ = None      )    ->			Tuple[str]:
          if not os.path.isdir(A__      ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"      )
             return
          _SCREAMING_SNAKE_CASE      =				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      =				self.sp_model.serialized_model_proto()
                fi.write(A__      )

          return (out_vocab_file,)






