text: # coding=utf-8
  # Copyright 2020 The Trax Authors and The HuggingFace Inc. team.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """ Tokenization class for model Reformer."""
  
  
  import os
  from shutil import copyfile
  from typing import Dict, Optional, Tuple
  
  import sentencepiece as spm
  
  from .tokenization_utils import PreTrainedTokenizer
  from .utils import logging
  
  
  logger = logging.get_logger(__name__)
  
  SPIECE_UNDERLINE = "▁"
  
  
  ####################################################
  # Mapping from the keyword arguments names of Tokenizer `__init__`
  # to file names for serializing Tokenizer instances
  ####################################################
  VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
  
  ####################################################
  # Mapping from the keyword arguments names of Tokenizer `__init__`
  # to pretrained vocabulary URL for all the model shortcut names.
  ####################################################
  PRETRAINED_VOCAB_FILES_MAP = {
      "vocab_file": {
          "google/reformer-crime-and-punishment": "https://cdn.huggingface.co/google/reformer-crime-and-punishment/spiece.model"
      }
  }
  
  ####################################################
  # Mapping from model shortcut names to max length of inputs
  ####################################################
  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
      "google/reformer-crime-and-punishment": 524288,
  }
  
  
  class ReformerTokenizer(PreTrainedTokenizer):
      """
      Construct a Reformer tokenizer. Based on `SentencePiece <https://github.com/google/sentencepiece>`__ .
  
      This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods.
      Users should refer to this superclass for more information regarding those methods.
  
      Args:
          vocab_file (:obj:`str`):
              `SentencePiece <https://github.com/google/sentencepiece>`__ file (generally has a `.spm` extension) that
              contains the vocabulary necessary to instantiate a tokenizer.
          eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
              The end of sequence token.
  
              .. note::
  
                  When building a sequence using special tokens, this is not the token that is used for the end
                  of sequence. The token used is the :obj:`sep_token`.
          unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`):
              The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
              token instead.
          pad_token (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`):
              The token used for padding, for example when batching sequences of different lengths.
          additional_special_tokens (:obj:`List[str]`, `optional`):
              Additional special tokens used by the tokenizer.
      """
  
      vocab_files_names = VOCAB_FILES_NAMES
      pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
      max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      model_input_names = ["attention_mask"]
  
      def __init__(
          self,
          vocab_file,
          eos_token="</s>",
          unk_token="<unk>",
          pad_token="<pad>",
          additional_special_tokens=[],
          **kwargs
      ):
          super().__init__(
              eos_token=eos_token,
              unk_token=unk_token,
              pad_token=pad_token,
              additional_special_tokens=additional_special_tokens,
              **kwargs,
          )
  
          self.vocab_file = vocab_file
          self.sp_model = spm.SentencePieceProcessor()
          self.sp_model.Load(vocab_file)
  
      @property
      def vocab_size(self):
          return self.sp_model.get_piece_size()
  
      def get_vocab(self) -> Dict[str, int]:
          vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
          vocab.update(self.added_tokens_encoder)
          return vocab
  
      def __getstate__(self):
          state = self.__dict__.copy()
          state["sp_model"] = None
          return state
  
      def __setstate__(self, d):
          self.__dict__ = d
          self.sp_model = spm.SentencePieceProcessor()
          self.sp_model.Load(self.vocab_file)
  
      def _tokenize(self, text, sample=False):
          """Take as input a string and return a list of strings (tokens) for words/sub-words"""
          if not sample:
              pieces = self.sp_model.EncodeAsPieces(text)
          else:
              pieces = self.sp_model.SampleEncodeAsPieces(text, 64, 0.1)
          return pieces
  
      def _convert_token_to_id(self, token):
          """ Converts a token (str) in an id using the vocab. """
          return self.sp_model.piece_to_id(token)
  
      def _convert_id_to_token(self, index):
          """Converts an index (integer) in a token (str) using the vocab."""
          if index < self.sp_model.get_piece_size():
              token = self.sp_model.IdToPiece(index)
          return token
  
      def convert_tokens_to_string(self, tokens):
          """ Converts a sequence of tokens (string) in a single string. """
          out_string = self.sp_model.decode_pieces(tokens)
          return out_string
  
      def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
          if not os.path.isdir(save_directory):
              logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
              return
          out_vocab_file = os.path.join(
              save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
          )
  
          if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
              copyfile(self.vocab_file, out_vocab_file)
  
          return (out_vocab_file,)
  