Source code for transformers.models.reformer.tokenization_reformer

# coding=utf-8
# Copyright 2020 The Trax Authors and The HuggingFace Inc. team.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" 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 ids.
####################################################
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 ids to max length of inputs
####################################################
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "google/reformer-crime-and-punishment": 524288,
}


[docs]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>", additional_special_tokens=[], **kwargs): super().__init__( eos_token=eos_token, unk_token=unk_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
[docs] 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,)