Source code for transformers.models.reformer.tokenization_reformer
# coding=utf-8
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""" Tokenization class for model Reformer."""
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
logger = logging.get_logger(__name__)
SPIECE_UNDERLINE = "▁"
VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"google/reformer-crime-and-punishment": "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
}
}
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.
sp_model_kwargs (:obj:`dict`, `optional`):
Will be passed to the ``SentencePieceProcessor.__init__()`` method. The `Python wrapper for SentencePiece
<https://github.com/google/sentencepiece/tree/master/python>`__ can be used, among other things, to set:
- ``enable_sampling``: Enable subword regularization.
- ``nbest_size``: Sampling parameters for unigram. Invalid for BPE-Dropout.
- ``nbest_size = {0,1}``: No sampling is performed.
- ``nbest_size > 1``: samples from the nbest_size results.
- ``nbest_size < 0``: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
- ``alpha``: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
"""
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 = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
eos_token="</s>",
unk_token="<unk>",
additional_special_tokens=[],
sp_model_kwargs: Optional[Dict[str, Any]] = None,
**kwargs
) -> None:
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=eos_token,
unk_token=unk_token,
additional_special_tokens=additional_special_tokens,
sp_model_kwargs=self.sp_model_kwargs,
**kwargs,
)
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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
# for backward compatibility
if not hasattr(self, "sp_model_kwargs"):
self.sp_model_kwargs = {}
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def _tokenize(self, text: str) -> List[str]:
"""Take as input a string and return a list of strings (tokens) for words/sub-words"""
return self.sp_model.encode(text, out_type=str)
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(f"Vocabulary path ({save_directory}) should be a 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,)