# coding=utf-8
# Copyright 2018 T5 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.
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""Tokenization class for Google T5 (sentence piece)."""
import logging
import os
import warnings
from shutil import copyfile
from typing import List, Optional
import regex as re
import sentencepiece as spm
from .tokenization_base import PreTrainedTokenizer
logger = logging.getLogger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {"t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model"}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"t5-base": 512,
}
[docs]class T5Tokenizer(PreTrainedTokenizer):
"""
Construct a T5 tokenizer. Based on `SentencePiece <https://github.com/google/sentencepiece>`.
Args:
vocab_file (:obj:`str`):
Path to the vocabulary file.
eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
The end of sequence 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.
extra_ids (:obj:`int`, `optional`, defaults to 100):
Add a number of extra ids added to the end of the vocabulary for use
as sentinels. These tokens are accessible as "<extra_id_{%d}>" where
"{%d}" is a number between 0 and extra_ids-1. Extra tokens are indexed
from the end of the vocabulary up to beginning ("<extra_id_0>" is the
last token in the vocabulary like in T5 preprocessing see `here
<https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117>`__).
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
def __init__(
self,
vocab_file,
eos_token="</s>",
unk_token="<unk>",
pad_token="<pad>",
extra_ids=100,
additional_special_tokens=None,
add_bos_token=False,
**kwargs,
):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
additional_special_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)]
elif extra_ids > 0 and additional_special_tokens is not None:
extra_tokens = len(
set(filter(lambda x: bool("extra_id" in str(x)), additional_special_tokens))
)
if extra_tokens != extra_ids:
raise ValueError(
f"Both extra_ids ({extra_ids}) and additional_special_tokens "
f"({additional_special_tokens}) are privided to T5Tokenizer. "
"In this case the additional_special_tokens must include the extra_ids tokens"
)
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._extra_ids = extra_ids
self.sp_model = spm.SentencePieceProcessor()
self.sp_model.Load(vocab_file)
self.add_bos_token = add_bos_token
@property
def vocab_size(self):
return self.sp_model.get_piece_size() + self._extra_ids
[docs] def get_vocab(self):
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def _tokenize(self, text):
"""Tokenize a string."""
pieces = self.sp_model.encode(text, out_type=str)
return pieces
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
if token.startswith("<extra_id_"):
match = re.match(r"<extra_id_(\d+)>", token)
num = int(match.group(1))
return self.vocab_size - num - 1
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)
else:
token = f"<extra_id_{self.vocab_size - 1 - index}>"
return token
[docs] def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) to a single string."""
current_sub_tokens = []
out_string = ""
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_pieces(current_sub_tokens) + token + " "
current_sub_tokens = []
else:
current_sub_tokens.append(token)
out_string += self.sp_model.decode_pieces(current_sub_tokens)
return out_string.strip()
def _add_eos_if_not_present(self, token_ids):
if not self.add_bos_token:
return token_ids
if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn("This sequence already has {self.eos_token}.")
return token_ids
else:
return token_ids + [self.eos_token_id]
[docs] def save_vocabulary(self, save_directory, filename_prefix=None):
"""Save the tokenizer vocabulary to a directory or file."""
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)
logger.info(f"Copy vocab file to {out_vocab_file}")
return (out_vocab_file,)