605 lines
24 KiB
Python
605 lines
24 KiB
Python
# coding=utf-8
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# Copyright 2019 The Open AI Team Authors and The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tokenization classes for XLM."""
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import json
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import os
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import re
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import sys
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import unicodedata
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from typing import List, Optional, Tuple
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from ...tokenization_utils import PreTrainedTokenizer
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from ...utils import logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {
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"vocab_file": "vocab.json",
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"merges_file": "merges.txt",
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}
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def get_pairs(word):
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"""
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Return set of symbol pairs in a word. word is represented as tuple of symbols (symbols being variable-length
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strings)
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"""
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pairs = set()
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prev_char = word[0]
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for char in word[1:]:
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pairs.add((prev_char, char))
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prev_char = char
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return pairs
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def lowercase_and_remove_accent(text):
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"""
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Lowercase and strips accents from a piece of text based on
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https://github.com/facebookresearch/XLM/blob/master/tools/lowercase_and_remove_accent.py
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"""
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text = " ".join(text)
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text = text.lower()
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text = unicodedata.normalize("NFD", text)
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output = []
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for char in text:
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cat = unicodedata.category(char)
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if cat == "Mn":
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continue
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output.append(char)
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return "".join(output).lower().split(" ")
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def replace_unicode_punct(text):
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"""
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Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/replace-unicode-punctuation.perl
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"""
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text = text.replace("，", ",")
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text = re.sub(r"。\s*", ". ", text)
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text = text.replace("、", ",")
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text = text.replace("”", '"')
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text = text.replace("“", '"')
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text = text.replace("∶", ":")
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text = text.replace("：", ":")
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text = text.replace("？", "?")
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text = text.replace("《", '"')
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text = text.replace("》", '"')
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text = text.replace("）", ")")
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text = text.replace("！", "!")
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text = text.replace("（", "(")
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text = text.replace("；", ";")
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text = text.replace("１", "1")
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text = text.replace("」", '"')
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text = text.replace("「", '"')
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text = text.replace("０", "0")
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text = text.replace("３", "3")
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text = text.replace("２", "2")
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text = text.replace("５", "5")
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text = text.replace("６", "6")
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text = text.replace("９", "9")
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text = text.replace("７", "7")
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text = text.replace("８", "8")
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text = text.replace("４", "4")
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text = re.sub(r"．\s*", ". ", text)
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text = text.replace("～", "~")
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text = text.replace("’", "'")
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text = text.replace("…", "...")
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text = text.replace("━", "-")
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text = text.replace("〈", "<")
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text = text.replace("〉", ">")
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text = text.replace("【", "[")
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text = text.replace("】", "]")
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text = text.replace("％", "%")
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return text
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def remove_non_printing_char(text):
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"""
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Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/remove-non-printing-char.perl
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"""
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output = []
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for char in text:
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cat = unicodedata.category(char)
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if cat.startswith("C"):
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continue
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output.append(char)
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return "".join(output)
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def romanian_preprocessing(text):
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"""Sennrich's WMT16 scripts for Romanian preprocessing, used by model `FacebookAI/xlm-mlm-enro-1024`"""
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# https://github.com/rsennrich/wmt16-scripts/blob/master/preprocess/normalise-romanian.py
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text = text.replace("\u015e", "\u0218").replace("\u015f", "\u0219")
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text = text.replace("\u0162", "\u021a").replace("\u0163", "\u021b")
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# https://github.com/rsennrich/wmt16-scripts/blob/master/preprocess/remove-diacritics.py
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text = text.replace("\u0218", "S").replace("\u0219", "s") # s-comma
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text = text.replace("\u021a", "T").replace("\u021b", "t") # t-comma
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text = text.replace("\u0102", "A").replace("\u0103", "a")
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text = text.replace("\u00C2", "A").replace("\u00E2", "a")
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text = text.replace("\u00CE", "I").replace("\u00EE", "i")
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return text
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class XLMTokenizer(PreTrainedTokenizer):
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"""
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Construct an XLM tokenizer. Based on Byte-Pair Encoding. The tokenization process is the following:
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- Moses preprocessing and tokenization for most supported languages.
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- Language specific tokenization for Chinese (Jieba), Japanese (KyTea) and Thai (PyThaiNLP).
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- Optionally lowercases and normalizes all inputs text.
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- The arguments `special_tokens` and the function `set_special_tokens`, can be used to add additional symbols (like
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"__classify__") to a vocabulary.
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- The `lang2id` attribute maps the languages supported by the model with their IDs if provided (automatically set
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for pretrained vocabularies).
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- The `id2lang` attributes does reverse mapping if provided (automatically set for pretrained vocabularies).
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
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this superclass for more information regarding those methods.
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Args:
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vocab_file (`str`):
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Vocabulary file.
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merges_file (`str`):
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Merges file.
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unk_token (`str`, *optional*, defaults to `"<unk>"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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bos_token (`str`, *optional*, defaults to `"<s>"`):
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The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the beginning of
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sequence. The token used is the `cls_token`.
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</Tip>
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sep_token (`str`, *optional*, defaults to `"</s>"`):
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The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
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sequence classification or for a text and a question for question answering. It is also used as the last
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token of a sequence built with special tokens.
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pad_token (`str`, *optional*, defaults to `"<pad>"`):
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The token used for padding, for example when batching sequences of different lengths.
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cls_token (`str`, *optional*, defaults to `"</s>"`):
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The classifier token which is used when doing sequence classification (classification of the whole sequence
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instead of per-token classification). It is the first token of the sequence when built with special tokens.
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mask_token (`str`, *optional*, defaults to `"<special1>"`):
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The token used for masking values. This is the token used when training this model with masked language
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modeling. This is the token which the model will try to predict.
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additional_special_tokens (`List[str]`, *optional*, defaults to `['<special0>', '<special1>', '<special2>', '<special3>', '<special4>', '<special5>', '<special6>', '<special7>', '<special8>', '<special9>']`):
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List of additional special tokens.
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lang2id (`Dict[str, int]`, *optional*):
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Dictionary mapping languages string identifiers to their IDs.
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id2lang (`Dict[int, str]`, *optional*):
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Dictionary mapping language IDs to their string identifiers.
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do_lowercase_and_remove_accent (`bool`, *optional*, defaults to `True`):
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Whether to lowercase and remove accents when tokenizing.
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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def __init__(
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self,
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vocab_file,
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merges_file,
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unk_token="<unk>",
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bos_token="<s>",
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sep_token="</s>",
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pad_token="<pad>",
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cls_token="</s>",
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mask_token="<special1>",
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additional_special_tokens=[
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"<special0>",
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"<special1>",
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"<special2>",
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"<special3>",
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"<special4>",
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"<special5>",
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"<special6>",
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"<special7>",
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"<special8>",
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"<special9>",
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],
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lang2id=None,
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id2lang=None,
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do_lowercase_and_remove_accent=True,
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**kwargs,
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):
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try:
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import sacremoses
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except ImportError:
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raise ImportError(
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"You need to install sacremoses to use XLMTokenizer. "
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"See https://pypi.org/project/sacremoses/ for installation."
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)
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self.sm = sacremoses
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# cache of sm.MosesPunctNormalizer instance
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self.cache_moses_punct_normalizer = {}
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# cache of sm.MosesTokenizer instance
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self.cache_moses_tokenizer = {}
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self.lang_with_custom_tokenizer = {"zh", "th", "ja"}
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# True for current supported model (v1.2.0), False for XLM-17 & 100
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self.do_lowercase_and_remove_accent = do_lowercase_and_remove_accent
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self.lang2id = lang2id
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self.id2lang = id2lang
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if lang2id is not None and id2lang is not None:
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assert len(lang2id) == len(id2lang)
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self.ja_word_tokenizer = None
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self.zh_word_tokenizer = None
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with open(vocab_file, encoding="utf-8") as vocab_handle:
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self.encoder = json.load(vocab_handle)
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self.decoder = {v: k for k, v in self.encoder.items()}
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with open(merges_file, encoding="utf-8") as merges_handle:
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merges = merges_handle.read().split("\n")[:-1]
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merges = [tuple(merge.split()[:2]) for merge in merges]
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self.bpe_ranks = dict(zip(merges, range(len(merges))))
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self.cache = {}
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super().__init__(
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unk_token=unk_token,
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bos_token=bos_token,
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sep_token=sep_token,
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pad_token=pad_token,
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cls_token=cls_token,
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mask_token=mask_token,
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additional_special_tokens=additional_special_tokens,
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lang2id=lang2id,
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id2lang=id2lang,
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do_lowercase_and_remove_accent=do_lowercase_and_remove_accent,
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**kwargs,
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)
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@property
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def do_lower_case(self):
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return self.do_lowercase_and_remove_accent
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def moses_punct_norm(self, text, lang):
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if lang not in self.cache_moses_punct_normalizer:
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punct_normalizer = self.sm.MosesPunctNormalizer(lang=lang)
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self.cache_moses_punct_normalizer[lang] = punct_normalizer
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else:
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punct_normalizer = self.cache_moses_punct_normalizer[lang]
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return punct_normalizer.normalize(text)
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def moses_tokenize(self, text, lang):
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if lang not in self.cache_moses_tokenizer:
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moses_tokenizer = self.sm.MosesTokenizer(lang=lang)
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self.cache_moses_tokenizer[lang] = moses_tokenizer
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else:
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moses_tokenizer = self.cache_moses_tokenizer[lang]
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return moses_tokenizer.tokenize(text, return_str=False, escape=False)
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def moses_pipeline(self, text, lang):
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text = replace_unicode_punct(text)
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text = self.moses_punct_norm(text, lang)
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text = remove_non_printing_char(text)
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return text
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def ja_tokenize(self, text):
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if self.ja_word_tokenizer is None:
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try:
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import Mykytea
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self.ja_word_tokenizer = Mykytea.Mykytea(
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f"-model {os.path.expanduser('~')}/local/share/kytea/model.bin"
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)
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except (AttributeError, ImportError):
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logger.error(
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"Make sure you install KyTea (https://github.com/neubig/kytea) and it's python wrapper"
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" (https://github.com/chezou/Mykytea-python) with the following steps"
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)
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logger.error("1. git clone git@github.com:neubig/kytea.git && cd kytea")
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logger.error("2. autoreconf -i")
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logger.error("3. ./configure --prefix=$HOME/local")
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logger.error("4. make && make install")
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logger.error("5. pip install kytea")
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raise
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return list(self.ja_word_tokenizer.getWS(text))
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@property
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def vocab_size(self):
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return len(self.encoder)
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def get_vocab(self):
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return dict(self.encoder, **self.added_tokens_encoder)
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def bpe(self, token):
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word = tuple(token[:-1]) + (token[-1] + "</w>",)
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if token in self.cache:
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return self.cache[token]
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pairs = get_pairs(word)
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if not pairs:
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return token + "</w>"
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while True:
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bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
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if bigram not in self.bpe_ranks:
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break
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first, second = bigram
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new_word = []
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i = 0
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while i < len(word):
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try:
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j = word.index(first, i)
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except ValueError:
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new_word.extend(word[i:])
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break
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else:
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new_word.extend(word[i:j])
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i = j
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if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
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new_word.append(first + second)
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i += 2
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else:
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new_word.append(word[i])
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i += 1
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new_word = tuple(new_word)
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word = new_word
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if len(word) == 1:
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break
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else:
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pairs = get_pairs(word)
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word = " ".join(word)
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if word == "\n </w>":
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word = "\n</w>"
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self.cache[token] = word
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return word
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def _tokenize(self, text, lang="en", bypass_tokenizer=False):
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"""
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Tokenize a string given language code. For Chinese, Japanese and Thai, we use a language specific tokenizer.
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Otherwise, we use Moses.
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Details of tokenization:
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- [sacremoses](https://github.com/alvations/sacremoses): port of Moses
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- Install with `pip install sacremoses`
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- [pythainlp](https://github.com/PyThaiNLP/pythainlp): Thai tokenizer
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- Install with `pip install pythainlp`
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- [kytea](https://github.com/chezou/Mykytea-python): Japanese tokenizer, wrapper of
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[KyTea](https://github.com/neubig/kytea)
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- Install with the following steps:
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::
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git clone git@github.com:neubig/kytea.git && cd kytea autoreconf -i ./configure --prefix=$HOME/local
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make && make install pip install kytea
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- [jieba](https://github.com/fxsjy/jieba): Chinese tokenizer (*)
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- Install with `pip install jieba`
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(*) The original XLM used [Stanford
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Segmenter](https://nlp.stanford.edu/software/stanford-segmenter-2018-10-16.zip). However, the wrapper
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(`nltk.tokenize.stanford_segmenter`) is slow due to JVM overhead, and it will be deprecated. Jieba is a lot
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faster and pip-installable. Note there is some mismatch with the Stanford Segmenter. It should be fine if you
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fine-tune the model with Chinese supervisionself. If you want the same exact behaviour, use the original XLM
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[preprocessing script](https://github.com/facebookresearch/XLM/tree/master/tools) to tokenize the sentence
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externally, and set `bypass_tokenizer=True` to bypass the tokenizer.
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Args:
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- lang: ISO language code (default = 'en') (string). Languages should belong of the model supported
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languages. However, we don't enforce it.
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- bypass_tokenizer: Allow users to preprocess and tokenize the sentences externally (default = False)
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(bool). If True, we only apply BPE.
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Returns:
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List of tokens.
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"""
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if lang and self.lang2id and lang not in self.lang2id:
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logger.error(
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"Supplied language code not found in lang2id mapping. Please check that your language is supported by"
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" the loaded pretrained model."
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)
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if bypass_tokenizer:
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text = text.split()
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elif lang not in self.lang_with_custom_tokenizer:
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text = self.moses_pipeline(text, lang=lang)
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# TODO: make sure we are using `FacebookAI/xlm-mlm-enro-1024`, since XLM-100 doesn't have this step
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if lang == "ro":
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text = romanian_preprocessing(text)
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text = self.moses_tokenize(text, lang=lang)
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elif lang == "th":
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text = self.moses_pipeline(text, lang=lang)
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try:
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if "pythainlp" not in sys.modules:
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from pythainlp.tokenize import word_tokenize as th_word_tokenize
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else:
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th_word_tokenize = sys.modules["pythainlp"].word_tokenize
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except (AttributeError, ImportError):
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logger.error(
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"Make sure you install PyThaiNLP (https://github.com/PyThaiNLP/pythainlp) with the following steps"
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)
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logger.error("1. pip install pythainlp")
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raise
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text = th_word_tokenize(text)
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elif lang == "zh":
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try:
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if "jieba" not in sys.modules:
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import jieba
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else:
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jieba = sys.modules["jieba"]
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except (AttributeError, ImportError):
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logger.error("Make sure you install Jieba (https://github.com/fxsjy/jieba) with the following steps")
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logger.error("1. pip install jieba")
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raise
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text = " ".join(jieba.cut(text))
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text = self.moses_pipeline(text, lang=lang)
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text = text.split()
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elif lang == "ja":
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text = self.moses_pipeline(text, lang=lang)
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text = self.ja_tokenize(text)
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else:
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raise ValueError("It should not reach here")
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if self.do_lowercase_and_remove_accent and not bypass_tokenizer:
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text = lowercase_and_remove_accent(text)
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split_tokens = []
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for token in text:
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if token:
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split_tokens.extend(list(self.bpe(token).split(" ")))
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return split_tokens
|
||
|
||
def _convert_token_to_id(self, token):
|
||
"""Converts a token (str) in an id using the vocab."""
|
||
return self.encoder.get(token, self.encoder.get(self.unk_token))
|
||
|
||
def _convert_id_to_token(self, index):
|
||
"""Converts an index (integer) in a token (str) using the vocab."""
|
||
return self.decoder.get(index, self.unk_token)
|
||
|
||
def convert_tokens_to_string(self, tokens):
|
||
"""Converts a sequence of tokens (string) in a single string."""
|
||
out_string = "".join(tokens).replace("</w>", " ").strip()
|
||
return out_string
|
||
|
||
def build_inputs_with_special_tokens(
|
||
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
||
) -> List[int]:
|
||
"""
|
||
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
|
||
adding special tokens. An XLM sequence has the following format:
|
||
|
||
- single sequence: `<s> X </s>`
|
||
- pair of sequences: `<s> A </s> B </s>`
|
||
|
||
Args:
|
||
token_ids_0 (`List[int]`):
|
||
List of IDs to which the special tokens will be added.
|
||
token_ids_1 (`List[int]`, *optional*):
|
||
Optional second list of IDs for sequence pairs.
|
||
|
||
Returns:
|
||
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
|
||
|
||
"""
|
||
bos = [self.bos_token_id]
|
||
sep = [self.sep_token_id]
|
||
|
||
if token_ids_1 is None:
|
||
return bos + token_ids_0 + sep
|
||
return bos + token_ids_0 + sep + token_ids_1 + sep
|
||
|
||
def get_special_tokens_mask(
|
||
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
|
||
) -> List[int]:
|
||
"""
|
||
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
|
||
special tokens using the tokenizer `prepare_for_model` method.
|
||
|
||
Args:
|
||
token_ids_0 (`List[int]`):
|
||
List of IDs.
|
||
token_ids_1 (`List[int]`, *optional*):
|
||
Optional second list of IDs for sequence pairs.
|
||
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
||
Whether or not the token list is already formatted with special tokens for the model.
|
||
|
||
Returns:
|
||
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
||
"""
|
||
|
||
if already_has_special_tokens:
|
||
return super().get_special_tokens_mask(
|
||
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
|
||
)
|
||
|
||
if token_ids_1 is not None:
|
||
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
|
||
return [1] + ([0] * len(token_ids_0)) + [1]
|
||
|
||
def create_token_type_ids_from_sequences(
|
||
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
||
) -> List[int]:
|
||
"""
|
||
Create a mask from the two sequences passed to be used in a sequence-pair classification task. An XLM sequence
|
||
pair mask has the following format:
|
||
|
||
```
|
||
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
|
||
| first sequence | second sequence |
|
||
```
|
||
|
||
If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
|
||
|
||
Args:
|
||
token_ids_0 (`List[int]`):
|
||
List of IDs.
|
||
token_ids_1 (`List[int]`, *optional*):
|
||
Optional second list of IDs for sequence pairs.
|
||
|
||
Returns:
|
||
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
|
||
"""
|
||
sep = [self.sep_token_id]
|
||
cls = [self.cls_token_id]
|
||
if token_ids_1 is None:
|
||
return len(cls + token_ids_0 + sep) * [0]
|
||
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
|
||
|
||
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
|
||
vocab_file = os.path.join(
|
||
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
||
)
|
||
merge_file = os.path.join(
|
||
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
|
||
)
|
||
|
||
with open(vocab_file, "w", encoding="utf-8") as f:
|
||
f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
|
||
|
||
index = 0
|
||
with open(merge_file, "w", encoding="utf-8") as writer:
|
||
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
|
||
if index != token_index:
|
||
logger.warning(
|
||
f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
|
||
" Please check that the tokenizer is not corrupted!"
|
||
)
|
||
index = token_index
|
||
writer.write(" ".join(bpe_tokens) + "\n")
|
||
index += 1
|
||
|
||
return vocab_file, merge_file
|
||
|
||
def __getstate__(self):
|
||
state = self.__dict__.copy()
|
||
state["sm"] = None
|
||
return state
|
||
|
||
def __setstate__(self, d):
|
||
self.__dict__ = d
|
||
|
||
try:
|
||
import sacremoses
|
||
except ImportError:
|
||
raise ImportError(
|
||
"You need to install sacremoses to use XLMTokenizer. "
|
||
"See https://pypi.org/project/sacremoses/ for installation."
|
||
)
|
||
|
||
self.sm = sacremoses
|