645 lines
24 KiB
Python
645 lines
24 KiB
Python
# coding=utf-8
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# Copyright 2020 The Google AI Language Team Authors, Allegro.pl, Facebook Inc. 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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import json
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import os
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import re
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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, _is_control, _is_punctuation, _is_whitespace
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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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# Copied from transformers.models.xlm.tokenization_xlm.get_pairs
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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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# Copied from transformers.models.xlm.tokenization_xlm.replace_unicode_punct
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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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# Copied from transformers.models.xlm.tokenization_xlm.remove_non_printing_char
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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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# Copied from transformers.models.bert.tokenization_bert.whitespace_tokenize
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def whitespace_tokenize(text):
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"""Runs basic whitespace cleaning and splitting on a piece of text."""
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text = text.strip()
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if not text:
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return []
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tokens = text.split()
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return tokens
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# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
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class BasicTokenizer(object):
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"""
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Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
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Args:
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do_lower_case (`bool`, *optional*, defaults to `True`):
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Whether or not to lowercase the input when tokenizing.
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never_split (`Iterable`, *optional*):
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Collection of tokens which will never be split during tokenization. Only has an effect when
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`do_basic_tokenize=True`
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tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
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Whether or not to tokenize Chinese characters.
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This should likely be deactivated for Japanese (see this
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[issue](https://github.com/huggingface/transformers/issues/328)).
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strip_accents (`bool`, *optional*):
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Whether or not to strip all accents. If this option is not specified, then it will be determined by the
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value for `lowercase` (as in the original BERT).
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do_split_on_punc (`bool`, *optional*, defaults to `True`):
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In some instances we want to skip the basic punctuation splitting so that later tokenization can capture
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the full context of the words, such as contractions.
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"""
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def __init__(
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self,
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do_lower_case=True,
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never_split=None,
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tokenize_chinese_chars=True,
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strip_accents=None,
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do_split_on_punc=True,
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):
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if never_split is None:
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never_split = []
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self.do_lower_case = do_lower_case
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self.never_split = set(never_split)
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self.tokenize_chinese_chars = tokenize_chinese_chars
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self.strip_accents = strip_accents
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self.do_split_on_punc = do_split_on_punc
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def tokenize(self, text, never_split=None):
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"""
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Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.
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Args:
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never_split (`List[str]`, *optional*)
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Kept for backward compatibility purposes. Now implemented directly at the base class level (see
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[`PreTrainedTokenizer.tokenize`]) List of token not to split.
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"""
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# union() returns a new set by concatenating the two sets.
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never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
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text = self._clean_text(text)
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# This was added on November 1st, 2018 for the multilingual and Chinese
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# models. This is also applied to the English models now, but it doesn't
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# matter since the English models were not trained on any Chinese data
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# and generally don't have any Chinese data in them (there are Chinese
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# characters in the vocabulary because Wikipedia does have some Chinese
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# words in the English Wikipedia.).
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if self.tokenize_chinese_chars:
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text = self._tokenize_chinese_chars(text)
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# prevents treating the same character with different unicode codepoints as different characters
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unicode_normalized_text = unicodedata.normalize("NFC", text)
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orig_tokens = whitespace_tokenize(unicode_normalized_text)
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split_tokens = []
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for token in orig_tokens:
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if token not in never_split:
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if self.do_lower_case:
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token = token.lower()
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if self.strip_accents is not False:
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token = self._run_strip_accents(token)
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elif self.strip_accents:
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token = self._run_strip_accents(token)
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split_tokens.extend(self._run_split_on_punc(token, never_split))
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output_tokens = whitespace_tokenize(" ".join(split_tokens))
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return output_tokens
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def _run_strip_accents(self, text):
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"""Strips accents from a piece of text."""
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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)
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def _run_split_on_punc(self, text, never_split=None):
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"""Splits punctuation on a piece of text."""
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if not self.do_split_on_punc or (never_split is not None and text in never_split):
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return [text]
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chars = list(text)
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i = 0
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start_new_word = True
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output = []
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while i < len(chars):
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char = chars[i]
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if _is_punctuation(char):
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output.append([char])
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start_new_word = True
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else:
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if start_new_word:
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output.append([])
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start_new_word = False
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output[-1].append(char)
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i += 1
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return ["".join(x) for x in output]
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def _tokenize_chinese_chars(self, text):
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"""Adds whitespace around any CJK character."""
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output = []
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for char in text:
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cp = ord(char)
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if self._is_chinese_char(cp):
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output.append(" ")
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output.append(char)
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output.append(" ")
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else:
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output.append(char)
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return "".join(output)
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def _is_chinese_char(self, cp):
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"""Checks whether CP is the codepoint of a CJK character."""
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# This defines a "chinese character" as anything in the CJK Unicode block:
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# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
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#
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# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
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# despite its name. The modern Korean Hangul alphabet is a different block,
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# as is Japanese Hiragana and Katakana. Those alphabets are used to write
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# space-separated words, so they are not treated specially and handled
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# like the all of the other languages.
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if (
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(cp >= 0x4E00 and cp <= 0x9FFF)
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or (cp >= 0x3400 and cp <= 0x4DBF) #
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or (cp >= 0x20000 and cp <= 0x2A6DF) #
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or (cp >= 0x2A700 and cp <= 0x2B73F) #
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or (cp >= 0x2B740 and cp <= 0x2B81F) #
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or (cp >= 0x2B820 and cp <= 0x2CEAF) #
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or (cp >= 0xF900 and cp <= 0xFAFF)
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or (cp >= 0x2F800 and cp <= 0x2FA1F) #
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): #
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return True
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return False
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def _clean_text(self, text):
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"""Performs invalid character removal and whitespace cleanup on text."""
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output = []
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for char in text:
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cp = ord(char)
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if cp == 0 or cp == 0xFFFD or _is_control(char):
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continue
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if _is_whitespace(char):
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output.append(" ")
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else:
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output.append(char)
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return "".join(output)
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class HerbertTokenizer(PreTrainedTokenizer):
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"""
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Construct a BPE tokenizer for HerBERT.
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Peculiarities:
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- uses BERT's pre-tokenizer: BaseTokenizer splits tokens on spaces, and also on punctuation. Each occurrence of a
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punctuation character will be treated separately.
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- Such pretokenized input is BPE subtokenized
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This tokenizer inherits from [`XLMTokenizer`] which contains most of the methods. Users should refer to the
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superclass for more information regarding methods.
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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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tokenizer_file=None,
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cls_token="<s>",
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unk_token="<unk>",
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pad_token="<pad>",
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mask_token="<mask>",
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sep_token="</s>",
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bos_token="<s>",
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do_lowercase_and_remove_accent=False,
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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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**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 HerbertTokenizer. "
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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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tokenizer_file=None,
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**kwargs,
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)
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self.bert_pre_tokenizer = BasicTokenizer(
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do_lower_case=False,
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never_split=self.all_special_tokens,
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tokenize_chinese_chars=False,
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strip_accents=False,
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)
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@property
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# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.do_lower_case
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def do_lower_case(self):
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return self.do_lowercase_and_remove_accent
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# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.moses_punct_norm
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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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# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.moses_tokenize
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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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# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.moses_pipeline
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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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# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.ja_tokenize
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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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# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.vocab_size
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def vocab_size(self):
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return len(self.encoder)
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# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.get_vocab
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def get_vocab(self):
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return dict(self.encoder, **self.added_tokens_encoder)
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# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.bpe
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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)
|
||
if word == "\n </w>":
|
||
word = "\n</w>"
|
||
self.cache[token] = word
|
||
return word
|
||
|
||
def _tokenize(self, text):
|
||
pre_tokens = self.bert_pre_tokenizer.tokenize(text)
|
||
|
||
split_tokens = []
|
||
for token in pre_tokens:
|
||
if token:
|
||
split_tokens.extend(list(self.bpe(token).split(" ")))
|
||
|
||
return split_tokens
|
||
|
||
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer._convert_token_to_id
|
||
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))
|
||
|
||
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer._convert_id_to_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)
|
||
|
||
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.convert_tokens_to_string
|
||
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
|
||
|
||
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.build_inputs_with_special_tokens
|
||
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
|
||
|
||
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.get_special_tokens_mask
|
||
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]
|
||
|
||
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.create_token_type_ids_from_sequences
|
||
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]
|
||
|
||
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.save_vocabulary
|
||
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
|
||
|
||
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.__getstate__
|
||
def __getstate__(self):
|
||
state = self.__dict__.copy()
|
||
state["sm"] = None
|
||
return state
|
||
|
||
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.__setstate__
|
||
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
|