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import json
import os
import time
from pathlib import Path
from typing import List
import tokenizers
import torch
from huggingface_hub import hf_hub_download
from huggingface_hub.file_download import http_user_agent
from pypinyin import pinyin, Style
try:
from tokenizers import BertWordPieceTokenizer
except:
from tokenizers.implementations import BertWordPieceTokenizer
from transformers import BertTokenizerFast
cache_path = Path(os.path.abspath(__file__)).parent
SOURCE_FILES_URL = {
"vocab.txt": "https://huggingface.co/iioSnail/chinesebert-base/resolve/main/vocab.txt",
"pinyin_map.json": "https://huggingface.co/iioSnail/chinesebert-base/resolve/main/config/pinyin_map.json",
"id2pinyin.json": "https://huggingface.co/iioSnail/chinesebert-base/resolve/main/config/id2pinyin.json",
"pinyin2tensor.json": "https://huggingface.co/iioSnail/chinesebert-base/resolve/main/config/id2pinyin.json",
}
def download_file(filename: str):
if os.path.exists(cache_path / filename):
return
hf_hub_download(
"iioSnail/chinesebert-base",
filename,
cache_dir=cache_path,
user_agent=http_user_agent(None),
)
time.sleep(0.2)
class ChineseBertTokenizer(BertTokenizerFast):
def __init__(self, **kwargs):
super(ChineseBertTokenizer, self).__init__(**kwargs)
vocab_file = os.path.join(cache_path, 'vocab.txt')
config_path = os.path.join(cache_path, 'config')
self.max_length = 512
download_file('vocab.txt')
self.tokenizer = BertWordPieceTokenizer(vocab_file)
# load pinyin map dict
download_file('config/pinyin_map.json')
with open(os.path.join(config_path, 'pinyin_map.json'), encoding='utf8') as fin:
self.pinyin_dict = json.load(fin)
# load char id map tensor
download_file('config/id2pinyin.json')
with open(os.path.join(config_path, 'id2pinyin.json'), encoding='utf8') as fin:
self.id2pinyin = json.load(fin)
# load pinyin map tensor
download_file('config/pinyin2tensor.json')
with open(os.path.join(config_path, 'pinyin2tensor.json'), encoding='utf8') as fin:
self.pinyin2tensor = json.load(fin)
def tokenize_sentence(self, sentence):
# convert sentence to ids
tokenizer_output = self.tokenizer.encode(sentence)
bert_tokens = tokenizer_output.ids
pinyin_tokens = self.convert_sentence_to_pinyin_ids(sentence, tokenizer_output)
# assert，token nums should be same as pinyin token nums
assert len(bert_tokens) <= self.max_length
assert len(bert_tokens) == len(pinyin_tokens)
# convert list to tensor
input_ids = torch.LongTensor(bert_tokens)
pinyin_ids = torch.LongTensor(pinyin_tokens).view(-1)
return input_ids, pinyin_ids
def convert_sentence_to_pinyin_ids(self, sentence: str, tokenizer_output: tokenizers.Encoding) -> List[List[int]]:
# get pinyin of a sentence
pinyin_list = pinyin(sentence, style=Style.TONE3, heteronym=True, errors=lambda x: [['not chinese'] for _ in x])
pinyin_locs = {}
# get pinyin of each location
for index, item in enumerate(pinyin_list):
pinyin_string = item[0]
# not a Chinese character, pass
if pinyin_string == "not chinese":
continue
if pinyin_string in self.pinyin2tensor:
pinyin_locs[index] = self.pinyin2tensor[pinyin_string]
else:
ids = [0] * 8
for i, p in enumerate(pinyin_string):
if p not in self.pinyin_dict["char2idx"]:
ids = [0] * 8
break
ids[i] = self.pinyin_dict["char2idx"][p]
pinyin_locs[index] = ids
# find chinese character location, and generate pinyin ids
pinyin_ids = []
for idx, (token, offset) in enumerate(zip(tokenizer_output.tokens, tokenizer_output.offsets)):
if offset[1] - offset[0] != 1:
pinyin_ids.append([0] * 8)
continue
if offset[0] in pinyin_locs:
pinyin_ids.append(pinyin_locs[offset[0]])
else:
pinyin_ids.append([0] * 8)
return pinyin_ids
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