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import json
import os
import shutil
import time
from pathlib import Path
from typing import List, Union, Optional
import tokenizers
import torch
from torch import NoneType
from huggingface_hub import hf_hub_download
from pypinyin import pinyin, Style
from transformers.tokenization_utils_base import TruncationStrategy
from transformers.utils import PaddingStrategy
from transformers.utils.generic import TensorType
try:
from tokenizers import BertWordPieceTokenizer
except:
from tokenizers.implementations import BertWordPieceTokenizer
from transformers import BertTokenizerFast, BatchEncoding
cache_path = Path(os.path.abspath(__file__)).parent
def download_file(filename: str, path: Path):
if os.path.exists(cache_path / filename):
return
if os.path.exists(path / filename):
shutil.copyfile(path / filename, cache_path / filename)
return
hf_hub_download(
"iioSnail/ChineseBERT-base",
filename,
local_dir=cache_path
)
time.sleep(0.2)
class ChineseBertTokenizer(BertTokenizerFast):
def __init__(self, **kwargs):
super(ChineseBertTokenizer, self).__init__(**kwargs)
self.path = Path(kwargs['name_or_path'])
vocab_file = cache_path / 'vocab.txt'
config_path = cache_path / 'config'
if not os.path.exists(config_path):
os.makedirs(config_path)
self.max_length = 512
download_file('vocab.txt', self.path)
self.tokenizer = BertWordPieceTokenizer(str(vocab_file))
# load pinyin map dict
download_file('config/pinyin_map.json', self.path)
with open(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', self.path)
with open(config_path / 'id2pinyin.json', encoding='utf8') as fin:
self.id2pinyin = json.load(fin)
# load pinyin map tensor
download_file('config/pinyin2tensor.json', self.path)
with open(config_path / 'pinyin2tensor.json', encoding='utf8') as fin:
self.pinyin2tensor = json.load(fin)
def __call__(self,
text: Union[str, List[str], List[List[str]]] = None,
text_pair: Union[str, List[str], List[List[str]], NoneType] = None,
text_target: Union[str, List[str], List[List[str]]] = None,
text_pair_target: Union[str, List[str], List[List[str]], NoneType] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
stride: int = 0,
is_split_into_words: bool = False,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Union[str, TensorType, NoneType] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True, **kwargs) -> BatchEncoding:
encoding = super(ChineseBertTokenizer, self).__call__(
text=text,
text_pair=text_pair,
text_target=text_target,
text_pair_target=text_pair_target,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
is_split_into_words=is_split_into_words,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
)
input_ids = encoding.input_ids
pinyin_ids = None
if type(text) == str:
pinyin_ids = self.convert_ids_to_pinyin_ids(input_ids)
if type(text) == list:
pinyin_ids = []
for ids in input_ids:
pinyin_ids.append(self.convert_ids_to_pinyin_ids(ids))
if torch.is_tensor(encoding.input_ids):
pinyin_ids = torch.LongTensor(pinyin_ids)
encoding['pinyin_ids'] = pinyin_ids
return encoding
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_ids_to_pinyin_ids(self, ids: List[int]):
pinyin_ids = []
tokens = self.convert_ids_to_tokens(ids)
for token in tokens:
if len(token) > 1:
pinyin_ids.append([0] * 8)
continue
pinyin_string = pinyin(token, style=Style.TONE3, errors=lambda x: [['not chinese'] for _ in x])[0][0]
if pinyin_string == "not chinese":
pinyin_ids.append([0] * 8)
continue
if pinyin_string in self.pinyin2tensor:
pinyin_ids.append(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_ids.append(pinyin_ids)
return 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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