process.py

""" Script to process raw SQuAD file for Question Generation format
You need to run `python -m spacy download en_core_web_sm`.
Split when uploading to dataset hub by
```
gsplit -l 3300 -d --additional-suffix=.jsonl train.jsonl train
gsplit -l 3300 -d --additional-suffix=.jsonl test.jsonl test
gsplit -l 3300 -d --additional-suffix=.jsonl dev.jsonl dev
```
"""
import json
import os
import re
from glob import glob
from tqdm import tqdm
from typing import List, Dict

import spacy

SPLITTER = spacy.load('en_core_web_sm')
HIGHLIGHT_TOKEN = '<hl>'


def get_sentence(document: str):
    return [str(s) for s in SPLITTER(document).sents]


def jsonline_reader(filename: str):
    with open(filename, 'r') as f_reader:
        examples = [json.loads(i) for i in f_reader.read().split('\n') if len(i) > 0]
    return examples


def process_single_data(data: Dict):
    """ Convert single raw json data into QG format """
    example = {'question': data["question"], 'paragraph': data["context"], 'answer': data["answer"]}

    # get sentence
    position = example['paragraph'].find(example['answer'])
    assert position != -1
    before_tmp = get_sentence(example['paragraph'][:position])
    if len(before_tmp) == 0:
        before = ''
        before_sentence = ''
    else:
        if before_tmp[-1].endswith('.'):
            before = ' '.join(before_tmp)
            before_sentence = ''
        else:
            before = ' '.join(before_tmp[:-1])
            before_sentence = before_tmp[-1]
            before_sentence = before_sentence if before_sentence.endswith(' ') else '{} '.format(before_sentence)
    after_tmp = get_sentence(example['paragraph'][position + len(example['answer']):])
    if len(after_tmp) == 0:
        after = ''
        after_sentence = ''
    else:
        after = ' '.join(after_tmp[1:])
        after_sentence = after_tmp[0]
        after_sentence = after_sentence if after_sentence.startswith(' ') else ' {}'.format(after_sentence)
    example['sentence'] = '{}{}{}'.format(before_sentence, example['answer'], after_sentence)

    # get paragraph_sentence
    before = '' if before == '' else '{} '.format(before)
    after = '' if after == '' else ' {}'.format(after)
    source_text = '{0}{1} {2} {1}{3}'.format(before, HIGHLIGHT_TOKEN, example['sentence'], after)
    example['paragraph_sentence'] = re.sub(r'\s+', ' ', source_text)

    # get paragraph_answer
    source_text = '{0}{1} {2} {1}{3}'.format(
        example['paragraph'][:position], HIGHLIGHT_TOKEN, example['answer'],
        example['paragraph'][position + len(example['answer']):])
    example['paragraph_answer'] = re.sub(r'\s+', ' ', source_text)

    # get sentence_answer
    if len(before_tmp) == 0 or before_tmp[-1].endswith('.'):
        before = ''
    else:
        before = before_tmp[-1] if before_tmp[-1].endswith(' ') else '{} '.format(before_tmp[-1])
    if len(after_tmp) == 0:
        after = ''
    else:
        after = after_tmp[0] if after_tmp[0].startswith(' ') else ' {}'.format(after_tmp[0])
    source_text = '{0}{1} {2} {1}{3}'.format(before, HIGHLIGHT_TOKEN, example['answer'], after)
    example['sentence_answer'] = re.sub(r'\s+', ' ', source_text)

    return example


if __name__ == '__main__':
    output = './data/processed'
    os.makedirs(output, exist_ok=True)
    path = {'train': 'data/raw/train*.jsonl', 'dev': 'data/raw/dev.jsonl', 'test': 'data/raw/test.jsonl'}
    for k, v in path.items():
        json_data = []
        for _file in sorted(glob(v)):
            json_data += jsonline_reader(_file)
        with open('{}/{}.jsonl'.format(output, k), 'w') as f:
            for single_data in tqdm(json_data):
                single_data = process_single_data(single_data)
                f.write(json.dumps(single_data) + '\n')