"""
gsplit -l 1500 -d --additional-suffix=.jsonl test.jsonl test
gsplit -l 1500 -d --additional-suffix=.jsonl train.jsonl train
gsplit -l 1500 -d --additional-suffix=.jsonl validation.jsonl validation
rm -rf test.jsonl
rm -rf train.jsonl
rm -rf validation.jsonl
"""
import json
import os
import re
import spacy
from random import seed, shuffle
from tqdm import tqdm
from datasets import load_dataset

DATASET_NAME = "squad_it"
DATASET_TYPES = None
HIGHLIGHT_TOKEN = '<hl>'
GENERATE_TEST_SPLIT = True
SPLITTER = spacy.load('it_core_news_sm')


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


def process_single_data(question: str, paragraph: str, answer: str):
    """ Convert single raw json data into QG format """
    example = {'question': question, 'paragraph': paragraph, 'answer': answer}
    start = example['paragraph'].find(example['answer'])
    end = start + len(answer)
    assert paragraph[start:end] == answer
    # get sentence
    before_tmp = get_sentence(example['paragraph'][:start])
    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 f'{before_sentence} '
    after_tmp = get_sentence(example['paragraph'][start + 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 f' {after_sentence}'
    example['sentence'] = f"{before_sentence}{example['answer']}{after_sentence}"

    # get paragraph_sentence
    before = '' if before == '' else f'{before} '
    after = '' if after == '' else f' {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'][:start], HIGHLIGHT_TOKEN, example['answer'],
        example['paragraph'][start + 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 f'{before_tmp[-1]} '
    if len(after_tmp) == 0:
        after = ''
    else:
        after = after_tmp[0] if after_tmp[0].startswith(' ') else f' {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)
    if DATASET_TYPES is not None:
        dataset = load_dataset(DATASET_NAME, DATASET_TYPES)
    else:
        dataset = load_dataset(DATASET_NAME)
    for _split in dataset.keys():
        tmp_dataset = dataset[_split]
        with open(f'{output}/{_split}.jsonl', 'w') as f:
            for single_data in tqdm(tmp_dataset):
                question_str = single_data['question']
                paragraph_str = single_data['context']
                answer_str = single_data['answers']['text']
                if type(answer_str) == list:
                    answer_str = answer_str[0]
                assert type(answer_str) is str, answer_str
                assert type(question_str) is str, question_str
                assert type(paragraph_str) is str, paragraph_str
                tmp_data = process_single_data(question=question_str, paragraph=paragraph_str, answer=answer_str)
                tmp_data['paragraph_id'] = single_data['id']
                f.write(json.dumps(tmp_data) + '\n')
    if GENERATE_TEST_SPLIT:
        # randomly sample for validation set
        with open(f'{output}/train.jsonl') as f:
            lines_train = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
        with open(f'{output}/test.jsonl') as f:
            size = len([i for i in f.read().split('\n') if len(i) > 0])
        paragraph_ids = list(set([i['paragraph_id'] for i in lines_train]))
        data_train = {p: [i for i in lines_train if i['paragraph_id'] == p] for p in paragraph_ids}
        seed(0)
        shuffle(paragraph_ids)
        data_test = []
        data_train_new = []
        for i in paragraph_ids:
            if len(data_test) < size:
                data_test += data_train[i]
            else:
                data_train_new += data_train[i]
        with open(f'{output}/train.jsonl', 'w') as f:
            f.write('\n'.join([json.dumps(i) for i in data_train_new]))
        with open(f'{output}/validation.jsonl', 'w') as f:
            f.write('\n'.join([json.dumps(i) for i in data_test]))