import os
import json
from tqdm import tqdm
import re
from convlab2.util import load_dataset


def create_lm_data(dataset, data_dir, args):
    data_by_split = dataset
    os.makedirs(data_dir, exist_ok=True)

    data_splits = data_by_split.keys()
    for data_split in data_splits:
        data = []
        for sample in tqdm(data_by_split[data_split], desc=f'{data_split} sample', leave=False):
            if args.model_type == 'dialogpt':
                dialogue = ' <|endoftext|> '.join([turn['utterance'] for turn in sample['turns']]) + ' <|endoftext|>'
            else:
                dialogue = ' '.join([f"{turn['speaker']}: {turn['utterance']}" for turn in sample['turns']])
            data.append(json.dumps({'dialogue': dialogue}, ensure_ascii=False)+'\n')

        file_name = os.path.join(data_dir, f"{data_split}.json")
        with open(file_name, "w", encoding='utf-8') as f:
            f.writelines(data)


if __name__ == '__main__':
    from argparse import ArgumentParser
    parser = ArgumentParser(description="create data for seq2seq training")
    parser.add_argument('--tasks', '-t', metavar='task_name', nargs='*', choices=['lm'], help='names of tasks')
    parser.add_argument('--datasets', '-d', metavar='dataset_name', nargs='*', help='names of unified datasets')
    parser.add_argument('--model_type', '-m', metavar='model_type', help='type of the language model: gpt, dialogpt, ..')
    args = parser.parse_args()
    print(args)
    for dataset_name in tqdm(args.datasets, desc='datasets'):
        dataset = load_dataset(dataset_name)
        for task_name in tqdm(args.tasks, desc='tasks', leave=False):
            data_dir = os.path.join('data', task_name, dataset_name)
            eval(f"create_{task_name}_data")(dataset, data_dir, args)