import json
import tqdm
import numpy as np
import multiprocessing as mp
import random
from collections import Counter
random.seed(13)


def _norm(x):
    return ' '.join(x.strip().split())


strategies = json.load(open('./strategy.json'))
strategies = [e[1:-1] for e in strategies]
strat2id = {strat: i for i, strat in enumerate(strategies)}
original = json.load(open('./ESConv.json'))

def process_data(d):
    dial = []
    for uttr in d['dialog']:
        text = _norm(uttr['content'])
        role = uttr['speaker']
        if role == 'seeker':
            dial.append({
                'text': text,
                'speaker': 'usr',
            })
        else:
            dial.append({
                'text': text,
                'speaker': 'sys',
                'strategy': uttr['annotation']['strategy'],
            })
    d['dialog'] = dial
    return d

data = []

for e in map(process_data, tqdm.tqdm(original, total=len(original))):
    data.append(e)

emotions = Counter([e['emotion_type'] for e in data])
problems = Counter([e['problem_type'] for e in data])
print('emotion', emotions)
print('problem', problems)


random.shuffle(data)
dev_size = int(0.15 * len(data))
test_size = int(0.15 * len(data))
valid = data[:dev_size]
test = data[dev_size: dev_size + test_size]
train = data[dev_size + test_size:]

print('train', len(train))
with open('./train.txt', 'w') as f:
    for e in train:
        f.write(json.dumps(e) + '\n')
with open('./sample.json', 'w') as f:
    json.dump(train[:10], f, ensure_ascii=False, indent=2)

print('valid', len(valid))
with open('./valid.txt', 'w') as f:
    for e in valid:
        f.write(json.dumps(e) + '\n')

print('test', len(test))
with open('./test.txt', 'w') as f:
    for e in test:
        f.write(json.dumps(e) + '\n')