#
# want data from all documents
# want data from all classes
# 

file_names = [
    "adjudications.txt",
    "blog.txt",
    "books.txt",
    "emails.txt",
    "fbl.txt",
    "laws.txt",
    "mbl.txt",
    "radio_tv_news.txt",
    "school_essays.txt",
    "scienceweb.txt",
    "webmedia.txt",
    "websites.txt",
    "written-to-be-spoken.txt"
]

def read_file(file_name):
    data = []
    sentence = []
    with open(file_name) as fh:
        for line in fh.readlines():
            if not line.strip() and sentence:
                data.append(sentence)
                sentence = []
                continue
            parts = line.strip().split()
            if len(parts) >= 2:
                w, t = parts[0], parts[1]  # Take the first two items only
                sentence.append((w, t))
    return data


from collections import defaultdict
def calc_stats(data):
    stats = defaultdict(int)
    for sent in data:
        stats["n_sentences"] += 1
        for token, label in sent:
            stats[label] += 1
    return stats


import pprint
def get_total_stats():
    total_stats = defaultdict(int)
    for file_name in file_names:
        d = read_file("data/"+file_name)
        stats = calc_stats(d)
        #print(f"--- [{file_name}]---")
        #pprint.pprint(stats)
        for k, v in stats.items():
            total_stats[k] += v
        #print("---- TOTAL ---- ")
        #pprint.pprint(total_stats)
    return total_stats

import random
random.seed(1)

def check_if_not_done(stats, total_stats, target):
    for k, v in total_stats.items():
        if v * target > stats[k]:
            return True
    return False

def create_splits(train=0.8, test=0.1, dev=0.1):
    train_data = []
    test_data = []
    dev_data = []

    total_stats = get_total_stats()

    for file_name in file_names:
        train_stats = defaultdict(int)
        test_stats = defaultdict(int)
        dev_stats = defaultdict(int)
 
        d = read_file("data/"+file_name)       
        stats = calc_stats(d)
        random.shuffle(d)

        file_train = []
        file_test = []
        file_dev = []

        for sent in d:
            if check_if_not_done(test_stats, stats, test):
                # TEST data
                use = False
                for token in sent:
                    w, tag = token
                    if tag == 'O':
                        continue
                    if test_stats[tag] < test * stats[tag] - 5:
                        use = True
                if test_stats['n_sentences'] < test * stats['n_sentences'] - 5:
                    use = True
                if use:
                    file_test.append(sent)
                    test_stats['n_sentences'] += 1
                    for w, t in sent:
                        test_stats[t] += 1                    
                elif check_if_not_done(dev_stats, stats, dev):
                    # DEV DATA
                    use = False
                    for token in sent:
                        w, tag = token
                        if tag == 'O':
                            continue
                        if dev_stats[tag] < dev * stats[tag] - 5:
                            use = True
                    if dev_stats['n_sentences'] < dev * stats['n_sentences'] - 5:
                        use = True
                    if use:
                        file_dev.append(sent)
                        dev_stats['n_sentences'] += 1
                        for w, t in sent:
                            dev_stats[t] += 1
                    else:
                        file_train.append(sent)
                        train_stats['n_sentences'] += 1
                        for w, t in sent:
                            train_stats[t] += 1
                else:
                    file_train.append(sent)
                    train_stats['n_sentences'] += 1
                    for w, t in sent:
                        train_stats[t] += 1
        try:
            assert len(d) == len(file_train) + len(file_dev) + len(file_test)
        except:
            import pdb; pdb.set_trace()
        train_data += file_train
        test_data += file_test
        dev_data += file_dev

    return train_data, test_data, dev_data

train, test, dev = create_splits()

total_stats = get_total_stats()
print("---- total -----")
pprint.pprint(total_stats)
print("----- test ----")
test_stats = calc_stats(test)
pprint.pprint(test_stats)
print("----- dev ----")
dev_stats = calc_stats(dev)
pprint.pprint(dev_stats)
print("----- train ----")
train_stats = calc_stats(train)
pprint.pprint(train_stats)


with open("train.txt", "w") as outf:
    for sent in train:
        for w, t in sent:
            outf.writelines(f"{w} {t}\n")
        outf.writelines("\n")


with open("test.txt", "w") as outf:
    for sent in test:
        for w, t in sent:
            outf.writelines(f"{w} {t}\n")
        outf.writelines("\n")


with open("dev.txt", "w") as outf:
    for sent in dev:
        for w, t in sent:
            outf.writelines(f"{w} {t}\n")
        outf.writelines("\n")