from glob import glob
import os
import json
import torchaudio
from tqdm import tqdm
from collections import defaultdict

from utils.util import has_existed


def statistics(utterance_dir):
    singers = []
    songs = []
    singers2songs = defaultdict(lambda: defaultdict(list))

    singer_infos = glob(utterance_dir + "/*")

    for singer_info in singer_infos:
        singer = singer_info.split("/")[-1]

        song_infos = glob(singer_info + "/*")

        for song_info in song_infos:
            song = song_info.split("/")[-1]

            singers.append(singer)
            songs.append(song)

            utts = glob(song_info + "/*.wav")

            for utt in utts:
                uid = utt.split("/")[-1].split(".")[0]
                singers2songs[singer][song].append(uid)

    unique_singers = list(set(singers))
    unique_songs = list(set(songs))
    unique_singers.sort()
    unique_songs.sort()

    print(
        "Statistics: {} singers, {} utterances ({} unique songs)".format(
            len(unique_singers), len(songs), len(unique_songs)
        )
    )
    print("Singers: \n{}".format("\t".join(unique_singers)))
    return singers2songs, unique_singers


def main(output_path, dataset_path, dataset_name):
    print("-" * 10)
    print("Preparing samples for {}...\n".format(dataset_name))

    save_dir = os.path.join(output_path, dataset_name)
    os.makedirs(save_dir, exist_ok=True)

    train_output_file = os.path.join(save_dir, "train.json")
    test_output_file = os.path.join(save_dir, "test.json")
    singer_dict_file = os.path.join(save_dir, "singers.json")
    utt2singer_file = os.path.join(save_dir, "utt2singer")
    if (
        has_existed(train_output_file)
        and has_existed(test_output_file)
        and has_existed(singer_dict_file)
        and has_existed(utt2singer_file)
    ):
        return
    utt2singer = open(utt2singer_file, "w")

    # Load
    singers2songs, unique_singers = statistics(dataset_path)

    # We select songs of standard samples as test songs
    train = []
    test = []
    test_songs = set()

    train_index_count = 0
    test_index_count = 0

    train_total_duration = 0
    test_total_duration = 0

    for singer, songs in singers2songs.items():
        song_names = list(songs.keys())

        print("Singer {}...".format(singer))
        for chosen_song in tqdm(song_names):
            for chosen_uid in songs[chosen_song]:
                res = {
                    "Dataset": dataset_name,
                    "Singer": singer,
                    "Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid),
                }
                res["Path"] = "{}/{}/{}.wav".format(singer, chosen_song, chosen_uid)
                res["Path"] = os.path.join(dataset_path, res["Path"])
                assert os.path.exists(res["Path"])

                waveform, sample_rate = torchaudio.load(res["Path"])
                duration = waveform.size(-1) / sample_rate
                res["Duration"] = duration

                # Remove the utterance whose duration is shorter than 0.1s
                if duration <= 1e-2:
                    continue

                # Place into train or test
                if "{}_{}".format(singer, chosen_song) not in test_songs:
                    test_songs.add("{}_{}".format(singer, chosen_song))

                    res["index"] = test_index_count
                    test_total_duration += duration
                    test.append(res)
                    test_index_count += 1
                else:
                    res["index"] = train_index_count
                    train_total_duration += duration
                    train.append(res)
                    train_index_count += 1

                utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"]))

    print("#Train = {}, #Test = {}".format(len(train), len(test)))
    print(
        "#Train hours= {}, #Test hours= {}".format(
            train_total_duration / 3600, test_total_duration / 3600
        )
    )

    # Save train.json and test.json
    with open(train_output_file, "w") as f:
        json.dump(train, f, indent=4, ensure_ascii=False)
    with open(test_output_file, "w") as f:
        json.dump(test, f, indent=4, ensure_ascii=False)

    # Save singers.json
    singer_lut = {name: i for i, name in enumerate(unique_singers)}
    with open(singer_dict_file, "w") as f:
        json.dump(singer_lut, f, indent=4, ensure_ascii=False)
