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

from utils.util import has_existed


def vocalist_statistics(data_dir):
    singers = []
    songs = []
    global2singer2songs = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))

    global_infos = glob(data_dir + "/*")

    for global_info in global_infos:
        global_split = global_info.split("/")[-1]

        singer_infos = glob(global_info + "/*")

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

            singers.append(singer)

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

                songs.append(song)

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

                for utt in utts:
                    uid = utt.split("/")[-1].split(".")[0]
                    global2singer2songs[global_split][singer][song].append(uid)

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

    print(
        "vocalist: {} singers, {} songs ({} unique songs)".format(
            len(unique_singers), len(songs), len(unique_songs)
        )
    )
    print("Singers: \n{}".format("\t".join(unique_singers)))
    return global2singer2songs, unique_singers


def main(output_path, dataset_path):
    print("-" * 10)
    print("Preparing test samples for vocalist...\n")

    save_dir = os.path.join(output_path, "vocalist")
    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
    vocalist_path = dataset_path

    global2singer2songs, unique_singers = vocalist_statistics(vocalist_path)

    train = []
    test = []

    train_index_count = 0
    test_index_count = 0

    train_total_duration = 0
    test_total_duration = 0

    for global_info, singer2songs in tqdm(global2singer2songs.items()):
        for singer, songs in tqdm(singer2songs.items()):
            song_names = list(songs.keys())

            for chosen_song in song_names:
                for chosen_uid in songs[chosen_song]:
                    res = {
                        "Dataset": "opensinger",
                        "Singer": singer,
                        "Song": chosen_song,
                        "Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid),
                    }
                    res["Path"] = "{}/{}/{}/{}.wav".format(
                        global_info, singer, chosen_song, chosen_uid
                    )
                    res["Path"] = os.path.join(vocalist_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

                    res["index"] = test_index_count
                    test_total_duration += duration
                    test.append(res)
                    test_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)
