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


from utils.io import save_audio
from utils.util import has_existed
from utils.audio_slicer import Slicer
from preprocessors import GOLDEN_TEST_SAMPLES


def split_to_utterances(dataset_path, singer, style, output_dir):
    data_dir = os.path.join(dataset_path, singer, style)

    print("Splitting to utterances for {}...".format(data_dir))

    wave_files = glob(data_dir + "/*.wav")

    for wav_file in tqdm(wave_files):
        # Load waveform
        song_name = wav_file.split("/")[-1].split(".")[0]
        waveform, fs = torchaudio.load(wav_file)

        # Split
        slicer = Slicer(sr=fs, threshold=-40.0, max_sil_kept=4000)
        chunks = slicer.slice(waveform)

        for i, chunk in enumerate(chunks):
            save_dir = os.path.join(output_dir, singer, style, song_name)
            os.makedirs(save_dir, exist_ok=True)

            output_file = os.path.join(save_dir, "{:04d}.wav".format(i))
            save_audio(output_file, chunk, fs)


def _main(dataset_path):
    """
    Split to utterances
    """
    utterance_dir = os.path.join(dataset_path, "utterances")

    singer_infos = glob(dataset_path + "/*")

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

        for style in ["read", "sing"]:
            split_to_utterances(dataset_path, singer, style, utterance_dir)


def get_test_songs():
    golden_samples = GOLDEN_TEST_SAMPLES["nus48e"]
    # every item is a tuple (singer, song)
    golden_songs = [s.split("#")[:2] for s in golden_samples]
    # singer_song, eg: Female1#Almost_lover_Amateur
    return golden_songs


def nus48e_statistics(data_dir):
    singers = []
    songs = []
    singer2songs = defaultdict(lambda: defaultdict(list))

    singer_infos = glob(data_dir + "/*")

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

        style_infos = glob(singer_info + "/*")

        for style_info in style_infos:
            style_info_split = style_info.split("/")[-1]

            singer = singer_info_split + "_" + style_info_split
            singers.append(singer)

            song_infos = glob(style_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]
                    singer2songs[singer][song].append(uid)

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

    print(
        "nus_48_e: {} singers, {} utterances ({} unique songs)".format(
            len(unique_singers), len(songs), len(unique_songs)
        )
    )
    print("Singers: \n{}".format("\t".join(unique_singers)))
    return singer2songs, unique_singers


def main(output_path, dataset_path):
    print("-" * 10)
    print("Preparing test samples for nus48e...\n")

    if not os.path.exists(os.path.join(dataset_path, "utterances")):
        print("Spliting into utterances...\n")
        _main(dataset_path)

    save_dir = os.path.join(output_path, "nus48e")
    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
    nus48e_path = os.path.join(dataset_path, "utterances")

    singer2songs, unique_singers = nus48e_statistics(nus48e_path)
    test_songs = get_test_songs()

    # We select songs of standard samples as test songs
    train = []
    test = []

    train_index_count = 0
    test_index_count = 0

    train_total_duration = 0
    test_total_duration = 0

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

        for chosen_song in song_names:
            for chosen_uid in songs[chosen_song]:
                res = {
                    "Dataset": "nus48e",
                    "Singer": singer,
                    "Uid": "{}#{}#{}".format(singer, chosen_song, chosen_uid),
                }
                res["Path"] = "{}/{}/{}/{}.wav".format(
                    singer.split("_")[0], singer.split("_")[-1], chosen_song, chosen_uid
                )
                res["Path"] = os.path.join(nus48e_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

                if duration <= 1e-8:
                    continue

                if ([singer, chosen_song]) in test_songs:
                    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)
