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

from utils.util import has_existed
from preprocessors import GOLDEN_TEST_SAMPLES


def get_test_songs():
    golden_samples = GOLDEN_TEST_SAMPLES["popbutfy"]
    # 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 popbutfy_statistics(data_dir):
    singers = []
    songs = []
    singer2songs = defaultdict(lambda: defaultdict(list))

    data_infos = glob(data_dir + "/*")

    for data_info in data_infos:
        data_info_split = data_info.split("/")[-1].split("#")

        singer, song = data_info_split[0], data_info_split[-1]
        singers.append(singer)
        songs.append(song)

        utts = glob(data_info + "/*")

        for utt in utts:
            uid = utt.split("/")[-1].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(
        "PopBuTFy: {} 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 popbutfy...\n")

    save_dir = os.path.join(output_path, "popbutfy")
    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
    popbutfy_dir = dataset_path

    singer2songs, unique_singers = popbutfy_statistics(popbutfy_dir)
    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 tqdm(singer2songs.items()):
        song_names = list(songs.keys())

        for chosen_song in song_names:
            for chosen_uid in songs[chosen_song]:
                res = {
                    "Dataset": "popbutfy",
                    "Singer": singer,
                    "Song": chosen_song,
                    "Uid": "{}#{}#".format(singer, chosen_song, chosen_uid),
                }
                res["Path"] = "{}#singing#{}/{}#singing#{}_{}.mp3".format(
                    singer, chosen_song, singer, chosen_song, chosen_uid
                )
                if not os.path.exists(os.path.join(popbutfy_dir, res["Path"])):
                    res["Path"] = "{}#singing#{}/{}#singing#{}_{}.wav".format(
                        singer, chosen_song, singer, chosen_song, chosen_uid
                    )
                res["Path"] = os.path.join(popbutfy_dir, res["Path"])
                assert os.path.exists(res["Path"])

                if res["Path"].split("/")[-1].split(".")[-1] == "wav":
                    waveform, sample_rate = torchaudio.load(res["Path"])
                    duration = waveform.size(-1) / sample_rate
                else:
                    waveform, sample_rate = librosa.load(res["Path"])
                    duration = waveform.shape[-1] / sample_rate
                res["Duration"] = duration

                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)
