import os
import argparse
import librosa
import numpy as np
import concurrent.futures
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
from multiprocessing import Pool, cpu_count
from scipy.io import wavfile
from tqdm import tqdm


def load_wav(wav_path):
    return librosa.load(wav_path, sr=None)

def trim_wav(wav, top_db=40):
    return librosa.effects.trim(wav, top_db=top_db)

def normalize_peak(wav, threshold=1.0):
    peak = np.abs(wav).max()
    if peak > threshold:
        wav = 0.98 * wav / peak
    return wav

def resample_wav(wav, sr, target_sr):
    return librosa.resample(wav, orig_sr=sr, target_sr=target_sr)

def save_wav_to_path(wav, save_path, sr):
    wavfile.write(
        save_path,
        sr,
        (wav * np.iinfo(np.int16).max).astype(np.int16)
    )

def process(item):
    spkdir, wav_name, args = item
    speaker = spkdir.replace("\\", "/").split("/")[-1]

    wav_path = os.path.join(args.in_dir, speaker, wav_name)
    if os.path.exists(wav_path) and '.wav' in wav_path:
        os.makedirs(os.path.join(args.out_dir2, speaker), exist_ok=True)

        wav, sr = load_wav(wav_path)
        wav, _ = trim_wav(wav)
        wav = normalize_peak(wav)
        resampled_wav = resample_wav(wav, sr, args.sr2)

        if not args.skip_loudnorm:
            resampled_wav /= max(resampled_wav.max(), -resampled_wav.min())

        save_path2 = os.path.join(args.out_dir2, speaker, wav_name)
        save_wav_to_path(resampled_wav, save_path2, args.sr2)

# def process_all_speakers(speakers, args):
#     process_count = 30 if os.cpu_count() > 60 else (os.cpu_count() - 2 if os.cpu_count() > 4 else 1)

#     with ThreadPoolExecutor(max_workers=process_count) as executor:
#         for speaker in speakers:
#             spk_dir = os.path.join(args.in_dir, speaker)
#             if os.path.isdir(spk_dir):
#                 print(spk_dir)
#                 futures = [executor.submit(process, (spk_dir, i, args)) for i in os.listdir(spk_dir) if i.endswith("wav")]
#                 for _ in tqdm(concurrent.futures.as_completed(futures), total=len(futures)):
#                     pass

# multi process
def process_all_speakers(speakers, args):
    process_count = 30 if os.cpu_count() > 60 else (os.cpu_count() - 2 if os.cpu_count() > 4 else 1)
    with ProcessPoolExecutor(max_workers=process_count) as executor:
        for speaker in speakers:
            spk_dir = os.path.join(args.in_dir, speaker)
            if os.path.isdir(spk_dir):
                print(spk_dir)
                futures = [executor.submit(process, (spk_dir, i, args)) for i in os.listdir(spk_dir) if i.endswith("wav")]
                for _ in tqdm(concurrent.futures.as_completed(futures), total=len(futures)):
                    pass

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--sr2", type=int, default=44100, help="sampling rate")
    parser.add_argument("--in_dir", type=str, default="./dataset_raw", help="path to source dir")
    parser.add_argument("--out_dir2", type=str, default="./dataset/44k", help="path to target dir")
    parser.add_argument("--skip_loudnorm", action="store_true", help="Skip loudness matching if you have done it")
    args = parser.parse_args()

    print(f"CPU count: {cpu_count()}")
    speakers = os.listdir(args.in_dir)
    process_all_speakers(speakers, args)