import os
import utils
import torch
import random
import librosa
import logging
import argparse
import multiprocessing
import numpy as np
import diffusion.logger.utils as du

from glob import glob
from tqdm import tqdm
from random import shuffle
from diffusion.vocoder import Vocoder
from concurrent.futures import ProcessPoolExecutor
from modules.mel_processing import spectrogram_torch

logging.getLogger("numba").setLevel(logging.WARNING)
logging.getLogger("matplotlib").setLevel(logging.WARNING)

hps = utils.get_hparams_from_file("configs/config.json")
dconfig = du.load_config("configs/diffusion.yaml")
sampling_rate = hps.data.sampling_rate
hop_length = hps.data.hop_length
speech_encoder = hps["model"]["speech_encoder"]


def process_one(filename, hmodel,f0p,diff=False,mel_extractor=None):
    # print(filename)
    wav, sr = librosa.load(filename, sr=sampling_rate)
    audio_norm = torch.FloatTensor(wav)
    audio_norm = audio_norm.unsqueeze(0)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    soft_path = filename + ".soft.pt"
    if not os.path.exists(soft_path):
        wav16k = librosa.resample(wav, orig_sr=sampling_rate, target_sr=16000)
        wav16k = torch.from_numpy(wav16k).to(device)
        c = hmodel.encoder(wav16k)
        torch.save(c.cpu(), soft_path)

    f0_path = filename + ".f0.npy"
    if not os.path.exists(f0_path):
        f0_predictor = utils.get_f0_predictor(f0p,sampling_rate=sampling_rate, hop_length=hop_length,device=None,threshold=0.05)
        f0,uv = f0_predictor.compute_f0_uv(
            wav
        )
        np.save(f0_path, np.asanyarray((f0,uv),dtype=object))


    spec_path = filename.replace(".wav", ".spec.pt")
    if not os.path.exists(spec_path):
        # Process spectrogram
        # The following code can't be replaced by torch.FloatTensor(wav)
        # because load_wav_to_torch return a tensor that need to be normalized

        if sr != hps.data.sampling_rate:
            raise ValueError(
                "{} SR doesn't match target {} SR".format(
                    sr, hps.data.sampling_rate
                )
            )

        #audio_norm = audio / hps.data.max_wav_value

        spec = spectrogram_torch(
            audio_norm,
            hps.data.filter_length,
            hps.data.sampling_rate,
            hps.data.hop_length,
            hps.data.win_length,
            center=False,
        )
        spec = torch.squeeze(spec, 0)
        torch.save(spec, spec_path)

    if diff or hps.model.vol_embedding:
        volume_path = filename + ".vol.npy"
        volume_extractor = utils.Volume_Extractor(hop_length)
        if not os.path.exists(volume_path):
            volume = volume_extractor.extract(audio_norm)
            np.save(volume_path, volume.to('cpu').numpy())

    if diff:
        mel_path = filename + ".mel.npy"
        if not os.path.exists(mel_path) and mel_extractor is not None:
            mel_t = mel_extractor.extract(audio_norm.to(device), sampling_rate)
            mel = mel_t.squeeze().to('cpu').numpy()
            np.save(mel_path, mel)
        aug_mel_path = filename + ".aug_mel.npy"
        aug_vol_path = filename + ".aug_vol.npy"
        max_amp = float(torch.max(torch.abs(audio_norm))) + 1e-5
        max_shift = min(1, np.log10(1/max_amp))
        log10_vol_shift = random.uniform(-1, max_shift)
        keyshift = random.uniform(-5, 5)
        if mel_extractor is not None:
            aug_mel_t = mel_extractor.extract(audio_norm * (10 ** log10_vol_shift), sampling_rate, keyshift = keyshift)
        aug_mel = aug_mel_t.squeeze().to('cpu').numpy()
        aug_vol = volume_extractor.extract(audio_norm * (10 ** log10_vol_shift))
        if not os.path.exists(aug_mel_path):
            np.save(aug_mel_path,np.asanyarray((aug_mel,keyshift),dtype=object))
        if not os.path.exists(aug_vol_path):
            np.save(aug_vol_path,aug_vol.to('cpu').numpy())

def process_batch(file_chunk, f0p, diff=False, mel_extractor=None):
    print("Loading speech encoder for content...")
    device = "cuda" if torch.cuda.is_available() else "cpu"
    hmodel = utils.get_speech_encoder(speech_encoder, device=device)
    print("Loaded speech encoder.")

    for filename in tqdm(file_chunk):
        process_one(filename, hmodel, f0p, diff, mel_extractor)

def parallel_process(filenames, num_processes, f0p, diff, mel_extractor):
    with ProcessPoolExecutor(max_workers=num_processes) as executor:
        tasks = []
        for i in range(num_processes):
            start = int(i * len(filenames) / num_processes)
            end = int((i + 1) * len(filenames) / num_processes)
            file_chunk = filenames[start:end]
            tasks.append(executor.submit(process_batch, file_chunk, f0p, diff, mel_extractor))

        for task in tqdm(tasks):
            task.result()

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--in_dir", type=str, default="dataset/44k", help="path to input dir"
    )
    parser.add_argument(
        '--use_diff',action='store_true', help='Whether to use the diffusion model'
    )
    parser.add_argument(
        '--f0_predictor', type=str, default="dio", help='Select F0 predictor, can select crepe,pm,dio,harvest, default pm(note: crepe is original F0 using mean filter)'
    )
    parser.add_argument(
        '--num_processes', type=int, default=1, help='You are advised to set the number of processes to the same as the number of CPU cores'
    )
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    args = parser.parse_args()
    f0p = args.f0_predictor
    print(speech_encoder)
    print(f0p)
    print(args.use_diff)
    if args.use_diff:
        print("use_diff")
        print("Loading Mel Extractor...")
        mel_extractor = Vocoder(dconfig.vocoder.type, dconfig.vocoder.ckpt, device = device)
        print("Loaded Mel Extractor.")
    else:
        mel_extractor = None
    filenames = glob(f"{args.in_dir}/*/*.wav", recursive=True)  # [:10]
    shuffle(filenames)
    multiprocessing.set_start_method("spawn", force=True)

    num_processes = args.num_processes
    if num_processes == 0:
        num_processes = os.cpu_count()
    
    parallel_process(filenames, num_processes, f0p, args.use_diff, mel_extractor)