import argparse

import torch
import torchaudio
from torchaudio.transforms import MelSpectrogram
from torchaudio.models import wavernn
from torchaudio.models.wavernn import _MODEL_CONFIG_AND_URLS
from torchaudio.datasets import LJSPEECH

from wavernn_inference_wrapper import WaveRNNInferenceWrapper
from processing import NormalizeDB


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--output-wav-path", default="./output.wav", type=str, metavar="PATH",
        help="The path to output the reconstructed wav file.",
    )
    parser.add_argument(
        "--jit", default=False, action="store_true",
        help="If used, the model and inference function is jitted."
    )
    parser.add_argument(
        "--loss", default="crossentropy", choices=["crossentropy"],
        type=str, help="The type of loss the pretrained model is trained on.",
    )
    parser.add_argument(
        "--no-batch-inference", default=False, action="store_true",
        help="Don't use batch inference."
    )
    parser.add_argument(
        "--no-mulaw", default=False, action="store_true",
        help="Don't use mulaw decoder to decoder the signal."
    )
    parser.add_argument(
        "--checkpoint-name", default="wavernn_10k_epochs_8bits_ljspeech",
        choices=list(_MODEL_CONFIG_AND_URLS.keys()),
        help="Select the WaveRNN checkpoint."
    )
    parser.add_argument(
        "--batch-timesteps", default=11000, type=int,
        help="The time steps for each batch. Only used when batch inference is used",
    )
    parser.add_argument(
        "--batch-overlap", default=550, type=int,
        help="The overlapping time steps between batches. Only used when batch inference is used",
    )
    args = parser.parse_args()
    return args


def main(args):
    device = "cuda" if torch.cuda.is_available() else "cpu"
    waveform, sample_rate, _, _ = LJSPEECH("./", download=True)[0]

    mel_kwargs = {
        'sample_rate': sample_rate,
        'n_fft': 2048,
        'f_min': 40.,
        'n_mels': 80,
        'win_length': 1100,
        'hop_length': 275,
        'mel_scale': 'slaney',
        'norm': 'slaney',
        'power': 1,
    }
    transforms = torch.nn.Sequential(
        MelSpectrogram(**mel_kwargs),
        NormalizeDB(min_level_db=-100, normalization=True),
    )
    mel_specgram = transforms(waveform)

    wavernn_model = wavernn(args.checkpoint_name).eval().to(device)
    wavernn_inference_model = WaveRNNInferenceWrapper(wavernn_model)

    if args.jit:
        wavernn_inference_model = torch.jit.script(wavernn_inference_model)

    with torch.no_grad():
        output = wavernn_inference_model(mel_specgram.to(device),
                                         loss_name=args.loss,
                                         mulaw=(not args.no_mulaw),
                                         batched=(not args.no_batch_inference),
                                         timesteps=args.batch_timesteps,
                                         overlap=args.batch_overlap,)

    torchaudio.save(args.output_wav_path, output.reshape(1, -1), sample_rate=sample_rate)


if __name__ == "__main__":
    args = parse_args()
    main(args)
