import torch
import torch.nn as nn
import torch.optim as optim

import torch.nn.functional as F
import torchaudio
import tqdm

import argparse
import math
import os

import AudioUtils
from generator import SISUGenerator


# Init script argument parser
parser = argparse.ArgumentParser(description="Training script")
parser.add_argument("--device", type=str, default="cpu",  help="Select device")
parser.add_argument("--model", type=str, help="Model to use for upscaling")
parser.add_argument("--clip_length", type=int, default=1024, help="Internal clip length, leave unspecified if unsure")
parser.add_argument("-i", "--input", type=str, help="Input audio file")
parser.add_argument("-o", "--output", type=str, help="Output audio file")

args = parser.parse_args()

device = torch.device(args.device if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

generator = SISUGenerator()

models_dir = args.model
clip_length = args.clip_length
input_audio = args.input
output_audio = args.output

if models_dir:
    generator.load_state_dict(torch.load(f"{models_dir}", map_location=device, weights_only=True))
else:
    print(f"Generator model (--model) isn't specified. Do you have the trained model? If not you need to train it OR acquire it from somewhere (DON'T ASK ME, YET!)")

generator = generator.to(device)

def start():
    # To Mono!
    audio, original_sample_rate = torchaudio.load(input_audio, normalize=True)
    audio = AudioUtils.stereo_tensor_to_mono(audio)

    splitted_audio = AudioUtils.split_audio(audio, clip_length)
    splitted_audio_on_device = [t.to(device) for t in splitted_audio]
    processed_audio = []

    for clip in tqdm.tqdm(splitted_audio_on_device, desc="Processing..."):
        processed_audio.append(generator(clip))

    reconstructed_audio = AudioUtils.reconstruct_audio(processed_audio)
    print(f"Saving {output_audio}!")
    torchaudio.save(output_audio, reconstructed_audio.cpu().detach(), original_sample_rate)

start()