"""
Extract 10-sec clip from YouTube and run SAM Audio separation.

Usage:
    python run_test.py <youtube_url> <start_time> <folder_name> <prompt>

Examples:
    python run_test.py "https://youtube.com/watch?v=xxx" 01:23 sitar_tanpura "sitar"
    python run_test.py "https://youtube.com/watch?v=xxx" 00:45 panche_baja "shehnai"
    python run_test.py "https://youtube.com/watch?v=xxx" 30 tabla_madal "tabla"

Output structure:
    tests/
      test1_sitar_tanpura/
        original.wav
        target.wav
        residual.wav
        prompt.txt
"""

import subprocess
import sys
import os
import torch
import torchaudio
from sam_audio import SAMAudio, SAMAudioProcessor



def extract_clip(url, start_time, output_path, duration=20):
    print(f"Downloading {duration}s from {start_time}...")

    cmd = [
        "yt-dlp",
        "-x",
        "--audio-format",
        "wav",
        "--download-sections",
        f"*{start_time}-{start_time}+{duration}",
        "-o",
        output_path,
        "--force-overwrite",
        "--no-playlist",
        url,
    ]
    result = subprocess.run(cmd, capture_output=True, text=True)

    if result.returncode != 0 or not os.path.exists(output_path):
        print("Section download failed. Downloading full audio then trimming...")

        temp_path = output_path + ".temp"
        cmd_dl = [
            "yt-dlp",
            "-x",
            "--audio-format",
            "wav",
            "-o",
            f"{temp_path}.%(ext)s",
            "--force-overwrite",
            "--no-playlist",
            url,
        ]
        subprocess.run(cmd_dl, check=True)

        # Find downloaded file
        temp_dir = os.path.dirname(temp_path)
        temp_name = os.path.basename(temp_path)
        temp_file = None
        for f in os.listdir(temp_dir):
            if f.startswith(os.path.basename(temp_path)):
                temp_file = os.path.join(temp_dir, f)
                break

        if not temp_file:
            print("Error: download failed")
            return False

        # Trim with ffmpeg
        cmd_trim = [
            "ffmpeg",
            "-y",
            "-i",
            temp_file,
            "-ss",
            str(start_time),
            "-t",
            str(duration),
            "-ar",
            "48000",
            "-ac",
            "1",
            output_path,
        ]
        subprocess.run(cmd_trim, check=True)
        os.remove(temp_file)
    else:
        # Convert to 48kHz mono for SAM Audio
        tmp = output_path + ".tmp.wav"
        cmd_convert = [
            "ffmpeg",
            "-y",
            "-i",
            output_path,
            "-ar",
            "48000",
            "-ac",
            "1",
            tmp,
        ]
        subprocess.run(cmd_convert, capture_output=True)
        if os.path.exists(tmp):
            os.replace(tmp, output_path)

    if os.path.exists(output_path):
        size = os.path.getsize(output_path) / 1024
        print(f"Saved: {output_path} ({size:.0f} KB)")
        return True

    print("Error: clip not created")
    return False



def load_model():
    print("Loading SAM Audio base model...")
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    model = (
        SAMAudio.from_pretrained(
            "facebook/sam-audio-base",
            visual_ranker=None,
            audio_ranker=None,
        )
        .to(device)
        .eval()
    )

    processor = SAMAudioProcessor.from_pretrained("facebook/sam-audio-base")
    print(f"Model loaded on {device}!")
    return model, processor, device



def separate(model, processor, device, audio_path, prompt, output_dir):
    print(f'Separating with prompt: "{prompt}"')

    inputs = processor(audios=[audio_path], descriptions=[prompt]).to(device)

    with torch.inference_mode():
        result = model.separate(inputs, predict_spans=True)

    sr = processor.audio_sampling_rate

    target_path = os.path.join(output_dir, "target.wav")
    residual_path = os.path.join(output_dir, "residual.wav")

    torchaudio.save(target_path, result.target[0].unsqueeze(0).cpu(), sr)
    torchaudio.save(residual_path, result.residual[0].unsqueeze(0).cpu(), sr)

    print(f"Target:   {target_path}")
    print(f"Residual: {residual_path}")



if __name__ == "__main__":
    if len(sys.argv) < 5:
        print(
            'Usage: python run_test.py <youtube_url> <start_time> <folder_name> "<prompt>"'
        )
        print(
            'Example: python run_test.py "https://youtube.com/watch?v=xxx" 01:23 sitar_tanpura "sitar"'
        )
        sys.exit(1)

    url = sys.argv[1]
    start = sys.argv[2]
    folder_name = sys.argv[3]
    prompt = sys.argv[4]

    # Create output folder with test prefix
    test_num = 1
    while os.path.exists(os.path.join("tests", f"test{test_num}_{folder_name}")):
        test_num += 1

    output_dir = os.path.join("tests", f"test{test_num}_{folder_name}")
    os.makedirs(output_dir, exist_ok=True)

    original_path = os.path.join(output_dir, "original.wav")

    # Extract clip
    ok = extract_clip(url, start, original_path)
    if not ok:
        sys.exit(1)

    # Load model and separate
    model, processor, device = load_model()
    separate(model, processor, device, original_path, prompt, output_dir)

    # Save prompt and metadata to txt file
    prompt_path = os.path.join(output_dir, "prompt.txt")
    with open(prompt_path, "w") as f:
        f.write(f"Prompt: {prompt}\n")
        f.write(f"YouTube URL: {url}\n")
        f.write(f"Start time: {start}\n")
        f.write(f"Duration: 10 seconds\n")

    print(f"\nDone! Check: {output_dir}/")
    print(f"  original.wav  — input clip")
    print(f"  target.wav    — isolated '{prompt}'")
    print(f"  residual.wav  — everything else")
    print(f"  prompt.txt    — input details")