"""LALAL.AI API wrapper for audio stem separation."""

import os
import shutil
import time
from pathlib import Path
from typing import Optional

import requests

API_BASE = "https://www.lalal.ai/api/v1"
DATA_DIR = Path(__file__).parent.parent / "data"

# Stems we need for the pipeline
STEMS_TO_EXTRACT = ["vocals", "drum"]
# Map LALAL.AI track labels to our file naming convention
LABEL_TO_FILENAME = {"vocals": "vocals.wav", "drum": "drums.wav"}


def _get_api_key() -> str:
    key = os.environ.get("LALAL_KEY")
    if not key:
        raise RuntimeError(
            "LALAL_KEY environment variable not set. "
            "Set it locally or as a HuggingFace Space secret."
        )
    return key


def _headers(api_key: str) -> dict:
    return {"X-License-Key": api_key}


def _next_run_dir(song_dir: Path) -> Path:
    """Find the next available run directory (run_001, run_002, ...)."""
    existing = sorted(song_dir.glob("run_*"))
    next_num = 1
    for d in existing:
        try:
            num = int(d.name.split("_")[1])
            next_num = max(next_num, num + 1)
        except (IndexError, ValueError):
            continue
    return song_dir / f"run_{next_num:03d}"


def _upload(audio_path: Path, api_key: str) -> str:
    """Upload audio file to LALAL.AI. Returns source_id."""
    with open(audio_path, "rb") as f:
        resp = requests.post(
            f"{API_BASE}/upload/",
            headers={
                **_headers(api_key),
                "Content-Disposition": f'attachment; filename="{audio_path.name}"',
            },
            data=f,
        )
    resp.raise_for_status()
    data = resp.json()
    source_id = data["id"]
    print(f"  Uploaded {audio_path.name} → source_id={source_id} "
          f"(duration: {data['duration']:.1f}s)")
    return source_id


def _split_stem(source_id: str, stem: str, api_key: str) -> str:
    """Start a stem separation task. Returns task_id."""
    # Andromeda is best for vocals but doesn't support all stems — use auto for others
    splitter = "andromeda" if stem == "vocals" else None
    resp = requests.post(
        f"{API_BASE}/split/stem_separator/",
        headers=_headers(api_key),
        json={
            "source_id": source_id,
            "presets": {
                "stem": stem,
                "splitter": splitter,
                "dereverb_enabled": False,
                "encoder_format": "wav",
                "extraction_level": "deep_extraction",
            },
        },
    )
    resp.raise_for_status()
    data = resp.json()
    task_id = data["task_id"]
    print(f"  Split task started: stem={stem}, task_id={task_id}")
    return task_id


def _poll_tasks(task_ids: list[str], api_key: str, poll_interval: float = 5.0) -> dict:
    """Poll tasks until all complete. Returns {task_id: result_data}."""
    pending = set(task_ids)
    results = {}

    while pending:
        resp = requests.post(
            f"{API_BASE}/check/",
            headers=_headers(api_key),
            json={"task_ids": list(pending)},
        )
        resp.raise_for_status()
        data = resp.json().get("result", resp.json())

        for task_id, info in data.items():
            status = info.get("status")
            if status == "success":
                results[task_id] = info
                pending.discard(task_id)
                print(f"  Task {task_id}: complete")
            elif status == "progress":
                print(f"  Task {task_id}: {info.get('progress', 0)}%")
            elif status == "error":
                error = info.get("error", {})
                raise RuntimeError(
                    f"LALAL.AI task {task_id} failed: "
                    f"{error.get('detail', 'unknown error')} "
                    f"(code: {error.get('code')})"
                )
            elif status == "cancelled":
                raise RuntimeError(f"LALAL.AI task {task_id} was cancelled")
            elif status == "server_error":
                raise RuntimeError(
                    f"LALAL.AI server error for task {task_id}: "
                    f"{info.get('error', 'unknown')}"
                )

        if pending:
            time.sleep(poll_interval)

    return results


def _download_track(url: str, output_path: Path) -> None:
    """Download a track from LALAL.AI CDN."""
    resp = requests.get(url, stream=True)
    resp.raise_for_status()
    with open(output_path, "wb") as f:
        for chunk in resp.iter_content(chunk_size=8192):
            f.write(chunk)
    print(f"  Downloaded → {output_path.name} ({output_path.stat().st_size / 1024:.0f} KB)")


def _delete_source(source_id: str, api_key: str) -> None:
    """Delete uploaded source file from LALAL.AI servers."""
    try:
        requests.post(
            f"{API_BASE}/delete/",
            headers=_headers(api_key),
            json={"source_id": source_id},
        )
        print(f"  Cleaned up remote source {source_id}")
    except Exception:
        pass  # non-critical


def separate_stems(
    audio_path: str | Path,
    output_dir: Optional[str | Path] = None,
) -> dict[str, Path]:
    """Separate an audio file into vocals and drums using LALAL.AI.

    Creates a new run directory for each invocation so multiple runs
    on the same song don't overwrite each other.

    Args:
        audio_path: Path to the input audio file (mp3/wav) from input/.
        output_dir: Directory to save stems. If None, auto-creates
            data/<song>/run_NNN/stems/.

    Returns:
        Dict mapping stem names to their file paths.
        Keys: "drums", "vocals", "run_dir"
    """
    audio_path = Path(audio_path)
    song_name = audio_path.stem
    song_dir = DATA_DIR / song_name
    api_key = _get_api_key()

    if output_dir is None:
        run_dir = _next_run_dir(song_dir)
        output_dir = run_dir / "stems"
    else:
        output_dir = Path(output_dir)
        run_dir = output_dir.parent

    output_dir.mkdir(parents=True, exist_ok=True)

    # Copy original song into song directory (shared across runs)
    song_copy = song_dir / audio_path.name
    if not song_copy.exists():
        shutil.copy2(audio_path, song_copy)

    # 1. Upload
    print("Stem separation (LALAL.AI):")
    source_id = _upload(audio_path, api_key)

    # 2. Start split tasks for each stem
    task_to_stem = {}
    for stem in STEMS_TO_EXTRACT:
        task_id = _split_stem(source_id, stem, api_key)
        task_to_stem[task_id] = stem

    # 3. Poll until all tasks complete
    results = _poll_tasks(list(task_to_stem.keys()), api_key)

    # 4. Download the separated stem tracks
    stem_paths = {"run_dir": run_dir}
    for task_id, result_data in results.items():
        stem = task_to_stem[task_id]
        filename = LABEL_TO_FILENAME[stem]
        tracks = result_data.get("result", {}).get("tracks", [])

        # Find the "stem" track (not the "back"/inverse track)
        stem_track = next((t for t in tracks if t["type"] == "stem"), None)
        if stem_track is None:
            raise RuntimeError(f"No stem track found in result for {stem}")

        output_path = output_dir / filename
        _download_track(stem_track["url"], output_path)

        # Map to our naming: "drum" API stem → "drums" key
        key = "drums" if stem == "drum" else stem
        stem_paths[key] = output_path

    # 5. Cleanup remote files
    _delete_source(source_id, api_key)

    return stem_paths


if __name__ == "__main__":
    import sys

    if len(sys.argv) < 2:
        print("Usage: python -m src.stem_separator <audio_file>")
        sys.exit(1)

    result = separate_stems(sys.argv[1])
    print(f"Run directory: {result['run_dir']}")
    for name, path in result.items():
        if name != "run_dir":
            print(f"  {name}: {path}")