"""Load configuration from environment / .env file."""

import os
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional


def _load_dotenv(path: Path) -> None:
    if not path.is_file():
        return
    for line in path.read_text().splitlines():
        line = line.strip()
        if not line or line.startswith("#") or "=" not in line:
            continue
        key, _, value = line.partition("=")
        os.environ.setdefault(key.strip(), value.strip())


@dataclass
class Config:
    # connection
    url: str = ""
    api_key: str = ""
    api_secret: str = ""
    room: str = "cameras"
    participant_prefix: str = "cam"
    # video
    width: int = 1280
    height: int = 720
    fps: int = 30
    min_fps: int = 5
    # audio
    audio_rate: int = 16000
    audio_channels: int = 1
    # speechbrain cleanup
    enhance_mode: str = "auto"  # auto | force | off
    enhance_model: str = "speechbrain/metricgan-plus-voicebank"
    enhance_chunk_s: float = 1.0
    enhance_hop_s: float = 0.2
    # SpeechBrain Delay-and-Sum (GCC-PHAT) on C920 stereo before enhance
    beamform_mode: str = "auto"  # auto | force | off
    torch_num_threads: int = 1
    beamform_tdoa_every: int = 5
    # misc
    publish_timeout_s: float = 60.0
    log_level: str = "INFO"
    cameras: list[str] = field(default_factory=lambda: ["all"])


def _parse_cameras(spec: str) -> list[str]:
    """Expand 'all' / '1,3,5' / '0-19' into a list of indices ('' = all)."""
    spec = (spec or "all").strip().lower()
    if spec in ("all", ""):
        return ["all"]
    out = []
    for part in spec.split(","):
        part = part.strip()
        if not part:
            continue
        if "-" in part:
            lo, hi = part.split("-", 1)
            out.extend(str(i) for i in range(int(lo), int(hi) + 1))
        else:
            out.append(part)
    return out


def load_config(env_path: Optional[Path] = None) -> Config:
    _load_dotenv(Path(env_path or Path(__file__).parent / ".env"))

    cfg = Config(
        url=os.environ.get("LIVEKIT_URL", ""),
        api_key=os.environ.get("LIVEKIT_API_KEY", ""),
        api_secret=os.environ.get("LIVEKIT_API_SECRET", ""),
        room=os.environ.get("LIVEKIT_ROOM", "cameras"),
        participant_prefix=os.environ.get("PARTICIPANT_PREFIX", "cam"),
        width=int(os.environ.get("VIDEO_WIDTH", "1280")),
        height=int(os.environ.get("VIDEO_HEIGHT", "720")),
        fps=int(os.environ.get("VIDEO_FPS", "30")),
        min_fps=int(os.environ.get("VIDEO_MIN_FPS", "5")),
        audio_rate=int(os.environ.get("AUDIO_SAMPLE_RATE", "16000")),
        audio_channels=int(os.environ.get("AUDIO_CHANNELS", "1")),
        enhance_mode=os.environ.get("ENHANCE_MODE", "auto").lower(),
        enhance_model=os.environ.get(
            "ENHANCE_MODEL", "speechbrain/metricgan-plus-voicebank"
        ),
        enhance_chunk_s=float(os.environ.get("ENHANCE_CHUNK_S", "1.0")),
        enhance_hop_s=float(os.environ.get("ENHANCE_HOP_S", "0.2")),
        beamform_mode=os.environ.get("BEAMFORM_MODE", "auto").lower(),
        torch_num_threads=int(os.environ.get("TORCH_NUM_THREADS", "1")),
        beamform_tdoa_every=int(os.environ.get("BEAMFORM_TDOA_EVERY", "5")),
        publish_timeout_s=float(os.environ.get("PUBLISH_TIMEOUT_S", "60")),
        log_level=os.environ.get("LOG_LEVEL", "INFO").upper(),
        cameras=_parse_cameras(os.environ.get("CAMERAS", "all")),
    )

    if cfg.enhance_mode not in ("auto", "force", "off"):
        raise ValueError(f"ENHANCE_MODE must be auto|force|off, got {cfg.enhance_mode}")
    if cfg.beamform_mode not in ("auto", "force", "off"):
        raise ValueError(
            f"BEAMFORM_MODE must be auto|force|off, got {cfg.beamform_mode}")
    if cfg.torch_num_threads < 1:
        raise ValueError("TORCH_NUM_THREADS must be >= 1")
    if cfg.beamform_tdoa_every < 1:
        raise ValueError("BEAMFORM_TDOA_EVERY must be >= 1")
    return cfg