import os
import torch
from pathlib import Path

BASE_DIR = Path(__file__).resolve().parent.parent
WEIGHTS_DIR = BASE_DIR / "weights"
WEIGHTS_DIR.mkdir(parents=True, exist_ok=True)


def _is_writable_dir(path: Path) -> bool:
    try:
        path.mkdir(parents=True, exist_ok=True)
        probe = path / ".write_probe"
        probe.write_text("ok", encoding="utf-8")
        probe.unlink(missing_ok=True)
        return True
    except OSError:
        return False


def resolve_log_dir() -> Path:
    env_dir = os.environ.get("AESTHETIC_LOG_DIR", "").strip()
    if env_dir:
        path = Path(env_dir).expanduser()
        path.mkdir(parents=True, exist_ok=True)
        return path
    hf_data = Path("/data/aesthetic_logs")
    if _is_writable_dir(hf_data):
        return hf_data
    fallback = BASE_DIR / "output"
    fallback.mkdir(parents=True, exist_ok=True)
    return fallback


LOG_DIR = resolve_log_dir()

# Device: MPS → CUDA → CPU (never hard-coded)
if torch.backends.mps.is_available():
    DEVICE = torch.device("mps")
elif torch.cuda.is_available():
    DEVICE = torch.device("cuda")
else:
    DEVICE = torch.device("cpu")

CLIP_MODEL_ID = "openai/clip-vit-large-patch14"
LAION_WEIGHTS_URL = (
    "https://github.com/LAION-AI/aesthetic-predictor/raw/main/sa_0_4_vit_l_14_linear.pth"
)
LAION_WEIGHTS_PATH = WEIGHTS_DIR / "sa_0_4_vit_l_14_linear.pth"

SHARE_LOCAL = os.environ.get("DV_SHARE", "0") == "1"
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "")
HF_TOKEN = os.environ.get("HF_TOKEN", os.environ.get("HUGGINGFACEHUB_API_TOKEN", ""))