"""Grade an answer for a row of the flat dataset, with no Docker and no LLM.

    from grade import grade
    reward, details = grade(row, answer_dict)

`row` is a dataset row (needs `family` and `instance`); `answer_dict` is the parsed JSON answer.
Construct tasks return 1.0 for any valid witness, else 0.0. Optimize tasks return 0 if invalid,
otherwise 0.1 + 0.9 * progress from a trivial baseline to the best known value (see graders/run.py).
Requires python>=3.10 with numpy, scipy, sympy, networkx.
"""
import importlib.util
import json
import os
import sys
from pathlib import Path

_HERE = Path(os.path.abspath(__file__)).parent / "graders"  # abspath, not resolve(): HF cache files are symlinks
_cache = {}


def _load(name, path):
    spec = importlib.util.spec_from_file_location(name, path)
    mod = importlib.util.module_from_spec(spec)
    sys.modules[name] = mod
    spec.loader.exec_module(mod)
    return mod


def _check_fn(family):
    if family not in _cache:
        if "run" not in sys.modules:
            _load("run", _HERE / "run.py")
        _cache[family] = _load(f"check_{family}", _HERE / family / "check.py").check
    return _cache[family]


def grade(row, answer):
    inst = row["instance"]
    if isinstance(inst, str):
        inst = json.loads(inst)
    run = sys.modules.get("run") or _load("run", _HERE / "run.py")
    return run.grade(inst, answer, _check_fn(row["family"]))