"""EvalHarness: a plugin-based LLM/agent evaluation harness (data layer first)."""

from .data import (
    ChatMessage,
    Dataset,
    DatasetSpec,
    FieldSpec,
    Sample,
    SandboxSpec,
    ToolInfo,
    get_dataset,
    list_datasets,
    register_dataset,
)

__version__ = '0.1.0'


async def arun(bench, model, **kwargs):
    """ASYNC entry: for callers already inside an event loop.

        import evalharness
        rep = await evalharness.arun('gsm8k', 'openai/http://...?m', limit=200)
    """
    return await _run_dispatch(bench, model, **kwargs)


def run(bench, model, **kwargs):
    """SYNC entry (primary API, mirroring inspect_ai.run / lm_eval).

        import evalharness
        rep = evalharness.run('gsm8k', 'openai/http://...?m', limit=200)

    Creates its own event loop; safe to call from scripts/notebooks.
    """
    import asyncio

    try:  # already inside a loop (notebook)? run in a thread
        asyncio.get_running_loop()
        import concurrent.futures

        with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
            return pool.submit(asyncio.run, _run_dispatch(bench, model, **kwargs)).result()
    except RuntimeError:
        return asyncio.run(_run_dispatch(bench, model, **kwargs))


async def _run_dispatch(bench, model, **kwargs):
    from .data import Dataset as _Dataset, get_dataset as _gd
    from .model import run_eval as _run_eval

    if isinstance(bench, str):
        ds = _gd(bench, subset=kwargs.pop('subset', None))
    elif isinstance(bench, (_Dataset, list)):
        ds = bench
    else:
        raise TypeError(f'bench must be str/Dataset/list, got {type(bench)}')
    return await _run_eval(ds, model, **kwargs)


__all__ = [
    'Dataset', 'DatasetSpec', 'FieldSpec', 'Sample', 'ChatMessage', 'SandboxSpec', 'ToolInfo',
    'get_dataset', 'list_datasets', 'register_dataset', 'run', 'arun',
]