"""MMMU importer — dev/validation rows, one config per subject.

MMMU ships 30 subjects, each with a 5-row ``dev`` and 30-row ``validation``
split (``docs/01`` §3.3). The subject is the Hugging Face config name, recorded
as ``source_config``. ``base_id`` is content-addressed (``docs/02`` §3.1) and
therefore excludes ``source_config``: two MMMU rows share an ID only when their
question, choices, and image family match — subject is metadata, not identity.
Only ``dev`` and ``validation`` are ingested; these are *limited* train
candidates that may enter silver training only after the full evaluation
registry is frozen and an explicit allowlist clears them of eval collisions
(enforced in P3).
"""

from __future__ import annotations

from collections.abc import Mapping
from typing import Any

from .base import (
    Choice,
    ImageResolver,
    ImageStore,
    IngestError,
    NormalizedItem,
    Policy,
    canonicalize_mc_answer,
    make_item,
    mc_choices,
)

NAME = "mmmu"
POLICY: Policy = "c2_train_candidate"
ALLOWED_SPLITS = ("dev", "validation")


def normalize(
    row: Mapping[str, Any],
    images: ImageStore,
    resolve: ImageResolver,
    *,
    revision: str,
    split: str,
    config: str = "default",
) -> NormalizedItem:
    """Normalize one MMMU native row to a :class:`NormalizedItem`."""
    if split not in ALLOWED_SPLITS:
        raise IngestError(f"mmmu: only splits {ALLOWED_SPLITS} may be ingested, got {split!r}")
    question = str(row["question"])
    option_texts = [str(o) for o in row["options"]]
    choices: list[Choice] = mc_choices(option_texts)
    answer_raw = str(row["answer"])
    answer_canonical = canonicalize_mc_answer(answer_raw, choices)
    image_ref = str(row["image"])
    rel, digest = images.store(resolve(image_ref))
    subject = str(row.get("subject", config))
    native_id = str(row.get("id", row.get("index")))
    return make_item(
        source=NAME,
        source_revision=revision,
        source_config=subject,  # MMMU config == subject
        source_split=split,
        source_native_id=native_id,
        question=question,
        choices=choices,
        answer_raw=answer_raw,
        answer_canonical=answer_canonical,
        answer_type="multiple_choice",
        image_paths=(rel,),
        image_sha256=(digest,),
        policy=POLICY,
        native_row=row,
        subject=subject,
    )