src/explicit_learning/sources/plotqa.py

"""PlotQA C1 source adapter (``docs/02`` §5).

PlotQA (``NiteshMethani/PlotQA`` @ ``e0f5c34``) ships scientific plots with
crowd-sourced QA instantiated from templates. The pinned GitHub revision carries
only docs/template definitions; the **data** (plot PNGs, ``annotations.json``,
``qa_pairs_v2.json``) is hosted on Google Drive, so the git revision is not a
data pin — SHA-256 of every downloaded archive is the integrity anchor and is
recorded in the ingest manifest.

Join key: ``image_index`` (qa pair ↔ plot annotation). Only rows whose template
is in the supported allowlist and whose referenced series/x label resolves
uniquely become candidates (§5.1); executor-tolerance answer reproduction is a
P3 gate. The template→PlotDSL mapping (§5.2) is dataset-level (per-template, not
per-example).

P1 scope: deterministic ingest + ``normalize`` to a schema-conforming
:class:`NormalizedItem`, a structural ``plot_world_v1`` builder, the released
``official_answer``, and the template allowlist + mapping table. The ≥50/template
reproduction unit gate is P3 (executor).

.. note:: The exact annotation/qa field shapes are confirmed against the real
   download at fetch time; the adapter is robust to the documented fields and
   unit-tested with fixtures that pin the schema it relies on.
"""

from __future__ import annotations

import json
from collections.abc import Iterator, Mapping
from pathlib import Path
from typing import Any

from ..dsl.ast import Program
from ..hashing import sha256_bytes
from ..ingest.base import (
    AnswerType,
    ImageStore,
    IngestError,
    NormalizedItem,
    Policy,
    infer_open_answer_type,
    make_item,
    mc_choices,
)
from .base import (
    AdapterError,
    CertificateTier,
    RawItem,
    World,
    extract_zip,
    gdrive_download,
    store_images,
)

SOURCE = "plotqa"
WORLD_SCHEMA = "plot_world_v1"
_VALID_SPLITS = frozenset({"train", "validation", "test"})
_MC_KEYS = ("A", "B", "C", "D", "E", "F", "G", "H")

# Supported template allowlist (§5.1). Templates not listed here are dropped
# wholesale — never patched per-row (§5.2). Extended as mappings are validated.
TEMPLATE_ALLOWLIST: frozenset[str] = frozenset(
    {"T_LOOKUP", "T_DIFFERENCE", "T_MAX_YEAR", "T_MIN_YEAR", "T_SUM"}
)

# Dataset-level template → PlotDSL mapping (§5.2). Per-template, not per-example.
# The ≥50-official-reproductions-per-template gate is a P3 executor test.
TEMPLATE_DSL_MAPPING: dict[str, str] = {
    "T_LOOKUP": "LOOKUP(SELECT_SERIES({series}), SELECT_X({x}))",
    "T_DIFFERENCE": "SUBTRACT(LOOKUP({series}, {x1}), LOOKUP({series}, {x2}))",
    "T_MAX_YEAR": "ARGMAX(POINTS(SELECT_SERIES({series})), value)",
    "T_MIN_YEAR": "ARGMIN(POINTS(SELECT_SERIES({series})), value)",
    "T_SUM": "SUM(POINTS(SELECT_SERIES({series})))",
}

# Google-Drive file ids per split (image archives). Annotation/qa-pair file ids
# are supplied via ``fetch``'s ``file_ids`` map; an unknown id is a hard error,
# never a silent skip. Image ids come from PlotQA_Dataset.md at the pinned rev.
_GDRIVE_IMAGE_IDS: dict[str, str] = {
    "train": "1AYuaPX-Lx7T0GZvnsPgN11Twq2FZbWXL",
    "validation": "1i74NRCEb-x44xqzAovuglex5d583qeiF",
    "test": "1D_WPUy91vOrFl6cJUkE55n3ZuB6Qrc4u",
}


class PlotQAAdapter:
    """C1 adapter for PlotQA."""

    def __init__(
        self,
        raw_dir: Path,
        store: ImageStore,
        *,
        revision: str,
        images_dir: Path | None = None,
    ) -> None:
        self.raw_dir = Path(raw_dir)
        self.store = store
        self.revision = revision
        # Plot images live under ``<raw_dir>/png/<index>.png`` by default; the
        # constructor lets a test pin an explicit images directory.
        self.images_dir = images_dir or (self.raw_dir / "png")

    # --- fetch -----------------------------------------------------------

    @classmethod
    def fetch(
        cls,
        raw_dir: Path,
        split: str,
        *,
        file_ids: Mapping[str, str],
        expected_sha256: Mapping[str, str] | None = None,
    ) -> Path:
        """Download one split's archives from Google Drive into ``raw_dir``.

        ``file_ids`` maps a local archive name (e.g. ``"train_images.zip"``,
        ``"train_annotations.json"``, ``"train_qa_pairs_v2.json"``) to a Drive
        file id. An id that is not supplied for a required artifact raises
        :class:`AdapterError` — never a silent skip. The image archive is
        extracted in place so ``_image_bytes`` can read ``png/<index>.png``.
        """
        if split not in _VALID_SPLITS:
            raise AdapterError(f"{SOURCE}: unsupported split {split!r}")
        raw_dir = Path(raw_dir)
        raw_dir.mkdir(parents=True, exist_ok=True)
        required = (f"{split}_images.zip", f"{split}_annotations.json", f"{split}_qa_pairs_v2.json")
        for archive in required:
            file_id = file_ids.get(archive)
            if not file_id:
                raise AdapterError(
                    f"{SOURCE}: missing Drive file id for {archive!r}; "
                    "supply it in resources/structured_sources.plotqa.file_ids"
                )
            dst = raw_dir / archive
            digest = (expected_sha256 or {}).get(archive)
            gdrive_download(file_id, dst, expected_sha256=digest)
            if archive.endswith(".zip"):
                extract_zip(dst, raw_dir)
        return raw_dir

    @classmethod
    def is_materialized(cls, raw_dir: Path, split: str) -> bool:
        """True when one split's annotation + qa files are already on disk."""
        raw_dir = Path(raw_dir)
        return (raw_dir / f"{split}_qa_pairs_v2.json").exists() and (
            raw_dir / f"{split}_annotations.json"
        ).exists()

    @classmethod
    def materialize(
        cls,
        raw_dir: Path,
        split: str,
        *,
        source_config: Mapping[str, Any],
        expected_sha256: Mapping[str, str] | None = None,
    ) -> Path:
        """Fetch a split from Drive using file ids from the resources config.

        The Drive file ids are not git-pinned, so they (and the per-archive
        sha256) must be supplied in ``source_config["file_ids"]``; their absence
        is a hard :class:`AdapterError`, never a silent skip.
        """
        file_ids = source_config.get("file_ids") or {}
        if not isinstance(file_ids, Mapping):
            raise AdapterError(f"{SOURCE}: file_ids must be a mapping in resources config")
        return cls.fetch(raw_dir, split, file_ids=file_ids, expected_sha256=expected_sha256)

    # --- iter ------------------------------------------------------------

    def _annotation_index(self) -> dict[int, dict[str, Any]]:
        path = self._first_split_file("annotations.json")
        records = json.loads(path.read_text(encoding="utf-8"))
        out: dict[int, dict[str, Any]] = {}
        for rec in records:
            idx = rec.get("image_index") if isinstance(rec, dict) else None
            if idx is None:
                continue
            out[int(idx)] = rec
        return out

    def _qa_pairs(self, split: str) -> list[dict[str, Any]]:
        path = self._first_split_file("qa_pairs_v2.json")
        data = json.loads(path.read_text(encoding="utf-8"))
        return [d for d in data if isinstance(d, dict)]

    def _first_split_file(self, suffix: str) -> Path:
        for prefix in ("train", "validation", "test"):
            candidate = self.raw_dir / f"{prefix}_{suffix}"
            if candidate.exists():
                return candidate
        raise AdapterError(f"{SOURCE}: missing {suffix} under {self.raw_dir}")

    def _image_bytes(self, image_index: int) -> bytes:
        for candidate in (
            self.images_dir / f"{image_index}.png",
            self.raw_dir / "png" / f"{image_index}.png",
        ):
            if candidate.exists():
                return candidate.read_bytes()
        raise AdapterError(f"{SOURCE}: plot image not found for image_index {image_index}")

    def iter_base_items(self, split: str) -> Iterator[RawItem]:
        if split not in _VALID_SPLITS:
            raise AdapterError(f"{SOURCE}: unsupported split {split!r}")
        annotations = self._annotation_index()
        qa_pairs = self._qa_pairs(split)
        # Deterministic order: (image_index, position in qa file).
        ordered = sorted(
            enumerate(qa_pairs),
            key=lambda pair: (
                int(pair[1].get("image_index", -1)) if isinstance(pair[1], dict) else -1,
                pair[0],
            ),
        )
        for qa_index, qa in ordered:
            if not isinstance(qa, dict):
                continue
            template = qa.get("template")
            if not isinstance(template, str) or template not in TEMPLATE_ALLOWLIST:
                continue  # out-of-allowlist template → whole-template skip (§5.2)
            image_index = qa.get("image_index")
            if image_index is None or int(image_index) not in annotations:
                continue  # referenced plot missing → not a candidate (§5.1)
            annotation = annotations[int(image_index)]
            if not _single_series_x_match(qa, annotation):
                continue  # referenced series/x not unique → not a candidate (§5.1)
            try:
                images = {"plot": self._image_bytes(int(image_index))}
            except AdapterError:
                continue  # image unavailable → drop, not a hard error at filter stage
            payload = {
                "image_index": int(image_index),
                "qa_index": qa_index,
                **qa,
                "annotation": annotation,
            }
            yield RawItem(
                source=SOURCE,
                split=split,
                source_revision=self.revision,
                native_id=f"{image_index}:{qa_index}",
                payload=payload,
                images=images,
            )

    # --- normalize -------------------------------------------------------

    def normalize(self, raw: RawItem) -> NormalizedItem:
        qa = raw.payload
        question = qa.get("question_string")
        if not isinstance(question, str) or not question.strip():
            raise IngestError(f"{SOURCE}/{raw.native_id}: question_string missing/empty")
        answer = qa.get("answer")
        if answer is None or (isinstance(answer, str) and not answer.strip()):
            raise IngestError(f"{SOURCE}/{raw.native_id}: answer missing/empty")
        if not raw.images:
            raise IngestError(f"{SOURCE}/{raw.native_id}: plot image missing")
        paths, shas = store_images(self.store, raw.images)

        choices_texts = qa.get("choices")
        answer_type: AnswerType
        if isinstance(choices_texts, list) and choices_texts:
            keys = list(_MC_KEYS[: len(choices_texts)])
            choices = mc_choices([str(c) for c in choices_texts], keys=keys)
            answer_type = "multiple_choice"
            answer_canonical = str(answer)
        else:
            choices = []
            answer_type = infer_open_answer_type(str(answer))
            answer_canonical = str(answer)

        annotation = qa.get("annotation") or {}
        policy: Policy = (
            "c1_train_candidate"
            if raw.split in ("train", "validation")
            else "c1_certified_eval_candidate"
        )
        extra = {
            "template": qa.get("template"),
            "type": qa.get("type"),
            "models": annotation.get("models"),
            "axes": {
                "x_axis": annotation.get("x_axis"),
                "y_axis": annotation.get("y_axis"),
                "legend": annotation.get("legend"),
                "title": annotation.get("title"),
            },
            # QA slot metadata consumed by the dataset-level program compiler
            # (§5.2). Carrying source-native slots is not per-example human
            # labeling; it is the template's structured input.
            "slots": {
                "series": qa.get("series") or qa.get("series_label"),
                "x": qa.get("x"),
                "x1": qa.get("x1"),
                "x2": qa.get("x2"),
            },
            "raw_payload_sha256": sha256_bytes(
                json.dumps(qa, sort_keys=True, separators=(",", ":")).encode("utf-8")
            ),
        }
        return make_item(
            source=SOURCE,
            source_revision=raw.source_revision,
            source_config="default",
            source_split=raw.split,
            source_native_id=raw.native_id,
            question=question,
            choices=choices,
            answer_raw=str(answer),
            answer_canonical=answer_canonical,
            answer_type=answer_type,
            image_paths=paths,
            image_sha256=shas,
            policy=policy,
            native_row=qa,
            extra_provenance=extra,
        )

    # --- world / answer / tier ------------------------------------------

    def build_world(self, item: NormalizedItem) -> World:
        prov = item.provenance
        models = prov.get("models") or []
        axes = prov.get("axes") or {}
        series = _series_from_models(models, axes)
        return {
            "world_schema": WORLD_SCHEMA,
            "plot_type": _infer_plot_type(models, axes),
            "series": series,
            "x_axis": axes.get("x_axis"),
            "y_axis": axes.get("y_axis"),
            "legend": axes.get("legend"),
            "title": axes.get("title"),
            "style_seed": 0,
            "node_visibility": {},
            "provenance": {
                "source": item.source,
                "source_native_id": item.source_native_id,
                "template": prov.get("template"),
            },
        }

    def get_or_compile_program(self, item: NormalizedItem) -> Program:
        """Compile the typed ``plotqa_dsl_v1`` program for ``item`` (P2).

        Builds the world, hands it to the dataset-level compiler, and returns a
        :class:`Program` (``compiled`` or ``unsupported``). No execution, no
        model calls — those are P3/P4.
        """
        from ..dsl.plotdsl import compile_plotqa

        return compile_plotqa(item, self.build_world(item))

    def official_answer(self, item: NormalizedItem) -> str:
        return str(item.answer_canonical)

    def source_certificate_tier(self, item: NormalizedItem) -> CertificateTier:
        return "C1_SOURCE_NATIVE"


# --- candidate-filter + world helpers -------------------------------------


def _single_series_x_match(qa: Mapping[str, Any], annotation: Mapping[str, Any]) -> bool:
    """§5.1: the referenced series/x label must resolve to exactly one in the plot.

    A best-effort structural check that the QA's referenced series label (if
    extractable from the template slots) matches exactly one model in the
    annotation. The full slot/template-metadata consistency check is completed
    with the executor (P3); here we reject only the clearly-ambiguous case.
    """
    models = annotation.get("models") or []
    if not isinstance(models, list) or not models:
        return False
    series = qa.get("series") or qa.get("series_label")
    if series is None:
        return True  # no series slot to check (e.g. aggregate templates)
    matches = [m for m in models if isinstance(m, dict) and m.get("label") == series]
    return len(matches) == 1


def _series_from_models(models: Any, axes: Mapping[str, Any]) -> list[dict[str, Any]]:
    if not isinstance(models, list):
        return []
    series: list[dict[str, Any]] = []
    for i, model in enumerate(models):
        if not isinstance(model, dict):
            continue
        points = [
            {"id": f"point:{i}:{j}", "x": str(p.get("x", "")), "y": str(p.get("y", ""))}
            for j, p in enumerate(model.get("points") or [])
            if isinstance(p, dict)
        ]
        series.append(
            {
                "id": f"series:{i}",
                "label": model.get("label") or model.get("name") or f"series_{i}",
                "points": points,
            }
        )
    return series


def _infer_plot_type(models: Any, axes: Mapping[str, Any]) -> str:
    legend = axes.get("legend")
    if isinstance(models, list) and len(models) > 1:
        return "multi_line" if legend else "bar"
    return "line"


__all__ = [
    "PlotQAAdapter",
    "SOURCE",
    "TEMPLATE_ALLOWLIST",
    "TEMPLATE_DSL_MAPPING",
    "WORLD_SCHEMA",
]