import json

from deg.extractbench import normalize_value, prepare, score_record

SCHEMA = {"name": "x", "description": "y", "schema_definition": {
    "type": "object", "properties": {
        "title": {"type": "string", "evaluation_config": "string_exact"},
        "year": {"anyOf": [{"type": "string"}, {"type": "integer"}, {"type": "null"}]},
        "kind": {"type": "string", "enum": ["a", "b"]},
        "authors": {"type": "array", "items": {"type": "string"}},
        "extra": {"type": "object", "additionalProperties": {"type": "string"}}}}}


def test_prepare_projects_gold_and_skips_image_only(tmp_path):
    d = tmp_path / "academic" / "research"
    (d / "pdf+gold").mkdir(parents=True)
    (d / "research-schema.json").write_text(json.dumps(SCHEMA))
    gold = {"title": "Deep Nets", "year": 1989, "kind": "a", "authors": ["Le Cun"],
            "extra": {"k": "v"}}
    words = " ".join(["word"] * 30)
    for name, text in (("doc1", f"Deep Nets (1989) by Le Cun. {words}"), ("scan", "")):
        (d / "pdf+gold" / f"{name}.pdf").write_bytes(b"%PDF")
        (d / "pdf+gold" / f"{name}.txt").write_text(text)
        (d / "pdf+gold" / f"{name}.gold.json").write_text(json.dumps(gold))
    out = tmp_path / "records.jsonl"
    report = prepare(tmp_path, out)
    assert report["documents"] == 1 and report["skipped"] == {"image_only": 1}
    info = report["schemas"]["research"]
    assert info["compiles_as_is"] is False and info["normalized"] == "ok"
    assert info["dropped"] == ["/extra"]
    row = json.loads(out.read_text())
    assert row["split"] == "eval" and row["uid"] == "extractbench:academic/research/doc1"
    assert "extra" not in row["value"] and row["value"]["year"] == 1989


def test_score_record_exact_and_normalized():
    schema = {"type": "object", "properties": {
        "title": {"type": "string"}, "n": {"type": "number"},
        "kind": {"enum": ["a", "b"]}, "tags": {"type": "array", "items": {"type": "string"}},
        "gone": {"type": ["string", "null"]}}}
    gold = {"title": "Deep  Nets.", "n": 3, "kind": "a", "tags": ["x", "y"], "gone": None}
    pred = {"title": "deep nets", "n": 3.0, "kind": "b", "tags": ["x"], "gone": None}
    c = score_record(schema, gold, pred)
    # title (normalized only), n (both), tags/0 (both), tags/1 (missing); kind is decide
    assert c == {"extract_n": 4, "exact": 2, "normalized": 3, "count_n": 1, "count_ok": 0}
    assert normalize_value("1,989") == normalize_value(1989) == 1989.0