import pytest

from rag.fusion import reciprocal_rank_fusion
from rag.models import RetrievalDocument, SearchHit, SourceRef


SOURCE = SourceRef(physical_page=1, precision="page")


def _hit(doc_id: str, score: float = 1.0) -> SearchHit:
    return SearchHit(
        RetrievalDocument(
            doc_id=doc_id,
            drug_id="drug",
            kind="prose",
            text=doc_id,
            section_key="section",
            source_refs=(SOURCE,),
        ),
        score,
    )


def test_rrf_promotes_candidate_supported_by_dense_and_lexical_rankings() -> None:
    fused = reciprocal_rank_fusion(
        [[_hit("dense-only"), _hit("shared")], [_hit("shared"), _hit("lexical-only")]],
        rank_constant=60,
    )
    assert [hit.document.doc_id for hit in fused] == [
        "shared", "dense-only", "lexical-only",
    ]


def test_rrf_deduplicates_a_document_within_one_ranking() -> None:
    fused = reciprocal_rank_fusion(
        [[_hit("duplicate"), _hit("duplicate")], [_hit("other")]],
        rank_constant=10,
    )
    duplicate = next(hit for hit in fused if hit.document.doc_id == "duplicate")
    assert duplicate.score == pytest.approx(1 / 11)


def test_rrf_limit_and_ties_are_deterministic() -> None:
    fused = reciprocal_rank_fusion(
        [[_hit("first")], [_hit("second")]], rank_constant=60, limit=1,
    )
    assert [hit.document.doc_id for hit in fused] == ["first"]


@pytest.mark.parametrize(
    ("kwargs", "message"),
    [({"rank_constant": 0}, "rank_constant"), ({"limit": -1}, "limit")],
)
def test_rrf_rejects_invalid_configuration(kwargs, message: str) -> None:
    with pytest.raises(ValueError, match=message):
        reciprocal_rank_fusion([], **kwargs)