"""Phase 0 — the `match` contract test.

The engine's `match` block must (a) validate against the Pydantic model and (b) serialize
to EXACTLY the keys the frontend `scoutJobToMatchRole` reads (scout/lib/jobMatching.ts →
`JobMatch`). If either drifts, backend ⇄ frontend have silently diverged — this test fails.
"""
import pytest
from pydantic import ValidationError

from app.engine.schema import Growth, MatchDim, MatchResult

# The exact field sets the frontend reads. Keep in lockstep with `JobMatch` / `MatchDim`.
FRONTEND_MATCH_KEYS = {"score", "fit", "archetype", "one_line", "reason", "breakdown", "growth",
                       "coach_note", "proofReady", "factors"}
FRONTEND_DIM_KEYS = {"name", "score", "level", "note"}
FRONTEND_GROWTH_KEYS = {"text", "from", "to"}


def _golden() -> MatchResult:
    return MatchResult(
        score=88, fit="stretch", reason="Matches 5/7 skills · Senior fit",
        breakdown=[MatchDim(name="Product Management", score=92, level="Strong"),
                   MatchDim(name="SQL", score=48, level="Light")],
        growth=Growth(text="Add an SQL proof point", **{"from": 88, "to": 95}),
        proofReady=True, factors={"skill": 0.71, "experience": 0.9, "salary": None},
    )


def test_match_block_keys_align_with_frontend():
    d = _golden().as_dict()
    assert set(d) == FRONTEND_MATCH_KEYS, f"match keys drifted: {set(d) ^ FRONTEND_MATCH_KEYS}"
    assert set(d["breakdown"][0]) == FRONTEND_DIM_KEYS
    assert set(d["growth"]) == FRONTEND_GROWTH_KEYS
    # the `from` keyword must serialize under its alias, not `from_`
    assert d["growth"]["from"] == 88 and d["growth"]["to"] == 95


def test_constrained_enums_reject_bad_values():
    with pytest.raises(ValidationError):
        MatchResult(score=1, fit="maybe", reason="x")        # fit ∉ {fit, stretch}
    with pytest.raises(ValidationError):
        MatchDim(name="x", score=1, level="Great")           # level ∉ {Strong, Solid, Light}


def test_minimal_match_is_valid_and_floor_free_shaped():
    d = MatchResult(score=50, fit="fit", reason="ok").as_dict()
    assert d["growth"] is None and d["breakdown"] == [] and d["factors"] == {}
    # factors may carry None (an *absent* factor) — that's the coverage-aware contract, not a floor
    d2 = MatchResult(score=50, fit="fit", reason="ok", factors={"salary": None}).as_dict()
    assert d2["factors"]["salary"] is None