"""End-to-end test that pass 2 can estimate a non-transport domain entirely
from domain-authored formulas, without touching the platform/actuator/
energy_storage physics model in Pipeline._estimate_physics."""

import pytest

from physcom.engine.constraint_resolver import ConstraintResolver
from physcom.engine.scorer import Scorer
from physcom.engine.pipeline import Pipeline
from physcom.models.domain import Domain, FreeVariable, MetricBound, MetricFormula
from physcom.models.entity import Dependency, Entity


def _build_archery_domain(repo):
    repo.add_entity(Entity(
        name="Recurve",
        dimension="bow",
        dependencies=[
            Dependency("physical", "draw_weight", "20", None, "range_min"),
            Dependency("physical", "draw_weight", "50", None, "range_max"),
        ],
    ))
    repo.add_entity(Entity(
        name="Carbon",
        dimension="arrow",
        dependencies=[
            Dependency("physical", "arrow_mass", "0.02", None, "provides"),
        ],
    ))
    return repo.add_domain(Domain(
        name="archery_test",
        metric_bounds=[
            MetricBound("drawback_force", weight=0.6, norm_min=0, norm_max=100),
            MetricBound("range", weight=0.4, norm_min=0, norm_max=300),
        ],
        free_variables=[
            FreeVariable(
                name="draw_weight_chosen",
                floor_formula='dep("draw_weight", "range_min")',
                ceiling_formula='dep("draw_weight", "range_max")',
                sort_order=0,
            ),
        ],
        metric_formulas=[
            MetricFormula(metric_name="drawback_force", formula="draw_weight_chosen * 2"),
            MetricFormula(
                metric_name="range",
                formula='draw_weight_chosen * 5 / dep("arrow_mass")',
            ),
        ],
    ))


def test_formula_domain_scores_without_platform_actuator_shape(repo):
    domain = _build_archery_domain(repo)
    resolver = ConstraintResolver()
    scorer = Scorer(domain)
    pipeline = Pipeline(repo, resolver, scorer)

    result = pipeline.run(
        domain, ["bow", "arrow"], score_threshold=0.01, passes=[1, 2, 3, 5],
    )

    assert result.total_generated == 1
    assert result.pass1_failed == 0
    assert result.pass2_estimated == 1
    assert result.pass3_above_threshold == 1

    combos = repo.list_combinations()
    assert len(combos) == 1
    combo = combos[0]
    scores = {
        s["metric_name"]: s["raw_value"]
        for s in repo.get_combination_scores(combo.id, domain.id)
    }
    # Both metrics increase monotonically with draw_weight_chosen and nothing
    # trades off against it, so the optimizer should push to the declared
    # ceiling (50) -- confirms _search_free_variables is actually searching,
    # not just evaluating at the floor.
    assert scores["drawback_force"] == pytest.approx(100.0, rel=0.02)


def test_formula_domain_zero_free_variables_direct_evaluation(repo):
    """A domain with metric_formulas but no free_variables should evaluate
    each formula once directly -- no search loop at all."""
    repo.add_entity(Entity(
        name="Recurve",
        dimension="bow",
        dependencies=[Dependency("physical", "draw_weight", "30", None, "provides")],
    ))
    repo.add_entity(Entity(name="Carbon", dimension="arrow"))
    domain = repo.add_domain(Domain(
        name="archery_direct_test",
        metric_bounds=[MetricBound("drawback_force", weight=1.0, norm_min=0, norm_max=100)],
        metric_formulas=[
            MetricFormula(metric_name="drawback_force", formula='dep("draw_weight") * 2'),
        ],
    ))
    resolver = ConstraintResolver()
    scorer = Scorer(domain)
    pipeline = Pipeline(repo, resolver, scorer)

    result = pipeline.run(
        domain, ["bow", "arrow"], score_threshold=0.01, passes=[1, 2, 3, 5],
    )

    assert result.pass2_estimated == 1
    combos = repo.list_combinations()
    scores = {
        s["metric_name"]: s["raw_value"]
        for s in repo.get_combination_scores(combos[0].id, domain.id)
    }
    assert scores["drawback_force"] == 60.0