"""Behaviour of the Fabric First two-phase Optimiser: phase 1 optimises the
fabric measures (wall / roof / floor insulation + glazing) with the full
budget; if the truthful post-fabric score meets the Scenario target the
package is fabric-only. Otherwise phase 2 optimises the remaining measures on
top, where the starting point is the dwelling with the phase-1 fabric applied
and only the leftover budget is spendable. Mirrors the legacy engine's
``enforce_fabric_first`` (funding_optimiser.optimise_with_scenarios) on the
new truthful-re-score core (ADR-0016).
"""

from __future__ import annotations

from typing import Sequence

from datatypes.epc.domain.epc_property_data import (
    BuildingPartIdentifier,
    EpcPropertyData,
)
from domain.modelling.measure_type import MeasureType
from domain.modelling.optimisation.optimiser import (
    OptimisedPackage,
    ScoredOption,
    optimise_package_fabric_first,
)
from domain.modelling.recommendation import Cost, MeasureOption
from domain.modelling.scoring.package_scorer import Score
from domain.modelling.simulation import (
    BuildingPartOverlay,
    EpcSimulation,
    HeatingOverlay,
)
from tests.domain.sap10_calculator.worksheet._elmhurst_worksheet_000490 import (
    build_epc,
)

_WALL_OVERLAY = EpcSimulation(
    building_parts={
        BuildingPartIdentifier.MAIN: BuildingPartOverlay(wall_insulation_type=2)
    }
)
_ROOF_OVERLAY = EpcSimulation(
    building_parts={
        BuildingPartIdentifier.MAIN: BuildingPartOverlay(roof_insulation_thickness=300)
    }
)
_HEATING_OVERLAY = EpcSimulation(heating=HeatingOverlay(sap_main_heating_code=201))
_BOILER_OVERLAY = EpcSimulation(heating=HeatingOverlay(sap_main_heating_code=201))
_ASHP_OVERLAY = EpcSimulation(
    heating=HeatingOverlay(main_heating_index_number=13000)
)


def _scored(
    measure_type: str, *, gain: float, cost: float, overlay: EpcSimulation
) -> ScoredOption:
    return ScoredOption(
        option=MeasureOption(
            measure_type=MeasureType(measure_type),
            description=measure_type,
            overlay=overlay,
            cost=Cost(total=cost, contingency_rate=0.0),
        ),
        sap_gain=gain,
    )


class _StubScorer:
    """Deterministic stand-in for PackageScorer: the package SAP is a base plus
    a fixed true gain per overlay kind present (wall / roof / heating), so the
    two-phase selection is exercised without the calculator."""

    def __init__(
        self, *, base: float, wall: float, roof: float, heating: float
    ) -> None:
        self._base = base
        self._wall = wall
        self._roof = roof
        self._heating = heating

    def score(
        self, baseline: EpcPropertyData, simulations: Sequence[EpcSimulation]
    ) -> Score:
        sap = self._base
        for sim in simulations:
            if sim.heating is not None:
                sap += self._heating
            for part in sim.building_parts.values():
                if part.wall_insulation_type is not None:
                    sap += self._wall
                if part.roof_insulation_thickness is not None:
                    sap += self._roof
        return Score(
            sap_continuous=sap, co2_kg_per_yr=0.0, primary_energy_kwh_per_yr=0.0
        )


def _selected_types(package: OptimisedPackage) -> set[str]:
    return {scored.option.measure_type for scored in package.selected}


def test_fabric_reaching_the_target_excludes_non_fabric_measures() -> None:
    # Arrange — the ASHP dominates on both gain and SAP-per-£ (a plain
    # least-cost-to-target run would take it alone), but the wall by itself
    # reaches the target: fabric first means the package stops at the fabric.
    groups: list[list[ScoredOption]] = [
        [_scored("cavity_wall_insulation", gain=10.0, cost=1000.0, overlay=_WALL_OVERLAY)],
        [_scored("air_source_heat_pump", gain=30.0, cost=500.0, overlay=_HEATING_OVERLAY)],
    ]
    scorer = _StubScorer(base=60.0, wall=10.0, roof=0.0, heating=30.0)

    # Act — target 69 (gain 9 over the 60 baseline).
    package: OptimisedPackage = optimise_package_fabric_first(
        groups=groups,
        scorer=scorer,
        baseline_epc=build_epc(),
        budget=10000.0,
        target_sap=69.0,
    )

    # Assert — fabric only: the wall (true 70 ≥ 69); the heat pump is never
    # considered because the upgrade requirement is already met.
    assert _selected_types(package) == {"cavity_wall_insulation"}
    assert abs(package.score.sap_continuous - 70.0) <= 1e-9


def test_fabric_short_of_target_is_topped_up_with_non_fabric_measures() -> None:
    # Arrange — all the fabric there is (the wall, +5) cannot reach the target;
    # phase 2 must add the heat pump on top of the retained fabric.
    groups: list[list[ScoredOption]] = [
        [_scored("cavity_wall_insulation", gain=5.0, cost=1000.0, overlay=_WALL_OVERLAY)],
        [_scored("air_source_heat_pump", gain=20.0, cost=8000.0, overlay=_HEATING_OVERLAY)],
    ]
    scorer = _StubScorer(base=60.0, wall=5.0, roof=0.0, heating=20.0)

    # Act — target 75 (gain 15); fabric alone tops out at 65.
    package: OptimisedPackage = optimise_package_fabric_first(
        groups=groups,
        scorer=scorer,
        baseline_epc=build_epc(),
        budget=20000.0,
        target_sap=75.0,
    )

    # Assert — the fabric is kept and the heat pump lands on top of it; the
    # score is the truthful whole-package figure (60 + 5 + 20).
    assert _selected_types(package) == {
        "cavity_wall_insulation",
        "air_source_heat_pump",
    }
    assert abs(package.score.sap_continuous - 85.0) <= 1e-9


def test_fabric_spend_comes_out_of_the_shared_budget_before_phase_two() -> None:
    # Arrange — the £8000 heat pump alone would fit the £8500 budget and reach
    # the target, but fabric first commits the £1000 wall first, leaving £7500:
    # the heat pump no longer fits. Fabric priority wins over the target.
    groups: list[list[ScoredOption]] = [
        [_scored("cavity_wall_insulation", gain=5.0, cost=1000.0, overlay=_WALL_OVERLAY)],
        [_scored("air_source_heat_pump", gain=20.0, cost=8000.0, overlay=_HEATING_OVERLAY)],
    ]
    scorer = _StubScorer(base=60.0, wall=5.0, roof=0.0, heating=20.0)

    # Act — target 78 (gain 18).
    package: OptimisedPackage = optimise_package_fabric_first(
        groups=groups,
        scorer=scorer,
        baseline_epc=build_epc(),
        budget=8500.0,
        target_sap=78.0,
    )

    # Assert — wall only; the target is missed rather than the fabric skipped.
    assert _selected_types(package) == {"cavity_wall_insulation"}
    assert abs(package.score.sap_continuous - 65.0) <= 1e-9


class _InteractionScorer:
    """A stub whose boiler gain collapses once the wall is insulated (+10 raw,
    +3 post-fabric) while the heat pump's holds (+8 either way) — so a phase 2
    that keeps valuing candidates against the raw baseline picks the wrong
    heating system."""

    def score(
        self, baseline: EpcPropertyData, simulations: Sequence[EpcSimulation]
    ) -> Score:
        wall_present = any(
            part.wall_insulation_type is not None
            for sim in simulations
            for part in sim.building_parts.values()
        )
        sap = 60.0 + (5.0 if wall_present else 0.0)
        for sim in simulations:
            if sim.heating is None:
                continue
            if sim.heating.sap_main_heating_code is not None:
                sap += 3.0 if wall_present else 10.0
            if sim.heating.main_heating_index_number is not None:
                sap += 8.0
        return Score(
            sap_continuous=sap, co2_kg_per_yr=0.0, primary_energy_kwh_per_yr=0.0
        )


def test_phase_two_values_candidates_against_the_post_fabric_dwelling() -> None:
    # Arrange — one heating Recommendation, two Options. The boiler's role-1
    # signal (vs the raw baseline, +10) beats the heat pump's (+8) and it is
    # cheaper — but on the insulated dwelling the boiler is only worth +3.
    # Only a heat pump gets the fabric-applied dwelling to the target.
    groups: list[list[ScoredOption]] = [
        [_scored("cavity_wall_insulation", gain=5.0, cost=1000.0, overlay=_WALL_OVERLAY)],
        [
            _scored("gas_boiler_upgrade", gain=10.0, cost=2000.0, overlay=_BOILER_OVERLAY),
            _scored("air_source_heat_pump", gain=8.0, cost=6000.0, overlay=_ASHP_OVERLAY),
        ],
    ]
    scorer = _InteractionScorer()

    # Act — target 73: wall (65) + boiler-post-fabric (+3) = 68 misses; wall +
    # heat pump (+8) = 73 reaches. The heating group is consumed by whichever
    # option phase 2 warm-starts with, so the choice must be made on
    # post-fabric values, not raw-baseline signals.
    package: OptimisedPackage = optimise_package_fabric_first(
        groups=groups,
        scorer=scorer,
        baseline_epc=build_epc(),
        budget=20000.0,
        target_sap=73.0,
    )

    # Assert
    assert _selected_types(package) == {
        "cavity_wall_insulation",
        "air_source_heat_pump",
    }
    assert abs(package.score.sap_continuous - 73.0) <= 1e-9