"""End-to-end through-repos integration for First Run (ADR-0012, #1138).

Real PostgresUnitOfWork over an ephemeral DB: Ingestion writes the EPC, Baseline
reads it back *through the repo* (not in memory), and a re-run replaces rather
than duplicates. Stub Modelling. The source clients are faked (no IO)."""

from __future__ import annotations

import dataclasses
import json
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Optional

from sqlalchemy import Engine
from sqlmodel import Session, select

from datatypes.epc.domain.epc import Epc
from datatypes.epc.domain.epc_property_data import EpcPropertyData
from datatypes.epc.domain.mapper import EpcPropertyDataMapper
from domain.property_baseline.rebaseliner import StubRebaseliner
from domain.geospatial.coordinates import Coordinates
from infrastructure.postgres.property_baseline_performance_table import (
    PropertyBaselinePerformanceModel,
)
from infrastructure.postgres.epc_property_table import EpcPropertyModel
from infrastructure.postgres.property_table import PropertyRow
from orchestration.property_baseline_orchestrator import PropertyBaselineOrchestrator
from orchestration.ara_first_run_pipeline import AraFirstRunPipeline
from orchestration.ingestion_orchestrator import IngestionOrchestrator
from orchestration.modelling_orchestrator import ModellingOrchestrator
from repositories.property_baseline.property_baseline_postgres_repository import (
    PropertyBaselinePostgresRepository,
)
from repositories.geospatial.geospatial_repository import GeospatialRepository
from repositories.materials.materials_repository import MaterialsRepository
from repositories.postgres_unit_of_work import PostgresUnitOfWork
from repositories.scenario.scenario_repository import ScenarioRepository

_JSON_SAMPLES = Path(__file__).resolve().parents[2] / "backend/epc_api/json_samples"


@dataclass
class _FakeCommand:
    portfolio_id: int
    property_ids: list[int]
    scenario_ids: list[int]


class _FetcherReturning:
    def __init__(self, epc: EpcPropertyData) -> None:
        self._epc = epc

    def get_by_uprn(self, uprn: int) -> Optional[EpcPropertyData]:
        return self._epc


class _NoCoordinates(GeospatialRepository):
    def coordinates_for(self, uprn: int) -> Optional[Coordinates]:
        return None  # skip the solar leg — not under test here


class _UnusedSolarFetcher:
    def get_building_insights(
        self, longitude: float, latitude: float
    ) -> dict[str, Any]:  # pragma: no cover
        return {}


def _lodged_epc() -> EpcPropertyData:
    # A real, persistable EPC (so it round-trips through the EPC repo), with the
    # recorded-performance fields the sample leaves blank filled in so Baseline
    # can read its Lodged Performance.
    raw: dict[str, Any] = json.loads(
        (_JSON_SAMPLES / "RdSAP-Schema-21.0.0" / "epc.json").read_text()
    )
    epc = EpcPropertyDataMapper.from_api_response(raw)
    return dataclasses.replace(
        epc,
        energy_rating_current=72,
        current_energy_efficiency_band=Epc.C,
        co2_emissions_current=1.8,
        energy_consumption_current=180,
    )


def test_first_run_baselines_through_repos_and_is_idempotent_on_rerun(
    db_engine: Engine,
) -> None:
    # Arrange — a property row to ingest against, and the EPC its fetcher returns.
    with Session(db_engine) as session:
        session.add(
            PropertyRow(
                id=10,
                portfolio_id=1,
                postcode="A0 0AA",
                address="1 Some Street",
                uprn=12345,
            )
        )
        session.commit()

    def unit_of_work() -> PostgresUnitOfWork:
        return PostgresUnitOfWork(lambda: Session(db_engine))

    pipeline = AraFirstRunPipeline(
        ingestion=IngestionOrchestrator(
            unit_of_work=unit_of_work,
            epc_fetcher=_FetcherReturning(_lodged_epc()),
            geospatial_repo=_NoCoordinates(),
            solar_fetcher=_UnusedSolarFetcher(),
        ),
        baseline=PropertyBaselineOrchestrator(
            unit_of_work=unit_of_work,
            rebaseliner=StubRebaseliner(),
        ),
        modelling=ModellingOrchestrator(
            scenario_repo=ScenarioRepository(),
            materials_repo=MaterialsRepository(),
        ),
    )
    command = _FakeCommand(portfolio_id=1, property_ids=[10], scenario_ids=[7])

    # Act — First Run, then a re-run over the same batch.
    pipeline.run(command)
    pipeline.run(command)

    # Assert — Baseline read the EPC Ingestion persisted (through the repo, only
    # property_ids crossed the stage boundary), and the re-run replaced rather
    # than duplicated either row.
    with Session(db_engine) as session:
        baseline = PropertyBaselinePostgresRepository(session).get_for_property(10)
        epc_rows = session.exec(
            select(EpcPropertyModel).where(EpcPropertyModel.property_id == 10)
        ).all()
        baseline_rows = session.exec(
            select(PropertyBaselinePerformanceModel).where(
                PropertyBaselinePerformanceModel.property_id == 10
            )
        ).all()

    assert baseline is not None
    assert baseline.lodged.sap_score == 72
    assert baseline.space_heating_kwh == 13120.0
    assert len(epc_rows) == 1
    assert len(baseline_rows) == 1