"""Loader for TRREB purchase data into fact_purchases."""

from sqlalchemy.orm import Session

from portfolio_app.toronto.models import DimTime, DimTRREBDistrict, FactPurchases
from portfolio_app.toronto.schemas import TRREBMonthlyRecord, TRREBMonthlyReport

from .base import get_session, upsert_by_key
from .dimensions import generate_date_key


def load_trreb_purchases(
    report: TRREBMonthlyReport,
    session: Session | None = None,
) -> int:
    """Load TRREB monthly report data into fact_purchases.

    Args:
        report: Validated TRREB monthly report containing records.
        session: Optional existing session.

    Returns:
        Number of records loaded.
    """

    def _load(sess: Session) -> int:
        # Get district key mapping
        districts = sess.query(DimTRREBDistrict).all()
        district_map = {d.district_code: d.district_key for d in districts}

        # Build date key from report date
        date_key = generate_date_key(report.report_date)

        # Verify time dimension exists
        time_dim = sess.query(DimTime).filter_by(date_key=date_key).first()
        if not time_dim:
            raise ValueError(
                f"Time dimension not found for date_key {date_key}. "
                "Load time dimension first."
            )

        records = []
        for record in report.records:
            district_key = district_map.get(record.area_code)
            if not district_key:
                # Skip records for unknown districts (e.g., aggregate rows)
                continue

            fact = FactPurchases(
                date_key=date_key,
                district_key=district_key,
                sales_count=record.sales,
                dollar_volume=record.dollar_volume,
                avg_price=record.avg_price,
                median_price=record.median_price,
                new_listings=record.new_listings,
                active_listings=record.active_listings,
                avg_dom=record.avg_dom,
                avg_sp_lp=record.avg_sp_lp,
            )
            records.append(fact)

        inserted, updated = upsert_by_key(
            sess, FactPurchases, records, ["date_key", "district_key"]
        )
        return inserted + updated

    if session:
        return _load(session)
    with get_session() as sess:
        return _load(sess)


def load_trreb_record(
    record: TRREBMonthlyRecord,
    session: Session | None = None,
) -> int:
    """Load a single TRREB record into fact_purchases.

    Args:
        record: Single validated TRREB monthly record.
        session: Optional existing session.

    Returns:
        Number of records loaded (0 or 1).
    """

    def _load(sess: Session) -> int:
        # Get district key
        district = (
            sess.query(DimTRREBDistrict)
            .filter_by(district_code=record.area_code)
            .first()
        )
        if not district:
            return 0

        date_key = generate_date_key(record.report_date)

        # Verify time dimension exists
        time_dim = sess.query(DimTime).filter_by(date_key=date_key).first()
        if not time_dim:
            raise ValueError(
                f"Time dimension not found for date_key {date_key}. "
                "Load time dimension first."
            )

        fact = FactPurchases(
            date_key=date_key,
            district_key=district.district_key,
            sales_count=record.sales,
            dollar_volume=record.dollar_volume,
            avg_price=record.avg_price,
            median_price=record.median_price,
            new_listings=record.new_listings,
            active_listings=record.active_listings,
            avg_dom=record.avg_dom,
            avg_sp_lp=record.avg_sp_lp,
        )

        inserted, updated = upsert_by_key(
            sess, FactPurchases, [fact], ["date_key", "district_key"]
        )
        return inserted + updated

    if session:
        return _load(session)
    with get_session() as sess:
        return _load(sess)