import pandas as pd
import numpy as np
from pathlib import Path
import time

from backend.export.property_scenarios.main import process_export
from backend.export.property_scenarios.input_schema import ExportRequest
from backend.app.db.models.portfolio import PropertyModel, Epc, Portfolio, PortfolioStatus, PortfolioGoal, \
    PropertyCreationStatus, PropertyDetailsEpcModel
from backend.app.db.models.recommendations import PlanModel, Recommendation, PlanRecommendations
from utils.logger import setup_logger

FIXTURE_PATH = Path("backend/export/tests/fixtures")
logger = setup_logger()


def load_csv(name: str) -> pd.DataFrame:
    df = pd.read_csv(FIXTURE_PATH / name)
    df = df.replace({np.nan: None})
    return df


def test_default_export_integration(db_session):
    # ----------------------------------------
    # 1) Load csvs
    # ----------------------------------------
    t0 = time.perf_counter()
    portfolio_df = load_csv("portfolio_569.csv")
    properties_df = load_csv("properties_569.csv")
    property_details_epc_df = load_csv("property_details_epc_569.csv")
    plans_df = load_csv("plans_569.csv")
    plan_recs_df = load_csv("plan_recs_569.csv")
    recommendations_df = load_csv("recommendations_569.csv")

    # Shrink down recommendations_df to speed up the data load. For this test, we only need
    # default recommendations so let's focus on those. We filter on where default is true
    recommendations_df = recommendations_df[
        recommendations_df["default"]
    ]
    valid_rec_ids = recommendations_df["id"].unique()

    plan_recs_df = plan_recs_df[
        plan_recs_df["recommendation_id"].isin(valid_rec_ids)
    ]

    logger.info(
        "Loaded CSVs in %.2f seconds | properties=%s plans=%s recs=%s",
        time.perf_counter() - t0,
        len(properties_df),
        len(plans_df),
        len(recommendations_df),
    )

    logger.info("Starting database load")
    db_load_t0 = time.perf_counter()

    # ----------------------------------------
    # 2) Insert test portfolio
    # ----------------------------------------

    portfolios = []
    for row in portfolio_df.itertuples(index=False):
        portfolios.append(
            Portfolio(
                id=row.id,
                name=row.name,
                status=PortfolioStatus[row.status.split(".")[-1]],
                goal=PortfolioGoal[row.goal.split(".")[-1]] if row.goal else None,
            )
        )

    db_session.bulk_save_objects(portfolios)
    db_session.flush()
    # ----------------------------------------
    # 3) Insert test property
    # ----------------------------------------

    properties = []

    for row in properties_df.itertuples(index=False):
        row_dict = row._asdict()

        row_dict["uprn"] = int(row_dict["uprn"]) if row_dict.get("uprn") else None
        row_dict["building_reference_number"] = (
            int(row_dict["building_reference_number"])
            if row_dict.get("building_reference_number")
            else None
        )

        prop = PropertyModel(**{
            col: row_dict[col]
            for col in PropertyModel.__table__.columns.keys()
            if col in row_dict
        })

        prop.creation_status = PropertyCreationStatus[
            row_dict["creation_status"].split(".")[-1]
        ]
        prop.status = PortfolioStatus[row_dict["status"].split(".")[-1]]

        if row_dict.get("current_epc_rating"):
            prop.current_epc_rating = Epc[
                row_dict["current_epc_rating"].split(".")[-1]
            ]

        properties.append(prop)

    db_session.bulk_save_objects(properties)
    db_session.flush()

    # ----------------------------------------
    # 4) Insert property details - EPC
    # ----------------------------------------

    property_lookup = {
        prop.uprn: prop
        for prop in db_session.query(PropertyModel).all()
    }

    epc_rows = []

    for row in property_details_epc_df.itertuples(index=False):
        row_dict = row._asdict()

        uprn = int(row_dict["uprn"]) if row_dict.get("uprn") else None
        property_obj = property_lookup.get(uprn)

        if not property_obj:
            continue  # skip if property not found

        # Build only fields that exist on the model
        epc_data = {
            col.name: row_dict[col.name]
            for col in PropertyDetailsEpcModel.__table__.columns
            if col.name in row_dict and col.name not in ["id", "property_id", "portfolio_id"]
        }

        epc = PropertyDetailsEpcModel(
            property_id=property_obj.id,
            portfolio_id=property_obj.portfolio_id,
            **epc_data,
        )

        epc_rows.append(epc)

    db_session.bulk_save_objects(epc_rows)
    db_session.flush()

    # ----------------------------------------
    # 4) Insert default plan
    # ----------------------------------------

    plans = []

    for row in plans_df.itertuples(index=False):
        row_dict = row._asdict()

        if row_dict.get("post_epc_rating"):
            row_dict["post_epc_rating"] = Epc[
                row_dict["post_epc_rating"].split(".")[-1]
            ]

        row_dict["scenario_id"] = None

        plan = PlanModel(**{
            col: row_dict[col]
            for col in PlanModel.__table__.columns.keys()
            if col in row_dict
        })

        plans.append(plan)

    db_session.bulk_save_objects(plans)
    db_session.flush()

    # ----------------------------------------
    # 5) Insert recommendation
    # ----------------------------------------

    recs = [
        Recommendation(**{
            col: row[col]
            for col in Recommendation.__table__.columns.keys()
            if col in row
        })
        for _, row in recommendations_df.iterrows()
    ]

    db_session.bulk_save_objects(recs)
    db_session.flush()

    # ----------------------------------------
    # 6) Insert PlanRecommendations
    # ----------------------------------------
    links = [
        PlanRecommendations(
            plan_id=row.plan_id,
            recommendation_id=row.recommendation_id,
        )
        for row in plan_recs_df.itertuples(index=False)
    ]

    db_session.bulk_save_objects(links)
    db_session.commit()
    logger.info("Inserted all data in %.2f seconds", time.perf_counter() - db_load_t0)

    # ----------------------------------------
    # 6) Build payload
    # ----------------------------------------

    body_dict = {
        "task_id": "test",
        "subtask_id": "test",
        "portfolio_id": 569,
        "scenario_ids": [],
        "default_plans_only": True,
    }

    payload = ExportRequest.model_validate(body_dict)

    # ----------------------------------------
    # 7) Call process_export
    # ----------------------------------------

    logger.info(
        "Recommendation count in DB: %s",
        db_session.query(Recommendation).count()
    )

    logger.info(
        "Default + not installed count: %s",
        db_session.query(Recommendation)
        .filter(
            Recommendation.default.is_(True),
            Recommendation.already_installed.is_(False)
        )
        .count()
    )

    logger.info("Starting process_export")
    process_t0 = time.perf_counter()

    result = process_export(payload, session=db_session)

    logger.info("process_export finished in %.2f seconds", time.perf_counter() - process_t0)

    # ----------------------------------------
    # 8) Assertions
    # ----------------------------------------

    assert "default_plans" in result

    df = result["default_plans"]

    assert not df.empty

    # This test was generated on a real portfolio and so we check the things we expect to do

    # 1) All packages are "compliant", where in this case, the properties should get to EPC C

    failed = df[df["predicted_post_works_sap"] < 69]
    failed_property_types = failed["property_type"].value_counts().to_dict()
    assert failed_property_types["Flat"] == 113
    assert failed_property_types["House"] == 8
    assert failed_property_types["Bungalow"] == 4
    assert failed_property_types["Maisonette"] == 1
    # Check the houses

    assert failed.shape[0]

    # Errors for me:
    # - should get to EPC C: https://ara.domna.homes/portfolio/569/building-passport/661051/plans
    # - Why doesn't this get to a C, under the plan?:
    # https://ara.domna.homes/portfolio/569/building-passport/660447/plans/1603913