"""Tests for the evidence-based price-performance functions (pure, no network)."""
from __future__ import annotations

from pricing_agent.cosmos.models import SalesDay
from pricing_agent.performance import (
    best_observed,
    fix_suggestion,
    loss_reason,
    monthly_performance,
    price_band_performance,
    recent_profit_per_unit,
    reconcile,
    root_cause_split,
    unprofitable_months,
)


def _day(date, price, units, profit, ad=0.0):
    return SalesDay(date=date, salePrice=price, unitOrders=units, profit=profit,
                    marketingCost=ad)


# Two months: June profitable at $20, July loss-making at $18.
HISTORY = (
    [_day(f"06/{d:02d}/2026", 20.0, 100, 500.0, 100.0) for d in range(1, 11)]
    + [_day(f"07/{d:02d}/2026", 18.0, 120, -240.0, 150.0) for d in range(1, 11)]
)


def test_monthly_performance_uses_actual_profit():
    m = monthly_performance(HISTORY)
    assert [x["month"] for x in m] == ["2026-06", "2026-07"]
    jun, jul = m
    assert jun["avg_price"] == 20.0 and jun["units_per_day"] == 100
    assert jun["actual_profit_per_day"] == 500.0
    assert jun["profit_per_unit"] == 5.0          # 500 / 100
    assert jul["actual_profit_per_day"] == -240.0
    assert jul["profit_per_unit"] == -2.0


def test_unprofitable_months_counts_losses():
    assert unprofitable_months(monthly_performance(HISTORY)) == 1


def test_price_bands_pool_by_price_not_month():
    bands = price_band_performance(HISTORY, band=0.50, min_days=5)
    prices = [b["price_band"] for b in bands]
    assert prices == [18.0, 20.0]
    hi = next(b for b in bands if b["price_band"] == 20.0)
    assert hi["days"] == 10 and hi["enough_data"] is True
    assert hi["actual_profit_per_day"] == 500.0


def test_best_observed_picks_max_actual_profit():
    bands = price_band_performance(HISTORY, band=0.50, min_days=5)
    best = best_observed(bands)
    assert best["price_band"] == 20.0            # not the cheaper, higher-volume $18
    assert best["actual_profit_per_day"] == 500.0


def test_best_observed_respects_min_days():
    # Only 3 days at $20 -> not enough sample; $18 has 10 days.
    hist = ([_day(f"06/{d:02d}/2026", 20.0, 100, 900.0) for d in range(1, 4)]
            + [_day(f"07/{d:02d}/2026", 18.0, 120, 100.0) for d in range(1, 11)])
    bands = price_band_performance(hist, band=0.50, min_days=7)
    best = best_observed(bands)
    assert best["price_band"] == 18.0            # the richer band is ineligible


def test_best_observed_none_when_no_sample():
    hist = [_day("06/01/2026", 20.0, 10, 5.0)]
    assert best_observed(price_band_performance(hist, min_days=7)) is None


def test_best_observed_can_still_be_loss_making():
    """A SKU unprofitable at every observed price still returns its least-bad band."""
    hist = ([_day(f"06/{d:02d}/2026", 20.0, 100, -100.0) for d in range(1, 11)]
            + [_day(f"07/{d:02d}/2026", 18.0, 120, -400.0) for d in range(1, 11)])
    best = best_observed(price_band_performance(hist, min_days=5))
    assert best["price_band"] == 20.0
    assert best["actual_profit_per_day"] < 0


def test_reconcile_reports_model_overstatement():
    # Model says $2.00/unit, reality is $0.50 -> model overstates by $1.50.
    assert reconcile(0.50, 2.00) == 1.50
    assert reconcile(None, 2.00) is None


def test_recent_profit_per_unit_is_units_weighted():
    m = monthly_performance(HISTORY)
    # last 1 month = July: -240/day over 10 days / (120*10 units) = -2.00
    assert recent_profit_per_unit(m, months=1) == -2.0


# ── Diagnosis: WHY it loses, and the fix ─────────────────────────────────
def test_reason_blames_ads_when_profitable_before_ads():
    # -2.50/u after 3.01/u ads => +0.51/u before ads -> ads are the cause.
    r = loss_reason(-2.50, 3.01, price=15.28)
    assert r.startswith("ADS:") and "$0.51" in r and "20% of price" in r


def test_reason_blames_cost_when_underwater_before_ads():
    # -4.56/u after 2.19/u ads => -2.37/u before ads -> below cost.
    r = loss_reason(-4.56, 2.19, price=14.19)
    assert r.startswith("BELOW COST:") and "$2.37" in r


def test_fix_for_ads_cause_is_an_ad_cut_no_price_change():
    f = fix_suggestion(-2.50, 3.01, price=15.28)
    assert "Cut ad/unit" in f and "$0.51" in f and "no price change" in f


def test_fix_for_below_cost_is_a_price_raise_sized_to_the_gap():
    # gap = 4.56/u; uplift = 4.56/0.83 = 5.49 -> target ~ 14.19 + 5.49 = 19.68
    f = fix_suggestion(-4.56, 2.19, price=14.19)
    assert "+$4.56/u" in f and "$19.6" in f


def test_healthy_sku_needs_no_fix():
    assert loss_reason(1.20, 0.30) == "healthy"
    assert fix_suggestion(1.20, 0.30) == "—"


def test_root_cause_split_uses_profit_before_ads():
    ads, cost = root_cause_split([
        {"profit_per_unit": -2.50, "ad_per_unit": 3.01},   # +0.51 before ads -> ads
        {"profit_per_unit": -4.56, "ad_per_unit": 2.19},   # -2.37 before ads -> cost
    ])
    assert len(ads) == 1 and len(cost) == 1