package eeo

import "testing"

// point builds a FairnessPoint with the given impact ratio string for the pure alert logic tests.
func point(ratio string) FairnessPoint {
	return FairnessPoint{ImpactRatio: ratio, SelectionRate: "0.5000"}
}

func TestAlertWhenLatestBelowThreshold(t *testing.T) {
	// The series ends below 0.80, so it must alert regardless of the prior trajectory.
	fs := &FairnessSeries{Points: []FairnessPoint{point("0.9000"), point("0.7500")}}
	evaluateAlert(fs)
	if !fs.Alert {
		t.Fatalf("a latest ratio of 0.7500 must alert, reason %q", fs.AlertReason)
	}
}

func TestAlertOnDriftDropEvenAboveThreshold(t *testing.T) {
	// Both points are at or above 0.80, but the drop 0.95 to 0.89 is 0.06, more than the 0.05 drift
	// limit, so a best in world tool warns before the group ever crosses the threshold.
	fs := &FairnessSeries{Points: []FairnessPoint{point("0.9500"), point("0.8900")}}
	evaluateAlert(fs)
	if !fs.Alert {
		t.Fatalf("a 0.06 drop above the threshold must alert, reason %q", fs.AlertReason)
	}
}

func TestNoAlertOnSmallDriftAboveThreshold(t *testing.T) {
	// A 0.03 drop, both above 0.80, is within the drift limit and above the threshold, so no alert.
	fs := &FairnessSeries{Points: []FairnessPoint{point("0.9500"), point("0.9200")}}
	evaluateAlert(fs)
	if fs.Alert {
		t.Fatalf("a 0.03 drop above the threshold must not alert, reason %q", fs.AlertReason)
	}
}

func TestExactDriftDeltaDoesNotAlert(t *testing.T) {
	// A drop of exactly 0.05 is NOT more than the limit (the alert is strictly greater than driftDelta),
	// so the boundary case does not alert when both points stay above the threshold.
	fs := &FairnessSeries{Points: []FairnessPoint{point("0.9500"), point("0.9000")}}
	evaluateAlert(fs)
	if fs.Alert {
		t.Fatalf("a drop of exactly 0.05 must not alert, reason %q", fs.AlertReason)
	}
}

func TestNAPointsDoNotContributeToDrift(t *testing.T) {
	// An n/a ratio (the reference group or a cut with too few known groups) is skipped, so a series of
	// only n/a points never alerts and never panics.
	fs := &FairnessSeries{Points: []FairnessPoint{point("n/a"), point("n/a")}}
	evaluateAlert(fs)
	if fs.Alert {
		t.Fatalf("a series of only n/a points must not alert")
	}
}

func TestSingleBelowThresholdPointAlerts(t *testing.T) {
	// One snapshot, already below 0.80, alerts without a prior to compare against.
	fs := &FairnessSeries{Points: []FairnessPoint{point("0.6000")}}
	evaluateAlert(fs)
	if !fs.Alert {
		t.Fatalf("a single below threshold point must alert")
	}
}

func TestSnapshotRowFromImpactReferenceGroupHasNoRatio(t *testing.T) {
	// The reference group's report ratio is "n/a", so the snapshot row carries a nil impact ratio and is
	// not below threshold (a null impact_ratio in the column, never a fabricated 1.0).
	row := snapshotRowFromImpact("selection :: by sex", ImpactRow{Group: "male", Selected: 8, Considered: 10, Rate: "0.8000", ImpactRatio: "n/a"})
	if row.impactRatio != nil {
		t.Fatalf("a reference group must store a null impact ratio, got %v", row.impactRatio)
	}
	if row.belowThreshold {
		t.Fatalf("a reference group must not be below threshold")
	}
	if !row.selectionRate.Equal(decimalOrZero("0.8000")) {
		t.Fatalf("selection rate not preserved, got %s", row.selectionRate)
	}
}

func TestSnapshotRowFromImpactFlagsBelowThreshold(t *testing.T) {
	// A group with ratio 0.5000 is below 0.80, so below_threshold must be true and the ratio preserved.
	row := snapshotRowFromImpact("selection :: by sex", ImpactRow{Group: "female", Selected: 4, Considered: 10, Rate: "0.4000", ImpactRatio: "0.5000"})
	if row.impactRatio == nil || !row.impactRatio.Equal(decimalOrZero("0.5000")) {
		t.Fatalf("impact ratio not preserved, got %v", row.impactRatio)
	}
	if !row.belowThreshold {
		t.Fatalf("a 0.5000 ratio must be below threshold")
	}
}

func TestSnapshotRowsCoverBothStagesAndAllCuts(t *testing.T) {
	// One ImpactRow in every cut of both stages must produce six snapshot rows, so the time series covers
	// selection and screening across by sex, by race or ethnicity, and intersectional.
	one := []ImpactRow{{Group: "g", Selected: 1, Considered: 2, Rate: "0.5000", ImpactRatio: "1.0000"}}
	rep := Report{
		Selection: AnalysisSection{Stage: "selection (recorded slate decisions)", ByGender: one, ByRace: one, Intersectional: one},
		Screening: AnalysisSection{Stage: "screening (served scores)", ByGender: one, ByRace: one, Intersectional: one},
	}
	rows := snapshotRowsFromReport(rep)
	if len(rows) != 6 {
		t.Fatalf("expected 6 snapshot rows across both stages and three cuts, got %d", len(rows))
	}
	seen := map[string]bool{}
	for _, r := range rows {
		seen[r.decisionContext] = true
	}
	if len(seen) != 6 {
		t.Fatalf("expected 6 distinct decision_context labels, got %d: %v", len(seen), seen)
	}
}