package sampling_test

import (
	"database/sql"
	"fmt"
	"math/rand"
	"testing"
	"time"

	"qbank/internal/models"
	"qbank/internal/sampling"
)

func TestSelectWeighted_Distribution(t *testing.T) {
	// Fixed reference time so weights don't drift with wall clock.
	now := time.Date(2026, 1, 1, 0, 0, 0, 0, time.UTC)
	recentSeen := sql.NullTime{Time: now, Valid: true}

	// Build a pool of 100 candidates:
	//   10 mastered  (seen=10, correct=10) → base=max(0.15, 1/12)=0.15, recency=1.0, w=0.15
	//   10 weak      (seen=10, correct=1)  → base=max(0.15, 10/12)≈0.833, recency=1.0, w≈0.833
	//   10 unseen    (no stat row)         → w=1.0 (UnseenBaseWeight*RecencyMaxMult)
	//   70 average   (seen=5,  correct=3)  → base≈(2+1)/(5+2)≈0.429, recency=1.0, w≈0.429
	type entry struct {
		id   string
		stat *models.UserQuestionStat
	}

	var entries []entry
	statWith := func(seen, correct int) *models.UserQuestionStat {
		return &models.UserQuestionStat{TimesSeen: seen, TimesCorrect: correct, LastSeenAt: recentSeen}
	}

	for i := 0; i < 10; i++ {
		entries = append(entries, entry{fmt.Sprintf("mastered-%d", i), statWith(10, 10)})
	}
	for i := 0; i < 10; i++ {
		entries = append(entries, entry{fmt.Sprintf("weak-%d", i), statWith(10, 1)})
	}
	for i := 0; i < 10; i++ {
		entries = append(entries, entry{fmt.Sprintf("unseen-%d", i), nil})
	}
	for i := 0; i < 70; i++ {
		entries = append(entries, entry{fmt.Sprintf("avg-%d", i), statWith(5, 3)})
	}

	// Build candidate list.
	candidates := make([]sampling.Candidate, len(entries))
	for i, e := range entries {
		candidates[i] = sampling.Candidate{
			ID:     e.id,
			Weight: sampling.ComputeWeight(e.stat, now),
		}
	}

	// Sample n=1 from the pool 10,000 times with a seeded RNG.
	rng := rand.New(rand.NewSource(42))
	counts := make(map[string]int, len(candidates))
	const runs = 10_000
	for range runs {
		sel := sampling.SelectWeighted(candidates, 1, rng)
		counts[sel[0].ID]++
	}

	// Compute group averages.
	masteredTotal, weakTotal := 0, 0
	for i := 0; i < 10; i++ {
		masteredTotal += counts[fmt.Sprintf("mastered-%d", i)]
		weakTotal += counts[fmt.Sprintf("weak-%d", i)]
	}
	masteredAvg := float64(masteredTotal) / 10.0
	weakAvg := float64(weakTotal) / 10.0

	t.Logf("mastered avg %.1f, weak avg %.1f, ratio %.2f", masteredAvg, weakAvg, weakAvg/masteredAvg)

	// Weak questions must appear >3× more often than mastered ones.
	if weakAvg < masteredAvg*3 {
		t.Errorf("want weakAvg > masteredAvg*3, got weakAvg=%.1f masteredAvg=%.1f", weakAvg, masteredAvg)
	}

	// Mastered questions must still appear (floor weight working).
	if masteredTotal < 50 {
		t.Errorf("want masteredTotal >= 50 (floor weight), got %d", masteredTotal)
	}
}

func TestComputeWeight_Unseen(t *testing.T) {
	w := sampling.ComputeWeight(nil, time.Now())
	if w != sampling.UnseenBaseWeight*sampling.RecencyMaxMult {
		t.Errorf("unseen weight: got %v, want %v", w, sampling.UnseenBaseWeight*sampling.RecencyMaxMult)
	}
}

func TestComputeWeight_FloorEnforced(t *testing.T) {
	now := time.Date(2026, 1, 1, 0, 0, 0, 0, time.UTC)
	stat := &models.UserQuestionStat{
		TimesSeen:    100,
		TimesCorrect: 100,
		LastSeenAt:   sql.NullTime{Time: now, Valid: true},
	}
	w := sampling.ComputeWeight(stat, now)
	if w < sampling.FloorWeight {
		t.Errorf("weight %v below FloorWeight %v", w, sampling.FloorWeight)
	}
}

func TestSelectWeighted_AllReturned_WhenNGeLen(t *testing.T) {
	rng := rand.New(rand.NewSource(1))
	cands := []sampling.Candidate{{ID: "a", Weight: 1}, {ID: "b", Weight: 2}}
	got := sampling.SelectWeighted(cands, 10, rng)
	if len(got) != 2 {
		t.Errorf("want 2, got %d", len(got))
	}
}