function findPublicTrainingShare(state, shareToken) {
  if (!shareToken || shareToken.length < 24) return null;
  const shares = Array.isArray(state?.training?.shares) ? state.training.shares : [];
  return shares.find((share) => share?.enabled !== false && String(share?.token || "") === shareToken) || null;
}

function hasPublicTrainingShare(state, shareToken) {
  return Boolean(findPublicTrainingShare(state, shareToken));
}

function buildPublicTrainingProfile(state, shareToken, updatedAt, sinceTimestamp = 0, windowMs = 0) {
  const share = findPublicTrainingShare(state, shareToken);
  if (!share) return null;
  const driverId = String(share.driverId || "");
  const drivers = Array.isArray(state?.drivers) ? state.drivers : [];
  const driver = drivers.find((item) => String(item?.id || "") === driverId);
  if (!driver) return null;

  const classes = Array.isArray(state?.classes) ? state.classes : [];
  const driverClass = classes.find((item) => String(item?.id || "") === String(driver.classId || ""));
  const ledger = Array.isArray(state?.training?.ledger) ? state.training.ledger : [];
  const driverLaps = ledger
    .filter((entry) => String(entry?.driverId || "") === driverId && Number(entry?.lapMs || 0) > 500)
    .map((entry) => ({
      timestamp: Math.max(0, Number(entry.timestamp || 0) || 0),
      lapMs: Math.max(0, Math.round(Number(entry.lapMs || 0) || 0)),
      sessionId: String(entry.sessionId || ""),
    }))
    .sort((left, right) => left.timestamp - right.timestamp);
  const stats = buildPublicTrainingStats(driverLaps);
  const goals = (Array.isArray(state?.training?.goals) ? state.training.goals : [])
    .filter((goal) => String(goal?.driverId || "") === driverId)
    .map((goal) => buildPublicTrainingGoal(goal, stats.totalLaps));
  const rankedTotals = getRankedTrainingTotals(ledger, new Set(drivers.map((item) => String(item?.id || "")).filter(Boolean)));
  const rankIndex = rankedTotals.findIndex(([id]) => id === driverId);
  const settings = state?.settings && typeof state.settings === "object" ? state.settings : {};
  const currentSession = sinceTimestamp
    ? buildPublicTrainingWindowStats(driverLaps.filter((lap) => lap.timestamp >= sinceTimestamp && (!windowMs || lap.timestamp <= sinceTimestamp + windowMs)))
    : null;

  return {
    updatedAt: String(updatedAt || ""),
    profile: {
      name: String(driver.name || "Driver").trim(),
      brand: String(driver.brand || "").trim(),
      className: String(driverClass?.name || "").trim(),
      clubRank: rankIndex >= 0 ? rankIndex + 1 : 0,
      rankedDrivers: rankedTotals.length,
    },
    branding: {
      clubName: String(settings.clubName || "JMK RB RaceController").trim(),
      clubTagline: String(settings.clubTagline || "RC Timing System").trim(),
      logoDataUrl: normalizePublicLogoDataUrl(settings.logoDataUrl),
    },
    stats,
    nextMilestone: getPublicTrainingMilestone(stats.totalLaps),
    recommendation: getPublicTrainingRecommendation(stats),
    goals,
    currentSession,
  };
}

function normalizePublicTrainingSince(value, startedAgoValue) {
  const now = Date.now();
  if (startedAgoValue !== undefined) {
    const startedAgoMs = Math.round(Number(startedAgoValue));
    if (Number.isFinite(startedAgoMs) && startedAgoMs >= 0 && startedAgoMs <= 24 * 60 * 60 * 1000) {
      return now - startedAgoMs;
    }
  }
  const timestamp = Math.round(Number(value || 0) || 0);
  return timestamp >= now - 24 * 60 * 60 * 1000 && timestamp <= now + 60 * 1000 ? timestamp : 0;
}

function normalizePublicTrainingWindow(value) {
  const windowMs = Math.round(Number(value || 0) || 0);
  return Math.min(24 * 60 * 60 * 1000, Math.max(0, windowMs));
}

function buildPublicTrainingWindowStats(laps) {
  const lapTimes = laps.map((lap) => Number(lap.lapMs || 0)).filter((lapMs) => lapMs > 500);
  return {
    laps: lapTimes.length,
    bestLapMs: lapTimes.length ? Math.min(...lapTimes) : 0,
    averageLapMs: lapTimes.length ? Math.round(lapTimes.reduce((sum, lapMs) => sum + lapMs, 0) / lapTimes.length) : 0,
  };
}

function buildPublicTrainingGoal(goal, currentLaps) {
  const targetLaps = Math.max(1, Math.round(Number(goal?.targetLaps || 1) || 1));
  return {
    title: String(goal?.title || "").trim(),
    reward: String(goal?.reward || "").trim(),
    targetLaps,
    currentLaps,
    remainingLaps: Math.max(0, targetLaps - currentLaps),
    percent: Math.min(100, Math.max(0, (currentLaps / targetLaps) * 100)),
    reached: currentLaps >= targetLaps,
    claimed: Boolean(goal?.claimedAt),
  };
}

function getRankedTrainingTotals(ledger, registeredDriverIds) {
  const totals = new Map();
  ledger.forEach((entry) => {
    const driverId = String(entry?.driverId || "");
    if (!driverId || !registeredDriverIds.has(driverId) || Number(entry?.lapMs || 0) <= 500) return;
    totals.set(driverId, (totals.get(driverId) || 0) + 1);
  });
  return [...totals.entries()].sort((left, right) => right[1] - left[1]);
}

function buildPublicTrainingStats(laps) {
  const lapTimes = laps.map((lap) => lap.lapMs);
  const recent = lapTimes.slice(-30);
  const average = (values) => values.length ? Math.round(values.reduce((sum, value) => sum + value, 0) / values.length) : 0;
  const sortedRecent = [...recent].sort((left, right) => left - right);
  const percentile = (ratio) => sortedRecent.length
    ? sortedRecent[Math.min(sortedRecent.length - 1, Math.max(0, Math.round((sortedRecent.length - 1) * ratio)))]
    : 0;
  const recentAverageMs = average(recent);
  const daily = new Map();
  laps.forEach((lap) => {
    const date = new Date(lap.timestamp);
    const dateKey = Number.isNaN(date.getTime()) ? "unknown" : `${date.getFullYear()}-${String(date.getMonth() + 1).padStart(2, "0")}-${String(date.getDate()).padStart(2, "0")}`;
    const day = daily.get(dateKey) || { dateKey, laps: 0, totalMs: 0, bestLapMs: 0, latestTimestamp: 0 };
    day.laps += 1;
    day.totalMs += lap.lapMs;
    day.bestLapMs = day.bestLapMs ? Math.min(day.bestLapMs, lap.lapMs) : lap.lapMs;
    day.latestTimestamp = Math.max(day.latestTimestamp, lap.timestamp);
    daily.set(dateKey, day);
  });
  const dailySummaries = [...daily.values()]
    .map((day) => ({
      dateKey: day.dateKey,
      laps: day.laps,
      bestLapMs: day.bestLapMs,
      latestTimestamp: day.latestTimestamp,
      averageLapMs: day.laps ? Math.round(day.totalMs / day.laps) : 0,
    }))
    .sort((left, right) => right.latestTimestamp - left.latestTimestamp)
    .slice(0, 30);
  const bestLapMs = lapTimes.length ? Math.min(...lapTimes) : 0;
  const controlTargetMs = bestLapMs ? Math.round(bestLapMs * 1.08) : 0;
  let controlledLapStreak = 0;
  for (let index = lapTimes.length - 1; index >= 0; index -= 1) {
    if (!controlTargetMs || lapTimes[index] > controlTargetMs) break;
    controlledLapStreak += 1;
  }

  return {
    totalLaps: laps.length,
    totalSessions: new Set(laps.map((lap) => lap.sessionId).filter(Boolean)).size,
    trainingDays: daily.size,
    bestLapMs,
    averageLapMs: average(lapTimes),
    recentAverageMs,
    recentLapCount: recent.length,
    consistencySpreadPct: recentAverageMs ? ((percentile(0.9) - percentile(0.1)) / recentAverageMs) * 100 : 0,
    controlledLapStreak,
    dailySummaries,
  };
}

function normalizePublicLogoDataUrl(value) {
  const logo = String(value || "").trim();
  return logo.length <= 2_000_000 && /^data:image\/(?:png|jpeg|webp);base64,/i.test(logo) ? logo : "";
}

function getPublicTrainingMilestone(currentLaps) {
  const presets = [100, 250, 500, 1000, 1500, 2500, 5000];
  const targetLaps = presets.find((target) => target > currentLaps) || (Math.floor(currentLaps / 1000) + 1) * 1000;
  return {
    currentLaps,
    targetLaps,
    remainingLaps: Math.max(0, targetLaps - currentLaps),
    percent: Math.min(100, Math.max(0, (currentLaps / targetLaps) * 100)),
  };
}

function getPublicTrainingRecommendation(stats) {
  if (stats.totalLaps < 10) return { level: "baseline", targetLapMs: 0 };
  if (stats.recentLapCount >= 10 && stats.consistencySpreadPct > 8) {
    return { level: "consistency", targetLapMs: Math.round(stats.bestLapMs * 1.08) };
  }
  return { level: "progression", targetLapMs: Math.round(stats.bestLapMs * 1.05) };
}

module.exports = {
  buildPublicTrainingProfile,
  findPublicTrainingShare,
  hasPublicTrainingShare,
  normalizePublicTrainingSince,
  normalizePublicTrainingWindow,
};