import { rankExactCooldownCombinations as rankExpectedCooldownCombinations } from './exact-optimizer-expected.ts';
import { effectiveActiveProbability } from './active-probability.ts';
import { calculateExactScore, finalNoteScore, specialScoreSupportForNote } from './score.ts';
import { passiveScoreSupportByCard } from './passive-support.ts';
import { effectiveCooldownSeconds } from './timeline.ts';
import { COOLDOWN_REDUCTIONS } from './types.ts';
import type {
  CooldownReduction,
  EffectiveSkillWindow,
  ExactOptimizationResult,
  ExactOptimizeOptions,
  ExactSongChart,
  OptimizerCard,
  SkillWindow
} from './types.ts';

type CandidateProfile = {
  boost: Float64Array;
  probability: Float64Array;
};

type NoteGroup = { note: ExactSongChart['notes'][number]; comboBonus: number; count: number };

/** Keep exact timing, note semantics and the original combo tier. */
export function groupExactOptimizationNotes(chart: ExactSongChart): NoteGroup[] {
  const groups = new Map<string, NoteGroup>();
  chart.notes.forEach((note, index) => {
    const bonus = comboBonus(index + 1);
    const key = `${note.timeSeconds}|${note.type === 'mid'}|${Boolean(note.isFlick)}|${Boolean(note.isHoldEnd)}|${bonus}`;
    const group = groups.get(key);
    if (group) group.count += 1;
    else groups.set(key, { note, comboBonus: bonus, count: 1 });
  });
  return [...groups.values()];
}

function optimizationNotes(cards: readonly OptimizerCard[], chart: ExactSongChart, options: ExactOptimizeOptions): NoteGroup[] {
  // Grouped integer additions are exact only within Number's integer precision.
  // For unusually large/invalid input, retain every note in its original order.
  const supportMagnitude = cards.reduce((sum, card) => sum + Math.abs(card.special.scoreUpPercent) +
    Math.abs(card.scoreSupportPercent ?? 0) + (card.passiveScoreSupportEffects ?? []).reduce((n, effect) => n + Math.abs(effect.percent), 0), 0);
  const boostMagnitude = Math.max(...cards.map((card) => Math.abs(card.active.scoreUpPercent))) / 100 * (1 + supportMagnitude / 100);
  const totalMagnitude = ((Math.abs(options.perfectNoteScore) + 1) * 1.155 * (1 + boostMagnitude) *
    Math.abs(1 + (options.directSongScoreBonusPercent ?? 0) / 100) + 1) * chart.notes.length;
  return Number.isFinite(totalMagnitude) && totalMagnitude < Number.MAX_SAFE_INTEGER / 2
    ? groupExactOptimizationNotes(chart)
    : chart.notes.map((note, index) => ({ note, comboBonus: comboBonus(index + 1), count: 1 }));
}

function permutations<T>(items: readonly T[]): T[][] {
  if (items.length <= 1) return [Array.from(items)];
  const result: T[][] = [];
  for (let index = 0; index < items.length; index += 1) {
    const rest = [...items.slice(0, index), ...items.slice(index + 1)];
    for (const permutation of permutations(rest)) result.push([items[index], ...permutation]);
  }
  return result;
}

function assignments(cards: readonly OptimizerCard[]): Array<Record<string, CooldownReduction>> {
  let result: Array<Record<string, CooldownReduction>> = [{}];
  for (const card of cards) {
    const allowed = card.allowedCooldownReductions?.length ? card.allowedCooldownReductions : COOLDOWN_REDUCTIONS;
    result = result.flatMap((current) => allowed.map((reduction) => ({ ...current, [card.id]: reduction })));
  }
  return result;
}

function specialActive(card: OptimizerCard, start: number | null | undefined, time: number): boolean {
  return start !== null && start !== undefined && start <= time && time < start + card.special.durationSeconds;
}

function specialRate(
  order: readonly OptimizerCard[],
  chart: ExactSongChart,
  time: number,
  field: 'scoreUpPercent' | 'activationRateUpPercent'
): number {
  let total = 0;
  for (let slot = 0; slot < order.length; slot += 1) {
    const card = order[slot];
    if (specialActive(card, chart.specialStartSeconds[slot], time)) total += card.special[field] ?? 0;
  }
  return total;
}

function comboBonus(combo: number): number {
  return combo >= 1_000 ? 0.1 : combo >= 100 ? Math.floor(combo / 100) * 0.01 : 0;
}

function baseScores(groups: readonly NoteGroup[], perfectNoteScore: number): Float64Array {
  return Float64Array.from(groups, ({ note, comboBonus }) => {
    const base = note.type === 'mid' ? Math.ceil(perfectNoteScore * 0.1) : perfectNoteScore * (note.isFlick ? 1.05 : 1);
    return base * (1 + comboBonus);
  });
}

function makeProfiles(order: readonly OptimizerCard[], chart: ExactSongChart, groups: readonly NoteGroup[]): Map<string, CandidateProfile> {
  const profiles = new Map<string, CandidateProfile>();
  const noteSupport = Float64Array.from(groups, ({ note }) => specialScoreSupportForNote(order, chart, note));
  const passiveSupport = passiveScoreSupportByCard(order);

  order.forEach((card, cardIndex) => {
    const reductions = card.allowedCooldownReductions?.length ? card.allowedCooldownReductions : COOLDOWN_REDUCTIONS;
    reductions.forEach((reduction) => {
      const interval = effectiveCooldownSeconds(card.active.cooldownSeconds, reduction);
      const boost = new Float64Array(groups.length);
      const probability = new Float64Array(groups.length);
      const probabilityByAttempt = new Map<number, number>();
      groups.forEach(({ note }, noteIndex) => {
        const attempt = Math.floor((note.timeSeconds + 1e-9) / interval);
        if (attempt <= 0) return;
        const activationTime = attempt * interval;
        if (note.timeSeconds >= activationTime + card.active.durationSeconds) return;
        let effectiveProbability = probabilityByAttempt.get(attempt);
        if (effectiveProbability === undefined) {
          const activationBonus = specialRate(order, chart, activationTime, 'activationRateUpPercent');
          effectiveProbability = effectiveActiveProbability(card, activationBonus);
          probabilityByAttempt.set(attempt, effectiveProbability);
        }
        boost[noteIndex] = card.active.scoreUpPercent / 100 *
          (1 + (noteSupport[noteIndex] + (card.scoreSupportPercent ?? 0) + passiveSupport[cardIndex]) / 100);
        probability[noteIndex] = effectiveProbability;
      });
      profiles.set(`${card.id}:${reduction}`, { boost, probability });
    });
  });
  return profiles;
}

export function buildExactSkillWindows(
  order: readonly OptimizerCard[],
  reductions: Readonly<Record<string, CooldownReduction>>,
  chart: ExactSongChart,
  excludedActiveWindows: readonly Pick<SkillWindow, 'cardId' | 'startSeconds'>[] = []
): { activeWindows: SkillWindow[]; effectiveActiveWindows: EffectiveSkillWindow[]; specialWindows: SkillWindow[] } {
  const activeWindows = order.flatMap((card) => {
    const interval = effectiveCooldownSeconds(card.active.cooldownSeconds, reductions[card.id] ?? 0);
    const windows: SkillWindow[] = [];
    for (let activation = 1; activation * interval < chart.durationSeconds - 1e-9; activation += 1) {
      const startSeconds = activation * interval;
      windows.push({
        cardId: card.id,
        startSeconds,
        endSeconds: Math.min(chart.durationSeconds, startSeconds + card.active.durationSeconds),
        scoreUpPercent: card.active.scoreUpPercent
      });
    }
    return windows;
  });
  const specialWindows = order.flatMap((card, slot) => {
    const startSeconds = chart.specialStartSeconds[slot];
    if (startSeconds === null || startSeconds === undefined) return [];
    return [{
      cardId: card.id,
      startSeconds,
      endSeconds: Math.min(chart.durationSeconds, startSeconds + card.special.durationSeconds),
      scoreUpPercent: card.special.scoreUpPercent
    }];
  });
  const passiveSupport = passiveScoreSupportByCard(order);
  const orderIndex = new Map(order.map((card, index) => [card.id, index]));
  const boundaries = Array.from(new Set([
    ...activeWindows.flatMap((window) => [window.startSeconds, window.endSeconds]),
    ...specialWindows.flatMap((window) => [window.startSeconds, window.endSeconds])
  ])).sort((left, right) => left - right);
  const effectiveActiveWindows: EffectiveSkillWindow[] = [];

  for (let index = 0; index < boundaries.length - 1; index += 1) {
    const startSeconds = boundaries[index];
    const endSeconds = boundaries[index + 1];
    if (endSeconds - startSeconds < 0.01) continue;
    const midpoint = (startSeconds + endSeconds) / 2;
    const specialSupport = specialRate(order, chart, midpoint, 'scoreUpPercent');
    const active = activeWindows
      .filter((window) => window.startSeconds <= midpoint && midpoint < window.endSeconds &&
        !excludedActiveWindows.some((excluded) => excluded.cardId === window.cardId && Math.abs(excluded.startSeconds - window.startSeconds) < 1e-9))
      .map((window) => {
        const cardIndex = orderIndex.get(window.cardId) ?? 0;
        const card = order[cardIndex];
        return {
          window,
          effectivePercent: window.scoreUpPercent *
            (1 + (specialSupport + (card.scoreSupportPercent ?? 0) + passiveSupport[cardIndex]) / 100)
        };
      })
      .sort((left, right) => right.effectivePercent - left.effectivePercent || (orderIndex.get(left.window.cardId) ?? 99) - (orderIndex.get(right.window.cardId) ?? 99));
    const winner = active[0];
    if (!winner) continue;
    const previous = effectiveActiveWindows.at(-1);
    if (previous && previous.cardId === winner.window.cardId && Math.abs(previous.scoreUpPercent - winner.effectivePercent) < 1e-9 && Math.abs(previous.endSeconds - startSeconds) < 0.01) {
      previous.endSeconds = endSeconds;
      previous.overlapCount = Math.max(previous.overlapCount, active.length);
    } else {
      effectiveActiveWindows.push({
        ...winner.window,
        startSeconds,
        endSeconds,
        baseScoreUpPercent: winner.window.scoreUpPercent,
        scoreUpPercent: winner.effectivePercent,
        overlapCount: active.length
      });
    }
  }
  return { activeWindows, effectiveActiveWindows, specialWindows };
}


/** Share prefix maxima across all cooldown assignments; round each note exactly as before. */
function scoreMaximumAssignments(
  cards: readonly OptimizerCard[], profiles: ReadonlyMap<string, CandidateProfile>,
  base: Float64Array, directBonus: number, counts: Float64Array
): Float64Array | null {
  if (!Number.isFinite(directBonus) || directBonus < -100 || base.some(value => !Number.isFinite(value) || value < 0)) return null;
  const choices = cards.map(card => (card.allowedCooldownReductions?.length ? card.allowedCooldownReductions : COOLDOWN_REDUCTIONS)
    .map(reduction => profiles.get(card.id + ':' + reduction)!));
  if (choices.some(row => row.some(profile => !profile || profile.boost.some(value => !Number.isFinite(value))))) return null;
  const widths = choices.map(row => row.length);
  const sizes = [1]; for (const width of widths) sizes.push(sizes.at(-1)! * width);
  const totals = new Float64Array(sizes.at(-1)!);
  const prefix = sizes.slice(0, -1).map(size => new Float64Array(size));
  const offsets = [1]; for (const width of widths) offsets.push(offsets.at(-1)! + width);
  // Notes with the same rounded scores for every selectable profile make identical contributions.
  const states = new Map<string, { scores: number[]; count: number }>();
  let magnitude = 0;
  for (let noteIndex = 0; noteIndex < base.length; noteIndex++) {
    const baseline = finalNoteScore(base[noteIndex], directBonus);
    const scores = [baseline];
    for (const row of choices) for (const profile of row) {
      scores.push(finalNoteScore(base[noteIndex] * (1 + Math.max(0, profile.boost[noteIndex])), directBonus));
    }
    const largest = Math.max(...scores);
    magnitude += largest * counts[noteIndex];
    if (!Number.isSafeInteger(largest) || magnitude >= Number.MAX_SAFE_INTEGER / 2) return null;
    const key = scores.join(','); const previous = states.get(key);
    if (previous) previous.count += counts[noteIndex]; else states.set(key, { scores, count: counts[noteIndex] });
  }
  for (const { scores, count } of states.values()) {
    prefix[0][0] = scores[0];
    for (let member = 0; member < cards.length - 1; member++) {
      const previous = prefix[member], next = prefix[member + 1], width = widths[member], offset = offsets[member];
      let index = 0;
      for (let p = 0; p < previous.length; p++) for (let choice = 0; choice < width; choice++) {
        next[index++] = Math.max(previous[p], scores[offset + choice]);
      }
    }
    const member = cards.length - 1, previous = prefix[member], width = widths[member], offset = offsets[member];
    let index = 0;
    for (let p = 0; p < previous.length; p++) for (let choice = 0; choice < width; choice++) {
      totals[index++] += Math.max(previous[p], scores[offset + choice]) * count;
    }
  }
  return totals;
}

function scoreMaximumAssignment(
  cards: readonly OptimizerCard[],
  reductions: Readonly<Record<string, CooldownReduction>>,
  profiles: ReadonlyMap<string, CandidateProfile>,
  base: Float64Array,
  directBonus: number,
  counts: Float64Array,
  selected: CandidateProfile[]
): number {
  cards.forEach((card, index) => {
    const profile = profiles.get(`${card.id}:${reductions[card.id] ?? 0}`);
    if (!profile) throw new Error(`${card.name}의 빈도 조합 프로파일이 없습니다.`);
    selected[index] = profile;
  });
  let total = 0;
  for (let noteIndex = 0; noteIndex < base.length; noteIndex += 1) {
    let largest = 0;
    for (let memberIndex = 0; memberIndex < selected.length; memberIndex += 1) {
      const boost = selected[memberIndex].boost[noteIndex];
      if (boost <= largest) continue;
      // Invalid numerical inputs retain the original combined evaluator.
      if (!Number.isFinite(boost)) return NaN;
      largest = boost;
    }
    total += finalNoteScore(base[noteIndex] * (1 + largest), directBonus) * counts[noteIndex];
  }
  return total;
}

function scoreAssignment(
  cards: readonly OptimizerCard[],
  reductions: Readonly<Record<string, CooldownReduction>>,
  profiles: ReadonlyMap<string, CandidateProfile>,
  base: Float64Array,
  directBonus: number,
  counts: Float64Array,
  selected: CandidateProfile[],
  boosts: Float64Array,
  probabilities: Float64Array
): { expectedScore: number; theoreticalMaxScore: number } {
  cards.forEach((card, index) => {
    const reduction = reductions[card.id] ?? 0;
    const profile = profiles.get(`${card.id}:${reduction}`);
    if (!profile) throw new Error(`${card.name}의 ${reduction}% 감소 프로필이 없습니다.`);
    selected[index] = profile;
  });
  let expectedScore = 0;
  let theoreticalMaxScore = 0;

  for (let noteIndex = 0; noteIndex < base.length; noteIndex += 1) {
    let count = 0;
    for (let memberIndex = 0; memberIndex < selected.length; memberIndex += 1) {
      const boost = selected[memberIndex].boost[noteIndex];
      if (boost <= 0) continue;
      let position = count;
      while (position > 0 && boosts[position - 1] < boost) {
        boosts[position] = boosts[position - 1];
        probabilities[position] = probabilities[position - 1];
        position -= 1;
      }
      boosts[position] = boost;
      probabilities[position] = selected[memberIndex].probability[noteIndex];
      count += 1;
    }
    let expectedBoost = 0;
    let previousFailed = 1;
    for (let position = 0; position < count; position += 1) {
      expectedBoost += previousFailed * probabilities[position] * boosts[position];
      previousFailed *= 1 - probabilities[position];
    }
    // Round each original note before applying the group's multiplicity.
    expectedScore += finalNoteScore(base[noteIndex] * (1 + expectedBoost), directBonus) * counts[noteIndex];
    theoreticalMaxScore += finalNoteScore(base[noteIndex] * (1 + (count > 0 ? boosts[0] : 0)), directBonus) * counts[noteIndex];
  }
  return { expectedScore, theoreticalMaxScore };
}

export function optimizeExactOrderAndCooldowns(
  cards: readonly OptimizerCard[],
  chart: ExactSongChart,
  options: ExactOptimizeOptions
): ExactOptimizationResult {
  return rankExactCooldownCombinations(cards, chart, options, 1)[0];
}

/** Keep the best special order for each distinct per-card cooldown assignment. */
export function rankExactCooldownCombinations(
  cards: readonly OptimizerCard[],
  chart: ExactSongChart,
  options: ExactOptimizeOptions,
  limit = 5
) : ExactOptimizationResult[] {
  if (options.objective === 'expected') return rankExpectedCooldownCombinations(cards, chart, options, limit);
  if (cards.length !== 5 || new Set(cards.map((card) => card.id)).size !== 5) {
    throw new Error('서로 다른 카드 5장이 필요합니다.');
  }
  const reductionAssignments = assignments(cards);
  const reductionTotals = new Map(reductionAssignments.map((reductions) => [reductions,
    Object.values(reductions).reduce<number>((sum, value) => sum + value, 0)]));
  const groups = optimizationNotes(cards, chart, options);
  const base = baseScores(groups, options.perfectNoteScore);
  const counts = Float64Array.from(groups, (group) => group.count);
  // Local to this call: callbacks may recursively invoke the optimizer.
  const selected: CandidateProfile[] = [];
  const boosts = new Float64Array(cards.length);
  const probabilities = new Float64Array(cards.length);
  const orderCount = cards.reduce((value, _, index) => value * (index + 1), 1);
  const totalCandidates = orderCount * reductionAssignments.length;
  let evaluatedCandidates = 0;
  type Candidate = {
    order: OptimizerCard[];
    reductions: Record<string, CooldownReduction>;
    theoreticalMaxScore: number;
    expectedScore: number | undefined;
    reductionTotal: number;
    ties?: Candidate[];
  };
  const bestByAssignment = new Map<Record<string, CooldownReduction>, Candidate>();
  const maximumObjective = true;
  // Keep a bounded number of order profiles for deferred expected-score ties.
  const profileCache = new Map<readonly OptimizerCard[], Map<string, CandidateProfile>>();
  let activeOrder: readonly OptimizerCard[] | null = null;
  let activeProfiles: Map<string, CandidateProfile> | null = null;
  const profilesFor = (order: readonly OptimizerCard[]): Map<string, CandidateProfile> => {
    if (!maximumObjective) return makeProfiles(order, chart, groups);
    let profiles = profileCache.get(order);
    if (profiles) profileCache.delete(order);
    else profiles = makeProfiles(order, chart, groups);
    profileCache.set(order, profiles);
    if (profileCache.size > 8) profileCache.delete(profileCache.keys().next().value!);
    return profiles;
  };
  const expectedFor = (candidate: Candidate): number => {
    if (candidate.expectedScore === undefined) {
      const profiles = candidate.order === activeOrder && activeProfiles ? activeProfiles : profilesFor(candidate.order);
      candidate.expectedScore = scoreAssignment(candidate.order, candidate.reductions, profiles, base,
        options.directSongScoreBonusPercent ?? 0, counts, selected, boosts, probabilities).expectedScore;
    }
    return candidate.expectedScore;
  };
  const compare = (left: Candidate, right: Candidate): number =>
    (options.objective === 'expected'
      ? expectedFor(right) - expectedFor(left) || right.theoreticalMaxScore - left.theoreticalMaxScore
      : right.theoreticalMaxScore - left.theoreticalMaxScore || expectedFor(right) - expectedFor(left)) ||
    left.reductionTotal - right.reductionTotal;

  const maximumCache = new Map<string, Float64Array>();
  for (const order of permutations(cards)) {
    const passive = passiveScoreSupportByCard(order);
    const supportById = new Map(order.map((card, index) => [card.id, passive[index]]));
    const inputs = [
      ...order.flatMap((card) => [card.special.durationSeconds, card.special.scoreUpPercent]),
      ...cards.flatMap((card) => [card.active.cooldownSeconds, card.active.durationSeconds,
        card.active.scoreUpPercent, card.scoreSupportPercent ?? 0, supportById.get(card.id)!]),
      options.directSongScoreBonusPercent ?? 0
    ];
    const cacheable = inputs.every(Number.isFinite) && cards.every((card) => typeof card.id === 'string') &&
      chart.specialStartSeconds.every((start) => start === null || start === undefined || Number.isFinite(start));
    const signature = cacheable ? JSON.stringify([inputs, chart.specialStartSeconds]) : null;
    let maxima = signature === null ? undefined : maximumCache.get(signature);
    if (signature !== null && !maxima) {
      maxima = new Float64Array(reductionAssignments.length).fill(NaN);
      maximumCache.set(signature, maxima);
    }
    const profiles = profilesFor(order);
    activeOrder = order;
    activeProfiles = profiles;
    if (!maxima || Number.isNaN(maxima[0])) {
      const shared = scoreMaximumAssignments(cards, profiles, base, options.directSongScoreBonusPercent ?? 0, counts);
      if (shared) { maxima = shared; if (signature !== null) maximumCache.set(signature, shared); }
    }
    for (let assignmentIndex = 0; assignmentIndex < reductionAssignments.length; assignmentIndex += 1) {
      const reductions = reductionAssignments[assignmentIndex];
      let maximum = maxima?.[assignmentIndex];
      if (maximum === undefined || Number.isNaN(maximum)) {
        maximum = scoreMaximumAssignment(order, reductions, profiles, base,
          options.directSongScoreBonusPercent ?? 0, counts, selected);
        if (maxima && Number.isFinite(maximum)) maxima[assignmentIndex] = maximum;
      }
      let score: { theoreticalMaxScore: number; expectedScore: number | undefined } = maximumObjective
        ? { theoreticalMaxScore: maximum, expectedScore: undefined }
        : scoreAssignment(order, reductions, profiles, base, options.directSongScoreBonusPercent ?? 0,
          counts, selected, boosts, probabilities);
      if (!Number.isFinite(score.theoreticalMaxScore)) score = scoreAssignment(order, reductions, profiles, base,
        options.directSongScoreBonusPercent ?? 0, counts, selected, boosts, probabilities);
      const reductionTotal = reductionTotals.get(reductions)!;
      const candidate = { ...score, order, reductions, reductionTotal };
      const previous = bestByAssignment.get(reductions);
      if (previous && (!Number.isFinite(candidate.theoreticalMaxScore) || !Number.isFinite(previous.theoreticalMaxScore))) {
        if (compare(candidate, previous) < 0) bestByAssignment.set(reductions, candidate);
      } else if (!previous || candidate.theoreticalMaxScore > previous.theoreticalMaxScore) {
        bestByAssignment.set(reductions, candidate);
      } else if (candidate.theoreticalMaxScore === previous.theoreticalMaxScore) {
        // Resolve expected-score ties only if this assignment can reach the requested ranks.
        (previous.ties ??= []).push(candidate);
      }
      evaluatedCandidates += 1;
    }
    options.onProgress?.({ evaluatedCandidates, totalCandidates });
  }

  const resultCount = Math.max(1, Math.floor(limit));
  let ranked = [...bestByAssignment.values()];
  if (Number.isFinite(resultCount) && ranked.every(candidate => Number.isFinite(candidate.theoreticalMaxScore))) {
    const maxima = ranked.map(candidate => candidate.theoreticalMaxScore).sort((left, right) => right - left);
    const cutoff = maxima[Math.min(maxima.length, resultCount) - 1];
    ranked = ranked.filter(candidate => candidate.theoreticalMaxScore >= cutoff);
  }
  ranked = ranked.map(candidate => {
    let best = candidate;
    for (const tied of candidate.ties ?? []) if (compare(tied, best) < 0) best = tied;
    return best;
  });
  return ranked.sort(compare).slice(0, resultCount).map((candidate) => ({
    ...calculateExactScore(candidate.order, candidate.reductions, chart, options),
    order: candidate.order.map((card) => card.id),
    formationOrder: cards.map((card) => card.id),
    cooldownReductions: { ...candidate.reductions },
    ...buildExactSkillWindows(candidate.order, candidate.reductions, chart),
    evaluatedCandidates
  }));
}