import type { Clock, Fs } from './io'
import { readJson, writeJson } from './io'

export type Layer = 'episodic' | 'semantic' | 'emotional' | 'procedural' | 'reflection'

export interface Pad {
  p: number
  a: number
  d: number
}

export interface Memory {
  id: number
  layer: Layer
  text: string
  /** 重要度 1–10 */
  importance: number
  source: string
  createdAt: number
  lastAccess: number
  accessCount: number
  emotion?: Pad
}

export interface MemoryDoc {
  items: Memory[]
  nextId: number
  /** 自上次鞏固以來累積的重要度 */
  pending: number
  lastConsolidated: number
  /** 已被反思涵蓋的最大記憶編號 */
  reflectedUpTo?: number
}

export const HALF_LIFE_H = 72
export const TOP_K = 6
export const WEIGHTS = { recency: 1, importance: 1, relevance: 2, emotion: 0.5 }
/** 累積重要度達此值就鞏固 */
export const CONSOLIDATE_THRESHOLD = 30
/** 保留率低於此值即遺忘 */
export const FORGET_BELOW = 0.1
/** 穩定度基準（小時） */
const BASE_STABILITY_H = 24
const HOUR = 3600_000

/** 穩定度（小時）隨重要度與取用次數成長 */
export const stability = (m: Pick<Memory, 'importance' | 'accessCount'>) =>
  BASE_STABILITY_H * m.importance * (1 + Math.log1p(m.accessCount))

/** 保留率：自最近一次取用起算 */
export function retention(m: Memory, now: number): number {
  const h = Math.max(0, now - m.lastAccess) / HOUR
  return Math.exp(-h / stability(m))
}

const tokens = (s: string) => {
  const out = new Set<string>()
  const latin = s.toLowerCase().match(/[a-z0-9]+/g) ?? []
  for (const w of latin) out.add(w)
  const cjk = s.replace(/[^一-鿿]/g, '')
  for (let i = 0; i < cjk.length; i++) {
    out.add(cjk[i]!)
    if (i + 1 < cjk.length) out.add(cjk.slice(i, i + 2))
  }
  return out
}

function relevance(query: string, text: string): number {
  const q = tokens(query)
  if (!q.size) return 0
  const t = tokens(text)
  let hit = 0
  for (const w of q) if (t.has(w)) hit++
  return hit / q.size
}

function moodFit(a?: Pad, b?: Pad): number {
  if (!a || !b) return 0
  const dist = Math.hypot(a.p - b.p, a.a - b.a, a.d - b.d) / Math.hypot(2, 2, 2)
  return 1 - dist
}

export function score(m: Memory, query: string, now: number, mood?: Pad): number {
  const recency = Math.pow(0.5, Math.max(0, now - m.lastAccess) / HOUR / HALF_LIFE_H)
  return (
    WEIGHTS.recency * recency +
    WEIGHTS.importance * (m.importance / 10) +
    WEIGHTS.relevance * relevance(query, m.text) +
    WEIGHTS.emotion * moodFit(m.emotion, mood)
  )
}

/** 疑似金鑰的字串在寫入前遮蔽 */
export function redact(s: string): string {
  return s
    .replace(/\b(sk|pk|ghp|gho|xox[bap]|AKIA)[-_A-Za-z0-9]{12,}/g, '[已過濾]')
    .replace(/\b[A-Za-z0-9+/_-]{32,}\b/g, '[已過濾]')
}

export type Summarize = (items: Memory[]) => string

/** 預設摘要：不呼叫模型，直接串接重點；實際反思文字可由外部注入模型版 */
const defaultSummarize: Summarize = items =>
  items
    .slice(0, 5)
    .map(i => i.text)
    .join('；')

export interface MemoryStore {
  add(input: { layer: Layer; text: string; importance: number; source: string; emotion?: Pad }): Promise<Memory>
  /** 檢索前 6 筆，並更新取用紀錄 */
  recall(query: string, mood?: Pad): Promise<Memory[]>
  /** 累積達門檻、會話結束或手動 reflect 時鞏固 */
  consolidate(opts?: { force?: boolean; summarize?: Summarize }): Promise<{ ran: boolean; forgotten: number; reflection?: Memory }>
  all(): Promise<Memory[]>
  pending(): Promise<number>
}

export function createMemory(fs: Fs, clock: Clock, path: string): MemoryStore {
  const load = () => readJson<MemoryDoc>(fs, path, { items: [], nextId: 1, pending: 0, lastConsolidated: 0 })
  const save = (d: MemoryDoc) => writeJson(fs, path, d)

  return {
    async add(i) {
      const d = await load()
      const now = clock()
      const m: Memory = {
        id: d.nextId++,
        layer: i.layer,
        text: redact(i.text),
        importance: Math.min(10, Math.max(1, i.importance)),
        source: i.source,
        createdAt: now,
        lastAccess: now,
        accessCount: 0,
        emotion: i.emotion,
      }
      d.items.push(m)
      d.pending += m.importance
      await save(d)
      return m
    },

    async recall(query, mood) {
      const d = await load()
      const now = clock()
      const top = d.items
        .map(m => ({ m, s: score(m, query, now, mood) }))
        .sort((a, b) => b.s - a.s || b.m.id - a.m.id)
        .slice(0, TOP_K)
        .map(x => x.m)
      for (const m of top) {
        m.lastAccess = now
        m.accessCount++
      }
      if (top.length) await save(d)
      return top
    },

    async consolidate(opts = {}) {
      const d = await load()
      const now = clock()
      if (!opts.force && d.pending < CONSOLIDATE_THRESHOLD) return { ran: false, forgotten: 0 }

      // 反思：把上次鞏固後新增、尚未被反思涵蓋的記憶濃縮成一筆
      const fresh = d.items.filter(m => m.layer !== 'reflection' && m.id > (d.reflectedUpTo ?? 0))
      let reflection: Memory | undefined
      if (fresh.length) {
        const top = [...fresh].sort((a, b) => b.importance - a.importance || b.id - a.id)
        reflection = {
          id: d.nextId++,
          layer: 'reflection',
          text: redact((opts.summarize ?? defaultSummarize)(top)),
          importance: Math.min(10, Math.max(...top.map(t => t.importance)) + 1),
          source: `reflect:${top.map(t => t.id).join(',')}`,
          createdAt: now,
          lastAccess: now,
          accessCount: 0,
        }
        d.items.push(reflection)
      }

      d.reflectedUpTo = d.nextId - 1

      // 遺忘：保留率過低的記憶淘汰（反思本身不在本輪淘汰）
      const before = d.items.length
      d.items = d.items.filter(m => m === reflection || retention(m, now) >= FORGET_BELOW)
      d.pending = 0
      d.lastConsolidated = now
      await save(d)
      return { ran: true, forgotten: before - d.items.length, reflection }
    },

    async all() {
      return (await load()).items
    },
    async pending() {
      return (await load()).pending
    },
  }
}