/* The AI's thread.
 *
 * Everything expensive happens here: loading the 13 MB ONNX graph, and running
 * PUCT search against it. The page keeps the game state and only ships the
 * position over (`toSetup()` is structured-clone-safe), so the UI thread never
 * blocks — the board stays interactive and the "thinking" clock keeps ticking
 * while the search runs.
 *
 * Backends. Two onnxruntime-web builds are vendored and exactly one is
 * downloaded: the WebGPU one when the browser can run it (`navigator.gpu` plus
 * JSPI), the pure-WASM one otherwise. The choice is made here rather than on the
 * page so that a WebGPU session that fails to create can quietly fall back to
 * WASM before the page has been told anything. See js/net.js for the measured
 * reason the two differ — and why the batch size differs with them.
 *
 * Protocol (all messages carry an `id` that is echoed back):
 *   -> {type:"init", backends, modelUrl}
 *                                            <- {type:"loading"} ... {type:"ready", backend, batch, margin} | {type:"error"}
 *   -> {type:"search", setup, budgetS}       <- {type:"progress"} ... {type:"result"}
 *   -> {type:"policy", setup}                <- {type:"result"}   (hint: no search)
 *   -> {type:"rate", setup, actionId, budgetS}
 *                                            <- {type:"progress"} ... {type:"result"} (coach)
 *   -> {type:"analyze", setup, budgetS}      <- {type:"result"} (coach, move not yet known)
 *   -> {type:"cancel"}
 */

import { AzulState, Rng } from "./engine.js";
import { MCTS, STALL_ROUNDS, selectAction } from "./mcts.js";
import { OnnxEvaluator, webgpuLikely } from "./net.js";
import { describeAction } from "./report.js";

/**
 * Leaves per forward pass, per backend.
 *
 * Measured on this laptop (Chrome, Apple M3 Pro), raw `session.run` throughput:
 *
 *   backend   batch 1     batch 16    batch 64    batch 256
 *   wasm      2.1 k/s     4.0 k/s     4.2 k/s     4.1 k/s
 *   webgpu    0.25 k/s    4.6 k/s     16.5 k/s    50 k/s
 *
 * WASM saturates by 16, so there is nothing to buy past it and a bigger batch
 * only blurs the search. A GPU dispatch costs ~4 ms whatever it carries, so
 * WebGPU is *worse* than WASM until about batch 8 and only then starts winning;
 * 64 is where the tree still stays sharp enough to be worth the extra breadth.
 *
 * `minBatch` is the floor the ramp may not go under (see mcts.js `batchRamp`):
 * on the CPU a batch of one is simply a small step, but on the GPU it is a 4 ms
 * dispatch carrying one position, so the GPU never goes below 8.
 */
export const BATCH_BY_BACKEND = {
  wasm: { batch: 16, minBatch: 1 },
  webgpu: { batch: 64, minBatch: 8 },
};

let ort = null;
let evaluator = null; // the OPPONENT's net: the one `search` plays with
/* The coach's net, when it differs from the opponent's. Advice — the coach,
 * the analysis head start, and "Suggest a move" — must come from the
 * strongest net we have, not from whichever weak opponent was chosen: Brick
 * is there to be beaten, not to teach. Loaded lazily by a "coach" message
 * the first time advice is asked for while a weaker net is playing; null
 * means the opponent IS the strongest net, which advises as itself. */
let coach = null;
let mainSpec = null; // the runtime/EP the main net settled on; the coach reuses it
let searchConfig = {};
/* Cancellation is a GENERATION, not a flag. A flag had a race: "cancel" then
 * "search" arrive back to back, the new search resets the flag before the
 * still-running analysis checks it, and the analysis runs its whole budget
 * interleaved with the opponent's search — on a phone, at half speed each.
 * With a generation, each search captures the count at its own start and
 * stops when any later cancel bumps it; nothing ever un-cancels. */
let cancelGen = 0;
const rng = new Rng((Date.now() ^ 0x5eed) >>> 0);

/**
 * Download the model, reporting bytes as they arrive.
 *
 * It is a 13 MB file, which on a phone is several seconds of nothing happening;
 * streaming the body lets the page show a real percentage instead of a spinner
 * that might as well be broken.
 */
async function fetchWithProgress(url, id) {
  const response = await fetch(url);
  if (!response.ok) throw new Error(`model fetch failed: ${response.status} ${response.statusText}`);
  const total = Number(response.headers.get("content-length")) || 0;
  if (!response.body) return new Uint8Array(await response.arrayBuffer());
  const reader = response.body.getReader();
  const chunks = [];
  let received = 0;
  for (;;) {
    const { done, value } = await reader.read();
    if (done) break;
    chunks.push(value);
    received += value.length;
    self.postMessage({ type: "loading", id, received, total });
  }
  const bytes = new Uint8Array(received);
  let offset = 0;
  for (const chunk of chunks) {
    bytes.set(chunk, offset);
    offset += chunk.length;
  }
  return bytes;
}

/** Load one onnxruntime-web build and point it at its own .wasm. */
async function loadRuntime(spec) {
  const runtime = await import(spec.module);
  // SharedArrayBuffer needs cross-origin isolation, which GitHub Pages does not
  // send; the CPU kernels therefore run on one thread either way.
  runtime.env.wasm.numThreads = 1;
  // Object form on purpose: a bare string prefix makes onnxruntime look for the
  // Emscripten glue .mjs next to the .wasm, and the bundled build has it inlined.
  runtime.env.wasm.wasmPaths = { wasm: spec.wasm };
  runtime.env.logLevel = "error";
  return runtime;
}

async function init(msg) {
  const bytes = await fetchWithProgress(msg.modelUrl, msg.id);
  // Ordered best-first by the page; each entry is {name, module, wasm, ep}.
  // Anything that fails — no adapter, a runtime that will not instantiate, a
  // graph the EP cannot take — falls through to the next, so the worst case is
  // the WASM path that shipped before.
  const tried = [];
  // `navigator.gpu` inside the worker is the authority — a page can be served to
  // a browser whose worker scope has no WebGPU at all — and skipping the entry
  // here means the 15 MB WebGPU runtime is never even requested.
  const wanted = msg.backends.filter((spec) => spec.ep !== "webgpu" || webgpuLikely());
  coach = null; // a new opponent net does not invalidate advice, but a fresh
  // init is the safe moment to drop the old coach session; the page reloads
  // it lazily if it is still wanted
  for (const spec of wanted) {
    try {
      ort = await loadRuntime(spec);
      mainSpec = spec;
      evaluator = await OnnxEvaluator.create(ort, bytes, {
        backend: spec.name,
        executionProviders: [spec.ep],
      });
      // Warm-up is also the real test: a WebGPU session can be created and then
      // fail on its first dispatch, and that must still fall back.
      const warm = AzulState.newGame(1, new Rng(1));
      await evaluator.evaluate(warm, warm.legalActions());
      break;
    } catch (err) {
      tried.push(`${spec.name}: ${String((err && err.message) || err)}`);
      ort = null;
      evaluator = null;
    }
  }
  if (!evaluator) throw new Error("no usable onnxruntime backend — " + tried.join("; "));

  searchConfig = { ...(BATCH_BY_BACKEND[evaluator.backend] || { batch: 1, minBatch: 1 }) };
  return {
    bytes: bytes.length,
    backend: evaluator.backend,
    batch: searchConfig.batch,
    margin: evaluator.hasMargin,
    outputs: evaluator.outputNames,
    fallbacks: tried,
  };
}

/** Load (or drop) the separate advice net. `modelUrl: null` frees it. */
async function coachInit(msg) {
  if (!msg.modelUrl) {
    coach = null;
    return { coach: false };
  }
  if (!ort || !mainSpec) throw new Error("the opponent's net must load first");
  const bytes = await fetchWithProgress(msg.modelUrl, msg.id);
  coach = await OnnxEvaluator.create(ort, bytes, {
    backend: mainSpec.name,
    executionProviders: [mainSpec.ep],
  });
  const warm = AzulState.newGame(1, new Rng(1));
  await coach.evaluate(warm, warm.legalActions());
  return { coach: true, bytes: bytes.length };
}

/** Whoever gives advice: the dedicated coach net, or the opponent itself. */
const adviser = () => coach || evaluator;

/** The search's own timing, so the page can show a real positions/s. */
let lastRate = null;

function noteRate(result) {
  if (result && result.sims > 32 && result.elapsedS > 0.2) {
    lastRate = result.sims / result.elapsedS;
  }
  return result;
}

async function search(msg) {
  const state = AzulState.fromSetup(msg.setup, new Rng(rng.next()));
  const legal = state.legalActions();
  if (!legal.length) throw new Error("no legal actions in the position sent to the worker");
  if (legal.length === 1) {
    return { action: legal[0], search: { sims: 0, elapsedS: 0, forced: true } };
  }

  const mcts = new MCTS(evaluator, searchConfig, new Rng(rng.next()));
  const gen = cancelGen;
  const result = await mcts.search(state, {
    timeLimitS: msg.budgetS,
    shouldStop: () => cancelGen !== gen,
    onProgress: ({ sims, elapsedS }) => {
      self.postMessage({ type: "progress", id: msg.id, sims, elapsedS });
    },
  });
  // A game that drags past STALL_ROUNDS gets randomised so that it terminates
  // (two arg-max players can otherwise loop forever — see mcts.js).
  const action =
    state.roundIndex >= STALL_ROUNDS ? selectAction(result.policy, 1, mcts.rng) : result.best;
  const top = [...result.visits.entries()].sort((a, b) => b[1] - a[1]).slice(0, 5);
  noteRate(result);
  return {
    action,
    search: {
      sims: result.sims,
      elapsedS: result.elapsedS,
      value: result.value,
      nodes: mcts.nodesCreated,
      forced: false,
      top,
      backend: evaluator.backend,
      batch: searchConfig.batch,
      rate: lastRate,
    },
  };
}

/**
 * Coach mode: rate one of *your* moves with the AI's own search.
 *
 * A port of ludometer/gui/coach.py, definition for definition. The same PUCT
 * search the opponent plays with is run on your position and the root's edge
 * statistics are read back out:
 *
 *     delta = Q(the move you played) − max over explored children Q
 *
 * `Q` is in the root player's frame — yours — on the net's [-1, 1] scale, so
 * 0.00 means "the move the AI would have played" and −0.06 means the search
 * values yours six hundredths of a win worse. A move the search never visited
 * has no `Q` and is reported `unrated` rather than given an invented number.
 */
async function rate(msg) {
  const state = AzulState.fromSetup(msg.setup, new Rng(rng.next()));
  const action = msg.actionId;
  const legal = state.legalActions();
  const base = { budgetS: msg.budgetS, legal: legal.length, sims: 0, elapsedS: 0 };
  if (legal.indexOf(action) === -1) {
    return { coach: { ...base, unrated: true, reason: "that move is not legal" } };
  }
  if (legal.length === 1) {
    return { coach: { ...base, delta: 0, forced: true } };
  }

  const mcts = new MCTS(adviser(), searchConfig, new Rng(rng.next()));
  // honors cancel like every other search: when the human moves, the opponent
  // gets the worker back and the verdict is read from the tree as it stands
  const gen = cancelGen;
  const result = await mcts.search(state, {
    timeLimitS: msg.budgetS,
    shouldStop: () => cancelGen !== gen,
    onProgress: ({ sims, elapsedS }) => {
      self.postMessage({ type: "progress", id: msg.id, sims, elapsedS });
    },
  });
  base.sims = result.sims;
  base.elapsedS = result.elapsedS;

  const explored = mcts.rootChildren().filter((c) => c.visits && c.q !== null);
  if (!explored.length) {
    return {
      coach: { ...base, unrated: true, reason: "the search had no time to explore this position" },
    };
  }
  const best = explored.reduce((a, b) => (b.q > a.q ? b : a));
  const mine = explored.find((c) => c.action === action);
  if (!mine) {
    return {
      coach: { ...base, unrated: true, reason: "the search never explored this move" },
    };
  }
  return {
    coach: {
      ...base,
      // Q is already in your frame, so this can only be <= 0; clamp the float
      // noise away rather than showing "+0.00"
      delta: Math.min(0, mine.q - best.q),
      your_q: mine.q,
      best_q: best.q,
      visits: mine.visits,
      best_visits: best.visits,
      best_text: describeAction(state, best.action).text,
      explored: explored.length,
    },
  };
}

/**
 * Coach mode's head start: the same search `rate` runs, but *before* the move
 * is known — it runs while the human is still thinking. The whole tree is
 * position-only, so the root's explored children can be shipped back whole and
 * the page can grade whichever move is eventually played. Honors `cancel`, so
 * the moment the human moves, the opponent's own search gets the worker back.
 */
async function analyze(msg) {
  const state = AzulState.fromSetup(msg.setup, new Rng(rng.next()));
  const legal = state.legalActions();
  const base = { budgetS: msg.budgetS, legal: legal.length, sims: 0, elapsedS: 0 };
  if (legal.length <= 1) return { analysis: { ...base, forced: true, children: [] } };

  const mcts = new MCTS(adviser(), searchConfig, new Rng(rng.next()));
  const gen = cancelGen;
  const result = await mcts.search(state, {
    timeLimitS: msg.budgetS,
    shouldStop: () => cancelGen !== gen,
    onProgress: ({ sims, elapsedS }) => {
      self.postMessage({ type: "progress", id: msg.id, sims, elapsedS });
    },
  });
  const children = mcts
    .rootChildren()
    .filter((c) => c.visits && c.q !== null)
    .map((c) => ({ action: c.action, q: c.q, visits: c.visits }));
  let bestAction = null;
  let bestText = null;
  if (children.length) {
    const best = children.reduce((a, b) => (b.q > a.q ? b : a));
    bestAction = best.action;
    bestText = describeAction(state, best.action).text;
  }
  return {
    analysis: {
      ...base,
      sims: result.sims,
      elapsedS: result.elapsedS,
      children,
      best_action: bestAction,
      best_text: bestText,
    },
  };
}

/** The policy head's own pick, no search — what the "Suggest a move" button asks. */
async function policy(msg) {
  const state = AzulState.fromSetup(msg.setup, new Rng(rng.next()));
  const legal = state.legalActions();
  if (!legal.length) throw new Error("no legal actions in the position sent to the worker");
  const { priors, value } = await adviser().evaluate(state, legal);
  let best = 0;
  for (let i = 1; i < priors.length; i++) if (priors[i] > priors[best]) best = i;
  return { action: legal[best], search: { sims: 0, elapsedS: 0, value, prior: priors[best], forced: false } };
}

self.onmessage = async (event) => {
  const msg = event.data;
  if (msg.type === "cancel") {
    cancelGen += 1;
    return;
  }
  try {
    let payload;
    if (msg.type === "init") payload = await init(msg);
    else if (msg.type === "coach") payload = await coachInit(msg);
    else if (msg.type === "search") payload = await search(msg);
    else if (msg.type === "policy") payload = await policy(msg);
    else if (msg.type === "rate") payload = await rate(msg);
    else if (msg.type === "analyze") payload = await analyze(msg);
    else throw new Error(`unknown message type ${msg.type}`);
    self.postMessage({ type: msg.type === "init" ? "ready" : "result", id: msg.id, ...payload });
  } catch (err) {
    self.postMessage({ type: "error", id: msg.id, message: String((err && err.message) || err) });
  }
};