/* GPU Lab: run a small GPU kernel on the GPU unit wired into the weights of Meta Muse Glimmer 30B (option 3 backend).
   The result is checked against the reference executor and plain Python. Honors the sleep/wake of the GPU backend. */
(() => {
'use strict';
const K = {
  vadd: {label:'Vector add', make:n => ({a:rnd(n, 50), b:rnd(n, 50)}), py:d => ({c:d.a.map((v, i) => v + d.b[i])})},
  dot: {label:'Dot product', make:n => ({a:rnd(n, 12), b:rnd(n, 12)}), py:d => ({out:[d.a.reduce((s, v, i) => s + v * d.b[i], 0)]})},
  reduce: {label:'Reduce (sum)', make:n => ({x:rnd(n, 99)}), py:d => ({out:[d.x.reduce((s, v) => s + v, 0)]})},
  histogram: {label:'Histogram (16 bins)', make:n => ({x:rnd(n, 15)}), py:d => ({bins:Array.from({length:16}, (_, i) => d.x.filter(v => v === i).length)})},
};
function rnd(n, m) { return Array.from({length:n}, () => Math.floor(Math.random() * (m + 1))); }
async function gpu(path, body, ms) {
  const ctl = new AbortController(); const to = setTimeout(() => ctl.abort(), ms || 300000);
  try {
    const r = await fetch('api/agx/' + path, body === undefined ? {cache:'no-store', signal:ctl.signal, headers:{'X-VK-Session':window.VK_SID}} : {method:'POST', headers:{'Content-Type':'application/json', 'X-VK-Session':window.VK_SID}, body:JSON.stringify(body), signal:ctl.signal});
    if (r.status === 429) throw new Error('rate');
    if (!r.ok) throw new Error('http ' + r.status);
    return await r.json();
  } finally { clearTimeout(to); }
}
const css = document.createElement('style');
css.textContent = `.gl{padding:14px;font-family:system-ui,'Segoe UI',sans-serif;display:flex;flex-direction:column;gap:12px;height:100%;overflow:auto}
.gl h3{margin:0;font-size:18px}.gl .mut{color:var(--muted);font-size:13px}.gl .row{display:flex;gap:8px;flex-wrap:wrap;align-items:center}
.gl .card{border:1px solid var(--border);border-radius:12px;padding:12px;background:var(--card)}.gl pre{margin:6px 0 0;white-space:pre-wrap;word-break:break-word;font:12px ui-monospace,Consolas,monospace;background:var(--bg);border-radius:8px;padding:8px}
.gl .ok{color:#16A34A;font-weight:800}.gl .bad{color:#E11D48;font-weight:800}.gl .tag{font:800 11px system-ui;padding:3px 8px;border-radius:999px;background:#EEF2FF;color:#4338CA}
.gl select,.gl input[type=range]{height:32px}.gl .grid{display:grid;grid-template-columns:repeat(auto-fit,minmax(150px,1fr));gap:8px}.gl .kv b{display:block;font-size:20px;white-space:nowrap}.gl .note{font-size:12px;color:var(--muted);border-left:3px solid #F59E0B;padding:4px 8px;background:var(--bg);border-radius:6px}`;
document.head.append(css);
window.VK_EXTRA_APPS = window.VK_EXTRA_APPS || [];
window.VK_EXTRA_APPS.push(h => ({id:'gpulab', name:() => 'GPU Lab', color:'#0F766E',
  icon:'<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><rect x="4" y="4" width="16" height="16" rx="2"/><rect x="8" y="8" width="8" height="8" rx="1"/><path d="M9 1v3M15 1v3M9 20v3M15 20v3M1 9h3M1 15h3M20 9h3M20 15h3"/></svg>',
  w:820, h:640, render(body) {
    body.style.padding = '0';
    const root = document.createElement('div'); root.className = 'gl'; body.append(root);
    root.innerHTML = `<div><h3>GPU Lab</h3><div class="mut">A GPU unit wired into the weights of Meta Muse Glimmer 30B runs a small kernel inside the model's own forward pass. The result is checked token by token against the reference executor and against plain Python.</div>
      <div class="note" style="margin-top:8px">Exact and auditable, not a GPU replacement: a real GPU is roughly a trillion times faster in raw throughput.</div></div>
      <div class="card row"><label>Kernel <select data-k="k">${Object.entries(K).map(([k, v]) => `<option value="${k}">${v.label}</option>`).join('')}</select></label>
        <label>Length n <input type="range" min="4" max="16" value="8" data-k="n"> <b data-k="nv">8</b></label>
        <label>Mode <select data-k="mode"><option value="verify">Verify in the weights</option><option value="reference">Reference executor only</option></select></label>
        <button class="btn" data-k="rand">Random inputs</button><button class="btn pri" data-k="run">Run on the GPU in the weights</button><span class="tag" data-k="state">checking backend...</span></div>
      <div class="card"><b>Inputs</b><pre data-k="in"></pre></div>
      <div class="card" data-k="out"><span class="mut">Pick a kernel and press Run. A run takes from a few seconds to about a minute, because every GPU step is computed by the model.</span></div>`;
    const q = k => root.querySelector(`[data-k="${k}"]`);
    let data = null, waking = false;
    const fresh = () => { data = K[q('k').value].make(+q('n').value); q('in').textContent = JSON.stringify(data); };
    q('k').onchange = fresh; q('n').oninput = () => { q('nv').textContent = q('n').value; fresh(); }; q('rand').onclick = fresh; fresh();
    const warm = window.VK_WARM.bar(); root.insertBefore(warm, root.children[1]);
    let isReady = false;
    async function health() { const h = await window.VK_WARM.health(); isReady = window.VK_WARM.ready(h, true); if (isReady) { warm.set({pct:100}); q('state').textContent = 'GPU backend ready'; } return isReady; }
    async function ensureAwake() {
      if (await health()) return true;
      q('state').textContent = 'loading the model...';
      if (waking) return false; waking = true;
      const ok = await window.VK_WARM.wait({needGpu:true, alive:() => root.isConnected, onProgress:i => { warm.set(i); q('state').textContent = i.pct >= 100 ? 'GPU backend ready' : 'loading the model ' + i.pct + '%'; }});
      waking = false; isReady = ok;
      if (ok) h.toast('GPU Lab: the in-weights model is ready'); else q('state').textContent = 'still loading; try again shortly';
      return ok;
    }
    ensureAwake();
    // run all 34: every kernel x 2 random inputs, checked in the weights, shown live as each launch finishes
    const all = document.createElement('div'); all.className = 'card';
    all.innerHTML = `<div class="row" style="justify-content:space-between"><b>Acceptance set, live</b><button class="btn pri" data-k="all">Run all 34 kernel launches</button></div>
      <div class="mut" style="margin-top:4px">Every kernel in the unit, twice, with fresh random inputs. Each launch is checked inside the weights against the reference executor, and against plain Python where one exists. Nothing is replayed.</div>
      <div data-k="allsum" style="margin-top:8px"></div><div data-k="allrows" style="max-height:260px;overflow:auto;margin-top:6px"></div>`;
    root.insertBefore(all, q('out'));
    const esc = v => String(v).replace(/[&<>]/g, c => ({'&':'&amp;', '<':'&lt;', '>':'&gt;'})[c]);
    function drawJob(j, wall) {
      const done = j.done || 0, tot = j.total || 34, pct = Math.round(100 * done / tot);
      q('allsum').innerHTML = `<div class="grid"><div class="kv"><span class="mut">exact in the weights</span><b class="${j.exact === done ? 'ok' : 'bad'}">${j.exact}/${tot}</b></div>
        <div class="kv"><span class="mut">equal to plain Python</span><b class="${j.python_equal === j.python_checked ? 'ok' : 'bad'}">${j.python_equal}/${j.python_checked}</b></div>
        <div class="kv"><span class="mut">launches done</span><b>${done}/${tot}</b></div><div class="kv"><span class="mut">time</span><b>${(j.elapsed == null ? wall : j.elapsed).toFixed(1)} s</b></div></div>
        <div class="vkwarm" style="margin-top:6px;padding:4px 8px"><div class="vkw-b" style="margin:0"><div class="vkw-f" style="width:${pct}%"></div></div></div>`;
      q('allrows').innerHTML = `<table style="width:100%;border-collapse:collapse;font:12px ui-monospace,Consolas,monospace"><tr style="text-align:left;color:var(--muted)"><th>#</th><th>kernel</th><th>n</th><th>output</th><th>tokens</th><th>in-weights</th><th>Python</th><th>s</th></tr>` +
        (j.rows || []).map((r, i) => `<tr style="border-top:1px solid var(--border)"><td>${i + 1}</td><td>${esc(r.kernel)}</td><td>${r.n}</td><td title="${esc(JSON.stringify(r.inputs))}">${esc(JSON.stringify(r.outputs)).slice(0, 60)}</td><td>${r.tokens}</td>
          <td class="${r.token_exact && r.equals_reference ? 'ok' : 'bad'}">${r.token_exact && r.equals_reference ? '&#10003; exact' : '&#10007;'}</td><td class="${r.equals_python == null ? '' : r.equals_python ? 'ok' : 'bad'}">${r.equals_python == null ? '-' : r.equals_python ? '&#10003;' : '&#10007;'}</td><td>${r.sec}</td></tr>`).join('') + '</table>';
    }
    async function runAll() {
      const btnAll = q('all');
      if (!isReady && !(await health())) { q('allsum').innerHTML = '<span class="mut">The in-weights model is loading; see the progress bar above. The run starts once it is ready.</span>'; btnAll.disabled = true; const ok = await ensureAwake(); btnAll.disabled = false; if (!ok) return; }
      btnAll.disabled = true; const t0 = performance.now();
      try {
        let j = await gpu('gpu/all', {}, 30000);
        if (j.error) throw new Error(j.error);
        if (!j.started) q('allsum').innerHTML = '<span class="mut">Another visitor started a run a moment ago; showing that run live.</span>';
        while (root.isConnected) {
          await new Promise(r => setTimeout(r, 700));
          j = await gpu('gpu/job', undefined, 15000); drawJob(j, (performance.now() - t0) / 1000);
          if (!j.running) break;
        }
        if (j.exact === j.total) h.toast(`GPU Lab: ${j.exact}/${j.total} exact in ${j.elapsed} s`);
        return j;
      } catch (e) { q('allsum').innerHTML = `<span class="mut">${e.message === 'rate' ? 'Too many requests from your network; try again in a few minutes.' : 'The in-weights model is busy or loading. Please try again in a minute.'}</span>`; }
      finally { btnAll.disabled = false; }
    }
    q('all').onclick = runAll;
    root.vkRunAll = runAll;
    q('run').onclick = async () => {
      const out = q('out'), k = q('k').value, n = +q('n').value;
      if (!isReady && !(await health())) { out.innerHTML = '<span class="mut">The in-weights model is loading (progress above). Agent Guard option 1 stays live meanwhile.</span>'; ensureAwake(); return; }
      q('run').disabled = true; out.innerHTML = '<span class="mut">Running inside the model...</span>';
      const t0 = performance.now();
      try {
        const mode = q('mode').value;
        const r = await gpu('gpu', {kernel:k, data, n, mode});
        if (r.error) throw new Error(r.error);
        const py = K[k].py(data), wall = (performance.now() - t0) / 1000;
        const okPy = JSON.stringify(r.outputs) === JSON.stringify(py);
        const fms = v => v == null ? 'n/a' : (+v).toFixed(1) + ' ms';
        const parity = r.parity == null ? `<div class="note" style="margin-top:8px">Parity: not checked in this mode. Reference executor only, ${fms(r.ms_reference)}.</div>`
          : `<div class="note" style="margin-top:8px">Parity: in-weights ${r.parity ? '&#10003; equals' : '&#10007; differs from'} reference. In the weights ${fms(r.ms_verify)}, reference ${fms(r.ms_reference)}.</div>`;
        out.innerHTML = `${parity}<div class="grid"><div class="kv"><span class="mut">exact vs reference</span><b class="${r.equals_reference ? 'ok' : 'bad'}">${r.equals_reference ? '&#10003; exact' : '&#10007; differs'}</b></div>
          <div class="kv"><span class="mut">token-exact vs executor</span><b class="${r.token_exact == null ? '' : r.token_exact ? 'ok' : 'bad'}">${r.token_exact == null ? 'n/a' : r.token_exact ? '&#10003;' : '&#10007;'}</b></div>
          <div class="kv"><span class="mut">matches plain Python</span><b class="${okPy ? 'ok' : 'bad'}">${okPy ? '&#10003;' : '&#10007;'}</b></div>
          <div class="kv"><span class="mut">model tokens</span><b>${(r.tokens || 0).toLocaleString('en-US')}</b></div>
          <div class="kv"><span class="mut">GPU time in the model</span><b>${r.sec == null ? 'n/a' : r.sec + ' s'}</b></div><div class="kv"><span class="mut">round trip</span><b>${wall.toFixed(1)} s</b></div></div>
          <b style="display:block;margin-top:10px">Output</b><pre>${JSON.stringify(r.outputs)}</pre><b>Plain Python</b><pre>${JSON.stringify(py)}</pre>`;
      } catch (e) {
        out.innerHTML = `<span class="mut">${e.message === 'rate' ? 'Too many requests from your network; try again in a few minutes.' : 'The in-weights model is busy or waking. Please try again in a minute.'}</span>`;
        ensureAwake();
      } finally { q('run').disabled = false; }
    };
  }}));
})();