"""Generate one megakernel task from a MegaSpec.

Produces the same file layout as _factory/build.py so harbor sees a uniform lane, but the grader is
different: stateful multi-step workload, measured fusion gates, throughput reward.

The generated grader is architecture-agnostic. Everything model-specific lives in the spec's
`model_src`, which must define:

    make_weights(cfg, seed, device)                 -> weights
    make_kv(cfg, batch, prefill_len, max_seq, seed) -> per-step state (may be [] / None)
    <entry_build>(weights, kv, cfg, max_seq_len)    -> handle
    <entry_step>(handle, *args)                     -> output tensor (or tuple of tensors)

and may override the two defaults injected by PRELUDE_SRC:

    make_step_args(cfg, batch, base_pos, seed, n)   -> list of arg tuples, one per step
    compare(got, exp)                               -> float relative error
"""
import pathlib
import shutil
import sys

HERE = pathlib.Path(__file__).resolve().parent
LANE = HERE.parent
sys.path.insert(0, str(HERE))
from model import MODEL_SRC                                                        # noqa: E402

SHARED = ["restrict-network.sh", "CLAUDE.md", "PROCESS_MANAGEMENT.md"]

PREAMBLE = "import torch\nimport torch.nn.functional as F"

# Injected BEFORE model_src, so a model source that defines either name overrides the default.
PRELUDE_SRC = r'''
def make_step_args(cfg, batch, base_pos, seed, n):
    """Arguments passed to each step, after the handle. Default: (token_ids, pos)."""
    g = torch.Generator(device="cuda").manual_seed(seed)
    return [(torch.randint(0, cfg["vocab"], (batch,), device="cuda", generator=g), base_pos + i)
            for i in range(n)]


def compare(got, exp):
    """Relative error (Frobenius) used by the correctness gate. Tuples compare worst-elementwise."""
    if isinstance(exp, (tuple, list)):
        return max(compare(g, e) for g, e in zip(got, exp))
    a, b = got.float(), exp.float()
    return ((a - b).norm() / b.norm().clamp(min=1e-9)).item()
'''

# --------------------------------------------------------------------------------------------------
VERIFY = '''"""{name} verifier — three gates, then an uncapped {metric} SPEED LEADERBOARD.

    reward = 0                if ANY gate fails
    reward = {metric}         otherwise

GATES
  1. correctness      full-output relative error <= {tol} at every compared step
  2. kernels/call     <= {max_kernels} CUDA kernel launches per timed call
  3. dominant share   the largest single kernel is >= {min_dom} of GPU busy time in a call

Gates 2 and 3 are what make this a FUSION task rather than a generic speed task. They are measured,
not inspected: nothing reads your source. CUDA Graphs do not help -- a graph replays the same nodes, so
graphed eager torch still launches the same number of kernels (measured), missing gate 2 by a wide
margin.

Gates 2/3 are measured in a SEPARATE profiled run; the reward comes from a clean unprofiled run, so
profiler overhead never contaminates your score.

A submission whose persistent kernel is launched once in {entry_build}() and driven by flags will show
0 launches/call. That is the ideal design and passes both gates.

ANTI-REPLAY: every timed rep uses fresh state and a fresh argument sequence, and the output of the
last timed rep is validated against the reference for that exact sequence.

GENERATED by _mega_factory/build.py — do not edit here; edit the spec and regenerate.
"""
import importlib.util
import json
import os
import sys
import traceback

{preamble}

REWARD_DIR = "/logs/verifier"
MODULE_PATH = "/app/{module}"
CFG = {cfg!r}
TOL = {tol!r}
BATCH, PREFILL, MAX_SEQ = {batch}, {prefill}, {max_seq}
DECODE_STEPS, CORRECT_STEPS, PROF_STEPS = {decode_steps}, {correct_steps}, {prof_steps}
MAX_KERNELS, MIN_DOM = {max_kernels!r}, {min_dom!r}
WEIGHT_BYTES, FLOOR_US = {weight_bytes}, {floor_us:.1f}
REWARD_WORK, METRIC = {reward_work!r}, "{metric}"

sys.path.insert(0, "/app")

{model_src}


def _args(seed, n, base=0):
    return make_step_args(CFG, BATCH, PREFILL + base, seed, n)


def _fresh(seed):
    """Independent weights + state. Built twice from the same seed rather than shared, so a submission
    that repacks or mutates its inputs cannot disturb the reference."""
    return (make_weights(CFG, seed=seed), make_kv(CFG, BATCH, PREFILL, MAX_SEQ, seed=seed))


def _teardown(mod, h):
    fn = getattr(mod, "teardown", None)
    if fn is not None:
        try:
            fn(h)
        except Exception:
            pass


def main():
    details, gates, thru = {{}}, {{}}, 0.0
    try:
        s = importlib.util.spec_from_file_location("submission", MODULE_PATH)
        m = importlib.util.module_from_spec(s)
        s.loader.exec_module(m)

        # ---- gate 1: correctness over CORRECT_STEPS ------------------------------------------------
        W, kv = _fresh(11)
        hs = m.{entry_build}(W, kv, CFG, MAX_SEQ)
        Wr, kvr = _fresh(11)
        hr = {entry_build}(Wr, kvr, CFG, MAX_SEQ)
        worst, bad = 0.0, ""
        for i, a in enumerate(_args(500, CORRECT_STEPS)):
            got = m.{entry_step}(hs, *a)
            exp = {entry_step}(hr, *a)
            e = compare(got, exp)
            worst = max(worst, e)
            if e > TOL:
                bad = f"step {{i}} relerr {{e:.5f}} > {{TOL}}"
                break
        gates["correct"] = not bad
        details["worst_relerr"] = round(worst, 6)
        details["correct_msg"] = bad or f"all {{CORRECT_STEPS}} steps within {{TOL}} (worst {{worst:.5f}})"
        _teardown(m, hs)
        del W, kv, hs, Wr, kvr, hr
        torch.cuda.empty_cache()

        # ---- gates 2+3: profiled run (never timed) -------------------------------------------------
        from torch.profiler import profile, ProfilerActivity
        W, kv = _fresh(12)
        h = m.{entry_build}(W, kv, CFG, MAX_SEQ)
        for a in _args(600, 4):
            m.{entry_step}(h, *a)
        torch.cuda.synchronize()
        pt = _args(601, PROF_STEPS, 4)
        with profile(activities=[ProfilerActivity.CUDA]) as prof:
            for a in pt:
                m.{entry_step}(h, *a)
            torch.cuda.synchronize()
        ka = [k for k in prof.key_averages() if k.self_device_time_total > 0]
        if ka:
            tot = sum(k.self_device_time_total for k in ka)
            per_step = sum(k.count for k in ka) / PROF_STEPS
            dom = max(k.self_device_time_total for k in ka) / max(tot, 1e-9)
            top = max(ka, key=lambda k: k.self_device_time_total).key[:48]
        else:
            # no kernel launched inside a call -> a persistent kernel launched in {entry_build}
            per_step, dom, top = 0.0, 1.0, "<persistent kernel spanning the timed region>"
        gates["kernels_per_step"] = per_step <= MAX_KERNELS
        gates["dominant_share"] = dom >= MIN_DOM
        details.update(kernels_per_step=round(per_step, 2), dominant_share=round(dom, 4),
                       dominant_kernel=top)
        _teardown(m, h)
        del W, kv, h
        torch.cuda.empty_cache()

        # ---- reward: clean unprofiled timing, fresh state per rep ----------------------------------
        best_s, timed_ok = float("inf"), True
        for rep in range(3):
            W, kv = _fresh(20 + rep)
            h = m.{entry_build}(W, kv, CFG, MAX_SEQ)
            seq = _args(900 + rep, DECODE_STEPS)
            for a in seq[:3]:                                    # warm on a throwaway prefix
                m.{entry_step}(h, *a)
            torch.cuda.synchronize()
            W2, kv2 = _fresh(20 + rep)
            h2 = m.{entry_build}(W2, kv2, CFG, MAX_SEQ)
            ev0 = torch.cuda.Event(enable_timing=True)
            ev1 = torch.cuda.Event(enable_timing=True)
            ev0.record()
            for a in seq:
                out = m.{entry_step}(h2, *a)
            ev1.record()
            torch.cuda.synchronize()
            best_s = min(best_s, ev0.elapsed_time(ev1) / 1e3)
            if rep == 2:                                          # validate the LAST timed rep
                Wr, kvr = _fresh(20 + rep)
                hr = {entry_build}(Wr, kvr, CFG, MAX_SEQ)
                for a in seq:
                    exp = {entry_step}(hr, *a)
                timed_ok = compare(out, exp) <= TOL
                del Wr, kvr, hr
            _teardown(m, h)
            _teardown(m, h2)
            del W, kv, h, W2, kv2, h2
            torch.cuda.empty_cache()
        gates["timed_output_valid"] = timed_ok
        thru = (REWARD_WORK * DECODE_STEPS) / best_s if best_s > 0 else 0.0
        details.update(throughput=round(thru, 2), metric=METRIC,
                       us_per_step=round(best_s / DECODE_STEPS * 1e6, 1),
                       floor_us=FLOOR_US,
                       x_above_floor=round((best_s / DECODE_STEPS * 1e6) / FLOOR_US, 2))
    except Exception as e:
        details["error"] = f"{{e.__class__.__name__}}: {{e}}"[:220]
        details["trace"] = traceback.format_exc()[-900:]

    ok = bool(gates) and all(gates.values())
    reward = round(thru, 3) if ok else 0.0
    details["gates"] = gates
    os.makedirs(REWARD_DIR, exist_ok=True)
    json.dump({{"reward": reward, "correct": 1.0 if ok else 0.0,
               "throughput": round(thru, 3), "metric": "{metric} (uncapped)"}},
              open(f"{{REWARD_DIR}}/reward.json", "w"), indent=2)
    open(f"{{REWARD_DIR}}/reward.txt", "w").write(str(reward))
    json.dump(details, open(f"{{REWARD_DIR}}/details.json", "w"), indent=2, default=str)
    print("reward:", reward, "{metric} | correct:", 1.0 if ok else 0.0, "| gates:", gates)


main()
'''

# --------------------------------------------------------------------------------------------------
MEASURE = '''"""Self-assessment — your three gates and your {metric}, same method as the grader.

Run:  python3 /app/measure.py
This does NOT set your score; it exists so you can iterate without guessing.
"""
import importlib.util
import sys

{preamble}

CFG = {cfg!r}
TOL = {tol!r}
BATCH, PREFILL, MAX_SEQ = {batch}, {prefill}, {max_seq}
DECODE_STEPS, CORRECT_STEPS, PROF_STEPS = {decode_steps}, {correct_steps}, {prof_steps}
MAX_KERNELS, MIN_DOM = {max_kernels!r}, {min_dom!r}
FLOOR_US, REWARD_WORK = {floor_us:.1f}, {reward_work!r}

sys.path.insert(0, "/app")
{model_src}


def main():
    s = importlib.util.spec_from_file_location("sub", "/app/{module}")
    m = importlib.util.module_from_spec(s)
    s.loader.exec_module(m)
    args = lambda seed, n, base=0: make_step_args(CFG, BATCH, PREFILL + base, seed, n)

    W = make_weights(CFG, seed=11); kv = make_kv(CFG, BATCH, PREFILL, MAX_SEQ, seed=11)
    Wr = make_weights(CFG, seed=11); kvr = make_kv(CFG, BATCH, PREFILL, MAX_SEQ, seed=11)
    hs, hr = m.{entry_build}(W, kv, CFG, MAX_SEQ), {entry_build}(Wr, kvr, CFG, MAX_SEQ)
    worst = 0.0
    for a in args(500, CORRECT_STEPS):
        worst = max(worst, compare(m.{entry_step}(hs, *a), {entry_step}(hr, *a)))
    print(f"gate 1 correctness : worst relerr {{worst:.5f}}  (limit {{TOL}})  "
          f"{{'PASS' if worst <= TOL else 'FAIL'}}")

    from torch.profiler import profile, ProfilerActivity
    for a in args(600, 4, CORRECT_STEPS):
        m.{entry_step}(hs, *a)
    torch.cuda.synchronize()
    with profile(activities=[ProfilerActivity.CUDA]) as prof:
        for a in args(601, PROF_STEPS, CORRECT_STEPS + 4):
            m.{entry_step}(hs, *a)
        torch.cuda.synchronize()
    ka = [k for k in prof.key_averages() if k.self_device_time_total > 0]
    if ka:
        tot = sum(k.self_device_time_total for k in ka)
        per_step = sum(k.count for k in ka) / PROF_STEPS
        dom = max(k.self_device_time_total for k in ka) / max(tot, 1e-9)
    else:
        per_step, dom = 0.0, 1.0
    print(f"gate 2 kernels/call: {{per_step:.1f}}  (limit {{MAX_KERNELS}})  "
          f"{{'PASS' if per_step <= MAX_KERNELS else 'FAIL'}}")
    print(f"gate 3 dominant    : {{dom:.3f}}  (limit {{MIN_DOM}})  "
          f"{{'PASS' if dom >= MIN_DOM else 'FAIL'}}")

    seq = args(900, DECODE_STEPS)
    W2 = make_weights(CFG, seed=20); kv2 = make_kv(CFG, BATCH, PREFILL, MAX_SEQ, seed=20)
    h2 = m.{entry_build}(W2, kv2, CFG, MAX_SEQ)
    for a in seq[:3]:
        m.{entry_step}(h2, *a)
    torch.cuda.synchronize()
    e0, e1 = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
    e0.record()
    for a in seq:
        m.{entry_step}(h2, *a)
    e1.record()
    torch.cuda.synchronize()
    sec = e0.elapsed_time(e1) / 1e3
    us = sec / DECODE_STEPS * 1e6
    print(f"\\nthroughput         : {{REWARD_WORK*DECODE_STEPS/sec:.1f}} {metric}   "
          f"({{us:.0f}} us/call, {{us/FLOOR_US:.1f}}x the {{FLOOR_US:.0f}}us bandwidth floor)")


main()
'''

# --------------------------------------------------------------------------------------------------
STUB = '''"""YOUR SUBMISSION. {stub_head}

Two entry points. `{entry_build}` is UNTIMED — do setup there (repack weights, allocate scratch, launch
a persistent kernel). `{entry_step}` is TIMED and is what the gates measure.

Optionally define `teardown(handle)`; the grader calls it if present, so a persistent daemon can be
stopped cleanly.
"""
import torch
import torch.nn.functional as F


def {entry_build}(weights, kv_cache, cfg, max_seq_len):
    """UNTIMED setup. Return any handle you like — the grader only passes it back to {entry_step}.

    {arg_doc}
    cfg       : the architecture dict
    """
    raise NotImplementedError("implement {entry_build}")


def {entry_step}({step_sig}):
    """TIMED. {step_doc}"""
    raise NotImplementedError("implement {entry_step}")
'''

# --------------------------------------------------------------------------------------------------
DOCKERFILE = '''# {title}
# {blurb_wrapped}
#
# Graded on 1 GPU by three gates (correctness, kernels/call, dominant-kernel share) and rewarded with
# an UNCAPPED {metric} number. Offline: no internet at run time, and only the permitted toolchain is
# installed — there is no flashinfer / vllm / flash-attn / cuBLASLt-fused-model to fall back on.
FROM {base_image}

RUN pip install --break-system-packages --no-cache-dir {pip_extra} && \\
    apt-get update && apt-get install -y --no-install-recommends \\
      iptables iproute2 curl ca-certificates build-essential git && \\
    curl -LsSf https://astral.sh/uv/install.sh | sh && \\
    /root/.local/bin/uv tool install mini-swe-agent
RUN curl -fsSL https://deb.nodesource.com/setup_22.x | bash - && \\
    apt-get install -y nodejs procps && \\
    npm install -g @anthropic-ai/claude-code && claude --version
ENV PATH=/root/.local/bin:$PATH
ENV DISABLE_TELEMETRY=1 DISABLE_AUTOUPDATER=1 DISABLE_ERROR_REPORTING=1 CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC=1
RUN rm -rf /var/lib/apt/lists/* /etc/apt/sources.list /etc/apt/sources.list.d
WORKDIR /app

COPY reference.py /app/reference.py
COPY measure.py /app/measure.py
COPY {module} /app/{module}
COPY restrict-network.sh /app/restrict-network.sh
RUN chmod +x /app/restrict-network.sh
COPY CLAUDE.md PROCESS_MANAGEMENT.md /app/
'''

TASK_TOML = '''schema_version = "1.1"

[task]
name = "mle-bench/{name}"
description = "{description}"
authors = []
keywords = [{keywords}]

[metadata]
suite = "mle-bench"
group = "kernel-generation"
level = "1.0"
difficulty = "hard"
category = "mle"
tags = [ "mle", "kernel-generation", "kernels", "gpu", "real-world",]

[verifier]
timeout_sec = {verifier_timeout_sec}

[agent]
timeout_sec = {agent_timeout_sec}

# GPU request: honored by Modal/GKE/Daytona. On the local docker backend set environment.override_gpus: 0
# in the job config and attach a GPU via configs/gpu_overlay_nvidia.yaml (see RUNNING.md §5).
# Profiling: `ncu` needs GPU performance counters — the runner must add the SYS_ADMIN capability
# (docker `--cap-add SYS_ADMIN`) or the host must set NVreg_RestrictProfilingToAdminUsers=0.
# Without it ncu exits with ERR_NVGPUCTRPERM. `nsys` works without any extra capability.
[environment]
build_timeout_sec = 3600.0
cpus = 8
memory_mb = {memory_mb}
storage_mb = 40960
gpus = {gpus}
network_mode = "public"
mcp_servers = []

[verifier.env]

[environment.env]

[solution.env]
'''

INSTRUCTION = '''# {title}

{intro_md}

Edit **`/app/{module}`**. Two entry points:

```python
def {entry_build}(weights, kv_cache, cfg, max_seq_len) -> handle     # UNTIMED setup
def {entry_step}({step_sig}) -> {step_ret}   # TIMED
def teardown(handle)                                     # OPTIONAL
```

{spec_md}

## The contract

{contract_md}

## Grading: three gates, then an uncapped speed leaderboard

| gate | limit | how it is measured |
|------|-------|--------------------|
| 1. correctness | full-output relative error <= `{tol}` | vs an embedded private copy of the reference |
| 2. kernels / call | <= **{max_kernels}** | CUDA kernel launches in a profiled timed call |
| 3. dominant share | >= **{min_dom}** | largest single kernel's fraction of GPU busy time |

**reward = {metric}, uncapped. Any gate failing scores 0.**

{gates_md}

Nothing reads your source code. Gates 2 and 3 are *measured* — they are properties of how your code
actually executes. Two consequences worth internalising:

* **CUDA Graphs will not get you past gate 2.** A graph replays the same nodes; it removes launch
  overhead, not kernels.{unfused_note}
* **A persistent kernel launched once in `{entry_build}` and driven by flags shows 0 launches/call.**
  That is the ideal design and passes gates 2 and 3 outright.

Gates 2/3 are measured in a separate profiled run; your reward comes from a clean unprofiled run, so
profiling overhead never costs you score.

{regime_md}

## Toolchain

**Write this in CUDA C++ if you can.** A megakernel is exactly the case where you want direct control
over the grid, shared memory, async copies, and the device-wide barrier, and that is easiest to express
in CUDA. `nvcc` and the full CUDA toolkit are installed; build with `torch.utils.cpp_extension` (JIT or
ahead-of-time) or drive `nvcc` yourself. CUTLASS and the CuTe DSL are available.

**Triton is also acceptable** — a grid-wide barrier built from `tl.atomic_add` / `tl.atomic_cas` with a
spin loop does work here, provided your grid stays co-resident (a grid larger than what fits deadlocks
the blocks already spinning). If you find Triton expressive enough for the fusion you want, use it.

**The exact GPU is deliberately not stated — query it.** You are guaranteed compute capability
**sm≥90**, so fp8, TMA and wgmma-class instructions exist; nothing beyond that is promised. The reward
is an absolute {metric} number, and the same submission is graded on whatever device it
lands on. A grid size, tile shape or bandwidth constant hardcoded to a part you assumed is just slower.

```python
p = torch.cuda.get_device_properties(0)
p.name, p.major, p.minor          # part and compute capability
p.multi_processor_count           # SM count -- size your persistent grid from this, never a constant
p.shared_memory_per_block_optin   # opt-in dynamic smem, the number that matters for big tiles
p.total_memory, p.regs_per_multiprocessor, p.max_threads_per_multi_processor
```

Measure achieved HBM bandwidth with a large stream-copy rather than trusting a datasheet figure — every
roofline quoted below is `bytes moved / (that measured bandwidth)`.

What is **not** available, by construction rather than by policy: there is no internet at run time and
no flashinfer, vLLM, TensorRT-LLM, flash-attn, or any pre-fused whole-model inference kernel installed.
`torch` is present and you may use it for setup and for anything with no efficient direct alternative,
but note that a torch-op implementation of `{entry_step}` cannot pass gate 2 no matter how it is wrapped.

**Profilers.** `nsys` works here and answers the first question — is the time actually inside your
kernel, or in launch gaps and dispatch? `ncu` reads GPU performance counters, which the container may
not be permitted to access: if it prints `ERR_NVGPUCTRPERM`, counters are unavailable on this run. That
is an environment permission, not something you can fix from inside — fall back to `nsys` plus A/B
ablation (change one thing, re-time it) rather than spending your budget fighting it. `ncu --version`
succeeds either way; it never touches a counter. Kernel *replay* also breaks a persistent
grid-synchronizing kernel — if you do profile one, use `ncu --replay-mode application`.

## Correctness

{correctness_md}

## Precision

{precision_md}

## Where the performance comes from

{perf_md}

## Self-assessment

`python3 /app/measure.py` reports all three gates and your {metric} using the grader's method. Use it
freely — it does not set your score.

## Faithfulness

{faithfulness}
'''

FAITHFULNESS = '''Your kernel must actually compute the reference computation. Specifically:

* Do **not** skip work, shrink a dimension, or approximate a reduction that the reference performs
  exactly.
* Do **not** cache outputs across calls and replay them — every timed rep uses fresh state and a fresh
  argument sequence, and the output of the last timed rep is validated.
* Do **not** mutate the reference's inputs to make the comparison easier; the grader builds its own
  independent copy of every fixture from the same seed.

You may repack, requantise, or re-layout the weights inside `{entry_build}` — that is untimed setup and
is exactly what a real serving stack does. You may allocate whatever scratch you need there too.
'''

E1_FAITHFULNESS = '''Your kernel must actually compute the model. Specifically:

* Do **not** skip layers, shrink the vocabulary, or approximate the attention over the KV cache.
* Do **not** cache logits across steps and replay them — every timed rep uses a fresh KV cache and a
  fresh token sequence, and the final logits of the last timed rep are validated.
* Do **not** mutate the reference's inputs to make the comparison easier; the grader builds its own
  independent copy of the weights and KV from the same seed.

You may repack, requantise, or re-layout the weights inside `{entry_build}` — that is untimed setup and
is exactly what a real serving stack does. You may allocate whatever scratch you need there too.
'''

E1_INTRO = '''You are writing a **megakernel**: the entire decode step of a transformer, fused into
(essentially) one persistent GPU kernel. This is not a "make it fast" task with a fusion hint — fusion
is **gated**.'''

E1_GATES = '''Gate 2 is set at a whole-model granularity on purpose: a decode step of this model is
~40 fusable operations per layer, and an unfused implementation launches hundreds of kernels per step.
Allowing a handful of launches leaves room for a token copy, a flag reset, or a trivial epilogue
without leaving room for a per-op implementation. Gate 3 then requires that whatever you do launch is
*one* kernel doing essentially all of the work, so the count cannot be gamed by batching the model into
a few large-but-still-unfused calls.'''

RUN_MD = '''# {name}

{blurb}

* **GPUs**: {gpus}
* **Edit**: `/app/{module}`
* **Reward**: {metric}, uncapped, gated on correctness + kernels/call + dominant-kernel share

```bash
docker build -t {name} environment
docker run --rm --gpus device=0 -v $PWD/tests:/tests:ro {name} bash /tests/test.sh
```
'''


def build(spec, out_root=LANE):
    spec.validate()
    d = pathlib.Path(out_root) / spec.name
    (d / "environment").mkdir(parents=True, exist_ok=True)
    (d / "tests").mkdir(parents=True, exist_ok=True)

    src = PRELUDE_SRC.strip("\n") + "\n\n\n" + (spec.model_src or MODEL_SRC).strip("\n")
    common = dict(name=spec.name, module=spec.module, cfg=spec.cfg, tol=spec.tol,
                  batch=spec.batch, prefill=spec.prefill_len, max_seq=spec.max_seq,
                  decode_steps=spec.decode_steps, correct_steps=spec.correct_steps,
                  prof_steps=spec.prof_steps, max_kernels=spec.max_kernels_per_step,
                  min_dom=spec.min_dominant_share, preamble=PREAMBLE, model_src=src,
                  entry_build=spec.entry_build, entry_step=spec.entry_step,
                  metric=spec.reward_metric, reward_work=spec.work_per_step(),
                  weight_bytes=int(spec.total_bytes()), floor_us=spec.floor_us())

    (d / "environment" / "reference.py").write_text(
        f'"""Reference implementation — the CORRECTNESS SPEC for `{spec.name}`.\n\n'
        f"Correct, deliberately unfused, and slow. Its speed has no bearing on your score, which is an\n"
        f"absolute {spec.reward_metric} number. GENERATED by _mega_factory/build.py.\n"
        f'"""\n{PREAMBLE}\n\n{src}\n')
    (d / "tests" / "verify_env.py").write_text(VERIFY.format(**common))
    (d / "tests" / "test.sh").write_text(
        "#!/bin/bash\n# GENERATED by _mega_factory/build.py.\n"
        "set -u\nmkdir -p /logs/verifier\npython3 /tests/verify_env.py\n")
    (d / "tests" / "test.sh").chmod(0o755)
    (d / "environment" / "measure.py").write_text(MEASURE.format(**common))
    (d / "environment" / spec.module).write_text(STUB.format(
        entry_build=spec.entry_build, entry_step=spec.entry_step, step_sig=spec.step_sig,
        step_doc=spec.step_doc, arg_doc=spec.arg_doc,
        stub_head=("Fuse the whole decode step into a megakernel." if spec.family == "e1"
                   else "Fuse this primitive into a single persistent kernel.")))
    (d / "environment" / "Dockerfile").write_text(DOCKERFILE.format(
        title=spec.title, blurb_wrapped=spec.blurb.replace("\n", "\n# "), metric=spec.reward_metric,
        base_image=spec.base_image, module=spec.module, pip_extra=spec.pip_extra))
    (d / "instruction.md").write_text(INSTRUCTION.format(
        title=spec.title, module=spec.module, tol=spec.tol,
        max_kernels=spec.max_kernels_per_step, min_dom=spec.min_dominant_share,
        entry_build=spec.entry_build, entry_step=spec.entry_step, step_sig=spec.step_sig,
        metric=spec.reward_metric, step_ret=spec.step_ret,
        unfused_note=(f" Eager torch here launches ~{spec.unfused_kernels} kernels/call — off by "
                      f"~{spec.unfused_kernels / max(spec.max_kernels_per_step, 1):.0f}x."
                      if spec.unfused_kernels else ""),
        intro_md=(spec.intro_md or E1_INTRO).strip(),
        spec_md=spec.spec_md.strip(), contract_md=spec.contract_md.strip(),
        gates_md=(spec.gates_md or (E1_GATES if spec.family == "e1" else "")).strip(),
        regime_md=spec.regime_md.strip(), correctness_md=spec.correctness_md.strip(),
        precision_md=spec.precision_md.strip(), perf_md=spec.perf_md.strip(),
        faithfulness=(spec.faithfulness_md
                      or (E1_FAITHFULNESS if spec.family == "e1" else FAITHFULNESS)).format(
            entry_build=spec.entry_build).strip()))
    (d / "task.toml").write_text(TASK_TOML.format(
        name=spec.name, description=spec.blurb.replace("\n", " ").replace('"', "'"),
        keywords=", ".join(f'"{k}"' for k in (spec.keywords or ["mle", "kernel-generation"])),
        verifier_timeout_sec=spec.verifier_timeout_sec, agent_timeout_sec=spec.agent_timeout_sec,
        memory_mb=spec.memory_mb, gpus=spec.gpus))
    (d / "RUN.md").write_text(RUN_MD.format(name=spec.name, blurb=spec.blurb, gpus=spec.gpus,
                                            module=spec.module, metric=spec.reward_metric))
    for f in SHARED:
        shutil.copy(LANE / "_factory" / "shared" / f, d / "environment" / f)
    return d


if __name__ == "__main__":
    import importlib.util
    path = pathlib.Path(sys.argv[1]).resolve()
    s = importlib.util.spec_from_file_location(path.stem, path)
    mod = importlib.util.module_from_spec(s)
    s.loader.exec_module(mod)
    out = build(mod.SPEC, out_root=sys.argv[2] if len(sys.argv) > 2 else LANE)
    print(f"generated {out}  floor={mod.SPEC.floor_us():.0f}us  metric={mod.SPEC.reward_metric}")