"""Self-contained bootstrap that runs ON a Vast.ai instance.

Replicates ``providers/runpod/train.py:_train_body`` semantics on the Vast substrate: install
extra pip deps, fetch the autoslm package from the HF dataset repo, then run the
substrate-neutral worker (``autoslm.engine.worker``) to train, uploading the console
tail on failure. There is NO return channel
from the instance: the worker's HF artifacts (DONE/metrics.json/heartbeat.json) are
the success signal, and this bootstrap's attempt-scoped ``vast_attempt<N>.json`` is
the terminal marker the control plane keys failures on.

This file is shipped verbatim inside the instance's onstart script (see
``providers/vast/jobs.py:build_onstart``), so it must stay self-contained: stdlib +
huggingface_hub (installed with the worker deps) only — never import autoslm here.
It reads its payload from ``/root/autoslm/payload.json``.
"""

from __future__ import annotations

import contextlib
import json
import os
import shutil
import signal
import subprocess
import sys
import threading
import time

PAYLOAD_PATH = "/root/autoslm/payload.json"
CODE_ROOT = "/runcode"
CODE_DIR = "/runcode/code"


def load_payload(path: str = PAYLOAD_PATH) -> dict:
    with open(path) as f:
        return json.load(f)


def hf_upload(payload: dict, local_path: str, repo_subpath: str) -> None:
    """Upload one artifact under the run's HF prefix; never raises."""
    try:
        from huggingface_hub import HfApi

        HfApi(token=(payload.get("env") or {}).get("HUGGINGFACE_TOKEN")).upload_file(
            path_or_fileobj=local_path,
            path_in_repo=f"{payload['hf_prefix']}/{repo_subpath}",
            repo_id=payload["hf_repo"],
            repo_type="dataset",
        )
    except Exception as exc:
        print(f"hf upload warn ({repo_subpath}): {exc}", flush=True)


def build_worker_env(payload: dict) -> dict:
    env = dict(os.environ)
    env.update({k: str(v) for k, v in (payload.get("env") or {}).items()})
    # Pass a large spec via a file, not the environment: a job spec with large inline
    # params can reach multiple hundred KB, and that big an env var trips execve's
    # "Argument list too long" when the worker subprocess starts. Mirrors
    # runpod/train.py:_train_body.
    spec_json = payload["job_spec_json"]
    if len(spec_json) > 96_000:
        with open("/tmp/job_spec.json", "w") as f:
            f.write(spec_json)
        env["AUTOSLM_JOB_SPEC_PATH"] = "/tmp/job_spec.json"
        env.pop("AUTOSLM_JOB_SPEC_JSON", None)
    else:
        env["AUTOSLM_JOB_SPEC_JSON"] = spec_json
    env["PHASE"] = payload["phase"]
    env["SEED"] = str(payload["seed"])
    # Compute substrate for the RunMetrics record (engine.worker reads AUTOSLM_ARM). The
    # payload env was built by the shared runpod env builder, which stamps "runpod"; this
    # bootstrap runs on the Vast instance, so override it to the real backend.
    env["AUTOSLM_ARM"] = "vast"
    env["PYTHONPATH"] = CODE_DIR + (os.pathsep + env["PYTHONPATH"] if env.get("PYTHONPATH") else "")
    return env


def fetch_code(payload: dict) -> None:
    from huggingface_hub import snapshot_download

    snapshot_download(
        repo_id=payload["hf_repo"],
        repo_type="dataset",
        allow_patterns=["code/**"],
        local_dir=CODE_ROOT,
        token=(payload.get("env") or {}).get("HUGGINGFACE_TOKEN"),
    )


def run_mode(payload: dict, env: dict, mode: str, deadline_ts: float) -> int:
    """One worker process; console teed to a file and streamed to the instance log.

    On failure the console tail is uploaded as console_<mode>.txt — like _train_body,
    because subprocess consoles are the only place engine-core crashes surface. On
    deadline the process is killed and we return a sentinel nonzero rc.
    """
    console = f"/tmp/console_{mode}.txt"
    timed_out = False
    with open(console, "w") as cf:
        proc = subprocess.Popen(
            [sys.executable, "-m", "autoslm.engine.worker"],
            cwd=CODE_DIR,
            env={**env, "RUN_MODE": mode},
            stdout=subprocess.PIPE,
            stderr=subprocess.STDOUT,
            text=True,
        )

        def pump():
            for line in proc.stdout:
                print(line, end="", flush=True)
                cf.write(line)

        t = threading.Thread(target=pump, daemon=True)
        t.start()
        try:
            proc.wait(timeout=max(10.0, deadline_ts - time.time()))
        except subprocess.TimeoutExpired:
            timed_out = True
            proc.kill()
            proc.wait()
        t.join(timeout=10)
    if proc.returncode != 0 or timed_out:
        try:
            tail_path = console + ".tail"
            with open(console) as f:
                tail = f.read()[-64_000:]
            if timed_out:
                tail += f"\n--- bootstrap: mode '{mode}' hit the wall-clock cap; killed ---\n"
            with open(tail_path, "w") as f:
                f.write(tail)
            hf_upload(payload, tail_path, f"console_{mode}.txt")
        except Exception as exc:
            print(f"console upload warn: {exc}", flush=True)
    if timed_out:
        raise TimeoutError(f"worker mode '{mode}' exceeded the wall-clock cap")
    return proc.returncode


def write_attempt_marker(payload: dict, ok: bool, error: str = "") -> None:
    """Attempt-scoped terminal marker: how the control plane distinguishes THIS
    attempt's failure from a prior attempt's leftovers under the same prefix."""
    marker = {
        "ok": bool(ok),
        "ts": time.time(),
        "attempt": int(payload.get("attempt") or 0),
        "error": error[:2000],
    }
    p = "/tmp/vast_attempt.json"
    with open(p, "w") as f:
        json.dump(marker, f)
    hf_upload(payload, p, f"vast_attempt{marker['attempt']}.json")


def main() -> int:
    # Make SIGTERM (vast stop / bash `timeout`) unwind through finally so the
    # terminal marker still gets uploaded.
    signal.signal(signal.SIGTERM, lambda *a: sys.exit(1))
    payload = load_payload()
    ok = False
    error = ""
    try:
        # Fast model downloads on Vast: RunPod's Flash runtime ships hf_transfer + sets
        # HF_HUB_ENABLE_HF_TRANSFER, but Vast hosts don't — so a cold model pull is serial and
        # slow (measured ~84s for a 2 GB model vs ~6s on RunPod, where setup is now the dominant
        # cost). Install it + enable so snapshot_download/from_pretrained saturate the NIC.
        # Best-effort: only enable the flag if the package is present (enabling it WITHOUT the
        # package makes huggingface_hub hard-error).
        try:
            import importlib.util

            if importlib.util.find_spec("hf_transfer") is None:
                subprocess.run([sys.executable, "-m", "pip", "install", "hf_transfer"], check=True)
            os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
        except Exception as _e:
            print("hf_transfer setup skipped (slow downloads):", _e)
        # W&B logging (restored post-autoslm-migration): the prebuilt image predates wandb being
        # added to the stack, so install it on-demand when a W&B key is present. The worker's
        # wandb_report_to() gates report_to on the package actually importing, so this is what makes
        # W&B logging real on the current image without a rebuild.
        try:
            import importlib.util  # local: the hf_transfer block above may fail before importing it

            _penv = payload.get("env") or {}
            if (_penv.get("WANDB_API_KEY") or os.environ.get("WANDB_API_KEY")) and (
                importlib.util.find_spec("wandb") is None
            ):
                subprocess.run([sys.executable, "-m", "pip", "install", "wandb>=0.17"], check=False)
                print("[wandb] installed wandb on-demand for W&B logging")
        except Exception as _e:
            print("wandb setup skipped:", _e)
        # NB: the Hopper fla guard lives in engine.worker._drop_fla_on_hopper (runs in the worker
        # process after all installs, before any model import) — not here, where a later
        # install could pull fla back in. The bootstrap just fetches code and runs the worker.

        extra_pip = payload.get("extra_pip") or []
        if extra_pip:
            # check=True: a deterministic dependency failure (GRPO / Prime Hub
            # / verifiers extras) must stop NOW with an actionable error, not proceed to
            # a later import crash while the paid instance runs (matches the RunPod path).
            subprocess.run([sys.executable, "-m", "pip", "install", *extra_pip], check=True)
        # Per-run package-version experiments: AUTOSLM_PIP_OVERRIDE is a whitespace-separated list
        # of pins installed ADDITIVELY on top of the (baked image) stack, so an A/B can try a
        # different transformers/vllm/kernels/fla/triton combo without rebuilding the image or a
        # global change. check=False: an experimental combo that fails resolution shouldn't abort
        # the run (it just trains on the existing stack); the worker logs what's actually installed.
        _pip_override = (payload.get("env") or {}).get("AUTOSLM_PIP_OVERRIDE") or os.environ.get(
            "AUTOSLM_PIP_OVERRIDE"
        )
        if _pip_override:
            pkgs = [p for p in _pip_override.split() if p.strip()]
            if pkgs:
                subprocess.run([sys.executable, "-m", "pip", "install", *pkgs], check=False)
                print(f"[pip-override] installed experiment pins: {pkgs}")
        _wenv = payload.get("env") or {}
        # Optional Triton pin (Hopper fla-fast experiment): fla's Gated-DeltaNet Triton backward is
        # CORRECT on triton<3.4 (#640 only regresses >=3.4), so pinning an older triton restores
        # fla's fast kernel on H100 instead of the slow native fallback. torch eager doesn't
        # need triton (only inductor/compile does), so a downgrade is viable for SFT. Per-run via
        # worker_env so it never globally downgrades the 4090/5090 GRPO runs (vLLM needs new triton).
        _triton_pin = _wenv.get("AUTOSLM_TRITON_PIN") or os.environ.get("AUTOSLM_TRITON_PIN")
        if _triton_pin:
            subprocess.run(
                [sys.executable, "-m", "pip", "install", f"triton=={_triton_pin}"], check=False
            )
            print(f"[triton] pinned triton=={_triton_pin} (Hopper fla-fast experiment)")
        # Hopper Gated-DeltaNet escape hatch: flash-linear-attention's Triton backward is wrong on
        # H100 (Triton>=3.4, fla #640) and its tilelang fallback aborts in TVM FFI. With
        # AUTOSLM_DISABLE_FLA=1, uninstall both so transformers uses its native (pure-PyTorch)
        # Gated-DeltaNet — correct everywhere (slower, but the only working Hopper path for Qwen3.5/3.6).
        # The knob travels in the PAYLOAD env (forwarded via the control plane allowlist), not the
        # container env — read it there (mirrors the PRIME_API_KEY lookup below). This MUST run AFTER
        # extra_pip: WORKER_DEPS lists flash-linear-attention, so an earlier uninstall gets silently
        # undone by the extra_pip reinstall — uninstalling here is the last word before training.
        if str(_wenv.get("AUTOSLM_DISABLE_FLA") or os.environ.get("AUTOSLM_DISABLE_FLA") or "") in (
            "1",
            "true",
            "True",
        ):
            subprocess.run(
                [
                    sys.executable,
                    "-m",
                    "pip",
                    "uninstall",
                    "-y",
                    "flash-linear-attention",
                    "tilelang",
                ],
                check=False,
            )
            print("[fla] uninstalled flash-linear-attention + tilelang (Hopper native delta-rule)")
        # Install the run's verifiers environment(s) from the Prime Hub via the
        # authenticated `prime` CLI (mirrors runpod/train.py:_train_body). The public pip
        # index does not serve PRIVATE env wheels; `prime env install` pulls/builds/installs
        # public + private alike, authenticated by PRIME_API_KEY forwarded in the payload env.
        hub_env_ids = payload.get("hub_env_ids") or []
        if hub_env_ids:
            worker_env = {k: str(v) for k, v in (payload.get("env") or {}).items()}
            prime_key = worker_env.get("PRIME_API_KEY") or os.environ.get("PRIME_API_KEY")
            if not prime_key:
                raise RuntimeError(
                    "PRIME_API_KEY is required to install the Prime Hub environment on the worker"
                )
            # Only install `prime` when it isn't already present (it's often baked into the
            # instance image) — an unconditional install adds latency and a per-run PyPI
            # failure point every run.
            if shutil.which("prime") is None:
                subprocess.run([sys.executable, "-m", "pip", "install", "prime"], check=True)
            # Resolve the prime binary (located path if present, else the bare name) so the env
            # install runs through the actually-installed CLI.
            prime_bin = shutil.which("prime") or "prime"
            install_env = {
                **os.environ,
                "PRIME_API_KEY": prime_key,
                "PRIME_DISABLE_VERSION_CHECK": "1",
                "PIP_BREAK_SYSTEM_PACKAGES": "1",
            }
            # --with pip: install the env into THIS python via pip, not prime's isolated uv env
            # (the default), so the trainer can import the env module at load_environment.
            for env_id in hub_env_ids:
                subprocess.run(
                    [prime_bin, "env", "install", env_id, "--with", "pip"],
                    check=True,
                    env=install_env,
                )
        fetch_code(payload)
        env = build_worker_env(payload)
        deadline = time.time() + float(payload.get("max_wall_s") or 24 * 3600)
        phase = payload["phase"]
        # A warm/retried Vast instance can carry a previous attempt's metrics file; a
        # stale one would let a crashed train phase report the previous run's metrics.
        # Clear before training (mirrors the RunPod Flash handler in runpod/train.py).
        for stale in ("/tmp/train_meta.json", "/tmp/metrics.json"):
            with contextlib.suppress(FileNotFoundError):
                os.remove(stale)
        # Train. Nonzero rc tolerated — RL's colocated vLLM can segfault at interpreter
        # exit AFTER the adapter + metrics.json + DONE are saved. The train phase writes
        # metrics.json + DONE itself (or restores them from an earlier attempt's DONE).
        run_mode(payload, env, phase, deadline)
        if not os.path.exists("/tmp/metrics.json"):
            raise RuntimeError(
                f"train phase '{phase}' produced no /tmp/metrics.json (it crashed before "
                f"finishing); see error_{phase}.txt and console_{phase}.txt in the HF "
                f"dataset repo"
            )
        ok = True
    except Exception as exc:
        # Record genuine failures in the attempt marker (written in `finally`). Don't catch
        # BaseException — KeyboardInterrupt/SystemExit must propagate after the marker write
        # rather than be swallowed into a `return 1`.
        error = f"{type(exc).__name__}: {exc}"
        print(f"bootstrap failed: {error}", flush=True)
    finally:
        write_attempt_marker(payload, ok, error)
    return 0 if ok else 1


if __name__ == "__main__":
    sys.exit(main())