Blame for inference.py

#!/usr/bin/env python3
"""Standalone chat inference for KerzgrAI/Senryu-2.2-1B.

Streams a live reasoning trace (`<think>` / `<|think|>`) and runs Hermes JSON
tool-call rounds. By default the chat starts the way Senryu-2.2 trained: its
Senryu system prompt, dated today, and the `web-search`, `fetch-page` and
`calculator` tools, called in Hermes JSON `<tool_call>` blocks. Those three run
automatically, like the built-in `python` tool, so the model can take several
tool rounds per reply. Other observations are pasted back and sent as
`<|tool_response|>`.

Examples:
  python inference.py --prompt "Explain recursion in two sentences."
  python inference.py --prompt "Summarize https://en.wikipedia.org/wiki/Senry%C5%AB in three bullets."
  python inference.py --tools none --system ""   # no tools, no system prompt
  python inference.py --tools web-search,fetch-page,calculator,python
  python inference.py --no-think --prompt "Reply in one sentence."
  python inference.py --ngram-device cpu   # keep the 4.3 GB n-gram tables in host RAM
"""

from __future__ import annotations

import argparse
import importlib
import importlib.util
import json
import os
import platform
import shutil
import subprocess
import sys
import time
import warnings
from pathlib import Path
from types import SimpleNamespace
from typing import Any

import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from tokenizers import Tokenizer


def _silence_runtime_warnings() -> None:
    patterns = (
        r"tl\.make_block_ptr is deprecated",
        r"Memory efficient kernel not used because",
        r"Memory Efficient attention has been runtime disabled",
        r"Flash attention kernel not used because",
        r"Torch was not compiled with flash attention",
        r"cuDNN attention kernel not used because",
        r"cuDNN attention has been runtime disabled",
    )
    for pattern in patterns:
        warnings.filterwarnings("ignore", message=pattern)


REPO_ID = "KerzgrAI/Senryu-2.2-1B"
MODEL_NAME = "Senryu-2.2-1B"
# The name the model knows itself by in its trained system prompt.
PROMPT_NAME = "Senryu-2.2"
# Hermes JSON <tool_call> blocks after a <tools> block in the system turn: the layout Senryu-2.2 trained on.
TOOL_FORMAT = "json"
# fetch-page's HTML and PDF readers, by import name.
TOOL_PACKAGES = {
    "trafilatura": "trafilatura>=2.0",
    "bs4": "beautifulsoup4>=4.12",
    "pypdf": "pypdf>=5.0",
    "urllib3": "urllib3>=2.0",
}
FLA_COMMIT = "cbb0a72efb55c18ca0ef4f298298317573ad2cb3"
FLA_REPO = "https://github.com/fla-org/flash-linear-attention.git"
FLA_RUNTIME_REQUIREMENT = "transformers>=4.45.0"
# Parallax attention is not in a flash-linear-attention release yet, so fla
# installs from git at the commit the model was trained with.
FLA_MODULES = (
    "fla.layers.gdn2",
    "fla.models.utils",
    "fla.modules.rotary",
    "fla.ops.parallax.parallel",
    "fla.ops.parallax.decode",
)
PATCH_FILES = (
    "fla/__init__.py",
    "fla/ops/__init__.py",
    "fla/layers/__init__.py",
    "fla/ops/simple_gla/__init__.py",
)
TINY_GDN_FILES = (
    "tiny_gdn/__init__.py",
    "tiny_gdn/config.py",
    "tiny_gdn/model.py",
    "tiny_gdn/haiku_layers.py",
    "tiny_gdn/nn_common.py",
    "tiny_gdn/fused_ops.py",
    "tiny_gdn/senryu2.py",
    "tiny_gdn/parallax_ops.py",
    "tiny_gdn/gated_residual_ops.py",
    "tiny_gdn/ngram.py",
    "tiny_gdn/tools.py",
    "tiny_gdn/smoltalk_chat.py",
    "tiny_gdn/chatml.py",
    "tiny_gdn/cli_chat.py",
    "tiny_gdn/detokenize.py",
    "tiny_gdn/repetition.py",
    "tiny_gdn/code_exec.py",
    "tiny_gdn/paths.py",
    "tiny_gdn/token_file.py",
    "tiny_gdn/web_search.py",
    "tiny_gdn/web_fetch.py",
    "tiny_gdn/senryu_chat.py",
)
NGRAM_TABLES_FILE = "ngram_tables.safetensors"


def _download(filename: str, local_dir: Path | None) -> Path:
    return Path(
        hf_hub_download(
            repo_id=REPO_ID,
            filename=filename,
            local_dir=str(local_dir) if local_dir else None,
        )
    )


def _run(cmd: list[str], *, cwd: Path | None = None, env: dict | None = None) -> None:
    print("+", " ".join(cmd), flush=True)
    merged = os.environ.copy()
    if env:
        merged.update(env)
    merged.setdefault("PYTHONUTF8", "1")
    merged.setdefault("PYTHONIOENCODING", "utf-8")
    subprocess.check_call(cmd, cwd=str(cwd) if cwd else None, env=merged)


def _pip_install(*args: str) -> None:
    _run([sys.executable, "-m", "pip", "install", *args])


def _fla_importable() -> tuple[bool, str]:
    try:
        for name in FLA_MODULES:
            importlib.import_module(name)
    except Exception as error:  # noqa: BLE001
        return False, str(error)
    return True, ""


def _cache_root() -> Path:
    override = os.environ.get("SENRYU_CACHE")
    if override:
        path = Path(override).expanduser().resolve()
    else:
        path = Path.home() / ".cache" / "senryu"
    path.mkdir(parents=True, exist_ok=True)
    return path


def _ensure_git() -> None:
    if shutil.which("git") is None:
        raise RuntimeError(
            "git is required to auto-install flash-linear-attention. "
            "Install Git and ensure it is on PATH."
        )


def _apply_windows_fla_patches(fla_root: Path, local_dir: Path | None) -> None:
    print("Applying Windows FLA import patches from the Hub …", flush=True)
    for relative in PATCH_FILES:
        source = _download(f"windows_fla_patches/{relative}", local_dir)
        target = fla_root / relative
        target.parent.mkdir(parents=True, exist_ok=True)
        shutil.copy2(source, target)
        print(f"  patched {relative}", flush=True)


def _install_fla(local_dir: Path | None) -> None:
    print("flash-linear-attention missing/broken — installing automatically …", flush=True)
    _pip_install("einops", "numpy", FLA_RUNTIME_REQUIREMENT)
    if platform.system() != "Windows":
        _pip_install("--no-deps", "--force-reinstall", f"git+{FLA_REPO}@{FLA_COMMIT}")
        return

    _ensure_git()
    fla_root = _cache_root() / "flash-linear-attention"
    if (fla_root / ".git").is_dir():
        _run(["git", "fetch", "--depth", "1", "origin", FLA_COMMIT], cwd=fla_root)
        _run(["git", "checkout", "--force", FLA_COMMIT], cwd=fla_root)
    else:
        if fla_root.exists():
            shutil.rmtree(fla_root)
        _run(["git", "clone", "--filter=blob:none", FLA_REPO, str(fla_root)])
        _run(["git", "fetch", "--depth", "1", "origin", FLA_COMMIT], cwd=fla_root)
        _run(["git", "checkout", "--force", FLA_COMMIT], cwd=fla_root)

    _apply_windows_fla_patches(fla_root, local_dir)
    _pip_install("--no-build-isolation", "--no-deps", "-e", str(fla_root))


def _ensure_fla(local_dir: Path | None) -> None:
    ok, error = _fla_importable()
    if ok:
        return
    print(f"FLA not ready ({error})", flush=True)
    try:
        _install_fla(local_dir)
    except Exception as install_error:  # noqa: BLE001
        raise RuntimeError(
            "Automatic flash-linear-attention install failed.\n"
            f"Original import error: {error}\n"
            f"Install error: {install_error}"
        ) from install_error

    for name in list(sys.modules):
        if name == "fla" or name.startswith("fla."):
            del sys.modules[name]
    importlib.invalidate_caches()

    ok, error = _fla_importable()
    if not ok:
        raise RuntimeError(
            "flash-linear-attention installed but still failed to import "
            f"Gated DeltaNet-2, Parallax attention and the decode cache: {error}"
        )
    print("flash-linear-attention ready.", flush=True)


def _ensure_tool_packages() -> None:
    missing = [
        requirement
        for module, requirement in TOOL_PACKAGES.items()
        if importlib.util.find_spec(module) is None
    ]
    if not missing:
        return
    print("Installing the fetch-page readers …", flush=True)
    _pip_install(*missing)
    importlib.invalidate_caches()


def _has_tiny_gdn(root: Path) -> bool:
    return all((root / name).is_file() for name in TINY_GDN_FILES)


def _ensure_tiny_gdn(local_dir: Path | None) -> Path:
    here = Path(__file__).resolve().parent
    if _has_tiny_gdn(here):
        return here
    if local_dir and _has_tiny_gdn(local_dir):
        return local_dir
    for name in TINY_GDN_FILES:
        _download(name, local_dir)
    return _download("tiny_gdn/__init__.py", local_dir).parent.parent


def _import_chat_stack() -> SimpleNamespace:
    # tiny_gdn is fetched from the Hub and FLA may be installed at runtime, so
    # these modules only become importable after _ensure_tiny_gdn/_ensure_fla.
    config = importlib.import_module("tiny_gdn.config")
    senryu2 = importlib.import_module("tiny_gdn.senryu2")
    chatml = importlib.import_module("tiny_gdn.chatml")
    cli_chat = importlib.import_module("tiny_gdn.cli_chat")
    detokenize = importlib.import_module("tiny_gdn.detokenize")
    repetition = importlib.import_module("tiny_gdn.repetition")
    tools = importlib.import_module("tiny_gdn.tools")
    senryu_chat = importlib.import_module("tiny_gdn.senryu_chat")
    return SimpleNamespace(
        TinyGDNConfig=config.TinyGDNConfig,
        Senryu2ForCausalLM=senryu2.Senryu2ForCausalLM,
        encode_chatml_messages=chatml.encode_chatml_messages,
        ChatLoopState=cli_chat.ChatLoopState,
        LiveReasoningStreamer=cli_chat.LiveReasoningStreamer,
        append_tool_round=cli_chat.append_tool_round,
        apply_slash_command=cli_chat.apply_slash_command,
        prompt_tool_results=cli_chat.prompt_tool_results,
        resolve_cli_tools=cli_chat.resolve_cli_tools,
        IncrementalUtf8Decoder=detokenize.IncrementalUtf8Decoder,
        apply_repetition_penalty=repetition.apply_repetition_penalty,
        apply_dry_penalty=repetition.apply_dry_penalty,
        cached_structural_token_flags=repetition.cached_structural_token_flags,
        cached_dry_vocabulary=repetition.cached_dry_vocabulary,
        parse_tool_calls=tools.parse_tool_calls,
        senryu2_2_1b_system_prompt=senryu_chat.senryu2_2_1b_system_prompt,
        SENRYU_TOOL_SPEC=senryu_chat.SENRYU_TOOL_SPEC,
    )


def _sample(
    logits: torch.Tensor,
    *,
    temperature: float,
    top_p: float,
    top_k: int,
    generator: torch.Generator,
) -> int:
    logits = logits.float()
    if temperature <= 1e-5:
        return int(torch.argmax(logits).item())
    logits = logits / temperature
    if 0 < top_k < logits.shape[-1]:
        threshold = torch.topk(logits, top_k).values[-1]
        logits = logits.masked_fill(logits < threshold, -torch.inf)
    if top_p < 1.0:
        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
        probs = torch.softmax(sorted_logits, dim=-1)
        remove = torch.cumsum(probs, dim=-1) > top_p
        remove[1:] = remove[:-1].clone()
        remove[0] = False
        sorted_logits = sorted_logits.masked_fill(remove, -torch.inf)
        logits = torch.full_like(logits, -torch.inf)
        logits.scatter_(0, sorted_indices, sorted_logits)
    probs = torch.softmax(logits, dim=-1).cpu()
    return int(torch.multinomial(probs, 1, generator=generator).item())


def _penalize_repeats(
    logits: torch.Tensor,
    token_ids: list[int],
    stack: SimpleNamespace,
    tokenizer: Tokenizer,
    *,
    repetition_penalty: float,
    repetition_window: int,
    dry_multiplier: float,
    dry_base: float,
    dry_allowed_length: int,
) -> torch.Tensor:
    """The KerzgrAI API's repetition control: a Markdown-safe penalty, then DRY."""

    logits = logits.float()
    if repetition_penalty != 1.0:
        stack.apply_repetition_penalty(
            logits,
            token_ids,
            penalty=repetition_penalty,
            window=repetition_window,
            structural=stack.cached_structural_token_flags(tokenizer),
        )
    if dry_multiplier != 0:
        stack.apply_dry_penalty(
            logits,
            token_ids,
            multiplier=dry_multiplier,
            base=dry_base,
            allowed_length=dry_allowed_length,
            window=0,
            vocabulary=stack.cached_dry_vocabulary(tokenizer),
        )
    return logits


@torch.inference_mode()
def generate_turn(
    model,
    tokenizer: Tokenizer,
    stack: SimpleNamespace,
    streamer,
    messages: list[dict[str, Any]],
    *,
    tools: Any | None,
    enable_thinking: bool,
    max_new_tokens: int,
    context_length: int,
    temperature: float,
    top_p: float,
    top_k: int,
    repetition_penalty: float,
    repetition_window: int,
    dry_multiplier: float,
    dry_base: float,
    dry_allowed_length: int,
    seed: int,
    device: torch.device,
) -> tuple[str, tuple]:
    prompt_ids = stack.encode_chatml_messages(
        tokenizer,
        messages,
        tools=tools,
        enable_thinking=enable_thinking,
        tool_format=TOOL_FORMAT,
    )
    if len(prompt_ids) > context_length:
        print(
            f"[warn] conversation is {len(prompt_ids)} tokens; "
            f"keeping the last {context_length} (--context-length)",
            file=sys.stderr,
            flush=True,
        )
    eos_id = int(model.config.eos_token_id)
    im_end = tokenizer.token_to_id("<|im_end|>")
    stop = {eos_id}
    if im_end is not None:
        stop.add(im_end)

    token_ids = list(prompt_ids[-context_length:])
    generated: list[int] = []
    decoder = stack.IncrementalUtf8Decoder(tokenizer)
    stop_reason = "max_new_tokens"
    generator = torch.Generator(device="cpu")
    generator.manual_seed(seed)
    started = time.perf_counter()

    try:
        input_ids = torch.tensor([token_ids], dtype=torch.long, device=device)
        output = model(
            input_ids,
            return_logits=True,
            logits_to_keep=1,
            use_cache=True,
        )

        for step in range(max_new_tokens):
            if output.logits is None:
                raise RuntimeError("Model returned no logits")
            next_logits = _penalize_repeats(
                output.logits[0, -1],
                token_ids,
                stack,
                tokenizer,
                repetition_penalty=repetition_penalty,
                repetition_window=repetition_window,
                dry_multiplier=dry_multiplier,
                dry_base=dry_base,
                dry_allowed_length=dry_allowed_length,
            )
            next_id = _sample(
                next_logits,
                temperature=temperature,
                top_p=top_p,
                top_k=top_k,
                generator=generator,
            )
            if next_id in stop:
                stop_reason = "stop"
                break
            token_ids.append(next_id)
            generated.append(next_id)
            decoder.push(next_id)
            if streamer is not None:
                streamer.update(decoder.text)
            if step + 1 < max_new_tokens:
                if output.past_key_values is None:
                    raise RuntimeError("Model returned no decode cache")
                if output.past_key_values.length + 1 > model.config.max_position_embeddings:
                    stop_reason = "context_full"
                    break
                input_ids = torch.tensor([[next_id]], dtype=torch.long, device=device)
                output = model(
                    input_ids,
                    return_logits=True,
                    logits_to_keep=1,
                    past_key_values=output.past_key_values,
                    use_cache=True,
                )
    except KeyboardInterrupt:
        stop_reason = "interrupted"

    decoded = decoder.finalize()
    if streamer is not None:
        streamer.finish()
    elapsed = time.perf_counter() - started
    tps = len(generated) / max(elapsed, 1e-9)
    print(
        f"[done] tokens={len(generated)} stop={stop_reason} {tps:.1f} tok/s",
        file=sys.stderr,
        flush=True,
    )
    if stop_reason == "interrupted":
        return decoded, ()
    return decoded, tuple(stack.parse_tool_calls(decoded))


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description=f"{MODEL_NAME} chat inference")
    parser.add_argument("--prompt", default=None, help="Single user prompt")
    parser.add_argument(
        "--system",
        default=None,
        help=f"System prompt (default: the {PROMPT_NAME} prompt the model trained with, dated today; '' for none)",
    )
    parser.add_argument(
        "--think",
        dest="enable_thinking",
        action="store_true",
        default=True,
    )
    parser.add_argument(
        "--no-think",
        dest="enable_thinking",
        action="store_false",
    )
    parser.add_argument(
        "--tools",
        default=None,
        help=(
            "Built-in tools, comma-separated: web-search, fetch-page, calculator, python "
            "(default: web-search,fetch-page,calculator, the tools the model trained with; none for no tools)"
        ),
    )
    parser.add_argument("--tools-json", default=None)
    parser.add_argument("--max-tool-rounds", type=int, default=8)
    parser.add_argument("--max-new-tokens", type=int, default=4096)
    parser.add_argument("--temperature", type=float, default=0.6)
    parser.add_argument("--top-p", type=float, default=0.95)
    parser.add_argument("--top-k", type=int, default=50)
    parser.add_argument("--repetition-penalty", type=float, default=1.08)
    parser.add_argument("--repetition-window", type=int, default=256)
    parser.add_argument(
        "--dry-multiplier",
        type=float,
        default=0.8,
        help="DRY penalty on verbatim loops and re-pasted replies (default 0.8; 0 disables)",
    )
    parser.add_argument(
        "--dry-base",
        type=float,
        default=1.75,
        help="How fast the DRY penalty grows per repeated token (default 1.75)",
    )
    parser.add_argument(
        "--dry-allowed-length",
        type=int,
        default=2,
        help="Tokens a phrase may repeat before DRY starts (default 2)",
    )
    parser.add_argument("--context-length", type=int, default=32768)
    parser.add_argument("--seed", type=int, default=42)
    parser.add_argument(
        "--device",
        default="cuda",
        help=f"CUDA device (cuda, cuda:1, …); {MODEL_NAME} needs a GPU",
    )
    parser.add_argument(
        "--ngram-device",
        default=None,
        help="Where the n-gram tables live: the GPU (default, --device) or cpu (host RAM, slower decode)",
    )
    parser.add_argument("--no-stream", action="store_true")
    parser.add_argument("--no-color", action="store_true")
    parser.add_argument("--local-dir", default=None)
    parser.add_argument("--repo-id", default=REPO_ID)
    return parser.parse_args()


def main() -> int:
    _silence_runtime_warnings()
    args = parse_args()
    global REPO_ID
    REPO_ID = args.repo_id
    local_dir = Path(args.local_dir).resolve() if args.local_dir else None

    try:
        device = torch.device(args.device)
        ngram_device = torch.device(args.ngram_device) if args.ngram_device else device
    except RuntimeError as error:
        print(f"Invalid device: {error}", file=sys.stderr)
        return 1
    if device.type != "cuda" or not torch.cuda.is_available():
        print(
            f"{MODEL_NAME} needs a CUDA GPU: its Gated DeltaNet-2 and Parallax "
            "layers run on flash-linear-attention Triton kernels. "
            f"torch {torch.__version__} reports CUDA available={torch.cuda.is_available()}.",
            file=sys.stderr,
        )
        return 1
    if ngram_device.type not in {"cuda", "cpu"}:
        print("--ngram-device must be a CUDA device or cpu", file=sys.stderr)
        return 1

    print(f"Loading {MODEL_NAME} from huggingface.co/{REPO_ID} …", flush=True)
    try:
        package_root = _ensure_tiny_gdn(local_dir)
    except Exception as error:  # noqa: BLE001
        print(f"Failed to resolve tiny_gdn package: {error}", file=sys.stderr)
        return 1

    if str(package_root) not in sys.path:
        sys.path.insert(0, str(package_root))

    try:
        _ensure_fla(local_dir)
        _ensure_tool_packages()
    except Exception as error:  # noqa: BLE001
        print(str(error), file=sys.stderr)
        return 1

    try:
        stack = _import_chat_stack()
    except ImportError as error:
        print(f"Could not import tiny_gdn chat stack: {error}", file=sys.stderr)
        return 1

    try:
        raw_json = (
            Path(args.tools_json).read_text(encoding="utf-8")
            if args.tools_json
            else None
        )
        tools = stack.resolve_cli_tools(
            stack.SENRYU_TOOL_SPEC if args.tools is None else args.tools,
            raw_json,
        )
    except (OSError, ValueError, json.JSONDecodeError) as error:
        print(error, file=sys.stderr)
        return 1

    weights_path = _download("model.safetensors", local_dir)
    tables_path = _download(NGRAM_TABLES_FILE, local_dir)
    tok_path = _download("tokenizer.json", local_dir)
    cfg_path = _download("config.json", local_dir)

    config = stack.TinyGDNConfig.from_json(cfg_path)
    print(
        f"Building {MODEL_NAME} ({config.num_hidden_layers}L / {config.hidden_size}d) "
        f"on {device}, n-gram tables on {ngram_device} …",
        flush=True,
    )
    with torch.device(device):
        model = stack.Senryu2ForCausalLM(config)
    model = model.to(dtype=torch.bfloat16)
    model.load_state_dict(load_file(str(weights_path), device=str(device)), strict=True)
    if model.ngram is not None:
        model.ngram.load_tables(tables_path, device=ngram_device, dtype=torch.bfloat16)
    model.eval()
    model.requires_grad_(False)

    tokenizer = Tokenizer.from_file(str(tok_path))
    context_length = min(args.context_length, config.max_position_embeddings)
    color = not args.no_color and sys.stdout.isatty()

    def read_line(prompt: str) -> str:
        return input(prompt)

    def run_turn(state) -> None:
        for round_index in range(max(1, args.max_tool_rounds + 1)):
            streamer = (
                None
                if args.no_stream
                else stack.LiveReasoningStreamer(sys.stdout, color=color)
            )
            if streamer is not None:
                print("assistant> ", end="", flush=True)
            text, calls = generate_turn(
                model,
                tokenizer,
                stack,
                streamer,
                state.messages,
                tools=state.tools,
                enable_thinking=state.enable_thinking,
                max_new_tokens=args.max_new_tokens,
                context_length=context_length,
                temperature=args.temperature,
                top_p=args.top_p,
                top_k=args.top_k,
                repetition_penalty=args.repetition_penalty,
                repetition_window=args.repetition_window,
                dry_multiplier=args.dry_multiplier,
                dry_base=args.dry_base,
                dry_allowed_length=args.dry_allowed_length,
                seed=args.seed,
                device=device,
            )
            if args.no_stream:
                print(f"assistant> {text}")
            if not calls or round_index >= args.max_tool_rounds:
                state.messages.append({"role": "assistant", "content": text})
                return
            results = stack.prompt_tool_results(calls, read_line=read_line)
            stack.append_tool_round(state.messages, text, results)

    system = stack.senryu2_2_1b_system_prompt() if args.system is None else args.system
    state = stack.ChatLoopState(
        system=system,
        enable_thinking=args.enable_thinking,
        tools=tools,
    )
    state.reset()

    if args.prompt is not None:
        state.add_user(args.prompt)
        try:
            run_turn(state)
        except KeyboardInterrupt:
            print("\n(interrupted)", file=sys.stderr)
            return 130
        return 0

    tool_names = ", ".join(tool["function"]["name"] for tool in tools or []) or "none"
    system_label = (
        f"the {PROMPT_NAME} default, dated today"
        if args.system is None
        else ("none" if not system.strip() else "custom")
    )
    print(
        f"Interactive {MODEL_NAME} chat. "
        "Commands: /think  /no_think  /system …  /reset  /exit\n"
        f"System prompt: {system_label}. Tools: {tool_names}.\n"
        "web-search, fetch-page, calculator and python run automatically when enabled. "
        "Other <tool_call> observations can be pasted at the prompt. "
        "Ctrl+C stops a reply.",
        flush=True,
    )
    while True:
        try:
            user_input = input("user> ")
        except (EOFError, KeyboardInterrupt):
            print()
            return 0
        text = user_input.strip()
        if not text:
            continue
        status = stack.apply_slash_command(state, text)
        if status == "exit":
            return 0
        if status is not None:
            print(f"({status})", flush=True)
            continue
        state.add_user(text)
        try:
            run_turn(state)
        except KeyboardInterrupt:
            print("\n(interrupted)", flush=True)
        print(flush=True)
    return 0


if __name__ == "__main__":
    raise SystemExit(main())