Instructions to use physicsrob/torchwright-doom-e1m1-80x50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use physicsrob/torchwright-doom-e1m1-80x50 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="physicsrob/torchwright-doom-e1m1-80x50")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("physicsrob/torchwright-doom-e1m1-80x50")
model = AutoModelForCausalLM.from_pretrained("physicsrob/torchwright-doom-e1m1-80x50", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use physicsrob/torchwright-doom-e1m1-80x50 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "physicsrob/torchwright-doom-e1m1-80x50"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "physicsrob/torchwright-doom-e1m1-80x50",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/physicsrob/torchwright-doom-e1m1-80x50

SGLang

How to use physicsrob/torchwright-doom-e1m1-80x50 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "physicsrob/torchwright-doom-e1m1-80x50" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "physicsrob/torchwright-doom-e1m1-80x50",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "physicsrob/torchwright-doom-e1m1-80x50" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "physicsrob/torchwright-doom-e1m1-80x50",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use physicsrob/torchwright-doom-e1m1-80x50 with Docker Model Runner:
```
docker model run hf.co/physicsrob/torchwright-doom-e1m1-80x50
```

physicsrob commited on Aug 9

Commit

1bedcfb

verified ·

1 Parent(s): 3e5aad7

Publish E1M1 70-layer seed-3 bundle

Browse files

This view is limited to 50 files because it contains too many changes. See raw diff

Files changed (50) hide show

.gitattributes +1 -0
README.md +61 -0
config.json +48 -0
doom_bundle_manifest.json +368 -0
doom_palette.json +1285 -0
doom_tables.json +297 -0
doom_vocab.json +3 -0
examples/e1m1_prompt.txt +1 -0
generation_config.json +8 -0
infer.py +302 -0
model-00001-of-00071.safetensors +3 -0
model-00002-of-00071.safetensors +3 -0
model-00003-of-00071.safetensors +3 -0
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.gitattributes CHANGED Viewed

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+doom_vocab.json filter=lfs diff=lfs merge=lfs -text

README.md ADDED Viewed

	@@ -0,0 +1,61 @@

+---
+library_name: transformers
+pipeline_tag: text-generation
+---
+# TorchWright Doom — E1M1
+This is a stock Hugging Face `Phi3ForCausalLM` that renders DOOM through
+ordinary autoregressive inference. The model and the data-only fast tokenizer
+load through the ordinary Transformers text-generation pipeline without
+remote code.
+The bundled `examples/e1m1_prompt.txt` is the executable prompt. Run
+`infer.py` (at the bundle root) to produce canonical emitted row ids and raw
+tokenizer text. `tools/pretty_text.py` formats that text for reading, while
+`tools/txt_to_png.py` independently decodes its cursor/pixel protocol into a
+PNG — every cursor move and pixel in that protocol is a model-emitted token.
+The protocol is specified in `PROTOCOL.md` in the source repo. Neither
+post-processing tool participates in inference or performs geometry,
+visibility, lighting, texture selection, or sorting.
+Ordinary Transformers pipeline inference works directly, with no custom or
+remote model code:
+```python
+from pathlib import Path
+from huggingface_hub import hf_hub_download
+from transformers import pipeline
+repo = "physicsrob/torchwright-doom-e1m1-80x50"
+prompt = Path(hf_hub_download(repo, "examples/e1m1_prompt.txt")).read_text()
+generate = pipeline("text-generation", model=repo, device_map="auto")
+generated_text = generate(prompt, return_full_text=False)[0]["generated_text"]
+```
+The saved generation defaults are greedy and cover the complete frame. Use
+the shipped `infer.py` when canonical integer row IDs, progress reporting, and
+the exact terminal-token-preserving raw text are required.
+Published checkpoints: [320×200](https://huggingface.co/physicsrob/torchwright-doom-e1m1)
+and [80×50](https://huggingface.co/physicsrob/torchwright-doom-e1m1-80x50).
+The compiler-facing source is
+[torchwright_doom](https://github.com/physicsrob/torchwright_doom).
+**This bundle:** screen 80×50, map
+E1M1, dense fp32 sharded safetensors, eager attention (the validated
+implementation), greedy decode, generation bound
+8000 new tokens.
+**What running it takes:** the fp32 weight shards total
+31.75 GiB (34.09 GB). A complete 7,007-token
+pipeline render peaked at 43.48 GiB reserved on one A100-80GB. At the
+configured generation cap, the stock KV cache is
+bounded at 12.41 GiB; one full layer's cache-growth copy adds
+0.18 GiB. This targets 64 GiB of total accelerator memory: one
+64-GiB-class device, or two 32-GiB consumer GPUs with `device_map="auto"`.
+The shipped `infer.py` records the allocator's measured peak in
+`output.ids.json`.
+Canonical numbers and their provenance: `FACTS.md` in the source repo
+(github.com/physicsrob/torchwright_doom).

config.json ADDED Viewed

	@@ -0,0 +1,48 @@

+{
+  "architectures": [
+    "Phi3ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "attn_implementation": "eager",
+  "bos_token_id": 93376,
+  "doom_screen_config": {
+    "detail": "low",
+    "height": 50,
+    "hud": true,
+    "scale": 4,
+    "width": 80
+  },
+  "doom_vocab_fingerprint": "ee25bcb4bd51bc5d62910c933953a7294847725a2d1754ec1e49ed53f4285ba1",
+  "embd_pdrop": 0.0,
+  "eos_token_id": 93309,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 6732,
+  "max_position_embeddings": 16384,
+  "model_type": "phi3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 70,
+  "num_key_value_heads": 16,
+  "original_max_position_embeddings": 16384,
+  "pad_token_id": 93309,
+  "resid_pdrop": 0.0,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "partial_rotary_factor": 0.5,
+    "rope_theta": 500000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": false,
+      "max_new_tokens": 8000
+    }
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.13.1",
+  "use_cache": true,
+  "vocab_size": 93378
+}

doom_bundle_manifest.json ADDED Viewed

	@@ -0,0 +1,368 @@

+{
+  "architecture": "Phi3ForCausalLM",
+  "artifact_kind": "hf_phi3_bundle",
+  "bos_token_id": 93376,
+  "bundle_identity": "torchwright_doom.phi3.v1:9e5152cdc4fab42c52d32e4f1ae99a9f8e3404a1833cc687b4a00736dbb347bd",
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+    "solver_workers": 16
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+    "viewz": 41.0,
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+  "profile": "phi3",
+  "prompt": {
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ssFloorPlane(s=1,p=4) ssCeilingPlane(s=1,p=11) ssFloorPlane(s=2,p=0) ssCeilingPlane(s=2,p=14) ssFloorPlane(s=3,p=0) ssCeilingPlane(s=3,p=14) ssFloorPlane(s=4,p=6) ssCeilingPlane(s=4,p=12) ssFloorPlane(s=5,p=6) ssCeilingPlane(s=5,p=12) ssFloorPlane(s=6,p=0) ssCeilingPlane(s=6,p=14) ssFloorPlane(s=7,p=2) ssCeilingPlane(s=7,p=10) ssFloorPlane(s=8,p=4) ssCeilingPlane(s=8,p=7) ssFloorPlane(s=9,p=4) ssCeilingPlane(s=9,p=7) ssFloorPlane(s=10,p=4) ssCeilingPlane(s=10,p=7) ssFloorPlane(s=11,p=4) ssCeilingPlane(s=11,p=7) ssFloorPlane(s=12,p=1) ssCeilingPlane(s=12,p=13) ssFloorPlane(s=13,p=4) ssCeilingPlane(s=13,p=7) ssFloorPlane(s=14,p=4) ssCeilingPlane(s=14,p=7) ssFloorPlane(s=15,p=5) ssCeilingPlane(s=15,p=8) ssFloorPlane(s=16,p=1) ssCeilingPlane(s=16,p=13) ssFloorPlane(s=17,p=2) ssCeilingPlane(s=17,p=10) ssFloorPlane(s=18,p=4) ssCeilingPlane(s=18,p=7) ssFloorPlane(s=19,p=2) ssCeilingPlane(s=19,p=10) ssFloorPlane(s=20,p=4) ssCeilingPlane(s=20,p=7) ssFloorPlane(s=21,p=4) ssCeilingPlane(s=21,p=7) ssFloorPlane(s=22,p=1) ssCeilingPlane(s=22,p=13) ssFloorPlane(s=23,p=4) ssCeilingPlane(s=23,p=7) ssFloorPlane(s=24,p=4) ssCeilingPlane(s=24,p=7) ssFloorPlane(s=25,p=4) ssCeilingPlane(s=25,p=7) ssFloorPlane(s=26,p=4) ssCeilingPlane(s=26,p=7) ssFloorPlane(s=27,p=4) ssCeilingPlane(s=27,p=11) ssFloorPlane(s=28,p=3) ssCeilingPlane(s=28,p=9) begin

generation_config.json ADDED Viewed

	@@ -0,0 +1,8 @@

+{
+  "bos_token_id": 93376,
+  "do_sample": false,
+  "eos_token_id": 93309,
+  "max_new_tokens": 8000,
+  "pad_token_id": 93309,
+  "transformers_version": "5.13.1"
+}

infer.py ADDED Viewed

	@@ -0,0 +1,302 @@

+"""The sole Doom inference program: portable stock-Hugging-Face generation.
+This file is copied byte-identical to the root of every published Doom
+bundle (``<bundle>/infer.py``) and executed there as a subprocess — by
+anyone who downloads a bundle and by production render orchestration
+(``run.py``) alike.  It is executed, never imported.  It intentionally
+imports no TorchWright or ``torchwright_doom`` code: text enters through
+the saved tokenizer, a stock ``Phi3ForCausalLM`` produces rows (a "row" is
+one tokenizer id — one row of the tied embedding matrix, one sequence
+position), and the only outputs are canonical integer ids plus their raw
+tokenizer text.  No pixels are produced here: the bundle's standalone
+tools (``tools/txt_to_png.py``; token protocol in ``PROTOCOL.md`` in the
+source repo) decode those ids into a frame afterward and do no inference.
+The bundle manifest's schema, field meanings, and completeness gate live
+in ``torchwright_doom/bundle/manifest.py`` in the source repo.
+"""
+from __future__ import annotations
+import argparse
+import hashlib
+import json
+import os
+import time
+from pathlib import Path
+# Multi-shard checkpoints otherwise load serially.  Keep these defaults in the
+# portable program so downloaded bundles and production use the same loader.
+os.environ.setdefault("HF_ENABLE_PARALLEL_LOADING", "true")
+os.environ.setdefault("HF_PARALLEL_LOADING_WORKERS", "8")
+import torch
+import transformers
+from transformers import TextGenerationPipeline, pipeline
+_PROGRESS_INTERVAL_SECONDS = 15.0
+def _canonical_json(value) -> bytes:
+    return json.dumps(value, ensure_ascii=False, separators=(",", ":")).encode("utf-8")
+def _sha(data: bytes) -> str:
+    return hashlib.sha256(data).hexdigest()
+def _cuda_devices(model) -> list[torch.device]:
+    return sorted(
+        {parameter.device for parameter in model.parameters() if parameter.is_cuda},
+        key=str,
+    )
+class _ProgressStreamer:
+    """Report generation throughput without changing or collecting tokens."""
+    def __init__(self, prompt_rows: int, max_new_tokens: int) -> None:
+        self.prompt_rows = prompt_rows
+        self.max_new_tokens = max_new_tokens
+        self.started = time.monotonic()
+        self.last_progress = self.started
+        self.prefill_seconds: float | None = None
+        self.finished: float | None = None
+        self.generated_rows = 0
+        self._saw_prompt = False
+    def put(self, value: torch.Tensor) -> None:
+        # GenerationMixin streams the complete prompt once before any emitted
+        # row.  It is already accounted for separately in ``prompt_rows``.
+        if not self._saw_prompt:
+            self._saw_prompt = True
+            return
+        now = time.monotonic()
+        if self.prefill_seconds is None:
+            self.prefill_seconds = now - self.started
+            print(
+                f"[infer] prefill complete; rows={self.prompt_rows} "
+                f"elapsed={self.prefill_seconds:.1f}s",
+                flush=True,
+            )
+        self.generated_rows += value.numel()
+        if now - self.last_progress >= _PROGRESS_INTERVAL_SECONDS:
+            elapsed = now - self.started - self.prefill_seconds
+            last_row = int(value.reshape(-1)[-1])
+            print(
+                f"[infer] decode rows={self.generated_rows}/{self.max_new_tokens} "
+                f"total_position={self.prompt_rows + self.generated_rows} "
+                f"elapsed={elapsed:.1f}s "
+                f"rows/s={self.generated_rows / elapsed:.1f} "
+                f"last_row={last_row}",
+                flush=True,
+            )
+            self.last_progress = now
+    def end(self) -> None:
+        self.finished = time.monotonic()
+    @property
+    def decode_seconds(self) -> float:
+        stopped = self.finished or time.monotonic()
+        prefill = self.prefill_seconds or 0.0
+        return stopped - self.started - prefill
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(description="Run stock Phi-3 Doom inference")
+    parser.add_argument("--model", type=Path)
+    parser.add_argument("--prompt", type=Path)
+    parser.add_argument("--output", type=Path, default=Path("out"))
+    parser.add_argument(
+        "--device", default="cuda" if torch.cuda.is_available() else "cpu"
+    )
+    parser.add_argument("--max-new-tokens", type=int)
+    args = parser.parse_args(argv)
+    # This file sits at the bundle root, so its own directory is the bundle.
+    model_dir = (args.model or Path(__file__).resolve().parent).resolve()
+    prompt_path = args.prompt or model_dir / "examples" / "e1m1_prompt.txt"
+    manifest = json.loads((model_dir / "doom_bundle_manifest.json").read_text())
+    if not manifest.get("validation", {}).get("complete"):
+        raise ValueError("Doom bundle manifest is not complete")
+    prompt_bytes = prompt_path.read_bytes()
+    prompt_sha256 = _sha(prompt_bytes)
+    bundled_prompt = prompt_sha256 == manifest["prompt"]["sha256"]
+    load_t0 = time.monotonic()
+    model_kwargs = {
+        "attn_implementation": "eager",
+        # Read each shard's bytes eagerly: deferring them to mmap page faults
+        # stalls badly on network filesystems, and eager reads are harmless on
+        # local disks.
+        "disable_mmap": True,
+    }
+    generate: TextGenerationPipeline
+    if args.device != "cpu":
+        # Accelerate builds the skeleton on meta and dispatches each shard
+        # directly to the target device.  This avoids a second full-model
+        # ``model.to(cuda)`` pass through CPU-backed mmap pages.
+        generate = pipeline(
+            "text-generation",
+            model=str(model_dir),
+            dtype=torch.float32,
+            model_kwargs=model_kwargs,
+            device_map=args.device,
+        )
+    else:
+        generate = pipeline(
+            "text-generation",
+            model=str(model_dir),
+            dtype=torch.float32,
+            model_kwargs=model_kwargs,
+        )
+    tokenizer = generate.tokenizer
+    if tokenizer is None:
+        raise RuntimeError("text-generation pipeline loaded without a tokenizer")
+    model = generate.model
+    model.eval()
+    cuda_devices = _cuda_devices(model)
+    for cuda_device in cuda_devices:
+        # Reset after loading: the current allocation still includes all
+        # weights, while the peak will additionally capture generation cache
+        # and runtime workspace. This is the consumer-fit measurement.
+        torch.cuda.reset_peak_memory_stats(cuda_device)
+    attention_implementation = getattr(model.config, "_attn_implementation", None)
+    if attention_implementation != "eager":
+        # Eager is the implementation the published render was validated
+        # under; fused kernels change fp accumulation order, and this check
+        # keeps every run on the validated numerics.
+        raise RuntimeError(
+            "Doom inference requires eager attention, got "
+            f"{attention_implementation!r}"
+        )
+    if (
+        model.config.original_max_position_embeddings
+        != model.config.max_position_embeddings
+    ):
+        raise RuntimeError(
+            "default-RoPE Doom model has inconsistent original/max position "
+            "capacity; GenerationMixin would discard its cache at the boundary"
+        )
+    load_seconds = time.monotonic() - load_t0
+    prompt_text = prompt_bytes.decode("utf-8")
+    encoded_prompt = tokenizer(
+        prompt_text,
+        return_tensors="pt",
+        add_special_tokens=False,
+    )
+    input_device = next(model.parameters()).device
+    prompt_ids = [int(row) for row in encoded_prompt.input_ids[0].tolist()]
+    prompt_ids_sha256 = _sha(_canonical_json(prompt_ids))
+    # Only the bundled prompt has a manifest row-id expectation; a custom
+    # prompt is permitted, never verified, and recorded in the payload as
+    # matches_bundled_prompt=false.
+    if bundled_prompt and prompt_ids_sha256 != manifest["prompt"]["row_ids_sha256"]:
+        raise ValueError("bundled prompt text does not reproduce its manifest rows")
+    default_new = int(manifest["generation"]["max_new_tokens"])
+    max_new = default_new if args.max_new_tokens is None else int(args.max_new_tokens)
+    if max_new < 1:
+        raise ValueError("max-new-tokens must be >= 1")
+    if len(prompt_ids) + max_new > model.config.max_position_embeddings:
+        raise ValueError("requested generation exceeds model position capacity")
+    print(
+        f"[infer] model ready in {load_seconds:.1f}s; prompt={len(prompt_ids)} "
+        f"max_new_tokens={max_new} device={input_device}",
+        flush=True,
+    )
+    generate_t0 = time.monotonic()
+    progress = _ProgressStreamer(len(prompt_ids), max_new)
+    print(f"[infer] generation started; max_new_tokens={max_new}", flush=True)
+    with torch.inference_mode():
+        records = generate(
+            prompt_text,
+            add_special_tokens=False,
+            return_tensors=True,
+            do_sample=False,
+            use_cache=True,
+            max_new_tokens=max_new,
+            eos_token_id=tokenizer.eos_token_id,
+            pad_token_id=tokenizer.pad_token_id,
+            streamer=progress,
+        )
+    generate_seconds = time.monotonic() - generate_t0
+    sequence = records[0]["generated_token_ids"]
+    generated = [int(row) for row in sequence[len(prompt_ids) :]]
+    for cuda_device in cuda_devices:
+        torch.cuda.synchronize(cuda_device)
+    cuda_memory = [
+        {
+            "device": str(cuda_device),
+            "peak_allocated_bytes": torch.cuda.max_memory_allocated(cuda_device),
+            "peak_reserved_bytes": torch.cuda.max_memory_reserved(cuda_device),
+        }
+        for cuda_device in cuda_devices
+    ]
+    prefill_seconds = progress.prefill_seconds or generate_seconds
+    decode_seconds = progress.decode_seconds
+    raw_text = tokenizer.decode(
+        generated, skip_special_tokens=False, clean_up_tokenization_spaces=False
+    )
+    if not isinstance(raw_text, str):
+        raise TypeError("tokenizer returned a batched decode for one row list")
+    if tokenizer(raw_text, add_special_tokens=False)["input_ids"] != generated:
+        raise ValueError("decoded output text does not round-trip to generated rows")
+    args.output.mkdir(parents=True, exist_ok=True)
+    emitted_ids_sha256 = _sha(_canonical_json(generated))
+    stopped = bool(generated and generated[-1] == tokenizer.eos_token_id)
+    payload = {
+        "format": "torchwright_doom.output_ids.v1",
+        "bundle": manifest.get("bundle_identity"),
+        "compile_payload_sha256": manifest.get("compile_payload_sha256"),
+        "row_vocab_fingerprint": manifest.get("row_vocab_fingerprint"),
+        "prompt": {
+            "sha256": prompt_sha256,
+            "matches_bundled_prompt": bundled_prompt,
+            "row_ids": prompt_ids,
+            "row_ids_sha256": prompt_ids_sha256,
+        },
+        "emitted_row_ids": generated,
+        "emitted_row_ids_sha256": emitted_ids_sha256,
+        "generation": {
+            "mode": "transformers_pipeline",
+            "max_new_tokens": max_new,
+            "termination_reason": "terminal" if stopped else "cap",
+        },
+        "timing_seconds": {
+            "load": load_seconds,
+            "prefill": prefill_seconds,
+            "decode": decode_seconds,
+            "generate": generate_seconds,
+        },
+        "attention_implementation": attention_implementation,
+        "cuda_memory": cuda_memory,
+        "transformers_version": transformers.__version__,
+    }
+    (args.output / "output.ids.json").write_text(
+        json.dumps(payload, indent=2, sort_keys=True) + "\n", encoding="utf-8"
+    )
+    (args.output / "output.txt").write_text(raw_text + "\n", encoding="utf-8")
+    print(
+        f"[infer] wrote {len(generated)} rows in {generate_seconds:.1f}s; "
+        f"stopped={payload['generation']['termination_reason']}",
+        flush=True,
+    )
+    for memory in cuda_memory:
+        peak_allocated = int(memory["peak_allocated_bytes"])
+        peak_reserved = int(memory["peak_reserved_bytes"])
+        print(
+            f"[infer] {memory['device']} peak allocated="
+            f"{peak_allocated / 1024**3:.2f} GiB "
+            f"reserved={peak_reserved / 1024**3:.2f} GiB",
+            flush=True,
+        )
+    return 0
+if __name__ == "__main__":
+    raise SystemExit(main())

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