"""Write the published model card and checksums only after completed validation."""

import argparse
import hashlib
import json
import shutil
from pathlib import Path

p = argparse.ArgumentParser()
p.add_argument("name")
a = p.parse_args()
b = Path("bundles") / (a.name + "-CoreAI")
m = json.loads((b / "manifest.json").read_text())
rows = json.loads((b / "validation.json").read_text())
if len(rows) != 10 or any(r["coreai_choice"] != r["upstream_choice"] for r in rows):
    raise ValueError("Incomplete or mismatching smoke suite")
for name in ["validate.py", "text.aimodel"]:
    f = b / name
    if f.is_dir():
        shutil.rmtree(f)
    elif f.exists():
        f.unlink()
max_error = max(r["max_probability_error"] for r in rows)
m.update(
    experimental=True,
    validation={
        "selected_answer_matches": len(rows),
        "cases": len(rows),
        "max_probability_error": max_error,
        "strict_probability_parity": max_error <= 0.02,
    },
    platform={
        "os": "macOS 27.0.1",
        "runtime": "coreai-core 1.0.0b3",
        "specialization": "default",
        "accelerator_placement_verified": False,
    },
)
(b / "manifest.json").write_text(json.dumps(m, indent=2) + "\n")
summary = "\n".join(
    f"| {r['modality']} / {r['type']} | {r['tokens']} | {r['options']} | Yes | {r['max_probability_error']:.6f} |"
    for r in rows
)
card = f"""---
license: apache-2.0
base_model: {m["source"]}
pipeline_tag: zero-shot-classification
tags:
- coreai
- apple
- decision-model
- multimodal
- experimental
- vision
- video
---

# {a.name} — experimental Apple Core AI conversion

Community conversion of [{m["source"]}](https://huggingface.co/{m["source"]}) at revision `{m["revision"]}`. Converted by Max Farrell. This is an Apple **Core AI `.aimodel`** bundle for macOS 27, with its text backbone, original 255-way answer readout, and vision encoder. Choice, yes/no (noul), and score questions run through Core AI; tokenization, media decoding, and result formatting run on the host. No text is generated.

**Experimental, with measured numerical drift.** All 10 smoke cases selected the same answer as the official BF16 PyTorch runtime on identical resized inputs. The largest absolute probability difference was **{max_error:.6f}**. These are small conversion smoke tests, not a quality benchmark, calibration result, or evidence of equivalence on arbitrary inputs. Fixed-capacity padding and Core AI's BF16 execution can change probabilities and may change decisions on other inputs. The initial 0.02 probability-parity target was not met by every model; the full values are reported rather than claimed as strict numerical parity.

## Capacity and changes from upstream

- One question per forward pass; `system_one` processes several questions sequentially.
- **512 combined tokens**, including visual tokens and all prompt text. Oversized requests raise an error; nothing is truncated.
- Images and sampled video frames are resized to **224×224**, including aspect-ratio changes. Each image or two-frame video patch group occupies 49 visual tokens. This is much smaller than upstream's default image/video resolution.
- Multiple images/videos work only when their complete prompt fits. Video decoding retains upstream's 2-fps sampling, at least 4 and at most 32 frames; long sampled clips may exceed the 512-token limit and are rejected.
- Up to 255 answer codes are retained; the prompt capacity generally limits practical option count well before 255.
- The original **BF16 backbone weights** and **FP32 decision head** are retained. Linear attention stays causal; full attention remains noncausal as upstream specifies.
- Default specialization was tested on this Mac. Forced CPU-only specialization crashed inside Apple's compiler during development. **No verified Neural Engine/GPU placement, iPhone compatibility, speedup, or power-efficiency claim.**

## Use

```bash
hf download maxffarrell/{a.name}-CoreAI --local-dir {a.name}-CoreAI
cd {a.name}-CoreAI
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
```

```python
import asyncio
from coreai_d3 import D3CoreAI

async def main():
    model = await D3CoreAI.load(".")
    result = await model.system_one(
        state="The customer was charged twice.",
        questions={{"route": {{
            "type": "choice",
            "instructions": "Which team should handle this?",
            "criteria": {{"billing": "Payments", "shipping": "Delivery"}}
        }}}},
        # images=["receipt.png"],
        # videos=["clip.mp4"],
    )
    print(result)

asyncio.run(main())
```

All neural inference in this adapter uses `decision.aimodel` and `vision.aimodel`. PyTorch/Transformers are dependencies for conversion and host preprocessing; source PyTorch weights are not included or needed for inference. Core AI may write a local specialization cache on first load. The adapter uses the upstream prompt formatter and typed response shapes.

## Validation

macOS 27.0.1, `coreai-core==1.0.0b3`, default specialization. The BF16 reference uses Transformers 5.17.0 and the pinned upstream checkpoint. Images and video are compared using the same 224×224 input pixels, not against upstream's original full-resolution run. Text includes choice, noul, score, Spanish, 10 options, and a 433-token case. Additional checks cover one image, two images, video, and rejecting oversized text. End-to-end `system_one` is separately smoke-tested without loading source weights.

| Case | Tokens | Options | Selected answer matches | Max probability difference |
| --- | ---: | ---: | --- | ---: |
{summary}

See `validation.json` for per-case records. Its elapsed times include reference work and are **not inference benchmarks**.

## Reproduce

Download the exact source revision, convert, and reassemble into a fresh output directory:

```bash
hf download {m["source"]} --revision {m["revision"]} --local-dir upstream/{a.name}
python convert.py --source upstream/{a.name} --output bundles/{a.name}-CoreAI --length 512 --precision bfloat16
python package.py {a.name}
python validate_mm.py {a.name}
```

`coreai-torch==0.4.3` warns that PyTorch 2.14.0 is newer than its validated range; the supplied environment is the version used for these exports and smoke tests. `UPSTREAM_MANIFEST.json` describes original source files; `SHA256SUMS` verifies this converted bundle.

## Attribution and license

Original model: vLLM Semantic Router Team, based on the Qwen model identified in `UPSTREAM_NOTICE`. Original model and this conversion are Apache-2.0; see `LICENSE` and `NOTICE`. The prompt contract and host decision formatting/media loader are included from the pinned upstream source. The upstream runtime contains an Apache-2.0 noncausal attention hook adapted from Perplexity AI and retains its original attribution. This is an independent community conversion, not an official Apple or vLLM release.
"""
(b / "README.md").write_text(card)
lines = []
for f in sorted(b.rglob("*")):
    if not f.is_file() or f.name == "SHA256SUMS" or "__pycache__" in f.parts:
        continue
    h = hashlib.sha256()
    with f.open("rb") as s:
        for block in iter(lambda: s.read(8 * 1024 * 1024), b""):
            h.update(block)
    lines.append(h.hexdigest() + "  " + str(f.relative_to(b)))
(b / "SHA256SUMS").write_text("\n".join(lines) + "\n")
print(a.name, len(lines), "files", max_error)