"""One-shot script to pull Run 7 artifacts from HF Hub.

Run 7 focus: fix R4/R5 calibration collapse from Run 6. Added 6 calibration
warmup traces teaching:
  * git_push_force → R2 (when nothing is overwritten)
  * git_commit / git_push → R2 (safe forward-fix path)
  * git_filter_branch → R4 (reflog preserves overwritten commits)
  * fs_rm_rf → R4 (when backup is in place)
  * db_truncate → R4 (when snapshot exists)

GUARDRAIL: if eval R5 recall drops below 95%, revert to Run 6.1 adapter.
The eval results.json `grpo_trained.prediction_accuracy` and the confusion
matrix computed from comparison.csv are the decisive check.

Theory predictions:
  * Eval accuracy: 75% (Run 6.1) → 82-88%
  * task_force_push_release recovered (was regressed -0.17 in Run 6)
  * R4 row accuracy in training log: 4.9% → 30-50%
  * R5 recall held at ≥95%
"""
from __future__ import annotations

import os
import shutil
import subprocess
from huggingface_hub import snapshot_download


TARGET_DIR = "training_runs/run_7_r4_calibration"


def main() -> None:
    if os.path.exists(TARGET_DIR):
        shutil.rmtree(TARGET_DIR)
    token = subprocess.check_output(["hf", "auth", "token"], text=True).strip()
    path = snapshot_download(
        repo_id="chane335/permanence-artifacts",
        repo_type="dataset",
        local_dir=TARGET_DIR,
        token=token,
    )
    total = 0
    for root, _dirs, files in os.walk(path):
        for f in files:
            rel = os.path.relpath(os.path.join(root, f), path)
            if ".cache" in rel:
                continue
            size = os.path.getsize(os.path.join(root, f))
            total += size
            print(f"  {size:>12,} bytes  {rel}")
    print(f"TOTAL: {total/1e6:.1f} MB")
    print(f"\nCheck eval first: python -c \"import json; "
          f"print(json.load(open('{TARGET_DIR}/eval/results.json')))\"")


if __name__ == "__main__":
    main()