#!/usr/bin/env python3
"""Upload the project code, docs and measurement results to the HF repo."""

from __future__ import annotations

import argparse
import os
import sys
from pathlib import Path

from huggingface_hub import HfApi

# this script lives in bench/, the repo root is its parent
REPO = Path(__file__).resolve().parent.parent

MSG = """EPM: expert paging CPU inference for MiniCPM-V-4.7-35B-A3B Q4_K_M

Port the 8 GiB expert pager to openbmb/MiniCPM-V-4.7-35B-A3B-Q4_K_M.gguf
(19.71 GiB, qwen35moe, 40 layers, 256 experts, top-8).

Measured under an 8 GiB / no-swap ceiling enforced by tools/strict8g.py:
5.83 tok/s median (N=9), peak anon RSS 7799 MiB, VmSwap 0, 9/9 strict_ok.
Unmodified llama.cpp cannot load the model at that ceiling.

Correctness: 3-layer verification passes; 5 runs x 48 tokens x 248144 vocab
logits bit-identical.

Port-specific changes: GGUF BOOL-array header fix, calibrated SDQ_RESERVE_MB,
two expert size classes, hybrid SSM/attention thread behaviour.
"""


def main() -> int:
    ap = argparse.ArgumentParser()
    ap.add_argument("--repo", default=os.environ.get("EPM_HF_REPO", "HelloSun/minicpm47woek"))
    ap.add_argument("--folder", default=str(REPO))
    ap.add_argument("--message", default=MSG)
    ap.add_argument("--dry-run", action="store_true")
    a = ap.parse_args()

    api = HfApi()
    print("whoami:", api.whoami()["name"])
    if a.dry_run:
        print("dry run, not uploading")
        return 0
    api.upload_folder(
        folder_path=a.folder,
        repo_id=a.repo,
        repo_type="model",
        ignore_patterns=[".git/*", "__pycache__/*", "*.pyc", "*.so",
                         "bench/results/.*", "*.gguf", "*.bin"],
        commit_message=a.message,
    )
    print(f"uploaded -> https://huggingface.co/{a.repo}")
    return 0


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