"""
╭──────────────────────────────────────────────────────────╮
│  WFGY SDK · Self-Healing Variance Gate for Any LLM       │
│----------------------------------------------------------│
│ 💌  Contact : hello@onestardao.com  /  TG @PSBigBig       │
│ 🌐  Docs    : https://onestardao.com/papers               │
│ 🐙  GitHub  : https://github.com/onestardao/WFGY          │
│                                                          │
│ ★ Star WFGY 1.0 → Unlock 2.0                             │
│   10k ⭐ by **Aug 1st** = next-gen AI alchemy             │
│   Your click = our quantum leap                          │
│                                                          │
│ 🔍  Official PDF of WFGY 1.0 (Zenodo DOI):               │
│     https://doi.org/10.5281/zenodo.15630969              │
│     (Hosted on Zenodo – trusted international archive)   │
│                                                          │
│ 🧬  WFGY BigBang Prompt Pack (v1.0):                     │
│     https://doi.org/10.5281/zenodo.15657016              │
│     (Prompts to trigger the gate; multilingual updates coming) │
│                                                          │
│ 🧠  Hidden folder inside repo: /I_am_not_lizardman        │
│     (X secret papers, wild prompts, and Einstein drama) │
│                                                          │
│ ⚠  GPT-2 demo is just the appetizer. With bigger LLMs,   │
│    WFGY activates variance-drop lasers and KL fireworks. │
│                                                          │
│ 🎮  Bonus: Honest Hero RPG Channel →                     │
│     https://www.youtube.com/@OneStarDao                  │
╰──────────────────────────────────────────────────────────╯
"""
# cli.py
import argparse, wfgy_sdk as w
from wfgy_sdk.evaluator import compare_logits, pretty_print
import numpy as np

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("prompt", help="text prompt to test")
    parser.add_argument("--model", default="gpt2",
                        help="huggingface model id (public)")
    args = parser.parse_args()

    logits = w.call_remote_model(args.prompt, model_id=args.model)
    G = np.random.randn(128); G /= np.linalg.norm(G)
    I = G + np.random.normal(scale=0.05, size=128)

    logits_mod = w.get_engine().run(input_vec=I, ground_vec=G, logits=logits)
    pretty_print(compare_logits(logits, logits_mod))

if __name__ == "__main__":
    main()