import os, json, argparse
HERE = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
os.environ["HF_HOME"] = os.path.join(HERE, "hf_cache")

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--model", default="Qwen/Qwen3-0.6B")
    ap.add_argument("--adapter", default=None)
    ap.add_argument("--eval", default=os.path.join(HERE, "eval", "chat_eval.jsonl"))
    ap.add_argument("--out", default=os.path.join(HERE, "eval", "baseline_chat.json"))
    a = ap.parse_args()

    device = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")
    tok = AutoTokenizer.from_pretrained(a.model)
    model = AutoModelForCausalLM.from_pretrained(a.model, dtype=torch.float32).to(device)
    if a.adapter:
        from peft import PeftModel
        model = PeftModel.from_pretrained(model, a.adapter).to(device)
    model.eval()

    cases = [json.loads(l) for l in open(a.eval) if l.strip()]
    results = []

    for c in cases:
        msgs = [{"role": "user", "content": c["query"]}]
        text = tok.apply_chat_template(msgs, tokenize=False,
                                       add_generation_prompt=True, enable_thinking=False)
        ids = tok(text, return_tensors="pt").to(device)
        out = model.generate(**ids, max_new_tokens=256, do_sample=False)
        reply = tok.decode(out[0][ids.input_ids.shape[1]:], skip_special_tokens=True)
        results.append({"id": c["id"], "kind": c["kind"],
                        "query": c["query"], "reply": reply})
        print(f"[{c['id']}] {c['kind']}: {reply[:80].strip()}...")

    json.dump({"model": a.model, "adapter": a.adapter, "n": len(cases), "results": results},
              open(a.out, "w"), indent=2)
    print(f"\nsaved {len(cases)} chat outputs -> {a.out}")

if __name__ == "__main__":
    main()