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
from tools_common import TOOLS, parse_tool_calls

def norm_args(d):
    return {k: (v.strip() if isinstance(v, str) else v) for k, v in (d or {}).items()}

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", "tools_eval.py"))
    ap.add_argument("--out", default=os.path.join(HERE, "eval", "baseline_tools.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, name_hits, full_hits = [], 0, 0

    for c in cases:
        msgs = [{"role": "user", "content": c["query"]}]
        text = tok.apply_chat_template(msgs, tools=TOOLS, tokenize=False,
                                       add_generation_prompt=True, enable_thinking=False)
        ids = tok(text, return_tensors="pt").to(device)
        out = model.generate(**ids, max_new_tokens=128, do_sample=False)
        reply = tok.decode(out[0][ids.input_ids.shape[1]:], skip_special_tokens=True)

        calls = parse_tool_calls(reply)
        got = calls[0] if calls else None
        exp = c["expected"]
        name_ok = bool(got) and got.get("name") == exp["name"]
        full_ok = name_ok and norm_args(got.get("arguments")) == norm_args(exp["arguments"])
        name_hits += name_ok
        full_hits += full_ok
        results.append({"query": c["query"], "expected": exp,
                        "got": got, "raw": reply,
                        "name_ok": name_ok, "full_ok": full_ok})

    n = len(cases)
    summary = {"model": a.model, "adapter": a.adapter, "n": n,
               "name_acc": round(name_hits / n, 3),
               "full_acc": round(full_hits / n, 3)}
    json.dump({"summary": summary, "results": results}, open(a.out, "w"), indent=2)
    print(json.dumps(summary, indent=2))

if __name__ == "__main__":
    main()