"""
Inference for docstring generation. Uses T5 (cached after first load).
"""
import torch

_cache = {}

def generate_docstring(
    code: str,
    model_name: str = "t5-small",
    max_length: int = 128,
    num_beams: int = 4,
    device: str = None,
) -> str:
    if device is None:
        device = "cuda" if torch.cuda.is_available() else "cpu"
    if model_name not in _cache:
        from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
        _cache[model_name] = {
            "tokenizer": AutoTokenizer.from_pretrained(model_name),
            "model": AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device),
        }
    tokenizer = _cache[model_name]["tokenizer"]
    model = _cache[model_name]["model"]
    input_text = "summarize: " + code
    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512).to(device)
    with torch.no_grad():
        out = model.generate(**inputs, max_length=max_length, num_beams=num_beams, early_stopping=True)
    return tokenizer.decode(out[0], skip_special_tokens=True)