"""Text generation wrapper."""
import torch
from typing import List, Dict
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

_model_cache = {}
_tok_cache = {}


def load_model(model_name: str, load_in_4bit: bool = True):
    cache_key = f"{model_name}:{load_in_4bit}"
    if cache_key in _model_cache:
        return _model_cache[cache_key], _tok_cache[cache_key]

    tok = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
    if tok.pad_token is None:
        tok.pad_token = tok.eos_token

    if load_in_4bit:
        bnb = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_compute_dtype=torch.bfloat16,
            bnb_4bit_use_double_quant=True,
            bnb_4bit_quant_type="nf4",
        )
        model = AutoModelForCausalLM.from_pretrained(
            model_name, quantization_config=bnb, device_map="auto",
            trust_remote_code=True, torch_dtype=torch.bfloat16,
        )
    else:
        model = AutoModelForCausalLM.from_pretrained(
            model_name, device_map="auto",
            trust_remote_code=True, torch_dtype=torch.bfloat16,
        )
    model.eval()
    _model_cache[cache_key] = model
    _tok_cache[cache_key] = tok
    return model, tok


def generate_text(messages: List[Dict[str, str]], model_name: str, max_new_tokens: int = 80):
    model, tokenizer = load_model(model_name)
    inputs = tokenizer.apply_chat_template(
        messages, tokenize=True, return_tensors="pt",
        add_generation_prompt=True, return_dict=True,
    )
    dev = next(model.parameters()).device
    inputs = {k: v.to(dev) for k, v in inputs.items()}
    with torch.no_grad():
        outputs = model.generate(
            **inputs, max_new_tokens=max_new_tokens,
            do_sample=False, pad_token_id=tokenizer.pad_token_id,
        )
    gen = outputs[0][inputs["input_ids"].shape[1]:]
    return tokenizer.decode(gen, skip_special_tokens=True).strip()