from typing import Any, Dict
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftConfig, PeftModel


class EndpointHandler:
    def __init__(self, path: str = ""):
        cfg = PeftConfig.from_pretrained(path)
        base = cfg.base_model_name_or_path
        self.tokenizer = AutoTokenizer.from_pretrained(base)
        if self.tokenizer.pad_token_id is None:
            self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
        model = AutoModelForCausalLM.from_pretrained(
            base,
            torch_dtype=torch.float16,
            device_map="auto",
        )
        self.model = PeftModel.from_pretrained(model, path)
        self.model.eval()

    def __call__(self, data: Dict[str, Any]):
        inputs = data.get("inputs", "")
        params = data.get("parameters", {}) or {}
        enc = self.tokenizer(inputs, return_tensors="pt").to(self.model.device)
        with torch.no_grad():
            out = self.model.generate(
                **enc,
                max_new_tokens=int(params.get("max_new_tokens", 256)),
                temperature=float(params.get("temperature", 0.7)),
                top_p=float(params.get("top_p", 0.9)),
                do_sample=bool(params.get("do_sample", True)),
                pad_token_id=self.tokenizer.pad_token_id,
            )
        text = self.tokenizer.decode(
            out[0][enc["input_ids"].shape[1]:], skip_special_tokens=True
        )
        return [{"generated_text": text}]