from typing import Any
import torch
from transformers import AutoModelForCausalLM, LlamaTokenizerFast, pipeline


class EndpointHandler:
    def __init__(self, path: str = ""):
        self.tokenizer = LlamaTokenizerFast.from_pretrained(path)
        self.model = AutoModelForCausalLM.from_pretrained(
            path,
            torch_dtype=torch.bfloat16,
            device_map="auto",
        )
        self.pipe = pipeline(
            "text-generation",
            model=self.model,
            tokenizer=self.tokenizer,
        )

    def __call__(self, data: dict[str, Any]) -> list[dict[str, Any]]:
        inputs = data.get("inputs", "")
        parameters = data.get("parameters", {})

        if isinstance(inputs, list):
            prompt = self.tokenizer.apply_chat_template(
                inputs, tokenize=False, add_generation_prompt=True
            )
        else:
            prompt = inputs

        outputs = self.pipe(
            prompt,
            max_new_tokens=parameters.get("max_new_tokens", 256),
            temperature=parameters.get("temperature", 0.7),
            top_p=parameters.get("top_p", 0.9),
            do_sample=parameters.get("do_sample", True),
            return_full_text=False,
        )

        return outputs