from typing import NamedTuple
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, TextStreamer
import torch

class InferenceResult(NamedTuple):
    reasoning: str
    actions: str

class LlmEngine:
    def __init__(self, model_path: str):
        """
        Initialize the LLM Engine with a model path.

        Args:
            model_path: Path to the model weights to be used.
        """
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
        print(f"device: {self.device}")
        self.set_model_path(model_path)

    def set_model_path(self, model_path: str):
        """
        Load the model from the specified path.

        Args:
            model_path: Path to the model weights to load.
        """
        tokenizer = AutoTokenizer.from_pretrained(model_path)
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            return_dict=True,
            low_cpu_mem_usage=True,
            torch_dtype=self.torch_dtype,
            device_map="auto",
            trust_remote_code=True,
        ).to(self.device)
        if tokenizer.pad_token_id is None:
            tokenizer.pad_token_id = tokenizer.eos_token_id
        if model.config.pad_token_id is None:
            model.config.pad_token_id = model.config.eos_token_id
        self.pipeline = pipeline(
            "text-generation",
            model=model,
            tokenizer=tokenizer,
            torch_dtype=torch.float16,
            device_map="auto",
        )

    def infer(self, system_prompt: str, main_context: str, action_schema: str) -> InferenceResult:
        """
        Run inference using the system prompt and main context, while validating actions against the provided XML schema.

        Args:
            system_prompt: The system prompt string
            main_context: The main context string after templating
            action_schema: XML schema to validate the generated actions

        Returns:
            InferenceResult: the actions validate against the schema
        """
        pass

    def finetune(self, dataset_paths: list, output_dir: str):
        """
        Fine-tune the model with new datasets and save the updated model weights.

        Args:
            dataset_paths: List of paths to datasets for fine-tuning.
            output_dir: Directory where the updated model weights will be saved.
        """
        pass