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

from . import util
from .inference_result import InferenceResult

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.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.
        """
        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            return_dict=True,
            low_cpu_mem_usage=True,
            torch_dtype=torch.bfloat16,
            device_map="auto",
            trust_remote_code=True,
        )
        if self.tokenizer.pad_token_id is None:
            self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
        if model.config.pad_token_id is None:
            model.config.pad_token_id = model.config.eos_token_id
        streamer = TextStreamer(
            self.tokenizer,
            skip_prompt=True
        )
        self.pipeline = pipeline(
            "text-generation",
            model=model,
            tokenizer=self.tokenizer,
            torch_dtype=torch.bfloat16,
            device_map="auto",
            streamer=streamer,
            return_full_text=False,
        )

    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: Tuple containing reasoning and actions that validate against the schema
        """
        valid_elements = util.get_valid_root_elements(action_schema)
        messages = [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": main_context}
        ]
        prompt = self.tokenizer.apply_chat_template(
            messages, tokenize=False, add_generation_prompt=True
        )
        outputs = self.pipeline(prompt, max_new_tokens=120, do_sample=True)
        generated_text = outputs[0]["generated_text"]
        #response = generated_text.split("<|start_header_id|>assistant<|end_header_id|>",1)[1].strip()
        result = util.split_response(generated_text, valid_elements)
        return result

    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