from threading import Thread
from typing import Iterator

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

from . import util

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
        self.pipeline = pipeline(
            "text-generation",
            model=model,
            tokenizer=self.tokenizer,
            torch_dtype=torch.bfloat16,
            device_map="auto",
            return_full_text=False,
        )

    def infer(self, system_prompt: str, main_context: str) -> Iterator[str]:
        """
        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

        Returns:
            Iterator[str]: An iterator that yields the generated text.
        """
        messages = [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": main_context}
        ]
        prompt = self.tokenizer.apply_chat_template(
            messages, tokenize=False, add_generation_prompt=True
        )
        streamer = TextIteratorStreamer(
            self.tokenizer,
            skip_prompt=True
        )
        pipeline_kwargs = dict(
            text_inputs=prompt,
            do_sample=True,
            max_new_tokens=1024,
            streamer=streamer
        )
        thread = Thread(target=self.pipeline, kwargs=pipeline_kwargs)
        thread.start()
        return util.stop_before_value(streamer, '<|eot_id|>')

    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