import os
from typing import Optional
from transformers import AutoConfig

import utils
from logger import logger
from modeling.inference_model import InferenceModel


class HFInferenceModel(InferenceModel):
    def __init__(self) -> None:
        super().__init__()
        self.model_config = None

    def get_local_model_path(
        self, legacy: bool = False, ignore_existance: bool = False
    ) -> Optional[str]:
        """
        Returns a string of the model's path locally, or None if it is not downloaded.
        If ignore_existance is true, it will always return a path.
        """

        basename = utils.koboldai_vars.model.replace("/", "_")
        if legacy:
            ret = basename
        else:
            ret = os.path.join("models", basename)

        if os.path.isdir(ret) or ignore_existance:
            return ret
        return None

    def init_model_config(self) -> None:
        # Get the model_type from the config or assume a model type if it isn't present
        try:
            self.model_config = AutoConfig.from_pretrained(
                self.get_local_model_path() or utils.koboldai_vars.model,
                revision=utils.koboldai_vars.revision,
                cache_dir="cache",
            )
            utils.koboldai_vars.model_type = self.model_config.model_type
        except ValueError:
            utils.koboldai_vars.model_type = {
                "NeoCustom": "gpt_neo",
                "GPT2Custom": "gpt2",
            }.get(utils.koboldai_vars.model)

            if not utils.koboldai_vars.model_type:
                logger.warning(
                    "No model type detected, assuming Neo (If this is a GPT2 model use the other menu option or --model GPT2Custom)"
                )
                utils.koboldai_vars.model_type = "gpt_neo"