from typing import Tuple, Union

import flask

from llm_server.cluster.cluster_config import cluster_config
from llm_server.custom_redis import redis
from llm_server.llm import get_token_count


class LLMBackend:
    _default_params: dict

    def __init__(self, backend_url: str):
        self.backend_url = backend_url
        self.backend_info = cluster_config.get_backend(self.backend_url)

    def handle_response(self, success, request: flask.Request, response_json_body: dict, response_status_code: int, client_ip, token, prompt, elapsed_time, parameters, headers):
        raise NotImplementedError

    def validate_params(self, params_dict: dict) -> Tuple[bool, str | None]:
        raise NotImplementedError

    # def get_model_info(self) -> Tuple[dict | bool, Exception | None]:
    #     raise NotImplementedError

    def get_parameters(self, parameters) -> Tuple[dict | None, str | None]:
        """
        Validate and return the parameters for this backend.
        Lets you set defaults for specific backends.
        :param parameters:
        :return:
        """
        raise NotImplementedError

    def validate_request(self, parameters: dict, prompt: str, request: flask.Request) -> Tuple[bool, Union[str, None]]:
        """
        If a backend needs to do other checks not related to the prompt or parameters.
        Default is no extra checks preformed.
        :param request:
        :param prompt:
        :param parameters:
        :return:
        """
        return True, None

    def validate_prompt(self, prompt: str) -> Tuple[bool, Union[str, None]]:
        prompt_len = get_token_count(prompt, self.backend_url)
        token_limit = self.backend_info['model_config']['max_position_embeddings']
        if prompt_len > token_limit - 10:
            return False, f'Token indices sequence length is longer than the specified maximum sequence length for this model ({prompt_len} > {token_limit}, model: {self.backend_info["model"]}). Please lower your context size'
        return True, None