from typing import Any, Literal, TypedDict

from neollm import MyLLM
from neollm.types import Functions
from neollm.utils.postprocess import json2dict
from neollm.utils.preprocess import optimize_token


class ProfileExtractorInputType(TypedDict):
    text: str


class ProfileExtractorOuputType(TypedDict):
    name: str
    birth_year: int
    domain: str
    lang: Literal["ENG", "JPN"]


class ProfileExtractor(MyLLM):
    """情報を抽出するMyLLM

    Notes:
        inputs:
            >>> {"text": str}
        outpus:
            >>> {"text_translated": str | None(うまくいかなかった場合)}
    """

    def _preprocess(self, inputs: ProfileExtractorInputType):
        system_prompt = "<input>より情報を抽出する。存在しない場合nullとする"
        user_prompt = "<input>\n" f"'''{inputs['text'].strip()}'''"
        messages = [
            {"role": "system", "content": optimize_token(system_prompt)},
            {"role": "user", "content": optimize_token(user_prompt)},
        ]
        return messages

    def _check_input(
        self, inputs: ProfileExtractorInputType, messages
    ) -> tuple[bool, ProfileExtractorOuputType | None]:
        # 入力がない場合の処理
        if inputs["text"].strip() == "":
            # requestしない, ルールベースのoutput
            return False, {"name": "", "birth_year": -1, "domain": "", "lang": "JPN"}
        # 入力が多い時に16kを使う
        if self.llm.count_tokens(messages) >= 1600:
            self.model = "gpt-3.5-turbo-16k"
        else:
            self.model = "gpt-3.5-turbo"
        # requestする, _
        return True, None

    def _postprocess(self, response) -> ProfileExtractorOuputType:
        if dict(response["choices"][0]["message"]).get("function_call"):
            try:
                extracted_data = json2dict(response["choices"][0]["message"]["function_call"]["arguments"])
            except Exception:
                extracted_data = {}
        else:
            extracted_data = {}

        lang_ = extracted_data.get("lang")
        if lang_ in {"ENG", "JPN"}:
            lang = lang_
        else:
            lang = "JPN"

        outputs: ProfileExtractorOuputType = {
            "name": str(extracted_data.get("name") or ""),
            "birth_year": int(extracted_data.get("birth_year") or -1),
            "domain": str(extracted_data.get("domain") or ""),
            "lang": lang,
        }
        return outputs

    # Function Callingを使う場合必要
    def _add_functions(self, inputs: Any) -> Functions | None:
        functions: Functions = [
            {
                "name": "extract_profile",
                "description": "extract profile of a person",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "name": {
                            "type": "string",
                            "description": "名前",
                        },
                        "domain": {
                            "type": "string",
                            "description": "研究ドメイン カンマ区切り",
                        },
                        "birth_year": {
                            "type": "integer",
                            "description": "the year of the birth YYYY",
                        },
                        "lang": {
                            "type": "string",
                            "description": "the language of the text",
                            "enum": ["ENG", "JPN"],
                        },
                    },
                    "required": ["name", "birth_year", "domain", "lang"],
                },
            }
        ]
        return functions

    # 型定義のために必要
    def __call__(self, inputs: ProfileExtractorInputType) -> ProfileExtractorOuputType:
        outputs: ProfileExtractorOuputType = super().__call__(inputs)
        return outputs