from __future__ import annotations

import asyncio
import os
import typing
from typing import Union, Mapping, Any, AsyncIterator
import uuid
import json
import base64

import async_lru
import ollama

from .. import entities, errors, requester
from ... import entities as llm_entities
from ...tools import entities as tools_entities
from ....core import app, entities as core_entities
from ....utils import image

REQUESTER_NAME: str = "ollama-chat"


class OllamaChatCompletions(requester.LLMAPIRequester):
    """Ollama平台 ChatCompletion API请求器"""

    client: ollama.AsyncClient

    default_config: dict[str, typing.Any] = {
        "base_url": "http://127.0.0.1:11434",
        "timeout": 120,
    }

    async def initialize(self):
        os.environ["OLLAMA_HOST"] = self.requester_cfg["base_url"]
        self.client = ollama.AsyncClient(timeout=self.requester_cfg["timeout"])

    async def _req(
        self,
        args: dict,
    ) -> Union[Mapping[str, Any], AsyncIterator[Mapping[str, Any]]]:
        return await self.client.chat(**args)

    async def _closure(
        self,
        query: core_entities.Query,
        req_messages: list[dict],
        use_model: requester.RuntimeLLMModel,
        user_funcs: list[tools_entities.LLMFunction] = None,
        extra_args: dict[str, typing.Any] = {},
    ) -> llm_entities.Message:
        args = extra_args.copy()
        args["model"] = use_model.model_entity.name

        messages: list[dict] = req_messages.copy()
        for msg in messages:
            if "content" in msg and isinstance(msg["content"], list):
                text_content: list = []
                image_urls: list = []
                for me in msg["content"]:
                    if me["type"] == "text":
                        text_content.append(me["text"])
                    elif me["type"] == "image_base64":
                        image_urls.append(me["image_base64"])

                msg["content"] = "\n".join(text_content)
                msg["images"] = [url.split(",")[1] for url in image_urls]
            if (
                "tool_calls" in msg
            ):  # LangBot 内部以 str 存储 tool_calls 的参数，这里需要转换为 dict
                for tool_call in msg["tool_calls"]:
                    tool_call["function"]["arguments"] = json.loads(
                        tool_call["function"]["arguments"]
                    )
        args["messages"] = messages

        args["tools"] = []
        if user_funcs:
            tools = await self.ap.tool_mgr.generate_tools_for_openai(user_funcs)
            if tools:
                args["tools"] = tools

        resp = await self._req(args)
        message: llm_entities.Message = await self._make_msg(resp)
        return message

    async def _make_msg(
        self, chat_completions: ollama.ChatResponse
    ) -> llm_entities.Message:
        message: ollama.Message = chat_completions.message
        if message is None:
            raise ValueError("chat_completions must contain a 'message' field")

        ret_msg: llm_entities.Message = None

        if message.content is not None:
            ret_msg = llm_entities.Message(role="assistant", content=message.content)
        if message.tool_calls is not None and len(message.tool_calls) > 0:
            tool_calls: list[llm_entities.ToolCall] = []

            for tool_call in message.tool_calls:
                tool_calls.append(
                    llm_entities.ToolCall(
                        id=uuid.uuid4().hex,
                        type="function",
                        function=llm_entities.FunctionCall(
                            name=tool_call.function.name,
                            arguments=json.dumps(tool_call.function.arguments),
                        ),
                    )
                )
            ret_msg.tool_calls = tool_calls

        return ret_msg

    async def invoke_llm(
        self,
        query: core_entities.Query,
        model: requester.RuntimeLLMModel,
        messages: typing.List[llm_entities.Message],
        funcs: typing.List[tools_entities.LLMFunction] = None,
        extra_args: dict[str, typing.Any] = {},
    ) -> llm_entities.Message:
        req_messages: list = []
        for m in messages:
            msg_dict: dict = m.dict(exclude_none=True)
            content: Any = msg_dict.get("content")
            if isinstance(content, list):
                if all(
                    isinstance(part, dict) and part.get("type") == "text"
                    for part in content
                ):
                    msg_dict["content"] = "\n".join(part["text"] for part in content)
            req_messages.append(msg_dict)
        try:
            return await self._closure(
                query=query,
                req_messages=req_messages,
                use_model=model,
                use_funcs=funcs,
                extra_args=extra_args,
            )
        except asyncio.TimeoutError:
            raise errors.RequesterError("请求超时")