from __future__ import annotations

import typing
import time
import traceback
import json

import mirai

from .. import handler
from ... import entities
from ....core import entities as core_entities
from ....provider import entities as llm_entities
from ....plugin import events


class ChatMessageHandler(handler.MessageHandler):

    async def handle(
        self,
        query: core_entities.Query,
    ) -> typing.AsyncGenerator[entities.StageProcessResult, None]:
        """处理
        """
        # 调API
        #   生成器

        # 触发插件事件
        event_class = events.PersonNormalMessageReceived if query.launcher_type == core_entities.LauncherTypes.PERSON else events.GroupNormalMessageReceived

        event_ctx = await self.ap.plugin_mgr.emit_event(
            event=event_class(
                launcher_type=query.launcher_type.value,
                launcher_id=query.launcher_id,
                sender_id=query.sender_id,
                text_message=str(query.message_chain),
                query=query
            )
        )

        if event_ctx.is_prevented_default():
            if event_ctx.event.reply is not None:
                mc = mirai.MessageChain(event_ctx.event.reply)

                query.resp_messages.append(mc)

                yield entities.StageProcessResult(
                    result_type=entities.ResultType.CONTINUE,
                    new_query=query
                )
            else:
                yield entities.StageProcessResult(
                    result_type=entities.ResultType.INTERRUPT,
                    new_query=query
                )
        else:

            if not self.ap.provider_cfg.data['enable-chat']:
                yield entities.StageProcessResult(
                    result_type=entities.ResultType.INTERRUPT,
                    new_query=query,
                )

            if event_ctx.event.alter is not None:
                query.message_chain = mirai.MessageChain([
                    mirai.Plain(event_ctx.event.alter)
                ])

            text_length = 0

            start_time = time.time()

            try:

                async for result in self.runner(query):
                    query.resp_messages.append(result)

                    self.ap.logger.info(f'对话({query.query_id})响应: {self.cut_str(result.readable_str())}')

                    if result.content is not None:
                        text_length += len(result.content)

                    yield entities.StageProcessResult(
                        result_type=entities.ResultType.CONTINUE,
                        new_query=query
                    )

                query.session.using_conversation.messages.append(query.user_message)
                query.session.using_conversation.messages.extend(query.resp_messages)
            except Exception as e:
                
                self.ap.logger.error(f'对话({query.query_id})请求失败: {str(e)}')

                yield entities.StageProcessResult(
                    result_type=entities.ResultType.INTERRUPT,
                    new_query=query,
                    user_notice='请求失败' if self.ap.platform_cfg.data['hide-exception-info'] else f'{e}',
                    error_notice=f'{e}',
                    debug_notice=traceback.format_exc()
                )
            finally:

                await self.ap.ctr_mgr.usage.post_query_record(
                    session_type=query.session.launcher_type.value,
                    session_id=str(query.session.launcher_id),
                    query_ability_provider="QChatGPT.Chat",
                    usage=text_length,
                    model_name=query.use_model.name,
                    response_seconds=int(time.time() - start_time),
                    retry_times=-1,
                )

    async def runner(
        self,
        query: core_entities.Query,
    ) -> typing.AsyncGenerator[llm_entities.Message, None]:
        """执行一个请求处理过程中的LLM接口请求、函数调用的循环
        
        这是临时处理方案，后续可能改为使用LangChain或者自研的工作流处理器
        """
        await query.use_model.requester.preprocess(query)

        pending_tool_calls = []

        req_messages = query.prompt.messages.copy() + query.messages.copy() + [query.user_message]

        # 首次请求
        msg = await query.use_model.requester.call(query.use_model, req_messages, query.use_funcs)

        yield msg

        pending_tool_calls = msg.tool_calls

        req_messages.append(msg)

        # 持续请求，只要还有待处理的工具调用就继续处理调用
        while pending_tool_calls:
            for tool_call in pending_tool_calls:
                try:
                    func = tool_call.function
                    
                    parameters = json.loads(func.arguments)

                    func_ret = await self.ap.tool_mgr.execute_func_call(
                        query, func.name, parameters
                    )

                    msg = llm_entities.Message(
                        role="tool", content=json.dumps(func_ret, ensure_ascii=False), tool_call_id=tool_call.id
                    )

                    yield msg

                    req_messages.append(msg)
                except Exception as e:
                    # 工具调用出错，添加一个报错信息到 req_messages
                    err_msg = llm_entities.Message(
                        role="tool", content=f"err: {e}", tool_call_id=tool_call.id
                    )

                    yield err_msg

                    req_messages.append(err_msg)

            # 处理完所有调用，再次请求
            msg = await query.use_model.requester.call(query.use_model, req_messages, query.use_funcs)

            yield msg

            pending_tool_calls = msg.tool_calls

            req_messages.append(msg)