# -*- coding: utf-8 -*-
"""
Pydantic AI 聊天智能体和相关模块
"""

# 列举导入模块
from asyncio import Queue, QueueEmpty, Task, Task, create_task, sleep
from typing import AsyncGenerator, Dict, List, Literal, Optional, Union

from pydantic import Field
from pydantic import BaseModel
from pydantic_ai import Agent as PydanticAIAgent, ModelMessage
from pydantic_ai.capabilities import AgentCapability
from pydantic_ai.messages import (
    AgentStreamEvent,
    FinalResultEvent,
    LoadCapabilityCallPart,
    ModelMessage,
    NativeToolCallPart,
    NativeToolReturnPart,
    NativeToolSearchCallPart,
    PartDeltaEvent,
    PartEndEvent,
    PartStartEvent,
    TextPart,
    TextPartDelta,
    ThinkingPart,
    ThinkingPartDelta,
    ToolCallPart,
    ToolCallPartDelta,
    ToolReturnPart,
    ToolSearchCallPart,
)
from pydantic_ai.models.openai import OpenAIChatModel
from pydantic_ai.output import OutputSpec
from pydantic_ai.providers.openai import OpenAIProvider
from pydantic_ai.run import AgentRunResultEvent


class PartBuffer(BaseModel):
    """
    片段缓冲类
    """

    part_type: Literal["thinking", "text"] = Field(..., description="片段类型")
    content_delta: str = Field(default="", description="片段内容增量")
    task: Optional[Task] = Field(default=None, description="延迟刷新异步协程任务")
    model_config = {"arbitrary_types_allowed": True}  # 允许任意类型


class Debouncer:
    """
    防抖器
    用于处理 run_stream_events 返回的模型消息事件
    """

    def __init__(self, delay: float = 0.25):
        """
        初始化
        :param delay: 延迟时长（单位为秒），停顿超该时长则刷新
        """
        self.delay = delay

        # 片段缓冲字典（键为片段索引，值为片段缓冲实例）
        self.part_buffers: Dict[int, PartBuffer] = {}
        # 待刷新异步队列
        self.pending_flush_queue = Queue()

    async def handle_event(
        self, event: Union[AgentStreamEvent, AgentRunResultEvent]
    ) -> AsyncGenerator[Union[AgentStreamEvent, AgentRunResultEvent]]:
        """
        处理模型消息事件
        在 Pydantic-AI 中模型流式输出包含若干类型片段，例如思考片段、文本片段等。每类型片段包含若干模型消息事件，例如片段开始事件、片段增量事件、片段结束事件等
        :param event: 模型消息事件
        :yield: AsyncGenerator
        """
        # 返回待刷新异步队列中模型消息事件
        while True:
            try:
                yield self.pending_flush_queue.get_nowait()
            except QueueEmpty:
                break

        # 若为思考或文本的片段开始事件则先缓存片段再返回，其它事件直接返回
        if isinstance(event, PartStartEvent):
            index, part = event.index, event.part  # 片段索引、片段
            match part:
                case ThinkingPart():
                    part_type = "thinking"
                case TextPart():
                    part_type = "text"
                case _:
                    yield event
                    return
            # 新增片段缓存
            self.buffers[index] = Buffer(
                part_type=part_type,
            )
            yield event
            return

        """
        原理：
        收到思考或文本片段增量事件时取消未完成的延迟刷新任务、追加增量并重设；若模型持续输出则先缓存，停顿超过阈值再返回，实现防抖
        """
        if isinstance(event, PartDeltaEvent):
            index, delta = event.index, event.delta
            buffer = self.buffers.get(index)
            if buffer and isinstance(delta, (ThinkingPartDelta, TextPartDelta)):
                # 追加增量
                buffer.content_delta += delta.content_delta or ""

                # 取消上一轮未完成的延迟刷新任务
                self._cancel_task(index=index)
                # 创建延迟刷新任务
                buffer.task = create_task(coro=self._delay_flush(index=index))
            else:
                yield event
            return

        # 若为其它事件则先批量刷新片段增量事件再返回该事件
        async for event_ in self._batch_flush():
            yield event_
        # 若为片段结束事件则删除该片段缓存
        if isinstance(event, PartEndEvent):
            self.buffers.pop(event.index, None)
        yield event

    def _cancel_task(self, index: int) -> None:
        """
        取消延迟刷新异步协程任务
        :param index: 片段索引
        :return: None
        """
        buffer = self.buffers.get(index)
        if not buffer or not buffer.task:
            return
        if not buffer.task.done():
            buffer.task.cancel()
        buffer.task = None

    async def _delay_flush(self, index: int) -> None:
        """
        延迟刷新
        :param index: 片段索引
        :return: None
        """
        await sleep(delay=self.delay)

        # 刷新片段增量事件
        event = await self._flush(index=index)
        if event:
            await self.pending_flush_queue.put(item=event)

    async def _flush(self, index: int) -> Optional[PartDeltaEvent]:
        """
        刷新片段增量事件
        :param index: 片段索引
        :return: Optional[PartDeltaEvent]
        """
        buffer = self.buffers.get(index)
        if not buffer or not buffer.content_delta:
            return

        # 构建片段增量事件中增量部分
        match buffer.part_type:
            case "thinking":
                delta = ThinkingPartDelta(content_delta=buffer.content_delta)
            case "text":
                delta = TextPartDelta(content_delta=buffer.content_delta)

        buffer.content_delta = ""
        buffer.task = None
        return PartDeltaEvent(index=index, delta=delta)

    async def _batch_flush(self) -> AsyncGenerator[PartDeltaEvent, None]:
        """
        批量刷新片段增量事件
        :yield: AsyncGenerator
        """
        for index in list(self.buffers.keys()):
            event = await self._flush(index=index)
            if event:
                yield event


class Agent:
    """
    Pydantic AI 智能体
    """

    def __init__(
        self,
        chat_id: str,
        instructions: str,
        output_type: OutputSpec = str,
        capabilities: Optional[List[AgentCapability]] = None,
        retries: int = 1,
    ):
        """
        初始化智能体
        :param chat_id: 聊天唯一标识
        :param instructions: 指令
        :param capabilities: 技能列表，默认为不使用技能
        :param output_type: 输出类型
        :param retries: 重试次数，默认为1次
        :return: 智能体实例
        """
        # 聊天唯一标识
        self.chat_id = chat_id

        # 本轮对话新增消息
        # 一次聊天（chat）包含若干论对话（dialog），每一轮对话由输入消息（message）和输出消息（message）组成
        self.new_messages: List[ModelMessage] = []

        # 实例智能体
        self.agent = PydanticAIAgent(
            model=OpenAIChatModel(
                model_name="deepseek-v4-flash",
                provider=OpenAIProvider(
                    base_url="https://tokenhub.tencentmaas.com/v1",
                    api_key="sk-D9Y1mCe8VlvNqLuSC4mAjqEwxJ2nW4C0h8a7EPn8kg9RLsHq",
                ),
            ),
            instructions=instructions,
            capabilities=capabilities,
            output_type=output_type,
            retries=retries,
        )

    async def stream_messages_events(
        self,
        user_prompt: str | List[str],
        message_history: Optional[List[ModelMessage]] = None,
        delay: float = 0.2,
    ) -> AsyncGenerator[str, None]:
        """
        流式输出消息事件
        :param user_prompt: 用户提示词（用户输入消息）
        :param delay: 延迟时长（单位为秒），停顿超该时长则刷新
        :yield: AsyncGenerator
        """
        # 实例防抖器
        debouncer = Debouncer(delay=delay)

        async with self.agent.run_stream_events(
            user_prompt=user_prompt,
            message_history=message_history,
        ) as events:
            async for event in events:
                # 处理模型消息事件
                async for event in debouncer.handle_event(event):
                    match event:
                        # 片段开始事件
                        case PartStartEvent(part=part):
                            match part:
                                case ThinkingPart(content=content):
                                    yield f"00:{content}"
                                case TextPart(content=content):
                                    yield f"01:{content}"
                                case (
                                    NativeToolSearchCallPart(tool_name=tool_name)
                                    | NativeToolCallPart(tool_name=tool_name)
                                    | ToolSearchCallPart(tool_name=tool_name)
                                    | ToolCallPart(tool_name=tool_name)
                                    | LoadCapabilityCallPart(tool_name=tool_name)
                                ):
                                    yield f"02:正在使用技能：{tool_name}"
                                case NativeToolReturnPart(
                                    content=content
                                ) | ToolReturnPart(content=content):
                                    yield f"04:技能返回：{content}"
                                case _:
                                    yield f"06:未知片段类型{type(part).__name__}"
                        # 片段增量事件
                        case PartDeltaEvent(delta=delta):
                            match delta:
                                case ThinkingPartDelta(content_delta=content_delta):
                                    yield f"00:{content_delta}"
                                case TextPartDelta(content_delta=content_delta):
                                    yield f"01:{content_delta}"
                                case ToolCallPartDelta(args_delta=args_delta):
                                    yield f"03:技能参数：{args_delta}"
                                case _:
                                    yield f"06:未知片段类型{type(delta).__name__}"
                        # 片段结束事件、最终结果事件无需处理
                        case PartEndEvent():
                            continue
                        case FinalResultEvent():
                            continue
                        case AgentRunResultEvent(result=result):
                            self.new_messages = result.new_messages()
                            yield "05:FinalResultEvent"
                        case _:
                            yield f"06:未知事件类型{type(event).__name__}"