# -*- coding: utf-8 -*-
"""
预定航班（范式）
"""
import asyncio
import datetime
from typing import AsyncGenerator

from logfire_api.variables import ValueDoesNotEqual
from pydantic import BaseModel, Field, field_validator
from pydantic_ai import (
    Agent,
    ApprovalRequired,
    DeferredToolRequests,
    DeferredToolResults,
    ModelMessage,
    ModelRetry,
    ModelSettings,
    RunContext,
    RunUsage,
    UsageLimits,
)
from pydantic_ai.models.openai import OpenAIChatModel
from pydantic_ai.providers.openai import OpenAIProvider
from sqlalchemy.sql.dml import ReturningDelete


DEEPSEEK_V4_FLASH_MODEL = OpenAIChatModel(
    model_name="deepseek-v4-flash",
    provider=OpenAIProvider(
        base_url="https://tokenhub.tencentmaas.com/v1",
        api_key="sk-D9Y1mCe8VlvNqLuSC4mAjqEwxJ2nW4C0h8a7EPn8kg9RLsHq",
    ),
)

MODEL_SETTINGS = ModelSettings(
    temperature=0, extra_body={"thinking": {"type": "disabled"}}  # 温度控制
)  # 禁用思考模式


class Deps(BaseModel):
    """
    依赖类
    """

    date: datetime.date = Field(..., description="日期")
    origin_airport_code: str = Field(..., description="出发机场代码")
    destination_airport_code: str = Field(..., description="到达机场代码")
    source_material: str = Field(..., description="来源资料")


class Flight(BaseModel):
    """
    航班类
    """

    number: str = Field(..., description="航班号")
    date: datetime.date = Field(..., description="日期")
    origin_airport_code: str = Field(..., description="出发机场代码")
    destination_airport_code: str = Field(..., description="到达机场代码")
    airfare: int = Field(..., description="机票价格")

    @field_validator("origin_airport_code", "destination_airport_code")
    @classmethod
    def validate_airport_code(cls, airport_code: str) -> str:
        """校验机场代码"""
        if (
            not airport_code.isalpha()
            or not airport_code.isupper()
            or len(airport_code) != 3
        ):
            raise ValueError(f"机场代码须为英文、大写、3位字符串")
        return airport_code


class FlightNoFound(BaseModel):
    """
    未查询到航班类
    """


system_prompt = """你的任务是根据用户提供的日期、出发机场和到达机场选择航班。
1. 使用 extract_flights 从来源资料提取所有航班信息；
2. 使用 select_flight 从所有航班信息中选择匹配航班；
"""

# 主智能体
agent = Agent[Deps, Flight | FlightNoFound](
    model=DEEPSEEK_V4_FLASH_MODEL,
    model_settings=MODEL_SETTINGS,
    deps_type=Deps,
    output_type=Flight | FlightNoFound,
    system_prompt=system_prompt,
)

# 航班信息提取智能体
extraction_agent = Agent(
    model=DEEPSEEK_V4_FLASH_MODEL,
    model_settings=MODEL_SETTINGS,
    output_type=list[Flight],
    system_prompt=(
        "你的任务是根据给定的来源资料提取出所有航班信息，包括航班号、日期、出发机场代码、到达机场代码和机票价格。",
        "其中，出发机场代码和到达机场代码须为英文、大写、3位字符串，例如 San Francisco International Airport (SFO) 的机场代码为 SFO。",
        "若某航班信息不全则跳过该航班，若没有任何航班信息则返回空列表。",
        "禁止编造。",
    ),
)  # 通过提示词约束输出


# 航班选择智能体
selection_agent = Agent(
    model=DEEPSEEK_V4_FLASH_MODEL,
    model_settings=MODEL_SETTINGS,
    output_type=Flight | FlightNoFound,
    system_prompt="你的任务是从所有航班信息中选择匹配用户提供的日期、出发机场和到达机场的航班：若有多个航班则选择最便宜的航班，选择完成需用户确认是否预定；若没有则返回 FlightNoFound。",
)

"""
如何提高提取准确率
1. 系统提示词规则约束
2. 降低模型温度
3. 输出类型约定和输出模型校验
4. 业务规则约束（例如，输出不可为空列表）
"""


@agent.tool
async def extract_flights(ctx: RunContext[Deps]) -> list[Flight]:
    """
    提取所有航班信息
    """
    result = await extraction_agent.run(
        ctx.deps.source_material,
        usage_limits=UsageLimits(request_limit=3),
        usage=ctx.usage,
    )
    if len(output := result.output) != 8:
        raise ModelRetry("提取到的所有航班信息应为 8 条")
    return output


@agent.tool
async def select_flight(ctx: RunContext[Deps]) -> Flight | FlightNoFound:
    """
    选择航班
    """
    result = await selection_agent.run(ctx.deps.source_material, usage=ctx.usage)
    if isinstance(result.output, Flight):
        if not ctx.tool_call_approved:
            raise ApprovalRequired(metadata={"reason": "protected"})
    return result.output


@agent.output_validator
async def validate_output(
    ctx: RunContext[Deps], output: Flight | FlightNoFound
) -> Flight | FlightNoFound:
    """
    输出校验
    """
    # 不校验未查询到航班
    if isinstance(output, FlightNoFound):
        return output

    errors = []
    if output.date != ctx.deps.date:
        errors.append(f"航班日期应为 {ctx.deps.date}, 不是 {output.date}")
    if output.origin != ctx.deps.origin:
        errors.append(f"航班出发机场应为 {ctx.deps.origin}, 不是 {output.origin}")
    if output.destination != ctx.deps.destination:
        errors.append(
            f"航班到达机场应为 {ctx.deps.destination}, 不是 {output.destination}"
        )
    if errors:
        raise ModelRetry("\n".join(errors))
    return output


source_material = """
1. Flight SFO-AK123
- Price: $350
- Origin: San Francisco International Airport (SFO)
- Destination: Ted Stevens Anchorage International Airport (ANC)
- Date: January 10, 2025
2. Flight SFO-AK456
- Price: $370
- Origin: San Francisco International Airport (SFO)
- Destination: Fairbanks International Airport (FAI)
- Date: January 10, 2025
3. Flight SFO-AK789
- Price: $400
- Origin: San Francisco International Airport (SFO)
- Destination: Juneau International Airport (JNU)
- Date: January 20, 2025
4. Flight NYC-LA101
- Price: $250
- Origin: San Francisco International Airport (SFO)
- Destination: Ted Stevens Anchorage International Airport (ANC)
- Date: January 10, 2025
5. Flight CHI-MIA202
- Price: $200
- Origin: Chicago O'Hare International Airport (ORD)
- Destination: Miami International Airport (MIA)
- Date: January 12, 2025
6. Flight BOS-SEA303
- Price: $120
- Origin: Boston Logan International Airport (BOS)
- Destination: Ted Stevens Anchorage International Airport (ANC)
- Date: January 12, 2025
7. Flight DFW-DEN404
- Price: $150
- Origin: Dallas/Fort Worth International Airport (DFW)
- Destination: Denver International Airport (DEN)
- Date: January 10, 2025
8. Flight ATL-HOU505
- Price: $180
- Origin: Hartsfield-Jackson Atlanta International Airport (ATL)
- Destination: George Bush Intercontinental Airport (IAH)
- Date: January 10, 2025
"""


async def run_stream_events(
    user_prompt: str,
    deps: Deps,
    message_history: list[ModelMessage],
    usage: RunUsage,
    deferred_tool_results: DeferredToolResults | None = None,
) -> AsyncGenerator:
    async with agent.run_stream_events(
        user_prompt=user_prompt,
        deps=deps,
        message_history=message_history,
        usage=usage,
        deferred_tool_results=deferred_tool_results,
    ) as events:
        async for event in events:
            yield event


async def main():
    deps = Deps(
        source_material=source_material,
        date=datetime.date(2025, 1, 10),
        origin="SFO",
        destination="ANC",
    )
    # message_history 传空列表，不要传 None
    msg_history: list[ModelMessage] = []
    prompt = f"Find me a flight from {deps.origin} to {deps.destination} on {deps.date}"

    gen = run_stream_events(
        user_prompt=prompt,
        deps=deps,
        message_history=msg_history,
        usage=RunUsage(),
    )
    # 消费异步生成器
    async for ev in gen:
        print(ev)


asyncio.run(main())

"""
if not isinstance(event, AgentRunResultEvent):
                    yield event
                else:
                    result = event.result
                    # 更新任务使用量
                    task.usage = usage_to_dict(result.usage)
                    if isinstance(result.output, FlightDetail):
                        content = "\n---\n已查询到航班，请回复 buy 购票 或 search 重新查询\n"
                        # 更新任务节点为提取座位偏好
                        task.node = "seat_preference_extraction"
                    else:
                        content = (
                            "\n---\n未查询到满足您需求的航班，流程结束！\n"
                        )
                        # 更新任务为空
                        task = None
                    # 返回任务节点结果事件
                    yield TaskNodeResultEvent(
                        task=task,
                        content=content,
                    )
                    yield event
                    return
            # 提取座位偏好
            case "seat_preference_extraction":
                if user_prompt == "buy":
                    # 返回任务节点结果事件
                    yield TaskNodeResultEvent(
                        task=task,
                        content="请和我说下您的座位偏好：\nA、F 座位是靠窗位；1 排、14 排、20 排腿部空间更大、更舒展，你更想要靠窗座位，宽敞大空间座位",
                    )
                    return

                elif user_prompt == "search":
                    # 更新任务节点为航班查询
                    task.node = "flight_search"

                else:
async with seat_preference_extraction_agent.run_stream_events(
                        user_prompt=user_prompt,
                        message_history=message_history,
                        usage=usage_to_object(dict(task.usage)),
                        usage_limits=usage_limits_to_object(dict(task.usage_limits)),
                    ) as events:
                        async for event in events:
                            if not isinstance(event, AgentRunResultEvent):
                                yield event
                            else:
                                result = event.result
                                # 更新任务使用量
                                task.usage = usage_to_dict(result.usage)
                                # 更新任务为空
                                task = None
                                # 返回任务节点结果事件
                                yield TaskNodeResultEvent(
                                    task=task,
                                    content="已帮您定好座位，流程结束！",
                                )
                                yield event
                                return


                            # 工具调用分片开始事件
                            case ToolCallPart(
                                tool_name=tool_name, tool_call_id=tool_call_id
                            ):
                                # 构建消息实例
                                message = Message(
                                    type=MessageType.TOOL_CALL,
                                    title=tool_name,
                                    content=",",
                                    is_running=True,
                                )
                                tool_call_ids.add(tool_call_id)
                                # 添加至消息字典
                                dialog.thoughts[index] = Thought(
                                    type="tool_call",
                                    content="正在生成调用参数",
                                )

                                                    # ========== 函数工具调用事件 ==========
                    case FunctionToolCallEvent(tool_call_id=tool_call_id, part=part):
                        # 获取分片索引
                        index = tool_call_ids[tool_call_id]
                        match dialog.thoughts[index].type:
                            # 工具检索
                            case "tool_search":
                                dialog.thoughts[index].content = (
                                    f"正在检索 {part.args_as_json_str()}"
                                )

                            # 能力加载
                            case "capability_load":
                                dialog.thoughts[index].content = (
                                    f"正在加载能力 {part.tool_name}"
                                )

                            # 工具调用
                            case "tool_call":
                                dialog.thoughts[index].content = (
                                    f"正在调用工具 {part.tool_name}"
                                )

                            case _:
                                continue


                              # ========== 函数工具结果事件 ==========
                    case FunctionToolResultEvent(
                        tool_call_id=tool_call_id,
                        content=content,
                    ):
                        index = tool_call_ids[tool_call_id]
                        match dialog.thoughts[index].type:
                            # 工具检索
                            case "tool_search":
                                dialog.thoughts[index].content = (
                                    content if isinstance(content, str) else ""
                                )  # 暂仅考虑文本内容

                            # 能力加载
                            case "capability_load":
                                dialog.thoughts[index].content = f"已加载 {content}"

                            # 工具调用
                            case "tool_call":
                                dialog.thoughts[index].content = f"已调用 {content}"

                            case _:
                                continue


                            def init_work_flow() -> WorkFlow:
    return WorkFlow(
        type=WorkFlowType.BOOK_FLIGHT,
        deps=Dependences(
            source_material=source_material,
            date=datetime.date(2025, 1, 10),
            origin="SFO",
            destination="ANC",
        ),
    )
from application.states.models import (
    WorkFlow,
    WorkFlowType,
)
from application.workshop.models import (
    DEEPSEEK_V4_FLASH_MODEL,
    MODEL_SETTINGS_DISABLED_THINKING,
)
"""