import os
from typing import Any
from datetime import datetime

from fastapi import APIRouter, Depends, HTTPException, Body
from pydantic import BaseModel, ConfigDict, Field
from starlette.concurrency import run_in_threadpool

from app.auth.keycloak_dependencies import get_current_keycloak_username
from app.services.dma_leakage_estimation import (
    run_leakage_identification,
)

router = APIRouter()
DEFAULT_N_WORKERS = max(1, min((os.cpu_count() or 1) - 1, 4))
MAX_POPULATION_SIZE = 1_000
MAX_GENERATIONS = 1_000
MAX_DURATION_HOURS = 168


class LeakageIdentifyRequest(BaseModel):
    """漏损识别请求模型"""

    model_config = ConfigDict(extra="forbid")

    network: str = Field(..., description="管网名称（或数据库名称）")
    observed_pressure_data: dict[str, list[Any]] | list[dict[str, Any]] | None = (
        Field(None, description="观测的压力数据；文件路径不属于公共 API 输入")
    )
    start_time: float = Field(0, ge=0, description="起始时间（小时）")
    duration: float = Field(
        24, gt=0, le=MAX_DURATION_HOURS, description="持续时间（小时）"
    )
    timestep: float = Field(5, gt=0, le=1440, description="时间步长（分钟）")
    q_sum: float = Field(0.2, ge=0, description="总流量（m3/s）")
    q_sum_unit: str = Field("m3/s", description="流量单位")
    pop_size: int = Field(
        50, ge=2, le=MAX_POPULATION_SIZE, description="种群大小"
    )
    max_gen: int = Field(100, ge=1, le=MAX_GENERATIONS, description="最大代数")
    n_workers: int = Field(
        DEFAULT_N_WORKERS,
        ge=1,
        le=DEFAULT_N_WORKERS,
        description="工作进程数",
    )
    output_flow_unit: str = Field("m3/s", description="输出流量单位")
    dma_count: int | None = Field(None, ge=1, description="DMA区域数量")
    scada_start: datetime | None = Field(None, description="SCADA数据起始时间")
    scada_end: datetime | None = Field(None, description="SCADA数据结束时间")
    sensor_nodes: list[str] | None = Field(None, description="传感器节点列表")
    scheme_name: str | None = Field(None, description="方案名称")


@router.post(
    "/leakage-identifications",
    summary="执行漏损识别",
    description="基于压力观测数据和遗传算法识别管网中的漏损位置和大小"
)
async def identify_leakage(
    data: LeakageIdentifyRequest = Body(..., description="漏损识别请求数据"),
    username: str = Depends(get_current_keycloak_username),
) -> dict[str, Any]:
    """
    执行漏损识别分析。
    
    使用遗传算法对比模型计算和实测压力数据，
    识别管网中的漏损节点和漏水量。
    
    Args:
        data: 包含管网名称（或数据库名称）、压力数据及优化参数的请求体
        username: 当前认证用户名
        
    Returns:
        包含识别结果的字典
        
    Raises:
        HTTPException: 当处理过程中发生错误时
    """
    try:
        return await run_in_threadpool(
            run_leakage_identification,
            **data.model_dump(),
            username=username,
        )
    except Exception as exc:
        raise HTTPException(status_code=400, detail=str(exc))