from __future__ import annotations

from collections import Counter

from sqlalchemy import func, select
from sqlalchemy.ext.asyncio import AsyncSession

from app.api.v1.bgp import BGP_SOURCES
from app.models.bgp_anomaly import BGPAnomaly
from app.models.bgp_incident import BGPIncident
from app.models.bgp_observation import BGPObservation
from app.schemas.ai import SituationalAnalysisRequest
from app.services.bgp_collectors import build_bgp_collector_coverage


def _format_counter(counter: dict[str, int], empty_text: str = "无") -> str:
    if not counter:
        return empty_text
    return "，".join(f"{key} {value}" for key, value in counter.items())


def _severity_rank(value: str | None) -> int:
    order = {
        "critical": 0,
        "high": 1,
        "medium": 2,
        "low": 3,
        "info": 4,
    }
    return order.get((value or "").lower(), 99)


async def build_bgp_brief_request(
    db: AsyncSession,
    *,
    incident_limit: int = 5,
    anomaly_limit: int = 6,
    collector_limit: int = 5,
) -> SituationalAnalysisRequest:
    incidents_result = await db.execute(
        select(BGPIncident)
        .order_by(BGPIncident.created_at.desc(), BGPIncident.id.desc())
        .limit(max(incident_limit, 1))
    )
    anomalies_result = await db.execute(
        select(BGPAnomaly)
        .order_by(BGPAnomaly.created_at.desc(), BGPAnomaly.id.desc())
        .limit(max(anomaly_limit, 1))
    )
    observations_result = await db.execute(
        select(BGPObservation).where(BGPObservation.source.in_(BGP_SOURCES))
    )
    incident_count_result = await db.execute(select(func.count(BGPIncident.id)))
    anomaly_count_result = await db.execute(select(func.count(BGPAnomaly.id)))

    incidents = incidents_result.scalars().all()
    anomalies = anomalies_result.scalars().all()
    observations = observations_result.scalars().all()
    collectors = await build_bgp_collector_coverage(db, source_filter=BGP_SOURCES)

    total_incidents = incident_count_result.scalar() or 0
    total_anomalies = anomaly_count_result.scalar() or 0
    total_observations = len(observations)
    active_collectors = [item for item in collectors if item["observation_count"] > 0]

    incident_status_counts = Counter((item.status or "unknown") for item in incidents)
    incident_severity_counts = Counter((item.severity or "unknown") for item in incidents)
    incident_type_counts = Counter((item.incident_type or "unknown") for item in incidents)
    anomaly_type_counts = Counter((item.anomaly_type or "unknown") for item in anomalies)
    event_type_counts = Counter((item.event_type or "unknown") for item in observations)

    top_collectors = sorted(
        active_collectors,
        key=lambda item: (
            -int(item["recent_24h_observation_count"]),
            -int(item["observation_count"]),
            str(item["collector"]),
        ),
    )[: max(collector_limit, 1)]

    observations_lines: list[str] = [
        f"当前共有 {total_incidents} 起 BGP incidents、{total_anomalies} 条 anomalies、{total_observations} 条原始观测事件。",
        f"活跃观测站 {len(active_collectors)} 个；近 24 小时事件数合计 {sum(int(item['recent_24h_observation_count']) for item in active_collectors)}。",
        f"最近 incidents 严重度分布：{_format_counter(dict(sorted(incident_severity_counts.items(), key=lambda item: _severity_rank(item[0]))))}。",
        f"最近 incidents 状态分布：{_format_counter(dict(incident_status_counts))}。",
        f"最近 incidents 类型分布：{_format_counter(dict(incident_type_counts.most_common(5)))}。",
        f"最近 anomalies 类型分布：{_format_counter(dict(anomaly_type_counts.most_common(6)))}。",
        f"观测事件类型分布：{_format_counter(dict(event_type_counts.most_common(6)))}。",
    ]

    if incidents:
        observations_lines.append(
            "最近 incident 摘要：" + "；".join(
                [
                    f"{item.incident_type} / {item.severity} / {item.status}"
                    f" / 前缀 {', '.join(item.affected_prefixes[:2]) if item.affected_prefixes else '-'}"
                    f" / 观测站 {len(item.affected_collectors or [])} 个"
                    for item in incidents
                ]
            )
        )

    if anomalies:
        observations_lines.append(
            "最近 anomaly 摘要：" + "；".join(
                [
                    f"{item.anomaly_type} / {item.severity}"
                    f" / 前缀 {item.prefix or '-'}"
                    f" / ASN {item.new_origin_asn or item.origin_asn or '-'}"
                    for item in anomalies
                ]
            )
        )

    if top_collectors:
        observations_lines.append(
            "重点观测站：" + "；".join(
                [
                    f"{item['collector']} ({', '.join([part for part in [item.get('city'), item.get('country')] if part]) or '未知位置'})"
                    f" / 近24h {item['recent_24h_observation_count']} 条"
                    f" / 前缀 {item['prefix_count']} 个"
                    for item in top_collectors
                ]
            )
        )

    return SituationalAnalysisRequest(
        title="BGP 态势 AI 简报",
        objective="基于当前 BGP incidents、anomalies、原始观测事件与观测站覆盖情况，生成一份面向操作员的简明态势简报，突出当前风险、证据和优先动作。",
        observations=observations_lines,
        constraints=[
            "明确区分事实、推断与建议。",
            "优先指出需要立即关注的高严重度 incident 或异常模式。",
            "结论应服务值班排障，不要写成泛泛的模型演示文案。",
            "如果证据不足，要明确指出缺失数据。",
        ],
        context={
            "source": "bgp-overview",
            "incident_total": total_incidents,
            "anomaly_total": total_anomalies,
            "observation_total": total_observations,
            "active_collectors": len(active_collectors),
            "top_incident_types": dict(incident_type_counts.most_common(5)),
            "top_anomaly_types": dict(anomaly_type_counts.most_common(6)),
            "top_event_types": dict(event_type_counts.most_common(6)),
        },
    )