"""Durable runtime for Campaign intelligence jobs.

智能分析与周期对比是两个领域工作 adapter；本 module 统一掌握它们的
持久排队、进程内幂等启动、重启恢复和关闭顺序。数据库行是耐久权威，
TaskRegistry 只保存当前进程中的任务句柄。
"""

import logging
from collections.abc import Callable
from typing import Any, Optional

from sqlmodel import Session

from agenteval.storage.db import get_session
from agenteval.storage.repository import (
    CampaignAnalysisRepository,
    CampaignPeriodComparisonRepository,
)
from agenteval.task_registry import TaskRegistry

_registry = TaskRegistry()
_logger = logging.getLogger("agenteval")
MAX_QUEUED_RECOVERY_ATTEMPTS = 3


def _job_key(kind: str, campaign_id: str) -> str:
    return f"{kind}:{campaign_id}"


def _launch_analysis(campaign_id: str, *, triggered_by: str) -> None:
    async def run(_cancel) -> None:
        await execute_campaign_analysis_job(campaign_id, triggered_by=triggered_by)

    _registry.launch(_job_key("analysis", campaign_id), run)


def _launch_comparison(
    campaign_id: str,
    *,
    triggered_by: str,
    baseline_campaign_id: str,
) -> None:
    async def run(_cancel) -> None:
        await execute_campaign_comparison_job(
            campaign_id,
            triggered_by=triggered_by,
            baseline_campaign_id=baseline_campaign_id,
        )

    _registry.launch(_job_key("comparison", campaign_id), run)


async def execute_campaign_analysis_job(
    campaign_id: str,
    *,
    triggered_by: str,
    chat_client: Any = None,
    session_factory: Optional[Callable[[], Session]] = None,
) -> None:
    """Claim and settle one intelligent-analysis job."""
    from agenteval.evaluation.analysis import (
        analyze_campaign,
        collect_failure_samples,
        gateway_chat_client,
        resolve_analysis_model,
    )
    from agenteval.evaluation.comparison import resolve_auto_baseline
    from agenteval.evaluation.report import load_campaign_view
    from agenteval.storage.repository import CampaignRepository, RunRepository

    session = (session_factory or get_session)()
    try:
        analyses = CampaignAnalysisRepository(session)
        row = analyses.get_by_campaign(campaign_id)
        if row is None:
            row = analyses.enqueue(campaign_id, triggered_by=triggered_by)
        if not analyses.claim_queued(campaign_id).claimed:
            return
        effective_trigger = row.triggered_by or triggered_by

        campaign = CampaignRepository(session).get(campaign_id)
        if campaign is None:
            analyses.upsert(
                campaign_id,
                status="failed",
                triggered_by=effective_trigger,
                error="campaign not found",
            )
            return
        runtime = resolve_analysis_model(campaign, session)
        if runtime is None:
            analyses.upsert(
                campaign_id,
                status="failed",
                triggered_by=effective_trigger,
                error="未配置分析模型：请在模型配置中心将某个 chat 配置设为「分析默认」",
            )
            return
        analyses.upsert(
            campaign_id,
            status="generating",
            model_config_id=runtime.id,
            triggered_by=effective_trigger,
        )
        try:
            client = chat_client or gateway_chat_client(runtime)
            runs = RunRepository(session).list_by_campaign(campaign_id)
            view = load_campaign_view(session, campaign)
            result = await analyze_campaign(
                campaign=campaign,
                report=view["report"],
                failure_samples=collect_failure_samples(campaign_id, session),
                valid_run_ids={run.id for run in runs if run.id},
                chat_client=client,
                exploration_summary=view["exploration"],
            )
        except Exception as exc:
            _logger.warning("活动 %s 智能分析失败: %s", campaign_id, exc)
            analyses.upsert(
                campaign_id,
                status="failed",
                model_config_id=runtime.id,
                error=str(exc)[:500],
                triggered_by=effective_trigger,
            )
            return
        analyses.upsert(
            campaign_id,
            status="completed",
            result=result,
            model_config_id=runtime.id,
            triggered_by=effective_trigger,
        )

        try:
            if campaign.time_scale != 1 or resolve_analysis_model(campaign, session) is None:
                return
            baseline = resolve_auto_baseline(campaign, session)
            if baseline is None:
                return
            enqueue_campaign_comparison(
                campaign.id,
                triggered_by="auto",
                baseline_campaign_id=baseline.id,
                session=session,
            )
        except Exception as exc:
            _logger.warning("活动 %s 自动周期对比跳过: %s", campaign.id, exc)
    finally:
        session.close()


async def execute_campaign_comparison_job(
    campaign_id: str,
    *,
    triggered_by: str,
    baseline_campaign_id: Optional[str] = None,
    chat_client: Any = None,
    session_factory: Optional[Callable[[], Session]] = None,
) -> None:
    """Claim and settle one period-comparison job."""
    from agenteval.evaluation.analysis import gateway_chat_client, resolve_analysis_model
    from agenteval.evaluation.comparison import (
        ComparisonError,
        compute_metric_diff,
        narrate_period_comparison,
        validate_comparison_request,
    )
    from agenteval.evaluation.report import load_campaign_report
    from agenteval.storage.repository import CampaignRepository

    session = (session_factory or get_session)()
    try:
        comparisons = CampaignPeriodComparisonRepository(session)
        campaign = CampaignRepository(session).get(campaign_id)
        if campaign is None:
            return

        row = comparisons.get_by_campaign(campaign_id)
        if row is None:
            try:
                initial_baseline = validate_comparison_request(
                    session,
                    campaign,
                    explicit_baseline_id=baseline_campaign_id,
                )
            except ComparisonError as exc:
                comparisons.upsert(
                    campaign_id,
                    status="failed",
                    triggered_by=triggered_by,
                    error=str(exc),
                )
                return
            row = comparisons.enqueue(
                campaign_id,
                baseline_campaign_id=initial_baseline.id,
                triggered_by=triggered_by,
            )
        if not comparisons.claim_queued(campaign_id).claimed:
            return

        effective_trigger = row.triggered_by or triggered_by
        effective_baseline_id = baseline_campaign_id or row.baseline_campaign_id
        try:
            baseline = validate_comparison_request(
                session,
                campaign,
                explicit_baseline_id=effective_baseline_id,
            )
        except ComparisonError as exc:
            comparisons.upsert(
                campaign_id,
                status="failed",
                baseline_campaign_id=effective_baseline_id,
                triggered_by=effective_trigger,
                error=str(exc),
            )
            return

        runtime = resolve_analysis_model(campaign, session)
        baseline_analysis = CampaignAnalysisRepository(session).get_by_campaign(baseline.id)
        current_analysis = CampaignAnalysisRepository(session).get_by_campaign(campaign_id)
        comparisons.upsert(
            campaign_id,
            status="generating",
            baseline_campaign_id=baseline.id,
            model_config_id=runtime.id,
            triggered_by=effective_trigger,
        )
        try:
            diff = compute_metric_diff(
                load_campaign_report(session, baseline),
                load_campaign_report(session, campaign),
            )
            result = await narrate_period_comparison(
                baseline_analysis=baseline_analysis.get_result(),
                current_analysis=current_analysis.get_result(),
                metric_diff=diff,
                valid_scenario_ids={item["scenario_id"] for item in diff["scenarios"]},
                chat_client=chat_client or gateway_chat_client(runtime),
            )
        except Exception as exc:
            _logger.warning("活动 %s 周期对比失败: %s", campaign_id, exc)
            comparisons.upsert(
                campaign_id,
                status="failed",
                baseline_campaign_id=baseline.id,
                model_config_id=runtime.id,
                error=str(exc)[:500],
                triggered_by=effective_trigger,
            )
            return
        comparisons.upsert(
            campaign_id,
            status="completed",
            baseline_campaign_id=baseline.id,
            result=result,
            model_config_id=runtime.id,
            triggered_by=effective_trigger,
        )
    finally:
        session.close()


def enqueue_campaign_analysis(
    campaign_id: str,
    *,
    triggered_by: str,
    session: Optional[Session] = None,
) -> None:
    """Persist an analysis job, then launch its process-local worker."""
    owns_session = session is None
    active_session = session or get_session()
    try:
        row = CampaignAnalysisRepository(active_session).enqueue(
            campaign_id,
            triggered_by=triggered_by,
        )
    finally:
        if owns_session:
            active_session.close()
    if row.status == "queued":
        _launch_analysis(campaign_id, triggered_by=row.triggered_by or triggered_by)


def enqueue_campaign_comparison(
    campaign_id: str,
    *,
    triggered_by: str,
    baseline_campaign_id: str,
    session: Optional[Session] = None,
) -> None:
    """Persist a comparison job, then launch its process-local worker."""
    owns_session = session is None
    active_session = session or get_session()
    try:
        row = CampaignPeriodComparisonRepository(active_session).enqueue(
            campaign_id,
            baseline_campaign_id=baseline_campaign_id,
            triggered_by=triggered_by,
        )
    finally:
        if owns_session:
            active_session.close()
    if row.status == "queued":
        _launch_comparison(
            campaign_id,
            triggered_by=row.triggered_by or triggered_by,
            baseline_campaign_id=row.baseline_campaign_id,
        )


def recover_campaign_intelligence_jobs(session: Session) -> tuple[int, int]:
    """Fail interrupted work and relaunch every durably queued job."""
    analyses = CampaignAnalysisRepository(session)
    comparisons = CampaignPeriodComparisonRepository(session)
    interrupted = analyses.mark_orphans_failed() + comparisons.mark_orphans_failed()

    queued_analyses = analyses.prepare_queued_recovery(
        MAX_QUEUED_RECOVERY_ATTEMPTS,
        "服务重启恢复次数超过上限，分析任务已终止",
    )
    for row in queued_analyses:
        _launch_analysis(row.campaign_id, triggered_by=row.triggered_by or "manual")

    queued_comparisons = comparisons.prepare_queued_recovery(
        MAX_QUEUED_RECOVERY_ATTEMPTS,
        "服务重启恢复次数超过上限，周期对比任务已终止",
    )
    for row in queued_comparisons:
        _launch_comparison(
            row.campaign_id,
            triggered_by=row.triggered_by or "manual",
            baseline_campaign_id=row.baseline_campaign_id,
        )
    return interrupted, len(queued_analyses) + len(queued_comparisons)


def is_intelligence_job_running(kind: str, campaign_id: str) -> bool:
    """Expose process-local liveness without exposing registry internals."""
    return _registry.is_running(_job_key(kind, campaign_id))


async def shutdown_campaign_intelligence_jobs() -> None:
    """Stop every live analysis and comparison worker."""
    await _registry.shutdown_all()