"""Period comparison — baseline pairing, metric diff and LLM narrative (周期对比).

机械半边：计划指纹（同活动串的判据）、自动基线解析、两期报告的指标
diff——全部确定性计算（ADR-0004 口径，经 ``generate_campaign_report`` 的
既有聚合结果现算）。叙述半边：单次 LLM 调用，在两期已消化的分析 JSON 与
机械 diff 之上产出结构化演进叙述（CONTEXT.md「周期对比」）。
"""

import json
from datetime import datetime, timezone
from typing import Any, Optional

from sqlmodel import Session

from agenteval.evaluation.analysis import ChatClient, resolve_analysis_model
from agenteval.models import Campaign, CampaignStatus
from agenteval.storage.async_job_repository import CampaignAnalysisRepository
from agenteval.storage.db import utc_now
from agenteval.storage.repository import CampaignRepository
from agenteval.utils.llm import parse_json_from_llm_text

_SAME_MOMENT_EPS = 1e-3

_VALID_TRENDS = {"improving", "stable", "regressing"}
_VALID_EVOLUTION_STATUSES = {"new", "persisting", "resolved"}
_VALID_SUGGESTION_STATUSES = {"addressed", "partial", "unaddressed", "new"}


class ComparisonError(RuntimeError):
    """对比生成失败（数据缺失或模型输出无法解析），可重试。"""


def campaign_plan_fingerprint(campaign: Campaign) -> str:
    """活动串判据：评测对象 + 计划条目集合 + 窗口秒数（条目顺序无关）。

    指纹相等的活动构成同一活动串（CONTEXT.md「周期对比」）。档位
    （time_scale）不参与指纹——正式/加速由基线解析另行过滤。
    """
    entries = sorted(
        ((entry.scenario_id, entry.offset_seconds, entry.count) for entry in campaign.plan),
        key=lambda e: (e[0], e[1], e[2]),
    )
    return json.dumps(
        {"target": campaign.target_id, "window": campaign.window_seconds, "plan": entries},
        separators=(",", ":"),
    )


def _to_utc(dt: Optional[datetime]) -> Optional[datetime]:
    if dt is None:
        return None
    return dt.replace(tzinfo=timezone.utc) if dt.tzinfo is None else dt


def resolve_auto_baseline(campaign: Campaign, session: Session) -> Optional[Campaign]:
    """自动基线：同活动串中最近一期已完成的正式线活动（须有 completed 分析）。

    条件（全部满足才可作基线）：指纹相等、双方均为正式线
    （time_scale == 1）、候选完成时间早于本期、候选已有 completed 分析。
    本期为加速线或无匹配候选时返回 None。
    """
    if campaign.time_scale != 1:
        return None
    reference = _to_utc(campaign.completed_at) or utc_now()
    fingerprint = campaign_plan_fingerprint(campaign)

    best: Optional[Campaign] = None
    best_completed: Optional[datetime] = None
    for candidate in CampaignRepository(session).list_all():
        if candidate.id == campaign.id or candidate.time_scale != 1:
            continue
        if campaign_plan_fingerprint(candidate) != fingerprint:
            continue
        completed = _to_utc(candidate.completed_at)
        if completed is None:
            continue
        if (reference - completed).total_seconds() < _SAME_MOMENT_EPS:
            continue
        analysis_row = CampaignAnalysisRepository(session).get_by_campaign(candidate.id)
        if analysis_row is None or analysis_row.status != "completed":
            continue
        if best_completed is None or completed > best_completed:
            best = candidate
            best_completed = completed
    return best


def _delta(base: Optional[float], current: Optional[float], ndigits: int) -> Optional[float]:
    if base is None or current is None:
        return None
    return round(current - base, ndigits)


def compute_metric_diff(
    baseline_report: dict[str, Any],
    current_report: dict[str, Any],
) -> dict[str, Any]:
    """两期 ``generate_campaign_report`` 结果 → 整窗 + 分场景指标 delta。

    通过率/可用性按 4 位小数、时延按 1 位小数取 delta；任一侧指标为 None
    或该侧缺失该场景时 delta 为 None（只保留存在的 baseline/current 值）。
    纯函数，无 I/O。
    """

    def _pair(base: Optional[float], current: Optional[float], ndigits: int) -> dict[str, Optional[float]]:
        return {"baseline": base, "current": current, "delta": _delta(base, current, ndigits)}

    base_summary = baseline_report.get("summary") or {}
    cur_summary = current_report.get("summary") or {}
    overall = {
        "pass_rate": _pair(base_summary.get("overall_pass_rate"), cur_summary.get("overall_pass_rate"), 4),
        "availability": _pair(base_summary.get("overall_availability"), cur_summary.get("overall_availability"), 4),
        "avg_latency_ms": _pair(base_summary.get("avg_latency_ms"), cur_summary.get("avg_latency_ms"), 1),
    }

    base_scenarios = {s.get("scenario_id"): s for s in baseline_report.get("capability_summary") or []}
    cur_scenarios = {s.get("scenario_id"): s for s in current_report.get("capability_summary") or []}

    scenarios = []
    for sid in sorted(set(base_scenarios) | set(cur_scenarios), key=lambda x: x or ""):
        base = base_scenarios.get(sid)
        cur = cur_scenarios.get(sid)
        entry: dict[str, Any] = {
            "scenario_id": sid,
            "scenario_name": (cur or base or {}).get("scenario_name") or "",
            "pass_rate": _pair(
                base.get("pass_rate") if base else None,
                cur.get("pass_rate") if cur else None,
                4,
            ),
            "availability": _pair(
                base.get("availability") if base else None,
                cur.get("availability") if cur else None,
                4,
            ),
            "avg_latency_ms": _pair(
                base.get("avg_latency_ms") if base else None,
                cur.get("avg_latency_ms") if cur else None,
                1,
            ),
        }
        scenarios.append(entry)

    return {"overall": overall, "scenarios": scenarios}


def validate_comparison_request(
    session: Session,
    campaign: Campaign,
    *,
    explicit_baseline_id: Optional[str] = None,
) -> Campaign:
    """共享校验入口：活动终态 → 模型 → 基线 → 分析。违规抛 ComparisonError。

    router 触发端点捕获映射 400；执行器捕获落 failed 行。校验顺序权威，
    两处不再漂移。
    """
    if campaign.status in (CampaignStatus.PLANNED, CampaignStatus.RUNNING):
        raise ComparisonError("活动完成后才能生成周期对比")
    if resolve_analysis_model(campaign, session) is None:
        raise ComparisonError(
            "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」，或为该活动指定分析模型"
        )
    current_analysis = CampaignAnalysisRepository(session).get_by_campaign(campaign.id)
    if current_analysis is None or current_analysis.status != "completed":
        raise ComparisonError("请先生成本期活动的智能分析")

    if explicit_baseline_id:
        baseline = CampaignRepository(session).get(explicit_baseline_id)
        if baseline is None:
            raise ComparisonError(f"基线活动不存在: {explicit_baseline_id}")
    else:
        baseline = resolve_auto_baseline(campaign, session)
        if baseline is None:
            raise ComparisonError("未找到自动基线：历史活动中没有同计划指纹且已完成分析的活动，可手动选择基线活动")

    baseline_analysis = CampaignAnalysisRepository(session).get_by_campaign(baseline.id)
    if baseline_analysis is None or baseline_analysis.status != "completed":
        raise ComparisonError("基线活动没有已完成的智能分析，请先生成基线活动的分析")

    return baseline


# ── 叙述半边：单次 LLM 调用编排 ─────────────────────────────────────────


async def narrate_period_comparison(
    *,
    baseline_analysis: dict[str, Any],
    current_analysis: dict[str, Any],
    metric_diff: dict[str, Any],
    valid_scenario_ids: set[str],
    chat_client: ChatClient,
) -> dict[str, Any]:
    """在两期分析结论与机械 diff 之上产出结构化演进叙述（单次调用）。

    输出 schema：trend / summary / problem_evolution / suggestion_tracking。
    模型虚构的 scenario_id 按白名单剔除；非法枚举归一（trend→stable、
    演化 status→persisting、建议 status→unaddressed）；解析失败抛
    ``ComparisonError``（由调用方落 failed 状态）。
    """
    system_prompt = (
        "你是智能客服质量评估平台的趋势研判专家，负责对照相邻两期评估活动的"
        "智能分析结论，产出结构化的演进叙述。只输出一个 JSON 对象："
        '{"trend": "improving|stable|regressing", '
        '"summary": "总体演进结论（一段话）", '
        '"problem_evolution": [{"status": "new|persisting|resolved", "title": "...", '
        '"detail": "该问题的演化说明", "scenario_ids": ["涉及场景 id"]}], '
        '"suggestion_tracking": [{"text": "上期建议原文", '
        '"status": "addressed|partial|unaddressed|new", "note": "落实情况的说明"}]}'
        "；problem_evolution 覆盖上期问题在本期的新增/持续/消解，"
        "suggestion_tracking 追踪上期每条建议的落实情况并列出本期新增建议。"
        "全部使用中文。"
    )
    user_prompt = json.dumps(
        {
            "上期分析": baseline_analysis,
            "本期分析": current_analysis,
            "指标变化（确定性计算，可信）": metric_diff,
        },
        ensure_ascii=False,
    )
    content = await chat_client(
        [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt},
        ]
    )
    try:
        parsed = parse_json_from_llm_text(content)
    except Exception as exc:
        raise ComparisonError(f"对比叙述输出解析失败: {exc}") from exc
    if not isinstance(parsed, dict):
        raise ComparisonError("对比叙述输出不是 JSON 对象")

    summary = parsed.get("summary")
    if not isinstance(summary, str) or not summary.strip():
        raise ComparisonError("对比叙述缺少 summary")

    trend = parsed.get("trend")
    if trend not in _VALID_TRENDS:
        trend = "stable"

    problem_evolution = []
    for item in parsed.get("problem_evolution") or []:
        if not isinstance(item, dict):
            continue
        status = item.get("status")
        problem_evolution.append(
            {
                "status": status if status in _VALID_EVOLUTION_STATUSES else "persisting",
                "title": str(item.get("title", "")),
                "detail": str(item.get("detail", "")),
                "scenario_ids": [s for s in item.get("scenario_ids") or [] if s in valid_scenario_ids],
            }
        )

    suggestion_tracking = []
    for item in parsed.get("suggestion_tracking") or []:
        if not isinstance(item, dict):
            continue
        status = item.get("status")
        suggestion_tracking.append(
            {
                "text": str(item.get("text", "")),
                "status": status if status in _VALID_SUGGESTION_STATUSES else "unaddressed",
                "note": str(item.get("note", "")),
            }
        )

    return {
        "trend": trend,
        "summary": summary,
        "problem_evolution": problem_evolution,
        "suggestion_tracking": suggestion_tracking,
    }