//! 对话持久化 + Token 累加器

use std::sync::Arc;

use tokio::sync::Mutex;

use df_storage::crud::AiConversationRepo;
use df_storage::db::Database;

use crate::commands::now_millis;

use super::AiSession;

/// Token 用量累加器(agent loop 生命周期内各轮叠加)
///
/// 纯结构 + 方法:抽自 run_agentic_loop 的 `total_prompt`/`total_completion` 双计数器,
/// 保证多轮累加、None 起始、跨 loop 实例叠加语义一致且可单测。
#[derive(Debug, Clone, Default)]
pub(crate) struct TokenAccumulator {
    prompt: u32,
    completion: u32,
}

impl TokenAccumulator {
    /// 叠加一轮用量(round_usage 为本轮流式末 chunk 的累计用量)
    ///
    /// saturating_add:恶意/异常 provider 返回巨大值时,避免 u32 += 溢出回绕打乱后续 budget 判定。
    pub(crate) fn add(&mut self, prompt: u32, completion: u32) {
        self.prompt = self.prompt.saturating_add(prompt);
        self.completion = self.completion.saturating_add(completion);
    }

    pub(crate) fn prompt(&self) -> u32 {
        self.prompt
    }

    pub(crate) fn completion(&self) -> u32 {
        self.completion
    }

    pub(crate) fn total(&self) -> u32 {
        self.prompt.saturating_add(self.completion)
    }
}

/// 把单轮增量叠加到 DB 的 Option<i64> 字段(读旧值+增量,跨 loop 实例防覆盖)
///
/// 纯函数:抽自 save_conversation 的 token 累加逻辑,None 起始当作 0。
pub(crate) fn accumulate_tokens(old: Option<i64>, add: u32) -> Option<i64> {
    Some(old.unwrap_or(0) + add as i64)
}

/// 持久化截断阈值:超过此长度的消息 content 落库前截断头尾各保 HEAD/TAIL 字符。
///
/// 防 read_file 1MB 洞 / list_directory 大体量结果落库后每轮重发累积致 token 暴增
/// (Sprint 19 实测单对话 in=115万 / 消息体 1.6MB)。仅作用于持久化视图,不污染内存真相源。
pub(crate) const TRUNCATE_THRESHOLD: usize = 50_000;
const TRUNCATE_HEAD: usize = 20_000;
const TRUNCATE_TAIL: usize = 20_000;

/// 落库前对超长 content 做截断(保留头尾各 ~20KB + 中段标注省略字符数)。
/// 50KB 阈值以下原样返回(零开销);按字符而非字节切避免 UTF-8 切坏中文。
pub(crate) fn truncate_for_persist(content: &str) -> String {
    let chars: Vec<char> = content.chars().collect();
    if chars.len() <= TRUNCATE_THRESHOLD {
        return content.to_string();
    }
    let head: String = chars.iter().take(TRUNCATE_HEAD).collect();
    let tail: String = chars[chars.len() - TRUNCATE_TAIL..].iter().collect();
    let omitted = chars.len() - TRUNCATE_HEAD - TRUNCATE_TAIL;
    format!(
        "{}\n\n[...省略 {} 字符(已截断,完整内容仅在内存态可读)...]\n\n{}",
        head, omitted, tail
    )
}

/// 保存对话到数据库（按 conv_id 写库，不受 active_conversation_id 切换影响）
///
/// 写 messages + updated_at + 累加 token 用量 + 首次落库的 model；标题由 ensure_conversation_title 单独生成。
/// token 走累加模式:upsert 读旧值叠加,保证审批暂停→恢复跨 loop 实例不覆盖丢失。
/// model 仅首次落库写入 + 旧记录缺值时补填(不覆盖历史已存值,兼容本次改造前的老对话)。
pub(crate) async fn save_conversation(
    session_arc: &Arc<Mutex<AiSession>>,
    db: &Arc<Database>,
    conv_id: &str,
    usage: Option<&df_ai::provider::TokenUsage>,
    model: Option<&str>,
) {
    // 取 messages + 懒创建首次落库所需的 provider_id/created_at
    // 工具结果(content)超 50KB 时截断头尾各 ~20KB + 中段标注,防大体量结果(read_file 1MB 洞 /
    // list_directory 13782 项)落库后每轮重发累积致 token 暴增。仅影响持久化视图,不污染
    // 内存真相源(ContextManager)——build_for_request 仍读全量 messages。
    let (messages_json, provider_id, created_at) = {
        let session = session_arc.lock().await;
        let mut msgs = session.messages.all_messages_clone();
        for m in &mut msgs {
            m.content = truncate_for_persist(&m.content);
        }
        (
            serde_json::to_string(&msgs).unwrap_or_else(|_| "[]".to_string()),
            session.active_provider_id.clone(),
            session.active_conv_created_at.clone(),
        )
    };

    let conv_repo = AiConversationRepo::new(db);
    match conv_repo.get_by_id(conv_id).await {
        Ok(Some(mut rec)) => {
            // 已落库:更新 messages + updated_at;token 累加(读旧值+新值,跨 loop 实例防覆盖)
            rec.messages = messages_json;
            rec.updated_at = now_millis();
            if let Some(u) = usage {
                rec.prompt_tokens = accumulate_tokens(rec.prompt_tokens, u.prompt_tokens);
                rec.completion_tokens = accumulate_tokens(rec.completion_tokens, u.completion_tokens);
            }
            // model: 旧记录缺值时补填(不覆盖已有);models: 去重追加用过的所有 model(JSON 数组)
            if let Some(m) = model {
                if rec.model.is_none() { rec.model = Some(m.to_string()); }
                let mut list: Vec<String> = rec.models
                    .as_deref()
                    .and_then(|s| serde_json::from_str(s).ok())
                    .unwrap_or_default();
                if !list.iter().any(|x| x == m) { list.push(m.to_string()); }
                rec.models = Some(serde_json::to_string(&list).unwrap_or_else(|_| "[]".to_string()));
            }
            if let Err(e) = conv_repo.update_full(&rec).await {
                tracing::warn!("更新对话失败 {conv_id}: {e}");
            }
        }
        Ok(None) => {
            // 懒创建首次落库(此为空对话不落库的落库点:走到这里 messages 必非空)
            let now = now_millis();
            let rec = df_storage::models::AiConversationRecord {
                id: conv_id.to_string(),
                title: None,
                messages: messages_json,
                provider_id,
                model: model.map(|m| m.to_string()),
                models: model.map(|m| serde_json::to_string(&[m]).unwrap_or_else(|_| "[]".to_string())),
                archived: false,
                pinned: false,
                prompt_tokens: usage.map(|u| u.prompt_tokens as i64),
                completion_tokens: usage.map(|u| u.completion_tokens as i64),
                created_at: created_at.unwrap_or_else(|| now.clone()),
                updated_at: now,
            };
            if let Err(e) = conv_repo.insert(rec).await {
                tracing::warn!("落库对话失败 {conv_id}: {e}");
            }
        }
        Err(e) => tracing::warn!("读取对话 {conv_id} 失败: {e}"),
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    // ---------- TokenAccumulator + accumulate_tokens ----------

    #[test]
    fn accumulator_starts_zero() {
        let acc = TokenAccumulator::default();
        assert_eq!(acc.prompt(), 0);
        assert_eq!(acc.completion(), 0);
        assert_eq!(acc.total(), 0);
    }

    #[test]
    fn accumulator_single_add() {
        let mut acc = TokenAccumulator::default();
        acc.add(100, 50);
        assert_eq!(acc.prompt(), 100);
        assert_eq!(acc.completion(), 50);
        assert_eq!(acc.total(), 150);
    }

    #[test]
    fn accumulator_multi_round_accumulation() {
        // 多轮累加(模拟 agent loop 多次迭代)
        let mut acc = TokenAccumulator::default();
        acc.add(100, 20); // 轮1
        acc.add(200, 40); // 轮2
        acc.add(50, 10);  // 轮3
        assert_eq!(acc.prompt(), 350);
        assert_eq!(acc.completion(), 70);
        assert_eq!(acc.total(), 420);
    }

    #[test]
    fn accumulator_add_zero_is_noop() {
        let mut acc = TokenAccumulator::default();
        acc.add(10, 5);
        acc.add(0, 0);
        assert_eq!(acc.total(), 15);
    }

    #[test]
    fn accumulate_tokens_from_none() {
        // 新记录(None 起始)落库
        assert_eq!(accumulate_tokens(None, 100), Some(100));
        assert_eq!(accumulate_tokens(None, 0), Some(0));
    }

    #[test]
    fn accumulate_tokens_adds_to_existing() {
        // 跨 loop 实例叠加:旧值 + 新增不覆盖
        assert_eq!(accumulate_tokens(Some(500), 100), Some(600));
        assert_eq!(accumulate_tokens(Some(0), 42), Some(42));
    }

    #[test]
    fn accumulate_tokens_multi_round_db_simulation() {
        // 模拟 save_conversation 多次落库累加(审批暂停→恢复跨 loop)
        let mut field: Option<i64> = None;
        field = accumulate_tokens(field, 100); // 首次
        field = accumulate_tokens(field, 200); // 二次
        field = accumulate_tokens(field, 50);  // 三次
        assert_eq!(field, Some(350));
    }

    #[test]
    fn accumulator_and_db_accumulate_are_consistent() {
        // loop 内 TokenAccumulator 与落库 accumulate_tokens 总量语义一致
        let mut acc = TokenAccumulator::default();
        let mut db_prompt: Option<i64> = None;
        let mut db_completion: Option<i64> = None;
        for (p, c) in [(100u32, 20u32), (200, 40), (50, 10)] {
            acc.add(p, c);
            db_prompt = accumulate_tokens(db_prompt, p);
            db_completion = accumulate_tokens(db_completion, c);
        }
        assert_eq!(acc.prompt() as i64, db_prompt.unwrap());
        assert_eq!(acc.completion() as i64, db_completion.unwrap());
    }

    // ---------- truncate_for_persist ----------

    #[test]
    fn truncate_short_content_unchanged() {
        // 阈值以下原样返回
        assert_eq!(truncate_for_persist("hello"), "hello");
        assert_eq!(truncate_for_persist(""), "");
        let near_limit: String = "a".repeat(TRUNCATE_THRESHOLD);
        assert_eq!(truncate_for_persist(&near_limit).len(), TRUNCATE_THRESHOLD);
    }

    #[test]
    fn truncate_long_content_keeps_head_and_tail() {
        // 超阈值:保留头尾各 TRUNCATE_HEAD/TAIL 字符 + 中段标注
        let long: String = "x".repeat(TRUNCATE_THRESHOLD + 1000);
        let result = truncate_for_persist(&long);
        // 头尾各 20k 字符应在结果中
        assert!(result.starts_with(&"x".repeat(TRUNCATE_HEAD)));
        assert!(result.ends_with(&"x".repeat(TRUNCATE_TAIL)));
        // 中段标注存在 + 标注省略字符数(中段 = 总长 - 头 - 尾 = 51000 - 20000 - 20000 = 11000)
        assert!(result.contains("已截断"));
        assert!(result.contains("省略 11000 字符"));
        // 结果总长应远小于原长(20k 头 + 20k 尾 + 标注)
        assert!(result.chars().count() < TRUNCATE_THRESHOLD + 1000);
    }

    #[test]
    fn truncate_preserves_utf8_chinese() {
        // 按字符切不切坏 UTF-8 中文
        let chinese: String = "中".repeat(TRUNCATE_THRESHOLD + 500);
        let result = truncate_for_persist(&chinese);
        assert!(result.starts_with('中'));
        assert!(result.ends_with('中'));
        assert!(result.contains("已截断"));
    }
}