"""
选题推导工具 - 图数据库游走

提供选题推导任务的状态管理和游走操作。
"""

import logging
import uuid
import json
import os
from pathlib import Path
from typing import List, Dict, Optional, Any
from agent.tools import tool, ToolResult, ToolContext

# 导入数据查询工具
from .search_library import (
    _search_class_by_point,
    _search_point_by_class,
    _search_relation_class_by_class
)
from .search_pattern import _search_pattern

logger = logging.getLogger(__name__)

# 状态存储目录
STATE_DIR = Path(__file__).parent.parent / ".db" / "derivation_states"
STATE_DIR.mkdir(parents=True, exist_ok=True)


def _get_state_file(derivation_id: str) -> Path:
    """获取状态文件路径"""
    return STATE_DIR / f"{derivation_id}.json"


def _save_state(derivation_id: str, state: Dict[str, Any], change_description: str = "") -> None:
    """
    保存状态到文件，并记录变更历史

    Args:
        derivation_id: 推导任务 ID
        state: 当前状态
        change_description: 变更描述
    """
    from datetime import datetime
    import copy

    # 初始化 history 字段（如果不存在）
    if "history" not in state:
        state["history"] = []

    # 记录本次变更（保留完整快照）
    if change_description:
        # 深拷贝当前状态作为快照（排除 history 本身，避免递归）
        snapshot = {
            "person_name": state.get("person_name"),
            "top_k_paths": state.get("top_k_paths"),
            "max_rounds": state.get("max_rounds"),
            "loop": state.get("loop", 0),
            "current_paths": copy.deepcopy(state.get("current_paths", [])),
            "discarded_paths": copy.deepcopy(state.get("discarded_paths", [])),
            "edges_to_expand": copy.deepcopy(state.get("edges_to_expand", [])),
            "candidate_paths": copy.deepcopy(state.get("candidate_paths", [])),
            "tool_call_stats": copy.deepcopy(state.get("tool_call_stats", {})),
            "prune_stats": copy.deepcopy(state.get("prune_stats", {})),
            "evidence_log": copy.deepcopy(state.get("evidence_log", []))
        }

        history_entry = {
            "timestamp": datetime.now().isoformat(),
            "round": state.get("loop", 0),
            "action": change_description,
            "snapshot": snapshot
        }
        state["history"].append(history_entry)

    # 保存到文件
    state_file = _get_state_file(derivation_id)
    with open(state_file, 'w', encoding='utf-8') as f:
        json.dump(state, f, ensure_ascii=False, indent=2)


def _load_state(derivation_id: str) -> Optional[Dict[str, Any]]:
    """
    从文件加载状态

    Args:
        derivation_id: 推导任务 ID

    Returns:
        状态字典，如果不存在则返回 None
    """
    state_file = _get_state_file(derivation_id)
    if not state_file.exists():
        return None

    with open(state_file, 'r', encoding='utf-8') as f:
        state = json.load(f)

    # 向后兼容：为旧状态文件添加 history 字段
    if "history" not in state:
        state["history"] = []

    return state


@tool(hidden_params=["context"])
async def init_topic_derivation(
    person_name: str,
    constants: List[Dict[str, str]],
    top_k_paths: int,
    max_rounds: int,
    context: Optional[ToolContext] = None,
) -> ToolResult:
    """
    初始化选题推导任务

    Args:
        person_name: 人设名称
        constants: 常量点列表，每个包含 {"名称": "...", "维度": "实质/形式/意图"}
        top_k_paths: 每轮保留路径数
        max_rounds: 最大推导轮次
        context: 工具上下文

    Returns:
        derivation_id 和初始状态
    """
    try:
        # 生成唯一任务 ID
        derivation_id = str(uuid.uuid4())

        # 为每个常量点创建初始路径
        initial_paths = []
        for constant in constants:
            path = [{
                "名称": constant["名称"],
                "类型": constant.get("类型", ""),
                "维度": constant["维度"]
            }]
            initial_paths.append(path)

        # 提取待扩展的末端点
        edges_to_expand = [
            {
                "名称": constant["名称"],
                "维度": constant["维度"],
                "类型": constant["类型"]
            }
            for constant in constants
        ]

        # 初始化状态
        state = {
            "person_name": person_name,
            "top_k_paths": top_k_paths,
            "max_rounds": max_rounds,
            "loop": 0,
            "current_paths": initial_paths,
            "discarded_paths": [],
            "edges_to_expand": edges_to_expand,
            "tool_call_stats": {},
            "prune_stats": {
                "语义冲突淘汰": 0,
                "低置信度淘汰": 0
            },
            "history": []
        }

        # 存储状态
        _save_state(derivation_id, state, "初始化推导任务")

        output = f"✅ 初始化成功\n\n"
        output += f"任务 ID: {derivation_id}\n"
        output += f"人设: {person_name}\n"
        output += f"常量点数量: {len(constants)}\n"
        output += f"初始路径数: {len(initial_paths)}\n"
        output += f"每轮保留: TOP {top_k_paths} 路径\n"
        output += f"最大轮次: {max_rounds}\n\n"
        output += f"常量点列表:\n"
        for idx, constant in enumerate(constants, 1):
            output += f"  {idx}. {constant['名称']} ({constant['维度']})  ({constant['类型']})\n"

        return ToolResult(
            title="✅ 推导任务已初始化",
            output=output,
            metadata={
                "derivation_id": derivation_id,
                "initial_path_count": len(initial_paths),
                "constants": constants
            }
        )

    except Exception as e:
        logger.error(f"初始化推导任务失败: {e}")
        return ToolResult(
            title="❌ 初始化失败",
            output=f"错误: {str(e)}",
            error=str(e)
        )


@tool(hidden_params=["context"])
async def get_current_state(
    derivation_id: str,
    context: Optional[ToolContext] = None,
) -> ToolResult:
    """
    获取当前推导状态

    Args:
        derivation_id: 推导任务 ID
        context: 工具上下文

    Returns:
        当前状态，包含：
        - loop: 当前轮次
        - active_paths: 活跃路径列表
        - edges_to_expand: 待扩展的末端点
    """
    try:
        # 加载状态
        state = _load_state(derivation_id)
        if state is None:
            return ToolResult(
                title="❌ 任务不存在",
                output=f"未找到任务 ID: {derivation_id}",
                error="任务不存在"
            )

        # 提取关键信息
        loop = state["loop"]
        current_paths = state["current_paths"]
        edges_to_expand = state["edges_to_expand"]
        max_rounds = state["max_rounds"]
        top_k_paths = state["top_k_paths"]

        # 构建输出
        output = f"📊 当前状态\n\n"
        output += f"任务 ID: {derivation_id}\n"
        output += f"人设: {state['person_name']}\n"
        output += f"当前轮次: {loop} / {max_rounds}\n"
        output += f"活跃路径数: {len(current_paths)}\n"
        output += f"待扩展末端点数: {len(edges_to_expand)}\n"
        output += f"已淘汰路径数: {len(state['discarded_paths'])}\n\n"

        if edges_to_expand:
            output += f"待扩展的末端点:\n"
            for idx, edge in enumerate(edges_to_expand[:10], 1):  # 最多显示10个
                output += f"  {idx}. {edge['名称']} ({edge.get('维度', '')})\n"
            if len(edges_to_expand) > 10:
                output += f"  ... 还有 {len(edges_to_expand) - 10} 个\n"
        else:
            output += "⚠️ 没有待扩展的末端点，无法继续游走\n"

        output += f"\n活跃路径示例 (前3条):\n"
        for idx, path in enumerate(current_paths[:3], 1):
            path_str = " → ".join([node["名称"] for node in path])
            output += f"  {idx}. {path_str} (长度: {len(path)})\n"

        return ToolResult(
            title=f"📊 轮次 {loop}/{max_rounds}",
            output=output,
            metadata={
                "loop": loop,
                "active_paths": current_paths,
                "edges_to_expand": edges_to_expand,
                "max_rounds": max_rounds,
                "top_k_paths": top_k_paths,
                "can_continue": len(edges_to_expand) > 0 and loop < max_rounds
            }
        )

    except Exception as e:
        logger.error(f"获取状态失败: {e}")
        return ToolResult(
            title="❌ 获取状态失败",
            output=f"错误: {str(e)}",
            error=str(e)
        )


@tool(hidden_params=["context"])
async def add_nodes_to_paths(
    derivation_id: str,
    path_extensions: List[Dict[str, Any]],
    context: Optional[ToolContext] = None,
) -> ToolResult:
    """
    将节点添加到指定路径，生成候选路径

    Agent 负责智能决策：
    - 调用数据查询工具获取候选节点
    - 分析和选择要添加的节点
    - 决定每条路径的扩展方式

    工具负责机械操作：
    - 将节点添加到路径中
    - 记录 Evidence 日志
    - 检查路径连续性和避免循环
    - 生成候选路径

    Args:
        derivation_id: 推导任务 ID
        path_extensions: 路径扩展列表，每个包含：
            - path_id: 路径索引
            - new_nodes: 要添加的节点列表，每个节点包含：
                - 名称: 节点名称
                - 类型: 节点类型（可选）
                - 维度: 节点维度（可选）
                - 分类: "point" 或 "class"
            - step_type: 游走方法（generalize/specialize/relate/pattern）
            - link_type: 推导关系类型
            - evidence: Evidence 信息
                - tool: 使用的工具名称
                - query: 查询参数
            - reasoning: 推理依据
        context: 工具上下文

    Returns:
        扩展后的候选路径信息
    """
    try:
        # 加载状态
        state = _load_state(derivation_id)
        if state is None:
            return ToolResult(
                title="❌ 任务不存在",
                output=f"未找到任务 ID: {derivation_id}",
                error="任务不存在"
            )

        current_paths = state["current_paths"]
        loop = state["loop"]

        # 存储新路径（每条路径可能产生多个候选）
        candidate_paths = []
        evidence_log = state.get("evidence_log", [])
        tool_call_stats = state.get("tool_call_stats", {})

        # 处理每个路径扩展
        for extension in path_extensions:
            path_id = extension["path_id"]
            new_nodes = extension["new_nodes"]
            step_type = extension["step_type"]
            link_type = extension["link_type"]
            evidence_info = extension.get("evidence", {})
            reasoning = extension["reasoning"]

            if path_id >= len(current_paths):
                logger.warning(f"路径 ID {path_id} 超出范围，跳过")
                continue

            # 获取当前路径和末端节点
            current_path = current_paths[path_id]
            end_node = current_path[-1]

            # 为每个新节点创建新路径
            for new_node_info in new_nodes:
                # 检查是否会造成循环
                node_names = [node["名称"] for node in current_path]
                if new_node_info["名称"] in node_names:
                    logger.info(f"跳过重复节点: {new_node_info['名称']}")
                    continue

                # 创建新节点
                new_node = {
                    "名称": new_node_info["名称"],
                    "类型": new_node_info.get("类型", ""),
                    "维度": new_node_info.get("维度", ""),
                    "分类": new_node_info["分类"],
                    "来源": end_node["名称"],
                    "step_type": step_type,
                    "link_type": link_type,
                    "推理": reasoning
                }

                # 创建新路径
                new_path = current_path + [new_node]
                candidate_paths.append(new_path)

                # 记录 Evidence
                evidence = {
                    "round": loop + 1,
                    "path_id": path_id,
                    "step_type": step_type,
                    "evidence_type": evidence_info.get("evidence_type", "unknown"),
                    "role": "expand",
                    "reference_detail": {
                        "tool": evidence_info.get("tool", "unknown"),
                        "query": evidence_info.get("query", {}),
                        "source_node": end_node["名称"],
                        "result_node": new_node_info["名称"]
                    }
                }
                evidence_log.append(evidence)

            # 更新工具调用统计
            tool_name = evidence_info.get("tool", "unknown")
            tool_call_stats[tool_name] = tool_call_stats.get(tool_name, 0) + 1

        # 更新状态（暂不保存，等待 evaluate_and_prune）
        state["candidate_paths"] = candidate_paths
        state["evidence_log"] = evidence_log
        state["tool_call_stats"] = tool_call_stats
        _save_state(derivation_id, state, f"添加节点到 {len(path_extensions)} 条路径，生成 {len(candidate_paths)} 条候选路径")

        # 构建输出
        output = f"🚶 节点已添加到路径\n\n"
        output += f"轮次: {loop + 1}\n"
        output += f"处理路径数: {len(path_extensions)}\n"
        output += f"生成候选路径数: {len(candidate_paths)}\n\n"

        output += f"候选路径示例 (前3条):\n"
        for idx, path in enumerate(candidate_paths[:3], 1):
            path_str = " → ".join([node["名称"] for node in path])
            output += f"  {idx}. {path_str}\n"

        return ToolResult(
            title="✅ 节点添加完成",
            output=output,
            metadata={
                "candidate_count": len(candidate_paths),
                "candidate_paths": candidate_paths
            }
        )

    except Exception as e:
        logger.error(f"添加节点失败: {e}")
        return ToolResult(
            title="❌ 添加节点失败",
            output=f"错误: {str(e)}",
            error=str(e)
        )


@tool(hidden_params=["context"])
async def evaluate_and_prune(
    derivation_id: str,
    path_evaluations: List[Dict[str, Any]],
    context: Optional[ToolContext] = None,
) -> ToolResult:
    """
    执行路径评估和全局 TopK 剪枝

    Agent 负责智能评估（语义矛盾、人设风格匹配），工具负责执行剪枝。

    Args:
        derivation_id: 推导任务 ID
        path_evaluations: Agent 的评估结果列表，每个包含：
            - path_id: 候选路径索引
            - score: 评分（0-10）
            - keep: 是否保留
            - reason: 评估理由
        context: 工具上下文

    Returns:
        剪枝结果，包含：
        - retained_paths: 保留的 TOP_K 路径
        - discarded_paths: 被淘汰的路径及原因
        - can_continue: 是否可以继续游走
    """
    try:
        # 加载状态
        state = _load_state(derivation_id)
        if state is None:
            return ToolResult(
                title="❌ 任务不存在",
                output=f"未找到任务 ID: {derivation_id}",
                error="任务不存在"
            )

        candidate_paths = state.get("candidate_paths", [])
        if not candidate_paths:
            return ToolResult(
                title="❌ 没有候选路径",
                output="请先调用 add_nodes_to_paths 生成候选路径",
                error="没有候选路径"
            )

        top_k_paths = state["top_k_paths"]
        loop = state["loop"]

        # 构建评估映射
        evaluation_map = {eval_item["path_id"]: eval_item for eval_item in path_evaluations}

        # 分类路径：保留 vs 淘汰
        paths_to_keep = []
        paths_to_discard = []

        for idx, path in enumerate(candidate_paths):
            evaluation = evaluation_map.get(idx)

            if evaluation is None:
                # 如果 Agent 没有评估这条路径，默认保留
                paths_to_keep.append({
                    "path": path,
                    "score": 5.0,
                    "reason": "未评估，默认保留"
                })
            elif evaluation.get("keep", True):
                paths_to_keep.append({
                    "path": path,
                    "score": evaluation.get("score", 5.0),
                    "reason": evaluation.get("reason", "")
                })
            else:
                paths_to_discard.append({
                    "path": path,
                    "reason": evaluation.get("reason", "未通过评估")
                })

        # 按分数降序排序
        paths_to_keep.sort(key=lambda x: x["score"], reverse=True)

        # 全局 TopK 剪枝
        retained_paths = [item["path"] for item in paths_to_keep[:top_k_paths]]
        additional_discarded = [item["path"] for item in paths_to_keep[top_k_paths:]]

        # 记录淘汰原因
        for path in additional_discarded:
            paths_to_discard.append({
                "path": path,
                "reason": "全局 TopK 剪枝淘汰"
            })

        # 提取新的待扩展末端点
        edges_to_expand = []
        for path in retained_paths:
            end_node = path[-1]
            edges_to_expand.append({
                "名称": end_node["名称"],
                "维度": end_node.get("维度", ""),
                "分类": end_node.get("分类", "")
            })

        # 更新状态
        state["current_paths"] = retained_paths
        state["edges_to_expand"] = edges_to_expand
        state["loop"] = loop + 1

        # 记录淘汰路径
        discarded_paths = state.get("discarded_paths", [])
        for item in paths_to_discard:
            discarded_paths.append({
                "round": loop + 1,
                "reason": item["reason"],
                "path": item["path"]
            })
        state["discarded_paths"] = discarded_paths

        # 更新剪枝统计
        prune_stats = state.get("prune_stats", {})
        for item in paths_to_discard:
            reason = item["reason"]
            if "矛盾" in reason:
                prune_stats["语义冲突淘汰"] = prune_stats.get("语义冲突淘汰", 0) + 1
            elif "TopK" in reason:
                prune_stats["低置信度淘汰"] = prune_stats.get("低置信度淘汰", 0) + 1
        state["prune_stats"] = prune_stats

        # 清空候选路径
        state["candidate_paths"] = []

        # 保存状态
        _save_state(derivation_id, state, f"轮次 {loop + 1} 评估与剪枝：保留 {len(retained_paths)} 条，淘汰 {len(paths_to_discard)} 条")

        # 检查是否可以继续
        can_continue = len(edges_to_expand) > 0 and (loop + 1) < state["max_rounds"]

        # 构建输出
        output = f"✂️ 评估与剪枝已完成\n\n"
        output += f"轮次: {loop + 1}\n"
        output += f"候选路径数: {len(candidate_paths)}\n"
        output += f"保留路径数: {len(retained_paths)}\n"
        output += f"淘汰路径数: {len(paths_to_discard)}\n"
        output += f"可继续游走: {'是' if can_continue else '否'}\n\n"

        if paths_to_discard:
            output += f"淘汰原因统计:\n"
            reason_counts = {}
            for item in paths_to_discard:
                reason = item["reason"]
                reason_counts[reason] = reason_counts.get(reason, 0) + 1
            for reason, count in reason_counts.items():
                output += f"  - {reason}: {count} 条\n"

        output += f"\n保留路径示例 (前3条):\n"
        for idx, path in enumerate(retained_paths[:3], 1):
            path_str = " → ".join([node["名称"] for node in path])
            score = paths_to_keep[idx-1]["score"] if idx-1 < len(paths_to_keep) else 0
            output += f"  {idx}. [{score:.1f}分] {path_str}\n"

        return ToolResult(
            title="✅ 评估与剪枝完成",
            output=output,
            metadata={
                "retained_count": len(retained_paths),
                "discarded_count": len(paths_to_discard),
                "can_continue": can_continue,
                "current_loop": loop + 1,
                "max_rounds": state["max_rounds"]
            }
        )

    except Exception as e:
        logger.error(f"评估与剪枝失败: {e}")
        return ToolResult(
            title="❌ 评估与剪枝失败",
            output=f"错误: {str(e)}",
            error=str(e)
        )


@tool(hidden_params=["context"])
async def get_final_paths(
    derivation_id: str,
    expand_class_nodes: bool = True,
    context: Optional[ToolContext] = None,
) -> ToolResult:
    """
    获取最终路径数据，准备生成选题

    工具负责数据准备：
    - 获取最终保留的 TOP_K_PATHS 条路径
    - 自动展开分类节点为具体点（如果需要）
    - 提取点组合
    - 生成执行摘要统计

    Agent 负责智能生成：
    - 分析路径数据
    - 为每条路径撰写 5-8 句话的创作指导
    - 说明预期效果和推理过程

    Args:
        derivation_id: 推导任务 ID
        expand_class_nodes: 是否自动展开分类节点为具体点（默认 true）
        context: 工具上下文

    Returns:
        最终路径数据和执行摘要
    """
    try:
        # 加载状态
        state = _load_state(derivation_id)
        if state is None:
            return ToolResult(
                title="❌ 任务不存在",
                output=f"未找到任务 ID: {derivation_id}",
                error="任务不存在"
            )

        current_paths = state["current_paths"]
        loop = state["loop"]
        max_rounds = state["max_rounds"]
        top_k_paths = state["top_k_paths"]
        tool_call_stats = state.get("tool_call_stats", {})
        prune_stats = state.get("prune_stats", {})
        discarded_paths = state.get("discarded_paths", [])

        # 准备最终路径列表
        final_paths = []

        for path_idx, path in enumerate(current_paths):
            # 如果需要展开分类节点
            expanded_path = []
            point_combination = []  # 只包含点，不包含分类

            for node in path:
                node_type = node.get("分类", "point")  # 默认为 point
                if node_type == "class" and expand_class_nodes:
                    # 调用 search_point_by_class 展开分类节点
                    try:
                        class_path = node["名称"]
                        results = _search_point_by_class([class_path])

                        # 获取该分类下的点（最多取3个）
                        points = []
                        for item in results:
                            points.extend(item.get("points", [])[:3])

                        if points:
                            # 为每个点创建节点
                            for point_name in points[:3]:  # 最多3个点
                                expanded_node = {
                                    "名称": point_name,
                                    "类型": node.get("类型", ""),
                                    "维度": node.get("维度", ""),
                                    "分类": "point",
                                    "来源": f"展开自分类: {class_path}"
                                }
                                expanded_path.append(expanded_node)
                                point_combination.append({
                                    "名称": point_name,
                                    "维度": node.get("维度", ""),
                                    "来源节点": class_path
                                })
                        else:
                            # 如果没有找到点，保留分类节点
                            expanded_path.append(node)
                    except Exception as e:
                        logger.warning(f"展开分类节点失败: {node['名称']}, 错误: {e}")
                        expanded_path.append(node)
                else:
                    # 保留原节点
                    expanded_path.append(node)
                    if node_type == "point":
                        point_combination.append({
                            "名称": node["名称"],
                            "维度": node.get("维度", ""),
                            "来源节点": node.get("来源", "起始常量点")
                        })

            # 构建完整路径信息
            final_path_info = {
                "路径编号": path_idx + 1,
                "点组合": point_combination,
                "完整路径": expanded_path,
                "路径长度": len(expanded_path),
                "原始路径长度": len(path)
            }
            final_paths.append(final_path_info)

        # 生成执行摘要
        execution_summary = {
            "总轮次": loop,
            "最大轮次": max_rounds,
            "最终路径数": len(final_paths),
            "目标路径数": top_k_paths,
            "工具调用统计": tool_call_stats,
            "路径统计": {
                "总生成路径数": len(current_paths) + len(discarded_paths),
                "保留路径数": len(current_paths),
                "淘汰路径数": len(discarded_paths)
            },
            "剪枝统计": prune_stats
        }

        # 构建输出
        output = f"📋 最终路径数据已准备\n\n"
        output += f"任务 ID: {derivation_id}\n"
        output += f"人设: {state['person_name']}\n"
        output += f"完成轮次: {loop} / {max_rounds}\n"
        output += f"最终路径数: {len(final_paths)}\n\n"

        output += f"执行摘要:\n"
        output += f"  - 总生成路径: {execution_summary['路径统计']['总生成路径数']} 条\n"
        output += f"  - 保留路径: {execution_summary['路径统计']['保留路径数']} 条\n"
        output += f"  - 淘汰路径: {execution_summary['路径统计']['淘汰路径数']} 条\n\n"

        output += f"工具调用统计:\n"
        for tool_name, count in tool_call_stats.items():
            output += f"  - {tool_name}: {count} 次\n"

        if prune_stats:
            output += f"\n剪枝统计:\n"
            for reason, count in prune_stats.items():
                output += f"  - {reason}: {count} 条\n"

        output += f"\n路径示例 (前3条):\n"
        for idx, path_info in enumerate(final_paths[:3], 1):
            points = [p["名称"] for p in path_info["点组合"]]
            output += f"  {idx}. {' → '.join(points)} ({len(points)} 个点)\n"

        output += f"\n💡 接下来请为每条路径生成选题:\n"
        output += f"  - 选题应该是 5-8 句话的完整创作指导\n"
        output += f"  - 说明预期效果\n"
        output += f"  - 解释推理过程\n"

        return ToolResult(
            title="✅ 最终路径数据已准备",
            output=output,
            metadata={
                "final_paths": final_paths,
                "execution_summary": execution_summary,
                "person_name": state["person_name"]
            }
        )

    except Exception as e:
        logger.error(f"获取最终路径失败: {e}")
        return ToolResult(
            title="❌ 获取最终路径失败",
            output=f"错误: {str(e)}",
            error=str(e)
        )