"""
Step 树 Debug 输出

将 Step 树以完整格式输出到文件，便于开发调试。

使用方式:
    1. 命令行实时查看:
       watch -n 0.5 cat .trace/tree.txt

    2. VS Code 打开文件自动刷新:
       code .trace/tree.txt

    3. 代码中使用:
       from agent.debug import dump_tree
       dump_tree(trace, steps)
"""

import json
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, List, Optional

# 默认输出路径
DEFAULT_DUMP_PATH = ".trace/tree.txt"
DEFAULT_JSON_PATH = ".trace/tree.json"
DEFAULT_MD_PATH = ".trace/tree.md"


class StepTreeDumper:
    """Step 树 Debug 输出器"""

    def __init__(self, output_path: str = DEFAULT_DUMP_PATH):
        self.output_path = Path(output_path)
        self.output_path.parent.mkdir(parents=True, exist_ok=True)

    def dump(
        self,
        trace: Optional[Dict[str, Any]] = None,
        steps: Optional[List[Dict[str, Any]]] = None,
        title: str = "Step Tree Debug",
    ) -> str:
        """
        输出完整的树形结构到文件

        Args:
            trace: Trace 字典（可选）
            steps: Step 字典列表
            title: 输出标题

        Returns:
            输出的文本内容
        """
        lines = []

        # 标题和时间
        lines.append("=" * 60)
        lines.append(f" {title}")
        lines.append(f" Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
        lines.append("=" * 60)
        lines.append("")

        # Trace 信息
        if trace:
            lines.append("## Trace")
            lines.append(f"  trace_id: {trace.get('trace_id', 'N/A')}")
            lines.append(f"  task: {trace.get('task', 'N/A')}")
            lines.append(f"  status: {trace.get('status', 'N/A')}")
            lines.append(f"  total_steps: {trace.get('total_steps', 0)}")
            lines.append(f"  total_tokens: {trace.get('total_tokens', 0)}")
            lines.append(f"  total_cost: {trace.get('total_cost', 0.0):.4f}")
            lines.append("")

        # Step 树
        if steps:
            lines.append("## Steps")
            lines.append("")

            # 构建树结构
            tree = self._build_tree(steps)
            tree_output = self._render_tree(tree, steps)
            lines.append(tree_output)

        content = "\n".join(lines)

        # 写入文件
        self.output_path.write_text(content, encoding="utf-8")

        return content

    def _build_tree(self, steps: List[Dict[str, Any]]) -> Dict[str, List[str]]:
        """构建父子关系映射"""
        # parent_id -> [child_ids]
        children: Dict[str, List[str]] = {"__root__": []}

        for step in steps:
            step_id = step.get("step_id", "")
            parent_id = step.get("parent_id")

            if parent_id is None:
                children["__root__"].append(step_id)
            else:
                if parent_id not in children:
                    children[parent_id] = []
                children[parent_id].append(step_id)

        return children

    def _render_tree(
        self,
        tree: Dict[str, List[str]],
        steps: List[Dict[str, Any]],
        parent_id: str = "__root__",
        indent: int = 0,
    ) -> str:
        """递归渲染树结构"""
        # step_id -> step 映射
        step_map = {s.get("step_id"): s for s in steps}

        lines = []
        child_ids = tree.get(parent_id, [])

        for i, step_id in enumerate(child_ids):
            step = step_map.get(step_id, {})
            is_last = i == len(child_ids) - 1

            # 渲染当前节点
            node_output = self._render_node(step, indent, is_last)
            lines.append(node_output)

            # 递归渲染子节点
            if step_id in tree:
                child_output = self._render_tree(tree, steps, step_id, indent + 1)
                lines.append(child_output)

        return "\n".join(lines)

    def _render_node(self, step: Dict[str, Any], indent: int, is_last: bool) -> str:
        """渲染单个节点的完整信息"""
        lines = []

        # 缩进和连接符
        prefix = "  " * indent
        connector = "└── " if is_last else "├── "
        child_prefix = "  " * indent + ("    " if is_last else "│   ")

        # 状态图标
        status = step.get("status", "unknown")
        status_icons = {
            "completed": "✓",
            "in_progress": "→",
            "planned": "○",
            "failed": "✗",
            "skipped": "⊘",
        }
        icon = status_icons.get(status, "?")

        # 类型和描述
        step_type = step.get("step_type", "unknown")
        description = step.get("description", "")

        # 第一行：类型和描述
        lines.append(f"{prefix}{connector}[{icon}] {step_type}: {description}")

        # 详细信息
        step_id = step.get("step_id", "")[:8]  # 只显示前 8 位
        lines.append(f"{child_prefix}id: {step_id}...")

        # 执行指标
        if step.get("duration_ms") is not None:
            lines.append(f"{child_prefix}duration: {step.get('duration_ms')}ms")
        if step.get("tokens") is not None:
            lines.append(f"{child_prefix}tokens: {step.get('tokens')}")
        if step.get("cost") is not None:
            lines.append(f"{child_prefix}cost: ${step.get('cost'):.4f}")

        # summary（如果有）
        if step.get("summary"):
            summary = step.get("summary", "")
            # 截断长 summary
            if len(summary) > 100:
                summary = summary[:100] + "..."
            lines.append(f"{child_prefix}summary: {summary}")

        # data 内容（格式化输出）
        data = step.get("data", {})
        if data:
            lines.append(f"{child_prefix}data:")
            data_lines = self._format_data(data, child_prefix + "  ")
            lines.append(data_lines)

        # 时间
        created_at = step.get("created_at", "")
        if created_at:
            if isinstance(created_at, str):
                # 只显示时间部分
                time_part = created_at.split("T")[-1][:8] if "T" in created_at else created_at
            else:
                time_part = created_at.strftime("%H:%M:%S")
            lines.append(f"{child_prefix}time: {time_part}")

        lines.append("")  # 空行分隔
        return "\n".join(lines)

    def _format_data(self, data: Dict[str, Any], prefix: str, max_value_len: int = 200) -> str:
        """格式化 data 字典"""
        lines = []

        for key, value in data.items():
            # 格式化值
            if isinstance(value, str):
                if len(value) > max_value_len:
                    value_str = value[:max_value_len] + f"... ({len(value)} chars)"
                else:
                    value_str = value
                # 处理多行字符串
                if "\n" in value_str:
                    first_line = value_str.split("\n")[0]
                    value_str = first_line + f"... ({value_str.count(chr(10))+1} lines)"
            elif isinstance(value, (dict, list)):
                value_str = json.dumps(value, ensure_ascii=False, indent=2)
                if len(value_str) > max_value_len:
                    value_str = value_str[:max_value_len] + "..."
                # 缩进多行
                value_str = value_str.replace("\n", "\n" + prefix + "  ")
            else:
                value_str = str(value)

            lines.append(f"{prefix}{key}: {value_str}")

        return "\n".join(lines)

    def dump_markdown(
        self,
        trace: Optional[Dict[str, Any]] = None,
        steps: Optional[List[Dict[str, Any]]] = None,
        title: str = "Step Tree Debug",
        output_path: Optional[str] = None,
    ) -> str:
        """
        输出 Markdown 格式（支持折叠，完整内容）

        Args:
            trace: Trace 字典（可选）
            steps: Step 字典列表
            title: 输出标题
            output_path: 输出路径（默认 .trace/tree.md）

        Returns:
            输出的 Markdown 内容
        """
        lines = []

        # 标题
        lines.append(f"# {title}")
        lines.append("")
        lines.append(f"*Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}*")
        lines.append("")

        # Trace 信息
        if trace:
            lines.append("## Trace")
            lines.append("")
            lines.append(f"- **trace_id**: `{trace.get('trace_id', 'N/A')}`")
            lines.append(f"- **task**: {trace.get('task', 'N/A')}")
            lines.append(f"- **status**: {trace.get('status', 'N/A')}")
            lines.append(f"- **total_steps**: {trace.get('total_steps', 0)}")
            lines.append(f"- **total_tokens**: {trace.get('total_tokens', 0)}")
            lines.append(f"- **total_cost**: ${trace.get('total_cost', 0.0):.4f}")
            lines.append("")

        # Steps
        if steps:
            lines.append("## Steps")
            lines.append("")

            # 构建树并渲染为 Markdown
            tree = self._build_tree(steps)
            step_map = {s.get("step_id"): s for s in steps}
            md_output = self._render_markdown_tree(tree, step_map, level=3)
            lines.append(md_output)

        content = "\n".join(lines)

        # 写入文件
        if output_path is None:
            output_path = str(self.output_path).replace(".txt", ".md")

        Path(output_path).write_text(content, encoding="utf-8")
        return content

    def _render_markdown_tree(
        self,
        tree: Dict[str, List[str]],
        step_map: Dict[str, Dict[str, Any]],
        parent_id: str = "__root__",
        level: int = 3,
    ) -> str:
        """递归渲染 Markdown 树"""
        lines = []
        child_ids = tree.get(parent_id, [])

        for step_id in child_ids:
            step = step_map.get(step_id, {})

            # 渲染节点
            node_md = self._render_markdown_node(step, level)
            lines.append(node_md)

            # 递归子节点
            if step_id in tree:
                child_md = self._render_markdown_tree(tree, step_map, step_id, level + 1)
                lines.append(child_md)

        return "\n".join(lines)

    def _render_markdown_node(self, step: Dict[str, Any], level: int) -> str:
        """渲染单个节点的 Markdown"""
        lines = []

        # 标题
        status = step.get("status", "unknown")
        status_icons = {
            "completed": "✓",
            "in_progress": "→",
            "planned": "○",
            "failed": "✗",
            "skipped": "⊘",
        }
        icon = status_icons.get(status, "?")

        step_type = step.get("step_type", "unknown")
        description = step.get("description", "")
        heading = "#" * level

        lines.append(f"{heading} [{icon}] {step_type}: {description}")
        lines.append("")

        # 基本信息
        lines.append("**基本信息**")
        lines.append("")
        step_id = step.get("step_id", "")[:16]
        lines.append(f"- **id**: `{step_id}...`")

        if step.get("duration_ms") is not None:
            lines.append(f"- **duration**: {step.get('duration_ms')}ms")
        if step.get("tokens") is not None:
            lines.append(f"- **tokens**: {step.get('tokens')}")
        if step.get("cost") is not None:
            lines.append(f"- **cost**: ${step.get('cost'):.4f}")

        created_at = step.get("created_at", "")
        if created_at:
            if isinstance(created_at, str):
                time_part = created_at.split("T")[-1][:8] if "T" in created_at else created_at
            else:
                time_part = created_at.strftime("%H:%M:%S")
            lines.append(f"- **time**: {time_part}")

        lines.append("")

        # Summary
        if step.get("summary"):
            lines.append("<details>")
            lines.append("<summary><b>📝 Summary</b></summary>")
            lines.append("")
            lines.append(f"```\n{step.get('summary')}\n```")
            lines.append("")
            lines.append("</details>")
            lines.append("")

        # Data（完整输出，不截断）
        data = step.get("data", {})
        if data:
            lines.append(self._render_markdown_data(data))
            lines.append("")

        return "\n".join(lines)

    def _render_markdown_data(self, data: Dict[str, Any]) -> str:
        """渲染 data 字典为可折叠的 Markdown"""
        lines = []

        # 定义输出顺序（重要的放前面）
        key_order = ["messages", "tools", "response", "content", "tool_calls", "model"]

        # 先按顺序输出重要的 key
        remaining_keys = set(data.keys())
        for key in key_order:
            if key in data:
                lines.append(self._render_data_item(key, data[key]))
                remaining_keys.remove(key)

        # 再输出剩余的 key
        for key in sorted(remaining_keys):
            lines.append(self._render_data_item(key, data[key]))

        return "\n".join(lines)

    def _render_data_item(self, key: str, value: Any) -> str:
        """渲染单个 data 项"""
        # 确定图标
        icon_map = {
            "messages": "📨",
            "response": "🤖",
            "tools": "🛠️",
            "tool_calls": "🔧",
            "model": "🎯",
            "error": "❌",
            "content": "💬",
        }
        icon = icon_map.get(key, "📄")

        # 特殊处理：跳过 None 值
        if value is None:
            return ""

        # 判断是否需要折叠（长内容或复杂结构）
        needs_collapse = False
        if isinstance(value, str):
            needs_collapse = len(value) > 100 or "\n" in value
        elif isinstance(value, (dict, list)):
            needs_collapse = True

        if needs_collapse:
            lines = []
            # 可折叠块
            lines.append("<details>")
            lines.append(f"<summary><b>{icon} {key.capitalize()}</b></summary>")
            lines.append("")

            # 格式化内容
            if isinstance(value, str):
                # 检查是否包含图片 base64
                if "data:image" in value or (isinstance(value, str) and len(value) > 10000):
                    lines.append("```")
                    lines.append(f"[IMAGE DATA: {len(value)} chars, truncated for display]")
                    lines.append(value[:200] + "...")
                    lines.append("```")
                else:
                    lines.append("```")
                    lines.append(value)
                    lines.append("```")
            elif isinstance(value, (dict, list)):
                # 递归截断图片 base64
                truncated_value = self._truncate_image_data(value)
                lines.append("```json")
                lines.append(json.dumps(truncated_value, ensure_ascii=False, indent=2))
                lines.append("```")

            lines.append("")
            lines.append("</details>")
            return "\n".join(lines)
        else:
            # 简单值，直接显示
            return f"- **{icon} {key}**: `{value}`"

    def _truncate_image_data(self, obj: Any, max_length: int = 200) -> Any:
        """递归截断对象中的图片 base64 数据"""
        if isinstance(obj, dict):
            result = {}
            for key, value in obj.items():
                # 检测图片 URL（data:image/...;base64,...）
                if isinstance(value, str) and value.startswith("data:image"):
                    # 提取 MIME 类型和数据长度
                    header_end = value.find(",")
                    if header_end > 0:
                        header = value[:header_end]
                        data = value[header_end+1:]
                        data_size_kb = len(data) / 1024
                        result[key] = f"<IMAGE_DATA: {data_size_kb:.1f}KB, {header}, preview: {data[:50]}...>"
                    else:
                        result[key] = value[:max_length] + f"... ({len(value)} chars)"
                else:
                    result[key] = self._truncate_image_data(value, max_length)
            return result
        elif isinstance(obj, list):
            return [self._truncate_image_data(item, max_length) for item in obj]
        elif isinstance(obj, str) and len(obj) > 100000:
            # 超长字符串（可能是未检测到的 base64）
            return obj[:max_length] + f"... (TRUNCATED: {len(obj)} chars total)"
        else:
            return obj


def dump_tree(
    trace: Optional[Any] = None,
    steps: Optional[List[Any]] = None,
    output_path: str = DEFAULT_DUMP_PATH,
    title: str = "Step Tree Debug",
) -> str:
    """
    便捷函数：输出 Step 树到文件

    Args:
        trace: Trace 对象或字典
        steps: Step 对象或字典列表
        output_path: 输出文件路径
        title: 输出标题

    Returns:
        输出的文本内容

    示例:
        from agent.debug import dump_tree

        # 每次 step 变化后调用
        dump_tree(trace, steps)

        # 自定义路径
        dump_tree(trace, steps, output_path=".debug/my_trace.txt")
    """
    # 转换为字典
    trace_dict = None
    if trace is not None:
        trace_dict = trace.to_dict() if hasattr(trace, "to_dict") else trace

    steps_list = []
    if steps:
        for step in steps:
            if hasattr(step, "to_dict"):
                steps_list.append(step.to_dict())
            else:
                steps_list.append(step)

    dumper = StepTreeDumper(output_path)
    return dumper.dump(trace_dict, steps_list, title)


def dump_json(
    trace: Optional[Any] = None,
    steps: Optional[List[Any]] = None,
    output_path: str = DEFAULT_JSON_PATH,
) -> str:
    """
    输出完整的 JSON 格式（用于程序化分析）

    Args:
        trace: Trace 对象或字典
        steps: Step 对象或字典列表
        output_path: 输出文件路径

    Returns:
        JSON 字符串
    """
    path = Path(output_path)
    path.parent.mkdir(parents=True, exist_ok=True)

    # 转换为字典
    trace_dict = None
    if trace is not None:
        trace_dict = trace.to_dict() if hasattr(trace, "to_dict") else trace

    steps_list = []
    if steps:
        for step in steps:
            if hasattr(step, "to_dict"):
                steps_list.append(step.to_dict())
            else:
                steps_list.append(step)

    data = {
        "generated_at": datetime.now().isoformat(),
        "trace": trace_dict,
        "steps": steps_list,
    }

    content = json.dumps(data, ensure_ascii=False, indent=2)
    path.write_text(content, encoding="utf-8")

    return content


def dump_markdown(
    trace: Optional[Any] = None,
    steps: Optional[List[Any]] = None,
    output_path: str = DEFAULT_MD_PATH,
    title: str = "Step Tree Debug",
) -> str:
    """
    便捷函数：输出 Markdown 格式（支持折叠，完整内容）

    Args:
        trace: Trace 对象或字典
        steps: Step 对象或字典列表
        output_path: 输出文件路径（默认 .trace/tree.md）
        title: 输出标题

    Returns:
        输出的 Markdown 内容

    示例:
        from agent.debug import dump_markdown

        # 输出完整可折叠的 Markdown
        dump_markdown(trace, steps)

        # 自定义路径
        dump_markdown(trace, steps, output_path=".debug/debug.md")
    """
    # 转换为字典
    trace_dict = None
    if trace is not None:
        trace_dict = trace.to_dict() if hasattr(trace, "to_dict") else trace

    steps_list = []
    if steps:
        for step in steps:
            if hasattr(step, "to_dict"):
                steps_list.append(step.to_dict())
            else:
                steps_list.append(step)

    dumper = StepTreeDumper(output_path)
    return dumper.dump_markdown(trace_dict, steps_list, title, output_path)