#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
LLM 结果处理脚本 - 合并版本
从 LLM 生成的 Markdown 结果中提取 templates，生成并验证 Alpha 表达式

使用方法:
    将 llm_result.md 和 dataset.csv 放在当前目录，然后运行:
    python decode_template.py

输出:
    在当前目录创建 output/ 文件夹，包含生成的表达式
"""

import json
import itertools
import re
import sys
from pathlib import Path
from typing import List, Dict, Any, Optional, Tuple


# ==================== 表达式处理工具函数 ====================

def detect_dataset_code(dataset_ids: List[str]) -> Optional[str]:
    """从字段ID中检测数据集代码前缀"""
    if not dataset_ids:
        return None
    counts: Dict[str, int] = {}
    for fid in dataset_ids:
        tok = (str(fid).split("_", 1)[0] or "").strip()
        if tok:
            counts[tok] = counts.get(tok, 0) + 1
    if not counts:
        return None
    return max(counts.items(), key=lambda kv: kv[1])[0]


def build_allowed_metric_suffixes(field_ids: List[str], max_suffixes: int = 300) -> List[str]:
    """从数据集字段ID中提取实用的占位符候选列表"""
    if not field_ids:
        return []

    # 过滤并转换字段ID
    field_ids = [str(fid) for fid in field_ids if fid]
    dataset_code = detect_dataset_code(field_ids)

    counts: Dict[str, int] = {}
    for raw in field_ids:
        parts = [p for p in str(raw).split("_") if p]
        if len(parts) < 2:
            continue

        for n in range(1, min(6, len(parts))):
            suffix = "_".join(parts[-n:])
            if suffix.replace("_", "").isdigit():
                continue
            if dataset_code and suffix.lower().startswith(dataset_code.lower() + "_"):
                continue
            if n == 1 and len(suffix) < 8:
                continue
            if len(suffix) < 6:
                continue
            counts[suffix] = counts.get(suffix, 0) + 1

    ranked = sorted(
        counts.items(),
        key=lambda kv: (kv[1], kv[0].count("_"), len(kv[0])),
        reverse=True,
    )

    suffixes: List[str] = []
    for suffix, _ in ranked:
        if suffix not in suffixes:
            suffixes.append(suffix)
        if len(suffixes) >= max_suffixes:
            break
    return suffixes


def compress_to_known_suffix(var: str, allowed_suffixes: List[str]) -> Optional[str]:
    """将变量名压缩为已知的后缀形式"""
    if not allowed_suffixes:
        return None
    var_lower = var.lower()
    for suffix in allowed_suffixes:
        if var_lower.endswith(suffix.lower()):
            return suffix
        if suffix.lower().endswith(var_lower):
            return suffix
    return None


def placeholder_is_reasonably_matchable(placeholder: str, dataset_ids: List[str]) -> bool:
    """检查占位符是否可以与数据集字段匹配"""
    if not dataset_ids:
        return True
    v = placeholder.strip()
    if not v:
        return False
    if len(v) <= 3:
        pat = re.compile(rf"(^|_){re.escape(v)}(_|$)", flags=re.IGNORECASE)
        return any(pat.search(str(fid)) for fid in dataset_ids)
    return any(v in str(fid) for fid in dataset_ids)


def normalize_template_placeholders(
        template: str,
        dataset_ids: List[str],
        allowed_suffixes: List[str],
        dataset_code: Optional[str],
) -> Tuple[str, bool]:
    """规范化模板中的占位符为后缀形式"""
    vars_in_template = re.findall(r"\{([A-Za-z0-9_]+)\}", template)
    if not vars_in_template:
        return template, False

    mapping: Dict[str, str] = {}
    for var in set(vars_in_template):
        new_var = var
        if dataset_code and new_var.lower().startswith(dataset_code.lower() + "_"):
            new_var = new_var[len(dataset_code) + 1:]

        compressed = compress_to_known_suffix(new_var, allowed_suffixes)
        if compressed:
            new_var = compressed

        mapping[var] = new_var

    normalized = template
    for src, dst in mapping.items():
        normalized = normalized.replace("{" + src + "}", "{" + dst + "}")

    vars_after = re.findall(r"\{([A-Za-z0-9_]+)\}", normalized)
    ok = all(placeholder_is_reasonably_matchable(v, dataset_ids) for v in vars_after)
    return normalized, ok


def extract_template_blocks(markdown_text: str) -> List[Dict[str, str]]:
    """
    从 Markdown 文本中解析 **Concept** 块并提取 {template, idea}

    返回列表，每个元素包含:
    - template: 实现示例模板（包含 {variable} 占位符）
    - idea: Concept 的完整描述
    """
    concept_re = re.compile(r"^\*\*Concept\*\*\s*:\s*(.*)\s*$")
    impl_re = re.compile(r"\*\*Implementation Example\*\*\s*:\s*(.*)$", flags=re.IGNORECASE)
    backtick_re = re.compile(r"`([^`]*)`")
    boundary_re = re.compile(r"^(?:-{3,}|#{1,6}\s+.*)\s*$")

    lines = markdown_text.splitlines()
    blocks: List[List[str]] = []
    current: List[str] = []

    def _flush():
        nonlocal current
        if current:
            while current and not current[0].strip():
                current.pop(0)
            while current and not current[-1].strip():
                current.pop()
            if current:
                blocks.append(current)
        current = []

    for line in lines:
        if concept_re.match(line.strip()):
            _flush()
            current = [line]
            continue

        if current and boundary_re.match(line.strip()):
            _flush()
            continue

        if current:
            current.append(line)

    _flush()

    out: List[Dict[str, str]] = []
    for block_lines in blocks:
        template: Optional[str] = None
        impl_line_idx: Optional[int] = None

        for i, raw in enumerate(block_lines):
            m = impl_re.search(raw)
            if not m:
                continue

            impl_line_idx = i
            tail = (m.group(1) or "").strip()

            bt = backtick_re.search(tail)
            if bt:
                template = bt.group(1).strip()
                break

            if tail and ("{" in tail and "}" in tail):
                template = tail.strip().strip("`")
                break

            for j in range(i + 1, min(i + 4, len(block_lines))):
                nxt = block_lines[j].strip()
                if not nxt:
                    continue
                bt2 = backtick_re.search(nxt)
                if bt2:
                    template = bt2.group(1).strip()
                    break
                if "{" in nxt and "}" in nxt:
                    template = nxt.strip().strip("`")
                    break
            break

        # 注释掉跳过逻辑，允许没有 {placeholder} 的模板
        # if not template or "{" not in template or "}" not in template:
        #     continue

        # 如果 template 为 None，跳过
        if not template:
            continue

        template = template.strip()
        if template.startswith('{') and template.endswith('}'):
            template = template[1:-1].strip()

        idea_lines: List[str] = []
        for i, raw in enumerate(block_lines):
            if impl_line_idx is not None and i == impl_line_idx:
                continue
            idea_lines.append(raw)

        idea = "\n".join(idea_lines).strip()
        out.append({"template": template.strip(), "idea": idea})

    return out


def match_single_horizon_auto(field_ids: List[str], template: str) -> List[str]:
    """
    从模板生成表达式，将每个 {variable} 匹配到数据集字段ID

    Args:
        field_ids: 数据集字段ID列表
        template: 包含 {variable} 占位符的模板字符串

    Returns:
        生成的表达式列表
    """
    metrics = re.findall(r'\{([A-Za-z0-9_]+)\}', template)
    if not metrics:
        return []

    metrics = sorted(metrics, key=len, reverse=True)
    primary = metrics[0]

    if not field_ids:
        return []

    ids = [str(fid) for fid in field_ids if fid]

    def _matches_metric(field_id: str, metric: str) -> bool:
        fid = str(field_id)
        m = str(metric)
        if len(m) <= 3:
            return re.search(rf"(^|_){re.escape(m)}(_|$)", fid, flags=re.IGNORECASE) is not None
        return m in fid

    def _common_prefix_len(a: str, b: str) -> int:
        n = min(len(a), len(b))
        i = 0
        while i < n and a[i] == b[i]:
            i += 1
        return i

    candidates_by_metric = {}
    for m in metrics:
        cands = [fid for fid in ids if _matches_metric(fid, m)]
        seen = set()
        uniq = []
        for x in cands:
            if x not in seen:
                seen.add(x)
                uniq.append(x)
        candidates_by_metric[m] = uniq

    if not candidates_by_metric.get(primary):
        return []
    for m in metrics[1:]:
        if not candidates_by_metric.get(m):
            return []

    MAX_PRIMARY_CANDIDATES = 30
    MAX_SECONDARY_CHOICES = 8
    MAX_EXPRESSIONS = 5000

    results = []
    seen_expr = set()

    primary_candidates = candidates_by_metric[primary][:MAX_PRIMARY_CANDIDATES]
    for primary_id in primary_candidates:
        chosen_by_metric = {primary: [primary_id]}
        for m in metrics[1:]:
            cands = candidates_by_metric[m]
            ranked = sorted(cands, key=lambda fid: _common_prefix_len(primary_id, fid), reverse=True)
            chosen_by_metric[m] = ranked[:MAX_SECONDARY_CHOICES]

        metric_order = metrics
        pools = [chosen_by_metric[m] for m in metric_order]
        for combo in itertools.product(*pools):
            field_map = dict(zip(metric_order, combo))
            try:
                expr = template.format(**field_map)
            except Exception:
                continue
            if expr in seen_expr:
                continue
            seen_expr.add(expr)
            results.append(expr)
            if len(results) >= MAX_EXPRESSIONS:
                return results

    return results


# ==================== 表达式验证器 ====================

# 尝试导入PLY库，如果不存在则提供安装提示
try:
    import ply.lex as lex
    import ply.yacc as yacc
except ImportError:
    print("错误: 需要安装PLY库。请运行 'pip install ply' 来安装。")
    sys.exit(1)

# 1. 定义支持的操作符和函数
supported_functions = {
    # Group 类别函数
    'group_min': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'category']},
    'group_mean': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'category']},
    'group_median': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'category']},
    'group_max': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'category']},
    'group_rank': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'category']},
    'group_vector_proj': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'category']},
    'group_normalize': {'min_args': 2, 'max_args': 5, 'arg_types': ['expression', 'category', 'expression', 'expression', 'expression']},
    'group_extra': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'category']},
    'group_backfill': {'min_args': 3, 'max_args': 4, 'arg_types': ['expression', 'expression', 'expression', 'expression'], 'param_names': ['x', 'cat', 'days', 'std']},
    'group_scale': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'category']},
    'group_count': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'category']},
    'group_zscore': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'category']},
    'group_std_dev': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'category']},
    'group_sum': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'category']},
    'group_neutralize': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'category']},
    'group_multi_regression': {'min_args': 4, 'max_args': 9, 'arg_types': ['expression'] * 9},
    'group_cartesian_product': {'min_args': 2, 'max_args': 2, 'arg_types': ['category', 'category']},
    'combo_a': {'min_args': 1, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression']},

    # Transformational 类别函数
    'right_tail': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression']},
    'bucket': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression']},
    'tail': {'min_args': 1, 'max_args': 4, 'arg_types': ['expression', 'expression', 'expression', 'expression']},
    'left_tail': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression']},
    'trade_when': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression']},
    'generate_stats': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},

    # Cross Sectional 类别函数
    'winsorize': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['x', 'std']},
    'rank': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression']},
    'regression_proj': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
    'vector_neut': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
    'regression_neut': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
    'multi_regression': {'min_args': 2, 'max_args': 100, 'arg_types': ['expression'] * 100},

    # Time Series 类别函数
    'ts_std_dev': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'ts_mean': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'ts_delay': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'ts_corr': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'number']},
    'ts_zscore': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'ts_returns': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'number', 'number'], 'param_names': ['x', 'd', 'mode'], 'keyword_only': True},
    'ts_product': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'ts_backfill': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number'], 'param_names': ['x', 'd']},
    'days_from_last_change': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'last_diff_value': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'ts_scale': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'number', 'number'], 'param_names': ['x', 'd', 'constant'], 'keyword_only': True},
    'ts_entropy': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number'], 'param_names': ['x', 'd']},
    'ts_step': {'min_args': 1, 'max_args': 1, 'arg_types': ['number']},
    'ts_sum': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'ts_co_kurtosis': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'number']},
    'inst_tvr': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'ts_decay_exp_window': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'number', 'number'], 'param_names': ['x', 'd', 'factor'], 'keyword_only': True},
    'ts_av_diff': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'ts_kurtosis': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'ts_min_max_diff': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'number', 'number'], 'param_names': ['x', 'd', 'f'], 'keyword_only': True},
    'ts_arg_max': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'ts_max': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'ts_min_max_cps': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'number', 'number'], 'param_names': ['x', 'd', 'f'], 'keyword_only': True},
    'ts_rank': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'number', 'number'], 'param_names': ['x', 'd', 'constant'], 'keyword_only': True},
    'ts_ir': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'ts_theilsen': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'number']},
    'hump_decay': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'number'], 'param_names': ['x', 'p'], 'keyword_only': True},
    'ts_weighted_decay': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'number'], 'param_names': ['x', 'k'], 'keyword_only': True},
    'ts_quantile': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'number', 'string'], 'param_names': ['x', 'd', 'driver'], 'keyword_only': True},
    'ts_min': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'ts_count_nans': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'ts_covariance': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'number']},
    'ts_co_skewness': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'number']},
    'ts_min_diff': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'ts_decay_linear': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'number', 'boolean'], 'param_names': ['x', 'd', 'dense'], 'keyword_only': True},
    'jump_decay': {'min_args': 2, 'max_args': 4, 'arg_types': ['expression', 'number', 'number', 'number'], 'param_names': ['x', 'd', 'sensitivity', 'force'],
                   'keyword_only': True},
    'ts_moment': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'number', 'number'], 'param_names': ['x', 'd', 'k'], 'keyword_only': True},
    'ts_arg_min': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'ts_regression': {'min_args': 3, 'max_args': 5, 'arg_types': ['expression', 'expression', 'number', 'number', 'number'], 'param_names': ['y', 'x', 'd', 'lag', 'rettype'],
                      'keyword_only': True},
    'ts_skewness': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'ts_max_diff': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'kth_element': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'number', 'number']},
    'hump': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'number'], 'param_names': ['x', 'hump']},
    'ts_median': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'ts_delta': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
    'ts_poly_regression': {'min_args': 3, 'max_args': 4, 'arg_types': ['expression', 'expression', 'number', 'number'], 'param_names': ['y', 'x', 'd', 'k'], 'keyword_only': True,
                           'keyword_only_from': 3},
    'ts_target_tvr_decay': {'min_args': 1, 'max_args': 4, 'arg_types': ['expression', 'number', 'number', 'number'], 'param_names': ['x', 'lambda_min', 'lambda_max', 'target_tvr'],
                            'keyword_only': True},
    'ts_target_tvr_delta_limit': {'min_args': 2, 'max_args': 5, 'arg_types': ['expression', 'expression', 'number', 'number', 'number'],
                                  'param_names': ['x', 'y', 'lambda_min', 'lambda_max', 'target_tvr'], 'keyword_only': True},
    'ts_target_tvr_hump': {'min_args': 1, 'max_args': 4, 'arg_types': ['expression', 'number', 'number', 'number'], 'param_names': ['x', 'lambda_min', 'lambda_max', 'target_tvr'],
                           'keyword_only': True},
    'ts_delta_limit': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'expression', 'number'], 'param_names': ['x', 'y', 'limit_volume'], 'keyword_only': True},

    # Special 类别函数
    'inst_pnl': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'self_corr': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'in': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
    'universe_size': {'min_args': 0, 'max_args': 0, 'arg_types': []},

    # Missing functions from operators.py
    'quantile': {'min_args': 1, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression'], 'param_names': ['x', 'driver', 'sigma']},
    'normalize': {'min_args': 1, 'max_args': 3, 'arg_types': ['expression', 'boolean', 'number']},
    'zscore': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},

    # Logical 类别函数
    'or': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
    'and': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
    'not': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'is_nan': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'is_not_nan': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'less': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
    'equal': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
    'greater': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
    'is_finite': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'if_else': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression']},
    'not_equal': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
    'less_equal': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
    'greater_equal': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},

    # Vector 类别函数
    'vec_kurtosis': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'vec_min': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'vec_count': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'vec_sum': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'vec_skewness': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'vec_max': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'vec_avg': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'vec_range': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'vec_choose': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number'], 'param_names': ['x', 'nth']},
    'vec_powersum': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'number'], 'param_names': ['x', 'constant']},
    'vec_stddev': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'vec_percentage': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'number'], 'param_names': ['x', 'percentage']},
    'vec_ir': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'vec_norm': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'ts_percentage': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'number', 'number'], 'param_names': ['x', 'd', 'percentage']},
    'signed_power': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},

    # Additional functions from test cases
    'rank_by_side': {'min_args': 1, 'max_args': 3, 'arg_types': ['expression', 'number', 'number'], 'param_names': ['x', 'rate', 'scale']},
    'log_diff': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'nan_mask': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
    'ts_partial_corr': {'min_args': 4, 'max_args': 4, 'arg_types': ['expression', 'expression', 'expression', 'number']},
    'ts_triple_corr': {'min_args': 4, 'max_args': 4, 'arg_types': ['expression', 'expression', 'expression', 'number']},
    'clamp': {'min_args': 1, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression'], 'param_names': ['x', 'lower', 'upper']},
    'keep': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'expression', 'number'], 'param_names': ['x', 'condition', 'period']},
    'replace': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression'], 'param_names': ['x', 'target', 'dest']},
    'filter': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression'], 'param_names': ['x', 'h', 't']},
    'one_side': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'string'], 'param_names': ['x', 'side']},
    'scale_down': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number'], 'param_names': ['x', 'constant']},

    # Arithmetic 类别函数
    'add': {'min_args': 2, 'max_args': 101, 'arg_types': ['expression'] * 101},
    'multiply': {'min_args': 2, 'max_args': 100, 'arg_types': ['expression'] * 99 + ['boolean'], 'param_names': ['x', 'y', 'filter']},
    'sign': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'subtract': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'expression', 'boolean']},
    'pasteurize': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'log': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'purify': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'arc_tan': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'max': {'min_args': 2, 'max_args': 100, 'arg_types': ['expression'] * 100},
    'to_nan': {'min_args': 1, 'max_args': 3, 'arg_types': ['expression', 'expression', 'boolean']},
    'abs': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'sigmoid': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'divide': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
    'min': {'min_args': 2, 'max_args': 100, 'arg_types': ['expression'] * 100},
    'tanh': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'nan_out': {'min_args': 1, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression'], 'param_names': ['x', 'lower', 'upper']},
    'signed_power': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
    'inverse': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'round': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'sqrt': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    's_log_1p': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'reverse': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'power': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
    'densify': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'floor': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
    'arc_cos': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['x']},
    'arc_sin': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['x']},
    'ceiling': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['x']},
    'exp': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['x']},
    'fraction': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['x']},
    'round_down': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['x', 'f']},
    'is_not_finite': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['input']},
    'negate': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['input']},
    'ts_rank_gmean_amean_diff': {'min_args': 5, 'max_args': 5, 'arg_types': ['expression', 'expression', 'expression', 'expression', 'number'],
                                 'param_names': ['input1', 'input2', 'input3', '...', 'd']},
    'ts_vector_neut': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'number'], 'param_names': ['x', 'y', 'd']},
    'ts_vector_proj': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'number'], 'param_names': ['x', 'y', 'd']},
    'scale': {'min_args': 1, 'max_args': 4, 'arg_types': ['expression', 'expression', 'expression', 'expression'], 'param_names': ['x', 'scale', 'longscale', 'shortscale']},
    'generalized_rank': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['open', 'm']},
    'rank_gmean_amean_diff': {'min_args': 4, 'max_args': 4, 'arg_types': ['expression', 'expression', 'expression', 'expression'],
                              'param_names': ['input1', 'input2', 'input3', '...']},
    'truncate': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['x', 'maxPercent']},
    'vector_proj': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['x', 'y']},
    'vec_filter': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['vec', 'value']},
    'group_coalesce': {'min_args': 4, 'max_args': 4, 'arg_types': ['expression', 'expression', 'expression', 'expression'],
                       'param_names': ['original_group', 'group2', 'group3', '…']},
    'group_percentage': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'category', 'expression'], 'param_names': ['x', 'group', 'percentage']},
    'group_vector_neut': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression'], 'param_names': ['x', 'y', 'g']},
    'convert': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['x', 'mode']},
    'reduce_avg': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['input', 'threshold']},
    'reduce_choose': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression'], 'param_names': ['input', 'nth', 'ignoreNan']},
    'reduce_count': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['input', 'threshold']},
    'reduce_ir': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['input']},
    'reduce_kurtosis': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['input']},
    'reduce_max': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['input']},
    'reduce_min': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['input']},
    'reduce_norm': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['input']},
    'reduce_percentage': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['input', 'percentage']},
    'reduce_powersum': {'min_args': 1, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression'], 'param_names': ['input', 'constant', 'precise']},
    'reduce_range': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['input']},
    'reduce_skewness': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['input']},
    'reduce_stddev': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['input', 'threshold']},
    'reduce_sum': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['input']},
}

# 2. 定义group类型字段
group_fields = {
    'sector', 'subindustry', 'industry', 'exchange', 'country', 'market'
}

# 3. 有效类别集合
valid_categories = group_fields

# 4. 字段命名模式 - 只校验字段是不是数字字母下划线组成
field_patterns = [
    re.compile(r'^[a-zA-Z0-9_]+$'),
]


# 4. 抽象语法树节点类型
class ASTNode:
    """抽象语法树节点基类"""

    def __init__(self, node_type: str, children: Optional[List['ASTNode']] = None,
                 value: Optional[Any] = None, line: Optional[int] = None):
        self.node_type = node_type
        self.children = children or []
        self.value = value
        self.line = line

    def __str__(self) -> str:
        return f"ASTNode({self.node_type}, {self.value}, line={self.line})"

    def __repr__(self) -> str:
        return self.__str__()


class ExpressionValidator:
    """表达式验证器类"""

    def __init__(self):
        """初始化词法分析器和语法分析器"""
        self.lexer = lex.lex(module=self, debug=False)
        self.parser = yacc.yacc(module=self, debug=False)
        self.errors = []
        self._unit_cache: Dict[int, str] = {}
        self._derived_category_cache: Dict[int, bool] = {}

    # 词法分析器规则
    tokens = ('FUNCTION', 'FIELD', 'NUMBER', 'LPAREN', 'RPAREN',
              'PLUS', 'MINUS', 'TIMES', 'DIVIDE', 'COMMA', 'CATEGORY',
              'EQUAL', 'ASSIGN', 'IDENTIFIER', 'STRING', 'GREATER', 'LESS', 'GREATEREQUAL', 'LESSEQUAL', 'NOTEQUAL', 'BOOLEAN')

    t_ignore = ' \t\n'

    t_PLUS = r'\+'
    t_MINUS = r'-'
    t_TIMES = r'\*'
    t_DIVIDE = r'/'
    t_LPAREN = r'\('
    t_RPAREN = r'\)'
    t_COMMA = r','
    t_EQUAL = r'=='
    t_NOTEQUAL = r'!='
    t_GREATEREQUAL = r'>='
    t_LESSEQUAL = r'<='
    t_GREATER = r'>'
    t_LESS = r'<'
    t_ASSIGN = r'='

    def t_NUMBER(self, t):
        r'\d+\.?\d*'
        if '.' in t.value:
            t.value = float(t.value)
        else:
            t.value = int(t.value)
        return t

    def t_STRING(self, t):
        r"'[^']*'|\"[^\"]*\""
        t.value = t.value[1:-1]
        return t

    def t_IDENTIFIER(self, t):
        r'[a-zA-Z_][a-zA-Z0-9_]*'
        if t.value.lower() in {'true', 'false'}:
            t.type = 'BOOLEAN'
            t.value = t.value.lower()
        else:
            lexpos = t.lexpos
            next_chars = ''
            if lexpos + len(t.value) < len(t.lexer.lexdata):
                next_pos = lexpos + len(t.value)
                while next_pos < len(t.lexer.lexdata) and t.lexer.lexdata[next_pos].isspace():
                    next_pos += 1
                if next_pos < len(t.lexer.lexdata):
                    next_chars = t.lexer.lexdata[next_pos:next_pos + 1]

            if next_chars == '=':
                t.type = 'IDENTIFIER'
            elif next_chars == '(':
                t.type = 'FUNCTION'
                t.value = t.value.lower()
            elif t.value in {'std', 'k', 'lambda_min', 'lambda_max', 'target_tvr', 'range', 'buckets', 'lag', 'rettype', 'mode', 'nth', 'constant', 'percentage', 'driver', 'sigma',
                             'rate', 'scale', 'filter', 'lower', 'upper', 'target', 'dest', 'event', 'sensitivity', 'force', 'h', 't', 'period', 'stddev', 'factor', 'k', 'useStd',
                             'limit', 'gaussian', 'uniform', 'cauchy'}:
                t.type = 'IDENTIFIER'
            elif t.value.lower() in supported_functions:
                t.type = 'FUNCTION'
                t.value = t.value.lower()
            elif t.value in valid_categories:
                t.type = 'CATEGORY'
            elif self._is_valid_field(t.value):
                t.type = 'FIELD'
            else:
                t.type = 'IDENTIFIER'
        return t

    def t_newline(self, t):
        r'\n+'
        t.lexer.lineno += len(t.value)

    def t_error(self, t):
        if t:
            if not re.match(r'[a-zA-Z0-9_\+\-\*/\(\)\,\s=<>!]', t.value[0]):
                self.errors.append(f"非法字符 '{t.value[0]}' (行 {t.lexer.lineno})")
            else:
                self.errors.append(f"非法标记 '{t.value}' (行 {t.lexer.lineno})")
            t.lexer.skip(1)
        else:
            self.errors.append("词法分析器到达文件末尾")

    # 语法分析器规则
    def p_expression(self, p):
        """expression : comparison
                      | expression EQUAL comparison
                      | expression NOTEQUAL comparison
                      | expression GREATER comparison
                      | expression LESS comparison
                      | expression GREATEREQUAL comparison
                      | expression LESSEQUAL comparison"""
        if len(p) == 2:
            p[0] = p[1]
        else:
            p[0] = ASTNode('binop', [p[1], p[3]], {'op': p[2]})

    def p_comparison(self, p):
        """comparison : term
                      | comparison PLUS term
                      | comparison MINUS term"""
        if len(p) == 2:
            p[0] = p[1]
        else:
            p[0] = ASTNode('binop', [p[1], p[3]], {'op': p[2]})

    def p_term(self, p):
        """term : factor
                | term TIMES factor
                | term DIVIDE factor"""
        if len(p) == 2:
            p[0] = p[1]
        else:
            p[0] = ASTNode('binop', [p[1], p[3]], {'op': p[2]})

    def p_factor(self, p):
        """factor : NUMBER
                  | STRING
                  | FIELD
                  | CATEGORY
                  | IDENTIFIER
                  | BOOLEAN
                  | MINUS factor
                  | LPAREN expression RPAREN
                  | function_call"""
        if len(p) == 2:
            if p.slice[1].type == 'NUMBER':
                p[0] = ASTNode('number', value=p[1])
            elif p.slice[1].type == 'STRING':
                p[0] = ASTNode('string', value=p[1])
            elif p.slice[1].type == 'FIELD':
                p[0] = ASTNode('field', value=p[1])
            elif p.slice[1].type == 'CATEGORY':
                p[0] = ASTNode('category', value=p[1])
            elif p.slice[1].type == 'BOOLEAN':
                p[0] = ASTNode('boolean', value=p[1])
            elif p.slice[1].type == 'IDENTIFIER':
                p[0] = ASTNode('identifier', value=p[1])
            else:
                p[0] = p[1]
        elif len(p) == 3:
            p[0] = ASTNode('unop', [p[2]], {'op': p[1]})
        elif len(p) == 4:
            p[0] = p[2]
        else:
            p[0] = p[1]

    def p_function_call(self, p):
        '''function_call : FUNCTION LPAREN args RPAREN'''
        p[0] = ASTNode('function', p[3], p[1])

    def p_args(self, p):
        '''args : arg_list
                | empty'''
        if len(p) == 2 and p[1] is not None:
            p[0] = p[1]
        else:
            p[0] = []

    def p_arg_list(self, p):
        '''arg_list : arg
                   | arg_list COMMA arg'''
        if len(p) == 2:
            p[0] = [p[1]]
        else:
            p[0] = p[1] + [p[3]]

    def p_arg(self, p):
        '''arg : expression
              | IDENTIFIER ASSIGN expression'''
        if len(p) == 2:
            p[0] = {'type': 'positional', 'value': p[1]}
        else:
            p[0] = {'type': 'named', 'name': p[1], 'value': p[3]}

    def p_empty(self, p):
        '''empty :'''
        p[0] = None

    def p_error(self, p):
        if p:
            self.errors.append(f"语法错误在位置 {p.lexpos}: 非法标记 '{p.value}'")
        else:
            self.errors.append("语法错误: 表达式不完整")

    def _is_valid_field(self, field_name: str) -> bool:
        """检查字段名是否符合模式"""
        for pattern in field_patterns:
            if pattern.match(field_name):
                return True
        return False

    def validate_function(self, node: ASTNode, is_in_group_arg: bool = False) -> List[str]:
        """验证函数调用的参数数量和类型"""
        function_name = node.value
        args = node.children
        function_info = supported_functions.get(function_name)

        if not function_info:
            return [f"未知函数: {function_name}"]

        if function_name == 'add':
            return self._validate_add(args, is_in_group_arg)

        errors = []

        keyword_only_from = function_info.get('keyword_only_from')
        if keyword_only_from is None and function_info.get('keyword_only'):
            keyword_only_from = function_info.get('min_args', 0)

        if len(args) < function_info['min_args']:
            errors.append(f"函数 {function_name} 需要至少 {function_info['min_args']} 个参数，但只提供了 {len(args)}")
        elif len(args) > function_info['max_args']:
            errors.append(f"函数 {function_name} 最多接受 {function_info['max_args']} 个参数，但提供了 {len(args)}")

        positional_index = 0

        for arg in args:
            if isinstance(arg, dict):
                if arg['type'] == 'named':
                    if 'param_names' in function_info and arg['name'] in function_info['param_names']:
                        param_index = function_info['param_names'].index(arg['name'])
                        if param_index < len(function_info['arg_types']):
                            expected_type = function_info['arg_types'][param_index]
                            arg_errors = self._validate_arg_type(arg['value'], expected_type, param_index, function_name, is_in_group_arg)
                            errors.extend(arg_errors)
                    elif function_name == 'winsorize' and arg['name'] in ['std', 'clip']:
                        arg_errors = self._validate_arg_type(arg['value'], 'number', 0, function_name, is_in_group_arg)
                        errors.extend(arg_errors)
                    elif function_name == 'bucket' and arg['name'] in ['range', 'buckets']:
                        arg_errors = self._validate_arg_type(arg['value'], 'string', 1, function_name, is_in_group_arg)
                        errors.extend(arg_errors)
                    else:
                        errors.append(f"函数 {function_name} 不存在参数 '{arg['name']}'")
                elif arg['type'] == 'positional':
                    if keyword_only_from is not None and positional_index >= keyword_only_from:
                        param_name = None
                        if 'param_names' in function_info and positional_index < len(function_info['param_names']):
                            param_name = function_info['param_names'][positional_index]
                        if param_name:
                            errors.append(f"函数 {function_name} 的第{positional_index + 1}个参数必须使用命名参数 '{param_name}='")
                        else:
                            errors.append(f"函数 {function_name} 的第{positional_index + 1}个参数必须使用命名参数")
                    else:
                        if positional_index < len(function_info['arg_types']):
                            expected_type = function_info['arg_types'][positional_index]
                            arg_errors = self._validate_arg_type(arg['value'], expected_type, positional_index, function_name, is_in_group_arg)
                            errors.extend(arg_errors)
                    positional_index += 1
                else:
                    errors.append(f"参数 {positional_index + 1} 格式错误")
                    positional_index += 1
            else:
                if keyword_only_from is not None and positional_index >= keyword_only_from:
                    param_name = None
                    if 'param_names' in function_info and positional_index < len(function_info['param_names']):
                        param_name = function_info['param_names'][positional_index]
                    if param_name:
                        errors.append(f"函数 {function_name} 的第{positional_index + 1}个参数必须使用命名参数 '{param_name}='")
                    else:
                        errors.append(f"函数 {function_name} 的第{positional_index + 1}个参数必须使用命名参数")
                else:
                    if positional_index < len(function_info['arg_types']):
                        expected_type = function_info['arg_types'][positional_index]
                        arg_errors = self._validate_arg_type(arg, expected_type, positional_index, function_name, is_in_group_arg)
                        errors.extend(arg_errors)
                positional_index += 1

        return errors

    def _validate_arg_type(self, arg: ASTNode, expected_type: str, arg_index: int, function_name: str, is_in_group_arg: bool = False) -> List[str]:
        """验证参数类型是否符合预期"""
        errors = []

        def _is_number_like(node: ASTNode) -> bool:
            if node is None:
                return False
            if node.node_type == 'number':
                return True
            if node.node_type == 'unop' and isinstance(node.value, dict) and node.value.get('op') in {'-', '+'}:
                if node.children and hasattr(node.children[0], 'node_type'):
                    return _is_number_like(node.children[0])
            return False

        if self._is_derived_category(arg) and expected_type != 'category':
            errors.append(
                f"Incompatible unit for input of \"{function_name}\" at index {arg_index}, expected \"Unit[]\", found \"Unit[Group:1]\""
            )
            return errors

        if arg.node_type == 'category' and arg.value in group_fields:
            if not (function_name.startswith('group_') or is_in_group_arg):
                errors.append(f"Group类型字段 '{arg.value}' 只能用于Group类型函数的参数中")

        if expected_type == 'expression':
            pass
        elif expected_type == 'number':
            if not _is_number_like(arg):
                errors.append(f"参数 {arg_index + 1} 应该是一个数字，但得到 {arg.node_type}")
        elif expected_type == 'boolean':
            if arg.node_type not in {'boolean', 'number'}:
                errors.append(f"参数 {arg_index + 1} 应该是一个布尔值（true/false 或 0/1），但得到 {arg.node_type}")
        elif expected_type == 'field':
            if arg.node_type != 'field' and arg.node_type != 'category':
                errors.append(f"参数 {arg_index + 1} 应该是一个字段，但得到 {arg.node_type}")
            elif arg.node_type == 'field' and not self._is_valid_field(arg.value):
                errors.append(f"无效的字段名: {arg.value}")
        elif expected_type == 'category':
            if not function_name.startswith('group_'):
                if arg.node_type != 'category':
                    errors.append(f"参数 {arg_index + 1} 应该是一个类别，但得到 {arg.node_type}")
                elif arg.value not in valid_categories:
                    errors.append(f"无效的类别: {arg.value}")

        return errors

    def _infer_unit(self, node: ASTNode) -> str:
        """Infer the Unit kind of an AST node."""
        if node is None:
            return 'unit'

        cache_key = id(node)
        cached = self._unit_cache.get(cache_key)
        if cached is not None:
            return cached

        unit = 'unit'

        if node.node_type in {'number', 'boolean', 'string'}:
            unit = 'scalar'
        elif node.node_type in {'field', 'identifier'}:
            unit = 'unit'
        elif node.node_type == 'category':
            unit = 'category'
        elif node.node_type in {'unop', 'binop'}:
            child_units = [self._infer_unit(child) for child in node.children if hasattr(child, 'node_type')]
            unit = 'category' if 'category' in child_units else 'unit'
        elif node.node_type == 'function':
            fname = node.value
            if fname in {'bucket', 'group_cartesian_product'}:
                unit = 'category'
            else:
                first_arg = None
                for child in node.children:
                    if isinstance(child, dict):
                        if child.get('type') == 'positional':
                            first_arg = child.get('value')
                            break
                    else:
                        first_arg = child
                        break

                if hasattr(first_arg, 'node_type'):
                    unit = self._infer_unit(first_arg)
                else:
                    unit = 'unit'

        self._unit_cache[cache_key] = unit
        return unit

    def _is_derived_category(self, node: ASTNode) -> bool:
        """Return True if node is a derived category/grouping key."""
        if node is None:
            return False

        cache_key = id(node)
        cached = self._derived_category_cache.get(cache_key)
        if cached is not None:
            return cached

        derived = False
        if node.node_type == 'function':
            if node.value in {'bucket', 'group_cartesian_product'}:
                derived = True
            else:
                function_info = supported_functions.get(node.value, {})
                arg_types = function_info.get('arg_types', [])
                param_names = function_info.get('param_names', [])

                positional_index = 0
                for child in node.children:
                    if isinstance(child, dict):
                        if child.get('type') == 'named':
                            name = child.get('name')
                            value = child.get('value')

                            expected_type = None
                            if name in param_names:
                                param_index = param_names.index(name)
                                if param_index < len(arg_types):
                                    expected_type = arg_types[param_index]

                            if expected_type == 'category':
                                continue

                            if self._is_derived_category(value):
                                derived = True
                                break
                        elif child.get('type') == 'positional':
                            value = child.get('value')
                            expected_type = arg_types[positional_index] if positional_index < len(arg_types) else None

                            if expected_type != 'category' and self._is_derived_category(value):
                                derived = True
                                break
                            positional_index += 1
                    else:
                        expected_type = arg_types[positional_index] if positional_index < len(arg_types) else None
                        if expected_type != 'category' and self._is_derived_category(child):
                            derived = True
                            break
                        positional_index += 1
        elif node.node_type in {'unop', 'binop'}:
            derived = any(
                self._is_derived_category(child)
                for child in node.children
                if hasattr(child, 'node_type')
            )

        self._derived_category_cache[cache_key] = derived
        return derived

    def _validate_add(self, args: List[Any], is_in_group_arg: bool = False) -> List[str]:
        """Validate add(x, y, ..., filter=false)."""
        errors: List[str] = []

        if len(args) < 2:
            return [f"函数 add 需要至少 2 个参数，但只提供了 {len(args)}"]

        named_filter_nodes: List[ASTNode] = []
        positional_nodes: List[ASTNode] = []

        for arg in args:
            if isinstance(arg, dict) and arg.get('type') == 'named':
                name = arg.get('name')
                value = arg.get('value')
                if name != 'filter':
                    errors.append(f"函数 add 不存在参数 '{name}'")
                    continue
                if not hasattr(value, 'node_type'):
                    errors.append("函数 add 的参数 filter 格式错误")
                    continue
                named_filter_nodes.append(value)
            elif isinstance(arg, dict) and arg.get('type') == 'positional':
                value = arg.get('value')
                if hasattr(value, 'node_type'):
                    positional_nodes.append(value)
                else:
                    errors.append("函数 add 的位置参数格式错误")
            elif hasattr(arg, 'node_type'):
                positional_nodes.append(arg)
            else:
                errors.append("函数 add 的参数格式错误")

        if len(named_filter_nodes) > 1:
            errors.append("函数 add 的参数 'filter' 只能出现一次")

        positional_filter_node: Optional[ASTNode] = None
        if not named_filter_nodes and len(positional_nodes) >= 3:
            last = positional_nodes[-1]
            if last.node_type == 'boolean' or (last.node_type == 'number' and last.value in {0, 1}):
                positional_filter_node = positional_nodes.pop()

        if len(positional_nodes) < 2:
            errors.append(f"函数 add 需要至少 2 个输入项（不含filter），但只提供了 {len(positional_nodes)}")

        for idx, node in enumerate(positional_nodes):
            errors.extend(self._validate_arg_type(node, 'expression', idx, 'add', is_in_group_arg))

        if positional_filter_node is not None and named_filter_nodes:
            errors.append("函数 add 的 filter 不能同时用位置参数和命名参数传递")
        if positional_filter_node is not None:
            errors.extend(self._validate_arg_type(positional_filter_node, 'boolean', len(positional_nodes), 'add', is_in_group_arg))
        if named_filter_nodes:
            errors.extend(self._validate_arg_type(named_filter_nodes[0], 'boolean', len(positional_nodes), 'add', is_in_group_arg))

        return errors

    def validate_ast(self, ast: Optional[ASTNode], is_in_group_arg: bool = False) -> List[str]:
        """递归验证抽象语法树"""
        if not ast:
            return ["无法解析表达式"]

        errors = []

        if ast.node_type == 'function':
            is_group_function = ast.value.startswith('group_')
            current_in_group_arg = is_in_group_arg or is_group_function
            function_errors = self.validate_function(ast, current_in_group_arg)
            errors.extend(function_errors)

            for child in ast.children:
                if isinstance(child, dict):
                    if 'value' in child and hasattr(child['value'], 'node_type'):
                        child_errors = self.validate_ast(child['value'], current_in_group_arg)
                        errors.extend(child_errors)
                elif hasattr(child, 'node_type'):
                    child_errors = self.validate_ast(child, current_in_group_arg)
                    errors.extend(child_errors)
        elif ast.node_type in ['unop', 'binop']:
            for child in ast.children:
                if hasattr(child, 'node_type'):
                    child_errors = self.validate_ast(child, is_in_group_arg)
                    errors.extend(child_errors)
        elif ast.node_type == 'field':
            if not self._is_valid_field(ast.value):
                errors.append(f"无效的字段名: {ast.value}")
        else:
            for child in ast.children:
                if isinstance(child, dict):
                    if 'value' in child and hasattr(child['value'], 'node_type'):
                        child_errors = self.validate_ast(child['value'], is_in_group_arg)
                        errors.extend(child_errors)
                elif hasattr(child, 'node_type'):
                    child_errors = self.validate_ast(child, is_in_group_arg)
                    errors.extend(child_errors)

        return errors

    def _process_semicolon_expression(self, expression: str) -> Tuple[bool, str]:
        """处理带有分号的表达式，将其转换为不带分号的简化形式"""

        def _top_level_equals_positions(stmt: str) -> List[int]:
            positions: List[int] = []
            paren_depth = 0
            bracket_depth = 0
            brace_depth = 0
            in_single_quote = False
            in_double_quote = False
            escape = False

            for i, ch in enumerate(stmt):
                if escape:
                    escape = False
                    continue
                if ch == '\\':
                    escape = True
                    continue

                if in_single_quote:
                    if ch == "'":
                        in_single_quote = False
                    continue
                if in_double_quote:
                    if ch == '"':
                        in_double_quote = False
                    continue

                if ch == "'":
                    in_single_quote = True
                    continue
                if ch == '"':
                    in_double_quote = True
                    continue

                if ch == '(':
                    paren_depth += 1
                    continue
                if ch == ')':
                    paren_depth = max(0, paren_depth - 1)
                    continue
                if ch == '[':
                    bracket_depth += 1
                    continue
                if ch == ']':
                    bracket_depth = max(0, bracket_depth - 1)
                    continue
                if ch == '{':
                    brace_depth += 1
                    continue
                if ch == '}':
                    brace_depth = max(0, brace_depth - 1)
                    continue

                if paren_depth or bracket_depth or brace_depth:
                    continue

                if ch != '=':
                    continue

                prev_ch = stmt[i - 1] if i > 0 else ''
                next_ch = stmt[i + 1] if i + 1 < len(stmt) else ''
                if prev_ch in ['=', '!', '<', '>'] or next_ch == '=':
                    continue

                positions.append(i)

            return positions

        def _keyword_arg_names(stmt: str):
            names = set()
            paren_depth = 0
            bracket_depth = 0
            brace_depth = 0
            in_single_quote = False
            in_double_quote = False
            escape = False

            i = 0
            while i < len(stmt):
                ch = stmt[i]

                if escape:
                    escape = False
                    i += 1
                    continue
                if ch == '\\':
                    escape = True
                    i += 1
                    continue

                if in_single_quote:
                    if ch == "'":
                        in_single_quote = False
                    i += 1
                    continue
                if in_double_quote:
                    if ch == '"':
                        in_double_quote = False
                    i += 1
                    continue

                if ch == "'":
                    in_single_quote = True
                    i += 1
                    continue
                if ch == '"':
                    in_double_quote = True
                    i += 1
                    continue

                if ch == '(':
                    paren_depth += 1
                    i += 1
                    continue
                if ch == ')':
                    paren_depth = max(0, paren_depth - 1)
                    i += 1
                    continue
                if ch == '[':
                    bracket_depth += 1
                    i += 1
                    continue
                if ch == ']':
                    bracket_depth = max(0, bracket_depth - 1)
                    i += 1
                    continue
                if ch == '{':
                    brace_depth += 1
                    i += 1
                    continue
                if ch == '}':
                    brace_depth = max(0, brace_depth - 1)
                    i += 1
                    continue

                inside_container = bool(paren_depth or bracket_depth or brace_depth)

                if inside_container and (ch.isalpha() or ch == '_'):
                    start = i
                    i += 1
                    while i < len(stmt) and (stmt[i].isalnum() or stmt[i] == '_'):
                        i += 1
                    name = stmt[start:i]

                    j = i
                    while j < len(stmt) and stmt[j].isspace():
                        j += 1

                    if j < len(stmt) and stmt[j] == '=':
                        next_ch = stmt[j + 1] if j + 1 < len(stmt) else ''
                        if next_ch != '=':
                            names.add(name.lower())
                    continue

                i += 1

            return names

        if expression.strip().endswith(';'):
            return False, "表达式不能以分号结尾"

        statements = [stmt.strip() for stmt in expression.split(';') if stmt.strip()]
        if not statements:
            return False, "表达式不能为空"

        variables = {}

        for i, stmt in enumerate(statements[:-1]):
            eq_positions = _top_level_equals_positions(stmt)
            if not eq_positions:
                return False, f"第{i + 1}个语句必须是赋值语句（使用=符号）"
            if len(eq_positions) > 1:
                return False, f"第{i + 1}个语句只能包含一个赋值符号（=）"

            real_equals_pos = eq_positions[0]

            var_name = stmt[:real_equals_pos].strip()
            var_value = stmt[real_equals_pos + 1:].strip()

            if not re.match(r'^[a-zA-Z_][a-zA-Z0-9_]*$', var_name):
                return False, f"第{i + 1}个语句的变量名'{var_name}'无效，只能包含字母、数字和下划线，且不能以数字开头"

            var_name_lower = var_name.lower()

            kw_names = _keyword_arg_names(var_value)
            used_vars = re.findall(r'\b[a-zA-Z_][a-zA-Z0-9_]*\b', var_value)
            for used_var in used_vars:
                used_var_lower = used_var.lower()
                if used_var_lower in kw_names:
                    continue
                if used_var_lower not in variables:
                    if used_var not in supported_functions:
                        if len(used_var) <= 2:
                            return False, f"第{i + 1}个语句中使用的变量'{used_var}'未在之前定义"
                        elif not self._is_valid_field(used_var):
                            return False, f"第{i + 1}个语句中使用的变量'{used_var}'未在之前定义"

            for existing_var, existing_val in variables.items():
                var_value = re.sub(rf'\b{existing_var}\b', existing_val, var_value)

            variables[var_name_lower] = var_value

        final_stmt = statements[-1]

        if _top_level_equals_positions(final_stmt):
            return False, "最后一个语句不能是赋值语句"

        kw_names = _keyword_arg_names(final_stmt)
        used_vars = re.findall(r'\b[a-zA-Z_][a-zA-Z0-9_]*\b', final_stmt)
        for used_var in used_vars:
            used_var_lower = used_var.lower()
            if used_var_lower in kw_names:
                continue
            if used_var_lower not in variables:
                if used_var not in supported_functions:
                    if len(used_var) <= 2:
                        return False, f"最后一个语句中使用的变量'{used_var}'未在之前定义"

                    if self._is_valid_field(used_var) or used_var_lower in valid_categories or used_var_lower in group_fields:
                        continue

                    return False, f"最后一个语句中使用的变量'{used_var}'未在之前定义"

        final_expr = final_stmt
        for var_name, var_value in variables.items():
            final_expr = re.sub(rf'\b{var_name}\b', var_value, final_expr)

        return True, final_expr

    def check_expression(self, expression: str) -> Dict[str, Any]:
        """检查表达式格式是否正确"""
        self.errors = []
        self._unit_cache = {}
        self._derived_category_cache = {}

        try:
            expression = expression.strip()
            if not expression:
                return {
                    'valid': False,
                    'errors': ['表达式不能为空'],
                    'tokens': [],
                    'ast': None
                }

            if ';' in expression:
                success, result = self._process_semicolon_expression(expression)
                if not success:
                    return {
                        'valid': False,
                        'errors': [result],
                        'tokens': [],
                        'ast': None
                    }
                expression = result

            self.lexer.lineno = 1

            self.lexer.input(expression)
            tokens = []
            for token in self.lexer:
                tokens.append(token)

            self.lexer.input(expression)
            self.lexer.lineno = 1

            ast = self.parser.parse(expression, lexer=self.lexer)

            validation_errors = self.validate_ast(ast)

            all_errors = self.errors + validation_errors

            bracket_count = 0
            for char in expression:
                if char == '(':
                    bracket_count += 1
                elif char == ')':
                    bracket_count -= 1
                if bracket_count < 0:
                    all_errors.append("括号不匹配: 右括号过多")
                    break
            if bracket_count > 0:
                all_errors.append("括号不匹配: 左括号过多")

            return {
                'valid': len(all_errors) == 0,
                'errors': all_errors,
                'tokens': tokens,
                'ast': ast
            }
        except Exception as e:
            return {
                'valid': False,
                'errors': [f"解析错误: {str(e)}"],
                'tokens': [],
                'ast': None
            }

    def validate_expressions(self, expressions: List[str]) -> Tuple[List[str], List[Dict]]:
        """批量验证表达式"""
        valid_expressions = []
        invalid_details = []

        for expr in expressions:
            if not isinstance(expr, str) or not expr.strip():
                invalid_details.append({
                    'expression': expr,
                    'error': '空表达式'
                })
                continue

            result = self.check_expression(expr.strip())
            if result['valid']:
                valid_expressions.append(expr.strip())
            else:
                invalid_details.append({
                    'expression': expr,
                    'errors': result['errors']
                })

        return valid_expressions, invalid_details


# ==================== 主处理流程 ====================

def process(data_sets_list: List[Dict], llm_template: str) -> Dict:
    """
    处理 LLM 生成的模板，生成 Alpha 表达式

    参数:
        data_sets_list: 数据集字段列表，格式 [{'id': 'field_name'}, ...]
        llm_template: LLM 生成的 Markdown 文本

    返回:
        包含生成结果的字典
    """
    # 提取 field_ids
    field_ids = [item['id'] for item in data_sets_list]

    # 提取 dataset_ids 和 allowed_suffixes
    dataset_ids = field_ids
    allowed_suffixes = build_allowed_metric_suffixes(field_ids)
    dataset_code = detect_dataset_code(dataset_ids)

    print(f"   数据集字段数: {len(dataset_ids)}")
    print(f"   提取的后缀数: {len(allowed_suffixes)}")
    print(f"   数据集代码: {dataset_code}")

    # Step 1: 提取 template blocks
    print("\n🔍 提取 Template Blocks...")
    block_pairs = extract_template_blocks(llm_template)
    print(f"   提取到 {len(block_pairs)} 个 Concept blocks")

    if not block_pairs:
        print("⚠️ 没有找到有效的 Concept blocks")
        return {
            "success": False,
            "error": "没有找到有效的 Concept blocks",
            "expressions": [],
            "templates": []
        }

    # Step 2: 规范化 templates
    print("\n📝 规范化 Templates...")
    normalized_pairs = []
    for item in block_pairs:
        template = str(item.get("template") or "").strip()
        idea_text = str(item.get("idea") or "").strip()

        if not template:
            continue

        normalized_t, ok = normalize_template_placeholders(
            template, dataset_ids, allowed_suffixes, dataset_code
        )

        # 允许没有 {placeholder} 的模板通过
        # if ok:
        normalized_pairs.append({
            "template": normalized_t,
            "idea": idea_text,
            "original_template": template
        })
        print(f"   ✓ {normalized_t[:60]}...")
        # else:
        #     print(f"   ✗ 跳过: {template[:60]}...")

    print(f"   规范化成功: {len(normalized_pairs)} 个")

    # Step 3: 生成表达式
    print("\n🎯 生成 Alpha 表达式...")
    all_expressions = []
    template_results = []

    for idx, pair in enumerate(normalized_pairs, 1):
        template = pair["template"]
        expressions = match_single_horizon_auto(field_ids, template)

        # 如果没有生成表达式（没有{placeholder}），直接把模板当作表达式
        if not expressions and template:
            expressions = [template]

        if expressions:
            result_item = {
                "template": template,
                "original_template": pair.get("original_template", template),
                "idea": pair.get("idea", ""),
                "expression_count": len(expressions),
                "expressions": expressions
            }
            template_results.append(result_item)
            all_expressions.extend(expressions)

            print(f"   Idea {idx}: {len(expressions):4d} 个表达式 - {template[:50]}...")

    print(f"\n   总计生成: {len(all_expressions)} 个表达式")

    # Step 4: 验证表达式
    print("\n🔍 验证表达式...")
    validator = ExpressionValidator()
    valid_expressions, invalid_details = validator.validate_expressions(all_expressions)
    invalid_count = len(invalid_details)

    print(f"   有效: {len(valid_expressions)}")
    print(f"   无效: {invalid_count}")

    if invalid_details:
        print(f"   无效表达式详情:")
        for detail in invalid_details[:5]:
            print(f"     - {detail['expression'][:50]}...: {detail.get('errors', detail.get('error', '未知错误'))}")
        if len(invalid_details) > 5:
            print(f"     ... 还有 {len(invalid_details) - 5} 个无效表达式")

    # Step 5: 去重
    print("\n🧹 去重...")
    unique_expressions = []
    seen = set()
    for expr in valid_expressions:
        if expr not in seen:
            unique_expressions.append(expr)
            seen.add(expr)

    print(f"   去重后: {len(unique_expressions)} 个")

    # Step 6: 过滤包含英文逗号的表达式和模板 过滤表达式：只保留包含英文逗号的
    print("\n🧹 过滤包含英文逗号的表达式和模板...")
    filtered_expressions = [expr for expr in unique_expressions if ',' in expr]
    print(f"   过滤后表达式: {len(filtered_expressions)} 个 (原 {len(unique_expressions)} 个)")

    # 过滤模板：只保留包含英文逗号的模板，且其表达式也要包含英文逗号
    filtered_template_results = []
    for template_item in template_results:
        template = template_item.get("template", "")
        # 检查模板是否包含英文逗号
        if ',' not in template:
            continue
        # 过滤表达式：只保留包含英文逗号的
        filtered_exprs = [expr for expr in template_item.get("expressions", []) if ',' in expr]
        if filtered_exprs:
            filtered_item = template_item.copy()
            filtered_item["expressions"] = filtered_exprs
            filtered_item["expression_count"] = len(filtered_exprs)
            filtered_template_results.append(filtered_item)

    print(f"   过滤后模板: {len(filtered_template_results)} 个 (原 {len(template_results)} 个)")

    print("\n✅ 处理完成!")

    # 打印统计
    print("\n" + "=" * 50)
    print("处理统计:")
    print("=" * 50)
    summary = {
        "total_templates": len(block_pairs),
        "normalized_templates": len(normalized_pairs),
        "total_expressions": len(all_expressions),
        "valid_expressions": len(valid_expressions),
        "unique_expressions": len(unique_expressions),
        "invalid_count": invalid_count
    }
    for key, value in summary.items():
        print(f"  {key}: {value}")

    # 返回结果字典
    return {
        "success": True,
        "summary": summary,
        "templates": filtered_template_results,
        "expressions": filtered_expressions,
        "invalid_details": invalid_details
    }