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diff --git a/utils/mpdocbench_patch/dataset/end2end_dataset.py b/utils/mpdocbench_patch/dataset/end2end_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..24c54546e8dbfcde73a2fe039414433634b8c347
--- /dev/null
+++ b/utils/mpdocbench_patch/dataset/end2end_dataset.py
@@ -0,0 +1,932 @@
+import json
+import os
+from collections import defaultdict
+from utils.extract import md_tex_filter, extract_figure_boxes
+from utils.match import match_gt2pred_simple, match_gt2pred_no_split
+from utils.match_quick import match_gt2pred_quick
+from utils.merge_tables import merge_tables
+from utils.relation_utils import (
+    extract_table_truncated_info, detect_pred_merges, compute_relation_f1,
+    split_pairs_by_page, _norm_table,
+    extract_text_truncated_info, detect_pred_text_merges, _norm_text,
+)
+from utils.read_files import read_md_file
+from utils.data_preprocess import normalized_table, clean_string
+from registry.registry import DATASET_REGISTRY
+from dataset.recog_dataset import *
+import Levenshtein
+from tqdm import tqdm
+from func_timeout import FunctionTimedOut, func_timeout
+from loguru import logger
+import signal
+import time
+import sys
+import multiprocessing as mp
+import pickle
+from pylatexenc.latex2text import LatexNodes2Text
+import traceback
+
+# ── Env switches (acceleration only; metric computation is unchanged) ──
+# MPDOC_MATCH_TIMEOUT: seconds for the text-match func_timeout (default 30; on
+#   timeout it falls back to the coarser match_gt2pred_simple). Raise it so the
+#   quality matcher finishes on very long documents.
+# MPDOC_EVAL_NPROC: documents matched in parallel (default 1 = serial, identical
+#   to the original). Per-document matching is independent and order-preserving.
+# MPDOC_DOC_BUDGET: hard per-document wall-clock cap (seconds, default
+#   max(timeout*3, 600)); over-budget documents are skipped and reported at the end.
+def _match_timeout():
+    try:
+        v = int(os.environ.get('MPDOC_MATCH_TIMEOUT', '30') or 30)
+        return v if v > 0 else 30
+    except ValueError:
+        return 30
+
+
+class _DocBudgetExceeded(BaseException):
+    """A single document's matching exceeded MPDOC_DOC_BUDGET.
+
+    Subclasses BaseException (not Exception) on purpose so it is not swallowed by
+    the broad `except Exception` fallbacks in the matching path; it signals a
+    control-flow abort, like KeyboardInterrupt / SystemExit.
+    """
+
+
+def _doc_budget():
+    raw = (os.environ.get('MPDOC_DOC_BUDGET', '') or '').strip()
+    if raw.isdigit():
+        return int(raw)
+    return max(_match_timeout() * 3, 600)
+
+
+def _run_with_budget(fn, *args):
+    """Run one document's matching in a child process; SIGKILL it past the budget.
+
+    An out-of-process hard kill does not rely on the callee's exception handling
+    and can interrupt C-extension calls that hold the GIL. Uses fork so `self`
+    need not be pickled.
+    """
+    budget = _doc_budget()
+    if budget <= 0:
+        return fn(*args)
+
+    ctx = mp.get_context('fork')
+    recv, send = ctx.Pipe(duplex=False)
+
+    def _child():
+        try:
+            payload = (True, fn(*args))
+        except BaseException as exc:  # 原样带回父进程重抛，语义与直接调用一致
+            payload = (False, exc)
+        try:
+            send.send_bytes(pickle.dumps(payload))
+        except Exception as exc:
+            # 结果不可序列化时至少让父进程拿到原因，而不是误判成超预算
+            send.send_bytes(pickle.dumps((False, exc)))
+
+    proc = ctx.Process(target=_child, daemon=True)
+    proc.start()
+    # 父进程不写：关掉自己这份写端，子进程异常退出时 poll 才会立刻返回而不是等满预算
+    send.close()
+    try:
+        if not recv.poll(budget):
+            proc.kill()
+            raise _DocBudgetExceeded()
+        try:
+            ok, payload = pickle.loads(recv.recv_bytes())
+        except EOFError:
+            # 子进程一个字节都没写就死了（如被 OOM killer 杀），按超预算处理
+            raise _DocBudgetExceeded()
+        if not ok:
+            raise payload
+        return payload
+    finally:
+        proc.join()
+        recv.close()
+
+
+def _mp_worker(payload):
+    ds, sample, pred_folder, save_time = payload
+    img_name = os.path.basename(sample['page_info']['image_path'])
+    try:
+        return _run_with_budget(ds._process_one_doc, sample, pred_folder, save_time)
+    except _DocBudgetExceeded:
+        print(f'[MPDocBench] 单篇匹配超预算 {_doc_budget()}s，整篇跳过: {img_name}', flush=True)
+        # 用独立标记而不是 None：None 已被"无预测文件"占用，混在一起就统计不出来
+        return {'img_name': img_name, 'pred_name': None, 'main': None, '_budget_exceeded': True}
+
+
+def remove_consecutive_duplicates(arr):
+    """
+    删除数组中连续的重复值，保留非连续的重复值
+    
+    Args:
+        arr: 输入的数组/列表
+        
+    Returns:
+        list: 删除连续重复值后的新列表
+    """
+    if not arr:
+        return []
+    
+    result = [arr[0]]  # 第一个元素总是保留
+    
+    for i in range(1, len(arr)):
+        if arr[i] != arr[i-1]:  # 只有当前元素与前一个不同时才添加
+            result.append(arr[i])
+    
+    return result
+
+
+def compute_iou(box_a, box_b):
+    """计算两个 [x1, y1, x2, y2] bbox 的 IoU。"""
+    x_left = max(box_a[0], box_b[0])
+    y_top = max(box_a[1], box_b[1])
+    x_right = min(box_a[2], box_b[2])
+    y_bottom = min(box_a[3], box_b[3])
+
+    intersection = max(0, x_right - x_left) * max(0, y_bottom - y_top)
+    area_a = (box_a[2] - box_a[0]) * (box_a[3] - box_a[1])
+    area_b = (box_b[2] - box_b[0]) * (box_b[3] - box_b[1])
+    union = area_a + area_b - intersection
+
+    return intersection / union if union > 0 else 0
+
+
+@DATASET_REGISTRY.register("end2end_dataset")
+class End2EndDataset():
+    def __init__(self, cfg_task):
+        gt_path = cfg_task['dataset']['ground_truth']['data_path']
+        pred_folder = cfg_task['dataset']['prediction']['data_path']
+        self.match_method = cfg_task['dataset'].get('match_method', 'quick_match')
+        filtered_types = cfg_task['dataset'].get('filter')
+        self.required_metrics = set(cfg_task.get('metrics', {}).keys())
+
+        with open(gt_path, 'r') as f:
+            gt_samples = json.load(f)
+
+        filtered_gt_samples = []
+        if filtered_types:
+            for gt_sample in gt_samples:
+                select_flag = True
+                for k, v in filtered_types.items():
+                    if gt_sample["page_info"]["page_attribute"][k] != v:
+                        select_flag = False
+                if select_flag:
+                    filtered_gt_samples.append(gt_sample)
+        else:
+            filtered_gt_samples = gt_samples
+
+        self.samples = self.get_matched_elements(filtered_gt_samples, pred_folder)
+        
+    def __getitem__(self, cat_name, idx):
+        return self.samples[cat_name][idx]
+    
+    # 处理json中标题中的父亲，儿子关系
+    def get_document_heads(self, selected_annos):
+        parent_son_list, all_nodes, head_nodes = [], {}, {}
+        for item in selected_annos['layout_dets']:
+            if item["category_type"] == "title":
+                head_nodes[item["anno_id"]] = item
+            all_nodes[item["anno_id"]] = item
+
+        for relation in selected_annos["extra"]["relation"]:   # Handle truncated text issues
+            if relation["relation_type"] == 'parent_son':
+                source_anno_id = relation["source_anno_id"]
+                target_anno_id = relation["target_anno_id"]
+                if (source_anno_id in head_nodes or source_anno_id == "root") and target_anno_id in head_nodes:
+                    parent_son_list.append(relation)
+        return parent_son_list, head_nodes
+
+    # 匹配元素 处理文本截断问题，将截断的文本块合并，并将元素按类别存储在字典中
+    def get_page_elements(self, selected_annos):
+
+        saved_element_dict = defaultdict(list) #存储元素
+        related_truncated = [] #存储需要合并的截断文本块列表
+        truncated_all = {} #存储截断文本块信息
+        for relation in selected_annos["extra"]["relation"]:   # Handle truncated text issues
+            if relation["relation_type"] == 'truncated':
+                truncated_all[relation["source_anno_id"]] = ""
+                truncated_all[relation["target_anno_id"]] = ""
+                exist_flag = False
+                for merge_list in related_truncated:
+                    if relation["source_anno_id"] in merge_list or relation["target_anno_id"] in merge_list:  # Consider cases where three text blocks may need to be merged
+                        merge_list.append(relation["source_anno_id"])
+                        merge_list.append(relation["target_anno_id"])
+                        exist_flag = True
+                if not exist_flag:
+                    related_truncated.append([relation["source_anno_id"], relation["target_anno_id"]])       
+        
+        for item in selected_annos['layout_dets']:
+            if item['anno_id'] not in truncated_all.keys():
+                saved_element_dict[item["category_type"]].append(item)
+            else:
+                truncated_all[item['anno_id']] = item
+        
+        for merge_list in related_truncated:
+            merge_list = remove_consecutive_duplicates(merge_list)
+            text_block_list = [truncated_all[key] for key in merge_list]
+            # order 字段在本批 GT 里为 None：裸键 sorted(None,None) 会触发 '<' not supported
+            # (CPython 序比较算子不走身份短路)。与 get_sorted_text_list 同口径把 None/空强转 0，
+            # 既兼容官方 int-order 数据，又让 None-order 切片在回填 truncated 边后可排序。
+            sorted_block = sorted(text_block_list, key=lambda x: (x.get('order') or 0))
+            page_ids = set([block['page_id'] for block in sorted_block])
+           
+            if sorted_block[0]['category_type'] == 'table':  # merge tabel blocks but keep category as text_block
+                if sorted_block[0].get("merged_html") is not None:
+                    html = sorted_block[0]["merged_html"]
+                else:
+                    print(f'{selected_annos["page_info"]["image_path"]} has a merged table group {"_".join([str(block["anno_id"]) for block in sorted_block])} with no merged_html annotation.')
+                    html = merge_tables([block['html'] for block in sorted_block])
+                merged_block = {
+                    "category_type": sorted_block[0]["category_type"], # Directly use information from the first block
+                    "order": sorted_block[0]["order"],
+                    "anno_id": sorted_block[0]["anno_id"],   
+                    "html": html,
+                    "merge_list": sorted_block,
+                    "is_merged": "table",
+                    "attribute": {"table_merge_cross_pages": str(len(page_ids))}
+                }
+            else:  # merge text blocks
+                text = ""
+                try:
+                    for block in sorted_block:
+                        text += block.get('text', "")
+                except Exception as e:
+                    print(sorted_block, selected_annos["page_info"])
+                merged_block = {
+                    "category_type": sorted_block[0]["category_type"], # Directly use information from the first block
+                    "order": sorted_block[0]["order"],
+                    "anno_id": sorted_block[0]["anno_id"],   
+                    "text": text,
+                    "merge_list": sorted_block,
+                # 2026-09-23: T-TextEdit(merged_text_block)从"仅 text_block"放宽到
+                # TextEdit 计分文本集，让文字类跨页(title/reference/code_txt 及经 RAW2NATIVE
+                # 归到 text_block 的 aside_text 等)都进跨页拼接评测。标记值仍用 'text_block'
+                # 以兼容下游 merged_gt_mix 的 is_merged=='text_block' 过滤。
+                "is_merged": "text_block" if sorted_block[0]["category_type"] in (
+                    "text_block", "title", "reference", "code_txt", "code_txt_caption") else "unknown",
+                }
+                if merged_block.get("is_merged") == "text_block":
+                    merged_block["attribute"] = {"text_merge_cross_pages": str(len(page_ids))}
+            
+            saved_element_dict[sorted_block[0]["category_type"]].append(merged_block)
+
+        return saved_element_dict
+    
+    # 根据类别列表 category_list 从 gt_page_elements 中提取元素，并将它们合并到一个列表中
+    def get_page_elements_list(self, gt_page_elements, category_list):
+        element_list = []
+        for category_type in category_list:
+            if gt_page_elements.get(category_type):
+                element_list.extend(gt_page_elements[category_type])
+        return element_list
+
+    # 根据元素的 order 字段对元素列表进行排序，并返回排序后的元素列表。
+    def get_sorted_text_list(self, selected_annos):
+        # txt_type: text, latex, html
+        text_list = []
+        for item in selected_annos:
+            if item.get('order'):
+                order = item['order']
+            else:
+                order = 0
+            text_list.append((order, item))
+        sorted_text_list = sorted(text_list, key=lambda x: x[0])
+        return [_[1] for _ in sorted_text_list]
+    
+    # 从元素列表 items 中过滤掉 gt_category_type 在 ignore_category_list 中的元素。
+    def filtered_out_ignore(self, items, ignore_category_list):
+        filted_items = []
+        for item in items:
+            if item['gt_category_type'] not in ignore_category_list:
+                filted_items.append(item)
+        return filted_items
+
+
+    def get_head_paired(self, parent_son_list, head_nodes, md_text_all_list, img_name):
+        md_text_all_list.sort(key=lambda x: x['position'][0])
+        md_head_list = []
+        for item in md_text_all_list:
+            if item['content'].strip().startswith('#'):
+                md_head_list.append(item['content'].strip())
+        return {
+            "gt": [parent_son_list, head_nodes],
+            "pred": md_head_list,
+            "img_id": img_name,
+            "edit": 1.0,
+        }
+
+    # 计算预测结果和地面真值的阅读顺序之间的编辑距离，并返回包含相关信息的字典。
+    def get_order_paired(self, order_match_s, img_name):
+        matched = [(item['gt_position'], item['pred_position']) for item in order_match_s if (item['gt_position'] != [""] and item['pred_position'] != "")]
+        gt_idx_all = [item['gt_position'] for item in order_match_s if (item['gt_position'] != [""])]
+        read_order_pred = [i[0] for i in sorted(matched, key=lambda x: x[1])]  # Sort by pred idx to get Pred ordered GT_idx
+        read_order_gt = sum(gt_idx_all, []) # Convert to one-dimensional list
+        read_order_gt = [x for x in read_order_gt if x]  # For truncated merges, some discarded classes may be merged in, remove them when calculating edit distance
+        gt = sorted(read_order_gt) # Sort by all GT idx to get GT ordered GT_idx
+        pred = sum(read_order_pred, [])
+        pred = [x for x in pred if x]
+        if len(pred) > 0 or len(gt) > 0:
+            edit = Levenshtein.distance(gt, pred)/ max(len(pred), len(gt))
+            return {
+                'gt': gt,  
+                'pred': pred,
+                'img_id': img_name,
+                'edit': edit
+            }
+        else:
+            return {}  # If both GT and pred are empty for the page, return empty
+
+    # 为公式匹配结果添加 img_id 信息。
+    def formula_format(self, formula_matches, img_name):
+        # formated_list = []
+        for i, item in enumerate(formula_matches):
+            item["img_id"] = img_name + '_' + str(i)
+        return formula_matches
+
+    def get_figure_matched(self, sample, pred_figures, img_name, iou_threshold=0.5):
+        """对单个文档进行 GT 与 Pred 的 figure IoU 匹配，计算召回。
+
+        Args:
+            sample: GT 文档数据（包含 layout_dets, page_info）
+            pred_figures: extract_figure_boxes() 返回的预测图片列表
+            img_name: 文档图片名
+            iou_threshold: IoU 匹配阈值
+
+        Returns:
+            匹配结果 dict，若无 GT figure 则返回 None
+        """
+        # 提取 GT figures
+        gt_figures = []
+        page_info = sample.get('page_info', {})
+        widths = page_info.get('width', [])
+        heights = page_info.get('height', [])
+
+        for det in sample.get('layout_dets', []):
+            if det.get('category_type') != 'figure':
+                continue
+            page_id = det.get('page_id')
+            poly = det.get('poly', [])
+            if page_id is None or len(poly) < 6:
+                continue
+
+            # 检查是否有该页的尺寸信息
+            page_idx = page_id - 1
+            if page_idx < 0 or page_idx >= len(widths) or page_idx >= len(heights):
+                continue
+            pw, ph = widths[page_idx], heights[page_idx]
+            if not pw or not ph:
+                continue
+
+            # poly 格式: [x1,y1, x2,y1, x2,y2, x1,y2] → 归一化到 0-1000
+            norm_x1 = poly[0] / pw * 1000
+            norm_y1 = poly[1] / ph * 1000
+            norm_x2 = poly[4] / pw * 1000
+            norm_y2 = poly[5] / ph * 1000
+            gt_figures.append({
+                "page_id": page_id,
+                "bbox_norm": [norm_x1, norm_y1, norm_x2, norm_y2],
+                "label": det.get('label', 'figure')
+            })
+
+        if not gt_figures:
+            return None
+
+        # 按 page_id 分组
+        gt_by_page = defaultdict(list)
+        for i, fig in enumerate(gt_figures):
+            gt_by_page[fig['page_id']].append((i, fig['bbox_norm']))
+
+        pred_by_page = defaultdict(list)
+        for j, fig in enumerate(pred_figures):
+            pred_by_page[fig['page_id']].append((j, fig['bbox_norm']))
+
+        # 贪心匹配：收集所有 (gt_idx, pred_idx, iou)，按 iou 降序，逐一匹配
+        all_pairs = []
+        for page_id in gt_by_page:
+            for gi, g_box in gt_by_page[page_id]:
+                for pi, p_box in pred_by_page.get(page_id, []):
+                    iou = compute_iou(g_box, p_box)
+                    if iou >= iou_threshold:
+                        all_pairs.append((gi, pi, iou))
+
+        all_pairs.sort(key=lambda x: x[2], reverse=True)
+        matched_gt = set()
+        matched_pred = set()
+        matches = []
+        for gi, pi, iou in all_pairs:
+            if gi not in matched_gt and pi not in matched_pred:
+                matched_gt.add(gi)
+                matched_pred.add(pi)
+                matches.append({"gt_idx": gi, "pred_idx": pi, "iou": iou})
+
+        gt_count = len(gt_figures)
+        matched_count = len(matches)
+
+        # 按 figure label 统计 gt_count 和 matched_count
+        label_stats = defaultdict(lambda: {'gt_count': 0, 'matched_count': 0})
+        for i, fig in enumerate(gt_figures):
+            label = fig.get('label', 'figure')
+            label_stats[label]['gt_count'] += 1
+            if i in matched_gt:
+                label_stats[label]['matched_count'] += 1
+
+        return {
+            "img_id": img_name,
+            "gt_count": gt_count,
+            "pred_count": len(pred_figures),
+            "matched_count": matched_count,
+            "gt": "",
+            "pred": "",
+            "gt_attribute": [page_info.get('page_attribute', {})],
+            "metric": {},
+            "label_stats": dict(label_stats),
+        }
+
+    def get_table_relation_matched(self, sample, pred_tables_html, img_name, match_threshold=0.15):
+        """计算单个文档的表格 truncated 关系 F1 数据。"""
+        # 1. 提取 GT truncated pairs
+        gt_pairs, groups, anno_map = extract_table_truncated_info(sample)
+
+        # 2. 获取全量 GT 表格，按文档顺序排列
+        all_gt_tables = [
+            item for item in sample["layout_dets"]
+            if item.get("category_type") == "table"
+        ]
+        all_gt_tables.sort(
+            key=lambda x: (x.get("page_id", 0), x.get("order", float("inf")))
+        )
+
+        # 3. 如果 GT 无表格或双方都没有 pair 潜力 -> 跳过
+        if not all_gt_tables or (not gt_pairs and not pred_tables_html):
+            return None
+
+        # 4. 归一化 pred 表格
+        pred_norm = [_norm_table(pt["content"]) for pt in pred_tables_html]
+
+        # 5. Pred->GT 跨度搜索
+        pred_pairs, pred_match_detail = detect_pred_merges(
+            all_gt_tables, pred_norm, match_threshold=match_threshold
+        )
+
+        # 6. 如果双方都没有合并对 -> 跳过
+        if not gt_pairs and not pred_pairs:
+            return None
+
+        # 7. 计算 F1
+        metrics = compute_relation_f1(gt_pairs, pred_pairs)
+
+        # 8. 按同页/跨页拆分
+        gt_same, gt_cross = split_pairs_by_page(gt_pairs, anno_map)
+        pred_same, pred_cross = split_pairs_by_page(pred_pairs, anno_map)
+        metrics_same = compute_relation_f1(gt_same, pred_same)
+        metrics_cross = compute_relation_f1(gt_cross, pred_cross)
+
+        page_info = sample.get("page_info", {})
+        return {
+            "img_id": img_name,
+            "gt_pairs_count": len(gt_pairs),
+            "pred_pairs_count": len(pred_pairs),
+            "gt_tables_count": len(all_gt_tables),
+            "pred_tables_count": len(pred_tables_html),
+            "tp": metrics["tp"],
+            "fp": metrics["fp"],
+            "fn": metrics["fn"],
+            # 同页分项
+            "same_page_tp": metrics_same["tp"],
+            "same_page_fp": metrics_same["fp"],
+            "same_page_fn": metrics_same["fn"],
+            # 跨页分项
+            "cross_page_tp": metrics_cross["tp"],
+            "cross_page_fp": metrics_cross["fp"],
+            "cross_page_fn": metrics_cross["fn"],
+            "gt": "",
+            "pred": "",
+            "gt_attribute": [page_info.get("page_attribute", {})],
+            "metric": {},
+        }
+
+    def get_text_relation_matched(self, sample, pred_text_blocks, img_name, match_threshold=0.15):
+        """计算单个文档的段落 truncated 关系 F1 数据。"""
+        # 1. 提取 GT truncated pairs
+        gt_pairs, groups, anno_map = extract_text_truncated_info(sample)
+
+        # 2. 获取全量 GT text_block，按文档顺序排列
+        all_gt_texts = [
+            item for item in sample["layout_dets"]
+            if item.get("category_type") == "text_block"
+        ]
+        all_gt_texts.sort(
+            key=lambda x: (x.get("page_id", 0), x.get("order", float("inf")))
+        )
+
+        # 3. 如果 GT 无文本块或双方都没有 pair 潜力 -> 跳过
+        if not all_gt_texts or (not gt_pairs and not pred_text_blocks):
+            return None
+
+        # 4. 归一化 pred 文本块
+        pred_norm = [_norm_text(pt["content"]) for pt in pred_text_blocks]
+
+        # 5. Pred->GT 跨度搜索
+        pred_pairs, pred_match_detail = detect_pred_text_merges(
+            all_gt_texts, pred_norm, match_threshold=match_threshold
+        )
+
+        # 6. 如果双方都没有合并对 -> 跳过
+        if not gt_pairs and not pred_pairs:
+            return None
+
+        # 7. 计算 F1
+        metrics = compute_relation_f1(gt_pairs, pred_pairs)
+
+        # 8. 按同页/跨页拆分
+        gt_same, gt_cross = split_pairs_by_page(gt_pairs, anno_map)
+        pred_same, pred_cross = split_pairs_by_page(pred_pairs, anno_map)
+        metrics_same = compute_relation_f1(gt_same, pred_same)
+        metrics_cross = compute_relation_f1(gt_cross, pred_cross)
+
+        page_info = sample.get("page_info", {})
+        return {
+            "img_id": img_name,
+            "gt_pairs_count": len(gt_pairs),
+            "pred_pairs_count": len(pred_pairs),
+            "gt_texts_count": len(all_gt_texts),
+            "pred_texts_count": len(pred_text_blocks),
+            "tp": metrics["tp"],
+            "fp": metrics["fp"],
+            "fn": metrics["fn"],
+            # 同页分项
+            "same_page_tp": metrics_same["tp"],
+            "same_page_fp": metrics_same["fp"],
+            "same_page_fn": metrics_same["fn"],
+            # 跨页分项
+            "cross_page_tp": metrics_cross["tp"],
+            "cross_page_fp": metrics_cross["fp"],
+            "cross_page_fn": metrics_cross["fn"],
+            "gt": "",
+            "pred": "",
+            "gt_attribute": [page_info.get("page_attribute", {})],
+            "metric": {},
+        }
+
+    # 对gt和预测结果进行匹配，调用 process_get_matched_elements 函数进行匹配处理，最终将匹配结果整理成一个字典返回
+    def _process_one_doc(self, sample, pred_folder, save_time=0):
+        """Match one document; shared by the serial and parallel paths
+        (returns None when there is no prediction file). Only difference from the
+        original inline version: table_relation / text_relation are computed only
+        when those metrics are actually requested."""
+        img_name = os.path.basename(sample["page_info"]["image_path"])
+        pred_path = os.path.join(pred_folder, img_name[:-4] + '.md')
+        if not os.path.exists(pred_path):
+            pred_path = os.path.join(pred_folder, img_name[:-4].replace('.pdf', "") + '.mmd')  # nougat
+            if not os.path.exists(pred_path):
+                pred_path = os.path.join(pred_folder, img_name[:-4].replace('.pdf', "") + '.md')  # marker
+                if not os.path.exists(pred_path):
+                    pred_path = os.path.join(pred_folder, img_name + '.md')
+                    if not os.path.exists(pred_path):  # mineru
+                        print(f'!!!WARNING: No prediction for {img_name}')
+                        return None
+
+        pred_content = read_md_file(pred_path)
+        need = self.required_metrics
+        out = {'img_name': img_name, 'pred_name': os.path.basename(pred_path), 'main': None}
+
+        # figure 匹配（在 md_tex_filter 之前提取，因为 md_tex_filter 会去掉图片引用）
+        if not need or 'figure' in need:
+            out['figure'] = self.get_figure_matched(sample, extract_figure_boxes(pred_content), img_name)
+
+        # relation 匹配（检测 pred 是否正确合并了 truncated 表格/段落）—— 按需计算
+        if not need or ({'table_relation', 'text_relation'} & set(need)):
+            pred_dataset_for_relation = md_tex_filter(pred_content)
+            if not need or 'table_relation' in need:
+                out['table_relation'] = self.get_table_relation_matched(
+                    sample, list(pred_dataset_for_relation.get("html_table", [])), img_name)
+            if not need or 'text_relation' in need:
+                out['text_relation'] = self.get_text_relation_matched(
+                    sample, list(pred_dataset_for_relation.get("text_all", [])), img_name)
+
+        # 如果只需要 figure / table_relation / text_relation 指标，跳过耗时的文本/表格/公式匹配
+        if need and need <= {'figure', 'table_relation', 'text_relation'}:
+            return out
+
+        out['main'] = self.process_get_matched_elements(sample, pred_content, img_name, save_time)
+        return out
+
+    def get_matched_elements(self, gt_samples, pred_folder):
+        plain_text_match = []
+        merged_plain_text_match = []
+        display_formula_match = []
+        html_table_match = []
+        merged_html_table_match = []
+        latex_table_match = []
+        merged_latex_table_match = []
+        order_match = []
+        head_match = []
+        figure_match = []
+        table_relation_match = []
+        text_relation_match = []
+       
+        save_time = time.time()
+        nproc = int(os.environ.get('MPDOC_EVAL_NPROC', '1') or 1)
+        if nproc > 1:
+            from concurrent.futures import ProcessPoolExecutor, as_completed
+            print(f'[MPDocBench] 按文档并行匹配: MPDOC_EVAL_NPROC={nproc}')
+            # 原来用 ex.map：它按提交顺序产出，进度条会被最慢的一篇顶住不动
+            # （现场出现过 63/100 停两小时，其后已算完的都不显示，外部误判成卡死）。
+            # 改成 submit + as_completed 实时推进度，再按下标回填，
+            # 结果顺序与 map 完全一致，指标逐位可复现。
+            doc_results = [None] * len(gt_samples)
+            with ProcessPoolExecutor(max_workers=nproc) as ex:
+                future_to_idx = {
+                    ex.submit(_mp_worker, (self, s, pred_folder, save_time)): i
+                    for i, s in enumerate(gt_samples)
+                }
+                with tqdm(total=len(gt_samples), ascii=True, ncols=140) as bar:
+                    for fut in as_completed(future_to_idx):
+                        doc_results[future_to_idx[fut]] = fut.result()
+                        bar.update(1)
+        else:
+            doc_results = []
+            process_bar = tqdm(gt_samples, ascii=True, ncols=140)
+            for sample in process_bar:
+                res = _mp_worker((self, sample, pred_folder, save_time))
+                if res and not res.get('_budget_exceeded'):
+                    process_bar.set_description(f"Processing {res['pred_name']}")
+                doc_results.append(res)
+
+        budget_skipped = [r['img_name'] for r in doc_results if r and r.get('_budget_exceeded')]
+        if budget_skipped:
+            print(f'[MPDocBench] 警告: {len(budget_skipped)} 篇文档超出单篇预算 '
+                  f'{_doc_budget()}s 被整篇跳过，其所有 element 均不计分，'
+                  f'本轮总分与历史跑分不可比：', flush=True)
+            for name in budget_skipped:
+                print(f'  - {name}', flush=True)
+
+        for res in doc_results:
+            if not res:
+                continue
+            if res.get('_budget_exceeded'):
+                continue
+            if res.get('figure'):
+                figure_match.append(res['figure'])
+            if res.get('table_relation'):
+                table_relation_match.append(res['table_relation'])
+            if res.get('text_relation'):
+                text_relation_match.append(res['text_relation'])
+            if res.get('main') is None:
+                continue
+
+            [plain_text_match_clean, merged_plain_text_match_clean, formated_display_formula, \
+                latex_table_match_s, merged_latex_table_match_s, html_table_match_s, merged_html_table_match_s, \
+                order_match_single, head_match_single] = res['main']
+
+            if order_match_single:
+                order_match.append(order_match_single)
+            if head_match_single:
+                head_match.append(head_match_single)
+            if plain_text_match_clean:
+                plain_text_match.extend(plain_text_match_clean)
+            if formated_display_formula:
+                display_formula_match.extend(formated_display_formula)
+            if latex_table_match_s:
+                latex_table_match.extend(latex_table_match_s)
+            if html_table_match_s:
+                html_table_match.extend(html_table_match_s)
+            if merged_html_table_match_s:
+                merged_html_table_match.extend(merged_html_table_match_s)
+            if merged_plain_text_match_clean:
+                merged_plain_text_match.extend(merged_plain_text_match_clean)
+            if merged_latex_table_match_s:
+                merged_latex_table_match.extend(merged_latex_table_match_s)
+
+        # 如果只需要 figure / table_relation / text_relation 指标，直接返回结果，跳过后续文本/表格后处理
+        if self.required_metrics and self.required_metrics <= {'figure', 'table_relation', 'text_relation'}:
+            matched_samples_all = {
+                'figure': DATASET_REGISTRY.get('recogition_end2end_base_dataset')(figure_match),
+                'table_relation': DATASET_REGISTRY.get('recogition_end2end_base_dataset')(table_relation_match),
+                'text_relation': DATASET_REGISTRY.get('recogition_end2end_base_dataset')(text_relation_match),
+            }
+            return matched_samples_all
+
+        display_formula_match_clean,display_formula_match_others = [],[]
+        for item in display_formula_match:
+            pred_category_type = item.get("pred_category_type",None)
+            # if pred_category_type == 'equation_inline':
+            if pred_category_type not in ['equation_inline','equation_isolated', '']:
+                gt = item.get('gt',None)
+                norm_gt = item.get('norm_gt',None)
+                ## latex2unicode
+                # pylatexenc 对含空矩阵环境(\begin{matrix}\end{matrix} 无行)等病态 latex 会抛
+                # ValueError("max() arg is an empty sequence"), 单条公式即可中断整轮评分。
+                # 这里捕获后回退原始文本, 仅该条指标受影响而非全量崩。
+                try:
+                    item['gt'] = LatexNodes2Text().latex_to_text(gt)
+                except Exception:
+                    item['gt'] = gt if gt is not None else ''
+                # item['norm_gt'] = LatexNodes2Text().latex_to_text(norm_gt)  # 错了，这里的norm gt是跑的normalized_formula函数，所以再跑latex2unicode会报错
+                # 这里的norm_gt应该是跑文本的nrom了
+                item['norm_gt'] = clean_string(item['gt'])
+                display_formula_match_others.append(item)
+            else:
+                display_formula_match_clean.append(item)
+        display_formula_match = display_formula_match_clean
+        if display_formula_match_others and plain_text_match:
+            plain_text_match.extend(display_formula_match_others)
+            
+        #  将latex合并到html 全量428
+        if latex_table_match:
+            latex_to_html = []
+            for latex_table in latex_table_match:
+                for k,v in latex_table.items():
+                    if 'pred' in k:
+                        latex_table[k] = ""
+                latex_table['edit'] = 1
+                latex_to_html.append(latex_table)
+            html_table_match.extend(latex_to_html)
+        
+        if merged_latex_table_match:
+            merged_latex_to_html = []
+            for latex_table in merged_latex_table_match:
+                for k,v in latex_table.items():
+                    if 'pred' in k:
+                        latex_table[k] = ""
+                latex_table['edit'] = 1
+                merged_latex_to_html.append(latex_table)
+            merged_html_table_match.extend(merged_latex_to_html)
+        
+        
+        if len(latex_table_match) > len(html_table_match): # Assume model won't randomly output both latex and html, but will choose one
+            table_match = latex_table_match
+            merged_table_match = merged_latex_table_match
+            table_format = 'latex'
+        else:
+            table_match = html_table_match
+            merged_table_match = merged_html_table_match
+            table_format = 'html'
+            
+        # with open('./qwen_latex_table_match.json','w',encoding='utf-8') as f:
+        #     json.dump(latex_table_match,f,indent=4,ensure_ascii=False)
+        # with open('./qwen_html_table_match.json','w',encoding='utf-8') as f:
+        #     json.dump(html_table_match,f,indent=4,ensure_ascii=False)
+
+
+        matched_samples_all = {
+            'text_block': DATASET_REGISTRY.get('recogition_end2end_base_dataset')(plain_text_match),
+            "merged_text_block": DATASET_REGISTRY.get('recogition_end2end_base_dataset')(merged_plain_text_match),
+            'display_formula':  DATASET_REGISTRY.get('recogition_end2end_base_dataset')(display_formula_match), 
+            'table': DATASET_REGISTRY.get('recogition_end2end_table_dataset')(table_match, table_format),
+            'merged_table': DATASET_REGISTRY.get('recogition_end2end_table_dataset')(merged_table_match, table_format),
+            'reading_order': DATASET_REGISTRY.get('recogition_end2end_base_dataset')(order_match),
+            "head": DATASET_REGISTRY.get('recogition_end2end_base_dataset')(head_match),
+            'figure': DATASET_REGISTRY.get('recogition_end2end_base_dataset')(figure_match),
+            'table_relation': DATASET_REGISTRY.get('recogition_end2end_base_dataset')(table_relation_match),
+            'text_relation': DATASET_REGISTRY.get('recogition_end2end_base_dataset')(text_relation_match),
+        }
+    
+        return matched_samples_all
+
+    
+    
+    #0403 提取gt的table跟pred的table进行匹配 -> 未匹配上的pred_table 去掉html格式然后丢进去混合匹配
+    def process_get_matched_elements(self, sample, pred_content, img_name, save_time):
+        if self.match_method == 'simple_match':   # add match choice
+            match_gt2pred = match_gt2pred_simple
+        elif self.match_method == 'quick_match':
+            match_gt2pred = match_gt2pred_quick
+        elif self.match_method == 'no_split':
+            match_gt2pred = match_gt2pred_no_split
+        else:
+            print('Invalid match method name. The quick_match will be used.')
+            match_gt2pred = match_gt2pred_quick
+
+      
+        pred_dataset = md_tex_filter(pred_content)
+        gt_page_elements = self.get_page_elements(sample)
+        parent_son_list, head_nodes = self.get_document_heads(sample)
+
+        gt_mix,pred_dataset_mix = [],[]
+        for category in pred_dataset:
+            if category not in ['html_table','latex_table','md2html_table']:
+                pred_dataset_mix.extend(pred_dataset[category])
+        # for category in gt_page_elements:
+        #     if category not in ['table']:
+        #         gt_mix.extend(gt_page_elements[category])
+        gt_mix = self.get_page_elements_list(gt_page_elements, ['text_block', 'title', 'code_txt', 'code_txt_caption', 'reference', 'equation_caption',
+                                                'figure_caption', 'figure_footnote', 'table_caption', 'table_footnote', 'code_algorithm', 'code_algorithm_caption',
+                                                'header', 'footer', 'page_footnote', 'page_number', 'equation_isolated'])
+        if gt_mix:
+            gt_mix = self.get_sorted_text_list(gt_mix)
+
+        display_formula_match_s = []
+        plain_text_match_clean = []
+        merged_plain_text_match_clean = []
+        latex_table_match_s = []
+        html_table_match_s = []
+        merged_latex_table_match_s = []
+        merged_html_table_match_s = []
+        order_match_single = []
+        head_match_single = []
+        
+
+        if gt_page_elements.get('table'):
+            gt_table = self.get_sorted_text_list(gt_page_elements['table'])
+            merged_gt_table = [table_sample for table_sample in self.get_sorted_text_list(gt_page_elements['table']) if table_sample.get('is_merged') == 'table']
+            latex_table_len = len(pred_dataset['latex_table']) if pred_dataset['latex_table'] else 0
+            html_table_len = len(pred_dataset['html_table']) if pred_dataset['html_table'] else 0
+            if latex_table_len == html_table_len and latex_table_len == 0:
+                html_table_match_s,unmatch_table_pred = match_gt2pred_simple(gt_table, [], 'html_table', img_name) # Don't consider truncated merging for tables
+                html_table_match_s = [x for x in html_table_match_s if x['gt_idx'] != [""]]  # Remove extra preds
+
+                merged_html_table_match_s, _ = match_gt2pred_simple(merged_gt_table, [], 'html_table', img_name) # Don't consider truncated merging for tables
+                merged_html_table_match_s = [x for x in merged_html_table_match_s if x['gt_idx'] != [""]]  # Remove extra preds
+
+            elif latex_table_len > html_table_len:
+                latex_table_match_s, unmatch_table_pred = match_gt2pred_simple(gt_table, pred_dataset['latex_table'], 'latex_table', img_name) # Don't consider truncated merging for tables
+                latex_table_match_s = [x for x in latex_table_match_s if x['gt_idx'] != [""]]  # Remove extra preds 
+
+                merged_latex_table_match_s, _ = match_gt2pred_simple(merged_gt_table, pred_dataset['latex_table'], 'latex_table', img_name) # Don't consider truncated merging for tables
+                merged_latex_table_match_s = [x for x in merged_latex_table_match_s if x['gt_idx'] != [""]]  # Remove extra preds
+            else:
+                html_table_match_s,unmatch_table_pred = match_gt2pred_simple(gt_table, pred_dataset['html_table'], 'html_table', img_name) # Don't consider truncated merging for tables
+                html_table_match_s = [x for x in html_table_match_s if x['gt_idx'] != [""]]  # Remove extra preds
+
+                merged_html_table_match_s, _ = match_gt2pred_simple(merged_gt_table, pred_dataset['html_table'], 'html_table', img_name) # Don't consider truncated merging for tables
+                merged_html_table_match_s = [x for x in merged_html_table_match_s if x['gt_idx'] != [""]]  # Remove extra preds
+
+            if unmatch_table_pred:
+                pred_dataset_mix.extend(unmatch_table_pred)
+
+        head_match_single = self.get_head_paired(parent_son_list, head_nodes, pred_dataset_mix, img_name)
+        
+        try:
+            match = func_timeout(_match_timeout(), match_gt2pred, args=(gt_mix, pred_dataset_mix, 'text_all', img_name))
+        except FunctionTimedOut as e1:
+            print(f'Time out for plain text match of {img_name}, match_gt2pred_simple will be used.')
+            match,_ = match_gt2pred_simple(gt_mix, pred_dataset_mix, 'text_all', img_name)
+        except Exception as e:
+            print(traceback.format_exc())
+            sys.exit() 
+
+        merged_gt_mix = [gt for gt in gt_mix if gt.get('is_merged') == 'text_block']
+        merged_plain_text_match_clean, _ = match_gt2pred_simple(merged_gt_mix, pred_dataset_mix, 'text_all', img_name) # Consider truncated merging for text blocks 
+        merged_plain_text_match_clean = [x for x in merged_plain_text_match_clean if x['gt_idx'] != [""]] 
+
+        plain_text_match_s = []
+        for item in match:
+            gt_category = item.get('gt_category_type',None)
+            if gt_category in ['text_block', 'title', 'code_txt', 'code_txt_caption', 'reference', 'equation_caption',
+                                                    'figure_caption', 'figure_footnote', 'table_caption', 'table_footnote', 'code_algorithm', 'code_algorithm_caption',
+                                                    'header', 'footer', 'page_footnote', 'page_number']:
+                plain_text_match_s.append(item)
+            elif gt_category == 'equation_isolated':
+                display_formula_match_s.append(item)
+
+        display_formula_match_s = [x for x in display_formula_match_s if x['gt_idx'] != [""]]
+
+        if not plain_text_match_s:
+            pass
+        else:
+            # Categories that need to be ignored for text
+            plain_text_match_clean = self.filtered_out_ignore(plain_text_match_s, ['figure_caption', 'figure_footnote', 'table_caption', 'table_footnote', 'code_algorithm', 'code_algorithm_caption', 'header', 'footer', 'page_footnote', 'page_number', 'equation_caption'])
+            # plain_text_match_clean = self.filtered_out_ignore(plain_text_match_s, ['figure_footnote', 'table_footnote', 'code_algorithm', 'code_algorithm_caption', 'header', 'footer', 'page_footnote', 'page_number'])
+        order_match_s = plain_text_match_clean
+        if order_match_s:
+            order_match_single = self.get_order_paired(order_match_s, img_name)
+
+
+        return [plain_text_match_clean, merged_plain_text_match_clean, display_formula_match_s, latex_table_match_s, merged_latex_table_match_s, html_table_match_s, merged_html_table_match_s, order_match_single, head_match_single]        
+
+
+
+@DATASET_REGISTRY.register("recogition_end2end_base_dataset")
+class RecognitionEnd2EndBaseDataset():
+    def __init__(self, samples):
+        img_id = 0
+        for sample in samples:
+            if not sample.get('img_id'):
+                sample['img_id'] = img_id
+            img_id += 1
+        self.samples = samples
+    def __getitem__(self, idx):
+        return self.samples[idx]
+
+@DATASET_REGISTRY.register("recogition_end2end_table_dataset")
+class RecognitionEnd2EndTableDataset(RecognitionTableDataset):
+    def __init__(self, samples, table_format):
+        self.pred_table_format = table_format
+        self.samples = self.normalize_data(samples)
+
+    def normalize_data(self, samples):
+        img_id = 0
+
+        for sample in samples:
+            p = sample['pred']
+            r = sample['gt']
+            p = normalized_table(p, self.pred_table_format)
+            r = normalized_table(r)
+            sample['norm_gt'] = r
+            sample['norm_pred'] = p
+            sample['img_id'] = sample['img_id'] if sample.get('img_id') else img_id
+            img_id += 1
+
+        return samples
diff --git a/utils/mpdocbench_patch/metrics/cal_metric.py b/utils/mpdocbench_patch/metrics/cal_metric.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae4bf70f1296c1e92c8724d212d906a3920f4bb8
--- /dev/null
+++ b/utils/mpdocbench_patch/metrics/cal_metric.py
@@ -0,0 +1,774 @@
+import json
+import os
+import time
+# from rapidfuzz.distance import Levenshtein
+import Levenshtein
+from .table_metric import TEDS
+import evaluate
+import random
+from utils.read_files import save_paired_result
+from registry.registry import METRIC_REGISTRY
+from collections import defaultdict
+import copy
+import pandas as pd
+from .cdm_metric import CDM
+from .head_metric import HeadTEDS
+from concurrent.futures import ProcessPoolExecutor, as_completed
+import multiprocessing
+import platform
+import re
+import signal
+
+def get_groups(samples, group_info):
+    group_samples = defaultdict(list)
+    for sample in samples:
+        group_samples['all'].append(sample)
+        for group in group_info:
+            select_flag = True
+            for k, v in group.items():
+                for gt_attribute in sample['gt_attribute']:   # gt_attribute is a list containing all merged gt attributes
+                    if not gt_attribute:   # if no GT attributes, don't include in calculation
+                        select_flag = False
+                    elif gt_attribute[k] != v:  # if any gt attribute doesn't meet criteria, don't select
+                        select_flag = False
+            if select_flag:
+                group_samples[str(group)].append(sample)
+    return group_samples
+
+
+_TEDS_CACHE = {}
+
+
+def _get_teds(structure_only):
+    """进程级缓存 TEDS 实例。
+
+    TEDS 无跨样本状态（原串行实现也是复用同一个对象），按进程缓存即可，
+    避免并行时每个样本都重建实例。
+    """
+    if structure_only not in _TEDS_CACHE:
+        _TEDS_CACHE[structure_only] = TEDS(structure_only=structure_only)
+    return _TEDS_CACHE[structure_only]
+
+
+class _TedsTimeout(Exception):
+    """单个 table sample 的 TEDS 计算超出 MPDOC_TEDS_TIMEOUT。"""
+
+
+def _teds_timeout():
+    """MPDOC_TEDS_TIMEOUT：单样本 TEDS 墙钟上界（秒），缺省 0 = 关闭。
+
+    默认关闭是刻意的：并行化本身不改分数，而超时降级会把该样本记 0、悄悄改分。
+    需要防单个畸形表格吃掉数小时时再显式打开，届时会打印受影响样本数并提示不可比。
+    """
+    raw = (os.environ.get('MPDOC_TEDS_TIMEOUT', '') or '').strip()
+    return int(raw) if raw.isdigit() else 0
+
+
+_SPAN_ATTR_RE = re.compile(r'\b(row|col)span\s*=\s*([^\s>]*)', re.I)
+
+
+def _fix_span_attrs(html):
+    """把 (row|col)span 的非法取值归一成纯数字，避免整张表因解析异常记 0 分。
+
+    模型退化时会写出 rowspan='3"'、colspan="</td"、colspan=' 这类坏属性，
+    TEDS 解析 span 时 int() 直接抛异常，整张表被判 0。这里只保留属性值里的
+    数字部分（去掉多余引号和粘连的标签片段），取不到数字则按 1 处理。
+    对合法取值是幂等的，所以 pred / gt 都可以无条件过一遍。
+    """
+    if not html or 'span' not in html:
+        return html
+
+    def _sub(m):
+        digits = re.search(r'\d+', m.group(2))
+        return '%sspan="%s"' % (m.group(1), digits.group(0) if digits else '1')
+
+    return _SPAN_ATTR_RE.sub(_sub, html)
+
+
+def _eval_teds_guarded(structure_only, pred, gt):
+    """返回 (score, timed_out)。timeout<=0 时行为与无保护路径完全一致。
+
+    apted 是纯 Python 实现（site-packages/apted 下无 .so），所以 SIGALRM
+    能在字节码边界可靠打断它的树编辑距离循环。
+    """
+    pred, gt = _fix_span_attrs(pred), _fix_span_attrs(gt)
+    timeout = _teds_timeout()
+    if timeout <= 0 or not hasattr(signal, 'SIGALRM'):
+        return _get_teds(structure_only).evaluate(pred, gt), False
+
+    def _on_alarm(signum, frame):
+        raise _TedsTimeout()
+
+    old_handler = signal.signal(signal.SIGALRM, _on_alarm)
+    signal.setitimer(signal.ITIMER_REAL, timeout)
+    try:
+        return _get_teds(structure_only).evaluate(pred, gt), False
+    except _TedsTimeout:
+        return 0, True
+    finally:
+        signal.setitimer(signal.ITIMER_REAL, 0)
+        signal.signal(signal.SIGALRM, old_handler)
+
+
+def _process_single_teds_sample(args):
+    """单个 table sample 的 TEDS 计算，与原串行循环体逐字对应。
+
+    不做 deepcopy：并行路径下 sample 已经是 pickle 过来的私有副本；串行重试路径下
+    直接改原对象，与原实现的就地修改语义一致。
+    """
+    idx, sample, group_info = args
+    gt = sample['norm_gt'] if sample.get('norm_gt') else sample['gt']
+    pred = sample['norm_pred'] if sample.get('norm_pred') else sample['pred']
+
+    timed_out = False
+    try:
+        score, _to = _eval_teds_guarded(False, pred, gt)
+        timed_out = timed_out or _to
+    except Exception:
+        score = 0
+        print(f'TEDS score error for table {sample["gt_idx"]} in {sample["img_id"]}. The score is set to 0.')
+    try:
+        score_structure_only, _to = _eval_teds_guarded(True, pred, gt)
+        timed_out = timed_out or _to
+    except Exception:
+        score_structure_only = 0
+        print(f'TEDS_structure_only score error for table {sample["gt_idx"]} in {sample["img_id"]}. '
+              f'The score is set to 0.')
+
+    if timed_out:
+        print(f'TEDS timeout ({_teds_timeout()}s) for table {sample["gt_idx"]} in {sample["img_id"]}. '
+              f'The score is set to 0.', flush=True)
+
+    if not sample.get('metric'):
+        sample['metric'] = {}
+    sample['metric']['TEDS'] = score
+    sample['metric']['TEDS_structure_only'] = score_structure_only
+
+    # group 匹配逻辑照搬原实现（注意：structure_only 原本只统计 'all'，不分组）
+    matched_groups = []
+    for group in group_info:
+        select_flag = True
+        for k, v in group.items():
+            for gt_attribute in sample['gt_attribute']:
+                if not gt_attribute:
+                    select_flag = False
+                elif gt_attribute[k] != v:
+                    select_flag = False
+        if select_flag:
+            matched_groups.append(str(group))
+
+    return {
+        'sample': sample,
+        'score': score,
+        'score_structure_only': score_structure_only,
+        'sample_key': sample['img_id'] + '_' + str(sample['gt_idx']),
+        'matched_groups': matched_groups,
+        'original_index': idx,
+        'timed_out': timed_out,
+    }
+
+
+@METRIC_REGISTRY.register("TEDS")
+class call_TEDS():
+    def __init__(self, samples):
+        self.samples = samples
+    def evaluate(self, group_info=[], save_name='default', max_workers=None):
+        group_scores = defaultdict(list)
+        group_scores_structure_only = defaultdict(list)
+        per_table_score = {}
+
+        if isinstance(self.samples, list):
+            original_samples = self.samples
+        else:
+            original_samples = self.samples.samples
+
+        worker_args = [(idx, sample, group_info) for idx, sample in enumerate(original_samples)]
+
+        # MPDOC_TEDS_NPROC: TEDS parallelism. Each sample runs APTED twice (full +
+        # structure-only); samples are independent, so evaluate them in parallel
+        # (same pattern as the CDM path).
+        if max_workers is None:
+            env_nproc = (os.environ.get('MPDOC_TEDS_NPROC', '') or '').strip()
+            if env_nproc.isdigit() and int(env_nproc) > 0:
+                max_workers = int(env_nproc)
+        if max_workers is None:
+            cpu_count = multiprocessing.cpu_count() or 4
+            if platform.system() == 'Darwin':
+                max_workers = max(1, min(cpu_count, 8))
+            else:
+                max_workers = max(1, min(cpu_count * 2, 32))
+
+        results = []
+        failed_indices = []
+
+        ### PARALLEL VERSION
+        with ProcessPoolExecutor(max_workers=max_workers) as executor:
+            future_to_idx = {executor.submit(_process_single_teds_sample, args): args[0]
+                             for args in worker_args}
+            for future in as_completed(future_to_idx):
+                try:
+                    results.append(future.result())
+                except Exception as exc:
+                    idx = future_to_idx[future]
+                    print(f'TEDS sample {idx} generated an exception in parallel: {exc}')
+                    failed_indices.append(idx)
+
+        ### RETRY failed samples sequentially —— 避免并行崩溃（OOM 等）造成假零分
+        if failed_indices:
+            print(f'[TEDS] Retrying {len(failed_indices)} failed samples sequentially ...')
+            for idx in failed_indices:
+                try:
+                    results.append(_process_single_teds_sample(worker_args[idx]))
+                except Exception as exc:
+                    print(f'TEDS sample {idx} also failed sequentially: {exc}')
+                    sample = original_samples[idx]
+                    if not sample.get('metric'):
+                        sample['metric'] = {}
+                    sample['metric']['TEDS'] = 0
+                    sample['metric']['TEDS_structure_only'] = 0
+                    results.append({
+                        'sample': sample, 'score': 0, 'score_structure_only': 0,
+                        'sample_key': sample['img_id'] + '_' + str(sample['gt_idx']),
+                        'matched_groups': [], 'original_index': idx, 'timed_out': False,
+                    })
+
+        # 按原下标重排：累加顺序与串行逐位一致，指标可复现
+        results.sort(key=lambda x: x['original_index'])
+
+        samples = []
+        for result in results:
+            score = result['score']
+            samples.append(result['sample'])
+            group_scores['all'].append(score)
+            group_scores_structure_only['all'].append(result['score_structure_only'])
+            per_table_score[result['sample_key']] = {
+                'TEDS': score, 'TEDS_structure_only': result['score_structure_only']}
+            for group_name in result['matched_groups']:
+                group_scores[group_name].append(score)
+
+        n_timeout = sum(1 for r in results if r.get('timed_out'))
+        if n_timeout:
+            print(f'[TEDS] 警告: {n_timeout}/{len(results)} 个样本超出 MPDOC_TEDS_TIMEOUT='
+                  f'{_teds_timeout()}s 被记 0 分，本轮 TEDS 与历史跑分不可比。', flush=True)
+
+        with open(f'./result/{save_name}_per_table_TEDS.json', 'w', encoding='utf-8') as f:
+            json.dump(per_table_score, f, indent=4, ensure_ascii=False)
+        result = {}
+        for group_name, scores in group_scores.items():
+            if len(scores) > 0:
+                result[group_name] = sum(scores) / len(scores)    # average of normalized scores at sample level
+            else:
+                result[group_name] = 'NaN'
+                print(f'Warning: Empyty matched samples for {group_name}.')
+        
+        structure_only_result = {}
+        for group_name, scores in group_scores_structure_only.items():
+            if len(scores) > 0:
+                structure_only_result[group_name] = sum(scores) / len(scores)    # average of normalized scores at sample level
+            else:
+                structure_only_result[group_name] = 'NaN'
+                print(f'Warning: Empyty matched samples for {group_name}.')
+
+        return samples, {'TEDS': result, 'TEDS_structure_only': structure_only_result}
+
+
+@METRIC_REGISTRY.register("BLEU")
+class call_BLEU():
+    def __init__(self, samples):
+        self.samples = samples
+    def evaluate(self, group_info=[], save_name='default'):
+        group_samples = get_groups(self.samples, group_info)
+        result = {}
+        for group_name, samples in group_samples.items():
+            predictions, references = [], []
+            for sample in samples:
+                gt = sample['norm_gt'] if sample.get('norm_gt') else sample['gt']
+                pred = sample['norm_pred'] if sample.get('norm_pred') else sample['pred']
+                predictions.append(gt)
+                references.append(pred)
+            bleu = evaluate.load("bleu", keep_in_memory=True, experiment_id=random.randint(1,1e8))
+            bleu_results = bleu.compute(predictions=predictions, references=references)
+            result[group_name] = bleu_results["bleu"]
+        
+        return self.samples, {'BLEU': result}
+    
+@METRIC_REGISTRY.register("METEOR")
+class call_METEOR():
+    def __init__(self, samples):
+        self.samples = samples
+    def evaluate(self, group_info=[], save_name='default'):
+        group_samples = get_groups(self.samples, group_info)
+        result = {}
+        for group_name, samples in group_samples.items():
+            predictions, references = [], []
+            for sample in samples:
+                gt = sample['norm_gt'] if sample.get('norm_gt') else sample['gt']
+                pred = sample['norm_pred'] if sample.get('norm_pred') else sample['pred']
+                predictions.append(gt)
+                references.append(pred)
+            meteor = evaluate.load('meteor', keep_in_memory=True, experiment_id=random.randint(1,1e8))
+            meteor_results = meteor.compute(predictions=predictions, references=references)
+            result[group_name] = meteor_results['meteor']
+        
+        return self.samples, {'METEOR': result}
+
+@METRIC_REGISTRY.register("Edit_dist")
+class call_Edit_dist():
+    def __init__(self, samples):
+        self.samples = samples
+    def evaluate(self, group_info=[], save_name='default'):
+        samples = self.samples
+        for sample in samples:
+            img_name = sample['img_id'] if sample['img_id'].endswith('.jpg') or sample['img_id'].endswith('.png') or sample['img_id'].endswith('.pdf') else '_'.join(sample['img_id'].split('_')[:-1])
+            sample['image_name'] = img_name
+            gt = sample['norm_gt'] if sample.get('norm_gt') else sample['gt']
+            pred = sample['norm_pred'] if sample.get('norm_pred') else sample['pred']
+            upper_len = max(len(pred), len(gt))
+            sample['upper_len'] = upper_len
+            if len(pred) > 0 or len(gt) > 0:
+                edit_dist = Levenshtein.distance(pred, gt)
+                if not sample.get('metric'):
+                    sample['metric'] = {}
+                sample['metric']['Edit_dist'] = edit_dist / upper_len
+                sample['Edit_num'] = edit_dist
+
+        if isinstance(samples, list):
+            saved_samples = samples
+        else:
+            saved_samples = samples.samples
+        
+        if not saved_samples:
+            return samples, {'Edit_dist': {'ALL_page_avg': 'NaN'}}
+
+        df = pd.DataFrame(saved_samples)
+        up_total_avg = df.groupby("image_name").apply(lambda x: x['Edit_num'].sum() / x['upper_len'].sum()) # page level, sum of edits divided by sum of max(gt,pred) lengths for each sample
+        all_total_avg = df['Edit_num'].sum() / df['upper_len'].sum()
+        # all_total_avg = df["Edit_dist"].mean()
+        per_img_score = up_total_avg.to_dict()
+        with open(f'./result/{save_name}_per_page_edit.json', 'w', encoding='utf-8') as f:
+            json.dump(per_img_score, f, indent=4, ensure_ascii=False)        
+        
+        edit_whole = df['Edit_num'].sum() / df['upper_len'].sum()
+        df['ratio'] = df['Edit_num'] / df['upper_len']
+        edit_sample_avg = df['ratio'].mean()
+        # edit_sample_avg = df['metric']['Edit_dist'].mean()
+        return samples, {'Edit_dist': {'ALL_page_avg': up_total_avg.mean(), 'edit_whole': edit_whole, 'edit_sample_avg': edit_sample_avg}}
+    
+def _process_single_cdm_sample(args):
+    """Worker function to process a single CDM sample"""
+    import re
+    idx, sample, output_root, group_info = args
+
+    # Prepare sample data
+    sample_copy = copy.deepcopy(sample)
+    sample_copy['img_id_cdm'] = str(idx)
+    sample_copy['gt'] = sample_copy['gt'].lstrip("$$").rstrip("$$").strip()
+    sample_copy['gt'] = sample_copy['gt'].lstrip("$").rstrip("$").strip()
+    sample_copy['pred'] = sample_copy['pred'].split("```latex")[-1].split("```")[0]
+    sample_copy['pred'] = sample_copy['pred'].lstrip("$$").rstrip("$$").strip()
+    sample_copy['pred'] = sample_copy['pred'].lstrip("$").rstrip("$").strip()
+    # Strip \[...\] and \(...\) display/inline math delimiters to avoid nested
+    # displaymath environments when the template already wraps with \begin{displaymath}
+    sample_copy['gt'] = re.sub(r'^\\\[|\\\]$', '', sample_copy['gt'].strip()).strip()
+    sample_copy['pred'] = re.sub(r'^\\\[|\\\]$', '', sample_copy['pred'].strip()).strip()
+    sample_copy['gt'] = re.sub(r'^\\\(|\\\)$', '', sample_copy['gt'].strip()).strip()
+    sample_copy['pred'] = re.sub(r'^\\\(|\\\)$', '', sample_copy['pred'].strip()).strip()
+    
+    # Calculate CDM score
+    # 空预测必然 0 分（precision = 匹配数/0 个 pred box，现网 1032/1133 条空 pred 全部拿 0），
+    # 直接短路，省掉一对 xelatex + magick 渲染 —— 分数与渲染路径完全一致。
+    if not sample_copy['pred'].strip():
+        cdm_score = 0
+    else:
+        # Create a new CDM instance for this worker to avoid thread safety issues
+        cal_cdm = CDM(output_root=output_root)
+        cdm_score = cal_cdm.evaluate(
+            sample_copy['gt'], sample_copy['pred'], sample_copy['img_id_cdm'])["F1_score"]
+    
+    # Add metric to sample
+    if not sample_copy.get('metric'):
+        sample_copy['metric'] = {}
+    sample_copy['metric']['CDM'] = cdm_score
+    
+    # Check which groups this sample belongs to
+    matched_groups = []
+    for group in group_info:
+        select_flag = True
+        for k, v in group.items():
+            for gt_attribute in sample_copy['gt_attribute']:
+                if not gt_attribute:
+                    select_flag = False
+                elif gt_attribute[k] != v:
+                    select_flag = False
+        if select_flag:
+            matched_groups.append(str(group))
+    
+    return {
+        'sample': sample_copy,
+        'cdm_score': cdm_score,
+        'sample_key': sample_copy['img_id'] + '_' + str(sample_copy['gt_idx']),
+        'matched_groups': matched_groups,
+        'original_index': idx
+    }
+
+
+@METRIC_REGISTRY.register("CDM")
+class call_CDM():
+    def __init__(self, samples):
+        self.samples = samples
+    def evaluate(self, group_info=[], save_name='default', max_workers=None):
+        group_scores = defaultdict(list)
+        output_root = f"result/{save_name}/CDM"
+        
+        if isinstance(self.samples, list):
+            original_samples = self.samples
+        else:
+            original_samples = self.samples.samples
+        
+        # Prepare arguments for concurrent processing
+        worker_args = []
+        for idx, sample in enumerate(original_samples):
+            worker_args.append((idx, sample, output_root, group_info))
+
+        # Auto-detect a safe number of parallel workers:
+        # Each worker spawns xelatex + magick sub-processes, so we keep
+        # concurrency moderate to avoid resource exhaustion / process crashes.
+        if max_workers is None:
+            # MPDOC_CDM_NPROC：显式指定 CDM 并行度（每个 worker 会拉起 xelatex + magick 子进程，
+            # 默认上限 32 是保守值；核多的机器可以调大）。
+            env_nproc = os.environ.get('MPDOC_CDM_NPROC', '')
+            if env_nproc.strip().isdigit() and int(env_nproc) > 0:
+                max_workers = int(env_nproc)
+        if max_workers is None:
+            cpu_count = multiprocessing.cpu_count() or 4
+            if platform.system() == 'Darwin':
+                # macOS 'spawn' is heavier; be conservative
+                max_workers = max(1, min(cpu_count, 8))
+            else:
+                max_workers = max(1, min(cpu_count * 2, 32))
+
+        # Use concurrent execution
+        per_sample_score = {}
+        cdm_samples = []
+        results = []
+        failed_indices = []  # track indices that crash in parallel
+
+        ### PARALLEL VERSION
+        with ProcessPoolExecutor(max_workers=max_workers) as executor:
+            # Submit all tasks
+            future_to_idx = {executor.submit(_process_single_cdm_sample, args): args[0] for args in worker_args}
+            
+            # Collect results as they complete
+            for future in as_completed(future_to_idx):
+                try:
+                    result = future.result()
+                    results.append(result)
+                except Exception as exc:
+                    idx = future_to_idx[future]
+                    print(f'Sample {idx} generated an exception in parallel: {exc}')
+                    failed_indices.append(idx)
+
+        ### RETRY failed samples sequentially — avoids false zeros from process crashes
+        if failed_indices:
+            print(f'[CDM] Retrying {len(failed_indices)} failed samples sequentially ...')
+            for idx in failed_indices:
+                try:
+                    result = _process_single_cdm_sample(worker_args[idx])
+                    results.append(result)
+                except Exception as exc:
+                    print(f'Sample {idx} also failed sequentially: {exc}')
+                    sample_copy = copy.deepcopy(original_samples[idx])
+                    sample_copy['img_id_cdm'] = str(idx)
+                    if not sample_copy.get('metric'):
+                        sample_copy['metric'] = {}
+                    sample_copy['metric']['CDM'] = 0.0
+                    results.append({
+                        'sample': sample_copy,
+                        'cdm_score': 0.0,
+                        'sample_key': sample_copy['img_id'] + '_' + str(sample_copy['gt_idx']),
+                        'matched_groups': [],
+                        'original_index': idx
+                    })
+        
+        # Sort results by original index to maintain order
+        results.sort(key=lambda x: x['original_index'])
+        
+        # Process results
+        for result in results:
+            sample = result['sample']
+            cdm_score = result['cdm_score']
+            sample_key = result['sample_key']
+            matched_groups = result['matched_groups']
+            
+            cdm_samples.append(sample)
+            per_sample_score[sample_key] = cdm_score
+            group_scores['all'].append(cdm_score)
+            
+            # Add scores to matched groups
+            for group_name in matched_groups:
+                group_scores[group_name].append(cdm_score)
+
+        # Save results to files
+        with open(f'./result/{save_name}_per_sample_CDM.json', 'w', encoding='utf-8') as f:
+            json.dump(per_sample_score, f, indent=4, ensure_ascii=False)
+
+        with open(f'result/{save_name}_result.json', 'w', encoding='utf-8') as f:
+            json.dump(cdm_samples, f, indent=4, ensure_ascii=False)
+
+        # Calculate final results
+        result = {}
+        for group_name, scores in group_scores.items():
+            if len(scores) > 0:
+                result[group_name] = sum(scores) / len(scores)    # average of normalized scores at sample level
+            else:
+                result[group_name] = 'NaN'
+                print(f'Warning: Empty matched samples for {group_name}.')
+        
+        return cdm_samples, {'CDM': result}
+
+
+@METRIC_REGISTRY.register("CDM_plain")
+class call_CDM_plain():
+    def __init__(self, samples):
+        self.samples = samples
+    def evaluate(self, group_info=[], save_name='default'):
+        if isinstance(self.samples, list):
+            cdm_samples = copy.deepcopy(self.samples)
+        else:
+            cdm_samples = copy.deepcopy(self.samples.samples)
+        for idx, sample in enumerate(cdm_samples):
+            sample['img_name'] = sample['img_id']
+            sample['img_id'] = str(idx)
+            sample['gt'] = sample['gt'].lstrip("$$").rstrip("$$").strip()
+            sample['pred'] = sample['pred'].split("```latex")[-1].split("```")[0]
+            sample['pred'] = sample['pred'].lstrip("$$").rstrip("$$").strip()
+
+        # time_stap = time.time()
+        with open(f'result/{save_name}_formula.json', 'w', encoding='utf-8') as f:
+            json.dump(cdm_samples, f, indent=4, ensure_ascii=False)
+        return self.samples, False
+
+
+@METRIC_REGISTRY.register("HeadTEDS")
+class call_HeadTEDS():
+    def __init__(self, samples):
+        self.samples = samples
+
+    def evaluate(self, group_info=[], save_name='default'):
+        head_teds = HeadTEDS()
+        group_scores = defaultdict(list)
+        samples = self.samples
+        per_sample_score = {}
+
+        for sample in samples:
+            gt = sample['norm_gt'] if sample.get('norm_gt') else sample['gt']
+            pred = sample['norm_pred'] if sample.get('norm_pred') else sample['pred']
+            sample_key = sample['img_id']
+
+            try:
+                score, gt_tree_depth = head_teds.evaluate(pred, gt)
+            except Exception as e:
+                score = 0
+                gt_tree_depth = 0
+                print(f'HeadTEDS score error for sample {sample_key}: {e}. The score is set to 0.')
+
+            if not sample.get('metric'):
+                sample['metric'] = {}
+            sample['metric']['HeadTEDS'] = score
+            sample["gt_attribute"] = [{"heading tree depth": gt_tree_depth}]
+            per_sample_score[sample_key] = score
+            group_scores['all'].append(score)
+
+            for group in group_info:
+                select_flag = True
+                for k, v in group.items():
+                    for gt_attribute in sample['gt_attribute']:
+                        if not gt_attribute:
+                            select_flag = False
+                        elif gt_attribute[k] != v:
+                            select_flag = False
+                if select_flag:
+                    group_scores[str(group)].append(score)
+
+        with open(f'./result/{save_name}_per_sample_HeadTEDS.json', 'w', encoding='utf-8') as f:
+            json.dump(per_sample_score, f, indent=4, ensure_ascii=False)
+
+        result = {}
+        for group_name, scores in group_scores.items():
+            if len(scores) > 0:
+                result[group_name] = sum(scores) / len(scores)
+            else:
+                result[group_name] = 'NaN'
+                print(f'Warning: Empty matched samples for {group_name}.')
+
+        return samples, {'HeadTEDS': result}
+
+
+
+@METRIC_REGISTRY.register("FigureF1")
+class call_FigureF1():
+    def __init__(self, samples):
+        self.samples = samples
+
+    def evaluate(self, group_info=[], save_name='default'):
+        """计算文档级图片 F1。"""
+        group_scores = defaultdict(list)
+        samples = self.samples
+        per_doc_score = {}
+
+        for sample in samples:
+            gt_count = sample.get('gt_count', 0)
+            pred_count = sample.get('pred_count', 0)
+            matched_count = sample.get('matched_count', 0)
+            precision = matched_count / pred_count if pred_count > 0 else 0
+            recall = matched_count / gt_count if gt_count > 0 else 0
+            f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0
+
+            if not sample.get('metric'):
+                sample['metric'] = {}
+            sample['metric']['FigureF1'] = f1
+
+            per_doc_score[sample['img_id']] = {
+                'f1': f1,
+                'precision': precision,
+                'recall': recall,
+                'gt_count': gt_count,
+                'pred_count': pred_count,
+                'matched_count': matched_count,
+            }
+
+            group_scores['all'].append(f1)
+
+            for group in group_info:
+                select_flag = True
+                for k, v in group.items():
+                    for gt_attribute in sample.get('gt_attribute', []):
+                        if not gt_attribute:
+                            select_flag = False
+                        elif gt_attribute.get(k) != v:
+                            select_flag = False
+                if select_flag:
+                    group_scores[str(group)].append(f1)
+
+        os.makedirs(f'./result/{save_name}', exist_ok=True)
+        with open(f'./result/{save_name}_per_doc_FigureF1.json', 'w', encoding='utf-8') as f:
+            json.dump(per_doc_score, f, indent=4, ensure_ascii=False)
+
+        result = {}
+        for group_name, scores in group_scores.items():
+            if len(scores) > 0:
+                result[group_name] = sum(scores) / len(scores)
+            else:
+                result[group_name] = 'NaN'
+                print(f'Warning: Empty matched samples for {group_name}.')
+
+        return samples, {'FigureF1': result}
+
+
+@METRIC_REGISTRY.register("Relation_F1")
+class call_RelationF1():
+    def __init__(self, samples):
+        self.samples = samples
+
+    @staticmethod
+    def _f1(tp, fp, fn):
+        p = tp / (tp + fp) if (tp + fp) > 0 else 0.0
+        r = tp / (tp + fn) if (tp + fn) > 0 else 0.0
+        f1 = 2 * p * r / (p + r) if (p + r) > 0 else 0.0
+        return p, r, f1
+
+    def evaluate(self, group_info=[], save_name='default'):
+        """计算文档级表格截断关系 F1，含同页/跨页分项。"""
+        group_scores = defaultdict(list)
+        group_scores_same = defaultdict(list)
+        group_scores_cross = defaultdict(list)
+        samples = self.samples
+        per_doc_score = {}
+
+        # Micro 聚合用
+        total_tp, total_fp, total_fn = 0, 0, 0
+        total_same_tp, total_same_fp, total_same_fn = 0, 0, 0
+        total_cross_tp, total_cross_fp, total_cross_fn = 0, 0, 0
+
+        for sample in samples:
+            tp = sample.get('tp', 0)
+            fp = sample.get('fp', 0)
+            fn = sample.get('fn', 0)
+            same_tp = sample.get('same_page_tp', 0)
+            same_fp = sample.get('same_page_fp', 0)
+            same_fn = sample.get('same_page_fn', 0)
+            cross_tp = sample.get('cross_page_tp', 0)
+            cross_fp = sample.get('cross_page_fp', 0)
+            cross_fn = sample.get('cross_page_fn', 0)
+
+            total_tp += tp; total_fp += fp; total_fn += fn
+            total_same_tp += same_tp; total_same_fp += same_fp; total_same_fn += same_fn
+            total_cross_tp += cross_tp; total_cross_fp += cross_fp; total_cross_fn += cross_fn
+
+            _, _, f1 = self._f1(tp, fp, fn)
+            _, _, same_f1 = self._f1(same_tp, same_fp, same_fn)
+            _, _, cross_f1 = self._f1(cross_tp, cross_fp, cross_fn)
+
+            if not sample.get('metric'):
+                sample['metric'] = {}
+            sample['metric']['Relation_F1'] = f1
+
+            per_doc_score[sample['img_id']] = {
+                'f1': f1, 'tp': tp, 'fp': fp, 'fn': fn,
+                'same_page_f1': same_f1, 'same_page_tp': same_tp, 'same_page_fp': same_fp, 'same_page_fn': same_fn,
+                'cross_page_f1': cross_f1, 'cross_page_tp': cross_tp, 'cross_page_fp': cross_fp, 'cross_page_fn': cross_fn,
+                'gt_pairs_count': sample.get('gt_pairs_count', 0),
+                'pred_pairs_count': sample.get('pred_pairs_count', 0),
+            }
+
+            group_scores['all'].append(f1)
+            # 只在有该分项 pair 时才计入 macro 平均
+            if (same_tp + same_fp + same_fn) > 0:
+                group_scores_same['all'].append(same_f1)
+            if (cross_tp + cross_fp + cross_fn) > 0:
+                group_scores_cross['all'].append(cross_f1)
+
+            for group in group_info:
+                select_flag = True
+                for k, v in group.items():
+                    for gt_attribute in sample.get('gt_attribute', []):
+                        if not gt_attribute:
+                            select_flag = False
+                        elif gt_attribute.get(k) != v:
+                            select_flag = False
+                if select_flag:
+                    group_scores[str(group)].append(f1)
+                    if (same_tp + same_fp + same_fn) > 0:
+                        group_scores_same[str(group)].append(same_f1)
+                    if (cross_tp + cross_fp + cross_fn) > 0:
+                        group_scores_cross[str(group)].append(cross_f1)
+
+        # 保存 per-doc 结果
+        os.makedirs(f'./result/{save_name}', exist_ok=True)
+        with open(f'./result/{save_name}_per_doc_RelationF1.json', 'w', encoding='utf-8') as f:
+            json.dump(per_doc_score, f, indent=4, ensure_ascii=False)
+
+        def _agg(group_dict):
+            out = {}
+            for gn, scores in group_dict.items():
+                out[gn] = sum(scores) / len(scores) if scores else 'NaN'
+            return out
+
+        # Macro F1（主指标）
+        result = _agg(group_scores)
+        result_same = _agg(group_scores_same)
+        result_cross = _agg(group_scores_cross)
+
+        # Micro F1（辅助参考）
+        _, _, micro_f1 = self._f1(total_tp, total_fp, total_fn)
+        _, _, micro_same_f1 = self._f1(total_same_tp, total_same_fp, total_same_fn)
+        _, _, micro_cross_f1 = self._f1(total_cross_tp, total_cross_fp, total_cross_fn)
+        result['micro_f1'] = micro_f1
+        result_same['micro_f1'] = micro_same_f1
+        result_cross['micro_f1'] = micro_cross_f1
+
+        return samples, {
+            'Relation_F1': result,
+            'Relation_F1_same_page': result_same,
+            'Relation_F1_cross_page': result_cross,
+        }
\ No newline at end of file
diff --git a/utils/mpdocbench_patch/utils/data_preprocess.py b/utils/mpdocbench_patch/utils/data_preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..c15acf3f66df80133fb7cb96fe0319259f7a4c58
--- /dev/null
+++ b/utils/mpdocbench_patch/utils/data_preprocess.py
@@ -0,0 +1,402 @@
+import re
+import unicodedata
+from pylatexenc.latex2text import LatexNodes2Text
+from bs4 import BeautifulSoup
+import subprocess
+import shutil
+import uuid
+import html
+import os
+
+def remove_markdown_fences(content):
+    content = re.sub(r'^```markdown\n?', '', content, flags=re.MULTILINE)
+    content = re.sub(r'^```html\n?', '', content, flags=re.MULTILINE)
+    content = re.sub(r'^```latex\n?', '', content, flags=re.MULTILINE)
+    content = re.sub(r'```\n?$', '', content, flags=re.MULTILINE)
+    return content
+
+# Standardize all consecutive characters
+def replace_repeated_chars(input_str):
+    input_str = re.sub(r'_{4,}', '____', input_str) # Replace more than 4 consecutive underscores with 4 underscores
+    input_str = re.sub(r' {4,}', '    ', input_str)   # Replace more than 4 consecutive spaces with 4 spaces
+    return input_str
+
+# Special Unicode handling
+def fullwidth_to_halfwidth(s):
+    result = []
+    for char in s:
+        code = ord(char)
+        # Convert full-width space to half-width space
+        if code == 0x3000:
+            code = 0x0020
+        # Convert other full-width characters to half-width
+        elif 0xFF01 <= code <= 0xFF5E:
+            code -= 0xFEE0
+        result.append(chr(code))
+    return ''.join(result)
+
+def find_special_unicode(s):
+    special_chars = {}
+    for char in s:
+        if ord(char) > 127:  # Non-ASCII characters
+            unicode_name = unicodedata.category(char)
+            special_chars[char] = f'U+{ord(char):04X} ({unicode_name})'
+    return special_chars
+
+inline_reg = re.compile(
+    r'\$(.*?)\$|'
+    r'\\\((.*?)\\\)',
+)
+
+# Skip pylatexenc on pathologically nested LaTeX: unbalanced braces make its
+# recursive-descent walker backtrack with cost exploding in nesting depth (it can
+# hang). Such strings don't yield meaningful unicode anyway, so above this brace
+# depth we keep the raw text for matching.
+_MAX_BRACE_DEPTH = 10
+
+
+def _latex_nesting_too_deep(s):
+    """花括号嵌套深度超 _MAX_BRACE_DEPTH → 判为病态 latex，不交给 pylatexenc。"""
+    depth = 0
+    for ch in s:
+        if ch == '{':
+            depth += 1
+            if depth > _MAX_BRACE_DEPTH:
+                return True
+        elif ch == '}':
+            depth -= 1
+    return False
+
+
+def textblock2unicode(text):
+    inline_matches = inline_reg.finditer(text)
+    removal_positions = []
+    for match in inline_matches:
+        position = [match.start(), match.end()]
+        content = match.group(1) if match.group(1) is not None else match.group(2)
+        # Remove escape characters \
+        clean_content = re.sub(r'\\([\\_&%^])', '', content)
+
+        try:
+            if any(char in clean_content for char in r'\^_') and \
+                    not _latex_nesting_too_deep(clean_content):
+                if clean_content.endswith('\\'):
+                    clean_content += ' '
+                unicode_content = LatexNodes2Text().latex_to_text(clean_content)
+                removal_positions.append((position[0], position[1], unicode_content))
+        except:
+            continue
+    
+    # Remove inline formulas from original text
+    for start, end, unicode_content in sorted(removal_positions, reverse=True):
+        text = text[:start] + unicode_content.strip() + text[end:]
+
+    return text
+
+def normalized_formula(text):
+    # Normalize math formulas before matching
+    filter_list = ['\\mathbf', '\\mathrm', '\\mathnormal', '\\mathit', '\\mathbb', '\\mathcal', '\\mathscr', '\\mathfrak', '\\mathsf', '\\mathtt', 
+                   '\\textbf', '\\text', '\\boldmath', '\\boldsymbol', '\\operatorname', '\\bm',
+                   '\\symbfit', '\\mathbfcal', '\\symbf', '\\scriptscriptstyle', '\\notag',
+                   '\\setlength', '\\coloneqq', '\\space', '\\thickspace', '\\thinspace', '\\medspace', '\\nobreakspace', '\\negmedspace',
+                   '\\quad', '\\qquad', '\\enspace', '\\substackw', ' ', '$$', '\\left', '\\right', '\\displaystyle', '\\text']
+                #    '\\left', '\\right', '{', '}', ' ']
+    
+    # delimiter_filter
+    text = text.strip().strip('$').strip('\n')
+    pattern = re.compile(r"\\\[(.+?)(?<!\\)\\\]")
+    match = pattern.search(text)
+
+    if match:
+        text = match.group(1).strip()
+    
+    tag_pattern = re.compile(r"\\tag\{.*?\}")
+    text = tag_pattern.sub('', text)
+    hspace_pattern = re.compile(r"\\hspace\{.*?\}")
+    text = hspace_pattern.sub('', text)
+    begin_pattern = re.compile(r"\\begin\{.*?\}")
+    text = begin_pattern.sub('', text)
+    end_pattern = re.compile(r"\\end\{.*?\}")
+    text = end_pattern.sub('', text)
+    col_sep = re.compile(r"\\arraycolsep.*?\}")
+    text = col_sep.sub('', text)
+    text = text.strip('.')
+    
+    for filter_text in filter_list:
+        text = text.replace(filter_text, '')
+        
+    # text = normalize_text(delimiter_filter(text))
+    # text = delimiter_filter(text)
+    text = text.lower()
+    return text
+
+def normalized_html_table(text):
+    def process_table_html(md_i):
+        """
+        pred_md format edit
+        """
+        def process_table_html(html_content):
+            soup = BeautifulSoup(html_content, 'html.parser')
+            th_tags = soup.find_all('th')
+            for th in th_tags:
+                th.name = 'td'
+            thead_tags = soup.find_all('thead')
+            for thead in thead_tags:
+                thead.unwrap()  # unwrap()会移除标签但保留其内容
+            math_tags = soup.find_all('math')
+            for math_tag in math_tags:
+                alttext = math_tag.get('alttext', '')
+                alttext = f'${alttext}$'
+                if alttext:
+                    math_tag.replace_with(alttext)
+            span_tags = soup.find_all('span')
+            for span in span_tags:
+                span.unwrap()
+            return str(soup)
+
+        table_res=''
+        table_res_no_space=''
+        if '<table' in md_i.replace(" ","").replace("'",'"'):
+            md_i = process_table_html(md_i)
+            table_res = html.unescape(md_i).replace('\n', '')
+            table_res = unicodedata.normalize('NFKC', table_res).strip()
+            pattern = r'<table\b[^>]*>(.*)</table>'
+            tables = re.findall(pattern, table_res, re.DOTALL | re.IGNORECASE)
+            table_res = ''.join(tables)
+            # table_res = re.sub('<table.*?>','',table_res)
+            table_res = re.sub('( style=".*?")', "", table_res)
+            table_res = re.sub('( height=".*?")', "", table_res)
+            table_res = re.sub('( width=".*?")', "", table_res)
+            table_res = re.sub('( align=".*?")', "", table_res)
+            table_res = re.sub('( class=".*?")', "", table_res)
+            table_res = re.sub('</?tbody>',"",table_res)
+            
+            table_res = re.sub(r'\s+', " ", table_res)
+            table_res_no_space = '<html><body><table border="1" >' + table_res.replace(' ','') + '</table></body></html>'
+            # table_res_no_space = re.sub(' (style=".*?")',"",table_res_no_space)
+            # table_res_no_space = re.sub(r'[ ]', " ", table_res_no_space)
+            table_res_no_space = re.sub('colspan="', ' colspan="', table_res_no_space)
+            table_res_no_space = re.sub('rowspan="', ' rowspan="', table_res_no_space)
+            table_res_no_space = re.sub('border="', ' border="', table_res_no_space)
+
+            table_res = '<html><body><table border="1" >' + table_res + '</table></body></html>'
+            # table_flow.append(table_res)
+            # table_flow_no_space.append(table_res_no_space)
+
+        return table_res, table_res_no_space
+    
+    def clean_table(input_str,flag=True):
+        if flag:
+            input_str = input_str.replace('<sup>', '').replace('</sup>', '')
+            input_str = input_str.replace('<sub>', '').replace('</sub>', '')
+            input_str = input_str.replace('<span>', '').replace('</span>', '')
+            input_str = input_str.replace('<div>', '').replace('</div>', '')
+            input_str = input_str.replace('<p>', '').replace('</p>', '')
+            input_str = input_str.replace('<spandata-span-identity="">', '')
+            input_str = re.sub('<colgroup>.*?</colgroup>','',input_str)
+        return input_str
+    
+    norm_text, _ = process_table_html(text)
+    norm_text = clean_table(norm_text)
+    return norm_text
+
+def normalized_latex_table(text):
+    def latex_template(latex_code):  
+        template = r'''
+        \documentclass[border=20pt]{article}
+        \usepackage{subcaption}
+        \usepackage{url}
+        \usepackage{graphicx}
+        \usepackage{caption}
+        \usepackage{multirow}
+        \usepackage{booktabs}
+        \usepackage{color}
+        \usepackage{colortbl}
+        \usepackage{xcolor,soul,framed}
+        \usepackage{fontspec}
+        \usepackage{amsmath,amssymb,mathtools,bm,mathrsfs,textcomp}
+        \setlength{\parindent}{0pt}''' + \
+        r'''
+        \begin{document}
+        ''' + \
+        latex_code + \
+        r'''
+        \end{document}'''
+    
+        return template
+
+    def process_table_latex(latex_code):
+        SPECIAL_STRINGS= [
+            ['\\\\vspace\\{.*?\\}', ''],
+            ['\\\\hspace\\{.*?\\}', ''],
+            ['\\\\rule\{.*?\\}\\{.*?\\}', ''],
+            ['\\\\addlinespace\\[.*?\\]', ''],
+            ['\\\\addlinespace', ''],
+            ['\\\\renewcommand\\{\\\\arraystretch\\}\\{.*?\\}', ''],
+            ['\\\\arraystretch\\{.*?\\}', ''],
+            ['\\\\(row|column)?colors?\\{[^}]*\\}(\\{[^}]*\\}){0,2}', ''],
+            ['\\\\color\\{.*?\\}', ''],
+            ['\\\\textcolor\\{.*?\\}', ''],
+            ['\\\\rowcolor(\\[.*?\\])?\\{.*?\\}', ''],
+            ['\\\\columncolor(\\[.*?\\])?\\{.*?\\}', ''],
+            ['\\\\cellcolor(\\[.*?\\])?\\{.*?\\}', ''],
+            ['\\\\colorbox\\{.*?\\}', ''],
+            ['\\\\(tiny|scriptsize|footnotesize|small|normalsize|large|Large|LARGE|huge|Huge)', ''],
+            [r'\s+', ' '],
+            ['\\\\centering', ''],
+            ['\\\\begin\\{table\\}\\[.*?\\]', '\\\\begin{table}'],
+            ['\t', ''],
+            ['@{}', ''],
+            ['\\\\toprule(\\[.*?\\])?', '\\\\hline'],
+            ['\\\\bottomrule(\\[.*?\\])?', '\\\\hline'],
+            ['\\\\midrule(\\[.*?\\])?', '\\\\hline'],
+            ['p\\{[^}]*\\}', 'l'],
+            ['m\\{[^}]*\\}', 'c'],
+            ['\\\\scalebox\\{[^}]*\\}\\{([^}]*)\\}', '\\1'],
+            ['\\\\textbf\\{([^}]*)\\}', '\\1'],
+            ['\\\\textit\\{([^}]*)\\}', '\\1'],
+            ['\\\\cmidrule(\\[.*?\\])?\\(.*?\\)\\{([0-9]-[0-9])\\}', '\\\\cline{\\2}'],
+            ['\\\\hline', ''],
+            [r'\\multicolumn\{1\}\{[^}]*\}\{((?:[^{}]|(?:\{[^{}]*\}))*)\}', r'\1']
+        ]
+        pattern = r'\\begin\{tabular\}.*\\end\{tabular\}'  # 注意这里不用 .*?
+        matches = re.findall(pattern, latex_code, re.DOTALL)
+        latex_code = ' '.join(matches)
+
+        for special_str in SPECIAL_STRINGS:
+            latex_code = re.sub(fr'{special_str[0]}', fr'{special_str[1]}', latex_code)
+
+        return latex_code
+    
+    def convert_latex_to_html(latex_content, cache_dir='./temp'):
+        if not os.path.exists(cache_dir):
+            os.makedirs(cache_dir)
+
+        uuid_str = str(uuid.uuid1())
+        with open(f'{cache_dir}/{uuid_str}.tex', 'w') as f:
+            f.write(latex_template(latex_content))
+
+        cmd = ['latexmlc', '--quiet', '--nocomments', f'--log={cache_dir}/{uuid_str}.log',
+               f'{cache_dir}/{uuid_str}.tex', f'--dest={cache_dir}/{uuid_str}.html']
+        try:
+            subprocess.run(cmd, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+            with open(f'{cache_dir}/{uuid_str}.html', 'r') as f:
+                html_content = f.read()
+
+            pattern = r'<table\b[^>]*>(.*)</table>'
+            tables = re.findall(pattern, html_content, re.DOTALL | re.IGNORECASE)
+            tables = [f'<table>{table}</table>' for table in tables]
+            html_content = '\n'.join(tables)
+        
+        except Exception as e:
+            html_content = ''
+        
+        shutil.rmtree(cache_dir)
+        return html_content
+    
+    html_text = convert_latex_to_html(text)
+    normlized_tables = normalized_html_table(html_text)
+    return normlized_tables
+
+
+def normalized_table(text, format='html'):
+    if format not in ['html', 'latex']:
+        raise ValueError('Invalid format: {}'.format(format))
+    else:
+        return globals()['normalized_{}_table'.format(format)](text)
+
+
+def textblock_with_norm_formula(text):
+    inline_matches = inline_reg.finditer(text)
+    removal_positions = []
+    for match in inline_matches:
+        position = [match.start(), match.end()]
+        content = match.group(1) if match.group(1) is not None else match.group(2)
+
+        norm_content = normalized_formula(content)
+        removal_positions.append((position[0], position[1], norm_content))
+    
+    # Remove inline formulas from original text
+    for start, end, norm_content in sorted(removal_positions, reverse=True):
+        text = text[:start] + norm_content.strip() + text[end:]
+
+    return text
+
+def inline_filter_unicode(text):
+    # Ensure text is string type
+    if not isinstance(text, str):
+        text = str(text)
+    
+    # Replace inline formula boundary markers
+    placeholder = '__INLINE_FORMULA_BOUNDARY__'
+    text_copy = text.replace('$', placeholder).replace('\\(', placeholder).replace('\\)', placeholder)
+    # Convert LaTeX content to Unicode representation
+    try:
+        text_copy = LatexNodes2Text().latex_to_text(text_copy)
+    except Exception:
+        # 同上: 空矩阵等病态 latex 会令 pylatexenc 崩溃, 这里回退为占位符版原文,
+        # 后续占位符还原与正则匹配仍可继续, 避免单个文档拖垮整体预处理。
+        pass
+    # Restore boundary markers
+    text_copy = text_copy.replace(placeholder, '$')
+    
+    inline_array = []
+    inline_matches = inline_reg.finditer(text_copy)
+    # Record positions of inline formulas to be removed
+    removal_positions = []
+    
+    for match in inline_matches:
+        position = [match.start(), match.end()]
+        content = match.group(1) if match.group(1) is not None else match.group(2)
+        print('-------- content-------', content)
+        # Remove escape characters \
+        clean_content = re.sub(r'\\([\\_&%^])', '', content)
+
+        if any(char in clean_content for char in r'\^_'):
+            inline_array.append({
+                'category_type': 'equation_inline',
+                'position': position,
+                'content': content,
+            })
+            removal_positions.append((position[0], position[1]))
+    
+    # Remove inline formulas from original text
+    for start, end in sorted(removal_positions, reverse=True):
+        text = text[:start] + text[end:]
+
+    return text, inline_array
+
+def inline_filter(text):
+    # Ensure text is string type
+    if not isinstance(text, str):
+        text = str(text)
+    
+    inline_array = []
+    inline_matches = inline_reg.finditer(text)
+    
+    for match in inline_matches:
+        position = [match.start(), match.end()]
+        content = match.group(1) if match.group(1) is not None else match.group(2)
+        
+        # Remove escape characters \
+        clean_content = re.sub(r'\\([\\_&%^])', '', content)
+
+        if any(char in clean_content for char in r'\^_'):
+            inline_array.append({
+                'category_type': 'equation_inline',
+                'position': position,
+                'content': match.group(0),
+            })
+            text = text.replace(match.group(0), '')
+        else:
+            text = text.replace(match.group(0), content)
+
+    return text, inline_array
+
+# Text OCR quality check processing:
+def clean_string(input_string):
+    # Use regex to keep Chinese characters, English letters and numbers
+    # input_string = input_string.replace('\\t', '').replace('\\n', '').replace('\t', '').replace('\n', '').replace('/t', '').replace('/n', '')
+    input_string = input_string.replace('\\t', '').replace('\\n', '').replace('\t', '').replace('\n', '').replace('/t', '').replace('/n', '').replace('_', '')
+    cleaned_string = re.sub(r'[^\w\u4e00-\u9fff]', '', input_string)   # 只保留中英文和数字
+    return cleaned_string
\ No newline at end of file