"""
表格识别个性化适配器 (v6 - 行内重叠合并修正版)

核心思想：
1. 废弃全局坐标聚类，改为按行分组和对齐，极大提升对倾斜、不规则表格的鲁棒性。
2. 结构生成与内容填充彻底分离：
   - `build_robust_html_from_cells`: 仅根据单元格几何位置，生成带`data-bbox`的HTML骨架。
   - `fill_html_with_ocr_by_bbox`: 根据`data-bbox`从全局OCR结果中查找文本并填充。
3. 通过适配器直接替换PaddleX Pipeline中的核心方法，实现无侵入式升级。
"""
import importlib
from typing import Any, Dict, List
import numpy as np

from paddlex.inference.pipelines.table_recognition.result import SingleTableRecognitionResult
from paddlex.inference.pipelines.table_recognition.pipeline_v2 import OCRResult

# --- 1. 核心算法：基于排序和行分组的HTML结构生成 ---
def filter_nested_boxes(boxes: List[list]) -> List[list]:
    """
    移除被其他框完全包含的框。
    boxes: List[[x1, y1, x2, y2]]
    """
    if not boxes:
        return []
    
    filtered = []
    # 按面积从大到小排序，优先保留大框
    boxes.sort(key=lambda b: (b[2] - b[0]) * (b[3] - b[1]), reverse=True)
    
    for i, box in enumerate(boxes):
        is_nested = False
        for j in range(i): # 只需和排在前面的（更大的）框比较
            outer_box = boxes[j]
            # 判断 box 是否被 outer_box 包含
            if outer_box[0] <= box[0] and outer_box[1] <= box[1] and \
               outer_box[2] >= box[2] and outer_box[3] >= box[3]:
                is_nested = True
                break
        if not is_nested:
            filtered.append(box)
    return filtered

def merge_overlapping_cells_in_row(row_cells: List[list], iou_threshold: float = 0.5) -> List[list]:
    """
    合并单行内水平方向上高度重叠的单元格。
    """
    if not row_cells:
        return []

    # 按x坐标排序
    cells = sorted(row_cells, key=lambda c: c[0])
    
    merged_cells = []
    i = 0
    while i < len(cells):
        current_cell = list(cells[i]) # 使用副本
        j = i + 1
        while j < len(cells):
            next_cell = cells[j]
            
            # 计算交集
            inter_x1 = max(current_cell[0], next_cell[0])
            inter_y1 = max(current_cell[1], next_cell[1])
            inter_x2 = min(current_cell[2], next_cell[2])
            inter_y2 = min(current_cell[3], next_cell[3])
            
            inter_area = max(0, inter_x2 - inter_x1) * max(0, inter_y2 - inter_y1)
            
            # 如果交集面积大于其中一个框面积的阈值，则认为是重叠
            current_area = (current_cell[2] - current_cell[0]) * (current_cell[3] - current_cell[1])
            next_area = (next_cell[2] - next_cell[0]) * (next_cell[3] - next_cell[1])
            
            if inter_area > min(current_area, next_area) * iou_threshold:
                # 合并两个框，取外包围框
                current_cell[0] = min(current_cell[0], next_cell[0])
                current_cell[1] = min(current_cell[1], next_cell[1])
                current_cell[2] = max(current_cell[2], next_cell[2])
                current_cell[3] = max(current_cell[3], next_cell[3])
                j += 1
            else:
                break # 不再与更远的单元格合并
        
        merged_cells.append(current_cell)
        i = j
        
    return merged_cells

def build_robust_html_from_cells(cells_det_results: List[list]) -> str:
    """
    通过按行排序、分组、合并和对齐，稳健地将单元格Bbox列表转换为带data-bbox的HTML结构。
    """
    if not cells_det_results:
        return "<table><tbody></tbody></table>"

    cells = filter_nested_boxes(cells_det_results)
    cells.sort(key=lambda c: (c[1], c[0]))

    rows = []
    if cells:
        current_row = [cells[0]]
        row_anchor_y = (cells[0][1] + cells[0][3]) / 2
        row_anchor_height = cells[0][3] - cells[0][1]

        for cell in cells[1:]:
            cell_y_center = (cell[1] + cell[3]) / 2
            if abs(cell_y_center - row_anchor_y) < row_anchor_height * 0.7:
                current_row.append(cell)
            else:
                rows.append(current_row)
                current_row = [cell]
                row_anchor_y = (cell[1] + cell[3]) / 2
                row_anchor_height = cell[3] - cell[1]
        rows.append(current_row)

    html = "<table><tbody>"
    for row_cells in rows:
        # 🎯 核心修正：在生成HTML前，合并行内的重叠单元格
        merged_row_cells = merge_overlapping_cells_in_row(row_cells)
        
        html += "<tr>"
        for cell in merged_row_cells:
            bbox_str = f"[{','.join(map(str, map(int, cell)))}]"
            html += f'<td data-bbox="{bbox_str}"></td>'
        html += "</tr>"
    html += "</tbody></table>"
    
    return html

# --- 2. 内容填充工具 ---

def fill_html_with_ocr_by_bbox(html_skeleton: str, ocr_dt_boxes: list, ocr_texts: list) -> str:
    """
    根据带有 data-bbox 的 HTML 骨架和全局 OCR 结果填充表格内容。
    """
    try:
        from bs4 import BeautifulSoup
    except ImportError:
        print("⚠️  BeautifulSoup not installed. Cannot fill table content. Returning skeleton.")
        return html_skeleton

    soup = BeautifulSoup(html_skeleton, 'html.parser')
    # # ocr_dt_boxes = cells_ocr_res.get("rec_boxes", [])
    # ocr_texts = cells_ocr_res.get("rec_texts", [])

    # 为快速查找，将OCR结果组织起来
    ocr_items = []
    for box, text in zip(ocr_dt_boxes, ocr_texts):
        center_x = (box[0] + box[2]) / 2
        center_y = (box[1] + box[3]) / 2
        ocr_items.append({'box': box, 'text': text, 'center': (center_x, center_y)})

    for td in soup.find_all('td'):
        if not td.has_attr('data-bbox'):
            continue
        
        bbox_str = td['data-bbox'].strip('[]')
        cell_box = list(map(float, bbox_str.split(',')))
        cx1, cy1, cx2, cy2 = cell_box

        cell_texts_with_pos = []
        # 查找所有中心点在该单元格内的OCR文本
        for item in ocr_items:
            if cx1 <= item['center'][0] <= cx2 and cy1 <= item['center'][1] <= cy2:
                # 记录文本和其y坐标，用于后续排序
                cell_texts_with_pos.append((item['text'], item['box'][1]))
        
        if cell_texts_with_pos:
            # 按y坐标排序，确保多行文本的顺序正确
            cell_texts_with_pos.sort(key=lambda x: x[1])
            # 合并文本
            td.string = " ".join([text for text, y in cell_texts_with_pos])
            
    return str(soup)

# --- 3. 适配器主函数和应用逻辑 ---

# 保存原始方法的引用
_original_predict_single = None

def enhanced_predict_single_table_recognition_res(
    # self, *args, **kwargs):
    self,
    image_array: np.ndarray,
    overall_ocr_res: OCRResult,
    table_box: list,
    use_e2e_wired_table_rec_model: bool = False,
    use_e2e_wireless_table_rec_model: bool = False,
    use_wired_table_cells_trans_to_html: bool = False,
    use_wireless_table_cells_trans_to_html: bool = False,
    use_ocr_results_with_table_cells: bool = True,
    flag_find_nei_text: bool = True,
) -> SingleTableRecognitionResult:
    """
    这是将被注入到 _TableRecognitionPipelineV2 实例中的增强版方法。
    它调用我们新的、解耦的结构生成和内容填充逻辑。
    """
    print(">>> [Adapter] enhanced_predict_single_table_recognition_res called")
    
    # 🎯 复用原始逻辑来获取 table_cells_result
    table_cls_pred = list(self.table_cls_model(image_array))[0]
    table_cls_result = self.extract_results(table_cls_pred, "cls")

    if table_cls_result == "wired_table":
        table_cells_pred = list(self.wired_table_cells_detection_model(image_array, threshold=0.3))[0]
    else: # wireless_table
        table_cells_pred = list(self.wireless_table_cells_detection_model(image_array, threshold=0.3))[0]
    
    table_cells_result, table_cells_score = self.extract_results(table_cells_pred, "det")
    table_cells_result, table_cells_score = self.cells_det_results_nms(table_cells_result, table_cells_score)
    cells_texts_list = self.gen_ocr_with_table_cells(image_array, table_cells_result)

    # 🎯 注入我们的核心逻辑
    # 只有当 use_wired_table_cells_trans_to_html 为 True 时，才使用我们的新方法
    # 这样可以保持与原始行为的兼容性，并提供一个开关
    if use_wired_table_cells_trans_to_html:
        print(">>> [Adapter] Using robust HTML generation from cells.")
        # 步骤1: 使用我们鲁棒的算法生成HTML骨架
        html_skeleton = build_robust_html_from_cells(table_cells_result)

        # 步骤2: 使用全局OCR结果和Bbox来填充内容
        pred_html = fill_html_with_ocr_by_bbox(html_skeleton, table_cells_result, cells_texts_list)

        single_img_res = {
            "cell_box_list": table_cells_result,
            "table_ocr_pred": {}, # 内容已填充，无需单独的 table_ocr_pred
            "pred_html": pred_html,
        }
        # 构造并返回结果
        res = SingleTableRecognitionResult(single_img_res)
        res["neighbor_texts"] = "" # 保持字段存在
        return res
    else:
        # 🎯 如果开关关闭，则调用原始的、未被补丁的方法
        print(">>> [Adapter] Falling back to original predict_single_table_recognition_res.")
        return _original_predict_single(self, *args, **kwargs)


def apply_table_recognition_adapter():
    """
    应用表格识别适配器。
    我们直接替换 _TableRecognitionPipelineV2 类中的 `predict_single_table_recognition_res` 方法。
    """
    global _original_predict_single
    
    try:
        # 导入目标类
        from paddlex.inference.pipelines.table_recognition.pipeline_v2 import _TableRecognitionPipelineV2
        
        # 保存原函数，防止重复应用补丁
        if _original_predict_single is None:
             _original_predict_single = _TableRecognitionPipelineV2.predict_single_table_recognition_res
        
        # 替换为增强版
        _TableRecognitionPipelineV2.predict_single_table_recognition_res = enhanced_predict_single_table_recognition_res
        
        print("✅ Table recognition adapter applied successfully (v3 - corrected).")
        return True
        
    except Exception as e:
        print(f"❌ Failed to apply table recognition adapter: {e}")
        return False


def restore_original_function():
    """恢复原始函数"""
    global _original_predict_single
    try:
        from paddlex.inference.pipelines.table_recognition.pipeline_v2 import _TableRecognitionPipelineV2
        
        if _original_predict_single is not None:
            _TableRecognitionPipelineV2.predict_single_table_recognition_res = _original_predict_single
            _original_predict_single = None # 重置状态
            print("✅ Original function restored.")
            return True
        return False
    except Exception as e:
        print(f"❌ Failed to restore original function: {e}")
        return False