import os
import json
import traceback
from typing import List, Dict, Any, Tuple
from openpyxl.utils import column_index_from_string
from openpyxl.workbook import Workbook
from openpyxl.worksheet.worksheet import Worksheet

from Config.Config import EXCEL_PATH
from Util.AreaUtil import query_area_info
from Util.DataUtil import (
    init_directories,
    process_value,
    print_conversion_stats,
    convert_area_name,
    save_to_json,
    load_workbook_sheet
)

# ======================== 配置常量 ======================== #
# 数据目录和JSON路径
DATA_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'Data')
JSON_PATH = os.path.join(DATA_DIR, 'ZaiXiaoShengCount.json')

# 工作表名称
SHEET_NAME = '在校生数'

# 区域名称所在列
REGION_NAME_COLUMN = 'B'

# 数据起始行
START_ROW = 4

# 年份范围
YEAR_RANGE = range(2015, 2025)

# 教育阶段配置 - 在校生数(2015-2024年)
EDUCATION_STAGES = [
    {
        'name': 'preschool',
        'chinese_name': '学前教育',
        'columns': [
            {'year': 2015, 'urban': 'D', 'town': 'E', 'rural': 'F', 'total': 'G'},
            {'year': 2016, 'urban': 'H', 'town': 'I', 'rural': 'J', 'total': 'K'},
            {'year': 2017, 'urban': 'L', 'town': 'M', 'rural': 'N', 'total': 'O'},
            {'year': 2018, 'urban': 'P', 'town': 'Q', 'rural': 'R', 'total': 'S'},
            {'year': 2019, 'urban': 'T', 'town': 'U', 'rural': 'V', 'total': 'W'},
            {'year': 2020, 'urban': 'X', 'town': 'Y', 'rural': 'Z', 'total': 'AA'},
            {'year': 2021, 'urban': 'AB', 'town': 'AC', 'rural': 'AD', 'total': 'AE'},
            {'year': 2022, 'urban': 'AF', 'town': 'AG', 'rural': 'AH', 'total': 'AI'},
            {'year': 2023, 'urban': 'AJ', 'town': 'AK', 'rural': 'AL', 'total': 'AM'},
            {'year': 2024, 'urban': 'AN', 'town': 'AO', 'rural': 'AP', 'total': 'AQ'}
        ]
    },
    {
        'name': 'primary',
        'chinese_name': '小学教育',
        'columns': [
            {'year': 2015, 'urban': 'AR', 'town': 'AS', 'rural': 'AT', 'total': 'AU'},
            {'year': 2016, 'urban': 'AV', 'town': 'AW', 'rural': 'AX', 'total': 'AY'},
            {'year': 2017, 'urban': 'AZ', 'town': 'BA', 'rural': 'BB', 'total': 'BC'},
            {'year': 2018, 'urban': 'BD', 'town': 'BE', 'rural': 'BF', 'total': 'BG'},
            {'year': 2019, 'urban': 'BH', 'town': 'BI', 'rural': 'BJ', 'total': 'BK'},
            {'year': 2020, 'urban': 'BL', 'town': 'BM', 'rural': 'BN', 'total': 'BO'},
            {'year': 2021, 'urban': 'BP', 'town': 'BQ', 'rural': 'BR', 'total': 'BS'},
            {'year': 2022, 'urban': 'BT', 'town': 'BU', 'rural': 'BV', 'total': 'BW'},
            {'year': 2023, 'urban': 'BX', 'town': 'BY', 'rural': 'BZ', 'total': 'CA'},
            {'year': 2024, 'urban': 'CB', 'town': 'CC', 'rural': 'CD', 'total': 'CE'}
        ]
    },
    {
        'name': 'junior',
        'chinese_name': '初中教育',
        'columns': [
            {'year': 2015, 'urban': 'CF', 'town': 'CG', 'rural': 'CH', 'total': 'CI'},
            {'year': 2016, 'urban': 'CJ', 'town': 'CK', 'rural': 'CL', 'total': 'CM'},
            {'year': 2017, 'urban': 'CN', 'town': 'CO', 'rural': 'CP', 'total': 'CQ'},
            {'year': 2018, 'urban': 'CR', 'town': 'CS', 'rural': 'CT', 'total': 'CU'},
            {'year': 2019, 'urban': 'CV', 'town': 'CW', 'rural': 'CX', 'total': 'CY'},
            {'year': 2020, 'urban': 'CZ', 'town': 'DA', 'rural': 'DB', 'total': 'DC'},
            {'year': 2021, 'urban': 'DD', 'town': 'DE', 'rural': 'DF', 'total': 'DG'},
            {'year': 2022, 'urban': 'DH', 'town': 'DI', 'rural': 'DJ', 'total': 'DK'},
            {'year': 2023, 'urban': 'DL', 'town': 'DM', 'rural': 'DN', 'total': 'DO'},
            {'year': 2024, 'urban': 'DP', 'town': 'DQ', 'rural': 'DR', 'total': 'DS'}
        ]
    },
    {
        'name': 'senior',
        'chinese_name': '高中教育',
        'columns': [
            {'year': 2015, 'urban': 'DT', 'town': 'DU', 'rural': 'DV', 'total': 'DW'},
            {'year': 2016, 'urban': 'DX', 'town': 'DY', 'rural': 'DZ', 'total': 'EA'},
            {'year': 2017, 'urban': 'EB', 'town': 'EC', 'rural': 'ED', 'total': 'EE'},
            {'year': 2018, 'urban': 'EF', 'town': 'EG', 'rural': 'EH', 'total': 'EI'},
            {'year': 2019, 'urban': 'EJ', 'town': 'EK', 'rural': 'EL', 'total': 'EM'},
            {'year': 2020, 'urban': 'EN', 'town': 'EO', 'rural': 'EP', 'total': 'EQ'},
            {'year': 2021, 'urban': 'ER', 'town': 'ES', 'rural': 'ET', 'total': 'EU'},
            {'year': 2022, 'urban': 'EV', 'town': 'EW', 'rural': 'EX', 'total': 'EY'},
            {'year': 2023, 'urban': 'EZ', 'town': 'FA', 'rural': 'FB', 'total': 'FC'},
            {'year': 2024, 'urban': 'FD', 'town': 'FE', 'rural': 'FF', 'total': 'FG'}
        ]
    },
    {
        'name': 'vocational',
        'chinese_name': '中职教育',
        'columns': [
            {'year': 2015, 'total': 'FH'},
            {'year': 2016, 'total': 'FI'},
            {'year': 2017, 'total': 'FJ'},
            {'year': 2018, 'total': 'FK'},
            {'year': 2019, 'total': 'FL'},
            {'year': 2020, 'total': 'FM'},
            {'year': 2021, 'total': 'FN'},
            {'year': 2022, 'total': 'FO'},
            {'year': 2023, 'total': 'FP'},
            {'year': 2024, 'total': 'FQ'}
        ]
    }
]

# ======================== 核心逻辑 ======================== #
def extract_stage_data(row: Tuple[Any, ...], stage: Dict[str, Any]) -> Dict[str, Dict[str, int]]:
    """
    提取单个教育阶段的在校生数据
    :param row: Excel行数据
    :param stage: 教育阶段配置
    :return: 格式化的阶段数据
    """
    stage_data = {}
    for year_config in stage['columns']:
        year = year_config['year']
        year_data = {}

        # 处理多类别教育阶段（学前到高中）
        if 'urban' in year_config:
            # 城区
            urban_col = column_index_from_string(year_config['urban']) - 1
            urban_val = row[urban_col] if len(row) > urban_col else None
            year_data['urban'] = process_value(urban_val)

            # 镇区
            town_col = column_index_from_string(year_config['town']) - 1
            town_val = row[town_col] if len(row) > town_col else None
            year_data['town'] = process_value(town_val)

            # 乡村
            rural_col = column_index_from_string(year_config['rural']) - 1
            rural_val = row[rural_col] if len(row) > rural_col else None
            year_data['rural'] = process_value(rural_val)

            # 总计
            total_col = column_index_from_string(year_config['total']) - 1
            total_val = row[total_col] if len(row) > total_col else None
            year_data['total'] = process_value(total_val)
        # 处理中职教育（单值）
        else:
            total_col = column_index_from_string(year_config['total']) - 1
            total_val = row[total_col] if len(row) > total_col else None
            year_data['total'] = process_value(total_val)

        stage_data[str(year)] = year_data
    return stage_data


# 修改函数定义，更新返回类型注解为4个值
def extract_student_data(sheet: Worksheet) -> Tuple[List[Dict[str, Any]], List[str], List[str], int]:
    """
    提取所有区域的在校生数据
    :param sheet: Excel工作表对象
    :return: 在校生数据列表、转换错误列表、处理总数
    """
    student_data = []
    name_conversion_errors = []
    conversion_records = []
    processed_count = 0
    region_col_index = column_index_from_string(REGION_NAME_COLUMN) - 1

    print(f"✅ 开始处理在校生数数据，共{sheet.max_row}行数据")

    # 遍历行数据
    for row_idx, row in enumerate(sheet.iter_rows(values_only=True), start=1):
        # 跳过表头行
        if row_idx < START_ROW:
            continue

        try:
            # 检查行是否有足够的列
            if len(row) <= region_col_index:
                print(f"⚠️ 第{row_idx}行数据不足，跳过")
                continue

            # 提取区域名称
            raw_name = row[region_col_index]
            # 修复：接收四个返回值并合并结果
            area_name, area_code, new_conversion, new_errors = convert_area_name(raw_name, row_idx)
            conversion_records.extend(new_conversion)
            name_conversion_errors.extend(new_errors)
            is_valid = len(new_errors) == 0

            # 记录转换结果
            if is_valid:
                # 将字符串记录改为字典格式
                conversion_records.append({
                    'row': row_idx,
                    'raw_name': raw_name,
                    'converted_name': area_name,
                    'status': 'success'
                })
                processed_count += 1
            else:
                error_msg = f"第{row_idx}行: {raw_name}"
                name_conversion_errors.append(error_msg)
                # 将字符串记录改为字典格式
                conversion_records.append({
                    'row': row_idx,
                    'raw_name': raw_name,
                    'converted_name': None,
                    'status': 'error'
                })
                continue

            # 创建区域数据对象
            area_data = {
                'area_name': area_name,
                'area_code': area_code,
                'raw_name': str(raw_name).strip(),
                'student_data': {}
            }

            # 提取各教育阶段数据
            for stage in EDUCATION_STAGES:
                stage_name = stage['name']
                area_data['student_data'][stage_name] = extract_stage_data(row, stage)

            student_data.append(area_data)

            # 进度提示
            if processed_count % 10 == 0 and processed_count > 0:
                print(f"🔄 已处理{processed_count}条数据...")

        except Exception as e:
            print(f"🔴 处理第{row_idx}行时发生错误：{str(e)}")
            continue

    # 修改return语句，添加name_conversion_errors返回值
    return student_data, conversion_records, name_conversion_errors, processed_count


def main() -> None:
    """主函数：执行在校生数数据处理流程"""
    try:
        # 初始化目录
        init_directories(DATA_DIR)  # 修复：移除列表括号，直接传入路径字符串

        # 加载Excel工作表
        sheet = load_workbook_sheet(EXCEL_PATH, SHEET_NAME)
        if not sheet:
            print(f"❌ 错误：未找到'{SHEET_NAME}'工作表")
            return

        # 提取数据
        # 修复：调整返回值顺序，获取转换记录列表
        student_data, conversion_records, name_conversion_errors, processed_count = extract_student_data(sheet)

        # 保存数据到JSON
        save_to_json(student_data, JSON_PATH)

        # 打印转换统计
        # 修复：传入转换记录列表而非处理数量
        print_conversion_stats(conversion_records, name_conversion_errors)
        print(f"💾 数据已保存至 {JSON_PATH}")

    except FileNotFoundError:
        print(f"🔴 错误：Excel文件 '{EXCEL_PATH}' 不存在")
    except Exception as e:
        print(f"🔴 处理数据时发生错误：{str(e)}{traceback.format_exc()}")


if __name__ == "__main__":
    main()