"""
测试京东账单清洗器
"""
import zipfile
import tempfile
import os
import csv
import sys

# 确保输出使用 UTF-8
sys.stdout.reconfigure(encoding='utf-8')

def test_jd_cleaner():
    zip_path = r'D:\Projects\BillAI\mock_data\京东交易流水(申请时间2026年01月26日13时29分47秒)(密码683263)_209.zip'
    
    with zipfile.ZipFile(zip_path, 'r') as zf:
        with tempfile.TemporaryDirectory() as tmpdir:
            zf.extractall(tmpdir, pwd=b'683263')
            
            # Find CSV file
            for f in os.listdir(tmpdir):
                if f.endswith('.csv'):
                    input_file = os.path.join(tmpdir, f)
                    output_file = os.path.join(tmpdir, 'output.csv')
                    
                    print(f"Input file: {f}")
                    print("-" * 60)
                    
                    # Run cleaner
                    from cleaners.jd import JDCleaner
                    cleaner = JDCleaner(input_file, output_file)
                    cleaner.clean()
                    
                    # Read output and show review levels
                    print("\n" + "=" * 60)
                    print("OUTPUT REVIEW LEVELS")
                    print("=" * 60)
                    
                    with open(output_file, 'r', encoding='utf-8') as of:
                        reader = csv.reader(of)
                        header = next(reader)
                        review_idx = header.index('复核等级') if '复核等级' in header else -1
                        cat_idx = header.index('交易分类') if '交易分类' in header else -1
                        merchant_idx = header.index('交易对方') if '交易对方' in header else -1
                        desc_idx = header.index('商品说明') if '商品说明' in header else -1
                        
                        stats = {'': 0, 'LOW': 0, 'HIGH': 0}
                        rows_needing_review = []
                        
                        for row in reader:
                            review = row[review_idx] if review_idx >= 0 else ''
                            stats[review] = stats.get(review, 0) + 1
                            if review:  # Collect rows that need review
                                cat = row[cat_idx] if cat_idx >= 0 else ''
                                merchant = row[merchant_idx][:20] if merchant_idx >= 0 else ''
                                desc = row[desc_idx][:25] if desc_idx >= 0 else ''
                                rows_needing_review.append((review, cat, merchant, desc))
                        
                        # Print rows needing review
                        print(f"{'Level':<5} | {'Category':<12} | {'Merchant':<20} | Description")
                        print("-" * 70)
                        for review, cat, merchant, desc in rows_needing_review:
                            print(f"{review:<5} | {cat:<12} | {merchant:<20} | {desc}")
                        
                        print("\n" + "=" * 60)
                        print("STATISTICS")
                        print("=" * 60)
                        print(f"No review (confident): {stats['']}")
                        print(f"LOW (keyword match):   {stats['LOW']}")
                        print(f"HIGH (needs manual):   {stats['HIGH']}")
                        print(f"Total:                 {sum(stats.values())}")


def test_infer_jd_category():
    """测试分类推断逻辑"""
    from cleaners.jd import infer_jd_category
    
    print("\n" + "=" * 60)
    print("INFER_JD_CATEGORY TESTS")
    print("=" * 60)
    
    tests = [
        # (商户, 商品, 原分类, 预期等级, 说明)
        ('京东外卖', '火鸡面', '', 0, '商户映射'),
        ('京东平台商户', 'xxx', '食品酒饮', 0, '原分类映射'),
        ('京东平台商户', 'xxx', '数码电器', 0, '原分类映射'),
        ('京东平台商户', 'xxx', '日用百货', 0, '原分类映射'),
        ('京东平台商户', 'xxx', '图书文娱', 0, '原分类映射'),
        ('京东平台商户', '猫粮', '其他', 1, '空映射+关键词成功'),
        ('京东平台商户', '咖啡', '其他网购', 1, '空映射+关键词成功'),
        ('京东平台商户', 'xxx', '其他', 2, '空映射+关键词失败'),
        ('京东平台商户', 'xxx', '家居用品', 2, '未知分类'),
        ('京东平台商户', 'xxx', '母婴', 2, '未知分类'),
        ('京东平台商户', 'xxx', '', 2, '无原分类+关键词失败'),
    ]
    
    level_map = {0: 'NONE', 1: 'LOW', 2: 'HIGH'}
    
    print(f"{'Merchant':<15} | {'Product':<8} | {'OrigCat':<10} | {'Result':<12} | {'Level':<5} | {'Expected':<5} | Note")
    print("-" * 90)
    
    all_pass = True
    for merchant, product, orig_cat, expected_level, note in tests:
        cat, certain, level = infer_jd_category(merchant, product, orig_cat)
        status = "✓" if level == expected_level else "✗"
        if level != expected_level:
            all_pass = False
        print(f"{merchant:<15} | {product:<8} | {orig_cat or '(empty)':<10} | {cat:<12} | {level_map[level]:<5} | {level_map[expected_level]:<5} | {note} {status}")
    
    print("\n" + ("All tests passed!" if all_pass else "Some tests FAILED!"))


if __name__ == '__main__':
    test_infer_jd_category()
    print("\n")
    test_jd_cleaner()