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import csv
import numpy as np
from scipy.stats import spearmanr, kendalltau

def add_to_csv(input_files,human_score_csv):
    output_rows = []
    for idx in [0,10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190]:
        for filename in input_files:
            with open(filename, newline='', encoding='utf-8') as f:
                reader = csv.reader(f)
                header = next(reader)  # 跳过表头
                rows = list(reader)
                if idx < len(rows):
                    for row in rows:
                        if row[0] == f"{idx+1:04d}.png":
                            target_row = row
                            break
                    # print(row)
                    # 倒数第三和倒数第二个元素
                    fourth_last = target_row[-4] #reason score
                    third_last = target_row[-3] #align score
                    second_last = target_row[-2] #quality score
                    output_rows.append([fourth_last,third_last, second_last])
                        
    # 2. 读取原CSV内容
    with open(human_score_csv, newline='', encoding='utf-8') as f:
        reader = csv.reader(f)
        original_rows = list(reader)

    # 3. 检查行数是否匹配
    if len(original_rows) - 1 != len(output_rows):
        raise ValueError("原CSV数据行数与新列数据行数不一致，请检查！")

    # 4. 拼接新列，写入新文件
    with open(human_score_csv, 'w', newline='', encoding='utf-8') as f:
        writer = csv.writer(f)
        # 写表头
        writer.writerow(original_rows[0][0:10] + ['qwen_reason','qwen_alignment', 'qwen_quality'])
        # 写数据行
        for orig_row, new_col in zip(original_rows[1:], output_rows):
            writer.writerow(orig_row[0:10] + new_col)
            
            
def corr(combined_csv):
    avg_align_list = []
    avg_quality_list= []
    qwen_reason_list = []
    qwen_align_list = []
    qwen_quality_list = []
    
    with open(combined_csv, newline='', encoding='utf-8') as f:
        reader = csv.reader(f)
        header = next(reader)  # 跳过表头
        for row in reader:
            # 取倒数第3、4、5列
            alignment_123 = [float(row[-8]), float(row[-7]), float(row[-6])]
            quality_123 = [float(row[-5]), float(row[-4]), float(row[-3])]
            alignment_avg = np.mean(alignment_123)
            quality_avg = np.mean(quality_123)
            avg_align_list.append(alignment_avg)
            avg_quality_list.append(quality_avg)
            # 取倒数第2列
            qwen_reason = float(row[-3])
            qwen_alignment = float(row[-2])
            qwen_quality = float(row[-1])
            qwen_reason_list.append(qwen_reason)
            qwen_align_list.append(qwen_alignment)
            qwen_quality_list.append(qwen_quality)

    # 2. 计算相关性
  
    weight1 = 0.7
    weight2 = 0.3
    qwen_total_align = [weight1 * v1 + weight2 * v2 for v1, v2 in zip(qwen_reason_list, qwen_align_list)]

    align_spearman_corr, align_spearman_p = spearmanr(avg_align_list, qwen_total_align)
    align_kendall_corr, align_kendall_p = kendalltau(avg_align_list, qwen_total_align)
    
    quality_spearman_corr, quality_spearman_p = spearmanr(avg_quality_list, qwen_quality_list)
    quality_kendall_corr, quality_kendall_p = kendalltau(avg_quality_list, qwen_quality_list)

    print(f"align Spearman correlation: {align_spearman_corr:.4f}, p-value: {align_spearman_p:.4g}")
    print(f"align Kendall correlation: {align_kendall_corr:.4f}, p-value: {align_kendall_p:.4g}")

    print(f"quality Spearman correlation: {quality_spearman_corr:.4f}, p-value: {quality_spearman_p:.4g}")
    print(f"quality Kendall correlation: {quality_kendall_corr:.4f}, p-value: {quality_kendall_p:.4g}")
    
    
if __name__ == "__main__":
    

    # input_files = [
    #     "/group/xihuiliu/sky/reasoning/csv/idiom/bagel.csv",
    #     "/group/xihuiliu/sky/reasoning/csv/idiom/GPT.csv",
    #     "/group/xihuiliu/sky/reasoning/csv/idiom/hidream.csv",
    #     "/group/xihuiliu/sky/reasoning/csv/idiom/janus_pro_7B.csv",
    #     "/group/xihuiliu/sky/reasoning/csv/idiom/sd30_medium.csv",
    # ]
    # human_score_csv = "/group/xihuiliu/sky/reasoning/human_eval/human_score/idiom.csv"
    
    # input_files = [
    #     "/group/xihuiliu/sky/reasoning/csv/text_image_new/bagel.csv",
    #     "/group/xihuiliu/sky/reasoning/csv/text_image_new/GPT.csv",
    #     "/group/xihuiliu/sky/reasoning/csv/text_image_new/hidream.csv",
    #     "/group/xihuiliu/sky/reasoning/csv/text_image_new/janus_pro_7B.csv",
    #     "/group/xihuiliu/sky/reasoning/csv/text_image_new/sd30_medium.csv",
    # ]
    # human_score_csv = "/group/xihuiliu/sky/reasoning/human_eval/human_score/text_image.csv"
    
    # input_files = [
    #     "/group/xihuiliu/sky/reasoning/csv/entity/bagel.csv",
    #     "/group/xihuiliu/sky/reasoning/csv/entity/GPT.csv",
    #     "/group/xihuiliu/sky/reasoning/csv/entity/hidream.csv",
    #     "/group/xihuiliu/sky/reasoning/csv/entity/janus_pro_7B.csv",
    #     "/group/xihuiliu/sky/reasoning/csv/entity/sd30_medium.csv",
    # ]
    # human_score_csv = "/group/xihuiliu/sky/reasoning/human_eval/human_score/entity.csv"
    
    
    input_files = [
        "/group/xihuiliu/sky/reasoning/csv/physics/bagel.csv",
        "/group/xihuiliu/sky/reasoning/csv/physics/GPT.csv",
        "/group/xihuiliu/sky/reasoning/csv/physics/hidream.csv",
        "/group/xihuiliu/sky/reasoning/csv/physics/janus_pro_7B.csv",
        "/group/xihuiliu/sky/reasoning/csv/physics/sd30_medium.csv",
    ]
    human_score_csv = "/group/xihuiliu/sky/reasoning/human_eval/human_score/physics.csv"
    
    add_to_csv(input_files,human_score_csv)

    corr(human_score_csv)
    
    
#idiom
# align Spearman correlation: 0.4674, p-value: 9.474e-07
# align Kendall correlation: 0.3498, p-value: 1.911e-06
# quality Spearman correlation: 0.3305, p-value: 0.0007828
# quality Kendall correlation: 0.2622, p-value: 0.0007976

#text image
# align Spearman correlation: 0.7675, p-value: 1.246e-20
# align Kendall correlation: 0.6081, p-value: 9.005e-17
# quality Spearman correlation: 0.4186, p-value: 1.465e-05
# quality Kendall correlation: 0.3257, p-value: 3.135e-05

# entity 0.9 0.1
# align Spearman correlation: 0.6091, p-value: 1.771e-11
# align Kendall correlation: 0.4673, p-value: 3.437e-10
# quality Spearman correlation: 0.3947, p-value: 4.824e-05
# quality Kendall correlation: 0.3141, p-value: 0.0001425

# entity 0.8 0.2
# align Spearman correlation: 0.6106, p-value: 1.529e-11
# align Kendall correlation: 0.4693, p-value: 2.989e-10
# quality Spearman correlation: 0.3947, p-value: 4.824e-05
# quality Kendall correlation: 0.3141, p-value: 0.0001425

# entity 0.7 0.3 THIS!!!!!!!!!!!!
# align Spearman correlation: 0.6148, p-value: 1.012e-11
# align Kendall correlation: 0.4732, p-value: 2.094e-10
# quality Spearman correlation: 0.3947, p-value: 4.824e-05
# quality Kendall correlation: 0.3141, p-value: 0.0001425

# entity 0.6 0.4
# align Spearman correlation: 0.6098, p-value: 1.646e-11
# align Kendall correlation: 0.4678, p-value: 3.428e-10
# quality Spearman correlation: 0.3947, p-value: 4.824e-05
# quality Kendall correlation: 0.3141, p-value: 0.0001425

# physics 0.9 0.1
# align Spearman correlation: 0.5756, p-value: 3.746e-10
# align Kendall correlation: 0.4477, p-value: 6.166e-10
# quality Spearman correlation: 0.2652, p-value: 0.007651
# quality Kendall correlation: 0.2080, p-value: 0.007783

# physics 0.8 0.2
# align Spearman correlation: 0.5773, p-value: 3.239e-10
# align Kendall correlation: 0.4496, p-value: 5.381e-10
# quality Spearman correlation: 0.2652, p-value: 0.007651
# quality Kendall correlation: 0.2080, p-value: 0.007783

# physics 0.7 0.3
# align Spearman correlation: 0.5777, p-value: 3.131e-10
# align Kendall correlation: 0.4490, p-value: 5.498e-10
# quality Spearman correlation: 0.2652, p-value: 0.007651
# quality Kendall correlation: 0.2080, p-value: 0.007783

# physics 0.6 0.4
# align Spearman correlation: 0.5839, p-value: 1.811e-10
# align Kendall correlation: 0.4566, p-value: 3.858e-10
# quality Spearman correlation: 0.2652, p-value: 0.007651
# quality Kendall correlation: 0.2080, p-value: 0.007783

# physics 0.5 0.5
# align Spearman correlation: 0.5827, p-value: 2.008e-10
# align Kendall correlation: 0.4594, p-value: 3.317e-10
# quality Spearman correlation: 0.2652, p-value: 0.007651
# quality Kendall correlation: 0.2080, p-value: 0.007783