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#Pearson correlation coefficient
import argparse
import numpy as np
import json
import pandas as pd
import scipy.stats
import os
import math

def compute_correlation(set1, set2):
    corr_coefficient = np.corrcoef(set1, set2)[0, 1]
    return corr_coefficient

def compute_max_correlation(fix, var): #(350,3)
    set1 = fix[:,0].flatten()
    set2 = var[:,0].flatten()
    max = np.corrcoef(set1,set2)[0, 1]
    selected_human_score = set2
    selected_human_index = np.zeros(num_images)

    for i in range(num_images):
        for j in range(3):
            set2[i] = var[i,j]
            corr_coefficient = np.corrcoef(set1, set2)[0, 1]
            if corr_coefficient > max:
                selected_human_score[i] = var[i,j]
                selected_human_index[i] = j
                max = corr_coefficient

    return max,selected_human_index,selected_human_score

# Normalizing function using Min-Max scaling
def min_max_normalize(scores):
    min_score = np.min(scores)
    max_score = np.max(scores)
    return (scores - min_score) / (max_score - min_score)

def scale_dict_values(input_dict):
    # 获取字典中所有值
    values = list(input_dict.values())
    
    # 找到最小值和最大值
    min_value = min(values)
    max_value = max(values)
    
    # 定义缩放函数
    def scale_value(value):
        return 1 + (value - min_value) * (5 - 1) / (max_value - min_value)
    
    # 创建一个新的字典，其中所有值都缩放到1到5之间
    scaled_dict = {key: scale_value(value) for key, value in input_dict.items()}
    
    return scaled_dict

def parse_args():
    parser = argparse.ArgumentParser(description="Demo")

    parser.add_argument(
        "--auto_evaluation",
        type=str,
        help="csv",
        # default="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_5_2/frame_score.csv" # TODO pkc
        # default="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_9_2/frame_score.csv" # TODO
        # default="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_2_2/frame_score_mechanism_3_1.csv" # TODO
        # default="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_2_2/frame_score_mechanism.csv" # TODO pick
        # default="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_6_2_gt/grid_score.csv" # TODO pick
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_6_2_gt/frame_score.csv"
        # default="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/pllava/csv_output_6_2/video_score.csv" # 
        # default="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_5_2/grid_score.csv" # TODO pick
        # default="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/clip/csv_output_6_2_gt/clip.csv" # TODO
        # default ="/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/csv_output_9_2_user_study/thresh_04_2_score.csv"
        # default ="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/b_clip/csv_output_2_2/b_clip.csv"
        # default ="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/clip/csv_output_2_2/clip.csv"
        # default ="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/pllava/csv_output_2_2/video_score.csv"
        
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_1_2/grid_score_again.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_1_2/grid_score_6_again.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_1_2/grid_score_4_adjust_again.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_1_2/grid_score_8.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_1_2/frame_score_8.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/BLIP-vqa/csv_output_1_2_blipvqa.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/pllava/csv_output_5_2/video_score.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_2_2/frame_score_mechanism_test.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_2_2/grid_score.csv"
        
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/csv_output_3_2_user_study_2d/user_study_2d_03_combine_frame.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/csv_output_3_2_user_study_2d/user_study_2d_03_321split_combine_frame.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/csv_output_3_2_user_study_2d/user_study_2d_03_321split_algorithm_035_combine_frame.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/csv_output_3_2_user_study_2d/user_study_2d_03_321split_algorithm_035_2_combine_frame.csv",
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/Grounded-Segment-Anything/csv_3_depth_user_study/algorithm_035_combine_frame.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/Grounded-Segment-Anything/csv_3_depth_user_study/algorithm_035_addscore_combine_frame.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/clip/csv_output_3_2/clip.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/b_clip/csv_output_6_2/b_clip.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/dot/csv_4_2_user_study/4_2_user_study_score.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/clip/csv_output_4_2/clip.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/b_clip/csv_output_4_2/b_clip.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_4_2/frame_score.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_4_2/grid_score.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_1_2/frame_score_corrected.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/csv_output_3_2_user_study_2d/user_study_2d_03_321split_algorithm_035_productscore_combine_frame.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/csv_output_3_2_user_study_2d/user_study_2d_03_321split_algorithm_035_2_combine_frame.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/Grounded-Segment-Anything/csv_3_depth_user_study/algorithm_035_addscore_combine_frame.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/groundingDINO_3/spatial_final_2.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/csv_output_3_2_user_study_2d/user_study_2d_03_321split_algorithm_035_corrected_combine_frame.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/csv_output_3_2_user_study_2d/user_study_2d_03_321split_algorithm_035_corrected_combine_frame.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/csv_output_3_2_user_study_2d/user_study_2d_03_321split_algorithm_035_corrected_2_combine_frame.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/Grounded-Segment-Anything/csv_3_depth_user_study/algorithm_035_corrected_combine_frame.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/b_clip/csv_output_3_2/b_clip.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_3_2/frame_score.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/pllava/csv_output_3_2/video_score.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/pllava/csv_output_4_2/video_score.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_1_2/grid_score_8.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_1_2/frame_score_corrected_8_withinitial.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/pllava/csv_output_1_2/video_score.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/BLIP-vqa/csv_output_1_2_blipvqa.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/b_clip/csv_output_1_2/b_clip.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/clip/csv_output_6_2_gt/clip.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/groundingDINO_3/spatial_method1.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/groundingDINO_3/spatial_method2.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_5_2/frame_score.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_5_2/grid_score_old.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/b_clip/csv_output_5_2/b_clip.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/clip/csv_output_5_2/clip.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/pllava/csv_output_5_2/video_score.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_5_2/frame_score_new.csv"
        
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/viclip/consistent_attr_rebuttal.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/viclip/dynamic_attr_rebuttal.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/viclip/spatial_relationship_rebuttal.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/viclip/motion_binding_rebuttal.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/viclip/action_rebuttal.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/viclip/interaction_rebuttal.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/viclip/numeracy_rebuttal.csv"
        
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/rebuttal_models/EvalCrafter/blipbleu_rebuttal/consistent_attr_rebuttal.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/rebuttal_models/EvalCrafter/blipbleu_rebuttal/dynamic_attr_rebuttal.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/rebuttal_models/EvalCrafter/blipbleu_rebuttal/spatial_relationship_rebuttal.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/rebuttal_models/EvalCrafter/blipbleu_rebuttal/motion_binding_rebuttal.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/rebuttal_models/EvalCrafter/blipbleu_rebuttal/action_rebuttal.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/rebuttal_models/EvalCrafter/blipbleu_rebuttal/interaction_rebuttal.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/rebuttal_models/EvalCrafter/blipbleu_rebuttal/numeracy_rebuttal.csv"
        
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/rebuttal_models/VPEval/csv_rebuttal/vpeval_user_study_video.csv"
        
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/rebuttal_videodirectorgpt_csv/M-GDino_user_study_rebuttal.csv"
        
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/LLaVA/cvpr_csv/csv_6/user_study/user-t0_object_interactions_score_0.0.csv",
        
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava_cvpr/csv_output_1_2/frame_score.csv",
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava_cvpr/csv_output_5_2/frame_score.csv",
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava_cvpr/csv_output_6_2_gt/frame_score.csv",
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava_cvpr/csv_output_1_2/frame_score_1st_last_2.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava_cvpr/csv_output_3_2/frame_score.csv",
        
        # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/LLaVA/cvpr_csv/csv_1/user_study/cvpr_conv/user-t005_consistent_attr_score_0.05_4.csv",
    )
    parser.add_argument(
        "--human_evaluation",
        type=str,
        help="csv",
        # default="/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/action_grid_corrected.csv" # pick!!!
        # default="/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/dynamic_attr.csv"
        # default="/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/interaction_llava_grid.csv"
        # default="/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/interaction_llava_grid_corrected_gt_filter2.csv" #pick!!!
        # default="/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/dynamic_attr_mechanism_corrected.csv" #pick!!!
        # default="/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/interaction_gt.csv"
        # default="/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/consistent spatial_human.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/consistent spatial_human_corrected2.csv"
        # default="/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/numeracy_human.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/numeracy_human_adjust.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/dynamic_spatial_human_1.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/consistent attribute_human.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/consistent attribute_human_2.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/consistent attribute_human_corrected.csv"
        
        default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/consistent attribute_human_corrected_kaiyue.csv", #ok final
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/dynamic_attr_mechanism_corrected_kaiyue.csv" #ok final
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/consistent spatial_human_corrected2_kaiyue.csv" #ok final
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/dynamic_spatial_human_1_kaiyue.csv"               #ok final
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/action_adjust_kaiyue_arxiv.csv"  #ok final
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/interaction_llava_grid_corrected_gt_filter2_kaiyue.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/numeracy_human_adjust_corrected_kaiyue.csv"
        # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/interaction_adjust_kaiyue.csv", #ok final
        
 
    )

    args = parser.parse_args()
    return args

if __name__ == "__main__":

    args = parse_args()
    auto_evaluation = args.auto_evaluation



    method = ["videocrafter2", "latte", "opensora11", "opensoraplan", "show1", "modelscope", "gt"]
    dic_human = {} # store by model name, for calculating avg score for each model
    dic_auto = {}# store by model name
    dic_score_category = {}
    
    dic_human_avg = {} # store by video name, for calculating human corr with avg score
    dic_auto_avg = {} # store by video name

    # hky
    
    # get AMT score
    # Define the path to your CSV file
    csv_file = args.human_evaluation 

    # Read the CSV file into a pandas DataFrame
    data_frame = pd.read_csv(csv_file)

    try:
        column_index = data_frame.columns.get_loc('Input.video')
    except:
        column_index = data_frame.columns.get_loc('Input.url')
    column_name = data_frame.iloc[:,column_index]
    column_name = column_name.to_numpy()

    try:
        score_index = data_frame.columns.get_loc('Answer.score')
    except:
        score_index = data_frame.columns.get_loc('Answer.correctness_answer')
    score_AMT = data_frame.iloc[:,score_index]
    score_AMT = score_AMT.to_numpy()


    score_dic = {} # to store {method: {id: score,... },... }
    # read from auto eval
    csv_file_auto = args.auto_evaluation
    data_frame_auto = pd.read_csv(csv_file_auto)
    # read key = id, value = Score from csv
    for i in range(len(data_frame_auto)):
        # print(data_frame_auto.iloc[i])
        # score_dic[(data_frame_auto.iloc[i]['name'].split(".")[0])] = float(data_frame_auto.iloc[i][-1])
        score_dic[(data_frame_auto.iloc[i]['name'].split(".")[0])] = float(data_frame_auto.iloc[i]['Score'])
        # score_dic[(data_frame_auto[0].split(".")[0])] = float(data_frame_auto.iloc[i]['Score'])
        
        # score_dic[(data_frame_auto.iloc[i]['name'])] = float(data_frame_auto.iloc[i]['Score'])
        # score_dic[(data_frame_auto.iloc[i]['id'])] = float(data_frame_auto.iloc[i]['Score'])
    # # read from human eval's csv (col. "Input.video"), store {method: [score,...],...}
    for me in range(len(method)): # initialize
        dic_human[method[me]] = []
        dic_auto[method[me]] = []
        dic_score_category[method[me]] = []

    print(score_dic)
    record = np.empty(0)
    score_human = np.empty(0) # for calculating human corr with all scores
    score_norm = np.empty(0)

    score_human_avg = np.empty(0) #f or calculating human corr with avg score
    score_norm_avg = np.empty(0)
    
    for i in range(len(column_name)): # initialize
        c = column_name[i]
        c = c.replace(".mp4","")
        dic_human_avg[c] = []
        dic_auto_avg[c] = []
        
    # import pdb; pdb.set_trace()
    # score_dic = scale_dict_values(score_dic) # TODO rescale to 1~5
    
    for i in range(len(column_name)):

        c = column_name[i]
        c = c.replace(".mp4","")
        s = score_AMT[i]
        
        method_name = c.split("_")[-1]
        
        # hky
        index_name = method.index(method_name)
        # print(method_name, index_num)
        # score_norm = np.append(score_norm, score_dic[method_name][index_num])
        # print(score_dic)
        # print(c)
        # print(score_dic[c])
        try:
            score_norm = np.append(score_norm, score_dic[c]) #TODO round or not
            # import pdb; pdb.set_trace()
            # auto_score = round(score_dic[c])
            # score_norm = np.append(score_norm, auto_score)

            score_human = np.append(score_human, (s/5))

        

            record = np.append(record, c)
            dic_human[method_name].append(s/5)  # length 150
            dic_auto[method_name].append(score_dic[c]) # 
            
            dic_score_category[method_name].append(score_dic[c])
            
            dic_human_avg[c].append(s/5)
            dic_auto_avg[c].append(score_dic[c]) #TODO round or not
            # dic_auto_avg[c].append(auto_score)
        except: # not for gt
            continue

    # import pdb; pdb.set_trace()

    for k, v in dic_human_avg.items():
        score_human_avg = np.append(score_human_avg, np.average(dic_human_avg[k]))
        score_norm_avg = np.append(score_norm_avg, np.average(dic_auto_avg[k]))
        


    Pearson_correlation_coefficient_all = compute_correlation(score_human,score_norm) #TODO change score_human, score_norm
    print("Pearson correlation coefficient all:",f'{Pearson_correlation_coefficient_all: .4}')

    score_diff = np.empty(0)
    # Normalize the scores to the range [0, 1], and pick the larger different score
    score_norm_norm = min_max_normalize(score_norm)
    score_human_norm = min_max_normalize(score_human)

    # Calculate the threshold
    threshold = np.mean(np.abs(score_human_norm - score_norm_norm))


    # Iterate through the scores and apply the condition
    for i in range(len(score_norm)):
        score_diff = np.append(score_diff, score_human_norm[i] - score_norm_norm[i])
        
        if abs(score_norm_norm[i] - score_human_norm[i]) > threshold * 2:
            score_diff = np.append(score_diff, [score_norm[i], score_human[i]])
            print("Not aligned:", i, "score ori:", score_norm[i], "score from AMT", score_human[i])
      
    print("##############################")

    # score_diff = np.reshape(score_diff,(num_images,3))
    # best_index = np.argmin(score_diff, axis=1)
    # #score_diff_best = np.choose(best_index,score_diff.T)

    # score_norm_reshape = np.reshape(score_norm,(num_images,3))
    # score_norm_best = np.choose(best_index,score_norm_reshape.T)

    # score_human_reshape = np.reshape(score_human,(num_images,3))
    # score_human_best = np.choose(best_index,score_human_reshape.T) * 5 #best human score

    # Pearson_correlation_coefficient_abs_diff = compute_correlation(score_human_best,score_norm_best)
    # print("Pearson correlation coefficient with min abs diff:",f'{Pearson_correlation_coefficient_abs_diff: .4}')


    # Pearson_correlation_coefficient_search_max,selected_human_index,selected_human_score = compute_max_correlation(score_norm_reshape, score_human_reshape) #(350,3)
    # print("Pearson correlation coefficient with search max:",f'{Pearson_correlation_coefficient_search_max: .4}')

    # score_norm_average = np.average(score_norm_reshape, axis=1)
    # score_human_average = np.average(score_human_reshape, axis=1)
    Pearson_correlation_coefficient_all = compute_correlation(score_norm, score_human) 
    print("Pearson correlation coefficient all:",f'{Pearson_correlation_coefficient_all: .4}')
    
    Pearson_correlation_coefficient_avg = compute_correlation(score_norm_avg, score_human_avg)
    print("Pearson correlation coefficient avg:",f'{Pearson_correlation_coefficient_avg: .4}')


    kendalltau_all,p_value_all = scipy.stats.kendalltau(score_human, score_norm)
    print("kendall's Tau_all: ", f'{kendalltau_all: .4}', "  p_value_all: ", f'{p_value_all: .4}')
    # kendalltau_abs_diff,p_value_abs_diff = scipy.stats.kendalltau(score_human_best,score_norm_best)
    # print("kendall's Tau with abs diff: ", f'{kendalltau_abs_diff: .4}', "  p_value: ", f'{p_value_abs_diff: .4}')
    #kendalltau_search_max = scipy.stats.kendalltau(x, y)
    kendalltau_avg, p_value_avg = scipy.stats.kendalltau(score_human_avg, score_norm_avg)
    print("kendall's Tau avg: ", f'{kendalltau_avg: .4}', "  p_value: ", f'{p_value_avg: .4}')

    spearmanr_all = scipy.stats.spearmanr(score_human,score_norm)
    print("spearmanr's rho all", f'{spearmanr_all.statistic: .4}', f'{spearmanr_all.pvalue: .4}')
    # spearmanr_abs_diff = scipy.stats.spearmanr(score_human_best, score_norm_best)
    # print("spearmanr's rho with abs diff", f'{spearmanr_abs_diff.statistic: .4}', f'{spearmanr_abs_diff.pvalue: .4}')
    spearmanr_avg = scipy.stats.spearmanr(score_human_avg, score_norm_avg)
    print("spearmanr's rho avg", f'{spearmanr_avg.statistic: .4}', f'{spearmanr_avg.pvalue: .4}')

    #auto  avg per model
    print("##############################")
    # import pdb; pdb.set_trace()
    for i in range(len(method)):
        auto_avg = np.average(dic_auto[method[i]])
        human_avg = np.average(dic_human[method[i]])
        print(method[i], " auto_avg: ", f'{auto_avg: .4}', "    human_avg:", f'{human_avg: .4}',"                human eval length / method", len(dic_human[method[i]]))


    # for i in range (len(method)):
    #     #corr within per model
    #     mPearson_all = compute_correlation(dic_human[method[i]], dic_score_category[method[i]])
    #     mkendalltau_all, mp_value_all = scipy.stats.kendalltau(dic_human[method[i]], dic_score_category[method[i]])
    #     mspearmanr_all = scipy.stats.spearmanr(dic_human[method[i]], dic_score_category[method[i]])

    #     m_human_reshape = np.reshape(dic_human[method[i]], (50, 3))
    #     m_auto_reshape = np.reshape(dic_score_category[method[i]], (50, 3))
    #     m_human_average = np.average(m_human_reshape, axis=1)
    #     m_auto_average = np.average(m_auto_reshape, axis=1)

    #     mPearson_average = compute_correlation(m_human_average, m_auto_average)
    #     mkendalltau_avg, mp_value_avg = scipy.stats.kendalltau(m_human_average, m_auto_average)
    #     mspearmanr_avg = scipy.stats.spearmanr(m_human_average, m_auto_average)

    #     print(method[i])
    #     print(" Pearson all:", f'{mPearson_all: .4}', " Pearson avg", f'{mPearson_average: .4}')
    #     print(" kendall all:", f'{mkendalltau_all: .4}', " p value", f'{mp_value_all: .4}',end="         ")
    #     print(" spearman all:", f'{mspearmanr_all.statistic: .4}', f'{mspearmanr_all.pvalue: .4}')
    #     print(" kendall avg:", f'{mkendalltau_avg: .4}', " p value", f'{mp_value_avg: .4}',end="         ")
    #     print(" spearman avg:", f'{mspearmanr_avg.statistic: .4}',f'{mspearmanr_avg.pvalue: .4}')