# import json
# import os
# import glob
# import argparse
# import csv
#
#
# def chatgpt_json(merge_file):
#     # chat results
#     merge_data = merge_file.decode("utf-8")
#     merge_data = eval(merge_data)
#     correct_answer_file = 'file/ANSWER.json'
#     with open(correct_answer_file, 'r', encoding='utf-8') as f:
#         correct_answer_data = json.load(f)
#
#     dataset_scores_dict = {}
#     for dataset_name, item in merge_data.items():
#
#         total_nums = len(item)
#         correct = 0
#         # assert len(item) >= len(correct_answer_data[dataset_name]), f'Video-Bench-Input.json---{dataset_name}---is incomplete!'
#         for id, sub_item in item.items():
#             if sub_item['output_chatgpt_choice'] == correct_answer_data[dataset_name][id]['answer']:
#                 correct += 1
#
#         dataset_scores_dict[dataset_name] = round(correct / total_nums * 100, 2)
#     return dataset_scores_dict
#
#
# def compute_scores(merge_file):
#     dataset_score_dict = chatgpt_json(merge_file)
#     dataset_weight = {
#         1:
#             {
#                 "ActivityNet": 1,
#                 "MSVD": 1,
#                 "MSRVTT": 1,
#                 "TGIF": 1,
#                 "Youcook2": 1,
#                 "Ucfcrime": 1,
#                 "MOT": 0.5,
#             },
#
#         2:
#             {
#                 "TVQA": 1,
#                 "MV": 1,
#                 "NBA": 1,
#             },
#
#         3:
#             {
#                 "Driving-exam": 0.5,
#                 "Driving-decision-making": 1,
#                 "SQA3D": 1,
#             }
#
#     }
#
#     # Video-exclusive Understanding score
#     exclusive_understanding_weight = dataset_weight[1]
#     weights_sum = sum(exclusive_understanding_weight.values())
#     exclusive_understanding_score = 0
#     # import ipdb; ipdb.set_trace()
#     for dataset_name, weight in exclusive_understanding_weight.items():
#         exclusive_understanding_score += weight * dataset_score_dict[dataset_name] / weights_sum
#
#     # Prior Knowledge-based Question-answer
#     prior_QA_weight = dataset_weight[2]
#     weights_sum = sum(prior_QA_weight.values())
#     prior_QA_score = 0
#     for dataset_name, weight in prior_QA_weight.items():
#         prior_QA_score += weight * dataset_score_dict[dataset_name] / weights_sum
#
#     # Comprehension and Decision-making
#     com_and_dec_QA_weight = dataset_weight[3]
#     weights_sum = sum(com_and_dec_QA_weight.values())
#     com_and_dec_QA_score = 0
#     for dataset_name, weight in com_and_dec_QA_weight.items():
#         com_and_dec_QA_score += weight * dataset_score_dict[dataset_name] / weights_sum
#
#     dataset_score_dict['Exclusive_understanding'] = exclusive_understanding_score
#     dataset_score_dict['Prior_Knowledge'] = prior_QA_score
#     dataset_score_dict['Comprehension_and_Decision-making'] = com_and_dec_QA_score
#
#     # final score
#     final_score = sum([exclusive_understanding_score, prior_QA_score, com_and_dec_QA_score]) / 3
#     dataset_score_dict['final_score'] = final_score
#
#     # print(dataset_score_dict)
#     # with open(args.score_output_file, 'w', encoding='utf-8') as f:
#     #   json.dump(dataset_score_dict, f, indent=2)
#     # print(f'{args.score_output_file} is saved!')
#     # ========================
#     data = [
#
#         ["Avg. All", "Avg. Video-Exclusive", "Avg. Prior-Knowledge QA", "Avg. Decision-Making",
#          "ActivityNet", "MSVD", "MSRVTT", "TGIF", "Youcook2", "Ucfcrime",
#          "MOT", "TVQA", "MV", "NBA", "Driving-exam", "Driving-decision-making", "SQA3D"],
#
#         [final_score, exclusive_understanding_score, prior_QA_score, com_and_dec_QA_score,
#          dataset_score_dict['ActivityNet'],
#          dataset_score_dict["MSVD"],
#          dataset_score_dict['MSRVTT'],
#          dataset_score_dict['TGIF'],
#          dataset_score_dict['Youcook2'],
#          dataset_score_dict['Ucfcrime'],
#          dataset_score_dict['MOT'],
#          dataset_score_dict['TVQA'],
#          dataset_score_dict['MV'],
#          dataset_score_dict['NBA'],
#          dataset_score_dict['Driving-exam'],
#          dataset_score_dict['Driving-decision-making'],
#          dataset_score_dict['SQA3D'],
#          ],
#     ]
#
#     return data
#

import json
import os
import glob
import argparse
import csv


def chatgpt_json(merge_file):
    # chat results
    merge_data = merge_file.decode("utf-8")
    merge_data = eval(merge_data)
    correct_answer_file = 'file/ANSWER.json'
    with open(correct_answer_file, 'r', encoding='utf-8') as f:
        correct_answer_data = json.load(f)

    dataset_scores_dict = {}
    for dataset_name, item in merge_data.items():

        total_nums = len(item)
        correct = 0
        # assert len(item) >= len(correct_answer_data[dataset_name]), f'Video-Bench-Input.json---{dataset_name}---is incomplete!'
        for id, sub_item in item.items():
            if sub_item['output_chatgpt_choice'] == correct_answer_data[dataset_name][id]['answer']:
                correct += 1

        # dataset_scores_dict[dataset_name] = round(correct / total_nums * 100, 2)
        dataset_scores_dict[dataset_name] = round(correct / total_nums , 4)
    return dataset_scores_dict


def compute_scores(merge_file):
    dataset_score_dict = chatgpt_json(merge_file)
    dataset_weight = {
        1:
            {
                "ActivityNet": 1,
                "MSVD": 1,
                "MSRVTT": 1,
                "TGIF": 1,
                "Youcook2": 1,
                "Ucfcrime": 1,
                "MOT": 0.5,
            },

        2:
            {
                "TVQA": 1,
                "MV": 1,
                "NBA": 1,
            },

        3:
            {
                "Driving-exam": 0.5,
                "Driving-decision-making": 1,
                "SQA3D": 1,
            }

    }

    # Video-exclusive Understanding score
    exclusive_understanding_weight = dataset_weight[1]
    weights_sum = sum(exclusive_understanding_weight.values())
    exclusive_understanding_score = 0
    # import ipdb; ipdb.set_trace()
    for dataset_name, weight in exclusive_understanding_weight.items():
        exclusive_understanding_score += weight * dataset_score_dict[dataset_name] / weights_sum * 100

    # Prior Knowledge-based Question-answer
    prior_QA_weight = dataset_weight[2]
    weights_sum = sum(prior_QA_weight.values())
    prior_QA_score = 0
    for dataset_name, weight in prior_QA_weight.items():
        prior_QA_score += weight * dataset_score_dict[dataset_name] / weights_sum *100

    # Comprehension and Decision-making
    com_and_dec_QA_weight = dataset_weight[3]
    weights_sum = sum(com_and_dec_QA_weight.values())
    com_and_dec_QA_score = 0
    for dataset_name, weight in com_and_dec_QA_weight.items():
        com_and_dec_QA_score += weight * dataset_score_dict[dataset_name] / weights_sum *100

    dataset_score_dict['Exclusive_understanding'] = exclusive_understanding_score
    dataset_score_dict['Prior_Knowledge'] = prior_QA_score
    dataset_score_dict['Comprehension_and_Decision-making'] = com_and_dec_QA_score

    # final score
    final_score = sum([exclusive_understanding_score, prior_QA_score, com_and_dec_QA_score]) / 3
    dataset_score_dict['final_score'] = final_score

    # print(dataset_score_dict)
    # with open(args.score_output_file, 'w', encoding='utf-8') as f:
    #   json.dump(dataset_score_dict, f, indent=2)
    # print(f'{args.score_output_file} is saved!')
    # ========================
    data = [

        ["Avg. All", "Avg. Video-Exclusive", "Avg. Prior-Knowledge QA", "Avg. Decision-Making",
         "ActivityNet", "MSVD", "MSRVTT", "TGIF", "Youcook2", "Ucfcrime",
         "MOT", "TVQA", "MV", "NBA", "Driving-exam", "Driving-decision-making", "SQA3D"],

        [final_score, exclusive_understanding_score, prior_QA_score, com_and_dec_QA_score,
         dataset_score_dict['ActivityNet'],
         dataset_score_dict["MSVD"],
         dataset_score_dict['MSRVTT'],
         dataset_score_dict['TGIF'],
         dataset_score_dict['Youcook2'],
         dataset_score_dict['Ucfcrime'],
         dataset_score_dict['MOT'],
         dataset_score_dict['TVQA'],
         dataset_score_dict['MV'],
         dataset_score_dict['NBA'],
         dataset_score_dict['Driving-exam'],
         dataset_score_dict['Driving-decision-making'],
         dataset_score_dict['SQA3D'],
         ],
    ]


    return data