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