automatic_extract.py

"""
https://github.com/MMMU-Benchmark/MMMU/blob/main/eval/utils/eval_utils.py
"""

import numpy as np
import random


random.seed(42)


def parse_multi_choice_response(response, all_choices, index2ans):
    """
    Parse the prediction from the generated response.
    Return the predicted index e.g., A, B, C, D.
    """
    for char in [',', '.', '!', '?', ';', ':', "'"]:
        response = response.strip(char)
    response = " " + response + " " # add space to avoid partial match

    index_ans = True
    ans_with_brack = False
    candidates = []
    for choice in all_choices:  # e.g., (A) (B) (C) (D)
        if f'({choice})' in response:
            candidates.append(choice)
            ans_with_brack = True

    if len(candidates) == 0:
        for choice in all_choices: # e.g., A B C D
            if f' {choice} ' in response:
                candidates.append(choice)

    # if all above doesn't get candidates, check if the content is larger than 5 tokens and try to parse the example
    if len(candidates) == 0 and len(response.split()) > 5:
        for index, ans in index2ans.items():
            if ans.lower() in response.lower():
                candidates.append(index)
                index_ans = False # it's content ans.

    if len(candidates) == 0: 
        return response
        #pred_index = random.choice(all_choices)
    elif len(candidates) > 1:
        start_indexes = []
        if index_ans:
            if ans_with_brack: 
                for can in candidates:
                    index = response.rfind(f'({can})')
                    start_indexes.append(index) # -1 will be ignored anyway
                # start_indexes = [generated_response.index(f'({can})') for can in candidates]
            else:
                for can in candidates:
                    index = response.rfind(f" {can} ")
                    start_indexes.append(index)
        else:
            for can in candidates:
                index = response.lower().rfind(index2ans[can].lower())
                start_indexes.append(index)
        # get the last one
        pred_index = candidates[np.argmax(start_indexes)]
    else: # if only one candidate, use it.
        pred_index = candidates[0]

    return pred_index