import re

PATTERN = re.compile(r'\d+\.?\d*%')


def find_percentage(s):
    match = PATTERN.search(s)
    if match is None:
        return None
    return match.group(0)


def to_int(s):
    percentage_string = find_percentage(s)
    if percentage_string is None:
        return None
    percentage_string = percentage_string.replace("%", "")
    percentage = float(percentage_string)
    return percentage


def exp_similarity_score(prediction, ground_truth):
    ground_truth_percentage = to_int(ground_truth)
    pred_percentage = to_int(str(prediction))

    if ground_truth_percentage is None:
        raise ValueError(f"ground_truth_percentage is None: {ground_truth_percentage}")

    if pred_percentage is None:
        return 0.0

    return 0.5 ** (abs(ground_truth_percentage - pred_percentage) / 10)


def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
    scores_for_ground_truths = []
    for ground_truth in ground_truths:
        score = metric_fn(prediction, ground_truth)
        scores_for_ground_truths.append(score)
    return max(scores_for_ground_truths)


def compute_exp_similarity(predictions, references):
    exp_similarity = 0
    for prediction, ground_truths in zip(predictions, references):
        exp_similarity += metric_max_over_ground_truths(exp_similarity_score, prediction, ground_truths)
    return 100 * exp_similarity / len(predictions)